From f846eccbfb0cc7cfe96610fc84deafe5df600e81 Mon Sep 17 00:00:00 2001
From: psamfass <psamfass@amd.com>
Date: Thu, 12 Dec 2024 16:37:22 +0000
Subject: [PATCH] (upstream branch, commit
 4dc8300c6104697b1d9313a48d1c4c7f5dabf81a:) GALI PREM SAGAR Thu Nov 16
 16:22:44 2023 -0600 Raise error in `reindex` when `index` is not unique
 (#14400) (#14429) Bacport of #14400 Fixes: #14398 This PR raises an error in
 `reindex` API when reindexing is performed on a non-unique index column.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Matthew Roeschke (https://github.com/mroeschke)
  - Lawrence Mitchell (https://github.com/wence-)

URL: https://github.com/rapidsai/cudf/pull/14400

Authors:
   - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
   - Richard (Rick) Zamora (https://github.com/rjzamora)
   - Ashwin Srinath (https://github.com/shwina)
   - Ray Douglass (https://github.com/raydouglass)
---
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 .../hashing/detail/murmurhash3_x64_128.cuh    |   223 +
 .../hashing/detail/murmurhash3_x86_32.cuh     |   194 +
 cpp/include/cudf/interop.hpp                  |   182 +
 cpp/include/cudf/io/arrow_io_source.hpp       |    85 +
 cpp/include/cudf/io/avro.hpp                  |   223 +
 cpp/include/cudf/io/csv.hpp                   |  1725 ++
 cpp/include/cudf/io/data_sink.hpp             |   212 +
 cpp/include/cudf/io/datasource.hpp            |   379 +
 cpp/include/cudf/io/detail/avro.hpp           |    47 +
 cpp/include/cudf/io/detail/csv.hpp            |    64 +
 cpp/include/cudf/io/detail/json.hpp           |    55 +
 cpp/include/cudf/io/detail/orc.hpp            |   133 +
 cpp/include/cudf/io/detail/parquet.hpp        |   254 +
 cpp/include/cudf/io/detail/tokenize_json.hpp  |   140 +
 cpp/include/cudf/io/detail/utils.hpp          |    28 +
 cpp/include/cudf/io/json.hpp                  |   871 +
 cpp/include/cudf/io/orc.hpp                   |  1256 ++
 cpp/include/cudf/io/orc_metadata.hpp          |   371 +
 cpp/include/cudf/io/orc_types.hpp             |    86 +
 cpp/include/cudf/io/parquet.hpp               |  1758 ++
 cpp/include/cudf/io/parquet_metadata.hpp      |   231 +
 cpp/include/cudf/io/text/byte_range_info.hpp  |   109 +
 .../cudf/io/text/data_chunk_source.hpp        |   115 +
 .../io/text/data_chunk_source_factories.hpp   |    87 +
 .../cudf/io/text/detail/bgzip_utils.hpp       |   112 +
 .../cudf/io/text/detail/multistate.hpp        |   128 +
 .../cudf/io/text/detail/tile_state.hpp        |   149 +
 cpp/include/cudf/io/text/detail/trie.hpp      |   250 +
 cpp/include/cudf/io/text/multibyte_split.hpp  |   103 +
 cpp/include/cudf/io/types.hpp                 |   936 ++
 cpp/include/cudf/join.hpp                     |  1181 ++
 cpp/include/cudf/labeling/label_bins.hpp      |    79 +
 cpp/include/cudf/lists/combine.hpp            |   100 +
 cpp/include/cudf/lists/contains.hpp           |   174 +
 cpp/include/cudf/lists/count_elements.hpp     |    58 +
 cpp/include/cudf/lists/detail/combine.hpp     |    47 +
 cpp/include/cudf/lists/detail/concatenate.hpp |    52 +
 cpp/include/cudf/lists/detail/contains.hpp    |    74 +
 cpp/include/cudf/lists/detail/copying.hpp     |    53 +
 cpp/include/cudf/lists/detail/dremel.hpp      |   216 +
 cpp/include/cudf/lists/detail/extract.hpp     |    47 +
 cpp/include/cudf/lists/detail/gather.cuh      |   315 +
 .../cudf/lists/detail/interleave_columns.hpp  |    54 +
 .../lists/detail/lists_column_factories.hpp   |    71 +
 cpp/include/cudf/lists/detail/reverse.hpp     |    30 +
 cpp/include/cudf/lists/detail/scatter.cuh     |   283 +
 .../cudf/lists/detail/scatter_helper.cuh      |   143 +
 .../cudf/lists/detail/set_operations.hpp      |    77 +
 cpp/include/cudf/lists/detail/sorting.hpp     |    50 +
 .../cudf/lists/detail/stream_compaction.hpp   |    47 +
 cpp/include/cudf/lists/explode.hpp            |   209 +
 cpp/include/cudf/lists/extract.hpp            |   111 +
 cpp/include/cudf/lists/filling.hpp            |   109 +
 cpp/include/cudf/lists/gather.hpp             |    80 +
 cpp/include/cudf/lists/list_device_view.cuh   |   380 +
 cpp/include/cudf/lists/list_view.hpp          |    32 +
 .../cudf/lists/lists_column_device_view.cuh   |   120 +
 cpp/include/cudf/lists/lists_column_view.hpp  |   139 +
 cpp/include/cudf/lists/reverse.hpp            |    54 +
 cpp/include/cudf/lists/set_operations.hpp     |   171 +
 cpp/include/cudf/lists/sorting.hpp            |    73 +
 cpp/include/cudf/lists/stream_compaction.hpp  |    92 +
 cpp/include/cudf/merge.hpp                    |   108 +
 cpp/include/cudf/null_mask.hpp                |   190 +
 cpp/include/cudf/partitioning.hpp             |   248 +
 cpp/include/cudf/quantiles.hpp                |   131 +
 cpp/include/cudf/reduction.hpp                |   222 +
 .../cudf/reduction/detail/histogram.hpp       |    57 +
 .../cudf/reduction/detail/reduction.cuh       |   235 +
 .../cudf/reduction/detail/reduction.hpp       |    40 +
 .../reduction/detail/reduction_functions.hpp  |   356 +
 .../reduction/detail/reduction_operators.cuh  |   295 +
 .../reduction/detail/segmented_reduction.cuh  |   198 +
 .../detail/segmented_reduction_functions.hpp  |   358 +
 cpp/include/cudf/replace.hpp                  |   310 +
 cpp/include/cudf/reshape.hpp                  |   107 +
 cpp/include/cudf/rolling.hpp                  |   597 +
 .../cudf/rolling/range_window_bounds.hpp      |   108 +
 cpp/include/cudf/round.hpp                    |    79 +
 cpp/include/cudf/scalar/scalar.hpp            |   893 +
 .../cudf/scalar/scalar_device_view.cuh        |   443 +
 cpp/include/cudf/scalar/scalar_factories.hpp  |   229 +
 cpp/include/cudf/search.hpp                   |   170 +
 cpp/include/cudf/sorting.hpp                  |   355 +
 cpp/include/cudf/stream_compaction.hpp        |   378 +
 cpp/include/cudf/strings/attributes.hpp       |    93 +
 cpp/include/cudf/strings/capitalize.hpp       |   131 +
 cpp/include/cudf/strings/case.hpp             |    91 +
 .../cudf/strings/char_types/char_cases.hpp    |    34 +
 .../cudf/strings/char_types/char_types.hpp    |   117 +
 .../strings/char_types/char_types_enum.hpp    |    83 +
 cpp/include/cudf/strings/combine.hpp          |   328 +
 cpp/include/cudf/strings/contains.hpp         |   201 +
 .../cudf/strings/convert/convert_booleans.hpp |    70 +
 .../cudf/strings/convert/convert_datetime.hpp |   251 +
 .../strings/convert/convert_durations.hpp     |   131 +
 .../strings/convert/convert_fixed_point.hpp   |   126 +
 .../cudf/strings/convert/convert_floats.hpp   |    99 +
 .../cudf/strings/convert/convert_integers.hpp |   223 +
 .../cudf/strings/convert/convert_ipv4.hpp     |   109 +
 .../cudf/strings/convert/convert_lists.hpp    |    68 +
 .../cudf/strings/convert/convert_urls.hpp     |    73 +
 .../cudf/strings/detail/char_tables.hpp       |   106 +
 cpp/include/cudf/strings/detail/combine.hpp   |    72 +
 .../cudf/strings/detail/concatenate.hpp       |    51 +
 .../strings/detail/convert/fixed_point.cuh    |   166 +
 .../detail/convert/fixed_point_to_string.cuh  |    80 +
 .../strings/detail/convert/int_to_string.cuh  |    97 +
 .../cudf/strings/detail/convert/is_float.cuh  |   120 +
 .../detail/convert/string_to_float.cuh        |   139 +
 .../strings/detail/convert/string_to_int.cuh  |    54 +
 .../cudf/strings/detail/converters.hpp        |   157 +
 .../cudf/strings/detail/copy_if_else.cuh      |   116 +
 .../cudf/strings/detail/copy_range.cuh        |   216 +
 cpp/include/cudf/strings/detail/copying.hpp   |    87 +
 cpp/include/cudf/strings/detail/fill.hpp      |    54 +
 cpp/include/cudf/strings/detail/gather.cuh    |   361 +
 cpp/include/cudf/strings/detail/json.hpp      |    43 +
 cpp/include/cudf/strings/detail/merge.cuh     |   112 +
 cpp/include/cudf/strings/detail/pad_impl.cuh  |   126 +
 cpp/include/cudf/strings/detail/replace.hpp   |   103 +
 cpp/include/cudf/strings/detail/scatter.cuh   |    89 +
 .../cudf/strings/detail/split_utils.cuh       |   161 +
 .../cudf/strings/detail/strings_children.cuh  |   127 +
 .../detail/strings_column_factories.cuh       |   199 +
 cpp/include/cudf/strings/detail/strip.cuh     |    71 +
 cpp/include/cudf/strings/detail/utf8.hpp      |   214 +
 cpp/include/cudf/strings/detail/utilities.cuh |   104 +
 cpp/include/cudf/strings/detail/utilities.hpp |    58 +
 cpp/include/cudf/strings/extract.hpp          |   102 +
 cpp/include/cudf/strings/find.hpp             |   264 +
 cpp/include/cudf/strings/find_multiple.hpp    |    65 +
 cpp/include/cudf/strings/findall.hpp          |    72 +
 cpp/include/cudf/strings/json.hpp             |   174 +
 cpp/include/cudf/strings/padding.hpp          |    94 +
 cpp/include/cudf/strings/regex/flags.hpp      |    89 +
 .../cudf/strings/regex/regex_program.hpp      |   138 +
 cpp/include/cudf/strings/repeat_strings.hpp   |   129 +
 cpp/include/cudf/strings/replace.hpp          |   158 +
 cpp/include/cudf/strings/replace_re.hpp       |   108 +
 cpp/include/cudf/strings/reverse.hpp          |    53 +
 cpp/include/cudf/strings/side_type.hpp        |    37 +
 cpp/include/cudf/strings/slice.hpp            |   112 +
 cpp/include/cudf/strings/split/partition.hpp  |    99 +
 cpp/include/cudf/strings/split/split.hpp      |   247 +
 cpp/include/cudf/strings/split/split_re.hpp   |   257 +
 cpp/include/cudf/strings/string_view.cuh      |   451 +
 cpp/include/cudf/strings/string_view.hpp      |   409 +
 .../cudf/strings/strings_column_view.hpp      |   153 +
 cpp/include/cudf/strings/strip.hpp            |    71 +
 cpp/include/cudf/strings/translate.hpp        |   107 +
 cpp/include/cudf/strings/wrap.hpp             |    72 +
 .../cudf/structs/detail/concatenate.hpp       |    57 +
 cpp/include/cudf/structs/struct_view.hpp      |    32 +
 .../structs/structs_column_device_view.cuh    |    87 +
 .../cudf/structs/structs_column_view.hpp      |   101 +
 .../cudf/table/experimental/row_operators.cuh |  1963 +++
 cpp/include/cudf/table/row_operators.cuh      |   639 +
 cpp/include/cudf/table/table.hpp              |   190 +
 cpp/include/cudf/table/table_device_view.cuh  |   274 +
 cpp/include/cudf/table/table_view.hpp         |   399 +
 .../cudf/tdigest/tdigest_column_view.hpp      |   127 +
 cpp/include/cudf/timezone.hpp                 |    54 +
 cpp/include/cudf/transform.hpp                |   228 +
 cpp/include/cudf/transpose.hpp                |    50 +
 cpp/include/cudf/types.hpp                    |   333 +
 cpp/include/cudf/unary.hpp                    |   156 +
 cpp/include/cudf/utilities/bit.hpp            |   218 +
 cpp/include/cudf/utilities/default_stream.hpp |    40 +
 cpp/include/cudf/utilities/error.hpp          |   284 +
 cpp/include/cudf/utilities/logger.hpp         |    46 +
 cpp/include/cudf/utilities/span.hpp           |   491 +
 cpp/include/cudf/utilities/traits.cuh         |    67 +
 cpp/include/cudf/utilities/traits.hpp         |   598 +
 cpp/include/cudf/utilities/type_checks.hpp    |    50 +
 .../cudf/utilities/type_dispatcher.hpp        |   615 +
 cpp/include/cudf/wrappers/dictionary.hpp      |   219 +
 cpp/include/cudf/wrappers/durations.hpp       |    68 +
 cpp/include/cudf/wrappers/timestamps.hpp      |    85 +
 cpp/include/cudf_test/base_fixture.hpp        |   399 +
 cpp/include/cudf_test/column_utilities.hpp    |   310 +
 cpp/include/cudf_test/column_wrapper.hpp      |  1910 +++
 cpp/include/cudf_test/cudf_gtest.hpp          |   139 +
 cpp/include/cudf_test/cxxopts.hpp             |  1504 ++
 cpp/include/cudf_test/default_stream.hpp      |    41 +
 .../cudf_test/detail/column_utilities.hpp     |    85 +
 cpp/include/cudf_test/file_utilities.hpp      |    71 +
 .../cudf_test/io_metadata_utilities.hpp       |    31 +
 cpp/include/cudf_test/iterator_utilities.hpp  |   139 +
 cpp/include/cudf_test/print_utilities.cuh     |   140 +
 .../stream_checking_resource_adaptor.hpp      |   202 +
 cpp/include/cudf_test/table_utilities.hpp     |    79 +
 cpp/include/cudf_test/tdigest_utilities.cuh   |   586 +
 cpp/include/cudf_test/timestamp_utilities.cuh |    78 +
 cpp/include/cudf_test/type_list_utilities.hpp |   630 +
 cpp/include/cudf_test/type_lists.hpp          |   435 +
 cpp/include/doxygen_groups.h                  |   184 +
 cpp/include/nvtext/bpe_tokenize.hpp           |   136 +
 cpp/include/nvtext/detail/generate_ngrams.hpp |    37 +
 cpp/include/nvtext/detail/load_hash_file.hpp  |    49 +
 cpp/include/nvtext/detail/tokenize.hpp        |    72 +
 cpp/include/nvtext/edit_distance.hpp          |   101 +
 cpp/include/nvtext/generate_ngrams.hpp        |   131 +
 cpp/include/nvtext/jaccard.hpp                |    79 +
 cpp/include/nvtext/minhash.hpp                |   152 +
 cpp/include/nvtext/ngrams_tokenize.hpp        |    87 +
 cpp/include/nvtext/normalize.hpp              |   105 +
 cpp/include/nvtext/replace.hpp                |   139 +
 cpp/include/nvtext/stemmer.hpp                |   167 +
 cpp/include/nvtext/subword_tokenize.hpp       |   161 +
 cpp/include/nvtext/tokenize.hpp               |   297 +
 cpp/libcudf_kafka/CMakeLists.txt              |   111 +
 .../cmake/thirdparty/get_cudf.cmake           |    55 +
 .../cmake/thirdparty/get_rdkafka.cmake        |    41 +
 .../include/cudf_kafka/kafka_callback.hpp     |    86 +
 .../include/cudf_kafka/kafka_consumer.hpp     |   231 +
 cpp/libcudf_kafka/src/kafka_callback.cpp      |    48 +
 cpp/libcudf_kafka/src/kafka_consumer.cpp      |   268 +
 cpp/libcudf_kafka/tests/CMakeLists.txt        |    49 +
 .../tests/kafka_consumer_tests.cpp            |    67 +
 cpp/scripts/gdb-pretty-printers.py            |    84 +
 cpp/scripts/load-pretty-printers.in           |     3 +
 cpp/scripts/run-clang-tidy.py                 |   253 +
 cpp/scripts/run-cmake-format.sh               |    84 +
 cpp/scripts/sort_ninja_log.py                 |   404 +
 cpp/src/aggregation/aggregation.cpp           |   888 +
 cpp/src/aggregation/aggregation.cu            |    36 +
 cpp/src/aggregation/result_cache.cpp          |    55 +
 cpp/src/ast/expression_parser.cpp             |   240 +
 cpp/src/ast/expressions.cpp                   |    83 +
 cpp/src/binaryop/binaryop.cpp                 |   442 +
 cpp/src/binaryop/compiled/ATan2.cu            |    26 +
 cpp/src/binaryop/compiled/Add.cu              |    26 +
 cpp/src/binaryop/compiled/BitwiseAnd.cu       |    26 +
 cpp/src/binaryop/compiled/BitwiseOr.cu        |    26 +
 cpp/src/binaryop/compiled/BitwiseXor.cu       |    26 +
 cpp/src/binaryop/compiled/Div.cu              |    26 +
 cpp/src/binaryop/compiled/FloorDiv.cu         |    26 +
 cpp/src/binaryop/compiled/Greater.cu          |    26 +
 cpp/src/binaryop/compiled/GreaterEqual.cu     |    26 +
 cpp/src/binaryop/compiled/IntPow.cu           |    26 +
 cpp/src/binaryop/compiled/Less.cu             |    26 +
 cpp/src/binaryop/compiled/LessEqual.cu        |    26 +
 cpp/src/binaryop/compiled/LogBase.cu          |    26 +
 cpp/src/binaryop/compiled/LogicalAnd.cu       |    26 +
 cpp/src/binaryop/compiled/LogicalOr.cu        |    26 +
 cpp/src/binaryop/compiled/Mod.cu              |    26 +
 cpp/src/binaryop/compiled/Mul.cu              |    26 +
 cpp/src/binaryop/compiled/NullEquals.cu       |    26 +
 cpp/src/binaryop/compiled/NullLogicalAnd.cu   |    26 +
 cpp/src/binaryop/compiled/NullLogicalOr.cu    |    26 +
 cpp/src/binaryop/compiled/NullMax.cu          |    26 +
 cpp/src/binaryop/compiled/NullMin.cu          |    26 +
 cpp/src/binaryop/compiled/PMod.cu             |    26 +
 cpp/src/binaryop/compiled/Pow.cu              |    26 +
 cpp/src/binaryop/compiled/PyMod.cu            |    26 +
 cpp/src/binaryop/compiled/ShiftLeft.cu        |    26 +
 cpp/src/binaryop/compiled/ShiftRight.cu       |    26 +
 .../binaryop/compiled/ShiftRightUnsigned.cu   |    26 +
 cpp/src/binaryop/compiled/Sub.cu              |    26 +
 cpp/src/binaryop/compiled/TrueDiv.cu          |    26 +
 cpp/src/binaryop/compiled/binary_ops.cu       |   469 +
 cpp/src/binaryop/compiled/binary_ops.cuh      |   307 +
 cpp/src/binaryop/compiled/binary_ops.hpp      |   218 +
 cpp/src/binaryop/compiled/equality_ops.cu     |    62 +
 cpp/src/binaryop/compiled/operation.cuh       |   527 +
 .../binaryop/compiled/struct_binary_ops.cuh   |   192 +
 cpp/src/binaryop/compiled/util.cpp            |   221 +
 cpp/src/binaryop/jit/kernel.cu                |    97 +
 cpp/src/binaryop/jit/operation-udf.hpp        |    20 +
 cpp/src/bitmask/is_element_valid.cpp          |    47 +
 cpp/src/bitmask/null_mask.cu                  |   543 +
 cpp/src/column/column.cu                      |   264 +
 cpp/src/column/column_device_view.cu          |   176 +
 cpp/src/column/column_factories.cpp           |   186 +
 cpp/src/column/column_factories.cu            |   121 +
 cpp/src/column/column_view.cpp                |   207 +
 cpp/src/copying/concatenate.cu                |   603 +
 cpp/src/copying/contiguous_split.cu           |  2086 +++
 cpp/src/copying/copy.cpp                      |   195 +
 cpp/src/copying/copy.cu                       |   451 +
 cpp/src/copying/copy_range.cu                 |   295 +
 cpp/src/copying/gather.cu                     |    94 +
 cpp/src/copying/get_element.cu                |   211 +
 cpp/src/copying/pack.cpp                      |   304 +
 cpp/src/copying/purge_nonempty_nulls.cu       |   141 +
 cpp/src/copying/reverse.cu                    |    73 +
 cpp/src/copying/sample.cu                     |    99 +
 cpp/src/copying/scatter.cu                    |   536 +
 cpp/src/copying/segmented_shift.cu            |   162 +
 cpp/src/copying/shift.cu                      |   180 +
 cpp/src/copying/slice.cu                      |   183 +
 cpp/src/copying/split.cpp                     |   116 +
 cpp/src/datetime/datetime_ops.cu              |   725 +
 cpp/src/datetime/timezone.cpp                 |   515 +
 cpp/src/dictionary/add_keys.cu                |   141 +
 cpp/src/dictionary/decode.cu                  |    76 +
 cpp/src/dictionary/detail/concatenate.cu      |   298 +
 cpp/src/dictionary/detail/merge.cu            |    76 +
 cpp/src/dictionary/dictionary_column_view.cpp |    55 +
 cpp/src/dictionary/dictionary_factories.cu    |   151 +
 cpp/src/dictionary/encode.cu                  |   100 +
 cpp/src/dictionary/remove_keys.cu             |   214 +
 cpp/src/dictionary/replace.cu                 |   145 +
 cpp/src/dictionary/search.cu                  |   185 +
 cpp/src/dictionary/set_keys.cu                |   261 +
 cpp/src/filling/calendrical_month_sequence.cu |    50 +
 cpp/src/filling/fill.cu                       |   267 +
 cpp/src/filling/repeat.cu                     |   175 +
 cpp/src/filling/sequence.cu                   |   169 +
 cpp/src/groupby/common/utils.hpp              |    62 +
 cpp/src/groupby/groupby.cu                    |   339 +
 cpp/src/groupby/hash/groupby.cu               |   682 +
 cpp/src/groupby/hash/groupby_kernels.cuh      |   119 +
 cpp/src/groupby/hash/multi_pass_kernels.cuh   |   116 +
 cpp/src/groupby/sort/aggregate.cpp            |   818 +
 cpp/src/groupby/sort/common_utils.cuh         |    62 +
 cpp/src/groupby/sort/functors.hpp             |   110 +
 cpp/src/groupby/sort/group_argmax.cu          |    64 +
 cpp/src/groupby/sort/group_argmin.cu          |    64 +
 cpp/src/groupby/sort/group_collect.cu         |   127 +
 cpp/src/groupby/sort/group_correlation.cu     |   214 +
 cpp/src/groupby/sort/group_count.cu           |    98 +
 cpp/src/groupby/sort/group_count_scan.cu      |    52 +
 cpp/src/groupby/sort/group_histogram.cu       |   152 +
 cpp/src/groupby/sort/group_m2.cu              |   147 +
 cpp/src/groupby/sort/group_max.cu             |    44 +
 cpp/src/groupby/sort/group_max_scan.cu        |    41 +
 cpp/src/groupby/sort/group_merge_lists.cu     |    74 +
 cpp/src/groupby/sort/group_merge_m2.cu        |   201 +
 cpp/src/groupby/sort/group_min.cu             |    44 +
 cpp/src/groupby/sort/group_min_scan.cu        |    41 +
 cpp/src/groupby/sort/group_nth_element.cu     |   135 +
 cpp/src/groupby/sort/group_nunique.cu         |   137 +
 cpp/src/groupby/sort/group_product.cu         |    46 +
 cpp/src/groupby/sort/group_quantiles.cu       |   180 +
 cpp/src/groupby/sort/group_rank_scan.cu       |   330 +
 cpp/src/groupby/sort/group_reductions.hpp     |   543 +
 cpp/src/groupby/sort/group_replace_nulls.cu   |    86 +
 cpp/src/groupby/sort/group_scan.hpp           |   199 +
 cpp/src/groupby/sort/group_scan_util.cuh      |   246 +
 .../sort/group_single_pass_reduction_util.cuh |   268 +
 cpp/src/groupby/sort/group_std.cu             |   190 +
 cpp/src/groupby/sort/group_sum.cu             |    46 +
 cpp/src/groupby/sort/group_sum_scan.cu        |    41 +
 cpp/src/groupby/sort/scan.cpp                 |   219 +
 cpp/src/groupby/sort/sort_helper.cu           |   319 +
 cpp/src/hash/concurrent_unordered_map.cuh     |   558 +
 cpp/src/hash/hashing.cu                       |    53 +
 cpp/src/hash/managed.cuh                      |    44 +
 cpp/src/hash/md5_hash.cu                      |   384 +
 cpp/src/hash/murmurhash3_x64_128.cu           |   150 +
 cpp/src/hash/murmurhash3_x86_32.cu            |    72 +
 cpp/src/hash/spark_murmurhash3_x86_32.cu      |   442 +
 cpp/src/hash/unordered_multiset.cuh           |   159 +
 cpp/src/hash/xxhash_64.cu                     |   337 +
 cpp/src/interop/detail/arrow_allocator.cpp    |    82 +
 cpp/src/interop/detail/arrow_allocator.hpp    |    31 +
 cpp/src/interop/dlpack.cpp                    |   313 +
 cpp/src/interop/from_arrow.cu                 |   561 +
 cpp/src/interop/to_arrow.cu                   |   473 +
 cpp/src/io/avro/avro.cpp                      |   495 +
 cpp/src/io/avro/avro.hpp                      |   184 +
 cpp/src/io/avro/avro_common.hpp               |   168 +
 cpp/src/io/avro/avro_gpu.cu                   |   444 +
 cpp/src/io/avro/avro_gpu.hpp                  |    62 +
 cpp/src/io/avro/reader_impl.cu                |   624 +
 cpp/src/io/comp/brotli_dict.cpp               |  6535 ++++++++
 cpp/src/io/comp/brotli_dict.hpp               |    85 +
 cpp/src/io/comp/brotli_tables.hpp             |  2653 +++
 cpp/src/io/comp/cpu_unbz2.cpp                 |   601 +
 cpp/src/io/comp/debrotli.cu                   |  2124 +++
 cpp/src/io/comp/gpuinflate.cu                 |  1230 ++
 cpp/src/io/comp/gpuinflate.hpp                |   168 +
 cpp/src/io/comp/io_uncomp.hpp                 |    59 +
 cpp/src/io/comp/nvcomp_adapter.cpp            |   655 +
 cpp/src/io/comp/nvcomp_adapter.cu             |   130 +
 cpp/src/io/comp/nvcomp_adapter.cuh            |    79 +
 cpp/src/io/comp/nvcomp_adapter.hpp            |   162 +
 cpp/src/io/comp/snap.cu                       |   360 +
 cpp/src/io/comp/statistics.cu                 |    62 +
 cpp/src/io/comp/unbz2.hpp                     |   106 +
 cpp/src/io/comp/uncomp.cpp                    |   562 +
 cpp/src/io/comp/unsnap.cu                     |   728 +
 cpp/src/io/csv/csv_common.hpp                 |    43 +
 cpp/src/io/csv/csv_gpu.cu                     |   866 +
 cpp/src/io/csv/csv_gpu.hpp                    |   236 +
 cpp/src/io/csv/datetime.cuh                   |   402 +
 cpp/src/io/csv/durations.cu                   |   235 +
 cpp/src/io/csv/durations.hpp                  |    38 +
 cpp/src/io/csv/reader_impl.cu                 |  1008 ++
 cpp/src/io/csv/writer_impl.cu                 |   495 +
 cpp/src/io/fst/agent_dfa.cuh                  |   695 +
 cpp/src/io/fst/device_dfa.cuh                 |    94 +
 cpp/src/io/fst/dispatch_dfa.cuh               |   456 +
 cpp/src/io/fst/in_reg_array.cuh               |   140 +
 cpp/src/io/fst/logical_stack.cuh              |   473 +
 cpp/src/io/fst/lookup_tables.cuh              |   924 ++
 cpp/src/io/functions.cpp                      |   892 +
 cpp/src/io/json/byte_range_info.cu            |    36 +
 cpp/src/io/json/json_column.cu                |  1037 ++
 cpp/src/io/json/json_tree.cu                  |   852 +
 cpp/src/io/json/legacy/json_gpu.cu            |   616 +
 cpp/src/io/json/legacy/json_gpu.hpp           |   100 +
 cpp/src/io/json/legacy/read_json.hpp          |    33 +
 cpp/src/io/json/legacy/reader_impl.cu         |   657 +
 cpp/src/io/json/nested_json.hpp               |   311 +
 cpp/src/io/json/nested_json_gpu.cu            |  2195 +++
 cpp/src/io/json/read_json.cu                  |   226 +
 cpp/src/io/json/read_json.hpp                 |    45 +
 cpp/src/io/json/write_json.cu                 |   912 ++
 cpp/src/io/orc/aggregate_orc_metadata.cpp     |   276 +
 cpp/src/io/orc/aggregate_orc_metadata.hpp     |   138 +
 cpp/src/io/orc/dict_enc.cu                    |   277 +
 cpp/src/io/orc/orc.cpp                        |   530 +
 cpp/src/io/orc/orc.hpp                        |   683 +
 cpp/src/io/orc/orc_field_reader.hpp           |    94 +
 cpp/src/io/orc/orc_field_writer.hpp           |   134 +
 cpp/src/io/orc/orc_gpu.hpp                    |   484 +
 cpp/src/io/orc/reader_impl.cu                 |  1366 ++
 cpp/src/io/orc/reader_impl.hpp                |    89 +
 cpp/src/io/orc/stats_enc.cu                   |   484 +
 cpp/src/io/orc/stripe_data.cu                 |  1902 +++
 cpp/src/io/orc/stripe_enc.cu                  |  1374 ++
 cpp/src/io/orc/stripe_init.cu                 |   612 +
 cpp/src/io/orc/writer_impl.cu                 |  2684 +++
 cpp/src/io/orc/writer_impl.hpp                |   369 +
 cpp/src/io/parquet/chunk_dict.cu              |   303 +
 .../io/parquet/compact_protocol_reader.cpp    |   875 +
 .../io/parquet/compact_protocol_reader.hpp    |   152 +
 .../io/parquet/compact_protocol_writer.cpp    |   396 +
 .../io/parquet/compact_protocol_writer.hpp    |   120 +
 cpp/src/io/parquet/decode_preprocess.cu       |   417 +
 cpp/src/io/parquet/delta_binary.cuh           |   294 +
 cpp/src/io/parquet/page_data.cu               |   654 +
 cpp/src/io/parquet/page_decode.cuh            |  1387 ++
 cpp/src/io/parquet/page_delta_decode.cu       |   187 +
 cpp/src/io/parquet/page_enc.cu                |  2349 +++
 cpp/src/io/parquet/page_hdr.cu                |   534 +
 cpp/src/io/parquet/page_string_decode.cu      |   808 +
 cpp/src/io/parquet/page_string_utils.cuh      |   110 +
 cpp/src/io/parquet/parquet.hpp                |   410 +
 cpp/src/io/parquet/parquet_common.hpp         |   161 +
 cpp/src/io/parquet/parquet_gpu.cuh            |    84 +
 cpp/src/io/parquet/parquet_gpu.hpp            |   867 +
 cpp/src/io/parquet/predicate_pushdown.cpp     |   530 +
 cpp/src/io/parquet/reader.cpp                 |    62 +
 cpp/src/io/parquet/reader_impl.cpp            |   574 +
 cpp/src/io/parquet/reader_impl.hpp            |   304 +
 cpp/src/io/parquet/reader_impl_helpers.cpp    |   659 +
 cpp/src/io/parquet/reader_impl_helpers.hpp    |   278 +
 cpp/src/io/parquet/reader_impl_preprocess.cu  |  2017 +++
 cpp/src/io/parquet/rle_stream.cuh             |   365 +
 cpp/src/io/parquet/writer_impl.cu             |  2398 +++
 cpp/src/io/parquet/writer_impl.hpp            |   186 +
 cpp/src/io/statistics/byte_array_view.cuh     |   180 +
 cpp/src/io/statistics/column_statistics.cuh   |   417 +
 .../io/statistics/conversion_type_select.cuh  |   142 +
 .../io/statistics/orc_column_statistics.cu    |    43 +
 .../statistics/parquet_column_statistics.cu   |    43 +
 cpp/src/io/statistics/statistics.cuh          |   142 +
 .../statistics_type_identification.cuh        |   288 +
 .../io/statistics/temp_storage_wrapper.cuh    |   112 +
 .../io/statistics/typed_statistics_chunk.cuh  |   276 +
 cpp/src/io/text/bgzip_data_chunk_source.cu    |   382 +
 cpp/src/io/text/bgzip_utils.cpp               |   179 +
 cpp/src/io/text/byte_range_info.cpp           |    47 +
 .../io/text/data_chunk_source_factories.cpp   |   339 +
 cpp/src/io/text/device_data_chunks.hpp        |    47 +
 cpp/src/io/text/multibyte_split.cu            |   589 +
 cpp/src/io/utilities/arrow_io_source.cpp      |    85 +
 cpp/src/io/utilities/block_utils.cuh          |   191 +
 cpp/src/io/utilities/column_buffer.cpp        |   356 +
 cpp/src/io/utilities/column_buffer.hpp        |   258 +
 .../io/utilities/column_type_histogram.hpp    |    44 +
 cpp/src/io/utilities/column_utils.cuh         |    92 +
 cpp/src/io/utilities/config_utils.cpp         |    83 +
 cpp/src/io/utilities/config_utils.hpp         |    81 +
 cpp/src/io/utilities/data_casting.cu          |   984 ++
 cpp/src/io/utilities/data_sink.cpp            |   217 +
 cpp/src/io/utilities/datasource.cpp           |   425 +
 cpp/src/io/utilities/file_io_utilities.cpp    |   348 +
 cpp/src/io/utilities/file_io_utilities.hpp    |   224 +
 cpp/src/io/utilities/hostdevice_span.hpp      |   179 +
 cpp/src/io/utilities/hostdevice_vector.hpp    |   258 +
 cpp/src/io/utilities/output_builder.cuh       |   357 +
 cpp/src/io/utilities/parsing_utils.cu         |   221 +
 cpp/src/io/utilities/parsing_utils.cuh        |   716 +
 cpp/src/io/utilities/row_selection.cpp        |    42 +
 cpp/src/io/utilities/row_selection.hpp        |    40 +
 cpp/src/io/utilities/string_parsing.hpp       |    79 +
 cpp/src/io/utilities/thread_pool.hpp          |   381 +
 cpp/src/io/utilities/time_utils.cuh           |    53 +
 cpp/src/io/utilities/trie.cu                  |   111 +
 cpp/src/io/utilities/trie.cuh                 |   104 +
 cpp/src/io/utilities/type_inference.cu        |   292 +
 cpp/src/jit/cache.cpp                         |   143 +
 cpp/src/jit/cache.hpp                         |    28 +
 cpp/src/jit/parser.cpp                        |   416 +
 cpp/src/jit/parser.hpp                        |   242 +
 cpp/src/jit/util.cpp                          |    71 +
 cpp/src/jit/util.hpp                          |    37 +
 cpp/src/join/conditional_join.cu              |   426 +
 cpp/src/join/conditional_join.hpp             |    74 +
 cpp/src/join/conditional_join_kernels.cuh     |   276 +
 cpp/src/join/cross_join.cu                    |    83 +
 cpp/src/join/hash_join.cu                     |   649 +
 cpp/src/join/join.cu                          |   150 +
 cpp/src/join/join_common_utils.cuh            |   327 +
 cpp/src/join/join_common_utils.hpp            |    69 +
 cpp/src/join/join_utils.cu                    |   158 +
 cpp/src/join/mixed_join.cu                    |   594 +
 cpp/src/join/mixed_join_common_utils.cuh      |   165 +
 cpp/src/join/mixed_join_kernel.cu             |    39 +
 cpp/src/join/mixed_join_kernel.cuh            |   114 +
 cpp/src/join/mixed_join_kernel_nulls.cu       |    39 +
 cpp/src/join/mixed_join_kernels.cuh           |   123 +
 cpp/src/join/mixed_join_kernels_semi.cu       |   110 +
 cpp/src/join/mixed_join_kernels_semi.cuh      |   121 +
 cpp/src/join/mixed_join_semi.cu               |   626 +
 cpp/src/join/mixed_join_size_kernel.cu        |    37 +
 cpp/src/join/mixed_join_size_kernel.cuh       |   105 +
 cpp/src/join/mixed_join_size_kernel_nulls.cu  |    37 +
 cpp/src/join/mixed_join_size_kernels_semi.cu  |   122 +
 cpp/src/join/semi_join.cu                     |   116 +
 cpp/src/labeling/label_bins.cu                |   250 +
 .../combine/concatenate_list_elements.cu      |   281 +
 cpp/src/lists/combine/concatenate_rows.cu     |   315 +
 cpp/src/lists/contains.cu                     |   406 +
 cpp/src/lists/copying/concatenate.cu          |   144 +
 cpp/src/lists/copying/copying.cu              |   102 +
 cpp/src/lists/copying/gather.cu               |   184 +
 cpp/src/lists/copying/scatter_helper.cu       |   483 +
 cpp/src/lists/copying/segmented_gather.cu     |   127 +
 cpp/src/lists/count_elements.cu               |    83 +
 cpp/src/lists/dremel.cu                       |   486 +
 cpp/src/lists/explode.cu                      |   347 +
 cpp/src/lists/extract.cu                      |   221 +
 cpp/src/lists/interleave_columns.cu           |   423 +
 cpp/src/lists/lists_column_factories.cu       |   156 +
 cpp/src/lists/lists_column_view.cu            |    69 +
 cpp/src/lists/reverse.cu                      |    95 +
 cpp/src/lists/segmented_sort.cu               |   139 +
 cpp/src/lists/sequences.cu                    |   226 +
 cpp/src/lists/set_operations.cu               |   319 +
 .../stream_compaction/apply_boolean_mask.cu   |   110 +
 cpp/src/lists/stream_compaction/distinct.cu   |    85 +
 cpp/src/lists/utilities.cu                    |    79 +
 cpp/src/lists/utilities.hpp                   |    67 +
 cpp/src/merge/merge.cu                        |   555 +
 cpp/src/partitioning/partitioning.cu          |   843 +
 cpp/src/partitioning/round_robin.cu           |   277 +
 cpp/src/quantiles/quantile.cu                 |   196 +
 cpp/src/quantiles/quantiles.cu                |   113 +
 cpp/src/quantiles/quantiles_util.hpp          |   219 +
 cpp/src/quantiles/tdigest/tdigest.cu          |   413 +
 .../quantiles/tdigest/tdigest_aggregation.cu  |  1294 ++
 .../quantiles/tdigest/tdigest_column_view.cpp |    78 +
 cpp/src/quantiles/tdigest/tdigest_util.cuh    |    56 +
 cpp/src/reductions/all.cu                     |   106 +
 cpp/src/reductions/any.cu                     |   106 +
 cpp/src/reductions/collect_ops.cu             |   118 +
 cpp/src/reductions/compound.cuh               |   159 +
 cpp/src/reductions/histogram.cu               |   273 +
 cpp/src/reductions/max.cu                     |    47 +
 cpp/src/reductions/mean.cu                    |    43 +
 cpp/src/reductions/min.cu                     |    43 +
 cpp/src/reductions/minmax.cu                  |   283 +
 .../reductions/nested_type_minmax_util.cuh    |   182 +
 cpp/src/reductions/nth_element.cu             |    66 +
 cpp/src/reductions/product.cu                 |    45 +
 cpp/src/reductions/reductions.cpp             |   224 +
 cpp/src/reductions/scan/rank_scan.cu          |   155 +
 cpp/src/reductions/scan/scan.cpp              |    67 +
 cpp/src/reductions/scan/scan.cuh              |    66 +
 cpp/src/reductions/scan/scan_exclusive.cu     |   107 +
 cpp/src/reductions/scan/scan_inclusive.cu     |   310 +
 cpp/src/reductions/segmented/all.cu           |    45 +
 cpp/src/reductions/segmented/any.cu           |    45 +
 cpp/src/reductions/segmented/compound.cuh     |   175 +
 cpp/src/reductions/segmented/counts.cu        |    54 +
 cpp/src/reductions/segmented/counts.hpp       |    55 +
 cpp/src/reductions/segmented/max.cu           |    42 +
 cpp/src/reductions/segmented/mean.cu          |    42 +
 cpp/src/reductions/segmented/min.cu           |    42 +
 cpp/src/reductions/segmented/nunique.cu       |   113 +
 cpp/src/reductions/segmented/product.cu       |    39 +
 cpp/src/reductions/segmented/reductions.cpp   |   170 +
 cpp/src/reductions/segmented/simple.cuh       |   496 +
 cpp/src/reductions/segmented/std.cu           |    42 +
 cpp/src/reductions/segmented/sum.cu           |    40 +
 .../reductions/segmented/sum_of_squares.cu    |    41 +
 .../reductions/segmented/update_validity.cu   |    49 +
 .../reductions/segmented/update_validity.hpp  |    58 +
 cpp/src/reductions/segmented/var.cu           |    41 +
 cpp/src/reductions/simple.cuh                 |   478 +
 cpp/src/reductions/std.cu                     |    49 +
 cpp/src/reductions/sum.cu                     |    46 +
 cpp/src/reductions/sum_of_squares.cu          |    45 +
 cpp/src/reductions/var.cu                     |    49 +
 cpp/src/replace/clamp.cu                      |   408 +
 cpp/src/replace/nans.cu                       |   249 +
 cpp/src/replace/nulls.cu                      |   474 +
 cpp/src/replace/replace.cu                    |   550 +
 cpp/src/reshape/byte_cast.cu                  |   192 +
 cpp/src/reshape/interleave_columns.cu         |   299 +
 cpp/src/reshape/tile.cu                       |    71 +
 cpp/src/rolling/detail/lead_lag_nested.cuh    |   208 +
 cpp/src/rolling/detail/nth_element.cuh        |   174 +
 .../detail/optimized_unbounded_window.cpp     |   161 +
 .../detail/optimized_unbounded_window.hpp     |    56 +
 .../rolling/detail/range_comparator_utils.cuh |   143 +
 .../rolling/detail/range_window_bounds.hpp    |   166 +
 cpp/src/rolling/detail/rolling.cuh            |  1390 ++
 cpp/src/rolling/detail/rolling.hpp            |    99 +
 .../rolling/detail/rolling_collect_list.cu    |   163 +
 .../rolling/detail/rolling_collect_list.cuh   |   229 +
 .../rolling/detail/rolling_fixed_window.cu    |    87 +
 cpp/src/rolling/detail/rolling_jit.hpp        |    61 +
 .../rolling/detail/rolling_variable_window.cu |    85 +
 cpp/src/rolling/grouped_rolling.cu            |  1292 ++
 cpp/src/rolling/jit/kernel.cu                 |   106 +
 cpp/src/rolling/jit/operation-udf.hpp         |    20 +
 cpp/src/rolling/jit/operation.hpp             |    41 +
 cpp/src/rolling/range_window_bounds.cpp       |    89 +
 cpp/src/rolling/rolling.cu                    |    80 +
 cpp/src/round/round.cu                        |   357 +
 cpp/src/scalar/scalar.cpp                     |   600 +
 cpp/src/scalar/scalar_factories.cpp           |   202 +
 cpp/src/search/contains_column.cu             |   164 +
 cpp/src/search/contains_scalar.cu             |   169 +
 cpp/src/search/contains_table.cu              |   286 +
 cpp/src/search/search_ordered.cu              |   165 +
 cpp/src/sort/is_sorted.cu                     |    96 +
 cpp/src/sort/rank.cu                          |   376 +
 cpp/src/sort/segmented_sort.cu                |   105 +
 cpp/src/sort/segmented_sort_impl.cuh          |   328 +
 cpp/src/sort/sort.cu                          |   140 +
 cpp/src/sort/sort_column.cu                   |    55 +
 cpp/src/sort/sort_column_impl.cuh             |   203 +
 cpp/src/sort/sort_impl.cuh                    |   100 +
 cpp/src/sort/stable_segmented_sort.cu         |    79 +
 cpp/src/sort/stable_sort.cu                   |    83 +
 cpp/src/sort/stable_sort_column.cu            |    55 +
 .../stream_compaction/apply_boolean_mask.cu   |    98 +
 cpp/src/stream_compaction/distinct.cu         |   179 +
 cpp/src/stream_compaction/distinct_count.cu   |   226 +
 cpp/src/stream_compaction/distinct_helpers.cu |   109 +
 .../stream_compaction/distinct_helpers.hpp    |    87 +
 cpp/src/stream_compaction/drop_nans.cu        |   135 +
 cpp/src/stream_compaction/drop_nulls.cu       |   108 +
 cpp/src/stream_compaction/stable_distinct.cu  |    87 +
 .../stream_compaction_common.cuh              |   113 +
 .../stream_compaction_common.hpp              |    38 +
 cpp/src/stream_compaction/unique.cu           |   128 +
 cpp/src/stream_compaction/unique_count.cu     |    76 +
 .../stream_compaction/unique_count_column.cu  |   110 +
 cpp/src/strings/attributes.cu                 |   284 +
 cpp/src/strings/capitalize.cu                 |   315 +
 cpp/src/strings/case.cu                       |   337 +
 cpp/src/strings/char_types/char_cases.cu      |   201 +
 cpp/src/strings/char_types/char_cases.h       |  5236 ++++++
 cpp/src/strings/char_types/char_flags.h       |  3488 ++++
 cpp/src/strings/char_types/char_types.cu      |   239 +
 cpp/src/strings/combine/concatenate.cu        |   295 +
 cpp/src/strings/combine/join.cu               |   190 +
 cpp/src/strings/combine/join_list_elements.cu |   336 +
 cpp/src/strings/contains.cu                   |   151 +
 cpp/src/strings/convert/convert_booleans.cu   |   166 +
 cpp/src/strings/convert/convert_datetime.cu   |  1178 ++
 cpp/src/strings/convert/convert_durations.cu  |   740 +
 .../strings/convert/convert_fixed_point.cu    |   351 +
 cpp/src/strings/convert/convert_floats.cu     |   484 +
 cpp/src/strings/convert/convert_hex.cu        |   304 +
 cpp/src/strings/convert/convert_integers.cu   |   417 +
 cpp/src/strings/convert/convert_ipv4.cu       |   240 +
 cpp/src/strings/convert/convert_lists.cu      |   243 +
 cpp/src/strings/convert/convert_urls.cu       |   439 +
 cpp/src/strings/copying/concatenate.cu        |   316 +
 cpp/src/strings/copying/copying.cu            |    88 +
 cpp/src/strings/copying/shift.cu              |   134 +
 cpp/src/strings/count_matches.cu              |    81 +
 cpp/src/strings/count_matches.hpp             |    52 +
 cpp/src/strings/extract/extract.cu            |   143 +
 cpp/src/strings/extract/extract_all.cu        |   176 +
 cpp/src/strings/filling/fill.cu               |   110 +
 cpp/src/strings/filter_chars.cu               |   169 +
 cpp/src/strings/json/json_path.cu             |  1057 ++
 cpp/src/strings/like.cu                       |   204 +
 cpp/src/strings/padding.cu                    |   186 +
 cpp/src/strings/regex/regcomp.cpp             |  1274 ++
 cpp/src/strings/regex/regcomp.h               |   162 +
 cpp/src/strings/regex/regex.cuh               |   311 +
 cpp/src/strings/regex/regex.inl               |   431 +
 cpp/src/strings/regex/regex_program.cpp       |    63 +
 cpp/src/strings/regex/regex_program_impl.h    |    51 +
 cpp/src/strings/regex/regexec.cpp             |   172 +
 cpp/src/strings/regex/utilities.cuh           |   156 +
 cpp/src/strings/repeat_strings.cu             |   286 +
 cpp/src/strings/replace/backref_re.cu         |   158 +
 cpp/src/strings/replace/backref_re.cuh        |   123 +
 cpp/src/strings/replace/multi.cu              |   500 +
 cpp/src/strings/replace/multi_re.cu           |   216 +
 cpp/src/strings/replace/replace.cu            |   772 +
 cpp/src/strings/replace/replace_re.cu         |   145 +
 cpp/src/strings/reverse.cu                    |    89 +
 cpp/src/strings/search/find.cu                |   676 +
 cpp/src/strings/search/find_multiple.cu       |    99 +
 cpp/src/strings/search/findall.cu             |   145 +
 cpp/src/strings/slice.cu                      |   268 +
 cpp/src/strings/split/partition.cu            |   259 +
 cpp/src/strings/split/split.cu                |   452 +
 cpp/src/strings/split/split.cuh               |   406 +
 cpp/src/strings/split/split_re.cu             |   377 +
 cpp/src/strings/split/split_record.cu         |   225 +
 cpp/src/strings/strings_column_factories.cu   |   183 +
 cpp/src/strings/strings_column_view.cpp       |    67 +
 cpp/src/strings/strings_scalar_factories.cpp  |    31 +
 cpp/src/strings/strip.cu                      |    96 +
 cpp/src/strings/translate.cu                  |   136 +
 cpp/src/strings/utilities.cu                  |   133 +
 cpp/src/strings/wrap.cu                       |   143 +
 cpp/src/structs/copying/concatenate.cu        |    79 +
 cpp/src/structs/structs_column_factories.cu   |    63 +
 cpp/src/structs/structs_column_view.cpp       |    52 +
 cpp/src/structs/utilities.cpp                 |   437 +
 cpp/src/table/row_operators.cu                |   876 +
 cpp/src/table/table.cpp                       |    92 +
 cpp/src/table/table_device_view.cu            |    56 +
 cpp/src/table/table_view.cpp                  |   144 +
 cpp/src/text/detokenize.cu                    |   181 +
 cpp/src/text/edit_distance.cu                 |   319 +
 cpp/src/text/generate_ngrams.cu               |   337 +
 cpp/src/text/jaccard.cu                       |   307 +
 cpp/src/text/minhash.cu                       |   279 +
 cpp/src/text/ngrams_tokenize.cu               |   275 +
 cpp/src/text/normalize.cu                     |   263 +
 cpp/src/text/replace.cu                       |   299 +
 cpp/src/text/stemmer.cu                       |   285 +
 cpp/src/text/subword/bpe_tokenizer.cu         |   564 +
 cpp/src/text/subword/bpe_tokenizer.cuh        |   114 +
 cpp/src/text/subword/data_normalizer.cu       |   349 +
 .../text/subword/detail/codepoint_metadata.ah | 13466 ++++++++++++++++
 cpp/src/text/subword/detail/cp_data.h         |    37 +
 .../text/subword/detail/data_normalizer.hpp   |   101 +
 cpp/src/text/subword/detail/hash_utils.cuh    |   172 +
 .../text/subword/detail/tokenizer_utils.cuh   |    76 +
 .../subword/detail/wordpiece_tokenizer.hpp    |   110 +
 cpp/src/text/subword/load_hash_file.cu        |   297 +
 cpp/src/text/subword/load_merges_file.cu      |   174 +
 cpp/src/text/subword/subword_tokenize.cu      |   312 +
 cpp/src/text/subword/wordpiece_tokenizer.cu   |   562 +
 cpp/src/text/tokenize.cu                      |   274 +
 cpp/src/text/utilities/tokenize_ops.cuh       |   255 +
 cpp/src/text/vocabulary_tokenize.cu           |   257 +
 cpp/src/transform/bools_to_mask.cu            |    67 +
 cpp/src/transform/compute_column.cu           |   146 +
 cpp/src/transform/encode.cu                   |    79 +
 cpp/src/transform/jit/kernel.cu               |    52 +
 cpp/src/transform/jit/operation-udf.hpp       |    20 +
 cpp/src/transform/mask_to_bools.cu            |    69 +
 cpp/src/transform/nans_to_nulls.cu            |   101 +
 cpp/src/transform/one_hot_encode.cu           |   118 +
 cpp/src/transform/row_bit_count.cu            |   546 +
 cpp/src/transform/transform.cpp               |   105 +
 cpp/src/transpose/transpose.cu                |    69 +
 cpp/src/unary/cast_ops.cu                     |   424 +
 cpp/src/unary/math_ops.cu                     |   651 +
 cpp/src/unary/nan_ops.cu                      |   107 +
 cpp/src/unary/null_ops.cu                     |    71 +
 cpp/src/unary/unary_ops.cuh                   |    79 +
 cpp/src/utilities/default_stream.cpp          |    46 +
 cpp/src/utilities/linked_column.cpp           |    55 +
 cpp/src/utilities/logger.cpp                  |    81 +
 cpp/src/utilities/stacktrace.cpp              |    88 +
 cpp/src/utilities/stream_pool.cpp             |   256 +
 cpp/src/utilities/traits.cpp                  |   409 +
 cpp/src/utilities/type_checks.cpp             |    78 +
 cpp/src/utilities/type_dispatcher.cpp         |    23 +
 cpp/tests/CMakeLists.txt                      |   644 +
 cpp/tests/ast/transform_tests.cpp             |   728 +
 cpp/tests/binaryop/assert-binops.h            |   227 +
 .../binop-compiled-fixed_point-test.cpp       |   845 +
 cpp/tests/binaryop/binop-compiled-test.cpp    |   786 +
 cpp/tests/binaryop/binop-fixture.hpp          |    76 +
 cpp/tests/binaryop/binop-generic-ptx-test.cpp |   212 +
 cpp/tests/binaryop/binop-null-test.cpp        |   138 +
 .../binaryop/binop-verify-input-test.cpp      |    46 +
 cpp/tests/binaryop/util/operation.h           |   447 +
 cpp/tests/binaryop/util/runtime_support.h     |    29 +
 cpp/tests/bitmask/bitmask_tests.cpp           |   752 +
 cpp/tests/bitmask/is_element_valid_tests.cpp  |    85 +
 cpp/tests/bitmask/set_nullmask_tests.cu       |   141 +
 cpp/tests/bitmask/valid_if_tests.cu           |   103 +
 cpp/tests/column/bit_cast_test.cpp            |   133 +
 cpp/tests/column/column_device_view_test.cu   |    69 +
 cpp/tests/column/column_test.cpp              |   648 +
 .../column/column_view_device_span_test.cpp   |    73 +
 cpp/tests/column/column_view_shallow_test.cpp |   436 +
 cpp/tests/column/compound_test.cu             |   216 +
 cpp/tests/column/factories_test.cpp           |   770 +
 cpp/tests/copying/concatenate_tests.cpp       |  1690 ++
 .../copying/copy_if_else_nested_tests.cpp     |   513 +
 cpp/tests/copying/copy_range_tests.cpp        |   520 +
 cpp/tests/copying/copy_tests.cpp              |   719 +
 cpp/tests/copying/detail_gather_tests.cu      |   122 +
 cpp/tests/copying/gather_list_tests.cpp       |   437 +
 cpp/tests/copying/gather_str_tests.cpp        |   159 +
 cpp/tests/copying/gather_struct_tests.cpp     |   472 +
 cpp/tests/copying/gather_tests.cpp            |   244 +
 cpp/tests/copying/get_value_tests.cpp         |   923 ++
 cpp/tests/copying/pack_tests.cpp              |   542 +
 .../copying/purge_nonempty_nulls_tests.cpp    |   485 +
 cpp/tests/copying/reverse_tests.cpp           |   182 +
 cpp/tests/copying/sample_tests.cpp            |   112 +
 .../copying/scatter_list_scalar_tests.cpp     |   453 +
 cpp/tests/copying/scatter_list_tests.cpp      |   968 ++
 .../copying/scatter_struct_scalar_tests.cpp   |   267 +
 cpp/tests/copying/scatter_struct_tests.cpp    |   259 +
 cpp/tests/copying/scatter_tests.cpp           |   847 +
 .../copying/segmented_gather_list_tests.cpp   |   621 +
 cpp/tests/copying/shift_tests.cpp             |   282 +
 cpp/tests/copying/slice_tests.cpp             |   532 +
 cpp/tests/copying/slice_tests.cuh             |   223 +
 cpp/tests/copying/split_tests.cpp             |  2531 +++
 cpp/tests/copying/utility_tests.cpp           |   225 +
 cpp/tests/datetime/datetime_ops_test.cpp      |  1053 ++
 .../device_atomics/device_atomics_test.cu     |   259 +
 cpp/tests/dictionary/add_keys_test.cpp        |    89 +
 cpp/tests/dictionary/decode_test.cpp          |    68 +
 cpp/tests/dictionary/encode_test.cpp          |    77 +
 cpp/tests/dictionary/factories_test.cpp       |   120 +
 cpp/tests/dictionary/fill_test.cpp            |    80 +
 cpp/tests/dictionary/gather_test.cpp          |    95 +
 cpp/tests/dictionary/remove_keys_test.cpp     |   124 +
 cpp/tests/dictionary/scatter_test.cpp         |   144 +
 cpp/tests/dictionary/search_test.cpp          |    84 +
 cpp/tests/dictionary/set_keys_test.cpp        |   110 +
 cpp/tests/dictionary/slice_test.cpp           |    88 +
 cpp/tests/encode/encode_tests.cpp             |   139 +
 cpp/tests/error/error_handling_test.cu        |   140 +
 cpp/tests/filling/fill_tests.cpp              |   407 +
 cpp/tests/filling/repeat_tests.cpp            |   307 +
 cpp/tests/filling/sequence_tests.cpp          |   179 +
 cpp/tests/fixed_point/fixed_point_tests.cpp   |   577 +
 cpp/tests/fixed_point/fixed_point_tests.cu    |   124 +
 cpp/tests/groupby/argmax_tests.cpp            |   256 +
 cpp/tests/groupby/argmin_tests.cpp            |   255 +
 cpp/tests/groupby/collect_list_tests.cpp      |   229 +
 cpp/tests/groupby/collect_set_tests.cpp       |   399 +
 cpp/tests/groupby/correlation_tests.cpp       |   241 +
 cpp/tests/groupby/count_scan_tests.cpp        |   208 +
 cpp/tests/groupby/count_tests.cpp             |   222 +
 cpp/tests/groupby/covariance_tests.cpp        |   255 +
 cpp/tests/groupby/groupby_test_util.cpp       |   141 +
 cpp/tests/groupby/groupby_test_util.hpp       |    52 +
 cpp/tests/groupby/groups_tests.cpp            |   118 +
 cpp/tests/groupby/histogram_tests.cpp         |   396 +
 cpp/tests/groupby/keys_tests.cpp              |   421 +
 cpp/tests/groupby/lists_tests.cpp             |   107 +
 cpp/tests/groupby/m2_tests.cpp                |   241 +
 cpp/tests/groupby/max_scan_tests.cpp          |   272 +
 cpp/tests/groupby/max_tests.cpp               |   551 +
 cpp/tests/groupby/mean_tests.cpp              |   206 +
 cpp/tests/groupby/median_tests.cpp            |   144 +
 cpp/tests/groupby/merge_lists_tests.cpp       |   386 +
 cpp/tests/groupby/merge_m2_tests.cpp          |   477 +
 cpp/tests/groupby/merge_sets_tests.cpp        |   358 +
 cpp/tests/groupby/min_scan_tests.cpp          |   273 +
 cpp/tests/groupby/min_tests.cpp               |   547 +
 cpp/tests/groupby/nth_element_tests.cpp       |   515 +
 cpp/tests/groupby/nunique_tests.cpp           |   228 +
 cpp/tests/groupby/product_tests.cpp           |   186 +
 cpp/tests/groupby/quantile_tests.cpp          |   214 +
 cpp/tests/groupby/rank_scan_tests.cpp         |   596 +
 cpp/tests/groupby/replace_nulls_tests.cpp     |   369 +
 cpp/tests/groupby/shift_tests.cpp             |   529 +
 cpp/tests/groupby/std_tests.cpp               |   163 +
 cpp/tests/groupby/structs_tests.cpp           |   318 +
 cpp/tests/groupby/sum_of_squares_tests.cpp    |   142 +
 cpp/tests/groupby/sum_scan_tests.cpp          |   172 +
 cpp/tests/groupby/sum_tests.cpp               |   231 +
 cpp/tests/groupby/tdigest_tests.cu            |   508 +
 cpp/tests/groupby/var_tests.cpp               |   199 +
 cpp/tests/hash_map/map_test.cu                |   216 +
 cpp/tests/hashing/md5_test.cpp                |   290 +
 .../hashing/murmurhash3_x64_128_test.cpp      |   113 +
 cpp/tests/hashing/murmurhash3_x86_32_test.cpp |   405 +
 .../hashing/spark_murmurhash3_x86_32_test.cpp |   576 +
 cpp/tests/hashing/xxhash_64_test.cpp          |   177 +
 .../test_default_stream_identification.cu     |    39 +
 cpp/tests/interop/arrow_utils.hpp             |   206 +
 cpp/tests/interop/dlpack_test.cpp             |   496 +
 cpp/tests/interop/from_arrow_test.cpp         |   553 +
 cpp/tests/interop/to_arrow_test.cpp           |   684 +
 cpp/tests/io/arrow_io_source_test.cpp         |   102 +
 cpp/tests/io/comp/decomp_test.cpp             |   226 +
 cpp/tests/io/csv_test.cpp                     |  2500 +++
 cpp/tests/io/file_io_test.cpp                 |    45 +
 cpp/tests/io/fst/common.hpp                   |    82 +
 cpp/tests/io/fst/fst_test.cu                  |   212 +
 cpp/tests/io/fst/logical_stack_test.cu        |   249 +
 cpp/tests/io/json_chunked_reader.cpp          |   126 +
 cpp/tests/io/json_test.cpp                    |  1994 +++
 cpp/tests/io/json_tree.cpp                    |   895 +
 cpp/tests/io/json_type_cast_test.cu           |   253 +
 cpp/tests/io/json_writer.cpp                  |   556 +
 cpp/tests/io/metadata_utilities.cpp           |    65 +
 cpp/tests/io/nested_json_test.cpp             |   926 ++
 cpp/tests/io/orc_test.cpp                     |  1937 +++
 cpp/tests/io/parquet_chunked_reader_test.cpp  |  1016 ++
 cpp/tests/io/parquet_test.cpp                 |  6735 ++++++++
 cpp/tests/io/row_selection_test.cpp           |   137 +
 cpp/tests/io/text/data_chunk_source_test.cpp  |   389 +
 cpp/tests/io/text/multibyte_split_test.cpp    |   561 +
 cpp/tests/io/type_inference_test.cu           |   273 +
 cpp/tests/iterator/README.md                  |    18 +
 cpp/tests/iterator/indexalator_test.cu        |    96 +
 cpp/tests/iterator/iterator_tests.cuh         |   132 +
 cpp/tests/iterator/optional_iterator_test.cuh |    95 +
 .../iterator/optional_iterator_test_chrono.cu |    27 +
 .../optional_iterator_test_numeric.cu         |   134 +
 cpp/tests/iterator/pair_iterator_test.cuh     |    84 +
 .../iterator/pair_iterator_test_chrono.cu     |    24 +
 .../iterator/pair_iterator_test_numeric.cu    |   138 +
 cpp/tests/iterator/scalar_iterator_test.cu    |    81 +
 .../sizes_to_offsets_iterator_test.cu         |    97 +
 cpp/tests/iterator/value_iterator.cpp         |    18 +
 cpp/tests/iterator/value_iterator_test.cuh    |    78 +
 .../iterator/value_iterator_test_chrono.cu    |    27 +
 .../iterator/value_iterator_test_numeric.cu   |    27 +
 .../iterator/value_iterator_test_strings.cu   |   140 +
 .../iterator/value_iterator_test_transform.cu |   142 +
 cpp/tests/join/conditional_join_tests.cu      |   891 +
 cpp/tests/join/cross_join_tests.cpp           |   141 +
 cpp/tests/join/join_tests.cpp                 |  2147 +++
 cpp/tests/join/mixed_join_tests.cu            |   943 ++
 cpp/tests/join/semi_anti_join_tests.cpp       |   311 +
 cpp/tests/labeling/label_bins_tests.cpp       |   436 +
 .../concatenate_list_elements_tests.cpp       |   809 +
 .../lists/combine/concatenate_rows_tests.cpp  |   961 ++
 cpp/tests/lists/contains_tests.cpp            |  1841 +++
 cpp/tests/lists/count_elements_tests.cpp      |   101 +
 cpp/tests/lists/explode_tests.cpp             |  1177 ++
 cpp/tests/lists/extract_tests.cpp             |   428 +
 cpp/tests/lists/reverse_tests.cpp             |   472 +
 cpp/tests/lists/sequences_tests.cpp           |   248 +
 .../difference_distinct_tests.cpp             |   670 +
 .../set_operations/have_overlap_tests.cpp     |   551 +
 .../intersect_distinct_tests.cpp              |   637 +
 .../set_operations/union_distinct_tests.cpp   |   622 +
 cpp/tests/lists/sort_lists_tests.cpp          |   291 +
 .../apply_boolean_mask_tests.cpp              |   232 +
 .../stream_compaction/distinct_tests.cpp      |   758 +
 cpp/tests/merge/merge_dictionary_test.cpp     |   150 +
 cpp/tests/merge/merge_string_test.cpp         |   413 +
 cpp/tests/merge/merge_test.cpp                |   908 ++
 .../partitioning/hash_partition_test.cpp      |   435 +
 cpp/tests/partitioning/partition_test.cpp     |   347 +
 cpp/tests/partitioning/round_robin_test.cpp   |   744 +
 .../quantiles/percentile_approx_test.cpp      |   455 +
 cpp/tests/quantiles/quantile_test.cpp         |   475 +
 cpp/tests/quantiles/quantiles_test.cpp        |   167 +
 cpp/tests/reductions/collect_ops_tests.cpp    |   369 +
 cpp/tests/reductions/list_rank_test.cpp       |   229 +
 cpp/tests/reductions/rank_tests.cpp           |   328 +
 cpp/tests/reductions/reduction_tests.cpp      |  3147 ++++
 cpp/tests/reductions/scan_tests.cpp           |   755 +
 cpp/tests/reductions/scan_tests.hpp           |   109 +
 .../reductions/segmented_reduction_tests.cpp  |  1494 ++
 cpp/tests/reductions/tdigest_tests.cu         |   162 +
 cpp/tests/replace/clamp_test.cpp              |   660 +
 cpp/tests/replace/normalize_replace_tests.cpp |    82 +
 cpp/tests/replace/replace_nans_tests.cpp      |   195 +
 cpp/tests/replace/replace_nulls_tests.cpp     |   752 +
 cpp/tests/replace/replace_tests.cpp           |   624 +
 cpp/tests/reshape/byte_cast_tests.cpp         |   408 +
 .../reshape/interleave_columns_tests.cpp      |  1365 ++
 cpp/tests/reshape/tile_tests.cpp              |   115 +
 cpp/tests/rolling/collect_ops_test.cpp        |  2288 +++
 cpp/tests/rolling/empty_input_test.cpp        |   411 +
 .../rolling/grouped_rolling_range_test.cpp    |   941 ++
 cpp/tests/rolling/grouped_rolling_test.cpp    |  2472 +++
 cpp/tests/rolling/lead_lag_test.cpp           |  1131 ++
 cpp/tests/rolling/nth_element_test.cpp        |   632 +
 cpp/tests/rolling/offset_row_window_test.cpp  |   343 +
 cpp/tests/rolling/range_comparator_test.cu    |   147 +
 .../rolling/range_rolling_window_test.cpp     |   596 +
 .../rolling/range_window_bounds_test.cpp      |   217 +
 cpp/tests/rolling/rolling_test.cpp            |  1628 ++
 cpp/tests/rolling/rolling_test.hpp            |    61 +
 cpp/tests/round/round_tests.cpp               |   785 +
 cpp/tests/scalar/factories_test.cpp           |   182 +
 cpp/tests/scalar/scalar_device_view_test.cu   |   139 +
 cpp/tests/scalar/scalar_test.cpp              |   295 +
 cpp/tests/search/search_dictionary_test.cpp   |   106 +
 cpp/tests/search/search_list_test.cpp         |   670 +
 cpp/tests/search/search_struct_test.cpp       |   749 +
 cpp/tests/search/search_test.cpp              |  1873 +++
 cpp/tests/sort/is_sorted_tests.cpp            |   500 +
 cpp/tests/sort/rank_test.cpp                  |   921 ++
 cpp/tests/sort/segmented_sort_tests.cpp       |   340 +
 cpp/tests/sort/sort_nested_types_tests.cpp    |   463 +
 cpp/tests/sort/sort_test.cpp                  |  1116 ++
 cpp/tests/sort/stable_sort_tests.cpp          |   291 +
 .../apply_boolean_mask_tests.cpp              |   375 +
 .../distinct_count_tests.cpp                  |   367 +
 .../stream_compaction/distinct_tests.cpp      |  1414 ++
 .../stream_compaction/drop_nans_tests.cpp     |   139 +
 .../stream_compaction/drop_nulls_tests.cpp    |   228 +
 .../stable_distinct_tests.cpp                 |  1354 ++
 .../stream_compaction/unique_count_tests.cpp  |   288 +
 cpp/tests/stream_compaction/unique_tests.cpp  |   811 +
 cpp/tests/streams/concatenate_test.cpp        |    51 +
 cpp/tests/streams/copying_test.cpp            |   339 +
 cpp/tests/streams/dictionary_test.cpp         |   105 +
 cpp/tests/streams/filling_test.cpp            |    76 +
 cpp/tests/streams/groupby_test.cpp            |    67 +
 cpp/tests/streams/hash_test.cpp               |    54 +
 cpp/tests/streams/interop_test.cpp            |    68 +
 cpp/tests/streams/replace_test.cpp            |   109 +
 cpp/tests/streams/search_test.cpp             |    69 +
 cpp/tests/streams/sorting_test.cpp            |   132 +
 cpp/tests/streams/strings/case_test.cpp       |    55 +
 cpp/tests/streams/strings/find_test.cpp       |    49 +
 cpp/tests/streams/text/ngrams_test.cpp        |    59 +
 cpp/tests/strings/array_tests.cpp             |   225 +
 cpp/tests/strings/attrs_tests.cpp             |   106 +
 cpp/tests/strings/booleans_tests.cpp          |    85 +
 cpp/tests/strings/case_tests.cpp              |   291 +
 cpp/tests/strings/chars_types_tests.cpp       |   336 +
 .../strings/combine/concatenate_tests.cpp     |   515 +
 .../combine/join_list_elements_tests.cpp      |   583 +
 .../strings/combine/join_strings_tests.cpp    |   100 +
 cpp/tests/strings/concatenate_tests.cpp       |   109 +
 cpp/tests/strings/contains_tests.cpp          |   813 +
 cpp/tests/strings/datetime_tests.cpp          |   641 +
 cpp/tests/strings/durations_tests.cpp         |   765 +
 cpp/tests/strings/extract_tests.cpp           |   322 +
 cpp/tests/strings/factories_test.cu           |   229 +
 cpp/tests/strings/fill_tests.cpp              |    85 +
 cpp/tests/strings/find_multiple_tests.cpp     |    82 +
 cpp/tests/strings/find_tests.cpp              |   438 +
 cpp/tests/strings/findall_tests.cpp           |   122 +
 cpp/tests/strings/fixed_point_tests.cpp       |   345 +
 cpp/tests/strings/floats_tests.cpp            |   209 +
 cpp/tests/strings/format_lists_tests.cpp      |   164 +
 cpp/tests/strings/integers_tests.cpp          |   458 +
 cpp/tests/strings/ipv4_tests.cpp              |   111 +
 cpp/tests/strings/json_tests.cpp              |  1024 ++
 cpp/tests/strings/like_tests.cpp              |   208 +
 cpp/tests/strings/pad_tests.cpp               |   221 +
 cpp/tests/strings/repeat_strings_tests.cpp    |   587 +
 cpp/tests/strings/replace_regex_tests.cpp     |   442 +
 cpp/tests/strings/replace_tests.cpp           |   480 +
 cpp/tests/strings/reverse_tests.cpp           |    53 +
 cpp/tests/strings/slice_tests.cpp             |   314 +
 cpp/tests/strings/split_tests.cpp             |   944 ++
 cpp/tests/strings/strip_tests.cpp             |   119 +
 cpp/tests/strings/translate_tests.cpp         |   121 +
 cpp/tests/strings/urls_tests.cpp              |   235 +
 cpp/tests/structs/structs_column_tests.cpp    |   642 +
 cpp/tests/structs/utilities_tests.cpp         |   661 +
 .../table/experimental_row_operator_tests.cu  |   297 +
 .../table/row_operator_tests_utilities.cu     |   214 +
 .../table/row_operator_tests_utilities.hpp    |    45 +
 cpp/tests/table/row_operators_tests.cpp       |    97 +
 cpp/tests/table/table_tests.cpp               |   153 +
 cpp/tests/table/table_view_tests.cu           |   145 +
 cpp/tests/text/bpe_tests.cpp                  |   110 +
 cpp/tests/text/edit_distance_tests.cpp        |   101 +
 cpp/tests/text/jaccard_tests.cpp              |    80 +
 cpp/tests/text/minhash_tests.cpp              |   172 +
 cpp/tests/text/ngrams_tests.cpp               |   172 +
 cpp/tests/text/ngrams_tokenize_tests.cpp      |   127 +
 cpp/tests/text/normalize_tests.cpp            |   161 +
 cpp/tests/text/replace_tests.cpp              |   152 +
 cpp/tests/text/stemmer_tests.cpp              |   179 +
 cpp/tests/text/subword_tests.cpp              |   442 +
 cpp/tests/text/tokenize_tests.cpp             |   250 +
 cpp/tests/transform/bools_to_mask_test.cpp    |    92 +
 .../transform/integration/assert_unary.h      |    51 +
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 cpp/tests/transform/mask_to_bools_test.cpp    |    79 +
 cpp/tests/transform/nans_to_null_test.cpp     |   138 +
 cpp/tests/transform/one_hot_encode_tests.cpp  |   272 +
 cpp/tests/transform/row_bit_count_test.cu     |   764 +
 cpp/tests/transpose/transpose_test.cpp        |   202 +
 cpp/tests/types/traits_test.cpp               |   134 +
 cpp/tests/types/type_dispatcher_test.cu       |   177 +
 cpp/tests/unary/cast_tests.cpp                |  1088 ++
 cpp/tests/unary/math_ops_test.cpp             |   438 +
 cpp/tests/unary/unary_ops_test.cpp            |   408 +
 cpp/tests/utilities/base_fixture.cpp          |    34 +
 cpp/tests/utilities/column_utilities.cu       |  1337 ++
 cpp/tests/utilities/default_stream.cpp        |    27 +
 cpp/tests/utilities/identify_stream_usage.cpp |   295 +
 cpp/tests/utilities/table_utilities.cu        |    48 +
 cpp/tests/utilities/tdigest_utilities.cu      |   157 +
 .../column_utilities_tests.cpp                |   563 +
 .../utilities_tests/column_wrapper_tests.cpp  |   282 +
 .../utilities_tests/default_stream_tests.cpp  |    25 +
 .../lists_column_wrapper_tests.cpp            |  1561 ++
 cpp/tests/utilities_tests/logger_tests.cpp    |    77 +
 cpp/tests/utilities_tests/span_tests.cu       |   456 +
 .../utilities_tests/type_check_tests.cpp      |   218 +
 cpp/tests/utilities_tests/type_list_tests.cpp |   250 +
 cpp/tests/wrappers/timestamps_test.cu         |   213 +
 dependencies.yaml                             |   676 +
 docs/cudf/Makefile                            |    20 +
 docs/cudf/README.md                           |     6 +
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 docs/cudf/source/user_guide/10min.ipynb       |  6162 +++++++
 docs/cudf/source/user_guide/PandasCompat.md   |     5 +
 .../source/user_guide/api_docs/dataframe.rst  |   280 +
 .../user_guide/api_docs/extension_dtypes.rst  |   170 +
 .../user_guide/api_docs/general_functions.rst |    48 +
 .../user_guide/api_docs/general_utilities.rst |    13 +
 .../source/user_guide/api_docs/groupby.rst    |   110 +
 .../cudf/source/user_guide/api_docs/index.rst |    25 +
 .../user_guide/api_docs/index_objects.rst     |   338 +
 docs/cudf/source/user_guide/api_docs/io.rst   |    83 +
 .../user_guide/api_docs/list_handling.rst     |    27 +
 .../source/user_guide/api_docs/options.rst    |    25 +
 .../source/user_guide/api_docs/series.rst     |   405 +
 .../user_guide/api_docs/string_handling.rst   |   105 +
 .../user_guide/api_docs/struct_handling.rst   |    19 +
 .../user_guide/api_docs/subword_tokenize.rst  |    12 +
 .../source/user_guide/api_docs/window.rst     |    25 +
 docs/cudf/source/user_guide/copy-on-write.md  |   179 +
 .../cudf/source/user_guide/cupy-interop.ipynb |  1429 ++
 docs/cudf/source/user_guide/data-types.md     |   155 +
 docs/cudf/source/user_guide/groupby.md        |   274 +
 .../source/user_guide/guide-to-udfs.ipynb     |  2658 +++
 docs/cudf/source/user_guide/index.md          |    19 +
 docs/cudf/source/user_guide/io/index.md       |     9 +
 docs/cudf/source/user_guide/io/io.md          |   180 +
 docs/cudf/source/user_guide/io/read-json.md   |   231 +
 .../cudf/source/user_guide/missing-data.ipynb |  3578 ++++
 docs/cudf/source/user_guide/options.md        |    14 +
 .../source/user_guide/pandas-comparison.md    |   188 +
 .../performance-comparisons/index.md          |     8 +
 .../performance-comparisons.ipynb             |  1651 ++
 docs/dask_cudf/Makefile                       |    21 +
 docs/dask_cudf/make.bat                       |    35 +
 .../source/_static/RAPIDS-logo-purple.png     |   Bin 0 -> 22593 bytes
 docs/dask_cudf/source/api.rst                 |    79 +
 docs/dask_cudf/source/conf.py                 |    82 +
 docs/dask_cudf/source/index.rst               |   112 +
 fetch_rapids.cmake                            |    19 +
 img/GDF_community.png                         |   Bin 0 -> 81567 bytes
 img/goai_logo.png                             |   Bin 0 -> 30695 bytes
 img/rapids_arrow.png                          |   Bin 0 -> 192477 bytes
 img/rapids_logo.png                           |   Bin 0 -> 113880 bytes
 java/README.md                                |   126 +
 java/buildscripts/build-info                  |    31 +
 java/ci/Dockerfile.centos7                    |    56 +
 java/ci/README.md                             |    50 +
 java/ci/build-in-docker.sh                    |    99 +
 java/dev/cudf_java_styles.xml                 |    22 +
 java/pom.xml                                  |   611 +
 .../main/java/ai/rapids/cudf/Aggregation.java |   993 ++
 .../ai/rapids/cudf/Aggregation128Utils.java   |    67 +
 .../ai/rapids/cudf/AggregationOverWindow.java |    73 +
 .../ai/rapids/cudf/ArrowColumnBuilder.java    |   113 +
 .../java/ai/rapids/cudf/ArrowIPCOptions.java  |    65 +
 .../ai/rapids/cudf/ArrowIPCWriterOptions.java |   138 +
 .../java/ai/rapids/cudf/AssertEmptyNulls.java |    36 +
 .../main/java/ai/rapids/cudf/AvroOptions.java |    41 +
 .../rapids/cudf/BaseDeviceMemoryBuffer.java   |   158 +
 .../main/java/ai/rapids/cudf/BinaryOp.java    |    79 +
 .../java/ai/rapids/cudf/BinaryOperable.java   |   577 +
 .../java/ai/rapids/cudf/BitVectorHelper.java  |   113 +
 .../main/java/ai/rapids/cudf/BufferType.java  |    28 +
 .../main/java/ai/rapids/cudf/CSVOptions.java  |   205 +
 .../java/ai/rapids/cudf/CSVWriterOptions.java |   162 +
 .../java/ai/rapids/cudf/CaptureGroups.java    |    36 +
 .../main/java/ai/rapids/cudf/ChunkedPack.java |   103 +
 .../java/ai/rapids/cudf/CloseableArray.java   |   106 +
 .../ai/rapids/cudf/ColumnFilterOptions.java   |    65 +
 .../java/ai/rapids/cudf/ColumnVector.java     |  1767 ++
 .../main/java/ai/rapids/cudf/ColumnView.java  |  5261 ++++++
 .../ai/rapids/cudf/ColumnWriterOptions.java   |   675 +
 .../cudf/CompressedMetadataWriterOptions.java |    82 +
 .../CompressionMetadataWriterOptions.java     |   128 +
 .../java/ai/rapids/cudf/CompressionType.java  |    64 +
 .../rapids/cudf/ContigSplitGroupByResult.java |   114 +
 .../java/ai/rapids/cudf/ContiguousTable.java  |   121 +
 java/src/main/java/ai/rapids/cudf/CuFile.java |   162 +
 .../java/ai/rapids/cudf/CuFileBuffer.java     |    84 +
 .../java/ai/rapids/cudf/CuFileDriver.java     |    38 +
 .../java/ai/rapids/cudf/CuFileHandle.java     |    44 +
 .../java/ai/rapids/cudf/CuFileReadHandle.java |    46 +
 .../ai/rapids/cudf/CuFileResourceCleaner.java |    78 +
 .../rapids/cudf/CuFileResourceDestroyer.java  |    24 +
 .../ai/rapids/cudf/CuFileWriteHandle.java     |    60 +
 java/src/main/java/ai/rapids/cudf/Cuda.java   |   605 +
 .../java/ai/rapids/cudf/CudaComputeMode.java  |    61 +
 .../java/ai/rapids/cudf/CudaException.java    |   329 +
 .../ai/rapids/cudf/CudaFatalException.java    |    35 +
 .../main/java/ai/rapids/cudf/CudaMemInfo.java |    35 +
 .../java/ai/rapids/cudf/CudaMemcpyKind.java   |    35 +
 .../java/ai/rapids/cudf/CudaMemoryBuffer.java |   124 +
 .../cudf/CudfColumnSizeOverflowException.java |    34 +
 .../java/ai/rapids/cudf/CudfException.java    |    41 +
 java/src/main/java/ai/rapids/cudf/DType.java  |   528 +
 .../java/ai/rapids/cudf/DecimalUtils.java     |   164 +
 .../cudf/DefaultHostMemoryAllocator.java      |    36 +
 .../ai/rapids/cudf/DeviceMemoryBuffer.java    |   174 +
 .../rapids/cudf/DeviceMemoryBufferView.java   |    39 +
 .../main/java/ai/rapids/cudf/GatherMap.java   |    85 +
 .../ai/rapids/cudf/GroupByAggregation.java    |   340 +
 .../cudf/GroupByAggregationOnColumn.java      |    56 +
 .../java/ai/rapids/cudf/GroupByOptions.java   |   124 +
 .../rapids/cudf/GroupByScanAggregation.java   |   125 +
 .../cudf/GroupByScanAggregationOnColumn.java  |    64 +
 .../main/java/ai/rapids/cudf/HashJoin.java    |   127 +
 .../main/java/ai/rapids/cudf/HashType.java    |    49 +
 .../ai/rapids/cudf/HostBufferConsumer.java    |    38 +
 .../ai/rapids/cudf/HostBufferProvider.java    |    38 +
 .../java/ai/rapids/cudf/HostColumnVector.java |  2202 +++
 .../ai/rapids/cudf/HostColumnVectorCore.java  |   663 +
 .../ai/rapids/cudf/HostMemoryAllocator.java   |    39 +
 .../java/ai/rapids/cudf/HostMemoryBuffer.java |   668 +
 .../cudf/HostMemoryBufferNativeUtils.java     |    57 +
 .../ai/rapids/cudf/HostMemoryReservation.java |    32 +
 .../ai/rapids/cudf/JCudfSerialization.java    |  2007 +++
 .../main/java/ai/rapids/cudf/JSONOptions.java |   118 +
 .../main/java/ai/rapids/cudf/MaskState.java   |    41 +
 .../java/ai/rapids/cudf/MemoryBuffer.java     |   284 +
 .../java/ai/rapids/cudf/MemoryCleaner.java    |   350 +
 .../java/ai/rapids/cudf/MixedJoinSize.java    |    43 +
 .../main/java/ai/rapids/cudf/NaNEquality.java |    41 +
 .../java/ai/rapids/cudf/NativeDepsLoader.java |   232 +
 .../java/ai/rapids/cudf/NullEquality.java     |    33 +
 .../main/java/ai/rapids/cudf/NullPolicy.java  |    33 +
 .../main/java/ai/rapids/cudf/NvtxColor.java   |    34 +
 .../main/java/ai/rapids/cudf/NvtxRange.java   |    69 +
 .../java/ai/rapids/cudf/NvtxUniqueRange.java  |    82 +
 .../main/java/ai/rapids/cudf/ORCOptions.java  |   107 +
 .../java/ai/rapids/cudf/ORCWriterOptions.java |    42 +
 .../main/java/ai/rapids/cudf/OrderByArg.java  |    59 +
 .../ai/rapids/cudf/OutOfBoundsPolicy.java     |    39 +
 .../ai/rapids/cudf/PackedColumnMetadata.java  |    74 +
 .../src/main/java/ai/rapids/cudf/PadSide.java |    42 +
 .../ai/rapids/cudf/ParquetChunkedReader.java  |   155 +
 .../java/ai/rapids/cudf/ParquetOptions.java   |   112 +
 .../ai/rapids/cudf/ParquetWriterOptions.java  |    75 +
 .../java/ai/rapids/cudf/PartitionedTable.java |    76 +
 .../java/ai/rapids/cudf/PinnedMemoryPool.java |   462 +
 .../java/ai/rapids/cudf/QuantileMethod.java   |    53 +
 .../main/java/ai/rapids/cudf/QuoteStyle.java  |    33 +
 java/src/main/java/ai/rapids/cudf/Range.java  |   247 +
 .../ai/rapids/cudf/ReductionAggregation.java  |   307 +
 .../main/java/ai/rapids/cudf/RegexFlag.java   |    37 +
 .../java/ai/rapids/cudf/RegexProgram.java     |   134 +
 .../java/ai/rapids/cudf/ReplacePolicy.java    |    46 +
 .../rapids/cudf/ReplacePolicyWithColumn.java  |    46 +
 java/src/main/java/ai/rapids/cudf/Rmm.java    |   584 +
 .../ai/rapids/cudf/RmmAllocationMode.java     |    39 +
 .../rapids/cudf/RmmArenaMemoryResource.java   |    67 +
 .../cudf/RmmCudaAsyncMemoryResource.java      |    59 +
 .../ai/rapids/cudf/RmmCudaMemoryResource.java |    44 +
 .../rapids/cudf/RmmDeviceMemoryResource.java  |    31 +
 .../java/ai/rapids/cudf/RmmEventHandler.java  |    98 +
 .../cudf/RmmEventHandlerResourceAdaptor.java  |    76 +
 .../java/ai/rapids/cudf/RmmException.java     |    30 +
 .../cudf/RmmLimitingResourceAdaptor.java      |    59 +
 .../cudf/RmmLoggingResourceAdaptor.java       |    58 +
 .../rapids/cudf/RmmManagedMemoryResource.java |    45 +
 .../ai/rapids/cudf/RmmPoolMemoryResource.java |    64 +
 .../cudf/RmmTrackingResourceAdaptor.java      |    69 +
 .../cudf/RmmWrappingDeviceMemoryResource.java |    56 +
 .../ai/rapids/cudf/RollingAggregation.java    |   230 +
 .../cudf/RollingAggregationOnColumn.java      |    65 +
 .../main/java/ai/rapids/cudf/RoundMode.java   |    31 +
 java/src/main/java/ai/rapids/cudf/Scalar.java |   969 ++
 .../java/ai/rapids/cudf/ScanAggregation.java  |   107 +
 .../main/java/ai/rapids/cudf/ScanType.java    |    39 +
 java/src/main/java/ai/rapids/cudf/Schema.java |   103 +
 .../cudf/SegmentedReductionAggregation.java   |   104 +
 .../ai/rapids/cudf/StreamedTableReader.java   |    42 +
 java/src/main/java/ai/rapids/cudf/Table.java  |  4782 ++++++
 .../main/java/ai/rapids/cudf/TableDebug.java  |   280 +
 .../java/ai/rapids/cudf/TableWithMeta.java    |    67 +
 .../main/java/ai/rapids/cudf/TableWriter.java |    75 +
 .../src/main/java/ai/rapids/cudf/UnaryOp.java |    60 +
 .../ai/rapids/cudf/UnsafeMemoryAccessor.java  |   374 +
 .../java/ai/rapids/cudf/WindowOptions.java    |   370 +
 .../java/ai/rapids/cudf/WriterOptions.java    |    72 +
 .../ai/rapids/cudf/ast/AstExpression.java     |    69 +
 .../ai/rapids/cudf/ast/BinaryOperation.java   |    48 +
 .../ai/rapids/cudf/ast/BinaryOperator.java    |    66 +
 .../ai/rapids/cudf/ast/ColumnReference.java   |    51 +
 .../rapids/cudf/ast/CompiledExpression.java   |   110 +
 .../main/java/ai/rapids/cudf/ast/Literal.java |   276 +
 .../ai/rapids/cudf/ast/TableReference.java    |    47 +
 .../ai/rapids/cudf/ast/UnaryOperation.java    |    44 +
 .../ai/rapids/cudf/ast/UnaryOperator.java     |    69 +
 .../cudf/nvcomp/BatchedLZ4Compressor.java     |   320 +
 .../cudf/nvcomp/BatchedLZ4Decompressor.java   |   199 +
 .../rapids/cudf/nvcomp/CompressionType.java   |    53 +
 .../cudf/nvcomp/NvcompCudaException.java      |    28 +
 .../rapids/cudf/nvcomp/NvcompException.java   |    28 +
 .../java/ai/rapids/cudf/nvcomp/NvcompJni.java |   117 +
 java/src/main/native/.clang-format            |   204 +
 java/src/main/native/CMakeLists.txt           |   266 +
 java/src/main/native/clang-format.README      |    13 +
 java/src/main/native/include/jni_utils.hpp    |   942 ++
 .../main/native/include/maps_column_view.hpp  |   152 +
 .../native/src/Aggregation128UtilsJni.cpp     |    47 +
 java/src/main/native/src/AggregationJni.cpp   |   280 +
 java/src/main/native/src/ChunkedPackJni.cpp   |    75 +
 java/src/main/native/src/ChunkedReaderJni.cpp |   124 +
 java/src/main/native/src/ColumnVectorJni.cpp  |   387 +
 java/src/main/native/src/ColumnViewJni.cpp    |  2575 +++
 java/src/main/native/src/ColumnViewJni.cu     |   224 +
 java/src/main/native/src/ColumnViewJni.hpp    |    94 +
 .../main/native/src/CompiledExpression.cpp    |   412 +
 .../main/native/src/ContiguousTableJni.cpp    |   146 +
 java/src/main/native/src/CuFileJni.cpp        |   517 +
 java/src/main/native/src/CudaJni.cpp          |   414 +
 java/src/main/native/src/CudfJni.cpp          |   197 +
 java/src/main/native/src/HashJoinJni.cpp      |    45 +
 .../src/HostMemoryBufferNativeUtilsJni.cpp    |    74 +
 java/src/main/native/src/NvcompJni.cpp        |   171 +
 java/src/main/native/src/NvtxRangeJni.cpp     |    42 +
 .../main/native/src/NvtxUniqueRangeJni.cpp    |    46 +
 .../native/src/PackedColumnMetadataJni.cpp    |    41 +
 java/src/main/native/src/RmmJni.cpp           |   765 +
 java/src/main/native/src/ScalarJni.cpp        |   576 +
 java/src/main/native/src/TableJni.cpp         |  3605 +++++
 .../main/native/src/aggregation128_utils.cu   |   129 +
 .../main/native/src/aggregation128_utils.hpp  |    70 +
 .../native/src/check_nvcomp_output_sizes.cu   |    47 +
 .../native/src/check_nvcomp_output_sizes.hpp  |    33 +
 .../main/native/src/csv_chunked_writer.hpp    |    73 +
 java/src/main/native/src/cudf_jni_apis.hpp    |   138 +
 java/src/main/native/src/dtype_utils.hpp      |    65 +
 java/src/main/native/src/emptyfile.cpp        |    17 +
 .../src/main/native/src/jni_compiled_expr.hpp |    66 +
 .../main/native/src/jni_writer_data_sink.hpp  |   170 +
 java/src/main/native/src/maps_column_view.cu  |   112 +
 java/src/main/native/src/nvtx_common.hpp      |    27 +
 java/src/main/native/src/row_conversion.cu    |  2341 +++
 java/src/main/native/src/row_conversion.hpp   |    52 +
 .../rapids/cudf/Aggregation128UtilsTest.java  |    80 +
 .../ai/rapids/cudf/ArrowColumnVectorTest.java |   337 +
 .../test/java/ai/rapids/cudf/AssertUtils.java |   272 +
 .../java/ai/rapids/cudf/BinaryOpTest.java     |  1925 +++
 .../ai/rapids/cudf/ByteColumnVectorTest.java  |   243 +
 .../ai/rapids/cudf/ColumnBuilderHelper.java   |   170 +
 .../java/ai/rapids/cudf/ColumnVectorTest.java |  6889 ++++++++
 .../cudf/ColumnViewNonEmptyNullsTest.java     |   138 +
 .../test/java/ai/rapids/cudf/CuFileTest.java  |   157 +
 .../java/ai/rapids/cudf/CudaFatalTest.java    |    67 +
 .../test/java/ai/rapids/cudf/CudaTest.java    |    55 +
 .../java/ai/rapids/cudf/CudfTestBase.java     |    96 +
 .../rapids/cudf/Date32ColumnVectorTest.java   |    72 +
 .../rapids/cudf/Date64ColumnVectorTest.java   |    95 +
 .../rapids/cudf/DecimalColumnVectorTest.java  |   425 +
 .../rapids/cudf/DoubleColumnVectorTest.java   |   174 +
 .../ai/rapids/cudf/FloatColumnVectorTest.java |   164 +
 .../java/ai/rapids/cudf/GatherMapTest.java    |   102 +
 .../java/ai/rapids/cudf/HashJoinTest.java     |    45 +
 .../ai/rapids/cudf/HostMemoryBufferTest.java  |   301 +
 .../test/java/ai/rapids/cudf/IfElseTest.java  |  1181 ++
 .../ai/rapids/cudf/IntColumnVectorTest.java   |   227 +
 .../java/ai/rapids/cudf/LargeTableTest.java   |    66 +
 .../ai/rapids/cudf/LongColumnVectorTest.java  |   217 +
 .../java/ai/rapids/cudf/MemoryBufferTest.java |   220 +
 .../test/java/ai/rapids/cudf/NvtxTest.java    |    72 +
 .../ai/rapids/cudf/PinnedMemoryPoolTest.java  |   134 +
 .../java/ai/rapids/cudf/ReductionTest.java    |   627 +
 .../ai/rapids/cudf/RmmMemoryAccessorTest.java |    99 +
 .../src/test/java/ai/rapids/cudf/RmmTest.java |   614 +
 .../test/java/ai/rapids/cudf/ScalarTest.java  |   461 +
 .../rapids/cudf/SegmentedReductionTest.java   |   115 +
 .../ai/rapids/cudf/ShortColumnVectorTest.java |   178 +
 .../test/java/ai/rapids/cudf/TableTest.java   |  9209 +++++++++++
 .../test/java/ai/rapids/cudf/TestUtils.java   |   229 +
 .../cudf/TimestampColumnVectorTest.java       |   492 +
 .../test/java/ai/rapids/cudf/UnaryOpTest.java |   345 +
 .../rapids/cudf/UnsafeMemoryAccessorTest.java |   100 +
 .../cudf/ast/CompiledExpressionTest.java      |   614 +
 .../ai/rapids/cudf/nvcomp/NvcompTest.java     |   129 +
 java/src/test/resources/TestOrcFile.orc       |   Bin 0 -> 1025 bytes
 java/src/test/resources/acq.parquet           |   Bin 0 -> 32027 bytes
 java/src/test/resources/alltypes_plain.avro   |   Bin 0 -> 868 bytes
 java/src/test/resources/binary.parquet        |   Bin 0 -> 653 bytes
 java/src/test/resources/decimal.parquet       |   Bin 0 -> 6604 bytes
 java/src/test/resources/people.json           |     3 +
 .../resources/people_with_invalid_lines.json  |     4 +
 java/src/test/resources/simple.csv            |    10 +
 java/src/test/resources/splittable.parquet    |   Bin 0 -> 320341 bytes
 .../test/resources/timestamp-date-test.orc    |   Bin 0 -> 409 bytes
 notebooks/10min.ipynb                         |     1 +
 notebooks/README.md                           |     6 +
 notebooks/cupy-interop.ipynb                  |     1 +
 notebooks/guide-to-udfs.ipynb                 |     1 +
 notebooks/missing-data.ipynb                  |     1 +
 notebooks/performance-comparisons             |     1 +
 print_env.sh                                  |    88 +
 pyproject.toml                                |    74 +
 python/cudf/.coveragerc                       |     3 +
 python/cudf/CMakeLists.txt                    |   121 +
 python/cudf/LICENSE                           |     1 +
 python/cudf/README.md                         |     1 +
 python/cudf/benchmarks/API/bench_dataframe.py |   183 +
 .../benchmarks/API/bench_dataframe_cases.py   |    14 +
 .../benchmarks/API/bench_frame_or_index.py    |    97 +
 python/cudf/benchmarks/API/bench_functions.py |    82 +
 .../benchmarks/API/bench_functions_cases.py   |   148 +
 python/cudf/benchmarks/API/bench_index.py     |    17 +
 .../benchmarks/API/bench_indexed_frame.py     |    30 +
 .../cudf/benchmarks/API/bench_multiindex.py   |    44 +
 .../cudf/benchmarks/API/bench_rangeindex.py   |    47 +
 python/cudf/benchmarks/API/bench_series.py    |    23 +
 python/cudf/benchmarks/common/config.py       |    69 +
 python/cudf/benchmarks/common/utils.py        |   257 +
 python/cudf/benchmarks/conftest.py            |   234 +
 .../cudf/benchmarks/internal/bench_column.py  |   115 +
 .../internal/bench_dataframe_internal.py      |    13 +
 .../internal/bench_rangeindex_internal.py     |    11 +
 python/cudf/benchmarks/internal/conftest.py   |    56 +
 python/cudf/benchmarks/pytest.ini             |     8 +
 .../cudf/cmake/Modules/ProtobufHelpers.cmake  |    50 +
 python/cudf/cmake/Modules/WheelHelpers.cmake  |    71 +
 python/cudf/cudf/__init__.py                  |   169 +
 python/cudf/cudf/_fuzz_testing/__init__.py    |     0
 python/cudf/cudf/_fuzz_testing/avro.py        |   116 +
 python/cudf/cudf/_fuzz_testing/csv.py         |   207 +
 python/cudf/cudf/_fuzz_testing/fuzzer.py      |   116 +
 python/cudf/cudf/_fuzz_testing/io.py          |   109 +
 python/cudf/cudf/_fuzz_testing/json.py        |   191 +
 python/cudf/cudf/_fuzz_testing/main.py        |    45 +
 python/cudf/cudf/_fuzz_testing/orc.py         |   209 +
 python/cudf/cudf/_fuzz_testing/parquet.py     |   169 +
 .../_fuzz_testing/tests/fuzz_test_avro.py     |    38 +
 .../cudf/_fuzz_testing/tests/fuzz_test_csv.py |   132 +
 .../_fuzz_testing/tests/fuzz_test_json.py     |    94 +
 .../cudf/_fuzz_testing/tests/fuzz_test_orc.py |    98 +
 .../_fuzz_testing/tests/fuzz_test_parquet.py  |   104 +
 .../cudf/cudf/_fuzz_testing/tests/readme.md   |   100 +
 python/cudf/cudf/_fuzz_testing/utils.py       |   407 +
 python/cudf/cudf/_lib/CMakeLists.txt          |   119 +
 python/cudf/cudf/_lib/__init__.pxd            |     0
 python/cudf/cudf/_lib/__init__.py             |    47 +
 python/cudf/cudf/_lib/aggregation.pxd         |    33 +
 python/cudf/cudf/_lib/aggregation.pyx         |   983 ++
 python/cudf/cudf/_lib/avro.pyx                |    55 +
 python/cudf/cudf/_lib/binaryop.pxd            |     5 +
 python/cudf/cudf/_lib/binaryop.pyx            |   239 +
 python/cudf/cudf/_lib/column.pxd              |    42 +
 python/cudf/cudf/_lib/column.pyi              |    75 +
 python/cudf/cudf/_lib/column.pyx              |   788 +
 python/cudf/cudf/_lib/concat.pyx              |    63 +
 python/cudf/cudf/_lib/copying.pxd             |    10 +
 python/cudf/cudf/_lib/copying.pyx             |   852 +
 python/cudf/cudf/_lib/cpp/CMakeLists.txt      |    23 +
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 python/cudf/cudf/_lib/cpp/aggregation.pxd     |   153 +
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 python/cudf/cudf/_lib/cpp/libcpp/optional.pxd |    50 +
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 python/cudf/cudf/_lib/cpp/lists/__init__.py   |     0
 python/cudf/cudf/_lib/cpp/lists/combine.pxd   |    29 +
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 python/cudf/cudf/_lib/cpp/lists/extract.pxd   |    18 +
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 python/cudf/cudf/_lib/cpp/merge.pxd           |    17 +
 python/cudf/cudf/_lib/cpp/null_mask.pxd       |    46 +
 python/cudf/cudf/_lib/cpp/nvtext/__init__.pxd |     0
 python/cudf/cudf/_lib/cpp/nvtext/__init__.py  |     0
 .../cudf/_lib/cpp/nvtext/edit_distance.pxd    |    19 +
 .../cudf/_lib/cpp/nvtext/generate_ngrams.pxd  |    27 +
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 python/cudf/cudf/_lib/cpp/nvtext/minhash.pxd  |    22 +
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 python/cudf/cudf/_lib/cpp/nvtext/stemmer.pxd  |    30 +
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 python/cudf/cudf/_lib/cpp/partitioning.pxd    |    28 +
 python/cudf/cudf/_lib/cpp/quantiles.pxd       |    37 +
 python/cudf/cudf/_lib/cpp/reduce.pxd          |    33 +
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 python/cudf/cudf/_lib/cpp/scalar/__init__.pxd |     0
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 python/cudf/cudf/_lib/cpp/scalar/scalar.pxd   |    75 +
 python/cudf/cudf/_lib/cpp/search.pxd          |    31 +
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 .../cudf/cudf/_lib/cpp/strings/__init__.pxd   |     0
 python/cudf/cudf/_lib/cpp/strings/__init__.py |     0
 .../cudf/cudf/_lib/cpp/strings/attributes.pxd |    18 +
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 python/cudf/cudf/_lib/cpp/strings/case.pxd    |    16 +
 .../cudf/cudf/_lib/cpp/strings/char_types.pxd |    37 +
 python/cudf/cudf/_lib/cpp/strings/combine.pxd |    44 +
 .../cudf/cudf/_lib/cpp/strings/contains.pxd   |    28 +
 .../_lib/cpp/strings/convert/__init__.pxd     |     0
 .../cudf/_lib/cpp/strings/convert/__init__.py |     0
 .../cpp/strings/convert/convert_booleans.pxd  |    18 +
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 .../cpp/strings/convert/convert_lists.pxd     |    15 +
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 python/cudf/cudf/_lib/cpp/strings/extract.pxd |    15 +
 python/cudf/cudf/_lib/cpp/strings/find.pxd    |    48 +
 .../cudf/_lib/cpp/strings/find_multiple.pxd   |    14 +
 python/cudf/cudf/_lib/cpp/strings/findall.pxd |    14 +
 python/cudf/cudf/_lib/cpp/strings/json.pxd    |    28 +
 python/cudf/cudf/_lib/cpp/strings/padding.pxd |    23 +
 .../cudf/_lib/cpp/strings/regex_flags.pxd     |     9 +
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 python/cudf/cudf/_lib/cpp/strings/repeat.pxd  |    19 +
 python/cudf/cudf/_lib/cpp/strings/replace.pxd |    29 +
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 .../cudf/cudf/_lib/cpp/strings/side_type.pxd  |    12 +
 .../cudf/_lib/cpp/strings/split/__init__.pxd  |     0
 .../cudf/_lib/cpp/strings/split/__init__.py   |     0
 .../cudf/_lib/cpp/strings/split/partition.pxd |    21 +
 .../cudf/_lib/cpp/strings/split/split.pxd     |    59 +
 python/cudf/cudf/_lib/cpp/strings/strip.pxd   |    16 +
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 .../cudf/cudf/_lib/cpp/strings/translate.pxd  |    28 +
 python/cudf/cudf/_lib/cpp/strings/wrap.pxd    |    14 +
 python/cudf/cudf/_lib/cpp/strings_udf.pxd     |    33 +
 python/cudf/cudf/_lib/cpp/table/__init__.pxd  |     0
 python/cudf/cudf/_lib/cpp/table/__init__.py   |     0
 python/cudf/cudf/_lib/cpp/table/table.pxd     |    19 +
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 python/cudf/cudf/_lib/cpp/types.pxd           |   106 +
 python/cudf/cudf/_lib/cpp/types.pyx           |     0
 python/cudf/cudf/_lib/cpp/unary.pxd           |    49 +
 .../cudf/cudf/_lib/cpp/utilities/__init__.pxd |     0
 .../cudf/cudf/_lib/cpp/utilities/__init__.py  |     0
 .../cudf/_lib/cpp/utilities/host_span.pxd     |     9 +
 .../cudf/cudf/_lib/cpp/wrappers/__init__.pxd  |     0
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 .../cudf/cudf/_lib/cpp/wrappers/decimals.pxd  |    17 +
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 .../cudf/_lib/cpp/wrappers/timestamps.pxd     |    10 +
 python/cudf/cudf/_lib/csv.pyx                 |   602 +
 python/cudf/cudf/_lib/datetime.pyx            |   214 +
 python/cudf/cudf/_lib/exception_handler.pxd   |    69 +
 python/cudf/cudf/_lib/expressions.pxd         |    31 +
 python/cudf/cudf/_lib/expressions.pyx         |   126 +
 python/cudf/cudf/_lib/filling.pyx             |   103 +
 python/cudf/cudf/_lib/groupby.pyx             |   404 +
 python/cudf/cudf/_lib/hash.pyx                |    63 +
 python/cudf/cudf/_lib/interop.pyx             |   277 +
 python/cudf/cudf/_lib/io/CMakeLists.txt       |    27 +
 python/cudf/cudf/_lib/io/__init__.pxd         |     0
 python/cudf/cudf/_lib/io/__init__.py          |     0
 python/cudf/cudf/_lib/io/datasource.pxd       |    15 +
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 python/cudf/cudf/_lib/join.pyx                |    74 +
 python/cudf/cudf/_lib/json.pyx                |   268 +
 python/cudf/cudf/_lib/labeling.pyx            |    43 +
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 python/cudf/cudf/_lib/nvtext/CMakeLists.txt   |    24 +
 python/cudf/cudf/_lib/nvtext/__init__.pxd     |     0
 python/cudf/cudf/_lib/nvtext/__init__.py      |     0
 .../cudf/cudf/_lib/nvtext/edit_distance.pyx   |    37 +
 .../cudf/cudf/_lib/nvtext/generate_ngrams.pyx |    75 +
 python/cudf/cudf/_lib/nvtext/jaccard.pyx      |    31 +
 python/cudf/cudf/_lib/nvtext/minhash.pyx      |    55 +
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 python/cudf/cudf/_lib/nvtext/normalize.pyx    |    37 +
 python/cudf/cudf/_lib/nvtext/replace.pyx      |    87 +
 python/cudf/cudf/_lib/nvtext/stemmer.pyx      |    68 +
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 python/cudf/cudf/_lib/nvtext/tokenize.pyx     |   162 +
 python/cudf/cudf/_lib/orc.pyx                 |   494 +
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 python/cudf/cudf/_lib/partitioning.pyx        |    73 +
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 python/cudf/cudf/_lib/quantiles.pyx           |   119 +
 python/cudf/cudf/_lib/reduce.pyx              |   163 +
 python/cudf/cudf/_lib/replace.pyx             |   261 +
 python/cudf/cudf/_lib/reshape.pyx             |    40 +
 python/cudf/cudf/_lib/rolling.pyx             |    95 +
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 python/cudf/cudf/_lib/scalar.pyx              |   318 +
 python/cudf/cudf/_lib/search.pyx              |   101 +
 python/cudf/cudf/_lib/sort.pyx                |   477 +
 python/cudf/cudf/_lib/stream_compaction.pyx   |   200 +
 python/cudf/cudf/_lib/string_casting.pyx      |   800 +
 python/cudf/cudf/_lib/strings/CMakeLists.txt  |    45 +
 python/cudf/cudf/_lib/strings/__init__.pxd    |     0
 python/cudf/cudf/_lib/strings/__init__.py     |   105 +
 python/cudf/cudf/_lib/strings/attributes.pyx  |    60 +
 python/cudf/cudf/_lib/strings/capitalize.pyx  |    48 +
 python/cudf/cudf/_lib/strings/case.pyx        |    48 +
 python/cudf/cudf/_lib/strings/char_types.pyx  |   202 +
 python/cudf/cudf/_lib/strings/combine.pyx     |   161 +
 python/cudf/cudf/_lib/strings/contains.pyx    |   122 +
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 .../cudf/_lib/strings/convert/__init__.pxd    |     0
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 .../strings/convert/convert_fixed_point.pyx   |   110 +
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 python/cudf/cudf/_lib/strings/extract.pyx     |    45 +
 python/cudf/cudf/_lib/strings/find.pyx        |   217 +
 .../cudf/cudf/_lib/strings/find_multiple.pyx  |    32 +
 python/cudf/cudf/_lib/strings/findall.pyx     |    39 +
 python/cudf/cudf/_lib/strings/json.pyx        |    83 +
 python/cudf/cudf/_lib/strings/padding.pyx     |   149 +
 python/cudf/cudf/_lib/strings/repeat.pyx      |    53 +
 python/cudf/cudf/_lib/strings/replace.pyx     |   135 +
 python/cudf/cudf/_lib/strings/replace_re.pyx  |   116 +
 .../cudf/_lib/strings/split/CMakeLists.txt    |    22 +
 .../cudf/cudf/_lib/strings/split/__init__.pxd |     0
 .../cudf/cudf/_lib/strings/split/__init__.py  |     0
 .../cudf/_lib/strings/split/partition.pyx     |    73 +
 python/cudf/cudf/_lib/strings/split/split.pyx |   263 +
 python/cudf/cudf/_lib/strings/strip.pyx       |    98 +
 python/cudf/cudf/_lib/strings/substring.pyx   |   119 +
 python/cudf/cudf/_lib/strings/translate.pyx   |   106 +
 python/cudf/cudf/_lib/strings/wrap.pyx        |    33 +
 python/cudf/cudf/_lib/strings_udf.pyx         |    66 +
 python/cudf/cudf/_lib/text.pyx                |    88 +
 python/cudf/cudf/_lib/timezone.pyx            |    28 +
 python/cudf/cudf/_lib/transform.pyx           |   201 +
 python/cudf/cudf/_lib/transpose.pyx           |    31 +
 python/cudf/cudf/_lib/types.pxd               |    21 +
 python/cudf/cudf/_lib/types.pyx               |   333 +
 python/cudf/cudf/_lib/unary.pyx               |   122 +
 python/cudf/cudf/_lib/utils.pxd               |    21 +
 python/cudf/cudf/_lib/utils.pyx               |   397 +
 python/cudf/cudf/_typing.py                   |    48 +
 python/cudf/cudf/api/__init__.py              |     5 +
 python/cudf/cudf/api/extensions/__init__.py   |    16 +
 python/cudf/cudf/api/extensions/accessor.py   |   161 +
 python/cudf/cudf/api/types.py                 |   506 +
 python/cudf/cudf/benchmarks/README.md         |    32 +
 python/cudf/cudf/benchmarks/bench_cudf_io.py  |    86 +
 python/cudf/cudf/benchmarks/conftest.py       |    20 +
 python/cudf/cudf/benchmarks/get_datasets.py   |    91 +
 python/cudf/cudf/comm/__init__.py             |     0
 python/cudf/cudf/comm/serialize.py            |    33 +
 python/cudf/cudf/core/__init__.py             |     1 +
 python/cudf/cudf/core/_base_index.py          |  2117 +++
 python/cudf/cudf/core/_compat.py              |    13 +
 python/cudf/cudf/core/_internals/__init__.py  |     1 +
 .../cudf/cudf/core/_internals/expressions.py  |   224 +
 python/cudf/cudf/core/_internals/timezones.py |   254 +
 python/cudf/cudf/core/_internals/where.py     |   127 +
 python/cudf/cudf/core/abc.py                  |   186 +
 python/cudf/cudf/core/algorithms.py           |   207 +
 python/cudf/cudf/core/buffer/__init__.py      |    10 +
 python/cudf/cudf/core/buffer/buffer.py        |   388 +
 .../core/buffer/exposure_tracked_buffer.py    |   311 +
 python/cudf/cudf/core/buffer/spill_manager.py |   446 +
 .../cudf/cudf/core/buffer/spillable_buffer.py |   598 +
 python/cudf/cudf/core/buffer/utils.py         |   135 +
 python/cudf/cudf/core/column/__init__.py      |    39 +
 python/cudf/cudf/core/column/categorical.py   |  1654 ++
 python/cudf/cudf/core/column/column.py        |  2835 ++++
 python/cudf/cudf/core/column/datetime.py      |   737 +
 python/cudf/cudf/core/column/decimal.py       |   443 +
 python/cudf/cudf/core/column/interval.py      |   147 +
 python/cudf/cudf/core/column/lists.py         |   728 +
 python/cudf/cudf/core/column/methods.py       |   104 +
 python/cudf/cudf/core/column/numerical.py     |   791 +
 .../cudf/cudf/core/column/numerical_base.py   |   230 +
 python/cudf/cudf/core/column/string.py        |  6009 +++++++
 python/cudf/cudf/core/column/struct.py        |   244 +
 python/cudf/cudf/core/column/timedelta.py     |   590 +
 python/cudf/cudf/core/column_accessor.py      |   750 +
 python/cudf/cudf/core/common.py               |    37 +
 python/cudf/cudf/core/copy_types.py           |   171 +
 python/cudf/cudf/core/cut.py                  |   308 +
 python/cudf/cudf/core/dataframe.py            |  8163 ++++++++++
 python/cudf/cudf/core/df_protocol.py          |   900 ++
 python/cudf/cudf/core/dtypes.py               |  1168 ++
 python/cudf/cudf/core/frame.py                |  2884 ++++
 python/cudf/cudf/core/groupby/__init__.py     |     8 +
 python/cudf/cudf/core/groupby/groupby.py      |  2800 ++++
 python/cudf/cudf/core/index.py                |  3627 +++++
 python/cudf/cudf/core/indexed_frame.py        |  5476 +++++++
 python/cudf/cudf/core/indexing_utils.py       |   243 +
 python/cudf/cudf/core/join/__init__.py        |     3 +
 python/cudf/cudf/core/join/_join_helpers.py   |   186 +
 python/cudf/cudf/core/join/join.py            |   451 +
 python/cudf/cudf/core/missing.py              |     9 +
 python/cudf/cudf/core/mixins/__init__.py      |     7 +
 python/cudf/cudf/core/mixins/binops.py        |    72 +
 python/cudf/cudf/core/mixins/binops.pyi       |    39 +
 python/cudf/cudf/core/mixins/mixin_factory.py |   263 +
 python/cudf/cudf/core/mixins/reductions.py    |    35 +
 python/cudf/cudf/core/mixins/reductions.pyi   |    29 +
 python/cudf/cudf/core/mixins/scans.py         |    16 +
 python/cudf/cudf/core/mixins/scans.pyi        |    11 +
 python/cudf/cudf/core/multiindex.py           |  2117 +++
 python/cudf/cudf/core/resample.py             |   421 +
 python/cudf/cudf/core/reshape.py              |  1455 ++
 python/cudf/cudf/core/scalar.py               |   404 +
 python/cudf/cudf/core/series.py               |  5282 ++++++
 python/cudf/cudf/core/single_column_frame.py  |   438 +
 python/cudf/cudf/core/subword_tokenizer.py    |   298 +
 python/cudf/cudf/core/tokenize_vocabulary.py  |    48 +
 python/cudf/cudf/core/tools/__init__.py       |     0
 python/cudf/cudf/core/tools/datetimes.py      |  1022 ++
 python/cudf/cudf/core/tools/numeric.py        |   254 +
 python/cudf/cudf/core/udf/__init__.py         |     9 +
 python/cudf/cudf/core/udf/_ops.py             |    66 +
 python/cudf/cudf/core/udf/api.py              |    27 +
 python/cudf/cudf/core/udf/groupby_lowering.py |   190 +
 python/cudf/cudf/core/udf/groupby_typing.py   |   284 +
 python/cudf/cudf/core/udf/groupby_utils.py    |   229 +
 python/cudf/cudf/core/udf/masked_lowering.py  |   400 +
 python/cudf/cudf/core/udf/masked_typing.py    |   677 +
 python/cudf/cudf/core/udf/row_function.py     |   164 +
 python/cudf/cudf/core/udf/scalar_function.py  |    69 +
 python/cudf/cudf/core/udf/strings_lowering.py |   723 +
 python/cudf/cudf/core/udf/strings_typing.py   |   280 +
 python/cudf/cudf/core/udf/strings_utils.py    |     0
 python/cudf/cudf/core/udf/templates.py        |    80 +
 python/cudf/cudf/core/udf/utils.py            |   362 +
 python/cudf/cudf/core/window/__init__.py      |     3 +
 python/cudf/cudf/core/window/rolling.py       |   562 +
 python/cudf/cudf/datasets.py                  |   177 +
 python/cudf/cudf/errors.py                    |     9 +
 python/cudf/cudf/io/__init__.py               |    16 +
 python/cudf/cudf/io/avro.py                   |    39 +
 python/cudf/cudf/io/csv.py                    |   255 +
 python/cudf/cudf/io/dlpack.py                 |    92 +
 python/cudf/cudf/io/feather.py                |    32 +
 python/cudf/cudf/io/hdf.py                    |    30 +
 python/cudf/cudf/io/json.py                   |   244 +
 python/cudf/cudf/io/orc.py                    |   474 +
 python/cudf/cudf/io/parquet.py                |  1458 ++
 python/cudf/cudf/io/text.py                   |    43 +
 python/cudf/cudf/options.py                   |   355 +
 python/cudf/cudf/pandas/__init__.py           |    34 +
 python/cudf/cudf/pandas/__main__.py           |    91 +
 python/cudf/cudf/pandas/_wrappers/__init__.py |     5 +
 python/cudf/cudf/pandas/_wrappers/common.py   |    50 +
 python/cudf/cudf/pandas/_wrappers/numpy.py    |   133 +
 python/cudf/cudf/pandas/_wrappers/pandas.py   |  1306 ++
 python/cudf/cudf/pandas/annotation.py         |    35 +
 python/cudf/cudf/pandas/fast_slow_proxy.py    |  1121 ++
 python/cudf/cudf/pandas/magics.py             |    33 +
 python/cudf/cudf/pandas/module_accelerator.py |   620 +
 python/cudf/cudf/pandas/profiler.py           |   322 +
 .../pandas/scripts/analyze-test-failures.py   |    70 +
 .../cudf/pandas/scripts/conftest-patch.py     |    39 +
 .../cudf/pandas/scripts/run-pandas-tests.sh   |   198 +
 .../pandas/scripts/summarize-test-results.py  |   115 +
 python/cudf/cudf/testing/__init__.py          |     7 +
 python/cudf/cudf/testing/_utils.py            |   475 +
 python/cudf/cudf/testing/dataset_generator.py |   857 +
 python/cudf/cudf/testing/testing.py           |   724 +
 python/cudf/cudf/tests/conftest.py            |   178 +
 python/cudf/cudf/tests/data/__init__.py       |     0
 python/cudf/cudf/tests/data/avro/__init__.py  |     0
 python/cudf/cudf/tests/data/avro/example.avro |   Bin 0 -> 313 bytes
 python/cudf/cudf/tests/data/ipums.pkl         |   Bin 0 -> 99199 bytes
 .../data/orc/TestOrcFile.Hive.AllNulls.orc    |   Bin 0 -> 293 bytes
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 .../bert_base_cased_sampled/vocab-hash.txt    |  4382 +++++
 .../bert_base_cased_sampled/vocab.txt         |  3500 ++++
 .../subword_tokenizer_data/test_sentences.txt |   100 +
 python/cudf/cudf/tests/data/text/__init__.py  |     0
 python/cudf/cudf/tests/data/text/chess.pgn    |    16 +
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 .../cudf/cudf/tests/dataframe/test_missing.py |     1 +
 .../cudf/tests/dataframe/test_reindexing.py   |     1 +
 .../cudf/tests/dataframe/test_reshaping.py    |     1 +
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 .../cudf/cudf/tests/dataframe/test_sorting.py |     1 +
 .../cudf/tests/dataframe/test_timeseries.py   |     1 +
 .../cudf/tests/general_functions/__init__.py  |     0
 .../general_functions/test_conversion.py      |     1 +
 .../test_data_manipulation.py                 |     1 +
 .../general_functions/test_datetimelike.py    |     1 +
 .../cudf/tests/general_utilities/__init__.py  |     0
 .../tests/general_utilities/test_testing.py   |     1 +
 python/cudf/cudf/tests/groupby/__init__.py    |     0
 .../cudf/tests/groupby/test_computation.py    |    18 +
 .../groupby/test_function_application.py      |     1 +
 .../cudf/cudf/tests/groupby/test_indexing.py  |     1 +
 python/cudf/cudf/tests/groupby/test_stats.py  |     1 +
 python/cudf/cudf/tests/indexes/__init__.py    |     0
 .../cudf/tests/indexes/datetime/__init__.py   |     0
 .../tests/indexes/datetime/test_components.py |     1 +
 .../indexes/datetime/test_constructing.py     |     1 +
 .../tests/indexes/datetime/test_conversion.py |     1 +
 .../tests/indexes/datetime/test_indexing.py   |    19 +
 .../indexes/datetime/test_time_specific.py    |    32 +
 .../cudf/tests/indexes/multiindex/__init__.py |     0
 .../indexes/multiindex/test_constructing.py   |     1 +
 .../indexes/multiindex/test_properties.py     |     1 +
 .../indexes/multiindex/test_selecting.py      |     1 +
 .../cudf/tests/indexes/test_categorical.py    |     1 +
 .../cudf/cudf/tests/indexes/test_combining.py |     1 +
 .../cudf/tests/indexes/test_computation.py    |     1 +
 .../cudf/tests/indexes/test_constructing.py   |     1 +
 .../cudf/tests/indexes/test_conversion.py     |     1 +
 .../cudf/cudf/tests/indexes/test_interval.py  |   317 +
 .../cudf/tests/indexes/test_memory_usage.py   |     1 +
 .../cudf/cudf/tests/indexes/test_missing.py   |     1 +
 .../cudf/cudf/tests/indexes/test_modifying.py |     1 +
 .../tests/indexes/test_multiindex_compat.py   |     1 +
 .../cudf/cudf/tests/indexes/test_numeric.py   |     1 +
 .../cudf/tests/indexes/test_properties.py     |     1 +
 .../cudf/cudf/tests/indexes/test_selecting.py |     1 +
 .../cudf/cudf/tests/indexes/test_sorting.py   |     1 +
 .../cudf/tests/indexes/test_time_specific.py  |     1 +
 .../cudf/tests/indexes/timedelta/__init__.py  |     0
 .../indexes/timedelta/test_components.py      |     1 +
 .../indexes/timedelta/test_constructing.py    |     1 +
 .../indexes/timedelta/test_conversion.py      |     1 +
 .../cudf/cudf/tests/input_output/__init__.py  |     0
 .../cudf/cudf/tests/input_output/test_avro.py |     1 +
 .../cudf/cudf/tests/input_output/test_csv.py  |     1 +
 .../cudf/tests/input_output/test_feather.py   |     1 +
 .../cudf/cudf/tests/input_output/test_hdf5.py |     1 +
 .../cudf/cudf/tests/input_output/test_json.py |     1 +
 .../cudf/cudf/tests/input_output/test_orc.py  |     1 +
 .../cudf/tests/input_output/test_parquet.py   |     1 +
 .../cudf/cudf/tests/input_output/test_text.py |   164 +
 python/cudf/cudf/tests/lists/__init__.py      |     0
 .../cudf/tests/lists/test_list_methods.py     |     1 +
 python/cudf/cudf/tests/options/__init__.py    |     0
 .../cudf/cudf/tests/options/test_options.py   |     1 +
 python/cudf/cudf/tests/pytest.ini             |    10 +
 python/cudf/cudf/tests/series/__init__.py     |     0
 .../cudf/cudf/tests/series/test_accessors.py  |     1 +
 .../cudf/cudf/tests/series/test_attributes.py |     1 +
 .../tests/series/test_binary_operations.py    |     1 +
 .../cudf/cudf/tests/series/test_categorial.py |     1 +
 .../cudf/cudf/tests/series/test_combining.py  |     1 +
 .../cudf/tests/series/test_computation.py     |     1 +
 .../cudf/tests/series/test_constructing.py    |    12 +
 .../cudf/cudf/tests/series/test_conversion.py |    34 +
 .../cudf/tests/series/test_datetimelike.py    |   192 +
 .../tests/series/test_function_application.py |     1 +
 .../cudf/cudf/tests/series/test_indexing.py   |     1 +
 .../tests/series/test_io_serialization.py     |     1 +
 python/cudf/cudf/tests/series/test_missing.py |     1 +
 .../cudf/cudf/tests/series/test_reshaping.py  |     1 +
 .../cudf/cudf/tests/series/test_selecting.py  |     1 +
 python/cudf/cudf/tests/series/test_sorting.py |     1 +
 .../cudf/cudf/tests/series/test_timeseries.py |     1 +
 python/cudf/cudf/tests/strings/__init__.py    |     0
 .../cudf/tests/strings/test_string_methods.py |     1 +
 python/cudf/cudf/tests/structs/__init__.py    |     0
 .../cudf/tests/structs/test_struct_methods.py |     1 +
 python/cudf/cudf/tests/test_api_types.py      |  1124 ++
 python/cudf/cudf/tests/test_apply_rows.py     |    54 +
 python/cudf/cudf/tests/test_applymap.py       |    48 +
 python/cudf/cudf/tests/test_array_function.py |   169 +
 python/cudf/cudf/tests/test_array_ufunc.py    |   437 +
 .../test_avro_reader_fastavro_integration.py  |   655 +
 python/cudf/cudf/tests/test_binops.py         |  3337 ++++
 python/cudf/cudf/tests/test_buffer.py         |   113 +
 python/cudf/cudf/tests/test_categorical.py    |   957 ++
 python/cudf/cudf/tests/test_column.py         |   551 +
 .../cudf/cudf/tests/test_column_accessor.py   |   287 +
 python/cudf/cudf/tests/test_compile_udf.py    |    67 +
 python/cudf/cudf/tests/test_concat.py         |  1884 +++
 python/cudf/cudf/tests/test_contains.py       |   123 +
 python/cudf/cudf/tests/test_copying.py        |   429 +
 python/cudf/cudf/tests/test_csv.py            |  2250 +++
 python/cudf/cudf/tests/test_cuda_apply.py     |   224 +
 .../cudf/tests/test_cuda_array_interface.py   |   229 +
 .../cudf/cudf/tests/test_custom_accessor.py   |    92 +
 python/cudf/cudf/tests/test_cut.py            |   316 +
 python/cudf/cudf/tests/test_dask.py           |    21 +
 python/cudf/cudf/tests/test_dataframe.py      | 10737 ++++++++++++
 python/cudf/cudf/tests/test_dataframe_copy.py |   175 +
 python/cudf/cudf/tests/test_datasets.py       |    57 +
 python/cudf/cudf/tests/test_datetime.py       |  2299 +++
 python/cudf/cudf/tests/test_decimal.py        |   391 +
 python/cudf/cudf/tests/test_df_protocol.py    |   285 +
 python/cudf/cudf/tests/test_dlpack.py         |   216 +
 python/cudf/cudf/tests/test_doctests.py       |   112 +
 python/cudf/cudf/tests/test_dropna.py         |   296 +
 python/cudf/cudf/tests/test_dtypes.py         |   370 +
 python/cudf/cudf/tests/test_duplicates.py     |   631 +
 .../cudf/tests/test_extension_compilation.py  |   335 +
 python/cudf/cudf/tests/test_factorize.py      |   176 +
 python/cudf/cudf/tests/test_feather.py        |    85 +
 python/cudf/cudf/tests/test_gcs.py            |    73 +
 python/cudf/cudf/tests/test_groupby.py        |  3542 ++++
 python/cudf/cudf/tests/test_hash_vocab.py     |    29 +
 python/cudf/cudf/tests/test_hdf.py            |   151 +
 python/cudf/cudf/tests/test_hdfs.py           |   300 +
 python/cudf/cudf/tests/test_index.py          |  2951 ++++
 python/cudf/cudf/tests/test_indexing.py       |  2197 +++
 python/cudf/cudf/tests/test_interpolate.py    |   121 +
 python/cudf/cudf/tests/test_interval.py       |   184 +
 python/cudf/cudf/tests/test_joining.py        |  2272 +++
 python/cudf/cudf/tests/test_json.py           |  1372 ++
 python/cudf/cudf/tests/test_list.py           |   932 ++
 python/cudf/cudf/tests/test_monotonic.py      |   394 +
 python/cudf/cudf/tests/test_multiindex.py     |  2081 +++
 python/cudf/cudf/tests/test_no_cuinit.py      |   110 +
 python/cudf/cudf/tests/test_numba_import.py   |    48 +
 python/cudf/cudf/tests/test_numerical.py      |   427 +
 python/cudf/cudf/tests/test_numpy_interop.py  |    95 +
 python/cudf/cudf/tests/test_offset.py         |    61 +
 python/cudf/cudf/tests/test_onehot.py         |   174 +
 python/cudf/cudf/tests/test_options.py        |   129 +
 python/cudf/cudf/tests/test_orc.py            |  1939 +++
 python/cudf/cudf/tests/test_pack.py           |   317 +
 python/cudf/cudf/tests/test_pandas_interop.py |    90 +
 python/cudf/cudf/tests/test_parquet.py        |  2882 ++++
 python/cudf/cudf/tests/test_pickling.py       |   151 +
 python/cudf/cudf/tests/test_quantiles.py      |    92 +
 python/cudf/cudf/tests/test_query.py          |   233 +
 python/cudf/cudf/tests/test_query_mask.py     |    71 +
 python/cudf/cudf/tests/test_rank.py           |   151 +
 python/cudf/cudf/tests/test_reductions.py     |   366 +
 python/cudf/cudf/tests/test_replace.py        |  1353 ++
 python/cudf/cudf/tests/test_repr.py           |  1484 ++
 python/cudf/cudf/tests/test_resampling.py     |   160 +
 python/cudf/cudf/tests/test_reshape.py        |   808 +
 python/cudf/cudf/tests/test_rolling.py        |   562 +
 python/cudf/cudf/tests/test_s3.py             |   535 +
 python/cudf/cudf/tests/test_scalar.py         |   472 +
 python/cudf/cudf/tests/test_scan.py           |   279 +
 python/cudf/cudf/tests/test_search.py         |   170 +
 python/cudf/cudf/tests/test_serialize.py      |   403 +
 python/cudf/cudf/tests/test_series.py         |  2652 +++
 python/cudf/cudf/tests/test_seriesmap.py      |    95 +
 python/cudf/cudf/tests/test_setitem.py        |   506 +
 python/cudf/cudf/tests/test_sorting.py        |   399 +
 python/cudf/cudf/tests/test_sparse_df.py      |    18 +
 python/cudf/cudf/tests/test_spilling.py       |   661 +
 python/cudf/cudf/tests/test_stats.py          |   650 +
 python/cudf/cudf/tests/test_string.py         |  3476 ++++
 python/cudf/cudf/tests/test_string_udfs.py    |   371 +
 python/cudf/cudf/tests/test_struct.py         |   450 +
 python/cudf/cudf/tests/test_testing.py        |   439 +
 python/cudf/cudf/tests/test_timedelta.py      |  1471 ++
 python/cudf/cudf/tests/test_transform.py      |    34 +
 python/cudf/cudf/tests/test_udf_binops.py     |    51 +
 python/cudf/cudf/tests/test_udf_masked_ops.py |   994 ++
 python/cudf/cudf/tests/test_unaops.py         |   131 +
 python/cudf/cudf/tests/text/__init__.py       |     0
 .../cudf/tests/text/test_subword_tokenizer.py |   238 +
 .../cudf/cudf/tests/text/test_text_methods.py |  1026 ++
 python/cudf/cudf/tests/window/__init__.py     |     0
 python/cudf/cudf/tests/window/test_rolling.py |     1 +
 python/cudf/cudf/utils/__init__.py            |     1 +
 python/cudf/cudf/utils/_numba.py              |   191 +
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 create mode 100644 python/cudf/cudf/tests/series/test_conversion.py
 create mode 100644 python/cudf/cudf/tests/series/test_datetimelike.py
 create mode 100644 python/cudf/cudf/tests/series/test_function_application.py
 create mode 100644 python/cudf/cudf/tests/series/test_indexing.py
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 create mode 100644 python/cudf/cudf/tests/test_column.py
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 create mode 100644 python/cudf/cudf/tests/test_compile_udf.py
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 create mode 100644 python/cudf/cudf/tests/test_cuda_array_interface.py
 create mode 100644 python/cudf/cudf/tests/test_custom_accessor.py
 create mode 100644 python/cudf/cudf/tests/test_cut.py
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 create mode 100644 python/cudf/cudf/tests/test_decimal.py
 create mode 100644 python/cudf/cudf/tests/test_df_protocol.py
 create mode 100644 python/cudf/cudf/tests/test_dlpack.py
 create mode 100644 python/cudf/cudf/tests/test_doctests.py
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 create mode 100644 python/cudf/cudf/tests/test_dtypes.py
 create mode 100644 python/cudf/cudf/tests/test_duplicates.py
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 create mode 100644 python/cudf/cudf/tests/test_factorize.py
 create mode 100644 python/cudf/cudf/tests/test_feather.py
 create mode 100644 python/cudf/cudf/tests/test_gcs.py
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 create mode 100644 python/cudf/cudf/tests/test_hash_vocab.py
 create mode 100644 python/cudf/cudf/tests/test_hdf.py
 create mode 100644 python/cudf/cudf/tests/test_hdfs.py
 create mode 100644 python/cudf/cudf/tests/test_index.py
 create mode 100644 python/cudf/cudf/tests/test_indexing.py
 create mode 100644 python/cudf/cudf/tests/test_interpolate.py
 create mode 100644 python/cudf/cudf/tests/test_interval.py
 create mode 100644 python/cudf/cudf/tests/test_joining.py
 create mode 100644 python/cudf/cudf/tests/test_json.py
 create mode 100644 python/cudf/cudf/tests/test_list.py
 create mode 100644 python/cudf/cudf/tests/test_monotonic.py
 create mode 100644 python/cudf/cudf/tests/test_multiindex.py
 create mode 100644 python/cudf/cudf/tests/test_no_cuinit.py
 create mode 100644 python/cudf/cudf/tests/test_numba_import.py
 create mode 100644 python/cudf/cudf/tests/test_numerical.py
 create mode 100644 python/cudf/cudf/tests/test_numpy_interop.py
 create mode 100644 python/cudf/cudf/tests/test_offset.py
 create mode 100644 python/cudf/cudf/tests/test_onehot.py
 create mode 100644 python/cudf/cudf/tests/test_options.py
 create mode 100644 python/cudf/cudf/tests/test_orc.py
 create mode 100644 python/cudf/cudf/tests/test_pack.py
 create mode 100644 python/cudf/cudf/tests/test_pandas_interop.py
 create mode 100644 python/cudf/cudf/tests/test_parquet.py
 create mode 100644 python/cudf/cudf/tests/test_pickling.py
 create mode 100644 python/cudf/cudf/tests/test_quantiles.py
 create mode 100644 python/cudf/cudf/tests/test_query.py
 create mode 100644 python/cudf/cudf/tests/test_query_mask.py
 create mode 100644 python/cudf/cudf/tests/test_rank.py
 create mode 100644 python/cudf/cudf/tests/test_reductions.py
 create mode 100644 python/cudf/cudf/tests/test_replace.py
 create mode 100644 python/cudf/cudf/tests/test_repr.py
 create mode 100644 python/cudf/cudf/tests/test_resampling.py
 create mode 100644 python/cudf/cudf/tests/test_reshape.py
 create mode 100644 python/cudf/cudf/tests/test_rolling.py
 create mode 100644 python/cudf/cudf/tests/test_s3.py
 create mode 100644 python/cudf/cudf/tests/test_scalar.py
 create mode 100644 python/cudf/cudf/tests/test_scan.py
 create mode 100644 python/cudf/cudf/tests/test_search.py
 create mode 100644 python/cudf/cudf/tests/test_serialize.py
 create mode 100644 python/cudf/cudf/tests/test_series.py
 create mode 100644 python/cudf/cudf/tests/test_seriesmap.py
 create mode 100644 python/cudf/cudf/tests/test_setitem.py
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 create mode 100644 python/cudf/cudf/tests/test_sparse_df.py
 create mode 100644 python/cudf/cudf/tests/test_spilling.py
 create mode 100644 python/cudf/cudf/tests/test_stats.py
 create mode 100644 python/cudf/cudf/tests/test_string.py
 create mode 100644 python/cudf/cudf/tests/test_string_udfs.py
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 create mode 100644 python/cudf/cudf/tests/test_testing.py
 create mode 100644 python/cudf/cudf/tests/test_timedelta.py
 create mode 100644 python/cudf/cudf/tests/test_transform.py
 create mode 100644 python/cudf/cudf/tests/test_udf_binops.py
 create mode 100644 python/cudf/cudf/tests/test_udf_masked_ops.py
 create mode 100644 python/cudf/cudf/tests/test_unaops.py
 create mode 100644 python/cudf/cudf/tests/text/__init__.py
 create mode 100644 python/cudf/cudf/tests/text/test_subword_tokenizer.py
 create mode 100644 python/cudf/cudf/tests/text/test_text_methods.py
 create mode 100644 python/cudf/cudf/tests/window/__init__.py
 create mode 100644 python/cudf/cudf/tests/window/test_rolling.py
 create mode 100644 python/cudf/cudf/utils/__init__.py
 create mode 100644 python/cudf/cudf/utils/_numba.py
 create mode 100644 python/cudf/cudf/utils/_ptxcompiler.py
 create mode 100644 python/cudf/cudf/utils/applyutils.py
 create mode 100755 python/cudf/cudf/utils/cudautils.py
 create mode 100644 python/cudf/cudf/utils/docutils.py
 create mode 100644 python/cudf/cudf/utils/dtypes.py
 create mode 100644 python/cudf/cudf/utils/gpu_utils.py
 create mode 100644 python/cudf/cudf/utils/hash_vocab_utils.py
 create mode 100644 python/cudf/cudf/utils/ioutils.py
 create mode 100644 python/cudf/cudf/utils/metadata/__init__.py
 create mode 100644 python/cudf/cudf/utils/metadata/orc_column_statistics.proto
 create mode 100644 python/cudf/cudf/utils/queryutils.py
 create mode 100644 python/cudf/cudf/utils/string.py
 create mode 100644 python/cudf/cudf/utils/utils.py
 create mode 100644 python/cudf/cudf_pandas_tests/_magics_cpu_test.py
 create mode 100755 python/cudf/cudf_pandas_tests/_magics_gpu_test.py
 create mode 100644 python/cudf/cudf_pandas_tests/test_array_function.py
 create mode 100644 python/cudf/cudf_pandas_tests/test_cudf_pandas.py
 create mode 100644 python/cudf/cudf_pandas_tests/test_cudf_pandas_cudf_interop.py
 create mode 100644 python/cudf/cudf_pandas_tests/test_fast_slow_proxy.py
 create mode 100644 python/cudf/cudf_pandas_tests/test_magics.py
 create mode 100644 python/cudf/cudf_pandas_tests/test_profiler.py
 create mode 100644 python/cudf/pyproject.toml
 create mode 100644 python/cudf/setup.py
 create mode 100644 python/cudf/udf_cpp/CMakeLists.txt
 create mode 100644 python/cudf/udf_cpp/shim.cu
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/case.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/char_types.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/numeric.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/pad.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/replace.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/search.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/split.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/starts_with.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/strip.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_apis.hpp
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.cuh
 create mode 100644 python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.hpp
 create mode 100644 python/cudf/udf_cpp/strings/src/strings/udf/udf_apis.cu
 create mode 100644 python/cudf_kafka/cudf_kafka/__init__.py
 create mode 100644 python/cudf_kafka/cudf_kafka/_lib/__init__.pxd
 create mode 100644 python/cudf_kafka/cudf_kafka/_lib/kafka.pxd
 create mode 100644 python/cudf_kafka/cudf_kafka/_lib/kafka.pyx
 create mode 100644 python/cudf_kafka/pyproject.toml
 create mode 100644 python/cudf_kafka/setup.py
 create mode 100644 python/custreamz/.coveragerc
 create mode 120000 python/custreamz/LICENSE
 create mode 100644 python/custreamz/README.md
 create mode 100644 python/custreamz/custreamz/__init__.py
 create mode 100644 python/custreamz/custreamz/kafka.py
 create mode 100644 python/custreamz/custreamz/tests/__init__.py
 create mode 100644 python/custreamz/custreamz/tests/conftest.py
 create mode 100644 python/custreamz/custreamz/tests/test_dataframes.py
 create mode 100644 python/custreamz/custreamz/tests/test_kafka.py
 create mode 100644 python/custreamz/pyproject.toml
 create mode 100644 python/custreamz/setup.py
 create mode 100644 python/dask_cudf/.coveragerc
 create mode 120000 python/dask_cudf/LICENSE
 create mode 120000 python/dask_cudf/README.md
 create mode 100644 python/dask_cudf/dask_cudf/DASK_LICENSE.txt
 create mode 100644 python/dask_cudf/dask_cudf/__init__.py
 create mode 100644 python/dask_cudf/dask_cudf/accessors.py
 create mode 100644 python/dask_cudf/dask_cudf/backends.py
 create mode 100644 python/dask_cudf/dask_cudf/core.py
 create mode 100644 python/dask_cudf/dask_cudf/groupby.py
 create mode 100644 python/dask_cudf/dask_cudf/io/__init__.py
 create mode 100644 python/dask_cudf/dask_cudf/io/csv.py
 create mode 100644 python/dask_cudf/dask_cudf/io/json.py
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 create mode 100644 python/dask_cudf/dask_cudf/io/parquet.py
 create mode 100644 python/dask_cudf/dask_cudf/io/tests/__init__.py
 create mode 100644 python/dask_cudf/dask_cudf/io/tests/data/orc/sample.orc
 create mode 100644 python/dask_cudf/dask_cudf/io/tests/data/text/sample.pgn
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 create mode 100644 python/dask_cudf/dask_cudf/tests/__init__.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_accessor.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_applymap.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_binops.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_core.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_delayed_io.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_dispatch.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_distributed.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_groupby.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_join.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_onehot.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_reductions.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_sort.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/test_struct.py
 create mode 100644 python/dask_cudf/dask_cudf/tests/utils.py
 create mode 100644 python/dask_cudf/pyproject.toml
 create mode 100644 python/dask_cudf/setup.py

diff --git a/.clang-format b/.clang-format
new file mode 100644
index 0000000..26b9a5b
--- /dev/null
+++ b/.clang-format
@@ -0,0 +1,155 @@
+---
+# Refer to the following link for the explanation of each params:
+#   http://releases.llvm.org/8.0.0/tools/clang/docs/ClangFormatStyleOptions.html
+Language: Cpp
+# BasedOnStyle: Google
+AccessModifierOffset: -1
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: true
+AlignConsecutiveBitFields: true
+AlignConsecutiveDeclarations: false
+AlignConsecutiveMacros: true
+AlignEscapedNewlines: Left
+AlignOperands: true
+AlignTrailingComments: true
+AllowAllArgumentsOnNextLine: true
+AllowAllConstructorInitializersOnNextLine: true
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortBlocksOnASingleLine: true
+AllowShortCaseLabelsOnASingleLine: true
+AllowShortEnumsOnASingleLine: true
+AllowShortFunctionsOnASingleLine: All
+AllowShortIfStatementsOnASingleLine: true
+AllowShortLambdasOnASingleLine: true
+AllowShortLoopsOnASingleLine: false
+# This is deprecated
+AlwaysBreakAfterDefinitionReturnType: None
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: Yes
+BinPackArguments:  false
+BinPackParameters: false
+BraceWrapping:
+  AfterClass:            false
+  AfterControlStatement: false
+  AfterEnum:             false
+  AfterFunction:         false
+  AfterNamespace:        false
+  AfterObjCDeclaration:  false
+  AfterStruct:           false
+  AfterUnion:            false
+  AfterExternBlock:      false
+  BeforeCatch:           false
+  BeforeElse:            false
+  IndentBraces:          false
+  # disabling the below splits, else, they'll just add to the vertical length of source files!
+  SplitEmptyFunction: false
+  SplitEmptyRecord: false
+  SplitEmptyNamespace: false
+BreakAfterJavaFieldAnnotations: false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: WebKit
+BreakBeforeInheritanceComma: false
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializersBeforeComma: false
+BreakConstructorInitializers: BeforeColon
+BreakInheritanceList: BeforeColon
+BreakStringLiterals: true
+ColumnLimit: 100
+CommentPragmas: '^ IWYU pragma:'
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+# Kept the below 2 to be the same as `IndentWidth` to keep everything uniform
+ConstructorInitializerIndentWidth: 2
+ContinuationIndentWidth: 2
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+DisableFormat: false
+ExperimentalAutoDetectBinPacking: false
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks: Preserve
+IncludeIsMainRegex: '([-_](test|unittest))?$'
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Never
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 1
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+PenaltyReturnTypeOnItsOwnLine: 200
+PointerAlignment: Left
+RawStringFormats:
+  - Language: Cpp
+    Delimiters:
+      - cc
+      - CC
+      - cpp
+      - Cpp
+      - CPP
+      - 'c++'
+      - 'C++'
+    CanonicalDelimiter: ''
+  - Language: TextProto
+    Delimiters:
+      - pb
+      - PB
+      - proto
+      - PROTO
+    EnclosingFunctions:
+      - EqualsProto
+      - EquivToProto
+      - PARSE_PARTIAL_TEXT_PROTO
+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
+    BasedOnStyle: google
+# Enabling comment reflow causes doxygen comments to be messed up in their formats!
+ReflowComments: true
+SortIncludes: true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: true
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: false
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceBeforeSquareBrackets: false
+SpaceInEmptyBlock: false
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 2
+SpacesInAngles: false
+SpacesInConditionalStatement: false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard: c++17
+StatementMacros:
+  - Q_UNUSED
+  - QT_REQUIRE_VERSION
+# Be consistent with indent-width, even for people who use tab for indentation!
+TabWidth: 2
+UseTab: Never
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
new file mode 100644
index 0000000..9578d32
--- /dev/null
+++ b/.github/CODEOWNERS
@@ -0,0 +1,22 @@
+#cpp code owners
+cpp/                 @rapidsai/cudf-cpp-codeowners
+python/cudf/udf_cpp/ @rapidsai/cudf-cpp-codeowners
+
+#python code owners
+python/            @rapidsai/cudf-python-codeowners
+notebooks/         @rapidsai/cudf-python-codeowners
+python/dask_cudf/  @rapidsai/cudf-dask-codeowners
+
+#cmake code owners
+cpp/CMakeLists.txt               @rapidsai/cudf-cmake-codeowners
+cpp/libcudf_kafka/CMakeLists.txt @rapidsai/cudf-cmake-codeowners
+**/cmake/                        @rapidsai/cudf-cmake-codeowners
+
+#java code owners
+java/              @rapidsai/cudf-java-codeowners
+
+#build/ops code owners
+.github/           @rapidsai/ops-codeowners
+/ci/               @rapidsai/ops-codeowners
+conda/             @rapidsai/ops-codeowners
+dependencies.yaml  @rapidsai/ops-codeowners
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000..bab613a
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,28 @@
+---
+name: Bug report
+about: Create a bug report to help us improve cuDF
+title: "[BUG]"
+labels: "? - Needs Triage, bug"
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Steps/Code to reproduce bug**
+Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Environment overview (please complete the following information)**
+ - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)]
+ - Method of cuDF install: [conda, Docker, or from source]
+   - If method of install is [Docker], provide `docker pull` & `docker run` commands used
+
+**Environment details**
+Please run and paste the output of the `cudf/print_env.sh` script here, to gather any other relevant environment details
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/documentation-request.md b/.github/ISSUE_TEMPLATE/documentation-request.md
new file mode 100644
index 0000000..89a026f
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documentation-request.md
@@ -0,0 +1,35 @@
+---
+name: Documentation request
+about: Report incorrect or needed documentation
+title: "[DOC]"
+labels: "? - Needs Triage, doc"
+assignees: ''
+
+---
+
+## Report incorrect documentation
+
+**Location of incorrect documentation**
+Provide links and line numbers if applicable.
+
+**Describe the problems or issues found in the documentation**
+A clear and concise description of what you found to be incorrect.
+
+**Steps taken to verify documentation is incorrect**
+List any steps you have taken:
+
+**Suggested fix for documentation**
+Detail proposed changes to fix the documentation if you have any.
+
+---
+
+## Report needed documentation
+
+**Report needed documentation**
+A clear and concise description of what documentation you believe it is needed and why.
+
+**Describe the documentation you'd like**
+A clear and concise description of what you want to happen.
+
+**Steps taken to search for needed documentation**
+List any steps you have taken:
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..b5bf48e
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for cuDF
+title: "[FEA]"
+labels: "? - Needs Triage, feature request"
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I wish I could use cuDF to do [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context, code examples, or references to existing implementations about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/pandas_function_request.md b/.github/ISSUE_TEMPLATE/pandas_function_request.md
new file mode 100644
index 0000000..1cecca7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/pandas_function_request.md
@@ -0,0 +1,22 @@
+---
+name: Request a Missing Pandas Function
+about: Request GPU support for a function executed on the CPU in pandas accelerator mode.
+title: "[FEA]"
+labels: "? - Needs Triage, feature request"
+assignees: ''
+
+---
+
+This issue template is intended to be used primarily for requests related to pandas accelerator mode. If you'd like to file a general cuDF feature request, please [click here](https://github.com/rapidsai/cudf/issues/new?assignees=&labels=%3F+-+Needs+Triage%2C+feature+request&projects=&template=feature_request.md&title=%5BFEA%5D).
+
+
+**Missing Pandas Feature Request**
+A clear and concise summary of the pandas function(s) you'd like to be able run with cuDF.
+
+
+**Profiler Output**
+If you used the profiler in pandas accelerator mode, please provide the full output of your profiling report.
+
+
+**Additional context**
+Add any other context, code examples, or references to existing implementations about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/submit-question.md b/.github/ISSUE_TEMPLATE/submit-question.md
new file mode 100644
index 0000000..ca1bfeb
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/submit-question.md
@@ -0,0 +1,10 @@
+---
+name: Submit question
+about: Ask a general question about cuDF
+title: "[QST]"
+labels: "? - Needs Triage, question"
+assignees: ''
+
+---
+
+**What is your question?**
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000..301037c
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,9 @@
+## Description
+<!-- Provide a standalone description of changes in this PR. -->
+<!-- Reference any issues closed by this PR with "closes #1234". -->
+<!-- Note: The pull request title will be included in the CHANGELOG. -->
+
+## Checklist
+- [ ] I am familiar with the [Contributing Guidelines](https://github.com/rapidsai/cudf/blob/HEAD/CONTRIBUTING.md).
+- [ ] New or existing tests cover these changes.
+- [ ] The documentation is up to date with these changes.
diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml
new file mode 100644
index 0000000..895ba83
--- /dev/null
+++ b/.github/copy-pr-bot.yaml
@@ -0,0 +1,4 @@
+# Configuration file for `copy-pr-bot` GitHub App
+# https://docs.gha-runners.nvidia.com/apps/copy-pr-bot/
+
+enabled: true
diff --git a/.github/labeler.yml b/.github/labeler.yml
new file mode 100644
index 0000000..b0b0db9
--- /dev/null
+++ b/.github/labeler.yml
@@ -0,0 +1,21 @@
+# Documentation for config - https://github.com/actions/labeler#common-examples
+
+cuDF (Python):
+  - 'python/**'
+  - 'notebooks/**'
+
+libcudf:
+  - 'cpp/**'
+
+CMake:
+  - '**/CMakeLists.txt'
+  - '**/cmake/**'
+
+cuDF (Java):
+  - 'java/**'
+
+ci:
+  - 'ci/**'
+
+conda:
+  - 'conda/**'
diff --git a/.github/ops-bot.yaml b/.github/ops-bot.yaml
new file mode 100644
index 0000000..d2ca789
--- /dev/null
+++ b/.github/ops-bot.yaml
@@ -0,0 +1,7 @@
+# This file controls which features from the `ops-bot` repository below are enabled.
+# - https://github.com/rapidsai/ops-bot
+
+auto_merger: true
+branch_checker: true
+label_checker: true
+release_drafter: true
diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
new file mode 100644
index 0000000..666d884
--- /dev/null
+++ b/.github/workflows/build.yaml
@@ -0,0 +1,109 @@
+name: build
+
+on:
+  push:
+    branches:
+      - "branch-*"
+    tags:
+      - v[0-9][0-9].[0-9][0-9].[0-9][0-9]
+  workflow_dispatch:
+    inputs:
+      branch:
+        required: true
+        type: string
+      date:
+        required: true
+        type: string
+      sha:
+        required: true
+        type: string
+      build_type:
+        type: string
+        default: nightly
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  cpp-build:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.10
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+  python-build:
+    needs: [cpp-build]
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-23.10
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+  upload-conda:
+    needs: [cpp-build, python-build]
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.10
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      skip_upload_pkgs: libcudf-example
+  docs-build:
+    if: github.ref_type == 'branch'
+    needs: python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      arch: "amd64"
+      branch: ${{ inputs.branch }}
+      build_type: ${{ inputs.build_type || 'branch' }}
+      container_image: "rapidsai/ci-conda:latest"
+      date: ${{ inputs.date }}
+      node_type: "gpu-v100-latest-1"
+      run_script: "ci/build_docs.sh"
+      sha: ${{ inputs.sha }}
+  wheel-build-cudf:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.10
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      script: ci/build_wheel_cudf.sh
+  wheel-publish-cudf:
+    needs: wheel-build-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.10
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      package-name: cudf
+  wheel-build-dask-cudf:
+    needs: wheel-publish-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64" and .PY_VER == "3.10" and (.CUDA_VER == "11.8.0" or .CUDA_VER == "12.0.1")))
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      script: ci/build_wheel_dask_cudf.sh
+  wheel-publish-dask-cudf:
+    needs: wheel-build-dask-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.10
+    with:
+      build_type: ${{ inputs.build_type || 'branch' }}
+      branch: ${{ inputs.branch }}
+      sha: ${{ inputs.sha }}
+      date: ${{ inputs.date }}
+      package-name: dask_cudf
diff --git a/.github/workflows/jni-docker-build.yml b/.github/workflows/jni-docker-build.yml
new file mode 100644
index 0000000..0bdc409
--- /dev/null
+++ b/.github/workflows/jni-docker-build.yml
@@ -0,0 +1,53 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: JNI Docker Build
+
+on:
+  workflow_dispatch: # manual trigger only
+
+concurrency:
+  group: jni-docker-build-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  docker-build:
+    if: github.repository == 'rapidsai/cudf'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.GPUCIBOT_DOCKERHUB_USER }}
+          password: ${{ secrets.GPUCIBOT_DOCKERHUB_TOKEN }}
+
+      - name: Set ENVs
+        run: |
+          echo "IMAGE_NAME=rapidsai/cudf-jni-build" >> $GITHUB_ENV
+          echo "IMAGE_REF=${GITHUB_REF_NAME}" >> $GITHUB_ENV
+
+      - name: Build and Push
+        uses: docker/build-push-action@v3
+        with:
+          push: true
+          file: java/ci/Dockerfile.centos7
+          tags: "${{ env.IMAGE_NAME }}:${{ env.IMAGE_REF }}"
diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml
new file mode 100644
index 0000000..23956a0
--- /dev/null
+++ b/.github/workflows/labeler.yml
@@ -0,0 +1,11 @@
+name: "Pull Request Labeler"
+on:
+- pull_request_target
+
+jobs:
+  triage:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/labeler@main
+      with:
+        repo-token: "${{ secrets.GITHUB_TOKEN }}"
diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml
new file mode 100644
index 0000000..abf5fcf
--- /dev/null
+++ b/.github/workflows/pr.yaml
@@ -0,0 +1,180 @@
+name: pr
+
+on:
+  push:
+    branches:
+      - "pull-request/[0-9]+"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  pr-builder:
+    needs:
+      - checks
+      - conda-cpp-build
+      - conda-cpp-tests
+      - conda-python-build
+      - conda-python-cudf-tests
+      - conda-python-other-tests
+      - conda-java-tests
+      - conda-notebook-tests
+      - docs-build
+      - wheel-build-cudf
+      - wheel-tests-cudf
+      - wheel-build-dask-cudf
+      - wheel-tests-dask-cudf
+      - unit-tests-cudf-pandas
+      - pandas-tests
+      #- pandas-tests-diff
+      #- pandas-tests-diff-comment
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-23.10
+  checks:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-23.10
+    with:
+      enable_check_generated_files: false
+  conda-cpp-build:
+    needs: checks
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.10
+    with:
+      build_type: pull-request
+  conda-cpp-tests:
+    needs: conda-cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.10
+    with:
+      build_type: pull-request
+  conda-python-build:
+    needs: conda-cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-23.10
+    with:
+      build_type: pull-request
+  conda-python-cudf-tests:
+    needs: conda-python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      test_script: "ci/test_python_cudf.sh"
+  conda-python-other-tests:
+    # Tests for dask_cudf, custreamz, cudf_kafka are separated for CI parallelism
+    needs: conda-python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      test_script: "ci/test_python_other.sh"
+  conda-java-tests:
+    needs: conda-cpp-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_java.sh"
+  conda-notebook-tests:
+    needs: conda-python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_notebooks.sh"
+  docs-build:
+    needs: conda-python-build
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/build_docs.sh"
+  wheel-build-cudf:
+    needs: checks
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      script: "ci/build_wheel_cudf.sh"
+  wheel-tests-cudf:
+    needs: wheel-build-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      build_type: pull-request
+      script: ci/test_wheel_cudf.sh
+  wheel-build-dask-cudf:
+    needs: wheel-tests-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64" and .PY_VER == "3.10" and (.CUDA_VER == "11.8.0" or .CUDA_VER == "12.0.1")))
+      build_type: pull-request
+      script: "ci/build_wheel_dask_cudf.sh"
+  wheel-tests-dask-cudf:
+    needs: wheel-build-dask-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64" and .PY_VER == "3.10" and (.CUDA_VER == "11.8.0" or .CUDA_VER == "12.0.1")))
+      build_type: pull-request
+      script: ci/test_wheel_dask_cudf.sh
+  unit-tests-cudf-pandas:
+    needs: wheel-build-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64" and .PY_VER == "3.10" and (.CUDA_VER == "11.8.0" or .CUDA_VER == "12.0.1")))
+      build_type: pull-request
+      script: ci/cudf_pandas_scripts/run_tests.sh
+  pandas-tests:
+    # run the Pandas unit tests using PR branch
+    needs: wheel-build-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64")) | max_by(.CUDA_VER) | [.]
+      build_type: pull-request
+      script: ci/cudf_pandas_scripts/pandas-tests/run.sh pr
+  #pandas-tests-diff:
+  #  # diff the results of running the Pandas unit tests and publish a job summary
+  #  needs: [pandas-tests-main, pandas-tests-pr]
+  #  secrets: inherit
+  #  # This branch exports a `job_output` output that the downstream job reads.
+  #  uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@wence/fea/custom-job-output
+  #  with:
+  #    node_type: cpu4
+  #    build_type: pull-request
+  #    run_script: ci/cudf_pandas_scripts/pandas-tests/diff.sh
+  #pandas-tests-diff-comment:
+  #  # Post comment of pass/fail rate on PR
+  #  runs-on: ubuntu-latest
+  #  needs: pandas-tests-diff
+  #  steps:
+  #    - uses: actions/github-script@v6
+  #      with:
+  #        script: |
+  #          const branch = process.env.GITHUB_REF_NAME;
+  #          const prBranchPattern = new RegExp("^pull-request/[0-9]+$");
+  #          if (!branch.match(prBranchPattern)) {
+  #            throw new Error(`${branch} does not match PR branch pattern.`);
+  #          }
+  #          const summary_url = `${context.serverUrl}/${context.repo.owner}/${context.repo.repo}/actions/runs/${context.runId}`;
+  #          const prNumber = branch.split("/")[1];
+  #          const summary_comment = `${{ needs.pandas-tests-diff.outputs.job_output }}`;
+  #          github.rest.issues.createComment({
+  #            issue_number: prNumber,
+  #            owner: context.repo.owner,
+  #            repo: context.repo.repo,
+  #            body: `${summary_comment}\n\nHere is [a link to the full test summary](${summary_url}).\n`
+  #          })
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
new file mode 100644
index 0000000..49a9c73
--- /dev/null
+++ b/.github/workflows/test.yaml
@@ -0,0 +1,120 @@
+name: test
+
+on:
+  workflow_dispatch:
+    inputs:
+      branch:
+        required: true
+        type: string
+      date:
+        required: true
+        type: string
+      sha:
+        required: true
+        type: string
+
+jobs:
+  conda-cpp-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+  conda-cpp-memcheck-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_cpp_memcheck.sh"
+  conda-python-cudf-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      test_script: "ci/test_python_cudf.sh"
+  conda-python-other-tests:
+    # Tests for dask_cudf, custreamz, cudf_kafka are separated for CI parallelism
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      test_script: "ci/test_python_other.sh"
+  conda-java-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_java.sh"
+  conda-notebook-tests:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: "ci/test_notebooks.sh"
+  wheel-tests-cudf:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      script: ci/test_wheel_cudf.sh
+  wheel-tests-dask-cudf:
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64" and .PY_VER == "3.10" and (.CUDA_VER == "11.8.0" or .CUDA_VER == "12.0.1")))
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      script: ci/test_wheel_dask_cudf.sh
+  unit-tests-cudf-pandas:
+    needs: wheel-build-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      script: ci/cudf_pandas_scripts/run_tests.sh
+  pandas-tests:
+    # run the Pandas unit tests
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.10
+    with:
+      matrix_filter: map(select(.ARCH == "amd64")) | max_by(.CUDA_VER) | [.]
+      build_type: nightly
+      branch: ${{ inputs.branch }}
+      date: ${{ inputs.date }}
+      sha: ${{ inputs.sha }}
+      # pr mode uses the HEAD of the branch, which is also correct for nightlies
+      script: ci/cudf_pandas_scripts/pandas-tests/run.sh pr
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..4a4a6a9
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,175 @@
+## Common
+__pycache__
+*.py[cod]
+*$py.class
+*.a
+*.o
+*.so
+*.dylib
+.cache
+.vscode
+*.swp
+*.pytest_cache
+DartConfiguration.tcl
+.DS_Store
+*.manifest
+*.spec
+.nfs*
+.clangd
+compile_commands.json
+
+## Python build directories & artifacts
+dask-worker-space/
+dist/
+cudf.egg-info/
+python/build
+python/*/build
+python/cudf/cudf-coverage.xml
+python/cudf/*/_lib/**/*.cpp
+python/cudf/*/_lib/**/*.h
+python/cudf/*/_lib/.nfs*
+python/cudf/*/_cuda/*.cpp
+python/cudf/*.ipynb
+python/cudf/.ipynb_checkpoints
+python/*/record.txt
+python/cudf/cudf/core/udf/*.ptx
+python/cudf_kafka/*/_lib/**/*.cpp
+python/cudf_kafka/*/_lib/**/*.h
+python/custreamz/*/_lib/**/*.cpp
+python/custreamz/*/_lib/**/*.h
+.Python
+env/
+develop-eggs/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+_skbuild/
+*.egg-info/
+.installed.cfg
+*.egg
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+junit-cudf.xml
+*.cover
+.hypothesis/
+test-results
+
+## Patching
+*.orig
+*.rej
+
+## C++ build directories & artifacts
+CMakeFiles/
+Debug
+build/
+cpp/build/
+cpp/include/cudf/ipc_generated/*.h
+cpp/thirdparty/googletest/
+
+## Eclipse IDE
+.project
+.cproject
+.settings
+
+## IntelliJ IDE
+.idea/
+.idea_modules/
+*.iml
+*.ipr
+*.iws
+
+## Doxygen
+cpp/doxygen/html
+
+#Java
+target
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+## VSCode IDE
+.vscode
+
+# Dask
+dask-worker-space/
+
+# protobuf
+**/*_pb2.py
+
+# Sphinx docs & build artifacts
+docs/cudf/source/api_docs/generated/*
+docs/cudf/source/user_guide/api_docs/api/*
+docs/cudf/source/user_guide/example_output/*
+docs/cudf/source/user_guide/cudf.*Dtype.*.rst
+_html
+_text
+jupyter_execute
+
+# cibuildwheel
+/wheelhouse
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..a621047
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,160 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+repos:
+      - repo: https://github.com/pre-commit/pre-commit-hooks
+        rev: v4.3.0
+        hooks:
+              - id: trailing-whitespace
+                exclude: |
+                  (?x)^(
+                    ^python/cudf/cudf/tests/data/subword_tokenizer_data/.*
+                  )
+              - id: end-of-file-fixer
+                exclude: |
+                  (?x)^(
+                    ^python/cudf/cudf/tests/data/subword_tokenizer_data/.*
+                  )
+      - repo: https://github.com/PyCQA/isort
+        rev: 5.12.0
+        hooks:
+              - id: isort
+                # Use the config file specific to each subproject so that each
+                # project can specify its own first/third-party packages.
+                args: ["--config-root=python/", "--resolve-all-configs"]
+                files: python/.*
+                types_or: [python, cython, pyi]
+      - repo: https://github.com/psf/black
+        rev: 22.3.0
+        hooks:
+              - id: black
+                files: python/.*
+                # Explicitly specify the pyproject.toml at the repo root, not per-project.
+                args: ["--config", "pyproject.toml"]
+      - repo: https://github.com/MarcoGorelli/cython-lint
+        rev: v0.15.0
+        hooks:
+              - id: cython-lint
+      - repo: https://github.com/pre-commit/mirrors-mypy
+        rev: 'v1.3.0'
+        hooks:
+              - id: mypy
+                additional_dependencies: [types-cachetools]
+                args: ["--config-file=pyproject.toml",
+                       "python/cudf/cudf",
+                       "python/custreamz/custreamz",
+                       "python/cudf_kafka/cudf_kafka",
+                       "python/dask_cudf/dask_cudf"]
+                pass_filenames: false
+      - repo: https://github.com/PyCQA/pydocstyle
+        rev: 6.1.1
+        hooks:
+              - id: pydocstyle
+                # https://github.com/PyCQA/pydocstyle/issues/603
+                additional_dependencies: [toml]
+                args: ["--config=pyproject.toml"]
+                exclude: |
+                  (?x)^(
+                    ^python/cudf/cudf/pandas/scripts/.*|
+                    ^python/cudf/cudf_pandas_tests/.*
+                  )
+      - repo: https://github.com/nbQA-dev/nbQA
+        rev: 1.6.3
+        hooks:
+              - id: nbqa-isort
+                # Use the cudf_kafka isort orderings in notebooks so that dask
+                # and RAPIDS packages have their own sections.
+                args: ["--settings-file=python/cudf_kafka/pyproject.toml"]
+              - id: nbqa-black
+                # Explicitly specify the pyproject.toml at the repo root, not per-project.
+                args: ["--config=pyproject.toml"]
+      - repo: https://github.com/pre-commit/mirrors-clang-format
+        rev: v16.0.6
+        hooks:
+              - id: clang-format
+                types_or: [c, c++, cuda]
+                args: ["-fallback-style=none", "-style=file", "-i"]
+      - repo: https://github.com/sirosen/texthooks
+        rev: 0.4.0
+        hooks:
+              - id: fix-smartquotes
+                exclude: |
+                  (?x)^(
+                    ^cpp/include/cudf_test/cxxopts.hpp|
+                    ^python/cudf/cudf/tests/data/subword_tokenizer_data/.*|
+                    ^python/cudf/cudf/tests/text/test_text_methods.py
+                  )
+      - repo: local
+        hooks:
+              - id: no-deprecationwarning
+                name: no-deprecationwarning
+                description: 'Enforce that DeprecationWarning is not introduced (use FutureWarning instead)'
+                entry: '(category=|\s)DeprecationWarning[,)]'
+                language: pygrep
+                types_or: [python, cython]
+              - id: no-programmatic-xfail
+                name: no-programmatic-xfail
+                description: 'Enforce that pytest.xfail is not introduced (see dev docs for details)'
+                entry: 'pytest\.xfail'
+                language: pygrep
+                types: [python]
+              - id: cmake-format
+                name: cmake-format
+                entry: ./cpp/scripts/run-cmake-format.sh cmake-format
+                language: python
+                types: [cmake]
+                # Note that pre-commit autoupdate does not update the versions
+                # of dependencies, so we'll have to update this manually.
+                additional_dependencies:
+                  - cmakelang==0.6.13
+                verbose: true
+                require_serial: true
+              - id: cmake-lint
+                name: cmake-lint
+                entry: ./cpp/scripts/run-cmake-format.sh cmake-lint
+                language: python
+                types: [cmake]
+                # Note that pre-commit autoupdate does not update the versions
+                # of dependencies, so we'll have to update this manually.
+                additional_dependencies:
+                  - cmakelang==0.6.13
+                verbose: true
+                require_serial: true
+              - id: copyright-check
+                name: copyright-check
+                entry: python ./ci/checks/copyright.py --git-modified-only --update-current-year
+                language: python
+                pass_filenames: false
+                additional_dependencies: [gitpython]
+              - id: doxygen-check
+                name: doxygen-check
+                entry: ./ci/checks/doxygen.sh
+                files: ^cpp/include/
+                types_or: [file]
+                language: system
+                pass_filenames: false
+                verbose: true
+      - repo: https://github.com/codespell-project/codespell
+        rev: v2.2.2
+        hooks:
+              - id: codespell
+                additional_dependencies: [tomli]
+                args: ["--toml", "pyproject.toml"]
+                exclude: |
+                  (?x)^(
+                    .*test.*|
+                    ^CHANGELOG.md$
+                  )
+      - repo: https://github.com/rapidsai/dependency-file-generator
+        rev: v1.5.1
+        hooks:
+            - id: rapids-dependency-file-generator
+              args: ["--clean"]
+      - repo: https://github.com/astral-sh/ruff-pre-commit
+        rev: v0.0.278
+        hooks:
+          - id: ruff
+            files: python/.*$
+
+
+default_language_version:
+      python: python3
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..ecd547a
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,6900 @@
+# cuDF 23.10.00 (11 Oct 2023)
+
+## 🚨 Breaking Changes
+
+- Expose stream parameter in public nvtext ngram APIs ([#14061](https://github.com/rapidsai/cudf/pull/14061)) [@davidwendt](https://github.com/davidwendt)
+- Raise `MixedTypeError` when a column of mixed-dtype is being constructed ([#14050](https://github.com/rapidsai/cudf/pull/14050)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise `NotImplementedError` for `MultiIndex.to_series` ([#14049](https://github.com/rapidsai/cudf/pull/14049)) [@galipremsagar](https://github.com/galipremsagar)
+- Create table_input_metadata from a table_metadata ([#13920](https://github.com/rapidsai/cudf/pull/13920)) [@etseidl](https://github.com/etseidl)
+- Enable RLE boolean encoding for v2 Parquet files ([#13886](https://github.com/rapidsai/cudf/pull/13886)) [@etseidl](https://github.com/etseidl)
+- Change `NA` to `NaT` for `datetime` and `timedelta` types ([#13868](https://github.com/rapidsai/cudf/pull/13868)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `any`, `all` reduction behavior for `axis=None` and warn for other reductions ([#13831](https://github.com/rapidsai/cudf/pull/13831)) [@galipremsagar](https://github.com/galipremsagar)
+- Add minhash support for MurmurHash3_x64_128 ([#13796](https://github.com/rapidsai/cudf/pull/13796)) [@davidwendt](https://github.com/davidwendt)
+- Remove the libcudf cudf::offset_type type ([#13788](https://github.com/rapidsai/cudf/pull/13788)) [@davidwendt](https://github.com/davidwendt)
+- Raise error when trying to join `datetime` and `timedelta` types with other types ([#13786](https://github.com/rapidsai/cudf/pull/13786)) [@galipremsagar](https://github.com/galipremsagar)
+- Update to Cython 3.0.0 ([#13777](https://github.com/rapidsai/cudf/pull/13777)) [@vyasr](https://github.com/vyasr)
+- Raise error on constructing an array from mixed type inputs ([#13768](https://github.com/rapidsai/cudf/pull/13768)) [@galipremsagar](https://github.com/galipremsagar)
+- Enforce deprecations in `23.10` ([#13732](https://github.com/rapidsai/cudf/pull/13732)) [@galipremsagar](https://github.com/galipremsagar)
+- Upgrade to arrow 12 ([#13728](https://github.com/rapidsai/cudf/pull/13728)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove Arrow dependency from the `datasource.hpp` public header ([#13698](https://github.com/rapidsai/cudf/pull/13698)) [@vuule](https://github.com/vuule)
+
+## 🐛 Bug Fixes
+
+- Fix inaccurate ceil/floor and inaccurate rescaling casts of fixed-point values. ([#14242](https://github.com/rapidsai/cudf/pull/14242)) [@bdice](https://github.com/bdice)
+- Fix inaccuracy in decimal128 rounding. ([#14233](https://github.com/rapidsai/cudf/pull/14233)) [@bdice](https://github.com/bdice)
+- Workaround for illegal instruction error in sm90 for warp instrinsics with mask ([#14201](https://github.com/rapidsai/cudf/pull/14201)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix pytorch related pytest ([#14198](https://github.com/rapidsai/cudf/pull/14198)) [@galipremsagar](https://github.com/galipremsagar)
+- Pin to `aws-sdk-cpp&lt;1.11` ([#14173](https://github.com/rapidsai/cudf/pull/14173)) [@pentschev](https://github.com/pentschev)
+- Fix assert failure for range window functions ([#14168](https://github.com/rapidsai/cudf/pull/14168)) [@mythrocks](https://github.com/mythrocks)
+- Fix Memcheck error found in JSON_TEST JsonReaderTest.ErrorStrings ([#14164](https://github.com/rapidsai/cudf/pull/14164)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix calls to copy_bitmask to pass stream parameter ([#14158](https://github.com/rapidsai/cudf/pull/14158)) [@davidwendt](https://github.com/davidwendt)
+- Fix DataFrame from Series with different CategoricalIndexes ([#14157](https://github.com/rapidsai/cudf/pull/14157)) [@mroeschke](https://github.com/mroeschke)
+- Pin to numpy&lt;1.25 and numba&lt;0.58 to avoid errors and deprecation warnings-as-errors. ([#14156](https://github.com/rapidsai/cudf/pull/14156)) [@bdice](https://github.com/bdice)
+- Fix kernel launch error for cudf::io::orc::gpu::rowgroup_char_counts_kernel ([#14139](https://github.com/rapidsai/cudf/pull/14139)) [@davidwendt](https://github.com/davidwendt)
+- Don&#39;t sort columns for DataFrame init from list of Series ([#14136](https://github.com/rapidsai/cudf/pull/14136)) [@mroeschke](https://github.com/mroeschke)
+- Fix DataFrame.values with no columns but index ([#14134](https://github.com/rapidsai/cudf/pull/14134)) [@mroeschke](https://github.com/mroeschke)
+- Avoid circular cimports in _lib/cpp/reduce.pxd ([#14125](https://github.com/rapidsai/cudf/pull/14125)) [@vyasr](https://github.com/vyasr)
+- Add support for nested dict in `DataFrame` constructor ([#14119](https://github.com/rapidsai/cudf/pull/14119)) [@galipremsagar](https://github.com/galipremsagar)
+- Restrict iterables of `DataFrame`&#39;s as input to `DataFrame` constructor ([#14118](https://github.com/rapidsai/cudf/pull/14118)) [@galipremsagar](https://github.com/galipremsagar)
+- Allow `numeric_only=True` for reduction operations on numeric types ([#14111](https://github.com/rapidsai/cudf/pull/14111)) [@galipremsagar](https://github.com/galipremsagar)
+- Preserve name of the column while initializing a `DataFrame` ([#14110](https://github.com/rapidsai/cudf/pull/14110)) [@galipremsagar](https://github.com/galipremsagar)
+- Correct numerous 20054-D: dynamic initialization errors found on arm+12.2 ([#14108](https://github.com/rapidsai/cudf/pull/14108)) [@robertmaynard](https://github.com/robertmaynard)
+- Drop `kwargs` from `Series.count` ([#14106](https://github.com/rapidsai/cudf/pull/14106)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix naming issues with `Index.to_frame` and `MultiIndex.to_frame` APIs ([#14105](https://github.com/rapidsai/cudf/pull/14105)) [@galipremsagar](https://github.com/galipremsagar)
+- Only use memory resources that haven&#39;t been freed ([#14103](https://github.com/rapidsai/cudf/pull/14103)) [@robertmaynard](https://github.com/robertmaynard)
+- Add support for `__round__` in `Series` and `DataFrame` ([#14099](https://github.com/rapidsai/cudf/pull/14099)) [@galipremsagar](https://github.com/galipremsagar)
+- Validate ignore_index type in drop_duplicates ([#14098](https://github.com/rapidsai/cudf/pull/14098)) [@mroeschke](https://github.com/mroeschke)
+- Fix renaming `Series` and `Index` ([#14080](https://github.com/rapidsai/cudf/pull/14080)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise NotImplementedError in to_datetime if Z (or tz component) in string ([#14074](https://github.com/rapidsai/cudf/pull/14074)) [@mroeschke](https://github.com/mroeschke)
+- Raise NotImplementedError for datetime strings with UTC offset ([#14070](https://github.com/rapidsai/cudf/pull/14070)) [@mroeschke](https://github.com/mroeschke)
+- Update pyarrow-related dispatch logic in dask_cudf ([#14069](https://github.com/rapidsai/cudf/pull/14069)) [@rjzamora](https://github.com/rjzamora)
+- Use `conda mambabuild` rather than `mamba mambabuild` ([#14067](https://github.com/rapidsai/cudf/pull/14067)) [@wence-](https://github.com/wence-)
+- Raise NotImplementedError in to_datetime with dayfirst without infer_format ([#14058](https://github.com/rapidsai/cudf/pull/14058)) [@mroeschke](https://github.com/mroeschke)
+- Fix various issues in `Index.intersection` ([#14054](https://github.com/rapidsai/cudf/pull/14054)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `Index.difference` to match with pandas ([#14053](https://github.com/rapidsai/cudf/pull/14053)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix empty string column construction ([#14052](https://github.com/rapidsai/cudf/pull/14052)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `IntervalIndex.union` to preserve type-metadata ([#14051](https://github.com/rapidsai/cudf/pull/14051)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise `MixedTypeError` when a column of mixed-dtype is being constructed ([#14050](https://github.com/rapidsai/cudf/pull/14050)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise `NotImplementedError` for `MultiIndex.to_series` ([#14049](https://github.com/rapidsai/cudf/pull/14049)) [@galipremsagar](https://github.com/galipremsagar)
+- Ignore compile_commands.json ([#14048](https://github.com/rapidsai/cudf/pull/14048)) [@harrism](https://github.com/harrism)
+- Raise TypeError for any non-parseable argument in to_datetime ([#14044](https://github.com/rapidsai/cudf/pull/14044)) [@mroeschke](https://github.com/mroeschke)
+- Raise NotImplementedError for to_datetime with z format ([#14037](https://github.com/rapidsai/cudf/pull/14037)) [@mroeschke](https://github.com/mroeschke)
+- Implement `sort_remaining` for `sort_index` ([#14033](https://github.com/rapidsai/cudf/pull/14033)) [@wence-](https://github.com/wence-)
+- Raise NotImplementedError for Categoricals with timezones ([#14032](https://github.com/rapidsai/cudf/pull/14032)) [@mroeschke](https://github.com/mroeschke)
+- Temporary fix Parquet metadata with empty value string being ignored from writing ([#14026](https://github.com/rapidsai/cudf/pull/14026)) [@ttnghia](https://github.com/ttnghia)
+- Preserve types of scalar being returned when possible in `quantile` ([#14014](https://github.com/rapidsai/cudf/pull/14014)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix return type of `MultiIndex.difference` ([#14009](https://github.com/rapidsai/cudf/pull/14009)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise an error when timezone subtypes are encountered in `pd.IntervalDtype` ([#14006](https://github.com/rapidsai/cudf/pull/14006)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix map column can not be non-nullable for java ([#14003](https://github.com/rapidsai/cudf/pull/14003)) [@res-life](https://github.com/res-life)
+- Fix `name` selection in `Index.difference` and `Index.intersection` ([#13986](https://github.com/rapidsai/cudf/pull/13986)) [@galipremsagar](https://github.com/galipremsagar)
+- Restore column type metadata with `dropna` to fix `factorize` API ([#13980](https://github.com/rapidsai/cudf/pull/13980)) [@galipremsagar](https://github.com/galipremsagar)
+- Use thread_index_type to avoid out of bounds accesses in conditional joins ([#13971](https://github.com/rapidsai/cudf/pull/13971)) [@vyasr](https://github.com/vyasr)
+- Fix `MultiIndex.to_numpy` to return numpy array with tuples ([#13966](https://github.com/rapidsai/cudf/pull/13966)) [@galipremsagar](https://github.com/galipremsagar)
+- Use cudf::thread_index_type in get_json_object and tdigest kernels ([#13962](https://github.com/rapidsai/cudf/pull/13962)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix an issue with `IntervalIndex.repr` when null values are present ([#13958](https://github.com/rapidsai/cudf/pull/13958)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix type metadata issue preservation with `Column.unique` ([#13957](https://github.com/rapidsai/cudf/pull/13957)) [@galipremsagar](https://github.com/galipremsagar)
+- Handle `Interval` scalars when passed in list-like inputs to `cudf.Index` ([#13956](https://github.com/rapidsai/cudf/pull/13956)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix setting of categories order when `dtype` is passed to a `CategoricalColumn` ([#13955](https://github.com/rapidsai/cudf/pull/13955)) [@galipremsagar](https://github.com/galipremsagar)
+- Handle `as_index` in `GroupBy.apply` ([#13951](https://github.com/rapidsai/cudf/pull/13951)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Raise error for string types in `nsmallest` and `nlargest` ([#13946](https://github.com/rapidsai/cudf/pull/13946)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `index` of `Groupby.apply` results when it is performed on empty objects ([#13944](https://github.com/rapidsai/cudf/pull/13944)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix integer overflow in shim `device_sum` functions ([#13943](https://github.com/rapidsai/cudf/pull/13943)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix type mismatch in groupby reduction for empty objects ([#13942](https://github.com/rapidsai/cudf/pull/13942)) [@galipremsagar](https://github.com/galipremsagar)
+- Fixed processed bytes calculation in APPLY_BOOLEAN_MASK benchmark. ([#13937](https://github.com/rapidsai/cudf/pull/13937)) [@Blonck](https://github.com/Blonck)
+- Fix construction of `Grouping` objects ([#13932](https://github.com/rapidsai/cudf/pull/13932)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix an issue with `loc` when column names is `MultiIndex` ([#13929](https://github.com/rapidsai/cudf/pull/13929)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix handling of typecasting in `searchsorted` ([#13925](https://github.com/rapidsai/cudf/pull/13925)) [@galipremsagar](https://github.com/galipremsagar)
+- Preserve index `name` in `reindex` ([#13917](https://github.com/rapidsai/cudf/pull/13917)) [@galipremsagar](https://github.com/galipremsagar)
+- Use `cudf::thread_index_type` in cuIO to prevent overflow in row indexing ([#13910](https://github.com/rapidsai/cudf/pull/13910)) [@vuule](https://github.com/vuule)
+- Fix for encodings listed in the Parquet column chunk metadata ([#13907](https://github.com/rapidsai/cudf/pull/13907)) [@etseidl](https://github.com/etseidl)
+- Use cudf::thread_index_type in concatenate.cu. ([#13906](https://github.com/rapidsai/cudf/pull/13906)) [@bdice](https://github.com/bdice)
+- Use cudf::thread_index_type in replace.cu. ([#13905](https://github.com/rapidsai/cudf/pull/13905)) [@bdice](https://github.com/bdice)
+- Add noSanitizer tag to Java reduction tests failing with sanitizer in CUDA 12 ([#13904](https://github.com/rapidsai/cudf/pull/13904)) [@jlowe](https://github.com/jlowe)
+- Remove the internal use of the cudf&#39;s default stream in cuIO ([#13903](https://github.com/rapidsai/cudf/pull/13903)) [@vuule](https://github.com/vuule)
+- Use cuda-nvtx-dev CUDA 12 package. ([#13901](https://github.com/rapidsai/cudf/pull/13901)) [@bdice](https://github.com/bdice)
+- Use `thread_index_type` to avoid index overflow in grid-stride loops ([#13895](https://github.com/rapidsai/cudf/pull/13895)) [@PointKernel](https://github.com/PointKernel)
+- Fix memory access error in cudf::shift for sliced strings ([#13894](https://github.com/rapidsai/cudf/pull/13894)) [@davidwendt](https://github.com/davidwendt)
+- Raise error when trying to construct a `DataFrame` with mixed types ([#13889](https://github.com/rapidsai/cudf/pull/13889)) [@galipremsagar](https://github.com/galipremsagar)
+- Return `nan` when one variable to be correlated has zero variance in JIT GroupBy Apply ([#13884](https://github.com/rapidsai/cudf/pull/13884)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Correctly detect the BOM mark in `read_csv` with compressed input ([#13881](https://github.com/rapidsai/cudf/pull/13881)) [@vuule](https://github.com/vuule)
+- Check for the presence of all values in `MultiIndex.isin` ([#13879](https://github.com/rapidsai/cudf/pull/13879)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix nvtext::generate_character_ngrams performance regression for longer strings ([#13874](https://github.com/rapidsai/cudf/pull/13874)) [@davidwendt](https://github.com/davidwendt)
+- Fix return type of `MultiIndex.levels` ([#13870](https://github.com/rapidsai/cudf/pull/13870)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix List&#39;s missing children metadata in JSON writer ([#13869](https://github.com/rapidsai/cudf/pull/13869)) [@karthikeyann](https://github.com/karthikeyann)
+- Disable construction of Index when `freq` is set in pandas-compatibility mode ([#13857](https://github.com/rapidsai/cudf/pull/13857)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix an issue with fetching `NA` from a `TimedeltaColumn` ([#13853](https://github.com/rapidsai/cudf/pull/13853)) [@galipremsagar](https://github.com/galipremsagar)
+- Simplify implementation of interval_range() and fix behaviour for floating `freq` ([#13844](https://github.com/rapidsai/cudf/pull/13844)) [@shwina](https://github.com/shwina)
+- Fix binary operations between `Series` and `Index` ([#13842](https://github.com/rapidsai/cudf/pull/13842)) [@galipremsagar](https://github.com/galipremsagar)
+- Update make_lists_column_from_scalar to use make_offsets_child_column utility ([#13841](https://github.com/rapidsai/cudf/pull/13841)) [@davidwendt](https://github.com/davidwendt)
+- Fix read out of bounds in string concatenate ([#13838](https://github.com/rapidsai/cudf/pull/13838)) [@pentschev](https://github.com/pentschev)
+- Raise error for more cases when `timezone-aware` data is passed to `as_column` ([#13835](https://github.com/rapidsai/cudf/pull/13835)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `any`, `all` reduction behavior for `axis=None` and warn for other reductions ([#13831](https://github.com/rapidsai/cudf/pull/13831)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise error when trying to construct time-zone aware timestamps ([#13830](https://github.com/rapidsai/cudf/pull/13830)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix cuFile I/O factories ([#13829](https://github.com/rapidsai/cudf/pull/13829)) [@vuule](https://github.com/vuule)
+- DataFrame with namedtuples uses ._field as column names ([#13824](https://github.com/rapidsai/cudf/pull/13824)) [@mroeschke](https://github.com/mroeschke)
+- Branch 23.10 merge 23.08 ([#13822](https://github.com/rapidsai/cudf/pull/13822)) [@vyasr](https://github.com/vyasr)
+- Return a Series from JIT GroupBy apply, rather than a DataFrame ([#13820](https://github.com/rapidsai/cudf/pull/13820)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- No need to dlsym EnsureS3Finalized we can call it directly ([#13819](https://github.com/rapidsai/cudf/pull/13819)) [@robertmaynard](https://github.com/robertmaynard)
+- Raise error when mixed types are being constructed ([#13816](https://github.com/rapidsai/cudf/pull/13816)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix unbounded sequence issue in `DataFrame` constructor ([#13811](https://github.com/rapidsai/cudf/pull/13811)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix Byte-Pair-Encoding usage of cuco static-map for storing merge-pairs ([#13807](https://github.com/rapidsai/cudf/pull/13807)) [@davidwendt](https://github.com/davidwendt)
+- Fix for Parquet writer when requested pages per row is smaller than fragment size ([#13806](https://github.com/rapidsai/cudf/pull/13806)) [@etseidl](https://github.com/etseidl)
+- Remove hangs from trying to construct un-bounded sequences ([#13799](https://github.com/rapidsai/cudf/pull/13799)) [@galipremsagar](https://github.com/galipremsagar)
+- Bug/update libcudf to handle arrow12 changes ([#13794](https://github.com/rapidsai/cudf/pull/13794)) [@robertmaynard](https://github.com/robertmaynard)
+- Update get_arrow to arrows 12 CMake target name of arrow::xsimd ([#13790](https://github.com/rapidsai/cudf/pull/13790)) [@robertmaynard](https://github.com/robertmaynard)
+- Raise error when trying to join `datetime` and `timedelta` types with other types ([#13786](https://github.com/rapidsai/cudf/pull/13786)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix negative unary operation for boolean type ([#13780](https://github.com/rapidsai/cudf/pull/13780)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix contains(`in`) method for `Series` ([#13779](https://github.com/rapidsai/cudf/pull/13779)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix binary operation column ordering and missing column issues ([#13778](https://github.com/rapidsai/cudf/pull/13778)) [@galipremsagar](https://github.com/galipremsagar)
+- Cast only time of day to nanos to avoid an overflow in Parquet INT96 write ([#13776](https://github.com/rapidsai/cudf/pull/13776)) [@gerashegalov](https://github.com/gerashegalov)
+- Preserve names of column object in various APIs ([#13772](https://github.com/rapidsai/cudf/pull/13772)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise error on constructing an array from mixed type inputs ([#13768](https://github.com/rapidsai/cudf/pull/13768)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix construction of DataFrames from dict when columns are provided ([#13766](https://github.com/rapidsai/cudf/pull/13766)) [@wence-](https://github.com/wence-)
+- Provide our own Cython declaration for make_unique ([#13746](https://github.com/rapidsai/cudf/pull/13746)) [@wence-](https://github.com/wence-)
+
+## 📖 Documentation
+
+- Fix typo in docstring: metadata. ([#14025](https://github.com/rapidsai/cudf/pull/14025)) [@bdice](https://github.com/bdice)
+- Fix typo in parquet/page_decode.cuh ([#13849](https://github.com/rapidsai/cudf/pull/13849)) [@XinyuZeng](https://github.com/XinyuZeng)
+- Simplify Python doc configuration ([#13826](https://github.com/rapidsai/cudf/pull/13826)) [@vyasr](https://github.com/vyasr)
+- Update documentation to reflect recent changes in JSON reader and writer ([#13791](https://github.com/rapidsai/cudf/pull/13791)) [@vuule](https://github.com/vuule)
+- Fix all warnings in Python docs ([#13789](https://github.com/rapidsai/cudf/pull/13789)) [@vyasr](https://github.com/vyasr)
+
+## 🚀 New Features
+
+- [Java] Add JNI bindings for `integers_to_hex` ([#14205](https://github.com/rapidsai/cudf/pull/14205)) [@razajafri](https://github.com/razajafri)
+- Propagate errors from Parquet reader kernels back to host ([#14167](https://github.com/rapidsai/cudf/pull/14167)) [@vuule](https://github.com/vuule)
+- JNI for `HISTOGRAM` and `MERGE_HISTOGRAM` aggregations ([#14154](https://github.com/rapidsai/cudf/pull/14154)) [@ttnghia](https://github.com/ttnghia)
+- Expose streams in all public sorting APIs ([#14146](https://github.com/rapidsai/cudf/pull/14146)) [@vyasr](https://github.com/vyasr)
+- Enable direct ingestion and production of Arrow scalars ([#14121](https://github.com/rapidsai/cudf/pull/14121)) [@vyasr](https://github.com/vyasr)
+- Implement `GroupBy.value_counts` to match pandas API ([#14114](https://github.com/rapidsai/cudf/pull/14114)) [@stmio](https://github.com/stmio)
+- Refactor parquet thrift reader ([#14097](https://github.com/rapidsai/cudf/pull/14097)) [@etseidl](https://github.com/etseidl)
+- Refactor `hash_reduce_by_row` ([#14095](https://github.com/rapidsai/cudf/pull/14095)) [@ttnghia](https://github.com/ttnghia)
+- Support negative preceding/following for ROW window functions ([#14093](https://github.com/rapidsai/cudf/pull/14093)) [@mythrocks](https://github.com/mythrocks)
+- Support for progressive parquet chunked reading. ([#14079](https://github.com/rapidsai/cudf/pull/14079)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Implement `HISTOGRAM` and `MERGE_HISTOGRAM` aggregations ([#14045](https://github.com/rapidsai/cudf/pull/14045)) [@ttnghia](https://github.com/ttnghia)
+- Expose streams in public search APIs ([#14034](https://github.com/rapidsai/cudf/pull/14034)) [@vyasr](https://github.com/vyasr)
+- Expose streams in public replace APIs ([#14010](https://github.com/rapidsai/cudf/pull/14010)) [@vyasr](https://github.com/vyasr)
+- Add stream parameter to public cudf::strings::split APIs ([#13997](https://github.com/rapidsai/cudf/pull/13997)) [@davidwendt](https://github.com/davidwendt)
+- Expose streams in public filling APIs ([#13990](https://github.com/rapidsai/cudf/pull/13990)) [@vyasr](https://github.com/vyasr)
+- Expose streams in public concatenate APIs ([#13987](https://github.com/rapidsai/cudf/pull/13987)) [@vyasr](https://github.com/vyasr)
+- Use HostMemoryAllocator in jni::allocate_host_buffer ([#13975](https://github.com/rapidsai/cudf/pull/13975)) [@gerashegalov](https://github.com/gerashegalov)
+- Enable fractional null probability for hashing benchmark ([#13967](https://github.com/rapidsai/cudf/pull/13967)) [@Blonck](https://github.com/Blonck)
+- Switch pylibcudf-enabled types to use enum class in Cython ([#13931](https://github.com/rapidsai/cudf/pull/13931)) [@vyasr](https://github.com/vyasr)
+- Add nvtext::tokenize_with_vocabulary API ([#13930](https://github.com/rapidsai/cudf/pull/13930)) [@davidwendt](https://github.com/davidwendt)
+- Rewrite `DataFrame.stack` to support multi level column names ([#13927](https://github.com/rapidsai/cudf/pull/13927)) [@isVoid](https://github.com/isVoid)
+- Add HostMemoryAllocator interface ([#13924](https://github.com/rapidsai/cudf/pull/13924)) [@gerashegalov](https://github.com/gerashegalov)
+- Global stream pool ([#13922](https://github.com/rapidsai/cudf/pull/13922)) [@etseidl](https://github.com/etseidl)
+- Create table_input_metadata from a table_metadata ([#13920](https://github.com/rapidsai/cudf/pull/13920)) [@etseidl](https://github.com/etseidl)
+- Translate column size overflow exception to JNI ([#13911](https://github.com/rapidsai/cudf/pull/13911)) [@mythrocks](https://github.com/mythrocks)
+- Enable RLE boolean encoding for v2 Parquet files ([#13886](https://github.com/rapidsai/cudf/pull/13886)) [@etseidl](https://github.com/etseidl)
+- Exclude some tests from running with the compute sanitizer ([#13872](https://github.com/rapidsai/cudf/pull/13872)) [@firestarman](https://github.com/firestarman)
+- Expand statistics support in ORC writer ([#13848](https://github.com/rapidsai/cudf/pull/13848)) [@vuule](https://github.com/vuule)
+- Register the memory mapped buffer in `datasource` to improve H2D throughput ([#13814](https://github.com/rapidsai/cudf/pull/13814)) [@vuule](https://github.com/vuule)
+- Add cudf::strings::find function with target per row ([#13808](https://github.com/rapidsai/cudf/pull/13808)) [@davidwendt](https://github.com/davidwendt)
+- Add minhash support for MurmurHash3_x64_128 ([#13796](https://github.com/rapidsai/cudf/pull/13796)) [@davidwendt](https://github.com/davidwendt)
+- Remove unnecessary pointer copying in JIT GroupBy Apply ([#13792](https://github.com/rapidsai/cudf/pull/13792)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add &#39;poll&#39; function to custreamz kafka consumer ([#13782](https://github.com/rapidsai/cudf/pull/13782)) [@jdye64](https://github.com/jdye64)
+- Support `corr` in `GroupBy.apply` through the jit engine ([#13767](https://github.com/rapidsai/cudf/pull/13767)) [@shwina](https://github.com/shwina)
+- Optionally write version 2 page headers in Parquet writer ([#13751](https://github.com/rapidsai/cudf/pull/13751)) [@etseidl](https://github.com/etseidl)
+- Support more numeric types in `Groupby.apply` with `engine=&#39;jit&#39;` ([#13729](https://github.com/rapidsai/cudf/pull/13729)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- [FEA] Add DELTA_BINARY_PACKED decoding support to Parquet reader ([#13637](https://github.com/rapidsai/cudf/pull/13637)) [@etseidl](https://github.com/etseidl)
+- Read FIXED_LEN_BYTE_ARRAY as binary in parquet reader ([#13437](https://github.com/rapidsai/cudf/pull/13437)) [@PointKernel](https://github.com/PointKernel)
+
+## 🛠️ Improvements
+
+- Pin `dask` and `distributed` for `23.10` release ([#14225](https://github.com/rapidsai/cudf/pull/14225)) [@galipremsagar](https://github.com/galipremsagar)
+- update rmm tag path ([#14195](https://github.com/rapidsai/cudf/pull/14195)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Disable `Recently Updated` Check ([#14193](https://github.com/rapidsai/cudf/pull/14193)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Move cpp/src/hash/hash_allocator.cuh to include/cudf/hashing/detail ([#14163](https://github.com/rapidsai/cudf/pull/14163)) [@davidwendt](https://github.com/davidwendt)
+- Add Parquet reader benchmarks for row selection ([#14147](https://github.com/rapidsai/cudf/pull/14147)) [@vuule](https://github.com/vuule)
+- Update image names ([#14145](https://github.com/rapidsai/cudf/pull/14145)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Support callables in DataFrame.assign ([#14142](https://github.com/rapidsai/cudf/pull/14142)) [@wence-](https://github.com/wence-)
+- Reduce memory usage of as_categorical_column ([#14138](https://github.com/rapidsai/cudf/pull/14138)) [@wence-](https://github.com/wence-)
+- Replace Python scalar conversions with libcudf ([#14124](https://github.com/rapidsai/cudf/pull/14124)) [@vyasr](https://github.com/vyasr)
+- Update to clang 16.0.6. ([#14120](https://github.com/rapidsai/cudf/pull/14120)) [@bdice](https://github.com/bdice)
+- Fix type of empty `Index` and raise warning in `Series` constructor ([#14116](https://github.com/rapidsai/cudf/pull/14116)) [@galipremsagar](https://github.com/galipremsagar)
+- Add stream parameter to external dict APIs ([#14115](https://github.com/rapidsai/cudf/pull/14115)) [@SurajAralihalli](https://github.com/SurajAralihalli)
+- Add fallback matrix for nvcomp. ([#14082](https://github.com/rapidsai/cudf/pull/14082)) [@bdice](https://github.com/bdice)
+- [Java] Add recoverWithNull to JSONOptions and pass to Table.readJSON ([#14078](https://github.com/rapidsai/cudf/pull/14078)) [@andygrove](https://github.com/andygrove)
+- Remove header tests ([#14072](https://github.com/rapidsai/cudf/pull/14072)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Refactor `contains_table` with cuco::static_set ([#14064](https://github.com/rapidsai/cudf/pull/14064)) [@PointKernel](https://github.com/PointKernel)
+- Remove debug print in a Parquet test ([#14063](https://github.com/rapidsai/cudf/pull/14063)) [@vuule](https://github.com/vuule)
+- Expose stream parameter in public nvtext ngram APIs ([#14061](https://github.com/rapidsai/cudf/pull/14061)) [@davidwendt](https://github.com/davidwendt)
+- Expose stream parameter in public strings find APIs ([#14060](https://github.com/rapidsai/cudf/pull/14060)) [@davidwendt](https://github.com/davidwendt)
+- Update doxygen to 1.9.1 ([#14059](https://github.com/rapidsai/cudf/pull/14059)) [@vyasr](https://github.com/vyasr)
+- Remove the mr from the base fixture ([#14057](https://github.com/rapidsai/cudf/pull/14057)) [@vyasr](https://github.com/vyasr)
+- Expose streams in public strings case APIs ([#14056](https://github.com/rapidsai/cudf/pull/14056)) [@davidwendt](https://github.com/davidwendt)
+- Refactor libcudf indexalator to typed normalator ([#14043](https://github.com/rapidsai/cudf/pull/14043)) [@davidwendt](https://github.com/davidwendt)
+- Use cudf::make_empty_column instead of column_view constructor ([#14030](https://github.com/rapidsai/cudf/pull/14030)) [@davidwendt](https://github.com/davidwendt)
+- Remove quadratic runtime due to accessing Frame._dtypes in loop ([#14028](https://github.com/rapidsai/cudf/pull/14028)) [@wence-](https://github.com/wence-)
+- Explicitly depend on zlib in conda recipes ([#14018](https://github.com/rapidsai/cudf/pull/14018)) [@wence-](https://github.com/wence-)
+- Use grid_stride for stride computations. ([#13996](https://github.com/rapidsai/cudf/pull/13996)) [@bdice](https://github.com/bdice)
+- Fix an issue where casting null-array to `object` dtype will result in a failure ([#13994](https://github.com/rapidsai/cudf/pull/13994)) [@galipremsagar](https://github.com/galipremsagar)
+- Add tab as literal to cudf::test::to_string output ([#13993](https://github.com/rapidsai/cudf/pull/13993)) [@davidwendt](https://github.com/davidwendt)
+- Enable `codes` dtype parity in pandas-compatibility mode for `factorize` API ([#13982](https://github.com/rapidsai/cudf/pull/13982)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `CategoricalIndex` ordering in `Groupby.agg` when pandas-compatibility mode is enabled ([#13978](https://github.com/rapidsai/cudf/pull/13978)) [@galipremsagar](https://github.com/galipremsagar)
+- Produce a fatal error if cudf is unable to find pyarrow include directory ([#13976](https://github.com/rapidsai/cudf/pull/13976)) [@cwharris](https://github.com/cwharris)
+- Use `thread_index_type` in `partitioning.cu` ([#13973](https://github.com/rapidsai/cudf/pull/13973)) [@divyegala](https://github.com/divyegala)
+- Use `cudf::thread_index_type` in `merge.cu` ([#13972](https://github.com/rapidsai/cudf/pull/13972)) [@divyegala](https://github.com/divyegala)
+- Use `copy-pr-bot` ([#13970](https://github.com/rapidsai/cudf/pull/13970)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Use cudf::thread_index_type in strings custom kernels ([#13968](https://github.com/rapidsai/cudf/pull/13968)) [@davidwendt](https://github.com/davidwendt)
+- Add `bytes_per_second` to hash_partition benchmark ([#13965](https://github.com/rapidsai/cudf/pull/13965)) [@Blonck](https://github.com/Blonck)
+- Added pinned pool reservation API for java ([#13964](https://github.com/rapidsai/cudf/pull/13964)) [@revans2](https://github.com/revans2)
+- Simplify wheel build scripts and allow alphas of RAPIDS dependencies ([#13963](https://github.com/rapidsai/cudf/pull/13963)) [@vyasr](https://github.com/vyasr)
+- Add `bytes_per_second` to copy_if_else benchmark ([#13960](https://github.com/rapidsai/cudf/pull/13960)) [@Blonck](https://github.com/Blonck)
+- Add pandas compatible output to `Series.unique` ([#13959](https://github.com/rapidsai/cudf/pull/13959)) [@galipremsagar](https://github.com/galipremsagar)
+- Add `bytes_per_second` to compiled binaryop benchmark ([#13938](https://github.com/rapidsai/cudf/pull/13938)) [@Blonck](https://github.com/Blonck)
+- Unpin `dask` and `distributed` for `23.10` development ([#13935](https://github.com/rapidsai/cudf/pull/13935)) [@galipremsagar](https://github.com/galipremsagar)
+- Make HostColumnVector.getRefCount public ([#13934](https://github.com/rapidsai/cudf/pull/13934)) [@abellina](https://github.com/abellina)
+- Use cuco::static_set in JSON tree algorithm ([#13928](https://github.com/rapidsai/cudf/pull/13928)) [@karthikeyann](https://github.com/karthikeyann)
+- Add java API to get size of host memory needed to copy column view ([#13919](https://github.com/rapidsai/cudf/pull/13919)) [@revans2](https://github.com/revans2)
+- Use cudf::size_type instead of int32 where appropriate in nvtext functions ([#13915](https://github.com/rapidsai/cudf/pull/13915)) [@davidwendt](https://github.com/davidwendt)
+- Enable hugepage for arrow host allocations ([#13914](https://github.com/rapidsai/cudf/pull/13914)) [@madsbk](https://github.com/madsbk)
+- Improve performance of nvtext::edit_distance ([#13912](https://github.com/rapidsai/cudf/pull/13912)) [@davidwendt](https://github.com/davidwendt)
+- Ensure cudf internals use pylibcudf in pure Python mode ([#13909](https://github.com/rapidsai/cudf/pull/13909)) [@vyasr](https://github.com/vyasr)
+- Use `empty()` instead of `size()` where possible ([#13908](https://github.com/rapidsai/cudf/pull/13908)) [@vuule](https://github.com/vuule)
+- [JNI] Adds HostColumnVector.EventHandler for spillability checks ([#13898](https://github.com/rapidsai/cudf/pull/13898)) [@abellina](https://github.com/abellina)
+- Return `Timestamp` &amp; `Timedelta` for fetching scalars in `DatetimeIndex` &amp; `TimedeltaIndex` ([#13896](https://github.com/rapidsai/cudf/pull/13896)) [@galipremsagar](https://github.com/galipremsagar)
+- Allow explicit `shuffle=&quot;p2p&quot;` within dask-cudf API ([#13893](https://github.com/rapidsai/cudf/pull/13893)) [@rjzamora](https://github.com/rjzamora)
+- Disable creation of `DatetimeIndex` when `freq` is passed to `cudf.date_range` ([#13890](https://github.com/rapidsai/cudf/pull/13890)) [@galipremsagar](https://github.com/galipremsagar)
+- Bring parity with pandas for `datetime` &amp; `timedelta` comparison operations ([#13877](https://github.com/rapidsai/cudf/pull/13877)) [@galipremsagar](https://github.com/galipremsagar)
+- Change `NA` to `NaT` for `datetime` and `timedelta` types ([#13868](https://github.com/rapidsai/cudf/pull/13868)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise error when `astype(object)` is called in pandas compatibility mode ([#13862](https://github.com/rapidsai/cudf/pull/13862)) [@galipremsagar](https://github.com/galipremsagar)
+- Fixes a performance regression in FST ([#13850](https://github.com/rapidsai/cudf/pull/13850)) [@elstehle](https://github.com/elstehle)
+- Set native handles to null on close in Java wrapper classes ([#13818](https://github.com/rapidsai/cudf/pull/13818)) [@jlowe](https://github.com/jlowe)
+- Avoid use of CUDF_EXPECTS in libcudf unit tests outside of helper functions with return values ([#13812](https://github.com/rapidsai/cudf/pull/13812)) [@vuule](https://github.com/vuule)
+- Update `lists::contains` to experimental row comparator ([#13810](https://github.com/rapidsai/cudf/pull/13810)) [@divyegala](https://github.com/divyegala)
+- Reduce `lists::contains` dispatches for scalars ([#13805](https://github.com/rapidsai/cudf/pull/13805)) [@divyegala](https://github.com/divyegala)
+- Long string optimization for string column parsing in JSON reader ([#13803](https://github.com/rapidsai/cudf/pull/13803)) [@karthikeyann](https://github.com/karthikeyann)
+- Raise NotImplementedError for pd.SparseDtype ([#13798](https://github.com/rapidsai/cudf/pull/13798)) [@mroeschke](https://github.com/mroeschke)
+- Remove the libcudf cudf::offset_type type ([#13788](https://github.com/rapidsai/cudf/pull/13788)) [@davidwendt](https://github.com/davidwendt)
+- Move Spark-indpendent Table debug to cudf Java ([#13783](https://github.com/rapidsai/cudf/pull/13783)) [@gerashegalov](https://github.com/gerashegalov)
+- Update to Cython 3.0.0 ([#13777](https://github.com/rapidsai/cudf/pull/13777)) [@vyasr](https://github.com/vyasr)
+- Refactor Parquet reader handling of V2 page header info ([#13775](https://github.com/rapidsai/cudf/pull/13775)) [@etseidl](https://github.com/etseidl)
+- Branch 23.10 merge 23.08 ([#13773](https://github.com/rapidsai/cudf/pull/13773)) [@vyasr](https://github.com/vyasr)
+- Restructure JSON code to correctly reflect legacy/experimental status ([#13757](https://github.com/rapidsai/cudf/pull/13757)) [@vuule](https://github.com/vuule)
+- Branch 23.10 merge 23.08 ([#13753](https://github.com/rapidsai/cudf/pull/13753)) [@vyasr](https://github.com/vyasr)
+- Enforce deprecations in `23.10` ([#13732](https://github.com/rapidsai/cudf/pull/13732)) [@galipremsagar](https://github.com/galipremsagar)
+- Upgrade to arrow 12 ([#13728](https://github.com/rapidsai/cudf/pull/13728)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactors JSON reader&#39;s pushdown automaton ([#13716](https://github.com/rapidsai/cudf/pull/13716)) [@elstehle](https://github.com/elstehle)
+- Remove Arrow dependency from the `datasource.hpp` public header ([#13698](https://github.com/rapidsai/cudf/pull/13698)) [@vuule](https://github.com/vuule)
+
+# cuDF 23.08.00 (9 Aug 2023)
+
+## 🚨 Breaking Changes
+
+- Enforce deprecations and add clarifications around existing deprecations ([#13710](https://github.com/rapidsai/cudf/pull/13710)) [@galipremsagar](https://github.com/galipremsagar)
+- Separate MurmurHash32 from hash_functions.cuh ([#13681](https://github.com/rapidsai/cudf/pull/13681)) [@davidwendt](https://github.com/davidwendt)
+- Avoid storing metadata in pointers in ORC and Parquet writers ([#13648](https://github.com/rapidsai/cudf/pull/13648)) [@vuule](https://github.com/vuule)
+- Expose streams in all public copying APIs ([#13629](https://github.com/rapidsai/cudf/pull/13629)) [@vyasr](https://github.com/vyasr)
+- Remove deprecated cudf::strings::slice_strings (by delimiter) functions ([#13628](https://github.com/rapidsai/cudf/pull/13628)) [@davidwendt](https://github.com/davidwendt)
+- Remove deprecated cudf.set_allocator. ([#13591](https://github.com/rapidsai/cudf/pull/13591)) [@bdice](https://github.com/bdice)
+- Change build.sh to use pip install instead of setup.py ([#13507](https://github.com/rapidsai/cudf/pull/13507)) [@vyasr](https://github.com/vyasr)
+- Remove unused max_rows_tensor parameter from subword tokenizer ([#13463](https://github.com/rapidsai/cudf/pull/13463)) [@davidwendt](https://github.com/davidwendt)
+- Fix decimal scale reductions in `_get_decimal_type` ([#13224](https://github.com/rapidsai/cudf/pull/13224)) [@charlesbluca](https://github.com/charlesbluca)
+
+## 🐛 Bug Fixes
+
+- Add CUDA version to cudf_kafka and libcudf-example build strings. ([#13769](https://github.com/rapidsai/cudf/pull/13769)) [@bdice](https://github.com/bdice)
+- Fix typo in wheels-test.yaml. ([#13763](https://github.com/rapidsai/cudf/pull/13763)) [@bdice](https://github.com/bdice)
+- Don&#39;t test strings shorter than the requested ngram size ([#13758](https://github.com/rapidsai/cudf/pull/13758)) [@vyasr](https://github.com/vyasr)
+- Add CUDA version to custreamz build string. ([#13754](https://github.com/rapidsai/cudf/pull/13754)) [@bdice](https://github.com/bdice)
+- Fix writing of ORC files with empty child string columns ([#13745](https://github.com/rapidsai/cudf/pull/13745)) [@vuule](https://github.com/vuule)
+- Remove the erroneous &quot;empty level&quot; short-circuit from ORC reader ([#13722](https://github.com/rapidsai/cudf/pull/13722)) [@vuule](https://github.com/vuule)
+- Fix character counting when writing sliced tables into ORC ([#13721](https://github.com/rapidsai/cudf/pull/13721)) [@vuule](https://github.com/vuule)
+- Parquet uses row group row count if missing from header ([#13712](https://github.com/rapidsai/cudf/pull/13712)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fix reading of RLE encoded boolean data from parquet files with V2 page headers ([#13707](https://github.com/rapidsai/cudf/pull/13707)) [@etseidl](https://github.com/etseidl)
+- Fix a corner case of list lexicographic comparator ([#13701](https://github.com/rapidsai/cudf/pull/13701)) [@ttnghia](https://github.com/ttnghia)
+- Fix combined filtering and column projection in `dask_cudf.read_parquet` ([#13697](https://github.com/rapidsai/cudf/pull/13697)) [@rjzamora](https://github.com/rjzamora)
+- Revert fetch-rapids changes ([#13696](https://github.com/rapidsai/cudf/pull/13696)) [@vyasr](https://github.com/vyasr)
+- Data generator - include offsets in the size estimate of list elments ([#13688](https://github.com/rapidsai/cudf/pull/13688)) [@vuule](https://github.com/vuule)
+- Add `cuda-nvcc-impl` to `cudf` for `numba` CUDA 12 ([#13673](https://github.com/rapidsai/cudf/pull/13673)) [@jakirkham](https://github.com/jakirkham)
+- Fix combined filtering and column projection in `read_parquet` ([#13666](https://github.com/rapidsai/cudf/pull/13666)) [@rjzamora](https://github.com/rjzamora)
+- Use `thrust::identity` as hash functions for byte pair encoding ([#13665](https://github.com/rapidsai/cudf/pull/13665)) [@PointKernel](https://github.com/PointKernel)
+- Fix loc-getitem ordering when index contains duplicate labels ([#13659](https://github.com/rapidsai/cudf/pull/13659)) [@wence-](https://github.com/wence-)
+- [REVIEW] Introduce parity with pandas for `MultiIndex.loc` ordering &amp; fix a bug in `Groupby` with `as_index` ([#13657](https://github.com/rapidsai/cudf/pull/13657)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix memcheck error found in nvtext tokenize functions ([#13649](https://github.com/rapidsai/cudf/pull/13649)) [@davidwendt](https://github.com/davidwendt)
+- Fix `has_nonempty_nulls` ignoring column offset ([#13647](https://github.com/rapidsai/cudf/pull/13647)) [@ttnghia](https://github.com/ttnghia)
+- [Java] Avoid double-free corruption in case of an Exception while creating a ColumnView ([#13645](https://github.com/rapidsai/cudf/pull/13645)) [@razajafri](https://github.com/razajafri)
+- Fix memcheck error in ORC reader call to cudf::io::copy_uncompressed_kernel ([#13643](https://github.com/rapidsai/cudf/pull/13643)) [@davidwendt](https://github.com/davidwendt)
+- Fix CUDA 12 conda environment to remove cubinlinker and ptxcompiler. ([#13636](https://github.com/rapidsai/cudf/pull/13636)) [@bdice](https://github.com/bdice)
+- Fix inf/NaN comparisons for FLOAT orderby in window functions ([#13635](https://github.com/rapidsai/cudf/pull/13635)) [@mythrocks](https://github.com/mythrocks)
+- Refactor `Index` search to simplify code and increase correctness ([#13625](https://github.com/rapidsai/cudf/pull/13625)) [@wence-](https://github.com/wence-)
+- Fix compile warning for unused variable in split_re.cu ([#13621](https://github.com/rapidsai/cudf/pull/13621)) [@davidwendt](https://github.com/davidwendt)
+- Fix tz_localize for dask_cudf Series ([#13610](https://github.com/rapidsai/cudf/pull/13610)) [@shwina](https://github.com/shwina)
+- Fix issue with no decompressed data in ORC reader ([#13609](https://github.com/rapidsai/cudf/pull/13609)) [@vuule](https://github.com/vuule)
+- Fix floating point window range extents. ([#13606](https://github.com/rapidsai/cudf/pull/13606)) [@mythrocks](https://github.com/mythrocks)
+- Fix `localize(None)` for timezone-naive columns ([#13603](https://github.com/rapidsai/cudf/pull/13603)) [@shwina](https://github.com/shwina)
+- Fixed a memory leak caused by Exception thrown while constructing a ColumnView ([#13597](https://github.com/rapidsai/cudf/pull/13597)) [@razajafri](https://github.com/razajafri)
+- Handle nullptr return value from bitmask_or in distinct_count ([#13590](https://github.com/rapidsai/cudf/pull/13590)) [@wence-](https://github.com/wence-)
+- Bring parity with pandas in Index.join ([#13589](https://github.com/rapidsai/cudf/pull/13589)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix cudf.melt when there are more than 255 columns ([#13588](https://github.com/rapidsai/cudf/pull/13588)) [@hcho3](https://github.com/hcho3)
+- Fix memory issues in cuIO due to removal of memory padding ([#13586](https://github.com/rapidsai/cudf/pull/13586)) [@ttnghia](https://github.com/ttnghia)
+- Fix Parquet multi-file reading ([#13584](https://github.com/rapidsai/cudf/pull/13584)) [@etseidl](https://github.com/etseidl)
+- Fix memcheck error found in LISTS_TEST ([#13579](https://github.com/rapidsai/cudf/pull/13579)) [@davidwendt](https://github.com/davidwendt)
+- Fix memcheck error found in STRINGS_TEST ([#13578](https://github.com/rapidsai/cudf/pull/13578)) [@davidwendt](https://github.com/davidwendt)
+- Fix memcheck error found in INTEROP_TEST ([#13577](https://github.com/rapidsai/cudf/pull/13577)) [@davidwendt](https://github.com/davidwendt)
+- Fix memcheck errors found in REDUCTION_TEST ([#13574](https://github.com/rapidsai/cudf/pull/13574)) [@davidwendt](https://github.com/davidwendt)
+- Preemptive fix for hive-partitioning change in dask ([#13564](https://github.com/rapidsai/cudf/pull/13564)) [@rjzamora](https://github.com/rjzamora)
+- Fix an issue with `dask_cudf.read_csv` when lines are needed to be skipped ([#13555](https://github.com/rapidsai/cudf/pull/13555)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix out-of-bounds memory write in cudf::dictionary::detail::concatenate ([#13554](https://github.com/rapidsai/cudf/pull/13554)) [@davidwendt](https://github.com/davidwendt)
+- Fix the null mask size in json reader ([#13537](https://github.com/rapidsai/cudf/pull/13537)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix cudf::strings::strip for all-empty input column ([#13533](https://github.com/rapidsai/cudf/pull/13533)) [@davidwendt](https://github.com/davidwendt)
+- Make sure to build without isolation or installing dependencies ([#13524](https://github.com/rapidsai/cudf/pull/13524)) [@vyasr](https://github.com/vyasr)
+- Remove preload lib from CMake for now ([#13519](https://github.com/rapidsai/cudf/pull/13519)) [@vyasr](https://github.com/vyasr)
+- Fix missing separator after null values in JSON writer ([#13503](https://github.com/rapidsai/cudf/pull/13503)) [@karthikeyann](https://github.com/karthikeyann)
+- Ensure `single_lane_block_sum_reduce` is safe to call in a loop ([#13488](https://github.com/rapidsai/cudf/pull/13488)) [@wence-](https://github.com/wence-)
+- Update all versions in pyproject.toml files. ([#13486](https://github.com/rapidsai/cudf/pull/13486)) [@bdice](https://github.com/bdice)
+- Remove applying nvbench that doesn&#39;t exist in 23.08 ([#13484](https://github.com/rapidsai/cudf/pull/13484)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix chunked Parquet reader benchmark ([#13482](https://github.com/rapidsai/cudf/pull/13482)) [@vuule](https://github.com/vuule)
+- Update JNI JSON reader column compatability for Spark ([#13477](https://github.com/rapidsai/cudf/pull/13477)) [@revans2](https://github.com/revans2)
+- Fix unsanitized output of scan with strings ([#13455](https://github.com/rapidsai/cudf/pull/13455)) [@davidwendt](https://github.com/davidwendt)
+- Reject functions without bytecode from `_can_be_jitted` in GroupBy Apply ([#13429](https://github.com/rapidsai/cudf/pull/13429)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix decimal scale reductions in `_get_decimal_type` ([#13224](https://github.com/rapidsai/cudf/pull/13224)) [@charlesbluca](https://github.com/charlesbluca)
+
+## 📖 Documentation
+
+- Fix doxygen groups for io data sources and sinks ([#13718](https://github.com/rapidsai/cudf/pull/13718)) [@davidwendt](https://github.com/davidwendt)
+- Add pandas compatibility note to DataFrame.query docstring ([#13693](https://github.com/rapidsai/cudf/pull/13693)) [@beckernick](https://github.com/beckernick)
+- Add pylibcudf to developer guide ([#13639](https://github.com/rapidsai/cudf/pull/13639)) [@vyasr](https://github.com/vyasr)
+- Fix repeated words in doxygen text ([#13598](https://github.com/rapidsai/cudf/pull/13598)) [@karthikeyann](https://github.com/karthikeyann)
+- Update docs for top-level API. ([#13592](https://github.com/rapidsai/cudf/pull/13592)) [@bdice](https://github.com/bdice)
+- Fix the the doxygen text for cudf::concatenate and other places ([#13561](https://github.com/rapidsai/cudf/pull/13561)) [@davidwendt](https://github.com/davidwendt)
+- Document stream validation approach used in testing ([#13556](https://github.com/rapidsai/cudf/pull/13556)) [@vyasr](https://github.com/vyasr)
+- Cleanup doc repetitions in libcudf ([#13470](https://github.com/rapidsai/cudf/pull/13470)) [@karthikeyann](https://github.com/karthikeyann)
+
+## 🚀 New Features
+
+- Support `min` and `max` aggregations for list type in groupby and reduction ([#13676](https://github.com/rapidsai/cudf/pull/13676)) [@ttnghia](https://github.com/ttnghia)
+- Add nvtext::jaccard_index API for strings columns ([#13669](https://github.com/rapidsai/cudf/pull/13669)) [@davidwendt](https://github.com/davidwendt)
+- Add read_parquet_metadata libcudf API ([#13663](https://github.com/rapidsai/cudf/pull/13663)) [@karthikeyann](https://github.com/karthikeyann)
+- Expose streams in all public copying APIs ([#13629](https://github.com/rapidsai/cudf/pull/13629)) [@vyasr](https://github.com/vyasr)
+- Add XXHash_64 hash function to cudf ([#13612](https://github.com/rapidsai/cudf/pull/13612)) [@davidwendt](https://github.com/davidwendt)
+- Java support: Floating point order-by columns for RANGE window functions ([#13595](https://github.com/rapidsai/cudf/pull/13595)) [@mythrocks](https://github.com/mythrocks)
+- Use `cuco::static_map` to build string dictionaries in ORC writer ([#13580](https://github.com/rapidsai/cudf/pull/13580)) [@vuule](https://github.com/vuule)
+- Add pylibcudf subpackage with gather implementation ([#13562](https://github.com/rapidsai/cudf/pull/13562)) [@vyasr](https://github.com/vyasr)
+- Add JNI for `lists::concatenate_list_elements` ([#13547](https://github.com/rapidsai/cudf/pull/13547)) [@ttnghia](https://github.com/ttnghia)
+- Enable nested types for `lists::concatenate_list_elements` ([#13545](https://github.com/rapidsai/cudf/pull/13545)) [@ttnghia](https://github.com/ttnghia)
+- Add unicode encoding for string columns in JSON writer ([#13539](https://github.com/rapidsai/cudf/pull/13539)) [@karthikeyann](https://github.com/karthikeyann)
+- Remove numba kernels from `find_index_of_val` ([#13517](https://github.com/rapidsai/cudf/pull/13517)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Floating point order-by columns for RANGE window functions ([#13512](https://github.com/rapidsai/cudf/pull/13512)) [@mythrocks](https://github.com/mythrocks)
+- Parse column chunk metadata statistics in parquet reader ([#13472](https://github.com/rapidsai/cudf/pull/13472)) [@karthikeyann](https://github.com/karthikeyann)
+- Add `abs` function to apply ([#13408](https://github.com/rapidsai/cudf/pull/13408)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- [FEA] AST filtering in parquet reader ([#13348](https://github.com/rapidsai/cudf/pull/13348)) [@karthikeyann](https://github.com/karthikeyann)
+- [FEA] Adds option to recover from invalid JSON lines in JSON tokenizer ([#13344](https://github.com/rapidsai/cudf/pull/13344)) [@elstehle](https://github.com/elstehle)
+- Ensure cccl packages don&#39;t clash with upstream version ([#13235](https://github.com/rapidsai/cudf/pull/13235)) [@robertmaynard](https://github.com/robertmaynard)
+- Update `struct_minmax_util` to experimental row comparator ([#13069](https://github.com/rapidsai/cudf/pull/13069)) [@divyegala](https://github.com/divyegala)
+- Add stream parameter to hashing APIs ([#12090](https://github.com/rapidsai/cudf/pull/12090)) [@vyasr](https://github.com/vyasr)
+
+## 🛠️ Improvements
+
+- Pin `dask` and `distributed` for `23.08` release ([#13802](https://github.com/rapidsai/cudf/pull/13802)) [@galipremsagar](https://github.com/galipremsagar)
+- Relax protobuf pinnings. ([#13770](https://github.com/rapidsai/cudf/pull/13770)) [@bdice](https://github.com/bdice)
+- Switch fully unbounded window functions to use aggregations ([#13727](https://github.com/rapidsai/cudf/pull/13727)) [@mythrocks](https://github.com/mythrocks)
+- Switch to new wheel building pipeline ([#13723](https://github.com/rapidsai/cudf/pull/13723)) [@vyasr](https://github.com/vyasr)
+- Revert CUDA 12.0 CI workflows to branch-23.08. ([#13719](https://github.com/rapidsai/cudf/pull/13719)) [@bdice](https://github.com/bdice)
+- Adding identify minimum version requirement ([#13713](https://github.com/rapidsai/cudf/pull/13713)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Enforce deprecations and add clarifications around existing deprecations ([#13710](https://github.com/rapidsai/cudf/pull/13710)) [@galipremsagar](https://github.com/galipremsagar)
+- Optimize ORC reader performance for list data ([#13708](https://github.com/rapidsai/cudf/pull/13708)) [@vyasr](https://github.com/vyasr)
+- fix limit overflow message in a docstring ([#13703](https://github.com/rapidsai/cudf/pull/13703)) [@ahmet-uyar](https://github.com/ahmet-uyar)
+- Alleviates JSON parser&#39;s need for multi-file sources to end with a newline ([#13702](https://github.com/rapidsai/cudf/pull/13702)) [@elstehle](https://github.com/elstehle)
+- Update cython-lint and replace flake8 with ruff ([#13699](https://github.com/rapidsai/cudf/pull/13699)) [@vyasr](https://github.com/vyasr)
+- Add `__dask_tokenize__` definitions to cudf classes ([#13695](https://github.com/rapidsai/cudf/pull/13695)) [@rjzamora](https://github.com/rjzamora)
+- Convert libcudf hashing benchmarks to nvbench ([#13694](https://github.com/rapidsai/cudf/pull/13694)) [@davidwendt](https://github.com/davidwendt)
+- Separate MurmurHash32 from hash_functions.cuh ([#13681](https://github.com/rapidsai/cudf/pull/13681)) [@davidwendt](https://github.com/davidwendt)
+- Improve performance of cudf::strings::split on whitespace ([#13680](https://github.com/rapidsai/cudf/pull/13680)) [@davidwendt](https://github.com/davidwendt)
+- Allow ORC and Parquet writers to write nullable columns without nulls as non-nullable ([#13675](https://github.com/rapidsai/cudf/pull/13675)) [@vuule](https://github.com/vuule)
+- Raise a NotImplementedError in to_datetime when utc is passed ([#13670](https://github.com/rapidsai/cudf/pull/13670)) [@shwina](https://github.com/shwina)
+- Add rmm_mode parameter to nvbench base fixture ([#13668](https://github.com/rapidsai/cudf/pull/13668)) [@davidwendt](https://github.com/davidwendt)
+- Fix multiindex loc ordering in pandas-compat mode ([#13660](https://github.com/rapidsai/cudf/pull/13660)) [@wence-](https://github.com/wence-)
+- Add nvtext hash_character_ngrams function ([#13654](https://github.com/rapidsai/cudf/pull/13654)) [@davidwendt](https://github.com/davidwendt)
+- Avoid storing metadata in pointers in ORC and Parquet writers ([#13648](https://github.com/rapidsai/cudf/pull/13648)) [@vuule](https://github.com/vuule)
+- Acquire spill lock in to/from_arrow ([#13646](https://github.com/rapidsai/cudf/pull/13646)) [@shwina](https://github.com/shwina)
+- Expose stable versions of libcudf sort routines ([#13634](https://github.com/rapidsai/cudf/pull/13634)) [@wence-](https://github.com/wence-)
+- Separate out hash_test.cpp source for each hash API ([#13633](https://github.com/rapidsai/cudf/pull/13633)) [@davidwendt](https://github.com/davidwendt)
+- Remove deprecated cudf::strings::slice_strings (by delimiter) functions ([#13628](https://github.com/rapidsai/cudf/pull/13628)) [@davidwendt](https://github.com/davidwendt)
+- Create separate libcudf hash APIs for each supported hash function ([#13626](https://github.com/rapidsai/cudf/pull/13626)) [@davidwendt](https://github.com/davidwendt)
+- Add convert_dtypes API ([#13623](https://github.com/rapidsai/cudf/pull/13623)) [@shwina](https://github.com/shwina)
+- Clean up cupy in dependencies.yaml. ([#13617](https://github.com/rapidsai/cudf/pull/13617)) [@bdice](https://github.com/bdice)
+- Use cuda-version to constrain cudatoolkit. ([#13615](https://github.com/rapidsai/cudf/pull/13615)) [@bdice](https://github.com/bdice)
+- Add murmurhash3_x64_128 function to libcudf ([#13604](https://github.com/rapidsai/cudf/pull/13604)) [@davidwendt](https://github.com/davidwendt)
+- Performance improvement for cudf::strings::like ([#13594](https://github.com/rapidsai/cudf/pull/13594)) [@davidwendt](https://github.com/davidwendt)
+- Remove deprecated cudf.set_allocator. ([#13591](https://github.com/rapidsai/cudf/pull/13591)) [@bdice](https://github.com/bdice)
+- Clean up cudf device atomic with `cuda::atomic_ref` ([#13583](https://github.com/rapidsai/cudf/pull/13583)) [@PointKernel](https://github.com/PointKernel)
+- Add java bindings for distinct count ([#13573](https://github.com/rapidsai/cudf/pull/13573)) [@revans2](https://github.com/revans2)
+- Use nvcomp conda package. ([#13566](https://github.com/rapidsai/cudf/pull/13566)) [@bdice](https://github.com/bdice)
+- Add exception to string_scalar if input string exceeds size_type ([#13560](https://github.com/rapidsai/cudf/pull/13560)) [@davidwendt](https://github.com/davidwendt)
+- Add dispatch for `cudf.Dataframe` to/from `pyarrow.Table` conversion ([#13558](https://github.com/rapidsai/cudf/pull/13558)) [@rjzamora](https://github.com/rjzamora)
+- Get rid of `cuco::pair_type` aliases ([#13553](https://github.com/rapidsai/cudf/pull/13553)) [@PointKernel](https://github.com/PointKernel)
+- Introduce parity with pandas when `sort=False` in `Groupby` ([#13551](https://github.com/rapidsai/cudf/pull/13551)) [@galipremsagar](https://github.com/galipremsagar)
+- Update CMake in docker to 3.26.4 ([#13550](https://github.com/rapidsai/cudf/pull/13550)) [@NvTimLiu](https://github.com/NvTimLiu)
+- Clarify source of error message in stream testing. ([#13541](https://github.com/rapidsai/cudf/pull/13541)) [@bdice](https://github.com/bdice)
+- Deprecate `strings_to_categorical` in `cudf.read_parquet` ([#13540](https://github.com/rapidsai/cudf/pull/13540)) [@galipremsagar](https://github.com/galipremsagar)
+- Update to CMake 3.26.4 ([#13538](https://github.com/rapidsai/cudf/pull/13538)) [@vyasr](https://github.com/vyasr)
+- s3 folder naming fix ([#13536](https://github.com/rapidsai/cudf/pull/13536)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Implement iloc-getitem using parse-don&#39;t-validate approach ([#13534](https://github.com/rapidsai/cudf/pull/13534)) [@wence-](https://github.com/wence-)
+- Make synchronization explicit in the names of `hostdevice_*` copying APIs ([#13530](https://github.com/rapidsai/cudf/pull/13530)) [@ttnghia](https://github.com/ttnghia)
+- Add benchmark (Google Benchmark) dependency to conda packages. ([#13528](https://github.com/rapidsai/cudf/pull/13528)) [@bdice](https://github.com/bdice)
+- Add libcufile to dependencies.yaml. ([#13523](https://github.com/rapidsai/cudf/pull/13523)) [@bdice](https://github.com/bdice)
+- Fix some memoization logic in groupby/sort/sort_helper.cu ([#13521](https://github.com/rapidsai/cudf/pull/13521)) [@davidwendt](https://github.com/davidwendt)
+- Use sizes_to_offsets_iterator in cudf::gather for strings ([#13520](https://github.com/rapidsai/cudf/pull/13520)) [@davidwendt](https://github.com/davidwendt)
+- use rapids-upload-docs script ([#13518](https://github.com/rapidsai/cudf/pull/13518)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Support UTF-8 BOM in CSV reader ([#13516](https://github.com/rapidsai/cudf/pull/13516)) [@davidwendt](https://github.com/davidwendt)
+- Move stream-related test configuration to CMake ([#13513](https://github.com/rapidsai/cudf/pull/13513)) [@vyasr](https://github.com/vyasr)
+- Implement `cudf.option_context` ([#13511](https://github.com/rapidsai/cudf/pull/13511)) [@galipremsagar](https://github.com/galipremsagar)
+- Unpin `dask` and `distributed` for development ([#13508](https://github.com/rapidsai/cudf/pull/13508)) [@galipremsagar](https://github.com/galipremsagar)
+- Change build.sh to use pip install instead of setup.py ([#13507](https://github.com/rapidsai/cudf/pull/13507)) [@vyasr](https://github.com/vyasr)
+- Use test default stream ([#13506](https://github.com/rapidsai/cudf/pull/13506)) [@vyasr](https://github.com/vyasr)
+- Remove documentation build scripts for Jenkins ([#13495](https://github.com/rapidsai/cudf/pull/13495)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Use east const in include files ([#13494](https://github.com/rapidsai/cudf/pull/13494)) [@karthikeyann](https://github.com/karthikeyann)
+- Use east const in src files ([#13493](https://github.com/rapidsai/cudf/pull/13493)) [@karthikeyann](https://github.com/karthikeyann)
+- Use east const in tests files ([#13492](https://github.com/rapidsai/cudf/pull/13492)) [@karthikeyann](https://github.com/karthikeyann)
+- Use east const in benchmarks files ([#13491](https://github.com/rapidsai/cudf/pull/13491)) [@karthikeyann](https://github.com/karthikeyann)
+- Performance improvement for nvtext tokenize/token functions ([#13480](https://github.com/rapidsai/cudf/pull/13480)) [@davidwendt](https://github.com/davidwendt)
+- Add pd.Float*Dtype to Avro and ORC mappings ([#13475](https://github.com/rapidsai/cudf/pull/13475)) [@mroeschke](https://github.com/mroeschke)
+- Use pandas public APIs where available ([#13467](https://github.com/rapidsai/cudf/pull/13467)) [@mroeschke](https://github.com/mroeschke)
+- Allow pd.ArrowDtype in cudf.from_pandas ([#13465](https://github.com/rapidsai/cudf/pull/13465)) [@mroeschke](https://github.com/mroeschke)
+- Rework libcudf regex benchmarks with nvbench ([#13464](https://github.com/rapidsai/cudf/pull/13464)) [@davidwendt](https://github.com/davidwendt)
+- Remove unused max_rows_tensor parameter from subword tokenizer ([#13463](https://github.com/rapidsai/cudf/pull/13463)) [@davidwendt](https://github.com/davidwendt)
+- Separate io-text and nvtext pytests into different files ([#13435](https://github.com/rapidsai/cudf/pull/13435)) [@davidwendt](https://github.com/davidwendt)
+- Add a move_to function to cudf::string_view::const_iterator ([#13428](https://github.com/rapidsai/cudf/pull/13428)) [@davidwendt](https://github.com/davidwendt)
+- Allow newer scikit-build ([#13424](https://github.com/rapidsai/cudf/pull/13424)) [@vyasr](https://github.com/vyasr)
+- Refactor sort_by_values to sort_values, drop indices from return values. ([#13419](https://github.com/rapidsai/cudf/pull/13419)) [@bdice](https://github.com/bdice)
+- Inline Cython exception handler ([#13411](https://github.com/rapidsai/cudf/pull/13411)) [@vyasr](https://github.com/vyasr)
+- Init JNI version 23.08.0-SNAPSHOT ([#13401](https://github.com/rapidsai/cudf/pull/13401)) [@pxLi](https://github.com/pxLi)
+- Refactor ORC reader ([#13396](https://github.com/rapidsai/cudf/pull/13396)) [@ttnghia](https://github.com/ttnghia)
+- JNI: Remove cleaned objects in memory cleaner ([#13378](https://github.com/rapidsai/cudf/pull/13378)) [@res-life](https://github.com/res-life)
+- Add tests of currently unsupported indexing ([#13338](https://github.com/rapidsai/cudf/pull/13338)) [@wence-](https://github.com/wence-)
+- Performance improvement for some libcudf regex functions for long strings ([#13322](https://github.com/rapidsai/cudf/pull/13322)) [@davidwendt](https://github.com/davidwendt)
+- Exposure Tracked Buffer (first step towards unifying copy-on-write and spilling) ([#13307](https://github.com/rapidsai/cudf/pull/13307)) [@madsbk](https://github.com/madsbk)
+- Write string data directly to column_buffer in Parquet reader ([#13302](https://github.com/rapidsai/cudf/pull/13302)) [@etseidl](https://github.com/etseidl)
+- Add stacktrace into cudf exception types ([#13298](https://github.com/rapidsai/cudf/pull/13298)) [@ttnghia](https://github.com/ttnghia)
+- cuDF: Build CUDA 12 packages ([#12922](https://github.com/rapidsai/cudf/pull/12922)) [@bdice](https://github.com/bdice)
+
+# cuDF 23.06.00 (7 Jun 2023)
+
+## 🚨 Breaking Changes
+
+- Fix batch processing for parquet writer ([#13438](https://github.com/rapidsai/cudf/pull/13438)) [@ttnghia](https://github.com/ttnghia)
+- Use &lt;NA&gt; instead of null to match pandas. ([#13415](https://github.com/rapidsai/cudf/pull/13415)) [@bdice](https://github.com/bdice)
+- Remove UNKNOWN_NULL_COUNT ([#13372](https://github.com/rapidsai/cudf/pull/13372)) [@vyasr](https://github.com/vyasr)
+- Remove default UNKNOWN_NULL_COUNT from cudf::column member functions ([#13341](https://github.com/rapidsai/cudf/pull/13341)) [@davidwendt](https://github.com/davidwendt)
+- Use std::overflow_error when output would exceed column size limit ([#13323](https://github.com/rapidsai/cudf/pull/13323)) [@davidwendt](https://github.com/davidwendt)
+- Remove null mask and null count from column_view constructors ([#13311](https://github.com/rapidsai/cudf/pull/13311)) [@vyasr](https://github.com/vyasr)
+- Change default value of the `observed=` argument in groupby to `True` to reflect the actual behaviour ([#13296](https://github.com/rapidsai/cudf/pull/13296)) [@shwina](https://github.com/shwina)
+- Throw error if UNINITIALIZED is passed to cudf::state_null_count ([#13292](https://github.com/rapidsai/cudf/pull/13292)) [@davidwendt](https://github.com/davidwendt)
+- Remove default null-count parameter from cudf::make_strings_column factory ([#13227](https://github.com/rapidsai/cudf/pull/13227)) [@davidwendt](https://github.com/davidwendt)
+- Remove UNKNOWN_NULL_COUNT where it can be easily computed ([#13205](https://github.com/rapidsai/cudf/pull/13205)) [@vyasr](https://github.com/vyasr)
+- Update minimum Python version to Python 3.9 ([#13196](https://github.com/rapidsai/cudf/pull/13196)) [@shwina](https://github.com/shwina)
+- Refactor contiguous_split API into contiguous_split.hpp ([#13186](https://github.com/rapidsai/cudf/pull/13186)) [@abellina](https://github.com/abellina)
+- Cleanup Parquet chunked writer ([#13094](https://github.com/rapidsai/cudf/pull/13094)) [@ttnghia](https://github.com/ttnghia)
+- Cleanup ORC chunked writer ([#13091](https://github.com/rapidsai/cudf/pull/13091)) [@ttnghia](https://github.com/ttnghia)
+- Raise `NotImplementedError` when attempting to construct cuDF objects from timezone-aware datetimes ([#13086](https://github.com/rapidsai/cudf/pull/13086)) [@shwina](https://github.com/shwina)
+- Remove deprecated regex functions from libcudf ([#13067](https://github.com/rapidsai/cudf/pull/13067)) [@davidwendt](https://github.com/davidwendt)
+- [REVIEW] Upgrade to `arrow-11` ([#12757](https://github.com/rapidsai/cudf/pull/12757)) [@galipremsagar](https://github.com/galipremsagar)
+- Implement Python drop_duplicates with cudf::stable_distinct. ([#11656](https://github.com/rapidsai/cudf/pull/11656)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+
+## 🐛 Bug Fixes
+
+- Fix valid count computation in offset_bitmask_binop kernel ([#13489](https://github.com/rapidsai/cudf/pull/13489)) [@davidwendt](https://github.com/davidwendt)
+- Fix writing of ORC files with empty rowgroups ([#13466](https://github.com/rapidsai/cudf/pull/13466)) [@vuule](https://github.com/vuule)
+- Fix cudf::repeat logic when count is zero ([#13459](https://github.com/rapidsai/cudf/pull/13459)) [@davidwendt](https://github.com/davidwendt)
+- Fix batch processing for parquet writer ([#13438](https://github.com/rapidsai/cudf/pull/13438)) [@ttnghia](https://github.com/ttnghia)
+- Fix invalid use of std::exclusive_scan in Parquet writer ([#13434](https://github.com/rapidsai/cudf/pull/13434)) [@etseidl](https://github.com/etseidl)
+- Patch numba if it is imported first to ensure minor version compatibility works. ([#13433](https://github.com/rapidsai/cudf/pull/13433)) [@bdice](https://github.com/bdice)
+- Fix cudf::strings::replace_with_backrefs hang on empty match result ([#13418](https://github.com/rapidsai/cudf/pull/13418)) [@davidwendt](https://github.com/davidwendt)
+- Use &lt;NA&gt; instead of null to match pandas. ([#13415](https://github.com/rapidsai/cudf/pull/13415)) [@bdice](https://github.com/bdice)
+- Fix tokenize with non-space delimiter ([#13403](https://github.com/rapidsai/cudf/pull/13403)) [@shwina](https://github.com/shwina)
+- Fix groupby head/tail for empty dataframe ([#13398](https://github.com/rapidsai/cudf/pull/13398)) [@shwina](https://github.com/shwina)
+- Default to closed=&quot;right&quot; in `IntervalIndex` constructor ([#13394](https://github.com/rapidsai/cudf/pull/13394)) [@shwina](https://github.com/shwina)
+- Correctly reorder and reindex scan groupbys with null keys ([#13389](https://github.com/rapidsai/cudf/pull/13389)) [@wence-](https://github.com/wence-)
+- Fix unused argument errors in nvcc 11.5 ([#13387](https://github.com/rapidsai/cudf/pull/13387)) [@abellina](https://github.com/abellina)
+- Updates needed to work with jitify that leverages libcudacxx ([#13383](https://github.com/rapidsai/cudf/pull/13383)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix unused parameter warning/error in parquet/page_data.cu ([#13367](https://github.com/rapidsai/cudf/pull/13367)) [@davidwendt](https://github.com/davidwendt)
+- Fix page size estimation in Parquet writer ([#13364](https://github.com/rapidsai/cudf/pull/13364)) [@etseidl](https://github.com/etseidl)
+- Fix subword_tokenize error when input contains no tokens ([#13320](https://github.com/rapidsai/cudf/pull/13320)) [@davidwendt](https://github.com/davidwendt)
+- Support gcc 12 as the C++ compiler ([#13316](https://github.com/rapidsai/cudf/pull/13316)) [@robertmaynard](https://github.com/robertmaynard)
+- Correctly set bitmask size in `from_column_view` ([#13315](https://github.com/rapidsai/cudf/pull/13315)) [@wence-](https://github.com/wence-)
+- Fix approach to detecting assignment for gte/lte operators ([#13285](https://github.com/rapidsai/cudf/pull/13285)) [@vyasr](https://github.com/vyasr)
+- Fix parquet schema interpretation issue ([#13277](https://github.com/rapidsai/cudf/pull/13277)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fix 64bit shift bug in avro reader ([#13276](https://github.com/rapidsai/cudf/pull/13276)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix unused variables/parameters in parquet/writer_impl.cu ([#13263](https://github.com/rapidsai/cudf/pull/13263)) [@davidwendt](https://github.com/davidwendt)
+- Clean up buffers in case AssertionError ([#13262](https://github.com/rapidsai/cudf/pull/13262)) [@razajafri](https://github.com/razajafri)
+- Allow empty input table in ast `compute_column` ([#13245](https://github.com/rapidsai/cudf/pull/13245)) [@wence-](https://github.com/wence-)
+- Fix structs_column_wrapper constructors to copy input column wrappers ([#13243](https://github.com/rapidsai/cudf/pull/13243)) [@davidwendt](https://github.com/davidwendt)
+- Fix the row index stream order in ORC reader ([#13242](https://github.com/rapidsai/cudf/pull/13242)) [@vuule](https://github.com/vuule)
+- Make `is_decompression_disabled` and `is_compression_disabled` thread-safe ([#13240](https://github.com/rapidsai/cudf/pull/13240)) [@vuule](https://github.com/vuule)
+- Add [[maybe_unused]] to nvbench environment. ([#13219](https://github.com/rapidsai/cudf/pull/13219)) [@bdice](https://github.com/bdice)
+- Fix race in ORC string dictionary creation ([#13214](https://github.com/rapidsai/cudf/pull/13214)) [@revans2](https://github.com/revans2)
+- Add scalar argtypes to udf cache keys ([#13194](https://github.com/rapidsai/cudf/pull/13194)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix unused parameter warning/error in grouped_rolling.cu ([#13192](https://github.com/rapidsai/cudf/pull/13192)) [@davidwendt](https://github.com/davidwendt)
+- Avoid skbuild 0.17.2 which affected the cmake -DPython_LIBRARY string ([#13188](https://github.com/rapidsai/cudf/pull/13188)) [@sevagh](https://github.com/sevagh)
+- Fix `hostdevice_vector::subspan` ([#13187](https://github.com/rapidsai/cudf/pull/13187)) [@ttnghia](https://github.com/ttnghia)
+- Use custom nvbench entry point to ensure `cudf::nvbench_base_fixture` usage ([#13183](https://github.com/rapidsai/cudf/pull/13183)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix slice_strings to return empty strings for stop &lt; start indices ([#13178](https://github.com/rapidsai/cudf/pull/13178)) [@davidwendt](https://github.com/davidwendt)
+- Allow compilation with any GTest version 1.11+ ([#13153](https://github.com/rapidsai/cudf/pull/13153)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix a few clang-format style check errors ([#13146](https://github.com/rapidsai/cudf/pull/13146)) [@davidwendt](https://github.com/davidwendt)
+- [REVIEW] Fix `Series` and `DataFrame` constructors to validate index lengths ([#13122](https://github.com/rapidsai/cudf/pull/13122)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix hash join when the input tables have nulls on only one side ([#13120](https://github.com/rapidsai/cudf/pull/13120)) [@ttnghia](https://github.com/ttnghia)
+- Fix GPU_ARCHS setting in Java CMake build and CMAKE_CUDA_ARCHITECTURES in Python package build. ([#13117](https://github.com/rapidsai/cudf/pull/13117)) [@davidwendt](https://github.com/davidwendt)
+- Adds checks to make sure json reader won&#39;t overflow ([#13115](https://github.com/rapidsai/cudf/pull/13115)) [@elstehle](https://github.com/elstehle)
+- Fix `null_count` of columns returned by `chunked_parquet_reader` ([#13111](https://github.com/rapidsai/cudf/pull/13111)) [@vuule](https://github.com/vuule)
+- Fixes sliced list and struct column bug  in JSON chunked writer ([#13108](https://github.com/rapidsai/cudf/pull/13108)) [@karthikeyann](https://github.com/karthikeyann)
+- [REVIEW] Fix missing confluent kafka version ([#13101](https://github.com/rapidsai/cudf/pull/13101)) [@galipremsagar](https://github.com/galipremsagar)
+- Use make_empty_lists_column instead of make_empty_column(type_id::LIST) ([#13099](https://github.com/rapidsai/cudf/pull/13099)) [@davidwendt](https://github.com/davidwendt)
+- Raise `NotImplementedError` when attempting to construct cuDF objects from timezone-aware datetimes ([#13086](https://github.com/rapidsai/cudf/pull/13086)) [@shwina](https://github.com/shwina)
+- Fix column selection `read_parquet` benchmarks ([#13082](https://github.com/rapidsai/cudf/pull/13082)) [@vuule](https://github.com/vuule)
+- Fix bugs in iterative groupby apply algorithm ([#13078](https://github.com/rapidsai/cudf/pull/13078)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add algorithm include in data_sink.hpp ([#13068](https://github.com/rapidsai/cudf/pull/13068)) [@ahendriksen](https://github.com/ahendriksen)
+- Fix tests/identify_stream_usage.cpp ([#13066](https://github.com/rapidsai/cudf/pull/13066)) [@ahendriksen](https://github.com/ahendriksen)
+- Prevent overflow with `skip_rows` in ORC and Parquet readers ([#13063](https://github.com/rapidsai/cudf/pull/13063)) [@vuule](https://github.com/vuule)
+- Add except declaration in Cython interface for regex_program::create ([#13054](https://github.com/rapidsai/cudf/pull/13054)) [@davidwendt](https://github.com/davidwendt)
+- [REVIEW] Fix branch version in CI scripts ([#13029](https://github.com/rapidsai/cudf/pull/13029)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix OOB memory access in CSV reader when reading without NA values ([#13011](https://github.com/rapidsai/cudf/pull/13011)) [@vuule](https://github.com/vuule)
+- Fix read_avro() skip_rows and num_rows. ([#12912](https://github.com/rapidsai/cudf/pull/12912)) [@tpn](https://github.com/tpn)
+- Purge nonempty nulls from byte_cast list outputs. ([#11971](https://github.com/rapidsai/cudf/pull/11971)) [@bdice](https://github.com/bdice)
+- Fix consumption of CPU-backed interchange protocol dataframes ([#11392](https://github.com/rapidsai/cudf/pull/11392)) [@shwina](https://github.com/shwina)
+
+## 🚀 New Features
+
+- Remove numba JIT kernel usage from dataframe copy tests ([#13385](https://github.com/rapidsai/cudf/pull/13385)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add JNI for ORC/Parquet writer compression statistics ([#13376](https://github.com/rapidsai/cudf/pull/13376)) [@ttnghia](https://github.com/ttnghia)
+- Use _compile_or_get in JIT groupby apply ([#13350](https://github.com/rapidsai/cudf/pull/13350)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- cuDF numba cuda 12 updates ([#13337](https://github.com/rapidsai/cudf/pull/13337)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add tz_convert method to convert between timestamps ([#13328](https://github.com/rapidsai/cudf/pull/13328)) [@shwina](https://github.com/shwina)
+- Optionally return compression statistics from ORC and Parquet writers ([#13294](https://github.com/rapidsai/cudf/pull/13294)) [@vuule](https://github.com/vuule)
+- Support the case=False argument to str.contains ([#13290](https://github.com/rapidsai/cudf/pull/13290)) [@shwina](https://github.com/shwina)
+- Add an event handler for ColumnVector.close ([#13279](https://github.com/rapidsai/cudf/pull/13279)) [@abellina](https://github.com/abellina)
+- JNI api for cudf::chunked_pack ([#13278](https://github.com/rapidsai/cudf/pull/13278)) [@abellina](https://github.com/abellina)
+- Implement a chunked_pack API ([#13260](https://github.com/rapidsai/cudf/pull/13260)) [@abellina](https://github.com/abellina)
+- Update cudf recipes to use GTest version to &gt;=1.13 ([#13207](https://github.com/rapidsai/cudf/pull/13207)) [@robertmaynard](https://github.com/robertmaynard)
+- JNI changes for range-extents in window functions. ([#13199](https://github.com/rapidsai/cudf/pull/13199)) [@mythrocks](https://github.com/mythrocks)
+- Add support for DatetimeTZDtype and tz_localize ([#13163](https://github.com/rapidsai/cudf/pull/13163)) [@shwina](https://github.com/shwina)
+- Add IS_NULL operator to AST ([#13145](https://github.com/rapidsai/cudf/pull/13145)) [@karthikeyann](https://github.com/karthikeyann)
+- STRING order-by column for RANGE window functions ([#13143](https://github.com/rapidsai/cudf/pull/13143)) [@mythrocks](https://github.com/mythrocks)
+- Update `contains_table` to experimental row hasher and equality comparator ([#13119](https://github.com/rapidsai/cudf/pull/13119)) [@divyegala](https://github.com/divyegala)
+- Automatically select `GroupBy.apply` algorithm based on if the UDF is jittable ([#13113](https://github.com/rapidsai/cudf/pull/13113)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Refactor Parquet chunked writer ([#13076](https://github.com/rapidsai/cudf/pull/13076)) [@ttnghia](https://github.com/ttnghia)
+- Add Python bindings for string literal support in AST ([#13073](https://github.com/rapidsai/cudf/pull/13073)) [@karthikeyann](https://github.com/karthikeyann)
+- Add Java bindings for string literal support in AST ([#13072](https://github.com/rapidsai/cudf/pull/13072)) [@karthikeyann](https://github.com/karthikeyann)
+- Add string scalar support in AST ([#13061](https://github.com/rapidsai/cudf/pull/13061)) [@karthikeyann](https://github.com/karthikeyann)
+- Log cuIO warnings using the libcudf logger ([#13043](https://github.com/rapidsai/cudf/pull/13043)) [@vuule](https://github.com/vuule)
+- Update `mixed_join` to use experimental row hasher and comparator ([#13028](https://github.com/rapidsai/cudf/pull/13028)) [@divyegala](https://github.com/divyegala)
+- Support structs of lists in row lexicographic comparator ([#13005](https://github.com/rapidsai/cudf/pull/13005)) [@ttnghia](https://github.com/ttnghia)
+- Adding `hostdevice_span` that is a span createable from `hostdevice_vector` ([#12981](https://github.com/rapidsai/cudf/pull/12981)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add nvtext::minhash function ([#12961](https://github.com/rapidsai/cudf/pull/12961)) [@davidwendt](https://github.com/davidwendt)
+- Support lists of structs in row lexicographic comparator ([#12953](https://github.com/rapidsai/cudf/pull/12953)) [@ttnghia](https://github.com/ttnghia)
+- Update `join` to use experimental row hasher and comparator ([#12787](https://github.com/rapidsai/cudf/pull/12787)) [@divyegala](https://github.com/divyegala)
+- Implement Python drop_duplicates with cudf::stable_distinct. ([#11656](https://github.com/rapidsai/cudf/pull/11656)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+
+## 🛠️ Improvements
+
+- Drop extraneous dependencies from cudf conda recipe. ([#13406](https://github.com/rapidsai/cudf/pull/13406)) [@bdice](https://github.com/bdice)
+- Handle some corner-cases in indexing with boolean masks ([#13402](https://github.com/rapidsai/cudf/pull/13402)) [@wence-](https://github.com/wence-)
+- Add cudf::stable_distinct public API, tests, and benchmarks. ([#13392](https://github.com/rapidsai/cudf/pull/13392)) [@bdice](https://github.com/bdice)
+- [JNI] Pass this ColumnVector to the onClosed event handler ([#13386](https://github.com/rapidsai/cudf/pull/13386)) [@abellina](https://github.com/abellina)
+- Fix JNI method with mismatched parameter list ([#13384](https://github.com/rapidsai/cudf/pull/13384)) [@ttnghia](https://github.com/ttnghia)
+- Split up experimental_row_operator_tests.cu to improve its compile time ([#13382](https://github.com/rapidsai/cudf/pull/13382)) [@davidwendt](https://github.com/davidwendt)
+- Deprecate cudf::strings::slice_strings APIs that accept delimiters ([#13373](https://github.com/rapidsai/cudf/pull/13373)) [@davidwendt](https://github.com/davidwendt)
+- Remove UNKNOWN_NULL_COUNT ([#13372](https://github.com/rapidsai/cudf/pull/13372)) [@vyasr](https://github.com/vyasr)
+- Move some nvtext benchmarks to nvbench ([#13368](https://github.com/rapidsai/cudf/pull/13368)) [@davidwendt](https://github.com/davidwendt)
+- run docs nightly too ([#13366](https://github.com/rapidsai/cudf/pull/13366)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Add warning for default `dtype` parameter in `get_dummies` ([#13365](https://github.com/rapidsai/cudf/pull/13365)) [@galipremsagar](https://github.com/galipremsagar)
+- Add log messages about kvikIO compatibility mode ([#13363](https://github.com/rapidsai/cudf/pull/13363)) [@vuule](https://github.com/vuule)
+- Switch back to using primary shared-action-workflows branch ([#13362](https://github.com/rapidsai/cudf/pull/13362)) [@vyasr](https://github.com/vyasr)
+- Deprecate `StringIndex` and use `Index` instead ([#13361](https://github.com/rapidsai/cudf/pull/13361)) [@galipremsagar](https://github.com/galipremsagar)
+- Ensure columns have valid null counts in CUDF JNI. ([#13355](https://github.com/rapidsai/cudf/pull/13355)) [@mythrocks](https://github.com/mythrocks)
+- Expunge most uses of `TypeVar(bound=&quot;Foo&quot;)` ([#13346](https://github.com/rapidsai/cudf/pull/13346)) [@wence-](https://github.com/wence-)
+- Remove all references to UNKNOWN_NULL_COUNT in Python ([#13345](https://github.com/rapidsai/cudf/pull/13345)) [@vyasr](https://github.com/vyasr)
+- Improve `distinct_count` with `cuco::static_set` ([#13343](https://github.com/rapidsai/cudf/pull/13343)) [@PointKernel](https://github.com/PointKernel)
+- Fix  `contiguous_split` performance ([#13342](https://github.com/rapidsai/cudf/pull/13342)) [@ttnghia](https://github.com/ttnghia)
+- Remove default UNKNOWN_NULL_COUNT from cudf::column member functions ([#13341](https://github.com/rapidsai/cudf/pull/13341)) [@davidwendt](https://github.com/davidwendt)
+- Update mypy to 1.3 ([#13340](https://github.com/rapidsai/cudf/pull/13340)) [@wence-](https://github.com/wence-)
+- [Java] Purge non-empty nulls when setting validity ([#13335](https://github.com/rapidsai/cudf/pull/13335)) [@razajafri](https://github.com/razajafri)
+- Add row-wise filtering step to `read_parquet` ([#13334](https://github.com/rapidsai/cudf/pull/13334)) [@rjzamora](https://github.com/rjzamora)
+- Performance improvement for nvtext::minhash ([#13333](https://github.com/rapidsai/cudf/pull/13333)) [@davidwendt](https://github.com/davidwendt)
+- Fix some libcudf functions to set the null count on returning columns ([#13331](https://github.com/rapidsai/cudf/pull/13331)) [@davidwendt](https://github.com/davidwendt)
+- Change cudf::detail::concatenate_masks to return null-count ([#13330](https://github.com/rapidsai/cudf/pull/13330)) [@davidwendt](https://github.com/davidwendt)
+- Move `meta` calculation in `dask_cudf.read_parquet` ([#13327](https://github.com/rapidsai/cudf/pull/13327)) [@rjzamora](https://github.com/rjzamora)
+- Changes to support Numpy &gt;= 1.24 ([#13325](https://github.com/rapidsai/cudf/pull/13325)) [@shwina](https://github.com/shwina)
+- Use std::overflow_error when output would exceed column size limit ([#13323](https://github.com/rapidsai/cudf/pull/13323)) [@davidwendt](https://github.com/davidwendt)
+- Clean up `distinct_count` benchmark ([#13321](https://github.com/rapidsai/cudf/pull/13321)) [@PointKernel](https://github.com/PointKernel)
+- Fix gtest pinning to 1.13.0. ([#13319](https://github.com/rapidsai/cudf/pull/13319)) [@bdice](https://github.com/bdice)
+- Remove null mask and null count from column_view constructors ([#13311](https://github.com/rapidsai/cudf/pull/13311)) [@vyasr](https://github.com/vyasr)
+- Address feedback from 13289 ([#13306](https://github.com/rapidsai/cudf/pull/13306)) [@vyasr](https://github.com/vyasr)
+- Change default value of the `observed=` argument in groupby to `True` to reflect the actual behaviour ([#13296](https://github.com/rapidsai/cudf/pull/13296)) [@shwina](https://github.com/shwina)
+- First check for `BaseDtype` when infering the data type of an arbitrary object ([#13295](https://github.com/rapidsai/cudf/pull/13295)) [@shwina](https://github.com/shwina)
+- Throw error if UNINITIALIZED is passed to cudf::state_null_count ([#13292](https://github.com/rapidsai/cudf/pull/13292)) [@davidwendt](https://github.com/davidwendt)
+- Support CUDA 12.0 for pip wheels ([#13289](https://github.com/rapidsai/cudf/pull/13289)) [@divyegala](https://github.com/divyegala)
+- Refactor `transform_lists_of_structs` in `row_operators.cu` ([#13288](https://github.com/rapidsai/cudf/pull/13288)) [@ttnghia](https://github.com/ttnghia)
+- Branch 23.06 merge 23.04 ([#13286](https://github.com/rapidsai/cudf/pull/13286)) [@vyasr](https://github.com/vyasr)
+- Update cupy dependency ([#13284](https://github.com/rapidsai/cudf/pull/13284)) [@vyasr](https://github.com/vyasr)
+- Performance improvement in cudf::strings::join_strings for long strings ([#13283](https://github.com/rapidsai/cudf/pull/13283)) [@davidwendt](https://github.com/davidwendt)
+- Fix unused variables and functions ([#13275](https://github.com/rapidsai/cudf/pull/13275)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix integer overflow in `partition` `scatter_map` construction ([#13272](https://github.com/rapidsai/cudf/pull/13272)) [@wence-](https://github.com/wence-)
+- Numba 0.57 compatibility fixes ([#13271](https://github.com/rapidsai/cudf/pull/13271)) [@gmarkall](https://github.com/gmarkall)
+- Performance improvement in cudf::strings::all_characters_of_type ([#13259](https://github.com/rapidsai/cudf/pull/13259)) [@davidwendt](https://github.com/davidwendt)
+- Remove default null-count parameter from some libcudf factory functions ([#13258](https://github.com/rapidsai/cudf/pull/13258)) [@davidwendt](https://github.com/davidwendt)
+- Roll our own generate_string() because mimesis&#39; has gone away ([#13257](https://github.com/rapidsai/cudf/pull/13257)) [@shwina](https://github.com/shwina)
+- Build wheels using new single image workflow ([#13249](https://github.com/rapidsai/cudf/pull/13249)) [@vyasr](https://github.com/vyasr)
+- Enable sccache hits from local builds ([#13248](https://github.com/rapidsai/cudf/pull/13248)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Revert to branch-23.06 for shared-action-workflows ([#13247](https://github.com/rapidsai/cudf/pull/13247)) [@shwina](https://github.com/shwina)
+- Introduce `pandas_compatible` option in `cudf` ([#13241](https://github.com/rapidsai/cudf/pull/13241)) [@galipremsagar](https://github.com/galipremsagar)
+- Add metadata_builder helper class ([#13232](https://github.com/rapidsai/cudf/pull/13232)) [@abellina](https://github.com/abellina)
+- Use libkvikio conda packages in libcudf, add explicit libcufile dependency. ([#13231](https://github.com/rapidsai/cudf/pull/13231)) [@bdice](https://github.com/bdice)
+- Remove default null-count parameter from cudf::make_strings_column factory ([#13227](https://github.com/rapidsai/cudf/pull/13227)) [@davidwendt](https://github.com/davidwendt)
+- Performance improvement in cudf::strings::find/rfind for long strings ([#13226](https://github.com/rapidsai/cudf/pull/13226)) [@davidwendt](https://github.com/davidwendt)
+- Add chunked reader benchmark ([#13223](https://github.com/rapidsai/cudf/pull/13223)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Set the null count in output columns in the CSV reader ([#13221](https://github.com/rapidsai/cudf/pull/13221)) [@vuule](https://github.com/vuule)
+- Skip Non-Empty nulls tests for the nightly build just like we skip CuFileTest and CudaFatalTest ([#13213](https://github.com/rapidsai/cudf/pull/13213)) [@razajafri](https://github.com/razajafri)
+- Fix string_scalar stream usage in write_json.cu ([#13212](https://github.com/rapidsai/cudf/pull/13212)) [@davidwendt](https://github.com/davidwendt)
+- Use canonicalized name for dlopen&#39;d libraries (libcufile) ([#13210](https://github.com/rapidsai/cudf/pull/13210)) [@shwina](https://github.com/shwina)
+- Refactor pinned memory vector and ORC+Parquet writers ([#13206](https://github.com/rapidsai/cudf/pull/13206)) [@ttnghia](https://github.com/ttnghia)
+- Remove UNKNOWN_NULL_COUNT where it can be easily computed ([#13205](https://github.com/rapidsai/cudf/pull/13205)) [@vyasr](https://github.com/vyasr)
+- Optimization to decoding of parquet level streams ([#13203](https://github.com/rapidsai/cudf/pull/13203)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Clean up and simplify `gpuDecideCompression` ([#13202](https://github.com/rapidsai/cudf/pull/13202)) [@vuule](https://github.com/vuule)
+- Use std::array for a statically sized vector in `create_serialized_trie` ([#13201](https://github.com/rapidsai/cudf/pull/13201)) [@vuule](https://github.com/vuule)
+- Update minimum Python version to Python 3.9 ([#13196](https://github.com/rapidsai/cudf/pull/13196)) [@shwina](https://github.com/shwina)
+- Refactor contiguous_split API into contiguous_split.hpp ([#13186](https://github.com/rapidsai/cudf/pull/13186)) [@abellina](https://github.com/abellina)
+- Remove usage of rapids-get-rapids-version-from-git ([#13184](https://github.com/rapidsai/cudf/pull/13184)) [@jjacobelli](https://github.com/jjacobelli)
+- Enable mixed-dtype decimal/scalar binary operations ([#13171](https://github.com/rapidsai/cudf/pull/13171)) [@shwina](https://github.com/shwina)
+- Split up unique_count.cu to improve build time ([#13169](https://github.com/rapidsai/cudf/pull/13169)) [@davidwendt](https://github.com/davidwendt)
+- Use nvtx3 includes in string examples. ([#13165](https://github.com/rapidsai/cudf/pull/13165)) [@bdice](https://github.com/bdice)
+- Change some .cu gtest files to .cpp ([#13155](https://github.com/rapidsai/cudf/pull/13155)) [@davidwendt](https://github.com/davidwendt)
+- Remove wheel pytest verbosity ([#13151](https://github.com/rapidsai/cudf/pull/13151)) [@sevagh](https://github.com/sevagh)
+- Fix libcudf to always pass null-count to set_null_mask ([#13149](https://github.com/rapidsai/cudf/pull/13149)) [@davidwendt](https://github.com/davidwendt)
+- Fix gtests to always pass null-count to set_null_mask calls ([#13148](https://github.com/rapidsai/cudf/pull/13148)) [@davidwendt](https://github.com/davidwendt)
+- Optimize JSON writer ([#13144](https://github.com/rapidsai/cudf/pull/13144)) [@karthikeyann](https://github.com/karthikeyann)
+- Performance improvement for libcudf upper/lower conversion for long strings ([#13142](https://github.com/rapidsai/cudf/pull/13142)) [@davidwendt](https://github.com/davidwendt)
+- [REVIEW] Deprecate `pad` and `backfill` methods ([#13140](https://github.com/rapidsai/cudf/pull/13140)) [@galipremsagar](https://github.com/galipremsagar)
+- Use CTAD instead of functions in ProtobufReader ([#13135](https://github.com/rapidsai/cudf/pull/13135)) [@vuule](https://github.com/vuule)
+- Remove more instances of `UNKNOWN_NULL_COUNT` ([#13134](https://github.com/rapidsai/cudf/pull/13134)) [@vyasr](https://github.com/vyasr)
+- Update clang-format to 16.0.1. ([#13133](https://github.com/rapidsai/cudf/pull/13133)) [@bdice](https://github.com/bdice)
+- Add log messages about cuIO&#39;s nvCOMP and cuFile use ([#13132](https://github.com/rapidsai/cudf/pull/13132)) [@vuule](https://github.com/vuule)
+- Branch 23.06 merge 23.04 ([#13131](https://github.com/rapidsai/cudf/pull/13131)) [@vyasr](https://github.com/vyasr)
+- Compute null-count in cudf::detail::slice ([#13124](https://github.com/rapidsai/cudf/pull/13124)) [@davidwendt](https://github.com/davidwendt)
+- Use ARC V2 self-hosted runners for GPU jobs ([#13123](https://github.com/rapidsai/cudf/pull/13123)) [@jjacobelli](https://github.com/jjacobelli)
+- Set null-count in linked_column_view conversion operator ([#13121](https://github.com/rapidsai/cudf/pull/13121)) [@davidwendt](https://github.com/davidwendt)
+- Adding ifdefs around nvcc-specific pragmas ([#13110](https://github.com/rapidsai/cudf/pull/13110)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add null-count parameter to json experimental parse_data utility ([#13107](https://github.com/rapidsai/cudf/pull/13107)) [@davidwendt](https://github.com/davidwendt)
+- Remove uses-setup-env-vars ([#13105](https://github.com/rapidsai/cudf/pull/13105)) [@vyasr](https://github.com/vyasr)
+- Explicitly compute null count in concatenate APIs ([#13104](https://github.com/rapidsai/cudf/pull/13104)) [@vyasr](https://github.com/vyasr)
+- Replace unnecessary uses of `UNKNOWN_NULL_COUNT` ([#13102](https://github.com/rapidsai/cudf/pull/13102)) [@vyasr](https://github.com/vyasr)
+- Performance improvement for cudf::string_view::find functions ([#13100](https://github.com/rapidsai/cudf/pull/13100)) [@davidwendt](https://github.com/davidwendt)
+- Use `.element()` instead of `.data()` for window range calculations ([#13095](https://github.com/rapidsai/cudf/pull/13095)) [@mythrocks](https://github.com/mythrocks)
+- Cleanup Parquet chunked writer ([#13094](https://github.com/rapidsai/cudf/pull/13094)) [@ttnghia](https://github.com/ttnghia)
+- Fix unused variable error/warning in page_data.cu ([#13093](https://github.com/rapidsai/cudf/pull/13093)) [@davidwendt](https://github.com/davidwendt)
+- Cleanup ORC chunked writer ([#13091](https://github.com/rapidsai/cudf/pull/13091)) [@ttnghia](https://github.com/ttnghia)
+- Remove using namespace cudf; from libcudf gtests source ([#13089](https://github.com/rapidsai/cudf/pull/13089)) [@davidwendt](https://github.com/davidwendt)
+- Change cudf::test::make_null_mask to also return null-count ([#13081](https://github.com/rapidsai/cudf/pull/13081)) [@davidwendt](https://github.com/davidwendt)
+- Resolved automerger from `branch-23.04` to `branch-23.06` ([#13080](https://github.com/rapidsai/cudf/pull/13080)) [@galipremsagar](https://github.com/galipremsagar)
+- Assert for non-empty nulls ([#13071](https://github.com/rapidsai/cudf/pull/13071)) [@razajafri](https://github.com/razajafri)
+- Remove deprecated regex functions from libcudf ([#13067](https://github.com/rapidsai/cudf/pull/13067)) [@davidwendt](https://github.com/davidwendt)
+- Refactor `cudf::detail::sorted_order` ([#13062](https://github.com/rapidsai/cudf/pull/13062)) [@ttnghia](https://github.com/ttnghia)
+- Improve performance of slice_strings for long strings ([#13057](https://github.com/rapidsai/cudf/pull/13057)) [@davidwendt](https://github.com/davidwendt)
+- Reduce shared memory usage in gpuComputePageSizes by 50% ([#13047](https://github.com/rapidsai/cudf/pull/13047)) [@nvdbaranec](https://github.com/nvdbaranec)
+- [REVIEW] Add notes to performance comparisons notebook ([#13044](https://github.com/rapidsai/cudf/pull/13044)) [@galipremsagar](https://github.com/galipremsagar)
+- Enable binary operations between scalars and columns of differing decimal types ([#13034](https://github.com/rapidsai/cudf/pull/13034)) [@shwina](https://github.com/shwina)
+- Remove console output from some libcudf gtests ([#13027](https://github.com/rapidsai/cudf/pull/13027)) [@davidwendt](https://github.com/davidwendt)
+- Remove underscore in build string. ([#13025](https://github.com/rapidsai/cudf/pull/13025)) [@bdice](https://github.com/bdice)
+- Bump up JNI version 23.06.0-SNAPSHOT ([#13021](https://github.com/rapidsai/cudf/pull/13021)) [@pxLi](https://github.com/pxLi)
+- Fix auto merger from `branch-23.04` to `branch-23.06` ([#13009](https://github.com/rapidsai/cudf/pull/13009)) [@galipremsagar](https://github.com/galipremsagar)
+- Reduce peak memory use when writing compressed ORC files. ([#12963](https://github.com/rapidsai/cudf/pull/12963)) [@vuule](https://github.com/vuule)
+- Add nvtx annotatations to groupby methods ([#12941](https://github.com/rapidsai/cudf/pull/12941)) [@wence-](https://github.com/wence-)
+- Compute column sizes in Parquet preprocess with single kernel ([#12931](https://github.com/rapidsai/cudf/pull/12931)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Add Python bindings for time zone data (TZiF) reader ([#12826](https://github.com/rapidsai/cudf/pull/12826)) [@shwina](https://github.com/shwina)
+- Optimize set-like operations ([#12769](https://github.com/rapidsai/cudf/pull/12769)) [@ttnghia](https://github.com/ttnghia)
+- [REVIEW] Upgrade to `arrow-11` ([#12757](https://github.com/rapidsai/cudf/pull/12757)) [@galipremsagar](https://github.com/galipremsagar)
+- Add empty test files for test reorganization ([#12288](https://github.com/rapidsai/cudf/pull/12288)) [@shwina](https://github.com/shwina)
+
+# cuDF 23.04.00 (6 Apr 2023)
+
+## 🚨 Breaking Changes
+
+- Pin `dask` and `distributed` for release ([#13070](https://github.com/rapidsai/cudf/pull/13070)) [@galipremsagar](https://github.com/galipremsagar)
+- Declare a different name for nan_equality.UNEQUAL to prevent Cython warnings. ([#12947](https://github.com/rapidsai/cudf/pull/12947)) [@bdice](https://github.com/bdice)
+- Update minimum `pandas` and `numpy` pinnings ([#12887](https://github.com/rapidsai/cudf/pull/12887)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `names` &amp; `dtype` in `Index.copy` ([#12825](https://github.com/rapidsai/cudf/pull/12825)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `Index.is_*` methods ([#12820](https://github.com/rapidsai/cudf/pull/12820)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `datetime_is_numeric` from `describe` ([#12818](https://github.com/rapidsai/cudf/pull/12818)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `na_sentinel` in `factorize` ([#12817](https://github.com/rapidsai/cudf/pull/12817)) [@galipremsagar](https://github.com/galipremsagar)
+- Make string methods return a Series with a useful Index ([#12814](https://github.com/rapidsai/cudf/pull/12814)) [@shwina](https://github.com/shwina)
+- Produce useful guidance on overflow error in `to_csv` ([#12705](https://github.com/rapidsai/cudf/pull/12705)) [@wence-](https://github.com/wence-)
+- Move `strings_udf` code into cuDF ([#12669](https://github.com/rapidsai/cudf/pull/12669)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Remove cudf::strings::repeat_strings_output_sizes and optional parameter from cudf::strings::repeat_strings ([#12609](https://github.com/rapidsai/cudf/pull/12609)) [@davidwendt](https://github.com/davidwendt)
+- Replace message parsing with throwing more specific exceptions ([#12426](https://github.com/rapidsai/cudf/pull/12426)) [@vyasr](https://github.com/vyasr)
+
+## 🐛 Bug Fixes
+
+- Fix memcheck script to execute only _TEST files found in bin/gtests/libcudf ([#13006](https://github.com/rapidsai/cudf/pull/13006)) [@davidwendt](https://github.com/davidwendt)
+- Fix `DataFrame` constructor to broadcast scalar inputs properly ([#12997](https://github.com/rapidsai/cudf/pull/12997)) [@galipremsagar](https://github.com/galipremsagar)
+- Drop `force_nullable_schema` from chunked parquet writer ([#12996](https://github.com/rapidsai/cudf/pull/12996)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix gtest column utility comparator diff reporting ([#12995](https://github.com/rapidsai/cudf/pull/12995)) [@davidwendt](https://github.com/davidwendt)
+- Handle index names while performing `groupby` ([#12992](https://github.com/rapidsai/cudf/pull/12992)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `__setitem__` on string columns when the scalar value ends in a null byte ([#12991](https://github.com/rapidsai/cudf/pull/12991)) [@wence-](https://github.com/wence-)
+- Fix `sort_values` when column is all empty strings ([#12988](https://github.com/rapidsai/cudf/pull/12988)) [@eriknw](https://github.com/eriknw)
+- Remove unused variable and fix memory issue in ORC writer ([#12984](https://github.com/rapidsai/cudf/pull/12984)) [@ttnghia](https://github.com/ttnghia)
+- Pre-emptive fix for upstream `dask.dataframe.read_parquet` changes ([#12983](https://github.com/rapidsai/cudf/pull/12983)) [@rjzamora](https://github.com/rjzamora)
+- Remove MANIFEST.in use auto-generated one for sdists and package_data for wheels ([#12960](https://github.com/rapidsai/cudf/pull/12960)) [@vyasr](https://github.com/vyasr)
+- Update to use rapids-export(COMPONENTS) feature. ([#12959](https://github.com/rapidsai/cudf/pull/12959)) [@robertmaynard](https://github.com/robertmaynard)
+- cudftestutil supports static gtest dependencies ([#12957](https://github.com/rapidsai/cudf/pull/12957)) [@robertmaynard](https://github.com/robertmaynard)
+- Include gtest in build environment. ([#12956](https://github.com/rapidsai/cudf/pull/12956)) [@vyasr](https://github.com/vyasr)
+- Correctly handle scalar indices in `Index.__getitem__` ([#12955](https://github.com/rapidsai/cudf/pull/12955)) [@wence-](https://github.com/wence-)
+- Avoid building cython twice ([#12945](https://github.com/rapidsai/cudf/pull/12945)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix set index error for Series rolling window operations ([#12942](https://github.com/rapidsai/cudf/pull/12942)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix calculation of null counts for Parquet statistics ([#12938](https://github.com/rapidsai/cudf/pull/12938)) [@etseidl](https://github.com/etseidl)
+- Preserve integer dtype of hive-partitioned column containing nulls ([#12930](https://github.com/rapidsai/cudf/pull/12930)) [@rjzamora](https://github.com/rjzamora)
+- Use get_current_device_resource for intermediate allocations in COLLECT_LIST window code ([#12927](https://github.com/rapidsai/cudf/pull/12927)) [@karthikeyann](https://github.com/karthikeyann)
+- Mark dlpack tensor deleter as noexcept to match PyCapsule_Destructor signature. ([#12921](https://github.com/rapidsai/cudf/pull/12921)) [@bdice](https://github.com/bdice)
+- Fix conda recipe post-link.sh typo ([#12916](https://github.com/rapidsai/cudf/pull/12916)) [@pentschev](https://github.com/pentschev)
+- min_rows and num_rows are swapped in ComputePageSizes declaration in Parquet reader ([#12886](https://github.com/rapidsai/cudf/pull/12886)) [@etseidl](https://github.com/etseidl)
+- Expect cupy to now support bool arrays for dlpack. ([#12883](https://github.com/rapidsai/cudf/pull/12883)) [@vyasr](https://github.com/vyasr)
+- Use python -m pytest for nightly wheel tests ([#12871](https://github.com/rapidsai/cudf/pull/12871)) [@bdice](https://github.com/bdice)
+- Parquet writer column_size() should return a size_t ([#12870](https://github.com/rapidsai/cudf/pull/12870)) [@etseidl](https://github.com/etseidl)
+- Fix cudf::hash_partition kernel launch error with decimal128 types ([#12863](https://github.com/rapidsai/cudf/pull/12863)) [@davidwendt](https://github.com/davidwendt)
+- Fix an issue with parquet chunked reader undercounting string lengths. ([#12859](https://github.com/rapidsai/cudf/pull/12859)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Remove tokenizers pre-install pinning. ([#12854](https://github.com/rapidsai/cudf/pull/12854)) [@vyasr](https://github.com/vyasr)
+- Fix parquet `RangeIndex` bug ([#12838](https://github.com/rapidsai/cudf/pull/12838)) [@rjzamora](https://github.com/rjzamora)
+- Remove KAFKA_HOST_TEST from compute-sanitizer check ([#12831](https://github.com/rapidsai/cudf/pull/12831)) [@davidwendt](https://github.com/davidwendt)
+- Make string methods return a Series with a useful Index ([#12814](https://github.com/rapidsai/cudf/pull/12814)) [@shwina](https://github.com/shwina)
+- Tell cudf_kafka to use header-only fmt ([#12796](https://github.com/rapidsai/cudf/pull/12796)) [@vyasr](https://github.com/vyasr)
+- Add `GroupBy.dtypes` ([#12783](https://github.com/rapidsai/cudf/pull/12783)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix a leak in a test and clarify some test names ([#12781](https://github.com/rapidsai/cudf/pull/12781)) [@revans2](https://github.com/revans2)
+- Fix bug in all-null list due to join_list_elements special handling ([#12767](https://github.com/rapidsai/cudf/pull/12767)) [@karthikeyann](https://github.com/karthikeyann)
+- Add try/except for expected null-schema error in read_parquet ([#12756](https://github.com/rapidsai/cudf/pull/12756)) [@rjzamora](https://github.com/rjzamora)
+- Throw an exception if an unsupported page encoding is detected in Parquet reader ([#12754](https://github.com/rapidsai/cudf/pull/12754)) [@etseidl](https://github.com/etseidl)
+- Fix a bug with `num_keys` in `_scatter_by_slice` ([#12749](https://github.com/rapidsai/cudf/pull/12749)) [@thomcom](https://github.com/thomcom)
+- Bump pinned rapids wheel deps to 23.4 ([#12735](https://github.com/rapidsai/cudf/pull/12735)) [@sevagh](https://github.com/sevagh)
+- Rework logic in cudf::strings::split_record to improve performance ([#12729](https://github.com/rapidsai/cudf/pull/12729)) [@davidwendt](https://github.com/davidwendt)
+- Add `always_nullable` flag to Dremel encoding ([#12727](https://github.com/rapidsai/cudf/pull/12727)) [@divyegala](https://github.com/divyegala)
+- Fix memcheck read error in compound segmented reduce ([#12722](https://github.com/rapidsai/cudf/pull/12722)) [@davidwendt](https://github.com/davidwendt)
+- Fix faulty conditional logic in JIT `GroupBy.apply` ([#12706](https://github.com/rapidsai/cudf/pull/12706)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Produce useful guidance on overflow error in `to_csv` ([#12705](https://github.com/rapidsai/cudf/pull/12705)) [@wence-](https://github.com/wence-)
+- Handle parquet list data corner case ([#12698](https://github.com/rapidsai/cudf/pull/12698)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix missing trailing comma in json writer ([#12688](https://github.com/rapidsai/cudf/pull/12688)) [@karthikeyann](https://github.com/karthikeyann)
+- Remove child fom newCudaAsyncMemoryResource ([#12681](https://github.com/rapidsai/cudf/pull/12681)) [@abellina](https://github.com/abellina)
+- Handle bool types in `round` API ([#12670](https://github.com/rapidsai/cudf/pull/12670)) [@galipremsagar](https://github.com/galipremsagar)
+- Ensure all of device bitmask is initialized in from_arrow ([#12668](https://github.com/rapidsai/cudf/pull/12668)) [@wence-](https://github.com/wence-)
+- Fix `from_arrow` to load a sliced arrow table ([#12665](https://github.com/rapidsai/cudf/pull/12665)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix dask-cudf read_parquet bug for multi-file aggregation ([#12663](https://github.com/rapidsai/cudf/pull/12663)) [@rjzamora](https://github.com/rjzamora)
+- Fix AllocateLikeTest gtests reading uninitialized null-mask ([#12643](https://github.com/rapidsai/cudf/pull/12643)) [@davidwendt](https://github.com/davidwendt)
+- Fix `find_common_dtype` and `values` to handle complex dtypes ([#12537](https://github.com/rapidsai/cudf/pull/12537)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix fetching of MultiIndex values when a label is passed ([#12521](https://github.com/rapidsai/cudf/pull/12521)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `Series` comparison vs scalars ([#12519](https://github.com/rapidsai/cudf/pull/12519)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Allow casting from `UDFString` back to `StringView` to call methods in `strings_udf` ([#12363](https://github.com/rapidsai/cudf/pull/12363)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+
+## 📖 Documentation
+
+- Fix `GroupBy.apply` doc examples rendering ([#12994](https://github.com/rapidsai/cudf/pull/12994)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- add sphinx building and s3 uploading for dask-cudf docs ([#12982](https://github.com/rapidsai/cudf/pull/12982)) [@quasiben](https://github.com/quasiben)
+- Add developer documentation forbidding default parameters in detail APIs ([#12978](https://github.com/rapidsai/cudf/pull/12978)) [@vyasr](https://github.com/vyasr)
+- Add README symlink for dask-cudf. ([#12946](https://github.com/rapidsai/cudf/pull/12946)) [@bdice](https://github.com/bdice)
+- Remove return type from [@return doxygen tags ([#12908](https://github.com/rapidsai/cudf/pull/12908)) @davidwendt](https://github.com/return doxygen tags ([#12908](https://github.com/rapidsai/cudf/pull/12908)) @davidwendt)
+- Fix docs build to be `pydata-sphinx-theme=0.13.0` compatible ([#12874](https://github.com/rapidsai/cudf/pull/12874)) [@galipremsagar](https://github.com/galipremsagar)
+- Add skeleton API and prose documentation for dask-cudf ([#12725](https://github.com/rapidsai/cudf/pull/12725)) [@wence-](https://github.com/wence-)
+- Enable doctests for GroupBy methods ([#12658](https://github.com/rapidsai/cudf/pull/12658)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add comment about CUB patch for SegmentedSortInt.Bool gtest ([#12611](https://github.com/rapidsai/cudf/pull/12611)) [@davidwendt](https://github.com/davidwendt)
+
+## 🚀 New Features
+
+- Add JNI method for strings::replace multi variety ([#12979](https://github.com/rapidsai/cudf/pull/12979)) [@NVnavkumar](https://github.com/NVnavkumar)
+- Add nunique aggregation support for cudf::segmented_reduce ([#12972](https://github.com/rapidsai/cudf/pull/12972)) [@davidwendt](https://github.com/davidwendt)
+- Refactor orc chunked writer ([#12949](https://github.com/rapidsai/cudf/pull/12949)) [@ttnghia](https://github.com/ttnghia)
+- Make Parquet writer `nullable` option application to single table writes ([#12933](https://github.com/rapidsai/cudf/pull/12933)) [@vuule](https://github.com/vuule)
+- Refactor `io::orc::ProtobufWriter` ([#12877](https://github.com/rapidsai/cudf/pull/12877)) [@ttnghia](https://github.com/ttnghia)
+- Make timezone table independent from ORC ([#12805](https://github.com/rapidsai/cudf/pull/12805)) [@vuule](https://github.com/vuule)
+- Cache JIT `GroupBy.apply` functions ([#12802](https://github.com/rapidsai/cudf/pull/12802)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Implement initial support for avro logical types ([#6482) (#12788](https://github.com/rapidsai/cudf/pull/6482) (#12788)) [@tpn](https://github.com/tpn)
+- Update `tests/column_utilities` to use `experimental::equality` row comparator ([#12777](https://github.com/rapidsai/cudf/pull/12777)) [@divyegala](https://github.com/divyegala)
+- Update `distinct/unique_count` to `experimental::row` hasher/comparator ([#12776](https://github.com/rapidsai/cudf/pull/12776)) [@divyegala](https://github.com/divyegala)
+- Update `hash_partition` to use `experimental::row::row_hasher` ([#12761](https://github.com/rapidsai/cudf/pull/12761)) [@divyegala](https://github.com/divyegala)
+- Update `is_sorted` to use `experimental::row::lexicographic` ([#12752](https://github.com/rapidsai/cudf/pull/12752)) [@divyegala](https://github.com/divyegala)
+- Update default data source in cuio reader benchmarks ([#12740](https://github.com/rapidsai/cudf/pull/12740)) [@PointKernel](https://github.com/PointKernel)
+- Reenable stream identification library in CI ([#12714](https://github.com/rapidsai/cudf/pull/12714)) [@vyasr](https://github.com/vyasr)
+- Add `regex_program` strings splitting java APIs and tests ([#12713](https://github.com/rapidsai/cudf/pull/12713)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Add `regex_program` strings replacing java APIs and tests ([#12701](https://github.com/rapidsai/cudf/pull/12701)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Add `regex_program` strings extract java APIs and tests ([#12699](https://github.com/rapidsai/cudf/pull/12699)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Variable fragment sizes for Parquet writer ([#12685](https://github.com/rapidsai/cudf/pull/12685)) [@etseidl](https://github.com/etseidl)
+- Add segmented reduction support for fixed-point types ([#12680](https://github.com/rapidsai/cudf/pull/12680)) [@davidwendt](https://github.com/davidwendt)
+- Move `strings_udf` code into cuDF ([#12669](https://github.com/rapidsai/cudf/pull/12669)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add `regex_program` searching APIs and related java classes ([#12666](https://github.com/rapidsai/cudf/pull/12666)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Add logging to libcudf ([#12637](https://github.com/rapidsai/cudf/pull/12637)) [@vuule](https://github.com/vuule)
+- Add compound aggregations to cudf::segmented_reduce ([#12573](https://github.com/rapidsai/cudf/pull/12573)) [@davidwendt](https://github.com/davidwendt)
+- Convert `rank` to use to experimental row comparators ([#12481](https://github.com/rapidsai/cudf/pull/12481)) [@divyegala](https://github.com/divyegala)
+- Use rapids-cmake parallel testing feature ([#12451](https://github.com/rapidsai/cudf/pull/12451)) [@robertmaynard](https://github.com/robertmaynard)
+- Enable detection of undesired stream usage ([#12089](https://github.com/rapidsai/cudf/pull/12089)) [@vyasr](https://github.com/vyasr)
+
+## 🛠️ Improvements
+
+- Pin `dask` and `distributed` for release ([#13070](https://github.com/rapidsai/cudf/pull/13070)) [@galipremsagar](https://github.com/galipremsagar)
+- Pin cupy in wheel tests to supported versions ([#13041](https://github.com/rapidsai/cudf/pull/13041)) [@vyasr](https://github.com/vyasr)
+- Pin numba version ([#13001](https://github.com/rapidsai/cudf/pull/13001)) [@vyasr](https://github.com/vyasr)
+- Rework gtests SequenceTest to remove using namepace cudf ([#12985](https://github.com/rapidsai/cudf/pull/12985)) [@davidwendt](https://github.com/davidwendt)
+- Stop setting package version attribute in wheels ([#12977](https://github.com/rapidsai/cudf/pull/12977)) [@vyasr](https://github.com/vyasr)
+- Move detail reduction functions to cudf::reduction::detail namespace ([#12971](https://github.com/rapidsai/cudf/pull/12971)) [@davidwendt](https://github.com/davidwendt)
+- Remove default detail mrs: part7 ([#12970](https://github.com/rapidsai/cudf/pull/12970)) [@vyasr](https://github.com/vyasr)
+- Remove default detail mrs: part6 ([#12969](https://github.com/rapidsai/cudf/pull/12969)) [@vyasr](https://github.com/vyasr)
+- Remove default detail mrs: part5 ([#12968](https://github.com/rapidsai/cudf/pull/12968)) [@vyasr](https://github.com/vyasr)
+- Remove default detail mrs: part4 ([#12967](https://github.com/rapidsai/cudf/pull/12967)) [@vyasr](https://github.com/vyasr)
+- Remove default detail mrs: part3 ([#12966](https://github.com/rapidsai/cudf/pull/12966)) [@vyasr](https://github.com/vyasr)
+- Remove default detail mrs: part2 ([#12965](https://github.com/rapidsai/cudf/pull/12965)) [@vyasr](https://github.com/vyasr)
+- Remove default detail mrs: part1 ([#12964](https://github.com/rapidsai/cudf/pull/12964)) [@vyasr](https://github.com/vyasr)
+- Add `force_nullable_schema` parameter to Parquet writer. ([#12952](https://github.com/rapidsai/cudf/pull/12952)) [@galipremsagar](https://github.com/galipremsagar)
+- Declare a different name for nan_equality.UNEQUAL to prevent Cython warnings. ([#12947](https://github.com/rapidsai/cudf/pull/12947)) [@bdice](https://github.com/bdice)
+- Remove remaining default stream parameters ([#12943](https://github.com/rapidsai/cudf/pull/12943)) [@vyasr](https://github.com/vyasr)
+- Fix cudf::segmented_reduce gtest for ANY aggregation ([#12940](https://github.com/rapidsai/cudf/pull/12940)) [@davidwendt](https://github.com/davidwendt)
+- Implement `groupby.head` and `groupby.tail` ([#12939](https://github.com/rapidsai/cudf/pull/12939)) [@wence-](https://github.com/wence-)
+- Fix libcudf gtests to pass null-count=0 for empty validity masks ([#12923](https://github.com/rapidsai/cudf/pull/12923)) [@davidwendt](https://github.com/davidwendt)
+- Migrate parquet encoding to use experimental row operators ([#12918](https://github.com/rapidsai/cudf/pull/12918)) [@PointKernel](https://github.com/PointKernel)
+- Fix benchmarks coded in namespace cudf and using namespace cudf ([#12915](https://github.com/rapidsai/cudf/pull/12915)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix io/text gtests coded in namespace cudf::test ([#12914](https://github.com/rapidsai/cudf/pull/12914)) [@karthikeyann](https://github.com/karthikeyann)
+- Pass `SCCACHE_S3_USE_SSL` to conda builds ([#12910](https://github.com/rapidsai/cudf/pull/12910)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix FST, JSON gtests &amp; benchmarks coded in namespace cudf::test ([#12907](https://github.com/rapidsai/cudf/pull/12907)) [@karthikeyann](https://github.com/karthikeyann)
+- Generate pyproject dependencies using dfg ([#12906](https://github.com/rapidsai/cudf/pull/12906)) [@vyasr](https://github.com/vyasr)
+- Update libcudf counting functions to specify cudf::size_type ([#12904](https://github.com/rapidsai/cudf/pull/12904)) [@davidwendt](https://github.com/davidwendt)
+- Fix `moto` env vars &amp; pass `AWS_SESSION_TOKEN` to conda builds ([#12902](https://github.com/rapidsai/cudf/pull/12902)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Rewrite CSV writer benchmark with nvbench ([#12901](https://github.com/rapidsai/cudf/pull/12901)) [@PointKernel](https://github.com/PointKernel)
+- Rework some code logic to reduce iterator and comparator inlining to improve compile time ([#12900](https://github.com/rapidsai/cudf/pull/12900)) [@davidwendt](https://github.com/davidwendt)
+- Deprecate `line_terminator` in favor of `lineterminator` in `to_csv` ([#12896](https://github.com/rapidsai/cudf/pull/12896)) [@wence-](https://github.com/wence-)
+- Add `stream` and `mr` parameters for `structs::detail::flatten_nested_columns` ([#12892](https://github.com/rapidsai/cudf/pull/12892)) [@ttnghia](https://github.com/ttnghia)
+- Deprecate libcudf regex APIs accepting pattern strings directly ([#12891](https://github.com/rapidsai/cudf/pull/12891)) [@davidwendt](https://github.com/davidwendt)
+- Remove default parameters from detail headers in include ([#12888](https://github.com/rapidsai/cudf/pull/12888)) [@vyasr](https://github.com/vyasr)
+- Update minimum `pandas` and `numpy` pinnings ([#12887](https://github.com/rapidsai/cudf/pull/12887)) [@galipremsagar](https://github.com/galipremsagar)
+- Implement `groupby.sample` ([#12882](https://github.com/rapidsai/cudf/pull/12882)) [@wence-](https://github.com/wence-)
+- Update JNI build ENV default to gcc 11 ([#12881](https://github.com/rapidsai/cudf/pull/12881)) [@pxLi](https://github.com/pxLi)
+- Change return type of `cudf::structs::detail::flatten_nested_columns` to smart pointer ([#12878](https://github.com/rapidsai/cudf/pull/12878)) [@ttnghia](https://github.com/ttnghia)
+- Fix passing seed parameter to MurmurHash3_32 in cudf::hash() function ([#12875](https://github.com/rapidsai/cudf/pull/12875)) [@davidwendt](https://github.com/davidwendt)
+- Remove manual artifact upload step in CI ([#12869](https://github.com/rapidsai/cudf/pull/12869)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Update to GCC 11 ([#12868](https://github.com/rapidsai/cudf/pull/12868)) [@bdice](https://github.com/bdice)
+- Fix null hive-partition behavior in dask-cudf parquet ([#12866](https://github.com/rapidsai/cudf/pull/12866)) [@rjzamora](https://github.com/rjzamora)
+- Update to protobuf&gt;=4.21.6,&lt;4.22. ([#12864](https://github.com/rapidsai/cudf/pull/12864)) [@bdice](https://github.com/bdice)
+- Update RMM allocators ([#12861](https://github.com/rapidsai/cudf/pull/12861)) [@pentschev](https://github.com/pentschev)
+- Improve performance for replace-multi for long strings ([#12858](https://github.com/rapidsai/cudf/pull/12858)) [@davidwendt](https://github.com/davidwendt)
+- Drop Python 3.7 handling for pickle protocol 4 ([#12857](https://github.com/rapidsai/cudf/pull/12857)) [@jakirkham](https://github.com/jakirkham)
+- Migrate as much as possible to pyproject.toml ([#12850](https://github.com/rapidsai/cudf/pull/12850)) [@vyasr](https://github.com/vyasr)
+- Enable nbqa pre-commit hooks for isort and black. ([#12848](https://github.com/rapidsai/cudf/pull/12848)) [@bdice](https://github.com/bdice)
+- Setting a threshold for KvikIO IO ([#12841](https://github.com/rapidsai/cudf/pull/12841)) [@madsbk](https://github.com/madsbk)
+- Update datasets download URL ([#12840](https://github.com/rapidsai/cudf/pull/12840)) [@jjacobelli](https://github.com/jjacobelli)
+- Make docs builds less verbose ([#12836](https://github.com/rapidsai/cudf/pull/12836)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Consolidate linter configs into pyproject.toml ([#12834](https://github.com/rapidsai/cudf/pull/12834)) [@vyasr](https://github.com/vyasr)
+- Deprecate `names` &amp; `dtype` in `Index.copy` ([#12825](https://github.com/rapidsai/cudf/pull/12825)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `inplace` parameters in categorical methods ([#12824](https://github.com/rapidsai/cudf/pull/12824)) [@galipremsagar](https://github.com/galipremsagar)
+- Add optional text file support to ninja-log utility ([#12823](https://github.com/rapidsai/cudf/pull/12823)) [@davidwendt](https://github.com/davidwendt)
+- Deprecate `Index.is_*` methods ([#12820](https://github.com/rapidsai/cudf/pull/12820)) [@galipremsagar](https://github.com/galipremsagar)
+- Add dfg as a pre-commit hook ([#12819](https://github.com/rapidsai/cudf/pull/12819)) [@vyasr](https://github.com/vyasr)
+- Deprecate `datetime_is_numeric` from `describe` ([#12818](https://github.com/rapidsai/cudf/pull/12818)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `na_sentinel` in `factorize` ([#12817](https://github.com/rapidsai/cudf/pull/12817)) [@galipremsagar](https://github.com/galipremsagar)
+- Shuffling read into a sub function in parquet read ([#12809](https://github.com/rapidsai/cudf/pull/12809)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fixing parquet coalescing of reads ([#12808](https://github.com/rapidsai/cudf/pull/12808)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- CI: Remove specification of manual stage for check_style.sh script. ([#12803](https://github.com/rapidsai/cudf/pull/12803)) [@csadorf](https://github.com/csadorf)
+- Add compute-sanitizer github workflow action to nightly tests ([#12800](https://github.com/rapidsai/cudf/pull/12800)) [@davidwendt](https://github.com/davidwendt)
+- Enable groupby std and variance aggregation types in libcudf Debug build ([#12799](https://github.com/rapidsai/cudf/pull/12799)) [@davidwendt](https://github.com/davidwendt)
+- Expose seed argument to hash_values ([#12795](https://github.com/rapidsai/cudf/pull/12795)) [@ayushdg](https://github.com/ayushdg)
+- Fix groupby gtests coded in namespace cudf::test ([#12784](https://github.com/rapidsai/cudf/pull/12784)) [@davidwendt](https://github.com/davidwendt)
+- Improve performance for cudf::strings::count_characters for long strings ([#12779](https://github.com/rapidsai/cudf/pull/12779)) [@davidwendt](https://github.com/davidwendt)
+- Deallocate encoded data in ORC writer immediately after compression ([#12770](https://github.com/rapidsai/cudf/pull/12770)) [@vuule](https://github.com/vuule)
+- Stop force pulling fmt in nvbench. ([#12768](https://github.com/rapidsai/cudf/pull/12768)) [@vyasr](https://github.com/vyasr)
+- Remove now redundant cuda initialization ([#12758](https://github.com/rapidsai/cudf/pull/12758)) [@vyasr](https://github.com/vyasr)
+- Adds JSON reader, writer io benchmark ([#12753](https://github.com/rapidsai/cudf/pull/12753)) [@karthikeyann](https://github.com/karthikeyann)
+- Use test paths relative to package directory. ([#12751](https://github.com/rapidsai/cudf/pull/12751)) [@bdice](https://github.com/bdice)
+- Add build metrics report as artifact to cpp-build workflow ([#12750](https://github.com/rapidsai/cudf/pull/12750)) [@davidwendt](https://github.com/davidwendt)
+- Add JNI methods for detecting and purging non-empty nulls from LIST and STRUCT ([#12742](https://github.com/rapidsai/cudf/pull/12742)) [@razajafri](https://github.com/razajafri)
+- Stop using versioneer to manage versions ([#12741](https://github.com/rapidsai/cudf/pull/12741)) [@vyasr](https://github.com/vyasr)
+- Reduce error handling verbosity in CI tests scripts ([#12738](https://github.com/rapidsai/cudf/pull/12738)) [@AjayThorve](https://github.com/AjayThorve)
+- Reduce the number of test cases in multibyte_split benchmark ([#12737](https://github.com/rapidsai/cudf/pull/12737)) [@PointKernel](https://github.com/PointKernel)
+- Update shared workflow branches ([#12733](https://github.com/rapidsai/cudf/pull/12733)) [@ajschmidt8](https://github.com/ajschmidt8)
+- JNI switches to nested JSON reader ([#12732](https://github.com/rapidsai/cudf/pull/12732)) [@res-life](https://github.com/res-life)
+- Changing `cudf::io::source_info` to use `cudf::host_span&lt;std::byte&gt;` in a non-breaking form ([#12730](https://github.com/rapidsai/cudf/pull/12730)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add nvbench environment class for initializing RMM in benchmarks ([#12728](https://github.com/rapidsai/cudf/pull/12728)) [@davidwendt](https://github.com/davidwendt)
+- Split C++ and Python build dependencies into separate lists. ([#12724](https://github.com/rapidsai/cudf/pull/12724)) [@bdice](https://github.com/bdice)
+- Add build dependencies to Java tests. ([#12723](https://github.com/rapidsai/cudf/pull/12723)) [@bdice](https://github.com/bdice)
+- Allow setting the seed argument for hash partition ([#12715](https://github.com/rapidsai/cudf/pull/12715)) [@firestarman](https://github.com/firestarman)
+- Remove gpuCI scripts. ([#12712](https://github.com/rapidsai/cudf/pull/12712)) [@bdice](https://github.com/bdice)
+- Unpin `dask` and `distributed` for development ([#12710](https://github.com/rapidsai/cudf/pull/12710)) [@galipremsagar](https://github.com/galipremsagar)
+- `partition_by_hash()`: use `_split()` ([#12704](https://github.com/rapidsai/cudf/pull/12704)) [@madsbk](https://github.com/madsbk)
+- Remove DataFrame.quantiles from docs. ([#12684](https://github.com/rapidsai/cudf/pull/12684)) [@bdice](https://github.com/bdice)
+- Fast path for `experimental::row::equality` ([#12676](https://github.com/rapidsai/cudf/pull/12676)) [@divyegala](https://github.com/divyegala)
+- Move date to build string in `conda` recipe ([#12661](https://github.com/rapidsai/cudf/pull/12661)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Refactor reduction logic for fixed-point types ([#12652](https://github.com/rapidsai/cudf/pull/12652)) [@davidwendt](https://github.com/davidwendt)
+- Pay off some JNI RMM API tech debt ([#12632](https://github.com/rapidsai/cudf/pull/12632)) [@revans2](https://github.com/revans2)
+- Merge `copy-on-write` feature branch into `branch-23.04` ([#12619](https://github.com/rapidsai/cudf/pull/12619)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove cudf::strings::repeat_strings_output_sizes and optional parameter from cudf::strings::repeat_strings ([#12609](https://github.com/rapidsai/cudf/pull/12609)) [@davidwendt](https://github.com/davidwendt)
+- Pin cuda-nvrtc. ([#12606](https://github.com/rapidsai/cudf/pull/12606)) [@bdice](https://github.com/bdice)
+- Remove cudf::test::print calls from libcudf gtests ([#12604](https://github.com/rapidsai/cudf/pull/12604)) [@davidwendt](https://github.com/davidwendt)
+- Init JNI version 23.04.0-SNAPSHOT ([#12599](https://github.com/rapidsai/cudf/pull/12599)) [@pxLi](https://github.com/pxLi)
+- Add performance benchmarks to user facing docs ([#12595](https://github.com/rapidsai/cudf/pull/12595)) [@galipremsagar](https://github.com/galipremsagar)
+- Add docs build job ([#12592](https://github.com/rapidsai/cudf/pull/12592)) [@AyodeAwe](https://github.com/AyodeAwe)
+- Replace message parsing with throwing more specific exceptions ([#12426](https://github.com/rapidsai/cudf/pull/12426)) [@vyasr](https://github.com/vyasr)
+- Support conversion to/from cudf in dask.dataframe.core.to_backend ([#12380](https://github.com/rapidsai/cudf/pull/12380)) [@rjzamora](https://github.com/rjzamora)
+
+# cuDF 23.02.00 (9 Feb 2023)
+
+## 🚨 Breaking Changes
+
+- Pin `dask` and `distributed` for release ([#12695](https://github.com/rapidsai/cudf/pull/12695)) [@galipremsagar](https://github.com/galipremsagar)
+- Change ways to access `ptr` in `Buffer` ([#12587](https://github.com/rapidsai/cudf/pull/12587)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove column names ([#12578](https://github.com/rapidsai/cudf/pull/12578)) [@vuule](https://github.com/vuule)
+- Default `cudf::io::read_json` to nested JSON parser ([#12544](https://github.com/rapidsai/cudf/pull/12544)) [@vuule](https://github.com/vuule)
+- Switch `engine=cudf` to the new `JSON` reader ([#12509](https://github.com/rapidsai/cudf/pull/12509)) [@galipremsagar](https://github.com/galipremsagar)
+- Add trailing comma support for nested JSON reader ([#12448](https://github.com/rapidsai/cudf/pull/12448)) [@karthikeyann](https://github.com/karthikeyann)
+- Upgrade to `arrow-10.0.1` ([#12327](https://github.com/rapidsai/cudf/pull/12327)) [@galipremsagar](https://github.com/galipremsagar)
+- Fail loudly to avoid data corruption with unsupported input in `read_orc` ([#12325](https://github.com/rapidsai/cudf/pull/12325)) [@vuule](https://github.com/vuule)
+- CSV, JSON reader to infer integer column with nulls as int64 instead of float64 ([#12309](https://github.com/rapidsai/cudf/pull/12309)) [@karthikeyann](https://github.com/karthikeyann)
+- Remove deprecated code for 23.02 ([#12281](https://github.com/rapidsai/cudf/pull/12281)) [@vyasr](https://github.com/vyasr)
+- Null element for parsing error in numeric types in JSON, CSV reader ([#12272](https://github.com/rapidsai/cudf/pull/12272)) [@karthikeyann](https://github.com/karthikeyann)
+- Purge non-empty nulls for `superimpose_nulls` and `push_down_nulls` ([#12239](https://github.com/rapidsai/cudf/pull/12239)) [@ttnghia](https://github.com/ttnghia)
+- Rename `cudf::structs::detail::superimpose_parent_nulls` APIs ([#12230](https://github.com/rapidsai/cudf/pull/12230)) [@ttnghia](https://github.com/ttnghia)
+- Remove JIT type names, refactor id_to_type. ([#12158](https://github.com/rapidsai/cudf/pull/12158)) [@bdice](https://github.com/bdice)
+- Floor division uses integer division for integral arguments ([#12131](https://github.com/rapidsai/cudf/pull/12131)) [@wence-](https://github.com/wence-)
+
+## 🐛 Bug Fixes
+
+- Fix a mask data corruption in UDF ([#12647](https://github.com/rapidsai/cudf/pull/12647)) [@galipremsagar](https://github.com/galipremsagar)
+- pre-commit: Update isort version to 5.12.0 ([#12645](https://github.com/rapidsai/cudf/pull/12645)) [@wence-](https://github.com/wence-)
+- tests: Skip cuInit tests if cuda-gdb is not found or not working ([#12644](https://github.com/rapidsai/cudf/pull/12644)) [@wence-](https://github.com/wence-)
+- Revert regex program java APIs and tests ([#12639](https://github.com/rapidsai/cudf/pull/12639)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Fix leaks in ColumnVectorTest ([#12625](https://github.com/rapidsai/cudf/pull/12625)) [@jlowe](https://github.com/jlowe)
+- Handle when spillable buffers own each other ([#12607](https://github.com/rapidsai/cudf/pull/12607)) [@madsbk](https://github.com/madsbk)
+- Fix incorrect null counts for sliced columns in JCudfSerialization ([#12589](https://github.com/rapidsai/cudf/pull/12589)) [@jlowe](https://github.com/jlowe)
+- lists: Transfer dtypes correctly through list.get ([#12586](https://github.com/rapidsai/cudf/pull/12586)) [@wence-](https://github.com/wence-)
+- timedelta: Don&#39;t go via float intermediates for floordiv ([#12585](https://github.com/rapidsai/cudf/pull/12585)) [@wence-](https://github.com/wence-)
+- Fixing BUG, `get_next_chunk()` should use the blocking function `device_read()` ([#12584](https://github.com/rapidsai/cudf/pull/12584)) [@madsbk](https://github.com/madsbk)
+- Make JNI QuoteStyle accessible outside ai.rapids.cudf ([#12572](https://github.com/rapidsai/cudf/pull/12572)) [@mythrocks](https://github.com/mythrocks)
+- `partition_by_hash()`: support index ([#12554](https://github.com/rapidsai/cudf/pull/12554)) [@madsbk](https://github.com/madsbk)
+- Mixed Join benchmark bug due to wrong conditional column ([#12553](https://github.com/rapidsai/cudf/pull/12553)) [@divyegala](https://github.com/divyegala)
+- Update List Lexicographical Comparator ([#12538](https://github.com/rapidsai/cudf/pull/12538)) [@divyegala](https://github.com/divyegala)
+- Dynamically read PTX version ([#12534](https://github.com/rapidsai/cudf/pull/12534)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- build.sh switch to use `RAPIDS` magic value ([#12525](https://github.com/rapidsai/cudf/pull/12525)) [@robertmaynard](https://github.com/robertmaynard)
+- Loosen runtime arrow pinning ([#12522](https://github.com/rapidsai/cudf/pull/12522)) [@vyasr](https://github.com/vyasr)
+- Enable metadata transfer for complex types in transpose ([#12491](https://github.com/rapidsai/cudf/pull/12491)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix issues with parquet chunked reader ([#12488](https://github.com/rapidsai/cudf/pull/12488)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix missing metadata transfer in concat for `ListColumn` ([#12487](https://github.com/rapidsai/cudf/pull/12487)) [@galipremsagar](https://github.com/galipremsagar)
+- Rename libcudf substring source files to slice ([#12484](https://github.com/rapidsai/cudf/pull/12484)) [@davidwendt](https://github.com/davidwendt)
+- Fix compile issue with arrow 10 ([#12465](https://github.com/rapidsai/cudf/pull/12465)) [@ttnghia](https://github.com/ttnghia)
+- Fix List offsets bug in mixed type list column in nested JSON reader ([#12447](https://github.com/rapidsai/cudf/pull/12447)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix xfail incompatibilities ([#12423](https://github.com/rapidsai/cudf/pull/12423)) [@vyasr](https://github.com/vyasr)
+- Fix bug in Parquet column index encoding ([#12404](https://github.com/rapidsai/cudf/pull/12404)) [@etseidl](https://github.com/etseidl)
+- When building Arrow shared look for a shared OpenSSL ([#12396](https://github.com/rapidsai/cudf/pull/12396)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix get_json_object to return empty column on empty input ([#12384](https://github.com/rapidsai/cudf/pull/12384)) [@davidwendt](https://github.com/davidwendt)
+- Pin arrow 9 in testing dependencies to prevent conda solve issues ([#12377](https://github.com/rapidsai/cudf/pull/12377)) [@vyasr](https://github.com/vyasr)
+- Fix reductions any/all return value for empty input ([#12374](https://github.com/rapidsai/cudf/pull/12374)) [@davidwendt](https://github.com/davidwendt)
+- Fix debug compile errors in parquet.hpp ([#12372](https://github.com/rapidsai/cudf/pull/12372)) [@davidwendt](https://github.com/davidwendt)
+- Purge non-empty nulls in `cudf::make_lists_column` ([#12370](https://github.com/rapidsai/cudf/pull/12370)) [@ttnghia](https://github.com/ttnghia)
+- Use correct memory resource in io::make_column ([#12364](https://github.com/rapidsai/cudf/pull/12364)) [@vyasr](https://github.com/vyasr)
+- Add code to detect possible malformed page data in parquet files. ([#12360](https://github.com/rapidsai/cudf/pull/12360)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fail loudly to avoid data corruption with unsupported input in `read_orc` ([#12325](https://github.com/rapidsai/cudf/pull/12325)) [@vuule](https://github.com/vuule)
+- Fix NumericPairIteratorTest for float values ([#12306](https://github.com/rapidsai/cudf/pull/12306)) [@davidwendt](https://github.com/davidwendt)
+- Fixes memory allocation in nested JSON tokenizer ([#12300](https://github.com/rapidsai/cudf/pull/12300)) [@elstehle](https://github.com/elstehle)
+- Reconstruct dtypes correctly for list aggs of struct columns ([#12290](https://github.com/rapidsai/cudf/pull/12290)) [@wence-](https://github.com/wence-)
+- Fix regex \A and \Z to strictly match string begin/end ([#12282](https://github.com/rapidsai/cudf/pull/12282)) [@davidwendt](https://github.com/davidwendt)
+- Fix compile issue in `json_chunked_reader.cpp` ([#12280](https://github.com/rapidsai/cudf/pull/12280)) [@ttnghia](https://github.com/ttnghia)
+- Change reductions any/all to return valid values for empty input ([#12279](https://github.com/rapidsai/cudf/pull/12279)) [@davidwendt](https://github.com/davidwendt)
+- Only exclude join keys that are indices from key columns ([#12271](https://github.com/rapidsai/cudf/pull/12271)) [@wence-](https://github.com/wence-)
+- Fix spill to device limit ([#12252](https://github.com/rapidsai/cudf/pull/12252)) [@madsbk](https://github.com/madsbk)
+- Correct behaviour of sort in `concat` for singleton concatenations ([#12247](https://github.com/rapidsai/cudf/pull/12247)) [@wence-](https://github.com/wence-)
+- Purge non-empty nulls for `superimpose_nulls` and `push_down_nulls` ([#12239](https://github.com/rapidsai/cudf/pull/12239)) [@ttnghia](https://github.com/ttnghia)
+- Patch CUB DeviceSegmentedSort and remove workaround ([#12234](https://github.com/rapidsai/cudf/pull/12234)) [@davidwendt](https://github.com/davidwendt)
+- Fix memory leak in udf_string::assign(&amp;&amp;) function ([#12206](https://github.com/rapidsai/cudf/pull/12206)) [@davidwendt](https://github.com/davidwendt)
+- Workaround thrust-copy-if limit in json get_tree_representation ([#12190](https://github.com/rapidsai/cudf/pull/12190)) [@davidwendt](https://github.com/davidwendt)
+- Fix page size calculation in Parquet writer ([#12182](https://github.com/rapidsai/cudf/pull/12182)) [@etseidl](https://github.com/etseidl)
+- Add cudf::detail::sizes_to_offsets_iterator to allow checking overflow in offsets ([#12180](https://github.com/rapidsai/cudf/pull/12180)) [@davidwendt](https://github.com/davidwendt)
+- Workaround thrust-copy-if limit in wordpiece-tokenizer ([#12168](https://github.com/rapidsai/cudf/pull/12168)) [@davidwendt](https://github.com/davidwendt)
+- Floor division uses integer division for integral arguments ([#12131](https://github.com/rapidsai/cudf/pull/12131)) [@wence-](https://github.com/wence-)
+
+## 📖 Documentation
+
+- Fix link to NVTX ([#12598](https://github.com/rapidsai/cudf/pull/12598)) [@sameerz](https://github.com/sameerz)
+- Include missing groupby functions in documentation ([#12580](https://github.com/rapidsai/cudf/pull/12580)) [@quasiben](https://github.com/quasiben)
+- Fix documentation author ([#12527](https://github.com/rapidsai/cudf/pull/12527)) [@bdice](https://github.com/bdice)
+- Update libcudf reduction docs for casting output types ([#12526](https://github.com/rapidsai/cudf/pull/12526)) [@davidwendt](https://github.com/davidwendt)
+- Add JSON reader page in user guide ([#12499](https://github.com/rapidsai/cudf/pull/12499)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Link unsupported iteration API docstrings ([#12482](https://github.com/rapidsai/cudf/pull/12482)) [@galipremsagar](https://github.com/galipremsagar)
+- `strings_udf` doc update ([#12469](https://github.com/rapidsai/cudf/pull/12469)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Update cudf_assert docs with correct NDEBUG behavior ([#12464](https://github.com/rapidsai/cudf/pull/12464)) [@robertmaynard](https://github.com/robertmaynard)
+- Update pre-commit hooks guide ([#12395](https://github.com/rapidsai/cudf/pull/12395)) [@bdice](https://github.com/bdice)
+- Update test docs to not use detail comparison utilities ([#12332](https://github.com/rapidsai/cudf/pull/12332)) [@PointKernel](https://github.com/PointKernel)
+- Fix doxygen description for regex_program::compute_working_memory_size ([#12329](https://github.com/rapidsai/cudf/pull/12329)) [@davidwendt](https://github.com/davidwendt)
+- Add eval to docs. ([#12322](https://github.com/rapidsai/cudf/pull/12322)) [@vyasr](https://github.com/vyasr)
+- Turn on xfail_strict=true ([#12244](https://github.com/rapidsai/cudf/pull/12244)) [@wence-](https://github.com/wence-)
+- Update 10 minutes to cuDF ([#12114](https://github.com/rapidsai/cudf/pull/12114)) [@wence-](https://github.com/wence-)
+
+## 🚀 New Features
+
+- Use kvikIO as the default IO backend ([#12574](https://github.com/rapidsai/cudf/pull/12574)) [@vuule](https://github.com/vuule)
+- Use `has_nonempty_nulls` instead of `may_contain_non_empty_nulls` in `superimpose_nulls` and `push_down_nulls` ([#12560](https://github.com/rapidsai/cudf/pull/12560)) [@ttnghia](https://github.com/ttnghia)
+- Add strings methods removeprefix and removesuffix ([#12557](https://github.com/rapidsai/cudf/pull/12557)) [@davidwendt](https://github.com/davidwendt)
+- Add `regex_program` java APIs and unit tests ([#12548](https://github.com/rapidsai/cudf/pull/12548)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Default `cudf::io::read_json` to nested JSON parser ([#12544](https://github.com/rapidsai/cudf/pull/12544)) [@vuule](https://github.com/vuule)
+- Make string quoting optional on CSV write ([#12539](https://github.com/rapidsai/cudf/pull/12539)) [@mythrocks](https://github.com/mythrocks)
+- Use new nvCOMP API to optimize the compression temp memory size ([#12533](https://github.com/rapidsai/cudf/pull/12533)) [@vuule](https://github.com/vuule)
+- Support &quot;values&quot; orient (array of arrays) in Nested JSON reader ([#12498](https://github.com/rapidsai/cudf/pull/12498)) [@karthikeyann](https://github.com/karthikeyann)
+- `one_hot_encode` to use experimental row comparators ([#12478](https://github.com/rapidsai/cudf/pull/12478)) [@divyegala](https://github.com/divyegala)
+- Support %W and %w format specifiers in cudf::strings::to_timestamps ([#12475](https://github.com/rapidsai/cudf/pull/12475)) [@davidwendt](https://github.com/davidwendt)
+- Add JSON Writer ([#12474](https://github.com/rapidsai/cudf/pull/12474)) [@karthikeyann](https://github.com/karthikeyann)
+- Refactor `thrust_copy_if` into `cudf::detail::copy_if_safe` ([#12455](https://github.com/rapidsai/cudf/pull/12455)) [@ttnghia](https://github.com/ttnghia)
+- Add trailing comma support for nested JSON reader ([#12448](https://github.com/rapidsai/cudf/pull/12448)) [@karthikeyann](https://github.com/karthikeyann)
+- Extract `tokenize_json.hpp` detail header from `src/io/json/nested_json.hpp` ([#12432](https://github.com/rapidsai/cudf/pull/12432)) [@ttnghia](https://github.com/ttnghia)
+- JNI bindings to write CSV ([#12425](https://github.com/rapidsai/cudf/pull/12425)) [@mythrocks](https://github.com/mythrocks)
+- Nested JSON depth benchmark ([#12371](https://github.com/rapidsai/cudf/pull/12371)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement `lists::reverse` ([#12336](https://github.com/rapidsai/cudf/pull/12336)) [@ttnghia](https://github.com/ttnghia)
+- Use `device_read` in experimental `read_json` ([#12314](https://github.com/rapidsai/cudf/pull/12314)) [@vuule](https://github.com/vuule)
+- Implement JNI for `strings::reverse` ([#12283](https://github.com/rapidsai/cudf/pull/12283)) [@ttnghia](https://github.com/ttnghia)
+- Null element for parsing error in numeric types in JSON, CSV reader ([#12272](https://github.com/rapidsai/cudf/pull/12272)) [@karthikeyann](https://github.com/karthikeyann)
+- Add cudf::strings:like function with multiple patterns ([#12269](https://github.com/rapidsai/cudf/pull/12269)) [@davidwendt](https://github.com/davidwendt)
+- Add environment variable to control host memory allocation in `hostdevice_vector` ([#12251](https://github.com/rapidsai/cudf/pull/12251)) [@vuule](https://github.com/vuule)
+- Add cudf::strings::reverse function ([#12227](https://github.com/rapidsai/cudf/pull/12227)) [@davidwendt](https://github.com/davidwendt)
+- Selectively use dictionary encoding in Parquet writer ([#12211](https://github.com/rapidsai/cudf/pull/12211)) [@etseidl](https://github.com/etseidl)
+- Support `replace` in `strings_udf` ([#12207](https://github.com/rapidsai/cudf/pull/12207)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add support to read binary encoded decimals in parquet ([#12205](https://github.com/rapidsai/cudf/pull/12205)) [@PointKernel](https://github.com/PointKernel)
+- Support regex EOL where the string ends with a new-line character ([#12181](https://github.com/rapidsai/cudf/pull/12181)) [@davidwendt](https://github.com/davidwendt)
+- Updating `stream_compaction/unique` to use new row comparators ([#12159](https://github.com/rapidsai/cudf/pull/12159)) [@divyegala](https://github.com/divyegala)
+- Add device buffer datasource ([#12024](https://github.com/rapidsai/cudf/pull/12024)) [@PointKernel](https://github.com/PointKernel)
+- Implement groupby apply with JIT ([#11452](https://github.com/rapidsai/cudf/pull/11452)) [@bwyogatama](https://github.com/bwyogatama)
+
+## 🛠️ Improvements
+
+- Update shared workflow branches ([#12696](https://github.com/rapidsai/cudf/pull/12696)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Pin `dask` and `distributed` for release ([#12695](https://github.com/rapidsai/cudf/pull/12695)) [@galipremsagar](https://github.com/galipremsagar)
+- Don&#39;t upload `libcudf-example` to Anaconda.org ([#12671](https://github.com/rapidsai/cudf/pull/12671)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Pin wheel dependencies to same RAPIDS release ([#12659](https://github.com/rapidsai/cudf/pull/12659)) [@sevagh](https://github.com/sevagh)
+- Use CTK 118/cp310 branch of wheel workflows ([#12602](https://github.com/rapidsai/cudf/pull/12602)) [@sevagh](https://github.com/sevagh)
+- Change ways to access `ptr` in `Buffer` ([#12587](https://github.com/rapidsai/cudf/pull/12587)) [@galipremsagar](https://github.com/galipremsagar)
+- Version a parquet writer xfail ([#12579](https://github.com/rapidsai/cudf/pull/12579)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove column names ([#12578](https://github.com/rapidsai/cudf/pull/12578)) [@vuule](https://github.com/vuule)
+- Parquet reader optimization to address V100 regression. ([#12577](https://github.com/rapidsai/cudf/pull/12577)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add support for `category` dtypes in CSV reader ([#12571](https://github.com/rapidsai/cudf/pull/12571)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove `spill_lock` parameter from `SpillableBuffer.get_ptr()` ([#12564](https://github.com/rapidsai/cudf/pull/12564)) [@madsbk](https://github.com/madsbk)
+- Optimize `cudf::make_lists_column` ([#12547](https://github.com/rapidsai/cudf/pull/12547)) [@ttnghia](https://github.com/ttnghia)
+- Remove `cudf::strings::repeat_strings_output_sizes` from Java and JNI ([#12546](https://github.com/rapidsai/cudf/pull/12546)) [@ttnghia](https://github.com/ttnghia)
+- Test that cuInit is not called when RAPIDS_NO_INITIALIZE is set ([#12545](https://github.com/rapidsai/cudf/pull/12545)) [@wence-](https://github.com/wence-)
+- Rework repeat_strings to use sizes-to-offsets utility ([#12543](https://github.com/rapidsai/cudf/pull/12543)) [@davidwendt](https://github.com/davidwendt)
+- Replace exclusive_scan with sizes_to_offsets in cudf::lists::sequences ([#12541](https://github.com/rapidsai/cudf/pull/12541)) [@davidwendt](https://github.com/davidwendt)
+- Rework nvtext::ngrams_tokenize to use sizes-to-offsets utility ([#12540](https://github.com/rapidsai/cudf/pull/12540)) [@davidwendt](https://github.com/davidwendt)
+- Fix binary-ops gtests coded in namespace cudf::test ([#12536](https://github.com/rapidsai/cudf/pull/12536)) [@davidwendt](https://github.com/davidwendt)
+- More `[@acquire_spill_lock()` and `as_buffer(..., exposed=False)` ([#12535](https://github.com/rapidsai/cudf/pull/12535)) @madsbk](https://github.com/acquire_spill_lock()` and `as_buffer(..., exposed=False)` ([#12535](https://github.com/rapidsai/cudf/pull/12535)) @madsbk)
+- Guard CUDA runtime APIs with error checking ([#12531](https://github.com/rapidsai/cudf/pull/12531)) [@PointKernel](https://github.com/PointKernel)
+- Update TODOs from issue 10432. ([#12528](https://github.com/rapidsai/cudf/pull/12528)) [@bdice](https://github.com/bdice)
+- Update rapids-cmake definitions version in GitHub Actions style checks. ([#12511](https://github.com/rapidsai/cudf/pull/12511)) [@bdice](https://github.com/bdice)
+- Switch `engine=cudf` to the new `JSON` reader ([#12509](https://github.com/rapidsai/cudf/pull/12509)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix SUM/MEAN aggregation type support. ([#12503](https://github.com/rapidsai/cudf/pull/12503)) [@bdice](https://github.com/bdice)
+- Stop using pandas._testing ([#12492](https://github.com/rapidsai/cudf/pull/12492)) [@vyasr](https://github.com/vyasr)
+- Fix ROLLING_TEST gtests coded in namespace cudf::test ([#12490](https://github.com/rapidsai/cudf/pull/12490)) [@davidwendt](https://github.com/davidwendt)
+- Fix erroneously skipped ORC ZSTD test ([#12486](https://github.com/rapidsai/cudf/pull/12486)) [@vuule](https://github.com/vuule)
+- Rework nvtext::generate_character_ngrams to use make_strings_children ([#12480](https://github.com/rapidsai/cudf/pull/12480)) [@davidwendt](https://github.com/davidwendt)
+- Raise warnings as errors in the test suite ([#12468](https://github.com/rapidsai/cudf/pull/12468)) [@vyasr](https://github.com/vyasr)
+- Remove `int32` hard-coding in python ([#12467](https://github.com/rapidsai/cudf/pull/12467)) [@galipremsagar](https://github.com/galipremsagar)
+- Use cudaMemcpyDefault. ([#12466](https://github.com/rapidsai/cudf/pull/12466)) [@bdice](https://github.com/bdice)
+- Update workflows for nightly tests ([#12462](https://github.com/rapidsai/cudf/pull/12462)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Build CUDA `11.8` and Python `3.10` Packages ([#12457](https://github.com/rapidsai/cudf/pull/12457)) [@ajschmidt8](https://github.com/ajschmidt8)
+- JNI build image default as cuda11.8 ([#12441](https://github.com/rapidsai/cudf/pull/12441)) [@pxLi](https://github.com/pxLi)
+- Re-enable `Recently Updated` Check ([#12435](https://github.com/rapidsai/cudf/pull/12435)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Rework remaining cudf::strings::from_xyz functions to use make_strings_children ([#12434](https://github.com/rapidsai/cudf/pull/12434)) [@vuule](https://github.com/vuule)
+- Build wheels alongside conda CI ([#12427](https://github.com/rapidsai/cudf/pull/12427)) [@sevagh](https://github.com/sevagh)
+- Remove arguments for checking exception messages in Python ([#12424](https://github.com/rapidsai/cudf/pull/12424)) [@vyasr](https://github.com/vyasr)
+- Clean up cuco usage ([#12421](https://github.com/rapidsai/cudf/pull/12421)) [@PointKernel](https://github.com/PointKernel)
+- Fix warnings in remaining modules ([#12406](https://github.com/rapidsai/cudf/pull/12406)) [@vyasr](https://github.com/vyasr)
+- Update `ops-bot.yaml` ([#12402](https://github.com/rapidsai/cudf/pull/12402)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Rework cudf::strings::integers_to_ipv4 to use make_strings_children utility ([#12401](https://github.com/rapidsai/cudf/pull/12401)) [@davidwendt](https://github.com/davidwendt)
+- Use `numpy.empty()` instead of `bytearray` to allocate host memory for spilling ([#12399](https://github.com/rapidsai/cudf/pull/12399)) [@madsbk](https://github.com/madsbk)
+- Deprecate chunksize from dask_cudf.read_csv ([#12394](https://github.com/rapidsai/cudf/pull/12394)) [@rjzamora](https://github.com/rjzamora)
+- Expose the RMM pool size in JNI ([#12390](https://github.com/rapidsai/cudf/pull/12390)) [@revans2](https://github.com/revans2)
+- Fix COPYING_TEST: gtests coded in namespace cudf::test ([#12387](https://github.com/rapidsai/cudf/pull/12387)) [@davidwendt](https://github.com/davidwendt)
+- Rework cudf::strings::url_encode to use make_strings_children utility ([#12385](https://github.com/rapidsai/cudf/pull/12385)) [@davidwendt](https://github.com/davidwendt)
+- Use make_strings_children in parse_data nested json reader ([#12382](https://github.com/rapidsai/cudf/pull/12382)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix warnings in test_datetime.py ([#12381](https://github.com/rapidsai/cudf/pull/12381)) [@vyasr](https://github.com/vyasr)
+- Mixed Join Benchmarks ([#12375](https://github.com/rapidsai/cudf/pull/12375)) [@divyegala](https://github.com/divyegala)
+- Fix warnings in dataframe.py ([#12369](https://github.com/rapidsai/cudf/pull/12369)) [@vyasr](https://github.com/vyasr)
+- Update conda recipes. ([#12368](https://github.com/rapidsai/cudf/pull/12368)) [@bdice](https://github.com/bdice)
+- Use gpu-latest-1 runner tag ([#12366](https://github.com/rapidsai/cudf/pull/12366)) [@bdice](https://github.com/bdice)
+- Rework cudf::strings::from_booleans to use make_strings_children ([#12365](https://github.com/rapidsai/cudf/pull/12365)) [@vuule](https://github.com/vuule)
+- Fix warnings in test modules up to test_dataframe.py ([#12355](https://github.com/rapidsai/cudf/pull/12355)) [@vyasr](https://github.com/vyasr)
+- JSON column performance optimization - struct column nulls ([#12354](https://github.com/rapidsai/cudf/pull/12354)) [@karthikeyann](https://github.com/karthikeyann)
+- Accelerate stable-segmented-sort with CUB segmented sort ([#12347](https://github.com/rapidsai/cudf/pull/12347)) [@davidwendt](https://github.com/davidwendt)
+- Add size check to make_offsets_child_column utility ([#12345](https://github.com/rapidsai/cudf/pull/12345)) [@davidwendt](https://github.com/davidwendt)
+- Enable max compression ratio small block optimization for ZSTD ([#12338](https://github.com/rapidsai/cudf/pull/12338)) [@vuule](https://github.com/vuule)
+- Fix warnings in test_monotonic.py ([#12334](https://github.com/rapidsai/cudf/pull/12334)) [@vyasr](https://github.com/vyasr)
+- Improve JSON column creation performance (list offsets) ([#12330](https://github.com/rapidsai/cudf/pull/12330)) [@karthikeyann](https://github.com/karthikeyann)
+- Upgrade to `arrow-10.0.1` ([#12327](https://github.com/rapidsai/cudf/pull/12327)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix warnings in test_orc.py ([#12326](https://github.com/rapidsai/cudf/pull/12326)) [@vyasr](https://github.com/vyasr)
+- Fix warnings in test_groupby.py ([#12324](https://github.com/rapidsai/cudf/pull/12324)) [@vyasr](https://github.com/vyasr)
+- Fix `test_notebooks.sh` ([#12323](https://github.com/rapidsai/cudf/pull/12323)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix transform gtests coded in namespace cudf::test ([#12321](https://github.com/rapidsai/cudf/pull/12321)) [@davidwendt](https://github.com/davidwendt)
+- Fix `check_style.sh` script ([#12320](https://github.com/rapidsai/cudf/pull/12320)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Rework cudf::strings::from_timestamps to use make_strings_children ([#12317](https://github.com/rapidsai/cudf/pull/12317)) [@davidwendt](https://github.com/davidwendt)
+- Fix warnings in test_index.py ([#12313](https://github.com/rapidsai/cudf/pull/12313)) [@vyasr](https://github.com/vyasr)
+- Fix warnings in test_multiindex.py ([#12310](https://github.com/rapidsai/cudf/pull/12310)) [@vyasr](https://github.com/vyasr)
+- CSV, JSON reader to infer integer column with nulls as int64 instead of float64 ([#12309](https://github.com/rapidsai/cudf/pull/12309)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix warnings in test_indexing.py ([#12305](https://github.com/rapidsai/cudf/pull/12305)) [@vyasr](https://github.com/vyasr)
+- Fix warnings in test_joining.py ([#12304](https://github.com/rapidsai/cudf/pull/12304)) [@vyasr](https://github.com/vyasr)
+- Unpin `dask` and `distributed` for development ([#12302](https://github.com/rapidsai/cudf/pull/12302)) [@galipremsagar](https://github.com/galipremsagar)
+- Re-enable `sccache` for Jenkins builds ([#12297](https://github.com/rapidsai/cudf/pull/12297)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Define needs for pr-builder workflow. ([#12296](https://github.com/rapidsai/cudf/pull/12296)) [@bdice](https://github.com/bdice)
+- Forward merge 22.12 into 23.02 ([#12294](https://github.com/rapidsai/cudf/pull/12294)) [@vyasr](https://github.com/vyasr)
+- Fix warnings in test_stats.py ([#12293](https://github.com/rapidsai/cudf/pull/12293)) [@vyasr](https://github.com/vyasr)
+- Fix table gtests coded in namespace cudf::test ([#12292](https://github.com/rapidsai/cudf/pull/12292)) [@davidwendt](https://github.com/davidwendt)
+- Change cython for regex calls to use cudf::strings::regex_program ([#12289](https://github.com/rapidsai/cudf/pull/12289)) [@davidwendt](https://github.com/davidwendt)
+- Improved error reporting when reading multiple JSON files ([#12285](https://github.com/rapidsai/cudf/pull/12285)) [@vuule](https://github.com/vuule)
+- Deprecate Frame.sum_of_squares ([#12284](https://github.com/rapidsai/cudf/pull/12284)) [@vyasr](https://github.com/vyasr)
+- Remove deprecated code for 23.02 ([#12281](https://github.com/rapidsai/cudf/pull/12281)) [@vyasr](https://github.com/vyasr)
+- Clean up handling of max_page_size_bytes in Parquet writer ([#12277](https://github.com/rapidsai/cudf/pull/12277)) [@etseidl](https://github.com/etseidl)
+- Fix replace gtests coded in namespace cudf::test ([#12270](https://github.com/rapidsai/cudf/pull/12270)) [@davidwendt](https://github.com/davidwendt)
+- Add pandas nullable type support in `Index.to_pandas` ([#12268](https://github.com/rapidsai/cudf/pull/12268)) [@galipremsagar](https://github.com/galipremsagar)
+- Rework nvtext::detokenize to use indexalator for row indices ([#12267](https://github.com/rapidsai/cudf/pull/12267)) [@davidwendt](https://github.com/davidwendt)
+- Fix reduction gtests coded in namespace cudf::test ([#12257](https://github.com/rapidsai/cudf/pull/12257)) [@davidwendt](https://github.com/davidwendt)
+- Remove default parameters from cudf::detail::sort function declarations ([#12254](https://github.com/rapidsai/cudf/pull/12254)) [@davidwendt](https://github.com/davidwendt)
+- Add `duplicated` support for `Series`, `DataFrame` and `Index` ([#12246](https://github.com/rapidsai/cudf/pull/12246)) [@galipremsagar](https://github.com/galipremsagar)
+- Replace column/table test utilities with macros ([#12242](https://github.com/rapidsai/cudf/pull/12242)) [@PointKernel](https://github.com/PointKernel)
+- Rework cudf::strings::pad and zfill to use make_strings_children ([#12238](https://github.com/rapidsai/cudf/pull/12238)) [@davidwendt](https://github.com/davidwendt)
+- Fix sort gtests coded in namespace cudf::test ([#12237](https://github.com/rapidsai/cudf/pull/12237)) [@davidwendt](https://github.com/davidwendt)
+- Wrapping concat and file writes in `[@acquire_spill_lock()` ([#12232](https://github.com/rapidsai/cudf/pull/12232)) @madsbk](https://github.com/acquire_spill_lock()` ([#12232](https://github.com/rapidsai/cudf/pull/12232)) @madsbk)
+- Rename `cudf::structs::detail::superimpose_parent_nulls` APIs ([#12230](https://github.com/rapidsai/cudf/pull/12230)) [@ttnghia](https://github.com/ttnghia)
+- Cover parsing to decimal types in `read_json` tests ([#12229](https://github.com/rapidsai/cudf/pull/12229)) [@vuule](https://github.com/vuule)
+- Spill Statistics ([#12223](https://github.com/rapidsai/cudf/pull/12223)) [@madsbk](https://github.com/madsbk)
+- Use CUDF_JNI_ENABLE_PROFILING to conditionally enable profiling support. ([#12221](https://github.com/rapidsai/cudf/pull/12221)) [@bdice](https://github.com/bdice)
+- Clean up of `test_spilling.py` ([#12220](https://github.com/rapidsai/cudf/pull/12220)) [@madsbk](https://github.com/madsbk)
+- Simplify repetitive boolean logic ([#12218](https://github.com/rapidsai/cudf/pull/12218)) [@vuule](https://github.com/vuule)
+- Add `Series.hasnans` and `Index.hasnans` ([#12214](https://github.com/rapidsai/cudf/pull/12214)) [@galipremsagar](https://github.com/galipremsagar)
+- Add cudf::strings:udf::replace function ([#12210](https://github.com/rapidsai/cudf/pull/12210)) [@davidwendt](https://github.com/davidwendt)
+- Adds in new java APIs for appending byte arrays to host columnar data ([#12208](https://github.com/rapidsai/cudf/pull/12208)) [@revans2](https://github.com/revans2)
+- Remove Python dependencies from Java CI. ([#12193](https://github.com/rapidsai/cudf/pull/12193)) [@bdice](https://github.com/bdice)
+- Fix null order in sort-based groupby and improve groupby tests ([#12191](https://github.com/rapidsai/cudf/pull/12191)) [@divyegala](https://github.com/divyegala)
+- Move strings children functions from cudf/strings/detail/utilities.cuh to new header ([#12185](https://github.com/rapidsai/cudf/pull/12185)) [@davidwendt](https://github.com/davidwendt)
+- Clean up existing JNI scalar to column code ([#12173](https://github.com/rapidsai/cudf/pull/12173)) [@revans2](https://github.com/revans2)
+- Remove JIT type names, refactor id_to_type. ([#12158](https://github.com/rapidsai/cudf/pull/12158)) [@bdice](https://github.com/bdice)
+- Update JNI version to 23.02.0-SNAPSHOT ([#12129](https://github.com/rapidsai/cudf/pull/12129)) [@pxLi](https://github.com/pxLi)
+- Minor refactor of cpp/src/io/parquet/page_data.cu ([#12126](https://github.com/rapidsai/cudf/pull/12126)) [@etseidl](https://github.com/etseidl)
+- Add codespell as a linter ([#12097](https://github.com/rapidsai/cudf/pull/12097)) [@benfred](https://github.com/benfred)
+- Enable specifying exceptions in error macros ([#12078](https://github.com/rapidsai/cudf/pull/12078)) [@vyasr](https://github.com/vyasr)
+- Move `_label_encoding` from Series to Column ([#12040](https://github.com/rapidsai/cudf/pull/12040)) [@shwina](https://github.com/shwina)
+- Add GitHub Actions Workflows ([#12002](https://github.com/rapidsai/cudf/pull/12002)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Consolidate dask-cudf `groupby_agg` calls in one place ([#10835](https://github.com/rapidsai/cudf/pull/10835)) [@charlesbluca](https://github.com/charlesbluca)
+
+# cuDF 22.12.00 (8 Dec 2022)
+
+## 🚨 Breaking Changes
+
+- Add JNI for `substring` without &#39;end&#39; parameter. ([#12113](https://github.com/rapidsai/cudf/pull/12113)) [@firestarman](https://github.com/firestarman)
+- Refactor `purge_nonempty_nulls` ([#12111](https://github.com/rapidsai/cudf/pull/12111)) [@ttnghia](https://github.com/ttnghia)
+- Create an `int8` column in `read_csv` when all elements are missing ([#12110](https://github.com/rapidsai/cudf/pull/12110)) [@vuule](https://github.com/vuule)
+- Throw an error when libcudf is built without cuFile and `LIBCUDF_CUFILE_POLICY` is set to `&quot;ALWAYS&quot;` ([#12080](https://github.com/rapidsai/cudf/pull/12080)) [@vuule](https://github.com/vuule)
+- Fix type promotion edge cases in numerical binops ([#12074](https://github.com/rapidsai/cudf/pull/12074)) [@wence-](https://github.com/wence-)
+- Reduce/Remove reliance on `**kwargs` and `*args` in `IO` readers &amp; writers ([#12025](https://github.com/rapidsai/cudf/pull/12025)) [@galipremsagar](https://github.com/galipremsagar)
+- Rollback of `DeviceBufferLike` ([#12009](https://github.com/rapidsai/cudf/pull/12009)) [@madsbk](https://github.com/madsbk)
+- Remove unused `managed_allocator` ([#12005](https://github.com/rapidsai/cudf/pull/12005)) [@vyasr](https://github.com/vyasr)
+- Pass column names to `write_csv` instead of `table_metadata` pointer ([#11972](https://github.com/rapidsai/cudf/pull/11972)) [@vuule](https://github.com/vuule)
+- Accept const refs instead of const unique_ptr refs in reduce and scan APIs. ([#11960](https://github.com/rapidsai/cudf/pull/11960)) [@vyasr](https://github.com/vyasr)
+- Default to equal NaNs in make_merge_sets_aggregation. ([#11952](https://github.com/rapidsai/cudf/pull/11952)) [@bdice](https://github.com/bdice)
+- Remove validation that requires introspection ([#11938](https://github.com/rapidsai/cudf/pull/11938)) [@vyasr](https://github.com/vyasr)
+- Trim quotes for non-string values in nested json parsing ([#11898](https://github.com/rapidsai/cudf/pull/11898)) [@karthikeyann](https://github.com/karthikeyann)
+- Add tests ensuring that cudf&#39;s default stream is always used ([#11875](https://github.com/rapidsai/cudf/pull/11875)) [@vyasr](https://github.com/vyasr)
+- Support nested types as groupby keys in libcudf ([#11792](https://github.com/rapidsai/cudf/pull/11792)) [@PointKernel](https://github.com/PointKernel)
+- Default to equal NaNs in make_collect_set_aggregation. ([#11621](https://github.com/rapidsai/cudf/pull/11621)) [@bdice](https://github.com/bdice)
+- Removing int8 column option from parquet byte_array writing ([#11539](https://github.com/rapidsai/cudf/pull/11539)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- part1: Simplify BaseIndex to an abstract class ([#10389](https://github.com/rapidsai/cudf/pull/10389)) [@skirui-source](https://github.com/skirui-source)
+
+## 🐛 Bug Fixes
+
+- Fix include line for IO Cython modules ([#12250](https://github.com/rapidsai/cudf/pull/12250)) [@vyasr](https://github.com/vyasr)
+- Make dask pinning looser ([#12231](https://github.com/rapidsai/cudf/pull/12231)) [@vyasr](https://github.com/vyasr)
+- Workaround for CUB segmented-sort bug with boolean keys ([#12217](https://github.com/rapidsai/cudf/pull/12217)) [@davidwendt](https://github.com/davidwendt)
+- Fix `from_dict` backend dispatch to match upstream `dask` ([#12203](https://github.com/rapidsai/cudf/pull/12203)) [@galipremsagar](https://github.com/galipremsagar)
+- Merge branch-22.10 into branch-22.12 ([#12198](https://github.com/rapidsai/cudf/pull/12198)) [@davidwendt](https://github.com/davidwendt)
+- Fix compression in ORC writer ([#12194](https://github.com/rapidsai/cudf/pull/12194)) [@vuule](https://github.com/vuule)
+- Don&#39;t use CMake 3.25.0 as it has a show stopping FindCUDAToolkit bug ([#12188](https://github.com/rapidsai/cudf/pull/12188)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix data corruption when reading ORC files with empty stripes ([#12160](https://github.com/rapidsai/cudf/pull/12160)) [@vuule](https://github.com/vuule)
+- Fix decimal binary operations ([#12142](https://github.com/rapidsai/cudf/pull/12142)) [@galipremsagar](https://github.com/galipremsagar)
+- Ensure dlpack include is provided to cudf interop lib ([#12139](https://github.com/rapidsai/cudf/pull/12139)) [@robertmaynard](https://github.com/robertmaynard)
+- Safely allocate `udf_string` pointers in `strings_udf` ([#12138](https://github.com/rapidsai/cudf/pull/12138)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix/disable jitify lto ([#12122](https://github.com/rapidsai/cudf/pull/12122)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix conditional_full_join benchmark ([#12121](https://github.com/rapidsai/cudf/pull/12121)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Fix regex working-memory-size refactor error ([#12119](https://github.com/rapidsai/cudf/pull/12119)) [@davidwendt](https://github.com/davidwendt)
+- Add in negative size checks for columns ([#12118](https://github.com/rapidsai/cudf/pull/12118)) [@revans2](https://github.com/revans2)
+- Add JNI for `substring` without &#39;end&#39; parameter. ([#12113](https://github.com/rapidsai/cudf/pull/12113)) [@firestarman](https://github.com/firestarman)
+- Fix reading of CSV files with blank second row ([#12098](https://github.com/rapidsai/cudf/pull/12098)) [@vuule](https://github.com/vuule)
+- Fix an error in IO with `GzipFile` type ([#12085](https://github.com/rapidsai/cudf/pull/12085)) [@galipremsagar](https://github.com/galipremsagar)
+- Workaround groupby aggregate thrust::copy_if overflow ([#12079](https://github.com/rapidsai/cudf/pull/12079)) [@davidwendt](https://github.com/davidwendt)
+- Fix alignment of compressed blocks in ORC writer ([#12077](https://github.com/rapidsai/cudf/pull/12077)) [@vuule](https://github.com/vuule)
+- Fix singleton-range `__setitem__` edge case ([#12075](https://github.com/rapidsai/cudf/pull/12075)) [@wence-](https://github.com/wence-)
+- Fix type promotion edge cases in numerical binops ([#12074](https://github.com/rapidsai/cudf/pull/12074)) [@wence-](https://github.com/wence-)
+- Force using old fmt in nvbench. ([#12067](https://github.com/rapidsai/cudf/pull/12067)) [@vyasr](https://github.com/vyasr)
+- Fixes List offset bug in Nested JSON reader ([#12060](https://github.com/rapidsai/cudf/pull/12060)) [@karthikeyann](https://github.com/karthikeyann)
+- Allow falling back to `shim_60.ptx` by default in `strings_udf` ([#12056](https://github.com/rapidsai/cudf/pull/12056)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Force black exclusions for pre-commit. ([#12036](https://github.com/rapidsai/cudf/pull/12036)) [@bdice](https://github.com/bdice)
+- Add `memory_usage` &amp; `items` implementation for `Struct` column &amp; dtype ([#12033](https://github.com/rapidsai/cudf/pull/12033)) [@galipremsagar](https://github.com/galipremsagar)
+- Reduce/Remove reliance on `**kwargs` and `*args` in `IO` readers &amp; writers ([#12025](https://github.com/rapidsai/cudf/pull/12025)) [@galipremsagar](https://github.com/galipremsagar)
+- Fixes bug in csv_reader_options construction in cython ([#12021](https://github.com/rapidsai/cudf/pull/12021)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix issues when both `usecols` and `names` options are used in `read_csv` ([#12018](https://github.com/rapidsai/cudf/pull/12018)) [@vuule](https://github.com/vuule)
+- Port thrust&#39;s pinned_allocator to cudf, since Thrust 1.17 removes the type ([#12004](https://github.com/rapidsai/cudf/pull/12004)) [@robertmaynard](https://github.com/robertmaynard)
+- Revert &quot;Replace most of preprocessor usage in nvcomp adapter with `constexpr`&quot; ([#11999](https://github.com/rapidsai/cudf/pull/11999)) [@vuule](https://github.com/vuule)
+- Fix bug where `df.loc` resulting in single row could give wrong index ([#11998](https://github.com/rapidsai/cudf/pull/11998)) [@eriknw](https://github.com/eriknw)
+- Switch to DISABLE_DEPRECATION_WARNINGS to match other RAPIDS projects ([#11989](https://github.com/rapidsai/cudf/pull/11989)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix maximum page size estimate in Parquet writer ([#11962](https://github.com/rapidsai/cudf/pull/11962)) [@vuule](https://github.com/vuule)
+- Fix local offset handling in bgzip reader ([#11918](https://github.com/rapidsai/cudf/pull/11918)) [@upsj](https://github.com/upsj)
+- Fix an issue reading struct-of-list types in Parquet. ([#11910](https://github.com/rapidsai/cudf/pull/11910)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix memcheck error in TypeInference.Timestamp gtest ([#11905](https://github.com/rapidsai/cudf/pull/11905)) [@davidwendt](https://github.com/davidwendt)
+- Fix type casting in Series.__setitem__ ([#11904](https://github.com/rapidsai/cudf/pull/11904)) [@wence-](https://github.com/wence-)
+- Fix memcheck error in get_dremel_data ([#11903](https://github.com/rapidsai/cudf/pull/11903)) [@davidwendt](https://github.com/davidwendt)
+- Fixes Unsupported column type error due to empty list columns in Nested JSON reader ([#11897](https://github.com/rapidsai/cudf/pull/11897)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix segmented-sort to ignore indices outside the offsets ([#11888](https://github.com/rapidsai/cudf/pull/11888)) [@davidwendt](https://github.com/davidwendt)
+- Fix cudf::stable_sorted_order for NaN and -NaN in FLOAT64 columns ([#11874](https://github.com/rapidsai/cudf/pull/11874)) [@davidwendt](https://github.com/davidwendt)
+- Fix writing of Parquet files with many fragments ([#11869](https://github.com/rapidsai/cudf/pull/11869)) [@etseidl](https://github.com/etseidl)
+- Fix RangeIndex unary operators. ([#11868](https://github.com/rapidsai/cudf/pull/11868)) [@vyasr](https://github.com/vyasr)
+- JNI Avoid NPE for reading host binary data ([#11865](https://github.com/rapidsai/cudf/pull/11865)) [@revans2](https://github.com/revans2)
+- Fix decimal benchmark input data generation ([#11863](https://github.com/rapidsai/cudf/pull/11863)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix pre-commit copyright check ([#11860](https://github.com/rapidsai/cudf/pull/11860)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix Parquet support for seconds and milliseconds duration types ([#11854](https://github.com/rapidsai/cudf/pull/11854)) [@vuule](https://github.com/vuule)
+- Ensure better compiler cache results between cudf cal-ver branches ([#11835](https://github.com/rapidsai/cudf/pull/11835)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix make_column_from_scalar for all-null strings column ([#11807](https://github.com/rapidsai/cudf/pull/11807)) [@davidwendt](https://github.com/davidwendt)
+- Tell jitify_preprocess where to search for libnvrtc ([#11787](https://github.com/rapidsai/cudf/pull/11787)) [@robertmaynard](https://github.com/robertmaynard)
+- add V2 page header support to parquet reader ([#11778](https://github.com/rapidsai/cudf/pull/11778)) [@etseidl](https://github.com/etseidl)
+- Parquet reader: bug fix for a num_rows/skip_rows corner case, w/optimization for nested preprocessing ([#11752](https://github.com/rapidsai/cudf/pull/11752)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Determine if Arrow has S3 support at runtime in unit test. ([#11560](https://github.com/rapidsai/cudf/pull/11560)) [@bdice](https://github.com/bdice)
+
+## 📖 Documentation
+
+- Use rapidsai CODE_OF_CONDUCT.md ([#12166](https://github.com/rapidsai/cudf/pull/12166)) [@bdice](https://github.com/bdice)
+- Add symlinks to notebooks. ([#12128](https://github.com/rapidsai/cudf/pull/12128)) [@bdice](https://github.com/bdice)
+- Add `truncate` API to python doc pages ([#12109](https://github.com/rapidsai/cudf/pull/12109)) [@galipremsagar](https://github.com/galipremsagar)
+- Update Numba docs links. ([#12107](https://github.com/rapidsai/cudf/pull/12107)) [@bdice](https://github.com/bdice)
+- Remove &quot;Multi-GPU with Dask-cuDF&quot; notebook. ([#12095](https://github.com/rapidsai/cudf/pull/12095)) [@bdice](https://github.com/bdice)
+- Fix link to c++ developer guide from `CONTRIBUTING.md` ([#12084](https://github.com/rapidsai/cudf/pull/12084)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add pivot_table and crosstab to docs. ([#12014](https://github.com/rapidsai/cudf/pull/12014)) [@bdice](https://github.com/bdice)
+- Fix doxygen text for cudf::dictionary::encode ([#11991](https://github.com/rapidsai/cudf/pull/11991)) [@davidwendt](https://github.com/davidwendt)
+- Replace default_stream_value with get_default_stream in docs. ([#11985](https://github.com/rapidsai/cudf/pull/11985)) [@vyasr](https://github.com/vyasr)
+- Add dtype docs pages and docstrings for `cudf` specific dtypes ([#11974](https://github.com/rapidsai/cudf/pull/11974)) [@galipremsagar](https://github.com/galipremsagar)
+- Update Unit Testing in libcudf guidelines to code tests outside the cudf::test namespace ([#11959](https://github.com/rapidsai/cudf/pull/11959)) [@davidwendt](https://github.com/davidwendt)
+- Rename libcudf++ to libcudf. ([#11953](https://github.com/rapidsai/cudf/pull/11953)) [@bdice](https://github.com/bdice)
+- Fix documentation referring to removed as_gpu_matrix method. ([#11937](https://github.com/rapidsai/cudf/pull/11937)) [@bdice](https://github.com/bdice)
+- Remove &quot;experimental&quot; warning for struct columns in ORC reader and writer ([#11880](https://github.com/rapidsai/cudf/pull/11880)) [@vuule](https://github.com/vuule)
+- Initial draft of policies and guidelines for libcudf usage. ([#11853](https://github.com/rapidsai/cudf/pull/11853)) [@vyasr](https://github.com/vyasr)
+- Add clear indication of non-GPU accelerated parameters in read_json docstring ([#11825](https://github.com/rapidsai/cudf/pull/11825)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Add developer docs for writing tests ([#11199](https://github.com/rapidsai/cudf/pull/11199)) [@vyasr](https://github.com/vyasr)
+
+## 🚀 New Features
+
+- Adds an EventHandler to Java MemoryBuffer to be invoked on close ([#12125](https://github.com/rapidsai/cudf/pull/12125)) [@abellina](https://github.com/abellina)
+- Support `+` in `strings_udf` ([#12117](https://github.com/rapidsai/cudf/pull/12117)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Support `upper` and `lower` in `strings_udf` ([#12099](https://github.com/rapidsai/cudf/pull/12099)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add wheel builds ([#12096](https://github.com/rapidsai/cudf/pull/12096)) [@vyasr](https://github.com/vyasr)
+- Allow setting malloc heap size in string udfs ([#12094](https://github.com/rapidsai/cudf/pull/12094)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Support `strip`, `lstrip`, and `rstrip` in `strings_udf` ([#12091](https://github.com/rapidsai/cudf/pull/12091)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Mark nvcomp zstd compression stable ([#12059](https://github.com/rapidsai/cudf/pull/12059)) [@jbrennan333](https://github.com/jbrennan333)
+- Add debug-only onAllocated/onDeallocated to RmmEventHandler ([#12054](https://github.com/rapidsai/cudf/pull/12054)) [@abellina](https://github.com/abellina)
+- Enable building against the libarrow contained in pyarrow ([#12034](https://github.com/rapidsai/cudf/pull/12034)) [@vyasr](https://github.com/vyasr)
+- Add strings `like` jni and native method ([#12032](https://github.com/rapidsai/cudf/pull/12032)) [@cindyyuanjiang](https://github.com/cindyyuanjiang)
+- Cleanup common parsing code in JSON, CSV reader ([#12022](https://github.com/rapidsai/cudf/pull/12022)) [@karthikeyann](https://github.com/karthikeyann)
+- byte_range support for JSON Lines format ([#12017](https://github.com/rapidsai/cudf/pull/12017)) [@karthikeyann](https://github.com/karthikeyann)
+- Minor cleanup of root CMakeLists.txt for better organization ([#11988](https://github.com/rapidsai/cudf/pull/11988)) [@robertmaynard](https://github.com/robertmaynard)
+- Add inplace arithmetic operators to `MaskedType` ([#11987](https://github.com/rapidsai/cudf/pull/11987)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Implement JNI for chunked Parquet reader ([#11961](https://github.com/rapidsai/cudf/pull/11961)) [@ttnghia](https://github.com/ttnghia)
+- Add method argument to DataFrame.quantile ([#11957](https://github.com/rapidsai/cudf/pull/11957)) [@rjzamora](https://github.com/rjzamora)
+- Add gpu memory watermark apis to JNI ([#11950](https://github.com/rapidsai/cudf/pull/11950)) [@abellina](https://github.com/abellina)
+- Adds retryCount to RmmEventHandler.onAllocFailure ([#11940](https://github.com/rapidsai/cudf/pull/11940)) [@abellina](https://github.com/abellina)
+- Enable returning string data from UDFs used through `apply` ([#11933](https://github.com/rapidsai/cudf/pull/11933)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Switch over to rapids-cmake patches for thrust ([#11921](https://github.com/rapidsai/cudf/pull/11921)) [@robertmaynard](https://github.com/robertmaynard)
+- Add strings udf C++ classes and functions for phase II ([#11912](https://github.com/rapidsai/cudf/pull/11912)) [@davidwendt](https://github.com/davidwendt)
+- Trim quotes for non-string values in nested json parsing ([#11898](https://github.com/rapidsai/cudf/pull/11898)) [@karthikeyann](https://github.com/karthikeyann)
+- Enable CEC for `strings_udf` ([#11884](https://github.com/rapidsai/cudf/pull/11884)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- ArrowIPCTableWriter writes en empty batch in the case of an empty table. ([#11883](https://github.com/rapidsai/cudf/pull/11883)) [@firestarman](https://github.com/firestarman)
+- Implement chunked Parquet reader ([#11867](https://github.com/rapidsai/cudf/pull/11867)) [@ttnghia](https://github.com/ttnghia)
+- Add `read_orc_metadata` to libcudf ([#11815](https://github.com/rapidsai/cudf/pull/11815)) [@vuule](https://github.com/vuule)
+- Support nested types as groupby keys in libcudf ([#11792](https://github.com/rapidsai/cudf/pull/11792)) [@PointKernel](https://github.com/PointKernel)
+- Adding feature Truncate to DataFrame and Series ([#11435](https://github.com/rapidsai/cudf/pull/11435)) [@VamsiTallam95](https://github.com/VamsiTallam95)
+
+## 🛠️ Improvements
+
+- Reduce number of tests marked `spilling` ([#12197](https://github.com/rapidsai/cudf/pull/12197)) [@madsbk](https://github.com/madsbk)
+- Pin `dask` and `distributed` for release ([#12165](https://github.com/rapidsai/cudf/pull/12165)) [@galipremsagar](https://github.com/galipremsagar)
+- Don&#39;t rely on GNU find in headers_test.sh ([#12164](https://github.com/rapidsai/cudf/pull/12164)) [@wence-](https://github.com/wence-)
+- Update cp.clip call ([#12148](https://github.com/rapidsai/cudf/pull/12148)) [@quasiben](https://github.com/quasiben)
+- Enable automatic column projection in groupby().agg ([#12124](https://github.com/rapidsai/cudf/pull/12124)) [@rjzamora](https://github.com/rjzamora)
+- Refactor `purge_nonempty_nulls` ([#12111](https://github.com/rapidsai/cudf/pull/12111)) [@ttnghia](https://github.com/ttnghia)
+- Create an `int8` column in `read_csv` when all elements are missing ([#12110](https://github.com/rapidsai/cudf/pull/12110)) [@vuule](https://github.com/vuule)
+- Spilling to host memory ([#12106](https://github.com/rapidsai/cudf/pull/12106)) [@madsbk](https://github.com/madsbk)
+- First pass of `pd.read_orc` changes in tests ([#12103](https://github.com/rapidsai/cudf/pull/12103)) [@galipremsagar](https://github.com/galipremsagar)
+- Expose engine argument in dask_cudf.read_json ([#12101](https://github.com/rapidsai/cudf/pull/12101)) [@rjzamora](https://github.com/rjzamora)
+- Remove CUDA 10 compatibility code. ([#12088](https://github.com/rapidsai/cudf/pull/12088)) [@bdice](https://github.com/bdice)
+- Move and update `dask` nigthly install in CI ([#12082](https://github.com/rapidsai/cudf/pull/12082)) [@galipremsagar](https://github.com/galipremsagar)
+- Throw an error when libcudf is built without cuFile and `LIBCUDF_CUFILE_POLICY` is set to `&quot;ALWAYS&quot;` ([#12080](https://github.com/rapidsai/cudf/pull/12080)) [@vuule](https://github.com/vuule)
+- Remove macros that inspect the contents of exceptions ([#12076](https://github.com/rapidsai/cudf/pull/12076)) [@vyasr](https://github.com/vyasr)
+- Fix ingest_raw_data performance issue in Nested JSON reader due to RVO ([#12070](https://github.com/rapidsai/cudf/pull/12070)) [@karthikeyann](https://github.com/karthikeyann)
+- Remove overflow error during decimal binops ([#12063](https://github.com/rapidsai/cudf/pull/12063)) [@galipremsagar](https://github.com/galipremsagar)
+- Change cudf::detail::tdigest to cudf::tdigest::detail ([#12050](https://github.com/rapidsai/cudf/pull/12050)) [@davidwendt](https://github.com/davidwendt)
+- Fix quantile gtests coded in namespace cudf::test ([#12049](https://github.com/rapidsai/cudf/pull/12049)) [@davidwendt](https://github.com/davidwendt)
+- Add support for `DataFrame.from_dict`\`to_dict` and `Series.to_dict` ([#12048](https://github.com/rapidsai/cudf/pull/12048)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactor Parquet reader ([#12046](https://github.com/rapidsai/cudf/pull/12046)) [@ttnghia](https://github.com/ttnghia)
+- Forward merge 22.10 into 22.12 ([#12045](https://github.com/rapidsai/cudf/pull/12045)) [@vyasr](https://github.com/vyasr)
+- Standardize newlines at ends of files. ([#12042](https://github.com/rapidsai/cudf/pull/12042)) [@bdice](https://github.com/bdice)
+- Trim trailing whitespace from all files. ([#12041](https://github.com/rapidsai/cudf/pull/12041)) [@bdice](https://github.com/bdice)
+- Use nosync policy in gather and scatter implementations. ([#12038](https://github.com/rapidsai/cudf/pull/12038)) [@bdice](https://github.com/bdice)
+- Remove smart quotes from all docstrings. ([#12035](https://github.com/rapidsai/cudf/pull/12035)) [@bdice](https://github.com/bdice)
+- Update cuda-python dependency to 11.7.1 ([#12030](https://github.com/rapidsai/cudf/pull/12030)) [@galipremsagar](https://github.com/galipremsagar)
+- Add cython-lint to pre-commit checks. ([#12020](https://github.com/rapidsai/cudf/pull/12020)) [@bdice](https://github.com/bdice)
+- Use pragma once ([#12019](https://github.com/rapidsai/cudf/pull/12019)) [@bdice](https://github.com/bdice)
+- New GHA to add issues/prs to project board ([#12016](https://github.com/rapidsai/cudf/pull/12016)) [@jarmak-nv](https://github.com/jarmak-nv)
+- Add DataFrame.pivot_table. ([#12015](https://github.com/rapidsai/cudf/pull/12015)) [@bdice](https://github.com/bdice)
+- Rollback of `DeviceBufferLike` ([#12009](https://github.com/rapidsai/cudf/pull/12009)) [@madsbk](https://github.com/madsbk)
+- Remove default parameters for nvtext::detail functions ([#12007](https://github.com/rapidsai/cudf/pull/12007)) [@davidwendt](https://github.com/davidwendt)
+- Remove default parameters for cudf::dictionary::detail functions ([#12006](https://github.com/rapidsai/cudf/pull/12006)) [@davidwendt](https://github.com/davidwendt)
+- Remove unused `managed_allocator` ([#12005](https://github.com/rapidsai/cudf/pull/12005)) [@vyasr](https://github.com/vyasr)
+- Remove default parameters for cudf::strings::detail functions ([#12003](https://github.com/rapidsai/cudf/pull/12003)) [@davidwendt](https://github.com/davidwendt)
+- Remove unnecessary code from dask-cudf _Frame ([#12001](https://github.com/rapidsai/cudf/pull/12001)) [@rjzamora](https://github.com/rjzamora)
+- Ignore python docs build artifacts ([#12000](https://github.com/rapidsai/cudf/pull/12000)) [@galipremsagar](https://github.com/galipremsagar)
+- Use rapids-cmake for google benchmark. ([#11997](https://github.com/rapidsai/cudf/pull/11997)) [@vyasr](https://github.com/vyasr)
+- Leverage rapids_cython for more automated RPATH handling ([#11996](https://github.com/rapidsai/cudf/pull/11996)) [@vyasr](https://github.com/vyasr)
+- Remove stale labeler ([#11995](https://github.com/rapidsai/cudf/pull/11995)) [@raydouglass](https://github.com/raydouglass)
+- Move protobuf compilation to CMake ([#11986](https://github.com/rapidsai/cudf/pull/11986)) [@vyasr](https://github.com/vyasr)
+- Replace most of preprocessor usage in nvcomp adapter with `constexpr` ([#11980](https://github.com/rapidsai/cudf/pull/11980)) [@vuule](https://github.com/vuule)
+- Add missing noexcepts to column_in_metadata methods ([#11973](https://github.com/rapidsai/cudf/pull/11973)) [@vyasr](https://github.com/vyasr)
+- Pass column names to `write_csv` instead of `table_metadata` pointer ([#11972](https://github.com/rapidsai/cudf/pull/11972)) [@vuule](https://github.com/vuule)
+- Accelerate libcudf segmented sort with CUB segmented sort ([#11969](https://github.com/rapidsai/cudf/pull/11969)) [@davidwendt](https://github.com/davidwendt)
+- Feature/remove default streams ([#11967](https://github.com/rapidsai/cudf/pull/11967)) [@vyasr](https://github.com/vyasr)
+- Add pool memory resource to libcudf basic example ([#11966](https://github.com/rapidsai/cudf/pull/11966)) [@davidwendt](https://github.com/davidwendt)
+- Fix some libcudf calls to cudf::detail::gather ([#11963](https://github.com/rapidsai/cudf/pull/11963)) [@davidwendt](https://github.com/davidwendt)
+- Accept const refs instead of const unique_ptr refs in reduce and scan APIs. ([#11960](https://github.com/rapidsai/cudf/pull/11960)) [@vyasr](https://github.com/vyasr)
+- Add deprecation warning for set_allocator. ([#11958](https://github.com/rapidsai/cudf/pull/11958)) [@vyasr](https://github.com/vyasr)
+- Fix lists and structs gtests coded in namespace cudf::test ([#11956](https://github.com/rapidsai/cudf/pull/11956)) [@davidwendt](https://github.com/davidwendt)
+- Add full page indexes to Parquet writer benchmarks ([#11955](https://github.com/rapidsai/cudf/pull/11955)) [@etseidl](https://github.com/etseidl)
+- Use gather-based strings factory in cudf::strings::strip ([#11954](https://github.com/rapidsai/cudf/pull/11954)) [@davidwendt](https://github.com/davidwendt)
+- Default to equal NaNs in make_merge_sets_aggregation. ([#11952](https://github.com/rapidsai/cudf/pull/11952)) [@bdice](https://github.com/bdice)
+- Add `strip_delimiters` option to `read_text` ([#11946](https://github.com/rapidsai/cudf/pull/11946)) [@upsj](https://github.com/upsj)
+- Refactor multibyte_split `output_builder` ([#11945](https://github.com/rapidsai/cudf/pull/11945)) [@upsj](https://github.com/upsj)
+- Remove validation that requires introspection ([#11938](https://github.com/rapidsai/cudf/pull/11938)) [@vyasr](https://github.com/vyasr)
+- Add `.str.find_multiple` API ([#11928](https://github.com/rapidsai/cudf/pull/11928)) [@galipremsagar](https://github.com/galipremsagar)
+- Add regex_program class for use with all regex APIs ([#11927](https://github.com/rapidsai/cudf/pull/11927)) [@davidwendt](https://github.com/davidwendt)
+- Enable backend dispatching for Dask-DataFrame creation ([#11920](https://github.com/rapidsai/cudf/pull/11920)) [@rjzamora](https://github.com/rjzamora)
+- Performance improvement in JSON Tree traversal ([#11919](https://github.com/rapidsai/cudf/pull/11919)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix some gtests incorrectly coded in namespace cudf::test (part I) ([#11917](https://github.com/rapidsai/cudf/pull/11917)) [@davidwendt](https://github.com/davidwendt)
+- Refactor pad/zfill functions for reuse with strings udf ([#11914](https://github.com/rapidsai/cudf/pull/11914)) [@davidwendt](https://github.com/davidwendt)
+- Add `nanosecond` &amp; `microsecond` to `DatetimeProperties` ([#11911](https://github.com/rapidsai/cudf/pull/11911)) [@galipremsagar](https://github.com/galipremsagar)
+- Pin mimesis version in setup.py. ([#11906](https://github.com/rapidsai/cudf/pull/11906)) [@bdice](https://github.com/bdice)
+- Error on `ListColumn` or any new unsupported column in `cudf.Index` ([#11902](https://github.com/rapidsai/cudf/pull/11902)) [@galipremsagar](https://github.com/galipremsagar)
+- Add thrust output iterator fix (1805) to thrust.patch ([#11900](https://github.com/rapidsai/cudf/pull/11900)) [@davidwendt](https://github.com/davidwendt)
+- Relax `codecov` threshold diff ([#11899](https://github.com/rapidsai/cudf/pull/11899)) [@galipremsagar](https://github.com/galipremsagar)
+- Use public APIs in STREAM_COMPACTION_NVBENCH ([#11892](https://github.com/rapidsai/cudf/pull/11892)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Add coverage for string UDF tests. ([#11891](https://github.com/rapidsai/cudf/pull/11891)) [@vyasr](https://github.com/vyasr)
+- Provide `data_chunk_source` wrapper for `datasource` ([#11886](https://github.com/rapidsai/cudf/pull/11886)) [@upsj](https://github.com/upsj)
+- Handle `multibyte_split` byte_range out-of-bounds offsets on host ([#11885](https://github.com/rapidsai/cudf/pull/11885)) [@upsj](https://github.com/upsj)
+- Add tests ensuring that cudf&#39;s default stream is always used ([#11875](https://github.com/rapidsai/cudf/pull/11875)) [@vyasr](https://github.com/vyasr)
+- Change expect_strings_empty into expect_column_empty libcudf test utility ([#11873](https://github.com/rapidsai/cudf/pull/11873)) [@davidwendt](https://github.com/davidwendt)
+- Add ngroup ([#11871](https://github.com/rapidsai/cudf/pull/11871)) [@shwina](https://github.com/shwina)
+- Reduce memory usage in nested JSON parser - tree generation ([#11864](https://github.com/rapidsai/cudf/pull/11864)) [@karthikeyann](https://github.com/karthikeyann)
+- Unpin `dask` and `distributed` for development ([#11859](https://github.com/rapidsai/cudf/pull/11859)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove unused includes for table/row_operators ([#11857](https://github.com/rapidsai/cudf/pull/11857)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Use conda-forge&#39;s `pyorc` ([#11855](https://github.com/rapidsai/cudf/pull/11855)) [@jakirkham](https://github.com/jakirkham)
+- Add libcudf strings examples ([#11849](https://github.com/rapidsai/cudf/pull/11849)) [@davidwendt](https://github.com/davidwendt)
+- Remove `cudf_io` namespace alias ([#11827](https://github.com/rapidsai/cudf/pull/11827)) [@vuule](https://github.com/vuule)
+- Test/remove thrust vector usage ([#11813](https://github.com/rapidsai/cudf/pull/11813)) [@vyasr](https://github.com/vyasr)
+- Add BGZIP reader to python `read_text` ([#11802](https://github.com/rapidsai/cudf/pull/11802)) [@upsj](https://github.com/upsj)
+- Merge branch-22.10 into branch-22.12 ([#11801](https://github.com/rapidsai/cudf/pull/11801)) [@davidwendt](https://github.com/davidwendt)
+- Fix compile warning from CUDF_FUNC_RANGE in a member function ([#11798](https://github.com/rapidsai/cudf/pull/11798)) [@davidwendt](https://github.com/davidwendt)
+- Update cudf JNI version to 22.12.0-SNAPSHOT ([#11764](https://github.com/rapidsai/cudf/pull/11764)) [@pxLi](https://github.com/pxLi)
+- Update flake8 to 5.0.4 and use flake8-force to check Cython. ([#11736](https://github.com/rapidsai/cudf/pull/11736)) [@bdice](https://github.com/bdice)
+- Add BGZIP multibyte_split benchmark ([#11723](https://github.com/rapidsai/cudf/pull/11723)) [@upsj](https://github.com/upsj)
+- Bifurcate Dependency Lists ([#11674](https://github.com/rapidsai/cudf/pull/11674)) [@bdice](https://github.com/bdice)
+- Default to equal NaNs in make_collect_set_aggregation. ([#11621](https://github.com/rapidsai/cudf/pull/11621)) [@bdice](https://github.com/bdice)
+- Conform &quot;bench_isin&quot; to match generator column names ([#11549](https://github.com/rapidsai/cudf/pull/11549)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Removing int8 column option from parquet byte_array writing ([#11539](https://github.com/rapidsai/cudf/pull/11539)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add checks for HLG layers in dask-cudf groupby tests ([#10853](https://github.com/rapidsai/cudf/pull/10853)) [@charlesbluca](https://github.com/charlesbluca)
+- part1: Simplify BaseIndex to an abstract class ([#10389](https://github.com/rapidsai/cudf/pull/10389)) [@skirui-source](https://github.com/skirui-source)
+- Make all `nvcc` warnings into errors ([#8916](https://github.com/rapidsai/cudf/pull/8916)) [@trxcllnt](https://github.com/trxcllnt)
+
+# cuDF 22.10.00 (12 Oct 2022)
+
+## 🚨 Breaking Changes
+
+- Disable Zstandard decompression on nvCOMP 2.4 and Pascal GPus ([#11856](https://github.com/rapidsai/cudf/pull/11856)) [@vuule](https://github.com/vuule)
+- Disable nvCOMP DEFLATE integration ([#11811](https://github.com/rapidsai/cudf/pull/11811)) [@vuule](https://github.com/vuule)
+- Fix return type of `Index.isna` &amp; `Index.notna` ([#11769](https://github.com/rapidsai/cudf/pull/11769)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove `kwargs` in `read_csv` &amp; `to_csv` ([#11762](https://github.com/rapidsai/cudf/pull/11762)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `cudf::partition*` APIs that do not return offsets for empty output table ([#11709](https://github.com/rapidsai/cudf/pull/11709)) [@ttnghia](https://github.com/ttnghia)
+- Fix regex negated classes to not automatically include new-lines ([#11644](https://github.com/rapidsai/cudf/pull/11644)) [@davidwendt](https://github.com/davidwendt)
+- Update zfill to match Python output ([#11634](https://github.com/rapidsai/cudf/pull/11634)) [@davidwendt](https://github.com/davidwendt)
+- Upgrade `pandas` to `1.5` ([#11617](https://github.com/rapidsai/cudf/pull/11617)) [@galipremsagar](https://github.com/galipremsagar)
+- Change default value of `ordered` to `False` in `CategoricalDtype` ([#11604](https://github.com/rapidsai/cudf/pull/11604)) [@galipremsagar](https://github.com/galipremsagar)
+- Move cudf::strings::findall_record to cudf::strings::findall ([#11575](https://github.com/rapidsai/cudf/pull/11575)) [@davidwendt](https://github.com/davidwendt)
+- Adding optional parquet reader schema ([#11524](https://github.com/rapidsai/cudf/pull/11524)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Deprecate `skiprows` and `num_rows` in `read_orc` ([#11522](https://github.com/rapidsai/cudf/pull/11522)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove support for skip_rows / num_rows options in the parquet reader. ([#11503](https://github.com/rapidsai/cudf/pull/11503)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Drop support for `skiprows` and `num_rows` in `cudf.read_parquet` ([#11480](https://github.com/rapidsai/cudf/pull/11480)) [@galipremsagar](https://github.com/galipremsagar)
+- Disable Arrow S3 support by default. ([#11470](https://github.com/rapidsai/cudf/pull/11470)) [@bdice](https://github.com/bdice)
+- Convert thrust::optional usages to std::optional ([#11455](https://github.com/rapidsai/cudf/pull/11455)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove unused is_struct trait. ([#11450](https://github.com/rapidsai/cudf/pull/11450)) [@bdice](https://github.com/bdice)
+- Refactor the `Buffer` class ([#11447](https://github.com/rapidsai/cudf/pull/11447)) [@madsbk](https://github.com/madsbk)
+- Return empty dataframe when reading an ORC file using empty `columns` option ([#11446](https://github.com/rapidsai/cudf/pull/11446)) [@vuule](https://github.com/vuule)
+- Refactor pad_side and strip_type enums into side_type enum ([#11438](https://github.com/rapidsai/cudf/pull/11438)) [@davidwendt](https://github.com/davidwendt)
+- Remove HASH_SERIAL_MURMUR3 / serial32BitMurmurHash3 ([#11383](https://github.com/rapidsai/cudf/pull/11383)) [@bdice](https://github.com/bdice)
+- Use the new JSON parser when the experimental reader is selected ([#11364](https://github.com/rapidsai/cudf/pull/11364)) [@vuule](https://github.com/vuule)
+- Remove deprecated Series.applymap. ([#11031](https://github.com/rapidsai/cudf/pull/11031)) [@bdice](https://github.com/bdice)
+- Remove deprecated expand parameter from str.findall. ([#11030](https://github.com/rapidsai/cudf/pull/11030)) [@bdice](https://github.com/bdice)
+
+## 🐛 Bug Fixes
+
+- Fixes bug in temporary decompression space estimation before calling nvcomp ([#11879](https://github.com/rapidsai/cudf/pull/11879)) [@abellina](https://github.com/abellina)
+- Handle `ptx` file paths during `strings_udf` import ([#11862](https://github.com/rapidsai/cudf/pull/11862)) [@galipremsagar](https://github.com/galipremsagar)
+- Disable Zstandard decompression on nvCOMP 2.4 and Pascal GPus ([#11856](https://github.com/rapidsai/cudf/pull/11856)) [@vuule](https://github.com/vuule)
+- Reset `strings_udf` CEC and solve several related issues ([#11846](https://github.com/rapidsai/cudf/pull/11846)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix  bug in new shuffle-based groupby implementation ([#11836](https://github.com/rapidsai/cudf/pull/11836)) [@rjzamora](https://github.com/rjzamora)
+- Fix `is_valid` checks in `Scalar._binaryop` ([#11818](https://github.com/rapidsai/cudf/pull/11818)) [@wence-](https://github.com/wence-)
+- Fix operator `NotImplemented` issue with `numpy` ([#11816](https://github.com/rapidsai/cudf/pull/11816)) [@galipremsagar](https://github.com/galipremsagar)
+- Disable nvCOMP DEFLATE integration ([#11811](https://github.com/rapidsai/cudf/pull/11811)) [@vuule](https://github.com/vuule)
+- Build `strings_udf` package with other python packages in nightlies ([#11808](https://github.com/rapidsai/cudf/pull/11808)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Revert problematic shuffle=explicit-comms changes ([#11803](https://github.com/rapidsai/cudf/pull/11803)) [@rjzamora](https://github.com/rjzamora)
+- Fix regex out-of-bounds write in strided rows logic ([#11797](https://github.com/rapidsai/cudf/pull/11797)) [@davidwendt](https://github.com/davidwendt)
+- Build `cudf` locally before building `strings_udf` conda packages in CI ([#11785](https://github.com/rapidsai/cudf/pull/11785)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix an issue in cudf::row_bit_count involving structs and lists at multiple levels. ([#11779](https://github.com/rapidsai/cudf/pull/11779)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix return type of `Index.isna` &amp; `Index.notna` ([#11769](https://github.com/rapidsai/cudf/pull/11769)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix issue with set-item in case of `list` and `struct` types ([#11760](https://github.com/rapidsai/cudf/pull/11760)) [@galipremsagar](https://github.com/galipremsagar)
+- Ensure all libcudf APIs run on cudf&#39;s default stream ([#11759](https://github.com/rapidsai/cudf/pull/11759)) [@vyasr](https://github.com/vyasr)
+- Resolve dask_cudf failures caused by upstream groupby changes ([#11755](https://github.com/rapidsai/cudf/pull/11755)) [@rjzamora](https://github.com/rjzamora)
+- Fix ORC string sum statistics ([#11740](https://github.com/rapidsai/cudf/pull/11740)) [@vuule](https://github.com/vuule)
+- Add `strings_udf` package for python 3.9 ([#11730](https://github.com/rapidsai/cudf/pull/11730)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Ensure that all tests launch kernels on cudf&#39;s default stream ([#11726](https://github.com/rapidsai/cudf/pull/11726)) [@vyasr](https://github.com/vyasr)
+- Don&#39;t assume stream is a compile-time constant expression ([#11725](https://github.com/rapidsai/cudf/pull/11725)) [@vyasr](https://github.com/vyasr)
+- Fix get_thrust.cmake format at patch command ([#11715](https://github.com/rapidsai/cudf/pull/11715)) [@davidwendt](https://github.com/davidwendt)
+- Fix `cudf::partition*` APIs that do not return offsets for empty output table ([#11709](https://github.com/rapidsai/cudf/pull/11709)) [@ttnghia](https://github.com/ttnghia)
+- Fix cudf::lists::sort_lists for NaN and Infinity values ([#11703](https://github.com/rapidsai/cudf/pull/11703)) [@davidwendt](https://github.com/davidwendt)
+- Modify ORC reader timestamp parsing to match the apache reader behavior ([#11699](https://github.com/rapidsai/cudf/pull/11699)) [@vuule](https://github.com/vuule)
+- Fix `DataFrame.from_arrow` to preserve type metadata ([#11698](https://github.com/rapidsai/cudf/pull/11698)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix compile error due to missing header ([#11697](https://github.com/rapidsai/cudf/pull/11697)) [@ttnghia](https://github.com/ttnghia)
+- Default to Snappy compression in `to_orc` when using cuDF or Dask ([#11690](https://github.com/rapidsai/cudf/pull/11690)) [@vuule](https://github.com/vuule)
+- Fix an issue related to `Multindex` when `group_keys=True` ([#11689](https://github.com/rapidsai/cudf/pull/11689)) [@galipremsagar](https://github.com/galipremsagar)
+- Transfer correct dtype to exploded column ([#11687](https://github.com/rapidsai/cudf/pull/11687)) [@wence-](https://github.com/wence-)
+- Ignore protobuf generated files in `mypy` checks ([#11685](https://github.com/rapidsai/cudf/pull/11685)) [@galipremsagar](https://github.com/galipremsagar)
+- Maintain the index name after `.loc` ([#11677](https://github.com/rapidsai/cudf/pull/11677)) [@shwina](https://github.com/shwina)
+- Fix issue with extracting nested column data &amp; dtype preservation ([#11671](https://github.com/rapidsai/cudf/pull/11671)) [@galipremsagar](https://github.com/galipremsagar)
+- Ensure that all cudf tests and benchmarks are conda env aware ([#11666](https://github.com/rapidsai/cudf/pull/11666)) [@robertmaynard](https://github.com/robertmaynard)
+- Update to Thrust 1.17.2 to fix cub ODR issues ([#11665](https://github.com/rapidsai/cudf/pull/11665)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix multi-file remote datasource bug ([#11655](https://github.com/rapidsai/cudf/pull/11655)) [@rjzamora](https://github.com/rjzamora)
+- Fix invalid regex quantifier check to not include alternation ([#11654](https://github.com/rapidsai/cudf/pull/11654)) [@davidwendt](https://github.com/davidwendt)
+- Fix bug in `device_write()`: it uses an incorrect size ([#11651](https://github.com/rapidsai/cudf/pull/11651)) [@madsbk](https://github.com/madsbk)
+- fixes overflows in benchmarks ([#11649](https://github.com/rapidsai/cudf/pull/11649)) [@elstehle](https://github.com/elstehle)
+- Fix regex negated classes to not automatically include new-lines ([#11644](https://github.com/rapidsai/cudf/pull/11644)) [@davidwendt](https://github.com/davidwendt)
+- Fix compile error in benchmark nested_json.cpp ([#11637](https://github.com/rapidsai/cudf/pull/11637)) [@davidwendt](https://github.com/davidwendt)
+- Update zfill to match Python output ([#11634](https://github.com/rapidsai/cudf/pull/11634)) [@davidwendt](https://github.com/davidwendt)
+- Removed converted type for INT32 and INT64 since they do not convert ([#11627](https://github.com/rapidsai/cudf/pull/11627)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fix host scalars construction of nested types ([#11612](https://github.com/rapidsai/cudf/pull/11612)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix compile warning in nested_json_gpu.cu ([#11607](https://github.com/rapidsai/cudf/pull/11607)) [@davidwendt](https://github.com/davidwendt)
+- Change default value of `ordered` to `False` in `CategoricalDtype` ([#11604](https://github.com/rapidsai/cudf/pull/11604)) [@galipremsagar](https://github.com/galipremsagar)
+- Preserve order if necessary when deduping categoricals internally ([#11597](https://github.com/rapidsai/cudf/pull/11597)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add is_timestamp test for leap second (60) ([#11594](https://github.com/rapidsai/cudf/pull/11594)) [@davidwendt](https://github.com/davidwendt)
+- Fix an issue with `to_arrow` when column name type is not a string ([#11590](https://github.com/rapidsai/cudf/pull/11590)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix exception in segmented-reduce benchmark ([#11588](https://github.com/rapidsai/cudf/pull/11588)) [@davidwendt](https://github.com/davidwendt)
+- Fix encode/decode of negative timestamps in ORC reader/writer ([#11586](https://github.com/rapidsai/cudf/pull/11586)) [@vuule](https://github.com/vuule)
+- Correct distribution data type in `quantiles` benchmark ([#11584](https://github.com/rapidsai/cudf/pull/11584)) [@vuule](https://github.com/vuule)
+- Fix multibyte_split benchmark for host buffers ([#11583](https://github.com/rapidsai/cudf/pull/11583)) [@upsj](https://github.com/upsj)
+- xfail custreamz display test for now ([#11567](https://github.com/rapidsai/cudf/pull/11567)) [@shwina](https://github.com/shwina)
+- Fix JNI for TableWithMeta to use schema_info instead of column_names ([#11566](https://github.com/rapidsai/cudf/pull/11566)) [@jlowe](https://github.com/jlowe)
+- Reduce code duplication for `dask` &amp; `distributed` nightly/stable installs ([#11565](https://github.com/rapidsai/cudf/pull/11565)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix groupby failures in dask_cudf CI ([#11561](https://github.com/rapidsai/cudf/pull/11561)) [@rjzamora](https://github.com/rjzamora)
+- Fix for pivot: error when &#39;values&#39; is a multicharacter string ([#11538](https://github.com/rapidsai/cudf/pull/11538)) [@shaswat-indian](https://github.com/shaswat-indian)
+- find_package(cudf) + arrow9 usable with cudf build directory ([#11535](https://github.com/rapidsai/cudf/pull/11535)) [@robertmaynard](https://github.com/robertmaynard)
+- Fixing crash when writing binary nested data in parquet ([#11526](https://github.com/rapidsai/cudf/pull/11526)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fix for: error when assigning a value to an empty series ([#11523](https://github.com/rapidsai/cudf/pull/11523)) [@shaswat-indian](https://github.com/shaswat-indian)
+- Fix invalid results from conditional-left-anti-join in debug build ([#11517](https://github.com/rapidsai/cudf/pull/11517)) [@davidwendt](https://github.com/davidwendt)
+- Fix cmake error after upgrading to Arrow 9 ([#11513](https://github.com/rapidsai/cudf/pull/11513)) [@ttnghia](https://github.com/ttnghia)
+- Fix reverse binary operators acting on a host value and cudf.Scalar ([#11512](https://github.com/rapidsai/cudf/pull/11512)) [@bdice](https://github.com/bdice)
+- Update parquet fuzz tests to drop support for `skiprows` &amp; `num_rows` ([#11505](https://github.com/rapidsai/cudf/pull/11505)) [@galipremsagar](https://github.com/galipremsagar)
+- Use rapids-cmake 22.10 best practice for RAPIDS.cmake location ([#11493](https://github.com/rapidsai/cudf/pull/11493)) [@robertmaynard](https://github.com/robertmaynard)
+- Handle some zero-sized corner cases in dlpack interop ([#11449](https://github.com/rapidsai/cudf/pull/11449)) [@wence-](https://github.com/wence-)
+- Return empty dataframe when reading an ORC file using empty `columns` option ([#11446](https://github.com/rapidsai/cudf/pull/11446)) [@vuule](https://github.com/vuule)
+- libcudf c++ example updated to CPM version 0.35.3 ([#11417](https://github.com/rapidsai/cudf/pull/11417)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix regex quantifier check to include capture groups ([#11373](https://github.com/rapidsai/cudf/pull/11373)) [@davidwendt](https://github.com/davidwendt)
+- Fix read_text when byte_range is aligned with field ([#11371](https://github.com/rapidsai/cudf/pull/11371)) [@upsj](https://github.com/upsj)
+- Fix to_timestamps truncated subsecond calculation ([#11367](https://github.com/rapidsai/cudf/pull/11367)) [@davidwendt](https://github.com/davidwendt)
+- column: calculate null_count before release()ing the cudf::column ([#11365](https://github.com/rapidsai/cudf/pull/11365)) [@wence-](https://github.com/wence-)
+
+## 📖 Documentation
+
+- Update `guide-to-udfs` notebook ([#11861](https://github.com/rapidsai/cudf/pull/11861)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Update docstring for cudf.read_text ([#11799](https://github.com/rapidsai/cudf/pull/11799)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Add doc section for `list` &amp; `struct` handling ([#11770](https://github.com/rapidsai/cudf/pull/11770)) [@galipremsagar](https://github.com/galipremsagar)
+- Document that minimum required CMake version is now 3.23.1 ([#11751](https://github.com/rapidsai/cudf/pull/11751)) [@robertmaynard](https://github.com/robertmaynard)
+- Update libcudf documentation build command in DOCUMENTATION.md ([#11735](https://github.com/rapidsai/cudf/pull/11735)) [@davidwendt](https://github.com/davidwendt)
+- Add docs for use of string data to `DataFrame.apply` and `Series.apply` and update guide to UDFs notebook ([#11733](https://github.com/rapidsai/cudf/pull/11733)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Enable more Pydocstyle rules ([#11582](https://github.com/rapidsai/cudf/pull/11582)) [@bdice](https://github.com/bdice)
+- Remove unused cpp/img folder ([#11554](https://github.com/rapidsai/cudf/pull/11554)) [@davidwendt](https://github.com/davidwendt)
+- Publish C++ developer docs ([#11475](https://github.com/rapidsai/cudf/pull/11475)) [@vyasr](https://github.com/vyasr)
+- Fix a misalignment in `cudf.get_dummies` docstring ([#11443](https://github.com/rapidsai/cudf/pull/11443)) [@galipremsagar](https://github.com/galipremsagar)
+- Update contributing doc to include links to the developer guides ([#11390](https://github.com/rapidsai/cudf/pull/11390)) [@davidwendt](https://github.com/davidwendt)
+- Fix table_view_base doxygen format ([#11340](https://github.com/rapidsai/cudf/pull/11340)) [@davidwendt](https://github.com/davidwendt)
+- Create main developer guide for Python ([#11235](https://github.com/rapidsai/cudf/pull/11235)) [@vyasr](https://github.com/vyasr)
+- Add developer documentation for benchmarking ([#11122](https://github.com/rapidsai/cudf/pull/11122)) [@vyasr](https://github.com/vyasr)
+- cuDF error handling document ([#7917](https://github.com/rapidsai/cudf/pull/7917)) [@isVoid](https://github.com/isVoid)
+
+## 🚀 New Features
+
+- Add hasNull statistic reading ability to ORC ([#11747](https://github.com/rapidsai/cudf/pull/11747)) [@devavret](https://github.com/devavret)
+- Add `istitle` to string UDFs ([#11738](https://github.com/rapidsai/cudf/pull/11738)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- JSON Column creation in GPU ([#11714](https://github.com/rapidsai/cudf/pull/11714)) [@karthikeyann](https://github.com/karthikeyann)
+- Adds option to take explicit nested schema for nested JSON reader ([#11682](https://github.com/rapidsai/cudf/pull/11682)) [@elstehle](https://github.com/elstehle)
+- Add BGZIP `data_chunk_reader` ([#11652](https://github.com/rapidsai/cudf/pull/11652)) [@upsj](https://github.com/upsj)
+- Support DECIMAL order-by for RANGE window functions ([#11645](https://github.com/rapidsai/cudf/pull/11645)) [@mythrocks](https://github.com/mythrocks)
+- changing version of cmake to 3.23.3 ([#11619](https://github.com/rapidsai/cudf/pull/11619)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Generate unique keys table in java JNI `contiguousSplitGroups` ([#11614](https://github.com/rapidsai/cudf/pull/11614)) [@res-life](https://github.com/res-life)
+- Generic type casting to support the new nested JSON reader ([#11613](https://github.com/rapidsai/cudf/pull/11613)) [@elstehle](https://github.com/elstehle)
+- JSON tree traversal ([#11610](https://github.com/rapidsai/cudf/pull/11610)) [@karthikeyann](https://github.com/karthikeyann)
+- Add casting operators to masked UDFs ([#11578](https://github.com/rapidsai/cudf/pull/11578)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Adds type inference and type conversion for leaf-columns to the nested JSON parser ([#11574](https://github.com/rapidsai/cudf/pull/11574)) [@elstehle](https://github.com/elstehle)
+- Add strings &#39;like&#39; function ([#11558](https://github.com/rapidsai/cudf/pull/11558)) [@davidwendt](https://github.com/davidwendt)
+- Handle hyphen as literal for regex cclass when incomplete range ([#11557](https://github.com/rapidsai/cudf/pull/11557)) [@davidwendt](https://github.com/davidwendt)
+- Enable ZSTD compression in ORC and Parquet writers ([#11551](https://github.com/rapidsai/cudf/pull/11551)) [@vuule](https://github.com/vuule)
+- Adds support for json lines format to the nested JSON reader ([#11534](https://github.com/rapidsai/cudf/pull/11534)) [@elstehle](https://github.com/elstehle)
+- Adding optional parquet reader schema ([#11524](https://github.com/rapidsai/cudf/pull/11524)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Adds GPU implementation of JSON-token-stream to JSON-tree ([#11518](https://github.com/rapidsai/cudf/pull/11518)) [@karthikeyann](https://github.com/karthikeyann)
+- Add `gdb` pretty-printers for simple types ([#11499](https://github.com/rapidsai/cudf/pull/11499)) [@upsj](https://github.com/upsj)
+- Add `create_random_column` function to the data generator ([#11490](https://github.com/rapidsai/cudf/pull/11490)) [@vuule](https://github.com/vuule)
+- Add fluent API builder to `data_profile` ([#11479](https://github.com/rapidsai/cudf/pull/11479)) [@vuule](https://github.com/vuule)
+- Adds Nested Json benchmark ([#11466](https://github.com/rapidsai/cudf/pull/11466)) [@karthikeyann](https://github.com/karthikeyann)
+- Convert thrust::optional usages to std::optional ([#11455](https://github.com/rapidsai/cudf/pull/11455)) [@robertmaynard](https://github.com/robertmaynard)
+- Python API for the future experimental JSON reader ([#11426](https://github.com/rapidsai/cudf/pull/11426)) [@vuule](https://github.com/vuule)
+- Return schema info from JSON reader ([#11419](https://github.com/rapidsai/cudf/pull/11419)) [@vuule](https://github.com/vuule)
+- Add regex ASCII flag support for matching builtin character classes ([#11404](https://github.com/rapidsai/cudf/pull/11404)) [@davidwendt](https://github.com/davidwendt)
+- Truncate parquet column indexes ([#11403](https://github.com/rapidsai/cudf/pull/11403)) [@etseidl](https://github.com/etseidl)
+- Adds the end-to-end JSON parser implementation ([#11388](https://github.com/rapidsai/cudf/pull/11388)) [@elstehle](https://github.com/elstehle)
+- Use the new JSON parser when the experimental reader is selected ([#11364](https://github.com/rapidsai/cudf/pull/11364)) [@vuule](https://github.com/vuule)
+- Add placeholder for the experimental JSON reader ([#11334](https://github.com/rapidsai/cudf/pull/11334)) [@vuule](https://github.com/vuule)
+- Add read-only functions on string dtypes to `DataFrame.apply` and `Series.apply` ([#11319](https://github.com/rapidsai/cudf/pull/11319)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Added &#39;crosstab&#39; and &#39;pivot_table&#39; features ([#11314](https://github.com/rapidsai/cudf/pull/11314)) [@shaswat-indian](https://github.com/shaswat-indian)
+- Quickly error out when trying to build with unsupported nvcc versions ([#11297](https://github.com/rapidsai/cudf/pull/11297)) [@robertmaynard](https://github.com/robertmaynard)
+- Adds JSON tokenizer ([#11264](https://github.com/rapidsai/cudf/pull/11264)) [@elstehle](https://github.com/elstehle)
+- List lexicographic comparator ([#11129](https://github.com/rapidsai/cudf/pull/11129)) [@devavret](https://github.com/devavret)
+- Add generic type inference for cuIO ([#11121](https://github.com/rapidsai/cudf/pull/11121)) [@PointKernel](https://github.com/PointKernel)
+- Fully support nested types in `cudf::contains` ([#10656](https://github.com/rapidsai/cudf/pull/10656)) [@ttnghia](https://github.com/ttnghia)
+- Support nested types in `lists::contains` ([#10548](https://github.com/rapidsai/cudf/pull/10548)) [@ttnghia](https://github.com/ttnghia)
+
+## 🛠️ Improvements
+
+- Pin `dask` and `distributed` for release ([#11822](https://github.com/rapidsai/cudf/pull/11822)) [@galipremsagar](https://github.com/galipremsagar)
+- Add examples for Nested JSON reader ([#11814](https://github.com/rapidsai/cudf/pull/11814)) [@GregoryKimball](https://github.com/GregoryKimball)
+- Support shuffle-based groupby aggregations in dask_cudf ([#11800](https://github.com/rapidsai/cudf/pull/11800)) [@rjzamora](https://github.com/rjzamora)
+- Update strings udf version updater script ([#11772](https://github.com/rapidsai/cudf/pull/11772)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove `kwargs` in `read_csv` &amp; `to_csv` ([#11762](https://github.com/rapidsai/cudf/pull/11762)) [@galipremsagar](https://github.com/galipremsagar)
+- Pass `dtype` param to avoid `pd.Series` warnings ([#11761](https://github.com/rapidsai/cudf/pull/11761)) [@galipremsagar](https://github.com/galipremsagar)
+- Enable `schema_element` &amp; `keep_quotes` support in json reader ([#11746](https://github.com/rapidsai/cudf/pull/11746)) [@galipremsagar](https://github.com/galipremsagar)
+- Add ability to construct `ListColumn` when size is `None` ([#11745](https://github.com/rapidsai/cudf/pull/11745)) [@galipremsagar](https://github.com/galipremsagar)
+- Reduces memory requirements in JSON parser and adds bytes/s and peak memory usage to benchmarks ([#11732](https://github.com/rapidsai/cudf/pull/11732)) [@elstehle](https://github.com/elstehle)
+- Add missing copyright headers. ([#11712](https://github.com/rapidsai/cudf/pull/11712)) [@bdice](https://github.com/bdice)
+- Fix copyright check issues in pre-commit ([#11711](https://github.com/rapidsai/cudf/pull/11711)) [@bdice](https://github.com/bdice)
+- Include decimal in supported types for range window order-by columns ([#11710](https://github.com/rapidsai/cudf/pull/11710)) [@mythrocks](https://github.com/mythrocks)
+- Disable very large column gtest for contiguous-split ([#11706](https://github.com/rapidsai/cudf/pull/11706)) [@davidwendt](https://github.com/davidwendt)
+- Drop split_out=None test from groupby.agg ([#11704](https://github.com/rapidsai/cudf/pull/11704)) [@wence-](https://github.com/wence-)
+- Use CubinLinker for CUDA Minor Version Compatibility ([#11701](https://github.com/rapidsai/cudf/pull/11701)) [@gmarkall](https://github.com/gmarkall)
+- Add regex capture-group parameter to auto convert to non-capture groups ([#11695](https://github.com/rapidsai/cudf/pull/11695)) [@davidwendt](https://github.com/davidwendt)
+- Add a `__dataframe__` method to the protocol dataframe object ([#11692](https://github.com/rapidsai/cudf/pull/11692)) [@rgommers](https://github.com/rgommers)
+- Special-case multibyte_split for single-byte delimiter ([#11681](https://github.com/rapidsai/cudf/pull/11681)) [@upsj](https://github.com/upsj)
+- Remove isort exclusions ([#11680](https://github.com/rapidsai/cudf/pull/11680)) [@bdice](https://github.com/bdice)
+- Refactor CSV reader benchmarks with nvbench ([#11678](https://github.com/rapidsai/cudf/pull/11678)) [@PointKernel](https://github.com/PointKernel)
+- Check conda recipe headers with pre-commit ([#11669](https://github.com/rapidsai/cudf/pull/11669)) [@bdice](https://github.com/bdice)
+- Remove redundant style check for clang-format. ([#11668](https://github.com/rapidsai/cudf/pull/11668)) [@bdice](https://github.com/bdice)
+- Add support for `group_keys` in `groupby` ([#11659](https://github.com/rapidsai/cudf/pull/11659)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix pandoc pinning. ([#11658](https://github.com/rapidsai/cudf/pull/11658)) [@bdice](https://github.com/bdice)
+- Revert removal of skip_rows / num_rows options from the Parquet reader. ([#11657](https://github.com/rapidsai/cudf/pull/11657)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Update git metadata ([#11647](https://github.com/rapidsai/cudf/pull/11647)) [@bdice](https://github.com/bdice)
+- Call set_null_count on a returning column if null-count is known ([#11646](https://github.com/rapidsai/cudf/pull/11646)) [@davidwendt](https://github.com/davidwendt)
+- Fix some libcudf detail calls not passing the stream variable ([#11642](https://github.com/rapidsai/cudf/pull/11642)) [@davidwendt](https://github.com/davidwendt)
+- Update to mypy 0.971 ([#11640](https://github.com/rapidsai/cudf/pull/11640)) [@wence-](https://github.com/wence-)
+- Refactor strings strip functor to details header ([#11635](https://github.com/rapidsai/cudf/pull/11635)) [@davidwendt](https://github.com/davidwendt)
+- Fix incorrect `nullCount` in `get_json_object` ([#11633](https://github.com/rapidsai/cudf/pull/11633)) [@trxcllnt](https://github.com/trxcllnt)
+- Simplify `hostdevice_vector` ([#11631](https://github.com/rapidsai/cudf/pull/11631)) [@upsj](https://github.com/upsj)
+- Refactor parquet writer benchmarks with nvbench ([#11623](https://github.com/rapidsai/cudf/pull/11623)) [@PointKernel](https://github.com/PointKernel)
+- Rework contains_scalar to check nulls at runtime ([#11622](https://github.com/rapidsai/cudf/pull/11622)) [@davidwendt](https://github.com/davidwendt)
+- Fix incorrect memory resource used in rolling temp columns ([#11618](https://github.com/rapidsai/cudf/pull/11618)) [@mythrocks](https://github.com/mythrocks)
+- Upgrade `pandas` to `1.5` ([#11617](https://github.com/rapidsai/cudf/pull/11617)) [@galipremsagar](https://github.com/galipremsagar)
+- Move type-dispatcher calls from traits.hpp to traits.cpp ([#11616](https://github.com/rapidsai/cudf/pull/11616)) [@davidwendt](https://github.com/davidwendt)
+- Refactor parquet reader benchmarks with nvbench ([#11611](https://github.com/rapidsai/cudf/pull/11611)) [@PointKernel](https://github.com/PointKernel)
+- Forward-merge branch-22.08 to branch-22.10 ([#11608](https://github.com/rapidsai/cudf/pull/11608)) [@bdice](https://github.com/bdice)
+- Use stream in Java API. ([#11601](https://github.com/rapidsai/cudf/pull/11601)) [@bdice](https://github.com/bdice)
+- Refactors of public/detail APIs, CUDF_FUNC_RANGE, stream handling. ([#11600](https://github.com/rapidsai/cudf/pull/11600)) [@bdice](https://github.com/bdice)
+- Improve ORC writer benchmark with nvbench ([#11598](https://github.com/rapidsai/cudf/pull/11598)) [@PointKernel](https://github.com/PointKernel)
+- Tune multibyte_split kernel ([#11587](https://github.com/rapidsai/cudf/pull/11587)) [@upsj](https://github.com/upsj)
+- Move split_utils.cuh to strings/detail ([#11585](https://github.com/rapidsai/cudf/pull/11585)) [@davidwendt](https://github.com/davidwendt)
+- Fix warnings due to compiler regression with `if constexpr` ([#11581](https://github.com/rapidsai/cudf/pull/11581)) [@ttnghia](https://github.com/ttnghia)
+- Add full 24-bit dictionary support to Parquet writer ([#11580](https://github.com/rapidsai/cudf/pull/11580)) [@etseidl](https://github.com/etseidl)
+- Expose &quot;explicit-comms&quot; option in shuffle-based dask_cudf functions ([#11576](https://github.com/rapidsai/cudf/pull/11576)) [@rjzamora](https://github.com/rjzamora)
+- Move cudf::strings::findall_record to cudf::strings::findall ([#11575](https://github.com/rapidsai/cudf/pull/11575)) [@davidwendt](https://github.com/davidwendt)
+- Refactor dask_cudf groupby to use apply_concat_apply ([#11571](https://github.com/rapidsai/cudf/pull/11571)) [@rjzamora](https://github.com/rjzamora)
+- Add ability to write `list(struct)` columns as `map` type in orc writer ([#11568](https://github.com/rapidsai/cudf/pull/11568)) [@galipremsagar](https://github.com/galipremsagar)
+- Add byte_range to multibyte_split benchmark + NVBench refactor ([#11562](https://github.com/rapidsai/cudf/pull/11562)) [@upsj](https://github.com/upsj)
+- JNI support for writing binary columns in parquet ([#11556](https://github.com/rapidsai/cudf/pull/11556)) [@revans2](https://github.com/revans2)
+- Support additional dictionary bit widths in Parquet writer ([#11547](https://github.com/rapidsai/cudf/pull/11547)) [@etseidl](https://github.com/etseidl)
+- Refactor string/numeric conversion utilities ([#11545](https://github.com/rapidsai/cudf/pull/11545)) [@davidwendt](https://github.com/davidwendt)
+- Removing unnecessary asserts in parquet tests ([#11544](https://github.com/rapidsai/cudf/pull/11544)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Clean up ORC reader benchmarks with NVBench ([#11543](https://github.com/rapidsai/cudf/pull/11543)) [@PointKernel](https://github.com/PointKernel)
+- Reuse MurmurHash3_32 in Parquet page data. ([#11528](https://github.com/rapidsai/cudf/pull/11528)) [@bdice](https://github.com/bdice)
+- Add hexadecimal value separators ([#11527](https://github.com/rapidsai/cudf/pull/11527)) [@bdice](https://github.com/bdice)
+- Deprecate `skiprows` and `num_rows` in `read_orc` ([#11522](https://github.com/rapidsai/cudf/pull/11522)) [@galipremsagar](https://github.com/galipremsagar)
+- Struct support for `NULL_EQUALS` binary operation ([#11520](https://github.com/rapidsai/cudf/pull/11520)) [@rwlee](https://github.com/rwlee)
+- Bump hadoop-common from 3.2.3 to 3.2.4 in /java ([#11516](https://github.com/rapidsai/cudf/pull/11516)) [@dependabot[bot]](https://github.com/dependabot[bot])
+- Fix Feather test warning. ([#11511](https://github.com/rapidsai/cudf/pull/11511)) [@bdice](https://github.com/bdice)
+- copy_range ballot_syncs to have no execution dependency ([#11508](https://github.com/rapidsai/cudf/pull/11508)) [@robertmaynard](https://github.com/robertmaynard)
+- Upgrade to `arrow-9.x` ([#11507](https://github.com/rapidsai/cudf/pull/11507)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove support for skip_rows / num_rows options in the parquet reader. ([#11503](https://github.com/rapidsai/cudf/pull/11503)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Single-pass `multibyte_split` ([#11500](https://github.com/rapidsai/cudf/pull/11500)) [@upsj](https://github.com/upsj)
+- Sanitize percentile_approx() output for empty input ([#11498](https://github.com/rapidsai/cudf/pull/11498)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Unpin `dask` and `distributed` for development ([#11492](https://github.com/rapidsai/cudf/pull/11492)) [@galipremsagar](https://github.com/galipremsagar)
+- Move SparkMurmurHash3_32 functor. ([#11489](https://github.com/rapidsai/cudf/pull/11489)) [@bdice](https://github.com/bdice)
+- Refactor group_nunique.cu to use nullate::DYNAMIC for reduce-by-key functor ([#11482](https://github.com/rapidsai/cudf/pull/11482)) [@davidwendt](https://github.com/davidwendt)
+- Drop support for `skiprows` and `num_rows` in `cudf.read_parquet` ([#11480](https://github.com/rapidsai/cudf/pull/11480)) [@galipremsagar](https://github.com/galipremsagar)
+- Add reduction `distinct_count` benchmark ([#11473](https://github.com/rapidsai/cudf/pull/11473)) [@ttnghia](https://github.com/ttnghia)
+- Add groupby `nunique` aggregation benchmark ([#11472](https://github.com/rapidsai/cudf/pull/11472)) [@ttnghia](https://github.com/ttnghia)
+- Disable Arrow S3 support by default. ([#11470](https://github.com/rapidsai/cudf/pull/11470)) [@bdice](https://github.com/bdice)
+- Add groupby `max` aggregation benchmark ([#11464](https://github.com/rapidsai/cudf/pull/11464)) [@ttnghia](https://github.com/ttnghia)
+- Extract Dremel encoding code from Parquet ([#11461](https://github.com/rapidsai/cudf/pull/11461)) [@vyasr](https://github.com/vyasr)
+- Add missing Thrust #includes. ([#11457](https://github.com/rapidsai/cudf/pull/11457)) [@bdice](https://github.com/bdice)
+- Make CMake hooks verbose ([#11456](https://github.com/rapidsai/cudf/pull/11456)) [@vyasr](https://github.com/vyasr)
+- Control Parquet page size through Python API ([#11454](https://github.com/rapidsai/cudf/pull/11454)) [@etseidl](https://github.com/etseidl)
+- Add control of Parquet column index creation to python ([#11453](https://github.com/rapidsai/cudf/pull/11453)) [@etseidl](https://github.com/etseidl)
+- Remove unused is_struct trait. ([#11450](https://github.com/rapidsai/cudf/pull/11450)) [@bdice](https://github.com/bdice)
+- Refactor the `Buffer` class ([#11447](https://github.com/rapidsai/cudf/pull/11447)) [@madsbk](https://github.com/madsbk)
+- Refactor pad_side and strip_type enums into side_type enum ([#11438](https://github.com/rapidsai/cudf/pull/11438)) [@davidwendt](https://github.com/davidwendt)
+- Update to Thrust 1.17.0 ([#11437](https://github.com/rapidsai/cudf/pull/11437)) [@bdice](https://github.com/bdice)
+- Add in JNI for parsing JSON data and getting the metadata back too. ([#11431](https://github.com/rapidsai/cudf/pull/11431)) [@revans2](https://github.com/revans2)
+- Convert byte_array_view to use std::byte ([#11424](https://github.com/rapidsai/cudf/pull/11424)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Deprecate unflatten_nested_columns ([#11421](https://github.com/rapidsai/cudf/pull/11421)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Remove HASH_SERIAL_MURMUR3 / serial32BitMurmurHash3 ([#11383](https://github.com/rapidsai/cudf/pull/11383)) [@bdice](https://github.com/bdice)
+- Add Spark list hashing Java tests ([#11379](https://github.com/rapidsai/cudf/pull/11379)) [@bdice](https://github.com/bdice)
+- Move cmake to the build section. ([#11376](https://github.com/rapidsai/cudf/pull/11376)) [@vyasr](https://github.com/vyasr)
+- Remove use of CUDA driver API calls from libcudf ([#11370](https://github.com/rapidsai/cudf/pull/11370)) [@shwina](https://github.com/shwina)
+- Add column constructor from device_uvector&amp;&amp; ([#11356](https://github.com/rapidsai/cudf/pull/11356)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Remove unused custreamz thirdparty directory ([#11343](https://github.com/rapidsai/cudf/pull/11343)) [@vyasr](https://github.com/vyasr)
+- Update jni version to 22.10.0-SNAPSHOT ([#11338](https://github.com/rapidsai/cudf/pull/11338)) [@pxLi](https://github.com/pxLi)
+- Enable using upstream jitify2 ([#11287](https://github.com/rapidsai/cudf/pull/11287)) [@shwina](https://github.com/shwina)
+- Cache cudf.Scalar ([#11246](https://github.com/rapidsai/cudf/pull/11246)) [@shwina](https://github.com/shwina)
+- Remove deprecated Series.applymap. ([#11031](https://github.com/rapidsai/cudf/pull/11031)) [@bdice](https://github.com/bdice)
+- Remove deprecated expand parameter from str.findall. ([#11030](https://github.com/rapidsai/cudf/pull/11030)) [@bdice](https://github.com/bdice)
+
+# cuDF 22.08.00 (17 Aug 2022)
+
+## 🚨 Breaking Changes
+
+- Remove legacy join APIs ([#11274](https://github.com/rapidsai/cudf/pull/11274)) [@vyasr](https://github.com/vyasr)
+- Remove `lists::drop_list_duplicates` ([#11236](https://github.com/rapidsai/cudf/pull/11236)) [@ttnghia](https://github.com/ttnghia)
+- Remove Index.replace API ([#11131](https://github.com/rapidsai/cudf/pull/11131)) [@vyasr](https://github.com/vyasr)
+- Remove deprecated Index methods from Frame ([#11073](https://github.com/rapidsai/cudf/pull/11073)) [@vyasr](https://github.com/vyasr)
+- Remove public API of cudf.merge_sorted. ([#11032](https://github.com/rapidsai/cudf/pull/11032)) [@bdice](https://github.com/bdice)
+- Drop python `3.7` in code-base ([#11029](https://github.com/rapidsai/cudf/pull/11029)) [@galipremsagar](https://github.com/galipremsagar)
+- Return empty dataframe when reading a Parquet file using empty `columns` option ([#11018](https://github.com/rapidsai/cudf/pull/11018)) [@vuule](https://github.com/vuule)
+- Remove Arrow CUDA IPC code ([#10995](https://github.com/rapidsai/cudf/pull/10995)) [@shwina](https://github.com/shwina)
+- Buffer: make `.ptr` read-only ([#10872](https://github.com/rapidsai/cudf/pull/10872)) [@madsbk](https://github.com/madsbk)
+
+## 🐛 Bug Fixes
+
+- Fix `distributed` error related to `loop_in_thread` ([#11428](https://github.com/rapidsai/cudf/pull/11428)) [@galipremsagar](https://github.com/galipremsagar)
+- Relax arrow pinning to just 8.x and remove cuda build dependency from cudf recipe ([#11412](https://github.com/rapidsai/cudf/pull/11412)) [@kkraus14](https://github.com/kkraus14)
+- Revert &quot;Allow CuPy 11&quot; ([#11409](https://github.com/rapidsai/cudf/pull/11409)) [@jakirkham](https://github.com/jakirkham)
+- Fix `moto` timeouts ([#11369](https://github.com/rapidsai/cudf/pull/11369)) [@galipremsagar](https://github.com/galipremsagar)
+- Set `+/-infinity` as the `identity` values for floating-point numbers in device operators `min` and `max` ([#11357](https://github.com/rapidsai/cudf/pull/11357)) [@ttnghia](https://github.com/ttnghia)
+- Fix memory_usage() for `ListSeries` ([#11355](https://github.com/rapidsai/cudf/pull/11355)) [@thomcom](https://github.com/thomcom)
+- Fix constructing Column from column_view with expired mask ([#11354](https://github.com/rapidsai/cudf/pull/11354)) [@shwina](https://github.com/shwina)
+- Handle parquet corner case:  Columns with more rows than are in the row group. ([#11353](https://github.com/rapidsai/cudf/pull/11353)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix `DatetimeIndex` &amp; `TimedeltaIndex` constructors ([#11342](https://github.com/rapidsai/cudf/pull/11342)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix unsigned-compare compile warning in IntPow binops ([#11339](https://github.com/rapidsai/cudf/pull/11339)) [@davidwendt](https://github.com/davidwendt)
+- Fix performance issue and add a new code path to `cudf::detail::contains` ([#11330](https://github.com/rapidsai/cudf/pull/11330)) [@ttnghia](https://github.com/ttnghia)
+- Pin `pytorch` to temporarily unblock from `libcupti` errors ([#11289](https://github.com/rapidsai/cudf/pull/11289)) [@galipremsagar](https://github.com/galipremsagar)
+- Workaround for nvcomp zstd overwriting blocks for orc due to underestimate of sizes ([#11288](https://github.com/rapidsai/cudf/pull/11288)) [@jbrennan333](https://github.com/jbrennan333)
+- Fix inconsistency when hashing two tables in `cudf::detail::contains` ([#11284](https://github.com/rapidsai/cudf/pull/11284)) [@ttnghia](https://github.com/ttnghia)
+- Fix issue related to numpy array and `category` dtype ([#11282](https://github.com/rapidsai/cudf/pull/11282)) [@galipremsagar](https://github.com/galipremsagar)
+- Add NotImplementedError when on is specified in DataFrame.join. ([#11275](https://github.com/rapidsai/cudf/pull/11275)) [@vyasr](https://github.com/vyasr)
+- Fix invalid allocate_like() and empty_like() tests. ([#11268](https://github.com/rapidsai/cudf/pull/11268)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Returns DataFrame When Concatenating Along Axis 1 ([#11263](https://github.com/rapidsai/cudf/pull/11263)) [@isVoid](https://github.com/isVoid)
+- Fix compile error due to missing header ([#11257](https://github.com/rapidsai/cudf/pull/11257)) [@ttnghia](https://github.com/ttnghia)
+- Fix a memory aliasing/crash issue in scatter for lists. ([#11254](https://github.com/rapidsai/cudf/pull/11254)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix `tests/rolling/empty_input_test` ([#11238](https://github.com/rapidsai/cudf/pull/11238)) [@ttnghia](https://github.com/ttnghia)
+- Fix const qualifier when using `host_span&lt;bitmask_type const*&gt;` ([#11220](https://github.com/rapidsai/cudf/pull/11220)) [@ttnghia](https://github.com/ttnghia)
+- Avoid using `nvcompBatchedDeflateDecompressGetTempSizeEx` in cuIO ([#11213](https://github.com/rapidsai/cudf/pull/11213)) [@vuule](https://github.com/vuule)
+- Generate benchmark data with correct run length regardless of cardinality ([#11205](https://github.com/rapidsai/cudf/pull/11205)) [@vuule](https://github.com/vuule)
+- Fix cumulative count index behavior ([#11188](https://github.com/rapidsai/cudf/pull/11188)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix assertion in dask_cudf test_struct_explode ([#11170](https://github.com/rapidsai/cudf/pull/11170)) [@rjzamora](https://github.com/rjzamora)
+- Provides a method for the user to remove the hook and re-register the hook in a custom shutdown hook manager ([#11161](https://github.com/rapidsai/cudf/pull/11161)) [@res-life](https://github.com/res-life)
+- Fix compatibility issues with pandas 1.4.3 ([#11152](https://github.com/rapidsai/cudf/pull/11152)) [@vyasr](https://github.com/vyasr)
+- Ensure cuco export set is installed in cmake build ([#11147](https://github.com/rapidsai/cudf/pull/11147)) [@jlowe](https://github.com/jlowe)
+- Avoid redundant deepcopy in `cudf.from_pandas` ([#11142](https://github.com/rapidsai/cudf/pull/11142)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix compile error due to missing header ([#11126](https://github.com/rapidsai/cudf/pull/11126)) [@ttnghia](https://github.com/ttnghia)
+- Fix `__cuda_array_interface__` failures ([#11113](https://github.com/rapidsai/cudf/pull/11113)) [@galipremsagar](https://github.com/galipremsagar)
+- Support octal and hex within regex character class pattern ([#11112](https://github.com/rapidsai/cudf/pull/11112)) [@davidwendt](https://github.com/davidwendt)
+- Fix split_re matching logic for word boundaries ([#11106](https://github.com/rapidsai/cudf/pull/11106)) [@davidwendt](https://github.com/davidwendt)
+- Handle multiple files metadata in `read_parquet` ([#11105](https://github.com/rapidsai/cudf/pull/11105)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix index alignment for Series objects with repeated index ([#11103](https://github.com/rapidsai/cudf/pull/11103)) [@shwina](https://github.com/shwina)
+- FindcuFile now searches in the current CUDA Toolkit location ([#11101](https://github.com/rapidsai/cudf/pull/11101)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix regex word boundary logic to include underline ([#11099](https://github.com/rapidsai/cudf/pull/11099)) [@davidwendt](https://github.com/davidwendt)
+- Exclude CudaFatalTest when selecting all Java tests ([#11083](https://github.com/rapidsai/cudf/pull/11083)) [@jlowe](https://github.com/jlowe)
+- Fix duplicate `cudatoolkit` pinning issue ([#11070](https://github.com/rapidsai/cudf/pull/11070)) [@galipremsagar](https://github.com/galipremsagar)
+- Maintain the input index in the result of a groupby-transform ([#11068](https://github.com/rapidsai/cudf/pull/11068)) [@shwina](https://github.com/shwina)
+- Fix bug with row count comparison for expect_columns_equivalent(). ([#11059](https://github.com/rapidsai/cudf/pull/11059)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix BPE uninitialized size value for null and empty input strings ([#11054](https://github.com/rapidsai/cudf/pull/11054)) [@davidwendt](https://github.com/davidwendt)
+- Include missing header for usage of `get_current_device_resource()` ([#11047](https://github.com/rapidsai/cudf/pull/11047)) [@AtlantaPepsi](https://github.com/AtlantaPepsi)
+- Fix warn_unused_result error in parquet test ([#11026](https://github.com/rapidsai/cudf/pull/11026)) [@karthikeyann](https://github.com/karthikeyann)
+- Return empty dataframe when reading a Parquet file using empty `columns` option ([#11018](https://github.com/rapidsai/cudf/pull/11018)) [@vuule](https://github.com/vuule)
+- Fix small error in page row count limiting ([#10991](https://github.com/rapidsai/cudf/pull/10991)) [@etseidl](https://github.com/etseidl)
+- Fix a row index entry error in ORC writer issue ([#10989](https://github.com/rapidsai/cudf/pull/10989)) [@vuule](https://github.com/vuule)
+- Fix grouped covariance to require both values to be convertible to double. ([#10891](https://github.com/rapidsai/cudf/pull/10891)) [@bdice](https://github.com/bdice)
+
+## 📖 Documentation
+
+- Fix issues with day &amp; night modes in python docs ([#11400](https://github.com/rapidsai/cudf/pull/11400)) [@galipremsagar](https://github.com/galipremsagar)
+- Update missing data handling APIs in docs ([#11345](https://github.com/rapidsai/cudf/pull/11345)) [@galipremsagar](https://github.com/galipremsagar)
+- Add lists filtering APIs to doxygen group. ([#11336](https://github.com/rapidsai/cudf/pull/11336)) [@bdice](https://github.com/bdice)
+- Remove unused import in README sample ([#11318](https://github.com/rapidsai/cudf/pull/11318)) [@vyasr](https://github.com/vyasr)
+- Note null behavior in `where` docs ([#11276](https://github.com/rapidsai/cudf/pull/11276)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Update docstring for spans in `get_row_data_range` ([#11271](https://github.com/rapidsai/cudf/pull/11271)) [@vyasr](https://github.com/vyasr)
+- Update nvCOMP integration table ([#11231](https://github.com/rapidsai/cudf/pull/11231)) [@vuule](https://github.com/vuule)
+- Add dev docs for documentation writing ([#11217](https://github.com/rapidsai/cudf/pull/11217)) [@vyasr](https://github.com/vyasr)
+- Documentation fix for concatenate ([#11187](https://github.com/rapidsai/cudf/pull/11187)) [@dagardner-nv](https://github.com/dagardner-nv)
+- Fix unresolved links in markdown ([#11173](https://github.com/rapidsai/cudf/pull/11173)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix cudf version in README.md install commands ([#11164](https://github.com/rapidsai/cudf/pull/11164)) [@jvanstraten](https://github.com/jvanstraten)
+- Switch `language` from `None` to `&quot;en&quot;` in docs build ([#11133](https://github.com/rapidsai/cudf/pull/11133)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove docs mentioning scalar_view since no such class exists. ([#11132](https://github.com/rapidsai/cudf/pull/11132)) [@bdice](https://github.com/bdice)
+- Add docstring entry for `DataFrame.value_counts` ([#11039](https://github.com/rapidsai/cudf/pull/11039)) [@galipremsagar](https://github.com/galipremsagar)
+- Add docs to rolling var, std, count. ([#11035](https://github.com/rapidsai/cudf/pull/11035)) [@bdice](https://github.com/bdice)
+- Fix docs for Numba UDFs. ([#11020](https://github.com/rapidsai/cudf/pull/11020)) [@bdice](https://github.com/bdice)
+- Replace column comparison utilities functions with macros ([#11007](https://github.com/rapidsai/cudf/pull/11007)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix Doxygen warnings in multiple headers files ([#11003](https://github.com/rapidsai/cudf/pull/11003)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix doxygen warnings in utilities/ headers ([#10974](https://github.com/rapidsai/cudf/pull/10974)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix Doxygen warnings in table header files ([#10964](https://github.com/rapidsai/cudf/pull/10964)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix Doxygen warnings in column header files ([#10963](https://github.com/rapidsai/cudf/pull/10963)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix Doxygen warnings in strings / header files ([#10937](https://github.com/rapidsai/cudf/pull/10937)) [@karthikeyann](https://github.com/karthikeyann)
+- Generate Doxygen Tag File for Libcudf ([#10932](https://github.com/rapidsai/cudf/pull/10932)) [@isVoid](https://github.com/isVoid)
+- Fix doxygen warnings in structs, lists headers ([#10923](https://github.com/rapidsai/cudf/pull/10923)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix doxygen warnings in fixed_point.hpp ([#10922](https://github.com/rapidsai/cudf/pull/10922)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix doxygen warnings in ast/, rolling, tdigest/, wrappers/, dictionary/ headers ([#10921](https://github.com/rapidsai/cudf/pull/10921)) [@karthikeyann](https://github.com/karthikeyann)
+- fix doxygen warnings in cudf/io/types.hpp, other header files ([#10913](https://github.com/rapidsai/cudf/pull/10913)) [@karthikeyann](https://github.com/karthikeyann)
+- fix doxygen warnings in cudf/io/ avro, csv, json, orc, parquet header files ([#10912](https://github.com/rapidsai/cudf/pull/10912)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix doxygen warnings in cudf/*.hpp ([#10896](https://github.com/rapidsai/cudf/pull/10896)) [@karthikeyann](https://github.com/karthikeyann)
+- Add missing documentation in aggregation.hpp ([#10887](https://github.com/rapidsai/cudf/pull/10887)) [@karthikeyann](https://github.com/karthikeyann)
+- Revise PR template. ([#10774](https://github.com/rapidsai/cudf/pull/10774)) [@bdice](https://github.com/bdice)
+
+## 🚀 New Features
+
+- Change cmake to allow controlling Arrow version via cmake variable ([#11429](https://github.com/rapidsai/cudf/pull/11429)) [@kkraus14](https://github.com/kkraus14)
+- Adding support for list&lt;int8&gt; columns to be written as byte arrays in parquet ([#11328](https://github.com/rapidsai/cudf/pull/11328)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Adding byte array view structure ([#11322](https://github.com/rapidsai/cudf/pull/11322)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Adding byte_array statistics ([#11303](https://github.com/rapidsai/cudf/pull/11303)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add column indexes to Parquet writer ([#11302](https://github.com/rapidsai/cudf/pull/11302)) [@etseidl](https://github.com/etseidl)
+- Provide an Option for Default Integer and Floating Bitwidth ([#11272](https://github.com/rapidsai/cudf/pull/11272)) [@isVoid](https://github.com/isVoid)
+- FST benchmark ([#11243](https://github.com/rapidsai/cudf/pull/11243)) [@karthikeyann](https://github.com/karthikeyann)
+- Adds the Finite-State Transducer algorithm ([#11242](https://github.com/rapidsai/cudf/pull/11242)) [@elstehle](https://github.com/elstehle)
+- Refactor `collect_set` to use `cudf::distinct` and `cudf::lists::distinct` ([#11228](https://github.com/rapidsai/cudf/pull/11228)) [@ttnghia](https://github.com/ttnghia)
+- Treat zstd as stable in nvcomp releases 2.3.2 and later ([#11226](https://github.com/rapidsai/cudf/pull/11226)) [@jbrennan333](https://github.com/jbrennan333)
+- Add 24 bit dictionary support to Parquet writer ([#11216](https://github.com/rapidsai/cudf/pull/11216)) [@devavret](https://github.com/devavret)
+- Enable positive group indices for extractAllRecord on JNI ([#11215](https://github.com/rapidsai/cudf/pull/11215)) [@anthony-chang](https://github.com/anthony-chang)
+- JNI bindings for NTH_ELEMENT window aggregation ([#11201](https://github.com/rapidsai/cudf/pull/11201)) [@mythrocks](https://github.com/mythrocks)
+- Add JNI bindings for extractAllRecord ([#11196](https://github.com/rapidsai/cudf/pull/11196)) [@anthony-chang](https://github.com/anthony-chang)
+- Add `cudf.options` ([#11193](https://github.com/rapidsai/cudf/pull/11193)) [@isVoid](https://github.com/isVoid)
+- Add thrift support for parquet column and offset indexes ([#11178](https://github.com/rapidsai/cudf/pull/11178)) [@etseidl](https://github.com/etseidl)
+- Adding binary read/write as options for parquet ([#11160](https://github.com/rapidsai/cudf/pull/11160)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Support `nth_element` for window functions ([#11158](https://github.com/rapidsai/cudf/pull/11158)) [@mythrocks](https://github.com/mythrocks)
+- Implement `lists::distinct` and `cudf::detail::stable_distinct` ([#11149](https://github.com/rapidsai/cudf/pull/11149)) [@ttnghia](https://github.com/ttnghia)
+- Implement Groupby pct_change ([#11144](https://github.com/rapidsai/cudf/pull/11144)) [@skirui-source](https://github.com/skirui-source)
+- Add JNI for set operations ([#11143](https://github.com/rapidsai/cudf/pull/11143)) [@ttnghia](https://github.com/ttnghia)
+- Remove deprecated PER_THREAD_DEFAULT_STREAM ([#11134](https://github.com/rapidsai/cudf/pull/11134)) [@jbrennan333](https://github.com/jbrennan333)
+- Added a Java method to check the existence of a list of keys in a map ([#11128](https://github.com/rapidsai/cudf/pull/11128)) [@razajafri](https://github.com/razajafri)
+- Feature/python benchmarking ([#11125](https://github.com/rapidsai/cudf/pull/11125)) [@vyasr](https://github.com/vyasr)
+- Support `nan_equality` in `cudf::distinct` ([#11118](https://github.com/rapidsai/cudf/pull/11118)) [@ttnghia](https://github.com/ttnghia)
+- Added JNI for getMapValueForKeys ([#11104](https://github.com/rapidsai/cudf/pull/11104)) [@razajafri](https://github.com/razajafri)
+- Refactor `semi_anti_join` ([#11100](https://github.com/rapidsai/cudf/pull/11100)) [@ttnghia](https://github.com/ttnghia)
+- Replace remaining instances of rmm::cuda_stream_default with cudf::default_stream_value ([#11082](https://github.com/rapidsai/cudf/pull/11082)) [@jbrennan333](https://github.com/jbrennan333)
+- Adds the Logical Stack algorithm ([#11078](https://github.com/rapidsai/cudf/pull/11078)) [@elstehle](https://github.com/elstehle)
+- Add doxygen-check pre-commit hook ([#11076](https://github.com/rapidsai/cudf/pull/11076)) [@karthikeyann](https://github.com/karthikeyann)
+- Use new nvCOMP API to optimize the decompression temp memory size ([#11064](https://github.com/rapidsai/cudf/pull/11064)) [@vuule](https://github.com/vuule)
+- Add Doxygen CI check ([#11057](https://github.com/rapidsai/cudf/pull/11057)) [@karthikeyann](https://github.com/karthikeyann)
+- Support `duplicate_keep_option` in `cudf::distinct` ([#11052](https://github.com/rapidsai/cudf/pull/11052)) [@ttnghia](https://github.com/ttnghia)
+- Support set operations ([#11043](https://github.com/rapidsai/cudf/pull/11043)) [@ttnghia](https://github.com/ttnghia)
+- Support for ZLIB compression in ORC writer ([#11036](https://github.com/rapidsai/cudf/pull/11036)) [@vuule](https://github.com/vuule)
+- Adding feature swaplevels ([#11027](https://github.com/rapidsai/cudf/pull/11027)) [@VamsiTallam95](https://github.com/VamsiTallam95)
+- Use nvCOMP for ZLIB decompression in ORC reader ([#11024](https://github.com/rapidsai/cudf/pull/11024)) [@vuule](https://github.com/vuule)
+- Function for bfill, ffill #9591 ([#11022](https://github.com/rapidsai/cudf/pull/11022)) [@Sreekiran096](https://github.com/Sreekiran096)
+- Generate group offsets from element labels ([#11017](https://github.com/rapidsai/cudf/pull/11017)) [@ttnghia](https://github.com/ttnghia)
+- Feature axes ([#10979](https://github.com/rapidsai/cudf/pull/10979)) [@VamsiTallam95](https://github.com/VamsiTallam95)
+- Generate group labels from offsets ([#10945](https://github.com/rapidsai/cudf/pull/10945)) [@ttnghia](https://github.com/ttnghia)
+- Add missing cuIO benchmark coverage for duration types ([#10933](https://github.com/rapidsai/cudf/pull/10933)) [@vuule](https://github.com/vuule)
+- Dask-cuDF cumulative groupby ops ([#10889](https://github.com/rapidsai/cudf/pull/10889)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Reindex Improvements ([#10815](https://github.com/rapidsai/cudf/pull/10815)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Implement value_counts for DataFrame ([#10813](https://github.com/rapidsai/cudf/pull/10813)) [@martinfalisse](https://github.com/martinfalisse)
+
+## 🛠️ Improvements
+
+- Pin `dask` &amp; `distributed` for release ([#11433](https://github.com/rapidsai/cudf/pull/11433)) [@galipremsagar](https://github.com/galipremsagar)
+- Use documented header template for `doxygen` ([#11430](https://github.com/rapidsai/cudf/pull/11430)) [@galipremsagar](https://github.com/galipremsagar)
+- Relax arrow version in dev env ([#11418](https://github.com/rapidsai/cudf/pull/11418)) [@galipremsagar](https://github.com/galipremsagar)
+- Allow CuPy 11 ([#11393](https://github.com/rapidsai/cudf/pull/11393)) [@jakirkham](https://github.com/jakirkham)
+- Improve multibyte_split performance ([#11347](https://github.com/rapidsai/cudf/pull/11347)) [@cwharris](https://github.com/cwharris)
+- Switch death test to use explicit trap. ([#11326](https://github.com/rapidsai/cudf/pull/11326)) [@vyasr](https://github.com/vyasr)
+- Add --output-on-failure to ctest args. ([#11321](https://github.com/rapidsai/cudf/pull/11321)) [@vyasr](https://github.com/vyasr)
+- Consolidate remaining DataFrame/Series APIs ([#11315](https://github.com/rapidsai/cudf/pull/11315)) [@vyasr](https://github.com/vyasr)
+- Add JNI support for the join_strings API ([#11309](https://github.com/rapidsai/cudf/pull/11309)) [@revans2](https://github.com/revans2)
+- Add cupy version to setup.py install_requires ([#11306](https://github.com/rapidsai/cudf/pull/11306)) [@vyasr](https://github.com/vyasr)
+- removing some unused code ([#11305](https://github.com/rapidsai/cudf/pull/11305)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add test of wildcard selection ([#11300](https://github.com/rapidsai/cudf/pull/11300)) [@vyasr](https://github.com/vyasr)
+- Update parquet reader to take stream parameter ([#11294](https://github.com/rapidsai/cudf/pull/11294)) [@PointKernel](https://github.com/PointKernel)
+- Spark list hashing ([#11292](https://github.com/rapidsai/cudf/pull/11292)) [@bdice](https://github.com/bdice)
+- Remove legacy join APIs ([#11274](https://github.com/rapidsai/cudf/pull/11274)) [@vyasr](https://github.com/vyasr)
+- Fix `cudf` recipes syntax ([#11273](https://github.com/rapidsai/cudf/pull/11273)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix `cudf` recipe ([#11267](https://github.com/rapidsai/cudf/pull/11267)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Cleanup config files ([#11266](https://github.com/rapidsai/cudf/pull/11266)) [@vyasr](https://github.com/vyasr)
+- Run mypy on all packages ([#11265](https://github.com/rapidsai/cudf/pull/11265)) [@vyasr](https://github.com/vyasr)
+- Update to isort 5.10.1. ([#11262](https://github.com/rapidsai/cudf/pull/11262)) [@vyasr](https://github.com/vyasr)
+- Consolidate flake8 and pydocstyle configuration ([#11260](https://github.com/rapidsai/cudf/pull/11260)) [@vyasr](https://github.com/vyasr)
+- Remove redundant black config specifications. ([#11258](https://github.com/rapidsai/cudf/pull/11258)) [@vyasr](https://github.com/vyasr)
+- Ensure DeprecationWarnings are not introduced via pre-commit ([#11255](https://github.com/rapidsai/cudf/pull/11255)) [@wence-](https://github.com/wence-)
+- Optimization to gpu::PreprocessColumnData in parquet reader. ([#11252](https://github.com/rapidsai/cudf/pull/11252)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Move rolling impl details to detail/ directory. ([#11250](https://github.com/rapidsai/cudf/pull/11250)) [@mythrocks](https://github.com/mythrocks)
+- Remove `lists::drop_list_duplicates` ([#11236](https://github.com/rapidsai/cudf/pull/11236)) [@ttnghia](https://github.com/ttnghia)
+- Use `cudf::lists::distinct` in Python binding ([#11234](https://github.com/rapidsai/cudf/pull/11234)) [@ttnghia](https://github.com/ttnghia)
+- Use `cudf::lists::distinct` in Java binding ([#11233](https://github.com/rapidsai/cudf/pull/11233)) [@ttnghia](https://github.com/ttnghia)
+- Use `cudf::distinct` in Java binding ([#11232](https://github.com/rapidsai/cudf/pull/11232)) [@ttnghia](https://github.com/ttnghia)
+- Pin `dask-cuda` in dev environment ([#11229](https://github.com/rapidsai/cudf/pull/11229)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove cruft in map_lookup ([#11221](https://github.com/rapidsai/cudf/pull/11221)) [@mythrocks](https://github.com/mythrocks)
+- Deprecate `skiprows` &amp; `num_rows` in parquet reader ([#11218](https://github.com/rapidsai/cudf/pull/11218)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove Frame._index ([#11210](https://github.com/rapidsai/cudf/pull/11210)) [@vyasr](https://github.com/vyasr)
+- Improve performance for `cudf::contains` when searching for a scalar ([#11202](https://github.com/rapidsai/cudf/pull/11202)) [@ttnghia](https://github.com/ttnghia)
+- Document why Development component is needing for CMake. ([#11200](https://github.com/rapidsai/cudf/pull/11200)) [@vyasr](https://github.com/vyasr)
+- cleanup unused code in rolling_test.hpp ([#11195](https://github.com/rapidsai/cudf/pull/11195)) [@karthikeyann](https://github.com/karthikeyann)
+- Standardize join internals around DataFrame ([#11184](https://github.com/rapidsai/cudf/pull/11184)) [@vyasr](https://github.com/vyasr)
+- Move character case table declarations from src to detail ([#11183](https://github.com/rapidsai/cudf/pull/11183)) [@davidwendt](https://github.com/davidwendt)
+- Remove usage of Frame in StringMethods ([#11181](https://github.com/rapidsai/cudf/pull/11181)) [@vyasr](https://github.com/vyasr)
+- Expose get_json_object_options to Python ([#11180](https://github.com/rapidsai/cudf/pull/11180)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Fix decimal128 stats in parquet writer ([#11179](https://github.com/rapidsai/cudf/pull/11179)) [@etseidl](https://github.com/etseidl)
+- Modify CheckPageRows in parquet_test to use datasources ([#11177](https://github.com/rapidsai/cudf/pull/11177)) [@etseidl](https://github.com/etseidl)
+- Pin max version of `cuda-python` to `11.7.0` ([#11174](https://github.com/rapidsai/cudf/pull/11174)) [@Ethyling](https://github.com/Ethyling)
+- Refactor and optimize Frame.where ([#11168](https://github.com/rapidsai/cudf/pull/11168)) [@vyasr](https://github.com/vyasr)
+- Add npos const static member to cudf::string_view ([#11166](https://github.com/rapidsai/cudf/pull/11166)) [@davidwendt](https://github.com/davidwendt)
+- Move _drop_rows_by_label from Frame to IndexedFrame ([#11157](https://github.com/rapidsai/cudf/pull/11157)) [@vyasr](https://github.com/vyasr)
+- Clean up _copy_type_metadata ([#11156](https://github.com/rapidsai/cudf/pull/11156)) [@vyasr](https://github.com/vyasr)
+- Add `nvcc` conda package in dev environment ([#11154](https://github.com/rapidsai/cudf/pull/11154)) [@galipremsagar](https://github.com/galipremsagar)
+- Struct binary comparison op functionality for spark rapids ([#11153](https://github.com/rapidsai/cudf/pull/11153)) [@rwlee](https://github.com/rwlee)
+- Refactor inline conditionals. ([#11151](https://github.com/rapidsai/cudf/pull/11151)) [@bdice](https://github.com/bdice)
+- Refactor Spark hashing tests ([#11145](https://github.com/rapidsai/cudf/pull/11145)) [@bdice](https://github.com/bdice)
+- Add new `_from_data_like_self` factory ([#11140](https://github.com/rapidsai/cudf/pull/11140)) [@vyasr](https://github.com/vyasr)
+- Update get_cucollections to use rapids-cmake ([#11139](https://github.com/rapidsai/cudf/pull/11139)) [@vyasr](https://github.com/vyasr)
+- Remove unnecessary extra function for libcudacxx detection ([#11138](https://github.com/rapidsai/cudf/pull/11138)) [@vyasr](https://github.com/vyasr)
+- Allow initial value for cudf::reduce and cudf::segmented_reduce. ([#11137](https://github.com/rapidsai/cudf/pull/11137)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Remove Index.replace API ([#11131](https://github.com/rapidsai/cudf/pull/11131)) [@vyasr](https://github.com/vyasr)
+- Move char-type table function declarations from src to detail ([#11127](https://github.com/rapidsai/cudf/pull/11127)) [@davidwendt](https://github.com/davidwendt)
+- Clean up repo root ([#11124](https://github.com/rapidsai/cudf/pull/11124)) [@bdice](https://github.com/bdice)
+- Improve print formatting of strings containing newline characters. ([#11108](https://github.com/rapidsai/cudf/pull/11108)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix cudf::string_view::find() to return pos for empty string argument ([#11107](https://github.com/rapidsai/cudf/pull/11107)) [@davidwendt](https://github.com/davidwendt)
+- Forward-merge branch-22.06 to branch-22.08 ([#11086](https://github.com/rapidsai/cudf/pull/11086)) [@bdice](https://github.com/bdice)
+- Take iterators by value in clamp.cu. ([#11084](https://github.com/rapidsai/cudf/pull/11084)) [@bdice](https://github.com/bdice)
+- Performance improvements for row to column conversions ([#11075](https://github.com/rapidsai/cudf/pull/11075)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Remove deprecated Index methods from Frame ([#11073](https://github.com/rapidsai/cudf/pull/11073)) [@vyasr](https://github.com/vyasr)
+- Use per-page max compressed size estimate for compression ([#11066](https://github.com/rapidsai/cudf/pull/11066)) [@devavret](https://github.com/devavret)
+- column to row refactor for performance ([#11063](https://github.com/rapidsai/cudf/pull/11063)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Include `skbuild` directory into `build.sh` `clean` operation ([#11060](https://github.com/rapidsai/cudf/pull/11060)) [@galipremsagar](https://github.com/galipremsagar)
+- Unpin `dask` &amp; `distributed` for development ([#11058](https://github.com/rapidsai/cudf/pull/11058)) [@galipremsagar](https://github.com/galipremsagar)
+- Add support for `Series.between` ([#11051](https://github.com/rapidsai/cudf/pull/11051)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix groupby include ([#11046](https://github.com/rapidsai/cudf/pull/11046)) [@bwyogatama](https://github.com/bwyogatama)
+- Regex cleanup internal reclass and reclass_device classes ([#11045](https://github.com/rapidsai/cudf/pull/11045)) [@davidwendt](https://github.com/davidwendt)
+- Remove public API of cudf.merge_sorted. ([#11032](https://github.com/rapidsai/cudf/pull/11032)) [@bdice](https://github.com/bdice)
+- Drop python `3.7` in code-base ([#11029](https://github.com/rapidsai/cudf/pull/11029)) [@galipremsagar](https://github.com/galipremsagar)
+- Addition &amp; integration of the integer power operator ([#11025](https://github.com/rapidsai/cudf/pull/11025)) [@AtlantaPepsi](https://github.com/AtlantaPepsi)
+- Refactor `lists::contains` ([#11019](https://github.com/rapidsai/cudf/pull/11019)) [@ttnghia](https://github.com/ttnghia)
+- Change build.sh to find C++ library by default and avoid shadowing CMAKE_ARGS ([#11013](https://github.com/rapidsai/cudf/pull/11013)) [@vyasr](https://github.com/vyasr)
+- Clean up parquet unit test ([#11005](https://github.com/rapidsai/cudf/pull/11005)) [@PointKernel](https://github.com/PointKernel)
+- Add missing #pragma once to header files ([#11004](https://github.com/rapidsai/cudf/pull/11004)) [@karthikeyann](https://github.com/karthikeyann)
+- Cleanup `iterator.cuh` and add fixed point support for `scalar_optional_accessor` ([#10999](https://github.com/rapidsai/cudf/pull/10999)) [@ttnghia](https://github.com/ttnghia)
+- Refactor `cudf::contains` ([#10997](https://github.com/rapidsai/cudf/pull/10997)) [@ttnghia](https://github.com/ttnghia)
+- Remove Arrow CUDA IPC code ([#10995](https://github.com/rapidsai/cudf/pull/10995)) [@shwina](https://github.com/shwina)
+- Change file extension for groupby benchmark ([#10985](https://github.com/rapidsai/cudf/pull/10985)) [@ttnghia](https://github.com/ttnghia)
+- Sort recipe include checks. ([#10984](https://github.com/rapidsai/cudf/pull/10984)) [@bdice](https://github.com/bdice)
+- Update cuCollections for thrust upgrade ([#10983](https://github.com/rapidsai/cudf/pull/10983)) [@PointKernel](https://github.com/PointKernel)
+- Expose row-group size options in cudf ParquetWriter ([#10980](https://github.com/rapidsai/cudf/pull/10980)) [@rjzamora](https://github.com/rjzamora)
+- Cleanup cudf::strings::detail::regex_parser class source ([#10975](https://github.com/rapidsai/cudf/pull/10975)) [@davidwendt](https://github.com/davidwendt)
+- Handle missing fields as nulls in get_json_object() ([#10970](https://github.com/rapidsai/cudf/pull/10970)) [@SrikarVanavasam](https://github.com/SrikarVanavasam)
+- Fix license families to match all-caps expected by conda-verify. ([#10931](https://github.com/rapidsai/cudf/pull/10931)) [@bdice](https://github.com/bdice)
+- Include &lt;optional&gt; for GCC 11 compatibility. ([#10927](https://github.com/rapidsai/cudf/pull/10927)) [@bdice](https://github.com/bdice)
+- Enable builds with scikit-build ([#10919](https://github.com/rapidsai/cudf/pull/10919)) [@vyasr](https://github.com/vyasr)
+- Improve `distinct` by using `cuco::static_map::retrieve_all` ([#10916](https://github.com/rapidsai/cudf/pull/10916)) [@PointKernel](https://github.com/PointKernel)
+- update cudfjni to 22.08.0-SNAPSHOT ([#10910](https://github.com/rapidsai/cudf/pull/10910)) [@pxLi](https://github.com/pxLi)
+- Improve the capture of fatal cuda error ([#10884](https://github.com/rapidsai/cudf/pull/10884)) [@sperlingxx](https://github.com/sperlingxx)
+- Cleanup regex compiler operators and operands source ([#10879](https://github.com/rapidsai/cudf/pull/10879)) [@davidwendt](https://github.com/davidwendt)
+- Buffer: make `.ptr` read-only ([#10872](https://github.com/rapidsai/cudf/pull/10872)) [@madsbk](https://github.com/madsbk)
+- Configurable NaN handling in device_row_comparators ([#10870](https://github.com/rapidsai/cudf/pull/10870)) [@rwlee](https://github.com/rwlee)
+- Register `cudf.core.groupby.Grouper` objects to dask `grouper_dispatch` ([#10838](https://github.com/rapidsai/cudf/pull/10838)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Upgrade to `arrow-8` ([#10816](https://github.com/rapidsai/cudf/pull/10816)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove _getattr_ method in RangeIndex class ([#10538](https://github.com/rapidsai/cudf/pull/10538)) [@skirui-source](https://github.com/skirui-source)
+- Adding bins to value counts ([#8247](https://github.com/rapidsai/cudf/pull/8247)) [@marlenezw](https://github.com/marlenezw)
+
+# cuDF 22.06.00 (7 Jun 2022)
+
+## 🚨 Breaking Changes
+
+- Enable Zstandard decompression only when all nvcomp integrations are enabled ([#10944](https://github.com/rapidsai/cudf/pull/10944)) [@vuule](https://github.com/vuule)
+- Rename `sliced_child` to `get_sliced_child`. ([#10885](https://github.com/rapidsai/cudf/pull/10885)) [@bdice](https://github.com/bdice)
+- Add parameters to control page size in Parquet writer ([#10882](https://github.com/rapidsai/cudf/pull/10882)) [@etseidl](https://github.com/etseidl)
+- Make cudf::test::expect_columns_equal() to fail when comparing unsanitary lists. ([#10880](https://github.com/rapidsai/cudf/pull/10880)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Cleanup regex compiler fixed quantifiers source ([#10843](https://github.com/rapidsai/cudf/pull/10843)) [@davidwendt](https://github.com/davidwendt)
+- Refactor `cudf::contains`, renaming and switching parameters role ([#10802](https://github.com/rapidsai/cudf/pull/10802)) [@ttnghia](https://github.com/ttnghia)
+- Generic serialization of all column types ([#10784](https://github.com/rapidsai/cudf/pull/10784)) [@wence-](https://github.com/wence-)
+- Return per-file metadata from readers ([#10782](https://github.com/rapidsai/cudf/pull/10782)) [@vuule](https://github.com/vuule)
+- HostColumnVectoreCore#isNull should return true for out-of-range rows ([#10779](https://github.com/rapidsai/cudf/pull/10779)) [@gerashegalov](https://github.com/gerashegalov)
+- Update `groupby::hash` to use new row operators for keys ([#10770](https://github.com/rapidsai/cudf/pull/10770)) [@PointKernel](https://github.com/PointKernel)
+- update mangle_dupe_cols behavior in csv reader to match pandas 1.4.0 behavior ([#10749](https://github.com/rapidsai/cudf/pull/10749)) [@karthikeyann](https://github.com/karthikeyann)
+- Rename CUDA_TRY macro to CUDF_CUDA_TRY, rename CHECK_CUDA macro to CUDF_CHECK_CUDA. ([#10589](https://github.com/rapidsai/cudf/pull/10589)) [@bdice](https://github.com/bdice)
+- Upgrade `cudf` to support `pandas` 1.4.x versions ([#10584](https://github.com/rapidsai/cudf/pull/10584)) [@galipremsagar](https://github.com/galipremsagar)
+- Move binop methods from Frame to IndexedFrame and standardize the docstring ([#10576](https://github.com/rapidsai/cudf/pull/10576)) [@vyasr](https://github.com/vyasr)
+- Add default= kwarg to .list.get() accessor method ([#10547](https://github.com/rapidsai/cudf/pull/10547)) [@shwina](https://github.com/shwina)
+- Remove deprecated `decimal_cols_as_float` in the ORC reader ([#10515](https://github.com/rapidsai/cudf/pull/10515)) [@vuule](https://github.com/vuule)
+- Support nvComp 2.3 if local, otherwise use nvcomp 2.2 ([#10513](https://github.com/rapidsai/cudf/pull/10513)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix findall_record to return empty list for no matches ([#10491](https://github.com/rapidsai/cudf/pull/10491)) [@davidwendt](https://github.com/davidwendt)
+- Namespace/Docstring Fixes for Reduction ([#10471](https://github.com/rapidsai/cudf/pull/10471)) [@isVoid](https://github.com/isVoid)
+- Additional refactoring of hash functions ([#10462](https://github.com/rapidsai/cudf/pull/10462)) [@bdice](https://github.com/bdice)
+- Fix default value of str.split expand parameter. ([#10457](https://github.com/rapidsai/cudf/pull/10457)) [@bdice](https://github.com/bdice)
+- Remove deprecated code. ([#10450](https://github.com/rapidsai/cudf/pull/10450)) [@vyasr](https://github.com/vyasr)
+
+## 🐛 Bug Fixes
+
+- Fix single column `MultiIndex` issue in `sort_index` ([#10957](https://github.com/rapidsai/cudf/pull/10957)) [@galipremsagar](https://github.com/galipremsagar)
+- Make SerializedTableHeader(numRows) public ([#10949](https://github.com/rapidsai/cudf/pull/10949)) [@gerashegalov](https://github.com/gerashegalov)
+- Fix `gcc_linux` version pinning in dev environment ([#10943](https://github.com/rapidsai/cudf/pull/10943)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix an issue with reading raw string in `cudf.read_json` ([#10924](https://github.com/rapidsai/cudf/pull/10924)) [@galipremsagar](https://github.com/galipremsagar)
+- Make cudf::test::expect_columns_equal() to fail when comparing unsanitary lists. ([#10880](https://github.com/rapidsai/cudf/pull/10880)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix segmented_reduce on empty column with non-empty offsets ([#10876](https://github.com/rapidsai/cudf/pull/10876)) [@davidwendt](https://github.com/davidwendt)
+- Fix dask-cudf groupby handling when grouping by all columns ([#10866](https://github.com/rapidsai/cudf/pull/10866)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix a bug in `distinct`: using nested nulls logic ([#10848](https://github.com/rapidsai/cudf/pull/10848)) [@PointKernel](https://github.com/PointKernel)
+- Fix constness / references in weak ordering operator() signatures. ([#10846](https://github.com/rapidsai/cudf/pull/10846)) [@bdice](https://github.com/bdice)
+- Suppress sizeof-array-div warnings in thrust found by gcc-11 ([#10840](https://github.com/rapidsai/cudf/pull/10840)) [@robertmaynard](https://github.com/robertmaynard)
+- Add handling for string by-columns in dask-cudf groupby ([#10830](https://github.com/rapidsai/cudf/pull/10830)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix compile warning in search.cu ([#10827](https://github.com/rapidsai/cudf/pull/10827)) [@davidwendt](https://github.com/davidwendt)
+- Fix element access const correctness in `hostdevice_vector` ([#10804](https://github.com/rapidsai/cudf/pull/10804)) [@vuule](https://github.com/vuule)
+- Update `cuco` git tag ([#10788](https://github.com/rapidsai/cudf/pull/10788)) [@PointKernel](https://github.com/PointKernel)
+- HostColumnVectoreCore#isNull should return true for out-of-range rows ([#10779](https://github.com/rapidsai/cudf/pull/10779)) [@gerashegalov](https://github.com/gerashegalov)
+- Fixing deprecation warnings in test_orc.py ([#10772](https://github.com/rapidsai/cudf/pull/10772)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Enable writing to `s3` storage in chunked parquet writer ([#10769](https://github.com/rapidsai/cudf/pull/10769)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix construction of nested structs with EMPTY child ([#10761](https://github.com/rapidsai/cudf/pull/10761)) [@shwina](https://github.com/shwina)
+- Fix replace error when regex has only zero match quantifiers ([#10760](https://github.com/rapidsai/cudf/pull/10760)) [@davidwendt](https://github.com/davidwendt)
+- Fix an issue with one_level_list schemas in parquet reader. ([#10750](https://github.com/rapidsai/cudf/pull/10750)) [@nvdbaranec](https://github.com/nvdbaranec)
+- update mangle_dupe_cols behavior in csv reader to match pandas 1.4.0 behavior ([#10749](https://github.com/rapidsai/cudf/pull/10749)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix `cupy` function in notebook ([#10737](https://github.com/rapidsai/cudf/pull/10737)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix `fillna` to retain `columns` when it is `MultiIndex` ([#10729](https://github.com/rapidsai/cudf/pull/10729)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix scatter for all-empty-string column case ([#10724](https://github.com/rapidsai/cudf/pull/10724)) [@davidwendt](https://github.com/davidwendt)
+- Retain series name in `Series.apply` ([#10716](https://github.com/rapidsai/cudf/pull/10716)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Correct build dir `cudf-config` dependency issues for static builds ([#10704](https://github.com/rapidsai/cudf/pull/10704)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix list of testing requirements in setup.py. ([#10678](https://github.com/rapidsai/cudf/pull/10678)) [@bdice](https://github.com/bdice)
+- Fix rounding to zero error in stod on very small float numbers ([#10672](https://github.com/rapidsai/cudf/pull/10672)) [@davidwendt](https://github.com/davidwendt)
+- cuco isn&#39;t a cudf dependency when we are built shared ([#10662](https://github.com/rapidsai/cudf/pull/10662)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix to_timestamps to support Z for %z format specifier ([#10617](https://github.com/rapidsai/cudf/pull/10617)) [@davidwendt](https://github.com/davidwendt)
+- Verify compression type in Parquet reader ([#10610](https://github.com/rapidsai/cudf/pull/10610)) [@vuule](https://github.com/vuule)
+- Fix struct row comparator&#39;s exception on empty structs ([#10604](https://github.com/rapidsai/cudf/pull/10604)) [@sperlingxx](https://github.com/sperlingxx)
+- Fix strings strip() to accept only str Scalar for to_strip parameter ([#10597](https://github.com/rapidsai/cudf/pull/10597)) [@davidwendt](https://github.com/davidwendt)
+- Fix has_atomic_support check in can_use_hash_groupby() ([#10588](https://github.com/rapidsai/cudf/pull/10588)) [@jbrennan333](https://github.com/jbrennan333)
+- Revert Thrust 1.16 to Thrust 1.15 ([#10586](https://github.com/rapidsai/cudf/pull/10586)) [@bdice](https://github.com/bdice)
+- Fix missing RMM_STATIC_CUDART define when compiling JNI with static CUDA runtime ([#10585](https://github.com/rapidsai/cudf/pull/10585)) [@jlowe](https://github.com/jlowe)
+- pin more cmake versions ([#10570](https://github.com/rapidsai/cudf/pull/10570)) [@robertmaynard](https://github.com/robertmaynard)
+- Re-enable Build Metrics Report ([#10562](https://github.com/rapidsai/cudf/pull/10562)) [@davidwendt](https://github.com/davidwendt)
+- Remove statically linked CUDA runtime check in Java build ([#10532](https://github.com/rapidsai/cudf/pull/10532)) [@jlowe](https://github.com/jlowe)
+- Fix temp data cleanup in `test_text.py` ([#10524](https://github.com/rapidsai/cudf/pull/10524)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Update pre-commit to run black 22.3.0 ([#10523](https://github.com/rapidsai/cudf/pull/10523)) [@vyasr](https://github.com/vyasr)
+- Remove deprecated `decimal_cols_as_float` in the ORC reader ([#10515](https://github.com/rapidsai/cudf/pull/10515)) [@vuule](https://github.com/vuule)
+- Fix findall_record to return empty list for no matches ([#10491](https://github.com/rapidsai/cudf/pull/10491)) [@davidwendt](https://github.com/davidwendt)
+- Allow users to specify data types for a subset of columns in `read_csv` ([#10484](https://github.com/rapidsai/cudf/pull/10484)) [@vuule](https://github.com/vuule)
+- Fix default value of str.split expand parameter. ([#10457](https://github.com/rapidsai/cudf/pull/10457)) [@bdice](https://github.com/bdice)
+- Improve coverage of dask-cudf&#39;s groupby aggregation, add tests for `dropna` support ([#10449](https://github.com/rapidsai/cudf/pull/10449)) [@charlesbluca](https://github.com/charlesbluca)
+- Allow string aggs for `dask_cudf.CudfDataFrameGroupBy.aggregate` ([#10222](https://github.com/rapidsai/cudf/pull/10222)) [@charlesbluca](https://github.com/charlesbluca)
+- In-place updates with loc or iloc don&#39;t work correctly when the LHS has more than one column ([#9918](https://github.com/rapidsai/cudf/pull/9918)) [@skirui-source](https://github.com/skirui-source)
+
+## 📖 Documentation
+
+- Clarify append deprecation notice. ([#10930](https://github.com/rapidsai/cudf/pull/10930)) [@bdice](https://github.com/bdice)
+- Use full name of GPUDirect Storage SDK in docs ([#10904](https://github.com/rapidsai/cudf/pull/10904)) [@vuule](https://github.com/vuule)
+- Update Dask + Pandas to Dask + cuDF path ([#10897](https://github.com/rapidsai/cudf/pull/10897)) [@miguelusque](https://github.com/miguelusque)
+- Add missing documentation in cudf/types.hpp ([#10895](https://github.com/rapidsai/cudf/pull/10895)) [@karthikeyann](https://github.com/karthikeyann)
+- Add strong index iterator docs. ([#10888](https://github.com/rapidsai/cudf/pull/10888)) [@bdice](https://github.com/bdice)
+- spell check fixes ([#10865](https://github.com/rapidsai/cudf/pull/10865)) [@karthikeyann](https://github.com/karthikeyann)
+- Add missing documentation in scalar/ headers ([#10861](https://github.com/rapidsai/cudf/pull/10861)) [@karthikeyann](https://github.com/karthikeyann)
+- Remove typo in ngram documentation ([#10859](https://github.com/rapidsai/cudf/pull/10859)) [@miguelusque](https://github.com/miguelusque)
+- fix doxygen warnings ([#10842](https://github.com/rapidsai/cudf/pull/10842)) [@karthikeyann](https://github.com/karthikeyann)
+- Add a library_design.md file documenting the core Python data structures and their relationship ([#10817](https://github.com/rapidsai/cudf/pull/10817)) [@vyasr](https://github.com/vyasr)
+- Add NumPy to intersphinx references. ([#10809](https://github.com/rapidsai/cudf/pull/10809)) [@bdice](https://github.com/bdice)
+- Add a section to the docs that compares cuDF with Pandas ([#10796](https://github.com/rapidsai/cudf/pull/10796)) [@shwina](https://github.com/shwina)
+- Mention 2 cpp-reviewer requirement in pull request template ([#10768](https://github.com/rapidsai/cudf/pull/10768)) [@davidwendt](https://github.com/davidwendt)
+- Enable pydocstyle for all packages. ([#10759](https://github.com/rapidsai/cudf/pull/10759)) [@bdice](https://github.com/bdice)
+- Enable pydocstyle rules involving quotes ([#10748](https://github.com/rapidsai/cudf/pull/10748)) [@vyasr](https://github.com/vyasr)
+- Revise 10 minutes notebook. ([#10738](https://github.com/rapidsai/cudf/pull/10738)) [@bdice](https://github.com/bdice)
+- Reorganize cuDF Python docs ([#10691](https://github.com/rapidsai/cudf/pull/10691)) [@shwina](https://github.com/shwina)
+- Fix sphinx/jupyter heading issue in UDF notebook ([#10690](https://github.com/rapidsai/cudf/pull/10690)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Migrated user guide notebooks to MyST-NB and added sphinx extension ([#10685](https://github.com/rapidsai/cudf/pull/10685)) [@mmccarty](https://github.com/mmccarty)
+- add data generation to benchmark documentation ([#10677](https://github.com/rapidsai/cudf/pull/10677)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix some docs build warnings ([#10674](https://github.com/rapidsai/cudf/pull/10674)) [@galipremsagar](https://github.com/galipremsagar)
+- Update UDF notebook in User Guide. ([#10668](https://github.com/rapidsai/cudf/pull/10668)) [@bdice](https://github.com/bdice)
+- Improve User Guide docs ([#10663](https://github.com/rapidsai/cudf/pull/10663)) [@bdice](https://github.com/bdice)
+- Fix some docstrings formatting ([#10660](https://github.com/rapidsai/cudf/pull/10660)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove implementation details from `apply` docstrings ([#10651](https://github.com/rapidsai/cudf/pull/10651)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Revise CONTRIBUTING.md ([#10644](https://github.com/rapidsai/cudf/pull/10644)) [@bdice](https://github.com/bdice)
+- Add missing APIs to documentation. ([#10643](https://github.com/rapidsai/cudf/pull/10643)) [@bdice](https://github.com/bdice)
+- Use cudf.read_json as documented API name. ([#10640](https://github.com/rapidsai/cudf/pull/10640)) [@bdice](https://github.com/bdice)
+- Fix docstring section headings. ([#10639](https://github.com/rapidsai/cudf/pull/10639)) [@bdice](https://github.com/bdice)
+- Document cudf.read_text and cudf.read_avro. ([#10638](https://github.com/rapidsai/cudf/pull/10638)) [@bdice](https://github.com/bdice)
+- Fix type-o in docstring for json_reader_options ([#10627](https://github.com/rapidsai/cudf/pull/10627)) [@dagardner-nv](https://github.com/dagardner-nv)
+- Update guide to UDFs with notes about `Series.applymap` deprecation and related changes ([#10607](https://github.com/rapidsai/cudf/pull/10607)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix doxygen Modules page for cudf::lists::sequences ([#10561](https://github.com/rapidsai/cudf/pull/10561)) [@davidwendt](https://github.com/davidwendt)
+- Add Replace Backreferences section to Regex Features page ([#10560](https://github.com/rapidsai/cudf/pull/10560)) [@davidwendt](https://github.com/davidwendt)
+- Introduce deprecation policy to developer guide. ([#10252](https://github.com/rapidsai/cudf/pull/10252)) [@vyasr](https://github.com/vyasr)
+
+## 🚀 New Features
+
+- Enable Zstandard decompression only when all nvcomp integrations are enabled ([#10944](https://github.com/rapidsai/cudf/pull/10944)) [@vuule](https://github.com/vuule)
+- Handle nested types in cudf::concatenate_rows() ([#10890](https://github.com/rapidsai/cudf/pull/10890)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Strong index types for equality comparator ([#10883](https://github.com/rapidsai/cudf/pull/10883)) [@ttnghia](https://github.com/ttnghia)
+- Add parameters to control page size in Parquet writer ([#10882](https://github.com/rapidsai/cudf/pull/10882)) [@etseidl](https://github.com/etseidl)
+- Support for Zstandard decompression in ORC reader ([#10873](https://github.com/rapidsai/cudf/pull/10873)) [@vuule](https://github.com/vuule)
+- Use pre-built nvcomp 2.3 binaries by default ([#10851](https://github.com/rapidsai/cudf/pull/10851)) [@robertmaynard](https://github.com/robertmaynard)
+- Support for Zstandard decompression in Parquet reader ([#10847](https://github.com/rapidsai/cudf/pull/10847)) [@vuule](https://github.com/vuule)
+- Add JNI support for apply_boolean_mask ([#10812](https://github.com/rapidsai/cudf/pull/10812)) [@res-life](https://github.com/res-life)
+- Segmented Min/Max for Fixed Point Types ([#10794](https://github.com/rapidsai/cudf/pull/10794)) [@isVoid](https://github.com/isVoid)
+- Return per-file metadata from readers ([#10782](https://github.com/rapidsai/cudf/pull/10782)) [@vuule](https://github.com/vuule)
+- Segmented `apply_boolean_mask` for `LIST` columns ([#10773](https://github.com/rapidsai/cudf/pull/10773)) [@mythrocks](https://github.com/mythrocks)
+- Update `groupby::hash` to use new row operators for keys ([#10770](https://github.com/rapidsai/cudf/pull/10770)) [@PointKernel](https://github.com/PointKernel)
+- Support purging non-empty null elements from LIST/STRING columns ([#10701](https://github.com/rapidsai/cudf/pull/10701)) [@mythrocks](https://github.com/mythrocks)
+- Add `detail::hash_join` ([#10695](https://github.com/rapidsai/cudf/pull/10695)) [@PointKernel](https://github.com/PointKernel)
+- Persist string statistics data across multiple calls to orc chunked write ([#10694](https://github.com/rapidsai/cudf/pull/10694)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add `.list.astype()` to cast list leaves to specified dtype ([#10693](https://github.com/rapidsai/cudf/pull/10693)) [@shwina](https://github.com/shwina)
+- JNI: Add generateListOffsets API ([#10683](https://github.com/rapidsai/cudf/pull/10683)) [@sperlingxx](https://github.com/sperlingxx)
+- Support `args` in groupby apply ([#10682](https://github.com/rapidsai/cudf/pull/10682)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Enable segmented_gather in Java package ([#10669](https://github.com/rapidsai/cudf/pull/10669)) [@sperlingxx](https://github.com/sperlingxx)
+- Add row hasher with nested column support ([#10641](https://github.com/rapidsai/cudf/pull/10641)) [@devavret](https://github.com/devavret)
+- Add support for numeric_only in DataFrame._reduce ([#10629](https://github.com/rapidsai/cudf/pull/10629)) [@martinfalisse](https://github.com/martinfalisse)
+- First step toward statistics in ORC files with chunked writes ([#10567](https://github.com/rapidsai/cudf/pull/10567)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add support for struct columns to the random table generator ([#10566](https://github.com/rapidsai/cudf/pull/10566)) [@vuule](https://github.com/vuule)
+- Enable passing a sequence for the `index` argument to `.list.get()` ([#10564](https://github.com/rapidsai/cudf/pull/10564)) [@shwina](https://github.com/shwina)
+- Add python bindings for cudf::list::index_of ([#10549](https://github.com/rapidsai/cudf/pull/10549)) [@ChrisJar](https://github.com/ChrisJar)
+- Add default= kwarg to .list.get() accessor method ([#10547](https://github.com/rapidsai/cudf/pull/10547)) [@shwina](https://github.com/shwina)
+- Add `cudf.DataFrame.applymap` ([#10542](https://github.com/rapidsai/cudf/pull/10542)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Support nvComp 2.3 if local, otherwise use nvcomp 2.2 ([#10513](https://github.com/rapidsai/cudf/pull/10513)) [@robertmaynard](https://github.com/robertmaynard)
+- Add column field ID control in parquet writer ([#10504](https://github.com/rapidsai/cudf/pull/10504)) [@PointKernel](https://github.com/PointKernel)
+- Deprecate `Series.applymap` ([#10497](https://github.com/rapidsai/cudf/pull/10497)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add option to drop cache in cuIO benchmarks ([#10488](https://github.com/rapidsai/cudf/pull/10488)) [@vuule](https://github.com/vuule)
+- move benchmark input generation in device in reduction nvbench ([#10486](https://github.com/rapidsai/cudf/pull/10486)) [@karthikeyann](https://github.com/karthikeyann)
+- Support Segmented Min/Max Reduction on String Type ([#10447](https://github.com/rapidsai/cudf/pull/10447)) [@isVoid](https://github.com/isVoid)
+- List element Equality comparator ([#10289](https://github.com/rapidsai/cudf/pull/10289)) [@devavret](https://github.com/devavret)
+- Implement all methods of groupby rank aggregation in libcudf, python ([#9569](https://github.com/rapidsai/cudf/pull/9569)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement DataFrame.eval using libcudf ASTs ([#8022](https://github.com/rapidsai/cudf/pull/8022)) [@vyasr](https://github.com/vyasr)
+
+## 🛠️ Improvements
+
+- Use `conda` compilers in env file ([#10915](https://github.com/rapidsai/cudf/pull/10915)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove C style artifacts in cuIO ([#10886](https://github.com/rapidsai/cudf/pull/10886)) [@vuule](https://github.com/vuule)
+- Rename `sliced_child` to `get_sliced_child`. ([#10885](https://github.com/rapidsai/cudf/pull/10885)) [@bdice](https://github.com/bdice)
+- Replace defaulted stream value for libcudf APIs that use NVCOMP ([#10877](https://github.com/rapidsai/cudf/pull/10877)) [@jbrennan333](https://github.com/jbrennan333)
+- Add more unit tests for `cudf::distinct` for nested types with sliced input ([#10860](https://github.com/rapidsai/cudf/pull/10860)) [@ttnghia](https://github.com/ttnghia)
+- Changing `list_view.cuh` to `list_view.hpp` ([#10854](https://github.com/rapidsai/cudf/pull/10854)) [@ttnghia](https://github.com/ttnghia)
+- More error checking in `from_dlpack` ([#10850](https://github.com/rapidsai/cudf/pull/10850)) [@wence-](https://github.com/wence-)
+- Cleanup regex compiler fixed quantifiers source ([#10843](https://github.com/rapidsai/cudf/pull/10843)) [@davidwendt](https://github.com/davidwendt)
+- Adds the JNI call for Cuda.deviceSynchronize ([#10839](https://github.com/rapidsai/cudf/pull/10839)) [@abellina](https://github.com/abellina)
+- Add missing cuda-python dependency to cudf ([#10833](https://github.com/rapidsai/cudf/pull/10833)) [@bdice](https://github.com/bdice)
+- Change std::string parameters in cudf::strings APIs to std::string_view ([#10832](https://github.com/rapidsai/cudf/pull/10832)) [@davidwendt](https://github.com/davidwendt)
+- Split up search.cu to improve compile time ([#10831](https://github.com/rapidsai/cudf/pull/10831)) [@davidwendt](https://github.com/davidwendt)
+- Add tests for null scalar binaryops ([#10828](https://github.com/rapidsai/cudf/pull/10828)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Cleanup regex compile optimize functions ([#10825](https://github.com/rapidsai/cudf/pull/10825)) [@davidwendt](https://github.com/davidwendt)
+- Use `ThreadedMotoServer` instead of `subprocess` in spinning up `s3` server ([#10822](https://github.com/rapidsai/cudf/pull/10822)) [@galipremsagar](https://github.com/galipremsagar)
+- Import `NA` from `missing` rather than using `cudf.NA` everywhere ([#10821](https://github.com/rapidsai/cudf/pull/10821)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Refactor regex builtin character-class identifiers ([#10814](https://github.com/rapidsai/cudf/pull/10814)) [@davidwendt](https://github.com/davidwendt)
+- Change pattern parameter for regex APIs from std::string to std::string_view ([#10810](https://github.com/rapidsai/cudf/pull/10810)) [@davidwendt](https://github.com/davidwendt)
+- Make the JNI API to get list offsets as a view public. ([#10807](https://github.com/rapidsai/cudf/pull/10807)) [@revans2](https://github.com/revans2)
+- Add cudf JNI docker build github action ([#10806](https://github.com/rapidsai/cudf/pull/10806)) [@pxLi](https://github.com/pxLi)
+- Removed `mr` parameter from inplace bitmask operations ([#10805](https://github.com/rapidsai/cudf/pull/10805)) [@AtlantaPepsi](https://github.com/AtlantaPepsi)
+- Refactor `cudf::contains`, renaming and switching parameters role ([#10802](https://github.com/rapidsai/cudf/pull/10802)) [@ttnghia](https://github.com/ttnghia)
+- Handle closed property in IntervalDtype.from_pandas ([#10798](https://github.com/rapidsai/cudf/pull/10798)) [@wence-](https://github.com/wence-)
+- Return weak orderings from `device_row_comparator`. ([#10793](https://github.com/rapidsai/cudf/pull/10793)) [@rwlee](https://github.com/rwlee)
+- Rework `Scalar` imports ([#10791](https://github.com/rapidsai/cudf/pull/10791)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Enable ccache for cudfjni build in Docker ([#10790](https://github.com/rapidsai/cudf/pull/10790)) [@gerashegalov](https://github.com/gerashegalov)
+- Generic serialization of all column types ([#10784](https://github.com/rapidsai/cudf/pull/10784)) [@wence-](https://github.com/wence-)
+- simplifying skiprows test in test_orc.py ([#10783](https://github.com/rapidsai/cudf/pull/10783)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Use column_views instead of column_device_views in binary operations. ([#10780](https://github.com/rapidsai/cudf/pull/10780)) [@bdice](https://github.com/bdice)
+- Add struct utility functions. ([#10776](https://github.com/rapidsai/cudf/pull/10776)) [@bdice](https://github.com/bdice)
+- Add multiple rows to subword tokenizer benchmark ([#10767](https://github.com/rapidsai/cudf/pull/10767)) [@davidwendt](https://github.com/davidwendt)
+- Refactor host decompression in ORC reader ([#10764](https://github.com/rapidsai/cudf/pull/10764)) [@vuule](https://github.com/vuule)
+- Flush output streams before creating a process to drop caches ([#10762](https://github.com/rapidsai/cudf/pull/10762)) [@vuule](https://github.com/vuule)
+- Refactor binaryop/compiled/util.cpp ([#10756](https://github.com/rapidsai/cudf/pull/10756)) [@bdice](https://github.com/bdice)
+- Use warp per string for long strings in cudf::strings::contains() ([#10739](https://github.com/rapidsai/cudf/pull/10739)) [@davidwendt](https://github.com/davidwendt)
+- Use generator expressions in any/all functions. ([#10736](https://github.com/rapidsai/cudf/pull/10736)) [@bdice](https://github.com/bdice)
+- Use canonical &quot;magic methods&quot; (replace `x.__repr__()` with `repr(x)`). ([#10735](https://github.com/rapidsai/cudf/pull/10735)) [@bdice](https://github.com/bdice)
+- Improve use of isinstance. ([#10734](https://github.com/rapidsai/cudf/pull/10734)) [@bdice](https://github.com/bdice)
+- Rename tests from multiIndex to multiindex. ([#10732](https://github.com/rapidsai/cudf/pull/10732)) [@bdice](https://github.com/bdice)
+- Two-table comparators with strong index types ([#10730](https://github.com/rapidsai/cudf/pull/10730)) [@bdice](https://github.com/bdice)
+- Replace std::make_pair with std::pair (C++17 CTAD) ([#10727](https://github.com/rapidsai/cudf/pull/10727)) [@karthikeyann](https://github.com/karthikeyann)
+- Use structured bindings instead of std::tie ([#10726](https://github.com/rapidsai/cudf/pull/10726)) [@karthikeyann](https://github.com/karthikeyann)
+- Missing `f` prefix on f-strings fix ([#10721](https://github.com/rapidsai/cudf/pull/10721)) [@code-review-doctor](https://github.com/code-review-doctor)
+- Add `max_file_size` parameter to chunked parquet dataset writer ([#10718](https://github.com/rapidsai/cudf/pull/10718)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `merge_sorted`, change dask cudf usage to internal method ([#10713](https://github.com/rapidsai/cudf/pull/10713)) [@isVoid](https://github.com/isVoid)
+- Prepare dask_cudf test_parquet.py for upcoming API changes ([#10709](https://github.com/rapidsai/cudf/pull/10709)) [@rjzamora](https://github.com/rjzamora)
+- Remove or simplify various utility functions ([#10705](https://github.com/rapidsai/cudf/pull/10705)) [@vyasr](https://github.com/vyasr)
+- Allow building arrow with parquet and not python ([#10702](https://github.com/rapidsai/cudf/pull/10702)) [@revans2](https://github.com/revans2)
+- Partial cuIO GPU decompression refactor ([#10699](https://github.com/rapidsai/cudf/pull/10699)) [@vuule](https://github.com/vuule)
+- Cython API refactor: `merge.pyx` ([#10698](https://github.com/rapidsai/cudf/pull/10698)) [@isVoid](https://github.com/isVoid)
+- Fix random string data length to become variable ([#10697](https://github.com/rapidsai/cudf/pull/10697)) [@galipremsagar](https://github.com/galipremsagar)
+- Add bindings for index_of with column search key ([#10696](https://github.com/rapidsai/cudf/pull/10696)) [@ChrisJar](https://github.com/ChrisJar)
+- Deprecate index merging ([#10689](https://github.com/rapidsai/cudf/pull/10689)) [@vyasr](https://github.com/vyasr)
+- Remove cudf::strings::string namespace ([#10684](https://github.com/rapidsai/cudf/pull/10684)) [@davidwendt](https://github.com/davidwendt)
+- Standardize imports. ([#10680](https://github.com/rapidsai/cudf/pull/10680)) [@bdice](https://github.com/bdice)
+- Standardize usage of collections.abc. ([#10679](https://github.com/rapidsai/cudf/pull/10679)) [@bdice](https://github.com/bdice)
+- Cython API Refactor: `transpose.pyx`, `sort.pyx` ([#10675](https://github.com/rapidsai/cudf/pull/10675)) [@isVoid](https://github.com/isVoid)
+- Add device_memory_resource parameter to create_string_vector_from_column ([#10673](https://github.com/rapidsai/cudf/pull/10673)) [@davidwendt](https://github.com/davidwendt)
+- Split up mixed-join kernels source files ([#10671](https://github.com/rapidsai/cudf/pull/10671)) [@davidwendt](https://github.com/davidwendt)
+- Use `std::filesystem` for temporary directory location and deletion ([#10664](https://github.com/rapidsai/cudf/pull/10664)) [@vuule](https://github.com/vuule)
+- cleanup benchmark includes ([#10661](https://github.com/rapidsai/cudf/pull/10661)) [@karthikeyann](https://github.com/karthikeyann)
+- Use upstream clang-format pre-commit hook. ([#10659](https://github.com/rapidsai/cudf/pull/10659)) [@bdice](https://github.com/bdice)
+- Clean up C++ includes to use &lt;&gt; instead of &quot;&quot;. ([#10658](https://github.com/rapidsai/cudf/pull/10658)) [@bdice](https://github.com/bdice)
+- Handle RuntimeError thrown by CUDA Python in `validate_setup` ([#10653](https://github.com/rapidsai/cudf/pull/10653)) [@shwina](https://github.com/shwina)
+- Rework JNI CMake to leverage rapids_find_package ([#10649](https://github.com/rapidsai/cudf/pull/10649)) [@jlowe](https://github.com/jlowe)
+- Use conda to build python packages during GPU tests ([#10648](https://github.com/rapidsai/cudf/pull/10648)) [@Ethyling](https://github.com/Ethyling)
+- Deprecate various functions that don&#39;t need to be defined for Index. ([#10647](https://github.com/rapidsai/cudf/pull/10647)) [@vyasr](https://github.com/vyasr)
+- Update pinning to allow newer CMake versions. ([#10646](https://github.com/rapidsai/cudf/pull/10646)) [@vyasr](https://github.com/vyasr)
+- Bump hadoop-common from 3.1.4 to 3.2.3 in /java ([#10645](https://github.com/rapidsai/cudf/pull/10645)) [@dependabot[bot]](https://github.com/dependabot[bot])
+- Remove `concurrent_unordered_multimap`. ([#10642](https://github.com/rapidsai/cudf/pull/10642)) [@bdice](https://github.com/bdice)
+- Improve parquet dictionary encoding ([#10635](https://github.com/rapidsai/cudf/pull/10635)) [@PointKernel](https://github.com/PointKernel)
+- Improve cudf::cuda_error ([#10630](https://github.com/rapidsai/cudf/pull/10630)) [@sperlingxx](https://github.com/sperlingxx)
+- Add support for null and non-numeric types in Series.diff and DataFrame.diff ([#10625](https://github.com/rapidsai/cudf/pull/10625)) [@Matt711](https://github.com/Matt711)
+- Branch 22.06 merge 22.04 ([#10624](https://github.com/rapidsai/cudf/pull/10624)) [@vyasr](https://github.com/vyasr)
+- Unpin `dask` &amp; `distributed` for development ([#10623](https://github.com/rapidsai/cudf/pull/10623)) [@galipremsagar](https://github.com/galipremsagar)
+- Slightly improve accuracy of stod in to_floats ([#10622](https://github.com/rapidsai/cudf/pull/10622)) [@davidwendt](https://github.com/davidwendt)
+- Allow libcudfjni to be built as a static library ([#10619](https://github.com/rapidsai/cudf/pull/10619)) [@jlowe](https://github.com/jlowe)
+- Change stack-based regex state data to use global memory ([#10600](https://github.com/rapidsai/cudf/pull/10600)) [@davidwendt](https://github.com/davidwendt)
+- Resolve Forward merging of `branch-22.04` into `branch-22.06` ([#10598](https://github.com/rapidsai/cudf/pull/10598)) [@galipremsagar](https://github.com/galipremsagar)
+- KvikIO as an alternative GDS backend ([#10593](https://github.com/rapidsai/cudf/pull/10593)) [@madsbk](https://github.com/madsbk)
+- Rename CUDA_TRY macro to CUDF_CUDA_TRY, rename CHECK_CUDA macro to CUDF_CHECK_CUDA. ([#10589](https://github.com/rapidsai/cudf/pull/10589)) [@bdice](https://github.com/bdice)
+- Upgrade `cudf` to support `pandas` 1.4.x versions ([#10584](https://github.com/rapidsai/cudf/pull/10584)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactor binary ops for timedelta and datetime columns ([#10581](https://github.com/rapidsai/cudf/pull/10581)) [@vyasr](https://github.com/vyasr)
+- Refactor cudf::strings::count_re API to use count_matches utility ([#10580](https://github.com/rapidsai/cudf/pull/10580)) [@davidwendt](https://github.com/davidwendt)
+- Update `Programming Language :: Python` Versions to 3.8 &amp; 3.9 ([#10579](https://github.com/rapidsai/cudf/pull/10579)) [@madsbk](https://github.com/madsbk)
+- Automate Java cudf jar build with statically linked dependencies ([#10578](https://github.com/rapidsai/cudf/pull/10578)) [@gerashegalov](https://github.com/gerashegalov)
+- Add patch for thrust-cub 1.16 to fix sort compile times ([#10577](https://github.com/rapidsai/cudf/pull/10577)) [@davidwendt](https://github.com/davidwendt)
+- Move binop methods from Frame to IndexedFrame and standardize the docstring ([#10576](https://github.com/rapidsai/cudf/pull/10576)) [@vyasr](https://github.com/vyasr)
+- Cleanup libcudf strings regex classes ([#10573](https://github.com/rapidsai/cudf/pull/10573)) [@davidwendt](https://github.com/davidwendt)
+- Simplify preprocessing of arguments for DataFrame binops ([#10563](https://github.com/rapidsai/cudf/pull/10563)) [@vyasr](https://github.com/vyasr)
+- Reduce kernel calls to build strings findall results ([#10559](https://github.com/rapidsai/cudf/pull/10559)) [@davidwendt](https://github.com/davidwendt)
+- Forward-merge branch-22.04 to branch-22.06 ([#10557](https://github.com/rapidsai/cudf/pull/10557)) [@bdice](https://github.com/bdice)
+- Update strings contains benchmark to measure varying match rates ([#10555](https://github.com/rapidsai/cudf/pull/10555)) [@davidwendt](https://github.com/davidwendt)
+- JNI: throw CUDA errors more specifically ([#10551](https://github.com/rapidsai/cudf/pull/10551)) [@sperlingxx](https://github.com/sperlingxx)
+- Enable building static libs ([#10545](https://github.com/rapidsai/cudf/pull/10545)) [@trxcllnt](https://github.com/trxcllnt)
+- Remove pip requirements files. ([#10543](https://github.com/rapidsai/cudf/pull/10543)) [@bdice](https://github.com/bdice)
+- Remove Click pinnings that are unnecessary after upgrading black. ([#10541](https://github.com/rapidsai/cudf/pull/10541)) [@vyasr](https://github.com/vyasr)
+- Refactor `memory_usage` to improve performance ([#10537](https://github.com/rapidsai/cudf/pull/10537)) [@galipremsagar](https://github.com/galipremsagar)
+- Adjust the valid range of group index for replace_with_backrefs ([#10530](https://github.com/rapidsai/cudf/pull/10530)) [@sperlingxx](https://github.com/sperlingxx)
+- add accidentally removed comment. ([#10526](https://github.com/rapidsai/cudf/pull/10526)) [@vyasr](https://github.com/vyasr)
+- Update conda environment. ([#10525](https://github.com/rapidsai/cudf/pull/10525)) [@vyasr](https://github.com/vyasr)
+- Remove ColumnBase.__getitem__ ([#10516](https://github.com/rapidsai/cudf/pull/10516)) [@vyasr](https://github.com/vyasr)
+- Optimize `left_semi_join` by materializing the gather mask ([#10511](https://github.com/rapidsai/cudf/pull/10511)) [@cheinger](https://github.com/cheinger)
+- Define proper binary operation APIs for columns ([#10509](https://github.com/rapidsai/cudf/pull/10509)) [@vyasr](https://github.com/vyasr)
+- Upgrade `arrow-cpp` &amp; `pyarrow` to `7.0.0` ([#10503](https://github.com/rapidsai/cudf/pull/10503)) [@galipremsagar](https://github.com/galipremsagar)
+- Update to Thrust 1.16 ([#10489](https://github.com/rapidsai/cudf/pull/10489)) [@bdice](https://github.com/bdice)
+- Namespace/Docstring Fixes for Reduction ([#10471](https://github.com/rapidsai/cudf/pull/10471)) [@isVoid](https://github.com/isVoid)
+- Update cudfjni 22.06.0-SNAPSHOT ([#10467](https://github.com/rapidsai/cudf/pull/10467)) [@pxLi](https://github.com/pxLi)
+- Use Lists of Columns for Various Files ([#10463](https://github.com/rapidsai/cudf/pull/10463)) [@isVoid](https://github.com/isVoid)
+- Additional refactoring of hash functions ([#10462](https://github.com/rapidsai/cudf/pull/10462)) [@bdice](https://github.com/bdice)
+- Fix Series.str.findall behavior for expand=False. ([#10459](https://github.com/rapidsai/cudf/pull/10459)) [@bdice](https://github.com/bdice)
+- Remove deprecated code. ([#10450](https://github.com/rapidsai/cudf/pull/10450)) [@vyasr](https://github.com/vyasr)
+- Update cmake-format version. ([#10440](https://github.com/rapidsai/cudf/pull/10440)) [@vyasr](https://github.com/vyasr)
+- Consolidate C++ `conda` recipes and add `libcudf-tests` package ([#10326](https://github.com/rapidsai/cudf/pull/10326)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Use conda compilers ([#10275](https://github.com/rapidsai/cudf/pull/10275)) [@Ethyling](https://github.com/Ethyling)
+- Add row bitmask as a `detail::hash_join` member ([#10248](https://github.com/rapidsai/cudf/pull/10248)) [@PointKernel](https://github.com/PointKernel)
+
+# cuDF 22.04.00 (6 Apr 2022)
+
+## 🚨 Breaking Changes
+
+- Drop unsupported method argument from nunique and distinct_count. ([#10411](https://github.com/rapidsai/cudf/pull/10411)) [@bdice](https://github.com/bdice)
+- Refactor stream compaction APIs ([#10370](https://github.com/rapidsai/cudf/pull/10370)) [@PointKernel](https://github.com/PointKernel)
+- Add scan_aggregation and reduce_aggregation derived types. ([#10357](https://github.com/rapidsai/cudf/pull/10357)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Avoid `decimal` type narrowing for decimal binops ([#10299](https://github.com/rapidsai/cudf/pull/10299)) [@galipremsagar](https://github.com/galipremsagar)
+- Rewrites `sample` API ([#10262](https://github.com/rapidsai/cudf/pull/10262)) [@isVoid](https://github.com/isVoid)
+- Remove probe-time null equality parameters in `cudf::hash_join` ([#10260](https://github.com/rapidsai/cudf/pull/10260)) [@PointKernel](https://github.com/PointKernel)
+- Enable proper `Index` round-tripping in `orc` reader and writer ([#10170](https://github.com/rapidsai/cudf/pull/10170)) [@galipremsagar](https://github.com/galipremsagar)
+- Add JNI for `strings::split_re` and `strings::split_record_re` ([#10139](https://github.com/rapidsai/cudf/pull/10139)) [@ttnghia](https://github.com/ttnghia)
+- Change cudf::strings::find_multiple to return a lists column ([#10134](https://github.com/rapidsai/cudf/pull/10134)) [@davidwendt](https://github.com/davidwendt)
+- Remove the option to completely disable decimal128 columns in the ORC reader ([#10127](https://github.com/rapidsai/cudf/pull/10127)) [@vuule](https://github.com/vuule)
+- Remove deprecated code ([#10124](https://github.com/rapidsai/cudf/pull/10124)) [@vyasr](https://github.com/vyasr)
+- Update gpu_utils.py to reflect current CUDA support. ([#10113](https://github.com/rapidsai/cudf/pull/10113)) [@bdice](https://github.com/bdice)
+- Optimize compaction operations ([#10030](https://github.com/rapidsai/cudf/pull/10030)) [@PointKernel](https://github.com/PointKernel)
+- Remove deprecated method Series.set_index. ([#9945](https://github.com/rapidsai/cudf/pull/9945)) [@bdice](https://github.com/bdice)
+- Add cudf::strings::findall_record API ([#9911](https://github.com/rapidsai/cudf/pull/9911)) [@davidwendt](https://github.com/davidwendt)
+- Upgrade `arrow` &amp; `pyarrow` to `6.0.1` ([#9686](https://github.com/rapidsai/cudf/pull/9686)) [@galipremsagar](https://github.com/galipremsagar)
+
+## 🐛 Bug Fixes
+
+- Fix an issue with tdigest merge aggregations. ([#10506](https://github.com/rapidsai/cudf/pull/10506)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Batch of fixes for index overflows in grid stride loops. ([#10448](https://github.com/rapidsai/cudf/pull/10448)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Update dask_cudf imports to be compatible with latest dask ([#10442](https://github.com/rapidsai/cudf/pull/10442)) [@rlratzel](https://github.com/rlratzel)
+- Fix for integer overflow in contiguous-split ([#10437](https://github.com/rapidsai/cudf/pull/10437)) [@jbrennan333](https://github.com/jbrennan333)
+- Fix has_null predicate for drop_list_duplicates on nested structs ([#10436](https://github.com/rapidsai/cudf/pull/10436)) [@sperlingxx](https://github.com/sperlingxx)
+- Fix empty reduce with List output and non-List input ([#10435](https://github.com/rapidsai/cudf/pull/10435)) [@sperlingxx](https://github.com/sperlingxx)
+- Fix `list` and `struct` meta generation issue in `dask-cudf` ([#10434](https://github.com/rapidsai/cudf/pull/10434)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix error in `cudf.to_numeric` when a `bool` input is passed ([#10431](https://github.com/rapidsai/cudf/pull/10431)) [@galipremsagar](https://github.com/galipremsagar)
+- Support cupy array in `quantile` input ([#10429](https://github.com/rapidsai/cudf/pull/10429)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix benchmarks to work with new aggregation types ([#10428](https://github.com/rapidsai/cudf/pull/10428)) [@davidwendt](https://github.com/davidwendt)
+- Fix cudf::shift to handle offset greater than column size ([#10414](https://github.com/rapidsai/cudf/pull/10414)) [@davidwendt](https://github.com/davidwendt)
+- Fix lifespan of the temporary directory that holds cuFile configuration file ([#10403](https://github.com/rapidsai/cudf/pull/10403)) [@vuule](https://github.com/vuule)
+- Fix error thrown in compiled-binaryop benchmark ([#10398](https://github.com/rapidsai/cudf/pull/10398)) [@davidwendt](https://github.com/davidwendt)
+- Limiting async allocator using alignment of 512 ([#10395](https://github.com/rapidsai/cudf/pull/10395)) [@rongou](https://github.com/rongou)
+- Include &lt;optional&gt; in multibyte split. ([#10385](https://github.com/rapidsai/cudf/pull/10385)) [@bdice](https://github.com/bdice)
+- Fix issue with column and scalar re-assignment ([#10377](https://github.com/rapidsai/cudf/pull/10377)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix floating point data generation in benchmarks ([#10372](https://github.com/rapidsai/cudf/pull/10372)) [@vuule](https://github.com/vuule)
+- Avoid overflow in fused_concatenate_kernel output_index ([#10344](https://github.com/rapidsai/cudf/pull/10344)) [@abellina](https://github.com/abellina)
+- Remove is_relationally_comparable for table device views ([#10342](https://github.com/rapidsai/cudf/pull/10342)) [@davidwendt](https://github.com/davidwendt)
+- Fix debug compile error in device_span to column_view conversion ([#10331](https://github.com/rapidsai/cudf/pull/10331)) [@davidwendt](https://github.com/davidwendt)
+- Add Pascal support to JCUDF transcode (row_conversion) ([#10329](https://github.com/rapidsai/cudf/pull/10329)) [@mythrocks](https://github.com/mythrocks)
+- Fix `std::bad_alloc` exception due to JIT reserving a huge buffer ([#10317](https://github.com/rapidsai/cudf/pull/10317)) [@ttnghia](https://github.com/ttnghia)
+- Fixes up the overflowed fixed-point round on nullable column ([#10316](https://github.com/rapidsai/cudf/pull/10316)) [@sperlingxx](https://github.com/sperlingxx)
+- Fix DataFrame slicing issues for empty cases ([#10310](https://github.com/rapidsai/cudf/pull/10310)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix documentation issues ([#10307](https://github.com/rapidsai/cudf/pull/10307)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Allow Java bindings to use default decimal precisions when writing columns ([#10276](https://github.com/rapidsai/cudf/pull/10276)) [@sperlingxx](https://github.com/sperlingxx)
+- Fix incorrect slicing of GDS read/write calls ([#10274](https://github.com/rapidsai/cudf/pull/10274)) [@vuule](https://github.com/vuule)
+- Fix out-of-memory error in compiled-binaryop benchmark ([#10269](https://github.com/rapidsai/cudf/pull/10269)) [@davidwendt](https://github.com/davidwendt)
+- Add tests of reflected ufuncs and fix behavior of logical reflected ufuncs ([#10261](https://github.com/rapidsai/cudf/pull/10261)) [@vyasr](https://github.com/vyasr)
+- Remove probe-time null equality parameters in `cudf::hash_join` ([#10260](https://github.com/rapidsai/cudf/pull/10260)) [@PointKernel](https://github.com/PointKernel)
+- Fix out-of-memory error in UrlDecode benchmark ([#10258](https://github.com/rapidsai/cudf/pull/10258)) [@davidwendt](https://github.com/davidwendt)
+- Fix groupby reductions that perform operations on source type instead of target type ([#10250](https://github.com/rapidsai/cudf/pull/10250)) [@ttnghia](https://github.com/ttnghia)
+- Fix small leak in explode ([#10245](https://github.com/rapidsai/cudf/pull/10245)) [@revans2](https://github.com/revans2)
+- Yet another small JNI memory leak ([#10238](https://github.com/rapidsai/cudf/pull/10238)) [@revans2](https://github.com/revans2)
+- Fix regex octal parsing to limit to 3 characters ([#10233](https://github.com/rapidsai/cudf/pull/10233)) [@davidwendt](https://github.com/davidwendt)
+- Fix string to decimal128 conversion handling large exponents ([#10231](https://github.com/rapidsai/cudf/pull/10231)) [@davidwendt](https://github.com/davidwendt)
+- Fix JNI leak on copy to device ([#10229](https://github.com/rapidsai/cudf/pull/10229)) [@revans2](https://github.com/revans2)
+- Fix the data generator element size for decimal types ([#10225](https://github.com/rapidsai/cudf/pull/10225)) [@vuule](https://github.com/vuule)
+- Fix `decimal` metadata in parquet writer ([#10224](https://github.com/rapidsai/cudf/pull/10224)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix strings handling of hex in regex pattern ([#10220](https://github.com/rapidsai/cudf/pull/10220)) [@davidwendt](https://github.com/davidwendt)
+- Fix docs builds ([#10216](https://github.com/rapidsai/cudf/pull/10216)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix a leftover _has_nulls change from Nullate ([#10211](https://github.com/rapidsai/cudf/pull/10211)) [@devavret](https://github.com/devavret)
+- Fix bitmask of the output for JNI of `lists::drop_list_duplicates` ([#10210](https://github.com/rapidsai/cudf/pull/10210)) [@ttnghia](https://github.com/ttnghia)
+- Fix compile error in `binaryop/compiled/util.cpp` ([#10209](https://github.com/rapidsai/cudf/pull/10209)) [@ttnghia](https://github.com/ttnghia)
+- Skip ORC and Parquet readers&#39; benchmark cases that are not currently supported ([#10194](https://github.com/rapidsai/cudf/pull/10194)) [@vuule](https://github.com/vuule)
+- Fix JNI leak of a cudf::column_view native class. ([#10171](https://github.com/rapidsai/cudf/pull/10171)) [@revans2](https://github.com/revans2)
+- Enable proper `Index` round-tripping in `orc` reader and writer ([#10170](https://github.com/rapidsai/cudf/pull/10170)) [@galipremsagar](https://github.com/galipremsagar)
+- Convert Column Name to String Before Using Struct Column Factory ([#10156](https://github.com/rapidsai/cudf/pull/10156)) [@isVoid](https://github.com/isVoid)
+- Preserve the correct `ListDtype` while creating an identical empty column ([#10151](https://github.com/rapidsai/cudf/pull/10151)) [@galipremsagar](https://github.com/galipremsagar)
+- benchmark fixture - static object pointer fix ([#10145](https://github.com/rapidsai/cudf/pull/10145)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix UDF Caching ([#10133](https://github.com/rapidsai/cudf/pull/10133)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Raise duplicate column error in `DataFrame.rename` ([#10120](https://github.com/rapidsai/cudf/pull/10120)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix flaky memory usage test by guaranteeing array size. ([#10114](https://github.com/rapidsai/cudf/pull/10114)) [@vyasr](https://github.com/vyasr)
+- Encode values from python callback for C++ ([#10103](https://github.com/rapidsai/cudf/pull/10103)) [@jdye64](https://github.com/jdye64)
+- Add check for regex instructions causing an infinite-loop ([#10095](https://github.com/rapidsai/cudf/pull/10095)) [@davidwendt](https://github.com/davidwendt)
+- Remove metadata singleton from nvtext normalizer ([#10090](https://github.com/rapidsai/cudf/pull/10090)) [@davidwendt](https://github.com/davidwendt)
+- Column equality testing fixes ([#10011](https://github.com/rapidsai/cudf/pull/10011)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Pin libcudf runtime dependency for cudf / libcudf-kafka nightlies ([#9847](https://github.com/rapidsai/cudf/pull/9847)) [@charlesbluca](https://github.com/charlesbluca)
+
+## 📖 Documentation
+
+- Fix documentation for DataFrame.corr and Series.corr. ([#10493](https://github.com/rapidsai/cudf/pull/10493)) [@bdice](https://github.com/bdice)
+- Add `cut` to API docs ([#10479](https://github.com/rapidsai/cudf/pull/10479)) [@shwina](https://github.com/shwina)
+- Remove documentation for methods removed in #10124. ([#10366](https://github.com/rapidsai/cudf/pull/10366)) [@bdice](https://github.com/bdice)
+- Fix documentation issues ([#10306](https://github.com/rapidsai/cudf/pull/10306)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix `fixed_point` binary operation documentation ([#10198](https://github.com/rapidsai/cudf/pull/10198)) [@codereport](https://github.com/codereport)
+- Remove cleaned up methods from docs ([#10189](https://github.com/rapidsai/cudf/pull/10189)) [@galipremsagar](https://github.com/galipremsagar)
+- Update developer guide to recommend no default stream parameter. ([#10136](https://github.com/rapidsai/cudf/pull/10136)) [@bdice](https://github.com/bdice)
+- Update benchmarking guide to use NVBench. ([#10093](https://github.com/rapidsai/cudf/pull/10093)) [@bdice](https://github.com/bdice)
+
+## 🚀 New Features
+
+- Add StringIO support to read_text ([#10465](https://github.com/rapidsai/cudf/pull/10465)) [@cwharris](https://github.com/cwharris)
+- Add support for tdigest and merge_tdigest aggregations through cudf::reduce ([#10433](https://github.com/rapidsai/cudf/pull/10433)) [@nvdbaranec](https://github.com/nvdbaranec)
+- JNI support for Collect Ops in Reduction ([#10427](https://github.com/rapidsai/cudf/pull/10427)) [@sperlingxx](https://github.com/sperlingxx)
+- Enable read_text with dask_cudf using byte_range ([#10407](https://github.com/rapidsai/cudf/pull/10407)) [@ChrisJar](https://github.com/ChrisJar)
+- Add `cudf::stable_sort_by_key` ([#10387](https://github.com/rapidsai/cudf/pull/10387)) [@PointKernel](https://github.com/PointKernel)
+- Implement `maps_column_view` abstraction over `LIST&lt;STRUCT&lt;K,V&gt;&gt;` ([#10380](https://github.com/rapidsai/cudf/pull/10380)) [@mythrocks](https://github.com/mythrocks)
+- Support Java bindings for Avro reader ([#10373](https://github.com/rapidsai/cudf/pull/10373)) [@HaoYang670](https://github.com/HaoYang670)
+- Refactor stream compaction APIs ([#10370](https://github.com/rapidsai/cudf/pull/10370)) [@PointKernel](https://github.com/PointKernel)
+- Support collect aggregations in reduction ([#10353](https://github.com/rapidsai/cudf/pull/10353)) [@sperlingxx](https://github.com/sperlingxx)
+- Refactor array_ufunc for Index and unify across all classes ([#10346](https://github.com/rapidsai/cudf/pull/10346)) [@vyasr](https://github.com/vyasr)
+- Add JNI for extract_list_element with index column ([#10341](https://github.com/rapidsai/cudf/pull/10341)) [@firestarman](https://github.com/firestarman)
+- Support `min` and `max` operations for structs in rolling window ([#10332](https://github.com/rapidsai/cudf/pull/10332)) [@ttnghia](https://github.com/ttnghia)
+- Add device create_sequence_table for benchmarks ([#10300](https://github.com/rapidsai/cudf/pull/10300)) [@karthikeyann](https://github.com/karthikeyann)
+- Enable numpy ufuncs for DataFrame ([#10287](https://github.com/rapidsai/cudf/pull/10287)) [@vyasr](https://github.com/vyasr)
+- move input generation for json benchmark to device ([#10281](https://github.com/rapidsai/cudf/pull/10281)) [@karthikeyann](https://github.com/karthikeyann)
+- move input generation for type dispatcher benchmark to device ([#10280](https://github.com/rapidsai/cudf/pull/10280)) [@karthikeyann](https://github.com/karthikeyann)
+- move input generation for copy benchmark to device ([#10279](https://github.com/rapidsai/cudf/pull/10279)) [@karthikeyann](https://github.com/karthikeyann)
+- generate url decode benchmark input in device ([#10278](https://github.com/rapidsai/cudf/pull/10278)) [@karthikeyann](https://github.com/karthikeyann)
+- device input generation in join bench ([#10277](https://github.com/rapidsai/cudf/pull/10277)) [@karthikeyann](https://github.com/karthikeyann)
+- Add nvtext::byte_pair_encoding API ([#10270](https://github.com/rapidsai/cudf/pull/10270)) [@davidwendt](https://github.com/davidwendt)
+- Prevent internal usage of expensive APIs ([#10263](https://github.com/rapidsai/cudf/pull/10263)) [@vyasr](https://github.com/vyasr)
+- Column to JCUDF row for tables with strings ([#10235](https://github.com/rapidsai/cudf/pull/10235)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Support `percent_rank()` aggregation ([#10227](https://github.com/rapidsai/cudf/pull/10227)) [@mythrocks](https://github.com/mythrocks)
+- Refactor Series.__array_ufunc__ ([#10217](https://github.com/rapidsai/cudf/pull/10217)) [@vyasr](https://github.com/vyasr)
+- Reduce pytest runtime ([#10203](https://github.com/rapidsai/cudf/pull/10203)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add regex flags parameter to python cudf strings split ([#10185](https://github.com/rapidsai/cudf/pull/10185)) [@davidwendt](https://github.com/davidwendt)
+- Support for `MOD`, `PMOD` and `PYMOD` for `decimal32/64/128` ([#10179](https://github.com/rapidsai/cudf/pull/10179)) [@codereport](https://github.com/codereport)
+- Adding string row size iterator for row to column and column to row conversion ([#10157](https://github.com/rapidsai/cudf/pull/10157)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add file size counter to cuIO benchmarks ([#10154](https://github.com/rapidsai/cudf/pull/10154)) [@vuule](https://github.com/vuule)
+- byte_range support for multibyte_split/read_text ([#10150](https://github.com/rapidsai/cudf/pull/10150)) [@cwharris](https://github.com/cwharris)
+- Add JNI for `strings::split_re` and `strings::split_record_re` ([#10139](https://github.com/rapidsai/cudf/pull/10139)) [@ttnghia](https://github.com/ttnghia)
+- Add `maxSplit` parameter to Java binding for `strings:split` ([#10137](https://github.com/rapidsai/cudf/pull/10137)) [@ttnghia](https://github.com/ttnghia)
+- Add libcudf strings split API that accepts regex pattern ([#10128](https://github.com/rapidsai/cudf/pull/10128)) [@davidwendt](https://github.com/davidwendt)
+- generate benchmark input in device ([#10109](https://github.com/rapidsai/cudf/pull/10109)) [@karthikeyann](https://github.com/karthikeyann)
+- Avoid `nan_as_null` op if `nan_count` is 0 ([#10082](https://github.com/rapidsai/cudf/pull/10082)) [@galipremsagar](https://github.com/galipremsagar)
+- Add Dataframe and Index nunique ([#10077](https://github.com/rapidsai/cudf/pull/10077)) [@martinfalisse](https://github.com/martinfalisse)
+- Support nanosecond timestamps in parquet ([#10063](https://github.com/rapidsai/cudf/pull/10063)) [@PointKernel](https://github.com/PointKernel)
+- Java bindings for mixed semi and anti joins ([#10040](https://github.com/rapidsai/cudf/pull/10040)) [@jlowe](https://github.com/jlowe)
+- Implement mixed equality/conditional semi/anti joins ([#10037](https://github.com/rapidsai/cudf/pull/10037)) [@vyasr](https://github.com/vyasr)
+- Optimize compaction operations ([#10030](https://github.com/rapidsai/cudf/pull/10030)) [@PointKernel](https://github.com/PointKernel)
+- Support `args=` in `Series.apply` ([#9982](https://github.com/rapidsai/cudf/pull/9982)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add cudf::strings::findall_record API ([#9911](https://github.com/rapidsai/cudf/pull/9911)) [@davidwendt](https://github.com/davidwendt)
+- Add covariance for sort groupby (python) ([#9889](https://github.com/rapidsai/cudf/pull/9889)) [@mayankanand007](https://github.com/mayankanand007)
+- Implement DataFrame diff() ([#9817](https://github.com/rapidsai/cudf/pull/9817)) [@skirui-source](https://github.com/skirui-source)
+- Implement DataFrame pct_change ([#9805](https://github.com/rapidsai/cudf/pull/9805)) [@skirui-source](https://github.com/skirui-source)
+- Support segmented reductions and null mask reductions ([#9621](https://github.com/rapidsai/cudf/pull/9621)) [@isVoid](https://github.com/isVoid)
+- Add &#39;spearman&#39; correlation method for `dataframe.corr` and `series.corr` ([#7141](https://github.com/rapidsai/cudf/pull/7141)) [@dominicshanshan](https://github.com/dominicshanshan)
+
+## 🛠️ Improvements
+
+- Add `scipy` skip for a test ([#10502](https://github.com/rapidsai/cudf/pull/10502)) [@galipremsagar](https://github.com/galipremsagar)
+- Temporarily disable new `ops-bot` functionality ([#10496](https://github.com/rapidsai/cudf/pull/10496)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Include &lt;cstddef&gt; to fix compilation of parquet reader on GCC 11. ([#10483](https://github.com/rapidsai/cudf/pull/10483)) [@bdice](https://github.com/bdice)
+- Pin `dask` and `distributed` ([#10481](https://github.com/rapidsai/cudf/pull/10481)) [@galipremsagar](https://github.com/galipremsagar)
+- MD5 refactoring. ([#10445](https://github.com/rapidsai/cudf/pull/10445)) [@bdice](https://github.com/bdice)
+- Remove or split up Frame methods that use the index ([#10439](https://github.com/rapidsai/cudf/pull/10439)) [@vyasr](https://github.com/vyasr)
+- Centralization of tdigest aggregation code. ([#10422](https://github.com/rapidsai/cudf/pull/10422)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Simplify column binary operations ([#10421](https://github.com/rapidsai/cudf/pull/10421)) [@vyasr](https://github.com/vyasr)
+- Add `.github/ops-bot.yaml` config file ([#10420](https://github.com/rapidsai/cudf/pull/10420)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Use list of columns for methods in `Groupby.pyx` ([#10419](https://github.com/rapidsai/cudf/pull/10419)) [@isVoid](https://github.com/isVoid)
+- Remove warnings in `test_timedelta.py` ([#10418](https://github.com/rapidsai/cudf/pull/10418)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix some warnings in `test_parquet.py` ([#10416](https://github.com/rapidsai/cudf/pull/10416)) [@galipremsagar](https://github.com/galipremsagar)
+- JNI support for segmented reduce ([#10413](https://github.com/rapidsai/cudf/pull/10413)) [@revans2](https://github.com/revans2)
+- Clean up null mask after purging null entries ([#10412](https://github.com/rapidsai/cudf/pull/10412)) [@sperlingxx](https://github.com/sperlingxx)
+- Drop unsupported method argument from nunique and distinct_count. ([#10411](https://github.com/rapidsai/cudf/pull/10411)) [@bdice](https://github.com/bdice)
+- Use str instead of builtins.str. ([#10410](https://github.com/rapidsai/cudf/pull/10410)) [@bdice](https://github.com/bdice)
+- Fix warnings in `test_rolling` ([#10405](https://github.com/rapidsai/cudf/pull/10405)) [@bdice](https://github.com/bdice)
+- Enable `codecov` github-check in CI ([#10404](https://github.com/rapidsai/cudf/pull/10404)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix warnings in test_cuda_apply, test_numerical, test_pickling, test_unaops. ([#10402](https://github.com/rapidsai/cudf/pull/10402)) [@bdice](https://github.com/bdice)
+- Set column names in `_from_columns_like_self` factory ([#10400](https://github.com/rapidsai/cudf/pull/10400)) [@isVoid](https://github.com/isVoid)
+- Refactor `nvtx` annotations in `cudf` &amp; `dask-cudf` ([#10396](https://github.com/rapidsai/cudf/pull/10396)) [@galipremsagar](https://github.com/galipremsagar)
+- Consolidate .cov and .corr for sort groupby ([#10386](https://github.com/rapidsai/cudf/pull/10386)) [@skirui-source](https://github.com/skirui-source)
+- Consolidate some Frame APIs ([#10381](https://github.com/rapidsai/cudf/pull/10381)) [@vyasr](https://github.com/vyasr)
+- Refactor hash functions and `hash_combine` ([#10379](https://github.com/rapidsai/cudf/pull/10379)) [@bdice](https://github.com/bdice)
+- Add `nvtx` annotations for `Series` and `Index` ([#10374](https://github.com/rapidsai/cudf/pull/10374)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactor `filling.repeat` API ([#10371](https://github.com/rapidsai/cudf/pull/10371)) [@isVoid](https://github.com/isVoid)
+- Move standalone UTF8 functions from string_view.hpp to utf8.hpp ([#10369](https://github.com/rapidsai/cudf/pull/10369)) [@davidwendt](https://github.com/davidwendt)
+- Remove doc for deprecated function `one_hot_encoding` ([#10367](https://github.com/rapidsai/cudf/pull/10367)) [@isVoid](https://github.com/isVoid)
+- Refactor array function ([#10364](https://github.com/rapidsai/cudf/pull/10364)) [@vyasr](https://github.com/vyasr)
+- Fix warnings in test_csv.py. ([#10362](https://github.com/rapidsai/cudf/pull/10362)) [@bdice](https://github.com/bdice)
+- Implement a mixin for binops ([#10360](https://github.com/rapidsai/cudf/pull/10360)) [@vyasr](https://github.com/vyasr)
+- Refactor cython interface: `copying.pyx` ([#10359](https://github.com/rapidsai/cudf/pull/10359)) [@isVoid](https://github.com/isVoid)
+- Implement a mixin for scans ([#10358](https://github.com/rapidsai/cudf/pull/10358)) [@vyasr](https://github.com/vyasr)
+- Add scan_aggregation and reduce_aggregation derived types. ([#10357](https://github.com/rapidsai/cudf/pull/10357)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add cleanup of python artifacts ([#10355](https://github.com/rapidsai/cudf/pull/10355)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix warnings in test_categorical.py. ([#10354](https://github.com/rapidsai/cudf/pull/10354)) [@bdice](https://github.com/bdice)
+- Create a dispatcher for invoking regex kernel functions ([#10349](https://github.com/rapidsai/cudf/pull/10349)) [@davidwendt](https://github.com/davidwendt)
+- Fix `codecov` in CI ([#10347](https://github.com/rapidsai/cudf/pull/10347)) [@galipremsagar](https://github.com/galipremsagar)
+- Enable caching for `memory_usage` calculation in `Column` ([#10345](https://github.com/rapidsai/cudf/pull/10345)) [@galipremsagar](https://github.com/galipremsagar)
+- C++17 cleanup: traits replace std::enable_if&lt;&gt;::type with std::enable_if_t ([#10343](https://github.com/rapidsai/cudf/pull/10343)) [@karthikeyann](https://github.com/karthikeyann)
+- JNI: Support appending DECIMAL128 into ColumnBuilder in terms of byte array ([#10338](https://github.com/rapidsai/cudf/pull/10338)) [@sperlingxx](https://github.com/sperlingxx)
+- multibyte_split test improvements ([#10328](https://github.com/rapidsai/cudf/pull/10328)) [@vuule](https://github.com/vuule)
+- Fix warnings in test_binops.py. ([#10327](https://github.com/rapidsai/cudf/pull/10327)) [@bdice](https://github.com/bdice)
+- Fix warnings from pandas in test_array_ufunc.py. ([#10324](https://github.com/rapidsai/cudf/pull/10324)) [@bdice](https://github.com/bdice)
+- Update upload script ([#10321](https://github.com/rapidsai/cudf/pull/10321)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Move hash type declarations to hashing.hpp ([#10320](https://github.com/rapidsai/cudf/pull/10320)) [@davidwendt](https://github.com/davidwendt)
+- C++17 cleanup: traits replace `::value` with `_v` ([#10319](https://github.com/rapidsai/cudf/pull/10319)) [@karthikeyann](https://github.com/karthikeyann)
+- Remove internal columns usage ([#10315](https://github.com/rapidsai/cudf/pull/10315)) [@vyasr](https://github.com/vyasr)
+- Remove extraneous `build.sh` parameter ([#10313](https://github.com/rapidsai/cudf/pull/10313)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Add const qualifier to MurmurHash3_32::hash_combine ([#10311](https://github.com/rapidsai/cudf/pull/10311)) [@davidwendt](https://github.com/davidwendt)
+- Remove `TODO` in `libcudf_kafka` recipe ([#10309](https://github.com/rapidsai/cudf/pull/10309)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Add conversions between column_view and device_span&lt;T const&gt;. ([#10302](https://github.com/rapidsai/cudf/pull/10302)) [@bdice](https://github.com/bdice)
+- Avoid `decimal` type narrowing for decimal binops ([#10299](https://github.com/rapidsai/cudf/pull/10299)) [@galipremsagar](https://github.com/galipremsagar)
+- Deprecate `DataFrame.iteritems` and introduce `.items` ([#10298](https://github.com/rapidsai/cudf/pull/10298)) [@galipremsagar](https://github.com/galipremsagar)
+- Explicitly request CMake use `gnu++17` over `c++17` ([#10297](https://github.com/rapidsai/cudf/pull/10297)) [@robertmaynard](https://github.com/robertmaynard)
+- Add copyright check as pre-commit hook. ([#10290](https://github.com/rapidsai/cudf/pull/10290)) [@vyasr](https://github.com/vyasr)
+- DataFrame `insert` and creation optimizations ([#10285](https://github.com/rapidsai/cudf/pull/10285)) [@galipremsagar](https://github.com/galipremsagar)
+- Improve hash join detail functions ([#10273](https://github.com/rapidsai/cudf/pull/10273)) [@PointKernel](https://github.com/PointKernel)
+- Replace custom `cached_property` implementation with functools ([#10272](https://github.com/rapidsai/cudf/pull/10272)) [@shwina](https://github.com/shwina)
+- Rewrites `sample` API ([#10262](https://github.com/rapidsai/cudf/pull/10262)) [@isVoid](https://github.com/isVoid)
+- Bump hadoop-common from 3.1.0 to 3.1.4 in /java ([#10259](https://github.com/rapidsai/cudf/pull/10259)) [@dependabot[bot]](https://github.com/dependabot[bot])
+- Remove making redundant `copy` across code-base ([#10257](https://github.com/rapidsai/cudf/pull/10257)) [@galipremsagar](https://github.com/galipremsagar)
+- Add more `nvtx` annotations ([#10256](https://github.com/rapidsai/cudf/pull/10256)) [@galipremsagar](https://github.com/galipremsagar)
+- Add `copyright` check in `cudf` ([#10253](https://github.com/rapidsai/cudf/pull/10253)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove redundant copies in `fillna` to improve performance ([#10241](https://github.com/rapidsai/cudf/pull/10241)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove `std::numeric_limit` specializations for timestamp &amp; durations ([#10239](https://github.com/rapidsai/cudf/pull/10239)) [@codereport](https://github.com/codereport)
+- Optimize `DataFrame` creation across code-base ([#10236](https://github.com/rapidsai/cudf/pull/10236)) [@galipremsagar](https://github.com/galipremsagar)
+- Change pytest distribution algorithm and increase parallelism in CI ([#10232](https://github.com/rapidsai/cudf/pull/10232)) [@galipremsagar](https://github.com/galipremsagar)
+- Add environment variables for I/O thread pool and slice sizes ([#10218](https://github.com/rapidsai/cudf/pull/10218)) [@vuule](https://github.com/vuule)
+- Add regex flags to strings findall functions ([#10208](https://github.com/rapidsai/cudf/pull/10208)) [@davidwendt](https://github.com/davidwendt)
+- Update dask-cudf parquet tests to reflect upstream bugfixes to `_metadata` ([#10206](https://github.com/rapidsai/cudf/pull/10206)) [@charlesbluca](https://github.com/charlesbluca)
+- Remove unnecessary nunique function in Series. ([#10205](https://github.com/rapidsai/cudf/pull/10205)) [@martinfalisse](https://github.com/martinfalisse)
+- Refactor DataFrame tests. ([#10204](https://github.com/rapidsai/cudf/pull/10204)) [@bdice](https://github.com/bdice)
+- Rewrites `column.__setitem__`, Use `boolean_mask_scatter` ([#10202](https://github.com/rapidsai/cudf/pull/10202)) [@isVoid](https://github.com/isVoid)
+- Java utilities to aid in accelerating aggregations on 128-bit types ([#10201](https://github.com/rapidsai/cudf/pull/10201)) [@jlowe](https://github.com/jlowe)
+- Fix docstrings alignment in `Frame` methods ([#10199](https://github.com/rapidsai/cudf/pull/10199)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix cuco pair issue in hash join ([#10195](https://github.com/rapidsai/cudf/pull/10195)) [@PointKernel](https://github.com/PointKernel)
+- Replace `dask` groupby `.index` usages with `.by` ([#10193](https://github.com/rapidsai/cudf/pull/10193)) [@galipremsagar](https://github.com/galipremsagar)
+- Add regex flags to strings extract function ([#10192](https://github.com/rapidsai/cudf/pull/10192)) [@davidwendt](https://github.com/davidwendt)
+- Forward-merge branch-22.02 to branch-22.04 ([#10191](https://github.com/rapidsai/cudf/pull/10191)) [@bdice](https://github.com/bdice)
+- Add CMake `install` rule for tests ([#10190](https://github.com/rapidsai/cudf/pull/10190)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Unpin `dask` &amp; `distributed` ([#10182](https://github.com/rapidsai/cudf/pull/10182)) [@galipremsagar](https://github.com/galipremsagar)
+- Add comments to explain test validation ([#10176](https://github.com/rapidsai/cudf/pull/10176)) [@galipremsagar](https://github.com/galipremsagar)
+- Reduce warnings in pytest output ([#10168](https://github.com/rapidsai/cudf/pull/10168)) [@bdice](https://github.com/bdice)
+- Some consolidation of indexed frame methods ([#10167](https://github.com/rapidsai/cudf/pull/10167)) [@vyasr](https://github.com/vyasr)
+- Refactor isin implementations ([#10165](https://github.com/rapidsai/cudf/pull/10165)) [@vyasr](https://github.com/vyasr)
+- Faster struct row comparator ([#10164](https://github.com/rapidsai/cudf/pull/10164)) [@devavret](https://github.com/devavret)
+- Refactor groupby::get_groups. ([#10161](https://github.com/rapidsai/cudf/pull/10161)) [@bdice](https://github.com/bdice)
+- Deprecate `decimal_cols_as_float` in ORC reader (C++ layer) ([#10152](https://github.com/rapidsai/cudf/pull/10152)) [@vuule](https://github.com/vuule)
+- Replace `ccache` with `sccache` ([#10146](https://github.com/rapidsai/cudf/pull/10146)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Murmur3 hash kernel cleanup ([#10143](https://github.com/rapidsai/cudf/pull/10143)) [@rwlee](https://github.com/rwlee)
+- Deprecate `decimal_cols_as_float` in ORC reader ([#10142](https://github.com/rapidsai/cudf/pull/10142)) [@galipremsagar](https://github.com/galipremsagar)
+- Run pyupgrade 2.31.0. ([#10141](https://github.com/rapidsai/cudf/pull/10141)) [@bdice](https://github.com/bdice)
+- Remove `drop_nan` from internal `IndexedFrame._drop_na_rows`. ([#10140](https://github.com/rapidsai/cudf/pull/10140)) [@bdice](https://github.com/bdice)
+- Change cudf::strings::find_multiple to return a lists column ([#10134](https://github.com/rapidsai/cudf/pull/10134)) [@davidwendt](https://github.com/davidwendt)
+- Update cmake-format script for branch 22.04. ([#10132](https://github.com/rapidsai/cudf/pull/10132)) [@bdice](https://github.com/bdice)
+- Accept r-value references in convert_table_for_return(): ([#10131](https://github.com/rapidsai/cudf/pull/10131)) [@mythrocks](https://github.com/mythrocks)
+- Remove the option to completely disable decimal128 columns in the ORC reader ([#10127](https://github.com/rapidsai/cudf/pull/10127)) [@vuule](https://github.com/vuule)
+- Remove deprecated code ([#10124](https://github.com/rapidsai/cudf/pull/10124)) [@vyasr](https://github.com/vyasr)
+- Update gpu_utils.py to reflect current CUDA support. ([#10113](https://github.com/rapidsai/cudf/pull/10113)) [@bdice](https://github.com/bdice)
+- Remove benchmarks suffix ([#10112](https://github.com/rapidsai/cudf/pull/10112)) [@bdice](https://github.com/bdice)
+- Update cudf java binding version to 22.04.0-SNAPSHOT ([#10084](https://github.com/rapidsai/cudf/pull/10084)) [@pxLi](https://github.com/pxLi)
+- Remove unnecessary docker files. ([#10069](https://github.com/rapidsai/cudf/pull/10069)) [@vyasr](https://github.com/vyasr)
+- Limit benchmark iterations using environment variable ([#10060](https://github.com/rapidsai/cudf/pull/10060)) [@karthikeyann](https://github.com/karthikeyann)
+- Add timing chart for libcudf build metrics report page ([#10038](https://github.com/rapidsai/cudf/pull/10038)) [@davidwendt](https://github.com/davidwendt)
+- JNI: Rewrite growBuffersAndRows to accelerate the HostColumnBuilder ([#10025](https://github.com/rapidsai/cudf/pull/10025)) [@sperlingxx](https://github.com/sperlingxx)
+- Reduce redundant code in CUDF JNI ([#10019](https://github.com/rapidsai/cudf/pull/10019)) [@mythrocks](https://github.com/mythrocks)
+- Make snappy decompress check more efficient ([#9995](https://github.com/rapidsai/cudf/pull/9995)) [@cheinger](https://github.com/cheinger)
+- Remove deprecated method Series.set_index. ([#9945](https://github.com/rapidsai/cudf/pull/9945)) [@bdice](https://github.com/bdice)
+- Implement a mixin for reductions ([#9925](https://github.com/rapidsai/cudf/pull/9925)) [@vyasr](https://github.com/vyasr)
+- JNI: Push back decimal utils from spark-rapids ([#9907](https://github.com/rapidsai/cudf/pull/9907)) [@sperlingxx](https://github.com/sperlingxx)
+- Add `assert_column_memory_*` ([#9882](https://github.com/rapidsai/cudf/pull/9882)) [@isVoid](https://github.com/isVoid)
+- Add CUDF_UNREACHABLE macro. ([#9727](https://github.com/rapidsai/cudf/pull/9727)) [@bdice](https://github.com/bdice)
+- Upgrade `arrow` &amp; `pyarrow` to `6.0.1` ([#9686](https://github.com/rapidsai/cudf/pull/9686)) [@galipremsagar](https://github.com/galipremsagar)
+
+# cuDF 22.02.00 (2 Feb 2022)
+
+## 🚨 Breaking Changes
+
+- ORC writer API changes for granular statistics ([#10058](https://github.com/rapidsai/cudf/pull/10058)) [@mythrocks](https://github.com/mythrocks)
+- `decimal128` Support for `to/from_arrow` ([#9986](https://github.com/rapidsai/cudf/pull/9986)) [@codereport](https://github.com/codereport)
+- Remove deprecated method `one_hot_encoding` ([#9977](https://github.com/rapidsai/cudf/pull/9977)) [@isVoid](https://github.com/isVoid)
+- Remove str.subword_tokenize ([#9968](https://github.com/rapidsai/cudf/pull/9968)) [@VibhuJawa](https://github.com/VibhuJawa)
+- Remove deprecated `method` parameter from `merge` and `join`. ([#9944](https://github.com/rapidsai/cudf/pull/9944)) [@bdice](https://github.com/bdice)
+- Remove deprecated method DataFrame.hash_columns. ([#9943](https://github.com/rapidsai/cudf/pull/9943)) [@bdice](https://github.com/bdice)
+- Remove deprecated method Series.hash_encode. ([#9942](https://github.com/rapidsai/cudf/pull/9942)) [@bdice](https://github.com/bdice)
+- Refactoring ceil/round/floor code for datetime64 types ([#9926](https://github.com/rapidsai/cudf/pull/9926)) [@mayankanand007](https://github.com/mayankanand007)
+- Introduce `nan_as_null` parameter for `cudf.Index` ([#9893](https://github.com/rapidsai/cudf/pull/9893)) [@galipremsagar](https://github.com/galipremsagar)
+- Add regex_flags parameter to strings replace_re functions ([#9878](https://github.com/rapidsai/cudf/pull/9878)) [@davidwendt](https://github.com/davidwendt)
+- Break tie for `top` categorical columns in `Series.describe` ([#9867](https://github.com/rapidsai/cudf/pull/9867)) [@isVoid](https://github.com/isVoid)
+- Add partitioning support in parquet writer ([#9810](https://github.com/rapidsai/cudf/pull/9810)) [@devavret](https://github.com/devavret)
+- Move `drop_duplicates`, `drop_na`, `_gather`, `take` to IndexFrame and create their `_base_index` counterparts ([#9807](https://github.com/rapidsai/cudf/pull/9807)) [@isVoid](https://github.com/isVoid)
+- Raise temporary error for `decimal128` types in parquet reader ([#9804](https://github.com/rapidsai/cudf/pull/9804)) [@galipremsagar](https://github.com/galipremsagar)
+- Change default `dtype` of all nulls column from `float` to `object` ([#9803](https://github.com/rapidsai/cudf/pull/9803)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove unused masked udf cython/c++ code ([#9792](https://github.com/rapidsai/cudf/pull/9792)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Pick smallest decimal type with required precision in ORC reader ([#9775](https://github.com/rapidsai/cudf/pull/9775)) [@vuule](https://github.com/vuule)
+- Add decimal128 support to Parquet reader and writer ([#9765](https://github.com/rapidsai/cudf/pull/9765)) [@vuule](https://github.com/vuule)
+- Refactor TableTest assertion methods to a separate utility class ([#9762](https://github.com/rapidsai/cudf/pull/9762)) [@jlowe](https://github.com/jlowe)
+- Use cuFile direct device reads/writes by default in cuIO ([#9722](https://github.com/rapidsai/cudf/pull/9722)) [@vuule](https://github.com/vuule)
+- Match pandas scalar result types in reductions ([#9717](https://github.com/rapidsai/cudf/pull/9717)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add parameters to control row group size in Parquet writer ([#9677](https://github.com/rapidsai/cudf/pull/9677)) [@vuule](https://github.com/vuule)
+- Refactor bit counting APIs, introduce valid/null count functions, and split host/device side code for segmented counts. ([#9588](https://github.com/rapidsai/cudf/pull/9588)) [@bdice](https://github.com/bdice)
+- Add support for `decimal128` in cudf python ([#9533](https://github.com/rapidsai/cudf/pull/9533)) [@galipremsagar](https://github.com/galipremsagar)
+- Implement `lists::index_of()` to find positions in list rows ([#9510](https://github.com/rapidsai/cudf/pull/9510)) [@mythrocks](https://github.com/mythrocks)
+- Rewriting row/column conversions for Spark &lt;-&gt; cudf data conversions ([#8444](https://github.com/rapidsai/cudf/pull/8444)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+
+## 🐛 Bug Fixes
+
+- Add check for negative stripe index in ORC reader ([#10074](https://github.com/rapidsai/cudf/pull/10074)) [@vuule](https://github.com/vuule)
+- Update Java tests to expect DECIMAL128 from Arrow ([#10073](https://github.com/rapidsai/cudf/pull/10073)) [@jlowe](https://github.com/jlowe)
+- Avoid index materialization when `DataFrame` is created with un-named `Series` objects ([#10071](https://github.com/rapidsai/cudf/pull/10071)) [@galipremsagar](https://github.com/galipremsagar)
+- fix gcc 11 compilation errors ([#10067](https://github.com/rapidsai/cudf/pull/10067)) [@rongou](https://github.com/rongou)
+- Fix `columns` ordering issue in parquet reader ([#10066](https://github.com/rapidsai/cudf/pull/10066)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix dataframe setitem with `ndarray` types ([#10056](https://github.com/rapidsai/cudf/pull/10056)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove implicit copy due to conversion from cudf::size_type and size_t ([#10045](https://github.com/rapidsai/cudf/pull/10045)) [@robertmaynard](https://github.com/robertmaynard)
+- Include &lt;optional&gt; in headers that use std::optional ([#10044](https://github.com/rapidsai/cudf/pull/10044)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix repr and concat of `StructColumn` ([#10042](https://github.com/rapidsai/cudf/pull/10042)) [@galipremsagar](https://github.com/galipremsagar)
+- Include row group level stats when writing ORC files ([#10041](https://github.com/rapidsai/cudf/pull/10041)) [@vuule](https://github.com/vuule)
+- build.sh respects the `--build_metrics` and `--incl_cache_stats` flags ([#10035](https://github.com/rapidsai/cudf/pull/10035)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix memory leaks in JNI native code. ([#10029](https://github.com/rapidsai/cudf/pull/10029)) [@mythrocks](https://github.com/mythrocks)
+- Update JNI to use new arena mr constructor ([#10027](https://github.com/rapidsai/cudf/pull/10027)) [@rongou](https://github.com/rongou)
+- Fix null check when comparing structs in `arg_min` operation of reduction/groupby ([#10026](https://github.com/rapidsai/cudf/pull/10026)) [@ttnghia](https://github.com/ttnghia)
+- Wrap CI script shell variables in quotes to fix local testing. ([#10018](https://github.com/rapidsai/cudf/pull/10018)) [@bdice](https://github.com/bdice)
+- cudftestutil no longer propagates compiler flags to external users ([#10017](https://github.com/rapidsai/cudf/pull/10017)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove `CUDA_DEVICE_CALLABLE` macro usage ([#10015](https://github.com/rapidsai/cudf/pull/10015)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add missing list filling header in meta.yaml ([#10007](https://github.com/rapidsai/cudf/pull/10007)) [@devavret](https://github.com/devavret)
+- Fix `conda` recipes for `custreamz` &amp; `cudf_kafka` ([#10003](https://github.com/rapidsai/cudf/pull/10003)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix matching regex word-boundary () in strings replace ([#9997](https://github.com/rapidsai/cudf/pull/9997)) [@davidwendt](https://github.com/davidwendt)
+- Fix null check when comparing structs in `min` and `max` reduction/groupby operations ([#9994](https://github.com/rapidsai/cudf/pull/9994)) [@ttnghia](https://github.com/ttnghia)
+- Fix octal pattern matching in regex string ([#9993](https://github.com/rapidsai/cudf/pull/9993)) [@davidwendt](https://github.com/davidwendt)
+- `decimal128` Support for `to/from_arrow` ([#9986](https://github.com/rapidsai/cudf/pull/9986)) [@codereport](https://github.com/codereport)
+- Fix groupby shift/diff/fill after selecting from a  `GroupBy` ([#9984](https://github.com/rapidsai/cudf/pull/9984)) [@shwina](https://github.com/shwina)
+- Fix the overflow problem of decimal rescale ([#9966](https://github.com/rapidsai/cudf/pull/9966)) [@sperlingxx](https://github.com/sperlingxx)
+- Use default value for decimal precision in parquet writer when not specified ([#9963](https://github.com/rapidsai/cudf/pull/9963)) [@devavret](https://github.com/devavret)
+- Fix cudf java build error. ([#9958](https://github.com/rapidsai/cudf/pull/9958)) [@firestarman](https://github.com/firestarman)
+- Use gpuci_mamba_retry to install local artifacts. ([#9951](https://github.com/rapidsai/cudf/pull/9951)) [@bdice](https://github.com/bdice)
+- Fix regression HostColumnVectorCore requiring native libs ([#9948](https://github.com/rapidsai/cudf/pull/9948)) [@jlowe](https://github.com/jlowe)
+- Rename aggregate_metadata in writer to fix name collision ([#9938](https://github.com/rapidsai/cudf/pull/9938)) [@devavret](https://github.com/devavret)
+- Fixed issue with percentile_approx where output tdigests could have uninitialized data at the end. ([#9931](https://github.com/rapidsai/cudf/pull/9931)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Resolve racecheck errors in ORC kernels ([#9916](https://github.com/rapidsai/cudf/pull/9916)) [@vuule](https://github.com/vuule)
+- Fix the java build after parquet partitioning support ([#9908](https://github.com/rapidsai/cudf/pull/9908)) [@revans2](https://github.com/revans2)
+- Fix compilation of benchmark for parquet writer. ([#9905](https://github.com/rapidsai/cudf/pull/9905)) [@bdice](https://github.com/bdice)
+- Fix a memcheck error in ORC writer ([#9896](https://github.com/rapidsai/cudf/pull/9896)) [@vuule](https://github.com/vuule)
+- Introduce `nan_as_null` parameter for `cudf.Index` ([#9893](https://github.com/rapidsai/cudf/pull/9893)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix fallback to sort aggregation for grouping only hash aggregate ([#9891](https://github.com/rapidsai/cudf/pull/9891)) [@abellina](https://github.com/abellina)
+- Add zlib to cudfjni link when using static libcudf library dependency ([#9890](https://github.com/rapidsai/cudf/pull/9890)) [@jlowe](https://github.com/jlowe)
+- TimedeltaIndex constructor raises an AttributeError. ([#9884](https://github.com/rapidsai/cudf/pull/9884)) [@skirui-source](https://github.com/skirui-source)
+- Fix cudf.Scalar string datetime construction ([#9875](https://github.com/rapidsai/cudf/pull/9875)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Load libcufile.so with RTLD_NODELETE flag ([#9872](https://github.com/rapidsai/cudf/pull/9872)) [@vuule](https://github.com/vuule)
+- Break tie for `top` categorical columns in `Series.describe` ([#9867](https://github.com/rapidsai/cudf/pull/9867)) [@isVoid](https://github.com/isVoid)
+- Fix null handling for structs `min` and `arg_min` in groupby, groupby scan, reduction, and inclusive_scan ([#9864](https://github.com/rapidsai/cudf/pull/9864)) [@ttnghia](https://github.com/ttnghia)
+- Add one-level list encoding support in parquet reader ([#9848](https://github.com/rapidsai/cudf/pull/9848)) [@PointKernel](https://github.com/PointKernel)
+- Fix an out-of-bounds read in validity copying in contiguous_split. ([#9842](https://github.com/rapidsai/cudf/pull/9842)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix join of MultiIndex to Index with one column and overlapping name. ([#9830](https://github.com/rapidsai/cudf/pull/9830)) [@vyasr](https://github.com/vyasr)
+- Fix caching in `Series.applymap` ([#9821](https://github.com/rapidsai/cudf/pull/9821)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Enforce boolean `ascending` for dask-cudf `sort_values` ([#9814](https://github.com/rapidsai/cudf/pull/9814)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix ORC writer crash with empty input columns ([#9808](https://github.com/rapidsai/cudf/pull/9808)) [@vuule](https://github.com/vuule)
+- Change default `dtype` of all nulls column from `float` to `object` ([#9803](https://github.com/rapidsai/cudf/pull/9803)) [@galipremsagar](https://github.com/galipremsagar)
+- Load native dependencies when Java ColumnView is loaded ([#9800](https://github.com/rapidsai/cudf/pull/9800)) [@jlowe](https://github.com/jlowe)
+- Fix dtype-argument bug in dask_cudf read_csv ([#9796](https://github.com/rapidsai/cudf/pull/9796)) [@rjzamora](https://github.com/rjzamora)
+- Fix overflow for min calculation in strings::from_timestamps ([#9793](https://github.com/rapidsai/cudf/pull/9793)) [@revans2](https://github.com/revans2)
+- Fix memory error due to lambda return type deduction limitation ([#9778](https://github.com/rapidsai/cudf/pull/9778)) [@karthikeyann](https://github.com/karthikeyann)
+- Revert regex $/EOL end-of-string new-line special case handling ([#9774](https://github.com/rapidsai/cudf/pull/9774)) [@davidwendt](https://github.com/davidwendt)
+- Fix missing streams ([#9767](https://github.com/rapidsai/cudf/pull/9767)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix make_empty_scalar_like on list_type ([#9759](https://github.com/rapidsai/cudf/pull/9759)) [@sperlingxx](https://github.com/sperlingxx)
+- Update cmake and conda to 22.02 ([#9746](https://github.com/rapidsai/cudf/pull/9746)) [@devavret](https://github.com/devavret)
+- Fix out-of-bounds memory write in decimal128-to-string conversion ([#9740](https://github.com/rapidsai/cudf/pull/9740)) [@davidwendt](https://github.com/davidwendt)
+- Match pandas scalar result types in reductions ([#9717](https://github.com/rapidsai/cudf/pull/9717)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix regex non-multiline EOL/$ matching strings ending with a new-line ([#9715](https://github.com/rapidsai/cudf/pull/9715)) [@davidwendt](https://github.com/davidwendt)
+- Fixed build by adding more checks for int8, int16 ([#9707](https://github.com/rapidsai/cudf/pull/9707)) [@razajafri](https://github.com/razajafri)
+- Fix `null` handling when `boolean` dtype is passed ([#9691](https://github.com/rapidsai/cudf/pull/9691)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix stream usage in `segmented_gather()` ([#9679](https://github.com/rapidsai/cudf/pull/9679)) [@mythrocks](https://github.com/mythrocks)
+
+## 📖 Documentation
+
+- Update `decimal` dtypes related docs entries ([#10072](https://github.com/rapidsai/cudf/pull/10072)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix regex doc describing hexadecimal escape characters ([#10009](https://github.com/rapidsai/cudf/pull/10009)) [@davidwendt](https://github.com/davidwendt)
+- Fix cudf compilation instructions. ([#9956](https://github.com/rapidsai/cudf/pull/9956)) [@esoha-nvidia](https://github.com/esoha-nvidia)
+- Fix see also links for IO APIs ([#9895](https://github.com/rapidsai/cudf/pull/9895)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix build instructions for libcudf doxygen ([#9837](https://github.com/rapidsai/cudf/pull/9837)) [@davidwendt](https://github.com/davidwendt)
+- Fix some doxygen warnings and add missing documentation ([#9770](https://github.com/rapidsai/cudf/pull/9770)) [@karthikeyann](https://github.com/karthikeyann)
+- update cuda version in local build ([#9736](https://github.com/rapidsai/cudf/pull/9736)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix doxygen for enum types in libcudf ([#9724](https://github.com/rapidsai/cudf/pull/9724)) [@davidwendt](https://github.com/davidwendt)
+- Spell check fixes ([#9682](https://github.com/rapidsai/cudf/pull/9682)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix links in C++ Developer Guide. ([#9675](https://github.com/rapidsai/cudf/pull/9675)) [@bdice](https://github.com/bdice)
+
+## 🚀 New Features
+
+- Remove libcudacxx patch needed for nvcc 11.4 ([#10057](https://github.com/rapidsai/cudf/pull/10057)) [@robertmaynard](https://github.com/robertmaynard)
+- Allow CuPy 10 ([#10048](https://github.com/rapidsai/cudf/pull/10048)) [@jakirkham](https://github.com/jakirkham)
+- Add in support for NULL_LOGICAL_AND and NULL_LOGICAL_OR binops ([#10016](https://github.com/rapidsai/cudf/pull/10016)) [@revans2](https://github.com/revans2)
+- Add `groupby.transform` (only support for aggregations) ([#10005](https://github.com/rapidsai/cudf/pull/10005)) [@shwina](https://github.com/shwina)
+- Add partitioning support to Parquet chunked writer ([#10000](https://github.com/rapidsai/cudf/pull/10000)) [@devavret](https://github.com/devavret)
+- Add jni for sequences ([#9972](https://github.com/rapidsai/cudf/pull/9972)) [@wbo4958](https://github.com/wbo4958)
+- Java bindings for mixed left, inner, and full joins ([#9941](https://github.com/rapidsai/cudf/pull/9941)) [@jlowe](https://github.com/jlowe)
+- Java bindings for JSON reader support ([#9940](https://github.com/rapidsai/cudf/pull/9940)) [@wbo4958](https://github.com/wbo4958)
+- Enable transpose for string columns in cudf python ([#9937](https://github.com/rapidsai/cudf/pull/9937)) [@galipremsagar](https://github.com/galipremsagar)
+- Support structs for `cudf::contains` with column/scalar input ([#9929](https://github.com/rapidsai/cudf/pull/9929)) [@ttnghia](https://github.com/ttnghia)
+- Implement mixed equality/conditional joins ([#9917](https://github.com/rapidsai/cudf/pull/9917)) [@vyasr](https://github.com/vyasr)
+- Add cudf::strings::extract_all API ([#9909](https://github.com/rapidsai/cudf/pull/9909)) [@davidwendt](https://github.com/davidwendt)
+- Implement JNI for `cudf::scatter` APIs ([#9903](https://github.com/rapidsai/cudf/pull/9903)) [@ttnghia](https://github.com/ttnghia)
+- JNI: Function to copy and set validity from bool column. ([#9901](https://github.com/rapidsai/cudf/pull/9901)) [@mythrocks](https://github.com/mythrocks)
+- Add dictionary support to cudf::copy_if_else ([#9887](https://github.com/rapidsai/cudf/pull/9887)) [@davidwendt](https://github.com/davidwendt)
+- add run_benchmarks target for running benchmarks with json output ([#9879](https://github.com/rapidsai/cudf/pull/9879)) [@karthikeyann](https://github.com/karthikeyann)
+- Add regex_flags parameter to strings replace_re functions ([#9878](https://github.com/rapidsai/cudf/pull/9878)) [@davidwendt](https://github.com/davidwendt)
+- Add_suffix and add_prefix for DataFrames and Series ([#9846](https://github.com/rapidsai/cudf/pull/9846)) [@mayankanand007](https://github.com/mayankanand007)
+- Add JNI for `cudf::drop_duplicates` ([#9841](https://github.com/rapidsai/cudf/pull/9841)) [@ttnghia](https://github.com/ttnghia)
+- Implement per-list sequence ([#9839](https://github.com/rapidsai/cudf/pull/9839)) [@ttnghia](https://github.com/ttnghia)
+- adding `series.transpose` ([#9835](https://github.com/rapidsai/cudf/pull/9835)) [@mayankanand007](https://github.com/mayankanand007)
+- Adding support for `Series.autocorr` ([#9833](https://github.com/rapidsai/cudf/pull/9833)) [@mayankanand007](https://github.com/mayankanand007)
+- Support round operation on datetime64 datatypes ([#9820](https://github.com/rapidsai/cudf/pull/9820)) [@mayankanand007](https://github.com/mayankanand007)
+- Add partitioning support in parquet writer ([#9810](https://github.com/rapidsai/cudf/pull/9810)) [@devavret](https://github.com/devavret)
+- Raise temporary error for `decimal128` types in parquet reader ([#9804](https://github.com/rapidsai/cudf/pull/9804)) [@galipremsagar](https://github.com/galipremsagar)
+- Add decimal128 support to Parquet reader and writer ([#9765](https://github.com/rapidsai/cudf/pull/9765)) [@vuule](https://github.com/vuule)
+- Optimize `groupby::scan` ([#9754](https://github.com/rapidsai/cudf/pull/9754)) [@PointKernel](https://github.com/PointKernel)
+- Add sample JNI API ([#9728](https://github.com/rapidsai/cudf/pull/9728)) [@res-life](https://github.com/res-life)
+- Support `min` and `max` in inclusive scan for structs ([#9725](https://github.com/rapidsai/cudf/pull/9725)) [@ttnghia](https://github.com/ttnghia)
+- Add `first` and `last` method to `IndexedFrame` ([#9710](https://github.com/rapidsai/cudf/pull/9710)) [@isVoid](https://github.com/isVoid)
+- Support `min` and `max` reduction for structs ([#9697](https://github.com/rapidsai/cudf/pull/9697)) [@ttnghia](https://github.com/ttnghia)
+- Add parameters to control row group size in Parquet writer ([#9677](https://github.com/rapidsai/cudf/pull/9677)) [@vuule](https://github.com/vuule)
+- Run compute-sanitizer in nightly build ([#9641](https://github.com/rapidsai/cudf/pull/9641)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement Series.datetime.floor ([#9571](https://github.com/rapidsai/cudf/pull/9571)) [@skirui-source](https://github.com/skirui-source)
+- ceil/floor for `DatetimeIndex` ([#9554](https://github.com/rapidsai/cudf/pull/9554)) [@mayankanand007](https://github.com/mayankanand007)
+- Add support for `decimal128` in cudf python ([#9533](https://github.com/rapidsai/cudf/pull/9533)) [@galipremsagar](https://github.com/galipremsagar)
+- Implement `lists::index_of()` to find positions in list rows ([#9510](https://github.com/rapidsai/cudf/pull/9510)) [@mythrocks](https://github.com/mythrocks)
+- custreamz oauth callback for kafka (librdkafka) ([#9486](https://github.com/rapidsai/cudf/pull/9486)) [@jdye64](https://github.com/jdye64)
+- Add Pearson correlation for sort groupby (python) ([#9166](https://github.com/rapidsai/cudf/pull/9166)) [@skirui-source](https://github.com/skirui-source)
+- Interchange dataframe protocol ([#9071](https://github.com/rapidsai/cudf/pull/9071)) [@iskode](https://github.com/iskode)
+- Rewriting row/column conversions for Spark &lt;-&gt; cudf data conversions ([#8444](https://github.com/rapidsai/cudf/pull/8444)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+
+## 🛠️ Improvements
+
+- Prepare upload scripts for Python 3.7 removal ([#10092](https://github.com/rapidsai/cudf/pull/10092)) [@Ethyling](https://github.com/Ethyling)
+- Simplify custreamz and cudf_kafka recipes files ([#10065](https://github.com/rapidsai/cudf/pull/10065)) [@Ethyling](https://github.com/Ethyling)
+- ORC writer API changes for granular statistics ([#10058](https://github.com/rapidsai/cudf/pull/10058)) [@mythrocks](https://github.com/mythrocks)
+- Remove python constraints in cutreamz and cudf_kafka recipes ([#10052](https://github.com/rapidsai/cudf/pull/10052)) [@Ethyling](https://github.com/Ethyling)
+- Unpin `dask` and `distributed` in CI ([#10028](https://github.com/rapidsai/cudf/pull/10028)) [@galipremsagar](https://github.com/galipremsagar)
+- Add `_from_column_like_self` factory ([#10022](https://github.com/rapidsai/cudf/pull/10022)) [@isVoid](https://github.com/isVoid)
+- Replace custom CUDA bindings previously provided by RMM with official CUDA Python bindings ([#10008](https://github.com/rapidsai/cudf/pull/10008)) [@shwina](https://github.com/shwina)
+- Use `cuda::std::is_arithmetic` in `cudf::is_numeric` trait. ([#9996](https://github.com/rapidsai/cudf/pull/9996)) [@bdice](https://github.com/bdice)
+- Clean up CUDA stream use in cuIO ([#9991](https://github.com/rapidsai/cudf/pull/9991)) [@vuule](https://github.com/vuule)
+- Use addressed-ordered first fit for the pinned memory pool ([#9989](https://github.com/rapidsai/cudf/pull/9989)) [@rongou](https://github.com/rongou)
+- Add strings tests to transpose_test.cpp ([#9985](https://github.com/rapidsai/cudf/pull/9985)) [@davidwendt](https://github.com/davidwendt)
+- Use gpuci_mamba_retry on Java CI. ([#9983](https://github.com/rapidsai/cudf/pull/9983)) [@bdice](https://github.com/bdice)
+- Remove deprecated method `one_hot_encoding` ([#9977](https://github.com/rapidsai/cudf/pull/9977)) [@isVoid](https://github.com/isVoid)
+- Minor cleanup of unused Python functions ([#9974](https://github.com/rapidsai/cudf/pull/9974)) [@vyasr](https://github.com/vyasr)
+- Use new efficient partitioned parquet writing in cuDF ([#9971](https://github.com/rapidsai/cudf/pull/9971)) [@devavret](https://github.com/devavret)
+- Remove str.subword_tokenize ([#9968](https://github.com/rapidsai/cudf/pull/9968)) [@VibhuJawa](https://github.com/VibhuJawa)
+- Forward-merge branch-21.12 to branch-22.02 ([#9947](https://github.com/rapidsai/cudf/pull/9947)) [@bdice](https://github.com/bdice)
+- Remove deprecated `method` parameter from `merge` and `join`. ([#9944](https://github.com/rapidsai/cudf/pull/9944)) [@bdice](https://github.com/bdice)
+- Remove deprecated method DataFrame.hash_columns. ([#9943](https://github.com/rapidsai/cudf/pull/9943)) [@bdice](https://github.com/bdice)
+- Remove deprecated method Series.hash_encode. ([#9942](https://github.com/rapidsai/cudf/pull/9942)) [@bdice](https://github.com/bdice)
+- use ninja in java ci build ([#9933](https://github.com/rapidsai/cudf/pull/9933)) [@rongou](https://github.com/rongou)
+- Add build-time publish step to cpu build script ([#9927](https://github.com/rapidsai/cudf/pull/9927)) [@davidwendt](https://github.com/davidwendt)
+- Refactoring ceil/round/floor code for datetime64 types ([#9926](https://github.com/rapidsai/cudf/pull/9926)) [@mayankanand007](https://github.com/mayankanand007)
+- Remove various unused functions ([#9922](https://github.com/rapidsai/cudf/pull/9922)) [@vyasr](https://github.com/vyasr)
+- Raise in `query` if dtype is not supported ([#9921](https://github.com/rapidsai/cudf/pull/9921)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add missing imports tests ([#9920](https://github.com/rapidsai/cudf/pull/9920)) [@Ethyling](https://github.com/Ethyling)
+- Spark Decimal128 hashing ([#9919](https://github.com/rapidsai/cudf/pull/9919)) [@rwlee](https://github.com/rwlee)
+- Replace `thrust/std::get` with structured bindings ([#9915](https://github.com/rapidsai/cudf/pull/9915)) [@codereport](https://github.com/codereport)
+- Upgrade thrust version to 1.15 ([#9912](https://github.com/rapidsai/cudf/pull/9912)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove conda envs for CUDA 11.0 and 11.2. ([#9910](https://github.com/rapidsai/cudf/pull/9910)) [@bdice](https://github.com/bdice)
+- Return count of set bits from inplace_bitmask_and. ([#9904](https://github.com/rapidsai/cudf/pull/9904)) [@bdice](https://github.com/bdice)
+- Use dynamic nullate for join hasher and equality comparator ([#9902](https://github.com/rapidsai/cudf/pull/9902)) [@davidwendt](https://github.com/davidwendt)
+- Update ucx-py version on release using rvc ([#9897](https://github.com/rapidsai/cudf/pull/9897)) [@Ethyling](https://github.com/Ethyling)
+- Remove `IncludeCategories` from `.clang-format` ([#9876](https://github.com/rapidsai/cudf/pull/9876)) [@codereport](https://github.com/codereport)
+- Support statically linking CUDA runtime for Java bindings ([#9873](https://github.com/rapidsai/cudf/pull/9873)) [@jlowe](https://github.com/jlowe)
+- Add `clang-tidy` to libcudf ([#9860](https://github.com/rapidsai/cudf/pull/9860)) [@codereport](https://github.com/codereport)
+- Remove deprecated methods from Java Table class ([#9853](https://github.com/rapidsai/cudf/pull/9853)) [@jlowe](https://github.com/jlowe)
+- Add test for map column metadata handling in ORC writer ([#9852](https://github.com/rapidsai/cudf/pull/9852)) [@vuule](https://github.com/vuule)
+- Use pandas `to_offset` to parse frequency string in `date_range` ([#9843](https://github.com/rapidsai/cudf/pull/9843)) [@isVoid](https://github.com/isVoid)
+- add templated benchmark with fixture ([#9838](https://github.com/rapidsai/cudf/pull/9838)) [@karthikeyann](https://github.com/karthikeyann)
+- Use list of column inputs for `apply_boolean_mask` ([#9832](https://github.com/rapidsai/cudf/pull/9832)) [@isVoid](https://github.com/isVoid)
+- Added a few more tests for Decimal to String cast ([#9818](https://github.com/rapidsai/cudf/pull/9818)) [@razajafri](https://github.com/razajafri)
+- Run doctests. ([#9815](https://github.com/rapidsai/cudf/pull/9815)) [@bdice](https://github.com/bdice)
+- Avoid overflow for fixed_point round ([#9809](https://github.com/rapidsai/cudf/pull/9809)) [@sperlingxx](https://github.com/sperlingxx)
+- Move `drop_duplicates`, `drop_na`, `_gather`, `take` to IndexFrame and create their `_base_index` counterparts ([#9807](https://github.com/rapidsai/cudf/pull/9807)) [@isVoid](https://github.com/isVoid)
+- Use vector factories for host-device copies. ([#9806](https://github.com/rapidsai/cudf/pull/9806)) [@bdice](https://github.com/bdice)
+- Refactor host device macros ([#9797](https://github.com/rapidsai/cudf/pull/9797)) [@vyasr](https://github.com/vyasr)
+- Remove unused masked udf cython/c++ code ([#9792](https://github.com/rapidsai/cudf/pull/9792)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Allow custom sort functions for dask-cudf `sort_values` ([#9789](https://github.com/rapidsai/cudf/pull/9789)) [@charlesbluca](https://github.com/charlesbluca)
+- Improve build time of libcudf iterator tests ([#9788](https://github.com/rapidsai/cudf/pull/9788)) [@davidwendt](https://github.com/davidwendt)
+- Copy Java native dependencies directly into classpath ([#9787](https://github.com/rapidsai/cudf/pull/9787)) [@jlowe](https://github.com/jlowe)
+- Add decimal types to cuIO benchmarks ([#9776](https://github.com/rapidsai/cudf/pull/9776)) [@vuule](https://github.com/vuule)
+- Pick smallest decimal type with required precision in ORC reader ([#9775](https://github.com/rapidsai/cudf/pull/9775)) [@vuule](https://github.com/vuule)
+- Avoid overflow for `fixed_point` `cudf::cast` and performance optimization ([#9772](https://github.com/rapidsai/cudf/pull/9772)) [@codereport](https://github.com/codereport)
+- Use CTAD with Thrust function objects ([#9768](https://github.com/rapidsai/cudf/pull/9768)) [@codereport](https://github.com/codereport)
+- Refactor TableTest assertion methods to a separate utility class ([#9762](https://github.com/rapidsai/cudf/pull/9762)) [@jlowe](https://github.com/jlowe)
+- Use Java classloader to find test resources ([#9760](https://github.com/rapidsai/cudf/pull/9760)) [@jlowe](https://github.com/jlowe)
+- Allow cast decimal128 to string and add tests ([#9756](https://github.com/rapidsai/cudf/pull/9756)) [@razajafri](https://github.com/razajafri)
+- Load balance optimization for contiguous_split ([#9755](https://github.com/rapidsai/cudf/pull/9755)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Consolidate and improve `reset_index` ([#9750](https://github.com/rapidsai/cudf/pull/9750)) [@isVoid](https://github.com/isVoid)
+- Update to UCX-Py 0.24 ([#9748](https://github.com/rapidsai/cudf/pull/9748)) [@pentschev](https://github.com/pentschev)
+- Skip cufile tests in JNI build script ([#9744](https://github.com/rapidsai/cudf/pull/9744)) [@pxLi](https://github.com/pxLi)
+- Enable string to decimal 128 cast ([#9742](https://github.com/rapidsai/cudf/pull/9742)) [@razajafri](https://github.com/razajafri)
+- Use stop instead of stop_. ([#9735](https://github.com/rapidsai/cudf/pull/9735)) [@bdice](https://github.com/bdice)
+- Forward-merge branch-21.12 to branch-22.02 ([#9730](https://github.com/rapidsai/cudf/pull/9730)) [@bdice](https://github.com/bdice)
+- Improve cmake format script ([#9723](https://github.com/rapidsai/cudf/pull/9723)) [@vyasr](https://github.com/vyasr)
+- Use cuFile direct device reads/writes by default in cuIO ([#9722](https://github.com/rapidsai/cudf/pull/9722)) [@vuule](https://github.com/vuule)
+- Add directory-partitioned data support to cudf.read_parquet ([#9720](https://github.com/rapidsai/cudf/pull/9720)) [@rjzamora](https://github.com/rjzamora)
+- Use stream allocator adaptor for hash join table ([#9704](https://github.com/rapidsai/cudf/pull/9704)) [@PointKernel](https://github.com/PointKernel)
+- Update check for inf/nan strings in libcudf float conversion to ignore case ([#9694](https://github.com/rapidsai/cudf/pull/9694)) [@davidwendt](https://github.com/davidwendt)
+- Update cudf JNI to 22.02.0-SNAPSHOT ([#9681](https://github.com/rapidsai/cudf/pull/9681)) [@pxLi](https://github.com/pxLi)
+- Replace cudf&#39;s concurrent_ordered_map with cuco::static_map in semi/anti joins ([#9666](https://github.com/rapidsai/cudf/pull/9666)) [@vyasr](https://github.com/vyasr)
+- Some improvements to `parse_decimal` function and bindings for `is_fixed_point` ([#9658](https://github.com/rapidsai/cudf/pull/9658)) [@razajafri](https://github.com/razajafri)
+- Add utility to format ninja-log build times ([#9631](https://github.com/rapidsai/cudf/pull/9631)) [@davidwendt](https://github.com/davidwendt)
+- Allow runtime has_nulls parameter for row operators ([#9623](https://github.com/rapidsai/cudf/pull/9623)) [@davidwendt](https://github.com/davidwendt)
+- Use fsspec.parquet for improved read_parquet performance from remote storage ([#9589](https://github.com/rapidsai/cudf/pull/9589)) [@rjzamora](https://github.com/rjzamora)
+- Refactor bit counting APIs, introduce valid/null count functions, and split host/device side code for segmented counts. ([#9588](https://github.com/rapidsai/cudf/pull/9588)) [@bdice](https://github.com/bdice)
+- Use List of Columns as Input for `drop_nulls`, `gather` and `drop_duplicates` ([#9558](https://github.com/rapidsai/cudf/pull/9558)) [@isVoid](https://github.com/isVoid)
+- Simplify merge internals and reduce overhead ([#9516](https://github.com/rapidsai/cudf/pull/9516)) [@vyasr](https://github.com/vyasr)
+- Add `struct` generation support in datagenerator &amp; fuzz tests ([#9180](https://github.com/rapidsai/cudf/pull/9180)) [@galipremsagar](https://github.com/galipremsagar)
+- Simplify write_csv by removing unnecessary writer/impl classes ([#9089](https://github.com/rapidsai/cudf/pull/9089)) [@cwharris](https://github.com/cwharris)
+
+# cuDF 21.12.00 (9 Dec 2021)
+
+## 🚨 Breaking Changes
+
+- Update `bitmask_and` and `bitmask_or` to return a pair of resulting mask and count of unset bits ([#9616](https://github.com/rapidsai/cudf/pull/9616)) [@PointKernel](https://github.com/PointKernel)
+- Remove sizeof and standardize on memory_usage ([#9544](https://github.com/rapidsai/cudf/pull/9544)) [@vyasr](https://github.com/vyasr)
+- Add support for single-line regex anchors ^/$ in contains_re ([#9482](https://github.com/rapidsai/cudf/pull/9482)) [@davidwendt](https://github.com/davidwendt)
+- Refactor sorting APIs ([#9464](https://github.com/rapidsai/cudf/pull/9464)) [@vyasr](https://github.com/vyasr)
+- Update Java nvcomp JNI bindings to nvcomp 2.x API ([#9384](https://github.com/rapidsai/cudf/pull/9384)) [@jbrennan333](https://github.com/jbrennan333)
+- Support Python UDFs written in terms of rows ([#9343](https://github.com/rapidsai/cudf/pull/9343)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- JNI: Support nested types in ORC writer ([#9334](https://github.com/rapidsai/cudf/pull/9334)) [@firestarman](https://github.com/firestarman)
+- Optionally nullify out-of-bounds indices in segmented_gather(). ([#9318](https://github.com/rapidsai/cudf/pull/9318)) [@mythrocks](https://github.com/mythrocks)
+- Refactor cuIO timestamp processing with `cuda::std::chrono` ([#9278](https://github.com/rapidsai/cudf/pull/9278)) [@PointKernel](https://github.com/PointKernel)
+- Various internal MultiIndex improvements ([#9243](https://github.com/rapidsai/cudf/pull/9243)) [@vyasr](https://github.com/vyasr)
+
+## 🐛 Bug Fixes
+
+- Fix read_parquet bug for bytes input ([#9669](https://github.com/rapidsai/cudf/pull/9669)) [@rjzamora](https://github.com/rjzamora)
+- Use `_gather` internal for `sort_*` ([#9668](https://github.com/rapidsai/cudf/pull/9668)) [@isVoid](https://github.com/isVoid)
+- Fix behavior of equals for non-DataFrame Frames and add tests. ([#9653](https://github.com/rapidsai/cudf/pull/9653)) [@vyasr](https://github.com/vyasr)
+- Dont recompute output size if it is already available ([#9649](https://github.com/rapidsai/cudf/pull/9649)) [@abellina](https://github.com/abellina)
+- Fix read_parquet bug for extended dtypes from remote storage ([#9638](https://github.com/rapidsai/cudf/pull/9638)) [@rjzamora](https://github.com/rjzamora)
+- add const when getting data from a JNI data wrapper ([#9637](https://github.com/rapidsai/cudf/pull/9637)) [@wjxiz1992](https://github.com/wjxiz1992)
+- Fix debrotli issue on CUDA 11.5 ([#9632](https://github.com/rapidsai/cudf/pull/9632)) [@vuule](https://github.com/vuule)
+- Use std::size_t when computing join output size ([#9626](https://github.com/rapidsai/cudf/pull/9626)) [@jlowe](https://github.com/jlowe)
+- Fix `usecols` parameter handling in `dask_cudf.read_csv` ([#9618](https://github.com/rapidsai/cudf/pull/9618)) [@galipremsagar](https://github.com/galipremsagar)
+- Add support for string `&#39;nan&#39;, &#39;inf&#39; &amp; &#39;-inf&#39;` values while type-casting to `float` ([#9613](https://github.com/rapidsai/cudf/pull/9613)) [@galipremsagar](https://github.com/galipremsagar)
+- Avoid passing NativeFileDatasource to pyarrow in read_parquet ([#9608](https://github.com/rapidsai/cudf/pull/9608)) [@rjzamora](https://github.com/rjzamora)
+- Fix test failure with cuda 11.5 in row_bit_count tests. ([#9581](https://github.com/rapidsai/cudf/pull/9581)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Correct _LIBCUDACXX_CUDACC_VER value computation ([#9579](https://github.com/rapidsai/cudf/pull/9579)) [@robertmaynard](https://github.com/robertmaynard)
+- Increase max RLE stream size estimate to avoid potential overflows ([#9568](https://github.com/rapidsai/cudf/pull/9568)) [@vuule](https://github.com/vuule)
+- Fix edge case in tdigest scalar generation for groups containing all nulls. ([#9551](https://github.com/rapidsai/cudf/pull/9551)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix pytests failing in `cuda-11.5` environment ([#9547](https://github.com/rapidsai/cudf/pull/9547)) [@galipremsagar](https://github.com/galipremsagar)
+- compile libnvcomp with PTDS if requested ([#9540](https://github.com/rapidsai/cudf/pull/9540)) [@jbrennan333](https://github.com/jbrennan333)
+- Fix `segmented_gather()` for null LIST rows ([#9537](https://github.com/rapidsai/cudf/pull/9537)) [@mythrocks](https://github.com/mythrocks)
+- Deprecate DataFrame.label_encoding, use private _label_encoding method internally. ([#9535](https://github.com/rapidsai/cudf/pull/9535)) [@bdice](https://github.com/bdice)
+- Fix several test and benchmark issues related to bitmask allocations. ([#9521](https://github.com/rapidsai/cudf/pull/9521)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix for inserting duplicates in groupby result cache ([#9508](https://github.com/rapidsai/cudf/pull/9508)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix mismatched types error in clip() when using non int64 numeric types ([#9498](https://github.com/rapidsai/cudf/pull/9498)) [@davidwendt](https://github.com/davidwendt)
+- Match conda pinnings for style checks (revert part of #9412, #9433). ([#9490](https://github.com/rapidsai/cudf/pull/9490)) [@bdice](https://github.com/bdice)
+- Make sure all dask-cudf supported aggs are handled in `_tree_node_agg` ([#9487](https://github.com/rapidsai/cudf/pull/9487)) [@charlesbluca](https://github.com/charlesbluca)
+- Resolve `hash_columns` `FutureWarning` in `dask_cudf` ([#9481](https://github.com/rapidsai/cudf/pull/9481)) [@pentschev](https://github.com/pentschev)
+- Add fixed point to AllTypes in libcudf unit tests ([#9472](https://github.com/rapidsai/cudf/pull/9472)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix regex handling of embedded null characters ([#9470](https://github.com/rapidsai/cudf/pull/9470)) [@davidwendt](https://github.com/davidwendt)
+- Fix memcheck error in copy-if-else ([#9467](https://github.com/rapidsai/cudf/pull/9467)) [@davidwendt](https://github.com/davidwendt)
+- Fix bug in dask_cudf.read_parquet for index=False ([#9453](https://github.com/rapidsai/cudf/pull/9453)) [@rjzamora](https://github.com/rjzamora)
+- Preserve the decimal scale when creating a default scalar ([#9449](https://github.com/rapidsai/cudf/pull/9449)) [@revans2](https://github.com/revans2)
+- Push down parent nulls when flattening nested columns. ([#9443](https://github.com/rapidsai/cudf/pull/9443)) [@mythrocks](https://github.com/mythrocks)
+- Fix memcheck error in gtest SegmentedGatherTest/GatherSliced ([#9442](https://github.com/rapidsai/cudf/pull/9442)) [@davidwendt](https://github.com/davidwendt)
+- Revert &quot;Fix quantile division / partition handling for dask-cudf sort… ([#9438](https://github.com/rapidsai/cudf/pull/9438)) [@charlesbluca](https://github.com/charlesbluca)
+- Allow int-like objects for the `decimals` argument in `round` ([#9428](https://github.com/rapidsai/cudf/pull/9428)) [@shwina](https://github.com/shwina)
+- Fix stream compaction&#39;s `drop_duplicates` API to use stable sort ([#9417](https://github.com/rapidsai/cudf/pull/9417)) [@ttnghia](https://github.com/ttnghia)
+- Skip Comparing Uniform Window Results in Var/std Tests ([#9416](https://github.com/rapidsai/cudf/pull/9416)) [@isVoid](https://github.com/isVoid)
+- Fix `StructColumn.to_pandas` type handling issues ([#9388](https://github.com/rapidsai/cudf/pull/9388)) [@galipremsagar](https://github.com/galipremsagar)
+- Correct issues in the build dir cudf-config.cmake ([#9386](https://github.com/rapidsai/cudf/pull/9386)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix Java table partition test to account for non-deterministic ordering ([#9385](https://github.com/rapidsai/cudf/pull/9385)) [@jlowe](https://github.com/jlowe)
+- Fix timestamp truncation/overflow bugs in orc/parquet ([#9382](https://github.com/rapidsai/cudf/pull/9382)) [@PointKernel](https://github.com/PointKernel)
+- Fix the crash in stats code ([#9368](https://github.com/rapidsai/cudf/pull/9368)) [@devavret](https://github.com/devavret)
+- Make Series.hash_encode results reproducible. ([#9366](https://github.com/rapidsai/cudf/pull/9366)) [@bdice](https://github.com/bdice)
+- Fix libcudf compile warnings on debug 11.4 build ([#9360](https://github.com/rapidsai/cudf/pull/9360)) [@davidwendt](https://github.com/davidwendt)
+- Fail gracefully when compiling python UDFs that attempt to access columns with unsupported dtypes ([#9359](https://github.com/rapidsai/cudf/pull/9359)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Set pass_filenames: false in mypy pre-commit configuration. ([#9349](https://github.com/rapidsai/cudf/pull/9349)) [@bdice](https://github.com/bdice)
+- Fix cudf_assert in cudf::io::orc::gpu::gpuDecodeOrcColumnData ([#9348](https://github.com/rapidsai/cudf/pull/9348)) [@davidwendt](https://github.com/davidwendt)
+- Fix memcheck error in groupby-tdigest get_scalar_minmax ([#9339](https://github.com/rapidsai/cudf/pull/9339)) [@davidwendt](https://github.com/davidwendt)
+- Optimizations for `cudf.concat` when `axis=1` ([#9333](https://github.com/rapidsai/cudf/pull/9333)) [@galipremsagar](https://github.com/galipremsagar)
+- Use f-string in join helper warning message. ([#9325](https://github.com/rapidsai/cudf/pull/9325)) [@bdice](https://github.com/bdice)
+- Avoid casting to list or struct dtypes in dask_cudf.read_parquet ([#9314](https://github.com/rapidsai/cudf/pull/9314)) [@rjzamora](https://github.com/rjzamora)
+- Fix null count in statistics for parquet ([#9303](https://github.com/rapidsai/cudf/pull/9303)) [@devavret](https://github.com/devavret)
+- Potential overflow of `decimal32` when casting to `int64_t` ([#9287](https://github.com/rapidsai/cudf/pull/9287)) [@codereport](https://github.com/codereport)
+- Fix quantile division / partition handling for dask-cudf sort on null dataframes ([#9259](https://github.com/rapidsai/cudf/pull/9259)) [@charlesbluca](https://github.com/charlesbluca)
+- Updating cudf version also updates rapids cmake branch ([#9249](https://github.com/rapidsai/cudf/pull/9249)) [@robertmaynard](https://github.com/robertmaynard)
+- Implement `one_hot_encoding` in libcudf and bind to python ([#9229](https://github.com/rapidsai/cudf/pull/9229)) [@isVoid](https://github.com/isVoid)
+- BUG FIX: CSV Writer ignores the header parameter when no metadata is provided ([#8740](https://github.com/rapidsai/cudf/pull/8740)) [@skirui-source](https://github.com/skirui-source)
+
+## 📖 Documentation
+
+- Update Documentation to use `TYPED_TEST_SUITE` ([#9654](https://github.com/rapidsai/cudf/pull/9654)) [@codereport](https://github.com/codereport)
+- Add dedicated page for `StringHandling` in python docs ([#9624](https://github.com/rapidsai/cudf/pull/9624)) [@galipremsagar](https://github.com/galipremsagar)
+- Update docstring of `DataFrame.merge` ([#9572](https://github.com/rapidsai/cudf/pull/9572)) [@galipremsagar](https://github.com/galipremsagar)
+- Use raw strings to avoid SyntaxErrors in parsed docstrings. ([#9526](https://github.com/rapidsai/cudf/pull/9526)) [@bdice](https://github.com/bdice)
+- Add example to docstrings in `rolling.apply` ([#9522](https://github.com/rapidsai/cudf/pull/9522)) [@isVoid](https://github.com/isVoid)
+- Update help message to escape quotes in ./build.sh --cmake-args. ([#9494](https://github.com/rapidsai/cudf/pull/9494)) [@bdice](https://github.com/bdice)
+- Improve Python docstring formatting. ([#9493](https://github.com/rapidsai/cudf/pull/9493)) [@bdice](https://github.com/bdice)
+- Update table of I/O supported types ([#9476](https://github.com/rapidsai/cudf/pull/9476)) [@vuule](https://github.com/vuule)
+- Document invalid regex patterns as undefined behavior ([#9473](https://github.com/rapidsai/cudf/pull/9473)) [@davidwendt](https://github.com/davidwendt)
+- Miscellaneous documentation fixes to `cudf` ([#9471](https://github.com/rapidsai/cudf/pull/9471)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix many documentation errors in libcudf. ([#9355](https://github.com/rapidsai/cudf/pull/9355)) [@karthikeyann](https://github.com/karthikeyann)
+- Fixing SubwordTokenizer docs issue ([#9354](https://github.com/rapidsai/cudf/pull/9354)) [@mayankanand007](https://github.com/mayankanand007)
+- Improved deprecation warnings. ([#9347](https://github.com/rapidsai/cudf/pull/9347)) [@bdice](https://github.com/bdice)
+- doc reorder mr, stream to stream, mr ([#9308](https://github.com/rapidsai/cudf/pull/9308)) [@karthikeyann](https://github.com/karthikeyann)
+- Deprecate method parameters to DataFrame.join, DataFrame.merge. ([#9291](https://github.com/rapidsai/cudf/pull/9291)) [@bdice](https://github.com/bdice)
+- Added deprecation warning for `.label_encoding()` ([#9289](https://github.com/rapidsai/cudf/pull/9289)) [@mayankanand007](https://github.com/mayankanand007)
+
+## 🚀 New Features
+
+- Enable Series.divide and DataFrame.divide ([#9630](https://github.com/rapidsai/cudf/pull/9630)) [@vyasr](https://github.com/vyasr)
+- Update `bitmask_and` and `bitmask_or` to return a pair of resulting mask and count of unset bits ([#9616](https://github.com/rapidsai/cudf/pull/9616)) [@PointKernel](https://github.com/PointKernel)
+- Add handling of mixed numeric types in `to_dlpack` ([#9585](https://github.com/rapidsai/cudf/pull/9585)) [@galipremsagar](https://github.com/galipremsagar)
+- Support re.Pattern object for pat arg in str.replace ([#9573](https://github.com/rapidsai/cudf/pull/9573)) [@davidwendt](https://github.com/davidwendt)
+- Add JNI for `lists::drop_list_duplicates` with keys-values input column ([#9553](https://github.com/rapidsai/cudf/pull/9553)) [@ttnghia](https://github.com/ttnghia)
+- Support structs column in `min`, `max`, `argmin` and `argmax` groupby aggregate() and scan() ([#9545](https://github.com/rapidsai/cudf/pull/9545)) [@ttnghia](https://github.com/ttnghia)
+- Move libcudacxx to use `rapids_cpm` and use newer versions ([#9539](https://github.com/rapidsai/cudf/pull/9539)) [@robertmaynard](https://github.com/robertmaynard)
+- Add scan min/max support for chrono types to libcudf reduction-scan (not groupby scan) ([#9518](https://github.com/rapidsai/cudf/pull/9518)) [@davidwendt](https://github.com/davidwendt)
+- Support `args=` in `apply` ([#9514](https://github.com/rapidsai/cudf/pull/9514)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add groupby scan min/max support for strings values ([#9502](https://github.com/rapidsai/cudf/pull/9502)) [@davidwendt](https://github.com/davidwendt)
+- Add list output option to character_ngrams() function ([#9499](https://github.com/rapidsai/cudf/pull/9499)) [@davidwendt](https://github.com/davidwendt)
+- More granular column selection in ORC reader ([#9496](https://github.com/rapidsai/cudf/pull/9496)) [@vuule](https://github.com/vuule)
+- add min_periods, ddof to groupby covariance, &amp; correlation aggregation ([#9492](https://github.com/rapidsai/cudf/pull/9492)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement Series.datetime.floor ([#9488](https://github.com/rapidsai/cudf/pull/9488)) [@skirui-source](https://github.com/skirui-source)
+- Enable linting of CMake files using pre-commit ([#9484](https://github.com/rapidsai/cudf/pull/9484)) [@vyasr](https://github.com/vyasr)
+- Add support for single-line regex anchors ^/$ in contains_re ([#9482](https://github.com/rapidsai/cudf/pull/9482)) [@davidwendt](https://github.com/davidwendt)
+- Augment `order_by` to Accept a List of `null_precedence` ([#9455](https://github.com/rapidsai/cudf/pull/9455)) [@isVoid](https://github.com/isVoid)
+- Add format API for list column of strings ([#9454](https://github.com/rapidsai/cudf/pull/9454)) [@davidwendt](https://github.com/davidwendt)
+- Enable Datetime/Timedelta dtypes in Masked UDFs ([#9451](https://github.com/rapidsai/cudf/pull/9451)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add cudf python groupby.diff ([#9446](https://github.com/rapidsai/cudf/pull/9446)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement `lists::stable_sort_lists` for stable sorting of elements within each row of lists column ([#9425](https://github.com/rapidsai/cudf/pull/9425)) [@ttnghia](https://github.com/ttnghia)
+- add ctest memcheck using cuda-sanitizer ([#9414](https://github.com/rapidsai/cudf/pull/9414)) [@karthikeyann](https://github.com/karthikeyann)
+- Support Unary Operations in Masked UDF ([#9409](https://github.com/rapidsai/cudf/pull/9409)) [@isVoid](https://github.com/isVoid)
+- Move Several Series Function to Frame ([#9394](https://github.com/rapidsai/cudf/pull/9394)) [@isVoid](https://github.com/isVoid)
+- MD5 Python hash API ([#9390](https://github.com/rapidsai/cudf/pull/9390)) [@bdice](https://github.com/bdice)
+- Add cudf strings is_title API ([#9380](https://github.com/rapidsai/cudf/pull/9380)) [@davidwendt](https://github.com/davidwendt)
+- Enable casting to int64, uint64, and double in AST code. ([#9379](https://github.com/rapidsai/cudf/pull/9379)) [@vyasr](https://github.com/vyasr)
+- Add support for writing ORC with map columns ([#9369](https://github.com/rapidsai/cudf/pull/9369)) [@vuule](https://github.com/vuule)
+- extract_list_elements() with column_view indices ([#9367](https://github.com/rapidsai/cudf/pull/9367)) [@mythrocks](https://github.com/mythrocks)
+- Reimplement `lists::drop_list_duplicates` for keys-values lists columns ([#9345](https://github.com/rapidsai/cudf/pull/9345)) [@ttnghia](https://github.com/ttnghia)
+- Support Python UDFs written in terms of rows ([#9343](https://github.com/rapidsai/cudf/pull/9343)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- JNI: Support nested types in ORC writer ([#9334](https://github.com/rapidsai/cudf/pull/9334)) [@firestarman](https://github.com/firestarman)
+- Optionally nullify out-of-bounds indices in segmented_gather(). ([#9318](https://github.com/rapidsai/cudf/pull/9318)) [@mythrocks](https://github.com/mythrocks)
+- Add shallow hash function and shallow equality comparison for column_view ([#9312](https://github.com/rapidsai/cudf/pull/9312)) [@karthikeyann](https://github.com/karthikeyann)
+- Add CudaMemoryBuffer for cudaMalloc memory using RMM cuda_memory_resource ([#9311](https://github.com/rapidsai/cudf/pull/9311)) [@rongou](https://github.com/rongou)
+- Add parameters to control row index stride and stripe size in ORC writer ([#9310](https://github.com/rapidsai/cudf/pull/9310)) [@vuule](https://github.com/vuule)
+- Add `na_position` param to dask-cudf `sort_values` ([#9264](https://github.com/rapidsai/cudf/pull/9264)) [@charlesbluca](https://github.com/charlesbluca)
+- Add `ascending` parameter for dask-cudf `sort_values` ([#9250](https://github.com/rapidsai/cudf/pull/9250)) [@charlesbluca](https://github.com/charlesbluca)
+- New array conversion methods ([#9236](https://github.com/rapidsai/cudf/pull/9236)) [@vyasr](https://github.com/vyasr)
+- Series `apply` method backed by masked UDFs ([#9217](https://github.com/rapidsai/cudf/pull/9217)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Grouping by frequency and resampling ([#9178](https://github.com/rapidsai/cudf/pull/9178)) [@shwina](https://github.com/shwina)
+- Pure-python masked UDFs ([#9174](https://github.com/rapidsai/cudf/pull/9174)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add Covariance, Pearson correlation for sort groupby (libcudf) ([#9154](https://github.com/rapidsai/cudf/pull/9154)) [@karthikeyann](https://github.com/karthikeyann)
+- Add `calendrical_month_sequence` in c++ and `date_range` in python ([#8886](https://github.com/rapidsai/cudf/pull/8886)) [@shwina](https://github.com/shwina)
+
+## 🛠️ Improvements
+
+- Followup to PR 9088 comments ([#9659](https://github.com/rapidsai/cudf/pull/9659)) [@cwharris](https://github.com/cwharris)
+- Update cuCollections to version that supports installed libcudacxx ([#9633](https://github.com/rapidsai/cudf/pull/9633)) [@robertmaynard](https://github.com/robertmaynard)
+- Add `11.5` dev.yml to `cudf` ([#9617](https://github.com/rapidsai/cudf/pull/9617)) [@galipremsagar](https://github.com/galipremsagar)
+- Add `xfail` for parquet reader `11.5` issue ([#9612](https://github.com/rapidsai/cudf/pull/9612)) [@galipremsagar](https://github.com/galipremsagar)
+- remove deprecated Rmm.initialize method ([#9607](https://github.com/rapidsai/cudf/pull/9607)) [@rongou](https://github.com/rongou)
+- Use HostColumnVectorCore for child columns in JCudfSerialization.unpackHostColumnVectors ([#9596](https://github.com/rapidsai/cudf/pull/9596)) [@sperlingxx](https://github.com/sperlingxx)
+- Set RMM pool to a fixed size in JNI ([#9583](https://github.com/rapidsai/cudf/pull/9583)) [@rongou](https://github.com/rongou)
+- Use nvCOMP for Snappy compression/decompression ([#9582](https://github.com/rapidsai/cudf/pull/9582)) [@vuule](https://github.com/vuule)
+- Build CUDA version agnostic packages for dask-cudf ([#9578](https://github.com/rapidsai/cudf/pull/9578)) [@Ethyling](https://github.com/Ethyling)
+- Fixed tests warning: &quot;TYPED_TEST_CASE is deprecated, please use TYPED_TEST_SUITE&quot; ([#9574](https://github.com/rapidsai/cudf/pull/9574)) [@ttnghia](https://github.com/ttnghia)
+- Enable CMake format in CI and fix style ([#9570](https://github.com/rapidsai/cudf/pull/9570)) [@vyasr](https://github.com/vyasr)
+- Add NVTX Start/End Ranges to JNI ([#9563](https://github.com/rapidsai/cudf/pull/9563)) [@abellina](https://github.com/abellina)
+- Add librdkafka and python-confluent-kafka to dev conda environments s… ([#9562](https://github.com/rapidsai/cudf/pull/9562)) [@jdye64](https://github.com/jdye64)
+- Add offsets_begin/end() to strings_column_view ([#9559](https://github.com/rapidsai/cudf/pull/9559)) [@davidwendt](https://github.com/davidwendt)
+- remove alignment options for RMM jni ([#9550](https://github.com/rapidsai/cudf/pull/9550)) [@rongou](https://github.com/rongou)
+- Add axis parameter passthrough to `DataFrame` and `Series` take for pandas API compatibility ([#9549](https://github.com/rapidsai/cudf/pull/9549)) [@dantegd](https://github.com/dantegd)
+- Remove sizeof and standardize on memory_usage ([#9544](https://github.com/rapidsai/cudf/pull/9544)) [@vyasr](https://github.com/vyasr)
+- Adds cudaProfilerStart/cudaProfilerStop in JNI api ([#9543](https://github.com/rapidsai/cudf/pull/9543)) [@abellina](https://github.com/abellina)
+- Generalize comparison binary operations ([#9542](https://github.com/rapidsai/cudf/pull/9542)) [@vyasr](https://github.com/vyasr)
+- Expose APIs to wrap CUDA or RMM allocations with a Java device buffer instance ([#9538](https://github.com/rapidsai/cudf/pull/9538)) [@jlowe](https://github.com/jlowe)
+- Add scan sum support for duration types to libcudf ([#9536](https://github.com/rapidsai/cudf/pull/9536)) [@davidwendt](https://github.com/davidwendt)
+- Force inlining to improve AST performance ([#9530](https://github.com/rapidsai/cudf/pull/9530)) [@vyasr](https://github.com/vyasr)
+- Generalize some more indexed frame methods ([#9529](https://github.com/rapidsai/cudf/pull/9529)) [@vyasr](https://github.com/vyasr)
+- Add Java bindings for rolling window stddev aggregation ([#9527](https://github.com/rapidsai/cudf/pull/9527)) [@razajafri](https://github.com/razajafri)
+- catch rmm::out_of_memory exceptions in jni ([#9525](https://github.com/rapidsai/cudf/pull/9525)) [@rongou](https://github.com/rongou)
+- Add an overload of `make_empty_column` with `type_id` parameter ([#9524](https://github.com/rapidsai/cudf/pull/9524)) [@ttnghia](https://github.com/ttnghia)
+- Accelerate conditional inner joins with larger right tables ([#9523](https://github.com/rapidsai/cudf/pull/9523)) [@vyasr](https://github.com/vyasr)
+- Initial pass of generalizing `decimal` support in `cudf` python layer ([#9517](https://github.com/rapidsai/cudf/pull/9517)) [@galipremsagar](https://github.com/galipremsagar)
+- Cleanup for flattening nested columns ([#9509](https://github.com/rapidsai/cudf/pull/9509)) [@rwlee](https://github.com/rwlee)
+- Enable running tests using RMM arena and async memory resources ([#9506](https://github.com/rapidsai/cudf/pull/9506)) [@rongou](https://github.com/rongou)
+- Remove dependency on six. ([#9495](https://github.com/rapidsai/cudf/pull/9495)) [@bdice](https://github.com/bdice)
+- Cleanup some libcudf strings gtests ([#9489](https://github.com/rapidsai/cudf/pull/9489)) [@davidwendt](https://github.com/davidwendt)
+- Rename strings/array_tests.cu to strings/array_tests.cpp ([#9480](https://github.com/rapidsai/cudf/pull/9480)) [@davidwendt](https://github.com/davidwendt)
+- Refactor sorting APIs ([#9464](https://github.com/rapidsai/cudf/pull/9464)) [@vyasr](https://github.com/vyasr)
+- Implement DataFrame.hash_values, deprecate DataFrame.hash_columns. ([#9458](https://github.com/rapidsai/cudf/pull/9458)) [@bdice](https://github.com/bdice)
+- Deprecate Series.hash_encode. ([#9457](https://github.com/rapidsai/cudf/pull/9457)) [@bdice](https://github.com/bdice)
+- Update `conda` recipes for Enhanced Compatibility effort ([#9456](https://github.com/rapidsai/cudf/pull/9456)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Small clean up to simplify column selection code in ORC reader ([#9444](https://github.com/rapidsai/cudf/pull/9444)) [@vuule](https://github.com/vuule)
+- add missing stream to scalar.is_valid() wherever stream is available ([#9436](https://github.com/rapidsai/cudf/pull/9436)) [@karthikeyann](https://github.com/karthikeyann)
+- Adds Deprecation Warnings to `one_hot_encoding` and Implement `get_dummies` with Cython API ([#9435](https://github.com/rapidsai/cudf/pull/9435)) [@isVoid](https://github.com/isVoid)
+- Update pre-commit hook URLs. ([#9433](https://github.com/rapidsai/cudf/pull/9433)) [@bdice](https://github.com/bdice)
+- Remove pyarrow import in `dask_cudf.io.parquet` ([#9429](https://github.com/rapidsai/cudf/pull/9429)) [@charlesbluca](https://github.com/charlesbluca)
+- Miscellaneous improvements for UDFs ([#9422](https://github.com/rapidsai/cudf/pull/9422)) [@isVoid](https://github.com/isVoid)
+- Use pre-commit for CI ([#9412](https://github.com/rapidsai/cudf/pull/9412)) [@vyasr](https://github.com/vyasr)
+- Update to UCX-Py 0.23 ([#9407](https://github.com/rapidsai/cudf/pull/9407)) [@pentschev](https://github.com/pentschev)
+- Expose OutOfBoundsPolicy in JNI for Table.gather ([#9406](https://github.com/rapidsai/cudf/pull/9406)) [@abellina](https://github.com/abellina)
+- Improvements to tdigest aggregation code. ([#9403](https://github.com/rapidsai/cudf/pull/9403)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add Java API to deserialize a table to host columns ([#9402](https://github.com/rapidsai/cudf/pull/9402)) [@jlowe](https://github.com/jlowe)
+- Frame copy to use __class__ instead of type() ([#9397](https://github.com/rapidsai/cudf/pull/9397)) [@madsbk](https://github.com/madsbk)
+- Change all DeprecationWarnings to FutureWarning. ([#9392](https://github.com/rapidsai/cudf/pull/9392)) [@bdice](https://github.com/bdice)
+- Update Java nvcomp JNI bindings to nvcomp 2.x API ([#9384](https://github.com/rapidsai/cudf/pull/9384)) [@jbrennan333](https://github.com/jbrennan333)
+- Add IndexedFrame class and move SingleColumnFrame to a separate module ([#9378](https://github.com/rapidsai/cudf/pull/9378)) [@vyasr](https://github.com/vyasr)
+- Support Arrow NativeFile and PythonFile for remote ORC storage ([#9377](https://github.com/rapidsai/cudf/pull/9377)) [@rjzamora](https://github.com/rjzamora)
+- Use Arrow PythonFile for remote CSV storage ([#9376](https://github.com/rapidsai/cudf/pull/9376)) [@rjzamora](https://github.com/rjzamora)
+- Add multi-threaded writing to GDS writes ([#9372](https://github.com/rapidsai/cudf/pull/9372)) [@devavret](https://github.com/devavret)
+- Miscellaneous column cleanup ([#9370](https://github.com/rapidsai/cudf/pull/9370)) [@vyasr](https://github.com/vyasr)
+- Use single kernel to extract all groups in cudf::strings::extract ([#9358](https://github.com/rapidsai/cudf/pull/9358)) [@davidwendt](https://github.com/davidwendt)
+- Consolidate binary ops into `Frame` ([#9357](https://github.com/rapidsai/cudf/pull/9357)) [@isVoid](https://github.com/isVoid)
+- Move rank scan implementations from scan_inclusive.cu to rank_scan.cu ([#9351](https://github.com/rapidsai/cudf/pull/9351)) [@davidwendt](https://github.com/davidwendt)
+- Remove usage of deprecated thrust::host_space_tag. ([#9350](https://github.com/rapidsai/cudf/pull/9350)) [@bdice](https://github.com/bdice)
+- Use Default Memory Resource for Temporaries in `reduction.cpp` ([#9344](https://github.com/rapidsai/cudf/pull/9344)) [@isVoid](https://github.com/isVoid)
+- Fix Cython compilation warnings. ([#9327](https://github.com/rapidsai/cudf/pull/9327)) [@bdice](https://github.com/bdice)
+- Fix some unused variable warnings in libcudf ([#9326](https://github.com/rapidsai/cudf/pull/9326)) [@davidwendt](https://github.com/davidwendt)
+- Use optional-iterator for copy-if-else kernel ([#9324](https://github.com/rapidsai/cudf/pull/9324)) [@davidwendt](https://github.com/davidwendt)
+- Remove Table class ([#9315](https://github.com/rapidsai/cudf/pull/9315)) [@vyasr](https://github.com/vyasr)
+- Unpin `dask` and `distributed` in CI ([#9307](https://github.com/rapidsai/cudf/pull/9307)) [@galipremsagar](https://github.com/galipremsagar)
+- Add optional-iterator support to indexalator ([#9306](https://github.com/rapidsai/cudf/pull/9306)) [@davidwendt](https://github.com/davidwendt)
+- Consolidate more methods in Frame ([#9305](https://github.com/rapidsai/cudf/pull/9305)) [@vyasr](https://github.com/vyasr)
+- Add Arrow-NativeFile and PythonFile support to read_parquet and read_csv in cudf ([#9304](https://github.com/rapidsai/cudf/pull/9304)) [@rjzamora](https://github.com/rjzamora)
+- Pin mypy in .pre-commit-config.yaml to match conda environment pinning. ([#9300](https://github.com/rapidsai/cudf/pull/9300)) [@bdice](https://github.com/bdice)
+- Use gather.hpp when gather-map exists in device memory ([#9299](https://github.com/rapidsai/cudf/pull/9299)) [@davidwendt](https://github.com/davidwendt)
+- Fix Automerger for `Branch-21.12` from `branch-21.10` ([#9285](https://github.com/rapidsai/cudf/pull/9285)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactor cuIO timestamp processing with `cuda::std::chrono` ([#9278](https://github.com/rapidsai/cudf/pull/9278)) [@PointKernel](https://github.com/PointKernel)
+- Change strings copy_if_else to use optional-iterator instead of pair-iterator ([#9266](https://github.com/rapidsai/cudf/pull/9266)) [@davidwendt](https://github.com/davidwendt)
+- Update cudf java bindings to 21.12.0-SNAPSHOT ([#9248](https://github.com/rapidsai/cudf/pull/9248)) [@pxLi](https://github.com/pxLi)
+- Various internal MultiIndex improvements ([#9243](https://github.com/rapidsai/cudf/pull/9243)) [@vyasr](https://github.com/vyasr)
+- Add detail interface for `split` and `slice(table_view)`, refactors both function with `host_span` ([#9226](https://github.com/rapidsai/cudf/pull/9226)) [@isVoid](https://github.com/isVoid)
+- Refactor MD5 implementation. ([#9212](https://github.com/rapidsai/cudf/pull/9212)) [@bdice](https://github.com/bdice)
+- Update groupby result_cache to allow sharing intermediate results based on column_view instead of requests. ([#9195](https://github.com/rapidsai/cudf/pull/9195)) [@karthikeyann](https://github.com/karthikeyann)
+- Use nvcomp&#39;s snappy decompressor in avro reader ([#9181](https://github.com/rapidsai/cudf/pull/9181)) [@devavret](https://github.com/devavret)
+- Add `isocalendar` API support ([#9169](https://github.com/rapidsai/cudf/pull/9169)) [@marlenezw](https://github.com/marlenezw)
+- Simplify read_json by removing unnecessary reader/impl classes ([#9088](https://github.com/rapidsai/cudf/pull/9088)) [@cwharris](https://github.com/cwharris)
+- Simplify read_csv by removing unnecessary reader/impl classes ([#9041](https://github.com/rapidsai/cudf/pull/9041)) [@cwharris](https://github.com/cwharris)
+- Refactor hash join with cuCollections multimap ([#8934](https://github.com/rapidsai/cudf/pull/8934)) [@PointKernel](https://github.com/PointKernel)
+
+# cuDF 21.10.00 (7 Oct 2021)
+
+## 🚨 Breaking Changes
+
+- Remove Cython APIs for table view generation ([#9199](https://github.com/rapidsai/cudf/pull/9199)) [@vyasr](https://github.com/vyasr)
+- Upgrade `pandas` version in `cudf` ([#9147](https://github.com/rapidsai/cudf/pull/9147)) [@galipremsagar](https://github.com/galipremsagar)
+- Make AST operators nullable ([#9096](https://github.com/rapidsai/cudf/pull/9096)) [@vyasr](https://github.com/vyasr)
+- Remove the option to pass data types as strings to `read_csv` and `read_json` ([#9079](https://github.com/rapidsai/cudf/pull/9079)) [@vuule](https://github.com/vuule)
+- Update JNI java CSV APIs to not use deprecated API ([#9066](https://github.com/rapidsai/cudf/pull/9066)) [@revans2](https://github.com/revans2)
+- Support additional format specifiers in from_timestamps ([#9047](https://github.com/rapidsai/cudf/pull/9047)) [@davidwendt](https://github.com/davidwendt)
+- Expose expression base class publicly and simplify public AST API ([#9045](https://github.com/rapidsai/cudf/pull/9045)) [@vyasr](https://github.com/vyasr)
+- Add support for struct type in ORC writer ([#9025](https://github.com/rapidsai/cudf/pull/9025)) [@vuule](https://github.com/vuule)
+- Remove aliases of various api.types APIs from utils.dtypes. ([#9011](https://github.com/rapidsai/cudf/pull/9011)) [@vyasr](https://github.com/vyasr)
+- Java bindings for conditional join output sizes ([#9002](https://github.com/rapidsai/cudf/pull/9002)) [@jlowe](https://github.com/jlowe)
+- Move compute_column API out of ast namespace ([#8957](https://github.com/rapidsai/cudf/pull/8957)) [@vyasr](https://github.com/vyasr)
+- `cudf.dtype` function ([#8949](https://github.com/rapidsai/cudf/pull/8949)) [@shwina](https://github.com/shwina)
+- Refactor Frame reductions ([#8944](https://github.com/rapidsai/cudf/pull/8944)) [@vyasr](https://github.com/vyasr)
+- Add nested column selection to parquet reader ([#8933](https://github.com/rapidsai/cudf/pull/8933)) [@devavret](https://github.com/devavret)
+- JNI Aggregation Type Changes ([#8919](https://github.com/rapidsai/cudf/pull/8919)) [@revans2](https://github.com/revans2)
+- Add groupby_aggregation and groupby_scan_aggregation classes and force their usage. ([#8906](https://github.com/rapidsai/cudf/pull/8906)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Expand CSV and JSON reader APIs to accept `dtypes` as a vector or map of `data_type` objects ([#8856](https://github.com/rapidsai/cudf/pull/8856)) [@vuule](https://github.com/vuule)
+- Change cudf docs theme to pydata theme ([#8746](https://github.com/rapidsai/cudf/pull/8746)) [@galipremsagar](https://github.com/galipremsagar)
+- Enable compiled binary ops in libcudf, python and java ([#8741](https://github.com/rapidsai/cudf/pull/8741)) [@karthikeyann](https://github.com/karthikeyann)
+- Make groupby transform-like op order match original data order ([#8720](https://github.com/rapidsai/cudf/pull/8720)) [@isVoid](https://github.com/isVoid)
+
+## 🐛 Bug Fixes
+
+- `fixed_point` `cudf::groupby` for `mean` aggregation ([#9296](https://github.com/rapidsai/cudf/pull/9296)) [@codereport](https://github.com/codereport)
+- Fix `interleave_columns` when the input string lists column having empty child column ([#9292](https://github.com/rapidsai/cudf/pull/9292)) [@ttnghia](https://github.com/ttnghia)
+- Update nvcomp to include fixes for installation of headers ([#9276](https://github.com/rapidsai/cudf/pull/9276)) [@devavret](https://github.com/devavret)
+- Fix Java column leak in testParquetWriteMap ([#9271](https://github.com/rapidsai/cudf/pull/9271)) [@jlowe](https://github.com/jlowe)
+- Fix call to thrust::reduce_by_key in argmin/argmax libcudf groupby ([#9263](https://github.com/rapidsai/cudf/pull/9263)) [@davidwendt](https://github.com/davidwendt)
+- Fixing empty input to getMapValue crashing ([#9262](https://github.com/rapidsai/cudf/pull/9262)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fix duplicate names issue in `MultiIndex.deserialize ` ([#9258](https://github.com/rapidsai/cudf/pull/9258)) [@galipremsagar](https://github.com/galipremsagar)
+- `Dataframe.sort_index` optimizations ([#9238](https://github.com/rapidsai/cudf/pull/9238)) [@galipremsagar](https://github.com/galipremsagar)
+- Temporarily disabling problematic test in parquet writer ([#9230](https://github.com/rapidsai/cudf/pull/9230)) [@devavret](https://github.com/devavret)
+- Explicitly disable groupby on unsupported key types. ([#9227](https://github.com/rapidsai/cudf/pull/9227)) [@mythrocks](https://github.com/mythrocks)
+- Fix `gather` for sliced input structs column ([#9218](https://github.com/rapidsai/cudf/pull/9218)) [@ttnghia](https://github.com/ttnghia)
+- Fix JNI code for left semi and anti joins ([#9207](https://github.com/rapidsai/cudf/pull/9207)) [@jlowe](https://github.com/jlowe)
+- Only install thrust when using a non &#39;system&#39; version ([#9206](https://github.com/rapidsai/cudf/pull/9206)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove zlib from libcudf public CMake dependencies ([#9204](https://github.com/rapidsai/cudf/pull/9204)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix out-of-bounds memory read in orc gpuEncodeOrcColumnData ([#9196](https://github.com/rapidsai/cudf/pull/9196)) [@davidwendt](https://github.com/davidwendt)
+- Fix `gather()` for `STRUCT` inputs with no nulls in members. ([#9194](https://github.com/rapidsai/cudf/pull/9194)) [@mythrocks](https://github.com/mythrocks)
+- get_cucollections properly uses rapids_cpm_find ([#9189](https://github.com/rapidsai/cudf/pull/9189)) [@robertmaynard](https://github.com/robertmaynard)
+- rapids-export correctly reference build code block and doc strings ([#9186](https://github.com/rapidsai/cudf/pull/9186)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix logic while parsing the sum statistic for numerical orc columns ([#9183](https://github.com/rapidsai/cudf/pull/9183)) [@ayushdg](https://github.com/ayushdg)
+- Add handling for nulls in `dask_cudf.sorting.quantile_divisions` ([#9171](https://github.com/rapidsai/cudf/pull/9171)) [@charlesbluca](https://github.com/charlesbluca)
+- Approximate overflow detection in ORC statistics ([#9163](https://github.com/rapidsai/cudf/pull/9163)) [@vuule](https://github.com/vuule)
+- Use decimal precision metadata when reading from parquet files ([#9162](https://github.com/rapidsai/cudf/pull/9162)) [@shwina](https://github.com/shwina)
+- Fix variable name in Java build script ([#9161](https://github.com/rapidsai/cudf/pull/9161)) [@jlowe](https://github.com/jlowe)
+- Import rapids-cmake modules using the correct cmake variable. ([#9149](https://github.com/rapidsai/cudf/pull/9149)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix conditional joins with empty left table ([#9146](https://github.com/rapidsai/cudf/pull/9146)) [@vyasr](https://github.com/vyasr)
+- Fix joining on indexes with duplicate level names ([#9137](https://github.com/rapidsai/cudf/pull/9137)) [@shwina](https://github.com/shwina)
+- Fixes missing child column name in dtype while reading ORC file. ([#9134](https://github.com/rapidsai/cudf/pull/9134)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Apply type metadata after column is slice-copied ([#9131](https://github.com/rapidsai/cudf/pull/9131)) [@isVoid](https://github.com/isVoid)
+- Fix a bug: inner_join_size return zero if build table is empty ([#9128](https://github.com/rapidsai/cudf/pull/9128)) [@PointKernel](https://github.com/PointKernel)
+- Fix multi hive-partition parquet reading in dask-cudf ([#9122](https://github.com/rapidsai/cudf/pull/9122)) [@rjzamora](https://github.com/rjzamora)
+- Support null literals in expressions ([#9117](https://github.com/rapidsai/cudf/pull/9117)) [@vyasr](https://github.com/vyasr)
+- Fix cudf::hash_join output size for struct joins ([#9107](https://github.com/rapidsai/cudf/pull/9107)) [@jlowe](https://github.com/jlowe)
+- Import fix ([#9104](https://github.com/rapidsai/cudf/pull/9104)) [@shwina](https://github.com/shwina)
+- Fix cudf::strings::is_fixed_point checking of overflow for decimal32 ([#9093](https://github.com/rapidsai/cudf/pull/9093)) [@davidwendt](https://github.com/davidwendt)
+- Fix branch_stack calculation in `row_bit_count()` ([#9076](https://github.com/rapidsai/cudf/pull/9076)) [@mythrocks](https://github.com/mythrocks)
+- Fetch rapids-cmake to work around cuCollection cmake issue ([#9075](https://github.com/rapidsai/cudf/pull/9075)) [@jlowe](https://github.com/jlowe)
+- Fix compilation errors in groupby benchmarks. ([#9072](https://github.com/rapidsai/cudf/pull/9072)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Preserve float16 upscaling ([#9069](https://github.com/rapidsai/cudf/pull/9069)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix memcheck read error in libcudf contiguous_split ([#9067](https://github.com/rapidsai/cudf/pull/9067)) [@davidwendt](https://github.com/davidwendt)
+- Add support for reading ORC file with no row group index ([#9060](https://github.com/rapidsai/cudf/pull/9060)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Various multiindex related fixes ([#9036](https://github.com/rapidsai/cudf/pull/9036)) [@shwina](https://github.com/shwina)
+- Avoid rebuilding cython in build.sh ([#9034](https://github.com/rapidsai/cudf/pull/9034)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add support for percentile dispatch in `dask_cudf` ([#9031](https://github.com/rapidsai/cudf/pull/9031)) [@galipremsagar](https://github.com/galipremsagar)
+- cudf resolve nvcc 11.0 compiler crashes during codegen ([#9028](https://github.com/rapidsai/cudf/pull/9028)) [@robertmaynard](https://github.com/robertmaynard)
+- Fetch correct grouping keys `agg` of dask groupby ([#9022](https://github.com/rapidsai/cudf/pull/9022)) [@galipremsagar](https://github.com/galipremsagar)
+- Allow `where()` to work with a Series and `other=cudf.NA` ([#9019](https://github.com/rapidsai/cudf/pull/9019)) [@sarahyurick](https://github.com/sarahyurick)
+- Use correct index when returning Series from `GroupBy.apply()` ([#9016](https://github.com/rapidsai/cudf/pull/9016)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix `Dataframe` indexer setitem when array is passed ([#9006](https://github.com/rapidsai/cudf/pull/9006)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix ORC reading of files with struct columns that have null values ([#9005](https://github.com/rapidsai/cudf/pull/9005)) [@vuule](https://github.com/vuule)
+- Ensure JNI native libraries load when CompiledExpression loads ([#8997](https://github.com/rapidsai/cudf/pull/8997)) [@jlowe](https://github.com/jlowe)
+- Fix memory read error in get_dremel_data in page_enc.cu ([#8995](https://github.com/rapidsai/cudf/pull/8995)) [@davidwendt](https://github.com/davidwendt)
+- Fix memory write error in get_list_child_to_list_row_mapping utility ([#8994](https://github.com/rapidsai/cudf/pull/8994)) [@davidwendt](https://github.com/davidwendt)
+- Fix debug compile error for csv_test.cpp ([#8981](https://github.com/rapidsai/cudf/pull/8981)) [@davidwendt](https://github.com/davidwendt)
+- Fix memory read/write error in concatenate_lists_ignore_null ([#8978](https://github.com/rapidsai/cudf/pull/8978)) [@davidwendt](https://github.com/davidwendt)
+- Fix concatenation of `cudf.RangeIndex` ([#8970](https://github.com/rapidsai/cudf/pull/8970)) [@galipremsagar](https://github.com/galipremsagar)
+- Java conditional joins should not require matching column counts ([#8955](https://github.com/rapidsai/cudf/pull/8955)) [@jlowe](https://github.com/jlowe)
+- Fix concatenate empty structs ([#8947](https://github.com/rapidsai/cudf/pull/8947)) [@sperlingxx](https://github.com/sperlingxx)
+- Fix cuda-memcheck errors for some libcudf functions ([#8941](https://github.com/rapidsai/cudf/pull/8941)) [@davidwendt](https://github.com/davidwendt)
+- Apply series name to result of `SeriesGroupby.apply()` ([#8939](https://github.com/rapidsai/cudf/pull/8939)) [@charlesbluca](https://github.com/charlesbluca)
+- `cdef packed_columns` as `cppclass` instead of `struct` ([#8936](https://github.com/rapidsai/cudf/pull/8936)) [@charlesbluca](https://github.com/charlesbluca)
+- Inserting a `cudf.NA` into a DataFrame ([#8923](https://github.com/rapidsai/cudf/pull/8923)) [@sarahyurick](https://github.com/sarahyurick)
+- Support casting with Pandas dtype aliases ([#8920](https://github.com/rapidsai/cudf/pull/8920)) [@sarahyurick](https://github.com/sarahyurick)
+- Allow `sort_values` to accept same `kind` values as Pandas ([#8912](https://github.com/rapidsai/cudf/pull/8912)) [@sarahyurick](https://github.com/sarahyurick)
+- Enable casting to pandas nullable dtypes ([#8889](https://github.com/rapidsai/cudf/pull/8889)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix libcudf memory errors ([#8884](https://github.com/rapidsai/cudf/pull/8884)) [@karthikeyann](https://github.com/karthikeyann)
+- Throw KeyError when accessing field from struct with nonexistent key ([#8880](https://github.com/rapidsai/cudf/pull/8880)) [@NV-jpt](https://github.com/NV-jpt)
+- replace auto with auto&amp; ref for cast&lt;&amp;&gt; ([#8866](https://github.com/rapidsai/cudf/pull/8866)) [@karthikeyann](https://github.com/karthikeyann)
+- Add missing include&lt;optional&gt; in binops ([#8864](https://github.com/rapidsai/cudf/pull/8864)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix `select_dtypes` to work when non-class dtypes present in dataframe ([#8849](https://github.com/rapidsai/cudf/pull/8849)) [@sarahyurick](https://github.com/sarahyurick)
+- Re-enable JSON tests ([#8843](https://github.com/rapidsai/cudf/pull/8843)) [@vuule](https://github.com/vuule)
+- Support header with embedded delimiter in csv writer ([#8798](https://github.com/rapidsai/cudf/pull/8798)) [@davidwendt](https://github.com/davidwendt)
+
+## 📖 Documentation
+
+- Add IO docs page in `cudf` documentation ([#9145](https://github.com/rapidsai/cudf/pull/9145)) [@galipremsagar](https://github.com/galipremsagar)
+- use correct namespace in cuio code examples ([#9037](https://github.com/rapidsai/cudf/pull/9037)) [@cwharris](https://github.com/cwharris)
+- Restructuring `Contributing doc` ([#9026](https://github.com/rapidsai/cudf/pull/9026)) [@iskode](https://github.com/iskode)
+- Update stable version in readme ([#9008](https://github.com/rapidsai/cudf/pull/9008)) [@galipremsagar](https://github.com/galipremsagar)
+- Add spans and more include guidelines to libcudf developer guide ([#8931](https://github.com/rapidsai/cudf/pull/8931)) [@harrism](https://github.com/harrism)
+- Update Java build instructions to mention Arrow S3 and Docker ([#8867](https://github.com/rapidsai/cudf/pull/8867)) [@jlowe](https://github.com/jlowe)
+- List GDS-enabled formats in the docs ([#8805](https://github.com/rapidsai/cudf/pull/8805)) [@vuule](https://github.com/vuule)
+- Change cudf docs theme to pydata theme ([#8746](https://github.com/rapidsai/cudf/pull/8746)) [@galipremsagar](https://github.com/galipremsagar)
+
+## 🚀 New Features
+
+- Revert &quot;Add shallow hash function and shallow equality comparison for column_view ([#9185)&quot; (#9283](https://github.com/rapidsai/cudf/pull/9185)&quot; (#9283)) [@karthikeyann](https://github.com/karthikeyann)
+- Align `DataFrame.apply` signature with pandas ([#9275](https://github.com/rapidsai/cudf/pull/9275)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add struct type support for `drop_list_duplicates` ([#9202](https://github.com/rapidsai/cudf/pull/9202)) [@ttnghia](https://github.com/ttnghia)
+- support CUDA async memory resource in JNI ([#9201](https://github.com/rapidsai/cudf/pull/9201)) [@rongou](https://github.com/rongou)
+- Add shallow hash function and shallow equality comparison for column_view ([#9185](https://github.com/rapidsai/cudf/pull/9185)) [@karthikeyann](https://github.com/karthikeyann)
+- Superimpose null masks for STRUCT columns. ([#9144](https://github.com/rapidsai/cudf/pull/9144)) [@mythrocks](https://github.com/mythrocks)
+- Implemented bindings for `ceil` timestamp operation ([#9141](https://github.com/rapidsai/cudf/pull/9141)) [@shaneding](https://github.com/shaneding)
+- Adding MAP type support for ORC Reader ([#9132](https://github.com/rapidsai/cudf/pull/9132)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Implement `interleave_columns` for lists with arbitrary nested type ([#9130](https://github.com/rapidsai/cudf/pull/9130)) [@ttnghia](https://github.com/ttnghia)
+- Add python bindings to fixed-size window and groupby `rolling.var`, `rolling.std` ([#9097](https://github.com/rapidsai/cudf/pull/9097)) [@isVoid](https://github.com/isVoid)
+- Make AST operators nullable ([#9096](https://github.com/rapidsai/cudf/pull/9096)) [@vyasr](https://github.com/vyasr)
+- Java bindings for approx_percentile ([#9094](https://github.com/rapidsai/cudf/pull/9094)) [@andygrove](https://github.com/andygrove)
+- Add `dseries.struct.explode` ([#9086](https://github.com/rapidsai/cudf/pull/9086)) [@isVoid](https://github.com/isVoid)
+- Add support for BaseIndexer in Rolling APIs ([#9085](https://github.com/rapidsai/cudf/pull/9085)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove the option to pass data types as strings to `read_csv` and `read_json` ([#9079](https://github.com/rapidsai/cudf/pull/9079)) [@vuule](https://github.com/vuule)
+- Add handling for nested dicts in dask-cudf groupby ([#9054](https://github.com/rapidsai/cudf/pull/9054)) [@charlesbluca](https://github.com/charlesbluca)
+- Added Series.dt.is_quarter_start and Series.dt.is_quarter_end ([#9046](https://github.com/rapidsai/cudf/pull/9046)) [@TravisHester](https://github.com/TravisHester)
+- Support nested types for nth_element reduction ([#9043](https://github.com/rapidsai/cudf/pull/9043)) [@sperlingxx](https://github.com/sperlingxx)
+- Update sort groupby to use non-atomic operation ([#9035](https://github.com/rapidsai/cudf/pull/9035)) [@karthikeyann](https://github.com/karthikeyann)
+- Add support for struct type in ORC writer ([#9025](https://github.com/rapidsai/cudf/pull/9025)) [@vuule](https://github.com/vuule)
+- Implement `interleave_columns` for structs columns ([#9012](https://github.com/rapidsai/cudf/pull/9012)) [@ttnghia](https://github.com/ttnghia)
+- Add groupby first and last aggregations ([#9004](https://github.com/rapidsai/cudf/pull/9004)) [@shwina](https://github.com/shwina)
+- Add `DecimalBaseColumn` and move `as_decimal_column` ([#9001](https://github.com/rapidsai/cudf/pull/9001)) [@isVoid](https://github.com/isVoid)
+- Python/Cython bindings for multibyte_split ([#8998](https://github.com/rapidsai/cudf/pull/8998)) [@jdye64](https://github.com/jdye64)
+- Support scalar `months` in `add_calendrical_months`, extends API to INT32 support ([#8991](https://github.com/rapidsai/cudf/pull/8991)) [@isVoid](https://github.com/isVoid)
+- Added Series.dt.is_month_end ([#8989](https://github.com/rapidsai/cudf/pull/8989)) [@TravisHester](https://github.com/TravisHester)
+- Support for using tdigests to compute approximate percentiles. ([#8983](https://github.com/rapidsai/cudf/pull/8983)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Support &quot;unflatten&quot; of columns flattened via `flatten_nested_columns()`: ([#8956](https://github.com/rapidsai/cudf/pull/8956)) [@mythrocks](https://github.com/mythrocks)
+- Implement timestamp ceil ([#8942](https://github.com/rapidsai/cudf/pull/8942)) [@shaneding](https://github.com/shaneding)
+- Add nested column selection to parquet reader ([#8933](https://github.com/rapidsai/cudf/pull/8933)) [@devavret](https://github.com/devavret)
+- Expose conditional join size calculation ([#8928](https://github.com/rapidsai/cudf/pull/8928)) [@vyasr](https://github.com/vyasr)
+- Support Nulls in Timeseries Generator ([#8925](https://github.com/rapidsai/cudf/pull/8925)) [@isVoid](https://github.com/isVoid)
+- Avoid index equality check in `_CPackedColumns.from_py_table()` ([#8917](https://github.com/rapidsai/cudf/pull/8917)) [@charlesbluca](https://github.com/charlesbluca)
+- Add dot product binary op ([#8909](https://github.com/rapidsai/cudf/pull/8909)) [@charlesbluca](https://github.com/charlesbluca)
+- Expose `days_in_month` function in libcudf and add python bindings ([#8892](https://github.com/rapidsai/cudf/pull/8892)) [@isVoid](https://github.com/isVoid)
+- Series string repeat ([#8882](https://github.com/rapidsai/cudf/pull/8882)) [@sarahyurick](https://github.com/sarahyurick)
+- Python binding for quarters ([#8862](https://github.com/rapidsai/cudf/pull/8862)) [@shaneding](https://github.com/shaneding)
+- Expand CSV and JSON reader APIs to accept `dtypes` as a vector or map of `data_type` objects ([#8856](https://github.com/rapidsai/cudf/pull/8856)) [@vuule](https://github.com/vuule)
+- Add Java bindings for AST transform ([#8846](https://github.com/rapidsai/cudf/pull/8846)) [@jlowe](https://github.com/jlowe)
+- Series datetime is_month_start ([#8844](https://github.com/rapidsai/cudf/pull/8844)) [@sarahyurick](https://github.com/sarahyurick)
+- Support bracket syntax for cudf::strings::replace_with_backrefs group index values ([#8841](https://github.com/rapidsai/cudf/pull/8841)) [@davidwendt](https://github.com/davidwendt)
+- Support `VARIANCE` and `STD` aggregation in rolling op ([#8809](https://github.com/rapidsai/cudf/pull/8809)) [@isVoid](https://github.com/isVoid)
+- Add quarters to libcudf datetime ([#8779](https://github.com/rapidsai/cudf/pull/8779)) [@shaneding](https://github.com/shaneding)
+- Linear Interpolation of `nan`s via `cupy` ([#8767](https://github.com/rapidsai/cudf/pull/8767)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Enable compiled binary ops in libcudf, python and java ([#8741](https://github.com/rapidsai/cudf/pull/8741)) [@karthikeyann](https://github.com/karthikeyann)
+- Make groupby transform-like op order match original data order ([#8720](https://github.com/rapidsai/cudf/pull/8720)) [@isVoid](https://github.com/isVoid)
+- multibyte_split ([#8702](https://github.com/rapidsai/cudf/pull/8702)) [@cwharris](https://github.com/cwharris)
+- Implement JNI for `strings:repeat_strings` that repeats each string separately by different numbers of times ([#8572](https://github.com/rapidsai/cudf/pull/8572)) [@ttnghia](https://github.com/ttnghia)
+
+## 🛠️ Improvements
+
+- Pin max `dask` and `distributed` versions to `2021.09.1` ([#9286](https://github.com/rapidsai/cudf/pull/9286)) [@galipremsagar](https://github.com/galipremsagar)
+- Optimized fsspec data transfer for remote file-systems ([#9265](https://github.com/rapidsai/cudf/pull/9265)) [@rjzamora](https://github.com/rjzamora)
+- Skip dask-cudf tests on arm64 ([#9252](https://github.com/rapidsai/cudf/pull/9252)) [@Ethyling](https://github.com/Ethyling)
+- Use nvcomp&#39;s snappy compressor in ORC writer ([#9242](https://github.com/rapidsai/cudf/pull/9242)) [@devavret](https://github.com/devavret)
+- Only run imports tests on x86_64 ([#9241](https://github.com/rapidsai/cudf/pull/9241)) [@Ethyling](https://github.com/Ethyling)
+- Remove unnecessary call to device_uvector::release() ([#9237](https://github.com/rapidsai/cudf/pull/9237)) [@harrism](https://github.com/harrism)
+- Use nvcomp&#39;s snappy decompression in ORC reader ([#9235](https://github.com/rapidsai/cudf/pull/9235)) [@devavret](https://github.com/devavret)
+- Add grouped_rolling test with STRUCT groupby keys. ([#9228](https://github.com/rapidsai/cudf/pull/9228)) [@mythrocks](https://github.com/mythrocks)
+- Optimize `cudf.concat` for `axis=0` ([#9222](https://github.com/rapidsai/cudf/pull/9222)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix some libcudf calls not passing the stream parameter ([#9220](https://github.com/rapidsai/cudf/pull/9220)) [@davidwendt](https://github.com/davidwendt)
+- Add min and max bounds for random dataframe generator numeric types ([#9211](https://github.com/rapidsai/cudf/pull/9211)) [@galipremsagar](https://github.com/galipremsagar)
+- Improve performance of expression evaluation ([#9210](https://github.com/rapidsai/cudf/pull/9210)) [@vyasr](https://github.com/vyasr)
+- Misc optimizations in `cudf` ([#9203](https://github.com/rapidsai/cudf/pull/9203)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove Cython APIs for table view generation ([#9199](https://github.com/rapidsai/cudf/pull/9199)) [@vyasr](https://github.com/vyasr)
+- Add JNI support for drop_list_duplicates ([#9198](https://github.com/rapidsai/cudf/pull/9198)) [@revans2](https://github.com/revans2)
+- Update pandas versions in conda recipes and requirements.txt files ([#9197](https://github.com/rapidsai/cudf/pull/9197)) [@galipremsagar](https://github.com/galipremsagar)
+- Minor C++17 cleanup of `groupby.cu`: structured bindings, more concise lambda, etc ([#9193](https://github.com/rapidsai/cudf/pull/9193)) [@codereport](https://github.com/codereport)
+- Explicit about bitwidth difference between cudf boolean and arrow boolean ([#9192](https://github.com/rapidsai/cudf/pull/9192)) [@isVoid](https://github.com/isVoid)
+- Remove _source_index from MultiIndex ([#9191](https://github.com/rapidsai/cudf/pull/9191)) [@vyasr](https://github.com/vyasr)
+- Fix typo in the name of `cudf-testing-targets.cmake` ([#9190](https://github.com/rapidsai/cudf/pull/9190)) [@trxcllnt](https://github.com/trxcllnt)
+- Add support for single-digits in cudf::to_timestamps ([#9173](https://github.com/rapidsai/cudf/pull/9173)) [@davidwendt](https://github.com/davidwendt)
+- Fix cufilejni build include path ([#9168](https://github.com/rapidsai/cudf/pull/9168)) [@pxLi](https://github.com/pxLi)
+- `dask_cudf` dispatch registering cleanup ([#9160](https://github.com/rapidsai/cudf/pull/9160)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove unneeded stream/mr from a cudf::make_strings_column ([#9148](https://github.com/rapidsai/cudf/pull/9148)) [@davidwendt](https://github.com/davidwendt)
+- Upgrade `pandas` version in `cudf` ([#9147](https://github.com/rapidsai/cudf/pull/9147)) [@galipremsagar](https://github.com/galipremsagar)
+- make data chunk reader return unique_ptr ([#9129](https://github.com/rapidsai/cudf/pull/9129)) [@cwharris](https://github.com/cwharris)
+- Add backend for `percentile_lookup` dispatch ([#9118](https://github.com/rapidsai/cudf/pull/9118)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactor implementation of column setitem ([#9110](https://github.com/rapidsai/cudf/pull/9110)) [@vyasr](https://github.com/vyasr)
+- Fix compile warnings found using nvcc 11.4 ([#9101](https://github.com/rapidsai/cudf/pull/9101)) [@davidwendt](https://github.com/davidwendt)
+- Update to UCX-Py 0.22 ([#9099](https://github.com/rapidsai/cudf/pull/9099)) [@pentschev](https://github.com/pentschev)
+- Simplify read_avro by removing unnecessary writer/impl classes ([#9090](https://github.com/rapidsai/cudf/pull/9090)) [@cwharris](https://github.com/cwharris)
+- Allowing %f in format to return nanoseconds ([#9081](https://github.com/rapidsai/cudf/pull/9081)) [@marlenezw](https://github.com/marlenezw)
+- Java bindings for cudf::hash_join ([#9080](https://github.com/rapidsai/cudf/pull/9080)) [@jlowe](https://github.com/jlowe)
+- Remove stale code in `ColumnBase._fill` ([#9078](https://github.com/rapidsai/cudf/pull/9078)) [@isVoid](https://github.com/isVoid)
+- Add support for `get_group` in GroupBy ([#9070](https://github.com/rapidsai/cudf/pull/9070)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove remaining &quot;support&quot; methods from DataFrame ([#9068](https://github.com/rapidsai/cudf/pull/9068)) [@vyasr](https://github.com/vyasr)
+- Update JNI java CSV APIs to not use deprecated API ([#9066](https://github.com/rapidsai/cudf/pull/9066)) [@revans2](https://github.com/revans2)
+- Added method to remove null_masks if the column has no nulls ([#9061](https://github.com/rapidsai/cudf/pull/9061)) [@razajafri](https://github.com/razajafri)
+- Consolidate Several Series and Dataframe Methods ([#9059](https://github.com/rapidsai/cudf/pull/9059)) [@isVoid](https://github.com/isVoid)
+- Remove usage of string based `set_dtypes` for `csv` &amp; `json` readers ([#9049](https://github.com/rapidsai/cudf/pull/9049)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove some debug print statements from gtests ([#9048](https://github.com/rapidsai/cudf/pull/9048)) [@davidwendt](https://github.com/davidwendt)
+- Support additional format specifiers in from_timestamps ([#9047](https://github.com/rapidsai/cudf/pull/9047)) [@davidwendt](https://github.com/davidwendt)
+- Expose expression base class publicly and simplify public AST API ([#9045](https://github.com/rapidsai/cudf/pull/9045)) [@vyasr](https://github.com/vyasr)
+- move filepath and mmap logic out of json/csv up to functions.cpp ([#9040](https://github.com/rapidsai/cudf/pull/9040)) [@cwharris](https://github.com/cwharris)
+- Refactor Index hierarchy ([#9039](https://github.com/rapidsai/cudf/pull/9039)) [@vyasr](https://github.com/vyasr)
+- cudf now leverages rapids-cmake to reduce CMake boilerplate ([#9030](https://github.com/rapidsai/cudf/pull/9030)) [@robertmaynard](https://github.com/robertmaynard)
+- Add support for `STRUCT` input to `groupby` ([#9024](https://github.com/rapidsai/cudf/pull/9024)) [@mythrocks](https://github.com/mythrocks)
+- Refactor Frame scans ([#9021](https://github.com/rapidsai/cudf/pull/9021)) [@vyasr](https://github.com/vyasr)
+- Remove duplicate `set_categories` code ([#9018](https://github.com/rapidsai/cudf/pull/9018)) [@isVoid](https://github.com/isVoid)
+- Map support for ParquetWriter ([#9013](https://github.com/rapidsai/cudf/pull/9013)) [@razajafri](https://github.com/razajafri)
+- Remove aliases of various api.types APIs from utils.dtypes. ([#9011](https://github.com/rapidsai/cudf/pull/9011)) [@vyasr](https://github.com/vyasr)
+- Java bindings for conditional join output sizes ([#9002](https://github.com/rapidsai/cudf/pull/9002)) [@jlowe](https://github.com/jlowe)
+- Remove _copy_construct factory ([#8999](https://github.com/rapidsai/cudf/pull/8999)) [@vyasr](https://github.com/vyasr)
+- ENH Allow arbitrary CMake config options in build.sh ([#8996](https://github.com/rapidsai/cudf/pull/8996)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- A small optimization for JNI copy column view to column vector ([#8985](https://github.com/rapidsai/cudf/pull/8985)) [@revans2](https://github.com/revans2)
+- Fix nvcc warnings in ORC writer ([#8975](https://github.com/rapidsai/cudf/pull/8975)) [@devavret](https://github.com/devavret)
+- Support nested structs in rank and dense rank ([#8962](https://github.com/rapidsai/cudf/pull/8962)) [@rwlee](https://github.com/rwlee)
+- Move compute_column API out of ast namespace ([#8957](https://github.com/rapidsai/cudf/pull/8957)) [@vyasr](https://github.com/vyasr)
+- Series datetime is_year_end and is_year_start ([#8954](https://github.com/rapidsai/cudf/pull/8954)) [@marlenezw](https://github.com/marlenezw)
+- Make Java AstNode public ([#8953](https://github.com/rapidsai/cudf/pull/8953)) [@jlowe](https://github.com/jlowe)
+- Replace allocate with device_uvector for subword_tokenize internal tables ([#8952](https://github.com/rapidsai/cudf/pull/8952)) [@davidwendt](https://github.com/davidwendt)
+- `cudf.dtype` function ([#8949](https://github.com/rapidsai/cudf/pull/8949)) [@shwina](https://github.com/shwina)
+- Refactor Frame reductions ([#8944](https://github.com/rapidsai/cudf/pull/8944)) [@vyasr](https://github.com/vyasr)
+- Add deprecation warning for `Series.set_mask` API ([#8943](https://github.com/rapidsai/cudf/pull/8943)) [@galipremsagar](https://github.com/galipremsagar)
+- Move AST evaluator into a separate header ([#8930](https://github.com/rapidsai/cudf/pull/8930)) [@vyasr](https://github.com/vyasr)
+- JNI Aggregation Type Changes ([#8919](https://github.com/rapidsai/cudf/pull/8919)) [@revans2](https://github.com/revans2)
+- Move template parameter to function parameter in cudf::detail::left_semi_anti_join ([#8914](https://github.com/rapidsai/cudf/pull/8914)) [@davidwendt](https://github.com/davidwendt)
+- Upgrade `arrow` &amp; `pyarrow` to `5.0.0` ([#8908](https://github.com/rapidsai/cudf/pull/8908)) [@galipremsagar](https://github.com/galipremsagar)
+- Add groupby_aggregation and groupby_scan_aggregation classes and force their usage. ([#8906](https://github.com/rapidsai/cudf/pull/8906)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Move `structs_column_tests.cu` to `.cpp`. ([#8902](https://github.com/rapidsai/cudf/pull/8902)) [@mythrocks](https://github.com/mythrocks)
+- Add stream and memory-resource parameters to struct-scalar copy ctor ([#8901](https://github.com/rapidsai/cudf/pull/8901)) [@davidwendt](https://github.com/davidwendt)
+- Combine linearizer and ast_plan ([#8900](https://github.com/rapidsai/cudf/pull/8900)) [@vyasr](https://github.com/vyasr)
+- Add Java bindings for conditional join gather maps ([#8888](https://github.com/rapidsai/cudf/pull/8888)) [@jlowe](https://github.com/jlowe)
+- Remove max version pin for `dask` &amp; `distributed` on development branch ([#8881](https://github.com/rapidsai/cudf/pull/8881)) [@galipremsagar](https://github.com/galipremsagar)
+- fix cufilejni build w/ c++17 ([#8877](https://github.com/rapidsai/cudf/pull/8877)) [@pxLi](https://github.com/pxLi)
+- Add struct accessor to dask-cudf ([#8874](https://github.com/rapidsai/cudf/pull/8874)) [@NV-jpt](https://github.com/NV-jpt)
+- Migrate dask-cudf CudfEngine to leverage ArrowDatasetEngine ([#8871](https://github.com/rapidsai/cudf/pull/8871)) [@rjzamora](https://github.com/rjzamora)
+- Add JNI for extract_quarter, add_calendrical_months, and is_leap_year ([#8863](https://github.com/rapidsai/cudf/pull/8863)) [@revans2](https://github.com/revans2)
+- Change cudf::scalar copy and move constructors to protected ([#8857](https://github.com/rapidsai/cudf/pull/8857)) [@davidwendt](https://github.com/davidwendt)
+- Replace `is_same&lt;&gt;::value` with `is_same_v&lt;&gt;` ([#8852](https://github.com/rapidsai/cudf/pull/8852)) [@codereport](https://github.com/codereport)
+- Add min `pytorch` version to `importorskip` in pytest ([#8851](https://github.com/rapidsai/cudf/pull/8851)) [@galipremsagar](https://github.com/galipremsagar)
+- Java bindings for regex replace ([#8847](https://github.com/rapidsai/cudf/pull/8847)) [@jlowe](https://github.com/jlowe)
+- Remove make strings children with null mask ([#8830](https://github.com/rapidsai/cudf/pull/8830)) [@davidwendt](https://github.com/davidwendt)
+- Refactor conditional joins ([#8815](https://github.com/rapidsai/cudf/pull/8815)) [@vyasr](https://github.com/vyasr)
+- Small cleanup (unused headers / commented code removals) ([#8799](https://github.com/rapidsai/cudf/pull/8799)) [@codereport](https://github.com/codereport)
+- ENH Replace gpuci_conda_retry with gpuci_mamba_retry ([#8770](https://github.com/rapidsai/cudf/pull/8770)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- Update cudf java bindings to 21.10.0-SNAPSHOT ([#8765](https://github.com/rapidsai/cudf/pull/8765)) [@pxLi](https://github.com/pxLi)
+- Refactor and improve join benchmarks with nvbench ([#8734](https://github.com/rapidsai/cudf/pull/8734)) [@PointKernel](https://github.com/PointKernel)
+- Refactor Python factories and remove usage of Table for libcudf output handling ([#8687](https://github.com/rapidsai/cudf/pull/8687)) [@vyasr](https://github.com/vyasr)
+- Optimize URL Decoding ([#8622](https://github.com/rapidsai/cudf/pull/8622)) [@gaohao95](https://github.com/gaohao95)
+- Parquet writer dictionary encoding refactor ([#8476](https://github.com/rapidsai/cudf/pull/8476)) [@devavret](https://github.com/devavret)
+- Use nvcomp&#39;s snappy decompression in parquet reader ([#8252](https://github.com/rapidsai/cudf/pull/8252)) [@devavret](https://github.com/devavret)
+- Use nvcomp&#39;s snappy compressor in parquet writer ([#8229](https://github.com/rapidsai/cudf/pull/8229)) [@devavret](https://github.com/devavret)
+
+# cuDF 21.08.00 (4 Aug 2021)
+
+## 🚨 Breaking Changes
+
+- Fix a crash in pack() when being handed tables with no columns. ([#8697](https://github.com/rapidsai/cudf/pull/8697)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Remove unused cudf::strings::create_offsets ([#8663](https://github.com/rapidsai/cudf/pull/8663)) [@davidwendt](https://github.com/davidwendt)
+- Add delimiter parameter to cudf::strings::capitalize() ([#8620](https://github.com/rapidsai/cudf/pull/8620)) [@davidwendt](https://github.com/davidwendt)
+- Change default datetime index resolution to ns to match pandas ([#8611](https://github.com/rapidsai/cudf/pull/8611)) [@vyasr](https://github.com/vyasr)
+- Add sequence_type parameter to cudf::strings::title function ([#8602](https://github.com/rapidsai/cudf/pull/8602)) [@davidwendt](https://github.com/davidwendt)
+- Add `strings::repeat_strings` API that can repeat each string a different number of times ([#8561](https://github.com/rapidsai/cudf/pull/8561)) [@ttnghia](https://github.com/ttnghia)
+- String-to-boolean conversion is different from Pandas ([#8549](https://github.com/rapidsai/cudf/pull/8549)) [@skirui-source](https://github.com/skirui-source)
+- Add accurate hash join size functions ([#8453](https://github.com/rapidsai/cudf/pull/8453)) [@PointKernel](https://github.com/PointKernel)
+- Expose a Decimal32Dtype in cuDF Python ([#8438](https://github.com/rapidsai/cudf/pull/8438)) [@skirui-source](https://github.com/skirui-source)
+- Update dask make_meta changes to be compatible with dask upstream ([#8426](https://github.com/rapidsai/cudf/pull/8426)) [@galipremsagar](https://github.com/galipremsagar)
+- Adapt `cudf::scalar` classes to changes in `rmm::device_scalar` ([#8411](https://github.com/rapidsai/cudf/pull/8411)) [@harrism](https://github.com/harrism)
+- Remove special Index class from the general index class hierarchy ([#8309](https://github.com/rapidsai/cudf/pull/8309)) [@vyasr](https://github.com/vyasr)
+- Add first-class dtype utilities ([#8308](https://github.com/rapidsai/cudf/pull/8308)) [@vyasr](https://github.com/vyasr)
+- ORC - Support reading multiple orc files/buffers in a single operation ([#8142](https://github.com/rapidsai/cudf/pull/8142)) [@jdye64](https://github.com/jdye64)
+- Upgrade arrow to 4.0.1 ([#7495](https://github.com/rapidsai/cudf/pull/7495)) [@galipremsagar](https://github.com/galipremsagar)
+
+## 🐛 Bug Fixes
+
+- Fix `contains` check in string column ([#8834](https://github.com/rapidsai/cudf/pull/8834)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove unused variable from `row_bit_count_test`. ([#8829](https://github.com/rapidsai/cudf/pull/8829)) [@mythrocks](https://github.com/mythrocks)
+- Fixes issue with null struct columns in ORC reader ([#8819](https://github.com/rapidsai/cudf/pull/8819)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Set CMake vars for python/parquet support in libarrow builds ([#8808](https://github.com/rapidsai/cudf/pull/8808)) [@vyasr](https://github.com/vyasr)
+- Handle empty child columns in row_bit_count() ([#8791](https://github.com/rapidsai/cudf/pull/8791)) [@mythrocks](https://github.com/mythrocks)
+- Revert &quot;Remove cudf unneeded build time requirement of the cuda driver&quot; ([#8784](https://github.com/rapidsai/cudf/pull/8784)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix isort error in utils.pyx ([#8771](https://github.com/rapidsai/cudf/pull/8771)) [@charlesbluca](https://github.com/charlesbluca)
+- Handle sliced struct/list columns properly in concatenate() bounds checking. ([#8760](https://github.com/rapidsai/cudf/pull/8760)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix issues with `_CPackedColumns.serialize()` handling of host and device data ([#8759](https://github.com/rapidsai/cudf/pull/8759)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix issues with `MultiIndex` in `dropna`, `stack` &amp; `reset_index` ([#8753](https://github.com/rapidsai/cudf/pull/8753)) [@galipremsagar](https://github.com/galipremsagar)
+- Write pandas extension types to parquet file metadata ([#8749](https://github.com/rapidsai/cudf/pull/8749)) [@devavret](https://github.com/devavret)
+- Fix `where` to handle `DataFrame` &amp; `Series` input combination ([#8747](https://github.com/rapidsai/cudf/pull/8747)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix `replace` to handle null values correctly ([#8744](https://github.com/rapidsai/cudf/pull/8744)) [@galipremsagar](https://github.com/galipremsagar)
+- Handle sliced structs properly in pack/contiguous_split. ([#8739](https://github.com/rapidsai/cudf/pull/8739)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix issue in slice() where columns with a positive offset were computing null counts incorrectly. ([#8738](https://github.com/rapidsai/cudf/pull/8738)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix `cudf.Series` constructor to handle list of sequences ([#8735](https://github.com/rapidsai/cudf/pull/8735)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix min/max sorted groupby aggregation on string column with nulls (argmin, argmax sentinel value missing on nulls) ([#8731](https://github.com/rapidsai/cudf/pull/8731)) [@karthikeyann](https://github.com/karthikeyann)
+- Fix orc reader assert on create data_type in debug ([#8706](https://github.com/rapidsai/cudf/pull/8706)) [@davidwendt](https://github.com/davidwendt)
+- Fix min/max inclusive cudf::scan for strings column ([#8705](https://github.com/rapidsai/cudf/pull/8705)) [@davidwendt](https://github.com/davidwendt)
+- JNI: Fix driver version assertion logic in testGetCudaRuntimeInfo ([#8701](https://github.com/rapidsai/cudf/pull/8701)) [@sperlingxx](https://github.com/sperlingxx)
+- Adding fix for skip_rows and crash in orc reader ([#8700](https://github.com/rapidsai/cudf/pull/8700)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Bug fix: `replace_nulls_policy` functor not returning correct indices for gathermap ([#8699](https://github.com/rapidsai/cudf/pull/8699)) [@isVoid](https://github.com/isVoid)
+- Fix a crash in pack() when being handed tables with no columns. ([#8697](https://github.com/rapidsai/cudf/pull/8697)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add post-processing steps to `dask_cudf.groupby.CudfSeriesGroupby.aggregate` ([#8694](https://github.com/rapidsai/cudf/pull/8694)) [@charlesbluca](https://github.com/charlesbluca)
+- JNI build no longer looks for Arrow in conda environment ([#8686](https://github.com/rapidsai/cudf/pull/8686)) [@jlowe](https://github.com/jlowe)
+- Handle arbitrarily different data in null list column rows when checking for equivalency. ([#8666](https://github.com/rapidsai/cudf/pull/8666)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add ConfigureNVBench to avoid concurrent main() entry points ([#8662](https://github.com/rapidsai/cudf/pull/8662)) [@PointKernel](https://github.com/PointKernel)
+- Pin `*arrow` to use `*cuda` in `run` ([#8651](https://github.com/rapidsai/cudf/pull/8651)) [@jakirkham](https://github.com/jakirkham)
+- Add proper support for tolerances in testing methods. ([#8649](https://github.com/rapidsai/cudf/pull/8649)) [@vyasr](https://github.com/vyasr)
+- Support multi-char case conversion in capitalize function ([#8647](https://github.com/rapidsai/cudf/pull/8647)) [@davidwendt](https://github.com/davidwendt)
+- Fix repeated mangled names in read_csv with duplicate column names ([#8645](https://github.com/rapidsai/cudf/pull/8645)) [@karthikeyann](https://github.com/karthikeyann)
+- Temporarily disable libcudf example build tests ([#8642](https://github.com/rapidsai/cudf/pull/8642)) [@isVoid](https://github.com/isVoid)
+- Use conda-sourced cudf artifacts for libcudf example in CI ([#8638](https://github.com/rapidsai/cudf/pull/8638)) [@isVoid](https://github.com/isVoid)
+- Ensure dev environment uses Arrow GPU packages ([#8637](https://github.com/rapidsai/cudf/pull/8637)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix bug that columns only initialized once when specified `columns` and `index` in dataframe ctor ([#8628](https://github.com/rapidsai/cudf/pull/8628)) [@isVoid](https://github.com/isVoid)
+- Propagate **kwargs through to as_*_column methods ([#8618](https://github.com/rapidsai/cudf/pull/8618)) [@shwina](https://github.com/shwina)
+- Fix orc_reader_benchmark.cpp compile error ([#8609](https://github.com/rapidsai/cudf/pull/8609)) [@davidwendt](https://github.com/davidwendt)
+- Fix missed renumbering of Aggregation values ([#8600](https://github.com/rapidsai/cudf/pull/8600)) [@revans2](https://github.com/revans2)
+- Update cmake to 3.20.5 in the Java Docker image ([#8593](https://github.com/rapidsai/cudf/pull/8593)) [@NvTimLiu](https://github.com/NvTimLiu)
+- Fix bug in replace_with_backrefs when group has greedy quantifier ([#8575](https://github.com/rapidsai/cudf/pull/8575)) [@davidwendt](https://github.com/davidwendt)
+- Apply metadata to keys before returning in `Frame._encode` ([#8560](https://github.com/rapidsai/cudf/pull/8560)) [@charlesbluca](https://github.com/charlesbluca)
+- Fix for strings containing special JSON characters in get_json_object(). ([#8556](https://github.com/rapidsai/cudf/pull/8556)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix debug compile error in gather_struct_tests.cpp ([#8554](https://github.com/rapidsai/cudf/pull/8554)) [@davidwendt](https://github.com/davidwendt)
+- String-to-boolean conversion is different from Pandas ([#8549](https://github.com/rapidsai/cudf/pull/8549)) [@skirui-source](https://github.com/skirui-source)
+- Fix `__repr__` output with `display.max_rows` is `None` ([#8547](https://github.com/rapidsai/cudf/pull/8547)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix size passed to column constructors in _with_type_metadata ([#8539](https://github.com/rapidsai/cudf/pull/8539)) [@shwina](https://github.com/shwina)
+- Properly retrieve last column when `-1` is specified for column index ([#8529](https://github.com/rapidsai/cudf/pull/8529)) [@isVoid](https://github.com/isVoid)
+- Fix importing `apply` from `dask` ([#8517](https://github.com/rapidsai/cudf/pull/8517)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix offset of the string dictionary length stream ([#8515](https://github.com/rapidsai/cudf/pull/8515)) [@vuule](https://github.com/vuule)
+- Fix double counting of selected columns in CSV reader ([#8508](https://github.com/rapidsai/cudf/pull/8508)) [@ochan1](https://github.com/ochan1)
+- Incorrect map size in scatter_to_gather corrupts struct columns ([#8507](https://github.com/rapidsai/cudf/pull/8507)) [@gerashegalov](https://github.com/gerashegalov)
+- replace_nulls properly propagates memory resource to gather calls ([#8500](https://github.com/rapidsai/cudf/pull/8500)) [@robertmaynard](https://github.com/robertmaynard)
+- Disallow groupby aggs for `StructColumns` ([#8499](https://github.com/rapidsai/cudf/pull/8499)) [@charlesbluca](https://github.com/charlesbluca)
+- Fixes out-of-bounds access for small files in unzip ([#8498](https://github.com/rapidsai/cudf/pull/8498)) [@elstehle](https://github.com/elstehle)
+- Adding support for writing empty dataframe ([#8490](https://github.com/rapidsai/cudf/pull/8490)) [@shaneding](https://github.com/shaneding)
+- Fix exclusive scan when including nulls and improve testing ([#8478](https://github.com/rapidsai/cudf/pull/8478)) [@harrism](https://github.com/harrism)
+- Add workaround for crash in libcudf debug build using output_indexalator in thrust::lower_bound ([#8432](https://github.com/rapidsai/cudf/pull/8432)) [@davidwendt](https://github.com/davidwendt)
+- Install only the same Thrust files that Thrust itself installs ([#8420](https://github.com/rapidsai/cudf/pull/8420)) [@robertmaynard](https://github.com/robertmaynard)
+- Add nightly version for ucx-py in ci script ([#8419](https://github.com/rapidsai/cudf/pull/8419)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix null_equality config of rolling_collect_set ([#8415](https://github.com/rapidsai/cudf/pull/8415)) [@sperlingxx](https://github.com/sperlingxx)
+- CollectSetAggregation: implement RollingAggregation interface ([#8406](https://github.com/rapidsai/cudf/pull/8406)) [@sperlingxx](https://github.com/sperlingxx)
+- Handle pre-sliced nested columns in contiguous_split. ([#8391](https://github.com/rapidsai/cudf/pull/8391)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix bitmask_tests.cpp host accessing device memory ([#8370](https://github.com/rapidsai/cudf/pull/8370)) [@davidwendt](https://github.com/davidwendt)
+- Fix concurrent_unordered_map to prevent accessing padding bits in pair_type ([#8348](https://github.com/rapidsai/cudf/pull/8348)) [@davidwendt](https://github.com/davidwendt)
+- BUG FIX: Raise appropriate strings error when concatenating strings column ([#8290](https://github.com/rapidsai/cudf/pull/8290)) [@skirui-source](https://github.com/skirui-source)
+- Make gpuCI and pre-commit style configurations consistent ([#8215](https://github.com/rapidsai/cudf/pull/8215)) [@charlesbluca](https://github.com/charlesbluca)
+- Add collect list to dask-cudf groupby aggregations ([#8045](https://github.com/rapidsai/cudf/pull/8045)) [@charlesbluca](https://github.com/charlesbluca)
+
+## 📖 Documentation
+
+- Update Python UDFs notebook ([#8810](https://github.com/rapidsai/cudf/pull/8810)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix dask.dataframe API docs  links after reorg ([#8772](https://github.com/rapidsai/cudf/pull/8772)) [@jsignell](https://github.com/jsignell)
+- Fix instructions for running cuDF/dask-cuDF tests in CONTRIBUTING.md ([#8724](https://github.com/rapidsai/cudf/pull/8724)) [@shwina](https://github.com/shwina)
+- Translate Markdown documentation to rST and remove recommonmark ([#8698](https://github.com/rapidsai/cudf/pull/8698)) [@vyasr](https://github.com/vyasr)
+- Fixed spelling mistakes in libcudf documentation ([#8664](https://github.com/rapidsai/cudf/pull/8664)) [@karthikeyann](https://github.com/karthikeyann)
+- Custom Sphinx Extension: `PandasCompat` ([#8643](https://github.com/rapidsai/cudf/pull/8643)) [@isVoid](https://github.com/isVoid)
+- Fix README.md ([#8535](https://github.com/rapidsai/cudf/pull/8535)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Change namespace contains_nulls to struct ([#8523](https://github.com/rapidsai/cudf/pull/8523)) [@davidwendt](https://github.com/davidwendt)
+- Add info about NVTX ranges to dev guide ([#8461](https://github.com/rapidsai/cudf/pull/8461)) [@jrhemstad](https://github.com/jrhemstad)
+- Fixed documentation bug in groupby agg method ([#8325](https://github.com/rapidsai/cudf/pull/8325)) [@ahmet-uyar](https://github.com/ahmet-uyar)
+
+## 🚀 New Features
+
+- Fix concatenating structs ([#8811](https://github.com/rapidsai/cudf/pull/8811)) [@shaneding](https://github.com/shaneding)
+- Implement JNI for groupby aggregations `M2` and `MERGE_M2` ([#8763](https://github.com/rapidsai/cudf/pull/8763)) [@ttnghia](https://github.com/ttnghia)
+- Bump `isort` to `5.6.4` and remove `isort` overrides made for 5.0.7 ([#8755](https://github.com/rapidsai/cudf/pull/8755)) [@charlesbluca](https://github.com/charlesbluca)
+- Implement `__setitem__` for `StructColumn` ([#8737](https://github.com/rapidsai/cudf/pull/8737)) [@shaneding](https://github.com/shaneding)
+- Add `is_leap_year` to `DateTimeProperties` and `DatetimeIndex` ([#8736](https://github.com/rapidsai/cudf/pull/8736)) [@isVoid](https://github.com/isVoid)
+- Add `struct.explode()` method ([#8729](https://github.com/rapidsai/cudf/pull/8729)) [@shwina](https://github.com/shwina)
+- Add `DataFrame.to_struct()` method to convert a DataFrame to a struct Series ([#8728](https://github.com/rapidsai/cudf/pull/8728)) [@shwina](https://github.com/shwina)
+- Add support for list type in ORC writer ([#8723](https://github.com/rapidsai/cudf/pull/8723)) [@vuule](https://github.com/vuule)
+- Fix slicing from struct columns and accessing struct columns ([#8719](https://github.com/rapidsai/cudf/pull/8719)) [@shaneding](https://github.com/shaneding)
+- Add `datetime::is_leap_year` ([#8711](https://github.com/rapidsai/cudf/pull/8711)) [@isVoid](https://github.com/isVoid)
+- Accessing struct columns from `dask_cudf` ([#8675](https://github.com/rapidsai/cudf/pull/8675)) [@shaneding](https://github.com/shaneding)
+- Added pct_change to Series ([#8650](https://github.com/rapidsai/cudf/pull/8650)) [@TravisHester](https://github.com/TravisHester)
+- Add strings support to cudf::shift function ([#8648](https://github.com/rapidsai/cudf/pull/8648)) [@davidwendt](https://github.com/davidwendt)
+- Support Scatter `struct_scalar` ([#8630](https://github.com/rapidsai/cudf/pull/8630)) [@isVoid](https://github.com/isVoid)
+- Struct scalar from host dictionary ([#8629](https://github.com/rapidsai/cudf/pull/8629)) [@shaneding](https://github.com/shaneding)
+- Add dayofyear and day_of_year to Series, DatetimeColumn, and DatetimeIndex ([#8626](https://github.com/rapidsai/cudf/pull/8626)) [@beckernick](https://github.com/beckernick)
+- JNI support for capitalize ([#8624](https://github.com/rapidsai/cudf/pull/8624)) [@firestarman](https://github.com/firestarman)
+- Add delimiter parameter to cudf::strings::capitalize() ([#8620](https://github.com/rapidsai/cudf/pull/8620)) [@davidwendt](https://github.com/davidwendt)
+- Add NVBench in CMake ([#8619](https://github.com/rapidsai/cudf/pull/8619)) [@PointKernel](https://github.com/PointKernel)
+- Change default datetime index resolution to ns to match pandas ([#8611](https://github.com/rapidsai/cudf/pull/8611)) [@vyasr](https://github.com/vyasr)
+- ListColumn `__setitem__` ([#8606](https://github.com/rapidsai/cudf/pull/8606)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Implement groupby aggregations `M2` and `MERGE_M2` ([#8605](https://github.com/rapidsai/cudf/pull/8605)) [@ttnghia](https://github.com/ttnghia)
+- Add sequence_type parameter to cudf::strings::title function ([#8602](https://github.com/rapidsai/cudf/pull/8602)) [@davidwendt](https://github.com/davidwendt)
+- Adding support for list and struct type in ORC Reader ([#8599](https://github.com/rapidsai/cudf/pull/8599)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Benchmark for `strings::repeat_strings` APIs ([#8589](https://github.com/rapidsai/cudf/pull/8589)) [@ttnghia](https://github.com/ttnghia)
+- Nested scalar support for copy if else ([#8588](https://github.com/rapidsai/cudf/pull/8588)) [@gerashegalov](https://github.com/gerashegalov)
+- User specified decimal columns to float64 ([#8587](https://github.com/rapidsai/cudf/pull/8587)) [@jdye64](https://github.com/jdye64)
+- Add `get_element` for struct column ([#8578](https://github.com/rapidsai/cudf/pull/8578)) [@isVoid](https://github.com/isVoid)
+- Python changes for adding `__getitem__` for `struct` ([#8577](https://github.com/rapidsai/cudf/pull/8577)) [@shaneding](https://github.com/shaneding)
+- Add `strings::repeat_strings` API that can repeat each string a different number of times ([#8561](https://github.com/rapidsai/cudf/pull/8561)) [@ttnghia](https://github.com/ttnghia)
+- Refactor `tests/iterator_utilities.hpp` functions ([#8540](https://github.com/rapidsai/cudf/pull/8540)) [@ttnghia](https://github.com/ttnghia)
+- Support MERGE_LISTS and MERGE_SETS in Java package ([#8516](https://github.com/rapidsai/cudf/pull/8516)) [@sperlingxx](https://github.com/sperlingxx)
+- Decimal support csv reader ([#8511](https://github.com/rapidsai/cudf/pull/8511)) [@elstehle](https://github.com/elstehle)
+- Add column type tests ([#8505](https://github.com/rapidsai/cudf/pull/8505)) [@isVoid](https://github.com/isVoid)
+- Warn when downscaling decimal columns ([#8492](https://github.com/rapidsai/cudf/pull/8492)) [@ChrisJar](https://github.com/ChrisJar)
+- Add JNI for `strings::repeat_strings` ([#8491](https://github.com/rapidsai/cudf/pull/8491)) [@ttnghia](https://github.com/ttnghia)
+- Add `Index.get_loc` for Numerical, String Index support ([#8489](https://github.com/rapidsai/cudf/pull/8489)) [@isVoid](https://github.com/isVoid)
+- Expose half_up rounding in cuDF ([#8477](https://github.com/rapidsai/cudf/pull/8477)) [@shwina](https://github.com/shwina)
+- Java APIs to fetch CUDA runtime info ([#8465](https://github.com/rapidsai/cudf/pull/8465)) [@sperlingxx](https://github.com/sperlingxx)
+- Add `str.edit_distance_matrix` ([#8463](https://github.com/rapidsai/cudf/pull/8463)) [@isVoid](https://github.com/isVoid)
+- Support constructing `cudf.Scalar` objects from host side lists ([#8459](https://github.com/rapidsai/cudf/pull/8459)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add accurate hash join size functions ([#8453](https://github.com/rapidsai/cudf/pull/8453)) [@PointKernel](https://github.com/PointKernel)
+- Add cudf::strings::integer_to_hex convert API ([#8450](https://github.com/rapidsai/cudf/pull/8450)) [@davidwendt](https://github.com/davidwendt)
+- Create objects from iterables that contain cudf.NA ([#8442](https://github.com/rapidsai/cudf/pull/8442)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- JNI bindings for sort_lists ([#8439](https://github.com/rapidsai/cudf/pull/8439)) [@sperlingxx](https://github.com/sperlingxx)
+- Expose a Decimal32Dtype in cuDF Python ([#8438](https://github.com/rapidsai/cudf/pull/8438)) [@skirui-source](https://github.com/skirui-source)
+- Replace `all_null()` and `all_valid()` by `iterator_all_nulls()` and `iterator_no_null()` in tests ([#8437](https://github.com/rapidsai/cudf/pull/8437)) [@ttnghia](https://github.com/ttnghia)
+- Implement groupby `MERGE_LISTS` and `MERGE_SETS` aggregates ([#8436](https://github.com/rapidsai/cudf/pull/8436)) [@ttnghia](https://github.com/ttnghia)
+- Add public libcudf match_dictionaries API ([#8429](https://github.com/rapidsai/cudf/pull/8429)) [@davidwendt](https://github.com/davidwendt)
+- Add move constructors for `string_scalar` and `struct_scalar` ([#8428](https://github.com/rapidsai/cudf/pull/8428)) [@ttnghia](https://github.com/ttnghia)
+- Implement `strings::repeat_strings` ([#8423](https://github.com/rapidsai/cudf/pull/8423)) [@ttnghia](https://github.com/ttnghia)
+- STRUCT column support for cudf::merge. ([#8422](https://github.com/rapidsai/cudf/pull/8422)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Implement reverse in libcudf ([#8410](https://github.com/rapidsai/cudf/pull/8410)) [@shaneding](https://github.com/shaneding)
+- Support multiple input files/buffers for read_json ([#8403](https://github.com/rapidsai/cudf/pull/8403)) [@jdye64](https://github.com/jdye64)
+- Improve test coverage for struct search ([#8396](https://github.com/rapidsai/cudf/pull/8396)) [@ttnghia](https://github.com/ttnghia)
+- Add `groupby.fillna` ([#8362](https://github.com/rapidsai/cudf/pull/8362)) [@isVoid](https://github.com/isVoid)
+- Enable AST-based joining ([#8214](https://github.com/rapidsai/cudf/pull/8214)) [@vyasr](https://github.com/vyasr)
+- Generalized null support in user defined functions ([#8213](https://github.com/rapidsai/cudf/pull/8213)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add compiled binary operation ([#8192](https://github.com/rapidsai/cudf/pull/8192)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement `.describe() ` for `DataFrameGroupBy` ([#8179](https://github.com/rapidsai/cudf/pull/8179)) [@skirui-source](https://github.com/skirui-source)
+- ORC - Support reading multiple orc files/buffers in a single operation ([#8142](https://github.com/rapidsai/cudf/pull/8142)) [@jdye64](https://github.com/jdye64)
+- Add Python bindings for `lists::concatenate_list_elements` and expose them as `.list.concat()` ([#8006](https://github.com/rapidsai/cudf/pull/8006)) [@shwina](https://github.com/shwina)
+- Use Arrow URI FileSystem backed instance to retrieve remote files ([#7709](https://github.com/rapidsai/cudf/pull/7709)) [@jdye64](https://github.com/jdye64)
+- Example to build custom application and link to libcudf ([#7671](https://github.com/rapidsai/cudf/pull/7671)) [@isVoid](https://github.com/isVoid)
+- Upgrade arrow to 4.0.1 ([#7495](https://github.com/rapidsai/cudf/pull/7495)) [@galipremsagar](https://github.com/galipremsagar)
+
+## 🛠️ Improvements
+
+- Provide a better error message when `CUDA::cuda_driver` not found ([#8794](https://github.com/rapidsai/cudf/pull/8794)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove anonymous namespace from null_mask.cuh ([#8786](https://github.com/rapidsai/cudf/pull/8786)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Allow cudf to be built without libcuda.so existing ([#8751](https://github.com/rapidsai/cudf/pull/8751)) [@robertmaynard](https://github.com/robertmaynard)
+- Pin `mimesis` to `&lt;4.1` ([#8745](https://github.com/rapidsai/cudf/pull/8745)) [@galipremsagar](https://github.com/galipremsagar)
+- Update `conda` environment name for CI ([#8692](https://github.com/rapidsai/cudf/pull/8692)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Remove flatbuffers dependency ([#8671](https://github.com/rapidsai/cudf/pull/8671)) [@Ethyling](https://github.com/Ethyling)
+- Add options to build Arrow with Python and Parquet support ([#8670](https://github.com/rapidsai/cudf/pull/8670)) [@trxcllnt](https://github.com/trxcllnt)
+- Remove unused cudf::strings::create_offsets ([#8663](https://github.com/rapidsai/cudf/pull/8663)) [@davidwendt](https://github.com/davidwendt)
+- Update GDS lib version to 1.0.0 ([#8654](https://github.com/rapidsai/cudf/pull/8654)) [@pxLi](https://github.com/pxLi)
+- Support for groupby/scan rank and dense_rank aggregations ([#8652](https://github.com/rapidsai/cudf/pull/8652)) [@rwlee](https://github.com/rwlee)
+- Fix usage of deprecated arrow ipc API ([#8632](https://github.com/rapidsai/cudf/pull/8632)) [@revans2](https://github.com/revans2)
+- Use absolute imports in `cudf` ([#8631](https://github.com/rapidsai/cudf/pull/8631)) [@galipremsagar](https://github.com/galipremsagar)
+- ENH Add Java CI build script ([#8627](https://github.com/rapidsai/cudf/pull/8627)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- Add DeprecationWarning to `ser.str.subword_tokenize` ([#8603](https://github.com/rapidsai/cudf/pull/8603)) [@VibhuJawa](https://github.com/VibhuJawa)
+- Rewrite binary operations for improved performance and additional type support ([#8598](https://github.com/rapidsai/cudf/pull/8598)) [@vyasr](https://github.com/vyasr)
+- Fix `mypy` errors surfacing because of `numpy-1.21.0` ([#8595](https://github.com/rapidsai/cudf/pull/8595)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove unneeded includes from cudf::string_view headers ([#8594](https://github.com/rapidsai/cudf/pull/8594)) [@davidwendt](https://github.com/davidwendt)
+- Use cmake 3.20.1 as it is now required by rmm ([#8586](https://github.com/rapidsai/cudf/pull/8586)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove device debug symbols from cmake CUDF_CUDA_FLAGS ([#8584](https://github.com/rapidsai/cudf/pull/8584)) [@davidwendt](https://github.com/davidwendt)
+- Dask-CuDF: use default Dask Dataframe optimizer ([#8581](https://github.com/rapidsai/cudf/pull/8581)) [@madsbk](https://github.com/madsbk)
+- Remove checking if an unsigned value is less than zero ([#8579](https://github.com/rapidsai/cudf/pull/8579)) [@robertmaynard](https://github.com/robertmaynard)
+- Remove strings_count parameter from cudf::strings::detail::create_chars_child_column ([#8576](https://github.com/rapidsai/cudf/pull/8576)) [@davidwendt](https://github.com/davidwendt)
+- Make `cudf.api.types` imports consistent ([#8571](https://github.com/rapidsai/cudf/pull/8571)) [@galipremsagar](https://github.com/galipremsagar)
+- Modernize libcudf basic example CMakeFile; updates CI build tests ([#8568](https://github.com/rapidsai/cudf/pull/8568)) [@isVoid](https://github.com/isVoid)
+- Rename concatenate_tests.cu to .cpp ([#8555](https://github.com/rapidsai/cudf/pull/8555)) [@davidwendt](https://github.com/davidwendt)
+- enable window lead/lag test on struct ([#8548](https://github.com/rapidsai/cudf/pull/8548)) [@wbo4958](https://github.com/wbo4958)
+- Add Java methods to split and write column views ([#8546](https://github.com/rapidsai/cudf/pull/8546)) [@razajafri](https://github.com/razajafri)
+- Small cleanup ([#8534](https://github.com/rapidsai/cudf/pull/8534)) [@codereport](https://github.com/codereport)
+- Unpin `dask` version in CI ([#8533](https://github.com/rapidsai/cudf/pull/8533)) [@galipremsagar](https://github.com/galipremsagar)
+- Added optional flag for building Arrow with S3 filesystem support ([#8531](https://github.com/rapidsai/cudf/pull/8531)) [@jdye64](https://github.com/jdye64)
+- Minor clean up of various internal column and frame utilities ([#8528](https://github.com/rapidsai/cudf/pull/8528)) [@vyasr](https://github.com/vyasr)
+- Rename some copying_test source files .cu to .cpp ([#8527](https://github.com/rapidsai/cudf/pull/8527)) [@davidwendt](https://github.com/davidwendt)
+- Correct the last warnings and issues when using newer cuda versions ([#8525](https://github.com/rapidsai/cudf/pull/8525)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in transform and unary ops ([#8521](https://github.com/rapidsai/cudf/pull/8521)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in string algorithms ([#8509](https://github.com/rapidsai/cudf/pull/8509)) [@robertmaynard](https://github.com/robertmaynard)
+- Add in JNI APIs for scan, replace_nulls, group_by.scan, and group_by.replace_nulls ([#8503](https://github.com/rapidsai/cudf/pull/8503)) [@revans2](https://github.com/revans2)
+- Fix `21.08` forward-merge conflicts ([#8502](https://github.com/rapidsai/cudf/pull/8502)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Fix Cython formatting command in Contributing.md. ([#8496](https://github.com/rapidsai/cudf/pull/8496)) [@marlenezw](https://github.com/marlenezw)
+- Bug/correct unused parameters in reshape and text ([#8495](https://github.com/rapidsai/cudf/pull/8495)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in partitioning and stream compact ([#8494](https://github.com/rapidsai/cudf/pull/8494)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in labelling and list algorithms ([#8493](https://github.com/rapidsai/cudf/pull/8493)) [@robertmaynard](https://github.com/robertmaynard)
+- Refactor index construction ([#8485](https://github.com/rapidsai/cudf/pull/8485)) [@vyasr](https://github.com/vyasr)
+- Correct unused parameter warnings in replace algorithms ([#8483](https://github.com/rapidsai/cudf/pull/8483)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in reduction algorithms ([#8481](https://github.com/rapidsai/cudf/pull/8481)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in io algorithms ([#8480](https://github.com/rapidsai/cudf/pull/8480)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in interop algorithms ([#8479](https://github.com/rapidsai/cudf/pull/8479)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in filling algorithms ([#8468](https://github.com/rapidsai/cudf/pull/8468)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameter warnings in groupby ([#8467](https://github.com/rapidsai/cudf/pull/8467)) [@robertmaynard](https://github.com/robertmaynard)
+- use libcu++ time_point as timestamp ([#8466](https://github.com/rapidsai/cudf/pull/8466)) [@karthikeyann](https://github.com/karthikeyann)
+- Modify reprog_device::extract to return groups in a single pass ([#8460](https://github.com/rapidsai/cudf/pull/8460)) [@davidwendt](https://github.com/davidwendt)
+- Update minimum Dask requirement to 2021.6.0 ([#8458](https://github.com/rapidsai/cudf/pull/8458)) [@pentschev](https://github.com/pentschev)
+- Fix failures when performing binary operations on DataFrames with empty columns ([#8452](https://github.com/rapidsai/cudf/pull/8452)) [@ChrisJar](https://github.com/ChrisJar)
+- Fix conflicts in `8447` ([#8448](https://github.com/rapidsai/cudf/pull/8448)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Add serialization methods for `List` and `StructDtype` ([#8441](https://github.com/rapidsai/cudf/pull/8441)) [@charlesbluca](https://github.com/charlesbluca)
+- Replace make_empty_strings_column with make_empty_column ([#8435](https://github.com/rapidsai/cudf/pull/8435)) [@davidwendt](https://github.com/davidwendt)
+- JNI bindings for get_element ([#8433](https://github.com/rapidsai/cudf/pull/8433)) [@revans2](https://github.com/revans2)
+- Update dask make_meta changes to be compatible with dask upstream ([#8426](https://github.com/rapidsai/cudf/pull/8426)) [@galipremsagar](https://github.com/galipremsagar)
+- Unpin dask version on CI ([#8425](https://github.com/rapidsai/cudf/pull/8425)) [@galipremsagar](https://github.com/galipremsagar)
+- Add benchmark for strings/fixed_point convert APIs ([#8417](https://github.com/rapidsai/cudf/pull/8417)) [@davidwendt](https://github.com/davidwendt)
+- Adapt `cudf::scalar` classes to changes in `rmm::device_scalar` ([#8411](https://github.com/rapidsai/cudf/pull/8411)) [@harrism](https://github.com/harrism)
+- Add benchmark for strings/integers convert APIs ([#8402](https://github.com/rapidsai/cudf/pull/8402)) [@davidwendt](https://github.com/davidwendt)
+- Enable multi-file partitioning in dask_cudf.read_parquet ([#8393](https://github.com/rapidsai/cudf/pull/8393)) [@rjzamora](https://github.com/rjzamora)
+- Correct unused parameter warnings in rolling algorithms ([#8390](https://github.com/rapidsai/cudf/pull/8390)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameters in column round and search ([#8389](https://github.com/rapidsai/cudf/pull/8389)) [@robertmaynard](https://github.com/robertmaynard)
+- Add functionality to apply `Dtype` metadata to `ColumnBase` ([#8373](https://github.com/rapidsai/cudf/pull/8373)) [@charlesbluca](https://github.com/charlesbluca)
+- Refactor setting stack size in regex code ([#8358](https://github.com/rapidsai/cudf/pull/8358)) [@davidwendt](https://github.com/davidwendt)
+- Update Java bindings to 21.08-SNAPSHOT ([#8344](https://github.com/rapidsai/cudf/pull/8344)) [@pxLi](https://github.com/pxLi)
+- Replace remaining uses of device_vector ([#8343](https://github.com/rapidsai/cudf/pull/8343)) [@harrism](https://github.com/harrism)
+- Statically link libnvcomp into libcudfjni ([#8334](https://github.com/rapidsai/cudf/pull/8334)) [@jlowe](https://github.com/jlowe)
+- Resolve auto merge conflicts for Branch 21.08 from branch 21.06 ([#8329](https://github.com/rapidsai/cudf/pull/8329)) [@galipremsagar](https://github.com/galipremsagar)
+- Minor code refactor for sorted_order ([#8326](https://github.com/rapidsai/cudf/pull/8326)) [@wbo4958](https://github.com/wbo4958)
+- Remove special Index class from the general index class hierarchy ([#8309](https://github.com/rapidsai/cudf/pull/8309)) [@vyasr](https://github.com/vyasr)
+- Add first-class dtype utilities ([#8308](https://github.com/rapidsai/cudf/pull/8308)) [@vyasr](https://github.com/vyasr)
+- Add option to link Java bindings with Arrow dynamically ([#8307](https://github.com/rapidsai/cudf/pull/8307)) [@jlowe](https://github.com/jlowe)
+- Refactor ColumnMethods and its subclasses to remove `column` argument and require `parent` argument ([#8306](https://github.com/rapidsai/cudf/pull/8306)) [@shwina](https://github.com/shwina)
+- Refactor `scatter` for list columns ([#8255](https://github.com/rapidsai/cudf/pull/8255)) [@isVoid](https://github.com/isVoid)
+- Expose pack/unpack API to Python ([#8153](https://github.com/rapidsai/cudf/pull/8153)) [@charlesbluca](https://github.com/charlesbluca)
+- Adding cudf.cut method ([#8002](https://github.com/rapidsai/cudf/pull/8002)) [@marlenezw](https://github.com/marlenezw)
+- Optimize string gather performance for large strings ([#7980](https://github.com/rapidsai/cudf/pull/7980)) [@gaohao95](https://github.com/gaohao95)
+- Add peak memory usage tracking to cuIO benchmarks ([#7770](https://github.com/rapidsai/cudf/pull/7770)) [@devavret](https://github.com/devavret)
+- Updating Clang Version to 11.0.0 ([#6695](https://github.com/rapidsai/cudf/pull/6695)) [@codereport](https://github.com/codereport)
+
+# cuDF 21.06.00 (9 Jun 2021)
+
+## 🚨 Breaking Changes
+
+- Add support for `make_meta_obj` dispatch in `dask-cudf` ([#8342](https://github.com/rapidsai/cudf/pull/8342)) [@galipremsagar](https://github.com/galipremsagar)
+- Add separator-on-null parameter to strings concatenate APIs ([#8282](https://github.com/rapidsai/cudf/pull/8282)) [@davidwendt](https://github.com/davidwendt)
+- Introduce a common parent class for NumericalColumn and DecimalColumn ([#8278](https://github.com/rapidsai/cudf/pull/8278)) [@vyasr](https://github.com/vyasr)
+- Update ORC statistics API to use C++17 standard library ([#8241](https://github.com/rapidsai/cudf/pull/8241)) [@vuule](https://github.com/vuule)
+- Preserve column hierarchy when getting NULL row from `LIST` column ([#8206](https://github.com/rapidsai/cudf/pull/8206)) [@isVoid](https://github.com/isVoid)
+- `Groupby.shift` c++ API refactor and python binding ([#8131](https://github.com/rapidsai/cudf/pull/8131)) [@isVoid](https://github.com/isVoid)
+
+## 🐛 Bug Fixes
+
+- Fix struct flattening to add a validity column only when the input column has null element ([#8374](https://github.com/rapidsai/cudf/pull/8374)) [@ttnghia](https://github.com/ttnghia)
+- Compilation fix: Remove redefinition for `std::is_same_v()` ([#8369](https://github.com/rapidsai/cudf/pull/8369)) [@mythrocks](https://github.com/mythrocks)
+- Add backward compatibility for `dask-cudf` to work with other versions of `dask` ([#8368](https://github.com/rapidsai/cudf/pull/8368)) [@galipremsagar](https://github.com/galipremsagar)
+- Handle empty results with nested types in copy_if_else ([#8359](https://github.com/rapidsai/cudf/pull/8359)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Handle nested column types properly for empty parquet files. ([#8350](https://github.com/rapidsai/cudf/pull/8350)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Raise error when unsupported arguments are passed to `dask_cudf.DataFrame.sort_values` ([#8349](https://github.com/rapidsai/cudf/pull/8349)) [@galipremsagar](https://github.com/galipremsagar)
+- Raise `NotImplementedError` for axis=1 in `rank` ([#8347](https://github.com/rapidsai/cudf/pull/8347)) [@galipremsagar](https://github.com/galipremsagar)
+- Add support for `make_meta_obj` dispatch in `dask-cudf` ([#8342](https://github.com/rapidsai/cudf/pull/8342)) [@galipremsagar](https://github.com/galipremsagar)
+- Update Java string concatenate test for single column ([#8330](https://github.com/rapidsai/cudf/pull/8330)) [@tgravescs](https://github.com/tgravescs)
+- Use empty_like in scatter ([#8314](https://github.com/rapidsai/cudf/pull/8314)) [@revans2](https://github.com/revans2)
+- Fix concatenate_lists_ignore_null on rows of all_nulls ([#8312](https://github.com/rapidsai/cudf/pull/8312)) [@sperlingxx](https://github.com/sperlingxx)
+- Add separator-on-null parameter to strings concatenate APIs ([#8282](https://github.com/rapidsai/cudf/pull/8282)) [@davidwendt](https://github.com/davidwendt)
+- COLLECT_LIST support returning empty output columns. ([#8279](https://github.com/rapidsai/cudf/pull/8279)) [@mythrocks](https://github.com/mythrocks)
+- Update io util to convert path like object to string ([#8275](https://github.com/rapidsai/cudf/pull/8275)) [@ayushdg](https://github.com/ayushdg)
+- Fix result column types for empty inputs to rolling window ([#8274](https://github.com/rapidsai/cudf/pull/8274)) [@mythrocks](https://github.com/mythrocks)
+- Actually test equality in assert_groupby_results_equal ([#8272](https://github.com/rapidsai/cudf/pull/8272)) [@shwina](https://github.com/shwina)
+- CMake always explicitly specify a source files extension ([#8270](https://github.com/rapidsai/cudf/pull/8270)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix struct binary search and struct flattening ([#8268](https://github.com/rapidsai/cudf/pull/8268)) [@ttnghia](https://github.com/ttnghia)
+- Revert &quot;patch thrust to fix intmax num elements limitation in scan_by_key&quot; ([#8263](https://github.com/rapidsai/cudf/pull/8263)) [@cwharris](https://github.com/cwharris)
+- upgrade dlpack to 0.5 ([#8262](https://github.com/rapidsai/cudf/pull/8262)) [@cwharris](https://github.com/cwharris)
+- Fixes CSV-reader type inference for thousands separator and decimal point ([#8261](https://github.com/rapidsai/cudf/pull/8261)) [@elstehle](https://github.com/elstehle)
+- Fix incorrect assertion in Java concat ([#8258](https://github.com/rapidsai/cudf/pull/8258)) [@sperlingxx](https://github.com/sperlingxx)
+- Copy nested types upon construction ([#8244](https://github.com/rapidsai/cudf/pull/8244)) [@isVoid](https://github.com/isVoid)
+- Preserve column hierarchy when getting NULL row from `LIST` column ([#8206](https://github.com/rapidsai/cudf/pull/8206)) [@isVoid](https://github.com/isVoid)
+- Clip decimal binary op precision at max precision ([#8194](https://github.com/rapidsai/cudf/pull/8194)) [@ChrisJar](https://github.com/ChrisJar)
+
+## 📖 Documentation
+
+- Add docstring for `dask_cudf.read_csv` ([#8355](https://github.com/rapidsai/cudf/pull/8355)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix cudf release version in readme ([#8331](https://github.com/rapidsai/cudf/pull/8331)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix structs column description in dev docs ([#8318](https://github.com/rapidsai/cudf/pull/8318)) [@isVoid](https://github.com/isVoid)
+- Update readme with correct CUDA versions ([#8315](https://github.com/rapidsai/cudf/pull/8315)) [@raydouglass](https://github.com/raydouglass)
+- Add description of the cuIO GDS integration ([#8293](https://github.com/rapidsai/cudf/pull/8293)) [@vuule](https://github.com/vuule)
+- Remove unused parameter from copy_partition kernel documentation ([#8283](https://github.com/rapidsai/cudf/pull/8283)) [@robertmaynard](https://github.com/robertmaynard)
+
+## 🚀 New Features
+
+- Add support merging b/w categorical data ([#8332](https://github.com/rapidsai/cudf/pull/8332)) [@galipremsagar](https://github.com/galipremsagar)
+- Java: Support struct scalar ([#8327](https://github.com/rapidsai/cudf/pull/8327)) [@sperlingxx](https://github.com/sperlingxx)
+- added _is_homogeneous property ([#8299](https://github.com/rapidsai/cudf/pull/8299)) [@shaneding](https://github.com/shaneding)
+- Added decimal writing for CSV writer ([#8296](https://github.com/rapidsai/cudf/pull/8296)) [@kaatish](https://github.com/kaatish)
+- Java: Support creating a scalar from utf8 string ([#8294](https://github.com/rapidsai/cudf/pull/8294)) [@firestarman](https://github.com/firestarman)
+- Add Java API for Concatenate strings with separator ([#8289](https://github.com/rapidsai/cudf/pull/8289)) [@tgravescs](https://github.com/tgravescs)
+- `strings::join_list_elements` options for empty list inputs ([#8285](https://github.com/rapidsai/cudf/pull/8285)) [@ttnghia](https://github.com/ttnghia)
+- Return python lists for __getitem__ calls to list type series ([#8265](https://github.com/rapidsai/cudf/pull/8265)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- add unit tests for lead/lag on list for row window ([#8259](https://github.com/rapidsai/cudf/pull/8259)) [@wbo4958](https://github.com/wbo4958)
+- Create a String column from UTF8 String byte arrays ([#8257](https://github.com/rapidsai/cudf/pull/8257)) [@firestarman](https://github.com/firestarman)
+- Support scattering `list_scalar` ([#8256](https://github.com/rapidsai/cudf/pull/8256)) [@isVoid](https://github.com/isVoid)
+- Implement `lists::concatenate_list_elements` ([#8231](https://github.com/rapidsai/cudf/pull/8231)) [@ttnghia](https://github.com/ttnghia)
+- Support for struct scalars. ([#8220](https://github.com/rapidsai/cudf/pull/8220)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add support for decimal types in ORC writer ([#8198](https://github.com/rapidsai/cudf/pull/8198)) [@vuule](https://github.com/vuule)
+- Support create lists column from a `list_scalar` ([#8185](https://github.com/rapidsai/cudf/pull/8185)) [@isVoid](https://github.com/isVoid)
+- `Groupby.shift` c++ API refactor and python binding ([#8131](https://github.com/rapidsai/cudf/pull/8131)) [@isVoid](https://github.com/isVoid)
+- Add `groupby::replace_nulls(replace_policy)` api ([#7118](https://github.com/rapidsai/cudf/pull/7118)) [@isVoid](https://github.com/isVoid)
+
+## 🛠️ Improvements
+
+- Support Dask + Distributed 2021.05.1 ([#8392](https://github.com/rapidsai/cudf/pull/8392)) [@jakirkham](https://github.com/jakirkham)
+- Add aliases for string methods ([#8353](https://github.com/rapidsai/cudf/pull/8353)) [@shwina](https://github.com/shwina)
+- Update environment variable used to determine `cuda_version` ([#8321](https://github.com/rapidsai/cudf/pull/8321)) [@ajschmidt8](https://github.com/ajschmidt8)
+- JNI: Refactor the code of making column from scalar ([#8310](https://github.com/rapidsai/cudf/pull/8310)) [@firestarman](https://github.com/firestarman)
+- Update `CHANGELOG.md` links for calver ([#8303](https://github.com/rapidsai/cudf/pull/8303)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Merge `branch-0.19` into `branch-21.06` ([#8302](https://github.com/rapidsai/cudf/pull/8302)) [@ajschmidt8](https://github.com/ajschmidt8)
+- use address and length for GDS reads/writes ([#8301](https://github.com/rapidsai/cudf/pull/8301)) [@rongou](https://github.com/rongou)
+- Update cudfjni version to 21.06.0 ([#8292](https://github.com/rapidsai/cudf/pull/8292)) [@pxLi](https://github.com/pxLi)
+- Update docs build script ([#8284](https://github.com/rapidsai/cudf/pull/8284)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Make device_buffer streams explicit and enforce move construction ([#8280](https://github.com/rapidsai/cudf/pull/8280)) [@harrism](https://github.com/harrism)
+- Introduce a common parent class for NumericalColumn and DecimalColumn ([#8278](https://github.com/rapidsai/cudf/pull/8278)) [@vyasr](https://github.com/vyasr)
+- Do not add nulls to the hash table when null_equality::NOT_EQUAL is passed to left_semi_join and left_anti_join ([#8277](https://github.com/rapidsai/cudf/pull/8277)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Enable implicit casting when concatenating mixed types ([#8276](https://github.com/rapidsai/cudf/pull/8276)) [@ChrisJar](https://github.com/ChrisJar)
+- Fix CMake FindPackage rmm, pin dev envs&#39; dlpack to v0.3 ([#8271](https://github.com/rapidsai/cudf/pull/8271)) [@trxcllnt](https://github.com/trxcllnt)
+- Update cudfjni version to 21.06 ([#8267](https://github.com/rapidsai/cudf/pull/8267)) [@pxLi](https://github.com/pxLi)
+- support RMM aligned resource adapter in JNI ([#8266](https://github.com/rapidsai/cudf/pull/8266)) [@rongou](https://github.com/rongou)
+- Pass compiler environment variables to conda python build ([#8260](https://github.com/rapidsai/cudf/pull/8260)) [@Ethyling](https://github.com/Ethyling)
+- Remove abc inheritance from Serializable ([#8254](https://github.com/rapidsai/cudf/pull/8254)) [@vyasr](https://github.com/vyasr)
+- Move more methods into SingleColumnFrame ([#8253](https://github.com/rapidsai/cudf/pull/8253)) [@vyasr](https://github.com/vyasr)
+- Update ORC statistics API to use C++17 standard library ([#8241](https://github.com/rapidsai/cudf/pull/8241)) [@vuule](https://github.com/vuule)
+- Correct unused parameter warnings in dictionary algorithms ([#8239](https://github.com/rapidsai/cudf/pull/8239)) [@robertmaynard](https://github.com/robertmaynard)
+- Correct unused parameters in the copying algorithms ([#8232](https://github.com/rapidsai/cudf/pull/8232)) [@robertmaynard](https://github.com/robertmaynard)
+- IO statistics cleanup ([#8191](https://github.com/rapidsai/cudf/pull/8191)) [@kaatish](https://github.com/kaatish)
+- Refactor of rolling_window implementation. ([#8158](https://github.com/rapidsai/cudf/pull/8158)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Add a flag for allowing single quotes in JSON strings. ([#8144](https://github.com/rapidsai/cudf/pull/8144)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Column refactoring 2 ([#8130](https://github.com/rapidsai/cudf/pull/8130)) [@vyasr](https://github.com/vyasr)
+- support space in workspace ([#7956](https://github.com/rapidsai/cudf/pull/7956)) [@jolorunyomi](https://github.com/jolorunyomi)
+- Support collect_set on rolling window ([#7881](https://github.com/rapidsai/cudf/pull/7881)) [@sperlingxx](https://github.com/sperlingxx)
+
+# cuDF 0.19.0 (21 Apr 2021)
+
+## 🚨 Breaking Changes
+
+- Allow hash_partition to take a seed value ([#7771](https://github.com/rapidsai/cudf/pull/7771)) [@magnatelee](https://github.com/magnatelee)
+- Allow merging index column with data column using keyword &quot;on&quot; ([#7736](https://github.com/rapidsai/cudf/pull/7736)) [@skirui-source](https://github.com/skirui-source)
+- Change JNI API to avoid loading native dependencies when creating sort order classes. ([#7729](https://github.com/rapidsai/cudf/pull/7729)) [@revans2](https://github.com/revans2)
+- Replace device_vector with device_uvector in null_mask ([#7715](https://github.com/rapidsai/cudf/pull/7715)) [@harrism](https://github.com/harrism)
+- Don&#39;t identify decimals as strings. ([#7710](https://github.com/rapidsai/cudf/pull/7710)) [@vyasr](https://github.com/vyasr)
+- Fix Java Parquet write after writer API changes ([#7655](https://github.com/rapidsai/cudf/pull/7655)) [@revans2](https://github.com/revans2)
+- Convert cudf::concatenate APIs to use spans and device_uvector ([#7621](https://github.com/rapidsai/cudf/pull/7621)) [@harrism](https://github.com/harrism)
+- Update missing docstring examples in python public APIs ([#7546](https://github.com/rapidsai/cudf/pull/7546)) [@galipremsagar](https://github.com/galipremsagar)
+- Remove unneeded step parameter from strings::detail::copy_slice ([#7525](https://github.com/rapidsai/cudf/pull/7525)) [@davidwendt](https://github.com/davidwendt)
+- Rename ARROW_STATIC_LIB because it conflicts with one in FindArrow.cmake ([#7518](https://github.com/rapidsai/cudf/pull/7518)) [@trxcllnt](https://github.com/trxcllnt)
+- Match Pandas logic for comparing two objects with nulls ([#7490](https://github.com/rapidsai/cudf/pull/7490)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add struct support to parquet writer ([#7461](https://github.com/rapidsai/cudf/pull/7461)) [@devavret](https://github.com/devavret)
+- Join APIs that return gathermaps ([#7454](https://github.com/rapidsai/cudf/pull/7454)) [@shwina](https://github.com/shwina)
+- `fixed_point` + `cudf::binary_operation` API Changes ([#7435](https://github.com/rapidsai/cudf/pull/7435)) [@codereport](https://github.com/codereport)
+- Fix BUG: Exception when PYTHONOPTIMIZE=2 ([#7434](https://github.com/rapidsai/cudf/pull/7434)) [@skirui-source](https://github.com/skirui-source)
+- Change nvtext::load_vocabulary_file to return a unique ptr ([#7424](https://github.com/rapidsai/cudf/pull/7424)) [@davidwendt](https://github.com/davidwendt)
+- Refactor strings column factories ([#7397](https://github.com/rapidsai/cudf/pull/7397)) [@harrism](https://github.com/harrism)
+- Use CMAKE_CUDA_ARCHITECTURES ([#7391](https://github.com/rapidsai/cudf/pull/7391)) [@robertmaynard](https://github.com/robertmaynard)
+- Upgrade pandas to 1.2 ([#7375](https://github.com/rapidsai/cudf/pull/7375)) [@galipremsagar](https://github.com/galipremsagar)
+- Rename `logical_cast` to `bit_cast` and allow additional conversions ([#7373](https://github.com/rapidsai/cudf/pull/7373)) [@ttnghia](https://github.com/ttnghia)
+- Rework libcudf CMakeLists.txt to export targets for CPM ([#7107](https://github.com/rapidsai/cudf/pull/7107)) [@trxcllnt](https://github.com/trxcllnt)
+
+## 🐛 Bug Fixes
+
+- Fix a `NameError` in meta dispatch API ([#7996](https://github.com/rapidsai/cudf/pull/7996)) [@galipremsagar](https://github.com/galipremsagar)
+- Reindex in `DataFrame.__setitem__` ([#7957](https://github.com/rapidsai/cudf/pull/7957)) [@galipremsagar](https://github.com/galipremsagar)
+- jitify direct-to-cubin compilation and caching. ([#7919](https://github.com/rapidsai/cudf/pull/7919)) [@cwharris](https://github.com/cwharris)
+- Use dynamic cudart for nvcomp in java build ([#7896](https://github.com/rapidsai/cudf/pull/7896)) [@abellina](https://github.com/abellina)
+- fix &quot;incompatible redefinition&quot; warnings ([#7894](https://github.com/rapidsai/cudf/pull/7894)) [@cwharris](https://github.com/cwharris)
+- cudf consistently specifies the cuda runtime ([#7887](https://github.com/rapidsai/cudf/pull/7887)) [@robertmaynard](https://github.com/robertmaynard)
+- disable verbose output for jitify_preprocess ([#7886](https://github.com/rapidsai/cudf/pull/7886)) [@cwharris](https://github.com/cwharris)
+- CMake jit_preprocess_files function only runs when needed ([#7872](https://github.com/rapidsai/cudf/pull/7872)) [@robertmaynard](https://github.com/robertmaynard)
+- Push DeviceScalar construction into cython for list.contains ([#7864](https://github.com/rapidsai/cudf/pull/7864)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- cudf now sets an install rpath of $ORIGIN ([#7863](https://github.com/rapidsai/cudf/pull/7863)) [@robertmaynard](https://github.com/robertmaynard)
+- Don&#39;t install Thrust examples, tests, docs, and python files ([#7811](https://github.com/rapidsai/cudf/pull/7811)) [@robertmaynard](https://github.com/robertmaynard)
+- Sort by index in groupby tests more consistently ([#7802](https://github.com/rapidsai/cudf/pull/7802)) [@shwina](https://github.com/shwina)
+- Revert &quot;Update conda recipes pinning of repo dependencies ([#7743)&quot; (#7793](https://github.com/rapidsai/cudf/pull/7743)&quot; (#7793)) [@raydouglass](https://github.com/raydouglass)
+- Add decimal column handling in copy_type_metadata ([#7788](https://github.com/rapidsai/cudf/pull/7788)) [@shwina](https://github.com/shwina)
+- Add column names validation in parquet writer ([#7786](https://github.com/rapidsai/cudf/pull/7786)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix Java explode outer unit tests ([#7782](https://github.com/rapidsai/cudf/pull/7782)) [@jlowe](https://github.com/jlowe)
+- Fix compiler warning about non-POD types passed through ellipsis ([#7781](https://github.com/rapidsai/cudf/pull/7781)) [@jrhemstad](https://github.com/jrhemstad)
+- User resource fix for replace_nulls ([#7769](https://github.com/rapidsai/cudf/pull/7769)) [@magnatelee](https://github.com/magnatelee)
+- Fix type dispatch for columnar replace_nulls ([#7768](https://github.com/rapidsai/cudf/pull/7768)) [@jlowe](https://github.com/jlowe)
+- Add `ignore_order` parameter to dask-cudf concat dispatch ([#7765](https://github.com/rapidsai/cudf/pull/7765)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix slicing and arrow representations of decimal columns ([#7755](https://github.com/rapidsai/cudf/pull/7755)) [@vyasr](https://github.com/vyasr)
+- Fixing issue with explode_outer position not nulling position entries of null rows ([#7754](https://github.com/rapidsai/cudf/pull/7754)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Implement scatter for struct columns ([#7752](https://github.com/rapidsai/cudf/pull/7752)) [@ttnghia](https://github.com/ttnghia)
+- Fix data corruption in string columns ([#7746](https://github.com/rapidsai/cudf/pull/7746)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix string length in stripe dictionary building ([#7744](https://github.com/rapidsai/cudf/pull/7744)) [@kaatish](https://github.com/kaatish)
+- Update conda recipes pinning of repo dependencies ([#7743](https://github.com/rapidsai/cudf/pull/7743)) [@mike-wendt](https://github.com/mike-wendt)
+- Enable dask dispatch to cuDF&#39;s `is_categorical_dtype` for cuDF objects ([#7740](https://github.com/rapidsai/cudf/pull/7740)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix dictionary size computation in ORC writer ([#7737](https://github.com/rapidsai/cudf/pull/7737)) [@vuule](https://github.com/vuule)
+- Fix `cudf::cast` overflow for `decimal64` to `int32_t` or smaller in certain cases ([#7733](https://github.com/rapidsai/cudf/pull/7733)) [@codereport](https://github.com/codereport)
+- Change JNI API to avoid loading native dependencies when creating sort order classes. ([#7729](https://github.com/rapidsai/cudf/pull/7729)) [@revans2](https://github.com/revans2)
+- Disable column_view data accessors for unsupported types ([#7725](https://github.com/rapidsai/cudf/pull/7725)) [@jrhemstad](https://github.com/jrhemstad)
+- Materialize `RangeIndex` when `index=True` in parquet writer ([#7711](https://github.com/rapidsai/cudf/pull/7711)) [@galipremsagar](https://github.com/galipremsagar)
+- Don&#39;t identify decimals as strings. ([#7710](https://github.com/rapidsai/cudf/pull/7710)) [@vyasr](https://github.com/vyasr)
+- Fix return type of `DataFrame.argsort` ([#7706](https://github.com/rapidsai/cudf/pull/7706)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix/correct cudf installed package requirements ([#7688](https://github.com/rapidsai/cudf/pull/7688)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix SparkMurmurHash3_32 hash inconsistencies with Apache Spark ([#7672](https://github.com/rapidsai/cudf/pull/7672)) [@jlowe](https://github.com/jlowe)
+- Fix ORC reader issue with reading empty string columns ([#7656](https://github.com/rapidsai/cudf/pull/7656)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Fix Java Parquet write after writer API changes ([#7655](https://github.com/rapidsai/cudf/pull/7655)) [@revans2](https://github.com/revans2)
+- Fixing empty null lists throwing explode_outer for a loop. ([#7649](https://github.com/rapidsai/cudf/pull/7649)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Fix internal compiler error during JNI Docker build ([#7645](https://github.com/rapidsai/cudf/pull/7645)) [@jlowe](https://github.com/jlowe)
+- Fix Debug build break with device_uvectors in grouped_rolling.cu ([#7633](https://github.com/rapidsai/cudf/pull/7633)) [@mythrocks](https://github.com/mythrocks)
+- Parquet reader:  Fix issue when using skip_rows on non-nested columns containing nulls ([#7627](https://github.com/rapidsai/cudf/pull/7627)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Fix ORC reader for empty DataFrame/Table ([#7624](https://github.com/rapidsai/cudf/pull/7624)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Fix specifying GPU architecture in JNI build ([#7612](https://github.com/rapidsai/cudf/pull/7612)) [@jlowe](https://github.com/jlowe)
+- Fix ORC writer OOM issue ([#7605](https://github.com/rapidsai/cudf/pull/7605)) [@vuule](https://github.com/vuule)
+- Fix 0.18 --&gt; 0.19 automerge ([#7589](https://github.com/rapidsai/cudf/pull/7589)) [@kkraus14](https://github.com/kkraus14)
+- Fix ORC issue with incorrect timestamp nanosecond values ([#7581](https://github.com/rapidsai/cudf/pull/7581)) [@vuule](https://github.com/vuule)
+- Fix missing Dask imports ([#7580](https://github.com/rapidsai/cudf/pull/7580)) [@kkraus14](https://github.com/kkraus14)
+- CMAKE_CUDA_ARCHITECTURES doesn&#39;t change when build-system invokes cmake ([#7579](https://github.com/rapidsai/cudf/pull/7579)) [@robertmaynard](https://github.com/robertmaynard)
+- Another fix for offsets_end() iterator in lists_column_view ([#7575](https://github.com/rapidsai/cudf/pull/7575)) [@ttnghia](https://github.com/ttnghia)
+- Fix ORC writer output corruption with string columns ([#7565](https://github.com/rapidsai/cudf/pull/7565)) [@vuule](https://github.com/vuule)
+- Fix cudf::lists::sort_lists failing for sliced column ([#7564](https://github.com/rapidsai/cudf/pull/7564)) [@ttnghia](https://github.com/ttnghia)
+- FIX Fix Anaconda upload args ([#7558](https://github.com/rapidsai/cudf/pull/7558)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- Fix index mismatch issue in equality related APIs ([#7555](https://github.com/rapidsai/cudf/pull/7555)) [@galipremsagar](https://github.com/galipremsagar)
+- FIX Revert gpuci_conda_retry on conda file output locations ([#7552](https://github.com/rapidsai/cudf/pull/7552)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- Fix offset_end iterator for lists_column_view, which was not correctl… ([#7551](https://github.com/rapidsai/cudf/pull/7551)) [@ttnghia](https://github.com/ttnghia)
+- Fix no such file dlpack.h error when build libcudf ([#7549](https://github.com/rapidsai/cudf/pull/7549)) [@chenrui17](https://github.com/chenrui17)
+- Update missing docstring examples in python public APIs ([#7546](https://github.com/rapidsai/cudf/pull/7546)) [@galipremsagar](https://github.com/galipremsagar)
+- Decimal32 Build Fix ([#7544](https://github.com/rapidsai/cudf/pull/7544)) [@razajafri](https://github.com/razajafri)
+- FIX Retry conda output location ([#7540](https://github.com/rapidsai/cudf/pull/7540)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- fix missing renames of dask git branches from master to main ([#7535](https://github.com/rapidsai/cudf/pull/7535)) [@kkraus14](https://github.com/kkraus14)
+- Remove detail from device_span ([#7533](https://github.com/rapidsai/cudf/pull/7533)) [@rwlee](https://github.com/rwlee)
+- Change dask and distributed branch to main ([#7532](https://github.com/rapidsai/cudf/pull/7532)) [@dantegd](https://github.com/dantegd)
+- Update JNI build to use CUDF_USE_ARROW_STATIC ([#7526](https://github.com/rapidsai/cudf/pull/7526)) [@jlowe](https://github.com/jlowe)
+- Make sure rmm::rmm CMake target is visible to cudf users ([#7524](https://github.com/rapidsai/cudf/pull/7524)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix contiguous_split not properly handling output partitions &gt; 2 GB. ([#7515](https://github.com/rapidsai/cudf/pull/7515)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Change jit launch to safe_launch ([#7510](https://github.com/rapidsai/cudf/pull/7510)) [@devavret](https://github.com/devavret)
+- Fix comparison between Datetime/Timedelta columns and NULL scalars ([#7504](https://github.com/rapidsai/cudf/pull/7504)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix off-by-one error in char-parallel string scalar replace ([#7502](https://github.com/rapidsai/cudf/pull/7502)) [@jlowe](https://github.com/jlowe)
+- Fix JNI deprecation of all, put it on the wrong version before ([#7501](https://github.com/rapidsai/cudf/pull/7501)) [@revans2](https://github.com/revans2)
+- Fix Series/Dataframe Mixed Arithmetic ([#7491](https://github.com/rapidsai/cudf/pull/7491)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Fix JNI build after removal of libcudf sub-libraries ([#7486](https://github.com/rapidsai/cudf/pull/7486)) [@jlowe](https://github.com/jlowe)
+- Correctly compile benchmarks ([#7485](https://github.com/rapidsai/cudf/pull/7485)) [@robertmaynard](https://github.com/robertmaynard)
+- Fix bool column corruption with ORC Reader ([#7483](https://github.com/rapidsai/cudf/pull/7483)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Fix `__repr__` for categorical dtype ([#7476](https://github.com/rapidsai/cudf/pull/7476)) [@galipremsagar](https://github.com/galipremsagar)
+- Java cleaner synchronization ([#7474](https://github.com/rapidsai/cudf/pull/7474)) [@abellina](https://github.com/abellina)
+- Fix java float/double parsing tests ([#7473](https://github.com/rapidsai/cudf/pull/7473)) [@revans2](https://github.com/revans2)
+- Pass stream and user resource to make_default_constructed_scalar ([#7469](https://github.com/rapidsai/cudf/pull/7469)) [@magnatelee](https://github.com/magnatelee)
+- Improve stability of dask_cudf.DataFrame.var and dask_cudf.DataFrame.std ([#7453](https://github.com/rapidsai/cudf/pull/7453)) [@rjzamora](https://github.com/rjzamora)
+- Missing `device_storage_dispatch` change affecting `cudf::gather` ([#7449](https://github.com/rapidsai/cudf/pull/7449)) [@codereport](https://github.com/codereport)
+- fix cuFile JNI compile errors ([#7445](https://github.com/rapidsai/cudf/pull/7445)) [@rongou](https://github.com/rongou)
+- Support `Series.__setitem__` with key to a new row ([#7443](https://github.com/rapidsai/cudf/pull/7443)) [@isVoid](https://github.com/isVoid)
+- Fix BUG: Exception when PYTHONOPTIMIZE=2 ([#7434](https://github.com/rapidsai/cudf/pull/7434)) [@skirui-source](https://github.com/skirui-source)
+- Make inclusive scan safe for cases with leading nulls ([#7432](https://github.com/rapidsai/cudf/pull/7432)) [@magnatelee](https://github.com/magnatelee)
+- Fix typo in list_device_view::pair_rep_end() ([#7423](https://github.com/rapidsai/cudf/pull/7423)) [@mythrocks](https://github.com/mythrocks)
+- Fix string to double conversion and row equivalent comparison ([#7410](https://github.com/rapidsai/cudf/pull/7410)) [@ttnghia](https://github.com/ttnghia)
+- Fix thrust failure when transferring data from device_vector to host_vector with vectors of size 1 ([#7382](https://github.com/rapidsai/cudf/pull/7382)) [@ttnghia](https://github.com/ttnghia)
+- Fix std::exception catch-by-reference gcc9 compile error ([#7380](https://github.com/rapidsai/cudf/pull/7380)) [@davidwendt](https://github.com/davidwendt)
+- Fix skiprows issue with ORC Reader ([#7359](https://github.com/rapidsai/cudf/pull/7359)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- fix Arrow CMake file ([#7358](https://github.com/rapidsai/cudf/pull/7358)) [@rongou](https://github.com/rongou)
+- Fix lists::contains() for NaN and Decimals ([#7349](https://github.com/rapidsai/cudf/pull/7349)) [@mythrocks](https://github.com/mythrocks)
+- Handle cupy array in `Dataframe.__setitem__` ([#7340](https://github.com/rapidsai/cudf/pull/7340)) [@galipremsagar](https://github.com/galipremsagar)
+- Fix invalid-device-fn error in cudf::strings::replace_re with multiple regex&#39;s ([#7336](https://github.com/rapidsai/cudf/pull/7336)) [@davidwendt](https://github.com/davidwendt)
+- FIX Add codecov upload block to gpu script ([#6860](https://github.com/rapidsai/cudf/pull/6860)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+
+## 📖 Documentation
+
+- Fix join API doxygen ([#7890](https://github.com/rapidsai/cudf/pull/7890)) [@shwina](https://github.com/shwina)
+- Add Resources to README. ([#7697](https://github.com/rapidsai/cudf/pull/7697)) [@bdice](https://github.com/bdice)
+- Add `isin` examples in Docstring ([#7479](https://github.com/rapidsai/cudf/pull/7479)) [@galipremsagar](https://github.com/galipremsagar)
+- Resolving unlinked type shorthands in cudf doc ([#7416](https://github.com/rapidsai/cudf/pull/7416)) [@isVoid](https://github.com/isVoid)
+- Fix typo in regex.md doc page ([#7363](https://github.com/rapidsai/cudf/pull/7363)) [@davidwendt](https://github.com/davidwendt)
+- Fix incorrect strings_column_view::chars_size documentation ([#7360](https://github.com/rapidsai/cudf/pull/7360)) [@jlowe](https://github.com/jlowe)
+
+## 🚀 New Features
+
+- Enable basic reductions for decimal columns ([#7776](https://github.com/rapidsai/cudf/pull/7776)) [@ChrisJar](https://github.com/ChrisJar)
+- Enable join on decimal columns ([#7764](https://github.com/rapidsai/cudf/pull/7764)) [@ChrisJar](https://github.com/ChrisJar)
+- Allow merging index column with data column using keyword &quot;on&quot; ([#7736](https://github.com/rapidsai/cudf/pull/7736)) [@skirui-source](https://github.com/skirui-source)
+- Implement DecimalColumn + Scalar and add cudf.Scalars of Decimal64Dtype ([#7732](https://github.com/rapidsai/cudf/pull/7732)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add support for `unique` groupby aggregation ([#7726](https://github.com/rapidsai/cudf/pull/7726)) [@shwina](https://github.com/shwina)
+- Expose libcudf&#39;s label_bins function to cudf ([#7724](https://github.com/rapidsai/cudf/pull/7724)) [@vyasr](https://github.com/vyasr)
+- Adding support for equi-join on struct ([#7720](https://github.com/rapidsai/cudf/pull/7720)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add decimal column comparison operations ([#7716](https://github.com/rapidsai/cudf/pull/7716)) [@isVoid](https://github.com/isVoid)
+- Implement scan operations for decimal columns ([#7707](https://github.com/rapidsai/cudf/pull/7707)) [@ChrisJar](https://github.com/ChrisJar)
+- Enable typecasting between decimal and int ([#7691](https://github.com/rapidsai/cudf/pull/7691)) [@ChrisJar](https://github.com/ChrisJar)
+- Enable decimal support in parquet writer ([#7673](https://github.com/rapidsai/cudf/pull/7673)) [@devavret](https://github.com/devavret)
+- Adds `list.unique` API ([#7664](https://github.com/rapidsai/cudf/pull/7664)) [@isVoid](https://github.com/isVoid)
+- Fix NaN handling in drop_list_duplicates ([#7662](https://github.com/rapidsai/cudf/pull/7662)) [@ttnghia](https://github.com/ttnghia)
+- Add `lists.sort_values` API ([#7657](https://github.com/rapidsai/cudf/pull/7657)) [@isVoid](https://github.com/isVoid)
+- Add is_integer API that can check for the validity of a string-to-integer conversion ([#7642](https://github.com/rapidsai/cudf/pull/7642)) [@ttnghia](https://github.com/ttnghia)
+- Adds `explode` API ([#7607](https://github.com/rapidsai/cudf/pull/7607)) [@isVoid](https://github.com/isVoid)
+- Adds `list.take`, python binding for `cudf::lists::segmented_gather` ([#7591](https://github.com/rapidsai/cudf/pull/7591)) [@isVoid](https://github.com/isVoid)
+- Implement cudf::label_bins() ([#7554](https://github.com/rapidsai/cudf/pull/7554)) [@vyasr](https://github.com/vyasr)
+- Add Python bindings for `lists::contains` ([#7547](https://github.com/rapidsai/cudf/pull/7547)) [@skirui-source](https://github.com/skirui-source)
+- cudf::row_bit_count() support. ([#7534](https://github.com/rapidsai/cudf/pull/7534)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Implement drop_list_duplicates ([#7528](https://github.com/rapidsai/cudf/pull/7528)) [@ttnghia](https://github.com/ttnghia)
+- Add Python bindings for `lists::extract_lists_element` ([#7505](https://github.com/rapidsai/cudf/pull/7505)) [@skirui-source](https://github.com/skirui-source)
+- Add explode_outer and explode_outer_position ([#7499](https://github.com/rapidsai/cudf/pull/7499)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Match Pandas logic for comparing two objects with nulls ([#7490](https://github.com/rapidsai/cudf/pull/7490)) [@brandon-b-miller](https://github.com/brandon-b-miller)
+- Add struct support to parquet writer ([#7461](https://github.com/rapidsai/cudf/pull/7461)) [@devavret](https://github.com/devavret)
+- Enable type conversion from float to decimal type ([#7450](https://github.com/rapidsai/cudf/pull/7450)) [@ChrisJar](https://github.com/ChrisJar)
+- Add cython for converting strings/fixed-point functions ([#7429](https://github.com/rapidsai/cudf/pull/7429)) [@davidwendt](https://github.com/davidwendt)
+- Add struct column support to cudf::sort and cudf::sorted_order ([#7422](https://github.com/rapidsai/cudf/pull/7422)) [@karthikeyann](https://github.com/karthikeyann)
+- Implement groupby collect_set ([#7420](https://github.com/rapidsai/cudf/pull/7420)) [@ttnghia](https://github.com/ttnghia)
+- Merge branch-0.18 into branch-0.19 ([#7411](https://github.com/rapidsai/cudf/pull/7411)) [@raydouglass](https://github.com/raydouglass)
+- Refactor strings column factories ([#7397](https://github.com/rapidsai/cudf/pull/7397)) [@harrism](https://github.com/harrism)
+- Add groupby scan operations (sort groupby) ([#7387](https://github.com/rapidsai/cudf/pull/7387)) [@karthikeyann](https://github.com/karthikeyann)
+- Add cudf::explode_position ([#7376](https://github.com/rapidsai/cudf/pull/7376)) [@hyperbolic2346](https://github.com/hyperbolic2346)
+- Add string conversion to/from decimal values libcudf APIs ([#7364](https://github.com/rapidsai/cudf/pull/7364)) [@davidwendt](https://github.com/davidwendt)
+- Add  groupby SUM_OF_SQUARES support ([#7362](https://github.com/rapidsai/cudf/pull/7362)) [@karthikeyann](https://github.com/karthikeyann)
+- Add `Series.drop` api ([#7304](https://github.com/rapidsai/cudf/pull/7304)) [@isVoid](https://github.com/isVoid)
+- get_json_object() implementation ([#7286](https://github.com/rapidsai/cudf/pull/7286)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Python API for `LIstMethods.len()` ([#7283](https://github.com/rapidsai/cudf/pull/7283)) [@isVoid](https://github.com/isVoid)
+- Support null_policy::EXCLUDE for COLLECT rolling aggregation ([#7264](https://github.com/rapidsai/cudf/pull/7264)) [@mythrocks](https://github.com/mythrocks)
+- Add support for special tokens in nvtext::subword_tokenizer ([#7254](https://github.com/rapidsai/cudf/pull/7254)) [@davidwendt](https://github.com/davidwendt)
+- Fix inplace update of data and add Series.update ([#7201](https://github.com/rapidsai/cudf/pull/7201)) [@galipremsagar](https://github.com/galipremsagar)
+- Implement `cudf::group_by` (hash) for `decimal32` and `decimal64` ([#7190](https://github.com/rapidsai/cudf/pull/7190)) [@codereport](https://github.com/codereport)
+- Adding support to specify &quot;level&quot; parameter  for `Dataframe.rename` ([#7135](https://github.com/rapidsai/cudf/pull/7135)) [@skirui-source](https://github.com/skirui-source)
+
+## 🛠️ Improvements
+
+- fix GDS include path for version 0.95 ([#7877](https://github.com/rapidsai/cudf/pull/7877)) [@rongou](https://github.com/rongou)
+- Update `dask` + `distributed` to `2021.4.0` ([#7858](https://github.com/rapidsai/cudf/pull/7858)) [@jakirkham](https://github.com/jakirkham)
+- Add ability to extract include dirs from `CUDF_HOME` ([#7848](https://github.com/rapidsai/cudf/pull/7848)) [@galipremsagar](https://github.com/galipremsagar)
+- Add USE_GDS as an option in build script ([#7833](https://github.com/rapidsai/cudf/pull/7833)) [@pxLi](https://github.com/pxLi)
+- add an allocate method with stream in java DeviceMemoryBuffer ([#7826](https://github.com/rapidsai/cudf/pull/7826)) [@rongou](https://github.com/rongou)
+- Constrain dask and distributed versions to 2021.3.1 ([#7825](https://github.com/rapidsai/cudf/pull/7825)) [@shwina](https://github.com/shwina)
+- Revert dask versioning of concat dispatch ([#7823](https://github.com/rapidsai/cudf/pull/7823)) [@galipremsagar](https://github.com/galipremsagar)
+- add copy methods in Java memory buffer ([#7791](https://github.com/rapidsai/cudf/pull/7791)) [@rongou](https://github.com/rongou)
+- Update README and CONTRIBUTING for 0.19 ([#7778](https://github.com/rapidsai/cudf/pull/7778)) [@robertmaynard](https://github.com/robertmaynard)
+- Allow hash_partition to take a seed value ([#7771](https://github.com/rapidsai/cudf/pull/7771)) [@magnatelee](https://github.com/magnatelee)
+- Turn on NVTX by default in java build ([#7761](https://github.com/rapidsai/cudf/pull/7761)) [@tgravescs](https://github.com/tgravescs)
+- Add Java bindings to join gather map APIs ([#7751](https://github.com/rapidsai/cudf/pull/7751)) [@jlowe](https://github.com/jlowe)
+- Add replacements column support for Java replaceNulls ([#7750](https://github.com/rapidsai/cudf/pull/7750)) [@jlowe](https://github.com/jlowe)
+- Add Java bindings for row_bit_count ([#7749](https://github.com/rapidsai/cudf/pull/7749)) [@jlowe](https://github.com/jlowe)
+- Remove unused JVM array creation ([#7748](https://github.com/rapidsai/cudf/pull/7748)) [@jlowe](https://github.com/jlowe)
+- Added JNI support for new is_integer ([#7739](https://github.com/rapidsai/cudf/pull/7739)) [@revans2](https://github.com/revans2)
+- Create and promote library aliases in libcudf installations ([#7734](https://github.com/rapidsai/cudf/pull/7734)) [@trxcllnt](https://github.com/trxcllnt)
+- Support groupby operations for decimal dtypes ([#7731](https://github.com/rapidsai/cudf/pull/7731)) [@vyasr](https://github.com/vyasr)
+- Memory map the input file only when GDS compatibility mode is not used ([#7717](https://github.com/rapidsai/cudf/pull/7717)) [@vuule](https://github.com/vuule)
+- Replace device_vector with device_uvector in null_mask ([#7715](https://github.com/rapidsai/cudf/pull/7715)) [@harrism](https://github.com/harrism)
+- Struct hashing support for SerialMurmur3 and SparkMurmur3 ([#7714](https://github.com/rapidsai/cudf/pull/7714)) [@jlowe](https://github.com/jlowe)
+- Add gbenchmark for nvtext replace-tokens function ([#7708](https://github.com/rapidsai/cudf/pull/7708)) [@davidwendt](https://github.com/davidwendt)
+- Use stream in groupby calls ([#7705](https://github.com/rapidsai/cudf/pull/7705)) [@karthikeyann](https://github.com/karthikeyann)
+- Update codeowners file ([#7701](https://github.com/rapidsai/cudf/pull/7701)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Cleanup groupby to use host_span, device_span, device_uvector ([#7698](https://github.com/rapidsai/cudf/pull/7698)) [@karthikeyann](https://github.com/karthikeyann)
+- Add gbenchmark for nvtext ngrams functions ([#7693](https://github.com/rapidsai/cudf/pull/7693)) [@davidwendt](https://github.com/davidwendt)
+- Misc Python/Cython optimizations ([#7686](https://github.com/rapidsai/cudf/pull/7686)) [@shwina](https://github.com/shwina)
+- Add gbenchmark for nvtext tokenize functions ([#7684](https://github.com/rapidsai/cudf/pull/7684)) [@davidwendt](https://github.com/davidwendt)
+- Add column_device_view to orc writer ([#7676](https://github.com/rapidsai/cudf/pull/7676)) [@kaatish](https://github.com/kaatish)
+- cudf_kafka now uses cuDF CMake export targets (CPM) ([#7674](https://github.com/rapidsai/cudf/pull/7674)) [@robertmaynard](https://github.com/robertmaynard)
+- Add gbenchmark for nvtext normalize functions ([#7668](https://github.com/rapidsai/cudf/pull/7668)) [@davidwendt](https://github.com/davidwendt)
+- Resolve unnecessary import of thrust/optional.hpp in types.hpp ([#7667](https://github.com/rapidsai/cudf/pull/7667)) [@vyasr](https://github.com/vyasr)
+- Feature/optimize accessor copy ([#7660](https://github.com/rapidsai/cudf/pull/7660)) [@vyasr](https://github.com/vyasr)
+- Fix `find_package(cudf)` ([#7658](https://github.com/rapidsai/cudf/pull/7658)) [@trxcllnt](https://github.com/trxcllnt)
+- Work-around for gcc7 compile error on Centos7 ([#7652](https://github.com/rapidsai/cudf/pull/7652)) [@davidwendt](https://github.com/davidwendt)
+- Add in JNI support for count_elements ([#7651](https://github.com/rapidsai/cudf/pull/7651)) [@revans2](https://github.com/revans2)
+- Fix issues with building cudf in a non-conda environment ([#7647](https://github.com/rapidsai/cudf/pull/7647)) [@galipremsagar](https://github.com/galipremsagar)
+- Refactor ConfigureCUDA to not conditionally insert compiler flags ([#7643](https://github.com/rapidsai/cudf/pull/7643)) [@robertmaynard](https://github.com/robertmaynard)
+- Add gbenchmark for converting strings to/from timestamps ([#7641](https://github.com/rapidsai/cudf/pull/7641)) [@davidwendt](https://github.com/davidwendt)
+- Handle constructing a `cudf.Scalar` from a `cudf.Scalar` ([#7639](https://github.com/rapidsai/cudf/pull/7639)) [@shwina](https://github.com/shwina)
+- Add in JNI support for table partition ([#7637](https://github.com/rapidsai/cudf/pull/7637)) [@revans2](https://github.com/revans2)
+- Add explicit fixed_point merge test ([#7635](https://github.com/rapidsai/cudf/pull/7635)) [@codereport](https://github.com/codereport)
+- Add JNI support for IDENTITY hash partitioning ([#7626](https://github.com/rapidsai/cudf/pull/7626)) [@revans2](https://github.com/revans2)
+- Java support on explode_outer ([#7625](https://github.com/rapidsai/cudf/pull/7625)) [@sperlingxx](https://github.com/sperlingxx)
+- Java support of casting string from/to decimal ([#7623](https://github.com/rapidsai/cudf/pull/7623)) [@sperlingxx](https://github.com/sperlingxx)
+- Convert cudf::concatenate APIs to use spans and device_uvector ([#7621](https://github.com/rapidsai/cudf/pull/7621)) [@harrism](https://github.com/harrism)
+- Add gbenchmark for cudf::strings::translate function ([#7617](https://github.com/rapidsai/cudf/pull/7617)) [@davidwendt](https://github.com/davidwendt)
+- Use file(COPY ) over file(INSTALL ) so cmake output is reduced ([#7616](https://github.com/rapidsai/cudf/pull/7616)) [@robertmaynard](https://github.com/robertmaynard)
+- Use rmm::device_uvector in place of rmm::device_vector for ORC reader/writer and cudf::io::column_buffer ([#7614](https://github.com/rapidsai/cudf/pull/7614)) [@vuule](https://github.com/vuule)
+- Refactor Java host-side buffer concatenation to expose separate steps ([#7610](https://github.com/rapidsai/cudf/pull/7610)) [@jlowe](https://github.com/jlowe)
+- Add gbenchmarks for string substrings functions ([#7603](https://github.com/rapidsai/cudf/pull/7603)) [@davidwendt](https://github.com/davidwendt)
+- Refactor string conversion check ([#7599](https://github.com/rapidsai/cudf/pull/7599)) [@ttnghia](https://github.com/ttnghia)
+- JNI: Pass names of children struct columns to native Arrow IPC writer ([#7598](https://github.com/rapidsai/cudf/pull/7598)) [@firestarman](https://github.com/firestarman)
+- Revert &quot;ENH Fix stale GHA and prevent duplicates &quot; ([#7595](https://github.com/rapidsai/cudf/pull/7595)) [@mike-wendt](https://github.com/mike-wendt)
+- ENH Fix stale GHA and prevent duplicates ([#7594](https://github.com/rapidsai/cudf/pull/7594)) [@mike-wendt](https://github.com/mike-wendt)
+- Fix auto-detecting GPU architectures ([#7593](https://github.com/rapidsai/cudf/pull/7593)) [@trxcllnt](https://github.com/trxcllnt)
+- Reduce cudf library size ([#7583](https://github.com/rapidsai/cudf/pull/7583)) [@robertmaynard](https://github.com/robertmaynard)
+- Optimize cudf::make_strings_column for long strings ([#7576](https://github.com/rapidsai/cudf/pull/7576)) [@davidwendt](https://github.com/davidwendt)
+- Always build and export the cudf::cudftestutil target ([#7574](https://github.com/rapidsai/cudf/pull/7574)) [@trxcllnt](https://github.com/trxcllnt)
+- Eliminate literal parameters to uvector::set_element_async and device_scalar::set_value ([#7563](https://github.com/rapidsai/cudf/pull/7563)) [@harrism](https://github.com/harrism)
+- Add gbenchmark for strings::concatenate ([#7560](https://github.com/rapidsai/cudf/pull/7560)) [@davidwendt](https://github.com/davidwendt)
+- Update Changelog Link ([#7550](https://github.com/rapidsai/cudf/pull/7550)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Add gbenchmarks for strings replace regex functions ([#7541](https://github.com/rapidsai/cudf/pull/7541)) [@davidwendt](https://github.com/davidwendt)
+- Add `__repr__` for Column and ColumnAccessor ([#7531](https://github.com/rapidsai/cudf/pull/7531)) [@shwina](https://github.com/shwina)
+- Support Decimal DIV changes in cudf ([#7527](https://github.com/rapidsai/cudf/pull/7527)) [@razajafri](https://github.com/razajafri)
+- Remove unneeded step parameter from strings::detail::copy_slice ([#7525](https://github.com/rapidsai/cudf/pull/7525)) [@davidwendt](https://github.com/davidwendt)
+- Use device_uvector, device_span in sort groupby ([#7523](https://github.com/rapidsai/cudf/pull/7523)) [@karthikeyann](https://github.com/karthikeyann)
+- Add gbenchmarks for strings extract function ([#7522](https://github.com/rapidsai/cudf/pull/7522)) [@davidwendt](https://github.com/davidwendt)
+- Rename ARROW_STATIC_LIB because it conflicts with one in FindArrow.cmake ([#7518](https://github.com/rapidsai/cudf/pull/7518)) [@trxcllnt](https://github.com/trxcllnt)
+- Reduce compile time/size for scan.cu ([#7516](https://github.com/rapidsai/cudf/pull/7516)) [@davidwendt](https://github.com/davidwendt)
+- Change device_vector to device_uvector in nvtext source files ([#7512](https://github.com/rapidsai/cudf/pull/7512)) [@davidwendt](https://github.com/davidwendt)
+- Removed unneeded includes from traits.hpp ([#7509](https://github.com/rapidsai/cudf/pull/7509)) [@davidwendt](https://github.com/davidwendt)
+- FIX Remove random build directory generation for ccache ([#7508](https://github.com/rapidsai/cudf/pull/7508)) [@dillon-cullinan](https://github.com/dillon-cullinan)
+- xfail failing pytest in pandas 1.2.3 ([#7507](https://github.com/rapidsai/cudf/pull/7507)) [@galipremsagar](https://github.com/galipremsagar)
+- JNI bit cast ([#7493](https://github.com/rapidsai/cudf/pull/7493)) [@revans2](https://github.com/revans2)
+- Combine rolling window function tests ([#7480](https://github.com/rapidsai/cudf/pull/7480)) [@mythrocks](https://github.com/mythrocks)
+- Prepare Changelog for Automation ([#7477](https://github.com/rapidsai/cudf/pull/7477)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Java support for explode position ([#7471](https://github.com/rapidsai/cudf/pull/7471)) [@sperlingxx](https://github.com/sperlingxx)
+- Update 0.18 changelog entry ([#7463](https://github.com/rapidsai/cudf/pull/7463)) [@ajschmidt8](https://github.com/ajschmidt8)
+- JNI: Support skipping nulls for collect aggregation ([#7457](https://github.com/rapidsai/cudf/pull/7457)) [@firestarman](https://github.com/firestarman)
+- Join APIs that return gathermaps ([#7454](https://github.com/rapidsai/cudf/pull/7454)) [@shwina](https://github.com/shwina)
+- Remove dependence on managed memory for multimap test ([#7451](https://github.com/rapidsai/cudf/pull/7451)) [@jrhemstad](https://github.com/jrhemstad)
+- Use cuFile for Parquet IO when available ([#7444](https://github.com/rapidsai/cudf/pull/7444)) [@vuule](https://github.com/vuule)
+- Statistics cleanup ([#7439](https://github.com/rapidsai/cudf/pull/7439)) [@kaatish](https://github.com/kaatish)
+- Add gbenchmarks for strings filter functions ([#7438](https://github.com/rapidsai/cudf/pull/7438)) [@davidwendt](https://github.com/davidwendt)
+- `fixed_point` + `cudf::binary_operation` API Changes ([#7435](https://github.com/rapidsai/cudf/pull/7435)) [@codereport](https://github.com/codereport)
+- Improve string gather performance ([#7433](https://github.com/rapidsai/cudf/pull/7433)) [@jlowe](https://github.com/jlowe)
+- Don&#39;t use user resource for a temporary allocation in sort_by_key ([#7431](https://github.com/rapidsai/cudf/pull/7431)) [@magnatelee](https://github.com/magnatelee)
+- Detail APIs for datetime functions ([#7430](https://github.com/rapidsai/cudf/pull/7430)) [@magnatelee](https://github.com/magnatelee)
+- Replace thrust::max_element with thrust::reduce in strings findall_re ([#7428](https://github.com/rapidsai/cudf/pull/7428)) [@davidwendt](https://github.com/davidwendt)
+- Add gbenchmark for strings split/split_record functions ([#7427](https://github.com/rapidsai/cudf/pull/7427)) [@davidwendt](https://github.com/davidwendt)
+- Update JNI build to use CMAKE_CUDA_ARCHITECTURES ([#7425](https://github.com/rapidsai/cudf/pull/7425)) [@jlowe](https://github.com/jlowe)
+- Change nvtext::load_vocabulary_file to return a unique ptr ([#7424](https://github.com/rapidsai/cudf/pull/7424)) [@davidwendt](https://github.com/davidwendt)
+- Simplify type dispatch with `device_storage_dispatch` ([#7419](https://github.com/rapidsai/cudf/pull/7419)) [@codereport](https://github.com/codereport)
+- Java support for casting of nested child columns ([#7417](https://github.com/rapidsai/cudf/pull/7417)) [@razajafri](https://github.com/razajafri)
+- Improve scalar string replace performance for long strings ([#7415](https://github.com/rapidsai/cudf/pull/7415)) [@jlowe](https://github.com/jlowe)
+- Remove unneeded temporary device vector for strings scatter specialization ([#7409](https://github.com/rapidsai/cudf/pull/7409)) [@davidwendt](https://github.com/davidwendt)
+- bitmask_or implementation with bitmask refactor ([#7406](https://github.com/rapidsai/cudf/pull/7406)) [@rwlee](https://github.com/rwlee)
+- Add other cudf::strings::replace functions to current strings replace gbenchmark ([#7403](https://github.com/rapidsai/cudf/pull/7403)) [@davidwendt](https://github.com/davidwendt)
+- Clean up included headers in `device_operators.cuh` ([#7401](https://github.com/rapidsai/cudf/pull/7401)) [@codereport](https://github.com/codereport)
+- Move nullable index iterator to indexalator factory ([#7399](https://github.com/rapidsai/cudf/pull/7399)) [@davidwendt](https://github.com/davidwendt)
+- ENH Pass ccache variables to conda recipe &amp; use Ninja in CI ([#7398](https://github.com/rapidsai/cudf/pull/7398)) [@Ethyling](https://github.com/Ethyling)
+- upgrade maven-antrun-plugin to support maven parallel builds ([#7393](https://github.com/rapidsai/cudf/pull/7393)) [@rongou](https://github.com/rongou)
+- Add gbenchmark for strings find/contains functions ([#7392](https://github.com/rapidsai/cudf/pull/7392)) [@davidwendt](https://github.com/davidwendt)
+- Use CMAKE_CUDA_ARCHITECTURES ([#7391](https://github.com/rapidsai/cudf/pull/7391)) [@robertmaynard](https://github.com/robertmaynard)
+- Refactor libcudf strings::replace to use make_strings_children utility ([#7384](https://github.com/rapidsai/cudf/pull/7384)) [@davidwendt](https://github.com/davidwendt)
+- Added in JNI support for out of core sort algorithm ([#7381](https://github.com/rapidsai/cudf/pull/7381)) [@revans2](https://github.com/revans2)
+- Upgrade pandas to 1.2 ([#7375](https://github.com/rapidsai/cudf/pull/7375)) [@galipremsagar](https://github.com/galipremsagar)
+- Rename `logical_cast` to `bit_cast` and allow additional conversions ([#7373](https://github.com/rapidsai/cudf/pull/7373)) [@ttnghia](https://github.com/ttnghia)
+- jitify 2 support ([#7372](https://github.com/rapidsai/cudf/pull/7372)) [@cwharris](https://github.com/cwharris)
+- compile_udf: Cache PTX for similar functions ([#7371](https://github.com/rapidsai/cudf/pull/7371)) [@gmarkall](https://github.com/gmarkall)
+- Add string scalar replace benchmark ([#7369](https://github.com/rapidsai/cudf/pull/7369)) [@jlowe](https://github.com/jlowe)
+- Add gbenchmark for strings contains_re/count_re functions ([#7366](https://github.com/rapidsai/cudf/pull/7366)) [@davidwendt](https://github.com/davidwendt)
+- Update orc reader and writer fuzz tests ([#7357](https://github.com/rapidsai/cudf/pull/7357)) [@galipremsagar](https://github.com/galipremsagar)
+- Improve url_decode performance for long strings ([#7353](https://github.com/rapidsai/cudf/pull/7353)) [@jlowe](https://github.com/jlowe)
+- `cudf::ast` Small Refactorings ([#7352](https://github.com/rapidsai/cudf/pull/7352)) [@codereport](https://github.com/codereport)
+- Remove std::cout and print in the scatter test function EmptyListsOfNullableStrings. ([#7342](https://github.com/rapidsai/cudf/pull/7342)) [@ttnghia](https://github.com/ttnghia)
+- Use `cudf::detail::make_counting_transform_iterator` ([#7338](https://github.com/rapidsai/cudf/pull/7338)) [@codereport](https://github.com/codereport)
+- Change block size parameter from a global to a template param. ([#7333](https://github.com/rapidsai/cudf/pull/7333)) [@nvdbaranec](https://github.com/nvdbaranec)
+- Partial clean up of ORC writer ([#7324](https://github.com/rapidsai/cudf/pull/7324)) [@vuule](https://github.com/vuule)
+- Add gbenchmark for cudf::strings::to_lower ([#7316](https://github.com/rapidsai/cudf/pull/7316)) [@davidwendt](https://github.com/davidwendt)
+- Update Java bindings version to 0.19-SNAPSHOT ([#7307](https://github.com/rapidsai/cudf/pull/7307)) [@pxLi](https://github.com/pxLi)
+- Move `cudf::test::make_counting_transform_iterator` to `cudf/detail/iterator.cuh` ([#7306](https://github.com/rapidsai/cudf/pull/7306)) [@codereport](https://github.com/codereport)
+- Use string literals in `fixed_point` `release_assert`s ([#7303](https://github.com/rapidsai/cudf/pull/7303)) [@codereport](https://github.com/codereport)
+- Fix merge conflicts for #7295 ([#7297](https://github.com/rapidsai/cudf/pull/7297)) [@ajschmidt8](https://github.com/ajschmidt8)
+- Add UTF-8 chars to create_random_column&lt;string_view&gt; benchmark utility ([#7292](https://github.com/rapidsai/cudf/pull/7292)) [@davidwendt](https://github.com/davidwendt)
+- Abstracting block reduce and block scan from cuIO kernels with `cub` apis ([#7278](https://github.com/rapidsai/cudf/pull/7278)) [@rgsl888prabhu](https://github.com/rgsl888prabhu)
+- Build.sh use cmake --build to drive build system invocation ([#7270](https://github.com/rapidsai/cudf/pull/7270)) [@robertmaynard](https://github.com/robertmaynard)
+- Refactor dictionary support for reductions any/all ([#7242](https://github.com/rapidsai/cudf/pull/7242)) [@davidwendt](https://github.com/davidwendt)
+- Replace stream.value() with stream for stream_view args ([#7236](https://github.com/rapidsai/cudf/pull/7236)) [@karthikeyann](https://github.com/karthikeyann)
+- Interval index and interval_range ([#7182](https://github.com/rapidsai/cudf/pull/7182)) [@marlenezw](https://github.com/marlenezw)
+- avro reader integration tests ([#7156](https://github.com/rapidsai/cudf/pull/7156)) [@cwharris](https://github.com/cwharris)
+- Rework libcudf CMakeLists.txt to export targets for CPM ([#7107](https://github.com/rapidsai/cudf/pull/7107)) [@trxcllnt](https://github.com/trxcllnt)
+- Adding Interval Dtype ([#6984](https://github.com/rapidsai/cudf/pull/6984)) [@marlenezw](https://github.com/marlenezw)
+- Cleaning up `for` loops with `make_(counting_)transform_iterator` ([#6546](https://github.com/rapidsai/cudf/pull/6546)) [@codereport](https://github.com/codereport)
+
+# cuDF 0.18.0 (24 Feb 2021)
+
+## Breaking Changes 🚨
+
+- Default `groupby` to `sort=False` (#7180) @isVoid
+- Add libcudf API for parsing of ORC statistics (#7136) @vuule
+- Replace ORC writer api with class (#7099) @rgsl888prabhu
+- Pack/unpack functionality to convert tables to and from a serialized format. (#7096) @nvdbaranec
+- Replace parquet writer api with class (#7058) @rgsl888prabhu
+- Add days check to cudf::is_timestamp using cuda::std::chrono classes (#7028) @davidwendt
+- Fix default parameter values of `write_csv` and `write_parquet` (#6967) @vuule
+- Align `Series.groupby` API to match Pandas (#6964) @kkraus14
+- Share `factorize` implementation with Index and cudf module (#6885) @brandon-b-miller
+
+## Bug Fixes 🐛
+
+- Remove incorrect std::move call on return variable (#7319) @davidwendt
+- Fix failing CI ORC test (#7313) @vuule
+- Disallow constructing frames from a ColumnAccessor (#7298) @shwina
+- fix java cuFile tests (#7296) @rongou
+- Fix style issues related to NumPy (#7279) @shwina
+- Fix bug when `iloc` slice terminates at before-the-zero position (#7277) @isVoid
+- Fix copying dtype metadata after calling libcudf functions (#7271) @shwina
+- Move lists utility function definition out of header (#7266) @mythrocks
+- Throw if bool column would cause incorrect result when writing to ORC (#7261) @vuule
+- Use `uvector` in `replace_nulls`; Fix `sort_helper::grouped_value` doc (#7256) @isVoid
+- Remove floating point types from cudf::sort fast-path (#7250) @davidwendt
+- Disallow picking output columns from nested columns. (#7248) @devavret
+- Fix `loc` for Series with a MultiIndex (#7243) @shwina
+- Fix Arrow column test leaks (#7241) @tgravescs
+- Fix test column vector leak (#7238) @kuhushukla
+- Fix some bugs in java scalar support for decimal (#7237) @revans2
+- Improve `assert_eq` handling of scalar (#7220) @isVoid
+- Fix missing null_count() comparison in test framework and related failures (#7219) @nvdbaranec
+- Remove floating point types from radix sort fast-path (#7215) @davidwendt
+- Fixing parquet benchmarks (#7214) @rgsl888prabhu
+- Handle various parameter combinations in `replace` API (#7207) @galipremsagar
+- Export mock aws credentials for s3 tests (#7176) @ayushdg
+- Add `MultiIndex.rename` API (#7172) @isVoid
+- Fix importing list &amp; struct types in `from_arrow` (#7162) @galipremsagar
+- Fixing parquet precision writing failing if scale is equal to precision (#7146) @hyperbolic2346
+- Update s3 tests to use moto_server (#7144) @ayushdg
+- Fix JIT cache multi-process test flakiness in slow drives (#7142) @devavret
+- Fix compilation errors in libcudf (#7138) @galipremsagar
+- Fix compilation failure caused by `-Wall` addition. (#7134) @codereport
+- Add informative error message for `sep` in CSV writer (#7095) @galipremsagar
+- Add JIT cache per compute capability (#7090) @devavret
+- Implement `__hash__` method for ListDtype (#7081) @galipremsagar
+- Only upload packages that were built (#7077) @raydouglass
+- Fix comparisons between Series and cudf.NA (#7072) @brandon-b-miller
+- Handle `nan` values correctly in `Series.one_hot_encoding` (#7059) @galipremsagar
+- Add `unstack()` support for non-multiindexed dataframes (#7054) @isVoid
+- Fix `read_orc` for decimal type (#7034) @rgsl888prabhu
+- Fix backward compatibility of loading a 0.16 pkl file (#7033) @galipremsagar
+- Decimal casts in JNI became a NOOP (#7032) @revans2
+- Restore usual instance/subclass checking to cudf.DateOffset (#7029) @shwina
+- Add days check to cudf::is_timestamp using cuda::std::chrono classes (#7028) @davidwendt
+- Fix to_csv delimiter handling of timestamp format (#7023) @davidwendt
+- Pin librdkakfa to gcc 7 compatible version (#7021) @raydouglass
+- Fix `fillna` &amp; `dropna` to also consider `np.nan` as a missing value (#7019) @galipremsagar
+- Fix round operator&#39;s HALF_EVEN computation for negative integers (#7014) @nartal1
+- Skip Thrust sort patch if already applied (#7009) @harrism
+- Fix `cudf::hash_partition` for `decimal32` and `decimal64` (#7006) @codereport
+- Fix Thrust unroll patch command (#7002) @harrism
+- Fix loc behaviour when key of incorrect type is used (#6993) @shwina
+- Fix int to datetime conversion in csv_read (#6991) @kaatish
+- fix excluding cufile tests by default (#6988) @rongou
+- Fix java cufile tests when cufile is not installed (#6987) @revans2
+- Make `cudf::round` for `fixed_point` when `scale = -decimal_places` a no-op (#6975) @codereport
+- Fix type comparison for java (#6970) @revans2
+- Fix default parameter values of `write_csv` and `write_parquet` (#6967) @vuule
+- Align `Series.groupby` API to match Pandas (#6964) @kkraus14
+- Fix timestamp parsing in ORC reader for timezones without transitions (#6959) @vuule
+- Fix typo in numerical.py (#6957) @rgsl888prabhu
+- `fixed_point_value` double-shifts in `fixed_point` construction (#6950) @codereport
+- fix libcu++ include path for jni (#6948) @rongou
+- Fix groupby agg/apply behaviour when no key columns are provided (#6945) @shwina
+- Avoid inserting null elements into join hash table when nulls are treated as unequal (#6943) @hyperbolic2346
+- Fix cudf::merge gtest for dictionary columns (#6942) @davidwendt
+- Pass numeric scalars of the same dtype through numeric binops (#6938) @brandon-b-miller
+- Fix N/A detection for empty fields in CSV reader (#6922) @vuule
+- Fix rmm_mode=managed parameter for gtests (#6912) @davidwendt
+- Fix nullmask offset handling in parquet and orc writer (#6889) @kaatish
+- Correct the sampling range when sampling with replacement (#6884) @ChrisJar
+- Handle nested string columns with no children in contiguous_split. (#6864) @nvdbaranec
+- Fix `columns` &amp; `index` handling in dataframe constructor (#6838) @galipremsagar
+
+## Documentation 📖
+
+- Update readme (#7318) @shwina
+- Fix typo in cudf.core.column.string.extract docs (#7253) @adelevie
+- Update doxyfile project number (#7161) @davidwendt
+- Update 10 minutes to cuDF and CuPy with new APIs (#7158) @ChrisJar
+- Cross link RMM &amp; libcudf Doxygen docs (#7149) @ajschmidt8
+- Add documentation for support dtypes in all IO formats (#7139) @galipremsagar
+- Add groupby docs (#7100) @shwina
+- Update cudf python docstrings with new null representation (`&lt;NA&gt;`) (#7050) @galipremsagar
+- Make Doxygen comments formatting consistent (#7041) @vuule
+- Add docs for working with missing data (#7010) @galipremsagar
+- Remove warning in from_dlpack and to_dlpack methods (#7001) @miguelusque
+- libcudf Developer Guide (#6977) @harrism
+- Add JNI wrapper for the cuFile API (GDS) (#6940) @rongou
+
+## New Features 🚀
+
+- Support `numeric_only` field for `rank()` (#7213) @isVoid
+- Add support for `cudf::binary_operation` `TRUE_DIV` for `decimal32` and `decimal64` (#7198) @codereport
+- Implement COLLECT rolling window aggregation (#7189) @mythrocks
+- Add support for array-like inputs in `cudf.get_dummies` (#7181) @galipremsagar
+- Default `groupby` to `sort=False` (#7180) @isVoid
+- Add libcudf lists column count_elements API (#7173) @davidwendt
+- Implement `cudf::group_by` (sort) for `decimal32` and `decimal64` (#7169) @codereport
+- Add encoding and compression argument to CSV writer (#7168) @VibhuJawa
+- `cudf::rolling_window` `SUM` support for `decimal32` and `decimal64` (#7147) @codereport
+- Adding support for explode to cuDF (#7140) @hyperbolic2346
+- Add libcudf API for parsing of ORC statistics (#7136) @vuule
+- update GDS/cuFile location for 0.9 release (#7131) @rongou
+- Add Segmented sort (#7122) @karthikeyann
+- Add `cudf::binary_operation` `NULL_MIN`, `NULL_MAX` &amp; `NULL_EQUALS` for `decimal32` and `decimal64` (#7119) @codereport
+- Add `scale` and `value` methods to `fixed_point` (#7109) @codereport
+- Replace ORC writer api with class (#7099) @rgsl888prabhu
+- Pack/unpack functionality to convert tables to and from a serialized format. (#7096) @nvdbaranec
+- Improve `digitize` API (#7071) @isVoid
+- Add List types support in data generator (#7064) @galipremsagar
+- `cudf::scan` support for `decimal32` and `decimal64` (#7063) @codereport
+- `cudf::rolling` `ROW_NUMBER` support for `decimal32` and `decimal64` (#7061) @codereport
+- Replace parquet writer api with class (#7058) @rgsl888prabhu
+- Support contains() on lists of primitives (#7039) @mythrocks
+- Implement `cudf::rolling` for `decimal32` and `decimal64` (#7037) @codereport
+- Add `ffill` and `bfill` to string columns (#7036) @isVoid
+- Enable round in cudf for DataFrame and Series (#7022) @ChrisJar
+- Extend `replace_nulls_policy` to `string` and `dictionary` type (#7004) @isVoid
+- Add segmented_gather(list_column, gather_list) (#7003) @karthikeyann
+- Add `method` field to `fillna` for fixed width columns (#6998) @isVoid
+- Manual merge of branch 0.17 into branch 0.18 (#6995) @shwina
+- Implement `cudf::reduce` for `decimal32` and `decimal64` (part 2) (#6980) @codereport
+- Add Ufunc alias look up for appropriate numpy ufunc dispatching (#6973) @VibhuJawa
+- Add pytest-xdist to dev environment.yml (#6958) @galipremsagar
+- Add `Index.set_names` api (#6929) @galipremsagar
+- Add `replace_null` API with `replace_policy` parameter, `fixed_width` column support (#6907) @isVoid
+- Share `factorize` implementation with Index and cudf module (#6885) @brandon-b-miller
+- Implement update() function (#6883) @skirui-source
+- Add groupby idxmin, idxmax aggregation (#6856) @karthikeyann
+- Implement `cudf::reduce` for `decimal32` and `decimal64` (part 1) (#6814) @codereport
+- Implement cudf.DateOffset for months (#6775) @brandon-b-miller
+- Add Python DecimalColumn (#6715) @shwina
+- Add dictionary support to libcudf groupby functions (#6585) @davidwendt
+
+## Improvements 🛠️
+
+- Update stale GHA with exemptions &amp; new labels (#7395) @mike-wendt
+- Add GHA to mark issues/prs as stale/rotten (#7388) @Ethyling
+- Unpin from numpy &lt; 1.20 (#7335) @shwina
+- Prepare Changelog for Automation (#7309) @galipremsagar
+- Prepare Changelog for Automation (#7272) @ajschmidt8
+- Add JNI support for converting Arrow buffers to CUDF ColumnVectors (#7222) @tgravescs
+- Add coverage for `skiprows` and `num_rows` in parquet reader fuzz testing (#7216) @galipremsagar
+- Define and implement more behavior for merging on categorical variables (#7209) @brandon-b-miller
+- Add CudfSeriesGroupBy to optimize dask_cudf groupby-mean (#7194) @rjzamora
+- Add dictionary column support to rolling_window (#7186) @davidwendt
+- Modify the semantics of `end` pointers in cuIO to match standard library (#7179) @vuule
+- Adding unit tests for `fixed_point` with extremely large `scale`s (#7178) @codereport
+- Fast path single column sort (#7167) @davidwendt
+- Fix -Werror=sign-compare errors in device code (#7164) @trxcllnt
+- Refactor cudf::string_view host and device code (#7159) @davidwendt
+- Enable logic for GPU auto-detection in cudfjni (#7155) @gerashegalov
+- Java bindings for Fixed-point type support for Parquet (#7153) @razajafri
+- Add Java interface for the new API &#39;explode&#39; (#7151) @firestarman
+- Replace offsets with iterators in cuIO utilities and CSV parser (#7150) @vuule
+- Add gbenchmarks for reduction aggregations any() and all() (#7129) @davidwendt
+- Update JNI for contiguous_split packed results (#7127) @jlowe
+- Add JNI and Java bindings for list_contains (#7125) @kuhushukla
+- Add Java unit tests for window aggregate &#39;collect&#39; (#7121) @firestarman
+- verify window operations on decimal with java tests (#7120) @sperlingxx
+- Adds in JNI support for creating an list column from existing columns (#7112) @revans2
+- Build libcudf with -Wall (#7105) @trxcllnt
+- Add column_device_view pointers to EncColumnDesc (#7097) @kaatish
+- Add `pyorc` to dev environment (#7085) @galipremsagar
+- JNI support for creating struct column from existing columns and fixed bug in struct with no children (#7084) @revans2
+- Fastpath single strings column in cudf::sort (#7075) @davidwendt
+- Upgrade nvcomp to 1.2.1 (#7069) @rongou
+- Refactor ORC `ProtobufReader` to make it more extendable (#7055) @vuule
+- Add Java tests for decimal casts (#7051) @sperlingxx
+- Auto-label PRs based on their content (#7044) @jolorunyomi
+- Create sort gbenchmark for strings column (#7040) @davidwendt
+- Refactor io memory fetches to use hostdevice_vector methods (#7035) @ChrisJar
+- Spark Murmur3 hash functionality (#7024) @rwlee
+- Fix libcudf strings logic where size_type is used to access INT32 column data (#7020) @davidwendt
+- Adding decimal writing support to parquet (#7017) @hyperbolic2346
+- Add compression=&quot;infer&quot; as default for dask_cudf.read_csv (#7013) @rjzamora
+- Correct ORC docstring; other minor cuIO improvements (#7012) @vuule
+- Reduce number of hostdevice_vector allocations in parquet reader (#7005) @devavret
+- Check output size overflow on strings gather (#6997) @davidwendt
+- Improve representation of `MultiIndex` (#6992) @galipremsagar
+- Disable some pragma unroll statements in thrust sort.h (#6982) @davidwendt
+- Minor `cudf::round` internal refactoring (#6976) @codereport
+- Add Java bindings for URL conversion (#6972) @jlowe
+- Enable strict_decimal_types in parquet reading (#6969) @sperlingxx
+- Add in basic support to JNI for logical_cast (#6954) @revans2
+- Remove duplicate file array_tests.cpp (#6953) @karthikeyann
+- Add null mask `fixed_point_column_wrapper` constructors (#6951) @codereport
+- Update Java bindings version to 0.18-SNAPSHOT (#6949) @jlowe
+- Use simplified `rmm::exec_policy` (#6939) @harrism
+- Add null count test for apply_boolean_mask (#6903) @harrism
+- Implement DataFrame.quantile for datetime and timedelta data types (#6902) @ChrisJar
+- Remove **kwargs from string/categorical methods (#6750) @shwina
+- Refactor rolling.cu to reduce compile time (#6512) @mythrocks
+- Add static type checking via Mypy (#6381) @shwina
+- Update to official libcu++ on Github (#6275) @trxcllnt
+
+# cuDF 0.17.0 (10 Dec 2020)
+
+## New Features
+
+- PR #6116 Add `filters` parameter to Python `read_orc` function or filtering
+- PR #6848 Added Java bindings for writing parquet files with INT96 timestamps
+- PR #6460 Add is_timestamp format check API
+- PR #6647 Implement `cudf::round` floating point and integer types (`HALF_EVEN`)
+- PR #6562 Implement `cudf::round` floating point and integer types (`HALF_UP`)
+- PR #6685 Implement `cudf::round` `decimal32` & `decimal64` (`HALF_UP` and `HALF_EVEN`)
+- PR #6711 Implement `cudf::cast` for `decimal32/64` to/from integer and floating point
+- PR #6777 Implement `cudf::unary_operation` for `decimal32` & `decimal64`
+- PR #6729 Implement `cudf::cast` for `decimal32/64` to/from different `type_id`
+- PR #6792 Implement `cudf::clamp` for `decimal32` and `decimal64`
+- PR #6845 Implement `cudf::copy_if_else` for `decimal32` and `decimal64`
+- PR #6805 Implement `cudf::detail::copy_if` for `decimal32` and `decimal64`
+- PR #6843 Implement `cudf::copy_range` for `decimal32` and `decimal64`
+- PR #6528 Enable `fixed_point` binary operations
+- PR #6460 Add is_timestamp format check API
+- PR #6568 Add function to create hashed vocabulary file from raw vocabulary
+- PR #6142 Add Python `read_orc_statistics` function for reading file- and stripe-level statistics
+- PR #6581 Add JNI API to check if PTDS is enabled
+- PR #6615 Add support for list and struct types to contiguous_split
+- PR #6625 Add INT96 timestamp writing option to parquet writer
+- PR #6592 Add `cudf.to_numeric` function
+- PR #6598 Add strings::contains API with target column parameter
+- PR #6638 Add support for `pipe` API
+- PR #6737 New build process (Project Flash)
+- PR #6652 Add support for struct columns in concatenate
+- PR #6675 Add DecimalDtype to cuDF
+- PR #6739 Add Java bindings for is_timestamp
+- PR #6808 Add support for reading decimal32 and decimal64 from parquet
+- PR #6781 Add serial murmur3 hashing
+- PR #6811 First class support for unbounded window function bounds
+- PR #6768 Add support for scatter() on list columns
+- PR #6796 Add create_metadata_file in dask_cudf
+- PR #6765 Cupy fallback for __array_function__ and __array_ufunc__ for cudf.Series
+- PR #6817 Add support for scatter() on lists-of-struct columns
+- PR #6805 Implement `cudf::detail::copy_if` for `decimal32` and `decimal64`
+- PR #6483 Add `agg` function to aggregate dataframe using one or more operations
+- PR #6726 Support selecting different hash functions in hash_partition
+- PR #6619 Improve Dockerfile
+- PR #6831 Added parquet chunked writing ability for list columns
+
+## Improvements
+
+- PR #6430 Add struct type support to `to_arrow` and `from_arrow`
+- PR #6384 Add CSV fuzz tests with varying function parameters
+- PR #6385 Add JSON fuzz tests with varying function parameters
+- PR #6398 Remove function constructor macros in parquet reader
+- PR #6432 Add dictionary support to `cudf::upper_bound` and `cudf::lower_bound`
+- PR #6461 Replace index type-dispatch call with indexalator in cudf::scatter
+- PR #6415 Support `datetime64` in row-wise op
+- PR #6457 Replace index type-dispatch call with indexalator in `cudf::gather`
+- PR #6413 Replace Python NVTX package with conda-forge source
+- PR #6442 Remove deprecated `DataFrame.from_gpu_matrix`, `DataFrame.to_gpu_matrix`, `DataFrame.add_column` APIs and method parameters
+- PR #6502 Add dictionary support to `cudf::merge`
+- PR #6471 Replace index type-dispatch call with indexalator in cudf::strings::substring
+- PR #6485 Add File IO to cuIO benchmarks
+- PR #6504 Update Java bindings version to 0.17-SNAPSHOT
+- PR #6875 Remove bounds check for `cudf::gather`
+- PR #6489 Add `AVRO` fuzz tests with varying function parameters
+- PR #6540 Add dictionary support to `cudf::unary_operation`
+- PR #6537 Refactor ORC timezone
+- PR #6527 Refactor DeviceColumnViewAccess to avoid JNI returning an array
+- PR #6690 Explicitly set legacy or per-thread default stream in JNI
+- PR #6545 Pin cmake policies to cmake 3.17 version
+- PR #6556 Add dictionary support to `cudf::inner_join`, `cudf::left_join` and `cudf::full_join`
+- PR #6557 Support nullable timestamp columns in time range window functions
+- PR #6566 Remove `reinterpret_cast` conversions between pointer types in ORC
+- PR #6544 Remove `fixed_point` precise round
+- PR #6552 Use `assert_exceptions_equal` to assert exceptions in pytests
+- PR #6555 Adapt JNI build to libcudf composition of multiple libraries
+- PR #6559 Refactoring cooperative loading with single thread loading.
+- PR #6564 Load JNI library dependencies with a thread pool
+- PR #6571 Add ORC fuzz tests with varying function parameters
+- PR #6578 Add in java column to row conversion
+- PR #6573 Create `cudf::detail::byte_cast` for `cudf::byte_cast`
+- PR #6597 Use thread-local to track CUDA device in JNI
+- PR #6599 Replace `size()==0` with `empty()`, `is_empty()`
+- PR #6514 Initial work for decimal type in Java/JNI
+- PR #6605 Reduce HtoD copies in `cudf::concatenate` of string columns
+- PR #6608 Improve subword tokenizer docs
+- PR #6610 Add ability to set scalar values in `cudf.DataFrame`
+- PR #6612 Update JNI to new RMM cuda_stream_view API
+- PR #6646 Replace `cudaStream_t` with `rmm::cuda_stream_view` (part 1)
+- PR #6648 Replace `cudaStream_t` with `rmm::cuda_stream_view` (part 2)
+- PR #6744 Replace `cudaStream_t` with `rmm::cuda_stream_view` (part 3)
+- PR #6579 Update scatter APIs to use reference wrapper / const scalar
+- PR #6614 Add support for conversion to Pandas nullable dtypes and fix related issue in `cudf.to_json`
+- PR #6622 Update `to_pandas` api docs
+- PR #6623 Add operator overloading to column and clean up error messages
+- PR #6644 Cover different CSV reader/writer options in benchmarks
+- PR #6741 Cover different ORC and Parquet reader/writer options in benchmarks
+- PR #6651 Add cudf::dictionary::make_dictionary_pair_iterator
+- PR #6666 Add dictionary support to `cudf::reduce`
+- PR #6635 Add cudf::test::dictionary_column_wrapper class
+- PR #6702 Fix orc read corruption on boolean column
+- PR #6676 Add dictionary support to `cudf::quantile`
+- PR #6673 Parameterize avro and json benchmark
+- PR #6609 Support fixed-point decimal for HostColumnVector
+- PR #6703 Add list column statistics writing to Parquet writer
+- PR #6662 `RangeIndex` supports `step` parameter
+- PR #6712 Remove `reinterpret_cast` conversions between pointer types in Avro
+- PR #6705 Add nested type support to Java table serialization
+- PR #6709 Raise informative error while converting a pandas dataframe with duplicate columns
+- PR #6727 Remove 2nd type-dispatcher call from cudf::reduce
+- PR #6749 Update nested JNI builder so we can do it incrementally
+- PR #6748 Add Java API to concatenate serialized tables to ContiguousTable
+- PR #6764 Add dictionary support to `cudf::minmax`
+- PR #6734 Binary operations support for decimal type in cudf Java
+- PR #6761 Add Java/JNI bindings for round
+- PR #6776 Use `void` return type for kernel wrapper functions instead of returning `cudaError_t`
+- PR #6786 Add nested type support to ColumnVector#getDeviceMemorySize
+- PR #6780 Move `cudf::cast` tests to separate test file
+- PR #6809 size_type overflow checking when concatenating columns
+- PR #6789 Rename `unary_op` to `unary_operator`
+- PR #6770 Support building decimal columns with Table.TestBuilder
+- PR #6815 Add wildcard path support to `read_parquet`
+- PR #6800 Push DeviceScalar to cython-only
+- PR #6822 Split out `cudf::distinct_count` from `drop_duplicates.cu`
+- PR #6813 Enable `expand=False` in `.str.split` and `.str.rsplit`
+- PR #6829 Enable workaround to write categorical columns in csv
+- PR #6819 Use CMake 3.19 for RMM when building cuDF jar
+- PR #6833 Use settings.xml if existing for internal build
+- PR #6839 Handle index when dispatching __array_function__ and __array_ufunc__ to cupy for cudf.Series
+- PR #6835 Move template param to member var to improve compile of hash/groupby.cu
+- PR #6837 Avoid gather when copying strings view from start of strings column
+- PR #6859 Move align_ptr_for_type() from cuda.cuh to alignment.hpp
+- PR #6807 Refactor `std::array` usage in row group index writing in ORC
+- PR #6914 Enable groupby `list` aggregation for strings
+- PR #6908 Parquet option for strictly decimal reading
+
+## Bug Fixes
+
+- PR #6446 Fix integer parsing in CSV and JSON for values outside of int64 range
+- PR #6506 Fix DateTime type value truncation while writing to csv
+- PR #6509 Disable JITIFY log printing
+- PR #6517 Handle index equality in `Series` and `DataFrame` equality checks
+- PR #6519 Fix end-of-string marking boundary condition in subword-tokenizer
+- PR #6543 Handle `np.nan` values in `isna`/`isnull`/`notna`/`notnull`
+- PR #6549 Fix memory_usage calls for list columns
+- PR #6575 Fix JNI RMM initialize with no pool allocator limit
+- PR #6636 Fix orc boolean column corruption issue
+- PR #6582 Add missing `device_scalar` stream parameters
+- PR #6596 Fix memory usage calculation
+- PR #6595 Fix JNI build, broken by to_arrow() signature change
+- PR #6601 Fix timezone offset when reading ORC files
+- PR #6603 Use correct stream in hash_join.
+- PR #6616 Block `fixed_point` `cudf::concatenate` with different scales
+- PR #6607 Fix integer overflow in ORC encoder
+- PR #6617 Fix JNI native dependency load order
+- PR #6621 Fix subword tokenizer metadata for token count equal to max_sequence_length
+- PR #6629 Fix JNI CMake
+- PR #6633 Fix Java HostColumnVector unnecessarily loading native dependencies
+- PR #6643 Fix csv writer handling embedded comma delimiter
+- PR #6640 Add error message for unsupported `axis` parameter in DataFrame APIs
+- PR #6686 Fix output size for orc read for skip_rows option
+- PR #6710 Fix an out-of-bounds indexing error in gather() for lists
+- PR #6670 Fix a bug where PTX parser fails to correctly parse a python lambda generated UDF
+- PR #6687 Fix issue where index name of caller object is being modified in csv writer
+- PR #6735 Fix hash join where row hash values would end up equal to the reserved empty key value
+- PR #6696 Fix release_assert.
+- PR #6692 Fix handling of empty column name in csv writer
+- PR #6693 Fix issue related to `na_values` input in `read_csv`
+- PR #6701 Fix issue when `numpy.str_` is given as input to string parameters in io APIs
+- PR #6704 Fix leak warnings in JNI unit tests
+- PR #6713 Fix missing call to cudaStreamSynchronize in get_value
+- PR #6708 Apply `na_rep` to column names in csv writer
+- PR #6720 Fix implementation of `dtype` parameter in `cudf.read_csv`
+- PR #6721 Add missing serialization methods for ListColumn
+- PR #6722 Fix index=False bug in dask_cudf.read_parquet
+- PR #6766 Fix race conditions in parquet
+- PR #6728 Fix cudf python docs and associated build warnings
+- PR #6732 Fix cuDF benchmarks build with static Arrow lib and fix rapids-compose cuDF JNI build
+- PR #6742 Fix concat bug in dask_cudf Series/Index creation
+- PR #6632 Fix DataFrame initialization from list of dicts
+- PR #6767 Fix sort order of parameters in `test_scalar_invalid_implicit_conversion` pytest
+- PR #6771 Fix index handling in parquet reader and writer
+- PR #6787 Update java reduction APIs to reflect C++ changes
+- PR #6790 Fix result representation in groupby.apply
+- PR #6794 Fix AVRO reader issues with empty input
+- PR #6798 Fix `read_avro` docs
+- PR #6824 Fix JNI build
+- PR #6826 Fix resource management in Java ColumnBuilder
+- PR #6830 Fix categorical scalar insertion
+- PR #6844 Fix uint32_t undefined errors
+- PR #6854 Fix the parameter order of writeParquetBufferBegin
+- PR #6855 Fix `.str.replace_with_backrefs` docs examples
+- PR #6853 Fix contiguous split of null string columns
+- PR #6860 Move codecov upload to build script
+- PR #6861 Fix compile error in type_dispatch_benchmark.cu
+- PR #6864 Handle contiguous_split corner case for nested string columns with no children
+- PR #6869 Avoid dependency resolution failure in latest version of pip by explicitly specifying versions for dask and distributed
+- PR #6806 Force install of local conda artifacts
+- PR #6887 Fix typo and `0-d` numpy array handling in binary operation
+- PR #6898 Fix missing clone overrides on derived aggregations
+- PR #6899 Update JNI to new gather boundary check API
+
+
+# cuDF 0.16.0 (21 Oct 2020)
+
+## New Features
+
+- PR #5779 Add DataFrame.pivot() and DataFrame.unstack()
+- PR #5975 Add strings `filter_characters` API
+- PR #5843 Add `filters` parameter to Python `read_parquet` function for filtering row groups
+- PR #5974 Use libcudf instead of cupy for `arange` or column creation from a scalar.
+- PR #5494 Add Abstract Syntax Tree (AST) evaluator.
+- PR #6076 Add durations type support for csv writer, reader
+- PR #5874 Add `COLLECT` groupby aggregation
+- PR #6330 Add ability to query if PTDS is enabled
+- PR #6119 Add support for `dayofweek` property in `DateTimeIndex` and `DatetimeProperties`
+- PR #6171 Java and Jni support for Struct columns
+- PR #6125 Add support for `Series.mode` and `DataFrame.mode`
+- PR #6271 Add support to deep-copy struct columns from struct column-view
+- PR #6262 Add nth_element series aggregation with null handling
+- PR #6316 Add StructColumn to Python API
+- PR #6247 Add `minmax` reduction function
+- PR #6232 `Json` and `Avro` benchmarking in python
+- PR #6139 Add column conversion to big endian byte list.
+- PR #6220 Add `list_topics()` to supply list of underlying Kafka connection topics
+- PR #6254 Add `cudf::make_dictionary_from_scalar` factory function
+- PR #6262 Add nth_element series aggregation with null handling
+- PR #6277 Add support for LEAD/LAG window functions for fixed-width types
+- PR #6318 Add support for reading Struct and map types from Parquet files
+- PR #6315 Native code for string-map lookups, for cudf-java
+- PR #6302 Add custom dataframe accessors
+- PR #6301 Add JNI bindings to nvcomp
+- PR #6328 Java and JNI bindings for getMapValue/map_lookup
+- PR #6371 Use ColumnViewAccess on Host side
+- PR #6392 add hash based groupby mean aggregation
+- PR #6511 Add LogicalType to Parquet reader
+- PR #6297 cuDF Python Scalars
+- PR #6723 Support creating decimal vectors from scalar
+
+## Improvements
+
+- PR #6393 Fix some misspelled words
+- PR #6292 Remove individual size tracking from JNI tracking resource adaptor
+- PR #5946 Add cython and python support for libcudf `to_arrow` and `from_arrow`
+- PR #5919 Remove max_strings and max_chars from nvtext::subword_tokenize
+- PR #5956 Add/Update tests for cuStreamz
+- PR #5953 Use stable sort when doing a sort groupby
+- PR #5973 Link to the Code of Conduct in CONTRIBUTING.md
+- PR #6354 Perform shallow clone of external projects
+- PR #6388 Add documentation for building `libboost_filesystem.a` from source
+- PR #5917 Just use `None` for `strides` in `Buffer`
+- PR #6015 Upgrade CUB/Thrust to the latest commit
+- PR #5971 Add cuStreamz README for basic installation and use
+- PR #6024 Expose selecting multiple ORC stripes to read from Python
+- PR #6155 Use the CUB submodule in Thrust instead of fetching CUB separately
+- PR #6321 Add option in JNI code to use `arena_memory_resource`
+- PR #6002 Add Java bindings for md5
+- PR #6311 Switch Thrust to use the NVIDIA/thrust repo
+- PR #6060 Add support for all types in `Series.describe` and `DataFrame.describe`
+- PR #6051 Add builder API for cuIO `parquet_writer_options` and `parquet_reader_options`
+- PR #6067 Added compute codes for aarch64 devices
+- PR #5861 `fixed_point` Column Optimization (store `scale` in `data_type`)
+- PR #6083 Small cleanup
+- PR #6355 Make sure PTDS mode is compatible between libcudf and JNI
+- PR #6120 Consolidate functionality in NestedHostColumnVector and HostColumnVector
+- PR #6092 Add `name` and `dtype` field to `Index.copy`
+- PR #5984 Support gather() on CUDF struct columns
+- PR #6103 Small refactor of `print_differences`
+- PR #6124 Fix gcc-9 compilation errors on tests
+- PR #6122 Add builder API for cuIO `csv_writer_options` and `csv_reader_options`
+- PR #6141 Fix typo in custreamz README that was a result of recent changes
+- PR #6162 Reduce output parameters in cuio csv and json reader internals
+- PR #6146 Added element/validity pair constructors for fixed_width and string wrappers
+- PR #6143 General improvements for java arrow IPC.
+- PR #6138 Add builder API for cuIO `orc_writer_options` and `orc_reader_options`
+- PR #6152 Change dictionary indices to uint32
+- PR #6099 Add fluent builder apis to `json_reader_options` and `avro_reader_options`
+- PR #6163 Use `Column.full` instead of `scalar_broadcast_to` or `cupy.zeros`
+- PR #6176 Fix cmake warnings for GoogleTest, GoogleBenchmark, and Arrow external projects
+- PR #6149 Update to Arrow v1.0.1
+- PR #6421 Use `pandas.testing` in `cudf`
+- PR #6357 Use `pandas.testing` in `dask-cudf`
+- PR #6201 Expose libcudf test utilities headers for external project use.
+- PR #6174 Data profile support in random data generator; Expand cuIO benchmarks
+- PR #6189 Avoid deprecated pyarrow.compat for parquet
+- PR #6184 Add cuda 11 dev environment.yml
+- PR #6186 Update JNI to look for cub in new location
+- PR #6194 Remove unnecessary memory-resource parameter in `cudf::contains` API
+- PR #6195 Update JNI to use parquet options builder
+- PR #6190 Avoid reading full csv files for metadata in dask_cudf
+- PR #6197 Remove librmm dependency for libcudf
+- PR #6205 Add dictionary support to cudf::contains
+- PR #6213 Reduce subscript usage in cuio in favor of pointer dereferencing
+- PR #6230 Support any unsigned int type for dictionary indices
+- PR #6202 Add additional parameter support to `DataFrame.drop`
+- PR #6214 Small clean up to use more algorithms
+- PR #6209 Remove CXX11 ABI handling from CMake
+- PR #6223 Remove CXX11 ABI flag from JNI build
+- PR #6114 Implement Fuzz tests for cuIO
+- PR #6231 Adds `inplace`, `append`, `verify_integrity` fields to `DataFrame.set_index`
+- PR #6215 Add cmake command-line setting for spdlog logging level
+- PR #6242 Added cudf::detail::host_span and device_span
+- PR #6240 Don't shallow copy index in as_index() unless necessary
+- PR #6204 Add dockerfile and script to build cuDF jar
+- PR #6248 Optimize groupby-agg in dask_cudf
+- PR #6243 Move `equals()` logic to `Frame`
+- PR #6245 Split up replace.cu into multiple source files
+- PR #6218 increase visibility/consistency for cuio reader writer private member variable names.
+- PR #6268 Add file tags to libcudf doxygen
+- PR #6265 Update JNI to use ORC options builder
+- PR #6273 Update JNI to use ORC options builder
+- PR #6293 Replace shuffle warp reduce with cub calls
+- PR #6287 Make java aggregate API follow C++ API
+- PR #6303 Use cudf test dtypes so timedelta tests are deterministic
+- PR #6329 Update and clean-up gpuCI scripts
+- PR #6299 Add lead and lag to java
+- PR #6327 Add dictionary specialization to `cudf::replace_nulls`
+- PR #6306 Remove cpw macros from page encode kernels
+- PR #6375 Parallelize Cython compilation in addition to Cythonization
+- PR #6303 Use cudf test dtypes so timedelta tests are deterministic
+- PR #6326 Simplify internal csv/json kernel parameters
+- PR #6308 Add dictionary support to cudf::scatter with scalar
+- PR #6367 Add JNI bindings for byte casting
+- PR #6312 Conda recipe dependency cleanup
+- PR #6346 Remove macros from CompactProtocolWriter
+- PR #6347 Add dictionary support to cudf::copy_range
+- PR #6352 Add specific Topic support for Kafka "list_topics()" metadata requests
+- PR #6332 Add support to return csv as string when `path=None` in `to_csv`
+- PR #6358 Add Parquet fuzz tests with varying function parameters
+- PR #6369 Add dictionary support to `cudf::find_and_replace`
+- PR #6373 Add dictionary support to `cudf::clamp`
+- PR #6377 Update ci/local/README.md
+- PR #6383 Removed `move.pxd`, use standard library `move`
+- PR #6400 Removed unused variables
+- PR #6409 Allow CuPy 8.x
+- PR #6407 Add RMM_LOGGING_LEVEL flag to Java docker build
+- PR #6425 Factor out csv parse_options creation to pure function
+- PR #6438 Fetch nvcomp v1.1.0 for JNI build
+- PR #6459 Add `map` method to series
+- PR #6379 Add list hashing functionality to MD5
+- PR #6498 Add helper method to ColumnBuilder with some nits
+- PR #6336 Add `join` functionality in cudf concat
+- PR #6653 Replaced SHFL_XOR calls with cub::WarpReduce
+- PR #6751 Rework ColumnViewAccess and its usage
+- PR #6698 Remove macros from ORC reader and writer
+- PR #6782 Replace cuio macros with constexpr and inline functions
+
+## Bug Fixes
+
+- PR #6073 Fix issue related to `.loc` in case of `DatetimeIndex`
+- PR #6081 Fix issue where fsspec thinks it has a protocol string
+- PR #6100 Fix issue in `Series.factorize` to correctly pick `na_sentinel` value
+- PR #6106 Fix datetime limit in csv due to 32-bit arithmetic
+- PR #6113 Fix to_timestamp to initialize default year to 1970
+- PR #6110 Handle `format` for other input types in `to_datetime`
+- PR #6118 Fix Java build for ORC read args change and update package version
+- PR #6121 Replace calls to get_default_resource with get_current_device_resource
+- PR #6128 Add support for numpy RandomState handling in `sample`
+- PR #6134 Fix CUDA C/C++ debug builds
+- PR #6137 Fix issue where `np.nan` is being return instead of `NAT` for datetime/duration types
+- PR #6298 Fix gcc-9 compilation error in dictionary/remove_keys.cu
+- PR #6172 Fix slice issue with empty column
+- PR #6342 Fix array out-of-bound errors in Orc writer
+- PR #6154 Warnings on row-wise op only when non-numeric columns are found.
+- PR #6150 Fix issue related to inferring `datetime64` format with UTC timezone in string data
+- PR #6179 `make_elements` copies to `iterator` without adjusting `size`
+- PR #6387 Remove extra `std::move` call in java/src/main/native/src/map_lookup.cu
+- PR #6182 Fix cmake build of arrow
+- PR #6288 Fix gcc-9 compilation error with `ColumnVectorJni.cpp`
+- PR #6173 Fix normalize_characters offset logic on sliced strings column
+- PR #6159 Fix issue related to empty `Dataframe` with columns input to `DataFrame.append`
+- PR #6199 Fix index preservation for dask_cudf parquet
+- PR #6207 Remove shared libs from Java sources jar
+- PR #6217 Fixed missing bounds checking when storing validity in parquet reader
+- PR #6212 Update codeowners file
+- PR #6389 Fix RMM logging level so that it can be turned off from the command line
+- PR #6157 Fix issue related to `Series.concat` to concat a non-empty and empty series.
+- PR #6226 Add in some JNI checks for null handles
+- PR #6183 Fix issues related to `Series.acos` for consistent output regardless of dtype
+- PR #6234 Add float infinity parsing in csv reader
+- PR #6251 Replace remaining calls to RMM `get_default_resource`
+- PR #6257 Support truncated fractions in `cudf::strings::to_timestamp`
+- PR #6259 Fix compilation error with GCC 8
+- PR #6258 Pin libcudf conda recipe to boost 1.72.0
+- PR #6264 Remove include statement for missing rmm/mr/device/default_memory_resource.hpp file
+- PR #6296 Handle double quote and escape character in json
+- PR #6294 Fix read parquet key error when reading empty pandas DataFrame with cudf
+- PR #6285 Removed unsafe `reinterpret_cast` and implicit pointer-to-bool casts
+- PR #6281 Fix unreachable code warning in datetime.cuh
+- PR #6286 Fix `read_csv` `int32` overflow
+- PR #6466 Fix ORC reader issue with decimal type
+- PR #6310 Replace a misspelled reference to `master` branch with `main` branch in a comment in changelog.sh
+- PR #6289 Revert #6206
+- PR #6291 Fix issue related to row-wise operations in `cudf.DataFrame`
+- PR #6304 Fix span_tests.cu includes
+- PR #6331 Avoids materializing `RangeIndex` during frame concatnation (when not needed)
+- PR #6278 Add filter tests for struct columns
+- PR #6344 Fix rolling-window count for null input
+- PR #6353 Rename `skip_rows` parameter to `skiprows` in `read_parquet`, `read_avro` and `read_orc`
+- PR #6361 Detect overflow in hash join
+- PR #6386 Removed c-style pointer casts and redundant `reinterpret_cast`s in cudf::io
+- PR #6397 Fix `build.sh` when `PARALLEL_LEVEL` environment variable isn't set
+- PR #6366 Fix Warp Reduce calls in cuio statistics calculation to account for NaNs
+- PR #6345 Fix ambiguous constructor compile error with devtoolset
+- PR #6335 Fix conda commands for outdated python version
+- PR #6372 Fix issue related to reading a nullable boolean column in `read_parquet` when `engine=pyarrow`
+- PR #6378 Fix index handling in `fillna` and incorrect pytests
+- PR #6380 Avoid problematic column-index check in dask_cudf.read_parquet test
+- PR #6403 Fix error handling in notebook tests
+- PR #6408 Avoid empty offset list in hash_partition output
+- PR #6402 Update JNI build to pull fixed nvcomp commit
+- PR #6410 Fix uses of dangerous default values in Python code
+- PR #6424 Check for null data in close for ColumnBuilder
+- PR #6426 Fix `RuntimeError` when `np.bool_` is passed as `header` in `to_csv`
+- PR #6443 Make java apis getList and getStruct public
+- PR #6445 Add `dlpack` to run section of libcudf conda recipe to fix downstream build issues
+- PR #6450 Make java Column Builder row agnostic
+- PR #6309 Make all CI `.sh` scripts have a consistent set of permissions
+- PR #6491 Remove repo URL from Java build-info
+- PR #6462 Bug fixes for ColumnBuilder
+- PR #6497 Fixes a data corruption issue reading list columns from Parquet files with multiple row groups.
+
+
+
+# cuDF 0.15.0 (26 Aug 2020)
+
+## New Features
+
+- PR #5292 Add unsigned int type columns to libcudf
+- PR #5287 Add `index.join` support
+- PR #5222 Adding clip feature support to DataFrame and Series
+- PR #5318 Support/leverage DataFrame.shuffle in dask_cudf
+- PR #4546 Support pandas 1.0+
+- PR #5331 Add `cudf::drop_nans`
+- PR #5327 Add `cudf::cross_join` feature
+- PR #5204 Concatenate strings columns using row separator as strings column
+- PR #5342 Add support for `StringMethods.__getitem__`
+- PR #5358 Add zero-copy `column_view` cast for compatible types
+- PR #3504 Add External Kafka Datasource
+- PR #5356 Use `size_type` instead of `scalar` in `cudf::repeat`.
+- PR #5397 Add internal implementation of nested loop equijoins.
+- PR #5303 Add slice_strings functionality using delimiter string
+- PR #5394 Enable cast and binops with duration types (builds on PR 5359)
+- PR #5301 Add Java bindings for `zfill`
+- PR #5411 Enable metadata collection for chunked parquet writer
+- PR #5359 Add duration types
+- PR #5364 Validate array interface during buffer construction
+- PR #5418 Add support for `DataFrame.info`
+- PR #5425 Add Python `Groupby.rolling()`
+- PR #5434 Add nvtext function generate_character_grams
+- PR #5442 Add support for `cudf.isclose`
+- PR #5444 Remove usage of deprecated RMM APIs and headers.
+- PR #5463 Add `.str.byte_count` python api and cython(bindings)
+- PR #5488 Add plumbings for `.str.replace_tokens`
+- PR #5502 Add Unsigned int types support in dlpack
+- PR #5497 Add `.str.isinteger` & `.str.isfloat`
+- PR #5511 Port of clx subword tokenizer to cudf
+- PR #5528 Add unsigned int reading and writing support to parquet
+- PR #5510 Add support for `cudf.Index` to create Indexes
+- PR #5618 Add Kafka as a cudf datasource
+- PR #5668 Adding support for `cudf.testing`
+- PR #5460 Add support to write to remote filesystems
+- PR #5454 Add support for `DataFrame.append`, `Index.append`, `Index.difference` and `Index.empty`
+- PR #5536 Parquet reader - add support for multiple sources
+- PR #5654 Adding support for `cudf.DataFrame.sample` and `cudf.Series.sample`
+- PR #5607 Add Java bindings for duration types
+- PR #5612 Add `is_hex` strings API
+- PR #5625 String conversion to and from duration types
+- PR #5659 Added support for rapids-compose for Java bindings and other enhancements
+- PR #5637 Parameterize Null comparator behaviour in Joins
+- PR #5623 Add `is_ipv4` strings API
+- PR #5723 Parquet reader - add support for nested LIST columns
+- PR #5669 Add support for reading JSON files with missing or out-of-order fields
+- PR #5674 Support JIT backend on PowerPC64
+- PR #5629 Add `ListColumn` and `ListDtype`
+- PR #5658 Add `filter_tokens` nvtext API
+- PR #5666 Add `filter_characters_of_type` strings API
+- PR #5778 Add support for `cudf::table` to `arrow::Table` and `arrow::Table` to `cudf::table`
+- PR #5673 Always build and test with per-thread default stream enabled in the GPU CI build
+- PR #5438 Add MD5 hash support
+- PR #5704 Initial `fixed_point` Column Support
+- PR #5716 Add `double_type_dispatcher` to libcudf
+- PR #5739 Add `nvtext::detokenize` API
+- PR #5645 Enforce pd.NA and Pandas nullable dtype parity
+- PR #5729 Create nvtext normalize_characters API from the subword_tokenize internal function
+- PR #5572 Add `cudf::encode` API.
+- PR #5767 Add `nvtext::porter_stemmer_measure` and `nvtext::is_letter` APIs
+- PR #5753 Add `cudf::lists::extract_list_element` API
+- PR #5568 Add support for `Series.keys()` and `DataFrame.keys()`
+- PR #5782 Add Kafka support to custreamz
+- PR #5642 Add `GroupBy.groups()`
+- PR #5811 Add `nvtext::edit_distance` API
+- PR #5789 Add groupby support for duration types
+- PR #5810 Make Cython subdirs packages and simplify package_data
+- PR #6005 Add support for Ampere
+- PR #5807 Initial support for struct columns
+- PR #5817 Enable more `fixed_point` unit tests by introducing "scale-less" constructor
+- PR #5822 Add `cudf_kafka` to `custreamz` run time conda dependency and fix bash syntax issue
+- PR #5903 Add duration support for Parquet reader, writer
+- PR #5845 Add support for `mask_to_bools`
+- PR #5851 Add support for `Index.sort_values`
+- PR #5904 Add slice/split support for LIST columns
+- PR #5857 Add dtypes information page in python docs
+- PR #5859 Add conversion form `fixed_point` to `bool`
+- PR #5781 Add duration types support in cudf(python/cython)
+- PR #5815 LIST Support for ColumnVector
+- PR #5931 Support for `add_calendrical_months` API
+- PR #5992 Add support for `.dt.strftime`
+- PR #6075 Parquet writer - add support for nested LIST columns
+
+## Improvements
+
+- PR #5492 compile_udf: compile straight to PTX instead of using @jit
+- PR #5605 Automatically flush RMM allocate/free logs in JNI
+- PR #5632 Switch JNI code to use `pool_memory_resource` instead of CNMeM
+- PR #5486 Link Boost libraries statically in the Java build
+- PR #5479 Link Arrow libraries statically
+- PR #5414 Use new release of Thrust/CUB in the JNI build
+- PR #5403 Update required CMake version to 3.14 in contribution guide
+- PR #5245 Add column reduction benchmark
+- PR #5315 Use CMake `FetchContent` to obtain `cub` and `thrust`
+- PR #5398 Use CMake `FetchContent` to obtain `jitify` and `libcudacxx`
+- PR #5268 Rely on NumPy arrays for out-of-band pickling
+- PR #5288 Drop `auto_pickle` decorator #5288
+- PR #5231 Type `Buffer` as `uint8`
+- PR #5305 Add support for `numpy`/`cupy` array in `DataFrame` construction
+- PR #5308 Coerce frames to `Buffer`s in deserialization
+- PR #5309 Handle host frames in serialization
+- PR #5312 Test serializing `Series` after `slice`
+- PR #5248 Support interleave_columns for string types
+- PR #5332 Remove outdated dask-xgboost docs
+- PR #5349 Improve libcudf documentation CSS style
+- PR #5317 Optimize fixed_point rounding shift for integers
+- PR #5386 Remove `cub` from `include_dirs` in `setup.py`
+- PR #5373 Remove legacy nvstrings/nvcategory/nvtext
+- PR #5362 Remove dependency on `rmm._DevicePointer`
+- PR #5302 Add missing comparison operators to `fixed_point` type
+- PR #5824 Mark host frames as not needing to be writeable
+- PR #5354 Split Dask deserialization methods by dask/cuda
+- PR #5363 Handle `0-dim` inputs while broadcasting to a column
+- PR #5396 Remove legacy tests env variable from build.sh
+- PR #5374 Port nvtext character_tokenize API to libcudf
+- PR #5389 Expose typed accessors for Java HostMemoryBuffer
+- PR #5379 Avoid chaining `Buffer`s
+- PR #5387 Port nvtext replace_tokens API to libcudf
+- PR #5381 Change numpy usages to cupy in `10min.ipynb`
+- PR #5408 Update pyrrow and arrow-cpp to 0.17.1
+- PR #5366 Add benchmarks for cuIO writers
+- PR #5913 Call cudaMemcpyAsync/cudaMemsetAsync in JNI
+- PR #5405 Add Error message to `StringColumn.unary_operator`
+- PR #5424 Add python plumbing for `.str.character_tokenize`
+- PR #5420 Aligning signature of `Series.value_counts` to Pandas
+- PR #5535 Update document for XGBoost usage with dask-cuda
+- PR #5431 Adding support for unsigned int
+- PR #5426 Refactor strings code to minimize calls to regex
+- PR #5433 Add support for column inputs in `strings::starts_with` and `strings::ends_with`
+- PR #5427 Add Java bindings for unsigned data types
+- PR #5429 Improve text wrapping in libcudf documentation
+- PR #5443 Remove unused `is_simple` trait
+- PR #5441 Update Java HostMemoryBuffer to only load native libs when necessary
+- PR #5452 Add support for strings conversion using negative timestamps
+- PR #5437 Improve libcudf join documentation
+- PR #5458 Install meta packages for dependencies
+- PR #5467 Move doc customization scripts to Jenkins
+- PR #5468 Add cudf::unique_count(table_view)
+- PR #5482 Use rmm::device_uvector in place of rmm::device_vector in copy_if
+- PR #5483 Add NVTX range calls to dictionary APIs
+- PR #5477 Add `is_index_type` trait
+- PR #5487 Use sorted lists instead of sets for pytest parameterization
+- PR #5491 allow build libcudf in custom dir
+- PR #5501 Adding only unsigned types support for categorical column codes
+- PR #5570 Add Index APIs such as `Int64Index`, `UInt64Index` and others
+- PR #5503 Change `unique_count` to `distinct_count`
+- PR #5514 `convert_datetime.cu` Small Cleanup
+- PR #5496 Rename .cu tests (zero cuda kernels) to .cpp files
+- PR #5518 split iterator and gather tests to speedup build tests
+- PR #5526 Change `type_id` to enum class
+- PR #5559 Java APIs for missing date/time operators
+- PR #5582 Add support for axis and other parameters to `DataFrame.sort_index` and fix other bunch of issues.
+- PR #5562 Add missing join type for java
+- PR #5584 Refactor `CompactProtocolReader::InitSchema`
+- PR #5591 Add `__arrow_array__` protocol and raise a descriptive error message
+- PR #5635 Ad cuIO reader benchmarks for CSV, ORC and Parquet
+- PR #5601 Instantiate Table instances in `Frame._concat` to avoid `DF.insert()` overhead
+- PR #5602 Add support for concatenation of `Series` & `DataFrame` in `cudf.concat` when `axis=0`
+- PR #5603 Refactor JIT `parser.cpp`
+- PR #5643 Update `isort` to 5.0.4
+- PR #5648 OO interface for hash join with explicit `build/probe` semantic
+- PR #5662 Make Java ColumnVector(long nativePointer) constructor public
+- PR #5681 Pin black, flake8 and isort
+- PR #5679 Use `pickle5` to test older Python versions
+- PR #5684 Use `pickle5` in `Serializable` (when available)
+- PR #5419 Support rolling, groupby_rolling for durations
+- PR #5687 Change strings::split_record to return a lists column
+- PR #5708 Add support for `dummy_na` in `get_dummies`
+- PR #5709 Update java build to help cu-spacial with java bindings
+- PR #5713 Remove old NVTX utilities
+- PR #5726 Replace use of `assert_frame_equal` in tests with `assert_eq`
+- PR #5720 Replace owning raw pointers with std::unique_ptr
+- PR #5702 Add inherited methods to python docs and other docs fixes
+- PR #5733 Add support for `size` property in `DataFrame`/ `Series` / `Index`/ `MultiIndex`
+- PR #5735 Force timestamp creation only with duration
+- PR #5743 Reduce number of test cases in concatenate benchmark
+- PR #5748 Disable `tolist` API in `Series` & `Index` and add `tolist` dispatch in `dask-cudf`
+- PR #5744 Reduce number of test cases in reduction benchmark
+- PR #5756 Switch JNI code to use the RMM owning wrapper
+- PR #5725 Integrate Gbenchmarks into CI
+- PR #5752 Add cuDF internals documentation (ColumnAccessor)
+- PR #5759 Fix documentation describing JIT cache default location
+- PR #5780 Add Java bindings for pad
+- PR #5775 Update dask_cudf.read_parquet to align with upstream improvements
+- PR #5785 Enable computing views of ListColumns
+- PR #5791 Get nullable_pd_dtype from kwargs if provided in assert_eq
+- PR #5786 JNI Header Cleanup for cuSpatial
+- PR #5800 Expose arrow datasource instead of directly taking a RandomAccessFile
+- PR #5795 Clarify documentation on Boost dependency
+- PR #5803 Add in Java support for the repeat command
+- PR #5806 Expose the error message from native exception when throwing an OOM exception
+- PR #5825 Enable ORC statistics generation by default
+- PR #5771 Enable gather/slicing/joins with ListColumns in Python
+- PR #5834 Add support for dictionary column in concatenate
+- PR #5832 Make dictionary_wrapper constructor from a value explicit
+- PR #5833 Pin `dask` and `distributed` version to `2.22.0`
+- PR #5856 Bump Pandas support to >=1.0,<1.2
+- PR #5855 Java interface to limit RMM maximum pool size
+- PR #5853 Disable `fixed_point` for use in `copy_if`
+- PR #5854 Raise informative error in `DataFrame.iterrows` and `DataFrame.itertuples`
+- PR #5864 Replace cnmem with pool_memory_resource in test/benchmark fixtures
+- PR #5863 Explicitly require `ucx-py` on CI
+- PR #5879 Added support of sub-types and object wrappers in concat()
+- PR #5884 Use S3 bucket directly for benchmark plugni
+- PR #5881 Add in JVM extractListElement and stringSplitRecord
+- PR #5885 Add in java support for merge sort
+- PR #5894 Small code improvement / cleanup
+- PR #5899 Add in gather support for Java
+- PR #5906 Add macros for showing line of failures in unit tests
+- PR #5933 Add in APIs to read/write arrow IPC formatted data from java
+- PR #3918 Update cuDF internals doc
+- PR #5970 Map data to pandas through arrow, always
+- PR #6012 Remove `cudf._cuda` and replace usages with `rmm._cuda`
+- PR #6045 Parametrize parquet_reader_list tests
+- PR #6053 Import traits.hpp for cudftestutils consumers
+
+## Bug Fixes
+
+- PR #6034 Specify `--basetemp` for `py.test` run
+- PR #5793 Fix leak in mutable_table_device_view by deleting _descendant_storage in table_device_view_base::destroy
+- PR #5525 Make sure to allocate bitmasks of string columns only once
+- PR #5336 Initialize conversion tables on a per-context basis
+- PR #5283 Fix strings::ipv4_to_integers overflow to negative
+- PR #5269 Explicitly require NumPy
+- PR #5271 Fix issue when different dtype values are passed to `.cat.add_categories`
+- PR #5333 Fix `DataFrame.loc` issue with list like argument
+- PR #5299 Update package version for Java bindings
+- PR #5300 Add support to ignore `None` in `cudf.concat` input
+- PR #5334 Fix pickling sizeof test
+- PR #5337 Fix broken alias from DataFrame.{at,iat} to {loc, iloc}
+- PR #5347 Fix APPLY_BOOLEAN_MASK_BENCH segfault
+- PR #5368 Fix loc indexing issue with `datetime` type index
+- PR #5367 Fix API for `cudf::repeat` in `cudf::cross_join`
+- PR #5377 Handle array of cupy scalars in to_column
+- PR #5326 Fix `DataFrame.__init__` for list of scalar inputs and related dask issue
+- PR #5383 Fix cython `type_id` enum mismatch
+- PR #5982 Fix gcc-9 compile errors under CUDA 11
+- PR #5382 Fix CategoricalDtype equality comparisons
+- PR #5989 Fix gcc-9 warnings on narrowing conversion
+- PR #5385 Fix index issues in `DataFrame.from_gpu_matrix`
+- PR #5390 Fix Java data type IDs and string interleave test
+- PR #5392 Fix documentation links
+- PR #5978 Fix option to turn off NVTX
+- PR #5410 Fix compile warning by disallowing bool column type for slice_strings
+- PR #5404 Fix issue with column creation when chunked arrays are passed
+- PR #5409 Use the correct memory resource when creating empty null masks
+- PR #5399 Fix cpp compiler warnings of unreachable code
+- PR #5439 Fix nvtext ngrams_tokenize performance for multi-byte UTF8
+- PR #5446 Fix compile error caused by out-of-date PR merge (4990)
+- PR #5983 Fix JNI gcc-9 compile error under CUDA 11
+- PR #5423 Fix any() reduction ignore nulls
+- PR #5459 Fix str.translate to convert table characters to UTF-8
+- PR #5480 Fix merge sort docs
+- PR #5465 Fix benchmark out of memory errors due to multiple initialization
+- PR #5473 Fix RLEv2 patched base in ORC reader
+- PR #5472 Fix str concat issue with indexed series
+- PR #5478 Fix `loc` and `iloc` doc
+- PR #5484 Ensure flat index after groupby if nlevels == 1
+- PR #5489 Fix drop_nulls/boolean_mask corruption for large columns
+- PR #5504 Remove some java assertions that are not needed
+- PR #5516 Update gpuCI image in local build script
+- PR #5529 Fix issue with negative timestamp in orc writer
+- PR #5523 Handle `dtype` of `Buffer` objects when not passed explicitly
+- PR #5534 Fix the java build around type_id
+- PR #5564 Fix CudfEngine.read_metadata API in dask_cudf
+- PR #5537 Fix issue related to using `set_index` on a string series
+- PR #5561 Fix `copy_bitmask` issue with offset
+- PR #5609 Fix loc and iloc issue with column like input
+- PR #5578 Fix getattr logic in GroupBy
+- PR #5490 Fix python column view
+- PR #5613 Fix assigning an equal length object into a masked out Series
+- PR #5608 Fix issue related to string types being represented as binary types
+- PR #5619 Fix issue related to typecasting when using a `CategoricalDtype`
+- PR #5649 Fix issue when empty Dataframe with index are passed to `cudf.concat`
+- PR #5644 Fix issue related to Dataframe init when passing in `columns`
+- PR #5340 Disable iteration in cudf objects and add support for `DataFrame` initialization with list of `Series`
+- PR #5663 Move Duration types under Timestamps in doxygen Modules page
+- PR #5664 Update conda upload versions for new supported CUDA/Python
+- PR #5656 Fix issue with incorrect docker image being used in local build script
+- PR #5671 Fix chunksize issue with `DataFrame.to_csv`
+- PR #5672 Fix crash in parquet writer while writing large string data
+- PR #5675 Allow lists_column_wrappers to be constructed from incomplete hierarchies.
+- PR #5691 Raise error on incompatible mixed-type input for a column
+- PR #5692 Fix compilation issue with gcc 7.4.0 and CUDA 10.1
+- PR #5693 Add fix missing from PR 5656 to update local docker image to py3.7
+- PR #5703 Small fix for dataframe constructor with cuda array interface objects that don't have `descr` field
+- PR #5727 Fix `Index.__repr__` to allow representation of null values
+- PR #5719 Fix Frame._concat() with categorical columns
+- PR #5736 Disable unsigned type in ORC writer benchmarks
+- PR #5745 Update JNI cast for inability to cast timestamp and integer types
+- PR #5750 Add RMM_ROOT/include to the spdlog search path in JNI build
+- PR #5763 Update Java slf4j version to match Spark 3.0
+- PR #5816 Always preserve list column hierarchies across operations.
+- PR #5766 Fix issue related to `iloc` and slicing a `DataFrame`
+- PR #5827 Revert fallback for `tolist` being absent
+- PR #5774 Add fallback for when `tolist` is absent
+- PR #5319 Disallow SUM and specialize MEAN of timestamp types
+- PR #5797 Fix a missing data issue in some Parquet files
+- PR #5787 Fix column create from dictionary column view
+- PR #5764 Remove repetition of install instructions
+- PR #5926 Fix SeriesGroupBy.nunique() to return a Series
+- PR #5813 Fix normalizer exception with all-null strings column
+- PR #5820 Fix ListColumn.to_arrow for all null case
+- PR #5837 Bash syntax error in prebuild.sh preventing `cudf_kafka` and `libcudf_kafka` from being uploaded to Anaconda
+- PR #5841 Added custreamz functions that were missing in interface layer
+- PR #5844 Fix `.str.cat` when objects with different index are passed
+- PR #5849 Modify custreamz api to integrate seamlessly with python streamz
+- PR #5866 cudf_kafka python version inconsistencies in Anaconda packages
+- PR #5872 libcudf_kafka r_path is causing docker build failures on centos7
+- PR #5869 Fix bug in parquet writer in writing string column with offset
+- PR #5910 Propagate `CUDA` insufficient driver error to the user
+- PR #5914 Link CUDA against libcudf_kafka
+- PR #5895 Do not break kafka client consumption loop on local client timeout
+- PR #5915 Fix reference count on Java DeviceMemoryBuffer after contiguousSplit
+- PR #5941 Fix issue related to `string` to `datetime64` column typecast
+- PR #5927 Fix return type of `MultiIndex.argsort`
+- PR #5942 Fix JIT cache multiprocess test failure
+- PR #5929 Revised assertEquals for List Columns in java tests
+- PR #5947 Fix null count for child device column vector
+- PR #5951 Fix mkdir error in benchmark build
+- PR #5949 Find Arrow include directory for JNI builds
+- PR #5964 Fix API doc page title tag
+- PR #5981 Handle `nat` in `fillna` for datetime and timedelta types
+- PR #6016 Fix benchmark fixture segfault
+- PR #6003 Fix concurrent JSON reads crash
+- PR #6032 Change black version to 19.10b0 in .pre-commit-config.yaml
+- PR #6041 Fix Java memory resource handler to rethrow original exception object
+- PR #6057 Fix issue in parquet reader with reading columns out of file-order
+- PR #6098 Patch Thrust to workaround CUDA_CUB_RET_IF_FAIL macro clearing CUDA errors
+
+
+# cuDF 0.14.0 (03 Jun 2020)
+
+## New Features
+
+- PR #5042 Use RMM for Numba
+- PR #4472 Add new `partition` API to replace `scatter_to_tables`.
+- PR #4626 LogBase binops
+- PR #4750 Normalize NANs and Zeroes (JNI Bindings)
+- PR #4689 Compute last day of the month for a given date
+- PR #4771 Added in an option to statically link against cudart
+- PR #4788 Add cudf::day_of_year API
+- PR #4789 Disallow timestamp sum and diffs via binary ops
+- PR #4815 Add JNI total memory allocated API
+- PR #4906 Add Java bindings for interleave_columns
+- PR #4900 Add `get_element` to obtain scalar from a column given an index
+- PR #4938 Add Java bindings for strip
+- PR #4923 Add Java and JNI bindings for string split
+- PR #4972 Add list_view (cudf::LIST) type
+- PR #4990 Add lists_column_view, list_column_wrapper, lists support for concatenate
+- PR #5073 gather support for cudf::LIST columns
+- PR #5004 Added a null considering min/max binary op
+- PR #4992 Add Java bindings for converting nans to nulls
+- PR #4975 Add Java bindings for first and last aggregate expressions based on nth
+- PR #5036 Add positive remainder binary op functionality
+- PR #5055 Add atan2 binary op
+- PR #5099 Add git commit hook for clang-format
+- PR #5072 Adding cython binding to `get_element`
+- PR #5092 Add `cudf::replace_nans`
+- PR #4881 Support row_number in rolling_window
+- PR #5068 Add Java bindings for arctan2
+- PR #5132 Support out-of-band buffers in Python pickling
+- PR #5139 Add ``Serializable`` ABC for Python
+- PR #5149 Add Java bindings for PMOD
+- PR #5153 Add Java bindings for extract
+- PR #5196 Add Java bindings for NULL_EQUALS, NULL_MAX and NULL_MIN
+- PR #5192 Add support for `cudf.to_datetime`
+- PR #5203 Add Java bindings for is_integer and is_float
+- PR #5205 Add ci test for libcudf, libnvstrings headers existence check in meta.yml
+- PR #5239 Support for custom cuIO datasource classes
+- PR #5293 Add Java bindings for replace_with_backrefs
+
+## Improvements
+
+- PR #5235 Make DataFrame.clean_renderable_dataframe() and DataFrame.get_renderable_dataframe non-public methods
+- PR #4995 Add CMake option for per-thread default stream
+- PR #5033 Fix Numba deprecations warnings with Numba 0.49+
+- PR #4950 Fix import errors with Numba 0.49+
+- PR #4825 Update the iloc exp in dataframe.py
+- PR #4450 Parquet writer: add parameter to retrieve the raw file metadata
+- PR #4531 Add doc note on conda `channel_priority`
+- PR #4479 Adding cuda 10.2 support via conda environment file addition
+- PR #4486 Remove explicit template parameter from detail::scatter.
+- PR #4471 Consolidate partitioning functionality into a single header.
+- PR #4483 Add support fill() on dictionary columns
+- PR #4498 Adds in support for chunked writers to java
+- PR #4073 Enable contiguous split java test
+- PR #4527 Add JNI and java bindings for matches_re
+- PR #4606 Fix `scan` unit test and upgrade to more appropriate algorithms
+- PR #4527 Add JNI and java bindings for `matches_re`
+- PR #4532 Parquet reader: add support for multiple pandas index columns
+- PR #4599 Add Java and JNI bindings for string replace
+- PR #4655 Raise error for list like dtypes in cudf
+- PR #4548 Remove string_view is_null method
+- PR #4645 Add Alias for `kurtosis` as `kurt`
+- PR #4703 Optimize strings concatenate for many columns
+- PR #4769 Remove legacy code from libcudf
+- PR #4668 Add Java bindings for log2/log10 unary ops and log_base binary op
+- PR #4616 Enable different RMM allocation modes in unit tests
+- PR #4520 Fix several single char -> single char case mapping values. Add support for single -> multi char mappings.
+- PR #4700 Expose events and more stream functionality in java
+- PR #4699 Make Java's MemoryBuffer public and add MemoryBuffer.slice
+- PR #4691 Fix compiler argument syntax for ccache
+- PR #4792 Port `gather`, `scatter`, and `type_dispatcher` benchmarks to libcudf++
+- PR #3581 Remove `bool8`
+- PR #4692 Add GPU and CUDA validations
+- PR #4705 quantile cython bindings
+- PR #4627 Remove legacy Cython
+- PR #4688 Add Java count aggregation to include null values
+- PR #4331 Improved test for double that considers an epsilon
+- PR #4731 Avoid redundant host->device copies when reading the entire CSV/JSON file
+- PR #4739 Add missing aggregations for cudf::experimental::reduce
+- PR #4738 Remove stop-gaps in StringMethods and enable related tests
+- PR #4745 Fix `fsspec` related issue and upgrade `fsspec` version
+- PR #4779 Allow reading arbitrary stripes/rowgroup lists in CPP columnar readers
+- PR #4766 Update to use header-only NVTX v3 and remove need to link against nvtx.
+- PR #4716 Remove direct calls to RMM_ALLOC/RMM_FREE
+- PR #4765 Add in java support for sequence
+- PR #4772 Cleanup `dask_cudf` `to_parquet` and enable `"_metadata"` creation
+- PR #4733 Fix `isin` docs for `DataFrame`, `Series`, `Index`, and add `DataFrame.isin` support
+- PR #4767 Remove linking against `gtest_main` and `gmock_main` in unit tests
+- PR #4660 Port `cudf::partition` api to python/cython
+- PR #4799 Remove null_count() and has_nulls() from column_device_view
+- PR #4778 Remove `scatter_to_tables` from libcudf, cython and python
+- PR #4783 Add support for child columns to mutable_column_device_view
+- PR #4802 Refactor `cudf::transpose` to increase performance.
+- PR #4776 Improve doxygen comments for libcudf string/timestamp conversion formats
+- PR #4793 Add `cudf._cuda` to setup.py
+- PR #4790 Replace the use of deprecated rmm APIs in the test environment
+- PR #4809 Improve libcudf doc rendering and add a new main page
+- PR #4811 Add precision to subsecond specifier in timestamp/string conversion format
+- PR #4543 Add `inplace` parameter support for `Series.replace` & `DataFrame.replace`
+- PR #4816 Remove java API use of deprecated RMM APIs
+- PR #4817 Fix `fixed_point` documentation
+- PR #4844 Change Doxygen color to RAPIDS purple and documentation improvement
+- PR #4840 Add docs for `T`, `empty` & `values`
+- PR #4841 Remove unused `single_lane_block_popc_reduce` function
+- PR #4842 Added Java bindings for titlizing a String column
+- PR #4847 Replace legacy NVTX calls with "standalone" NVTX bindings calls
+- PR #4851 Performance improvements relating to `concat`
+- PR #4852 Add NVTX range calls to strings and nvtext APIs
+- PR #4849 Update Java bindings to use new NVTX API
+- PR #4845 Add CUDF_FUNC_RANGE to top-level cuIO function APIs
+- PR #4848 Side step `unique_count` calculation in `scatter_by_map`
+- PR #4863 Create is_integer/is_float functions for checking characters before calling to_integers/to_floats
+- PR #4864 Add support for `__array__` method in cuDF
+- PR #4853 Added CUDA_TRY to multiple places in libcudf code
+- PR #4870 Add chunked parquet file writing from python
+- PR #4865 Add docs and clarify limitations of `applymap`
+- PR #4867 Parquet reader: coalesce adjacent column chunk reads
+- PR #4871 Add in the build information when building the java jar file
+- PR #4869 Expose contiguous table when deserializing from Java
+- PR #4878 Remove obsolete string_from_host utility
+- PR #4873 Prevent mutable_view() from invoking null count
+- PR #4806 Modify doc and correct cupy array conversions in `10min-cudf-cupy.ipynb`
+- PR #4877 Fix `DataFrame.mask` and align `mask` & `where` behavior with pandas
+- PR #4884 Add more NVTX annotations in cuDF Python
+- PR #4902 Use ContextDecorator instead of contextmanager for nvtx.annotate
+- PR #4894 Add annotations for the `.columns` property and setter
+- PR #4901 Improve unit tests for casting Java numeric types to string
+- PR #4888 Handle dropping of nan's & nulls using `skipna` parameter in Statistical reduction ops
+- PR #4903 Improve internal documentation of cudf-io compression/decompression kernels
+- PR #4905 Get decorated function name as message when annotating
+- PR #4907 Reuse EventAttributes across NVTX annotations
+- PR #4912 Drop old `valid` check in `element_indexing`
+- PR #4924 Properly handle npartition argument in rearrange_by_hash
+- PR #4918 Adding support for `cupy.ndarray` in `series.loc`
+- PR #4909 Added ability to transform a column using cuda method in Java bindings
+- PR #3259 Add .clang-format file & format all files
+- PR #4943 Fix-up error handling in GPU detection
+- PR #4917 Add support for casting unsupported `dtypes` of same kind
+- PR #4928 Misc performance improvements for `scatter_by_map`
+- PR #4927 Use stack for memory in `deviceGetName`
+- P# #4933 Enable nop annotate
+- PR #4929 Java methods ensure calling thread's CUDA device matches RMM device
+- PR #4956 Dropping `find_first_value` and `find_last_value`
+- PR #4962 Add missing parameters to `DataFrame.replace` & `Series.replace`
+- PR #4960 Return the result of `to_json`
+- PR #4963 Use `cudaDeviceAttr` in `getDeviceAttribute`
+- PR #4953 add documentation for supported NVIDIA GPUs and CUDA versions for cuDF
+- PR #4967 Add more comments to top-level gpuinflate and debrotli kernels
+- PR #4968 Add CODE_OF_CONDUCT.md
+- PR #4980 Change Java HostMemoryBuffer default to prefer pinned memory
+- PR #4994 clang-format "cpp/tests" directory
+- PR #4993 Remove Java memory prediction code
+- PR #4985 Add null_count to Python Column ctors and use already computed null_count when possible
+- PR #4998 Clean up dispatch of aggregation methods in result_cache
+- PR #5000 Performance improvements in `isin` and dask_cudf backend
+- PR #5002 Fix Column.__reduce__ to accept `null_count`
+- PR #5006 Add Java bindings for strip, lstrip and rstrip
+- PR #5047 Add Cython binding for libcudf++ CSV reader
+- PR #5027 Move nvstrings standalone docs pages to libcudf doxygen pages
+- PR #4947 Add support for `CategoricalColumn` to be type-casted with different categories
+- PR #4822 Add constructor to `pq_chunked_state` to enable using RAII idiom
+- PR #5024 CSV reader input stage optimizations
+- PR #5061 Add support for writing parquet to python file-like objects
+- PR #5034 Use loc to apply boolmask to frame efficiently when constructing query result
+- PR #5039 Make `annotate` picklable
+- PR #5045 Remove call to `unique()` in concat when `axis=1`
+- PR #5023 Object oriented join and column agnostic typecasting
+- PR #5049 Add grouping of libcudf apis into doxygen modules
+- PR #5069 Remove duplicate documentation from detail headers
+- PR #5075 Add simple row-group aggregation mechanism in dask_cudf read_parquet
+- PR #5084 Improve downcasting in `Series.label_encoding()` to reduce memory usage
+- PR #5085 Print more precise numerical strings in unit tests
+- PR #5028 Add Docker 19 support to local gpuci build
+- PR #5093 Add `.cat.as_known` related test in `dask_cudf`
+- PR #5100 Add documentation on libcudf doxygen guidelines
+- PR #5106 Add detail API for `cudf::concatenate` with tables
+- PR #5104 Add missing `.inl` files to clang-format and git commit hook
+- PR #5112 Adding `htoi` and `ip2int` support to `StringMethods`
+- PR #5101 Add POSITION_INDEPENDENT_CODE flag to static cudftestutil library
+- PR #5109 Update CONTRIBUTING.md for `clang-format` pre-commit hook
+- PR #5054 Change String typecasting to be inline with Pandas
+- PR #5123 Display more useful info on `clang-format` CI Failure
+- PR #5058 Adding cython binding for CSV writer
+- PR #5156 Raise error when applying boolean mask containing null values.
+- PR #5137 Add java bindings for getSizeInBytes in DType
+- PR #5194 Update Series.fillna to reflect dtype behavior
+- PR #5159 Add `make_meta_object` in `dask_cudf` backend and add `str.split` test
+- PR #5147 Use logging_resource_adaptor from RMM in the JNI code
+- PR #5184 Fix style checks
+- PR #5198 Add detail headers for strings converter functions
+- PR #5199 Add index support in `DataFrame.query`
+- PR #5227 Refactor `detail::gather` API to make use of scoped enumerators
+- PR #5218 Reduce memory usage when categorifying column with null values.
+- PR #5209 Add `nan_as_null` support to `cudf.from_pandas`
+- PR #5207 Break up backref_re.cu into multiple source files to improve compile time
+- PR #5155 Fix cudf documentation misspellings
+- PR #5208 Port search and join benchmark to libcudf++
+- PR #5214 Move docs build script into repository
+- PR #5219 Add per context cache for JIT kernels
+- PR #5250 Improve `to_csv()` support for writing to buffers
+- PR #5233 Remove experimental namespace used during libcudf++ refactor
+- PR #5213 Documentation enhancements to `cudf` python APIs
+- PR #5251 Fix more mispellings in cpp comments and strings
+- PR #5261 Add short git commit to conda package name
+- PR #5254 Deprecate nvstrings, nvcategory and nvtext
+- PR #5270 Add support to check for "NaT" and "None" strings while typecasting to `datetime64`
+- PR #5298 Remove unused native deps from java library
+- PR #5216 Make documentation uniform for params
+
+## Bug Fixes
+
+- PR #5221 Fix the use of user-provided resource on temporary values
+- PR #5181 Allocate null count using the default resource in `copy_if`
+- PR #5141 Use user-provided resource correctly in `unary_operation()` and `shift()`
+- PR #5064 Fix `hash()` and `construct_join_output_df()` to use user-provided memory resource correctly
+- PR #4386 Update Java package to 0.14
+- PR #4466 Fix merge key column sorting
+- PR #4402 Fix `cudf::strings::join_strings` logic with all-null strings and null narep
+- PR #4610 Fix validity bug in string scalar factory
+- PR #4570 Fixing loc ordering issue in dataframe
+- PR #4612 Fix invalid index handling in cudf:dictionary:add-keys call to gather
+- PR #4614 Fix cuda-memcheck errors found in column_tests.cu and copying/utility_tests.cu
+- PR #4614 Fix cuda-memcheck errors found in `column_tests.cu` and `copying/utility_tests.cu`
+- PR #4639 Fix java column of empty strings issue
+- PR #4613 Fix issue related to downcasting in `.loc`
+- PR #4615 Fix potential OOB write in ORC writer compression stage
+- PR #4587 Fix non-regex libcudf contains methods to return true when target is an empty string
+- PR #4617 Fix memory leak in aggregation object destructor
+- PR #4633 String concatenation fix in `DataFrame.rename`
+- PR #4609 Fix to handle `Series.factorize` when index is set
+- PR #4659 Fix strings::replace_re handling empty regex pattern
+- PR #4652 Fix misaligned error when computing regex device structs
+- PR #4651 Fix hashing benchmark missing includes
+- PR #4672 Fix docs for `value_counts` and update test cases
+- PR #4672 Fix `__setitem__` handling list of column names
+- PR #4673 Fix regex infinite loop while parsing invalid quantifier pattern
+- PR #4679 Fix comments for make_dictionary_column factory functions
+- PR #4711 Fix column leaks in Java unit test
+- pR #4721 Fix string binop to update nulls appropriately
+- PR #4722 Fix strings::pad when using pad::both with odd width
+- PR #4743 Fix loc issue with Multiindex on DataFrame and Series
+- PR #4725 Fix issue java with not setting GPU on background thread
+- PR #4701 Fix issue related to mixed input types in `as_column`
+- PR #4748 Fix strings::all_characters_of_type to allow verify-types mask
+- PR #4747 Fix random failures of decompression gtests
+- PR #4749 Setting `nan_as_null=True` while creating a column in DataFrame creation
+- PR #4761 Fix issues with `nan_as_null` in certain case
+- PR #4650 Fix type mismatch & result format issue in `searchsorted`
+- PR #4755 Fix Java build to deal with new quantiles API
+- PR #4720 Fix issue related to `dtype` param not being adhered in case of cuda arrays
+- PR #4756 Fix regex error checking for valid quantifier condition
+- PR #4777 Fix data pointer for column slices of zero length
+- PR #4770 Fix readonly flag in `Column. __cuda_array_interface__`
+- PR #4800 Fix dataframe slicing with strides
+- PR #4796 Fix groupby apply for operations that fail on empty groups
+- PR #4801 gitignore `_cuda/*.cpp` files
+- PR #4805 Fix hash_object_dispatch definitions in dask_cudf
+- PR #4813 Fix `GenericIndex` printing
+- PR #4804 Fix issue related `repartition` during hash based repartition
+- PR #4814 Raise error if `to_csv` does not get `filename/path`
+- PR #4821 Port apply_boolean_mask_benchmark to new cudf::column types
+- PR #4826 Move memory resource from RmmTestEnvironment to the custom gtest main() scope
+- PR #4839 Update Java bindings for timestamp cast formatting changes
+- PR #4797 Fix string timestamp to datetime conversion with `ms` and `ns`
+- PR #4854 Fix several cases of incorrect downcasting of operands in binops
+- PR #4834 Fix bug in transform in handling single line UDFs
+- PR #4857 Change JIT cache default directory to $HOME/.cudf
+- PR #4807 Fix `categories` duplication in `dask_cudf`
+- PR #4846 Fix CSV parsing with byte_range parameter and string columns
+- PR #4883 Fix series get/set to match pandas
+- PR #4861 Fix to_integers illegal-memory-access with all-empty strings column
+- PR #4860 Fix issues in HostMemoryBufferTest, and testNormalizeNANsAndZeros
+- PR #4879 Fix output for `cudf.concat` with `axis=1` for pandas parity
+- PR #4838 Fix to support empty inputs to `replace` method
+- PR #4859 JSON reader: fix data type inference for string columns
+- PR #4868 Temporary fix to skip validation on Dask related runs
+- PR #4872 Fix broken column wrapper constructors in merge benchmark
+- PR #4875 Fix cudf::strings::from_integer logic converting min integer to string
+- PR #4876 Mark Java cleaner objects as being cleaned even if exception is thrown
+- PR #4780 Handle nulls in Statistical column operations
+- PR #4886 Minimize regex-find calls in multi-replace cudf::strings::replace_re function
+- PR #4887 Remove `developer.rst` and any links
+- PR #4915 Fix to `reset_index` inplace in MultiIndex and other places
+- PR #4899 Fix series inplace handling
+- PR #4940 Fix boolean mask issue with large sized Dataframe
+- PR #4889 Fix multi-index merging
+- PR #4922 Fix cudf::strings:split logic for many columns
+- PR #4949 Fix scatter, gather benchmark constructor call
+- PR #4958 Fix strings::replace perf for long strings
+- PR #4965 Raise Error when there are duplicate columns sent to `cudf.concat`
+- PR #4983 Fix from_cudf in dask_cudf
+- PR #4996 Parquet writer: fix potentially zero-sized string dictionary
+- PR #5009 Fix pickling for string and categorical columns
+- PR #4984 Fix groupby nth aggregation negative n and exclude nulls
+- PR #5011 Fix DataFrame loc issue with boolean masking
+- PR #4977 Fix compilation of cuDF benchmarks with build.sh
+- PR #5018 Fix crash when JIT cache dir inaccessible. Fix inter version cache clash for custom cache path.
+- PR #5005 Fix CSV reader error when only one of the row selection parameters is set
+- PR #5022 Add timestamp header to transform
+- PR #5021 Fix bug with unsigned right shift and scalar lhs
+- PR #5020 Fix `conda install pre_commit` not found when setting up dev environment
+- PR #5030 Fix Groupby sort=True
+- PR #5029 Change temporary dir to working dir for cudf io tests
+- PR #5040 Fix `make_scalar_iterator()` and `make_pair_iterator(scalar)` to not copy values to host
+- PR #5041 Fix invalid java test for shift right unsigned
+- PR #5043 Remove invalid examples page libcudf doxygen
+- PR #5060 Fix unsigned char limits issue in JIT by updating Jitify
+- PR #5070 Fix libcudf++ csv reader support for hex dtypes, doublequotes and empty columns
+- PR #5057 Fix metadata_out parameter not reaching parquet `write_all`
+- PR #5076 Fix JNI code for null_policy enum change
+- PR #5031 grouped_time_range_rolling_window assumes ASC sort order
+- PR #5032 grouped_time_range_rolling_window should permit invocation without specifying grouping_keys
+- PR #5103 Fix `read_csv` issue with names and header
+- PR #5090 Fix losing nulls while creating DataFrame from dictionary
+- PR #5089 Return false for sign-only string in libcudf is_float and is_integer
+- PR #5124 `DataFrame.rename` support for renaming indexes w/ default for `index`
+- PR #5108 Fix float-to-string convert for -0.0
+- PR #5111 Fix header not being included in legacy jit transform.
+- PR #5115 Fix hex-to-integer logic when string has prefix '0x'
+- PR #5118 Fix naming for java string length operators
+- PR #5129 Fix missed reference in tests from 5118
+- PR #5122 Fix `clang-format` `custrings` bug
+- PR #5138 Install `contextvars` backport on Python 3.6
+- PR #5145 Fix an issue with calling an aggregation operation on `SeriesGroupBy`
+- PR #5148 Fix JNI build for GCC 8
+- PR #5162 Fix issues related to empty `Dataframe` in `as_gpu_matrix` & `astype`
+- PR #5167 Fix regex extract match to return empty string
+- PR #5163 Fix parquet INT96 timestamps before the epoch
+- PR #5165 Fix potentially missing last row in libcudf++ csv reader
+- PR #5185 Fix flake8 configuration and issues from new flake8 version
+- PR #5193 Fix OOB read in csv reader
+- PR #5191 Fix the use of the device memory resource
+- PR #5212 Fix memory leak in `dlpack.pyx:from_dlpack()`
+- PR #5224 Add new headers from 5198 to libcudf/meta.yaml
+- PR #5228 Fix datetime64 scalar dtype handling for unsupported time units
+- PR #5256 ORC reader: fix loading individual timestamp columns
+- PR #5285 Fix DEBUG compilation failure due to `fixed_point.hpp`
+
+
+# cuDF 0.13.0 (31 Mar 2020)
+
+## New Features
+
+- PR #4360 Added Java bindings for bitwise shift operators
+- PR #3577 Add initial dictionary support to column classes
+- PR #3777 Add support for dictionary column in gather
+- PR #3693 add string support, skipna to scan operation
+- PR #3662 Define and implement `shift`.
+- PR #3861 Added Series.sum feature for String
+- PR #4069 Added cast of numeric columns from/to String
+- PR #3681 Add cudf::experimental::boolean_mask_scatter
+- PR #4040 Add support for n-way merge of sorted tables
+- PR #4053 Multi-column quantiles.
+- PR #4100 Add set_keys function for dictionary columns
+- PR #3894 Add remove_keys functions for dictionary columns
+- PR #4107 Add groupby nunique aggregation
+- PR #4235 Port nvtx.pyx to use non-legacy libcudf APIs
+- PR #4153 Support Dask serialization protocol on cuDF objects
+- PR #4127 Add python API for n-way sorted merge (merge_sorted)
+- PR #4164 Add Buffer "constructor-kwargs" header
+- PR #4172 Add groupby nth aggregation
+- PR #4159 Add COUNT aggregation that includes null values
+- PR #4190 Add libcudf++ transpose Cython implementation
+- PR #4063 Define and implement string capitalize and title API
+- PR #4217 Add libcudf++ quantiles Cython implementation
+- PR #4216 Add cudf.Scalar Python type
+- PR #3782 Add `fixed_point` class to support DecimalType
+- PR #4272 Add stable sorted order
+- PR #4129 Add libcudf++ interleave_columns and tile Cython implementation
+- PR #4262 Port unaryops.pyx to use libcudf++ APIs
+- PR #4276 Port avro.pyx to libcudf++
+- PR #4259 Ability to create Java host buffers from memory-mapped files
+- PR #4240 Add groupby::groups()
+- PR #4294 Add Series rank and Dataframe rank
+- PR #4304 Add new NVTX infrastructure and add ranges to all top-level compute APIs.
+- PR #4319 Add repartition_by_hash API to dask_cudf
+- PR #4315 ShiftLeft, ShiftRight, ShiftRightUnsigned binops
+- PR #4321 Expose Python Semi and Anti Joins
+- PR #4291 Add Java callback support for RMM events
+- PR #4298 Port orc.pyx to libcudf++
+- PR #4344 Port concat.pyx to libcudf++
+- PR #4329 Add support for dictionary columns in scatter
+- PR #4352 Add factory function make_column_from_scalar
+- PR #4381 Add Java support for copying buffers with asynchronous streams
+- PR #4288 Add libcudf++ shift Cython implementation
+- PR #4338 Add cudf::sequence() for generating an incrementing list of numeric values
+- PR #4456 Add argmin/max and string min/max to sort groupby
+- PR #4564 Added Java bindings for clamp operator.
+- PR #4602 Add Cython bindings for functions in `datetime.hpp`
+- PR #4670 Add java and JNI bindings for contains_re
+- PR #4363 Grouped Rolling Window support
+- PR #4798 Add UDF support to grouped rolling window
+- PR #3917 Add dictionary add_keys function
+- PR #3842 ORC writer: add support for column statistics
+- PR #4088 Added asString() on ColumnVector in Java that takes a format string
+- PR #4484 Port CSV writer to libcudf++
+
+## Improvements
+
+- PR #4641 Add replace example in dataframe.py and update 10min.ipynb
+- PR #4140 Add cudf series examples and corr() method for dataframe in dataframe.py
+- PR #4187 exposed getNativeView method in Java bindings
+- PR #3525 build.sh option to disable nvtx
+- PR #3748 Optimize hash_partition using shared memory
+- PR #3808 Optimize hash_partition using shared memory and cub block scan
+- PR #3698 Add count_(un)set_bits functions taking multiple ranges and updated slice to compute null counts at once.
+- PR #3909 Move java backend to libcudf++
+- PR #3971 Adding `as_table` to convert Column to Table in python
+- PR #3910 Adding sinh, cosh, tanh, asinh, acosh, atanh cube root and rint unary support.
+- PR #3972 Add Java bindings for left_semi_join and left_anti_join
+- PR #3975 Simplify and generalize data handling in `Buffer`
+- PR #3985 Update RMM include files and remove extraneously included header files.
+- PR #3601 Port UDF functionality for rolling windows to libcudf++
+- PR #3911 Adding null boolean handling for copy_if_else
+- PR #4003 Drop old `to_device` utility wrapper function
+- PR #4002 Adding to_frame and fix for categorical column issue
+- PR #4009 build script update to enable cudf build without installing
+- PR #3897 Port cuIO JSON reader to cudf::column types
+- PR #4008 Eliminate extra copy in column constructor
+- PR #4013 Add cython definition for io readers cudf/io/io_types.hpp
+- PR #4028 Port json.pyx to use new libcudf APIs
+- PR #4014 ORC/Parquet: add count parameter to stripe/rowgroup-based reader API
+- PR #3880 Add aggregation infrastructure support for cudf::reduce
+- PR #4059 Add aggregation infrastructure support for cudf::scan
+- PR #4021 Change quantiles signature for clarity.
+- PR #4057 Handle offsets in cython Column class
+- PR #4045 Reorganize `libxx` directory
+- PR #4029 Port stream_compaction.pyx to use libcudf++ APIs
+- PR #4031 Docs build scripts and instructions update
+- PR #4062 Improve how java classifiers are produced
+- PR #4038 JNI and Java support for is_nan and is_not_nan
+- PR #3786 Adding string support to rolling_windows
+- PR #4067 Removed unused `CATEGORY` type ID.
+- PR #3891 Port NVStrings (r)split_record to contiguous_(r)split_record
+- PR #4070 Port NVText normalize_spaces to use libcudf strings column
+- PR #4072 Allow round_robin_partition to single partition
+- PR #4064 Add cudaGetDeviceCount to JNI layer
+- PR #4075 Port nvtext ngrams-tokenize to libcudf++
+- PR #4087 Add support for writing large Parquet files in a chunked manner.
+- PR #3716 Update cudf.to_parquet to use new GPU accelerated Parquet writer
+- PR #4083 Use two partitions in test_groupby_multiindex_reset_index
+- PR #4071 Add Java bindings for round robin partition
+- PR #4079 Simply use `mask.size` to create the array view
+- PR #4092 Keep mask on GPU for bit unpacking
+- PR #4081 Copy from `Buffer`'s pointer directly to host
+- PR #4105 Change threshold of using optimized hash partition code
+- PR #4101 Redux serialize `Buffer` directly with `__cuda_array_interface__`
+- PR #4098 Remove legacy calls from libcudf strings column code
+- PR #4044 Port join.pyx to use libcudf++ APIs
+- PR #4111 Use `Buffer`'s to serialize `StringColumn`
+- PR #4567 Optimize `__reduce__` in `StringColumn`
+- PR #4590 Register a few more types for Dask serialization
+- PR #4113 Get `len` of `StringColumn`s without `nvstrings`
+- PR #4147 Remove workaround for UNKNOWN_NULL_COUNT in contiguous_split.
+- PR #4130 Renames in-place `cudf::experimental::fill` to `cudf::experimental::fill_in_place`
+- PR #4136 Add `Index.names` property
+- PR #4139 Port rolling.pyx to new libcudf APIs
+- PR #4143 Renames in-place `cudf::experimental::copy_range` to `cudf::experimental::copy_range_in_place`
+- PR #4144 Release GIL when calling libcudf++ functions
+- PR #4082 Rework MultiColumns in cuDF
+- PR #4149 Use "type-serialized" for pickled types like Dask
+- PR #4174 Port hash groupby to libcudf++
+- PR #4171 Split java host and device vectors to make a vector truly immutable
+- PR #4167 Port `search` to libcudf++ (support multi-column searchsorted)
+- PR #4163 Assert Dask CUDA serializers have `Buffer` frames
+- PR #4165 List serializable classes once
+- PR #4168 IO readers: do not create null mask for non-nullable columns
+- PR #4177 Use `uint8` type for host array copy of `Buffer`
+- PR #4183 Update Google Test Execution
+- PR #4182 Rename cuDF serialize functions to be more generic
+- PR #4176 Add option to parallelize setup.py's cythonize
+- PR #4191 Porting sort.pyx to use new libcudf APIs
+- PR #4196 reduce CHANGELOG.md merge conflicts
+- PR #4197 Added notebook testing to gpuCI gpu build
+- PR #4220 Port strings wrap functionality.
+- PR #4204 Port nvtext create-ngrams function
+- PR #4219 Port dlpack.pyx to use new libcudf APIs
+- PR #4225 Remove stale notebooks
+- PR #4233 Porting replace.pyx to use new libcudf APIs
+- PR #4223 Fix a few of the Cython warnings
+- PR #4224 Optimize concatenate for many columns
+- PR #4234 Add BUILD_LEGACY_TESTS cmake option
+- PR #4231 Support for custom cuIO data_sink classes.
+- PR #4251 Add class to docs in `dask-cudf` `derived_from`
+- PR #4261 libxx Cython reorganization
+- PR #4274 Support negative position values in slice_strings
+- PR #4282 Porting nvstrings conversion functions from new libcudf++ to Python/Cython
+- PR #4290 Port Parquet to use new libcudf APIs
+- PR #4299 Convert cudf::shift to column-based api
+- PR #4301 Add support for writing large ORC files in a chunked manner
+- PR #4306 Use libcudf++ `unary.pyx` cast instead of legacy cast
+- PR #4295 Port reduce.pyx to libcudf++ API
+- PR #4305 Move gpuarrow.pyx and related libarrow_cuda files into `_libxx`
+- PR #4244 Port nvstrings Substring Gather/Scatter functions to cuDF Python/Cython
+- PR #4280 Port nvstrings Numeric Handling functions to cuDF Python/Cython
+- PR #4278 Port filling.pyx to libcudf++ API
+- PR #4328 Add memory threshold callbacks for Java RMM event handler
+- PR #4336 Move a bunch of internal nvstrings code to use native StringColumns
+- PR #4166 Port `is_sorted.pyx` to use libcudf++ APIs
+- PR #4351 Remove a bunch of internal usage of Numba; set rmm as cupy allocator
+- PR #4333 nvstrings case/capitalization cython bindings
+- PR #4345 Removed an undesirable backwards include from /include to /src in cuIO writers.hpp
+- PR #4367 Port copying.pyx to use new libcudf
+- PR #4362 Move pq_chunked_state struct into it's own header to match how orc writer is doing it.
+- PR #4339 Port libcudf strings `wrap` api to cython/python
+- PR #4236 Update dask_cudf.io.to_parquet to use cudf to_parquet
+- PR #4311 Port nvstrings String Manipulations functions to cuDF Python/Cython
+- PR #4373 Port nvstrings Regular Expressions functions to cuDF Python/Cython
+- PR #4308 Replace dask_cudf sort_values and improve set_index
+- PR #4407 Enable `.str.slice` & `.str.get` and `.str.zfill` unit-tests
+- PR #4412 Require Dask + Distributed 2.12.0+
+- PR #4377 Support loading avro files that contain nested arrays
+- PR #4436 Enable `.str.cat` and fix `.str.split` on python side
+- PR #4405 Port nvstrings (Sub)string Comparisons functions to cuDF Python/Cython
+- PR #4316 Add Java and JNI bindings for substring expression
+- PR #4314 Add Java and JNI bindings for string contains
+- PR #4461 Port nvstrings Miscellaneous functions to cuDF Python/Cython
+- PR #4495 Port nvtext to cuDF Python/Cython
+- PR #4503 Port binaryop.pyx to libcudf++ API
+- PR #4499 Adding changes to handle include `keep_index` and `RangeIndex`
+- PR #4533 Import `tlz` for optional `cytoolz` support
+- PR #4493 Skip legacy testing in CI
+- PR #4346 Port groupby Cython/Python to use libcudf++ API
+- PR #4524 Updating `__setitem__` for DataFrame to use scalar scatter
+- PR #4611 Fix to use direct slicing in iloc for multiindex than using gather under `_get_row_major`
+- PR #4534 Disable deprecation warnings as errors.
+- PR #4542 Remove RMM init/finalize in cudf test fixture.
+- PR #4506 Check for multi-dimensional data in column/Series creation
+- PR #4549 Add option to disable deprecation warnings.
+- PR #4516 Add negative value support for `.str.get`
+- PR #4563 Remove copying to host for metadata generation in `generate_pandas_metadata`
+- PR #4554 Removed raw RMM allocation from `column_device_view`
+- PR #4619 Remove usage of `nvstrings` in `data_array_view`
+- PR #4654 Upgrade version of `numba` required to `>=0.48.0`
+- PR #4035 Port NVText tokenize function to libcudf++
+- PR #4042 Port cudf/io/functions.hpp to Cython for use in IO bindings
+- PR #4058 Port hash.pyx to use libcudf++ APIs
+- PR #4133 Mask cleanup and fixes: use `int32` dtype, ensure 64 byte padding, handle offsets
+
+## Bug Fixes
+
+- PR #3888 Drop `ptr=None` from `DeviceBuffer` call
+- PR #3976 Fix string serialization and memory_usage method to be consistent
+- PR #3902 Fix conversion of large size GPU array to dataframe
+- PR #3953 Fix overflow in column_buffer when computing the device buffer size
+- PR #3959 Add missing hash-dispatch function for cudf.Series
+- PR #3970 Fix for Series Pickle
+- PR #3964 Restore legacy NVStrings and NVCategory dependencies in Java jar
+- PR #3982 Fix java unary op enum and add missing ops
+- PR #3999 Fix issue serializing empty string columns (java)
+- PR #3979 Add `name` to Series serialize and deserialize
+- PR #4005 Fix null mask allocation bug in gather_bitmask
+- PR #4000 Fix dask_cudf sort_values performance for single partitions
+- PR #4007 Fix for copy_bitmask issue with uninitialized device_buffer
+- PR #4037 Fix JNI quantile compile issue
+- PR #4054 Fixed JNI to deal with reduction API changes
+- PR #4052 Fix for round-robin when num_partitions divides nrows.
+- PR #4061 Add NDEBUG guard on `constexpr_assert`.
+- PR #4049 Fix `cudf::split` issue returning one less than expected column vectors
+- PR #4065 Parquet writer: fix for out-of-range dictionary indices
+- PR #4066 Fixed mismatch with dtype enums
+- PR #4078 Fix joins for when column_in_common input parameter is empty
+- PR #4080 Fix multi-index dask test with sort issue
+- PR #4084 Update Java for removal of CATEGORY type
+- PR #4086 ORC reader: fix potentially incorrect timestamp decoding in the last rowgroup
+- PR #4089 Fix dask groupby mutliindex test case issues in join
+- PR #4097 Fix strings concatenate logic with column offsets
+- PR #4076 All null string entries should have null data buffer
+- PR #4109 Use rmm::device_vector instead of thrust::device_vector
+- PR #4113 Use `.nvstrings` in `StringColumn.sum(...)`
+- PR #4116 Fix a bug in contiguous_split() where tables with mixed column types could corrupt string output
+- PR #4125 Fix type enum to account for added Dictionary type in `types.hpp`
+- PR #4132 Fix `hash_partition` null mask allocation
+- PR #4137 Update Java for mutating fill and rolling window changes
+- PR #4184 Add missing except+ to Cython bindings
+- PR #4141 Fix NVStrings test_convert failure in 10.2 build
+- PR #4156 Make fill/copy_range no-op on empty columns
+- PR #4158 Fix merge issue with empty table return if one of the two tables are empty
+- PR #4162 Properly handle no index metadata generation for to_parquet
+- PR #4175 Fix `__sizeof__` calculation in `StringColumn`
+- PR #4155 Update groupby group_offsets size and fix unnecessary device dispatch.
+- PR #4186 Fix from_timestamps 12-hour specifiers support
+- PR #4198 Fix constructing `RangeIndex` from `range`
+- PR #4192 Parquet writer: fix OOB read when computing string hash
+- PR #4201 Fix java window tests
+- PR #4199 Fix potential race condition in memcpy_block
+- PR #4221 Fix series dict alignment to not drop index name
+- PR #4218 Fix `get_aggregation` definition with `except *`
+- PR #4215 Fix performance regression in strings::detail::concatenate
+- PR #4214 Alter ValueError exception for GPU accelerated Parquet writer to properly report `categorical` columns are not supported.
+- PR #4232 Fix handling empty tuples of children in string columns
+- PR #4222 Fix no-return compile error in binop-null-test
+- PR #4242 Fix for rolling tests CI failure
+- PR #4245 Fix race condition in parquet reader
+- PR #4253 Fix dictionary decode and set_keys with column offset
+- PR #4258 Fix dask-cudf losing index name in `reset_index`
+- PR #4268 Fix java build for hash aggregate
+- PR #4275 Fix bug in searching nullable values in non-nullable search space in `upper_bound`
+- PR #4273 Fix losing `StringIndex` name in dask `_meta_nonempty`
+- PR #4279 Fix converting `np.float64` to Scalar
+- PR #4285 Add init files for cython pkgs and fix `setup.py`
+- PR #4287 Parquet reader: fix empty string potentially read as null
+- PR #4310 Fix empty values case in groupby
+- PR #4297 Fix specification of package_data in setup.py
+- PR #4302 Fix `_is_local_filesystem` check
+- PR #4303 Parquet reader: fix empty columns missing from table
+- PR #4317 Fix fill() when using string_scalar with an empty string
+- PR #4324 Fix slice_strings for out-of-range start position value
+- PR #4115 Serialize an empty column table with non zero rows
+- PR #4327 Preemptive dispatch fix for changes in dask#5973
+- PR #4379 Correct regex reclass count variable to number of pairs instead of the number of literals
+- PR #4364 Fix libcudf zfill strings to ignore '+/-' chars
+- PR #4358 Fix strings::concat where narep is an empty string
+- PR #4369 Fix race condition in gpuinflate
+- PR #4390 Disable ScatterValid and ScatterNull legacy tests
+- PR #4399 Make scalar destructor virtual.
+- PR #4398 Fixes the failure in groupby in MIN/MAX on strings when some groups are empty
+- PR #4406 Fix sorted merge issue with null values and ascending=False
+- PR #4445 Fix string issue for parquet reader and support `keep_index` for `scatter_to_tables`
+- PR #4423 Tighten up Dask serialization checks
+- PR #4537 Use `elif` in Dask deserialize check
+- PR #4682 Include frame lengths in Dask serialized header
+- PR #4438 Fix repl-template error for replace_with_backrefs
+- PR #4434 Fix join_strings logic with all-null strings and non-null narep
+- PR #4465 Fix use_pandas_index having no effect in libcudf++ parquet reader
+- PR #4464 Update Cmake to always link in libnvToolsExt
+- PR #4467 Fix dropna issue for a DataFrame having np.nan
+- PR #4480 Fix string_scalar.value to return an empty string_view for empty string-scalar
+- PR #4474 Fix to not materialize RangeIndex in copy_categories
+- PR #4496 Skip tests which require 2+ GPUs
+- PR #4494 Update Java memory event handler for new RMM resource API
+- PR #4505 Fix 0 length buffers during serialization
+- PR #4482 Fix `.str.rsplit`, `.str.split`, `.str.find`, `.str.rfind`, `.str.index`, `.str.rindex` and enable related tests
+- PR #4513 Backport scalar virtual destructor fix
+- PR #4519 Remove `n` validation for `nlargest` & `nsmallest` and add negative support for `n`
+- PR #4596 Fix `_popn` issue with performance
+- PR #4526 Fix index slicing issue for index in case of an empty dataframe
+- PR #4538 Fix cudf::strings::slice_strings(step=-1) for empty strings
+- PR #4557 Disable compile-errors on deprecation warnings, for JNI
+- PR #4669 Fix `dask_cudf` categorical nonempty meta handling
+- PR #4576 Fix typo in `serialize.py`
+- PR #4571 Load JNI native dependencies for Scalar class
+- PR #4598 Fix to handle `pd.DataFrame` in `DataFrame.__init__`
+- PR #4594 Fix exec dangling pointer issue in legacy groupby
+- PR #4591 Fix issue when reading consecutive rowgroups
+- PR #4600 Fix missing include in benchmark_fixture.hpp
+- PR #4588 Fix ordering issue in `MultiIndex`
+- PR #4632 Fix handling of empty inputs to concatenate
+- PR #4630 Remove dangling reference to RMM exec policy in drop duplicates tests.
+- PR #4625 Fix hash-based repartition bug in dask_cudf
+- PR #4662 Fix to handle `keep_index` in `partition_by_hash`
+- PR #4683 Fix Slicing issue with categorical column in DataFrame
+- PR #4676 Fix bug in `_shuffle_group` for repartition
+- PR #4681 Fix `test_repr` tests that were generating a `RangeIndex` for column names
+- PR #4729 Fix `fsspec` versioning to prevent dask test failures
+- PR #4145 Support empty index case in DataFrame._from_table
+- PR #4108 Fix dtype bugs in dask_cudf metadata (metadata_nonempty overhaul)
+- PR #4138 Really fix strings concatenate logic with column offsets
+- PR #4119 Fix binary ops slowdown using jitify -remove-unused-globals
+
+
+# cuDF 0.12.0 (04 Feb 2020)
+
+## New Features
+
+- PR #3759 Updated 10 Minutes with clarification on how `dask_cudf` uses `cudf` API
+- PR #3224 Define and implement new join APIs.
+- PR #3284 Add gpu-accelerated parquet writer
+- PR #3254 Python redesign for libcudf++
+- PR #3336 Add `from_dlpack` and `to_dlpack`
+- PR #3555 Add column names support to libcudf++ io readers and writers
+- PR #3527 Add string functionality for merge API
+- PR #3610 Add memory_usage to DataFrame and Series APIs
+- PR #3557 Add contiguous_split() function.
+- PR #3619 Support CuPy 7
+- PR #3604 Add nvtext ngrams-tokenize function
+- PR #3403 Define and implement new stack + tile APIs
+- PR #3627 Adding cudf::sort and cudf::sort_by_key
+- PR #3597 Implement new sort based groupby
+- PR #3776 Add column equivalence comparator (using epsilon for float equality)
+- PR #3667 Define and implement round-robin partition API.
+- PR #3690 Add bools_to_mask
+- PR #3761 Introduce a Frame class and make Index, DataFrame and Series subclasses
+- PR #3538 Define and implement left semi join and left anti join
+- PR #3683 Added support for multiple delimiters in `nvtext.token_count()`
+- PR #3792 Adding is_nan and is_notnan
+- PR #3594 Adding clamp support to libcudf++
+
+## Improvements
+
+- PR #3124 Add support for grand-children in cudf column classes
+- PR #3292 Port NVStrings regex contains function
+- PR #3409 Port NVStrings regex replace function
+- PR #3417 Port NVStrings regex findall function
+- PR #3351 Add warning when filepath resolves to multiple files in cudf readers
+- PR #3370 Port NVStrings strip functions
+- PR #3453 Port NVStrings IPv4 convert functions to cudf strings column
+- PR #3441 Port NVStrings url encode/decode to cudf strings column
+- PR #3364 Port NVStrings split functions
+- PR #3463 Port NVStrings partition/rpartition to cudf strings column
+- PR #3502 ORC reader: add option to read DECIMALs as INT64
+- PR #3461 Add a new overload to allocate_like() that takes explicit type and size params.
+- PR #3590 Specialize hash functions for floating point
+- PR #3569 Use `np.asarray` in `StringColumn.deserialize`
+- PR #3553 Support Python NoneType in numeric binops
+- PR #3511 Support DataFrame / Series mixed arithmetic
+- PR #3567 Include `strides` in `__cuda_array_interface__`
+- PR #3608 Update OPS codeowner group name
+- PR #3431 Port NVStrings translate to cudf strings column
+- PR #3507 Define and implement new binary operation APIs
+- PR #3620 Add stream parameter to unary ops detail API
+- PR #3593 Adding begin/end for mutable_column_device_view
+- PR #3587 Merge CHECK_STREAM & CUDA_CHECK_LAST to CHECK_CUDA
+- PR #3733 Rework `hash_partition` API
+- PR #3655 Use move with make_pair to avoid copy construction
+- PR #3402 Define and implement new quantiles APIs
+- PR #3612 Add ability to customize the JIT kernel cache path
+- PR #3647 Remove PatchedNumbaDeviceArray with CuPy 6.6.0
+- PR #3641 Remove duplicate definitions of CUDA_DEVICE_CALLABLE
+- PR #3640 Enable memory_usage in dask_cudf (also adds pd.Index from_pandas)
+- PR #3654 Update Jitify submodule ref to include gcc-8 fix
+- PR #3639 Define and implement `nans_to_nulls`
+- PR #3561 Rework contains implementation in search
+- PR #3616 Add aggregation infrastructure for argmax/argmin.
+- PR #3673 Parquet reader: improve rounding of timestamp conversion to seconds
+- PR #3699 Stringify libcudacxx headers for binary op JIT
+- PR #3697 Improve column insert performance for wide frames
+- PR #3653 Make `gather_bitmask_kernel` more reusable.
+- PR #3710 Remove multiple CMake configuration steps from root build script
+- PR #3657 Define and implement compiled binops for string column comparisons
+- PR #3520 Change read_parquet defaults and add warnings
+- PR #3780 Java APIs for selecting a GPU
+- PR #3796 Improve on round-robin with the case when number partitions greater than number of rows.
+- PR #3805 Avoid CuPy 7.1.0 for now
+- PR #3758 detail::scatter variant with map iterator support
+- PR #3882 Fail loudly when creating a StringColumn from nvstrings with > MAX_VAL(int32) bytes
+- PR #3823 Add header file for detail search functions
+- PR #2438 Build GBench Benchmarks in CI
+- PR #3713 Adding aggregation support to rolling_window
+- PR #3875 Add abstract sink for IO writers, used by ORC and Parquet writers for now
+- PR #3916 Refactor gather bindings
+
+## Bug Fixes
+
+- PR #3618 Update 10 minutes to cudf and cupy to hide warning that were being shown in the docs
+- PR #3550 Update Java package to 0.12
+- PR #3549 Fix index name issue with iloc with RangeIndex
+- PR #3562 Fix 4GB limit for gzipped-compressed csv files
+- PR #2981 enable build.sh to build all targets without installation
+- PR #3563 Use `__cuda_array_interface__` for serialization
+- PR #3564 Fix cuda memory access error in gather_bitmask_kernel
+- PR #3548 Replaced CUDA_RT_CALL with CUDA_TRY
+- PR #3486 Pandas > 0.25 compatibility
+- PR #3622 Fix new warnings and errors when building with gcc-8
+- PR #3588 Remove avro reader column order reversal
+- PR #3629 Fix hash map test failure
+- PR #3637 Fix sorted set_index operations in dask_cudf
+- PR #3663 Fix libcudf++ ORC reader microseconds and milliseconds conversion
+- PR #3668 Fixing CHECK_CUDA debug build issue
+- PR #3684 Fix ends_with logic for matching string case
+- PR #3691 Fix create_offsets to handle offset correctly
+- PR #3687 Fixed bug while passing input GPU memory pointer in `nvtext.scatter_count()`
+- PR #3701 Fix hash_partition hashing all columns instead of columns_to_hash
+- PR #3694 Allow for null columns parameter in `csv_writer`
+- PR #3706 Removed extra type-dispatcher call from merge
+- PR #3704 Changed the default delimiter to `whitespace` for nvtext methods.
+- PR #3741 Construct DataFrame from dict-of-Series with alignment
+- PR #3724 Update rmm version to match release
+- PR #3743 Fix for `None` data in `__array_interface__`
+- PR #3731 Fix performance of zero sized dataframe slice
+- PR #3709 Fix inner_join incorrect result issue
+- PR #3734 Update numba to 0.46 in conda files
+- PR #3738 Update libxx cython types.hpp path
+- PR #3672 Fix to_host issue with column_view having offset
+- PR #3730 CSV reader: Set invalid float values to NaN/null
+- PR #3670 Floor when casting between timestamps of different precisions
+- PR #3728 Fix apply_boolean_mask issue with non-null string column
+- PR #3769 Don't look for a `name` attribute in column
+- PR #3783 Bind cuDF operators to Dask Dataframe
+- PR #3775 Fix segfault when reading compressed CSV files larger than 4GB
+- PR #3799 Align indices of Series inputs when adding as columns to DataFrame
+- PR #3803 Keep name when unpickling Index objects
+- PR #3804 Fix cuda crash in AVRO reader
+- PR #3766 Remove references to cudf::type_id::CATEGORY from IO code
+- PR #3817 Don't always deepcopy an index
+- PR #3821 Fix OOB read in gpuinflate prefetcher
+- PR #3829 Parquet writer: fix empty dataframe causing cuda launch errors
+- PR #3835 Fix memory leak in Cython when dealing with nulls in string columns
+- PR #3866 Remove unnecessary if check in NVStrings.create_offsets
+- PR #3858 Fixes the broken debug build after #3728
+- PR #3850 Fix merge typecast scope issue and resulting memory leak
+- PR #3855 Fix MultiColumn recreation with reset_index
+- PR #3869 Fixed size calculation in NVStrings::byte_count()
+- PR #3868 Fix apply_grouped moving average example
+- PR #3900 Properly link `NVStrings` and `NVCategory` into tests
+- PR #3868 Fix apply_grouped moving average example
+- PR #3871 Fix `split_out` error
+- PR #3886 Fix string column materialization from column view
+- PR #3893 Parquet reader: fix segfault reading empty parquet file
+- PR #3931 Dask-cudf groupby `.agg` multicolumn handling fix
+- PR #4017 Fix memory leaks in `GDF_STRING` cython handling and `nans_to_nulls` cython
+
+
+# cuDF 0.11.0 (11 Dec 2019)
+
+## New Features
+
+- PR #2905 Added `Series.median()` and null support for `Series.quantile()`
+- PR #2930 JSON Reader: Support ARROW_RANDOM_FILE input
+- PR #2956 Add `cudf::stack` and `cudf::tile`
+- PR #2980 Added nvtext is_vowel/is_consonant functions
+- PR #2987 Add `inplace` arg to `DataFrame.reset_index` and `Series`
+- PR #3011 Added libcudf++ transition guide
+- PR #3129 Add strings column factory from `std::vector`s
+- PR #3054 Add parquet reader support for decimal data types
+- PR #3022 adds DataFrame.astype for cuDF dataframes
+- PR #2962 Add isnull(), notnull() and related functions
+- PR #3025 Move search files to legacy
+- PR #3068 Add `scalar` class
+- PR #3094 Adding `any` and `all` support from libcudf
+- PR #3130 Define and implement new `column_wrapper`
+- PR #3143 Define and implement new copying APIs `slice` and `split`
+- PR #3161 Move merge files to legacy
+- PR #3079 Added support to write ORC files given a local path
+- PR #3192 Add dtype param to cast `DataFrame` on init
+- PR #3213 Port cuIO to libcudf++
+- PR #3222 Add nvtext character tokenizer
+- PR #3223 Java expose underlying buffers
+- PR #3300 Add `DataFrame.insert`
+- PR #3263 Define and implement new `valid_if`
+- PR #3278 Add `to_host` utility to copy `column_view` to host
+- PR #3087 Add new cudf::experimental bool8 wrapper
+- PR #3219 Construct column from column_view
+- PR #3250 Define and implement new merge APIs
+- PR #3144 Define and implement new hashing APIs `hash` and `hash_partition`
+- PR #3229 Define and implement new search APIs
+- PR #3308 java add API for memory usage callbacks
+- PR #2691 Row-wise reduction and scan operations via CuPy
+- PR #3291 Add normalize_nans_and_zeros
+- PR #3187 Define and implement new replace APIs
+- PR #3356 Add vertical concatenation for table/columns
+- PR #3344 java split API
+- PR #2791 Add `groupby.std()`
+- PR #3368 Enable dropna argument in dask_cudf groupby
+- PR #3298 add null replacement iterator for column_device_view
+- PR #3297 Define and implement new groupby API.
+- PR #3396 Update device_atomics with new bool8 and timestamp specializations
+- PR #3411 Java host memory management API
+- PR #3393 Implement df.cov and enable covariance/correlation in dask_cudf
+- PR #3401 Add dask_cudf ORC writer (to_orc)
+- PR #3331 Add copy_if_else
+- PR #3427 Define and Implement new multi-search API
+- PR #3442 Add Bool-index + Multi column + DataFrame support for set-item
+- PR #3172 Define and implement new fill/repeat/copy_range APIs
+- PR #3490 Add pair iterators for columns
+- PR #3497 Add DataFrame.drop(..., inplace=False) argument
+- PR #3469 Add string functionality for replace API
+- PR #3273 Define and implement new reduction APIs
+
+## Improvements
+
+- PR #2904 Move gpu decompressors to cudf::io namespace
+- PR #2977 Moved old C++ test utilities to legacy directory.
+- PR #2965 Fix slow orc reader perf with large uncompressed blocks
+- PR #2995 Move JIT type utilities to legacy directory
+- PR #2927 Add ``Table`` and ``TableView`` extension classes that wrap legacy cudf::table
+- PR #3005 Renames `cudf::exp` namespace to `cudf::experimental`
+- PR #3008 Make safe versions of `is_null` and `is_valid` in `column_device_view`
+- PR #3026 Move fill and repeat files to legacy
+- PR #3027 Move copying.hpp and related source to legacy folder
+- PR #3014 Snappy decompression optimizations
+- PR #3032 Use `asarray` to coerce indices to a NumPy array
+- PR #2996 IO Readers: Replace `cuio::device_buffer` with `rmm::device_buffer`
+- PR #3051 Specialized hash function for strings column
+- PR #3065 Select and Concat for cudf::experimental::table
+- PR #3080 Move `valid_if.cuh` to `legacy/`
+- PR #3052 Moved replace.hpp functionality to legacy
+- PR #3091 Move join files to legacy
+- PR #3092 Implicitly init RMM if Java allocates before init
+- PR #3029 Update gdf_ numeric types with stdint and move to cudf namespace
+- PR #3052 Moved replace.hpp functionality to legacy
+- PR #2955 Add cmake option to only build for present GPU architecture
+- PR #3070 Move functions.h and related source to legacy
+- PR #2951 Allow set_index to handle a list of column names
+- PR #3093 Move groupby files to legacy
+- PR #2988 Removing GIS functionality (now part of cuSpatial library)
+- PR #3067 Java method to return size of device memory buffer
+- PR #3083 Improved some binary operation tests to include null testing.
+- PR #3084 Update to arrow-cpp and pyarrow 0.15.0
+- PR #3071 Move cuIO to legacy
+- PR #3126 Round 2 of snappy decompression optimizations
+- PR #3046 Define and implement new copying APIs `empty_like` and `allocate_like`
+- PR #3128 Support MultiIndex in DataFrame.join
+- PR #2971 Added initial gather and scatter methods for strings_column_view
+- PR #3133 Port NVStrings to cudf column: count_characters and count_bytes
+- PR #2991 Added strings column functions concatenate and join_strings
+- PR #3028 Define and implement new `gather` APIs.
+- PR #3135 Add nvtx utilities to cudf::nvtx namespace
+- PR #3021 Java host side concat of serialized buffers
+- PR #3138 Move unary files to legacy
+- PR #3170 Port NVStrings substring functions to cudf strings column
+- PR #3159 Port NVStrings is-chars-types function to cudf strings column
+- PR #3154 Make `table_view_base.column()` const and add `mutable_table_view.column()`
+- PR #3175 Set cmake cuda version variables
+- PR #3171 Move deprecated error macros to legacy
+- PR #3191 Port NVStrings integer convert ops to cudf column
+- PR #3189 Port NVStrings find ops to cudf column
+- PR #3352 Port NVStrings convert float functions to cudf strings column
+- PR #3193 Add cuPy as a formal dependency
+- PR #3195 Support for zero columned `table_view`
+- PR #3165 Java device memory size for string category
+- PR #3205 Move transform files to legacy
+- PR #3202 Rename and move error.hpp to public headers
+- PR #2878 Use upstream merge code in dask_cudf
+- PR #3217 Port NVStrings upper and lower case conversion functions
+- PR #3350 Port NVStrings booleans convert functions
+- PR #3231 Add `column::release()` to give up ownership of contents.
+- PR #3157 Use enum class rather than enum for mask_allocation_policy
+- PR #3232 Port NVStrings datetime conversion to cudf strings column
+- PR #3136 Define and implement new transpose API
+- PR #3237 Define and implement new transform APIs
+- PR #3245 Move binaryop files to legacy
+- PR #3241 Move stream_compaction files to legacy
+- PR #3166 Move reductions to legacy
+- PR #3261 Small cleanup: remove `== true`
+- PR #3271 Update rmm API based on `rmm.reinitialize(...)` change
+- PR #3266 Remove optional checks for CuPy
+- PR #3268 Adding null ordering per column feature when sorting
+- PR #3239 Adding floating point specialization to comparators for NaNs
+- PR #3270 Move predicates files to legacy
+- PR #3281 Add to_host specialization for strings in column test utilities
+- PR #3282 Add `num_bitmask_words`
+- PR #3252 Add new factory methods to include passing an existing null mask
+- PR #3288 Make `bit.cuh` utilities usable from host code.
+- PR #3287 Move rolling windows files to legacy
+- PR #3182 Define and implement new unary APIs `is_null` and `is_not_null`
+- PR #3314 Drop `cython` from run requirements
+- PR #3301 Add tests for empty column wrapper.
+- PR #3294 Update to arrow-cpp and pyarrow 0.15.1
+- PR #3310 Add `row_hasher` and `element_hasher` utilities
+- PR #3272 Support non-default streams when creating/destroying hash maps
+- PR #3286 Clean up the starter code on README
+- PR #3332 Port NVStrings replace to cudf strings column
+- PR #3354 Define and implement new `scatter` APIs
+- PR #3322 Port NVStrings pad operations to cudf strings column
+- PR #3345 Add cache member for number of characters in string_view class
+- PR #3299 Define and implement new `is_sorted` APIs
+- PR #3328 Partition by stripes in dask_cudf ORC reader
+- PR #3243 Use upstream join code in dask_cudf
+- PR #3371 Add `select` method to `table_view`
+- PR #3309 Add java and JNI bindings for search bounds
+- PR #3305 Define and implement new rolling window APIs
+- PR #3380 Concatenate columns of strings
+- PR #3382 Add fill function for strings column
+- PR #3391 Move device_atomics_tests.cu files to legacy
+- PR #3303 Define and implement new stream compaction APIs `copy_if`, `drop_nulls`,
+           `apply_boolean_mask`, `drop_duplicate` and `unique_count`.
+- PR #3387 Strings column gather function
+- PR #3440 Strings column scatter function
+- PR #3389 Move quantiles.hpp + group_quantiles.hpp files to legacy
+- PR #3397 Port unary cast to libcudf++
+- PR #3398 Move reshape.hpp files to legacy
+- PR #3395 Port NVStrings regex extract to cudf strings column
+- PR #3423 Port NVStrings htoi to cudf strings column
+- PR #3425 Strings column copy_if_else implementation
+- PR #3422 Move utilities to legacy
+- PR #3201 Define and implement new datetime_ops APIs
+- PR #3421 Port NVStrings find_multiple to cudf strings column
+- PR #3448 Port scatter_to_tables to libcudf++
+- PR #3458 Update strings sections in the transition guide
+- PR #3462 Add `make_empty_column` and update `empty_like`.
+- PR #3465 Port `aggregation` traits and utilities.
+- PR #3214 Define and implement new unary operations APIs
+- PR #3475 Add `bitmask_to_host` column utility
+- PR #3487 Add is_boolean trait and random timestamp generator for testing
+- PR #3492 Small cleanup (remove std::abs) and comment
+- PR #3407 Allow multiple row-groups per task in dask_cudf read_parquet
+- PR #3512 Remove unused CUDA conda labels
+- PR #3500 cudf::fill()/cudf::repeat() support for strings columns.
+- PR #3438 Update scalar and scalar_device_view to better support strings
+- PR #3414 Add copy_range function for strings column
+- PR #3685 Add string support to contiguous_split.
+- PR #3471 Add scalar/column, column/scalar and scalar/scalar overloads to copy_if_else.
+- PR #3451 Add support for implicit typecasting of join columns
+
+## Bug Fixes
+
+- PR #2895 Fixed dask_cudf group_split behavior to handle upstream rearrange_by_divisions
+- PR #3048 Support for zero columned tables
+- PR #3030 Fix snappy decoding regression in PR #3014
+- PR #3041 Fixed exp to experimental namespace name change issue
+- PR #3056 Add additional cmake hint for finding local build of RMM files
+- PR #3060 Move copying.hpp includes to legacy
+- PR #3139 Fixed java RMM auto initialization
+- PR #3141 Java fix for relocated IO headers
+- PR #3149 Rename column_wrapper.cuh to column_wrapper.hpp
+- PR #3168 Fix mutable_column_device_view head const_cast
+- PR #3199 Update JNI includes for legacy moves
+- PR #3204 ORC writer: Fix ByteRLE encoding of NULLs
+- PR #2994 Fix split_out-support but with hash_object_dispatch
+- PR #3212 Fix string to date casting when format is not specified
+- PR #3218 Fixes `row_lexicographic_comparator` issue with handling two tables
+- PR #3228 Default initialize RMM when Java native dependencies are loaded
+- PR #3012 replacing instances of `to_gpu_array` with `mem`
+- PR #3236 Fix Numba 0.46+/CuPy 6.3 interface compatibility
+- PR #3276 Update JNI includes for legacy moves
+- PR #3256 Fix orc writer crash with multiple string columns
+- PR #3211 Fix breaking change caused by rapidsai/rmm#167
+- PR #3265 Fix dangling pointer in `is_sorted`
+- PR #3267 ORC writer: fix incorrect ByteRLE encoding of long literal runs
+- PR #3277 Fix invalid reference to deleted temporary in `is_sorted`.
+- PR #3274 ORC writer: fix integer RLEv2 mode2 unsigned base value encoding
+- PR #3279 Fix shutdown hang issues with pinned memory pool init executor
+- PR #3280 Invalid children check in mutable_column_device_view
+- PR #3289 fix java memory usage API for empty columns
+- PR #3293 Fix loading of csv files zipped on MacOS (disabled zip min version check)
+- PR #3295 Fix storing storing invalid RMM exec policies.
+- PR #3307 Add pd.RangeIndex to from_pandas to fix dask_cudf meta_nonempty bug
+- PR #3313 Fix public headers including non-public headers
+- PR #3318 Revert arrow to 0.15.0 temporarily to unblock downstream projects CI
+- PR #3317 Fix index-argument bug in dask_cudf parquet reader
+- PR #3323 Fix `insert` non-assert test case
+- PR #3341 Fix `Series` constructor converting NoneType to "None"
+- PR #3326 Fix and test for detail::gather map iterator type inference
+- PR #3334 Remove zero-size exception check from make_strings_column factories
+- PR #3333 Fix compilation issues with `constexpr` functions not marked `__device__`
+- PR #3340 Make all benchmarks use cudf base fixture to initialize RMM pool
+- PR #3337 Fix Java to pad validity buffers to 64-byte boundary
+- PR #3362 Fix `find_and_replace` upcasting series for python scalars and lists
+- PR #3357 Disabling `column_view` iterators for non fixed-width types
+- PR #3383 Fix : properly compute null counts for rolling_window.
+- PR #3386 Removing external includes from `column_view.hpp`
+- PR #3369 Add write_partition to dask_cudf to fix to_parquet bug
+- PR #3388 Support getitem with bools when DataFrame has a MultiIndex
+- PR #3408 Fix String and Column (De-)Serialization
+- PR #3372 Fix dask-distributed scatter_by_map bug
+- PR #3419 Fix a bug in parse_into_parts (incomplete input causing walking past the end of string).
+- PR #3413 Fix dask_cudf read_csv file-list bug
+- PR #3416 Fix memory leak in ColumnVector when pulling strings off the GPU
+- PR #3424 Fix benchmark build by adding libcudacxx to benchmark's CMakeLists.txt
+- PR #3435 Fix diff and shift for empty series
+- PR #3439 Fix index-name bug in StringColumn concat
+- PR #3445 Fix ORC Writer default stripe size
+- PR #3459 Fix printing of invalid entries
+- PR #3466 Fix gather null mask allocation for invalid index
+- PR #3468 Fix memory leak issue in `drop_duplicates`
+- PR #3474 Fix small doc error in capitalize Docs
+- PR #3491 Fix more doc errors in NVStrings
+- PR #3478 Fix as_index deep copy via Index.rename inplace arg
+- PR #3476 Fix ORC reader timezone conversion
+- PR #3188 Repr slices up large DataFrames
+- PR #3519 Fix strings column concatenate handling zero-sized columns
+- PR #3530 Fix copy_if_else test case fail issue
+- PR #3523 Fix lgenfe issue with debug build
+- PR #3532 Fix potential use-after-free in cudf parquet reader
+- PR #3540 Fix unary_op null_mask bug and add missing test cases
+- PR #3559 Use HighLevelGraph api in DataFrame constructor (Fix upstream compatibility)
+- PR #3572 Fix CI Issue with hypothesis tests that are flaky
+
+
+# cuDF 0.10.0 (16 Oct 2019)
+
+## New Features
+
+- PR #2423 Added `groupby.quantile()`
+- PR #2522 Add Java bindings for NVStrings backed upper and lower case mutators
+- PR #2605 Added Sort based groupby in libcudf
+- PR #2607 Add Java bindings for parsing JSON
+- PR #2629 Add dropna= parameter to groupby
+- PR #2585 ORC & Parquet Readers: Remove millisecond timestamp restriction
+- PR #2507 Add GPU-accelerated ORC Writer
+- PR #2559 Add Series.tolist()
+- PR #2653 Add Java bindings for rolling window operations
+- PR #2480 Merge `custreamz` codebase into `cudf` repo
+- PR #2674 Add __contains__ for Index/Series/Column
+- PR #2635 Add support to read from remote and cloud sources like s3, gcs, hdfs
+- PR #2722 Add Java bindings for NVTX ranges
+- PR #2702 Add make_bool to dataset generation functions
+- PR #2394 Move `rapidsai/custrings` into `cudf`
+- PR #2734 Final sync of custrings source into cudf
+- PR #2724 Add libcudf support for __contains__
+- PR #2777 Add python bindings for porter stemmer measure functionality
+- PR #2781 Add issorted to is_monotonic
+- PR #2685 Add cudf::scatter_to_tables and cython binding
+- PR #2743 Add Java bindings for NVStrings timestamp2long as part of String ColumnVector casting
+- PR #2785 Add nvstrings Python docs
+- PR #2786 Add benchmarks option to root build.sh
+- PR #2802 Add `cudf::repeat()` and `cudf.Series.repeat()`
+- PR #2773 Add Fisher's unbiased kurtosis and skew for Series/DataFrame
+- PR #2748 Parquet Reader: Add option to specify loading of PANDAS index
+- PR #2807 Add scatter_by_map to DataFrame python API
+- PR #2836 Add nvstrings.code_points method
+- PR #2844 Add Series/DataFrame notnull
+- PR #2858 Add GTest type list utilities
+- PR #2870 Add support for grouping by Series of arbitrary length
+- PR #2719 Series covariance and Pearson correlation
+- PR #2207 Beginning of libcudf overhaul: introduce new column and table types
+- PR #2869 Add `cudf.CategoricalDtype`
+- PR #2838 CSV Reader: Support ARROW_RANDOM_FILE input
+- PR #2655 CuPy-based Series and Dataframe .values property
+- PR #2803 Added `edit_distance_matrix()` function to calculate pairwise edit distance for each string on a given nvstrings object.
+- PR #2811 Start of cudf strings column work based on 2207
+- PR #2872 Add Java pinned memory pool allocator
+- PR #2969 Add findAndReplaceAll to ColumnVector
+- PR #2814 Add Datetimeindex.weekday
+- PR #2999 Add timestamp conversion support for string categories
+- PR #2918 Add cudf::column timestamp wrapper types
+
+## Improvements
+
+- PR #2578 Update legacy_groupby to use libcudf group_by_without_aggregation
+- PR #2581 Removed `managed` allocator from hash map classes.
+- PR #2571 Remove unnecessary managed memory from gdf_column_concat
+- PR #2648 Cython/Python reorg
+- PR #2588 Update Series.append documentation
+- PR #2632 Replace dask-cudf set_index code with upstream
+- PR #2682 Add cudf.set_allocator() function for easier allocator init
+- PR #2642 Improve null printing and testing
+- PR #2747 Add missing Cython headers / cudftestutil lib to conda package for cuspatial build
+- PR #2706 Compute CSV format in device code to speedup performance
+- PR #2673 Add support for np.longlong type
+- PR #2703 move dask serialization dispatch into cudf
+- PR #2728 Add YYMMDD to version tag for nightly conda packages
+- PR #2729 Handle file-handle input in to_csv
+- PR #2741 CSV Reader: Move kernel functions into its own file
+- PR #2766 Improve nvstrings python cmake flexibility
+- PR #2756 Add out_time_unit option to csv reader, support timestamp resolutions
+- PR #2771 Stopgap alias for to_gpu_matrix()
+- PR #2783 Support mapping input columns to function arguments in apply kernels
+- PR #2645 libcudf unique_count for Series.nunique
+- PR #2817 Dask-cudf: `read_parquet` support for remote filesystems
+- PR #2823 improve java data movement debugging
+- PR #2806 CSV Reader: Clean-up row offset operations
+- PR #2640 Add dask wait/persist example to 10 minute guide
+- PR #2828 Optimizations of kernel launch configuration for `DataFrame.apply_rows` and `DataFrame.apply_chunks`
+- PR #2831 Add `column` argument to `DataFrame.drop`
+- PR #2775 Various optimizations to improve __getitem__ and __setitem__ performance
+- PR #2810 cudf::allocate_like can optionally always allocate a mask.
+- PR #2833 Parquet reader: align page data allocation sizes to 4-bytes to satisfy cuda-memcheck
+- PR #2832 Using the new Python bindings for UCX
+- PR #2856 Update group_split_cudf to use scatter_by_map
+- PR #2890 Optionally keep serialized table data on the host.
+- PR #2778 Doc: Updated and fixed some docstrings that were formatted incorrectly.
+- PR #2830 Use YYMMDD tag in custreamz nightly build
+- PR #2875 Java: Remove synchronized from register methods in MemoryCleaner
+- PR #2887 Minor snappy decompression optimization
+- PR #2899 Use new RMM API based on Cython
+- PR #2788 Guide to Python UDFs
+- PR #2919 Change java API to use operators in groupby namespace
+- PR #2909 CSV Reader: Avoid row offsets host vector default init
+- PR #2834 DataFrame supports setting columns via attribute syntax `df.x = col`
+- PR #3147 DataFrame can be initialized from rows via list of tuples
+- PR #3539 Restrict CuPy to 6
+
+## Bug Fixes
+
+- PR #2584 ORC Reader: fix parsing of `DECIMAL` index positions
+- PR #2619 Fix groupby serialization/deserialization
+- PR #2614 Update Java version to match
+- PR #2601 Fixes nlargest(1) issue in Series and Dataframe
+- PR #2610 Fix a bug in index serialization (properly pass DeviceNDArray)
+- PR #2621 Fixes the floordiv issue of not promoting float type when rhs is 0
+- PR #2611 Types Test: fix static casting from negative int to string
+- PR #2618 IO Readers: Fix datasource memory map failure for multiple reads
+- PR #2628 groupby_without_aggregation non-nullable input table produces non-nullable output
+- PR #2615 fix string category partitioning in java API
+- PR #2641 fix string category and timeunit concat in the java API
+- PR #2649 Fix groupby issue resulting from column_empty bug
+- PR #2658 Fix astype() for null categorical columns
+- PR #2660 fix column string category and timeunit concat in the java API
+- PR #2664 ORC reader: fix `skip_rows` larger than first stripe
+- PR #2654 Allow Java gdfOrderBy to work with string categories
+- PR #2669 AVRO reader: fix non-deterministic output
+- PR #2668 Update Java bindings to specify timestamp units for ORC and Parquet readers
+- PR #2679 AVRO reader: fix cuda errors when decoding compressed streams
+- PR #2692 Add concatenation for data-frame with different headers (empty and non-empty)
+- PR #2651 Remove nvidia driver installation from ci/cpu/build.sh
+- PR #2697 Ensure csv reader sets datetime column time units
+- PR #2698 Return RangeIndex from contiguous slice of RangeIndex
+- PR #2672 Fix null and integer handling in round
+- PR #2704 Parquet Reader: Fix crash when loading string column with nulls
+- PR #2725 Fix Jitify issue with running on Turing using CUDA version < 10
+- PR #2731 Fix building of benchmarks
+- PR #2738 Fix java to find new NVStrings locations
+- PR #2736 Pin Jitify branch to v0.10 version
+- PR #2742 IO Readers: Fix possible silent failures when creating `NvStrings` instance
+- PR #2753 Fix java quantile API calls
+- PR #2762 Fix validity processing for time in java
+- PR #2796 Fix handling string slicing and other nvstrings delegated methods with dask
+- PR #2769 Fix link to API docs in README.md
+- PR #2772 Handle multiindex pandas Series #2772
+- PR #2749 Fix apply_rows/apply_chunks pessimistic null mask to use in_cols null masks only
+- PR #2752 CSV Reader: Fix exception when there's no rows to process
+- PR #2716 Added Exception for `StringMethods` in string methods
+- PR #2787 Fix Broadcasting `None` to `cudf-series`
+- PR #2794 Fix async race in NVCategory::get_value and get_value_bounds
+- PR #2795 Fix java build/cast error
+- PR #2496 Fix improper merge of two dataframes when names differ
+- PR #2824 Fix issue with incorrect result when Numeric Series replace is called several times
+- PR #2751 Replace value with null
+- PR #2765 Fix Java inequality comparisons for string category
+- PR #2818 Fix java join API to use new C++ join API
+- PR #2841 Fix nvstrings.slice and slice_from for range (0,0)
+- PR #2837 Fix join benchmark
+- PR #2809 Add hash_df and group_split dispatch functions for dask
+- PR #2843 Parquet reader: fix skip_rows when not aligned with page or row_group boundaries
+- PR #2851 Deleted existing dask-cudf/record.txt
+- PR #2854 Fix column creation from ephemeral objects exposing __cuda_array_interface__
+- PR #2860 Fix boolean indexing when the result is a single row
+- PR #2859 Fix tail method issue for string columns
+- PR #2852 Fixed `cumsum()` and `cumprod()` on boolean series.
+- PR #2865 DaskIO: Fix `read_csv` and `read_orc` when input is list of files
+- PR #2750 Fixed casting values to cudf::bool8 so non-zero values always cast to true
+- PR #2873 Fixed dask_cudf read_partition bug by generating ParquetDatasetPiece
+- PR #2850 Fixes dask_cudf.read_parquet on partitioned datasets
+- PR #2896 Properly handle `axis` string keywords in `concat`
+- PR #2926 Update rounding algorithm to avoid using fmod
+- PR #2968 Fix Java dependency loading when using NVTX
+- PR #2963 Fix ORC writer uncompressed block indexing
+- PR #2928 CSV Reader: Fix using `byte_range` for large datasets
+- PR #2983 Fix sm_70+ race condition in gpu_unsnap
+- PR #2964 ORC Writer: Segfault when writing mixed numeric and string columns
+- PR #3007 Java: Remove unit test that frees RMM invalid pointer
+- PR #3009 Fix orc reader RLEv2 patch position regression from PR #2507
+- PR #3002 Fix CUDA invalid configuration errors reported after loading an ORC file without data
+- PR #3035 Update update-version.sh for new docs locations
+- PR #3038 Fix uninitialized stream parameter in device_table deleter
+- PR #3064 Fixes groupby performance issue
+- PR #3061 Add rmmInitialize to nvstrings gtests
+- PR #3058 Fix UDF doc markdown formatting
+- PR #3059 Add nvstrings python build instructions to contributing.md
+
+
+# cuDF 0.9.0 (21 Aug 2019)
+
+## New Features
+
+- PR #1993 Add CUDA-accelerated series aggregations: mean, var, std
+- PR #2111 IO Readers: Support memory buffer, file-like object, and URL inputs
+- PR #2012 Add `reindex()` to DataFrame and Series
+- PR #2097 Add GPU-accelerated AVRO reader
+- PR #2098 Support binary ops on DFs and Series with mismatched indices
+- PR #2160 Merge `dask-cudf` codebase into `cudf` repo
+- PR #2149 CSV Reader: Add `hex` dtype for explicit hexadecimal parsing
+- PR #2156 Add `upper_bound()` and `lower_bound()` for libcudf tables and `searchsorted()` for cuDF Series
+- PR #2158 CSV Reader: Support single, non-list/dict argument for `dtype`
+- PR #2177 CSV Reader: Add `parse_dates` parameter for explicit date inference
+- PR #1744 cudf::apply_boolean_mask and cudf::drop_nulls support for cudf::table inputs (multi-column)
+- PR #2196 Add `DataFrame.dropna()`
+- PR #2197 CSV Writer: add `chunksize` parameter for `to_csv`
+- PR #2215 `type_dispatcher` benchmark
+- PR #2179 Add Java quantiles
+- PR #2157 Add __array_function__ to DataFrame and Series
+- PR #2212 Java support for ORC reader
+- PR #2224 Add DataFrame isna, isnull, notna functions
+- PR #2236 Add Series.drop_duplicates
+- PR #2105 Add hash-based join benchmark
+- PR #2316 Add unique, nunique, and value_counts for datetime columns
+- PR #2337 Add Java support for slicing a ColumnVector
+- PR #2049 Add cudf::merge (sorted merge)
+- PR #2368 Full cudf+dask Parquet Support
+- PR #2380 New cudf::is_sorted checks whether cudf::table is sorted
+- PR #2356 Java column vector standard deviation support
+- PR #2221 MultiIndex full indexing - Support iloc and wildcards for loc
+- PR #2429 Java support for getting length of strings in a ColumnVector
+- PR #2415 Add `value_counts` for series of any type
+- PR #2446 Add __array_function__ for index
+- PR #2437 ORC reader: Add 'use_np_dtypes' option
+- PR #2382 Add CategoricalAccessor add, remove, rename, and ordering methods
+- PR #2464 Native implement `__cuda_array_interface__` for Series/Index/Column objects
+- PR #2425 Rolling window now accepts array-based user-defined functions
+- PR #2442 Add __setitem__
+- PR #2449 Java support for getting byte count of strings in a ColumnVector
+- PR #2492 Add groupby.size() method
+- PR #2358 Add cudf::nans_to_nulls: convert floating point column into bitmask
+- PR #2489 Add drop argument to set_index
+- PR #2491 Add Java bindings for ORC reader 'use_np_dtypes' option
+- PR #2213 Support s/ms/us/ns DatetimeColumn time unit resolutions
+- PR #2536 Add _constructor properties to Series and DataFrame
+
+## Improvements
+
+- PR #2103 Move old `column` and `bitmask` files into `legacy/` directory
+- PR #2109 added name to Python column classes
+- PR #1947 Cleanup serialization code
+- PR #2125 More aggregate in java API
+- PR #2127 Add in java Scalar tests
+- PR #2088 Refactor of Python groupby code
+- PR #2130 Java serialization and deserialization of tables.
+- PR #2131 Chunk rows logic added to csv_writer
+- PR #2129 Add functions in the Java API to support nullable column filtering
+- PR #2165 made changes to get_dummies api for it to be available in MethodCache
+- PR #2171 Add CodeCov integration, fix doc version, make --skip-tests work when invoking with source
+- PR #2184 handle remote orc files for dask-cudf
+- PR #2186 Add `getitem` and `getattr` style access to Rolling objects
+- PR #2168 Use cudf.Column for CategoricalColumn's categories instead of a tuple
+- PR #2193 DOC: cudf::type_dispatcher documentation for specializing dispatched functors
+- PR #2199 Better java support for appending strings
+- PR #2176 Added column dtype support for datetime, int8, int16 to csv_writer
+- PR #2209 Matching `get_dummies` & `select_dtypes` behavior to pandas
+- PR #2217 Updated Java bindings to use the new groupby API
+- PR #2214 DOC: Update doc instructions to build/install `cudf` and `dask-cudf`
+- PR #2220 Update Java bindings for reduction rename
+- PR #2232 Move CodeCov upload from build script to Jenkins
+- PR #2225 refactor to use libcudf for gathering columns in dataframes
+- PR #2293 Improve join performance (faster compute_join_output_size)
+- PR #2300 Create separate dask codeowners for dask-cudf codebase
+- PR #2304 gdf_group_by_without_aggregations returns gdf_column
+- PR #2309 Java readers: remove redundant copy of result pointers
+- PR #2307 Add `black` and `isort` to style checker script
+- PR #2345 Restore removal of old groupby implementation
+- PR #2342 Improve `astype()` to operate all ways
+- PR #2329 using libcudf cudf::copy for column deep copy
+- PR #2344 DOC: docs on code formatting for contributors
+- PR #2376 Add inoperative axis= and win_type= arguments to Rolling()
+- PR #2378 remove dask for (de-)serialization of cudf objects
+- PR #2353 Bump Arrow and Dask versions
+- PR #2377 Replace `standard_python_slice` with just `slice.indices()`
+- PR #2373 cudf.DataFrame enhancements & Series.values support
+- PR #2392 Remove dlpack submodule; make cuDF's Cython API externally accessible
+- PR #2430 Updated Java bindings to use the new unary API
+- PR #2406 Moved all existing `table` related files to a `legacy/` directory
+- PR #2350 Performance related changes to get_dummies
+- PR #2420 Remove `cudautils.astype` and replace with `typecast.apply_cast`
+- PR #2456 Small improvement to typecast utility
+- PR #2458 Fix handling of thirdparty packages in `isort` config
+- PR #2459 IO Readers: Consolidate all readers to use `datasource` class
+- PR #2475 Exposed type_dispatcher.hpp, nvcategory_util.hpp and wrapper_types.hpp in the include folder
+- PR #2484 Enabled building libcudf as a static library
+- PR #2453 Streamline CUDA_REL environment variable
+- PR #2483 Bundle Boost filesystem dependency in the Java jar
+- PR #2486 Java API hash functions
+- PR #2481 Adds the ignore_null_keys option to the java api
+- PR #2490 Java api: support multiple aggregates for the same column
+- PR #2510 Java api: uses table based apply_boolean_mask
+- PR #2432 Use pandas formatting for console, html, and latex output
+- PR #2573 Bump numba version to 0.45.1
+- PR #2606 Fix references to notebooks-contrib
+
+## Bug Fixes
+
+- PR #2086 Fixed quantile api behavior mismatch in series & dataframe
+- PR #2128 Add offset param to host buffer readers in java API.
+- PR #2145 Work around binops validity checks for java
+- PR #2146 Work around unary_math validity checks for java
+- PR #2151 Fixes bug in cudf::copy_range where null_count was invalid
+- PR #2139 matching to pandas describe behavior & fixing nan values issue
+- PR #2161 Implicitly convert unsigned to signed integer types in binops
+- PR #2154 CSV Reader: Fix bools misdetected as strings dtype
+- PR #2178 Fix bug in rolling bindings where a view of an ephemeral column was being taken
+- PR #2180 Fix issue with isort reordering `importorskip` below imports depending on them
+- PR #2187 fix to honor dtype when numpy arrays are passed to columnops.as_column
+- PR #2190 Fix issue in astype conversion of string column to 'str'
+- PR #2208 Fix issue with calling `head()` on one row dataframe
+- PR #2229 Propagate exceptions from Cython cdef functions
+- PR #2234 Fix issue with local build script not properly building
+- PR #2223 Fix CUDA invalid configuration errors reported after loading small compressed ORC files
+- PR #2162 Setting is_unique and is_monotonic-related attributes
+- PR #2244 Fix ORC RLEv2 delta mode decoding with nonzero residual delta width
+- PR #2297 Work around `var/std` unsupported only at debug build
+- PR #2302 Fixed java serialization corner case
+- PR #2355 Handle float16 in binary operations
+- PR #2311 Fix copy behaviour for GenericIndex
+- PR #2349 Fix issues with String filter in java API
+- PR #2323 Fix groupby on categoricals
+- PR #2328 Ensure order is preserved in CategoricalAccessor._set_categories
+- PR #2202 Fix issue with unary ops mishandling empty input
+- PR #2326 Fix for bug in DLPack when reading multiple columns
+- PR #2324 Fix cudf Docker build
+- PR #2325 Fix ORC RLEv2 patched base mode decoding with nonzero patch width
+- PR #2235 Fix get_dummies to be compatible with dask
+- PR #2332 Zero initialize gdf_dtype_extra_info
+- PR #2355 Handle float16 in binary operations
+- PR #2360 Fix missing dtype handling in cudf.Series & columnops.as_column
+- PR #2364 Fix quantile api and other trivial issues around it
+- PR #2361 Fixed issue with `codes` of CategoricalIndex
+- PR #2357 Fixed inconsistent type of index created with from_pandas vs direct construction
+- PR #2389 Fixed Rolling __getattr__ and __getitem__ for offset based windows
+- PR #2402 Fixed bug in valid mask computation in cudf::copy_if (apply_boolean_mask)
+- PR #2401 Fix to a scalar datetime(of type Days) issue
+- PR #2386 Correctly allocate output valids in groupby
+- PR #2411 Fixed failures on binary op on single element string column
+- PR #2422 Fix Pandas logical binary operation incompatibilites
+- PR #2447 Fix CodeCov posting build statuses temporarily
+- PR #2450 Fix erroneous null handling in `cudf.DataFrame`'s `apply_rows`
+- PR #2470 Fix issues with empty strings and string categories (Java)
+- PR #2471 Fix String Column Validity.
+- PR #2481 Fix java validity buffer serialization
+- PR #2485 Updated bytes calculation to use size_t to avoid overflow in column concat
+- PR #2461 Fix groupby multiple aggregations same column
+- PR #2514 Fix cudf::drop_nulls threshold handling in Cython
+- PR #2516 Fix utilities include paths and meta.yaml header paths
+- PR #2517 Fix device memory leak in to_dlpack tensor deleter
+- PR #2431 Fix local build generated file ownerships
+- PR #2511 Added import of orc, refactored exception handlers to not squash fatal exceptions
+- PR #2527 Fix index and column input handling in dask_cudf read_parquet
+- PR #2466 Fix `dataframe.query` returning null rows erroneously
+- PR #2548 Orc reader: fix non-deterministic data decoding at chunk boundaries
+- PR #2557 fix cudautils import in string.py
+- PR #2521 Fix casting datetimes from/to the same resolution
+- PR #2545 Fix MultiIndexes with datetime levels
+- PR #2560 Remove duplicate `dlpack` definition in conda recipe
+- PR #2567 Fix ColumnVector.fromScalar issues while dealing with null scalars
+- PR #2565 Orc reader: fix incorrect data decoding of int64 data types
+- PR #2577 Fix search benchmark compilation error by adding necessary header
+- PR #2604 Fix a bug in copying.pyx:_normalize_types that upcasted int32 to int64
+
+
+# cuDF 0.8.0 (27 June 2019)
+
+## New Features
+
+- PR #1524 Add GPU-accelerated JSON Lines parser with limited feature set
+- PR #1569 Add support for Json objects to the JSON Lines reader
+- PR #1622 Add Series.loc
+- PR #1654 Add cudf::apply_boolean_mask: faster replacement for gdf_apply_stencil
+- PR #1487 cython gather/scatter
+- PR #1310 Implemented the slice/split functionality.
+- PR #1630 Add Python layer to the GPU-accelerated JSON reader
+- PR #1745 Add rounding of numeric columns via Numba
+- PR #1772 JSON reader: add support for BytesIO and StringIO input
+- PR #1527 Support GDF_BOOL8 in readers and writers
+- PR #1819 Logical operators (AND, OR, NOT) for libcudf and cuDF
+- PR #1813 ORC Reader: Add support for stripe selection
+- PR #1828 JSON Reader: add support for bool8 columns
+- PR #1833 Add column iterator with/without nulls
+- PR #1665 Add the point-in-polygon GIS function
+- PR #1863 Series and Dataframe methods for all and any
+- PR #1908 cudf::copy_range and cudf::fill for copying/assigning an index or range to a constant
+- PR #1921 Add additional formats for typecasting to/from strings
+- PR #1807 Add Series.dropna()
+- PR #1987 Allow user defined functions in the form of ptx code to be passed to binops
+- PR #1948 Add operator functions like `Series.add()` to DataFrame and Series
+- PR #1954 Add skip test argument to GPU build script
+- PR #2018 Add bindings for new groupby C++ API
+- PR #1984 Add rolling window operations Series.rolling() and DataFrame.rolling()
+- PR #1542 Python method and bindings for to_csv
+- PR #1995 Add Java API
+- PR #1998 Add google benchmark to cudf
+- PR #1845 Add cudf::drop_duplicates, DataFrame.drop_duplicates
+- PR #1652 Added `Series.where()` feature
+- PR #2074 Java Aggregates, logical ops, and better RMM support
+- PR #2140 Add a `cudf::transform` function
+- PR #2068 Concatenation of different typed columns
+
+## Improvements
+
+- PR #1538 Replacing LesserRTTI with inequality_comparator
+- PR #1703 C++: Added non-aggregating `insert` to `concurrent_unordered_map` with specializations to store pairs with a single atomicCAS when possible.
+- PR #1422 C++: Added a RAII wrapper for CUDA streams
+- PR #1701 Added `unique` method for stringColumns
+- PR #1713 Add documentation for Dask-XGBoost
+- PR #1666 CSV Reader: Improve performance for files with large number of columns
+- PR #1725 Enable the ability to use a single column groupby as its own index
+- PR #1759 Add an example showing simultaneous rolling averages to `apply_grouped` documentation
+- PR #1746 C++: Remove unused code: `windowed_ops.cu`, `sorting.cu`, `hash_ops.cu`
+- PR #1748 C++: Add `bool` nullability flag to `device_table` row operators
+- PR #1764 Improve Numerical column: `mean_var` and `mean`
+- PR #1767 Speed up Python unit tests
+- PR #1770 Added build.sh script, updated CI scripts and documentation
+- PR #1739 ORC Reader: Add more pytest coverage
+- PR #1696 Added null support in `Series.replace()`.
+- PR #1390 Added some basic utility functions for `gdf_column`'s
+- PR #1791 Added general column comparison code for testing
+- PR #1795 Add printing of git submodule info to `print_env.sh`
+- PR #1796 Removing old sort based group by code and gdf_filter
+- PR #1811 Added functions for copying/allocating `cudf::table`s
+- PR #1838 Improve columnops.column_empty so that it returns typed columns instead of a generic Column
+- PR #1890 Add utils.get_dummies- a pandas-like wrapper around one_hot-encoding
+- PR #1823 CSV Reader: default the column type to string for empty dataframes
+- PR #1827 Create bindings for scalar-vector binops, and update one_hot_encoding to use them
+- PR #1817 Operators now support different sized dataframes as long as they don't share different sized columns
+- PR #1855 Transition replace_nulls to new C++ API and update corresponding Cython/Python code
+- PR #1858 Add `std::initializer_list` constructor to `column_wrapper`
+- PR #1846 C++ type-erased gdf_equal_columns test util; fix gdf_equal_columns logic error
+- PR #1390 Added some basic utility functions for `gdf_column`s
+- PR #1391 Tidy up bit-resolution-operation and bitmask class code
+- PR #1882 Add iloc functionality to MultiIndex dataframes
+- PR #1884 Rolling windows: general enhancements and better coverage for unit tests
+- PR #1886 support GDF_STRING_CATEGORY columns in apply_boolean_mask, drop_nulls and other libcudf functions
+- PR #1896 Improve performance of groupby with levels specified in dask-cudf
+- PR #1915 Improve iloc performance for non-contiguous row selection
+- PR #1859 Convert read_json into a C++ API
+- PR #1919 Rename libcudf namespace gdf to namespace cudf
+- PR #1850 Support left_on and right_on for DataFrame merge operator
+- PR #1930 Specialize constructor for `cudf::bool8` to cast argument to `bool`
+- PR #1938 Add default constructor for `column_wrapper`
+- PR #1930 Specialize constructor for `cudf::bool8` to cast argument to `bool`
+- PR #1952 consolidate libcudf public API headers in include/cudf
+- PR #1949 Improved selection with boolmask using libcudf `apply_boolean_mask`
+- PR #1956 Add support for nulls in `query()`
+- PR #1973 Update `std::tuple` to `std::pair` in top-most libcudf APIs and C++ transition guide
+- PR #1981 Convert read_csv into a C++ API
+- PR #1868 ORC Reader: Support row index for speed up on small/medium datasets
+- PR #1964 Added support for list-like types in Series.str.cat
+- PR #2005 Use HTML5 details tag in bug report issue template
+- PR #2003 Removed few redundant unit-tests from test_string.py::test_string_cat
+- PR #1944 Groupby design improvements
+- PR #2017 Convert `read_orc()` into a C++ API
+- PR #2011 Convert `read_parquet()` into a C++ API
+- PR #1756 Add documentation "10 Minutes to cuDF and dask_cuDF"
+- PR #2034 Adding support for string columns concatenation using "add" binary operator
+- PR #2042 Replace old "10 Minutes" guide with new guide for docs build process
+- PR #2036 Make library of common test utils to speed up tests compilation
+- PR #2022 Facilitating get_dummies to be a high level api too
+- PR #2050 Namespace IO readers and add back free-form `read_xxx` functions
+- PR #2104 Add a functional ``sort=`` keyword argument to groupby
+- PR #2108 Add `find_and_replace` for StringColumn for replacing single values
+- PR #1803 cuDF/CuPy interoperability documentation
+
+## Bug Fixes
+
+- PR #1465 Fix for test_orc.py and test_sparse_df.py test failures
+- PR #1583 Fix underlying issue in `as_index()` that was causing `Series.quantile()` to fail
+- PR #1680 Add errors= keyword to drop() to fix cudf-dask bug
+- PR #1651 Fix `query` function on empty dataframe
+- PR #1616 Fix CategoricalColumn to access categories by index instead of iteration
+- PR #1660 Fix bug in `loc` when indexing with a column name (a string)
+- PR #1683 ORC reader: fix timestamp conversion to UTC
+- PR #1613 Improve CategoricalColumn.fillna(-1) performance
+- PR #1642 Fix failure of CSV_TEST gdf_csv_test.SkiprowsNrows on multiuser systems
+- PR #1709 Fix handling of `datetime64[ms]` in `dataframe.select_dtypes`
+- PR #1704 CSV Reader: Add support for the plus sign in number fields
+- PR #1687 CSV reader: return an empty dataframe for zero size input
+- PR #1757 Concatenating columns with null columns
+- PR #1755 Add col_level keyword argument to melt
+- PR #1758 Fix df.set_index() when setting index from an empty column
+- PR #1749 ORC reader: fix long strings of NULL values resulting in incorrect data
+- PR #1742 Parquet Reader: Fix index column name to match PANDAS compat
+- PR #1782 Update libcudf doc version
+- PR #1783 Update conda dependencies
+- PR #1786 Maintain the original series name in series.unique output
+- PR #1760 CSV Reader: fix segfault when dtype list only includes columns from usecols list
+- PR #1831 build.sh: Assuming python is in PATH instead of using PYTHON env var
+- PR #1839 Raise an error instead of segfaulting when transposing a DataFrame with StringColumns
+- PR #1840 Retain index correctly during merge left_on right_on
+- PR #1825 cuDF: Multiaggregation Groupby Failures
+- PR #1789 CSV Reader: Fix missing support for specifying `int8` and `int16` dtypes
+- PR #1857 Cython Bindings: Handle `bool` columns while calling `column_view_from_NDArrays`
+- PR #1849 Allow DataFrame support methods to pass arguments to the methods
+- PR #1847 Fixed #1375 by moving the nvstring check into the wrapper function
+- PR #1864 Fixing cudf reduction for POWER platform
+- PR #1869 Parquet reader: fix Dask timestamps not matching with Pandas (convert to milliseconds)
+- PR #1876 add dtype=bool for `any`, `all` to treat integer column correctly
+- PR #1875 CSV reader: take NaN values into account in dtype detection
+- PR #1873 Add column dtype checking for the all/any methods
+- PR #1902 Bug with string iteration in _apply_basic_agg
+- PR #1887 Fix for initialization issue in pq_read_arg,orc_read_arg
+- PR #1867 JSON reader: add support for null/empty fields, including the 'null' literal
+- PR #1891 Fix bug #1750 in string column comparison
+- PR #1909 Support of `to_pandas()` of boolean series with null values
+- PR #1923 Use prefix removal when two aggs are called on a SeriesGroupBy
+- PR #1914 Zero initialize gdf_column local variables
+- PR #1959 Add support for comparing boolean Series to scalar
+- PR #1966 Ignore index fix in series append
+- PR #1967 Compute index __sizeof__ only once for DataFrame __sizeof__
+- PR #1977 Support CUDA installation in default system directories
+- PR #1982 Fixes incorrect index name after join operation
+- PR #1985 Implement `GDF_PYMOD`, a special modulo that follows python's sign rules
+- PR #1991 Parquet reader: fix decoding of NULLs
+- PR #1990 Fixes a rendering bug in the `apply_grouped` documentation
+- PR #1978 Fix for values being filled in an empty dataframe
+- PR #2001 Correctly create MultiColumn from Pandas MultiColumn
+- PR #2006 Handle empty dataframe groupby construction for dask
+- PR #1965 Parquet Reader: Fix duplicate index column when it's already in `use_cols`
+- PR #2033 Add pip to conda environment files to fix warning
+- PR #2028 CSV Reader: Fix reading of uncompressed files without a recognized file extension
+- PR #2073 Fix an issue when gathering columns with NVCategory and nulls
+- PR #2053 cudf::apply_boolean_mask return empty column for empty boolean mask
+- PR #2066 exclude `IteratorTest.mean_var_output` test from debug build
+- PR #2069 Fix JNI code to use read_csv and read_parquet APIs
+- PR #2071 Fix bug with unfound transitive dependencies for GTests in Ubuntu 18.04
+- PR #2089 Configure Sphinx to render params correctly
+- PR #2091 Fix another bug with unfound transitive dependencies for `cudftestutils` in Ubuntu 18.04
+- PR #2115 Just apply `--disable-new-dtags` instead of trying to define all the transitive dependencies
+- PR #2106 Fix errors in JitCache tests caused by sharing of device memory between processes
+- PR #2120 Fix errors in JitCache tests caused by running multiple threads on the same data
+- PR #2102 Fix memory leak in groupby
+- PR #2113 fixed typo in to_csv code example
+
+
+# cudf 0.7.2 (16 May 2019)
+
+## New Features
+
+- PR #1735 Added overload for atomicAdd on int64. Streamlined implementation of custom atomic overloads.
+- PR #1741 Add MultiIndex concatenation
+
+## Bug Fixes
+
+- PR #1718 Fix issue with SeriesGroupBy MultiIndex in dask-cudf
+- PR #1734 Python: fix performance regression for groupby count() aggregations
+- PR #1768 Cython: fix handling read only schema buffers in gpuarrow reader
+
+
+# cudf 0.7.1 (11 May 2019)
+
+## New Features
+
+- PR #1702 Lazy load MultiIndex to return groupby performance to near optimal.
+
+## Bug Fixes
+
+- PR #1708 Fix handling of `datetime64[ms]` in `dataframe.select_dtypes`
+
+
+# cuDF 0.7.0 (10 May 2019)
+
+## New Features
+
+- PR #982 Implement gdf_group_by_without_aggregations and gdf_unique_indices functions
+- PR #1142 Add `GDF_BOOL` column type
+- PR #1194 Implement overloads for CUDA atomic operations
+- PR #1292 Implemented Bitwise binary ops AND, OR, XOR (&, |, ^)
+- PR #1235 Add GPU-accelerated Parquet Reader
+- PR #1335 Added local_dict arg in `DataFrame.query()`.
+- PR #1282 Add Series and DataFrame.describe()
+- PR #1356 Rolling windows
+- PR #1381 Add DataFrame._get_numeric_data
+- PR #1388 Add CODEOWNERS file to auto-request reviews based on where changes are made
+- PR #1396 Add DataFrame.drop method
+- PR #1413 Add DataFrame.melt method
+- PR #1412 Add DataFrame.pop()
+- PR #1419 Initial CSV writer function
+- PR #1441 Add Series level cumulative ops (cumsum, cummin, cummax, cumprod)
+- PR #1420 Add script to build and test on a local gpuCI image
+- PR #1440 Add DatetimeColumn.min(), DatetimeColumn.max()
+- PR #1455 Add Series.Shift via Numba kernel
+- PR #1441 Add Series level cumulative ops (cumsum, cummin, cummax, cumprod)
+- PR #1461 Add Python coverage test to gpu build
+- PR #1445 Parquet Reader: Add selective reading of rows and row group
+- PR #1532 Parquet Reader: Add support for INT96 timestamps
+- PR #1516 Add Series and DataFrame.ndim
+- PR #1556 Add libcudf C++ transition guide
+- PR #1466 Add GPU-accelerated ORC Reader
+- PR #1565 Add build script for nightly doc builds
+- PR #1508 Add Series isna, isnull, and notna
+- PR #1456 Add Series.diff() via Numba kernel
+- PR #1588 Add Index `astype` typecasting
+- PR #1301 MultiIndex support
+- PR #1599 Level keyword supported in groupby
+- PR #929 Add support operations to dataframe
+- PR #1609 Groupby accept list of Series
+- PR #1658 Support `group_keys=True` keyword in groupby method
+
+## Improvements
+
+- PR #1531 Refactor closures as private functions in gpuarrow
+- PR #1404 Parquet reader page data decoding speedup
+- PR #1076 Use `type_dispatcher` in join, quantiles, filter, segmented sort, radix sort and hash_groupby
+- PR #1202 Simplify README.md
+- PR #1149 CSV Reader: Change convertStrToValue() functions to `__device__` only
+- PR #1238 Improve performance of the CUDA trie used in the CSV reader
+- PR #1245 Use file cache for JIT kernels
+- PR #1278 Update CONTRIBUTING for new conda environment yml naming conventions
+- PR #1163 Refactored UnaryOps. Reduced API to two functions: `gdf_unary_math` and `gdf_cast`. Added `abs`, `-`, and `~` ops. Changed bindings to Cython
+- PR #1284 Update docs version
+- PR #1287 add exclude argument to cudf.select_dtype function
+- PR #1286 Refactor some of the CSV Reader kernels into generic utility functions
+- PR #1291 fillna in `Series.to_gpu_array()` and `Series.to_array()` can accept the scalar too now.
+- PR #1005 generic `reduction` and `scan` support
+- PR #1349 Replace modernGPU sort join with thrust.
+- PR #1363 Add a dataframe.mean(...) that raises NotImplementedError to satisfy `dask.dataframe.utils.is_dataframe_like`
+- PR #1319 CSV Reader: Use column wrapper for gdf_column output alloc/dealloc
+- PR #1376 Change series quantile default to linear
+- PR #1399 Replace CFFI bindings for NVTX functions with Cython bindings
+- PR #1389 Refactored `set_null_count()`
+- PR #1386 Added macros `GDF_TRY()`, `CUDF_TRY()` and `ASSERT_CUDF_SUCCEEDED()`
+- PR #1435 Rework CMake and conda recipes to depend on installed libraries
+- PR #1391 Tidy up bit-resolution-operation and bitmask class code
+- PR #1439 Add cmake variable to enable compiling CUDA code with -lineinfo
+- PR #1462 Add ability to read parquet files from arrow::io::RandomAccessFile
+- PR #1453 Convert CSV Reader CFFI to Cython
+- PR #1479 Convert Parquet Reader CFFI to Cython
+- PR #1397 Add a utility function for producing an overflow-safe kernel launch grid configuration
+- PR #1382 Add GPU parsing of nested brackets to cuIO parsing utilities
+- PR #1481 Add cudf::table constructor to allocate a set of `gdf_column`s
+- PR #1484 Convert GroupBy CFFI to Cython
+- PR #1463 Allow and default melt keyword argument var_name to be None
+- PR #1486 Parquet Reader: Use device_buffer rather than device_ptr
+- PR #1525 Add cudatoolkit conda dependency
+- PR #1520 Renamed `src/dataframe` to `src/table` and moved `table.hpp`. Made `types.hpp` to be type declarations only.
+- PR #1492 Convert transpose CFFI to Cython
+- PR #1495 Convert binary and unary ops CFFI to Cython
+- PR #1503 Convert sorting and hashing ops CFFI to Cython
+- PR #1522 Use latest release version in update-version CI script
+- PR #1533 Remove stale join CFFI, fix memory leaks in join Cython
+- PR #1521 Added `row_bitmask` to compute bitmask for rows of a table. Merged `valids_ops.cu` and `bitmask_ops.cu`
+- PR #1553 Overload `hash_row` to avoid using initial hash values. Updated `gdf_hash` to select between overloads
+- PR #1585 Updated `cudf::table` to maintain own copy of wrapped `gdf_column*`s
+- PR #1559 Add `except +` to all Cython function definitions to catch C++ exceptions properly
+- PR #1617 `has_nulls` and `column_dtypes` for `cudf::table`
+- PR #1590 Remove CFFI from the build / install process entirely
+- PR #1536 Convert gpuarrow CFFI to Cython
+- PR #1655 Add `Column._pointer` as a way to access underlying `gdf_column*` of a `Column`
+- PR #1655 Update readme conda install instructions for cudf version 0.6 and 0.7
+
+
+## Bug Fixes
+
+- PR #1233 Fix dtypes issue while adding the column to `str` dataframe.
+- PR #1254 CSV Reader: fix data type detection for floating-point numbers in scientific notation
+- PR #1289 Fix looping over each value instead of each category in concatenation
+- PR #1293 Fix Inaccurate error message in join.pyx
+- PR #1308 Add atomicCAS overload for `int8_t`, `int16_t`
+- PR #1317 Fix catch polymorphic exception by reference in ipc.cu
+- PR #1325 Fix dtype of null bitmasks to int8
+- PR #1326 Update build documentation to use -DCMAKE_CXX11_ABI=ON
+- PR #1334 Add "na_position" argument to CategoricalColumn sort_by_values
+- PR #1321 Fix out of bounds warning when checking Bzip2 header
+- PR #1359 Add atomicAnd/Or/Xor for integers
+- PR #1354 Fix `fillna()` behaviour when replacing values with different dtypes
+- PR #1347 Fixed core dump issue while passing dict_dtypes without column names in `cudf.read_csv()`
+- PR #1379 Fixed build failure caused due to error: 'col_dtype' may be used uninitialized
+- PR #1392 Update cudf Dockerfile and package_versions.sh
+- PR #1385 Added INT8 type to `_schema_to_dtype` for use in GpuArrowReader
+- PR #1393 Fixed a bug in `gdf_count_nonzero_mask()` for the case of 0 bits to count
+- PR #1395 Update CONTRIBUTING to use the environment variable CUDF_HOME
+- PR #1416 Fix bug at gdf_quantile_exact and gdf_quantile_appox
+- PR #1421 Fix remove creation of series multiple times during `add_column()`
+- PR #1405 CSV Reader: Fix memory leaks on read_csv() failure
+- PR #1328 Fix CategoricalColumn to_arrow() null mask
+- PR #1433 Fix NVStrings/categories includes
+- PR #1432 Update NVStrings to 0.7.* to coincide with 0.7 development
+- PR #1483 Modify CSV reader to avoid cropping blank quoted characters in non-string fields
+- PR #1446 Merge 1275 hotfix from master into branch-0.7
+- PR #1447 Fix legacy groupby apply docstring
+- PR #1451 Fix hash join estimated result size is not correct
+- PR #1454 Fix local build script improperly change directory permissions
+- PR #1490 Require Dask 1.1.0+ for `is_dataframe_like` test or skip otherwise.
+- PR #1491 Use more specific directories & groups in CODEOWNERS
+- PR #1497 Fix Thrust issue on CentOS caused by missing default constructor of host_vector elements
+- PR #1498 Add missing include guard to device_atomics.cuh and separated DEVICE_ATOMICS_TEST
+- PR #1506 Fix csv-write call to updated NVStrings method
+- PR #1510 Added nvstrings `fillna()` function
+- PR #1507 Parquet Reader: Default string data to GDF_STRING
+- PR #1535 Fix doc issue to ensure correct labelling of cudf.series
+- PR #1537 Fix `undefined reference` link error in HashPartitionTest
+- PR #1548 Fix ci/local/build.sh README from using an incorrect image example
+- PR #1551 CSV Reader: Fix integer column name indexing
+- PR #1586 Fix broken `scalar_wrapper::operator==`
+- PR #1591 ORC/Parquet Reader: Fix missing import for FileNotFoundError exception
+- PR #1573 Parquet Reader: Fix crash due to clash with ORC reader datasource
+- PR #1607 Revert change of `column.to_dense_buffer` always return by copy for performance concerns
+- PR #1618 ORC reader: fix assert & data output when nrows/skiprows isn't aligned to stripe boundaries
+- PR #1631 Fix failure of TYPES_TEST on some gcc-7 based systems.
+- PR #1641 CSV Reader: Fix skip_blank_lines behavior with Windows line terminators (
+)
+- PR #1648 ORC reader: fix non-deterministic output when skiprows is non-zero
+- PR #1676 Fix groupby `as_index` behaviour with `MultiIndex`
+- PR #1659 Fix bug caused by empty groupbys and multiindex slicing throwing exceptions
+- PR #1656 Correct Groupby failure in dask when un-aggregable columns are left in dataframe.
+- PR #1689 Fix groupby performance regression
+- PR #1694 Add Cython as a runtime dependency since it's required in `setup.py`
+
+
+# cuDF 0.6.1 (25 Mar 2019)
+
+## Bug Fixes
+
+- PR #1275 Fix CentOS exception in DataFrame.hash_partition from using value "returned" by a void function
+
+
+# cuDF 0.6.0 (22 Mar 2019)
+
+## New Features
+
+- PR #760 Raise `FileNotFoundError` instead of `GDF_FILE_ERROR` in `read_csv` if the file does not exist
+- PR #539 Add Python bindings for replace function
+- PR #823 Add Doxygen configuration to enable building HTML documentation for libcudf C/C++ API
+- PR #807 CSV Reader: Add byte_range parameter to specify the range in the input file to be read
+- PR #857 Add Tail method for Series/DataFrame and update Head method to use iloc
+- PR #858 Add series feature hashing support
+- PR #871 CSV Reader: Add support for NA values, including user specified strings
+- PR #893 Adds PyArrow based parquet readers / writers to Python, fix category dtype handling, fix arrow ingest buffer size issues
+- PR #867 CSV Reader: Add support for ignoring blank lines and comment lines
+- PR #887 Add Series digitize method
+- PR #895 Add Series groupby
+- PR #898 Add DataFrame.groupby(level=0) support
+- PR #920 Add feather, JSON, HDF5 readers / writers from PyArrow / Pandas
+- PR #888 CSV Reader: Add prefix parameter for column names, used when parsing without a header
+- PR #913 Add DLPack support: convert between cuDF DataFrame and DLTensor
+- PR #939 Add ORC reader from PyArrow
+- PR #918 Add Series.groupby(level=0) support
+- PR #906 Add binary and comparison ops to DataFrame
+- PR #958 Support unary and binary ops on indexes
+- PR #964 Add `rename` method to `DataFrame`, `Series`, and `Index`
+- PR #985 Add `Series.to_frame` method
+- PR #985 Add `drop=` keyword to reset_index method
+- PR #994 Remove references to pygdf
+- PR #990 Add external series groupby support
+- PR #988 Add top-level merge function to cuDF
+- PR #992 Add comparison binaryops to DateTime columns
+- PR #996 Replace relative path imports with absolute paths in tests
+- PR #995 CSV Reader: Add index_col parameter to specify the column name or index to be used as row labels
+- PR #1004 Add `from_gpu_matrix` method to DataFrame
+- PR #997 Add property index setter
+- PR #1007 Replace relative path imports with absolute paths in cudf
+- PR #1013 select columns with df.columns
+- PR #1016 Rename Series.unique_count() to nunique() to match pandas API
+- PR #947 Prefixsum to handle nulls and float types
+- PR #1029 Remove rest of relative path imports
+- PR #1021 Add filtered selection with assignment for Dataframes
+- PR #872 Adding NVCategory support to cudf apis
+- PR #1052 Add left/right_index and left/right_on keywords to merge
+- PR #1091 Add `indicator=` and `suffixes=` keywords to merge
+- PR #1107 Add unsupported keywords to Series.fillna
+- PR #1032 Add string support to cuDF python
+- PR #1136 Removed `gdf_concat`
+- PR #1153 Added function for getting the padded allocation size for valid bitmask
+- PR #1148 Add cudf.sqrt for dataframes and Series
+- PR #1159 Add Python bindings for libcudf dlpack functions
+- PR #1155 Add __array_ufunc__ for DataFrame and Series for sqrt
+- PR #1168 to_frame for series accepts a name argument
+
+
+## Improvements
+
+- PR #1218 Add dask-cudf page to API docs
+- PR #892 Add support for heterogeneous types in binary ops with JIT
+- PR #730 Improve performance of `gdf_table` constructor
+- PR #561 Add Doxygen style comments to Join CUDA functions
+- PR #813 unified libcudf API functions by replacing gpu_ with gdf_
+- PR #822 Add support for `__cuda_array_interface__` for ingest
+- PR #756 Consolidate common helper functions from unordered map and multimap
+- PR #753 Improve performance of groupby sum and average, especially for cases with few groups.
+- PR #836 Add ingest support for arrow chunked arrays in Column, Series, DataFrame creation
+- PR #763 Format doxygen comments for csv_read_arg struct
+- PR #532 CSV Reader: Use type dispatcher instead of switch block
+- PR #694 Unit test utilities improvements
+- PR #878 Add better indexing to Groupby
+- PR #554 Add `empty` method and `is_monotonic` attribute to `Index`
+- PR #1040 Fixed up Doxygen comment tags
+- PR #909 CSV Reader: Avoid host->device->host copy for header row data
+- PR #916 Improved unit testing and error checking for `gdf_column_concat`
+- PR #941 Replace `numpy` call in `Series.hash_encode` with `numba`
+- PR #942 Added increment/decrement operators for wrapper types
+- PR #943 Updated `count_nonzero_mask` to return `num_rows` when the mask is null
+- PR #952 Added trait to map C++ type to `gdf_dtype`
+- PR #966 Updated RMM submodule.
+- PR #998 Add IO reader/writer modules to API docs, fix for missing cudf.Series docs
+- PR #1017 concatenate along columns for Series and DataFrames
+- PR #1002 Support indexing a dataframe with another boolean dataframe
+- PR #1018 Better concatenation for Series and Dataframes
+- PR #1036 Use Numpydoc style docstrings
+- PR #1047 Adding gdf_dtype_extra_info to gdf_column_view_augmented
+- PR #1054 Added default ctor to SerialTrieNode to overcome Thrust issue in CentOS7 + CUDA10
+- PR #1024 CSV Reader: Add support for hexadecimal integers in integral-type columns
+- PR #1033 Update `fillna()` to use libcudf function `gdf_replace_nulls`
+- PR #1066 Added inplace assignment for columns and select_dtypes for dataframes
+- PR #1026 CSV Reader: Change the meaning and type of the quoting parameter to match Pandas
+- PR #1100 Adds `CUDF_EXPECTS` error-checking macro
+- PR #1092 Fix select_dtype docstring
+- PR #1111 Added cudf::table
+- PR #1108 Sorting for datetime columns
+- PR #1120 Return a `Series` (not a `Column`) from `Series.cat.set_categories()`
+- PR #1128 CSV Reader: The last data row does not need to be line terminated
+- PR #1183 Bump Arrow version to 0.12.1
+- PR #1208 Default to CXX11_ABI=ON
+- PR #1252 Fix NVStrings dependencies for cuda 9.2 and 10.0
+- PR #2037 Optimize the existing `gather` and `scatter` routines in `libcudf`
+
+## Bug Fixes
+
+- PR #821 Fix flake8 issues revealed by flake8 update
+- PR #808 Resolved renamed `d_columns_valids` variable name
+- PR #820 CSV Reader: fix the issue where reader adds additional rows when file uses
+ as a line terminator
+- PR #780 CSV Reader: Fix scientific notation parsing and null values for empty quotes
+- PR #815 CSV Reader: Fix data parsing when tabs are present in the input CSV file
+- PR #850 Fix bug where left joins where the left df has 0 rows causes a crash
+- PR #861 Fix memory leak by preserving the boolean mask index
+- PR #875 Handle unnamed indexes in to/from arrow functions
+- PR #877 Fix ingest of 1 row arrow tables in from arrow function
+- PR #876 Added missing `<type_traits>` include
+- PR #889 Deleted test_rmm.py which has now moved to RMM repo
+- PR #866 Merge v0.5.1 numpy ABI hotfix into 0.6
+- PR #917 value_counts return int type on empty columns
+- PR #611 Renamed `gdf_reduce_optimal_output_size()` -> `gdf_reduction_get_intermediate_output_size()`
+- PR #923 fix index for negative slicing for cudf dataframe and series
+- PR #927 CSV Reader: Fix category GDF_CATEGORY hashes not being computed properly
+- PR #921 CSV Reader: Fix parsing errors with delim_whitespace, quotations in the header row, unnamed columns
+- PR #933 Fix handling objects of all nulls in series creation
+- PR #940 CSV Reader: Fix an issue where the last data row is missing when using byte_range
+- PR #945 CSV Reader: Fix incorrect datetime64 when milliseconds or space separator are used
+- PR #959 Groupby: Problem with column name lookup
+- PR #950 Converting dataframe/recarry with non-contiguous arrays
+- PR #963 CSV Reader: Fix another issue with missing data rows when using byte_range
+- PR #999 Fix 0 sized kernel launches and empty sort_index exception
+- PR #993 Fix dtype in selecting 0 rows from objects
+- PR #1009 Fix performance regression in `to_pandas` method on DataFrame
+- PR #1008 Remove custom dask communication approach
+- PR #1001 CSV Reader: Fix a memory access error when reading a large (>2GB) file with date columns
+- PR #1019 Binary Ops: Fix error when one input column has null mask but other doesn't
+- PR #1014 CSV Reader: Fix false positives in bool value detection
+- PR #1034 CSV Reader: Fix parsing floating point precision and leading zero exponents
+- PR #1044 CSV Reader: Fix a segfault when byte range aligns with a page
+- PR #1058 Added support for `DataFrame.loc[scalar]`
+- PR #1060 Fix column creation with all valid nan values
+- PR #1073 CSV Reader: Fix an issue where a column name includes the return character
+- PR #1090 Updating Doxygen Comments
+- PR #1080 Fix dtypes returned from loc / iloc because of lists
+- PR #1102 CSV Reader: Minor fixes and memory usage improvements
+- PR #1174: Fix release script typo
+- PR #1137 Add prebuild script for CI
+- PR #1118 Enhanced the `DataFrame.from_records()` feature
+- PR #1129 Fix join performance with index parameter from using numpy array
+- PR #1145 Issue with .agg call on multi-column dataframes
+- PR #908 Some testing code cleanup
+- PR #1167 Fix issue with null_count not being set after inplace fillna()
+- PR #1184 Fix iloc performance regression
+- PR #1185 Support left_on/right_on and also on=str in merge
+- PR #1200 Fix allocating bitmasks with numba instead of rmm in allocate_mask function
+- PR #1213 Fix bug with csv reader requesting subset of columns using wrong datatype
+- PR #1223 gpuCI: Fix label on rapidsai channel on gpu build scripts
+- PR #1242 Add explicit Thrust exec policy to fix NVCATEGORY_TEST segfault on some platforms
+- PR #1246 Fix categorical tests that failed due to bad implicit type conversion
+- PR #1255 Fix overwriting conda package main label uploads
+- PR #1259 Add dlpack includes to pip build
+
+
+# cuDF 0.5.1 (05 Feb 2019)
+
+## Bug Fixes
+
+- PR #842 Avoid using numpy via cimport to prevent ABI issues in Cython compilation
+
+
+# cuDF 0.5.0 (28 Jan 2019)
+
+## New Features
+
+- PR #722 Add bzip2 decompression support to `read_csv()`
+- PR #693 add ZLIB-based GZIP/ZIP support to `read_csv_strings()`
+- PR #411 added null support to gdf_order_by (new API) and cudf_table::sort
+- PR #525 Added GitHub Issue templates for bugs, documentation, new features, and questions
+- PR #501 CSV Reader: Add support for user-specified decimal point and thousands separator to read_csv_strings()
+- PR #455 CSV Reader: Add support for user-specified decimal point and thousands separator to read_csv()
+- PR #439 add `DataFrame.drop` method similar to pandas
+- PR #356 add `DataFrame.transpose` method and `DataFrame.T` property similar to pandas
+- PR #505 CSV Reader: Add support for user-specified boolean values
+- PR #350 Implemented Series replace function
+- PR #490 Added print_env.sh script to gather relevant environment details when reporting cuDF issues
+- PR #474 add ZLIB-based GZIP/ZIP support to `read_csv()`
+- PR #547 Added melt similar to `pandas.melt()`
+- PR #491 Add CI test script to check for updates to CHANGELOG.md in PRs
+- PR #550 Add CI test script to check for style issues in PRs
+- PR #558 Add CI scripts for cpu-based conda and gpu-based test builds
+- PR #524 Add Boolean Indexing
+- PR #564 Update python `sort_values` method to use updated libcudf `gdf_order_by` API
+- PR #509 CSV Reader: Input CSV file can now be passed in as a text or a binary buffer
+- PR #607 Add `__iter__` and iteritems to DataFrame class
+- PR #643 added a new api gdf_replace_nulls that allows a user to replace nulls in a column
+
+## Improvements
+
+- PR #426 Removed sort-based groupby and refactored existing groupby APIs. Also improves C++/CUDA compile time.
+- PR #461 Add `CUDF_HOME` variable in README.md to replace relative pathing.
+- PR #472 RMM: Created centralized rmm::device_vector alias and rmm::exec_policy
+- PR #500 Improved the concurrent hash map class to support partitioned (multi-pass) hash table building.
+- PR #454 Improve CSV reader docs and examples
+- PR #465 Added templated C++ API for RMM to avoid explicit cast to `void**`
+- PR #513 `.gitignore` tweaks
+- PR #521 Add `assert_eq` function for testing
+- PR #502 Simplify Dockerfile for local dev, eliminate old conda/pip envs
+- PR #549 Adds `-rdynamic` compiler flag to nvcc for Debug builds
+- PR #472 RMM: Created centralized rmm::device_vector alias and rmm::exec_policy
+- PR #577 Added external C++ API for scatter/gather functions
+- PR #500 Improved the concurrent hash map class to support partitioned (multi-pass) hash table building
+- PR #583 Updated `gdf_size_type` to `int`
+- PR #500 Improved the concurrent hash map class to support partitioned (multi-pass) hash table building
+- PR #617 Added .dockerignore file. Prevents adding stale cmake cache files to the docker container
+- PR #658 Reduced `JOIN_TEST` time by isolating overflow test of hash table size computation
+- PR #664 Added Debugging instructions to README
+- PR #651 Remove noqa marks in `__init__.py` files
+- PR #671 CSV Reader: uncompressed buffer input can be parsed without explicitly specifying compression as None
+- PR #684 Make RMM a submodule
+- PR #718 Ensure sum, product, min, max methods pandas compatibility on empty datasets
+- PR #720 Refactored Index classes to make them more Pandas-like, added CategoricalIndex
+- PR #749 Improve to_arrow and from_arrow Pandas compatibility
+- PR #766 Remove TravisCI references, remove unused variables from CMake, fix ARROW_VERSION in Cmake
+- PR #773 Add build-args back to Dockerfile and handle dependencies based on environment yml file
+- PR #781 Move thirdparty submodules to root and symlink in /cpp
+- PR #843 Fix broken cudf/python API examples, add new methods to the API index
+
+## Bug Fixes
+
+- PR #569 CSV Reader: Fix days being off-by-one when parsing some dates
+- PR #531 CSV Reader: Fix incorrect parsing of quoted numbers
+- PR #465 Added templated C++ API for RMM to avoid explicit cast to `void**`
+- PR #473 Added missing <random> include
+- PR #478 CSV Reader: Add api support for auto column detection, header, mangle_dupe_cols, usecols
+- PR #495 Updated README to correct where cffi pytest should be executed
+- PR #501 Fix the intermittent segfault caused by the `thousands` and `compression` parameters in the csv reader
+- PR #502 Simplify Dockerfile for local dev, eliminate old conda/pip envs
+- PR #512 fix bug for `on` parameter in `DataFrame.merge` to allow for None or single column name
+- PR #511 Updated python/cudf/bindings/join.pyx to fix cudf merge printing out dtypes
+- PR #513 `.gitignore` tweaks
+- PR #521 Add `assert_eq` function for testing
+- PR #537 Fix CMAKE_CUDA_STANDARD_REQURIED typo in CMakeLists.txt
+- PR #447 Fix silent failure in initializing DataFrame from generator
+- PR #545 Temporarily disable csv reader thousands test to prevent segfault (test re-enabled in PR #501)
+- PR #559 Fix Assertion error while using `applymap` to change the output dtype
+- PR #575 Update `print_env.sh` script to better handle missing commands
+- PR #612 Prevent an exception from occurring with true division on integer series.
+- PR #630 Fix deprecation warning for `pd.core.common.is_categorical_dtype`
+- PR #622 Fix Series.append() behaviour when appending values with different numeric dtype
+- PR #603 Fix error while creating an empty column using None.
+- PR #673 Fix array of strings not being caught in from_pandas
+- PR #644 Fix return type and column support of dataframe.quantile()
+- PR #634 Fix create `DataFrame.from_pandas()` with numeric column names
+- PR #654 Add resolution check for GDF_TIMESTAMP in Join
+- PR #648 Enforce one-to-one copy required when using `numba>=0.42.0`
+- PR #645 Fix cmake build type handling not setting debug options when CMAKE_BUILD_TYPE=="Debug"
+- PR #669 Fix GIL deadlock when launching multiple python threads that make Cython calls
+- PR #665 Reworked the hash map to add a way to report the destination partition for a key
+- PR #670 CMAKE: Fix env include path taking precedence over libcudf source headers
+- PR #674 Check for gdf supported column types
+- PR #677 Fix 'gdf_csv_test_Dates' gtest failure due to missing nrows parameter
+- PR #604 Fix the parsing errors while reading a csv file using `sep` instead of `delimiter`.
+- PR #686 Fix converting nulls to NaT values when converting Series to Pandas/Numpy
+- PR #689 CSV Reader: Fix behavior with skiprows+header to match pandas implementation
+- PR #691 Fixes Join on empty input DFs
+- PR #706 CSV Reader: Fix broken dtype inference when whitespace is in data
+- PR #717 CSV reader: fix behavior when parsing a csv file with no data rows
+- PR #724 CSV Reader: fix build issue due to parameter type mismatch in a std::max call
+- PR #734 Prevents reading undefined memory in gpu_expand_mask_bits numba kernel
+- PR #747 CSV Reader: fix an issue where CUDA allocations fail with some large input files
+- PR #750 Fix race condition for handling NVStrings in CMake
+- PR #719 Fix merge column ordering
+- PR #770 Fix issue where RMM submodule pointed to wrong branch and pin other to correct branches
+- PR #778 Fix hard coded ABI off setting
+- PR #784 Update RMM submodule commit-ish and pip paths
+- PR #794 Update `rmm::exec_policy` usage to fix segmentation faults when used as temporary allocator.
+- PR #800 Point git submodules to branches of forks instead of exact commits
+
+
+# cuDF 0.4.0 (05 Dec 2018)
+
+## New Features
+
+- PR #398 add pandas-compatible `DataFrame.shape()` and `Series.shape()`
+- PR #394 New documentation feature "10 Minutes to cuDF"
+- PR #361 CSV Reader: Add support for strings with delimiters
+
+## Improvements
+
+ - PR #436 Improvements for type_dispatcher and wrapper structs
+ - PR #429 Add CHANGELOG.md (this file)
+ - PR #266 use faster CUDA-accelerated DataFrame column/Series concatenation.
+ - PR #379 new C++ `type_dispatcher` reduces code complexity in supporting many data types.
+ - PR #349 Improve performance for creating columns from memoryview objects
+ - PR #445 Update reductions to use type_dispatcher. Adds integer types support to sum_of_squares.
+ - PR #448 Improve installation instructions in README.md
+ - PR #456 Change default CMake build to Release, and added option for disabling compilation of tests
+
+## Bug Fixes
+
+ - PR #444 Fix csv_test CUDA too many resources requested fail.
+ - PR #396 added missing output buffer in validity tests for groupbys.
+ - PR #408 Dockerfile updates for source reorganization
+ - PR #437 Add cffi to Dockerfile conda env, fixes "cannot import name 'librmm'"
+ - PR #417 Fix `map_test` failure with CUDA 10
+ - PR #414 Fix CMake installation include file paths
+ - PR #418 Properly cast string dtypes to programmatic dtypes when instantiating columns
+ - PR #427 Fix and tests for Concatenation illegal memory access with nulls
+
+
+# cuDF 0.3.0 (23 Nov 2018)
+
+## New Features
+
+ - PR #336 CSV Reader string support
+
+## Improvements
+
+ - PR #354 source code refactored for better organization. CMake build system overhaul. Beginning of transition to Cython bindings.
+ - PR #290 Add support for typecasting to/from datetime dtype
+ - PR #323 Add handling pyarrow boolean arrays in input/out, add tests
+ - PR #325 GDF_VALIDITY_UNSUPPORTED now returned for algorithms that don't support non-empty valid bitmasks
+ - PR #381 Faster InputTooLarge Join test completes in ms rather than minutes.
+ - PR #373 .gitignore improvements
+ - PR #367 Doc cleanup & examples for DataFrame methods
+ - PR #333 Add Rapids Memory Manager documentation
+ - PR #321 Rapids Memory Manager adds file/line location logging and convenience macros
+ - PR #334 Implement DataFrame `__copy__` and `__deepcopy__`
+ - PR #271 Add NVTX ranges to pygdf
+ - PR #311 Document system requirements for conda install
+
+## Bug Fixes
+
+ - PR #337 Retain index on `scale()` function
+ - PR #344 Fix test failure due to PyArrow 0.11 Boolean handling
+ - PR #364 Remove noexcept from managed_allocator;  CMakeLists fix for NVstrings
+ - PR #357 Fix bug that made all series be considered booleans for indexing
+ - PR #351 replace conda env configuration for developers
+ - PRs #346 #360 Fix CSV reading of negative numbers
+ - PR #342 Fix CMake to use conda-installed nvstrings
+ - PR #341 Preserve categorical dtype after groupby aggregations
+ - PR #315 ReadTheDocs build update to fix missing libcuda.so
+ - PR #320 FIX out-of-bounds access error in reductions.cu
+ - PR #319 Fix out-of-bounds memory access in libcudf count_valid_bits
+ - PR #303 Fix printing empty dataframe
+
+
+# cuDF 0.2.0 and cuDF 0.1.0
+
+These were initial releases of cuDF based on previously separate pyGDF and libGDF libraries.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..07537e7
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,398 @@
+# Contributing to cuDF
+
+Contributions to cuDF fall into the following categories:
+
+1. To report a bug, request a new feature, or report a problem with documentation, please file an
+   [issue](https://github.com/rapidsai/cudf/issues/new/choose) describing the problem or new feature
+   in detail. The RAPIDS team evaluates and triages issues, and schedules them for a release. If you
+   believe the issue needs priority attention, please comment on the issue to notify the team.
+2. To propose and implement a new feature, please file a new feature request
+   [issue](https://github.com/rapidsai/cudf/issues/new/choose). Describe the intended feature and
+   discuss the design and implementation with the team and community. Once the team agrees that the
+   plan looks good, go ahead and implement it, using the [code contributions](#code-contributions)
+   guide below.
+3. To implement a feature or bug fix for an existing issue, please follow the [code
+   contributions](#code-contributions) guide below. If you need more context on a particular issue,
+   please ask in a comment.
+
+As contributors and maintainers to this project, you are expected to abide by cuDF's code of
+conduct. More information can be found at:
+[Contributor Code of Conduct](https://docs.rapids.ai/resources/conduct/).
+
+## Code contributions
+
+### Your first issue
+
+1. Follow the guide at the bottom of this page for
+   [Setting up your build environment](#setting-up-your-build-environment).
+2. Find an issue to work on. The best way is to look for the
+   [good first issue](https://github.com/rapidsai/cudf/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
+   or [help wanted](https://github.com/rapidsai/cudf/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22)
+   labels.
+3. Comment on the issue stating that you are going to work on it.
+4. Create a fork of the cudf repository and check out a branch with a name that
+   describes your planned work. For example, `fix-documentation`.
+5. Write code to address the issue or implement the feature.
+6. Add unit tests and unit benchmarks.
+7. [Create your pull request](https://github.com/rapidsai/cudf/compare). To run continuous integration (CI) tests without requesting review, open a draft pull request.
+8. Verify that CI passes all [status checks](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/collaborating-on-repositories-with-code-quality-features/about-status-checks).
+   Fix if needed.
+9. Wait for other developers to review your code and update code as needed.
+10. Once reviewed and approved, a RAPIDS developer will merge your pull request.
+
+If you are unsure about anything, don't hesitate to comment on issues and ask for clarification!
+
+### Seasoned developers
+
+Once you have gotten your feet wet and are more comfortable with the code, you can look at the
+prioritized issues for our next release in our
+[project boards](https://github.com/rapidsai/cudf/projects).
+
+**Note:** Always look at the release board that is
+[currently under development](https://docs.rapids.ai/maintainers) for issues to work on. This is
+where RAPIDS developers also focus their efforts.
+
+Look at the unassigned issues, and find an issue to which you are comfortable contributing. Start
+with _Step 3_ above, commenting on the issue to let others know you are working on it. If you have
+any questions related to the implementation of the issue, ask them in the issue instead of the PR.
+
+## Setting up your build environment
+
+The following instructions are for developers and contributors to cuDF development. These
+instructions are tested on Ubuntu Linux LTS releases. Use these instructions to build cuDF from
+source and contribute to its development. Other operating systems may be compatible, but are not
+currently tested.
+
+Building cudf with the provided conda environment is recommended for users who wish to enable all
+library features. The following instructions are for building with a conda environment. Dependencies
+for a minimal build of libcudf without using conda are also listed below.
+
+### General requirements
+
+Compilers:
+
+* `gcc` version 9.3+
+* `nvcc` version 11.5+
+* `cmake` version 3.26.4+
+
+CUDA/GPU:
+
+* CUDA 11.5+
+* NVIDIA driver 450.80.02+
+* Pascal architecture or better
+
+You can obtain CUDA from
+[https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads).
+
+### Create the build environment
+
+- Clone the repository:
+
+```bash
+CUDF_HOME=$(pwd)/cudf
+git clone https://github.com/rapidsai/cudf.git $CUDF_HOME
+cd $CUDF_HOME
+```
+
+#### Building with a conda environment
+
+**Note:** Using a conda environment is the easiest way to satisfy the library's dependencies.
+Instructions for a minimal build environment without conda are included below.
+
+- Create the conda development environment:
+
+```bash
+# create the conda environment (assuming in base `cudf` directory)
+# note: RAPIDS currently doesn't support `channel_priority: strict`;
+# use `channel_priority: flexible` instead
+conda env create --name cudf_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml
+# activate the environment
+conda activate cudf_dev
+```
+
+- **Note**: the conda environment files are updated frequently, so the
+  development environment may also need to be updated if dependency versions or
+  pinnings are changed.
+
+#### Building without a conda environment
+
+- libcudf has the following minimal dependencies (in addition to those listed in the [General
+  requirements](#general-requirements)). The packages listed below use Ubuntu package names:
+
+  - `build-essential`
+  - `libssl-dev`
+  - `libz-dev`
+  - `libpython3-dev` (required if building cudf)
+
+### Build cuDF from source
+
+- A `build.sh` script is provided in `$CUDF_HOME`. Running the script with no additional arguments
+  will install the `libcudf`, `cudf` and `dask_cudf` libraries. By default, the libraries are
+  installed to the `$CONDA_PREFIX` directory. To install into a different location, set the location
+  in `$INSTALL_PREFIX`. Finally, note that the script depends on the `nvcc` executable being on your
+  path, or defined in `$CUDACXX`.
+
+```bash
+cd $CUDF_HOME
+
+# Choose one of the following commands, depending on whether
+# you want to build and install the libcudf C++ library only,
+# or include the cudf and/or dask_cudf Python libraries:
+
+./build.sh  # libcudf, cudf and dask_cudf
+./build.sh libcudf  # libcudf only
+./build.sh libcudf cudf  # libcudf and cudf only
+```
+
+- Other libraries like `cudf-kafka` and `custreamz` can be installed with this script. For the
+  complete list of libraries as well as details about the script usage, run the `help` command:
+
+```bash
+./build.sh --help
+```
+
+### Build, install and test cuDF libraries for contributors
+
+The general workflow is provided below. Please also see the last section about
+[code formatting](#code-formatting).
+
+#### `libcudf` (C++)
+
+- If you're only interested in building the library (and not the unit tests):
+
+```bash
+cd $CUDF_HOME
+./build.sh libcudf
+```
+
+- If, in addition, you want to build tests:
+
+```bash
+./build.sh libcudf tests
+```
+
+- To run the tests:
+
+```bash
+make test
+```
+
+#### `cudf` (Python)
+
+- First, build the `libcudf` C++ library following the steps above
+
+- To build and install in edit/develop `cudf` Python package:
+```bash
+cd $CUDF_HOME/python/cudf
+python setup.py build_ext --inplace
+python setup.py develop
+```
+
+- To run `cudf` tests:
+```bash
+cd $CUDF_HOME/python
+pytest -v cudf/cudf/tests
+```
+
+#### `dask-cudf` (Python)
+
+- First, build the `libcudf` C++ and `cudf` Python libraries following the steps above
+
+- To install the `dask-cudf` Python package in editable/develop mode:
+```bash
+cd $CUDF_HOME/python/dask_cudf
+python setup.py build_ext --inplace
+python setup.py develop
+```
+
+- To run `dask_cudf` tests:
+```bash
+cd $CUDF_HOME/python
+pytest -v dask_cudf
+```
+
+#### `libcudf_kafka` (C++)
+
+- If you're only interested in building the library (and not the unit tests):
+
+```bash
+cd $CUDF_HOME
+./build.sh libcudf_kafka
+```
+
+- If, in addition, you want to build tests:
+
+```bash
+./build.sh libcudf_kafka tests
+```
+
+- To run the tests:
+
+```bash
+make test
+```
+
+#### `cudf-kafka` (Python)
+
+- First, build the `libcudf` and `libcudf_kafka` libraries following the steps above
+
+- To install the `cudf-kafka` Python package in editable/develop mode:
+
+```bash
+cd $CUDF_HOME/python/cudf_kafka
+python setup.py build_ext --inplace
+python setup.py develop
+```
+
+#### `custreamz` (Python)
+
+- First, build `libcudf`, `libcudf_kafka`, and `cudf_kafka` following the steps above
+
+- To install the `custreamz` Python package in editable/develop mode:
+
+```bash
+cd $CUDF_HOME/python/custreamz
+python setup.py build_ext --inplace
+python setup.py develop
+```
+
+- To run `custreamz` tests :
+
+```bash
+cd $CUDF_HOME/python
+pytest -v custreamz
+```
+
+#### `cudf` (Java):
+
+- First, build the `libcudf` C++ library following the steps above
+
+- Then, refer to the [Java README](java/README.md)
+
+Done! You are ready to develop for the cuDF project. Please review the project's
+[code formatting guidelines](#code-formatting).
+
+## Debugging cuDF
+
+### Building in debug mode from source
+
+Follow the instructions to [build from source](#build-cudf-from-source) and add `-g` to the
+`./build.sh` command.
+
+For example:
+
+```bash
+./build.sh libcudf -g
+```
+
+This builds `libcudf` in debug mode which enables some `assert` safety checks and includes symbols
+in the library for debugging.
+
+All other steps for installing `libcudf` into your environment are the same.
+
+### Debugging with `cuda-gdb` and `cuda-memcheck`
+
+When you have a debug build of `libcudf` installed, debugging with the `cuda-gdb` and
+`cuda-memcheck` is easy.
+
+If you are debugging a Python script, run the following:
+
+```bash
+cuda-gdb -ex r --args python <program_name>.py <program_arguments>
+```
+
+```bash
+cuda-memcheck python <program_name>.py <program_arguments>
+```
+
+### Device debug symbols
+
+The device debug symbols are not automatically added with the cmake `Debug` build type because it
+causes a runtime delay of several minutes when loading the libcudf.so library.
+
+Therefore, it is recommended to add device debug symbols only to specific files by setting the `-G`
+compile option locally in your `cpp/CMakeLists.txt` for that file. Here is an example of adding the
+`-G` option to the compile command for `src/copying/copy.cu` source file:
+
+```cmake
+set_source_files_properties(src/copying/copy.cu PROPERTIES COMPILE_OPTIONS "-G")
+```
+
+This will add the device debug symbols for this object file in `libcudf.so`.  You can then use
+`cuda-dbg` to debug into the kernels in that source file.
+
+## Code Formatting
+
+### Using pre-commit hooks
+
+cuDF uses [pre-commit](https://pre-commit.com/) to execute all code linters and formatters. These
+tools ensure a consistent code format throughout the project. Using pre-commit ensures that linter
+versions and options are aligned for all developers. Additionally, there is a CI check in place to
+enforce that committed code follows our standards.
+
+To use `pre-commit`, install via `conda` or `pip`:
+
+```bash
+conda install -c conda-forge pre-commit
+```
+
+```bash
+pip install pre-commit
+```
+
+Then run pre-commit hooks before committing code:
+
+```bash
+pre-commit run
+```
+
+By default, pre-commit runs on staged files (only changes and additions that will be committed).
+To run pre-commit checks on all files, execute:
+
+```bash
+pre-commit run --all-files
+```
+
+Optionally, you may set up the pre-commit hooks to run automatically when you make a git commit. This can be done by running:
+
+```bash
+pre-commit install
+```
+
+Now code linters and formatters will be run each time you commit changes.
+
+You can skip these checks with `git commit --no-verify` or with the short version `git commit -n`.
+
+### Summary of pre-commit hooks
+
+The following section describes some of the core pre-commit hooks used by the repository.
+See `.pre-commit-config.yaml` for a full list.
+
+C++/CUDA is formatted with [`clang-format`](https://clang.llvm.org/docs/ClangFormat.html).
+
+[`doxygen`](https://doxygen.nl/) is used as documentation generator and also as a documentation linter.
+In order to run doxygen as a linter on C++/CUDA code, run
+
+```bash
+./ci/checks/doxygen.sh
+```
+
+Python code runs several linters including [Black](https://black.readthedocs.io/en/stable/),
+[isort](https://pycqa.github.io/isort/), and [flake8](https://flake8.pycqa.org/en/latest/).
+
+cuDF also uses [codespell](https://github.com/codespell-project/codespell) to find spelling
+mistakes, and this check is run as a pre-commit hook. To apply the suggested spelling fixes,
+you can run  `codespell -i 3 -w .` from the repository root directory.
+This will bring up an interactive prompt to select which spelling fixes to apply.
+
+## Developer Guidelines
+
+The [C++ Developer Guide](cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md) includes details on contributing to libcudf C++ code.
+
+The [Python Developer Guide](https://docs.rapids.ai/api/cudf/stable/developer_guide/index.html) includes details on contributing to cuDF Python code.
+
+
+## Attribution
+
+Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md
+Portions adopted from https://github.com/dask/dask/blob/master/docs/source/develop.rst
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..18bcb43
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright 2018 NVIDIA Corporation
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..64c980d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,97 @@
+# <div align="left"><img src="img/rapids_logo.png" width="90px"/>&nbsp;cuDF - GPU DataFrames</div>
+
+**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cudf/blob/main/README.md) ensure you are on the `main` branch.
+
+## Resources
+
+- [cuDF Reference Documentation](https://docs.rapids.ai/api/cudf/stable/): Python API reference, tutorials, and topic guides.
+- [libcudf Reference Documentation](https://docs.rapids.ai/api/libcudf/stable/): C/C++ CUDA library API reference.
+- [Getting Started](https://rapids.ai/start.html): Instructions for installing cuDF.
+- [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
+- [GitHub repository](https://github.com/rapidsai/cudf): Download the cuDF source code.
+- [Issue tracker](https://github.com/rapidsai/cudf/issues): Report issues or request features.
+
+## Overview
+
+Built based on the [Apache Arrow](http://arrow.apache.org/) columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
+
+cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
+
+For example, the following snippet downloads a CSV, then uses the GPU to parse it into rows and columns and run calculations:
+```python
+import cudf, requests
+from io import StringIO
+
+url = "https://github.com/plotly/datasets/raw/master/tips.csv"
+content = requests.get(url).content.decode('utf-8')
+
+tips_df = cudf.read_csv(StringIO(content))
+tips_df['tip_percentage'] = tips_df['tip'] / tips_df['total_bill'] * 100
+
+# display average tip by dining party size
+print(tips_df.groupby('size').tip_percentage.mean())
+```
+
+Output:
+```
+size
+1    21.729201548727808
+2    16.571919173482897
+3    15.215685473711837
+4    14.594900639351332
+5    14.149548965142023
+6    15.622920072028379
+Name: tip_percentage, dtype: float64
+```
+
+For additional examples, browse our complete [API documentation](https://docs.rapids.ai/api/cudf/stable/), or check out our more detailed [notebooks](https://github.com/rapidsai/notebooks-contrib).
+
+## Quick Start
+
+Please see the [Demo Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you're running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize cuDF.
+
+## Installation
+
+
+### CUDA/GPU requirements
+
+* CUDA 11.2+
+* NVIDIA driver 450.80.02+
+* Pascal architecture or better (Compute Capability >=6.0)
+
+### Conda
+
+cuDF can be installed with conda (via [miniconda](https://conda.io/miniconda.html) or the full [Anaconda distribution](https://www.anaconda.com/download)) from the `rapidsai` channel:
+
+```bash
+conda install -c rapidsai -c conda-forge -c nvidia \
+    cudf=23.10 python=3.10 cuda-version=11.8
+```
+
+We also provide [nightly Conda packages](https://anaconda.org/rapidsai-nightly) built from the HEAD
+of our latest development branch.
+
+Note: cuDF is supported only on Linux, and with Python versions 3.9 and later.
+
+See the [Get RAPIDS version picker](https://rapids.ai/start.html) for more OS and version info.
+
+## Build/Install from Source
+See build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).
+
+## Contributing
+
+Please see our [guide for contributing to cuDF](CONTRIBUTING.md).
+
+## Contact
+
+Find out more details on the [RAPIDS site](https://rapids.ai/community.html)
+
+## <div align="left"><img src="img/rapids_logo.png" width="265px"/></div> Open GPU Data Science
+
+The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
+
+<p align="center"><img src="img/rapids_arrow.png" width="80%"/></p>
+
+### Apache Arrow on GPU
+
+The GPU version of [Apache Arrow](https://arrow.apache.org/) is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.
diff --git a/build.sh b/build.sh
new file mode 100755
index 0000000..2ad6971
--- /dev/null
+++ b/build.sh
@@ -0,0 +1,383 @@
+#!/bin/bash
+
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+# cuDF build script
+
+# This script is used to build the component(s) in this repo from
+# source, and can be called with various options to customize the
+# build as needed (see the help output for details)
+# Abort script on first error
+set -e
+
+NUMARGS=$#
+ARGS=$*
+
+# NOTE: ensure all dir changes are relative to the location of this
+# script, and that this script resides in the repo dir!
+REPODIR=$(cd $(dirname $0); pwd)
+
+VALIDARGS="clean libcudf cudf cudfjar dask_cudf benchmarks tests libcudf_kafka cudf_kafka custreamz -v -g -n -l --allgpuarch --disable_nvtx --opensource_nvcomp  --show_depr_warn --ptds -h --build_metrics --incl_cache_stats"
+HELP="$0 [clean] [libcudf] [cudf] [cudfjar] [dask_cudf] [benchmarks] [tests] [libcudf_kafka] [cudf_kafka] [custreamz] [-v] [-g] [-n] [-h] [--cmake-args=\\\"<args>\\\"]
+   clean                         - remove all existing build artifacts and configuration (start
+                                   over)
+   libcudf                       - build the cudf C++ code only
+   cudf                          - build the cudf Python package
+   cudfjar                       - build cudf JAR with static libcudf using devtoolset toolchain
+   dask_cudf                     - build the dask_cudf Python package
+   benchmarks                    - build benchmarks
+   tests                         - build tests
+   libcudf_kafka                 - build the libcudf_kafka C++ code only
+   cudf_kafka                    - build the cudf_kafka Python package
+   custreamz                     - build the custreamz Python package
+   -v                            - verbose build mode
+   -g                            - build for debug
+   -n                            - no install step (does not affect Python)
+   --allgpuarch                  - build for all supported GPU architectures
+   --disable_nvtx                - disable inserting NVTX profiling ranges
+   --opensource_nvcomp           - disable use of proprietary nvcomp extensions
+   --show_depr_warn              - show cmake deprecation warnings
+   --ptds                        - enable per-thread default stream
+   --build_metrics               - generate build metrics report for libcudf
+   --incl_cache_stats            - include cache statistics in build metrics report
+   --cmake-args=\\\"<args>\\\"   - pass arbitrary list of CMake configuration options (escape all quotes in argument)
+   -h | --h[elp]                 - print this text
+
+   default action (no args) is to build and install 'libcudf' then 'cudf'
+   then 'dask_cudf' targets
+"
+LIB_BUILD_DIR=${LIB_BUILD_DIR:=${REPODIR}/cpp/build}
+KAFKA_LIB_BUILD_DIR=${KAFKA_LIB_BUILD_DIR:=${REPODIR}/cpp/libcudf_kafka/build}
+CUDF_KAFKA_BUILD_DIR=${REPODIR}/python/cudf_kafka/build
+CUDF_BUILD_DIR=${REPODIR}/python/cudf/build
+DASK_CUDF_BUILD_DIR=${REPODIR}/python/dask_cudf/build
+CUSTREAMZ_BUILD_DIR=${REPODIR}/python/custreamz/build
+CUDF_JAR_JAVA_BUILD_DIR="$REPODIR/java/target"
+
+BUILD_DIRS="${LIB_BUILD_DIR} ${CUDF_BUILD_DIR} ${DASK_CUDF_BUILD_DIR} ${KAFKA_LIB_BUILD_DIR} ${CUDF_KAFKA_BUILD_DIR} ${CUSTREAMZ_BUILD_DIR} ${CUDF_JAR_JAVA_BUILD_DIR}"
+
+# Set defaults for vars modified by flags to this script
+VERBOSE_FLAG=""
+BUILD_TYPE=Release
+INSTALL_TARGET=install
+BUILD_BENCHMARKS=OFF
+BUILD_ALL_GPU_ARCH=0
+BUILD_NVTX=ON
+BUILD_TESTS=OFF
+BUILD_DISABLE_DEPRECATION_WARNINGS=ON
+BUILD_PER_THREAD_DEFAULT_STREAM=OFF
+BUILD_REPORT_METRICS=OFF
+BUILD_REPORT_INCL_CACHE_STATS=OFF
+USE_PROPRIETARY_NVCOMP=ON
+
+# Set defaults for vars that may not have been defined externally
+#  FIXME: if INSTALL_PREFIX is not set, check PREFIX, then check
+#         CONDA_PREFIX, but there is no fallback from there!
+INSTALL_PREFIX=${INSTALL_PREFIX:=${PREFIX:=${CONDA_PREFIX}}}
+PARALLEL_LEVEL=${PARALLEL_LEVEL:=$(nproc)}
+
+function hasArg {
+    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+}
+
+function cmakeArgs {
+    # Check for multiple cmake args options
+    if [[ $(echo $ARGS | { grep -Eo "\-\-cmake\-args" || true; } | wc -l ) -gt 1 ]]; then
+        echo "Multiple --cmake-args options were provided, please provide only one: ${ARGS}"
+        exit 1
+    fi
+
+    # Check for cmake args option
+    if [[ -n $(echo $ARGS | { grep -E "\-\-cmake\-args" || true; } ) ]]; then
+        # There are possible weird edge cases that may cause this regex filter to output nothing and fail silently
+        # the true pipe will catch any weird edge cases that may happen and will cause the program to fall back
+        # on the invalid option error
+        EXTRA_CMAKE_ARGS=$(echo $ARGS | { grep -Eo "\-\-cmake\-args=\".+\"" || true; })
+        if [[ -n ${EXTRA_CMAKE_ARGS} ]]; then
+            # Remove the full  EXTRA_CMAKE_ARGS argument from list of args so that it passes validArgs function
+            ARGS=${ARGS//$EXTRA_CMAKE_ARGS/}
+            # Filter the full argument down to just the extra string that will be added to cmake call
+            EXTRA_CMAKE_ARGS=$(echo $EXTRA_CMAKE_ARGS | grep -Eo "\".+\"" | sed -e 's/^"//' -e 's/"$//')
+        fi
+    fi
+}
+
+function buildAll {
+    ((${NUMARGS} == 0 )) || !(echo " ${ARGS} " | grep -q " [^-]\+ ")
+}
+
+function buildLibCudfJniInDocker {
+    local cudaVersion="11.5.0"
+    local imageName="cudf-build:${cudaVersion}-devel-centos7"
+    local CMAKE_GENERATOR="${CMAKE_GENERATOR:-Ninja}"
+    local workspaceDir="/rapids"
+    local localMavenRepo=${LOCAL_MAVEN_REPO:-"$HOME/.m2/repository"}
+    local workspaceRepoDir="$workspaceDir/cudf"
+    local workspaceMavenRepoDir="$workspaceDir/.m2/repository"
+    local workspaceCcacheDir="$workspaceDir/.ccache"
+    mkdir -p "$CUDF_JAR_JAVA_BUILD_DIR/libcudf-cmake-build"
+    mkdir -p "$HOME/.ccache" "$HOME/.m2"
+    nvidia-docker build \
+        -f java/ci/Dockerfile.centos7 \
+        --build-arg CUDA_VERSION=${cudaVersion} \
+        -t $imageName .
+    nvidia-docker run -it -u $(id -u):$(id -g) --rm \
+        -e PARALLEL_LEVEL \
+        -e CCACHE_DISABLE \
+        -e CCACHE_DIR="$workspaceCcacheDir" \
+        -v "/etc/group:/etc/group:ro" \
+        -v "/etc/passwd:/etc/passwd:ro" \
+        -v "/etc/shadow:/etc/shadow:ro" \
+        -v "/etc/sudoers.d:/etc/sudoers.d:ro" \
+        -v "$HOME/.ccache:$workspaceCcacheDir:rw" \
+        -v "$REPODIR:$workspaceRepoDir:rw" \
+        -v "$localMavenRepo:$workspaceMavenRepoDir:rw" \
+        --workdir "$workspaceRepoDir/java/target/libcudf-cmake-build" \
+        ${imageName} \
+        scl enable devtoolset-9 \
+            "cmake $workspaceRepoDir/cpp \
+                -G${CMAKE_GENERATOR} \
+                -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+                -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+                -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+                -DCMAKE_CXX_LINKER_LAUNCHER=ccache \
+                -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                -DCUDA_STATIC_RUNTIME=ON \
+                -DCMAKE_CUDA_ARCHITECTURES=${CUDF_CMAKE_CUDA_ARCHITECTURES} \
+                -DCMAKE_INSTALL_PREFIX=/usr/local/rapids \
+                -DUSE_NVTX=ON \
+                -DCUDF_USE_PROPRIETARY_NVCOMP=ON \
+                -DCUDF_USE_ARROW_STATIC=ON \
+                -DCUDF_ENABLE_ARROW_S3=OFF \
+                -DBUILD_TESTS=OFF \
+                -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=ON \
+                -DRMM_LOGGING_LEVEL=OFF \
+                -DBUILD_SHARED_LIBS=OFF && \
+             cmake --build . --parallel ${PARALLEL_LEVEL} && \
+             cd $workspaceRepoDir/java && \
+             mvn ${MVN_PHASES:-"package"} \
+                -Dmaven.repo.local=$workspaceMavenRepoDir \
+                -DskipTests=${SKIP_TESTS:-false} \
+                -Dparallel.level=${PARALLEL_LEVEL} \
+                -Dcmake.ccache.opts='-DCMAKE_C_COMPILER_LAUNCHER=ccache \
+                                     -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+                                     -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache \
+                                     -DCMAKE_CXX_LINKER_LAUNCHER=ccache' \
+                -DCUDF_CPP_BUILD_DIR=$workspaceRepoDir/java/target/libcudf-cmake-build \
+                -DCUDA_STATIC_RUNTIME=ON \
+                -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=ON \
+                -DUSE_GDS=ON \
+                -DGPU_ARCHS=${CUDF_CMAKE_CUDA_ARCHITECTURES} \
+                -DCUDF_JNI_LIBCUDF_STATIC=ON \
+                -Dtest=*,!CuFileTest,!CudaFatalTest,!ColumnViewNonEmptyNullsTest"
+}
+
+if hasArg -h || hasArg --h || hasArg --help; then
+    echo "${HELP}"
+    exit 0
+fi
+
+# Check for valid usage
+if (( ${NUMARGS} != 0 )); then
+    # Check for cmake args
+    cmakeArgs
+    for a in ${ARGS}; do
+    if ! (echo " ${VALIDARGS} " | grep -q " ${a} "); then
+        echo "Invalid option or formatting, check --help: ${a}"
+        exit 1
+    fi
+    done
+fi
+
+# Process flags
+if hasArg -v; then
+    VERBOSE_FLAG="-v"
+fi
+if hasArg -g; then
+    BUILD_TYPE=Debug
+fi
+if hasArg -n; then
+    INSTALL_TARGET=""
+    LIBCUDF_BUILD_DIR=${LIB_BUILD_DIR}
+fi
+if hasArg --allgpuarch; then
+    BUILD_ALL_GPU_ARCH=1
+fi
+if hasArg benchmarks; then
+    BUILD_BENCHMARKS=ON
+fi
+if hasArg tests; then
+    BUILD_TESTS=ON
+fi
+if hasArg --disable_nvtx; then
+    BUILD_NVTX="OFF"
+fi
+if hasArg --opensource_nvcomp; then
+    USE_PROPRIETARY_NVCOMP="OFF"
+fi
+if hasArg --show_depr_warn; then
+    BUILD_DISABLE_DEPRECATION_WARNINGS=OFF
+fi
+if hasArg --ptds; then
+    BUILD_PER_THREAD_DEFAULT_STREAM=ON
+fi
+if hasArg --build_metrics; then
+    BUILD_REPORT_METRICS=ON
+fi
+
+if hasArg --incl_cache_stats; then
+    BUILD_REPORT_INCL_CACHE_STATS=ON
+fi
+
+# Append `-DFIND_CUDF_CPP=ON` to EXTRA_CMAKE_ARGS unless a user specified the option.
+if [[ "${EXTRA_CMAKE_ARGS}" != *"DFIND_CUDF_CPP"* ]]; then
+    EXTRA_CMAKE_ARGS="${EXTRA_CMAKE_ARGS} -DFIND_CUDF_CPP=ON"
+fi
+
+
+# If clean given, run it prior to any other steps
+if hasArg clean; then
+    # If the dirs to clean are mounted dirs in a container, the
+    # contents should be removed but the mounted dirs will remain.
+    # The find removes all contents but leaves the dirs, the rmdir
+    # attempts to remove the dirs but can fail safely.
+    for bd in ${BUILD_DIRS}; do
+    if [ -d ${bd} ]; then
+        find ${bd} -mindepth 1 -delete
+        rmdir ${bd} || true
+    fi
+    done
+
+    # Cleaning up python artifacts
+    find ${REPODIR}/python/ | grep -E "(__pycache__|\.pyc|\.pyo|\.so|\_skbuild$)"  | xargs rm -rf
+
+fi
+
+
+################################################################################
+# Configure, build, and install libcudf
+
+if buildAll || hasArg libcudf || hasArg cudf || hasArg cudfjar; then
+    if (( ${BUILD_ALL_GPU_ARCH} == 0 )); then
+        CUDF_CMAKE_CUDA_ARCHITECTURES="${CUDF_CMAKE_CUDA_ARCHITECTURES:-NATIVE}"
+        if [[ "$CUDF_CMAKE_CUDA_ARCHITECTURES" == "NATIVE" ]]; then
+            echo "Building for the architecture of the GPU in the system..."
+        else
+            echo "Building for the GPU architecture(s) $CUDF_CMAKE_CUDA_ARCHITECTURES ..."
+        fi
+    else
+        CUDF_CMAKE_CUDA_ARCHITECTURES="RAPIDS"
+        echo "Building for *ALL* supported GPU architectures..."
+    fi
+fi
+
+if buildAll || hasArg libcudf; then
+    # get the current count before the compile starts
+    if [[ "$BUILD_REPORT_INCL_CACHE_STATS" == "ON" && -x "$(command -v sccache)" ]]; then
+        # zero the sccache statistics
+        sccache --zero-stats
+    fi
+
+    cmake -S $REPODIR/cpp -B ${LIB_BUILD_DIR} \
+          -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \
+          -DCMAKE_CUDA_ARCHITECTURES=${CUDF_CMAKE_CUDA_ARCHITECTURES} \
+          -DUSE_NVTX=${BUILD_NVTX} \
+          -DCUDF_USE_PROPRIETARY_NVCOMP=${USE_PROPRIETARY_NVCOMP} \
+          -DBUILD_TESTS=${BUILD_TESTS} \
+          -DBUILD_BENCHMARKS=${BUILD_BENCHMARKS} \
+          -DDISABLE_DEPRECATION_WARNINGS=${BUILD_DISABLE_DEPRECATION_WARNINGS} \
+          -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=${BUILD_PER_THREAD_DEFAULT_STREAM} \
+          -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+          ${EXTRA_CMAKE_ARGS}
+
+    cd ${LIB_BUILD_DIR}
+
+    compile_start=$(date +%s)
+    cmake --build . -j${PARALLEL_LEVEL} ${VERBOSE_FLAG}
+    compile_end=$(date +%s)
+    compile_total=$(( compile_end - compile_start ))
+
+    # Record build times
+    if [[ "$BUILD_REPORT_METRICS" == "ON" && -f "${LIB_BUILD_DIR}/.ninja_log" ]]; then
+        echo "Formatting build metrics"
+        MSG=""
+        # get some sccache stats after the compile
+        if [[ "$BUILD_REPORT_INCL_CACHE_STATS" == "ON" && -x "$(command -v sccache)" ]]; then
+           COMPILE_REQUESTS=$(sccache -s | grep "Compile requests \+ [0-9]\+$" | awk '{ print $NF }')
+           CACHE_HITS=$(sccache -s | grep "Cache hits \+ [0-9]\+$" | awk '{ print $NF }')
+           HIT_RATE=$(echo - | awk "{printf \"%.2f\n\", $CACHE_HITS / $COMPILE_REQUESTS * 100}")
+           MSG="${MSG}<br/>cache hit rate ${HIT_RATE} %"
+        fi
+        MSG="${MSG}<br/>parallel setting: $PARALLEL_LEVEL"
+        MSG="${MSG}<br/>parallel build time: $compile_total seconds"
+        if [[ -f "${LIB_BUILD_DIR}/libcudf.so" ]]; then
+           LIBCUDF_FS=$(ls -lh ${LIB_BUILD_DIR}/libcudf.so | awk '{print $5}')
+           MSG="${MSG}<br/>libcudf.so size: $LIBCUDF_FS"
+        fi
+        BMR_DIR=${RAPIDS_ARTIFACTS_DIR:-"${LIB_BUILD_DIR}"}
+        echo "Metrics output dir: [$BMR_DIR]"
+        mkdir -p ${BMR_DIR}
+        MSG_OUTFILE="$(mktemp)"
+        echo "$MSG" > "${MSG_OUTFILE}"
+        python ${REPODIR}/cpp/scripts/sort_ninja_log.py ${LIB_BUILD_DIR}/.ninja_log --fmt html --msg "${MSG_OUTFILE}" > ${BMR_DIR}/ninja_log.html
+        cp ${LIB_BUILD_DIR}/.ninja_log ${BMR_DIR}/ninja.log
+    fi
+
+    if [[ ${INSTALL_TARGET} != "" ]]; then
+        cmake --build . -j${PARALLEL_LEVEL} --target install ${VERBOSE_FLAG}
+    fi
+fi
+
+# Build and install the cudf Python package
+if buildAll || hasArg cudf; then
+
+    cd ${REPODIR}/python/cudf
+    SKBUILD_CONFIGURE_OPTIONS="-DCMAKE_PREFIX_PATH=${INSTALL_PREFIX} -DCMAKE_LIBRARY_PATH=${LIBCUDF_BUILD_DIR} -DCMAKE_CUDA_ARCHITECTURES=${CUDF_CMAKE_CUDA_ARCHITECTURES} ${EXTRA_CMAKE_ARGS}" \
+        SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL:-1}" \
+        python -m pip install --no-build-isolation --no-deps .
+fi
+
+
+# Build and install the dask_cudf Python package
+if buildAll || hasArg dask_cudf; then
+
+    cd ${REPODIR}/python/dask_cudf
+    python -m pip install --no-build-isolation --no-deps .
+fi
+
+if hasArg cudfjar; then
+    buildLibCudfJniInDocker
+fi
+
+# Build libcudf_kafka library
+if hasArg libcudf_kafka; then
+    cmake -S $REPODIR/cpp/libcudf_kafka -B ${KAFKA_LIB_BUILD_DIR} \
+          -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \
+          -DBUILD_TESTS=${BUILD_TESTS} \
+          -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+          ${EXTRA_CMAKE_ARGS}
+
+
+    cd ${KAFKA_LIB_BUILD_DIR}
+    cmake --build . -j${PARALLEL_LEVEL} ${VERBOSE_FLAG}
+
+    if [[ ${INSTALL_TARGET} != "" ]]; then
+        cmake --build . -j${PARALLEL_LEVEL} --target install ${VERBOSE_FLAG}
+    fi
+fi
+
+# build cudf_kafka Python package
+if hasArg cudf_kafka; then
+    cd ${REPODIR}/python/cudf_kafka
+    SKBUILD_CONFIGURE_OPTIONS="-DCMAKE_LIBRARY_PATH=${LIBCUDF_BUILD_DIR}" \
+        SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL:-1}" \
+        python -m pip install --no-build-isolation --no-deps .
+fi
+
+# build custreamz Python package
+if hasArg custreamz; then
+    cd ${REPODIR}/python/custreamz
+    SKBUILD_CONFIGURE_OPTIONS="-DCMAKE_LIBRARY_PATH=${LIBCUDF_BUILD_DIR}" \
+        SKBUILD_BUILD_OPTIONS="-j${PARALLEL_LEVEL:-1}" \
+        python -m pip install --no-build-isolation --no-deps .
+fi
diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh
new file mode 100755
index 0000000..8b757fe
--- /dev/null
+++ b/ci/build_cpp.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+source rapids-env-update
+
+export CMAKE_GENERATOR=Ninja
+
+rapids-print-env
+
+rapids-logger "Begin cpp build"
+
+# With boa installed conda build forward to boa
+rapids-conda-retry mambabuild \
+    conda/recipes/libcudf
+
+rapids-upload-conda-to-s3 cpp
diff --git a/ci/build_docs.sh b/ci/build_docs.sh
new file mode 100755
index 0000000..9149b5e
--- /dev/null
+++ b/ci/build_docs.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+rapids-logger "Create test conda environment"
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key docs \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n docs
+conda activate docs
+
+rapids-print-env
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${PYTHON_CHANNEL}" \
+  libcudf cudf dask-cudf
+
+export RAPIDS_VERSION_NUMBER="23.10"
+export RAPIDS_DOCS_DIR="$(mktemp -d)"
+
+rapids-logger "Build CPP docs"
+pushd cpp/doxygen
+aws s3 cp s3://rapidsai-docs/librmm/html/${RAPIDS_VERSION_NUMBER}/rmm.tag . || echo "Failed to download rmm Doxygen tag"
+doxygen Doxyfile
+mkdir -p "${RAPIDS_DOCS_DIR}/libcudf/html"
+mv html/* "${RAPIDS_DOCS_DIR}/libcudf/html"
+popd
+
+rapids-logger "Build Python docs"
+pushd docs/cudf
+make dirhtml
+make text
+mkdir -p "${RAPIDS_DOCS_DIR}/cudf/"{html,txt}
+mv build/dirhtml/* "${RAPIDS_DOCS_DIR}/cudf/html"
+mv build/text/* "${RAPIDS_DOCS_DIR}/cudf/txt"
+popd
+
+rapids-logger "Build dask-cuDF Sphinx docs"
+pushd docs/dask_cudf
+make dirhtml
+make text
+mkdir -p "${RAPIDS_DOCS_DIR}/dask-cudf/"{html,txt}
+mv build/dirhtml/* "${RAPIDS_DOCS_DIR}/dask-cudf/html"
+mv build/text/* "${RAPIDS_DOCS_DIR}/dask-cudf/txt"
+popd
+
+rapids-upload-docs
diff --git a/ci/build_python.sh b/ci/build_python.sh
new file mode 100755
index 0000000..61f160b
--- /dev/null
+++ b/ci/build_python.sh
@@ -0,0 +1,43 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+source rapids-env-update
+
+export CMAKE_GENERATOR=Ninja
+
+rapids-print-env
+
+rapids-logger "Begin py build"
+
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
+# TODO: Remove `--no-test` flag once importing on a CPU
+# node works correctly
+# With boa installed conda build forwards to the boa builder
+rapids-conda-retry mambabuild \
+  --no-test \
+  --channel "${CPP_CHANNEL}" \
+  conda/recipes/cudf
+
+rapids-conda-retry mambabuild \
+  --no-test \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
+  conda/recipes/dask-cudf
+
+rapids-conda-retry mambabuild \
+  --no-test \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
+  conda/recipes/cudf_kafka
+
+rapids-conda-retry mambabuild \
+  --no-test \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${RAPIDS_CONDA_BLD_OUTPUT_DIR}" \
+  conda/recipes/custreamz
+
+
+rapids-upload-conda-to-s3 python
diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh
new file mode 100755
index 0000000..a1d52c5
--- /dev/null
+++ b/ci/build_wheel.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+package_name=$1
+package_dir=$2
+
+source rapids-configure-sccache
+source rapids-date-string
+
+# Use gha-tools rapids-pip-wheel-version to generate wheel version then
+# update the necessary files
+version_override="$(rapids-pip-wheel-version ${RAPIDS_DATE_STRING})"
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+
+# This is the version of the suffix with a preceding hyphen. It's used
+# everywhere except in the final wheel name.
+PACKAGE_CUDA_SUFFIX="-${RAPIDS_PY_CUDA_SUFFIX}"
+
+# Patch project metadata files to include the CUDA version suffix and version override.
+pyproject_file="${package_dir}/pyproject.toml"
+
+sed -i "s/^version = .*/version = \"${version_override}\"/g" ${pyproject_file}
+sed -i "s/^name = .*/name = \"${package_name}${PACKAGE_CUDA_SUFFIX}\"/g" ${pyproject_file}
+
+# For nightlies we want to ensure that we're pulling in alphas as well. The
+# easiest way to do so is to augment the spec with a constraint containing a
+# min alpha version that doesn't affect the version bounds but does allow usage
+# of alpha versions for that dependency without --pre
+alpha_spec=''
+if ! rapids-is-release-build; then
+    alpha_spec=',>=0.0.0a0'
+fi
+
+if [[ ${package_name} == "dask_cudf" ]]; then
+    sed -r -i "s/cudf==(.*)\"/cudf${PACKAGE_CUDA_SUFFIX}==\1${alpha_spec}\"/g" ${pyproject_file}
+else
+    sed -r -i "s/rmm(.*)\"/rmm${PACKAGE_CUDA_SUFFIX}\1${alpha_spec}\"/g" ${pyproject_file}
+    # ptxcompiler and cubinlinker aren't version constrained
+    sed -r -i "s/ptxcompiler\"/ptxcompiler${PACKAGE_CUDA_SUFFIX}\"/g" ${pyproject_file}
+    sed -r -i "s/cubinlinker\"/cubinlinker${PACKAGE_CUDA_SUFFIX}\"/g" ${pyproject_file}
+fi
+
+if [[ $PACKAGE_CUDA_SUFFIX == "-cu12" ]]; then
+    sed -i "s/cuda-python[<=>\.,0-9a]*/cuda-python>=12.0,<13.0a0/g" ${pyproject_file}
+    sed -i "s/cupy-cuda11x/cupy-cuda12x/g" ${pyproject_file}
+    sed -i "/ptxcompiler/d" ${pyproject_file}
+    sed -i "/cubinlinker/d" ${pyproject_file}
+fi
+
+cd "${package_dir}"
+
+python -m pip wheel . -w dist -vvv --no-deps --disable-pip-version-check
diff --git a/ci/build_wheel_cudf.sh b/ci/build_wheel_cudf.sh
new file mode 100755
index 0000000..1b2285b
--- /dev/null
+++ b/ci/build_wheel_cudf.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+package_dir="python/cudf"
+
+export SKBUILD_CONFIGURE_OPTIONS="-DCUDF_BUILD_WHEELS=ON -DDETECT_CONDA_ENV=OFF"
+
+# Force a build using the latest version of the code before this PR
+CUDF_BUILD_BRANCH=${1:-""}
+WHEEL_NAME="cudf"
+if [[ "${CUDF_BUILD_BRANCH}" == "main" ]]; then
+    MAIN_COMMIT=$(git merge-base HEAD origin/branch-23.10-xdf)
+    git checkout $MAIN_COMMIT
+    WHEEL_NAME="${WHEEL_NAME}_${CUDF_BUILD_BRANCH}"
+fi
+
+./ci/build_wheel.sh ${WHEEL_NAME} ${package_dir}
+
+mkdir -p ${package_dir}/final_dist
+python -m auditwheel repair -w ${package_dir}/final_dist ${package_dir}/dist/*
+
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_WHEEL_NAME="${WHEEL_NAME}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 ${package_dir}/final_dist
diff --git a/ci/build_wheel_dask_cudf.sh b/ci/build_wheel_dask_cudf.sh
new file mode 100755
index 0000000..47e35c4
--- /dev/null
+++ b/ci/build_wheel_dask_cudf.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+package_dir="python/dask_cudf"
+
+./ci/build_wheel.sh dask_cudf ${package_dir}
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_WHEEL_NAME="dask_cudf_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 ${package_dir}/dist
diff --git a/ci/check_style.sh b/ci/check_style.sh
new file mode 100755
index 0000000..e96ad8b
--- /dev/null
+++ b/ci/check_style.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+rapids-logger "Create checks conda environment"
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key checks \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n checks
+conda activate checks
+
+FORMAT_FILE_URL=https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-23.10/cmake-format-rapids-cmake.json
+export RAPIDS_CMAKE_FORMAT_FILE=/tmp/rapids_cmake_ci/cmake-formats-rapids-cmake.json
+mkdir -p $(dirname ${RAPIDS_CMAKE_FORMAT_FILE})
+wget -O ${RAPIDS_CMAKE_FORMAT_FILE} ${FORMAT_FILE_URL}
+
+# Run pre-commit checks
+pre-commit run --all-files --show-diff-on-failure
diff --git a/ci/checks/copyright.py b/ci/checks/copyright.py
new file mode 100644
index 0000000..dd89b09
--- /dev/null
+++ b/ci/checks/copyright.py
@@ -0,0 +1,277 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import argparse
+import datetime
+import os
+import re
+import sys
+
+import git
+
+FilesToCheck = [
+    re.compile(r"[.](cmake|cpp|cu|cuh|h|hpp|sh|pxd|py|pyx)$"),
+    re.compile(r"CMakeLists[.]txt$"),
+    re.compile(r"CMakeLists_standalone[.]txt$"),
+    re.compile(r"setup[.]cfg$"),
+    re.compile(r"meta[.]yaml$"),
+]
+ExemptFiles = [
+    re.compile(r"cpp/include/cudf_test/cxxopts.hpp"),
+]
+
+# this will break starting at year 10000, which is probably OK :)
+CheckSimple = re.compile(
+    r"Copyright *(?:\(c\))? *(\d{4}),? *NVIDIA C(?:ORPORATION|orporation)"
+)
+CheckDouble = re.compile(
+    r"Copyright *(?:\(c\))? *(\d{4})-(\d{4}),? *NVIDIA C(?:ORPORATION|orporation)"  # noqa: E501
+)
+
+
+def checkThisFile(f):
+    if isinstance(f, git.Diff):
+        if f.deleted_file or f.b_blob.size == 0:
+            return False
+        f = f.b_path
+    elif not os.path.exists(f) or os.stat(f).st_size == 0:
+        # This check covers things like symlinks which point to files that DNE
+        return False
+    for exempt in ExemptFiles:
+        if exempt.search(f):
+            return False
+    for checker in FilesToCheck:
+        if checker.search(f):
+            return True
+    return False
+
+
+def modifiedFiles():
+    """Get a set of all modified files, as Diff objects.
+
+    The files returned have been modified in git since the merge base of HEAD
+    and the upstream of the target branch. We return the Diff objects so that
+    we can read only the staged changes.
+    """
+    repo = git.Repo()
+    # Use the environment variable TARGET_BRANCH or RAPIDS_BASE_BRANCH (defined in CI) if possible
+    target_branch = os.environ.get("TARGET_BRANCH", os.environ.get("RAPIDS_BASE_BRANCH"))
+    if target_branch is None:
+        # Fall back to the closest branch if not on CI
+        target_branch = repo.git.describe(
+            all=True, tags=True, match="branch-*", abbrev=0
+        ).lstrip("heads/")
+
+    upstream_target_branch = None
+    if target_branch in repo.heads:
+        # Use the tracking branch of the local reference if it exists. This
+        # returns None if no tracking branch is set.
+        upstream_target_branch = repo.heads[target_branch].tracking_branch()
+    if upstream_target_branch is None:
+        # Fall back to the remote with the newest target_branch. This code
+        # path is used on CI because the only local branch reference is
+        # current-pr-branch, and thus target_branch is not in repo.heads.
+        # This also happens if no tracking branch is defined for the local
+        # target_branch. We use the remote with the latest commit if
+        # multiple remotes are defined.
+        candidate_branches = [
+            remote.refs[target_branch] for remote in repo.remotes
+            if target_branch in remote.refs
+        ]
+        if len(candidate_branches) > 0:
+            upstream_target_branch = sorted(
+                candidate_branches,
+                key=lambda branch: branch.commit.committed_datetime,
+            )[-1]
+        else:
+            # If no remotes are defined, try to use the local version of the
+            # target_branch. If this fails, the repo configuration must be very
+            # strange and we can fix this script on a case-by-case basis.
+            upstream_target_branch = repo.heads[target_branch]
+    merge_base = repo.merge_base("HEAD", upstream_target_branch.commit)[0]
+    diff = merge_base.diff()
+    changed_files = {f for f in diff if f.b_path is not None}
+    return changed_files
+
+
+def getCopyrightYears(line):
+    res = CheckSimple.search(line)
+    if res:
+        return int(res.group(1)), int(res.group(1))
+    res = CheckDouble.search(line)
+    if res:
+        return int(res.group(1)), int(res.group(2))
+    return None, None
+
+
+def replaceCurrentYear(line, start, end):
+    # first turn a simple regex into double (if applicable). then update years
+    res = CheckSimple.sub(r"Copyright (c) \1-\1, NVIDIA CORPORATION", line)
+    res = CheckDouble.sub(
+        rf"Copyright (c) {start:04d}-{end:04d}, NVIDIA CORPORATION",
+        res,
+    )
+    return res
+
+
+def checkCopyright(f, update_current_year):
+    """Checks for copyright headers and their years."""
+    errs = []
+    thisYear = datetime.datetime.now().year
+    lineNum = 0
+    crFound = False
+    yearMatched = False
+
+    if isinstance(f, git.Diff):
+        path = f.b_path
+        lines = f.b_blob.data_stream.read().decode().splitlines(keepends=True)
+    else:
+        path = f
+        with open(f, encoding="utf-8") as fp:
+            lines = fp.readlines()
+
+    for line in lines:
+        lineNum += 1
+        start, end = getCopyrightYears(line)
+        if start is None:
+            continue
+        crFound = True
+        if start > end:
+            e = [
+                path,
+                lineNum,
+                "First year after second year in the copyright "
+                "header (manual fix required)",
+                None,
+            ]
+            errs.append(e)
+        elif thisYear < start or thisYear > end:
+            e = [
+                path,
+                lineNum,
+                "Current year not included in the copyright header",
+                None,
+            ]
+            if thisYear < start:
+                e[-1] = replaceCurrentYear(line, thisYear, end)
+            if thisYear > end:
+                e[-1] = replaceCurrentYear(line, start, thisYear)
+            errs.append(e)
+        else:
+            yearMatched = True
+    # copyright header itself not found
+    if not crFound:
+        e = [
+            path,
+            0,
+            "Copyright header missing or formatted incorrectly "
+            "(manual fix required)",
+            None,
+        ]
+        errs.append(e)
+    # even if the year matches a copyright header, make the check pass
+    if yearMatched:
+        errs = []
+
+    if update_current_year:
+        errs_update = [x for x in errs if x[-1] is not None]
+        if len(errs_update) > 0:
+            lines_changed = ", ".join(str(x[1]) for x in errs_update)
+            print(f"File: {path}. Changing line(s) {lines_changed}")
+            for _, lineNum, __, replacement in errs_update:
+                lines[lineNum - 1] = replacement
+            with open(path, "w", encoding="utf-8") as out_file:
+                out_file.writelines(lines)
+
+    return errs
+
+
+def getAllFilesUnderDir(root, pathFilter=None):
+    retList = []
+    for dirpath, dirnames, filenames in os.walk(root):
+        for fn in filenames:
+            filePath = os.path.join(dirpath, fn)
+            if pathFilter(filePath):
+                retList.append(filePath)
+    return retList
+
+
+def checkCopyright_main():
+    """
+    Checks for copyright headers in all the modified files. In case of local
+    repo, this script will just look for uncommitted files and in case of CI
+    it compares between branches "$PR_TARGET_BRANCH" and "current-pr-branch"
+    """
+    retVal = 0
+
+    argparser = argparse.ArgumentParser(
+        "Checks for a consistent copyright header in git's modified files"
+    )
+    argparser.add_argument(
+        "--update-current-year",
+        dest="update_current_year",
+        action="store_true",
+        required=False,
+        help="If set, "
+        "update the current year if a header is already "
+        "present and well formatted.",
+    )
+    argparser.add_argument(
+        "--git-modified-only",
+        dest="git_modified_only",
+        action="store_true",
+        required=False,
+        help="If set, "
+        "only files seen as modified by git will be "
+        "processed.",
+    )
+
+    args, dirs = argparser.parse_known_args()
+
+    if args.git_modified_only:
+        files = [f for f in modifiedFiles() if checkThisFile(f)]
+    else:
+        files = []
+        for d in [os.path.abspath(d) for d in dirs]:
+            if not os.path.isdir(d):
+                raise ValueError(f"{d} is not a directory.")
+            files += getAllFilesUnderDir(d, pathFilter=checkThisFile)
+
+    errors = []
+    for f in files:
+        errors += checkCopyright(f, args.update_current_year)
+
+    if len(errors) > 0:
+        if any(e[-1] is None for e in errors):
+            print("Copyright headers incomplete in some of the files!")
+        for e in errors:
+            print("  %s:%d Issue: %s" % (e[0], e[1], e[2]))
+        print("")
+        n_fixable = sum(1 for e in errors if e[-1] is not None)
+        path_parts = os.path.abspath(__file__).split(os.sep)
+        file_from_repo = os.sep.join(path_parts[path_parts.index("ci") :])
+        if n_fixable > 0 and not args.update_current_year:
+            print(
+                f"You can run `python {file_from_repo} --git-modified-only "
+                "--update-current-year` and stage the results in git to "
+                f"fix {n_fixable} of these errors.\n"
+            )
+        retVal = 1
+
+    return retVal
+
+
+if __name__ == "__main__":
+    sys.exit(checkCopyright_main())
diff --git a/ci/checks/doxygen.sh b/ci/checks/doxygen.sh
new file mode 100755
index 0000000..d932fa0
--- /dev/null
+++ b/ci/checks/doxygen.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+###############################
+# cuDF doxygen warnings check #
+###############################
+
+# skip if doxygen is not installed
+if ! [ -x "$(command -v doxygen)" ]; then
+  echo -e "warning: doxygen is not installed"
+  exit 0
+fi
+
+# Utility to return version as number for comparison
+function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
+
+# doxygen supported version 1.9.1
+DOXYGEN_VERSION=`doxygen --version`
+if [ ! $(version "$DOXYGEN_VERSION") -eq $(version "1.9.1") ] ; then
+  echo -e "warning: Unsupported doxygen version $DOXYGEN_VERSION"
+  echo -e "Expecting doxygen version 1.9.1"
+  exit 0
+fi
+
+# Run doxygen, ignore missing tag files error
+TAG_ERROR1="error: Tag file '.*.tag' does not exist or is not a file. Skipping it..."
+TAG_ERROR2="error: cannot open tag file .*.tag for writing"
+DOXYGEN_STDERR=`cd cpp/doxygen && { cat Doxyfile ; echo QUIET = YES; echo GENERATE_HTML = NO; }  | doxygen - 2>&1 | sed "/\($TAG_ERROR1\|$TAG_ERROR2\)/d"`
+RETVAL=$?
+
+if [ "$RETVAL" != "0" ] || [ ! -z "$DOXYGEN_STDERR" ]; then
+  echo -e "$DOXYGEN_STDERR"
+  RETVAL=1 #because return value is not generated by doxygen 1.8.20
+fi
+
+exit $RETVAL
diff --git a/ci/cudf_pandas_scripts/pandas-tests/diff.sh b/ci/cudf_pandas_scripts/pandas-tests/diff.sh
new file mode 100755
index 0000000..37adabd
--- /dev/null
+++ b/ci/cudf_pandas_scripts/pandas-tests/diff.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Download the summarized results of running the Pandas tests on both the main
+# branch and the PR branch:
+
+# Hard-coded needs to match the version deduced by rapids-upload-artifacts-dir
+MAIN_ARTIFACT=$(rapids-s3-path)cuda12_$(arch)_py310.main-results.json
+PR_ARTIFACT=$(rapids-s3-path)cuda12_$(arch)_py310.pr-results.json
+aws s3 cp $MAIN_ARTIFACT main-results.json
+aws s3 cp $PR_ARTIFACT pr-results.json
+
+# Compute the diff and prepare job summary:
+python -m pip install pandas tabulate
+python ci/cudf_pandas_scripts/pandas-tests/job-summary.py main-results.json pr-results.json | tee summary.txt >> "$GITHUB_STEP_SUMMARY"
+
+COMMENT=$(head -1 summary.txt)
+
+echo "$COMMENT"
+
+# Magic name that the custom-job.yaml workflow reads and re-exports
+echo "job_output=${COMMENT}" >> "${GITHUB_OUTPUT}"
diff --git a/ci/cudf_pandas_scripts/pandas-tests/job-summary.py b/ci/cudf_pandas_scripts/pandas-tests/job-summary.py
new file mode 100644
index 0000000..1e83e51
--- /dev/null
+++ b/ci/cudf_pandas_scripts/pandas-tests/job-summary.py
@@ -0,0 +1,100 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import sys
+
+import pandas as pd
+
+
+def get_total_and_passed(results):
+    total_failed = 0
+    total_errored = 0
+    total_passed = 0
+    for module_name, row in results.items():
+        total_failed += row.get("failed", 0)
+        total_errored += row.get("errored", 0)
+        total_passed += row.get("passed", 0)
+    total_tests = total_failed + total_errored + total_passed
+    return total_tests, total_passed
+
+
+main_json = sys.argv[1]
+pr_json = sys.argv[2]
+
+# read the results of summarize-test-results.py --summary
+with open(main_json) as f:
+    main_results = json.load(f)
+main_total, main_passed = get_total_and_passed(main_results)
+
+with open(pr_json) as f:
+    pr_results = json.load(f)
+pr_total, pr_passed = get_total_and_passed(pr_results)
+
+passing_percentage = pr_passed / pr_total * 100
+pass_rate_change = abs(pr_passed - main_passed) / main_passed * 100
+rate_change_type = "a decrease" if pr_passed < main_passed else "an increase"
+
+comment = (
+    "Merging this PR would result in "
+    f"{pr_passed}/{pr_total} ({passing_percentage:.2f}%) "
+    "Pandas tests passing, "
+    f"{rate_change_type} in the test pass rate by "
+    f"{pass_rate_change:.2f}%. "
+    f"Trunk stats: {main_passed}/{main_total}."
+)
+
+
+def emoji_passed(x):
+    if x > 0:
+        return f"{x}✅"
+    elif x < 0:
+        return f"{x}❌"
+    else:
+        return f"{x}"
+
+
+def emoji_failed(x):
+    if x > 0:
+        return f"{x}❌"
+    elif x < 0:
+        return f"{x}✅"
+    else:
+        return f"{x}"
+
+
+# convert pr_results to a pandas DataFrame and then a markdown table
+pr_df = pd.DataFrame.from_dict(pr_results, orient="index").sort_index()
+main_df = pd.DataFrame.from_dict(main_results, orient="index").sort_index()
+diff_df = pr_df - main_df
+
+pr_df = pr_df[["total", "passed", "failed", "skipped"]]
+diff_df = diff_df[["total", "passed", "failed", "skipped"]]
+diff_df.columns = diff_df.columns + "_diff"
+diff_df["passed_diff"] = diff_df["passed_diff"].map(emoji_passed)
+diff_df["failed_diff"] = diff_df["failed_diff"].map(emoji_failed)
+diff_df["skipped_diff"] = diff_df["skipped_diff"].map(emoji_failed)
+
+df = pd.concat([pr_df, diff_df], axis=1)
+df = df.rename_axis("Test module")
+
+df = df.rename(
+    columns={
+        "total": "Total tests",
+        "passed": "Passed tests",
+        "failed": "Failed tests",
+        "skipped": "Skipped tests",
+        "total_diff": "Total delta",
+        "passed_diff": "Passed delta",
+        "failed_diff": "Failed delta",
+        "skipped_diff": "Skipped delta",
+    }
+)
+df = df.sort_values(by=["Failed tests", "Skipped tests"], ascending=False)
+
+print(comment)
+print()
+print("Here are the results of running the Pandas tests against this PR:")
+print()
+print(df.to_markdown())
diff --git a/ci/cudf_pandas_scripts/pandas-tests/run.sh b/ci/cudf_pandas_scripts/pandas-tests/run.sh
new file mode 100755
index 0000000..920625b
--- /dev/null
+++ b/ci/cudf_pandas_scripts/pandas-tests/run.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+PANDAS_TESTS_BRANCH=${1}
+
+rapids-logger "Running Pandas tests using $PANDAS_TESTS_BRANCH branch"
+rapids-logger "PR number: $RAPIDS_REF_NAME"
+
+
+COMMIT=$(git rev-parse HEAD)
+WHEEL_NAME="cudf"
+if [[ "${PANDAS_TESTS_BRANCH}" == "main" ]]; then
+    COMMIT=$(git merge-base HEAD origin/branch-23.10-xdf)
+    WHEEL_NAME="${WHEEL_NAME}_${PANDAS_TESTS_BRANCH}"
+fi
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_WHEEL_NAME="${WHEEL_NAME}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./local-cudf-dep
+python -m pip install $(ls ./local-cudf-dep/cudf*.whl)[test,pandas_tests]
+
+git checkout $COMMIT
+
+bash python/cudf/cudf/pandas/scripts/run-pandas-tests.sh \
+  -n 10 \
+  --tb=line \
+  --skip-slow \
+  --max-worker-restart=3 \
+  --import-mode=importlib \
+  --report-log=${PANDAS_TESTS_BRANCH}.json 2>&1
+
+# summarize the results and save them to artifacts:
+python python/cudf/cudf/pandas/scripts/summarize-test-results.py --output json pandas-testing/${PANDAS_TESTS_BRANCH}.json > pandas-testing/${PANDAS_TESTS_BRANCH}-results.json
+RAPIDS_ARTIFACTS_DIR=${RAPIDS_ARTIFACTS_DIR:-"${PWD}/artifacts"}
+mkdir -p "${RAPIDS_ARTIFACTS_DIR}"
+mv pandas-testing/${PANDAS_TESTS_BRANCH}-results.json ${RAPIDS_ARTIFACTS_DIR}/
diff --git a/ci/cudf_pandas_scripts/run_tests.sh b/ci/cudf_pandas_scripts/run_tests.sh
new file mode 100755
index 0000000..cc578b5
--- /dev/null
+++ b/ci/cudf_pandas_scripts/run_tests.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+set -eoxu pipefail
+
+# Function to display script usage
+function display_usage {
+    echo "Usage: $0 [--no-cudf]"
+}
+
+# Default value for the --no-cudf option
+no_cudf=false
+
+# Parse command-line arguments
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --no-cudf)
+            no_cudf=true
+            shift
+            ;;
+        *)
+            echo "Error: Unknown option $1"
+            display_usage
+            exit 1
+            ;;
+    esac
+done
+
+if [ "$no_cudf" = true ]; then
+    echo "Skipping cudf install"
+else
+    RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+    RAPIDS_PY_WHEEL_NAME="cudf_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./local-cudf-dep
+    python -m pip install $(ls ./local-cudf-dep/cudf*.whl)[test,cudf_pandas_tests]
+fi
+
+python -m pytest -p cudf.pandas ./python/cudf/cudf_pandas_tests/
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
new file mode 100755
index 0000000..eac64fe
--- /dev/null
+++ b/ci/release/update-version.sh
@@ -0,0 +1,125 @@
+#!/bin/bash
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+########################
+# cuDF Version Updater #
+########################
+
+## Usage
+# bash update-version.sh <new_version>
+
+
+# Format is YY.MM.PP - no leading 'v' or trailing 'a'
+NEXT_FULL_TAG=$1
+
+# Get current version
+CURRENT_TAG=$(git tag --merged HEAD | grep -xE '^v.*' | sort --version-sort | tail -n 1 | tr -d 'v')
+CURRENT_MAJOR=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[1]}')
+CURRENT_MINOR=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[2]}')
+CURRENT_PATCH=$(echo $CURRENT_TAG | awk '{split($0, a, "."); print a[3]}')
+CURRENT_SHORT_TAG=${CURRENT_MAJOR}.${CURRENT_MINOR}
+
+#Get <major>.<minor> for next version
+NEXT_MAJOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[1]}')
+NEXT_MINOR=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[2]}')
+NEXT_PATCH=$(echo $NEXT_FULL_TAG | awk '{split($0, a, "."); print a[3]}')
+NEXT_SHORT_TAG=${NEXT_MAJOR}.${NEXT_MINOR}
+NEXT_UCX_PY_VERSION="$(curl -sL https://version.gpuci.io/rapids/${NEXT_SHORT_TAG}).*"
+
+# Need to distutils-normalize the versions for some use cases
+CURRENT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${CURRENT_SHORT_TAG}'))")
+NEXT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_SHORT_TAG}'))")
+PATCH_PEP440=$(python -c "from setuptools.extern import packaging; print(packaging.version.Version('${NEXT_PATCH}'))")
+echo "current is ${CURRENT_SHORT_TAG_PEP440}, next is ${NEXT_SHORT_TAG_PEP440}"
+
+echo "Preparing release $CURRENT_TAG => $NEXT_FULL_TAG"
+
+# Inplace sed replace; workaround for Linux and Mac
+function sed_runner() {
+    sed -i.bak ''"$1"'' $2 && rm -f ${2}.bak
+}
+
+# cpp update
+sed_runner 's/'"VERSION ${CURRENT_SHORT_TAG}.*"'/'"VERSION ${NEXT_FULL_TAG}"'/g' cpp/CMakeLists.txt
+
+# Python CMakeLists updates
+sed_runner 's/'"cudf_version .*)"'/'"cudf_version ${NEXT_FULL_TAG})"'/g' python/cudf/CMakeLists.txt
+
+# cpp libcudf_kafka update
+sed_runner 's/'"VERSION ${CURRENT_SHORT_TAG}.*"'/'"VERSION ${NEXT_FULL_TAG}"'/g' cpp/libcudf_kafka/CMakeLists.txt
+
+# cpp cudf_jni update
+sed_runner 's/'"VERSION ${CURRENT_SHORT_TAG}.*"'/'"VERSION ${NEXT_FULL_TAG}"'/g' java/src/main/native/CMakeLists.txt
+
+# Python __init__.py updates
+sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cudf/cudf/__init__.py
+sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/dask_cudf/dask_cudf/__init__.py
+sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cudf_kafka/cudf_kafka/__init__.py
+sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/custreamz/custreamz/__init__.py
+
+# Python pyproject.toml updates
+sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cudf/pyproject.toml
+sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/dask_cudf/pyproject.toml
+sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cudf_kafka/pyproject.toml
+sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/custreamz/pyproject.toml
+
+# Wheel testing script
+sed_runner "s/branch-.*/branch-${NEXT_SHORT_TAG}/g" ci/test_wheel_dask_cudf.sh
+
+# rapids-cmake version
+sed_runner 's/'"branch-.*\/RAPIDS.cmake"'/'"branch-${NEXT_SHORT_TAG}\/RAPIDS.cmake"'/g' fetch_rapids.cmake
+
+# cmake-format rapids-cmake definitions
+sed_runner 's/'"branch-.*\/cmake-format-rapids-cmake.json"'/'"branch-${NEXT_SHORT_TAG}\/cmake-format-rapids-cmake.json"'/g' ci/check_style.sh
+
+# doxyfile update
+sed_runner 's/PROJECT_NUMBER         = .*/PROJECT_NUMBER         = '${NEXT_FULL_TAG}'/g' cpp/doxygen/Doxyfile
+
+# sphinx docs update
+sed_runner 's/version = .*/version = '"'${NEXT_SHORT_TAG}'"'/g' docs/cudf/source/conf.py
+sed_runner 's/release = .*/release = '"'${NEXT_FULL_TAG}'"'/g' docs/cudf/source/conf.py
+sed_runner 's/version = .*/version = '"'${NEXT_SHORT_TAG}'"'/g' docs/dask_cudf/source/conf.py
+sed_runner 's/release = .*/release = '"'${NEXT_FULL_TAG}'"'/g' docs/dask_cudf/source/conf.py
+
+DEPENDENCIES=(
+  cudf
+  cudf_kafka
+  custreamz
+  dask-cuda
+  dask-cudf
+  kvikio
+  libkvikio
+  librmm
+  rmm
+)
+for DEP in "${DEPENDENCIES[@]}"; do
+  for FILE in dependencies.yaml conda/environments/*.yaml; do
+    sed_runner "/-.* ${DEP}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*/g" ${FILE}
+  done
+  for FILE in python/*/pyproject.toml; do
+    sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*\"/g" ${FILE}
+  done
+done
+
+# Doxyfile update
+sed_runner "s|\(TAGFILES.*librmm/\).*|\1${NEXT_SHORT_TAG}|" cpp/doxygen/Doxyfile
+
+# README.md update
+sed_runner "s/version == ${CURRENT_SHORT_TAG}/version == ${NEXT_SHORT_TAG}/g" README.md
+sed_runner "s/cudf=${CURRENT_SHORT_TAG}/cudf=${NEXT_SHORT_TAG}/g" README.md
+
+# Libcudf examples update
+sed_runner "s/CUDF_TAG branch-${CURRENT_SHORT_TAG}/CUDF_TAG branch-${NEXT_SHORT_TAG}/" cpp/examples/basic/CMakeLists.txt
+sed_runner "s/CUDF_TAG branch-${CURRENT_SHORT_TAG}/CUDF_TAG branch-${NEXT_SHORT_TAG}/" cpp/examples/strings/CMakeLists.txt
+
+# CI files
+for FILE in .github/workflows/*.yaml; do
+  sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}"
+  sed_runner "s/dask-cuda.git@branch-[^\"\s]\+/dask-cuda.git@branch-${NEXT_SHORT_TAG}/g" ${FILE};
+done
+sed_runner "s/RAPIDS_VERSION_NUMBER=\".*/RAPIDS_VERSION_NUMBER=\"${NEXT_SHORT_TAG}\"/g" ci/build_docs.sh
+
+# Java files
+NEXT_FULL_JAVA_TAG="${NEXT_SHORT_TAG}.${PATCH_PEP440}-SNAPSHOT"
+sed_runner "s|<version>.*-SNAPSHOT</version>|<version>${NEXT_FULL_JAVA_TAG}</version>|g" java/pom.xml
+sed_runner "s/branch-.*/branch-${NEXT_SHORT_TAG}/g" java/ci/README.md
+sed_runner "s/cudf-.*-SNAPSHOT/cudf-${NEXT_FULL_JAVA_TAG}/g" java/ci/README.md
diff --git a/ci/test_cpp.sh b/ci/test_cpp.sh
new file mode 100755
index 0000000..30172b7
--- /dev/null
+++ b/ci/test_cpp.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+source "$(dirname "$0")/test_cpp_common.sh"
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+# Run libcudf and libcudf_kafka gtests from libcudf-tests package
+export GTEST_OUTPUT=xml:${RAPIDS_TESTS_DIR}/
+
+pushd $CONDA_PREFIX/bin/gtests/libcudf/
+rapids-logger "Run libcudf gtests"
+ctest -j20 --output-on-failure
+SUITEERROR=$?
+popd
+
+if (( ${SUITEERROR} == 0 )); then
+    pushd $CONDA_PREFIX/bin/gtests/libcudf_kafka/
+    rapids-logger "Run libcudf_kafka gtests"
+    ctest -j20 --output-on-failure
+    SUITEERROR=$?
+    popd
+fi
+
+# Ensure that benchmarks are runnable
+pushd $CONDA_PREFIX/bin/benchmarks/libcudf/
+rapids-logger "Run tests of libcudf benchmarks"
+
+if (( ${SUITEERROR} == 0 )); then
+    # Run a small Google benchmark
+    ./MERGE_BENCH --benchmark_filter=/2/
+    SUITEERROR=$?
+fi
+
+if (( ${SUITEERROR} == 0 )); then
+    # Run a small nvbench benchmark
+    ./STRINGS_NVBENCH --run-once --benchmark 0 --devices 0
+    SUITEERROR=$?
+fi
+popd
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/test_cpp_common.sh b/ci/test_cpp_common.sh
new file mode 100644
index 0000000..c7c095d
--- /dev/null
+++ b/ci/test_cpp_common.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-logger "Generate C++ testing dependencies"
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key test_cpp \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n test
+
+# Temporarily allow unbound variables for conda activation.
+set +u
+conda activate test
+set -u
+
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+RAPIDS_TESTS_DIR=${RAPIDS_TESTS_DIR:-"${PWD}/test-results"}/
+mkdir -p "${RAPIDS_TESTS_DIR}"
+
+rapids-print-env
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  libcudf libcudf_kafka libcudf-tests
+
+rapids-logger "Check GPU usage"
+nvidia-smi
diff --git a/ci/test_cpp_memcheck.sh b/ci/test_cpp_memcheck.sh
new file mode 100755
index 0000000..0e85268
--- /dev/null
+++ b/ci/test_cpp_memcheck.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+source "$(dirname "$0")/test_cpp_common.sh"
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+# Run gtests with compute-sanitizer
+rapids-logger "Memcheck gtests with rmm_mode=cuda"
+export GTEST_CUDF_RMM_MODE=cuda
+COMPUTE_SANITIZER_CMD="compute-sanitizer --tool memcheck"
+for gt in "$CONDA_PREFIX"/bin/gtests/libcudf/*_TEST ; do
+    test_name=$(basename ${gt})
+    if [[ "$test_name" == "ERROR_TEST" ]] || [[ "$test_name" == "STREAM_IDENTIFICATION_TEST" ]]; then
+        continue
+    fi
+    echo "Running compute-sanitizer on $test_name"
+    ${COMPUTE_SANITIZER_CMD} ${gt} --gtest_output=xml:"${RAPIDS_TESTS_DIR}${test_name}.xml"
+done
+unset GTEST_CUDF_RMM_MODE
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/test_java.sh b/ci/test_java.sh
new file mode 100755
index 0000000..e4df625
--- /dev/null
+++ b/ci/test_java.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-logger "Generate Java testing dependencies"
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key test_java \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch)" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n test
+
+export CMAKE_GENERATOR=Ninja
+
+# Temporarily allow unbound variables for conda activation.
+set +u
+conda activate test
+set -u
+
+rapids-print-env
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  libcudf
+
+rapids-logger "Check GPU usage"
+nvidia-smi
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+rapids-logger "Run Java tests"
+pushd java
+mvn test -B -DCUDF_JNI_ENABLE_PROFILING=OFF
+popd
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/test_notebooks.sh b/ci/test_notebooks.sh
new file mode 100755
index 0000000..348428f
--- /dev/null
+++ b/ci/test_notebooks.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+set -euo pipefail
+
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-logger "Generate notebook testing dependencies"
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key test_notebooks \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n test
+
+# Temporarily allow unbound variables for conda activation.
+set +u
+conda activate test
+set -u
+
+rapids-print-env
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${PYTHON_CHANNEL}" \
+  cudf libcudf
+
+NBTEST="$(realpath "$(dirname "$0")/utils/nbtest.sh")"
+pushd notebooks
+
+# Add notebooks that should be skipped here
+# (space-separated list of filenames without paths)
+SKIPNBS="performance-comparisons.ipynb"
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+for nb in $(find . -name "*.ipynb"); do
+    nbBasename=$(basename ${nb})
+    # Skip all notebooks that use dask (in the code or even in their name)
+    if ((echo ${nb} | grep -qi dask) || \
+        (grep -q dask ${nb})); then
+        echo "--------------------------------------------------------------------------------"
+        echo "SKIPPING: ${nb} (suspected Dask usage, not currently automatable)"
+        echo "--------------------------------------------------------------------------------"
+    elif (echo " ${SKIPNBS} " | grep -q " ${nbBasename} "); then
+        echo "--------------------------------------------------------------------------------"
+        echo "SKIPPING: ${nb} (listed in skip list)"
+        echo "--------------------------------------------------------------------------------"
+    else
+        nvidia-smi
+        ${NBTEST} ${nbBasename}
+    fi
+done
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/test_python_common.sh b/ci/test_python_common.sh
new file mode 100755
index 0000000..0e922c1
--- /dev/null
+++ b/ci/test_python_common.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+# Common setup steps shared by Python test jobs
+
+set -euo pipefail
+
+. /opt/conda/etc/profile.d/conda.sh
+
+rapids-logger "Generate Python testing dependencies"
+rapids-dependency-file-generator \
+  --output conda \
+  --file_key test_python \
+  --matrix "cuda=${RAPIDS_CUDA_VERSION%.*};arch=$(arch);py=${RAPIDS_PY_VERSION}" | tee env.yaml
+
+rapids-mamba-retry env create --force -f env.yaml -n test
+
+# Temporarily allow unbound variables for conda activation.
+set +u
+conda activate test
+set -u
+
+rapids-logger "Downloading artifacts from previous jobs"
+CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp)
+PYTHON_CHANNEL=$(rapids-download-conda-from-s3 python)
+
+RAPIDS_TESTS_DIR=${RAPIDS_TESTS_DIR:-"${PWD}/test-results"}
+RAPIDS_COVERAGE_DIR=${RAPIDS_COVERAGE_DIR:-"${PWD}/coverage-results"}
+mkdir -p "${RAPIDS_TESTS_DIR}" "${RAPIDS_COVERAGE_DIR}"
+
+rapids-print-env
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${PYTHON_CHANNEL}" \
+  cudf libcudf
diff --git a/ci/test_python_cudf.sh b/ci/test_python_cudf.sh
new file mode 100755
index 0000000..bb33d84
--- /dev/null
+++ b/ci/test_python_cudf.sh
@@ -0,0 +1,62 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+# Common setup steps shared by Python test jobs
+source "$(dirname "$0")/test_python_common.sh"
+
+rapids-logger "Check GPU usage"
+nvidia-smi
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+rapids-logger "pytest cudf"
+pushd python/cudf/cudf
+# It is essential to cd into python/cudf/cudf as `pytest-xdist` + `coverage` seem to work only at this directory level.
+pytest \
+  --cache-clear \
+  --ignore="benchmarks" \
+  --junitxml="${RAPIDS_TESTS_DIR}/junit-cudf.xml" \
+  --numprocesses=8 \
+  --dist=loadscope \
+  --cov-config=../.coveragerc \
+  --cov=cudf \
+  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cudf-coverage.xml" \
+  --cov-report=term \
+  tests
+popd
+
+# Run benchmarks with both cudf and pandas to ensure compatibility is maintained.
+# Benchmarks are run in DEBUG_ONLY mode, meaning that only small data sizes are used.
+# Therefore, these runs only verify that benchmarks are valid.
+# They do not generate meaningful performance measurements.
+pushd python/cudf
+rapids-logger "pytest for cudf benchmarks"
+CUDF_BENCHMARKS_DEBUG_ONLY=ON \
+pytest \
+  --cache-clear \
+  --numprocesses=8 \
+  --dist=loadscope \
+  --cov-config=.coveragerc \
+  --cov=cudf \
+  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cudf-benchmark-coverage.xml" \
+  --cov-report=term \
+  benchmarks
+
+rapids-logger "pytest for cudf benchmarks using pandas"
+CUDF_BENCHMARKS_USE_PANDAS=ON \
+CUDF_BENCHMARKS_DEBUG_ONLY=ON \
+pytest \
+  --cache-clear \
+  --numprocesses=8 \
+  --dist=loadscope \
+  --cov-config=.coveragerc \
+  --cov=cudf \
+  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cudf-benchmark-pandas-coverage.xml" \
+  --cov-report=term \
+  benchmarks
+popd
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/test_python_other.sh b/ci/test_python_other.sh
new file mode 100755
index 0000000..25c1d68
--- /dev/null
+++ b/ci/test_python_other.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+# Common setup steps shared by Python test jobs
+source "$(dirname "$0")/test_python_common.sh"
+
+rapids-mamba-retry install \
+  --channel "${CPP_CHANNEL}" \
+  --channel "${PYTHON_CHANNEL}" \
+  dask-cudf cudf_kafka custreamz
+
+rapids-logger "Check GPU usage"
+nvidia-smi
+
+EXITCODE=0
+trap "EXITCODE=1" ERR
+set +e
+
+rapids-logger "pytest dask_cudf"
+pushd python/dask_cudf/dask_cudf
+pytest \
+  --cache-clear \
+  --junitxml="${RAPIDS_TESTS_DIR}/junit-dask-cudf.xml" \
+  --numprocesses=8 \
+  --dist=loadscope \
+  --cov-config=../.coveragerc \
+  --cov=dask_cudf \
+  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/dask-cudf-coverage.xml" \
+  --cov-report=term \
+  .
+popd
+
+rapids-logger "pytest custreamz"
+pushd python/custreamz/custreamz
+pytest \
+  --cache-clear \
+  --junitxml="${RAPIDS_TESTS_DIR}/junit-custreamz.xml" \
+  --numprocesses=8 \
+  --dist=loadscope \
+  --cov-config=../.coveragerc \
+  --cov=custreamz \
+  --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/custreamz-coverage.xml" \
+  --cov-report=term \
+  tests
+popd
+
+rapids-logger "Test script exiting with value: $EXITCODE"
+exit ${EXITCODE}
diff --git a/ci/test_wheel_cudf.sh b/ci/test_wheel_cudf.sh
new file mode 100755
index 0000000..83e24ab
--- /dev/null
+++ b/ci/test_wheel_cudf.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -eou pipefail
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_WHEEL_NAME="cudf_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./dist
+
+# echo to expand wildcard before adding `[extra]` requires for pip
+python -m pip install $(echo ./dist/cudf*.whl)[test]
+
+# Run smoke tests for aarch64 pull requests
+if [[ "$(arch)" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then
+    python ./ci/wheel_smoke_test_cudf.py
+else
+    python -m pytest -n 8 ./python/cudf/cudf/tests
+fi
diff --git a/ci/test_wheel_dask_cudf.sh b/ci/test_wheel_dask_cudf.sh
new file mode 100755
index 0000000..a0a6fbe
--- /dev/null
+++ b/ci/test_wheel_dask_cudf.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+set -eou pipefail
+
+RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})"
+RAPIDS_PY_WHEEL_NAME="dask_cudf_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./dist
+
+# Download the cudf built in the previous step
+RAPIDS_PY_WHEEL_NAME="cudf_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./local-cudf-dep
+python -m pip install --no-deps ./local-cudf-dep/cudf*.whl
+
+# Always install latest dask for testing
+python -m pip install git+https://github.com/dask/dask.git@2023.9.2 git+https://github.com/dask/distributed.git@2023.9.2 git+https://github.com/rapidsai/dask-cuda.git@branch-23.10
+
+# echo to expand wildcard before adding `[extra]` requires for pip
+python -m pip install $(echo ./dist/dask_cudf*.whl)[test]
+
+python -m pytest -n 8 ./python/dask_cudf/dask_cudf/
diff --git a/ci/utils/nbtest.sh b/ci/utils/nbtest.sh
new file mode 100755
index 0000000..2a94e2d
--- /dev/null
+++ b/ci/utils/nbtest.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+MAGIC_OVERRIDE_CODE="
+def my_run_line_magic(*args, **kwargs):
+    g=globals()
+    l={}
+    for a in args:
+        try:
+            exec(str(a),g,l)
+        except Exception as e:
+            print('WARNING: %s\n   While executing this magic function code:\n%s\n   continuing...\n' % (e, a))
+        else:
+            g.update(l)
+
+def my_run_cell_magic(*args, **kwargs):
+    my_run_line_magic(*args, **kwargs)
+
+get_ipython().run_line_magic=my_run_line_magic
+get_ipython().run_cell_magic=my_run_cell_magic
+
+"
+
+NO_COLORS=--colors=NoColor
+EXITCODE=0
+NBTMPDIR="$WORKSPACE/tmp"
+mkdir -p ${NBTMPDIR}
+
+for nb in $*; do
+    NBFILENAME=$1
+    NBNAME=${NBFILENAME%.*}
+    NBNAME=${NBNAME##*/}
+    NBTESTSCRIPT=${NBTMPDIR}/${NBNAME}-test.py
+    shift
+
+    echo --------------------------------------------------------------------------------
+    echo STARTING: ${NBNAME}
+    echo --------------------------------------------------------------------------------
+    jupyter nbconvert --to script ${NBFILENAME} --output ${NBTMPDIR}/${NBNAME}-test
+    echo "${MAGIC_OVERRIDE_CODE}" > ${NBTMPDIR}/tmpfile
+    cat ${NBTESTSCRIPT} >> ${NBTMPDIR}/tmpfile
+    mv ${NBTMPDIR}/tmpfile ${NBTESTSCRIPT}
+
+    echo "Running \"ipython ${NO_COLORS} ${NBTESTSCRIPT}\" on $(date)"
+    echo
+    time bash -c "ipython ${NO_COLORS} ${NBTESTSCRIPT}; EC=\$?; echo -------------------------------------------------------------------------------- ; echo DONE: ${NBNAME}; exit \$EC"
+    NBEXITCODE=$?
+    echo EXIT CODE: ${NBEXITCODE}
+    echo
+    EXITCODE=$((EXITCODE | ${NBEXITCODE}))
+done
+
+exit ${EXITCODE}
diff --git a/ci/utils/nbtestlog2junitxml.py b/ci/utils/nbtestlog2junitxml.py
new file mode 100644
index 0000000..14384af
--- /dev/null
+++ b/ci/utils/nbtestlog2junitxml.py
@@ -0,0 +1,163 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Generate a junit-xml file from parsing a nbtest log
+
+import re
+from xml.etree.ElementTree import Element, ElementTree
+from os import path
+import string
+from enum import Enum
+
+
+startingPatt = re.compile(r"^STARTING: ([\w\.\-]+)$")
+skippingPatt = re.compile(r"^SKIPPING: ([\w\.\-]+)\s*(\(([\w\.\-\ \,]+)\))?\s*$")
+exitCodePatt = re.compile(r"^EXIT CODE: (\d+)$")
+folderPatt = re.compile(r"^FOLDER: ([\w\.\-]+)$")
+timePatt = re.compile(r"^real\s+([\d\.ms]+)$")
+linePatt = re.compile("^" + ("-" * 80) + "$")
+
+
+def getFileBaseName(filePathName):
+    return path.splitext(path.basename(filePathName))[0]
+
+
+def makeTestCaseElement(attrDict):
+    return Element("testcase", attrib=attrDict)
+
+
+def makeSystemOutElement(outputLines):
+    e = Element("system-out")
+    e.text = "".join(filter(lambda c: c in string.printable, outputLines))
+    return e
+
+
+def makeFailureElement(outputLines):
+    e = Element("failure", message="failed")
+    e.text = "".join(filter(lambda c: c in string.printable, outputLines))
+    return e
+
+
+def setFileNameAttr(attrDict, fileName):
+    attrDict.update(file=fileName,
+                    classname="",
+                    line="",
+                    name="",
+                    time=""
+                   )
+
+def setClassNameAttr(attrDict, className):
+    attrDict["classname"] = className
+
+
+def setTestNameAttr(attrDict, testName):
+    attrDict["name"] = testName
+
+
+def setTimeAttr(attrDict, timeVal):
+    (mins, seconds) = timeVal.split("m")
+    seconds = float(seconds.strip("s")) + (60 * int(mins))
+    attrDict["time"] = str(seconds)
+
+
+def incrNumAttr(element, attr):
+    newVal = int(element.attrib.get(attr)) + 1
+    element.attrib[attr] = str(newVal)
+
+
+def parseLog(logFile, testSuiteElement):
+    # Example attrs:
+    # errors="0" failures="0" hostname="a437d6835edf" name="pytest" skipped="2" tests="6" time="6.174" timestamp="2019-11-18T19:49:47.946307"
+
+    with open(logFile) as lf:
+        testSuiteElement.attrib["tests"] = "0"
+        testSuiteElement.attrib["errors"] = "0"
+        testSuiteElement.attrib["failures"] = "0"
+        testSuiteElement.attrib["skipped"] = "0"
+        testSuiteElement.attrib["time"] = "0"
+        testSuiteElement.attrib["timestamp"] = ""
+
+        attrDict = {}
+        #setFileNameAttr(attrDict, logFile)
+        setFileNameAttr(attrDict, "nbtest")
+
+        parserStateEnum = Enum("parserStateEnum",
+                               "newTest startingLine finishLine exitCode")
+        parserState = parserStateEnum.newTest
+
+        testOutput = ""
+
+        for line in lf.readlines():
+            if parserState == parserStateEnum.newTest:
+                m = folderPatt.match(line)
+                if m:
+                    setClassNameAttr(attrDict, m.group(1))
+                    continue
+
+                m = skippingPatt.match(line)
+                if m:
+                    setTestNameAttr(attrDict, getFileBaseName(m.group(1)))
+                    setTimeAttr(attrDict, "0m0s")
+                    skippedElement = makeTestCaseElement(attrDict)
+                    message = m.group(3) or ""
+                    skippedElement.append(Element("skipped", message=message, type=""))
+                    testSuiteElement.append(skippedElement)
+                    incrNumAttr(testSuiteElement, "skipped")
+                    incrNumAttr(testSuiteElement, "tests")
+                    continue
+
+                m = startingPatt.match(line)
+                if m:
+                    parserState = parserStateEnum.startingLine
+                    testOutput = ""
+                    setTestNameAttr(attrDict, m.group(1))
+                    setTimeAttr(attrDict, "0m0s")
+                    continue
+
+                continue
+
+            elif parserState == parserStateEnum.startingLine:
+                if linePatt.match(line):
+                    parserState = parserStateEnum.finishLine
+                    testOutput = ""
+                continue
+
+            elif parserState == parserStateEnum.finishLine:
+                if linePatt.match(line):
+                    parserState = parserStateEnum.exitCode
+                else:
+                    testOutput += line
+                continue
+
+            elif parserState == parserStateEnum.exitCode:
+                m = exitCodePatt.match(line)
+                if m:
+                    testCaseElement = makeTestCaseElement(attrDict)
+                    if m.group(1) != "0":
+                        failureElement = makeFailureElement(testOutput)
+                        testCaseElement.append(failureElement)
+                        incrNumAttr(testSuiteElement, "failures")
+                    else:
+                        systemOutElement = makeSystemOutElement(testOutput)
+                        testCaseElement.append(systemOutElement)
+
+                    testSuiteElement.append(testCaseElement)
+                    parserState = parserStateEnum.newTest
+                    testOutput = ""
+                    incrNumAttr(testSuiteElement, "tests")
+                    continue
+
+                m = timePatt.match(line)
+                if m:
+                    setTimeAttr(attrDict, m.group(1))
+                    continue
+
+                continue
+
+
+if __name__ == "__main__":
+    import sys
+
+    testSuitesElement = Element("testsuites")
+    testSuiteElement = Element("testsuite", name="nbtest", hostname="")
+    parseLog(sys.argv[1], testSuiteElement)
+    testSuitesElement.append(testSuiteElement)
+    ElementTree(testSuitesElement).write(sys.argv[1]+".xml", xml_declaration=True)
diff --git a/ci/wheel_smoke_test_cudf.py b/ci/wheel_smoke_test_cudf.py
new file mode 100644
index 0000000..a11a970
--- /dev/null
+++ b/ci/wheel_smoke_test_cudf.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import cudf
+import pyarrow as pa
+
+if __name__ == '__main__':
+    n_legs = pa.array([2, 4, 5, 100])
+    animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
+    names = ["n_legs", "animals"]
+    foo = pa.table([n_legs, animals], names=names)
+    df = cudf.DataFrame.from_arrow(foo)
+    assert df.loc[df["animals"] == "Centipede"]["n_legs"].iloc[0] == 100
+    assert df.loc[df["animals"] == "Flamingo"]["n_legs"].iloc[0] == 2
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 0000000..344d4f3
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,11 @@
+#Configuration File for CodeCov
+coverage:
+  status:
+    project: off
+    patch:
+      default:
+        target: auto
+        threshold: 5%
+
+github_checks:
+    annotations: true
diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml
new file mode 100644
index 0000000..27a3a84
--- /dev/null
+++ b/conda/environments/all_cuda-118_arch-x86_64.yaml
@@ -0,0 +1,103 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- pytorch
+- conda-forge
+- nvidia
+dependencies:
+- aiobotocore>=2.2.0
+- benchmark==1.8.0
+- boto3>=1.21.21
+- botocore>=1.24.21
+- c-compiler
+- cachetools
+- cmake>=3.26.4
+- cramjam
+- cubinlinker
+- cuda-nvtx=11.8
+- cuda-python>=11.7.1,<12.0a0
+- cuda-sanitizer-api=11.8.86
+- cuda-version=11.8
+- cudatoolkit
+- cupy>=12.0.0
+- cxx-compiler
+- cython>=3.0.0
+- dask-core==2023.9.2
+- dask-cuda==23.10.*
+- dask==2023.9.2
+- distributed==2023.9.2
+- dlpack>=0.5,<0.6.0a0
+- doxygen=1.9.1
+- fastavro>=0.22.9
+- fmt>=9.1.0,<10
+- fsspec>=0.6.0
+- gcc_linux-64=11.*
+- gmock>=1.13.0
+- gtest>=1.13.0
+- hypothesis
+- identify>=2.5.20
+- ipython
+- libarrow==12.0.1.*
+- libcufile-dev=1.4.0.31
+- libcufile=1.4.0.31
+- libcurand-dev=10.3.0.86
+- libcurand=10.3.0.86
+- libkvikio==23.10.*
+- librdkafka>=1.9.0,<1.10.0a0
+- librmm==23.10.*
+- make
+- mimesis>=4.1.0
+- moto>=4.0.8
+- msgpack-python
+- myst-nb
+- nbsphinx
+- ninja
+- notebook
+- numba>=0.57,<0.58
+- numpy>=1.21,<1.25
+- numpydoc
+- nvcc_linux-64=11.8
+- nvcomp==2.6.1
+- nvtx>=0.2.1
+- packaging
+- pandas>=1.3,<1.6.0dev0
+- pandoc
+- pip
+- pre-commit
+- protobuf>=4.21,<5
+- ptxcompiler
+- pyarrow==12.0.1.*
+- pydata-sphinx-theme
+- pyorc
+- pytest
+- pytest-benchmark
+- pytest-cases
+- pytest-cov
+- pytest-xdist
+- python-confluent-kafka>=1.9.0,<1.10.0a0
+- python-snappy>=0.6.0
+- python>=3.9,<3.11
+- pytorch<1.12.0
+- rich
+- rmm==23.10.*
+- s3fs>=2022.3.0
+- scikit-build>=0.13.1
+- scipy
+- spdlog>=1.11.0,<1.12
+- sphinx
+- sphinx-autobuild
+- sphinx-copybutton
+- sphinx-markdown-tables
+- sphinxcontrib-websupport
+- streamz
+- sysroot_linux-64==2.17
+- tokenizers==0.13.1
+- transformers==4.24.0
+- typing_extensions>=4.0.0
+- zlib>=1.2.13
+- pip:
+  - git+https://github.com/python-streamz/streamz.git@master
+name: all_cuda-118_arch-x86_64
diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml
new file mode 100644
index 0000000..eb229f1
--- /dev/null
+++ b/conda/environments/all_cuda-120_arch-x86_64.yaml
@@ -0,0 +1,100 @@
+# This file is generated by `rapids-dependency-file-generator`.
+# To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+channels:
+- rapidsai
+- rapidsai-nightly
+- dask/label/dev
+- pytorch
+- conda-forge
+- nvidia
+dependencies:
+- aiobotocore>=2.2.0
+- benchmark==1.8.0
+- boto3>=1.21.21
+- botocore>=1.24.21
+- c-compiler
+- cachetools
+- cmake>=3.26.4
+- cramjam
+- cuda-cudart-dev
+- cuda-nvcc
+- cuda-nvrtc-dev
+- cuda-nvtx-dev
+- cuda-python>=12.0,<13.0a0
+- cuda-sanitizer-api
+- cuda-version=12.0
+- cupy>=12.0.0
+- cxx-compiler
+- cython>=3.0.0
+- dask-core==2023.9.2
+- dask-cuda==23.10.*
+- dask==2023.9.2
+- distributed==2023.9.2
+- dlpack>=0.5,<0.6.0a0
+- doxygen=1.9.1
+- fastavro>=0.22.9
+- fmt>=9.1.0,<10
+- fsspec>=0.6.0
+- gcc_linux-64=11.*
+- gmock>=1.13.0
+- gtest>=1.13.0
+- hypothesis
+- identify>=2.5.20
+- ipython
+- libarrow==12.0.1.*
+- libcufile-dev
+- libcurand-dev
+- libkvikio==23.10.*
+- librdkafka>=1.9.0,<1.10.0a0
+- librmm==23.10.*
+- make
+- mimesis>=4.1.0
+- moto>=4.0.8
+- msgpack-python
+- myst-nb
+- nbsphinx
+- ninja
+- notebook
+- numba>=0.57,<0.58
+- numpy>=1.21,<1.25
+- numpydoc
+- nvcomp==2.6.1
+- nvtx>=0.2.1
+- packaging
+- pandas>=1.3,<1.6.0dev0
+- pandoc
+- pip
+- pre-commit
+- protobuf>=4.21,<5
+- pyarrow==12.0.1.*
+- pydata-sphinx-theme
+- pyorc
+- pytest
+- pytest-benchmark
+- pytest-cases
+- pytest-cov
+- pytest-xdist
+- python-confluent-kafka>=1.9.0,<1.10.0a0
+- python-snappy>=0.6.0
+- python>=3.9,<3.11
+- pytorch<1.12.0
+- rich
+- rmm==23.10.*
+- s3fs>=2022.3.0
+- scikit-build>=0.13.1
+- scipy
+- spdlog>=1.11.0,<1.12
+- sphinx
+- sphinx-autobuild
+- sphinx-copybutton
+- sphinx-markdown-tables
+- sphinxcontrib-websupport
+- streamz
+- sysroot_linux-64==2.17
+- tokenizers==0.13.1
+- transformers==4.24.0
+- typing_extensions>=4.0.0
+- zlib>=1.2.13
+- pip:
+  - git+https://github.com/python-streamz/streamz.git@master
+name: all_cuda-120_arch-x86_64
diff --git a/conda/recipes/cudf/build.sh b/conda/recipes/cudf/build.sh
new file mode 100644
index 0000000..43d0464
--- /dev/null
+++ b/conda/recipes/cudf/build.sh
@@ -0,0 +1,4 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+# This assumes the script is executed from the root of the repo directory
+./build.sh cudf
diff --git a/conda/recipes/cudf/conda_build_config.yaml b/conda/recipes/cudf/conda_build_config.yaml
new file mode 100644
index 0000000..c98c270
--- /dev/null
+++ b/conda/recipes/cudf/conda_build_config.yaml
@@ -0,0 +1,17 @@
+c_compiler_version:
+  - 11
+
+cxx_compiler_version:
+  - 11
+
+sysroot_version:
+  - "2.17"
+
+cmake_version:
+  - ">=3.26.4"
+
+cuda_compiler:
+  - cuda-nvcc
+
+cuda11_compiler:
+  - nvcc
diff --git a/conda/recipes/cudf/meta.yaml b/conda/recipes/cudf/meta.yaml
new file mode 100644
index 0000000..d3e15f7
--- /dev/null
+++ b/conda/recipes/cudf/meta.yaml
@@ -0,0 +1,119 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set py_version = environ['CONDA_PY'] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: cudf
+  version: {{ version }}
+
+source:
+  git_url: ../../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=cudf-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=cudf-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+    - SCCACHE_S3_NO_CREDENTIALS
+  ignore_run_exports:
+    # libcudf's run_exports pinning is looser than we would like
+    - libcudf
+  ignore_run_exports_from:
+    {% if cuda_major == "11" %}
+    - {{ compiler('cuda11') }}
+    {% endif %}
+
+requirements:
+  build:
+    - cmake {{ cmake_version }}
+    - ninja
+    - {{ compiler('c') }}
+    - {{ compiler('cxx') }}
+    {% if cuda_major == "11" %}
+    - {{ compiler('cuda11') }} ={{ cuda_version }}
+    {% else %}
+    - {{ compiler('cuda') }}
+    {% endif %}
+    - cuda-version ={{ cuda_version }}
+    - sysroot_{{ target_platform }} {{ sysroot_version }}
+  host:
+    - protobuf ==4.21.*
+    - python
+    - cython >=3.0.0
+    - scikit-build >=0.13.1
+    - setuptools
+    - dlpack >=0.5,<0.6.0a0
+    - pyarrow =12
+    - libcudf ={{ version }}
+    - rmm ={{ minor_version }}
+    {% if cuda_major == "11" %}
+    - cudatoolkit
+    {% else %}
+    - cuda-cudart-dev
+    - cuda-nvrtc
+    - libcufile-dev  # [linux64]
+    {% endif %}
+    - cuda-version ={{ cuda_version }}
+  run:
+    - {{ pin_compatible('protobuf', min_pin='x.x', max_pin='x') }}
+    - python
+    - typing_extensions >=4.0.0
+    - pandas >=1.3,<1.6.0dev0
+    - cupy >=12.0.0
+    # TODO: Pin to numba<0.58 until #14160 is resolved
+    - numba >=0.57,<0.58
+    # TODO: Pin to numpy<1.25 until cudf requires pandas 2
+    - numpy >=1.21,<1.25
+    - {{ pin_compatible('pyarrow', max_pin='x.x.x') }}
+    - libcudf ={{ version }}
+    - {{ pin_compatible('rmm', max_pin='x.x') }}
+    - fsspec >=0.6.0
+    {% if cuda_major == "11" %}
+    - cudatoolkit
+    - ptxcompiler >=0.7.0
+    - cubinlinker  # CUDA enhanced compatibility.
+    - cuda-python >=11.7.1,<12.0a0
+    {% else %}
+    # Needed by Numba for CUDA support
+    - cuda-nvcc-impl
+    # TODO: Add nvjitlink here
+    # xref: https://github.com/rapidsai/cudf/issues/12822
+    - cuda-nvrtc
+    - cuda-python >=12.0,<13.0a0
+    {% endif %}
+    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+    - nvtx >=0.2.1
+    - packaging
+    - cachetools
+    - rich
+
+test:
+  requires:
+    - cuda-version ={{ cuda_version }}
+  imports:
+    - cudf
+
+about:
+  home: https://rapids.ai/
+  license: Apache-2.0
+  license_family: APACHE
+  license_file: LICENSE
+  summary: cuDF GPU DataFrame core library
diff --git a/conda/recipes/cudf_kafka/build.sh b/conda/recipes/cudf_kafka/build.sh
new file mode 100644
index 0000000..f4bb6e1
--- /dev/null
+++ b/conda/recipes/cudf_kafka/build.sh
@@ -0,0 +1,16 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+# This assumes the script is executed from the root of the repo directory
+# Need to set CUDA_HOME inside conda environments because the hacked together
+# setup.py for cudf-kafka searches that way.
+# TODO: Remove after https://github.com/rapidsai/cudf/pull/14292 updates
+# cudf_kafka to use scikit-build
+CUDA_MAJOR=${RAPIDS_CUDA_VERSION%%.*}
+if [[ ${CUDA_MAJOR} == "12" ]]; then
+    target_name="x86_64-linux"
+    if [[ ! $(arch) == "x86_64" ]]; then
+        target_name="sbsa-linux"
+    fi
+    export CUDA_HOME="${PREFIX}/targets/${target_name}/"
+fi
+./build.sh -v cudf_kafka
diff --git a/conda/recipes/cudf_kafka/conda_build_config.yaml b/conda/recipes/cudf_kafka/conda_build_config.yaml
new file mode 100644
index 0000000..b63a136
--- /dev/null
+++ b/conda/recipes/cudf_kafka/conda_build_config.yaml
@@ -0,0 +1,11 @@
+c_compiler_version:
+  - 11
+
+cxx_compiler_version:
+  - 11
+
+sysroot_version:
+  - "2.17"
+
+cmake_version:
+  - ">=3.26.4"
diff --git a/conda/recipes/cudf_kafka/meta.yaml b/conda/recipes/cudf_kafka/meta.yaml
new file mode 100644
index 0000000..a79c23b
--- /dev/null
+++ b/conda/recipes/cudf_kafka/meta.yaml
@@ -0,0 +1,79 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set py_version = environ['CONDA_PY'] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: cudf_kafka
+  version: {{ version }}
+
+source:
+  git_url: ../../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=cudf-kafka-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=cudf-kafka-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+    - SCCACHE_S3_NO_CREDENTIALS
+    # TODO: Remove after https://github.com/rapidsai/cudf/pull/14292 updates
+    # cudf_kafka to use scikit-build
+    - RAPIDS_CUDA_VERSION
+
+requirements:
+  build:
+    - cmake {{ cmake_version }}
+    - {{ compiler('c') }}
+    - {{ compiler('cxx') }}
+    - ninja
+    - sysroot_{{ target_platform }} {{ sysroot_version }}
+    # TODO: Remove after https://github.com/rapidsai/cudf/pull/14292 updates
+    # cudf_kafka to use scikit-build
+    {% if cuda_major == "12" %}
+    - cuda-gdb
+    {% endif %}
+  host:
+    - python
+    - cython >=3.0.0
+    - cuda-version ={{ cuda_version }}
+    - cudf ={{ version }}
+    - libcudf_kafka ={{ version }}
+    - setuptools
+    {% if cuda_major == "12" %}
+    - cuda-cudart-dev
+    {% endif %}
+  run:
+    - python
+    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+    - libcudf_kafka ={{ version }}
+    - cudf ={{ version }}
+
+test:
+  requires:
+    - cuda-version ={{ cuda_version }}
+  imports:
+    - cudf_kafka
+
+about:
+  home: https://rapids.ai/
+  license: Apache-2.0
+  license_family: APACHE
+  license_file: LICENSE
+  summary: libcudf_kafka library
diff --git a/conda/recipes/custreamz/build.sh b/conda/recipes/custreamz/build.sh
new file mode 100644
index 0000000..88fccf9
--- /dev/null
+++ b/conda/recipes/custreamz/build.sh
@@ -0,0 +1,4 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+# This assumes the script is executed from the root of the repo directory
+./build.sh -v custreamz
diff --git a/conda/recipes/custreamz/meta.yaml b/conda/recipes/custreamz/meta.yaml
new file mode 100644
index 0000000..233d51b
--- /dev/null
+++ b/conda/recipes/custreamz/meta.yaml
@@ -0,0 +1,65 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set py_version = environ['CONDA_PY'] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: custreamz
+  version: {{ version }}
+
+source:
+  git_url: ../../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=custreamz-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=custreamz-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+    - SCCACHE_S3_NO_CREDENTIALS
+
+requirements:
+  host:
+    - python
+    - python-confluent-kafka >=1.9.0,<1.10.0a0
+    - cudf_kafka ={{ version }}
+    - cuda-version ={{ cuda_version }}
+  run:
+    - python
+    - streamz
+    - cudf ={{ version }}
+    - cudf_kafka ={{ version }}
+    - dask ==2023.9.2
+    - dask-core ==2023.9.2
+    - distributed ==2023.9.2
+    - python-confluent-kafka >=1.9.0,<1.10.0a0
+    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+
+test:
+  requires:
+    - cuda-version ={{ cuda_version }}
+  imports:
+    - custreamz
+
+about:
+  home: https://rapids.ai/
+  license: Apache-2.0
+  license_family: APACHE
+  license_file: LICENSE
+  summary: cuStreamz library
diff --git a/conda/recipes/dask-cudf/build.sh b/conda/recipes/dask-cudf/build.sh
new file mode 100644
index 0000000..473f52c
--- /dev/null
+++ b/conda/recipes/dask-cudf/build.sh
@@ -0,0 +1,4 @@
+# Copyright (c) 2018-2019, NVIDIA CORPORATION.
+
+# This assumes the script is executed from the root of the repo directory
+./build.sh dask_cudf
diff --git a/conda/recipes/dask-cudf/meta.yaml b/conda/recipes/dask-cudf/meta.yaml
new file mode 100644
index 0000000..4c8af07
--- /dev/null
+++ b/conda/recipes/dask-cudf/meta.yaml
@@ -0,0 +1,65 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set py_version = environ['CONDA_PY'] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: dask-cudf
+  version: {{ version }}
+
+source:
+  git_url: ../../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda{{ cuda_major }}_py{{ py_version }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=dask-cudf-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=dask-cudf-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+    - SCCACHE_S3_NO_CREDENTIALS
+
+requirements:
+  host:
+    - python
+    - cudf ={{ version }}
+    - dask ==2023.9.2
+    - dask-core ==2023.9.2
+    - distributed ==2023.9.2
+    - cuda-version ={{ cuda_version }}
+  run:
+    - python
+    - cudf ={{ version }}
+    - dask ==2023.9.2
+    - dask-core ==2023.9.2
+    - distributed ==2023.9.2
+    - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
+
+test:
+  requires:
+    - cuda-version ={{ cuda_version }}
+  imports:
+    - dask_cudf
+
+
+about:
+  home: https://rapids.ai/
+  license: Apache-2.0
+  license_family: APACHE
+  license_file: LICENSE
+  summary: dask-cudf library
diff --git a/conda/recipes/dask-cudf/run_test.sh b/conda/recipes/dask-cudf/run_test.sh
new file mode 100644
index 0000000..c79c014
--- /dev/null
+++ b/conda/recipes/dask-cudf/run_test.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+set -e
+
+# Logger function for build status output
+function logger() {
+  echo -e "\n>>>> $@\n"
+}
+
+# Importing cudf on arm64 CPU only nodes is currently not working due to a
+# difference in reported gpu devices between arm64 and amd64
+ARCH=$(arch)
+
+if [ "${ARCH}" = "aarch64" ]; then
+  logger "Skipping tests on arm64"
+  exit 0
+fi
+
+# Dask & Distributed option to install main(nightly) or `conda-forge` packages.
+export INSTALL_DASK_MAIN=0
+
+# Dask version to install when `INSTALL_DASK_MAIN=0`
+export DASK_STABLE_VERSION="2023.9.2"
+
+# Install the conda-forge or nightly version of dask and distributed
+if [[ "${INSTALL_DASK_MAIN}" == 1 ]]; then
+    rapids-logger "rapids-mamba-retry install -c dask/label/dev 'dask/label/dev::dask' 'dask/label/dev::distributed'"
+    rapids-mamba-retry install -c dask/label/dev "dask/label/dev::dask" "dask/label/dev::distributed"
+else
+    rapids-logger "rapids-mamba-retry install conda-forge::dask=={$DASK_STABLE_VERSION} conda-forge::distributed=={$DASK_STABLE_VERSION} conda-forge::dask-core=={$DASK_STABLE_VERSION} --force-reinstall"
+    rapids-mamba-retry install conda-forge::dask=={$DASK_STABLE_VERSION} conda-forge::distributed=={$DASK_STABLE_VERSION} conda-forge::dask-core=={$DASK_STABLE_VERSION} --force-reinstall
+fi
+
+logger "python -c 'import dask_cudf'"
+python -c "import dask_cudf"
diff --git a/conda/recipes/libcudf/build.sh b/conda/recipes/libcudf/build.sh
new file mode 100644
index 0000000..47047f4
--- /dev/null
+++ b/conda/recipes/libcudf/build.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+export cudf_ROOT="$(realpath ./cpp/build)"
+
+./build.sh -n -v \
+    libcudf libcudf_kafka benchmarks tests \
+    --build_metrics --incl_cache_stats \
+    --cmake-args=\"-DCMAKE_INSTALL_LIBDIR=lib -DCUDF_ENABLE_ARROW_S3=ON -DNVBench_ENABLE_CUPTI=OFF\"
diff --git a/conda/recipes/libcudf/conda_build_config.yaml b/conda/recipes/libcudf/conda_build_config.yaml
new file mode 100644
index 0000000..25b3f19
--- /dev/null
+++ b/conda/recipes/libcudf/conda_build_config.yaml
@@ -0,0 +1,59 @@
+c_compiler_version:
+  - 11
+
+cxx_compiler_version:
+  - 11
+
+cuda_compiler:
+  - cuda-nvcc
+
+cuda11_compiler:
+  - nvcc
+
+sysroot_version:
+  - "2.17"
+
+cmake_version:
+  - ">=3.26.4"
+
+gbench_version:
+  - "==1.8.0"
+
+gtest_version:
+  - ">=1.13.0"
+
+libarrow_version:
+  - "=12"
+
+dlpack_version:
+  - ">=0.5,<0.6.0a0"
+
+librdkafka_version:
+  - ">=1.9.0,<1.10.0a0"
+
+fmt_version:
+  - ">=9.1.0,<10"
+
+spdlog_version:
+  - ">=1.11.0,<1.12"
+
+nvcomp_version:
+  - "=2.6.1"
+
+zlib_version:
+  - ">=1.2.13"
+# The CTK libraries below are missing from the conda-forge::cudatoolkit package
+# for CUDA 11. The "*_host_*" version specifiers correspond to `11.8` packages
+# and the "*_run_*" version specifiers correspond to `11.x` packages.
+
+cuda11_libcufile_host_version:
+  - "1.4.0.31"
+
+cuda11_libcufile_run_version:
+  - ">=1.0.0.82,<=1.4.0.31"
+
+cuda11_libcurand_host_version:
+  - "=10.3.0.86"
+
+cuda11_libcurand_run_version:
+  - ">=10.2.5.43,<10.3.1"
diff --git a/conda/recipes/libcudf/install_libcudf.sh b/conda/recipes/libcudf/install_libcudf.sh
new file mode 100644
index 0000000..173f8cf
--- /dev/null
+++ b/conda/recipes/libcudf/install_libcudf.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+cmake --install cpp/build
diff --git a/conda/recipes/libcudf/install_libcudf_example.sh b/conda/recipes/libcudf/install_libcudf_example.sh
new file mode 100644
index 0000000..e249688
--- /dev/null
+++ b/conda/recipes/libcudf/install_libcudf_example.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+./cpp/examples/build.sh
diff --git a/conda/recipes/libcudf/install_libcudf_kafka.sh b/conda/recipes/libcudf/install_libcudf_kafka.sh
new file mode 100644
index 0000000..9eae251
--- /dev/null
+++ b/conda/recipes/libcudf/install_libcudf_kafka.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+cmake --install cpp/libcudf_kafka/build
diff --git a/conda/recipes/libcudf/install_libcudf_tests.sh b/conda/recipes/libcudf/install_libcudf_tests.sh
new file mode 100644
index 0000000..069462e
--- /dev/null
+++ b/conda/recipes/libcudf/install_libcudf_tests.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+cmake --install cpp/build --component testing
+cmake --install cpp/libcudf_kafka/build --component testing
diff --git a/conda/recipes/libcudf/meta.yaml b/conda/recipes/libcudf/meta.yaml
new file mode 100644
index 0000000..6270658
--- /dev/null
+++ b/conda/recipes/libcudf/meta.yaml
@@ -0,0 +1,227 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %}
+{% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %}
+{% set cuda_major = cuda_version.split('.')[0] %}
+{% set cuda_spec = ">=" + cuda_major ~ ",<" + (cuda_major | int + 1) ~ ".0a0" %} # i.e. >=11,<12.0a0
+{% set date_string = environ['RAPIDS_DATE_STRING'] %}
+
+package:
+  name: libcudf-split
+
+source:
+  git_url: ../../..
+
+build:
+  script_env:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN
+    - CMAKE_C_COMPILER_LAUNCHER
+    - CMAKE_CUDA_COMPILER_LAUNCHER
+    - CMAKE_CXX_COMPILER_LAUNCHER
+    - CMAKE_GENERATOR
+    - PARALLEL_LEVEL
+    - RAPIDS_ARTIFACTS_DIR
+    - SCCACHE_BUCKET
+    - SCCACHE_IDLE_TIMEOUT
+    - SCCACHE_REGION
+    - SCCACHE_S3_KEY_PREFIX=libcudf-aarch64 # [aarch64]
+    - SCCACHE_S3_KEY_PREFIX=libcudf-linux64 # [linux64]
+    - SCCACHE_S3_USE_SSL
+    - SCCACHE_S3_NO_CREDENTIALS
+
+requirements:
+  build:
+    - cmake {{ cmake_version }}
+    - {{ compiler('c') }}
+    - {{ compiler('cxx') }}
+    {% if cuda_major == "11" %}
+    - {{ compiler('cuda11') }} ={{ cuda_version }}
+    {% else %}
+    - {{ compiler('cuda') }}
+    {% endif %}
+    - cuda-version ={{ cuda_version }}
+    - ninja
+    - sysroot_{{ target_platform }} {{ sysroot_version }}
+  host:
+    - librmm ={{ minor_version }}
+    - libkvikio ={{ minor_version }}
+    {% if cuda_major == "11" %}
+    - cudatoolkit
+    - libcufile {{ cuda11_libcufile_host_version }}  # [linux64]
+    - libcufile-dev {{ cuda11_libcufile_host_version }}  # [linux64]
+    - libcurand {{ cuda11_libcurand_host_version }}
+    - libcurand-dev {{ cuda11_libcurand_host_version }}
+    - cuda-nvrtc ={{ cuda_version }}
+    - cuda-nvrtc-dev ={{ cuda_version }}
+    - cuda-nvtx ={{ cuda_version }}
+    {% else %}
+    - cuda-nvrtc-dev
+    - cuda-nvtx-dev
+    - libcufile-dev  # [linux64]
+    - libcurand-dev
+    {% endif %}
+    - cuda-version ={{ cuda_version }}
+    - nvcomp {{ nvcomp_version }}
+    - libarrow {{ libarrow_version }}
+    - dlpack {{ dlpack_version }}
+    - librdkafka {{ librdkafka_version }}
+    - fmt {{ fmt_version }}
+    - spdlog {{ spdlog_version }}
+    - benchmark {{ gbench_version }}
+    - gtest {{ gtest_version }}
+    - gmock {{ gtest_version }}
+    - zlib {{ zlib_version }}
+
+outputs:
+  - name: libcudf
+    version: {{ version }}
+    script: install_libcudf.sh
+    build:
+      number: {{ GIT_DESCRIBE_NUMBER }}
+      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+      run_exports:
+        - {{ pin_subpackage("libcudf", max_pin="x.x") }}
+      ignore_run_exports_from:
+        {% if cuda_major == "11" %}
+        - {{ compiler('cuda11') }}
+        {% endif %}
+    requirements:
+      build:
+        - cmake {{ cmake_version }}
+      run:
+        {% if cuda_major == "11" %}
+        - cudatoolkit
+        - libcufile {{ cuda11_libcufile_run_version }}  # [linux64]
+        {% else %}
+        - cuda-nvrtc
+        - libcufile  # [linux64]
+        {% endif %}
+        - cuda-version {{ cuda_spec }}
+        - nvcomp {{ nvcomp_version }}
+        - librmm ={{ minor_version }}
+        - libkvikio ={{ minor_version }}
+        - libarrow {{ libarrow_version }}
+        - dlpack {{ dlpack_version }}
+        - gtest {{ gtest_version }}
+        - gmock {{ gtest_version }}
+    test:
+      commands:
+        - test -f $PREFIX/lib/libcudf.so
+        - test -f $PREFIX/include/cudf/column/column.hpp
+    about:
+      home: https://rapids.ai/
+      license: Apache-2.0
+      license_family: APACHE
+      license_file: LICENSE
+      summary: libcudf library
+  - name: libcudf_kafka
+    version: {{ version }}
+    script: install_libcudf_kafka.sh
+    build:
+      number: {{ GIT_DESCRIBE_NUMBER }}
+      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+      ignore_run_exports_from:
+        {% if cuda_major == "11" %}
+        - {{ compiler('cuda11') }}
+        {% endif %}
+    requirements:
+      build:
+        - cmake {{ cmake_version }}
+      host:
+        - librdkafka {{ librdkafka_version }}
+        - {{ pin_subpackage('libcudf', exact=True) }}
+      run:
+        - librdkafka {{ librdkafka_version }}
+        - {{ pin_subpackage('libcudf', exact=True) }}
+    test:
+      commands:
+        - test -f $PREFIX/lib/libcudf_kafka.so
+    about:
+      home: https://rapids.ai/
+      license: Apache-2.0
+      license_family: APACHE
+      license_file: LICENSE
+      summary: libcudf_kafka library
+  - name: libcudf-example
+    version: {{ version }}
+    script: install_libcudf_example.sh
+    build:
+      number: {{ GIT_DESCRIBE_NUMBER }}
+      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+      ignore_run_exports_from:
+        {% if cuda_major == "11" %}
+        - {{ compiler('cuda11') }}
+        {% endif %}
+    requirements:
+      build:
+        - cmake {{ cmake_version }}
+        - {{ compiler('c') }}
+        - {{ compiler('cxx') }}
+        {% if cuda_major == "11" %}
+        - {{ compiler('cuda11') }} ={{ cuda_version }}
+        {% else %}
+        - {{ compiler('cuda') }}
+        {% endif %}
+        - cuda-version ={{ cuda_version }}
+        - ninja
+        - sysroot_{{ target_platform }} {{ sysroot_version }}
+      host:
+        - {{ pin_subpackage('libcudf', exact=True) }}
+        {% if cuda_major == "11" %}
+        - cuda-nvtx ={{ cuda_version }}
+        {% else %}
+        - cuda-nvtx-dev
+        {% endif %}
+        - cuda-version ={{ cuda_version }}
+      run:
+        - {{ pin_subpackage('libcudf', exact=True) }}
+    about:
+      home: https://rapids.ai/
+      license: Apache-2.0
+      license_family: APACHE
+      license_file: LICENSE
+      summary: libcudf_example library
+  - name: libcudf-tests
+    version: {{ version }}
+    script: install_libcudf_tests.sh
+    build:
+      number: {{ GIT_DESCRIBE_NUMBER }}
+      string: cuda{{ cuda_major }}_{{ date_string }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+      ignore_run_exports_from:
+        {% if cuda_major == "11" %}
+        - {{ compiler('cuda11') }}
+        {% endif %}
+    requirements:
+      build:
+        - cmake {{ cmake_version }}
+      host:
+        - {{ pin_subpackage('libcudf', exact=True) }}
+        - {{ pin_subpackage('libcudf_kafka', exact=True) }}
+        - cuda-version {{ cuda_spec }}
+        {% if cuda_major == "11" %}
+        - libcurand {{ cuda11_libcurand_run_version }}
+        {% else %}
+        - libcurand-dev
+        {% endif %}
+        - benchmark {{ gbench_version }}
+        - gtest {{ gtest_version }}
+        - gmock {{ gtest_version }}
+      run:
+        - {{ pin_subpackage('libcudf', exact=True) }}
+        - {{ pin_subpackage('libcudf_kafka', exact=True) }}
+        - cuda-version {{ cuda_spec }}
+        {% if cuda_major == "11" %}
+        - libcurand {{ cuda11_libcurand_run_version }}
+        {% endif %}
+        - benchmark {{ gbench_version }}
+        - gtest {{ gtest_version }}
+        - gmock {{ gtest_version }}
+    about:
+      home: https://rapids.ai/
+      license: Apache-2.0
+      license_family: APACHE
+      license_file: LICENSE
+      summary: libcudf test & benchmark executables
diff --git a/cpp/.clang-tidy b/cpp/.clang-tidy
new file mode 100644
index 0000000..d766d98
--- /dev/null
+++ b/cpp/.clang-tidy
@@ -0,0 +1,27 @@
+---
+Checks:
+      'modernize-*,
+       -modernize-use-equals-default,
+       -modernize-concat-nested-namespaces,
+       -modernize-use-trailing-return-type'
+
+      # -modernize-use-equals-default        # auto-fix is broken (doesn't insert =default correctly)
+      # -modernize-concat-nested-namespaces  # auto-fix is broken (can delete code)
+      # -modernize-use-trailing-return-type  # just a preference
+
+WarningsAsErrors: ''
+HeaderFilterRegex: ''
+AnalyzeTemporaryDtors: false
+FormatStyle:     none
+CheckOptions:
+ - key:             modernize-loop-convert.MaxCopySize
+   value:           '16'
+ - key:             modernize-loop-convert.MinConfidence
+   value:           reasonable
+ - key:             modernize-pass-by-value.IncludeStyle
+   value:           llvm
+ - key:             modernize-replace-auto-ptr.IncludeStyle
+   value:           llvm
+ - key:             modernize-use-nullptr.NullMacros
+   value:           'NULL'
+...
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
new file mode 100644
index 0000000..ec58c39
--- /dev/null
+++ b/cpp/CMakeLists.txt
@@ -0,0 +1,1099 @@
+# =============================================================================
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+include(../fetch_rapids.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-cuda)
+include(rapids-export)
+include(rapids-find)
+
+rapids_cuda_init_architectures(CUDF)
+
+project(
+  CUDF
+  VERSION 23.10.00
+  LANGUAGES C CXX CUDA
+)
+if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA" AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.5)
+  message(
+    FATAL_ERROR
+      "libcudf requires CUDA Toolkit 11.5+ to compile (nvcc ${CMAKE_CUDA_COMPILER_VERSION} provided)"
+  )
+endif()
+
+# Needed because GoogleBenchmark changes the state of FindThreads.cmake, causing subsequent runs to
+# have different values for the `Threads::Threads` target. Setting this flag ensures
+# `Threads::Threads` is the same value in first run and subsequent runs.
+set(THREADS_PREFER_PTHREAD_FLAG ON)
+
+# ##################################################################################################
+# * build options ---------------------------------------------------------------------------------
+
+option(USE_NVTX "Build with NVTX support" ON)
+option(BUILD_TESTS "Configure CMake to build tests" ON)
+option(BUILD_BENCHMARKS "Configure CMake to build (google & nvbench) benchmarks" OFF)
+option(BUILD_SHARED_LIBS "Build cuDF shared libraries" ON)
+option(JITIFY_USE_CACHE "Use a file cache for JIT compiled kernels" ON)
+option(CUDF_BUILD_TESTUTIL "Whether to build the test utilities contained in libcudf" ON)
+mark_as_advanced(CUDF_BUILD_TESTUTIL)
+option(CUDF_USE_PROPRIETARY_NVCOMP "Download and use NVCOMP with proprietary extensions" ON)
+option(CUDF_USE_ARROW_STATIC "Build and statically link Arrow libraries" OFF)
+option(CUDF_ENABLE_ARROW_ORC "Build the Arrow ORC adapter" OFF)
+option(CUDF_ENABLE_ARROW_PYTHON "Find (or build) Arrow with Python support" OFF)
+option(CUDF_ENABLE_ARROW_PARQUET "Find (or build) Arrow with Parquet support" OFF)
+option(CUDF_ENABLE_ARROW_S3 "Build/Enable AWS S3 Arrow filesystem support" OFF)
+option(
+  CUDF_USE_PER_THREAD_DEFAULT_STREAM
+  "Build cuDF with per-thread default stream, including passing the per-thread default
+         stream to external libraries."
+  OFF
+)
+# Option to add all symbols to the dynamic symbol table in the library file, allowing to retrieve
+# human-readable stacktrace for debugging.
+option(
+  CUDF_BUILD_STACKTRACE_DEBUG
+  "Replace the current optimization flags by the options '-rdynamic -Og -NDEBUG', useful for debugging with stacktrace retrieval"
+  OFF
+)
+option(DISABLE_DEPRECATION_WARNINGS "Disable warnings generated from deprecated declarations." OFF)
+# Option to enable line info in CUDA device compilation to allow introspection when profiling /
+# memchecking
+option(CUDA_ENABLE_LINEINFO
+       "Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler)" OFF
+)
+option(CUDA_WARNINGS_AS_ERRORS "Enable -Werror=all-warnings for all CUDA compilation" ON)
+# cudart can be statically linked or dynamically linked. The python ecosystem wants dynamic linking
+option(CUDA_STATIC_RUNTIME "Statically link the CUDA runtime" OFF)
+
+set(DEFAULT_CUDF_BUILD_STREAMS_TEST_UTIL ON)
+if(CUDA_STATIC_RUNTIME OR NOT BUILD_SHARED_LIBS)
+  set(DEFAULT_CUDF_BUILD_STREAMS_TEST_UTIL OFF)
+endif()
+option(
+  CUDF_BUILD_STREAMS_TEST_UTIL
+  "Whether to build the utilities for stream testing contained in libcudf"
+  ${DEFAULT_CUDF_BUILD_STREAMS_TEST_UTIL}
+)
+mark_as_advanced(CUDF_BUILD_STREAMS_TEST_UTIL)
+
+option(USE_LIBARROW_FROM_PYARROW "Use the libarrow contained within pyarrow." OFF)
+mark_as_advanced(USE_LIBARROW_FROM_PYARROW)
+
+message(VERBOSE "CUDF: Build with NVTX support: ${USE_NVTX}")
+message(VERBOSE "CUDF: Configure CMake to build tests: ${BUILD_TESTS}")
+message(VERBOSE "CUDF: Configure CMake to build (google & nvbench) benchmarks: ${BUILD_BENCHMARKS}")
+message(VERBOSE "CUDF: Build cuDF shared libraries: ${BUILD_SHARED_LIBS}")
+message(VERBOSE "CUDF: Use a file cache for JIT compiled kernels: ${JITIFY_USE_CACHE}")
+message(VERBOSE "CUDF: Build and statically link Arrow libraries: ${CUDF_USE_ARROW_STATIC}")
+message(VERBOSE "CUDF: Build and enable S3 filesystem support for Arrow: ${CUDF_ENABLE_ARROW_S3}")
+message(VERBOSE "CUDF: Build with per-thread default stream: ${CUDF_USE_PER_THREAD_DEFAULT_STREAM}")
+message(
+  VERBOSE
+  "CUDF: Replace the current optimization flags by the options '-rdynamic -Og' (useful for debugging with stacktrace retrieval): ${CUDF_BUILD_STACKTRACE_DEBUG}"
+)
+message(
+  VERBOSE
+  "CUDF: Disable warnings generated from deprecated declarations: ${DISABLE_DEPRECATION_WARNINGS}"
+)
+message(
+  VERBOSE
+  "CUDF: Enable the -lineinfo option for nvcc (useful for cuda-memcheck / profiler): ${CUDA_ENABLE_LINEINFO}"
+)
+message(VERBOSE "CUDF: Statically link the CUDA runtime: ${CUDA_STATIC_RUNTIME}")
+
+# Set a default build type if none was specified
+rapids_cmake_build_type("Release")
+set(CUDF_BUILD_TESTS ${BUILD_TESTS})
+set(CUDF_BUILD_BENCHMARKS ${BUILD_BENCHMARKS})
+if(BUILD_TESTS AND NOT CUDF_BUILD_TESTUTIL)
+  message(
+    FATAL_ERROR
+      "Tests cannot be built without building cudf test utils. Please set CUDF_BUILD_TESTUTIL=ON or BUILD_TESTS=OFF"
+  )
+endif()
+
+if(CUDF_BUILD_STACKTRACE_DEBUG AND NOT CMAKE_COMPILER_IS_GNUCXX)
+  message(FATAL_ERROR "CUDF_BUILD_STACKTRACE_DEBUG is only supported with GCC compiler")
+endif()
+
+set(CUDF_CXX_FLAGS "")
+set(CUDF_CUDA_FLAGS "")
+set(CUDF_CXX_DEFINITIONS "")
+set(CUDF_CUDA_DEFINITIONS "")
+
+# Set logging level
+set(LIBCUDF_LOGGING_LEVEL
+    "INFO"
+    CACHE STRING "Choose the logging level."
+)
+set_property(
+  CACHE LIBCUDF_LOGGING_LEVEL PROPERTY STRINGS "TRACE" "DEBUG" "INFO" "WARN" "ERROR" "CRITICAL"
+                                       "OFF"
+)
+message(VERBOSE "CUDF: LIBCUDF_LOGGING_LEVEL = '${LIBCUDF_LOGGING_LEVEL}'.")
+
+if(NOT CUDF_GENERATED_INCLUDE_DIR)
+  set(CUDF_GENERATED_INCLUDE_DIR ${CUDF_BINARY_DIR})
+endif()
+
+# ##################################################################################################
+# * conda environment -----------------------------------------------------------------------------
+rapids_cmake_support_conda_env(conda_env MODIFY_PREFIX_PATH)
+
+# ##################################################################################################
+# * compiler options ------------------------------------------------------------------------------
+rapids_find_package(
+  CUDAToolkit REQUIRED
+  BUILD_EXPORT_SET cudf-exports
+  INSTALL_EXPORT_SET cudf-exports
+)
+include(cmake/Modules/ConfigureCUDA.cmake) # set other CUDA compilation flags
+
+# ##################################################################################################
+# * dependencies ----------------------------------------------------------------------------------
+
+# find zlib
+rapids_find_package(ZLIB REQUIRED)
+
+if(CUDF_BUILD_TESTUTIL)
+  # find Threads (needed by cudftestutil)
+  rapids_find_package(
+    Threads REQUIRED
+    BUILD_EXPORT_SET cudf-exports
+    INSTALL_EXPORT_SET cudf-exports
+  )
+endif()
+
+# add third party dependencies using CPM
+rapids_cpm_init()
+# find jitify
+include(cmake/thirdparty/get_jitify.cmake)
+# find nvCOMP
+include(cmake/thirdparty/get_nvcomp.cmake)
+# find thrust/cub
+include(cmake/thirdparty/get_thrust.cmake)
+# find rmm
+include(cmake/thirdparty/get_rmm.cmake)
+# find arrow
+include(cmake/thirdparty/get_arrow.cmake)
+# find dlpack
+include(cmake/thirdparty/get_dlpack.cmake)
+# find libcu++
+include(cmake/thirdparty/get_libcudacxx.cmake)
+# find cuCollections Should come after including thrust and libcudacxx
+include(cmake/thirdparty/get_cucollections.cmake)
+# find or install GoogleTest
+if(CUDF_BUILD_TESTUTIL)
+  include(cmake/thirdparty/get_gtest.cmake)
+endif()
+# preprocess jitify-able kernels
+include(cmake/Modules/JitifyPreprocessKernels.cmake)
+# find cuFile
+include(cmake/thirdparty/get_cufile.cmake)
+# find KvikIO
+include(cmake/thirdparty/get_kvikio.cmake)
+# find fmt
+include(cmake/thirdparty/get_fmt.cmake)
+# find spdlog
+include(cmake/thirdparty/get_spdlog.cmake)
+
+# Workaround until https://github.com/rapidsai/rapids-cmake/issues/176 is resolved
+if(NOT BUILD_SHARED_LIBS)
+  include("${rapids-cmake-dir}/export/find_package_file.cmake")
+  list(APPEND METADATA_KINDS BUILD INSTALL)
+  list(APPEND dependencies KvikIO ZLIB nvcomp)
+  if(TARGET cufile::cuFile_interface)
+    list(APPEND dependencies cuFile)
+  endif()
+
+  foreach(METADATA_KIND IN LISTS METADATA_KINDS)
+    foreach(dep IN LISTS dependencies)
+      rapids_export_package(${METADATA_KIND} ${dep} cudf-exports)
+    endforeach()
+  endforeach()
+
+  if(TARGET conda_env)
+    install(TARGETS conda_env EXPORT cudf-exports)
+  endif()
+endif()
+
+# ##################################################################################################
+# * library targets -------------------------------------------------------------------------------
+
+add_library(
+  cudf
+  src/aggregation/aggregation.cpp
+  src/aggregation/aggregation.cu
+  src/aggregation/result_cache.cpp
+  src/ast/expression_parser.cpp
+  src/ast/expressions.cpp
+  src/binaryop/binaryop.cpp
+  src/binaryop/compiled/ATan2.cu
+  src/binaryop/compiled/Add.cu
+  src/binaryop/compiled/BitwiseAnd.cu
+  src/binaryop/compiled/BitwiseOr.cu
+  src/binaryop/compiled/BitwiseXor.cu
+  src/binaryop/compiled/Div.cu
+  src/binaryop/compiled/FloorDiv.cu
+  src/binaryop/compiled/Greater.cu
+  src/binaryop/compiled/GreaterEqual.cu
+  src/binaryop/compiled/IntPow.cu
+  src/binaryop/compiled/Less.cu
+  src/binaryop/compiled/LessEqual.cu
+  src/binaryop/compiled/LogBase.cu
+  src/binaryop/compiled/LogicalAnd.cu
+  src/binaryop/compiled/LogicalOr.cu
+  src/binaryop/compiled/Mod.cu
+  src/binaryop/compiled/Mul.cu
+  src/binaryop/compiled/NullEquals.cu
+  src/binaryop/compiled/NullLogicalAnd.cu
+  src/binaryop/compiled/NullLogicalOr.cu
+  src/binaryop/compiled/NullMax.cu
+  src/binaryop/compiled/NullMin.cu
+  src/binaryop/compiled/PMod.cu
+  src/binaryop/compiled/Pow.cu
+  src/binaryop/compiled/PyMod.cu
+  src/binaryop/compiled/ShiftLeft.cu
+  src/binaryop/compiled/ShiftRight.cu
+  src/binaryop/compiled/ShiftRightUnsigned.cu
+  src/binaryop/compiled/Sub.cu
+  src/binaryop/compiled/TrueDiv.cu
+  src/binaryop/compiled/binary_ops.cu
+  src/binaryop/compiled/equality_ops.cu
+  src/binaryop/compiled/util.cpp
+  src/labeling/label_bins.cu
+  src/bitmask/null_mask.cu
+  src/bitmask/is_element_valid.cpp
+  src/column/column.cu
+  src/column/column_device_view.cu
+  src/column/column_factories.cpp
+  src/column/column_factories.cu
+  src/column/column_view.cpp
+  src/copying/concatenate.cu
+  src/copying/contiguous_split.cu
+  src/copying/copy.cpp
+  src/copying/copy.cu
+  src/copying/copy_range.cu
+  src/copying/gather.cu
+  src/copying/get_element.cu
+  src/copying/pack.cpp
+  src/copying/purge_nonempty_nulls.cu
+  src/copying/reverse.cu
+  src/copying/sample.cu
+  src/copying/scatter.cu
+  src/copying/shift.cu
+  src/copying/slice.cu
+  src/copying/split.cpp
+  src/copying/segmented_shift.cu
+  src/datetime/datetime_ops.cu
+  src/dictionary/add_keys.cu
+  src/dictionary/decode.cu
+  src/dictionary/detail/concatenate.cu
+  src/dictionary/detail/merge.cu
+  src/dictionary/dictionary_column_view.cpp
+  src/dictionary/dictionary_factories.cu
+  src/dictionary/encode.cu
+  src/dictionary/remove_keys.cu
+  src/dictionary/replace.cu
+  src/dictionary/search.cu
+  src/dictionary/set_keys.cu
+  src/filling/calendrical_month_sequence.cu
+  src/filling/fill.cu
+  src/filling/repeat.cu
+  src/filling/sequence.cu
+  src/groupby/groupby.cu
+  src/groupby/hash/groupby.cu
+  src/groupby/sort/aggregate.cpp
+  src/groupby/sort/group_argmax.cu
+  src/groupby/sort/group_argmin.cu
+  src/groupby/sort/group_collect.cu
+  src/groupby/sort/group_correlation.cu
+  src/groupby/sort/group_count.cu
+  src/groupby/sort/group_histogram.cu
+  src/groupby/sort/group_m2.cu
+  src/groupby/sort/group_max.cu
+  src/groupby/sort/group_min.cu
+  src/groupby/sort/group_merge_lists.cu
+  src/groupby/sort/group_merge_m2.cu
+  src/groupby/sort/group_nth_element.cu
+  src/groupby/sort/group_nunique.cu
+  src/groupby/sort/group_product.cu
+  src/groupby/sort/group_quantiles.cu
+  src/groupby/sort/group_std.cu
+  src/groupby/sort/group_sum.cu
+  src/groupby/sort/scan.cpp
+  src/groupby/sort/group_count_scan.cu
+  src/groupby/sort/group_max_scan.cu
+  src/groupby/sort/group_min_scan.cu
+  src/groupby/sort/group_rank_scan.cu
+  src/groupby/sort/group_replace_nulls.cu
+  src/groupby/sort/group_sum_scan.cu
+  src/groupby/sort/sort_helper.cu
+  src/hash/hashing.cu
+  src/hash/md5_hash.cu
+  src/hash/murmurhash3_x86_32.cu
+  src/hash/murmurhash3_x64_128.cu
+  src/hash/spark_murmurhash3_x86_32.cu
+  src/hash/xxhash_64.cu
+  src/interop/dlpack.cpp
+  src/interop/from_arrow.cu
+  src/interop/to_arrow.cu
+  src/interop/detail/arrow_allocator.cpp
+  src/io/avro/avro.cpp
+  src/io/avro/avro_gpu.cu
+  src/io/avro/reader_impl.cu
+  src/io/comp/brotli_dict.cpp
+  src/io/comp/cpu_unbz2.cpp
+  src/io/comp/debrotli.cu
+  src/io/comp/gpuinflate.cu
+  src/io/comp/nvcomp_adapter.cpp
+  src/io/comp/nvcomp_adapter.cu
+  src/io/comp/snap.cu
+  src/io/comp/statistics.cu
+  src/io/comp/uncomp.cpp
+  src/io/comp/unsnap.cu
+  src/io/csv/csv_gpu.cu
+  src/io/csv/durations.cu
+  src/io/csv/reader_impl.cu
+  src/io/csv/writer_impl.cu
+  src/io/functions.cpp
+  src/io/json/byte_range_info.cu
+  src/io/json/json_column.cu
+  src/io/json/json_tree.cu
+  src/io/json/nested_json_gpu.cu
+  src/io/json/read_json.cu
+  src/io/json/legacy/json_gpu.cu
+  src/io/json/legacy/reader_impl.cu
+  src/io/json/write_json.cu
+  src/io/orc/aggregate_orc_metadata.cpp
+  src/io/orc/dict_enc.cu
+  src/io/orc/orc.cpp
+  src/io/orc/reader_impl.cu
+  src/io/orc/stats_enc.cu
+  src/io/orc/stripe_data.cu
+  src/io/orc/stripe_enc.cu
+  src/io/orc/stripe_init.cu
+  src/datetime/timezone.cpp
+  src/io/orc/writer_impl.cu
+  src/io/parquet/compact_protocol_reader.cpp
+  src/io/parquet/compact_protocol_writer.cpp
+  src/io/parquet/decode_preprocess.cu
+  src/io/parquet/page_data.cu
+  src/io/parquet/chunk_dict.cu
+  src/io/parquet/page_enc.cu
+  src/io/parquet/page_hdr.cu
+  src/io/parquet/page_delta_decode.cu
+  src/io/parquet/page_string_decode.cu
+  src/io/parquet/predicate_pushdown.cpp
+  src/io/parquet/reader.cpp
+  src/io/parquet/reader_impl.cpp
+  src/io/parquet/reader_impl_helpers.cpp
+  src/io/parquet/reader_impl_preprocess.cu
+  src/io/parquet/writer_impl.cu
+  src/io/statistics/orc_column_statistics.cu
+  src/io/statistics/parquet_column_statistics.cu
+  src/io/text/byte_range_info.cpp
+  src/io/text/data_chunk_source_factories.cpp
+  src/io/text/bgzip_data_chunk_source.cu
+  src/io/text/bgzip_utils.cpp
+  src/io/text/multibyte_split.cu
+  src/io/utilities/arrow_io_source.cpp
+  src/io/utilities/column_buffer.cpp
+  src/io/utilities/config_utils.cpp
+  src/io/utilities/data_casting.cu
+  src/io/utilities/data_sink.cpp
+  src/io/utilities/datasource.cpp
+  src/io/utilities/file_io_utilities.cpp
+  src/io/utilities/parsing_utils.cu
+  src/io/utilities/row_selection.cpp
+  src/io/utilities/type_inference.cu
+  src/io/utilities/trie.cu
+  src/jit/cache.cpp
+  src/jit/parser.cpp
+  src/jit/util.cpp
+  src/join/conditional_join.cu
+  src/join/cross_join.cu
+  src/join/hash_join.cu
+  src/join/join.cu
+  src/join/join_utils.cu
+  src/join/mixed_join.cu
+  src/join/mixed_join_kernel.cu
+  src/join/mixed_join_kernel_nulls.cu
+  src/join/mixed_join_kernels_semi.cu
+  src/join/mixed_join_semi.cu
+  src/join/mixed_join_size_kernel.cu
+  src/join/mixed_join_size_kernel_nulls.cu
+  src/join/mixed_join_size_kernels_semi.cu
+  src/join/semi_join.cu
+  src/lists/contains.cu
+  src/lists/combine/concatenate_list_elements.cu
+  src/lists/combine/concatenate_rows.cu
+  src/lists/copying/concatenate.cu
+  src/lists/copying/copying.cu
+  src/lists/copying/gather.cu
+  src/lists/copying/segmented_gather.cu
+  src/lists/copying/scatter_helper.cu
+  src/lists/count_elements.cu
+  src/lists/dremel.cu
+  src/lists/explode.cu
+  src/lists/extract.cu
+  src/lists/interleave_columns.cu
+  src/lists/lists_column_factories.cu
+  src/lists/lists_column_view.cu
+  src/lists/reverse.cu
+  src/lists/segmented_sort.cu
+  src/lists/sequences.cu
+  src/lists/set_operations.cu
+  src/lists/stream_compaction/apply_boolean_mask.cu
+  src/lists/stream_compaction/distinct.cu
+  src/lists/utilities.cu
+  src/merge/merge.cu
+  src/partitioning/partitioning.cu
+  src/partitioning/round_robin.cu
+  src/quantiles/tdigest/tdigest.cu
+  src/quantiles/tdigest/tdigest_aggregation.cu
+  src/quantiles/tdigest/tdigest_column_view.cpp
+  src/quantiles/quantile.cu
+  src/quantiles/quantiles.cu
+  src/reductions/all.cu
+  src/reductions/any.cu
+  src/reductions/collect_ops.cu
+  src/reductions/histogram.cu
+  src/reductions/max.cu
+  src/reductions/mean.cu
+  src/reductions/min.cu
+  src/reductions/minmax.cu
+  src/reductions/nth_element.cu
+  src/reductions/product.cu
+  src/reductions/reductions.cpp
+  src/reductions/scan/rank_scan.cu
+  src/reductions/scan/scan.cpp
+  src/reductions/scan/scan_exclusive.cu
+  src/reductions/scan/scan_inclusive.cu
+  src/reductions/segmented/all.cu
+  src/reductions/segmented/any.cu
+  src/reductions/segmented/counts.cu
+  src/reductions/segmented/max.cu
+  src/reductions/segmented/mean.cu
+  src/reductions/segmented/min.cu
+  src/reductions/segmented/nunique.cu
+  src/reductions/segmented/product.cu
+  src/reductions/segmented/reductions.cpp
+  src/reductions/segmented/std.cu
+  src/reductions/segmented/sum.cu
+  src/reductions/segmented/sum_of_squares.cu
+  src/reductions/segmented/update_validity.cu
+  src/reductions/segmented/var.cu
+  src/reductions/std.cu
+  src/reductions/sum.cu
+  src/reductions/sum_of_squares.cu
+  src/reductions/var.cu
+  src/replace/clamp.cu
+  src/replace/nans.cu
+  src/replace/nulls.cu
+  src/replace/replace.cu
+  src/reshape/byte_cast.cu
+  src/reshape/interleave_columns.cu
+  src/reshape/tile.cu
+  src/rolling/detail/optimized_unbounded_window.cpp
+  src/rolling/detail/rolling_collect_list.cu
+  src/rolling/detail/rolling_fixed_window.cu
+  src/rolling/detail/rolling_variable_window.cu
+  src/rolling/grouped_rolling.cu
+  src/rolling/range_window_bounds.cpp
+  src/rolling/rolling.cu
+  src/round/round.cu
+  src/scalar/scalar.cpp
+  src/scalar/scalar_factories.cpp
+  src/search/contains_column.cu
+  src/search/contains_scalar.cu
+  src/search/contains_table.cu
+  src/search/search_ordered.cu
+  src/sort/is_sorted.cu
+  src/sort/rank.cu
+  src/sort/segmented_sort.cu
+  src/sort/sort_column.cu
+  src/sort/sort.cu
+  src/sort/stable_segmented_sort.cu
+  src/sort/stable_sort_column.cu
+  src/sort/stable_sort.cu
+  src/stream_compaction/apply_boolean_mask.cu
+  src/stream_compaction/distinct.cu
+  src/stream_compaction/distinct_count.cu
+  src/stream_compaction/distinct_helpers.cu
+  src/stream_compaction/drop_nans.cu
+  src/stream_compaction/drop_nulls.cu
+  src/stream_compaction/stable_distinct.cu
+  src/stream_compaction/unique.cu
+  src/stream_compaction/unique_count.cu
+  src/stream_compaction/unique_count_column.cu
+  src/strings/attributes.cu
+  src/strings/capitalize.cu
+  src/strings/case.cu
+  src/strings/char_types/char_cases.cu
+  src/strings/char_types/char_types.cu
+  src/strings/combine/concatenate.cu
+  src/strings/combine/join.cu
+  src/strings/combine/join_list_elements.cu
+  src/strings/contains.cu
+  src/strings/convert/convert_booleans.cu
+  src/strings/convert/convert_datetime.cu
+  src/strings/convert/convert_durations.cu
+  src/strings/convert/convert_fixed_point.cu
+  src/strings/convert/convert_floats.cu
+  src/strings/convert/convert_hex.cu
+  src/strings/convert/convert_integers.cu
+  src/strings/convert/convert_ipv4.cu
+  src/strings/convert/convert_urls.cu
+  src/strings/convert/convert_lists.cu
+  src/strings/copying/concatenate.cu
+  src/strings/copying/copying.cu
+  src/strings/copying/shift.cu
+  src/strings/count_matches.cu
+  src/strings/extract/extract.cu
+  src/strings/extract/extract_all.cu
+  src/strings/filling/fill.cu
+  src/strings/filter_chars.cu
+  src/strings/like.cu
+  src/strings/padding.cu
+  src/strings/json/json_path.cu
+  src/strings/regex/regcomp.cpp
+  src/strings/regex/regexec.cpp
+  src/strings/regex/regex_program.cpp
+  src/strings/repeat_strings.cu
+  src/strings/replace/backref_re.cu
+  src/strings/replace/multi.cu
+  src/strings/replace/multi_re.cu
+  src/strings/replace/replace.cu
+  src/strings/replace/replace_re.cu
+  src/strings/reverse.cu
+  src/strings/search/findall.cu
+  src/strings/search/find.cu
+  src/strings/search/find_multiple.cu
+  src/strings/slice.cu
+  src/strings/split/partition.cu
+  src/strings/split/split.cu
+  src/strings/split/split_re.cu
+  src/strings/split/split_record.cu
+  src/strings/strings_column_factories.cu
+  src/strings/strings_column_view.cpp
+  src/strings/strings_scalar_factories.cpp
+  src/strings/strip.cu
+  src/strings/translate.cu
+  src/strings/utilities.cu
+  src/strings/wrap.cu
+  src/structs/copying/concatenate.cu
+  src/structs/structs_column_factories.cu
+  src/structs/structs_column_view.cpp
+  src/structs/utilities.cpp
+  src/table/row_operators.cu
+  src/table/table.cpp
+  src/table/table_device_view.cu
+  src/table/table_view.cpp
+  src/text/detokenize.cu
+  src/text/edit_distance.cu
+  src/text/generate_ngrams.cu
+  src/text/jaccard.cu
+  src/text/minhash.cu
+  src/text/ngrams_tokenize.cu
+  src/text/normalize.cu
+  src/text/replace.cu
+  src/text/stemmer.cu
+  src/text/subword/bpe_tokenizer.cu
+  src/text/subword/data_normalizer.cu
+  src/text/subword/load_hash_file.cu
+  src/text/subword/load_merges_file.cu
+  src/text/subword/subword_tokenize.cu
+  src/text/subword/wordpiece_tokenizer.cu
+  src/text/tokenize.cu
+  src/text/vocabulary_tokenize.cu
+  src/transform/bools_to_mask.cu
+  src/transform/compute_column.cu
+  src/transform/encode.cu
+  src/transform/mask_to_bools.cu
+  src/transform/nans_to_nulls.cu
+  src/transform/one_hot_encode.cu
+  src/transform/row_bit_count.cu
+  src/transform/transform.cpp
+  src/transpose/transpose.cu
+  src/unary/cast_ops.cu
+  src/unary/math_ops.cu
+  src/unary/nan_ops.cu
+  src/unary/null_ops.cu
+  src/utilities/default_stream.cpp
+  src/utilities/linked_column.cpp
+  src/utilities/logger.cpp
+  src/utilities/stacktrace.cpp
+  src/utilities/stream_pool.cpp
+  src/utilities/traits.cpp
+  src/utilities/type_checks.cpp
+  src/utilities/type_dispatcher.cpp
+)
+
+# Anything that includes jitify needs to be compiled with _FILE_OFFSET_BITS=64 due to a limitation
+# in how conda builds glibc
+set_source_files_properties(
+  src/binaryop/binaryop.cpp
+  src/jit/cache.cpp
+  src/rolling/detail/rolling_fixed_window.cu
+  src/rolling/detail/rolling_variable_window.cu
+  src/rolling/grouped_rolling.cu
+  src/rolling/rolling.cu
+  src/transform/transform.cpp
+  PROPERTIES COMPILE_DEFINITIONS "_FILE_OFFSET_BITS=64"
+)
+
+set_target_properties(
+  cudf
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             INSTALL_RPATH "\$ORIGIN"
+             # set target compile options
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             # For std:: support of __int128_t. Can be removed once using cuda::std
+             CXX_EXTENSIONS ON
+             CUDA_STANDARD 17
+             CUDA_STANDARD_REQUIRED ON
+             POSITION_INDEPENDENT_CODE ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+)
+
+target_compile_options(
+  cudf PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_FLAGS}>"
+               "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>"
+)
+
+if(CUDF_BUILD_STACKTRACE_DEBUG)
+  # Remove any optimization level to avoid nvcc warning "incompatible redefinition for option
+  # 'optimize'".
+  string(REGEX REPLACE "(\-O[0123])" "" CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}")
+  string(REGEX REPLACE "(\-O[0123])" "" CMAKE_CUDA_FLAGS_RELEASE "${CMAKE_CUDA_FLAGS_RELEASE}")
+  string(REGEX REPLACE "(\-O[0123])" "" CMAKE_CUDA_FLAGS_MINSIZEREL
+                       "${CMAKE_CUDA_FLAGS_MINSIZEREL}"
+  )
+  string(REGEX REPLACE "(\-O[0123])" "" CMAKE_CUDA_FLAGS_RELWITHDEBINFO
+                       "${CMAKE_CUDA_FLAGS_RELWITHDEBINFO}"
+  )
+
+  add_library(cudf_backtrace INTERFACE)
+  target_compile_definitions(cudf_backtrace INTERFACE CUDF_BUILD_STACKTRACE_DEBUG)
+  target_compile_options(
+    cudf_backtrace INTERFACE "$<$<COMPILE_LANGUAGE:CXX>:-Og>"
+                             "$<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=-Og>"
+  )
+  target_link_options(
+    cudf_backtrace INTERFACE "$<$<LINK_LANGUAGE:CXX>:-rdynamic>"
+    "$<$<LINK_LANGUAGE:CUDA>:-Xlinker=-rdynamic>"
+  )
+  target_link_libraries(cudf PRIVATE cudf_backtrace)
+endif()
+
+# Specify include paths for the current target and dependents
+target_include_directories(
+  cudf
+  PUBLIC "$<BUILD_INTERFACE:${DLPACK_INCLUDE_DIR}>"
+         "$<BUILD_INTERFACE:${JITIFY_INCLUDE_DIR}>"
+         "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}/include>"
+         "$<BUILD_INTERFACE:${CUDF_GENERATED_INCLUDE_DIR}/include>"
+  PRIVATE "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}/src>"
+  INTERFACE "$<INSTALL_INTERFACE:include>"
+)
+
+target_compile_definitions(
+  cudf PUBLIC "$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_DEFINITIONS}>"
+              "$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_DEFINITIONS}>>"
+)
+
+# Disable Jitify log printing. See https://github.com/NVIDIA/jitify/issues/79
+target_compile_definitions(cudf PRIVATE "JITIFY_PRINT_LOG=0")
+
+if(JITIFY_USE_CACHE)
+  # Instruct src/jit/cache what version of cudf we are building so it can compute a cal-ver cache
+  # directory. We isolate this definition to the single source so it doesn't effect compiling
+  # caching for all of libcudf
+  set_property(
+    SOURCE src/jit/cache.cpp
+    APPEND
+    PROPERTY COMPILE_DEFINITIONS "JITIFY_USE_CACHE" "CUDF_VERSION=${PROJECT_VERSION}"
+  )
+endif()
+
+# Per-thread default stream
+if(CUDF_USE_PER_THREAD_DEFAULT_STREAM)
+  target_compile_definitions(
+    cudf PUBLIC CUDA_API_PER_THREAD_DEFAULT_STREAM CUDF_USE_PER_THREAD_DEFAULT_STREAM
+  )
+endif()
+
+# Disable NVTX if necessary
+if(NOT USE_NVTX)
+  target_compile_definitions(cudf PUBLIC NVTX_DISABLE)
+endif()
+
+# Define RMM logging level
+target_compile_definitions(cudf PRIVATE "RMM_LOGGING_LEVEL=LIBCUDF_LOGGING_LEVEL")
+
+# Define spdlog level
+target_compile_definitions(cudf PUBLIC "SPDLOG_ACTIVE_LEVEL=SPDLOG_LEVEL_${LIBCUDF_LOGGING_LEVEL}")
+
+# Compile stringified JIT sources first
+add_dependencies(cudf jitify_preprocess_run)
+
+# Specify the target module library dependencies
+target_link_libraries(
+  cudf
+  PUBLIC ${ARROW_LIBRARIES} libcudacxx::libcudacxx cudf::Thrust rmm::rmm
+  PRIVATE cuco::cuco ZLIB::ZLIB nvcomp::nvcomp kvikio::kvikio
+          $<TARGET_NAME_IF_EXISTS:cuFile_interface>
+)
+
+# Add Conda library, and include paths if specified
+if(TARGET conda_env)
+  target_link_libraries(cudf PRIVATE conda_env)
+endif()
+
+if(CUDA_STATIC_RUNTIME)
+  # Tell CMake what CUDA language runtime to use
+  set_target_properties(cudf PROPERTIES CUDA_RUNTIME_LIBRARY Static)
+  # Make sure to export to consumers what runtime we used
+  target_link_libraries(cudf PUBLIC CUDA::cudart_static)
+else()
+  # Tell CMake what CUDA language runtime to use
+  set_target_properties(cudf PROPERTIES CUDA_RUNTIME_LIBRARY Shared)
+  # Make sure to export to consumers what runtime we used
+  target_link_libraries(cudf PUBLIC CUDA::cudart)
+endif()
+
+file(
+  WRITE "${CUDF_BINARY_DIR}/fatbin.ld"
+  [=[
+SECTIONS
+{
+  .nvFatBinSegment : { *(.nvFatBinSegment) }
+  .nv_fatbin : { *(.nv_fatbin) }
+}
+]=]
+)
+target_link_options(cudf PRIVATE "$<HOST_LINK:${CUDF_BINARY_DIR}/fatbin.ld>")
+
+add_library(cudf::cudf ALIAS cudf)
+
+# ##################################################################################################
+# * tests and benchmarks --------------------------------------------------------------------------
+# ##################################################################################################
+
+# ##################################################################################################
+# * build cudftestutil ----------------------------------------------------------------------------
+
+if(CUDF_BUILD_TESTUTIL)
+  add_library(
+    cudftest_default_stream
+    # When compiled as a dynamic library allows us to use LD_PRELOAD injection of symbols. We
+    # currently leverage this for stream-related library validation and may make use of it for
+    # other similar features in the future.
+    tests/utilities/default_stream.cpp
+  )
+  set_target_properties(
+    cudftest_default_stream
+    PROPERTIES BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+               # set target compile options
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+  )
+  target_link_libraries(
+    cudftest_default_stream
+    PUBLIC cudf
+    PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>
+  )
+
+  add_library(cudf::cudftest_default_stream ALIAS cudftest_default_stream)
+
+  # Needs to be static so that we support usage of static builds of gtest which doesn't compile with
+  # fPIC enabled and therefore can't be embedded into shared libraries.
+  add_library(
+    cudftestutil STATIC
+    tests/io/metadata_utilities.cpp
+    tests/utilities/base_fixture.cpp
+    tests/utilities/column_utilities.cu
+    tests/utilities/table_utilities.cu
+    tests/utilities/tdigest_utilities.cu
+  )
+
+  set_target_properties(
+    cudftestutil
+    PROPERTIES BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+               # set target compile options
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+  )
+
+  target_compile_options(
+    cudftestutil PUBLIC "$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_FLAGS}>>"
+                        "$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>>"
+  )
+
+  target_link_libraries(
+    cudftestutil
+    PUBLIC GTest::gmock GTest::gtest Threads::Threads cudf cudftest_default_stream
+    PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>
+  )
+
+  target_include_directories(
+    cudftestutil PUBLIC "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}>"
+                        "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}/src>"
+  )
+  add_library(cudf::cudftestutil ALIAS cudftestutil)
+
+endif()
+
+# * build cudf_identify_stream_usage --------------------------------------------------------------
+
+if(CUDF_BUILD_STREAMS_TEST_UTIL)
+  if(CUDA_STATIC_RUNTIME)
+    message(
+      FATAL_ERROR
+        "Stream identification cannot be used with a static CUDA runtime. Please set CUDA_STATIC_RUNTIME=OFF or CUDF_BUILD_STREAMS_TEST_UTIL=OFF."
+    )
+  endif()
+
+  # Libraries for stream-related testing. We build the library twice, one with STREAM_MODE_TESTING
+  # on and one with it set to off. Each test will then be configured to use the appropriate library
+  # depending via ctest and whether it has been updated to expose public stream APIs.
+  foreach(_mode cudf testing)
+    set(_tgt "cudf_identify_stream_usage_mode_${_mode}")
+    add_library(
+      ${_tgt} SHARED src/utilities/stacktrace.cpp tests/utilities/identify_stream_usage.cpp
+    )
+
+    set_target_properties(
+      ${_tgt}
+      PROPERTIES # set target compile options
+                 CXX_STANDARD 17
+                 CXX_STANDARD_REQUIRED ON
+                 POSITION_INDEPENDENT_CODE ON
+    )
+    target_compile_options(
+      ${_tgt} PRIVATE "$<BUILD_INTERFACE:$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_FLAGS}>>"
+    )
+    target_include_directories(${_tgt} PRIVATE "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}/include>")
+    target_link_libraries(${_tgt} PUBLIC CUDA::cudart rmm::rmm)
+    if(CUDF_BUILD_STACKTRACE_DEBUG)
+      target_link_libraries(${_tgt} PRIVATE cudf_backtrace)
+    endif()
+    add_library(cudf::${_tgt} ALIAS ${_tgt})
+
+    if("${_mode}" STREQUAL "testing")
+      target_compile_definitions(${_tgt} PUBLIC STREAM_MODE_TESTING)
+    endif()
+  endforeach()
+endif()
+
+# ##################################################################################################
+# * add tests -------------------------------------------------------------------------------------
+
+if(CUDF_BUILD_TESTS)
+  # include CTest module -- automatically calls enable_testing()
+  include(CTest)
+
+  # ctest cuda memcheck
+  find_program(CUDA_SANITIZER compute-sanitizer)
+  set(MEMORYCHECK_COMMAND ${CUDA_SANITIZER})
+  set(MEMORYCHECK_TYPE CudaSanitizer)
+  set(CUDA_SANITIZER_COMMAND_OPTIONS "--tool memcheck")
+
+  # Always print verbose output when tests fail if run using `make test`.
+  list(APPEND CMAKE_CTEST_ARGUMENTS "--output-on-failure")
+  add_subdirectory(tests)
+endif()
+
+# ##################################################################################################
+# * add benchmarks --------------------------------------------------------------------------------
+
+if(CUDF_BUILD_BENCHMARKS)
+  # Find or install GoogleBench
+  include(${rapids-cmake-dir}/cpm/gbench.cmake)
+  rapids_cpm_gbench()
+
+  # Find or install nvbench
+  include(cmake/thirdparty/get_nvbench.cmake)
+
+  add_subdirectory(benchmarks)
+endif()
+
+# ##################################################################################################
+# * install targets -------------------------------------------------------------------------------
+rapids_cmake_install_lib_dir(lib_dir)
+include(CPack)
+include(GNUInstallDirs)
+
+set(CMAKE_INSTALL_DEFAULT_COMPONENT_NAME cudf)
+
+# install target for cudf_base and the proxy libcudf.so
+install(
+  TARGETS cudf
+  DESTINATION ${lib_dir}
+  EXPORT cudf-exports
+)
+
+set(_components_export_string)
+if(TARGET cudftestutil)
+  install(
+    TARGETS cudftest_default_stream cudftestutil
+    DESTINATION ${lib_dir}
+    EXPORT cudf-testing-exports
+  )
+  set(_components_export_string COMPONENTS testing COMPONENTS_EXPORT_SET cudf-testing-exports)
+endif()
+
+install(DIRECTORY ${CUDF_SOURCE_DIR}/include/cudf ${CUDF_SOURCE_DIR}/include/cudf_test
+                  ${CUDF_SOURCE_DIR}/include/nvtext DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+)
+
+if(CUDF_BUILD_STREAMS_TEST_UTIL)
+  install(TARGETS cudf_identify_stream_usage_mode_cudf DESTINATION ${lib_dir})
+  install(TARGETS cudf_identify_stream_usage_mode_testing DESTINATION ${lib_dir})
+endif()
+
+set(doc_string
+    [=[
+Provide targets for the cudf library.
+
+Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame
+library for loading, joining, aggregating, filtering, and otherwise
+manipulating data.
+
+cuDF provides a pandas-like API that will be familiar to data engineers &
+data scientists, so they can use it to easily accelerate their workflows
+without going into the details of CUDA programming.
+
+
+Imported Targets
+^^^^^^^^^^^^^^^^
+
+If cudf is found, this module defines the following IMPORTED GLOBAL
+targets:
+
+ cudf::cudf             - The main cudf library.
+
+This module offers an optional testing component which defines the
+following IMPORTED GLOBAL  targets:
+
+ cudf::cudftestutil     - The main cudf testing library
+    ]=]
+)
+
+set(common_code_string
+    [=[
+if(NOT TARGET cudf::Thrust)
+  thrust_create_target(cudf::Thrust FROM_OPTIONS)
+endif()
+]=]
+)
+
+if(CUDF_ENABLE_ARROW_PARQUET)
+  string(
+    APPEND
+    install_code_string
+    [=[
+  if(NOT Parquet_DIR)
+    set(Parquet_DIR "${Arrow_DIR}")
+  endif()
+  set(ArrowDataset_DIR "${Arrow_DIR}")
+  find_dependency(ArrowDataset)
+  ]=]
+  )
+endif()
+
+string(
+  APPEND
+  install_code_string
+  [=[
+if(testing IN_LIST cudf_FIND_COMPONENTS)
+  enable_language(CUDA)
+endif()
+]=]
+)
+string(APPEND install_code_string "${common_code_string}")
+
+rapids_export(
+  INSTALL cudf
+  EXPORT_SET cudf-exports ${_components_export_string}
+  GLOBAL_TARGETS cudf cudftestutil
+  NAMESPACE cudf::
+  DOCUMENTATION doc_string
+  FINAL_CODE_BLOCK install_code_string
+)
+
+# ##################################################################################################
+# * build export -------------------------------------------------------------------------------
+set(build_code_string
+    [=[
+if(EXISTS "${CMAKE_CURRENT_LIST_DIR}/cudf-testing-dependencies.cmake")
+  include("${CMAKE_CURRENT_LIST_DIR}/cudf-testing-dependencies.cmake")
+endif()
+if(EXISTS "${CMAKE_CURRENT_LIST_DIR}/cudf-testing-targets.cmake")
+  include("${CMAKE_CURRENT_LIST_DIR}/cudf-testing-targets.cmake")
+endif()
+]=]
+)
+
+string(APPEND build_code_string "${common_code_string}")
+
+rapids_export(
+  BUILD cudf
+  EXPORT_SET cudf-exports ${_components_export_string}
+  GLOBAL_TARGETS cudf cudftestutil
+  NAMESPACE cudf::
+  DOCUMENTATION doc_string
+  FINAL_CODE_BLOCK build_code_string
+)
+
+# ##################################################################################################
+# * make documentation ----------------------------------------------------------------------------
+
+# doc targets for cuDF
+add_custom_command(
+  OUTPUT CUDF_DOXYGEN
+  WORKING_DIRECTORY ${CUDF_SOURCE_DIR}/doxygen
+  COMMAND doxygen Doxyfile
+  VERBATIM
+  COMMENT "Custom command for building cudf doxygen docs."
+)
+
+add_custom_target(
+  docs_cudf
+  DEPENDS CUDF_DOXYGEN
+  COMMENT "Custom command for building cudf doxygen docs."
+)
+
+# ##################################################################################################
+# * make gdb helper scripts ------------------------------------------------------------------------
+
+# build pretty-printer load script
+if(Thrust_SOURCE_DIR AND rmm_SOURCE_DIR)
+  configure_file(scripts/load-pretty-printers.in load-pretty-printers @ONLY)
+endif()
diff --git a/cpp/benchmarks/CMakeLists.txt b/cpp/benchmarks/CMakeLists.txt
new file mode 100644
index 0000000..cd6b3cf
--- /dev/null
+++ b/cpp/benchmarks/CMakeLists.txt
@@ -0,0 +1,336 @@
+# =============================================================================
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+find_package(Threads REQUIRED)
+
+add_library(cudf_datagen STATIC common/generate_input.cu)
+target_compile_features(cudf_datagen PUBLIC cxx_std_17 cuda_std_17)
+
+target_compile_options(
+  cudf_datagen PUBLIC "$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_FLAGS}>"
+                      "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>"
+)
+
+target_link_libraries(
+  cudf_datagen
+  PUBLIC GTest::gmock GTest::gtest benchmark::benchmark nvbench::nvbench Threads::Threads cudf
+         cudftestutil
+  PRIVATE $<TARGET_NAME_IF_EXISTS:conda_env>
+)
+
+target_include_directories(
+  cudf_datagen
+  PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>" "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}>"
+         "$<BUILD_INTERFACE:${CUDF_SOURCE_DIR}/src>"
+)
+
+# ##################################################################################################
+# * compiler function -----------------------------------------------------------------------------
+
+# Use an OBJECT library so we only compile these helper source files only once
+add_library(
+  cudf_benchmark_common OBJECT "${CUDF_SOURCE_DIR}/tests/utilities/base_fixture.cpp"
+                               synchronization/synchronization.cpp io/cuio_common.cpp
+)
+target_link_libraries(cudf_benchmark_common PRIVATE cudf_datagen $<TARGET_NAME_IF_EXISTS:conda_env>)
+add_custom_command(
+  OUTPUT CUDF_BENCHMARKS
+  COMMAND echo Running benchmarks
+  COMMAND mkdir -p results
+  VERBATIM
+  COMMENT "Running cudf benchmarks."
+  USES_TERMINAL
+)
+
+# This function takes in a benchmark name and benchmark source and handles setting all of the
+# associated properties and linking to build the benchmark
+function(ConfigureBench CMAKE_BENCH_NAME)
+  add_executable(${CMAKE_BENCH_NAME} ${ARGN})
+  set_target_properties(
+    ${CMAKE_BENCH_NAME}
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUDF_BINARY_DIR}/benchmarks>"
+               INSTALL_RPATH "\$ORIGIN/../../../lib"
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               # For std:: support of __int128_t. Can be removed once using cuda::std
+               CXX_EXTENSIONS ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+  )
+  target_link_libraries(
+    ${CMAKE_BENCH_NAME} PRIVATE cudf_benchmark_common cudf_datagen benchmark::benchmark_main
+                                $<TARGET_NAME_IF_EXISTS:conda_env>
+  )
+  add_custom_command(
+    OUTPUT CUDF_BENCHMARKS
+    COMMAND ${CMAKE_BENCH_NAME} --benchmark_out_format=json
+            --benchmark_out=results/${CMAKE_BENCH_NAME}.json
+    APPEND
+    COMMENT "Adding ${CMAKE_BENCH_NAME}"
+  )
+
+  install(
+    TARGETS ${CMAKE_BENCH_NAME}
+    COMPONENT testing
+    DESTINATION bin/benchmarks/libcudf
+    EXCLUDE_FROM_ALL
+  )
+endfunction()
+
+# This function takes in a benchmark name and benchmark source for nvbench benchmarks and handles
+# setting all of the associated properties and linking to build the benchmark
+function(ConfigureNVBench CMAKE_BENCH_NAME)
+  add_executable(${CMAKE_BENCH_NAME} ${ARGN} fixture/nvbench_main.cpp)
+  set_target_properties(
+    ${CMAKE_BENCH_NAME}
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUDF_BINARY_DIR}/benchmarks>"
+               INSTALL_RPATH "\$ORIGIN/../../../lib"
+  )
+  target_link_libraries(
+    ${CMAKE_BENCH_NAME} PRIVATE cudf_benchmark_common cudf_datagen nvbench::nvbench
+                                $<TARGET_NAME_IF_EXISTS:conda_env>
+  )
+  install(
+    TARGETS ${CMAKE_BENCH_NAME}
+    COMPONENT testing
+    DESTINATION bin/benchmarks/libcudf
+    EXCLUDE_FROM_ALL
+  )
+endfunction()
+
+# ##################################################################################################
+# * column benchmarks -----------------------------------------------------------------------------
+ConfigureBench(COLUMN_CONCAT_BENCH column/concatenate.cpp)
+
+# ##################################################################################################
+# * gather benchmark ------------------------------------------------------------------------------
+ConfigureBench(GATHER_BENCH copying/gather.cu)
+
+# ##################################################################################################
+# * scatter benchmark -----------------------------------------------------------------------------
+ConfigureBench(SCATTER_BENCH copying/scatter.cu)
+
+# ##################################################################################################
+# * lists scatter benchmark -----------------------------------------------------------------------
+ConfigureBench(SCATTER_LISTS_BENCH lists/copying/scatter_lists.cu)
+
+# ##################################################################################################
+# * Other list-related operartions benchmark ------------------------------------------------------
+ConfigureNVBench(SET_OPS_NVBENCH lists/set_operations.cpp)
+
+# ##################################################################################################
+# * contiguous_split benchmark  -------------------------------------------------------------------
+ConfigureBench(CONTIGUOUS_SPLIT_BENCH copying/contiguous_split.cu)
+
+# ##################################################################################################
+# * shift benchmark -------------------------------------------------------------------------------
+ConfigureBench(SHIFT_BENCH copying/shift.cu)
+
+# ##################################################################################################
+# * copy-if-else benchmark
+# -----------------------------------------------------------------------------
+ConfigureBench(COPY_IF_ELSE_BENCH copying/copy_if_else.cpp)
+
+# ##################################################################################################
+# * transpose benchmark ---------------------------------------------------------------------------
+ConfigureBench(TRANSPOSE_BENCH transpose/transpose.cpp)
+
+# ##################################################################################################
+# * apply_boolean_mask benchmark ------------------------------------------------------------------
+ConfigureBench(APPLY_BOOLEAN_MASK_BENCH stream_compaction/apply_boolean_mask.cpp)
+
+# ##################################################################################################
+# * stream_compaction benchmark -------------------------------------------------------------------
+ConfigureNVBench(
+  STREAM_COMPACTION_NVBENCH
+  stream_compaction/distinct.cpp
+  stream_compaction/distinct_count.cpp
+  stream_compaction/stable_distinct.cpp
+  stream_compaction/unique.cpp
+  stream_compaction/unique_count.cpp
+)
+
+# ##################################################################################################
+# * join benchmark --------------------------------------------------------------------------------
+ConfigureBench(JOIN_BENCH join/left_join.cu join/conditional_join.cu)
+ConfigureNVBench(JOIN_NVBENCH join/join.cu join/mixed_join.cu)
+
+# ##################################################################################################
+# * iterator benchmark ----------------------------------------------------------------------------
+ConfigureBench(ITERATOR_BENCH iterator/iterator.cu)
+
+# ##################################################################################################
+# * search benchmark ------------------------------------------------------------------------------
+ConfigureBench(SEARCH_BENCH search/search.cpp)
+ConfigureNVBench(SEARCH_NVBENCH search/contains_scalar.cpp search/contains_table.cpp)
+
+# ##################################################################################################
+# * sort benchmark --------------------------------------------------------------------------------
+ConfigureBench(SORT_BENCH sort/rank.cpp sort/sort.cpp sort/sort_strings.cpp)
+ConfigureNVBench(
+  SORT_NVBENCH sort/rank_lists.cpp sort/rank_structs.cpp sort/segmented_sort.cpp
+  sort/sort_lists.cpp sort/sort_structs.cpp
+)
+
+# ##################################################################################################
+# * quantiles benchmark
+# --------------------------------------------------------------------------------
+ConfigureBench(QUANTILES_BENCH quantiles/quantiles.cpp)
+
+# ##################################################################################################
+# * type_dispatcher benchmark ---------------------------------------------------------------------
+ConfigureBench(TYPE_DISPATCHER_BENCH type_dispatcher/type_dispatcher.cu)
+
+# ##################################################################################################
+# * reduction benchmark ---------------------------------------------------------------------------
+ConfigureBench(
+  REDUCTION_BENCH reduction/anyall.cpp reduction/dictionary.cpp reduction/minmax.cpp
+  reduction/reduce.cpp reduction/scan.cpp
+)
+ConfigureNVBench(
+  REDUCTION_NVBENCH reduction/rank.cpp reduction/scan_structs.cpp reduction/segmented_reduce.cpp
+)
+
+# ##################################################################################################
+# * reduction benchmark ---------------------------------------------------------------------------
+ConfigureBench(REPLACE_BENCH replace/clamp.cpp replace/nans.cpp)
+
+# ##################################################################################################
+# * filling benchmark -----------------------------------------------------------------------------
+ConfigureBench(FILL_BENCH filling/repeat.cpp)
+
+# ##################################################################################################
+# * groupby benchmark -----------------------------------------------------------------------------
+ConfigureBench(
+  GROUPBY_BENCH groupby/group_sum.cpp groupby/group_nth.cpp groupby/group_shift.cpp
+  groupby/group_struct_values.cpp groupby/group_no_requests.cpp groupby/group_scan.cpp
+)
+
+ConfigureNVBench(
+  GROUPBY_NVBENCH groupby/group_max.cpp groupby/group_nunique.cpp groupby/group_rank.cpp
+  groupby/group_struct_keys.cpp
+)
+
+# ##################################################################################################
+# * hashing benchmark -----------------------------------------------------------------------------
+ConfigureBench(HASHING_BENCH hashing/partition.cpp)
+ConfigureNVBench(HASHING_NVBENCH hashing/hash.cpp)
+
+# ##################################################################################################
+# * merge benchmark -------------------------------------------------------------------------------
+ConfigureBench(MERGE_BENCH merge/merge.cpp)
+
+# ##################################################################################################
+# * null_mask benchmark ---------------------------------------------------------------------------
+ConfigureBench(NULLMASK_BENCH null_mask/set_null_mask.cpp)
+
+# ##################################################################################################
+# * parquet writer benchmark ----------------------------------------------------------------------
+ConfigureNVBench(
+  PARQUET_WRITER_NVBENCH io/parquet/parquet_writer.cpp io/parquet/parquet_writer_chunks.cpp
+)
+
+# ##################################################################################################
+# * parquet reader benchmark ----------------------------------------------------------------------
+ConfigureNVBench(
+  PARQUET_READER_NVBENCH io/parquet/parquet_reader_input.cpp io/parquet/parquet_reader_options.cpp
+)
+
+# ##################################################################################################
+# * orc reader benchmark --------------------------------------------------------------------------
+ConfigureNVBench(ORC_READER_NVBENCH io/orc/orc_reader_input.cpp io/orc/orc_reader_options.cpp)
+
+# ##################################################################################################
+# * csv reader benchmark --------------------------------------------------------------------------
+ConfigureNVBench(CSV_READER_NVBENCH io/csv/csv_reader_input.cpp io/csv/csv_reader_options.cpp)
+
+# ##################################################################################################
+# * orc writer benchmark --------------------------------------------------------------------------
+ConfigureNVBench(ORC_WRITER_NVBENCH io/orc/orc_writer.cpp io/orc/orc_writer_chunks.cpp)
+
+# ##################################################################################################
+# * csv writer benchmark --------------------------------------------------------------------------
+ConfigureNVBench(CSV_WRITER_NVBENCH io/csv/csv_writer.cpp)
+
+# ##################################################################################################
+# * ast benchmark ---------------------------------------------------------------------------------
+ConfigureBench(AST_BENCH ast/transform.cpp)
+
+# ##################################################################################################
+# * binaryop benchmark ----------------------------------------------------------------------------
+ConfigureBench(BINARYOP_BENCH binaryop/binaryop.cpp binaryop/compiled_binaryop.cpp)
+
+# ##################################################################################################
+# * nvtext benchmark -------------------------------------------------------------------
+ConfigureBench(TEXT_BENCH text/ngrams.cpp text/subword.cpp)
+
+ConfigureNVBench(
+  TEXT_NVBENCH text/edit_distance.cpp text/hash_ngrams.cpp text/jaccard.cpp text/minhash.cpp
+  text/normalize.cpp text/replace.cpp text/tokenize.cpp
+)
+
+# ##################################################################################################
+# * strings benchmark -------------------------------------------------------------------
+ConfigureBench(
+  STRINGS_BENCH
+  string/combine.cpp
+  string/convert_datetime.cpp
+  string/convert_durations.cpp
+  string/convert_fixed_point.cpp
+  string/convert_numerics.cpp
+  string/copy.cu
+  string/factory.cu
+  string/filter.cpp
+  string/find.cpp
+  string/repeat_strings.cpp
+  string/replace.cpp
+  string/slice.cpp
+  string/translate.cpp
+  string/url_decode.cu
+)
+
+ConfigureNVBench(
+  STRINGS_NVBENCH
+  string/case.cpp
+  string/char_types.cpp
+  string/contains.cpp
+  string/count.cpp
+  string/extract.cpp
+  string/gather.cpp
+  string/join_strings.cpp
+  string/lengths.cpp
+  string/like.cpp
+  string/replace_re.cpp
+  string/reverse.cpp
+  string/split.cpp
+  string/split_re.cpp
+)
+
+# ##################################################################################################
+# * json benchmark -------------------------------------------------------------------
+ConfigureBench(JSON_BENCH string/json.cu)
+ConfigureNVBench(FST_NVBENCH io/fst.cu)
+ConfigureNVBench(JSON_READER_NVBENCH io/json/nested_json.cpp io/json/json_reader_input.cpp)
+ConfigureNVBench(JSON_WRITER_NVBENCH io/json/json_writer.cpp)
+
+# ##################################################################################################
+# * io benchmark ---------------------------------------------------------------------
+ConfigureNVBench(MULTIBYTE_SPLIT_NVBENCH io/text/multibyte_split.cpp)
+target_link_libraries(MULTIBYTE_SPLIT_NVBENCH PRIVATE ZLIB::ZLIB)
+
+add_custom_target(
+  run_benchmarks
+  DEPENDS CUDF_BENCHMARKS
+  COMMENT "Custom command for running cudf benchmarks."
+)
diff --git a/cpp/benchmarks/ast/transform.cpp b/cpp/benchmarks/ast/transform.cpp
new file mode 100644
index 0000000..65a4453
--- /dev/null
+++ b/cpp/benchmarks/ast/transform.cpp
@@ -0,0 +1,134 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <list>
+#include <memory>
+#include <optional>
+#include <vector>
+
+enum class TreeType {
+  IMBALANCED_LEFT  // All operator expressions have a left child operator expression and a right
+                   // child column reference
+};
+
+template <typename key_type, TreeType tree_type, bool reuse_columns, bool Nullable>
+class AST : public cudf::benchmark {};
+
+template <typename key_type, TreeType tree_type, bool reuse_columns, bool Nullable>
+static void BM_ast_transform(benchmark::State& state)
+{
+  auto const table_size{static_cast<cudf::size_type>(state.range(0))};
+  auto const tree_levels{static_cast<cudf::size_type>(state.range(1))};
+
+  // Create table data
+  auto const n_cols = reuse_columns ? 1 : tree_levels + 1;
+  auto const source_table =
+    create_sequence_table(cycle_dtypes({cudf::type_to_id<key_type>()}, n_cols),
+                          row_count{table_size},
+                          Nullable ? std::optional<double>{0.5} : std::nullopt);
+  auto table = source_table->view();
+
+  // Create column references
+  auto column_refs = std::vector<cudf::ast::column_reference>();
+  std::transform(thrust::make_counting_iterator(0),
+                 thrust::make_counting_iterator(n_cols),
+                 std::back_inserter(column_refs),
+                 [](auto const& column_id) {
+                   return cudf::ast::column_reference(reuse_columns ? 0 : column_id);
+                 });
+
+  // Create expression trees
+
+  // Note that a std::list is required here because of its guarantees against reference invalidation
+  // when items are added or removed. References to items in a std::vector are not safe if the
+  // vector must re-allocate.
+  auto expressions = std::list<cudf::ast::operation>();
+
+  // Construct tree that chains additions like (((a + b) + c) + d)
+  auto const op = cudf::ast::ast_operator::ADD;
+  if (reuse_columns) {
+    expressions.push_back(cudf::ast::operation(op, column_refs.at(0), column_refs.at(0)));
+    for (cudf::size_type i = 0; i < tree_levels - 1; i++) {
+      expressions.push_back(cudf::ast::operation(op, expressions.back(), column_refs.at(0)));
+    }
+  } else {
+    expressions.push_back(cudf::ast::operation(op, column_refs.at(0), column_refs.at(1)));
+    std::transform(std::next(column_refs.cbegin(), 2),
+                   column_refs.cend(),
+                   std::back_inserter(expressions),
+                   [&](auto const& column_ref) {
+                     return cudf::ast::operation(op, expressions.back(), column_ref);
+                   });
+  }
+
+  auto const& expression_tree_root = expressions.back();
+
+  // Execute benchmark
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::compute_column(table, expression_tree_root);
+  }
+
+  // Use the number of bytes read from global memory
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * state.range(0) *
+                          (tree_levels + 1) * sizeof(key_type));
+}
+
+static void CustomRanges(benchmark::internal::Benchmark* b)
+{
+  auto row_counts       = std::vector<cudf::size_type>{100'000, 1'000'000, 10'000'000, 100'000'000};
+  auto operation_counts = std::vector<cudf::size_type>{1, 5, 10};
+  for (auto const& row_count : row_counts) {
+    for (auto const& operation_count : operation_counts) {
+      b->Args({row_count, operation_count});
+    }
+  }
+}
+
+#define AST_TRANSFORM_BENCHMARK_DEFINE(name, key_type, tree_type, reuse_columns, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(AST, name, key_type, tree_type, reuse_columns, nullable)     \
+  (::benchmark::State & st)                                                                \
+  {                                                                                        \
+    BM_ast_transform<key_type, tree_type, reuse_columns, nullable>(st);                    \
+  }                                                                                        \
+  BENCHMARK_REGISTER_F(AST, name)                                                          \
+    ->Apply(CustomRanges)                                                                  \
+    ->Unit(benchmark::kMillisecond)                                                        \
+    ->UseManualTime();
+
+AST_TRANSFORM_BENCHMARK_DEFINE(
+  ast_int32_imbalanced_unique, int32_t, TreeType::IMBALANCED_LEFT, false, false);
+AST_TRANSFORM_BENCHMARK_DEFINE(
+  ast_int32_imbalanced_reuse, int32_t, TreeType::IMBALANCED_LEFT, true, false);
+AST_TRANSFORM_BENCHMARK_DEFINE(
+  ast_double_imbalanced_unique, double, TreeType::IMBALANCED_LEFT, false, false);
+
+AST_TRANSFORM_BENCHMARK_DEFINE(
+  ast_int32_imbalanced_unique_nulls, int32_t, TreeType::IMBALANCED_LEFT, false, true);
+AST_TRANSFORM_BENCHMARK_DEFINE(
+  ast_int32_imbalanced_reuse_nulls, int32_t, TreeType::IMBALANCED_LEFT, true, true);
+AST_TRANSFORM_BENCHMARK_DEFINE(
+  ast_double_imbalanced_unique_nulls, double, TreeType::IMBALANCED_LEFT, false, true);
diff --git a/cpp/benchmarks/binaryop/binaryop.cpp b/cpp/benchmarks/binaryop/binaryop.cpp
new file mode 100644
index 0000000..fa98d9e
--- /dev/null
+++ b/cpp/benchmarks/binaryop/binaryop.cpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/binaryop.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <algorithm>
+#include <vector>
+
+// This set of benchmarks is designed to be a comparison for the AST benchmarks
+
+enum class TreeType {
+  IMBALANCED_LEFT  // All operator expressions have a left child operator expression and a right
+                   // child column reference
+};
+
+template <typename key_type, TreeType tree_type, bool reuse_columns>
+class BINARYOP : public cudf::benchmark {};
+
+template <typename key_type, TreeType tree_type, bool reuse_columns>
+static void BM_binaryop_transform(benchmark::State& state)
+{
+  auto const table_size{static_cast<cudf::size_type>(state.range(0))};
+  auto const tree_levels{static_cast<cudf::size_type>(state.range(1))};
+
+  // Create table data
+  auto const n_cols       = reuse_columns ? 1 : tree_levels + 1;
+  auto const source_table = create_sequence_table(
+    cycle_dtypes({cudf::type_to_id<key_type>()}, n_cols), row_count{table_size});
+  cudf::table_view table{*source_table};
+
+  // Execute benchmark
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    // Execute tree that chains additions like (((a + b) + c) + d)
+    auto const op               = cudf::binary_operator::ADD;
+    auto const result_data_type = cudf::data_type(cudf::type_to_id<key_type>());
+    if (reuse_columns) {
+      auto result = cudf::binary_operation(table.column(0), table.column(0), op, result_data_type);
+      for (cudf::size_type i = 0; i < tree_levels - 1; i++) {
+        result = cudf::binary_operation(result->view(), table.column(0), op, result_data_type);
+      }
+    } else {
+      auto result = cudf::binary_operation(table.column(0), table.column(1), op, result_data_type);
+      std::for_each(std::next(table.begin(), 2), table.end(), [&](auto const& col) {
+        result = cudf::binary_operation(result->view(), col, op, result_data_type);
+      });
+    }
+  }
+
+  // Use the number of bytes read from global memory
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * state.range(0) *
+                          (tree_levels + 1) * sizeof(key_type));
+}
+
+#define BINARYOP_TRANSFORM_BENCHMARK_DEFINE(name, key_type, tree_type, reuse_columns) \
+  BENCHMARK_TEMPLATE_DEFINE_F(BINARYOP, name, key_type, tree_type, reuse_columns)     \
+  (::benchmark::State & st) { BM_binaryop_transform<key_type, tree_type, reuse_columns>(st); }
+
+BINARYOP_TRANSFORM_BENCHMARK_DEFINE(binaryop_int32_imbalanced_unique,
+                                    int32_t,
+                                    TreeType::IMBALANCED_LEFT,
+                                    false);
+BINARYOP_TRANSFORM_BENCHMARK_DEFINE(binaryop_int32_imbalanced_reuse,
+                                    int32_t,
+                                    TreeType::IMBALANCED_LEFT,
+                                    true);
+BINARYOP_TRANSFORM_BENCHMARK_DEFINE(binaryop_double_imbalanced_unique,
+                                    double,
+                                    TreeType::IMBALANCED_LEFT,
+                                    false);
+
+static void CustomRanges(benchmark::internal::Benchmark* b)
+{
+  auto row_counts       = std::vector<cudf::size_type>{100'000, 1'000'000, 10'000'000, 100'000'000};
+  auto operation_counts = std::vector<cudf::size_type>{1, 2, 5, 10};
+  for (auto const& row_count : row_counts) {
+    for (auto const& operation_count : operation_counts) {
+      b->Args({row_count, operation_count});
+    }
+  }
+}
+
+BENCHMARK_REGISTER_F(BINARYOP, binaryop_int32_imbalanced_unique)
+  ->Apply(CustomRanges)
+  ->Unit(benchmark::kMillisecond)
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(BINARYOP, binaryop_int32_imbalanced_reuse)
+  ->Apply(CustomRanges)
+  ->Unit(benchmark::kMillisecond)
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(BINARYOP, binaryop_double_imbalanced_unique)
+  ->Apply(CustomRanges)
+  ->Unit(benchmark::kMillisecond)
+  ->UseManualTime();
diff --git a/cpp/benchmarks/binaryop/compiled_binaryop.cpp b/cpp/benchmarks/binaryop/compiled_binaryop.cpp
new file mode 100644
index 0000000..a1131df
--- /dev/null
+++ b/cpp/benchmarks/binaryop/compiled_binaryop.cpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/binaryop.hpp>
+
+class COMPILED_BINARYOP : public cudf::benchmark {};
+
+template <typename TypeLhs, typename TypeRhs, typename TypeOut>
+void BM_compiled_binaryop(benchmark::State& state, cudf::binary_operator binop)
+{
+  auto const column_size{static_cast<cudf::size_type>(state.range(0))};
+
+  auto const source_table = create_random_table(
+    {cudf::type_to_id<TypeLhs>(), cudf::type_to_id<TypeRhs>()}, row_count{column_size});
+
+  auto lhs = cudf::column_view(source_table->get_column(0));
+  auto rhs = cudf::column_view(source_table->get_column(1));
+
+  auto output_dtype = cudf::data_type(cudf::type_to_id<TypeOut>());
+
+  // Call once for hot cache.
+  cudf::binary_operation(lhs, rhs, binop, output_dtype);
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    cudf::binary_operation(lhs, rhs, binop, output_dtype);
+  }
+
+  // use number of bytes read and written to global memory
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * column_size *
+                          (sizeof(TypeLhs) + sizeof(TypeRhs) + sizeof(TypeOut)));
+}
+
+// TODO tparam boolean for null.
+#define BM_BINARYOP_BENCHMARK_DEFINE(name, lhs, rhs, bop, tout)           \
+  BENCHMARK_DEFINE_F(COMPILED_BINARYOP, name)                             \
+  (::benchmark::State & st)                                               \
+  {                                                                       \
+    BM_compiled_binaryop<lhs, rhs, tout>(st, cudf::binary_operator::bop); \
+  }                                                                       \
+  BENCHMARK_REGISTER_F(COMPILED_BINARYOP, name)                           \
+    ->Unit(benchmark::kMicrosecond)                                       \
+    ->UseManualTime()                                                     \
+    ->Arg(10000)      /* 10k */                                           \
+    ->Arg(100000)     /* 100k */                                          \
+    ->Arg(1000000)    /* 1M */                                            \
+    ->Arg(10000000)   /* 10M */                                           \
+    ->Arg(100000000); /* 100M */
+
+#define build_name(a, b, c, d) a##_##b##_##c##_##d
+
+#define BINARYOP_BENCHMARK_DEFINE(lhs, rhs, bop, tout) \
+  BM_BINARYOP_BENCHMARK_DEFINE(build_name(bop, lhs, rhs, tout), lhs, rhs, bop, tout)
+
+using cudf::duration_D;
+using cudf::duration_ms;
+using cudf::duration_ns;
+using cudf::duration_s;
+using cudf::timestamp_D;
+using cudf::timestamp_ms;
+using cudf::timestamp_s;
+using numeric::decimal32;
+
+// clang-format off
+BINARYOP_BENCHMARK_DEFINE(float,        int64_t,      ADD,                  int32_t);
+BINARYOP_BENCHMARK_DEFINE(float,        float,        ADD,                  float);
+BINARYOP_BENCHMARK_DEFINE(timestamp_s,  duration_s,   ADD,                  timestamp_s);
+BINARYOP_BENCHMARK_DEFINE(duration_s,   duration_D,   SUB,                  duration_ms);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int64_t,      SUB,                  int64_t);
+BINARYOP_BENCHMARK_DEFINE(float,        float,        MUL,                  int64_t);
+BINARYOP_BENCHMARK_DEFINE(duration_s,   int64_t,      MUL,                  duration_s);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int64_t,      DIV,                  int64_t);
+BINARYOP_BENCHMARK_DEFINE(duration_ms,  int32_t,      DIV,                  duration_ms);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int64_t,      TRUE_DIV,             int64_t);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int64_t,      FLOOR_DIV,            int64_t);
+BINARYOP_BENCHMARK_DEFINE(double,       double,       MOD,                  double);
+BINARYOP_BENCHMARK_DEFINE(duration_ms,  int64_t,      MOD,                  duration_ms);
+BINARYOP_BENCHMARK_DEFINE(int32_t,      int64_t,      PMOD,                 double);
+BINARYOP_BENCHMARK_DEFINE(int32_t,      uint8_t,      PYMOD,                int64_t);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int64_t,      POW,                  double);
+BINARYOP_BENCHMARK_DEFINE(float,        double,       LOG_BASE,             double);
+BINARYOP_BENCHMARK_DEFINE(float,        double,       ATAN2,                double);
+BINARYOP_BENCHMARK_DEFINE(int,          int,          SHIFT_LEFT,           int);
+BINARYOP_BENCHMARK_DEFINE(int16_t,      int64_t,      SHIFT_RIGHT,          int);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int32_t,      SHIFT_RIGHT_UNSIGNED, int64_t);
+BINARYOP_BENCHMARK_DEFINE(int64_t,      int32_t,      BITWISE_AND,          int16_t);
+BINARYOP_BENCHMARK_DEFINE(int16_t,      int32_t,      BITWISE_OR,           int64_t);
+BINARYOP_BENCHMARK_DEFINE(int16_t,      int64_t,      BITWISE_XOR,          int32_t);
+BINARYOP_BENCHMARK_DEFINE(double,       int8_t,       LOGICAL_AND,          bool);
+BINARYOP_BENCHMARK_DEFINE(int16_t,      int64_t,      LOGICAL_OR,           bool);
+BINARYOP_BENCHMARK_DEFINE(int32_t,      int64_t,      EQUAL,                bool);
+BINARYOP_BENCHMARK_DEFINE(duration_ms,  duration_ns,  EQUAL,                bool);
+BINARYOP_BENCHMARK_DEFINE(decimal32,    decimal32,    NOT_EQUAL,            bool);
+BINARYOP_BENCHMARK_DEFINE(timestamp_s,  timestamp_s,  LESS,                 bool);
+BINARYOP_BENCHMARK_DEFINE(timestamp_ms, timestamp_s,  GREATER,              bool);
+BINARYOP_BENCHMARK_DEFINE(duration_ms,  duration_ns,  NULL_EQUALS,          bool);
+BINARYOP_BENCHMARK_DEFINE(decimal32,    decimal32,    NULL_MAX,             decimal32);
+BINARYOP_BENCHMARK_DEFINE(timestamp_D,  timestamp_s,  NULL_MIN,             timestamp_s);
diff --git a/cpp/benchmarks/column/concatenate.cpp b/cpp/benchmarks/column/concatenate.cpp
new file mode 100644
index 0000000..51106c7
--- /dev/null
+++ b/cpp/benchmarks/column/concatenate.cpp
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/fixture/templated_benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/concatenate.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+#include <vector>
+
+class Concatenate : public cudf::benchmark {};
+
+template <typename T, bool Nullable>
+static void BM_concatenate(benchmark::State& state)
+{
+  cudf::size_type const num_rows = state.range(0);
+  cudf::size_type const num_cols = state.range(1);
+
+  auto input         = create_sequence_table(cycle_dtypes({cudf::type_to_id<T>()}, num_cols),
+                                     row_count{num_rows},
+                                     Nullable ? std::optional<double>{2.0 / 3.0} : std::nullopt);
+  auto input_columns = input->view();
+  std::vector<cudf::column_view> column_views(input_columns.begin(), input_columns.end());
+
+  CUDF_CHECK_CUDA(0);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    auto result = cudf::concatenate(column_views);
+  }
+
+  state.SetBytesProcessed(state.iterations() * num_cols * num_rows * sizeof(T));
+}
+
+#define CONCAT_BENCHMARK_DEFINE(type, nullable)                             \
+  BENCHMARK_DEFINE_F(Concatenate, BM_concatenate##_##nullable_##nullable)   \
+  (::benchmark::State & st) { BM_concatenate<type, nullable>(st); }         \
+  BENCHMARK_REGISTER_F(Concatenate, BM_concatenate##_##nullable_##nullable) \
+    ->RangeMultiplier(8)                                                    \
+    ->Ranges({{1 << 6, 1 << 18}, {2, 1024}})                                \
+    ->Unit(benchmark::kMillisecond)                                         \
+    ->UseManualTime();
+
+CONCAT_BENCHMARK_DEFINE(int64_t, false)
+CONCAT_BENCHMARK_DEFINE(int64_t, true)
+
+template <typename T, bool Nullable>
+static void BM_concatenate_tables(benchmark::State& state)
+{
+  cudf::size_type const num_rows   = state.range(0);
+  cudf::size_type const num_cols   = state.range(1);
+  cudf::size_type const num_tables = state.range(2);
+
+  std::vector<std::unique_ptr<cudf::table>> tables(num_tables);
+  std::generate_n(tables.begin(), num_tables, [&]() {
+    return create_sequence_table(cycle_dtypes({cudf::type_to_id<T>()}, num_cols),
+                                 row_count{num_rows},
+                                 Nullable ? std::optional<double>{2.0 / 3.0} : std::nullopt);
+  });
+
+  // Generate table views
+  std::vector<cudf::table_view> table_views(num_tables);
+  std::transform(tables.begin(), tables.end(), table_views.begin(), [](auto& table) mutable {
+    return table->view();
+  });
+
+  CUDF_CHECK_CUDA(0);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    auto result = cudf::concatenate(table_views);
+  }
+
+  state.SetBytesProcessed(state.iterations() * num_cols * num_rows * num_tables * sizeof(T));
+}
+
+#define CONCAT_TABLES_BENCHMARK_DEFINE(type, nullable)                             \
+  BENCHMARK_DEFINE_F(Concatenate, BM_concatenate_tables##_##nullable_##nullable)   \
+  (::benchmark::State & st) { BM_concatenate_tables<type, nullable>(st); }         \
+  BENCHMARK_REGISTER_F(Concatenate, BM_concatenate_tables##_##nullable_##nullable) \
+    ->RangeMultiplier(8)                                                           \
+    ->Ranges({{1 << 8, 1 << 12}, {2, 32}, {2, 128}})                               \
+    ->Unit(benchmark::kMillisecond)                                                \
+    ->UseManualTime();
+
+CONCAT_TABLES_BENCHMARK_DEFINE(int64_t, false)
+CONCAT_TABLES_BENCHMARK_DEFINE(int64_t, true)
+
+class ConcatenateStrings : public cudf::benchmark {};
+
+template <bool Nullable>
+static void BM_concatenate_strings(benchmark::State& state)
+{
+  using column_wrapper = cudf::test::strings_column_wrapper;
+
+  auto const num_rows  = state.range(0);
+  auto const num_chars = state.range(1);
+  auto const num_cols  = state.range(2);
+
+  std::string str(num_chars, 'a');
+
+  // Create owning columns
+  std::vector<column_wrapper> columns;
+  columns.reserve(num_cols);
+  std::generate_n(std::back_inserter(columns), num_cols, [num_rows, c_str = str.c_str()]() {
+    auto iter = thrust::make_constant_iterator(c_str);
+    if (Nullable) {
+      auto count_it = thrust::make_counting_iterator(0);
+      auto valid_iter =
+        thrust::make_transform_iterator(count_it, [](auto i) { return i % 3 == 0; });
+      return column_wrapper(iter, iter + num_rows, valid_iter);
+    } else {
+      return column_wrapper(iter, iter + num_rows);
+    }
+  });
+
+  // Generate column views
+  std::vector<cudf::column_view> column_views;
+  column_views.reserve(columns.size());
+  std::transform(
+    columns.begin(), columns.end(), std::back_inserter(column_views), [](auto const& col) {
+      return static_cast<cudf::column_view>(col);
+    });
+
+  CUDF_CHECK_CUDA(0);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    auto result = cudf::concatenate(column_views);
+  }
+
+  state.SetBytesProcessed(state.iterations() * num_cols * num_rows *
+                          (sizeof(int32_t) + num_chars));  // offset + chars
+}
+
+#define CONCAT_STRINGS_BENCHMARK_DEFINE(nullable)                                   \
+  BENCHMARK_DEFINE_F(Concatenate, BM_concatenate_strings##_##nullable_##nullable)   \
+  (::benchmark::State & st) { BM_concatenate_strings<nullable>(st); }               \
+  BENCHMARK_REGISTER_F(Concatenate, BM_concatenate_strings##_##nullable_##nullable) \
+    ->RangeMultiplier(8)                                                            \
+    ->Ranges({{1 << 8, 1 << 14}, {8, 128}, {2, 256}})                               \
+    ->Unit(benchmark::kMillisecond)                                                 \
+    ->UseManualTime();
+
+CONCAT_STRINGS_BENCHMARK_DEFINE(false)
+CONCAT_STRINGS_BENCHMARK_DEFINE(true)
diff --git a/cpp/benchmarks/common/generate_input.cu b/cpp/benchmarks/common/generate_input.cu
new file mode 100644
index 0000000..aef3d92
--- /dev/null
+++ b/cpp/benchmarks/common/generate_input.cu
@@ -0,0 +1,946 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "generate_input.hpp"
+#include "random_distribution_factory.cuh"
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/filling.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/gather.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/transform_output_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/random/linear_congruential_engine.h>
+#include <thrust/random/uniform_int_distribution.h>
+#include <thrust/random/uniform_real_distribution.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <algorithm>
+#include <cstdint>
+#include <memory>
+#include <optional>
+#include <random>
+#include <utility>
+#include <vector>
+
+/**
+ * @brief Mersenne Twister pseudo-random engine.
+ */
+auto deterministic_engine(unsigned seed) { return thrust::minstd_rand{seed}; }
+
+/**
+ *  Computes the mean value for a distribution of given type and value bounds.
+ */
+template <typename T>
+T get_distribution_mean(distribution_params<T> const& dist)
+{
+  switch (dist.id) {
+    case distribution_id::NORMAL:
+    case distribution_id::UNIFORM: return (dist.lower_bound / 2.) + (dist.upper_bound / 2.);
+    case distribution_id::GEOMETRIC: {
+      auto const range_size = dist.lower_bound < dist.upper_bound
+                                ? dist.upper_bound - dist.lower_bound
+                                : dist.lower_bound - dist.upper_bound;
+      auto const p          = geometric_dist_p(range_size);
+      if (dist.lower_bound < dist.upper_bound)
+        return dist.lower_bound + (1. / p);
+      else
+        return dist.lower_bound - (1. / p);
+    }
+    default: CUDF_FAIL("Unsupported distribution type.");
+  }
+}
+
+/**
+ * @brief Computes the average element size in a column, given the data profile.
+ *
+ * Random distribution parameters like average string length and maximum list nesting level affect
+ * the element size of non-fixed-width columns. For lists and structs, `avg_element_size` is called
+ * recursively to determine the size of nested columns.
+ */
+size_t avg_element_size(data_profile const& profile, cudf::data_type dtype);
+
+// Utilities to determine the mean size of an element, given the data profile
+template <typename T, CUDF_ENABLE_IF(cudf::is_fixed_width<T>())>
+size_t non_fixed_width_size(data_profile const& profile)
+{
+  CUDF_FAIL("Should not be called, use `size_of` for this type instead");
+}
+
+template <typename T, CUDF_ENABLE_IF(!cudf::is_fixed_width<T>())>
+size_t non_fixed_width_size(data_profile const& profile)
+{
+  CUDF_FAIL("not implemented!");
+}
+
+template <>
+size_t non_fixed_width_size<cudf::string_view>(data_profile const& profile)
+{
+  auto const dist = profile.get_distribution_params<cudf::string_view>().length_params;
+  return get_distribution_mean(dist);
+}
+
+double geometric_sum(size_t n, double p)
+{
+  if (p == 1) { return n; }
+  return (1 - std::pow(p, n)) / (1 - p);
+}
+
+template <>
+size_t non_fixed_width_size<cudf::list_view>(data_profile const& profile)
+{
+  auto const dist_params       = profile.get_distribution_params<cudf::list_view>();
+  auto const single_level_mean = get_distribution_mean(dist_params.length_params);
+
+  auto const element_size  = avg_element_size(profile, cudf::data_type{dist_params.element_type});
+  auto const element_count = std::pow(single_level_mean, dist_params.max_depth);
+
+  // Each nesting level includes offsets, this is the sum of all levels
+  // Also include an additional offset per level for the size of the last element
+  auto const total_offset_count =
+    geometric_sum(dist_params.max_depth, single_level_mean) + dist_params.max_depth;
+
+  return sizeof(cudf::size_type) * total_offset_count + element_size * element_count;
+}
+
+template <>
+size_t non_fixed_width_size<cudf::struct_view>(data_profile const& profile)
+{
+  auto const dist_params = profile.get_distribution_params<cudf::struct_view>();
+  return std::accumulate(dist_params.leaf_types.cbegin(),
+                         dist_params.leaf_types.cend(),
+                         0ul,
+                         [&](auto& sum, auto type_id) {
+                           return sum + avg_element_size(profile, cudf::data_type{type_id});
+                         });
+}
+
+struct non_fixed_width_size_fn {
+  template <typename T>
+  size_t operator()(data_profile const& profile)
+  {
+    return non_fixed_width_size<T>(profile);
+  }
+};
+
+size_t avg_element_size(data_profile const& profile, cudf::data_type dtype)
+{
+  if (cudf::is_fixed_width(dtype)) { return cudf::size_of(dtype); }
+  return cudf::type_dispatcher(dtype, non_fixed_width_size_fn{}, profile);
+}
+
+/**
+ * @brief bool generator with given probability [0.0 - 1.0] of returning true.
+ */
+struct bool_generator {
+  thrust::minstd_rand engine;
+  thrust::uniform_real_distribution<float> dist;
+  double probability_true;
+  bool_generator(thrust::minstd_rand engine, double probability_true)
+    : engine(engine), dist{0, 1}, probability_true{probability_true}
+  {
+  }
+  bool_generator(unsigned seed, double probability_true)
+    : engine(seed), dist{0, 1}, probability_true{probability_true}
+  {
+  }
+
+  __device__ bool operator()(size_t n)
+  {
+    engine.discard(n);
+    return dist(engine) < probability_true;
+  }
+};
+
+/**
+ * @brief Functor that computes a random column element with the given data profile.
+ *
+ * The implementation is SFINAEd for different type groups. Currently only used for fixed-width
+ * types.
+ */
+template <typename T, typename Enable = void>
+struct random_value_fn;
+
+/**
+ * @brief Creates an random timestamp/duration value
+ */
+template <typename T>
+struct random_value_fn<T, std::enable_if_t<cudf::is_chrono<T>()>> {
+  distribution_fn<int64_t> seconds_gen;
+  distribution_fn<int64_t> nanoseconds_gen;
+
+  random_value_fn(distribution_params<T> params)
+  {
+    using cuda::std::chrono::duration_cast;
+
+    std::pair<cudf::duration_s, cudf::duration_s> const range_s = {
+      duration_cast<cuda::std::chrono::seconds>(typename T::duration{params.lower_bound}),
+      duration_cast<cuda::std::chrono::seconds>(typename T::duration{params.upper_bound})};
+    if (range_s.first != range_s.second) {
+      seconds_gen =
+        make_distribution<int64_t>(params.id, range_s.first.count(), range_s.second.count());
+
+      nanoseconds_gen = make_distribution<int64_t>(distribution_id::UNIFORM, 0l, 1000000000l);
+    } else {
+      // Don't need a random seconds generator for sub-second intervals
+      seconds_gen = [range_s](thrust::minstd_rand&, size_t size) {
+        rmm::device_uvector<int64_t> result(size, cudf::get_default_stream());
+        thrust::fill(thrust::device, result.begin(), result.end(), range_s.second.count());
+        return result;
+      };
+
+      std::pair<cudf::duration_ns, cudf::duration_ns> const range_ns = {
+        duration_cast<cudf::duration_ns>(typename T::duration{params.lower_bound}),
+        duration_cast<cudf::duration_ns>(typename T::duration{params.upper_bound})};
+      nanoseconds_gen = make_distribution<int64_t>(distribution_id::UNIFORM,
+                                                   std::min(range_ns.first.count(), 0l),
+                                                   std::max(range_ns.second.count(), 0l));
+    }
+  }
+
+  rmm::device_uvector<T> operator()(thrust::minstd_rand& engine, unsigned size)
+  {
+    auto const sec = seconds_gen(engine, size);
+    auto const ns  = nanoseconds_gen(engine, size);
+    rmm::device_uvector<T> result(size, cudf::get_default_stream());
+    thrust::transform(
+      thrust::device,
+      sec.begin(),
+      sec.end(),
+      ns.begin(),
+      result.begin(),
+      [] __device__(int64_t sec_value, int64_t nanoseconds_value) {
+        auto const timestamp_ns =
+          cudf::duration_s{sec_value} + cudf::duration_ns{nanoseconds_value};
+        // Return value in the type's precision
+        return T(cuda::std::chrono::duration_cast<typename T::duration>(timestamp_ns));
+      });
+    return result;
+  }
+};
+
+/**
+ * @brief Creates an random fixed_point value.
+ */
+template <typename T>
+struct random_value_fn<T, std::enable_if_t<cudf::is_fixed_point<T>()>> {
+  using DeviceType = cudf::device_storage_type_t<T>;
+  DeviceType const lower_bound;
+  DeviceType const upper_bound;
+  distribution_fn<DeviceType> dist;
+  std::optional<numeric::scale_type> scale;
+
+  random_value_fn(distribution_params<DeviceType> const& desc)
+    : lower_bound{desc.lower_bound},
+      upper_bound{desc.upper_bound},
+      dist{make_distribution<DeviceType>(desc.id, desc.lower_bound, desc.upper_bound)}
+  {
+  }
+
+  [[nodiscard]] numeric::scale_type get_scale(thrust::minstd_rand& engine)
+  {
+    if (not scale.has_value()) {
+      constexpr int max_scale = std::numeric_limits<DeviceType>::digits10;
+      std::uniform_int_distribution<int> scale_dist{-max_scale, max_scale};
+      std::mt19937 engine_scale(engine());
+      scale = numeric::scale_type{scale_dist(engine_scale)};
+    }
+    return scale.value_or(numeric::scale_type{0});
+  }
+
+  rmm::device_uvector<DeviceType> operator()(thrust::minstd_rand& engine, unsigned size)
+  {
+    return dist(engine, size);
+  }
+};
+
+/**
+ * @brief Creates an random numeric value with the given distribution.
+ */
+template <typename T>
+struct random_value_fn<T, std::enable_if_t<!std::is_same_v<T, bool> && cudf::is_numeric<T>()>> {
+  T const lower_bound;
+  T const upper_bound;
+  distribution_fn<T> dist;
+
+  random_value_fn(distribution_params<T> const& desc)
+    : lower_bound{desc.lower_bound},
+      upper_bound{desc.upper_bound},
+      dist{make_distribution<T>(desc.id, desc.lower_bound, desc.upper_bound)}
+  {
+  }
+
+  auto operator()(thrust::minstd_rand& engine, unsigned size) { return dist(engine, size); }
+};
+
+/**
+ * @brief Creates an boolean value with given probability of returning `true`.
+ */
+template <typename T>
+struct random_value_fn<T, typename std::enable_if_t<std::is_same_v<T, bool>>> {
+  // Bernoulli distribution
+  distribution_fn<bool> dist;
+
+  random_value_fn(distribution_params<bool> const& desc)
+    : dist{[valid_prob = desc.probability_true](thrust::minstd_rand& engine,
+                                                size_t size) -> rmm::device_uvector<bool> {
+        rmm::device_uvector<bool> result(size, cudf::get_default_stream());
+        thrust::tabulate(
+          thrust::device, result.begin(), result.end(), bool_generator(engine, valid_prob));
+        return result;
+      }}
+  {
+  }
+  auto operator()(thrust::minstd_rand& engine, unsigned size) { return dist(engine, size); }
+};
+
+auto create_run_length_dist(cudf::size_type avg_run_len)
+{
+  // Distribution with low probability of generating 0-1 even with a low `avg_run_len` value
+  static constexpr float alpha = 4.f;
+  return std::gamma_distribution<float>{alpha, avg_run_len / alpha};
+}
+
+/**
+ * @brief Generate indices within range [0 , cardinality) repeating with average run length
+ * `avg_run_len`
+ *
+ * @param avg_run_len  Average run length of the generated indices
+ * @param cardinality  Number of unique values in the output vector
+ * @param num_rows     Number of indices to generate
+ * @param engine       Random engine
+ * @return Generated indices of type `cudf::size_type`
+ */
+rmm::device_uvector<cudf::size_type> sample_indices_with_run_length(cudf::size_type avg_run_len,
+                                                                    cudf::size_type cardinality,
+                                                                    cudf::size_type num_rows,
+                                                                    thrust::minstd_rand& engine)
+{
+  auto sample_dist = random_value_fn<cudf::size_type>{
+    distribution_params<cudf::size_type>{distribution_id::UNIFORM, 0, cardinality - 1}};
+  if (avg_run_len > 1) {
+    auto avglen_dist =
+      random_value_fn<int>{distribution_params<int>{distribution_id::UNIFORM, 1, 2 * avg_run_len}};
+    auto const approx_run_len = num_rows / avg_run_len + 1;
+    auto run_lens             = avglen_dist(engine, approx_run_len);
+    thrust::inclusive_scan(
+      thrust::device, run_lens.begin(), run_lens.end(), run_lens.begin(), std::plus<int>{});
+    auto const samples_indices = sample_dist(engine, approx_run_len + 1);
+    // This is gather.
+    auto avg_repeated_sample_indices_iterator = thrust::make_transform_iterator(
+      thrust::make_counting_iterator(0),
+      [rb              = run_lens.begin(),
+       re              = run_lens.end(),
+       samples_indices = samples_indices.begin()] __device__(cudf::size_type i) {
+        auto sample_idx = thrust::upper_bound(thrust::seq, rb, re, i) - rb;
+        return samples_indices[sample_idx];
+      });
+    rmm::device_uvector<cudf::size_type> repeated_sample_indices(num_rows,
+                                                                 cudf::get_default_stream());
+    thrust::copy(thrust::device,
+                 avg_repeated_sample_indices_iterator,
+                 avg_repeated_sample_indices_iterator + num_rows,
+                 repeated_sample_indices.begin());
+    return repeated_sample_indices;
+  } else {
+    // generate n samples.
+    return sample_dist(engine, num_rows);
+  }
+}
+
+/**
+ * @brief Creates a column with random content of type @ref T.
+ *
+ * @param profile Parameters for the random generator
+ * @param engine Pseudo-random engine
+ * @param num_rows Size of the output column
+ *
+ * @tparam T Data type of the output column
+ * @return Column filled with random data
+ */
+template <typename T>
+std::unique_ptr<cudf::column> create_random_column(data_profile const& profile,
+                                                   thrust::minstd_rand& engine,
+                                                   cudf::size_type num_rows)
+{
+  // Bernoulli distribution
+  auto valid_dist = random_value_fn<bool>(
+    distribution_params<bool>{1. - profile.get_null_probability().value_or(0)});
+  auto value_dist = random_value_fn<T>{profile.get_distribution_params<T>()};
+
+  using DeviceType            = cudf::device_storage_type_t<T>;
+  cudf::data_type const dtype = [&]() {
+    if constexpr (cudf::is_fixed_point<T>())
+      return cudf::data_type{cudf::type_to_id<T>(), value_dist.get_scale(engine)};
+    else
+      return cudf::data_type{cudf::type_to_id<T>()};
+  }();
+
+  // Distribution for picking elements from the array of samples
+  auto const avg_run_len = profile.get_avg_run_length();
+  rmm::device_uvector<DeviceType> data(0, cudf::get_default_stream());
+  rmm::device_uvector<bool> null_mask(0, cudf::get_default_stream());
+
+  if (profile.get_cardinality() == 0 and avg_run_len == 1) {
+    data      = value_dist(engine, num_rows);
+    null_mask = valid_dist(engine, num_rows);
+  } else {
+    auto const cardinality = [profile_cardinality = profile.get_cardinality(), num_rows] {
+      return (profile_cardinality == 0 or profile_cardinality > num_rows) ? num_rows
+                                                                          : profile_cardinality;
+    }();
+    rmm::device_uvector<bool> samples_null_mask = valid_dist(engine, cardinality);
+    rmm::device_uvector<DeviceType> samples     = value_dist(engine, cardinality);
+
+    // generate n samples and gather.
+    auto const sample_indices =
+      sample_indices_with_run_length(avg_run_len, cardinality, num_rows, engine);
+    data      = rmm::device_uvector<DeviceType>(num_rows, cudf::get_default_stream());
+    null_mask = rmm::device_uvector<bool>(num_rows, cudf::get_default_stream());
+    thrust::gather(
+      thrust::device, sample_indices.begin(), sample_indices.end(), samples.begin(), data.begin());
+    thrust::gather(thrust::device,
+                   sample_indices.begin(),
+                   sample_indices.end(),
+                   samples_null_mask.begin(),
+                   null_mask.begin());
+  }
+
+  auto [result_bitmask, null_count] =
+    cudf::detail::valid_if(null_mask.begin(),
+                           null_mask.end(),
+                           thrust::identity<bool>{},
+                           cudf::get_default_stream(),
+                           rmm::mr::get_current_device_resource());
+
+  return std::make_unique<cudf::column>(
+    dtype,
+    num_rows,
+    data.release(),
+    profile.get_null_probability().has_value() ? std::move(result_bitmask) : rmm::device_buffer{},
+    profile.get_null_probability().has_value() ? null_count : 0);
+}
+
+struct valid_or_zero {
+  template <typename T>
+  __device__ T operator()(thrust::tuple<T, bool> len_valid) const
+  {
+    return thrust::get<1>(len_valid) ? thrust::get<0>(len_valid) : T{0};
+  }
+};
+
+struct string_generator {
+  char* chars;
+  thrust::minstd_rand engine;
+  thrust::uniform_int_distribution<unsigned char> char_dist;
+  string_generator(char* c, thrust::minstd_rand& engine)
+    : chars(c), engine(engine), char_dist(32, 137)
+  // ~90% ASCII, ~10% UTF-8.
+  // ~80% not-space, ~20% space.
+  // range 32-127 is ASCII; 127-136 will be multi-byte UTF-8
+  {
+  }
+  __device__ void operator()(thrust::tuple<cudf::size_type, cudf::size_type> str_begin_end)
+  {
+    auto begin = thrust::get<0>(str_begin_end);
+    auto end   = thrust::get<1>(str_begin_end);
+    engine.discard(begin);
+    for (auto i = begin; i < end; ++i) {
+      auto ch = char_dist(engine);
+      if (i == end - 1 && ch >= '\x7F') ch = ' ';  // last element ASCII only.
+      if (ch >= '\x7F')                            // x7F is at the top edge of ASCII
+        chars[i++] = '\xC4';                       // these characters are assigned two bytes
+      chars[i] = static_cast<char>(ch + (ch >= '\x7F'));
+    }
+  }
+};
+
+/**
+ * @brief Create a UTF-8 string column with the average length.
+ *
+ */
+std::unique_ptr<cudf::column> create_random_utf8_string_column(data_profile const& profile,
+                                                               thrust::minstd_rand& engine,
+                                                               cudf::size_type num_rows)
+{
+  auto len_dist =
+    random_value_fn<uint32_t>{profile.get_distribution_params<cudf::string_view>().length_params};
+  auto valid_dist = random_value_fn<bool>(
+    distribution_params<bool>{1. - profile.get_null_probability().value_or(0)});
+  auto lengths   = len_dist(engine, num_rows + 1);
+  auto null_mask = valid_dist(engine, num_rows + 1);
+  thrust::transform_if(
+    thrust::device,
+    lengths.begin(),
+    lengths.end(),
+    null_mask.begin(),
+    lengths.begin(),
+    [] __device__(auto) { return 0; },
+    thrust::logical_not<bool>{});
+  auto valid_lengths = thrust::make_transform_iterator(
+    thrust::make_zip_iterator(thrust::make_tuple(lengths.begin(), null_mask.begin())),
+    valid_or_zero{});
+  rmm::device_uvector<cudf::size_type> offsets(num_rows + 1, cudf::get_default_stream());
+  thrust::exclusive_scan(
+    thrust::device, valid_lengths, valid_lengths + lengths.size(), offsets.begin());
+  // offsets are ready.
+  auto chars_length = *thrust::device_pointer_cast(offsets.end() - 1);
+  rmm::device_uvector<char> chars(chars_length, cudf::get_default_stream());
+  thrust::for_each_n(thrust::device,
+                     thrust::make_zip_iterator(offsets.begin(), offsets.begin() + 1),
+                     num_rows,
+                     string_generator{chars.data(), engine});
+  auto [result_bitmask, null_count] =
+    cudf::detail::valid_if(null_mask.begin(),
+                           null_mask.end() - 1,
+                           thrust::identity<bool>{},
+                           cudf::get_default_stream(),
+                           rmm::mr::get_current_device_resource());
+  return cudf::make_strings_column(
+    num_rows,
+    std::move(offsets),
+    std::move(chars),
+    profile.get_null_probability().has_value() ? std::move(result_bitmask) : rmm::device_buffer{},
+    null_count);
+}
+
+/**
+ * @brief Creates a string column with random content.
+ *
+ * @param profile Parameters for the random generator
+ * @param engine Pseudo-random engine
+ * @param num_rows Size of the output column
+ *
+ * @return Column filled with random strings
+ */
+template <>
+std::unique_ptr<cudf::column> create_random_column<cudf::string_view>(data_profile const& profile,
+                                                                      thrust::minstd_rand& engine,
+                                                                      cudf::size_type num_rows)
+{
+  auto const cardinality = std::min(profile.get_cardinality(), num_rows);
+  auto const avg_run_len = profile.get_avg_run_length();
+
+  auto sample_strings =
+    create_random_utf8_string_column(profile, engine, cardinality == 0 ? num_rows : cardinality);
+  if (cardinality == 0) { return sample_strings; }
+  auto sample_indices = sample_indices_with_run_length(avg_run_len, cardinality, num_rows, engine);
+  auto str_table      = cudf::detail::gather(cudf::table_view{{sample_strings->view()}},
+                                        sample_indices,
+                                        cudf::out_of_bounds_policy::DONT_CHECK,
+                                        cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                        cudf::get_default_stream(),
+                                        rmm::mr::get_current_device_resource());
+  return std::move(str_table->release()[0]);
+}
+
+template <>
+std::unique_ptr<cudf::column> create_random_column<cudf::dictionary32>(data_profile const& profile,
+                                                                       thrust::minstd_rand& engine,
+                                                                       cudf::size_type num_rows)
+{
+  CUDF_FAIL("not implemented yet");
+}
+
+/**
+ * @brief Functor to dispatch create_random_column calls.
+ */
+struct create_rand_col_fn {
+ public:
+  template <typename T>
+  std::unique_ptr<cudf::column> operator()(data_profile const& profile,
+                                           thrust::minstd_rand& engine,
+                                           cudf::size_type num_rows)
+  {
+    return create_random_column<T>(profile, engine, num_rows);
+  }
+};
+
+/**
+ * @brief Calculates the number of direct parents needed to generate a struct column hierarchy with
+ * lowest maximum number of children in any nested column.
+ *
+ * Used to generate an "evenly distributed" struct column hierarchy with the given number of leaf
+ * columns and nesting levels. The column tree is considered evenly distributed if all columns have
+ * nearly the same number of child columns (difference not larger than one).
+ */
+int num_direct_parents(int num_lvls, int num_leaf_columns)
+{
+  // Estimated average number of children in the hierarchy;
+  auto const num_children_avg = std::pow(num_leaf_columns, 1. / num_lvls);
+  // Minimum number of children columns for any column in the hierarchy
+  int const num_children_min = std::floor(num_children_avg);
+  // Maximum number of children columns for any column in the hierarchy
+  int const num_children_max = num_children_min + 1;
+
+  // Minimum number of columns needed so that their number of children does not exceed the maximum
+  int const min_for_current_nesting = std::ceil((double)num_leaf_columns / num_children_max);
+  // Minimum number of columns needed so that columns at the higher levels have at least the minimum
+  // number of children
+  int const min_for_upper_nesting = std::pow(num_children_min, num_lvls - 1);
+  // Both conditions need to be satisfied
+  return std::max(min_for_current_nesting, min_for_upper_nesting);
+}
+
+template <>
+std::unique_ptr<cudf::column> create_random_column<cudf::struct_view>(data_profile const& profile,
+                                                                      thrust::minstd_rand& engine,
+                                                                      cudf::size_type num_rows)
+{
+  auto const dist_params = profile.get_distribution_params<cudf::struct_view>();
+
+  // Generate leaf columns
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.reserve(dist_params.leaf_types.size());
+  std::transform(dist_params.leaf_types.cbegin(),
+                 dist_params.leaf_types.cend(),
+                 std::back_inserter(children),
+                 [&](auto& type_id) {
+                   return cudf::type_dispatcher(
+                     cudf::data_type(type_id), create_rand_col_fn{}, profile, engine, num_rows);
+                 });
+
+  auto valid_dist = random_value_fn<bool>(
+    distribution_params<bool>{1. - profile.get_null_probability().value_or(0)});
+
+  // Generate the column bottom-up
+  for (int lvl = dist_params.max_depth; lvl > 0; --lvl) {
+    // Generating the next level
+    std::vector<std::unique_ptr<cudf::column>> parents;
+    parents.resize(num_direct_parents(lvl, children.size()));
+
+    auto current_child = children.begin();
+    for (auto current_parent = parents.begin(); current_parent != parents.end(); ++current_parent) {
+      auto [null_mask, null_count] = [&]() {
+        if (profile.get_null_probability().has_value()) {
+          auto valids = valid_dist(engine, num_rows);
+          return cudf::detail::valid_if(valids.begin(),
+                                        valids.end(),
+                                        thrust::identity<bool>{},
+                                        cudf::get_default_stream(),
+                                        rmm::mr::get_current_device_resource());
+        }
+        return std::pair<rmm::device_buffer, cudf::size_type>{};
+      }();
+
+      // Adopt remaining children as evenly as possible
+      auto const num_to_adopt = cudf::util::div_rounding_up_unsafe(
+        std::distance(current_child, children.end()), std::distance(current_parent, parents.end()));
+      CUDF_EXPECTS(num_to_adopt > 0, "No children columns left to adopt");
+
+      std::vector<std::unique_ptr<cudf::column>> children_to_adopt;
+      children_to_adopt.insert(children_to_adopt.end(),
+                               std::make_move_iterator(current_child),
+                               std::make_move_iterator(current_child + num_to_adopt));
+      current_child += children_to_adopt.size();
+
+      *current_parent = cudf::make_structs_column(
+        num_rows, std::move(children_to_adopt), null_count, std::move(null_mask));
+    }
+
+    if (lvl == 1) {
+      CUDF_EXPECTS(parents.size() == 1, "There should be one top-level column");
+      return std::move(parents.front());
+    }
+    children = std::move(parents);
+  }
+  CUDF_FAIL("Reached unreachable code in struct column creation");
+}
+
+template <typename T>
+struct clamp_down : public thrust::unary_function<T, T> {
+  T max;
+  clamp_down(T max) : max(max) {}
+  __host__ __device__ T operator()(T x) const { return min(x, max); }
+};
+/**
+ * @brief Creates a list column with random content.
+ *
+ * The data profile determines the list length distribution, number of nested level, and the data
+ * type of the bottom level.
+ *
+ * @param profile Parameters for the random generator
+ * @param engine Pseudo-random engine
+ * @param num_rows Size of the output column
+ *
+ * @return Column filled with random lists
+ */
+template <>
+std::unique_ptr<cudf::column> create_random_column<cudf::list_view>(data_profile const& profile,
+                                                                    thrust::minstd_rand& engine,
+                                                                    cudf::size_type num_rows)
+{
+  auto const dist_params       = profile.get_distribution_params<cudf::list_view>();
+  auto const single_level_mean = get_distribution_mean(dist_params.length_params);
+  auto const num_elements      = num_rows * pow(single_level_mean, dist_params.max_depth);
+
+  auto leaf_column = cudf::type_dispatcher(
+    cudf::data_type(dist_params.element_type), create_rand_col_fn{}, profile, engine, num_elements);
+  auto len_dist =
+    random_value_fn<uint32_t>{profile.get_distribution_params<cudf::list_view>().length_params};
+  auto valid_dist = random_value_fn<bool>(
+    distribution_params<bool>{1. - profile.get_null_probability().value_or(0)});
+
+  // Generate the list column bottom-up
+  auto list_column = std::move(leaf_column);
+  for (int lvl = 0; lvl < dist_params.max_depth; ++lvl) {
+    // Generating the next level - offsets point into the current list column
+    auto current_child_column      = std::move(list_column);
+    cudf::size_type const num_rows = current_child_column->size() / single_level_mean;
+
+    auto offsets = len_dist(engine, num_rows + 1);
+    auto valids  = valid_dist(engine, num_rows);
+    // to ensure these values <= current_child_column->size()
+    auto output_offsets = thrust::make_transform_output_iterator(
+      offsets.begin(), clamp_down{current_child_column->size()});
+
+    thrust::exclusive_scan(thrust::device, offsets.begin(), offsets.end(), output_offsets);
+    thrust::device_pointer_cast(offsets.end())[-1] =
+      current_child_column->size();  // Always include all elements
+
+    auto offsets_column = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                                         num_rows + 1,
+                                                         offsets.release(),
+                                                         rmm::device_buffer{},
+                                                         0);
+
+    auto [null_mask, null_count] = cudf::detail::valid_if(valids.begin(),
+                                                          valids.end(),
+                                                          thrust::identity<bool>{},
+                                                          cudf::get_default_stream(),
+                                                          rmm::mr::get_current_device_resource());
+    list_column                  = cudf::make_lists_column(
+      num_rows,
+      std::move(offsets_column),
+      std::move(current_child_column),
+      profile.get_null_probability().has_value() ? null_count : 0,
+      profile.get_null_probability().has_value() ? std::move(null_mask) : rmm::device_buffer{});
+  }
+  return list_column;  // return the top-level column
+}
+
+using columns_vector = std::vector<std::unique_ptr<cudf::column>>;
+
+/**
+ * @brief Creates a vector of columns with random content.
+ *
+ * @param profile Parameters for the random generator
+ * @param dtype_ids vector of data type ids, one for each output column
+ * @param engine Pseudo-random engine
+ * @param num_rows Size of the output columns
+ *
+ * @return Column filled with random lists
+ */
+columns_vector create_random_columns(data_profile const& profile,
+                                     std::vector<cudf::type_id> dtype_ids,
+                                     thrust::minstd_rand engine,
+                                     cudf::size_type num_rows)
+{
+  columns_vector output_columns;
+  std::transform(
+    dtype_ids.begin(), dtype_ids.end(), std::back_inserter(output_columns), [&](auto tid) {
+      engine.discard(num_rows);
+      return cudf::type_dispatcher(
+        cudf::data_type(tid), create_rand_col_fn{}, profile, engine, num_rows);
+    });
+  return output_columns;
+}
+
+/**
+ * @brief Repeats the input data types cyclically order to fill a vector of @ref num_cols
+ * elements.
+ */
+std::vector<cudf::type_id> cycle_dtypes(std::vector<cudf::type_id> const& dtype_ids,
+                                        cudf::size_type num_cols)
+{
+  if (dtype_ids.size() == static_cast<std::size_t>(num_cols)) { return dtype_ids; }
+  std::vector<cudf::type_id> out_dtypes;
+  out_dtypes.reserve(num_cols);
+  for (cudf::size_type col = 0; col < num_cols; ++col)
+    out_dtypes.push_back(dtype_ids[col % dtype_ids.size()]);
+  return out_dtypes;
+}
+
+/**
+ * @brief Repeat the given two data types with a given ratio of a:b.
+ *
+ * The first dtype will have 'first_num' columns and the second will have 'num_cols - first_num'
+ * columns.
+ */
+std::vector<cudf::type_id> mix_dtypes(std::pair<cudf::type_id, cudf::type_id> const& dtype_ids,
+                                      cudf::size_type num_cols,
+                                      int first_num)
+{
+  std::vector<cudf::type_id> out_dtypes;
+  out_dtypes.reserve(num_cols);
+  for (cudf::size_type col = 0; col < first_num; ++col)
+    out_dtypes.push_back(dtype_ids.first);
+  for (cudf::size_type col = first_num; col < num_cols; ++col)
+    out_dtypes.push_back(dtype_ids.second);
+  return out_dtypes;
+}
+
+std::unique_ptr<cudf::table> create_random_table(std::vector<cudf::type_id> const& dtype_ids,
+                                                 table_size_bytes table_bytes,
+                                                 data_profile const& profile,
+                                                 unsigned seed)
+{
+  size_t const avg_row_bytes =
+    std::accumulate(dtype_ids.begin(), dtype_ids.end(), 0ul, [&](size_t sum, auto tid) {
+      return sum + avg_element_size(profile, cudf::data_type(tid));
+    });
+  cudf::size_type const num_rows = table_bytes.size / avg_row_bytes;
+
+  return create_random_table(dtype_ids, row_count{num_rows}, profile, seed);
+}
+
+std::unique_ptr<cudf::table> create_random_table(std::vector<cudf::type_id> const& dtype_ids,
+                                                 row_count num_rows,
+                                                 data_profile const& profile,
+                                                 unsigned seed)
+{
+  auto seed_engine = deterministic_engine(seed);
+  thrust::uniform_int_distribution<unsigned> seed_dist;
+
+  columns_vector output_columns;
+  std::transform(
+    dtype_ids.begin(), dtype_ids.end(), std::back_inserter(output_columns), [&](auto tid) mutable {
+      return create_random_column(tid, num_rows, profile, seed_dist(seed_engine));
+    });
+  return std::make_unique<cudf::table>(std::move(output_columns));
+}
+
+std::unique_ptr<cudf::column> create_random_column(cudf::type_id dtype_id,
+                                                   row_count num_rows,
+                                                   data_profile const& profile,
+                                                   unsigned seed)
+{
+  auto engine = deterministic_engine(seed);
+  return cudf::type_dispatcher(
+    cudf::data_type(dtype_id), create_rand_col_fn{}, profile, engine, num_rows.count);
+}
+
+std::unique_ptr<cudf::table> create_sequence_table(std::vector<cudf::type_id> const& dtype_ids,
+                                                   row_count num_rows,
+                                                   std::optional<double> null_probability,
+                                                   unsigned seed)
+{
+  auto seed_engine = deterministic_engine(seed);
+  thrust::uniform_int_distribution<unsigned> seed_dist;
+
+  auto columns = std::vector<std::unique_ptr<cudf::column>>(dtype_ids.size());
+  std::transform(dtype_ids.begin(), dtype_ids.end(), columns.begin(), [&](auto dtype) mutable {
+    auto init = cudf::make_default_constructed_scalar(cudf::data_type{dtype});
+    auto col  = cudf::sequence(num_rows.count, *init);
+    auto [mask, count] =
+      create_random_null_mask(num_rows.count, null_probability, seed_dist(seed_engine));
+    col->set_null_mask(std::move(mask), count);
+    return col;
+  });
+  return std::make_unique<cudf::table>(std::move(columns));
+}
+
+std::pair<rmm::device_buffer, cudf::size_type> create_random_null_mask(
+  cudf::size_type size, std::optional<double> null_probability, unsigned seed)
+{
+  if (not null_probability.has_value()) { return {rmm::device_buffer{}, 0}; }
+  CUDF_EXPECTS(*null_probability >= 0.0 and *null_probability <= 1.0,
+               "Null probability must be within the range [0.0, 1.0]");
+  if (*null_probability == 0.0f) {
+    return {cudf::create_null_mask(size, cudf::mask_state::ALL_VALID), 0};
+  } else if (*null_probability == 1.0) {
+    return {cudf::create_null_mask(size, cudf::mask_state::ALL_NULL), size};
+  } else {
+    return cudf::detail::valid_if(thrust::make_counting_iterator<cudf::size_type>(0),
+                                  thrust::make_counting_iterator<cudf::size_type>(size),
+                                  bool_generator{seed, 1.0 - *null_probability},
+                                  cudf::get_default_stream(),
+                                  rmm::mr::get_current_device_resource());
+  }
+}
+
+std::vector<cudf::type_id> get_type_or_group(int32_t id)
+{
+  // identity transformation when passing a concrete type_id
+  if (id < static_cast<int32_t>(cudf::type_id::NUM_TYPE_IDS))
+    return {static_cast<cudf::type_id>(id)};
+
+  // if the value is larger that type_id::NUM_TYPE_IDS, it's a group id
+  type_group_id const group_id = static_cast<type_group_id>(id);
+
+  using trait_fn       = bool (*)(cudf::data_type);
+  trait_fn is_integral = [](cudf::data_type type) {
+    return cudf::is_numeric(type) && !cudf::is_floating_point(type);
+  };
+  trait_fn is_integral_signed = [](cudf::data_type type) {
+    return cudf::is_numeric(type) && !cudf::is_floating_point(type) && !cudf::is_unsigned(type);
+  };
+  auto fn = [&]() -> trait_fn {
+    switch (group_id) {
+      case type_group_id::FLOATING_POINT: return cudf::is_floating_point;
+      case type_group_id::INTEGRAL: return is_integral;
+      case type_group_id::INTEGRAL_SIGNED: return is_integral_signed;
+      case type_group_id::NUMERIC: return cudf::is_numeric;
+      case type_group_id::TIMESTAMP: return cudf::is_timestamp;
+      case type_group_id::DURATION: return cudf::is_duration;
+      case type_group_id::FIXED_POINT: return cudf::is_fixed_point;
+      case type_group_id::COMPOUND: return cudf::is_compound;
+      case type_group_id::NESTED: return cudf::is_nested;
+      default: CUDF_FAIL("Invalid data type group");
+    }
+  }();
+  std::vector<cudf::type_id> types;
+  for (int type_int = 0; type_int < static_cast<int32_t>(cudf::type_id::NUM_TYPE_IDS); ++type_int) {
+    auto const type = static_cast<cudf::type_id>(type_int);
+    if (type != cudf::type_id::EMPTY && fn(cudf::data_type(type))) types.push_back(type);
+  }
+  return types;
+}
+
+std::vector<cudf::type_id> get_type_or_group(std::vector<int32_t> const& ids)
+{
+  std::vector<cudf::type_id> all_type_ids;
+  for (auto& id : ids) {
+    auto const type_ids = get_type_or_group(id);
+    all_type_ids.insert(std::end(all_type_ids), std::cbegin(type_ids), std::cend(type_ids));
+  }
+  return all_type_ids;
+}
diff --git a/cpp/benchmarks/common/generate_input.hpp b/cpp/benchmarks/common/generate_input.hpp
new file mode 100644
index 0000000..a2efdb8
--- /dev/null
+++ b/cpp/benchmarks/common/generate_input.hpp
@@ -0,0 +1,694 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <map>
+#include <optional>
+
+/**
+ * @file generate_input.hpp
+ * @brief Contains declarations of functions that generate columns filled with random data.
+ *
+ * Also includes the data profile descriptor classes.
+ *
+ * The create_random_table functions take a data profile, the information about table size and a
+ * seed to deterministically generate a table with given parameters.
+ *
+ * Currently, the data generation is done on the CPU and the data is then copied to the device
+ * memory.
+ */
+
+/**
+ * @brief Identifies a group of related column's logical element types.
+ */
+enum class type_group_id : int32_t {
+  INTEGRAL = static_cast<int32_t>(cudf::type_id::NUM_TYPE_IDS),
+  INTEGRAL_SIGNED,
+  FLOATING_POINT,
+  NUMERIC,
+  TIMESTAMP,
+  DURATION,
+  FIXED_POINT,
+  COMPOUND,
+  NESTED,
+};
+
+/**
+ * @brief Identifies a probability distribution type.
+ */
+enum class distribution_id : int8_t {
+  UNIFORM,    ///< Uniform sampling between the given bounds. Provides the best coverage of the
+              ///< overall value range. Real data rarely has this distribution.
+  NORMAL,     ///< Gaussian sampling - most samples are close to the middle of the range. Good for
+              ///< simulating real-world numeric data.
+  GEOMETRIC,  ///< Geometric sampling - highest chance to sample close to the lower bound. Good for
+              ///< simulating real data with asymmetric distribution (unsigned values, timestamps).
+};
+
+// Default distribution types for each type
+namespace {
+template <typename T, std::enable_if_t<cudf::is_chrono<T>()>* = nullptr>
+distribution_id default_distribution_id()
+{
+  return distribution_id::GEOMETRIC;
+}
+
+template <typename T, std::enable_if_t<!std::is_unsigned_v<T> && cudf::is_numeric<T>()>* = nullptr>
+distribution_id default_distribution_id()
+{
+  return distribution_id::NORMAL;
+}
+
+template <typename T,
+          std::enable_if_t<!std::is_same_v<T, bool> && std::is_unsigned_v<T> &&
+                           cudf::is_numeric<T>()>* = nullptr>
+distribution_id default_distribution_id()
+{
+  return distribution_id::GEOMETRIC;
+}
+
+/**
+ * @brief Default range for the timestamp types: 1970 - 2020.
+ *
+ * The 2020 timestamp is used as a lower bound to bias the geometric distribution to recent
+ * timestamps.
+ */
+template <typename T, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+std::pair<int64_t, int64_t> default_range()
+{
+  using cuda::std::chrono::duration_cast;
+  auto const year = duration_cast<typename T::duration>(cudf::duration_D{365l});
+  return {50 * year.count(), 0};
+}
+
+/**
+ * @brief Default range for the duration types.
+ *
+ * If a geometric distribution is used, it will bias towards short duration values.
+ */
+template <typename T, std::enable_if_t<cudf::is_duration<T>()>* = nullptr>
+std::pair<int64_t, int64_t> default_range()
+{
+  using cuda::std::chrono::duration_cast;
+  auto const year = duration_cast<typename T::duration>(cudf::duration_D{365l});
+  return {0, 2 * year.count()};
+}
+
+template <typename T, std::enable_if_t<cudf::is_numeric<T>()>* = nullptr>
+std::pair<T, T> default_range()
+{
+  // Limits need to be such that `upper - lower` does not overflow
+  return {std::numeric_limits<T>::lowest() / 2, std::numeric_limits<T>::max() / 2};
+}
+}  // namespace
+
+/**
+ * @brief Enables partial specializations with SFINAE.
+ */
+template <typename T, typename Enable = void>
+struct distribution_params;
+
+/**
+ * @brief Numeric values are parameterized with a distribution type and bounds of the same type.
+ */
+template <typename T>
+struct distribution_params<T, std::enable_if_t<!std::is_same_v<T, bool> && cudf::is_numeric<T>()>> {
+  distribution_id id;
+  T lower_bound;
+  T upper_bound;
+};
+
+/**
+ * @brief Booleans are parameterized with the probability of getting `true` value.
+ */
+template <typename T>
+struct distribution_params<T, std::enable_if_t<std::is_same_v<T, bool>>> {
+  double probability_true;
+};
+
+/**
+ * @brief Timestamps and durations are parameterized with a distribution type and int64_t bounds.
+ */
+template <typename T>
+struct distribution_params<T, std::enable_if_t<cudf::is_chrono<T>()>> {
+  distribution_id id;
+  int64_t lower_bound;
+  int64_t upper_bound;
+};
+
+/**
+ * @brief Strings are parameterized by the distribution of their length, as an integral value.
+ */
+template <typename T>
+struct distribution_params<T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>> {
+  distribution_params<uint32_t> length_params;
+};
+
+/**
+ * @brief Lists are parameterized by the distribution of their length, maximal nesting level, and
+ * the element type.
+ */
+template <typename T>
+struct distribution_params<T, std::enable_if_t<std::is_same_v<T, cudf::list_view>>> {
+  cudf::type_id element_type;
+  distribution_params<uint32_t> length_params;
+  cudf::size_type max_depth;
+};
+
+/**
+ * @brief Structs are parameterized by the maximal nesting level, and the leaf column types.
+ */
+template <typename T>
+struct distribution_params<T, std::enable_if_t<std::is_same_v<T, cudf::struct_view>>> {
+  std::vector<cudf::type_id> leaf_types;
+  cudf::size_type max_depth;
+};
+
+// Present for compilation only. To be implemented once reader/writers support the fixed width type.
+template <typename T>
+struct distribution_params<T, std::enable_if_t<cudf::is_fixed_point<T>()>> {};
+
+/**
+ * @brief Returns a vector of types, corresponding to the input type or a type group.
+ *
+ * If the input is a `cudf::type_id` enumerator, function simply returns a vector containing this
+ * type. If the input value corresponds to a `type_group_id` enumerator, function returns a vector
+ * containing all types in the input group.
+ *
+ * @param id Integer equal to either a `cudf::type_id` enumerator or a `type_group_id` enumerator.
+ */
+std::vector<cudf::type_id> get_type_or_group(int32_t id);
+
+/**
+ * @brief Returns a vector of types, corresponding to the input types or type groups.
+ *
+ * If an element of the input vector is a `cudf::type_id` enumerator, function return value simply
+ * includes this type. If an element of the input vector is a `type_group_id` enumerator, function
+ * return value includes all types corresponding to the group enumerator.
+ *
+ * @param ids Vector of integers equal to either a `cudf::type_id` enumerator or a `type_group_id`
+ * enumerator.
+ */
+std::vector<cudf::type_id> get_type_or_group(std::vector<int32_t> const& ids);
+
+/**
+ * @brief Contains data parameters for all types.
+ *
+ * This class exposes APIs to set and get distribution parameters for each supported type.
+ * Parameters can be set for multiple types with a single call by passing a `type_group_id` instead
+ * of `cudf::type_id`.
+ *
+ * All types have default parameters so it's not necessary to set the parameters before using them.
+ */
+class data_profile {
+  std::map<cudf::type_id, distribution_params<uint64_t>> int_params;
+  std::map<cudf::type_id, distribution_params<double>> float_params;
+  distribution_params<cudf::string_view> string_dist_desc{{distribution_id::NORMAL, 0, 32}};
+  distribution_params<cudf::list_view> list_dist_desc{
+    cudf::type_id::INT32, {distribution_id::GEOMETRIC, 0, 100}, 2};
+  distribution_params<cudf::struct_view> struct_dist_desc{
+    {cudf::type_id::INT32, cudf::type_id::FLOAT32, cudf::type_id::STRING}, 2};
+  std::map<cudf::type_id, distribution_params<__uint128_t>> decimal_params;
+
+  double bool_probability_true           = 0.5;
+  std::optional<double> null_probability = 0.01;
+  cudf::size_type cardinality            = 2000;
+  cudf::size_type avg_run_length         = 4;
+
+ public:
+  template <typename T,
+            std::enable_if_t<!std::is_same_v<T, bool> && cuda::std::is_integral_v<T>, T>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    auto it = int_params.find(cudf::type_to_id<T>());
+    if (it == int_params.end()) {
+      auto const range = default_range<T>();
+      return distribution_params<T>{default_distribution_id<T>(), range.first, range.second};
+    } else {
+      auto& desc = it->second;
+      return {desc.id, static_cast<T>(desc.lower_bound), static_cast<T>(desc.upper_bound)};
+    }
+  }
+
+  template <typename T, std::enable_if_t<std::is_floating_point_v<T>, T>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    auto it = float_params.find(cudf::type_to_id<T>());
+    if (it == float_params.end()) {
+      auto const range = default_range<T>();
+      return distribution_params<T>{default_distribution_id<T>(), range.first, range.second};
+    } else {
+      auto& desc = it->second;
+      return {desc.id, static_cast<T>(desc.lower_bound), static_cast<T>(desc.upper_bound)};
+    }
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, bool>>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    return distribution_params<T>{bool_probability_true};
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_chrono<T>()>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    auto it = int_params.find(cudf::type_to_id<T>());
+    if (it == int_params.end()) {
+      auto const range = default_range<T>();
+      return distribution_params<T>{default_distribution_id<T>(), range.first, range.second};
+    } else {
+      auto& desc = it->second;
+      return {
+        desc.id, static_cast<int64_t>(desc.lower_bound), static_cast<int64_t>(desc.upper_bound)};
+    }
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    return string_dist_desc;
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::list_view>>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    return list_dist_desc;
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::struct_view>>* = nullptr>
+  distribution_params<T> get_distribution_params() const
+  {
+    return struct_dist_desc;
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  distribution_params<typename T::rep> get_distribution_params() const
+  {
+    using rep = typename T::rep;
+    auto it   = decimal_params.find(cudf::type_to_id<T>());
+    if (it == decimal_params.end()) {
+      auto const range = default_range<rep>();
+      return distribution_params<rep>{default_distribution_id<rep>(), range.first, range.second};
+    } else {
+      auto& desc = it->second;
+      return {desc.id, static_cast<rep>(desc.lower_bound), static_cast<rep>(desc.upper_bound)};
+    }
+  }
+
+  auto get_bool_probability_true() const { return bool_probability_true; }
+  auto get_null_probability() const { return null_probability; };
+  [[nodiscard]] auto get_cardinality() const { return cardinality; };
+  [[nodiscard]] auto get_avg_run_length() const { return avg_run_length; };
+
+  // Users should pass integral values for bounds when setting the parameters for types that have
+  // discrete distributions (integers, strings, lists). Otherwise the call with have no effect.
+  template <typename T,
+            typename Type_enum,
+            std::enable_if_t<cuda::std::is_integral_v<T>, T>* = nullptr>
+  void set_distribution_params(Type_enum type_or_group,
+                               distribution_id dist,
+                               T lower_bound,
+                               T upper_bound)
+  {
+    for (auto tid : get_type_or_group(static_cast<int32_t>(type_or_group))) {
+      if (tid == cudf::type_id::STRING) {
+        string_dist_desc.length_params = {
+          dist, static_cast<uint32_t>(lower_bound), static_cast<uint32_t>(upper_bound)};
+      } else if (tid == cudf::type_id::LIST) {
+        list_dist_desc.length_params = {
+          dist, static_cast<uint32_t>(lower_bound), static_cast<uint32_t>(upper_bound)};
+      } else {
+        int_params[tid] = {
+          dist, static_cast<uint64_t>(lower_bound), static_cast<uint64_t>(upper_bound)};
+      }
+    }
+  }
+
+  // Users should pass floating point values for bounds when setting the parameters for types that
+  // have continuous distributions (floating point types). Otherwise the call with have no effect.
+  template <typename T,
+            typename Type_enum,
+            std::enable_if_t<std::is_floating_point_v<T>, T>* = nullptr>
+  void set_distribution_params(Type_enum type_or_group,
+                               distribution_id dist,
+                               T lower_bound,
+                               T upper_bound)
+  {
+    for (auto tid : get_type_or_group(static_cast<int32_t>(type_or_group))) {
+      float_params[tid] = {
+        dist, static_cast<double>(lower_bound), static_cast<double>(upper_bound)};
+    }
+  }
+
+  template <typename T, typename Type_enum, std::enable_if_t<cudf::is_chrono<T>(), T>* = nullptr>
+  void set_distribution_params(Type_enum type_or_group,
+                               distribution_id dist,
+                               typename T::rep lower_bound,
+                               typename T::rep upper_bound)
+  {
+    for (auto tid : get_type_or_group(static_cast<int32_t>(type_or_group))) {
+      int_params[tid] = {
+        dist, static_cast<uint64_t>(lower_bound), static_cast<uint64_t>(upper_bound)};
+    }
+  }
+
+  void set_bool_probability_true(double p)
+  {
+    CUDF_EXPECTS(p >= 0. and p <= 1., "probability must be in range [0...1]");
+    bool_probability_true = p;
+  }
+  void set_null_probability(std::optional<double> p)
+  {
+    CUDF_EXPECTS(p.value_or(0.) >= 0. and p.value_or(0.) <= 1.,
+                 "probability must be in range [0...1]");
+    null_probability = p;
+  }
+  void set_cardinality(cudf::size_type c) { cardinality = c; }
+  void set_avg_run_length(cudf::size_type avg_rl) { avg_run_length = avg_rl; }
+
+  void set_list_depth(cudf::size_type max_depth)
+  {
+    CUDF_EXPECTS(max_depth > 0, "List depth must be positive");
+    list_dist_desc.max_depth = max_depth;
+  }
+
+  void set_list_type(cudf::type_id type) { list_dist_desc.element_type = type; }
+
+  void set_struct_depth(cudf::size_type max_depth)
+  {
+    CUDF_EXPECTS(max_depth > 0, "Struct depth must be positive");
+    struct_dist_desc.max_depth = max_depth;
+  }
+
+  void set_struct_types(cudf::host_span<cudf::type_id const> types)
+  {
+    CUDF_EXPECTS(
+      std::none_of(
+        types.begin(), types.end(), [](auto& type) { return type == cudf::type_id::STRUCT; }),
+      "Cannot include STRUCT as its own subtype");
+    struct_dist_desc.leaf_types.assign(types.begin(), types.end());
+  }
+};
+
+/**
+ * @brief Builder to construct data profiles for the random data generator.
+ *
+ * Setters can be chained to set multiple properties in a single expression.
+ * For example, `data_profile` initialization
+ * @code{.pseudo}
+ * data_profile profile;
+ * profile.set_null_probability(0.0);
+ * profile.set_cardinality(0);
+ * profile.set_distribution_params(cudf::type_id::INT32, distribution_id::UNIFORM, 0, 100);
+ * @endcode
+ * becomes
+ * @code{.pseudo}
+ * data_profile const profile =
+ *   data_profile_builder().cardinality(0).null_probability(0.0).distribution(
+ *     cudf::type_id::INT32, distribution_id::UNIFORM, 0, 100);
+ * @endcode
+ * The builder makes it easier to have immutable `data_profile` objects even with the complex
+ * initialization. The `profile` object in the example above is initialized from
+ * `data_profile_builder` using an implicit conversion operator.
+ *
+ * The builder API also includes a few additional convenience setters:
+ * Overload of `distribution` that only takes the distribution type (not the range).
+ * `no_validity`, which is a simpler equivalent of `null_probability(std::nullopr)`.
+ */
+class data_profile_builder {
+  data_profile profile;
+
+ public:
+  /**
+   * @brief Sets random distribution type for a given set of data types.
+   *
+   * Only the distribution type is set; the distribution will use the default range.
+   *
+   * @param type_or_group  Type or group ID, depending on whether the new distribution
+   * applies to a single type or a subset of types
+   * @param dist  Random distribution type
+   * @tparam T Data type of the distribution range; does not need to match the data type
+   * @return this for chaining
+   */
+  template <typename T, typename Type_enum>
+  data_profile_builder& distribution(Type_enum type_or_group, distribution_id dist)
+  {
+    auto const range = default_range<T>();
+    profile.set_distribution_params(type_or_group, dist, range.first, range.second);
+    return *this;
+  }
+
+  /**
+   * @brief Sets random distribution type and value range for a given set of data types.
+   *
+   * @tparam T Parameters that are forwarded to set_distribution_params
+   * @return this for chaining
+   */
+  template <class... T>
+  data_profile_builder& distribution(T&&... t)
+  {
+    profile.set_distribution_params(std::forward<T>(t)...);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the probability that a randomly generated boolean element with be `true`.
+   *
+   * For example, passing `0.9` means that 90% of values in boolean columns with be `true`.
+   *
+   * @param p Probability of `true` values, in range [0..1]
+   * @return this for chaining
+   */
+  data_profile_builder& bool_probability_true(double p)
+  {
+    profile.set_bool_probability_true(p);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the probability that a randomly generated element will be `null`.
+   *
+   * @param p Probability of `null` values, in range [0..1]
+   * @return this for chaining
+   */
+  data_profile_builder& null_probability(std::optional<double> p)
+  {
+    profile.set_null_probability(p);
+    return *this;
+  }
+
+  /**
+   * @brief Disables the creation of null mask in the output columns.
+   *
+   * @return this for chaining
+   */
+  data_profile_builder& no_validity()
+  {
+    profile.set_null_probability(std::nullopt);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum number of unique values in each output column.
+   *
+   * @param c Maximum number of unique values
+   * @return this for chaining
+   */
+  data_profile_builder& cardinality(cudf::size_type c)
+  {
+    profile.set_cardinality(c);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the average length of sequences of equal elements in output columns.
+   *
+   * @param avg_rl Average sequence length (run-length)
+   * @return this for chaining
+   */
+  data_profile_builder& avg_run_length(cudf::size_type avg_rl)
+  {
+    profile.set_avg_run_length(avg_rl);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum nesting depth of generated list columns.
+   *
+   * @param max_depth maximum nesting depth
+   * @return this for chaining
+   */
+  data_profile_builder& list_depth(cudf::size_type max_depth)
+  {
+    profile.set_list_depth(max_depth);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the data type of list elements.
+   *
+   * @param type data type ID
+   * @return this for chaining
+   */
+  data_profile_builder& list_type(cudf::type_id type)
+  {
+    profile.set_list_type(type);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum nesting depth of generated struct columns.
+   *
+   * @param max_depth maximum nesting depth
+   * @return this for chaining
+   */
+  data_profile_builder& struct_depth(cudf::size_type max_depth)
+  {
+    profile.set_struct_depth(max_depth);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the data types of struct fields.
+   *
+   * @param types data type IDs
+   * @return this for chaining
+   */
+  data_profile_builder& struct_types(cudf::host_span<cudf::type_id const> types)
+  {
+    profile.set_struct_types(types);
+    return *this;
+  }
+
+  /**
+   * @brief move data_profile member once it's built.
+   */
+  operator data_profile&&() { return std::move(profile); }
+};
+
+/**
+ * @brief Strongly typed table size in bytes. Used to disambiguate overloads of
+ * `create_random_table`.
+ */
+struct table_size_bytes {
+  size_t size;
+};
+
+/**
+ * @brief Strongly typed row count. Used to disambiguate overloads of `create_random_table`.
+ */
+struct row_count {
+  cudf::size_type count;
+};
+
+/**
+ * @brief Deterministically generates a table filled with data with the given parameters.
+ *
+ * @param dtype_ids Vector of requested column types
+ * @param table_bytes Target size of the output table, in bytes. Some type may not produce columns
+ * of exact size
+ * @param data_params Optional, set of data parameters describing the data profile for each type
+ * @param seed Optional, seed for the pseudo-random engine
+ */
+std::unique_ptr<cudf::table> create_random_table(std::vector<cudf::type_id> const& dtype_ids,
+                                                 table_size_bytes table_bytes,
+                                                 data_profile const& data_params = data_profile{},
+                                                 unsigned seed                   = 1);
+
+/**
+ * @brief Deterministically generates a table filled with data with the given parameters.
+ *
+ * @param dtype_ids Vector of requested column types
+ * @param num_rows Number of rows in the output table
+ * @param data_params Optional, set of data parameters describing the data profile for each type
+ * @param seed Optional, seed for the pseudo-random engine
+ */
+std::unique_ptr<cudf::table> create_random_table(std::vector<cudf::type_id> const& dtype_ids,
+                                                 row_count num_rows,
+                                                 data_profile const& data_params = data_profile{},
+                                                 unsigned seed                   = 1);
+
+/**
+ * @brief Deterministically generates a column filled with data with the given parameters.
+ *
+ * @param dtype_id Requested column type
+ * @param num_rows Number of rows in the output column
+ * @param data_params Optional, set of data parameters describing the data profile
+ * @param seed Optional, seed for the pseudo-random engine
+ */
+std::unique_ptr<cudf::column> create_random_column(cudf::type_id dtype_id,
+                                                   row_count num_rows,
+                                                   data_profile const& data_params = data_profile{},
+                                                   unsigned seed                   = 1);
+
+/**
+ * @brief Generate sequence columns starting with value 0 in first row and increasing by 1 in
+ * subsequent rows.
+ *
+ * @param dtype_ids Vector of requested column types
+ * @param num_rows Number of rows in the output table
+ * @param null_probability Optional, probability of a null value
+ *  no value implies no null mask, =0 implies all valids, >=1 implies all nulls
+ * @param seed Optional, seed for the pseudo-random engine
+ * @return A table with the sequence columns.
+ */
+std::unique_ptr<cudf::table> create_sequence_table(
+  std::vector<cudf::type_id> const& dtype_ids,
+  row_count num_rows,
+  std::optional<double> null_probability = std::nullopt,
+  unsigned seed                          = 1);
+
+/**
+ * @brief Repeats the input data types cyclically to fill a vector of @ref num_cols
+ * elements.
+ *
+ * @param dtype_ids Vector of requested column types
+ * @param num_cols Number of types in the output vector
+ * @return A vector of type_ids
+ */
+std::vector<cudf::type_id> cycle_dtypes(std::vector<cudf::type_id> const& dtype_ids,
+                                        cudf::size_type num_cols);
+
+/**
+ * @brief Repeat the given two data types with a given ratio of a:b.
+ *
+ * The first dtype will have 'first_num' columns and the second will have 'num_cols - first_num'
+ * columns.
+ *
+ * @param dtype_ids Pair of requested column types
+ * @param num_cols Total number of columns in the output vector
+ * @param first_num Total number of columns of type `dtype_ids.first`
+ * @return A vector of type_ids
+ */
+std::vector<cudf::type_id> mix_dtypes(std::pair<cudf::type_id, cudf::type_id> const& dtype_ids,
+                                      cudf::size_type num_cols,
+                                      int first_num);
+/**
+ * @brief Create a random null mask object
+ *
+ * @param size number of rows
+ * @param null_probability probability of a null value
+ *  no value implies no null mask, =0 implies all valids, >=1 implies all nulls
+ * @param seed Optional, seed for the pseudo-random engine
+ * @return null mask device buffer with random null mask data and null count
+ */
+std::pair<rmm::device_buffer, cudf::size_type> create_random_null_mask(
+  cudf::size_type size, std::optional<double> null_probability = std::nullopt, unsigned seed = 1);
diff --git a/cpp/benchmarks/common/random_distribution_factory.cuh b/cpp/benchmarks/common/random_distribution_factory.cuh
new file mode 100644
index 0000000..a548e4c
--- /dev/null
+++ b/cpp/benchmarks/common/random_distribution_factory.cuh
@@ -0,0 +1,181 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "generate_input.hpp"
+
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/random.h>
+#include <thrust/random/normal_distribution.h>
+#include <thrust/random/uniform_int_distribution.h>
+#include <thrust/tabulate.h>
+
+#include <algorithm>
+#include <memory>
+#include <type_traits>
+
+/**
+ * @brief Real Type that has at least number of bits of integral type in its mantissa.
+ *  number of bits of integrals < 23 bits of mantissa in float
+ * to allow full range of integer bits to be generated.
+ * @tparam T integral type
+ */
+template <typename T>
+using integral_to_realType =
+  std::conditional_t<cuda::std::is_floating_point_v<T>,
+                     T,
+                     std::conditional_t<sizeof(T) * 8 <= 23, float, double>>;
+
+/**
+ * @brief Generates a normal distribution between zero and upper_bound.
+ */
+template <typename T>
+auto make_normal_dist(T lower_bound, T upper_bound)
+{
+  using realT    = integral_to_realType<T>;
+  T const mean   = lower_bound + (upper_bound - lower_bound) / 2;
+  T const stddev = (upper_bound - lower_bound) / 6;
+  return thrust::random::normal_distribution<realT>(mean, stddev);
+}
+
+template <typename T, std::enable_if_t<cuda::std::is_integral_v<T>, T>* = nullptr>
+auto make_uniform_dist(T range_start, T range_end)
+{
+  return thrust::uniform_int_distribution<T>(range_start, range_end);
+}
+
+template <typename T, std::enable_if_t<cudf::is_floating_point<T>()>* = nullptr>
+auto make_uniform_dist(T range_start, T range_end)
+{
+  return thrust::uniform_real_distribution<T>(range_start, range_end);
+}
+
+template <typename T>
+double geometric_dist_p(T range_size)
+{
+  constexpr double percentage_in_range = 0.99;
+  double const p                       = 1 - exp(log(1 - percentage_in_range) / range_size);
+  return p ? p : std::numeric_limits<double>::epsilon();
+}
+
+/**
+ * @brief Generates a geometric distribution between lower_bound and upper_bound.
+ * This distribution is an approximation generated using normal distribution.
+ *
+ * @tparam T Result type of the number to produce.
+ */
+template <typename T>
+class geometric_distribution : public thrust::random::normal_distribution<integral_to_realType<T>> {
+  using realType = integral_to_realType<T>;
+  using super_t  = thrust::random::normal_distribution<realType>;
+  T _lower_bound;
+  T _upper_bound;
+
+ public:
+  using result_type = T;
+  __host__ __device__ explicit geometric_distribution(T lower_bound, T upper_bound)
+    : super_t(0, std::labs(upper_bound - lower_bound) / 4.0),
+      _lower_bound(lower_bound),
+      _upper_bound(upper_bound)
+  {
+  }
+
+  template <typename UniformRandomNumberGenerator>
+  __host__ __device__ result_type operator()(UniformRandomNumberGenerator& urng)
+  {
+    return _lower_bound < _upper_bound ? std::abs(super_t::operator()(urng)) + _lower_bound
+                                       : _lower_bound - std::abs(super_t::operator()(urng));
+  }
+};
+
+template <typename T, typename Generator>
+struct value_generator {
+  using result_type = T;
+
+  value_generator(T lower_bound, T upper_bound, thrust::minstd_rand& engine, Generator gen)
+    : lower_bound(std::min(lower_bound, upper_bound)),
+      upper_bound(std::max(lower_bound, upper_bound)),
+      engine(engine),
+      dist(gen)
+  {
+  }
+
+  __device__ T operator()(size_t n)
+  {
+    engine.discard(n);
+    if constexpr (cuda::std::is_integral_v<T> &&
+                  cuda::std::is_floating_point_v<decltype(dist(engine))>) {
+      return std::clamp(static_cast<T>(std::round(dist(engine))), lower_bound, upper_bound);
+    } else {
+      return std::clamp(dist(engine), lower_bound, upper_bound);
+    }
+    // Note: uniform does not need clamp, because already range is guaranteed to be within bounds.
+  }
+
+  T lower_bound;
+  T upper_bound;
+  thrust::minstd_rand engine;
+  Generator dist;
+};
+
+template <typename T>
+using distribution_fn = std::function<rmm::device_uvector<T>(thrust::minstd_rand&, size_t)>;
+
+template <
+  typename T,
+  std::enable_if_t<cuda::std::is_integral_v<T> or cuda::std::is_floating_point_v<T>, T>* = nullptr>
+distribution_fn<T> make_distribution(distribution_id dist_id, T lower_bound, T upper_bound)
+{
+  switch (dist_id) {
+    case distribution_id::NORMAL:
+      return [lower_bound, upper_bound, dist = make_normal_dist(lower_bound, upper_bound)](
+               thrust::minstd_rand& engine, size_t size) -> rmm::device_uvector<T> {
+        rmm::device_uvector<T> result(size, cudf::get_default_stream());
+        thrust::tabulate(thrust::device,
+                         result.begin(),
+                         result.end(),
+                         value_generator{lower_bound, upper_bound, engine, dist});
+        return result;
+      };
+    case distribution_id::UNIFORM:
+      return [lower_bound, upper_bound, dist = make_uniform_dist(lower_bound, upper_bound)](
+               thrust::minstd_rand& engine, size_t size) -> rmm::device_uvector<T> {
+        rmm::device_uvector<T> result(size, cudf::get_default_stream());
+        thrust::tabulate(thrust::device,
+                         result.begin(),
+                         result.end(),
+                         value_generator{lower_bound, upper_bound, engine, dist});
+        return result;
+      };
+    case distribution_id::GEOMETRIC:
+      // kind of exponential distribution from lower_bound to upper_bound.
+      return [lower_bound, upper_bound, dist = geometric_distribution<T>(lower_bound, upper_bound)](
+               thrust::minstd_rand& engine, size_t size) -> rmm::device_uvector<T> {
+        rmm::device_uvector<T> result(size, cudf::get_default_stream());
+        thrust::tabulate(thrust::device,
+                         result.begin(),
+                         result.end(),
+                         value_generator{lower_bound, upper_bound, engine, dist});
+        return result;
+      };
+    default: CUDF_FAIL("Unsupported probability distribution");
+  }
+}
diff --git a/cpp/benchmarks/copying/contiguous_split.cu b/cpp/benchmarks/copying/contiguous_split.cu
new file mode 100644
index 0000000..910fc68
--- /dev/null
+++ b/cpp/benchmarks/copying/contiguous_split.cu
@@ -0,0 +1,263 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/contiguous_split.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+void contiguous_split(cudf::table_view const& src_table, std::vector<cudf::size_type> const& splits)
+{
+  auto result = cudf::contiguous_split(src_table, splits);
+}
+
+void chunked_pack(cudf::table_view const& src_table, std::vector<cudf::size_type> const&)
+{
+  auto const mr     = rmm::mr::get_current_device_resource();
+  auto const stream = cudf::get_default_stream();
+  auto user_buffer  = rmm::device_uvector<std::uint8_t>(100L * 1024 * 1024, stream, mr);
+  auto chunked_pack = cudf::chunked_pack::create(src_table, user_buffer.size(), mr);
+  while (chunked_pack->has_next()) {
+    auto iter_size = chunked_pack->next(user_buffer);
+  }
+  stream.synchronize();
+}
+
+template <typename T, typename ContigSplitImpl>
+void BM_contiguous_split_common(benchmark::State& state,
+                                std::vector<T>& src_cols,
+                                int64_t num_rows,
+                                int64_t num_splits,
+                                int64_t bytes_total,
+                                ContigSplitImpl& impl)
+{
+  // generate splits
+  std::vector<cudf::size_type> splits;
+  if (num_splits > 0) {
+    cudf::size_type const split_stride = num_rows / num_splits;
+    // start after the first element.
+    auto iter = thrust::make_counting_iterator(1);
+    splits.reserve(num_splits);
+    std::transform(iter,
+                   iter + num_splits,
+                   std::back_inserter(splits),
+                   [split_stride, num_rows](cudf::size_type i) {
+                     return std::min(i * split_stride, static_cast<cudf::size_type>(num_rows));
+                   });
+  }
+
+  for (auto const& col : src_cols)
+    // computing the null count is not a part of the benchmark's target code path, and we want the
+    // property to be pre-computed so that we measure the performance of only the intended code path
+    [[maybe_unused]]
+    auto const nulls = col->null_count();
+
+  auto const src_table = cudf::table(std::move(src_cols));
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    impl(src_table, splits);
+  }
+
+  // it's 2x bytes_total because we're both reading and writing.
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * bytes_total * 2);
+}
+
+class ContiguousSplit : public cudf::benchmark {};
+class ChunkedPack : public cudf::benchmark {};
+
+template <typename ContiguousSplitImpl>
+void BM_contiguous_split(benchmark::State& state, ContiguousSplitImpl& impl)
+{
+  int64_t const total_desired_bytes = state.range(0);
+  cudf::size_type const num_cols    = state.range(1);
+  cudf::size_type const num_splits  = state.range(2);
+  bool const include_validity       = state.range(3) != 0;
+
+  cudf::size_type el_size = 4;  // ints and floats
+  int64_t const num_rows  = total_desired_bytes / (num_cols * el_size);
+
+  // generate input table
+  auto builder = data_profile_builder().cardinality(0).distribution<int>(cudf::type_id::INT32,
+                                                                         distribution_id::UNIFORM);
+  if (not include_validity) builder.no_validity();
+
+  auto src_cols = create_random_table(cycle_dtypes({cudf::type_id::INT32}, num_cols),
+                                      row_count{static_cast<cudf::size_type>(num_rows)},
+                                      data_profile{builder})
+                    ->release();
+
+  int64_t const total_bytes =
+    total_desired_bytes +
+    (include_validity ? (max(int64_t{1}, (num_rows / 32)) * sizeof(cudf::bitmask_type) * num_cols)
+                      : 0);
+
+  BM_contiguous_split_common(state, src_cols, num_rows, num_splits, total_bytes, impl);
+}
+
+class ContiguousSplitStrings : public cudf::benchmark {};
+class ChunkedPackStrings : public cudf::benchmark {};
+
+template <typename ContiguousSplitImpl>
+void BM_contiguous_split_strings(benchmark::State& state, ContiguousSplitImpl& impl)
+{
+  int64_t const total_desired_bytes = state.range(0);
+  cudf::size_type const num_cols    = state.range(1);
+  cudf::size_type const num_splits  = state.range(2);
+  bool const include_validity       = state.range(3) != 0;
+
+  constexpr int64_t string_len = 8;
+  std::vector<char const*> h_strings{
+    "aaaaaaaa", "bbbbbbbb", "cccccccc", "dddddddd", "eeeeeeee", "ffffffff", "gggggggg", "hhhhhhhh"};
+
+  int64_t const col_len_bytes = total_desired_bytes / num_cols;
+  int64_t const num_rows      = col_len_bytes / string_len;
+
+  // generate input table
+  data_profile profile = data_profile_builder().no_validity().cardinality(0).distribution(
+    cudf::type_id::INT32,
+    distribution_id::UNIFORM,
+    0ul,
+    include_validity ? h_strings.size() * 2 : h_strings.size() - 1);  // out of bounds nullified
+  cudf::test::strings_column_wrapper one_col(h_strings.begin(), h_strings.end());
+  std::vector<std::unique_ptr<cudf::column>> src_cols(num_cols);
+  for (int64_t idx = 0; idx < num_cols; idx++) {
+    auto random_indices = create_random_column(
+      cudf::type_id::INT32, row_count{static_cast<cudf::size_type>(num_rows)}, profile);
+    auto str_table = cudf::gather(cudf::table_view{{one_col}},
+                                  *random_indices,
+                                  (include_validity ? cudf::out_of_bounds_policy::NULLIFY
+                                                    : cudf::out_of_bounds_policy::DONT_CHECK));
+    src_cols[idx]  = std::move(str_table->release()[0]);
+  }
+
+  int64_t const total_bytes =
+    total_desired_bytes + ((num_rows + 1) * sizeof(cudf::size_type)) +
+    (include_validity ? (max(int64_t{1}, (num_rows / 32)) * sizeof(cudf::bitmask_type) * num_cols)
+                      : 0);
+
+  BM_contiguous_split_common(state, src_cols, num_rows, num_splits, total_bytes, impl);
+}
+
+#define CSBM_BENCHMARK_DEFINE(name, size, num_columns, num_splits, validity) \
+  BENCHMARK_DEFINE_F(ContiguousSplit, name)(::benchmark::State & state)      \
+  {                                                                          \
+    BM_contiguous_split(state, contiguous_split);                            \
+  }                                                                          \
+  BENCHMARK_REGISTER_F(ContiguousSplit, name)                                \
+    ->Args({size, num_columns, num_splits, validity})                        \
+    ->Unit(benchmark::kMillisecond)                                          \
+    ->UseManualTime()                                                        \
+    ->Iterations(8)
+CSBM_BENCHMARK_DEFINE(6Gb512ColsNoValidity, (int64_t)6 * 1024 * 1024 * 1024, 512, 256, 0);
+CSBM_BENCHMARK_DEFINE(6Gb512ColsValidity, (int64_t)6 * 1024 * 1024 * 1024, 512, 256, 1);
+CSBM_BENCHMARK_DEFINE(6Gb10ColsNoValidity, (int64_t)6 * 1024 * 1024 * 1024, 10, 256, 0);
+CSBM_BENCHMARK_DEFINE(6Gb10ColsValidity, (int64_t)6 * 1024 * 1024 * 1024, 10, 256, 1);
+
+CSBM_BENCHMARK_DEFINE(4Gb512ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 256, 0);
+CSBM_BENCHMARK_DEFINE(4Gb512ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 256, 1);
+CSBM_BENCHMARK_DEFINE(4Gb10ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 256, 0);
+CSBM_BENCHMARK_DEFINE(4Gb10ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 256, 1);
+CSBM_BENCHMARK_DEFINE(4Gb4ColsNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 4, 0, 1);
+CSBM_BENCHMARK_DEFINE(4Gb4ColsValidityNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 4, 0, 1);
+
+CSBM_BENCHMARK_DEFINE(1Gb512ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 256, 0);
+CSBM_BENCHMARK_DEFINE(1Gb512ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 256, 1);
+CSBM_BENCHMARK_DEFINE(1Gb10ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 256, 0);
+CSBM_BENCHMARK_DEFINE(1Gb10ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 256, 1);
+CSBM_BENCHMARK_DEFINE(1Gb1ColNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 1, 0, 1);
+CSBM_BENCHMARK_DEFINE(1Gb1ColValidityNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 1, 0, 1);
+
+#define CSBM_STRINGS_BENCHMARK_DEFINE(name, size, num_columns, num_splits, validity) \
+  BENCHMARK_DEFINE_F(ContiguousSplitStrings, name)(::benchmark::State & state)       \
+  {                                                                                  \
+    BM_contiguous_split_strings(state, contiguous_split);                            \
+  }                                                                                  \
+  BENCHMARK_REGISTER_F(ContiguousSplitStrings, name)                                 \
+    ->Args({size, num_columns, num_splits, validity})                                \
+    ->Unit(benchmark::kMillisecond)                                                  \
+    ->UseManualTime()                                                                \
+    ->Iterations(8)
+
+CSBM_STRINGS_BENCHMARK_DEFINE(4Gb512ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 256, 0);
+CSBM_STRINGS_BENCHMARK_DEFINE(4Gb512ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 256, 1);
+CSBM_STRINGS_BENCHMARK_DEFINE(4Gb10ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 256, 0);
+CSBM_STRINGS_BENCHMARK_DEFINE(4Gb10ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 256, 1);
+CSBM_STRINGS_BENCHMARK_DEFINE(4Gb4ColsNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 4, 0, 0);
+CSBM_STRINGS_BENCHMARK_DEFINE(4Gb4ColsValidityNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 4, 0, 1);
+
+CSBM_STRINGS_BENCHMARK_DEFINE(1Gb512ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 256, 0);
+CSBM_STRINGS_BENCHMARK_DEFINE(1Gb512ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 256, 1);
+CSBM_STRINGS_BENCHMARK_DEFINE(1Gb10ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 256, 0);
+CSBM_STRINGS_BENCHMARK_DEFINE(1Gb10ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 256, 1);
+CSBM_STRINGS_BENCHMARK_DEFINE(1Gb1ColNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 1, 0, 0);
+CSBM_STRINGS_BENCHMARK_DEFINE(1Gb1ColValidityNoSplits, (int64_t)1 * 1024 * 1024 * 1024, 1, 0, 1);
+
+#define CCSBM_BENCHMARK_DEFINE(name, size, num_columns, num_splits, validity) \
+  BENCHMARK_DEFINE_F(ChunkedPack, name)(::benchmark::State & state)           \
+  {                                                                           \
+    BM_contiguous_split(state, chunked_pack);                                 \
+  }                                                                           \
+  BENCHMARK_REGISTER_F(ChunkedPack, name)                                     \
+    ->Args({size, num_columns, num_splits, validity})                         \
+    ->Unit(benchmark::kMillisecond)                                           \
+    ->UseManualTime()                                                         \
+    ->Iterations(8)
+CCSBM_BENCHMARK_DEFINE(6Gb512ColsNoValidity, (int64_t)6 * 1024 * 1024 * 1024, 512, 0, 0);
+CCSBM_BENCHMARK_DEFINE(6Gb512ColsValidity, (int64_t)6 * 1024 * 1024 * 1024, 512, 0, 1);
+CCSBM_BENCHMARK_DEFINE(6Gb10ColsNoValidity, (int64_t)6 * 1024 * 1024 * 1024, 10, 0, 0);
+CCSBM_BENCHMARK_DEFINE(6Gb10ColsValidity, (int64_t)6 * 1024 * 1024 * 1024, 10, 0, 1);
+
+CCSBM_BENCHMARK_DEFINE(4Gb512ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 0, 0);
+CCSBM_BENCHMARK_DEFINE(4Gb512ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 0, 1);
+CCSBM_BENCHMARK_DEFINE(4Gb10ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 0, 0);
+CCSBM_BENCHMARK_DEFINE(4Gb10ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 0, 1);
+CCSBM_BENCHMARK_DEFINE(4Gb4ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 4, 0, 1);
+
+CCSBM_BENCHMARK_DEFINE(1Gb512ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 0, 0);
+CCSBM_BENCHMARK_DEFINE(1Gb512ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 0, 1);
+CCSBM_BENCHMARK_DEFINE(1Gb10ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 0, 0);
+CCSBM_BENCHMARK_DEFINE(1Gb10ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 0, 1);
+CCSBM_BENCHMARK_DEFINE(1Gb1ColValidity, (int64_t)1 * 1024 * 1024 * 1024, 1, 0, 1);
+
+#define CCSBM_STRINGS_BENCHMARK_DEFINE(name, size, num_columns, num_splits, validity) \
+  BENCHMARK_DEFINE_F(ChunkedPackStrings, name)(::benchmark::State & state)            \
+  {                                                                                   \
+    BM_contiguous_split_strings(state, chunked_pack);                                 \
+  }                                                                                   \
+  BENCHMARK_REGISTER_F(ChunkedPackStrings, name)                                      \
+    ->Args({size, num_columns, num_splits, validity})                                 \
+    ->Unit(benchmark::kMillisecond)                                                   \
+    ->UseManualTime()                                                                 \
+    ->Iterations(8)
+
+CCSBM_STRINGS_BENCHMARK_DEFINE(4Gb512ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 0, 0);
+CCSBM_STRINGS_BENCHMARK_DEFINE(4Gb512ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 512, 0, 1);
+CCSBM_STRINGS_BENCHMARK_DEFINE(4Gb10ColsNoValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 0, 0);
+CCSBM_STRINGS_BENCHMARK_DEFINE(4Gb10ColsValidity, (int64_t)4 * 1024 * 1024 * 1024, 10, 0, 1);
+CCSBM_STRINGS_BENCHMARK_DEFINE(4Gb4ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 4, 0, 1);
+
+CCSBM_STRINGS_BENCHMARK_DEFINE(1Gb512ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 0, 0);
+CCSBM_STRINGS_BENCHMARK_DEFINE(1Gb512ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 512, 0, 1);
+CCSBM_STRINGS_BENCHMARK_DEFINE(1Gb10ColsNoValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 0, 0);
+CCSBM_STRINGS_BENCHMARK_DEFINE(1Gb10ColsValidity, (int64_t)1 * 1024 * 1024 * 1024, 10, 0, 1);
+CCSBM_STRINGS_BENCHMARK_DEFINE(1Gb1ColValidity, (int64_t)1 * 1024 * 1024 * 1024, 1, 0, 1);
diff --git a/cpp/benchmarks/copying/copy_if_else.cpp b/cpp/benchmarks/copying/copy_if_else.cpp
new file mode 100644
index 0000000..50ddfb8
--- /dev/null
+++ b/cpp/benchmarks/copying/copy_if_else.cpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+class CopyIfElse : public cudf::benchmark {};
+
+template <class TypeParam>
+static void BM_copy_if_else(benchmark::State& state, bool nulls)
+{
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+  auto input_type  = cudf::type_to_id<TypeParam>();
+  auto bool_type   = cudf::type_id::BOOL8;
+  auto const input = create_random_table({input_type, input_type, bool_type}, row_count{n_rows});
+
+  if (!nulls) {
+    input->get_column(2).set_null_mask(rmm::device_buffer{}, 0);
+    input->get_column(1).set_null_mask(rmm::device_buffer{}, 0);
+    input->get_column(0).set_null_mask(rmm::device_buffer{}, 0);
+  }
+
+  cudf::column_view decision(input->view().column(2));
+  cudf::column_view rhs(input->view().column(1));
+  cudf::column_view lhs(input->view().column(0));
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::copy_if_else(lhs, rhs, decision);
+  }
+
+  auto const bytes_read    = n_rows * (sizeof(TypeParam) + sizeof(bool));
+  auto const bytes_written = n_rows * sizeof(TypeParam);
+  auto const null_bytes    = nulls ? 2 * cudf::bitmask_allocation_size_bytes(n_rows) : 0;
+
+  // Use number of bytes read and written.
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) *
+                          (bytes_read + bytes_written + null_bytes));
+}
+
+#define COPY_BENCHMARK_DEFINE(name, type, b)                  \
+  BENCHMARK_DEFINE_F(CopyIfElse, name)                        \
+  (::benchmark::State & st) { BM_copy_if_else<type>(st, b); } \
+  BENCHMARK_REGISTER_F(CopyIfElse, name)                      \
+    ->RangeMultiplier(8)                                      \
+    ->Ranges({{1 << 12, 1 << 27}})                            \
+    ->UseManualTime()                                         \
+    ->Unit(benchmark::kMillisecond);
+
+COPY_BENCHMARK_DEFINE(int16, int16_t, true)
+COPY_BENCHMARK_DEFINE(uint32, uint32_t, true)
+COPY_BENCHMARK_DEFINE(float64, double, true)
+COPY_BENCHMARK_DEFINE(int16_no_nulls, int16_t, false)
+COPY_BENCHMARK_DEFINE(uint32_no_nulls, uint32_t, false)
+COPY_BENCHMARK_DEFINE(float64_no_nulls, double, false)
diff --git a/cpp/benchmarks/copying/gather.cu b/cpp/benchmarks/copying/gather.cu
new file mode 100644
index 0000000..eeb0149
--- /dev/null
+++ b/cpp/benchmarks/copying/gather.cu
@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/random.h>
+#include <thrust/reverse.h>
+#include <thrust/shuffle.h>
+
+class Gather : public cudf::benchmark {};
+
+template <class TypeParam, bool coalesce>
+void BM_gather(benchmark::State& state)
+{
+  cudf::size_type const source_size{(cudf::size_type)state.range(0)};
+  auto const n_cols = (cudf::size_type)state.range(1);
+
+  // Gather indices
+  auto gather_map_table =
+    create_sequence_table({cudf::type_to_id<cudf::size_type>()}, row_count{source_size});
+  auto gather_map = gather_map_table->get_column(0).mutable_view();
+
+  if (coalesce) {
+    thrust::reverse(
+      thrust::device, gather_map.begin<cudf::size_type>(), gather_map.end<cudf::size_type>());
+  } else {
+    thrust::shuffle(thrust::device,
+                    gather_map.begin<cudf::size_type>(),
+                    gather_map.end<cudf::size_type>(),
+                    thrust::default_random_engine());
+  }
+
+  // Every element is valid
+  auto source_table = create_sequence_table(cycle_dtypes({cudf::type_to_id<TypeParam>()}, n_cols),
+                                            row_count{source_size});
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::gather(*source_table, gather_map);
+  }
+
+  state.SetBytesProcessed(state.iterations() * state.range(0) * n_cols * 2 * sizeof(TypeParam));
+}
+
+#define GBM_BENCHMARK_DEFINE(name, type, coalesce)             \
+  BENCHMARK_DEFINE_F(Gather, name)(::benchmark::State & state) \
+  {                                                            \
+    BM_gather<type, coalesce>(state);                          \
+  }                                                            \
+  BENCHMARK_REGISTER_F(Gather, name)                           \
+    ->RangeMultiplier(2)                                       \
+    ->Ranges({{1 << 10, 1 << 26}, {1, 8}})                     \
+    ->UseManualTime();
+
+GBM_BENCHMARK_DEFINE(double_coalesce_x, double, true);
+GBM_BENCHMARK_DEFINE(double_coalesce_o, double, false);
diff --git a/cpp/benchmarks/copying/scatter.cu b/cpp/benchmarks/copying/scatter.cu
new file mode 100644
index 0000000..a521dc8
--- /dev/null
+++ b/cpp/benchmarks/copying/scatter.cu
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/random.h>
+#include <thrust/reverse.h>
+#include <thrust/shuffle.h>
+
+class Scatter : public cudf::benchmark {};
+
+template <class TypeParam, bool coalesce>
+void BM_scatter(benchmark::State& state)
+{
+  auto const source_size{static_cast<cudf::size_type>(state.range(0))};
+  auto const n_cols{static_cast<cudf::size_type>(state.range(1))};
+
+  // Gather indices
+  auto scatter_map_table =
+    create_sequence_table({cudf::type_to_id<cudf::size_type>()}, row_count{source_size});
+  auto scatter_map = scatter_map_table->get_column(0).mutable_view();
+
+  if (coalesce) {
+    thrust::reverse(
+      thrust::device, scatter_map.begin<cudf::size_type>(), scatter_map.end<cudf::size_type>());
+  } else {
+    thrust::shuffle(thrust::device,
+                    scatter_map.begin<cudf::size_type>(),
+                    scatter_map.end<cudf::size_type>(),
+                    thrust::default_random_engine());
+  }
+
+  // Every element is valid
+  auto source_table = create_sequence_table(cycle_dtypes({cudf::type_to_id<TypeParam>()}, n_cols),
+                                            row_count{source_size});
+  auto target_table = create_sequence_table(cycle_dtypes({cudf::type_to_id<TypeParam>()}, n_cols),
+                                            row_count{source_size});
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::scatter(*source_table, scatter_map, *target_table);
+  }
+
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * state.range(0) * n_cols * 2 *
+                          sizeof(TypeParam));
+}
+
+#define SBM_BENCHMARK_DEFINE(name, type, coalesce)              \
+  BENCHMARK_DEFINE_F(Scatter, name)(::benchmark::State & state) \
+  {                                                             \
+    BM_scatter<type, coalesce>(state);                          \
+  }                                                             \
+  BENCHMARK_REGISTER_F(Scatter, name)                           \
+    ->RangeMultiplier(2)                                        \
+    ->Ranges({{1 << 10, 1 << 25}, {1, 8}})                      \
+    ->UseManualTime();
+
+SBM_BENCHMARK_DEFINE(double_coalesce_x, double, true);
+SBM_BENCHMARK_DEFINE(double_coalesce_o, double, false);
diff --git a/cpp/benchmarks/copying/shift.cu b/cpp/benchmarks/copying/shift.cu
new file mode 100644
index 0000000..460100a
--- /dev/null
+++ b/cpp/benchmarks/copying/shift.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+template <typename T, typename ScalarType = cudf::scalar_type_t<T>>
+std::unique_ptr<cudf::scalar> make_scalar(
+  T value                             = 0,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  auto s = new ScalarType(value, true, stream, mr);
+  return std::unique_ptr<cudf::scalar>(s);
+}
+
+template <typename T>
+struct value_func {
+  T* data;
+  cudf::size_type offset;
+
+  __device__ T operator()(int idx) { return data[idx - offset]; }
+};
+
+struct validity_func {
+  cudf::size_type size;
+  cudf::size_type offset;
+
+  __device__ bool operator()(int idx)
+  {
+    auto source_idx = idx - offset;
+    return source_idx < 0 || source_idx >= size;
+  }
+};
+
+template <bool use_validity, int shift_factor>
+static void BM_shift(benchmark::State& state)
+{
+  cudf::size_type size   = state.range(0);
+  cudf::size_type offset = size * (static_cast<double>(shift_factor) / 100.0);
+
+  auto const input_table =
+    create_sequence_table({cudf::type_to_id<int>()},
+                          row_count{size},
+                          use_validity ? std::optional<double>{1.0} : std::nullopt);
+  cudf::column_view input{input_table->get_column(0)};
+
+  auto fill = use_validity ? make_scalar<int>() : make_scalar<int>(777);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    auto output = cudf::shift(input, offset, *fill);
+  }
+}
+
+class Shift : public cudf::benchmark {};
+
+#define SHIFT_BM_BENCHMARK_DEFINE(name, use_validity, shift_factor) \
+  BENCHMARK_DEFINE_F(Shift, name)(::benchmark::State & state)       \
+  {                                                                 \
+    BM_shift<use_validity, shift_factor>(state);                    \
+  }                                                                 \
+  BENCHMARK_REGISTER_F(Shift, name)                                 \
+    ->RangeMultiplier(32)                                           \
+    ->Range(1 << 10, 1 << 30)                                       \
+    ->UseManualTime()                                               \
+    ->Unit(benchmark::kMillisecond);
+
+SHIFT_BM_BENCHMARK_DEFINE(shift_zero, false, 0);
+SHIFT_BM_BENCHMARK_DEFINE(shift_zero_nullable_out, true, 0);
+
+SHIFT_BM_BENCHMARK_DEFINE(shift_ten_percent, false, 10);
+SHIFT_BM_BENCHMARK_DEFINE(shift_ten_percent_nullable_out, true, 10);
+
+SHIFT_BM_BENCHMARK_DEFINE(shift_half, false, 50);
+SHIFT_BM_BENCHMARK_DEFINE(shift_half_nullable_out, true, 50);
+
+SHIFT_BM_BENCHMARK_DEFINE(shift_full, false, 100);
+SHIFT_BM_BENCHMARK_DEFINE(shift_full_nullable_out, true, 100);
diff --git a/cpp/benchmarks/filling/repeat.cpp b/cpp/benchmarks/filling/repeat.cpp
new file mode 100644
index 0000000..0abef46
--- /dev/null
+++ b/cpp/benchmarks/filling/repeat.cpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/filling.hpp>
+
+class Repeat : public cudf::benchmark {};
+
+template <class TypeParam, bool nulls>
+void BM_repeat(benchmark::State& state)
+{
+  auto const n_rows = static_cast<cudf::size_type>(state.range(0));
+  auto const n_cols = static_cast<cudf::size_type>(state.range(1));
+
+  auto const input_table =
+    create_sequence_table(cycle_dtypes({cudf::type_to_id<TypeParam>()}, n_cols),
+                          row_count{n_rows},
+                          nulls ? std::optional<double>{1.0} : std::nullopt);
+  // Create table view
+  auto input = cudf::table_view(*input_table);
+
+  // repeat counts
+  using sizeT                = cudf::size_type;
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<sizeT>(), distribution_id::UNIFORM, 0, 3);
+  auto repeat_count = create_random_column(cudf::type_to_id<sizeT>(), row_count{n_rows}, profile);
+
+  // warm up
+  auto output = cudf::repeat(input, *repeat_count);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::repeat(input, *repeat_count);
+  }
+
+  auto data_bytes =
+    (input.num_columns() * input.num_rows() + output->num_columns() * output->num_rows()) *
+    sizeof(TypeParam);
+  auto null_bytes =
+    nulls ? input.num_columns() * cudf::bitmask_allocation_size_bytes(input.num_rows()) +
+              output->num_columns() * cudf::bitmask_allocation_size_bytes(output->num_rows())
+          : 0;
+  state.SetBytesProcessed(state.iterations() * (data_bytes + null_bytes));
+}
+
+#define REPEAT_BENCHMARK_DEFINE(name, type, nulls)                                                \
+  BENCHMARK_DEFINE_F(Repeat, name)(::benchmark::State & state) { BM_repeat<type, nulls>(state); } \
+  BENCHMARK_REGISTER_F(Repeat, name)                                                              \
+    ->RangeMultiplier(8)                                                                          \
+    ->Ranges({{1 << 10, 1 << 26}, {1, 8}})                                                        \
+    ->UseManualTime()                                                                             \
+    ->Unit(benchmark::kMillisecond);
+
+REPEAT_BENCHMARK_DEFINE(double_nulls, double, true);
+REPEAT_BENCHMARK_DEFINE(double_no_nulls, double, false);
diff --git a/cpp/benchmarks/fixture/benchmark_fixture.hpp b/cpp/benchmarks/fixture/benchmark_fixture.hpp
new file mode 100644
index 0000000..bc6c2e5
--- /dev/null
+++ b/cpp/benchmarks/fixture/benchmark_fixture.hpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <benchmark/benchmark.h>
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/device/owning_wrapper.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+#include <rmm/mr/device/statistics_resource_adaptor.hpp>
+
+namespace cudf {
+
+namespace {
+// memory resource factory helpers
+inline auto make_cuda() { return std::make_shared<rmm::mr::cuda_memory_resource>(); }
+
+inline auto make_pool_instance()
+{
+  static rmm::mr::cuda_memory_resource cuda_mr;
+  static auto pool_mr =
+    std::make_shared<rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource>>(&cuda_mr);
+  return pool_mr;
+}
+}  // namespace
+
+/**
+ * @brief Google Benchmark fixture for libcudf benchmarks
+ *
+ * libcudf benchmarks should use a fixture derived from this fixture class to
+ * ensure that the RAPIDS Memory Manager pool mode is used in benchmarks, which
+ * eliminates memory allocation / deallocation performance overhead from the
+ * benchmark.
+ *
+ * The SetUp and TearDown methods of this fixture initialize RMM into pool mode
+ * and finalize it, respectively. These methods are called automatically by
+ * Google Benchmark
+ *
+ * Example:
+ *
+ * template <class T>
+ * class my_benchmark : public cudf::benchmark {
+ * public:
+ *   using TypeParam = T;
+ * };
+ *
+ * Then:
+ *
+ * BENCHMARK_TEMPLATE_DEFINE_F(my_benchmark, my_test_name, int)
+ *   (::benchmark::State& state) {
+ *     for (auto _ : state) {
+ *       // benchmark stuff
+ *     }
+ * }
+ *
+ * BENCHMARK_REGISTER_F(my_benchmark, my_test_name)->Range(128, 512);
+ */
+class benchmark : public ::benchmark::Fixture {
+ public:
+  benchmark() : ::benchmark::Fixture()
+  {
+    char const* env_iterations = std::getenv("CUDF_BENCHMARK_ITERATIONS");
+    if (env_iterations != nullptr) { this->Iterations(std::max(0L, atol(env_iterations))); }
+  }
+
+  void SetUp(::benchmark::State const& state) override
+  {
+    mr = make_pool_instance();
+    rmm::mr::set_current_device_resource(mr.get());  // set default resource to pool
+  }
+
+  void TearDown(::benchmark::State const& state) override
+  {
+    // reset default resource to the initial resource
+    rmm::mr::set_current_device_resource(nullptr);
+    mr.reset();
+  }
+
+  // eliminate partial override warnings (see benchmark/benchmark.h)
+  void SetUp(::benchmark::State& st) override { SetUp(const_cast<::benchmark::State const&>(st)); }
+  void TearDown(::benchmark::State& st) override
+  {
+    TearDown(const_cast<::benchmark::State const&>(st));
+  }
+
+  std::shared_ptr<rmm::mr::device_memory_resource> mr;
+};
+
+class memory_stats_logger {
+ public:
+  memory_stats_logger()
+    : existing_mr(rmm::mr::get_current_device_resource()),
+      statistics_mr(rmm::mr::make_statistics_adaptor(existing_mr))
+  {
+    rmm::mr::set_current_device_resource(&statistics_mr);
+  }
+
+  ~memory_stats_logger() { rmm::mr::set_current_device_resource(existing_mr); }
+
+  [[nodiscard]] size_t peak_memory_usage() const noexcept
+  {
+    return statistics_mr.get_bytes_counter().peak;
+  }
+
+ private:
+  rmm::mr::device_memory_resource* existing_mr;
+  rmm::mr::statistics_resource_adaptor<rmm::mr::device_memory_resource> statistics_mr;
+};
+
+}  // namespace cudf
diff --git a/cpp/benchmarks/fixture/nvbench_fixture.hpp b/cpp/benchmarks/fixture/nvbench_fixture.hpp
new file mode 100644
index 0000000..e08f910
--- /dev/null
+++ b/cpp/benchmarks/fixture/nvbench_fixture.hpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/mr/device/arena_memory_resource.hpp>
+#include <rmm/mr/device/cuda_async_memory_resource.hpp>
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <rmm/mr/device/owning_wrapper.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <string>
+
+namespace cudf {
+namespace detail {
+static std::string rmm_mode_param{"--rmm_mode"};  ///< RMM mode command-line parameter name
+}  // namespace detail
+
+/**
+ * Base fixture for cudf benchmarks using nvbench.
+ *
+ * Initializes the default memory resource to use the RMM pool device resource.
+ */
+struct nvbench_base_fixture {
+  inline auto make_cuda() { return std::make_shared<rmm::mr::cuda_memory_resource>(); }
+
+  inline auto make_pool()
+  {
+    return rmm::mr::make_owning_wrapper<rmm::mr::pool_memory_resource>(make_cuda());
+  }
+
+  inline auto make_async() { return std::make_shared<rmm::mr::cuda_async_memory_resource>(); }
+
+  inline auto make_managed() { return std::make_shared<rmm::mr::managed_memory_resource>(); }
+
+  inline auto make_arena()
+  {
+    return rmm::mr::make_owning_wrapper<rmm::mr::arena_memory_resource>(make_cuda());
+  }
+
+  inline auto make_managed_pool()
+  {
+    return rmm::mr::make_owning_wrapper<rmm::mr::pool_memory_resource>(make_managed());
+  }
+
+  inline std::shared_ptr<rmm::mr::device_memory_resource> create_memory_resource(
+    std::string const& mode)
+  {
+    if (mode == "cuda") return make_cuda();
+    if (mode == "pool") return make_pool();
+    if (mode == "async") return make_async();
+    if (mode == "arena") return make_arena();
+    if (mode == "managed") return make_managed();
+    if (mode == "managed_pool") return make_managed_pool();
+    CUDF_FAIL("Unknown rmm_mode parameter: " + mode +
+              "\nExpecting: cuda, pool, async, arena, managed, or managed_pool");
+  }
+
+  nvbench_base_fixture(int argc, char const* const* argv)
+  {
+    for (int i = 1; i < argc - 1; ++i) {
+      std::string arg = argv[i];
+      if (arg == detail::rmm_mode_param) {
+        i++;
+        rmm_mode = argv[i];
+      }
+    }
+
+    mr = create_memory_resource(rmm_mode);
+    rmm::mr::set_current_device_resource(mr.get());
+    std::cout << "RMM memory resource = " << rmm_mode << "\n";
+  }
+
+  std::shared_ptr<rmm::mr::device_memory_resource> mr;
+  std::string rmm_mode{"pool"};
+};
+
+}  // namespace cudf
diff --git a/cpp/benchmarks/fixture/nvbench_main.cpp b/cpp/benchmarks/fixture/nvbench_main.cpp
new file mode 100644
index 0000000..64c4d83
--- /dev/null
+++ b/cpp/benchmarks/fixture/nvbench_main.cpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/nvbench_fixture.hpp>
+#define NVBENCH_ENVIRONMENT cudf::nvbench_base_fixture
+
+#include <nvbench/main.cuh>
+
+#include <vector>
+
+// strip off the rmm_mode parameter before passing the
+// remaining arguments to nvbench::option_parser
+#undef NVBENCH_MAIN_PARSE
+#define NVBENCH_MAIN_PARSE(argc, argv)         \
+  nvbench::option_parser parser;               \
+  std::vector<std::string> m_args;             \
+  for (int i = 0; i < argc; ++i) {             \
+    std::string arg = argv[i];                 \
+    if (arg == cudf::detail::rmm_mode_param) { \
+      i += 2;                                  \
+    } else {                                   \
+      m_args.push_back(arg);                   \
+    }                                          \
+  }                                            \
+  parser.parse(m_args)
+
+// this declares/defines the main() function using the definitions above
+NVBENCH_MAIN
diff --git a/cpp/benchmarks/fixture/templated_benchmark_fixture.hpp b/cpp/benchmarks/fixture/templated_benchmark_fixture.hpp
new file mode 100644
index 0000000..57f5286
--- /dev/null
+++ b/cpp/benchmarks/fixture/templated_benchmark_fixture.hpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <benchmark/benchmark.h>
+
+namespace cudf {
+/**
+ * @brief Templated Google Benchmark with fixture
+ *
+ * Extends Google benchmarks to support templated Benchmarks with non-templated fixture class.
+ *
+ * The SetUp and TearDown methods is called before each templated benchmark function is run.
+ * These methods are called automatically by Google Benchmark
+ *
+ * Example:
+ *
+ * @code
+ * template <class T, class U>
+ * void  my_benchmark(::benchmark::State& state) {
+ *     std::vector<T> v1(state.range(0));
+ *     std::vector<U> v2(state.range(0));
+ *     for (auto _ : state) {
+ *       // benchmark stuff
+ *     }
+ * }
+ *
+ * TEMPLATED_BENCHMARK_F(cudf::benchmark, my_benchmark, int, double)->Range(128, 512);
+ * @endcode
+ */
+template <class Fixture>
+class FunctionTemplateBenchmark : public Fixture {
+ public:
+  FunctionTemplateBenchmark(char const* name, ::benchmark::internal::Function* func)
+    : Fixture(), func_(func)
+  {
+    this->SetName(name);
+  }
+
+  virtual void Run(::benchmark::State& st)
+  {
+    this->SetUp(st);
+    this->BenchmarkCase(st);
+    this->TearDown(st);
+  }
+
+ private:
+  ::benchmark::internal::Function* func_;
+
+ protected:
+  virtual void BenchmarkCase(::benchmark::State& st) { func_(st); }
+};
+
+#define TEMPLATED_BENCHMARK_F(BaseClass, n, ...)                                           \
+  BENCHMARK_PRIVATE_DECLARE(n) = (::benchmark::internal::RegisterBenchmarkInternal(        \
+    new cudf::FunctionTemplateBenchmark<BaseClass>(#BaseClass "/" #n "<" #__VA_ARGS__ ">", \
+                                                   n<__VA_ARGS__>)))
+
+}  // namespace cudf
diff --git a/cpp/benchmarks/groupby/group_common.hpp b/cpp/benchmarks/groupby/group_common.hpp
new file mode 100644
index 0000000..fba5bc2
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_common.hpp
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <random>
+
+template <typename T>
+T random_int(T min, T max)
+{
+  static unsigned seed = 13377331;
+  static std::mt19937 engine{seed};
+  static std::uniform_int_distribution<T> uniform{min, max};
+
+  return uniform(engine);
+}
diff --git a/cpp/benchmarks/groupby/group_max.cpp b/cpp/benchmarks/groupby/group_max.cpp
new file mode 100644
index 0000000..e65c37f
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_max.cpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/groupby.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <typename Type>
+void bench_groupby_max(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const size = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+
+  auto const keys = [&] {
+    data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+      cudf::type_to_id<int32_t>(), distribution_id::UNIFORM, 0, 100);
+    return create_random_column(cudf::type_to_id<int32_t>(), row_count{size}, profile);
+  }();
+
+  auto const vals = [&] {
+    auto builder = data_profile_builder().cardinality(0).distribution(
+      cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 1000);
+    if (const auto null_freq = state.get_float64("null_probability"); null_freq > 0) {
+      builder.null_probability(null_freq);
+    } else {
+      builder.no_validity();
+    }
+    return create_random_column(cudf::type_to_id<Type>(), row_count{size}, data_profile{builder});
+  }();
+
+  auto keys_view = keys->view();
+  auto gb_obj    = cudf::groupby::groupby(cudf::table_view({keys_view, keys_view, keys_view}));
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(cudf::make_max_aggregation<cudf::groupby_aggregation>());
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto const result = gb_obj.aggregate(requests); });
+}
+
+NVBENCH_BENCH_TYPES(bench_groupby_max,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, int64_t, float, double>))
+  .set_name("groupby_max")
+  .add_int64_power_of_two_axis("num_rows", {12, 18, 24})
+  .add_float64_axis("null_probability", {0, 0.1, 0.9});
diff --git a/cpp/benchmarks/groupby/group_no_requests.cpp b/cpp/benchmarks/groupby/group_no_requests.cpp
new file mode 100644
index 0000000..34618ac
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_no_requests.cpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/groupby/group_common.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+
+class Groupby : public cudf::benchmark {};
+
+void BM_basic_no_requests(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    cudf::groupby::groupby gb_obj(*keys_table);
+    auto result = gb_obj.aggregate(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, BasicNoRequest)(::benchmark::State& state)
+{
+  BM_basic_no_requests(state);
+}
+
+BENCHMARK_REGISTER_F(Groupby, BasicNoRequest)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(10000)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000);
+
+void BM_pre_sorted_no_requests(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+
+  auto sort_order  = cudf::sorted_order(*keys_table);
+  auto sorted_keys = cudf::gather(*keys_table, *sort_order);
+  // No need to sort values using sort_order because they were generated randomly
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    cudf::groupby::groupby gb_obj(*sorted_keys, cudf::null_policy::EXCLUDE, cudf::sorted::YES);
+    auto result = gb_obj.aggregate(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, PreSortedNoRequests)(::benchmark::State& state)
+{
+  BM_pre_sorted_no_requests(state);
+}
+
+BENCHMARK_REGISTER_F(Groupby, PreSortedNoRequests)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000);
diff --git a/cpp/benchmarks/groupby/group_nth.cpp b/cpp/benchmarks/groupby/group_nth.cpp
new file mode 100644
index 0000000..f2c2443
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_nth.cpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/groupby/group_common.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+
+class Groupby : public cudf::benchmark {};
+
+void BM_pre_sorted_nth(benchmark::State& state)
+{
+  // const cudf::size_type num_columns{(cudf::size_type)state.range(0)};
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+  auto vals = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+
+  auto sort_order  = cudf::sorted_order(*keys_table);
+  auto sorted_keys = cudf::gather(*keys_table, *sort_order);
+  // No need to sort values using sort_order because they were generated randomly
+
+  cudf::groupby::groupby gb_obj(*sorted_keys, cudf::null_policy::EXCLUDE, cudf::sorted::YES);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(
+    cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-1));
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = gb_obj.aggregate(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, PreSortedNth)(::benchmark::State& state) { BM_pre_sorted_nth(state); }
+
+BENCHMARK_REGISTER_F(Groupby, PreSortedNth)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(1000000)    /*   1M */
+  ->Arg(10000000)   /*  10M */
+  ->Arg(100000000); /* 100M */
diff --git a/cpp/benchmarks/groupby/group_nunique.cpp b/cpp/benchmarks/groupby/group_nunique.cpp
new file mode 100644
index 0000000..63d738b
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_nunique.cpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/groupby.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+namespace {
+
+template <typename... Args>
+auto make_aggregation_request_vector(cudf::column_view const& values, Args&&... args)
+{
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  (aggregations.emplace_back(std::forward<Args>(args)), ...);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request{values, std::move(aggregations)});
+
+  return requests;
+}
+
+}  // namespace
+
+template <typename Type>
+void bench_groupby_nunique(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const size = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+
+  auto const keys = [&] {
+    data_profile profile = data_profile_builder().cardinality(0).no_validity().distribution(
+      cudf::type_to_id<int32_t>(), distribution_id::UNIFORM, 0, 100);
+    return create_random_column(cudf::type_to_id<int32_t>(), row_count{size}, profile);
+  }();
+
+  auto const vals = [&] {
+    data_profile profile = data_profile_builder().cardinality(0).distribution(
+      cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 1000);
+    if (const auto null_freq = state.get_float64("null_probability"); null_freq > 0) {
+      profile.set_null_probability(null_freq);
+    } else {
+      profile.set_null_probability(std::nullopt);
+    }
+    return create_random_column(cudf::type_to_id<Type>(), row_count{size}, profile);
+  }();
+
+  auto gb_obj =
+    cudf::groupby::groupby(cudf::table_view({keys->view(), keys->view(), keys->view()}));
+  auto const requests = make_aggregation_request_vector(
+    *vals, cudf::make_nunique_aggregation<cudf::groupby_aggregation>());
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto const result = gb_obj.aggregate(requests); });
+}
+
+NVBENCH_BENCH_TYPES(bench_groupby_nunique, NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, int64_t>))
+  .set_name("groupby_nunique")
+  .add_int64_power_of_two_axis("num_rows", {12, 16, 20, 24})
+  .add_float64_axis("null_probability", {0, 0.5});
diff --git a/cpp/benchmarks/groupby/group_rank.cpp b/cpp/benchmarks/groupby/group_rank.cpp
new file mode 100644
index 0000000..2122720
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_rank.cpp
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <cudf::rank_method method>
+static void nvbench_groupby_rank(nvbench::state& state,
+                                 nvbench::type_list<nvbench::enum_type<method>>)
+{
+  constexpr auto dtype = cudf::type_to_id<int64_t>();
+
+  bool const is_sorted              = state.get_int64("is_sorted");
+  cudf::size_type const column_size = state.get_int64("data_size");
+  constexpr int num_groups          = 100;
+
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    dtype, distribution_id::UNIFORM, 0, num_groups);
+
+  auto source_table = create_random_table({dtype, dtype}, row_count{column_size}, profile);
+
+  // values to be pre-sorted too for groupby rank
+  if (is_sorted) source_table = cudf::sort(*source_table);
+
+  cudf::table_view keys{{source_table->view().column(0)}};
+  cudf::column_view order_by{source_table->view().column(1)};
+
+  auto agg = cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(method);
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = order_by;
+  requests[0].aggregations.push_back(std::move(agg));
+
+  cudf::groupby::groupby gb_obj(
+    keys, cudf::null_policy::EXCLUDE, is_sorted ? cudf::sorted::YES : cudf::sorted::NO);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    rmm::cuda_stream_view stream_view{launch.get_stream()};
+    // groupby scan uses sort implementation
+    auto result = gb_obj.scan(requests);
+  });
+}
+
+enum class rank_method : int32_t {};
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::rank_method,
+  [](cudf::rank_method value) {
+    switch (value) {
+      case cudf::rank_method::FIRST: return "FIRST";
+      case cudf::rank_method::AVERAGE: return "AVERAGE";
+      case cudf::rank_method::MIN: return "MIN";
+      case cudf::rank_method::MAX: return "MAX";
+      case cudf::rank_method::DENSE: return "DENSE";
+      default: return "unknown";
+    }
+  },
+  [](cudf::rank_method value) {
+    switch (value) {
+      case cudf::rank_method::FIRST: return "cudf::rank_method::FIRST";
+      case cudf::rank_method::AVERAGE: return "cudf::rank_method::AVERAGE";
+      case cudf::rank_method::MIN: return "cudf::rank_method::MIN";
+      case cudf::rank_method::MAX: return "cudf::rank_method::MAX";
+      case cudf::rank_method::DENSE: return "cudf::rank_method::DENSE";
+      default: return "unknown";
+    }
+  })
+
+using methods = nvbench::enum_type_list<cudf::rank_method::AVERAGE,
+                                        cudf::rank_method::DENSE,
+                                        cudf::rank_method::FIRST,
+                                        cudf::rank_method::MAX,
+                                        cudf::rank_method::MIN>;
+
+NVBENCH_BENCH_TYPES(nvbench_groupby_rank, NVBENCH_TYPE_AXES(methods))
+  .set_type_axes_names({"rank_method"})
+  .set_name("groupby_rank")
+  .add_int64_axis("data_size",
+                  {
+                    1000000,    // 1M
+                    10000000,   // 10M
+                    100000000,  // 100M
+                  })
+
+  .add_int64_axis("is_sorted", {0, 1});
diff --git a/cpp/benchmarks/groupby/group_scan.cpp b/cpp/benchmarks/groupby/group_scan.cpp
new file mode 100644
index 0000000..2ae5b6f
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_scan.cpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/groupby/group_common.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+
+class Groupby : public cudf::benchmark {};
+
+void BM_basic_sum_scan(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+  auto vals = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+
+  cudf::groupby::groupby gb_obj(cudf::table_view({keys->view(), keys->view(), keys->view()}));
+
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>());
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+
+    auto result = gb_obj.scan(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, BasicSumScan)(::benchmark::State& state) { BM_basic_sum_scan(state); }
+
+BENCHMARK_REGISTER_F(Groupby, BasicSumScan)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000);
+
+void BM_pre_sorted_sum_scan(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+  profile.set_null_probability(0.1);
+  auto vals = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+
+  auto sort_order  = cudf::sorted_order(*keys_table);
+  auto sorted_keys = cudf::gather(*keys_table, *sort_order);
+  // No need to sort values using sort_order because they were generated randomly
+
+  cudf::groupby::groupby gb_obj(*sorted_keys, cudf::null_policy::EXCLUDE, cudf::sorted::YES);
+
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>());
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+
+    auto result = gb_obj.scan(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, PreSortedSumScan)(::benchmark::State& state)
+{
+  BM_pre_sorted_sum_scan(state);
+}
+
+BENCHMARK_REGISTER_F(Groupby, PreSortedSumScan)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000);
diff --git a/cpp/benchmarks/groupby/group_shift.cpp b/cpp/benchmarks/groupby/group_shift.cpp
new file mode 100644
index 0000000..eda2b3d
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_shift.cpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/groupby/group_common.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/groupby.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+class Groupby : public cudf::benchmark {};
+
+void BM_group_shift(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+  int const num_groups = 100;
+
+  data_profile const profile =
+    data_profile_builder().cardinality(0).null_probability(0.01).distribution(
+      cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, num_groups);
+
+  auto keys_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+  auto vals_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+
+  cudf::groupby::groupby gb_obj(*keys_table);
+
+  std::vector<cudf::size_type> offsets{
+    static_cast<cudf::size_type>(column_size / float(num_groups) * 0.5)};  // forward shift half way
+  // null fill value
+  auto fill_value = cudf::make_default_constructed_scalar(cudf::data_type(cudf::type_id::INT64));
+  // non null fill value
+  // auto fill_value = cudf::make_fixed_width_scalar(static_cast<int64_t>(42));
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = gb_obj.shift(*vals_table, offsets, {*fill_value});
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, Shift)(::benchmark::State& state) { BM_group_shift(state); }
+
+BENCHMARK_REGISTER_F(Groupby, Shift)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond);
diff --git a/cpp/benchmarks/groupby/group_struct_keys.cpp b/cpp/benchmarks/groupby/group_struct_keys.cpp
new file mode 100644
index 0000000..44a12c1
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_struct_keys.cpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <random>
+
+void bench_groupby_struct_keys(nvbench::state& state)
+{
+  using Type           = int;
+  using column_wrapper = cudf::test::fixed_width_column_wrapper<Type>;
+  std::default_random_engine generator;
+  std::uniform_int_distribution<int> distribution(0, 100);
+
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.get_int64("NumRows"))};
+  cudf::size_type const n_cols{1};
+  cudf::size_type const depth{static_cast<cudf::size_type>(state.get_int64("Depth"))};
+  bool const nulls{static_cast<bool>(state.get_int64("Nulls"))};
+
+  // Create columns with values in the range [0,100)
+  std::vector<column_wrapper> columns;
+  columns.reserve(n_cols);
+  std::generate_n(std::back_inserter(columns), n_cols, [&]() {
+    auto const elements = cudf::detail::make_counting_transform_iterator(
+      0, [&](auto row) { return distribution(generator); });
+    if (!nulls) return column_wrapper(elements, elements + n_rows);
+    auto valids =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 10 != 0; });
+    return column_wrapper(elements, elements + n_rows, valids);
+  });
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  std::transform(columns.begin(), columns.end(), std::back_inserter(cols), [](column_wrapper& col) {
+    return col.release();
+  });
+
+  std::vector<std::unique_ptr<cudf::column>> child_cols = std::move(cols);
+  // Add some layers
+  for (int i = 0; i < depth; i++) {
+    std::vector<bool> struct_validity;
+    std::uniform_int_distribution<int> bool_distribution(0, 100 * (i + 1));
+    std::generate_n(
+      std::back_inserter(struct_validity), n_rows, [&]() { return bool_distribution(generator); });
+    cudf::test::structs_column_wrapper struct_col(std::move(child_cols), struct_validity);
+    child_cols = std::vector<std::unique_ptr<cudf::column>>{};
+    child_cols.push_back(struct_col.release());
+  }
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+
+  auto const keys_table = cudf::table(std::move(child_cols));
+  auto const vals = create_random_column(cudf::type_to_id<int64_t>(), row_count{n_rows}, profile);
+
+  cudf::groupby::groupby gb_obj(keys_table.view());
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(cudf::make_min_aggregation<cudf::groupby_aggregation>());
+
+  // Set up nvbench default stream
+  auto stream = cudf::get_default_stream();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto const result = gb_obj.aggregate(requests); });
+}
+
+NVBENCH_BENCH(bench_groupby_struct_keys)
+  .set_name("groupby_struct_keys")
+  .add_int64_power_of_two_axis("NumRows", {10, 16, 20})
+  .add_int64_axis("Depth", {0, 1, 8})
+  .add_int64_axis("Nulls", {0, 1});
diff --git a/cpp/benchmarks/groupby/group_struct_values.cpp b/cpp/benchmarks/groupby/group_struct_values.cpp
new file mode 100644
index 0000000..024fd37
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_struct_values.cpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+
+static constexpr cudf::size_type num_struct_members = 8;
+static constexpr cudf::size_type max_int            = 100;
+static constexpr cudf::size_type max_str_length     = 32;
+
+static auto create_data_table(cudf::size_type n_rows)
+{
+  data_profile const table_profile =
+    data_profile_builder()
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, max_int)
+      .distribution(cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+
+  // The first two struct members are int32 and string.
+  // The first column is also used as keys in groupby.
+  // The subsequent struct members are int32 and string again.
+  return create_random_table(
+    cycle_dtypes({cudf::type_id::INT32, cudf::type_id::STRING}, num_struct_members),
+    row_count{n_rows},
+    table_profile);
+}
+
+// Max aggregation/scan technically has the same performance as min.
+template <typename OpType>
+void BM_groupby_min_struct(benchmark::State& state)
+{
+  auto const n_rows = static_cast<cudf::size_type>(state.range(0));
+  auto data_cols    = create_data_table(n_rows)->release();
+
+  auto const keys_view = data_cols.front()->view();
+  auto const values =
+    cudf::make_structs_column(keys_view.size(), std::move(data_cols), 0, rmm::device_buffer());
+
+  using RequestType = std::conditional_t<std::is_same_v<OpType, cudf::groupby_aggregation>,
+                                         cudf::groupby::aggregation_request,
+                                         cudf::groupby::scan_request>;
+
+  auto gb_obj   = cudf::groupby::groupby(cudf::table_view({keys_view}));
+  auto requests = std::vector<RequestType>();
+  requests.emplace_back(RequestType());
+  requests.front().values = values->view();
+  requests.front().aggregations.push_back(cudf::make_min_aggregation<OpType>());
+
+  for (auto _ : state) {
+    [[maybe_unused]] auto const timer = cuda_event_timer(state, true);
+    if constexpr (std::is_same_v<OpType, cudf::groupby_aggregation>) {
+      [[maybe_unused]] auto const result = gb_obj.aggregate(requests);
+    } else {
+      [[maybe_unused]] auto const result = gb_obj.scan(requests);
+    }
+  }
+}
+
+class Groupby : public cudf::benchmark {};
+
+#define MIN_RANGE 10'000
+#define MAX_RANGE 10'000'000
+
+#define REGISTER_BENCHMARK(name, op_type)                       \
+  BENCHMARK_DEFINE_F(Groupby, name)(::benchmark::State & state) \
+  {                                                             \
+    BM_groupby_min_struct<op_type>(state);                      \
+  }                                                             \
+  BENCHMARK_REGISTER_F(Groupby, name)                           \
+    ->UseManualTime()                                           \
+    ->Unit(benchmark::kMillisecond)                             \
+    ->RangeMultiplier(4)                                        \
+    ->Ranges({{MIN_RANGE, MAX_RANGE}});
+
+REGISTER_BENCHMARK(Aggregation, cudf::groupby_aggregation)
+REGISTER_BENCHMARK(Scan, cudf::groupby_scan_aggregation)
diff --git a/cpp/benchmarks/groupby/group_sum.cpp b/cpp/benchmarks/groupby/group_sum.cpp
new file mode 100644
index 0000000..b3fd881
--- /dev/null
+++ b/cpp/benchmarks/groupby/group_sum.cpp
@@ -0,0 +1,98 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/groupby/group_common.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+
+class Groupby : public cudf::benchmark {};
+
+void BM_basic_sum(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+  auto vals = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+
+  cudf::groupby::groupby gb_obj(cudf::table_view({keys->view(), keys->view(), keys->view()}));
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+
+    auto result = gb_obj.aggregate(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, Basic)(::benchmark::State& state) { BM_basic_sum(state); }
+
+BENCHMARK_REGISTER_F(Groupby, Basic)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(10000)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000);
+
+void BM_pre_sorted_sum(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+
+  data_profile profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<int64_t>(), distribution_id::UNIFORM, 0, 100);
+  auto keys_table =
+    create_random_table({cudf::type_to_id<int64_t>()}, row_count{column_size}, profile);
+  profile.set_null_probability(0.1);
+  auto vals = create_random_column(cudf::type_to_id<int64_t>(), row_count{column_size}, profile);
+
+  auto sort_order  = cudf::sorted_order(*keys_table);
+  auto sorted_keys = cudf::gather(*keys_table, *sort_order);
+  // No need to sort values using sort_order because they were generated randomly
+
+  cudf::groupby::groupby gb_obj(*sorted_keys, cudf::null_policy::EXCLUDE, cudf::sorted::YES);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals->view();
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+
+    auto result = gb_obj.aggregate(requests);
+  }
+}
+
+BENCHMARK_DEFINE_F(Groupby, PreSorted)(::benchmark::State& state) { BM_pre_sorted_sum(state); }
+
+BENCHMARK_REGISTER_F(Groupby, PreSorted)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(1000000)
+  ->Arg(10000000)
+  ->Arg(100000000);
diff --git a/cpp/benchmarks/hashing/hash.cpp b/cpp/benchmarks/hashing/hash.cpp
new file mode 100644
index 0000000..e679b4b
--- /dev/null
+++ b/cpp/benchmarks/hashing/hash.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/hashing.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <optional>
+
+static void bench_hash(nvbench::state& state)
+{
+  auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const nulls    = state.get_float64("nulls");
+  // disable null bitmask if probability is exactly 0.0
+  bool const no_nulls  = nulls == 0.0;
+  auto const hash_name = state.get_string("hash_name");
+
+  data_profile const profile =
+    data_profile_builder().null_probability(no_nulls ? std::nullopt : std::optional<double>{nulls});
+  auto const data = create_random_table(
+    {cudf::type_id::INT64, cudf::type_id::STRING}, row_count{num_rows}, profile);
+
+  auto stream = cudf::get_default_stream();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+
+  // collect statistics
+  cudf::strings_column_view input(data->get_column(1).view());
+  auto const chars_size = input.chars_size();
+  // add memory read from string column
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  // add memory read from int64_t column
+  state.add_global_memory_reads<nvbench::int64_t>(num_rows);
+  // add memory read from bitmaks
+  if (!no_nulls) {
+    state.add_global_memory_reads<nvbench::int8_t>(2 *
+                                                   cudf::bitmask_allocation_size_bytes(num_rows));
+  }
+  // memory written depends on used hash
+
+  if (hash_name == "murmurhash3_x86_32") {
+    state.add_global_memory_writes<nvbench::uint32_t>(num_rows);
+
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = cudf::hashing::murmurhash3_x86_32(data->view());
+    });
+  } else if (hash_name == "md5") {
+    // md5 creates a 32-byte string
+    state.add_global_memory_writes<nvbench::int8_t>(32 * num_rows);
+
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { auto result = cudf::hashing::md5(data->view()); });
+  } else if (hash_name == "spark_murmurhash3_x86_32") {
+    state.add_global_memory_writes<nvbench::int32_t>(num_rows);
+
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = cudf::hashing::spark_murmurhash3_x86_32(data->view());
+    });
+  } else {
+    state.skip(hash_name + ": unknown hash name");
+  }
+}
+
+NVBENCH_BENCH(bench_hash)
+  .set_name("hashing")
+  .add_int64_axis("num_rows", {65536, 16777216})
+  .add_float64_axis("nulls", {0.0, 0.1})
+  .add_string_axis("hash_name", {"murmurhash3_x86_32", "md5", "spark_murmurhash3_x86_32"});
diff --git a/cpp/benchmarks/hashing/partition.cpp b/cpp/benchmarks/hashing/partition.cpp
new file mode 100644
index 0000000..0bec439
--- /dev/null
+++ b/cpp/benchmarks/hashing/partition.cpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/partitioning.hpp>
+
+#include <algorithm>
+
+class Hashing : public cudf::benchmark {};
+
+template <class T>
+void BM_hash_partition(benchmark::State& state)
+{
+  auto const num_rows       = state.range(0);
+  auto const num_cols       = state.range(1);
+  auto const num_partitions = state.range(2);
+
+  // Create owning columns
+  auto input_table = create_sequence_table(cycle_dtypes({cudf::type_to_id<T>()}, num_cols),
+                                           row_count{static_cast<cudf::size_type>(num_rows)});
+  auto input       = cudf::table_view(*input_table);
+
+  auto columns_to_hash = std::vector<cudf::size_type>(num_cols);
+  std::iota(columns_to_hash.begin(), columns_to_hash.end(), 0);
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto output = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  }
+
+  auto const bytes_read      = num_rows * num_cols * sizeof(T);
+  auto const bytes_written   = num_rows * num_cols * sizeof(T);
+  auto const partition_bytes = num_partitions * sizeof(cudf::size_type);
+
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) *
+                          (bytes_read + bytes_written + partition_bytes));
+}
+
+BENCHMARK_DEFINE_F(Hashing, hash_partition)
+(::benchmark::State& state) { BM_hash_partition<double>(state); }
+
+static void CustomRanges(benchmark::internal::Benchmark* b)
+{
+  for (int columns = 1; columns <= 256; columns *= 16) {
+    for (int partitions = 64; partitions <= 1024; partitions *= 2) {
+      for (int rows = 1 << 17; rows <= 1 << 21; rows *= 2) {
+        b->Args({rows, columns, partitions});
+      }
+    }
+  }
+}
+
+BENCHMARK_REGISTER_F(Hashing, hash_partition)
+  ->Apply(CustomRanges)
+  ->Unit(benchmark::kMillisecond)
+  ->UseManualTime();
diff --git a/cpp/benchmarks/io/csv/csv_reader_input.cpp b/cpp/benchmarks/io/csv/csv_reader_input.cpp
new file mode 100644
index 0000000..6216a9e
--- /dev/null
+++ b/cpp/benchmarks/io/csv/csv_reader_input.cpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/csv.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+constexpr size_t data_size         = 256 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+template <typename DataType>
+void csv_read_common(DataType const& data_types,
+                     cudf::io::io_type const& source_type,
+                     nvbench::state& state)
+{
+  auto const tbl =
+    create_random_table(cycle_dtypes(data_types, num_cols), table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(source_type);
+  cudf::io::csv_writer_options options =
+    cudf::io::csv_writer_options::builder(source_sink.make_sink_info(), view).include_header(true);
+
+  cudf::io::write_csv(options);
+
+  cudf::io::csv_reader_options const read_options =
+    cudf::io::csv_reader_options::builder(source_sink.make_source_info());
+
+  auto const mem_stats_logger = cudf::memory_stats_logger();  // init stats logger
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();  // Drop L3 cache for accurate measurement
+
+               timer.start();
+               cudf::io::read_csv(read_options);
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+template <data_type DataType, cudf::io::io_type IOType>
+void BM_csv_read_input(nvbench::state& state,
+                       nvbench::type_list<nvbench::enum_type<DataType>, nvbench::enum_type<IOType>>)
+{
+  auto const d_type      = get_type_or_group(static_cast<int32_t>(DataType));
+  auto const source_type = IOType;
+
+  csv_read_common(d_type, source_type, state);
+}
+
+template <cudf::io::io_type IOType>
+void BM_csv_read_io(nvbench::state& state, nvbench::type_list<nvbench::enum_type<IOType>>)
+{
+  auto const d_type      = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                              static_cast<int32_t>(data_type::FLOAT),
+                                              static_cast<int32_t>(data_type::DECIMAL),
+                                              static_cast<int32_t>(data_type::TIMESTAMP),
+                                              static_cast<int32_t>(data_type::DURATION),
+                                              static_cast<int32_t>(data_type::STRING)});
+  auto const source_type = IOType;
+
+  csv_read_common(d_type, source_type, state);
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::DURATION,
+                                            data_type::STRING>;
+
+using io_list =
+  nvbench::enum_type_list<cudf::io::io_type::FILEPATH, cudf::io::io_type::HOST_BUFFER>;
+
+NVBENCH_BENCH_TYPES(BM_csv_read_input,
+                    NVBENCH_TYPE_AXES(d_type_list,
+                                      nvbench::enum_type_list<cudf::io::io_type::DEVICE_BUFFER>))
+  .set_name("csv_read_data_type")
+  .set_type_axes_names({"data_type", "io"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH_TYPES(BM_csv_read_io, NVBENCH_TYPE_AXES(io_list))
+  .set_name("csv_read_io")
+  .set_type_axes_names({"io"})
+  .set_min_samples(4);
diff --git a/cpp/benchmarks/io/csv/csv_reader_options.cpp b/cpp/benchmarks/io/csv/csv_reader_options.cpp
new file mode 100644
index 0000000..93ef5be
--- /dev/null
+++ b/cpp/benchmarks/io/csv/csv_reader_options.cpp
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/csv.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+constexpr size_t data_size = 256 << 20;
+
+template <column_selection ColSelection, row_selection RowSelection>
+void BM_csv_read_varying_options(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<ColSelection>, nvbench::enum_type<RowSelection>>)
+{
+  auto const data_types =
+    dtypes_for_column_selection(get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                                   static_cast<int32_t>(data_type::FLOAT),
+                                                   static_cast<int32_t>(data_type::DECIMAL),
+                                                   static_cast<int32_t>(data_type::TIMESTAMP),
+                                                   static_cast<int32_t>(data_type::DURATION),
+                                                   static_cast<int32_t>(data_type::STRING)}),
+                                ColSelection);
+  auto const cols_to_read = select_column_indexes(data_types.size(), ColSelection);
+  auto const num_chunks   = state.get_int64("num_chunks");
+
+  auto const tbl  = create_random_table(data_types, table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(io_type::HOST_BUFFER);
+  cudf::io::csv_writer_options options =
+    cudf::io::csv_writer_options::builder(source_sink.make_sink_info(), view)
+      .include_header(true)
+      .line_terminator("\r\n");
+  cudf::io::write_csv(options);
+
+  cudf::io::csv_reader_options read_options =
+    cudf::io::csv_reader_options::builder(source_sink.make_source_info())
+      .use_cols_indexes(cols_to_read)
+      .thousands('\'')
+      .windowslinetermination(true)
+      .comment('#')
+      .prefix("BM_");
+
+  size_t const chunk_size             = source_sink.size() / num_chunks;
+  cudf::size_type const chunk_row_cnt = view.num_rows() / num_chunks;
+  auto const mem_stats_logger         = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();  // Drop L3 cache for accurate measurement
+
+               timer.start();
+               for (int32_t chunk = 0; chunk < num_chunks; ++chunk) {
+                 // only read the header in the first chunk
+                 read_options.set_header(chunk == 0 ? 0 : -1);
+
+                 auto const is_last_chunk = chunk == (num_chunks - 1);
+                 switch (RowSelection) {
+                   case row_selection::ALL: break;
+                   case row_selection::BYTE_RANGE:
+                     read_options.set_byte_range_offset(chunk * chunk_size);
+                     read_options.set_byte_range_size(chunk_size);
+                     if (is_last_chunk) read_options.set_byte_range_size(0);
+                     break;
+                   case row_selection::NROWS:
+                     read_options.set_skiprows(chunk * chunk_row_cnt);
+                     read_options.set_nrows(chunk_row_cnt);
+                     if (is_last_chunk) read_options.set_nrows(-1);
+                     break;
+                   case row_selection::SKIPFOOTER:
+                     read_options.set_skiprows(chunk * chunk_row_cnt);
+                     read_options.set_skipfooter(view.num_rows() - (chunk + 1) * chunk_row_cnt);
+                     if (is_last_chunk) read_options.set_skipfooter(0);
+                     break;
+                   default: CUDF_FAIL("Unsupported row selection method");
+                 }
+
+                 cudf::io::read_csv(read_options);
+               }
+               timer.stop();
+             });
+
+  auto const elapsed_time   = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  auto const data_processed = data_size * cols_to_read.size() / view.num_columns();
+  state.add_element_count(static_cast<double>(data_processed) / elapsed_time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+using col_selections = nvbench::enum_type_list<column_selection::ALL,
+                                               column_selection::ALTERNATE,
+                                               column_selection::FIRST_HALF,
+                                               column_selection::SECOND_HALF>;
+
+using row_selections = nvbench::
+  enum_type_list<row_selection::BYTE_RANGE, row_selection::NROWS, row_selection::SKIPFOOTER>;
+
+NVBENCH_BENCH_TYPES(BM_csv_read_varying_options,
+                    NVBENCH_TYPE_AXES(col_selections, nvbench::enum_type_list<row_selection::ALL>))
+  .set_name("csv_read_column_selection")
+  .set_type_axes_names({"column_selection", "row_selection"})
+  .set_min_samples(4)
+  .add_int64_axis("num_chunks", {1});
+
+NVBENCH_BENCH_TYPES(BM_csv_read_varying_options,
+                    NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
+                                      row_selections))
+  .set_name("csv_read_row_selection")
+  .set_type_axes_names({"column_selection", "row_selection"})
+  .set_min_samples(4)
+  .add_int64_axis("num_chunks", {1, 8});
diff --git a/cpp/benchmarks/io/csv/csv_writer.cpp b/cpp/benchmarks/io/csv/csv_writer.cpp
new file mode 100644
index 0000000..8ff07be
--- /dev/null
+++ b/cpp/benchmarks/io/csv/csv_writer.cpp
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/csv.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr size_t data_size         = 256 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+template <data_type DataType, cudf::io::io_type IO>
+void BM_csv_write_dtype_io(nvbench::state& state,
+                           nvbench::type_list<nvbench::enum_type<DataType>, nvbench::enum_type<IO>>)
+{
+  auto const data_types = get_type_or_group(static_cast<int32_t>(DataType));
+  auto const sink_type  = IO;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(data_types, num_cols), table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto const mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(sink_type);
+
+               timer.start();
+               cudf::io::csv_writer_options options =
+                 cudf::io::csv_writer_options::builder(source_sink.make_sink_info(), view);
+               cudf::io::write_csv(options);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+void BM_csv_write_varying_options(nvbench::state& state)
+{
+  auto const na_per_len     = state.get_int64("na_per_len");
+  auto const rows_per_chunk = state.get_int64("rows_per_chunk");
+
+  auto const data_types = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                             static_cast<int32_t>(data_type::FLOAT),
+                                             static_cast<int32_t>(data_type::DECIMAL),
+                                             static_cast<int32_t>(data_type::TIMESTAMP),
+                                             static_cast<int32_t>(data_type::DURATION),
+                                             static_cast<int32_t>(data_type::STRING)});
+
+  auto const tbl  = create_random_table(data_types, table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  std::string const na_per(na_per_len, '#');
+  std::size_t encoded_file_size = 0;
+
+  auto const mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(io_type::HOST_BUFFER);
+
+               timer.start();
+               cudf::io::csv_writer_options options =
+                 cudf::io::csv_writer_options::builder(source_sink.make_sink_info(), view)
+                   .na_rep(na_per)
+                   .rows_per_chunk(rows_per_chunk);
+               cudf::io::write_csv(options);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::DURATION,
+                                            data_type::STRING>;
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::VOID>;
+
+NVBENCH_BENCH_TYPES(BM_csv_write_dtype_io, NVBENCH_TYPE_AXES(d_type_list, io_list))
+  .set_name("csv_write_dtype_io")
+  .set_type_axes_names({"data_type", "io"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH(BM_csv_write_varying_options)
+  .set_name("csv_write_options")
+  .set_min_samples(4)
+  .add_int64_axis("na_per_len", {0, 16})
+  .add_int64_power_of_two_axis("rows_per_chunk", nvbench::range(8, 20, 2));
diff --git a/cpp/benchmarks/io/cuio_common.cpp b/cpp/benchmarks/io/cuio_common.cpp
new file mode 100644
index 0000000..b1aaef4
--- /dev/null
+++ b/cpp/benchmarks/io/cuio_common.cpp
@@ -0,0 +1,203 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/io/cuio_common.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/logger.hpp>
+
+#include <cstdio>
+#include <fstream>
+#include <numeric>
+#include <string>
+
+#include <unistd.h>
+
+temp_directory const cuio_source_sink_pair::tmpdir{"cudf_gbench"};
+
+std::string random_file_in_dir(std::string const& dir_path)
+{
+  // `mkstemp` modifies the template in place
+  std::string filename = dir_path + "io.XXXXXX";
+
+  // `mkstemp` opens the file; closing immediately, only need the name
+  close(mkstemp(const_cast<char*>(filename.data())));
+
+  return filename;
+}
+
+cuio_source_sink_pair::cuio_source_sink_pair(io_type type)
+  : type{type},
+    d_buffer{0, cudf::get_default_stream()},
+    file_name{random_file_in_dir(tmpdir.path())}
+{
+}
+
+cudf::io::source_info cuio_source_sink_pair::make_source_info()
+{
+  switch (type) {
+    case io_type::FILEPATH: return cudf::io::source_info(file_name);
+    case io_type::HOST_BUFFER: return cudf::io::source_info(h_buffer.data(), h_buffer.size());
+    case io_type::DEVICE_BUFFER: {
+      // TODO: make cuio_source_sink_pair stream-friendly and avoid implicit use of the default
+      // stream
+      auto const stream = cudf::get_default_stream();
+      d_buffer.resize(h_buffer.size(), stream);
+      CUDF_CUDA_TRY(cudaMemcpyAsync(
+        d_buffer.data(), h_buffer.data(), h_buffer.size(), cudaMemcpyDefault, stream.value()));
+
+      return cudf::io::source_info(d_buffer);
+    }
+    default: CUDF_FAIL("invalid input type");
+  }
+}
+
+cudf::io::sink_info cuio_source_sink_pair::make_sink_info()
+{
+  switch (type) {
+    case io_type::VOID: return cudf::io::sink_info(&void_sink);
+    case io_type::FILEPATH: return cudf::io::sink_info(file_name);
+    case io_type::HOST_BUFFER: [[fallthrough]];
+    case io_type::DEVICE_BUFFER: return cudf::io::sink_info(&h_buffer);
+    default: CUDF_FAIL("invalid output type");
+  }
+}
+
+size_t cuio_source_sink_pair::size()
+{
+  switch (type) {
+    case io_type::VOID: return void_sink.bytes_written();
+    case io_type::FILEPATH:
+      return static_cast<size_t>(
+        std::ifstream(file_name, std::ifstream::ate | std::ifstream::binary).tellg());
+    case io_type::HOST_BUFFER: [[fallthrough]];
+    case io_type::DEVICE_BUFFER: return h_buffer.size();
+    default: CUDF_FAIL("invalid output type");
+  }
+}
+
+std::vector<cudf::type_id> dtypes_for_column_selection(std::vector<cudf::type_id> const& data_types,
+                                                       column_selection col_sel)
+{
+  std::vector<cudf::type_id> out_dtypes;
+  out_dtypes.reserve(2 * data_types.size());
+  switch (col_sel) {
+    case column_selection::ALL:
+    case column_selection::FIRST_HALF:
+    case column_selection::SECOND_HALF:
+      std::copy(data_types.begin(), data_types.end(), std::back_inserter(out_dtypes));
+      std::copy(data_types.begin(), data_types.end(), std::back_inserter(out_dtypes));
+      break;
+    case column_selection::ALTERNATE:
+      for (auto const& type : data_types) {
+        out_dtypes.push_back(type);
+        out_dtypes.push_back(type);
+      }
+      break;
+  }
+  return out_dtypes;
+}
+
+std::vector<int> select_column_indexes(int num_cols, column_selection col_sel)
+{
+  std::vector<int> col_idxs(num_cols / 2);
+  switch (col_sel) {
+    case column_selection::ALL: col_idxs.resize(num_cols);
+    case column_selection::FIRST_HALF:
+    case column_selection::SECOND_HALF:
+      std::iota(std::begin(col_idxs),
+                std::end(col_idxs),
+                (col_sel == column_selection::SECOND_HALF) ? num_cols / 2 : 0);
+      break;
+    case column_selection::ALTERNATE:
+      for (size_t i = 0; i < col_idxs.size(); ++i)
+        col_idxs[i] = 2 * i;
+      break;
+  }
+  return col_idxs;
+}
+
+std::vector<std::string> select_column_names(std::vector<std::string> const& col_names,
+                                             column_selection col_sel)
+{
+  auto const col_idxs_to_read = select_column_indexes(col_names.size(), col_sel);
+
+  std::vector<std::string> col_names_to_read;
+  std::transform(col_idxs_to_read.begin(),
+                 col_idxs_to_read.end(),
+                 std::back_inserter(col_names_to_read),
+                 [&](auto& idx) { return col_names[idx]; });
+  return col_names_to_read;
+}
+
+std::vector<cudf::size_type> segments_in_chunk(int num_segments, int num_chunks, int chunk_idx)
+{
+  CUDF_EXPECTS(num_segments >= num_chunks,
+               "Number of chunks cannot be greater than the number of segments in the file");
+  CUDF_EXPECTS(chunk_idx < num_chunks,
+               "Chunk index must be smaller than the number of chunks in the file");
+
+  auto const segments_in_chunk = cudf::util::div_rounding_up_unsafe(num_segments, num_chunks);
+  auto const begin_segment     = std::min(chunk_idx * segments_in_chunk, num_segments);
+  auto const end_segment       = std::min(begin_segment + segments_in_chunk, num_segments);
+  std::vector<cudf::size_type> selected_segments(end_segment - begin_segment);
+  std::iota(selected_segments.begin(), selected_segments.end(), begin_segment);
+
+  return selected_segments;
+}
+
+// Executes the command and returns stderr output
+std::string exec_cmd(std::string_view cmd)
+{
+  // Prevent the output from the command from mixing with the original process' output
+  std::fflush(nullptr);
+  // Switch stderr and stdout to only capture stderr
+  auto const redirected_cmd = std::string{"( "}.append(cmd).append(" 3>&2 2>&1 1>&3) 2>/dev/null");
+  std::unique_ptr<FILE, decltype(&pclose)> pipe(popen(redirected_cmd.c_str(), "r"), pclose);
+  CUDF_EXPECTS(pipe != nullptr, "popen() failed");
+
+  std::array<char, 128> buffer;
+  std::string error_out;
+  while (fgets(buffer.data(), buffer.size(), pipe.get()) != nullptr) {
+    error_out += buffer.data();
+  }
+  return error_out;
+}
+
+void log_l3_warning_once()
+{
+  static bool is_logged = false;
+  if (not is_logged) {
+    CUDF_LOG_WARN(
+      "Running benchmarks without dropping the L3 cache; results may not reflect file IO "
+      "throughput");
+    is_logged = true;
+  }
+}
+
+void try_drop_l3_cache()
+{
+  static bool is_drop_cache_enabled = std::getenv("CUDF_BENCHMARK_DROP_CACHE") != nullptr;
+  if (not is_drop_cache_enabled) {
+    log_l3_warning_once();
+    return;
+  }
+
+  std::array drop_cache_cmds{"/sbin/sysctl vm.drop_caches=3", "sudo /sbin/sysctl vm.drop_caches=3"};
+  CUDF_EXPECTS(std::any_of(drop_cache_cmds.cbegin(),
+                           drop_cache_cmds.cend(),
+                           [](auto& cmd) { return exec_cmd(cmd).empty(); }),
+               "Failed to execute the drop cache command");
+}
diff --git a/cpp/benchmarks/io/cuio_common.hpp b/cpp/benchmarks/io/cuio_common.hpp
new file mode 100644
index 0000000..34adae3
--- /dev/null
+++ b/cpp/benchmarks/io/cuio_common.hpp
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/file_utilities.hpp>
+
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/types.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+using cudf::io::io_type;
+
+std::string random_file_in_dir(std::string const& dir_path);
+
+/**
+ * @brief Class to create a coupled `source_info` and `sink_info` of given type.
+ */
+class cuio_source_sink_pair {
+  class bytes_written_only_sink : public cudf::io::data_sink {
+    size_t _bytes_written = 0;
+
+   public:
+    void host_write(void const* data, size_t size) override { _bytes_written += size; }
+    void flush() override {}
+    size_t bytes_written() override { return _bytes_written; }
+  };
+
+ public:
+  cuio_source_sink_pair(io_type type);
+  ~cuio_source_sink_pair()
+  {
+    // delete the temporary file
+    std::remove(file_name.c_str());
+  }
+  /**
+   * @brief Created a source info of the set type
+   *
+   * The `datasource` created using the returned `source_info` will read data from the same location
+   * that the result of a @ref `make_sink_info` call writes to.
+   *
+   * @return The description of the data source
+   */
+  cudf::io::source_info make_source_info();
+
+  /**
+   * @brief Created a sink info of the set type
+   *
+   * The `data_sink` created using the returned `sink_info` will write data to the same location
+   * that the result of a @ref `make_source_info` call reads from.
+   *
+   * `io_type::DEVICE_BUFFER` source/sink is an exception where a host buffer sink will be created.
+   *
+   * @return The description of the data sink
+   */
+  cudf::io::sink_info make_sink_info();
+
+  [[nodiscard]] size_t size();
+
+ private:
+  static temp_directory const tmpdir;
+
+  io_type const type;
+  std::vector<char> h_buffer;
+  rmm::device_uvector<std::byte> d_buffer;
+  std::string const file_name;
+  bytes_written_only_sink void_sink;
+};
+
+/**
+ * @brief Column selection strategy.
+ */
+enum class column_selection { ALL, ALTERNATE, FIRST_HALF, SECOND_HALF };
+
+/**
+ * @brief Row selection strategy.
+ *
+ * Not all strategies are applicable to all readers.
+ */
+enum class row_selection { ALL, BYTE_RANGE, NROWS, SKIPFOOTER, STRIPES, ROW_GROUPS };
+
+/**
+ * @brief Modify data types such that total selected columns size is a fix fraction of the total
+ * size.
+ *
+ * The data types are multiplied/rearranged such that the columns selected with the given column
+ * selection enumerator add up to a fixed fraction of the total table size, regardless of the data
+ * types.
+ *
+ * @param ids Array of column type IDs
+ * @param cs The column selection enumerator
+ *
+ * @return The duplicated/rearranged array of type IDs
+ */
+std::vector<cudf::type_id> dtypes_for_column_selection(std::vector<cudf::type_id> const& ids,
+                                                       column_selection col_sel);
+
+/**
+ * @brief Selects a subset of columns based on the input enumerator.
+ */
+std::vector<int> select_column_indexes(int num_cols, column_selection col_sel);
+
+/**
+ * @brief Selects a subset of columns from the array of names, based on the input enumerator.
+ */
+std::vector<std::string> select_column_names(std::vector<std::string> const& col_names,
+                                             column_selection col_sel);
+
+/**
+ * @brief Returns file segments that belong to the given chunk if the file is split into a given
+ * number of chunks.
+ *
+ * The segments could be Parquet row groups or ORC stripes.
+ */
+std::vector<cudf::size_type> segments_in_chunk(int num_segments, int num_chunks, int chunk);
+
+/**
+ * @brief Drops L3 cache if `CUDF_BENCHMARK_DROP_CACHE` environment variable is set.
+ *
+ * Has no effect if the environment variable is not set.
+ * May require sudo access ro run successfully.
+ *
+ * @throw cudf::logic_error if the environment variable is set and the command fails
+ */
+void try_drop_l3_cache();
diff --git a/cpp/benchmarks/io/fst.cu b/cpp/benchmarks/io/fst.cu
new file mode 100644
index 0000000..c0c8851
--- /dev/null
+++ b/cpp/benchmarks/io/fst.cu
@@ -0,0 +1,243 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <io/fst/lookup_tables.cuh>
+#include <io/utilities/hostdevice_vector.hpp>  //TODO find better replacement
+
+#include <tests/io/fst/common.hpp>
+
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/discard_iterator.h>
+
+#include <nvbench/nvbench.cuh>
+
+#include <cstdlib>
+
+namespace {
+auto make_test_json_data(nvbench::state& state)
+{
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+
+  // Test input
+  std::string input = R"(  {)"
+                      R"("category": "reference",)"
+                      R"("index:" [4,12,42],)"
+                      R"("author": "Nigel Rees",)"
+                      R"("title": "Sayings of the Century",)"
+                      R"("price": 8.95)"
+                      R"(}  )"
+                      R"({)"
+                      R"("category": "reference",)"
+                      R"("index:" [4,{},null,{"a":[]}],)"
+                      R"("author": "Nigel Rees",)"
+                      R"("title": "Sayings of the Century",)"
+                      R"("price": 8.95)"
+                      R"(}  {} [] [ ])";
+
+  auto d_input_scalar                = cudf::make_string_scalar(input);
+  auto& d_string_scalar              = static_cast<cudf::string_scalar&>(*d_input_scalar);
+  cudf::size_type const repeat_times = string_size / input.size();
+  return cudf::strings::repeat_string(d_string_scalar, repeat_times);
+}
+
+// Type used to represent the atomic symbol type used within the finite-state machine
+using SymbolT = char;
+// Type sufficiently large to index symbols within the input and output (may be unsigned)
+using SymbolOffsetT                       = uint32_t;
+constexpr std::size_t single_item         = 1;
+constexpr auto max_translation_table_size = TT_NUM_STATES * NUM_SYMBOL_GROUPS;
+
+}  // namespace
+
+void BM_FST_JSON(nvbench::state& state)
+{
+  CUDF_EXPECTS(state.get_int64("string_size") <= std::numeric_limits<cudf::size_type>::max(),
+               "Benchmarks only support up to size_type's maximum number of items");
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+  // Prepare cuda stream for data transfers & kernels
+  rmm::cuda_stream stream{};
+  rmm::cuda_stream_view stream_view(stream);
+
+  auto input_string = make_test_json_data(state);
+  auto& d_input     = static_cast<cudf::scalar_type_t<std::string>&>(*input_string);
+
+  state.add_element_count(d_input.size());
+
+  // Prepare input & output buffers
+  cudf::detail::hostdevice_vector<SymbolT> output_gpu(d_input.size(), stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> output_gpu_size(single_item, stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> out_indexes_gpu(d_input.size(), stream_view);
+
+  // Run algorithm
+  auto parser = cudf::io::fst::detail::make_fst(
+    cudf::io::fst::detail::make_symbol_group_lut(pda_sgs),
+    cudf::io::fst::detail::make_transition_table(pda_state_tt),
+    cudf::io::fst::detail::make_translation_table<max_translation_table_size>(pda_out_tt),
+    stream);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    // Allocate device-side temporary storage & run algorithm
+    parser.Transduce(d_input.data(),
+                     static_cast<SymbolOffsetT>(d_input.size()),
+                     output_gpu.device_ptr(),
+                     out_indexes_gpu.device_ptr(),
+                     output_gpu_size.device_ptr(),
+                     start_state,
+                     stream.value());
+  });
+}
+
+void BM_FST_JSON_no_outidx(nvbench::state& state)
+{
+  CUDF_EXPECTS(state.get_int64("string_size") <= std::numeric_limits<cudf::size_type>::max(),
+               "Benchmarks only support up to size_type's maximum number of items");
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+  // Prepare cuda stream for data transfers & kernels
+  rmm::cuda_stream stream{};
+  rmm::cuda_stream_view stream_view(stream);
+
+  auto input_string = make_test_json_data(state);
+  auto& d_input     = static_cast<cudf::scalar_type_t<std::string>&>(*input_string);
+
+  state.add_element_count(d_input.size());
+
+  // Prepare input & output buffers
+  cudf::detail::hostdevice_vector<SymbolT> output_gpu(d_input.size(), stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> output_gpu_size(single_item, stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> out_indexes_gpu(d_input.size(), stream_view);
+
+  // Run algorithm
+  auto parser = cudf::io::fst::detail::make_fst(
+    cudf::io::fst::detail::make_symbol_group_lut(pda_sgs),
+    cudf::io::fst::detail::make_transition_table(pda_state_tt),
+    cudf::io::fst::detail::make_translation_table<max_translation_table_size>(pda_out_tt),
+    stream);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    // Allocate device-side temporary storage & run algorithm
+    parser.Transduce(d_input.data(),
+                     static_cast<SymbolOffsetT>(d_input.size()),
+                     output_gpu.device_ptr(),
+                     thrust::make_discard_iterator(),
+                     output_gpu_size.device_ptr(),
+                     start_state,
+                     stream.value());
+  });
+}
+
+void BM_FST_JSON_no_out(nvbench::state& state)
+{
+  CUDF_EXPECTS(state.get_int64("string_size") <= std::numeric_limits<cudf::size_type>::max(),
+               "Benchmarks only support up to size_type's maximum number of items");
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+  // Prepare cuda stream for data transfers & kernels
+  rmm::cuda_stream stream{};
+  rmm::cuda_stream_view stream_view(stream);
+
+  auto input_string = make_test_json_data(state);
+  auto& d_input     = static_cast<cudf::scalar_type_t<std::string>&>(*input_string);
+
+  state.add_element_count(d_input.size());
+
+  // Prepare input & output buffers
+  cudf::detail::hostdevice_vector<SymbolOffsetT> output_gpu_size(single_item, stream_view);
+
+  // Run algorithm
+  auto parser = cudf::io::fst::detail::make_fst(
+    cudf::io::fst::detail::make_symbol_group_lut(pda_sgs),
+    cudf::io::fst::detail::make_transition_table(pda_state_tt),
+    cudf::io::fst::detail::make_translation_table<max_translation_table_size>(pda_out_tt),
+    stream);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    // Allocate device-side temporary storage & run algorithm
+    parser.Transduce(d_input.data(),
+                     static_cast<SymbolOffsetT>(d_input.size()),
+                     thrust::make_discard_iterator(),
+                     thrust::make_discard_iterator(),
+                     output_gpu_size.device_ptr(),
+                     start_state,
+                     stream.value());
+  });
+}
+
+void BM_FST_JSON_no_str(nvbench::state& state)
+{
+  CUDF_EXPECTS(state.get_int64("string_size") <= std::numeric_limits<cudf::size_type>::max(),
+               "Benchmarks only support up to size_type's maximum number of items");
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+  // Prepare cuda stream for data transfers & kernels
+  rmm::cuda_stream stream{};
+  rmm::cuda_stream_view stream_view(stream);
+
+  auto input_string = make_test_json_data(state);
+  auto& d_input     = static_cast<cudf::scalar_type_t<std::string>&>(*input_string);
+
+  state.add_element_count(d_input.size());
+
+  // Prepare input & output buffers
+  cudf::detail::hostdevice_vector<SymbolOffsetT> output_gpu_size(single_item, stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> out_indexes_gpu(d_input.size(), stream_view);
+
+  // Run algorithm
+  auto parser = cudf::io::fst::detail::make_fst(
+    cudf::io::fst::detail::make_symbol_group_lut(pda_sgs),
+    cudf::io::fst::detail::make_transition_table(pda_state_tt),
+    cudf::io::fst::detail::make_translation_table<max_translation_table_size>(pda_out_tt),
+    stream);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    // Allocate device-side temporary storage & run algorithm
+    parser.Transduce(d_input.data(),
+                     static_cast<SymbolOffsetT>(d_input.size()),
+                     thrust::make_discard_iterator(),
+                     out_indexes_gpu.device_ptr(),
+                     output_gpu_size.device_ptr(),
+                     start_state,
+                     stream.value());
+  });
+}
+
+NVBENCH_BENCH(BM_FST_JSON)
+  .set_name("FST_JSON")
+  .add_int64_power_of_two_axis("string_size", nvbench::range(20, 30, 1));
+
+NVBENCH_BENCH(BM_FST_JSON_no_outidx)
+  .set_name("FST_JSON_no_outidx")
+  .add_int64_power_of_two_axis("string_size", nvbench::range(20, 30, 1));
+
+NVBENCH_BENCH(BM_FST_JSON_no_out)
+  .set_name("FST_JSON_no_out")
+  .add_int64_power_of_two_axis("string_size", nvbench::range(20, 30, 1));
+
+NVBENCH_BENCH(BM_FST_JSON_no_str)
+  .set_name("FST_JSON_no_str")
+  .add_int64_power_of_two_axis("string_size", nvbench::range(20, 30, 1));
diff --git a/cpp/benchmarks/io/json/json_reader_input.cpp b/cpp/benchmarks/io/json/json_reader_input.cpp
new file mode 100644
index 0000000..31bb5da
--- /dev/null
+++ b/cpp/benchmarks/io/json/json_reader_input.cpp
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/json.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr size_t data_size         = 512 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+void json_read_common(cuio_source_sink_pair& source_sink, nvbench::state& state)
+{
+  cudf::io::json_reader_options read_opts =
+    cudf::io::json_reader_options::builder(source_sink.make_source_info());
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();
+
+               timer.start();
+               cudf::io::read_json(read_opts);
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+template <cudf::io::io_type IO>
+void BM_json_read_io(nvbench::state& state, nvbench::type_list<nvbench::enum_type<IO>>)
+{
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::DURATION),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST),
+                                         static_cast<int32_t>(data_type::STRUCT)});
+
+  auto const source_type = IO;
+  cuio_source_sink_pair source_sink(source_type);
+
+  {
+    auto const tbl = create_random_table(
+      cycle_dtypes(d_type, num_cols), table_size_bytes{data_size}, data_profile_builder());
+    auto const view = tbl->view();
+
+    cudf::io::json_writer_options const write_opts =
+      cudf::io::json_writer_options::builder(source_sink.make_sink_info(), view)
+        .na_rep("null")
+        .rows_per_chunk(100'000);
+    cudf::io::write_json(write_opts);
+  }
+
+  json_read_common(source_sink, state);
+}
+
+template <data_type DataType, cudf::io::io_type IO>
+void BM_json_read_data_type(
+  nvbench::state& state, nvbench::type_list<nvbench::enum_type<DataType>, nvbench::enum_type<IO>>)
+{
+  auto const d_type      = get_type_or_group(static_cast<int32_t>(DataType));
+  auto const source_type = IO;
+  cuio_source_sink_pair source_sink(source_type);
+  {
+    auto const tbl = create_random_table(
+      cycle_dtypes(d_type, num_cols), table_size_bytes{data_size}, data_profile_builder());
+    auto const view = tbl->view();
+
+    cudf::io::json_writer_options const write_opts =
+      cudf::io::json_writer_options::builder(source_sink.make_sink_info(), view)
+        .na_rep("null")
+        .rows_per_chunk(100'000);
+    cudf::io::write_json(write_opts);
+  }
+  json_read_common(source_sink, state);
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::DURATION,
+                                            data_type::STRING,
+                                            data_type::LIST,
+                                            data_type::STRUCT>;
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::DEVICE_BUFFER>;
+
+using compression_list =
+  nvbench::enum_type_list<cudf::io::compression_type::SNAPPY, cudf::io::compression_type::NONE>;
+
+NVBENCH_BENCH_TYPES(BM_json_read_data_type,
+                    NVBENCH_TYPE_AXES(d_type_list,
+                                      nvbench::enum_type_list<cudf::io::io_type::DEVICE_BUFFER>))
+  .set_name("json_read_data_type")
+  .set_type_axes_names({"data_type", "io"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH_TYPES(BM_json_read_io, NVBENCH_TYPE_AXES(io_list))
+  .set_name("json_read_io")
+  .set_type_axes_names({"io"})
+  .set_min_samples(4);
diff --git a/cpp/benchmarks/io/json/json_writer.cpp b/cpp/benchmarks/io/json/json_writer.cpp
new file mode 100644
index 0000000..ae6bb81
--- /dev/null
+++ b/cpp/benchmarks/io/json/json_writer.cpp
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/json.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr size_t data_size         = 512 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+void json_write_common(cudf::io::json_writer_options const& write_opts,
+                       cuio_source_sink_pair& source_sink,
+                       size_t const data_size,
+                       nvbench::state& state)
+{
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();
+
+               timer.start();
+               cudf::io::write_json(write_opts);
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+template <cudf::io::io_type IO>
+void BM_json_write_io(nvbench::state& state, nvbench::type_list<nvbench::enum_type<IO>>)
+{
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::DURATION),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST),
+                                         static_cast<int32_t>(data_type::STRUCT)});
+
+  auto const source_type = IO;
+
+  auto const tbl = create_random_table(
+    cycle_dtypes(d_type, num_cols), table_size_bytes{data_size}, data_profile_builder());
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(source_type);
+  cudf::io::json_writer_options write_opts =
+    cudf::io::json_writer_options::builder(source_sink.make_sink_info(), view)
+      .na_rep("null")
+      .rows_per_chunk(view.num_rows() / 10);
+
+  json_write_common(write_opts, source_sink, data_size, state);
+}
+
+void BM_json_writer_options(nvbench::state& state)
+{
+  auto const source_type    = io_type::HOST_BUFFER;
+  bool const json_lines     = state.get_int64("json_lines");
+  bool const include_nulls  = state.get_int64("include_nulls");
+  auto const rows_per_chunk = state.get_int64("rows_per_chunk");
+
+  if ((json_lines or include_nulls) and rows_per_chunk != 1 << 20) {
+    state.skip("Skipping for unrequired rows_per_chunk combinations");
+    return;
+  }
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::DURATION),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST),
+                                         static_cast<int32_t>(data_type::STRUCT)});
+
+  auto const tbl = create_random_table(
+    cycle_dtypes(d_type, num_cols), table_size_bytes{data_size}, data_profile_builder());
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(source_type);
+  cudf::io::json_writer_options write_opts =
+    cudf::io::json_writer_options::builder(source_sink.make_sink_info(), view)
+      .na_rep("null")
+      .lines(json_lines)
+      .include_nulls(include_nulls)
+      .rows_per_chunk(rows_per_chunk);
+
+  json_write_common(write_opts, source_sink, data_size, state);
+}
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::DEVICE_BUFFER>;
+
+NVBENCH_BENCH_TYPES(BM_json_write_io, NVBENCH_TYPE_AXES(io_list))
+  .set_name("json_write_io")
+  .set_type_axes_names({"io"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH(BM_json_writer_options)
+  .set_name("json_write_options")
+  .set_min_samples(4)
+  .add_int64_axis("json_lines", {false, true})
+  .add_int64_axis("include_nulls", {false, true})
+  .add_int64_power_of_two_axis("rows_per_chunk", {10, 15, 16, 18, 20});
diff --git a/cpp/benchmarks/io/json/nested_json.cpp b/cpp/benchmarks/io/json/nested_json.cpp
new file mode 100644
index 0000000..03ccd4e
--- /dev/null
+++ b/cpp/benchmarks/io/json/nested_json.cpp
@@ -0,0 +1,217 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <io/json/nested_json.hpp>
+
+#include <tests/io/fst/common.hpp>
+
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <string>
+#include <vector>
+
+namespace {
+
+// pre-generate all the number strings
+std::vector<std::string> _num_to_string;
+std::string num_to_string(int32_t num) { return _num_to_string.at(num); }
+
+// List of List nested.
+std::string generate_list_of_lists(int32_t max_depth, int32_t max_rows, std::string elem)
+{
+  std::string json = "[";
+  if (max_depth > 1) json += std::string(max_depth - 1, '[');
+  for (int32_t row = 0; row < max_rows; ++row) {
+    json += elem;
+    if (row < max_rows - 1) { json += ", "; }
+  }
+  if (max_depth > 1) json += std::string(max_depth - 1, ']');
+  json += "]";
+  return json;
+}
+
+// Struct of Struct nested.
+std::string generate_struct_of_structs(int32_t max_depth, int32_t max_rows, std::string elem)
+{
+  if (max_depth <= 0) return "{}";
+  std::string json;
+  for (int32_t depth = 0; depth < max_depth / 2; ++depth) {
+    json += R"({"a)" + num_to_string(depth) + R"(": )";
+  }
+  if (max_rows == 0) json += "{}";
+
+  for (int32_t row = 0; row < max_rows; ++row) {
+    json += elem;
+    if (row < max_rows - 1) {
+      json += R"(, "a)" + num_to_string(max_depth / 2 - 1) + "_" + num_to_string(row) + R"(": )";
+    }
+  }
+  if (max_depth > 0) json += std::string(max_depth / 2, '}');
+  return json;
+}
+
+// Memoize the generated rows so we don't have to regenerate them.
+std::map<std::tuple<int, int, int, int>, std::string> _row_cache;
+
+std::string generate_row(
+  int num_columns, int max_depth, int max_list_size, int max_struct_size, size_t max_bytes)
+{
+  std::string s = "{";
+  std::vector<std::string> const elems{
+    R"(1)", R"(-2)", R"(3.4)", R"("5")", R"("abcdefghij")", R"(true)", R"(null)"};
+  for (int i = 0; i < num_columns; i++) {
+    s += R"("col)" + num_to_string(i) + R"(": )";
+    if (auto it = _row_cache.find({i % 2, max_depth - 2, max_struct_size, i % elems.size()});
+        it != _row_cache.end()) {
+      s += it->second;
+    } else {
+      auto r =
+        (i % 2 == 0)
+          ? generate_struct_of_structs(max_depth - 2, max_struct_size, elems[i % elems.size()])
+          : generate_list_of_lists(max_depth - 2, max_struct_size, elems[i % elems.size()]);
+      _row_cache[{i % 2, max_depth - 2, max_struct_size, i % elems.size()}] = r;
+      s += r;
+    }
+    if (s.length() > max_bytes) break;
+    if (i < num_columns - 1) s += ", ";
+  }
+  s += "}";
+  return s;
+}
+
+std::string generate_json(int num_rows,
+                          int num_columns,
+                          int max_depth,
+                          int max_list_size,
+                          int max_struct_size,
+                          size_t max_json_bytes)
+{
+  // std::to_string is slow, so we pre-generate all number strings we need.
+  _num_to_string.clear();
+  auto max_num_str =
+    std::max(std::max(num_columns, max_depth), std::max(max_list_size, max_struct_size));
+  for (int i = 0; i < max_num_str; i++)
+    _num_to_string.emplace_back(std::to_string(i));
+  _row_cache.clear();
+
+  std::string s = "[\n";
+  s.reserve(max_json_bytes + 1024);
+  for (int i = 0; i < num_rows; i++) {
+    s += generate_row(
+      num_columns, max_depth - 2, max_list_size, max_struct_size, max_json_bytes - s.length());
+    if (s.length() > max_json_bytes) break;
+    if (i != num_rows - 1) s += ",\n";
+  }
+  s += "\n]";
+  return s;
+}
+
+auto make_test_json_data(cudf::size_type string_size, rmm::cuda_stream_view stream)
+{
+  // Test input
+  std::string input = R"(
+                      {"a":1,"b":2,"c":[3], "d": {}},
+                      {"a":1,"b":4.0,"c":[], "d": {"year":1882,"author": "Bharathi"}},
+                      {"a":1,"b":6.0,"c":[5, 7], "d": null},
+                      {"a":1,"b":null,"c":null},
+                      {
+                        "a" : 1
+                      },
+                      {"a":1,"b":Infinity,"c":[null], "d": {"year":-600,"author": "Kaniyan"}},
+                      {"a": 1, "b": 8.0, "d": { "author": "Jean-Jacques Rousseau"}},)";
+
+  cudf::size_type const repeat_times = string_size / input.size();
+
+  auto d_input_scalar   = cudf::make_string_scalar(input, stream);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_input_scalar);
+  auto d_scalar         = cudf::strings::repeat_string(d_string_scalar, repeat_times);
+
+  auto data = const_cast<char*>(d_scalar->data());
+  CUDF_CUDA_TRY(cudaMemsetAsync(data, '[', 1, stream.value()));
+  CUDF_CUDA_TRY(cudaMemsetAsync(data + d_scalar->size() - 1, ']', 1, stream.value()));
+
+  return d_scalar;
+}
+}  // namespace
+
+void BM_NESTED_JSON(nvbench::state& state)
+{
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+  auto const default_options = cudf::io::json_reader_options{};
+
+  auto input = make_test_json_data(string_size, cudf::get_default_stream());
+  state.add_element_count(input->size());
+
+  // Run algorithm
+  auto const mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    // Allocate device-side temporary storage & run algorithm
+    cudf::io::json::detail::device_parse_nested_json(
+      cudf::device_span<char const>{input->data(), static_cast<size_t>(input->size())},
+      default_options,
+      cudf::get_default_stream(),
+      rmm::mr::get_current_device_resource());
+  });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(string_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+}
+
+NVBENCH_BENCH(BM_NESTED_JSON)
+  .set_name("nested_json_gpu_parser")
+  .add_int64_power_of_two_axis("string_size", nvbench::range(20, 30, 1));
+
+void BM_NESTED_JSON_DEPTH(nvbench::state& state)
+{
+  auto const string_size{cudf::size_type(state.get_int64("string_size"))};
+  auto const depth{cudf::size_type(state.get_int64("depth"))};
+
+  auto d_scalar = cudf::string_scalar(
+    generate_json(100'000'000, 10, depth, 10, 10, string_size), true, cudf::get_default_stream());
+  auto input = cudf::device_span<char const>(d_scalar.data(), d_scalar.size());
+
+  state.add_element_count(input.size());
+  auto const default_options = cudf::io::json_reader_options{};
+
+  // Run algorithm
+  auto const mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    // Allocate device-side temporary storage & run algorithm
+    cudf::io::json::detail::device_parse_nested_json(
+      input, default_options, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(string_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+}
+
+NVBENCH_BENCH(BM_NESTED_JSON_DEPTH)
+  .set_name("nested_json_gpu_parser_depth")
+  .add_int64_power_of_two_axis("depth", nvbench::range(1, 4, 1))
+  .add_int64_power_of_two_axis("string_size", nvbench::range(20, 30, 2));
diff --git a/cpp/benchmarks/io/nvbench_helpers.hpp b/cpp/benchmarks/io/nvbench_helpers.hpp
new file mode 100644
index 0000000..dd96f6f
--- /dev/null
+++ b/cpp/benchmarks/io/nvbench_helpers.hpp
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+
+#include <cudf/io/types.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+enum class data_type : int32_t {
+  INTEGRAL        = static_cast<int32_t>(type_group_id::INTEGRAL),
+  INTEGRAL_SIGNED = static_cast<int32_t>(type_group_id::INTEGRAL_SIGNED),
+  FLOAT           = static_cast<int32_t>(type_group_id::FLOATING_POINT),
+  DECIMAL         = static_cast<int32_t>(type_group_id::FIXED_POINT),
+  TIMESTAMP       = static_cast<int32_t>(type_group_id::TIMESTAMP),
+  DURATION        = static_cast<int32_t>(type_group_id::DURATION),
+  STRING          = static_cast<int32_t>(cudf::type_id::STRING),
+  LIST            = static_cast<int32_t>(cudf::type_id::LIST),
+  STRUCT          = static_cast<int32_t>(cudf::type_id::STRUCT)
+};
+
+// NVBENCH_DECLARE_ENUM_TYPE_STRINGS macro must be used from global namespace scope
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  data_type,
+  [](data_type value) {
+    switch (value) {
+      case data_type::INTEGRAL: return "INTEGRAL";
+      case data_type::INTEGRAL_SIGNED: return "INTEGRAL_SIGNED";
+      case data_type::FLOAT: return "FLOAT";
+      case data_type::DECIMAL: return "DECIMAL";
+      case data_type::TIMESTAMP: return "TIMESTAMP";
+      case data_type::DURATION: return "DURATION";
+      case data_type::STRING: return "STRING";
+      case data_type::LIST: return "LIST";
+      case data_type::STRUCT: return "STRUCT";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::io::io_type,
+  [](auto value) {
+    switch (value) {
+      case cudf::io::io_type::FILEPATH: return "FILEPATH";
+      case cudf::io::io_type::HOST_BUFFER: return "HOST_BUFFER";
+      case cudf::io::io_type::DEVICE_BUFFER: return "DEVICE_BUFFER";
+      case cudf::io::io_type::VOID: return "VOID";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::io::compression_type,
+  [](auto value) {
+    switch (value) {
+      case cudf::io::compression_type::SNAPPY: return "SNAPPY";
+      case cudf::io::compression_type::NONE: return "NONE";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+enum class uses_index : bool { YES, NO };
+
+enum class uses_numpy_dtype : bool { YES, NO };
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  uses_index,
+  [](auto value) {
+    switch (value) {
+      case uses_index::YES: return "YES";
+      case uses_index::NO: return "NO";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  uses_numpy_dtype,
+  [](auto value) {
+    switch (value) {
+      case uses_numpy_dtype::YES: return "YES";
+      case uses_numpy_dtype::NO: return "NO";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  column_selection,
+  [](auto value) {
+    switch (value) {
+      case column_selection::ALL: return "ALL";
+      case column_selection::ALTERNATE: return "ALTERNATE";
+      case column_selection::FIRST_HALF: return "FIRST_HALF";
+      case column_selection::SECOND_HALF: return "SECOND_HALF";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  row_selection,
+  [](auto value) {
+    switch (value) {
+      case row_selection::ALL: return "ALL";
+      case row_selection::BYTE_RANGE: return "BYTE_RANGE";
+      case row_selection::NROWS: return "NROWS";
+      case row_selection::SKIPFOOTER: return "SKIPFOOTER";
+      case row_selection::STRIPES: return "STRIPES";
+      case row_selection::ROW_GROUPS: return "ROW_GROUPS";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::type_id,
+  [](auto value) {
+    switch (value) {
+      case cudf::type_id::EMPTY: return "EMPTY";
+      case cudf::type_id::TIMESTAMP_NANOSECONDS: return "TIMESTAMP_NANOSECONDS";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+enum class converts_strings : bool { YES, NO };
+
+enum class uses_pandas_metadata : bool { YES, NO };
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  converts_strings,
+  [](auto value) {
+    switch (value) {
+      case converts_strings::YES: return "YES";
+      case converts_strings::NO: return "NO";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  uses_pandas_metadata,
+  [](auto value) {
+    switch (value) {
+      case uses_pandas_metadata::YES: return "YES";
+      case uses_pandas_metadata::NO: return "NO";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
diff --git a/cpp/benchmarks/io/orc/orc_reader_input.cpp b/cpp/benchmarks/io/orc/orc_reader_input.cpp
new file mode 100644
index 0000000..b6e15fb
--- /dev/null
+++ b/cpp/benchmarks/io/orc/orc_reader_input.cpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/orc.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr int64_t data_size        = 512 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+void orc_read_common(cudf::io::orc_writer_options const& opts,
+                     cuio_source_sink_pair& source_sink,
+                     nvbench::state& state)
+{
+  cudf::io::write_orc(opts);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(source_sink.make_source_info());
+
+  auto mem_stats_logger = cudf::memory_stats_logger();  // init stats logger
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();
+
+               timer.start();
+               cudf::io::read_orc(read_opts);
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+template <data_type DataType, cudf::io::io_type IOType>
+void BM_orc_read_data(nvbench::state& state,
+                      nvbench::type_list<nvbench::enum_type<DataType>, nvbench::enum_type<IOType>>)
+{
+  auto const d_type                 = get_type_or_group(static_cast<int32_t>(DataType));
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(IOType);
+  cudf::io::orc_writer_options opts =
+    cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view);
+
+  orc_read_common(opts, source_sink, state);
+}
+
+template <cudf::io::io_type IOType, cudf::io::compression_type Compression>
+void BM_orc_read_io_compression(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<IOType>, nvbench::enum_type<Compression>>)
+{
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST),
+                                         static_cast<int32_t>(data_type::STRUCT)});
+
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(IOType);
+  cudf::io::orc_writer_options opts =
+    cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view)
+      .compression(Compression);
+
+  orc_read_common(opts, source_sink, state);
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL_SIGNED,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::STRING,
+                                            data_type::LIST,
+                                            data_type::STRUCT>;
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::DEVICE_BUFFER>;
+
+using compression_list =
+  nvbench::enum_type_list<cudf::io::compression_type::SNAPPY, cudf::io::compression_type::NONE>;
+
+NVBENCH_BENCH_TYPES(BM_orc_read_data,
+                    NVBENCH_TYPE_AXES(d_type_list,
+                                      nvbench::enum_type_list<cudf::io::io_type::DEVICE_BUFFER>))
+  .set_name("orc_read_decode")
+  .set_type_axes_names({"data_type", "io"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_orc_read_io_compression, NVBENCH_TYPE_AXES(io_list, compression_list))
+  .set_name("orc_read_io_compression")
+  .set_type_axes_names({"io", "compression"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
diff --git a/cpp/benchmarks/io/orc/orc_reader_options.cpp b/cpp/benchmarks/io/orc/orc_reader_options.cpp
new file mode 100644
index 0000000..1f656f7
--- /dev/null
+++ b/cpp/benchmarks/io/orc/orc_reader_options.cpp
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/io/orc.hpp>
+#include <cudf/io/orc_metadata.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr int64_t data_size = 512 << 20;
+// The number of separate read calls to use when reading files in multiple chunks
+// Each call reads roughly equal amounts of data
+constexpr int32_t chunked_read_num_chunks = 4;
+
+std::vector<std::string> get_top_level_col_names(cudf::io::source_info const& source)
+{
+  auto const top_lvl_cols = cudf::io::read_orc_metadata(source).schema().root().children();
+  std::vector<std::string> col_names;
+  std::transform(top_lvl_cols.cbegin(),
+                 top_lvl_cols.cend(),
+                 std::back_inserter(col_names),
+                 [](auto const& col_meta) { return col_meta.name(); });
+  return col_names;
+}
+
+template <column_selection ColSelection,
+          row_selection RowSelection,
+          uses_index UsesIndex,
+          uses_numpy_dtype UsesNumpyDType,
+          cudf::type_id Timestamp>
+void BM_orc_read_varying_options(nvbench::state& state,
+                                 nvbench::type_list<nvbench::enum_type<ColSelection>,
+                                                    nvbench::enum_type<RowSelection>,
+                                                    nvbench::enum_type<UsesIndex>,
+                                                    nvbench::enum_type<UsesNumpyDType>,
+                                                    nvbench::enum_type<Timestamp>>)
+{
+  auto const num_chunks = RowSelection == row_selection::ALL ? 1 : chunked_read_num_chunks;
+
+  auto const use_index     = UsesIndex == uses_index::YES;
+  auto const use_np_dtypes = UsesNumpyDType == uses_numpy_dtype::YES;
+  auto const ts_type       = cudf::data_type{Timestamp};
+
+  // skip_rows is not supported on nested types
+  auto const data_types =
+    dtypes_for_column_selection(get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                                   static_cast<int32_t>(data_type::FLOAT),
+                                                   static_cast<int32_t>(data_type::DECIMAL),
+                                                   static_cast<int32_t>(data_type::TIMESTAMP),
+                                                   static_cast<int32_t>(data_type::STRING)}),
+                                ColSelection);
+  auto const tbl  = create_random_table(data_types, table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(io_type::HOST_BUFFER);
+  cudf::io::orc_writer_options options =
+    cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view);
+  cudf::io::write_orc(options);
+
+  auto const cols_to_read =
+    select_column_names(get_top_level_col_names(source_sink.make_source_info()), ColSelection);
+  cudf::io::orc_reader_options read_options =
+    cudf::io::orc_reader_options::builder(source_sink.make_source_info())
+      .columns(cols_to_read)
+      .use_index(use_index)
+      .use_np_dtypes(use_np_dtypes)
+      .timestamp_type(ts_type);
+
+  auto const num_stripes =
+    cudf::io::read_orc_metadata(source_sink.make_source_info()).num_stripes();
+  auto const chunk_row_cnt = cudf::util::div_rounding_up_unsafe(view.num_rows(), num_chunks);
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(
+    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
+      try_drop_l3_cache();
+
+      timer.start();
+      cudf::size_type rows_read = 0;
+      for (int32_t chunk = 0; chunk < num_chunks; ++chunk) {
+        switch (RowSelection) {
+          case row_selection::ALL: break;
+          case row_selection::STRIPES:
+            read_options.set_stripes({segments_in_chunk(num_stripes, num_chunks, chunk)});
+            break;
+          case row_selection::NROWS:
+            read_options.set_skip_rows(chunk * chunk_row_cnt);
+            read_options.set_num_rows(chunk_row_cnt);
+            break;
+          default: CUDF_FAIL("Unsupported row selection method");
+        }
+
+        rows_read += cudf::io::read_orc(read_options).tbl->num_rows();
+      }
+
+      CUDF_EXPECTS(rows_read == view.num_rows(), "Benchmark did not read the entire table");
+      timer.stop();
+    });
+
+  auto const elapsed_time   = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  auto const data_processed = data_size * cols_to_read.size() / view.num_columns();
+  state.add_element_count(static_cast<double>(data_processed) / elapsed_time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+using col_selections = nvbench::enum_type_list<column_selection::ALL,
+                                               column_selection::ALTERNATE,
+                                               column_selection::FIRST_HALF,
+                                               column_selection::SECOND_HALF>;
+NVBENCH_BENCH_TYPES(BM_orc_read_varying_options,
+                    NVBENCH_TYPE_AXES(col_selections,
+                                      nvbench::enum_type_list<row_selection::ALL>,
+                                      nvbench::enum_type_list<uses_index::YES>,
+                                      nvbench::enum_type_list<uses_numpy_dtype::YES>,
+                                      nvbench::enum_type_list<cudf::type_id::EMPTY>))
+  .set_name("orc_read_column_selection")
+  .set_type_axes_names(
+    {"column_selection", "row_selection", "uses_index", "uses_numpy_dtype", "timestamp_type"})
+  .set_min_samples(4);
+
+using row_selections =
+  nvbench::enum_type_list<row_selection::ALL, row_selection::NROWS, row_selection::STRIPES>;
+NVBENCH_BENCH_TYPES(BM_orc_read_varying_options,
+                    NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
+                                      row_selections,
+                                      nvbench::enum_type_list<uses_index::YES>,
+                                      nvbench::enum_type_list<uses_numpy_dtype::YES>,
+                                      nvbench::enum_type_list<cudf::type_id::EMPTY>))
+  .set_name("orc_read_row_selection")
+  .set_type_axes_names(
+    {"column_selection", "row_selection", "uses_index", "uses_numpy_dtype", "timestamp_type"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH_TYPES(
+  BM_orc_read_varying_options,
+  NVBENCH_TYPE_AXES(
+    nvbench::enum_type_list<column_selection::ALL>,
+    nvbench::enum_type_list<row_selection::ALL>,
+    nvbench::enum_type_list<uses_index::YES, uses_index::NO>,
+    nvbench::enum_type_list<uses_numpy_dtype::YES, uses_numpy_dtype::NO>,
+    nvbench::enum_type_list<cudf::type_id::EMPTY, cudf::type_id::TIMESTAMP_NANOSECONDS>))
+  .set_name("orc_read_misc_options")
+  .set_type_axes_names(
+    {"column_selection", "row_selection", "uses_index", "uses_numpy_dtype", "timestamp_type"})
+  .set_min_samples(4);
diff --git a/cpp/benchmarks/io/orc/orc_writer.cpp b/cpp/benchmarks/io/orc/orc_writer.cpp
new file mode 100644
index 0000000..bb37329
--- /dev/null
+++ b/cpp/benchmarks/io/orc/orc_writer.cpp
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/orc.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::io::statistics_freq,
+  [](auto value) {
+    switch (value) {
+      case cudf::io::statistics_freq::STATISTICS_NONE: return "STATISTICS_NONE";
+      case cudf::io::statistics_freq::STATISTICS_ROWGROUP: return "ORC_STATISTICS_STRIPE";
+      case cudf::io::statistics_freq::STATISTICS_PAGE: return "ORC_STATISTICS_ROW_GROUP";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr int64_t data_size        = 512 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+template <data_type DataType>
+void BM_orc_write_encode(nvbench::state& state, nvbench::type_list<nvbench::enum_type<DataType>>)
+{
+  auto const d_type                 = get_type_or_group(static_cast<int32_t>(DataType));
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  auto const compression            = cudf::io::compression_type::SNAPPY;
+  auto const sink_type              = io_type::VOID;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(sink_type);
+
+               timer.start();
+               cudf::io::orc_writer_options options =
+                 cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view)
+                   .compression(compression);
+               cudf::io::write_orc(options);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+template <cudf::io::io_type IO, cudf::io::compression_type Compression>
+void BM_orc_write_io_compression(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<IO>, nvbench::enum_type<Compression>>)
+{
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST),
+                                         static_cast<int32_t>(data_type::STRUCT)});
+
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  auto const compression            = Compression;
+  auto const sink_type              = IO;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(sink_type);
+
+               timer.start();
+               cudf::io::orc_writer_options options =
+                 cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view)
+                   .compression(compression);
+               cudf::io::write_orc(options);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+template <cudf::io::statistics_freq Statistics, cudf::io::compression_type Compression>
+void BM_orc_write_statistics(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<Statistics>, nvbench::enum_type<Compression>>)
+{
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST)});
+
+  auto const compression = Compression;
+  auto const stats_freq  = Statistics;
+
+  auto const tbl  = create_random_table(d_type, table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(io_type::FILEPATH);
+
+               timer.start();
+               cudf::io::orc_writer_options const options =
+                 cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view)
+                   .compression(compression)
+                   .enable_statistics(stats_freq);
+               cudf::io::write_orc(options);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL_SIGNED,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::STRING,
+                                            data_type::LIST,
+                                            data_type::STRUCT>;
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::VOID>;
+
+using compression_list =
+  nvbench::enum_type_list<cudf::io::compression_type::SNAPPY, cudf::io::compression_type::NONE>;
+
+using stats_list = nvbench::enum_type_list<cudf::io::STATISTICS_NONE,
+                                           cudf::io::ORC_STATISTICS_STRIPE,
+                                           cudf::io::ORC_STATISTICS_ROW_GROUP>;
+
+NVBENCH_BENCH_TYPES(BM_orc_write_encode, NVBENCH_TYPE_AXES(d_type_list))
+  .set_name("orc_write_encode")
+  .set_type_axes_names({"data_type"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_orc_write_io_compression, NVBENCH_TYPE_AXES(io_list, compression_list))
+  .set_name("orc_write_io_compression")
+  .set_type_axes_names({"io", "compression"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_orc_write_statistics, NVBENCH_TYPE_AXES(stats_list, compression_list))
+  .set_name("orc_write_statistics")
+  .set_type_axes_names({"statistics", "compression"})
+  .set_min_samples(4);
diff --git a/cpp/benchmarks/io/orc/orc_writer_chunks.cpp b/cpp/benchmarks/io/orc/orc_writer_chunks.cpp
new file mode 100644
index 0000000..dff88d7
--- /dev/null
+++ b/cpp/benchmarks/io/orc/orc_writer_chunks.cpp
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/io/orc.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr int64_t data_size = 512 << 20;
+
+void nvbench_orc_write(nvbench::state& state)
+{
+  cudf::size_type num_cols = state.get_int64("num_columns");
+
+  auto tbl = create_random_table(
+    cycle_dtypes(get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                    static_cast<int32_t>(data_type::FLOAT),
+                                    static_cast<int32_t>(data_type::DECIMAL),
+                                    static_cast<int32_t>(data_type::TIMESTAMP),
+                                    static_cast<int32_t>(data_type::STRING),
+                                    static_cast<int32_t>(data_type::STRUCT),
+                                    static_cast<int32_t>(data_type::LIST)}),
+                 num_cols),
+    table_size_bytes{data_size});
+  cudf::table_view view = tbl->view();
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+
+  state.add_global_memory_reads<int64_t>(data_size);
+  state.add_element_count(view.num_columns() * view.num_rows());
+
+  size_t encoded_file_size = 0;
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(io_type::VOID);
+               timer.start();
+
+               cudf::io::orc_writer_options opts =
+                 cudf::io::orc_writer_options::builder(source_sink.make_sink_info(), view);
+               cudf::io::write_orc(opts);
+
+               timer.stop();
+               encoded_file_size = source_sink.size();
+             });
+
+  state.add_buffer_size(mem_stats_logger.peak_memory_usage(), "pmu", "Peak Memory Usage");
+  state.add_buffer_size(encoded_file_size, "efs", "Encoded File Size");
+  state.add_element_count(view.num_rows(), "Total Rows");
+}
+
+void nvbench_orc_chunked_write(nvbench::state& state)
+{
+  cudf::size_type num_cols   = state.get_int64("num_columns");
+  cudf::size_type num_tables = state.get_int64("num_chunks");
+
+  std::vector<std::unique_ptr<cudf::table>> tables;
+  for (cudf::size_type idx = 0; idx < num_tables; idx++) {
+    tables.push_back(create_random_table(
+      cycle_dtypes(get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                      static_cast<int32_t>(data_type::FLOAT),
+                                      static_cast<int32_t>(data_type::DECIMAL),
+                                      static_cast<int32_t>(data_type::TIMESTAMP),
+                                      static_cast<int32_t>(data_type::STRING),
+                                      static_cast<int32_t>(data_type::STRUCT),
+                                      static_cast<int32_t>(data_type::LIST)}),
+                   num_cols),
+      table_size_bytes{size_t(data_size / num_tables)}));
+  }
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+
+  auto size_iter = thrust::make_transform_iterator(
+    tables.begin(), [](auto const& i) { return i->num_columns() * i->num_rows(); });
+  auto row_count_iter =
+    thrust::make_transform_iterator(tables.begin(), [](auto const& i) { return i->num_rows(); });
+  auto total_elements = std::accumulate(size_iter, size_iter + num_tables, 0);
+  auto total_rows     = std::accumulate(row_count_iter, row_count_iter + num_tables, 0);
+
+  state.add_global_memory_reads<int64_t>(data_size);
+  state.add_element_count(total_elements);
+
+  size_t encoded_file_size = 0;
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(
+    nvbench::exec_tag::timer | nvbench::exec_tag::sync, [&](nvbench::launch& launch, auto& timer) {
+      cuio_source_sink_pair source_sink(io_type::VOID);
+      timer.start();
+
+      cudf::io::chunked_orc_writer_options opts =
+        cudf::io::chunked_orc_writer_options::builder(source_sink.make_sink_info());
+      cudf::io::orc_chunked_writer writer(opts);
+      std::for_each(tables.begin(),
+                    tables.end(),
+                    [&writer](std::unique_ptr<cudf::table> const& tbl) { writer.write(*tbl); });
+      writer.close();
+
+      timer.stop();
+      encoded_file_size = source_sink.size();
+    });
+
+  state.add_buffer_size(mem_stats_logger.peak_memory_usage(), "pmu", "Peak Memory Usage");
+  state.add_buffer_size(encoded_file_size, "efs", "Encoded File Size");
+  state.add_element_count(total_rows, "Total Rows");
+}
+
+NVBENCH_BENCH(nvbench_orc_write)
+  .set_name("orc_write")
+  .set_min_samples(4)
+  .add_int64_axis("num_columns", {8, 64});
+
+NVBENCH_BENCH(nvbench_orc_chunked_write)
+  .set_name("orc_chunked_write")
+  .set_min_samples(4)
+  .add_int64_axis("num_columns", {8, 64})
+  .add_int64_axis("num_chunks", {8, 64});
diff --git a/cpp/benchmarks/io/parquet/parquet_reader_input.cpp b/cpp/benchmarks/io/parquet/parquet_reader_input.cpp
new file mode 100644
index 0000000..80303ea
--- /dev/null
+++ b/cpp/benchmarks/io/parquet/parquet_reader_input.cpp
@@ -0,0 +1,245 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/parquet.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr size_t data_size         = 512 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+void parquet_read_common(cudf::io::parquet_writer_options const& write_opts,
+                         cuio_source_sink_pair& source_sink,
+                         nvbench::state& state)
+{
+  cudf::io::write_parquet(write_opts);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(source_sink.make_source_info());
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();
+
+               timer.start();
+               cudf::io::read_parquet(read_opts);
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+template <data_type DataType, cudf::io::io_type IOType>
+void BM_parquet_read_data(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<DataType>, nvbench::enum_type<IOType>>)
+{
+  auto const d_type                 = get_type_or_group(static_cast<int32_t>(DataType));
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  auto const compression            = cudf::io::compression_type::SNAPPY;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(IOType);
+  cudf::io::parquet_writer_options write_opts =
+    cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+      .compression(compression);
+
+  parquet_read_common(write_opts, source_sink, state);
+}
+
+template <cudf::io::io_type IOType, cudf::io::compression_type Compression>
+void BM_parquet_read_io_compression(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<IOType>, nvbench::enum_type<Compression>>)
+{
+  auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::DURATION),
+                                         static_cast<int32_t>(data_type::STRING),
+                                         static_cast<int32_t>(data_type::LIST),
+                                         static_cast<int32_t>(data_type::STRUCT)});
+
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  auto const compression            = Compression;
+  auto const source_type            = IOType;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(source_type);
+  cudf::io::parquet_writer_options write_opts =
+    cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+      .compression(compression);
+
+  parquet_read_common(write_opts, source_sink, state);
+}
+
+template <cudf::io::io_type IOType>
+void BM_parquet_read_io_small_mixed(nvbench::state& state,
+                                    nvbench::type_list<nvbench::enum_type<IOType>>)
+{
+  auto const d_type =
+    std::pair<cudf::type_id, cudf::type_id>{cudf::type_id::STRING, cudf::type_id::INT32};
+
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  cudf::size_type const num_strings = state.get_int64("num_string_cols");
+  auto const source_type            = IOType;
+
+  // want 80 pages total, across 4 columns, so 20 pages per column
+  cudf::size_type constexpr n_col          = 4;
+  cudf::size_type constexpr page_size_rows = 10'000;
+  cudf::size_type constexpr num_rows       = page_size_rows * (80 / n_col);
+
+  auto const tbl =
+    create_random_table(mix_dtypes(d_type, n_col, num_strings),
+                        row_count{num_rows},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(source_type);
+  cudf::io::parquet_writer_options write_opts =
+    cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+      .max_page_size_rows(10'000)
+      .compression(cudf::io::compression_type::NONE);
+
+  parquet_read_common(write_opts, source_sink, state);
+}
+
+template <data_type DataType, cudf::io::io_type IOType>
+void BM_parquet_read_chunks(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<DataType>, nvbench::enum_type<IOType>>)
+{
+  auto const d_type                 = get_type_or_group(static_cast<int32_t>(DataType));
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  cudf::size_type const byte_limit  = state.get_int64("byte_limit");
+  auto const compression            = cudf::io::compression_type::SNAPPY;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(d_type, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(IOType);
+  cudf::io::parquet_writer_options write_opts =
+    cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+      .compression(compression);
+
+  cudf::io::write_parquet(write_opts);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(source_sink.make_source_info());
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();
+
+               timer.start();
+               auto reader = cudf::io::chunked_parquet_reader(byte_limit, read_opts);
+               do {
+                 [[maybe_unused]] auto const chunk = reader.read_chunk();
+               } while (reader.has_next());
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::DURATION,
+                                            data_type::STRING,
+                                            data_type::LIST,
+                                            data_type::STRUCT>;
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::DEVICE_BUFFER>;
+
+using compression_list =
+  nvbench::enum_type_list<cudf::io::compression_type::SNAPPY, cudf::io::compression_type::NONE>;
+
+NVBENCH_BENCH_TYPES(BM_parquet_read_data,
+                    NVBENCH_TYPE_AXES(d_type_list,
+                                      nvbench::enum_type_list<cudf::io::io_type::DEVICE_BUFFER>))
+  .set_name("parquet_read_decode")
+  .set_type_axes_names({"data_type", "io"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_parquet_read_io_compression, NVBENCH_TYPE_AXES(io_list, compression_list))
+  .set_name("parquet_read_io_compression")
+  .set_type_axes_names({"io", "compression"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_parquet_read_chunks,
+                    NVBENCH_TYPE_AXES(d_type_list,
+                                      nvbench::enum_type_list<cudf::io::io_type::DEVICE_BUFFER>))
+  .set_name("parquet_read_chunks")
+  .set_type_axes_names({"data_type", "io"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32})
+  .add_int64_axis("byte_limit", {0, 500'000});
+
+NVBENCH_BENCH_TYPES(BM_parquet_read_io_small_mixed,
+                    NVBENCH_TYPE_AXES(nvbench::enum_type_list<cudf::io::io_type::FILEPATH>))
+  .set_name("parquet_read_io_small_mixed")
+  .set_type_axes_names({"io"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32})
+  .add_int64_axis("num_string_cols", {1, 2, 3});
diff --git a/cpp/benchmarks/io/parquet/parquet_reader_options.cpp b/cpp/benchmarks/io/parquet/parquet_reader_options.cpp
new file mode 100644
index 0000000..9f221de
--- /dev/null
+++ b/cpp/benchmarks/io/parquet/parquet_reader_options.cpp
@@ -0,0 +1,179 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr std::size_t data_size = 512 << 20;
+// The number of separate read calls to use when reading files in multiple chunks
+// Each call reads roughly equal amounts of data
+constexpr int32_t chunked_read_num_chunks = 4;
+
+std::vector<std::string> get_top_level_col_names(cudf::io::source_info const& source)
+{
+  auto const top_lvl_cols = cudf::io::read_parquet_metadata(source).schema().root().children();
+  std::vector<std::string> col_names;
+  std::transform(top_lvl_cols.cbegin(),
+                 top_lvl_cols.cend(),
+                 std::back_inserter(col_names),
+                 [](auto const& col_meta) { return col_meta.name(); });
+
+  return col_names;
+}
+
+template <column_selection ColSelection,
+          row_selection RowSelection,
+          converts_strings ConvertsStrings,
+          uses_pandas_metadata UsesPandasMetadata,
+          cudf::type_id Timestamp>
+void BM_parquet_read_options(nvbench::state& state,
+                             nvbench::type_list<nvbench::enum_type<ColSelection>,
+                                                nvbench::enum_type<RowSelection>,
+                                                nvbench::enum_type<ConvertsStrings>,
+                                                nvbench::enum_type<UsesPandasMetadata>,
+                                                nvbench::enum_type<Timestamp>>)
+{
+  auto const num_chunks = RowSelection == row_selection::ALL ? 1 : chunked_read_num_chunks;
+
+  auto constexpr str_to_categories = ConvertsStrings == converts_strings::YES;
+  auto constexpr uses_pd_metadata  = UsesPandasMetadata == uses_pandas_metadata::YES;
+
+  auto const ts_type = cudf::data_type{Timestamp};
+
+  auto const data_types =
+    dtypes_for_column_selection(get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                                   static_cast<int32_t>(data_type::FLOAT),
+                                                   static_cast<int32_t>(data_type::DECIMAL),
+                                                   static_cast<int32_t>(data_type::TIMESTAMP),
+                                                   static_cast<int32_t>(data_type::DURATION),
+                                                   static_cast<int32_t>(data_type::STRING),
+                                                   static_cast<int32_t>(data_type::LIST),
+                                                   static_cast<int32_t>(data_type::STRUCT)}),
+                                ColSelection);
+  auto const tbl  = create_random_table(data_types, table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(io_type::HOST_BUFFER);
+  cudf::io::parquet_writer_options options =
+    cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view);
+  cudf::io::write_parquet(options);
+
+  auto const cols_to_read =
+    select_column_names(get_top_level_col_names(source_sink.make_source_info()), ColSelection);
+  cudf::io::parquet_reader_options read_options =
+    cudf::io::parquet_reader_options::builder(source_sink.make_source_info())
+      .columns(cols_to_read)
+      .convert_strings_to_categories(str_to_categories)
+      .use_pandas_metadata(uses_pd_metadata)
+      .timestamp_type(ts_type);
+
+  auto const num_row_groups = read_parquet_metadata(source_sink.make_source_info()).num_rowgroups();
+  auto const chunk_row_cnt  = cudf::util::div_rounding_up_unsafe(view.num_rows(), num_chunks);
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(
+    nvbench::exec_tag::sync | nvbench::exec_tag::timer, [&](nvbench::launch& launch, auto& timer) {
+      try_drop_l3_cache();
+
+      timer.start();
+      cudf::size_type rows_read = 0;
+      for (int32_t chunk = 0; chunk < num_chunks; ++chunk) {
+        switch (RowSelection) {
+          case row_selection::ALL: break;
+          case row_selection::ROW_GROUPS: {
+            read_options.set_row_groups({segments_in_chunk(num_row_groups, num_chunks, chunk)});
+          } break;
+          case row_selection::NROWS:
+            read_options.set_skip_rows(chunk * chunk_row_cnt);
+            read_options.set_num_rows(chunk_row_cnt);
+            break;
+          default: CUDF_FAIL("Unsupported row selection method");
+        }
+
+        rows_read += cudf::io::read_parquet(read_options).tbl->num_rows();
+      }
+
+      CUDF_EXPECTS(rows_read == view.num_rows(), "Benchmark did not read the entire table");
+      timer.stop();
+    });
+
+  auto const elapsed_time   = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  auto const data_processed = data_size * cols_to_read.size() / view.num_columns();
+  state.add_element_count(static_cast<double>(data_processed) / elapsed_time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+using row_selections =
+  nvbench::enum_type_list<row_selection::ALL, row_selection::NROWS, row_selection::ROW_GROUPS>;
+NVBENCH_BENCH_TYPES(BM_parquet_read_options,
+                    NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
+                                      row_selections,
+                                      nvbench::enum_type_list<converts_strings::YES>,
+                                      nvbench::enum_type_list<uses_pandas_metadata::YES>,
+                                      nvbench::enum_type_list<cudf::type_id::EMPTY>))
+  .set_name("parquet_read_row_selection")
+  .set_type_axes_names({"column_selection",
+                        "row_selection",
+                        "str_to_categories",
+                        "uses_pandas_metadata",
+                        "timestamp_type"})
+  .set_min_samples(4);
+
+using col_selections = nvbench::enum_type_list<column_selection::ALL,
+                                               column_selection::ALTERNATE,
+                                               column_selection::FIRST_HALF,
+                                               column_selection::SECOND_HALF>;
+NVBENCH_BENCH_TYPES(BM_parquet_read_options,
+                    NVBENCH_TYPE_AXES(col_selections,
+                                      nvbench::enum_type_list<row_selection::ALL>,
+                                      nvbench::enum_type_list<converts_strings::YES>,
+                                      nvbench::enum_type_list<uses_pandas_metadata::YES>,
+                                      nvbench::enum_type_list<cudf::type_id::EMPTY>))
+  .set_name("parquet_read_column_selection")
+  .set_type_axes_names({"column_selection",
+                        "row_selection",
+                        "str_to_categories",
+                        "uses_pandas_metadata",
+                        "timestamp_type"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH_TYPES(
+  BM_parquet_read_options,
+  NVBENCH_TYPE_AXES(nvbench::enum_type_list<column_selection::ALL>,
+                    nvbench::enum_type_list<row_selection::ALL>,
+                    nvbench::enum_type_list<converts_strings::YES, converts_strings::NO>,
+                    nvbench::enum_type_list<uses_pandas_metadata::YES, uses_pandas_metadata::NO>,
+                    nvbench::enum_type_list<cudf::type_id::EMPTY>))
+  .set_name("parquet_read_misc_options")
+  .set_type_axes_names({"column_selection",
+                        "row_selection",
+                        "str_to_categories",
+                        "uses_pandas_metadata",
+                        "timestamp_type"})
+  .set_min_samples(4);
diff --git a/cpp/benchmarks/io/parquet/parquet_writer.cpp b/cpp/benchmarks/io/parquet/parquet_writer.cpp
new file mode 100644
index 0000000..13b396e
--- /dev/null
+++ b/cpp/benchmarks/io/parquet/parquet_writer.cpp
@@ -0,0 +1,221 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/parquet.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::io::statistics_freq,
+  [](auto value) {
+    switch (value) {
+      case cudf::io::statistics_freq::STATISTICS_NONE: return "STATISTICS_NONE";
+      case cudf::io::statistics_freq::STATISTICS_ROWGROUP: return "STATISTICS_ROWGROUP";
+      case cudf::io::statistics_freq::STATISTICS_PAGE: return "STATISTICS_PAGE";
+      case cudf::io::statistics_freq::STATISTICS_COLUMN: return "STATISTICS_COLUMN";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr size_t data_size         = 512 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+template <data_type DataType>
+void BM_parq_write_encode(nvbench::state& state, nvbench::type_list<nvbench::enum_type<DataType>>)
+{
+  auto const data_types             = get_type_or_group(static_cast<int32_t>(DataType));
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  auto const compression            = cudf::io::compression_type::SNAPPY;
+  auto const sink_type              = io_type::VOID;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(data_types, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto const mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(sink_type);
+
+               timer.start();
+               cudf::io::parquet_writer_options opts =
+                 cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+                   .compression(compression);
+               cudf::io::write_parquet(opts);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+template <cudf::io::io_type IO, cudf::io::compression_type Compression>
+void BM_parq_write_io_compression(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<IO>, nvbench::enum_type<Compression>>)
+{
+  auto const data_types = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                             static_cast<int32_t>(data_type::FLOAT),
+                                             static_cast<int32_t>(data_type::DECIMAL),
+                                             static_cast<int32_t>(data_type::TIMESTAMP),
+                                             static_cast<int32_t>(data_type::DURATION),
+                                             static_cast<int32_t>(data_type::STRING),
+                                             static_cast<int32_t>(data_type::LIST),
+                                             static_cast<int32_t>(data_type::STRUCT)});
+
+  cudf::size_type const cardinality = state.get_int64("cardinality");
+  cudf::size_type const run_length  = state.get_int64("run_length");
+  auto const compression            = Compression;
+  auto const sink_type              = IO;
+
+  auto const tbl =
+    create_random_table(cycle_dtypes(data_types, num_cols),
+                        table_size_bytes{data_size},
+                        data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto const mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(sink_type);
+
+               timer.start();
+               cudf::io::parquet_writer_options opts =
+                 cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+                   .compression(compression);
+               cudf::io::write_parquet(opts);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+template <cudf::io::statistics_freq Statistics, cudf::io::compression_type Compression>
+void BM_parq_write_varying_options(
+  nvbench::state& state,
+  nvbench::type_list<nvbench::enum_type<Statistics>, nvbench::enum_type<Compression>>)
+{
+  auto const enable_stats = Statistics;
+  auto const compression  = Compression;
+  auto const file_path    = state.get_string("file_path");
+
+  auto const data_types = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL_SIGNED),
+                                             static_cast<int32_t>(data_type::FLOAT),
+                                             static_cast<int32_t>(data_type::DECIMAL),
+                                             static_cast<int32_t>(data_type::TIMESTAMP),
+                                             static_cast<int32_t>(data_type::DURATION),
+                                             static_cast<int32_t>(data_type::STRING),
+                                             static_cast<int32_t>(data_type::LIST)});
+
+  auto const tbl  = create_random_table(data_types, table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(io_type::FILEPATH);
+
+               timer.start();
+               cudf::io::parquet_writer_options const options =
+                 cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view)
+                   .compression(compression)
+                   .stats_level(enable_stats)
+                   .column_chunks_file_paths({file_path});
+               cudf::io::write_parquet(options);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::DURATION,
+                                            data_type::STRING,
+                                            data_type::LIST,
+                                            data_type::STRUCT>;
+
+using io_list = nvbench::enum_type_list<cudf::io::io_type::FILEPATH,
+                                        cudf::io::io_type::HOST_BUFFER,
+                                        cudf::io::io_type::VOID>;
+
+using compression_list =
+  nvbench::enum_type_list<cudf::io::compression_type::SNAPPY, cudf::io::compression_type::NONE>;
+
+using stats_list = nvbench::enum_type_list<cudf::io::STATISTICS_NONE,
+                                           cudf::io::STATISTICS_ROWGROUP,
+                                           cudf::io::STATISTICS_COLUMN,
+                                           cudf::io::STATISTICS_PAGE>;
+
+NVBENCH_BENCH_TYPES(BM_parq_write_encode, NVBENCH_TYPE_AXES(d_type_list))
+  .set_name("parquet_write_encode")
+  .set_type_axes_names({"data_type"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_parq_write_io_compression, NVBENCH_TYPE_AXES(io_list, compression_list))
+  .set_name("parquet_write_io_compression")
+  .set_type_axes_names({"io", "compression"})
+  .set_min_samples(4)
+  .add_int64_axis("cardinality", {0, 1000})
+  .add_int64_axis("run_length", {1, 32});
+
+NVBENCH_BENCH_TYPES(BM_parq_write_varying_options, NVBENCH_TYPE_AXES(stats_list, compression_list))
+  .set_name("parquet_write_options")
+  .set_type_axes_names({"statistics", "compression"})
+  .set_min_samples(4)
+  .add_string_axis("file_path", {"unused_path.parquet", ""});
diff --git a/cpp/benchmarks/io/parquet/parquet_writer_chunks.cpp b/cpp/benchmarks/io/parquet/parquet_writer_chunks.cpp
new file mode 100644
index 0000000..b85c97f
--- /dev/null
+++ b/cpp/benchmarks/io/parquet/parquet_writer_chunks.cpp
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// Size of the data in the benchmark dataframe; chosen to be low enough to allow benchmarks to
+// run on most GPUs, but large enough to allow highest throughput
+constexpr int64_t data_size = 512 << 20;
+
+void PQ_write(nvbench::state& state)
+{
+  cudf::size_type const num_cols = state.get_int64("num_cols");
+
+  auto const tbl  = create_random_table(cycle_dtypes({cudf::type_id::INT32}, num_cols),
+                                       table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  std::size_t encoded_file_size = 0;
+  auto const mem_stats_logger   = cudf::memory_stats_logger();
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::timer | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch, auto& timer) {
+               cuio_source_sink_pair source_sink(io_type::VOID);
+
+               timer.start();
+               cudf::io::parquet_writer_options opts =
+                 cudf::io::parquet_writer_options::builder(source_sink.make_sink_info(), view);
+               cudf::io::write_parquet(opts);
+               timer.stop();
+
+               encoded_file_size = source_sink.size();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+void PQ_write_chunked(nvbench::state& state)
+{
+  cudf::size_type const num_cols   = state.get_int64("num_cols");
+  cudf::size_type const num_tables = state.get_int64("num_chunks");
+
+  std::vector<std::unique_ptr<cudf::table>> tables;
+  for (cudf::size_type idx = 0; idx < num_tables; idx++) {
+    tables.push_back(create_random_table(cycle_dtypes({cudf::type_id::INT32}, num_cols),
+                                         table_size_bytes{size_t(data_size / num_tables)}));
+  }
+
+  auto const mem_stats_logger   = cudf::memory_stats_logger();
+  std::size_t encoded_file_size = 0;
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(
+    nvbench::exec_tag::timer | nvbench::exec_tag::sync, [&](nvbench::launch& launch, auto& timer) {
+      cuio_source_sink_pair source_sink(io_type::VOID);
+
+      timer.start();
+      cudf::io::chunked_parquet_writer_options opts =
+        cudf::io::chunked_parquet_writer_options::builder(source_sink.make_sink_info());
+      cudf::io::parquet_chunked_writer writer(opts);
+      std::for_each(tables.begin(),
+                    tables.end(),
+                    [&writer](std::unique_ptr<cudf::table> const& tbl) { writer.write(*tbl); });
+      writer.close();
+      timer.stop();
+
+      encoded_file_size = source_sink.size();
+    });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(encoded_file_size, "encoded_file_size", "encoded_file_size");
+}
+
+NVBENCH_BENCH(PQ_write)
+  .set_name("parquet_write_num_cols")
+  .set_min_samples(4)
+  .add_int64_axis("num_cols", {8, 1024});
+
+NVBENCH_BENCH(PQ_write_chunked)
+  .set_name("parquet_chunked_write")
+  .set_min_samples(4)
+  .add_int64_axis("num_cols", {8, 1024})
+  .add_int64_axis("num_chunks", {8, 64});
diff --git a/cpp/benchmarks/io/text/multibyte_split.cpp b/cpp/benchmarks/io/text/multibyte_split.cpp
new file mode 100644
index 0000000..b5d855d
--- /dev/null
+++ b/cpp/benchmarks/io/text/multibyte_split.cpp
@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/file_utilities.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/utilities/pinned_host_vector.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/text/data_chunk_source_factories.hpp>
+#include <cudf/io/text/detail/bgzip_utils.hpp>
+#include <cudf/io/text/multibyte_split.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/transform.h>
+
+#include <nvbench/nvbench.cuh>
+
+#include <cstdio>
+#include <fstream>
+#include <memory>
+#include <random>
+
+temp_directory const temp_dir("cudf_nvbench");
+
+enum class data_chunk_source_type { device, file, file_datasource, host, host_pinned, file_bgzip };
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  data_chunk_source_type,
+  [](auto value) {
+    switch (value) {
+      case data_chunk_source_type::device: return "device";
+      case data_chunk_source_type::file: return "file";
+      case data_chunk_source_type::file_datasource: return "file_datasource";
+      case data_chunk_source_type::host: return "host";
+      case data_chunk_source_type::host_pinned: return "host_pinned";
+      case data_chunk_source_type::file_bgzip: return "file_bgzip";
+      default: return "Unknown";
+    }
+  },
+  [](auto) { return std::string{}; })
+
+static cudf::string_scalar create_random_input(int32_t num_chars,
+                                               double delim_factor,
+                                               double deviation,
+                                               std::string delim)
+{
+  auto const num_delims      = static_cast<int32_t>((num_chars * delim_factor) / delim.size());
+  auto const num_delim_chars = num_delims * delim.size();
+  auto const num_value_chars = num_chars - num_delim_chars;
+  auto const num_rows        = num_delims;
+  auto const value_size_avg  = static_cast<int32_t>(num_value_chars / num_rows);
+  auto const value_size_min  = static_cast<int32_t>(value_size_avg * (1 - deviation));
+  auto const value_size_max  = static_cast<int32_t>(value_size_avg * (1 + deviation));
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, value_size_min, value_size_max);
+
+  auto const values =
+    create_random_column(cudf::type_id::STRING, row_count{num_rows}, table_profile);
+
+  auto delim_scalar  = cudf::make_string_scalar(delim);
+  auto delims_column = cudf::make_column_from_scalar(*delim_scalar, num_rows);
+  auto input_table   = cudf::table_view({values->view(), delims_column->view()});
+  auto input_column  = cudf::strings::concatenate(input_table);
+
+  // extract the chars from the returned strings column.
+  auto input_column_contents = input_column->release();
+  auto chars_column_contents = input_column_contents.children[1]->release();
+  auto chars_buffer          = chars_column_contents.data.release();
+
+  // turn the chars in to a string scalar.
+  return cudf::string_scalar(std::move(*chars_buffer));
+}
+
+static void write_bgzip_file(cudf::host_span<char const> host_data, std::ostream& output_stream)
+{
+  // a bit of variability with a decent amount of padding so we don't overflow 16 bit block sizes
+  std::uniform_int_distribution<std::size_t> chunk_size_dist{64000, 65000};
+  std::default_random_engine rng{};
+  std::size_t pos = 0;
+  while (pos < host_data.size()) {
+    auto const remainder  = host_data.size() - pos;
+    auto const chunk_size = std::min(remainder, chunk_size_dist(rng));
+    cudf::io::text::detail::bgzip::write_compressed_block(output_stream,
+                                                          {host_data.data() + pos, chunk_size});
+    pos += chunk_size;
+  }
+  // empty block denotes EOF
+  cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+}
+
+template <data_chunk_source_type source_type>
+static void bench_multibyte_split(nvbench::state& state,
+                                  nvbench::type_list<nvbench::enum_type<source_type>>)
+{
+  auto const delim_size         = state.get_int64("delim_size");
+  auto const delim_percent      = state.get_int64("delim_percent");
+  auto const file_size_approx   = state.get_int64("size_approx");
+  auto const byte_range_percent = state.get_int64("byte_range_percent");
+  auto const strip_delimiters   = bool(state.get_int64("strip_delimiters"));
+
+  auto const byte_range_factor = static_cast<double>(byte_range_percent) / 100;
+  CUDF_EXPECTS(delim_percent >= 1, "delimiter percent must be at least 1");
+  CUDF_EXPECTS(delim_percent <= 50, "delimiter percent must be at most 50");
+  CUDF_EXPECTS(byte_range_percent >= 1, "byte range percent must be at least 1");
+  CUDF_EXPECTS(byte_range_percent <= 100, "byte range percent must be at most 100");
+
+  auto delim = std::string(delim_size, '0');
+  // the algorithm can only support 7 equal characters, so use different chars in the delimiter
+  std::iota(delim.begin(), delim.end(), '1');
+
+  auto const delim_factor = static_cast<double>(delim_percent) / 100;
+  std::unique_ptr<cudf::io::datasource> datasource;
+  auto device_input      = create_random_input(file_size_approx, delim_factor, 0.05, delim);
+  auto host_input        = std::vector<char>{};
+  auto host_pinned_input = cudf::detail::pinned_host_vector<char>{};
+
+  if (source_type != data_chunk_source_type::device &&
+      source_type != data_chunk_source_type::host_pinned) {
+    host_input = cudf::detail::make_std_vector_sync<char>(
+      {device_input.data(), static_cast<std::size_t>(device_input.size())},
+      cudf::get_default_stream());
+  }
+  if (source_type == data_chunk_source_type::host_pinned) {
+    host_pinned_input.resize(static_cast<std::size_t>(device_input.size()));
+    CUDF_CUDA_TRY(cudaMemcpy(
+      host_pinned_input.data(), device_input.data(), host_pinned_input.size(), cudaMemcpyDefault));
+  }
+
+  auto source = [&] {
+    switch (source_type) {
+      case data_chunk_source_type::file:
+      case data_chunk_source_type::file_datasource: {
+        auto const temp_file_name = random_file_in_dir(temp_dir.path());
+        std::ofstream(temp_file_name, std::ofstream::out)
+          .write(host_input.data(), host_input.size());
+        if (source_type == data_chunk_source_type::file) {
+          return cudf::io::text::make_source_from_file(temp_file_name);
+        } else {
+          datasource = cudf::io::datasource::create(temp_file_name);
+          return cudf::io::text::make_source(*datasource);
+        }
+      }
+      case data_chunk_source_type::host:  //
+        return cudf::io::text::make_source(host_input);
+      case data_chunk_source_type::host_pinned:
+        return cudf::io::text::make_source(host_pinned_input);
+      case data_chunk_source_type::device:  //
+        return cudf::io::text::make_source(device_input);
+      case data_chunk_source_type::file_bgzip: {
+        auto const temp_file_name = random_file_in_dir(temp_dir.path());
+        {
+          std::ofstream output_stream(temp_file_name, std::ofstream::out);
+          write_bgzip_file(host_input, output_stream);
+        }
+        return cudf::io::text::make_source_from_bgzip_file(temp_file_name);
+      }
+      default: CUDF_FAIL();
+    }
+  }();
+
+  auto mem_stats_logger   = cudf::memory_stats_logger();
+  auto const range_size   = static_cast<int64_t>(device_input.size() * byte_range_factor);
+  auto const range_offset = (device_input.size() - range_size) / 2;
+  cudf::io::text::byte_range_info range{range_offset, range_size};
+  cudf::io::text::parse_options options{range, strip_delimiters};
+  std::unique_ptr<cudf::column> output;
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    try_drop_l3_cache();
+    output = cudf::io::text::multibyte_split(*source, delim, options);
+  });
+
+  state.add_buffer_size(mem_stats_logger.peak_memory_usage(), "pmu", "Peak Memory Usage");
+  // TODO adapt to consistent naming scheme once established
+  state.add_buffer_size(range_size, "efs", "Encoded file size");
+}
+
+using source_type_list = nvbench::enum_type_list<data_chunk_source_type::device,
+                                                 data_chunk_source_type::file,
+                                                 data_chunk_source_type::file_datasource,
+                                                 data_chunk_source_type::host,
+                                                 data_chunk_source_type::host_pinned,
+                                                 data_chunk_source_type::file_bgzip>;
+
+NVBENCH_BENCH_TYPES(bench_multibyte_split,
+                    NVBENCH_TYPE_AXES(nvbench::enum_type_list<data_chunk_source_type::file>))
+  .set_name("multibyte_split_delimiters")
+  .set_min_samples(4)
+  .add_int64_axis("strip_delimiters", {0, 1})
+  .add_int64_axis("delim_size", {1, 4, 7})
+  .add_int64_axis("delim_percent", {1, 25})
+  .add_int64_power_of_two_axis("size_approx", {15})
+  .add_int64_axis("byte_range_percent", {50});
+
+NVBENCH_BENCH_TYPES(bench_multibyte_split, NVBENCH_TYPE_AXES(source_type_list))
+  .set_name("multibyte_split_source")
+  .set_min_samples(4)
+  .add_int64_axis("strip_delimiters", {1})
+  .add_int64_axis("delim_size", {1})
+  .add_int64_axis("delim_percent", {1})
+  .add_int64_power_of_two_axis("size_approx", {15, 30})
+  .add_int64_axis("byte_range_percent", {10, 100});
diff --git a/cpp/benchmarks/iterator/iterator.cu b/cpp/benchmarks/iterator/iterator.cu
new file mode 100644
index 0000000..dcd13cf
--- /dev/null
+++ b/cpp/benchmarks/iterator/iterator.cu
@@ -0,0 +1,255 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cub/device/device_reduce.cuh>
+
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/reduce.h>
+
+#include <random>
+
+template <typename T>
+T random_int(T min, T max)
+{
+  static unsigned seed = 13377331;
+  static std::mt19937 engine{seed};
+  static std::uniform_int_distribution<T> uniform{min, max};
+
+  return uniform(engine);
+}
+
+// -----------------------------------------------------------------------------
+template <typename InputIterator, typename OutputIterator, typename T>
+inline auto reduce_by_cub(OutputIterator result, InputIterator d_in, int num_items, T init)
+{
+  size_t temp_storage_bytes = 0;
+
+  cub::DeviceReduce::Reduce(
+    nullptr, temp_storage_bytes, d_in, result, num_items, cudf::DeviceSum{}, init);
+
+  // Allocate temporary storage
+  rmm::device_buffer d_temp_storage(temp_storage_bytes, cudf::get_default_stream());
+
+  // Run reduction
+  cub::DeviceReduce::Reduce(
+    d_temp_storage.data(), temp_storage_bytes, d_in, result, num_items, cudf::DeviceSum{}, init);
+
+  return temp_storage_bytes;
+}
+
+// -----------------------------------------------------------------------------
+template <typename T>
+void raw_stream_bench_cub(cudf::column_view& col, rmm::device_uvector<T>& result)
+{
+  // std::cout << "raw stream cub: " << "\t";
+
+  T init{0};
+  auto begin    = col.data<T>();
+  int num_items = col.size();
+
+  reduce_by_cub(result.begin(), begin, num_items, init);
+};
+
+template <typename T, bool has_null>
+void iterator_bench_cub(cudf::column_view& col, rmm::device_uvector<T>& result)
+{
+  // std::cout << "iterator cub " << ( (has_null) ? "<true>: " : "<false>: " ) << "\t";
+
+  T init{0};
+  auto d_col    = cudf::column_device_view::create(col);
+  int num_items = col.size();
+  if (has_null) {
+    auto begin = cudf::detail::make_null_replacement_iterator(*d_col, init);
+    reduce_by_cub(result.begin(), begin, num_items, init);
+  } else {
+    auto begin = d_col->begin<T>();
+    reduce_by_cub(result.begin(), begin, num_items, init);
+  }
+}
+
+// -----------------------------------------------------------------------------
+template <typename T>
+void raw_stream_bench_thrust(cudf::column_view& col, rmm::device_uvector<T>& result)
+{
+  // std::cout << "raw stream thust: " << "\t\t";
+
+  T init{0};
+  auto d_in  = col.data<T>();
+  auto d_end = d_in + col.size();
+  thrust::reduce(thrust::device, d_in, d_end, init, cudf::DeviceSum{});
+}
+
+template <typename T, bool has_null>
+void iterator_bench_thrust(cudf::column_view& col, rmm::device_uvector<T>& result)
+{
+  // std::cout << "iterator thust " << ( (has_null) ? "<true>: " : "<false>: " ) << "\t";
+
+  T init{0};
+  auto d_col = cudf::column_device_view::create(col);
+  if (has_null) {
+    auto d_in  = cudf::detail::make_null_replacement_iterator(*d_col, init);
+    auto d_end = d_in + col.size();
+    thrust::reduce(thrust::device, d_in, d_end, init, cudf::DeviceSum{});
+  } else {
+    auto d_in  = d_col->begin<T>();
+    auto d_end = d_in + col.size();
+    thrust::reduce(thrust::device, d_in, d_end, init, cudf::DeviceSum{});
+  }
+}
+
+// -----------------------------------------------------------------------------
+class Iterator : public cudf::benchmark {};
+
+template <class TypeParam, bool cub_or_thrust, bool raw_or_iterator>
+void BM_iterator(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+  using T      = TypeParam;
+  auto num_gen = thrust::counting_iterator<cudf::size_type>(0);
+
+  cudf::test::fixed_width_column_wrapper<T> wrap_hasnull_F(num_gen, num_gen + column_size);
+  cudf::column_view hasnull_F = wrap_hasnull_F;
+
+  // Initialize dev_result to false
+  auto dev_result = cudf::detail::make_zeroed_device_uvector_sync<TypeParam>(
+    1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    if (cub_or_thrust) {
+      if (raw_or_iterator) {
+        raw_stream_bench_cub<T>(hasnull_F, dev_result);  // driven by raw pointer
+      } else {
+        iterator_bench_cub<T, false>(hasnull_F, dev_result);  // driven by riterator without nulls
+      }
+    } else {
+      if (raw_or_iterator) {
+        raw_stream_bench_thrust<T>(hasnull_F, dev_result);  // driven by raw pointer
+      } else {
+        iterator_bench_thrust<T, false>(hasnull_F,
+                                        dev_result);  // driven by riterator without nulls
+      }
+    }
+  }
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * column_size *
+                          sizeof(TypeParam));
+}
+
+// operator+ defined for pair iterator reduction
+template <typename T>
+__device__ thrust::pair<T, bool> operator+(thrust::pair<T, bool> lhs, thrust::pair<T, bool> rhs)
+{
+  return thrust::pair<T, bool>{lhs.first * lhs.second + rhs.first * rhs.second,
+                               lhs.second + rhs.second};
+}
+// -----------------------------------------------------------------------------
+template <typename T, bool has_null>
+void pair_iterator_bench_cub(cudf::column_view& col,
+                             rmm::device_uvector<thrust::pair<T, bool>>& result)
+{
+  thrust::pair<T, bool> init{0, false};
+  auto d_col    = cudf::column_device_view::create(col);
+  int num_items = col.size();
+  auto begin    = d_col->pair_begin<T, has_null>();
+  reduce_by_cub(result.begin(), begin, num_items, init);
+}
+
+template <typename T, bool has_null>
+void pair_iterator_bench_thrust(cudf::column_view& col,
+                                rmm::device_uvector<thrust::pair<T, bool>>& result)
+{
+  thrust::pair<T, bool> init{0, false};
+  auto d_col = cudf::column_device_view::create(col);
+  auto d_in  = d_col->pair_begin<T, has_null>();
+  auto d_end = d_in + col.size();
+  thrust::reduce(thrust::device, d_in, d_end, init, cudf::DeviceSum{});
+}
+
+template <class TypeParam, bool cub_or_thrust>
+void BM_pair_iterator(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+  using T      = TypeParam;
+  auto num_gen = thrust::counting_iterator<cudf::size_type>(0);
+  auto null_gen =
+    thrust::make_transform_iterator(num_gen, [](cudf::size_type row) { return row % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<T> wrap_hasnull_F(num_gen, num_gen + column_size);
+  cudf::test::fixed_width_column_wrapper<T> wrap_hasnull_T(
+    num_gen, num_gen + column_size, null_gen);
+  cudf::column_view hasnull_F = wrap_hasnull_F;
+  cudf::column_view hasnull_T = wrap_hasnull_T;
+
+  // Initialize dev_result to false
+  auto dev_result = cudf::detail::make_zeroed_device_uvector_sync<thrust::pair<T, bool>>(
+    1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    if (cub_or_thrust) {
+      pair_iterator_bench_cub<T, false>(hasnull_T,
+                                        dev_result);  // driven by pair iterator with nulls
+    } else {
+      pair_iterator_bench_thrust<T, false>(hasnull_T,
+                                           dev_result);  // driven by pair iterator with nulls
+    }
+  }
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * column_size *
+                          sizeof(TypeParam));
+}
+
+#define ITER_BM_BENCHMARK_DEFINE(name, type, cub_or_thrust, raw_or_iterator) \
+  BENCHMARK_DEFINE_F(Iterator, name)(::benchmark::State & state)             \
+  {                                                                          \
+    BM_iterator<type, cub_or_thrust, raw_or_iterator>(state);                \
+  }                                                                          \
+  BENCHMARK_REGISTER_F(Iterator, name)                                       \
+    ->RangeMultiplier(10)                                                    \
+    ->Range(1000, 10000000)                                                  \
+    ->UseManualTime()                                                        \
+    ->Unit(benchmark::kMillisecond);
+
+ITER_BM_BENCHMARK_DEFINE(double_cub_raw, double, true, true);
+ITER_BM_BENCHMARK_DEFINE(double_cub_iter, double, true, false);
+ITER_BM_BENCHMARK_DEFINE(double_thrust_raw, double, false, true);
+ITER_BM_BENCHMARK_DEFINE(double_thrust_iter, double, false, false);
+
+#define PAIRITER_BM_BENCHMARK_DEFINE(name, type, cub_or_thrust)  \
+  BENCHMARK_DEFINE_F(Iterator, name)(::benchmark::State & state) \
+  {                                                              \
+    BM_pair_iterator<type, cub_or_thrust>(state);                \
+  }                                                              \
+  BENCHMARK_REGISTER_F(Iterator, name)                           \
+    ->RangeMultiplier(10)                                        \
+    ->Range(1000, 10000000)                                      \
+    ->UseManualTime()                                            \
+    ->Unit(benchmark::kMillisecond);
+
+PAIRITER_BM_BENCHMARK_DEFINE(double_cub_pair, double, true);
+PAIRITER_BM_BENCHMARK_DEFINE(double_thrust_pair, double, false);
diff --git a/cpp/benchmarks/join/conditional_join.cu b/cpp/benchmarks/join/conditional_join.cu
new file mode 100644
index 0000000..d721de0
--- /dev/null
+++ b/cpp/benchmarks/join/conditional_join.cu
@@ -0,0 +1,287 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/join/join_common.hpp>
+
+template <typename key_type, typename payload_type>
+class ConditionalJoin : public cudf::benchmark {};
+
+// For compatibility with the shared logic for equality (hash) joins, all of
+// the join lambdas defined by these macros accept a null_equality parameter
+// but ignore it (don't forward it to the underlying join implementation)
+// because conditional joins do not use this parameter.
+#define CONDITIONAL_INNER_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(ConditionalJoin, name, key_type, payload_type)            \
+  (::benchmark::State & st)                                                             \
+  {                                                                                     \
+    auto join = [](cudf::table_view const& left,                                        \
+                   cudf::table_view const& right,                                       \
+                   cudf::ast::operation binary_pred,                                    \
+                   cudf::null_equality compare_nulls) {                                 \
+      return cudf::conditional_inner_join(left, right, binary_pred);                    \
+    };                                                                                  \
+    BM_join<key_type, payload_type, nullable, join_t::CONDITIONAL>(st, join);           \
+  }
+
+CONDITIONAL_INNER_JOIN_BENCHMARK_DEFINE(conditional_inner_join_32bit, int32_t, int32_t, false);
+CONDITIONAL_INNER_JOIN_BENCHMARK_DEFINE(conditional_inner_join_64bit, int64_t, int64_t, false);
+CONDITIONAL_INNER_JOIN_BENCHMARK_DEFINE(conditional_inner_join_32bit_nulls, int32_t, int32_t, true);
+CONDITIONAL_INNER_JOIN_BENCHMARK_DEFINE(conditional_inner_join_64bit_nulls, int64_t, int64_t, true);
+
+#define CONDITIONAL_LEFT_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(ConditionalJoin, name, key_type, payload_type)           \
+  (::benchmark::State & st)                                                            \
+  {                                                                                    \
+    auto join = [](cudf::table_view const& left,                                       \
+                   cudf::table_view const& right,                                      \
+                   cudf::ast::operation binary_pred,                                   \
+                   cudf::null_equality compare_nulls) {                                \
+      return cudf::conditional_left_join(left, right, binary_pred);                    \
+    };                                                                                 \
+    BM_join<key_type, payload_type, nullable, join_t::CONDITIONAL>(st, join);          \
+  }
+
+CONDITIONAL_LEFT_JOIN_BENCHMARK_DEFINE(conditional_left_join_32bit, int32_t, int32_t, false);
+CONDITIONAL_LEFT_JOIN_BENCHMARK_DEFINE(conditional_left_join_64bit, int64_t, int64_t, false);
+CONDITIONAL_LEFT_JOIN_BENCHMARK_DEFINE(conditional_left_join_32bit_nulls, int32_t, int32_t, true);
+CONDITIONAL_LEFT_JOIN_BENCHMARK_DEFINE(conditional_left_join_64bit_nulls, int64_t, int64_t, true);
+
+#define CONDITIONAL_FULL_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(ConditionalJoin, name, key_type, payload_type)           \
+  (::benchmark::State & st)                                                            \
+  {                                                                                    \
+    auto join = [](cudf::table_view const& left,                                       \
+                   cudf::table_view const& right,                                      \
+                   cudf::ast::operation binary_pred,                                   \
+                   cudf::null_equality compare_nulls) {                                \
+      return cudf::conditional_full_join(left, right, binary_pred);                    \
+    };                                                                                 \
+    BM_join<key_type, payload_type, nullable, join_t::CONDITIONAL>(st, join);          \
+  }
+
+CONDITIONAL_FULL_JOIN_BENCHMARK_DEFINE(conditional_full_join_32bit, int32_t, int32_t, false);
+CONDITIONAL_FULL_JOIN_BENCHMARK_DEFINE(conditional_full_join_64bit, int64_t, int64_t, false);
+CONDITIONAL_FULL_JOIN_BENCHMARK_DEFINE(conditional_full_join_32bit_nulls, int32_t, int32_t, true);
+CONDITIONAL_FULL_JOIN_BENCHMARK_DEFINE(conditional_full_join_64bit_nulls, int64_t, int64_t, true);
+
+#define CONDITIONAL_LEFT_ANTI_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(ConditionalJoin, name, key_type, payload_type)                \
+  (::benchmark::State & st)                                                                 \
+  {                                                                                         \
+    auto join = [](cudf::table_view const& left,                                            \
+                   cudf::table_view const& right,                                           \
+                   cudf::ast::operation binary_pred,                                        \
+                   cudf::null_equality compare_nulls) {                                     \
+      return cudf::conditional_left_anti_join(left, right, binary_pred);                    \
+    };                                                                                      \
+    BM_join<key_type, payload_type, nullable, join_t::CONDITIONAL>(st, join);               \
+  }
+
+CONDITIONAL_LEFT_ANTI_JOIN_BENCHMARK_DEFINE(conditional_left_anti_join_32bit,
+                                            int32_t,
+                                            int32_t,
+                                            false);
+CONDITIONAL_LEFT_ANTI_JOIN_BENCHMARK_DEFINE(conditional_left_anti_join_64bit,
+                                            int64_t,
+                                            int64_t,
+                                            false);
+CONDITIONAL_LEFT_ANTI_JOIN_BENCHMARK_DEFINE(conditional_left_anti_join_32bit_nulls,
+                                            int32_t,
+                                            int32_t,
+                                            true);
+CONDITIONAL_LEFT_ANTI_JOIN_BENCHMARK_DEFINE(conditional_left_anti_join_64bit_nulls,
+                                            int64_t,
+                                            int64_t,
+                                            true);
+
+#define CONDITIONAL_LEFT_SEMI_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(ConditionalJoin, name, key_type, payload_type)                \
+  (::benchmark::State & st)                                                                 \
+  {                                                                                         \
+    auto join = [](cudf::table_view const& left,                                            \
+                   cudf::table_view const& right,                                           \
+                   cudf::ast::operation binary_pred,                                        \
+                   cudf::null_equality compare_nulls) {                                     \
+      return cudf::conditional_left_semi_join(left, right, binary_pred);                    \
+    };                                                                                      \
+    BM_join<key_type, payload_type, nullable, join_t::CONDITIONAL>(st, join);               \
+  }
+
+CONDITIONAL_LEFT_SEMI_JOIN_BENCHMARK_DEFINE(conditional_left_semi_join_32bit,
+                                            int32_t,
+                                            int32_t,
+                                            false);
+CONDITIONAL_LEFT_SEMI_JOIN_BENCHMARK_DEFINE(conditional_left_semi_join_64bit,
+                                            int64_t,
+                                            int64_t,
+                                            false);
+CONDITIONAL_LEFT_SEMI_JOIN_BENCHMARK_DEFINE(conditional_left_semi_join_32bit_nulls,
+                                            int32_t,
+                                            int32_t,
+                                            true);
+CONDITIONAL_LEFT_SEMI_JOIN_BENCHMARK_DEFINE(conditional_left_semi_join_64bit_nulls,
+                                            int64_t,
+                                            int64_t,
+                                            true);
+
+// inner join -----------------------------------------------------------------------
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_inner_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({400'000, 100'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_inner_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({400'000, 100'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_inner_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({400'000, 100'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_inner_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({400'000, 100'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+// left join -----------------------------------------------------------------------
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+// full join -----------------------------------------------------------------------
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_full_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_full_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_full_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_full_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+// left anti-join -------------------------------------------------------------
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_anti_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_anti_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_anti_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_anti_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+// left semi-join -------------------------------------------------------------
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_semi_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_semi_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_semi_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(ConditionalJoin, conditional_left_semi_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->UseManualTime();
diff --git a/cpp/benchmarks/join/generate_input_tables.cuh b/cpp/benchmarks/join/generate_input_tables.cuh
new file mode 100644
index 0000000..84e607a
--- /dev/null
+++ b/cpp/benchmarks/join/generate_input_tables.cuh
@@ -0,0 +1,181 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/utilities/device_atomics.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/sequence.h>
+
+#include <curand.h>
+#include <curand_kernel.h>
+
+#include <cassert>
+
+__global__ static void init_curand(curandState* state, int const nstates)
+{
+  int ithread = threadIdx.x + blockIdx.x * blockDim.x;
+
+  if (ithread < nstates) { curand_init(1234ULL, ithread, 0, state + ithread); }
+}
+
+template <typename key_type, typename size_type>
+__global__ static void init_build_tbl(key_type* const build_tbl,
+                                      size_type const build_tbl_size,
+                                      int const multiplicity,
+                                      curandState* state,
+                                      int const num_states)
+{
+  auto const start_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  auto const stride    = blockDim.x * gridDim.x;
+  assert(start_idx < num_states);
+
+  curandState localState = state[start_idx];
+
+  for (size_type idx = start_idx; idx < build_tbl_size; idx += stride) {
+    double const x = curand_uniform_double(&localState);
+
+    build_tbl[idx] = static_cast<key_type>(x * (build_tbl_size / multiplicity));
+  }
+
+  state[start_idx] = localState;
+}
+
+template <typename key_type, typename size_type>
+__global__ void init_probe_tbl(key_type* const probe_tbl,
+                               size_type const probe_tbl_size,
+                               size_type const build_tbl_size,
+                               key_type const rand_max,
+                               double const selectivity,
+                               int const multiplicity,
+                               curandState* state,
+                               int const num_states)
+{
+  auto const start_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  auto const stride    = blockDim.x * gridDim.x;
+  assert(start_idx < num_states);
+
+  curandState localState = state[start_idx];
+
+  for (size_type idx = start_idx; idx < probe_tbl_size; idx += stride) {
+    key_type val;
+    double x = curand_uniform_double(&localState);
+
+    if (x <= selectivity) {
+      // x <= selectivity means this key in the probe table should be present in the build table, so
+      // we pick a key from [0, build_tbl_size / multiplicity]
+      x   = curand_uniform_double(&localState);
+      val = static_cast<key_type>(x * (build_tbl_size / multiplicity));
+    } else {
+      // This key in the probe table should not be present in the build table, so we pick a key from
+      // [build_tbl_size, rand_max].
+      x   = curand_uniform_double(&localState);
+      val = static_cast<key_type>(x * (rand_max - build_tbl_size) + build_tbl_size);
+    }
+    probe_tbl[idx] = val;
+  }
+
+  state[start_idx] = localState;
+}
+
+/**
+ * generate_input_tables generates random integer input tables for database benchmarks.
+ *
+ * generate_input_tables generates two random integer input tables for database benchmark
+ * mainly designed to benchmark join operations. The templates key_type and size_type needed
+ * to be builtin integer types (e.g. short, int, longlong) and key_type needs to be signed
+ * as the lottery used internally relies on being able to use negative values to mark drawn
+ * numbers. The tables need to be preallocated in a memory region accessible by the GPU
+ * (e.g. device memory, zero copy memory or unified memory). Each value in the build table
+ * will be from [0,rand_max] and if uniq_build_tbl_keys is true it is ensured that each value
+ * will be uniq in the build table. Each value in the probe table will be also in the build
+ * table with a probability of selectivity and a random number from
+ * [0,rand_max] \setminus \{build_tbl\} otherwise.
+ *
+ * @param[out] build_tbl            The build table to generate. Usually the smaller table used to
+ *                                  "build" the hash table in a hash based join implementation.
+ * @param[in] build_tbl_size        number of keys in the build table
+ * @param[out] probe_tbl            The probe table to generate. Usually the larger table used to
+ *                                  probe into the hash table created from the build table.
+ * @param[in] build_tbl_size        number of keys in the build table
+ * @param[in] selectivity           probability with which an element of the probe table is
+ *                                  present in the build table.
+ * @param[in] multiplicity          number of matches for each key.
+ */
+template <typename key_type, typename size_type>
+void generate_input_tables(key_type* const build_tbl,
+                           size_type const build_tbl_size,
+                           key_type* const probe_tbl,
+                           size_type const probe_tbl_size,
+                           double const selectivity,
+                           int const multiplicity)
+{
+  // With large values of rand_max the a lot of temporary storage is needed for the lottery. At the
+  // expense of not being that accurate with applying the selectivity an especially more memory
+  // efficient implementations would be to partition the random numbers into two intervals and then
+  // let one table choose random numbers from only one interval and the other only select with
+  // selective probability from the same interval and from the other in the other cases.
+
+  constexpr int block_size = 128;
+
+  // Maximize exposed parallelism while minimizing storage for curand state
+  int num_blocks_init_build_tbl{-1};
+  CUDF_CUDA_TRY(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &num_blocks_init_build_tbl, init_build_tbl<key_type, size_type>, block_size, 0));
+
+  int num_blocks_init_probe_tbl{-1};
+  CUDF_CUDA_TRY(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+    &num_blocks_init_probe_tbl, init_probe_tbl<key_type, size_type>, block_size, 0));
+
+  int dev_id{-1};
+  CUDF_CUDA_TRY(cudaGetDevice(&dev_id));
+
+  int num_sms{-1};
+  CUDF_CUDA_TRY(cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id));
+
+  int const num_states =
+    num_sms * std::max(num_blocks_init_build_tbl, num_blocks_init_probe_tbl) * block_size;
+  rmm::device_uvector<curandState> devStates(num_states, cudf::get_default_stream());
+
+  init_curand<<<(num_states - 1) / block_size + 1, block_size>>>(devStates.data(), num_states);
+
+  CUDF_CHECK_CUDA(0);
+
+  init_build_tbl<key_type, size_type><<<num_sms * num_blocks_init_build_tbl, block_size>>>(
+    build_tbl, build_tbl_size, multiplicity, devStates.data(), num_states);
+
+  CUDF_CHECK_CUDA(0);
+
+  auto const rand_max = std::numeric_limits<key_type>::max();
+
+  init_probe_tbl<key_type, size_type>
+    <<<num_sms * num_blocks_init_build_tbl, block_size>>>(probe_tbl,
+                                                          probe_tbl_size,
+                                                          build_tbl_size,
+                                                          rand_max,
+                                                          selectivity,
+                                                          multiplicity,
+                                                          devStates.data(),
+                                                          num_states);
+
+  CUDF_CHECK_CUDA(0);
+}
diff --git a/cpp/benchmarks/join/join.cu b/cpp/benchmarks/join/join.cu
new file mode 100644
index 0000000..1c02a44
--- /dev/null
+++ b/cpp/benchmarks/join/join.cu
@@ -0,0 +1,194 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/join/join_common.hpp>
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_inner_join(nvbench::state& state,
+                        nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_input,
+                 cudf::table_view const& right_input,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    auto const has_nulls = cudf::has_nested_nulls(left_input) || cudf::has_nested_nulls(right_input)
+                             ? cudf::nullable_join::YES
+                             : cudf::nullable_join::NO;
+    cudf::hash_join hj_obj(left_input, has_nulls, compare_nulls, stream);
+    return hj_obj.inner_join(right_input, std::nullopt, stream);
+  };
+
+  BM_join<key_type, payload_type, Nullable>(state, join);
+}
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_left_join(nvbench::state& state,
+                       nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_input,
+                 cudf::table_view const& right_input,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    auto const has_nulls = cudf::has_nested_nulls(left_input) || cudf::has_nested_nulls(right_input)
+                             ? cudf::nullable_join::YES
+                             : cudf::nullable_join::NO;
+    cudf::hash_join hj_obj(left_input, has_nulls, compare_nulls, stream);
+    return hj_obj.left_join(right_input, std::nullopt, stream);
+  };
+
+  BM_join<key_type, payload_type, Nullable>(state, join);
+}
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_full_join(nvbench::state& state,
+                       nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_input,
+                 cudf::table_view const& right_input,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    auto const has_nulls = cudf::has_nested_nulls(left_input) || cudf::has_nested_nulls(right_input)
+                             ? cudf::nullable_join::YES
+                             : cudf::nullable_join::NO;
+    cudf::hash_join hj_obj(left_input, has_nulls, compare_nulls, stream);
+    return hj_obj.full_join(right_input, std::nullopt, stream);
+  };
+
+  BM_join<key_type, payload_type, Nullable>(state, join);
+}
+
+// inner join -----------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("inner_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("inner_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("inner_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("inner_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+// left join ------------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("left_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("left_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("left_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("left_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+// full join ------------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("full_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("full_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("full_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("full_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
diff --git a/cpp/benchmarks/join/join_common.hpp b/cpp/benchmarks/join/join_common.hpp
new file mode 100644
index 0000000..7d1b1c7
--- /dev/null
+++ b/cpp/benchmarks/join/join_common.hpp
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "generate_input_tables.cuh"
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/filling.hpp>
+#include <cudf/join.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/random/linear_congruential_engine.h>
+#include <thrust/random/uniform_int_distribution.h>
+
+#include <vector>
+
+struct null75_generator {
+  thrust::minstd_rand engine;
+  thrust::uniform_int_distribution<unsigned> rand_gen;
+  null75_generator() : engine(), rand_gen() {}
+  __device__ bool operator()(size_t i)
+  {
+    engine.discard(i);
+    // roughly 75% nulls
+    return (rand_gen(engine) & 3) == 0;
+  }
+};
+
+enum class join_t { CONDITIONAL, MIXED, HASH };
+
+inline void skip_helper(nvbench::state& state)
+{
+  auto const build_table_size = state.get_int64("Build Table Size");
+  auto const probe_table_size = state.get_int64("Probe Table Size");
+
+  if (build_table_size > probe_table_size) {
+    state.skip("Large build tables are skipped.");
+    return;
+  }
+
+  if (build_table_size * 100 <= probe_table_size) {
+    state.skip("Large probe tables are skipped.");
+    return;
+  }
+}
+
+template <typename key_type,
+          typename payload_type,
+          bool Nullable,
+          join_t join_type = join_t::HASH,
+          typename state_type,
+          typename Join>
+void BM_join(state_type& state, Join JoinFunc)
+{
+  auto const build_table_size = [&]() {
+    if constexpr (std::is_same_v<state_type, benchmark::State>) {
+      return static_cast<cudf::size_type>(state.range(0));
+    }
+    if constexpr (std::is_same_v<state_type, nvbench::state>) {
+      return static_cast<cudf::size_type>(state.get_int64("Build Table Size"));
+    }
+  }();
+  auto const probe_table_size = [&]() {
+    if constexpr (std::is_same_v<state_type, benchmark::State>) {
+      return static_cast<cudf::size_type>(state.range(1));
+    }
+    if constexpr (std::is_same_v<state_type, nvbench::state>) {
+      return static_cast<cudf::size_type>(state.get_int64("Probe Table Size"));
+    }
+  }();
+
+  double const selectivity = 0.3;
+  int const multiplicity   = 1;
+
+  // Generate build and probe tables
+  auto build_random_null_mask = [](int size) {
+    // roughly 75% nulls
+    auto validity =
+      thrust::make_transform_iterator(thrust::make_counting_iterator(0), null75_generator{});
+    return cudf::detail::valid_if(validity,
+                                  validity + size,
+                                  thrust::identity<bool>{},
+                                  cudf::get_default_stream(),
+                                  rmm::mr::get_current_device_resource());
+  };
+
+  std::unique_ptr<cudf::column> build_key_column0 = [&]() {
+    auto [null_mask, null_count] = build_random_null_mask(build_table_size);
+    return Nullable ? cudf::make_numeric_column(cudf::data_type(cudf::type_to_id<key_type>()),
+                                                build_table_size,
+                                                std::move(null_mask),
+                                                null_count)
+                    : cudf::make_numeric_column(cudf::data_type(cudf::type_to_id<key_type>()),
+                                                build_table_size);
+  }();
+  std::unique_ptr<cudf::column> probe_key_column0 = [&]() {
+    auto [null_mask, null_count] = build_random_null_mask(probe_table_size);
+    return Nullable ? cudf::make_numeric_column(cudf::data_type(cudf::type_to_id<key_type>()),
+                                                probe_table_size,
+                                                std::move(null_mask),
+                                                null_count)
+                    : cudf::make_numeric_column(cudf::data_type(cudf::type_to_id<key_type>()),
+                                                probe_table_size);
+  }();
+
+  generate_input_tables<key_type, cudf::size_type>(
+    build_key_column0->mutable_view().data<key_type>(),
+    build_table_size,
+    probe_key_column0->mutable_view().data<key_type>(),
+    probe_table_size,
+    selectivity,
+    multiplicity);
+
+  // Copy build_key_column0 and probe_key_column0 into new columns.
+  // If Nullable, the new columns will be assigned new nullmasks.
+  auto const build_key_column1 = [&]() {
+    auto col = std::make_unique<cudf::column>(build_key_column0->view());
+    if (Nullable) {
+      auto [null_mask, null_count] = build_random_null_mask(build_table_size);
+      col->set_null_mask(std::move(null_mask), null_count);
+    }
+    return col;
+  }();
+  auto const probe_key_column1 = [&]() {
+    auto col = std::make_unique<cudf::column>(probe_key_column0->view());
+    if (Nullable) {
+      auto [null_mask, null_count] = build_random_null_mask(probe_table_size);
+      col->set_null_mask(std::move(null_mask), null_count);
+    }
+    return col;
+  }();
+
+  auto init = cudf::make_fixed_width_scalar<payload_type>(static_cast<payload_type>(0));
+  auto build_payload_column = cudf::sequence(build_table_size, *init);
+  auto probe_payload_column = cudf::sequence(probe_table_size, *init);
+
+  CUDF_CHECK_CUDA(0);
+
+  cudf::table_view build_table(
+    {build_key_column0->view(), build_key_column1->view(), *build_payload_column});
+  cudf::table_view probe_table(
+    {probe_key_column0->view(), probe_key_column1->view(), *probe_payload_column});
+
+  // Setup join parameters and result table
+  [[maybe_unused]] std::vector<cudf::size_type> columns_to_join = {0};
+
+  // Benchmark the inner join operation
+  if constexpr (std::is_same_v<state_type, benchmark::State> and
+                (join_type != join_t::CONDITIONAL)) {
+    for (auto _ : state) {
+      cuda_event_timer raii(state, true, cudf::get_default_stream());
+
+      auto result = JoinFunc(probe_table.select(columns_to_join),
+                             build_table.select(columns_to_join),
+                             cudf::null_equality::UNEQUAL);
+    }
+  }
+  if constexpr (std::is_same_v<state_type, nvbench::state> and (join_type != join_t::CONDITIONAL)) {
+    if constexpr (join_type == join_t::MIXED) {
+      auto const col_ref_left_0 = cudf::ast::column_reference(0);
+      auto const col_ref_right_0 =
+        cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+      auto left_zero_eq_right_zero =
+        cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_left_0, col_ref_right_0);
+      state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+        rmm::cuda_stream_view stream_view{launch.get_stream()};
+        auto result = JoinFunc(probe_table.select(columns_to_join),
+                               build_table.select(columns_to_join),
+                               probe_table.select({1}),
+                               build_table.select({1}),
+                               left_zero_eq_right_zero,
+                               cudf::null_equality::UNEQUAL,
+                               stream_view);
+      });
+    }
+    if constexpr (join_type == join_t::HASH) {
+      state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+        rmm::cuda_stream_view stream_view{launch.get_stream()};
+        auto result = JoinFunc(probe_table.select(columns_to_join),
+                               build_table.select(columns_to_join),
+                               cudf::null_equality::UNEQUAL,
+                               stream_view);
+      });
+    }
+  }
+
+  // Benchmark conditional join
+  if constexpr (std::is_same_v<state_type, benchmark::State> and join_type == join_t::CONDITIONAL) {
+    // Common column references.
+    auto const col_ref_left_0  = cudf::ast::column_reference(0);
+    auto const col_ref_right_0 = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+    auto left_zero_eq_right_zero =
+      cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_left_0, col_ref_right_0);
+
+    for (auto _ : state) {
+      cuda_event_timer raii(state, true, cudf::get_default_stream());
+
+      auto result =
+        JoinFunc(probe_table, build_table, left_zero_eq_right_zero, cudf::null_equality::UNEQUAL);
+    }
+  }
+}
diff --git a/cpp/benchmarks/join/left_join.cu b/cpp/benchmarks/join/left_join.cu
new file mode 100644
index 0000000..96bbd1b
--- /dev/null
+++ b/cpp/benchmarks/join/left_join.cu
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/join/join_common.hpp>
+
+template <typename key_type, typename payload_type>
+class Join : public cudf::benchmark {};
+
+#define LEFT_ANTI_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(Join, name, key_type, payload_type)               \
+  (::benchmark::State & st)                                                     \
+  {                                                                             \
+    auto join = [](cudf::table_view const& left,                                \
+                   cudf::table_view const& right,                               \
+                   cudf::null_equality compare_nulls) {                         \
+      return cudf::left_anti_join(left, right, compare_nulls);                  \
+    };                                                                          \
+    BM_join<key_type, payload_type, nullable>(st, join);                        \
+  }
+
+LEFT_ANTI_JOIN_BENCHMARK_DEFINE(left_anti_join_32bit, int32_t, int32_t, false);
+LEFT_ANTI_JOIN_BENCHMARK_DEFINE(left_anti_join_64bit, int64_t, int64_t, false);
+LEFT_ANTI_JOIN_BENCHMARK_DEFINE(left_anti_join_32bit_nulls, int32_t, int32_t, true);
+LEFT_ANTI_JOIN_BENCHMARK_DEFINE(left_anti_join_64bit_nulls, int64_t, int64_t, true);
+
+#define LEFT_SEMI_JOIN_BENCHMARK_DEFINE(name, key_type, payload_type, nullable) \
+  BENCHMARK_TEMPLATE_DEFINE_F(Join, name, key_type, payload_type)               \
+  (::benchmark::State & st)                                                     \
+  {                                                                             \
+    auto join = [](cudf::table_view const& left,                                \
+                   cudf::table_view const& right,                               \
+                   cudf::null_equality compare_nulls) {                         \
+      return cudf::left_semi_join(left, right, compare_nulls);                  \
+    };                                                                          \
+    BM_join<key_type, payload_type, nullable>(st, join);                        \
+  }
+
+LEFT_SEMI_JOIN_BENCHMARK_DEFINE(left_semi_join_32bit, int32_t, int32_t, false);
+LEFT_SEMI_JOIN_BENCHMARK_DEFINE(left_semi_join_64bit, int64_t, int64_t, false);
+LEFT_SEMI_JOIN_BENCHMARK_DEFINE(left_semi_join_32bit_nulls, int32_t, int32_t, true);
+LEFT_SEMI_JOIN_BENCHMARK_DEFINE(left_semi_join_64bit_nulls, int64_t, int64_t, true);
+
+// left anti-join -------------------------------------------------------------
+BENCHMARK_REGISTER_F(Join, left_anti_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->Args({10'000'000, 10'000'000})
+  ->Args({10'000'000, 40'000'000})
+  ->Args({10'000'000, 100'000'000})
+  ->Args({100'000'000, 100'000'000})
+  ->Args({80'000'000, 240'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(Join, left_anti_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({50'000'000, 50'000'000})
+  ->Args({40'000'000, 120'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(Join, left_anti_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->Args({10'000'000, 10'000'000})
+  ->Args({10'000'000, 40'000'000})
+  ->Args({10'000'000, 100'000'000})
+  ->Args({100'000'000, 100'000'000})
+  ->Args({80'000'000, 240'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(Join, left_anti_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({50'000'000, 50'000'000})
+  ->Args({40'000'000, 120'000'000})
+  ->UseManualTime();
+
+// left semi-join -------------------------------------------------------------
+BENCHMARK_REGISTER_F(Join, left_semi_join_32bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->Args({10'000'000, 10'000'000})
+  ->Args({10'000'000, 40'000'000})
+  ->Args({10'000'000, 100'000'000})
+  ->Args({100'000'000, 100'000'000})
+  ->Args({80'000'000, 240'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(Join, left_semi_join_64bit)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({50'000'000, 50'000'000})
+  ->Args({40'000'000, 120'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(Join, left_semi_join_32bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({100'000, 100'000})
+  ->Args({100'000, 400'000})
+  ->Args({100'000, 1'000'000})
+  ->Args({10'000'000, 10'000'000})
+  ->Args({10'000'000, 40'000'000})
+  ->Args({10'000'000, 100'000'000})
+  ->Args({100'000'000, 100'000'000})
+  ->Args({80'000'000, 240'000'000})
+  ->UseManualTime();
+
+BENCHMARK_REGISTER_F(Join, left_semi_join_64bit_nulls)
+  ->Unit(benchmark::kMillisecond)
+  ->Args({50'000'000, 50'000'000})
+  ->Args({40'000'000, 120'000'000})
+  ->UseManualTime();
diff --git a/cpp/benchmarks/join/mixed_join.cu b/cpp/benchmarks/join/mixed_join.cu
new file mode 100644
index 0000000..67be464
--- /dev/null
+++ b/cpp/benchmarks/join/mixed_join.cu
@@ -0,0 +1,332 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/join/join_common.hpp>
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_mixed_inner_join(
+  nvbench::state& state, nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_equality_input,
+                 cudf::table_view const& right_equality_input,
+                 cudf::table_view const& left_conditional_input,
+                 cudf::table_view const& right_conditional_input,
+                 cudf::ast::operation binary_pred,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    return cudf::mixed_inner_join(left_equality_input,
+                                  right_equality_input,
+                                  left_conditional_input,
+                                  right_conditional_input,
+                                  binary_pred,
+                                  compare_nulls);
+  };
+
+  BM_join<key_type, payload_type, Nullable, join_t::MIXED>(state, join);
+}
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_mixed_left_join(
+  nvbench::state& state, nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_equality_input,
+                 cudf::table_view const& right_equality_input,
+                 cudf::table_view const& left_conditional_input,
+                 cudf::table_view const& right_conditional_input,
+                 cudf::ast::operation binary_pred,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    return cudf::mixed_left_join(left_equality_input,
+                                 right_equality_input,
+                                 left_conditional_input,
+                                 right_conditional_input,
+                                 binary_pred,
+                                 compare_nulls);
+  };
+
+  BM_join<key_type, payload_type, Nullable, join_t::MIXED>(state, join);
+}
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_mixed_full_join(
+  nvbench::state& state, nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_equality_input,
+                 cudf::table_view const& right_equality_input,
+                 cudf::table_view const& left_conditional_input,
+                 cudf::table_view const& right_conditional_input,
+                 cudf::ast::operation binary_pred,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    return cudf::mixed_full_join(left_equality_input,
+                                 right_equality_input,
+                                 left_conditional_input,
+                                 right_conditional_input,
+                                 binary_pred,
+                                 compare_nulls);
+  };
+
+  BM_join<key_type, payload_type, Nullable, join_t::MIXED>(state, join);
+}
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_mixed_left_semi_join(
+  nvbench::state& state, nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_equality_input,
+                 cudf::table_view const& right_equality_input,
+                 cudf::table_view const& left_conditional_input,
+                 cudf::table_view const& right_conditional_input,
+                 cudf::ast::operation binary_pred,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    return cudf::mixed_left_semi_join(left_equality_input,
+                                      right_equality_input,
+                                      left_conditional_input,
+                                      right_conditional_input,
+                                      binary_pred,
+                                      compare_nulls);
+  };
+
+  BM_join<key_type, payload_type, Nullable, join_t::MIXED>(state, join);
+}
+
+template <typename key_type, typename payload_type, bool Nullable>
+void nvbench_mixed_left_anti_join(
+  nvbench::state& state, nvbench::type_list<key_type, payload_type, nvbench::enum_type<Nullable>>)
+{
+  skip_helper(state);
+
+  auto join = [](cudf::table_view const& left_equality_input,
+                 cudf::table_view const& right_equality_input,
+                 cudf::table_view const& left_conditional_input,
+                 cudf::table_view const& right_conditional_input,
+                 cudf::ast::operation binary_pred,
+                 cudf::null_equality compare_nulls,
+                 rmm::cuda_stream_view stream) {
+    return cudf::mixed_left_anti_join(left_equality_input,
+                                      right_equality_input,
+                                      left_conditional_input,
+                                      right_conditional_input,
+                                      binary_pred,
+                                      compare_nulls);
+  };
+
+  BM_join<key_type, payload_type, Nullable, join_t::MIXED>(state, join);
+}
+
+// inner join -----------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_mixed_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_inner_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_inner_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_inner_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_inner_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_inner_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+// left join ------------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_left_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_left_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_left_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_left_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+// full join ------------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_mixed_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_full_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_full_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_full_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_full_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_full_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+// left semi join ------------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_semi_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_left_semi_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_semi_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_left_semi_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_semi_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_left_semi_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_semi_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_left_semi_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+// left anti join ------------------------------------------------------------------------
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_anti_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_left_anti_join_32bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_anti_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<false>))
+  .set_name("mixed_left_anti_join_64bit")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_anti_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::type_list<nvbench::int32_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_left_anti_join_32bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {100'000, 10'000'000, 80'000'000, 100'000'000})
+  .add_int64_axis("Probe Table Size",
+                  {100'000, 400'000, 10'000'000, 40'000'000, 100'000'000, 240'000'000});
+
+NVBENCH_BENCH_TYPES(nvbench_mixed_left_anti_join,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::type_list<nvbench::int64_t>,
+                                      nvbench::enum_type_list<true>))
+  .set_name("mixed_left_anti_join_64bit_nulls")
+  .set_type_axes_names({"Key Type", "Payload Type", "Nullable"})
+  .add_int64_axis("Build Table Size", {40'000'000, 50'000'000})
+  .add_int64_axis("Probe Table Size", {50'000'000, 120'000'000});
diff --git a/cpp/benchmarks/lists/copying/scatter_lists.cu b/cpp/benchmarks/lists/copying/scatter_lists.cu
new file mode 100644
index 0000000..dbc3234
--- /dev/null
+++ b/cpp/benchmarks/lists/copying/scatter_lists.cu
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <thrust/random.h>
+#include <thrust/sequence.h>
+#include <thrust/shuffle.h>
+
+#include <cmath>
+
+class ScatterLists : public cudf::benchmark {};
+
+template <class TypeParam, bool coalesce>
+void BM_lists_scatter(::benchmark::State& state)
+{
+  auto stream = cudf::get_default_stream();
+  auto mr     = rmm::mr::get_current_device_resource();
+
+  cudf::size_type const base_size{(cudf::size_type)state.range(0)};
+  cudf::size_type const num_elements_per_row{(cudf::size_type)state.range(1)};
+  auto const num_rows = (cudf::size_type)ceil(double(base_size) / num_elements_per_row);
+
+  auto source_base_col = make_fixed_width_column(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                                 base_size,
+                                                 cudf::mask_state::UNALLOCATED,
+                                                 stream,
+                                                 mr);
+  auto target_base_col = make_fixed_width_column(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                                 base_size,
+                                                 cudf::mask_state::UNALLOCATED,
+                                                 stream,
+                                                 mr);
+  thrust::sequence(rmm::exec_policy(stream),
+                   source_base_col->mutable_view().begin<TypeParam>(),
+                   source_base_col->mutable_view().end<TypeParam>());
+  thrust::sequence(rmm::exec_policy(stream),
+                   target_base_col->mutable_view().begin<TypeParam>(),
+                   target_base_col->mutable_view().end<TypeParam>());
+
+  auto source_offsets =
+    make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                            num_rows + 1,
+                            cudf::mask_state::UNALLOCATED,
+                            stream,
+                            mr);
+  auto target_offsets =
+    make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                            num_rows + 1,
+                            cudf::mask_state::UNALLOCATED,
+                            stream,
+                            mr);
+
+  thrust::sequence(rmm::exec_policy(stream),
+                   source_offsets->mutable_view().begin<cudf::size_type>(),
+                   source_offsets->mutable_view().end<cudf::size_type>(),
+                   0,
+                   num_elements_per_row);
+  thrust::sequence(rmm::exec_policy(stream),
+                   target_offsets->mutable_view().begin<cudf::size_type>(),
+                   target_offsets->mutable_view().end<cudf::size_type>(),
+                   0,
+                   num_elements_per_row);
+
+  auto source = make_lists_column(num_rows,
+                                  std::move(source_offsets),
+                                  std::move(source_base_col),
+                                  0,
+                                  cudf::create_null_mask(num_rows, cudf::mask_state::UNALLOCATED),
+                                  stream,
+                                  mr);
+  auto target = make_lists_column(num_rows,
+                                  std::move(target_offsets),
+                                  std::move(target_base_col),
+                                  0,
+                                  cudf::create_null_mask(num_rows, cudf::mask_state::UNALLOCATED),
+                                  stream,
+                                  mr);
+
+  auto scatter_map   = make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                             num_rows,
+                                             cudf::mask_state::UNALLOCATED,
+                                             stream,
+                                             mr);
+  auto m_scatter_map = scatter_map->mutable_view();
+  thrust::sequence(rmm::exec_policy(stream),
+                   m_scatter_map.begin<cudf::size_type>(),
+                   m_scatter_map.end<cudf::size_type>(),
+                   num_rows - 1,
+                   -1);
+
+  if (not coalesce) {
+    thrust::default_random_engine g;
+    thrust::shuffle(rmm::exec_policy(stream),
+                    m_scatter_map.begin<cudf::size_type>(),
+                    m_scatter_map.begin<cudf::size_type>(),
+                    g);
+  }
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    scatter(cudf::table_view{{*source}},
+            *scatter_map,
+            cudf::table_view{{*target}},
+            cudf::get_default_stream(),
+            mr);
+  }
+
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * state.range(0) * 2 *
+                          sizeof(TypeParam));
+}
+
+#define SBM_BENCHMARK_DEFINE(name, type, coalesce)                                \
+  BENCHMARK_DEFINE_F(ScatterLists, name)(::benchmark::State & state)              \
+  {                                                                               \
+    BM_lists_scatter<type, coalesce>(state);                                      \
+  }                                                                               \
+  BENCHMARK_REGISTER_F(ScatterLists, name)                                        \
+    ->RangeMultiplier(8)                                                          \
+    ->Ranges({{1 << 10, 1 << 25}, {64, 2048}}) /* 1K-1B rows, 64-2048 elements */ \
+    ->UseManualTime();
+
+SBM_BENCHMARK_DEFINE(double_type_colesce_o, double, true);
+SBM_BENCHMARK_DEFINE(double_type_colesce_x, double, false);
diff --git a/cpp/benchmarks/lists/set_operations.cpp b/cpp/benchmarks/lists/set_operations.cpp
new file mode 100644
index 0000000..5b24092
--- /dev/null
+++ b/cpp/benchmarks/lists/set_operations.cpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/lists/set_operations.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+namespace {
+
+constexpr auto max_list_size = 20;
+
+auto generate_random_lists(cudf::size_type num_rows, cudf::size_type depth, double null_freq)
+{
+  auto builder =
+    data_profile_builder()
+      .cardinality(0)
+      .distribution(cudf::type_id::LIST, distribution_id::UNIFORM, 0, max_list_size)
+      .list_depth(depth)
+      .null_probability(null_freq > 0 ? std::optional<double>{null_freq} : std::nullopt);
+
+  auto data_table =
+    create_random_table({cudf::type_id::LIST}, row_count{num_rows}, data_profile{builder});
+  return std::move(data_table->release().front());
+}
+
+template <typename BenchFuncPtr>
+void nvbench_set_op(nvbench::state& state, BenchFuncPtr bfunc)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const depth     = static_cast<cudf::size_type>(state.get_int64("depth"));
+  auto const null_freq = state.get_float64("null_frequency");
+
+  auto const lhs = generate_random_lists(num_rows, depth, null_freq);
+  auto const rhs = generate_random_lists(num_rows, depth, null_freq);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    bfunc(cudf::lists_column_view{*lhs},
+          cudf::lists_column_view{*rhs},
+          cudf::null_equality::EQUAL,
+          cudf::nan_equality::ALL_EQUAL,
+          rmm::mr::get_current_device_resource());
+  });
+}
+
+}  // namespace
+
+void nvbench_have_overlap(nvbench::state& state)
+{
+  nvbench_set_op(state, &cudf::lists::have_overlap);
+}
+
+void nvbench_intersect_distinct(nvbench::state& state)
+{
+  nvbench_set_op(state, &cudf::lists::intersect_distinct);
+}
+
+NVBENCH_BENCH(nvbench_have_overlap)
+  .set_name("have_overlap")
+  .add_int64_power_of_two_axis("num_rows", {10, 13, 16})
+  .add_int64_axis("depth", {1, 4})
+  .add_float64_axis("null_frequency", {0, 0.2, 0.8});
+
+NVBENCH_BENCH(nvbench_intersect_distinct)
+  .set_name("intersect_distinct")
+  .add_int64_power_of_two_axis("num_rows", {10, 13, 16})
+  .add_int64_axis("depth", {1, 4})
+  .add_float64_axis("null_frequency", {0, 0.2, 0.8});
diff --git a/cpp/benchmarks/merge/merge.cpp b/cpp/benchmarks/merge/merge.cpp
new file mode 100644
index 0000000..2d2f4fd
--- /dev/null
+++ b/cpp/benchmarks/merge/merge.cpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/merge.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+
+#include <random>
+
+// to enable, run cmake with -DBUILD_BENCHMARKS=ON
+
+// Fixture that enables RMM pool mode
+class Merge : public cudf::benchmark {};
+
+using IntColWrap = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+void BM_merge(benchmark::State& state)
+{
+  cudf::size_type const avg_rows = 1 << 19;  // 512K rows
+  int const num_tables           = state.range(0);
+
+  // Content is irrelevant for the benchmark
+  auto data_sequence = thrust::make_constant_iterator(0);
+
+  // Using 0 seed to ensure consistent pseudo-numbers on each run
+  std::mt19937 rand_gen(0);
+  // Gaussian distribution with 98% of elements are in range [0, avg_rows*2]
+  std::normal_distribution<> table_size_dist(avg_rows, avg_rows / 2);
+  // Used to generate a random monotonic sequence for each table key column
+  std::uniform_int_distribution<> key_dist(0, 10);
+
+  std::vector<std::pair<IntColWrap, IntColWrap>> columns;
+  size_t total_rows = 0;
+  std::vector<cudf::table_view> tables;
+  for (int i = 0; i < num_tables; ++i) {
+    cudf::size_type const rows = std::round(table_size_dist(rand_gen));
+    // Ensure size in range [0, avg_rows*2]
+    auto const clamped_rows = std::clamp(rows, 0, avg_rows * 2);
+
+    int32_t prev_key  = 0;
+    auto key_sequence = cudf::detail::make_counting_transform_iterator(0, [&](auto row) {
+      prev_key += key_dist(rand_gen);
+      return prev_key;
+    });
+
+    columns.emplace_back(
+      std::pair<IntColWrap, IntColWrap>{IntColWrap(key_sequence, key_sequence + clamped_rows),
+                                        IntColWrap(data_sequence, data_sequence + clamped_rows)});
+    tables.push_back(cudf::table_view{{columns.back().first, columns.back().second}});
+    total_rows += clamped_rows;
+  }
+  std::vector<cudf::size_type> const key_cols{0};
+  std::vector<cudf::order> const column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> const null_precedence{};
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    auto result = cudf::merge(tables, key_cols, column_order, null_precedence);
+  }
+
+  state.SetBytesProcessed(state.iterations() * 2 * sizeof(int32_t) * total_rows);
+}
+
+#define MBM_BENCHMARK_DEFINE(name)                                                 \
+  BENCHMARK_DEFINE_F(Merge, name)(::benchmark::State & state) { BM_merge(state); } \
+  BENCHMARK_REGISTER_F(Merge, name)                                                \
+    ->Unit(benchmark::kMillisecond)                                                \
+    ->UseManualTime()                                                              \
+    ->RangeMultiplier(2)                                                           \
+    ->Ranges({{2, 128}});
+
+MBM_BENCHMARK_DEFINE(pow2tables);
diff --git a/cpp/benchmarks/null_mask/set_null_mask.cpp b/cpp/benchmarks/null_mask/set_null_mask.cpp
new file mode 100644
index 0000000..4ac4c96
--- /dev/null
+++ b/cpp/benchmarks/null_mask/set_null_mask.cpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/null_mask.hpp>
+
+class SetNullmask : public cudf::benchmark {};
+
+void BM_setnullmask(benchmark::State& state)
+{
+  cudf::size_type const size{(cudf::size_type)state.range(0)};
+  rmm::device_buffer mask = cudf::create_null_mask(size, cudf::mask_state::UNINITIALIZED);
+  auto begin = 0, end = size;
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask.data()), begin, end, true);
+  }
+
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * size / 8);
+}
+
+#define NBM_BENCHMARK_DEFINE(name)                                                             \
+  BENCHMARK_DEFINE_F(SetNullmask, name)(::benchmark::State & state) { BM_setnullmask(state); } \
+  BENCHMARK_REGISTER_F(SetNullmask, name)                                                      \
+    ->RangeMultiplier(1 << 10)                                                                 \
+    ->Range(1 << 10, 1 << 30)                                                                  \
+    ->UseManualTime();
+
+NBM_BENCHMARK_DEFINE(SetNullMaskKernel);
diff --git a/cpp/benchmarks/quantiles/quantiles.cpp b/cpp/benchmarks/quantiles/quantiles.cpp
new file mode 100644
index 0000000..24f9cc9
--- /dev/null
+++ b/cpp/benchmarks/quantiles/quantiles.cpp
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/quantiles.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/tabulate.h>
+
+class Quantiles : public cudf::benchmark {};
+
+static void BM_quantiles(benchmark::State& state, bool nulls)
+{
+  using Type = int;
+
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+  cudf::size_type const n_cols{(cudf::size_type)state.range(1)};
+  cudf::size_type const n_quantiles{(cudf::size_type)state.range(2)};
+
+  // Create columns with values in the range [0,100)
+  data_profile profile = data_profile_builder().cardinality(0).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100);
+  profile.set_null_probability(nulls ? std::optional{0.01}
+                                     : std::nullopt);  // 1% nulls or no null mask (<0)
+
+  auto input_table = create_random_table(
+    cycle_dtypes({cudf::type_to_id<Type>()}, n_cols), row_count{n_rows}, profile);
+  auto input = cudf::table_view(*input_table);
+
+  std::vector<double> q(n_quantiles);
+  thrust::tabulate(
+    thrust::seq, q.begin(), q.end(), [n_quantiles](auto i) { return i * (1.0f / n_quantiles); });
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+
+    auto result = cudf::quantiles(input, q);
+    // auto result = (stable) ? cudf::stable_sorted_order(input) : cudf::sorted_order(input);
+  }
+}
+
+#define QUANTILES_BENCHMARK_DEFINE(name, nulls)          \
+  BENCHMARK_DEFINE_F(Quantiles, name)                    \
+  (::benchmark::State & st) { BM_quantiles(st, nulls); } \
+  BENCHMARK_REGISTER_F(Quantiles, name)                  \
+    ->RangeMultiplier(4)                                 \
+    ->Ranges({{1 << 16, 1 << 26}, {1, 8}, {1, 12}})      \
+    ->UseManualTime()                                    \
+    ->Unit(benchmark::kMillisecond);
+
+QUANTILES_BENCHMARK_DEFINE(no_nulls, false)
+QUANTILES_BENCHMARK_DEFINE(nulls, true)
diff --git a/cpp/benchmarks/reduction/anyall.cpp b/cpp/benchmarks/reduction/anyall.cpp
new file mode 100644
index 0000000..8b1e71c
--- /dev/null
+++ b/cpp/benchmarks/reduction/anyall.cpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/types.hpp>
+
+#include <memory>
+
+class Reduction : public cudf::benchmark {};
+
+template <typename type>
+void BM_reduction_anyall(benchmark::State& state,
+                         std::unique_ptr<cudf::reduce_aggregation> const& agg)
+{
+  cudf::size_type const column_size{static_cast<cudf::size_type>(state.range(0))};
+  auto const dtype           = cudf::type_to_id<type>();
+  data_profile const profile = data_profile_builder().no_validity().distribution(
+    dtype, distribution_id::UNIFORM, 0, agg->kind == cudf::aggregation::ANY ? 0 : 100);
+  auto const values = create_random_column(dtype, row_count{column_size}, profile);
+
+  cudf::data_type output_dtype{cudf::type_id::BOOL8};
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::reduce(*values, *agg, output_dtype);
+  }
+}
+
+#define concat(a, b, c) a##b##c
+#define get_agg(op)     concat(cudf::make_, op, _aggregation<cudf::reduce_aggregation>())
+
+// TYPE, OP
+#define RBM_BENCHMARK_DEFINE(name, type, aggregation)             \
+  BENCHMARK_DEFINE_F(Reduction, name)(::benchmark::State & state) \
+  {                                                               \
+    BM_reduction_anyall<type>(state, get_agg(aggregation));       \
+  }                                                               \
+  BENCHMARK_REGISTER_F(Reduction, name)                           \
+    ->UseManualTime()                                             \
+    ->Arg(10000)      /* 10k */                                   \
+    ->Arg(100000)     /* 100k */                                  \
+    ->Arg(1000000)    /* 1M */                                    \
+    ->Arg(10000000)   /* 10M */                                   \
+    ->Arg(100000000); /* 100M */
+
+#define REDUCE_BENCHMARK_DEFINE(type, aggregation) \
+  RBM_BENCHMARK_DEFINE(concat(type, _, aggregation), type, aggregation)
+
+REDUCE_BENCHMARK_DEFINE(bool, all);
+REDUCE_BENCHMARK_DEFINE(int8_t, all);
+REDUCE_BENCHMARK_DEFINE(int32_t, all);
+REDUCE_BENCHMARK_DEFINE(float, all);
+REDUCE_BENCHMARK_DEFINE(bool, any);
+REDUCE_BENCHMARK_DEFINE(int8_t, any);
+REDUCE_BENCHMARK_DEFINE(int32_t, any);
+REDUCE_BENCHMARK_DEFINE(float, any);
diff --git a/cpp/benchmarks/reduction/dictionary.cpp b/cpp/benchmarks/reduction/dictionary.cpp
new file mode 100644
index 0000000..c1c44c9
--- /dev/null
+++ b/cpp/benchmarks/reduction/dictionary.cpp
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+
+class ReductionDictionary : public cudf::benchmark {};
+
+template <typename T>
+void BM_reduction_dictionary(benchmark::State& state,
+                             std::unique_ptr<cudf::reduce_aggregation> const& agg)
+{
+  cudf::size_type const column_size{static_cast<cudf::size_type>(state.range(0))};
+
+  // int column and encoded dictionary column
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_to_id<long>(),
+    distribution_id::UNIFORM,
+    (agg->kind == cudf::aggregation::ALL ? 1 : 0),
+    (agg->kind == cudf::aggregation::ANY ? 0 : 100));
+  auto int_column = create_random_column(cudf::type_to_id<long>(), row_count{column_size}, profile);
+  auto number_col = cudf::cast(*int_column, cudf::data_type{cudf::type_to_id<T>()});
+  auto values     = cudf::dictionary::encode(*number_col);
+
+  cudf::data_type output_dtype = [&] {
+    if (agg->kind == cudf::aggregation::ANY || agg->kind == cudf::aggregation::ALL)
+      return cudf::data_type{cudf::type_id::BOOL8};
+    if (agg->kind == cudf::aggregation::MEAN) return cudf::data_type{cudf::type_id::FLOAT64};
+    return cudf::data_type{cudf::type_to_id<T>()};
+  }();
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::reduce(*values, *agg, output_dtype);
+  }
+}
+
+#define concat(a, b, c) a##b##c
+#define get_agg(op)     concat(cudf::make_, op, _aggregation<cudf::reduce_aggregation>())
+
+// TYPE, OP
+#define RBM_BENCHMARK_DEFINE(name, type, aggregation)                       \
+  BENCHMARK_DEFINE_F(ReductionDictionary, name)(::benchmark::State & state) \
+  {                                                                         \
+    BM_reduction_dictionary<type>(state, get_agg(aggregation));             \
+  }                                                                         \
+  BENCHMARK_REGISTER_F(ReductionDictionary, name)                           \
+    ->UseManualTime()                                                       \
+    ->Arg(10000)      /* 10k */                                             \
+    ->Arg(100000)     /* 100k */                                            \
+    ->Arg(1000000)    /* 1M */                                              \
+    ->Arg(10000000)   /* 10M */                                             \
+    ->Arg(100000000); /* 100M */
+
+#define REDUCE_BENCHMARK_DEFINE(type, aggregation) \
+  RBM_BENCHMARK_DEFINE(concat(type, _, aggregation), type, aggregation)
+
+REDUCE_BENCHMARK_DEFINE(int32_t, all);
+REDUCE_BENCHMARK_DEFINE(float, all);
+REDUCE_BENCHMARK_DEFINE(int32_t, any);
+REDUCE_BENCHMARK_DEFINE(float, any);
+REDUCE_BENCHMARK_DEFINE(int32_t, min);
+REDUCE_BENCHMARK_DEFINE(float, min);
+REDUCE_BENCHMARK_DEFINE(int32_t, max);
+REDUCE_BENCHMARK_DEFINE(float, max);
+REDUCE_BENCHMARK_DEFINE(int32_t, mean);
+REDUCE_BENCHMARK_DEFINE(float, mean);
diff --git a/cpp/benchmarks/reduction/minmax.cpp b/cpp/benchmarks/reduction/minmax.cpp
new file mode 100644
index 0000000..963c266
--- /dev/null
+++ b/cpp/benchmarks/reduction/minmax.cpp
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/types.hpp>
+
+class Reduction : public cudf::benchmark {};
+
+template <typename type>
+void BM_reduction(benchmark::State& state)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+  auto const dtype = cudf::type_to_id<type>();
+  auto const input_column =
+    create_random_column(dtype, row_count{column_size}, data_profile_builder().no_validity());
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::minmax(*input_column);
+  }
+}
+
+#define concat(a, b, c) a##b##c
+#define get_agg(op)     concat(cudf::make_, op, _aggregation())
+
+// TYPE, OP
+#define RBM_BENCHMARK_DEFINE(name, type, aggregation)                                            \
+  BENCHMARK_DEFINE_F(Reduction, name)(::benchmark::State & state) { BM_reduction<type>(state); } \
+  BENCHMARK_REGISTER_F(Reduction, name)                                                          \
+    ->UseManualTime()                                                                            \
+    ->Arg(10000)      /* 10k */                                                                  \
+    ->Arg(100000)     /* 100k */                                                                 \
+    ->Arg(1000000)    /* 1M */                                                                   \
+    ->Arg(10000000)   /* 10M */                                                                  \
+    ->Arg(100000000); /* 100M */
+
+#define REDUCE_BENCHMARK_DEFINE(type, aggregation) \
+  RBM_BENCHMARK_DEFINE(concat(type, _, aggregation), type, aggregation)
+
+REDUCE_BENCHMARK_DEFINE(bool, minmax);
+REDUCE_BENCHMARK_DEFINE(int8_t, minmax);
+REDUCE_BENCHMARK_DEFINE(int32_t, minmax);
+using cudf::timestamp_ms;
+REDUCE_BENCHMARK_DEFINE(timestamp_ms, minmax);
+REDUCE_BENCHMARK_DEFINE(float, minmax);
diff --git a/cpp/benchmarks/reduction/rank.cpp b/cpp/benchmarks/reduction/rank.cpp
new file mode 100644
index 0000000..e55f3b9
--- /dev/null
+++ b/cpp/benchmarks/reduction/rank.cpp
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/detail/scan.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/lists/list_view.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <typename type>
+static void nvbench_reduction_scan(nvbench::state& state, nvbench::type_list<type>)
+{
+  auto const dtype = cudf::type_to_id<type>();
+
+  double const null_probability = state.get_float64("null_probability");
+  size_t const size             = state.get_int64("data_size");
+
+  data_profile const profile = data_profile_builder()
+                                 .null_probability(null_probability)
+                                 .distribution(dtype, distribution_id::UNIFORM, 0, 5);
+
+  auto const table = create_random_table({dtype}, table_size_bytes{size / 2}, profile);
+
+  auto const new_tbl = cudf::repeat(table->view(), 2);
+  cudf::column_view input(new_tbl->view().column(0));
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    rmm::cuda_stream_view stream_view{launch.get_stream()};
+    auto result = cudf::detail::inclusive_dense_rank_scan(
+      input, stream_view, rmm::mr::get_current_device_resource());
+  });
+}
+
+using data_type = nvbench::type_list<int32_t, cudf::list_view>;
+
+NVBENCH_BENCH_TYPES(nvbench_reduction_scan, NVBENCH_TYPE_AXES(data_type))
+  .set_name("rank_scan")
+  .add_float64_axis("null_probability", {0, 0.1, 0.5, 0.9})
+  .add_int64_axis("data_size",
+                  {
+                    10000,      // 10k
+                    100000,     // 100k
+                    1000000,    // 1M
+                    10000000,   // 10M
+                    100000000,  // 100M
+                  });
diff --git a/cpp/benchmarks/reduction/reduce.cpp b/cpp/benchmarks/reduction/reduce.cpp
new file mode 100644
index 0000000..5bd3e2e
--- /dev/null
+++ b/cpp/benchmarks/reduction/reduce.cpp
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/types.hpp>
+
+#include <memory>
+
+class Reduction : public cudf::benchmark {};
+
+template <typename type>
+void BM_reduction(benchmark::State& state, std::unique_ptr<cudf::reduce_aggregation> const& agg)
+{
+  cudf::size_type const column_size{(cudf::size_type)state.range(0)};
+  auto const dtype = cudf::type_to_id<type>();
+  data_profile const profile =
+    data_profile_builder().no_validity().distribution(dtype, distribution_id::UNIFORM, 0, 100);
+  auto const input_column = create_random_column(dtype, row_count{column_size}, profile);
+
+  cudf::data_type output_dtype =
+    (agg->kind == cudf::aggregation::MEAN || agg->kind == cudf::aggregation::VARIANCE ||
+     agg->kind == cudf::aggregation::STD)
+      ? cudf::data_type{cudf::type_id::FLOAT64}
+      : input_column->type();
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::reduce(*input_column, *agg, output_dtype);
+  }
+}
+
+#define concat(a, b, c) a##b##c
+#define get_agg(op)     concat(cudf::make_, op, _aggregation<cudf::reduce_aggregation>())
+
+// TYPE, OP
+#define RBM_BENCHMARK_DEFINE(name, type, aggregation)             \
+  BENCHMARK_DEFINE_F(Reduction, name)(::benchmark::State & state) \
+  {                                                               \
+    BM_reduction<type>(state, get_agg(aggregation));              \
+  }                                                               \
+  BENCHMARK_REGISTER_F(Reduction, name)                           \
+    ->UseManualTime()                                             \
+    ->Arg(10000)      /* 10k */                                   \
+    ->Arg(100000)     /* 100k */                                  \
+    ->Arg(1000000)    /* 1M */                                    \
+    ->Arg(10000000)   /* 10M */                                   \
+    ->Arg(100000000); /* 100M */
+
+#define REDUCE_BENCHMARK_DEFINE(type, aggregation) \
+  RBM_BENCHMARK_DEFINE(concat(type, _, aggregation), type, aggregation)
+
+#define REDUCE_BENCHMARK_NUMERIC(aggregation)    \
+  REDUCE_BENCHMARK_DEFINE(bool, aggregation);    \
+  REDUCE_BENCHMARK_DEFINE(int8_t, aggregation);  \
+  REDUCE_BENCHMARK_DEFINE(int32_t, aggregation); \
+  REDUCE_BENCHMARK_DEFINE(int64_t, aggregation); \
+  REDUCE_BENCHMARK_DEFINE(float, aggregation);   \
+  REDUCE_BENCHMARK_DEFINE(double, aggregation);
+
+REDUCE_BENCHMARK_NUMERIC(sum);
+REDUCE_BENCHMARK_DEFINE(int32_t, product);
+REDUCE_BENCHMARK_DEFINE(float, product);
+REDUCE_BENCHMARK_DEFINE(int64_t, min);
+REDUCE_BENCHMARK_DEFINE(double, min);
+using cudf::timestamp_ms;
+REDUCE_BENCHMARK_DEFINE(timestamp_ms, min);
+REDUCE_BENCHMARK_DEFINE(int8_t, mean);
+REDUCE_BENCHMARK_DEFINE(float, mean);
+REDUCE_BENCHMARK_DEFINE(int32_t, variance);
+REDUCE_BENCHMARK_DEFINE(double, variance);
+REDUCE_BENCHMARK_DEFINE(int64_t, std);
+REDUCE_BENCHMARK_DEFINE(float, std);
diff --git a/cpp/benchmarks/reduction/scan.cpp b/cpp/benchmarks/reduction/scan.cpp
new file mode 100644
index 0000000..8c9883e
--- /dev/null
+++ b/cpp/benchmarks/reduction/scan.cpp
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+class ReductionScan : public cudf::benchmark {};
+
+template <typename type>
+static void BM_reduction_scan(benchmark::State& state, bool include_nulls)
+{
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+  auto const dtype  = cudf::type_to_id<type>();
+  auto const column = create_random_column(dtype, row_count{n_rows});
+  if (!include_nulls) column->set_null_mask(rmm::device_buffer{}, 0);
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::scan(
+      *column, *cudf::make_min_aggregation<cudf::scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+  }
+}
+
+#define SCAN_BENCHMARK_DEFINE(name, type, nulls)                          \
+  BENCHMARK_DEFINE_F(ReductionScan, name)                                 \
+  (::benchmark::State & state) { BM_reduction_scan<type>(state, nulls); } \
+  BENCHMARK_REGISTER_F(ReductionScan, name)                               \
+    ->UseManualTime()                                                     \
+    ->Arg(10000)      /* 10k */                                           \
+    ->Arg(100000)     /* 100k */                                          \
+    ->Arg(1000000)    /* 1M */                                            \
+    ->Arg(10000000)   /* 10M */                                           \
+    ->Arg(100000000); /* 100M */
+
+SCAN_BENCHMARK_DEFINE(int8_no_nulls, int8_t, false);
+SCAN_BENCHMARK_DEFINE(int32_no_nulls, int32_t, false);
+SCAN_BENCHMARK_DEFINE(uint64_no_nulls, uint64_t, false);
+SCAN_BENCHMARK_DEFINE(float_no_nulls, float, false);
+SCAN_BENCHMARK_DEFINE(int16_nulls, int16_t, true);
+SCAN_BENCHMARK_DEFINE(uint32_nulls, uint32_t, true);
+SCAN_BENCHMARK_DEFINE(double_nulls, double, true);
diff --git a/cpp/benchmarks/reduction/scan_structs.cpp b/cpp/benchmarks/reduction/scan_structs.cpp
new file mode 100644
index 0000000..ee97b54
--- /dev/null
+++ b/cpp/benchmarks/reduction/scan_structs.cpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/scan.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static constexpr cudf::size_type num_struct_members = 8;
+static constexpr cudf::size_type max_int            = 100;
+static constexpr cudf::size_type max_str_length     = 32;
+
+static void nvbench_structs_scan(nvbench::state& state)
+{
+  auto const null_probability = [&] {
+    auto const null_prob_val = state.get_float64("null_probability");
+    return null_prob_val > 0 ? std::optional{null_prob_val} : std::nullopt;
+  }();
+  auto const size    = static_cast<cudf::size_type>(state.get_int64("data_size"));
+  auto const profile = static_cast<data_profile>(
+    data_profile_builder()
+      .null_probability(null_probability)
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, max_int)
+      .distribution(cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length));
+
+  auto data_table = create_random_table(
+    cycle_dtypes({cudf::type_id::INT32, cudf::type_id::STRING}, num_struct_members),
+    row_count{size},
+    profile);
+  auto [null_mask, null_count] = create_random_null_mask(size, null_probability);
+  auto const input             = cudf::make_structs_column(
+    size, std::move(data_table->release()), null_count, std::move(null_mask));
+
+  auto const agg         = cudf::make_min_aggregation<cudf::scan_aggregation>();
+  auto const null_policy = static_cast<cudf::null_policy>(state.get_int64("null_policy"));
+  auto const stream      = cudf::get_default_stream();
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto const result = cudf::detail::scan_inclusive(
+      *input, *agg, null_policy, stream, rmm::mr::get_current_device_resource());
+  });
+}
+
+NVBENCH_BENCH(nvbench_structs_scan)
+  .set_name("structs_scan")
+  .add_float64_axis("null_probability", {0, 0.1, 0.5, 0.9})
+  .add_int64_axis("null_policy", {0, 1})
+  .add_int64_axis("data_size",
+                  {
+                    10000,     // 10k
+                    100000,    // 100k
+                    1000000,   // 1M
+                    10000000,  // 10M
+                  });
diff --git a/cpp/benchmarks/reduction/segmented_reduce.cpp b/cpp/benchmarks/reduction/segmented_reduce.cpp
new file mode 100644
index 0000000..7accb82
--- /dev/null
+++ b/cpp/benchmarks/reduction/segmented_reduce.cpp
@@ -0,0 +1,150 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <memory>
+
+bool constexpr is_boolean_output_agg(cudf::segmented_reduce_aggregation::Kind kind)
+{
+  return kind == cudf::segmented_reduce_aggregation::ALL ||
+         kind == cudf::segmented_reduce_aggregation::ANY;
+}
+
+bool constexpr is_float_output_agg(cudf::segmented_reduce_aggregation::Kind kind)
+{
+  return kind == cudf::segmented_reduce_aggregation::MEAN ||
+         kind == cudf::segmented_reduce_aggregation::VARIANCE ||
+         kind == cudf::segmented_reduce_aggregation::STD;
+}
+
+template <cudf::segmented_reduce_aggregation::Kind kind>
+std::unique_ptr<cudf::segmented_reduce_aggregation> make_reduce_aggregation()
+{
+  switch (kind) {
+    case cudf::segmented_reduce_aggregation::SUM:
+      return cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::PRODUCT:
+      return cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::MIN:
+      return cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::MAX:
+      return cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::ALL:
+      return cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::ANY:
+      return cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::SUM_OF_SQUARES:
+      return cudf::make_sum_of_squares_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::MEAN:
+      return cudf::make_mean_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::VARIANCE:
+      return cudf::make_variance_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::STD:
+      return cudf::make_std_aggregation<cudf::segmented_reduce_aggregation>();
+    case cudf::segmented_reduce_aggregation::NUNIQUE:
+      return cudf::make_nunique_aggregation<cudf::segmented_reduce_aggregation>();
+    default: CUDF_FAIL("Unsupported segmented reduce aggregation in this benchmark");
+  }
+}
+
+template <typename DataType>
+std::pair<std::unique_ptr<cudf::column>, std::unique_ptr<cudf::column>> make_test_data(
+  nvbench::state& state)
+{
+  auto const column_size{cudf::size_type(state.get_int64("column_size"))};
+  auto const num_segments{cudf::size_type(state.get_int64("num_segments"))};
+
+  auto segment_length = column_size / num_segments;
+
+  auto const dtype     = cudf::type_to_id<DataType>();
+  data_profile profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    dtype, distribution_id::UNIFORM, 0, 100);
+  auto input = create_random_column(dtype, row_count{column_size}, profile);
+
+  auto offsets = cudf::sequence(num_segments + 1,
+                                cudf::numeric_scalar<cudf::size_type>(0),
+                                cudf::numeric_scalar<cudf::size_type>(segment_length));
+  return std::pair(std::move(input), std::move(offsets));
+}
+
+template <typename DataType, cudf::aggregation::Kind kind>
+void BM_Segmented_Reduction(nvbench::state& state,
+                            nvbench::type_list<DataType, nvbench::enum_type<kind>>)
+{
+  auto const column_size{cudf::size_type(state.get_int64("column_size"))};
+  auto const num_segments{cudf::size_type(state.get_int64("num_segments"))};
+
+  auto [input, offsets] = make_test_data<DataType>(state);
+  auto agg              = make_reduce_aggregation<kind>();
+
+  auto const output_type = [] {
+    if (is_boolean_output_agg(kind)) { return cudf::data_type{cudf::type_id::BOOL8}; }
+    if (is_float_output_agg(kind)) { return cudf::data_type{cudf::type_id::FLOAT64}; }
+    if (kind == cudf::segmented_reduce_aggregation::NUNIQUE) {
+      return cudf::data_type{cudf::type_to_id<cudf::size_type>()};
+    }
+    return cudf::data_type{cudf::type_to_id<DataType>()};
+  }();
+
+  state.add_element_count(column_size);
+  state.add_global_memory_reads<DataType>(column_size);
+  if (is_boolean_output_agg(kind)) {
+    state.add_global_memory_writes<nvbench::int8_t>(num_segments);  // BOOL8
+  } else {
+    state.add_global_memory_writes<DataType>(num_segments);
+  }
+
+  auto const input_view   = input->view();
+  auto const offsets_view = offsets->view();
+  auto const offset_span  = cudf::device_span<cudf::size_type const>{
+    offsets_view.template data<cudf::size_type>(), static_cast<std::size_t>(offsets_view.size())};
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(
+    nvbench::exec_tag::sync, [input_view, output_type, offset_span, &agg](nvbench::launch& launch) {
+      segmented_reduce(input_view, offset_span, *agg, output_type, cudf::null_policy::INCLUDE);
+    });
+}
+
+using Types = nvbench::type_list<bool, int32_t, float, double>;
+// Skip benchmarking MAX/ANY since they are covered by MIN/ALL respectively.
+// Also VARIANCE includes STD calculation.
+using AggKinds = nvbench::enum_type_list<cudf::aggregation::SUM,
+                                         cudf::aggregation::PRODUCT,
+                                         cudf::aggregation::MIN,
+                                         cudf::aggregation::ALL,
+                                         cudf::aggregation::MEAN,
+                                         cudf::aggregation::VARIANCE,
+                                         cudf::aggregation::NUNIQUE>;
+
+NVBENCH_BENCH_TYPES(BM_Segmented_Reduction, NVBENCH_TYPE_AXES(Types, AggKinds))
+  .set_name("segmented_reduction")
+  .set_type_axes_names({"DataType", "AggregationKinds"})
+  .add_int64_axis("column_size", {100'000, 1'000'000, 10'000'000, 100'000'000})
+  .add_int64_axis("num_segments", {1'000, 10'000, 100'000});
diff --git a/cpp/benchmarks/replace/clamp.cpp b/cpp/benchmarks/replace/clamp.cpp
new file mode 100644
index 0000000..601c65a
--- /dev/null
+++ b/cpp/benchmarks/replace/clamp.cpp
@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+class ReplaceClamp : public cudf::benchmark {};
+
+template <typename type>
+static void BM_clamp(benchmark::State& state, bool include_nulls)
+{
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+  auto const dtype = cudf::type_to_id<type>();
+  auto const input = create_random_column(dtype, row_count{n_rows});
+  if (!include_nulls) input->set_null_mask(rmm::device_buffer{}, 0);
+
+  auto [low_scalar, high_scalar] = cudf::minmax(*input);
+
+  // set the clamps 2 in from the min and max
+  {
+    using ScalarType = cudf::scalar_type_t<type>;
+    auto lvalue      = static_cast<ScalarType*>(low_scalar.get());
+    auto hvalue      = static_cast<ScalarType*>(high_scalar.get());
+
+    // super heavy clamp
+    auto mid = lvalue->value() + (hvalue->value() - lvalue->value()) / 2;
+    lvalue->set_value(mid - 10);
+    hvalue->set_value(mid + 10);
+  }
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::clamp(*input, *low_scalar, *high_scalar);
+  }
+}
+
+#define CLAMP_BENCHMARK_DEFINE(name, type, nulls)                \
+  BENCHMARK_DEFINE_F(ReplaceClamp, name)                         \
+  (::benchmark::State & state) { BM_clamp<type>(state, nulls); } \
+  BENCHMARK_REGISTER_F(ReplaceClamp, name)                       \
+    ->UseManualTime()                                            \
+    ->Arg(10000)      /* 10k */                                  \
+    ->Arg(100000)     /* 100k */                                 \
+    ->Arg(1000000)    /* 1M */                                   \
+    ->Arg(10000000)   /* 10M */                                  \
+    ->Arg(100000000); /* 100M */
+
+CLAMP_BENCHMARK_DEFINE(int8_no_nulls, int8_t, false);
+CLAMP_BENCHMARK_DEFINE(int32_no_nulls, int32_t, false);
+CLAMP_BENCHMARK_DEFINE(uint64_no_nulls, uint64_t, false);
+CLAMP_BENCHMARK_DEFINE(float_no_nulls, float, false);
+CLAMP_BENCHMARK_DEFINE(int16_nulls, int16_t, true);
+CLAMP_BENCHMARK_DEFINE(uint32_nulls, uint32_t, true);
+CLAMP_BENCHMARK_DEFINE(double_nulls, double, true);
diff --git a/cpp/benchmarks/replace/nans.cpp b/cpp/benchmarks/replace/nans.cpp
new file mode 100644
index 0000000..d33de36
--- /dev/null
+++ b/cpp/benchmarks/replace/nans.cpp
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+class ReplaceNans : public cudf::benchmark {};
+
+template <typename type>
+static void BM_replace_nans(benchmark::State& state, bool include_nulls)
+{
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+  auto const dtype = cudf::type_to_id<type>();
+  auto const input = create_random_column(dtype, row_count{n_rows});
+  if (!include_nulls) input->set_null_mask(rmm::device_buffer{}, 0);
+
+  auto zero = cudf::make_fixed_width_scalar<type>(0);
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto result = cudf::replace_nans(*input, *zero);
+  }
+}
+
+#define NANS_BENCHMARK_DEFINE(name, type, nulls)                        \
+  BENCHMARK_DEFINE_F(ReplaceNans, name)                                 \
+  (::benchmark::State & state) { BM_replace_nans<type>(state, nulls); } \
+  BENCHMARK_REGISTER_F(ReplaceNans, name)                               \
+    ->UseManualTime()                                                   \
+    ->Arg(10000)      /* 10k */                                         \
+    ->Arg(100000)     /* 100k */                                        \
+    ->Arg(1000000)    /* 1M */                                          \
+    ->Arg(10000000)   /* 10M */                                         \
+    ->Arg(100000000); /* 100M */
+
+NANS_BENCHMARK_DEFINE(float32_nulls, float, true);
+NANS_BENCHMARK_DEFINE(float64_nulls, double, true);
+NANS_BENCHMARK_DEFINE(float32_no_nulls, float, false);
+NANS_BENCHMARK_DEFINE(float64_no_nulls, double, false);
diff --git a/cpp/benchmarks/search/contains_scalar.cpp b/cpp/benchmarks/search/contains_scalar.cpp
new file mode 100644
index 0000000..8d3c3f5
--- /dev/null
+++ b/cpp/benchmarks/search/contains_scalar.cpp
@@ -0,0 +1,57 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/detail/search.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+namespace {
+template <typename Type>
+std::unique_ptr<cudf::column> create_column_data(cudf::size_type n_rows, bool has_nulls = false)
+{
+  data_profile profile = data_profile_builder().cardinality(0).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 1000);
+  profile.set_null_probability(has_nulls ? std::optional{0.1} : std::nullopt);
+
+  return create_random_column(cudf::type_to_id<Type>(), row_count{n_rows}, profile);
+}
+
+}  // namespace
+
+static void nvbench_contains_scalar(nvbench::state& state)
+{
+  using Type = int;
+
+  auto const has_nulls = static_cast<bool>(state.get_int64("has_nulls"));
+  auto const size      = state.get_int64("data_size");
+
+  auto const haystack = create_column_data<Type>(size, has_nulls);
+  auto const needle   = cudf::make_fixed_width_scalar<Type>(size / 2);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto const stream_view             = rmm::cuda_stream_view{launch.get_stream()};
+    [[maybe_unused]] auto const result = cudf::detail::contains(*haystack, *needle, stream_view);
+  });
+}
+
+NVBENCH_BENCH(nvbench_contains_scalar)
+  .set_name("contains_scalar")
+  .add_int64_power_of_two_axis("data_size", {10, 12, 14, 16, 18, 20, 22, 24, 26})
+  .add_int64_axis("has_nulls", {0, 1});
diff --git a/cpp/benchmarks/search/contains_table.cpp b/cpp/benchmarks/search/contains_table.cpp
new file mode 100644
index 0000000..17702d0
--- /dev/null
+++ b/cpp/benchmarks/search/contains_table.cpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf/detail/search.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+auto constexpr num_unique_elements = 1000;
+
+template <typename Type>
+static void nvbench_contains_table(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const size               = state.get_int64("table_size");
+  auto const dtype              = cudf::type_to_id<Type>();
+  double const null_probability = state.get_float64("null_probability");
+
+  auto builder = data_profile_builder().null_probability(null_probability);
+  if (dtype == cudf::type_id::LIST) {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, num_unique_elements)
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, num_unique_elements)
+      .list_depth(1);
+  } else {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, num_unique_elements);
+  }
+
+  auto const haystack = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 0);
+  auto const needles = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 1);
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto const stream_view = rmm::cuda_stream_view{launch.get_stream()};
+    [[maybe_unused]] auto const result =
+      cudf::detail::contains(haystack->view(),
+                             needles->view(),
+                             cudf::null_equality::EQUAL,
+                             cudf::nan_equality::ALL_EQUAL,
+                             stream_view,
+                             rmm::mr::get_current_device_resource());
+  });
+
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+}
+
+NVBENCH_BENCH_TYPES(nvbench_contains_table,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, cudf::list_view>))
+  .set_name("contains_table")
+  .set_type_axes_names({"type"})
+  .add_float64_axis("null_probability", {0.0, 0.1})
+  .add_int64_axis("table_size", {10'000, 100'000, 1'000'000, 10'000'000});
diff --git a/cpp/benchmarks/search/search.cpp b/cpp/benchmarks/search/search.cpp
new file mode 100644
index 0000000..68bfa26
--- /dev/null
+++ b/cpp/benchmarks/search/search.cpp
@@ -0,0 +1,151 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/search.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/types.hpp>
+
+class Search : public cudf::benchmark {};
+
+void BM_column(benchmark::State& state, bool nulls)
+{
+  auto const column_size{static_cast<cudf::size_type>(state.range(0))};
+  auto const values_size = column_size;
+
+  auto init_data  = cudf::make_fixed_width_scalar<float>(static_cast<float>(0));
+  auto init_value = cudf::make_fixed_width_scalar<float>(static_cast<float>(values_size));
+  auto step       = cudf::make_fixed_width_scalar<float>(static_cast<float>(-1));
+  auto column     = cudf::sequence(column_size, *init_data);
+  auto values     = cudf::sequence(values_size, *init_value, *step);
+  if (nulls) {
+    auto [column_null_mask, column_null_count] = create_random_null_mask(column->size(), 0.1, 1);
+    column->set_null_mask(std::move(column_null_mask), column_null_count);
+    auto [values_null_mask, values_null_count] = create_random_null_mask(values->size(), 0.1, 2);
+    values->set_null_mask(std::move(values_null_mask), values_null_count);
+  }
+
+  auto data_table = cudf::sort(cudf::table_view({*column}));
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto col = cudf::upper_bound(data_table->view(),
+                                 cudf::table_view({*values}),
+                                 {cudf::order::ASCENDING},
+                                 {cudf::null_order::BEFORE});
+  }
+}
+
+BENCHMARK_DEFINE_F(Search, Column_AllValid)(::benchmark::State& state) { BM_column(state, false); }
+BENCHMARK_DEFINE_F(Search, Column_Nulls)(::benchmark::State& state) { BM_column(state, true); }
+
+BENCHMARK_REGISTER_F(Search, Column_AllValid)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(100000000);
+
+BENCHMARK_REGISTER_F(Search, Column_Nulls)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Arg(100000000);
+
+void BM_table(benchmark::State& state)
+{
+  using Type = float;
+
+  auto const num_columns{static_cast<cudf::size_type>(state.range(0))};
+  auto const column_size{static_cast<cudf::size_type>(state.range(1))};
+  auto const values_size = column_size;
+
+  data_profile profile = data_profile_builder().cardinality(0).null_probability(0.1).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100);
+  auto data_table = create_random_table(
+    cycle_dtypes({cudf::type_to_id<Type>()}, num_columns), row_count{column_size}, profile);
+  auto values_table = create_random_table(
+    cycle_dtypes({cudf::type_to_id<Type>()}, num_columns), row_count{values_size}, profile);
+
+  std::vector<cudf::order> orders(num_columns, cudf::order::ASCENDING);
+  std::vector<cudf::null_order> null_orders(num_columns, cudf::null_order::BEFORE);
+  auto sorted = cudf::sort(*data_table);
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto col = cudf::lower_bound(sorted->view(), *values_table, orders, null_orders);
+  }
+}
+
+BENCHMARK_DEFINE_F(Search, Table)(::benchmark::State& state) { BM_table(state); }
+
+static void CustomArguments(benchmark::internal::Benchmark* b)
+{
+  for (int num_cols = 1; num_cols <= 10; num_cols *= 2)
+    for (int col_size = 1000; col_size <= 100000000; col_size *= 10)
+      b->Args({num_cols, col_size});
+}
+
+BENCHMARK_REGISTER_F(Search, Table)
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond)
+  ->Apply(CustomArguments);
+
+void BM_contains(benchmark::State& state, bool nulls)
+{
+  auto const column_size{static_cast<cudf::size_type>(state.range(0))};
+  auto const values_size = column_size;
+
+  auto init_data  = cudf::make_fixed_width_scalar<float>(static_cast<float>(0));
+  auto init_value = cudf::make_fixed_width_scalar<float>(static_cast<float>(values_size));
+  auto step       = cudf::make_fixed_width_scalar<float>(static_cast<float>(-1));
+  auto column     = cudf::sequence(column_size, *init_data);
+  auto values     = cudf::sequence(values_size, *init_value, *step);
+  if (nulls) {
+    auto [column_null_mask, column_null_count] = create_random_null_mask(column->size(), 0.1, 1);
+    column->set_null_mask(std::move(column_null_mask), column_null_count);
+    auto [values_null_mask, values_null_count] = create_random_null_mask(values->size(), 0.1, 2);
+    values->set_null_mask(std::move(values_null_mask), values_null_count);
+  }
+
+  for (auto _ : state) {
+    cuda_event_timer timer(state, true);
+    auto col = cudf::contains(*column, *values);
+  }
+}
+
+BENCHMARK_DEFINE_F(Search, ColumnContains_AllValid)(::benchmark::State& state)
+{
+  BM_contains(state, false);
+}
+BENCHMARK_DEFINE_F(Search, ColumnContains_Nulls)(::benchmark::State& state)
+{
+  BM_contains(state, true);
+}
+
+BENCHMARK_REGISTER_F(Search, ColumnContains_AllValid)
+  ->RangeMultiplier(8)
+  ->Ranges({{1 << 10, 1 << 26}})
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond);
+
+BENCHMARK_REGISTER_F(Search, ColumnContains_Nulls)
+  ->RangeMultiplier(8)
+  ->Ranges({{1 << 10, 1 << 26}})
+  ->UseManualTime()
+  ->Unit(benchmark::kMillisecond);
diff --git a/cpp/benchmarks/sort/nested_types_common.hpp b/cpp/benchmarks/sort/nested_types_common.hpp
new file mode 100644
index 0000000..93853ba
--- /dev/null
+++ b/cpp/benchmarks/sort/nested_types_common.hpp
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+// This error appears in GCC 11.3 and may be a compiler bug or nvbench bug.
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#include <nvbench/nvbench.cuh>
+#pragma GCC diagnostic pop
+
+#include <random>
+
+inline std::unique_ptr<cudf::table> create_lists_data(nvbench::state& state,
+                                                      cudf::size_type const num_columns = 1,
+                                                      cudf::size_type const min_val     = 0,
+                                                      cudf::size_type const max_val     = 5)
+{
+  size_t const size_bytes(state.get_int64("size_bytes"));
+  cudf::size_type const depth{static_cast<cudf::size_type>(state.get_int64("depth"))};
+  auto const null_frequency{state.get_float64("null_frequency")};
+
+  data_profile table_profile;
+  table_profile.set_distribution_params(
+    cudf::type_id::LIST, distribution_id::UNIFORM, min_val, max_val);
+  table_profile.set_list_depth(depth);
+  table_profile.set_null_probability(null_frequency);
+  return create_random_table(std::vector<cudf::type_id>(num_columns, cudf::type_id::LIST),
+                             table_size_bytes{size_bytes},
+                             table_profile);
+}
+
+inline std::unique_ptr<cudf::table> create_structs_data(nvbench::state& state,
+                                                        cudf::size_type const n_cols = 1)
+{
+  using Type           = int;
+  using column_wrapper = cudf::test::fixed_width_column_wrapper<Type>;
+  std::default_random_engine generator;
+  std::uniform_int_distribution<int> distribution(0, 100);
+
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.get_int64("NumRows"))};
+  cudf::size_type const depth{static_cast<cudf::size_type>(state.get_int64("Depth"))};
+  bool const nulls{static_cast<bool>(state.get_int64("Nulls"))};
+
+  // Create columns with values in the range [0,100)
+  std::vector<column_wrapper> columns;
+  columns.reserve(n_cols);
+  std::generate_n(std::back_inserter(columns), n_cols, [&]() {
+    auto const elements = cudf::detail::make_counting_transform_iterator(
+      0, [&](auto row) { return distribution(generator); });
+    if (!nulls) return column_wrapper(elements, elements + n_rows);
+    auto valids =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 10 != 0; });
+    return column_wrapper(elements, elements + n_rows, valids);
+  });
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  std::transform(columns.begin(), columns.end(), std::back_inserter(cols), [](column_wrapper& col) {
+    return col.release();
+  });
+
+  std::vector<std::unique_ptr<cudf::column>> child_cols = std::move(cols);
+  // Nest the child columns in a struct, then nest that struct column inside another
+  // struct column up to the desired depth
+  for (int i = 0; i < depth; i++) {
+    std::vector<bool> struct_validity;
+    std::uniform_int_distribution<int> bool_distribution(0, 100 * (i + 1));
+    std::generate_n(
+      std::back_inserter(struct_validity), n_rows, [&]() { return bool_distribution(generator); });
+    cudf::test::structs_column_wrapper struct_col(std::move(child_cols), struct_validity);
+    child_cols = std::vector<std::unique_ptr<cudf::column>>{};
+    child_cols.push_back(struct_col.release());
+  }
+
+  // Create table view
+  return std::make_unique<cudf::table>(std::move(child_cols));
+}
diff --git a/cpp/benchmarks/sort/rank.cpp b/cpp/benchmarks/sort/rank.cpp
new file mode 100644
index 0000000..6231c70
--- /dev/null
+++ b/cpp/benchmarks/sort/rank.cpp
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+class Rank : public cudf::benchmark {};
+
+static void BM_rank(benchmark::State& state, bool nulls)
+{
+  using Type = int;
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+
+  // Create columns with values in the range [0,100)
+  data_profile profile = data_profile_builder().cardinality(0).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100);
+  profile.set_null_probability(nulls ? std::optional{0.2} : std::nullopt);
+  auto keys = create_random_column(cudf::type_to_id<Type>(), row_count{n_rows}, profile);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+
+    auto result = cudf::rank(keys->view(),
+                             cudf::rank_method::FIRST,
+                             cudf::order::ASCENDING,
+                             nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE,
+                             cudf::null_order::AFTER,
+                             false);
+  }
+}
+
+#define RANK_BENCHMARK_DEFINE(name, nulls)          \
+  BENCHMARK_DEFINE_F(Rank, name)                    \
+  (::benchmark::State & st) { BM_rank(st, nulls); } \
+  BENCHMARK_REGISTER_F(Rank, name)                  \
+    ->RangeMultiplier(8)                            \
+    ->Ranges({{1 << 10, 1 << 26}})                  \
+    ->UseManualTime()                               \
+    ->Unit(benchmark::kMillisecond);
+
+RANK_BENCHMARK_DEFINE(no_nulls, false)
+RANK_BENCHMARK_DEFINE(nulls, true)
diff --git a/cpp/benchmarks/sort/rank_lists.cpp b/cpp/benchmarks/sort/rank_lists.cpp
new file mode 100644
index 0000000..49dc409
--- /dev/null
+++ b/cpp/benchmarks/sort/rank_lists.cpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_types_common.hpp"
+#include "rank_types_common.hpp"
+
+#include <cudf/sorting.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <cudf::rank_method method>
+void nvbench_rank_lists(nvbench::state& state, nvbench::type_list<nvbench::enum_type<method>>)
+{
+  auto const table = create_lists_data(state);
+
+  auto const null_frequency{state.get_float64("null_frequency")};
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    cudf::rank(table->view().column(0),
+               method,
+               cudf::order::ASCENDING,
+               null_frequency ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE,
+               cudf::null_order::AFTER,
+               rmm::mr::get_current_device_resource());
+  });
+}
+
+NVBENCH_BENCH_TYPES(nvbench_rank_lists, NVBENCH_TYPE_AXES(methods))
+  .set_name("rank_lists")
+  .add_int64_power_of_two_axis("size_bytes", {10, 18, 24, 28})
+  .add_int64_axis("depth", {1, 4})
+  .add_float64_axis("null_frequency", {0, 0.2});
diff --git a/cpp/benchmarks/sort/rank_structs.cpp b/cpp/benchmarks/sort/rank_structs.cpp
new file mode 100644
index 0000000..85427e2
--- /dev/null
+++ b/cpp/benchmarks/sort/rank_structs.cpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_types_common.hpp"
+#include "rank_types_common.hpp"
+
+#include <cudf/sorting.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <cudf::rank_method method>
+void nvbench_rank_structs(nvbench::state& state, nvbench::type_list<nvbench::enum_type<method>>)
+{
+  auto const table = create_structs_data(state);
+
+  bool const nulls{static_cast<bool>(state.get_int64("Nulls"))};
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    cudf::rank(table->view().column(0),
+               method,
+               cudf::order::ASCENDING,
+               nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE,
+               cudf::null_order::AFTER,
+               rmm::mr::get_current_device_resource());
+  });
+}
+
+NVBENCH_BENCH_TYPES(nvbench_rank_structs, NVBENCH_TYPE_AXES(methods))
+  .set_name("rank_structs")
+  .add_int64_power_of_two_axis("NumRows", {10, 18, 26})
+  .add_int64_axis("Depth", {0, 1, 8})
+  .add_int64_axis("Nulls", {0, 1});
diff --git a/cpp/benchmarks/sort/rank_types_common.hpp b/cpp/benchmarks/sort/rank_types_common.hpp
new file mode 100644
index 0000000..adb5860
--- /dev/null
+++ b/cpp/benchmarks/sort/rank_types_common.hpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+enum class rank_method : int32_t {};
+
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  cudf::rank_method,
+  [](cudf::rank_method value) {
+    switch (value) {
+      case cudf::rank_method::FIRST: return "FIRST";
+      case cudf::rank_method::AVERAGE: return "AVERAGE";
+      case cudf::rank_method::MIN: return "MIN";
+      case cudf::rank_method::MAX: return "MAX";
+      case cudf::rank_method::DENSE: return "DENSE";
+      default: return "unknown";
+    }
+  },
+  [](cudf::rank_method value) {
+    switch (value) {
+      case cudf::rank_method::FIRST: return "cudf::rank_method::FIRST";
+      case cudf::rank_method::AVERAGE: return "cudf::rank_method::AVERAGE";
+      case cudf::rank_method::MIN: return "cudf::rank_method::MIN";
+      case cudf::rank_method::MAX: return "cudf::rank_method::MAX";
+      case cudf::rank_method::DENSE: return "cudf::rank_method::DENSE";
+      default: return "unknown";
+    }
+  })
+
+using methods = nvbench::enum_type_list<cudf::rank_method::AVERAGE,
+                                        cudf::rank_method::DENSE,
+                                        cudf::rank_method::FIRST,
+                                        cudf::rank_method::MAX,
+                                        cudf::rank_method::MIN>;
diff --git a/cpp/benchmarks/sort/segmented_sort.cpp b/cpp/benchmarks/sort/segmented_sort.cpp
new file mode 100644
index 0000000..2e83525
--- /dev/null
+++ b/cpp/benchmarks/sort/segmented_sort.cpp
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+void nvbench_segmented_sort(nvbench::state& state)
+{
+  auto const stable     = static_cast<bool>(state.get_int64("stable"));
+  auto const dtype      = cudf::type_to_id<int32_t>();
+  auto const size_bytes = static_cast<size_t>(state.get_int64("size_bytes"));
+  auto const null_freq  = state.get_float64("null_frequency");
+  auto const row_width  = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  data_profile const table_profile =
+    data_profile_builder().null_probability(null_freq).distribution(
+      dtype, distribution_id::UNIFORM, 0, 10);
+  auto const input =
+    create_random_table({cudf::type_id::INT32}, table_size_bytes{size_bytes}, table_profile);
+  auto const rows = input->num_rows();
+
+  auto const segments = cudf::sequence((rows / row_width) + 1,
+                                       cudf::numeric_scalar<int32_t>(0),
+                                       cudf::numeric_scalar<int32_t>(row_width));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.add_element_count(size_bytes, "bytes");
+  state.add_global_memory_reads<nvbench::int32_t>(rows * row_width);
+  state.add_global_memory_writes<nvbench::int32_t>(rows);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    if (stable)
+      cudf::stable_segmented_sorted_order(*input, *segments);
+    else
+      cudf::segmented_sorted_order(*input, *segments);
+  });
+}
+
+NVBENCH_BENCH(nvbench_segmented_sort)
+  .set_name("segmented_sort")
+  .add_int64_axis("stable", {0, 1})
+  .add_int64_power_of_two_axis("size_bytes", {16, 18, 20, 22, 24, 28})
+  .add_float64_axis("null_frequency", {0, 0.1})
+  .add_int64_axis("row_width", {16, 128, 1024});
diff --git a/cpp/benchmarks/sort/sort.cpp b/cpp/benchmarks/sort/sort.cpp
new file mode 100644
index 0000000..267a740
--- /dev/null
+++ b/cpp/benchmarks/sort/sort.cpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/sorting.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+template <bool stable>
+class Sort : public cudf::benchmark {};
+
+template <bool stable>
+static void BM_sort(benchmark::State& state, bool nulls)
+{
+  using Type       = int;
+  auto const dtype = cudf::type_to_id<Type>();
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+  cudf::size_type const n_cols{(cudf::size_type)state.range(1)};
+
+  // Create table with values in the range [0,100)
+  data_profile const profile = data_profile_builder()
+                                 .cardinality(0)
+                                 .null_probability(nulls ? std::optional{0.01} : std::nullopt)
+                                 .distribution(dtype, distribution_id::UNIFORM, 0, 100);
+  auto input_table = create_random_table(cycle_dtypes({dtype}, n_cols), row_count{n_rows}, profile);
+  cudf::table_view input{*input_table};
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+
+    auto result = (stable) ? cudf::stable_sorted_order(input) : cudf::sorted_order(input);
+  }
+}
+
+#define SORT_BENCHMARK_DEFINE(name, stable, nulls)          \
+  BENCHMARK_TEMPLATE_DEFINE_F(Sort, name, stable)           \
+  (::benchmark::State & st) { BM_sort<stable>(st, nulls); } \
+  BENCHMARK_REGISTER_F(Sort, name)                          \
+    ->RangeMultiplier(8)                                    \
+    ->Ranges({{1 << 10, 1 << 26}, {1, 8}})                  \
+    ->UseManualTime()                                       \
+    ->Unit(benchmark::kMillisecond);
+
+SORT_BENCHMARK_DEFINE(unstable_no_nulls, false, false)
+SORT_BENCHMARK_DEFINE(stable_no_nulls, true, false)
+SORT_BENCHMARK_DEFINE(unstable, false, true)
+SORT_BENCHMARK_DEFINE(stable, true, true)
diff --git a/cpp/benchmarks/sort/sort_lists.cpp b/cpp/benchmarks/sort/sort_lists.cpp
new file mode 100644
index 0000000..4b04323
--- /dev/null
+++ b/cpp/benchmarks/sort/sort_lists.cpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_types_common.hpp"
+
+#include <cudf/detail/sorting.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+namespace {
+constexpr cudf::size_type min_val = 0;
+constexpr cudf::size_type max_val = 100;
+
+void sort_multiple_lists(nvbench::state& state)
+{
+  auto const num_columns = static_cast<cudf::size_type>(state.get_int64("num_columns"));
+  auto const input_table = create_lists_data(state, num_columns, min_val, max_val);
+  auto const stream      = cudf::get_default_stream();
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    cudf::detail::sorted_order(
+      *input_table, {}, {}, stream, rmm::mr::get_current_device_resource());
+  });
+}
+
+void sort_lists_of_structs(nvbench::state& state)
+{
+  auto const num_columns = static_cast<cudf::size_type>(state.get_int64("num_columns"));
+  auto const lists_table = create_lists_data(state, num_columns, min_val, max_val);
+
+  // After having a table of (multiple) lists columns, convert those lists columns into lists of
+  // structs columns. The children of these structs columns are also children of the original lists
+  // columns.
+  // Such resulted lists-of-structs columns are very similar to the original lists-of-integers
+  // columns so their benchmarks can be somewhat comparable.
+  std::vector<cudf::column_view> lists_of_structs;
+  for (auto const& col : lists_table->view()) {
+    auto const child = col.child(cudf::lists_column_view::child_column_index);
+
+    // Put the child column under a struct column having the same null mask/null count.
+    auto const new_child = cudf::column_view{cudf::data_type{cudf::type_id::STRUCT},
+                                             child.size(),
+                                             nullptr,
+                                             child.null_mask(),
+                                             child.null_count(),
+                                             child.offset(),
+                                             {child}};
+    auto const converted_col =
+      cudf::column_view{cudf::data_type{cudf::type_id::LIST},
+                        col.size(),
+                        nullptr,
+                        col.null_mask(),
+                        col.null_count(),
+                        col.offset(),
+                        {col.child(cudf::lists_column_view::offsets_column_index), new_child}};
+    lists_of_structs.push_back(converted_col);
+  }
+
+  auto const input_table = cudf::table_view{lists_of_structs};
+  auto const stream      = cudf::get_default_stream();
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(stream.value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    rmm::cuda_stream_view stream_view{launch.get_stream()};
+    cudf::detail::sorted_order(input_table, {}, {}, stream, rmm::mr::get_current_device_resource());
+  });
+}
+
+}  // namespace
+
+void nvbench_sort_lists(nvbench::state& state)
+{
+  auto const has_lists_of_structs = state.get_int64("lists_of_structs") > 0;
+  if (has_lists_of_structs) {
+    sort_lists_of_structs(state);
+  } else {
+    sort_multiple_lists(state);
+  }
+}
+
+NVBENCH_BENCH(nvbench_sort_lists)
+  .set_name("sort_list")
+  .add_int64_power_of_two_axis("size_bytes", {10, 18, 24, 28})
+  .add_int64_axis("depth", {1, 4})
+  .add_int64_axis("num_columns", {1})
+  .add_int64_axis("lists_of_structs", {0, 1})
+  .add_float64_axis("null_frequency", {0, 0.2});
diff --git a/cpp/benchmarks/sort/sort_strings.cpp b/cpp/benchmarks/sort/sort_strings.cpp
new file mode 100644
index 0000000..dde1068
--- /dev/null
+++ b/cpp/benchmarks/sort/sort_strings.cpp
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/sorting.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+class Sort : public cudf::benchmark {};
+
+static void BM_sort(benchmark::State& state)
+{
+  cudf::size_type const n_rows{(cudf::size_type)state.range(0)};
+
+  auto const table = create_random_table({cudf::type_id::STRING}, row_count{n_rows});
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::sort(table->view());
+  }
+}
+
+#define SORT_BENCHMARK_DEFINE(name)          \
+  BENCHMARK_DEFINE_F(Sort, name)             \
+  (::benchmark::State & st) { BM_sort(st); } \
+  BENCHMARK_REGISTER_F(Sort, name)           \
+    ->RangeMultiplier(8)                     \
+    ->Ranges({{1 << 10, 1 << 24}})           \
+    ->UseManualTime()                        \
+    ->Unit(benchmark::kMillisecond);
+
+SORT_BENCHMARK_DEFINE(strings)
diff --git a/cpp/benchmarks/sort/sort_structs.cpp b/cpp/benchmarks/sort/sort_structs.cpp
new file mode 100644
index 0000000..1d54fa4
--- /dev/null
+++ b/cpp/benchmarks/sort/sort_structs.cpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_types_common.hpp"
+
+#include <cudf/detail/sorting.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+void nvbench_sort_struct(nvbench::state& state)
+{
+  auto const input = create_structs_data(state);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    rmm::cuda_stream_view stream_view{launch.get_stream()};
+    cudf::detail::sorted_order(*input, {}, {}, stream_view, rmm::mr::get_current_device_resource());
+  });
+}
+
+NVBENCH_BENCH(nvbench_sort_struct)
+  .set_name("sort_struct")
+  .add_int64_power_of_two_axis("NumRows", {10, 18, 26})
+  .add_int64_axis("Depth", {0, 1, 8})
+  .add_int64_axis("Nulls", {0, 1});
diff --git a/cpp/benchmarks/stream_compaction/apply_boolean_mask.cpp b/cpp/benchmarks/stream_compaction/apply_boolean_mask.cpp
new file mode 100644
index 0000000..f78aa9f
--- /dev/null
+++ b/cpp/benchmarks/stream_compaction/apply_boolean_mask.cpp
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <fixture/benchmark_fixture.hpp>
+#include <synchronization/synchronization.hpp>
+
+#include <cudf/stream_compaction.hpp>
+
+namespace {
+
+constexpr cudf::size_type hundredM      = 1e8;
+constexpr cudf::size_type tenM          = 1e7;
+constexpr cudf::size_type tenK          = 1e4;
+constexpr cudf::size_type fifty_percent = 50;
+
+void percent_range(benchmark::internal::Benchmark* b)
+{
+  b->Unit(benchmark::kMillisecond);
+  for (int percent = 0; percent <= 100; percent += 10)
+    b->Args({hundredM, percent});
+}
+
+void size_range(benchmark::internal::Benchmark* b)
+{
+  b->Unit(benchmark::kMillisecond);
+  for (int size = tenK; size <= hundredM; size *= 10)
+    b->Args({size, fifty_percent});
+}
+
+template <typename T>
+void calculate_bandwidth(benchmark::State& state, cudf::size_type num_columns)
+{
+  cudf::size_type const column_size{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const percent_true{static_cast<cudf::size_type>(state.range(1))};
+
+  float const fraction                  = percent_true / 100.f;
+  cudf::size_type const column_size_out = fraction * column_size;
+  int64_t const mask_size =
+    sizeof(bool) * column_size + cudf::bitmask_allocation_size_bytes(column_size);
+  int64_t const validity_bytes_in  = (fraction >= 1.0f / 32)
+                                       ? cudf::bitmask_allocation_size_bytes(column_size)
+                                       : 4 * column_size_out;
+  int64_t const validity_bytes_out = cudf::bitmask_allocation_size_bytes(column_size_out);
+  int64_t const column_bytes_out   = sizeof(T) * column_size_out;
+  int64_t const column_bytes_in    = column_bytes_out;  // we only read unmasked inputs
+
+  int64_t const bytes_read =
+    (column_bytes_in + validity_bytes_in) * num_columns +  // reading columns
+    mask_size;                                             // reading boolean mask
+  int64_t const bytes_written =
+    (column_bytes_out + validity_bytes_out) * num_columns;  // writing columns
+
+  state.SetItemsProcessed(state.iterations() * column_size * num_columns);
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * (bytes_read + bytes_written));
+}
+
+}  // namespace
+
+template <class T>
+void BM_apply_boolean_mask(benchmark::State& state, cudf::size_type num_columns)
+{
+  cudf::size_type const column_size{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const percent_true{static_cast<cudf::size_type>(state.range(1))};
+
+  data_profile profile = data_profile_builder().cardinality(0).null_probability(0.0).distribution(
+    cudf::type_to_id<T>(), distribution_id::UNIFORM, 0, 100);
+
+  auto source_table = create_random_table(
+    cycle_dtypes({cudf::type_to_id<T>()}, num_columns), row_count{column_size}, profile);
+
+  profile.set_bool_probability_true(percent_true / 100.0);
+  profile.set_null_probability(std::nullopt);  // no null mask
+  auto mask = create_random_column(cudf::type_id::BOOL8, row_count{column_size}, profile);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    auto result = cudf::apply_boolean_mask(*source_table, mask->view());
+  }
+
+  calculate_bandwidth<T>(state, num_columns);
+}
+
+template <class T>
+class ApplyBooleanMask : public cudf::benchmark {
+ public:
+  using TypeParam = T;
+};
+
+#define ABM_BENCHMARK_DEFINE(name, type, n_columns)                                  \
+  BENCHMARK_TEMPLATE_DEFINE_F(ApplyBooleanMask, name, type)(::benchmark::State & st) \
+  {                                                                                  \
+    BM_apply_boolean_mask<TypeParam>(st, n_columns);                                 \
+  }
+
+ABM_BENCHMARK_DEFINE(float_1_col, float, 1);
+ABM_BENCHMARK_DEFINE(float_2_col, float, 2);
+ABM_BENCHMARK_DEFINE(float_4_col, float, 4);
+
+// shmoo 1, 2, 4 column float across percentage true
+BENCHMARK_REGISTER_F(ApplyBooleanMask, float_1_col)->Apply(percent_range);
+BENCHMARK_REGISTER_F(ApplyBooleanMask, float_2_col)->Apply(percent_range);
+BENCHMARK_REGISTER_F(ApplyBooleanMask, float_4_col)->Apply(percent_range);
+
+// shmoo 1, 2, 4 column float across column sizes with 50% true
+BENCHMARK_REGISTER_F(ApplyBooleanMask, float_1_col)->Apply(size_range);
+BENCHMARK_REGISTER_F(ApplyBooleanMask, float_2_col)->Apply(size_range);
+BENCHMARK_REGISTER_F(ApplyBooleanMask, float_4_col)->Apply(size_range);
+
+// spot benchmark other types
+ABM_BENCHMARK_DEFINE(int8_1_col, int8_t, 1);
+ABM_BENCHMARK_DEFINE(int16_1_col, int16_t, 1);
+ABM_BENCHMARK_DEFINE(int32_1_col, int32_t, 1);
+ABM_BENCHMARK_DEFINE(int64_1_col, int64_t, 1);
+ABM_BENCHMARK_DEFINE(double_1_col, double, 1);
+BENCHMARK_REGISTER_F(ApplyBooleanMask, int8_1_col)->Args({tenM, fifty_percent});
+BENCHMARK_REGISTER_F(ApplyBooleanMask, int16_1_col)->Args({tenM, fifty_percent});
+BENCHMARK_REGISTER_F(ApplyBooleanMask, int32_1_col)->Args({tenM, fifty_percent});
+BENCHMARK_REGISTER_F(ApplyBooleanMask, int64_1_col)->Args({tenM, fifty_percent});
+BENCHMARK_REGISTER_F(ApplyBooleanMask, double_1_col)->Args({tenM, fifty_percent});
diff --git a/cpp/benchmarks/stream_compaction/distinct.cpp b/cpp/benchmarks/stream_compaction/distinct.cpp
new file mode 100644
index 0000000..c04b651
--- /dev/null
+++ b/cpp/benchmarks/stream_compaction/distinct.cpp
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+NVBENCH_DECLARE_TYPE_STRINGS(cudf::timestamp_ms, "cudf::timestamp_ms", "cudf::timestamp_ms");
+
+template <typename Type>
+void nvbench_distinct(nvbench::state& state, nvbench::type_list<Type>)
+{
+  cudf::size_type const num_rows = state.get_int64("NumRows");
+
+  data_profile profile = data_profile_builder().cardinality(0).null_probability(0.01).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100);
+
+  auto source_column = create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile);
+
+  auto input_column = source_column->view();
+  auto input_table  = cudf::table_view({input_column, input_column, input_column, input_column});
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::distinct(input_table,
+                                 {0},
+                                 cudf::duplicate_keep_option::KEEP_ANY,
+                                 cudf::null_equality::EQUAL,
+                                 cudf::nan_equality::ALL_EQUAL);
+  });
+}
+
+using data_type = nvbench::type_list<bool, int8_t, int32_t, int64_t, float, cudf::timestamp_ms>;
+
+NVBENCH_BENCH_TYPES(nvbench_distinct, NVBENCH_TYPE_AXES(data_type))
+  .set_name("distinct")
+  .set_type_axes_names({"Type"})
+  .add_int64_axis("NumRows", {10'000, 100'000, 1'000'000, 10'000'000});
+
+template <typename Type>
+void nvbench_distinct_list(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const size               = state.get_int64("ColumnSize");
+  auto const dtype              = cudf::type_to_id<Type>();
+  double const null_probability = state.get_float64("null_probability");
+
+  auto builder = data_profile_builder().null_probability(null_probability);
+  if (dtype == cudf::type_id::LIST) {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, 4)
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, 4)
+      .list_depth(1);
+  } else {
+    // We're comparing distinct() on a non-nested column to that on a list column with the same
+    // number of distinct rows. The max list size is 4 and the number of distinct values in the
+    // list's child is 5. So the number of distinct rows in the list = 1 + 5 + 5^2 + 5^3 + 5^4 = 781
+    // We want this column to also have 781 distinct values.
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, 781);
+  }
+
+  auto const table = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 0);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::distinct(*table,
+                                 {0},
+                                 cudf::duplicate_keep_option::KEEP_ANY,
+                                 cudf::null_equality::EQUAL,
+                                 cudf::nan_equality::ALL_EQUAL);
+  });
+}
+
+NVBENCH_BENCH_TYPES(nvbench_distinct_list,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, cudf::list_view>))
+  .set_name("distinct_list")
+  .set_type_axes_names({"Type"})
+  .add_float64_axis("null_probability", {0.0, 0.1})
+  .add_int64_axis("ColumnSize", {100'000'000});
diff --git a/cpp/benchmarks/stream_compaction/distinct_count.cpp b/cpp/benchmarks/stream_compaction/distinct_count.cpp
new file mode 100644
index 0000000..2b2c901
--- /dev/null
+++ b/cpp/benchmarks/stream_compaction/distinct_count.cpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf/stream_compaction.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <typename Type>
+static void bench_distinct_count(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const dtype            = cudf::type_to_id<Type>();
+  auto const size             = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const null_probability = state.get_float64("null_probability");
+
+  data_profile profile =
+    data_profile_builder().distribution(dtype, distribution_id::UNIFORM, 0, size / 100);
+  if (null_probability > 0) {
+    profile.set_null_probability({null_probability});
+  } else {
+    profile.set_null_probability(std::nullopt);
+  }
+
+  auto const data_table   = create_random_table({dtype}, row_count{size}, profile);
+  auto const& data_column = data_table->get_column(0);
+  auto const input_table  = cudf::table_view{{data_column, data_column, data_column}};
+
+  auto mem_stats_logger = cudf::memory_stats_logger();  // init stats logger
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    cudf::distinct_count(input_table, cudf::null_equality::EQUAL);
+  });
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+}
+
+using data_type = nvbench::type_list<int32_t, int64_t, float, double>;
+
+NVBENCH_BENCH_TYPES(bench_distinct_count, NVBENCH_TYPE_AXES(data_type))
+  .set_name("distinct_count")
+  .add_int64_axis("num_rows",
+                  {
+                    10000,      // 10k
+                    100000,     // 100k
+                    1000000,    // 1M
+                    10000000,   // 10M
+                    100000000,  // 100M
+                  })
+  .add_float64_axis("null_probability", {0, 0.5});
diff --git a/cpp/benchmarks/stream_compaction/stable_distinct.cpp b/cpp/benchmarks/stream_compaction/stable_distinct.cpp
new file mode 100644
index 0000000..bcee304
--- /dev/null
+++ b/cpp/benchmarks/stream_compaction/stable_distinct.cpp
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+NVBENCH_DECLARE_TYPE_STRINGS(cudf::timestamp_ms, "cudf::timestamp_ms", "cudf::timestamp_ms");
+
+template <typename Type>
+void nvbench_stable_distinct(nvbench::state& state, nvbench::type_list<Type>)
+{
+  cudf::size_type const num_rows = state.get_int64("NumRows");
+
+  data_profile profile = data_profile_builder().cardinality(0).null_probability(0.01).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100);
+
+  auto source_column = create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile);
+
+  auto input_column = source_column->view();
+  auto input_table  = cudf::table_view({input_column, input_column, input_column, input_column});
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::stable_distinct(input_table,
+                                        {0},
+                                        cudf::duplicate_keep_option::KEEP_ANY,
+                                        cudf::null_equality::EQUAL,
+                                        cudf::nan_equality::ALL_EQUAL);
+  });
+}
+
+using data_type = nvbench::type_list<bool, int8_t, int32_t, int64_t, float, cudf::timestamp_ms>;
+
+NVBENCH_BENCH_TYPES(nvbench_stable_distinct, NVBENCH_TYPE_AXES(data_type))
+  .set_name("stable_distinct")
+  .set_type_axes_names({"Type"})
+  .add_int64_axis("NumRows", {10'000, 100'000, 1'000'000, 10'000'000});
+
+template <typename Type>
+void nvbench_stable_distinct_list(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const size               = state.get_int64("ColumnSize");
+  auto const dtype              = cudf::type_to_id<Type>();
+  double const null_probability = state.get_float64("null_probability");
+
+  auto builder = data_profile_builder().null_probability(null_probability);
+  if (dtype == cudf::type_id::LIST) {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, 4)
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, 4)
+      .list_depth(1);
+  } else {
+    // We're comparing stable_distinct() on a non-nested column to that on a list column with the
+    // same number of stable_distinct rows. The max list size is 4 and the number of distinct values
+    // in the list's child is 5. So the number of distinct rows in the list = 1 + 5 + 5^2 + 5^3 +
+    // 5^4 = 781 We want this column to also have 781 distinct values.
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, 781);
+  }
+
+  auto const table = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 0);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::stable_distinct(*table,
+                                        {0},
+                                        cudf::duplicate_keep_option::KEEP_ANY,
+                                        cudf::null_equality::EQUAL,
+                                        cudf::nan_equality::ALL_EQUAL);
+  });
+}
+
+NVBENCH_BENCH_TYPES(nvbench_stable_distinct_list,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, cudf::list_view>))
+  .set_name("stable_distinct_list")
+  .set_type_axes_names({"Type"})
+  .add_float64_axis("null_probability", {0.0, 0.1})
+  .add_int64_axis("ColumnSize", {100'000'000});
diff --git a/cpp/benchmarks/stream_compaction/unique.cpp b/cpp/benchmarks/stream_compaction/unique.cpp
new file mode 100644
index 0000000..854bc17
--- /dev/null
+++ b/cpp/benchmarks/stream_compaction/unique.cpp
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+// necessary for custom enum types
+// see: https://github.com/NVIDIA/nvbench/blob/main/examples/enums.cu
+NVBENCH_DECLARE_ENUM_TYPE_STRINGS(
+  // Enum type:
+  cudf::duplicate_keep_option,
+  // Callable to generate input strings:
+  [](cudf::duplicate_keep_option option) {
+    switch (option) {
+      case cudf::duplicate_keep_option::KEEP_FIRST: return "KEEP_FIRST";
+      case cudf::duplicate_keep_option::KEEP_LAST: return "KEEP_LAST";
+      case cudf::duplicate_keep_option::KEEP_NONE: return "KEEP_NONE";
+      default: return "ERROR";
+    }
+  },
+  // Callable to generate descriptions:
+  [](auto) { return std::string{}; })
+
+NVBENCH_DECLARE_TYPE_STRINGS(cudf::timestamp_ms, "cudf::timestamp_ms", "cudf::timestamp_ms");
+
+template <typename Type, cudf::duplicate_keep_option Keep>
+void nvbench_unique(nvbench::state& state, nvbench::type_list<Type, nvbench::enum_type<Keep>>)
+{
+  // KEEP_FIRST and KEEP_ANY are equivalent for unique
+  if constexpr (not std::is_same_v<Type, int32_t> and
+                Keep == cudf::duplicate_keep_option::KEEP_ANY) {
+    state.skip("Skip unwanted benchmarks.");
+  }
+
+  cudf::size_type const num_rows = state.get_int64("NumRows");
+  auto const sorting             = state.get_int64("Sort");
+
+  data_profile profile = data_profile_builder().cardinality(0).null_probability(0.01).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, num_rows / 100);
+
+  auto source_column = create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile);
+
+  auto input_column = source_column->view();
+  auto input_table  = cudf::table_view({input_column, input_column, input_column, input_column});
+
+  auto const run_bench = [&](auto const input) {
+    state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = cudf::unique(input, {0}, Keep, cudf::null_equality::EQUAL);
+    });
+  };
+
+  if (sorting) {
+    auto const sort_order = cudf::sorted_order(input_table);
+    auto const sort_table = cudf::gather(input_table, *sort_order);
+    run_bench(*sort_table);
+  } else {
+    run_bench(input_table);
+  }
+}
+
+using data_type   = nvbench::type_list<bool, int8_t, int32_t, int64_t, float, cudf::timestamp_ms>;
+using keep_option = nvbench::enum_type_list<cudf::duplicate_keep_option::KEEP_FIRST,
+                                            cudf::duplicate_keep_option::KEEP_LAST,
+                                            cudf::duplicate_keep_option::KEEP_NONE>;
+
+NVBENCH_BENCH_TYPES(nvbench_unique, NVBENCH_TYPE_AXES(data_type, keep_option))
+  .set_name("unique")
+  .set_type_axes_names({"Type", "KeepOption"})
+  .add_int64_axis("NumRows", {10'000, 100'000, 1'000'000, 10'000'000})
+  .add_int64_axis("Sort", {0, 1});
+
+template <typename Type, cudf::duplicate_keep_option Keep>
+void nvbench_unique_list(nvbench::state& state, nvbench::type_list<Type, nvbench::enum_type<Keep>>)
+{
+  // KEEP_FIRST and KEEP_ANY are equivalent for unique
+  if constexpr (Keep == cudf::duplicate_keep_option::KEEP_ANY) {
+    state.skip("Skip unwanted benchmarks.");
+  }
+
+  auto const size               = state.get_int64("ColumnSize");
+  auto const dtype              = cudf::type_to_id<Type>();
+  double const null_probability = state.get_float64("null_probability");
+  auto const sorting            = state.get_int64("Sort");
+
+  auto builder = data_profile_builder().null_probability(null_probability);
+  if (dtype == cudf::type_id::LIST) {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, 4)
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, 4)
+      .list_depth(1);
+  } else {
+    // We're comparing unique() on a non-nested column to that on a list column with the same
+    // number of unique rows. The max list size is 4 and the number of unique values in the
+    // list's child is 5. So the number of unique rows in the list = 1 + 5 + 5^2 + 5^3 + 5^4 = 781
+    // We want this column to also have 781 unique values.
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, 781);
+  }
+
+  auto const input_table = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 0);
+
+  auto const run_bench = [&](auto const input) {
+    state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = cudf::unique(input, {0}, Keep, cudf::null_equality::EQUAL);
+    });
+  };
+
+  if (sorting) {
+    auto const sort_order = cudf::sorted_order(*input_table);
+    auto const sort_table = cudf::gather(*input_table, *sort_order);
+    run_bench(*sort_table);
+  } else {
+    run_bench(*input_table);
+  }
+}
+
+NVBENCH_BENCH_TYPES(nvbench_unique_list,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, cudf::list_view>, keep_option))
+  .set_name("unique_list")
+  .set_type_axes_names({"Type", "KeepOption"})
+  .add_float64_axis("null_probability", {0.0, 0.1})
+  .add_int64_axis("ColumnSize", {10'000, 100'000, 1'000'000, 10'000'000, 100'000'000})
+  .add_int64_axis("Sort", {0, 1});
diff --git a/cpp/benchmarks/stream_compaction/unique_count.cpp b/cpp/benchmarks/stream_compaction/unique_count.cpp
new file mode 100644
index 0000000..e003c47
--- /dev/null
+++ b/cpp/benchmarks/stream_compaction/unique_count.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/types.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+template <typename Type>
+void nvbench_unique_count(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const num_rows = static_cast<cudf::size_type>(state.get_int64("NumRows"));
+  auto const nulls    = state.get_float64("NullProbability");
+
+  data_profile profile = data_profile_builder().cardinality(0).null_probability(nulls).distribution(
+    cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, num_rows / 100);
+
+  auto source_column = create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile);
+  auto sorted_table  = cudf::sort(cudf::table_view({source_column->view()}));
+
+  auto input = sorted_table->view();
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    cudf::unique_count(input, cudf::null_equality::EQUAL);
+  });
+}
+
+using data_type = nvbench::type_list<bool, int8_t, int32_t, int64_t, float, cudf::timestamp_ms>;
+
+NVBENCH_BENCH_TYPES(nvbench_unique_count, NVBENCH_TYPE_AXES(data_type))
+  .set_name("unique_count")
+  .set_type_axes_names({"Type"})
+  .add_int64_axis("NumRows", {10'000, 100'000, 1'000'000, 10'000'000})
+  .add_float64_axis("NullProbability", {0.0, 0.1});
diff --git a/cpp/benchmarks/string/case.cpp b/cpp/benchmarks/string/case.cpp
new file mode 100644
index 0000000..385bb76
--- /dev/null
+++ b/cpp/benchmarks/string/case.cpp
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/case.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+void bench_case(nvbench::state& state)
+{
+  auto const n_rows    = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const max_width = static_cast<int32_t>(state.get_int64("row_width"));
+  auto const encoding  = state.get_string("encoding");
+
+  if (static_cast<std::size_t>(n_rows) * static_cast<std::size_t>(max_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_width);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+
+  auto col_view = column->view();
+
+  cudf::column::contents ascii_contents;
+  if (encoding == "ascii") {
+    data_profile ascii_profile = data_profile_builder().no_validity().distribution(
+      cudf::type_id::INT8, distribution_id::UNIFORM, 32, 126);  // nice ASCII range
+    auto input = cudf::strings_column_view(col_view);
+    auto ascii_column =
+      create_random_column(cudf::type_id::INT8, row_count{input.chars_size()}, ascii_profile);
+    auto ascii_data = ascii_column->view();
+
+    col_view = cudf::column_view(col_view.type(),
+                                 col_view.size(),
+                                 nullptr,
+                                 col_view.null_mask(),
+                                 col_view.null_count(),
+                                 0,
+                                 {input.offsets(), ascii_data});
+
+    ascii_contents = ascii_column->release();
+  }
+  auto input = cudf::strings_column_view(col_view);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  state.add_element_count(input.chars_size(), "chars_size");
+  state.add_global_memory_reads<nvbench::int8_t>(input.chars_size());
+  state.add_global_memory_writes<nvbench::int8_t>(input.chars_size());
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto result = cudf::strings::to_lower(input); });
+}
+
+NVBENCH_BENCH(bench_case)
+  .set_name("case")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216})
+  .add_string_axis("encoding", {"ascii", "utf8"});
diff --git a/cpp/benchmarks/string/char_types.cpp b/cpp/benchmarks/string/char_types.cpp
new file mode 100644
index 0000000..59e6245
--- /dev/null
+++ b/cpp/benchmarks/string/char_types.cpp
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/char_types/char_types.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_char_types(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const api_type  = state.get_string("api");
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, table_profile);
+  cudf::strings_column_view input(table->view().column(0));
+  auto input_types = cudf::strings::string_character_types::SPACE;
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);  // all bytes are read;
+  if (api_type == "all") {
+    state.add_global_memory_writes<nvbench::int8_t>(num_rows);  // output is a bool8 per row
+  } else {
+    state.add_global_memory_writes<nvbench::int8_t>(chars_size);
+  }
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    if (api_type == "all") {
+      auto result = cudf::strings::all_characters_of_type(input, input_types);
+    } else {
+      auto result = cudf::strings::filter_characters_of_type(input, input_types);
+    }
+  });
+}
+
+NVBENCH_BENCH(bench_char_types)
+  .set_name("char_types")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048, 4096})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216})
+  .add_string_axis("api", {"all", "filter"});
diff --git a/cpp/benchmarks/string/combine.cpp b/cpp/benchmarks/string/combine.cpp
new file mode 100644
index 0000000..4ed54a3
--- /dev/null
+++ b/cpp/benchmarks/string/combine.cpp
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+class StringCombine : public cudf::benchmark {};
+
+static void BM_combine(benchmark::State& state)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const table = create_random_table(
+    {cudf::type_id::STRING, cudf::type_id::STRING}, row_count{n_rows}, table_profile);
+  cudf::strings_column_view input1(table->view().column(0));
+  cudf::strings_column_view input2(table->view().column(1));
+  cudf::string_scalar separator("+");
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::strings::concatenate(table->view(), separator);
+  }
+
+  state.SetBytesProcessed(state.iterations() * (input1.chars_size() + input2.chars_size()));
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 4;
+  int const max_rowlen = 1 << 11;
+  int const len_mult   = 4;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name)          \
+  BENCHMARK_DEFINE_F(StringCombine, name)       \
+  (::benchmark::State & st) { BM_combine(st); } \
+  BENCHMARK_REGISTER_F(StringCombine, name)     \
+    ->Apply(generate_bench_args)                \
+    ->UseManualTime()                           \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(concat)
diff --git a/cpp/benchmarks/string/contains.cpp b/cpp/benchmarks/string/contains.cpp
new file mode 100644
index 0000000..af45d5d
--- /dev/null
+++ b/cpp/benchmarks/string/contains.cpp
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/filling.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+std::unique_ptr<cudf::column> build_input_column(cudf::size_type n_rows,
+                                                 cudf::size_type row_width,
+                                                 int32_t hit_rate)
+{
+  // build input table using the following data
+  auto raw_data = cudf::test::strings_column_wrapper(
+                    {
+                      "123 abc 4567890 DEFGHI 0987 5W43",  // matches both patterns;
+                      "012345 6789 01234 56789 0123 456",  // the rest do not match
+                      "abc 4567890 DEFGHI 0987 Wxyz 123",
+                      "abcdefghijklmnopqrstuvwxyz 01234",
+                      "",
+                      "AbcéDEFGHIJKLMNOPQRSTUVWXYZ 01",
+                      "9876543210,abcdefghijklmnopqrstU",
+                      "9876543210,abcdefghijklmnopqrstU",
+                      "123 édf 4567890 DéFG 0987 X5",
+                      "1",
+                    })
+                    .release();
+
+  if (row_width / 32 > 1) {
+    std::vector<cudf::column_view> columns;
+    for (int i = 0; i < row_width / 32; ++i) {
+      columns.push_back(raw_data->view());
+    }
+    raw_data = cudf::strings::concatenate(cudf::table_view(columns));
+  }
+  auto data_view = raw_data->view();
+
+  // compute number of rows in n_rows that should match
+  auto matches = static_cast<int32_t>(n_rows * hit_rate) / 100;
+
+  // Create a randomized gather-map to build a column out of the strings in data.
+  data_profile gather_profile =
+    data_profile_builder().cardinality(0).null_probability(0.0).distribution(
+      cudf::type_id::INT32, distribution_id::UNIFORM, 1, data_view.size() - 1);
+  auto gather_table =
+    create_random_table({cudf::type_id::INT32}, row_count{n_rows}, gather_profile);
+  gather_table->get_column(0).set_null_mask(rmm::device_buffer{}, 0);
+
+  // Create scatter map by placing 0-index values throughout the gather-map
+  auto scatter_data = cudf::sequence(
+    matches, cudf::numeric_scalar<int32_t>(0), cudf::numeric_scalar<int32_t>(n_rows / matches));
+  auto zero_scalar = cudf::numeric_scalar<int32_t>(0);
+  auto table       = cudf::scatter({zero_scalar}, scatter_data->view(), gather_table->view());
+  auto gather_map  = table->view().column(0);
+  table            = cudf::gather(cudf::table_view({data_view}), gather_map);
+
+  return std::move(table->release().front());
+}
+
+// longer pattern lengths demand more working memory per string
+std::string patterns[] = {"^\\d+ [a-z]+", "[A-Z ]+\\d+ +\\d+[A-Z]+\\d+$"};
+
+static void bench_contains(nvbench::state& state)
+{
+  auto const n_rows        = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width     = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const pattern_index = static_cast<cudf::size_type>(state.get_int64("pattern"));
+  auto const hit_rate      = static_cast<cudf::size_type>(state.get_int64("hit_rate"));
+
+  if (static_cast<std::size_t>(n_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  auto col   = build_input_column(n_rows, row_width, hit_rate);
+  auto input = cudf::strings_column_view(col->view());
+
+  auto pattern = patterns[pattern_index];
+  auto program = cudf::strings::regex_program::create(pattern);
+
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int32_t>(input.size());
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { cudf::strings::contains_re(input, *program); });
+}
+
+NVBENCH_BENCH(bench_contains)
+  .set_name("contains")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512})
+  .add_int64_axis("num_rows", {32768, 262144, 2097152, 16777216})
+  .add_int64_axis("hit_rate", {50, 100})  // percentage
+  .add_int64_axis("pattern", {0, 1});
diff --git a/cpp/benchmarks/string/convert_datetime.cpp b/cpp/benchmarks/string/convert_datetime.cpp
new file mode 100644
index 0000000..5f332a3
--- /dev/null
+++ b/cpp/benchmarks/string/convert_datetime.cpp
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/strings/convert/convert_datetime.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+class StringDateTime : public cudf::benchmark {};
+
+enum class direction { to, from };
+
+template <class TypeParam>
+void BM_convert_datetime(benchmark::State& state, direction dir)
+{
+  auto const n_rows    = static_cast<cudf::size_type>(state.range(0));
+  auto const data_type = cudf::data_type(cudf::type_to_id<TypeParam>());
+
+  auto const column = create_random_column(data_type.id(), row_count{n_rows});
+  cudf::column_view input(column->view());
+
+  auto source = dir == direction::to ? cudf::strings::from_timestamps(input, "%Y-%m-%d %H:%M:%S")
+                                     : make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view source_string(source->view());
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    if (dir == direction::to)
+      cudf::strings::to_timestamps(source_string, data_type, "%Y-%m-%d %H:%M:%S");
+    else
+      cudf::strings::from_timestamps(input, "%Y-%m-%d %H:%M:%S");
+  }
+
+  auto const bytes = dir == direction::to ? source_string.chars_size() : n_rows * sizeof(TypeParam);
+  state.SetBytesProcessed(state.iterations() * bytes);
+}
+
+#define STR_BENCHMARK_DEFINE(name, type, dir)                          \
+  BENCHMARK_DEFINE_F(StringDateTime, name)(::benchmark::State & state) \
+  {                                                                    \
+    BM_convert_datetime<type>(state, dir);                             \
+  }                                                                    \
+  BENCHMARK_REGISTER_F(StringDateTime, name)                           \
+    ->RangeMultiplier(1 << 5)                                          \
+    ->Range(1 << 10, 1 << 25)                                          \
+    ->UseManualTime()                                                  \
+    ->Unit(benchmark::kMicrosecond);
+
+STR_BENCHMARK_DEFINE(from_days, cudf::timestamp_D, direction::from);
+STR_BENCHMARK_DEFINE(from_seconds, cudf::timestamp_s, direction::from);
+STR_BENCHMARK_DEFINE(from_mseconds, cudf::timestamp_ms, direction::from);
+STR_BENCHMARK_DEFINE(from_useconds, cudf::timestamp_us, direction::from);
+STR_BENCHMARK_DEFINE(from_nseconds, cudf::timestamp_ns, direction::from);
+
+STR_BENCHMARK_DEFINE(to_days, cudf::timestamp_D, direction::to);
+STR_BENCHMARK_DEFINE(to_seconds, cudf::timestamp_s, direction::to);
+STR_BENCHMARK_DEFINE(to_mseconds, cudf::timestamp_ms, direction::to);
+STR_BENCHMARK_DEFINE(to_useconds, cudf::timestamp_us, direction::to);
+STR_BENCHMARK_DEFINE(to_nseconds, cudf::timestamp_ns, direction::to);
diff --git a/cpp/benchmarks/string/convert_durations.cpp b/cpp/benchmarks/string/convert_durations.cpp
new file mode 100644
index 0000000..f12d292
--- /dev/null
+++ b/cpp/benchmarks/string/convert_durations.cpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/strings/convert/convert_durations.hpp>
+#include <cudf/types.hpp>
+#include <cudf/wrappers/durations.hpp>
+
+#include <algorithm>
+#include <random>
+
+class DurationsToString : public cudf::benchmark {};
+template <class TypeParam>
+void BM_convert_from_durations(benchmark::State& state)
+{
+  cudf::size_type const source_size = state.range(0);
+
+  // Every element is valid
+  auto data = cudf::detail::make_counting_transform_iterator(
+    0, [source_size](auto i) { return TypeParam{i - source_size / 2}; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_durations(data, data + source_size);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::strings::from_durations(source_durations, "%D days %H:%M:%S");
+  }
+
+  state.SetBytesProcessed(state.iterations() * source_size * sizeof(TypeParam));
+}
+
+class StringToDurations : public cudf::benchmark {};
+template <class TypeParam>
+void BM_convert_to_durations(benchmark::State& state)
+{
+  cudf::size_type const source_size = state.range(0);
+
+  // Every element is valid
+  auto data = cudf::detail::make_counting_transform_iterator(
+    0, [source_size](auto i) { return TypeParam{i - source_size / 2}; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_durations(data, data + source_size);
+  auto results = cudf::strings::from_durations(source_durations, "%D days %H:%M:%S");
+  cudf::strings_column_view source_string(*results);
+  auto output_type = cudf::data_type(cudf::type_to_id<TypeParam>());
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    cudf::strings::to_durations(source_string, output_type, "%D days %H:%M:%S");
+  }
+
+  state.SetBytesProcessed(state.iterations() * source_size * sizeof(TypeParam));
+}
+
+#define DSBM_BENCHMARK_DEFINE(name, type)                                 \
+  BENCHMARK_DEFINE_F(DurationsToString, name)(::benchmark::State & state) \
+  {                                                                       \
+    BM_convert_from_durations<type>(state);                               \
+  }                                                                       \
+  BENCHMARK_REGISTER_F(DurationsToString, name)                           \
+    ->RangeMultiplier(1 << 5)                                             \
+    ->Range(1 << 10, 1 << 25)                                             \
+    ->UseManualTime()                                                     \
+    ->Unit(benchmark::kMicrosecond);
+
+#define SDBM_BENCHMARK_DEFINE(name, type)                                 \
+  BENCHMARK_DEFINE_F(StringToDurations, name)(::benchmark::State & state) \
+  {                                                                       \
+    BM_convert_to_durations<type>(state);                                 \
+  }                                                                       \
+  BENCHMARK_REGISTER_F(StringToDurations, name)                           \
+    ->RangeMultiplier(1 << 5)                                             \
+    ->Range(1 << 10, 1 << 25)                                             \
+    ->UseManualTime()                                                     \
+    ->Unit(benchmark::kMicrosecond);
+
+DSBM_BENCHMARK_DEFINE(from_durations_D, cudf::duration_D);
+DSBM_BENCHMARK_DEFINE(from_durations_s, cudf::duration_s);
+DSBM_BENCHMARK_DEFINE(from_durations_ms, cudf::duration_ms);
+DSBM_BENCHMARK_DEFINE(from_durations_us, cudf::duration_us);
+DSBM_BENCHMARK_DEFINE(from_durations_ns, cudf::duration_ns);
+
+SDBM_BENCHMARK_DEFINE(to_durations_D, cudf::duration_D);
+SDBM_BENCHMARK_DEFINE(to_durations_s, cudf::duration_s);
+SDBM_BENCHMARK_DEFINE(to_durations_ms, cudf::duration_ms);
+SDBM_BENCHMARK_DEFINE(to_durations_us, cudf::duration_us);
+SDBM_BENCHMARK_DEFINE(to_durations_ns, cudf::duration_ns);
diff --git a/cpp/benchmarks/string/convert_fixed_point.cpp b/cpp/benchmarks/string/convert_fixed_point.cpp
new file mode 100644
index 0000000..0cc98ee
--- /dev/null
+++ b/cpp/benchmarks/string/convert_fixed_point.cpp
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/strings/convert/convert_fixed_point.hpp>
+#include <cudf/strings/convert/convert_floats.hpp>
+#include <cudf/types.hpp>
+
+namespace {
+
+std::unique_ptr<cudf::column> get_strings_column(cudf::size_type rows)
+{
+  auto result =
+    create_random_column(cudf::type_id::FLOAT32, row_count{static_cast<cudf::size_type>(rows)});
+  return cudf::strings::from_floats(result->view());
+}
+
+}  // anonymous namespace
+
+class StringsToFixedPoint : public cudf::benchmark {};
+
+template <typename fixed_point_type>
+void convert_to_fixed_point(benchmark::State& state)
+{
+  auto const rows         = static_cast<cudf::size_type>(state.range(0));
+  auto const strings_col  = get_strings_column(rows);
+  auto const strings_view = cudf::strings_column_view(strings_col->view());
+  auto const dtype = cudf::data_type{cudf::type_to_id<fixed_point_type>(), numeric::scale_type{-2}};
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    auto volatile results = cudf::strings::to_fixed_point(strings_view, dtype);
+  }
+
+  // bytes_processed = bytes_input + bytes_output
+  state.SetBytesProcessed(state.iterations() *
+                          (strings_view.chars_size() + rows * cudf::size_of(dtype)));
+}
+
+class StringsFromFixedPoint : public cudf::benchmark {};
+
+template <typename fixed_point_type>
+void convert_from_fixed_point(benchmark::State& state)
+{
+  auto const rows        = static_cast<cudf::size_type>(state.range(0));
+  auto const strings_col = get_strings_column(rows);
+  auto const dtype = cudf::data_type{cudf::type_to_id<fixed_point_type>(), numeric::scale_type{-2}};
+  auto const fp_col =
+    cudf::strings::to_fixed_point(cudf::strings_column_view(strings_col->view()), dtype);
+
+  std::unique_ptr<cudf::column> results = nullptr;
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    results = cudf::strings::from_fixed_point(fp_col->view());
+  }
+
+  // bytes_processed = bytes_input + bytes_output
+  state.SetBytesProcessed(
+    state.iterations() *
+    (cudf::strings_column_view(results->view()).chars_size() + rows * cudf::size_of(dtype)));
+}
+
+#define CONVERT_TO_FIXED_POINT_BMD(name, fixed_point_type)                  \
+  BENCHMARK_DEFINE_F(StringsToFixedPoint, name)(::benchmark::State & state) \
+  {                                                                         \
+    convert_to_fixed_point<fixed_point_type>(state);                        \
+  }                                                                         \
+  BENCHMARK_REGISTER_F(StringsToFixedPoint, name)                           \
+    ->RangeMultiplier(4)                                                    \
+    ->Range(1 << 12, 1 << 24)                                               \
+    ->UseManualTime()                                                       \
+    ->Unit(benchmark::kMicrosecond);
+
+#define CONVERT_FROM_FIXED_POINT_BMD(name, fixed_point_type)                  \
+  BENCHMARK_DEFINE_F(StringsFromFixedPoint, name)(::benchmark::State & state) \
+  {                                                                           \
+    convert_from_fixed_point<fixed_point_type>(state);                        \
+  }                                                                           \
+  BENCHMARK_REGISTER_F(StringsFromFixedPoint, name)                           \
+    ->RangeMultiplier(4)                                                      \
+    ->Range(1 << 12, 1 << 24)                                                 \
+    ->UseManualTime()                                                         \
+    ->Unit(benchmark::kMicrosecond);
+
+CONVERT_TO_FIXED_POINT_BMD(strings_to_decimal32, numeric::decimal32);
+CONVERT_TO_FIXED_POINT_BMD(strings_to_decimal64, numeric::decimal64);
+
+CONVERT_FROM_FIXED_POINT_BMD(strings_from_decimal32, numeric::decimal32);
+CONVERT_FROM_FIXED_POINT_BMD(strings_from_decimal64, numeric::decimal64);
diff --git a/cpp/benchmarks/string/convert_numerics.cpp b/cpp/benchmarks/string/convert_numerics.cpp
new file mode 100644
index 0000000..cce5d0f
--- /dev/null
+++ b/cpp/benchmarks/string/convert_numerics.cpp
@@ -0,0 +1,130 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/strings/convert/convert_floats.hpp>
+#include <cudf/strings/convert/convert_integers.hpp>
+#include <cudf/types.hpp>
+
+namespace {
+
+template <typename NumericType>
+std::unique_ptr<cudf::column> get_numerics_column(cudf::size_type rows)
+{
+  return create_random_column(cudf::type_to_id<NumericType>(), row_count{rows});
+}
+
+template <typename NumericType>
+std::unique_ptr<cudf::column> get_strings_column(cudf::size_type rows)
+{
+  auto const numerics_col = get_numerics_column<NumericType>(rows);
+  if constexpr (std::is_floating_point_v<NumericType>) {
+    return cudf::strings::from_floats(numerics_col->view());
+  } else {
+    return cudf::strings::from_integers(numerics_col->view());
+  }
+}
+}  // anonymous namespace
+
+class StringsToNumeric : public cudf::benchmark {};
+
+template <typename NumericType>
+void convert_to_number(benchmark::State& state)
+{
+  auto const rows = static_cast<cudf::size_type>(state.range(0));
+
+  auto const strings_col  = get_strings_column<NumericType>(rows);
+  auto const strings_view = cudf::strings_column_view(strings_col->view());
+  auto const col_type     = cudf::type_to_id<NumericType>();
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    if constexpr (std::is_floating_point_v<NumericType>) {
+      cudf::strings::to_floats(strings_view, cudf::data_type{col_type});
+    } else {
+      cudf::strings::to_integers(strings_view, cudf::data_type{col_type});
+    }
+  }
+
+  // bytes_processed = bytes_input + bytes_output
+  state.SetBytesProcessed(state.iterations() *
+                          (strings_view.chars_size() + rows * sizeof(NumericType)));
+}
+
+class StringsFromNumeric : public cudf::benchmark {};
+
+template <typename NumericType>
+void convert_from_number(benchmark::State& state)
+{
+  auto const rows = static_cast<cudf::size_type>(state.range(0));
+
+  auto const numerics_col  = get_numerics_column<NumericType>(rows);
+  auto const numerics_view = numerics_col->view();
+
+  std::unique_ptr<cudf::column> results = nullptr;
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    if constexpr (std::is_floating_point_v<NumericType>)
+      results = cudf::strings::from_floats(numerics_view);
+    else
+      results = cudf::strings::from_integers(numerics_view);
+  }
+
+  // bytes_processed = bytes_input + bytes_output
+  state.SetBytesProcessed(
+    state.iterations() *
+    (cudf::strings_column_view(results->view()).chars_size() + rows * sizeof(NumericType)));
+}
+
+#define CONVERT_TO_NUMERICS_BD(name, type)                               \
+  BENCHMARK_DEFINE_F(StringsToNumeric, name)(::benchmark::State & state) \
+  {                                                                      \
+    convert_to_number<type>(state);                                      \
+  }                                                                      \
+  BENCHMARK_REGISTER_F(StringsToNumeric, name)                           \
+    ->RangeMultiplier(4)                                                 \
+    ->Range(1 << 10, 1 << 17)                                            \
+    ->UseManualTime()                                                    \
+    ->Unit(benchmark::kMicrosecond);
+
+#define CONVERT_FROM_NUMERICS_BD(name, type)                               \
+  BENCHMARK_DEFINE_F(StringsFromNumeric, name)(::benchmark::State & state) \
+  {                                                                        \
+    convert_from_number<type>(state);                                      \
+  }                                                                        \
+  BENCHMARK_REGISTER_F(StringsFromNumeric, name)                           \
+    ->RangeMultiplier(4)                                                   \
+    ->Range(1 << 10, 1 << 17)                                              \
+    ->UseManualTime()                                                      \
+    ->Unit(benchmark::kMicrosecond);
+
+CONVERT_TO_NUMERICS_BD(strings_to_float32, float);
+CONVERT_TO_NUMERICS_BD(strings_to_float64, double);
+CONVERT_TO_NUMERICS_BD(strings_to_int32, int32_t);
+CONVERT_TO_NUMERICS_BD(strings_to_int64, int64_t);
+CONVERT_TO_NUMERICS_BD(strings_to_uint8, uint8_t);
+CONVERT_TO_NUMERICS_BD(strings_to_uint16, uint16_t);
+
+CONVERT_FROM_NUMERICS_BD(strings_from_float32, float);
+CONVERT_FROM_NUMERICS_BD(strings_from_float64, double);
+CONVERT_FROM_NUMERICS_BD(strings_from_int32, int32_t);
+CONVERT_FROM_NUMERICS_BD(strings_from_int64, int64_t);
+CONVERT_FROM_NUMERICS_BD(strings_from_uint8, uint8_t);
+CONVERT_FROM_NUMERICS_BD(strings_from_uint16, uint16_t);
diff --git a/cpp/benchmarks/string/copy.cu b/cpp/benchmarks/string/copy.cu
new file mode 100644
index 0000000..27438f8
--- /dev/null
+++ b/cpp/benchmarks/string/copy.cu
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/random.h>
+#include <thrust/shuffle.h>
+
+class StringCopy : public cudf::benchmark {};
+
+enum copy_type { gather, scatter };
+
+static void BM_copy(benchmark::State& state, copy_type ct)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+
+  auto const source =
+    create_random_table({cudf::type_id::STRING}, row_count{n_rows}, table_profile);
+  auto const target =
+    create_random_table({cudf::type_id::STRING}, row_count{n_rows}, table_profile);
+
+  // scatter indices
+  auto index_map_col = make_numeric_column(
+    cudf::data_type{cudf::type_id::INT32}, n_rows, cudf::mask_state::UNALLOCATED);
+  auto index_map = index_map_col->mutable_view();
+  thrust::shuffle_copy(thrust::device,
+                       thrust::counting_iterator<cudf::size_type>(0),
+                       thrust::counting_iterator<cudf::size_type>(n_rows),
+                       index_map.begin<cudf::size_type>(),
+                       thrust::default_random_engine());
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    switch (ct) {
+      case gather: cudf::gather(source->view(), index_map); break;
+      case scatter: cudf::scatter(source->view(), index_map, target->view()); break;
+    }
+  }
+
+  state.SetBytesProcessed(state.iterations() *
+                          cudf::strings_column_view(source->view().column(0)).chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 5;
+  int const max_rowlen = 1 << 13;
+  int const len_mult   = 4;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+
+  // Benchmark for very small strings
+  b->Args({67108864, 2});
+}
+
+#define COPY_BENCHMARK_DEFINE(name)                           \
+  BENCHMARK_DEFINE_F(StringCopy, name)                        \
+  (::benchmark::State & st) { BM_copy(st, copy_type::name); } \
+  BENCHMARK_REGISTER_F(StringCopy, name)                      \
+    ->Apply(generate_bench_args)                              \
+    ->UseManualTime()                                         \
+    ->Unit(benchmark::kMillisecond);
+
+COPY_BENCHMARK_DEFINE(gather)
+COPY_BENCHMARK_DEFINE(scatter)
diff --git a/cpp/benchmarks/string/count.cpp b/cpp/benchmarks/string/count.cpp
new file mode 100644
index 0000000..0840646
--- /dev/null
+++ b/cpp/benchmarks/string/count.cpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_count(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, table_profile);
+  cudf::strings_column_view input(table->view().column(0));
+
+  std::string pattern = "\\d+";
+
+  auto prog = cudf::strings::regex_program::create(pattern);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int32_t>(input.size());
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto result = cudf::strings::count_re(input, *prog); });
+}
+
+NVBENCH_BENCH(bench_count)
+  .set_name("count")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/string/extract.cpp b/cpp/benchmarks/string/extract.cpp
new file mode 100644
index 0000000..135dada
--- /dev/null
+++ b/cpp/benchmarks/string/extract.cpp
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/extract.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <random>
+
+static void bench_extract(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  auto groups = static_cast<cudf::size_type>(state.get_int64("groups"));
+
+  std::default_random_engine generator;
+  std::uniform_int_distribution<int> words_dist(0, 999);
+  std::vector<std::string> samples(100);  // 100 unique rows of data to reuse
+  std::generate(samples.begin(), samples.end(), [&]() {
+    std::string row;  // build a row of random tokens
+    while (static_cast<cudf::size_type>(row.size()) < row_width) {
+      row += std::to_string(words_dist(generator)) + " ";
+    }
+    return row;
+  });
+
+  std::string pattern{""};
+  while (groups--) {
+    pattern += "(\\d+) ";
+  }
+
+  cudf::test::strings_column_wrapper samples_column(samples.begin(), samples.end());
+  data_profile const profile = data_profile_builder().no_validity().distribution(
+    cudf::type_to_id<cudf::size_type>(), distribution_id::UNIFORM, 0ul, samples.size() - 1);
+  auto map =
+    create_random_column(cudf::type_to_id<cudf::size_type>(), row_count{num_rows}, profile);
+  auto input = cudf::gather(
+    cudf::table_view{{samples_column}}, map->view(), cudf::out_of_bounds_policy::DONT_CHECK);
+  cudf::strings_column_view strings_view(input->get_column(0).view());
+  auto prog = cudf::strings::regex_program::create(pattern);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = strings_view.chars_size();
+  state.add_element_count(chars_size, "chars_size");            // number of bytes;
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);   // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);  // all bytes are written
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::strings::extract(strings_view, *prog);
+  });
+}
+
+NVBENCH_BENCH(bench_extract)
+  .set_name("extract")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216})
+  .add_int64_axis("groups", {1, 2, 4});
diff --git a/cpp/benchmarks/string/factory.cu b/cpp/benchmarks/string/factory.cu
new file mode 100644
index 0000000..c73bcb0
--- /dev/null
+++ b/cpp/benchmarks/string/factory.cu
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+#include <limits>
+
+namespace {
+using string_pair = thrust::pair<char const*, cudf::size_type>;
+struct string_view_to_pair {
+  __device__ string_pair operator()(thrust::pair<cudf::string_view, bool> const& p)
+  {
+    return (p.second) ? string_pair{p.first.data(), p.first.size_bytes()} : string_pair{nullptr, 0};
+  }
+};
+}  // namespace
+
+class StringsFactory : public cudf::benchmark {};
+
+static void BM_factory(benchmark::State& state)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  auto d_column     = cudf::column_device_view::create(column->view());
+  rmm::device_uvector<string_pair> pairs(d_column->size(), cudf::get_default_stream());
+  thrust::transform(thrust::device,
+                    d_column->pair_begin<cudf::string_view, true>(),
+                    d_column->pair_end<cudf::string_view, true>(),
+                    pairs.data(),
+                    string_view_to_pair{});
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::make_strings_column(pairs, cudf::get_default_stream());
+  }
+
+  cudf::strings_column_view input(column->view());
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 5;
+  int const max_rowlen = 1 << 13;
+  int const len_mult   = 4;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name)          \
+  BENCHMARK_DEFINE_F(StringsFactory, name)      \
+  (::benchmark::State & st) { BM_factory(st); } \
+  BENCHMARK_REGISTER_F(StringsFactory, name)    \
+    ->Apply(generate_bench_args)                \
+    ->UseManualTime()                           \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(factory)
diff --git a/cpp/benchmarks/string/filter.cpp b/cpp/benchmarks/string/filter.cpp
new file mode 100644
index 0000000..b935fc4
--- /dev/null
+++ b/cpp/benchmarks/string/filter.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/char_types/char_types.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/strip.hpp>
+#include <cudf/strings/translate.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <vector>
+
+enum FilterAPI { filter, filter_chars, strip };
+
+class StringFilterChars : public cudf::benchmark {};
+
+static void BM_filter_chars(benchmark::State& state, FilterAPI api)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+
+  auto const types = cudf::strings::string_character_types::SPACE;
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> filter_table{
+    {cudf::char_utf8{'a'}, cudf::char_utf8{'c'}}};
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    switch (api) {
+      case filter: cudf::strings::filter_characters_of_type(input, types); break;
+      case filter_chars: cudf::strings::filter_characters(input, filter_table); break;
+      case strip: cudf::strings::strip(input); break;
+    }
+  }
+
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows          = 1 << 12;
+  int const max_rows          = 1 << 24;
+  int const row_multiplier    = 8;
+  int const min_length        = 1 << 5;
+  int const max_length        = 1 << 13;
+  int const length_multiplier = 2;
+  generate_string_bench_args(
+    b, min_rows, max_rows, row_multiplier, min_length, max_length, length_multiplier);
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name)                                \
+  BENCHMARK_DEFINE_F(StringFilterChars, name)                         \
+  (::benchmark::State & st) { BM_filter_chars(st, FilterAPI::name); } \
+  BENCHMARK_REGISTER_F(StringFilterChars, name)                       \
+    ->Apply(generate_bench_args)                                      \
+    ->UseManualTime()                                                 \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(filter)
+STRINGS_BENCHMARK_DEFINE(filter_chars)
+STRINGS_BENCHMARK_DEFINE(strip)
diff --git a/cpp/benchmarks/string/find.cpp b/cpp/benchmarks/string/find.cpp
new file mode 100644
index 0000000..5f2e694
--- /dev/null
+++ b/cpp/benchmarks/string/find.cpp
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/find.hpp>
+#include <cudf/strings/find_multiple.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <limits>
+
+enum FindAPI { find, find_multi, contains, starts_with, ends_with };
+
+class StringFindScalar : public cudf::benchmark {};
+
+static void BM_find_scalar(benchmark::State& state, FindAPI find_api)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+  cudf::string_scalar target("+");
+  cudf::test::strings_column_wrapper targets({"+", "-"});
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    switch (find_api) {
+      case find: cudf::strings::find(input, target); break;
+      case find_multi:
+        cudf::strings::find_multiple(input, cudf::strings_column_view(targets));
+        break;
+      case contains: cudf::strings::contains(input, target); break;
+      case starts_with: cudf::strings::starts_with(input, target); break;
+      case ends_with: cudf::strings::ends_with(input, target); break;
+    }
+  }
+
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 5;
+  int const max_rowlen = 1 << 13;
+  int const len_mult   = 2;
+  for (int row_count = min_rows; row_count <= max_rows; row_count *= row_mult) {
+    for (int rowlen = min_rowlen; rowlen <= max_rowlen; rowlen *= len_mult) {
+      // avoid generating combinations that exceed the cudf column limit
+      size_t total_chars = static_cast<size_t>(row_count) * rowlen;
+      if (total_chars < static_cast<size_t>(std::numeric_limits<cudf::size_type>::max())) {
+        b->Args({row_count, rowlen});
+      }
+    }
+  }
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name)                    \
+  BENCHMARK_DEFINE_F(StringFindScalar, name)              \
+  (::benchmark::State & st) { BM_find_scalar(st, name); } \
+  BENCHMARK_REGISTER_F(StringFindScalar, name)            \
+    ->Apply(generate_bench_args)                          \
+    ->UseManualTime()                                     \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(find)
+STRINGS_BENCHMARK_DEFINE(find_multi)
+STRINGS_BENCHMARK_DEFINE(contains)
+STRINGS_BENCHMARK_DEFINE(starts_with)
+STRINGS_BENCHMARK_DEFINE(ends_with)
diff --git a/cpp/benchmarks/string/gather.cpp b/cpp/benchmarks/string/gather.cpp
new file mode 100644
index 0000000..530b09b
--- /dev/null
+++ b/cpp/benchmarks/string/gather.cpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_gather(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const input_table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, table_profile);
+
+  data_profile const map_profile = data_profile_builder().no_validity().distribution(
+    cudf::type_id::INT32, distribution_id::UNIFORM, 0, num_rows);
+  auto const map_table =
+    create_random_table({cudf::type_id::INT32}, row_count{num_rows}, map_profile);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  auto chars_size = cudf::strings_column_view(input_table->view().column(0)).chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);  // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::gather(
+      input_table->view(), map_table->view().column(0), cudf::out_of_bounds_policy::NULLIFY);
+  });
+}
+
+NVBENCH_BENCH(bench_gather)
+  .set_name("gather")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048, 4096})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/string/join_strings.cpp b/cpp/benchmarks/string/join_strings.cpp
new file mode 100644
index 0000000..a122c00
--- /dev/null
+++ b/cpp/benchmarks/string/join_strings.cpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_join(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, table_profile);
+  cudf::strings_column_view input(table->view().column(0));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto const chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");            // number of bytes;
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);   // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);  // all bytes are written
+
+  std::string separator(":");
+  std::string narep("null");
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::strings::join_strings(input, separator, narep);
+  });
+}
+
+NVBENCH_BENCH(bench_join)
+  .set_name("strings_join")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/string/json.cu b/cpp/benchmarks/string/json.cu
new file mode 100644
index 0000000..7e89edf
--- /dev/null
+++ b/cpp/benchmarks/string/json.cu
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/json.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/random.h>
+
+class JsonPath : public cudf::benchmark {};
+
+std::vector<std::string> const Books{
+  R"json({
+"category": "reference",
+"author": "Nigel Rees",
+"title": "Sayings of the Century",
+"price": 8.95
+})json",
+  R"json({
+"category": "fiction",
+"author": "Evelyn Waugh",
+"title": "Sword of Honour",
+"price": 12.99
+})json",
+  R"json({
+"category": "fiction",
+"author": "Herman Melville",
+"title": "Moby Dick",
+"isbn": "0-553-21311-3",
+"price": 8.99
+})json",
+  R"json({
+"category": "fiction",
+"author": "J. R. R. Tolkien",
+"title": "The Lord of the Rings",
+"isbn": "0-395-19395-8",
+"price": 22.99
+})json"};
+constexpr int Approx_book_size = 110;
+std::vector<std::string> const Bicycles{
+  R"json({"color": "red", "price": 9.95})json",
+  R"json({"color": "green", "price": 29.95})json",
+  R"json({"color": "blue", "price": 399.95})json",
+  R"json({"color": "yellow", "price": 99.95})json",
+  R"json({"color": "mauve", "price": 199.95})json",
+};
+constexpr int Approx_bicycle_size = 33;
+std::string Misc{"\n\"expensive\": 10\n"};
+
+struct json_benchmark_row_builder {
+  int const desired_bytes;
+  cudf::size_type const num_rows;
+  cudf::column_device_view const d_books_bicycles[2];  // Books, Bicycles strings
+  cudf::column_device_view const d_book_pct;           // Book percentage
+  cudf::column_device_view const d_misc_order;         // Misc-Store order
+  cudf::column_device_view const d_store_order;        // Books-Bicycles order
+  int32_t* d_offsets{};
+  char* d_chars{};
+  thrust::minstd_rand rng{5236};
+  thrust::uniform_int_distribution<int> dist{};
+
+  // internal data structure for {bytes, out_ptr} with operator+=
+  struct bytes_and_ptr {
+    cudf::size_type bytes;
+    char* ptr;
+    __device__ bytes_and_ptr& operator+=(cudf::string_view const& str_append)
+    {
+      bytes += str_append.size_bytes();
+      if (ptr) { ptr = cudf::strings::detail::copy_string(ptr, str_append); }
+      return *this;
+    }
+  };
+
+  __device__ inline void copy_items(int this_idx,
+                                    cudf::size_type num_items,
+                                    bytes_and_ptr& output_str)
+  {
+    using param_type = thrust::uniform_int_distribution<int>::param_type;
+    dist.param(param_type{0, d_books_bicycles[this_idx].size() - 1});
+    cudf::string_view comma(",\n", 2);
+    for (int i = 0; i < num_items; i++) {
+      if (i > 0) { output_str += comma; }
+      int idx   = dist(rng);
+      auto item = d_books_bicycles[this_idx].element<cudf::string_view>(idx);
+      output_str += item;
+    }
+  }
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    int num_books       = 2;
+    int num_bicycles    = 2;
+    int remaining_bytes = max(
+      0, desired_bytes - ((num_books * Approx_book_size) + (num_bicycles * Approx_bicycle_size)));
+
+    // divide up the remainder between books and bikes
+    auto book_pct = d_book_pct.element<float>(idx);
+    // {Misc, store} OR {store, Misc}
+    // store: {books, bicycles} OR store: {bicycles, books}
+    float bicycle_pct = 1.0f - book_pct;
+    num_books += (remaining_bytes * book_pct) / Approx_book_size;
+    num_bicycles += (remaining_bytes * bicycle_pct) / Approx_bicycle_size;
+
+    char* out_ptr = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    bytes_and_ptr output_str{0, out_ptr};
+    //
+    cudf::string_view comma(",\n", 2);
+    cudf::string_view brace1("{\n", 2);
+    cudf::string_view store_member_start[2]{{"\"book\": [\n", 10}, {"\"bicycle\": [\n", 13}};
+    cudf::string_view store("\"store\": {\n", 11);
+    cudf::string_view Misc{"\"expensive\": 10", 15};
+    cudf::string_view brace2("\n}", 2);
+    cudf::string_view square2{"\n]", 2};
+
+    output_str += brace1;
+    if (d_misc_order.element<bool>(idx)) {  // Misc. first.
+      output_str += Misc;
+      output_str += comma;
+    }
+    output_str += store;
+    for (int store_order = 0; store_order < 2; store_order++) {
+      if (store_order > 0) { output_str += comma; }
+      int this_idx    = (d_store_order.element<bool>(idx) == store_order);
+      auto& mem_start = store_member_start[this_idx];
+      output_str += mem_start;
+      copy_items(this_idx, this_idx == 0 ? num_books : num_bicycles, output_str);
+      output_str += square2;
+    }
+    output_str += brace2;
+    if (!d_misc_order.element<bool>(idx)) {  // Misc, if not first.
+      output_str += comma;
+      output_str += Misc;
+    }
+    output_str += brace2;
+    if (!output_str.ptr) d_offsets[idx] = output_str.bytes;
+  }
+};
+
+auto build_json_string_column(int desired_bytes, int num_rows)
+{
+  data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution(
+    cudf::type_id::FLOAT32, distribution_id::UNIFORM, 0.0, 1.0);
+  auto float_2bool_columns =
+    create_random_table({cudf::type_id::FLOAT32, cudf::type_id::BOOL8, cudf::type_id::BOOL8},
+                        row_count{num_rows},
+                        profile);
+
+  cudf::test::strings_column_wrapper books(Books.begin(), Books.end());
+  cudf::test::strings_column_wrapper bicycles(Bicycles.begin(), Bicycles.end());
+  auto d_books       = cudf::column_device_view::create(books);
+  auto d_bicycles    = cudf::column_device_view::create(bicycles);
+  auto d_book_pct    = cudf::column_device_view::create(float_2bool_columns->get_column(0));
+  auto d_misc_order  = cudf::column_device_view::create(float_2bool_columns->get_column(1));
+  auto d_store_order = cudf::column_device_view::create(float_2bool_columns->get_column(2));
+  json_benchmark_row_builder jb{
+    desired_bytes, num_rows, {*d_books, *d_bicycles}, *d_book_pct, *d_misc_order, *d_store_order};
+  auto children = cudf::strings::detail::make_strings_children(
+    jb, num_rows, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  return cudf::make_strings_column(
+    num_rows, std::move(children.first), std::move(children.second), 0, {});
+}
+
+void BM_case(benchmark::State& state, std::string query_arg)
+{
+  srand(5236);
+  int num_rows      = state.range(0);
+  int desired_bytes = state.range(1);
+  auto input        = build_json_string_column(desired_bytes, num_rows);
+  cudf::strings_column_view scv(input->view());
+  size_t num_chars = scv.chars().size();
+
+  std::string json_path(query_arg);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    auto result = cudf::strings::get_json_object(scv, json_path);
+    CUDF_CUDA_TRY(cudaStreamSynchronize(0));
+  }
+
+  // this isn't strictly 100% accurate. a given query isn't necessarily
+  // going to visit every single incoming character.  but in spirit it does.
+  state.SetBytesProcessed(state.iterations() * num_chars);
+}
+
+#define JSON_BENCHMARK_DEFINE(name, query)                                                  \
+  BENCHMARK_DEFINE_F(JsonPath, name)(::benchmark::State & state) { BM_case(state, query); } \
+  BENCHMARK_REGISTER_F(JsonPath, name)                                                      \
+    ->ArgsProduct({{100, 1000, 100000, 400000}, {300, 600, 4096}})                          \
+    ->UseManualTime()                                                                       \
+    ->Unit(benchmark::kMillisecond);
+
+JSON_BENCHMARK_DEFINE(query0, "$");
+JSON_BENCHMARK_DEFINE(query1, "$.store");
+JSON_BENCHMARK_DEFINE(query2, "$.store.book");
+JSON_BENCHMARK_DEFINE(query3, "$.store.*");
+JSON_BENCHMARK_DEFINE(query4, "$.store.book[*]");
+JSON_BENCHMARK_DEFINE(query5, "$.store.book[*].category");
+JSON_BENCHMARK_DEFINE(query6, "$.store['bicycle']");
+JSON_BENCHMARK_DEFINE(query7, "$.store.book[*]['isbn']");
+JSON_BENCHMARK_DEFINE(query8, "$.store.bicycle[1]");
diff --git a/cpp/benchmarks/string/lengths.cpp b/cpp/benchmarks/string/lengths.cpp
new file mode 100644
index 0000000..36c4bf6
--- /dev/null
+++ b/cpp/benchmarks/string/lengths.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/attributes.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_lengths(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, table_profile);
+  cudf::strings_column_view input(table->view().column(0));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);  // all bytes are read;
+  state.add_global_memory_writes<nvbench::int32_t>(num_rows);  // output is an integer per row
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::strings::count_characters(input);
+  });
+}
+
+NVBENCH_BENCH(bench_lengths)
+  .set_name("lengths")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048, 4096})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/string/like.cpp b/cpp/benchmarks/string/like.cpp
new file mode 100644
index 0000000..6ac8324
--- /dev/null
+++ b/cpp/benchmarks/string/like.cpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+namespace {
+std::unique_ptr<cudf::column> build_input_column(cudf::size_type n_rows,
+                                                 cudf::size_type row_width,
+                                                 int32_t hit_rate)
+{
+  // build input table using the following data
+  auto raw_data = cudf::test::strings_column_wrapper(
+                    {
+                      "123 abc 4567890 DEFGHI 0987 5W43",  // matches always;
+                      "012345 6789 01234 56789 0123 456",  // the rest do not match
+                      "abc 4567890 DEFGHI 0987 Wxyz 123",
+                      "abcdefghijklmnopqrstuvwxyz 01234",
+                      "",
+                      "AbcéDEFGHIJKLMNOPQRSTUVWXYZ 01",
+                      "9876543210,abcdefghijklmnopqrstU",
+                      "9876543210,abcdefghijklmnopqrstU",
+                      "123 édf 4567890 DéFG 0987 X5",
+                      "1",
+                    })
+                    .release();
+  if (row_width / 32 > 1) {
+    std::vector<cudf::column_view> columns;
+    for (int i = 0; i < row_width / 32; ++i) {
+      columns.push_back(raw_data->view());
+    }
+    raw_data = cudf::strings::concatenate(cudf::table_view(columns));
+  }
+  auto data_view = raw_data->view();
+
+  // compute number of rows in n_rows that should match
+  auto matches = static_cast<int32_t>(n_rows * hit_rate) / 100;
+
+  // Create a randomized gather-map to build a column out of the strings in data.
+  data_profile gather_profile =
+    data_profile_builder().cardinality(0).null_probability(0.0).distribution(
+      cudf::type_id::INT32, distribution_id::UNIFORM, 1, data_view.size() - 1);
+  auto gather_table =
+    create_random_table({cudf::type_id::INT32}, row_count{n_rows}, gather_profile);
+  gather_table->get_column(0).set_null_mask(rmm::device_buffer{}, 0);
+
+  // Create scatter map by placing 0-index values throughout the gather-map
+  auto scatter_data = cudf::sequence(
+    matches, cudf::numeric_scalar<int32_t>(0), cudf::numeric_scalar<int32_t>(n_rows / matches));
+  auto zero_scalar = cudf::numeric_scalar<int32_t>(0);
+  auto table       = cudf::scatter({zero_scalar}, scatter_data->view(), gather_table->view());
+  auto gather_map  = table->view().column(0);
+  table            = cudf::gather(cudf::table_view({data_view}), gather_map);
+
+  return std::move(table->release().front());
+}
+
+}  // namespace
+
+static void bench_like(nvbench::state& state)
+{
+  auto const n_rows    = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const hit_rate  = static_cast<int32_t>(state.get_int64("hit_rate"));
+
+  if (static_cast<std::size_t>(n_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  auto col   = build_input_column(n_rows, row_width, hit_rate);
+  auto input = cudf::strings_column_view(col->view());
+
+  // This pattern forces reading the entire target string (when matched expected)
+  auto pattern = std::string("% 5W4_");  // regex equivalent: ".* 5W4.$"
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");           // number of bytes;
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);  // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(n_rows);     // writes are BOOL8
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto result = cudf::strings::like(input, pattern); });
+}
+
+NVBENCH_BENCH(bench_like)
+  .set_name("strings_like")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512})
+  .add_int64_axis("num_rows", {32768, 262144, 2097152, 16777216})
+  .add_int64_axis("hit_rate", {10, 25, 70, 100});
diff --git a/cpp/benchmarks/string/repeat_strings.cpp b/cpp/benchmarks/string/repeat_strings.cpp
new file mode 100644
index 0000000..9264552
--- /dev/null
+++ b/cpp/benchmarks/string/repeat_strings.cpp
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+static constexpr cudf::size_type default_repeat_times = 16;
+static constexpr cudf::size_type min_repeat_times     = -16;
+static constexpr cudf::size_type max_repeat_times     = 16;
+
+static std::unique_ptr<cudf::table> create_data_table(cudf::size_type n_cols,
+                                                      cudf::size_type n_rows,
+                                                      cudf::size_type max_str_length)
+{
+  CUDF_EXPECTS(n_cols == 1 || n_cols == 2, "Invalid number of columns.");
+
+  std::vector<cudf::type_id> dtype_ids{cudf::type_id::STRING};
+  auto builder = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+
+  if (n_cols == 2) {
+    dtype_ids.push_back(cudf::type_id::INT32);
+    builder.distribution(
+      cudf::type_id::INT32, distribution_id::NORMAL, min_repeat_times, max_repeat_times);
+  }
+
+  return create_random_table(dtype_ids, row_count{n_rows}, data_profile{builder});
+}
+
+static void BM_repeat_strings_scalar_times(benchmark::State& state)
+{
+  auto const n_rows         = static_cast<cudf::size_type>(state.range(0));
+  auto const max_str_length = static_cast<cudf::size_type>(state.range(1));
+  auto const table          = create_data_table(1, n_rows, max_str_length);
+  auto const strings_col    = cudf::strings_column_view(table->view().column(0));
+
+  for ([[maybe_unused]] auto _ : state) {
+    [[maybe_unused]] cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::strings::repeat_strings(strings_col, default_repeat_times);
+  }
+
+  state.SetBytesProcessed(state.iterations() * strings_col.chars_size());
+}
+
+static void BM_repeat_strings_column_times(benchmark::State& state)
+{
+  auto const n_rows           = static_cast<cudf::size_type>(state.range(0));
+  auto const max_str_length   = static_cast<cudf::size_type>(state.range(1));
+  auto const table            = create_data_table(2, n_rows, max_str_length);
+  auto const strings_col      = cudf::strings_column_view(table->view().column(0));
+  auto const repeat_times_col = table->view().column(1);
+
+  for ([[maybe_unused]] auto _ : state) {
+    [[maybe_unused]] cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::strings::repeat_strings(strings_col, repeat_times_col);
+  }
+
+  state.SetBytesProcessed(state.iterations() *
+                          (strings_col.chars_size() + repeat_times_col.size() * sizeof(int32_t)));
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 8;
+  int const max_rows   = 1 << 18;
+  int const row_mult   = 4;
+  int const min_strlen = 1 << 4;
+  int const max_strlen = 1 << 8;
+  int const len_mult   = 4;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_strlen, max_strlen, len_mult);
+}
+
+class RepeatStrings : public cudf::benchmark {};
+
+#define REPEAT_STRINGS_SCALAR_TIMES_BENCHMARK_DEFINE(name)          \
+  BENCHMARK_DEFINE_F(RepeatStrings, name)                           \
+  (::benchmark::State & st) { BM_repeat_strings_scalar_times(st); } \
+  BENCHMARK_REGISTER_F(RepeatStrings, name)                         \
+    ->Apply(generate_bench_args)                                    \
+    ->UseManualTime()                                               \
+    ->Unit(benchmark::kMillisecond);
+
+#define REPEAT_STRINGS_COLUMN_TIMES_BENCHMARK_DEFINE(name)          \
+  BENCHMARK_DEFINE_F(RepeatStrings, name)                           \
+  (::benchmark::State & st) { BM_repeat_strings_column_times(st); } \
+  BENCHMARK_REGISTER_F(RepeatStrings, name)                         \
+    ->Apply(generate_bench_args)                                    \
+    ->UseManualTime()                                               \
+    ->Unit(benchmark::kMillisecond);
+
+REPEAT_STRINGS_SCALAR_TIMES_BENCHMARK_DEFINE(scalar_times)
+REPEAT_STRINGS_COLUMN_TIMES_BENCHMARK_DEFINE(column_times)
diff --git a/cpp/benchmarks/string/replace.cpp b/cpp/benchmarks/string/replace.cpp
new file mode 100644
index 0000000..5ddf09f
--- /dev/null
+++ b/cpp/benchmarks/string/replace.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/replace.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <limits>
+
+class StringReplace : public cudf::benchmark {};
+
+enum replace_type { scalar, slice, multi };
+
+static void BM_replace(benchmark::State& state, replace_type rt)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+  cudf::string_scalar target("+");
+  cudf::string_scalar repl("");
+  cudf::test::strings_column_wrapper targets({"+", "-"});
+  cudf::test::strings_column_wrapper repls({"", ""});
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    switch (rt) {
+      case scalar: cudf::strings::replace(input, target, repl); break;
+      case slice: cudf::strings::replace_slice(input, repl, 1, 10); break;
+      case multi:
+        cudf::strings::replace(
+          input, cudf::strings_column_view(targets), cudf::strings_column_view(repls));
+        break;
+    }
+  }
+
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 5;
+  int const max_rowlen = 1 << 13;
+  int const len_mult   = 2;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name)                              \
+  BENCHMARK_DEFINE_F(StringReplace, name)                           \
+  (::benchmark::State & st) { BM_replace(st, replace_type::name); } \
+  BENCHMARK_REGISTER_F(StringReplace, name)                         \
+    ->Apply(generate_bench_args)                                    \
+    ->UseManualTime()                                               \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(scalar)
+STRINGS_BENCHMARK_DEFINE(slice)
+STRINGS_BENCHMARK_DEFINE(multi)
diff --git a/cpp/benchmarks/string/replace_re.cpp b/cpp/benchmarks/string/replace_re.cpp
new file mode 100644
index 0000000..b8efd76
--- /dev/null
+++ b/cpp/benchmarks/string/replace_re.cpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/replace_re.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_replace(nvbench::state& state)
+{
+  auto const n_rows    = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const rtype     = state.get_string("type");
+
+  if (static_cast<std::size_t>(n_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+
+  auto program = cudf::strings::regex_program::create("(\\d+)");
+
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);
+
+  if (rtype == "backref") {
+    auto replacement = std::string("#\\1X");
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      cudf::strings::replace_with_backrefs(input, *program, replacement);
+    });
+  } else {
+    auto replacement = std::string("77");
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      cudf::strings::replace_re(input, *program, replacement);
+    });
+  }
+}
+
+NVBENCH_BENCH(bench_replace)
+  .set_name("replace_re")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512})
+  .add_int64_axis("num_rows", {32768, 262144, 2097152, 16777216})
+  .add_string_axis("type", {"replace", "backref"});
diff --git a/cpp/benchmarks/string/reverse.cpp b/cpp/benchmarks/string/reverse.cpp
new file mode 100644
index 0000000..31cd463
--- /dev/null
+++ b/cpp/benchmarks/string/reverse.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/reverse.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_reverse(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const table_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, table_profile);
+  cudf::strings_column_view input(table->view().column(0));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");            // number of bytes;
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);   // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);  // all bytes are written
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto result = cudf::strings::reverse(input); });
+}
+
+NVBENCH_BENCH(bench_reverse)
+  .set_name("reverse")
+  .add_int64_axis("row_width", {8, 16, 32, 64, 128})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/string/slice.cpp b/cpp/benchmarks/string/slice.cpp
new file mode 100644
index 0000000..6c1d7d9
--- /dev/null
+++ b/cpp/benchmarks/string/slice.cpp
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/slice.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+
+#include <limits>
+
+class StringSlice : public cudf::benchmark {};
+
+enum slice_type { position, multi_position };
+
+static void BM_slice(benchmark::State& state, slice_type rt)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+  auto starts_itr = thrust::constant_iterator<cudf::size_type>(max_str_length / 3);
+  auto stops_itr  = thrust::constant_iterator<cudf::size_type>(max_str_length / 2);
+  cudf::test::fixed_width_column_wrapper<int32_t> starts(starts_itr, starts_itr + n_rows);
+  cudf::test::fixed_width_column_wrapper<int32_t> stops(stops_itr, stops_itr + n_rows);
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    switch (rt) {
+      case position:
+        cudf::strings::slice_strings(input, max_str_length / 3, max_str_length / 2);
+        break;
+      case multi_position: cudf::strings::slice_strings(input, starts, stops); break;
+    }
+  }
+
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 5;
+  int const max_rowlen = 1 << 13;
+  int const len_mult   = 2;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name)                          \
+  BENCHMARK_DEFINE_F(StringSlice, name)                         \
+  (::benchmark::State & st) { BM_slice(st, slice_type::name); } \
+  BENCHMARK_REGISTER_F(StringSlice, name)                       \
+    ->Apply(generate_bench_args)                                \
+    ->UseManualTime()                                           \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(position)
+STRINGS_BENCHMARK_DEFINE(multi_position)
diff --git a/cpp/benchmarks/string/split.cpp b/cpp/benchmarks/string/split.cpp
new file mode 100644
index 0000000..eb724fa
--- /dev/null
+++ b/cpp/benchmarks/string/split.cpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/split/split.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_split(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const stype     = state.get_string("type");
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{num_rows}, profile);
+  cudf::strings_column_view input(column->view());
+  cudf::string_scalar target("+");
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");            // number of bytes;
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);   // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);  // all bytes are written
+
+  if (stype == "split") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { cudf::strings::split(input, target); });
+  } else if (stype == "split_ws") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { cudf::strings::split(input); });
+  } else if (stype == "record") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { cudf::strings::split_record(input, target); });
+  } else {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { cudf::strings::split_record(input); });
+  }
+}
+
+NVBENCH_BENCH(bench_split)
+  .set_name("split")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216})
+  .add_string_axis("type", {"split", "split_ws", "record", "record_ws"});
diff --git a/cpp/benchmarks/string/split_re.cpp b/cpp/benchmarks/string/split_re.cpp
new file mode 100644
index 0000000..67aa6f0
--- /dev/null
+++ b/cpp/benchmarks/string/split_re.cpp
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/split/split_re.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_split(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  auto prog = cudf::strings::regex_program::create("\\d+");
+
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{num_rows}, profile);
+  cudf::strings_column_view input(column->view());
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+  // gather some throughput statistics as well
+  auto chars_size = input.chars_size();
+  state.add_element_count(chars_size, "chars_size");            // number of bytes;
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);   // all bytes are read;
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);  // all bytes are written
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = cudf::strings::split_record_re(input, *prog);
+  });
+}
+
+NVBENCH_BENCH(bench_split)
+  .set_name("split_re")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024, 2048})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/string/string_bench_args.hpp b/cpp/benchmarks/string/string_bench_args.hpp
new file mode 100644
index 0000000..92a4637
--- /dev/null
+++ b/cpp/benchmarks/string/string_bench_args.hpp
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <benchmark/benchmark.h>
+
+#include <cudf/types.hpp>
+
+#include <limits>
+
+/**
+ * @brief Generate row count and row length argument ranges for a string benchmark.
+ *
+ * Generates a series of row count and row length arguments for string benchmarks.
+ * Combinations of row count and row length that would exceed the maximum string character
+ * column data length are not generated.
+ *
+ * @param b           Benchmark to update with row count and row length arguments.
+ * @param min_rows    Minimum row count argument to generate.
+ * @param max_rows    Maximum row count argument to generate.
+ * @param rows_mult   Row count multiplier to generate intermediate row count arguments.
+ * @param min_rowlen  Minimum row length argument to generate.
+ * @param max_rowlen  Maximum row length argument to generate.
+ * @param rowlen_mult Row length multiplier to generate intermediate row length arguments.
+ */
+inline void generate_string_bench_args(benchmark::internal::Benchmark* b,
+                                       int min_rows,
+                                       int max_rows,
+                                       int rows_mult,
+                                       int min_rowlen,
+                                       int max_rowlen,
+                                       int rowlen_mult)
+{
+  for (int row_count = min_rows; row_count <= max_rows; row_count *= rows_mult) {
+    for (int rowlen = min_rowlen; rowlen <= max_rowlen; rowlen *= rowlen_mult) {
+      // avoid generating combinations that exceed the cudf column limit
+      size_t total_chars = static_cast<size_t>(row_count) * rowlen;
+      if (total_chars < static_cast<size_t>(std::numeric_limits<cudf::size_type>::max())) {
+        b->Args({row_count, rowlen});
+      }
+    }
+  }
+}
diff --git a/cpp/benchmarks/string/translate.cpp b/cpp/benchmarks/string/translate.cpp
new file mode 100644
index 0000000..00ca745
--- /dev/null
+++ b/cpp/benchmarks/string/translate.cpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "string_bench_args.hpp"
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/translate.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+
+class StringTranslate : public cudf::benchmark {};
+
+using entry_type = std::pair<cudf::char_utf8, cudf::char_utf8>;
+
+static void BM_translate(benchmark::State& state, int entry_count)
+{
+  cudf::size_type const n_rows{static_cast<cudf::size_type>(state.range(0))};
+  cudf::size_type const max_str_length{static_cast<cudf::size_type>(state.range(1))};
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+
+  std::vector<entry_type> entries(entry_count);
+  std::transform(thrust::counting_iterator<int>(0),
+                 thrust::counting_iterator<int>(entry_count),
+                 entries.begin(),
+                 [](auto idx) -> entry_type {
+                   return entry_type{'!' + idx, '~' - idx};
+                 });
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    cudf::strings::translate(input, entries);
+  }
+
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 1 << 5;
+  int const max_rowlen = 1 << 13;
+  int const len_mult   = 4;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+}
+
+#define STRINGS_BENCHMARK_DEFINE(name, entries)            \
+  BENCHMARK_DEFINE_F(StringTranslate, name)                \
+  (::benchmark::State & st) { BM_translate(st, entries); } \
+  BENCHMARK_REGISTER_F(StringTranslate, name)              \
+    ->Apply(generate_bench_args)                           \
+    ->UseManualTime()                                      \
+    ->Unit(benchmark::kMillisecond);
+
+STRINGS_BENCHMARK_DEFINE(translate_small, 5)
+STRINGS_BENCHMARK_DEFINE(translate_medium, 25)
+STRINGS_BENCHMARK_DEFINE(translate_large, 50)
diff --git a/cpp/benchmarks/string/url_decode.cu b/cpp/benchmarks/string/url_decode.cu
new file mode 100644
index 0000000..9ede89b
--- /dev/null
+++ b/cpp/benchmarks/string/url_decode.cu
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/strings/convert/convert_urls.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/random.h>
+#include <thrust/tuple.h>
+
+struct url_string_generator {
+  char* chars;
+  double esc_seq_chance;
+  thrust::minstd_rand engine;
+  thrust::uniform_real_distribution<float> esc_seq_dist;
+  url_string_generator(char* c, double esc_seq_chance, thrust::minstd_rand& engine)
+    : chars(c), esc_seq_chance(esc_seq_chance), engine(engine), esc_seq_dist(0, 1)
+  {
+  }
+
+  __device__ void operator()(thrust::tuple<cudf::size_type, cudf::size_type> str_begin_end)
+  {
+    auto begin = thrust::get<0>(str_begin_end);
+    auto end   = thrust::get<1>(str_begin_end);
+    engine.discard(begin);
+    for (auto i = begin; i < end; ++i) {
+      if (esc_seq_dist(engine) < esc_seq_chance and i < end - 3) {
+        chars[i]     = '%';
+        chars[i + 1] = '2';
+        chars[i + 2] = '0';
+        i += 2;
+      } else {
+        chars[i] = 'a';
+      }
+    }
+  }
+};
+
+auto generate_column(cudf::size_type num_rows, cudf::size_type chars_per_row, double esc_seq_chance)
+{
+  std::vector<std::string> strings{std::string(chars_per_row, 'a')};
+  auto col_1a     = cudf::test::strings_column_wrapper(strings.begin(), strings.end());
+  auto table_a    = cudf::repeat(cudf::table_view{{col_1a}}, num_rows);
+  auto result_col = std::move(table_a->release()[0]);  // string column with num_rows  aaa...
+  auto chars_col  = result_col->child(cudf::strings_column_view::chars_column_index).mutable_view();
+  auto offset_col = result_col->child(cudf::strings_column_view::offsets_column_index).view();
+
+  auto engine = thrust::default_random_engine{};
+  thrust::for_each_n(thrust::device,
+                     thrust::make_zip_iterator(offset_col.begin<cudf::size_type>(),
+                                               offset_col.begin<cudf::size_type>() + 1),
+                     num_rows,
+                     url_string_generator{chars_col.begin<char>(), esc_seq_chance, engine});
+  return result_col;
+}
+
+class UrlDecode : public cudf::benchmark {};
+
+void BM_url_decode(benchmark::State& state, int esc_seq_pct)
+{
+  cudf::size_type const num_rows      = state.range(0);
+  cudf::size_type const chars_per_row = state.range(1);
+
+  auto column       = generate_column(num_rows, chars_per_row, esc_seq_pct / 100.0);
+  auto strings_view = cudf::strings_column_view(column->view());
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true, cudf::get_default_stream());
+    auto result = cudf::strings::url_decode(strings_view);
+  }
+
+  state.SetBytesProcessed(state.iterations() * num_rows *
+                          (chars_per_row + sizeof(cudf::size_type)));
+}
+
+#define URLD_BENCHMARK_DEFINE(esc_seq_pct)                      \
+  BENCHMARK_DEFINE_F(UrlDecode, esc_seq_pct)                    \
+  (::benchmark::State & st) { BM_url_decode(st, esc_seq_pct); } \
+  BENCHMARK_REGISTER_F(UrlDecode, esc_seq_pct)                  \
+    ->Args({100000000, 10})                                     \
+    ->Args({10000000, 100})                                     \
+    ->Args({1000000, 1000})                                     \
+    ->Unit(benchmark::kMillisecond)                             \
+    ->UseManualTime();
+
+URLD_BENCHMARK_DEFINE(10)
+URLD_BENCHMARK_DEFINE(50)
diff --git a/cpp/benchmarks/synchronization/synchronization.cpp b/cpp/benchmarks/synchronization/synchronization.cpp
new file mode 100644
index 0000000..5993bb2
--- /dev/null
+++ b/cpp/benchmarks/synchronization/synchronization.cpp
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "synchronization.hpp"
+
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+cuda_event_timer::cuda_event_timer(benchmark::State& state,
+                                   bool flush_l2_cache,
+                                   rmm::cuda_stream_view stream)
+  : stream(stream), p_state(&state)
+{
+  // flush all of L2$
+  if (flush_l2_cache) {
+    int current_device = 0;
+    CUDF_CUDA_TRY(cudaGetDevice(&current_device));
+
+    int l2_cache_bytes = 0;
+    CUDF_CUDA_TRY(cudaDeviceGetAttribute(&l2_cache_bytes, cudaDevAttrL2CacheSize, current_device));
+
+    if (l2_cache_bytes > 0) {
+      int const memset_value = 0;
+      rmm::device_buffer l2_cache_buffer(l2_cache_bytes, stream);
+      CUDF_CUDA_TRY(
+        cudaMemsetAsync(l2_cache_buffer.data(), memset_value, l2_cache_bytes, stream.value()));
+    }
+  }
+
+  CUDF_CUDA_TRY(cudaEventCreate(&start));
+  CUDF_CUDA_TRY(cudaEventCreate(&stop));
+  CUDF_CUDA_TRY(cudaEventRecord(start, stream.value()));
+}
+
+cuda_event_timer::~cuda_event_timer()
+{
+  CUDF_CUDA_TRY(cudaEventRecord(stop, stream.value()));
+  CUDF_CUDA_TRY(cudaEventSynchronize(stop));
+
+  float milliseconds = 0.0f;
+  CUDF_CUDA_TRY(cudaEventElapsedTime(&milliseconds, start, stop));
+  p_state->SetIterationTime(milliseconds / (1000.0f));
+  CUDF_CUDA_TRY(cudaEventDestroy(start));
+  CUDF_CUDA_TRY(cudaEventDestroy(stop));
+}
diff --git a/cpp/benchmarks/synchronization/synchronization.hpp b/cpp/benchmarks/synchronization/synchronization.hpp
new file mode 100644
index 0000000..e56d881
--- /dev/null
+++ b/cpp/benchmarks/synchronization/synchronization.hpp
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file synchronization.hpp
+ * @brief This is the header file for `cuda_event_timer`.
+ */
+
+/**
+ * @brief  This class serves as a wrapper for using `cudaEvent_t` as the user
+ * defined timer within the framework of google benchmark
+ * (https://github.com/google/benchmark).
+ *
+ * It is built on top of the idea of Resource acquisition is initialization
+ * (RAII). In the following we show a minimal example of how to use this class.
+
+    #include <benchmark/benchmark.h>
+    #include <cudf/utilities/default_stream.hpp>
+
+    static void sample_cuda_benchmark(benchmark::State& state) {
+
+      for (auto _ : state){
+
+        // default stream, could be another stream
+        rmm::cuda_stream_view stream{cudf::get_default_stream()};
+
+        // Create (Construct) an object of this class. You HAVE to pass in the
+        // benchmark::State object you are using. It measures the time from its
+        // creation to its destruction that is spent on the specified CUDA stream.
+        // It also clears the L2 cache by cudaMemset'ing a device buffer that is of
+        // the size of the L2 cache (if flush_l2_cache is set to true and there is
+        // an L2 cache on the current device).
+        cuda_event_timer raii(state, true, stream); // flush_l2_cache = true
+
+        // Now perform the operations that is to be benchmarked
+        sample_kernel<<<1, 256, 0, stream.value()>>>(); // Possibly launching a CUDA kernel
+
+      }
+    }
+
+    // Register the function as a benchmark. You will need to set the `UseManualTime()`
+    // flag in order to use the timer embedded in this class.
+    BENCHMARK(sample_cuda_benchmark)->UseManualTime();
+
+
+ */
+
+#pragma once
+
+// Google Benchmark library
+#include <benchmark/benchmark.h>
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <driver_types.h>
+
+class cuda_event_timer {
+ public:
+  /**
+   * @brief This c'tor clears the L2$ by cudaMemset'ing a buffer of L2$ size
+   * and starts the timer.
+   *
+   * @param[in,out] state  This is the benchmark::State whose timer we are going
+   * to update.
+   * @param[in] flush_l2_cache_ whether or not to flush the L2 cache before
+   *                            every iteration.
+   * @param[in] stream_ The CUDA stream we are measuring time on.
+   */
+  cuda_event_timer(benchmark::State& state,
+                   bool flush_l2_cache,
+                   rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  // The user must provide a benchmark::State object to set
+  // the timer so we disable the default c'tor.
+  cuda_event_timer() = delete;
+
+  // The d'tor stops the timer and performs a synchronization.
+  // Time of the benchmark::State object provided to the c'tor
+  // will be set to the value given by `cudaEventElapsedTime`.
+  ~cuda_event_timer();
+
+ private:
+  cudaEvent_t start;
+  cudaEvent_t stop;
+  rmm::cuda_stream_view stream;
+  benchmark::State* p_state;
+};
diff --git a/cpp/benchmarks/text/edit_distance.cpp b/cpp/benchmarks/text/edit_distance.cpp
new file mode 100644
index 0000000..8a8bd9a
--- /dev/null
+++ b/cpp/benchmarks/text/edit_distance.cpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/edit_distance.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <rmm/device_buffer.hpp>
+
+static void bench_edit_distance(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const strings_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const strings_table = create_random_table(
+    {cudf::type_id::STRING, cudf::type_id::STRING}, row_count{num_rows}, strings_profile);
+  cudf::strings_column_view input1(strings_table->view().column(0));
+  cudf::strings_column_view input2(strings_table->view().column(1));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = input1.chars_size() + input2.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  // output are integers (one per row)
+  state.add_global_memory_writes<nvbench::int32_t>(num_rows);
+
+  state.exec(nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) { auto result = nvtext::edit_distance(input1, input2); });
+}
+
+NVBENCH_BENCH(bench_edit_distance)
+  .set_name("edit_distance")
+  .add_int64_axis("num_rows", {1024, 4096, 8192, 16364, 32768, 262144})
+  .add_int64_axis("row_width", {8, 16, 32, 64, 128, 256});
diff --git a/cpp/benchmarks/text/hash_ngrams.cpp b/cpp/benchmarks/text/hash_ngrams.cpp
new file mode 100644
index 0000000..5bbd2fc
--- /dev/null
+++ b/cpp/benchmarks/text/hash_ngrams.cpp
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/generate_ngrams.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <rmm/device_buffer.hpp>
+
+static void bench_hash_ngrams(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const ngrams    = static_cast<cudf::size_type>(state.get_int64("ngrams"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const strings_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const strings_table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, strings_profile);
+  cudf::strings_column_view input(strings_table->view().column(0));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = input.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  // output are hashes: approximate total number of hashes
+  state.add_global_memory_writes<nvbench::int32_t>(num_rows * ngrams);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = nvtext::hash_character_ngrams(input, ngrams);
+  });
+}
+
+NVBENCH_BENCH(bench_hash_ngrams)
+  .set_name("hash_ngrams")
+  .add_int64_axis("num_rows", {1024, 4096, 8192, 16364, 32768, 262144})
+  .add_int64_axis("row_width", {128, 512, 2048})
+  .add_int64_axis("ngrams", {5, 10});
diff --git a/cpp/benchmarks/text/jaccard.cpp b/cpp/benchmarks/text/jaccard.cpp
new file mode 100644
index 0000000..70470b8
--- /dev/null
+++ b/cpp/benchmarks/text/jaccard.cpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/jaccard.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <rmm/device_buffer.hpp>
+
+static void bench_jaccard(nvbench::state& state)
+{
+  auto const num_rows        = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width       = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const substring_width = static_cast<cudf::size_type>(state.get_int64("substring_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const strings_profile =
+    data_profile_builder()
+      .distribution(cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width)
+      .no_validity();
+  auto const input_table = create_random_table(
+    {cudf::type_id::STRING, cudf::type_id::STRING}, row_count{num_rows}, strings_profile);
+  cudf::strings_column_view input1(input_table->view().column(0));
+  cudf::strings_column_view input2(input_table->view().column(1));
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = input1.chars_size() + input2.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::float32_t>(num_rows);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = nvtext::jaccard_index(input1, input2, substring_width);
+  });
+}
+
+NVBENCH_BENCH(bench_jaccard)
+  .set_name("jaccard")
+  .add_int64_axis("num_rows", {1024, 4096, 8192, 16364, 32768, 262144})
+  .add_int64_axis("row_width", {128, 512, 2048})
+  .add_int64_axis("substring_width", {5, 10});
diff --git a/cpp/benchmarks/text/minhash.cpp b/cpp/benchmarks/text/minhash.cpp
new file mode 100644
index 0000000..1b60caa
--- /dev/null
+++ b/cpp/benchmarks/text/minhash.cpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/minhash.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <rmm/device_buffer.hpp>
+
+static void bench_minhash(nvbench::state& state)
+{
+  auto const num_rows   = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width  = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const hash_width = static_cast<cudf::size_type>(state.get_int64("hash_width"));
+  auto const seed_count = static_cast<cudf::size_type>(state.get_int64("seed_count"));
+  auto const base64     = state.get_int64("hash_type") == 64;
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const strings_profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const strings_table =
+    create_random_table({cudf::type_id::STRING}, row_count{num_rows}, strings_profile);
+  cudf::strings_column_view input(strings_table->view().column(0));
+
+  data_profile const seeds_profile = data_profile_builder().null_probability(0).distribution(
+    cudf::type_to_id<cudf::hash_value_type>(), distribution_id::NORMAL, 0, row_width);
+  auto const seed_type   = base64 ? cudf::type_id::UINT64 : cudf::type_id::UINT32;
+  auto const seeds_table = create_random_table({seed_type}, row_count{seed_count}, seeds_profile);
+  auto seeds             = seeds_table->get_column(0);
+  seeds.set_null_mask(rmm::device_buffer{}, 0);
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = input.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int32_t>(num_rows);  // output are hashes
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = base64 ? nvtext::minhash64(input, seeds.view(), hash_width)
+                         : nvtext::minhash(input, seeds.view(), hash_width);
+  });
+}
+
+NVBENCH_BENCH(bench_minhash)
+  .set_name("minhash")
+  .add_int64_axis("num_rows", {1024, 8192, 16364, 131072})
+  .add_int64_axis("row_width", {128, 512, 2048})
+  .add_int64_axis("hash_width", {5, 10})
+  .add_int64_axis("seed_count", {2, 26})
+  .add_int64_axis("hash_type", {32, 64});
diff --git a/cpp/benchmarks/text/ngrams.cpp b/cpp/benchmarks/text/ngrams.cpp
new file mode 100644
index 0000000..f3fd5cc
--- /dev/null
+++ b/cpp/benchmarks/text/ngrams.cpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/string/string_bench_args.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/generate_ngrams.hpp>
+
+class TextNGrams : public cudf::benchmark {};
+
+enum class ngrams_type { tokens, characters };
+
+static void BM_ngrams(benchmark::State& state, ngrams_type nt)
+{
+  auto const n_rows          = static_cast<cudf::size_type>(state.range(0));
+  auto const max_str_length  = static_cast<cudf::size_type>(state.range(1));
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, max_str_length);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{n_rows}, profile);
+  cudf::strings_column_view input(column->view());
+  auto const separator = cudf::string_scalar("_");
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    switch (nt) {
+      case ngrams_type::tokens: nvtext::generate_ngrams(input, 2, separator); break;
+      case ngrams_type::characters: nvtext::generate_character_ngrams(input); break;
+    }
+  }
+
+  state.SetBytesProcessed(state.iterations() * input.chars_size());
+}
+
+static void generate_bench_args(benchmark::internal::Benchmark* b)
+{
+  int const min_rows   = 1 << 12;
+  int const max_rows   = 1 << 24;
+  int const row_mult   = 8;
+  int const min_rowlen = 5;
+  int const max_rowlen = 40;
+  int const len_mult   = 2;
+  generate_string_bench_args(b, min_rows, max_rows, row_mult, min_rowlen, max_rowlen, len_mult);
+}
+
+#define NVTEXT_BENCHMARK_DEFINE(name)                             \
+  BENCHMARK_DEFINE_F(TextNGrams, name)                            \
+  (::benchmark::State & st) { BM_ngrams(st, ngrams_type::name); } \
+  BENCHMARK_REGISTER_F(TextNGrams, name)                          \
+    ->Apply(generate_bench_args)                                  \
+    ->UseManualTime()                                             \
+    ->Unit(benchmark::kMillisecond);
+
+NVTEXT_BENCHMARK_DEFINE(tokens)
+NVTEXT_BENCHMARK_DEFINE(characters)
diff --git a/cpp/benchmarks/text/normalize.cpp b/cpp/benchmarks/text/normalize.cpp
new file mode 100644
index 0000000..6878fa4
--- /dev/null
+++ b/cpp/benchmarks/text/normalize.cpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvtext/normalize.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_normalize(nvbench::state& state)
+{
+  auto const num_rows       = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width      = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const normalize_type = state.get_string("type");
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{num_rows}, profile);
+  cudf::strings_column_view input(column->view());
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = input.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);
+
+  if (normalize_type == "spaces") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { auto result = nvtext::normalize_spaces(input); });
+  } else {
+    bool const to_lower = (normalize_type == "to_lower");
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = nvtext::normalize_characters(input, to_lower);
+    });
+  }
+}
+
+NVBENCH_BENCH(bench_normalize)
+  .set_name("normalize")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216})
+  .add_string_axis("type", {"spaces", "characters", "to_lower"});
diff --git a/cpp/benchmarks/text/replace.cpp b/cpp/benchmarks/text/replace.cpp
new file mode 100644
index 0000000..257f62a
--- /dev/null
+++ b/cpp/benchmarks/text/replace.cpp
@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/replace.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <random>
+
+static void bench_replace(nvbench::state& state)
+{
+  auto const num_rows  = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width = static_cast<cudf::size_type>(state.get_int64("row_width"));
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  std::vector<std::string> words{" ",        "one  ",    "two ",       "three ",     "four ",
+                                 "five ",    "six  ",    "sevén  ",    "eight ",     "nine ",
+                                 "ten   ",   "eleven ",  "twelve ",    "thirteen  ", "fourteen ",
+                                 "fifteen ", "sixteen ", "seventeen ", "eighteen ",  "nineteen "};
+
+  std::default_random_engine generator;
+  std::uniform_int_distribution<int> tokens_dist(0, words.size() - 1);
+  std::string row;  // build a row of random tokens
+  while (static_cast<cudf::size_type>(row.size()) < row_width)
+    row += words[tokens_dist(generator)];
+
+  std::uniform_int_distribution<int> position_dist(0, 16);
+
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [&](auto idx) { return row.c_str() + position_dist(generator); });
+  cudf::test::strings_column_wrapper input(elements, elements + num_rows);
+  cudf::strings_column_view view(input);
+
+  cudf::test::strings_column_wrapper targets({"one", "two", "sevén", "zero"});
+  cudf::test::strings_column_wrapper replacements({"1", "2", "7", "0"});
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = view.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto result = nvtext::replace_tokens(
+      view, cudf::strings_column_view(targets), cudf::strings_column_view(replacements));
+  });
+}
+
+NVBENCH_BENCH(bench_replace)
+  .set_name("replace")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216});
diff --git a/cpp/benchmarks/text/subword.cpp b/cpp/benchmarks/text/subword.cpp
new file mode 100644
index 0000000..1dd7322
--- /dev/null
+++ b/cpp/benchmarks/text/subword.cpp
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+#include <nvtext/subword_tokenize.hpp>
+
+#include <filesystem>
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+static std::string create_hash_vocab_file()
+{
+  std::string dir_template{std::filesystem::temp_directory_path().string()};
+  if (char const* env_p = std::getenv("WORKSPACE")) dir_template = env_p;
+  std::string hash_file = dir_template + "/hash_vocab.txt";
+  // create a fake hashed vocab text file for this test
+  // this only works with words in the strings in the benchmark code below
+  std::vector<std::pair<int, int>> coefficients(23, {65559, 0});
+  std::ofstream outfile(hash_file, std::ofstream::out);
+  outfile << "1\n0\n" << coefficients.size() << "\n";
+  for (auto c : coefficients)
+    outfile << c.first << " " << c.second << "\n";
+  std::vector<uint64_t> hash_table(23, 0);
+  outfile << hash_table.size() << "\n";
+  hash_table[0]  = 3015668L;
+  hash_table[1]  = 6205475701751155871L;
+  hash_table[5]  = 6358029;
+  hash_table[16] = 451412625363L;
+  hash_table[20] = 6206321707968235495L;
+  for (auto h : hash_table)
+    outfile << h << "\n";
+  outfile << "100\n101\n102\n\n";
+  return hash_file;
+}
+
+static void BM_subword_tokenizer(benchmark::State& state)
+{
+  auto const nrows = static_cast<cudf::size_type>(state.range(0));
+  std::vector<char const*> h_strings(nrows, "This is a test ");
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  std::string hash_file = create_hash_vocab_file();
+  std::vector<uint32_t> offsets{14};
+  uint32_t max_sequence_length = 64;
+  uint32_t stride              = 48;
+  uint32_t do_truncate         = 0;
+  uint32_t do_lower            = 1;
+  //
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                           *vocab,
+                                           max_sequence_length,
+                                           stride,
+                                           do_lower,
+                                           do_truncate);
+  }
+}
+
+class Subword : public cudf::benchmark {};
+
+#define SUBWORD_BM_BENCHMARK_DEFINE(name)                                                        \
+  BENCHMARK_DEFINE_F(Subword, name)(::benchmark::State & state) { BM_subword_tokenizer(state); } \
+  BENCHMARK_REGISTER_F(Subword, name)                                                            \
+    ->RangeMultiplier(2)                                                                         \
+    ->Range(1 << 10, 1 << 17)                                                                    \
+    ->UseManualTime()                                                                            \
+    ->Unit(benchmark::kMillisecond);
+
+SUBWORD_BM_BENCHMARK_DEFINE(BM_subword_tokenizer);
+
+// BENCHMARK_MAIN();
diff --git a/cpp/benchmarks/text/tokenize.cpp b/cpp/benchmarks/text/tokenize.cpp
new file mode 100644
index 0000000..b556a84
--- /dev/null
+++ b/cpp/benchmarks/text/tokenize.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvtext/ngrams_tokenize.hpp>
+#include <nvtext/tokenize.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+static void bench_tokenize(nvbench::state& state)
+{
+  auto const num_rows      = static_cast<cudf::size_type>(state.get_int64("num_rows"));
+  auto const row_width     = static_cast<cudf::size_type>(state.get_int64("row_width"));
+  auto const tokenize_type = state.get_string("type");
+
+  if (static_cast<std::size_t>(num_rows) * static_cast<std::size_t>(row_width) >=
+      static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max())) {
+    state.skip("Skip benchmarks greater than size_type limit");
+  }
+
+  data_profile const profile = data_profile_builder().distribution(
+    cudf::type_id::STRING, distribution_id::NORMAL, 0, row_width);
+  auto const column = create_random_column(cudf::type_id::STRING, row_count{num_rows}, profile);
+  cudf::strings_column_view input(column->view());
+
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value()));
+
+  auto chars_size = input.chars_size();
+  state.add_global_memory_reads<nvbench::int8_t>(chars_size);
+  state.add_global_memory_writes<nvbench::int8_t>(chars_size);
+
+  if (tokenize_type == "whitespace") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { auto result = nvtext::tokenize(input); });
+  } else if (tokenize_type == "multi") {
+    cudf::test::strings_column_wrapper delimiters({" ", "+", "-"});
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = nvtext::tokenize(input, cudf::strings_column_view(delimiters));
+    });
+  } else if (tokenize_type == "count") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { auto result = nvtext::count_tokens(input); });
+  } else if (tokenize_type == "count_multi") {
+    cudf::test::strings_column_wrapper delimiters({" ", "+", "-"});
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = nvtext::count_tokens(input, cudf::strings_column_view(delimiters));
+    });
+  } else if (tokenize_type == "ngrams") {
+    auto const delimiter = cudf::string_scalar("");
+    auto const separator = cudf::string_scalar("_");
+    state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      auto result = nvtext::ngrams_tokenize(input, 2, delimiter, separator);
+    });
+  } else if (tokenize_type == "characters") {
+    state.exec(nvbench::exec_tag::sync,
+               [&](nvbench::launch& launch) { auto result = nvtext::character_tokenize(input); });
+  }
+}
+
+NVBENCH_BENCH(bench_tokenize)
+  .set_name("tokenize")
+  .add_int64_axis("row_width", {32, 64, 128, 256, 512, 1024})
+  .add_int64_axis("num_rows", {4096, 32768, 262144, 2097152, 16777216})
+  .add_string_axis("type", {"whitespace", "multi", "count", "count_multi", "ngrams", "characters"});
diff --git a/cpp/benchmarks/transpose/transpose.cpp b/cpp/benchmarks/transpose/transpose.cpp
new file mode 100644
index 0000000..2f41bda
--- /dev/null
+++ b/cpp/benchmarks/transpose/transpose.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/transpose.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+static void BM_transpose(benchmark::State& state)
+{
+  auto count = state.range(0);
+  auto int_column_generator =
+    thrust::make_transform_iterator(thrust::counting_iterator(0), [count](int i) {
+      return cudf::make_numeric_column(
+        cudf::data_type{cudf::type_id::INT32}, count, cudf::mask_state::ALL_VALID);
+    });
+
+  auto input_table = cudf::table(std::vector(int_column_generator, int_column_generator + count));
+  auto input       = input_table.view();
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);
+    auto output = cudf::transpose(input);
+  }
+}
+
+class Transpose : public cudf::benchmark {};
+
+#define TRANSPOSE_BM_BENCHMARK_DEFINE(name)                                                \
+  BENCHMARK_DEFINE_F(Transpose, name)(::benchmark::State & state) { BM_transpose(state); } \
+  BENCHMARK_REGISTER_F(Transpose, name)                                                    \
+    ->RangeMultiplier(4)                                                                   \
+    ->Range(4, 4 << 13)                                                                    \
+    ->UseManualTime()                                                                      \
+    ->Unit(benchmark::kMillisecond);
+
+TRANSPOSE_BM_BENCHMARK_DEFINE(transpose_simple);
diff --git a/cpp/benchmarks/type_dispatcher/type_dispatcher.cu b/cpp/benchmarks/type_dispatcher/type_dispatcher.cu
new file mode 100644
index 0000000..3f985cf
--- /dev/null
+++ b/cpp/benchmarks/type_dispatcher/type_dispatcher.cu
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/synchronization/synchronization.hpp>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <type_traits>
+
+enum DispatchingType { HOST_DISPATCHING, DEVICE_DISPATCHING, NO_DISPATCHING };
+
+enum FunctorType { BANDWIDTH_BOUND, COMPUTE_BOUND };
+
+template <class NotFloat, FunctorType ft, class DisableNotFloat = void>
+struct Functor {
+  static __device__ NotFloat f(NotFloat x) { return x; }
+};
+
+template <class Float, FunctorType ft>
+struct Functor<Float, ft, std::enable_if_t<std::is_floating_point_v<Float>>> {
+  static __device__ Float f(Float x)
+  {
+    if (ft == BANDWIDTH_BOUND) {
+      return x + static_cast<Float>(1) - static_cast<Float>(1);
+    } else {
+      for (int i = 0; i < 1000; i++) {
+        x = (x * x + static_cast<Float>(1)) - x * x - static_cast<Float>(1);
+      }
+      return x;
+    }
+  }
+};
+
+constexpr int block_size = 256;
+
+// This is for NO_DISPATCHING
+template <FunctorType functor_type, class T>
+__global__ void no_dispatching_kernel(T** A, cudf::size_type n_rows, cudf::size_type n_cols)
+{
+  using F               = Functor<T, functor_type>;
+  cudf::size_type index = blockIdx.x * blockDim.x + threadIdx.x;
+  while (index < n_rows) {
+    for (int c = 0; c < n_cols; c++) {
+      A[c][index] = F::f(A[c][index]);
+    }
+    index += blockDim.x * gridDim.x;
+  }
+}
+
+// This is for HOST_DISPATCHING
+template <FunctorType functor_type, class T>
+__global__ void host_dispatching_kernel(cudf::mutable_column_device_view source_column)
+{
+  using F               = Functor<T, functor_type>;
+  T* A                  = source_column.data<T>();
+  cudf::size_type index = blockIdx.x * blockDim.x + threadIdx.x;
+  while (index < source_column.size()) {
+    A[index] = F::f(A[index]);
+    index += blockDim.x * gridDim.x;
+  }
+}
+
+template <FunctorType functor_type>
+struct ColumnHandle {
+  template <typename ColumnType, CUDF_ENABLE_IF(cudf::is_rep_layout_compatible<ColumnType>())>
+  void operator()(cudf::mutable_column_device_view source_column, int work_per_thread)
+  {
+    cudf::detail::grid_1d grid_config{source_column.size(), block_size};
+    int grid_size = grid_config.num_blocks;
+    // Launch the kernel.
+    host_dispatching_kernel<functor_type, ColumnType><<<grid_size, block_size>>>(source_column);
+  }
+
+  template <typename ColumnType, CUDF_ENABLE_IF(not cudf::is_rep_layout_compatible<ColumnType>())>
+  void operator()(cudf::mutable_column_device_view source_column, int work_per_thread)
+  {
+    CUDF_FAIL("Invalid type to benchmark.");
+  }
+};
+
+// The following is for DEVICE_DISPATCHING:
+// The dispatching is done on device. The loop loops over
+// each row (across different columns). Type is dispatched each time
+// a column is visited so the total number of dispatching is
+// n_rows * n_cols.
+template <FunctorType functor_type>
+struct RowHandle {
+  template <typename T, CUDF_ENABLE_IF(cudf::is_rep_layout_compatible<T>())>
+  __device__ void operator()(cudf::mutable_column_device_view source, cudf::size_type index)
+  {
+    using F                 = Functor<T, functor_type>;
+    source.data<T>()[index] = F::f(source.data<T>()[index]);
+  }
+
+  template <typename T, CUDF_ENABLE_IF(not cudf::is_rep_layout_compatible<T>())>
+  __device__ void operator()(cudf::mutable_column_device_view source, cudf::size_type index)
+  {
+    CUDF_UNREACHABLE("Unsupported type.");
+  }
+};
+
+// This is for DEVICE_DISPATCHING
+template <FunctorType functor_type>
+__global__ void device_dispatching_kernel(cudf::mutable_table_device_view source)
+{
+  cudf::size_type const n_rows = source.num_rows();
+  cudf::size_type index        = threadIdx.x + blockIdx.x * blockDim.x;
+
+  while (index < n_rows) {
+    for (cudf::size_type i = 0; i < source.num_columns(); i++) {
+      cudf::type_dispatcher(
+        source.column(i).type(), RowHandle<functor_type>{}, source.column(i), index);
+    }
+    index += blockDim.x * gridDim.x;
+  }  // while
+}
+
+template <FunctorType functor_type, DispatchingType dispatching_type, class T>
+void launch_kernel(cudf::mutable_table_view input, T** d_ptr, int work_per_thread)
+{
+  cudf::size_type const n_rows = input.num_rows();
+  cudf::size_type const n_cols = input.num_columns();
+
+  cudf::detail::grid_1d grid_config{n_rows, block_size};
+  int grid_size = grid_config.num_blocks;
+
+  if (dispatching_type == HOST_DISPATCHING) {
+    // std::vector<cudf::util::cuda::scoped_stream> v_stream(n_cols);
+    for (int c = 0; c < n_cols; c++) {
+      auto d_column = cudf::mutable_column_device_view::create(input.column(c));
+      cudf::type_dispatcher(
+        d_column->type(), ColumnHandle<functor_type>{}, *d_column, work_per_thread);
+    }
+  } else if (dispatching_type == DEVICE_DISPATCHING) {
+    auto d_table_view = cudf::mutable_table_device_view::create(input);
+    auto f            = device_dispatching_kernel<functor_type>;
+    // Launch the kernel
+    f<<<grid_size, block_size>>>(*d_table_view);
+  } else if (dispatching_type == NO_DISPATCHING) {
+    auto f = no_dispatching_kernel<functor_type, T>;
+    // Launch the kernel
+    f<<<grid_size, block_size>>>(d_ptr, n_rows, n_cols);
+  }
+}
+
+template <class TypeParam, FunctorType functor_type, DispatchingType dispatching_type>
+void type_dispatcher_benchmark(::benchmark::State& state)
+{
+  auto const n_cols          = static_cast<cudf::size_type>(state.range(0));
+  auto const source_size     = static_cast<cudf::size_type>(state.range(1));
+  auto const work_per_thread = static_cast<cudf::size_type>(state.range(2));
+
+  auto init = cudf::make_fixed_width_scalar<TypeParam>(static_cast<TypeParam>(0));
+
+  std::vector<std::unique_ptr<cudf::column>> source_column_wrappers;
+  std::vector<cudf::mutable_column_view> source_columns;
+
+  for (int i = 0; i < n_cols; ++i) {
+    source_column_wrappers.push_back(cudf::sequence(source_size, *init));
+    source_columns.push_back(*source_column_wrappers[i]);
+  }
+  cudf::mutable_table_view source_table{source_columns};
+
+  // For no dispatching
+  std::vector<rmm::device_buffer> h_vec(n_cols);
+  std::vector<TypeParam*> h_vec_p(n_cols);
+  std::transform(h_vec.begin(), h_vec.end(), h_vec_p.begin(), [source_size](auto& col) {
+    col.resize(source_size * sizeof(TypeParam), cudf::get_default_stream());
+    return static_cast<TypeParam*>(col.data());
+  });
+  rmm::device_uvector<TypeParam*> d_vec(n_cols, cudf::get_default_stream());
+
+  if (dispatching_type == NO_DISPATCHING) {
+    CUDF_CUDA_TRY(
+      cudaMemcpy(d_vec.data(), h_vec_p.data(), sizeof(TypeParam*) * n_cols, cudaMemcpyDefault));
+  }
+
+  // Warm up
+  launch_kernel<functor_type, dispatching_type>(source_table, d_vec.data(), work_per_thread);
+  CUDF_CUDA_TRY(cudaDeviceSynchronize());
+
+  for (auto _ : state) {
+    cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
+    launch_kernel<functor_type, dispatching_type>(source_table, d_vec.data(), work_per_thread);
+  }
+
+  state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) * source_size * n_cols * 2 *
+                          sizeof(TypeParam));
+}
+
+class TypeDispatcher : public cudf::benchmark {};
+
+#define TBM_BENCHMARK_DEFINE(name, TypeParam, functor_type, dispatching_type)    \
+  BENCHMARK_DEFINE_F(TypeDispatcher, name)(::benchmark::State & state)           \
+  {                                                                              \
+    type_dispatcher_benchmark<TypeParam, functor_type, dispatching_type>(state); \
+  }                                                                              \
+  BENCHMARK_REGISTER_F(TypeDispatcher, name)                                     \
+    ->RangeMultiplier(2)                                                         \
+    ->Ranges({{1, 8}, {1 << 10, 1 << 26}, {1, 1}})                               \
+    ->UseManualTime();
+
+TBM_BENCHMARK_DEFINE(fp64_bandwidth_host, double, BANDWIDTH_BOUND, HOST_DISPATCHING);
+TBM_BENCHMARK_DEFINE(fp64_bandwidth_device, double, BANDWIDTH_BOUND, DEVICE_DISPATCHING);
+TBM_BENCHMARK_DEFINE(fp64_bandwidth_no, double, BANDWIDTH_BOUND, NO_DISPATCHING);
+TBM_BENCHMARK_DEFINE(fp64_compute_host, double, COMPUTE_BOUND, HOST_DISPATCHING);
+TBM_BENCHMARK_DEFINE(fp64_compute_device, double, COMPUTE_BOUND, DEVICE_DISPATCHING);
+TBM_BENCHMARK_DEFINE(fp64_compute_no, double, COMPUTE_BOUND, NO_DISPATCHING);
diff --git a/cpp/cmake/Modules/ConfigureCUDA.cmake b/cpp/cmake/Modules/ConfigureCUDA.cmake
new file mode 100644
index 0000000..f79e4c3
--- /dev/null
+++ b/cpp/cmake/Modules/ConfigureCUDA.cmake
@@ -0,0 +1,47 @@
+# =============================================================================
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+if(CMAKE_COMPILER_IS_GNUCXX)
+  list(APPEND CUDF_CXX_FLAGS -Wall -Werror -Wno-unknown-pragmas -Wno-error=deprecated-declarations)
+endif()
+
+list(APPEND CUDF_CUDA_FLAGS --expt-extended-lambda --expt-relaxed-constexpr)
+
+# set warnings as errors
+if(CUDA_WARNINGS_AS_ERRORS)
+  list(APPEND CUDF_CUDA_FLAGS -Werror=all-warnings)
+else()
+  list(APPEND CUDF_CUDA_FLAGS -Werror=cross-execution-space-call)
+endif()
+list(APPEND CUDF_CUDA_FLAGS -Xcompiler=-Wall,-Werror,-Wno-error=deprecated-declarations)
+
+if(DISABLE_DEPRECATION_WARNINGS)
+  list(APPEND CUDF_CXX_FLAGS -Wno-deprecated-declarations)
+  list(APPEND CUDF_CUDA_FLAGS -Xcompiler=-Wno-deprecated-declarations)
+endif()
+
+# make sure we produce smallest binary size
+list(APPEND CUDF_CUDA_FLAGS -Xfatbin=-compress-all)
+
+# Option to enable line info in CUDA device compilation to allow introspection when profiling /
+# memchecking
+if(CUDA_ENABLE_LINEINFO)
+  list(APPEND CUDF_CUDA_FLAGS -lineinfo)
+endif()
+
+# Debug options
+if(CMAKE_BUILD_TYPE MATCHES Debug)
+  message(VERBOSE "CUDF: Building with debugging flags")
+  list(APPEND CUDF_CUDA_FLAGS -Xcompiler=-rdynamic)
+endif()
diff --git a/cpp/cmake/Modules/FindcuFile.cmake b/cpp/cmake/Modules/FindcuFile.cmake
new file mode 100644
index 0000000..1df4f12
--- /dev/null
+++ b/cpp/cmake/Modules/FindcuFile.cmake
@@ -0,0 +1,120 @@
+# =============================================================================
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+
+#[=======================================================================[.rst:
+FindcuFile
+----------
+
+Find cuFile headers and libraries.
+
+Imported Targets
+^^^^^^^^^^^^^^^^
+
+``cufile::cuFile``
+  The cuFile library, if found.
+``cufile::cuFileRDMA``
+  The cuFile RDMA library, if found.
+
+Result Variables
+^^^^^^^^^^^^^^^^
+
+This will define the following variables in your project:
+
+``cuFile_FOUND``
+  true if (the requested version of) cuFile is available.
+``cuFile_VERSION``
+  the version of cuFile.
+``cuFile_LIBRARIES``
+  the libraries to link against to use cuFile.
+``cuFileRDMA_LIBRARIES``
+  the libraries to link against to use cuFile RDMA.
+``cuFile_INCLUDE_DIRS``
+  where to find the cuFile headers.
+``cuFile_COMPILE_OPTIONS``
+  this should be passed to target_compile_options(), if the
+  target is not used for linking
+
+#]=======================================================================]
+
+# use pkg-config to get the directories and then use these values in the FIND_PATH() and
+# FIND_LIBRARY() calls
+find_package(PkgConfig QUIET)
+pkg_check_modules(PKG_cuFile QUIET cuFile)
+
+set(cuFile_COMPILE_OPTIONS ${PKG_cuFile_CFLAGS_OTHER})
+set(cuFile_VERSION ${PKG_cuFile_VERSION})
+
+# Find the location of the CUDA Toolkit
+find_package(CUDAToolkit QUIET)
+find_path(
+  cuFile_INCLUDE_DIR
+  NAMES cufile.h
+  HINTS ${PKG_cuFile_INCLUDE_DIRS} ${CUDAToolkit_INCLUDE_DIRS}
+)
+
+find_library(
+  cuFile_LIBRARY
+  NAMES cufile
+  HINTS ${PKG_cuFile_LIBRARY_DIRS} ${CUDAToolkit_LIBRARY_DIR}
+)
+
+find_library(
+  cuFileRDMA_LIBRARY
+  NAMES cufile_rdma
+  HINTS ${PKG_cuFile_LIBRARY_DIRS} ${CUDAToolkit_LIBRARY_DIR}
+)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(
+  cuFile
+  FOUND_VAR cuFile_FOUND
+  REQUIRED_VARS cuFile_LIBRARY cuFileRDMA_LIBRARY cuFile_INCLUDE_DIR
+  VERSION_VAR cuFile_VERSION
+)
+
+if(cuFile_INCLUDE_DIR AND NOT TARGET cufile::cuFile_interface)
+  add_library(cufile::cuFile_interface INTERFACE IMPORTED GLOBAL)
+  target_include_directories(
+    cufile::cuFile_interface INTERFACE "$<BUILD_INTERFACE:${cuFile_INCLUDE_DIR}>"
+  )
+  target_compile_options(cufile::cuFile_interface INTERFACE "${cuFile_COMPILE_OPTIONS}")
+  target_compile_definitions(cufile::cuFile_interface INTERFACE CUFILE_FOUND)
+endif()
+
+if(cuFile_FOUND AND NOT TARGET cufile::cuFile)
+  add_library(cufile::cuFile UNKNOWN IMPORTED GLOBAL)
+  set_target_properties(
+    cufile::cuFile
+    PROPERTIES IMPORTED_LOCATION "${cuFile_LIBRARY}"
+               INTERFACE_COMPILE_OPTIONS "${cuFile_COMPILE_OPTIONS}"
+               INTERFACE_INCLUDE_DIRECTORIES "${cuFile_INCLUDE_DIR}"
+  )
+endif()
+
+if(cuFile_FOUND AND NOT TARGET cufile::cuFileRDMA)
+  add_library(cufile::cuFileRDMA UNKNOWN IMPORTED GLOBAL)
+  set_target_properties(
+    cufile::cuFileRDMA
+    PROPERTIES IMPORTED_LOCATION "${cuFileRDMA_LIBRARY}"
+               INTERFACE_COMPILE_OPTIONS "${cuFile_COMPILE_OPTIONS}"
+               INTERFACE_INCLUDE_DIRECTORIES "${cuFile_INCLUDE_DIR}"
+  )
+endif()
+
+mark_as_advanced(cuFile_LIBRARY cuFileRDMA_LIBRARY cuFile_INCLUDE_DIR)
+
+if(cuFile_FOUND)
+  set(cuFile_LIBRARIES ${cuFile_LIBRARY})
+  set(cuFileRDMA_LIBRARIES ${cuFileRDMA_LIBRARY})
+  set(cuFile_INCLUDE_DIRS ${cuFile_INCLUDE_DIR})
+endif()
diff --git a/cpp/cmake/Modules/JitifyPreprocessKernels.cmake b/cpp/cmake/Modules/JitifyPreprocessKernels.cmake
new file mode 100644
index 0000000..df285bd
--- /dev/null
+++ b/cpp/cmake/Modules/JitifyPreprocessKernels.cmake
@@ -0,0 +1,66 @@
+# =============================================================================
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Create `jitify_preprocess` executable
+add_executable(jitify_preprocess "${JITIFY_INCLUDE_DIR}/jitify2_preprocess.cpp")
+
+target_compile_definitions(jitify_preprocess PRIVATE "_FILE_OFFSET_BITS=64")
+target_link_libraries(jitify_preprocess CUDA::cudart ${CMAKE_DL_LIBS})
+
+# Take a list of files to JIT-compile and run them through jitify_preprocess.
+function(jit_preprocess_files)
+  cmake_parse_arguments(ARG "" "SOURCE_DIRECTORY" "FILES" ${ARGN})
+
+  foreach(inc IN LISTS libcudacxx_raw_includes)
+    list(APPEND libcudacxx_includes "-I${inc}")
+  endforeach()
+  foreach(ARG_FILE ${ARG_FILES})
+    set(ARG_OUTPUT ${CUDF_GENERATED_INCLUDE_DIR}/include/jit_preprocessed_files/${ARG_FILE}.jit.hpp)
+    get_filename_component(jit_output_directory "${ARG_OUTPUT}" DIRECTORY)
+    list(APPEND JIT_PREPROCESSED_FILES "${ARG_OUTPUT}")
+
+    # Note: need to pass _FILE_OFFSET_BITS=64 in COMMAND due to a limitation in how conda builds
+    # glibc
+    add_custom_command(
+      OUTPUT ${ARG_OUTPUT}
+      DEPENDS jitify_preprocess "${ARG_SOURCE_DIRECTORY}/${ARG_FILE}"
+      WORKING_DIRECTORY ${ARG_SOURCE_DIRECTORY}
+      VERBATIM
+      COMMAND ${CMAKE_COMMAND} -E make_directory "${jit_output_directory}"
+      COMMAND
+        "${CMAKE_COMMAND}" -E env LD_LIBRARY_PATH=${CUDAToolkit_LIBRARY_DIR}
+        $<TARGET_FILE:jitify_preprocess> ${ARG_FILE} -o
+        ${CUDF_GENERATED_INCLUDE_DIR}/include/jit_preprocessed_files -i -m -std=c++17
+        -remove-unused-globals -D_FILE_OFFSET_BITS=64 -D__CUDACC_RTC__ -I${CUDF_SOURCE_DIR}/include
+        -I${CUDF_SOURCE_DIR}/src ${libcudacxx_includes} -I${CUDAToolkit_INCLUDE_DIRS}
+        --no-preinclude-workarounds --no-replace-pragma-once
+      COMMENT "Custom command to JIT-compile files."
+    )
+  endforeach()
+  set(JIT_PREPROCESSED_FILES
+      "${JIT_PREPROCESSED_FILES}"
+      PARENT_SCOPE
+  )
+endfunction()
+
+jit_preprocess_files(
+  SOURCE_DIRECTORY ${CUDF_SOURCE_DIR}/src FILES binaryop/jit/kernel.cu transform/jit/kernel.cu
+  rolling/jit/kernel.cu
+)
+
+add_custom_target(
+  jitify_preprocess_run
+  DEPENDS ${JIT_PREPROCESSED_FILES}
+  COMMENT "Target representing jitified files."
+)
diff --git a/cpp/cmake/config.json b/cpp/cmake/config.json
new file mode 100644
index 0000000..a65afe9
--- /dev/null
+++ b/cpp/cmake/config.json
@@ -0,0 +1,47 @@
+{
+  "parse": {
+    "additional_commands": {
+      "CPMFindPackage": {
+        "kwargs": {
+          "NAME": 1,
+          "GITHUB_REPOSITORY": "?",
+          "GIT_TAG": "?",
+          "VERSION": "?",
+          "GIT_SHALLOW": "?",
+          "OPTIONS": "*",
+          "FIND_PACKAGE_ARGUMENTS": "*"
+        }
+      },
+      "ConfigureTest": {
+        "flags": ["TEST_NAME", "TEST_SRC"],
+        "kwargs": {
+          "GPUS": 1,
+          "PERCENT": 1
+        }
+      },
+      "ConfigureBench": {
+        "flags": ["BENCH_NAME", "BENCH_SRC"]
+      }
+    }
+  },
+  "format": {
+    "line_width": 100,
+    "tab_size": 2,
+    "command_case": "unchanged",
+    "max_lines_hwrap": 1,
+    "max_pargs_hwrap": 999,
+    "dangle_parens": true
+  },
+  "lint": {
+    "disabled_codes": ["C0301"],
+    "function_pattern": "[0-9A-z_]+",
+    "macro_pattern": "[0-9A-z_]+",
+    "global_var_pattern": "[A-z][0-9A-z_]+",
+    "internal_var_pattern": "_[A-z][0-9A-z_]+",
+    "local_var_pattern": "[A-z][A-z0-9_]+",
+    "private_var_pattern": "_[0-9A-z_]+",
+    "public_var_pattern": "[A-z][0-9A-z_]+",
+    "argument_var_pattern": "[A-z][A-z0-9_]+",
+    "keyword_pattern": "[A-z][0-9A-z_]+"
+  }
+}
diff --git a/cpp/cmake/thirdparty/get_arrow.cmake b/cpp/cmake/thirdparty/get_arrow.cmake
new file mode 100644
index 0000000..894dc96
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_arrow.cmake
@@ -0,0 +1,414 @@
+# =============================================================================
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Finding arrow is far more complex than it should be, and as a result we violate multiple linting
+# rules aiming to limit complexity. Since all our other CMake scripts conform to expectations
+# without undue difficulty, disabling those rules for just this function is our best approach for
+# now. The spacing between this comment, the cmake-lint directives, and the function docstring is
+# necessary to prevent cmake-format from trying to combine the lines.
+
+# cmake-lint: disable=R0912,R0913,R0915
+
+include_guard(GLOBAL)
+
+# Generate a FindArrow module for the case where we need to search for arrow within a pip install
+# pyarrow.
+function(find_libarrow_in_python_wheel PYARROW_VERSION)
+  string(REPLACE "." ";" PYARROW_VER_COMPONENTS "${PYARROW_VERSION}")
+  list(GET PYARROW_VER_COMPONENTS 0 PYARROW_SO_VER)
+  # The soname for Arrow libraries is constructed using the major version plus "00". Note that,
+  # although it may seem like it due to Arrow almost exclusively releasing new major versions (i.e.
+  # `${MINOR_VERSION}${PATCH_VERSION}` is almost always equivalent to "00"),
+  # the soname is not generated by concatenating the major, minor, and patch versions into a single
+  # version number soname, just `${MAJOR_VERSION}00`
+  set(PYARROW_LIB "libarrow.so.${PYARROW_SO_VER}00")
+
+  find_package(Python REQUIRED)
+  execute_process(
+    COMMAND "${Python_EXECUTABLE}" -c "import pyarrow; print(pyarrow.get_library_dirs()[0])"
+    OUTPUT_VARIABLE CUDF_PYARROW_WHEEL_DIR
+    OUTPUT_STRIP_TRAILING_WHITESPACE
+  )
+  list(APPEND CMAKE_PREFIX_PATH "${CUDF_PYARROW_WHEEL_DIR}")
+  rapids_find_generate_module(
+    Arrow NO_CONFIG
+    VERSION "${PYARROW_VERSION}"
+    LIBRARY_NAMES "${PYARROW_LIB}"
+    BUILD_EXPORT_SET cudf-exports
+    INSTALL_EXPORT_SET cudf-exports
+    HEADER_NAMES arrow/python/arrow_to_pandas.h
+  )
+
+  find_package(Arrow ${PYARROW_VERSION} MODULE REQUIRED GLOBAL)
+  add_library(arrow_shared ALIAS Arrow::Arrow)
+
+  # When using the libarrow inside a wheel we must build libcudf with the old ABI because pyarrow's
+  # `libarrow.so` is compiled for manylinux2014 (centos7 toolchain) which uses the old ABI. Note
+  # that these flags will often be redundant because we build wheels in manylinux containers that
+  # actually have the old libc++ anyway, but setting them explicitly ensures correct and consistent
+  # behavior in all other cases such as aarch builds on newer manylinux or testing builds in newer
+  # containers. Note that tests will not build successfully without also propagating these options
+  # to builds of GTest. Similarly, benchmarks will not work without updating GBench (and possibly
+  # NVBench) builds. We are currently ignoring these limitations since we don't anticipate using
+  # this feature except for building wheels.
+  target_compile_options(
+    Arrow::Arrow INTERFACE "$<$<COMPILE_LANGUAGE:CXX>:-D_GLIBCXX_USE_CXX11_ABI=0>"
+                           "$<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=-D_GLIBCXX_USE_CXX11_ABI=0>"
+  )
+
+  rapids_export_package(BUILD Arrow cudf-exports)
+  rapids_export_package(INSTALL Arrow cudf-exports)
+
+  list(POP_BACK CMAKE_PREFIX_PATH)
+endfunction()
+
+# This function finds arrow and sets any additional necessary environment variables.
+function(find_and_configure_arrow VERSION BUILD_STATIC ENABLE_S3 ENABLE_ORC ENABLE_PYTHON
+         ENABLE_PARQUET
+)
+
+  if(USE_LIBARROW_FROM_PYARROW)
+    # Generate a FindArrow.cmake to find pyarrow's libarrow.so
+    find_libarrow_in_python_wheel(${VERSION})
+    set(ARROW_FOUND
+        TRUE
+        PARENT_SCOPE
+    )
+    set(ARROW_LIBRARIES
+        arrow_shared
+        PARENT_SCOPE
+    )
+    return()
+  endif()
+
+  if(BUILD_STATIC)
+    if(TARGET arrow_static)
+      set(ARROW_FOUND
+          TRUE
+          PARENT_SCOPE
+      )
+      set(ARROW_LIBRARIES
+          arrow_static
+          PARENT_SCOPE
+      )
+      return()
+    endif()
+  else()
+    if(TARGET arrow_shared)
+      set(ARROW_FOUND
+          TRUE
+          PARENT_SCOPE
+      )
+      set(ARROW_LIBRARIES
+          arrow_shared
+          PARENT_SCOPE
+      )
+      return()
+    endif()
+  endif()
+
+  if(NOT ARROW_ARMV8_ARCH)
+    set(ARROW_ARMV8_ARCH "armv8-a")
+  endif()
+
+  if(NOT ARROW_SIMD_LEVEL)
+    set(ARROW_SIMD_LEVEL "NONE")
+  endif()
+
+  if(BUILD_STATIC)
+    set(ARROW_BUILD_STATIC ON)
+    set(ARROW_BUILD_SHARED OFF)
+    # Turn off CPM using `find_package` so we always download and make sure we get proper static
+    # library.
+    set(CPM_DOWNLOAD_Arrow TRUE)
+    # By default ARROW will try to search for a static version of OpenSSL which is a bad idea given
+    # that shared linking is advised for critical components like SSL. If a static build is
+    # requested, we honor ARROW's default of static linking, but users may consider setting
+    # ARROW_OPENSSL_USE_SHARED even in static builds.
+  else()
+    set(ARROW_BUILD_SHARED ON)
+    set(ARROW_BUILD_STATIC OFF)
+    # By default ARROW will try to search for a static version of OpenSSL which is a bad idea given
+    # that shared linking is advised for critical components like SSL
+    set(ARROW_OPENSSL_USE_SHARED ON)
+  endif()
+
+  set(ARROW_PYTHON_OPTIONS "")
+  if(ENABLE_PYTHON)
+    list(APPEND ARROW_PYTHON_OPTIONS "ARROW_PYTHON ON")
+    # Arrow's logic to build Boost from source is busted, so we have to get it from the system.
+    list(APPEND ARROW_PYTHON_OPTIONS "BOOST_SOURCE SYSTEM")
+    list(APPEND ARROW_PYTHON_OPTIONS "ARROW_DEPENDENCY_SOURCE AUTO")
+  endif()
+
+  set(ARROW_PARQUET_OPTIONS "")
+  if(ENABLE_PARQUET)
+    # Arrow's logic to build Boost from source is busted, so we have to get it from the system.
+    list(APPEND ARROW_PARQUET_OPTIONS "BOOST_SOURCE SYSTEM")
+    list(APPEND ARROW_PARQUET_OPTIONS "Thrift_SOURCE BUNDLED")
+    list(APPEND ARROW_PARQUET_OPTIONS "ARROW_DEPENDENCY_SOURCE AUTO")
+  endif()
+
+  rapids_cpm_find(
+    Arrow ${VERSION}
+    GLOBAL_TARGETS arrow_shared parquet_shared arrow_acero_shared arrow_dataset_shared arrow_static
+                   parquet_static arrow_acero_static arrow_dataset_static
+    CPM_ARGS
+    GIT_REPOSITORY https://github.com/apache/arrow.git
+    GIT_TAG apache-arrow-${VERSION}
+    GIT_SHALLOW TRUE SOURCE_SUBDIR cpp
+    OPTIONS "CMAKE_VERBOSE_MAKEFILE ON"
+            "ARROW_ACERO ON"
+            "ARROW_IPC ON"
+            "ARROW_DATASET ON"
+            "ARROW_WITH_BACKTRACE ON"
+            "ARROW_CXXFLAGS -w"
+            "ARROW_JEMALLOC OFF"
+            "ARROW_S3 ${ENABLE_S3}"
+            "ARROW_ORC ${ENABLE_ORC}"
+            # e.g. needed by blazingsql-io
+            ${ARROW_PARQUET_OPTIONS}
+            "ARROW_PARQUET ${ENABLE_PARQUET}"
+            "ARROW_FILESYSTEM ON"
+            ${ARROW_PYTHON_OPTIONS}
+            # Arrow modifies CMake's GLOBAL RULE_LAUNCH_COMPILE unless this is off
+            "ARROW_USE_CCACHE OFF"
+            "ARROW_ARMV8_ARCH ${ARROW_ARMV8_ARCH}"
+            "ARROW_SIMD_LEVEL ${ARROW_SIMD_LEVEL}"
+            "ARROW_BUILD_STATIC ${ARROW_BUILD_STATIC}"
+            "ARROW_BUILD_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_POSITION_INDEPENDENT_CODE ON"
+            "ARROW_DEPENDENCY_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_BOOST_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_BROTLI_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_GFLAGS_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_GRPC_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_PROTOBUF_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "ARROW_ZSTD_USE_SHARED ${ARROW_BUILD_SHARED}"
+            "xsimd_SOURCE AUTO"
+  )
+
+  set(ARROW_FOUND
+      TRUE
+      PARENT_SCOPE
+  )
+
+  if(BUILD_STATIC)
+    set(ARROW_LIBRARIES arrow_static)
+  else()
+    set(ARROW_LIBRARIES arrow_shared)
+  endif()
+
+  # Arrow_DIR:   set if CPM found Arrow on the system/conda/etc.
+  if(Arrow_DIR)
+    # This extra find_package is necessary because rapids_cpm_find does not propagate all the
+    # variables from find_package that we might need. This is especially problematic when
+    # rapids_cpm_find builds from source.
+    find_package(Arrow REQUIRED QUIET)
+    if(ENABLE_PARQUET)
+      # Setting Parquet_DIR is conditional because parquet may be installed independently of arrow.
+      if(NOT Parquet_DIR)
+        # Set this to enable `find_package(Parquet)`
+        set(Parquet_DIR "${Arrow_DIR}")
+      endif()
+      # Set this to enable `find_package(ArrowDataset)`. This will call find_package(ArrowAcero) for
+      # us
+      set(ArrowDataset_DIR "${Arrow_DIR}")
+      find_package(ArrowDataset REQUIRED QUIET)
+    endif()
+    # Arrow_ADDED: set if CPM downloaded Arrow from Github
+  elseif(Arrow_ADDED)
+    # Copy these files so we can avoid adding paths in Arrow_BINARY_DIR to
+    # target_include_directories. That defeats ccache.
+    file(INSTALL "${Arrow_BINARY_DIR}/src/arrow/util/config.h"
+         DESTINATION "${Arrow_SOURCE_DIR}/cpp/src/arrow/util"
+    )
+    if(ENABLE_PARQUET)
+      file(INSTALL "${Arrow_BINARY_DIR}/src/parquet/parquet_version.h"
+           DESTINATION "${Arrow_SOURCE_DIR}/cpp/src/parquet"
+      )
+    endif()
+    # Arrow populates INTERFACE_INCLUDE_DIRECTORIES for the `arrow_static` and `arrow_shared`
+    # targets in FindArrow, so for static source-builds, we have to do it after-the-fact.
+    #
+    # This only works because we know exactly which components we're using. Don't forget to update
+    # this list if we add more!
+    #
+    foreach(ARROW_LIBRARY ${ARROW_LIBRARIES})
+      target_include_directories(
+        ${ARROW_LIBRARY}
+        INTERFACE "$<BUILD_INTERFACE:${Arrow_SOURCE_DIR}/cpp/src>"
+                  "$<BUILD_INTERFACE:${Arrow_SOURCE_DIR}/cpp/src/generated>"
+                  "$<BUILD_INTERFACE:${Arrow_SOURCE_DIR}/cpp/thirdparty/hadoop/include>"
+                  "$<BUILD_INTERFACE:${Arrow_SOURCE_DIR}/cpp/thirdparty/flatbuffers/include>"
+      )
+    endforeach()
+  else()
+    set(ARROW_FOUND
+        FALSE
+        PARENT_SCOPE
+    )
+    message(FATAL_ERROR "CUDF: Arrow library not found or downloaded.")
+  endif()
+
+  if(Arrow_ADDED)
+
+    set(arrow_code_string
+        [=[
+          if (TARGET cudf::arrow_shared AND (NOT TARGET arrow_shared))
+              add_library(arrow_shared ALIAS cudf::arrow_shared)
+          endif()
+          if (TARGET cudf::arrow_static AND (NOT TARGET arrow_static))
+              add_library(arrow_static ALIAS cudf::arrow_static)
+          endif()
+          if (NOT TARGET arrow::flatbuffers)
+            add_library(arrow::flatbuffers INTERFACE IMPORTED)
+          endif()
+          if (NOT TARGET arrow::hadoop)
+            add_library(arrow::hadoop INTERFACE IMPORTED)
+          endif()
+        ]=]
+    )
+    if(ENABLE_PARQUET)
+      string(
+        APPEND
+        arrow_code_string
+        "
+          find_package(Boost)
+          if (NOT TARGET Boost::headers)
+            add_library(Boost::headers INTERFACE IMPORTED)
+          endif()
+        "
+      )
+    endif()
+    if(NOT TARGET xsimd)
+      string(
+        APPEND
+        arrow_code_string
+        "
+          if(NOT TARGET arrow::xsimd)
+            add_library(arrow::xsimd INTERFACE IMPORTED)
+            target_include_directories(arrow::xsimd INTERFACE \"${Arrow_BINARY_DIR}/xsimd_ep/src/xsimd_ep-install/include\")
+          endif()
+        "
+      )
+    endif()
+
+    rapids_export(
+      BUILD Arrow
+      VERSION ${VERSION}
+      EXPORT_SET arrow_targets
+      GLOBAL_TARGETS arrow_shared arrow_static
+      NAMESPACE cudf::
+      FINAL_CODE_BLOCK arrow_code_string
+    )
+
+    if(ENABLE_PARQUET)
+
+      set(arrow_acero_code_string
+          [=[
+              if (TARGET cudf::arrow_acero_shared AND (NOT TARGET arrow_acero_shared))
+                  add_library(arrow_acero_shared ALIAS cudf::arrow_acero_shared)
+              endif()
+              if (TARGET cudf::arrow_acero_static AND (NOT TARGET arrow_acero_static))
+                  add_library(arrow_acero_static ALIAS cudf::arrow_acero_static)
+              endif()
+            ]=]
+      )
+
+      rapids_export(
+        BUILD ArrowAcero
+        VERSION ${VERSION}
+        EXPORT_SET arrow_acero_targets
+        GLOBAL_TARGETS arrow_acero_shared arrow_acero_static
+        NAMESPACE cudf::
+        FINAL_CODE_BLOCK arrow_acero_code_string
+      )
+
+      set(arrow_dataset_code_string
+          [=[
+              if (TARGET cudf::arrow_dataset_shared AND (NOT TARGET arrow_dataset_shared))
+                  add_library(arrow_dataset_shared ALIAS cudf::arrow_dataset_shared)
+              endif()
+              if (TARGET cudf::arrow_dataset_static AND (NOT TARGET arrow_dataset_static))
+                  add_library(arrow_dataset_static ALIAS cudf::arrow_dataset_static)
+              endif()
+            ]=]
+      )
+
+      rapids_export(
+        BUILD ArrowDataset
+        VERSION ${VERSION}
+        EXPORT_SET arrow_dataset_targets
+        GLOBAL_TARGETS arrow_dataset_shared arrow_dataset_static
+        NAMESPACE cudf::
+        FINAL_CODE_BLOCK arrow_dataset_code_string
+      )
+
+      set(parquet_code_string
+          [=[
+              if (TARGET cudf::parquet_shared AND (NOT TARGET parquet_shared))
+                  add_library(parquet_shared ALIAS cudf::parquet_shared)
+              endif()
+              if (TARGET cudf::parquet_static AND (NOT TARGET parquet_static))
+                  add_library(parquet_static ALIAS cudf::parquet_static)
+              endif()
+            ]=]
+      )
+
+      rapids_export(
+        BUILD Parquet
+        VERSION ${VERSION}
+        EXPORT_SET parquet_targets
+        GLOBAL_TARGETS parquet_shared parquet_static
+        NAMESPACE cudf::
+        FINAL_CODE_BLOCK parquet_code_string
+      )
+    endif()
+  endif()
+  # We generate the arrow-configfiles when we built arrow locally, so always do `find_dependency`
+  rapids_export_package(BUILD Arrow cudf-exports)
+  rapids_export_package(INSTALL Arrow cudf-exports)
+
+  if(ENABLE_PARQUET)
+    rapids_export_package(BUILD Parquet cudf-exports)
+    rapids_export_package(BUILD ArrowDataset cudf-exports)
+  endif()
+
+  include("${rapids-cmake-dir}/export/find_package_root.cmake")
+  rapids_export_find_package_root(BUILD Arrow [=[${CMAKE_CURRENT_LIST_DIR}]=] cudf-exports)
+  if(ENABLE_PARQUET)
+    rapids_export_find_package_root(BUILD Parquet [=[${CMAKE_CURRENT_LIST_DIR}]=] cudf-exports)
+    rapids_export_find_package_root(BUILD ArrowDataset [=[${CMAKE_CURRENT_LIST_DIR}]=] cudf-exports)
+  endif()
+
+  set(ARROW_LIBRARIES
+      "${ARROW_LIBRARIES}"
+      PARENT_SCOPE
+  )
+endfunction()
+
+if(NOT DEFINED CUDF_VERSION_Arrow)
+  set(CUDF_VERSION_Arrow
+      # This version must be kept in sync with the libarrow version pinned for builds in
+      # dependencies.yaml.
+      12.0.1
+      CACHE STRING "The version of Arrow to find (or build)"
+  )
+endif()
+
+find_and_configure_arrow(
+  ${CUDF_VERSION_Arrow} ${CUDF_USE_ARROW_STATIC} ${CUDF_ENABLE_ARROW_S3} ${CUDF_ENABLE_ARROW_ORC}
+  ${CUDF_ENABLE_ARROW_PYTHON} ${CUDF_ENABLE_ARROW_PARQUET}
+)
diff --git a/cpp/cmake/thirdparty/get_cucollections.cmake b/cpp/cmake/thirdparty/get_cucollections.cmake
new file mode 100644
index 0000000..9758958
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_cucollections.cmake
@@ -0,0 +1,25 @@
+# =============================================================================
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds cuCollections and performs any additional configuration.
+function(find_and_configure_cucollections)
+  include(${rapids-cmake-dir}/cpm/cuco.cmake)
+  if(BUILD_SHARED_LIBS)
+    rapids_cpm_cuco(BUILD_EXPORT_SET cudf-exports)
+  else()
+    rapids_cpm_cuco(BUILD_EXPORT_SET cudf-exports INSTALL_EXPORT_SET cudf-exports)
+  endif()
+endfunction()
+
+find_and_configure_cucollections()
diff --git a/cpp/cmake/thirdparty/get_cufile.cmake b/cpp/cmake/thirdparty/get_cufile.cmake
new file mode 100644
index 0000000..c0235eb
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_cufile.cmake
@@ -0,0 +1,32 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds nvcomp and sets any additional necessary environment variables.
+function(find_and_configure_cufile)
+
+  list(APPEND CMAKE_MODULE_PATH ${CUDF_SOURCE_DIR}/cmake/Modules)
+  rapids_find_package(cuFile)
+
+  if(cuFile_FOUND AND NOT BUILD_SHARED_LIBS)
+    include("${rapids-cmake-dir}/export/find_package_file.cmake")
+    rapids_export_find_package_file(
+      BUILD "${CUDF_SOURCE_DIR}/cmake/Modules/FindcuFile.cmake" cudf-exports
+    )
+    rapids_export_find_package_file(
+      INSTALL "${CUDF_SOURCE_DIR}/cmake/Modules/FindcuFile.cmake" cudf-exports
+    )
+  endif()
+endfunction()
+
+find_and_configure_cufile()
diff --git a/cpp/cmake/thirdparty/get_dlpack.cmake b/cpp/cmake/thirdparty/get_dlpack.cmake
new file mode 100644
index 0000000..65b5f4f
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_dlpack.cmake
@@ -0,0 +1,41 @@
+# =============================================================================
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds dlpack and sets any additional necessary environment variables.
+function(find_and_configure_dlpack VERSION)
+
+  include(${rapids-cmake-dir}/find/generate_module.cmake)
+  rapids_find_generate_module(DLPACK HEADER_NAMES dlpack.h)
+
+  rapids_cpm_find(
+    dlpack ${VERSION}
+    GIT_REPOSITORY https://github.com/dmlc/dlpack.git
+    GIT_TAG v${VERSION}
+    GIT_SHALLOW TRUE
+    DOWNLOAD_ONLY TRUE
+    OPTIONS "BUILD_MOCK OFF"
+  )
+
+  if(DEFINED dlpack_SOURCE_DIR)
+    # otherwise find_package(DLPACK) will set this variable
+    set(DLPACK_INCLUDE_DIR
+        "${dlpack_SOURCE_DIR}/include"
+        PARENT_SCOPE
+    )
+  endif()
+endfunction()
+
+set(CUDF_MIN_VERSION_dlpack 0.5)
+
+find_and_configure_dlpack(${CUDF_MIN_VERSION_dlpack})
diff --git a/cpp/cmake/thirdparty/get_fmt.cmake b/cpp/cmake/thirdparty/get_fmt.cmake
new file mode 100644
index 0000000..083dd1d
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_fmt.cmake
@@ -0,0 +1,22 @@
+# =============================================================================
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Use CPM to find or clone fmt
+function(find_and_configure_fmt)
+
+  include(${rapids-cmake-dir}/cpm/fmt.cmake)
+  rapids_cpm_fmt(INSTALL_EXPORT_SET cudf-exports BUILD_EXPORT_SET cudf-exports)
+endfunction()
+
+find_and_configure_fmt()
diff --git a/cpp/cmake/thirdparty/get_gtest.cmake b/cpp/cmake/thirdparty/get_gtest.cmake
new file mode 100644
index 0000000..1363f43
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_gtest.cmake
@@ -0,0 +1,39 @@
+# =============================================================================
+# Copyright (c) 2021, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds gtest and sets any additional necessary environment variables.
+function(find_and_configure_gtest)
+  include(${rapids-cmake-dir}/cpm/gtest.cmake)
+
+  # Find or install GoogleTest
+  rapids_cpm_gtest(BUILD_EXPORT_SET cudf-testing-exports INSTALL_EXPORT_SET cudf-testing-exports)
+
+  if(GTest_ADDED)
+    rapids_export(
+      BUILD GTest
+      VERSION ${GTest_VERSION}
+      EXPORT_SET GTestTargets
+      GLOBAL_TARGETS gtest gmock gtest_main gmock_main
+      NAMESPACE GTest::
+    )
+
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    rapids_export_find_package_root(
+      BUILD GTest [=[${CMAKE_CURRENT_LIST_DIR}]=] cudf-testing-exports
+    )
+  endif()
+
+endfunction()
+
+find_and_configure_gtest()
diff --git a/cpp/cmake/thirdparty/get_jitify.cmake b/cpp/cmake/thirdparty/get_jitify.cmake
new file mode 100644
index 0000000..d98abdf
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_jitify.cmake
@@ -0,0 +1,32 @@
+# =============================================================================
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Jitify doesn't have a version :/
+
+# This function finds Jitify and sets any additional necessary environment variables.
+function(find_and_configure_jitify)
+  rapids_cpm_find(
+    jitify 2.0.0
+    GIT_REPOSITORY https://github.com/rapidsai/jitify.git
+    GIT_TAG jitify2
+    GIT_SHALLOW TRUE
+    DOWNLOAD_ONLY TRUE
+  )
+  set(JITIFY_INCLUDE_DIR
+      "${jitify_SOURCE_DIR}"
+      PARENT_SCOPE
+  )
+endfunction()
+
+find_and_configure_jitify()
diff --git a/cpp/cmake/thirdparty/get_kvikio.cmake b/cpp/cmake/thirdparty/get_kvikio.cmake
new file mode 100644
index 0000000..e94e024
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_kvikio.cmake
@@ -0,0 +1,36 @@
+# =============================================================================
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds KvikIO
+function(find_and_configure_kvikio VERSION)
+
+  rapids_cpm_find(
+    KvikIO ${VERSION}
+    GLOBAL_TARGETS kvikio::kvikio
+    CPM_ARGS
+    GIT_REPOSITORY https://github.com/rapidsai/kvikio.git
+    GIT_TAG branch-${VERSION}
+    GIT_SHALLOW TRUE SOURCE_SUBDIR cpp
+    OPTIONS "KvikIO_BUILD_EXAMPLES OFF"
+  )
+
+  if(KvikIO_BINARY_DIR)
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    rapids_export_find_package_root(BUILD KvikIO "${KvikIO_BINARY_DIR}" cudf-exports)
+  endif()
+
+endfunction()
+
+set(KVIKIO_MIN_VERSION_cudf "${CUDF_VERSION_MAJOR}.${CUDF_VERSION_MINOR}")
+find_and_configure_kvikio(${KVIKIO_MIN_VERSION_cudf})
diff --git a/cpp/cmake/thirdparty/get_libcudacxx.cmake b/cpp/cmake/thirdparty/get_libcudacxx.cmake
new file mode 100644
index 0000000..0e03352
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_libcudacxx.cmake
@@ -0,0 +1,37 @@
+# =============================================================================
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds thrust and sets any additional necessary environment variables.
+function(find_and_configure_libcudacxx)
+  # Make sure we install libcudacxx beside our patched version of thrust
+  include(GNUInstallDirs)
+  set(CMAKE_INSTALL_INCLUDEDIR "${CMAKE_INSTALL_INCLUDEDIR}/libcudf")
+  set(CMAKE_INSTALL_LIBDIR "${CMAKE_INSTALL_INCLUDEDIR}/lib")
+
+  include(${rapids-cmake-dir}/cpm/libcudacxx.cmake)
+  rapids_cpm_libcudacxx(BUILD_EXPORT_SET cudf-exports INSTALL_EXPORT_SET cudf-exports)
+
+  if(libcudacxx_SOURCE_DIR)
+    # Store where CMake can find our custom Thrust install
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    rapids_export_find_package_root(
+      INSTALL
+      libcudacxx
+      [=[${CMAKE_CURRENT_LIST_DIR}/../../../include/libcudf/lib/rapids/cmake/libcudacxx]=]
+      cudf-exports
+    )
+  endif()
+endfunction()
+
+find_and_configure_libcudacxx()
diff --git a/cpp/cmake/thirdparty/get_nvbench.cmake b/cpp/cmake/thirdparty/get_nvbench.cmake
new file mode 100644
index 0000000..f064214
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_nvbench.cmake
@@ -0,0 +1,28 @@
+# =============================================================================
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds nvbench and applies any needed patches.
+function(find_and_configure_nvbench)
+
+  include(${rapids-cmake-dir}/cpm/nvbench.cmake)
+  include(${rapids-cmake-dir}/cpm/package_override.cmake)
+
+  set(cudf_patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/patches")
+  rapids_cpm_package_override("${cudf_patch_dir}/nvbench_override.json")
+
+  rapids_cpm_nvbench()
+
+endfunction()
+
+find_and_configure_nvbench()
diff --git a/cpp/cmake/thirdparty/get_nvcomp.cmake b/cpp/cmake/thirdparty/get_nvcomp.cmake
new file mode 100644
index 0000000..41bbf44
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_nvcomp.cmake
@@ -0,0 +1,31 @@
+# =============================================================================
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds nvcomp and sets any additional necessary environment variables.
+function(find_and_configure_nvcomp)
+
+  include(${rapids-cmake-dir}/cpm/nvcomp.cmake)
+  rapids_cpm_nvcomp(
+    BUILD_EXPORT_SET cudf-exports
+    INSTALL_EXPORT_SET cudf-exports
+    USE_PROPRIETARY_BINARY ${CUDF_USE_PROPRIETARY_NVCOMP}
+  )
+
+  # Per-thread default stream
+  if(TARGET nvcomp AND CUDF_USE_PER_THREAD_DEFAULT_STREAM)
+    target_compile_definitions(nvcomp PRIVATE CUDA_API_PER_THREAD_DEFAULT_STREAM)
+  endif()
+endfunction()
+
+find_and_configure_nvcomp()
diff --git a/cpp/cmake/thirdparty/get_rmm.cmake b/cpp/cmake/thirdparty/get_rmm.cmake
new file mode 100644
index 0000000..854bd3d
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_rmm.cmake
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds rmm and sets any additional necessary environment variables.
+function(find_and_configure_rmm)
+  include(${rapids-cmake-dir}/cpm/rmm.cmake)
+
+  # Find or install RMM
+  rapids_cpm_rmm(BUILD_EXPORT_SET cudf-exports INSTALL_EXPORT_SET cudf-exports)
+
+endfunction()
+
+find_and_configure_rmm()
diff --git a/cpp/cmake/thirdparty/get_spdlog.cmake b/cpp/cmake/thirdparty/get_spdlog.cmake
new file mode 100644
index 0000000..fff5b84
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_spdlog.cmake
@@ -0,0 +1,34 @@
+# =============================================================================
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# Use CPM to find or clone speedlog
+function(find_and_configure_spdlog)
+
+  include(${rapids-cmake-dir}/cpm/spdlog.cmake)
+  rapids_cpm_spdlog(FMT_OPTION "EXTERNAL_FMT_HO" INSTALL_EXPORT_SET cudf-exports)
+  rapids_export_package(BUILD spdlog cudf-exports)
+
+  if(spdlog_ADDED)
+    rapids_export(
+      BUILD spdlog
+      EXPORT_SET spdlog
+      GLOBAL_TARGETS spdlog spdlog_header_only
+      NAMESPACE spdlog::
+    )
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    rapids_export_find_package_root(BUILD spdlog [=[${CMAKE_CURRENT_LIST_DIR}]=] cudf-exports)
+  endif()
+endfunction()
+
+find_and_configure_spdlog()
diff --git a/cpp/cmake/thirdparty/get_thrust.cmake b/cpp/cmake/thirdparty/get_thrust.cmake
new file mode 100644
index 0000000..39a9de1
--- /dev/null
+++ b/cpp/cmake/thirdparty/get_thrust.cmake
@@ -0,0 +1,46 @@
+# =============================================================================
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds thrust and sets any additional necessary environment variables.
+function(find_and_configure_thrust)
+
+  include(${rapids-cmake-dir}/cpm/thrust.cmake)
+  include(${rapids-cmake-dir}/cpm/package_override.cmake)
+
+  set(cudf_patch_dir "${CMAKE_CURRENT_FUNCTION_LIST_DIR}/patches")
+  rapids_cpm_package_override("${cudf_patch_dir}/thrust_override.json")
+
+  # Make sure we install thrust into the `include/libcudf` subdirectory instead of the default
+  include(GNUInstallDirs)
+  set(CMAKE_INSTALL_INCLUDEDIR "${CMAKE_INSTALL_INCLUDEDIR}/libcudf")
+  set(CMAKE_INSTALL_LIBDIR "${CMAKE_INSTALL_INCLUDEDIR}/lib")
+
+  # Find or install Thrust with our custom set of patches
+  rapids_cpm_thrust(
+    NAMESPACE cudf
+    BUILD_EXPORT_SET cudf-exports
+    INSTALL_EXPORT_SET cudf-exports
+  )
+
+  if(Thrust_SOURCE_DIR)
+    # Store where CMake can find our custom Thrust install
+    include("${rapids-cmake-dir}/export/find_package_root.cmake")
+    rapids_export_find_package_root(
+      INSTALL Thrust
+      [=[${CMAKE_CURRENT_LIST_DIR}/../../../include/libcudf/lib/rapids/cmake/thrust]=] cudf-exports
+    )
+  endif()
+endfunction()
+
+find_and_configure_thrust()
diff --git a/cpp/cmake/thirdparty/patches/cub_segmented_sort_with_bool_key.diff b/cpp/cmake/thirdparty/patches/cub_segmented_sort_with_bool_key.diff
new file mode 100644
index 0000000..7c40fd4
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/cub_segmented_sort_with_bool_key.diff
@@ -0,0 +1,14 @@
+diff --git a/dependencies/cub/cub/agent/agent_sub_warp_merge_sort.cuh b/dependencies/cub/cub/agent/agent_sub_warp_merge_sort.cuh
+index ad65f2a3..ad45a21e 100644
+--- a/dependencies/cub/cub/agent/agent_sub_warp_merge_sort.cuh
++++ b/dependencies/cub/cub/agent/agent_sub_warp_merge_sort.cuh
+@@ -221,7 +221,8 @@ public:
+       using UnsignedBitsT = typename Traits<KeyT>::UnsignedBits;
+       UnsignedBitsT default_key_bits = IS_DESCENDING ? Traits<KeyT>::LOWEST_KEY
+                                                      : Traits<KeyT>::MAX_KEY;
+-      KeyT oob_default = reinterpret_cast<KeyT &>(default_key_bits);
++      KeyT oob_default = std::is_same_v<KeyT, bool> ? !IS_DESCENDING
++                                                   : reinterpret_cast<KeyT &>(default_key_bits);
+
+       WarpLoadKeysT(storage.load_keys)
+         .Load(keys_input, keys, segment_size, oob_default);
diff --git a/cpp/cmake/thirdparty/patches/nvbench_global_setup.diff b/cpp/cmake/thirdparty/patches/nvbench_global_setup.diff
new file mode 100644
index 0000000..04f96f4
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/nvbench_global_setup.diff
@@ -0,0 +1,29 @@
+diff --git a/nvbench/main.cuh b/nvbench/main.cuh
+index 0ba82d7..cca5273 100644
+--- a/nvbench/main.cuh
++++ b/nvbench/main.cuh
+@@ -54,6 +54,16 @@
+ // clang-format on
+ #endif
+
++#ifndef NVBENCH_ENVIRONMENT
++namespace nvbench {
++struct no_environment
++{
++  no_environment(int, char const *const *) {}
++};
++}
++#define NVBENCH_ENVIRONMENT nvbench::no_environment
++#endif
++
+ #define NVBENCH_MAIN_PARSE(argc, argv)                                                             \
+   nvbench::option_parser parser;                                                                   \
+   parser.parse(argc, argv)
+@@ -77,6 +87,7 @@
+     printer.set_total_state_count(total_states);                                                   \
+                                                                                                    \
+     printer.set_completed_state_count(0);                                                          \
++    [[maybe_unused]] auto env_state = NVBENCH_ENVIRONMENT(argc, argv);                             \
+     for (auto &bench_ptr : benchmarks)                                                             \
+     {                                                                                              \
+       bench_ptr->set_printer(printer);                                                             \
diff --git a/cpp/cmake/thirdparty/patches/nvbench_override.json b/cpp/cmake/thirdparty/patches/nvbench_override.json
new file mode 100644
index 0000000..7be8680
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/nvbench_override.json
@@ -0,0 +1,19 @@
+
+{
+  "packages" : {
+    "nvbench" : {
+      "patches" : [
+        {
+          "file" : "${current_json_dir}/nvbench_global_setup.diff",
+          "issue" : "Fix add support for global setup to initialize RMM in nvbench [https://github.com/NVIDIA/nvbench/pull/123]",
+          "fixed_in" : ""
+        },
+        {
+          "file" : "nvbench/use_existing_fmt.diff",
+          "issue" : "Fix add support for using an existing fmt [https://github.com/NVIDIA/nvbench/pull/125]",
+          "fixed_in" : ""
+        }
+      ]
+    }
+  }
+}
diff --git a/cpp/cmake/thirdparty/patches/thrust_disable_64bit_dispatching.diff b/cpp/cmake/thirdparty/patches/thrust_disable_64bit_dispatching.diff
new file mode 100644
index 0000000..382f7dc
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/thrust_disable_64bit_dispatching.diff
@@ -0,0 +1,29 @@
+diff --git a/thrust/system/cuda/detail/dispatch.h b/thrust/system/cuda/detail/dispatch.h
+index d0e3f94..76774b0 100644
+--- a/thrust/system/cuda/detail/dispatch.h
++++ b/thrust/system/cuda/detail/dispatch.h
+@@ -32,9 +32,8 @@
+         status = call arguments; \
+     } \
+     else { \
+-        auto THRUST_PP_CAT2(count, _fixed) = static_cast<thrust::detail::int64_t>(count); \
+-        status = call arguments; \
+-    }
++       throw std::runtime_error("THRUST_INDEX_TYPE_DISPATCH 64-bit count is unsupported in libcudf"); \
++    }
+
+ /**
+  * Dispatch between 32-bit and 64-bit index based versions of the same algorithm
+@@ -52,10 +51,8 @@
+         status = call arguments; \
+     } \
+     else { \
+-        auto THRUST_PP_CAT2(count1, _fixed) = static_cast<thrust::detail::int64_t>(count1); \
+-        auto THRUST_PP_CAT2(count2, _fixed) = static_cast<thrust::detail::int64_t>(count2); \
+-        status = call arguments; \
+-    }
++       throw std::runtime_error("THRUST_DOUBLE_INDEX_TYPE_DISPATCH 64-bit count is unsupported in libcudf"); \
++    }
+ /**
+  * Dispatch between 32-bit and 64-bit index based versions of the same algorithm
+  * implementation. This version allows using different token sequences for callables
diff --git a/cpp/cmake/thirdparty/patches/thrust_faster_scan_compile_times.diff b/cpp/cmake/thirdparty/patches/thrust_faster_scan_compile_times.diff
new file mode 100644
index 0000000..6bf1658
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/thrust_faster_scan_compile_times.diff
@@ -0,0 +1,39 @@
+diff --git a/dependencies/cub/cub/device/dispatch/dispatch_radix_sort.cuh b/dependencies/cub/cub/device/dispatch/dispatch_radix_sort.cuh
+index b188c75f..3f36656f 100644
+--- a/dependencies/cub/cub/device/dispatch/dispatch_radix_sort.cuh
++++ b/dependencies/cub/cub/device/dispatch/dispatch_radix_sort.cuh
+@@ -736,7 +736,7 @@ struct DeviceRadixSortPolicy
+
+
+     /// SM60 (GP100)
+-    struct Policy600 : ChainedPolicy<600, Policy600, Policy500>
++    struct Policy600 : ChainedPolicy<600, Policy600, Policy600>
+     {
+         enum {
+             PRIMARY_RADIX_BITS      = (sizeof(KeyT) > 1) ? 7 : 5,    // 6.9B 32b keys/s (Quadro P100)
+diff --git a/dependencies/cub/cub/device/dispatch/dispatch_reduce.cuh b/dependencies/cub/cub/device/dispatch/dispatch_reduce.cuh
+index e0470ccb..6a0c2ed6 100644
+--- a/dependencies/cub/cub/device/dispatch/dispatch_reduce.cuh
++++ b/dependencies/cub/cub/device/dispatch/dispatch_reduce.cuh
+@@ -280,7 +280,7 @@ struct DeviceReducePolicy
+     };
+
+     /// SM60
+-    struct Policy600 : ChainedPolicy<600, Policy600, Policy350>
++    struct Policy600 : ChainedPolicy<600, Policy600, Policy600>
+     {
+         // ReducePolicy (P100: 591 GB/s @ 64M 4B items; 583 GB/s @ 256M 1B items)
+         typedef AgentReducePolicy<
+diff --git a/dependencies/cub/cub/device/dispatch/dispatch_scan.cuh b/dependencies/cub/cub/device/dispatch/dispatch_scan.cuh
+index c2d04588..ac2d10e0 100644
+--- a/dependencies/cub/cub/device/dispatch/dispatch_scan.cuh
++++ b/dependencies/cub/cub/device/dispatch/dispatch_scan.cuh
+@@ -177,7 +177,7 @@ struct DeviceScanPolicy
+     };
+
+     /// SM600
+-    struct Policy600 : ChainedPolicy<600, Policy600, Policy520>
++    struct Policy600 : ChainedPolicy<600, Policy600, Policy600>
+     {
+         typedef AgentScanPolicy<
+                 128, 15,                                        ///< Threads per block, items per thread
diff --git a/cpp/cmake/thirdparty/patches/thrust_faster_sort_compile_times.diff b/cpp/cmake/thirdparty/patches/thrust_faster_sort_compile_times.diff
new file mode 100644
index 0000000..864c89d
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/thrust_faster_sort_compile_times.diff
@@ -0,0 +1,48 @@
+diff --git a/dependencies/cub/cub/block/block_merge_sort.cuh b/dependencies/cub/cub/block/block_merge_sort.cuh
+index 4769df36..d86d6342 100644
+--- a/dependencies/cub/cub/block/block_merge_sort.cuh
++++ b/dependencies/cub/cub/block/block_merge_sort.cuh
+@@ -91,7 +91,7 @@ __device__ __forceinline__ void SerialMerge(KeyT *keys_shared,
+   KeyT key1 = keys_shared[keys1_beg];
+   KeyT key2 = keys_shared[keys2_beg];
+
+-#pragma unroll
++#pragma unroll 1
+   for (int item = 0; item < ITEMS_PER_THREAD; ++item)
+   {
+     bool p = (keys2_beg < keys2_end) &&
+@@ -383,7 +383,7 @@ public:
+       //
+       KeyT max_key = oob_default;
+
+-      #pragma unroll
++      #pragma unroll 1
+       for (int item = 1; item < ITEMS_PER_THREAD; ++item)
+       {
+         if (ITEMS_PER_THREAD * linear_tid + item < valid_items)
+@@ -407,7 +407,7 @@ public:
+     // each thread has sorted keys
+     // merge sort keys in shared memory
+     //
+-    #pragma unroll
++    #pragma unroll 1
+     for (int target_merged_threads_number = 2;
+          target_merged_threads_number <= NUM_THREADS;
+          target_merged_threads_number *= 2)
+diff --git a/dependencies/cub/cub/thread/thread_sort.cuh b/dependencies/cub/cub/thread/thread_sort.cuh
+index 5d486789..b42fb5f0 100644
+--- a/dependencies/cub/cub/thread/thread_sort.cuh
++++ b/dependencies/cub/cub/thread/thread_sort.cuh
+@@ -83,10 +83,10 @@ StableOddEvenSort(KeyT (&keys)[ITEMS_PER_THREAD],
+ {
+   constexpr bool KEYS_ONLY = std::is_same<ValueT, NullType>::value;
+
+-  #pragma unroll
++  #pragma unroll 1
+   for (int i = 0; i < ITEMS_PER_THREAD; ++i)
+   {
+-  #pragma unroll
++  #pragma unroll 1
+     for (int j = 1 & i; j < ITEMS_PER_THREAD - 1; j += 2)
+     {
+       if (compare_op(keys[j + 1], keys[j]))
diff --git a/cpp/cmake/thirdparty/patches/thrust_override.json b/cpp/cmake/thirdparty/patches/thrust_override.json
new file mode 100644
index 0000000..ded2b90
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/thrust_override.json
@@ -0,0 +1,39 @@
+
+{
+  "packages" : {
+    "Thrust" : {
+      "patches" : [
+        {
+          "file" : "Thrust/install_rules.diff",
+          "issue" : "Thrust 1.X installs incorrect files [https://github.com/NVIDIA/thrust/issues/1790]",
+          "fixed_in" : "2.0.0"
+        },
+        {
+          "file" : "${current_json_dir}/thrust_transform_iter_with_reduce_by_key.diff",
+          "issue" : "Support transform_output_iterator as output of reduce by key [https://github.com/NVIDIA/thrust/pull/1805]",
+          "fixed_in" : "2.1"
+        },
+        {
+          "file" : "${current_json_dir}/thrust_disable_64bit_dispatching.diff",
+          "issue" : "Remove 64bit dispatching as not needed by libcudf and results in compiling twice as many kernels [https://github.com/rapidsai/cudf/pull/11437]",
+          "fixed_in" : ""
+        },
+        {
+          "file" : "${current_json_dir}/thrust_faster_sort_compile_times.diff",
+          "issue" : "Improve Thrust sort compile times by not unrolling loops for inlined comparators [https://github.com/rapidsai/cudf/pull/10577]",
+          "fixed_in" : ""
+        },
+        {
+          "file" : "${current_json_dir}/thrust_faster_scan_compile_times.diff",
+          "issue" : "Improve Thrust scan compile times by reducing the number of kernels generated [https://github.com/rapidsai/cudf/pull/8183]",
+          "fixed_in" : ""
+        },
+        {
+          "file" : "${current_json_dir}/cub_segmented_sort_with_bool_key.diff",
+          "issue" : "Fix an error in CUB DeviceSegmentedSort when the keys are bool type [https://github.com/NVIDIA/cub/issues/594]",
+          "fixed_in" : "2.1"
+        }
+      ]
+    }
+  }
+}
diff --git a/cpp/cmake/thirdparty/patches/thrust_transform_iter_with_reduce_by_key.diff b/cpp/cmake/thirdparty/patches/thrust_transform_iter_with_reduce_by_key.diff
new file mode 100644
index 0000000..6a56af9
--- /dev/null
+++ b/cpp/cmake/thirdparty/patches/thrust_transform_iter_with_reduce_by_key.diff
@@ -0,0 +1,26 @@
+diff --git a/thrust/iterator/transform_input_output_iterator.h b/thrust/iterator/transform_input_output_iterator.h
+index f512a36..a5f725d 100644
+--- a/thrust/iterator/transform_input_output_iterator.h
++++ b/thrust/iterator/transform_input_output_iterator.h
+@@ -102,6 +102,8 @@ template <typename InputFunction, typename OutputFunction, typename Iterator>
+   /*! \endcond
+    */
+
++  transform_input_output_iterator() = default;
++
+   /*! This constructor takes as argument a \c Iterator an \c InputFunction and an
+    * \c OutputFunction and copies them to a new \p transform_input_output_iterator
+    *
+diff --git a/thrust/iterator/transform_output_iterator.h b/thrust/iterator/transform_output_iterator.h
+index 66fb46a..4a68cb5 100644
+--- a/thrust/iterator/transform_output_iterator.h
++++ b/thrust/iterator/transform_output_iterator.h
+@@ -104,6 +104,8 @@ template <typename UnaryFunction, typename OutputIterator>
+   /*! \endcond
+    */
+
++  transform_output_iterator() = default;
++
+   /*! This constructor takes as argument an \c OutputIterator and an \c
+    * UnaryFunction and copies them to a new \p transform_output_iterator
+    *
diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile
new file mode 100644
index 0000000..b072d25
--- /dev/null
+++ b/cpp/doxygen/Doxyfile
@@ -0,0 +1,2590 @@
+# Doxyfile 1.9.1
+
+# This file describes the settings to be used by the documentation system
+# doxygen (www.doxygen.org) for a project.
+#
+# All text after a double hash (##) is considered a comment and is placed in
+# front of the TAG it is preceding.
+#
+# All text after a single hash (#) is considered a comment and will be ignored.
+# The format is:
+# TAG = value [value, ...]
+# For lists, items can also be appended using:
+# TAG += value [value, ...]
+# Values that contain spaces should be placed between quotes (\" \").
+
+#---------------------------------------------------------------------------
+# Project related configuration options
+#---------------------------------------------------------------------------
+
+# This tag specifies the encoding used for all characters in the configuration
+# file that follow. The default is UTF-8 which is also the encoding used for all
+# text before the first occurrence of this tag. Doxygen uses libiconv (or the
+# iconv built into libc) for the transcoding. See
+# https://www.gnu.org/software/libiconv/ for the list of possible encodings.
+# The default value is: UTF-8.
+
+DOXYFILE_ENCODING      = UTF-8
+
+# The PROJECT_NAME tag is a single word (or a sequence of words surrounded by
+# double-quotes, unless you are using Doxywizard) that should identify the
+# project for which the documentation is generated. This name is used in the
+# title of most generated pages and in a few other places.
+# The default value is: My Project.
+
+PROJECT_NAME           = libcudf
+
+# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
+# could be handy for archiving the generated documentation or if some version
+# control system is used.
+
+PROJECT_NUMBER         = 23.10.00
+
+# Using the PROJECT_BRIEF tag one can provide an optional one line description
+# for a project that appears at the top of each page and should give viewer a
+# quick idea about the purpose of the project. Keep the description short.
+
+PROJECT_BRIEF          =
+
+# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
+# in the documentation. The maximum height of the logo should not exceed 55
+# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
+# the logo to the output directory.
+
+PROJECT_LOGO           =
+
+# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
+# into which the generated documentation will be written. If a relative path is
+# entered, it will be relative to the location where doxygen was started. If
+# left blank the current directory will be used.
+
+OUTPUT_DIRECTORY       =
+
+# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
+# directories (in 2 levels) under the output directory of each output format and
+# will distribute the generated files over these directories. Enabling this
+# option can be useful when feeding doxygen a huge amount of source files, where
+# putting all generated files in the same directory would otherwise causes
+# performance problems for the file system.
+# The default value is: NO.
+
+CREATE_SUBDIRS         = NO
+
+# If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII
+# characters to appear in the names of generated files. If set to NO, non-ASCII
+# characters will be escaped, for example _xE3_x81_x84 will be used for Unicode
+# U+3044.
+# The default value is: NO.
+
+ALLOW_UNICODE_NAMES    = NO
+
+# The OUTPUT_LANGUAGE tag is used to specify the language in which all
+# documentation generated by doxygen is written. Doxygen will use this
+# information to generate all constant output in the proper language.
+# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese,
+# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States),
+# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian,
+# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages),
+# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian,
+# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian,
+# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish,
+# Ukrainian and Vietnamese.
+# The default value is: English.
+
+OUTPUT_LANGUAGE        = English
+
+# The OUTPUT_TEXT_DIRECTION tag is used to specify the direction in which all
+# documentation generated by doxygen is written. Doxygen will use this
+# information to generate all generated output in the proper direction.
+# Possible values are: None, LTR, RTL and Context.
+# The default value is: None.
+
+OUTPUT_TEXT_DIRECTION  = None
+
+# If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member
+# descriptions after the members that are listed in the file and class
+# documentation (similar to Javadoc). Set to NO to disable this.
+# The default value is: YES.
+
+BRIEF_MEMBER_DESC      = YES
+
+# If the REPEAT_BRIEF tag is set to YES, doxygen will prepend the brief
+# description of a member or function before the detailed description
+#
+# Note: If both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the
+# brief descriptions will be completely suppressed.
+# The default value is: YES.
+
+REPEAT_BRIEF           = YES
+
+# This tag implements a quasi-intelligent brief description abbreviator that is
+# used to form the text in various listings. Each string in this list, if found
+# as the leading text of the brief description, will be stripped from the text
+# and the result, after processing the whole list, is used as the annotated
+# text. Otherwise, the brief description is used as-is. If left blank, the
+# following values are used ($name is automatically replaced with the name of
+# the entity):The $name class, The $name widget, The $name file, is, provides,
+# specifies, contains, represents, a, an and the.
+
+ABBREVIATE_BRIEF       =
+
+# If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then
+# doxygen will generate a detailed section even if there is only a brief
+# description.
+# The default value is: NO.
+
+ALWAYS_DETAILED_SEC    = NO
+
+# If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all
+# inherited members of a class in the documentation of that class as if those
+# members were ordinary class members. Constructors, destructors and assignment
+# operators of the base classes will not be shown.
+# The default value is: NO.
+
+INLINE_INHERITED_MEMB  = NO
+
+# If the FULL_PATH_NAMES tag is set to YES, doxygen will prepend the full path
+# before files name in the file list and in the header files. If set to NO the
+# shortest path that makes the file name unique will be used
+# The default value is: YES.
+
+FULL_PATH_NAMES        = NO
+
+# The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
+# Stripping is only done if one of the specified strings matches the left-hand
+# part of the path. The tag can be used to show relative paths in the file list.
+# If left blank the directory from which doxygen is run is used as the path to
+# strip.
+#
+# Note that you can specify absolute paths here, but also relative paths, which
+# will be relative from the directory where doxygen is started.
+# This tag requires that the tag FULL_PATH_NAMES is set to YES.
+
+STRIP_FROM_PATH        =
+
+# The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
+# path mentioned in the documentation of a class, which tells the reader which
+# header file to include in order to use a class. If left blank only the name of
+# the header file containing the class definition is used. Otherwise one should
+# specify the list of include paths that are normally passed to the compiler
+# using the -I flag.
+
+STRIP_FROM_INC_PATH    =
+
+# If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
+# less readable) file names. This can be useful is your file systems doesn't
+# support long names like on DOS, Mac, or CD-ROM.
+# The default value is: NO.
+
+SHORT_NAMES            = NO
+
+# If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
+# first line (until the first dot) of a Javadoc-style comment as the brief
+# description. If set to NO, the Javadoc-style will behave just like regular Qt-
+# style comments (thus requiring an explicit @brief command for a brief
+# description.)
+# The default value is: NO.
+
+JAVADOC_AUTOBRIEF      = NO
+
+# If the JAVADOC_BANNER tag is set to YES then doxygen will interpret a line
+# such as
+# /***************
+# as being the beginning of a Javadoc-style comment "banner". If set to NO, the
+# Javadoc-style will behave just like regular comments and it will not be
+# interpreted by doxygen.
+# The default value is: NO.
+
+JAVADOC_BANNER         = NO
+
+# If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
+# line (until the first dot) of a Qt-style comment as the brief description. If
+# set to NO, the Qt-style will behave just like regular Qt-style comments (thus
+# requiring an explicit \brief command for a brief description.)
+# The default value is: NO.
+
+QT_AUTOBRIEF           = NO
+
+# The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
+# multi-line C++ special comment block (i.e. a block of //! or /// comments) as
+# a brief description. This used to be the default behavior. The new default is
+# to treat a multi-line C++ comment block as a detailed description. Set this
+# tag to YES if you prefer the old behavior instead.
+#
+# Note that setting this tag to YES also means that rational rose comments are
+# not recognized any more.
+# The default value is: NO.
+
+MULTILINE_CPP_IS_BRIEF = NO
+
+# By default Python docstrings are displayed as preformatted text and doxygen's
+# special commands cannot be used. By setting PYTHON_DOCSTRING to NO the
+# doxygen's special commands can be used and the contents of the docstring
+# documentation blocks is shown as doxygen documentation.
+# The default value is: YES.
+
+PYTHON_DOCSTRING       = YES
+
+# If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
+# documentation from any documented member that it re-implements.
+# The default value is: YES.
+
+INHERIT_DOCS           = YES
+
+# If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
+# page for each member. If set to NO, the documentation of a member will be part
+# of the file/class/namespace that contains it.
+# The default value is: NO.
+
+SEPARATE_MEMBER_PAGES  = NO
+
+# The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
+# uses this value to replace tabs by spaces in code fragments.
+# Minimum value: 1, maximum value: 16, default value: 4.
+
+TAB_SIZE               = 4
+
+# This tag can be used to specify a number of aliases that act as commands in
+# the documentation. An alias has the form:
+# name=value
+# For example adding
+# "sideeffect=@par Side Effects:\n"
+# will allow you to put the command \sideeffect (or @sideeffect) in the
+# documentation, which will result in a user-defined paragraph with heading
+# "Side Effects:". You can put \n's in the value part of an alias to insert
+# newlines (in the resulting output). You can put ^^ in the value part of an
+# alias to insert a newline as if a physical newline was in the original file.
+# When you need a literal { or } or , in the value part of an alias you have to
+# escape them by means of a backslash (\), this can lead to conflicts with the
+# commands \{ and \} for these it is advised to use the version @{ and @} or use
+# a double escape (\\{ and \\})
+
+ALIASES                =
+
+# Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
+# only. Doxygen will then generate output that is more tailored for C. For
+# instance, some of the names that are used will be different. The list of all
+# members will be omitted, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_FOR_C  = NO
+
+# Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
+# Python sources only. Doxygen will then generate output that is more tailored
+# for that language. For instance, namespaces will be presented as packages,
+# qualified scopes will look different, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_JAVA   = NO
+
+# Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
+# sources. Doxygen will then generate output that is tailored for Fortran.
+# The default value is: NO.
+
+OPTIMIZE_FOR_FORTRAN   = NO
+
+# Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
+# sources. Doxygen will then generate output that is tailored for VHDL.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_VHDL   = NO
+
+# Set the OPTIMIZE_OUTPUT_SLICE tag to YES if your project consists of Slice
+# sources only. Doxygen will then generate output that is more tailored for that
+# language. For instance, namespaces will be presented as modules, types will be
+# separated into more groups, etc.
+# The default value is: NO.
+
+OPTIMIZE_OUTPUT_SLICE  = NO
+
+# Doxygen selects the parser to use depending on the extension of the files it
+# parses. With this tag you can assign which parser to use for a given
+# extension. Doxygen has a built-in mapping, but you can override or extend it
+# using this tag. The format is ext=language, where ext is a file extension, and
+# language is one of the parsers supported by doxygen: IDL, Java, JavaScript,
+# Csharp (C#), C, C++, D, PHP, md (Markdown), Objective-C, Python, Slice, VHDL,
+# Fortran (fixed format Fortran: FortranFixed, free formatted Fortran:
+# FortranFree, unknown formatted Fortran: Fortran. In the later case the parser
+# tries to guess whether the code is fixed or free formatted code, this is the
+# default for Fortran type files). For instance to make doxygen treat .inc files
+# as Fortran files (default is PHP), and .f files as C (default is Fortran),
+# use: inc=Fortran f=C.
+#
+# Note: For files without extension you can use no_extension as a placeholder.
+#
+# Note that for custom extensions you also need to set FILE_PATTERNS otherwise
+# the files are not read by doxygen. When specifying no_extension you should add
+# * to the FILE_PATTERNS.
+#
+# Note see also the list of default file extension mappings.
+
+EXTENSION_MAPPING      = cu=C++ \
+                         cuh=C++
+
+# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
+# according to the Markdown format, which allows for more readable
+# documentation. See https://daringfireball.net/projects/markdown/ for details.
+# The output of markdown processing is further processed by doxygen, so you can
+# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
+# case of backward compatibilities issues.
+# The default value is: YES.
+
+MARKDOWN_SUPPORT       = YES
+
+# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up
+# to that level are automatically included in the table of contents, even if
+# they do not have an id attribute.
+# Note: This feature currently applies only to Markdown headings.
+# Minimum value: 0, maximum value: 99, default value: 5.
+# This tag requires that the tag MARKDOWN_SUPPORT is set to YES.
+
+TOC_INCLUDE_HEADINGS   = 5
+
+# When enabled doxygen tries to link words that correspond to documented
+# classes, or namespaces to their corresponding documentation. Such a link can
+# be prevented in individual cases by putting a % sign in front of the word or
+# globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+AUTOLINK_SUPPORT       = YES
+
+# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
+# diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
+
+BUILTIN_STL_SUPPORT    = NO
+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+# The default value is: NO.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate
+# getter and setter methods for a property. Setting this option to YES will make
+# doxygen to replace the get and set methods by a property in the documentation.
+# This will only work if the methods are indeed getting or setting a simple
+# type. If this is not the case, or you want to show the methods anyway, you
+# should set this option to NO.
+# The default value is: YES.
+
+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES then doxygen will reuse the documentation of the first
+# member in the group (if any) for the other members of the group. By default
+# all members of a group must be documented explicitly.
+# The default value is: NO.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# If one adds a struct or class to a group and this option is enabled, then also
+# any nested class or struct is added to the same group. By default this option
+# is disabled and one has to add nested compounds explicitly via \ingroup.
+# The default value is: NO.
+
+GROUP_NESTED_COMPOUNDS = NO
+
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
+# subgrouping. Alternatively, this can be done per class using the
+# \nosubgrouping command.
+# The default value is: YES.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
+# the documentation of the scope in which they are defined (i.e. file,
+# namespace, or group documentation), provided this scope is documented. If set
+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
+
+INLINE_SIMPLE_STRUCTS  = NO
+
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
+# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
+# with name TypeT. When disabled the typedef will appear as a member of a file,
+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
+
+LOOKUP_CACHE_SIZE      = 0
+
+# The NUM_PROC_THREADS specifies the number threads doxygen is allowed to use
+# during processing. When set to 0 doxygen will based this on the number of
+# cores available in the system. You can set it explicitly to a value larger
+# than 0 to get more control over the balance between CPU load and processing
+# speed. At this moment only the input processing can be done using multiple
+# threads. Since this is still an experimental feature the default is set to 1,
+# which efficively disables parallel processing. Please report any issues you
+# encounter. Generating dot graphs in parallel is controlled by the
+# DOT_NUM_THREADS setting.
+# Minimum value: 0, maximum value: 32, default value: 1.
+
+NUM_PROC_THREADS       = 1
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
+
+EXTRACT_ALL            = NO
+
+# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIVATE        = NO
+
+# If the EXTRACT_PRIV_VIRTUAL tag is set to YES, documented private virtual
+# methods of a class will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIV_VIRTUAL   = NO
+
+# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PACKAGE        = NO
+
+# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
+
+EXTRACT_STATIC         = NO
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO,
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. If set to YES, local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO, only methods in the interface are
+# included.
+# The default value is: NO.
+
+EXTRACT_LOCAL_METHODS  = NO
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If this flag is set to YES, the name of an unnamed parameter in a declaration
+# will be determined by the corresponding definition. By default unnamed
+# parameters remain unnamed in the output.
+# The default value is: YES.
+
+RESOLVE_UNNAMED_PARAMS = YES
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO, these classes will be included in the various overviews. This option
+# has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_CLASSES     = NO
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# declarations. If set to NO, these declarations will be included in the
+# documentation.
+# The default value is: NO.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO, these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# With the correct setting of option CASE_SENSE_NAMES doxygen will better be
+# able to match the capabilities of the underlying filesystem. In case the
+# filesystem is case sensitive (i.e. it supports files in the same directory
+# whose names only differ in casing), the option must be set to YES to properly
+# deal with such files in case they appear in the input. For filesystems that
+# are not case sensitive the option should be be set to NO to properly deal with
+# output files written for symbols that only differ in casing, such as for two
+# classes, one named CLASS and the other named Class, and to also support
+# references to files without having to specify the exact matching casing. On
+# Windows (including Cygwin) and MacOS, users should typically set this option
+# to NO, whereas on Linux or other Unix flavors it should typically be set to
+# YES.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES, the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# append additional text to a page's title, such as Class Reference. If set to
+# YES the compound reference will be hidden.
+# The default value is: NO.
+
+HIDE_COMPOUND_REFERENCE= NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
+# list. This list is created by putting \todo commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
+# list. This list is created by putting \test commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES, the
+# list will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            = DoxygenLayout.xml
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO, doxygen will only warn about wrong or incomplete
+# parameter documentation, but not about the absence of documentation. If
+# EXTRACT_ALL is set to YES then this flag will automatically be disabled.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = YES
+
+# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
+# a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS
+# then doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but
+# at the end of the doxygen process doxygen will return with a non-zero status.
+# Possible values are: NO, YES and FAIL_ON_WARNINGS.
+# The default value is: NO.
+
+WARN_AS_ERROR          = NO
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  = main_page.md \
+                         regex.md \
+                         unicode.md \
+                         developer_guide/BENCHMARKING.md \
+                         developer_guide/DOCUMENTATION.md \
+                         developer_guide/DEVELOPER_GUIDE.md \
+                         developer_guide/TESTING.md \
+                         ../include \
+                         ../include/cudf_test/column_wrapper.hpp \
+                         ../include/cudf_test/column_utilities.hpp \
+                         ../include/cudf_test/iterator_utilities.hpp \
+                         ../include/cudf_test/table_utilities.hpp \
+                         ../include/cudf_test/type_lists.hpp \
+                         ../include/cudf_test/type_list_utilities.hpp \
+                         ../libcudf_kafka/include
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see:
+# https://www.gnu.org/software/libiconv/) for the list of possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# read by doxygen.
+#
+# Note the list of default checked file patterns might differ from the list of
+# default file extension mappings.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
+# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
+# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
+# *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C comment),
+# *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f18, *.f, *.for, *.vhd, *.vhdl,
+# *.ucf, *.qsf and *.ice.
+
+FILE_PATTERNS          = *.cpp \
+                         *.hpp \
+                         *.h \
+                         *.c \
+                         *.cu \
+                         *.cuh
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = YES
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       = */nvtx/* \
+                         */detail/* \
+                         */cudf_test/*
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        = org::apache \
+                         *_impl \
+                         *Impl
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       =
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+FILTER_PATTERNS        = *.md=./modify_fences.sh
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE = main_page.md
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = YES
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# entity all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see https://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            = header.html
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefore more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the style sheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# https://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 266
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 255
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 52
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to YES can help to show when doxygen was last run and thus if the
+# documentation is up to date.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = NO
+
+# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
+# documentation will contain a main index with vertical navigation menus that
+# are dynamically created via JavaScript. If disabled, the navigation index will
+# consists of multiple levels of tabs that are statically embedded in every HTML
+# page. Disable this option to support browsers that do not have JavaScript,
+# like the Qt help browser.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_MENUS     = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see:
+# https://developer.apple.com/xcode/), introduced with OSX 10.5 (Leopard). To
+# create a documentation set, doxygen will generate a Makefile in the HTML
+# output directory. Running make will produce the docset in that directory and
+# running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
+# genXcode/_index.html for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see:
+# https://www.microsoft.com/en-us/download/details.aspx?id=21138) on Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler (hhc.exe). If non-empty,
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated
+# (YES) or that it should be included in the main .chm file (NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated
+# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location (absolute path
+# including file name) of Qt's qhelpgenerator. If non-empty doxygen will try to
+# run qhelpgenerator on the generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# If the HTML_FORMULA_FORMAT option is set to svg, doxygen will use the pdf2svg
+# tool (see https://github.com/dawbarton/pdf2svg) or inkscape (see
+# https://inkscape.org) to generate formulas as SVG images instead of PNGs for
+# the HTML output. These images will generally look nicer at scaled resolutions.
+# Possible values are: png (the default) and svg (looks nicer but requires the
+# pdf2svg or inkscape tool).
+# The default value is: png.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FORMULA_FORMAT    = png
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
+# to create new LaTeX commands to be used in formulas as building blocks. See
+# the section "Including formulas" for details.
+
+FORMULA_MACROFILE      =
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# https://www.mathjax.org) which uses client side JavaScript for the rendering
+# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = NO
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/v2.7-latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from https://www.mathjax.org before deployment.
+# The default value is: https://cdn.jsdelivr.net/npm/mathjax@2.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see:
+# http://docs.mathjax.org/en/v2.7-latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using JavaScript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see:
+# https://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see:
+# https://xapian.org/). See the section "External Indexing and Searching" for
+# details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = NO
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when not enabling USE_PDFLATEX the default is latex when enabling
+# USE_PDFLATEX the default is pdflatex and when in the later case latex is
+# chosen this is overwritten by pdflatex. For specific output languages the
+# default can have been set differently, this depends on the implementation of
+# the output language.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         = latex
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# Note: This tag is used in the Makefile / make.bat.
+# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file
+# (.tex).
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to
+# generate index for LaTeX. In case there is no backslash (\) as first character
+# it will be automatically added in the LaTeX code.
+# Note: This tag is used in the generated output file (.tex).
+# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.
+# The default value is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_MAKEINDEX_CMD    = makeindex
+
+# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. The package can be specified just
+# by its name or with the correct syntax as to be used with the LaTeX
+# \usepackage command. To get the times font for instance you can specify :
+# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
+# To use the option intlimits with the amsmath package you can specify:
+# EXTRA_PACKAGES=[intlimits]{amsmath}
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
+# string, for the replacement values of the other commands the user is referred
+# to HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# LaTeX style sheets that are included after the standard style sheets created
+# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list).
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_STYLESHEET =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use the engine as
+# specified with LATEX_CMD_NAME to generate the PDF file directly from the LaTeX
+# files. Set this option to YES, to get a higher quality PDF documentation.
+#
+# See also section LATEX_CMD_NAME for selecting the engine.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_TIMESTAMP        = NO
+
+# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
+# path from which the emoji images will be read. If a relative path is entered,
+# it will be relative to the LATEX_OUTPUT directory. If left blank the
+# LATEX_OUTPUT directory will be used.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EMOJI_DIRECTORY  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's
+# configuration file, i.e. a series of assignments. You only have to provide
+# replacements, missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's configuration file. A template extensions file can be
+# generated using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
+# with syntax highlighting in the RTF output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_SOURCE_CODE        = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+
+GENERATE_XML           = NO
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
+# namespace members in file scope as well, matching the HTML output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_NS_MEMB_FILE_SCOPE = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
+# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# the structure of the code including all documentation. Note that this feature
+# is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO, the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
+
+ENABLE_PREPROCESSING   = YES
+
+# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
+# in the source code. If set to NO, only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+MACRO_EXPANSION        = YES
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_ONLY_PREDEF     = YES
+
+# If the SEARCH_INCLUDES tag is set to YES, the include files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+PREDEFINED             = __device__= \
+                         __host__= \
+                         DOXYGEN_SHOULD_SKIP_THIS
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
+# the path). If a tag file is not located in the directory in which doxygen is
+# run, you must also specify the path to the tagfile here.
+
+TAGFILES               = rmm.tag=https://docs.rapids.ai/api/librmm/23.10
+
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# tag file that is based on the input files it reads. See section "Linking to
+# external documentation" for more information about the usage of tag files.
+
+GENERATE_TAGFILE       = html/libcudf.tag
+
+# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
+# the class index. If set to NO, only the inherited external classes will be
+# listed.
+# The default value is: NO.
+
+ALLEXTERNALS           = NO
+
+# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
+# in the modules index. If set to NO, only the current project's groups will be
+# listed.
+# The default value is: YES.
+
+EXTERNAL_GROUPS        = YES
+
+# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
+# the related pages index. If set to NO, only the current project's pages will
+# be listed.
+# The default value is: YES.
+
+EXTERNAL_PAGES         = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to the dot tool
+#---------------------------------------------------------------------------
+
+# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
+# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
+# NO turns the diagrams off. Note that this option also works with HAVE_DOT
+# disabled, but it is recommended to install and use dot, since it yields more
+# powerful graphs.
+# The default value is: YES.
+
+CLASS_DIAGRAMS         = YES
+
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
+
+DIA_PATH               =
+
+# If set to YES the inheritance and collaboration graphs will hide inheritance
+# and usage relations if the target is undocumented or is not a class.
+# The default value is: YES.
+
+HIDE_UNDOC_RELATIONS   = YES
+
+# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# available from the path. This tool is part of Graphviz (see:
+# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# Bell Labs. The other options in this section have no effect if this option is
+# set to NO
+# The default value is: NO.
+
+HAVE_DOT               = NO
+
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
+# to run in parallel. When set to 0 doxygen will base this on the number of
+# processors available in the system. You can set it explicitly to a value
+# larger than 0 to get control over the balance between CPU load and processing
+# speed.
+# Minimum value: 0, maximum value: 32, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_NUM_THREADS        = 0
+
+# When you want a differently looking font in the dot files that doxygen
+# generates you can specify the font name using DOT_FONTNAME. You need to make
+# sure dot is able to find the font, which can be done by putting it in a
+# standard location or by setting the DOTFONTPATH environment variable or by
+# setting DOT_FONTPATH to the directory containing the font.
+# The default value is: Helvetica.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTNAME           = Helvetica
+
+# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
+# dot graphs.
+# Minimum value: 4, maximum value: 24, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTSIZE           = 10
+
+# By default doxygen will tell dot to use the default font as specified with
+# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
+# the path where dot can find it using this tag.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTPATH           =
+
+# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
+# each documented class showing the direct and indirect inheritance relations.
+# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CLASS_GRAPH            = YES
+
+# If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
+# graph for each documented class showing the direct and indirect implementation
+# dependencies (inheritance, containment, and class references variables) of the
+# class with other documented classes.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+COLLABORATION_GRAPH    = YES
+
+# If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
+# groups, showing the direct groups dependencies.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GROUP_GRAPHS           = YES
+
+# If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
+# collaboration diagrams in a style similar to the OMG's Unified Modeling
+# Language.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+UML_LOOK               = NO
+
+# If the UML_LOOK tag is enabled, the fields and methods are shown inside the
+# class node. If there are many fields or methods and many nodes the graph may
+# become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
+# number of items for each type to make the size more manageable. Set this to 0
+# for no limit. Note that the threshold may be exceeded by 50% before the limit
+# is enforced. So when you set the threshold to 10, up to 15 fields may appear,
+# but if the number exceeds 15, the total amount of fields shown is limited to
+# 10.
+# Minimum value: 0, maximum value: 100, default value: 10.
+# This tag requires that the tag UML_LOOK is set to YES.
+
+UML_LIMIT_NUM_FIELDS   = 10
+
+# If the DOT_UML_DETAILS tag is set to NO, doxygen will show attributes and
+# methods without types and arguments in the UML graphs. If the DOT_UML_DETAILS
+# tag is set to YES, doxygen will add type and arguments for attributes and
+# methods in the UML graphs. If the DOT_UML_DETAILS tag is set to NONE, doxygen
+# will not generate fields with class member information in the UML graphs. The
+# class diagrams will look similar to the default class diagrams but using UML
+# notation for the relationships.
+# Possible values are: NO, YES and NONE.
+# The default value is: NO.
+# This tag requires that the tag UML_LOOK is set to YES.
+
+DOT_UML_DETAILS        = NO
+
+# The DOT_WRAP_THRESHOLD tag can be used to set the maximum number of characters
+# to display on a single line. If the actual line length exceeds this threshold
+# significantly it will wrapped across multiple lines. Some heuristics are apply
+# to avoid ugly line breaks.
+# Minimum value: 0, maximum value: 1000, default value: 17.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_WRAP_THRESHOLD     = 17
+
+# If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
+# collaboration graphs will show the relations between templates and their
+# instances.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+TEMPLATE_RELATIONS     = NO
+
+# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
+# YES then doxygen will generate a graph for each documented file showing the
+# direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDE_GRAPH          = YES
+
+# If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
+# set to YES then doxygen will generate a graph for each documented file showing
+# the direct and indirect include dependencies of the file with other documented
+# files.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INCLUDED_BY_GRAPH      = YES
+
+# If the CALL_GRAPH tag is set to YES then doxygen will generate a call
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command. Disabling a call graph can be
+# accomplished by means of the command \hidecallgraph.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALL_GRAPH             = NO
+
+# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable caller graphs for selected
+# functions only using the \callergraph command. Disabling a caller graph can be
+# accomplished by means of the command \hidecallergraph.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALLER_GRAPH           = NO
+
+# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# hierarchy of all classes instead of a textual one.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GRAPHICAL_HIERARCHY    = YES
+
+# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
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diff --git a/cpp/doxygen/DoxygenLayout.xml b/cpp/doxygen/DoxygenLayout.xml
new file mode 100644
index 0000000..ded88df
--- /dev/null
+++ b/cpp/doxygen/DoxygenLayout.xml
@@ -0,0 +1,227 @@
+<doxygenlayout version="1.0">
+  <!-- Generated by doxygen 1.8.20 -->
+  <!-- Navigation index tabs for HTML output -->
+  <navindex>
+    <tab type="mainpage" visible="yes" title=""/>
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+    <tab type="files" visible="yes" title="">
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+      <tab type="globals" visible="yes" title="" intro=""/>
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+    <tab type="examples" visible="yes" title="" intro=""/>
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+
+  <!-- Layout definition for a directory page -->
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diff --git a/cpp/doxygen/developer_guide/BENCHMARKING.md b/cpp/doxygen/developer_guide/BENCHMARKING.md
new file mode 100644
index 0000000..98e2f42
--- /dev/null
+++ b/cpp/doxygen/developer_guide/BENCHMARKING.md
@@ -0,0 +1,56 @@
+# Unit Benchmarking in libcudf
+
+Unit benchmarks in libcudf are written using [NVBench](https://github.com/NVIDIA/nvbench).
+While many existing benchmarks are written using
+[Google Benchmark](https://github.com/google/benchmark), new benchmarks should use NVBench.
+
+The NVBench library is similar to Google Benchmark, but has several quality of life improvements
+when doing GPU benchmarking such as displaying the fraction of peak memory bandwidth achieved and
+details about the GPU hardware.
+
+Both NVBench and Google Benchmark provide many options for specifying ranges of parameters to
+benchmark, as well as to control the time unit reported, among other options. Refer to existing
+benchmarks in `cpp/benchmarks` to understand the options.
+
+## Directory and File Naming
+
+The naming of unit benchmark directories and source files should be consistent with the feature
+being benchmarked. For example, the benchmarks for APIs in `copying.hpp` should live in
+`cpp/benchmarks/copying`. Each feature (or set of related features) should have its own
+benchmark source file named `<feature>.cu/cpp`. For example, `cpp/src/copying/scatter.cu` has
+benchmarks in `cpp/benchmarks/copying/scatter.cu`.
+
+In the interest of improving compile time, whenever possible, test source files should be `.cpp`
+files because `nvcc` is slower than `gcc` in compiling host code. Note that `thrust::device_vector`
+includes device code, and so must only be used in `.cu` files. `rmm::device_uvector`,
+`rmm::device_buffer` and the various `column_wrapper` types described in [Testing](TESTING.md)
+can be used in `.cpp` files, and are therefore preferred in test code over `thrust::device_vector`.
+
+## CUDA Asynchrony and benchmark accuracy
+
+CUDA computations and operations like copies are typically asynchronous with respect to host code,
+so it is important to carefully synchronize in order to ensure the benchmark timing is not stopped
+before the feature you are benchmarking has completed. An RAII helper class `cuda_event_timer` is
+provided in `cpp/benchmarks/synchronization/synchronization.hpp` to help with this. This class
+can also optionally clear the GPU L2 cache in order to ensure cache hits do not artificially inflate
+performance in repeated iterations.
+
+## Data generation
+
+For generating benchmark input data, helper functions are available at [cpp/benchmarks/common/generate_input.hpp](/cpp/benchmarks/common/generate_input.hpp). The input data generation happens on device, in contrast to any `column_wrapper` where data generation happens on the host.
+* `create_sequence_table` can generate sequence columns starting with value 0 in first row and increasing by 1 in subsequent rows.
+* `create_random_column` can generate a column filled with random data. The random data parameters are configurable.
+* `create_random_table` can generate a table of columns filled with random data. The random data parameters are configurable.
+
+## What should we benchmark?
+
+In general, we should benchmark all features over a range of data sizes and types, so that we can
+catch regressions across libcudf changes. However, running many benchmarks is expensive, so ideally
+we should sample the parameter space in such a way to get good coverage without having to test
+exhaustively.
+
+A rule of thumb is that we should benchmark with enough data to reach the point where the algorithm
+reaches its saturation bottleneck, whether that bottleneck is bandwidth or computation. Using data
+sets larger than this point is generally not helpful, except in specific cases where doing so
+exercises different code and can therefore uncover regressions that smaller benchmarks will not
+(this should be rare).
diff --git a/cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md b/cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md
new file mode 100644
index 0000000..fc2f72d
--- /dev/null
+++ b/cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md
@@ -0,0 +1,1375 @@
+# libcudf C++ Developer Guide {#DEVELOPER_GUIDE}
+
+This document serves as a guide for contributors to libcudf C++ code. Developers should also refer
+to these additional files for further documentation of libcudf best practices.
+
+* [Documentation Guide](DOCUMENTATION.md) for guidelines on documenting libcudf code.
+* [Testing Guide](TESTING.md) for guidelines on writing unit tests.
+* [Benchmarking Guide](BENCHMARKING.md) for guidelines on writing unit benchmarks.
+
+# Overview
+
+libcudf is a C++ library that provides GPU-accelerated data-parallel algorithms for processing
+column-oriented tabular data. libcudf provides algorithms including slicing, filtering, sorting,
+various types of aggregations, and database-type operations such as grouping and joins. libcudf
+serves a number of clients via multiple language interfaces, including Python and Java. Users may
+also use libcudf directly from C++ code.
+
+## Lexicon
+
+This section defines terminology used within libcudf.
+
+### Column
+
+A column is an array of data of a single type. Along with Tables, columns are the fundamental data
+structures used in libcudf. Most libcudf algorithms operate on columns. Columns may have a validity
+mask representing whether each element is valid or null (invalid). Columns of nested types are
+supported, meaning that a column may have child columns. A column is the C++ equivalent to a cuDF
+Python [Series](https://docs.rapids.ai/api/cudf/stable/api_docs/series.html).
+
+### Element
+
+An individual data item within a column. Also known as a row.
+
+### Scalar
+
+A type representing a single element of a data type.
+
+### Table
+
+A table is a collection of columns with equal number of elements. A table is the C++ equivalent to
+a cuDF Python [DataFrame](https://docs.rapids.ai/api/cudf/stable/api_docs/dataframe.html).
+
+### View
+
+A view is a non-owning object that provides zero-copy access (possibly with slicing or offsets) to
+data owned by another object. Examples are column views and table views.
+
+# Directory Structure and File Naming
+
+External/public libcudf APIs are grouped based on functionality into an appropriately titled
+header file in `cudf/cpp/include/cudf/`. For example, `cudf/cpp/include/cudf/copying.hpp`
+contains the APIs for functions related to copying from one column to another. Note the `.hpp`
+file extension used to indicate a C++ header file.
+
+Header files should use the `#pragma once` include guard.
+
+The naming of external API headers should be consistent with the name of the folder that contains
+the source files that implement the API. For example, the implementation of the APIs found in
+`cudf/cpp/include/cudf/copying.hpp` are located in `cudf/src/copying`. Likewise, the unit tests for
+the APIs reside in `cudf/tests/copying/`.
+
+Internal API headers containing `detail` namespace definitions that are used across translation
+units inside libcudf should be placed in `include/cudf/detail`.
+
+## File extensions
+
+- `.hpp` : C++ header files
+- `.cpp` : C++ source files
+- `.cu`  : CUDA C++ source files
+- `.cuh` : Headers containing CUDA device code
+
+Only use `.cu` and `.cuh` if necessary. A good indicator is the inclusion of `__device__` and other
+symbols that are only recognized by `nvcc`. Another indicator is Thrust algorithm APIs with a device
+execution policy (always `rmm::exec_policy` in libcudf).
+
+## Code and Documentation Style and Formatting
+
+libcudf code uses [snake_case](https://en.wikipedia.org/wiki/Snake_case) for all names except in a
+few cases: template parameters, unit tests and test case names may use Pascal case, aka
+[UpperCamelCase](https://en.wikipedia.org/wiki/Camel_case). We do not use
+[Hungarian notation](https://en.wikipedia.org/wiki/Hungarian_notation), except sometimes when naming
+device data variables and their corresponding host copies. Private member variables are typically
+prefixed with an underscore.
+
+```c++
+template <typename IteratorType>
+void algorithm_function(int x, rmm::cuda_stream_view s, rmm::device_memory_resource* mr)
+{
+  ...
+}
+
+class utility_class
+{
+  ...
+private:
+  int _rating{};
+  std::unique_ptr<cudf::column> _column{};
+}
+
+TYPED_TEST_SUITE(RepeatTypedTestFixture, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(RepeatTypedTestFixture, RepeatScalarCount)
+{
+  ...
+}
+```
+
+C++ formatting is enforced using `clang-format`. You should configure `clang-format` on your
+machine to use the `cudf/cpp/.clang-format` configuration file, and run `clang-format` on all
+changed code before committing it. The easiest way to do this is to configure your editor to
+"format on save."
+
+Aspects of code style not discussed in this document and not automatically enforceable are typically
+caught during code review, or not enforced.
+
+### C++ Guidelines
+
+In general, we recommend following
+[C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines). We also
+recommend watching Sean Parent's [C++ Seasoning talk](https://www.youtube.com/watch?v=W2tWOdzgXHA),
+and we try to follow his rules: "No raw loops. No raw pointers. No raw synchronization primitives."
+
+ * Prefer algorithms from STL and Thrust to raw loops.
+ * Prefer libcudf and RMM [owning data structures and views](#libcudf-data-structures) to raw
+   pointers and raw memory allocation.
+ * libcudf doesn't have a lot of CPU-thread concurrency, but there is some. And currently libcudf
+   does use raw synchronization primitives. So we should revisit Parent's third rule and improve
+   here.
+
+Additional style guidelines for libcudf code include:
+
+ * [NL.11: Make Literals
+   Readable](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines#nl11-make-literals-readable):
+   Decimal values should use integer separators every thousands place, like
+   `1'234'567`. Hexadecimal values should use separators every 4 characters,
+   like `0x0123'ABCD`.
+
+Documentation is discussed in the [Documentation Guide](DOCUMENTATION.md).
+
+### Includes
+
+The following guidelines apply to organizing `#include` lines.
+
+ * Group includes by library (e.g. cuDF, RMM, Thrust, STL). `clang-format` will respect the
+   groupings and sort the individual includes within a group lexicographically.
+ * Separate groups by a blank line.
+ * Order the groups from "nearest" to "farthest". In other words, local includes, then includes
+   from other RAPIDS libraries, then includes from related libraries, like `<thrust/...>`, then
+   includes from dependencies installed with cuDF, and then standard headers (for example
+   `<string>`, `<iostream>`).
+ * Use `<>` instead of `""` unless the header is in the same directory as the source file.
+ * Tools like `clangd` often auto-insert includes when they can, but they usually get the grouping
+   and brackets wrong.
+ * Always check that includes are only necessary for the file in which they are included.
+   Try to avoid excessive including especially in header files. Double check this when you remove
+   code.
+ * Use quotes `"` to include local headers from the same relative source directory. This should only
+   occur in source files and non-public header files. Otherwise use angle brackets `<>` around
+   included header filenames.
+ * Avoid relative paths with `..` when possible. Paths with `..` are necessary when including
+   (internal) headers from source paths not in the same directory as the including file,
+   because source paths are not passed with `-I`.
+ * Avoid including library internal headers from non-internal files. For example, try not to include
+   headers from libcudf `src` directories in tests or in libcudf public headers. If you find
+   yourself doing this, start a discussion about moving (parts of) the included internal header
+   to a public header.
+
+# libcudf Data Structures
+
+Application data in libcudf is contained in Columns and Tables, but there are a variety of other
+data structures you will use when developing libcudf code.
+
+## Views and Ownership
+
+Resource ownership is an essential concept in libcudf. In short, an "owning" object owns a
+resource (such as device memory). It acquires that resource during construction and releases the
+resource in destruction ([RAII](https://en.cppreference.com/w/cpp/language/raii)). A "non-owning"
+object does not own resources. Any class in libcudf with the `*_view` suffix is non-owning. For more
+detail see the [`libcudf` presentation.](https://docs.google.com/presentation/d/1zKzAtc1AWFKfMhiUlV5yRZxSiPLwsObxMlWRWz_f5hA/edit?usp=sharing)
+
+libcudf functions typically take views as input (`column_view` or `table_view`)
+and produce `unique_ptr`s to owning objects as output. For example,
+
+```c++
+std::unique_ptr<table> sort(table_view const& input);
+```
+
+## rmm::device_memory_resource
+
+libcudf allocates all device memory via RMM memory resources (MR). See the
+[RMM documentation](https://github.com/rapidsai/rmm/blob/main/README.md) for details.
+
+### Current Device Memory Resource
+
+RMM provides a "default" memory resource for each device that can be accessed and updated via the
+`rmm::mr::get_current_device_resource()` and `rmm::mr::set_current_device_resource(...)` functions,
+respectively. All memory resource parameters should be defaulted to use the return value of
+`rmm::mr::get_current_device_resource()`.
+
+## cudf::column
+
+`cudf::column` is a core owning data structure in libcudf. Most libcudf public APIs produce either
+a `cudf::column` or a `cudf::table` as output. A `column` contains `device_buffer`s which own the
+device memory for the elements of a column and an optional null indicator bitmask.
+
+Implicitly convertible to `column_view` and `mutable_column_view`.
+
+Movable and copyable. A copy performs a deep copy of the column's contents, whereas a move moves
+the contents from one column to another.
+
+Example:
+
+```c++
+cudf::column col{...};
+
+cudf::column copy{col}; // Copies the contents of `col`
+cudf::column const moved_to{std::move(col)}; // Moves contents from `col`
+
+column_view v = moved_to; // Implicit conversion to non-owning column_view
+// mutable_column_view m = moved_to; // Cannot create mutable view to const column
+```
+
+A `column` may have nested (child) columns, depending on the data type of the column. For example,
+`LIST`, `STRUCT`, and `STRING` type columns.
+
+### cudf::column_view
+
+`cudf::column_view` is a core non-owning data structure in libcudf. It is an immutable,
+non-owning view of device memory as a column. Most libcudf public APIs take views as inputs.
+
+A `column_view` may be a view of a "slice" of a column. For example, it might view rows 75-150 of a
+column with 1000 rows. The `size()` of this `column_view` would be `75`, and accessing index `0` of
+the view would return the element at index `75` of the owning `column`. Internally, this is
+implemented by storing in the view a pointer, an offset, and a size. `column_view::data<T>()`
+returns a pointer iterator to `column_view::head<T>() + offset`.
+
+### cudf::mutable_column_view
+
+A *mutable*, non-owning view of device memory as a column. Used for detail APIs and (rare) public
+APIs that modify columns in place.
+
+### cudf::column_device_view
+
+An immutable, non-owning view of device data as a column of elements that is trivially copyable and
+usable in CUDA device code. Used to pass `column_view` data as input to CUDA kernels and device
+functions (including Thrust algorithms)
+
+### cudf::mutable_column_device_view
+
+A mutable, non-owning view of device data as a column of elements that is trivially copyable and
+usable in CUDA device code. Used to pass `column_view` data to be modified on the device by CUDA
+kernels and device functions (including Thrust algorithms).
+
+## cudf::table
+
+Owning class for a set of `cudf::column`s all with equal number of elements. This is the C++
+equivalent to a data frame.
+
+Implicitly convertible to `cudf::table_view` and `cudf::mutable_table_view`
+
+Movable and copyable. A copy performs a deep copy of all columns, whereas a move moves all columns
+from one table to another.
+
+### cudf::table_view
+
+An *immutable*, non-owning view of a table.
+
+### cudf::mutable_table_view
+
+A *mutable*, non-owning view of a table.
+
+## cudf::size_type
+
+The `cudf::size_type` is the type used for the number of elements in a column, offsets to elements
+within a column, indices to address specific elements, segments for subsets of column elements, etc.
+It is equivalent to a signed, 32-bit integer type and therefore has a maximum value of 2147483647.
+Some APIs also accept negative index values and those functions support a minimum value of
+-2147483648. This fundamental type also influences output values not just for column size limits
+but for counting elements as well.
+
+## Spans
+
+libcudf provides `span` classes that mimic C++20 `std::span`, which is a lightweight
+view of a contiguous sequence of objects. libcudf provides two classes, `host_span` and
+`device_span`, which can be constructed from multiple container types, or from a pointer
+(host or device, respectively) and size, or from iterators. `span` types are useful for defining
+generic (internal) interfaces which work with multiple input container types. `device_span` can be
+constructed from `thrust::device_vector`, `rmm::device_vector`, or `rmm::device_uvector`.
+`host_span` can be constructed from `thrust::host_vector`, `std::vector`, or `std::basic_string`.
+
+If you are defining internal (detail) functions that operate on vectors, use spans for the input
+vector parameters rather than a specific vector type, to make your functions more widely applicable.
+
+When a `span` refers to immutable elements, use `span<T const>`, not `span<T> const`. Since a span
+is lightweight view, it does not propagate `const`-ness. Therefore, `const` should be applied to
+the template type parameter, not to the `span` itself. Also, `span` should be passed by value
+because it is a lightweight view. APIS in libcudf that take spans as input will look like the
+following function that copies device data to a host `std::vector`.
+
+```c++
+template <typename T>
+std::vector<T> make_std_vector_async(device_span<T const> v, rmm::cuda_stream_view stream)
+```
+
+## cudf::scalar
+
+A `cudf::scalar` is an object that can represent a singular, nullable value of any of the types
+currently supported by cudf. Each type of value is represented by a separate type of scalar class
+which are all derived from `cudf::scalar`. e.g. A `numeric_scalar` holds a single numerical value,
+a `string_scalar` holds a single string. The data for the stored value resides in device memory.
+
+A `list_scalar` holds the underlying data of a single list. This means the underlying data can be
+any type that cudf supports. For example, a `list_scalar` representing a list of integers stores a
+`cudf::column` of type `INT32`. A `list_scalar` representing a list of lists of integers stores a
+`cudf::column` of type `LIST`, which in turn stores a column of type `INT32`.
+
+|Value type|Scalar class|Notes|
+|-|-|-|
+|fixed-width|`fixed_width_scalar<T>`| `T` can be any fixed-width type|
+|numeric|`numeric_scalar<T>` | `T` can be `int8_t`, `int16_t`, `int32_t`, `int_64_t`, `float` or `double`|
+|fixed-point|`fixed_point_scalar<T>` | `T` can be `numeric::decimal32` or `numeric::decimal64`|
+|timestamp|`timestamp_scalar<T>` | `T` can be `timestamp_D`, `timestamp_s`, etc.|
+|duration|`duration_scalar<T>` | `T` can be `duration_D`, `duration_s`, etc.|
+|string|`string_scalar`| This class object is immutable|
+|list|`list_scalar`| Underlying data can be any type supported by cudf |
+
+### Construction
+`scalar`s can be created using either their respective constructors or using factory functions like
+`make_numeric_scalar()`, `make_timestamp_scalar()` or `make_string_scalar()`.
+
+### Casting
+All the factory methods return a `unique_ptr<scalar>` which needs to be statically downcasted to
+its respective scalar class type before accessing its value. Their validity (nullness) can be
+accessed without casting. Generally, the value needs to be accessed from a function that is aware
+of the value type e.g. a functor that is dispatched from `type_dispatcher`. To cast to the
+requisite scalar class type given the value type, use the mapping utility `scalar_type_t` provided
+in `type_dispatcher.hpp` :
+
+```c++
+//unique_ptr<scalar> s = make_numeric_scalar(...);
+
+using ScalarType = cudf::scalar_type_t<T>;
+// ScalarType is now numeric_scalar<T>
+auto s1 = static_cast<ScalarType *>(s.get());
+```
+
+### Passing to device
+Each scalar type, except `list_scalar`, has a corresponding non-owning device view class which
+allows access to the value and its validity from the device. This can be obtained using the function
+`get_scalar_device_view(ScalarType s)`. Note that a device view is not provided for a base scalar
+object, only for the derived typed scalar class objects.
+
+The underlying data for `list_scalar` can be accessed via `view()` method. For non-nested data,
+the device view can be obtained via function `column_device_view::create(column_view)`. For nested
+data, a specialized device view for list columns can be constructed via
+`lists_column_device_view(column_device_view)`.
+
+# libcudf Policies and Design Principles
+
+`libcudf` is designed to provide thread-safe, single-GPU accelerated algorithm primitives for
+solving a wide variety of problems that arise in data science.  APIs are written to execute on the
+default GPU, which can be controlled by the caller through standard CUDA device APIs or environment
+variables like `CUDA_VISIBLE_DEVICES`.  Our goal is to enable diverse use cases like Spark or Pandas
+to benefit from the performance of GPUs, and libcudf relies on these higher-level layers like Spark
+or Dask to orchestrate multi-GPU tasks.
+
+To best satisfy these use-cases, libcudf prioritizes performance and flexibility, which sometimes
+may come at the cost of convenience.  While we welcome users to use libcudf directly, we design with
+the expectation that most users will be consuming libcudf through higher-level layers like Spark or
+cuDF Python that handle some of details that direct users of libcudf must handle on their own.  We
+document these policies and the reasons behind them here.
+
+## libcudf does not introspect data
+
+libcudf APIs generally do not perform deep introspection and validation of input data.
+There are numerous reasons for this:
+1. It violates the single responsibility principle: validation is separate from execution.
+2. Since libcudf data structures store data on the GPU, any validation incurs _at minimum_ the
+   overhead of a kernel launch, and may in general be prohibitively expensive.
+3. API promises around data introspection often significantly complicate implementation.
+
+Users are therefore responsible for passing valid data into such APIs.
+_Note that this policy does not mean that libcudf performs no validation whatsoever_.
+libcudf APIs should still perform any validation that does not require introspection.
+To give some idea of what should or should not be validated, here are (non-exhaustive) lists of
+examples.
+
+**Things that libcudf should validate**:
+- Input column/table sizes or data types
+
+**Things that libcudf should not validate**:
+- Integer overflow
+- Ensuring that outputs will not exceed the [2GB size](#cudfsize_type) limit for a given set of
+  inputs
+
+
+## libcudf expects nested types to have sanitized null masks
+
+Various libcudf APIs accepting columns of nested data types (such as `LIST` or `STRUCT`) may assume
+that these columns have been sanitized. In this context, sanitization refers to ensuring that the
+null elements in a column with a nested dtype are compatible with the elements of nested columns.
+Specifically:
+- Null elements of list columns should also be empty. The starting offset of a null element should
+  be equal to the ending offset.
+- Null elements of struct columns should also be null elements in the underlying structs.
+- For compound columns, nulls should only be present at the level of the parent column. Child
+  columns should not contain nulls.
+- Slice operations on nested columns do not propagate offsets to child columns.
+
+libcudf APIs _should_ promise to never return "dirty" columns, i.e. columns containing unsanitized
+data. Therefore, the only problem is if users construct input columns that are not correctly
+sanitized and then pass those into libcudf APIs.
+
+## Treat libcudf APIs as if they were asynchronous
+
+libcudf APIs called on the host do not guarantee that the stream is synchronized before returning.
+Work in libcudf occurs on `cudf::get_default_stream().value`, which defaults to the CUDA default
+stream (stream 0). Note that the stream 0 behavior differs if [per-thread default stream is
+enabled](https://docs.nvidia.com/cuda/cuda-runtime-api/stream-sync-behavior.html) via
+`CUDF_USE_PER_THREAD_DEFAULT_STREAM`. Any data provided to or returned by libcudf that uses a
+separate non-blocking stream requires synchronization with the default libcudf stream to ensure
+stream safety.
+
+## libcudf generally does not make ordering guarantees
+
+Functions like merge or groupby in libcudf make no guarantees about the order of entries in the
+output. Promising deterministic ordering is not, in general, conducive to fast parallel algorithms.
+Calling code is responsible for performing sorts after the fact if sorted outputs are needed.
+
+## libcudf does not promise specific exception messages
+
+libcudf documents the exceptions that will be thrown by an API for different kinds of invalid
+inputs. The types of those exceptions (e.g. `cudf::logic_error`) are part of the public API.
+However, the explanatory string returned by the `what` method of those exceptions is not part of the
+API and is subject to change. Calling code should not rely on the contents of libcudf error
+messages to determine the nature of the error. For information on the types of exceptions that
+libcudf throws under different circumstances, see the [section on error handling](#errors).
+
+# libcudf API and Implementation
+
+## Streams
+
+libcudf is in the process of adding support for asynchronous execution using
+CUDA streams. In order to facilitate the usage of streams, all new libcudf APIs
+that allocate device memory or execute a kernel should accept an
+`rmm::cuda_stream_view` parameter at the end with a default value of
+`cudf::get_default_stream()`.  There is one exception to this rule: if the API
+also accepts a memory resource parameter, the stream parameter should be placed
+just *before* the memory resource. This API should then forward the call to a
+corresponding `detail` API with an identical signature, except that the
+`detail` API should not have a default parameter for the stream ([detail APIs
+should always avoid default parameters](#default-parameters)). The
+implementation should be wholly contained in the `detail` API definition and
+use only asynchronous versions of CUDA APIs with the stream parameter.
+
+In order to make the `detail` API callable from other libcudf functions, it should be exposed in a
+header placed in the `cudf/cpp/include/detail/` directory.
+
+For example:
+
+```c++
+// cpp/include/cudf/header.hpp
+void external_function(...);
+
+// cpp/include/cudf/detail/header.hpp
+namespace detail{
+void external_function(..., rmm::cuda_stream_view stream)
+} // namespace detail
+
+// cudf/src/implementation.cpp
+namespace detail{
+    // Use the stream parameter in the detail implementation.
+    void external_function(..., rmm::cuda_stream_view stream){
+        // Implementation uses the stream with async APIs.
+        rmm::device_buffer buff(...,stream);
+        CUDF_CUDA_TRY(cudaMemcpyAsync(...,stream.value()));
+        kernel<<<..., stream>>>(...);
+        thrust::algorithm(rmm::exec_policy(stream), ...);
+    }
+} // namespace detail
+
+void external_function(...){
+    CUDF_FUNC_RANGE(); // Generates an NVTX range for the lifetime of this function.
+    detail::external_function(..., cudf::get_default_stream());
+}
+```
+
+**Note:** It is important to synchronize the stream if *and only if* it is necessary. For example,
+when a non-pointer value is returned from the API that is the result of an asynchronous
+device-to-host copy, the stream used for the copy should be synchronized before returning. However,
+when a column is returned, the stream should not be synchronized because doing so will break
+asynchrony.
+
+**Note:** `cudaDeviceSynchronize()` should *never* be used.
+This limits the ability to do any multi-stream/multi-threaded work with libcudf APIs.
+
+ ### Stream Creation
+
+There may be times in implementing libcudf features where it would be advantageous to use streams
+*internally*, i.e., to accomplish overlap in implementing an algorithm. However, dynamically
+creating a stream can be expensive. RMM has a stream pool class to help avoid dynamic stream
+creation. However, this is not yet exposed in libcudf, so for the time being, libcudf features
+should avoid creating streams (even if it is slightly less efficient). It is a good idea to leave a
+`// TODO:` note indicating where using a stream would be beneficial.
+
+## Memory Allocation
+
+Device [memory resources](#rmmdevice_memory_resource) are used in libcudf to abstract and control
+how device memory is allocated.
+
+### Output Memory
+
+Any libcudf API that allocates memory that is *returned* to a user must accept a pointer to a
+`device_memory_resource` as the last parameter. Inside the API, this memory resource must be used
+to allocate any memory for returned objects. It should therefore be passed into functions whose
+outputs will be returned. Example:
+
+```c++
+// Returned `column` contains newly allocated memory,
+// therefore the API must accept a memory resource pointer
+std::unique_ptr<column> returns_output_memory(
+  ..., rmm::device_memory_resource * mr = rmm::mr::get_current_device_resource());
+
+// This API does not allocate any new *output* memory, therefore
+// a memory resource is unnecessary
+void does_not_allocate_output_memory(...);
+```
+
+This rule automatically applies to all detail APIs that allocates memory. Any detail API may be
+called by any public API, and therefore could be allocating memory that is returned to the user.
+To support such uses cases, all detail APIs allocating memory resources should accept an `mr`
+parameter. Callers are responsible for either passing through a provided `mr` or
+`rmm::mr::get_current_device_resource()` as needed.
+
+### Temporary Memory
+
+Not all memory allocated within a libcudf API is returned to the caller. Often algorithms must
+allocate temporary, scratch memory for intermediate results. Always use the default resource
+obtained from `rmm::mr::get_current_device_resource()` for temporary memory allocations. Example:
+
+```c++
+rmm::device_buffer some_function(
+  ..., rmm::mr::device_memory_resource mr * = rmm::mr::get_current_device_resource()) {
+    rmm::device_buffer returned_buffer(..., mr); // Returned buffer uses the passed in MR
+    ...
+    rmm::device_buffer temporary_buffer(...); // Temporary buffer uses default MR
+    ...
+    return returned_buffer;
+}
+```
+
+### Memory Management
+
+libcudf code generally eschews raw pointers and direct memory allocation. Use RMM classes built to
+use `device_memory_resource`s for device memory allocation with automated lifetime management.
+
+#### rmm::device_buffer
+Allocates a specified number of bytes of untyped, uninitialized device memory using a
+`device_memory_resource`. If no resource is explicitly provided, uses
+`rmm::mr::get_current_device_resource()`.
+
+`rmm::device_buffer` is movable and copyable on a stream. A copy performs a deep copy of the
+`device_buffer`'s device memory on the specified stream, whereas a move moves ownership of the
+device memory from one `device_buffer` to another.
+
+```c++
+// Allocates at least 100 bytes of uninitialized device memory
+// using the specified resource and stream
+rmm::device_buffer buff(100, stream, mr);
+void * raw_data = buff.data(); // Raw pointer to underlying device memory
+
+// Deep copies `buff` into `copy` on `stream`
+rmm::device_buffer copy(buff, stream);
+
+// Moves contents of `buff` into `moved_to`
+rmm::device_buffer moved_to(std::move(buff));
+
+custom_memory_resource *mr...;
+// Allocates 100 bytes from the custom_memory_resource
+rmm::device_buffer custom_buff(100, mr, stream);
+```
+
+#### rmm::device_scalar<T>
+Allocates a single element of the specified type initialized to the specified value. Use this for
+scalar input/outputs into device kernels, e.g., reduction results, null count, etc. This is
+effectively a convenience wrapper around a `rmm::device_vector<T>` of length 1.
+
+```c++
+// Allocates device memory for a single int using the specified resource and stream
+// and initializes the value to 42
+rmm::device_scalar<int> int_scalar{42, stream, mr};
+
+// scalar.data() returns pointer to value in device memory
+kernel<<<...>>>(int_scalar.data(),...);
+
+// scalar.value() synchronizes the scalar's stream and copies the
+// value from device to host and returns the value
+int host_value = int_scalar.value();
+```
+
+#### rmm::device_vector<T>
+
+Allocates a specified number of elements of the specified type. If no initialization value is
+provided, all elements are default initialized (this incurs a kernel launch).
+
+**Note**: We have removed all usage of `rmm::device_vector` and `thrust::device_vector` from
+libcudf, and you should not use it in new code in libcudf without careful consideration. Instead,
+use `rmm::device_uvector` along with the utility factories in `device_factories.hpp`. These
+utilities enable creation of `uvector`s from host-side vectors, or creating zero-initialized
+`uvector`s, so that they are as convenient to use as `device_vector`. Avoiding `device_vector` has
+a number of benefits, as described in the following section on `rmm::device_uvector`.
+
+#### rmm::device_uvector<T>
+
+Similar to a `device_vector`, allocates a contiguous set of elements in device memory but with key
+differences:
+- As an optimization, elements are uninitialized and no synchronization occurs at construction.
+This limits the types `T` to trivially copyable types.
+- All operations are stream ordered (i.e., they accept a `cuda_stream_view` specifying the stream
+on which the operation is performed). This improves safety when using non-default streams.
+- `device_uvector.hpp` does not include any `__device__` code, unlike `thrust/device_vector.hpp`,
+  which means `device_uvector`s can be used in `.cpp` files, rather than just in `.cu` files.
+
+```c++
+cuda_stream s;
+// Allocates uninitialized storage for 100 `int32_t` elements on stream `s` using the
+// default resource
+rmm::device_uvector<int32_t> v(100, s);
+// Initializes the elements to 0
+thrust::uninitialized_fill(thrust::cuda::par.on(s.value()), v.begin(), v.end(), int32_t{0});
+
+rmm::mr::device_memory_resource * mr = new my_custom_resource{...};
+// Allocates uninitialized storage for 100 `int32_t` elements on stream `s` using the resource `mr`
+rmm::device_uvector<int32_t> v2{100, s, mr};
+```
+
+## Default Parameters
+
+While public libcudf APIs are free to include default function parameters, detail functions should
+not. Default memory resource parameters make it easy for developers to accidentally allocate memory
+using the incorrect resource. Avoiding default memory resources forces developers to consider each
+memory allocation carefully.
+
+While streams are not currently exposed in libcudf's API, we plan to do so eventually. As a result,
+the same reasons for memory resources also apply to streams. Public APIs default to using
+`cudf::get_default_stream()`. However, including the same default in detail APIs opens the door for
+developers to forget to pass in a user-provided stream if one is passed to a public API. Forcing
+every detail API call to explicitly pass a stream is intended to prevent such mistakes.
+
+The memory resources (and eventually, the stream) are the final parameters for essentially all
+public APIs. For API consistency, the same is true throughout libcudf's internals. Therefore, a
+consequence of not allowing default streams or MRs is that no parameters in detail APIs may have
+defaults.
+
+## NVTX Ranges
+
+In order to aid in performance optimization and debugging, all compute intensive libcudf functions
+should have a corresponding NVTX range. libcudf has a convenience macro `CUDF_FUNC_RANGE()` that
+automatically annotates the lifetime of the enclosing function and uses the function's name as
+the name of the NVTX range. For more information about NVTX, see
+[here](https://github.com/NVIDIA/NVTX/tree/dev/c).
+
+## Input/Output Style
+
+The preferred style for how inputs are passed in and outputs are returned is the following:
+- Inputs
+  - Columns:
+    - `column_view const&`
+  - Tables:
+    - `table_view const&`
+    - Scalar:
+        - `scalar const&`
+    - Everything else:
+       - Trivial or inexpensively copied types
+          - Pass by value
+       - Non-trivial or expensive to copy types
+          - Pass by `const&`
+- In/Outs
+  - Columns:
+    - `mutable_column_view&`
+  - Tables:
+    - `mutable_table_view&`
+    - Everything else:
+        - Pass by via raw pointer
+- Outputs
+  - Outputs should be *returned*, i.e., no output parameters
+  - Columns:
+    - `std::unique_ptr<column>`
+  - Tables:
+    - `std::unique_ptr<table>`
+    - Scalars:
+        - `std::unique_ptr<scalar>`
+
+
+### Multiple Return Values
+
+Sometimes it is necessary for functions to have multiple outputs. There are a few ways this can be
+done in C++ (including creating a `struct` for the output). One convenient way to do this is
+using `std::tie`  and `std::pair`. Note that objects passed to `std::pair` will invoke
+either the copy constructor or the move constructor of the object, and it may be preferable to move
+non-trivially copyable objects (and required for types with deleted copy constructors, like
+`std::unique_ptr`).
+
+```c++
+std::pair<table, table> return_two_tables(void){
+  cudf::table out0;
+  cudf::table out1;
+  ...
+  // Do stuff with out0, out1
+
+  // Return a std::pair of the two outputs
+  return std::pair(std::move(out0), std::move(out1));
+}
+
+cudf::table out0;
+cudf::table out1;
+std::tie(out0, out1) = cudf::return_two_outputs();
+```
+
+Note: `std::tuple` _could_ be used if not for the fact that Cython does not support
+`std::tuple`. Therefore, libcudf APIs must use `std::pair`, and are therefore limited to return
+only two objects of different types. Multiple objects of the same type may be returned via a
+`std::vector<T>`.
+
+Alternatively, with C++17 (supported from cudf v0.20),
+[structured binding](https://en.cppreference.com/w/cpp/language/structured_binding)
+may be used to disaggregate multiple return values:
+
+```c++
+auto [out0, out1] = cudf::return_two_outputs();
+```
+
+Note that the compiler might not support capturing aliases defined in a structured binding
+in a lambda. One may work around this by using a capture with an initializer instead:
+
+```c++
+auto [out0, out1] = cudf::return_two_outputs();
+
+// Direct capture of alias from structured binding might fail with:
+// "error: structured binding cannot be captured"
+// auto foo = [out0]() {...};
+
+// Use an initializing capture:
+auto foo = [&out0 = out0] {
+  // Use out0 to compute something.
+  // ...
+};
+```
+
+## Iterator-based interfaces
+
+Increasingly, libcudf is moving toward internal (`detail`) APIs with iterator parameters rather
+than explicit `column`/`table`/`scalar` parameters. As with STL, iterators enable generic
+algorithms to be applied to arbitrary containers. A good example of this is `cudf::copy_if_else`.
+This function takes two inputs, and a Boolean mask. It copies the corresponding element from the
+first or second input depending on whether the mask at that index is `true` or `false`. Implementing
+`copy_if_else` for all combinations of `column` and `scalar` parameters is simplified by using
+iterators in the `detail` API.
+
+```c++
+template <typename FilterFn, typename LeftIter, typename RightIter>
+std::unique_ptr<column> copy_if_else(
+  bool nullable,
+  LeftIter lhs_begin,
+  LeftIter lhs_end,
+  RightIter rhs,
+  FilterFn filter,
+  ...);
+```
+
+`LeftIter` and `RightIter` need only implement the necessary interface for an iterator. libcudf
+provides a number of iterator types and utilities that are useful with iterator-based APIs from
+libcudf as well as Thrust algorithms. Most are defined in `include/detail/iterator.cuh`.
+
+### Pair iterator
+
+The pair iterator is used to access elements of nullable columns as a pair containing an element's
+value and validity. `cudf::detail::make_pair_iterator` can be used to create a pair iterator from a
+`column_device_view` or a `cudf::scalar`. `make_pair_iterator` is not available for
+`mutable_column_device_view`.
+
+### Null-replacement iterator
+
+This iterator replaces the null/validity value for each element with a specified constant (`true` or
+`false`). Created using `cudf::detail::make_null_replacement_iterator`.
+
+### Validity iterator
+
+This iterator returns the validity of the underlying element (`true` or `false`). Created using
+`cudf::detail::make_validity_iterator`.
+
+### Index-normalizing iterators
+
+The proliferation of data types supported by libcudf can result in long compile times. One area
+where compile time was a problem is in types used to store indices, which can be any integer type.
+The "Indexalator", or index-normalizing iterator (`include/cudf/detail/indexalator.cuh`), can be
+used for index types (integers) without requiring a type-specific instance. It can be used for any
+iterator interface for reading an array of integer values of type `int8`, `int16`, `int32`,
+`int64`, `uint8`, `uint16`, `uint32`, or `uint64`. Reading specific elements always returns a
+[`cudf::size_type`](#cudfsize_type) integer.
+
+Use the `indexalator_factory` to create an appropriate input iterator from a column_view. Example
+input iterator usage:
+
+```c++
+auto begin = indexalator_factory::create_input_iterator(gather_map);
+auto end   = begin + gather_map.size();
+auto result = detail::gather( source, begin, end, IGNORE, stream, mr );
+```
+
+Example output iterator usage:
+
+```c++
+auto result_itr = indexalator_factory::create_output_iterator(indices->mutable_view());
+thrust::lower_bound(rmm::exec_policy(stream),
+                    input->begin<Element>(),
+                    input->end<Element>(),
+                    values->begin<Element>(),
+                    values->end<Element>(),
+                    result_itr,
+                    thrust::less<Element>());
+```
+
+## Namespaces
+
+### External
+All public libcudf APIs should be placed in the `cudf` namespace. Example:
+
+```c++
+namespace cudf{
+   void public_function(...);
+} // namespace cudf
+```
+
+The top-level `cudf` namespace is sufficient for most of the public API. However, to logically
+group a broad set of functions, further namespaces may be used. For example, there are numerous
+functions that are specific to columns of Strings. These functions reside in the `cudf::strings::`
+namespace. Similarly, functionality used exclusively for unit testing is in the `cudf::test::`
+namespace.
+
+### Internal
+
+Many functions are not meant for public use, so place them in either the `detail` or an *anonymous*
+namespace, depending on the situation.
+
+#### detail namespace
+
+Functions or objects that will be used across *multiple* translation units (i.e., source files),
+should be exposed in an internal header file and placed in the `detail` namespace. Example:
+
+```c++
+// some_utilities.hpp
+namespace cudf{
+namespace detail{
+void reusable_helper_function(...);
+} // namespace detail
+} // namespace cudf
+```
+
+#### Anonymous namespace
+
+Functions or objects that will only be used in a *single* translation unit should be defined in an
+*anonymous* namespace in the source file where it is used. Example:
+
+```c++
+// some_file.cpp
+namespace{
+void isolated_helper_function(...);
+} // anonymous namespace
+```
+
+[**Anonymous namespaces should *never* be used in a header file.**](https://wiki.sei.cmu.edu/confluence/display/cplusplus/DCL59-CPP.+Do+not+define+an+unnamed+namespace+in+a+header+file)
+
+# Deprecating and Removing Code
+
+libcudf is constantly evolving to improve performance and better meet our users' needs. As a
+result, we occasionally need to break or entirely remove APIs to respond to new and improved
+understanding of the functionality we provide. Remaining free to do this is essential to making
+libcudf an agile library that can rapidly accommodate our users needs. As a result, we do not
+always provide a warning or any lead time prior to releasing breaking changes. On a best effort
+basis, the libcudf team will notify users of changes that we expect to have significant or
+widespread effects.
+
+Where possible, indicate pending API removals using the
+[deprecated](https://en.cppreference.com/w/cpp/language/attributes/deprecated) attribute and
+document them using Doxygen's
+[deprecated](https://www.doxygen.nl/manual/commands.html#cmddeprecated) command prior to removal.
+When a replacement API is available for a deprecated API, mention the replacement in both the
+deprecation message and the deprecation documentation. Pull requests that introduce deprecations
+should be labeled "deprecation" to facilitate discovery and removal in the subsequent release.
+
+Advertise breaking changes by labeling any pull request that breaks or removes an existing API with
+the "breaking" tag. This ensures that the "Breaking" section of the release notes includes a
+description of what has broken from the past release. Label pull requests that contain deprecations
+with the "non-breaking" tag.
+
+
+# Error Handling {#errors}
+
+libcudf follows conventions (and provides utilities) enforcing compile-time and run-time
+conditions and detecting and handling CUDA errors. Communication of errors is always via C++
+exceptions.
+
+## Runtime Conditions
+
+Use the `CUDF_EXPECTS` macro to enforce runtime conditions necessary for correct execution.
+
+Example usage:
+
+```c++
+CUDF_EXPECTS(lhs.type() == rhs.type(), "Column type mismatch");
+```
+
+The first argument is the conditional expression expected to resolve to `true` under normal
+conditions. If the conditional evaluates to `false`, then an error has occurred and an instance of
+`cudf::logic_error` is thrown. The second argument to `CUDF_EXPECTS` is a short description of the
+error that has occurred and is used for the exception's `what()` message.
+
+There are times where a particular code path, if reached, should indicate an error no matter what.
+For example, often the `default` case of a `switch` statement represents an invalid alternative.
+Use the `CUDF_FAIL` macro for such errors. This is effectively the same as calling
+`CUDF_EXPECTS(false, reason)`.
+
+Example:
+
+```c++
+CUDF_FAIL("This code path should not be reached.");
+```
+
+### CUDA Error Checking
+
+Use the `CUDF_CUDA_TRY` macro to check for the successful completion of CUDA runtime API functions.
+This macro throws a `cudf::cuda_error` exception if the CUDA API return value is not `cudaSuccess`.
+The thrown exception includes a description of the CUDA error code in its `what()` message.
+
+Example:
+
+```c++
+CUDF_CUDA_TRY( cudaMemcpy(&dst, &src, num_bytes) );
+```
+
+## Compile-Time Conditions
+
+Use `static_assert` to enforce compile-time conditions. For example,
+
+```c++
+template <typename T>
+void trivial_types_only(T t){
+  static_assert(std::is_trivial<T>::value, "This function requires a trivial type.");
+...
+}
+```
+
+# Logging
+
+libcudf includes logging utilities (built on top of [spdlog](https://github.com/gabime/spdlog)
+library), which should be used to log important events (e.g. user warnings). This utility can also
+be used to log debug information, as long as the correct logging level is used. There are six macros
+that should be used for logging at different levels:
+
+* `CUDF_LOG_TRACE` - verbose debug messages (targeted at developers)
+* `CUDF_LOG_DEBUG` - debug messages (targeted at developers)
+* `CUDF_LOG_INFO` - information about rare events (e.g. once per run) that occur during normal
+execution
+* `CUDF_LOG_WARN` - user warnings about potentially unexpected behavior or deprecations
+* `CUDF_LOG_ERROR` - recoverable errors
+* `CUDF_LOG_CRITICAL` - unrecoverable errors (e.g. memory corruption)
+
+By default, `TRACE`, `DEBUG` and `INFO` messages are excluded from the log. In addition, in public
+builds, the code that logs at `TRACE` and `DEBUG` levels is compiled out. This prevents logging of
+potentially sensitive data that might be done for debug purposes. Also, this allows developers to
+include expensive computation in the trace/debug logs, as the overhead will not be present in the
+public builds.
+The minimum enabled logging level is `WARN`, and it can be modified in multiple ways:
+
+* CMake configuration variable `LIBCUDF_LOGGING_LEVEL` - sets the minimum level of logging that
+will be compiled in the build.
+Available levels are `TRACE`, `DEBUG`, `INFO`, `WARN`, `ERROR`, `CRITICAL`, and `OFF`.
+* Environment variable `LIBCUDF_LOGGING_LEVEL` - sets the minimum logging level during
+initialization. If this setting is higher than the compile-time CMake variable, any logging levels
+in between the two settings will be excluded from the written log. The available levels are the same
+as for the CMake variable.
+* Global logger object exposed via `cudf::logger()` - sets the minimum logging level at runtime.
+For example, calling `cudf::logger().set_level(spdlog::level::err)`, will exclude any messages that
+are not errors or critical errors. This API should not be used within libcudf to manipulate logging,
+its purpose is to allow upstream users to configure libcudf logging to fit their application.
+
+By default, logging messages are output to stderr.
+Setting the environment variable `LIBCUDF_DEBUG_LOG_FILE` redirects the log to a file with the
+specified path (can be relative to the current directory).
+Upstream users can also manipulate `cudf::logger().sinks()` to add sinks or divert the log to
+standard output or even a custom spdlog sink.
+
+# Data Types
+
+Columns may contain data of a number of types (see `enum class type_id` in `include/cudf/types.hpp`)
+
+ * Numeric data: signed and unsigned integers (8-, 16-, 32-, or 64-bit), floats (32- or 64-bit), and
+   Booleans (8-bit).
+ * Timestamp data with resolution of days, seconds, milliseconds, microseconds, or nanoseconds.
+ * Duration data with resolution of days, seconds, milliseconds, microseconds, or nanoseconds.
+ * Decimal fixed-point data (32- or 64-bit).
+ * Strings
+ * Dictionaries
+ * Lists of any type
+ * Structs of columns of any type
+
+Most algorithms must support columns of any data type. This leads to complexity in the code, and
+is one of the primary challenges a libcudf developer faces. Sometimes we develop new algorithms with
+gradual support for more data types to make this easier. Typically we start with fixed-width data
+types such as numeric types and timestamps/durations, adding support for nested types later.
+
+Enabling an algorithm differently for different types uses either template specialization or SFINAE,
+as discussed in [Specializing Type-Dispatched Code Paths](#specializing-type-dispatched-code-paths).
+
+# Type Dispatcher
+
+libcudf stores data (for columns and scalars) "type erased" in `void*` device memory. This
+*type-erasure* enables interoperability with other languages and type systems, such as Python and
+Java. In order to determine the type, libcudf algorithms must use the run-time information stored
+in the column `type()` to reconstruct the data type `T` by casting the `void*` to the appropriate
+`T*`.
+
+This so-called *type dispatch* is pervasive throughout libcudf. The `type_dispatcher` is a
+central utility that automates the process of mapping the runtime type information in `data_type`
+to a concrete C++ type.
+
+At a high level, you call the `type_dispatcher` with a `data_type` and a function object (also
+known as a *functor*) with an `operator()` template. Based on the value of `data_type::id()`, the
+type dispatcher invokes the corresponding instantiation of the `operator()` template.
+
+This simplified example shows how the value of `data_type::id()` determines which instantiation of
+the `F::operator()` template is invoked.
+
+```c++
+template <typename F>
+void type_dispatcher(data_type t, F f){
+    switch(t.id())
+       case type_id::INT32: f.template operator()<int32_t>()
+       case type_id::INT64: f.template operator()<int64_t>()
+       case type_id::FLOAT: f.template operator()<float>()
+       ...
+}
+```
+
+The following example shows a function object called `size_of_functor` that returns the size of the
+dispatched type.
+
+```c++
+struct size_of_functor{
+  template <typename T>
+  int operator()(){ return sizeof(T); }
+};
+
+cudf::type_dispatcher(data_type{type_id::INT8}, size_of_functor{});  // returns 1
+cudf::type_dispatcher(data_type{type_id::INT32}, size_of_functor{});  // returns 4
+cudf::type_dispatcher(data_type{type_id::FLOAT64}, size_of_functor{});  // returns 8
+```
+
+By default, `type_dispatcher` uses `cudf::type_to_id<t>` to provide the mapping of `cudf::type_id`
+to dispatched C++ types. However, this mapping may be customized by explicitly specifying a
+user-defined trait for the `IdTypeMap`. For example, to always dispatch `int32_t` for all values of
+`cudf::type_id`:
+
+```c++
+template<cudf::type_id t> struct always_int{ using type = int32_t; }
+
+// This will always invoke `operator()<int32_t>`
+cudf::type_dispatcher<always_int>(data_type, f);
+```
+
+## Avoid Multiple Type Dispatch
+
+Avoid multiple type-dispatch if possible. The compiler creates a code path for every type
+dispatched, so a second-level type dispatch results in quadratic growth in compilation time and
+object code size. As a large library with many types and functions, we are constantly working to
+reduce compilation time and code size.
+
+## Specializing Type-Dispatched Code Paths
+
+It is often necessary to customize the dispatched `operator()` for different types. This can be
+done in several ways.
+
+The first method is to use explicit, full template specialization. This is useful for specializing
+behavior for single types. The following example function object prints `"int32_t"` or `"double"`
+when invoked with either of those types, or `"unhandled type"` otherwise.
+
+```c++
+struct type_printer {
+template <typename ColumnType>
+void operator()() { std::cout << "unhandled type\n"; }
+};
+
+// Due to a bug in g++, explicit member function specializations need to be
+// defined outside of the class definition
+template <>
+void type_printer::operator()<int32_t>() { std::cout << "int32_t\n"; }
+
+template <>
+void type_printer::operator()<double>() { std::cout << "double\n"; }
+```
+
+The second method is to use [SFINAE](https://en.cppreference.com/w/cpp/language/sfinae) with
+`std::enable_if_t`. This is useful to partially specialize for a set of types with a common trait.
+The following example functor prints `integral` or `floating point` for integral or floating point
+types, respectively.
+
+```c++
+struct integral_or_floating_point {
+template <typename ColumnType,
+          std::enable_if_t<not std::is_integral<ColumnType>::value and
+                           not std::is_floating_point<ColumnType>::value>* = nullptr>
+void operator()() { std::cout << "neither integral nor floating point\n"; }
+
+template <typename ColumnType,
+          std::enable_if_t<std::is_integral<ColumnType>::value>* = nullptr>
+void operator()() { std::cout << "integral\n"; }
+
+template < typename ColumnType,
+           std::enable_if_t<std::is_floating_point<ColumnType>::value>* = nullptr>
+void operator()() { std::cout << "floating point\n"; }
+};
+```
+
+For more info on SFINAE with `std::enable_if`, [see this post](https://eli.thegreenplace.net/2014/sfinae-and-enable_if).
+
+There are a number of traits defined in `include/cudf/utilities/traits.hpp` that are useful for
+partial specialization of dispatched function objects. For example `is_numeric<T>()` can be used to
+specialize for any numeric type.
+
+# Variable-Size and Nested Data Types
+
+libcudf supports a number of variable-size and nested data types, including strings, lists, and
+structs.
+
+ * `string`: Simply a character string, but a column of strings may have a different-length string
+   in each row.
+ * `list`: A list of elements of any type, so a column of lists of integers has rows with a list of
+   integers, possibly of a different length, in each row.
+ * `struct`: In a column of structs, each row is a structure comprising one or more fields. These
+   fields are stored in structure-of-arrays format, so that the column of structs has a nested
+   column for each field of the structure.
+
+As the heading implies, list and struct columns may be nested arbitrarily. One may create a column
+of lists of structs, where the fields of the struct may be of any type, including strings, lists and
+structs. Thinking about deeply nested data types can be confusing for column-based data, even with
+experience. Therefore it is important to carefully write algorithms, and to test and document them
+well.
+
+## List columns
+
+In order to represent variable-width elements, libcudf columns contain a vector of child columns.
+For list columns, the parent column's type is `LIST` and contains no data, but its size represents
+the number of lists in the column, and its null mask represents the validity of each list element.
+The parent has two children.
+
+1. A non-nullable column of [`size_type`](#cudfsize_type) elements that indicates the offset to the
+   beginning of each list in a dense column of elements.
+2. A column containing the actual data and optional null mask for all elements of all the lists
+   packed together.
+
+With this representation, `data[offsets[i]]` is the first element of list `i`, and the size of list
+`i` is given by `offsets[i+1] - offsets[i]`.
+
+Note that the data may be of any type, and therefore the data column may itself be a nested column
+of any type. Note also that not only is each list nullable (using the null mask of the parent), but
+each list element may be nullable. So you may have a lists column with null row 3, and also null
+element 2 of row 4.
+
+The underlying data for a lists column is always bundled into a single leaf column at the very
+bottom of the hierarchy (ignoring structs, which conceptually "reset" the root of the hierarchy),
+regardless of the level of nesting. So a `List<List<List<List<int>>>>` column has a single `int`
+column at the very bottom. The following is a visual representation of this.
+
+```
+lists_column = { {{{1, 2}, {3, 4}}, NULL}, {{{10, 20}, {30, 40}}, {{50, 60, 70}, {0}}} }
+
+   List<List<List<int>>>  (2 rows):
+   Length : 2
+   Offsets : 0, 2, 4
+   Children :
+      List<List<int>>:
+      Length : 4
+      Offsets : 0, 2, 2, 4, 6
+      Null count: 1
+        1101
+      Children :
+        List<int>:
+        Length : 6
+        Offsets : 0, 2, 4, 6, 8, 11, 12
+        Children :
+          Column of ints
+          1, 2, 3, 4, 10, 20, 30, 40, 50, 60, 70, 0
+```
+
+This is related to [Arrow's "Variable-Size List" memory layout](https://arrow.apache.org/docs/format/Columnar.html?highlight=nested%20types#physical-memory-layout).
+
+## Strings columns
+
+Strings are represented in much the same way as lists, except that the data child column is always
+a non-nullable column of `INT8` data. The parent column's type is `STRING` and contains no data,
+but its size represents the number of strings in the column, and its null mask represents the
+validity of each string. To summarize, the strings column children are:
+
+1. A non-nullable column of [`size_type`](#cudfsize_type) elements that indicates the offset to the beginning of each
+   string in a dense column of all characters.
+2. A non-nullable column of `INT8` elements of all the characters across all the strings packed
+   together.
+
+With this representation, `characters[offsets[i]]` is the first character of string `i`, and the
+size of string `i` is given by `offsets[i+1] - offsets[i]`. The following image shows an example of
+this compound column representation of strings.
+
+![strings](strings.png)
+
+## Structs columns
+
+A struct is a nested data type with a set of child columns each representing an individual field
+of a logical struct. Field names are not represented.
+
+A structs column with `N` fields has `N` children. Each child is a column storing all the data
+of a single field packed column-wise, with an optional null mask. The parent column's type is
+`STRUCT` and contains no data, its size represents the number of struct rows in the column, and its
+null mask represents the validity of each struct element.
+
+With this representation, `child[0][10]` is row 10 of the first field of the struct, `child[1][42]`
+is row 42 of the second field of the struct.
+
+Notice that in addition to the struct column's null mask, each struct field column has its own
+optional null mask. A struct field's validity can vary independently from the corresponding struct
+row. For instance, a non-null struct row might have a null field. However, the fields of a null
+struct row are deemed to be null as well. For example, consider a struct column of type
+`STRUCT<FLOAT32, INT32>`. If the contents are `[ {1.0, 2}, {4.0, 5}, null, {8.0, null} ]`, the
+struct column's layout is as follows. (Note that null masks should be read from right to left.)
+
+```
+{
+  type = STRUCT
+  null_mask = [1, 1, 0, 1]
+  null_count = 1
+  children = {
+    {
+      type = FLOAT32
+      data =       [1.0, 4.0, X, 8.0]
+      null_mask  = [  1,   1, 0,   1]
+      null_count = 1
+    },
+    {
+      type = INT32
+      data =       [2, 5, X, X]
+      null_mask  = [1, 1, 0, 0]
+      null_count = 2
+    }
+  }
+}
+```
+
+The last struct row (index 3) is not null, but has a null value in the INT32 field. Also, row 2 of
+the struct column is null, making its corresponding fields also null. Therefore, bit 2 is unset in
+the null masks of both struct fields.
+
+## Dictionary columns
+
+Dictionaries provide an efficient way to represent low-cardinality data by storing a single copy
+of each value. A dictionary comprises a column of sorted keys and a column containing an index into
+the keys column for each row of the parent column. The keys column may have any libcudf data type,
+such as a numerical type or strings. The indices represent the corresponding positions of each
+element's value in the keys. The indices child column can have any unsigned integer type
+(`UINT8`, `UINT16`, `UINT32`, or `UINT64`).
+
+## Nested column challenges
+
+The first challenge with nested columns is that it is effectively impossible to do any operation
+that modifies the length of any string or list in place. For example, consider trying to append the
+character `'a'` to the end of each string. This requires dynamically resizing the characters column
+to allow inserting `'a'` at the end of each string, and then modifying the offsets column to
+indicate the new size of each element. As a result, every operation that can modify the strings or
+lists in a column must be done out-of-place.
+
+The second challenge is that in an out-of-place operation on a strings column, unlike with fixed-
+width elements, the size of the output cannot be known *a priori*. For example, consider scattering
+into a column of strings:
+
+    destination:    {"this", "is", "a", "column", "of", "strings"}
+    scatter_map:    {1, 3, 5}
+    scatter_values: {"red", "green", "blue"}
+
+    result:         {"this", "red", "a", "green", "of", "blue"}
+
+In this example, the strings "red", "green", and "blue" will respectively be scattered into
+positions `1`, `3`, and `5` of `destination`. Recall from above that this operation cannot be done
+in place, therefore `result` will be generated by selectively copying strings from `destination` and
+`scatter_values`. Notice that `result`'s child column of characters requires storage for `19`
+characters. However, there is no way to know ahead of time that `result` will require `19`
+characters. Therefore, most operations that produce a new output column of strings use a two-phase
+approach:
+
+1. Determine the number and size of each string in the result. This amounts to materializing the
+   output offsets column.
+2. Allocate sufficient storage for all of the output characters and materialize each output string.
+
+In scatter, the first phase consists of using the `scatter_map` to determine whether string `i` in
+the output will come from `destination` or from `scatter_values` and use the corresponding size(s)
+to materialize the offsets column and determine the size of the output. Then, in the second phase,
+sufficient storage is allocated for the output's characters, and then the characters are filled
+with the corresponding strings from either `destination` or `scatter_values`.
+
+## Nested Type Views
+
+libcudf provides view types for nested column types as well as for the data elements within them.
+
+### cudf::strings_column_view and cudf::string_view
+
+`cudf::strings_column_view` is a view of a strings column, like `cudf::column_view` is a view of
+any `cudf::column`. `cudf::string_view` is a view of a single string, and therefore
+`cudf::string_view` is the data type of a `cudf::column` of type `STRING` just like `int32_t` is the
+data type for a `cudf::column` of type [`size_type`](#cudfsize_type). As its name implies, this is a
+read-only object instance that points to device memory inside the strings column. It's lifespan is
+the same (or less) as the column it views.
+
+Use the `column_device_view::element` method to access an individual row element. Like any other
+column, do not call `element()` on a row that is null.
+
+```c++
+   cudf::column_device_view d_strings;
+   ...
+   if( d_strings.is_valid(row_index) ) {
+      string_view d_str = d_strings.element<string_view>(row_index);
+      ...
+   }
+```
+
+A null string is not the same as an empty string. Use the `string_scalar` class if you need an
+instance of a class object to represent a null string.
+
+The `string_view` contains comparison operators `<,>,==,<=,>=` that can be used in many cudf
+functions like `sort` without string-specific code. The data for a `string_view` instance is
+required to be [UTF-8](#utf-8) and all operators and methods expect this encoding. Unless documented
+otherwise, position and length parameters are specified in characters and not bytes. The class also
+includes a `string_view::const_iterator` which can be used to navigate through individual characters
+within the string.
+
+`cudf::type_dispatcher` dispatches to the `string_view` data type when invoked on a `STRING` column.
+
+#### UTF-8
+
+The libcudf strings column only supports UTF-8 encoding for strings data.
+[UTF-8](https://en.wikipedia.org/wiki/UTF-8) is a variable-length character encoding wherein each
+character can be 1-4 bytes. This means the length of a string is not the same as its size in bytes.
+For this reason, it is recommended to use the `string_view` class to access these characters for
+most operations.
+
+The `string_view.cuh` header also includes some utility methods for reading and writing
+(`to_char_utf8/from_char_utf8`) individual UTF-8 characters to/from byte arrays.
+
+### cudf::lists_column_view and cudf::lists_view
+
+`cudf::lists_column_view` is a view of a lists column. `cudf::list_view` is a view of a single list,
+and therefore `cudf::list_view` is the data type of a `cudf::column` of type `LIST`.
+
+`cudf::type_dispatcher` dispatches to the `list_view` data type when invoked on a `LIST` column.
+
+### cudf::structs_column_view and cudf::struct_view
+
+`cudf::structs_column_view` is a view of a structs column. `cudf::struct_view` is a view of a single
+struct, and therefore `cudf::struct_view` is the data type of a `cudf::column` of type `STRUCT`.
+
+`cudf::type_dispatcher` dispatches to the `struct_view` data type when invoked on a `STRUCT` column.
+
+# cuIO: file reading and writing
+
+cuIO is a component of libcudf that provides GPU-accelerated reading and writing of data file
+formats commonly used in data analytics, including CSV, Parquet, ORC, Avro, and JSON_Lines.
+
+// TODO: add more detail and move to a separate file.
diff --git a/cpp/doxygen/developer_guide/DOCUMENTATION.md b/cpp/doxygen/developer_guide/DOCUMENTATION.md
new file mode 100644
index 0000000..b86f7db
--- /dev/null
+++ b/cpp/doxygen/developer_guide/DOCUMENTATION.md
@@ -0,0 +1,450 @@
+# libcudf C++ Documentation Guide
+
+These guidelines apply to documenting all libcudf C++ source files using doxygen style formatting although only public APIs and classes are actually [published](https://docs.rapids.ai/api/libcudf/stable/index.html).
+
+## Copyright License
+
+The copyright comment is included here but may also be mentioned in a coding guideline document as well.
+The following is the license header comment that should appear at the beginning of every C++ source file.
+
+    /*
+     * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+     *
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     *     http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     */
+
+The comment should start with `/*` and not `/**` so it is not processed by doxygen.
+
+Also, here are the rules for the copyright year.
+
+- A new file should have the year in which it was created
+- A modified file should span the year it was created and the year it was modified (e.g. `2019-2021`)
+
+Changing the copyright year may not be necessary if no content has changed (e.g. reformatting only).
+
+## Doxygen
+
+The [doxygen tool](https://www.doxygen.nl/manual/index.html) is used to generate HTML pages from the C++ comments in the source code.
+Doxygen recognizes and parses block comments and performs specialized output formatting when it encounters [doxygen commands](https://www.doxygen.nl/manual/commands.html).
+
+There are almost 200 commands (also called tags in this document) that doxygen recognizes in comment blocks.
+This document provides guidance on which commands/tags to use and how to use them in the libcudf C++ source code.
+
+The doxygen process can be customized using options in the [Doxyfile](../doxygen/Doxyfile).
+
+Here are some of the custom options in the Doxyfile for libcudf.
+| Option | Setting | Description |
+| ------ | ------- | ----------- |
+| PROJECT_NAME | libcudf | Title used on the main page |
+| PROJECT_NUMBER | 22.02.00 | Version number |
+| EXTENSION_MAPPING | cu=C++ cuh=C++ | Process `cu` and `cuh` as C++ |
+| INPUT | main_page.md regex.md unicode.md ../include | Embedded markdown files and source code directories to process |
+| FILE_PATTERNS | *.cpp *.hpp *.h *.c *.cu *.cuh | File extensions to process |
+
+## Block Comments
+
+Use the following style for block comments describing functions, classes and other types, groups, and files.
+
+    /**
+     * description text and
+     * doxygen tags go here
+     */
+
+Doxygen comment blocks start with `/**` and end with `*/` only, and with nothing else on those lines.
+Do not add dashes `-----` or extra asterisks `*****` to the first and last lines of a doxygen block.
+The block must be placed immediately before the source code line to which it refers.
+The block may be indented to line up vertically with the item it documents as appropriate.
+See the [Example](#the-example) section below.
+
+Each line in the comment block between the `/**` and `*/` lines should start with a space followed by an asterisk.
+Any text on these lines, including tag declarations, should start after a single space after the asterisk.
+
+## Tag/Command names
+
+Use @ to prefix doxygen commands (e.g. \@brief, \@code, etc.)
+
+## Markdown
+
+The doxygen tool supports a limited set of markdown format in the comment block including links, tables, lists, etc.
+In some cases a trade-off may be required for readability in the source text file versus the readability in the doxygen formatted web pages.
+For example, there are some limitations on readability with '%' character and pipe character '|' within a markdown table.
+
+Avoid using direct HTML tags.
+Although doxygen supports markdown and markdown supports HTML tags, the HTML support for doxygen's markdown is also limited.
+
+## The Example
+
+The following example covers most of the doxygen block comment and tag styles
+for documenting C++ code in libcudf.
+
+    /**
+     * @file source_file.cpp
+     * @brief Description of source file contents
+     *
+     * Longer description of the source file contents.
+     */
+
+    /**
+     * @brief One sentence description of the class.
+     *
+     * @ingroup optional_predefined_group_id
+     *
+     * Longer, more detailed description of the class.
+     *
+     * @tparam T Short description of each template parameter
+     * @tparam U Short description of each template parameter
+     */
+    template <typename T, typename U>
+    class example_class {
+
+      void get_my_int();            ///< Simple members can be documented like this
+      void set_my_int( int value ); ///< Try to use descriptive member names
+
+      /**
+       * @brief Short, one sentence description of the member function.
+       *
+       * A more detailed description of what this function does and what
+       * its logic does.
+       *
+       * @code
+       * example_class<int> inst;
+       * inst.set_my_int(5);
+       * int output = inst.complicated_function(1,dptr,fptr);
+       * @endcode
+       *
+       * @param[in]     first  This parameter is an input parameter to the function
+       * @param[in,out] second This parameter is used both as an input and output
+       * @param[out]    third  This parameter is an output of the function
+       *
+       * @return The result of the complex function
+       */
+      T complicated_function(int first, double* second, float* third)
+      {
+          // Do not use doxygen-style block comments
+          // for code logic documentation.
+      }
+
+     private:
+      int my_int;                ///< An example private member variable
+    };
+
+    /**
+     * @brief Short, one sentence description of this free function.
+     *
+     * @ingroup optional_predefined_group_id
+     *
+     * A detailed description must start after a blank line.
+     *
+     * @code
+     * template<typename T>
+     * struct myfunctor {
+     *   bool operator()(T input) { return input % 2 > 0; }
+     * };
+     * free_function<myfunctor,int>(myfunctor{},12);
+     * @endcode
+     *
+     * @throw cudf::logic_error if `input_argument` is negative or zero
+     *
+     * @tparam functor_type The type of the functor
+     * @tparam input_type The datatype of the input argument
+     *
+     * @param[in] functor        The functor to be called on the input argument
+     * @param[in] input_argument The input argument passed into the functor
+     * @return The result of calling the functor on the input argument
+     */
+    template <class functor_type, typename input_type>
+    bool free_function(functor_type functor, input_type input_argument)
+    {
+      CUDF_EXPECTS( input_argument > 0, "input_argument must be positive");
+      return functor(input_argument);
+    }
+
+    /**
+     * @brief Short, one sentence description.
+     *
+     * @ingroup optional_predefined_group_id
+     *
+     * Optional, longer description.
+     */
+    enum class example_enum {
+      first_enum,   ///< Description of the first enum
+      second_enum,  ///< Description of the second enum
+      third_enum    ///< Description of the third enum
+    };
+
+## Descriptions
+
+The comment description should clearly detail how the output(s) are created from any inputs.
+Include any performance and any boundary considerations.
+Also include any limits on parameter values and if any default values are declared.
+Don't forget to specify how nulls are handled or produced.
+Also, try to include a short [example](#inline-examples) if possible.
+
+### @brief
+
+The [\@brief](https://www.doxygen.nl/manual/commands.html#cmdbrief) text should be a short, one sentence description.
+Doxygen does not provide much space to show this text in the output pages.
+Always follow the \@brief line with a blank comment line.
+
+The longer description is the rest of the comment text that is not tagged with any doxygen command.
+
+    /**
+     * @brief Short description.
+     *
+     * Long description.
+     *
+
+### \@copydoc
+
+Documentation for declarations in headers should be clear and complete.
+You can use the [\@copydoc](https://www.doxygen.nl/manual/commands.html#cmdcopydoc) tag to avoid duplicating the comment block for a function definition.
+
+      /**
+       * @copydoc complicated_function(int,double*,float*)
+       *
+       * Any extra documentation.
+       */
+
+Also, \@copydoc is useful when documenting a `detail` function that differs only by the `stream` parameter.
+
+    /**
+     * @copydoc cudf::segmented_count_set_bits(bitmask_type const*,std::vector<size_type> const&)
+     *
+     * @param[in] stream Optional CUDA stream on which to execute kernels
+     */
+    std::vector<size_type> segmented_count_set_bits(bitmask_type const* bitmask,
+                                                    std::vector<size_type> const& indices,
+                                                    rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+Note, you must specify the whole signature of the function, including optional parameters, so that doxygen will be able to locate it.
+
+### Function parameters
+
+The following tags should appear near the end of function comment block in the order specified here:
+
+| Command | Description |
+| ------- | ----------- |
+| [\@throw](#throw) | Specify the conditions in which the function may throw an exception |
+| [\@tparam](#tparam) | Description for each template parameter |
+| [\@param](#param) | Description for each function parameter |
+| [\@return](#return) | Short description of object or value returned |
+
+#### \@throw
+
+Add an [\@throw](https://www.doxygen.nl/manual/commands.html#cmdthrow) comment line in the doxygen block for each exception that the function may throw.
+You only need to include exceptions thrown by the function itself.
+If the function calls another function that may throw an exception, you do not need to document those exceptions here.
+
+Include the name of the exception without backtick marks so doxygen can add reference links correctly.
+
+     *
+     * @throw cudf::logic_error if `input_argument` is negative or zero
+     *
+
+Using \@throws is also acceptable but VS Code and other tools only do syntax highlighting on \@throw.
+
+#### @tparam
+
+Add a [\@tparam](https://www.doxygen.nl/manual/commands.html#cmdtparam) comment line for each template parameter declared by this function.
+The name of the parameter specified after the doxygen tag must match exactly to the template parameter name.
+
+     *
+     * @tparam functor_type The type of the functor
+     * @tparam input_type The datatype of the input argument
+     *
+
+The definition should detail the requirements of the parameter.
+For example, if the template is for a functor or predicate, then describe the expected input types and output.
+
+#### @param
+
+Add a [\@param](https://www.doxygen.nl/manual/commands.html#cmdparam) comment line for each function parameter passed to this function.
+The name of the parameter specified after the doxygen tag must match the function's parameter name.
+Also include append `[in]`, `[out]` or `[in,out]` to the `@param` if it is not clear from the declaration and the parameter name itself.
+
+     *
+     * @param[in]     first  This parameter is an input parameter to the function
+     * @param[in,out] second This parameter is used both as an input and output
+     * @param[out]    third  This parameter is an output of the function
+     *
+
+It is also recommended to vertically aligning the 3 columns of text if possible to make it easier to read in a source code editor.
+
+#### @return
+
+Add a single [\@return](https://www.doxygen.nl/manual/commands.html#cmdreturn) comment line at the end of the comment block if the function returns an object or value.
+Include a brief description of what is returned.
+
+    /**
+     * ...
+     *
+     * @return A new column of type INT32 and no nulls
+     */
+
+Do not include the type of the object returned with the `@return` comment.
+
+### Inline Examples
+
+It is usually helpful to include a source code example inside your comment block when documenting a function or other declaration.
+Use the [\@code](https://www.doxygen.nl/manual/commands.html#cmdcode) and [\@endcode](https://www.doxygen.nl/manual/commands.html#cmdendcode) pair to include inline examples.
+
+Doxygen supports syntax highlighting for C++ and several other programming languages (e.g. Python, Java).
+By default, the \@code tag uses syntax highlighting based on the source code in which it is found.
+
+     *
+     * @code
+     * auto result = cudf::make_column( );
+     * @endcode
+     *
+
+You can specify a different language by indicating the file extension in the tag:
+
+     *
+     * @code{.py}
+     * import cudf
+     * s = cudf.Series([1,2,3])
+     * @endcode
+     *
+
+If you wish to use pseudocode in your example, use the following:
+
+     *
+     * Sometimes pseudocode is clearer.
+     * @code{.pseudo}
+     * s = int column of [ 1, 2, null, 4 ]
+     * r = fill( s, [1, 2], 0 )
+     * r is now [ 1, 0, 0, 4 ]
+     * @endcode
+     *
+
+When writing example snippets, using fully qualified class names allows doxygen to add reference links to the example.
+
+     *
+     * @code
+     * auto result1 = make_column( ); // reference link will not be created
+     * auto result2 = cudf::make_column( ); // reference link will be created
+     * @endcode
+     *
+
+Although using 3 backtick marks \`\`\` for example blocks will work too, they do not stand out as well in VS Code and other source editors.
+
+Do not use the `@example` tag in the comments for a declaration, or doxygen will interpret the entire source file as example source code.
+The source file is then published under a separate _Examples_ page in the output.
+
+### Deprecations
+
+Add a single [\@deprecated](https://www.doxygen.nl/manual/commands.html#cmddeprecated) comment line
+to comment blocks for APIs that will be removed in future releases. Mention alternative /
+replacement APIs in the deprecation comment.
+
+    /**
+     * ...
+     *
+     * @deprecated This function is deprecated. Use another new function instead.
+     */
+
+## Namespaces
+
+Doxygen output includes a _Namespaces_ page that shows all the namespaces declared with comment blocks in the processed files.
+Here is an example of a doxygen description comment for a namespace declaration.
+
+    /**
+     * @brief cuDF interfaces
+     *
+     * This is the top-level namespace which contains all cuDF functions and types.
+     */
+    namespace cudf {
+
+A description comment should be included only once for each unique namespace declaration.
+Otherwise, if more than one description is found, doxygen aggregates the descriptions in an arbitrary order in the output pages.
+
+If you introduce a new namespace, provide a description block for only one declaration and not for every occurrence.
+
+## Groups/Modules
+
+Grouping declarations into modules helps users to find APIs in the doxygen pages.
+Generally, common functions are already grouped logically into header files but doxygen does not automatically group them this way in its output.
+The doxygen output includes a _Modules_ page that organizes items into groups specified using the [Grouping doxygen commands](https://www.doxygen.nl/manual/grouping.html).
+These commands can group common functions across header files, source files, and even namespaces.
+Groups can also be nested by defining new groups within existing groups.
+
+For libcudf, all the group hierarchy is defined in the [doxygen_groups.h](../include/doxygen_groups.h) header file.
+The [doxygen_groups.h](../include/doxygen_groups.h) file does not need to be included in any other source file, because the definitions in this file are used only by the doxygen tool to generate groups in the _Modules_ page.
+Modify this file only to add or update groups.
+The existing groups have been carefully structured and named, so new groups should be added thoughtfully.
+
+When creating a new API, specify its group using the [\@ingroup](https://www.doxygen.nl/manual/commands.html#cmdingroup) tag and the group reference id from the [doxygen_groups.h](../include/doxygen_groups.h) file.
+
+    namespace cudf {
+
+    /**
+     * @brief ...
+     *
+     * @ingroup transformation_fill
+     *
+     * @param ...
+     * @return ...
+     */
+    std::unique_ptr<column> fill(table_view const& input,...);
+
+    }  // namespace cudf
+
+You can also use the \@addtogroup with a `@{ ... @}` pair to automatically include doxygen comment blocks as part of a group.
+
+    namespace cudf {
+    /**
+     * @addtogroup transformation_fill
+     * @{
+     */
+
+    /**
+     * @brief ...
+     *
+     * @param ...
+     * @return ...
+     */
+    std::unique_ptr<column> fill(table_view const& input,...);
+
+    /** @} */
+    }  // namespace cudf
+
+This just saves adding \@ingroup to individual doxygen comment blocks within a file.
+Make sure a blank line is included after the \@addtogroup command block so doxygen knows it does not apply to whatever follows in the source code.
+Note that doxygen will not assign groups to items if the \@addtogroup with `@{ ... @}` pair includes a namespace declaration.
+So include the `@addtogroup` and `@{ ... @}` between the namespace declaration braces as shown in the example above.
+
+Summary of groups tags
+| Tag/Command | Where to use |
+| ----------- | ------------ |
+| `@defgroup` | For use only in [doxygen_groups.h](../include/doxygen_groups.h) and should include the group's title. |
+| `@ingroup` | Use inside individual doxygen block comments for declaration statements in a header file. |
+| `@addtogroup` | Use instead of `@ingroup` for multiple declarations in the same file within a namespace declaration. Do not specify a group title. |
+| `@{ ... @}` |  Use only with `@addtogroup`. |
+
+## Build Doxygen Output
+
+We recommend installing Doxygen using conda (`conda install doxygen`) or a Linux package manager (`sudo apt install doxygen`).
+Alternatively you can [build and install doxygen from source](https://www.doxygen.nl/manual/install.html).
+
+To build the libcudf HTML documentation simply run the `doxygen` command from the `cpp/doxygen` directory containing the `Doxyfile`.
+The libcudf documentation can also be built using `cmake --build . --target docs_cudf` from the cmake build directory (e.g. `cpp/build`).
+Doxygen reads and processes all appropriate source files under the `cpp/include/` directory.
+The output is generated in the `cpp/doxygen/html/` directory.
+You can load the local `index.html` file generated there into any web browser to view the result.
+
+To view docs built on a remote server, you can run a simple HTTP server using Python: `cd html && python -m http.server`.
+Then open `<IP address>:8000` in your local web browser, inserting the IP address of the machine on which you ran the HTTP server.
+
+The doxygen output is intended for building documentation only for the public APIs and classes.
+For example, the output should not include documentation for `detail` or `/src` files, and these directories are excluded in the `Doxyfile` configuration.
+When published by the build/CI system, the doxygen output will appear on our external [RAPIDS web site](https://docs.rapids.ai/api/libcudf/stable/index.html).
diff --git a/cpp/doxygen/developer_guide/TESTING.md b/cpp/doxygen/developer_guide/TESTING.md
new file mode 100644
index 0000000..c19976a
--- /dev/null
+++ b/cpp/doxygen/developer_guide/TESTING.md
@@ -0,0 +1,526 @@
+# Unit Testing in libcudf
+
+Unit tests in libcudf are written using
+[Google Test](https://github.com/google/googletest/blob/master/docs/primer.md).
+
+**Important:** Instead of including `gtest/gtest.h` directly, use
+`#include <cudf_test/cudf_gtest.hpp>`.
+
+Also, write test code in the global namespace. That is,
+do not write test code in the `cudf` or the `cudf::test` namespace or their
+sub-namespaces.
+Likewise, do not use `using namespace cudf;` or `using namespace cudf::test;`
+in the global namespace.
+
+
+## Best Practices: What Should We Test?
+
+In general we should test to make sure all code paths are covered. This is not always easy or
+possible. But generally this means we test all supported combinations of algorithms and data types,
+and all operators supported by algorithms that support multiple operators (e.g. reductions,
+groupby).  Here are some other guidelines.
+
+ * In general empty input is not an error in libcudf. Typically empty input results in empty output.
+   Tests should verify this.
+
+ * Anything that involves manipulating bitmasks (especially hand-rolled kernels) should have tests
+   that check varying number of rows, especially around boundaries like the warp size (32). So, test
+   fewer than 32 rows, more than 32 rows, exactly 32 rows, and greater than 64 rows.
+
+ * Most algorithms should have one or more tests exercising inputs with a large enough number of
+   rows to require launching multiple thread blocks, especially when values are ultimately
+   communicated between blocks (e.g. reductions). This is especially important for custom kernels
+   but also applies to Thrust and CUB algorithm calls with lambdas / functors.
+
+ * For anything involving strings or lists, test exhaustive combinations of empty strings/lists,
+   null strings/lists and strings/lists with null elements.
+
+ * Strings tests should include a mixture of non-ASCII UTF-8 characters like `é` in test data.
+
+ * Test sliced columns as input (that is, columns that have a nonzero `offset`). This is an easy to
+   forget case.
+
+ * Tests that verify various forms of "degenerate" column inputs, for example: empty
+   string columns that have no children (not many paths in cudf can generate these but it
+   does happen); columns with zero size but that somehow have non-null data pointers; and struct
+   columns with no children.
+
+ * Decimal types are not included in the `cudf::test::NumericTypes` type list, but are included in
+   `cudf::test::FixedWidthTypes`, so be careful that tests either include or exclude decimal types as
+   appropriate.
+
+
+## Directory and File Naming
+
+The naming of unit test directories and source files should be consistent with the feature being
+tested. For example, the tests for APIs in `copying.hpp` should live in `cudf/cpp/tests/copying`.
+Each feature (or set of related features) should have its own test source file named
+`<feature>_tests.cu/cpp`. For example, `cudf/cpp/src/copying/scatter.cu` has tests in
+`cudf/cpp/tests/copying/scatter_tests.cu`.
+
+In the interest of improving compile time, whenever possible, test source files should be `.cpp`
+files because `nvcc` is slower than `gcc` in compiling host code. Note that `thrust::device_vector`
+includes device code, and so must only be used in `.cu` files. `rmm::device_uvector`,
+`rmm::device_buffer` and the various `column_wrapper` types described later can be used in `.cpp`
+files, and are therefore preferred in test code over `thrust::device_vector`.
+
+## Base Fixture
+
+All libcudf unit tests should make use of a GTest ["Test Fixture"](https://github.com/google/googletest/blob/master/docs/primer.md#test-fixtures-using-the-same-data-configuration-for-multiple-tests-same-data-multiple-tests).
+Even if the fixture is empty, it should inherit from the base fixture `cudf::test::BaseFixture`
+found in `include/cudf_test/base_fixture.hpp`. This ensures that RMM is properly initialized and
+finalized. `cudf::test::BaseFixture` already inherits from `testing::Test` and therefore it is
+not necessary for your test fixtures to inherit from it.
+
+Example:
+
+    class MyTestFixture : public cudf::test::BaseFixture {...};
+
+## Typed Tests
+
+In general, libcudf features must work across all of the supported types (there are exceptions e.g.
+not all binary operations are supported for all types). In order to automate the process of running
+the same tests across multiple types, we use GTest's
+[Typed Tests](https://github.com/google/googletest/blob/master/docs/advanced.md#typed-tests).
+Typed tests allow you to write a test once and run it across a list of types.
+
+For example:
+
+```c++
+// Fixture must be a template
+template <typename T>
+class TypedTestFixture : cudf::test::BaseFixture {...};
+using TestTypes = cudf::test:Types<int,float,double>; // Notice custom cudf type list type
+TYPED_TEST_SUITE(TypedTestFixture, TestTypes);
+TYPED_TEST(TypedTestFixture, FirstTest){
+    // Access the current type using `TypeParam`
+    using T = TypeParam;
+}
+```
+
+To specify the list of types to use, instead of GTest's `testing::Types<...>`, libcudf provides `cudf::test::Types<...>` which is a custom, drop-in replacement for `testing::Types`.
+In this example, all tests using the `TypedTestFixture` fixture will run once for each type in the
+list defined in `TestTypes` (`int, float, double`).
+
+### Type Lists
+
+The list of types that are used in tests should be consistent across all tests. To ensure
+consistency, several sets of common type lists are provided in
+`include/cudf_test/type_lists.hpp`. For example, `cudf::test::NumericTypes` is a type list of all numeric types,
+`FixedWidthTypes` is a list of all fixed-width element types, and `cudf::test::AllTypes` is a list of every
+element type that libcudf supports.
+
+```c++
+#include <cudf_test/type_lists.hpp>
+
+// All tests using TypeTestFixture will be invoked once for each numeric type
+TYPED_TEST_SUITE(TypedTestFixture, cudf::test::NumericTypes);
+```
+
+Whenever possible, use one of the type list provided in `include/utilities/test/type_lists.hpp`
+rather than creating new custom lists.
+
+#### Advanced Type Lists
+
+Sometimes it is necessary to generate more advanced type lists than the simple lists of single types
+in the `TypeList` example above. libcudf provides a set of meta-programming utilities in
+`include/cudf_test/type_list_utilities.hpp` for generating and composing more advanced type lists.
+
+For example, it may be useful to generate a *nested* type list where each element in the list is two
+types. In a nested type list, each element in the list is itself another list. In order to access
+the `N`th type within the nested list, use `GetType<NestedList, N>`.
+
+Imagine testing all possible two-type combinations of `<int,float>`. This could be done manually:
+
+```c++
+template <typename TwoTypes>
+TwoTypesFixture : cudf::test::BaseFixture{...};
+using TwoTypesList = Types< Types<int, int>, Types<int, float>,
+                            Types<float, int>, Types<float, float> >;
+TYPED_TEST_SUITE(TwoTypesFixture, TwoTypesList);
+TYPED_TEST(TwoTypesFixture, FirstTest){
+    // TypeParam is a list of two types, i.e., a "nested" type list
+    // Use `cudf::test::GetType` to retrieve the individual types
+    using FirstType = GetType<TypeParam,0>;
+    using SecondType = GetType<TypeParam,1>;
+}
+```
+
+The above example manually specifies all pairs composed of `int` and `float`. `CrossProduct` is a
+utility in `type_list_utilities.hpp` which materializes this cross product automatically.
+
+```c++
+using TwoTypesList = Types< Types<int, int>, Types<int, float>,
+                            Types<float, int>, Types<float, float> >;
+using CrossProductTypeList = CrossProduct< Types<int, float>, Types<int, float> >;
+// TwoTypesList and CrossProductTypeList are identical
+```
+
+`CrossProduct` can be used with an arbitrary number of type lists to generate nested type lists of
+two or more types. **However**, overuse of `CrossProduct` can dramatically inflate compile time.
+The cross product of two type lists of size `n` and `m` will result in a new list with
+`n*m` nested type lists. This means `n*m` templates will be instantiated; `n` and `m` need not be
+large before compile time becomes unreasonable.
+
+There are a number of other utilities in `type_list_utilities.hpp`. For more details, see the
+documentation in that file and their associated tests in
+`cudf/cpp/tests/utilities_tests/type_list_tests.cpp`.
+
+## Utilities
+
+libcudf provides a number of utilities in `include/cudf_test` to make common testing operations more
+convenient. Before creating your own test utilities, look to see if one already exists that does
+what you need. If not, consider adding a new utility to do what you need. However, make sure that
+the utility is generic enough to be useful for other tests and is not overly tailored to your
+specific testing need.
+
+### Column Wrappers
+
+In order to make generating input columns easier, libcudf provides the `*_column_wrapper` classes in
+`include/cudf_test/column_wrapper.hpp`. These classes wrap a `cudf::column` and provide constructors
+for initializing a `cudf::column` object usable with libcudf APIs. Any `*_column_wrapper` class is
+implicitly convertible to a `column_view` or `mutable_column_view` and therefore may be
+transparently passed to any API expecting a `column_view` or `mutable_column_view` argument.
+
+#### fixed_width_column_wrapper
+
+The `cudf::test::fixed_width_column_wrapper` class should be used for constructing and initializing columns of
+any fixed-width element type, e.g., numeric types, timestamp types, Boolean, etc.
+`cudf::test::fixed_width_column_wrapper` provides constructors that accept an iterator range to generate each
+element in the column. For nullable columns, an additional iterator can be provided to indicate the
+validity of each element. There are also constructors that accept a `std::initializer_list<T>` for
+the column elements and optionally for the validity of each element.
+
+Example:
+
+```c++
+// Creates a non-nullable column of INT32 elements with 5 elements: {0, 1, 2, 3, 4}
+auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i;});
+cudf::test::fixed_width_column_wrapper<int32_t> w(elements, elements + 5);
+
+// Creates a nullable column of INT32 elements with 5 elements: {null, 1, null, 3, null}
+auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i;});
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;})
+cudf::test::fixed_width_column_wrapper<int32_t> w(elements, elements + 5, validity);
+
+// Creates a non-nullable INT32 column with 4 elements: {1, 2, 3, 4}
+cudf::test::fixed_width_column_wrapper<int32_t> w{{1, 2, 3, 4}};
+
+// Creates a nullable INT32 column with 4 elements: {1, NULL, 3, NULL}
+cudf::test::fixed_width_column_wrapper<int32_t> w{ {1,2,3,4}, {1, 0, 1, 0}};
+```
+
+#### fixed_point_column_wrapper
+
+The `cudf::test::fixed_point_column_wrapper` class should be used for constructing and initializing columns of
+any fixed-point element type (DECIMAL32 or DECIMAL64). `cudf::test::fixed_point_column_wrapper` provides
+constructors that accept an iterator range to generate each element in the column. For nullable
+columns, an additional iterator can be provided to indicate the validity of each element.
+Constructors also take the scale of the fixed-point values to create.
+
+Example:
+
+```c++
+// Creates a non-nullable column of 4 DECIMAL32 elements of scale 3: {1000, 2000, 3000, 4000}
+auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i){ return i; });
+cudf::test::fixed_point_column_wrapper<int32_t> w(elements, elements + 4, 3);
+
+// Creates a nullable column of 5 DECIMAL32 elements of scale 2: {null, 100, null, 300, null}
+auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i){ return i; });
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){ return i % 2; });
+cudf::test::fixed_point_column_wrapper<int32_t> w(elements, elements + 5, validity, 2);
+```
+
+#### dictionary_column_wrapper
+
+The `cudf::test::dictionary_column_wrapper` class should be used to create dictionary columns.
+`cudf::test::dictionary_column_wrapper` provides constructors that accept an iterator range to generate each
+element in the column. For nullable columns, an additional iterator can be provided to indicate the
+validity of each element. There are also constructors that accept a `std::initializer_list<T>` for
+the column elements and optionally for the validity of each element.
+
+Example:
+
+```c++
+// Creates a non-nullable dictionary column of INT32 elements with 5 elements
+// keys = {0, 2, 6}, indices = {0, 1, 1, 2, 2}
+std::vector<int32_t> elements{0, 2, 2, 6, 6};
+cudf::test::dictionary_column_wrapper<int32_t> w(element.begin(), elements.end());
+
+// Creates a nullable dictionary column with 5 elements and a validity iterator.
+std::vector<int32_t> elements{0, 2, 0, 6, 0};
+// Validity iterator here sets even rows to null.
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;})
+// keys = {2, 6}, indices = {NULL, 0, NULL, 1, NULL}
+cudf::test::dictionary_column_wrapper<int32_t> w(elements, elements + 5, validity);
+
+// Creates a non-nullable dictionary column with 4 elements.
+// keys = {1, 2, 3}, indices = {0, 1, 2, 0}
+cudf::test::dictionary_column_wrapper<int32_t> w{{1, 2, 3, 1}};
+
+// Creates a nullable dictionary column with 4 elements and validity initializer.
+// keys = {1, 3}, indices = {0, NULL, 1, NULL}
+cudf::test::dictionary_column_wrapper<int32_t> w{ {1, 0, 3, 0}, {1, 0, 1, 0}};
+
+// Creates a nullable column of dictionary elements with 5 elements and validity initializer.
+std::vector<int32_t> elements{0, 2, 2, 6, 6};
+// keys = {2, 6}, indices = {NULL, 0, NULL, 1, NULL}
+cudf::test::dictionary_width_column_wrapper<int32_t> w(elements, elements + 5, {0, 1, 0, 1, 0});
+
+// Creates a non-nullable dictionary column with 7 string elements
+std::vector<std::string> strings{"", "aaa", "bbb", "aaa", "bbb", "ccc", "bbb"};
+// keys = {"","aaa","bbb","ccc"}, indices = {0, 1, 2, 1, 2, 3, 2}
+cudf::test::dictionary_column_wrapper<std::string> d(strings.begin(), strings.end());
+
+// Creates a nullable dictionary column with 7 string elements and a validity iterator.
+// Validity iterator here sets even rows to null.
+// keys = {"a", "bb"}, indices = {NULL, 1, NULL, 1, NULL, 0, NULL}
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;});
+cudf::test::dictionary_column_wrapper<std::string> d({"", "bb", "", "bb", "", "a", ""}, validity);
+```
+
+#### strings_column_wrapper
+
+The `cudf::test::strings_column_wrapper` class should be used to create columns of strings. It provides
+constructors that accept an iterator range to generate each string in the column. For nullable
+columns, an additional iterator can be provided to indicate the validity of each string. There are
+also constructors that accept a `std::initializer_list<std::string>` for the column's strings and
+optionally for the validity of each element.
+
+Example:
+
+```c++
+// Creates a non-nullable STRING column with 7 string elements:
+// {"", "this", "is", "a", "column", "of", "strings"}
+std::vector<std::string> strings{"", "this", "is", "a", "column", "of", "strings"};
+cudf::test::strings_column_wrapper s(strings.begin(), strings.end());
+
+// Creates a nullable STRING column with 7 string elements:
+// {NULL, "this", NULL, "a", NULL, "of", NULL}
+std::vector<std::string> strings{"", "this", "is", "a", "column", "of", "strings"};
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;});
+cudf::test::strings_column_wrapper s(strings.begin(), strings.end(), validity);
+
+// Creates a non-nullable STRING column with 7 string elements:
+// {"", "this", "is", "a", "column", "of", "strings"}
+cudf::test::strings_column_wrapper s({"", "this", "is", "a", "column", "of", "strings"});
+
+// Creates a nullable STRING column with 7 string elements:
+// {NULL, "this", NULL, "a", NULL, "of", NULL}
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;});
+cudf::test::strings_column_wrapper s({"", "this", "is", "a", "column", "of", "strings"}, validity);
+```
+
+#### lists_column_wrapper
+
+The `cudf::test::lists_column_wrapper` class should be used to create columns of lists. It provides
+constructors that accept an iterator range to generate each list in the column. For nullable
+columns, an additional iterator can be provided to indicate the validity of each list. There are
+also constructors that accept a `std::initializer_list<T>` for the column's lists and
+optionally for the validity of each element. A number of other constructors are available.
+
+Example:
+
+```c++
+// Creates an empty LIST column
+// []
+cudf::test::lists_column_wrapper l{};
+
+// Creates a LIST column with 1 list composed of 2 total integers
+// [{0, 1}]
+cudf::test::lists_column_wrapper l{0, 1};
+
+// Creates a LIST column with 3 lists
+// [{0, 1}, {2, 3}, {4, 5}]
+cudf::test::lists_column_wrapper l{ {0, 1}, {2, 3}, {4, 5} };
+
+// Creates a LIST of LIST columns with 2 lists on the top level and
+// 4 below
+// [ {{0, 1}, {2, 3}}, {{4, 5}, {6, 7}} ]
+cudf::test::lists_column_wrapper l{ {{0, 1}, {2, 3}}, {{4, 5}, {6, 7}} };
+
+// Creates a LIST column with 1 list composed of 5 total integers
+// [{0, 1, 2, 3, 4}]
+auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i*2;});
+cudf::test::lists_column_wrapper l(elements, elements+5);
+
+// Creates a LIST column with 1 lists composed of 2 total integers
+// [{0, NULL}]
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;});
+cudf::test::lists_column_wrapper l{{0, 1}, validity};
+
+// Creates a LIST column with 1 lists composed of 5 total integers
+// [{0, NULL, 2, NULL, 4}]
+auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i*2;});
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;});
+cudf::test::lists_column_wrapper l(elements, elements+5, validity);
+
+// Creates a LIST column with 1 list composed of 2 total strings
+// [{"abc", "def"}]
+cudf::test::lists_column_wrapper l{"abc", "def"};
+
+// Creates a LIST of LIST columns with 2 lists on the top level and 4 below
+// [ {{0, 1}, NULL}, {{4, 5}, NULL} ]
+auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i){return i % 2;});
+cudf::test::lists_column_wrapper l{ {{{0, 1}, {2, 3}}, validity}, {{{4, 5}, {6, 7}}, validity} };
+```
+
+#### structs_column_wrapper
+
+The `cudf::test::structs_column_wrapper` class should be used to create columns of structs. It provides
+constructors that accept a vector or initializer list of pre-constructed columns or column wrappers
+for child columns. For nullable columns, an additional iterator can be provided to indicate the
+validity of each struct.
+
+Examples:
+
+```c++
+// The following constructs a column for struct< int, string >.
+auto child_int_col = cudf::test::fixed_width_column_wrapper<int32_t>{ 1, 2, 3, 4, 5 }.release();
+auto child_string_col = cudf::test::string_column_wrapper {"All", "the", "leaves", "are", "brown"}.release();
+
+std::vector<std::unique_ptr<cudf::column>> child_columns;
+child_columns.push_back(std::move(child_int_col));
+child_columns.push_back(std::move(child_string_col));
+
+cudf::test::struct_col wrapper wrapper{
+  child_cols,
+  {1,0,1,0,1} // Validity
+};
+
+auto struct_col {wrapper.release()};
+
+// The following constructs a column for struct< int, string >.
+cudf::test::fixed_width_column_wrapper<int32_t> child_int_col_wrapper{ 1, 2, 3, 4, 5 };
+cudf::test::string_column_wrapper child_string_col_wrapper {"All", "the", "leaves", "are", "brown"};
+
+cudf::test::struct_column_wrapper wrapper{
+  {child_int_col_wrapper, child_string_col_wrapper}
+  {1,0,1,0,1} // Validity
+};
+
+auto struct_col {wrapper.release()};
+
+// The following constructs a column for struct< int, string >.
+cudf::test::fixed_width_column_wrapper<int32_t> child_int_col_wrapper{ 1, 2, 3, 4, 5 };
+cudf::test::string_column_wrapper child_string_col_wrapper {"All", "the", "leaves", "are", "brown"};
+
+cudf::test::struct_column_wrapper wrapper{
+  {child_int_col_wrapper, child_string_col_wrapper}
+  cudf::detail::make_counting_transform_iterator(0, [](auto i){ return i % 2; }) // Validity
+};
+
+auto struct_col {wrapper.release()};
+```
+
+### Column Comparison Utilities
+
+A common operation in testing is verifying that two columns are equal, or equivalent, or that they
+have the same metadata.
+
+#### CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL
+
+Verifies that two columns have the same type, size, and nullability. For nested types, recursively
+verifies the equality of type, size and nullability of all nested children.
+
+#### CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT
+
+Verifies that two columns have equivalent type and equal size, ignoring nullability. For nested
+types, recursively verifies the equivalence of type, and equality of size of all nested children,
+ignoring nullability.
+
+Note "equivalent type". Most types are equivalent if and only they are equal. `fixed_point` types
+are one exception. They are equivalent if the representation type is equal, even if they have
+different scales. Nested type columns can be equivalent in the case where they both have zero size,
+but one has children (also empty) and the other does not. For columns with nonzero size, both equals
+and equivalent expect equal number of children.
+
+#### CUDF_TEST_EXPECT_COLUMNS_EQUAL
+
+Verifies that two columns have equal properties and verifies elementwise equality of the column
+data. Null elements are treated as equal.
+
+#### CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT
+
+Verifies that two columns have equivalent properties and verifies elementwise equivalence of the
+column data. Null elements are treated as equivalent.
+
+#### CUDF_TEST_EXPECT_EQUAL_BUFFERS
+
+Verifies the bitwise equality of two device memory buffers.
+
+#### Caveats
+
+Column comparison functions in the `cudf::test::detail` namespace should **NOT** be used directly.
+
+### Printing and accessing column data
+
+`include/cudf_test/column_utilities.hpp` defines various functions and overloads for printing
+columns (`print`), converting column data to string (`to_string`, `to_strings`), and copying data to
+the host (`to_host`).
+
+
+## Validating Stream Usage
+
+### Background
+
+libcudf employs a custom-built [preload library
+docs](https://man7.org/linux/man-pages/man8/ld.so.8.html) to validate its internal stream usage (the
+code may be found
+[`here`](https://github.com/rapidsai/cudf/blob/main/cpp/tests/utilities/identify_stream_usage.cpp)).
+This library wraps every asynchronous CUDA runtime API call that accepts a stream with a check to
+ensure that the passed CUDA stream is a valid one, immediately throwing an exception if an invalid
+stream is detected. Running tests with this library loaded immediately triggers errors if any test
+accidentally runs code on an invalid stream.
+
+Stream validity is determined by overloading the definition of libcudf's default stream. Normally, in
+libcudf `cudf::get_default_stream` returns one of `rmm`'s default stream values (depending on
+whether or not libcudf is compiled with per thread default stream enabled). In the preload library,
+this function is redefined to instead return a new user-created stream managed using a
+function-local static `rmm::cuda_stream`. An invalid stream in this situation is defined as any of
+CUDA's default stream values (cudaStreamLegacy, cudaStreamDefault, or cudaStreamPerThread), since
+any kernel that properly uses `cudf::get_default_stream` will now instead be using the custom stream
+created by the preload library.
+
+The preload library supports two different modes, `cudf` mode and `testing` mode. The previous
+paragraph describes the behavior of `cudf` mode, where `cudf::get_default_stream` is overloaded. In
+`cudf` mode, the preload library ensures that all CUDA runtime APIs are being provided cudf's
+default stream. This will detect oversights where, for example, a Thrust call has no stream specified, or
+when one of CUDA's default stream values is explicitly specified to a kernel. However, it will not
+detect cases where a stream is not correctly forwarded down the call stack, for instance if
+some `detail` function that accepts a stream parameter fails to forward it along and instead
+erroneously calls `cudf::get_default_stream` instead.
+
+In `testing` mode, the library instead overloads `cudf::test::get_default_stream`. This function
+defined in the `cudf::test` namespace enables a more stringent mode of testing. In `testing` mode,
+the preload library instead verifies that all CUDA runtime APIs are instead called using the test
+namespace's default stream. This distinction is important because cudf internals never use
+`cudf::test::get_default_stream`, so this stream value can only appear internally if it was provided
+to a public API and forwarded properly all the way down the call stack. While `testing` mode is more
+strict than `cudf` mode, it is also more intrusive. `cudf` mode can operate with no changes to the
+library or the tests because the preload library overwrites the relevant APIs in place. `testing`
+mode, however, can only be used to validate tests that are correctly passing
+`cudf::test::get_default_stream` to public libcudf APIs.
+
+In addition to the preload library, the test suite also implements a [custom memory
+resource](https://github.com/rapidsai/cudf/blob/main/cpp/include/cudf_test/stream_checking_resource_adaptor.hpp)
+that performs analogous stream verification when its `do_allocate` method is called. During testing
+this rmm's default memory resource is set to use this adaptor for additional stream validation.
+
+### Usage
+
+When writing tests for a libcudf API, a special set of additional tests should be added to validate
+the API's stream usage. These tests should be placed in the `cpp/tests/streams` directory in a file
+corresponding to the header containing the tested APIs, e.g. `cpp/tests/streams/copying_test.cpp`
+for all APIs declared in `cpp/include/cudf/copying.hpp`. These tests should contain a minimal
+invocation of the tested API with no additional assertions since they are solely designed to check
+stream usage. When adding these tests to `cpp/tests/CMakeLists.txt`, the `ConfigureTest` CMake
+function should be provided the arguments `STREAM_MODE testing`. This change is sufficient for
+CTest to set up the test to automatically load the preload library compiled in `testing` mode when
+running the test.
+
+The rest of the test suite is configured to run with the preload library in `cudf` mode. As a
+result, all test runs with `ctest` will always include stream validation. Since this configuration
+is managed via CMake and CTest, direct execution of the test executables will not use the preload
+library at all. Tests will still run and pass normally in this situation, however (with the
+exception of the test of the preload library itself).
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diff --git a/cpp/doxygen/header.html b/cpp/doxygen/header.html
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--- /dev/null
+++ b/cpp/doxygen/header.html
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+<!-- HTML header for doxygen 1.8.20-->
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
+<meta http-equiv="X-UA-Compatible" content="IE=9"/>
+<meta name="generator" content="Doxygen $doxygenversion"/>
+<meta name="viewport" content="width=device-width, initial-scale=1"/>
+<!--BEGIN PROJECT_NAME--><title>$projectname: $title</title><!--END PROJECT_NAME-->
+<!--BEGIN !PROJECT_NAME--><title>$title</title><!--END !PROJECT_NAME-->
+<link href="$relpath^tabs.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="$relpath^jquery.js"></script>
+<script type="text/javascript" src="$relpath^dynsections.js"></script>
+$treeview
+$search
+$mathjax
+<link href="$relpath^$stylesheet" rel="stylesheet" type="text/css" />
+$extrastylesheet
+
+<!-- RAPIDS CUSTOM JS & CSS: START, Please add these two lines back after every version upgrade -->
+<script defer src="https://docs.rapids.ai/assets/js/custom.js"></script>
+<link rel="stylesheet" href="https://docs.rapids.ai/assets/css/custom.css">
+<!-- RAPIDS CUSTOM JS & CSS: END -->
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+
+<!--BEGIN TITLEAREA-->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <!--BEGIN PROJECT_LOGO-->
+  <td id="projectlogo"><img alt="Logo" src="$relpath^$projectlogo"/></td>
+  <!--END PROJECT_LOGO-->
+  <!--BEGIN PROJECT_NAME-->
+  <td id="projectalign" style="padding-left: 0.5em;">
+   <div id="projectname">$projectname
+   <!--BEGIN PROJECT_NUMBER-->&#160;<span id="projectnumber">$projectnumber</span><!--END PROJECT_NUMBER-->
+   </div>
+   <!--BEGIN PROJECT_BRIEF--><div id="projectbrief">$projectbrief</div><!--END PROJECT_BRIEF-->
+  </td>
+  <!--END PROJECT_NAME-->
+  <!--BEGIN !PROJECT_NAME-->
+   <!--BEGIN PROJECT_BRIEF-->
+    <td style="padding-left: 0.5em;">
+    <div id="projectbrief">$projectbrief</div>
+    </td>
+   <!--END PROJECT_BRIEF-->
+  <!--END !PROJECT_NAME-->
+  <!--BEGIN DISABLE_INDEX-->
+   <!--BEGIN SEARCHENGINE-->
+   <td>$searchbox</td>
+   <!--END SEARCHENGINE-->
+  <!--END DISABLE_INDEX-->
+ </tr>
+ </tbody>
+</table>
+</div>
+<!--END TITLEAREA-->
+<!-- end header part -->
diff --git a/cpp/doxygen/main_page.md b/cpp/doxygen/main_page.md
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index 0000000..308d106
--- /dev/null
+++ b/cpp/doxygen/main_page.md
@@ -0,0 +1,5 @@
+# libcudf
+
+libcudf is a C++ GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise
+manipulating data. A GPU DataFrame is a column-oriented tabular data structure, so libcudf provides
+two core data structures: cudf::column, and cudf::table.
diff --git a/cpp/doxygen/modify_fences.sh b/cpp/doxygen/modify_fences.sh
new file mode 100755
index 0000000..195f60d
--- /dev/null
+++ b/cpp/doxygen/modify_fences.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+# This script modifies the GitHub Markdown style code fences in our MD files
+# into the PHP style that Doxygen supports, allowing us to display code
+# properly both on the GitHub GUI and in published Doxygen documentation.
+
+sed 's/```c++/```{.cpp}/g' "$@"
diff --git a/cpp/doxygen/regex.md b/cpp/doxygen/regex.md
new file mode 100644
index 0000000..8d206f2
--- /dev/null
+++ b/cpp/doxygen/regex.md
@@ -0,0 +1,124 @@
+# Regex Features
+
+This page specifies which regular expression (regex) features are currently supported by libcudf strings column APIs that accept regex patterns:
+
+- cudf::strings::contains_re()
+- cudf::strings::matches_re()
+- cudf::strings::count_re()
+- cudf::strings::extract()
+- cudf::strings::extract_all_record()
+- cudf::strings::findall()
+- cudf::strings::replace_re()
+- cudf::strings::replace_with_backrefs()
+- cudf::strings::split_re()
+- cudf::strings::split_record_re()
+
+The details are based on features documented at https://www.regular-expressions.info/reference.html
+
+**Note:** The alternation character is the pipe character `|` and not the character included in the tables on this page. There is an issue including the pipe character inside the table markdown that is rendered by doxygen.
+
+**Invalid regex patterns will result in undefined behavior**. This includes but is not limited to the following:
+- Unescaped special characters (listed in the third row of the Characters table below) when they are intended to match as literals.
+- Unmatched paired special characters like `()`, `[]`, and `{}`.
+- Empty groups, classes, or quantifiers. That is, `()` and `[]` without an enclosing expression and `{}` without a valid integer.
+- Incomplete ranges in character classes like `[-z]`, `[a-]`, and `[-]`.
+- Unqualified quantifiers. That is, a quantifier with no preceding item to match like `*a`, `a⎮?`, `(+)`, `{2}a`, etc.
+
+## Features Supported
+
+### Characters
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Literal character | Any character except `[\^$.⎮?*+()` | All characters except the listed special characters match a single instance of themselves | `a` matches `a` |
+| Literal curly braces | `{` and `}` | `{` and `}` are literal characters, unless they are part of a valid regular expression token such as a quantifier `{3}` | `{` matches `{` |
+| Backslash escapes a metacharacter | `\` followed by any of `[\^$.⎮?*+(){}` | A backslash escapes special characters to suppress their special meaning | `\*` matches `*` |
+| Hexadecimal escape | `\xFF` where `FF` are 2 hexadecimal digits | Matches the character at the specified position in the ASCII table | `\x40` matches `@` |
+| Character escape | `\n`, `\r` and `\t` | Match an line-feed (LF) character, carriage return (CR) character and a tab character respectively | `\r\n` matches a Windows CRLF line break |
+| Character escape | `\a` | Match the "alert" or "bell" control character (ASCII 0x07) | |
+| Character escape | `\f` | Match the form-feed control character (ASCII 0x0C) | |
+| NULL escape      | `\0` | Match the NULL character ||
+| Octal escape     | `\100` through `\177` <br/> `\200` through `\377` <br/> `\01` through `\07` <br/> `\010` through `\077` | Matches the character at the specified position in the ASCII table | `\100` matches `@` |
+
+### Basic Features
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Dot | . (dot) | Matches any single character except line break characters. Optionally match line break characters. The behavior of the dot when encountering a  `\n` character can be controlled by cudf::strings::regex_flags for some regex APIs. | . matches x or (almost) any other character |
+| Alternation | `⎮` (pipe) | Causes the regex engine to match either the part on the left side, or the part on the right side. Can be strung together into a series of alternations. | `abc⎮def⎮xyz` matches `abc`, `def` or `xyz` |
+
+
+### Character Classes
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Character class | `[` | `[` begins a character class. | |
+| Literal character | Any character except `\^-]` | All characters except the listed special characters are literal characters that add themselves to the character class. | `[abc]` matches `a`, `b` or `c` |
+| Backslash escapes a metacharacter	| `\` (backslash) followed by any of `\^-]` | A backslash escapes special characters to suppress their special meaning. | `[\^\]]` matches `^` or `]` |
+| Range | `-` (hyphen) between two tokens that each specify a single character. | Adds a range of characters to the character class. If '`-`' is the first or last character (e.g. `[a-]` or `[-z]`), it will match a literal '`-`' and not infer a range. | `[a-zA-Z0-9]` matches any ASCII letter or digit |
+| Negated character class | `^` (caret) immediately after the opening `[` | Negates the character class, causing it to match a single character not listed in the character class. | `[^a-d]` matches `x` (any character except `a`, `b`, `c` or `d`) |
+| Literal opening bracket | `[` | An opening square bracket is a literal character that adds an opening square bracket to the character class. | `[ab[cd]ef]` matches `aef]`, `bef]`, `[ef]`, `cef]`, and `def]` |
+| Character escape | `\n`, `\r` and `\t` | Add an LF character, a CR character, or a tab character to the character class, respectively. | `[\n\r\t]` matches a line feed, a carriage return, or a tab character |
+| Character escape | `\a` | Add the "alert" or "bell" control character (ASCII 0x07) to the character class. | `[\a\t]` matches a bell or a tab character |
+| Character escape | `\b` | Add the backspace control character (ASCII 0x08) to the character class. | `[\b\t]` matches a backspace or a tab character |
+| Character escape | `\f` | Add the form-feed control character (ASCII 0x0C) to the character class. | `[\f\t]` matches a form-feed or a tab character |
+
+### Shorthand Character Classes
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Shorthand | `\d` | Adds all digits to the character class. Matches a single digit if used outside character classes. The behavior can be controlled by [cudf::strings::regex_flags::ASCII](@ref cudf::strings::regex_flags) to include only `[0-9]` | `\d` matches a character that is a digit |
+| Shorthand | `\w` | Adds all word characters to the character class. Matches a single word character if used outside character classes. The behavior can be controlled by [cudf::strings::regex_flags::ASCII](@ref cudf::strings::regex_flags) to include only `[0-9A-Za-z_]` | `\w` matches any single word character |
+| Shorthand | `\s` | Adds all whitespace to the character class. Matches a single whitespace character if used outside character classes. The behavior can be controlled by [cudf::strings::regex_flags::ASCII](@ref cudf::strings::regex_flags) to include only `[\t- ]` | `\s` matches any single whitespace character |
+| Shorthand | `\D` | Adds all non-digits to the character class. Matches a single character that is not a digit character if used outside character classes. The behavior can be controlled by [cudf::strings::regex_flags::ASCII](@ref cudf::strings::regex_flags) | `[\D]` matches a single character that is not a digit character |
+| Shorthand | `\W` | Adds all non-word characters to the character class. Matches a single character that is not a word character if used outside character classes. The behavior can be controlled by [cudf::strings::regex_flags::ASCII](@ref cudf::strings::regex_flags) | [`\W`] matches a single character that is not a word character |
+| Shorthand | `\S` | Adds all non-whitespace to the character class. Matches a single character that is not a whitespace character if used outside character classes. The behavior can be controlled by [cudf::strings::regex_flags::ASCII](@ref cudf::strings::regex_flags) | `[\S]` matches a single character that is not a whitespace character |
+
+### Anchors
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| String anchor | `^` (caret) | Matches at the start of the string | `^.` matches `a` in `abcdef` |
+| Line anchor | `^` (caret) | When [cudf::strings::regex_flags::MULTILINE](@ref cudf::strings::regex_flags) is specified: Matches after each line break in addition to matching at the start of the string, thus matching at the start of each line in the string. | `^.` matches `a` and `d` in `abc\ndef` |
+| String anchor | `$` (dollar) | Matches at the end of the string as well as before the final line break in the string | `.$` matches `f` in `abcdef` and in `abcdef\n` |
+| Line anchor | `$` (dollar) | When [cudf::strings::regex_flags::MULTILINE](@ref cudf::strings::regex_flags) is specified: Matches before each line break in addition to matching at the end of the string, thus matching at the end of each line in the string. | `.$` matches `c` and `f` in `abc\ndef` and in `abc\ndef\n`　|
+| String anchor | `\A` | Matches at the start of the string | `\A\w` matches only `a` in `abc` |
+| String anchor | `\Z` | Matches at the end of the string | `\w\Z` matches `f` in `abc\ndef` but fails to match `abc\ndef\n` |
+
+### Word Boundaries
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Word boundary | `\b` | Matches at a position that is followed by a word character but not preceded by a word character, or that is preceded by a word character but not followed by a word character. | `\b.` matches `a`, the space, and `d` in `abc def` |
+| Word boundary | `\B`　| Matches at a position that is preceded and followed by a word character, or that is not preceded and not followed by a word character. | `\B.` matches `b`, `c`, `e`, and `f` in `abc def` |
+
+### Quantifiers
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Greedy quantifier | `?` (question mark) | Makes the preceding item optional. Greedy, so the optional item is included in the match if possible. | `abc?` matches `abc` or `ab` |
+| Greedy quantifier | `*` (star) | Repeats the previous item zero or more times. Greedy, so as many items as possible will be matched before trying permutations with fewer matches of the preceding item, up to the point where the preceding item is not matched at all. | `".*"` matches `"def"` and `"ghi"` in `abc "def" "ghi" jkl` |
+| Greedy quantifier | `+` (plus)　| Repeats the previous item once or more. Greedy, so as many items as possible will be matched before trying permutations with fewer matches of the preceding item, up to the point where the preceding item is matched only once. | `".+"` matches `"def"` and `"ghi"` in `abc "def" "ghi" jkl` |
+| Lazy quantifier | `??` | Makes the preceding item optional. Lazy, so the optional item is excluded in the match if possible. | `abc??` matches `ab` or `abc` |
+| Lazy quantifier | `*?` | Repeats the previous item zero or more times. Lazy, so the engine first attempts to skip the previous item, before trying permutations with ever increasing matches of the preceding item. | `".*?"` matches `"def"` and `"ghi"` in `abc "def" "ghi" jkl` |
+| Lazy quantifier | `+?` | Repeats the previous item once or more. Lazy, so the engine first matches the previous item only once, before trying permutations with ever increasing matches of the preceding item. | `".+?"` matches `"def"` and `"ghi"` in `abc "def" "ghi" jkl` |
+| Fixed quantifier | `{n}` where `n` is an integer: `0 ≤ n ≤ 999` | Repeats the previous item exactly `n` times. | `a{5}` matches `aaaaa` |
+| Greedy quantifier | `{n,m}` where `n` and `m` are integers: `0 ≤ n ≤ m ≤ 999` | Repeats the previous item between `n` and `m` times. Greedy, so repeating `m` times is tried before reducing the repetition to `n` times. | `a{2,4}` matches `aaaa`, `aaa` or `aa` |
+| Greedy quantifier | `{n,}` where `n` is an integer: `0 ≤ n ≤ 999` | Repeats the previous item at least `n` times. Greedy, so as many items as possible will be matched before trying permutations with fewer matches of the preceding item, up to the point where the preceding item is matched only `n` times. | `a{2,}` matches `aaaaa` in `aaaaa` |
+| Lazy quantifier | `{n,m}?` where `n` and `m` are integers `0 ≤ n ≤ m ≤ 999` | Repeats the previous item between `n` and `m` times. Lazy, so repeating `n` times is tried before increasing the repetition to `m` times. | `a{2,4}?` matches `aa`, `aaa`, or `aaaa` |
+| Lazy quantifier | `{n,}?` where `n` is an integer: `0 ≤ n ≤ 999` | Repeats the previous item `n` or more times. Lazy, so the engine first matches the previous item `n` times, before trying permutations with ever increasing matches of the preceding item. | `a{2,}?` matches `aa` in `aaaaa` |
+
+### Groups
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Capturing group | `(regex)` | Parentheses group the regex between them. They capture the text matched by the regex inside them into a numbered group. They allow you to apply regex operators to the entire grouped regex. | `(abc⎮def)ghi` matches `abcghi` or `defghi` |
+| Non-capturing group | `(?:regex)` | Non-capturing parentheses group the regex so you can apply regex operators, but do not capture anything. | `(?:abc⎮def)ghi` matches `abcghi` or `defghi` |
+
+### Replacement Backreferences
+
+| Feature  | Syntax | Description | Example |
+| ---------- | ------------- | ------------- | ------------- |
+| Backreference | `\1` through `\99` | Insert the text matched by capturing groups 1 through 99 | Replacing `(a)(b)(c)` with `\3\3\1` in `abc` yields `cca` |
+| Backreference | `${1}` through `${99}` | Insert the text matched by capturing groups 1 through 99 | Replacing `(a)(b)(c)` with `${2}.${2}:{$3}` in `abc` yields `b.b:c` |
+| Whole match | `${0}` | Insert the whole regex match | Replacing `(\d)(a)` with `[${0}]:-${2}_${1};` in `123abc` yields `12[3a]:-a_3;bc`
diff --git a/cpp/doxygen/unicode.md b/cpp/doxygen/unicode.md
new file mode 100644
index 0000000..1ab09e1
--- /dev/null
+++ b/cpp/doxygen/unicode.md
@@ -0,0 +1,23 @@
+# Unicode Limitations
+
+The strings column currently supports only UTF-8 characters internally.
+For functions that require character testing (e.g. cudf::strings::all_characters_of_type()) or
+case conversion (e.g. cudf::strings::capitalize(), etc) only the 16-bit [Unicode 13.0](http://www.unicode.org/versions/Unicode13.0.0)
+character code-points (0-65535) values are supported.
+Case conversion and character testing on characters above code-point 65535 are not supported.
+
+Case conversions that are context-sensitive are not supported. Also, case conversions that result
+in multiple characters are not reversible. That is, adjacent individual characters will not be case converted
+to a single character. For example, converting character ß to upper case will result in the characters "SS". But converting "SS" to lower case will produce "ss".
+
+Strings case and type APIs:
+
+- cudf::strings::all_characters_of_type()
+- cudf::strings::to_upper()
+- cudf::strings::to_lower()
+- cudf::strings::capitalize()
+- cudf::strings::title()
+- cudf::strings::swapcase()
+
+Also, using regex patterns that use the shorthand character classes `\d \D \w \W \s \S` will include only appropriate characters with
+code-points between (0-65535).
diff --git a/cpp/examples/README.md b/cpp/examples/README.md
new file mode 100644
index 0000000..b2e8dd3
--- /dev/null
+++ b/cpp/examples/README.md
@@ -0,0 +1,9 @@
+# Libcudf Examples
+
+This folder contains examples to demonstrate libcudf use cases. Running `build.sh` builds all
+libcudf examples.
+
+Current examples:
+
+- Basic: demonstrates a basic use case with libcudf and building a custom application with libcudf
+- Strings: demonstrates using libcudf for accessing and creating strings columns and for building custom kernels for strings
diff --git a/cpp/examples/basic/4stock_5day.csv b/cpp/examples/basic/4stock_5day.csv
new file mode 100644
index 0000000..471912a
--- /dev/null
+++ b/cpp/examples/basic/4stock_5day.csv
@@ -0,0 +1,21 @@
+Company,Date,Open,High,Low,Close,Volume
+MSFT,2021-03-03,232.16000366210938,233.5800018310547,227.25999450683594,227.55999755859375,33950400.0
+MSFT,2021-03-04,226.74000549316406,232.49000549316406,224.25999450683594,226.72999572753906,44584200.0
+MSFT,2021-03-05,229.52000427246094,233.27000427246094,226.4600067138672,231.60000610351562,41842100.0
+MSFT,2021-03-08,231.3699951171875,233.3699951171875,227.1300048828125,227.38999938964844,35245900.0
+MSFT,2021-03-09,232.8800048828125,235.3800048828125,231.6699981689453,233.77999877929688,33034000.0
+GOOG,2021-03-03,2067.2099609375,2088.51806640625,2010.0,2026.7099609375,1483100.0
+GOOG,2021-03-04,2023.3699951171875,2089.239990234375,2020.27001953125,2049.090087890625,2116100.0
+GOOG,2021-03-05,2073.1201171875,2118.110107421875,2046.4150390625,2108.5400390625,2193800.0
+GOOG,2021-03-08,2101.1298828125,2128.81005859375,2021.6099853515625,2024.1700439453125,1646000.0
+GOOG,2021-03-09,2070.0,2078.0400390625,2047.8299560546875,2052.699951171875,1696400.0
+AMZN,2021-03-03,3081.179931640625,3107.780029296875,2995.0,3005.0,3967200.0
+AMZN,2021-03-04,3012.0,3058.1298828125,2945.429931640625,2977.570068359375,5458700.0
+AMZN,2021-03-05,3005.0,3009.0,2881.0,3000.4599609375,5383400.0
+AMZN,2021-03-08,3015.0,3064.590087890625,2951.31005859375,2951.949951171875,4178500.0
+AMZN,2021-03-09,3017.989990234375,3090.9599609375,3005.14990234375,3062.85009765625,4023500.0
+AAPL,2021-03-03,124.80999755859375,125.70999908447266,121.83999633789062,122.05999755859375,112430400.0
+AAPL,2021-03-04,121.75,123.5999984741211,118.62000274658203,120.12999725341797,177275300.0
+AAPL,2021-03-05,120.9800033569336,121.94000244140625,117.56999969482422,121.41999816894531,153590400.0
+AAPL,2021-03-08,120.93000030517578,121.0,116.20999908447266,116.36000061035156,153918600.0
+AAPL,2021-03-09,119.02999877929688,122.05999755859375,118.79000091552734,121.08999633789062,129159600.0
diff --git a/cpp/examples/basic/CMakeLists.txt b/cpp/examples/basic/CMakeLists.txt
new file mode 100644
index 0000000..1c1952c
--- /dev/null
+++ b/cpp/examples/basic/CMakeLists.txt
@@ -0,0 +1,32 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+cmake_minimum_required(VERSION 3.26.4)
+
+project(
+  basic_example
+  VERSION 0.0.1
+  LANGUAGES CXX CUDA
+)
+
+set(CPM_DOWNLOAD_VERSION v0.35.3)
+file(
+  DOWNLOAD
+  https://github.com/cpm-cmake/CPM.cmake/releases/download/${CPM_DOWNLOAD_VERSION}/get_cpm.cmake
+  ${CMAKE_BINARY_DIR}/cmake/get_cpm.cmake
+)
+include(${CMAKE_BINARY_DIR}/cmake/get_cpm.cmake)
+
+set(CUDF_TAG branch-23.10)
+CPMFindPackage(
+  NAME cudf GIT_REPOSITORY https://github.com/rapidsai/cudf
+  GIT_TAG ${CUDF_TAG}
+  GIT_SHALLOW
+    TRUE
+    SOURCE_SUBDIR
+    cpp
+)
+
+# Configure your project here
+add_executable(basic_example src/process_csv.cpp)
+target_link_libraries(basic_example PRIVATE cudf::cudf)
+target_compile_features(basic_example PRIVATE cxx_std_17)
diff --git a/cpp/examples/basic/README.md b/cpp/examples/basic/README.md
new file mode 100644
index 0000000..471dcf6
--- /dev/null
+++ b/cpp/examples/basic/README.md
@@ -0,0 +1,23 @@
+# Basic Standalone libcudf C++ application
+
+This C++ example demonstrates a basic libcudf use case and provides a minimal
+example of building your own application based on libcudf using CMake.
+
+The example source code loads a csv file that contains stock prices from 4
+companies spanning across 5 days, computes the average of the closing price
+for each company and writes the result in csv format.
+
+## Compile and execute
+
+```bash
+# Configure project
+cmake -S . -B build/
+# Build
+cmake --build build/ --parallel $PARALLEL_LEVEL
+# Execute
+build/basic_example
+```
+
+If your machine does not come with a pre-built libcudf binary, expect the
+first build to take some time, as it would build libcudf on the host machine.
+It may be sped up by configuring the proper `PARALLEL_LEVEL` number.
diff --git a/cpp/examples/basic/src/process_csv.cpp b/cpp/examples/basic/src/process_csv.cpp
new file mode 100644
index 0000000..edd14d9
--- /dev/null
+++ b/cpp/examples/basic/src/process_csv.cpp
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/io/csv.hpp>
+#include <cudf/table/table.hpp>
+
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+cudf::io::table_with_metadata read_csv(std::string const& file_path)
+{
+  auto source_info = cudf::io::source_info(file_path);
+  auto builder     = cudf::io::csv_reader_options::builder(source_info);
+  auto options     = builder.build();
+  return cudf::io::read_csv(options);
+}
+
+void write_csv(cudf::table_view const& tbl_view, std::string const& file_path)
+{
+  auto sink_info = cudf::io::sink_info(file_path);
+  auto builder   = cudf::io::csv_writer_options::builder(sink_info, tbl_view);
+  auto options   = builder.build();
+  cudf::io::write_csv(options);
+}
+
+std::vector<cudf::groupby::aggregation_request> make_single_aggregation_request(
+  std::unique_ptr<cudf::groupby_aggregation>&& agg, cudf::column_view value)
+{
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].aggregations.push_back(std::move(agg));
+  requests[0].values = value;
+  return requests;
+}
+
+std::unique_ptr<cudf::table> average_closing_price(cudf::table_view stock_info_table)
+{
+  // Schema: | Company | Date | Open | High | Low | Close | Volume |
+  auto keys = cudf::table_view{{stock_info_table.column(0)}};  // Company
+  auto val  = stock_info_table.column(5);                      // Close
+
+  // Compute the average of each company's closing price with entire column
+  cudf::groupby::groupby grpby_obj(keys);
+  auto requests =
+    make_single_aggregation_request(cudf::make_mean_aggregation<cudf::groupby_aggregation>(), val);
+
+  auto agg_results = grpby_obj.aggregate(requests);
+
+  // Assemble the result
+  auto result_key = std::move(agg_results.first);
+  auto result_val = std::move(agg_results.second[0].results[0]);
+  std::vector<cudf::column_view> columns{result_key->get_column(0), *result_val};
+  return std::make_unique<cudf::table>(cudf::table_view(columns));
+}
+
+int main(int argc, char** argv)
+{
+  // Construct a CUDA memory resource using RAPIDS Memory Manager (RMM)
+  // This is the default memory resource for libcudf for allocating device memory.
+  rmm::mr::cuda_memory_resource cuda_mr{};
+  // Construct a memory pool using the CUDA memory resource
+  // Using a memory pool for device memory allocations is important for good performance in libcudf.
+  // The pool defaults to allocating half of the available GPU memory.
+  rmm::mr::pool_memory_resource mr{&cuda_mr};
+
+  // Set the pool resource to be used by default for all device memory allocations
+  // Note: It is the user's responsibility to ensure the `mr` object stays alive for the duration of
+  // it being set as the default
+  // Also, call this before the first libcudf API call to ensure all data is allocated by the same
+  // memory resource.
+  rmm::mr::set_current_device_resource(&mr);
+
+  // Read data
+  auto stock_table_with_metadata = read_csv("4stock_5day.csv");
+
+  // Process
+  auto result = average_closing_price(*stock_table_with_metadata.tbl);
+
+  // Write out result
+  write_csv(*result, "4stock_5day_avg_close.csv");
+
+  return 0;
+}
diff --git a/cpp/examples/build.sh b/cpp/examples/build.sh
new file mode 100755
index 0000000..7d389cd
--- /dev/null
+++ b/cpp/examples/build.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+# libcudf examples build script
+
+# Parallelism control
+PARALLEL_LEVEL=${PARALLEL_LEVEL:-4}
+
+# Root of examples
+EXAMPLES_DIR=$(dirname "$(realpath "$0")")
+LIB_BUILD_DIR=${LIB_BUILD_DIR:-$(readlink -f "${EXAMPLES_DIR}/../build")}
+
+################################################################################
+# Add individual libcudf examples build scripts down below
+
+# Basic example
+BASIC_EXAMPLE_DIR=${EXAMPLES_DIR}/basic
+BASIC_EXAMPLE_BUILD_DIR=${BASIC_EXAMPLE_DIR}/build
+# Configure
+cmake -S ${BASIC_EXAMPLE_DIR} -B ${BASIC_EXAMPLE_BUILD_DIR} -Dcudf_ROOT="${LIB_BUILD_DIR}"
+# Build
+cmake --build ${BASIC_EXAMPLE_BUILD_DIR} -j${PARALLEL_LEVEL}
+
+# Strings example
+STRINGS_EXAMPLE_DIR=${EXAMPLES_DIR}/strings
+STRINGS_EXAMPLE_BUILD_DIR=${STRINGS_EXAMPLE_DIR}/build
+# Configure
+cmake -S ${STRINGS_EXAMPLE_DIR} -B ${STRINGS_EXAMPLE_BUILD_DIR} -Dcudf_ROOT="${LIB_BUILD_DIR}"
+# Build
+cmake --build ${STRINGS_EXAMPLE_BUILD_DIR} -j${PARALLEL_LEVEL}
diff --git a/cpp/examples/strings/CMakeLists.txt b/cpp/examples/strings/CMakeLists.txt
new file mode 100644
index 0000000..31a6b12
--- /dev/null
+++ b/cpp/examples/strings/CMakeLists.txt
@@ -0,0 +1,49 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+cmake_minimum_required(VERSION 3.26.4)
+
+project(
+  strings_examples
+  VERSION 0.0.1
+  LANGUAGES CXX CUDA
+)
+
+set(CPM_DOWNLOAD_VERSION v0.35.3)
+file(
+  DOWNLOAD
+  https://github.com/cpm-cmake/CPM.cmake/releases/download/${CPM_DOWNLOAD_VERSION}/get_cpm.cmake
+  ${CMAKE_BINARY_DIR}/cmake/get_cpm.cmake
+)
+include(${CMAKE_BINARY_DIR}/cmake/get_cpm.cmake)
+
+set(CUDF_TAG branch-23.10)
+CPMFindPackage(
+  NAME cudf GIT_REPOSITORY https://github.com/rapidsai/cudf
+  GIT_TAG ${CUDF_TAG}
+  GIT_SHALLOW
+    TRUE
+    SOURCE_SUBDIR
+    cpp
+)
+
+list(APPEND CUDF_CUDA_FLAGS --expt-extended-lambda --expt-relaxed-constexpr)
+
+#
+add_executable(libcudf_apis libcudf_apis.cpp)
+target_compile_features(libcudf_apis PRIVATE cxx_std_17)
+target_link_libraries(libcudf_apis PRIVATE cudf::cudf nvToolsExt)
+
+add_executable(custom_with_malloc custom_with_malloc.cu)
+target_compile_features(custom_with_malloc PRIVATE cxx_std_17)
+target_compile_options(custom_with_malloc PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>")
+target_link_libraries(custom_with_malloc PRIVATE cudf::cudf nvToolsExt)
+
+add_executable(custom_prealloc custom_prealloc.cu)
+target_compile_features(custom_prealloc PRIVATE cxx_std_17)
+target_compile_options(custom_prealloc PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>")
+target_link_libraries(custom_prealloc PRIVATE cudf::cudf nvToolsExt)
+
+add_executable(custom_optimized custom_optimized.cu)
+target_compile_features(custom_optimized PRIVATE cxx_std_17)
+target_compile_options(custom_optimized PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>")
+target_link_libraries(custom_optimized PRIVATE cudf::cudf nvToolsExt)
diff --git a/cpp/examples/strings/README.md b/cpp/examples/strings/README.md
new file mode 100644
index 0000000..241aa06
--- /dev/null
+++ b/cpp/examples/strings/README.md
@@ -0,0 +1,37 @@
+# libcudf C++ examples using strings columns
+
+This C++ example demonstrates using libcudf APIs to access and create
+strings columns.
+
+The example source code loads a csv file and produces a redacted strings
+column from the names column using the values from the visibilities column.
+
+Four examples are included:
+1. Using libcudf APIs to build the output
+2. Using a simple custom kernel with dynamic memory
+3. Using a custom kernel with pre-allocated device memory
+4. Using a two-pass approach to improve performance
+
+These examples are described in more detail in
+https://developer.nvidia.com/blog/mastering-string-transformations-in-rapids-libcudf/
+
+## Compile and execute
+
+```bash
+# Configure project
+cmake -S . -B build/
+# Build
+cmake --build build/ --parallel $PARALLEL_LEVEL
+# Execute
+build/libcudf_apis names.csv
+--OR--
+build/custom_with_malloc names.csv
+--OR--
+build/custom_prealloc names.csv
+--OR--
+build/custom_optimized names.csv
+```
+
+If your machine does not come with a pre-built libcudf binary, expect the
+first build to take some time, as it would build libcudf on the host machine.
+It may be sped up by configuring the proper `PARALLEL_LEVEL` number.
diff --git a/cpp/examples/strings/common.hpp b/cpp/examples/strings/common.hpp
new file mode 100644
index 0000000..2fd9daf
--- /dev/null
+++ b/cpp/examples/strings/common.hpp
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/io/csv.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/owning_wrapper.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include <chrono>
+#include <iostream>
+#include <memory>
+#include <string>
+
+/**
+ * @brief Main example function returns redacted strings column.
+ *
+ * This function returns a redacted version of the input `names` column
+ * using the `visibilities` column as in the following example
+ * ```
+ * names        visibility  --> redacted
+ * John Doe     public          D John
+ * Bobby Joe    private         X X
+ * ```
+ *
+ * @param names First and last names separated with a single space
+ * @param visibilities String values `public` or `private` only
+ * @return Redacted strings column
+ */
+std::unique_ptr<cudf::column> redact_strings(cudf::column_view const& names,
+                                             cudf::column_view const& visibilities);
+
+/**
+ * @brief Create CUDA memory resource
+ */
+auto make_cuda_mr() { return std::make_shared<rmm::mr::cuda_memory_resource>(); }
+
+/**
+ * @brief Create a pool device memory resource
+ */
+auto make_pool_mr()
+{
+  return rmm::mr::make_owning_wrapper<rmm::mr::pool_memory_resource>(make_cuda_mr());
+}
+
+/**
+ * @brief Create memory resource for libcudf functions
+ */
+std::shared_ptr<rmm::mr::device_memory_resource> create_memory_resource(std::string const& name)
+{
+  if (name == "pool") { return make_pool_mr(); }
+  return make_cuda_mr();
+}
+
+/**
+ * @brief Main for strings examples
+ *
+ * Command line parameters:
+ * 1. CSV file name/path
+ * 2. Memory resource (optional): 'pool' or 'cuda'
+ *
+ * The stdout includes the number of rows in the input and the output size in bytes.
+ */
+int main(int argc, char const** argv)
+{
+  if (argc < 2) {
+    std::cout << "required parameter: csv-file-path\n";
+    return 1;
+  }
+
+  auto const mr_name = std::string{argc > 2 ? std::string(argv[2]) : std::string("cuda")};
+  auto resource      = create_memory_resource(mr_name);
+  rmm::mr::set_current_device_resource(resource.get());
+
+  auto const csv_file   = std::string{argv[1]};
+  auto const csv_result = [csv_file] {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_file}).header(-1);
+    return cudf::io::read_csv(in_opts).tbl;
+  }();
+  auto const csv_table = csv_result->view();
+
+  std::cout << "table: " << csv_table.num_rows() << " rows " << csv_table.num_columns()
+            << " columns\n";
+
+  auto st     = std::chrono::steady_clock::now();
+  auto result = redact_strings(csv_table.column(0), csv_table.column(1));
+
+  std::chrono::duration<double> elapsed = std::chrono::steady_clock::now() - st;
+  std::cout << "Wall time: " << elapsed.count() << " seconds\n";
+  std::cout << "Output size " << result->view().child(1).size() << " bytes\n";
+
+  return 0;
+}
diff --git a/cpp/examples/strings/custom_optimized.cu b/cpp/examples/strings/custom_optimized.cu
new file mode 100644
index 0000000..3652187
--- /dev/null
+++ b/cpp/examples/strings/custom_optimized.cu
@@ -0,0 +1,166 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/scan.h>
+
+#include <cuda_runtime.h>
+#include <nvtx3/nvToolsExt.h>
+
+/**
+ * @brief Computes the size of each output row
+ *
+ * This thread is called once per row in d_names.
+ *
+ * @param d_names Column of names
+ * @param d_visibilities Column of visibilities
+ * @param d_sizes Output sizes for each row
+ */
+__global__ void sizes_kernel(cudf::column_device_view const d_names,
+                             cudf::column_device_view const d_visibilities,
+                             cudf::size_type* d_sizes)
+{
+  // The row index is resolved from the CUDA thread/block objects
+  auto index = threadIdx.x + blockIdx.x * blockDim.x;
+  // There may be more threads than actual rows
+  if (index >= d_names.size()) return;
+
+  auto const visible   = cudf::string_view("public", 6);
+  auto const redaction = cudf::string_view("X X", 3);
+
+  auto const name = d_names.element<cudf::string_view>(index);
+  auto const vis  = d_visibilities.element<cudf::string_view>(index);
+
+  cudf::size_type result = redaction.size_bytes();  // init to redaction size
+  if (vis == visible) {
+    auto const space_idx    = name.find(' ');
+    auto const first        = name.substr(0, space_idx);
+    auto const last_initial = name.substr(space_idx + 1, 1);
+
+    result = first.size_bytes() + last_initial.size_bytes() + 1;
+  }
+
+  d_sizes[index] = result;
+}
+
+/**
+ * @brief Builds the output for each row
+ *
+ * This thread is called once per row in d_names.
+ *
+ * @param d_names Column of names
+ * @param d_visibilities Column of visibilities
+ * @param d_offsets Byte offset in `d_chars` for each row
+ * @param d_chars Output memory for all rows
+ */
+__global__ void redact_kernel(cudf::column_device_view const d_names,
+                              cudf::column_device_view const d_visibilities,
+                              cudf::size_type const* d_offsets,
+                              char* d_chars)
+{
+  // The row index is resolved from the CUDA thread/block objects
+  auto index = threadIdx.x + blockIdx.x * blockDim.x;
+  // There may be more threads than actual rows
+  if (index >= d_names.size()) return;
+
+  auto const visible   = cudf::string_view("public", 6);
+  auto const redaction = cudf::string_view("X X", 3);
+
+  // resolve output_ptr using the offsets vector
+  char* output_ptr = d_chars + d_offsets[index];
+
+  auto const name = d_names.element<cudf::string_view>(index);
+  auto const vis  = d_visibilities.element<cudf::string_view>(index);
+
+  if (vis == visible) {
+    auto const space_idx    = name.find(' ');
+    auto const first        = name.substr(0, space_idx);
+    auto const last_initial = name.substr(space_idx + 1, 1);
+    auto const output_size  = first.size_bytes() + last_initial.size_bytes() + 1;
+
+    // build output string
+    memcpy(output_ptr, last_initial.data(), last_initial.size_bytes());
+    output_ptr += last_initial.size_bytes();
+    *output_ptr++ = ' ';
+    memcpy(output_ptr, first.data(), first.size_bytes());
+  } else {
+    memcpy(output_ptr, redaction.data(), redaction.size_bytes());
+  }
+}
+
+/**
+ * @brief Redacts each name per the corresponding visibility entry
+ *
+ * This implementation builds the strings column children (offsets and chars)
+ * directly into device memory for libcudf.
+ *
+ * @param names Column of names
+ * @param visibilities Column of visibilities
+ * @return Redacted column of names
+ */
+std::unique_ptr<cudf::column> redact_strings(cudf::column_view const& names,
+                                             cudf::column_view const& visibilities)
+{
+  // all device memory operations and kernel functions will run on this stream
+  auto stream = rmm::cuda_stream_default;
+
+  auto const d_names        = cudf::column_device_view::create(names, stream);
+  auto const d_visibilities = cudf::column_device_view::create(visibilities, stream);
+
+  constexpr int block_size = 128;  // this arbitrary size should be a power of 2
+  int const blocks         = (names.size() + block_size - 1) / block_size;
+
+  nvtxRangePushA("redact_strings");
+
+  // create offsets vector
+  auto offsets = rmm::device_uvector<cudf::size_type>(names.size() + 1, stream);
+
+  // compute output sizes
+  sizes_kernel<<<blocks, block_size, 0, stream.value()>>>(
+    *d_names, *d_visibilities, offsets.data());
+
+  // convert sizes to offsets (in place)
+  thrust::exclusive_scan(rmm::exec_policy(stream), offsets.begin(), offsets.end(), offsets.begin());
+
+  // last element is the total output size
+  // (device-to-host copy of 1 integer -- includes syncing the stream)
+  cudf::size_type output_size = offsets.back_element(stream);
+
+  //  create chars vector
+  auto chars = rmm::device_uvector<char>(output_size, stream);
+
+  // build chars output
+  redact_kernel<<<blocks, block_size, 0, stream.value()>>>(
+    *d_names, *d_visibilities, offsets.data(), chars.data());
+
+  // create column from offsets and chars vectors (no copy is performed)
+  auto result =
+    cudf::make_strings_column(names.size(), std::move(offsets), std::move(chars), {}, 0);
+
+  // wait for all of the above to finish
+  stream.synchronize();
+
+  nvtxRangePop();
+  return result;
+}
diff --git a/cpp/examples/strings/custom_prealloc.cu b/cpp/examples/strings/custom_prealloc.cu
new file mode 100644
index 0000000..0af4c47
--- /dev/null
+++ b/cpp/examples/strings/custom_prealloc.cu
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cuda_runtime.h>
+#include <nvtx3/nvToolsExt.h>
+
+/**
+ * @brief Builds the output for each row
+ *
+ * This thread is called once per row in d_names.
+ *
+ * @param d_names Column of names
+ * @param d_visibilities Column of visibilities
+ * @param redaction Redacted string replacement
+ * @param working_memory Output memory for all rows
+ * @param d_offsets Byte offset in `d_chars` for each row
+ * @param d_output Output array of string_view objects
+ */
+__global__ void redact_kernel(cudf::column_device_view const d_names,
+                              cudf::column_device_view const d_visibilities,
+                              cudf::string_view redaction,
+                              char* working_memory,
+                              cudf::size_type const* d_offsets,
+                              cudf::string_view* d_output)
+{
+  // The row index is resolved from the CUDA thread/block objects
+  auto index = threadIdx.x + blockIdx.x * blockDim.x;
+  // There may be more threads than actual rows
+  if (index >= d_names.size()) return;
+
+  auto const visible = cudf::string_view("public", 6);
+
+  auto const name = d_names.element<cudf::string_view>(index);
+  auto const vis  = d_visibilities.element<cudf::string_view>(index);
+  if (vis == visible) {
+    auto const space_idx    = name.find(' ');
+    auto const first        = name.substr(0, space_idx);
+    auto const last_initial = name.substr(space_idx + 1, 1);
+    auto const output_size  = first.size_bytes() + last_initial.size_bytes() + 1;
+
+    char* output_ptr = working_memory + d_offsets[index];
+    d_output[index]  = cudf::string_view{output_ptr, output_size};
+
+    // build output string
+    memcpy(output_ptr, last_initial.data(), last_initial.size_bytes());
+    output_ptr += last_initial.size_bytes();
+    *output_ptr++ = ' ';
+    memcpy(output_ptr, first.data(), first.size_bytes());
+  } else {
+    d_output[index] = cudf::string_view{redaction.data(), redaction.size_bytes()};
+  }
+}
+
+/**
+ * @brief Redacts each name per the corresponding visibility entry
+ *
+ * This implementation builds the individual strings into a fixed memory buffer
+ * and then calls a factory function to gather them into a strings column.
+ *
+ * @param names Column of names
+ * @param visibilities Column of visibilities
+ * @return Redacted column of names
+ */
+std::unique_ptr<cudf::column> redact_strings(cudf::column_view const& names,
+                                             cudf::column_view const& visibilities)
+{
+  // all device memory operations and kernel functions will run on this stream
+  auto stream = rmm::cuda_stream_default;
+
+  auto const d_names        = cudf::column_device_view::create(names, stream);
+  auto const d_visibilities = cudf::column_device_view::create(visibilities, stream);
+  auto const d_redaction    = cudf::string_scalar(std::string("X X"), true, stream);
+
+  constexpr int block_size = 128;  // this arbitrary size should be a power of 2
+  auto const blocks        = (names.size() + block_size - 1) / block_size;
+
+  nvtxRangePushA("redact_strings");
+
+  auto const scv     = cudf::strings_column_view(names);
+  auto const offsets = scv.offsets_begin();
+
+  // create working memory to hold the output of each string
+  auto working_memory = rmm::device_uvector<char>(scv.chars_size(), stream);
+  // create a vector for the output strings' pointers
+  auto str_ptrs = rmm::device_uvector<cudf::string_view>(names.size(), stream);
+
+  // build the output strings
+  redact_kernel<<<blocks, block_size, 0, stream.value()>>>(*d_names,
+                                                           *d_visibilities,
+                                                           d_redaction.value(),
+                                                           working_memory.data(),
+                                                           offsets,
+                                                           str_ptrs.data());
+
+  // create strings column from the string_pairs;
+  // this copies all the individual strings into a single output column
+  auto result = cudf::make_strings_column(str_ptrs, cudf::string_view{nullptr, 0}, stream);
+  // temporary memory cleanup cost here for str_ptrs and working_memory
+
+  // wait for all of the above to finish
+  stream.synchronize();
+
+  nvtxRangePop();
+  return result;
+}
diff --git a/cpp/examples/strings/custom_with_malloc.cu b/cpp/examples/strings/custom_with_malloc.cu
new file mode 100644
index 0000000..32f7bf7
--- /dev/null
+++ b/cpp/examples/strings/custom_with_malloc.cu
@@ -0,0 +1,158 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <cuda_runtime.h>
+#include <nvtx3/nvToolsExt.h>
+
+/**
+ * @brief Reserve CUDA malloc heap size
+ *
+ * Call this function to change the CUDA malloc heap size limit.
+ * This value depends on the total size of all the malloc()
+ * calls needed for redact_kernel.
+ *
+ * @param heap_size Number of bytes to reserve
+ *                  Default is 1GB
+ */
+void set_malloc_heap_size(size_t heap_size = 1073741824)  // 1GB
+{
+  size_t max_malloc_heap_size = 0;
+  cudaDeviceGetLimit(&max_malloc_heap_size, cudaLimitMallocHeapSize);
+  if (max_malloc_heap_size < heap_size) {
+    max_malloc_heap_size = heap_size;
+    if (cudaDeviceSetLimit(cudaLimitMallocHeapSize, max_malloc_heap_size) != cudaSuccess) {
+      fprintf(stderr, "could not set malloc heap size to %ldMB\n", (heap_size / (1024 * 1024)));
+      throw std::runtime_error("");
+    }
+  }
+}
+
+/**
+ * @brief Builds the output for each row
+ *
+ * This thread is called once per row in d_names.
+ *
+ * Note: This uses malloc() in a device kernel which works great
+ * but is not very efficient. This can be useful for prototyping
+ * on functions where performance is not yet important.
+ * All calls to malloc() must have a corresponding free() call.
+ * The separate free_kernel is launched for this purpose.
+ *
+ * @param d_names Column of names
+ * @param d_visibilities Column of visibilities
+ * @param redaction Redacted string replacement
+ * @param d_output Output array of string_view objects
+ */
+__global__ void redact_kernel(cudf::column_device_view const d_names,
+                              cudf::column_device_view const d_visibilities,
+                              cudf::string_view redaction,
+                              cudf::string_view* d_output)
+{
+  // The row index is resolved from the CUDA thread/block objects
+  auto index = threadIdx.x + blockIdx.x * blockDim.x;
+  // There may be more threads than actual rows
+  if (index >= d_names.size()) return;
+
+  auto const visible = cudf::string_view("public", 6);
+
+  auto const name = d_names.element<cudf::string_view>(index);
+  auto const vis  = d_visibilities.element<cudf::string_view>(index);
+  if (vis == visible) {
+    auto const space_idx    = name.find(' ');
+    auto const first        = name.substr(0, space_idx);
+    auto const last_initial = name.substr(space_idx + 1, 1);
+    auto const output_size  = first.size_bytes() + last_initial.size_bytes() + 1;
+
+    char* output_ptr = static_cast<char*>(malloc(output_size));
+    d_output[index]  = cudf::string_view{output_ptr, output_size};
+
+    // build output string
+    memcpy(output_ptr, last_initial.data(), last_initial.size_bytes());
+    output_ptr += last_initial.size_bytes();
+    *output_ptr++ = ' ';
+    memcpy(output_ptr, first.data(), first.size_bytes());
+  } else {
+    d_output[index] = cudf::string_view{redaction.data(), redaction.size_bytes()};
+  }
+}
+
+/**
+ * @brief Frees the temporary individual string objects created in the
+ * redact_kernel
+ *
+ * Like malloc(), free() is not very efficient but must be called for
+ * each malloc() to return the memory to the CUDA malloc heap.
+ *
+ * @param redaction Redacted string replacement (not to be freed)
+ * @param d_output Output array of string_view objects to free
+ */
+__global__ void free_kernel(cudf::string_view redaction, cudf::string_view* d_output, int count)
+{
+  auto index = threadIdx.x + blockIdx.x * blockDim.x;
+  if (index >= count) return;
+
+  auto ptr = const_cast<char*>(d_output[index].data());
+  if (ptr != redaction.data()) { free(ptr); }
+}
+
+std::unique_ptr<cudf::column> redact_strings(cudf::column_view const& names,
+                                             cudf::column_view const& visibilities)
+{
+  // all device memory operations and kernel functions will run on this stream
+  auto stream = rmm::cuda_stream_default;
+
+  set_malloc_heap_size();  // to illustrate adjusting the malloc heap
+
+  auto const d_names        = cudf::column_device_view::create(names, stream);
+  auto const d_visibilities = cudf::column_device_view::create(visibilities, stream);
+  auto const d_redaction    = cudf::string_scalar(std::string("X X"), true, stream);
+
+  constexpr int block_size = 128;  // this arbitrary size should be a power of 2
+  auto const blocks        = (names.size() + block_size - 1) / block_size;
+
+  nvtxRangePushA("redact_strings");
+
+  // create a vector for the output strings' pointers
+  auto str_ptrs = new rmm::device_uvector<cudf::string_view>(names.size(), stream);
+
+  auto result = [&] {
+    // build the output strings
+    redact_kernel<<<blocks, block_size, 0, stream.value()>>>(
+      *d_names, *d_visibilities, d_redaction.value(), str_ptrs->data());
+    // create strings column from the string_view vector
+    // this copies all the individual strings into a single output column
+    return cudf::make_strings_column(*str_ptrs, cudf::string_view{nullptr, 0}, stream);
+  }();
+
+  // free the individual temporary memory pointers
+  free_kernel<<<blocks, block_size, 0, stream.value()>>>(
+    d_redaction.value(), str_ptrs->data(), names.size());
+  delete str_ptrs;
+
+  // wait for all of the above to finish
+  stream.synchronize();
+
+  nvtxRangePop();
+  return result;
+}
diff --git a/cpp/examples/strings/libcudf_apis.cpp b/cpp/examples/strings/libcudf_apis.cpp
new file mode 100644
index 0000000..f5f1eb0
--- /dev/null
+++ b/cpp/examples/strings/libcudf_apis.cpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common.hpp"
+
+#include <cudf/copying.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/find.hpp>
+#include <cudf/strings/slice.hpp>
+#include <cudf/strings/split/split.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cuda_runtime.h>
+#include <nvtx3/nvToolsExt.h>
+
+/**
+ * @brief Redacts each name per the corresponding visibility entry
+ *
+ * This implementation uses libcudf APIs to create the output result.
+ *
+ * @param names Column of names
+ * @param visibilities Column of visibilities
+ * @return Redacted column of names
+ */
+std::unique_ptr<cudf::column> redact_strings(cudf::column_view const& names,
+                                             cudf::column_view const& visibilities)
+{
+  auto const visible   = cudf::string_scalar(std::string("public"));
+  auto const redaction = cudf::string_scalar(std::string("X X"));
+
+  nvtxRangePushA("redact_strings");
+
+  auto const allowed      = cudf::strings::contains(visibilities, visible);
+  auto const redacted     = cudf::copy_if_else(names, redaction, allowed->view());
+  auto const first_last   = cudf::strings::split(redacted->view());
+  auto const first        = first_last->view().column(0);
+  auto const last         = first_last->view().column(1);
+  auto const last_initial = cudf::strings::slice_strings(last, 0, 1);
+
+  auto const last_initial_first = cudf::table_view({last_initial->view(), first});
+
+  auto result = cudf::strings::concatenate(last_initial_first, std::string(" "));
+
+  cudaStreamSynchronize(0);
+
+  nvtxRangePop();
+  return result;
+}
diff --git a/cpp/examples/strings/names.csv b/cpp/examples/strings/names.csv
new file mode 100644
index 0000000..77dca3e
--- /dev/null
+++ b/cpp/examples/strings/names.csv
@@ -0,0 +1,20 @@
+John Doe,public
+Jane Doe,private
+Billy Joe,private
+James James,public
+Michael Frederick,public
+Christopher Cheryl,public
+Jessica Autumn,public
+Matthew Tyrone,public
+Ashley Martha,public
+Jennifer Omar,public
+Joshua Lydia,public
+Amanda Jerome,public
+Daniel Theodore,public
+David Abby,public
+James Neil,public
+Robert Shawna,private
+John Sierra,private
+Joseph Nina,private
+Andrew Tammy,private
+Ryan Nikki,public
diff --git a/cpp/include/cudf/aggregation.hpp b/cpp/include/cudf/aggregation.hpp
new file mode 100644
index 0000000..d458c83
--- /dev/null
+++ b/cpp/include/cudf/aggregation.hpp
@@ -0,0 +1,734 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <functional>
+#include <memory>
+#include <vector>
+
+/**
+ * @file aggregation.hpp
+ * @brief Representation for specifying desired aggregations from
+ * aggregation-based APIs, e.g., groupby, reductions, rolling, etc.
+ *
+ * @note Not all aggregation APIs support all aggregation operations. See
+ * individual function documentation to see what aggregations are supported.
+ */
+
+namespace cudf {
+/**
+ * @addtogroup aggregation_factories
+ * @{
+ * @file
+ */
+
+// forward declaration
+namespace detail {
+class simple_aggregations_collector;
+class aggregation_finalizer;
+}  // namespace detail
+
+/**
+ * @brief Tie-breaker method to use for ranking the column.
+ *
+ * @see cudf::make_rank_aggregation for more details.
+ * @ingroup column_sort
+ */
+enum class rank_method : int32_t {
+  FIRST,    ///< stable sort order ranking (no ties)
+  AVERAGE,  ///< mean of first in the group
+  MIN,      ///< min of first in the group
+  MAX,      ///< max of first in the group
+  DENSE     ///< rank always increases by 1 between groups
+};
+
+/**
+ * @brief Whether returned rank should be percentage or not and
+ *  mention the type of percentage normalization.
+ *
+ */
+enum class rank_percentage : int32_t {
+  NONE,             ///< rank
+  ZERO_NORMALIZED,  ///< rank / count
+  ONE_NORMALIZED    ///< (rank - 1) / (count - 1)
+};
+
+/**
+ * @brief Abstract base class for specifying the desired aggregation in an
+ * `aggregation_request`.
+ *
+ * All aggregations must derive from this class to implement the pure virtual
+ * functions and potentially encapsulate additional information needed to
+ * compute the aggregation.
+ */
+class aggregation {
+ public:
+  /**
+   * @brief Possible aggregation operations
+   */
+  enum Kind {
+    SUM,             ///< sum reduction
+    PRODUCT,         ///< product reduction
+    MIN,             ///< min reduction
+    MAX,             ///< max reduction
+    COUNT_VALID,     ///< count number of valid elements
+    COUNT_ALL,       ///< count number of elements
+    ANY,             ///< any reduction
+    ALL,             ///< all reduction
+    SUM_OF_SQUARES,  ///< sum of squares reduction
+    MEAN,            ///< arithmetic mean reduction
+    M2,              ///< sum of squares of differences from the mean
+    VARIANCE,        ///< variance
+    STD,             ///< standard deviation
+    MEDIAN,          ///< median reduction
+    QUANTILE,        ///< compute specified quantile(s)
+    ARGMAX,          ///< Index of max element
+    ARGMIN,          ///< Index of min element
+    NUNIQUE,         ///< count number of unique elements
+    NTH_ELEMENT,     ///< get the nth element
+    ROW_NUMBER,      ///< get row-number of current index (relative to rolling window)
+    RANK,            ///< get rank of current index
+    COLLECT_LIST,    ///< collect values into a list
+    COLLECT_SET,     ///< collect values into a list without duplicate entries
+    LEAD,            ///< window function, accesses row at specified offset following current row
+    LAG,             ///< window function, accesses row at specified offset preceding current row
+    PTX,             ///< PTX  UDF based reduction
+    CUDA,            ///< CUDA UDF based reduction
+    MERGE_LISTS,     ///< merge multiple lists values into one list
+    MERGE_SETS,      ///< merge multiple lists values into one list then drop duplicate entries
+    MERGE_M2,        ///< merge partial values of M2 aggregation,
+    COVARIANCE,      ///< covariance between two sets of elements
+    CORRELATION,     ///< correlation between two sets of elements
+    TDIGEST,         ///< create a tdigest from a set of input values
+    MERGE_TDIGEST,   ///< create a tdigest by merging multiple tdigests together
+    HISTOGRAM,       ///< compute frequency of each element
+    MERGE_HISTOGRAM  ///< merge partial values of HISTOGRAM aggregation,
+  };
+
+  aggregation() = delete;
+
+  /**
+   * @brief Construct a new aggregation object
+   *
+   * @param a aggregation::Kind enum value
+   */
+  aggregation(aggregation::Kind a) : kind{a} {}
+  Kind kind;  ///< The aggregation to perform
+  virtual ~aggregation() = default;
+
+  /**
+   * @brief Compares two aggregation objects for equality
+   *
+   * @param other The other aggregation to compare with
+   * @return True if the two aggregations are equal
+   */
+  [[nodiscard]] virtual bool is_equal(aggregation const& other) const { return kind == other.kind; }
+
+  /**
+   * @brief Computes the hash value of the aggregation
+   *
+   * @return The hash value of the aggregation
+   */
+  [[nodiscard]] virtual size_t do_hash() const { return std::hash<int>{}(kind); }
+
+  /**
+   * @pure @brief Clones the aggregation object
+   *
+   * @return A copy of the aggregation object
+   */
+  [[nodiscard]] virtual std::unique_ptr<aggregation> clone() const = 0;
+
+  // override functions for compound aggregations
+  /**
+   * @pure @brief Get the simple aggregations that this aggregation requires to compute.
+   *
+   * @param col_type The type of the column to aggregate
+   * @param collector The collector visitor pattern to use to collect the simple aggregations
+   * @return Vector of pre-requisite simple aggregations
+   */
+  virtual std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, cudf::detail::simple_aggregations_collector& collector) const = 0;
+
+  /**
+   * @pure @brief Compute the aggregation after pre-requisite simple aggregations have been
+   * computed.
+   *
+   * @param finalizer The finalizer visitor pattern to use to compute the aggregation
+   */
+  virtual void finalize(cudf::detail::aggregation_finalizer& finalizer) const = 0;
+};
+
+/**
+ * @brief Derived class intended for rolling_window specific aggregation usage.
+ *
+ * As an example, rolling_window will only accept rolling_aggregation inputs,
+ * and the appropriate derived classes (sum_aggregation, mean_aggregation, etc)
+ * derive from this interface to represent these valid options.
+ */
+class rolling_aggregation : public virtual aggregation {
+ public:
+  ~rolling_aggregation() override = default;
+
+ protected:
+  rolling_aggregation() {}
+  /// constructor inherited from cudf::aggregation
+  using aggregation::aggregation;
+};
+
+/**
+ * @brief Derived class intended for groupby specific aggregation usage.
+ */
+class groupby_aggregation : public virtual aggregation {
+ public:
+  ~groupby_aggregation() override = default;
+
+ protected:
+  groupby_aggregation() {}
+};
+
+/**
+ * @brief Derived class intended for groupby specific scan usage.
+ */
+class groupby_scan_aggregation : public virtual aggregation {
+ public:
+  ~groupby_scan_aggregation() override = default;
+
+ protected:
+  groupby_scan_aggregation() {}
+};
+
+/**
+ * @brief Derived class intended for reduction usage.
+ */
+class reduce_aggregation : public virtual aggregation {
+ public:
+  ~reduce_aggregation() override = default;
+
+ protected:
+  reduce_aggregation() {}
+};
+
+/**
+ * @brief Derived class intended for scan usage.
+ */
+class scan_aggregation : public virtual aggregation {
+ public:
+  ~scan_aggregation() override = default;
+
+ protected:
+  scan_aggregation() {}
+};
+
+/**
+ * @brief Derived class intended for segmented reduction usage.
+ */
+class segmented_reduce_aggregation : public virtual aggregation {
+ public:
+  ~segmented_reduce_aggregation() override = default;
+
+ protected:
+  segmented_reduce_aggregation() {}
+};
+
+/// Type of code in the user defined function string.
+enum class udf_type : bool { CUDA, PTX };
+/// Type of correlation method.
+enum class correlation_type : int32_t { PEARSON, KENDALL, SPEARMAN };
+
+/// Factory to create a SUM aggregation
+/// @return A SUM aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_sum_aggregation();
+
+/// Factory to create a PRODUCT aggregation
+/// @return A PRODUCT aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_product_aggregation();
+
+/// Factory to create a MIN aggregation
+/// @return A MIN aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_min_aggregation();
+
+/// Factory to create a MAX aggregation
+/// @return A MAX aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_max_aggregation();
+
+/**
+ * @brief Factory to create a COUNT aggregation
+ *
+ * @param null_handling Indicates if null values will be counted
+ * @return A COUNT aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_count_aggregation(null_policy null_handling = null_policy::EXCLUDE);
+
+/// Factory to create an ANY aggregation
+/// @return A ANY aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_any_aggregation();
+
+/// Factory to create a ALL aggregation
+/// @return A ALL aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_all_aggregation();
+
+/// Factory to create a HISTOGRAM aggregation
+/// @return A HISTOGRAM aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_histogram_aggregation();
+
+/// Factory to create a SUM_OF_SQUARES aggregation
+/// @return A SUM_OF_SQUARES aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_sum_of_squares_aggregation();
+
+/// Factory to create a MEAN aggregation
+/// @return A MEAN aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_mean_aggregation();
+
+/**
+ * @brief Factory to create a M2 aggregation
+ *
+ * A M2 aggregation is sum of squares of differences from the mean. That is:
+ *  `M2 = SUM((x - MEAN) * (x - MEAN))`.
+ *
+ * This aggregation produces the intermediate values that are used to compute variance and standard
+ * deviation across multiple discrete sets. See
+ * `https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm` for more
+ * detail.
+ * @return A M2 aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_m2_aggregation();
+
+/**
+ * @brief Factory to create a VARIANCE aggregation
+ *
+ * @param ddof Delta degrees of freedom. The divisor used in calculation of
+ *             `variance` is `N - ddof`, where `N` is the population size.
+ *
+ * @throw cudf::logic_error if input type is chrono or compound types.
+ * @return A VARIANCE aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_variance_aggregation(size_type ddof = 1);
+
+/**
+ * @brief Factory to create a STD aggregation
+ *
+ * @param ddof Delta degrees of freedom. The divisor used in calculation of
+ *             `std` is `N - ddof`, where `N` is the population size.
+ *
+ * @throw cudf::logic_error if input type is chrono or compound types.
+ * @return A STD aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_std_aggregation(size_type ddof = 1);
+
+/// Factory to create a MEDIAN aggregation
+/// @return A MEDIAN aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_median_aggregation();
+
+/**
+ * @brief Factory to create a QUANTILE aggregation
+ *
+ * @param quantiles The desired quantiles
+ * @param interp The desired interpolation
+ * @return A QUANTILE aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_quantile_aggregation(std::vector<double> const& quantiles,
+                                                interpolation interp = interpolation::LINEAR);
+
+/**
+ * @brief Factory to create an ARGMAX aggregation
+ *
+ * ARGMAX returns the index of the maximum element.
+ * @return A ARGMAX aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_argmax_aggregation();
+
+/**
+ * @brief Factory to create an ARGMIN aggregation
+ *
+ * `argmin` returns the index of the minimum element.
+ * @return A ARGMIN aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_argmin_aggregation();
+
+/**
+ * @brief Factory to create a NUNIQUE aggregation
+ *
+ * NUNIQUE returns the number of unique elements.
+ * @param null_handling Indicates if null values will be counted
+ * @return A NUNIQUE aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_nunique_aggregation(null_policy null_handling = null_policy::EXCLUDE);
+
+/**
+ * @brief Factory to create a NTH_ELEMENT aggregation
+ *
+ * NTH_ELEMENT returns the n'th element of the group/series.
+ *
+ * If @p n is not within the range `[-group_size, group_size)`, the result of
+ * the respective group will be null. Negative indices `[-group_size, -1]`
+ * corresponds to `[0, group_size-1]` indices respectively where `group_size` is
+ * the size of each group.
+ *
+ * @param n index of nth element in each group
+ * @param null_handling Indicates to include/exclude nulls during indexing
+ * @return A NTH_ELEMENT aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_nth_element_aggregation(
+  size_type n, null_policy null_handling = null_policy::INCLUDE);
+
+/// Factory to create a ROW_NUMBER aggregation
+/// @return A ROW_NUMBER aggregation object
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_row_number_aggregation();
+
+/**
+ * @brief Factory to create a RANK aggregation
+ *
+ * `RANK` returns a column of size_type or double "ranks" (see note 3 below for how the
+ * data type is determined) for a given rank method and column order.
+ * If nulls are excluded, the rank will be null for those rows, otherwise a non-nullable column is
+ * returned. Double precision column is returned only when percentage!=NONE and when rank method is
+ * average.
+ *
+ * This aggregation only works with "scan" algorithms. The input column into the group or
+ * ungrouped scan is an orderby column that orders the rows that the aggregate function ranks.
+ * If rows are ordered by more than one column, the orderby input column should be a struct
+ * column containing the ordering columns.
+ *
+ * Note:
+ *  1. This method could work faster with the rows that are presorted by the group keys and order_by
+ *     columns. Though groupby object does not require order_by column to be sorted, groupby rank
+ *     scan aggregation does require the order_by column to be sorted if the keys are sorted.
+ *  2. `RANK` aggregations are not compatible with exclusive scans.
+ *  3. All rank methods except AVERAGE method and percentage!=NONE returns size_type column.
+ *     For AVERAGE method and percentage!=NONE, the return type is double column.
+ *
+ * @code{.pseudo}
+ * Example: Consider a motor-racing statistics dataset, containing the following columns:
+ *   1. venue:  (STRING) Location of the race event
+ *   2. driver: (STRING) Name of the car driver (abbreviated to 3 characters)
+ *   3. time:   (INT32)  Time taken to complete the circuit
+ *
+ * For the following presorted data:
+ *
+ *  [ //      venue,           driver,           time
+ *    {   "silverstone",  "HAM" ("hamilton"),   15823},
+ *    {   "silverstone",  "LEC" ("leclerc"),    15827},
+ *    {   "silverstone",  "BOT" ("bottas"),     15834},  // <-- Tied for 3rd place.
+ *    {   "silverstone",  "NOR" ("norris"),     15834},  // <-- Tied for 3rd place.
+ *    {   "silverstone",  "RIC" ("ricciardo"),  15905},
+ *    {      "monza",     "RIC" ("ricciardo"),  12154},
+ *    {      "monza",     "NOR" ("norris"),     12156},  // <-- Tied for 2nd place.
+ *    {      "monza",     "BOT" ("bottas"),     12156},  // <-- Tied for 2nd place.
+ *    {      "monza",     "LEC" ("leclerc"),    12201},
+ *    {      "monza",     "PER" ("perez"),      12203}
+ *  ]
+ *
+ * A grouped rank aggregation scan with:
+ *   groupby column      : venue
+ *   input orderby column: time
+ * Produces the following rank column for each methods:
+ * first:   {   1,     2,     3,     4,     5,      1,     2,     3,     4,     5}
+ * average: {   1,     2,   3.5,   3.5,     5,      1,   2.5,   2.5,     4,     5}
+ * min:     {   1,     2,     3,     3,     5,      1,     2,     2,     4,     5}
+ * max:     {   1,     2,     4,     4,     5,      1,     3,     3,     4,     5}
+ * dense:   {   1,     2,     3,     3,     4,      1,     2,     2,     3,     4}
+ * This corresponds to the following grouping and `driver` rows:
+ *          { "HAM", "LEC", "BOT", "NOR", "RIC",  "RIC", "NOR", "BOT", "LEC", "PER" }
+ *            <----------silverstone----------->|<-------------monza-------------->
+ *
+ * min rank for each percentage types:
+ * NONE:             {   1,      2,     3,     3,     5,      1,     2,     2,     4,     5 }
+ * ZERO_NORMALIZED : { 0.16,  0.33,  0.50,  0.50,  0.83,   0.16,  0.33,  0.33,  0.66,  0.83 }
+ * ONE_NORMALIZED:   { 0.00,  0.25,  0.50,  0.50,  1.00,   0.00,  0.25,  0.25,  0.75,  1.00 }
+ * where count corresponds to the number of rows in the group. @see cudf::rank_percentage
+ *
+ * @endcode
+ *
+ * @param method The ranking method used for tie breaking (same values)
+ * @param column_order The desired sort order for ranking
+ * @param null_handling  flag to include nulls during ranking If nulls are not included,
+ * the corresponding rank will be null.
+ * @param null_precedence The desired order of null compared to other elements for column
+ * @param percentage enum to denote the type of conversion of ranks to percentage in range (0,1]
+ * @return A RANK aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_rank_aggregation(rank_method method,
+                                            order column_order         = order::ASCENDING,
+                                            null_policy null_handling  = null_policy::EXCLUDE,
+                                            null_order null_precedence = null_order::AFTER,
+                                            rank_percentage percentage = rank_percentage::NONE);
+
+/**
+ * @brief Factory to create a COLLECT_LIST aggregation
+ *
+ * `COLLECT_LIST` returns a list column of all included elements in the group/series.
+ *
+ * If `null_handling` is set to `EXCLUDE`, null elements are dropped from each
+ * of the list rows.
+ *
+ * @param null_handling Indicates whether to include/exclude nulls in list elements
+ * @return A COLLECT_LIST aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_collect_list_aggregation(
+  null_policy null_handling = null_policy::INCLUDE);
+
+/**
+ * @brief Factory to create a COLLECT_SET aggregation
+ *
+ * `COLLECT_SET` returns a lists column of all included elements in the group/series. Within each
+ * list, the duplicated entries are dropped out such that each entry appears only once.
+ *
+ * If `null_handling` is set to `EXCLUDE`, null elements are dropped from each
+ * of the list rows.
+ *
+ * @param null_handling Indicates whether to include/exclude nulls during collection
+ * @param nulls_equal Flag to specify whether null entries within each list should be considered
+ *        equal.
+ * @param nans_equal Flag to specify whether NaN values in floating point column should be
+ *        considered equal.
+ * @return A COLLECT_SET aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_collect_set_aggregation(
+  null_policy null_handling = null_policy::INCLUDE,
+  null_equality nulls_equal = null_equality::EQUAL,
+  nan_equality nans_equal   = nan_equality::ALL_EQUAL);
+
+/**
+ * @brief Factory to create a LAG aggregation
+ *
+ * @param offset The number of rows to lag the input
+ * @return A LAG aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_lag_aggregation(size_type offset);
+
+/**
+ * @brief Factory to create a LEAD aggregation
+ *
+ * @param offset The number of rows to lead the input
+ * @return A LEAD aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_lead_aggregation(size_type offset);
+
+/**
+ * @brief Factory to create an aggregation base on UDF for PTX or CUDA
+ *
+ * @param[in] type: either udf_type::PTX or udf_type::CUDA
+ * @param[in] user_defined_aggregator A string containing the aggregator code
+ * @param[in] output_type expected output type
+ *
+ * @return An aggregation containing a user-defined aggregator string
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_udf_aggregation(udf_type type,
+                                           std::string const& user_defined_aggregator,
+                                           data_type output_type);
+
+/**
+ * @brief Factory to create a MERGE_LISTS aggregation.
+ *
+ * Given a lists column, this aggregation merges all the lists corresponding to the same key value
+ * into one list. It is designed specifically to merge the partial results of multiple (distributed)
+ * groupby `COLLECT_LIST` aggregations into a final `COLLECT_LIST` result. As such, it requires the
+ * input lists column to be non-nullable (the child column containing list entries is not subjected
+ * to this requirement).
+ *
+ * @return A MERGE_LISTS aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_merge_lists_aggregation();
+
+/**
+ * @brief Factory to create a MERGE_SETS aggregation.
+ *
+ * Given a lists column, this aggregation firstly merges all the lists corresponding to the same key
+ * value into one list, then it drops all the duplicate entries in each lists, producing a lists
+ * column containing non-repeated entries.
+ *
+ * This aggregation is designed specifically to merge the partial results of multiple (distributed)
+ * groupby `COLLECT_LIST` or `COLLECT_SET` aggregations into a final `COLLECT_SET` result. As such,
+ * it requires the input lists column to be non-nullable (the child column containing list entries
+ * is not subjected to this requirement).
+ *
+ * In practice, the input (partial results) to this aggregation should be generated by (distributed)
+ * `COLLECT_LIST` aggregations, not `COLLECT_SET`, to avoid unnecessarily removing duplicate entries
+ * for the partial results.
+ *
+ * @param nulls_equal Flag to specify whether nulls within each list should be considered equal
+ *        during dropping duplicate list entries.
+ * @param nans_equal Flag to specify whether NaN values in floating point column should be
+ *        considered equal during dropping duplicate list entries.
+ * @return A MERGE_SETS aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_merge_sets_aggregation(
+  null_equality nulls_equal = null_equality::EQUAL,
+  nan_equality nans_equal   = nan_equality::ALL_EQUAL);
+
+/**
+ * @brief Factory to create a MERGE_M2 aggregation
+ *
+ * Merges the results of `M2` aggregations on independent sets into a new `M2` value equivalent to
+ * if a single `M2` aggregation was done across all of the sets at once. This aggregation is only
+ * valid on structs whose members are the result of the `COUNT_VALID`, `MEAN`, and `M2` aggregations
+ * on the same sets. The output of this aggregation is a struct containing the merged `COUNT_VALID`,
+ * `MEAN`, and `M2` aggregations.
+ *
+ * The input `M2` aggregation values are expected to be all non-negative numbers, since they
+ * were output from `M2` aggregation.
+ *
+ * @return A MERGE_M2 aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_merge_m2_aggregation();
+
+/**
+ * @brief Factory to create a MERGE_HISTOGRAM aggregation
+ *
+ * Merges the results of `HISTOGRAM` aggregations on independent sets into a new `HISTOGRAM` value
+ * equivalent to if a single `HISTOGRAM` aggregation was done across all of the sets at once.
+ *
+ * @return A MERGE_HISTOGRAM aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_merge_histogram_aggregation();
+
+/**
+ * @brief Factory to create a COVARIANCE aggregation
+ *
+ * Compute covariance between two columns.
+ * The input columns are child columns of a non-nullable struct columns.
+ * @param min_periods Minimum number of non-null observations required to produce a result
+ * @param ddof Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N is
+ *        the number of non-null observations.
+ * @return A COVARIANCE aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_covariance_aggregation(size_type min_periods = 1, size_type ddof = 1);
+
+/**
+ * @brief Factory to create a CORRELATION aggregation
+ *
+ * Compute correlation coefficient between two columns.
+ * The input columns are child columns of a non-nullable struct columns.
+ *
+ * @param type correlation_type
+ * @param min_periods Minimum number of non-null observations required to produce a result
+ * @return A CORRELATION aggregation object
+ */
+template <typename Base = aggregation>
+std::unique_ptr<Base> make_correlation_aggregation(correlation_type type,
+                                                   size_type min_periods = 1);
+
+/**
+ * @brief Factory to create a TDIGEST aggregation
+ *
+ * Produces a tdigest (https://arxiv.org/pdf/1902.04023.pdf) column from input values.
+ * The input aggregation values are expected to be fixed-width numeric types.
+ *
+ * The tdigest column produced is of the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * Each output row is a single tdigest.  The length of the row is the "size" of the
+ * tdigest, each element of which represents a weighted centroid (mean, weight).
+ *
+ * @param max_centroids Parameter controlling compression level and accuracy on subsequent
+ * queries on the output tdigest data.  `max_centroids` places an upper bound on the size of
+ * the computed tdigests: A value of 1000 will result in a tdigest containing no
+ * more than 1000 centroids (32 bytes each). Higher result in more accurate tdigest information.
+ *
+ * @return A TDIGEST aggregation object
+ */
+template <typename Base>
+std::unique_ptr<Base> make_tdigest_aggregation(int max_centroids = 1000);
+
+/**
+ * @brief Factory to create a MERGE_TDIGEST aggregation
+ *
+ * Merges the results from a previous aggregation resulting from a `make_tdigest_aggregation`
+ * or `make_merge_tdigest_aggregation` to produce a new a tdigest
+ * (https://arxiv.org/pdf/1902.04023.pdf) column.
+ *
+ * The tdigest column produced is of the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * Each output row is a single tdigest.  The length of the row is the "size" of the
+ * tdigest, each element of which represents a weighted centroid (mean, weight).
+ *
+ * @param max_centroids Parameter controlling compression level and accuracy on subsequent
+ * queries on the output tdigest data.  `max_centroids` places an upper bound on the size of
+ * the computed tdigests: A value of 1000 will result in a tdigest containing no
+ * more than 1000 centroids (32 bytes each). Higher result in more accurate tdigest information.
+ *
+ * @return A MERGE_TDIGEST aggregation object
+ */
+template <typename Base>
+std::unique_ptr<Base> make_merge_tdigest_aggregation(int max_centroids = 1000);
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/ast/detail/expression_evaluator.cuh b/cpp/include/cudf/ast/detail/expression_evaluator.cuh
new file mode 100644
index 0000000..105d87f
--- /dev/null
+++ b/cpp/include/cudf/ast/detail/expression_evaluator.cuh
@@ -0,0 +1,710 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/detail/operators.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/optional.h>
+
+namespace cudf {
+
+namespace ast {
+
+namespace detail {
+
+/**
+ * @brief A container for capturing the output of an evaluated expression.
+ *
+ * This class is designed to be passed by reference as the first argument to
+ * expression_evaluator::evaluate. The API is designed such that template
+ * specializations for specific output types will be able to customize setting
+ * behavior if necessary. The class leverages CRTP to define a suitable interface
+ * for the `expression_evaluator` at compile-time and enforce this API on its
+ * subclasses to get around the lack of device-side polymorphism.
+ *
+ * @tparam Subclass The subclass to dispatch methods to.
+ * @tparam T The underlying data type.
+ * @tparam has_nulls Whether or not the result data is nullable.
+ */
+template <typename Subclass, typename T, bool has_nulls>
+struct expression_result {
+  /**
+   * Helper function to get the subclass type to dispatch methods to.
+   */
+  __device__ inline Subclass& subclass() { return static_cast<Subclass&>(*this); }
+  __device__ inline Subclass const& subclass() const { return static_cast<Subclass const&>(*this); }
+
+  // TODO: The index is ignored by the value subclass, but is included in this
+  // signature because it is required by the implementation in the template
+  // specialization for column views. It would be nice to clean this up, see
+  // the related TODO below. Note that storing the index in the class on
+  // construction (which would result in a cleaner delineation of the API for
+  // the derived types) results in a significant performance penalty because
+  // the index is pushed down the memory hierarchy by the time it needs to be
+  // used, whereas passing it as a parameter keeps it in registers for fast
+  // access at the point where indexing occurs.
+  template <typename Element>
+  __device__ inline void set_value(cudf::size_type index,
+                                   possibly_null_value_t<Element, has_nulls> const& result)
+  {
+    subclass().template set_value<Element>(index, result);
+  }
+
+  [[nodiscard]] __device__ inline bool is_valid() const { return subclass().is_valid(); }
+
+  __device__ inline T value() const { return subclass().value(); }
+};
+
+/**
+ * @brief A container for capturing the output of an evaluated expression in a scalar.
+ *
+ * This subclass of `expression_result` functions as an owning container of a
+ * (possibly nullable) scalar type that can be written to by the
+ * expression_evaluator. The data (and its validity) can then be accessed.
+ *
+ * @tparam T The underlying data type.
+ * @tparam has_nulls Whether or not the result data is nullable.
+ */
+template <typename T, bool has_nulls>
+struct value_expression_result
+  : public expression_result<value_expression_result<T, has_nulls>, T, has_nulls> {
+  __device__ inline value_expression_result() {}
+
+  template <typename Element>
+  __device__ inline void set_value(cudf::size_type index,
+                                   possibly_null_value_t<Element, has_nulls> const& result)
+  {
+    if constexpr (std::is_same_v<Element, T>) {
+      _obj = result;
+    } else {
+      CUDF_UNREACHABLE("Output type does not match container type.");
+    }
+  }
+
+  /**
+   * @brief Returns true if the underlying data is valid and false otherwise.
+   */
+  [[nodiscard]] __device__ inline bool is_valid() const
+  {
+    if constexpr (has_nulls) { return _obj.has_value(); }
+    return true;
+  }
+
+  /**
+   * @brief Returns the underlying data.
+   *
+   * If the underlying data is not valid, behavior is undefined. Callers should
+   * use is_valid to check for validity before accessing the value.
+   */
+  __device__ inline T value() const
+  {
+    // Using two separate constexprs silences compiler warnings, whereas an
+    // if/else does not. An unconditional return is not ignored by the compiler
+    // when has_nulls is true and therefore raises a compiler error.
+    if constexpr (has_nulls) { return *_obj; }
+    if constexpr (!has_nulls) { return _obj; }
+  }
+
+  possibly_null_value_t<T, has_nulls>
+    _obj;  ///< The underlying data value, or a nullable version of it.
+};
+
+// TODO: The below implementation significantly differs from the default
+// implementation above due to the non-owning nature of the container and the
+// usage of the index. It would be ideal to unify these further if possible.
+
+/**
+ * @brief A container for capturing the output of an evaluated expression in a column.
+ *
+ * This subclass of `expression_result` functions as a non-owning container
+ * that transparently passes calls through to an underlying mutable view to a
+ * column. Not all methods are implemented
+ *
+ * @tparam has_nulls Whether or not the result data is nullable.
+ */
+template <bool has_nulls>
+struct mutable_column_expression_result
+  : public expression_result<mutable_column_expression_result<has_nulls>,
+                             mutable_column_device_view,
+                             has_nulls> {
+  __device__ inline mutable_column_expression_result(mutable_column_device_view& obj) : _obj(obj) {}
+
+  template <typename Element>
+  __device__ inline void set_value(cudf::size_type index,
+                                   possibly_null_value_t<Element, has_nulls> const& result)
+  {
+    if constexpr (has_nulls) {
+      if (result.has_value()) {
+        _obj.template element<Element>(index) = *result;
+        _obj.set_valid(index);
+      } else {
+        _obj.set_null(index);
+      }
+    } else {
+      _obj.template element<Element>(index) = result;
+    }
+  }
+
+  /**
+   * @brief Not implemented for this specialization.
+   */
+  [[nodiscard]] __device__ inline bool is_valid() const
+  {
+    // Not implemented since it would require modifying the API in the parent class to accept an
+    // index.
+    CUDF_UNREACHABLE("This method is not implemented.");
+  }
+
+  /**
+   * @brief Not implemented for this specialization.
+   */
+  [[nodiscard]] __device__ inline mutable_column_device_view value() const
+  {
+    // Not implemented since it would require modifying the API in the parent class to accept an
+    // index.
+    CUDF_UNREACHABLE("This method is not implemented.");
+  }
+
+  mutable_column_device_view& _obj;  ///< The column to which the data is written.
+};
+
+/**
+ * @brief Dispatch to a binary operator based on a single data type.
+ *
+ * This functor is a dispatcher for binary operations that assumes that both
+ * operands are of the same type. This assumption is encoded in the
+ * non-deducible template parameter LHS, the type of the left-hand operand,
+ * which is then used as the template parameter for both the left and right
+ * operands to the binary operator f.
+ */
+struct single_dispatch_binary_operator {
+  /**
+   * @brief Single-type dispatch to a binary operation.
+   *
+   * @tparam LHS Left input type.
+   * @tparam F Type of forwarded binary operator functor.
+   * @tparam Ts Parameter pack of forwarded arguments.
+   *
+   * @param f Binary operator functor.
+   * @param args Forwarded arguments to `operator()` of `f`.
+   */
+  template <typename LHS, typename F, typename... Ts>
+  __device__ inline auto operator()(F&& f, Ts&&... args)
+  {
+    f.template operator()<LHS, LHS>(std::forward<Ts>(args)...);
+  }
+};
+
+/**
+ * @brief The principal object for evaluating AST expressions on device.
+ *
+ * This class is designed for n-ary transform evaluation. It operates on two
+ * tables.
+ */
+template <bool has_nulls>
+struct expression_evaluator {
+ public:
+  /**
+   * @brief Construct an expression evaluator acting on two tables.
+   *
+   * @param left View of the left table view used for evaluation.
+   * @param right View of the right table view used for evaluation.
+   * @param plan The collection of device references representing the expression to evaluate.
+   * @param thread_intermediate_storage Pointer to this thread's portion of shared memory for
+   * storing intermediates.
+
+   */
+  __device__ inline expression_evaluator(table_device_view const& left,
+                                         table_device_view const& right,
+                                         expression_device_view const& plan)
+    : left(left), right(right), plan(plan)
+  {
+  }
+
+  /**
+   * @brief Construct an expression evaluator acting on one table.
+   *
+   * @param table View of the table view used for evaluation.
+   * @param plan The collection of device references representing the expression to evaluate.
+   * @param thread_intermediate_storage Pointer to this thread's portion of shared memory for
+   * storing intermediates.
+   */
+  __device__ inline expression_evaluator(table_device_view const& table,
+                                         expression_device_view const& plan)
+    : expression_evaluator(table, table, plan)
+  {
+  }
+
+  /**
+   * @brief Resolves an input data reference into a value.
+   *
+   * Only input columns (COLUMN), literal values (LITERAL), and intermediates (INTERMEDIATE) are
+   * supported as input data references. Intermediates must be of fixed width less than or equal to
+   * sizeof(std::int64_t). This requirement on intermediates is enforced by the linearizer.
+   *
+   * @tparam Element Type of element to return.
+   * @tparam has_nulls Whether or not the result data is nullable.
+   * @param device_data_reference Data reference to resolve.
+   * @param row_index Row index of data column.
+   * @return Element The type- and null-resolved data.
+   */
+  template <typename Element, CUDF_ENABLE_IF(column_device_view::has_element_accessor<Element>())>
+  __device__ inline possibly_null_value_t<Element, has_nulls> resolve_input(
+    detail::device_data_reference const& input_reference,
+    IntermediateDataType<has_nulls>* thread_intermediate_storage,
+    cudf::size_type left_row_index,
+    thrust::optional<cudf::size_type> right_row_index = {}) const
+  {
+    // TODO: Everywhere in the code assumes that the table reference is either
+    // left or right. Should we error-check somewhere to prevent
+    // table_reference::OUTPUT from being specified?
+    using ReturnType = possibly_null_value_t<Element, has_nulls>;
+    if (input_reference.reference_type == detail::device_data_reference_type::COLUMN) {
+      // If we have nullable data, return an empty nullable type with no value if the data is null.
+      auto const& table = (input_reference.table_source == table_reference::LEFT) ? left : right;
+      // Note that the code below assumes that a right index has been passed in
+      // any case where input_reference.table_source == table_reference::RIGHT.
+      // Otherwise, behavior is undefined.
+      auto const row_index =
+        (input_reference.table_source == table_reference::LEFT) ? left_row_index : *right_row_index;
+      if constexpr (has_nulls) {
+        return table.column(input_reference.data_index).is_valid(row_index)
+                 ? ReturnType(table.column(input_reference.data_index).element<Element>(row_index))
+                 : ReturnType();
+
+      } else {
+        return ReturnType(table.column(input_reference.data_index).element<Element>(row_index));
+      }
+    } else if (input_reference.reference_type == detail::device_data_reference_type::LITERAL) {
+      if constexpr (has_nulls) {
+        return plan.literals[input_reference.data_index].is_valid()
+                 ? ReturnType(plan.literals[input_reference.data_index].value<Element>())
+                 : ReturnType();
+
+      } else {
+        return ReturnType(plan.literals[input_reference.data_index].value<Element>());
+      }
+    } else {  // Assumes input_reference.reference_type ==
+              // detail::device_data_reference_type::INTERMEDIATE
+      // Using memcpy instead of reinterpret_cast<Element*> for safe type aliasing
+      // Using a temporary variable ensures that the compiler knows the result is aligned
+      IntermediateDataType<has_nulls> intermediate =
+        thread_intermediate_storage[input_reference.data_index];
+      ReturnType tmp;
+      memcpy(&tmp, &intermediate, sizeof(ReturnType));
+      return tmp;
+    }
+    // Unreachable return used to silence compiler warnings.
+    return {};
+  }
+
+  template <typename Element,
+            CUDF_ENABLE_IF(not column_device_view::has_element_accessor<Element>())>
+  __device__ inline possibly_null_value_t<Element, has_nulls> resolve_input(
+    detail::device_data_reference const& device_data_reference,
+    IntermediateDataType<has_nulls>* thread_intermediate_storage,
+    cudf::size_type left_row_index,
+    thrust::optional<cudf::size_type> right_row_index = {}) const
+  {
+    CUDF_UNREACHABLE("Unsupported type in resolve_input.");
+  }
+
+  /**
+   * @brief Callable to perform a unary operation.
+   *
+   * @tparam Input Type of input value.
+   * @tparam OutputType The container type that data will be inserted into.
+   *
+   * @param output_object The container that data will be inserted into.
+   * @param input_row_index The row to pull the data from the input table.
+   * @param input Input data reference.
+   * @param output Output data reference.
+   * @param output_row_index The row in the output to insert the result.
+   * @param op The operator to act with.
+   */
+  template <typename Input, typename ResultSubclass, typename T, bool result_has_nulls>
+  __device__ inline void operator()(
+    expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+    cudf::size_type const input_row_index,
+    detail::device_data_reference const& input,
+    detail::device_data_reference const& output,
+    cudf::size_type const output_row_index,
+    ast_operator const op,
+    IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+  {
+    auto const typed_input =
+      resolve_input<Input>(input, thread_intermediate_storage, input_row_index);
+    ast_operator_dispatcher(op,
+                            unary_expression_output_handler<Input>{},
+                            output_object,
+                            output_row_index,
+                            typed_input,
+                            output,
+                            thread_intermediate_storage);
+  }
+
+  /**
+   * @brief Callable to perform a binary operation.
+   *
+   * @tparam LHS Type of the left input value.
+   * @tparam RHS Type of the right input value.
+   * @tparam OutputType The container type that data will be inserted into.
+   *
+   * @param output_object The container that data will be inserted into.
+   * @param left_row_index The row to pull the data from the left table.
+   * @param right_row_index The row to pull the data from the right table.
+   * @param lhs Left input data reference.
+   * @param rhs Right input data reference.
+   * @param output Output data reference.
+   * @param output_row_index The row in the output to insert the result.
+   * @param op The operator to act with.
+   */
+  template <typename LHS, typename RHS, typename ResultSubclass, typename T, bool result_has_nulls>
+  __device__ inline void operator()(
+    expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+    cudf::size_type const left_row_index,
+    cudf::size_type const right_row_index,
+    detail::device_data_reference const& lhs,
+    detail::device_data_reference const& rhs,
+    detail::device_data_reference const& output,
+    cudf::size_type const output_row_index,
+    ast_operator const op,
+    IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+  {
+    auto const typed_lhs =
+      resolve_input<LHS>(lhs, thread_intermediate_storage, left_row_index, right_row_index);
+    auto const typed_rhs =
+      resolve_input<RHS>(rhs, thread_intermediate_storage, left_row_index, right_row_index);
+    ast_operator_dispatcher(op,
+                            binary_expression_output_handler<LHS, RHS>{},
+                            output_object,
+                            output_row_index,
+                            typed_lhs,
+                            typed_rhs,
+                            output,
+                            thread_intermediate_storage);
+  }
+
+  /**
+   * @brief Evaluate an expression applied to a row.
+   *
+   * This function performs an n-ary transform for one row on one thread.
+   *
+   * @tparam OutputType The container type that data will be inserted into.
+   *
+   * @param output_object The container that data will be inserted into.
+   * @param row_index Row index of all input and output data column(s).
+   */
+  template <typename ResultSubclass, typename T, bool result_has_nulls>
+  __device__ __forceinline__ void evaluate(
+    expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+    cudf::size_type const row_index,
+    IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+  {
+    evaluate(output_object, row_index, row_index, row_index, thread_intermediate_storage);
+  }
+
+  /**
+   * @brief Evaluate an expression applied to a row.
+   *
+   * This function performs an n-ary transform for one row on one thread.
+   *
+   * @tparam OutputType The container type that data will be inserted into.
+   *
+   * @param output_object The container that data will be inserted into.
+   * @param left_row_index The row to pull the data from the left table.
+   * @param right_row_index The row to pull the data from the right table.
+   * @param output_row_index The row in the output to insert the result.
+   */
+  template <typename ResultSubclass, typename T, bool result_has_nulls>
+  __device__ __forceinline__ void evaluate(
+    expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+    cudf::size_type const left_row_index,
+    cudf::size_type const right_row_index,
+    cudf::size_type const output_row_index,
+    IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+  {
+    cudf::size_type operator_source_index{0};
+    for (cudf::size_type operator_index = 0; operator_index < plan.operators.size();
+         ++operator_index) {
+      // Execute operator
+      auto const op    = plan.operators[operator_index];
+      auto const arity = ast_operator_arity(op);
+      if (arity == 1) {
+        // Unary operator
+        auto const& input =
+          plan.data_references[plan.operator_source_indices[operator_source_index++]];
+        auto const& output =
+          plan.data_references[plan.operator_source_indices[operator_source_index++]];
+        auto input_row_index =
+          input.table_source == table_reference::LEFT ? left_row_index : right_row_index;
+        type_dispatcher(input.data_type,
+                        *this,
+                        output_object,
+                        input_row_index,
+                        input,
+                        output,
+                        output_row_index,
+                        op,
+                        thread_intermediate_storage);
+      } else if (arity == 2) {
+        // Binary operator
+        auto const& lhs =
+          plan.data_references[plan.operator_source_indices[operator_source_index++]];
+        auto const& rhs =
+          plan.data_references[plan.operator_source_indices[operator_source_index++]];
+        auto const& output =
+          plan.data_references[plan.operator_source_indices[operator_source_index++]];
+        type_dispatcher(lhs.data_type,
+                        detail::single_dispatch_binary_operator{},
+                        *this,
+                        output_object,
+                        left_row_index,
+                        right_row_index,
+                        lhs,
+                        rhs,
+                        output,
+                        output_row_index,
+                        op,
+                        thread_intermediate_storage);
+      } else {
+        CUDF_UNREACHABLE("Invalid operator arity.");
+      }
+    }
+  }
+
+ private:
+  /**
+   * @brief Helper struct for type dispatch on the result of an expression.
+   *
+   * Evaluating an expression requires multiple levels of type dispatch to
+   * determine the input types, the operation type, and the output type. This
+   * helper class is a functor that handles the operator dispatch, invokes the
+   * operator, and dispatches output writing based on the resulting data type.
+   */
+  struct expression_output_handler {
+   public:
+    __device__ inline expression_output_handler() {}
+
+    /**
+     * @brief Resolves an output data reference and assigns result value.
+     *
+     * Only output columns (COLUMN) and intermediates (INTERMEDIATE) are supported as output
+     * reference types. Intermediates must be of fixed width less than or equal to
+     * sizeof(std::int64_t). This requirement on intermediates is enforced by the linearizer.
+     *
+     * @tparam Element Type of result element.
+     * @tparam OutputType The container type that data will be inserted into.
+     *
+     * @param output_object The container that data will be inserted into.
+     * @param device_data_reference Data reference to resolve.
+     * @param row_index Row index of data column.
+     * @param result Value to assign to output.
+     */
+    template <typename Element,
+              typename ResultSubclass,
+              typename T,
+              bool result_has_nulls,
+              CUDF_ENABLE_IF(is_rep_layout_compatible<Element>())>
+    __device__ inline void resolve_output(
+      expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+      detail::device_data_reference const& device_data_reference,
+      cudf::size_type const row_index,
+      IntermediateDataType<has_nulls>* thread_intermediate_storage,
+      possibly_null_value_t<Element, has_nulls> const& result) const
+    {
+      if (device_data_reference.reference_type == detail::device_data_reference_type::COLUMN) {
+        output_object.template set_value<Element>(row_index, result);
+      } else {  // Assumes device_data_reference.reference_type ==
+                // detail::device_data_reference_type::INTERMEDIATE
+        // Using memcpy instead of reinterpret_cast<Element*> for safe type aliasing.
+        // Using a temporary variable ensures that the compiler knows the result is aligned.
+        IntermediateDataType<has_nulls> tmp;
+        memcpy(&tmp, &result, sizeof(possibly_null_value_t<Element, has_nulls>));
+        thread_intermediate_storage[device_data_reference.data_index] = tmp;
+      }
+    }
+
+    template <typename Element,
+              typename ResultSubclass,
+              typename T,
+              bool result_has_nulls,
+              CUDF_ENABLE_IF(!is_rep_layout_compatible<Element>())>
+    __device__ inline void resolve_output(
+      expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+      detail::device_data_reference const& device_data_reference,
+      cudf::size_type const row_index,
+      IntermediateDataType<has_nulls>* thread_intermediate_storage,
+      possibly_null_value_t<Element, has_nulls> const& result) const
+    {
+      CUDF_UNREACHABLE("Invalid type in resolve_output.");
+    }
+  };
+
+  /**
+   * @brief Subclass of the expression output handler for unary operations.
+   *
+   * This functor's call operator is specialized to handle unary operations,
+   * which only require a single operand.
+   */
+  template <typename Input>
+  struct unary_expression_output_handler : public expression_output_handler {
+    __device__ inline unary_expression_output_handler() {}
+
+    /**
+     * @brief Callable to perform a unary operation.
+     *
+     * @tparam op The operation to perform.
+     * @tparam OutputType The container type that data will be inserted into.
+     *
+     * @param output_object The container that data will be inserted into.
+     * @param outputrow_index The row in the output object to insert the data.
+     * @param input Input to the operation.
+     * @param output Output data reference.
+     */
+    template <ast_operator op,
+              typename ResultSubclass,
+              typename T,
+              bool result_has_nulls,
+              std::enable_if_t<
+                detail::is_valid_unary_op<detail::operator_functor<op, has_nulls>,
+                                          possibly_null_value_t<Input, has_nulls>>>* = nullptr>
+    __device__ inline void operator()(
+      expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+      cudf::size_type const output_row_index,
+      possibly_null_value_t<Input, has_nulls> const& input,
+      detail::device_data_reference const& output,
+      IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+    {
+      // The output data type is the same whether or not nulls are present, so
+      // pull from the non-nullable operator.
+      using Out = cuda::std::invoke_result_t<detail::operator_functor<op, false>, Input>;
+      this->template resolve_output<Out>(output_object,
+                                         output,
+                                         output_row_index,
+                                         thread_intermediate_storage,
+                                         detail::operator_functor<op, has_nulls>{}(input));
+    }
+
+    template <ast_operator op,
+              typename ResultSubclass,
+              typename T,
+              bool result_has_nulls,
+              std::enable_if_t<
+                !detail::is_valid_unary_op<detail::operator_functor<op, has_nulls>,
+                                           possibly_null_value_t<Input, has_nulls>>>* = nullptr>
+    __device__ inline void operator()(
+      expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+      cudf::size_type const output_row_index,
+      possibly_null_value_t<Input, has_nulls> const& input,
+      detail::device_data_reference const& output,
+      IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+    {
+      CUDF_UNREACHABLE("Invalid unary dispatch operator for the provided input.");
+    }
+  };
+
+  /**
+   * @brief Subclass of the expression output handler for binary operations.
+   *
+   * This functor's call operator is specialized to handle binary operations,
+   * which require two operands.
+   */
+  template <typename LHS, typename RHS>
+  struct binary_expression_output_handler : public expression_output_handler {
+    __device__ inline binary_expression_output_handler() {}
+
+    /**
+     * @brief Callable to perform a binary operation.
+     *
+     * @tparam op The operation to perform.
+     * @tparam OutputType The container type that data will be inserted into.
+     *
+     * @param output_object The container that data will be inserted into.
+     * @param output_row_index The row in the output to insert the result.
+     * @param lhs Left input to the operation.
+     * @param rhs Right input to the operation.
+     * @param output Output data reference.
+     */
+    template <ast_operator op,
+              typename ResultSubclass,
+              typename T,
+              bool result_has_nulls,
+              std::enable_if_t<detail::is_valid_binary_op<detail::operator_functor<op, has_nulls>,
+                                                          possibly_null_value_t<LHS, has_nulls>,
+                                                          possibly_null_value_t<RHS, has_nulls>>>* =
+                nullptr>
+    __device__ inline void operator()(
+      expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+      cudf::size_type const output_row_index,
+      possibly_null_value_t<LHS, has_nulls> const& lhs,
+      possibly_null_value_t<RHS, has_nulls> const& rhs,
+      detail::device_data_reference const& output,
+      IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+    {
+      // The output data type is the same whether or not nulls are present, so
+      // pull from the non-nullable operator.
+      using Out = cuda::std::invoke_result_t<detail::operator_functor<op, false>, LHS, RHS>;
+      this->template resolve_output<Out>(output_object,
+                                         output,
+                                         output_row_index,
+                                         thread_intermediate_storage,
+                                         detail::operator_functor<op, has_nulls>{}(lhs, rhs));
+    }
+
+    template <ast_operator op,
+              typename ResultSubclass,
+              typename T,
+              bool result_has_nulls,
+              std::enable_if_t<
+                !detail::is_valid_binary_op<detail::operator_functor<op, has_nulls>,
+                                            possibly_null_value_t<LHS, has_nulls>,
+                                            possibly_null_value_t<RHS, has_nulls>>>* = nullptr>
+    __device__ inline void operator()(
+      expression_result<ResultSubclass, T, result_has_nulls>& output_object,
+      cudf::size_type const output_row_index,
+      possibly_null_value_t<LHS, has_nulls> const& lhs,
+      possibly_null_value_t<RHS, has_nulls> const& rhs,
+      detail::device_data_reference const& output,
+      IntermediateDataType<has_nulls>* thread_intermediate_storage) const
+    {
+      CUDF_UNREACHABLE("Invalid binary dispatch operator for the provided input.");
+    }
+  };
+
+  table_device_view const& left;   ///< The left table to operate on.
+  table_device_view const& right;  ///< The right table to operate on.
+  expression_device_view const&
+    plan;  ///< The container of device data representing the expression to evaluate.
+};
+
+}  // namespace detail
+
+}  // namespace ast
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/ast/detail/expression_parser.hpp b/cpp/include/cudf/ast/detail/expression_parser.hpp
new file mode 100644
index 0000000..db0abe4
--- /dev/null
+++ b/cpp/include/cudf/ast/detail/expression_parser.hpp
@@ -0,0 +1,333 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/ast/detail/operators.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/scan.h>
+
+#include <functional>
+#include <numeric>
+#include <optional>
+
+namespace cudf {
+namespace ast {
+namespace detail {
+
+/**
+ * @brief Node data reference types.
+ *
+ * This enum is device-specific. For instance, intermediate data references are generated by the
+ * linearization process but cannot be explicitly created by the user.
+ */
+enum class device_data_reference_type {
+  COLUMN,       ///< A value in a table column
+  LITERAL,      ///< A literal value
+  INTERMEDIATE  ///< An internal temporary value
+};
+
+/**
+ * @brief A device data reference describes a source of data used by a expression.
+ *
+ * This is a POD class used to create references describing data type and locations for consumption
+ * by the `row_evaluator`.
+ */
+struct alignas(8) device_data_reference {
+  device_data_reference(device_data_reference_type reference_type,
+                        cudf::data_type data_type,
+                        cudf::size_type data_index,
+                        table_reference table_source);
+
+  device_data_reference(device_data_reference_type reference_type,
+                        cudf::data_type data_type,
+                        cudf::size_type data_index);
+
+  device_data_reference_type const reference_type;  // Source of data
+  cudf::data_type const data_type;                  // Type of data
+  cudf::size_type const data_index;                 // The column index of a table, index of a
+                                                    // literal, or index of an intermediate
+  table_reference const table_source;
+
+  bool operator==(device_data_reference const& rhs) const
+  {
+    return std::tie(data_index, reference_type, table_source) ==
+           std::tie(rhs.data_index, rhs.reference_type, rhs.table_source);
+  }
+};
+
+// Type used for intermediate storage in expression evaluation.
+template <bool has_nulls>
+using IntermediateDataType = possibly_null_value_t<std::int64_t, has_nulls>;
+
+/**
+ * @brief A container of all device data required to evaluate an expression on tables.
+ *
+ * This struct should never be instantiated directly. It is created by the
+ * `expression_parser` on construction, and the resulting member is publicly accessible
+ * for passing to kernels for constructing an `expression_evaluator`.
+ *
+ */
+struct expression_device_view {
+  device_span<detail::device_data_reference const> data_references;
+  device_span<generic_scalar_device_view const> literals;
+  device_span<ast_operator const> operators;
+  device_span<cudf::size_type const> operator_source_indices;
+  cudf::size_type num_intermediates;
+};
+
+/**
+ * @brief The expression_parser traverses an expression and converts it into a form suitable for
+ * execution on the device.
+ *
+ * This class is part of a "visitor" pattern with the `expression` class.
+ *
+ * This class does pre-processing work on the host, validating operators and operand data types. It
+ * traverses downward from a root expression in a depth-first fashion, capturing information about
+ * the expressions and constructing vectors of information that are later used by the device for
+ * evaluating the abstract syntax tree as a "linear" list of operators whose input dependencies are
+ * resolved into intermediate data storage in shared memory.
+ */
+class expression_parser {
+ public:
+  /**
+   * @brief Construct a new expression_parser object
+   *
+   * @param expr The expression to create an evaluable expression_parser for.
+   * @param left The left table used for evaluating the abstract syntax tree.
+   * @param right The right table used for evaluating the abstract syntax tree.
+   */
+  expression_parser(expression const& expr,
+                    cudf::table_view const& left,
+                    std::optional<std::reference_wrapper<cudf::table_view const>> right,
+                    bool has_nulls,
+                    rmm::cuda_stream_view stream,
+                    rmm::mr::device_memory_resource* mr)
+    : _left{left},
+      _right{right},
+      _expression_count{0},
+      _intermediate_counter{},
+      _has_nulls(has_nulls)
+  {
+    expr.accept(*this);
+    move_to_device(stream, mr);
+  }
+
+  /**
+   * @brief Construct a new expression_parser object
+   *
+   * @param expr The expression to create an evaluable expression_parser for.
+   * @param table The table used for evaluating the abstract syntax tree.
+   */
+  expression_parser(expression const& expr,
+                    cudf::table_view const& table,
+                    bool has_nulls,
+                    rmm::cuda_stream_view stream,
+                    rmm::mr::device_memory_resource* mr)
+    : expression_parser(expr, table, {}, has_nulls, stream, mr)
+  {
+  }
+
+  /**
+   * @brief Get the root data type of the abstract syntax tree.
+   *
+   * @return cudf::data_type
+   */
+  [[nodiscard]] cudf::data_type output_type() const;
+
+  /**
+   * @brief Visit a literal expression.
+   *
+   * @param expr Literal expression.
+   * @return cudf::size_type Index of device data reference for the expression.
+   */
+  cudf::size_type visit(literal const& expr);
+
+  /**
+   * @brief Visit a column reference expression.
+   *
+   * @param expr Column reference expression.
+   * @return cudf::size_type Index of device data reference for the expression.
+   */
+  cudf::size_type visit(column_reference const& expr);
+
+  /**
+   * @brief Visit an expression expression.
+   *
+   * @param expr Expression expression.
+   * @return cudf::size_type Index of device data reference for the expression.
+   */
+  cudf::size_type visit(operation const& expr);
+
+  /**
+   * @brief Visit a column name reference expression.
+   *
+   * @param expr Column name reference expression.
+   * @return cudf::size_type Index of device data reference for the expression.
+   */
+  cudf::size_type visit(column_name_reference const& expr);
+  /**
+   * @brief Internal class used to track the utilization of intermediate storage locations.
+   *
+   * As expressions are being evaluated, they may generate "intermediate" data that is immediately
+   * consumed. Rather than manifesting this data in global memory, we can store intermediates of any
+   * fixed width type (up to 8 bytes) by placing them in shared memory. This class helps to track
+   * the number and indices of intermediate data in shared memory using a give-take model. Locations
+   * in shared memory can be "taken" and used for storage, "given back," and then later re-used.
+   * This aims to minimize the maximum amount of shared memory needed at any point during the
+   * evaluation.
+   *
+   */
+  class intermediate_counter {
+   public:
+    intermediate_counter() : used_values() {}
+    cudf::size_type take();
+    void give(cudf::size_type value);
+    [[nodiscard]] cudf::size_type get_max_used() const { return max_used; }
+
+   private:
+    /**
+     * @brief Find the first missing value in a contiguous sequence of integers.
+     *
+     * From a sorted container of integers, find the first "missing" value.
+     * For example, {0, 1, 2, 4, 5} is missing 3, and {1, 2, 3} is missing 0.
+     * If there are no missing values, return the size of the container.
+     *
+     * @return cudf::size_type Smallest value not already in the container.
+     */
+    [[nodiscard]] cudf::size_type find_first_missing() const;
+
+    std::vector<cudf::size_type> used_values;
+    cudf::size_type max_used{0};
+  };
+
+  expression_device_view device_expression_data;  ///< The collection of data required to evaluate
+                                                  ///< the expression on the device.
+  int shmem_per_thread;
+
+ private:
+  /**
+   * @brief Helper function for adding components (operators, literals, etc) to AST plan
+   *
+   * @tparam T  The underlying type of the input `std::vector`
+   * @param[in]  v  The `std::vector` containing components (operators, literals, etc).
+   * @param[in,out]  sizes  The `std::vector` containing the size of each data buffer.
+   * @param[in,out]  data_pointers  The `std::vector` containing pointers to each data buffer.
+   */
+  template <typename T>
+  void extract_size_and_pointer(std::vector<T> const& v,
+                                std::vector<cudf::size_type>& sizes,
+                                std::vector<void const*>& data_pointers)
+  {
+    auto const data_size = sizeof(T) * v.size();
+    sizes.push_back(data_size);
+    data_pointers.push_back(v.data());
+  }
+
+  void move_to_device(rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+  {
+    std::vector<cudf::size_type> sizes;
+    std::vector<void const*> data_pointers;
+
+    extract_size_and_pointer(_data_references, sizes, data_pointers);
+    extract_size_and_pointer(_literals, sizes, data_pointers);
+    extract_size_and_pointer(_operators, sizes, data_pointers);
+    extract_size_and_pointer(_operator_source_indices, sizes, data_pointers);
+
+    // Create device buffer
+    auto const buffer_size = std::accumulate(sizes.cbegin(), sizes.cend(), 0);
+    auto buffer_offsets    = std::vector<int>(sizes.size());
+    thrust::exclusive_scan(sizes.cbegin(), sizes.cend(), buffer_offsets.begin(), 0);
+
+    auto h_data_buffer = std::vector<char>(buffer_size);
+    for (unsigned int i = 0; i < data_pointers.size(); ++i) {
+      std::memcpy(h_data_buffer.data() + buffer_offsets[i], data_pointers[i], sizes[i]);
+    }
+
+    _device_data_buffer = rmm::device_buffer(h_data_buffer.data(), buffer_size, stream, mr);
+
+    stream.synchronize();
+
+    // Create device pointers to components of plan
+    auto device_data_buffer_ptr            = static_cast<char const*>(_device_data_buffer.data());
+    device_expression_data.data_references = device_span<detail::device_data_reference const>(
+      reinterpret_cast<detail::device_data_reference const*>(device_data_buffer_ptr +
+                                                             buffer_offsets[0]),
+      _data_references.size());
+    device_expression_data.literals = device_span<generic_scalar_device_view const>(
+      reinterpret_cast<generic_scalar_device_view const*>(device_data_buffer_ptr +
+                                                          buffer_offsets[1]),
+      _literals.size());
+    device_expression_data.operators = device_span<ast_operator const>(
+      reinterpret_cast<ast_operator const*>(device_data_buffer_ptr + buffer_offsets[2]),
+      _operators.size());
+    device_expression_data.operator_source_indices = device_span<cudf::size_type const>(
+      reinterpret_cast<cudf::size_type const*>(device_data_buffer_ptr + buffer_offsets[3]),
+      _operator_source_indices.size());
+    device_expression_data.num_intermediates = _intermediate_counter.get_max_used();
+    shmem_per_thread                         = static_cast<int>(
+      (_has_nulls ? sizeof(IntermediateDataType<true>) : sizeof(IntermediateDataType<false>)) *
+      device_expression_data.num_intermediates);
+  }
+
+  /**
+   * @brief Helper function for recursive traversal of expressions.
+   *
+   * When parsing an expression composed of subexpressions, all subexpressions
+   * must be evaluated before an operator can be applied to them. This method
+   * performs that recursive traversal (in conjunction with the
+   * `expression_parser.visit` and `expression.accept` methods if necessary to
+   * descend deeper into an expression tree).
+   *
+   * @param  operands  The operands to visit.
+   *
+   * @return The indices of the operands stored in the data references.
+   */
+  std::vector<cudf::size_type> visit_operands(
+    std::vector<std::reference_wrapper<expression const>> operands);
+
+  /**
+   * @brief Add a data reference to the internal list.
+   *
+   * @param  data_ref  The data reference to add.
+   *
+   * @return The index of the added data reference in the internal data references list.
+   */
+  cudf::size_type add_data_reference(detail::device_data_reference data_ref);
+
+  rmm::device_buffer
+    _device_data_buffer;  ///< The device-side data buffer containing the plan information, which is
+                          ///< owned by this class and persists until it is destroyed.
+
+  cudf::table_view const& _left;
+  std::optional<std::reference_wrapper<cudf::table_view const>> _right;
+  cudf::size_type _expression_count;
+  intermediate_counter _intermediate_counter;
+  bool _has_nulls;
+  std::vector<detail::device_data_reference> _data_references;
+  std::vector<ast_operator> _operators;
+  std::vector<cudf::size_type> _operator_source_indices;
+  std::vector<generic_scalar_device_view> _literals;
+};
+
+}  // namespace detail
+
+}  // namespace ast
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/ast/detail/expression_transformer.hpp b/cpp/include/cudf/ast/detail/expression_transformer.hpp
new file mode 100644
index 0000000..a6529c3
--- /dev/null
+++ b/cpp/include/cudf/ast/detail/expression_transformer.hpp
@@ -0,0 +1,64 @@
+
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/ast/expressions.hpp>
+
+namespace cudf::ast::detail {
+/**
+ * @brief Base "visitor" pattern class with the `expression` class for expression transformer.
+ *
+ * This class can be used to implement recursive traversal of AST tree, and used to validate or
+ * translate an AST expression.
+ */
+class expression_transformer {
+ public:
+  /**
+   * @brief Visit a literal expression.
+   *
+   * @param expr Literal expression
+   * @return Reference wrapper of transformed expression
+   */
+  virtual std::reference_wrapper<expression const> visit(literal const& expr) = 0;
+
+  /**
+   * @brief Visit a column reference expression.
+   *
+   * @param expr Column reference expression
+   * @return Reference wrapper of transformed expression
+   */
+  virtual std::reference_wrapper<expression const> visit(column_reference const& expr) = 0;
+
+  /**
+   * @brief Visit an expression expression
+   *
+   * @param expr Expression expression
+   * @return Reference wrapper of transformed expression
+   */
+  virtual std::reference_wrapper<expression const> visit(operation const& expr) = 0;
+
+  /**
+   * @brief Visit a column name reference expression.
+   *
+   * @param expr Column name reference expression
+   * @return Reference wrapper of transformed expression
+   */
+  virtual std::reference_wrapper<expression const> visit(column_name_reference const& expr) = 0;
+
+  virtual ~expression_transformer() {}
+};
+}  // namespace cudf::ast::detail
diff --git a/cpp/include/cudf/ast/detail/operators.hpp b/cpp/include/cudf/ast/detail/operators.hpp
new file mode 100644
index 0000000..ed7f2d9
--- /dev/null
+++ b/cpp/include/cudf/ast/detail/operators.hpp
@@ -0,0 +1,1226 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/optional.h>
+
+#include <cuda/std/type_traits>
+
+#include <cmath>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace cudf {
+
+namespace ast {
+
+namespace detail {
+
+// Type trait for wrapping nullable types in a thrust::optional. Non-nullable
+// types are returned as is.
+template <typename T, bool has_nulls>
+struct possibly_null_value;
+
+template <typename T>
+struct possibly_null_value<T, true> {
+  using type = thrust::optional<T>;
+};
+
+template <typename T>
+struct possibly_null_value<T, false> {
+  using type = T;
+};
+
+template <typename T, bool has_nulls>
+using possibly_null_value_t = typename possibly_null_value<T, has_nulls>::type;
+
+// Traits for valid operator / type combinations
+template <typename Op, typename LHS, typename RHS>
+constexpr bool is_valid_binary_op = cuda::std::is_invocable_v<Op, LHS, RHS>;
+
+template <typename Op, typename T>
+constexpr bool is_valid_unary_op = cuda::std::is_invocable_v<Op, T>;
+
+/**
+ * @brief Operator dispatcher
+ *
+ * @tparam F Type of forwarded functor.
+ * @tparam Ts Parameter pack of forwarded arguments.
+ * @param f Forwarded functor to be called.
+ * @param args Forwarded arguments to `operator()` of `f`.
+ */
+template <typename F, typename... Ts>
+CUDF_HOST_DEVICE inline constexpr void ast_operator_dispatcher(ast_operator op, F&& f, Ts&&... args)
+{
+  switch (op) {
+    case ast_operator::ADD:
+      f.template operator()<ast_operator::ADD>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::SUB:
+      f.template operator()<ast_operator::SUB>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::MUL:
+      f.template operator()<ast_operator::MUL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::DIV:
+      f.template operator()<ast_operator::DIV>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::TRUE_DIV:
+      f.template operator()<ast_operator::TRUE_DIV>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::FLOOR_DIV:
+      f.template operator()<ast_operator::FLOOR_DIV>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::MOD:
+      f.template operator()<ast_operator::MOD>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::PYMOD:
+      f.template operator()<ast_operator::PYMOD>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::POW:
+      f.template operator()<ast_operator::POW>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::EQUAL:
+      f.template operator()<ast_operator::EQUAL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::NULL_EQUAL:
+      f.template operator()<ast_operator::NULL_EQUAL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::NOT_EQUAL:
+      f.template operator()<ast_operator::NOT_EQUAL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::LESS:
+      f.template operator()<ast_operator::LESS>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::GREATER:
+      f.template operator()<ast_operator::GREATER>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::LESS_EQUAL:
+      f.template operator()<ast_operator::LESS_EQUAL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::GREATER_EQUAL:
+      f.template operator()<ast_operator::GREATER_EQUAL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::BITWISE_AND:
+      f.template operator()<ast_operator::BITWISE_AND>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::BITWISE_OR:
+      f.template operator()<ast_operator::BITWISE_OR>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::BITWISE_XOR:
+      f.template operator()<ast_operator::BITWISE_XOR>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::LOGICAL_AND:
+      f.template operator()<ast_operator::LOGICAL_AND>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::NULL_LOGICAL_AND:
+      f.template operator()<ast_operator::NULL_LOGICAL_AND>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::LOGICAL_OR:
+      f.template operator()<ast_operator::LOGICAL_OR>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::NULL_LOGICAL_OR:
+      f.template operator()<ast_operator::NULL_LOGICAL_OR>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::IDENTITY:
+      f.template operator()<ast_operator::IDENTITY>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::IS_NULL:
+      f.template operator()<ast_operator::IS_NULL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::SIN:
+      f.template operator()<ast_operator::SIN>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::COS:
+      f.template operator()<ast_operator::COS>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::TAN:
+      f.template operator()<ast_operator::TAN>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ARCSIN:
+      f.template operator()<ast_operator::ARCSIN>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ARCCOS:
+      f.template operator()<ast_operator::ARCCOS>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ARCTAN:
+      f.template operator()<ast_operator::ARCTAN>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::SINH:
+      f.template operator()<ast_operator::SINH>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::COSH:
+      f.template operator()<ast_operator::COSH>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::TANH:
+      f.template operator()<ast_operator::TANH>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ARCSINH:
+      f.template operator()<ast_operator::ARCSINH>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ARCCOSH:
+      f.template operator()<ast_operator::ARCCOSH>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ARCTANH:
+      f.template operator()<ast_operator::ARCTANH>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::EXP:
+      f.template operator()<ast_operator::EXP>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::LOG:
+      f.template operator()<ast_operator::LOG>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::SQRT:
+      f.template operator()<ast_operator::SQRT>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::CBRT:
+      f.template operator()<ast_operator::CBRT>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::CEIL:
+      f.template operator()<ast_operator::CEIL>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::FLOOR:
+      f.template operator()<ast_operator::FLOOR>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::ABS:
+      f.template operator()<ast_operator::ABS>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::RINT:
+      f.template operator()<ast_operator::RINT>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::BIT_INVERT:
+      f.template operator()<ast_operator::BIT_INVERT>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::NOT:
+      f.template operator()<ast_operator::NOT>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::CAST_TO_INT64:
+      f.template operator()<ast_operator::CAST_TO_INT64>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::CAST_TO_UINT64:
+      f.template operator()<ast_operator::CAST_TO_UINT64>(std::forward<Ts>(args)...);
+      break;
+    case ast_operator::CAST_TO_FLOAT64:
+      f.template operator()<ast_operator::CAST_TO_FLOAT64>(std::forward<Ts>(args)...);
+      break;
+    default: {
+#ifndef __CUDA_ARCH__
+      CUDF_FAIL("Invalid operator.");
+#else
+      CUDF_UNREACHABLE("Invalid operator.");
+#endif
+    }
+  }
+}
+
+/**
+ * @brief Operator functor.
+ *
+ * This functor is templated on an `ast_operator`, with each template specialization defining a
+ * callable `operator()` that executes the operation. The functor specialization also has a member
+ * `arity` defining the number of operands that are accepted by the call to `operator()`. The
+ * `operator()` is templated on the types of its inputs (e.g. `typename LHS` and `typename RHS` for
+ * a binary operator). Trailing return types are defined as `decltype(result)` where `result` is
+ * the returned value. The trailing return types allow SFINAE to only consider template
+ * instantiations for valid combinations of types. This, in turn, allows the operator functors to be
+ * used with traits like `is_valid_binary_op` that rely on `std::is_invocable` and related features.
+ *
+ * @tparam op AST operator.
+ */
+template <ast_operator op, bool has_nulls>
+struct operator_functor {};
+
+template <>
+struct operator_functor<ast_operator::ADD, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs + rhs)
+  {
+    return lhs + rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::SUB, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs - rhs)
+  {
+    return lhs - rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::MUL, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs * rhs)
+  {
+    return lhs * rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::DIV, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs / rhs)
+  {
+    return lhs / rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::TRUE_DIV, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(static_cast<double>(lhs) / static_cast<double>(rhs))
+  {
+    return static_cast<double>(lhs) / static_cast<double>(rhs);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::FLOOR_DIV, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(floor(static_cast<double>(lhs) / static_cast<double>(rhs)))
+  {
+    return floor(static_cast<double>(lhs) / static_cast<double>(rhs));
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::MOD, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS,
+            typename RHS,
+            typename CommonType                               = std::common_type_t<LHS, RHS>,
+            std::enable_if_t<std::is_integral_v<CommonType>>* = nullptr>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(static_cast<CommonType>(lhs) % static_cast<CommonType>(rhs))
+  {
+    return static_cast<CommonType>(lhs) % static_cast<CommonType>(rhs);
+  }
+
+  template <typename LHS,
+            typename RHS,
+            typename CommonType                                  = std::common_type_t<LHS, RHS>,
+            std::enable_if_t<std::is_same_v<CommonType, float>>* = nullptr>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(fmodf(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs)))
+  {
+    return fmodf(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs));
+  }
+
+  template <typename LHS,
+            typename RHS,
+            typename CommonType                                   = std::common_type_t<LHS, RHS>,
+            std::enable_if_t<std::is_same_v<CommonType, double>>* = nullptr>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(fmod(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs)))
+  {
+    return fmod(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs));
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::PYMOD, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS,
+            typename RHS,
+            typename CommonType                               = std::common_type_t<LHS, RHS>,
+            std::enable_if_t<std::is_integral_v<CommonType>>* = nullptr>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(((static_cast<CommonType>(lhs) % static_cast<CommonType>(rhs)) +
+                 static_cast<CommonType>(rhs)) %
+                static_cast<CommonType>(rhs))
+  {
+    return ((static_cast<CommonType>(lhs) % static_cast<CommonType>(rhs)) +
+            static_cast<CommonType>(rhs)) %
+           static_cast<CommonType>(rhs);
+  }
+
+  template <typename LHS,
+            typename RHS,
+            typename CommonType                                  = std::common_type_t<LHS, RHS>,
+            std::enable_if_t<std::is_same_v<CommonType, float>>* = nullptr>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(fmodf(fmodf(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs)) +
+                        static_cast<CommonType>(rhs),
+                      static_cast<CommonType>(rhs)))
+  {
+    return fmodf(fmodf(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs)) +
+                   static_cast<CommonType>(rhs),
+                 static_cast<CommonType>(rhs));
+  }
+
+  template <typename LHS,
+            typename RHS,
+            typename CommonType                                   = std::common_type_t<LHS, RHS>,
+            std::enable_if_t<std::is_same_v<CommonType, double>>* = nullptr>
+  __device__ inline auto operator()(LHS lhs, RHS rhs)
+    -> decltype(fmod(fmod(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs)) +
+                       static_cast<CommonType>(rhs),
+                     static_cast<CommonType>(rhs)))
+  {
+    return fmod(fmod(static_cast<CommonType>(lhs), static_cast<CommonType>(rhs)) +
+                  static_cast<CommonType>(rhs),
+                static_cast<CommonType>(rhs));
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::POW, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(std::pow(lhs, rhs))
+  {
+    return std::pow(lhs, rhs);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::EQUAL, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs == rhs)
+  {
+    return lhs == rhs;
+  }
+};
+
+// Alias NULL_EQUAL = EQUAL in the non-nullable case.
+template <>
+struct operator_functor<ast_operator::NULL_EQUAL, false>
+  : public operator_functor<ast_operator::EQUAL, false> {};
+
+template <>
+struct operator_functor<ast_operator::NOT_EQUAL, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs != rhs)
+  {
+    return lhs != rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::LESS, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs < rhs)
+  {
+    return lhs < rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::GREATER, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs > rhs)
+  {
+    return lhs > rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::LESS_EQUAL, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs <= rhs)
+  {
+    return lhs <= rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::GREATER_EQUAL, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs >= rhs)
+  {
+    return lhs >= rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::BITWISE_AND, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs & rhs)
+  {
+    return lhs & rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::BITWISE_OR, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs | rhs)
+  {
+    return lhs | rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::BITWISE_XOR, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs ^ rhs)
+  {
+    return lhs ^ rhs;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::LOGICAL_AND, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs && rhs)
+  {
+    return lhs && rhs;
+  }
+};
+
+// Alias NULL_LOGICAL_AND = LOGICAL_AND in the non-nullable case.
+template <>
+struct operator_functor<ast_operator::NULL_LOGICAL_AND, false>
+  : public operator_functor<ast_operator::LOGICAL_AND, false> {};
+
+template <>
+struct operator_functor<ast_operator::LOGICAL_OR, false> {
+  static constexpr auto arity{2};
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS lhs, RHS rhs) -> decltype(lhs || rhs)
+  {
+    return lhs || rhs;
+  }
+};
+
+// Alias NULL_LOGICAL_OR = LOGICAL_OR in the non-nullable case.
+template <>
+struct operator_functor<ast_operator::NULL_LOGICAL_OR, false>
+  : public operator_functor<ast_operator::LOGICAL_OR, false> {};
+
+template <>
+struct operator_functor<ast_operator::IDENTITY, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(input)
+  {
+    return input;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::IS_NULL, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> bool
+  {
+    return false;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::SIN, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::sin(input))
+  {
+    return std::sin(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::COS, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::cos(input))
+  {
+    return std::cos(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::TAN, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::tan(input))
+  {
+    return std::tan(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ARCSIN, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::asin(input))
+  {
+    return std::asin(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ARCCOS, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::acos(input))
+  {
+    return std::acos(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ARCTAN, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::atan(input))
+  {
+    return std::atan(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::SINH, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::sinh(input))
+  {
+    return std::sinh(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::COSH, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::cosh(input))
+  {
+    return std::cosh(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::TANH, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::tanh(input))
+  {
+    return std::tanh(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ARCSINH, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::asinh(input))
+  {
+    return std::asinh(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ARCCOSH, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::acosh(input))
+  {
+    return std::acosh(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ARCTANH, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT, std::enable_if_t<std::is_floating_point_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::atanh(input))
+  {
+    return std::atanh(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::EXP, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::exp(input))
+  {
+    return std::exp(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::LOG, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::log(input))
+  {
+    return std::log(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::SQRT, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::sqrt(input))
+  {
+    return std::sqrt(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::CBRT, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::cbrt(input))
+  {
+    return std::cbrt(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::CEIL, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::ceil(input))
+  {
+    return std::ceil(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::FLOOR, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::floor(input))
+  {
+    return std::floor(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::ABS, false> {
+  static constexpr auto arity{1};
+
+  // Only accept signed or unsigned types (both require is_arithmetic<T> to be true)
+  template <typename InputT, std::enable_if_t<std::is_signed_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(std::abs(input))
+  {
+    return std::abs(input);
+  }
+
+  template <typename InputT, std::enable_if_t<std::is_unsigned_v<InputT>>* = nullptr>
+  __device__ inline auto operator()(InputT input) -> decltype(input)
+  {
+    return input;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::RINT, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(std::rint(input))
+  {
+    return std::rint(input);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::BIT_INVERT, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(~input)
+  {
+    return ~input;
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::NOT, false> {
+  static constexpr auto arity{1};
+
+  template <typename InputT>
+  __device__ inline auto operator()(InputT input) -> decltype(!input)
+  {
+    return !input;
+  }
+};
+
+template <typename To>
+struct cast {
+  static constexpr auto arity{1};
+  template <typename From>
+  __device__ inline auto operator()(From f) -> decltype(static_cast<To>(f))
+  {
+    return static_cast<To>(f);
+  }
+};
+
+template <>
+struct operator_functor<ast_operator::CAST_TO_INT64, false> : cast<int64_t> {};
+template <>
+struct operator_functor<ast_operator::CAST_TO_UINT64, false> : cast<uint64_t> {};
+template <>
+struct operator_functor<ast_operator::CAST_TO_FLOAT64, false> : cast<double> {};
+
+/*
+ * The default specialization of nullable operators is to fall back to the non-nullable
+ * implementation
+ */
+template <ast_operator op>
+struct operator_functor<op, true> {
+  using NonNullOperator       = operator_functor<op, false>;
+  static constexpr auto arity = NonNullOperator::arity;
+
+  template <typename LHS,
+            typename RHS,
+            std::size_t arity_placeholder             = arity,
+            std::enable_if_t<arity_placeholder == 2>* = nullptr>
+  __device__ inline auto operator()(LHS const lhs, RHS const rhs)
+    -> possibly_null_value_t<decltype(NonNullOperator{}(*lhs, *rhs)), true>
+  {
+    using Out = possibly_null_value_t<decltype(NonNullOperator{}(*lhs, *rhs)), true>;
+    return (lhs.has_value() && rhs.has_value()) ? Out{NonNullOperator{}(*lhs, *rhs)} : Out{};
+  }
+
+  template <typename Input,
+            std::size_t arity_placeholder             = arity,
+            std::enable_if_t<arity_placeholder == 1>* = nullptr>
+  __device__ inline auto operator()(Input const input)
+    -> possibly_null_value_t<decltype(NonNullOperator{}(*input)), true>
+  {
+    using Out = possibly_null_value_t<decltype(NonNullOperator{}(*input)), true>;
+    return input.has_value() ? Out{NonNullOperator{}(*input)} : Out{};
+  }
+};
+
+// IS_NULL(null) is true, IS_NULL(valid) is false
+template <>
+struct operator_functor<ast_operator::IS_NULL, true> {
+  using NonNullOperator       = operator_functor<ast_operator::IS_NULL, false>;
+  static constexpr auto arity = NonNullOperator::arity;
+
+  template <typename LHS>
+  __device__ inline auto operator()(LHS const lhs) -> decltype(!lhs.has_value())
+  {
+    return !lhs.has_value();
+  }
+};
+
+// NULL_EQUAL(null, null) is true, NULL_EQUAL(null, valid) is false, and NULL_EQUAL(valid, valid) ==
+// EQUAL(valid, valid)
+template <>
+struct operator_functor<ast_operator::NULL_EQUAL, true> {
+  using NonNullOperator       = operator_functor<ast_operator::NULL_EQUAL, false>;
+  static constexpr auto arity = NonNullOperator::arity;
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS const lhs, RHS const rhs)
+    -> possibly_null_value_t<decltype(NonNullOperator{}(*lhs, *rhs)), true>
+  {
+    // Case 1: Neither is null, so the output is given by the operation.
+    if (lhs.has_value() && rhs.has_value()) { return {NonNullOperator{}(*lhs, *rhs)}; }
+    // Case 2: Two nulls compare equal.
+    if (!lhs.has_value() && !rhs.has_value()) { return {true}; }
+    // Case 3: One value is null, while the other is not, so we return false.
+    return {false};
+  }
+};
+
+///< NULL_LOGICAL_AND(null, null) is null, NULL_LOGICAL_AND(null, true) is null,
+///< NULL_LOGICAL_AND(null, false) is false, and NULL_LOGICAL_AND(valid, valid) ==
+///< LOGICAL_AND(valid, valid)
+template <>
+struct operator_functor<ast_operator::NULL_LOGICAL_AND, true> {
+  using NonNullOperator       = operator_functor<ast_operator::NULL_LOGICAL_AND, false>;
+  static constexpr auto arity = NonNullOperator::arity;
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS const lhs, RHS const rhs)
+    -> possibly_null_value_t<decltype(NonNullOperator{}(*lhs, *rhs)), true>
+  {
+    // Case 1: Neither is null, so the output is given by the operation.
+    if (lhs.has_value() && rhs.has_value()) { return {NonNullOperator{}(*lhs, *rhs)}; }
+    // Case 2: Two nulls return null.
+    if (!lhs.has_value() && !rhs.has_value()) { return {}; }
+    // Case 3: One value is null, while the other is not. If it's true we return null, otherwise we
+    // return false.
+    auto const& valid_element = lhs.has_value() ? lhs : rhs;
+    if (*valid_element) { return {}; }
+    return {false};
+  }
+};
+
+///< NULL_LOGICAL_OR(null, null) is null, NULL_LOGICAL_OR(null, true) is true, NULL_LOGICAL_OR(null,
+///< false) is null, and NULL_LOGICAL_OR(valid, valid) == LOGICAL_OR(valid, valid)
+template <>
+struct operator_functor<ast_operator::NULL_LOGICAL_OR, true> {
+  using NonNullOperator       = operator_functor<ast_operator::NULL_LOGICAL_OR, false>;
+  static constexpr auto arity = NonNullOperator::arity;
+
+  template <typename LHS, typename RHS>
+  __device__ inline auto operator()(LHS const lhs, RHS const rhs)
+    -> possibly_null_value_t<decltype(NonNullOperator{}(*lhs, *rhs)), true>
+  {
+    // Case 1: Neither is null, so the output is given by the operation.
+    if (lhs.has_value() && rhs.has_value()) { return {NonNullOperator{}(*lhs, *rhs)}; }
+    // Case 2: Two nulls return null.
+    if (!lhs.has_value() && !rhs.has_value()) { return {}; }
+    // Case 3: One value is null, while the other is not. If it's true we return true, otherwise we
+    // return null.
+    auto const& valid_element = lhs.has_value() ? lhs : rhs;
+    if (*valid_element) { return {true}; }
+    return {};
+  }
+};
+
+/**
+ * @brief Functor used to single-type-dispatch binary operators.
+ *
+ * This functor's `operator()` is templated to validate calls to its operators based on the input
+ * type, as determined by the `is_valid_binary_op` trait. This function assumes that both inputs are
+ * the same type, and dispatches based on the type of the left input.
+ *
+ * @tparam OperatorFunctor Binary operator functor.
+ */
+template <typename OperatorFunctor>
+struct single_dispatch_binary_operator_types {
+  template <typename LHS,
+            typename F,
+            typename... Ts,
+            std::enable_if_t<is_valid_binary_op<OperatorFunctor, LHS, LHS>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(F&& f, Ts&&... args)
+  {
+    f.template operator()<OperatorFunctor, LHS, LHS>(std::forward<Ts>(args)...);
+  }
+
+  template <typename LHS,
+            typename F,
+            typename... Ts,
+            std::enable_if_t<!is_valid_binary_op<OperatorFunctor, LHS, LHS>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(F&& f, Ts&&... args)
+  {
+#ifndef __CUDA_ARCH__
+    CUDF_FAIL("Invalid binary operation.");
+#else
+    CUDF_UNREACHABLE("Invalid binary operation.");
+#endif
+  }
+};
+
+/**
+ * @brief Functor performing a type dispatch for a binary operator.
+ *
+ * This functor performs single dispatch, which assumes lhs_type == rhs_type. This may not be true
+ * for all binary operators but holds for all currently implemented operators.
+ */
+struct type_dispatch_binary_op {
+  /**
+   * @brief Performs type dispatch for a binary operator.
+   *
+   * @tparam op AST operator.
+   * @tparam F Type of forwarded functor.
+   * @tparam Ts Parameter pack of forwarded arguments.
+   * @param lhs_type Type of left input data.
+   * @param rhs_type Type of right input data.
+   * @param f Forwarded functor to be called.
+   * @param args Forwarded arguments to `operator()` of `f`.
+   */
+  template <ast_operator op, typename F, typename... Ts>
+  CUDF_HOST_DEVICE inline void operator()(cudf::data_type lhs_type,
+                                          cudf::data_type rhs_type,
+                                          F&& f,
+                                          Ts&&... args)
+  {
+    // Single dispatch (assume lhs_type == rhs_type)
+    type_dispatcher(
+      lhs_type,
+      // Always dispatch to the non-null operator for the purpose of type determination.
+      detail::single_dispatch_binary_operator_types<operator_functor<op, false>>{},
+      std::forward<F>(f),
+      std::forward<Ts>(args)...);
+  }
+};
+
+/**
+ * @brief Dispatches a runtime binary operator to a templated type dispatcher.
+ *
+ * @tparam F Type of forwarded functor.
+ * @tparam Ts Parameter pack of forwarded arguments.
+ * @param lhs_type Type of left input data.
+ * @param rhs_type Type of right input data.
+ * @param f Forwarded functor to be called.
+ * @param args Forwarded arguments to `operator()` of `f`.
+ */
+template <typename F, typename... Ts>
+CUDF_HOST_DEVICE inline constexpr void binary_operator_dispatcher(
+  ast_operator op, cudf::data_type lhs_type, cudf::data_type rhs_type, F&& f, Ts&&... args)
+{
+  ast_operator_dispatcher(op,
+                          detail::type_dispatch_binary_op{},
+                          lhs_type,
+                          rhs_type,
+                          std::forward<F>(f),
+                          std::forward<Ts>(args)...);
+}
+
+/**
+ * @brief Functor used to type-dispatch unary operators.
+ *
+ * This functor's `operator()` is templated to validate calls to its operators based on the input
+ * type, as determined by the `is_valid_unary_op` trait.
+ *
+ * @tparam OperatorFunctor Unary operator functor.
+ */
+template <typename OperatorFunctor>
+struct dispatch_unary_operator_types {
+  template <typename InputT,
+            typename F,
+            typename... Ts,
+            std::enable_if_t<is_valid_unary_op<OperatorFunctor, InputT>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(F&& f, Ts&&... args)
+  {
+    f.template operator()<OperatorFunctor, InputT>(std::forward<Ts>(args)...);
+  }
+
+  template <typename InputT,
+            typename F,
+            typename... Ts,
+            std::enable_if_t<!is_valid_unary_op<OperatorFunctor, InputT>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(F&& f, Ts&&... args)
+  {
+#ifndef __CUDA_ARCH__
+    CUDF_FAIL("Invalid unary operation.");
+#else
+    CUDF_UNREACHABLE("Invalid unary operation.");
+#endif
+  }
+};
+
+/**
+ * @brief Functor performing a type dispatch for a unary operator.
+ */
+struct type_dispatch_unary_op {
+  template <ast_operator op, typename F, typename... Ts>
+  CUDF_HOST_DEVICE inline void operator()(cudf::data_type input_type, F&& f, Ts&&... args)
+  {
+    type_dispatcher(
+      input_type,
+      // Always dispatch to the non-null operator for the purpose of type determination.
+      detail::dispatch_unary_operator_types<operator_functor<op, false>>{},
+      std::forward<F>(f),
+      std::forward<Ts>(args)...);
+  }
+};
+
+/**
+ * @brief Dispatches a runtime unary operator to a templated type dispatcher.
+ *
+ * @tparam F Type of forwarded functor.
+ * @tparam Ts Parameter pack of forwarded arguments.
+ * @param input_type Type of input data.
+ * @param f Forwarded functor to be called.
+ * @param args Forwarded arguments to `operator()` of `f`.
+ */
+template <typename F, typename... Ts>
+CUDF_HOST_DEVICE inline constexpr void unary_operator_dispatcher(ast_operator op,
+                                                                 cudf::data_type input_type,
+                                                                 F&& f,
+                                                                 Ts&&... args)
+{
+  ast_operator_dispatcher(op,
+                          detail::type_dispatch_unary_op{},
+                          input_type,
+                          std::forward<F>(f),
+                          std::forward<Ts>(args)...);
+}
+
+/**
+ * @brief Functor to determine the return type of an operator from its input types.
+ */
+struct return_type_functor {
+  /**
+   * @brief Callable for binary operators to determine return type.
+   *
+   * @tparam OperatorFunctor Operator functor to perform.
+   * @tparam LHS Left input type.
+   * @tparam RHS Right input type.
+   * @param result Reference whose value is assigned to the result data type.
+   */
+  template <typename OperatorFunctor,
+            typename LHS,
+            typename RHS,
+            std::enable_if_t<is_valid_binary_op<OperatorFunctor, LHS, RHS>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(cudf::data_type& result)
+  {
+    using Out = cuda::std::invoke_result_t<OperatorFunctor, LHS, RHS>;
+    result    = cudf::data_type(cudf::type_to_id<Out>());
+  }
+
+  template <typename OperatorFunctor,
+            typename LHS,
+            typename RHS,
+            std::enable_if_t<!is_valid_binary_op<OperatorFunctor, LHS, RHS>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(cudf::data_type& result)
+  {
+#ifndef __CUDA_ARCH__
+    CUDF_FAIL("Invalid binary operation. Return type cannot be determined.");
+#else
+    CUDF_UNREACHABLE("Invalid binary operation. Return type cannot be determined.");
+#endif
+  }
+
+  /**
+   * @brief Callable for unary operators to determine return type.
+   *
+   * @tparam OperatorFunctor Operator functor to perform.
+   * @tparam T Input type.
+   * @param result Pointer whose value is assigned to the result data type.
+   */
+  template <typename OperatorFunctor,
+            typename T,
+            std::enable_if_t<is_valid_unary_op<OperatorFunctor, T>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(cudf::data_type& result)
+  {
+    using Out = cuda::std::invoke_result_t<OperatorFunctor, T>;
+    result    = cudf::data_type(cudf::type_to_id<Out>());
+  }
+
+  template <typename OperatorFunctor,
+            typename T,
+            std::enable_if_t<!is_valid_unary_op<OperatorFunctor, T>>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(cudf::data_type& result)
+  {
+#ifndef __CUDA_ARCH__
+    CUDF_FAIL("Invalid unary operation. Return type cannot be determined.");
+#else
+    CUDF_UNREACHABLE("Invalid unary operation. Return type cannot be determined.");
+#endif
+  }
+};
+
+/**
+ * @brief Gets the return type of an AST operator.
+ *
+ * @param op Operator used to evaluate return type.
+ * @param operand_types Vector of input types to the operator.
+ * @return cudf::data_type Return type of the operator.
+ */
+inline cudf::data_type ast_operator_return_type(ast_operator op,
+                                                std::vector<cudf::data_type> const& operand_types)
+{
+  auto result = cudf::data_type(cudf::type_id::EMPTY);
+  switch (operand_types.size()) {
+    case 1:
+      unary_operator_dispatcher(op, operand_types[0], detail::return_type_functor{}, result);
+      break;
+    case 2:
+      binary_operator_dispatcher(
+        op, operand_types[0], operand_types[1], detail::return_type_functor{}, result);
+      break;
+    default: CUDF_FAIL("Unsupported operator return type."); break;
+  }
+  return result;
+}
+
+/**
+ * @brief Functor to determine the arity (number of operands) of an operator.
+ */
+struct arity_functor {
+  template <ast_operator op>
+  CUDF_HOST_DEVICE inline void operator()(cudf::size_type& result)
+  {
+    // Arity is not dependent on null handling, so just use the false implementation here.
+    result = operator_functor<op, false>::arity;
+  }
+};
+
+/**
+ * @brief Gets the arity (number of operands) of an AST operator.
+ *
+ * @param op Operator used to determine arity.
+ * @return Arity of the operator.
+ */
+CUDF_HOST_DEVICE inline cudf::size_type ast_operator_arity(ast_operator op)
+{
+  auto result = cudf::size_type(0);
+  ast_operator_dispatcher(op, detail::arity_functor{}, result);
+  return result;
+}
+
+}  // namespace detail
+
+}  // namespace ast
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/ast/expressions.hpp b/cpp/include/cudf/ast/expressions.hpp
new file mode 100644
index 0000000..c517248
--- /dev/null
+++ b/cpp/include/cudf/ast/expressions.hpp
@@ -0,0 +1,549 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <cstdint>
+
+namespace cudf {
+namespace ast {
+
+// Forward declaration.
+namespace detail {
+class expression_parser;
+class expression_transformer;
+}  // namespace detail
+
+/**
+ * @brief A generic expression that can be evaluated to return a value.
+ *
+ * This class is a part of a "visitor" pattern with the `expression_parser` class.
+ * Expressions inheriting from this class can accept parsers as visitors.
+ */
+struct expression {
+  /**
+   * @brief Accepts a visitor class.
+   *
+   * @param visitor The `expression_parser` parsing this expression tree
+   * @return Index of device data reference for this instance
+   */
+  virtual cudf::size_type accept(detail::expression_parser& visitor) const = 0;
+
+  /**
+   * @brief Accepts a visitor class.
+   *
+   * @param visitor The `expression_transformer` transforming this expression tree
+   * @return Reference wrapper of transformed expression
+   */
+  virtual std::reference_wrapper<expression const> accept(
+    detail::expression_transformer& visitor) const = 0;
+
+  /**
+   * @brief Returns true if the expression may evaluate to null.
+   *
+   * @param left The left operand of the expression (The same is used as right operand)
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return `true` if the expression may evaluate to null, otherwise false
+   */
+  [[nodiscard]] bool may_evaluate_null(table_view const& left, rmm::cuda_stream_view stream) const
+  {
+    return may_evaluate_null(left, left, stream);
+  }
+
+  /**
+   * @brief Returns true if the expression may evaluate to null.
+   *
+   * @param left The left operand of the expression
+   * @param right The right operand of the expression
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return `true` if the expression may evaluate to null, otherwise false
+   */
+  [[nodiscard]] virtual bool may_evaluate_null(table_view const& left,
+                                               table_view const& right,
+                                               rmm::cuda_stream_view stream) const = 0;
+
+  virtual ~expression() {}
+};
+
+/**
+ * @brief Enum of supported operators.
+ */
+enum class ast_operator : int32_t {
+  // Binary operators
+  ADD,         ///< operator +
+  SUB,         ///< operator -
+  MUL,         ///< operator *
+  DIV,         ///< operator / using common type of lhs and rhs
+  TRUE_DIV,    ///< operator / after promoting type to floating point
+  FLOOR_DIV,   ///< operator / after promoting to 64 bit floating point and then
+               ///< flooring the result
+  MOD,         ///< operator %
+  PYMOD,       ///< operator % using Python's sign rules for negatives
+  POW,         ///< lhs ^ rhs
+  EQUAL,       ///< operator ==
+  NULL_EQUAL,  ///< operator == with Spark rules: NULL_EQUAL(null, null) is true, NULL_EQUAL(null,
+               ///< valid) is false, and
+               ///< NULL_EQUAL(valid, valid) == EQUAL(valid, valid)
+  NOT_EQUAL,   ///< operator !=
+  LESS,        ///< operator <
+  GREATER,     ///< operator >
+  LESS_EQUAL,  ///< operator <=
+  GREATER_EQUAL,     ///< operator >=
+  BITWISE_AND,       ///< operator &
+  BITWISE_OR,        ///< operator |
+  BITWISE_XOR,       ///< operator ^
+  LOGICAL_AND,       ///< operator &&
+  NULL_LOGICAL_AND,  ///< operator && with Spark rules: NULL_LOGICAL_AND(null, null) is null,
+                     ///< NULL_LOGICAL_AND(null, true) is
+                     ///< null, NULL_LOGICAL_AND(null, false) is false, and NULL_LOGICAL_AND(valid,
+                     ///< valid) == LOGICAL_AND(valid, valid)
+  LOGICAL_OR,        ///< operator ||
+  NULL_LOGICAL_OR,   ///< operator || with Spark rules: NULL_LOGICAL_OR(null, null) is null,
+                     ///< NULL_LOGICAL_OR(null, true) is true,
+                     ///< NULL_LOGICAL_OR(null, false) is null, and NULL_LOGICAL_OR(valid, valid) ==
+                     ///< LOGICAL_OR(valid, valid)
+  // Unary operators
+  IDENTITY,        ///< Identity function
+  IS_NULL,         ///< Check if operand is null
+  SIN,             ///< Trigonometric sine
+  COS,             ///< Trigonometric cosine
+  TAN,             ///< Trigonometric tangent
+  ARCSIN,          ///< Trigonometric sine inverse
+  ARCCOS,          ///< Trigonometric cosine inverse
+  ARCTAN,          ///< Trigonometric tangent inverse
+  SINH,            ///< Hyperbolic sine
+  COSH,            ///< Hyperbolic cosine
+  TANH,            ///< Hyperbolic tangent
+  ARCSINH,         ///< Hyperbolic sine inverse
+  ARCCOSH,         ///< Hyperbolic cosine inverse
+  ARCTANH,         ///< Hyperbolic tangent inverse
+  EXP,             ///< Exponential (base e, Euler number)
+  LOG,             ///< Natural Logarithm (base e)
+  SQRT,            ///< Square-root (x^0.5)
+  CBRT,            ///< Cube-root (x^(1.0/3))
+  CEIL,            ///< Smallest integer value not less than arg
+  FLOOR,           ///< largest integer value not greater than arg
+  ABS,             ///< Absolute value
+  RINT,            ///< Rounds the floating-point argument arg to an integer value
+  BIT_INVERT,      ///< Bitwise Not (~)
+  NOT,             ///< Logical Not (!)
+  CAST_TO_INT64,   ///< Cast value to int64_t
+  CAST_TO_UINT64,  ///< Cast value to uint64_t
+  CAST_TO_FLOAT64  ///< Cast value to double
+};
+
+/**
+ * @brief Enum of table references.
+ *
+ * This determines which table to use in cases with two tables (e.g. joins).
+ */
+enum class table_reference {
+  LEFT,   ///< Column index in the left table
+  RIGHT,  ///< Column index in the right table
+  OUTPUT  ///< Column index in the output table
+};
+
+/**
+ * @brief A type-erased scalar_device_view where the value is a fixed width type or a string
+ */
+class generic_scalar_device_view : public cudf::detail::scalar_device_view_base {
+ public:
+  /**
+   * @brief Returns the stored value.
+   *
+   * @tparam T The desired type
+   * @returns The stored value
+   */
+  template <typename T>
+  __device__ T const value() const noexcept
+  {
+    if constexpr (std::is_same_v<T, cudf::string_view>) {
+      return string_view(static_cast<char const*>(_data), _size);
+    }
+    return *static_cast<T const*>(_data);
+  }
+
+  /** @brief Construct a new generic scalar device view object from a numeric scalar
+   *
+   * @param s The numeric scalar to construct from
+   */
+  template <typename T>
+  generic_scalar_device_view(numeric_scalar<T>& s)
+    : generic_scalar_device_view(s.type(), s.data(), s.validity_data())
+  {
+  }
+
+  /** @brief Construct a new generic scalar device view object from a timestamp scalar
+   *
+   * @param s The timestamp scalar to construct from
+   */
+  template <typename T>
+  generic_scalar_device_view(timestamp_scalar<T>& s)
+    : generic_scalar_device_view(s.type(), s.data(), s.validity_data())
+  {
+  }
+
+  /** @brief Construct a new generic scalar device view object from a duration scalar
+   *
+   * @param s The duration scalar to construct from
+   */
+  template <typename T>
+  generic_scalar_device_view(duration_scalar<T>& s)
+    : generic_scalar_device_view(s.type(), s.data(), s.validity_data())
+  {
+  }
+
+  /** @brief Construct a new generic scalar device view object from a string scalar
+   *
+   * @param s The string scalar to construct from
+   */
+  generic_scalar_device_view(string_scalar& s)
+    : generic_scalar_device_view(s.type(), s.data(), s.validity_data(), s.size())
+  {
+  }
+
+ protected:
+  void const* _data{};      ///< Pointer to device memory containing the value
+  size_type const _size{};  ///< Size of the string in bytes for string scalar
+
+  /**
+   * @brief Construct a new fixed width scalar device view object
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  generic_scalar_device_view(data_type type, void const* data, bool* is_valid)
+    : cudf::detail::scalar_device_view_base(type, is_valid), _data(data)
+  {
+  }
+
+  /** @brief Construct a new string scalar device view object
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   * @param size The size of the string in bytes
+   */
+  generic_scalar_device_view(data_type type, void const* data, bool* is_valid, size_type size)
+    : cudf::detail::scalar_device_view_base(type, is_valid), _data(data), _size(size)
+  {
+  }
+};
+
+/**
+ * @brief A literal value used in an abstract syntax tree.
+ */
+class literal : public expression {
+ public:
+  /**
+   * @brief Construct a new literal object.
+   *
+   * @tparam T Numeric scalar template type
+   * @param value A numeric scalar value
+   */
+  template <typename T>
+  literal(cudf::numeric_scalar<T>& value) : scalar(value), value(value)
+  {
+  }
+
+  /**
+   * @brief Construct a new literal object.
+   *
+   * @tparam T Timestamp scalar template type
+   * @param value A timestamp scalar value
+   */
+  template <typename T>
+  literal(cudf::timestamp_scalar<T>& value) : scalar(value), value(value)
+  {
+  }
+
+  /**
+   * @brief Construct a new literal object.
+   *
+   * @tparam T Duration scalar template type
+   * @param value A duration scalar value
+   */
+  template <typename T>
+  literal(cudf::duration_scalar<T>& value) : scalar(value), value(value)
+  {
+  }
+
+  /**
+   * @brief Construct a new literal object.
+   *
+   * @param value A string scalar value
+   */
+  literal(cudf::string_scalar& value) : scalar(value), value(value) {}
+
+  /**
+   * @brief Get the data type.
+   *
+   * @return The data type of the literal
+   */
+  [[nodiscard]] cudf::data_type get_data_type() const { return get_value().type(); }
+
+  /**
+   * @brief Get the value object.
+   *
+   * @return The device scalar object
+   */
+  [[nodiscard]] generic_scalar_device_view get_value() const { return value; }
+
+  /**
+   * @copydoc expression::accept
+   */
+  cudf::size_type accept(detail::expression_parser& visitor) const override;
+
+  /**
+   * @copydoc expression::accept
+   */
+  std::reference_wrapper<expression const> accept(
+    detail::expression_transformer& visitor) const override;
+
+  [[nodiscard]] bool may_evaluate_null(table_view const& left,
+                                       table_view const& right,
+                                       rmm::cuda_stream_view stream) const override
+  {
+    return !is_valid(stream);
+  }
+
+  /**
+   * @brief Check if the underlying scalar is valid.
+   *
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return true if the underlying scalar is valid
+   */
+  [[nodiscard]] bool is_valid(rmm::cuda_stream_view stream) const
+  {
+    return scalar.is_valid(stream);
+  }
+
+ private:
+  cudf::scalar const& scalar;
+  generic_scalar_device_view const value;
+};
+
+/**
+ * @brief A expression referring to data from a column in a table.
+ */
+class column_reference : public expression {
+ public:
+  /**
+   * @brief Construct a new column reference object
+   *
+   * @param column_index Index of this column in the table (provided when the expression is
+   * evaluated).
+   * @param table_source Which table to use in cases with two tables (e.g. joins)
+   */
+  column_reference(cudf::size_type column_index,
+                   table_reference table_source = table_reference::LEFT)
+    : column_index(column_index), table_source(table_source)
+  {
+  }
+
+  /**
+   * @brief Get the column index.
+   *
+   * @return The column index of the column reference
+   */
+  [[nodiscard]] cudf::size_type get_column_index() const { return column_index; }
+
+  /**
+   * @brief Get the table source.
+   *
+   * @return table_reference The reference to the table containing this column
+   */
+  [[nodiscard]] table_reference get_table_source() const { return table_source; }
+
+  /**
+   * @brief Get the data type.
+   *
+   * @param table Table used to determine types
+   * @return The data type of the column
+   */
+  [[nodiscard]] cudf::data_type get_data_type(table_view const& table) const
+  {
+    return table.column(get_column_index()).type();
+  }
+
+  /**
+   * @brief Get the data type.
+   *
+   * @param left_table Left table used to determine types
+   * @param right_table Right table used to determine types
+   * @return The data type of the column
+   */
+  [[nodiscard]] cudf::data_type get_data_type(table_view const& left_table,
+                                              table_view const& right_table) const
+  {
+    auto const table = [&] {
+      if (get_table_source() == table_reference::LEFT) {
+        return left_table;
+      } else if (get_table_source() == table_reference::RIGHT) {
+        return right_table;
+      } else {
+        CUDF_FAIL("Column reference data type cannot be determined from unknown table.");
+      }
+    }();
+    return table.column(get_column_index()).type();
+  }
+
+  /**
+   * @copydoc expression::accept
+   */
+  cudf::size_type accept(detail::expression_parser& visitor) const override;
+
+  /**
+   * @copydoc expression::accept
+   */
+  std::reference_wrapper<expression const> accept(
+    detail::expression_transformer& visitor) const override;
+
+  [[nodiscard]] bool may_evaluate_null(table_view const& left,
+                                       table_view const& right,
+                                       rmm::cuda_stream_view stream) const override
+  {
+    return (table_source == table_reference::LEFT ? left : right).column(column_index).has_nulls();
+  }
+
+ private:
+  cudf::size_type column_index;
+  table_reference table_source;
+};
+
+/**
+ * @brief An operation expression holds an operator and zero or more operands.
+ */
+class operation : public expression {
+ public:
+  /**
+   * @brief Construct a new unary operation object.
+   *
+   * @param op Operator
+   * @param input Input expression (operand)
+   */
+  operation(ast_operator op, expression const& input);
+
+  /**
+   * @brief Construct a new binary operation object.
+   *
+   * @param op Operator
+   * @param left Left input expression (left operand)
+   * @param right Right input expression (right operand)
+   */
+  operation(ast_operator op, expression const& left, expression const& right);
+
+  // operation only stores references to expressions, so it does not accept r-value
+  // references: the calling code must own the expressions.
+  operation(ast_operator op, expression&& input)                         = delete;
+  operation(ast_operator op, expression&& left, expression&& right)      = delete;
+  operation(ast_operator op, expression&& left, expression const& right) = delete;
+  operation(ast_operator op, expression const& left, expression&& right) = delete;
+
+  /**
+   * @brief Get the operator.
+   *
+   * @return The operator
+   */
+  [[nodiscard]] ast_operator get_operator() const { return op; }
+
+  /**
+   * @brief Get the operands.
+   *
+   * @return Vector of operands
+   */
+  std::vector<std::reference_wrapper<expression const>> get_operands() const { return operands; }
+
+  /**
+   * @copydoc expression::accept
+   */
+  cudf::size_type accept(detail::expression_parser& visitor) const override;
+
+  /**
+   * @copydoc expression::accept
+   */
+  std::reference_wrapper<expression const> accept(
+    detail::expression_transformer& visitor) const override;
+
+  [[nodiscard]] bool may_evaluate_null(table_view const& left,
+                                       table_view const& right,
+                                       rmm::cuda_stream_view stream) const override
+  {
+    return std::any_of(operands.cbegin(),
+                       operands.cend(),
+                       [&left, &right, &stream](std::reference_wrapper<expression const> subexpr) {
+                         return subexpr.get().may_evaluate_null(left, right, stream);
+                       });
+  };
+
+ private:
+  ast_operator const op;
+  std::vector<std::reference_wrapper<expression const>> const operands;
+};
+
+/**
+ * @brief A expression referring to data from a column in a table.
+ */
+class column_name_reference : public expression {
+ public:
+  /**
+   * @brief Construct a new column name reference object
+   *
+   * @param column_name Name of this column in the table metadata (provided when the expression is
+   * evaluated).
+   */
+  column_name_reference(std::string column_name) : column_name(std::move(column_name)) {}
+
+  /**
+   * @brief Get the column name.
+   *
+   * @return The name of this column reference
+   */
+  [[nodiscard]] std::string get_column_name() const { return column_name; }
+
+  /**
+   * @copydoc expression::accept
+   */
+  cudf::size_type accept(detail::expression_parser& visitor) const override;
+
+  /**
+   * @copydoc expression::accept
+   */
+  std::reference_wrapper<expression const> accept(
+    detail::expression_transformer& visitor) const override;
+
+  [[nodiscard]] bool may_evaluate_null(table_view const& left,
+                                       table_view const& right,
+                                       rmm::cuda_stream_view stream) const override
+  {
+    return true;
+  }
+
+ private:
+  std::string column_name;
+};
+
+}  // namespace ast
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/binaryop.hpp b/cpp/include/cudf/binaryop.hpp
new file mode 100644
index 0000000..77d6a4d
--- /dev/null
+++ b/cpp/include/cudf/binaryop.hpp
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+
+/**
+ * @addtogroup transformation_binaryops
+ * @{
+ * @file
+ * @brief Column APIs for binary ops
+ */
+
+/**
+ * @brief Types of binary operations that can be performed on data.
+ */
+enum class binary_operator : int32_t {
+  ADD,          ///< operator +
+  SUB,          ///< operator -
+  MUL,          ///< operator *
+  DIV,          ///< operator / using common type of lhs and rhs
+  TRUE_DIV,     ///< operator / after promoting type to floating point
+  FLOOR_DIV,    ///< operator //
+                ///< integer division rounding towards negative
+                ///< infinity if both arguments are integral;
+                ///< floor division for floating types (using C++ type
+                ///< promotion for mixed integral/floating arguments)
+                ///< If different promotion semantics are required, it
+                ///< is the responsibility of the caller to promote
+                ///< manually before calling in to this function.
+  MOD,          ///< operator %
+  PMOD,         ///< positive modulo operator
+                ///< If remainder is negative, this returns (remainder + divisor) % divisor
+                ///< else, it returns (dividend % divisor)
+  PYMOD,        ///< operator % but following Python's sign rules for negatives
+  POW,          ///< lhs ^ rhs
+  INT_POW,      ///< int ^ int, used to avoid floating point precision loss. Returns 0 for negative
+                ///< exponents.
+  LOG_BASE,     ///< logarithm to the base
+  ATAN2,        ///< 2-argument arctangent
+  SHIFT_LEFT,   ///< operator <<
+  SHIFT_RIGHT,  ///< operator >>
+  SHIFT_RIGHT_UNSIGNED,  ///< operator >>> (from Java)
+                         ///< Logical right shift. Casts to an unsigned value before shifting.
+  BITWISE_AND,           ///< operator &
+  BITWISE_OR,            ///< operator |
+  BITWISE_XOR,           ///< operator ^
+  LOGICAL_AND,           ///< operator &&
+  LOGICAL_OR,            ///< operator ||
+  EQUAL,                 ///< operator ==
+  NOT_EQUAL,             ///< operator !=
+  LESS,                  ///< operator <
+  GREATER,               ///< operator >
+  LESS_EQUAL,            ///< operator <=
+  GREATER_EQUAL,         ///< operator >=
+  NULL_EQUALS,           ///< Returns true when both operands are null; false when one is null; the
+                         ///< result of equality when both are non-null
+  NULL_MAX,              ///< Returns max of operands when both are non-null; returns the non-null
+                         ///< operand when one is null; or invalid when both are null
+  NULL_MIN,              ///< Returns min of operands when both are non-null; returns the non-null
+                         ///< operand when one is null; or invalid when both are null
+  GENERIC_BINARY,        ///< generic binary operator to be generated with input
+                         ///< ptx code
+  NULL_LOGICAL_AND,  ///< operator && with Spark rules: (null, null) is null, (null, true) is null,
+                     ///< (null, false) is false, and (valid, valid) == LOGICAL_AND(valid, valid)
+  NULL_LOGICAL_OR,   ///< operator || with Spark rules: (null, null) is null, (null, true) is true,
+                     ///< (null, false) is null, and (valid, valid) == LOGICAL_OR(valid, valid)
+  INVALID_BINARY     ///< invalid operation
+};
+/**
+ * @brief Performs a binary operation between a scalar and a column.
+ *
+ * The output contains the result of `op(lhs, rhs[i])` for all `0 <= i < rhs.size()`
+ * The scalar is the left operand and the column elements are the right operand.
+ * This distinction is significant in case of non-commutative binary operations
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands except NullMin and NullMax (logical OR).
+ *
+ * @param lhs         The left operand scalar
+ * @param rhs         The right operand column
+ * @param op          The binary operator
+ * @param output_type The desired data type of the output column
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return            Output column of `output_type` type containing the result of
+ *                    the binary operation
+ * @throw cudf::logic_error if @p output_type dtype isn't fixed-width
+ * @throw cudf::logic_error if @p output_type dtype isn't boolean for comparison and logical
+ * operations.
+ * @throw cudf::data_type_error if the operation is not supported for the types of @p lhs and @p rhs
+ */
+std::unique_ptr<column> binary_operation(
+  scalar const& lhs,
+  column_view const& rhs,
+  binary_operator op,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a binary operation between a column and a scalar.
+ *
+ * The output contains the result of `op(lhs[i], rhs)` for all `0 <= i < lhs.size()`
+ * The column elements are the left operand and the scalar is the right operand.
+ * This distinction is significant in case of non-commutative binary operations
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands except NullMin and NullMax (logical OR).
+ *
+ * @param lhs         The left operand column
+ * @param rhs         The right operand scalar
+ * @param op          The binary operator
+ * @param output_type The desired data type of the output column
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return            Output column of `output_type` type containing the result of
+ *                    the binary operation
+ * @throw cudf::logic_error if @p output_type dtype isn't fixed-width
+ * @throw cudf::logic_error if @p output_type dtype isn't boolean for comparison and logical
+ * operations.
+ * @throw cudf::data_type_error if the operation is not supported for the types of @p lhs and @p rhs
+ */
+std::unique_ptr<column> binary_operation(
+  column_view const& lhs,
+  scalar const& rhs,
+  binary_operator op,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a binary operation between two columns.
+ *
+ * The output contains the result of `op(lhs[i], rhs[i])` for all `0 <= i < lhs.size()`
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands except NullMin and NullMax (logical OR).
+ *
+ * @param lhs         The left operand column
+ * @param rhs         The right operand column
+ * @param op          The binary operator
+ * @param output_type The desired data type of the output column
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return            Output column of `output_type` type containing the result of
+ *                    the binary operation
+ * @throw cudf::logic_error if @p lhs and @p rhs are different sizes
+ * @throw cudf::logic_error if @p output_type dtype isn't boolean for comparison and logical
+ * operations.
+ * @throw cudf::logic_error if @p output_type dtype isn't fixed-width
+ * @throw cudf::data_type_error if the operation is not supported for the types of @p lhs and @p rhs
+ */
+std::unique_ptr<column> binary_operation(
+  column_view const& lhs,
+  column_view const& rhs,
+  binary_operator op,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a binary operation between two columns using a
+ * user-defined PTX function.
+ *
+ * The output contains the result of `op(lhs[i], rhs[i])` for all `0 <= i < lhs.size()`
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands
+ *
+ * @param lhs         The left operand column
+ * @param rhs         The right operand column
+ * @param ptx         String containing the PTX of a binary function
+ * @param output_type The desired data type of the output column. It is assumed
+ *                    that output_type is compatible with the output data type
+ *                    of the function in the PTX code
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return            Output column of `output_type` type containing the result of
+ *                    the binary operation
+ * @throw cudf::logic_error if @p lhs and @p rhs are different sizes
+ * @throw cudf::logic_error if @p lhs and @p rhs dtypes aren't numeric
+ * @throw cudf::logic_error if @p output_type dtype isn't numeric
+ */
+std::unique_ptr<column> binary_operation(
+  column_view const& lhs,
+  column_view const& rhs,
+  std::string const& ptx,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the `scale` for a `fixed_point` number based on given binary operator `op`
+ *
+ * @param op           The binary_operator used for two `fixed_point` numbers
+ * @param left_scale   Scale of left `fixed_point` number
+ * @param right_scale  Scale of right `fixed_point` number
+ * @return             The resulting `scale` of the computed `fixed_point` number
+ */
+int32_t binary_operation_fixed_point_scale(binary_operator op,
+                                           int32_t left_scale,
+                                           int32_t right_scale);
+
+/**
+ * @brief Computes the `data_type` for a `fixed_point` number based on given binary operator `op`
+ *
+ * @param op   The binary_operator used for two `fixed_point` numbers
+ * @param lhs  `cudf::data_type` of left `fixed_point` number
+ * @param rhs  `cudf::data_type` of right `fixed_point` number
+ * @return     The resulting `cudf::data_type` of the computed `fixed_point` number
+ */
+cudf::data_type binary_operation_fixed_point_output_type(binary_operator op,
+                                                         cudf::data_type const& lhs,
+                                                         cudf::data_type const& rhs);
+
+namespace binops {
+
+/**
+ * @brief Computes output valid mask for op between a column and a scalar
+ *
+ * @param col     Column to compute the valid mask from
+ * @param s       Scalar to compute the valid mask from
+ * @param stream  CUDA stream used for device memory operations and kernel launches
+ * @param mr      Device memory resource used to allocate the returned valid mask
+ * @return        Computed validity mask
+ */
+std::pair<rmm::device_buffer, size_type> scalar_col_valid_mask_and(
+  column_view const& col,
+  scalar const& s,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+namespace compiled {
+namespace detail {
+
+/**
+ * @brief struct binary operation using `NaN` aware sorting physical element comparators
+ *
+ * @param out mutable view of output column
+ * @param lhs view of left operand column
+ * @param rhs view of right operand column
+ * @param is_lhs_scalar true if @p lhs is a single element column representing a scalar
+ * @param is_rhs_scalar true if @p rhs is a single element column representing a scalar
+ * @param op binary operator identifier
+ * @param stream CUDA stream used for device memory operations
+ */
+void apply_sorting_struct_binary_op(mutable_column_view& out,
+                                    column_view const& lhs,
+                                    column_view const& rhs,
+                                    bool is_lhs_scalar,
+                                    bool is_rhs_scalar,
+                                    binary_operator op,
+                                    rmm::cuda_stream_view stream);
+}  // namespace detail
+}  // namespace compiled
+}  // namespace binops
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/column/column.hpp b/cpp/include/cudf/column/column.hpp
new file mode 100644
index 0000000..a381864
--- /dev/null
+++ b/cpp/include/cudf/column/column.hpp
@@ -0,0 +1,335 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+/**
+ * @file
+ * @brief Class definition for cudf::column
+ */
+
+namespace cudf {
+
+/**
+ * @brief A container of nullable device data as a column of elements.
+ *
+ * @ingroup column_classes Column
+ * @{
+ */
+
+class column {
+ public:
+  column()                               = default;
+  ~column()                              = default;
+  column& operator=(column const& other) = delete;
+  column& operator=(column&& other)      = delete;
+
+  /**
+   * @brief Construct a new column object by deep copying the contents of
+   *`other`.
+   *
+   * Uses the specified `stream` and device_memory_resource for all allocations
+   * and copies.
+   *
+   * @param other The `column` to copy
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for all device memory allocations
+   */
+  column(column const& other,
+         rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+         rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Move the contents from `other` to create a new column.
+   *
+   * After the move, `other.size() == 0` and `other.type() = {EMPTY}`
+   *
+   * @param other The column whose contents will be moved into the new column
+   */
+  column(column&& other) noexcept;
+
+  /**
+   * @brief Construct a new column by taking ownership of the contents of a device_uvector.
+   *
+   * @param other The device_uvector whose contents will be moved into the new column.
+   * @param null_mask Column's null value indicator bitmask. May be empty if `null_count` is 0.
+   * @param null_count The count of null elements.
+   */
+  template <typename T, CUDF_ENABLE_IF(cudf::is_numeric<T>() or cudf::is_chrono<T>())>
+  column(rmm::device_uvector<T>&& other, rmm::device_buffer&& null_mask, size_type null_count)
+    : _type{cudf::data_type{cudf::type_to_id<T>()}},
+      _size{[&]() {
+        CUDF_EXPECTS(
+          other.size() <= static_cast<std::size_t>(std::numeric_limits<size_type>::max()),
+          "The device_uvector size exceeds the column size limit",
+          std::overflow_error);
+        return static_cast<size_type>(other.size());
+      }()},
+      _data{other.release()},
+      _null_mask{std::move(null_mask)},
+      _null_count{null_count}
+  {
+  }
+
+  /**
+   * @brief Construct a new column from existing device memory.
+   *
+   * @note This constructor is primarily intended for use in column factory
+   * functions.
+   *
+   * @throws cudf::logic_error if `size < 0`
+   *
+   * @param dtype The element type
+   * @param size The number of elements in the column
+   * @param data The column's data
+   * @param null_mask Column's null value indicator bitmask. May be empty if `null_count` is 0.
+   * @param null_count Optional, the count of null elements.
+   * @param children Optional, vector of child columns
+   */
+  template <typename B1, typename B2 = rmm::device_buffer>
+  column(data_type dtype,
+         size_type size,
+         B1&& data,
+         B2&& null_mask,
+         size_type null_count,
+         std::vector<std::unique_ptr<column>>&& children = {})
+    : _type{dtype},
+      _size{size},
+      _data{std::forward<B1>(data)},
+      _null_mask{std::forward<B2>(null_mask)},
+      _null_count{null_count},
+      _children{std::move(children)}
+  {
+    CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+  }
+
+  /**
+   * @brief Construct a new column by deep copying the contents of a
+   * `column_view`.
+   *
+   * This accounts for the `column_view`'s offset.
+   *
+   * @param view The view to copy
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for all device memory allocations
+   */
+  explicit column(column_view view,
+                  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Returns the column's logical element type
+   *
+   * @return The column's logical element type
+   */
+  [[nodiscard]] data_type type() const noexcept { return _type; }
+
+  /**
+   * @brief Returns the number of elements
+   *
+   * @return The number of elements
+   */
+  [[nodiscard]] size_type size() const noexcept { return _size; }
+
+  /**
+   * @brief Returns the count of null elements.
+   *
+   * @return The number of null elements
+   */
+  [[nodiscard]] size_type null_count() const { return _null_count; }
+
+  /**
+   * @brief Sets the column's null value indicator bitmask to `new_null_mask`.
+   *
+   * @throws cudf::logic_error if new_null_count is larger than 0 and the size
+   * of `new_null_mask` does not match the size of this column.
+   *
+   * @param new_null_mask New null value indicator bitmask (rvalue overload &
+   * moved) to set the column's null value indicator mask. May be empty if
+   * `new_null_count` is 0.
+   * @param new_null_count The count of null elements.
+   */
+  void set_null_mask(rmm::device_buffer&& new_null_mask, size_type new_null_count);
+
+  /**
+   * @brief Sets the column's null value indicator bitmask to `new_null_mask`.
+   *
+   * @throws cudf::logic_error if new_null_count is larger than 0 and the size of `new_null_mask`
+   * does not match the size of this column.
+   *
+   * @param new_null_mask New null value indicator bitmask (lvalue overload & copied) to set the
+   * column's null value indicator mask. May be empty if `new_null_count` is 0.
+   * @param new_null_count The count of null elements
+   * @param stream The stream on which to perform the allocation and copy. Uses the default CUDF
+   * stream if none is specified.
+   */
+  void set_null_mask(rmm::device_buffer const& new_null_mask,
+                     size_type new_null_count,
+                     rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * @brief Updates the count of null elements.
+   *
+   * @throws cudf::logic_error if `new_null_count > 0 and nullable() == false`
+   *
+   * @param new_null_count The new null count.
+   */
+  void set_null_count(size_type new_null_count);
+
+  /**
+   * @brief Indicates whether it is possible for the column to contain null
+   * values, i.e., it has an allocated null mask.
+   *
+   * This may return `false` iff `null_count() == 0`.
+   *
+   * May return true even if `null_count() == 0`. This function simply indicates
+   * whether the column has an allocated null mask.
+   *
+   * @return true The column can hold null values
+   * @return false The column cannot hold null values
+   */
+  [[nodiscard]] bool nullable() const noexcept { return (_null_mask.size() > 0); }
+
+  /**
+   * @brief Indicates whether the column contains null elements.
+   *
+   * @return true One or more elements are null
+   * @return false Zero elements are null
+   */
+  [[nodiscard]] bool has_nulls() const noexcept { return (null_count() > 0); }
+
+  /**
+   * @brief Returns the number of child columns
+   *
+   * @return The number of child columns
+   */
+  [[nodiscard]] size_type num_children() const noexcept { return _children.size(); }
+
+  /**
+   * @brief Returns a reference to the specified child
+   *
+   * @param child_index Index of the desired child
+   * @return Reference to the desired child
+   */
+  column& child(size_type child_index) noexcept { return *_children[child_index]; };
+
+  /**
+   * @brief Returns a const reference to the specified child
+   *
+   * @param child_index Index of the desired child
+   * @return Const reference to the desired child
+   */
+  [[nodiscard]] column const& child(size_type child_index) const noexcept
+  {
+    return *_children[child_index];
+  };
+
+  /**
+   * @brief Wrapper for the contents of a column.
+   *
+   * Returned by `column::release()`.
+   */
+  struct contents {
+    std::unique_ptr<rmm::device_buffer> data;       ///< data device memory buffer
+    std::unique_ptr<rmm::device_buffer> null_mask;  ///< null mask device memory buffer
+    std::vector<std::unique_ptr<column>> children;  ///< child columns
+  };
+
+  /**
+   * @brief Releases ownership of the column's contents.
+   *
+   * It is the caller's responsibility to query the `size(), null_count(),
+   * type()` before invoking `release()`.
+   *
+   * After calling `release()` on a column it will be empty, i.e.:
+   * - `type() == data_type{EMPTY}`
+   * - `size() == 0`
+   * - `null_count() == 0`
+   * - `num_children() == 0`
+   *
+   * @return A `contents` struct containing the data, null mask, and children of
+   * the column.
+   */
+  contents release() noexcept;
+
+  /**
+   * @brief Creates an immutable, non-owning view of the column's data and
+   * children.
+   *
+   * @return The immutable, non-owning view
+   */
+  [[nodiscard]] column_view view() const;
+
+  /**
+   * @brief Implicit conversion operator to a `column_view`.
+   *
+   * This allows passing a `column` object directly into a function that
+   * requires a `column_view`. The conversion is automatic.
+   *
+   * @return Immutable, non-owning `column_view`
+   */
+  operator column_view() const { return this->view(); };
+
+  /**
+   * @brief Creates a mutable, non-owning view of the column's data, null mask,
+   * and children
+   *
+   * @return The mutable, non-owning view
+   */
+  mutable_column_view mutable_view();
+
+  /**
+   * @brief Implicit conversion operator to a `mutable_column_view`.
+   *
+   * This allows passing a `column` object into a function that accepts a
+   * `mutable_column_view`. The conversion is automatic.
+   *
+   * The caller is expected to update the null count appropriately if the null mask
+   * is modified.
+   *
+   * @return Mutable, non-owning `mutable_column_view`
+   */
+  operator mutable_column_view() { return this->mutable_view(); };
+
+ private:
+  cudf::data_type _type{type_id::EMPTY};  ///< Logical type of elements in the column
+  cudf::size_type _size{};                ///< The number of elements in the column
+  rmm::device_buffer _data{};             ///< Dense, contiguous, type erased device memory
+                                          ///< buffer containing the column elements
+  rmm::device_buffer _null_mask{};        ///< Bitmask used to represent null values.
+                                          ///< May be empty if `null_count() == 0`
+  mutable cudf::size_type _null_count{};  ///< The number of null elements
+  std::vector<std::unique_ptr<column>> _children{};  ///< Depending on element type, child
+                                                     ///< columns may contain additional data
+};
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/column/column_device_view.cuh b/cpp/include/cudf/column/column_device_view.cuh
new file mode 100644
index 0000000..35851a9
--- /dev/null
+++ b/cpp/include/cudf/column/column_device_view.cuh
@@ -0,0 +1,1526 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/utilities/alignment.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+#include <algorithm>
+
+/**
+ * @file column_device_view.cuh
+ * @brief Column device view class definitions
+ */
+
+namespace cudf {
+
+/**
+ * @brief Indicates the presence of nulls at compile-time or runtime.
+ *
+ * If used at compile-time, this indicator can tell the optimizer
+ * to include or exclude any null-checking clauses.
+ *
+ */
+struct nullate {
+  struct YES : std::bool_constant<true> {};
+  struct NO : std::bool_constant<false> {};
+  /**
+   * @brief `nullate::DYNAMIC` defers the determination of nullability to run time rather than
+   * compile time. The calling code is responsible for specifying whether or not nulls are
+   * present using the constructor parameter at run time.
+   */
+  struct DYNAMIC {
+    DYNAMIC() = delete;
+    /**
+     * @brief Create a runtime nullate object.
+     *
+     * @see cudf::column_device_view::optional_begin for example usage
+     *
+     * @param b True if nulls are expected in the operation in which this
+     *          object is applied.
+     */
+    constexpr explicit DYNAMIC(bool b) noexcept : value{b} {}
+    /**
+     * @brief Returns true if nulls are expected in the operation in which this object is applied.
+     *
+     * @return `true` if nulls are expected in the operation in which this object is applied,
+     * otherwise false
+     */
+    constexpr operator bool() const noexcept { return value; }
+    bool value;  ///< True if nulls are expected
+  };
+};
+
+namespace detail {
+/**
+ * @brief An immutable, non-owning view of device data as a column of elements
+ * that is trivially copyable and usable in CUDA device code.
+ *
+ * column_device_view_base and derived classes do not support has_nulls() or
+ * null_count().  The primary reason for this is that creation of column_device_views
+ * from column_views that have UNKNOWN null counts would require an on-the-spot, and
+ * not-obvious computation of null count, which could lead to undesirable performance issues.
+ * This information is also generally not needed in device code, and on the host-side
+ * is easily accessible from the associated column_view.
+ */
+class alignas(16) column_device_view_base {
+ public:
+  column_device_view_base()                               = delete;
+  ~column_device_view_base()                              = default;
+  column_device_view_base(column_device_view_base const&) = default;  ///< Copy constructor
+  column_device_view_base(column_device_view_base&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  column_device_view_base& operator=(column_device_view_base const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  column_device_view_base& operator=(column_device_view_base&&) = default;
+
+  /**
+   * @brief Returns pointer to the base device memory allocation casted to
+   * the specified type.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * @note It should be rare to need to access the `head<T>()` allocation of
+   * a column, and instead, accessing the elements should be done via
+   *`data<T>()`.
+   *
+   * This function will only participate in overload resolution if `is_rep_layout_compatible<T>()`
+   * or `std::is_same_v<T,void>` are true.
+   *
+   * @tparam The type to cast to
+   * @return Typed pointer to underlying data
+   */
+  template <typename T = void,
+            CUDF_ENABLE_IF(std::is_same_v<T, void> or is_rep_layout_compatible<T>())>
+  [[nodiscard]] CUDF_HOST_DEVICE T const* head() const noexcept
+  {
+    return static_cast<T const*>(_data);
+  }
+
+  /**
+   * @brief Returns the underlying data casted to the specified type, plus the
+   * offset.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * For columns with children, the pointer returned is undefined
+   * and should not be used.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @tparam T The type to cast to
+   * @return Typed pointer to underlying data, including the offset
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  [[nodiscard]] CUDF_HOST_DEVICE T const* data() const noexcept
+  {
+    return head<T>() + _offset;
+  }
+
+  /**
+   * @brief Returns the number of elements in the column.
+   *
+   * @return The number of elements in the column
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE size_type size() const noexcept { return _size; }
+
+  /**
+   * @brief Returns the element type
+   *
+   * @return The element type
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE data_type type() const noexcept { return _type; }
+
+  /**
+   * @brief Indicates whether the column can contain null elements, i.e., if it
+   *has an allocated bitmask.
+   *
+   * @note If `null_count() > 0`, this function must always return `true`.
+   *
+   * @return true The bitmask is allocated
+   * @return false The bitmask is not allocated
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE bool nullable() const noexcept { return nullptr != _null_mask; }
+
+  /**
+   * @brief Returns raw pointer to the underlying bitmask allocation.
+   *
+   * @note This function does *not* account for the `offset()`.
+   *
+   * @note If `null_count() == 0`, this may return `nullptr`.
+   *
+   * @return Raw pointer to the underlying bitmask allocation
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE bitmask_type const* null_mask() const noexcept
+  {
+    return _null_mask;
+  }
+
+  /**
+   * @brief Returns the index of the first element relative to the base memory
+   * allocation, i.e., what is returned from `head<T>()`.
+   *
+   * @return The index of the first element relative to the `head<T>()`
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE size_type offset() const noexcept { return _offset; }
+
+  /**
+   * @brief Returns whether the specified element holds a valid value (i.e., not
+   * null).
+   *
+   * Checks first for the existence of the null bitmask. If `nullable() ==
+   * false`, this function always returns true.
+   *
+   * @note If `nullable() == true` can be guaranteed, then it is more performant
+   * to use `is_valid_nocheck()`.
+   *
+   * @param element_index The index of the element to query
+   * @return true The element is valid
+   * @return false The element is null
+   */
+  [[nodiscard]] __device__ bool is_valid(size_type element_index) const noexcept
+  {
+    return not nullable() or is_valid_nocheck(element_index);
+  }
+
+  /**
+   * @brief Returns whether the specified element holds a valid value (i.e., not
+   * null)
+   *
+   * This function does *not* verify the existence of the bitmask before
+   * attempting to read it. Therefore, it is undefined behavior to call this
+   * function if `nullable() == false`.
+   *
+   * @param element_index The index of the element to query
+   * @return true The element is valid
+   * @return false The element is null
+   */
+  [[nodiscard]] __device__ bool is_valid_nocheck(size_type element_index) const noexcept
+  {
+    return bit_is_set(_null_mask, offset() + element_index);
+  }
+
+  /**
+   * @brief Returns whether the specified element is null.
+   *
+   * Checks first for the existence of the null bitmask. If `nullable() ==
+   * false`, this function always returns false.
+   *
+   * @note If `nullable() == true` can be guaranteed, then it is more performant
+   * to use `is_null_nocheck()`.
+   *
+   * @param element_index The index of the element to query
+   * @return true The element is null
+   * @return false The element is valid
+   */
+  [[nodiscard]] __device__ bool is_null(size_type element_index) const noexcept
+  {
+    return not is_valid(element_index);
+  }
+
+  /**
+   * @brief Returns whether the specified element is null
+   *
+   * This function does *not* verify the existence of the bitmask before
+   * attempting to read it. Therefore, it is undefined behavior to call this
+   * function if `nullable() == false`.
+   *
+   * @param element_index The index of the element to query
+   * @return true The element is null
+   * @return false The element is valid
+   */
+  [[nodiscard]] __device__ bool is_null_nocheck(size_type element_index) const noexcept
+  {
+    return not is_valid_nocheck(element_index);
+  }
+
+  /**
+   * @brief Returns the specified bitmask word from the `null_mask()`.
+   *
+   * @note It is undefined behavior to call this function if `nullable() ==
+   * false`.
+   *
+   * @param word_index The index of the word to get
+   * @return bitmask word for the given word_index
+   */
+  [[nodiscard]] __device__ bitmask_type get_mask_word(size_type word_index) const noexcept
+  {
+    return null_mask()[word_index];
+  }
+
+ protected:
+  data_type _type{type_id::EMPTY};   ///< Element type
+  cudf::size_type _size{};           ///< Number of elements
+  void const* _data{};               ///< Pointer to device memory containing elements
+  bitmask_type const* _null_mask{};  ///< Pointer to device memory containing
+                                     ///< bitmask representing null elements.
+  size_type _offset{};               ///< Index position of the first element.
+                                     ///< Enables zero-copy slicing
+
+  /**
+   * @brief Constructs a column with the specified type, size, data, nullmask and offset.
+   *
+   * @param type The type of the column
+   * @param size The number of elements in the column
+   * @param data Pointer to device memory containing elements
+   * @param null_mask Pointer to device memory containing bitmask representing valid elements
+   * @param offset Index position of the first element
+   */
+  CUDF_HOST_DEVICE column_device_view_base(data_type type,
+                                           size_type size,
+                                           void const* data,
+                                           bitmask_type const* null_mask,
+                                           size_type offset)
+    : _type{type}, _size{size}, _data{data}, _null_mask{null_mask}, _offset{offset}
+  {
+  }
+
+  template <typename C, typename T, typename = void>
+  struct has_element_accessor_impl : std::false_type {};
+
+  template <typename C, typename T>
+  struct has_element_accessor_impl<
+    C,
+    T,
+    void_t<decltype(std::declval<C>().template element<T>(std::declval<size_type>()))>>
+    : std::true_type {};
+};
+// @cond
+// Forward declaration
+template <typename T>
+struct value_accessor;
+template <typename T, typename Nullate>
+struct optional_accessor;
+template <typename T, bool has_nulls>
+struct pair_accessor;
+template <typename T, bool has_nulls>
+struct pair_rep_accessor;
+template <typename T>
+struct mutable_value_accessor;
+// @endcond
+}  // namespace detail
+
+/**
+ * @brief An immutable, non-owning view of device data as a column of elements
+ * that is trivially copyable and usable in CUDA device code.
+ *
+ * @ingroup column_classes
+ */
+class alignas(16) column_device_view : public detail::column_device_view_base {
+ public:
+  column_device_view()                          = delete;
+  ~column_device_view()                         = default;
+  column_device_view(column_device_view const&) = default;  ///< Copy constructor
+  column_device_view(column_device_view&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  column_device_view& operator=(column_device_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  column_device_view& operator=(column_device_view&&) = default;
+
+  /**
+   * @brief Creates an instance of this class using the specified host memory
+   * pointer (h_ptr) to store child objects and the device memory pointer
+   * (d_ptr) as a base for any child object pointers.
+   *
+   * @param column Column view from which to create this instance.
+   * @param h_ptr Host memory pointer on which to place any child data.
+   * @param d_ptr Device memory pointer on which to base any child pointers.
+   */
+  column_device_view(column_view column, void* h_ptr, void* d_ptr);
+
+  /**
+   * @brief Get a new column_device_view which is a slice of this column.
+   *
+   * Example:
+   * @code{.cpp}
+   * // column = column_device_view([1, 2, 3, 4, 5, 6, 7])
+   * auto c = column.slice(1, 3);
+   * // c = column_device_view([2, 3, 4])
+   * auto c1 = column.slice(2, 3);
+   * // c1 = column_device_view([3, 4, 5])
+   * @endcode
+   *
+   * @param offset The index of the first element in the slice
+   * @param size The number of elements in the slice
+   * @return A slice of this column
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE column_device_view slice(size_type offset,
+                                                          size_type size) const noexcept
+  {
+    return column_device_view{this->type(),
+                              size,
+                              this->head(),
+                              this->null_mask(),
+                              this->offset() + offset,
+                              d_children,
+                              this->num_child_columns()};
+  }
+
+  /**
+   * @brief Returns reference to element at the specified index.
+   *
+   * If the element at the specified index is NULL, i.e.,
+   * `is_null(element_index) == true`, then any attempt to use the result will
+   * lead to undefined behavior.
+   *
+   * This function accounts for the offset.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false. Specializations of this function may exist for types `T` where
+   *`is_rep_layout_compatible<T>` is false.
+   *
+   * @tparam T The element type
+   * @param element_index Position of the desired element
+   * @return reference to the element at the specified index
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  [[nodiscard]] __device__ T element(size_type element_index) const noexcept
+  {
+    return data<T>()[element_index];
+  }
+
+  /**
+   * @brief Returns `string_view` to the string element at the specified index.
+   *
+   * If the element at the specified index is NULL, i.e., `is_null(element_index)
+   * == true`, then any attempt to use the result will lead to undefined behavior.
+   *
+   * This function accounts for the offset.
+   *
+   * @param element_index Position of the desired string element
+   * @return string_view instance representing this element at this index
+   */
+  template <typename T, CUDF_ENABLE_IF(std::is_same_v<T, string_view>)>
+  __device__ T element(size_type element_index) const noexcept
+  {
+    size_type index       = element_index + offset();  // account for this view's _offset
+    auto const* d_offsets = d_children[strings_column_view::offsets_column_index].data<int32_t>();
+    char const* d_strings = d_children[strings_column_view::chars_column_index].data<char>();
+    size_type offset      = d_offsets[index];
+    return string_view{d_strings + offset, d_offsets[index + 1] - offset};
+  }
+
+ private:
+  /**
+   * @brief Dispatch functor for resolving the index value for a dictionary element.
+   *
+   * The basic dictionary elements are the indices which can be any index type.
+   */
+  struct index_element_fn {
+    template <typename IndexType,
+              CUDF_ENABLE_IF(is_index_type<IndexType>() and std::is_unsigned_v<IndexType>)>
+    __device__ size_type operator()(column_device_view const& indices, size_type index)
+    {
+      return static_cast<size_type>(indices.element<IndexType>(index));
+    }
+
+    template <typename IndexType,
+              typename... Args,
+              CUDF_ENABLE_IF(not(is_index_type<IndexType>() and std::is_unsigned_v<IndexType>))>
+    __device__ size_type operator()(Args&&... args)
+    {
+      CUDF_UNREACHABLE("dictionary indices must be an unsigned integral type");
+    }
+  };
+
+ public:
+  /**
+   * @brief Returns `dictionary32` element at the specified index for a
+   * dictionary column.
+   *
+   * `dictionary32` is a strongly typed wrapper around an `int32_t` value that holds the
+   * offset into the dictionary keys for the specified element.
+   *
+   * For example, given a dictionary column `d` with:
+   * ```c++
+   * keys: {"foo", "bar", "baz"}
+   * indices: {2, 0, 2, 1, 0}
+   *
+   * d.element<dictionary32>(0) == dictionary32{2};
+   * d.element<dictionary32>(1) == dictionary32{0};
+   * ```
+   *
+   * If the element at the specified index is NULL, i.e., `is_null(element_index) == true`,
+   * then any attempt to use the result will lead to undefined behavior.
+   *
+   * This function accounts for the offset.
+   *
+   * @param element_index Position of the desired element
+   * @return dictionary32 instance representing this element at this index
+   */
+  template <typename T, CUDF_ENABLE_IF(std::is_same_v<T, dictionary32>)>
+  __device__ T element(size_type element_index) const noexcept
+  {
+    size_type index    = element_index + offset();  // account for this view's _offset
+    auto const indices = d_children[0];
+    return dictionary32{type_dispatcher(indices.type(), index_element_fn{}, indices, index)};
+  }
+
+  /**
+   * @brief Returns a `numeric::fixed_point` element at the specified index for a `fixed_point`
+   * column.
+   *
+   * If the element at the specified index is NULL, i.e., `is_null(element_index) == true`,
+   * then any attempt to use the result will lead to undefined behavior.
+   *
+   * @param element_index Position of the desired element
+   * @return numeric::fixed_point representing the element at this index
+   */
+  template <typename T, CUDF_ENABLE_IF(cudf::is_fixed_point<T>())>
+  __device__ T element(size_type element_index) const noexcept
+  {
+    using namespace numeric;
+    using rep        = typename T::rep;
+    auto const scale = scale_type{_type.scale()};
+    return T{scaled_integer<rep>{data<rep>()[element_index], scale}};
+  }
+
+  /**
+   * @brief For a given `T`, indicates if `column_device_view::element<T>()` has a valid overload.
+   *
+   * @tparam T The element type
+   * @return `true` if `column_device_view::element<T>()` has a valid overload, `false` otherwise
+   */
+  template <typename T>
+  static constexpr bool has_element_accessor()
+  {
+    return has_element_accessor_impl<column_device_view, T>::value;
+  }
+
+  /// Counting iterator
+  using count_it = thrust::counting_iterator<size_type>;
+  /**
+   * @brief Iterator for navigating this column
+   */
+  template <typename T>
+  using const_iterator = thrust::transform_iterator<detail::value_accessor<T>, count_it>;
+
+  /**
+   * @brief Return an iterator to the first element of the column.
+   *
+   * This iterator only supports columns where `has_nulls() == false`. Using it
+   * with columns where `has_nulls() == true` will result in undefined behavior
+   * when accessing null elements.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * For columns with null elements, use `make_null_replacement_iterator`.
+   *
+   * @tparam T Type of the elements in the column
+   * @return An iterator to the first element of the column
+   */
+  template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  [[nodiscard]] const_iterator<T> begin() const
+  {
+    return const_iterator<T>{count_it{0}, detail::value_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Returns an iterator to the element following the last element of the column.
+   *
+   * This iterator only supports columns where `has_nulls() == false`. Using it
+   * with columns where `has_nulls() == true` will result in undefined behavior
+   * when accessing null elements.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * For columns with null elements, use `make_null_replacement_iterator`.
+   *
+   * @return An iterator to the element following the last element of the column
+   */
+  template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  [[nodiscard]] const_iterator<T> end() const
+  {
+    return const_iterator<T>{count_it{size()}, detail::value_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Optional iterator for navigating this column
+   */
+  template <typename T, typename Nullate>
+  using const_optional_iterator =
+    thrust::transform_iterator<detail::optional_accessor<T, Nullate>, count_it>;
+
+  /**
+   * @brief Pair iterator for navigating this column
+   */
+  template <typename T, bool has_nulls>
+  using const_pair_iterator =
+    thrust::transform_iterator<detail::pair_accessor<T, has_nulls>, count_it>;
+
+  /**
+   * @brief Pair rep iterator for navigating this column
+   *
+   * Each row value is accessed in its representative form.
+   */
+  template <typename T, bool has_nulls>
+  using const_pair_rep_iterator =
+    thrust::transform_iterator<detail::pair_rep_accessor<T, has_nulls>, count_it>;
+
+  /**
+   * @brief Return an optional iterator to the first element of the column.
+   *
+   * Dereferencing the returned iterator returns a `thrust::optional<T>`.
+   *
+   * The element of this iterator contextually converts to bool. The conversion returns true
+   * if the object contains a value and false if it does not contain a value.
+   *
+   * Calling this method with `nullate::DYNAMIC` defers the assumption of nullability to
+   * runtime with the caller indicating if the column has nulls. The `nullate::DYNAMIC` is
+   * useful when an algorithm is going to execute on multiple iterators and all the combinations of
+   * iterator types are not required at compile time.
+   *
+   * @code{.cpp}
+   * template<typename T>
+   * void some_function(cudf::column_view<T> const& col_view){
+   *    auto d_col = cudf::column_device_view::create(col_view);
+   *    // Create a `DYNAMIC` optional iterator
+   *    auto optional_iterator =
+   *       d_col->optional_begin<T>(cudf::nullate::DYNAMIC{col_view.has_nulls()});
+   * }
+   * @endcode
+   *
+   * Calling this method with `nullate::YES` means that the column supports nulls and
+   * the optional returned might not contain a value.
+   *
+   * Calling this method with `nullate::NO` means that the column has no null values
+   * and the optional returned will always contain a value.
+   *
+   * @code{.cpp}
+   * template<typename T, bool has_nulls>
+   * void some_function(cudf::column_view<T> const& col_view){
+   *    auto d_col = cudf::column_device_view::create(col_view);
+   *    if constexpr(has_nulls) {
+   *      auto optional_iterator = d_col->optional_begin<T>(cudf::nullate::YES{});
+   *      //use optional_iterator
+   *    } else {
+   *      auto optional_iterator = d_col->optional_begin<T>(cudf::nullate::NO{});
+   *      //use optional_iterator
+   *    }
+   * }
+   * @endcode
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @throws cudf::logic_error if the column is not nullable and `has_nulls` evaluates to true.
+   * @throws cudf::logic_error if column datatype and Element type mismatch.
+   *
+   * @tparam T The type of elements in the column
+   * @tparam Nullate A cudf::nullate type describing how to check for nulls
+   * @param has_nulls  A cudf::nullate type describing how to check for nulls
+   * @return An optional iterator to the first element of the column
+   */
+  template <typename T,
+            typename Nullate,
+            CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  auto optional_begin(Nullate has_nulls) const
+  {
+    return const_optional_iterator<T, Nullate>{
+      count_it{0}, detail::optional_accessor<T, Nullate>{*this, has_nulls}};
+  }
+
+  /**
+   * @brief Return a pair iterator to the first element of the column.
+   *
+   * Dereferencing the returned iterator returns a `thrust::pair<T, bool>`.
+   *
+   * If an element at position `i` is valid (or `has_nulls == false`), then
+   * for `p = *(iter + i)`, `p.first` contains the value of the element at `i`
+   * and `p.second == true`.
+   *
+   * Else, if the element at `i` is null, then the value of `p.first` is
+   * undefined and `p.second == false`.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @throws cudf::logic_error if tparam `has_nulls == true` and
+   * `nullable() == false`
+   * @throws cudf::logic_error if column datatype and Element type mismatch.
+   *
+   * @return A pair iterator to the first element of the column
+   */
+  template <typename T,
+            bool has_nulls,
+            CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  [[nodiscard]] const_pair_iterator<T, has_nulls> pair_begin() const
+  {
+    return const_pair_iterator<T, has_nulls>{count_it{0},
+                                             detail::pair_accessor<T, has_nulls>{*this}};
+  }
+
+  /**
+   * @brief Return a pair iterator to the first element of the column.
+   *
+   * Dereferencing the returned iterator returns a `thrust::pair<rep_type, bool>`,
+   * where `rep_type` is `device_storage_type<T>`, the type used to store
+   * the value on the device.
+   *
+   * If an element at position `i` is valid (or `has_nulls == false`), then
+   * for `p = *(iter + i)`, `p.first` contains the value of the element at `i`
+   * and `p.second == true`.
+   *
+   * Else, if the element at `i` is null, then the value of `p.first` is
+   * undefined and `p.second == false`.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @throws cudf::logic_error if tparam `has_nulls == true` and
+   * `nullable() == false`
+   * @throws cudf::logic_error if column datatype and Element type mismatch.
+   *
+   * @return A pair iterator to the first element of the column
+   */
+  template <typename T,
+            bool has_nulls,
+            CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  [[nodiscard]] const_pair_rep_iterator<T, has_nulls> pair_rep_begin() const
+  {
+    return const_pair_rep_iterator<T, has_nulls>{count_it{0},
+                                                 detail::pair_rep_accessor<T, has_nulls>{*this}};
+  }
+
+  /**
+   * @brief Return an optional iterator to the element following the last element of the column.
+   *
+   * The returned iterator represents a `thrust::optional<T>` element.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @throws cudf::logic_error if the column is not nullable and `has_nulls` is true
+   * @throws cudf::logic_error if column datatype and Element type mismatch.
+   *
+   * @tparam T The type of elements in the column
+   * @tparam Nullate A cudf::nullate type describing how to check for nulls
+   * @param has_nulls  A cudf::nullate type describing how to check for nulls
+   * @return An optional iterator to the element following the last element of the column
+   */
+  template <typename T,
+            typename Nullate,
+            CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  auto optional_end(Nullate has_nulls) const
+  {
+    return const_optional_iterator<T, Nullate>{
+      count_it{size()}, detail::optional_accessor<T, Nullate>{*this, has_nulls}};
+  }
+
+  /**
+   * @brief Return a pair iterator to the element following the last element of the column.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @throws cudf::logic_error if tparam `has_nulls == true` and
+   * `nullable() == false`
+   * @throws cudf::logic_error if column datatype and Element type mismatch.
+   * @return A pair iterator to the element following the last element of the column
+   */
+  template <typename T,
+            bool has_nulls,
+            CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  [[nodiscard]] const_pair_iterator<T, has_nulls> pair_end() const
+  {
+    return const_pair_iterator<T, has_nulls>{count_it{size()},
+                                             detail::pair_accessor<T, has_nulls>{*this}};
+  }
+
+  /**
+   * @brief Return a pair iterator to the element following the last element of the column.
+   *
+   * This function does not participate in overload resolution if
+   * `column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @throws cudf::logic_error if tparam `has_nulls == true` and
+   * `nullable() == false`
+   * @throws cudf::logic_error if column datatype and Element type mismatch.
+   *
+   * @return A pair iterator to the element following the last element of the column
+   */
+  template <typename T,
+            bool has_nulls,
+            CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  [[nodiscard]] const_pair_rep_iterator<T, has_nulls> pair_rep_end() const
+  {
+    return const_pair_rep_iterator<T, has_nulls>{count_it{size()},
+                                                 detail::pair_rep_accessor<T, has_nulls>{*this}};
+  }
+
+  /**
+   * @brief Factory to construct a column view that is usable in device memory.
+   *
+   * Allocates and copies views of `source_view`'s children to device memory to
+   * make them accessible in device code.
+   *
+   * If `source_view.num_children() == 0`, then no device memory is allocated.
+   *
+   * Returns a `std::unique_ptr<column_device_view>` with a custom deleter to
+   * free the device memory allocated for the children.
+   *
+   * A `column_device_view` should be passed by value into GPU kernels.
+   *
+   * @param source_view The `column_view` to make usable in device code
+   * @param stream CUDA stream used for device memory operations for children columns.
+   * @return A `unique_ptr` to a `column_device_view` that makes the data from
+   *`source_view` available in device memory.
+   */
+  static std::unique_ptr<column_device_view, std::function<void(column_device_view*)>> create(
+    column_view source_view, rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * @brief Destroy the `column_device_view` object.
+   *
+   * @note Does not free the column data, simply frees the device memory
+   * allocated to hold the child views.
+   */
+  void destroy();
+
+  /**
+   * @brief Return the size in bytes of the amount of memory needed to hold a
+   * device view of the specified column and it's children.
+   *
+   * @param source_view The `column_view` to use for this calculation.
+   * @return number of bytes to store device view in GPU memory
+   */
+  static std::size_t extent(column_view const& source_view);
+
+  /**
+   * @brief Returns the specified child
+   *
+   * @param child_index The index of the desired child
+   * @return column_view The requested child `column_view`
+   */
+  [[nodiscard]] __device__ column_device_view child(size_type child_index) const noexcept
+  {
+    return d_children[child_index];
+  }
+
+  /**
+   * @brief Returns a span containing the children of this column
+   *
+   * @return A span containing the children of this column
+   */
+  [[nodiscard]] __device__ device_span<column_device_view const> children() const noexcept
+  {
+    return device_span<column_device_view const>(d_children, _num_children);
+  }
+
+  /**
+   * @brief Returns the number of child columns
+   *
+   * @return The number of child columns
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE size_type num_child_columns() const noexcept
+  {
+    return _num_children;
+  }
+
+ private:
+  /**
+   * @brief Creates an instance of this class using pre-existing device memory pointers to data,
+   * nullmask, and offset.
+   *
+   * @param type The type of the column
+   * @param size The number of elements in the column
+   * @param data Pointer to the device memory containing the data
+   * @param null_mask Pointer to the device memory containing the null bitmask
+   * @param offset The index of the first element in the column
+   * @param children Pointer to the device memory containing child data
+   * @param num_children The number of child columns
+   */
+  CUDF_HOST_DEVICE column_device_view(data_type type,
+                                      size_type size,
+                                      void const* data,
+                                      bitmask_type const* null_mask,
+                                      size_type offset,
+                                      column_device_view* children,
+                                      size_type num_children)
+    : column_device_view_base(type, size, data, null_mask, offset),
+      d_children(children),
+      _num_children(num_children)
+  {
+  }
+
+ protected:
+  column_device_view* d_children{};  ///< Array of `column_device_view`
+                                     ///< objects in device memory.
+                                     ///< Based on element type, children
+                                     ///< may contain additional data
+  size_type _num_children{};         ///< The number of child columns
+
+  /**
+   * @brief Construct's a `column_device_view` from a `column_view` populating
+   * all but the children.
+   *
+   * @note This constructor is for internal use only. To create a
+   *`column_device_view` from a `column_view`, the
+   *`column_device_view::create()` function should be used.
+   *
+   * @param source The `column_view` to use for this construction
+   */
+  column_device_view(column_view source);
+};
+
+/**
+ * @brief A mutable, non-owning view of device data as a column of elements
+ * that is trivially copyable and usable in CUDA device code.
+ *
+ * @ingroup column_classes
+ */
+class alignas(16) mutable_column_device_view : public detail::column_device_view_base {
+ public:
+  mutable_column_device_view()                                  = delete;
+  ~mutable_column_device_view()                                 = default;
+  mutable_column_device_view(mutable_column_device_view const&) = default;  ///< Copy constructor
+  mutable_column_device_view(mutable_column_device_view&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  mutable_column_device_view& operator=(mutable_column_device_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  mutable_column_device_view& operator=(mutable_column_device_view&&) = default;
+
+  /**
+   * @brief Creates an instance of this class using the specified host memory
+   * pointer (h_ptr) to store child objects and the device memory pointer
+   * (d_ptr) as a base for any child object pointers.
+   *
+   * @param column Column view from which to create this instance.
+   * @param h_ptr Host memory pointer on which to place any child data.
+   * @param d_ptr Device memory pointer on which to base any child pointers.
+   */
+  mutable_column_device_view(mutable_column_view column, void* h_ptr, void* d_ptr);
+
+  /**
+   * @brief Factory to construct a column view that is usable in device memory.
+   *
+   * Allocates and copies views of `source_view`'s children to device memory to
+   * make them accessible in device code.
+   *
+   * If `source_view.num_children() == 0`, then no device memory is allocated.
+   *
+   * Returns a `std::unique_ptr<mutable_column_device_view>` with a custom
+   * deleter to free the device memory allocated for the children.
+   *
+   * A `mutable_column_device_view` should be passed by value into GPU kernels.
+   *
+   * @param source_view The `column_view` to make usable in device code
+   * @param stream CUDA stream used for device memory operations for children columns.
+   * @return A `unique_ptr` to a `mutable_column_device_view` that makes the
+   * data from `source_view` available in device memory.
+   */
+  static std::unique_ptr<mutable_column_device_view,
+                         std::function<void(mutable_column_device_view*)>>
+  create(mutable_column_view source_view,
+         rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * @brief Returns pointer to the base device memory allocation casted to
+   * the specified type.
+   *
+   * This function will only participate in overload resolution if `is_rep_layout_compatible<T>()`
+   * or `std::is_same_v<T,void>` are true.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * @note It should be rare to need to access the `head<T>()` allocation of
+   * a column, and instead, accessing the elements should be done via
+   * `data<T>()`.
+   *
+   * @tparam The type to cast to
+   * @return Typed pointer to underlying data
+   */
+  template <typename T = void,
+            CUDF_ENABLE_IF(std::is_same_v<T, void> or is_rep_layout_compatible<T>())>
+  CUDF_HOST_DEVICE T* head() const noexcept
+  {
+    return const_cast<T*>(detail::column_device_view_base::head<T>());
+  }
+
+  /**
+   * @brief Returns the underlying data casted to the specified type, plus the
+   * offset.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * @tparam T The type to cast to
+   * @return Typed pointer to underlying data, including the offset
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  CUDF_HOST_DEVICE T* data() const noexcept
+  {
+    return const_cast<T*>(detail::column_device_view_base::data<T>());
+  }
+
+  /**
+   * @brief Returns reference to element at the specified index.
+   *
+   * This function accounts for the offset.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false. Specializations of this function may exist for types `T` where
+   *`is_rep_layout_compatible<T>` is false.
+   *
+   *
+   * @tparam T The element type
+   * @param element_index Position of the desired element
+   * @return Reference to the element at the specified index
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  __device__ T& element(size_type element_index) const noexcept
+  {
+    return data<T>()[element_index];
+  }
+
+  /**
+   * @brief For a given `T`, indicates if `mutable_column_device_view::element<T>()` has a valid
+   * overload.
+   *
+   * @return `true` if `mutable_column_device_view::element<T>()` has a valid overload, `false`
+   */
+  template <typename T>
+  static constexpr bool has_element_accessor()
+  {
+    return has_element_accessor_impl<mutable_column_device_view, T>::value;
+  }
+
+  /**
+   * @brief Returns raw pointer to the underlying bitmask allocation.
+   *
+   * @note This function does *not* account for the `offset()`.
+   *
+   * @note If `null_count() == 0`, this may return `nullptr`.
+   * @return Raw pointer to the underlying bitmask allocation
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE bitmask_type* null_mask() const noexcept
+  {
+    return const_cast<bitmask_type*>(detail::column_device_view_base::null_mask());
+  }
+
+  /// Counting iterator
+  using count_it = thrust::counting_iterator<size_type>;
+  /**
+   * @brief Iterator for navigating this column
+   */
+  template <typename T>
+  using iterator = thrust::transform_iterator<detail::mutable_value_accessor<T>, count_it>;
+
+  /**
+   * @brief Return first element (accounting for offset) after underlying data
+   * is casted to the specified type.
+   *
+   * This function does not participate in overload resolution if
+   * `mutable_column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @tparam T The desired type
+   * @return Pointer to the first element after casting
+   */
+  template <typename T, CUDF_ENABLE_IF(mutable_column_device_view::has_element_accessor<T>())>
+  iterator<T> begin()
+  {
+    return iterator<T>{count_it{0}, detail::mutable_value_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Return one past the last element after underlying data is casted to
+   * the specified type.
+   *
+   * This function does not participate in overload resolution if
+   * `mutable_column_device_view::has_element_accessor<T>()` is false.
+   *
+   * @tparam T The desired type
+   * @return Pointer to one past the last element after casting
+   */
+  template <typename T, CUDF_ENABLE_IF(mutable_column_device_view::has_element_accessor<T>())>
+  iterator<T> end()
+  {
+    return iterator<T>{count_it{size()}, detail::mutable_value_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Returns the specified child
+   *
+   * @param child_index The index of the desired child
+   * @return The requested child `column_view`
+   */
+  [[nodiscard]] __device__ mutable_column_device_view child(size_type child_index) const noexcept
+  {
+    return d_children[child_index];
+  }
+
+#ifdef __CUDACC__  // because set_bit in bit.hpp is wrapped with __CUDACC__
+  /**
+   * @brief Updates the null mask to indicate that the specified element is
+   * valid
+   *
+   * @note This operation requires a global atomic operation. Therefore, it is
+   * not recommended to use this function in performance critical regions. When
+   * possible, it is more efficient to compute and update an entire word at
+   * once using `set_mask_word`.
+   *
+   * @note It is undefined behavior to call this function if `nullable() ==
+   * false`.
+   *
+   * @param element_index The index of the element to update
+   */
+  __device__ void set_valid(size_type element_index) const noexcept
+  {
+    return set_bit(null_mask(), element_index);
+  }
+
+  /**
+   * @brief Updates the null mask to indicate that the specified element is null
+   *
+   * @note This operation requires a global atomic operation. Therefore, it is
+   * not recommended to use this function in performance critical regions. When
+   * possible, it is more efficient to compute and update an entire word at
+   * once using `set_mask_word`.
+   *
+   * @note It is undefined behavior to call this function if `nullable() ==
+   * false`.
+   *
+   * @param element_index The index of the element to update
+   */
+  __device__ void set_null(size_type element_index) const noexcept
+  {
+    return clear_bit(null_mask(), element_index);
+  }
+
+#endif
+
+  /**
+   * @brief Updates the specified bitmask word in the `null_mask()` with a
+   * new word.
+   *
+   * @note It is undefined behavior to call this function if `nullable() ==
+   * false`.
+   *
+   * @param word_index The index of the word to update
+   * @param new_word The new bitmask word
+   */
+  __device__ void set_mask_word(size_type word_index, bitmask_type new_word) const noexcept
+  {
+    null_mask()[word_index] = new_word;
+  }
+
+  /**
+   * @brief Return the size in bytes of the amount of memory needed to hold a
+   * device view of the specified column and it's children.
+   *
+   * @param source_view The `column_view` to use for this calculation.
+   * @return The size in bytes of the amount of memory needed to hold a
+   * device view of the specified column and it's children
+   */
+  static std::size_t extent(mutable_column_view source_view);
+
+  /**
+   * @brief Destroy the `mutable_column_device_view` object.
+   *
+   * @note Does not free the column data, simply frees the device memory
+   * allocated to hold the child views.
+   */
+  void destroy();
+
+ private:
+  mutable_column_device_view* d_children{};  ///< Array of `mutable_column_device_view`
+                                             ///< objects in device memory.
+                                             ///< Based on element type, children
+                                             ///< may contain additional data
+  size_type _num_children{};                 ///< The number of child columns
+
+  /**
+   * @brief Construct's a `mutable_column_device_view` from a
+   *`mutable_column_view` populating all but the children.
+   *
+   * @note This constructor is for internal use only. To create a
+   *`mutable_column_device_view` from a `column_view`, the
+   *`mutable_column_device_view::create()` function should be used.
+   */
+  mutable_column_device_view(mutable_column_view source);
+};
+
+namespace detail {
+
+#ifdef __CUDACC__  // because set_bit in bit.hpp is wrapped with __CUDACC__
+
+/**
+ * @brief Convenience function to get offset word from a bitmask
+ *
+ * @see copy_offset_bitmask
+ * @see offset_bitmask_binop
+ */
+__device__ inline bitmask_type get_mask_offset_word(bitmask_type const* __restrict__ source,
+                                                    size_type destination_word_index,
+                                                    size_type source_begin_bit,
+                                                    size_type source_end_bit)
+{
+  size_type source_word_index = destination_word_index + word_index(source_begin_bit);
+  bitmask_type curr_word      = source[source_word_index];
+  bitmask_type next_word      = 0;
+  if (word_index(source_end_bit - 1) >
+      word_index(source_begin_bit +
+                 destination_word_index * detail::size_in_bits<bitmask_type>())) {
+    next_word = source[source_word_index + 1];
+  }
+  return __funnelshift_r(curr_word, next_word, source_begin_bit);
+}
+
+#endif
+
+/**
+ * @brief value accessor of column without null bitmask
+ *
+ * A unary functor returns scalar value at `id`.
+ * `operator() (cudf::size_type id)` computes `element`
+ * This functor is only allowed for non-nullable columns.
+ *
+ * the return value for element `i` will return `column[i]`
+ *
+ * @throws cudf::logic_error if the column is nullable.
+ * @throws cudf::logic_error if column datatype and template T type mismatch.
+ *
+ * @tparam T The type of elements in the column
+ */
+template <typename T>
+struct value_accessor {
+  column_device_view const col;  ///< column view of column in device
+
+  /**
+   * @brief constructor
+   *
+   * @param[in] _col column device view of cudf column
+   */
+  value_accessor(column_device_view const& _col) : col{_col}
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<T>(col.type().id()), "the data type mismatch");
+  }
+
+  /**
+   * @brief Returns the value of element at index `i`
+   * @param[in] i index of element
+   * @return value of element at index `i`
+   */
+  __device__ T operator()(cudf::size_type i) const { return col.element<T>(i); }
+};
+
+/**
+ * @brief optional accessor of a column
+ *
+ *
+ * The optional_accessor always returns a `thrust::optional` of `column[i]`. The validity
+ * of the optional is determined by the `Nullate` parameter which may be one of the following:
+ *
+ * - `nullate::YES` means that the column supports nulls and the optional returned
+ *    might be valid or invalid.
+ *
+ * - `nullate::NO` means the caller attests that the column has no null values,
+ *    no checks will occur and `thrust::optional{column[i]}` will be
+ *    return for each `i`.
+ *
+ * - `nullate::DYNAMIC` defers the assumption of nullability to runtime and the caller
+ *    specifies if the column has nulls at runtime.
+ *    For `DYNAMIC{true}` the return value will be `thrust::optional{column[i]}` if
+ *      element `i` is not null and `thrust::optional{}` if element `i` is null.
+ *    For `DYNAMIC{false}` the return value will always be `thrust::optional{column[i]}`.
+ *
+ * @throws cudf::logic_error if column datatype and template T type mismatch.
+ * @throws cudf::logic_error if the column is not nullable and `with_nulls` evaluates to true
+ *
+ * @tparam T The type of elements in the column
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <typename T, typename Nullate>
+struct optional_accessor {
+  column_device_view const col;  ///< column view of column in device
+
+  /**
+   * @brief Constructor
+   *
+   * @param _col Column on which to iterator over its elements.
+   * @param with_nulls Indicates if the `col` should be checked for nulls.
+   */
+  optional_accessor(column_device_view const& _col, Nullate with_nulls)
+    : col{_col}, has_nulls{with_nulls}
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<T>(col.type().id()), "the data type mismatch");
+    if (with_nulls) { CUDF_EXPECTS(_col.nullable(), "Unexpected non-nullable column."); }
+  }
+
+  /**
+   * @brief Returns a `thrust::optional` of `column[i]`.
+   *
+   * @param i The index of the element to return
+   * @return A `thrust::optional` that contains the value of `column[i]` is not null. If that
+   * element is null, the resulting optional will not contain a value.
+   */
+  __device__ inline thrust::optional<T> operator()(cudf::size_type i) const
+  {
+    if (has_nulls) {
+      return (col.is_valid_nocheck(i)) ? thrust::optional<T>{col.element<T>(i)}
+                                       : thrust::optional<T>{thrust::nullopt};
+    }
+    return thrust::optional<T>{col.element<T>(i)};
+  }
+
+  Nullate has_nulls{};  ///< Indicates if the `col` should be checked for nulls.
+};
+
+/**
+ * @brief pair accessor of column with/without null bitmask
+ *
+ * A unary functor returns pair with scalar value at `id` and boolean validity
+ * `operator() (cudf::size_type id)` computes `element`  and
+ * returns a `pair(element, validity)`
+ *
+ * the return value for element `i` will return `pair(column[i], validity)`
+ * `validity` is `true` if `has_nulls=false`.
+ * `validity` is validity of the element at `i` if `has_nulls=true` and the
+ * column is nullable.
+ *
+ * @throws cudf::logic_error if `has_nulls==true` and the column is not
+ * nullable.
+ * @throws cudf::logic_error if column datatype and template T type mismatch.
+ *
+ * @tparam T The type of elements in the column
+ * @tparam has_nulls boolean indicating to treat the column is nullable
+ */
+template <typename T, bool has_nulls = false>
+struct pair_accessor {
+  column_device_view const col;  ///< column view of column in device
+
+  /**
+   * @brief constructor
+   *
+   * @param[in] _col column device view of cudf column
+   */
+  pair_accessor(column_device_view const& _col) : col{_col}
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<T>(col.type().id()), "the data type mismatch");
+    if (has_nulls) { CUDF_EXPECTS(_col.nullable(), "Unexpected non-nullable column."); }
+  }
+
+  /**
+   * @brief Pair accessor
+   *
+   * @param[in] i index of the element
+   * @return pair(element, validity)
+   */
+  __device__ inline thrust::pair<T, bool> operator()(cudf::size_type i) const
+  {
+    return {col.element<T>(i), (has_nulls ? col.is_valid_nocheck(i) : true)};
+  }
+};
+
+/**
+ * @brief pair accessor of column with/without null bitmask
+ *
+ * A unary functor returns pair with representative scalar value at `id` and boolean validity
+ * `operator() (cudf::size_type id)` computes `element`  and
+ * returns a `pair(element, validity)`
+ *
+ * the return value for element `i` will return `pair(column[i], validity)`
+ * `validity` is `true` if `has_nulls=false`.
+ * `validity` is validity of the element at `i` if `has_nulls=true` and the
+ * column is nullable.
+ *
+ * @throws cudf::logic_error if `has_nulls==true` and the column is not
+ * nullable.
+ * @throws cudf::logic_error if column datatype and template T type mismatch.
+ *
+ * @tparam T The type of elements in the column
+ * @tparam has_nulls boolean indicating to treat the column is nullable
+ */
+template <typename T, bool has_nulls = false>
+struct pair_rep_accessor {
+  column_device_view const col;  ///< column view of column in device
+
+  using rep_type = device_storage_type_t<T>;  ///< representation type
+
+  /**
+   * @brief constructor
+   *
+   * @param[in] _col column device view of cudf column
+   */
+  pair_rep_accessor(column_device_view const& _col) : col{_col}
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<T>(col.type().id()), "the data type mismatch");
+    if (has_nulls) { CUDF_EXPECTS(_col.nullable(), "Unexpected non-nullable column."); }
+  }
+
+  /**
+   * @brief Pair accessor
+   *
+   * @param[in] i index of element to access
+   * @return pair of element and validity
+   */
+  __device__ inline thrust::pair<rep_type, bool> operator()(cudf::size_type i) const
+  {
+    return {get_rep<T>(i), (has_nulls ? col.is_valid_nocheck(i) : true)};
+  }
+
+ private:
+  template <typename R, std::enable_if_t<std::is_same_v<R, rep_type>, void>* = nullptr>
+  __device__ inline auto get_rep(cudf::size_type i) const
+  {
+    return col.element<R>(i);
+  }
+
+  template <typename R, std::enable_if_t<not std::is_same_v<R, rep_type>, void>* = nullptr>
+  __device__ inline auto get_rep(cudf::size_type i) const
+  {
+    return col.element<R>(i).value();
+  }
+};
+
+/**
+ * @brief Mutable value accessor of column without null bitmask
+ *
+ * A unary functor that accepts an index and returns a reference to the element at that index in the
+ * column.
+ *
+ * @throws cudf::logic_error if the column is nullable
+ * @throws cudf::logic_error if column datatype and template T type mismatch
+ *
+ * @tparam T The type of elements in the column
+ */
+template <typename T>
+struct mutable_value_accessor {
+  mutable_column_device_view col;  ///< mutable column view of column in device
+
+  /**
+   * @brief Constructor
+   *
+   * @param[in] _col mutable column device view of cudf column
+   */
+  mutable_value_accessor(mutable_column_device_view& _col) : col{_col}
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<T>(col.type().id()), "the data type mismatch");
+  }
+
+  /**
+   * @brief Accessor
+   *
+   * @param i index of element to access
+   * @return reference to element at `i`
+   */
+  __device__ T& operator()(cudf::size_type i) { return col.element<T>(i); }
+};
+
+/**
+ * @brief Helper function for use by column_device_view and mutable_column_device_view
+ * constructors to build device_views from views.
+ *
+ * It is used to build the array of child columns in device memory. Since child columns can
+ * also have child columns, this uses recursion to build up the flat device buffer to contain
+ * all the children and set the member pointers appropriately.
+ *
+ * This is accomplished by laying out all the children and grand-children into a flat host
+ * buffer first but also keep a running device pointer to use when setting the
+ * d_children array result.
+ *
+ * This function is provided both the host pointer in which to insert its children (and
+ * by recursion its grand-children) and the device pointer to be used when calculating
+ * ultimate device pointer for the d_children member.
+ *
+ * @tparam ColumnView is either column_view or mutable_column_view
+ * @tparam ColumnDeviceView is either column_device_view or mutable_column_device_view
+ *
+ * @param child_begin Iterator pointing to begin of child columns to make into a device view
+ * @param child_end   Iterator pointing to end   of child columns to make into a device view
+ * @param h_ptr The host memory where to place any child data
+ * @param d_ptr The device pointer for calculating the d_children member of any child data
+ * @return The device pointer to be used for the d_children member of the given column
+ */
+template <typename ColumnDeviceView, typename ColumnViewIterator>
+ColumnDeviceView* child_columns_to_device_array(ColumnViewIterator child_begin,
+                                                ColumnViewIterator child_end,
+                                                void* h_ptr,
+                                                void* d_ptr)
+{
+  ColumnDeviceView* d_children = detail::align_ptr_for_type<ColumnDeviceView>(d_ptr);
+  auto num_children            = std::distance(child_begin, child_end);
+  if (num_children > 0) {
+    // The beginning of the memory must be the fixed-sized ColumnDeviceView
+    // struct objects in order for d_children to be used as an array.
+    auto h_column = detail::align_ptr_for_type<ColumnDeviceView>(h_ptr);
+    auto d_column = d_children;
+
+    // Any child data is assigned past the end of this array: h_end and d_end.
+    auto h_end = reinterpret_cast<int8_t*>(h_column + num_children);
+    auto d_end = reinterpret_cast<int8_t*>(d_column + num_children);
+    std::for_each(child_begin, child_end, [&](auto const& col) {
+      // inplace-new each child into host memory
+      new (h_column) ColumnDeviceView(col, h_end, d_end);
+      h_column++;  // advance to next child
+      // update the pointers for holding this child column's child data
+      auto col_child_data_size = ColumnDeviceView::extent(col) - sizeof(ColumnDeviceView);
+      h_end += col_child_data_size;
+      d_end += col_child_data_size;
+    });
+  }
+  return d_children;
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/column/column_factories.hpp b/cpp/include/cudf/column/column_factories.hpp
new file mode 100644
index 0000000..68d7df7
--- /dev/null
+++ b/cpp/include/cudf/column/column_factories.hpp
@@ -0,0 +1,627 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/pair.h>
+
+namespace cudf {
+/**
+ * @addtogroup column_factories
+ * @{
+ * @file
+ * @brief Column factory APIs
+ */
+
+/**
+ * @brief Creates an empty column of the specified @p type
+ *
+ * An empty column contains zero elements and no validity mask.
+ *
+ * @param[in] type The column data type
+ * @return Empty column with desired type
+ */
+std::unique_ptr<column> make_empty_column(data_type type);
+
+/**
+ * @brief Creates an empty column of the specified type.
+ *
+ * An empty column contains zero elements and no validity mask.
+ *
+ * @param[in] id The column type id
+ * @return Empty column with specified type
+ */
+std::unique_ptr<column> make_empty_column(type_id id);
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified numeric `data_type` with an optional null mask.
+ *
+ * @note `null_count()` is determined by the requested null mask `state`
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a numeric type
+ * @throws cudf::logic_error if `size < 0`
+ *
+ * @param[in] type The desired numeric element type
+ * @param[in] size The number of elements in the column
+ * @param[in] state Optional, controls allocation/initialization of the
+ * column's null mask. By default, no null mask is allocated.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed numeric column
+ */
+std::unique_ptr<column> make_numeric_column(
+  data_type type,
+  size_type size,
+  mask_state state                    = mask_state::UNALLOCATED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified numeric `data_type` with a null mask.
+ *
+ * @note null_count is optional and will be computed if not provided.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a numeric type
+ *
+ * @param[in] type The desired numeric element type
+ * @param[in] size The number of elements in the column
+ * @param[in] null_mask Null mask to use for this column.
+ * @param[in] null_count Optional number of nulls in the null_mask.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed numeric column
+ */
+template <typename B>
+std::unique_ptr<column> make_numeric_column(
+  data_type type,
+  size_type size,
+  B&& null_mask,
+  size_type null_count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  CUDF_EXPECTS(is_numeric(type), "Invalid, non-numeric type.");
+  return std::make_unique<column>(type,
+                                  size,
+                                  rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                                  std::forward<B>(null_mask),
+                                  null_count);
+}
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified `fixed_point` `data_type` with an optional null mask.
+ *
+ * @note The column's null count is determined by the requested null mask `state`.
+ *
+ * @throws cudf::logic_error if `type` is not a `fixed_point` type.
+ * @throws cudf::logic_error if `size < 0`
+ *
+ * @param[in] type The desired `fixed_point` element type.
+ * @param[in] size The number of elements in the column.
+ * @param[in] state Optional, controls allocation/initialization of the.
+ * column's null mask. By default, no null mask is allocated.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory.
+ * @return Constructed fixed-point type column
+ */
+std::unique_ptr<column> make_fixed_point_column(
+  data_type type,
+  size_type size,
+  mask_state state                    = mask_state::UNALLOCATED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified `fixed_point` `data_type` with a null mask.
+ *
+ * @note null_count is optional and will be computed if not provided.
+ *
+ * @throws cudf::logic_error if `type` is not a `fixed_point` type.
+ *
+ * @param[in] type The desired `fixed_point` element type.
+ * @param[in] size The number of elements in the column.
+ * @param[in] null_mask Null mask to use for this column.
+ * @param[in] null_count Optional number of nulls in the null_mask.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory.
+ * @return Constructed fixed-point type column
+ */
+template <typename B>
+std::unique_ptr<column> make_fixed_point_column(
+  data_type type,
+  size_type size,
+  B&& null_mask,
+  size_type null_count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  CUDF_EXPECTS(is_fixed_point(type), "Invalid, non-fixed_point type.");
+  return std::make_unique<column>(type,
+                                  size,
+                                  rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                                  std::forward<B>(null_mask),
+                                  null_count);
+}
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified timestamp `data_type` with an optional null mask.
+ *
+ * @note `null_count()` is determined by the requested null mask `state`
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a timestamp type
+ * @throws cudf::logic_error if `size < 0`
+ *
+ * @param[in] type The desired timestamp element type
+ * @param[in] size The number of elements in the column
+ * @param[in] state Optional, controls allocation/initialization of the
+ * column's null mask. By default, no null mask is allocated.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed timestamp type column
+ */
+std::unique_ptr<column> make_timestamp_column(
+  data_type type,
+  size_type size,
+  mask_state state                    = mask_state::UNALLOCATED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified timestamp `data_type` with a null mask.
+ *
+ * @note null_count is optional and will be computed if not provided.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a timestamp type
+ *
+ * @param[in] type The desired timestamp element type
+ * @param[in] size The number of elements in the column
+ * @param[in] null_mask Null mask to use for this column.
+ * @param[in] null_count Optional number of nulls in the null_mask.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed timestamp type column
+ */
+template <typename B>
+std::unique_ptr<column> make_timestamp_column(
+  data_type type,
+  size_type size,
+  B&& null_mask,
+  size_type null_count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  CUDF_EXPECTS(is_timestamp(type), "Invalid, non-timestamp type.");
+  return std::make_unique<column>(type,
+                                  size,
+                                  rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                                  std::forward<B>(null_mask),
+                                  null_count);
+}
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified duration `data_type` with an optional null mask.
+ *
+ * @note `null_count()` is determined by the requested null mask `state`
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a duration type
+ * @throws cudf::logic_error if `size < 0`
+ *
+ * @param[in] type The desired duration element type
+ * @param[in] size The number of elements in the column
+ * @param[in] state Optional, controls allocation/initialization of the
+ * column's null mask. By default, no null mask is allocated.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed duration type column
+ */
+std::unique_ptr<column> make_duration_column(
+  data_type type,
+  size_type size,
+  mask_state state                    = mask_state::UNALLOCATED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified duration `data_type` with a null mask.
+ *
+ * @note null_count is optional and will be computed if not provided.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a duration type
+ *
+ * @param[in] type The desired duration element type
+ * @param[in] size The number of elements in the column
+ * @param[in] null_mask Null mask to use for this column.
+ * @param[in] null_count Optional number of nulls in the null_mask.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed duration type column
+ */
+template <typename B>
+std::unique_ptr<column> make_duration_column(
+  data_type type,
+  size_type size,
+  B&& null_mask,
+  size_type null_count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  CUDF_EXPECTS(is_duration(type), "Invalid, non-duration type.");
+  return std::make_unique<column>(type,
+                                  size,
+                                  rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                                  std::forward<B>(null_mask),
+                                  null_count);
+}
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified fixed width `data_type` with an optional null mask.
+ *
+ * @note `null_count()` is determined by the requested null mask `state`
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a fixed width type
+ * @throws cudf::logic_error if `size < 0`
+ *
+ * @param[in] type The desired fixed width type
+ * @param[in] size The number of elements in the column
+ * @param[in] state Optional, controls allocation/initialization of the
+ * column's null mask. By default, no null mask is allocated.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed fixed-width type column
+ */
+std::unique_ptr<column> make_fixed_width_column(
+  data_type type,
+  size_type size,
+  mask_state state                    = mask_state::UNALLOCATED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct column with sufficient uninitialized storage to hold `size` elements of the
+ * specified fixed width `data_type` with a null mask.
+ *
+ * @note null_count is optional and will be computed if not provided.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a fixed width type
+ *
+ * @param[in] type The desired fixed width element type
+ * @param[in] size The number of elements in the column
+ * @param[in] null_mask Null mask to use for this column.
+ * @param[in] null_count Optional number of nulls in the null_mask.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Constructed fixed-width type column
+ */
+template <typename B>
+std::unique_ptr<column> make_fixed_width_column(
+  data_type type,
+  size_type size,
+  B&& null_mask,
+  size_type null_count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  CUDF_EXPECTS(is_fixed_width(type), "Invalid, non-fixed-width type.");
+  if (is_timestamp(type)) {
+    return make_timestamp_column(type, size, std::forward<B>(null_mask), null_count, stream, mr);
+  } else if (is_duration(type)) {
+    return make_duration_column(type, size, std::forward<B>(null_mask), null_count, stream, mr);
+  } else if (is_fixed_point(type)) {
+    return make_fixed_point_column(type, size, std::forward<B>(null_mask), null_count, stream, mr);
+  }
+  return make_numeric_column(type, size, std::forward<B>(null_mask), null_count, stream, mr);
+}
+
+/**
+ * @brief Construct a STRING type column given a device span of pointer/size pairs.
+ *
+ * The total number of char bytes must not exceed the maximum size of size_type.
+ * The string characters are expected to be UTF-8 encoded sequence of char
+ * bytes. Use the strings_column_view class to perform strings operations on
+ * this type of column.
+ *
+ * @note `null_count()` and `null_bitmask` are determined if a pair contains
+ * a null string. That is, for each pair, if `.first` is null, that string
+ * is considered null. Likewise, a string is considered empty (not null)
+ * if `.first` is not null and `.second` is 0. Otherwise the `.first` member
+ * must be a valid device address pointing to `.second` consecutive bytes.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ *
+ * @param[in] strings The device span of pointer/size pairs. Each pointer must be a device memory
+   address or `nullptr` (indicating a null string). The size must be the number of bytes.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used for allocation of the column's `null_mask` and children
+ * columns' device memory.
+ * @return Constructed strings column
+ */
+std::unique_ptr<column> make_strings_column(
+  cudf::device_span<thrust::pair<char const*, size_type> const> strings,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a STRING type column given a device span of string_view.
+ *
+ * The total number of char bytes must not exceed the maximum size of size_type.
+ * The string characters are expected to be UTF-8 encoded sequence of char
+ * bytes. Use the strings_column_view class to perform strings operations on
+ * this type of column.
+ *
+ * @note For each string_view, if `.data()` is `null_placeholder.data()`, that
+ * string is considered null. Likewise, a string is considered empty (not null)
+ * if `.data()` is not `null_placeholder.data()` and `.size_bytes()` is 0.
+ * Otherwise the `.data()` must be a valid device address pointing to
+ * `.size_bytes()` consecutive bytes. The `null_count()` for the output column
+ * will be equal to the number of input `string_view`s that are null.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ *
+ * @param[in] string_views The span of string_view. Each string_view must point to a device memory
+   address or `null_placeholder` (indicating a null string). The size must be the number of bytes.
+ * @param[in] null_placeholder string_view indicating null string in given list of
+ * string_views.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used for allocation of the column's `null_mask` and children
+ * columns' device memory.
+  * @return Constructed strings column
+ */
+std::unique_ptr<column> make_strings_column(
+  cudf::device_span<string_view const> string_views,
+  string_view const null_placeholder,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a STRING type column given a device span of chars encoded as UTF-8, a device
+ * span of byte offsets identifying individual strings within the char vector, and an optional
+ * null bitmask.
+ *
+ * `offsets.front()` must always be zero.
+ *
+ * The total number of char bytes must not exceed the maximum size of size_type. Use the
+ * strings_column_view class to perform strings operations on this type of column.
+ *
+ * This function makes a deep copy of the strings, offsets, null_mask to create a new column.
+ *
+ * @param strings The device span of chars in device memory. This char vector is expected to be
+ *  UTF-8 encoded characters.
+ * @param offsets The device span of byte offsets in device memory. The number of elements is
+ *  one more than the total number of strings so the `offsets.back()` is the total number of bytes
+ *  in the strings array. `offsets.front()` must always be 0 to point to the beginning of `strings`.
+ * @param null_mask Device span containing the null element indicator bitmask. Arrow format for
+ *  nulls is used for interpreting this bitmask.
+ * @param null_count The number of null string entries
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used for allocation of the column's `null_mask` and children
+ * columns' device memory
+ * @return Constructed strings column
+ */
+std::unique_ptr<column> make_strings_column(
+  cudf::device_span<char const> strings,
+  cudf::device_span<size_type const> offsets,
+  cudf::device_span<bitmask_type const> null_mask,
+  size_type null_count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a STRING type column given offsets column, chars columns, and null mask and null
+ * count.
+ *
+ * The columns and mask are moved into the resulting strings column.
+ *
+ * @param num_strings The number of strings the column represents.
+ * @param offsets_column The column of offset values for this column. The number of elements is
+ *  one more than the total number of strings so the `offset[last] - offset[0]` is the total number
+ *  of bytes in the strings vector.
+ * @param chars_column The column of char bytes for all the strings for this column. Individual
+ *  strings are identified by the offsets and the nullmask.
+ * @param null_count The number of null string entries.
+ * @param null_mask The bits specifying the null strings in device memory. Arrow format for
+ *  nulls is used for interpreting this bitmask.
+ * @return Constructed strings column
+ */
+std::unique_ptr<column> make_strings_column(size_type num_strings,
+                                            std::unique_ptr<column> offsets_column,
+                                            std::unique_ptr<column> chars_column,
+                                            size_type null_count,
+                                            rmm::device_buffer&& null_mask);
+
+/**
+ * @brief Construct a STRING type column given offsets, columns, and optional null count and null
+ * mask.
+ *
+ * @param[in] num_strings The number of strings the column represents.
+ * @param[in] offsets The offset values for this column. The number of elements is one more than the
+ * total number of strings so the `offset[last] - offset[0]` is the total number of bytes in the
+ * strings vector.
+ * @param[in] chars The char bytes for all the strings for this column. Individual strings are
+ * identified by the offsets and the nullmask.
+ * @param[in] null_mask The bits specifying the null strings in device memory. Arrow format for
+ *  nulls is used for interpreting this bitmask.
+ * @param[in] null_count The number of null string entries.
+ * @return Constructed strings column
+ */
+std::unique_ptr<column> make_strings_column(size_type num_strings,
+                                            rmm::device_uvector<size_type>&& offsets,
+                                            rmm::device_uvector<char>&& chars,
+                                            rmm::device_buffer&& null_mask,
+                                            size_type null_count);
+
+/**
+ * @brief Construct a LIST type column given offsets column, child column, null mask and null
+ * count.
+ *
+ * The columns and mask are moved into the resulting lists column.
+ *
+ * List columns are structured similarly to strings columns.  They contain
+ * a set of offsets which represents the lengths of the lists in each row, and
+ * a "child" column of data that is referenced by the offsets.  Since lists
+ * are a nested type, the child column may itself be further nested.
+ *
+ * When child column at depth N+1 is itself a list, the offsets column at
+ * depth N references the offsets column for depth N+1.  When the child column at depth
+ * N+1 is a leaf type (int, float, etc), the offsets column at depth N references
+ * the data for depth N+1.
+ *
+ * @code{.pseudo}
+ * Example:
+ * List<int>
+ * input:              {{1, 2}, {3, 4, 5}}
+ * offsets (depth 0)   {0, 2, 5}
+ * data    (depth 0)
+ * offsets (depth 1)
+ * data    (depth 1)   {1, 2, 3, 4, 5}
+ * @endcode
+ *
+ * @code{.pseudo}
+ * Example:
+ * List<List<int>>
+ * input:              { {{1, 2}}, {{3, 4, 5}, {6, 7}} }
+ * offsets (depth 0)   {0, 1, 3}
+ * data    (depth 0)
+ * offsets (depth 1)   {0, 2, 5, 7}
+ * data    (depth 1)
+ * offsets (depth 2)
+ * data    (depth 1)   {1, 2, 3, 4, 5, 6, 7}
+ * @endcode
+ *
+ * @param[in] num_rows The number of lists the column represents.
+ * @param[in] offsets_column The column of offset values for this column. Each value should
+ * represent the starting offset into the child elements that corresponds to the beginning of the
+ * row, with the first row starting at 0. The length of row N can be determined by subtracting
+ * offsets[N+1] - offsets[N]. The total number of offsets should be 1 longer than the # of rows in
+ * the column.
+ * @param[in] child_column The column of nested data referenced by the lists represented by the
+ *                     offsets_column. Note: the child column may itself be
+ *                     further nested.
+ * @param[in] null_count The number of null list entries.
+ * @param[in] null_mask The bits specifying the null lists in device memory.
+ *                  Arrow format for nulls is used for interpreting this bitmask.
+ * @param[in] stream Optional stream for use with all memory allocation
+ *               and device kernels
+ * @param[in] mr Optional resource to use for device memory
+ *           allocation of the column's `null_mask` and children.
+ * @return Constructed lists column
+ */
+std::unique_ptr<cudf::column> make_lists_column(
+  size_type num_rows,
+  std::unique_ptr<column> offsets_column,
+  std::unique_ptr<column> child_column,
+  size_type null_count,
+  rmm::device_buffer&& null_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a STRUCT column using specified child columns as members.
+ *
+ * Specified child/member columns and null_mask are adopted by resultant
+ * struct column.
+ *
+ * A struct column requires that all specified child columns have the same
+ * number of rows. A struct column's row count equals that of any/all
+ * of its child columns. A single struct row at any index is comprised of
+ * all the individual child column values at the same index, in the order
+ * specified in the list of child columns.
+ *
+ * The specified null mask governs which struct row has a null value. This
+ * is orthogonal to the null values of individual child columns.
+ *
+ * @param[in] num_rows The number of struct values in the struct column.
+ * @param[in] child_columns The list of child/members that the struct is comprised of.
+ * @param[in] null_count The number of null values in the struct column.
+ * @param[in] null_mask The bits specifying the null struct values in the column.
+ * @param[in] stream Optional stream for use with all memory allocation and device kernels.
+ * @param[in] mr Optional resource to use for device memory allocation.
+ * @return Constructed structs column
+ */
+std::unique_ptr<cudf::column> make_structs_column(
+  size_type num_rows,
+  std::vector<std::unique_ptr<column>>&& child_columns,
+  size_type null_count,
+  rmm::device_buffer&& null_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a column with size elements that are all equal to the given scalar.
+ *
+ * The output column will have the same type as `s.type()`
+ * The output column will contain all null rows if `s.invalid()==false`
+ * The output column will be empty if `size==0`. For LIST scalars, the column hierarchy
+ * from @p s is preserved.
+ *
+ * @param[in] s The scalar to use for values in the column.
+ * @param[in] size The number of rows for the output column.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory.
+ * @return Constructed column whose rows all contain the scalar value
+ */
+std::unique_ptr<column> make_column_from_scalar(
+  scalar const& s,
+  size_type size,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a dictionary column with size elements that are all equal to the given scalar.
+ *
+ * The output column will have keys of type `s.type()`
+ * The output column will be empty if `size==0`.
+ *
+ * @throw cudf::logic_error if `s.is_valid()==false`
+ *
+ * @param[in] s The scalar to use for values in the column.
+ * @param[in] size The number of rows for the output column.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory.
+ * @return Constructed dictionary column
+ */
+std::unique_ptr<column> make_dictionary_from_scalar(
+  scalar const& s,
+  size_type size,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/column/column_view.hpp b/cpp/include/cudf/column/column_view.hpp
new file mode 100644
index 0000000..d80c720
--- /dev/null
+++ b/cpp/include/cudf/column/column_view.hpp
@@ -0,0 +1,766 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <limits>
+#include <type_traits>
+#include <vector>
+
+/**
+ * @file column_view.hpp
+ * @brief column view class definitions
+ */
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief A non-owning, immutable view of device data as a column of elements,
+ * some of which may be null as indicated by a bitmask.
+ *
+ * A `column_view_base` can be constructed implicitly from a `cudf::column`, or
+ *may be constructed explicitly from a pointer to pre-existing device memory.
+ *
+ * Unless otherwise noted, the memory layout of the `column_view_base`'s data
+ *and bitmask is expected to adhere to the Arrow Physical Memory Layout
+ * Specification: https://arrow.apache.org/docs/memory_layout.html
+ *
+ * Because `column_view_base` is non-owning, no device memory is allocated nor
+ *freed when `column_view_base` objects are created or destroyed.
+ *
+ * To enable zero-copy slicing, a `column_view_base` has an `offset` that
+ *indicates the index of the first element in the column relative to the base
+ *device memory allocation. By default, `offset()` is zero.
+ */
+class column_view_base {
+ public:
+  /**
+   * @brief Returns pointer to the base device memory allocation casted to
+   * the specified type.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * @note It should be rare to need to access the `head<T>()` allocation of
+   *a column, and instead, accessing the elements should be done via
+   *`data<T>()`.
+   *
+   * This function will only participate in overload resolution if `is_rep_layout_compatible<T>()`
+   * or `std::is_same_v<T,void>` are true.
+   *
+   * @tparam The type to cast to
+   * @return Typed pointer to underlying data
+   */
+  template <typename T = void,
+            CUDF_ENABLE_IF(std::is_same_v<T, void> or is_rep_layout_compatible<T>())>
+  T const* head() const noexcept
+  {
+    return static_cast<T const*>(_data);
+  }
+
+  /**
+   * @brief Returns the underlying data casted to the specified type, plus the
+   * offset.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @tparam T The type to cast to
+   * @return Typed pointer to underlying data, including the offset
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  T const* data() const noexcept
+  {
+    return head<T>() + _offset;
+  }
+
+  /**
+   * @brief Return first element (accounting for offset) after underlying data
+   * is casted to the specified type.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @tparam T The desired type
+   * @return Pointer to the first element after casting
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  T const* begin() const noexcept
+  {
+    return data<T>();
+  }
+
+  /**
+   * @brief Return one past the last element after underlying data is casted to
+   * the specified type.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @tparam T The desired type
+   * @return Pointer to one past the last element after casting
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  T const* end() const noexcept
+  {
+    return begin<T>() + size();
+  }
+
+  /**
+   * @brief Returns the number of elements in the column
+   *
+   * @return The number of elements in the column
+   */
+  [[nodiscard]] size_type size() const noexcept { return _size; }
+
+  /**
+   * @brief Returns true if `size()` returns zero, or false otherwise
+   *
+   * @return True if `size()` returns zero, or false otherwise
+   */
+  [[nodiscard]] bool is_empty() const noexcept { return size() == 0; }
+
+  /**
+   * @brief Returns the element `data_type`
+   *
+   * @return The `data_type` of the elements in the column
+   */
+  [[nodiscard]] data_type type() const noexcept { return _type; }
+
+  /**
+   * @brief Indicates if the column can contain null elements, i.e., if it has
+   * an allocated bitmask.
+   *
+   * @note If `null_count() > 0`, this function must always return `true`.
+   *
+   * @return true The bitmask is allocated
+   * @return false The bitmask is not allocated
+   */
+  [[nodiscard]] bool nullable() const noexcept { return nullptr != _null_mask; }
+
+  /**
+   * @brief Returns the count of null elements
+   *
+   * @return The count of null elements
+   */
+  [[nodiscard]] size_type null_count() const { return _null_count; }
+
+  /**
+   * @brief Returns the count of null elements in the range [begin, end)
+   *
+   * @note If `null_count() != 0`, every invocation of `null_count(begin, end)`
+   * will recompute the count of null elements indicated by the `null_mask` in
+   * the range [begin, end).
+   *
+   * @throws cudf::logic_error for invalid range (if `begin < 0`,
+   * `begin > end`, `begin >= size()`, or `end > size()`).
+   *
+   * @param[in] begin The starting index of the range (inclusive).
+   * @param[in] end The index of the last element in the range (exclusive).
+   * @return The count of null elements in the given range
+   */
+  [[nodiscard]] size_type null_count(size_type begin, size_type end) const;
+
+  /**
+   * @brief Indicates if the column contains null elements,
+   * i.e., `null_count() > 0`
+   *
+   * @return true One or more elements are null
+   * @return false All elements are valid
+   */
+  [[nodiscard]] bool has_nulls() const { return null_count() > 0; }
+
+  /**
+   * @brief Indicates if the column contains null elements in the range
+   * [begin, end), i.e., `null_count(begin, end) > 0`
+   *
+   * @throws cudf::logic_error for invalid range (if `begin < 0`,
+   * `begin > end`, `begin >= size()`, or `end > size()`).
+   *
+   * @param begin The starting index of the range (inclusive).
+   * @param end The index of the last element in the range (exclusive).
+   * @return true One or more elements are null in the range [begin, end)
+   * @return false All elements are valid in the range [begin, end)
+   */
+  [[nodiscard]] bool has_nulls(size_type begin, size_type end) const
+  {
+    return null_count(begin, end) > 0;
+  }
+
+  /**
+   * @brief Returns raw pointer to the underlying bitmask allocation.
+   *
+   * @note This function does *not* account for the `offset()`.
+   *
+   * @note If `null_count() == 0`, this may return `nullptr`.
+   * @return Raw pointer to the bitmask
+   */
+  [[nodiscard]] bitmask_type const* null_mask() const noexcept { return _null_mask; }
+
+  /**
+   * @brief Returns the index of the first element relative to the base memory
+   * allocation, i.e., what is returned from `head<T>()`.
+   *
+   * @return The index of the first element relative to `head<T>()`
+   */
+  [[nodiscard]] size_type offset() const noexcept { return _offset; }
+
+ protected:
+  data_type _type{type_id::EMPTY};   ///< Element type
+  size_type _size{};                 ///< Number of elements
+  void const* _data{};               ///< Pointer to device memory containing elements
+  bitmask_type const* _null_mask{};  ///< Pointer to device memory containing
+                                     ///< bitmask representing null elements.
+                                     ///< Optional if `null_count() == 0`
+  mutable size_type _null_count{};   ///< The number of null elements
+  size_type _offset{};               ///< Index position of the first element.
+                                     ///< Enables zero-copy slicing
+
+  column_view_base()                        = default;
+  ~column_view_base()                       = default;
+  column_view_base(column_view_base const&) = default;  ///< Copy constructor
+  column_view_base(column_view_base&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  column_view_base& operator=(column_view_base const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  column_view_base& operator=(column_view_base&&) = default;
+
+  /**
+   * @brief Construct a `column_view_base` from pointers to device memory for
+   *the elements and bitmask of the column.
+   *
+   * If `null_count()` is zero, `null_mask` is optional.
+   *
+   * If `type` is `EMPTY`, the specified `null_count` will be ignored and
+   * `null_count()` will always return the same value as `size()`
+   *
+   * @throws cudf::logic_error if `size < 0`
+   * @throws cudf::logic_error if `size > 0` but `data == nullptr`
+   * @throws cudf::logic_error if `type.id() == EMPTY` but `data != nullptr`
+   *or `null_mask != nullptr`
+   * @throws cudf::logic_error if `null_count > 0`, but `null_mask == nullptr`
+   * @throws cudf::logic_error if `offset < 0`
+   *
+   * @param type The element type
+   * @param size The number of elements
+   * @param data Pointer to device memory containing the column elements
+   * @param null_mask Pointer to device memory containing the null
+   * indicator bitmask
+   * @param null_count The number of null elements.
+   * @param offset Optional, index of the first element
+   */
+  column_view_base(data_type type,
+                   size_type size,
+                   void const* data,
+                   bitmask_type const* null_mask,
+                   size_type null_count,
+                   size_type offset = 0);
+};
+
+class mutable_column_view_base : public column_view_base {
+ public:
+ protected:
+};
+}  // namespace detail
+
+/**
+ * @brief A non-owning, immutable view of device data as a column of elements,
+ * some of which may be null as indicated by a bitmask.
+ *
+ * @ingroup column_classes
+ *
+ * A `column_view` can be constructed implicitly from a `cudf::column`, or may
+ * be constructed explicitly from a pointer to pre-existing device memory.
+ *
+ * Unless otherwise noted, the memory layout of the `column_view`'s data and
+ * bitmask is expected to adhere to the Arrow Physical Memory Layout
+ * Specification: https://arrow.apache.org/docs/memory_layout.html
+ *
+ * Because `column_view` is non-owning, no device memory is allocated nor freed
+ * when `column_view` objects are created or destroyed.
+ *
+ * To enable zero-copy slicing, a `column_view` has an `offset` that indicates
+ * the index of the first element in the column relative to the base device
+ * memory allocation. By default, `offset()` is zero.
+ */
+class column_view : public detail::column_view_base {
+ public:
+  column_view() = default;
+
+  // these pragmas work around the nvcc issue where if a column_view is used
+  // inside of a __device__ code path, these functions will end up being created
+  // as __host__ __device__ because they are explicitly defaulted.  However, if
+  // they then end up being called by a simple __host__ function
+  // (eg std::vector destructor) you get a compile error because you're trying to
+  // call a __host__ __device__ function from a __host__ function.
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+  ~column_view() = default;
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+  column_view(column_view const&) = default;  ///< Copy constructor
+  column_view(column_view&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  column_view& operator=(column_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object
+   */
+  column_view& operator=(column_view&&) = default;
+
+  /**
+   * @brief Construct a `column_view` from pointers to device memory for the
+   * elements and bitmask of the column.
+   *
+   * If `null_count()` is zero, `null_mask` is optional.
+   *
+   * If `type` is `EMPTY`, the specified `null_count` will be ignored and
+   * `null_count()` will always return the same value as `size()`
+   *
+   * @throws cudf::logic_error if `size < 0`
+   * @throws cudf::logic_error if `size > 0` but `data == nullptr`
+   * @throws cudf::logic_error if `type.id() == EMPTY` but `data != nullptr`
+   *or `null_mask != nullptr`
+   * @throws cudf::logic_error if `null_count > 0`, but `null_mask == nullptr`
+   * @throws cudf::logic_error if `offset < 0`
+   *
+   * @param type The element type
+   * @param size The number of elements
+   * @param data Pointer to device memory containing the column elements
+   * @param null_mask Pointer to device memory containing the null
+   * indicator bitmask
+   * @param null_count The number of null elements.
+   * @param offset Optional, index of the first element
+   * @param children Optional, depending on the element type, child columns may
+   * contain additional data
+   */
+  column_view(data_type type,
+              size_type size,
+              void const* data,
+              bitmask_type const* null_mask,
+              size_type null_count,
+              size_type offset                         = 0,
+              std::vector<column_view> const& children = {});
+
+  /**
+   * @brief Returns the specified child
+   *
+   * @param child_index The index of the desired child
+   * @return The requested child `column_view`
+   */
+  [[nodiscard]] column_view child(size_type child_index) const noexcept
+  {
+    return _children[child_index];
+  }
+
+  /**
+   * @brief Returns the number of child columns.
+   *
+   * @return The number of child columns
+   */
+  [[nodiscard]] size_type num_children() const noexcept { return _children.size(); }
+
+  /**
+   * @brief Returns iterator to the beginning of the ordered sequence of child column-views.
+   *
+   * @return An iterator to a `column_view` referencing the first child column
+   */
+  auto child_begin() const noexcept { return _children.cbegin(); }
+
+  /**
+   * @brief Returns iterator to the end of the ordered sequence of child column-views.
+   *
+   * @return An iterator to a `column_view` one past the end of the child columns
+   */
+  auto child_end() const noexcept { return _children.cend(); }
+
+  /**
+   * @brief Construct a column view from a device_span<T>.
+   *
+   * Only numeric and chrono types are supported.
+   *
+   * @tparam T The device span type. Must be const and match the column view's type.
+   * @param data A typed device span containing the column view's data.
+   */
+  template <typename T, CUDF_ENABLE_IF(cudf::is_numeric<T>() or cudf::is_chrono<T>())>
+  column_view(device_span<T const> data)
+    : column_view(
+        cudf::data_type{cudf::type_to_id<T>()}, data.size(), data.data(), nullptr, 0, 0, {})
+  {
+    CUDF_EXPECTS(
+      data.size() <= static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max()),
+      "Data exceeds the column size limit",
+      std::overflow_error);
+  }
+
+  /**
+   * @brief Converts a column view into a device span.
+   *
+   * Only numeric and chrono data types are supported. The column view must not
+   * be nullable.
+   *
+   * @tparam T The device span type. Must be const and match the column view's type.
+   * @throws cudf::logic_error if the column view type does not match the span type.
+   * @throws cudf::logic_error if the column view is nullable.
+   * @return A typed device span of the column view's data.
+   */
+  template <typename T, CUDF_ENABLE_IF(cudf::is_numeric<T>() or cudf::is_chrono<T>())>
+  [[nodiscard]] operator device_span<T const>() const
+  {
+    CUDF_EXPECTS(type() == cudf::data_type{cudf::type_to_id<T>()},
+                 "Device span type must match column view type.");
+    CUDF_EXPECTS(!nullable(), "A nullable column view cannot be converted to a device span.");
+    return device_span<T const>(data<T>(), size());
+  }
+
+ private:
+  friend column_view bit_cast(column_view const& input, data_type type);
+
+  std::vector<column_view> _children{};  ///< Based on element type, children
+                                         ///< may contain additional data
+};                                       // namespace cudf
+
+/**
+ * @brief A non-owning, mutable view of device data as a column of elements,
+ * some of which may be null as indicated by a bitmask.
+ *
+ * @ingroup column_classes
+ *
+ * A `mutable_column_view` can be constructed implicitly from a `cudf::column`,
+ * or may be constructed explicitly from a pointer to pre-existing device memory.
+ *
+ * Unless otherwise noted, the memory layout of the `mutable_column_view`'s data
+ * and bitmask is expected to adhere to the Arrow Physical Memory Layout
+ * Specification: https://arrow.apache.org/docs/memory_layout.html
+ *
+ * Because `mutable_column_view` is non-owning, no device memory is allocated
+ * nor freed when `mutable_column_view` objects are created or destroyed.
+ *
+ * To enable zero-copy slicing, a `mutable_column_view` has an `offset` that
+ * indicates the index of the first element in the column relative to the base
+ * device memory allocation. By default, `offset()` is zero.
+ */
+class mutable_column_view : public detail::column_view_base {
+ public:
+  mutable_column_view() = default;
+
+  ~mutable_column_view() = default;
+
+  mutable_column_view(mutable_column_view const&) = default;  ///< Copy constructor
+  mutable_column_view(mutable_column_view&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  mutable_column_view& operator=(mutable_column_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  mutable_column_view& operator=(mutable_column_view&&) = default;
+
+  /**
+   * @brief Construct a `mutable_column_view` from pointers to device memory for
+   * the elements and bitmask of the column.
+
+   * If `type` is `EMPTY`, the specified `null_count` will be ignored and
+   * `null_count()` will always return the same value as `size()`
+   *
+   * @throws cudf::logic_error if `size < 0`
+   * @throws cudf::logic_error if `size > 0` but `data == nullptr`
+   * @throws cudf::logic_error if `type.id() == EMPTY` but `data != nullptr`
+   *or `null_mask != nullptr`
+   * @throws cudf::logic_error if `null_count > 0`, but `null_mask == nullptr`
+   * @throws cudf::logic_error if `offset < 0`
+   *
+   * @param type The element type
+   * @param size The number of elements
+   * @param data Pointer to device memory containing the column elements
+   * @param null_mask Pointer to device memory containing the null
+   indicator
+   * bitmask
+   * @param null_count The number of null elements.
+   * @param offset Optional, index of the first element
+   * @param children Optional, depending on the element type, child columns may
+   * contain additional data
+   */
+  mutable_column_view(data_type type,
+                      size_type size,
+                      void* data,
+                      bitmask_type* null_mask,
+                      size_type null_count,
+                      size_type offset                                 = 0,
+                      std::vector<mutable_column_view> const& children = {});
+
+  /**
+   * @brief Returns pointer to the base device memory allocation casted to
+   * the specified type.
+   *
+   * This function will only participate in overload resolution if `is_rep_layout_compatible<T>()`
+   * or `std::is_same_v<T,void>` are true.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * @note It should be rare to need to access the `head<T>()` allocation of a
+   * column, and instead, accessing the elements should be done via `data<T>()`.
+   *
+   * @tparam The type to cast to
+   * @return Typed pointer to underlying data
+   */
+  template <typename T = void,
+            CUDF_ENABLE_IF(std::is_same_v<T, void> or is_rep_layout_compatible<T>())>
+  T* head() const noexcept
+  {
+    return const_cast<T*>(detail::column_view_base::head<T>());
+  }
+
+  /**
+   * @brief Returns the underlying data casted to the specified type, plus the
+   * offset.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @note If `offset() == 0`, then `head<T>() == data<T>()`
+   *
+   * @tparam T The type to cast to
+   * @return Typed pointer to underlying data, including the offset
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  T* data() const noexcept
+  {
+    return const_cast<T*>(detail::column_view_base::data<T>());
+  }
+
+  /**
+   * @brief Return first element (accounting for offset) when underlying data is
+   * casted to the specified type.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @tparam T The desired type
+   * @return Pointer to the first element after casting
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  T* begin() const noexcept
+  {
+    return const_cast<T*>(detail::column_view_base::begin<T>());
+  }
+
+  /**
+   * @brief Return one past the last element after underlying data is casted to
+   * the specified type.
+   *
+   * This function does not participate in overload resolution if `is_rep_layout_compatible<T>` is
+   * false.
+   *
+   * @tparam T The desired type
+   * @return Pointer to one past the last element after casting
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  T* end() const noexcept
+  {
+    return const_cast<T*>(detail::column_view_base::end<T>());
+  }
+
+  /**
+   * @brief Returns raw pointer to the underlying bitmask allocation.
+   *
+   * @note This function does *not* account for the `offset()`.
+   *
+   * @note If `null_count() == 0`, this may return `nullptr`.
+   *
+   * @return Raw pointer to the underlying bitmask allocation
+   */
+  [[nodiscard]] bitmask_type* null_mask() const noexcept
+  {
+    return const_cast<bitmask_type*>(detail::column_view_base::null_mask());
+  }
+
+  /**
+   * @brief Set the null count
+   *
+   * @throws cudf::logic_error if `new_null_count > 0` and `nullable() == false`
+   *
+   * @param new_null_count The new null count
+   */
+  void set_null_count(size_type new_null_count);
+
+  /**
+   * @brief Returns a reference to the specified child
+   *
+   * @param child_index The index of the desired child
+   * @return The requested child `mutable_column_view`
+   */
+  [[nodiscard]] mutable_column_view child(size_type child_index) const noexcept
+  {
+    return mutable_children[child_index];
+  }
+
+  /**
+   * @brief Returns the number of child columns.
+   *
+   * @return The number of child columns
+   */
+  [[nodiscard]] size_type num_children() const noexcept { return mutable_children.size(); }
+
+  /**
+   * @brief Returns iterator to the beginning of the ordered sequence of child column-views.
+   *
+   * @return An iterator to a `mutable_column_view` referencing the first child column
+   */
+  auto child_begin() const noexcept { return mutable_children.begin(); }
+
+  /**
+   * @brief Returns iterator to the end of the ordered sequence of child column-views.
+   *
+   * @return An iterator to a `mutable_column_view` to the element following the last child column
+   */
+  auto child_end() const noexcept { return mutable_children.end(); }
+
+  /**
+   * @brief Converts a mutable view into an immutable view
+   *
+   * @return An immutable view of the mutable view's elements
+   */
+  operator column_view() const;
+
+ private:
+  friend mutable_column_view bit_cast(mutable_column_view const& input, data_type type);
+
+  std::vector<mutable_column_view> mutable_children;
+};
+
+/**
+ * @brief Counts the number of descendants of the specified parent.
+ *
+ * @param parent The parent whose descendants will be counted
+ * @return The number of descendants of the parent
+ */
+size_type count_descendants(column_view parent);
+
+/**
+ * @brief Zero-copy cast between types with the same size and compatible underlying representations.
+ *
+ * This is similar to `reinterpret_cast` or `bit_cast` in that it gives a view of the same raw bits
+ * as a different type. Unlike `reinterpret_cast` however, this cast is only allowed on types that
+ * have the same width and compatible representations. For example, the way timestamp types are laid
+ * out in memory is equivalent to an integer representing a duration since a fixed epoch;
+ * bit-casting to the same integer type (INT32 for days, INT64 for others) results in a raw view of
+ * the duration count. A FLOAT32 can also be bit-casted into INT32 and treated as an integer value.
+ * However, an INT32 column cannot be bit-casted to INT64 as the sizes differ, nor can a string_view
+ * column be casted into a numeric type column as their data representations are not compatible.
+ *
+ * The validity of the conversion can be checked with `cudf::is_bit_castable()`.
+ *
+ * @throws cudf::logic_error if the specified cast is not possible, i.e.,
+ * `is_bit_castable(input.type(), type)` is false.
+ *
+ * @param input The `column_view` to cast from
+ * @param type The `data_type` to cast to
+ * @return New `column_view` wrapping the same data as `input` but cast to `type`
+ */
+column_view bit_cast(column_view const& input, data_type type);
+
+/**
+ * @brief Zero-copy cast between types with the same size and compatible underlying representations.
+ *
+ * This is similar to `reinterpret_cast` or `bit_cast` in that it gives a view of the same raw bits
+ * as a different type. Unlike `reinterpret_cast` however, this cast is only allowed on types that
+ * have the same width and compatible representations. For example, the way timestamp types are laid
+ * out in memory is equivalent to an integer representing a duration since a fixed epoch;
+ * bit-casting to the same integer type (INT32 for days, INT64 for others) results in a raw view of
+ * the duration count. A FLOAT32 can also be bit-casted into INT32 and treated as an integer value.
+ * However, an INT32 column cannot be bit-casted to INT64 as the sizes differ, nor can a string_view
+ * column be casted into a numeric type column as their data representations are not compatible.
+ *
+ * The validity of the conversion can be checked with `cudf::is_bit_castable()`.
+ *
+ * @throws cudf::logic_error if the specified cast is not possible, i.e.,
+ * `is_bit_castable(input.type(), type)` is false.
+ *
+ * @param input The `mutable_column_view` to cast from
+ * @param type The `data_type` to cast to
+ * @return New `mutable_column_view` wrapping the same data as `input` but cast to `type`
+ */
+mutable_column_view bit_cast(mutable_column_view const& input, data_type type);
+
+namespace detail {
+/**
+ * @brief Computes a hash value from the shallow state of the specified column
+ *
+ * For any two columns, if `is_shallow_equivalent(c0,c1)` then `shallow_hash(c0) ==
+ * shallow_hash(c1)`.
+ *
+ * The complexity of computing the hash value of `input` is `O( count_descendants(input) )`, i.e.,
+ * it is independent of the number of elements in the column.
+ *
+ * This function does _not_ inspect the elements of `input` nor access any device memory or launch
+ * any kernels.
+ *
+ * @param input The `column_view` to compute hash
+ * @return The hash value derived from the shallow state of `input`.
+ */
+std::size_t shallow_hash(column_view const& input);
+
+/**
+ * @brief Uses only shallow state to determine if two `column_view`s view equivalent columns
+ *
+ *  Two columns are equivalent if for any operation `F` then:
+ *   ```
+ *    is_shallow_equivalent(c0, c1) ==> The results of F(c0) and F(c1) are equivalent
+ *   ```
+ * For any two non-empty columns, `is_shallow_equivalent(c0,c1)` is true only if they view the exact
+ * same physical column. In other words, two physically independent columns may have exactly
+ * equivalent elements but their shallow state would not be equivalent.
+ *
+ * The complexity of this function is `O( min(count_descendants(lhs), count_descendants(rhs)) )`,
+ * i.e., it is independent of the number of elements in either column.
+ *
+ * This function does _not_ inspect the elements of `lhs` or `rhs` nor access any device memory nor
+ * launch any kernels.
+ *
+ * @param lhs The left `column_view` to compare
+ * @param rhs The right `column_view` to compare
+ * @return If `lhs` and `rhs` have equivalent shallow state
+ */
+bool is_shallow_equivalent(column_view const& lhs, column_view const& rhs);
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/concatenate.hpp b/cpp/include/cudf/concatenate.hpp
new file mode 100644
index 0000000..9ee5527
--- /dev/null
+++ b/cpp/include/cudf/concatenate.hpp
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup copy_concatenate
+ * @{
+ * @file
+ * @brief Concatenate columns APIs
+ */
+
+/**
+ * @brief Concatenates `views[i]`'s bitmask from the bits
+ * `[views[i].offset(), views[i].offset() + views[i].size())` for all elements
+ * `views` into an `rmm::device_buffer`
+ *
+ * Returns an empty buffer if the column is not nullable.
+ *
+ * @param views Column views whose bitmasks will be concatenated
+ * @param mr Device memory resource used for allocating the returned memory
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Bitmasks of all the column views in the views vector
+ */
+rmm::device_buffer concatenate_masks(
+  host_span<column_view const> views,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Concatenates multiple columns into a single column
+ *
+ * @throws cudf::logic_error If types of the input columns mismatch
+ * @throws std::overflow_error If the total number of output rows exceeds cudf::size_type
+ *
+ * @param columns_to_concat Column views to be concatenated into a single column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A single column having all the rows from the elements of `columns_to_concat` respectively
+ * in the same order.
+ */
+std::unique_ptr<column> concatenate(
+  host_span<column_view const> columns_to_concat,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Columns of `tables_to_concat` are concatenated vertically to return a
+ * single table
+ *
+ * @code{.pseudo}
+ * column_view c0 is {0,1,2,3}
+ * column_view c1 is {4,5,6,7}
+ * table_view t0{{c0, c0}};
+ * table_view t1{{c1, c1}};
+ * ...
+ * auto t = concatenate({t0.view(), t1.view()});
+ * column_view tc0 = (t->view()).column(0) is {0,1,2,3,4,5,6,7}
+ * column_view tc1 = (t->view()).column(1) is {0,1,2,3,4,5,6,7}
+ * @endcode
+ *
+ * @throws cudf::logic_error If number of columns mismatch
+ * @throws std::overflow_error If the total number of output rows exceeds cudf::size_type
+ *
+ * @param tables_to_concat Table views to be concatenated into a single table
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return A single table having all the rows from the elements of
+ * `tables_to_concat` respectively in the same order.
+ */
+std::unique_ptr<table> concatenate(
+  host_span<table_view const> tables_to_concat,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/contiguous_split.hpp b/cpp/include/cudf/contiguous_split.hpp
new file mode 100644
index 0000000..bf10f1f
--- /dev/null
+++ b/cpp/include/cudf/contiguous_split.hpp
@@ -0,0 +1,345 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+
+/**
+ * @addtogroup column_copy
+ * @{
+ * @file
+ * @brief Table APIs for contiguous_split, pack, unpack, and metadata
+ */
+
+/**
+ * @brief Column data in a serialized format
+ *
+ * @ingroup copy_split
+ *
+ * Contains data from an array of columns in two contiguous buffers: one on host, which contains
+ * table metadata and one on device which contains the table data.
+ */
+struct packed_columns {
+  packed_columns()
+    : metadata(std::make_unique<std::vector<uint8_t>>()),
+      gpu_data(std::make_unique<rmm::device_buffer>())
+  {
+  }
+
+  /**
+   * @brief Construct a new packed columns object
+   *
+   * @param md Host-side metadata buffer
+   * @param gd Device-side data buffer
+   */
+  packed_columns(std::unique_ptr<std::vector<uint8_t>>&& md,
+                 std::unique_ptr<rmm::device_buffer>&& gd)
+    : metadata(std::move(md)), gpu_data(std::move(gd))
+  {
+  }
+
+  std::unique_ptr<std::vector<uint8_t>> metadata;  ///< Host-side metadata buffer
+  std::unique_ptr<rmm::device_buffer> gpu_data;    ///< Device-side data buffer
+};
+
+/**
+ * @brief The result(s) of a cudf::contiguous_split
+ *
+ * @ingroup copy_split
+ *
+ * Each table_view resulting from a split operation performed by contiguous_split,
+ * will be returned wrapped in a `packed_table`. The table_view and internal
+ * column_views in this struct are not owned by a top level cudf::table or cudf::column.
+ * The backing memory and metadata is instead owned by the `data` field and is in one
+ * contiguous block.
+ *
+ * The user is responsible for assuring that the `table` or any derived table_views do
+ * not outlive the memory owned by `data`.
+ */
+struct packed_table {
+  cudf::table_view table;  ///< Result table_view of a cudf::contiguous_split
+  packed_columns data;     ///< Column data owned
+};
+
+/**
+ * @brief Performs a deep-copy split of a `table_view` into a vector of `packed_table` where each
+ * `packed_table` is using a single contiguous block of memory for all of the split's column data.
+ *
+ * @ingroup copy_split
+ *
+ * The memory for the output views is allocated in a single contiguous `rmm::device_buffer` returned
+ * in the `packed_table`. There is no top-level owning table.
+ *
+ * The returned views of `input` are constructed from a vector of indices, that indicate
+ * where each split should occur. The `i`th returned `table_view` is sliced as
+ * `[0, splits[i])` if `i`=0, else `[splits[i], input.size())` if `i` is the last view and
+ * `[splits[i-1], splits[i]]` otherwise.
+ *
+ * For all `i` it is expected `splits[i] <= splits[i+1] <= input.size()`.
+ * For a `splits` size N, there will always be N+1 splits in the output.
+ *
+ * @note It is the caller's responsibility to ensure that the returned views
+ * do not outlive the viewed device memory contained in the `all_data` field of the
+ * returned packed_table.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input:   [{10, 12, 14, 16, 18, 20, 22, 24, 26, 28},
+ *           {50, 52, 54, 56, 58, 60, 62, 64, 66, 68}]
+ * splits:  {2, 5, 9}
+ * output:  [{{10, 12}, {14, 16, 18}, {20, 22, 24, 26}, {28}},
+ *           {{50, 52}, {54, 56, 58}, {60, 62, 64, 66}, {68}}]
+ * @endcode
+ *
+ *
+ * @throws cudf::logic_error if `splits` has end index > size of `input`.
+ * @throws cudf::logic_error When the value in `splits` is not in the range [0, input.size()).
+ * @throws cudf::logic_error When the values in the `splits` are 'strictly decreasing'.
+ *
+ * @param input View of a table to split
+ * @param splits A vector of indices where the view will be split
+ * @param mr An optional memory resource to use for all returned device allocations
+ * @return The set of requested views of `input` indicated by the `splits` and the viewed memory
+ * buffer
+ */
+std::vector<packed_table> contiguous_split(
+  cudf::table_view const& input,
+  std::vector<size_type> const& splits,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+namespace detail {
+struct contiguous_split_state;
+};
+
+/**
+ * @brief Perform a chunked "pack" operation of the input `table_view` using a user provided
+ * buffer of size `user_buffer_size`.
+ *
+ * The intent of this operation is to be used in a streamed fashion at times of GPU
+ * out-of-memory, where we want to minimize the number of small cudaMemcpy calls and
+ * tracking of all the metadata associated with cudf tables. Because of the memory constraints,
+ * all thrust and scratch memory allocations are using the passed-in memory resource exclusively,
+ * not a per-device memory resource.
+ *
+ * This class defines two methods that must be used in concert to carry out the chunked_pack:
+ * has_next and next. Here is an example:
+ *
+ * @code{.pseudo}
+ * // Create a table_view
+ * cudf::table_view tv = ...;
+ *
+ * // Choose a memory resource (optional). This memory resource is used for scratch/thrust temporary
+ * // data. In memory constrained cases, this can be used to set aside scratch memory
+ * // for `chunked_pack` at the beginning of a program.
+ * auto mr = rmm::mr::get_current_device_resource();
+ *
+ * // Define a buffer size for each chunk: the larger the buffer is, the more SMs can be
+ * // occupied by this algorithm.
+ * //
+ * // Internally, the GPU unit of work is a 1MB batch. When we instantiate `cudf::chunked_pack`,
+ * // all the 1MB batches for the source table_view are computed up front. Additionally,
+ * // chunked_pack calculates the number of iterations that are required to go through all those
+ * // batches given a `user_buffer_size` buffer. The number of 1MB batches in each iteration (chunk)
+ * // equals the number of CUDA blocks that will be used for the main kernel launch.
+ * //
+ * std::size_t user_buffer_size = 128*1024*1024;
+ *
+ * auto chunked_packer = cudf::chunked_pack::create(tv, user_buffer_size, mr);
+ *
+ * std::size_t host_offset = 0;
+ * auto host_buffer = ...; // obtain a host buffer you would like to copy to
+ *
+ * while (chunked_packer->has_next()) {
+ *   // get a user buffer of size `user_buffer_size`
+ *   cudf::device_span<uint8_t> user_buffer = ...;
+ *   std::size_t bytes_copied = chunked_packer->next(user_buffer);
+ *
+ *   // buffer will hold the contents of at most `user_buffer_size` bytes
+ *   // of the contiguously packed input `table_view`. You are now free to copy
+ *   // this memory somewhere else, for example, to host.
+ *   cudaMemcpyAsync(
+ *     host_buffer.data() + host_offset,
+ *     user_buffer.data(),
+ *     bytes_copied,
+ *     cudaMemcpyDefault,
+ *     stream);
+ *
+ *   host_offset += bytes_copied;
+ * }
+ * @endcode
+ */
+class chunked_pack {
+ public:
+  /**
+   * @brief Construct a `chunked_pack` class.
+   *
+   * @param input source `table_view` to pack
+   * @param user_buffer_size buffer size (in bytes) that will be passed on `next`. Must be
+   *                         at least 1MB
+   * @param temp_mr An optional memory resource to be used for temporary and scratch allocations
+   * only
+   */
+  explicit chunked_pack(
+    cudf::table_view const& input,
+    std::size_t user_buffer_size,
+    rmm::mr::device_memory_resource* temp_mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Destructor that will be implemented as default. Declared with definition here because
+   * contiguous_split_state is incomplete at this stage.
+   */
+  ~chunked_pack();
+
+  /**
+   * @brief Obtain the total size of the contiguously packed `table_view`.
+   *
+   * @return total size (in bytes) of all the chunks
+   */
+  [[nodiscard]] std::size_t get_total_contiguous_size() const;
+
+  /**
+   * @brief Function to check if there are chunks left to be copied.
+   *
+   * @return true if there are chunks left to be copied, and false otherwise
+   */
+  [[nodiscard]] bool has_next() const;
+
+  /**
+   * @brief Packs the next chunk into `user_buffer`. This should be called as long as
+   * `has_next` returns true. If `next` is called when `has_next` is false, an exception
+   * is thrown.
+   *
+   * @throws cudf::logic_error If the size of `user_buffer` is different than `user_buffer_size`
+   * @throws cudf::logic_error If called after all chunks have been copied
+   *
+   * @param user_buffer device span target for the chunk. The size of this span must equal
+   *                    the `user_buffer_size` parameter passed at construction
+   * @return The number of bytes that were written to `user_buffer` (at most
+   *          `user_buffer_size`)
+   */
+  [[nodiscard]] std::size_t next(cudf::device_span<uint8_t> const& user_buffer);
+
+  /**
+   * @brief Build the opaque metadata for all added columns.
+   *
+   * @return A vector containing the serialized column metadata
+   */
+  [[nodiscard]] std::unique_ptr<std::vector<uint8_t>> build_metadata() const;
+
+  /**
+   * @brief Creates a `chunked_pack` instance to perform a "pack" of the `table_view`
+   * "input", where a buffer of `user_buffer_size` is filled with chunks of the
+   * overall operation. This operation can be used in cases where GPU memory is constrained.
+   *
+   * The memory resource (`temp_mr`) could be a special memory resource to be used in
+   * situations when GPU memory is low and we want scratch and temporary allocations to
+   * happen from a small reserved pool of memory. Note that it defaults to the regular cuDF
+   * per-device resource.
+   *
+   * @throws cudf::logic_error When user_buffer_size is less than 1MB
+   *
+   * @param input source `table_view` to pack
+   * @param user_buffer_size buffer size (in bytes) that will be passed on `next`. Must be
+   *                         at least 1MB
+   * @param temp_mr RMM memory resource to be used for temporary and scratch allocations only
+   * @return a unique_ptr of chunked_pack
+   */
+  [[nodiscard]] static std::unique_ptr<chunked_pack> create(
+    cudf::table_view const& input,
+    std::size_t user_buffer_size,
+    rmm::mr::device_memory_resource* temp_mr = rmm::mr::get_current_device_resource());
+
+ private:
+  // internal state of contiguous split
+  std::unique_ptr<detail::contiguous_split_state> state;
+};
+
+/**
+ * @brief Deep-copy a `table_view` into a serialized contiguous memory format.
+ *
+ * The metadata from the `table_view` is copied into a host vector of bytes and the data from the
+ * `table_view` is copied into a `device_buffer`. Pass the output of this function into
+ * `cudf::unpack` to deserialize.
+ *
+ * @param input View of the table to pack
+ * @param mr An optional memory resource to use for all returned device allocations
+ * @return packed_columns A struct containing the serialized metadata and data in contiguous host
+ *         and device memory respectively
+ */
+packed_columns pack(cudf::table_view const& input,
+                    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Produce the metadata used for packing a table stored in a contiguous buffer.
+ *
+ * The metadata from the `table_view` is copied into a host vector of bytes which can be used to
+ * construct a `packed_columns` or `packed_table` structure. The caller is responsible for
+ * guaranteeing that all of the columns in the table point into `contiguous_buffer`.
+ *
+ * @param table View of the table to pack
+ * @param contiguous_buffer A contiguous buffer of device memory which contains the data referenced
+ *        by the columns in `table`
+ * @param buffer_size The size of `contiguous_buffer`
+ * @return Vector of bytes representing the metadata used to `unpack` a packed_columns struct
+ */
+std::vector<uint8_t> pack_metadata(table_view const& table,
+                                   uint8_t const* contiguous_buffer,
+                                   size_t buffer_size);
+
+/**
+ * @brief Deserialize the result of `cudf::pack`.
+ *
+ * Converts the result of a serialized table into a `table_view` that points to the data stored in
+ * the contiguous device buffer contained in `input`.
+ *
+ * It is the caller's responsibility to ensure that the `table_view` in the output does not outlive
+ * the data in the input.
+ *
+ * No new device memory is allocated in this function.
+ *
+ * @param input The packed columns to unpack
+ * @return The unpacked `table_view`
+ */
+table_view unpack(packed_columns const& input);
+
+/**
+ * @brief Deserialize the result of `cudf::pack`.
+ *
+ * Converts the result of a serialized table into a `table_view` that points to the data stored in
+ * the contiguous device buffer contained in `gpu_data` using the metadata contained in the host
+ * buffer `metadata`.
+ *
+ * It is the caller's responsibility to ensure that the `table_view` in the output does not outlive
+ * the data in the input.
+ *
+ * No new device memory is allocated in this function.
+ *
+ * @param metadata The host-side metadata buffer resulting from the initial pack() call
+ * @param gpu_data The device-side contiguous buffer storing the data that will be referenced by
+ *        the resulting `table_view`
+ * @return The unpacked `table_view`
+ */
+table_view unpack(uint8_t const* metadata, uint8_t const* gpu_data);
+
+/** @} */
+}  // namespace cudf
diff --git a/cpp/include/cudf/copying.hpp b/cpp/include/cudf/copying.hpp
new file mode 100644
index 0000000..6368047
--- /dev/null
+++ b/cpp/include/cudf/copying.hpp
@@ -0,0 +1,958 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+
+/**
+ * @addtogroup column_copy
+ * @{
+ * @file
+ * @brief Column APIs for gather, scatter, split, slice, etc.
+ */
+
+/**
+ * @brief Policy to account for possible out-of-bounds indices
+ *
+ * `NULLIFY` means to nullify output values corresponding to out-of-bounds gather_map values.
+ * `DONT_CHECK` means do not check whether the indices are out-of-bounds, for better performance.
+ */
+
+enum class out_of_bounds_policy : bool {
+  NULLIFY,    ///< Output values corresponding to out-of-bounds indices are null
+  DONT_CHECK  ///< No bounds checking is performed, better performance
+};
+
+/**
+ * @brief Gathers the specified rows (including null values) of a set of columns.
+ *
+ * @ingroup copy_gather
+ *
+ * Gathers the rows of the source columns according to `gather_map` such that row "i"
+ * in the resulting table's columns will contain row "gather_map[i]" from the source columns.
+ * The number of rows in the result table will be equal to the number of elements in
+ * `gather_map`.
+ *
+ * A negative value `i` in the `gather_map` is interpreted as `i+n`, where
+ * `n` is the number of rows in the `source_table`.
+ *
+ * For dictionary columns, the keys column component is copied and not trimmed
+ * if the gather results in abandoned key elements.
+ *
+ * @throws cudf::logic_error if gather_map contains null values.
+ *
+ * @param source_table The input columns whose rows will be gathered
+ * @param gather_map View into a non-nullable column of integral indices that maps the
+ * rows in the source columns to rows in the destination columns.
+ * @param bounds_policy Policy to apply to account for possible out-of-bounds indices
+ * `DONT_CHECK` skips all bounds checking for gather map values. `NULLIFY` coerces rows that
+ * corresponds to out-of-bounds indices in the gather map to be null elements. Callers should
+ * use `DONT_CHECK` when they are certain that the gather_map contains only valid indices for
+ * better performance. If `policy` is set to `DONT_CHECK` and there are out-of-bounds indices
+ * in the gather map, the behavior is undefined. Defaults to `DONT_CHECK`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Result of the gather
+ */
+std::unique_ptr<table> gather(
+  table_view const& source_table,
+  column_view const& gather_map,
+  out_of_bounds_policy bounds_policy  = out_of_bounds_policy::DONT_CHECK,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Reverses the rows within a table.
+ *
+ * Creates a new table that is the reverse of @p source_table.
+ * Example:
+ * ```
+ * source = [[4,5,6], [7,8,9], [10,11,12]]
+ * return = [[6,5,4], [9,8,7], [12,11,10]]
+ * ```
+ *
+ * @param source_table Table that will be reversed
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Reversed table
+ */
+std::unique_ptr<table> reverse(
+  table_view const& source_table,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Reverses the elements of a column
+ *
+ * Creates a new column that is the reverse of @p source_column.
+ * Example:
+ * ```
+ * source = [4,5,6]
+ * return = [6,5,4]
+ * ```
+ *
+ * @param source_column Column that will be reversed
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Reversed column
+ */
+std::unique_ptr<column> reverse(
+  column_view const& source_column,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Scatters the rows of the source table into a copy of the target table
+ * according to a scatter map.
+ *
+ * @ingroup copy_scatter
+ *
+ * Scatters values from the source table into the target table out-of-place,
+ * returning a "destination table". The scatter is performed according to a
+ * scatter map such that row `scatter_map[i]` of the destination table gets row
+ * `i` of the source table. All other rows of the destination table equal
+ * corresponding rows of the target table.
+ *
+ * The number of columns in source must match the number of columns in target
+ * and their corresponding datatypes must be the same.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ *
+ * If any values in `scatter_map` are outside of the interval [-n, n) where `n`
+ * is the number of rows in the `target` table, behavior is undefined.
+ *
+ * A negative value `i` in the `scatter_map` is interpreted as `i+n`, where `n`
+ * is the number of rows in the `target` table.
+ *
+ * @param source The input columns containing values to be scattered into the
+ * target columns
+ * @param scatter_map A non-nullable column of integral indices that maps the
+ * rows in the source table to rows in the target table. The size must be equal
+ * to or less than the number of elements in the source columns.
+ * @param target The set of columns into which values from the source_table
+ * are to be scattered
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Result of scattering values from source to target
+ */
+std::unique_ptr<table> scatter(
+  table_view const& source,
+  column_view const& scatter_map,
+  table_view const& target,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Scatters a row of scalar values into a copy of the target table
+ * according to a scatter map.
+ *
+ * @ingroup copy_scatter
+ *
+ * Scatters values from the source row into the target table out-of-place,
+ * returning a "destination table". The scatter is performed according to a
+ * scatter map such that row `scatter_map[i]` of the destination table is
+ * replaced by the source row. All other rows of the destination table equal
+ * corresponding rows of the target table.
+ *
+ * The number of elements in source must match the number of columns in target
+ * and their corresponding datatypes must be the same.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ *
+ * If any values in `scatter_map` are outside of the interval [-n, n) where `n`
+ * is the number of rows in the `target` table, behavior is undefined.
+ *
+ * @param source The input scalars containing values to be scattered into the
+ * target columns
+ * @param indices A non-nullable column of integral indices that indicate
+ * the rows in the target table to be replaced by source.
+ * @param target The set of columns into which values from the source_table
+ * are to be scattered
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Result of scattering values from source to target
+ */
+std::unique_ptr<table> scatter(
+  std::vector<std::reference_wrapper<scalar const>> const& source,
+  column_view const& indices,
+  table_view const& target,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Indicates when to allocate a mask, based on an existing mask.
+ */
+enum class mask_allocation_policy {
+  NEVER,   ///< Do not allocate a null mask, regardless of input
+  RETAIN,  ///< Allocate a null mask if the input contains one
+  ALWAYS   ///< Allocate a null mask, regardless of input
+};
+
+/**
+ * @brief Initializes and returns an empty column of the same type as the `input`.
+ *
+ * @param[in] input Immutable view of input column to emulate
+ * @return An empty column of same type as `input`
+ */
+std::unique_ptr<column> empty_like(column_view const& input);
+
+/**
+ * @brief Initializes and returns an empty column of the same type as the `input`.
+ *
+ * @param[in] input Scalar to emulate
+ * @return An empty column of same type as `input`
+ */
+std::unique_ptr<column> empty_like(scalar const& input);
+
+/**
+ * @brief Creates an uninitialized new column of the same size and type as the `input`.
+ *
+ * Supports only fixed-width types.
+ *
+ * If the `mask_alloc` allocates a validity mask that mask is also uninitialized
+ * and the validity bits and the null count should be set by the caller.
+ *
+ * @param input Immutable view of input column to emulate
+ * @param mask_alloc Optional, Policy for allocating null mask. Defaults to RETAIN
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return A column with sufficient uninitialized capacity to hold the same
+ * number of elements as `input` of the same type as `input.type()`
+ */
+std::unique_ptr<column> allocate_like(
+  column_view const& input,
+  mask_allocation_policy mask_alloc   = mask_allocation_policy::RETAIN,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates an uninitialized new column of the specified size and same type as the `input`.
+ *
+ * Supports only fixed-width types.
+ *
+ * If the `mask_alloc` allocates a validity mask that mask is also uninitialized
+ * and the validity bits and the null count should be set by the caller.
+ *
+ * @param input Immutable view of input column to emulate
+ * @param size The desired number of elements that the new column should have capacity for
+ * @param mask_alloc Optional, Policy for allocating null mask. Defaults to RETAIN
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A column with sufficient uninitialized capacity to hold the specified number of elements
+ * as `input` of the same type as `input.type()`
+ */
+std::unique_ptr<column> allocate_like(
+  column_view const& input,
+  size_type size,
+  mask_allocation_policy mask_alloc   = mask_allocation_policy::RETAIN,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a table of empty columns with the same types as the `input_table`
+ *
+ * Creates the `cudf::column` objects, but does not allocate any underlying device
+ * memory for the column's data or bitmask.
+ *
+ * @param[in] input_table Immutable view of input table to emulate
+ * @return A table of empty columns with the same types as the columns in
+ * `input_table`
+ */
+std::unique_ptr<table> empty_like(table_view const& input_table);
+
+/**
+ * @brief Copies a range of elements in-place from one column to another.
+ *
+ * Overwrites the range of elements in @p target indicated by the indices
+ * [@p target_begin, @p target_begin + N) with the elements from @p source
+ * indicated by the indices [@p source_begin, @p source_end) (where N =
+ * (@p source_end - @p source_begin)). Use the out-of-place copy function
+ * returning std::unique_ptr<column> for uses cases requiring memory
+ * reallocation. For example for strings columns and other variable-width types.
+ *
+ * If @p source and @p target refer to the same elements and the ranges overlap,
+ * the behavior is undefined.
+ *
+ * @throws cudf::logic_error if memory reallocation is required (e.g. for
+ * variable width types).
+ * @throws cudf::logic_error for invalid range (if
+ * @p source_begin > @p source_end, @p source_begin < 0,
+ * @p source_begin >= @p source.size(), @p source_end > @p source.size(),
+ * @p target_begin < 0, target_begin >= @p target.size(), or
+ * @p target_begin + (@p source_end - @p source_begin) > @p target.size()).
+ * @throws cudf::logic_error if @p target and @p source have different types.
+ * @throws cudf::logic_error if @p source has null values and @p target is not
+ * nullable.
+ *
+ * @param source The column to copy from
+ * @param target The preallocated column to copy into
+ * @param source_begin The starting index of the source range (inclusive)
+ * @param source_end The index of the last element in the source range
+ * (exclusive)
+ * @param target_begin The starting index of the target range (inclusive)
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void copy_range_in_place(column_view const& source,
+                         mutable_column_view& target,
+                         size_type source_begin,
+                         size_type source_end,
+                         size_type target_begin,
+                         rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Copies a range of elements out-of-place from one column to another.
+ *
+ * Creates a new column as if an in-place copy was performed into @p target.
+ * A copy of @p target is created first and then the elements indicated by the
+ * indices [@p target_begin, @p target_begin + N) were copied from the elements
+ * indicated by the indices [@p source_begin, @p source_end) of @p source
+ * (where N = (@p source_end - @p source_begin)). Elements outside the range are
+ * copied from @p target into the returned new column target.
+ *
+ * If @p source and @p target refer to the same elements and the ranges overlap,
+ * the behavior is undefined.
+ *
+ * @throws cudf::logic_error for invalid range (if
+ * @p source_begin > @p source_end, @p source_begin < 0,
+ * @p source_begin >= @p source.size(), @p source_end > @p source.size(),
+ * @p target_begin < 0, target_begin >= @p target.size(), or
+ * @p target_begin + (@p source_end - @p source_begin) > @p target.size()).
+ * @throws cudf::logic_error if @p target and @p source have different types.
+ *
+ * @param source The column to copy from inside the range
+ * @param target The column to copy from outside the range
+ * @param source_begin The starting index of the source range (inclusive)
+ * @param source_end The index of the last element in the source range
+ * (exclusive)
+ * @param target_begin The starting index of the target range (inclusive)
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return The result target column
+ */
+std::unique_ptr<column> copy_range(
+  column_view const& source,
+  column_view const& target,
+  size_type source_begin,
+  size_type source_end,
+  size_type target_begin,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a new column by shifting all values by an offset.
+ *
+ * @ingroup copy_shift
+ *
+ * Elements will be determined by `output[idx] = input[idx - offset]`.
+ * Some elements in the output may be indeterminable from the input. For those
+ * elements, the value will be determined by `fill_values`.
+ *
+ * @code{.pseudo}
+ * Examples
+ * -------------------------------------------------
+ * input       = [0, 1, 2, 3, 4]
+ * offset      = 3
+ * fill_values = @
+ * return      = [@, @, @, 0, 1]
+ * -------------------------------------------------
+ * input       = [5, 4, 3, 2, 1]
+ * offset      = -2
+ * fill_values = 7
+ * return      = [3, 2, 1, 7, 7]
+ * @endcode
+ *
+ * @note if the input is nullable, the output will be nullable.
+ * @note if the fill value is null, the output will be nullable.
+ *
+ * @param input      Column to be shifted
+ * @param offset     The offset by which to shift the input
+ * @param fill_value Fill value for indeterminable outputs
+ * @param stream     CUDA stream used for device memory operations and kernel launches
+ * @param mr         Device memory resource used to allocate the returned result's device memory
+ *
+ * @throw cudf::logic_error if @p input dtype is neither fixed-width nor string type
+ * @throw cudf::logic_error if @p fill_value dtype does not match @p input dtype.
+ *
+ * @return The shifted column
+ */
+std::unique_ptr<column> shift(
+  column_view const& input,
+  size_type offset,
+  scalar const& fill_value,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Slices a `column_view` into a set of `column_view`s according to a set of indices.
+ *
+ * @ingroup copy_slice
+ *
+ * The returned views of `input` are constructed from an even number indices where
+ * the `i`th returned `column_view` views the elements in `input` indicated by the range
+ * `[indices[2*i], indices[(2*i)+1])`.
+ *
+ * For all `i` it is expected `indices[i] <= input.size()`
+ * For all `i%2==0`, it is expected that `indices[i] <= indices[i+1]`
+ *
+ * @note It is the caller's responsibility to ensure that the returned views
+ * do not outlive the viewed device memory.
+ *
+ * @code{.pseudo}
+ * input:   {10, 12, 14, 16, 18, 20, 22, 24, 26, 28}
+ * indices: {1, 3, 5, 9, 2, 4, 8, 8}
+ * output:  {{12, 14}, {20, 22, 24, 26}, {14, 16}, {}}
+ * @endcode
+ *
+ * @throws cudf::logic_error if `indices` size is not even.
+ * @throws cudf::logic_error When the values in the pair are strictly decreasing.
+ * @throws cudf::logic_error When any of the values in the pair don't belong to
+ * the range [0, input.size()).
+ *
+ * @param input View of column to slice
+ * @param indices Indices used to take slices of `input`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Vector of views of `input` indicated by the ranges in `indices`
+ */
+std::vector<column_view> slice(column_view const& input,
+                               host_span<size_type const> indices,
+                               rmm::cuda_stream_view stream = cudf::get_default_stream());
+/**
+ * @ingroup copy_slice
+ * @copydoc cudf::slice(column_view const&, host_span<size_type const>, rmm::cuda_stream_view)
+ */
+std::vector<column_view> slice(column_view const& input,
+                               std::initializer_list<size_type> indices,
+                               rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Slices a `table_view` into a set of `table_view`s according to a set of indices.
+ *
+ * @ingroup copy_slice
+ *
+ * The returned views of `input` are constructed from an even number indices where
+ * the `i`th returned `table_view` views the elements in `input` indicated by the range
+ * `[indices[2*i], indices[(2*i)+1])`.
+ *
+ * For all `i` it is expected `indices[i] <= input.size()`
+ * For all `i%2==0`, it is expected that `indices[i] <= indices[i+1]`
+ *
+ * @note It is the caller's responsibility to ensure that the returned views
+ * do not outlive the viewed device memory.
+ *
+ * @code{.pseudo}
+ * input:   [{10, 12, 14, 16, 18, 20, 22, 24, 26, 28},
+ *           {50, 52, 54, 56, 58, 60, 62, 64, 66, 68}]
+ * indices: {1, 3, 5, 9, 2, 4, 8, 8}
+ * output:  [{{12, 14}, {20, 22, 24, 26}, {14, 16}, {}},
+ *           {{52, 54}, {60, 22, 24, 26}, {14, 16}, {}}]
+ * @endcode
+ *
+ * @throws cudf::logic_error if `indices` size is not even.
+ * @throws cudf::logic_error When the values in the pair are strictly decreasing.
+ * @throws cudf::logic_error When any of the values in the pair don't belong to
+ * the range [0, input.size()).
+ *
+ * @param input View of table to slice
+ * @param indices Indices used to take slices of `input`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Vector of views of `input` indicated by the ranges in `indices`
+ */
+std::vector<table_view> slice(table_view const& input,
+                              host_span<size_type const> indices,
+                              rmm::cuda_stream_view stream = cudf::get_default_stream());
+/**
+ * @ingroup copy_slice
+ * @copydoc cudf::slice(table_view const&, host_span<size_type const>, rmm::cuda_stream_view stream)
+ */
+std::vector<table_view> slice(table_view const& input,
+                              std::initializer_list<size_type> indices,
+                              rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Splits a `column_view` into a set of `column_view`s according to a set of indices
+ * derived from expected splits.
+ *
+ * @ingroup copy_split
+ *
+ * The returned view's of `input` are constructed from vector of splits, which indicates
+ * where the split should occur. The `i`th returned `column_view` is sliced as
+ * `[0, splits[i])` if `i`=0, else `[splits[i], input.size())` if `i` is the last view and
+ * `[splits[i-1], splits[i]]` otherwise.
+ *
+ * For all `i` it is expected `splits[i] <= splits[i+1] <= input.size()`
+ * For a `splits` size N, there will always be N+1 splits in the output
+ *
+ * @note It is the caller's responsibility to ensure that the returned views
+ * do not outlive the viewed device memory.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input:   {10, 12, 14, 16, 18, 20, 22, 24, 26, 28}
+ * splits:  {2, 5, 9}
+ * output:  {{10, 12}, {14, 16, 18}, {20, 22, 24, 26}, {28}}
+ * @endcode
+ *
+ * @throws cudf::logic_error if `splits` has end index > size of `input`.
+ * @throws cudf::logic_error When the value in `splits` is not in the range [0, input.size()).
+ * @throws cudf::logic_error When the values in the `splits` are 'strictly decreasing'.
+ *
+ * @param input View of column to split
+ * @param splits Indices where the view will be split
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return The set of requested views of `input` indicated by the `splits`
+ */
+std::vector<column_view> split(column_view const& input,
+                               host_span<size_type const> splits,
+                               rmm::cuda_stream_view stream = cudf::get_default_stream());
+/**
+ * @ingroup copy_split
+ * @copydoc cudf::split(column_view const&, host_span<size_type const>, rmm::cuda_stream_view)
+ */
+std::vector<column_view> split(column_view const& input,
+                               std::initializer_list<size_type> splits,
+                               rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Splits a `table_view` into a set of `table_view`s according to a set of indices
+ * derived from expected splits.
+ *
+ * @ingroup copy_split
+ *
+ * The returned views of `input` are constructed from vector of splits, which indicates
+ * where the split should occur. The `i`th returned `table_view` is sliced as
+ * `[0, splits[i])` if `i`=0, else `[splits[i], input.size())` if `i` is the last view and
+ * `[splits[i-1], splits[i]]` otherwise.
+ *
+ * For all `i` it is expected `splits[i] <= splits[i+1] <= input.size()`
+ * For a `splits` size N, there will always be N+1 splits in the output
+ *
+ * @note It is the caller's responsibility to ensure that the returned views
+ * do not outlive the viewed device memory.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input:   [{10, 12, 14, 16, 18, 20, 22, 24, 26, 28},
+ *           {50, 52, 54, 56, 58, 60, 62, 64, 66, 68}]
+ * splits:  {2, 5, 9}
+ * output:  [{{10, 12}, {14, 16, 18}, {20, 22, 24, 26}, {28}},
+ *           {{50, 52}, {54, 56, 58}, {60, 62, 64, 66}, {68}}]
+ * @endcode
+ *
+ * @throws cudf::logic_error if `splits` has end index > size of `input`.
+ * @throws cudf::logic_error When the value in `splits` is not in the range [0, input.size()).
+ * @throws cudf::logic_error When the values in the `splits` are 'strictly decreasing'.
+ *
+ * @param input View of a table to split
+ * @param splits Indices where the view will be split
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return The set of requested views of `input` indicated by the `splits`
+ */
+std::vector<table_view> split(table_view const& input,
+                              host_span<size_type const> splits,
+                              rmm::cuda_stream_view stream = cudf::get_default_stream());
+/**
+ * @ingroup copy_split
+ * @copydoc cudf::split(table_view const&, host_span<size_type const>, rmm::cuda_stream_view)
+ */
+std::vector<table_view> split(table_view const& input,
+                              std::initializer_list<size_type> splits,
+                              rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief   Returns a new column, where each element is selected from either @p lhs or
+ *          @p rhs based on the value of the corresponding element in @p boolean_mask
+ *
+ * Selects each element i in the output column from either @p rhs or @p lhs using the following
+ * rule: `output[i] = (boolean_mask.valid(i) and boolean_mask[i]) ? lhs[i] : rhs[i]`
+ *
+ * @throws cudf::logic_error if lhs and rhs are not of the same type
+ * @throws cudf::logic_error if lhs and rhs are not of the same length
+ * @throws cudf::logic_error if boolean mask is not of type bool
+ * @throws cudf::logic_error if boolean mask is not of the same length as lhs and rhs
+ * @param lhs left-hand column_view
+ * @param rhs right-hand column_view
+ * @param boolean_mask column of `type_id::BOOL8` representing "left (true) / right (false)"
+ * boolean for each element. Null element represents false.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns new column with the selected elements
+ */
+std::unique_ptr<column> copy_if_else(
+  column_view const& lhs,
+  column_view const& rhs,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief   Returns a new column, where each element is selected from either @p lhs or
+ *          @p rhs based on the value of the corresponding element in @p boolean_mask
+ *
+ * Selects each element i in the output column from either @p rhs or @p lhs using the following
+ * rule: `output[i] = (boolean_mask.valid(i) and boolean_mask[i]) ? lhs : rhs[i]`
+ *
+ * @throws cudf::logic_error if lhs and rhs are not of the same type
+ * @throws cudf::logic_error if boolean mask is not of type bool
+ * @throws cudf::logic_error if boolean mask is not of the same length as rhs
+ * @param lhs left-hand scalar
+ * @param rhs right-hand column_view
+ * @param boolean_mask column of `type_id::BOOL8` representing "left (true) / right (false)"
+ * boolean for each element. Null element represents false.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns new column with the selected elements
+ */
+std::unique_ptr<column> copy_if_else(
+  scalar const& lhs,
+  column_view const& rhs,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief   Returns a new column, where each element is selected from either @p lhs or
+ *          @p rhs based on the value of the corresponding element in @p boolean_mask
+ *
+ * Selects each element i in the output column from either @p rhs or @p lhs using the following
+ * rule: `output[i] = (boolean_mask.valid(i) and boolean_mask[i]) ? lhs[i] : rhs`
+ *
+ * @throws cudf::logic_error if lhs and rhs are not of the same type
+ * @throws cudf::logic_error if boolean mask is not of type bool
+ * @throws cudf::logic_error if boolean mask is not of the same length as lhs
+ * @param lhs left-hand column_view
+ * @param rhs right-hand scalar
+ * @param boolean_mask column of `type_id::BOOL8` representing "left (true) / right (false)"
+ * boolean for each element. Null element represents false.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns new column with the selected elements
+ */
+std::unique_ptr<column> copy_if_else(
+  column_view const& lhs,
+  scalar const& rhs,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief   Returns a new column, where each element is selected from either @p lhs or
+ *          @p rhs based on the value of the corresponding element in @p boolean_mask
+ *
+ * Selects each element i in the output column from either @p rhs or @p lhs using the following
+ * rule: `output[i] = (boolean_mask.valid(i) and boolean_mask[i]) ? lhs : rhs`
+ *
+ * @throws cudf::logic_error if boolean mask is not of type bool
+ * @param lhs left-hand scalar
+ * @param rhs right-hand scalar
+ * @param boolean_mask column of `type_id::BOOL8` representing "left (true) / right (false)"
+ * boolean for each element. null element represents false.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns new column with the selected elements
+ */
+std::unique_ptr<column> copy_if_else(
+  scalar const& lhs,
+  scalar const& rhs,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Scatters rows from the input table to rows of the output corresponding
+ * to true values in a boolean mask.
+ *
+ * @ingroup copy_scatter
+ *
+ * The `i`th row of `input` will be written to the output table at the location
+ * of the `i`th true value in `boolean_mask`. All other rows in the output will
+ * equal the same row in `target`.
+ *
+ * `boolean_mask` should have number of `true`s <= number of rows in `input`.
+ * If boolean mask is `true`, corresponding value in target is updated with
+ * value from corresponding `input` column, else it is left untouched.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input: {{1, 5, 6, 8, 9}}
+ * boolean_mask: {true, false, false, false, true, true, false, true, true, false}
+ * target:       {{   2,     2,     3,     4,    4,     7,    7,    7,    8,    10}}
+ *
+ * output:       {{   1,     2,     3,     4,    5,     6,    7,    8,    9,    10}}
+ * @endcode
+ *
+ * @throw  cudf::logic_error if input.num_columns() != target.num_columns()
+ * @throws cudf::logic_error if any `i`th input_column type != `i`th target_column type
+ * @throws cudf::logic_error if boolean_mask.type() != bool
+ * @throws cudf::logic_error if boolean_mask.size() != target.num_rows()
+ * @throws cudf::logic_error if number of `true` in `boolean_mask` > input.num_rows()
+ *
+ * @param input table_view (set of dense columns) to scatter
+ * @param target table_view to modify with scattered values from `input`
+ * @param boolean_mask column_view which acts as boolean mask
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate device memory of the returned table
+ *
+ * @returns Returns a table by scattering `input` into `target` as per `boolean_mask`
+ */
+std::unique_ptr<table> boolean_mask_scatter(
+  table_view const& input,
+  table_view const& target,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Scatters scalar values to rows of the output corresponding
+ * to true values in a boolean mask.
+ *
+ * @ingroup copy_scatter
+ *
+ * The `i`th scalar in `input` will be written to all columns of the output
+ * table at the location of the `i`th true value in `boolean_mask`.
+ * All other rows in the output will equal the same row in `target`.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input: {11}
+ * boolean_mask: {true, false, false, false, true, true, false, true, true, false}
+ * target:      {{   2,     2,     3,     4,    4,     7,    7,    7,    8,    10}}
+ *
+ * output:       {{   11,    2,     3,     4,   11,    11,    7,   11,   11,    10}}
+ * @endcode
+ *
+ * @throw  cudf::logic_error if input.size() != target.num_columns()
+ * @throws cudf::logic_error if any `i`th input_scalar type != `i`th target_column type
+ * @throws cudf::logic_error if boolean_mask.type() != bool
+ * @throws cudf::logic_error if boolean_mask.size() != target.size()
+ *
+ * @param input scalars to scatter
+ * @param target table_view to modify with scattered values from `input`
+ * @param boolean_mask column_view which acts as boolean mask
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate device memory of the returned table
+ *
+ * @returns Returns a table by scattering `input` into `target` as per `boolean_mask`
+ */
+std::unique_ptr<table> boolean_mask_scatter(
+  std::vector<std::reference_wrapper<scalar const>> const& input,
+  table_view const& target,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Get the element at specified index from a column
+ *
+ * @warning This function is expensive (invokes a kernel launch). So, it is not
+ * recommended to be used in performance sensitive code or inside a loop.
+ *
+ * @throws cudf::logic_error if `index` is not within the range `[0, input.size())`
+ *
+ * @param input Column view to get the element from
+ * @param index Index into `input` to get the element at
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Scalar containing the single value
+ */
+std::unique_ptr<scalar> get_element(
+  column_view const& input,
+  size_type index,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Indicates whether a row can be sampled more than once.
+ */
+enum class sample_with_replacement : bool {
+  FALSE,  ///< A row can be sampled only once
+  TRUE    ///< A row can be sampled more than once
+};
+
+/**
+ * @brief Gather `n` samples from given `input` randomly
+ *
+ * @code{.pseudo}
+ * Example:
+ * input: {col1: {1, 2, 3, 4, 5}, col2: {6, 7, 8, 9, 10}}
+ * n: 3
+ * replacement: false
+ *
+ * output:       {col1: {3, 1, 4}, col2: {8, 6, 9}}
+ *
+ * replacement: true
+ *
+ * output:       {col1: {3, 1, 1}, col2: {8, 6, 6}}
+ * @endcode
+ *
+ * @throws cudf::logic_error if `n` > `input.num_rows()` and `replacement` == FALSE.
+ * @throws cudf::logic_error if `n` < 0.
+ *
+ * @param input View of a table to sample
+ * @param n non-negative number of samples expected from `input`
+ * @param replacement Allow or disallow sampling of the same row more than once
+ * @param seed Seed value to initiate random number generator
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @return Table containing samples from `input`
+ */
+std::unique_ptr<table> sample(
+  table_view const& input,
+  size_type const n,
+  sample_with_replacement replacement = sample_with_replacement::FALSE,
+  int64_t const seed                  = 0,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Checks if a column or its descendants have non-empty null rows
+ *
+ * @note This function is exact. If it returns `true`, there exists one or more
+ * non-empty null elements.
+ *
+ * A LIST or STRING column might have non-empty rows that are marked as null.
+ * A STRUCT OR LIST column might have child columns that have non-empty null rows.
+ * Other types of columns are deemed incapable of having non-empty null rows.
+ * E.g. Fixed width columns have no concept of an "empty" row.
+ *
+ * @param input The column which is (and whose descendants are) to be checked for
+ * non-empty null rows.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return true If either the column or its descendants have non-empty null rows
+ * @return false If neither the column or its descendants have non-empty null rows
+ */
+bool has_nonempty_nulls(column_view const& input,
+                        rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Approximates if a column or its descendants *may* have non-empty null elements
+ *
+ * @note This function is approximate.
+ * - `true`: Non-empty null elements could exist
+ * - `false`: Non-empty null elements definitely do not exist
+ *
+ * False positives are possible, but false negatives are not.
+ *
+ * Compared to the exact `has_nonempty_nulls()` function, this function is typically
+ * more efficient.
+ *
+ * Complexity:
+ * - Best case: `O(count_descendants(input))`
+ * - Worst case: `O(count_descendants(input)) * m`, where `m` is the number of rows in the largest
+ * descendant
+ *
+ * @param input The column which is (and whose descendants are) to be checked for
+ * non-empty null rows
+ * @return true If either the column or its descendants have null rows
+ * @return false If neither the column nor its descendants have null rows
+ */
+bool may_have_nonempty_nulls(column_view const& input);
+
+/**
+ * @brief Copy `input` into output while purging any non-empty null rows in the column or its
+ * descendants.
+ *
+ * If the input column is not of compound type (LIST/STRING/STRUCT/DICTIONARY), the output will be
+ * the same as input.
+ *
+ * The purge operation only applies directly to LIST and STRING columns, but it applies indirectly
+ * to STRUCT/DICTIONARY columns as well, since these columns may have child columns that
+ * are LIST or STRING.
+ *
+ * Examples:
+ *
+ * @code{.pseudo}
+ * auto const lists   = lists_column_wrapper<int32_t>{ {0,1}, {2,3}, {4,5} }.release();
+ * cudf::detail::set_null_mask(lists->null_mask(), 1, 2, false);
+ *
+ * lists[1] is now null, but the lists child column still stores `{2,3}`.
+ * The lists column contents will be:
+ *   Validity: 101
+ *   Offsets:  [0, 2, 4, 6]
+ *   Child:    [0, 1, 2, 3, 4, 5]
+ *
+ * After purging the contents of the list's null rows, the column's contents will be:
+ *   Validity: 101
+ *   Offsets:  [0, 2, 2, 4]
+ *   Child:    [0, 1, 4, 5]
+ * @endcode
+ *
+ * @code{.pseudo}
+ * auto const strings = strings_column_wrapper{ "AB", "CD", "EF" }.release();
+ * cudf::detail::set_null_mask(strings->null_mask(), 1, 2, false);
+ *
+ * strings[1] is now null, but the strings column still stores `"CD"`.
+ * The lists column contents will be:
+ *   Validity: 101
+ *   Offsets:  [0, 2, 4, 6]
+ *   Child:    [A, B, C, D, E, F]
+ *
+ * After purging the contents of the list's null rows, the column's contents
+ * will be:
+ *   Validity: 101
+ *   Offsets:  [0, 2, 2, 4]
+ *   Child:    [A, B, E, F]
+ * @endcode
+ *
+ * @code{.pseudo}
+ * auto const lists   = lists_column_wrapper<int32_t>{ {0,1}, {2,3}, {4,5} };
+ * auto const structs = structs_column_wrapper{ {lists}, null_at(1) };
+ *
+ * structs[1].child is now null, but the lists column still stores `{2,3}`.
+ * The lists column contents will be:
+ *   Validity: 101
+ *   Offsets:  [0, 2, 4, 6]
+ *   Child:    [0, 1, 2, 3, 4, 5]
+ *
+ * After purging the contents of the list's null rows, the column's contents
+ * will be:
+ *   Validity: 101
+ *   Offsets:  [0, 2, 2, 4]
+ *   Child:    [0, 1, 4, 5]
+ * @endcode
+ *
+ * @param input The column whose null rows are to be checked and purged
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A new column with equivalent contents to `input`, but with null rows purged
+ */
+std::unique_ptr<column> purge_nonempty_nulls(
+  column_view const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */
+}  // namespace cudf
diff --git a/cpp/include/cudf/datetime.hpp b/cpp/include/cudf/datetime.hpp
new file mode 100644
index 0000000..44736ca
--- /dev/null
+++ b/cpp/include/cudf/datetime.hpp
@@ -0,0 +1,403 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+/**
+ * @file datetime.hpp
+ * @brief DateTime column APIs.
+ */
+
+namespace cudf {
+namespace datetime {
+/**
+ * @addtogroup datetime_extract
+ * @{
+ * @file
+ */
+
+/**
+ * @brief  Extracts year from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t years
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_year(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts month from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t months
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_month(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts day from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t days
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_day(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts a weekday from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t days
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_weekday(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts hour from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t hours
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_hour(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts minute from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t minutes
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_minute(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts second from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t seconds
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_second(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts millisecond fraction from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * A millisecond fraction is only the 3 digits that make up the millisecond portion of a duration.
+ * For example, the millisecond fraction of 1.234567890 seconds is 234.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t milliseconds
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_millisecond_fraction(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts microsecond fraction from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * A microsecond fraction is only the 3 digits that make up the microsecond portion of a duration.
+ * For example, the microsecond fraction of 1.234567890 seconds is 567.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t microseconds
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_microsecond_fraction(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Extracts nanosecond fraction from any datetime type and returns an int16_t
+ * cudf::column.
+ *
+ * A nanosecond fraction is only the 3 digits that make up the nanosecond portion of a duration.
+ * For example, the nanosecond fraction of 1.234567890 seconds is 890.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of the extracted int16_t nanoseconds
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> extract_nanosecond_fraction(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+/**
+ * @addtogroup datetime_compute
+ * @{
+ * @file
+ */
+
+/**
+ * @brief  Computes the last day of the month in datetime type and returns a TIMESTAMP_DAYS
+ * cudf::column.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column containing last day of the month as TIMESTAMP_DAYS
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP
+ */
+std::unique_ptr<cudf::column> last_day_of_month(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Computes the day number since the start of the year from the datetime and
+ * returns an int16_t cudf::column. The value is between [1, {365-366}]
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of datatype INT16 containing the day number since the start of the year
+ * @throw cudf::logic_error if input column datatype is not a TIMESTAMP
+ */
+std::unique_ptr<cudf::column> day_of_year(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Adds or subtracts a number of months from the datetime type and returns a
+ * timestamp column that is of the same type as the input `timestamps` column.
+ *
+ * For a given row, if the `timestamps` or the `months` column value is null,
+ * the output for that row is null.
+ * This method preserves the input time and the day where applicable. The date is rounded
+ * down to the last day of the month for that year, if the new day is invalid for that month.
+ *
+ * @code{.pseudo}
+ * Example:
+ * timestamps = [5/31/20 08:00:00, 5/31/20 00:00:00, 5/31/20 13:00:00, 5/31/20 23:00:00,
+ *               6/30/20 00:00:01, 6/30/20 14:12:13]
+ * months     = [1               , -1              , -3              , -15             ,
+ *               -1              , 1]
+ * r = add_calendrical_months(timestamp_column, months_column)
+ * r is [6/30/20 08:00:00, 4/30/20 00:00:00, 2/29/20 13:00:00, 2/28/19 23:00:00,
+ *       5/30/20 00:00:01, 7/30/20 14:12:13]
+ * @endcode
+
+ * @throw cudf::logic_error if `timestamps` datatype is not a TIMESTAMP or if `months` datatype
+ * is not INT16 or INT32.
+ * @throw cudf::logic_error if `timestamps` column size is not equal to `months` column size.
+ *
+ * @param timestamps cudf::column_view of timestamp type
+ * @param months cudf::column_view of integer type containing the number of months to add
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of timestamp type containing the computed timestamps
+ */
+std::unique_ptr<cudf::column> add_calendrical_months(
+  cudf::column_view const& timestamps,
+  cudf::column_view const& months,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Adds or subtracts a number of months from the datetime type and returns a
+ * timestamp column that is of the same type as the input `timestamps` column.
+ *
+ * For a given row, if the `timestamps` value is null, the output for that row is null.
+ * A null months scalar would result in an all null column.
+ * This method preserves the input time and the day where applicable. The date is rounded
+ * down to the last day of the month for that year, if the new day is invalid for that month.
+ *
+ * @code{.pseudo}
+ * Example:
+ * timestamps = [5/31/20 08:00:00, 6/30/20 00:00:00, 7/31/20 13:00:00]
+ * months     = -3
+ * output is [2/29/20 08:00:00, 3/30/20 00:00:00, 4/30/20 13:00:00]
+ *
+ * timestamps = [4/28/20 04:00:00, 5/30/20 01:00:00, 6/30/20 21:00:00]
+ * months     = 1
+ * output is [5/28/20 04:00:00, 6/30/20 01:00:00, 7/30/20 21:00:00]
+ * @endcode
+ *
+ * @throw cudf::logic_error if `timestamps` datatype is not a TIMESTAMP or if `months` datatype
+ * is not INT16 or INT32.
+ * @throw cudf::logic_error if `timestamps` column size is not equal to `months` column size.
+ *
+ * @param timestamps cudf::column_view of timestamp type
+ * @param months cudf::scalar of integer type containing the number of months to add
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @return cudf::column of timestamp type containing the computed timestamps
+ */
+std::unique_ptr<cudf::column> add_calendrical_months(
+  cudf::column_view const& timestamps,
+  cudf::scalar const& months,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Check if the year of the given date is a leap year
+ *
+ * `output[i] == true` if year of `column[i]` is a leap year
+ * `output[i] == false` if year of `column[i]` is not a leap year
+ * `output[i] is null` if `column[i]` is null
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns cudf::column of datatype BOOL8 truth value of the corresponding date
+ * @throw cudf::logic_error if input column datatype is not a TIMESTAMP
+ */
+std::unique_ptr<cudf::column> is_leap_year(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Extract the number of days in the month
+ *
+ * output[i] contains the number of days in the month of date `column[i]`
+ * output[i] is null if `column[i]` is null
+ *
+ * @throw cudf::logic_error if input column datatype is not a TIMESTAMP
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ * @return cudf::column of datatype INT16 of days in month of the corresponding date
+ */
+std::unique_ptr<cudf::column> days_in_month(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Returns the quarter of the date
+ *
+ * `output[i]` will be a value from {1, 2, 3, 4} corresponding to the quarter of month given by
+ * `column[i]`. It will be null if the input row at `column[i]` is null.
+ *
+ * @throw cudf::logic_error if input column datatype is not a TIMESTAMP
+ *
+ * @param column The input column containing datetime values
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ * @return A column of INT16 type indicating which quarter the date is in
+ */
+std::unique_ptr<cudf::column> extract_quarter(
+  cudf::column_view const& column,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Fixed frequencies supported by datetime rounding functions ceil, floor, round.
+ *
+ */
+enum class rounding_frequency : int32_t {
+  DAY,
+  HOUR,
+  MINUTE,
+  SECOND,
+  MILLISECOND,
+  MICROSECOND,
+  NANOSECOND
+};
+
+/**
+ * @brief Round datetimes up to the nearest multiple of the given frequency.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param freq rounding_frequency indicating the frequency to round up to
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP.
+ * @return cudf::column of the same datetime resolution as the input column
+ */
+std::unique_ptr<cudf::column> ceil_datetimes(
+  cudf::column_view const& column,
+  rounding_frequency freq,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Round datetimes down to the nearest multiple of the given frequency.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param freq rounding_frequency indicating the frequency to round down to
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP.
+ * @return cudf::column of the same datetime resolution as the input column
+ */
+std::unique_ptr<cudf::column> floor_datetimes(
+  cudf::column_view const& column,
+  rounding_frequency freq,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Round datetimes to the nearest multiple of the given frequency.
+ *
+ * @param column cudf::column_view of the input datetime values
+ * @param freq rounding_frequency indicating the frequency to round to
+ * @param mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @throw cudf::logic_error if input column datatype is not TIMESTAMP.
+ * @return cudf::column of the same datetime resolution as the input column
+ */
+std::unique_ptr<cudf::column> round_datetimes(
+  cudf::column_view const& column,
+  rounding_frequency freq,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+}  // namespace datetime
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/aggregation/aggregation.cuh b/cpp/include/cudf/detail/aggregation/aggregation.cuh
new file mode 100644
index 0000000..f13166d
--- /dev/null
+++ b/cpp/include/cudf/detail/aggregation/aggregation.cuh
@@ -0,0 +1,676 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/detail/utilities/device_atomics.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/traits.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Maps an `aggregation::Kind` value to it's corresponding binary
+ * operator.
+ *
+ * @note Not all values of `aggregation::Kind` have a valid corresponding binary
+ * operator. For these values `E`,
+ * `std::is_same_v<corresponding_operator<E>::type, void>`.
+ *
+ * @tparam k The `aggregation::Kind` value to map to its corresponding operator
+ */
+template <aggregation::Kind k>
+struct corresponding_operator {
+  using type = void;
+};
+
+template <>
+struct corresponding_operator<aggregation::MIN> {
+  using type = DeviceMin;
+};
+template <>
+struct corresponding_operator<aggregation::MAX> {
+  using type = DeviceMax;
+};
+template <>
+struct corresponding_operator<aggregation::ARGMIN> {
+  using type = DeviceMin;
+};
+template <>
+struct corresponding_operator<aggregation::ARGMAX> {
+  using type = DeviceMax;
+};
+template <>
+struct corresponding_operator<aggregation::ANY> {
+  using type = DeviceMax;
+};
+template <>
+struct corresponding_operator<aggregation::ALL> {
+  using type = DeviceMin;
+};
+template <>
+struct corresponding_operator<aggregation::SUM> {
+  using type = DeviceSum;
+};
+template <>
+struct corresponding_operator<aggregation::PRODUCT> {
+  using type = DeviceProduct;
+};
+template <>
+struct corresponding_operator<aggregation::SUM_OF_SQUARES> {
+  using type = DeviceSum;
+};
+template <>
+struct corresponding_operator<aggregation::STD> {
+  using type = DeviceSum;
+};
+template <>
+struct corresponding_operator<aggregation::VARIANCE> {
+  using type = DeviceSum;
+};
+template <>
+struct corresponding_operator<aggregation::MEAN> {
+  using type = DeviceSum;
+};
+template <>
+struct corresponding_operator<aggregation::COUNT_VALID> {
+  using type = DeviceCount;
+};
+template <>
+struct corresponding_operator<aggregation::COUNT_ALL> {
+  using type = DeviceCount;
+};
+
+template <aggregation::Kind k>
+using corresponding_operator_t = typename corresponding_operator<k>::type;
+
+template <aggregation::Kind k>
+constexpr bool has_corresponding_operator()
+{
+  return !std::is_same_v<typename corresponding_operator<k>::type, void>;
+}
+
+template <typename Source,
+          aggregation::Kind k,
+          bool target_has_nulls,
+          bool source_has_nulls,
+          typename Enable = void>
+struct update_target_element {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    CUDF_UNREACHABLE("Invalid source type and aggregation combination.");
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::MIN,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_fixed_width<Source>() && cudf::has_atomic_support<Source>() &&
+                   !is_fixed_point<Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::MIN>;
+    atomicMin(&target.element<Target>(target_index),
+              static_cast<Target>(source.element<Source>(source_index)));
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::MIN,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_fixed_point<Source>() &&
+                   cudf::has_atomic_support<device_storage_type_t<Source>>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target       = target_type_t<Source, aggregation::MIN>;
+    using DeviceTarget = device_storage_type_t<Target>;
+    using DeviceSource = device_storage_type_t<Source>;
+
+    atomicMin(&target.element<DeviceTarget>(target_index),
+              static_cast<DeviceTarget>(source.element<DeviceSource>(source_index)));
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::MAX,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_fixed_width<Source>() && cudf::has_atomic_support<Source>() &&
+                   !is_fixed_point<Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::MAX>;
+    atomicMax(&target.element<Target>(target_index),
+              static_cast<Target>(source.element<Source>(source_index)));
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::MAX,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_fixed_point<Source>() &&
+                   cudf::has_atomic_support<device_storage_type_t<Source>>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target       = target_type_t<Source, aggregation::MAX>;
+    using DeviceTarget = device_storage_type_t<Target>;
+    using DeviceSource = device_storage_type_t<Source>;
+
+    atomicMax(&target.element<DeviceTarget>(target_index),
+              static_cast<DeviceTarget>(source.element<DeviceSource>(source_index)));
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::SUM,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<cudf::is_fixed_width<Source>() && cudf::has_atomic_support<Source>() &&
+                   !cudf::is_fixed_point<Source>() && !cudf::is_timestamp<Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::SUM>;
+    atomicAdd(&target.element<Target>(target_index),
+              static_cast<Target>(source.element<Source>(source_index)));
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::SUM,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_fixed_point<Source>() &&
+                   cudf::has_atomic_support<device_storage_type_t<Source>>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target       = target_type_t<Source, aggregation::SUM>;
+    using DeviceTarget = device_storage_type_t<Target>;
+    using DeviceSource = device_storage_type_t<Source>;
+
+    atomicAdd(&target.element<DeviceTarget>(target_index),
+              static_cast<DeviceTarget>(source.element<DeviceSource>(source_index)));
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+/**
+ * @brief Function object to update a single element in a target column using
+ * the dictionary key addressed by the specific index.
+ *
+ * SFINAE is used to prevent recursion for dictionary type. Dictionary keys cannot be a
+ * dictionary.
+ *
+ */
+template <bool target_has_nulls = true>
+struct update_target_from_dictionary {
+  template <typename Source,
+            aggregation::Kind k,
+            std::enable_if_t<!is_dictionary<Source>()>* = nullptr>
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    update_target_element<Source, k, target_has_nulls, false>{}(
+      target, target_index, source, source_index);
+  }
+  template <typename Source,
+            aggregation::Kind k,
+            std::enable_if_t<is_dictionary<Source>()>* = nullptr>
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+  }
+};
+
+/**
+ * @brief Specialization function for dictionary type and aggregations.
+ *
+ * The `source` column is a dictionary type. This functor de-references the
+ * dictionary's keys child column and maps the input source index through
+ * the dictionary's indices child column to pass to the `update_target_element`
+ * in the above `update_target_from_dictionary` using the type-dispatcher to
+ * resolve the keys column type.
+ *
+ * `update_target_element( target, target_index, source.keys(), source.indices()[source_index] )`
+ *
+ * @tparam target_has_nulls Indicates presence of null elements in `target`
+ * @tparam source_has_nulls Indicates presence of null elements in `source`.
+ */
+template <aggregation::Kind k, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  dictionary32,
+  k,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<not(k == aggregation::ARGMIN or k == aggregation::ARGMAX or
+                       k == aggregation::COUNT_VALID or k == aggregation::COUNT_ALL)>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    dispatch_type_and_aggregation(
+      source.child(cudf::dictionary_column_view::keys_column_index).type(),
+      k,
+      update_target_from_dictionary<target_has_nulls>{},
+      target,
+      target_index,
+      source.child(cudf::dictionary_column_view::keys_column_index),
+      static_cast<cudf::size_type>(source.element<dictionary32>(source_index)));
+  }
+};
+
+template <typename T>
+constexpr bool is_product_supported()
+{
+  return is_numeric<T>();
+}
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<Source,
+                             aggregation::SUM_OF_SQUARES,
+                             target_has_nulls,
+                             source_has_nulls,
+                             std::enable_if_t<is_product_supported<Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::SUM_OF_SQUARES>;
+    auto value   = static_cast<Target>(source.element<Source>(source_index));
+    atomicAdd(&target.element<Target>(target_index), value * value);
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<Source,
+                             aggregation::PRODUCT,
+                             target_has_nulls,
+                             source_has_nulls,
+                             std::enable_if_t<is_product_supported<Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::PRODUCT>;
+    atomicMul(&target.element<Target>(target_index),
+              static_cast<Target>(source.element<Source>(source_index)));
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::COUNT_VALID,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_valid_aggregation<Source, aggregation::COUNT_VALID>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::COUNT_VALID>;
+    atomicAdd(&target.element<Target>(target_index), Target{1});
+
+    // It is assumed the output for COUNT_VALID is initialized to be all valid
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::COUNT_ALL,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_valid_aggregation<Source, aggregation::COUNT_ALL>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    using Target = target_type_t<Source, aggregation::COUNT_ALL>;
+    atomicAdd(&target.element<Target>(target_index), Target{1});
+
+    // It is assumed the output for COUNT_ALL is initialized to be all valid
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::ARGMAX,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_valid_aggregation<Source, aggregation::ARGMAX>() and
+                   cudf::is_relationally_comparable<Source, Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::ARGMAX>;
+    auto old     = atomicCAS(&target.element<Target>(target_index), ARGMAX_SENTINEL, source_index);
+    if (old != ARGMAX_SENTINEL) {
+      while (source.element<Source>(source_index) > source.element<Source>(old)) {
+        old = atomicCAS(&target.element<Target>(target_index), old, source_index);
+      }
+    }
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+template <typename Source, bool target_has_nulls, bool source_has_nulls>
+struct update_target_element<
+  Source,
+  aggregation::ARGMIN,
+  target_has_nulls,
+  source_has_nulls,
+  std::enable_if_t<is_valid_aggregation<Source, aggregation::ARGMIN>() and
+                   cudf::is_relationally_comparable<Source, Source>()>> {
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    if (source_has_nulls and source.is_null(source_index)) { return; }
+
+    using Target = target_type_t<Source, aggregation::ARGMIN>;
+    auto old     = atomicCAS(&target.element<Target>(target_index), ARGMIN_SENTINEL, source_index);
+    if (old != ARGMIN_SENTINEL) {
+      while (source.element<Source>(source_index) < source.element<Source>(old)) {
+        old = atomicCAS(&target.element<Target>(target_index), old, source_index);
+      }
+    }
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+};
+
+/**
+ * @brief Function object to update a single element in a target column by
+ * performing an aggregation operation with a single element from a source
+ * column.
+ *
+ * @tparam target_has_nulls Indicates presence of null elements in `target`
+ * @tparam source_has_nulls Indicates presence of null elements in `source`.
+ */
+template <bool target_has_nulls = true, bool source_has_nulls = true>
+struct elementwise_aggregator {
+  template <typename Source, aggregation::Kind k>
+  __device__ void operator()(mutable_column_device_view target,
+                             size_type target_index,
+                             column_device_view source,
+                             size_type source_index) const noexcept
+  {
+    update_target_element<Source, k, target_has_nulls, source_has_nulls>{}(
+      target, target_index, source, source_index);
+  }
+};
+
+/**
+ * @brief Updates a row in `target` by performing elementwise aggregation
+ * operations with a row in `source`.
+ *
+ * For the row in `target` specified by `target_index`, each element at `i` is
+ * updated by:
+ * ```c++
+ * target_row[i] = aggs[i](target_row[i], source_row[i])
+ * ```
+ *
+ * This function only supports aggregations that can be done in a "single pass",
+ * i.e., given an initial value `R`, the aggregation `op` can be computed on a series
+ * of elements `e[i] for i in [0,n)` by computing `R = op(e[i],R)` for any order
+ * of the values of `i`.
+ *
+ * The initial value and validity of `R` depends on the aggregation:
+ * SUM: 0 and NULL
+ * MIN: Max value of type and NULL
+ * MAX: Min value of type and NULL
+ * COUNT_VALID: 0 and VALID
+ * COUNT_ALL:   0 and VALID
+ * ARGMAX: `ARGMAX_SENTINEL` and NULL
+ * ARGMIN: `ARGMIN_SENTINEL` and NULL
+ *
+ * It is required that the elements of `target` be initialized with the corresponding
+ * initial values and validity specified above.
+ *
+ * Handling of null elements in both `source` and `target` depends on the aggregation:
+ * SUM, MIN, MAX, ARGMIN, ARGMAX:
+ *  - `source`: Skipped
+ *  - `target`: Updated from null to valid upon first successful aggregation
+ * COUNT_VALID, COUNT_ALL:
+ *  - `source`: Skipped
+ *  - `target`: Cannot be null
+ *
+ * @param target Table containing the row to update
+ * @param target_index Index of the row to update in `target`
+ * @param source Table containing the row used to update the row in `target`.
+ * The invariant `source.num_columns() >= target.num_columns()` must hold.
+ * @param source_index Index of the row to use in `source`
+ * @param aggs Array of aggregations to perform between elements of the `target`
+ * and `source` rows. Must contain at least `target.num_columns()` valid
+ * `aggregation::Kind` values.
+ */
+template <bool target_has_nulls = true, bool source_has_nulls = true>
+__device__ inline void aggregate_row(mutable_table_device_view target,
+                                     size_type target_index,
+                                     table_device_view source,
+                                     size_type source_index,
+                                     aggregation::Kind const* aggs)
+{
+  for (auto i = 0; i < target.num_columns(); ++i) {
+    dispatch_type_and_aggregation(source.column(i).type(),
+                                  aggs[i],
+                                  elementwise_aggregator<target_has_nulls, source_has_nulls>{},
+                                  target.column(i),
+                                  target_index,
+                                  source.column(i),
+                                  source_index);
+  }
+}
+
+/**
+ * @brief Dispatched functor to initialize a column with the identity of an
+ * aggregation operation.
+ *
+ * Given a type `T` and `aggregation kind k`, determines and sets the value of
+ * each element of the passed column to the appropriate initial value for the
+ * aggregation.
+ *
+ * The initial values set as per aggregation are:
+ * SUM: 0
+ * COUNT_VALID: 0 and VALID
+ * COUNT_ALL:   0 and VALID
+ * MIN: Max value of type `T`
+ * MAX: Min value of type `T`
+ * ARGMAX: `ARGMAX_SENTINEL`
+ * ARGMIN: `ARGMIN_SENTINEL`
+ *
+ * Only works on columns of fixed-width types.
+ */
+struct identity_initializer {
+ private:
+  template <typename T, aggregation::Kind k>
+  static constexpr bool is_supported()
+  {
+    return cudf::is_fixed_width<T>() and
+           (k == aggregation::SUM or k == aggregation::MIN or k == aggregation::MAX or
+            k == aggregation::COUNT_VALID or k == aggregation::COUNT_ALL or
+            k == aggregation::ARGMAX or k == aggregation::ARGMIN or
+            k == aggregation::SUM_OF_SQUARES or k == aggregation::STD or
+            k == aggregation::VARIANCE or
+            (k == aggregation::PRODUCT and is_product_supported<T>()));
+  }
+
+  template <typename T, aggregation::Kind k>
+  std::enable_if_t<not std::is_same_v<corresponding_operator_t<k>, void>, T>
+  identity_from_operator()
+  {
+    using DeviceType = device_storage_type_t<T>;
+    return corresponding_operator_t<k>::template identity<DeviceType>();
+  }
+
+  template <typename T, aggregation::Kind k>
+  std::enable_if_t<std::is_same_v<corresponding_operator_t<k>, void>, T> identity_from_operator()
+  {
+    CUDF_FAIL("Unable to get identity/sentinel from device operator");
+  }
+
+  template <typename T, aggregation::Kind k>
+  T get_identity()
+  {
+    if (k == aggregation::ARGMAX || k == aggregation::ARGMIN) {
+      if constexpr (cudf::is_timestamp<T>())
+        return k == aggregation::ARGMAX ? T{typename T::duration(ARGMAX_SENTINEL)}
+                                        : T{typename T::duration(ARGMIN_SENTINEL)};
+      else {
+        using DeviceType = device_storage_type_t<T>;
+        return k == aggregation::ARGMAX ? static_cast<DeviceType>(ARGMAX_SENTINEL)
+                                        : static_cast<DeviceType>(ARGMIN_SENTINEL);
+      }
+    }
+    return identity_from_operator<T, k>();
+  }
+
+ public:
+  template <typename T, aggregation::Kind k>
+  std::enable_if_t<is_supported<T, k>(), void> operator()(mutable_column_view const& col,
+                                                          rmm::cuda_stream_view stream)
+  {
+    using DeviceType = device_storage_type_t<T>;
+    thrust::fill(rmm::exec_policy(stream),
+                 col.begin<DeviceType>(),
+                 col.end<DeviceType>(),
+                 get_identity<DeviceType, k>());
+  }
+
+  template <typename T, aggregation::Kind k>
+  std::enable_if_t<not is_supported<T, k>(), void> operator()(mutable_column_view const& col,
+                                                              rmm::cuda_stream_view stream)
+  {
+    CUDF_FAIL("Unsupported aggregation for initializing values");
+  }
+};
+
+/**
+ * @brief Initializes each column in a table with a corresponding identity value
+ * of an aggregation operation.
+ *
+ * The `i`th column will be initialized with the identity value of the `i`th
+ * aggregation operation in `aggs`.
+ *
+ * @throw cudf::logic_error if column type and corresponding agg are incompatible
+ * @throw cudf::logic_error if column type is not fixed-width
+ *
+ * @param table The table of columns to initialize.
+ * @param aggs A vector of aggregation operations corresponding to the table
+ * columns. The aggregations determine the identity value for each column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+void initialize_with_identity(mutable_table_view& table,
+                              std::vector<aggregation::Kind> const& aggs,
+                              rmm::cuda_stream_view stream);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/aggregation/aggregation.hpp b/cpp/include/cudf/detail/aggregation/aggregation.hpp
new file mode 100644
index 0000000..784f05a
--- /dev/null
+++ b/cpp/include/cudf/detail/aggregation/aggregation.hpp
@@ -0,0 +1,1625 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <functional>
+#include <numeric>
+
+namespace cudf {
+namespace detail {
+
+// Visitor pattern
+class simple_aggregations_collector {  // Declares the interface for the simple aggregations
+                                       // collector
+ public:
+  // Declare overloads for each kind of a agg to dispatch
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class sum_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class product_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class min_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class max_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class count_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class histogram_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class any_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class all_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(
+    data_type col_type, class sum_of_squares_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class mean_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class m2_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class var_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class std_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class median_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class quantile_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class argmax_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class argmin_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class nunique_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class nth_element_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class row_number_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class rank_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(
+    data_type col_type, class collect_list_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class collect_set_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class lead_lag_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class udf_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class merge_lists_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class merge_sets_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class merge_m2_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(
+    data_type col_type, class merge_histogram_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class covariance_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class correlation_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                          class tdigest_aggregation const& agg);
+  virtual std::vector<std::unique_ptr<aggregation>> visit(
+    data_type col_type, class merge_tdigest_aggregation const& agg);
+};
+
+class aggregation_finalizer {  // Declares the interface for the finalizer
+ public:
+  // Declare overloads for each kind of a agg to dispatch
+  virtual void visit(aggregation const& agg);
+  virtual void visit(class sum_aggregation const& agg);
+  virtual void visit(class product_aggregation const& agg);
+  virtual void visit(class min_aggregation const& agg);
+  virtual void visit(class max_aggregation const& agg);
+  virtual void visit(class count_aggregation const& agg);
+  virtual void visit(class histogram_aggregation const& agg);
+  virtual void visit(class any_aggregation const& agg);
+  virtual void visit(class all_aggregation const& agg);
+  virtual void visit(class sum_of_squares_aggregation const& agg);
+  virtual void visit(class mean_aggregation const& agg);
+  virtual void visit(class m2_aggregation const& agg);
+  virtual void visit(class var_aggregation const& agg);
+  virtual void visit(class std_aggregation const& agg);
+  virtual void visit(class median_aggregation const& agg);
+  virtual void visit(class quantile_aggregation const& agg);
+  virtual void visit(class argmax_aggregation const& agg);
+  virtual void visit(class argmin_aggregation const& agg);
+  virtual void visit(class nunique_aggregation const& agg);
+  virtual void visit(class nth_element_aggregation const& agg);
+  virtual void visit(class row_number_aggregation const& agg);
+  virtual void visit(class rank_aggregation const& agg);
+  virtual void visit(class collect_list_aggregation const& agg);
+  virtual void visit(class collect_set_aggregation const& agg);
+  virtual void visit(class lead_lag_aggregation const& agg);
+  virtual void visit(class udf_aggregation const& agg);
+  virtual void visit(class merge_lists_aggregation const& agg);
+  virtual void visit(class merge_sets_aggregation const& agg);
+  virtual void visit(class merge_m2_aggregation const& agg);
+  virtual void visit(class merge_histogram_aggregation const& agg);
+  virtual void visit(class covariance_aggregation const& agg);
+  virtual void visit(class correlation_aggregation const& agg);
+  virtual void visit(class tdigest_aggregation const& agg);
+  virtual void visit(class merge_tdigest_aggregation const& agg);
+};
+
+/**
+ * @brief Derived class for specifying a sum aggregation
+ */
+class sum_aggregation final : public rolling_aggregation,
+                              public groupby_aggregation,
+                              public groupby_scan_aggregation,
+                              public reduce_aggregation,
+                              public scan_aggregation,
+                              public segmented_reduce_aggregation {
+ public:
+  sum_aggregation() : aggregation(SUM) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<sum_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a product aggregation
+ */
+class product_aggregation final : public groupby_aggregation,
+                                  public reduce_aggregation,
+                                  public scan_aggregation,
+                                  public segmented_reduce_aggregation {
+ public:
+  product_aggregation() : aggregation(PRODUCT) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<product_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a min aggregation
+ */
+class min_aggregation final : public rolling_aggregation,
+                              public groupby_aggregation,
+                              public groupby_scan_aggregation,
+                              public reduce_aggregation,
+                              public scan_aggregation,
+                              public segmented_reduce_aggregation {
+ public:
+  min_aggregation() : aggregation(MIN) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<min_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a max aggregation
+ */
+class max_aggregation final : public rolling_aggregation,
+                              public groupby_aggregation,
+                              public groupby_scan_aggregation,
+                              public reduce_aggregation,
+                              public scan_aggregation,
+                              public segmented_reduce_aggregation {
+ public:
+  max_aggregation() : aggregation(MAX) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<max_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a count aggregation
+ */
+class count_aggregation final : public rolling_aggregation,
+                                public groupby_aggregation,
+                                public groupby_scan_aggregation {
+ public:
+  count_aggregation(aggregation::Kind kind) : aggregation(kind) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<count_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a histogram aggregation
+ */
+class histogram_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  histogram_aggregation() : aggregation(HISTOGRAM) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<histogram_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying an any aggregation
+ */
+class any_aggregation final : public reduce_aggregation, public segmented_reduce_aggregation {
+ public:
+  any_aggregation() : aggregation(ANY) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<any_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying an all aggregation
+ */
+class all_aggregation final : public reduce_aggregation, public segmented_reduce_aggregation {
+ public:
+  all_aggregation() : aggregation(ALL) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<all_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a sum_of_squares aggregation
+ */
+class sum_of_squares_aggregation final : public groupby_aggregation,
+                                         public reduce_aggregation,
+                                         public segmented_reduce_aggregation {
+ public:
+  sum_of_squares_aggregation() : aggregation(SUM_OF_SQUARES) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<sum_of_squares_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a mean aggregation
+ */
+class mean_aggregation final : public rolling_aggregation,
+                               public groupby_aggregation,
+                               public reduce_aggregation,
+                               public segmented_reduce_aggregation {
+ public:
+  mean_aggregation() : aggregation(MEAN) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<mean_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a m2 aggregation
+ */
+class m2_aggregation : public groupby_aggregation {
+ public:
+  m2_aggregation() : aggregation{M2} {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<m2_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a standard deviation/variance aggregation
+ */
+class std_var_aggregation : public rolling_aggregation,
+                            public groupby_aggregation,
+                            public reduce_aggregation,
+                            public segmented_reduce_aggregation {
+ public:
+  size_type _ddof;  ///< Delta degrees of freedom
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<std_var_aggregation const&>(_other);
+    return _ddof == other._ddof;
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+ protected:
+  std_var_aggregation(aggregation::Kind k, size_type ddof) : rolling_aggregation(k), _ddof{ddof}
+  {
+    CUDF_EXPECTS(k == aggregation::STD or k == aggregation::VARIANCE,
+                 "std_var_aggregation can accept only STD, VARIANCE");
+  }
+  [[nodiscard]] size_type hash_impl() const { return std::hash<size_type>{}(_ddof); }
+};
+
+/**
+ * @brief Derived class for specifying a variance aggregation
+ */
+class var_aggregation final : public std_var_aggregation {
+ public:
+  var_aggregation(size_type ddof)
+    : aggregation{aggregation::VARIANCE}, std_var_aggregation{aggregation::VARIANCE, ddof}
+  {
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<var_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a standard deviation aggregation
+ */
+class std_aggregation final : public std_var_aggregation {
+ public:
+  std_aggregation(size_type ddof)
+    : aggregation{aggregation::STD}, std_var_aggregation{aggregation::STD, ddof}
+  {
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<std_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a median aggregation
+ */
+class median_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  median_aggregation() : aggregation(MEDIAN) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<median_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a quantile aggregation
+ */
+class quantile_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  quantile_aggregation(std::vector<double> const& q, interpolation i)
+    : aggregation{QUANTILE}, _quantiles{q}, _interpolation{i}
+  {
+  }
+  std::vector<double> _quantiles;  ///< Desired quantile(s)
+  interpolation _interpolation;    ///< Desired interpolation
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+
+    auto const& other = dynamic_cast<quantile_aggregation const&>(_other);
+
+    return _interpolation == other._interpolation &&
+           std::equal(_quantiles.begin(), _quantiles.end(), other._quantiles.begin());
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<quantile_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ private:
+  size_t hash_impl() const
+  {
+    return std::hash<int>{}(static_cast<int>(_interpolation)) ^
+           std::accumulate(
+             _quantiles.cbegin(), _quantiles.cend(), size_t{0}, [](size_t a, double b) {
+               return a ^ std::hash<double>{}(b);
+             });
+  }
+};
+
+/**
+ * @brief Derived class for specifying an argmax aggregation
+ */
+class argmax_aggregation final : public rolling_aggregation, public groupby_aggregation {
+ public:
+  argmax_aggregation() : aggregation(ARGMAX) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<argmax_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying an argmin aggregation
+ */
+class argmin_aggregation final : public rolling_aggregation, public groupby_aggregation {
+ public:
+  argmin_aggregation() : aggregation(ARGMIN) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<argmin_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a nunique aggregation
+ */
+class nunique_aggregation final : public groupby_aggregation,
+                                  public reduce_aggregation,
+                                  public segmented_reduce_aggregation {
+ public:
+  nunique_aggregation(null_policy null_handling)
+    : aggregation{NUNIQUE}, _null_handling{null_handling}
+  {
+  }
+
+  null_policy _null_handling;  ///< include or exclude nulls
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<nunique_aggregation const&>(_other);
+    return _null_handling == other._null_handling;
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<nunique_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ private:
+  size_t hash_impl() const { return std::hash<int>{}(static_cast<int>(_null_handling)); }
+};
+
+/**
+ * @brief Derived class for specifying a nth element aggregation
+ */
+class nth_element_aggregation final : public groupby_aggregation,
+                                      public reduce_aggregation,
+                                      public rolling_aggregation {
+ public:
+  nth_element_aggregation(size_type n, null_policy null_handling)
+    : aggregation{NTH_ELEMENT}, _n{n}, _null_handling{null_handling}
+  {
+  }
+
+  size_type _n;                ///< nth index to return
+  null_policy _null_handling;  ///< include or exclude nulls
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<nth_element_aggregation const&>(_other);
+    return _n == other._n and _null_handling == other._null_handling;
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<nth_element_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ private:
+  size_t hash_impl() const
+  {
+    return std::hash<size_type>{}(_n) ^ std::hash<int>{}(static_cast<int>(_null_handling));
+  }
+};
+
+/**
+ * @brief Derived class for specifying a row_number aggregation
+ */
+class row_number_aggregation final : public rolling_aggregation {
+ public:
+  row_number_aggregation() : aggregation(ROW_NUMBER) {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<row_number_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived class for specifying a rank aggregation
+ */
+class rank_aggregation final : public rolling_aggregation,
+                               public groupby_scan_aggregation,
+                               public scan_aggregation {
+ public:
+  rank_aggregation(rank_method method,
+                   order column_order,
+                   null_policy null_handling,
+                   null_order null_precedence,
+                   rank_percentage percentage)
+    : aggregation{RANK},
+      _method{method},
+      _column_order{column_order},
+      _null_handling{null_handling},
+      _null_precedence{null_precedence},
+      _percentage(percentage)
+  {
+  }
+  rank_method const _method;          ///< rank method
+  order const _column_order;          ///< order of the column to rank
+  null_policy const _null_handling;   ///< include or exclude nulls in ranks
+  null_order const _null_precedence;  ///< order of nulls in ranks
+  rank_percentage const _percentage;  ///< whether to return percentage ranks
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<rank_aggregation const&>(_other);
+    return _method == other._method and _null_handling == other._null_handling and
+           _column_order == other._column_order and _null_precedence == other._null_precedence and
+           _percentage == other._percentage;
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<rank_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ private:
+  [[nodiscard]] size_t hash_impl() const
+  {
+    return std::hash<int>{}(static_cast<int>(_method)) ^
+           std::hash<int>{}(static_cast<int>(_column_order)) ^
+           std::hash<int>{}(static_cast<int>(_null_handling)) ^
+           std::hash<int>{}(static_cast<int>(_null_precedence)) ^
+           std::hash<int>{}(static_cast<int>(_percentage));
+  }
+};
+
+/**
+ * @brief Derived aggregation class for specifying COLLECT_LIST aggregation
+ */
+class collect_list_aggregation final : public rolling_aggregation,
+                                       public groupby_aggregation,
+                                       public reduce_aggregation {
+ public:
+  explicit collect_list_aggregation(null_policy null_handling = null_policy::INCLUDE)
+    : aggregation{COLLECT_LIST}, _null_handling{null_handling}
+  {
+  }
+
+  null_policy _null_handling;  ///< include or exclude nulls
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<collect_list_aggregation const&>(_other);
+    return (_null_handling == other._null_handling);
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<collect_list_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ private:
+  size_t hash_impl() const { return std::hash<int>{}(static_cast<int>(_null_handling)); }
+};
+
+/**
+ * @brief Derived aggregation class for specifying COLLECT_SET aggregation
+ */
+class collect_set_aggregation final : public rolling_aggregation,
+                                      public groupby_aggregation,
+                                      public reduce_aggregation {
+ public:
+  explicit collect_set_aggregation(null_policy null_handling = null_policy::INCLUDE,
+                                   null_equality nulls_equal = null_equality::EQUAL,
+                                   nan_equality nans_equal   = nan_equality::UNEQUAL)
+    : aggregation{COLLECT_SET},
+      _null_handling{null_handling},
+      _nulls_equal(nulls_equal),
+      _nans_equal(nans_equal)
+  {
+  }
+
+  null_policy _null_handling;  ///< include or exclude nulls
+  null_equality _nulls_equal;  ///< whether to consider nulls as equal values
+  nan_equality _nans_equal;    ///< whether to consider NaNs as equal value (applicable only to
+                               ///< floating point types)
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<collect_set_aggregation const&>(_other);
+    return (_null_handling == other._null_handling && _nulls_equal == other._nulls_equal &&
+            _nans_equal == other._nans_equal);
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<collect_set_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ protected:
+  size_t hash_impl() const
+  {
+    return std::hash<int>{}(static_cast<int>(_null_handling) ^ static_cast<int>(_nulls_equal) ^
+                            static_cast<int>(_nans_equal));
+  }
+};
+
+/**
+ * @brief Derived aggregation class for specifying LEAD/LAG window aggregations
+ */
+class lead_lag_aggregation final : public rolling_aggregation {
+ public:
+  lead_lag_aggregation(Kind kind, size_type offset)
+    : aggregation{offset < 0 ? (kind == LAG ? LEAD : LAG) : kind}, row_offset{std::abs(offset)}
+  {
+  }
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<lead_lag_aggregation const&>(_other);
+    return (row_offset == other.row_offset);
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<lead_lag_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+  size_type row_offset;
+
+ private:
+  [[nodiscard]] size_t hash_impl() const { return std::hash<size_type>()(row_offset); }
+};
+
+/**
+ * @brief Derived class for specifying a custom aggregation
+ * specified in udf
+ */
+class udf_aggregation final : public rolling_aggregation {
+ public:
+  udf_aggregation(aggregation::Kind type,
+                  std::string const& user_defined_aggregator,
+                  data_type output_type)
+    : aggregation{type},
+      _source{user_defined_aggregator},
+      _operator_name{(type == aggregation::PTX) ? "rolling_udf_ptx" : "rolling_udf_cuda"},
+      _function_name{"rolling_udf"},
+      _output_type{output_type}
+  {
+    CUDF_EXPECTS(type == aggregation::PTX or type == aggregation::CUDA,
+                 "udf_aggregation can accept only PTX, CUDA");
+  }
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<udf_aggregation const&>(_other);
+    return (_source == other._source and _operator_name == other._operator_name and
+            _function_name == other._function_name and _output_type == other._output_type);
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<udf_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+  std::string const _source;
+  std::string const _operator_name;
+  std::string const _function_name;
+  data_type _output_type;
+
+ protected:
+  [[nodiscard]] size_t hash_impl() const
+  {
+    return std::hash<std::string>{}(_source) ^ std::hash<std::string>{}(_operator_name) ^
+           std::hash<std::string>{}(_function_name) ^
+           std::hash<int>{}(static_cast<int32_t>(_output_type.id()));
+  }
+};
+
+/**
+ * @brief Derived aggregation class for specifying MERGE_LISTS aggregation
+ */
+class merge_lists_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  explicit merge_lists_aggregation() : aggregation{MERGE_LISTS} {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<merge_lists_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived aggregation class for specifying MERGE_SETS aggregation
+ */
+class merge_sets_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  explicit merge_sets_aggregation(null_equality nulls_equal, nan_equality nans_equal)
+    : aggregation{MERGE_SETS}, _nulls_equal(nulls_equal), _nans_equal(nans_equal)
+  {
+  }
+
+  null_equality _nulls_equal;  ///< whether to consider nulls as equal value
+  nan_equality _nans_equal;    ///< whether to consider NaNs as equal value (applicable only to
+                               ///< floating point types)
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<merge_sets_aggregation const&>(_other);
+    return (_nulls_equal == other._nulls_equal && _nans_equal == other._nans_equal);
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<merge_sets_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ protected:
+  size_t hash_impl() const
+  {
+    return std::hash<int>{}(static_cast<int>(_nulls_equal) ^ static_cast<int>(_nans_equal));
+  }
+};
+
+/**
+ * @brief Derived aggregation class for specifying MERGE_M2 aggregation
+ */
+class merge_m2_aggregation final : public groupby_aggregation {
+ public:
+  explicit merge_m2_aggregation() : aggregation{MERGE_M2} {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<merge_m2_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived aggregation class for specifying MERGE_HISTOGRAM aggregation
+ */
+class merge_histogram_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  explicit merge_histogram_aggregation() : aggregation{MERGE_HISTOGRAM} {}
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<merge_histogram_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived aggregation class for specifying COVARIANCE aggregation
+ */
+class covariance_aggregation final : public groupby_aggregation {
+ public:
+  explicit covariance_aggregation(size_type min_periods, size_type ddof)
+    : aggregation{COVARIANCE}, _min_periods{min_periods}, _ddof(ddof)
+  {
+  }
+  size_type _min_periods;
+  size_type _ddof;
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<covariance_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ protected:
+  size_t hash_impl() const
+  {
+    return std::hash<size_type>{}(_min_periods) ^ std::hash<size_type>{}(_ddof);
+  }
+};
+
+/**
+ * @brief Derived aggregation class for specifying CORRELATION aggregation
+ */
+class correlation_aggregation final : public groupby_aggregation {
+ public:
+  explicit correlation_aggregation(correlation_type type, size_type min_periods)
+    : aggregation{CORRELATION}, _type{type}, _min_periods{min_periods}
+  {
+  }
+  correlation_type _type;
+  size_type _min_periods;
+
+  [[nodiscard]] bool is_equal(aggregation const& _other) const override
+  {
+    if (!this->aggregation::is_equal(_other)) { return false; }
+    auto const& other = dynamic_cast<correlation_aggregation const&>(_other);
+    return (_type == other._type);
+  }
+
+  [[nodiscard]] size_t do_hash() const override
+  {
+    return this->aggregation::do_hash() ^ hash_impl();
+  }
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<correlation_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+
+ protected:
+  size_t hash_impl() const
+  {
+    return std::hash<int>{}(static_cast<int>(_type)) ^ std::hash<size_type>{}(_min_periods);
+  }
+};
+
+/**
+ * @brief Derived aggregation class for specifying TDIGEST aggregation
+ */
+class tdigest_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  explicit tdigest_aggregation(int max_centroids_)
+    : aggregation{TDIGEST}, max_centroids{max_centroids_}
+  {
+  }
+
+  int const max_centroids;
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<tdigest_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Derived aggregation class for specifying MERGE_TDIGEST aggregation
+ */
+class merge_tdigest_aggregation final : public groupby_aggregation, public reduce_aggregation {
+ public:
+  explicit merge_tdigest_aggregation(int max_centroids_)
+    : aggregation{MERGE_TDIGEST}, max_centroids{max_centroids_}
+  {
+  }
+
+  int const max_centroids;
+
+  [[nodiscard]] std::unique_ptr<aggregation> clone() const override
+  {
+    return std::make_unique<merge_tdigest_aggregation>(*this);
+  }
+  std::vector<std::unique_ptr<aggregation>> get_simple_aggregations(
+    data_type col_type, simple_aggregations_collector& collector) const override
+  {
+    return collector.visit(col_type, *this);
+  }
+  void finalize(aggregation_finalizer& finalizer) const override { finalizer.visit(*this); }
+};
+
+/**
+ * @brief Sentinel value used for `ARGMAX` aggregation.
+ *
+ * The output column for an `ARGMAX` aggregation is initialized with the
+ * sentinel value to indicate an unused element.
+ */
+constexpr size_type ARGMAX_SENTINEL{-1};
+
+/**
+ * @brief Sentinel value used for `ARGMIN` aggregation.
+ *
+ * The output column for an `ARGMIN` aggregation is initialized with the
+ * sentinel value to indicate an unused element.
+ */
+constexpr size_type ARGMIN_SENTINEL{-1};
+
+/**
+ * @brief Determines accumulator type based on input type and aggregation.
+ *
+ * @tparam Source The type on which the aggregation is computed
+ * @tparam k The aggregation performed
+ */
+template <typename Source, aggregation::Kind k, typename Enable = void>
+struct target_type_impl {
+  using type = void;
+};
+
+// Computing MIN of Source, use Source accumulator
+template <typename Source>
+struct target_type_impl<Source, aggregation::MIN> {
+  using type = Source;
+};
+
+// Computing MAX of Source, use Source accumulator
+template <typename Source>
+struct target_type_impl<Source, aggregation::MAX> {
+  using type = Source;
+};
+
+// Always use size_type accumulator for COUNT_VALID
+template <typename Source>
+struct target_type_impl<Source, aggregation::COUNT_VALID> {
+  using type = size_type;
+};
+
+// Always use size_type accumulator for COUNT_ALL
+template <typename Source>
+struct target_type_impl<Source, aggregation::COUNT_ALL> {
+  using type = size_type;
+};
+
+// Use list for HISTOGRAM
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::HISTOGRAM> {
+  using type = list_view;
+};
+
+// Computing ANY of any type, use bool accumulator
+template <typename Source>
+struct target_type_impl<Source, aggregation::ANY> {
+  using type = bool;
+};
+
+// Computing ALL of any type, use bool accumulator
+template <typename Source>
+struct target_type_impl<Source, aggregation::ALL> {
+  using type = bool;
+};
+
+// Always use `double` for MEAN except for durations and fixed point types.
+template <typename Source, aggregation::Kind k>
+struct target_type_impl<
+  Source,
+  k,
+  std::enable_if_t<is_fixed_width<Source>() and not is_chrono<Source>() and
+                   not is_fixed_point<Source>() and (k == aggregation::MEAN)>> {
+  using type = double;
+};
+
+template <typename Source, aggregation::Kind k>
+struct target_type_impl<Source,
+                        k,
+                        std::enable_if_t<(is_duration<Source>() or is_fixed_point<Source>()) &&
+                                         (k == aggregation::MEAN)>> {
+  using type = Source;
+};
+
+constexpr bool is_sum_product_agg(aggregation::Kind k)
+{
+  return (k == aggregation::SUM) || (k == aggregation::PRODUCT) ||
+         (k == aggregation::SUM_OF_SQUARES);
+}
+
+// Summing/Multiplying integers of any type, always use int64_t accumulator
+template <typename Source, aggregation::Kind k>
+struct target_type_impl<Source,
+                        k,
+                        std::enable_if_t<std::is_integral_v<Source> && is_sum_product_agg(k)>> {
+  using type = int64_t;
+};
+
+// Summing fixed_point numbers
+template <typename Source, aggregation::Kind k>
+struct target_type_impl<
+  Source,
+  k,
+  std::enable_if_t<cudf::is_fixed_point<Source>() && (k == aggregation::SUM)>> {
+  using type = Source;
+};
+
+// Summing/Multiplying float/doubles, use same type accumulator
+template <typename Source, aggregation::Kind k>
+struct target_type_impl<
+  Source,
+  k,
+  std::enable_if_t<std::is_floating_point_v<Source> && is_sum_product_agg(k)>> {
+  using type = Source;
+};
+
+// Summing duration types, use same type accumulator
+template <typename Source, aggregation::Kind k>
+struct target_type_impl<Source,
+                        k,
+                        std::enable_if_t<is_duration<Source>() && (k == aggregation::SUM)>> {
+  using type = Source;
+};
+
+// Always use `double` for M2
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::M2> {
+  using type = double;
+};
+
+// Always use `double` for VARIANCE
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::VARIANCE> {
+  using type = double;
+};
+
+// Always use `double` for STD
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::STD> {
+  using type = double;
+};
+
+// Always use `double` for quantile
+template <typename Source>
+struct target_type_impl<Source, aggregation::QUANTILE> {
+  using type = double;
+};
+
+// MEDIAN is a special case of a QUANTILE
+template <typename Source>
+struct target_type_impl<Source, aggregation::MEDIAN> {
+  using type = typename target_type_impl<Source, aggregation::QUANTILE>::type;
+};
+
+// Always use `size_type` for ARGMAX index
+template <typename Source>
+struct target_type_impl<Source, aggregation::ARGMAX> {
+  using type = size_type;
+};
+
+// Always use `size_type` for ARGMIN index
+template <typename Source>
+struct target_type_impl<Source, aggregation::ARGMIN> {
+  using type = size_type;
+};
+
+// Always use size_type accumulator for NUNIQUE
+template <typename Source>
+struct target_type_impl<Source, aggregation::NUNIQUE> {
+  using type = size_type;
+};
+
+// Always use Source for NTH_ELEMENT
+template <typename Source>
+struct target_type_impl<Source, aggregation::NTH_ELEMENT> {
+  using type = Source;
+};
+
+// Always use size_type accumulator for ROW_NUMBER
+template <typename Source>
+struct target_type_impl<Source, aggregation::ROW_NUMBER> {
+  using type = size_type;
+};
+
+// Always use size_type accumulator for RANK
+template <typename Source>
+struct target_type_impl<Source, aggregation::RANK> {
+  using type = size_type;  // double for percentage=true.
+};
+
+// Always use list for COLLECT_LIST
+template <typename Source>
+struct target_type_impl<Source, aggregation::COLLECT_LIST> {
+  using type = list_view;
+};
+
+// Always use list for COLLECT_SET
+template <typename Source>
+struct target_type_impl<Source, aggregation::COLLECT_SET> {
+  using type = list_view;
+};
+
+// Always use Source for LEAD
+template <typename Source>
+struct target_type_impl<Source, aggregation::LEAD> {
+  using type = Source;
+};
+
+// Always use Source for LAG
+template <typename Source>
+struct target_type_impl<Source, aggregation::LAG> {
+  using type = Source;
+};
+
+// Always use list for MERGE_LISTS
+template <typename Source>
+struct target_type_impl<Source, aggregation::MERGE_LISTS> {
+  using type = list_view;
+};
+
+// Always use list for MERGE_SETS
+template <typename Source>
+struct target_type_impl<Source, aggregation::MERGE_SETS> {
+  using type = list_view;
+};
+
+// Always use struct for MERGE_M2
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::MERGE_M2> {
+  using type = struct_view;
+};
+
+// Use list for MERGE_HISTOGRAM
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::MERGE_HISTOGRAM> {
+  using type = list_view;
+};
+
+// Always use double for COVARIANCE
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::COVARIANCE> {
+  using type = double;
+};
+
+// Always use double for CORRELATION
+template <typename SourceType>
+struct target_type_impl<SourceType, aggregation::CORRELATION> {
+  using type = double;
+};
+
+// Always use numeric types for TDIGEST
+template <typename Source>
+struct target_type_impl<Source,
+                        aggregation::TDIGEST,
+                        std::enable_if_t<(is_numeric<Source>() || is_fixed_point<Source>())>> {
+  using type = struct_view;
+};
+
+// TDIGEST_MERGE. The root column type for a tdigest column is a list_view. Strictly
+// speaking, this check is not sufficient to guarantee we are actually being given a
+// real tdigest column, but we will do further verification inside the aggregation code.
+template <typename Source>
+struct target_type_impl<Source,
+                        aggregation::MERGE_TDIGEST,
+                        std::enable_if_t<std::is_same_v<Source, cudf::struct_view>>> {
+  using type = struct_view;
+};
+
+/**
+ * @brief Helper alias to get the accumulator type for performing aggregation
+ * `k` on elements of type `Source`
+ *
+ * @tparam Source The type on which the aggregation is computed
+ * @tparam k The aggregation performed
+ */
+template <typename Source, aggregation::Kind k>
+using target_type_t = typename target_type_impl<Source, k>::type;
+
+template <aggregation::Kind k>
+struct kind_to_type_impl {
+  using type = aggregation;
+};
+
+template <aggregation::Kind k>
+using kind_to_type = typename kind_to_type_impl<k>::type;
+
+#ifndef AGG_KIND_MAPPING
+#define AGG_KIND_MAPPING(k, Type) \
+  template <>                     \
+  struct kind_to_type_impl<k> {   \
+    using type = Type;            \
+  }
+#endif
+
+AGG_KIND_MAPPING(aggregation::QUANTILE, quantile_aggregation);
+AGG_KIND_MAPPING(aggregation::STD, std_aggregation);
+AGG_KIND_MAPPING(aggregation::VARIANCE, var_aggregation);
+
+/**
+ * @brief Dispatches `k` as a non-type template parameter to a callable,  `f`.
+ *
+ * @tparam F Type of callable
+ * @param k The `aggregation::Kind` value to dispatch
+ * aram f The callable that accepts an `aggregation::Kind` non-type template
+ * argument.
+ * @param args Parameter pack forwarded to the `operator()` invocation
+ * @return Forwards the return value of the callable.
+ */
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+template <typename F, typename... Ts>
+CUDF_HOST_DEVICE inline decltype(auto) aggregation_dispatcher(aggregation::Kind k,
+                                                              F&& f,
+                                                              Ts&&... args)
+{
+  switch (k) {
+    case aggregation::SUM:
+      return f.template operator()<aggregation::SUM>(std::forward<Ts>(args)...);
+    case aggregation::PRODUCT:
+      return f.template operator()<aggregation::PRODUCT>(std::forward<Ts>(args)...);
+    case aggregation::MIN:
+      return f.template operator()<aggregation::MIN>(std::forward<Ts>(args)...);
+    case aggregation::MAX:
+      return f.template operator()<aggregation::MAX>(std::forward<Ts>(args)...);
+    case aggregation::COUNT_VALID:
+      return f.template operator()<aggregation::COUNT_VALID>(std::forward<Ts>(args)...);
+    case aggregation::COUNT_ALL:
+      return f.template operator()<aggregation::COUNT_ALL>(std::forward<Ts>(args)...);
+    case aggregation::HISTOGRAM:
+      return f.template operator()<aggregation::HISTOGRAM>(std::forward<Ts>(args)...);
+    case aggregation::ANY:
+      return f.template operator()<aggregation::ANY>(std::forward<Ts>(args)...);
+    case aggregation::ALL:
+      return f.template operator()<aggregation::ALL>(std::forward<Ts>(args)...);
+    case aggregation::SUM_OF_SQUARES:
+      return f.template operator()<aggregation::SUM_OF_SQUARES>(std::forward<Ts>(args)...);
+    case aggregation::MEAN:
+      return f.template operator()<aggregation::MEAN>(std::forward<Ts>(args)...);
+    case aggregation::M2: return f.template operator()<aggregation::M2>(std::forward<Ts>(args)...);
+    case aggregation::VARIANCE:
+      return f.template operator()<aggregation::VARIANCE>(std::forward<Ts>(args)...);
+    case aggregation::STD:
+      return f.template operator()<aggregation::STD>(std::forward<Ts>(args)...);
+    case aggregation::MEDIAN:
+      return f.template operator()<aggregation::MEDIAN>(std::forward<Ts>(args)...);
+    case aggregation::QUANTILE:
+      return f.template operator()<aggregation::QUANTILE>(std::forward<Ts>(args)...);
+    case aggregation::ARGMAX:
+      return f.template operator()<aggregation::ARGMAX>(std::forward<Ts>(args)...);
+    case aggregation::ARGMIN:
+      return f.template operator()<aggregation::ARGMIN>(std::forward<Ts>(args)...);
+    case aggregation::NUNIQUE:
+      return f.template operator()<aggregation::NUNIQUE>(std::forward<Ts>(args)...);
+    case aggregation::NTH_ELEMENT:
+      return f.template operator()<aggregation::NTH_ELEMENT>(std::forward<Ts>(args)...);
+    case aggregation::ROW_NUMBER:
+      return f.template operator()<aggregation::ROW_NUMBER>(std::forward<Ts>(args)...);
+    case aggregation::RANK:
+      return f.template operator()<aggregation::RANK>(std::forward<Ts>(args)...);
+    case aggregation::COLLECT_LIST:
+      return f.template operator()<aggregation::COLLECT_LIST>(std::forward<Ts>(args)...);
+    case aggregation::COLLECT_SET:
+      return f.template operator()<aggregation::COLLECT_SET>(std::forward<Ts>(args)...);
+    case aggregation::LEAD:
+      return f.template operator()<aggregation::LEAD>(std::forward<Ts>(args)...);
+    case aggregation::LAG:
+      return f.template operator()<aggregation::LAG>(std::forward<Ts>(args)...);
+    case aggregation::MERGE_LISTS:
+      return f.template operator()<aggregation::MERGE_LISTS>(std::forward<Ts>(args)...);
+    case aggregation::MERGE_SETS:
+      return f.template operator()<aggregation::MERGE_SETS>(std::forward<Ts>(args)...);
+    case aggregation::MERGE_M2:
+      return f.template operator()<aggregation::MERGE_M2>(std::forward<Ts>(args)...);
+    case aggregation::MERGE_HISTOGRAM:
+      return f.template operator()<aggregation::MERGE_HISTOGRAM>(std::forward<Ts>(args)...);
+    case aggregation::COVARIANCE:
+      return f.template operator()<aggregation::COVARIANCE>(std::forward<Ts>(args)...);
+    case aggregation::CORRELATION:
+      return f.template operator()<aggregation::CORRELATION>(std::forward<Ts>(args)...);
+    case aggregation::TDIGEST:
+      return f.template operator()<aggregation::TDIGEST>(std::forward<Ts>(args)...);
+    case aggregation::MERGE_TDIGEST:
+      return f.template operator()<aggregation::MERGE_TDIGEST>(std::forward<Ts>(args)...);
+    default: {
+#ifndef __CUDA_ARCH__
+      CUDF_FAIL("Unsupported aggregation.");
+#else
+      CUDF_UNREACHABLE("Unsupported aggregation.");
+#endif
+    }
+  }
+}
+
+template <typename Element>
+struct dispatch_aggregation {
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+  template <aggregation::Kind k, typename F, typename... Ts>
+  CUDF_HOST_DEVICE inline decltype(auto) operator()(F&& f, Ts&&... args) const
+  {
+    return f.template operator()<Element, k>(std::forward<Ts>(args)...);
+  }
+};
+
+struct dispatch_source {
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+  template <typename Element, typename F, typename... Ts>
+  CUDF_HOST_DEVICE inline decltype(auto) operator()(aggregation::Kind k, F&& f, Ts&&... args) const
+  {
+    return aggregation_dispatcher(
+      k, dispatch_aggregation<Element>{}, std::forward<F>(f), std::forward<Ts>(args)...);
+  }
+};
+
+/**
+ * @brief Dispatches both a type and `aggregation::Kind` template parameters to
+ * a callable.
+ *
+ * This function expects a callable `f` with an `operator()` template accepting
+ * two template parameters. The first is a type dispatched from `type`. The
+ * second is an `aggregation::Kind` dispatched from `k`.
+ *
+ * @param type The `data_type` used to dispatch a type for the first template
+ * parameter of the callable `F`
+ * @param k The `aggregation::Kind` used to dispatch an `aggregation::Kind`
+ * non-type template parameter for the second template parameter of the callable
+ * @param args Parameter pack forwarded to the `operator()` invocation
+ * `F`.
+ */
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+template <typename F, typename... Ts>
+CUDF_HOST_DEVICE inline constexpr decltype(auto) dispatch_type_and_aggregation(data_type type,
+                                                                               aggregation::Kind k,
+                                                                               F&& f,
+                                                                               Ts&&... args)
+{
+  return type_dispatcher(type, dispatch_source{}, k, std::forward<F>(f), std::forward<Ts>(args)...);
+}
+/**
+ * @brief Returns the target `data_type` for the specified aggregation  k
+ * performed on elements of type  source_type.
+ *
+ * aram source_type The element type to be aggregated
+ * aram k The aggregation
+ * @return data_type The target_type of  k performed on  source_type
+ * elements
+ */
+data_type target_type(data_type source_type, aggregation::Kind k);
+
+/**
+ * @brief Indicates whether the specified aggregation `k` is valid to perform on
+ * the type `Source`.
+ *
+ * @tparam Source Type on which the aggregation is performed
+ * @tparam k The aggregation to perform
+ */
+template <typename Source, aggregation::Kind k>
+constexpr inline bool is_valid_aggregation()
+{
+  return (not std::is_void_v<target_type_t<Source, k>>);
+}
+
+/**
+ * @brief Indicates whether the specified aggregation `k` is valid to perform on
+ * the `data_type` `source`.
+ *
+ * @param source Source `data_type` on which the aggregation is performed
+ * @param k The aggregation to perform
+ */
+bool is_valid_aggregation(data_type source, aggregation::Kind k);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/aggregation/result_cache.hpp b/cpp/include/cudf/detail/aggregation/result_cache.hpp
new file mode 100644
index 0000000..41eec15
--- /dev/null
+++ b/cpp/include/cudf/detail/aggregation/result_cache.hpp
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/types.hpp>
+
+#include <unordered_map>
+
+namespace cudf {
+namespace detail {
+struct pair_column_aggregation_equal_to {
+  bool operator()(std::pair<column_view, aggregation const&> const& lhs,
+                  std::pair<column_view, aggregation const&> const& rhs) const
+  {
+    return is_shallow_equivalent(lhs.first, rhs.first) and lhs.second.is_equal(rhs.second);
+  }
+};
+
+struct pair_column_aggregation_hash {
+  size_t operator()(std::pair<column_view, aggregation const&> const& key) const
+  {
+    return cudf::hashing::detail::hash_combine(shallow_hash(key.first), key.second.do_hash());
+  }
+};
+
+class result_cache {
+ public:
+  result_cache()                                     = delete;
+  ~result_cache()                                    = default;
+  result_cache(result_cache const&)                  = delete;
+  result_cache& operator=(result_cache const& other) = delete;
+
+  result_cache(size_t num_columns) : _cache(num_columns) {}
+
+  [[nodiscard]] bool has_result(column_view const& input, aggregation const& agg) const;
+
+  void add_result(column_view const& input, aggregation const& agg, std::unique_ptr<column>&& col);
+
+  [[nodiscard]] column_view get_result(column_view const& input, aggregation const& agg) const;
+
+  std::unique_ptr<column> release_result(column_view const& input, aggregation const& agg);
+
+ private:
+  std::unordered_map<std::pair<column_view, std::reference_wrapper<aggregation const>>,
+                     std::pair<std::unique_ptr<aggregation>, std::unique_ptr<column>>,
+                     pair_column_aggregation_hash,
+                     pair_column_aggregation_equal_to>
+    _cache;
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/binaryop.hpp b/cpp/include/cudf/detail/binaryop.hpp
new file mode 100644
index 0000000..e560956
--- /dev/null
+++ b/cpp/include/cudf/detail/binaryop.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/binaryop.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+//! Inner interfaces and implementations
+namespace detail {
+
+/**
+ * @copydoc cudf::binary_operation(column_view const&, column_view const&,
+ * std::string const&, data_type, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         std::string const& ptx,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::binary_operation(scalar const&, column_view const&, binary_operator,
+ * data_type, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> binary_operation(scalar const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::binary_operation(column_view const&, scalar const&, binary_operator,
+ * data_type, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         scalar const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::binary_operation(column_view const&, column_view const&,
+ * binary_operator, data_type, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/calendrical_month_sequence.cuh b/cpp/include/cudf/detail/calendrical_month_sequence.cuh
new file mode 100644
index 0000000..59fb675
--- /dev/null
+++ b/cpp/include/cudf/detail/calendrical_month_sequence.cuh
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/datetime_ops.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+struct calendrical_month_sequence_functor {
+  template <typename T>
+  std::enable_if_t<cudf::is_timestamp_t<T>::value, std::unique_ptr<cudf::column>> operator()(
+    size_type n,
+    scalar const& input,
+    size_type months,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    // Return empty column if n = 0
+    if (n == 0) return cudf::make_empty_column(input.type());
+
+    auto const device_input =
+      get_scalar_device_view(static_cast<cudf::scalar_type_t<T>&>(const_cast<scalar&>(input)));
+    auto output_column_type = cudf::data_type{cudf::type_to_id<T>()};
+    auto output             = cudf::make_fixed_width_column(
+      output_column_type, n, cudf::mask_state::UNALLOCATED, stream, mr);
+
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(n),
+                      output->mutable_view().begin<T>(),
+                      [initial = device_input, months] __device__(size_type i) {
+                        return datetime::detail::add_calendrical_months_with_scale_back(
+                          initial.value(), cuda::std::chrono::months{i * months});
+                      });
+
+    return output;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!cudf::is_timestamp_t<T>::value, std::unique_ptr<cudf::column>> operator()(
+    Args&&...)
+  {
+    CUDF_FAIL("Cannot make a date_range of a non-datetime type");
+  }
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/concatenate.hpp b/cpp/include/cudf/detail/concatenate.hpp
new file mode 100644
index 0000000..442814b
--- /dev/null
+++ b/cpp/include/cudf/detail/concatenate.hpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <vector>
+
+namespace cudf {
+//! Inner interfaces and implementations
+namespace detail {
+/**
+ * @copydoc cudf::concatenate(host_span<column_view const>,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns_to_concat,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::concatenate(host_span<table_view const>,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> concatenate(host_span<table_view const> tables_to_concat,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/concatenate_masks.hpp b/cpp/include/cudf/detail/concatenate_masks.hpp
new file mode 100644
index 0000000..e7086ea
--- /dev/null
+++ b/cpp/include/cudf/detail/concatenate_masks.hpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+namespace cudf {
+//! Inner interfaces and implementations
+namespace detail {
+
+/**
+ * @brief Concatenates the null mask bits of all the column device views in the
+ * `views` array to the destination bitmask.
+ *
+ * @param d_views Column device views whose null masks will be concatenated
+ * @param d_offsets Prefix sum of sizes of elements of `d_views`
+ * @param dest_mask The output buffer to copy null masks into
+ * @param output_size The total number of null masks bits that are being copied
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return The number of nulls
+ */
+size_type concatenate_masks(device_span<column_device_view const> d_views,
+                            device_span<size_t const> d_offsets,
+                            bitmask_type* dest_mask,
+                            size_type output_size,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Concatenates `views[i]`'s bitmask from the bits
+ * `[views[i].offset(), views[i].offset() + views[i].size())` for all elements
+ * views[i] in views into a destination bitmask pointer.
+ *
+ * @param views Column views whose bitmasks will be concatenated
+ * @param dest_mask The output buffer to copy null masks into
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return The number of nulls
+ */
+size_type concatenate_masks(host_span<column_view const> views,
+                            bitmask_type* dest_mask,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::concatenate_masks(host_span<column_view const>, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+rmm::device_buffer concatenate_masks(host_span<column_view const> views,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/contiguous_split.hpp b/cpp/include/cudf/detail/contiguous_split.hpp
new file mode 100644
index 0000000..d9a3547
--- /dev/null
+++ b/cpp/include/cudf/detail/contiguous_split.hpp
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/contiguous_split.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::contiguous_split
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ **/
+std::vector<packed_table> contiguous_split(cudf::table_view const& input,
+                                           std::vector<size_type> const& splits,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::pack
+ *
+ * @param stream Optional CUDA stream on which to execute kernels
+ **/
+packed_columns pack(cudf::table_view const& input,
+                    rmm::cuda_stream_view stream,
+                    rmm::mr::device_memory_resource* mr);
+
+// opaque implementation of `metadata_builder` since it needs to use
+// `serialized_column`, which is only defined in pack.cpp
+class metadata_builder_impl;
+
+/**
+ * @brief Helper class that creates packed column metadata.
+ *
+ * This class is an interface to the opaque metadata that is used to
+ * describe `contiguous_split` and `pack` results.
+ */
+class metadata_builder {
+ public:
+  /**
+   * @brief Construct a new metadata_builder.
+   *
+   * @param num_root_columns is the number of top-level columns
+   */
+  explicit metadata_builder(size_type const num_root_columns);
+
+  /**
+   * @brief Destructor that will be implemented as default, required because metadata_builder_impl
+   * is incomplete at this stage.
+   */
+  ~metadata_builder();
+
+  /**
+   * @brief Add a column to this metadata builder.
+   *
+   * Callers must call this function for the parent column and followed by any children,
+   * in the order maintained in the column/column_view.
+   *
+   * Example: given a table with a nested column "a" with 2 children, and a non-nested column "b":
+   *
+   *   1) add_column_info_to_meta(col_a)
+   *   2) add_column_info_to_meta(col_a_child_1)
+   *   3) add_column_info_to_meta(col_a_child_2)
+   *   4) add_column_info_to_meta(col_b)
+   *
+   * @param col_type column data type
+   * @param col_size column row count
+   * @param col_null_count column null count
+   * @param data_offset data offset from the column's base ptr,
+   *                    or -1 for an empty column
+   * @param null_mask_offset null mask offset from the column's base ptr,
+   *                    or -1 for a column that isn't nullable
+   * @param num_children number of children columns
+   */
+  void add_column_info_to_meta(data_type const col_type,
+                               size_type const col_size,
+                               size_type const col_null_count,
+                               int64_t const data_offset,
+                               int64_t const null_mask_offset,
+                               size_type const num_children);
+
+  /**
+   * @brief Builds the opaque metadata for all added columns.
+   *
+   * @returns A vector containing the serialized column metadata
+   */
+  std::vector<uint8_t> build() const;
+
+  /**
+   * @brief Clear the internal buffer containing all added metadata.
+   */
+  void clear();
+
+ private:
+  std::unique_ptr<metadata_builder_impl> impl;
+};
+
+/**
+ * @copydoc pack_metadata
+ * @param builder The reusable builder object to create packed column metadata.
+ */
+std::vector<uint8_t> pack_metadata(table_view const& table,
+                                   uint8_t const* contiguous_buffer,
+                                   size_t buffer_size,
+                                   metadata_builder& builder);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/copy.hpp b/cpp/include/cudf/detail/copy.hpp
new file mode 100644
index 0000000..1158221
--- /dev/null
+++ b/cpp/include/cudf/detail/copy.hpp
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <initializer_list>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Constructs a zero-copy `column_view`/`mutable_column_view` of the
+ * elements in the range `[begin,end)` in `input`.
+ *
+ * @note It is the caller's responsibility to ensure that the returned view
+ * does not outlive the viewed device memory.
+ *
+ * @throws cudf::logic_error if `begin < 0`, `end < begin` or
+ * `end > input.size()`.
+ *
+ * @tparam ColumnView Must be either cudf::column_view or cudf::mutable_column_view
+ * @param input View of input column to slice
+ * @param begin Index of the first desired element in the slice (inclusive).
+ * @param end Index of the last desired element in the slice (exclusive).
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return ColumnView View of the elements `[begin,end)` from `input`.
+ */
+template <typename ColumnView>
+ColumnView slice(ColumnView const& input,
+                 size_type begin,
+                 size_type end,
+                 rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::slice(column_view const&, host_span<size_type const>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<column_view> slice(column_view const& input,
+                               host_span<size_type const> indices,
+                               rmm::cuda_stream_view stream);
+/**
+ * @copydoc cudf::slice(column_view const&, std::initializer_list<size_type>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<column_view> slice(column_view const& input,
+                               std::initializer_list<size_type> indices,
+                               rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::slice(table_view const&, host_span<size_type const>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<table_view> slice(table_view const& input,
+                              host_span<size_type const> indices,
+                              rmm::cuda_stream_view stream);
+/**
+ * @copydoc cudf::slice(table_view const&, std::initializer_list<size_type>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<table_view> slice(table_view const& input,
+                              std::initializer_list<size_type> indices,
+                              rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::split(column_view const&, host_span<size_type const>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<column_view> split(column_view const& input,
+                               host_span<size_type const> splits,
+                               rmm::cuda_stream_view stream);
+/**
+ * @copydoc cudf::split(column_view const&, std::initializer_list<size_type>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<column_view> split(column_view const& input,
+                               std::initializer_list<size_type> splits,
+                               rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::split(table_view const&, host_span<size_type const>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<table_view> split(table_view const& input,
+                              host_span<size_type const> splits,
+                              rmm::cuda_stream_view stream);
+/**
+ * @copydoc cudf::split(table_view const&, std::initializer_list<size_type>)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<table_view> split(table_view const& input,
+                              std::initializer_list<size_type> splits,
+                              rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::shift(column_view const&,size_type,scalar const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> shift(column_view const& input,
+                              size_type offset,
+                              scalar const& fill_value,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Performs segmented shifts for specified values.
+ *
+ * For each segment, `i`th element is determined by the `i - offset`th element
+ * of the segment. If `i - offset < 0 or >= segment_size`, the value is determined by
+ * @p fill_value.
+ *
+ * Example:
+ * @code{.pseudo}
+ * segmented_values: { 3 1 2 | 3 5 3 | 2 6 }
+ * segment_offsets: {0 3 6 8}
+ * offset: 2
+ * fill_value: @
+ * result: { @ @ 3 | @ @ 3 | @ @ }
+ * -------------------------------------------------
+ * segmented_values: { 3 1 2 | 3 5 3 | 2 6 }
+ * segment_offsets: {0 3 6 8}
+ * offset: -1
+ * fill_value: -1
+ * result: { 1 2 -1 | 5 3 -1 | 6 -1 }
+ * @endcode
+ *
+ * @param segmented_values Segmented column, specified by @p segment_offsets
+ * @param segment_offsets Each segment's offset of @p segmented_values. A list of offsets
+ * with size `num_segments + 1`. The size of each segment is `segment_offsets[i+1] -
+ * segment_offsets[i]`.
+ * @param offset The offset by which to shift the input
+ * @param fill_value Fill value for indeterminable outputs
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @note If `offset == 0`, a copy of @p segmented_values is returned.
+ */
+std::unique_ptr<column> segmented_shift(column_view const& segmented_values,
+                                        device_span<size_type const> segment_offsets,
+                                        size_type offset,
+                                        scalar const& fill_value,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::allocate_like(column_view const&, size_type, mask_allocation_policy,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> allocate_like(column_view const& input,
+                                      size_type size,
+                                      mask_allocation_policy mask_alloc,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::copy_if_else( column_view const&, column_view const&,
+ * column_view const&, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> copy_if_else(column_view const& lhs,
+                                     column_view const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::copy_if_else( scalar const&, column_view const&,
+ * column_view const&, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> copy_if_else(scalar const& lhs,
+                                     column_view const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::copy_if_else( column_view const&, scalar const&,
+ * column_view const&, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> copy_if_else(column_view const& lhs,
+                                     scalar const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::copy_if_else( scalar const&, scalar const&,
+ * column_view const&, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> copy_if_else(scalar const& lhs,
+                                     scalar const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::sample
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> sample(table_view const& input,
+                              size_type const n,
+                              sample_with_replacement replacement,
+                              int64_t const seed,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::get_element
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<scalar> get_element(column_view const& input,
+                                    size_type index,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::has_nonempty_nulls
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+bool has_nonempty_nulls(column_view const& input, rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::may_have_nonempty_nulls
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+bool may_have_nonempty_nulls(column_view const& input, rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::purge_nonempty_nulls
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> purge_nonempty_nulls(column_view const& input,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/copy_if.cuh b/cpp/include/cudf/detail/copy_if.cuh
new file mode 100644
index 0000000..ebe7e05
--- /dev/null
+++ b/cpp/include/cudf/detail/copy_if.cuh
@@ -0,0 +1,409 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <cub/cub.cuh>
+
+#include <cuda/atomic>
+
+#include <algorithm>
+
+namespace cudf {
+namespace detail {
+
+// Compute the count of elements that pass the mask within each block
+template <typename Filter, int block_size>
+__global__ void compute_block_counts(cudf::size_type* __restrict__ block_counts,
+                                     cudf::size_type size,
+                                     cudf::size_type per_thread,
+                                     Filter filter)
+{
+  int tid   = threadIdx.x + per_thread * block_size * blockIdx.x;
+  int count = 0;
+
+  for (int i = 0; i < per_thread; i++) {
+    bool mask_true = (tid < size) && filter(tid);
+    count += __syncthreads_count(mask_true);
+    tid += block_size;
+  }
+
+  if (threadIdx.x == 0) block_counts[blockIdx.x] = count;
+}
+
+// Compute the exclusive prefix sum of each thread's mask value within each block
+template <int block_size>
+__device__ cudf::size_type block_scan_mask(bool mask_true, cudf::size_type& block_sum)
+{
+  int offset = 0;
+
+  using BlockScan = cub::BlockScan<cudf::size_type, block_size>;
+  __shared__ typename BlockScan::TempStorage temp_storage;
+  BlockScan(temp_storage).ExclusiveSum(mask_true, offset, block_sum);
+
+  return offset;
+}
+
+// This kernel scatters data and validity mask of a column based on the
+// scan of the boolean mask. The block offsets for the scan are already computed.
+// Just compute the scan of the mask in each block and add it to the block's
+// output offset. This is the output index of each element. Scattering
+// the valid mask is not as easy, because each thread is only responsible for
+// one bit. Warp-level processing (ballot) makes this simpler.
+// To make scattering efficient, we "coalesce" the block's scattered data and
+// valids in shared memory, and then write from shared memory to global memory
+// in a contiguous manner.
+// The has_validity template parameter specializes this kernel for the
+// non-nullable case for performance without writing another kernel.
+//
+// Note: `filter` is not run on indices larger than the input column size
+template <typename T, typename Filter, int block_size, bool has_validity>
+__launch_bounds__(block_size) __global__
+  void scatter_kernel(cudf::mutable_column_device_view output_view,
+                      cudf::size_type* output_null_count,
+                      cudf::column_device_view input_view,
+                      cudf::size_type const* __restrict__ block_offsets,
+                      cudf::size_type size,
+                      cudf::size_type per_thread,
+                      Filter filter)
+{
+  T* __restrict__ output_data                   = output_view.data<T>();
+  cudf::bitmask_type* __restrict__ output_valid = output_view.null_mask();
+  static_assert(block_size <= 1024, "Maximum thread block size exceeded");
+
+  int tid                      = threadIdx.x + per_thread * block_size * blockIdx.x;
+  cudf::size_type block_offset = block_offsets[blockIdx.x];
+
+  // one extra warp worth in case the block is not aligned
+  __shared__ bool temp_valids[has_validity ? block_size + cudf::detail::warp_size : 1];
+  __shared__ T temp_data[block_size];
+
+  cudf::size_type warp_valid_counts{0};  // total valid sum over the `per_thread` loop below
+  cudf::size_type block_sum = 0;         // count passing filter over the `per_thread` loop below
+
+  // Note that since the maximum gridDim.x on all supported GPUs is as big as
+  // cudf::size_type, this loop is sufficient to cover our maximum column size
+  // regardless of the value of block_size and per_thread.
+  for (int i = 0; i < per_thread; i++) {
+    bool mask_true = (tid < size) && filter(tid);
+
+    cudf::size_type tmp_block_sum = 0;
+    // get output location using a scan of the mask result
+    cudf::size_type const local_index = block_scan_mask<block_size>(mask_true, tmp_block_sum);
+    block_sum += tmp_block_sum;
+
+    if (has_validity) {
+      temp_valids[threadIdx.x] = false;  // init shared memory
+      if (threadIdx.x < cudf::detail::warp_size) temp_valids[block_size + threadIdx.x] = false;
+      __syncthreads();  // wait for init
+    }
+
+    if (mask_true) {
+      temp_data[local_index] = input_view.data<T>()[tid];  // scatter data to shared
+
+      // scatter validity mask to shared memory
+      if (has_validity and input_view.is_valid(tid)) {
+        // determine aligned offset for this warp's output
+        cudf::size_type const aligned_offset      = block_offset % cudf::detail::warp_size;
+        temp_valids[local_index + aligned_offset] = true;
+      }
+    }
+
+    __syncthreads();  // wait for shared data and validity mask to be complete
+
+    // Copy output data coalesced from shared to global
+    if (threadIdx.x < tmp_block_sum)
+      output_data[block_offset + threadIdx.x] = temp_data[threadIdx.x];
+
+    if (has_validity) {
+      // Since the valid bools are contiguous in shared memory now, we can use
+      // __popc to combine them into a single mask element.
+      // Then, most mask elements can be directly copied from shared to global
+      // memory. Only the first and last 32-bit mask elements of each block must
+      // use an atomicOr, because these are where other blocks may overlap.
+
+      constexpr int num_warps = block_size / cudf::detail::warp_size;
+      // account for partial blocks with non-warp-aligned offsets
+      int const last_index = tmp_block_sum + (block_offset % cudf::detail::warp_size) - 1;
+      int const last_warp  = min(num_warps, last_index / cudf::detail::warp_size);
+      int const wid        = threadIdx.x / cudf::detail::warp_size;
+      int const lane       = threadIdx.x % cudf::detail::warp_size;
+
+      cudf::size_type tmp_warp_valid_counts{0};
+
+      if (tmp_block_sum > 0 && wid <= last_warp) {
+        int valid_index = (block_offset / cudf::detail::warp_size) + wid;
+
+        // compute the valid mask for this warp
+        uint32_t valid_warp = __ballot_sync(0xffff'ffffu, temp_valids[threadIdx.x]);
+
+        // Note the atomicOr's below assume that output_valid has been set to
+        // all zero before the kernel
+        if (lane == 0 && valid_warp != 0) {
+          tmp_warp_valid_counts = __popc(valid_warp);
+          if (wid > 0 && wid < last_warp)
+            output_valid[valid_index] = valid_warp;
+          else {
+            cuda::atomic_ref<cudf::bitmask_type, cuda::thread_scope_device> ref{
+              output_valid[valid_index]};
+            ref.fetch_or(valid_warp, cuda::std::memory_order_relaxed);
+          }
+        }
+
+        // if the block is full and not aligned then we have one more warp to cover
+        if ((wid == 0) && (last_warp == num_warps)) {
+          uint32_t valid_warp = __ballot_sync(0xffff'ffffu, temp_valids[block_size + threadIdx.x]);
+          if (lane == 0 && valid_warp != 0) {
+            tmp_warp_valid_counts += __popc(valid_warp);
+            cuda::atomic_ref<cudf::bitmask_type, cuda::thread_scope_device> ref{
+              output_valid[valid_index + num_warps]};
+            ref.fetch_or(valid_warp, cuda::std::memory_order_relaxed);
+          }
+        }
+      }
+      warp_valid_counts += tmp_warp_valid_counts;
+    }
+
+    block_offset += tmp_block_sum;
+    tid += block_size;
+  }
+  // Compute total null_count for this block and add it to global count
+  constexpr cudf::size_type leader_lane{0};
+  cudf::size_type block_valid_count =
+    cudf::detail::single_lane_block_sum_reduce<block_size, leader_lane>(warp_valid_counts);
+
+  if (threadIdx.x == 0) {  // one thread computes and adds to null count
+    cuda::atomic_ref<size_type, cuda::thread_scope_device> ref{*output_null_count};
+    ref.fetch_add(block_sum - block_valid_count, cuda::std::memory_order_relaxed);
+  }
+}
+
+template <typename T, typename Enable = void>
+struct DeviceType {
+  using type = T;
+};
+
+template <typename T>
+struct DeviceType<T, std::enable_if_t<cudf::is_timestamp<T>()>> {
+  using type = typename T::rep;
+};
+
+template <typename T>
+struct DeviceType<T, std::enable_if_t<cudf::is_fixed_point<T>()>> {
+  using type = typename cudf::device_storage_type_t<T>;
+};
+
+// Dispatch functor which performs the scatter for fixed column types and gather for other
+template <typename Filter, int block_size>
+struct scatter_gather_functor {
+  template <typename T, std::enable_if_t<cudf::is_fixed_width<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           cudf::size_type const& output_size,
+                                           cudf::size_type const* block_offsets,
+                                           Filter filter,
+                                           cudf::size_type per_thread,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    auto output_column = cudf::detail::allocate_like(
+      input, output_size, cudf::mask_allocation_policy::RETAIN, stream, mr);
+    auto output = output_column->mutable_view();
+
+    bool has_valid = input.nullable();
+
+    using Type = typename DeviceType<T>::type;
+
+    auto scatter = (has_valid) ? scatter_kernel<Type, Filter, block_size, true>
+                               : scatter_kernel<Type, Filter, block_size, false>;
+
+    cudf::detail::grid_1d grid{input.size(), block_size, per_thread};
+
+    rmm::device_scalar<cudf::size_type> null_count{0, stream};
+    if (output.nullable()) {
+      // Have to initialize the output mask to all zeros because we may update
+      // it with atomicOr().
+      CUDF_CUDA_TRY(cudaMemsetAsync(static_cast<void*>(output.null_mask()),
+                                    0,
+                                    cudf::bitmask_allocation_size_bytes(output.size()),
+                                    stream.value()));
+    }
+
+    auto output_device_view = cudf::mutable_column_device_view::create(output, stream);
+    auto input_device_view  = cudf::column_device_view::create(input, stream);
+    scatter<<<grid.num_blocks, block_size, 0, stream.value()>>>(*output_device_view,
+                                                                null_count.data(),
+                                                                *input_device_view,
+                                                                block_offsets,
+                                                                input.size(),
+                                                                per_thread,
+                                                                filter);
+
+    if (has_valid) { output_column->set_null_count(null_count.value(stream)); }
+    return output_column;
+  }
+
+  template <typename T,
+            std::enable_if_t<!cudf::is_fixed_width<T>() and !cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           cudf::size_type const& output_size,
+                                           cudf::size_type const*,
+                                           Filter filter,
+                                           cudf::size_type,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    rmm::device_uvector<cudf::size_type> indices(output_size, stream);
+
+    thrust::copy_if(rmm::exec_policy(stream),
+                    thrust::counting_iterator<cudf::size_type>(0),
+                    thrust::counting_iterator<cudf::size_type>(input.size()),
+                    indices.begin(),
+                    filter);
+
+    auto output_table = cudf::detail::gather(cudf::table_view{{input}},
+                                             indices,
+                                             cudf::out_of_bounds_policy::DONT_CHECK,
+                                             cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                             stream,
+                                             mr);
+
+    // There will be only one column
+    return std::make_unique<cudf::column>(std::move(output_table->get_column(0)));
+  }
+};
+
+/**
+ * @brief Filters `input` using a Filter function object
+ *
+ * @p filter must be a functor or lambda with the following signature:
+ * __device__ bool operator()(cudf::size_type i);
+ * It will return true if element i of @p input should be copied,
+ * false otherwise.
+ *
+ * @tparam Filter the filter functor type
+ * @param[in] input The table_view to filter
+ * @param[in] filter A function object that takes an index and returns a bool
+ * @return unique_ptr<table> The table generated from filtered `input`.
+ */
+template <typename Filter>
+std::unique_ptr<table> copy_if(table_view const& input,
+                               Filter filter,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (0 == input.num_rows() || 0 == input.num_columns()) { return empty_like(input); }
+
+  constexpr int block_size = 256;
+  cudf::size_type per_thread =
+    elements_per_thread(compute_block_counts<Filter, block_size>, input.num_rows(), block_size);
+  cudf::detail::grid_1d grid{input.num_rows(), block_size, per_thread};
+
+  // temp storage for block counts and offsets
+  rmm::device_uvector<cudf::size_type> block_counts(grid.num_blocks, stream);
+  rmm::device_uvector<cudf::size_type> block_offsets(grid.num_blocks + 1, stream);
+
+  // 1. Find the count of elements in each block that "pass" the mask
+  compute_block_counts<Filter, block_size><<<grid.num_blocks, block_size, 0, stream.value()>>>(
+    block_counts.begin(), input.num_rows(), per_thread, filter);
+
+  // initialize just the first element of block_offsets to 0 since the InclusiveSum below
+  // starts at the second element.
+  CUDF_CUDA_TRY(cudaMemsetAsync(block_offsets.begin(), 0, sizeof(cudf::size_type), stream.value()));
+
+  // 2. Find the offset for each block's output using a scan of block counts
+  if (grid.num_blocks > 1) {
+    // Determine and allocate temporary device storage
+    size_t temp_storage_bytes = 0;
+    cub::DeviceScan::InclusiveSum(nullptr,
+                                  temp_storage_bytes,
+                                  block_counts.begin(),
+                                  block_offsets.begin() + 1,
+                                  grid.num_blocks,
+                                  stream.value());
+    rmm::device_buffer d_temp_storage(temp_storage_bytes, stream);
+
+    // Run exclusive prefix sum
+    cub::DeviceScan::InclusiveSum(d_temp_storage.data(),
+                                  temp_storage_bytes,
+                                  block_counts.begin(),
+                                  block_offsets.begin() + 1,
+                                  grid.num_blocks,
+                                  stream.value());
+  }
+
+  // As it is InclusiveSum, last value in block_offsets will be output_size
+  // unless num_blocks == 1, in which case output_size is just block_counts[0]
+  cudf::size_type output_size{0};
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    &output_size,
+    grid.num_blocks > 1 ? block_offsets.begin() + grid.num_blocks : block_counts.begin(),
+    sizeof(cudf::size_type),
+    cudaMemcpyDefault,
+    stream.value()));
+
+  stream.synchronize();
+
+  if (output_size == input.num_rows()) {
+    return std::make_unique<table>(input, stream, mr);
+  } else if (output_size > 0) {
+    std::vector<std::unique_ptr<column>> out_columns(input.num_columns());
+    std::transform(input.begin(), input.end(), out_columns.begin(), [&](auto col_view) {
+      return cudf::type_dispatcher(col_view.type(),
+                                   scatter_gather_functor<Filter, block_size>{},
+                                   col_view,
+                                   output_size,
+                                   block_offsets.begin(),
+                                   filter,
+                                   per_thread,
+                                   stream,
+                                   mr);
+    });
+
+    return std::make_unique<table>(std::move(out_columns));
+  } else {
+    return empty_like(input);
+  }
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/copy_if_else.cuh b/cpp/include/cudf/detail/copy_if_else.cuh
new file mode 100644
index 0000000..04ad1f2
--- /dev/null
+++ b/cpp/include/cudf/detail/copy_if_else.cuh
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+
+#include <rmm/device_scalar.hpp>
+
+#include <thrust/iterator/iterator_traits.h>
+#include <thrust/optional.h>
+
+namespace cudf {
+namespace detail {
+namespace {  // anonymous
+
+template <size_type block_size,
+          typename T,
+          typename LeftIter,
+          typename RightIter,
+          typename Filter,
+          bool has_nulls>
+__launch_bounds__(block_size) __global__
+  void copy_if_else_kernel(LeftIter lhs,
+                           RightIter rhs,
+                           Filter filter,
+                           mutable_column_device_view out,
+                           size_type* __restrict__ const valid_count)
+{
+  size_type const tid            = threadIdx.x + blockIdx.x * block_size;
+  int const warp_id              = tid / warp_size;
+  size_type const warps_per_grid = gridDim.x * block_size / warp_size;
+
+  // begin/end indices for the column data
+  size_type begin = 0;
+  size_type end   = out.size();
+  // warp indices.  since 1 warp == 32 threads == sizeof(bitmask_type) * 8,
+  // each warp will process one (32 bit) of the validity mask via
+  // __ballot_sync()
+  size_type warp_begin = cudf::word_index(begin);
+  size_type warp_end   = cudf::word_index(end - 1);
+
+  // lane id within the current warp
+  constexpr size_type leader_lane{0};
+  int const lane_id = threadIdx.x % warp_size;
+
+  size_type warp_valid_count{0};
+
+  // current warp.
+  size_type warp_cur = warp_begin + warp_id;
+  size_type index    = tid;
+  while (warp_cur <= warp_end) {
+    auto const opt_value =
+      (index < end) ? (filter(index) ? lhs[index] : rhs[index]) : thrust::nullopt;
+    if (opt_value) { out.element<T>(index) = static_cast<T>(*opt_value); }
+
+    // update validity
+    if (has_nulls) {
+      // the final validity mask for this warp
+      int warp_mask = __ballot_sync(0xFFFF'FFFFu, opt_value.has_value());
+      // only one guy in the warp needs to update the mask and count
+      if (lane_id == 0) {
+        out.set_mask_word(warp_cur, warp_mask);
+        warp_valid_count += __popc(warp_mask);
+      }
+    }
+
+    // next grid
+    warp_cur += warps_per_grid;
+    index += block_size * gridDim.x;
+  }
+
+  if (has_nulls) {
+    // sum all null counts across all warps
+    size_type block_valid_count =
+      single_lane_block_sum_reduce<block_size, leader_lane>(warp_valid_count);
+    // block_valid_count will only be valid on thread 0
+    if (threadIdx.x == 0) {
+      // using an atomic here because there are multiple blocks doing this work
+      atomicAdd(valid_count, block_valid_count);
+    }
+  }
+}
+
+}  // anonymous namespace
+
+/**
+ * @brief Returns a new column, where each element is selected from either of two input ranges based
+ * on a filter
+ *
+ * Given two ranges lhs and rhs, and a unary filter function, this function will allocate and return
+ * an output column that contains `lhs[i]` if `function(i) == true` or `rhs[i]` if `function(i) ==
+ * false`. The validity of the elements is propagated to the output.
+ *
+ * The range lhs is defined by iterators `[lhs_begin, lhs_end)`. The `size` of output is
+ * determined by the distance between `lhs_begin` and `lhs_end`.
+ *
+ * The range rhs is defined by `[rhs, rhs + size)`
+ *
+ * Example:
+ * @code{.pseudo}
+ * lhs = {1, 2, 3, -, 5}
+ * rhs = {-, 6, 7, 8, 9}
+ *
+ * filter = [](i) {
+ *   bool arr[5] = {1, 1, 0, 1, 0}
+ *   return arr[i];
+ * }
+ *
+ * output = {1, 2, 7, -, 9}
+ * @endcode
+ *
+ * @tparam FilterFn   A function of type `bool(size_type)`
+ * @tparam LeftIter   An iterator of pair type where `first` is the value and `second` is the
+ *                    validity
+ * @tparam RightIter  An iterator of pair type where `first` is the value and `second` is the
+ *                    validity
+ * @param nullable    Indicate whether either input range can contain nulls
+ * @param lhs_begin   Begin iterator of lhs range
+ * @param lhs_end     End iterator of lhs range
+ * @param rhs         Begin iterator of rhs range
+ * @param filter      Function of type `FilterFn` which determines for index `i` where to get the
+ *                    corresponding output value from
+ * @param out_type    `cudf::data_type` of the returned column
+ * @param stream      CUDA stream used for device memory operations and kernel launches.
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return            A new column that contains the values from either `lhs` or `rhs` as determined
+ *                    by `filter[i]`
+ */
+template <typename FilterFn, typename LeftIter, typename RightIter>
+std::unique_ptr<column> copy_if_else(bool nullable,
+                                     LeftIter lhs_begin,
+                                     LeftIter lhs_end,
+                                     RightIter rhs,
+                                     FilterFn filter,
+                                     cudf::data_type output_type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  // This is the type of the thrust::optional element in the passed iterators
+  using Element = typename thrust::iterator_traits<LeftIter>::value_type::value_type;
+
+  size_type size           = std::distance(lhs_begin, lhs_end);
+  size_type num_els        = cudf::util::round_up_safe(size, warp_size);
+  constexpr int block_size = 256;
+  cudf::detail::grid_1d grid{num_els, block_size, 1};
+
+  std::unique_ptr<column> out = make_fixed_width_column(
+    output_type, size, nullable ? mask_state::UNINITIALIZED : mask_state::UNALLOCATED, stream, mr);
+
+  auto out_v = mutable_column_device_view::create(*out, stream);
+
+  // if we have validity in the output
+  if (nullable) {
+    rmm::device_scalar<size_type> valid_count{0, stream};
+
+    // call the kernel
+    copy_if_else_kernel<block_size, Element, LeftIter, RightIter, FilterFn, true>
+      <<<grid.num_blocks, block_size, 0, stream.value()>>>(
+        lhs_begin, rhs, filter, *out_v, valid_count.data());
+
+    out->set_null_count(size - valid_count.value(stream));
+  } else {
+    // call the kernel
+    copy_if_else_kernel<block_size, Element, LeftIter, RightIter, FilterFn, false>
+      <<<grid.num_blocks, block_size, 0, stream.value()>>>(lhs_begin, rhs, filter, *out_v, nullptr);
+  }
+
+  return out;
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/copy_range.cuh b/cpp/include/cudf/detail/copy_range.cuh
new file mode 100644
index 0000000..16e4e7a
--- /dev/null
+++ b/cpp/include/cudf/detail/copy_range.cuh
@@ -0,0 +1,210 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+
+#include <cub/cub.cuh>
+
+#include <cuda_runtime.h>
+
+#include <memory>
+
+namespace {
+template <cudf::size_type block_size,
+          typename SourceValueIterator,
+          typename SourceValidityIterator,
+          typename T,
+          bool has_validity>
+__global__ void copy_range_kernel(SourceValueIterator source_value_begin,
+                                  SourceValidityIterator source_validity_begin,
+                                  cudf::mutable_column_device_view target,
+                                  cudf::size_type target_begin,
+                                  cudf::size_type target_end,
+                                  cudf::size_type* __restrict__ const null_count)
+{
+  using cudf::detail::warp_size;
+
+  static_assert(block_size <= 1024, "copy_range_kernel assumes block_size is not larger than 1024");
+  static_assert(warp_size == cudf::detail::size_in_bits<cudf::bitmask_type>(),
+                "copy_range_kernel assumes bitmask element size in bits == warp size");
+
+  constexpr cudf::size_type leader_lane{0};
+  int const lane_id = threadIdx.x % warp_size;
+
+  cudf::size_type const tid = threadIdx.x + blockIdx.x * blockDim.x;
+  int const warp_id         = tid / warp_size;
+
+  cudf::size_type const offset         = target.offset();
+  cudf::size_type const begin_mask_idx = cudf::word_index(offset + target_begin);
+  cudf::size_type const end_mask_idx   = cudf::word_index(offset + target_end);
+
+  cudf::size_type mask_idx             = begin_mask_idx + warp_id;
+  cudf::size_type const masks_per_grid = gridDim.x * blockDim.x / warp_size;
+
+  cudf::size_type target_offset = begin_mask_idx * warp_size - (offset + target_begin);
+  cudf::size_type source_idx    = tid + target_offset;
+
+  cudf::size_type warp_null_change{0};
+
+  while (mask_idx <= end_mask_idx) {
+    cudf::size_type index = mask_idx * warp_size + lane_id - offset;
+    bool in_range         = (index >= target_begin && index < target_end);
+
+    // write data
+    if (in_range) target.element<T>(index) = *(source_value_begin + source_idx);
+
+    if (has_validity) {  // update bitmask
+      bool const valid      = in_range && *(source_validity_begin + source_idx);
+      int const active_mask = __ballot_sync(0xFFFF'FFFFu, in_range);
+      int const valid_mask  = __ballot_sync(0xFFFF'FFFFu, valid);
+      int const warp_mask   = active_mask & valid_mask;
+
+      cudf::bitmask_type old_mask = target.get_mask_word(mask_idx);
+      if (lane_id == leader_lane) {
+        cudf::bitmask_type new_mask = (old_mask & ~active_mask) | warp_mask;
+        target.set_mask_word(mask_idx, new_mask);
+        warp_null_change += __popc(active_mask & old_mask) - __popc(active_mask & new_mask);
+      }
+    }
+
+    source_idx += blockDim.x * gridDim.x;
+    mask_idx += masks_per_grid;
+  }
+
+  if (has_validity) {
+    auto block_null_change =
+      cudf::detail::single_lane_block_sum_reduce<block_size, leader_lane>(warp_null_change);
+    if (threadIdx.x == 0) {  // if the first thread in a block
+      atomicAdd(null_count, block_null_change);
+    }
+  }
+}
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Internal API to copy a range of values from source iterators to a
+ * target column.
+ *
+ * The elements indicated by the indices [@p target_begin, @p target_end) were
+ * replaced with the elements retrieved from source iterators;
+ * *(@p source_value_begin + idx) if *(@p source_validity_begin + idx) is true,
+ * invalidate otherwise (where idx = [0, @p target_end - @p target_begin)).
+ * @p target is modified in place.
+ *
+ * @tparam SourceValueIterator Iterator for retrieving source values
+ * @tparam SourceValidityIterator Iterator for retrieving source validities
+ * @param source_value_begin Start of source value iterator
+ * @param source_validity_begin Start of source validity iterator
+ * @param target the column to copy into
+ * @param target_begin The starting index of the target range (inclusive)
+ * @param target_end The index of the last element in the target range
+ * (exclusive)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename SourceValueIterator, typename SourceValidityIterator>
+void copy_range(SourceValueIterator source_value_begin,
+                SourceValidityIterator source_validity_begin,
+                mutable_column_view& target,
+                size_type target_begin,
+                size_type target_end,
+                rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS((target_begin <= target_end) && (target_begin >= 0) &&
+                 (target_begin < target.size()) && (target_end <= target.size()),
+               "Range is out of bounds.");
+  using T = typename std::iterator_traits<SourceValueIterator>::value_type;
+
+  // this code assumes that source and target have the same type.
+  CUDF_EXPECTS(type_id_matches_device_storage_type<T>(target.type().id()), "data type mismatch");
+
+  auto warp_aligned_begin_lower_bound = cudf::util::round_down_safe(target_begin, warp_size);
+  auto warp_aligned_end_upper_bound   = cudf::util::round_up_safe(target_end, warp_size);
+  auto num_items = warp_aligned_end_upper_bound - warp_aligned_begin_lower_bound;
+
+  constexpr size_type block_size{256};
+
+  auto grid = cudf::detail::grid_1d{num_items, block_size, 1};
+
+  if (target.nullable()) {
+    rmm::device_scalar<size_type> null_count(target.null_count(), stream);
+
+    auto kernel =
+      copy_range_kernel<block_size, SourceValueIterator, SourceValidityIterator, T, true>;
+    kernel<<<grid.num_blocks, block_size, 0, stream.value()>>>(
+      source_value_begin,
+      source_validity_begin,
+      *mutable_column_device_view::create(target, stream),
+      target_begin,
+      target_end,
+      null_count.data());
+
+    target.set_null_count(null_count.value(stream));
+  } else {
+    auto kernel =
+      copy_range_kernel<block_size, SourceValueIterator, SourceValidityIterator, T, false>;
+    kernel<<<grid.num_blocks, block_size, 0, stream.value()>>>(
+      source_value_begin,
+      source_validity_begin,
+      *mutable_column_device_view::create(target, stream),
+      target_begin,
+      target_end,
+      nullptr);
+  }
+
+  CUDF_CHECK_CUDA(stream.value());
+}
+
+/**
+ * @copydoc cudf::copy_range_in_place
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+void copy_range_in_place(column_view const& source,
+                         mutable_column_view& target,
+                         size_type source_begin,
+                         size_type source_end,
+                         size_type target_begin,
+                         rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::copy_range
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return std::unique_ptr<column> The result target column
+ */
+std::unique_ptr<column> copy_range(column_view const& source,
+                                   column_view const& target,
+                                   size_type source_begin,
+                                   size_type source_end,
+                                   size_type target_begin,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/datetime.hpp b/cpp/include/cudf/detail/datetime.hpp
new file mode 100644
index 0000000..c516095
--- /dev/null
+++ b/cpp/include/cudf/detail/datetime.hpp
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace datetime {
+namespace detail {
+/**
+ * @copydoc cudf::extract_year(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_year(cudf::column_view const& column,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_month(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_month(cudf::column_view const& column,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_day(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_day(cudf::column_view const& column,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_weekday(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_weekday(cudf::column_view const& column,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_hour(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_hour(cudf::column_view const& column,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_minute(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_minute(cudf::column_view const& column,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_second(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_second(cudf::column_view const& column,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_millisecond_fraction(cudf::column_view const&,
+ * rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_millisecond_fraction(cudf::column_view const& column,
+                                                           rmm::cuda_stream_view stream,
+                                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_microsecond_fraction(cudf::column_view const&,
+ * rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_microsecond_fraction(cudf::column_view const& column,
+                                                           rmm::cuda_stream_view stream,
+                                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::extract_nanosecond_fraction(cudf::column_view const&,
+ * rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> extract_nanosecond_fraction(cudf::column_view const& column,
+                                                          rmm::cuda_stream_view stream,
+                                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::last_day_of_month(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> last_day_of_month(cudf::column_view const& column,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::day_of_year(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> day_of_year(cudf::column_view const& column,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::add_calendrical_months(cudf::column_view const&, cudf::column_view const&,
+ * rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> add_calendrical_months(cudf::column_view const& timestamps,
+                                                     cudf::column_view const& months,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::add_calendrical_months(cudf::column_view const&, cudf::scalar const&,
+ * rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> add_calendrical_months(cudf::column_view const& timestamps,
+                                                     cudf::scalar const& months,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::is_leap_year(cudf::column_view const&, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> is_leap_year(cudf::column_view const& column,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<cudf::column> extract_quarter(cudf::column_view const& column,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace datetime
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/datetime_ops.cuh b/cpp/include/cudf/detail/datetime_ops.cuh
new file mode 100644
index 0000000..e68785e
--- /dev/null
+++ b/cpp/include/cudf/detail/datetime_ops.cuh
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuda/std/chrono>
+
+namespace cudf {
+namespace datetime {
+namespace detail {
+using namespace cuda::std::chrono;
+
+template <typename Timestamp>
+__device__ Timestamp add_calendrical_months_with_scale_back(Timestamp time_val, months months_val)
+{
+  auto const days_since_epoch = floor<days>(time_val);
+
+  auto const date = [&]() {
+    auto const ymd = year_month_day{days_since_epoch} + months_val;
+    return ymd.ok() ? ymd : ymd.year() / ymd.month() / last;
+  }();
+
+  auto const time = (time_val - days_since_epoch);
+
+  return sys_days{date} + time;
+}
+
+}  // namespace detail
+}  // namespace datetime
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/fill.hpp b/cpp/include/cudf/detail/fill.hpp
new file mode 100644
index 0000000..caaccfb
--- /dev/null
+++ b/cpp/include/cudf/detail/fill.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/filling.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::fill_in_place
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+void fill_in_place(mutable_column_view& destination,
+                   size_type begin,
+                   size_type end,
+                   scalar const& value,
+                   rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::fill
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> fill(column_view const& input,
+                             size_type begin,
+                             size_type end,
+                             scalar const& value,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/gather.cuh b/cpp/include/cudf/detail/gather.cuh
new file mode 100644
index 0000000..955f991
--- /dev/null
+++ b/cpp/include/cudf/detail/gather.cuh
@@ -0,0 +1,690 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/lists/detail/gather.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/strings/detail/gather.cuh>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <algorithm>
+
+#include <thrust/functional.h>
+#include <thrust/gather.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/logical.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Function object to check if an index is within the bounds [begin, end).
+ */
+template <typename map_type>
+struct bounds_checker {
+  size_type begin;
+  size_type end;
+
+  __device__ bounds_checker(size_type begin_, size_type end_) : begin{begin_}, end{end_} {}
+
+  __device__ bool operator()(map_type const index) { return ((index >= begin) && (index < end)); }
+};
+
+/**
+ * @brief The operation to perform when a gather map index is out of bounds
+ */
+enum class gather_bitmask_op {
+  DONT_CHECK,   ///< Don't check for out of bounds indices
+  PASSTHROUGH,  ///< Preserve mask at rows with out of bounds indices
+  NULLIFY,      ///< Nullify rows with out of bounds indices
+};
+
+template <gather_bitmask_op Op, typename MapIterator>
+struct gather_bitmask_functor {
+  table_device_view input;
+  bitmask_type** masks;
+  MapIterator gather_map;
+
+  __device__ bool operator()(size_type mask_idx, size_type bit_idx)
+  {
+    auto row_idx = gather_map[bit_idx];
+    auto col     = input.column(mask_idx);
+
+    if (Op != gather_bitmask_op::DONT_CHECK) {
+      bool out_of_range = is_signed_iterator<MapIterator>() ? (row_idx < 0 || row_idx >= col.size())
+                                                            : row_idx >= col.size();
+      if (out_of_range) {
+        if (Op == gather_bitmask_op::PASSTHROUGH) {
+          return bit_is_set(masks[mask_idx], bit_idx);
+        } else if (Op == gather_bitmask_op::NULLIFY) {
+          return false;
+        }
+      }
+    }
+
+    return col.is_valid(row_idx);
+  }
+};
+
+/**
+ * @brief Function for calling gather using iterators.
+ *
+ * Used by column_gatherer_impl definitions below.
+ *
+ * @tparam InputIterator Type for gather source data
+ * @tparam OutputIterator Type for gather results
+ * @tparam MapIterator Iterator type for the gather map
+ *
+ * @param source_itr Source data up to `source_size`
+ * @param source_size Maximum index value for source data
+ * @param target_itr Output iterator for gather result
+ * @param gather_map_begin Start of the gather map
+ * @param gather_map_end End of the gather map
+ * @param nullify_out_of_bounds True if map values are checked against `source_size`
+ * @param stream CUDA stream used for kernel launches.
+ */
+template <typename InputItr, typename OutputItr, typename MapIterator>
+void gather_helper(InputItr source_itr,
+                   size_type source_size,
+                   OutputItr target_itr,
+                   MapIterator gather_map_begin,
+                   MapIterator gather_map_end,
+                   bool nullify_out_of_bounds,
+                   rmm::cuda_stream_view stream)
+{
+  using map_type = typename std::iterator_traits<MapIterator>::value_type;
+  if (nullify_out_of_bounds) {
+    thrust::gather_if(rmm::exec_policy_nosync(stream),
+                      gather_map_begin,
+                      gather_map_end,
+                      gather_map_begin,
+                      source_itr,
+                      target_itr,
+                      bounds_checker<map_type>{0, source_size});
+  } else {
+    thrust::gather(
+      rmm::exec_policy_nosync(stream), gather_map_begin, gather_map_end, source_itr, target_itr);
+  }
+}
+
+// Error case when no other overload or specialization is available
+template <typename Element, typename Enable = void>
+struct column_gatherer_impl {
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type in gather.");
+  }
+};
+
+/**
+ * @brief Function object for gathering a type-erased
+ * column. To be used with the cudf::type_dispatcher.
+ */
+struct column_gatherer {
+  /**
+   * @brief Type-dispatched function to gather from one column to another based
+   * on a `gather_map`.
+   *
+   * @tparam Element Dispatched type for the column being gathered
+   * @tparam MapIterator Iterator type for the gather map
+   * @param source_column View into the column to gather from
+   * @param gather_map_begin Beginning of iterator range of integral values representing the gather
+   * map
+   * @param gather_map_end End of iterator range of integral values representing the gather map
+   * @param nullify_out_of_bounds Nullify values in `gather_map` that are out of bounds
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   */
+  template <typename Element, typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source_column,
+                                     MapIterator gather_map_begin,
+                                     MapIterator gather_map_end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    column_gatherer_impl<Element> gatherer{};
+
+    return gatherer(
+      source_column, gather_map_begin, gather_map_end, nullify_out_of_bounds, stream, mr);
+  }
+};
+
+/**
+ * @brief Function object for gathering a type-erased column.
+ *
+ * To be used with column_gatherer to provide specialization to handle
+ * fixed-width, string and other types.
+ *
+ * @tparam Element Dispatched type for the column being gathered
+ * @tparam MapIterator Iterator type for the gather map
+ */
+template <typename Element>
+struct column_gatherer_impl<Element, std::enable_if_t<is_rep_layout_compatible<Element>()>> {
+  /**
+   * @brief Type-dispatched function to gather from one column to another based
+   * on a `gather_map`.
+   *
+   * This handles fixed width type column_views only.
+   *
+   * @param source_column View into the column to gather from
+   * @param gather_map_begin Beginning of iterator range of integral values representing the gather
+   * map
+   * @param gather_map_end End of iterator range of integral values representing the gather map
+   * @param nullify_out_of_bounds Nullify values in `gather_map` that are out of bounds
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   */
+  template <typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source_column,
+                                     MapIterator gather_map_begin,
+                                     MapIterator gather_map_end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const num_rows = cudf::distance(gather_map_begin, gather_map_end);
+    auto const policy   = cudf::mask_allocation_policy::NEVER;
+    auto destination_column =
+      cudf::detail::allocate_like(source_column, num_rows, policy, stream, mr);
+
+    gather_helper(source_column.data<Element>(),
+                  source_column.size(),
+                  destination_column->mutable_view().template begin<Element>(),
+                  gather_map_begin,
+                  gather_map_end,
+                  nullify_out_of_bounds,
+                  stream);
+
+    return destination_column;
+  }
+};
+
+/**
+ * @brief Function object for gathering a type-erased
+ * column. To be used with column_gatherer to provide specialization for
+ * string_view.
+ *
+ * @tparam MapIterator Iterator type for the gather map
+ */
+template <>
+struct column_gatherer_impl<string_view> {
+  /**
+   * @brief Type-dispatched function to gather from one column to another based
+   * on a `gather_map`. This handles string_view type column_views only.
+   *
+   * @param source_column View into the column to gather from
+   * @param gather_map_begin Beginning of iterator range of integral values representing the gather
+   * map
+   * @param gather_map_end End of iterator range of integral values representing the gather map
+   * @param nullify_out_of_bounds Nullify values in `gather_map` that are out of bounds
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   */
+  template <typename MapItType>
+  std::unique_ptr<column> operator()(column_view const& source_column,
+                                     MapItType gather_map_begin,
+                                     MapItType gather_map_end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    if (true == nullify_out_of_bounds) {
+      return cudf::strings::detail::gather<true>(
+        strings_column_view(source_column), gather_map_begin, gather_map_end, stream, mr);
+    } else {
+      return cudf::strings::detail::gather<false>(
+        strings_column_view(source_column), gather_map_begin, gather_map_end, stream, mr);
+    }
+  }
+};
+
+/**
+ * @brief Column gather specialization for list_view column type.
+ *
+ * @tparam MapItRoot Iterator type to access the incoming root column.
+ *
+ * This functor is invoked only on the root column of a hierarchy of list
+ * columns. Recursion is handled internally.
+ */
+template <>
+struct column_gatherer_impl<list_view> {
+  /**
+   * @brief Gather a list column from a hierarchy of list columns.
+   *
+   * This function is similar to gather_list_nested() but the difference is
+   * significant.  This particular level takes a templated gather map iterator of
+   * any type.  As we start recursing, we need to be able to generate new gather
+   * maps for each level.  To do this requires manifesting a buffer of intermediate
+   * data. If we were to do that at level N and then wrap it in an anonymous iterator
+   * to be passed to level N+1, these buffers of data would remain resident for the
+   * entirety of the recursion.  But if level N+1 could create it's own iterator
+   * internally from a buffer passed to it by level N, it could then -delete- that
+   * buffer of data after using it, keeping the amount of extra memory needed
+   * to a minimum. see comment on "memory optimization" inside cudf::list::gather_list_nested
+   *
+   * The tree of calls can be visualized like this:
+   *
+   * @code{.pseudo}
+   * R :  this operator
+   * N :  lists::detail::gather_list_nested
+   * L :  lists::detail::gather_list_leaf
+   *
+   *        R
+   *       / \
+   *      L   N
+   *           \
+   *            N
+   *             \
+   *              ...
+   *               \
+   *                L
+   * @endcode
+   *
+   * This is the start of the recursion - we will only ever get in here once.
+   * We will only ever travel down the left branch or the right branch, and we
+   * will always end up in a final call to gather_list_leaf.
+   *
+   * @param column View into the column to gather from
+   * @param gather_map_begin iterator representing the start of the range to gather from
+   * @param gather_map_end iterator representing the end of the range to gather from
+   * @param nullify_out_of_bounds Nullify values in the gather map that are out of bounds
+   * @param stream CUDA stream on which to execute kernels
+   * @param mr Memory resource to use for all allocations
+   *
+   * @returns column with elements gathered based on the gather map
+   *
+   */
+  template <typename MapItRoot>
+  std::unique_ptr<column> operator()(column_view const& column,
+                                     MapItRoot gather_map_begin,
+                                     MapItRoot gather_map_end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    lists_column_view list(column);
+    auto gather_map_size = std::distance(gather_map_begin, gather_map_end);
+    // if the gather map is empty, return an empty column
+    if (gather_map_size == 0) { return empty_like(column); }
+
+    // generate gather_data for the next level (N+1)
+    lists::detail::gather_data gd = nullify_out_of_bounds
+                                      ? lists::detail::make_gather_data<true>(
+                                          column, gather_map_begin, gather_map_size, stream, mr)
+                                      : lists::detail::make_gather_data<false>(
+                                          column, gather_map_begin, gather_map_size, stream, mr);
+
+    // the nesting case.
+    if (list.child().type() == cudf::data_type{type_id::LIST}) {
+      // gather children
+      auto child = lists::detail::gather_list_nested(list.get_sliced_child(stream), gd, stream, mr);
+
+      // return the final column
+      return make_lists_column(gather_map_size,
+                               std::move(gd.offsets),
+                               std::move(child),
+                               0,
+                               rmm::device_buffer{0, stream, mr});
+    }
+
+    // it's a leaf.  do a regular gather
+    auto child = lists::detail::gather_list_leaf(list.get_sliced_child(stream), gd, stream, mr);
+
+    // assemble final column
+    return make_lists_column(gather_map_size,
+                             std::move(gd.offsets),
+                             std::move(child),
+                             0,
+                             rmm::device_buffer{0, stream, mr});
+  }
+};
+
+/**
+ * @brief Column gather specialization for dictionary column type.
+ */
+template <>
+struct column_gatherer_impl<dictionary32> {
+  /**
+   * @brief Type-dispatched function to gather from one column to another based
+   * on a `gather_map`.
+   *
+   * @param source_column View into the column to gather from
+   * @param gather_map_begin Beginning of iterator range of integral values representing the gather
+   * map
+   * @param gather_map_end End of iterator range of integral values representing the gather map
+   * @param nullify_out_of_bounds Nullify values in `gather_map` that are out of bounds
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   * @return New dictionary column with gathered rows.
+   */
+  template <typename MapItType>
+  std::unique_ptr<column> operator()(column_view const& source_column,
+                                     MapItType gather_map_begin,
+                                     MapItType gather_map_end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    dictionary_column_view dictionary(source_column);
+    auto output_count = std::distance(gather_map_begin, gather_map_end);
+    if (output_count == 0) return make_empty_column(type_id::DICTIONARY32);
+    // The gather could cause some keys to be abandoned -- no indices point to them.
+    // In this case, we could do further work to remove the abandoned keys and
+    // reshuffle the indices values.
+    // We decided we will copy the keys for gather since the keys column should
+    // be relatively smallish.
+    // Also, there are scenarios where the keys are common with other dictionaries
+    // and the original intention was to share the keys here.
+    auto keys_copy = std::make_unique<column>(dictionary.keys(), stream, mr);
+    // Perform gather on just the indices
+    column_view indices = dictionary.get_indices_annotated();
+    auto new_indices    = cudf::detail::allocate_like(
+      indices, output_count, cudf::mask_allocation_policy::NEVER, stream, mr);
+    gather_helper(
+      cudf::detail::indexalator_factory::make_input_iterator(indices),
+      indices.size(),
+      cudf::detail::indexalator_factory::make_output_iterator(new_indices->mutable_view()),
+      gather_map_begin,
+      gather_map_end,
+      nullify_out_of_bounds,
+      stream);
+    // dissect the column's contents
+    auto indices_type = new_indices->type();
+    auto null_count   = new_indices->null_count();  // get this before calling release()
+    auto contents     = new_indices->release();     // new_indices will now be empty
+    // build the output indices column from the contents' data component
+    auto indices_column = std::make_unique<column>(indices_type,
+                                                   static_cast<size_type>(output_count),
+                                                   std::move(*(contents.data.release())),
+                                                   rmm::device_buffer{0, stream, mr},
+                                                   0);  // set null count to 0
+    // finally, build the dictionary with the null_mask component and the keys and indices
+    return make_dictionary_column(std::move(keys_copy),
+                                  std::move(indices_column),
+                                  std::move(*(contents.null_mask.release())),
+                                  null_count);
+  }
+};
+
+template <>
+struct column_gatherer_impl<struct_view> {
+  template <typename MapItRoot>
+  std::unique_ptr<column> operator()(column_view const& column,
+                                     MapItRoot gather_map_begin,
+                                     MapItRoot gather_map_end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const gather_map_size = std::distance(gather_map_begin, gather_map_end);
+    if (gather_map_size == 0) { return empty_like(column); }
+
+    // Gathering needs to operate on the sliced children since they need to take into account the
+    // offset of the parent structs column.
+    std::vector<cudf::column_view> sliced_children;
+    std::transform(thrust::make_counting_iterator(0),
+                   thrust::make_counting_iterator(column.num_children()),
+                   std::back_inserter(sliced_children),
+                   [&stream, structs_view = structs_column_view{column}](auto const idx) {
+                     return structs_view.get_sliced_child(idx, stream);
+                   });
+
+    std::vector<std::unique_ptr<cudf::column>> output_struct_members;
+    std::transform(sliced_children.begin(),
+                   sliced_children.end(),
+                   std::back_inserter(output_struct_members),
+                   [&](auto const& col) {
+                     return cudf::type_dispatcher<dispatch_storage_type>(col.type(),
+                                                                         column_gatherer{},
+                                                                         col,
+                                                                         gather_map_begin,
+                                                                         gather_map_end,
+                                                                         nullify_out_of_bounds,
+                                                                         stream,
+                                                                         mr);
+                   });
+
+    auto const nullable =
+      nullify_out_of_bounds || std::any_of(sliced_children.begin(),
+                                           sliced_children.end(),
+                                           [](auto const& col) { return col.nullable(); });
+
+    if (nullable) {
+      gather_bitmask(
+        // Table view of struct column.
+        cudf::table_view{
+          std::vector<cudf::column_view>{sliced_children.begin(), sliced_children.end()}},
+        gather_map_begin,
+        output_struct_members,
+        nullify_out_of_bounds ? gather_bitmask_op::NULLIFY : gather_bitmask_op::DONT_CHECK,
+        stream,
+        mr);
+    }
+
+    return cudf::make_structs_column(
+      gather_map_size,
+      std::move(output_struct_members),
+      0,
+      rmm::device_buffer{0, stream, mr},  // Null mask will be fixed up in cudf::gather().
+      stream,
+      mr);
+  }
+};
+
+/**
+ * @brief Function object for applying a transformation on the gathermap
+ * that converts negative indices to positive indices
+ *
+ * A negative index `i` is transformed to `i + size`, where `size` is
+ * the number of elements in the column being gathered from.
+ * Allowable values for the index `i` are in the range `[-size, size)`.
+ * Thus, when gathering from a column of size `10`, the index `-1`
+ * is transformed to `9` (i.e., the last element), `-2` is transformed
+ * to `8` (the second-to-last element) and so on.
+ * Positive indices are unchanged by this transformation.
+ */
+template <typename map_type>
+struct index_converter : public thrust::unary_function<map_type, map_type> {
+  index_converter(size_type n_rows) : n_rows(n_rows) {}
+
+  __device__ map_type operator()(map_type in) const { return ((in % n_rows) + n_rows) % n_rows; }
+  size_type n_rows;
+};
+
+template <gather_bitmask_op Op, typename GatherMap>
+void gather_bitmask(table_device_view input,
+                    GatherMap gather_map_begin,
+                    bitmask_type** masks,
+                    size_type mask_count,
+                    size_type mask_size,
+                    size_type* valid_counts,
+                    rmm::cuda_stream_view stream)
+{
+  if (mask_size == 0) { return; }
+
+  constexpr size_type block_size = 256;
+  using Selector                 = gather_bitmask_functor<Op, decltype(gather_map_begin)>;
+  auto selector                  = Selector{input, masks, gather_map_begin};
+  auto counting_it               = thrust::make_counting_iterator(0);
+  auto kernel =
+    valid_if_n_kernel<decltype(counting_it), decltype(counting_it), Selector, block_size>;
+
+  cudf::detail::grid_1d grid{mask_size, block_size, 1};
+  kernel<<<grid.num_blocks, block_size, 0, stream.value()>>>(
+    counting_it, counting_it, selector, masks, mask_count, mask_size, valid_counts);
+}
+
+template <typename MapIterator>
+void gather_bitmask(table_view const& source,
+                    MapIterator gather_map,
+                    std::vector<std::unique_ptr<column>>& target,
+                    gather_bitmask_op op,
+                    rmm::cuda_stream_view stream,
+                    rmm::mr::device_memory_resource* mr)
+{
+  if (target.empty()) { return; }
+
+  // Validate that all target columns have the same size
+  auto const target_rows = target.front()->size();
+  CUDF_EXPECTS(std::all_of(target.begin(),
+                           target.end(),
+                           [target_rows](auto const& col) { return target_rows == col->size(); }),
+               "Column size mismatch");
+
+  // Create null mask if source is nullable but target is not
+  for (size_t i = 0; i < target.size(); ++i) {
+    if ((source.column(i).nullable() or op == gather_bitmask_op::NULLIFY) and
+        not target[i]->nullable()) {
+      auto const state =
+        op == gather_bitmask_op::PASSTHROUGH ? mask_state::ALL_VALID : mask_state::UNINITIALIZED;
+      auto mask = detail::create_null_mask(target[i]->size(), state, stream, mr);
+      target[i]->set_null_mask(std::move(mask), 0);
+    }
+  }
+
+  // Make device array of target bitmask pointers
+  std::vector<bitmask_type*> target_masks(target.size());
+  std::transform(target.begin(), target.end(), target_masks.begin(), [](auto const& col) {
+    return col->mutable_view().null_mask();
+  });
+  auto d_target_masks =
+    make_device_uvector_async(target_masks, stream, rmm::mr::get_current_device_resource());
+
+  auto const device_source = table_device_view::create(source, stream);
+  auto d_valid_counts      = make_zeroed_device_uvector_async<size_type>(
+    target.size(), stream, rmm::mr::get_current_device_resource());
+
+  // Dispatch operation enum to get implementation
+  auto const impl = [op]() {
+    switch (op) {
+      case gather_bitmask_op::DONT_CHECK:
+        return gather_bitmask<gather_bitmask_op::DONT_CHECK, MapIterator>;
+      case gather_bitmask_op::PASSTHROUGH:
+        return gather_bitmask<gather_bitmask_op::PASSTHROUGH, MapIterator>;
+      case gather_bitmask_op::NULLIFY:
+        return gather_bitmask<gather_bitmask_op::NULLIFY, MapIterator>;
+      default: CUDF_FAIL("Invalid gather_bitmask_op");
+    }
+  }();
+  impl(*device_source,
+       gather_map,
+       d_target_masks.data(),
+       target.size(),
+       target_rows,
+       d_valid_counts.data(),
+       stream);
+
+  // Copy the valid counts into each column
+  auto const valid_counts = make_std_vector_sync(d_valid_counts, stream);
+  for (size_t i = 0; i < target.size(); ++i) {
+    if (target[i]->nullable()) {
+      auto const null_count = target_rows - valid_counts[i];
+      target[i]->set_null_count(null_count);
+    }
+  }
+}
+
+/**
+ * @brief Gathers the specified rows of a set of columns according to a gather map.
+ *
+ * Gathers the rows of the source columns according to `gather_map` such that row "i"
+ * in the resulting table's columns will contain row "gather_map[i]" from the source columns.
+ * The number of rows in the result table will be equal to the number of elements in
+ * `gather_map`.
+ *
+ * A negative value `i` in the `gather_map` is interpreted as `i+n`, where
+ * `n` is the number of rows in the `source_table`.
+ *
+ * tparam MapIterator Iterator type for the gather map
+ * @param[in] source_table View into the table containing the input columns whose rows will be
+ * gathered
+ * @param[in] gather_map_begin Beginning of iterator range of integer indices that map the rows in
+ * the source columns to rows in the destination columns
+ * @param[in] gather_map_end End of iterator range of integer indices that map the rows in the
+ * source columns to rows in the destination columns
+ * @param[in] bounds_policy Policy to apply to account for possible out-of-bound indices
+ * `DONT_CHECK` skips all bound checking for gather map values. `NULLIFY` coerces rows that
+ * corresponds to out-of-bound indices in the gather map to be null elements. Callers should
+ * use `DONT_CHECK` when they are certain that the gather_map contains only valid indices for
+ * better performance. In case there are out-of-bound indices in the gather map, the behavior
+ * is undefined. Defaults to `DONT_CHECK`.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return cudf::table Result of the gather
+ */
+template <typename MapIterator>
+std::unique_ptr<table> gather(table_view const& source_table,
+                              MapIterator gather_map_begin,
+                              MapIterator gather_map_end,
+                              out_of_bounds_policy bounds_policy,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  std::vector<std::unique_ptr<column>> destination_columns;
+
+  // TODO: Could be beneficial to use streams internally here
+
+  for (auto const& source_column : source_table) {
+    // The data gather for n columns will be put on the first n streams
+    destination_columns.push_back(
+      cudf::type_dispatcher<dispatch_storage_type>(source_column.type(),
+                                                   column_gatherer{},
+                                                   source_column,
+                                                   gather_map_begin,
+                                                   gather_map_end,
+                                                   bounds_policy == out_of_bounds_policy::NULLIFY,
+                                                   stream,
+                                                   mr));
+  }
+
+  auto const nullable = bounds_policy == out_of_bounds_policy::NULLIFY ||
+                        std::any_of(source_table.begin(), source_table.end(), [](auto const& col) {
+                          return col.nullable();
+                        });
+  if (nullable) {
+    auto const op = bounds_policy == out_of_bounds_policy::NULLIFY ? gather_bitmask_op::NULLIFY
+                                                                   : gather_bitmask_op::DONT_CHECK;
+    gather_bitmask(source_table, gather_map_begin, destination_columns, op, stream, mr);
+  }
+
+  return std::make_unique<table>(std::move(destination_columns));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/gather.hpp b/cpp/include/cudf/detail/gather.hpp
new file mode 100644
index 0000000..034eb6c
--- /dev/null
+++ b/cpp/include/cudf/detail/gather.hpp
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+
+namespace detail {
+
+enum class negative_index_policy : bool { ALLOWED, NOT_ALLOWED };
+
+/**
+ * @brief Gathers the specified rows of a set of columns according to a gather map.
+ *
+ * Gathers the rows of the source columns according to `gather_map` such that row "i"
+ * in the resulting table's columns will contain row "gather_map[i]" from the source columns.
+ * The number of rows in the result table will be equal to the number of elements in
+ * `gather_map`.
+ *
+ * A negative value `i` in the `gather_map` is interpreted as `i+n`, where
+ * `n` is the number of rows in the `source_table`.
+ *
+ * @throws cudf::logic_error if `check_bounds == true` and an index exists in
+ * `gather_map` outside the range `[-n, n)`, where `n` is the number of rows in
+ * the source table. If `check_bounds == false`, the behavior is undefined.
+ *
+ * @param[in] source_table The input columns whose rows will be gathered
+ * @param[in] gather_map View into a non-nullable column of integral indices that maps the
+ * rows in the source columns to rows in the destination columns.
+ * @param[in] bounds_policy How to treat out-of-bounds indices. `NULLIFY` coerces rows that
+ * correspond to out-of-bounds indices in the gather map to be null elements. For better
+ * performance, use `DONT_CHECK` when the `gather_map` is known to contain only valid
+ * indices. If `policy` is set to `DONT_CHECK` and there are out-of-bounds indices in `gather_map`,
+ * the behavior is undefined.
+ * @param[in] negative_index_policy Interpret each negative index `i` in the
+ * `gather_map` as the positive index `i+num_source_rows`.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return Result of the gather
+ */
+std::unique_ptr<table> gather(table_view const& source_table,
+                              column_view const& gather_map,
+                              out_of_bounds_policy bounds_policy,
+                              negative_index_policy neg_indices,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::detail::gather(table_view const&,column_view const&,table_view
+ * const&,cudf::out_of_bounds_policy,cudf::detail::negative_index_policy,rmm::cuda_stream_view,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @throws cudf::logic_error if `gather_map` span size is larger than max of `size_type`.
+ */
+std::unique_ptr<table> gather(table_view const& source_table,
+                              device_span<size_type const> const gather_map,
+                              out_of_bounds_policy bounds_policy,
+                              negative_index_policy neg_indices,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/get_value.cuh b/cpp/include/cudf/detail/get_value.cuh
new file mode 100644
index 0000000..5ea0d06
--- /dev/null
+++ b/cpp/include/cudf/detail/get_value.cuh
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Return a fixed-width value from column.
+ *
+ * Retrieves the specified value from device memory. This function
+ * synchronizes the stream.
+ *
+ * @throw cudf::logic_error if `col_view` is not a fixed-width column
+ * @throw cudf::logic_error if `element_index < 0 or >= col_view.size()`
+ *
+ * @tparam T Fixed-width type to return.
+ * @param col_view The column to retrieve the element from.
+ * @param element_index The specific element to retrieve
+ * @param stream The stream to use for copying the value to the host.
+ * @return Value from the `col_view[element_index]`
+ */
+template <typename T>
+T get_value(column_view const& col_view, size_type element_index, rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(cudf::is_fixed_width(col_view.type()), "get_value supports only fixed-width types");
+  CUDF_EXPECTS(data_type(type_to_id<T>()) == col_view.type(), "get_value data type mismatch");
+  CUDF_EXPECTS(element_index >= 0 && element_index < col_view.size(),
+               "invalid element_index value");
+  T result;
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    &result, col_view.data<T>() + element_index, sizeof(T), cudaMemcpyDefault, stream.value()));
+  stream.synchronize();
+  return result;
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/groupby.hpp b/cpp/include/cudf/detail/groupby.hpp
new file mode 100644
index 0000000..0afa69b
--- /dev/null
+++ b/cpp/include/cudf/detail/groupby.hpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/groupby.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+#include <utility>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace hash {
+/**
+ * @brief Indicates if a set of aggregation requests can be satisfied with a
+ * hash-based groupby implementation.
+ *
+ * @param requests The set of columns to aggregate and the aggregations to
+ * perform
+ * @return true A hash-based groupby can be used
+ * @return false A hash-based groupby cannot be used
+ */
+bool can_use_hash_groupby(host_span<aggregation_request const> requests);
+
+// Hash-based groupby
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby(
+  table_view const& keys,
+  host_span<aggregation_request const> requests,
+  null_policy include_null_keys,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+}  // namespace hash
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/groupby/group_replace_nulls.hpp b/cpp/include/cudf/detail/groupby/group_replace_nulls.hpp
new file mode 100644
index 0000000..e081a62
--- /dev/null
+++ b/cpp/include/cudf/detail/groupby/group_replace_nulls.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/exec_policy.hpp>
+namespace cudf {
+namespace groupby {
+namespace detail {
+
+/**
+ * @brief Internal API to replace nulls with preceding/following non-null values in @p value
+ *
+ * @param[in] grouped_value A column whose null values will be replaced.
+ * @param[in] group_labels Group labels for @p grouped_value, corresponding to group keys.
+ * @param[in] replace_policy Specify the position of replacement values relative to null values.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate device memory of the returned column.
+ */
+std::unique_ptr<column> group_replace_nulls(cudf::column_view const& grouped_value,
+                                            device_span<size_type const> group_labels,
+                                            cudf::replace_policy replace_policy,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/groupby/sort_helper.hpp b/cpp/include/cudf/detail/groupby/sort_helper.hpp
new file mode 100644
index 0000000..7b386eb
--- /dev/null
+++ b/cpp/include/cudf/detail/groupby/sort_helper.hpp
@@ -0,0 +1,234 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace sort {
+/**
+ * @brief Helper class for computing sort-based groupby
+ *
+ * This class serves the purpose of sorting the keys and values and provides
+ * building blocks for aggregations. It can provide:
+ * 1. On-demand grouping or sorting of a value column based on `keys`
+ *   which is provided at construction
+ * 2. Group offsets: starting offsets of all groups in sorted key table
+ * 3. Group valid sizes: The number of valid values in each group in a sorted
+ *   value column
+ */
+struct sort_groupby_helper {
+  using index_vector       = rmm::device_uvector<size_type>;
+  using bitmask_vector     = rmm::device_uvector<bitmask_type>;
+  using column_ptr         = std::unique_ptr<column>;
+  using index_vector_ptr   = std::unique_ptr<index_vector>;
+  using bitmask_vector_ptr = std::unique_ptr<bitmask_vector>;
+
+  /**
+   * @brief Construct a new helper object
+   *
+   * If `include_null_keys == NO`, then any row in `keys` containing a null
+   * value will effectively be discarded. I.e., any values corresponding to
+   * discarded rows in `keys` will not contribute to any aggregation.
+   *
+   * @param keys table to group by
+   * @param include_null_keys Include rows in keys with nulls
+   * @param keys_pre_sorted Indicate if the keys are already sorted. Enables
+   *                        optimizations to help skip re-sorting keys.
+   * @param null_precedence Indicates the ordering of nulls in each column.
+   *                        Default behavior for each column is
+   *                        `null_order::AFTER`
+   */
+  sort_groupby_helper(table_view const& keys,
+                      null_policy include_null_keys,
+                      sorted keys_pre_sorted,
+                      std::vector<null_order> const& null_precedence);
+
+  ~sort_groupby_helper()                                     = default;
+  sort_groupby_helper(sort_groupby_helper const&)            = delete;
+  sort_groupby_helper& operator=(sort_groupby_helper const&) = delete;
+  sort_groupby_helper(sort_groupby_helper&&)                 = default;
+  sort_groupby_helper& operator=(sort_groupby_helper&&)      = default;
+
+  /**
+   * @brief Groups a column of values according to `keys` and sorts within each
+   *  group.
+   *
+   * Groups the @p values where the groups are dictated by key table and each
+   * group is sorted in ascending order, with NULL elements positioned at the
+   * end of each group.
+   *
+   * @throw cudf::logic_error if `values.size() != keys.num_rows()`
+   *
+   * @param values The value column to group and sort
+   * @return the sorted and grouped column
+   */
+  std::unique_ptr<column> sorted_values(column_view const& values,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Groups a column of values according to `keys`
+   *
+   * The values within each group maintain their original order.
+   *
+   * @throw cudf::logic_error if `values.size() != keys.num_rows()`
+   *
+   * @param values The value column to group
+   * @return the grouped column
+   */
+  std::unique_ptr<column> grouped_values(column_view const& values,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Get a table of sorted unique keys
+   *
+   * @return a new table in which each row is a unique row in the sorted key table.
+   */
+  std::unique_ptr<table> unique_keys(rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Get a table of sorted keys
+   *
+   * @return a new table containing the sorted keys.
+   */
+  std::unique_ptr<table> sorted_keys(rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Get the number of groups in `keys`
+   */
+  size_type num_groups(rmm::cuda_stream_view stream) { return group_offsets(stream).size() - 1; }
+
+  /**
+   * @brief check if the groupby keys are presorted
+   */
+  bool is_presorted() { return _keys_pre_sorted == sorted::YES; }
+
+  /**
+   * @brief Return the effective number of keys
+   *
+   * When include_null_keys = YES, returned value is same as `keys.num_rows()`
+   * When include_null_keys = NO, returned value is the number of rows in `keys`
+   *  in which no element is null
+   */
+  size_type num_keys(rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Get the sorted order of `keys`.
+   *
+   * Gathering `keys` by sort order indices will produce the sorted key table.
+   *
+   * When ignore_null_keys = true, the result will not include indices
+   * for null keys.
+   *
+   * Computes and stores the key sorted order on first invocation, and returns
+   * the stored order on subsequent calls.
+   *
+   * @return the sort order indices for `keys`.
+   */
+  column_view key_sort_order(rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Get each group's offset into the sorted order of `keys`.
+   *
+   * Computes and stores the group offsets on first invocation and returns
+   * the stored group offsets on subsequent calls.
+   * This returns a vector of size `num_groups() + 1` such that the size of
+   * group `i` is `group_offsets[i+1] - group_offsets[i]`
+   *
+   * @return vector of offsets of the starting point of each group in the sorted
+   * key table
+   */
+  index_vector const& group_offsets(rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Get the group labels corresponding to the sorted order of `keys`.
+   *
+   * Each group is assigned a unique numerical "label" in
+   * `[0, 1, 2, ... , num_groups() - 1, num_groups(stream))`.
+   * For a row in sorted `keys`, its corresponding group label indicates which
+   * group it belongs to.
+   *
+   * Computes and stores labels on first invocation and returns stored labels on
+   * subsequent calls.
+   *
+   * @return vector of group labels for each row in the sorted key column
+   */
+  index_vector const& group_labels(rmm::cuda_stream_view stream);
+
+ private:
+  /**
+   * @brief Get the group labels for unsorted keys
+   *
+   * Returns the group label for every row in the original `keys` table. For a
+   * given unique key row, its group label is equivalent to what is returned by
+   * `group_labels(stream)`. However, if a row contains a null value, and
+   * `include_null_keys == NO`, then its label is NULL.
+   *
+   * Computes and stores unsorted labels on first invocation and returns stored
+   * labels on subsequent calls.
+   *
+   * @return A nullable column of `INT32` containing group labels in the order
+   *         of the unsorted key table
+   */
+  column_view unsorted_keys_labels(rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Get the column representing the row bitmask for the `keys`
+   *
+   * Computes a bitmask corresponding to the rows of `keys` where if bit `i` is
+   * zero, then row `i` contains one or more null values. If bit `i` is one,
+   * then row `i` does not contain null values. This bitmask is added as null
+   * mask of a column of type `INT8` where all the data values are the same and
+   * the elements differ only in validity.
+   *
+   * Computes and stores bitmask on first invocation and returns stored column
+   * on subsequent calls.
+   */
+  column_view keys_bitmask_column(rmm::cuda_stream_view stream);
+
+ private:
+  column_ptr _key_sorted_order;      ///< Indices to produce _keys in sorted order
+  column_ptr _unsorted_keys_labels;  ///< Group labels for unsorted _keys
+  column_ptr _keys_bitmask_column;   ///< Column representing rows with one or more nulls values
+  table_view _keys;                  ///< Input keys to sort by
+
+  index_vector_ptr
+    _group_offsets;  ///< Indices into sorted _keys indicating starting index of each groups
+  index_vector_ptr _group_labels;  ///< Group labels for sorted _keys
+
+  size_type _num_keys;      ///< Number of effective rows in _keys (adjusted for _include_null_keys)
+  sorted _keys_pre_sorted;  ///< Whether _keys are pre-sorted
+  null_policy _include_null_keys;  ///< Whether to use rows with nulls in _keys for grouping
+  std::vector<null_order> _null_precedence;  ///< How to sort NULLs
+};
+
+}  // namespace sort
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/hash_reduce_by_row.cuh b/cpp/include/cudf/detail/hash_reduce_by_row.cuh
new file mode 100644
index 0000000..f63d192
--- /dev/null
+++ b/cpp/include/cudf/detail/hash_reduce_by_row.cuh
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/uninitialized_fill.h>
+
+#include <cuco/static_map.cuh>
+
+namespace cudf::detail {
+
+using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+using hash_map_type =
+  cuco::static_map<size_type, size_type, cuda::thread_scope_device, hash_table_allocator_type>;
+
+/**
+ * @brief The base struct for customized reduction functor to perform reduce-by-key with keys are
+ * rows that compared equal.
+ *
+ * TODO: We need to switch to use `static_reduction_map` when it is ready
+ * (https://github.com/NVIDIA/cuCollections/pull/98).
+ */
+template <typename MapView, typename KeyHasher, typename KeyEqual, typename OutputType>
+struct reduce_by_row_fn_base {
+ protected:
+  MapView const d_map;
+  KeyHasher const d_hasher;
+  KeyEqual const d_equal;
+  OutputType* const d_output;
+
+  reduce_by_row_fn_base(MapView const& d_map,
+                        KeyHasher const& d_hasher,
+                        KeyEqual const& d_equal,
+                        OutputType* const d_output)
+    : d_map{d_map}, d_hasher{d_hasher}, d_equal{d_equal}, d_output{d_output}
+  {
+  }
+
+  /**
+   * @brief Return a pointer to the output array at the given index.
+   *
+   * @param idx The access index
+   * @return A pointer to the given index in the output array
+   */
+  __device__ OutputType* get_output_ptr(size_type const idx) const
+  {
+    auto const iter = d_map.find(idx, d_hasher, d_equal);
+
+    if (iter != d_map.end()) {
+      // Only one (undetermined) index value of the duplicate rows could be inserted into the map.
+      // As such, looking up for all indices of duplicate rows always returns the same value.
+      auto const inserted_idx = iter->second.load(cuda::std::memory_order_relaxed);
+
+      // All duplicate rows will have concurrent access to this same output slot.
+      return &d_output[inserted_idx];
+    } else {
+      // All input `idx` values have been inserted into the map before.
+      // Thus, searching for an `idx` key resulting in the `end()` iterator only happens if
+      // `d_equal(idx, idx) == false`.
+      // Such situations are due to comparing nulls or NaNs which are considered as always unequal.
+      // In those cases, all rows containing nulls or NaNs are distinct. Just return their direct
+      // output slot.
+      return &d_output[idx];
+    }
+  }
+};
+
+/**
+ * @brief Perform a reduction on groups of rows that are compared equal.
+ *
+ * This is essentially a reduce-by-key operation with keys are non-contiguous rows and are compared
+ * equal. A hash table is used to find groups of equal rows.
+ *
+ * At the beginning of the operation, the entire output array is filled with a value given by
+ * the `init` parameter. Then, the reduction result for each row group is written into the output
+ * array at the index of an unspecified row in the group.
+ *
+ * @tparam ReduceFuncBuilder The builder class that must have a `build()` method returning a
+ *         reduction functor derived from `reduce_by_row_fn_base`
+ * @tparam OutputType Type of the reduction results
+ * @param map The auxiliary map to perform reduction
+ * @param preprocessed_input The preprocessed of the input rows for computing row hashing and row
+ *        comparisons
+ * @param num_rows The number of all input rows
+ * @param has_nulls Indicate whether the input rows has any nulls at any nested levels
+ * @param has_nested_columns Indicates whether the input table has any nested columns
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether NaN values in floating point column should be
+ *        considered equal.
+ * @param init The initial value for reduction of each row group
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned vector
+ * @return A device_uvector containing the reduction results
+ */
+template <typename ReduceFuncBuilder, typename OutputType>
+rmm::device_uvector<OutputType> hash_reduce_by_row(
+  hash_map_type const& map,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const preprocessed_input,
+  size_type num_rows,
+  cudf::nullate::DYNAMIC has_nulls,
+  bool has_nested_columns,
+  null_equality nulls_equal,
+  nan_equality nans_equal,
+  ReduceFuncBuilder func_builder,
+  OutputType init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto const map_dview  = map.get_device_view();
+  auto const row_hasher = cudf::experimental::row::hash::row_hasher(preprocessed_input);
+  auto const key_hasher = row_hasher.device_hasher(has_nulls);
+  auto const row_comp   = cudf::experimental::row::equality::self_comparator(preprocessed_input);
+
+  auto reduction_results = rmm::device_uvector<OutputType>(num_rows, stream, mr);
+  thrust::uninitialized_fill(
+    rmm::exec_policy(stream), reduction_results.begin(), reduction_results.end(), init);
+
+  auto const reduce_by_row = [&](auto const value_comp) {
+    if (has_nested_columns) {
+      auto const key_equal = row_comp.equal_to<true>(has_nulls, nulls_equal, value_comp);
+      thrust::for_each(
+        rmm::exec_policy(stream),
+        thrust::make_counting_iterator(0),
+        thrust::make_counting_iterator(num_rows),
+        func_builder.build(map_dview, key_hasher, key_equal, reduction_results.begin()));
+    } else {
+      auto const key_equal = row_comp.equal_to<false>(has_nulls, nulls_equal, value_comp);
+      thrust::for_each(
+        rmm::exec_policy(stream),
+        thrust::make_counting_iterator(0),
+        thrust::make_counting_iterator(num_rows),
+        func_builder.build(map_dview, key_hasher, key_equal, reduction_results.begin()));
+    }
+  };
+
+  if (nans_equal == nan_equality::ALL_EQUAL) {
+    using nan_equal_comparator =
+      cudf::experimental::row::equality::nan_equal_physical_equality_comparator;
+    reduce_by_row(nan_equal_comparator{});
+  } else {
+    using nan_unequal_comparator = cudf::experimental::row::equality::physical_equality_comparator;
+    reduce_by_row(nan_unequal_comparator{});
+  }
+
+  return reduction_results;
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/indexalator.cuh b/cpp/include/cudf/detail/indexalator.cuh
new file mode 100644
index 0000000..6532dae
--- /dev/null
+++ b/cpp/include/cudf/detail/indexalator.cuh
@@ -0,0 +1,308 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/normalizing_iterator.cuh>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief The index normalizing input iterator.
+ *
+ * This is an iterator that can be used for index types (integers) without
+ * requiring a type-specific instance. It can be used for any iterator
+ * interface for reading an array of integer values of type
+ * int8, int16, int32, int64, uint8, uint16, uint32, or uint64.
+ * Reading specific elements always return a `size_type` integer.
+ *
+ * Use the indexalator_factory to create an appropriate input iterator
+ * from a column_view.
+ *
+ * Example input iterator usage.
+ * @code
+ *  auto begin = indexalator_factory::create_input_iterator(gather_map);
+ *  auto end   = begin + gather_map.size();
+ *  auto result = detail::gather( source, begin, end, IGNORE, stream, mr );
+ * @endcode
+ *
+ * @code
+ *  auto begin = indexalator_factory::create_input_iterator(indices);
+ *  auto end   = begin + indices.size();
+ *  auto result = thrust::find(thrust::device, begin, end, size_type{12} );
+ * @endcode
+ */
+using input_indexalator = input_normalator<cudf::size_type>;
+
+/**
+ * @brief The index normalizing output iterator.
+ *
+ * This is an iterator that can be used for index types (integers) without
+ * requiring a type-specific instance. It can be used for any iterator
+ * interface for writing an array of integer values of type
+ * int8, int16, int32, int64, uint8, uint16, uint32, or uint64.
+ * Setting specific elements always accept `size_type` integer values.
+ *
+ * Use the indexalator_factory to create an appropriate output iterator
+ * from a mutable_column_view.
+ *
+ * Example output iterator usage.
+ * @code
+ *  auto result_itr = indexalator_factory::create_output_iterator(indices->mutable_view());
+ *  thrust::lower_bound(rmm::exec_policy(stream),
+ *                      input->begin<Element>(),
+ *                      input->end<Element>(),
+ *                      values->begin<Element>(),
+ *                      values->end<Element>(),
+ *                      result_itr,
+ *                      thrust::less<Element>());
+ * @endcode
+ */
+using output_indexalator = output_normalator<cudf::size_type>;
+
+/**
+ * @brief Use this class to create an indexalator instance.
+ */
+struct indexalator_factory {
+  /**
+   * @brief A type_dispatcher functor to create an input iterator from an indices column.
+   */
+  struct input_indexalator_fn {
+    template <typename IndexType, std::enable_if_t<is_index_type<IndexType>()>* = nullptr>
+    input_indexalator operator()(column_view const& indices)
+    {
+      return input_indexalator(indices.data<IndexType>(), indices.type());
+    }
+    template <typename IndexType,
+              typename... Args,
+              std::enable_if_t<not is_index_type<IndexType>()>* = nullptr>
+    input_indexalator operator()(Args&&... args)
+    {
+      CUDF_FAIL("indices must be an index type");
+    }
+  };
+
+  /**
+   * @brief Use this class to create an indexalator to a scalar index.
+   */
+  struct input_indexalator_scalar_fn {
+    template <typename IndexType, std::enable_if_t<is_index_type<IndexType>()>* = nullptr>
+    input_indexalator operator()(scalar const& index)
+    {
+      // note: using static_cast<scalar_type_t<IndexType> const&>(index) creates a copy
+      auto const scalar_impl = static_cast<scalar_type_t<IndexType> const*>(&index);
+      return input_indexalator(scalar_impl->data(), index.type());
+    }
+    template <typename IndexType,
+              typename... Args,
+              std::enable_if_t<not is_index_type<IndexType>()>* = nullptr>
+    input_indexalator operator()(Args&&... args)
+    {
+      CUDF_FAIL("scalar must be an index type");
+    }
+  };
+
+  /**
+   * @brief A type_dispatcher functor to create an output iterator from an indices column.
+   */
+  struct output_indexalator_fn {
+    template <typename IndexType, std::enable_if_t<is_index_type<IndexType>()>* = nullptr>
+    output_indexalator operator()(mutable_column_view const& indices)
+    {
+      return output_indexalator(indices.data<IndexType>(), indices.type());
+    }
+    template <typename IndexType,
+              typename... Args,
+              std::enable_if_t<not is_index_type<IndexType>()>* = nullptr>
+    output_indexalator operator()(Args&&... args)
+    {
+      CUDF_FAIL("indices must be an index type");
+    }
+  };
+
+  /**
+   * @brief Create an input indexalator instance from an indices column.
+   */
+  static input_indexalator make_input_iterator(column_view const& indices)
+  {
+    return type_dispatcher(indices.type(), input_indexalator_fn{}, indices);
+  }
+
+  /**
+   * @brief Create an input indexalator instance from an index scalar.
+   */
+  static input_indexalator make_input_iterator(cudf::scalar const& index)
+  {
+    return type_dispatcher(index.type(), input_indexalator_scalar_fn{}, index);
+  }
+
+  /**
+   * @brief Create an output indexalator instance from an indices column.
+   */
+  static output_indexalator make_output_iterator(mutable_column_view const& indices)
+  {
+    return type_dispatcher(indices.type(), output_indexalator_fn{}, indices);
+  }
+
+  /**
+   * @brief An index accessor that returns a validity flag along with the index value.
+   *
+   * This is suitable as a `pair_iterator` for calling functions like `copy_if_else`.
+   */
+  struct nullable_index_accessor {
+    input_indexalator iter;
+    bitmask_type const* null_mask{};
+    size_type const offset{};
+    bool const has_nulls{};
+
+    /**
+     * @brief Create an accessor from a column_view.
+     */
+    nullable_index_accessor(column_view const& col, bool has_nulls = false)
+      : null_mask{col.null_mask()}, offset{col.offset()}, has_nulls{has_nulls}
+    {
+      if (has_nulls) { CUDF_EXPECTS(col.nullable(), "Unexpected non-nullable column."); }
+      iter = make_input_iterator(col);
+    }
+
+    __device__ thrust::pair<size_type, bool> operator()(size_type i) const
+    {
+      return {iter[i], (has_nulls ? bit_is_set(null_mask, i + offset) : true)};
+    }
+  };
+
+  /**
+   * @brief An index accessor that returns a validity flag along with the index value.
+   *
+   * This is suitable as a `pair_iterator`.
+   */
+  struct scalar_nullable_index_accessor {
+    input_indexalator iter;
+    bool const is_null;
+
+    /**
+     * @brief Create an accessor from a scalar.
+     */
+    scalar_nullable_index_accessor(scalar const& input) : is_null{!input.is_valid()}
+    {
+      iter = indexalator_factory::make_input_iterator(input);
+    }
+
+    __device__ thrust::pair<size_type, bool> operator()(size_type) const
+    {
+      return {*iter, is_null};
+    }
+  };
+
+  /**
+   * @brief Create an index iterator with a nullable index accessor.
+   */
+  static auto make_input_pair_iterator(column_view const& col)
+  {
+    return make_counting_transform_iterator(0, nullable_index_accessor{col, col.has_nulls()});
+  }
+
+  /**
+   * @brief Create an index iterator with a nullable index accessor for a scalar.
+   */
+  static auto make_input_pair_iterator(scalar const& input)
+  {
+    return thrust::make_transform_iterator(thrust::make_constant_iterator<size_type>(0),
+                                           scalar_nullable_index_accessor{input});
+  }
+
+  /**
+   * @brief An index accessor that returns an index value if corresponding validity flag is true.
+   *
+   * This is suitable as an `optional_iterator`.
+   */
+  struct optional_index_accessor {
+    input_indexalator iter;
+    bitmask_type const* null_mask{};
+    size_type const offset{};
+    bool const has_nulls{};
+
+    /**
+     * @brief Create an accessor from a column_view.
+     */
+    optional_index_accessor(column_view const& col, bool has_nulls = false)
+      : null_mask{col.null_mask()}, offset{col.offset()}, has_nulls{has_nulls}
+    {
+      if (has_nulls) { CUDF_EXPECTS(col.nullable(), "Unexpected non-nullable column."); }
+      iter = make_input_iterator(col);
+    }
+
+    __device__ thrust::optional<size_type> operator()(size_type i) const
+    {
+      return has_nulls && !bit_is_set(null_mask, i + offset) ? thrust::nullopt
+                                                             : thrust::make_optional(iter[i]);
+    }
+  };
+
+  /**
+   * @brief An index accessor that returns an index value if the scalar's validity flag is true.
+   *
+   * This is suitable as an `optional_iterator`.
+   */
+  struct scalar_optional_index_accessor {
+    input_indexalator iter;
+    bool const is_null;
+
+    /**
+     * @brief Create an accessor from a scalar.
+     */
+    scalar_optional_index_accessor(scalar const& input) : is_null{!input.is_valid()}
+    {
+      iter = indexalator_factory::make_input_iterator(input);
+    }
+
+    __device__ thrust::optional<size_type> operator()(size_type) const
+    {
+      return is_null ? thrust::nullopt : thrust::make_optional(*iter);
+    }
+  };
+
+  /**
+   * @brief Create an index iterator with an optional index accessor.
+   */
+  static auto make_input_optional_iterator(column_view const& col)
+  {
+    return make_counting_transform_iterator(0, optional_index_accessor{col, col.has_nulls()});
+  }
+
+  /**
+   * @brief Create an index iterator with an optional index accessor for a scalar.
+   */
+  static auto make_input_optional_iterator(scalar const& input)
+  {
+    return thrust::make_transform_iterator(thrust::make_constant_iterator<size_type>(0),
+                                           scalar_optional_index_accessor{input});
+  }
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/interop.hpp b/cpp/include/cudf/detail/interop.hpp
new file mode 100644
index 0000000..4402433
--- /dev/null
+++ b/cpp/include/cudf/detail/interop.hpp
@@ -0,0 +1,198 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// We disable warning 611 because the `arrow::TableBatchReader` only partially
+// override the `ReadNext` method of `arrow::RecordBatchReader::ReadNext`
+// triggering warning 611-D from nvcc.
+#ifdef __CUDACC__
+#pragma nv_diag_suppress 611
+#pragma nv_diag_suppress 2810
+#endif
+#include <arrow/api.h>
+#ifdef __CUDACC__
+#pragma nv_diag_default 611
+#pragma nv_diag_default 2810
+#endif
+
+#include <cudf/interop.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <string>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::from_dlpack
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> from_dlpack(DLManagedTensor const* managed_tensor,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::to_dlpack
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+DLManagedTensor* to_dlpack(table_view const& input,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr);
+
+// Creating arrow as per given type_id and buffer arguments
+template <typename... Ts>
+std::shared_ptr<arrow::Array> to_arrow_array(cudf::type_id id, Ts&&... args)
+{
+  switch (id) {
+    case type_id::BOOL8: return std::make_shared<arrow::BooleanArray>(std::forward<Ts>(args)...);
+    case type_id::INT8: return std::make_shared<arrow::Int8Array>(std::forward<Ts>(args)...);
+    case type_id::INT16: return std::make_shared<arrow::Int16Array>(std::forward<Ts>(args)...);
+    case type_id::INT32: return std::make_shared<arrow::Int32Array>(std::forward<Ts>(args)...);
+    case type_id::INT64: return std::make_shared<arrow::Int64Array>(std::forward<Ts>(args)...);
+    case type_id::UINT8: return std::make_shared<arrow::UInt8Array>(std::forward<Ts>(args)...);
+    case type_id::UINT16: return std::make_shared<arrow::UInt16Array>(std::forward<Ts>(args)...);
+    case type_id::UINT32: return std::make_shared<arrow::UInt32Array>(std::forward<Ts>(args)...);
+    case type_id::UINT64: return std::make_shared<arrow::UInt64Array>(std::forward<Ts>(args)...);
+    case type_id::FLOAT32: return std::make_shared<arrow::FloatArray>(std::forward<Ts>(args)...);
+    case type_id::FLOAT64: return std::make_shared<arrow::DoubleArray>(std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_DAYS:
+      return std::make_shared<arrow::Date32Array>(std::make_shared<arrow::Date32Type>(),
+                                                  std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_SECONDS:
+      return std::make_shared<arrow::TimestampArray>(arrow::timestamp(arrow::TimeUnit::SECOND),
+                                                     std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_MILLISECONDS:
+      return std::make_shared<arrow::TimestampArray>(arrow::timestamp(arrow::TimeUnit::MILLI),
+                                                     std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_MICROSECONDS:
+      return std::make_shared<arrow::TimestampArray>(arrow::timestamp(arrow::TimeUnit::MICRO),
+                                                     std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_NANOSECONDS:
+      return std::make_shared<arrow::TimestampArray>(arrow::timestamp(arrow::TimeUnit::NANO),
+                                                     std::forward<Ts>(args)...);
+    case type_id::DURATION_SECONDS:
+      return std::make_shared<arrow::DurationArray>(arrow::duration(arrow::TimeUnit::SECOND),
+                                                    std::forward<Ts>(args)...);
+    case type_id::DURATION_MILLISECONDS:
+      return std::make_shared<arrow::DurationArray>(arrow::duration(arrow::TimeUnit::MILLI),
+                                                    std::forward<Ts>(args)...);
+    case type_id::DURATION_MICROSECONDS:
+      return std::make_shared<arrow::DurationArray>(arrow::duration(arrow::TimeUnit::MICRO),
+                                                    std::forward<Ts>(args)...);
+    case type_id::DURATION_NANOSECONDS:
+      return std::make_shared<arrow::DurationArray>(arrow::duration(arrow::TimeUnit::NANO),
+                                                    std::forward<Ts>(args)...);
+    default: CUDF_FAIL("Unsupported type_id conversion to arrow");
+  }
+}
+
+/**
+ * @brief Invokes an `operator()` template with the type instantiation based on
+ * the specified `arrow::DataType`'s `id()`.
+ *
+ * This function is analogous to libcudf's type_dispatcher, but instead applies
+ * to Arrow functions. Its primary use case is to leverage Arrow's
+ * metaprogramming facilities like arrow::TypeTraits that require translating
+ * the runtime dtype information into compile-time types.
+ */
+template <typename Functor, typename... Ts>
+constexpr decltype(auto) arrow_type_dispatcher(arrow::DataType const& dtype,
+                                               Functor f,
+                                               Ts&&... args)
+{
+  switch (dtype.id()) {
+    case arrow::Type::INT8:
+      return f.template operator()<arrow::Int8Type>(std::forward<Ts>(args)...);
+    case arrow::Type::INT16:
+      return f.template operator()<arrow::Int16Type>(std::forward<Ts>(args)...);
+    case arrow::Type::INT32:
+      return f.template operator()<arrow::Int32Type>(std::forward<Ts>(args)...);
+    case arrow::Type::INT64:
+      return f.template operator()<arrow::Int64Type>(std::forward<Ts>(args)...);
+    case arrow::Type::UINT8:
+      return f.template operator()<arrow::UInt8Type>(std::forward<Ts>(args)...);
+    case arrow::Type::UINT16:
+      return f.template operator()<arrow::UInt16Type>(std::forward<Ts>(args)...);
+    case arrow::Type::UINT32:
+      return f.template operator()<arrow::UInt32Type>(std::forward<Ts>(args)...);
+    case arrow::Type::UINT64:
+      return f.template operator()<arrow::UInt64Type>(std::forward<Ts>(args)...);
+    case arrow::Type::FLOAT:
+      return f.template operator()<arrow::FloatType>(std::forward<Ts>(args)...);
+    case arrow::Type::DOUBLE:
+      return f.template operator()<arrow::DoubleType>(std::forward<Ts>(args)...);
+    case arrow::Type::BOOL:
+      return f.template operator()<arrow::BooleanType>(std::forward<Ts>(args)...);
+    case arrow::Type::TIMESTAMP:
+      return f.template operator()<arrow::TimestampType>(std::forward<Ts>(args)...);
+    case arrow::Type::DURATION:
+      return f.template operator()<arrow::DurationType>(std::forward<Ts>(args)...);
+    case arrow::Type::STRING:
+      return f.template operator()<arrow::StringType>(std::forward<Ts>(args)...);
+    case arrow::Type::LIST:
+      return f.template operator()<arrow::ListType>(std::forward<Ts>(args)...);
+    case arrow::Type::DECIMAL128:
+      return f.template operator()<arrow::Decimal128Type>(std::forward<Ts>(args)...);
+    case arrow::Type::STRUCT:
+      return f.template operator()<arrow::StructType>(std::forward<Ts>(args)...);
+    default: {
+      CUDF_FAIL("Invalid type.");
+    }
+  }
+}
+
+// Converting arrow type to cudf type
+data_type arrow_to_cudf_type(arrow::DataType const& arrow_type);
+
+/**
+ * @copydoc cudf::to_arrow(table_view input, std::vector<column_metadata> const& metadata,
+ * rmm::cuda_stream_view stream, arrow::MemoryPool* ar_mr)
+ */
+std::shared_ptr<arrow::Table> to_arrow(table_view input,
+                                       std::vector<column_metadata> const& metadata,
+                                       rmm::cuda_stream_view stream,
+                                       arrow::MemoryPool* ar_mr);
+
+/**
+ * @copydoc cudf::to_arrow(cudf::scalar const& input, column_metadata const& metadata,
+ * rmm::cuda_stream_view stream, arrow::MemoryPool* ar_mr)
+ */
+std::shared_ptr<arrow::Scalar> to_arrow(cudf::scalar const& input,
+                                        column_metadata const& metadata,
+                                        rmm::cuda_stream_view stream,
+                                        arrow::MemoryPool* ar_mr);
+/**
+ * @copydoc cudf::from_arrow(arrow::Table const& input_table, rmm::cuda_stream_view stream,
+ * rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<table> from_arrow(arrow::Table const& input_table,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::from_arrow(arrow::Scalar const& input, rmm::cuda_stream_view stream,
+ * rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<cudf::scalar> from_arrow(arrow::Scalar const& input,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/is_element_valid.hpp b/cpp/include/cudf/detail/is_element_valid.hpp
new file mode 100644
index 0000000..72a85d4
--- /dev/null
+++ b/cpp/include/cudf/detail/is_element_valid.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Return validity of a row
+ *
+ * Retrieves the validity (NULL or non-NULL) of the specified row from device memory.
+ *
+ * @note Synchronizes `stream`.
+ *
+ * @throw cudf::logic_error if `element_index < 0 or >= col_view.size()`
+ *
+ * @param col_view The column to retrieve the validity from.
+ * @param element_index The index of the row to retrieve.
+ * @param stream The stream to use for copying the validity to the host.
+ * @return Host boolean that indicates the validity of the row.
+ */
+
+bool is_element_valid_sync(column_view const& col_view,
+                           size_type element_index,
+                           rmm::cuda_stream_view stream);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/iterator.cuh b/cpp/include/cudf/detail/iterator.cuh
new file mode 100644
index 0000000..9e6227e
--- /dev/null
+++ b/cpp/include/cudf/detail/iterator.cuh
@@ -0,0 +1,656 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @brief provides column input iterator with nulls replaced with a specified value
+ * @file iterator.cuh
+ *
+ * The column input iterator is designed to be used as an input
+ * iterator for thrust and cub.
+ *
+ * Usage:
+ * auto iter = make_null_replacement_iterator(column, null_value);
+ *
+ * The column input iterator returns only a scalar value of data at [id] or
+ * the null_replacement value passed while creating the iterator.
+ * For non-null column, use
+ * auto iter = column.begin<Element>();
+ *
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+#include <utility>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Convenience wrapper for creating a `thrust::transform_iterator` over a
+ * `thrust::counting_iterator`.
+ *
+ * Example:
+ * @code{.cpp}
+ * // Returns square of the value of the counting iterator
+ * auto iter = make_counting_transform_iterator(0, [](auto i){ return (i * i);});
+ * iter[0] == 0
+ * iter[1] == 1
+ * iter[2] == 4
+ * ...
+ * iter[n] == n * n
+ * @endcode
+ *
+ * @param start The starting value of the counting iterator
+ * @param f The unary function to apply to the counting iterator.
+ * @return A transform iterator that applies `f` to a counting iterator
+ */
+template <typename UnaryFunction>
+CUDF_HOST_DEVICE inline auto make_counting_transform_iterator(cudf::size_type start,
+                                                              UnaryFunction f)
+{
+  return thrust::make_transform_iterator(thrust::make_counting_iterator(start), f);
+}
+
+/**
+ * @brief Value accessor of column that may have a null bitmask.
+ *
+ * This unary functor returns scalar value at `id`.
+ * The `operator()(cudf::size_type id)` computes the `element` and valid flag at `id`.
+ *
+ * The return value for element `i` will return `column[i]`
+ * if it is valid, or `null_replacement` if it is null.
+ *
+ * @tparam Element The type of elements in the column
+ */
+template <typename Element>
+struct null_replaced_value_accessor {
+  column_device_view const col;      ///< column view of column in device
+  Element const null_replacement{};  ///< value returned when element is null
+  bool const has_nulls;              ///< true if col has null elements
+
+  /**
+   * @brief Creates an accessor for a null-replacement iterator.
+   *
+   * @throws cudf::logic_error if `col` type does not match Element type.
+   * @throws cudf::logic_error if `has_nulls` is true but `col` does not have a validity mask.
+   *
+   * @param[in] col column device view of cudf column
+   * @param[in] null_replacement The value to return for null elements
+   * @param[in] has_nulls Must be set to true if `col` has nulls.
+   */
+  null_replaced_value_accessor(column_device_view const& col,
+                               Element null_val,
+                               bool has_nulls = true)
+    : col{col}, null_replacement{null_val}, has_nulls{has_nulls}
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<Element>(col.type().id()),
+                 "the data type mismatch");
+    if (has_nulls) CUDF_EXPECTS(col.nullable(), "column with nulls must have a validity bitmask");
+  }
+
+  __device__ inline Element const operator()(cudf::size_type i) const
+  {
+    return has_nulls && col.is_null_nocheck(i) ? null_replacement : col.element<Element>(i);
+  }
+};
+
+/**
+ * @brief validity accessor of column with null bitmask
+ * A unary functor that returns validity at index `i`.
+ *
+ * @tparam safe If false, the accessor will throw a logic_error if the column is not nullable. If
+ * true, the accessor checks for nullability and if col is not nullable, returns true.
+ */
+template <bool safe = false>
+struct validity_accessor {
+  column_device_view const col;
+
+  /**
+   * @brief constructor
+   *
+   * @throws cudf::logic_error if not safe and `col` does not have a validity bitmask
+   *
+   * @param[in] _col column device view of cudf column
+   */
+  CUDF_HOST_DEVICE validity_accessor(column_device_view const& _col) : col{_col}
+  {
+    if constexpr (not safe) {
+      // verify col is nullable, otherwise, is_valid_nocheck() will crash
+#if defined(__CUDA_ARCH__)
+      cudf_assert(_col.nullable() && "Unexpected non-nullable column.");
+#else
+      CUDF_EXPECTS(_col.nullable(), "Unexpected non-nullable column.");
+#endif
+    }
+  }
+
+  __device__ inline bool operator()(cudf::size_type i) const
+  {
+    if constexpr (safe) {
+      return col.is_valid(i);
+    } else {
+      return col.is_valid_nocheck(i);
+    }
+  }
+};
+
+/**
+ * @brief Constructs an iterator over a column's values that replaces null
+ * elements with a specified value.
+ *
+ * Dereferencing the returned iterator for element `i` will return `column[i]`
+ * if it is valid, or `null_replacement` if it is null.
+ * This iterator is only allowed for both nullable and non-nullable columns.
+ *
+ * @throws cudf::logic_error if the column is not nullable.
+ * @throws cudf::logic_error if column datatype and Element type mismatch.
+ *
+ * @tparam Element The type of elements in the column
+ * @param column The column to iterate
+ * @param null_replacement The value to return for null elements
+ * @param has_nulls Must be set to true if `column` has nulls.
+ * @return Iterator that returns valid column elements, or a null
+ * replacement value for null elements.
+ */
+template <typename Element>
+auto make_null_replacement_iterator(column_device_view const& column,
+                                    Element const null_replacement = Element{0},
+                                    bool has_nulls                 = true)
+{
+  return make_counting_transform_iterator(
+    0, null_replaced_value_accessor<Element>{column, null_replacement, has_nulls});
+}
+
+/**
+ * @brief Constructs an optional iterator over a column's values and its validity.
+ *
+ * Dereferencing the returned iterator returns a `thrust::optional<Element>`.
+ *
+ * The element of this iterator contextually converts to bool. The conversion returns true
+ * if the object contains a value and false if it does not contain a value.
+ *
+ * Calling this function with `nullate::DYNAMIC` defers the assumption
+ * of nullability to runtime with the caller indicating if the column has nulls.
+ * This is useful when an algorithm is going to execute on multiple iterators and all
+ * the combinations of iterator types are not required at compile time.
+ *
+ * @code{.cpp}
+ * template<typename T>
+ * void some_function(cudf::column_view<T> const& col_view){
+ *    auto d_col = cudf::column_device_view::create(col_view);
+ *    // Create a `DYNAMIC` optional iterator
+ *    auto optional_iterator =
+ *      cudf::detail::make_optional_iterator<T>(
+ *        d_col, cudf::nullate::DYNAMIC{col_view.has_nulls()});
+ * }
+ * @endcode
+ *
+ * Calling this function with `nullate::YES` means that the column supports
+ * nulls and the optional returned might not contain a value.
+ * Calling this function with `nullate::NO` means that the column has no
+ * null values and the optional returned will always contain a value.
+ *
+ * @code{.cpp}
+ * template<typename T, bool has_nulls>
+ * void some_function(cudf::column_view<T> const& col_view){
+ *    auto d_col = cudf::column_device_view::create(col_view);
+ *    if constexpr(has_nulls) {
+ *      auto optional_iterator =
+ *        cudf::detail::make_optional_iterator<T>(d_col, cudf::nullate::YES{});
+ *      //use optional_iterator
+ *    } else {
+ *      auto optional_iterator =
+ *        cudf::detail::make_optional_iterator<T>(d_col, cudf::nullate::NO{});
+ *      //use optional_iterator
+ *    }
+ * }
+ * @endcode
+ *
+ * @throws cudf::logic_error if the column is not nullable and `has_nulls` is true.
+ * @throws cudf::logic_error if column datatype and Element type mismatch.
+ *
+ * @tparam Element The type of elements in the column.
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ *
+ * @param column The column to iterate
+ * @param has_nulls Indicates whether `column` is checked for nulls.
+ * @return Iterator that returns valid column elements and the validity of the
+ * element in a `thrust::optional`
+ */
+template <typename Element, typename Nullate>
+auto make_optional_iterator(column_device_view const& column, Nullate has_nulls)
+{
+  return column.optional_begin<Element, Nullate>(has_nulls);
+}
+
+/**
+ * @brief Constructs a pair iterator over a column's values and its validity.
+ *
+ * Dereferencing the returned iterator returns a `thrust::pair<Element, bool>`.
+ *
+ * If an element at position `i` is valid (or `has_nulls == false`), then for `p = *(iter + i)`,
+ * `p.first` contains the value of the element at `i` and `p.second == true`.
+ *
+ * Else, if the element at `i` is null, then the value of `p.first` is undefined and `p.second ==
+ * false`. `pair(column[i], validity)`. `validity` is `true` if `has_nulls=false`. `validity` is
+ * validity of the element at `i` if `has_nulls=true` and the column is nullable.
+ *
+ * @throws cudf::logic_error if the column is nullable.
+ * @throws cudf::logic_error if column datatype and Element type mismatch.
+ *
+ * @tparam Element The type of elements in the column
+ * @tparam has_nulls boolean indicating to treat the column is nullable
+ * @param column The column to iterate
+ * @return auto Iterator that returns valid column elements, and validity of the
+ * element in a pair
+ */
+template <typename Element, bool has_nulls = false>
+auto make_pair_iterator(column_device_view const& column)
+{
+  return column.pair_begin<Element, has_nulls>();
+}
+
+/**
+ * @brief Constructs a pair rep iterator over a column's representative values and its validity.
+ *
+ * Dereferencing the returned iterator returns a `thrust::pair<rep_type, bool>`,
+ * where `rep_type` is `device_storage_type<T>`, the type used to store
+ * the value on the device.
+ *
+ * If an element at position `i` is valid (or `has_nulls == false`), then for `p = *(iter + i)`,
+ * `p.first` contains the value of the element at `i` and `p.second == true`.
+ *
+ * Else, if the element at `i` is null, then the value of `p.first` is undefined and `p.second ==
+ * false`. `pair(column[i], validity)`. `validity` is `true` if `has_nulls=false`. `validity` is
+ * validity of the element at `i` if `has_nulls=true` and the column is nullable.
+ *
+ * @throws cudf::logic_error if the column is nullable.
+ * @throws cudf::logic_error if column datatype and Element type mismatch.
+ *
+ * @tparam Element The type of elements in the column
+ * @tparam has_nulls boolean indicating to treat the column is nullable
+ * @param column The column to iterate
+ * @return auto Iterator that returns valid column elements, and validity of the
+ * element in a pair
+ */
+template <typename Element, bool has_nulls = false>
+auto make_pair_rep_iterator(column_device_view const& column)
+{
+  return column.pair_rep_begin<Element, has_nulls>();
+}
+
+/**
+ * @brief Constructs an iterator over a column's validities.
+ *
+ * Dereferencing the returned iterator for element `i` will return the validity
+ * of `column[i]`
+ * If `safe` = false, the column must be nullable.
+ * When safe = true, if the column is not nullable then the validity is always true.
+ *
+ * @throws cudf::logic_error if the column is not nullable and safe = false
+ *
+ * @tparam safe If false, the accessor will throw a logic_error if the column is not nullable. If
+ * true, the accessor checks for nullability and if col is not nullable, returns true.
+ * @param column The column to iterate
+ * @return auto Iterator that returns validities of column elements.
+ */
+template <bool safe = false>
+CUDF_HOST_DEVICE auto inline make_validity_iterator(column_device_view const& column)
+{
+  return make_counting_transform_iterator(cudf::size_type{0}, validity_accessor<safe>{column});
+}
+
+/**
+ * @brief Constructs a constant device iterator over a scalar's validity.
+ *
+ * Dereferencing the returned iterator returns a `bool`.
+ *
+ * For `p = *(iter + i)`, `p` is the validity of the scalar.
+ *
+ * @tparam bool unused. This template parameter exists to enforce the same
+ *         template interface as @ref make_validity_iterator(column_device_view const&).
+ * @param scalar_value The scalar to iterate
+ * @return auto Iterator that returns scalar validity
+ */
+template <bool safe = false>
+auto inline make_validity_iterator(scalar const& scalar_value)
+{
+  return thrust::make_constant_iterator(scalar_value.is_valid());
+}
+
+/**
+ * @brief value accessor for scalar with valid data.
+ * The unary functor returns data of Element type of the scalar.
+ *
+ * @throws `cudf::logic_error` if scalar datatype and Element type mismatch.
+ *
+ * @tparam Element The type of return type of functor
+ */
+template <typename Element>
+struct scalar_value_accessor {
+  using ScalarType       = scalar_type_t<Element>;
+  using ScalarDeviceType = scalar_device_type_t<Element>;
+  ScalarDeviceType const dscalar;  ///< scalar device view
+
+  scalar_value_accessor(scalar const& scalar_value)
+    : dscalar(get_scalar_device_view(static_cast<ScalarType&>(const_cast<scalar&>(scalar_value))))
+  {
+    CUDF_EXPECTS(type_id_matches_device_storage_type<Element>(scalar_value.type().id()),
+                 "the data type mismatch");
+  }
+
+  __device__ inline Element const operator()(size_type) const { return dscalar.value(); }
+};
+
+/**
+ * @brief Constructs a constant device iterator over a scalar's value.
+ *
+ * Dereferencing the returned iterator returns a `Element`.
+ *
+ * For `p = *(iter + i)`, `p` is the value stored in the scalar.
+ *
+ * The behavior is undefined if the scalar is destroyed before iterator dereferencing.
+ *
+ * @throws cudf::logic_error if scalar datatype and Element type mismatch.
+ * @throws cudf::logic_error if scalar is null.
+ * @throws cudf::logic_error if the returned iterator is dereferenced in host
+ *
+ * @tparam Element The type of element in the scalar
+ * @param scalar_value The scalar to iterate
+ * @return auto Iterator that returns scalar value
+ */
+template <typename Element>
+auto inline make_scalar_iterator(scalar const& scalar_value)
+{
+  CUDF_EXPECTS(data_type(type_to_id<Element>()) == scalar_value.type(), "the data type mismatch");
+  CUDF_EXPECTS(scalar_value.is_valid(), "the scalar value must be valid");
+  return thrust::make_transform_iterator(thrust::make_constant_iterator<size_type>(0),
+                                         scalar_value_accessor<Element>{scalar_value});
+}
+
+/**
+ * @brief Optional accessor for a scalar
+ *
+ * The `scalar_optional_accessor` always returns a `thrust::optional` of the scalar.
+ * The validity of the optional is determined by the `Nullate` parameter which may
+ * be one of the following:
+ *
+ * - `nullate::YES` means that the scalar may be valid or invalid and the optional returned
+ *    will contain a value only if the scalar is valid.
+ *
+ * - `nullate::NO` means the caller attests that the scalar will always be valid,
+ *    no checks will occur and `thrust::optional{column[i]}` will return a value
+ *    for each `i`.
+ *
+ * - `nullate::DYNAMIC` defers the assumption of nullability to runtime and the caller
+ *    specifies if the scalar may be valid or invalid.
+ *    For `DYNAMIC{true}` the return value will be a `thrust::optional{scalar}` when the
+ *      scalar is valid and a `thrust::optional{}` when the scalar is invalid.
+ *    For `DYNAMIC{false}` the return value will always be a `thrust::optional{scalar}`.
+ *
+ * @throws `cudf::logic_error` if scalar datatype and Element type mismatch.
+ *
+ * @tparam Element The type of return type of functor
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <typename Element, typename Nullate>
+struct scalar_optional_accessor : public scalar_value_accessor<Element> {
+  using super_t    = scalar_value_accessor<Element>;
+  using value_type = thrust::optional<Element>;
+
+  scalar_optional_accessor(scalar const& scalar_value, Nullate with_nulls)
+    : scalar_value_accessor<Element>(scalar_value), has_nulls{with_nulls}
+  {
+  }
+
+  __device__ inline value_type const operator()(size_type) const
+  {
+    if (has_nulls && !super_t::dscalar.is_valid()) { return value_type{thrust::nullopt}; }
+
+    if constexpr (cudf::is_fixed_point<Element>()) {
+      using namespace numeric;
+      using rep        = typename Element::rep;
+      auto const value = super_t::dscalar.rep();
+      auto const scale = scale_type{super_t::dscalar.type().scale()};
+      return Element{scaled_integer<rep>{value, scale}};
+    } else {
+      return Element{super_t::dscalar.value()};
+    }
+  }
+
+  Nullate has_nulls{};
+};
+
+/**
+ * @brief pair accessor for scalar.
+ * The unary functor returns a pair of data of Element type and bool validity of the scalar.
+ *
+ * @throws `cudf::logic_error` if scalar datatype and Element type mismatch.
+ *
+ * @tparam Element The type of return type of functor
+ */
+template <typename Element>
+struct scalar_pair_accessor : public scalar_value_accessor<Element> {
+  using super_t    = scalar_value_accessor<Element>;
+  using value_type = thrust::pair<Element, bool>;
+  scalar_pair_accessor(scalar const& scalar_value) : scalar_value_accessor<Element>(scalar_value) {}
+
+  __device__ inline value_type const operator()(size_type) const
+  {
+    return {Element(super_t::dscalar.value()), super_t::dscalar.is_valid()};
+  }
+};
+
+/**
+ * @brief Utility to discard template type arguments.
+ *
+ * Substitute for std::void_t.
+ *
+ * @tparam T Ignored template parameter
+ */
+template <typename... T>
+using void_t = void;
+
+/**
+ * @brief Compile-time reflection to check if `Element` type has a `rep()` member.
+ */
+template <typename Element, typename = void>
+struct has_rep_member : std::false_type {};
+
+template <typename Element>
+struct has_rep_member<Element, void_t<decltype(std::declval<Element>().rep())>> : std::true_type {};
+
+/**
+ * @brief Pair accessor for scalar's representation value and validity.
+ *
+ * @tparam Element The type of element in the scalar.
+ */
+template <typename Element>
+struct scalar_representation_pair_accessor : public scalar_value_accessor<Element> {
+  using base       = scalar_value_accessor<Element>;
+  using rep_type   = device_storage_type_t<Element>;
+  using value_type = thrust::pair<rep_type, bool>;
+
+  scalar_representation_pair_accessor(scalar const& scalar_value) : base(scalar_value) {}
+
+  __device__ inline value_type const operator()(size_type) const
+  {
+    return {get_rep(base::dscalar), base::dscalar.is_valid()};
+  }
+
+ private:
+  template <typename DeviceScalar,
+            std::enable_if_t<!has_rep_member<DeviceScalar>::value, void>* = nullptr>
+  __device__ inline rep_type get_rep(DeviceScalar const& dscalar) const
+  {
+    return dscalar.value();
+  }
+
+  template <typename DeviceScalar,
+            std::enable_if_t<has_rep_member<DeviceScalar>::value, void>* = nullptr>
+  __device__ inline rep_type get_rep(DeviceScalar const& dscalar) const
+  {
+    return dscalar.rep();
+  }
+};
+
+/**
+ * @brief Constructs an optional iterator over a scalar's values and its validity.
+ *
+ * Dereferencing the returned iterator returns a `thrust::optional<Element>`.
+ *
+ * The element of this iterator contextually converts to bool. The conversion returns true
+ * if the object contains a value and false if it does not contain a value.
+ *
+ * The iterator behavior is undefined if the scalar is destroyed before iterator dereferencing.
+ *
+ * Calling this function with `nullate::DYNAMIC` defers the assumption
+ * of nullability to runtime with the caller indicating if the scalar is valid.
+ *
+ * @code{.cpp}
+ * template<typename T>
+ * void some_function(cudf::column_view<T> const& col_view,
+ *                    scalar const& scalar_value,
+ *                    bool col_has_nulls){
+ *    auto d_col = cudf::column_device_view::create(col_view);
+ *    auto column_iterator = cudf::detail::make_optional_iterator<T>(
+ *      d_col, cudf::nullate::DYNAMIC{col_has_nulls});
+ *    auto scalar_iterator = cudf::detail::make_optional_iterator<T>(
+ *      scalar_value, cudf::nullate::DYNAMIC{scalar_value.is_valid()});
+ *    //use iterators
+ * }
+ * @endcode
+ *
+ * Calling this function with `nullate::YES` means that the scalar maybe invalid
+ * and the optional return might not contain a value.
+ * Calling this function with `nullate::NO` means that the scalar is valid
+ * and the optional returned will always contain a value.
+ *
+ * @code{.cpp}
+ * template<typename T, bool any_nulls>
+ * void some_function(cudf::column_view<T> const& col_view, scalar const& scalar_value){
+ *    auto d_col = cudf::column_device_view::create(col_view);
+ *    if constexpr(any_nulls) {
+ *      auto column_iterator =
+ *        cudf::detail::make_optional_iterator<T>(d_col, cudf::nullate::YES{});
+ *      auto scalar_iterator =
+ *        cudf::detail::make_optional_iterator<T>(scalar_value, cudf::nullate::YES{});
+ *      //use iterators
+ *    } else {
+ *      auto column_iterator =
+ *        cudf::detail::make_optional_iterator<T>(d_col, cudf::nullate::NO{});
+ *      auto scalar_iterator =
+ *        cudf::detail::make_optional_iterator<T>(scalar_value, cudf::nullate::NO{});
+ *      //use iterators
+ *    }
+ * }
+ * @endcode
+ *
+ * @throws cudf::logic_error if scalar datatype and Element type mismatch.
+ *
+ * @tparam Element The type of elements in the scalar
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ *
+ * @param scalar_value The scalar to be returned by the iterator.
+ * @param has_nulls Indicates if the scalar value may be invalid.
+ * @return Iterator that returns scalar and the validity of the scalar in a thrust::optional
+ */
+template <typename Element, typename Nullate>
+auto inline make_optional_iterator(scalar const& scalar_value, Nullate has_nulls)
+{
+  CUDF_EXPECTS(type_id_matches_device_storage_type<Element>(scalar_value.type().id()),
+               "the data type mismatch");
+  return thrust::make_transform_iterator(
+    thrust::make_constant_iterator<size_type>(0),
+    scalar_optional_accessor<Element, Nullate>{scalar_value, has_nulls});
+}
+
+/**
+ * @brief Constructs a constant device pair iterator over a scalar's value and its validity.
+ *
+ * Dereferencing the returned iterator returns a `thrust::pair<Element, bool>`.
+ *
+ * If scalar is valid, then for `p = *(iter + i)`, `p.first` contains
+ * the value of the scalar and `p.second == true`.
+ *
+ * Else, if the scalar is null, then the value of `p.first` is undefined and `p.second == false`.
+ *
+ * The behavior is undefined if the scalar is destroyed before iterator dereferencing.
+ *
+ * @throws cudf::logic_error if scalar datatype and Element type mismatch.
+ * @throws cudf::logic_error if the returned iterator is dereferenced in host
+ *
+ * @tparam Element The type of elements in the scalar
+ * @tparam bool unused. This template parameter exists to enforce same
+ * template interface as @ref make_pair_iterator(column_device_view const&).
+ * @param scalar_value The scalar to iterate
+ * @return auto Iterator that returns scalar, and validity of the scalar in a pair
+ */
+template <typename Element, bool = false>
+auto inline make_pair_iterator(scalar const& scalar_value)
+{
+  CUDF_EXPECTS(type_id_matches_device_storage_type<Element>(scalar_value.type().id()),
+               "the data type mismatch");
+  return thrust::make_transform_iterator(thrust::make_constant_iterator<size_type>(0),
+                                         scalar_pair_accessor<Element>{scalar_value});
+}
+
+/**
+ * @brief Constructs a constant device pair iterator over a scalar's representative value
+ *        and its validity.
+ *
+ * Dereferencing the returned iterator returns a `thrust::pair<Element::rep, bool>`.
+ * E.g. For a valid `decimal32` row, a `thrust::pair<int32_t, bool>` is returned,
+ * with the value set to the `int32_t` representative value of the decimal,
+ * and validity `true`, indicating that the row is valid.
+ *
+ * If scalar is valid, then for `p = *(iter + i)`, `p.first` contains
+ * the representative value of the scalar and `p.second == true`.
+ *
+ * Else, if the scalar is null, then the value of `p.first` is undefined and `p.second == false`.
+ *
+ * The behavior is undefined if the scalar is destroyed before iterator dereferencing.
+ *
+ * @throws cudf::logic_error if scalar datatype and Element type mismatch.
+ * @throws cudf::logic_error if the returned iterator is dereferenced in host
+ *
+ * @tparam Element The type of elements in the scalar
+ * @tparam bool unused. This template parameter exists to enforce same
+ * template interface as @ref make_pair_iterator(column_device_view const&).
+ * @param scalar_value The scalar to iterate
+ * @return auto Iterator that returns scalar's representative value,
+ *         and validity of the scalar in a pair
+ */
+template <typename Element, bool = false>
+auto make_pair_rep_iterator(scalar const& scalar_value)
+{
+  CUDF_EXPECTS(type_id_matches_device_storage_type<Element>(scalar_value.type().id()),
+               "the data type mismatch");
+  return make_counting_transform_iterator(
+    0, scalar_representation_pair_accessor<Element>{scalar_value});
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/join.hpp b/cpp/include/cudf/detail/join.hpp
new file mode 100644
index 0000000..b69632c
--- /dev/null
+++ b/cpp/include/cudf/detail/join.hpp
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <cuco/static_multimap.cuh>
+
+#include <cstddef>
+#include <memory>
+#include <optional>
+
+// Forward declaration
+template <typename T>
+class default_allocator;
+
+namespace cudf::experimental::row::equality {
+class preprocessed_table;
+}
+
+namespace cudf {
+namespace detail {
+
+constexpr int DEFAULT_JOIN_CG_SIZE = 2;
+
+enum class join_kind { INNER_JOIN, LEFT_JOIN, FULL_JOIN, LEFT_SEMI_JOIN, LEFT_ANTI_JOIN };
+
+/**
+ * @brief Hash join that builds hash table in creation and probes results in subsequent `*_join`
+ * member functions.
+ *
+ * User-defined hash function can be passed via the template parameter `Hasher`
+ *
+ * @tparam Hasher Unary callable type
+ */
+template <typename Hasher>
+struct hash_join {
+ public:
+  using map_type =
+    cuco::static_multimap<hash_value_type,
+                          cudf::size_type,
+                          cuda::thread_scope_device,
+                          rmm::mr::stream_allocator_adaptor<default_allocator<char>>,
+                          cuco::double_hashing<DEFAULT_JOIN_CG_SIZE, Hasher, Hasher>>;
+
+  hash_join()                            = delete;
+  ~hash_join()                           = default;
+  hash_join(hash_join const&)            = delete;
+  hash_join(hash_join&&)                 = delete;
+  hash_join& operator=(hash_join const&) = delete;
+  hash_join& operator=(hash_join&&)      = delete;
+
+ private:
+  bool const _is_empty;   ///< true if `_hash_table` is empty
+  bool const _has_nulls;  ///< true if nulls are present in either build table or any probe table
+  cudf::null_equality const _nulls_equal;  ///< whether to consider nulls as equal
+  cudf::table_view _build;                 ///< input table to build the hash map
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table>
+    _preprocessed_build;  ///< input table preprocssed for row operators
+  map_type _hash_table;   ///< hash table built on `_build`
+
+ public:
+  /**
+   * @brief Constructor that internally builds the hash table based on the given `build` table.
+   *
+   * @throw cudf::logic_error if the number of columns in `build` table is 0.
+   *
+   * @param build The build table, from which the hash table is built.
+   * @param has_nulls Flag to indicate if the there exists any nulls in the `build` table or
+   *        any `probe` table that will be used later for join.
+   * @param compare_nulls Controls whether null join-key values should match or not.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   */
+  hash_join(cudf::table_view const& build,
+            bool has_nulls,
+            cudf::null_equality compare_nulls,
+            rmm::cuda_stream_view stream);
+
+  /**
+   * @copydoc cudf::hash_join::inner_join
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  inner_join(cudf::table_view const& probe,
+             std::optional<std::size_t> output_size,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr) const;
+
+  /**
+   * @copydoc cudf::hash_join::left_join
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  left_join(cudf::table_view const& probe,
+            std::optional<std::size_t> output_size,
+            rmm::cuda_stream_view stream,
+            rmm::mr::device_memory_resource* mr) const;
+
+  /**
+   * @copydoc cudf::hash_join::full_join
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  full_join(cudf::table_view const& probe,
+            std::optional<std::size_t> output_size,
+            rmm::cuda_stream_view stream,
+            rmm::mr::device_memory_resource* mr) const;
+
+  /**
+   * @copydoc cudf::hash_join::inner_join_size
+   */
+  [[nodiscard]] std::size_t inner_join_size(cudf::table_view const& probe,
+                                            rmm::cuda_stream_view stream) const;
+
+  /**
+   * @copydoc cudf::hash_join::left_join_size
+   */
+  [[nodiscard]] std::size_t left_join_size(cudf::table_view const& probe,
+                                           rmm::cuda_stream_view stream) const;
+
+  /**
+   * @copydoc cudf::hash_join::full_join_size
+   */
+  std::size_t full_join_size(cudf::table_view const& probe,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr) const;
+
+ private:
+  /**
+   * @brief Probes the `_hash_table` built from `_build` for tuples in `probe_table`,
+   * and returns the output indices of `build_table` and `probe_table` as a combined table,
+   * i.e. if full join is specified as the join type then left join is called. Behavior
+   * is undefined if the provided `output_size` is smaller than the actual output size.
+   *
+   * @throw cudf::logic_error if build table is empty and `join == INNER_JOIN`.
+   *
+   * @param probe_table Table of probe side columns to join.
+   * @param join The type of join to be performed.
+   * @param output_size Optional value which allows users to specify the exact output size.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned vectors.
+   *
+   * @return Join output indices vector pair.
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  probe_join_indices(cudf::table_view const& probe_table,
+                     join_kind join,
+                     std::optional<std::size_t> output_size,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr) const;
+
+  /**
+   * @copydoc cudf::detail::hash_join::probe_join_indices
+   *
+   * @throw cudf::logic_error if probe table is empty.
+   * @throw cudf::logic_error if the number of columns in build table and probe table do not match.
+   * @throw cudf::logic_error if the column data types in build table and probe table do not match.
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  compute_hash_join(cudf::table_view const& probe,
+                    join_kind join,
+                    std::optional<std::size_t> output_size,
+                    rmm::cuda_stream_view stream,
+                    rmm::mr::device_memory_resource* mr) const;
+};
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/label_bins.hpp b/cpp/include/cudf/detail/label_bins.hpp
new file mode 100644
index 0000000..7f3cf03
--- /dev/null
+++ b/cpp/include/cudf/detail/label_bins.hpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/labeling/label_bins.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+
+namespace detail {
+
+/**
+ * @addtogroup label_bins
+ * @{
+ * @file
+ * @brief Internal APIs for labeling values by bin.
+ */
+
+/**
+ * @copydoc cudf::label_bins(column_view const& input, column_view const& left_edges, inclusive
+ * left_inclusive, column_view const& right_edges, inclusive right_inclusive, rmm::cuda_stream_view,
+ * rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream Stream view on which to allocate resources and queue execution.
+ */
+std::unique_ptr<column> label_bins(column_view const& input,
+                                   column_view const& left_edges,
+                                   inclusive left_inclusive,
+                                   column_view const& right_edges,
+                                   inclusive right_inclusive,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+/** @} */  // end of group
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/labeling/label_segments.cuh b/cpp/include/cudf/detail/labeling/label_segments.cuh
new file mode 100644
index 0000000..9051230
--- /dev/null
+++ b/cpp/include/cudf/detail/labeling/label_segments.cuh
@@ -0,0 +1,198 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/iterator_traits.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/uninitialized_fill.h>
+
+namespace cudf::detail {
+
+/**
+ * @brief Fill label values for segments defined by a given offsets array.
+ *
+ * Given a pair of iterators accessing to an offset array, generate label values for segments
+ * defined by the offset values. The output will be an array containing consecutive groups of
+ * identical labels, the number of elements in each group `i` is defined by
+ * `offsets[i+1] - offsets[i]`.
+ *
+ * The labels always start from `0` regardless of the offset values.
+ * In case there are empty segments, their corresponding label values will be skipped in the output.
+ *
+ * Note that the caller is responsible to make sure the output range have the correct size, which is
+ * the total segment sizes (i.e., `size = *(offsets_end - 1) - *offsets_begin`). Otherwise, the
+ * result is undefined.
+ *
+ * @code{.pseudo}
+ * Examples:
+ *
+ * offsets = [ 0, 4, 6, 6, 6, 10 ]
+ * output  = [ 0, 0, 0, 0, 1, 1, 4, 4, 4, 4 ]
+ *
+ * offsets = [ 5, 10, 12 ]
+ * output  = [ 0, 0, 0, 0, 0, 1, 1 ]
+ * @endcode
+ *
+ * @param offsets_begin The beginning of the offsets that define segments.
+ * @param offsets_end The end of the offsets that define segments.
+ * @param label_begin The beginning of the output label range.
+ * @param label_end The end of the output label range.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename InputIterator, typename OutputIterator>
+void label_segments(InputIterator offsets_begin,
+                    InputIterator offsets_end,
+                    OutputIterator label_begin,
+                    OutputIterator label_end,
+                    rmm::cuda_stream_view stream)
+{
+  auto const num_labels = thrust::distance(label_begin, label_end);
+
+  // If the output array is empty, that means we have all empty segments.
+  // In such cases, we must terminate immediately. Otherwise, the `for_each` loop below may try to
+  // access memory of the output array, resulting in "illegal memory access" error.
+  if (num_labels == 0) { return; }
+
+  // When the output array is not empty, always fill it with `0` value first.
+  using OutputType = typename thrust::iterator_value<OutputIterator>::type;
+  thrust::uninitialized_fill(rmm::exec_policy(stream), label_begin, label_end, OutputType{0});
+
+  // If the offsets array has no more than 2 offset values, there will be at max 1 segment.
+  // In such cases, the output will just be an array of all `0` values (which we already filled).
+  // We should terminate from here, otherwise the `inclusive_scan` call below still does its entire
+  // computation. That is unnecessary and may be expensive if we have the input offsets defining a
+  // very large segment.
+  if (thrust::distance(offsets_begin, offsets_end) <= 2) { return; }
+
+  thrust::for_each(
+    rmm::exec_policy(stream),
+    offsets_begin + 1,  // exclude the first offset value
+    offsets_end - 1,    // exclude the last offset value
+    [num_labels = static_cast<typename thrust::iterator_value<InputIterator>::type>(num_labels),
+     offsets    = offsets_begin,
+     output     = label_begin] __device__(auto const idx) {
+      // Zero-normalized offsets.
+      auto const dst_idx = idx - (*offsets);
+
+      // Scatter value `1` to the index at (idx - offsets[0]).
+      // Note that we need to check for out of bound, since the offset values may be invalid due to
+      // empty segments at the end.
+      // In case we have repeated offsets (i.e., we have empty segments), this `atomicAdd` call will
+      // make sure the label values corresponding to these empty segments will be skipped in the
+      // output.
+      if (dst_idx < num_labels) { atomicAdd(&output[dst_idx], OutputType{1}); }
+    });
+  thrust::inclusive_scan(rmm::exec_policy(stream), label_begin, label_end, label_begin);
+}
+
+/**
+ * @brief Generate segment offsets from groups of identical label values.
+ *
+ * Given a pair of iterators accessing to an array containing groups of identical label values,
+ * generate offsets for segments defined by these label.
+ *
+ * Empty segments are also taken into account. If the input label values are discontinuous, the
+ * segments corresponding to the missing labels will be inferred as empty segments and their offsets
+ * will also be generated.
+ *
+ * Note that the caller is responsible to make sure the output range for offsets have the correct
+ * size, which is the maximum label value plus two (i.e., `size = *(labels_end - 1) + 2`).
+ * Otherwise, the result is undefined.
+ *
+ * @code{.pseudo}
+ * Examples:
+ *
+ * labels = [ 0, 0, 0, 0, 1, 1, 4, 4, 4, 4 ]
+ * output = [ 0, 4, 6, 6, 6, 10 ]
+ *
+ * labels = [ 0, 0, 0, 0, 0, 1, 1 ]
+ * output = [ 0, 5, 7 ]
+ * @endcode
+ *
+ * @param labels_begin The beginning of the labels that define segments.
+ * @param labels_end The end of the labels that define segments.
+ * @param offsets_begin The beginning of the output offset range.
+ * @param offsets_end The end of the output offset range.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename InputIterator, typename OutputIterator>
+void labels_to_offsets(InputIterator labels_begin,
+                       InputIterator labels_end,
+                       OutputIterator offsets_begin,
+                       OutputIterator offsets_end,
+                       rmm::cuda_stream_view stream)
+{
+  // Always fill the entire output array with `0` value regardless of the input.
+  using OutputType = typename thrust::iterator_value<OutputIterator>::type;
+  thrust::uninitialized_fill(rmm::exec_policy(stream), offsets_begin, offsets_end, OutputType{0});
+
+  // If there is not any label value, we will have zero segment or all empty segments. We should
+  // terminate from here because:
+  //  - If we have zero segment, the output array is empty thus `num_segments` computed below is
+  //    wrong and may cascade to undefined behavior if we continue.
+  //  - If we have all empty segments, the output offset values will be all `0`, which we already
+  //    filled above. If we continue, the `exclusive_scan` call below still does its entire
+  //    computation. That is unnecessary and may be expensive if we have the input labels defining
+  //    a very large number of segments.
+  if (thrust::distance(labels_begin, labels_end) == 0) { return; }
+
+  auto const num_segments = thrust::distance(offsets_begin, offsets_end) - 1;
+
+  //================================================================================
+  // Let's consider an example: Given input labels = [ 0, 0, 0, 0, 1, 1, 4, 4, 4, 4 ].
+
+  // This stores the unique label values.
+  // Given the example above, we will have this array containing [0, 1, 4].
+  auto list_indices = rmm::device_uvector<OutputType>(num_segments, stream);
+
+  // Stores the non-zero segment sizes.
+  // Given the example above, we will have this array containing [4, 2, 4].
+  auto list_sizes = rmm::device_uvector<OutputType>(num_segments, stream);
+
+  // Count the numbers of labels in the each segment.
+  auto const end                    = thrust::reduce_by_key(rmm::exec_policy(stream),
+                                         labels_begin,  // keys
+                                         labels_end,    // keys
+                                         thrust::make_constant_iterator<OutputType>(1),
+                                         list_indices.begin(),  // output unique label values
+                                         list_sizes.begin());  // count for each label
+  auto const num_non_empty_segments = thrust::distance(list_indices.begin(), end.first);
+
+  // Scatter segment sizes into the end position of their corresponding segment indices.
+  // Given the example above, we scatter [4, 2, 4] by the scatter map [0, 1, 4], resulting
+  // output = [4, 2, 0, 0, 4, 0].
+  thrust::scatter(rmm::exec_policy(stream),
+                  list_sizes.begin(),
+                  list_sizes.begin() + num_non_empty_segments,
+                  list_indices.begin(),
+                  offsets_begin);
+
+  // Generate offsets from sizes.
+  // Given the example above, the final output is [0, 4, 6, 6, 6, 10].
+  thrust::exclusive_scan(rmm::exec_policy(stream), offsets_begin, offsets_end, offsets_begin);
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/merge.cuh b/cpp/include/cudf/detail/merge.cuh
new file mode 100644
index 0000000..e8e9b08
--- /dev/null
+++ b/cpp/include/cudf/detail/merge.cuh
@@ -0,0 +1,166 @@
+/*
+ * Copyright (c) 2018-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/table/row_operators.cuh>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/merge.h>
+#include <thrust/pair.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Source table identifier to copy data from.
+ */
+enum class side : bool { LEFT, RIGHT };
+
+/**
+ * @brief Tagged index type: `thrust::get<0>` indicates left/right side,
+ * `thrust::get<1>` indicates the row index
+ */
+using index_type = thrust::pair<side, cudf::size_type>;
+
+/**
+ * @brief Vector of `index_type` values.
+ */
+using index_vector = rmm::device_uvector<index_type>;
+
+/**
+ * @brief tagged_element_relational_comparator uses element_relational_comparator to provide
+ * "tagged-index" comparison logic.
+ *
+ * Special treatment is necessary in several thrust algorithms (e.g., merge()) where
+ * the index affinity to the side is not guaranteed; i.e., the algorithms rely on
+ * binary functors (predicates) where the operands may transparently switch sides.
+ *
+ * For example,
+ *         thrust::merge(left_container,
+ *                       right_container,
+ *                       predicate(lhs, rhs){...});
+ *         can create 4 different use-cases, inside predicate(...):
+ *
+ *         1. lhs refers to the left container; rhs to the right container;
+ *         2. vice-versa;
+ *         3. both lhs and rhs actually refer to the left container;
+ *         4. both lhs and rhs actually refer to the right container;
+ *
+ * Because of that, one cannot rely on the predicate having *fixed* references to the containers.
+ * Each invocation may land in a different situation (among the 4 above) than any other invocation.
+ * Also, one cannot just manipulate lhs, rhs (indices) alone; because, if predicate always applies
+ * one index to one container and the other index to the other container,
+ * switching the indices alone won't suffice in the cases (3) or (4),
+ * where the also the containers must be changed (to just one instead of two)
+ * independently of indices;
+ *
+ * As a result, a special comparison logic is necessary whereby the index is "tagged" with side
+ * information and consequently comparator functors (predicates) must operate on these tagged
+ * indices rather than on raw indices.
+ */
+template <bool has_nulls = true>
+struct tagged_element_relational_comparator {
+  __host__ __device__ tagged_element_relational_comparator(column_device_view lhs,
+                                                           column_device_view rhs,
+                                                           null_order null_precedence)
+    : lhs{lhs}, rhs{rhs}, null_precedence{null_precedence}
+  {
+  }
+
+  [[nodiscard]] __device__ weak_ordering compare(index_type lhs_tagged_index,
+                                                 index_type rhs_tagged_index) const noexcept
+  {
+    auto const [l_side, l_indx] = lhs_tagged_index;
+    auto const [r_side, r_indx] = rhs_tagged_index;
+
+    column_device_view const* ptr_left_dview{l_side == side::LEFT ? &lhs : &rhs};
+    column_device_view const* ptr_right_dview{r_side == side::LEFT ? &lhs : &rhs};
+
+    auto erl_comparator = element_relational_comparator(
+      nullate::DYNAMIC{has_nulls}, *ptr_left_dview, *ptr_right_dview, null_precedence);
+
+    return cudf::type_dispatcher(lhs.type(), erl_comparator, l_indx, r_indx);
+  }
+
+ private:
+  column_device_view lhs;
+  column_device_view rhs;
+  null_order null_precedence;
+};
+
+/**
+ * @brief The equivalent of `row_lexicographic_comparator` for tagged indices.
+ */
+template <bool has_nulls = true>
+struct row_lexicographic_tagged_comparator {
+  row_lexicographic_tagged_comparator(table_device_view lhs,
+                                      table_device_view rhs,
+                                      order const* column_order         = nullptr,
+                                      null_order const* null_precedence = nullptr)
+    : _lhs{lhs}, _rhs{rhs}, _column_order{column_order}, _null_precedence{null_precedence}
+  {
+    // Add check for types to be the same.
+    CUDF_EXPECTS(_lhs.num_columns() == _rhs.num_columns(), "Mismatched number of columns.");
+  }
+
+  __device__ bool operator()(index_type lhs_tagged_index,
+                             index_type rhs_tagged_index) const noexcept
+  {
+    for (size_type i = 0; i < _lhs.num_columns(); ++i) {
+      bool ascending = (_column_order == nullptr) or (_column_order[i] == order::ASCENDING);
+
+      null_order null_precedence =
+        _null_precedence == nullptr ? null_order::BEFORE : _null_precedence[i];
+
+      auto comparator = tagged_element_relational_comparator<has_nulls>{
+        _lhs.column(i), _rhs.column(i), null_precedence};
+
+      weak_ordering state = comparator.compare(lhs_tagged_index, rhs_tagged_index);
+
+      if (state == weak_ordering::EQUIVALENT) { continue; }
+
+      return state == (ascending ? weak_ordering::LESS : weak_ordering::GREATER);
+    }
+    return false;
+  }
+
+ private:
+  table_device_view _lhs;
+  table_device_view _rhs;
+  null_order const* _null_precedence{};
+  order const* _column_order{};
+};
+
+/**
+ * @copydoc std::unique_ptr<cudf::table> merge(
+ *            std::vector<table_view> const& tables_to_merge,
+ *            std::vector<cudf::size_type> const& key_cols,
+ *            std::vector<cudf::order> const& column_order,
+ *            std::vector<cudf::null_order> const& null_precedence,
+ *            rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::table> merge(std::vector<table_view> const& tables_to_merge,
+                                   std::vector<cudf::size_type> const& key_cols,
+                                   std::vector<cudf::order> const& column_order,
+                                   std::vector<cudf::null_order> const& null_precedence,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/normalizing_iterator.cuh b/cpp/include/cudf/detail/normalizing_iterator.cuh
new file mode 100644
index 0000000..51b3133
--- /dev/null
+++ b/cpp/include/cudf/detail/normalizing_iterator.cuh
@@ -0,0 +1,367 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief The base class for the input or output normalizing iterator
+ *
+ * The base class mainly manages updating the `p_` member variable while the
+ * subclasses handle accessing individual elements in device memory.
+ *
+ * @tparam Derived The derived class type for the iterator
+ * @tparam Integer The type the iterator normalizes to
+ */
+template <class Derived, typename Integer>
+struct base_normalator {
+  static_assert(std::is_integral_v<Integer>);
+  using difference_type   = std::ptrdiff_t;
+  using value_type        = Integer;
+  using pointer           = Integer*;
+  using iterator_category = std::random_access_iterator_tag;
+
+  base_normalator()                                  = default;
+  base_normalator(base_normalator const&)            = default;
+  base_normalator(base_normalator&&)                 = default;
+  base_normalator& operator=(base_normalator const&) = default;
+  base_normalator& operator=(base_normalator&&)      = default;
+
+  /**
+   * @brief Prefix increment operator.
+   */
+  CUDF_HOST_DEVICE inline Derived& operator++()
+  {
+    Derived& derived = static_cast<Derived&>(*this);
+    derived.p_ += width_;
+    return derived;
+  }
+
+  /**
+   * @brief Postfix increment operator.
+   */
+  CUDF_HOST_DEVICE inline Derived operator++(int)
+  {
+    Derived tmp{static_cast<Derived&>(*this)};
+    operator++();
+    return tmp;
+  }
+
+  /**
+   * @brief Prefix decrement operator.
+   */
+  CUDF_HOST_DEVICE inline Derived& operator--()
+  {
+    Derived& derived = static_cast<Derived&>(*this);
+    derived.p_ -= width_;
+    return derived;
+  }
+
+  /**
+   * @brief Postfix decrement operator.
+   */
+  CUDF_HOST_DEVICE inline Derived operator--(int)
+  {
+    Derived tmp{static_cast<Derived&>(*this)};
+    operator--();
+    return tmp;
+  }
+
+  /**
+   * @brief Compound assignment by sum operator.
+   */
+  CUDF_HOST_DEVICE inline Derived& operator+=(difference_type offset)
+  {
+    Derived& derived = static_cast<Derived&>(*this);
+    derived.p_ += offset * width_;
+    return derived;
+  }
+
+  /**
+   * @brief Increment by offset operator.
+   */
+  CUDF_HOST_DEVICE inline Derived operator+(difference_type offset) const
+  {
+    auto tmp = Derived{static_cast<Derived const&>(*this)};
+    tmp.p_ += (offset * width_);
+    return tmp;
+  }
+
+  /**
+   * @brief Addition assignment operator.
+   */
+  CUDF_HOST_DEVICE inline friend Derived operator+(difference_type offset, Derived const& rhs)
+  {
+    Derived tmp{rhs};
+    tmp.p_ += (offset * rhs.width_);
+    return tmp;
+  }
+
+  /**
+   * @brief Compound assignment by difference operator.
+   */
+  CUDF_HOST_DEVICE inline Derived& operator-=(difference_type offset)
+  {
+    Derived& derived = static_cast<Derived&>(*this);
+    derived.p_ -= offset * width_;
+    return derived;
+  }
+
+  /**
+   * @brief Decrement by offset operator.
+   */
+  CUDF_HOST_DEVICE inline Derived operator-(difference_type offset) const
+  {
+    auto tmp = Derived{static_cast<Derived const&>(*this)};
+    tmp.p_ -= (offset * width_);
+    return tmp;
+  }
+
+  /**
+   * @brief Subtraction assignment operator.
+   */
+  CUDF_HOST_DEVICE inline friend Derived operator-(difference_type offset, Derived const& rhs)
+  {
+    Derived tmp{rhs};
+    tmp.p_ -= (offset * rhs.width_);
+    return tmp;
+  }
+
+  /**
+   * @brief Compute offset from iterator difference operator.
+   */
+  CUDF_HOST_DEVICE inline difference_type operator-(Derived const& rhs) const
+  {
+    return (static_cast<Derived const&>(*this).p_ - rhs.p_) / width_;
+  }
+
+  /**
+   * @brief Equals to operator.
+   */
+  CUDF_HOST_DEVICE inline bool operator==(Derived const& rhs) const
+  {
+    return rhs.p_ == static_cast<Derived const&>(*this).p_;
+  }
+
+  /**
+   * @brief Not equals to operator.
+   */
+  CUDF_HOST_DEVICE inline bool operator!=(Derived const& rhs) const
+  {
+    return rhs.p_ != static_cast<Derived const&>(*this).p_;
+  }
+
+  /**
+   * @brief Less than operator.
+   */
+  CUDF_HOST_DEVICE inline bool operator<(Derived const& rhs) const
+  {
+    return static_cast<Derived const&>(*this).p_ < rhs.p_;
+  }
+
+  /**
+   * @brief Greater than operator.
+   */
+  CUDF_HOST_DEVICE inline bool operator>(Derived const& rhs) const
+  {
+    return static_cast<Derived const&>(*this).p_ > rhs.p_;
+  }
+
+  /**
+   * @brief Less than or equals to operator.
+   */
+  CUDF_HOST_DEVICE inline bool operator<=(Derived const& rhs) const
+  {
+    return static_cast<Derived const&>(*this).p_ <= rhs.p_;
+  }
+
+  /**
+   * @brief Greater than or equals to operator.
+   */
+  CUDF_HOST_DEVICE inline bool operator>=(Derived const& rhs) const
+  {
+    return static_cast<Derived const&>(*this).p_ >= rhs.p_;
+  }
+
+ protected:
+  /**
+   * @brief Constructor assigns width and type member variables for base class.
+   */
+  explicit base_normalator(data_type dtype) : width_(size_of(dtype)), dtype_(dtype) {}
+
+  int width_;        /// integer type width = 1,2,4, or 8
+  data_type dtype_;  /// for type-dispatcher calls
+};
+
+/**
+ * @brief The integer normalizing input iterator
+ *
+ * This is an iterator that can be used for index types (integers) without
+ * requiring a type-specific instance. It can be used for any iterator
+ * interface for reading an array of integer values of type
+ * int8, int16, int32, int64, uint8, uint16, uint32, or uint64.
+ * Reading specific elements always return a type of `Integer`
+ *
+ * @tparam Integer Type returned by all read functions
+ */
+template <typename Integer>
+struct input_normalator : base_normalator<input_normalator<Integer>, Integer> {
+  friend struct base_normalator<input_normalator<Integer>, Integer>;  // for CRTP
+
+  using reference = Integer const;  // this keeps STL and thrust happy
+
+  input_normalator()                                   = default;
+  input_normalator(input_normalator const&)            = default;
+  input_normalator(input_normalator&&)                 = default;
+  input_normalator& operator=(input_normalator const&) = default;
+  input_normalator& operator=(input_normalator&&)      = default;
+
+  /**
+   * @brief Indirection operator returns the value at the current iterator position
+   */
+  __device__ inline Integer operator*() const { return operator[](0); }
+
+  /**
+   * @brief Dispatch functor for resolving a Integer value from any integer type
+   */
+  struct normalize_type {
+    template <typename T, std::enable_if_t<cuda::std::is_integral_v<T>>* = nullptr>
+    __device__ Integer operator()(void const* tp)
+    {
+      return static_cast<Integer>(*static_cast<T const*>(tp));
+    }
+    template <typename T, std::enable_if_t<not cuda::std::is_integral_v<T>>* = nullptr>
+    __device__ Integer operator()(void const*)
+    {
+      CUDF_UNREACHABLE("only integral types are supported");
+    }
+  };
+
+  /**
+   * @brief Array subscript operator returns a value at the input
+   * `idx` position as a `Integer` value.
+   */
+  __device__ inline Integer operator[](size_type idx) const
+  {
+    void const* tp = p_ + (idx * this->width_);
+    return type_dispatcher(this->dtype_, normalize_type{}, tp);
+  }
+
+  /**
+   * @brief Create an input index normalizing iterator.
+   *
+   * Use the indexalator_factory to create an iterator instance.
+   *
+   * @param data      Pointer to an integer array in device memory.
+   * @param data_type Type of data in data
+   */
+  input_normalator(void const* data, data_type dtype)
+    : base_normalator<input_normalator<Integer>, Integer>(dtype), p_{static_cast<char const*>(data)}
+  {
+  }
+
+  char const* p_;  /// pointer to the integer data in device memory
+};
+
+/**
+ * @brief The integer normalizing output iterator
+ *
+ * This is an iterator that can be used for index types (integers) without
+ * requiring a type-specific instance. It can be used for any iterator
+ * interface for writing an array of integer values of type
+ * int8, int16, int32, int64, uint8, uint16, uint32, or uint64.
+ * Setting specific elements always accept the `Integer` type values.
+ *
+ * @tparam Integer The type used for all write functions
+ */
+template <typename Integer>
+struct output_normalator : base_normalator<output_normalator<Integer>, Integer> {
+  friend struct base_normalator<output_normalator<Integer>, Integer>;  // for CRTP
+
+  using reference = output_normalator const&;  // required for output iterators
+
+  output_normalator()                                    = default;
+  output_normalator(output_normalator const&)            = default;
+  output_normalator(output_normalator&&)                 = default;
+  output_normalator& operator=(output_normalator const&) = default;
+  output_normalator& operator=(output_normalator&&)      = default;
+
+  /**
+   * @brief Indirection operator returns this iterator instance in order
+   * to capture the `operator=(Integer)` calls.
+   */
+  __device__ inline output_normalator const& operator*() const { return *this; }
+
+  /**
+   * @brief Array subscript operator returns an iterator instance at the specified `idx` position.
+   *
+   * This allows capturing the subsequent `operator=(Integer)` call in this class.
+   */
+  __device__ inline output_normalator const operator[](size_type idx) const
+  {
+    output_normalator tmp{*this};
+    tmp.p_ += (idx * this->width_);
+    return tmp;
+  }
+
+  /**
+   * @brief Dispatch functor for setting the index value from a size_type value.
+   */
+  struct normalize_type {
+    template <typename T, std::enable_if_t<std::is_integral_v<T>>* = nullptr>
+    __device__ void operator()(void* tp, Integer const value)
+    {
+      (*static_cast<T*>(tp)) = static_cast<T>(value);
+    }
+    template <typename T, std::enable_if_t<not std::is_integral_v<T>>* = nullptr>
+    __device__ void operator()(void*, Integer const)
+    {
+      CUDF_UNREACHABLE("only index types are supported");
+    }
+  };
+
+  /**
+   * @brief Assign an Integer value to the current iterator position
+   */
+  __device__ inline output_normalator const& operator=(Integer const value) const
+  {
+    void* tp = p_;
+    type_dispatcher(this->dtype_, normalize_type{}, tp, value);
+    return *this;
+  }
+
+  /**
+   * @brief Create an output normalizing iterator
+   *
+   * @param data      Pointer to an integer array in device memory.
+   * @param data_type Type of data in data
+   */
+  output_normalator(void* data, data_type dtype)
+    : base_normalator<output_normalator<Integer>, Integer>(dtype), p_{static_cast<char*>(data)}
+  {
+  }
+
+  char* p_;  /// pointer to the integer data in device memory
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/null_mask.cuh b/cpp/include/cudf/detail/null_mask.cuh
new file mode 100644
index 0000000..78cd3d7
--- /dev/null
+++ b/cpp/include/cudf/detail/null_mask.cuh
@@ -0,0 +1,591 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <cub/block/block_reduce.cuh>
+#include <cub/device/device_segmented_reduce.cuh>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <algorithm>
+#include <iterator>
+#include <optional>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Computes the merger of an array of bitmasks using a binary operator
+ *
+ * @tparam block_size Number of threads in each thread block
+ * @tparam Binop Type of binary operator
+ *
+ * @param op The binary operator used to combine the bitmasks
+ * @param destination The bitmask to write result into
+ * @param source Array of source mask pointers. All masks must be of same size
+ * @param source_begin_bits Array of offsets into corresponding @p source masks.
+ *                          Must be same size as source array
+ * @param source_size_bits Number of bits in each mask in @p source
+ * @param count_ptr Pointer to counter of set bits
+ */
+template <int block_size, typename Binop>
+__global__ void offset_bitmask_binop(Binop op,
+                                     device_span<bitmask_type> destination,
+                                     device_span<bitmask_type const* const> source,
+                                     device_span<size_type const> source_begin_bits,
+                                     size_type source_size_bits,
+                                     size_type* count_ptr)
+{
+  auto const tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+  auto const last_bit_index  = source_size_bits - 1;
+  auto const last_word_index = cudf::word_index(last_bit_index);
+
+  size_type thread_count = 0;
+
+  for (size_type destination_word_index = tid; destination_word_index < destination.size();
+       destination_word_index += blockDim.x * gridDim.x) {
+    bitmask_type destination_word =
+      detail::get_mask_offset_word(source[0],
+                                   destination_word_index,
+                                   source_begin_bits[0],
+                                   source_begin_bits[0] + source_size_bits);
+    for (size_type i = 1; i < source.size(); i++) {
+      destination_word = op(destination_word,
+                            detail::get_mask_offset_word(source[i],
+                                                         destination_word_index,
+                                                         source_begin_bits[i],
+                                                         source_begin_bits[i] + source_size_bits));
+    }
+
+    if (destination_word_index == last_word_index) {
+      // mask out any bits not part of this word
+      auto const num_bits_in_last_word = intra_word_index(last_bit_index);
+      if (num_bits_in_last_word <
+          static_cast<size_type>(detail::size_in_bits<bitmask_type>() - 1)) {
+        destination_word &= set_least_significant_bits(num_bits_in_last_word + 1);
+      }
+    }
+
+    destination[destination_word_index] = destination_word;
+    thread_count += __popc(destination_word);
+  }
+
+  using BlockReduce = cub::BlockReduce<size_type, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  size_type block_count = BlockReduce(temp_storage).Sum(thread_count);
+
+  if (threadIdx.x == 0) { atomicAdd(count_ptr, block_count); }
+}
+
+/**
+ * @copydoc bitmask_binop(Binop op, host_span<bitmask_type const* const>, host_span<size_type>
+ * const, size_type, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+template <typename Binop>
+std::pair<rmm::device_buffer, size_type> bitmask_binop(Binop op,
+                                                       host_span<bitmask_type const* const> masks,
+                                                       host_span<size_type const> masks_begin_bits,
+                                                       size_type mask_size_bits,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  auto dest_mask = rmm::device_buffer{bitmask_allocation_size_bytes(mask_size_bits), stream, mr};
+  auto null_count =
+    mask_size_bits -
+    inplace_bitmask_binop(op,
+                          device_span<bitmask_type>(static_cast<bitmask_type*>(dest_mask.data()),
+                                                    num_bitmask_words(mask_size_bits)),
+                          masks,
+                          masks_begin_bits,
+                          mask_size_bits,
+                          stream);
+
+  return std::pair(std::move(dest_mask), null_count);
+}
+
+/**
+ * @brief Performs a merge of the specified bitmasks using the binary operator
+ *        provided, writes in place to destination and returns count of set bits
+ *
+ * @param[in] op The binary operator used to combine the bitmasks
+ * @param[out] dest_mask Destination to which the merged result is written
+ * @param[in] masks The list of data pointers of the bitmasks to be merged
+ * @param[in] masks_begin_bits The bit offsets from which each mask is to be merged
+ * @param[in] mask_size_bits The number of bits to be ANDed in each mask
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ * @return size_type Count of set bits
+ */
+template <typename Binop>
+size_type inplace_bitmask_binop(Binop op,
+                                device_span<bitmask_type> dest_mask,
+                                host_span<bitmask_type const* const> masks,
+                                host_span<size_type const> masks_begin_bits,
+                                size_type mask_size_bits,
+                                rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(
+    std::all_of(masks_begin_bits.begin(), masks_begin_bits.end(), [](auto b) { return b >= 0; }),
+    "Invalid range.");
+  CUDF_EXPECTS(mask_size_bits > 0, "Invalid bit range.");
+  CUDF_EXPECTS(std::all_of(masks.begin(), masks.end(), [](auto p) { return p != nullptr; }),
+               "Mask pointer cannot be null");
+
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource();
+  rmm::device_scalar<size_type> d_counter{0, stream, mr};
+  rmm::device_uvector<bitmask_type const*> d_masks(masks.size(), stream, mr);
+  rmm::device_uvector<size_type> d_begin_bits(masks_begin_bits.size(), stream, mr);
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    d_masks.data(), masks.data(), masks.size_bytes(), cudaMemcpyDefault, stream.value()));
+  CUDF_CUDA_TRY(cudaMemcpyAsync(d_begin_bits.data(),
+                                masks_begin_bits.data(),
+                                masks_begin_bits.size_bytes(),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  auto constexpr block_size = 256;
+  cudf::detail::grid_1d config(dest_mask.size(), block_size);
+  offset_bitmask_binop<block_size>
+    <<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+      op, dest_mask, d_masks, d_begin_bits, mask_size_bits, d_counter.data());
+  CUDF_CHECK_CUDA(stream.value());
+  return d_counter.value(stream);
+}
+
+/**
+ * @brief Enum indicating whether to count unset (0) bits or set (1) bits.
+ */
+enum class count_bits_policy : bool {
+  UNSET_BITS,  /// Count unset (0) bits
+  SET_BITS     /// Count set (1) bits
+};
+
+/**
+ * For each range `[first_bit_indices[i], last_bit_indices[i])`
+ * (where 0 <= i < `num_ranges`), count the number of bits set outside the range
+ * in the boundary words (i.e. words that include either the first or last bit)
+ * and subtract the count from the range's null count.
+ *
+ * Expects `0 <= first_bit_indices[i] <= last_bit_indices[i]`.
+ *
+ * @param[in] bitmask The bitmask whose non-zero bits outside the range in the
+ * boundary words will be counted.
+ * @param[in] num_ranges The number of ranges.
+ * @param[in] first_bit_indices Random-access input iterator to the sequence of indices (inclusive)
+ * of the first bit in each range.
+ * @param[in] last_bit_indices Random-access input iterator to the sequence of indices (exclusive)
+ * of the last bit in each range.
+ * @param[in,out] null_counts Random-access input/output iterator where the number of non-zero bits
+ * in each range is updated.
+ */
+template <typename OffsetIterator, typename OutputIterator>
+__global__ void subtract_set_bits_range_boundaries_kernel(bitmask_type const* bitmask,
+                                                          size_type num_ranges,
+                                                          OffsetIterator first_bit_indices,
+                                                          OffsetIterator last_bit_indices,
+                                                          OutputIterator null_counts)
+{
+  constexpr size_type const word_size_in_bits{detail::size_in_bits<bitmask_type>()};
+
+  size_type const tid = threadIdx.x + blockIdx.x * blockDim.x;
+  size_type range_id  = tid;
+
+  while (range_id < num_ranges) {
+    size_type const first_bit_index = *(first_bit_indices + range_id);
+    size_type const last_bit_index  = *(last_bit_indices + range_id);
+    size_type delta                 = 0;
+
+    // Compute delta due to the preceding bits in the first word in the range.
+    size_type const first_num_slack_bits = intra_word_index(first_bit_index);
+    if (first_num_slack_bits > 0) {
+      bitmask_type const word       = bitmask[word_index(first_bit_index)];
+      bitmask_type const slack_mask = set_least_significant_bits(first_num_slack_bits);
+      delta -= __popc(word & slack_mask);
+    }
+
+    // Compute delta due to the following bits in the last word in the range.
+    size_type const last_num_slack_bits = (last_bit_index % word_size_in_bits) == 0
+                                            ? 0
+                                            : word_size_in_bits - intra_word_index(last_bit_index);
+    if (last_num_slack_bits > 0) {
+      bitmask_type const word       = bitmask[word_index(last_bit_index)];
+      bitmask_type const slack_mask = set_most_significant_bits(last_num_slack_bits);
+      delta -= __popc(word & slack_mask);
+    }
+
+    // Update the null count with the computed delta.
+    size_type updated_null_count = *(null_counts + range_id) + delta;
+    *(null_counts + range_id)    = updated_null_count;
+    range_id += blockDim.x * gridDim.x;
+  }
+}
+
+/**
+ * @brief Functor that converts bit segment indices to word segment indices.
+ *
+ * Converts [first_bit_index, last_bit_index) to [first_word_index,
+ * last_word_index). The flag `inclusive` indicates whether the indices are inclusive or exclusive.
+ * the end of a segment, in which case the word index should be incremented for
+ * bits at the start of a word.
+ */
+struct bit_to_word_index {
+  bit_to_word_index(bool inclusive) : inclusive(inclusive) {}
+  __device__ inline size_type operator()(size_type const& bit_index) const
+  {
+    return word_index(bit_index) + ((inclusive || intra_word_index(bit_index) == 0) ? 0 : 1);
+  }
+  bool const inclusive;
+};
+
+struct popc {
+  __device__ inline size_type operator()(bitmask_type word) const { return __popc(word); }
+};
+
+// Count set/unset bits in a segmented null mask, using offset iterators accessible by the device.
+template <typename OffsetIterator>
+rmm::device_uvector<size_type> segmented_count_bits(bitmask_type const* bitmask,
+                                                    OffsetIterator first_bit_indices_begin,
+                                                    OffsetIterator first_bit_indices_end,
+                                                    OffsetIterator last_bit_indices_begin,
+                                                    count_bits_policy count_bits,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  auto const num_ranges =
+    static_cast<size_type>(std::distance(first_bit_indices_begin, first_bit_indices_end));
+  rmm::device_uvector<size_type> d_bit_counts(num_ranges, stream);
+
+  auto num_set_bits_in_word = thrust::make_transform_iterator(bitmask, popc{});
+  auto first_word_indices =
+    thrust::make_transform_iterator(first_bit_indices_begin, bit_to_word_index{true});
+  auto last_word_indices =
+    thrust::make_transform_iterator(last_bit_indices_begin, bit_to_word_index{false});
+
+  // Allocate temporary memory.
+  size_t temp_storage_bytes{0};
+  CUDF_CUDA_TRY(cub::DeviceSegmentedReduce::Sum(nullptr,
+                                                temp_storage_bytes,
+                                                num_set_bits_in_word,
+                                                d_bit_counts.begin(),
+                                                num_ranges,
+                                                first_word_indices,
+                                                last_word_indices,
+                                                stream.value()));
+  rmm::device_buffer d_temp_storage(temp_storage_bytes, stream);
+
+  // Perform segmented reduction.
+  CUDF_CUDA_TRY(cub::DeviceSegmentedReduce::Sum(d_temp_storage.data(),
+                                                temp_storage_bytes,
+                                                num_set_bits_in_word,
+                                                d_bit_counts.begin(),
+                                                num_ranges,
+                                                first_word_indices,
+                                                last_word_indices,
+                                                stream.value()));
+
+  // Adjust counts in segment boundaries (if segments are not word-aligned).
+  constexpr size_type block_size{256};
+  cudf::detail::grid_1d grid(num_ranges, block_size);
+  subtract_set_bits_range_boundaries_kernel<<<grid.num_blocks,
+                                              grid.num_threads_per_block,
+                                              0,
+                                              stream.value()>>>(
+    bitmask, num_ranges, first_bit_indices_begin, last_bit_indices_begin, d_bit_counts.begin());
+
+  if (count_bits == count_bits_policy::UNSET_BITS) {
+    // Convert from set bits counts to unset bits by subtracting the number of
+    // set bits from the length of the segment.
+    auto segments_begin =
+      thrust::make_zip_iterator(first_bit_indices_begin, last_bit_indices_begin);
+    auto segment_length_iterator =
+      thrust::transform_iterator(segments_begin, [] __device__(auto const& segment) {
+        auto const begin = thrust::get<0>(segment);
+        auto const end   = thrust::get<1>(segment);
+        return end - begin;
+      });
+    thrust::transform(rmm::exec_policy(stream),
+                      segment_length_iterator,
+                      segment_length_iterator + num_ranges,
+                      d_bit_counts.data(),
+                      d_bit_counts.data(),
+                      [] __device__(auto segment_size, auto segment_bit_count) {
+                        return segment_size - segment_bit_count;
+                      });
+  }
+
+  CUDF_CHECK_CUDA(stream.value());
+  return d_bit_counts;
+}
+
+/**
+ * @brief Given two iterators, validate that the iterators represent valid ranges of
+ * indices and return the number of ranges.
+ *
+ * @throws cudf::logic_error if `std::distance(indices_begin, indices_end) % 2 != 0`
+ * @throws cudf::logic_error if `indices_begin[2*i] < 0 or indices_begin[2*i] >
+ * indices_begin[(2*i)+1]`
+ *
+ * @param indices_begin An iterator representing the beginning of the ranges of indices
+ * @param indices_end An iterator representing the end of the ranges of indices
+ *
+ * @return The number of segments specified by the input iterators.
+ */
+template <typename IndexIterator>
+size_type validate_segmented_indices(IndexIterator indices_begin, IndexIterator indices_end)
+{
+  auto const num_indices = static_cast<size_type>(std::distance(indices_begin, indices_end));
+  CUDF_EXPECTS(num_indices % 2 == 0, "Array of indices needs to have an even number of elements.");
+  size_type const num_segments = num_indices / 2;
+  for (size_type i = 0; i < num_segments; i++) {
+    auto begin = indices_begin[2 * i];
+    auto end   = indices_begin[2 * i + 1];
+    CUDF_EXPECTS(begin >= 0, "Starting index cannot be negative.");
+    CUDF_EXPECTS(end >= begin, "End index cannot be smaller than the starting index.");
+  }
+  return num_segments;
+}
+
+struct index_alternator {
+  __device__ inline size_type operator()(size_type const& i) const
+  {
+    return *(d_indices + 2 * i + (is_end ? 1 : 0));
+  }
+
+  bool const is_end = false;
+  size_type const* d_indices;
+};
+
+/**
+ * @brief Given a bitmask, counts the number of set (1) or unset (0) bits in every range
+ * `[indices_begin[2*i], indices_begin[(2*i)+1])` (where 0 <= i < std::distance(indices_begin,
+ * indices_end) / 2).
+ *
+ * If `bitmask == nullptr`, this function returns a vector containing the
+ * segment lengths, or a vector of zeros if counting unset bits.
+ *
+ * @throws cudf::logic_error if `bitmask == nullptr`.
+ * @throws cudf::logic_error if `std::distance(indices_begin, indices_end) % 2 != 0`.
+ * @throws cudf::logic_error if `indices_begin[2*i] < 0 or indices_begin[2*i] >
+ * indices_begin[(2*i)+1]`.
+ *
+ * @param bitmask Bitmask residing in device memory whose bits will be counted.
+ * @param indices_begin An iterator representing the beginning of the range of indices specifying
+ * ranges to count the number of set/unset bits within.
+ * @param indices_end An iterator representing the end of the range of indices specifying ranges to
+ * count the number of set/unset bits within.
+ * @param count_bits If SET_BITS, count set (1) bits. If UNSET_BITS, count unset (0) bits.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return A vector storing the number of non-zero bits in the specified ranges
+ */
+template <typename IndexIterator>
+std::vector<size_type> segmented_count_bits(bitmask_type const* bitmask,
+                                            IndexIterator indices_begin,
+                                            IndexIterator indices_end,
+                                            count_bits_policy count_bits,
+                                            rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(bitmask != nullptr, "Invalid bitmask.");
+  auto const num_segments = validate_segmented_indices(indices_begin, indices_end);
+
+  // Return an empty vector if there are zero segments.
+  if (num_segments == 0) { return std::vector<size_type>{}; }
+
+  // Construct a contiguous host buffer of indices and copy to device.
+  auto const h_indices = std::vector<size_type>(indices_begin, indices_end);
+  auto const d_indices =
+    make_device_uvector_async(h_indices, stream, rmm::mr::get_current_device_resource());
+
+  // Compute the bit counts over each segment.
+  auto first_bit_indices_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0), index_alternator{false, d_indices.data()});
+  auto const first_bit_indices_end = first_bit_indices_begin + num_segments;
+  auto last_bit_indices_begin      = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0), index_alternator{true, d_indices.data()});
+  rmm::device_uvector<size_type> d_bit_counts =
+    cudf::detail::segmented_count_bits(bitmask,
+                                       first_bit_indices_begin,
+                                       first_bit_indices_end,
+                                       last_bit_indices_begin,
+                                       count_bits,
+                                       stream,
+                                       rmm::mr::get_current_device_resource());
+
+  // Copy the results back to the host.
+  return make_std_vector_sync(d_bit_counts, stream);
+}
+
+// Count non-zero bits in the specified ranges.
+template <typename IndexIterator>
+std::vector<size_type> segmented_count_set_bits(bitmask_type const* bitmask,
+                                                IndexIterator indices_begin,
+                                                IndexIterator indices_end,
+                                                rmm::cuda_stream_view stream)
+{
+  return detail::segmented_count_bits(
+    bitmask, indices_begin, indices_end, count_bits_policy::SET_BITS, stream);
+}
+
+// Count zero bits in the specified ranges.
+template <typename IndexIterator>
+std::vector<size_type> segmented_count_unset_bits(bitmask_type const* bitmask,
+                                                  IndexIterator indices_begin,
+                                                  IndexIterator indices_end,
+                                                  rmm::cuda_stream_view stream)
+{
+  return detail::segmented_count_bits(
+    bitmask, indices_begin, indices_end, count_bits_policy::UNSET_BITS, stream);
+}
+
+// Count valid elements in the specified ranges of a validity bitmask.
+template <typename IndexIterator>
+std::vector<size_type> segmented_valid_count(bitmask_type const* bitmask,
+                                             IndexIterator indices_begin,
+                                             IndexIterator indices_end,
+                                             rmm::cuda_stream_view stream)
+{
+  if (bitmask == nullptr) {
+    // Return a vector of segment lengths.
+    auto const num_segments = validate_segmented_indices(indices_begin, indices_end);
+    auto ret                = std::vector<size_type>(num_segments, 0);
+    for (size_type i = 0; i < num_segments; i++) {
+      ret[i] = indices_begin[2 * i + 1] - indices_begin[2 * i];
+    }
+    return ret;
+  }
+
+  return detail::segmented_count_set_bits(bitmask, indices_begin, indices_end, stream);
+}
+
+// Count null elements in the specified ranges of a validity bitmask.
+template <typename IndexIterator>
+std::vector<size_type> segmented_null_count(bitmask_type const* bitmask,
+                                            IndexIterator indices_begin,
+                                            IndexIterator indices_end,
+                                            rmm::cuda_stream_view stream)
+{
+  if (bitmask == nullptr) {
+    // Return a vector of zeros.
+    auto const num_segments = validate_segmented_indices(indices_begin, indices_end);
+    return std::vector<size_type>(num_segments, 0);
+  }
+  return detail::segmented_count_unset_bits(bitmask, indices_begin, indices_end, stream);
+}
+
+/**
+ * @brief Create an output null mask whose validity is determined by the
+ * validity of any/all elements of segments of an input null mask.
+ *
+ * @tparam OffsetIterator Random-access input iterator type.
+ * @param bitmask Null mask residing in device memory whose segments will be reduced into a new
+ * mask.
+ * @param first_bit_indices_begin Random-access input iterator to the beginning of a sequence of
+ * indices of the first bit in each segment (inclusive).
+ * @param first_bit_indices_end Random-access input iterator to the end of a sequence of indices of
+ * the first bit in each segment (inclusive).
+ * @param last_bit_indices_begin Random-access input iterator to the beginning of a sequence of
+ * indices of the last bit in each segment (exclusive).
+ * @param null_handling If `null_policy::INCLUDE`, all elements in a segment must be valid for the
+ * reduced value to be valid. If `null_policy::EXCLUDE`, the reduction is valid if any element in
+ * the segment is valid.
+ * @param valid_initial_value Indicates whether a valid initial value was provided to the reduction.
+ * True indicates a valid initial value, false indicates a null initial value, and null indicates no
+ * initial value was provided.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned buffer's device memory.
+ * @return A pair containing the reduced null mask and number of nulls.
+ */
+template <typename OffsetIterator>
+std::pair<rmm::device_buffer, size_type> segmented_null_mask_reduction(
+  bitmask_type const* bitmask,
+  OffsetIterator first_bit_indices_begin,
+  OffsetIterator first_bit_indices_end,
+  OffsetIterator last_bit_indices_begin,
+  null_policy null_handling,
+  std::optional<bool> valid_initial_value,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto const segments_begin =
+    thrust::make_zip_iterator(first_bit_indices_begin, last_bit_indices_begin);
+  auto const segment_length_iterator =
+    thrust::make_transform_iterator(segments_begin, [] __device__(auto const& segment) {
+      auto const begin = thrust::get<0>(segment);
+      auto const end   = thrust::get<1>(segment);
+      return end - begin;
+    });
+
+  auto const num_segments =
+    static_cast<size_type>(std::distance(first_bit_indices_begin, first_bit_indices_end));
+
+  if (bitmask == nullptr) {
+    return cudf::detail::valid_if(
+      segment_length_iterator,
+      segment_length_iterator + num_segments,
+      [valid_initial_value] __device__(auto const& length) {
+        return valid_initial_value.value_or(length > 0);
+      },
+      stream,
+      mr);
+  }
+
+  auto const segment_valid_counts =
+    cudf::detail::segmented_count_bits(bitmask,
+                                       first_bit_indices_begin,
+                                       first_bit_indices_end,
+                                       last_bit_indices_begin,
+                                       cudf::detail::count_bits_policy::SET_BITS,
+                                       stream,
+                                       rmm::mr::get_current_device_resource());
+  auto const length_and_valid_count =
+    thrust::make_zip_iterator(segment_length_iterator, segment_valid_counts.begin());
+  return cudf::detail::valid_if(
+    length_and_valid_count,
+    length_and_valid_count + num_segments,
+    [null_handling, valid_initial_value] __device__(auto const& length_and_valid_count) {
+      auto const length      = thrust::get<0>(length_and_valid_count);
+      auto const valid_count = thrust::get<1>(length_and_valid_count);
+      return (null_handling == null_policy::EXCLUDE)
+               ? (valid_initial_value.value_or(false) || valid_count > 0)
+               : (valid_initial_value.value_or(length > 0) && valid_count == length);
+    },
+    stream,
+    mr);
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/null_mask.hpp b/cpp/include/cudf/detail/null_mask.hpp
new file mode 100644
index 0000000..8c10bbe
--- /dev/null
+++ b/cpp/include/cudf/detail/null_mask.hpp
@@ -0,0 +1,264 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <vector>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::create_null_mask(size_type, mask_state, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+rmm::device_buffer create_null_mask(size_type size,
+                                    mask_state state,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::set_null_mask(bitmask_type*, size_type, size_type, bool)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+void set_null_mask(bitmask_type* bitmask,
+                   size_type begin_bit,
+                   size_type end_bit,
+                   bool valid,
+                   rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a bitmask, counts the number of set (1) bits in the range
+ * `[start, stop)`.
+ *
+ * @throws cudf::logic_error if `bitmask == nullptr`
+ * @throws cudf::logic_error if `start > stop`
+ * @throws cudf::logic_error if `start < 0`
+ *
+ * @param bitmask Bitmask residing in device memory whose bits will be counted.
+ * @param start Index of the first bit to count (inclusive).
+ * @param stop Index of the last bit to count (exclusive).
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return The number of non-zero bits in the specified range.
+ */
+cudf::size_type count_set_bits(bitmask_type const* bitmask,
+                               size_type start,
+                               size_type stop,
+                               rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a bitmask, counts the number of unset (0) bits in the range
+ * `[start, stop)`.
+ *
+ * @throws cudf::logic_error if `bitmask == nullptr`
+ * @throws cudf::logic_error if `start > stop`
+ * @throws cudf::logic_error if `start < 0`
+ *
+ * @param bitmask Bitmask residing in device memory whose bits will be counted.
+ * @param start Index of the first bit to count (inclusive).
+ * @param stop Index of the last bit to count (exclusive).
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return The number of zero bits in the specified range.
+ */
+cudf::size_type count_unset_bits(bitmask_type const* bitmask,
+                                 size_type start,
+                                 size_type stop,
+                                 rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a bitmask, counts the number of set (1) bits in every range
+ * `[indices[2*i], indices[(2*i)+1])` (where 0 <= i < indices.size() / 2).
+ *
+ * @throws cudf::logic_error if `bitmask == nullptr`
+ * @throws cudf::logic_error if `indices.size() % 2 != 0`
+ * @throws cudf::logic_error if `indices[2*i] < 0 or indices[2*i] > indices[(2*i)+1]`
+ *
+ * @param[in] bitmask Bitmask residing in device memory whose bits will be counted.
+ * @param[in] indices A host_span of indices specifying ranges to count the number of set bits.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @return A vector storing the number of non-zero bits in the specified ranges.
+ */
+std::vector<size_type> segmented_count_set_bits(bitmask_type const* bitmask,
+                                                host_span<size_type const> indices,
+                                                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a bitmask, counts the number of unset (0) bits in every range
+ * `[indices[2*i], indices[(2*i)+1])` (where 0 <= i < indices.size() / 2).
+ *
+ * @throws cudf::logic_error if `bitmask == nullptr`
+ * @throws cudf::logic_error if `indices.size() % 2 != 0`
+ * @throws cudf::logic_error if `indices[2*i] < 0 or indices[2*i] > indices[(2*i)+1]`
+ *
+ * @param[in] bitmask Bitmask residing in device memory whose bits will be counted.
+ * @param[in] indices A host_span of indices specifying ranges to count the number of unset bits.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @return A vector storing the number of zero bits in the specified ranges.
+ */
+std::vector<size_type> segmented_count_unset_bits(bitmask_type const* bitmask,
+                                                  host_span<size_type const> indices,
+                                                  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a validity bitmask, counts the number of valid elements (set bits)
+ * in the range `[start, stop)`.
+ *
+ * If `bitmask == nullptr`, all elements are assumed to be valid and the
+ * function returns `stop-start`.
+ *
+ * @throws cudf::logic_error if `start > stop`
+ * @throws cudf::logic_error if `start < 0`
+ *
+ * @param[in] bitmask Validity bitmask residing in device memory.
+ * @param[in] start Index of the first bit to count (inclusive).
+ * @param[in] stop Index of the last bit to count (exclusive).
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @return The number of valid elements in the specified range.
+ */
+cudf::size_type valid_count(bitmask_type const* bitmask,
+                            size_type start,
+                            size_type stop,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc null_count(bitmask_type const* bitmask, size_type start, size_type stop)
+ *
+ * @param stream Stream view on which to allocate resources and queue execution.
+ */
+cudf::size_type null_count(bitmask_type const* bitmask,
+                           size_type start,
+                           size_type stop,
+                           rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a validity bitmask, counts the number of valid elements (set
+ * bits) in every range `[indices[2*i], indices[(2*i)+1])` (where 0 <= i <
+ * indices.size() / 2).
+ *
+ * If `bitmask == nullptr`, all elements are assumed to be valid and a vector of
+ * length `indices.size()` containing segment lengths is returned.
+ *
+ * @throws cudf::logic_error if `indices.size() % 2 != 0`.
+ * @throws cudf::logic_error if `indices[2*i] < 0 or indices[2*i] > indices[(2*i)+1]`.
+ *
+ * @param[in] bitmask Validity bitmask residing in device memory.
+ * @param[in] indices A host_span of indices specifying ranges to count the number of valid
+ * elements.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @return A vector storing the number of valid elements in each specified range.
+ */
+std::vector<size_type> segmented_valid_count(bitmask_type const* bitmask,
+                                             host_span<size_type const> indices,
+                                             rmm::cuda_stream_view stream);
+
+/**
+ * @brief Given a validity bitmask, counts the number of null elements (unset
+ * bits) in every range `[indices[2*i], indices[(2*i)+1])` (where 0 <= i <
+ * indices.size() / 2).
+ *
+ * If `bitmask == nullptr`, all elements are assumed to be valid and a vector of
+ * length `indices.size()` containing all zeros is returned.
+ *
+ * @throws cudf::logic_error if `indices.size() % 2 != 0`
+ * @throws cudf::logic_error if `indices[2*i] < 0 or indices[2*i] > indices[(2*i)+1]`
+ *
+ * @param[in] bitmask Validity bitmask residing in device memory.
+ * @param[in] indices A host_span of indices specifying ranges to count the number of null elements.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @return A vector storing the number of null elements in each specified range.
+ */
+std::vector<size_type> segmented_null_count(bitmask_type const* bitmask,
+                                            host_span<size_type const> indices,
+                                            rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::copy_bitmask(bitmask_type const*, size_type, size_type,
+ *rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+rmm::device_buffer copy_bitmask(bitmask_type const* mask,
+                                size_type begin_bit,
+                                size_type end_bit,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::copy_bitmask(column_view const& view, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+rmm::device_buffer copy_bitmask(column_view const& view,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc bitmask_and(host_span<bitmask_type const* const>, host_span<size_type> const,
+ * size_type, rmm::mr::device_memory_resource *)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::pair<rmm::device_buffer, size_type> bitmask_and(host_span<bitmask_type const* const> masks,
+                                                     host_span<size_type const> masks_begin_bits,
+                                                     size_type mask_size_bits,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::bitmask_and
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<rmm::device_buffer, size_type> bitmask_and(table_view const& view,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::bitmask_or
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<rmm::device_buffer, size_type> bitmask_or(table_view const& view,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Performs a bitwise AND of the specified bitmasks,
+ *        and writes in place to destination
+ *
+ * @param dest_mask Destination to which the AND result is written
+ * @param masks The list of data pointers of the bitmasks to be ANDed
+ * @param masks_begin_bits The bit offsets from which each mask is to be ANDed
+ * @param mask_size_bits The number of bits to be ANDed in each mask
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Count of set bits
+ */
+cudf::size_type inplace_bitmask_and(device_span<bitmask_type> dest_mask,
+                                    host_span<bitmask_type const* const> masks,
+                                    host_span<size_type const> masks_begin_bits,
+                                    size_type mask_size_bits,
+                                    rmm::cuda_stream_view stream);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/nvtx/nvtx3.hpp b/cpp/include/cudf/detail/nvtx/nvtx3.hpp
new file mode 100644
index 0000000..4b84072
--- /dev/null
+++ b/cpp/include/cudf/detail/nvtx/nvtx3.hpp
@@ -0,0 +1,1933 @@
+/*
+ *  Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ */
+#pragma once
+
+#if defined(NVTX3_MINOR_VERSION) and NVTX3_MINOR_VERSION < 0
+#error \
+  "Trying to #include NVTX version 3 in a source file where an older NVTX version has already been included.  If you are not directly using NVTX (the NVIDIA Tools Extension library), you are getting this error because libraries you are using have included different versions of NVTX.  Suggested solutions are: (1) reorder #includes so the newest NVTX version is included first, (2) avoid using the conflicting libraries in the same .c/.cpp file, or (3) update the library using the older NVTX version to use the newer version instead."
+#endif
+
+/**
+ * @brief Semantic minor version number.
+ *
+ * Major version number is hardcoded into the "nvtx3" namespace/prefix.
+ *
+ * If this value is incremented, the above version include guard needs to be
+ * updated.
+ */
+#define NVTX3_MINOR_VERSION 0
+
+#include <nvtx3/nvToolsExt.h>
+
+#include <string>
+
+/**
+ * @file nvtx3.hpp
+ *
+ * @brief Provides C++ constructs making the NVTX library safer and easier to
+ * use with zero overhead.
+ */
+
+/**
+ * \mainpage
+ * \tableofcontents
+ *
+ * \section QUICK_START Quick Start
+ *
+ * To add NVTX ranges to your code, use the `nvtx3::thread_range` RAII object. A
+ * range begins when the object is created, and ends when the object is
+ * destroyed.
+ *
+ * \code{.cpp}
+ * #include "nvtx3.hpp"
+ * void some_function(){
+ *    // Begins a NVTX range with the message "some_function"
+ *    // The range ends when some_function() returns and `r` is destroyed
+ *    nvtx3::thread_range r{"some_function"};
+ *
+ *    for(int i = 0; i < 6; ++i){
+ *       nvtx3::thread_range loop{"loop range"};
+ *       std::this_thread::sleep_for(std::chrono::seconds{1});
+ *    }
+ * } // Range ends when `r` is destroyed
+ * \endcode
+ *
+ * The example code above generates the following timeline view in Nsight
+ * Systems:
+ *
+ * \image html
+ * https://raw.githubusercontent.com/jrhemstad/nvtx_wrappers/master/docs/example_range.png
+ *
+ * Alternatively, use the \ref MACROS like `NVTX3_FUNC_RANGE()` to add
+ * ranges to your code that automatically use the name of the enclosing function
+ * as the range's message.
+ *
+ * \code{.cpp}
+ * #include "nvtx3.hpp"
+ * void some_function(){
+ *    // Creates a range with a message "some_function" that ends when the
+ * enclosing
+ *    // function returns
+ *    NVTX3_FUNC_RANGE();
+ *    ...
+ * }
+ * \endcode
+ *
+ *
+ * \section Overview
+ *
+ * The NVTX library provides a set of functions for users to annotate their code
+ * to aid in performance profiling and optimization. These annotations provide
+ * information to tools like Nsight Systems to improve visualization of
+ * application timelines.
+ *
+ * \ref RANGES are one of the most commonly used NVTX constructs for annotating
+ * a span of time. For example, imagine a user wanted to see every time a
+ * function, `my_function`, is called and how long it takes to execute. This can
+ * be accomplished with an NVTX range created on the entry to the function and
+ * terminated on return from `my_function` using the push/pop C APIs:
+ *
+ * ```
+ * void my_function(...){
+ *    nvtxRangePushA("my_function"); // Begins NVTX range
+ *    // do work
+ *    nvtxRangePop(); // Ends NVTX range
+ * }
+ * ```
+ *
+ * One of the challenges with using the NVTX C API is that it requires manually
+ * terminating the end of the range with `nvtxRangePop`. This can be challenging
+ * if `my_function()` has multiple returns or can throw exceptions as it
+ * requires calling `nvtxRangePop()` before all possible return points.
+ *
+ * NVTX++ solves this inconvenience through the "RAII" technique by providing a
+ * `nvtx3::thread_range` class that begins a range at construction and ends the
+ * range on destruction. The above example then becomes:
+ *
+ * ```
+ * void my_function(...){
+ *    nvtx3::thread_range r{"my_function"}; // Begins NVTX range
+ *    // do work
+ * } // Range ends on exit from `my_function` when `r` is destroyed
+ * ```
+ *
+ * The range object `r` is deterministically destroyed whenever `my_function`
+ * returns---ending the NVTX range without manual intervention. For more
+ * information, see \ref RANGES and `nvtx3::domain_thread_range`.
+ *
+ * Another inconvenience of the NVTX C APIs are the several constructs where the
+ * user is expected to initialize an object at the beginning of an application
+ * and reuse that object throughout the lifetime of the application. For example
+ * Domains, Categories, and Registered messages.
+ *
+ * Example:
+ * ```
+ * nvtxDomainHandle_t D = nvtxDomainCreateA("my domain");
+ * // Reuse `D` throughout the rest of the application
+ * ```
+ *
+ * This can be problematic if the user application or library does not have an
+ * explicit initialization function called before all other functions to
+ * ensure that these long-lived objects are initialized before being used.
+ *
+ * NVTX++ makes use of the "construct on first use" technique to alleviate this
+ * inconvenience. In short, a function local static object is constructed upon
+ * the first invocation of a function and returns a reference to that object on
+ * all future invocations. See the documentation for
+ * `nvtx3::registered_message`, `nvtx3::domain`, `nvtx3::named_category`,  and
+ * https://isocpp.org/wiki/faq/ctors#static-init-order-on-first-use for more
+ * information.
+ *
+ * Using construct on first use, the above example becomes:
+ * ```
+ * struct my_domain{ static constexpr char const* name{"my domain"}; };
+ *
+ * // The first invocation of `domain::get` for the type `my_domain` will
+ * // construct a `nvtx3::domain` object and return a reference to it. Future
+ * // invocations simply return a reference.
+ * nvtx3::domain const& D = nvtx3::domain::get<my_domain>();
+ * ```
+ * For more information about NVTX and how it can be used, see
+ * https://docs.nvidia.com/cuda/profiler-users-guide/index.html#nvtx and
+ * https://devblogs.nvidia.com/cuda-pro-tip-generate-custom-application-profile-timelines-nvtx/
+ * for more information.
+ *
+ * \section RANGES Ranges
+ *
+ * Ranges are used to describe a span of time during the execution of an
+ * application. Common examples are using ranges to annotate the time it takes
+ * to execute a function or an iteration of a loop.
+ *
+ * NVTX++ uses RAII to automate the generation of ranges that are tied to the
+ * lifetime of objects. Similar to `std::lock_guard` in the C++ Standard
+ * Template Library.
+ *
+ * \subsection THREAD_RANGE Thread Range
+ *
+ * `nvtx3::domain_thread_range` is a class that begins a range upon construction
+ * and ends the range at destruction. This is one of the most commonly used
+ * constructs in NVTX++ and is useful for annotating spans of time on a
+ * particular thread. These ranges can be nested to arbitrary depths.
+ *
+ * `nvtx3::thread_range` is an alias for a `nvtx3::domain_thread_range` in the
+ * global NVTX domain. For more information about Domains, see \ref DOMAINS.
+ *
+ * Various attributes of a range can be configured constructing a
+ * `nvtx3::domain_thread_range` with a `nvtx3::event_attributes` object. For
+ * more information, see \ref ATTRIBUTES.
+ *
+ * Example:
+ *
+ * \code{.cpp}
+ * void some_function(){
+ *    // Creates a range for the duration of `some_function`
+ *    nvtx3::thread_range r{};
+ *
+ *    while(true){
+ *       // Creates a range for every loop iteration
+ *       // `loop_range` is nested inside `r`
+ *       nvtx3::thread_range loop_range{};
+ *    }
+ * }
+ * \endcode
+ *
+ * \subsection PROCESS_RANGE Process Range
+ *
+ * `nvtx3::domain_process_range` is identical to `nvtx3::domain_thread_range`
+ * with the exception that a `domain_process_range` can be created and destroyed
+ * on different threads. This is useful to annotate spans of time that can
+ * bridge multiple threads.
+ *
+ * `nvtx3::domain_thread_range`s should be preferred unless one needs the
+ * ability to begin and end a range on different threads.
+ *
+ * \section MARKS Marks
+ *
+ * `nvtx3::mark` allows annotating an instantaneous event in an application's
+ * timeline. For example, indicating when a mutex is locked or unlocked.
+ *
+ * \code{.cpp}
+ * std::mutex global_lock;
+ * void lock_mutex(){
+ *    global_lock.lock();
+ *    // Marks an event immediately after the mutex is locked
+ *    nvtx3::mark<my_domain>("lock_mutex");
+ * }
+ * \endcode
+ *
+ * \section DOMAINS Domains
+ *
+ * Similar to C++ namespaces, Domains allow for scoping NVTX events. By default,
+ * all NVTX events belong to the "global" domain. Libraries and applications
+ * should scope their events to use a custom domain to differentiate where the
+ * events originate from.
+ *
+ * It is common for a library or application to have only a single domain and
+ * for the name of that domain to be known at compile time. Therefore, Domains
+ * in NVTX++ are represented by _tag types_.
+ *
+ * For example, to define a custom  domain, simply define a new concrete type
+ * (a `class` or `struct`) with a `static` member called `name` that contains
+ * the desired name of the domain.
+ *
+ * ```
+ * struct my_domain{ static constexpr char const* name{"my domain"}; };
+ * ```
+ *
+ * For any NVTX++ construct that can be scoped to a domain, the type `my_domain`
+ * can be passed as an explicit template argument to scope it to the custom
+ * domain.
+ *
+ * The tag type `nvtx3::domain::global` represents the global NVTX domain.
+ *
+ * \code{.cpp}
+ * // By default, `domain_thread_range` belongs to the global domain
+ * nvtx3::domain_thread_range<> r0{};
+ *
+ * // Alias for a `domain_thread_range` in the global domain
+ * nvtx3::thread_range r1{};
+ *
+ * // `r` belongs to the custom domain
+ * nvtx3::domain_thread_range<my_domain> r{};
+ * \endcode
+ *
+ * When using a custom domain, it is recommended to define type aliases for NVTX
+ * constructs in the custom domain.
+ * ```
+ * using my_thread_range = nvtx3::domain_thread_range<my_domain>;
+ * using my_registered_message = nvtx3::registered_message<my_domain>;
+ * using my_named_category = nvtx3::named_category<my_domain>;
+ * ```
+ *
+ * See `nvtx3::domain` for more information.
+ *
+ * \section ATTRIBUTES Event Attributes
+ *
+ * NVTX events can be customized with various attributes to provide additional
+ * information (such as a custom message) or to control visualization of the
+ * event (such as the color used). These attributes can be specified per-event
+ * via arguments to a `nvtx3::event_attributes` object.
+ *
+ * NVTX events can be customized via four "attributes":
+ * - \ref COLOR : color used to visualize the event in tools.
+ * - \ref MESSAGES :  Custom message string.
+ * - \ref PAYLOAD :  User-defined numerical value.
+ * - \ref CATEGORY : Intra-domain grouping.
+ *
+ * It is possible to construct a `nvtx3::event_attributes` from any number of
+ * attribute objects (nvtx3::color, nvtx3::message, nvtx3::payload,
+ * nvtx3::category) in any order. If an attribute is not specified, a tool
+ * specific default value is used. See `nvtx3::event_attributes` for more
+ * information.
+ *
+ * \code{.cpp}
+ * // Custom color, message
+ * event_attributes attr{nvtx3::rgb{127, 255, 0},
+ *                      "message"};
+ *
+ * // Custom color, message, payload, category
+ * event_attributes attr{nvtx3::rgb{127, 255, 0},
+ *                      nvtx3::payload{42},
+ *                      "message",
+ *                      nvtx3::category{1}};
+ *
+ * // Arguments can be in any order
+ * event_attributes attr{nvtx3::payload{42},
+ *                      nvtx3::category{1},
+ *                      "message",
+ *                      nvtx3::rgb{127, 255, 0}};
+ *
+ * // "First wins" with multiple arguments of the same type
+ * event_attributes attr{ nvtx3::payload{42}, nvtx3::payload{7} }; // payload is
+ * 42 \endcode
+ *
+ * \subsection MESSAGES message
+ *
+ * A `nvtx3::message` allows associating a custom message string with an NVTX
+ * event.
+ *
+ * Example:
+ * \code{.cpp}
+ * // Create an `event_attributes` with the custom message "my message"
+ * nvtx3::event_attributes attr{nvtx3::message{"my message"}};
+ *
+ * // strings and string literals implicitly assumed to be a `nvtx3::message`
+ * nvtx3::event_attributes attr{"my message"};
+ * \endcode
+ *
+ * \subsubsection REGISTERED_MESSAGE Registered Messages
+ *
+ * Associating a `nvtx3::message` with an event requires copying the contents of
+ * the message every time the message is used, i.e., copying the entire message
+ * string. This may cause non-trivial overhead in performance sensitive code.
+ *
+ * To eliminate this overhead, NVTX allows registering a message string,
+ * yielding a "handle" that is inexpensive to copy that may be used in place of
+ * a message string. When visualizing the events, tools such as Nsight Systems
+ * will take care of mapping the message handle to its string.
+ *
+ * A message should be registered once and the handle reused throughout the rest
+ * of the application. This can be done by either explicitly creating static
+ * `nvtx3::registered_message` objects, or using the
+ * `nvtx3::registered_message::get` construct on first use helper (recommended).
+ *
+ * Similar to \ref DOMAINS, `nvtx3::registered_message::get` requires defining a
+ * custom tag type with a static `message` member whose value will be the
+ * contents of the registered string.
+ *
+ * Example:
+ * \code{.cpp}
+ * // Explicitly constructed, static `registered_message`
+ * static registered_message<my_domain> static_message{"my message"};
+ *
+ * // Or use construct on first use:
+ * // Define a tag type with a `message` member string to register
+ * struct my_message{ static constexpr char const* message{ "my message" }; };
+ *
+ * // Uses construct on first use to register the contents of
+ * // `my_message::message`
+ * nvtx3::registered_message<my_domain> const& msg =
+ * nvtx3::registered_message<my_domain>::get<my_message>(); \endcode
+ *
+ * \subsection COLOR color
+ *
+ * Associating a `nvtx3::color` with an event allows controlling how the event
+ * is visualized in a tool such as Nsight Systems. This is a convenient way to
+ * visually differentiate among different events.
+ *
+ * \code{.cpp}
+ * // Define a color via rgb color values
+ * nvtx3::color c{nvtx3::rgb{127, 255, 0}};
+ * nvtx3::event_attributes attr{c};
+ *
+ * // rgb color values can be passed directly to an `event_attributes`
+ * nvtx3::event_attributes attr1{nvtx3::rgb{127,255,0}};
+ * \endcode
+ *
+ * \subsection CATEGORY category
+ *
+ * A `nvtx3::category` is simply an integer id that allows for fine-grain
+ * grouping of NVTX events. For example, one might use separate categories for
+ * IO, memory allocation, compute, etc.
+ *
+ * \code{.cpp}
+ * nvtx3::event_attributes{nvtx3::category{1}};
+ * \endcode
+ *
+ * \subsubsection NAMED_CATEGORIES Named Categories
+ *
+ * Associates a `name` string with a category `id` to help differentiate among
+ * categories.
+ *
+ * For any given category id `Id`, a `named_category{Id, "name"}` should only
+ * be constructed once and reused throughout an application. This can be done by
+ * either explicitly creating static `nvtx3::named_category` objects, or using
+ * the `nvtx3::named_category::get` construct on first use helper (recommended).
+ *
+ * Similar to \ref DOMAINS, `nvtx3::named_category::get` requires defining a
+ * custom tag type with static `name` and `id` members.
+ *
+ * \code{.cpp}
+ * // Explicitly constructed, static `named_category`
+ * static nvtx3::named_category static_category{42, "my category"};
+ *
+ * // OR use construct on first use:
+ * // Define a tag type with `name` and `id` members
+ * struct my_category{
+ *    static constexpr char const* name{"my category"}; // category name
+ *    static constexpr category::id_type id{42}; // category id
+ * };
+ *
+ * // Use construct on first use to name the category id `42`
+ * // with name "my category"
+ * nvtx3::named_category const& my_category =
+ * named_category<my_domain>::get<my_category>();
+ *
+ * // Range `r` associated with category id `42`
+ * nvtx3::event_attributes attr{my_category};
+ * \endcode
+ *
+ * \subsection PAYLOAD payload
+ *
+ * Allows associating a user-defined numerical value with an event.
+ *
+ * ```
+ * nvtx3:: event_attributes attr{nvtx3::payload{42}}; // Constructs a payload
+ * from
+ *                                                 // the `int32_t` value 42
+ * ```
+ *
+ *
+ * \section EXAMPLE Example
+ *
+ * Putting it all together:
+ * \code{.cpp}
+ * // Define a custom domain tag type
+ * struct my_domain{ static constexpr char const* name{"my domain"}; };
+ *
+ * // Define a named category tag type
+ * struct my_category{
+ *    static constexpr char const* name{"my category"};
+ *    static constexpr uint32_t id{42};
+ * };
+ *
+ * // Define a registered message tag type
+ * struct my_message{ static constexpr char const* message{"my message"}; };
+ *
+ * // For convenience, use aliases for domain scoped objects
+ * using my_thread_range = nvtx3::domain_thread_range<my_domain>;
+ * using my_registered_message = nvtx3::registered_message<my_domain>;
+ * using my_named_category = nvtx3::named_category<my_domain>;
+ *
+ * // Default values for all attributes
+ * nvtx3::event_attributes attr{};
+ * my_thread_range r0{attr};
+ *
+ * // Custom (unregistered) message, and unnamed category
+ * nvtx3::event_attributes attr1{"message", nvtx3::category{2}};
+ * my_thread_range r1{attr1};
+ *
+ * // Alternatively, pass arguments of `event_attributes` ctor directly to
+ * // `my_thread_range`
+ * my_thread_range r2{"message", nvtx3::category{2}};
+ *
+ * // construct on first use a registered message
+ * auto msg = my_registered_message::get<my_message>();
+ *
+ * // construct on first use a named category
+ * auto category = my_named_category::get<my_category>();
+ *
+ * // Use registered message and named category
+ * my_thread_range r3{msg, category, nvtx3::rgb{127, 255, 0},
+ *                    nvtx3::payload{42}};
+ *
+ * // Any number of arguments in any order
+ * my_thread_range r{nvtx3::rgb{127, 255,0}, msg};
+ *
+ * \endcode
+ * \section MACROS Convenience Macros
+ *
+ * Oftentimes users want to quickly and easily add NVTX ranges to their library
+ * or application to aid in profiling and optimization.
+ *
+ * A convenient way to do this is to use the \ref NVTX3_FUNC_RANGE and
+ * \ref NVTX3_FUNC_RANGE_IN macros. These macros take care of constructing an
+ * `nvtx3::domain_thread_range` with the name of the enclosing function as the
+ * range's message.
+ *
+ * \code{.cpp}
+ * void some_function(){
+ *    // Automatically generates an NVTX range for the duration of the function
+ *    // using "some_function" as the event's message.
+ *    NVTX3_FUNC_RANGE();
+ * }
+ * \endcode
+ */
+
+/**
+ * @brief Enables the use of constexpr when support for C++14 relaxed constexpr
+ * is present.
+ *
+ * Initializing a legacy-C (i.e., no constructor) union member requires
+ * initializing in the constructor body. Non-empty constexpr constructors
+ * require C++14 relaxed constexpr.
+ */
+#if __cpp_constexpr >= 201304L
+#define NVTX3_RELAXED_CONSTEXPR constexpr
+#else
+#define NVTX3_RELAXED_CONSTEXPR
+#endif
+
+namespace nvtx3 {
+namespace detail {
+/**
+ * @brief Verifies if a type `T` contains a member `T::name` of type `const
+ * char*` or `const wchar_t*`.
+ *
+ * @tparam T The type to verify
+ * @return True if `T` contains a member `T::name` of type `const char*` or
+ * `const wchar_t*`.
+ */
+template <typename T>
+constexpr auto has_name_member() noexcept -> decltype(T::name, bool())
+{
+  return (std::is_same_v<char const*, typename std::decay<decltype(T::name)>::type> or
+          std::is_same_v<wchar_t const*, typename std::decay<decltype(T::name)>::type>);
+}
+}  // namespace detail
+
+/**
+ * @brief `domain`s allow for grouping NVTX events into a single scope to
+ * differentiate them from events in other `domain`s.
+ *
+ * By default, all NVTX constructs are placed in the "global" NVTX domain.
+ *
+ * A custom `domain` may be used in order to differentiate a library's or
+ * application's NVTX events from other events.
+ *
+ * `domain`s are expected to be long-lived and unique to a library or
+ * application. As such, it is assumed a domain's name is known at compile
+ * time. Therefore, all NVTX constructs that can be associated with a domain
+ * require the domain to be specified via a *type* `DomainName` passed as an
+ * explicit template parameter.
+ *
+ * The type `domain::global` may be used to indicate that the global NVTX
+ * domain should be used.
+ *
+ * None of the C++ NVTX constructs require the user to manually construct a
+ * `domain` object. Instead, if a custom domain is desired, the user is
+ * expected to define a type `DomainName` that contains a member
+ * `DomainName::name` which resolves to either a `char const*` or `wchar_t
+ * const*`. The value of `DomainName::name` is used to name and uniquely
+ * identify the custom domain.
+ *
+ * Upon the first use of an NVTX construct associated with the type
+ * `DomainName`, the "construct on first use" pattern is used to construct a
+ * function local static `domain` object. All future NVTX constructs
+ * associated with `DomainType` will use a reference to the previously
+ * constructed `domain` object. See `domain::get`.
+ *
+ * Example:
+ * ```
+ * // The type `my_domain` defines a `name` member used to name and identify
+ * the
+ * // `domain` object identified by `my_domain`.
+ * struct my_domain{ static constexpr char const* name{"my_domain"}; };
+ *
+ * // The NVTX range `r` will be grouped with all other NVTX constructs
+ * // associated with  `my_domain`.
+ * nvtx3::domain_thread_range<my_domain> r{};
+ *
+ * // An alias can be created for a `domain_thread_range` in the custom domain
+ * using my_thread_range = nvtx3::domain_thread_range<my_domain>;
+ * my_thread_range my_range{};
+ *
+ * // `domain::global` indicates that the global NVTX domain is used
+ * nvtx3::domain_thread_range<domain::global> r2{};
+ *
+ * // For convenience, `nvtx3::thread_range` is an alias for a range in the
+ * // global domain
+ * nvtx3::thread_range r3{};
+ * ```
+ */
+class domain {
+ public:
+  domain(domain const&)            = delete;
+  domain& operator=(domain const&) = delete;
+  domain(domain&&)                 = delete;
+  domain& operator=(domain&&)      = delete;
+
+  /**
+   * @brief Returns reference to an instance of a function local static
+   * `domain` object.
+   *
+   * Uses the "construct on first use" idiom to safely ensure the `domain`
+   * object is initialized exactly once upon first invocation of
+   * `domain::get<DomainName>()`. All following invocations will return a
+   * reference to the previously constructed `domain` object. See
+   * https://isocpp.org/wiki/faq/ctors#static-init-order-on-first-use
+   *
+   * None of the constructs in this header require the user to directly invoke
+   * `domain::get`. It is automatically invoked when constructing objects like
+   * a `domain_thread_range` or `category`. Advanced users may wish to use
+   * `domain::get` for the convenience of the "construct on first use" idiom
+   * when using domains with their own use of the NVTX C API.
+   *
+   * This function is threadsafe as of C++11. If two or more threads call
+   * `domain::get<DomainName>` concurrently, exactly one of them is guaranteed
+   * to construct the `domain` object and the other(s) will receive a
+   * reference to the object after it is fully constructed.
+   *
+   * The domain's name is specified via the type `DomainName` pass as an
+   * explicit template parameter. `DomainName` is required to contain a
+   * member `DomainName::name` that resolves to either a `char const*` or
+   * `wchar_t const*`. The value of `DomainName::name` is used to name and
+   * uniquely identify the `domain`.
+   *
+   * Example:
+   * ```
+   * // The type `my_domain` defines a `name` member used to name and identify
+   * // the `domain` object identified by `my_domain`.
+   * struct my_domain{ static constexpr char const* name{"my domain"}; };
+   *
+   * auto D = domain::get<my_domain>(); // First invocation constructs a
+   *                                    // `domain` with the name "my domain"
+   *
+   * auto D1 = domain::get<my_domain>(); // Simply returns reference to
+   *                                     // previously constructed `domain`.
+   * ```
+   *
+   * @tparam DomainName Type that contains a `DomainName::name` member used to
+   * name the `domain` object.
+   * @return Reference to the `domain` corresponding to the type `DomainName`.
+   */
+  template <typename DomainName>
+  static domain const& get()
+  {
+    static_assert(detail::has_name_member<DomainName>(),
+                  "Type used to identify a domain must contain a name member of"
+                  "type const char* or const wchar_t*");
+    static domain const d{DomainName::name};
+    return d;
+  }
+
+  /**
+   * @brief Conversion operator to `nvtxDomainHandle_t`.
+   *
+   * Allows transparently passing a domain object into an API expecting a
+   * native `nvtxDomainHandle_t` object.
+   */
+  operator nvtxDomainHandle_t() const noexcept { return _domain; }
+
+  /**
+   * @brief Tag type for the "global" NVTX domain.
+   *
+   * This type may be passed as a template argument to any function/class
+   * expecting a type to identify a domain to indicate that the global domain
+   * should be used.
+   *
+   * All NVTX events in the global domain across all libraries and
+   * applications will be grouped together.
+   *
+   */
+  struct global {};
+
+ private:
+  /**
+   * @brief Construct a new domain with the specified `name`.
+   *
+   * This constructor is private as it is intended that `domain` objects only
+   * be created through the `domain::get` function.
+   *
+   * @param name A unique name identifying the domain
+   */
+  explicit domain(char const* name) noexcept : _domain{nvtxDomainCreateA(name)} {}
+
+  /**
+   * @brief Construct a new domain with the specified `name`.
+   *
+   * This constructor is private as it is intended that `domain` objects only
+   * be created through the `domain::get` function.
+   *
+   * @param name A unique name identifying the domain
+   */
+  explicit domain(wchar_t const* name) noexcept : _domain{nvtxDomainCreateW(name)} {}
+
+  /**
+   * @brief Construct a new domain with the specified `name`.
+   *
+   * This constructor is private as it is intended that `domain` objects only
+   * be created through the `domain::get` function.
+   *
+   * @param name A unique name identifying the domain
+   */
+  explicit domain(std::string const& name) noexcept : domain{name.c_str()} {}
+
+  /**
+   * @brief Construct a new domain with the specified `name`.
+   *
+   * This constructor is private as it is intended that `domain` objects only
+   * be created through the `domain::get` function.
+   *
+   * @param name A unique name identifying the domain
+   */
+  explicit domain(std::wstring const& name) noexcept : domain{name.c_str()} {}
+
+  /**
+   * @brief Default constructor creates a `domain` representing the
+   * "global" NVTX domain.
+   *
+   * All events not associated with a custom `domain` are grouped in the
+   * "global" NVTX domain.
+   *
+   */
+  domain() = default;
+
+  /**
+   * @brief Destroy the domain object, unregistering and freeing all domain
+   * specific resources.
+   */
+  ~domain() noexcept { nvtxDomainDestroy(_domain); }
+
+ private:
+  nvtxDomainHandle_t const _domain{};  ///< The `domain`s NVTX handle
+};
+
+/**
+ * @brief Returns reference to the `domain` object that represents the global
+ * NVTX domain.
+ *
+ * This specialization for `domain::global` returns a default constructed,
+ * `domain` object for use when the "global" domain is desired.
+ *
+ * All NVTX events in the global domain across all libraries and applications
+ * will be grouped together.
+ *
+ * @return Reference to the `domain` corresponding to the global NVTX domain.
+ */
+template <>
+inline domain const& domain::get<domain::global>()
+{
+  static domain const d{};
+  return d;
+}
+
+/**
+ * @brief Indicates the values of the red, green, blue color channels for
+ * a rgb color code.
+ */
+struct rgb {
+  /// Type used for component values
+  using component_type = uint8_t;
+
+  /**
+   * @brief Construct a rgb with red, green, and blue channels
+   * specified by `red_`, `green_`, and `blue_`, respectively.
+   *
+   * Valid values are in the range `[0,255]`.
+   *
+   * @param red_ Value of the red channel
+   * @param green_ Value of the green channel
+   * @param blue_ Value of the blue channel
+   */
+  constexpr rgb(component_type red_, component_type green_, component_type blue_) noexcept
+    : red{red_}, green{green_}, blue{blue_}
+  {
+  }
+
+  component_type const red{};    ///< Red channel value
+  component_type const green{};  ///< Green channel value
+  component_type const blue{};   ///< Blue channel value
+};
+
+/**
+ * @brief Indicates the value of the alpha, red, green, and blue color
+ * channels for an argb color code.
+ */
+struct argb final : rgb {
+  /**
+   * @brief Construct an argb with alpha, red, green, and blue channels
+   * specified by `alpha_`, `red_`, `green_`, and `blue_`, respectively.
+   *
+   * Valid values are in the range `[0,255]`.
+   *
+   * @param alpha_  Value of the alpha channel (opacity)
+   * @param red_  Value of the red channel
+   * @param green_  Value of the green channel
+   * @param blue_  Value of the blue channel
+   *
+   */
+  constexpr argb(component_type alpha_,
+                 component_type red_,
+                 component_type green_,
+                 component_type blue_) noexcept
+    : rgb{red_, green_, blue_}, alpha{alpha_}
+  {
+  }
+
+  component_type const alpha{};  ///< Alpha channel value
+};
+
+/**
+ * @brief Represents a custom color that can be associated with an NVTX event
+ * via it's `event_attributes`.
+ *
+ * Specifying colors for NVTX events is a convenient way to visually
+ * differentiate among different events in a visualization tool such as Nsight
+ * Systems.
+ */
+class color {
+ public:
+  /// Type used for the color's value
+  using value_type = uint32_t;
+
+  /**
+   * @brief Constructs a `color` using the value provided by `hex_code`.
+   *
+   * `hex_code` is expected to be a 4 byte argb hex code.
+   *
+   * The most significant byte indicates the value of the alpha channel
+   * (opacity) (0-255)
+   *
+   * The next byte indicates the value of the red channel (0-255)
+   *
+   * The next byte indicates the value of the green channel (0-255)
+   *
+   * The least significant byte indicates the value of the blue channel
+   * (0-255)
+   *
+   * @param hex_code The hex code used to construct the `color`
+   */
+  constexpr explicit color(value_type hex_code) noexcept : _value{hex_code} {}
+
+  /**
+   * @brief Construct a `color` using the alpha, red, green, blue components
+   * in `argb`.
+   *
+   * @param argb The alpha, red, green, blue components of the desired `color`
+   */
+  constexpr color(argb argb) noexcept
+    : color{from_bytes_msb_to_lsb(argb.alpha, argb.red, argb.green, argb.blue)}
+  {
+  }
+
+  /**
+   * @brief Construct a `color` using the red, green, blue components in
+   * `rgb`.
+   *
+   * Uses maximum value for the alpha channel (opacity) of the `color`.
+   *
+   * @param rgb The red, green, blue components of the desired `color`
+   */
+  constexpr color(rgb rgb) noexcept
+    : color{from_bytes_msb_to_lsb(0xFF, rgb.red, rgb.green, rgb.blue)}
+  {
+  }
+
+  /**
+   * @brief Returns the `color`s argb hex code
+   *
+   */
+  constexpr value_type get_value() const noexcept { return _value; }
+
+  /**
+   * @brief Return the NVTX color type of the color.
+   *
+   */
+  constexpr nvtxColorType_t get_type() const noexcept { return _type; }
+
+  color()                        = delete;
+  ~color()                       = default;
+  color(color const&)            = default;
+  color& operator=(color const&) = default;
+  color(color&&)                 = default;
+  color& operator=(color&&)      = default;
+
+ private:
+  /**
+   * @brief Constructs an unsigned, 4B integer from the component bytes in
+   * most to least significant byte order.
+   *
+   */
+  constexpr static value_type from_bytes_msb_to_lsb(uint8_t byte3,
+                                                    uint8_t byte2,
+                                                    uint8_t byte1,
+                                                    uint8_t byte0) noexcept
+  {
+    return uint32_t{byte3} << 24 | uint32_t{byte2} << 16 | uint32_t{byte1} << 8 | uint32_t{byte0};
+  }
+
+  value_type const _value{};                     ///< color's argb color code
+  nvtxColorType_t const _type{NVTX_COLOR_ARGB};  ///< NVTX color type code
+};
+
+/**
+ * @brief Object for intra-domain grouping of NVTX events.
+ *
+ * A `category` is simply an integer id that allows for fine-grain grouping of
+ * NVTX events. For example, one might use separate categories for IO, memory
+ * allocation, compute, etc.
+ *
+ * Example:
+ * \code{.cpp}
+ * nvtx3::category cat1{1};
+ *
+ * // Range `r1` belongs to the category identified by the value `1`.
+ * nvtx3::thread_range r1{cat1};
+ *
+ * // Range `r2` belongs to the same category as `r1`
+ * nvtx3::thread_range r2{nvtx3::category{1}};
+ * \endcode
+ *
+ * To associate a name string with a category id, see `named_category`.
+ */
+class category {
+ public:
+  /// Type used for `category`s integer id.
+  using id_type = uint32_t;
+
+  /**
+   * @brief Construct a `category` with the specified `id`.
+   *
+   * The `category` will be unnamed and identified only by its `id` value.
+   *
+   * All `category` objects sharing the same `id` are equivalent.
+   *
+   * @param[in] id The `category`'s identifying value
+   */
+  constexpr explicit category(id_type id) noexcept : id_{id} {}
+
+  /**
+   * @brief Returns the id of the category.
+   *
+   */
+  constexpr id_type get_id() const noexcept { return id_; }
+
+  category()                           = delete;
+  ~category()                          = default;
+  category(category const&)            = default;
+  category& operator=(category const&) = default;
+  category(category&&)                 = default;
+  category& operator=(category&&)      = default;
+
+ private:
+  id_type const id_{};  ///< category's unique identifier
+};
+
+/**
+ * @brief A `category` with an associated name string.
+ *
+ * Associates a `name` string with a category `id` to help differentiate among
+ * categories.
+ *
+ * For any given category id `Id`, a `named_category(Id, "name")` should only
+ * be constructed once and reused throughout an application. This can be done
+ * by either explicitly creating static `named_category` objects, or using the
+ * `named_category::get` construct on first use helper (recommended).
+ *
+ * Creating two or more `named_category` objects with the same value for `id`
+ * in the same domain results in undefined behavior.
+ *
+ * Similarly, behavior is undefined when a `named_category` and `category`
+ * share the same value of `id`.
+ *
+ * Example:
+ * \code{.cpp}
+ * // Explicitly constructed, static `named_category`
+ * static nvtx3::named_category static_category{42, "my category"};
+ *
+ * // Range `r` associated with category id `42`
+ * nvtx3::thread_range r{static_category};
+ *
+ * // OR use construct on first use:
+ *
+ * // Define a type with `name` and `id` members
+ * struct my_category{
+ *    static constexpr char const* name{"my category"}; // category name
+ *    static constexpr category::id_type id{42}; // category id
+ * };
+ *
+ * // Use construct on first use to name the category id `42`
+ * // with name "my category"
+ * auto my_category = named_category<my_domain>::get<my_category>();
+ *
+ * // Range `r` associated with category id `42`
+ * nvtx3::thread_range r{my_category};
+ * \endcode
+ *
+ * `named_category`'s association of a name to a category id is local to the
+ * domain specified by the type `D`. An id may have a different name in
+ * another domain.
+ *
+ * @tparam D Type containing `name` member used to identify the `domain` to
+ * which the `named_category` belongs. Else, `domain::global` to  indicate
+ * that the global NVTX domain should be used.
+ */
+template <typename D = domain::global>
+class named_category final : public category {
+ public:
+  /**
+   * @brief Returns a global instance of a `named_category` as a
+   * function-local static.
+   *
+   * Creates a `named_category` with name and id specified by the contents of
+   * a type `C`. `C::name` determines the name and `C::id` determines the
+   * category id.
+   *
+   * This function is useful for constructing a named `category` exactly once
+   * and reusing the same instance throughout an application.
+   *
+   * Example:
+   * \code{.cpp}
+   * // Define a type with `name` and `id` members
+   * struct my_category{
+   *    static constexpr char const* name{"my category"}; // category name
+   *    static constexpr uint32_t id{42}; // category id
+   * };
+   *
+   * // Use construct on first use to name the category id `42`
+   * // with name "my category"
+   * auto cat = named_category<my_domain>::get<my_category>();
+   *
+   * // Range `r` associated with category id `42`
+   * nvtx3::thread_range r{cat};
+   * \endcode
+   *
+   * Uses the "construct on first use" idiom to safely ensure the `category`
+   * object is initialized exactly once. See
+   * https://isocpp.org/wiki/faq/ctors#static-init-order-on-first-use
+   *
+   * @tparam C Type containing a member `C::name` that resolves  to either a
+   * `char const*` or `wchar_t const*` and `C::id`.
+   */
+  template <typename C>
+  static named_category<D> const& get() noexcept
+  {
+    static_assert(detail::has_name_member<C>(),
+                  "Type used to name a category must contain a name member.");
+    static named_category<D> const category{C::id, C::name};
+    return category;
+  }
+  /**
+   * @brief Construct a `category` with the specified `id` and `name`.
+   *
+   * The name `name` will be registered with `id`.
+   *
+   * Every unique value of `id` should only be named once.
+   *
+   * @param[in] id The category id to name
+   * @param[in] name The name to associated with `id`
+   */
+  named_category(id_type id, char const* name) noexcept : category{id}
+  {
+    nvtxDomainNameCategoryA(domain::get<D>(), get_id(), name);
+  };
+
+  /**
+   * @brief Construct a `category` with the specified `id` and `name`.
+   *
+   * The name `name` will be registered with `id`.
+   *
+   * Every unique value of `id` should only be named once.
+   *
+   * @param[in] id The category id to name
+   * @param[in] name The name to associated with `id`
+   */
+  named_category(id_type id, wchar_t const* name) noexcept : category{id}
+  {
+    nvtxDomainNameCategoryW(domain::get<D>(), get_id(), name);
+  };
+};
+
+/**
+ * @brief A message registered with NVTX.
+ *
+ * Normally, associating a `message` with an NVTX event requires copying the
+ * contents of the message string. This may cause non-trivial overhead in
+ * highly performance sensitive regions of code.
+ *
+ * message registration is an optimization to lower the overhead of
+ * associating a message with an NVTX event. Registering a message yields a
+ * handle that is inexpensive to copy that may be used in place of a message
+ * string.
+ *
+ * A particular message should only be registered once and the handle
+ * reused throughout the rest of the application. This can be done by either
+ * explicitly creating static `registered_message` objects, or using the
+ * `registered_message::get` construct on first use helper (recommended).
+ *
+ * Example:
+ * \code{.cpp}
+ * // Explicitly constructed, static `registered_message`
+ * static registered_message<my_domain> static_message{"message"};
+ *
+ * // "message" is associated with the range `r`
+ * nvtx3::thread_range r{static_message};
+ *
+ * // Or use construct on first use:
+ *
+ * // Define a type with a `message` member that defines the contents of the
+ * // registered message
+ * struct my_message{ static constexpr char const* message{ "my message" }; };
+ *
+ * // Uses construct on first use to register the contents of
+ * // `my_message::message`
+ * auto msg = registered_message<my_domain>::get<my_message>();
+ *
+ * // "my message" is associated with the range `r`
+ * nvtx3::thread_range r{msg};
+ * \endcode
+ *
+ * `registered_message`s are local to a particular domain specified via
+ * the type `D`.
+ *
+ * @tparam D Type containing `name` member used to identify the `domain` to
+ * which the `registered_message` belongs. Else, `domain::global` to  indicate
+ * that the global NVTX domain should be used.
+ */
+template <typename D = domain::global>
+class registered_message {
+ public:
+  /**
+   * @brief Returns a global instance of a `registered_message` as a function
+   * local static.
+   *
+   * Provides a convenient way to register a message with NVTX without having
+   * to explicitly register the message.
+   *
+   * Upon first invocation, constructs a `registered_message` whose contents
+   * are specified by `message::message`.
+   *
+   * All future invocations will return a reference to the object constructed
+   * in the first invocation.
+   *
+   * Example:
+   * \code{.cpp}
+   * // Define a type with a `message` member that defines the contents of the
+   * // registered message
+   * struct my_message{ static constexpr char const* message{ "my message" };
+   * };
+   *
+   * // Uses construct on first use to register the contents of
+   * // `my_message::message`
+   * auto msg = registered_message<my_domain>::get<my_message>();
+   *
+   * // "my message" is associated with the range `r`
+   * nvtx3::thread_range r{msg};
+   * \endcode
+   *
+   * @tparam M Type required to contain a member `M::message` that
+   * resolves to either a `char const*` or `wchar_t const*` used as the
+   * registered message's contents.
+   * @return Reference to a `registered_message` associated with the type `M`.
+   */
+  template <typename M>
+  static registered_message<D> const& get() noexcept
+  {
+    static registered_message<D> const registered_message{M::message};
+    return registered_message;
+  }
+
+  /**
+   * @brief Constructs a `registered_message` from the specified `msg` string.
+   *
+   * Registers `msg` with NVTX and associates a handle with the registered
+   * message.
+   *
+   * A particular message should only be registered once and the handle
+   * reused throughout the rest of the application.
+   *
+   * @param msg The contents of the message
+   */
+  explicit registered_message(char const* msg) noexcept
+    : handle_{nvtxDomainRegisterStringA(domain::get<D>(), msg)}
+  {
+  }
+
+  /**
+   * @brief Constructs a `registered_message` from the specified `msg` string.
+   *
+   * Registers `msg` with NVTX and associates a handle with the registered
+   * message.
+   *
+   * A particular message should only be registered once and the handle
+   * reused throughout the rest of the application.
+   *
+   * @param msg The contents of the message
+   */
+  explicit registered_message(std::string const& msg) noexcept : registered_message{msg.c_str()} {}
+
+  /**
+   * @brief Constructs a `registered_message` from the specified `msg` string.
+   *
+   * Registers `msg` with NVTX and associates a handle with the registered
+   * message.
+   *
+   * A particular message should only be registered once and the handle
+   * reused throughout the rest of the application.
+   *
+   * @param msg The contents of the message
+   */
+  explicit registered_message(wchar_t const* msg) noexcept
+    : handle_{nvtxDomainRegisterStringW(domain::get<D>(), msg)}
+  {
+  }
+
+  /**
+   * @brief Constructs a `registered_message` from the specified `msg` string.
+   *
+   * Registers `msg` with NVTX and associates a handle with the registered
+   * message.
+   *
+   * A particular message should only be registered once and the handle
+   * reused throughout the rest of the application.
+   *
+   * @param msg The contents of the message
+   */
+  explicit registered_message(std::wstring const& msg) noexcept : registered_message{msg.c_str()} {}
+
+  /**
+   * @brief Returns the registered message's handle
+   *
+   */
+  nvtxStringHandle_t get_handle() const noexcept { return handle_; }
+
+  registered_message()                                     = delete;
+  ~registered_message()                                    = default;
+  registered_message(registered_message const&)            = default;
+  registered_message& operator=(registered_message const&) = default;
+  registered_message(registered_message&&)                 = default;
+  registered_message& operator=(registered_message&&)      = default;
+
+ private:
+  nvtxStringHandle_t const handle_{};  ///< The handle returned from
+                                       ///< registering the message with NVTX
+};
+
+/**
+ * @brief Allows associating a message string with an NVTX event via
+ * its `EventAttribute`s.
+ *
+ * Associating a `message` with an NVTX event through its `event_attributes`
+ * allows for naming events to easily differentiate them from other events.
+ *
+ * Every time an NVTX event is created with an associated `message`, the
+ * contents of the message string must be copied.  This may cause non-trivial
+ * overhead in highly performance sensitive sections of code. Use of a
+ * `nvtx3::registered_message` is recommended in these situations.
+ *
+ * Example:
+ * \code{.cpp}
+ * // Creates an `event_attributes` with message "message 0"
+ * nvtx3::event_attributes attr0{nvtx3::message{"message 0"}};
+ *
+ * // `range0` contains message "message 0"
+ * nvtx3::thread_range range0{attr0};
+ *
+ * // `std::string` and string literals are implicitly assumed to be
+ * // the contents of an `nvtx3::message`
+ * // Creates an `event_attributes` with message "message 1"
+ * nvtx3::event_attributes attr1{"message 1"};
+ *
+ * // `range1` contains message "message 1"
+ * nvtx3::thread_range range1{attr1};
+ *
+ * // `range2` contains message "message 2"
+ * nvtx3::thread_range range2{nvtx3::message{"message 2"}};
+ *
+ * // `std::string` and string literals are implicitly assumed to be
+ * // the contents of an `nvtx3::message`
+ * // `range3` contains message "message 3"
+ * nvtx3::thread_range range3{"message 3"};
+ * \endcode
+ */
+class message {
+ public:
+  using value_type = nvtxMessageValue_t;
+
+  /**
+   * @brief Construct a `message` whose contents are specified by `msg`.
+   *
+   * @param msg The contents of the message
+   */
+  NVTX3_RELAXED_CONSTEXPR message(char const* msg) noexcept : type_{NVTX_MESSAGE_TYPE_ASCII}
+  {
+    value_.ascii = msg;
+  }
+
+  /**
+   * @brief Construct a `message` whose contents are specified by `msg`.
+   *
+   * @param msg The contents of the message
+   */
+  message(std::string const& msg) noexcept : message{msg.c_str()} {}
+
+  /**
+   * @brief Disallow construction for `std::string` r-value
+   *
+   * `message` is a non-owning type and therefore cannot take ownership of an
+   * r-value. Therefore, constructing from an r-value is disallowed to prevent
+   * a dangling pointer.
+   *
+   */
+  message(std::string&&) = delete;
+
+  /**
+   * @brief Construct a `message` whose contents are specified by `msg`.
+   *
+   * @param msg The contents of the message
+   */
+  NVTX3_RELAXED_CONSTEXPR message(wchar_t const* msg) noexcept : type_{NVTX_MESSAGE_TYPE_UNICODE}
+  {
+    value_.unicode = msg;
+  }
+
+  /**
+   * @brief Construct a `message` whose contents are specified by `msg`.
+   *
+   * @param msg The contents of the message
+   */
+  message(std::wstring const& msg) noexcept : message{msg.c_str()} {}
+
+  /**
+   * @brief Disallow construction for `std::wstring` r-value
+   *
+   * `message` is a non-owning type and therefore cannot take ownership of an
+   * r-value. Therefore, constructing from an r-value is disallowed to prevent
+   * a dangling pointer.
+   *
+   */
+  message(std::wstring&&) = delete;
+
+  /**
+   * @brief Construct a `message` from a `registered_message`.
+   *
+   * @tparam D Type containing `name` member used to identify the `domain`
+   * to which the `registered_message` belongs. Else, `domain::global` to
+   * indicate that the global NVTX domain should be used.
+   * @param msg The message that has already been registered with NVTX.
+   */
+  template <typename D>
+  message(registered_message<D> const& msg) noexcept : type_{NVTX_MESSAGE_TYPE_REGISTERED}
+  {
+    value_.registered = msg.get_handle();
+  }
+
+  /**
+   * @brief Return the union holding the value of the message.
+   *
+   */
+  NVTX3_RELAXED_CONSTEXPR value_type get_value() const noexcept { return value_; }
+
+  /**
+   * @brief Return the type information about the value the union holds.
+   *
+   */
+  NVTX3_RELAXED_CONSTEXPR nvtxMessageType_t get_type() const noexcept { return type_; }
+
+ private:
+  nvtxMessageType_t const type_{};  ///< message type
+  nvtxMessageValue_t value_{};      ///< message contents
+};
+
+/**
+ * @brief A numerical value that can be associated with an NVTX event via
+ * its `event_attributes`.
+ *
+ * Example:
+ * ```
+ * nvtx3:: event_attributes attr{nvtx3::payload{42}}; // Constructs a payload
+ * from
+ *                                                 // the `int32_t` value 42
+ *
+ * // `range0` will have an int32_t payload of 42
+ * nvtx3::thread_range range0{attr};
+ *
+ * // range1 has double payload of 3.14
+ * nvtx3::thread_range range1{ nvtx3::payload{3.14} };
+ * ```
+ */
+class payload {
+ public:
+  using value_type = typename nvtxEventAttributes_v2::payload_t;
+
+  /**
+   * @brief Construct a `payload` from a signed, 8 byte integer.
+   *
+   * @param value Value to use as contents of the payload
+   */
+  NVTX3_RELAXED_CONSTEXPR explicit payload(int64_t value) noexcept
+    : type_{NVTX_PAYLOAD_TYPE_INT64}, value_{}
+  {
+    value_.llValue = value;
+  }
+
+  /**
+   * @brief Construct a `payload` from a signed, 4 byte integer.
+   *
+   * @param value Value to use as contents of the payload
+   */
+  NVTX3_RELAXED_CONSTEXPR explicit payload(int32_t value) noexcept
+    : type_{NVTX_PAYLOAD_TYPE_INT32}, value_{}
+  {
+    value_.iValue = value;
+  }
+
+  /**
+   * @brief Construct a `payload` from an unsigned, 8 byte integer.
+   *
+   * @param value Value to use as contents of the payload
+   */
+  NVTX3_RELAXED_CONSTEXPR explicit payload(uint64_t value) noexcept
+    : type_{NVTX_PAYLOAD_TYPE_UNSIGNED_INT64}, value_{}
+  {
+    value_.ullValue = value;
+  }
+
+  /**
+   * @brief Construct a `payload` from an unsigned, 4 byte integer.
+   *
+   * @param value Value to use as contents of the payload
+   */
+  NVTX3_RELAXED_CONSTEXPR explicit payload(uint32_t value) noexcept
+    : type_{NVTX_PAYLOAD_TYPE_UNSIGNED_INT32}, value_{}
+  {
+    value_.uiValue = value;
+  }
+
+  /**
+   * @brief Construct a `payload` from a single-precision floating point
+   * value.
+   *
+   * @param value Value to use as contents of the payload
+   */
+  NVTX3_RELAXED_CONSTEXPR explicit payload(float value) noexcept
+    : type_{NVTX_PAYLOAD_TYPE_FLOAT}, value_{}
+  {
+    value_.fValue = value;
+  }
+
+  /**
+   * @brief Construct a `payload` from a double-precision floating point
+   * value.
+   *
+   * @param value Value to use as contents of the payload
+   */
+  NVTX3_RELAXED_CONSTEXPR explicit payload(double value) noexcept
+    : type_{NVTX_PAYLOAD_TYPE_DOUBLE}, value_{}
+  {
+    value_.dValue = value;
+  }
+
+  /**
+   * @brief Return the union holding the value of the payload
+   *
+   */
+  NVTX3_RELAXED_CONSTEXPR value_type get_value() const noexcept { return value_; }
+
+  /**
+   * @brief Return the information about the type the union holds.
+   *
+   */
+  NVTX3_RELAXED_CONSTEXPR nvtxPayloadType_t get_type() const noexcept { return type_; }
+
+ private:
+  nvtxPayloadType_t const type_;  ///< Type of the payload value
+  value_type value_;              ///< Union holding the payload value
+};
+
+/**
+ * @brief Describes the attributes of a NVTX event.
+ *
+ * NVTX events can be customized via four "attributes":
+ *
+ * - color:    color used to visualize the event in tools such as Nsight
+ *             Systems. See `color`.
+ * - message:  Custom message string. See `message`.
+ * - payload:  User-defined numerical value. See `payload`.
+ * - category: Intra-domain grouping. See `category`.
+ *
+ * These component attributes are specified via an `event_attributes` object.
+ * See `nvtx3::color`, `nvtx3::message`, `nvtx3::payload`, and
+ * `nvtx3::category` for how these individual attributes are constructed.
+ *
+ * While it is possible to specify all four attributes, it is common to want
+ * to only specify a subset of attributes and use default values for the
+ * others. For convenience, `event_attributes` can be constructed from any
+ * number of attribute components in any order.
+ *
+ * Example:
+ * \code{.cpp}
+ * event_attributes attr{}; // No arguments, use defaults for all attributes
+ *
+ * event_attributes attr{"message"}; // Custom message, rest defaulted
+ *
+ * // Custom color & message
+ * event_attributes attr{"message", nvtx3::rgb{127, 255, 0}};
+ *
+ * /// Custom color & message, can use any order of arguments
+ * event_attributes attr{nvtx3::rgb{127, 255, 0}, "message"};
+ *
+ *
+ * // Custom color, message, payload, category
+ * event_attributes attr{nvtx3::rgb{127, 255, 0},
+ *                      "message",
+ *                      nvtx3::payload{42},
+ *                      nvtx3::category{1}};
+ *
+ * // Custom color, message, payload, category, can use any order of arguments
+ * event_attributes attr{nvtx3::payload{42},
+ *                      nvtx3::category{1},
+ *                      "message",
+ *                      nvtx3::rgb{127, 255, 0}};
+ *
+ * // Multiple arguments of the same type are allowed, but only the first is
+ * // used. All others are ignored
+ * event_attributes attr{ nvtx3::payload{42}, nvtx3::payload{7} }; // payload
+ * is 42
+ *
+ * // Range `r` will be customized according the attributes in `attr`
+ * nvtx3::thread_range r{attr};
+ *
+ * // For convenience, the arguments that can be passed to the
+ * `event_attributes`
+ * // constructor may be passed to the `domain_thread_range` constructor where
+ * // they will be forwarded to the `EventAttribute`s constructor
+ * nvtx3::thread_range r{nvtx3::payload{42}, nvtx3::category{1}, "message"};
+ * \endcode
+ */
+class event_attributes {
+ public:
+  using value_type = nvtxEventAttributes_t;
+
+  /**
+   * @brief Default constructor creates an `event_attributes` with no
+   * category, color, payload, nor message.
+   */
+  constexpr event_attributes() noexcept
+    : attributes_{
+        NVTX_VERSION,                   // version
+        sizeof(nvtxEventAttributes_t),  // size
+        0,                              // category
+        NVTX_COLOR_UNKNOWN,             // color type
+        0,                              // color value
+        NVTX_PAYLOAD_UNKNOWN,           // payload type
+        {},                             // payload value (union)
+        NVTX_MESSAGE_UNKNOWN,           // message type
+        {}                              // message value (union)
+      }
+  {
+  }
+
+  /**
+   * @brief Variadic constructor where the first argument is a `category`.
+   *
+   * Sets the value of the `EventAttribute`s category based on `c` and
+   * forwards the remaining variadic parameter pack to the next constructor.
+   *
+   */
+  template <typename... Args>
+  NVTX3_RELAXED_CONSTEXPR explicit event_attributes(category const& c, Args const&... args) noexcept
+    : event_attributes(args...)
+  {
+    attributes_.category = c.get_id();
+  }
+
+  /**
+   * @brief Variadic constructor where the first argument is a `color`.
+   *
+   * Sets the value of the `EventAttribute`s color based on `c` and forwards
+   * the remaining variadic parameter pack to the next constructor.
+   *
+   */
+  template <typename... Args>
+  NVTX3_RELAXED_CONSTEXPR explicit event_attributes(color const& c, Args const&... args) noexcept
+    : event_attributes(args...)
+  {
+    attributes_.color     = c.get_value();
+    attributes_.colorType = c.get_type();
+  }
+
+  /**
+   * @brief Variadic constructor where the first argument is a `payload`.
+   *
+   * Sets the value of the `EventAttribute`s payload based on `p` and forwards
+   * the remaining variadic parameter pack to the next constructor.
+   *
+   */
+  template <typename... Args>
+  NVTX3_RELAXED_CONSTEXPR explicit event_attributes(payload const& p, Args const&... args) noexcept
+    : event_attributes(args...)
+  {
+    attributes_.payload     = p.get_value();
+    attributes_.payloadType = p.get_type();
+  }
+
+  /**
+   * @brief Variadic constructor where the first argument is a `message`.
+   *
+   * Sets the value of the `EventAttribute`s message based on `m` and forwards
+   * the remaining variadic parameter pack to the next constructor.
+   *
+   */
+  template <typename... Args>
+  explicit event_attributes(message const& m, Args const&... args) noexcept
+    : event_attributes(args...)
+  {
+    attributes_.message     = m.get_value();
+    attributes_.messageType = m.get_type();
+  }
+
+  ~event_attributes()                                  = default;
+  event_attributes(event_attributes const&)            = default;
+  event_attributes& operator=(event_attributes const&) = default;
+  event_attributes(event_attributes&&)                 = default;
+  event_attributes& operator=(event_attributes&&)      = default;
+
+  /**
+   * @brief Get raw pointer to underlying NVTX attributes object.
+   *
+   */
+  constexpr value_type const* get() const noexcept { return &attributes_; }
+
+ private:
+  value_type attributes_{};  ///< The NVTX attributes structure
+};
+
+/**
+ * @brief A RAII object for creating a NVTX range local to a thread within a
+ * domain.
+ *
+ * When constructed, begins a nested NVTX range on the calling thread in the
+ * specified domain. Upon destruction, ends the NVTX range.
+ *
+ * Behavior is undefined if a `domain_thread_range` object is
+ * created/destroyed on different threads.
+ *
+ * `domain_thread_range` is neither moveable nor copyable.
+ *
+ * `domain_thread_range`s may be nested within other ranges.
+ *
+ * The domain of the range is specified by the template type parameter `D`.
+ * By default, the `domain::global` is used, which scopes the range to the
+ * global NVTX domain. The convenience alias `thread_range` is provided for
+ * ranges scoped to the global domain.
+ *
+ * A custom domain can be defined by creating a type, `D`, with a static
+ * member `D::name` whose value is used to name the domain associated with
+ * `D`. `D::name` must resolve to either `char const*` or `wchar_t const*`
+ *
+ * Example:
+ * ```
+ * // Define a type `my_domain` with a member `name` used to name the domain
+ * // associated with the type `my_domain`.
+ * struct my_domain{
+ *    static constexpr const char * name{"my domain"};
+ * };
+ * ```
+ *
+ * Usage:
+ * ```
+ * nvtx3::domain_thread_range<> r0{"range 0"}; // Range in global domain
+ *
+ * nvtx3::thread_range r1{"range 1"}; // Alias for range in global domain
+ *
+ * nvtx3::domain_thread_range<my_domain> r2{"range 2"}; // Range in custom
+ * domain
+ *
+ * // specify an alias to a range that uses a custom domain
+ * using my_thread_range = nvtx3::domain_thread_range<my_domain>;
+ *
+ * my_thread_range r3{"range 3"}; // Alias for range in custom domain
+ * ```
+ */
+template <class D = domain::global>
+class domain_thread_range {
+ public:
+  /**
+   * @brief Construct a `domain_thread_range` with the specified
+   * `event_attributes`
+   *
+   * Example:
+   * ```
+   * nvtx3::event_attributes attr{"msg", nvtx3::rgb{127,255,0}};
+   * nvtx3::domain_thread_range<> range{attr}; // Creates a range with message
+   * contents
+   *                                    // "msg" and green color
+   * ```
+   *
+   * @param[in] attr `event_attributes` that describes the desired attributes
+   * of the range.
+   */
+  explicit domain_thread_range(event_attributes const& attr) noexcept
+  {
+    nvtxDomainRangePushEx(domain::get<D>(), attr.get());
+  }
+
+  /**
+   * @brief Constructs a `domain_thread_range` from the constructor arguments
+   * of an `event_attributes`.
+   *
+   * Forwards the arguments `first, args...` to construct an
+   * `event_attributes` object. The `event_attributes` object is then
+   * associated with the `domain_thread_range`.
+   *
+   * For more detail, see `event_attributes` documentation.
+   *
+   * Example:
+   * ```
+   * // Creates a range with message "message" and green color
+   * nvtx3::domain_thread_range<> r{"message", nvtx3::rgb{127,255,0}};
+   * ```
+   *
+   * @note To prevent making needless copies of `event_attributes` objects,
+   * this constructor is disabled when the first argument is an
+   * `event_attributes` object, instead preferring the explicit
+   * `domain_thread_range(event_attributes const&)` constructor.
+   *
+   * @param[in] first First argument to forward to the `event_attributes`
+   * constructor.
+   * @param[in] args Variadic parameter pack of additional arguments to
+   * forward.
+   *
+   */
+  template <typename First,
+            typename... Args,
+            typename = typename std::enable_if<
+              not std::is_same_v<event_attributes, typename std::decay<First>>>>
+  explicit domain_thread_range(First const& first, Args const&... args) noexcept
+    : domain_thread_range{event_attributes{first, args...}}
+  {
+  }
+
+  /**
+   * @brief Default constructor creates a `domain_thread_range` with no
+   * message, color, payload, nor category.
+   *
+   */
+  domain_thread_range() : domain_thread_range{event_attributes{}} {}
+
+  domain_thread_range(domain_thread_range const&)            = delete;
+  domain_thread_range& operator=(domain_thread_range const&) = delete;
+  domain_thread_range(domain_thread_range&&)                 = delete;
+  domain_thread_range& operator=(domain_thread_range&&)      = delete;
+
+  /**
+   * @brief Destroy the domain_thread_range, ending the NVTX range event.
+   */
+  ~domain_thread_range() noexcept { nvtxDomainRangePop(domain::get<D>()); }
+};
+
+/**
+ * @brief Alias for a `domain_thread_range` in the global NVTX domain.
+ */
+using thread_range = domain_thread_range<>;
+
+/**
+ * @brief A RAII object for creating a NVTX range within a domain that can be
+ * created and destroyed on different threads.
+ *
+ * When constructed, begins a NVTX range in the specified domain. Upon
+ * destruction, ends the NVTX range.
+ *
+ * Similar to `nvtx3::domain_thread_range`, the only difference being that
+ * `domain_process_range` can start and end on different threads.
+ *
+ * Use of `nvtx3::domain_thread_range` should be preferred unless one needs
+ * the ability to start and end a range on different threads.
+ *
+ * `domain_process_range` is moveable, but not copyable.
+ *
+ * @tparam D Type containing `name` member used to identify the `domain`
+ * to which the `domain_process_range` belongs. Else, `domain::global` to
+ * indicate that the global NVTX domain should be used.
+ */
+template <typename D = domain::global>
+class domain_process_range {
+ public:
+  /**
+   * @brief Construct a new domain process range object
+   *
+   * @param attr
+   */
+  explicit domain_process_range(event_attributes const& attr) noexcept
+    : range_id_{nvtxDomainRangeStartEx(domain::get<D>(), attr.get())}
+  {
+  }
+
+  /**
+   * @brief Construct a new domain process range object
+   *
+   * @param first
+   * @param args
+   */
+  template <typename First,
+            typename... Args,
+            typename = typename std::enable_if<
+              not std::is_same_v<event_attributes, typename std::decay<First>>>>
+  explicit domain_process_range(First const& first, Args const&... args) noexcept
+    : domain_process_range{event_attributes{first, args...}}
+  {
+  }
+
+  /**
+   * @brief Construct a new domain process range object
+   *
+   */
+  constexpr domain_process_range() noexcept : domain_process_range{event_attributes{}} {}
+
+  /**
+   * @brief Destroy the `domain_process_range` ending the range.
+   *
+   */
+  ~domain_process_range() noexcept
+  {
+    if (not moved_from_) { nvtxRangeEnd(range_id_); }
+  }
+
+  domain_process_range(domain_process_range const&)            = delete;
+  domain_process_range& operator=(domain_process_range const&) = delete;
+
+  domain_process_range(domain_process_range&& other) noexcept : range_id_{other.range_id_}
+  {
+    other.moved_from_ = true;
+  }
+
+  domain_process_range& operator=(domain_process_range&& other) noexcept
+  {
+    range_id_         = other.range_id_;
+    other.moved_from_ = true;
+  }
+
+ private:
+  nvtxRangeId_t range_id_;  ///< Range id used to correlate
+                            ///< the start/end of the range
+  bool moved_from_{false};  ///< Indicates if the object has had
+                            ///< it's contents moved from it,
+                            ///< indicating it should not attempt
+                            ///< to end the NVTX range.
+};
+
+/**
+ * @brief Alias for a `domain_process_range` in the global NVTX domain.
+ */
+using process_range = domain_process_range<>;
+
+/**
+ * @brief Annotates an instantaneous point in time with the attributes specified
+ * by `attr`.
+ *
+ * Unlike a "range", a mark is an instantaneous event in an application, e.g.,
+ * locking/unlocking a mutex.
+ *
+ * \code{.cpp}
+ * std::mutex global_lock;
+ * void lock_mutex(){
+ *    global_lock.lock();
+ *    nvtx3::mark("lock_mutex");
+ * }
+ * \endcode
+ *
+ * @tparam D Type containing `name` member used to identify the `domain`
+ * to which the `domain_process_range` belongs. Else, `domain::global` to
+ * indicate that the global NVTX domain should be used.
+ * @param[in] attr `event_attributes` that describes the desired attributes
+ * of the mark.
+ */
+template <typename D = nvtx3::domain::global>
+inline void mark(event_attributes const& attr) noexcept
+{
+  nvtxDomainMarkEx(domain::get<D>(), attr.get());
+}
+
+}  // namespace nvtx3
+
+/**
+ * @brief Convenience macro for generating a range in the specified `domain`
+ * from the lifetime of a function
+ *
+ * This macro is useful for generating an NVTX range in `domain` from
+ * the entry point of a function to its exit. It is intended to be the first
+ * line of the function.
+ *
+ * Constructs a static `registered_message` using the name of the immediately
+ * enclosing function returned by `__func__` and constructs a
+ * `nvtx3::thread_range` using the registered function name as the range's
+ * message.
+ *
+ * Example:
+ * ```
+ * struct my_domain{static constexpr char const* name{"my_domain"};};
+ *
+ * void foo(...){
+ *    NVTX3_FUNC_RANGE_IN(my_domain); // Range begins on entry to foo()
+ *    // do stuff
+ *    ...
+ * } // Range ends on return from foo()
+ * ```
+ *
+ * @param[in] D Type containing `name` member used to identify the
+ * `domain` to which the `registered_message` belongs. Else,
+ * `domain::global` to  indicate that the global NVTX domain should be used.
+ */
+#define NVTX3_FUNC_RANGE_IN(D)                                                 \
+  static ::nvtx3::registered_message<D> const nvtx3_func_name__{__func__};     \
+  static ::nvtx3::event_attributes const nvtx3_func_attr__{nvtx3_func_name__}; \
+  [[maybe_unused]] ::nvtx3::domain_thread_range<D> const nvtx3_range__{nvtx3_func_attr__};
+
+/**
+ * @brief Convenience macro for generating a range in the global domain from the
+ * lifetime of a function.
+ *
+ * This macro is useful for generating an NVTX range in the global domain from
+ * the entry point of a function to its exit. It is intended to be the first
+ * line of the function.
+ *
+ * Constructs a static `registered_message` using the name of the immediately
+ * enclosing function returned by `__func__` and constructs a
+ * `nvtx3::thread_range` using the registered function name as the range's
+ * message.
+ *
+ * Example:
+ * ```
+ * void foo(...){
+ *    NVTX3_FUNC_RANGE(); // Range begins on entry to foo()
+ *    // do stuff
+ *    ...
+ * } // Range ends on return from foo()
+ * ```
+ */
+#define NVTX3_FUNC_RANGE() NVTX3_FUNC_RANGE_IN(::nvtx3::domain::global)
diff --git a/cpp/include/cudf/detail/nvtx/ranges.hpp b/cpp/include/cudf/detail/nvtx/ranges.hpp
new file mode 100644
index 0000000..de5f990
--- /dev/null
+++ b/cpp/include/cudf/detail/nvtx/ranges.hpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "nvtx3.hpp"
+
+namespace cudf {
+/**
+ * @brief Tag type for libcudf's NVTX domain.
+ */
+struct libcudf_domain {
+  static constexpr char const* name{"libcudf"};  ///< Name of the libcudf domain
+};
+
+/**
+ * @brief Alias for an NVTX range in the libcudf domain.
+ */
+using thread_range = ::nvtx3::domain_thread_range<libcudf_domain>;
+
+}  // namespace cudf
+
+/**
+ * @brief Convenience macro for generating an NVTX range in the `libcudf` domain
+ * from the lifetime of a function.
+ *
+ * Uses the name of the immediately enclosing function returned by `__func__` to
+ * name the range.
+ *
+ * Example:
+ * ```
+ * void some_function(){
+ *    CUDF_FUNC_RANGE();
+ *    ...
+ * }
+ * ```
+ */
+#define CUDF_FUNC_RANGE() NVTX3_FUNC_RANGE_IN(cudf::libcudf_domain)
diff --git a/cpp/include/cudf/detail/quantiles.hpp b/cpp/include/cudf/detail/quantiles.hpp
new file mode 100644
index 0000000..ac37d92
--- /dev/null
+++ b/cpp/include/cudf/detail/quantiles.hpp
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/quantiles.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::quantile()
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> quantile(column_view const& input,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 column_view const& ordered_indices,
+                                 bool exact,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::quantiles()
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> quantiles(table_view const& input,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 cudf::sorted is_input_sorted,
+                                 std::vector<order> const& column_order,
+                                 std::vector<null_order> const& null_precedence,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::percentile_approx(tdigest_column_view const&, column_view const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> percentile_approx(tdigest::tdigest_column_view const& input,
+                                          column_view const& percentiles,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/repeat.hpp b/cpp/include/cudf/detail/repeat.hpp
new file mode 100644
index 0000000..883d5d1
--- /dev/null
+++ b/cpp/include/cudf/detail/repeat.hpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::repeat(table_view const&, column_view const&, bool,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> repeat(table_view const& input_table,
+                              column_view const& count,
+                              bool check_count,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::repeat(table_view const&, size_type,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> repeat(table_view const& input_table,
+                              size_type count,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/replace.hpp b/cpp/include/cudf/detail/replace.hpp
new file mode 100644
index 0000000..da83f7b
--- /dev/null
+++ b/cpp/include/cudf/detail/replace.hpp
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/replace.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::replace_nulls(column_view const&, column_view const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace_nulls(column_view const& input,
+                                      cudf::column_view const& replacement,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::replace_nulls(column_view const&, scalar const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace_nulls(column_view const& input,
+                                      scalar const& replacement,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::replace_nulls(column_view const&, replace_policy const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace_nulls(column_view const& input,
+                                      replace_policy const& replace_policy,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::replace_nans(column_view const&, column_view const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace_nans(column_view const& input,
+                                     column_view const& replacement,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::replace_nans(column_view const&, scalar const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace_nans(column_view const& input,
+                                     scalar const& replacement,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::find_and_replace_all
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> find_and_replace_all(column_view const& input_col,
+                                             column_view const& values_to_replace,
+                                             column_view const& replacement_values,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::normalize_nans_and_zeros
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> normalize_nans_and_zeros(column_view const& input,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/replace/nulls.cuh b/cpp/include/cudf/detail/replace/nulls.cuh
new file mode 100644
index 0000000..d691ef5
--- /dev/null
+++ b/cpp/include/cudf/detail/replace/nulls.cuh
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace detail {
+
+using idx_valid_pair_t = thrust::tuple<cudf::size_type, bool>;
+
+/**
+ * @brief Functor used by `replace_nulls(replace_policy)` to determine the index to gather from in
+ * the result column.
+ *
+ * Binary functor passed to `inclusive_scan` or `inclusive_scan_by_key`. Arguments are a tuple of
+ * index and validity of a row. Returns a tuple of current index and a discarded boolean if current
+ * row is valid, otherwise a tuple of the nearest non-null row index and a discarded boolean.
+ */
+struct replace_policy_functor {
+  __device__ idx_valid_pair_t operator()(idx_valid_pair_t const& lhs, idx_valid_pair_t const& rhs)
+  {
+    return thrust::get<1>(rhs) ? rhs : lhs;
+  }
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/reshape.hpp b/cpp/include/cudf/detail/reshape.hpp
new file mode 100644
index 0000000..5ab5369
--- /dev/null
+++ b/cpp/include/cudf/detail/reshape.hpp
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::tile
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<table> tile(table_view const& input,
+                            size_type count,
+                            rmm::cuda_stream_view,
+                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::interleave_columns
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<column> interleave_columns(table_view const& input,
+                                           rmm::cuda_stream_view,
+                                           rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/rolling.hpp b/cpp/include/cudf/detail/rolling.hpp
new file mode 100644
index 0000000..da90217
--- /dev/null
+++ b/cpp/include/cudf/detail/rolling.hpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc std::unique_ptr<column> rolling_window(
+ *            column_view const& input,
+ *            column_view const& preceding_window,
+ *            column_view const& following_window,
+ *            size_type min_periods,
+ *            rolling_aggregation const& agg,
+ *            rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& preceding_window,
+                                       column_view const& following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/round.hpp b/cpp/include/cudf/detail/round.hpp
new file mode 100644
index 0000000..cdfc7ca
--- /dev/null
+++ b/cpp/include/cudf/detail/round.hpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/round.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+//! Inner interfaces and implementations
+namespace detail {
+
+/**
+ * @copydoc cudf::round(column_view const&, int32_t, rounding_method,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> round(column_view const& input,
+                              int32_t decimal_places,
+                              rounding_method method,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/scan.hpp b/cpp/include/cudf/detail/scan.hpp
new file mode 100644
index 0000000..f4b2d51
--- /dev/null
+++ b/cpp/include/cudf/detail/scan.hpp
@@ -0,0 +1,119 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Computes the exclusive scan of a column.
+ *
+ * The null values are skipped for the operation, and if an input element at `i` is null, then the
+ * output element at `i` will also be null.
+ *
+ * The identity value for the column type as per the aggregation type is used for the value of the
+ * first element in the output column.
+ *
+ * Struct columns are allowed with aggregation types Min and Max.
+ *
+ * @throws cudf::logic_error if column data_type is not an arithmetic type or struct type but the
+ *                           `agg` is not Min or Max.
+ *
+ * @param input The input column view for the scan.
+ * @param agg Aggregation operator applied by the scan
+ * @param null_handling Exclude null values when computing the result if null_policy::EXCLUDE.
+ *                      Include nulls if null_policy::INCLUDE. Any operation with a null results in
+ *                      a null.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned scalar's device memory.
+ * @returns Column with scan results.
+ */
+std::unique_ptr<column> scan_exclusive(column_view const& input,
+                                       scan_aggregation const& agg,
+                                       null_policy null_handling,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes the inclusive scan of a column.
+ *
+ * The null values are skipped for the operation, and if an input element at `i` is null, then the
+ * output element at `i` will also be null.
+ *
+ * String and struct columns are allowed with aggregation types Min and Max.
+ *
+ * @throws cudf::logic_error if column data_type is not an arithmetic type or string/struct types
+ *                           but the `agg` is not Min or Max.
+ *
+ * @param input The input column view for the scan.
+ * @param agg Aggregation operator applied by the scan
+ * @param null_handling Exclude null values when computing the result if null_policy::EXCLUDE.
+ *                      Include nulls if null_policy::INCLUDE. Any operation with a null results in
+ *                      a null.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned scalar's device memory.
+ * @returns Column with scan results.
+ */
+std::unique_ptr<column> scan_inclusive(column_view const& input,
+                                       scan_aggregation const& agg,
+                                       null_policy null_handling,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Generate row ranks for a column.
+ *
+ * @param order_by Input column to generate ranks for.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return rank values.
+ */
+std::unique_ptr<column> inclusive_rank_scan(column_view const& order_by,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Generate row dense ranks for a column.
+ *
+ * @param order_by Input column to generate ranks for.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return rank values.
+ */
+std::unique_ptr<column> inclusive_dense_rank_scan(column_view const& order_by,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Generate row ONE_NORMALIZED percent ranks for a column.
+ * Also, knowns as ANSI SQL PERCENT RANK.
+ * Calculated by (rank - 1) / (count - 1).
+ *
+ * @param order_by Input column to generate ranks for.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return rank values.
+ */
+std::unique_ptr<column> inclusive_one_normalized_percent_rank_scan(
+  column_view const& order_by, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/scatter.cuh b/cpp/include/cudf/detail/scatter.cuh
new file mode 100644
index 0000000..dbf7bfa
--- /dev/null
+++ b/cpp/include/cudf/detail/scatter.cuh
@@ -0,0 +1,457 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/lists/detail/scatter.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/strings/detail/scatter.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/iterator_traits.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+#include <thrust/uninitialized_fill.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Convert a scatter map into a gather map.
+ *
+ * The caller is expected to use the output map on a subsequent gather_bitmask()
+ * function using the PASSTHROUGH op since the resulting map may contain index
+ * values outside the target's range.
+ *
+ * First, the gather-map is initialized with an invalid index.
+ * The value `numeric_limits::lowest()` is used since it should always be outside the target size.
+ * Then, `output[scatter_map[i]] = i` for each `i`.
+ *
+ * @tparam MapIterator Iterator type of the input scatter map.
+ * @param scatter_map_begin Beginning of scatter map.
+ * @param scatter_map_end End of the scatter map.
+ * @param gather_rows Number of rows in the output map.
+ * @param stream Stream used for CUDA kernel calls.
+ * @return Output gather map.
+ */
+template <typename MapIterator>
+auto scatter_to_gather(MapIterator scatter_map_begin,
+                       MapIterator scatter_map_end,
+                       size_type gather_rows,
+                       rmm::cuda_stream_view stream)
+{
+  using MapValueType = typename thrust::iterator_traits<MapIterator>::value_type;
+
+  // The gather_map is initialized with `numeric_limits::lowest()` value to identify pass-through
+  // entries when calling the gather_bitmask() which applies a pass-through whenever it finds a
+  // value outside the range of the target column.
+  // We'll use the `numeric_limits::lowest()` value for this since it should always be outside the
+  // valid range.
+  auto gather_map = rmm::device_uvector<size_type>(gather_rows, stream);
+  thrust::uninitialized_fill(rmm::exec_policy_nosync(stream),
+                             gather_map.begin(),
+                             gather_map.end(),
+                             std::numeric_limits<size_type>::lowest());
+
+  // Convert scatter map to a gather map
+  thrust::scatter(
+    rmm::exec_policy_nosync(stream),
+    thrust::make_counting_iterator<MapValueType>(0),
+    thrust::make_counting_iterator<MapValueType>(std::distance(scatter_map_begin, scatter_map_end)),
+    scatter_map_begin,
+    gather_map.begin());
+
+  return gather_map;
+}
+
+/**
+ * @brief Create a complement map of `scatter_to_gather` map
+ *
+ * The purpose of this map is to create an identity-mapping for the rows that are not
+ * touched by the `scatter_map`.
+ *
+ * The output result of this mapping is firstly initialized as an identity-mapping
+ * (i.e., `output[i] = i`). Then, for each value `idx` from `scatter_map`, the value `output[idx]`
+ * is set to `numeric_limits::lowest()`, which is an invalid, out-of-bound index to identify the
+ * pass-through entries when calling the `gather_bitmask()` function.
+ *
+ */
+template <typename MapIterator>
+auto scatter_to_gather_complement(MapIterator scatter_map_begin,
+                                  MapIterator scatter_map_end,
+                                  size_type gather_rows,
+                                  rmm::cuda_stream_view stream)
+{
+  auto gather_map = rmm::device_uvector<size_type>(gather_rows, stream);
+  thrust::sequence(rmm::exec_policy_nosync(stream), gather_map.begin(), gather_map.end(), 0);
+
+  auto const out_of_bounds_begin =
+    thrust::make_constant_iterator(std::numeric_limits<size_type>::lowest());
+  auto const out_of_bounds_end =
+    out_of_bounds_begin + thrust::distance(scatter_map_begin, scatter_map_end);
+  thrust::scatter(rmm::exec_policy_nosync(stream),
+                  out_of_bounds_begin,
+                  out_of_bounds_end,
+                  scatter_map_begin,
+                  gather_map.begin());
+  return gather_map;
+}
+
+template <typename Element, typename Enable = void>
+struct column_scatterer_impl {
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...) const
+  {
+    CUDF_FAIL("Unsupported type for scatter.");
+  }
+};
+
+template <typename Element>
+struct column_scatterer_impl<Element, std::enable_if_t<cudf::is_fixed_width<Element>()>> {
+  template <typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source,
+                                     MapIterator scatter_map_begin,
+                                     MapIterator scatter_map_end,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto result      = std::make_unique<column>(target, stream, mr);
+    auto result_view = result->mutable_view();
+
+    // NOTE use source.begin + scatter rows rather than source.end in case the
+    // scatter map is smaller than the number of source rows
+    thrust::scatter(rmm::exec_policy_nosync(stream),
+                    source.begin<Element>(),
+                    source.begin<Element>() + cudf::distance(scatter_map_begin, scatter_map_end),
+                    scatter_map_begin,
+                    result_view.begin<Element>());
+
+    return result;
+  }
+};
+
+template <>
+struct column_scatterer_impl<string_view> {
+  template <typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source,
+                                     MapIterator scatter_map_begin,
+                                     MapIterator scatter_map_end,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto d_column    = column_device_view::create(source, stream);
+    auto const begin = d_column->begin<string_view>();
+    auto const end   = begin + cudf::distance(scatter_map_begin, scatter_map_end);
+    return strings::detail::scatter(begin, end, scatter_map_begin, target, stream, mr);
+  }
+};
+
+template <>
+struct column_scatterer_impl<list_view> {
+  template <typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source,
+                                     MapIterator scatter_map_begin,
+                                     MapIterator scatter_map_end,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    return cudf::lists::detail::scatter(
+      source, scatter_map_begin, scatter_map_end, target, stream, mr);
+  }
+};
+
+template <>
+struct column_scatterer_impl<dictionary32> {
+  template <typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source_in,
+                                     MapIterator scatter_map_begin,
+                                     MapIterator scatter_map_end,
+                                     column_view const& target_in,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    if (target_in.is_empty())  // empty begets empty
+      return make_empty_column(type_id::DICTIONARY32);
+    if (source_in.is_empty())  // no input, just make a copy
+      return std::make_unique<column>(target_in, stream, mr);
+
+    // check the keys match
+    dictionary_column_view const source(source_in);
+    dictionary_column_view const target(target_in);
+    CUDF_EXPECTS(source.keys().type() == target.keys().type(),
+                 "scatter dictionary keys must be the same type");
+
+    // first combine keys so both dictionaries have the same set
+    auto target_matched    = dictionary::detail::add_keys(target, source.keys(), stream, mr);
+    auto const target_view = dictionary_column_view(target_matched->view());
+    auto source_matched    = dictionary::detail::set_keys(
+      source, target_view.keys(), stream, rmm::mr::get_current_device_resource());
+    auto const source_view = dictionary_column_view(source_matched->view());
+
+    // now build the new indices by doing a scatter on just the matched indices
+    auto source_itr  = indexalator_factory::make_input_iterator(source_view.indices());
+    auto new_indices = std::make_unique<column>(target_view.get_indices_annotated(), stream, mr);
+    auto target_itr  = indexalator_factory::make_output_iterator(new_indices->mutable_view());
+    thrust::scatter(rmm::exec_policy_nosync(stream),
+                    source_itr,
+                    source_itr + std::distance(scatter_map_begin, scatter_map_end),
+                    scatter_map_begin,
+                    target_itr);
+
+    // record some data before calling release()
+    auto const indices_type = new_indices->type();
+    auto const output_size  = new_indices->size();
+    auto const null_count   = new_indices->null_count();
+    auto contents           = new_indices->release();
+    auto indices_column     = std::make_unique<column>(indices_type,
+                                                   static_cast<size_type>(output_size),
+                                                   std::move(*(contents.data.release())),
+                                                   rmm::device_buffer{0, stream, mr},
+                                                   0);
+
+    // take the keys from the matched column allocated using mr
+    std::unique_ptr<column> keys_column(std::move(target_matched->release().children.back()));
+
+    // create column with keys_column and indices_column
+    return make_dictionary_column(std::move(keys_column),
+                                  std::move(indices_column),
+                                  std::move(*(contents.null_mask.release())),
+                                  null_count);
+  }
+};
+
+struct column_scatterer {
+  template <typename Element, typename MapIterator>
+  std::unique_ptr<column> operator()(column_view const& source,
+                                     MapIterator scatter_map_begin,
+                                     MapIterator scatter_map_end,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    column_scatterer_impl<Element> scatterer{};
+    return scatterer(source, scatter_map_begin, scatter_map_end, target, stream, mr);
+  }
+};
+
+template <>
+struct column_scatterer_impl<struct_view> {
+  template <typename MapItRoot>
+  std::unique_ptr<column> operator()(column_view const& source,
+                                     MapItRoot scatter_map_begin,
+                                     MapItRoot scatter_map_end,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    CUDF_EXPECTS(source.num_children() == target.num_children(),
+                 "Scatter source and target are not of the same type.");
+
+    auto const scatter_map_size = std::distance(scatter_map_begin, scatter_map_end);
+    if (scatter_map_size == 0) { return std::make_unique<column>(target, stream, mr); }
+
+    structs_column_view const structs_src(source);
+    structs_column_view const structs_target(target);
+    std::vector<std::unique_ptr<column>> output_struct_members(structs_src.num_children());
+
+    std::transform(structs_src.child_begin(),
+                   structs_src.child_end(),
+                   structs_target.child_begin(),
+                   output_struct_members.begin(),
+                   [&scatter_map_begin, &scatter_map_end, stream, mr](auto const& source_col,
+                                                                      auto const& target_col) {
+                     return type_dispatcher<dispatch_storage_type>(source_col.type(),
+                                                                   column_scatterer{},
+                                                                   source_col,
+                                                                   scatter_map_begin,
+                                                                   scatter_map_end,
+                                                                   target_col,
+                                                                   stream,
+                                                                   mr);
+                   });
+
+    // We still need to call `gather_bitmask` even when the source's children are not nullable,
+    // as if the target's children have null_masks, those null_masks need to be updated after
+    // being scattered onto.
+    auto const child_nullable = std::any_of(structs_src.child_begin(),
+                                            structs_src.child_end(),
+                                            [](auto const& col) { return col.nullable(); }) or
+                                std::any_of(structs_target.child_begin(),
+                                            structs_target.child_end(),
+                                            [](auto const& col) { return col.nullable(); });
+    if (child_nullable) {
+      auto const gather_map =
+        scatter_to_gather(scatter_map_begin, scatter_map_end, target.size(), stream);
+      gather_bitmask(cudf::table_view{std::vector<cudf::column_view>{structs_src.child_begin(),
+                                                                     structs_src.child_end()}},
+                     gather_map.begin(),
+                     output_struct_members,
+                     gather_bitmask_op::PASSTHROUGH,
+                     stream,
+                     mr);
+    }
+
+    // Need to put the result column in a vector to call `gather_bitmask`.
+    std::vector<std::unique_ptr<column>> result;
+    result.emplace_back(cudf::make_structs_column(target.size(),
+                                                  std::move(output_struct_members),
+                                                  0,
+                                                  rmm::device_buffer{0, stream, mr},
+                                                  stream,
+                                                  mr));
+
+    // Only gather bitmask from the target column for the rows that have not been scattered onto
+    // The bitmask from the source column will be gathered at the top level `scatter()` call.
+    if (target.nullable()) {
+      auto const gather_map =
+        scatter_to_gather_complement(scatter_map_begin, scatter_map_end, target.size(), stream);
+      gather_bitmask(table_view{std::vector<cudf::column_view>{target}},
+                     gather_map.begin(),
+                     result,
+                     gather_bitmask_op::PASSTHROUGH,
+                     stream,
+                     mr);
+    }
+
+    return std::move(result.front());
+  }
+};
+
+/**
+ * @brief Scatters the rows of the source table into a copy of the target table
+ * according to a scatter map.
+ *
+ * Scatters values from the source table into the target table out-of-place,
+ * returning a "destination table". The scatter is performed according to a
+ * scatter map such that row `scatter_begin[i]` of the destination table gets row
+ * `i` of the source table. All other rows of the destination table equal
+ * corresponding rows of the target table.
+ *
+ * The number of columns in source must match the number of columns in target
+ * and their corresponding datatypes must be the same.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined. This range might have negative values, which will be modified by adding target.size()
+ *
+ * @throws cudf::logic_error if scatter map index is out of bounds
+ * @throws cudf::logic_error if scatter_map.size() > source.num_rows()
+ *
+ * @param[in] source The input columns containing values to be scattered into the
+ * target columns
+ * @param[in] scatter_map_begin Beginning of iterator range of integer indices that has been
+ *provided.
+ * @param[in] scatter_map_end End of iterator range of integer indices that has been provided.
+ * source columns to rows in the target columns
+ * @param[in] target The set of columns into which values from the source_table
+ * are to be scattered
+ * @param[in] check_bounds Optionally perform bounds checking on the values of
+ * `scatter_map` and throw an error if any of its values are out of bounds.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @return Result of scattering values from source to target
+ */
+template <typename MapIterator>
+std::unique_ptr<table> scatter(table_view const& source,
+                               MapIterator scatter_map_begin,
+                               MapIterator scatter_map_end,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  using MapType = typename thrust::iterator_traits<MapIterator>::value_type;
+
+  CUDF_EXPECTS(std::distance(scatter_map_begin, scatter_map_end) <= source.num_rows(),
+               "scatter map size should be <= to number of rows in source");
+
+  // Transform negative indices to index + target size.
+  auto updated_scatter_map_begin =
+    thrust::make_transform_iterator(scatter_map_begin, index_converter<MapType>{target.num_rows()});
+  auto updated_scatter_map_end =
+    thrust::make_transform_iterator(scatter_map_end, index_converter<MapType>{target.num_rows()});
+  auto result = std::vector<std::unique_ptr<column>>(target.num_columns());
+
+  std::transform(source.begin(),
+                 source.end(),
+                 target.begin(),
+                 result.begin(),
+                 [=](auto const& source_col, auto const& target_col) {
+                   return type_dispatcher<dispatch_storage_type>(source_col.type(),
+                                                                 column_scatterer{},
+                                                                 source_col,
+                                                                 updated_scatter_map_begin,
+                                                                 updated_scatter_map_end,
+                                                                 target_col,
+                                                                 stream,
+                                                                 mr);
+                 });
+
+  // We still need to call `gather_bitmask` even when the source columns are not nullable,
+  // as if the target has null_mask, that null_mask needs to be updated after scattering.
+  auto const nullable =
+    std::any_of(source.begin(), source.end(), [](auto const& col) { return col.nullable(); }) or
+    std::any_of(target.begin(), target.end(), [](auto const& col) { return col.nullable(); });
+  if (nullable) {
+    auto const gather_map = scatter_to_gather(
+      updated_scatter_map_begin, updated_scatter_map_end, target.num_rows(), stream);
+    gather_bitmask(source, gather_map.begin(), result, gather_bitmask_op::PASSTHROUGH, stream, mr);
+
+    // For struct columns, we need to superimpose the null_mask of the parent over the null_mask of
+    // the children.
+    std::for_each(result.begin(), result.end(), [=](auto& col) {
+      auto const col_view = col->view();
+      if (col_view.type().id() == type_id::STRUCT and col_view.nullable()) {
+        auto const num_rows   = col_view.size();
+        auto const null_count = col_view.null_count();
+        auto contents         = col->release();
+
+        // Children null_mask will be superimposed during structs column construction.
+        col = cudf::make_structs_column(num_rows,
+                                        std::move(contents.children),
+                                        null_count,
+                                        std::move(*contents.null_mask),
+                                        stream,
+                                        mr);
+      }
+    });
+  }
+  return std::make_unique<table>(std::move(result));
+}
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/scatter.hpp b/cpp/include/cudf/detail/scatter.hpp
new file mode 100644
index 0000000..94c795f
--- /dev/null
+++ b/cpp/include/cudf/detail/scatter.hpp
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Scatters the rows of the source table into a copy of the target table
+ * according to a scatter map.
+ *
+ * Scatters values from the source table into the target table out-of-place,
+ * returning a "destination table". The scatter is performed according to a
+ * scatter map such that row `scatter_map[i]` of the destination table gets row
+ * `i` of the source table. All other rows of the destination table equal
+ * corresponding rows of the target table.
+ *
+ * The number of columns in source must match the number of columns in target
+ * and their corresponding datatypes must be the same.
+ *
+ * A negative value `i` in the `scatter_map` is interpreted as `i+n`, where `n`
+ * is the number of rows in the `target` table.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ * If any values in `scatter_map` are outside of the interval [-n, n) where `n`
+ * is the number of rows in the `target` table, behavior is undefined.
+ *
+ * @param source The input columns containing values to be scattered into the
+ * target columns
+ * @param scatter_map A non-nullable column of integral indices that maps the
+ * rows in the source table to rows in the target table. The size must be equal
+ * to or less than the number of elements in the source columns.
+ * @param target The set of columns into which values from the source_table
+ * are to be scattered
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Result of scattering values from source to target
+ */
+std::unique_ptr<table> scatter(table_view const& source,
+                               column_view const& scatter_map,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::detail::scatter(table_view const&,column_view const&,table_view
+ * const&,bool,rmm::cuda_stream_view,rmm::mr::device_memory_resource*)
+ *
+ * @throws cudf::logic_error if `scatter_map` span size is larger than max of `size_type`.
+ */
+std::unique_ptr<table> scatter(table_view const& source,
+                               device_span<size_type const> const scatter_map,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Scatters a row of scalar values into a copy of the target table
+ * according to a scatter map.
+ *
+ * Scatters values from the source row into the target table out-of-place,
+ * returning a "destination table". The scatter is performed according to a
+ * scatter map such that row `scatter_map[i]` of the destination table is
+ * replaced by the source row. All other rows of the destination table equal
+ * corresponding rows of the target table.
+ *
+ * The number of elements in source must match the number of columns in target
+ * and their corresponding datatypes must be the same.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ *
+ * If any values in `indices` are outside of the interval [-n, n) where `n`
+ * is the number of rows in the `target` table, behavior is undefined.
+ *
+ * @param source The input scalars containing values to be scattered into the
+ * target columns
+ * @param indices A non-nullable column of integral indices that indicate
+ * the rows in the target table to be replaced by source.
+ * @param target The set of columns into which values from the source_table
+ * are to be scattered
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Result of scattering values from source to target
+ */
+std::unique_ptr<table> scatter(std::vector<std::reference_wrapper<scalar const>> const& source,
+                               column_view const& indices,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::boolean_mask_scatter(
+                      table_view const& source, table_view const& target,
+ *                    column_view const& boolean_mask,
+ *                    rmm::mr::device_memory_resource *mr)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> boolean_mask_scatter(table_view const& source,
+                                            table_view const& target,
+                                            column_view const& boolean_mask,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::boolean_mask_scatter(
+ *                    std::vector<std::reference_wrapper<scalar>> const& source,
+ *                    table_view const& target,
+ *                    column_view const& boolean_mask,
+ *                    rmm::mr::device_memory_resource *mr)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> boolean_mask_scatter(
+  std::vector<std::reference_wrapper<scalar const>> const& source,
+  table_view const& target,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/search.hpp b/cpp/include/cudf/detail/search.hpp
new file mode 100644
index 0000000..4277baf
--- /dev/null
+++ b/cpp/include/cudf/detail/search.hpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf::detail {
+/**
+ * @copydoc cudf::lower_bound
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> lower_bound(table_view const& haystack,
+                                    table_view const& needles,
+                                    std::vector<order> const& column_order,
+                                    std::vector<null_order> const& null_precedence,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::upper_bound
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> upper_bound(table_view const& haystack,
+                                    table_view const& needles,
+                                    std::vector<order> const& column_order,
+                                    std::vector<null_order> const& null_precedence,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::contains(column_view const&, scalar const&, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+bool contains(column_view const& haystack, scalar const& needle, rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::contains(column_view const&, column_view const&, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> contains(column_view const& haystack,
+                                 column_view const& needles,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Check if rows in the given `needles` table exist in the `haystack` table.
+ *
+ * Given two tables, each row in the `needles` table is checked to see if there is any matching row
+ * (i.e., compared equal to it) in the `haystack` table. The boolean search results are written into
+ * the corresponding rows of the output array.
+ *
+ * @code{.pseudo}
+ * Example:
+ *
+ * haystack = { { 5, 4, 1, 2, 3 } }
+ * needles  = { { 0, 1, 2 } }
+ * output   = { false, true, true }
+ * @endcode
+ *
+ * @throws cudf::logic_error If column types of haystack and needles don't match
+ *
+ * @param haystack The table containing the search space
+ * @param needles A table of rows whose existence to check in the search space
+ * @param compare_nulls Control whether nulls should be compared as equal or not
+ * @param compare_nans Control whether floating-point NaNs values should be compared as equal or not
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned vector
+ * @return A vector of bools indicating if each row in `needles` has matching rows in `haystack`
+ */
+rmm::device_uvector<bool> contains(table_view const& haystack,
+                                   table_view const& needles,
+                                   null_equality compare_nulls,
+                                   nan_equality compare_nans,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/sequence.hpp b/cpp/include/cudf/detail/sequence.hpp
new file mode 100644
index 0000000..3c3d1d0
--- /dev/null
+++ b/cpp/include/cudf/detail/sequence.hpp
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/sequence.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::sequence(size_type size, scalar const& init, scalar const& step,
+ *                                       rmm::mr::device_memory_resource* mr =
+ *rmm::mr::get_current_device_resource())
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> sequence(size_type size,
+                                 scalar const& init,
+                                 scalar const& step,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::sequence(size_type size, scalar const& init,
+                                         rmm::mr::device_memory_resource* mr =
+ rmm::mr::get_current_device_resource())
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> sequence(size_type size,
+                                 scalar const& init,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::calendrical_month_sequence(size_type size,
+ *                                           scalar const& init,
+ *                                           size_type months,
+ *                                           rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> calendrical_month_sequence(size_type size,
+                                                         scalar const& init,
+                                                         size_type months,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/sizes_to_offsets_iterator.cuh b/cpp/include/cudf/detail/sizes_to_offsets_iterator.cuh
new file mode 100644
index 0000000..155b1ce
--- /dev/null
+++ b/cpp/include/cudf/detail/sizes_to_offsets_iterator.cuh
@@ -0,0 +1,330 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/scan.h>
+
+#include <stdexcept>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Iterator that can be used with a scan algorithm and also return the last element
+ *
+ * Use cudf::detail::make_sizes_to_offsets_iterator to create an instance of this class.
+ *
+ * @tparam ScanIterator Output iterator type for use in a scan operation
+ * @tparam LastType Type used for final scan element
+ */
+template <typename ScanIterator, typename LastType>
+struct sizes_to_offsets_iterator {
+  using difference_type   = ptrdiff_t;
+  using value_type        = LastType;
+  using pointer           = LastType*;
+  using reference         = sizes_to_offsets_iterator const&;
+  using iterator_category = std::random_access_iterator_tag;
+
+  using ScanType = typename thrust::iterator_traits<ScanIterator>::value_type;
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator& operator++()
+  {
+    ++itr_;
+    return *this;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator operator++(int)
+  {
+    sizes_to_offsets_iterator tmp(*this);
+    operator++();
+    return tmp;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator& operator--()
+  {
+    --itr_;
+    return *this;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator operator--(int)
+  {
+    sizes_to_offsets_iterator tmp(*this);
+    operator--();
+    return tmp;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator& operator+=(difference_type offset)
+  {
+    itr_ += offset;
+    return *this;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator operator+(difference_type offset) const
+  {
+    sizes_to_offsets_iterator tmp(*this);
+    tmp.itr_ += offset;
+    return tmp;
+  }
+
+  CUDF_HOST_DEVICE inline friend sizes_to_offsets_iterator operator+(
+    difference_type offset, sizes_to_offsets_iterator const& rhs)
+  {
+    sizes_to_offsets_iterator tmp{rhs};
+    tmp.itr_ += offset;
+    return tmp;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator& operator-=(difference_type offset)
+  {
+    itr_ -= offset;
+    return *this;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator operator-(difference_type offset) const
+  {
+    sizes_to_offsets_iterator tmp(*this);
+    tmp.itr_ -= offset;
+    return tmp;
+  }
+
+  CUDF_HOST_DEVICE inline friend sizes_to_offsets_iterator operator-(
+    difference_type offset, sizes_to_offsets_iterator const& rhs)
+  {
+    sizes_to_offsets_iterator tmp{rhs};
+    tmp.itr_ -= offset;
+    return tmp;
+  }
+
+  CUDF_HOST_DEVICE inline difference_type operator-(sizes_to_offsets_iterator const& rhs) const
+  {
+    return itr_ - rhs.itr_;
+  }
+  CUDF_HOST_DEVICE inline bool operator==(sizes_to_offsets_iterator const& rhs) const
+  {
+    return rhs.itr_ == itr_;
+  }
+  CUDF_HOST_DEVICE inline bool operator!=(sizes_to_offsets_iterator const& rhs) const
+  {
+    return rhs.itr_ != itr_;
+  }
+  CUDF_HOST_DEVICE inline bool operator<(sizes_to_offsets_iterator const& rhs) const
+  {
+    return itr_ < rhs.itr_;
+  }
+  CUDF_HOST_DEVICE inline bool operator>(sizes_to_offsets_iterator const& rhs) const
+  {
+    return itr_ > rhs.itr_;
+  }
+  CUDF_HOST_DEVICE inline bool operator<=(sizes_to_offsets_iterator const& rhs) const
+  {
+    return itr_ <= rhs.itr_;
+  }
+  CUDF_HOST_DEVICE inline bool operator>=(sizes_to_offsets_iterator const& rhs) const
+  {
+    return itr_ >= rhs.itr_;
+  }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator const& operator*() const { return *this; }
+
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator const operator[](int idx) const
+  {
+    sizes_to_offsets_iterator tmp{*this};
+    tmp.itr_ += idx;
+    return tmp;
+  }
+
+  /**
+   * @brief Called to set the output of the scan operation to the current iterator position
+   *
+   * @param value Value to set to the current output
+   * @return This iterator instance
+   */
+  CUDF_HOST_DEVICE inline sizes_to_offsets_iterator const& operator=(LastType const value) const
+  {
+    *itr_ = static_cast<ScanType>(value);  // place into the output
+    if (itr_ == end_) { *last_ = value; }  // also save the last value
+    return *this;
+  }
+
+  sizes_to_offsets_iterator()                                            = default;
+  sizes_to_offsets_iterator(sizes_to_offsets_iterator const&)            = default;
+  sizes_to_offsets_iterator(sizes_to_offsets_iterator&&)                 = default;
+  sizes_to_offsets_iterator& operator=(sizes_to_offsets_iterator const&) = default;
+  sizes_to_offsets_iterator& operator=(sizes_to_offsets_iterator&&)      = default;
+
+ protected:
+  template <typename S, typename R>
+  friend sizes_to_offsets_iterator<S, R> make_sizes_to_offsets_iterator(S, S, R*);
+
+  /**
+   * @brief Iterator constructor
+   *
+   * Use the make_sizes_to_offsets_iterator() to create an instance of this class
+   */
+  sizes_to_offsets_iterator(ScanIterator begin, ScanIterator end, LastType* last)
+    : itr_{begin}, end_{thrust::prev(end)}, last_{last}
+  {
+  }
+
+  ScanIterator itr_{};
+  ScanIterator end_{};
+  LastType* last_{};
+};
+
+/**
+ * @brief Create an instance of a sizes_to_offsets_iterator
+ *
+ * @code{.pseudo}
+ *  auto begin = // begin input iterator
+ *  auto end = // end input iterator
+ *  auto result = rmm::device_uvector(std::distance(begin,end), stream);
+ *  auto last = rmm::device_scalar<int64_t>(0, stream);
+ *  auto itr = make_sizes_to_offsets_iterator(result.begin(),
+ *                                            result.end(),
+ *                                            last.data());
+ *  thrust::exclusive_scan(rmm::exec_policy(stream), begin, end, itr, int64_t{0});
+ *  // last contains the value of the final element in the scan result
+ * @endcode
+ *
+ * @tparam ScanIterator Output iterator type for use in a scan operation
+ * @tparam LastType Type used for holding the final element value
+ *
+ * @param begin Output iterator for scan
+ * @param end End of the output iterator for scan
+ * @param last Last element in the scan is stored here
+ * @return Instance of iterator
+ */
+template <typename ScanIterator, typename LastType>
+static sizes_to_offsets_iterator<ScanIterator, LastType> make_sizes_to_offsets_iterator(
+  ScanIterator begin, ScanIterator end, LastType* last)
+{
+  return sizes_to_offsets_iterator<ScanIterator, LastType>(begin, end, last);
+}
+
+/**
+ * @brief Perform an exclusive-scan and capture the final element value
+ *
+ * This performs an exclusive-scan (addition only) on the given input `[begin, end)`.
+ * The output of the scan is placed in `result` and the value of the last element is returned.
+ *
+ * This implementation will return the last element in `int64_t` or `uint64_t` precision
+ * as appropriate regardless of the input or result types.
+ * This can be used to check if the scan operation overflowed when the input and result are
+ * declared as smaller types.
+ *
+ * Only integral types for input and result types are supported.
+ *
+ * Note that `begin == result` is allowed but `result` may not overlap `[begin,end)` otherwise the
+ * behavior is undefined.
+ *
+ * @code{.pseudo}
+ *   auto const bytes = cudf::detail::sizes_to_offsets(
+ *     d_offsets, d_offsets + strings_count + 1, d_offsets, stream);
+ *   CUDF_EXPECTS(bytes <= static_cast<int64_t>(std::numeric_limits<size_type>::max()),
+ *               "Size of output exceeds the column size limit", std::overflow_error);
+ * @endcode
+ *
+ * @tparam SizesIterator Iterator type for input of the scan using addition operation
+ * @tparam OffsetsIterator Iterator type for the output of the scan
+ *
+ * @param begin Input iterator for scan
+ * @param end End of the input iterator
+ * @param result Output iterator for scan result
+ * @return The last element of the scan
+ */
+template <typename SizesIterator, typename OffsetsIterator>
+auto sizes_to_offsets(SizesIterator begin,
+                      SizesIterator end,
+                      OffsetsIterator result,
+                      rmm::cuda_stream_view stream)
+{
+  using SizeType = typename thrust::iterator_traits<SizesIterator>::value_type;
+  static_assert(std::is_integral_v<SizeType>,
+                "Only numeric types are supported by sizes_to_offsets");
+
+  using LastType    = std::conditional_t<std::is_signed_v<SizeType>, int64_t, uint64_t>;
+  auto last_element = rmm::device_scalar<LastType>(0, stream);
+  auto output_itr =
+    make_sizes_to_offsets_iterator(result, result + std::distance(begin, end), last_element.data());
+  // This function uses the type of the initialization parameter as the accumulator type
+  // when computing the individual scan output elements.
+  thrust::exclusive_scan(rmm::exec_policy(stream), begin, end, output_itr, LastType{0});
+  return last_element.value(stream);
+}
+
+/**
+ * @brief Create an offsets column to be a child of a compound column
+ *
+ * This function sets the offsets values by executing scan over the sizes in the provided
+ * Iterator.
+ *
+ * The return also includes the total number of elements -- the last element value from the
+ * scan.
+ *
+ * @throw std::overflow_error if the total size of the scan (last element) greater than maximum
+ * value of `size_type`
+ *
+ * @tparam InputIterator Used as input to scan to set the offset values
+ * @param begin The beginning of the input sequence
+ * @param end The end of the input sequence
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Offsets column and total elements
+ */
+template <typename InputIterator>
+std::pair<std::unique_ptr<column>, size_type> make_offsets_child_column(
+  InputIterator begin,
+  InputIterator end,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto count          = static_cast<size_type>(std::distance(begin, end));
+  auto offsets_column = make_numeric_column(
+    data_type{type_to_id<size_type>()}, count + 1, mask_state::UNALLOCATED, stream, mr);
+  auto offsets_view = offsets_column->mutable_view();
+  auto d_offsets    = offsets_view.template data<size_type>();
+
+  // The number of offsets is count+1 so to build the offsets from the sizes
+  // using exclusive-scan technically requires count+1 input values even though
+  // the final input value is never used.
+  // The input iterator is wrapped here to allow the last value to be safely read.
+  auto map_fn = [begin, count] __device__(size_type idx) -> size_type {
+    return idx < count ? static_cast<size_type>(begin[idx]) : size_type{0};
+  };
+  auto input_itr = cudf::detail::make_counting_transform_iterator(0, map_fn);
+  // Use the sizes-to-offsets iterator to compute the total number of elements
+  auto const total_elements = sizes_to_offsets(input_itr, input_itr + count + 1, d_offsets, stream);
+  CUDF_EXPECTS(
+    total_elements <= static_cast<decltype(total_elements)>(std::numeric_limits<size_type>::max()),
+    "Size of output exceeds the column size limit",
+    std::overflow_error);
+
+  offsets_column->set_null_count(0);
+  return std::pair(std::move(offsets_column), static_cast<size_type>(total_elements));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/sorting.hpp b/cpp/include/cudf/detail/sorting.hpp
new file mode 100644
index 0000000..8f92b66
--- /dev/null
+++ b/cpp/include/cudf/detail/sorting.hpp
@@ -0,0 +1,154 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/sorting.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc cudf::sorted_order
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> sorted_order(table_view const& input,
+                                     std::vector<order> const& column_order,
+                                     std::vector<null_order> const& null_precedence,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::stable_sorted_order
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> stable_sorted_order(table_view const& input,
+                                            std::vector<order> const& column_order,
+                                            std::vector<null_order> const& null_precedence,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::sort_by_key
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> sort_by_key(table_view const& values,
+                                   table_view const& keys,
+                                   std::vector<order> const& column_order,
+                                   std::vector<null_order> const& null_precedence,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::rank
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> rank(column_view const& input,
+                             rank_method method,
+                             order column_order,
+                             null_policy null_handling,
+                             null_order null_precedence,
+                             bool percentage,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::stable_sort_by_key
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> stable_sort_by_key(table_view const& values,
+                                          table_view const& keys,
+                                          std::vector<order> const& column_order,
+                                          std::vector<null_order> const& null_precedence,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::segmented_sorted_order
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> segmented_sorted_order(table_view const& keys,
+                                               column_view const& segment_offsets,
+                                               std::vector<order> const& column_order,
+                                               std::vector<null_order> const& null_precedence,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::stable_segmented_sorted_order
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> stable_segmented_sorted_order(
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::segmented_sort_by_key
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> segmented_sort_by_key(table_view const& values,
+                                             table_view const& keys,
+                                             column_view const& segment_offsets,
+                                             std::vector<order> const& column_order,
+                                             std::vector<null_order> const& null_precedence,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::stable_segmented_sort_by_key
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> stable_segmented_sort_by_key(table_view const& values,
+                                                    table_view const& keys,
+                                                    column_view const& segment_offsets,
+                                                    std::vector<order> const& column_order,
+                                                    std::vector<null_order> const& null_precedence,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::sort
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> sort(table_view const& values,
+                            std::vector<order> const& column_order,
+                            std::vector<null_order> const& null_precedence,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/stream_compaction.hpp b/cpp/include/cudf/detail/stream_compaction.hpp
new file mode 100644
index 0000000..5476000
--- /dev/null
+++ b/cpp/include/cudf/detail/stream_compaction.hpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::drop_nulls(table_view const&, std::vector<size_type> const&,
+ *                           cudf::size_type, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> drop_nulls(table_view const& input,
+                                  std::vector<size_type> const& keys,
+                                  cudf::size_type keep_threshold,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::drop_nans(table_view const&, std::vector<size_type> const&,
+ *                          cudf::size_type, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> drop_nans(table_view const& input,
+                                 std::vector<size_type> const& keys,
+                                 cudf::size_type keep_threshold,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::apply_boolean_mask
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> apply_boolean_mask(table_view const& input,
+                                          column_view const& boolean_mask,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::unique
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> unique(table_view const& input,
+                              std::vector<size_type> const& keys,
+                              duplicate_keep_option keep,
+                              null_equality nulls_equal,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::distinct
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> distinct(table_view const& input,
+                                std::vector<size_type> const& keys,
+                                duplicate_keep_option keep,
+                                null_equality nulls_equal,
+                                nan_equality nans_equal,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::stable_distinct
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> stable_distinct(table_view const& input,
+                                       std::vector<size_type> const& keys,
+                                       duplicate_keep_option keep,
+                                       null_equality nulls_equal,
+                                       nan_equality nans_equal,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create a column of indices of all distinct rows in the input table.
+ *
+ * Given an `input` table_view, an output vector of all row indices of the distinct rows is
+ * generated. If there are duplicate rows, which index is kept depends on the `keep` parameter.
+ *
+ * @param input The input table
+ * @param keep Get index of any, first, last, or none of the found duplicates
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether NaN elements should be considered as equal
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned vector
+ * @return A device_uvector containing the result indices
+ */
+rmm::device_uvector<size_type> get_distinct_indices(table_view const& input,
+                                                    duplicate_keep_option keep,
+                                                    null_equality nulls_equal,
+                                                    nan_equality nans_equal,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::unique_count(column_view const&, null_policy, nan_policy)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+cudf::size_type unique_count(column_view const& input,
+                             null_policy null_handling,
+                             nan_policy nan_handling,
+                             rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::unique_count(table_view const&, null_equality)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+cudf::size_type unique_count(table_view const& input,
+                             null_equality nulls_equal,
+                             rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::distinct_count(column_view const&, null_policy, nan_policy)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+cudf::size_type distinct_count(column_view const& input,
+                               null_policy null_handling,
+                               nan_policy nan_handling,
+                               rmm::cuda_stream_view stream);
+
+/**
+ * @copydoc cudf::distinct_count(table_view const&, null_equality)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+cudf::size_type distinct_count(table_view const& input,
+                               null_equality nulls_equal,
+                               rmm::cuda_stream_view stream);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/structs/utilities.hpp b/cpp/include/cudf/detail/structs/utilities.hpp
new file mode 100644
index 0000000..c0a7914
--- /dev/null
+++ b/cpp/include/cudf/detail/structs/utilities.hpp
@@ -0,0 +1,268 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+namespace cudf::structs::detail {
+
+enum class column_nullability {
+  MATCH_INCOMING,  ///< generate a null column if the incoming column has nulls
+  FORCE            ///< always generate a null column
+};
+
+/**
+ * @brief The struct to hold temporary data that is not directly used but needs to be kept alive to
+ * support the output generated by `superimpose_nulls(column_view)` and
+ * `superimpose_nulls(table_view)`.
+ */
+struct temporary_nullable_data {
+  /**
+   * @brief Store the newly generated null masks (if any).
+   */
+  std::vector<rmm::device_buffer> new_null_masks;
+
+  /**
+   * @brief Store the newly generated columns with new null masks (if any).
+   */
+  std::vector<std::unique_ptr<column>> new_columns;
+
+  /**
+   * @brief Take over the content of another instance and append it into the internal data.
+   *
+   * @param other The other instance to take over its content
+   */
+  void emplace_back(temporary_nullable_data&& other);
+};
+
+/**
+ * @brief Flatten the children of the input columns into a vector where the i'th element
+ * is a vector of column_views representing the i'th child from each input column_view.
+ *
+ * @code{.pseudo}
+ * s1 = [ col0 : {0, 1}
+ *        col1 : {2, 3, 4, 5, 6}
+ *        col2 : {"abc", "def", "ghi"} ]
+ *
+ * s2 = [ col0 : {7, 8}
+ *        col1 : {-4, -5, -6}
+ *        col2 : {"uvw", "xyz"} ]
+ *
+ * e = extract_ordered_struct_children({s1, s2})
+ *
+ * e is now [ {{0, 1}, {7, 8}}
+ *            {{2, 3, 4, 5, 6}, {-4, -5, -6}}
+ *            {{"abc", "def", "ghi"}, {"uvw", "xyz"} ]
+ * @endcode
+ *
+ * @param columns Vector of structs columns to extract from.
+ * @return New column with concatenated results.
+ */
+std::vector<std::vector<column_view>> extract_ordered_struct_children(
+  host_span<column_view const> struct_cols, rmm::cuda_stream_view stream);
+
+/**
+ * @brief Check whether the specified column is of type LIST, or any LISTs in its descendent
+ * columns.
+ * @param col column to check for lists.
+ * @return true if the column or any of it's children is a list, false otherwise.
+ */
+bool is_or_has_nested_lists(cudf::column_view const& col);
+
+/**
+ * @brief Result of `flatten_nested_columns()`, where all `STRUCT` columns are replaced with
+ * their non-nested member columns, and `BOOL8` columns for their null masks.
+ *
+ * `flatten_nested_columns()` produces a "flattened" table_view with all `STRUCT` columns
+ * replaced with their child column_views, preceded by their null masks.
+ * All newly allocated columns and device_buffers that back the returned table_view
+ * are also encapsulated in `flatten_result`.
+ *
+ * Objects of `flatten_result` need to kept alive while its table_view is accessed.
+ */
+class flattened_table {
+ public:
+  /**
+   * @brief Constructor, to be used from `flatten_nested_columns()`.
+   *
+   * @param flattened_columns_ table_view resulting from `flatten_nested_columns()`
+   * @param orders_ Per-column ordering of the table_view
+   * @param null_orders_ Per-column null_order of the table_view
+   * @param columns_ Newly allocated columns to back the table_view
+   * @param nullable_data_ Newly generated temporary data that needs to be kept alive
+   */
+  flattened_table(table_view const& flattened_columns_,
+                  std::vector<order> const& orders_,
+                  std::vector<null_order> const& null_orders_,
+                  std::vector<std::unique_ptr<column>>&& columns_,
+                  temporary_nullable_data&& nullable_data_)
+    : _flattened_columns{flattened_columns_},
+      _orders{orders_},
+      _null_orders{null_orders_},
+      _columns{std::move(columns_)},
+      _nullable_data{std::move(nullable_data_)}
+  {
+  }
+
+  flattened_table() = default;
+
+  /**
+   * @brief Getter for the flattened columns, as a `table_view`.
+   */
+  [[nodiscard]] table_view flattened_columns() const { return _flattened_columns; }
+
+  /**
+   * @brief Getter for the cudf::order of the table_view's columns.
+   */
+  [[nodiscard]] std::vector<order> orders() const { return _orders; }
+
+  /**
+   * @brief Getter for the cudf::null_order of the table_view's columns.
+   */
+  [[nodiscard]] std::vector<null_order> null_orders() const { return _null_orders; }
+
+  /**
+   * @brief Conversion to `table_view`, to fetch flattened columns.
+   */
+  operator table_view() const { return flattened_columns(); }
+
+ private:
+  table_view _flattened_columns;
+  std::vector<order> _orders;
+  std::vector<null_order> _null_orders;
+  std::vector<std::unique_ptr<column>> _columns;
+  temporary_nullable_data _nullable_data;
+};
+
+/**
+ * @brief Flatten table with struct columns to table with constituent columns of struct columns.
+ *
+ * If a table does not have struct columns, same input arguments are returned.
+ *
+ * @param input input table to be flattened
+ * @param column_order column order for input table
+ * @param null_precedence null order for input table
+ * @param nullability force output to have nullability columns even if input columns are all valid
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate new device memory
+ * @return A pointer of type `flattened_table` containing flattened columns, flattened column
+ *         orders, flattened null precedence, alongside the supporting columns and device_buffers
+ *         for the flattened table.
+ */
+[[nodiscard]] std::unique_ptr<flattened_table> flatten_nested_columns(
+  table_view const& input,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  column_nullability nullability,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Superimpose nulls from a given null mask into the input column, using bitwise AND.
+ *
+ * This function will recurse through all struct descendants. It is expected that the size of
+ * the given null mask in bits is the same as size of the input column.
+ *
+ * Any null strings/lists in the input (if any) will also be sanitized to make sure nulls in the
+ * output always have their sizes equal to 0.
+ *
+ * @param null_mask Null mask to be applied to the input column
+ * @param null_count Null count in the given null mask
+ * @param input Column to apply the null mask to
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate new device memory
+ * @return A new column with potentially new null mask
+ */
+[[nodiscard]] std::unique_ptr<column> superimpose_nulls(bitmask_type const* null_mask,
+                                                        size_type null_count,
+                                                        std::unique_ptr<column>&& input,
+                                                        rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Push down nulls from the given input column into its children columns, using bitwise AND.
+ *
+ * This function constructs a new column_view instance equivalent to the input column_view,
+ * with possibly new child column_view, all with possibly new null mask reflecting null rows from
+ * the parent column:
+ * 1. If the specified column is not STRUCT, the column_view is returned unmodified, with no
+ *    temporary data allocated.
+ * 2. If the column is STRUCT, the null masks of the parent and child are bitwise-ANDed, and a
+ *    modified column_view is returned. This applies recursively.
+ *
+ * Any null strings/lists in the input (if any) will also be sanitized to make sure nulls in the
+ * output always have their sizes equal to 0.
+ *
+ * @param input The input (possibly STRUCT) column whose nulls need to be pushed to its children
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate new device memory
+ * @return A pair of:
+ *         1. column_view with nulls pushed down to child columns, as appropriate.
+ *         2. An instance of `temporary_nullable_data` holding the temporary data that needs
+ *         to be kept alive.
+ */
+[[nodiscard]] std::pair<column_view, temporary_nullable_data> push_down_nulls(
+  column_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Push down nulls from columns of the input table into their children columns, using
+ * bitwise AND.
+ *
+ * This function constructs a new table_view containing new column_view instances equivalent to
+ * the corresponding column_views in the input table. Each column_view might contain possibly new
+ * child column_views, all with possibly new null mask reflecting null rows from the parent column:
+ * 1. If the specified column is not STRUCT, the column_view is returned unmodified, with no
+ *    temporary data allocated.
+ * 2. If the column is STRUCT, the null masks of the parent and child are bitwise-ANDed, and a
+ *    modified column_view is returned. This applies recursively.
+ *
+ * Any null strings/lists in the input (if any) will also be sanitized to make sure nulls in the
+ * output always have their sizes equal to 0.
+ *
+ * @param input The table_view of (possibly STRUCT) columns whose nulls need to be pushed to their
+ *        children
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate new device memory
+ * @return A pair of:
+ *         1. table_view of columns with nulls pushed down to child columns, as appropriate.
+ *         2. An instance of `temporary_nullable_data` holding the temporary data that needs
+ *         to be kept alive.
+ */
+[[nodiscard]] std::pair<table_view, temporary_nullable_data> push_down_nulls(
+  table_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Checks if a column or any of its children is a struct column with structs that are null.
+ *
+ * This function searches for structs that are null -- differentiating between structs that are null
+ * and structs containing null values. Null structs add a column to the result of the flatten column
+ * utility and necessitates column_nullability::FORCE when flattening the column for comparison
+ * operations.
+ *
+ * @param col Column to check for null structs
+ * @return A boolean indicating if the column is or contains a struct column that contains a null
+ * struct.
+ */
+bool contains_null_structs(column_view const& col);
+
+}  // namespace cudf::structs::detail
diff --git a/cpp/include/cudf/detail/tdigest/tdigest.hpp b/cpp/include/cudf/detail/tdigest/tdigest.hpp
new file mode 100644
index 0000000..b529d4a
--- /dev/null
+++ b/cpp/include/cudf/detail/tdigest/tdigest.hpp
@@ -0,0 +1,240 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace tdigest {
+namespace detail {
+
+/**
+ * @brief Generate a tdigest column from a grouped, sorted set of numeric input values.
+ *
+ * The input is expected to be sorted in ascending order within each group, with
+ * nulls at the end.
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * Each output row is a single tdigest.  The length of the row is the "size" of the
+ * tdigest, each element of which represents a weighted centroid (mean, weight).
+ *
+ * @param values Grouped (and sorted) values to merge.
+ * @param group_offsets Offsets of groups' starting points within @p values.
+ * @param group_labels 0-based ID of group that the corresponding value belongs to
+ * @param group_valid_counts Per-group counts of valid elements.
+ * @param num_groups Number of groups.
+ * @param max_centroids Parameter controlling the level of compression of the tdigest. Higher
+ * values result in a larger, more precise tdigest.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns tdigest column, with 1 tdigest per row
+ */
+std::unique_ptr<column> group_tdigest(column_view const& values,
+                                      cudf::device_span<size_type const> group_offsets,
+                                      cudf::device_span<size_type const> group_labels,
+                                      cudf::device_span<size_type const> group_valid_counts,
+                                      size_type num_groups,
+                                      int max_centroids,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Merges tdigests within the same group to generate a new tdigest.
+ *
+ * The tdigest column produced is of the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * Each output row is a single tdigest.  The length of the row is the "size" of the
+ * tdigest, each element of which represents a weighted centroid (mean, weight).
+ *
+ * @param values Grouped tdigests to merge.
+ * @param group_offsets Offsets of groups' starting points within @p values.
+ * @param group_labels 0-based ID of group that the corresponding value belongs to
+ * @param num_groups Number of groups.
+ * @param max_centroids Parameter controlling the level of compression of the tdigest. Higher
+ * values result in a larger, more precise tdigest.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns tdigest column, with 1 tdigest per row
+ */
+std::unique_ptr<column> group_merge_tdigest(column_view const& values,
+                                            cudf::device_span<size_type const> group_offsets,
+                                            cudf::device_span<size_type const> group_labels,
+                                            size_type num_groups,
+                                            int max_centroids,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create a tdigest column from its constituent components.
+ *
+ * @param num_rows The number of rows in the output column.
+ * @param centroid_means The inner means column.  These values are partitioned into lists by the
+ * `tdigest_offsets` column.
+ * @param centroid_weights The inner weights column.  These values are partitioned into lists by the
+ * `tdigest_offsets` column.
+ * @param tdigest_offsets Offsets representing each individual tdigest in the output column. The
+ * offsets partition the centroid means and weights.
+ * @param min_values Column representing the minimum input value for each tdigest.
+ * @param max_values Column representing the maximum input value for each tdigest.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @returns The constructed tdigest column.
+ */
+std::unique_ptr<column> make_tdigest_column(size_type num_rows,
+                                            std::unique_ptr<column>&& centroid_means,
+                                            std::unique_ptr<column>&& centroid_weights,
+                                            std::unique_ptr<column>&& tdigest_offsets,
+                                            std::unique_ptr<column>&& min_values,
+                                            std::unique_ptr<column>&& max_values,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create an empty tdigest column.
+ *
+ * An empty tdigest column contains a single row of length 0
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @returns An empty tdigest column.
+ */
+std::unique_ptr<column> make_empty_tdigest_column(rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create an empty tdigest scalar.
+ *
+ * An empty tdigest scalar is a struct_scalar that contains a single row of length 0
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @returns An empty tdigest scalar.
+ */
+std::unique_ptr<scalar> make_empty_tdigest_scalar(rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Generate a tdigest scalar from a set of numeric input values.
+ *
+ * The tdigest scalar produced is of the following structure:
+ ** struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ *
+ * @param values Values to merge.
+ * @param max_centroids Parameter controlling the level of compression of the tdigest. Higher
+ * values result in a larger, more precise tdigest.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ *
+ * @returns tdigest scalar
+ */
+std::unique_ptr<scalar> reduce_tdigest(column_view const& values,
+                                       int max_centroids,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Merges multiple tdigest columns to generate a new tdigest scalar.
+ *
+ * The tdigest scalar produced is of the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * @param values tdigests to merge.
+ * @param max_centroids Parameter controlling the level of compression of the tdigest. Higher
+ * values result in a larger, more precise tdigest.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ *
+ * @returns tdigest column, with 1 tdigest per row
+ */
+std::unique_ptr<scalar> reduce_merge_tdigest(column_view const& input,
+                                             int max_centroids,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace tdigest
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/timezone.cuh b/cpp/include/cudf/detail/timezone.cuh
new file mode 100644
index 0000000..830ee1a
--- /dev/null
+++ b/cpp/include/cudf/detail/timezone.cuh
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/timezone.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+
+namespace cudf::detail {
+
+/**
+ * @brief Returns the UT offset for a given date and given timezone table.
+ *
+ * @param transition_times Transition times; trailing `solar_cycle_entry_count` entries are used for
+ * all times beyond the one covered by the TZif file
+ * @param offsets Time offsets in specific intervals; trailing `solar_cycle_entry_count` entries are
+ * used for all times beyond the one covered by the TZif file
+ * @param ts ORC timestamp
+ *
+ * @return offset from UT, in seconds
+ */
+inline __device__ duration_s get_ut_offset(table_device_view tz_table, timestamp_s ts)
+{
+  if (tz_table.num_rows() == 0) { return duration_s{0}; }
+
+  cudf::device_span<timestamp_s const> transition_times(tz_table.column(0).head<timestamp_s>(),
+                                                        static_cast<size_t>(tz_table.num_rows()));
+
+  auto const ts_ttime_it = [&]() {
+    auto last_less_equal = [](auto begin, auto end, auto value) {
+      auto const first_larger = thrust::upper_bound(thrust::seq, begin, end, value);
+      // Return start of the range if all elements are larger than the value
+      if (first_larger == begin) return begin;
+      // Element before the first larger element is the last one less or equal
+      return first_larger - 1;
+    };
+
+    auto const file_entry_end =
+      transition_times.begin() + (transition_times.size() - solar_cycle_entry_count);
+
+    if (ts <= *(file_entry_end - 1)) {
+      // Search the file entries if the timestamp is in range
+      return last_less_equal(transition_times.begin(), file_entry_end, ts);
+    } else {
+      auto project_to_cycle = [](timestamp_s ts) {
+        // Years divisible by four are leap years
+        // Exceptions are years divisible by 100, but not divisible by 400
+        static constexpr int32_t num_leap_years_in_cycle =
+          solar_cycle_years / 4 - (solar_cycle_years / 100 - solar_cycle_years / 400);
+        static constexpr duration_s cycle_s = cuda::std::chrono::duration_cast<duration_s>(
+          duration_D{365 * solar_cycle_years + num_leap_years_in_cycle});
+        return timestamp_s{(ts.time_since_epoch() + cycle_s) % cycle_s};
+      };
+      // Search the 400-year cycle if outside of the file entries range
+      return last_less_equal(file_entry_end, transition_times.end(), project_to_cycle(ts));
+    }
+  }();
+
+  return tz_table.column(1).element<duration_s>(ts_ttime_it - transition_times.begin());
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/timezone.hpp b/cpp/include/cudf/detail/timezone.hpp
new file mode 100644
index 0000000..f7f97c0
--- /dev/null
+++ b/cpp/include/cudf/detail/timezone.hpp
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/timezone.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::detail {
+
+/**
+ * @copydoc cudf::make_timezone_transition_table(std::optional<std::string_view>, std::string_view,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<table> make_timezone_transition_table(
+  std::optional<std::string_view> tzif_dir,
+  std::string_view timezone_name,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/transform.hpp b/cpp/include/cudf/detail/transform.hpp
new file mode 100644
index 0000000..215ad50
--- /dev/null
+++ b/cpp/include/cudf/detail/transform.hpp
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::transform
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> transform(column_view const& input,
+                                  std::string const& unary_udf,
+                                  data_type output_type,
+                                  bool is_ptx,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::compute_column
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> compute_column(table_view const& table,
+                                       ast::expression const& expr,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::nans_to_nulls
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<std::unique_ptr<rmm::device_buffer>, size_type> nans_to_nulls(
+  column_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::bools_to_mask
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type> bools_to_mask(
+  column_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::encode
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<std::unique_ptr<cudf::table>, std::unique_ptr<cudf::column>> encode(
+  cudf::table_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::one_hot_encode
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<std::unique_ptr<column>, table_view> one_hot_encode(column_view const& input,
+                                                              column_view const& categories,
+                                                              rmm::cuda_stream_view stream,
+                                                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::mask_to_bools
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> mask_to_bools(bitmask_type const* null_mask,
+                                      size_type begin_bit,
+                                      size_type end_bit,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::row_bit_count
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> row_bit_count(table_view const& t,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/transpose.hpp b/cpp/include/cudf/detail/transpose.hpp
new file mode 100644
index 0000000..d0be518
--- /dev/null
+++ b/cpp/include/cudf/detail/transpose.hpp
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::transpose
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<std::unique_ptr<column>, table_view> transpose(table_view const& input,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/unary.hpp b/cpp/include/cudf/detail/unary.hpp
new file mode 100644
index 0000000..3fbdf4a
--- /dev/null
+++ b/cpp/include/cudf/detail/unary.hpp
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Creates a column of `type_id::BOOL8` elements by applying a predicate to every element
+ * between
+ * [`begin, `end`) `true` indicates the value is satisfies the predicate and `false` indicates it
+ * doesn't.
+ *
+ * @tparam InputIterator Iterator type for `begin` and `end`
+ * @tparam Predicate A predicator type which will be evaluated
+ * @param begin Beginning of the sequence of elements
+ * @param end End of the sequence of elements
+ * @param p Predicate to be applied to each element in `[begin,end)`
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A column of type `type_id::BOOL8,` with `true` representing predicate is satisfied.
+ */
+
+template <typename InputIterator, typename Predicate>
+std::unique_ptr<column> true_if(InputIterator begin,
+                                InputIterator end,
+                                size_type size,
+                                Predicate p,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  auto output =
+    make_numeric_column(data_type(type_id::BOOL8), size, mask_state::UNALLOCATED, stream, mr);
+  auto output_mutable_view = output->mutable_view();
+  auto output_data         = output_mutable_view.data<bool>();
+
+  thrust::transform(rmm::exec_policy(stream), begin, end, output_data, p);
+
+  return output;
+}
+
+/**
+ * @copydoc cudf::unary_operation
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> unary_operation(cudf::column_view const& input,
+                                              cudf::unary_operator op,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::is_valid
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> is_valid(cudf::column_view const& input,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::cast
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> cast(column_view const& input,
+                             data_type type,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::is_nan
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> is_nan(cudf::column_view const& input,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::is_not_nan
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> is_not_nan(cudf::column_view const& input,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/algorithm.cuh b/cpp/include/cudf/detail/utilities/algorithm.cuh
new file mode 100644
index 0000000..fab8617
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/algorithm.cuh
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+
+namespace cudf::detail {
+
+template <typename Iterator, typename T, typename BinaryOp>
+__device__ __forceinline__ T accumulate(Iterator first, Iterator last, T init, BinaryOp op)
+{
+  for (; first != last; ++first) {
+    init = op(std::move(init), *first);
+  }
+  return init;
+}
+
+/**
+ * @copydoc cudf::detail::copy_if_safe(rmm::exec_policy, InputIterator, InputIterator,
+ * OutputIterator, Predicate, rmm::cuda_stream_view)
+ *
+ * @tparam StencilIterator Type of the stencil iterator
+ * @param stencil The beginning of the stencil sequence
+ */
+template <typename InputIterator,
+          typename StencilIterator,
+          typename OutputIterator,
+          typename Predicate>
+OutputIterator copy_if_safe(InputIterator first,
+                            InputIterator last,
+                            StencilIterator stencil,
+                            OutputIterator result,
+                            Predicate pred,
+                            rmm::cuda_stream_view stream)
+{
+  auto const copy_size = std::min(static_cast<std::size_t>(std::distance(first, last)),
+                                  static_cast<std::size_t>(std::numeric_limits<int>::max()));
+
+  auto itr = first;
+  while (itr != last) {
+    auto const copy_end =
+      static_cast<std::size_t>(std::distance(itr, last)) <= copy_size ? last : itr + copy_size;
+    result = thrust::copy_if(rmm::exec_policy(stream), itr, copy_end, stencil, result, pred);
+    stencil += std::distance(itr, copy_end);
+    itr = copy_end;
+  }
+  return result;
+}
+
+/**
+ * @brief Utility for calling `thrust::copy_if`.
+ *
+ * This is a proxy for `thrust::copy_if` which is a workaround for its bug
+ * (https://github.com/NVIDIA/thrust/issues/1302) where it cannot iterate over int-max values
+ * `distance(first,last) > int-max` This calls thrust::copy_if in 2B chunks instead.
+ *
+ * @tparam InputIterator Type of the input iterator
+ * @tparam OutputIterator Type of the output iterator
+ * @tparam Predicate Type of the binary predicate used to determine elements to copy
+ *
+ * @param first The beginning of the sequence from which to copy
+ * @param last The end of the sequence from which to copy
+ * @param result The beginning of the sequence into which to copy
+ * @param pred The predicate to test on every value of the range `[first, last)`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return An iterator pointing to the position `result + n`, where `n` is equal to the number of
+ *         times `pred` evaluated to `true` in the range `[first, last)`.
+ */
+template <typename InputIterator, typename OutputIterator, typename Predicate>
+OutputIterator copy_if_safe(InputIterator first,
+                            InputIterator last,
+                            OutputIterator result,
+                            Predicate pred,
+                            rmm::cuda_stream_view stream)
+{
+  return copy_if_safe(first, last, first, result, pred, stream);
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/utilities/alignment.hpp b/cpp/include/cudf/detail/utilities/alignment.hpp
new file mode 100644
index 0000000..e52032f
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/alignment.hpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <memory>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Returns the aligned address for holding array of type T in pre-allocated memory.
+ *
+ * @tparam T The data type to align upon.
+ *
+ * @param destination pointer to pre-allocated contiguous storage to store type T.
+ * @return Pointer of type T, aligned to alignment of type T.
+ */
+template <typename T>
+T* align_ptr_for_type(void* destination)
+{
+  constexpr std::size_t bytes_needed{sizeof(T)};
+  constexpr std::size_t alignment{alignof(T)};
+
+  // pad the allocation for aligning the first pointer
+  auto padded_bytes_needed = bytes_needed + (alignment - 1);
+  // std::align captures last argument by reference and modifies it, but we don't want it modified
+  return reinterpret_cast<T*>(
+    std::align(alignment, bytes_needed, destination, padded_bytes_needed));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/assert.cuh b/cpp/include/cudf/detail/utilities/assert.cuh
new file mode 100644
index 0000000..da2bc3d
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/assert.cuh
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda_runtime.h>
+
+/**
+ * @brief `assert`-like macro for device code
+ *
+ * This is effectively the same as the standard `assert` macro, except it
+ * relies on the `__PRETTY_FUNCTION__` macro which is specific to GCC and Clang
+ * to produce better assert messages.
+ */
+#if !defined(NDEBUG) && defined(__CUDA_ARCH__) && (defined(__clang__) || defined(__GNUC__))
+#define __ASSERT_STR_HELPER(x) #x
+#define cudf_assert(e)        \
+  ((e) ? static_cast<void>(0) \
+       : __assert_fail(__ASSERT_STR_HELPER(e), __FILE__, __LINE__, __PRETTY_FUNCTION__))
+#else
+#define cudf_assert(e) (static_cast<void>(0))
+#endif
+
+/**
+ * @brief Macro indicating that a location in the code is unreachable.
+ *
+ * The CUDF_UNREACHABLE macro should only be used where CUDF_FAIL cannot be used
+ * due to performance or due to being used in device code. In the majority of
+ * host code situations, an exception should be thrown in "unreachable" code
+ * paths as those usually aren't tight inner loops like they are in device code.
+ *
+ * One example where this macro may be used is in conjunction with dispatchers
+ * to indicate that a function does not need to return a default value because
+ * it has already exhausted all possible cases in a `switch` statement.
+ *
+ * The assert in this macro can be used when compiling in debug mode to help
+ * debug functions that may reach the supposedly unreachable code.
+ *
+ * Example usage:
+ * ```
+ * CUDF_UNREACHABLE("Invalid type_id.");
+ * ```
+ */
+#define CUDF_UNREACHABLE(msg)             \
+  do {                                    \
+    assert(false && "Unreachable: " msg); \
+    __builtin_unreachable();              \
+  } while (0)
diff --git a/cpp/include/cudf/detail/utilities/cuda.cuh b/cpp/include/cudf/detail/utilities/cuda.cuh
new file mode 100644
index 0000000..264302d
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/cuda.cuh
@@ -0,0 +1,233 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cub/cub.cuh>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Size of a warp in a CUDA kernel.
+ */
+static constexpr size_type warp_size{32};
+
+/**
+ * @brief A kernel grid configuration construction gadget for simple
+ * one-dimensional kernels, with protection against integer overflow.
+ */
+class grid_1d {
+ public:
+  int const num_threads_per_block;
+  int const num_blocks;
+  /**
+   * @param overall_num_elements The number of elements the kernel needs to
+   * handle/process, in its main, one-dimensional/linear input (e.g. one or more
+   * cuDF columns)
+   * @param num_threads_per_block The grid block size, determined according to
+   * the kernel's specific features (amount of shared memory necessary, SM
+   * functional units use pattern etc.); this can't be determined
+   * generically/automatically (as opposed to the number of blocks)
+   * @param elements_per_thread Typically, a single kernel thread processes more
+   * than a single element; this affects the number of threads the grid must
+   * contain
+   */
+  grid_1d(cudf::size_type overall_num_elements,
+          cudf::size_type num_threads_per_block,
+          cudf::size_type elements_per_thread = 1)
+    : num_threads_per_block(num_threads_per_block),
+      num_blocks(util::div_rounding_up_safe(overall_num_elements,
+                                            elements_per_thread * num_threads_per_block))
+  {
+    CUDF_EXPECTS(num_threads_per_block > 0, "num_threads_per_block must be > 0");
+    CUDF_EXPECTS(num_blocks > 0, "num_blocks must be > 0");
+  }
+
+  /**
+   * @brief Returns the global thread index in a 1D grid.
+   *
+   * The returned index is unique across the entire grid.
+   *
+   * @param thread_id The thread index within the block
+   * @param block_id The block index within the grid
+   * @param num_threads_per_block The number of threads per block
+   * @return thread_index_type The global thread index
+   */
+  static constexpr thread_index_type global_thread_id(thread_index_type thread_id,
+                                                      thread_index_type block_id,
+                                                      thread_index_type num_threads_per_block)
+  {
+    return thread_id + block_id * num_threads_per_block;
+  }
+
+  /**
+   * @brief Returns the global thread index of the current thread in a 1D grid.
+   *
+   * @return thread_index_type The global thread index
+   */
+  static __device__ thread_index_type global_thread_id()
+  {
+    return global_thread_id(threadIdx.x, blockIdx.x, blockDim.x);
+  }
+
+  /**
+   * @brief Returns the stride of a 1D grid.
+   *
+   * The returned stride is the total number of threads in the grid.
+   *
+   * @param thread_id The thread index within the block
+   * @param block_id The block index within the grid
+   * @param num_threads_per_block The number of threads per block
+   * @return thread_index_type The global thread index
+   */
+  static constexpr thread_index_type grid_stride(thread_index_type num_threads_per_block,
+                                                 thread_index_type num_blocks_per_grid)
+  {
+    return num_threads_per_block * num_blocks_per_grid;
+  }
+
+  /**
+   * @brief Returns the stride of the current 1D grid.
+   *
+   * @return thread_index_type The number of threads in the grid.
+   */
+  static __device__ thread_index_type grid_stride() { return grid_stride(blockDim.x, gridDim.x); }
+};
+
+/**
+ * @brief Performs a sum reduction of values from the same lane across all
+ * warps in a thread block and returns the result on thread 0 of the block.
+ *
+ * All threads in a block must call this function, but only values from the
+ * threads indicated by `leader_lane` will contribute to the result. Similarly,
+ * the returned result is only defined on `threadIdx.x==0`.
+ *
+ * @tparam block_size The number of threads in the thread block (must be less
+ * than or equal to 1024)
+ * @tparam leader_lane The id of the lane in the warp whose value contributes to
+ * the reduction
+ * @tparam T Arithmetic type
+ * @param lane_value The value from the lane that contributes to the reduction
+ * @return The sum reduction of the values from each lane. Only valid on
+ * `threadIdx.x == 0`. The returned value on all other threads is undefined.
+ */
+template <int32_t block_size, int32_t leader_lane = 0, typename T>
+__device__ T single_lane_block_sum_reduce(T lane_value)
+{
+  static_assert(block_size <= 1024, "Invalid block size.");
+  static_assert(std::is_arithmetic_v<T>, "Invalid non-arithmetic type.");
+  constexpr auto warps_per_block{block_size / warp_size};
+  auto const lane_id{threadIdx.x % warp_size};
+  auto const warp_id{threadIdx.x / warp_size};
+  __shared__ T lane_values[warp_size];
+
+  // Load each lane's value into a shared memory array
+  if (lane_id == leader_lane) { lane_values[warp_id] = lane_value; }
+  __syncthreads();
+
+  // Use a single warp to do the reduction, result is only defined on
+  // threadId.x == 0
+  T result{0};
+  if (warp_id == 0) {
+    __shared__ typename cub::WarpReduce<T>::TempStorage temp;
+    lane_value = (lane_id < warps_per_block) ? lane_values[lane_id] : T{0};
+    result     = cub::WarpReduce<T>(temp).Sum(lane_value);
+  }
+  // Shared memory has block scope, so sync here to ensure no data
+  // races between successive calls to this function in the same
+  // kernel.
+  __syncthreads();
+  return result;
+}
+
+/**
+ * @brief Get the number of elements that can be processed per thread.
+ *
+ * @param[in] kernel The kernel for which the elements per thread needs to be assessed
+ * @param[in] total_size Number of elements
+ * @param[in] block_size Expected block size
+ *
+ * @return cudf::size_type Elements per thread that can be processed for given specification.
+ */
+template <typename Kernel>
+cudf::size_type elements_per_thread(Kernel kernel,
+                                    cudf::size_type total_size,
+                                    cudf::size_type block_size,
+                                    cudf::size_type max_per_thread = 32)
+{
+  CUDF_FUNC_RANGE();
+
+  // calculate theoretical occupancy
+  int max_blocks = 0;
+  CUDF_CUDA_TRY(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks, kernel, block_size, 0));
+
+  int device = 0;
+  CUDF_CUDA_TRY(cudaGetDevice(&device));
+  int num_sms = 0;
+  CUDF_CUDA_TRY(cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, device));
+  int per_thread = total_size / (max_blocks * num_sms * block_size);
+  return std::clamp(per_thread, 1, max_per_thread);
+}
+
+/**
+ * @brief Finds the smallest value not less than `number_to_round` and modulo `modulus` is
+ * zero. Expects modulus to be a power of 2.
+ *
+ * @note Does not throw or otherwise verify the user has passed in a modulus that is a
+ * power of 2.
+ *
+ * @param[in] number_to_round The value to be rounded up
+ * @param[in] modulus The modulus to be rounded up to.  Must be a power of 2.
+ *
+ * @return cudf::size_type Elements per thread that can be processed for given specification.
+ */
+template <typename T>
+__device__ inline T round_up_pow2(T number_to_round, T modulus)
+{
+  return (number_to_round + (modulus - 1)) & -modulus;
+}
+
+template <class F>
+__global__ void single_thread_kernel(F f)
+{
+  f();
+}
+
+/**
+ * @brief single thread cuda kernel
+ *
+ * @tparam Functor Device functor type
+ * @param functor device functor object or device lambda function
+ * @param stream CUDA stream used for the kernel launch
+ */
+template <class Functor>
+void device_single_thread(Functor functor, rmm::cuda_stream_view stream)
+{
+  single_thread_kernel<<<1, 1, 0, stream.value()>>>(functor);
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/default_stream.hpp b/cpp/include/cudf/detail/utilities/default_stream.hpp
new file mode 100644
index 0000000..fa438f1
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/default_stream.hpp
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+
+namespace detail {
+
+/**
+ * @brief Default stream for cudf
+ *
+ * Use this value to ensure the correct stream is used when compiled with per
+ * thread default stream.
+ */
+extern rmm::cuda_stream_view const default_stream_value;
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/device_atomics.cuh b/cpp/include/cudf/detail/utilities/device_atomics.cuh
new file mode 100644
index 0000000..c56e88f
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/device_atomics.cuh
@@ -0,0 +1,541 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+/**
+ * @brief overloads for CUDA atomic operations
+ * @file device_atomics.cuh
+ *
+ * Provides the overloads for all of possible cudf's data types,
+ * where cudf's data types are, int8_t, int16_t, int32_t, int64_t, float, double,
+ * cudf::timestamp_D, cudf::timestamp_s, cudf::timestamp_ms, cudf::timestamp_us,
+ * cudf::timestamp_ns, cudf::duration_D, cudf::duration_s, cudf::duration_ms,
+ * cudf::duration_us, cudf::duration_ns and bool
+ * where CUDA atomic operations are, `atomicAdd`, `atomicMin`, `atomicMax`,
+ * `atomicCAS`.
+ * Also provides `cudf::genericAtomicOperation` which performs atomic operation
+ * with the given binary operator.
+ */
+
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+template <typename T_output, typename T_input>
+__forceinline__ __device__ T_output type_reinterpret(T_input value)
+{
+  static_assert(sizeof(T_output) == sizeof(T_input), "type_reinterpret for different size");
+  return *(reinterpret_cast<T_output*>(&value));
+}
+
+// -----------------------------------------------------------------------
+// the implementation of `genericAtomicOperation`
+template <typename T, typename Op, size_t N = sizeof(T)>
+struct genericAtomicOperationImpl;
+
+// single byte atomic operation
+template <typename T, typename Op>
+struct genericAtomicOperationImpl<T, Op, 1> {
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, Op op)
+  {
+    using T_int = unsigned int;
+
+    auto* address_uint32 = reinterpret_cast<T_int*>(addr - (reinterpret_cast<size_t>(addr) & 3));
+    T_int shift          = ((reinterpret_cast<size_t>(addr) & 3) * 8);
+
+    T_int old = *address_uint32;
+    T_int assumed;
+
+    do {
+      assumed                = old;
+      T target_value         = T((old >> shift) & 0xff);
+      uint8_t updating_value = type_reinterpret<uint8_t, T>(op(target_value, update_value));
+      T_int new_value        = (old & ~(0x0000'00ff << shift)) | (T_int(updating_value) << shift);
+      old                    = atomicCAS(address_uint32, assumed, new_value);
+    } while (assumed != old);
+
+    return T((old >> shift) & 0xff);
+  }
+};
+
+// 2 bytes atomic operation
+template <typename T, typename Op>
+struct genericAtomicOperationImpl<T, Op, 2> {
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, Op op)
+  {
+    using T_int      = unsigned int;
+    bool is_32_align = (reinterpret_cast<size_t>(addr) & 2) == 0;
+    auto* address_uint32 =
+      reinterpret_cast<T_int*>(reinterpret_cast<size_t>(addr) - (is_32_align ? 0 : 2));
+
+    T_int old = *address_uint32;
+    T_int assumed;
+
+    do {
+      assumed                 = old;
+      T const target_value    = (is_32_align) ? T(old & 0xffff) : T(old >> 16);
+      uint16_t updating_value = type_reinterpret<uint16_t, T>(op(target_value, update_value));
+
+      T_int const new_value = (is_32_align) ? (old & 0xffff'0000) | updating_value
+                                            : (old & 0xffff) | (T_int(updating_value) << 16);
+      old                   = atomicCAS(address_uint32, assumed, new_value);
+    } while (assumed != old);
+
+    return (is_32_align) ? T(old & 0xffff) : T(old >> 16);
+    ;
+  }
+};
+
+// 4 bytes atomic operation
+template <typename T, typename Op>
+struct genericAtomicOperationImpl<T, Op, 4> {
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, Op op)
+  {
+    using T_int = unsigned int;
+
+    T old_value = *addr;
+    T_int assumed;
+    T_int ret;
+
+    do {
+      T_int const new_value = type_reinterpret<T_int, T>(op(old_value, update_value));
+
+      assumed   = type_reinterpret<T_int, T>(old_value);
+      ret       = atomicCAS(reinterpret_cast<T_int*>(addr), assumed, new_value);
+      old_value = type_reinterpret<T, T_int>(ret);
+
+    } while (assumed != ret);
+
+    return old_value;
+  }
+};
+
+// 8 bytes atomic operation
+template <typename T, typename Op>
+struct genericAtomicOperationImpl<T, Op, 8> {
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, Op op)
+  {
+    using T_int = unsigned long long int;
+    static_assert(sizeof(T) == sizeof(T_int));
+
+    T old_value = *addr;
+    T_int assumed;
+    T_int ret;
+
+    do {
+      T_int const new_value = type_reinterpret<T_int, T>(op(old_value, update_value));
+
+      assumed   = type_reinterpret<T_int, T>(old_value);
+      ret       = atomicCAS(reinterpret_cast<T_int*>(addr), assumed, new_value);
+      old_value = type_reinterpret<T, T_int>(ret);
+
+    } while (assumed != ret);
+
+    return old_value;
+  }
+};
+
+// -----------------------------------------------------------------------
+// specialized functions for operators
+// `atomicAdd` supports int32, float, double (signed int64 is not supported.)
+// `atomicMin`, `atomicMax` support int32_t, int64_t
+template <>
+struct genericAtomicOperationImpl<float, DeviceSum, 4> {
+  using T = float;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceSum op)
+  {
+    return atomicAdd(addr, update_value);
+  }
+};
+
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600)
+// `atomicAdd(double)` is supported after cuda architecture 6.0
+template <>
+struct genericAtomicOperationImpl<double, DeviceSum, 8> {
+  using T = double;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceSum op)
+  {
+    return atomicAdd(addr, update_value);
+  }
+};
+#endif
+
+template <>
+struct genericAtomicOperationImpl<int32_t, DeviceSum, 4> {
+  using T = int32_t;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceSum op)
+  {
+    return atomicAdd(addr, update_value);
+  }
+};
+
+// Cuda natively supports `unsigned long long int` for `atomicAdd`,
+// but doesn't supports `signed long long int`.
+// However, since the signed integer is represented as Two's complement,
+// the fundamental arithmetic operations of addition are identical to
+// those for unsigned binary numbers.
+// Then, this computes as `unsigned long long int` with `atomicAdd`
+// @sa https://en.wikipedia.org/wiki/Two%27s_complement
+template <>
+struct genericAtomicOperationImpl<int64_t, DeviceSum, 8> {
+  using T = int64_t;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceSum op)
+  {
+    using T_int = unsigned long long int;
+    static_assert(sizeof(T) == sizeof(T_int));
+    T ret = atomicAdd(reinterpret_cast<T_int*>(addr), type_reinterpret<T_int, T>(update_value));
+    return ret;
+  }
+};
+
+template <>
+struct genericAtomicOperationImpl<int32_t, DeviceMin, 4> {
+  using T = int32_t;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceMin op)
+  {
+    return atomicMin(addr, update_value);
+  }
+};
+
+template <>
+struct genericAtomicOperationImpl<int32_t, DeviceMax, 4> {
+  using T = int32_t;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceMax op)
+  {
+    return atomicMax(addr, update_value);
+  }
+};
+
+template <>
+struct genericAtomicOperationImpl<int64_t, DeviceMin, 8> {
+  using T = int64_t;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceMin op)
+  {
+    using T_int = long long int;
+    static_assert(sizeof(T) == sizeof(T_int));
+    T ret = atomicMin(reinterpret_cast<T_int*>(addr), type_reinterpret<T_int, T>(update_value));
+    return ret;
+  }
+};
+
+template <>
+struct genericAtomicOperationImpl<int64_t, DeviceMax, 8> {
+  using T = int64_t;
+  __forceinline__ __device__ T operator()(T* addr, T const& update_value, DeviceMax op)
+  {
+    using T_int = long long int;
+    static_assert(sizeof(T) == sizeof(T_int));
+    T ret = atomicMax(reinterpret_cast<T_int*>(addr), type_reinterpret<T_int, T>(update_value));
+    return ret;
+  }
+};
+// -----------------------------------------------------------------------
+// the implementation of `typesAtomicCASImpl`
+template <typename T, size_t N = sizeof(T)>
+struct typesAtomicCASImpl;
+
+template <typename T>
+struct typesAtomicCASImpl<T, 1> {
+  __forceinline__ __device__ T operator()(T* addr, T const& compare, T const& update_value)
+  {
+    using T_int = unsigned int;
+
+    T_int shift          = ((reinterpret_cast<size_t>(addr) & 3) * 8);
+    auto* address_uint32 = reinterpret_cast<T_int*>(addr - (reinterpret_cast<size_t>(addr) & 3));
+
+    // the 'target_value' in `old` can be different from `compare`
+    // because other thread may update the value
+    // before fetching a value from `address_uint32` in this function
+    T_int old = *address_uint32;
+    T_int assumed;
+    T target_value;
+    uint8_t u_val = type_reinterpret<uint8_t, T>(update_value);
+
+    do {
+      assumed      = old;
+      target_value = T((old >> shift) & 0xff);
+      // have to compare `target_value` and `compare` before calling atomicCAS
+      // the `target_value` in `old` can be different with `compare`
+      if (target_value != compare) break;
+
+      T_int new_value = (old & ~(0x0000'00ff << shift)) | (T_int(u_val) << shift);
+      old             = atomicCAS(address_uint32, assumed, new_value);
+    } while (assumed != old);
+
+    return target_value;
+  }
+};
+
+template <typename T>
+struct typesAtomicCASImpl<T, 2> {
+  __forceinline__ __device__ T operator()(T* addr, T const& compare, T const& update_value)
+  {
+    using T_int = unsigned int;
+
+    bool is_32_align = (reinterpret_cast<size_t>(addr) & 2) == 0;
+    auto* address_uint32 =
+      reinterpret_cast<T_int*>(reinterpret_cast<size_t>(addr) - (is_32_align ? 0 : 2));
+
+    T_int old = *address_uint32;
+    T_int assumed;
+    T target_value;
+    uint16_t u_val = type_reinterpret<uint16_t, T>(update_value);
+
+    do {
+      assumed      = old;
+      target_value = (is_32_align) ? T(old & 0xffff) : T(old >> 16);
+      if (target_value != compare) break;
+
+      T_int new_value =
+        (is_32_align) ? (old & 0xffff'0000) | u_val : (old & 0xffff) | (T_int(u_val) << 16);
+      old = atomicCAS(address_uint32, assumed, new_value);
+    } while (assumed != old);
+
+    return target_value;
+  }
+};
+
+template <typename T>
+struct typesAtomicCASImpl<T, 4> {
+  __forceinline__ __device__ T operator()(T* addr, T const& compare, T const& update_value)
+  {
+    using T_int = unsigned int;
+
+    T_int ret = atomicCAS(reinterpret_cast<T_int*>(addr),
+                          type_reinterpret<T_int, T>(compare),
+                          type_reinterpret<T_int, T>(update_value));
+
+    return type_reinterpret<T, T_int>(ret);
+  }
+};
+
+// 8 bytes atomic operation
+template <typename T>
+struct typesAtomicCASImpl<T, 8> {
+  __forceinline__ __device__ T operator()(T* addr, T const& compare, T const& update_value)
+  {
+    using T_int = unsigned long long int;
+    static_assert(sizeof(T) == sizeof(T_int));
+
+    T_int ret = atomicCAS(reinterpret_cast<T_int*>(addr),
+                          type_reinterpret<T_int, T>(compare),
+                          type_reinterpret<T_int, T>(update_value));
+
+    return type_reinterpret<T, T_int>(ret);
+  }
+};
+
+}  // namespace detail
+
+/**
+ * @brief compute atomic binary operation
+ * reads the `old` located at the `address` in global or shared memory,
+ * computes 'BinaryOp'('old', 'update_value'),
+ * and stores the result back to memory at the same address.
+ * These three operations are performed in one atomic transaction.
+ *
+ * The supported cudf types for `genericAtomicOperation` are:
+ * int8_t, int16_t, int32_t, int64_t, float, double
+ *
+ * @param[in] address The address of old value in global or shared memory
+ * @param[in] val The value to be computed
+ * @param[in] op  The binary operator used for compute
+ *
+ * @returns The old value at `address`
+ */
+template <typename T, typename BinaryOp>
+std::enable_if_t<cudf::is_numeric<T>(), T> __forceinline__ __device__
+genericAtomicOperation(T* address, T const& update_value, BinaryOp op)
+{
+  auto fun = cudf::detail::genericAtomicOperationImpl<T, BinaryOp>{};
+  return T(fun(address, update_value, op));
+}
+
+// specialization for cudf::detail::timestamp types
+template <typename T, typename BinaryOp>
+std::enable_if_t<cudf::is_timestamp<T>(), T> __forceinline__ __device__
+genericAtomicOperation(T* address, T const& update_value, BinaryOp op)
+{
+  using R = typename T::rep;
+  // Unwrap the input timestamp to its underlying duration value representation.
+  // Use the underlying representation's type to apply operation for the cudf::detail::timestamp
+  auto update_value_rep = update_value.time_since_epoch().count();
+  auto fun              = cudf::detail::genericAtomicOperationImpl<R, BinaryOp>{};
+  return T{T::duration(fun(reinterpret_cast<R*>(address), update_value_rep, op))};
+}
+
+// specialization for cudf::detail::duration types
+template <typename T, typename BinaryOp>
+std::enable_if_t<cudf::is_duration<T>(), T> __forceinline__ __device__
+genericAtomicOperation(T* address, T const& update_value, BinaryOp op)
+{
+  using R = typename T::rep;
+  // Unwrap the input duration to its underlying duration value representation.
+  // Use the underlying representation's type to apply operation for the cudf::detail::duration
+  auto update_value_rep = update_value.count();
+  auto fun              = cudf::detail::genericAtomicOperationImpl<R, BinaryOp>{};
+  return T(fun(reinterpret_cast<R*>(address), update_value_rep, op));
+}
+
+// specialization for bool types
+template <typename BinaryOp>
+__forceinline__ __device__ bool genericAtomicOperation(bool* address,
+                                                       bool const& update_value,
+                                                       BinaryOp op)
+{
+  using T = bool;
+  // don't use underlying type to apply operation for bool
+  auto fun = cudf::detail::genericAtomicOperationImpl<T, BinaryOp>{};
+  return T(fun(address, update_value, op));
+}
+
+}  // namespace cudf
+
+/**
+ * @brief Overloads for `atomicAdd`
+ * reads the `old` located at the `address` in global or shared memory,
+ * computes (old + val), and stores the result back to memory at the same
+ * address. These three operations are performed in one atomic transaction.
+ *
+ * The supported cudf types for `atomicAdd` are:
+ * int8_t, int16_t, int32_t, int64_t, float, double,
+ * cudf::timestamp_D, cudf::timestamp_s, cudf::timestamp_ms cudf::timestamp_us,
+ * cudf::timestamp_ns, cudf::duration_D, cudf::duration_s, cudf::duration_ms,
+ * cudf::duration_us, cudf::duration_ns and bool
+ *
+ * Cuda natively supports `sint32`, `uint32`, `uint64`, `float`, `double.
+ * (`double` is supported after Pascal).
+ * Other types are implemented by `atomicCAS`.
+ *
+ * @param[in] address The address of old value in global or shared memory
+ * @param[in] val The value to be added
+ *
+ * @returns The old value at `address`
+ */
+template <typename T>
+__forceinline__ __device__ T atomicAdd(T* address, T val)
+{
+  return cudf::genericAtomicOperation(address, val, cudf::DeviceSum{});
+}
+
+/**
+ * @brief Overloads for `atomicMul`
+ * reads the `old` located at the `address` in global or shared memory,
+ * computes (old * val), and stores the result back to memory at the same
+ * address. These three operations are performed in one atomic transaction.
+ *
+ * The supported cudf types for `atomicMul` are:
+ * int8_t, int16_t, int32_t, int64_t, float, double, and bool
+ *
+ * All types are implemented by `atomicCAS`.
+ *
+ * @param[in] address The address of old value in global or shared memory
+ * @param[in] val The value to be multiplied
+ *
+ * @returns The old value at `address`
+ */
+template <typename T>
+__forceinline__ __device__ T atomicMul(T* address, T val)
+{
+  return cudf::genericAtomicOperation(address, val, cudf::DeviceProduct{});
+}
+
+/**
+ * @brief Overloads for `atomicMin`
+ * reads the `old` located at the `address` in global or shared memory,
+ * computes the minimum of old and val, and stores the result back to memory
+ * at the same address.
+ * These three operations are performed in one atomic transaction.
+ *
+ * The supported cudf types for `atomicMin` are:
+ * int8_t, int16_t, int32_t, int64_t, float, double,
+ * cudf::timestamp_D, cudf::timestamp_s, cudf::timestamp_ms, cudf::timestamp_us,
+ * cudf::timestamp_ns, cudf::duration_D, cudf::duration_s, cudf::duration_ms,
+ * cudf::duration_us, cudf::duration_ns and bool
+ * Cuda natively supports `sint32`, `uint32`, `sint64`, `uint64`.
+ * Other types are implemented by `atomicCAS`.
+ *
+ * @param[in] address The address of old value in global or shared memory
+ * @param[in] val The value to be computed
+ *
+ * @returns The old value at `address`
+ */
+template <typename T>
+__forceinline__ __device__ T atomicMin(T* address, T val)
+{
+  return cudf::genericAtomicOperation(address, val, cudf::DeviceMin{});
+}
+
+/**
+ * @brief Overloads for `atomicMax`
+ * reads the `old` located at the `address` in global or shared memory,
+ * computes the maximum of old and val, and stores the result back to memory
+ * at the same address.
+ * These three operations are performed in one atomic transaction.
+ *
+ * The supported cudf types for `atomicMax` are:
+ * int8_t, int16_t, int32_t, int64_t, float, double,
+ * cudf::timestamp_D, cudf::timestamp_s, cudf::timestamp_ms, cudf::timestamp_us,
+ * cudf::timestamp_ns, cudf::duration_D, cudf::duration_s, cudf::duration_ms,
+ * cudf::duration_us, cudf::duration_ns and bool
+ * Cuda natively supports `sint32`, `uint32`, `sint64`, `uint64`.
+ * Other types are implemented by `atomicCAS`.
+ *
+ * @param[in] address The address of old value in global or shared memory
+ * @param[in] val The value to be computed
+ *
+ * @returns The old value at `address`
+ */
+template <typename T>
+__forceinline__ __device__ T atomicMax(T* address, T val)
+{
+  return cudf::genericAtomicOperation(address, val, cudf::DeviceMax{});
+}
+
+/**
+ * @brief Overloads for `atomicCAS`
+ * reads the `old` located at the `address` in global or shared memory,
+ * computes (`old` == `compare` ? `val` : `old`),
+ * and stores the result back to memory at the same address.
+ * These three operations are performed in one atomic transaction.
+ *
+ * The supported cudf types for `atomicCAS` are:
+ * int8_t, int16_t, int32_t, int64_t, float, double,
+ * cudf::timestamp_D, cudf::timestamp_s, cudf::timestamp_ms, cudf::timestamp_us,
+ * cudf::timestamp_ns, cudf::duration_D, cudf::duration_s, cudf::duration_ms,
+ * cudf::duration_us, cudf::duration_ns and bool
+ * Cuda natively supports `sint32`, `uint32`, `uint64`.
+ * Other types are implemented by `atomicCAS`.
+ *
+ * @param[in] address The address of old value in global or shared memory
+ * @param[in] compare The value to be compared
+ * @param[in] val The value to be computed
+ *
+ * @returns The old value at `address`
+ */
+template <typename T>
+__forceinline__ __device__ T atomicCAS(T* address, T compare, T val)
+{
+  return cudf::detail::typesAtomicCASImpl<T>()(address, compare, val);
+}
diff --git a/cpp/include/cudf/detail/utilities/device_operators.cuh b/cpp/include/cudf/detail/utilities/device_operators.cuh
new file mode 100644
index 0000000..46f424e
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/device_operators.cuh
@@ -0,0 +1,242 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+/**
+ * @brief Definition of the device operators
+ * @file
+ */
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/fixed_point/temporary.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief SFINAE enabled min function suitable for std::is_invocable
+ */
+template <typename LHS,
+          typename RHS,
+          std::enable_if_t<cudf::is_relationally_comparable<LHS, RHS>()>* = nullptr>
+CUDF_HOST_DEVICE inline auto min(LHS const& lhs, RHS const& rhs)
+{
+  return std::min(lhs, rhs);
+}
+
+/**
+ * @brief SFINAE enabled max function suitable for std::is_invocable
+ */
+template <typename LHS,
+          typename RHS,
+          std::enable_if_t<cudf::is_relationally_comparable<LHS, RHS>()>* = nullptr>
+CUDF_HOST_DEVICE inline auto max(LHS const& lhs, RHS const& rhs)
+{
+  return std::max(lhs, rhs);
+}
+}  // namespace detail
+
+/**
+ * @brief Binary `sum` operator
+ */
+struct DeviceSum {
+  template <typename T, std::enable_if_t<!cudf::is_timestamp<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline auto operator()(T const& lhs, T const& rhs) -> decltype(lhs + rhs)
+  {
+    return lhs + rhs;
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    return T{typename T::duration{0}};
+  }
+
+  template <typename T,
+            std::enable_if_t<!cudf::is_timestamp<T>() && !cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    return T{0};
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    CUDF_FAIL("fixed_point does not yet support device operator identity");
+    return T{};
+  }
+};
+
+/**
+ * @brief `count` operator - used in rolling windows
+ */
+struct DeviceCount {
+  template <typename T, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline T operator()(T const& lhs, T const& rhs)
+  {
+    return T{DeviceCount{}(lhs.time_since_epoch(), rhs.time_since_epoch())};
+  }
+
+  template <typename T, std::enable_if_t<!cudf::is_timestamp<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline T operator()(T const&, T const& rhs)
+  {
+    return rhs + T{1};
+  }
+
+  template <typename T>
+  static constexpr T identity()
+  {
+    return T{};
+  }
+};
+
+/**
+ * @brief binary `min` operator
+ */
+struct DeviceMin {
+  template <typename T>
+  CUDF_HOST_DEVICE inline auto operator()(T const& lhs, T const& rhs)
+    -> decltype(cudf::detail::min(lhs, rhs))
+  {
+    return numeric::detail::min(lhs, rhs);
+  }
+
+  template <typename T,
+            std::enable_if_t<!std::is_same_v<T, cudf::string_view> && !cudf::is_dictionary<T>() &&
+                             !cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    // chrono types do not have std::numeric_limits specializations and should use T::max()
+    // https://eel.is/c++draft/numeric.limits.general#6
+    if constexpr (cudf::is_chrono<T>()) {
+      return T::max();
+    } else if constexpr (cuda::std::numeric_limits<T>::has_infinity) {
+      return cuda::std::numeric_limits<T>::infinity();
+    } else {
+      return cuda::std::numeric_limits<T>::max();
+    }
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    CUDF_FAIL("fixed_point does not yet support DeviceMin identity");
+    return cuda::std::numeric_limits<T>::max();
+  }
+
+  // @brief identity specialized for string_view
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>* = nullptr>
+  CUDF_HOST_DEVICE inline static constexpr T identity()
+  {
+    return string_view::max();
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_dictionary<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    return static_cast<T>(T::max_value());
+  }
+};
+
+/**
+ * @brief binary `max` operator
+ */
+struct DeviceMax {
+  template <typename T>
+  CUDF_HOST_DEVICE inline auto operator()(T const& lhs, T const& rhs)
+    -> decltype(cudf::detail::max(lhs, rhs))
+  {
+    return numeric::detail::max(lhs, rhs);
+  }
+
+  template <typename T,
+            std::enable_if_t<!std::is_same_v<T, cudf::string_view> && !cudf::is_dictionary<T>() &&
+                             !cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    // chrono types do not have std::numeric_limits specializations and should use T::min()
+    // https://eel.is/c++draft/numeric.limits.general#6
+    if constexpr (cudf::is_chrono<T>()) {
+      return T::min();
+    } else if constexpr (cuda::std::numeric_limits<T>::has_infinity) {
+      return -cuda::std::numeric_limits<T>::infinity();
+    } else {
+      return cuda::std::numeric_limits<T>::lowest();
+    }
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    CUDF_FAIL("fixed_point does not yet support DeviceMax identity");
+    return cuda::std::numeric_limits<T>::lowest();
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>* = nullptr>
+  CUDF_HOST_DEVICE inline static constexpr T identity()
+  {
+    return string_view::min();
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_dictionary<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    return static_cast<T>(T::lowest_value());
+  }
+};
+
+/**
+ * @brief binary `product` operator
+ */
+struct DeviceProduct {
+  template <typename T, std::enable_if_t<!cudf::is_timestamp<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline auto operator()(T const& lhs, T const& rhs) -> decltype(lhs * rhs)
+  {
+    return lhs * rhs;
+  }
+
+  template <typename T, std::enable_if_t<!cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    return T{1};
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  static constexpr T identity()
+  {
+    CUDF_FAIL("fixed_point does not yet support DeviceProduct identity");
+    return T{1, numeric::scale_type{0}};
+  }
+};
+
+/**
+ * @brief Operator for calculating Lead/Lag window function.
+ */
+struct DeviceLeadLag {
+  size_type const row_offset;
+
+  explicit CUDF_HOST_DEVICE inline DeviceLeadLag(size_type offset_) : row_offset(offset_) {}
+};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/element_argminmax.cuh b/cpp/include/cudf/detail/utilities/element_argminmax.cuh
new file mode 100644
index 0000000..45b5627
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/element_argminmax.cuh
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Binary `argmin`/`argmax` operator
+ *
+ * @tparam T Type of the underlying column. Must support '<' operator.
+ */
+template <typename T>
+struct element_argminmax_fn {
+  column_device_view const d_col;
+  bool const has_nulls;
+  bool const arg_min;
+
+  __device__ inline auto operator()(size_type const& lhs_idx, size_type const& rhs_idx) const
+  {
+    // The extra bounds checking is due to issue github.com/rapidsai/cudf/9156 and
+    // github.com/NVIDIA/thrust/issues/1525
+    // where invalid random values may be passed here by thrust::reduce_by_key
+    auto out_of_bound_or_null = [this] __device__(size_type const& idx) {
+      return idx < 0 || idx >= this->d_col.size() ||
+             (this->has_nulls && this->d_col.is_null_nocheck(idx));
+    };
+    if (out_of_bound_or_null(lhs_idx)) { return rhs_idx; }
+    if (out_of_bound_or_null(rhs_idx)) { return lhs_idx; }
+
+    // Return `lhs_idx` iff:
+    //   row(lhs_idx) <  row(rhs_idx) and finding ArgMin, or
+    //   row(lhs_idx) >= row(rhs_idx) and finding ArgMax.
+    auto const less = d_col.element<T>(lhs_idx) < d_col.element<T>(rhs_idx);
+    return less == arg_min ? lhs_idx : rhs_idx;
+  }
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/int_fastdiv.h b/cpp/include/cudf/detail/utilities/int_fastdiv.h
new file mode 100644
index 0000000..ff442af
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/int_fastdiv.h
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2014 Maxim Milakov
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+class int_fastdiv {
+ public:
+  // divisor != 0
+  __host__ __device__ __forceinline__ int_fastdiv(int divisor = 0) : d(divisor)
+  {
+    update_magic_numbers();
+  }
+
+  __host__ __device__ __forceinline__ int_fastdiv& operator=(int divisor)
+  {
+    this->d = divisor;
+    update_magic_numbers();
+    return *this;
+  }
+
+  __host__ __device__ __forceinline__ operator int() const { return d; }
+
+ private:
+  int d;
+  int M;
+  int s;
+  int n_add_sign;
+
+  // Hacker's Delight, Second Edition, Chapter 10, Integer Division By Constants
+  __host__ __device__ __forceinline__ void update_magic_numbers()
+  {
+    if (d == 1) {
+      M          = 0;
+      s          = -1;
+      n_add_sign = 1;
+      return;
+    } else if (d == -1) {
+      M          = 0;
+      s          = -1;
+      n_add_sign = -1;
+      return;
+    }
+
+    int p;
+    unsigned int ad, anc, delta, q1, r1, q2, r2, t;
+    unsigned const two31 = 0x8000'0000u;
+    ad                   = (d == 0) ? 1 : abs(d);
+    t                    = two31 + ((unsigned int)d >> 31);
+    anc                  = t - 1 - t % ad;
+    p                    = 31;
+    q1                   = two31 / anc;
+    r1                   = two31 - q1 * anc;
+    q2                   = two31 / ad;
+    r2                   = two31 - q2 * ad;
+    do {
+      ++p;
+      q1 = 2 * q1;
+      r1 = 2 * r1;
+      if (r1 >= anc) {
+        ++q1;
+        r1 -= anc;
+      }
+      q2 = 2 * q2;
+      r2 = 2 * r2;
+      if (r2 >= ad) {
+        ++q2;
+        r2 -= ad;
+      }
+      delta = ad - r2;
+    } while (q1 < delta || (q1 == delta && r1 == 0));
+    this->M = q2 + 1;
+    if (d < 0) this->M = -this->M;
+    this->s = p - 32;
+
+    if ((d > 0) && (M < 0))
+      n_add_sign = 1;
+    else if ((d < 0) && (M > 0))
+      n_add_sign = -1;
+    else
+      n_add_sign = 0;
+  }
+
+  __host__ __device__ __forceinline__ friend int operator/(int const divident,
+                                                           int_fastdiv const& divisor);
+};
+
+__host__ __device__ __forceinline__ int operator/(int const n, int_fastdiv const& divisor)
+{
+  int q;
+#ifdef __CUDA_ARCH__
+  asm("mul.hi.s32 %0, %1, %2;" : "=r"(q) : "r"(divisor.M), "r"(n));
+#else
+  q = (((unsigned long long)((long long)divisor.M * (long long)n)) >> 32);
+#endif
+  q += n * divisor.n_add_sign;
+  if (divisor.s >= 0) {
+    q >>= divisor.s;  // we rely on this to be implemented as arithmetic shift
+    q += (((unsigned int)q) >> 31);
+  }
+  return q;
+}
+
+__host__ __device__ __forceinline__ int operator%(int const n, int_fastdiv const& divisor)
+{
+  int quotient  = n / divisor;
+  int remainder = n - quotient * divisor;
+  return remainder;
+}
+
+__host__ __device__ __forceinline__ int operator/(unsigned int const n, int_fastdiv const& divisor)
+{
+  return ((int)n) / divisor;
+}
+
+__host__ __device__ __forceinline__ int operator%(unsigned int const n, int_fastdiv const& divisor)
+{
+  return ((int)n) % divisor;
+}
+
+__host__ __device__ __forceinline__ int operator/(short const n, int_fastdiv const& divisor)
+{
+  return ((int)n) / divisor;
+}
+
+__host__ __device__ __forceinline__ int operator%(short const n, int_fastdiv const& divisor)
+{
+  return ((int)n) % divisor;
+}
+
+__host__ __device__ __forceinline__ int operator/(unsigned short const n,
+                                                  int_fastdiv const& divisor)
+{
+  return ((int)n) / divisor;
+}
+
+__host__ __device__ __forceinline__ int operator%(unsigned short const n,
+                                                  int_fastdiv const& divisor)
+{
+  return ((int)n) % divisor;
+}
+
+__host__ __device__ __forceinline__ int operator/(char const n, int_fastdiv const& divisor)
+{
+  return ((int)n) / divisor;
+}
+
+__host__ __device__ __forceinline__ int operator%(char const n, int_fastdiv const& divisor)
+{
+  return ((int)n) % divisor;
+}
+
+__host__ __device__ __forceinline__ int operator/(unsigned char const n, int_fastdiv const& divisor)
+{
+  return ((int)n) / divisor;
+}
+
+__host__ __device__ __forceinline__ int operator%(unsigned char const n, int_fastdiv const& divisor)
+{
+  return ((int)n) % divisor;
+}
diff --git a/cpp/include/cudf/detail/utilities/integer_utils.hpp b/cpp/include/cudf/detail/utilities/integer_utils.hpp
new file mode 100644
index 0000000..8b709f2
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/integer_utils.hpp
@@ -0,0 +1,193 @@
+/*
+ * Copyright 2019 BlazingDB, Inc.
+ *     Copyright 2019 Eyal Rozenberg <eyalroz@blazingdb.com>
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+/**
+ * @file Utility code involving integer arithmetic
+ */
+
+#include <cudf/fixed_point/temporary.hpp>
+
+#include <cmath>
+#include <cstdlib>
+#include <stdexcept>
+#include <type_traits>
+
+namespace cudf {
+//! Utility functions
+namespace util {
+/**
+ * @brief Rounds `number_to_round` up to the next multiple of modulus
+ *
+ * @tparam S type to return
+ * @param number_to_round number that is being rounded
+ * @param modulus value to which to round
+ * @return smallest integer greater than `number_to_round` and modulo `S` is zero.
+ *
+ * @note This function assumes that `number_to_round` is non-negative and
+ * `modulus` is positive. The safety is in regard to rollover.
+ */
+template <typename S>
+constexpr S round_up_safe(S number_to_round, S modulus)
+{
+  auto remainder = number_to_round % modulus;
+  if (remainder == 0) { return number_to_round; }
+  auto rounded_up = number_to_round - remainder + modulus;
+  if (rounded_up < number_to_round) {
+    throw std::invalid_argument("Attempt to round up beyond the type's maximum value");
+  }
+  return rounded_up;
+}
+
+/**
+ * @brief Rounds `number_to_round` down to the last multiple of modulus
+ *
+ * @tparam S type to return
+ * @param number_to_round number that is being rounded
+ * @param modulus value to which to round
+ * @return largest integer not greater than `number_to_round` and modulo `S` is zero.
+ *
+ * @note This function assumes that `number_to_round` is non-negative and
+ * `modulus` is positive and does not check for overflow.
+ */
+template <typename S>
+constexpr S round_down_safe(S number_to_round, S modulus) noexcept
+{
+  auto remainder    = number_to_round % modulus;
+  auto rounded_down = number_to_round - remainder;
+  return rounded_down;
+}
+
+/**
+ * @brief Rounds `number_to_round` up to the next multiple of modulus
+ *
+ * @tparam S type to return
+ * @param number_to_round number that is being rounded
+ * @param modulus value to which to round
+ * @return smallest integer greater than `number_to_round` and modulo `S` is zero.
+ *
+ * @note This function assumes that `number_to_round` is non-negative and
+ * `modulus` is positive and does not check for overflow.
+ */
+template <typename S>
+constexpr S round_up_unsafe(S number_to_round, S modulus) noexcept
+{
+  auto remainder = number_to_round % modulus;
+  if (remainder == 0) { return number_to_round; }
+  auto rounded_up = number_to_round - remainder + modulus;
+  return rounded_up;
+}
+
+/**
+ * Divides the left-hand-side by the right-hand-side, rounding up
+ * to an integral multiple of the right-hand-side, e.g. (9,5) -> 2 , (10,5) -> 2, (11,5) -> 3.
+ *
+ * @param dividend the number to divide
+ * @param divisor the number by which to divide
+ * @return The least integer multiple of {@link divisor} which is greater than or equal to
+ * the non-integral division dividend/divisor.
+ *
+ * @note sensitive to overflow, i.e. if dividend > std::numeric_limits<S>::max() - divisor,
+ * the result will be incorrect
+ */
+template <typename S, typename T>
+constexpr S div_rounding_up_unsafe(S const& dividend, T const& divisor) noexcept
+{
+  return (dividend + divisor - 1) / divisor;
+}
+
+namespace detail {
+template <typename I>
+constexpr I div_rounding_up_safe(std::integral_constant<bool, false>,
+                                 I dividend,
+                                 I divisor) noexcept
+{
+  // TODO: This could probably be implemented faster
+  return (dividend > divisor) ? 1 + div_rounding_up_unsafe(dividend - divisor, divisor)
+                              : (dividend > 0);
+}
+
+template <typename I>
+constexpr I div_rounding_up_safe(std::integral_constant<bool, true>, I dividend, I divisor) noexcept
+{
+  auto quotient  = dividend / divisor;
+  auto remainder = dividend % divisor;
+  return quotient + (remainder != 0);
+}
+
+}  // namespace detail
+
+/**
+ * Divides the left-hand-side by the right-hand-side, rounding up
+ * to an integral multiple of the right-hand-side, e.g. (9,5) -> 2 , (10,5) -> 2, (11,5) -> 3.
+ *
+ * @param dividend the number to divide
+ * @param divisor the number of by which to divide
+ * @return The least integer multiple of {@link divisor} which is greater than or equal to
+ * the non-integral division dividend/divisor.
+ *
+ * @note will not overflow, and may _or may not_ be slower than the intuitive
+ * approach of using (dividend + divisor - 1) / divisor
+ */
+template <typename I>
+constexpr I div_rounding_up_safe(I dividend, I divisor) noexcept
+{
+  using i_is_a_signed_type = std::integral_constant<bool, std::is_signed_v<I>>;
+  return detail::div_rounding_up_safe(i_is_a_signed_type{}, dividend, divisor);
+}
+
+template <typename I>
+constexpr bool is_a_power_of_two(I val) noexcept
+{
+  static_assert(std::is_integral_v<I>, "This function only applies to integral types");
+  return ((val - 1) & val) == 0;
+}
+
+/**
+ * @brief Return the absolute value of a number.
+ *
+ * This calls `std::abs()` which performs equivalent: `(value < 0) ? -value : value`.
+ *
+ * This was created to prevent compile errors calling `std::abs()` with unsigned integers.
+ * An example compile error appears as follows:
+ * @code{.pseudo}
+ * error: more than one instance of overloaded function "std::abs" matches the argument list:
+ *          function "abs(int)"
+ *          function "std::abs(long)"
+ *          function "std::abs(long long)"
+ *          function "std::abs(double)"
+ *          function "std::abs(float)"
+ *          function "std::abs(long double)"
+ *          argument types are: (uint64_t)
+ * @endcode
+ *
+ * Not all cases could be if-ed out using `std::is_signed_v<T>` and satisfy the compiler.
+ *
+ * @param value Numeric value can be either integer or float type.
+ * @return Absolute value if value type is signed.
+ */
+template <typename T>
+constexpr auto absolute_value(T value) -> T
+{
+  if constexpr (cuda::std::is_signed<T>()) return numeric::detail::abs(value);
+  return value;
+}
+
+}  // namespace util
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/linked_column.hpp b/cpp/include/cudf/detail/utilities/linked_column.hpp
new file mode 100644
index 0000000..0feef0f
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/linked_column.hpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf::detail {
+
+struct linked_column_view;
+
+using LinkedColPtr    = std::shared_ptr<linked_column_view>;
+using LinkedColVector = std::vector<LinkedColPtr>;
+
+/**
+ * @brief A column_view class with pointer to parent's column_view
+ */
+struct linked_column_view : public column_view_base {
+  linked_column_view(linked_column_view const&)            = delete;
+  linked_column_view& operator=(linked_column_view const&) = delete;
+
+  /**
+   * @brief Construct from column_view
+   *
+   * @param col column_view to wrap
+   */
+  linked_column_view(column_view const& col);
+
+  /**
+   * @brief Construct from column_view with it's parent
+   *
+   * @param parent Pointer to the column_view's parent column_view
+   * @param col column_view to wrap
+   */
+  linked_column_view(linked_column_view* parent, column_view const& col);
+
+  /**
+   * @brief Conversion operator to cast this instance to it's column_view
+   */
+  operator column_view() const;
+
+  linked_column_view* parent;  ///< Pointer to parent of this column; nullptr if root
+  LinkedColVector children;    ///< Vector of children of this instance
+};
+
+/**
+ * @brief Converts all column_views of a table into linked_column_views
+ *
+ * @param table table of columns to convert
+ * @return Vector of converted linked_column_views
+ */
+LinkedColVector table_to_linked_columns(table_view const& table);
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/utilities/logger.hpp b/cpp/include/cudf/detail/utilities/logger.hpp
new file mode 100644
index 0000000..8c1c3c2
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/logger.hpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/logger.hpp>
+
+// Log messages that require computation should only be used at level TRACE and DEBUG
+#define CUDF_LOG_TRACE(...)    SPDLOG_LOGGER_TRACE(&cudf::logger(), __VA_ARGS__)
+#define CUDF_LOG_DEBUG(...)    SPDLOG_LOGGER_DEBUG(&cudf::logger(), __VA_ARGS__)
+#define CUDF_LOG_INFO(...)     SPDLOG_LOGGER_INFO(&cudf::logger(), __VA_ARGS__)
+#define CUDF_LOG_WARN(...)     SPDLOG_LOGGER_WARN(&cudf::logger(), __VA_ARGS__)
+#define CUDF_LOG_ERROR(...)    SPDLOG_LOGGER_ERROR(&cudf::logger(), __VA_ARGS__)
+#define CUDF_LOG_CRITICAL(...) SPDLOG_LOGGER_CRITICAL(&cudf::logger(), __VA_ARGS__)
diff --git a/cpp/include/cudf/detail/utilities/pinned_host_vector.hpp b/cpp/include/cudf/detail/utilities/pinned_host_vector.hpp
new file mode 100644
index 0000000..9e2b85e
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/pinned_host_vector.hpp
@@ -0,0 +1,211 @@
+/*
+ *  Copyright 2008-2023 NVIDIA Corporation
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ */
+
+#pragma once
+
+#include <cstddef>
+#include <limits>
+#include <new>  // for bad_alloc
+
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/host_vector.h>
+
+namespace cudf::detail {
+
+/*! \p pinned_allocator is a CUDA-specific host memory allocator
+ *  that employs \c cudaMallocHost for allocation.
+ *
+ * This implementation is ported from the experimental/pinned_allocator
+ * that Thrust used to provide.
+ *
+ *  \see https://en.cppreference.com/w/cpp/memory/allocator
+ */
+template <typename T>
+class pinned_allocator;
+
+/*! \p pinned_allocator is a CUDA-specific host memory allocator
+ *  that employs \c cudaMallocHost for allocation.
+ *
+ * This implementation is ported from the experimental/pinned_allocator
+ * that Thrust used to provide.
+ *
+ *  \see https://en.cppreference.com/w/cpp/memory/allocator
+ */
+template <>
+class pinned_allocator<void> {
+ public:
+  using value_type      = void;            ///< The type of the elements in the allocator
+  using pointer         = void*;           ///< The type returned by address() / allocate()
+  using const_pointer   = void const*;     ///< The type returned by address()
+  using size_type       = std::size_t;     ///< The type used for the size of the allocation
+  using difference_type = std::ptrdiff_t;  ///< The type of the distance between two pointers
+
+  /**
+   * @brief converts a `pinned_allocator<void>` to `pinned_allocator<U>`
+   */
+  template <typename U>
+  struct rebind {
+    using other = pinned_allocator<U>;  ///< The rebound type
+  };
+};
+
+/*! \p pinned_allocator is a CUDA-specific host memory allocator
+ *  that employs \c cudaMallocHost for allocation.
+ *
+ * This implementation is ported from the experimental/pinned_allocator
+ * that Thrust used to provide.
+ *
+ *  \see https://en.cppreference.com/w/cpp/memory/allocator
+ */
+template <typename T>
+class pinned_allocator {
+ public:
+  using value_type      = T;               ///< The type of the elements in the allocator
+  using pointer         = T*;              ///< The type returned by address() / allocate()
+  using const_pointer   = T const*;        ///< The type returned by address()
+  using reference       = T&;              ///< The parameter type for address()
+  using const_reference = T const&;        ///< The parameter type for address()
+  using size_type       = std::size_t;     ///< The type used for the size of the allocation
+  using difference_type = std::ptrdiff_t;  ///< The type of the distance between two pointers
+
+  /**
+   * @brief converts a `pinned_allocator<T>` to `pinned_allocator<U>`
+   */
+  template <typename U>
+  struct rebind {
+    using other = pinned_allocator<U>;  ///< The rebound type
+  };
+
+  /**
+   * @brief pinned_allocator's null constructor does nothing.
+   */
+  __host__ __device__ inline pinned_allocator() {}
+
+  /**
+   * @brief pinned_allocator's null destructor does nothing.
+   */
+  __host__ __device__ inline ~pinned_allocator() {}
+
+  /**
+   * @brief pinned_allocator's copy constructor does nothing.
+   */
+  __host__ __device__ inline pinned_allocator(pinned_allocator const&) {}
+
+  /**
+   * @brief  pinned_allocator's copy constructor does nothing.
+   *
+   *  This version of pinned_allocator's copy constructor
+   *  is templated on the \c value_type of the pinned_allocator
+   *  to copy from.  It is provided merely for convenience; it
+   *  does nothing.
+   */
+  template <typename U>
+  __host__ __device__ inline pinned_allocator(pinned_allocator<U> const&)
+  {
+  }
+
+  /**
+   * @brief This method returns the address of a \c reference of
+   *  interest.
+   *
+   *  @param r The \c reference of interest.
+   *  @return \c r's address.
+   */
+  __host__ __device__ inline pointer address(reference r) { return &r; }
+
+  /**
+   * @brief This method returns the address of a \c const_reference
+   *  of interest.
+   *
+   *  @param r The \c const_reference of interest.
+   *  @return \c r's address.
+   */
+  __host__ __device__ inline const_pointer address(const_reference r) { return &r; }
+
+  /**
+   * @brief This method allocates storage for objects in pinned host
+   *  memory.
+   *
+   *  @param cnt The number of objects to allocate.
+   *  @return a \c pointer to the newly allocated objects.
+   *  @note The second parameter to this function is meant as a
+   *        hint pointer to a nearby memory location, but is
+   *        not used by this allocator.
+   *  @note This method does not invoke \p value_type's constructor.
+   *        It is the responsibility of the caller to initialize the
+   *        objects at the returned \c pointer.
+   */
+  __host__ inline pointer allocate(size_type cnt, const_pointer /*hint*/ = 0)
+  {
+    if (cnt > this->max_size()) { throw std::bad_alloc(); }  // end if
+
+    pointer result(0);
+    CUDF_CUDA_TRY(cudaMallocHost(reinterpret_cast<void**>(&result), cnt * sizeof(value_type)));
+    return result;
+  }
+
+  /**
+   * @brief This method deallocates pinned host memory previously allocated
+   *  with this \c pinned_allocator.
+   *
+   *  @param p A \c pointer to the previously allocated memory.
+   *  @note The second parameter is the number of objects previously allocated
+   *        but is ignored by this allocator.
+   *  @note This method does not invoke \p value_type's destructor.
+   *        It is the responsibility of the caller to destroy
+   *        the objects stored at \p p.
+   */
+  __host__ inline void deallocate(pointer p, size_type /*cnt*/) { CUDF_CUDA_TRY(cudaFreeHost(p)); }
+
+  /**
+   * @brief This method returns the maximum size of the \c cnt parameter
+   *  accepted by the \p allocate() method.
+   *
+   *  @return The maximum number of objects that may be allocated
+   *          by a single call to \p allocate().
+   */
+  inline size_type max_size() const { return (std::numeric_limits<size_type>::max)() / sizeof(T); }
+
+  /**
+   * @brief This method tests this \p pinned_allocator for equality to
+   *  another.
+   *
+   *  @param x The other \p pinned_allocator of interest.
+   *  @return This method always returns \c true.
+   */
+  __host__ __device__ inline bool operator==(pinned_allocator const& x) const { return true; }
+
+  /**
+   * @brief This method tests this \p pinned_allocator for inequality
+   *  to another.
+   *
+   *  @param x The other \p pinned_allocator of interest.
+   *  @return This method always returns \c false.
+   */
+  __host__ __device__ inline bool operator!=(pinned_allocator const& x) const
+  {
+    return !operator==(x);
+  }
+};
+
+/**
+ * @brief A vector class with pinned host memory allocator
+ */
+template <typename T>
+using pinned_host_vector = thrust::host_vector<T, pinned_allocator<T>>;
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/utilities/stacktrace.hpp b/cpp/include/cudf/detail/utilities/stacktrace.hpp
new file mode 100644
index 0000000..c3ec9ce
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/stacktrace.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <string>
+
+namespace cudf::detail {
+/**
+ * @addtogroup utility_stacktrace
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Specify whether the last stackframe is included in the stacktrace.
+ */
+enum class capture_last_stackframe : bool { YES, NO };
+
+/**
+ * @brief Query the current stacktrace and return the whole stacktrace as one string.
+ *
+ * Depending on the value of the flag `capture_last_frame`, the caller that executes stacktrace
+ * retrieval can be included in the output result.
+ *
+ * @param capture_last_frame Flag to specify if the current stackframe will be included into
+ *        the output
+ * @return A string storing the whole current stacktrace
+ */
+std::string get_stacktrace(capture_last_stackframe capture_last_frame);
+
+/** @} */  // end of group
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/utilities/stream_pool.hpp b/cpp/include/cudf/detail/utilities/stream_pool.hpp
new file mode 100644
index 0000000..95384a9
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/stream_pool.hpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cstddef>
+#include <vector>
+
+namespace cudf::detail {
+
+/**
+ * @brief Acquire a set of `cuda_stream_view` objects and synchronize them to an event on another
+ * stream.
+ *
+ * By default an underlying `rmm::cuda_stream_pool` is used to obtain the streams. The only other
+ * implementation at present is a debugging version that always returns the stream returned by
+ * `cudf::get_default_stream()`. To use this debugging version, set the environment variable
+ * `LIBCUDF_USE_DEBUG_STREAM_POOL`.
+ *
+ * Example usage:
+ * @code{.cpp}
+ * auto stream = cudf::get_default_stream();
+ * auto const num_streams = 2;
+ * // do work on stream
+ * // allocate streams and wait for an event on stream before executing on any of streams
+ * auto streams = cudf::detail::fork_stream(stream, num_streams);
+ * // do work on streams[0] and streams[1]
+ * // wait for event on streams before continuing to do work on stream
+ * cudf::detail::join_streams(streams, stream);
+ * @endcode
+ *
+ * @param stream Stream that the returned streams will wait on.
+ * @param count The number of `cuda_stream_view` objects to return.
+ * @return Vector containing `count` stream views.
+ */
+[[nodiscard]] std::vector<rmm::cuda_stream_view> fork_streams(rmm::cuda_stream_view stream,
+                                                              std::size_t count);
+
+/**
+ * @brief Synchronize a stream to an event on a set of streams.
+ *
+ * @param streams Streams to wait on.
+ * @param stream Joined stream that synchronizes with the waited-on streams.
+ */
+void join_streams(host_span<rmm::cuda_stream_view const> streams, rmm::cuda_stream_view stream);
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/utilities/transform_unary_functions.cuh b/cpp/include/cudf/detail/utilities/transform_unary_functions.cuh
new file mode 100644
index 0000000..12774f5
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/transform_unary_functions.cuh
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @brief unary functions for thrust::transform_iterator
+ * @file transform_unary_functions.cuh
+ *
+ * These are designed for using as AdaptableUnaryFunction
+ * for thrust::transform_iterator.
+ * For the detail of example cases,
+ * @see iterator.cuh iterator_test.cu
+ */
+
+#pragma once
+
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+
+namespace cudf {
+/**
+ * @brief Transforms non-null input using `Functor`, and for null, returns `null_replacement`.
+ *
+ * This functor argument is considered null if second value of functor argument pair is false.
+ *
+ * @tparam ResultType Output type of `Functor` and null replacement type.
+ * @tparam Functor functor to transform first value of argument pair to ResultType.
+ */
+template <typename ResultType, typename Functor>
+struct null_replacing_transformer {
+  using type = ResultType;
+  Functor f;
+  type replacement;
+  CUDF_HOST_DEVICE inline null_replacing_transformer(type null_replacement, Functor transformer)
+    : f(transformer), replacement(null_replacement)
+  {
+  }
+
+  template <typename ElementType>
+  CUDF_HOST_DEVICE inline type operator()(thrust::pair<ElementType, bool> const& pair_value)
+  {
+    if (pair_value.second)
+      return f(pair_value.first);
+    else
+      return replacement;
+  }
+};
+
+/**
+ * @brief intermediate struct to calculate mean and variance
+ * This is an example case to output a struct from column input.
+ *
+ * this will be used to calculate and hold `sum of values`, 'sum of squares',
+ * 'sum of valid count'.
+ * Those will be used to compute `mean` (= sum / count)
+ * and `variance` (= sum of squares / count - mean^2).
+ *
+ * @tparam ElementType  element data type of value and value_squared.
+ */
+template <typename ElementType>
+struct meanvar {
+  ElementType value;          /// the value
+  ElementType value_squared;  /// the value of squared
+  cudf::size_type count;      /// the count
+
+  CUDF_HOST_DEVICE inline meanvar(ElementType _value         = 0,
+                                  ElementType _value_squared = 0,
+                                  cudf::size_type _count     = 0)
+    : value(_value), value_squared(_value_squared), count(_count){};
+
+  using this_t = cudf::meanvar<ElementType>;
+
+  CUDF_HOST_DEVICE inline this_t operator+(this_t const& rhs) const
+  {
+    return this_t((this->value + rhs.value),
+                  (this->value_squared + rhs.value_squared),
+                  (this->count + rhs.count));
+  };
+
+  CUDF_HOST_DEVICE inline bool operator==(this_t const& rhs) const
+  {
+    return ((this->value == rhs.value) && (this->value_squared == rhs.value_squared) &&
+            (this->count == rhs.count));
+  };
+};
+
+// --------------------------------------------------------------------------
+// transformers
+
+/**
+ * @brief Transforms a scalar by first casting to another type, and then squaring the result.
+ *
+ * This struct transforms the output value as
+ * `value * value`.
+ *
+ * This will be used to compute "sum of squares".
+ *
+ * @tparam  ResultType  scalar data type of output
+ */
+template <typename ElementType>
+struct transformer_squared {
+  CUDF_HOST_DEVICE inline ElementType operator()(ElementType const& value)
+  {
+    return (value * value);
+  };
+};
+
+/**
+ * @brief Uses a scalar value to construct a `meanvar` object.
+ * This transforms `thrust::pair<ElementType, bool>` into
+ * `ResultType = meanvar<ElementType>` form.
+ *
+ * This struct transforms the value and the squared value and the count at once.
+ *
+ * @tparam  ElementType         scalar data type of input
+ */
+template <typename ElementType>
+struct transformer_meanvar {
+  using ResultType = meanvar<ElementType>;
+
+  CUDF_HOST_DEVICE inline ResultType operator()(thrust::pair<ElementType, bool> const& pair)
+  {
+    ElementType v = pair.first;
+    return meanvar<ElementType>(v, v * v, (pair.second) ? 1 : 0);
+  };
+};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/vector_factories.hpp b/cpp/include/cudf/detail/utilities/vector_factories.hpp
new file mode 100644
index 0000000..90ad987
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/vector_factories.hpp
@@ -0,0 +1,443 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+/**
+ * @brief Convenience factories for creating device vectors from host spans
+ * @file vector_factories.hpp
+ */
+
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <thrust/host_vector.h>
+
+#include <vector>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Asynchronously construct a `device_uvector` and set all elements to zero.
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param size The number of elements in the created vector
+ * @param stream The stream on which to allocate memory and perform the memset
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing zeros
+ */
+template <typename T>
+rmm::device_uvector<T> make_zeroed_device_uvector_async(std::size_t size,
+                                                        rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_uvector<T> ret(size, stream, mr);
+  CUDF_CUDA_TRY(cudaMemsetAsync(ret.data(), 0, size * sizeof(T), stream.value()));
+  return ret;
+}
+
+/**
+ * @brief Synchronously construct a `device_uvector` and set all elements to zero.
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param size The number of elements in the created vector
+ * @param stream The stream on which to allocate memory and perform the memset
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing zeros
+ */
+template <typename T>
+rmm::device_uvector<T> make_zeroed_device_uvector_sync(std::size_t size,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_uvector<T> ret(size, stream, mr);
+  CUDF_CUDA_TRY(cudaMemsetAsync(ret.data(), 0, size * sizeof(T), stream.value()));
+  stream.synchronize();
+  return ret;
+}
+
+/**
+ * @brief Asynchronously construct a `device_uvector` containing a deep copy of data from a
+ * `host_span`
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The host_span of data to deep copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <typename T>
+rmm::device_uvector<T> make_device_uvector_async(host_span<T const> source_data,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_uvector<T> ret(source_data.size(), stream, mr);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(ret.data(),
+                                source_data.data(),
+                                source_data.size() * sizeof(T),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  return ret;
+}
+
+/**
+ * @brief Asynchronously construct a `device_uvector` containing a deep copy of data from a host
+ * container
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input host container from which to copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, host_span<typename Container::value_type const>>>* = nullptr>
+rmm::device_uvector<typename Container::value_type> make_device_uvector_async(
+  Container const& c, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  return make_device_uvector_async(host_span<typename Container::value_type const>{c}, stream, mr);
+}
+
+/**
+ * @brief Asynchronously construct a `device_uvector` containing a deep copy of data from a
+ * `device_span`
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The device_span of data to deep copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <typename T>
+rmm::device_uvector<T> make_device_uvector_async(device_span<T const> source_data,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_uvector<T> ret(source_data.size(), stream, mr);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(ret.data(),
+                                source_data.data(),
+                                source_data.size() * sizeof(T),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  return ret;
+}
+
+/**
+ * @brief Asynchronously construct a `device_uvector` containing a deep copy of data from a device
+ * container
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input device container from which to copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, device_span<typename Container::value_type const>>>* = nullptr>
+rmm::device_uvector<typename Container::value_type> make_device_uvector_async(
+  Container const& c, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  return make_device_uvector_async(
+    device_span<typename Container::value_type const>{c}, stream, mr);
+}
+
+/**
+ * @brief Synchronously construct a `device_uvector` containing a deep copy of data from a
+ * `host_span`
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The host_span of data to deep copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <typename T>
+rmm::device_uvector<T> make_device_uvector_sync(host_span<T const> source_data,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  auto ret = make_device_uvector_async(source_data, stream, mr);
+  stream.synchronize();
+  return ret;
+}
+
+/**
+ * @brief Synchronously construct a `device_uvector` containing a deep copy of data from a host
+ * container
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input host container from which to copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, host_span<typename Container::value_type const>>>* = nullptr>
+rmm::device_uvector<typename Container::value_type> make_device_uvector_sync(
+  Container const& c, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  return make_device_uvector_sync(host_span<typename Container::value_type const>{c}, stream, mr);
+}
+
+/**
+ * @brief Synchronously construct a `device_uvector` containing a deep copy of data from a
+ * `device_span`
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The device_span of data to deep copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <typename T>
+rmm::device_uvector<T> make_device_uvector_sync(device_span<T const> source_data,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  auto ret = make_device_uvector_async(source_data, stream, mr);
+  stream.synchronize();
+  return ret;
+}
+
+/**
+ * @brief Synchronously construct a `device_uvector` containing a deep copy of data from a device
+ * container
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input device container from which to copy
+ * @param stream The stream on which to allocate memory and perform the copy
+ * @param mr The memory resource to use for allocating the returned device_uvector
+ * @return A device_uvector containing the copied data
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, device_span<typename Container::value_type const>>>* = nullptr>
+rmm::device_uvector<typename Container::value_type> make_device_uvector_sync(
+  Container const& c, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  return make_device_uvector_sync(device_span<typename Container::value_type const>{c}, stream, mr);
+}
+
+// Utility function template to allow copying to either a thrust::host_vector or std::vector
+template <typename T, typename OutContainer>
+OutContainer make_vector_async(device_span<T const> v, rmm::cuda_stream_view stream)
+{
+  OutContainer result(v.size());
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    result.data(), v.data(), v.size() * sizeof(T), cudaMemcpyDefault, stream.value()));
+  return result;
+}
+
+/**
+ * @brief Asynchronously construct a `std::vector` containing a copy of data from a
+ * `device_span`
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The device data to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <typename T>
+std::vector<T> make_std_vector_async(device_span<T const> v, rmm::cuda_stream_view stream)
+{
+  return make_vector_async<T, std::vector<T>>(v, stream);
+}
+
+/**
+ * @brief Asynchronously construct a `std::vector` containing a copy of data from a device
+ * container
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input device container from which to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, device_span<typename Container::value_type const>>>* = nullptr>
+std::vector<typename Container::value_type> make_std_vector_async(Container const& c,
+                                                                  rmm::cuda_stream_view stream)
+{
+  return make_std_vector_async(device_span<typename Container::value_type const>{c}, stream);
+}
+
+/**
+ * @brief Synchronously construct a `std::vector` containing a copy of data from a
+ * `device_span`
+ *
+ * @note This function does a synchronize on `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The device data to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <typename T>
+std::vector<T> make_std_vector_sync(device_span<T const> v, rmm::cuda_stream_view stream)
+{
+  auto result = make_std_vector_async(v, stream);
+  stream.synchronize();
+  return result;
+}
+
+/**
+ * @brief Synchronously construct a `std::vector` containing a copy of data from a device
+ * container
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input device container from which to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, device_span<typename Container::value_type const>>>* = nullptr>
+std::vector<typename Container::value_type> make_std_vector_sync(Container const& c,
+                                                                 rmm::cuda_stream_view stream)
+{
+  return make_std_vector_sync(device_span<typename Container::value_type const>{c}, stream);
+}
+
+/**
+ * @brief Asynchronously construct a `thrust::host_vector` containing a copy of data from a
+ * `device_span`
+ *
+ * @note This function does not synchronize `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The device data to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <typename T>
+thrust::host_vector<T> make_host_vector_async(device_span<T const> v, rmm::cuda_stream_view stream)
+{
+  return make_vector_async<T, thrust::host_vector<T>>(v, stream);
+}
+
+/**
+ * @brief Asynchronously construct a `std::vector` containing a copy of data from a device
+ * container
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input device container from which to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, device_span<typename Container::value_type const>>>* = nullptr>
+thrust::host_vector<typename Container::value_type> make_host_vector_async(
+  Container const& c, rmm::cuda_stream_view stream)
+{
+  return make_host_vector_async(device_span<typename Container::value_type const>{c}, stream);
+}
+
+/**
+ * @brief Synchronously construct a `thrust::host_vector` containing a copy of data from a
+ * `device_span`
+ *
+ * @note This function does a synchronize on `stream`.
+ *
+ * @tparam T The type of the data to copy
+ * @param source_data The device data to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <typename T>
+thrust::host_vector<T> make_host_vector_sync(device_span<T const> v, rmm::cuda_stream_view stream)
+{
+  auto result = make_host_vector_async(v, stream);
+  stream.synchronize();
+  return result;
+}
+
+/**
+ * @brief Synchronously construct a `thrust::host_vector` containing a copy of data from a device
+ * container
+ *
+ * @note This function synchronizes `stream`.
+ *
+ * @tparam Container The type of the container to copy from
+ * @tparam T The type of the data to copy
+ * @param c The input device container from which to copy
+ * @param stream The stream on which to perform the copy
+ * @return The data copied to the host
+ */
+template <
+  typename Container,
+  std::enable_if_t<
+    std::is_convertible_v<Container, device_span<typename Container::value_type const>>>* = nullptr>
+thrust::host_vector<typename Container::value_type> make_host_vector_sync(
+  Container const& c, rmm::cuda_stream_view stream)
+{
+  return make_host_vector_sync(device_span<typename Container::value_type const>{c}, stream);
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/detail/utilities/visitor_overload.hpp b/cpp/include/cudf/detail/utilities/visitor_overload.hpp
new file mode 100644
index 0000000..a55ca32
--- /dev/null
+++ b/cpp/include/cudf/detail/utilities/visitor_overload.hpp
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+namespace cudf::detail {
+
+/**
+ * @brief Helper class to support inline-overloading for all of a variant's alternative types
+ */
+template <class... Ts>
+struct visitor_overload : Ts... {
+  using Ts::operator()...;
+};
+template <class... Ts>
+visitor_overload(Ts...) -> visitor_overload<Ts...>;
+
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/detail/valid_if.cuh b/cpp/include/cudf/detail/valid_if.cuh
new file mode 100644
index 0000000..f3f95da
--- /dev/null
+++ b/cpp/include/cudf/detail/valid_if.cuh
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+
+#include <thrust/distance.h>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Generate a bitmask where every bit is set for which a predicate is
+ * `true` over the elements in `[begin, begin + size)`.
+ *
+ * Bit `i` in the output mask will be set if `p(*(begin+i)) == true`.
+ *
+ * @tparam block_size The number of threads in the block
+ * @param[out] output The output bitmask
+ * @param[in] begin The beginning of the sequence of elements
+ * @param[in] size The number of elements
+ * @param[in] p The predicate to apply to each element
+ * @param[out] valid_count The count of set bits in the output bitmask
+ */
+template <size_type block_size, typename InputIterator, typename Predicate>
+__global__ void valid_if_kernel(
+  bitmask_type* output, InputIterator begin, size_type size, Predicate p, size_type* valid_count)
+{
+  constexpr size_type leader_lane{0};
+  auto const lane_id{threadIdx.x % warp_size};
+  auto i            = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+  size_type warp_valid_count{0};
+
+  auto active_mask = __ballot_sync(0xFFFF'FFFFu, i < size);
+  while (i < size) {
+    bitmask_type ballot = __ballot_sync(active_mask, p(*(begin + i)));
+    if (lane_id == leader_lane) {
+      output[cudf::word_index(i)] = ballot;
+      warp_valid_count += __popc(ballot);
+    }
+    i += stride;
+    active_mask = __ballot_sync(active_mask, i < size);
+  }
+
+  size_type block_count = single_lane_block_sum_reduce<block_size, leader_lane>(warp_valid_count);
+  if (threadIdx.x == 0) { atomicAdd(valid_count, block_count); }
+}
+
+/**
+ * @brief Generate a bitmask where every bit is set for which a predicate is
+ * `true` over the elements in `[begin,end)`.
+ *
+ * Bit `i` in the output mask will be set if `p(*(begin+i)) == true`.
+ *
+ * If `distance(begin,end) == 0`, returns an empty `rmm::device_buffer`.
+ *
+ * @throws cudf::logic_error if `(begin > end)`
+ *
+ * @param begin The beginning of the sequence
+ * @param end The end of the sequence
+ * @param p The predicate
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return A pair containing a `device_buffer` with the new bitmask and it's
+ * null count
+ */
+template <typename InputIterator, typename Predicate>
+std::pair<rmm::device_buffer, size_type> valid_if(InputIterator begin,
+                                                  InputIterator end,
+                                                  Predicate p,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(begin <= end, "Invalid range.");
+
+  size_type size = thrust::distance(begin, end);
+
+  auto null_mask = detail::create_null_mask(size, mask_state::UNINITIALIZED, stream, mr);
+
+  size_type null_count{0};
+  if (size > 0) {
+    rmm::device_scalar<size_type> valid_count{0, stream};
+
+    constexpr size_type block_size{256};
+    grid_1d grid{size, block_size};
+
+    valid_if_kernel<block_size><<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+      static_cast<bitmask_type*>(null_mask.data()), begin, size, p, valid_count.data());
+
+    null_count = size - valid_count.value(stream);
+  }
+  return std::pair(std::move(null_mask), null_count);
+}
+
+/**
+ * @brief Populates a set of bitmasks by applying a binary predicate to two
+*         input ranges.
+
+ * Given a set of bitmasks, `masks`, the state of bit `j` in mask `i` is
+ * determined by `p( *(begin1 + i), *(begin2 + j))`. If the predicate evaluates
+ * to true, the bit is set to `1`. If false, set to `0`.
+ *
+ * Example Arguments:
+ * begin1:        zero-based counting iterator,
+ * begin2:        zero-based counting iterator,
+ * p:             [](size_type col, size_type row){ return col == row; }
+ * masks:         [[b00...], [b00...], [b00...]]
+ * mask_count:    3
+ * mask_num_bits: 2
+ * valid_counts:  [0, 0, 0]
+ *
+ * Example Results:
+ * masks:         [[b10...], [b01...], [b00...]]
+ * valid_counts:  [1, 1, 0]
+ *
+ * @note If any mask in `masks` is `nullptr`, that mask will be ignored.
+ *
+ * @param begin1        LHS arguments to binary predicate. ex: column/mask idx
+ * @param begin2        RHS arguments to binary predicate. ex: row/bit idx
+ * @param p             Predicate: `bit = p(begin1 + mask_idx, begin2 + bit_idx)`
+ * @param masks         Masks for which bits will be obtained and assigned.
+ * @param mask_count    The number of `masks`.
+ * @param mask_num_bits The number of bits to assign for each mask. If this
+ *                      number is smaller than the total number of bits, the
+ *                      remaining bits may not be initialized.
+ * @param valid_counts  Used to obtain the total number of valid bits for each
+ *                      mask.
+ */
+template <typename InputIterator1,
+          typename InputIterator2,
+          typename BinaryPredicate,
+          int32_t block_size>
+__global__ void valid_if_n_kernel(InputIterator1 begin1,
+                                  InputIterator2 begin2,
+                                  BinaryPredicate p,
+                                  bitmask_type* masks[],
+                                  size_type mask_count,
+                                  size_type mask_num_bits,
+                                  size_type* valid_counts)
+{
+  for (size_type mask_idx = 0; mask_idx < mask_count; mask_idx++) {
+    auto const mask = masks[mask_idx];
+    if (mask == nullptr) { continue; }
+
+    auto block_offset     = blockIdx.x * blockDim.x;
+    auto warp_valid_count = static_cast<size_type>(0);
+
+    while (block_offset < mask_num_bits) {
+      auto const thread_idx    = block_offset + threadIdx.x;
+      auto const thread_active = thread_idx < mask_num_bits;
+      auto const arg_1         = *(begin1 + mask_idx);
+      auto const arg_2         = *(begin2 + thread_idx);
+      auto const bit_is_valid  = thread_active && p(arg_1, arg_2);
+      auto const warp_validity = __ballot_sync(0xffff'ffffu, bit_is_valid);
+      auto const mask_idx      = word_index(thread_idx);
+
+      if (thread_active && threadIdx.x % warp_size == 0) { mask[mask_idx] = warp_validity; }
+
+      warp_valid_count += __popc(warp_validity);
+      block_offset += blockDim.x * gridDim.x;
+    }
+
+    auto block_valid_count = single_lane_block_sum_reduce<block_size, 0>(warp_valid_count);
+
+    if (threadIdx.x == 0) { atomicAdd(valid_counts + mask_idx, block_valid_count); }
+  }
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/concatenate.hpp b/cpp/include/cudf/dictionary/detail/concatenate.hpp
new file mode 100644
index 0000000..d744294
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/concatenate.hpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+/**
+ * @brief Returns a single column by vertically concatenating the given vector of
+ * dictionary columns.
+ *
+ * @throw cudf::logic_error if `columns.size()==0`
+ * @throw cudf::logic_error if dictionary column keys are not all the same type.
+ *
+ * @param columns Vector of dictionary columns to concatenate.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with concatenated results.
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/encode.hpp b/cpp/include/cudf/dictionary/detail/encode.hpp
new file mode 100644
index 0000000..2aad7dd
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/encode.hpp
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+/**
+ * @brief Construct a dictionary column by dictionary encoding an existing column.
+ *
+ * The output column is a DICTIONARY type with a keys column of non-null, unique values
+ * that are in a strict, total order. Meaning, `keys[i]` is ordered before
+ * `keys[i+1]` for all `i in [0,n-1)` where `n` is the number of keys.
+
+ * The output column has a child indices column that is of integer type and with
+ * the same size as the input column.
+ *
+ * The null_mask and null count are copied from the input column to the output column.
+ *
+ * @throw cudf::logic_error if indices_type is not INT32
+ *
+ * ```
+ * c = [429,111,213,111,213,429,213]
+ * d = make_dictionary_column(c)
+ * d now has keys [111,213,429] and indices [2,0,1,0,1,2,1]
+ * ```
+ *
+ * @param column The column to dictionary encode.
+ * @param indices_type The integer type to use for the indices.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return Returns a dictionary column.
+ */
+std::unique_ptr<column> encode(column_view const& column,
+                               data_type indices_type,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create a column by gathering the keys from the provided
+ * dictionary_column into a new column using the indices from that column.
+ *
+ * ```
+ * d1 = {["a","c","d"],[2,0,1,0]}
+ * s = decode(d1)
+ * s is now ["d","a","c","a"]
+ * ```
+ *
+ * @param dictionary_column Existing dictionary column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with type matching the dictionary_column's keys.
+ */
+std::unique_ptr<column> decode(dictionary_column_view const& dictionary_column,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Return minimal integer type for the given number of elements.
+ *
+ * @param keys_size Number of elements in the keys
+ * @return Minimal type that can hold `keys_size` values
+ */
+data_type get_indices_type_for_size(size_type keys_size);
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/iterator.cuh b/cpp/include/cudf/dictionary/detail/iterator.cuh
new file mode 100644
index 0000000..cf08b8e
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/iterator.cuh
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+
+#include <thrust/pair.h>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+
+/**
+ * @brief Accessor functor for returning a dictionary key element in a dictionary iterator.
+ *
+ * @tparam KeyType The type of the dictionary's key element.
+ */
+template <typename KeyType>
+struct dictionary_access_fn {
+  dictionary_access_fn(column_device_view const& d_dictionary) : d_dictionary{d_dictionary} {}
+
+  __device__ KeyType operator()(size_type idx) const
+  {
+    if (d_dictionary.is_null(idx)) return KeyType{};
+    auto keys = d_dictionary.child(dictionary_column_view::keys_column_index);
+    return keys.element<KeyType>(static_cast<size_type>(d_dictionary.element<dictionary32>(idx)));
+  };
+
+ private:
+  column_device_view const d_dictionary;
+};
+
+/**
+ * @brief Create dictionary iterator that produces key elements.
+ *
+ * The iterator returns `keys[indices[i]]` where the `keys` are the dictionary's key
+ * elements and the `indices` are the dictionary's index elements.
+ *
+ * @throw cudf::logic_error if `dictionary_column` is not a dictionary column.
+ *
+ * @tparam KeyType The type of the dictionary's key element.
+ * @param dictionary_column The dictionary device view to iterate.
+ * @return Iterator
+ */
+template <typename KeyType>
+auto make_dictionary_iterator(column_device_view const& dictionary_column)
+{
+  CUDF_EXPECTS(is_dictionary(dictionary_column.type()),
+               "Dictionary iterator is only for dictionary columns");
+  return cudf::detail::make_counting_transform_iterator(
+    size_type{0}, dictionary_access_fn<KeyType>{dictionary_column});
+}
+
+/**
+ * @brief Accessor functor for returning a dictionary pair iterator.
+ *
+ * @tparam KeyType The type of the dictionary's key element.
+ *
+ * @throw cudf::logic_error if `has_nulls==true` and `d_dictionary` is not nullable.
+ */
+template <typename KeyType>
+struct dictionary_access_pair_fn {
+  dictionary_access_pair_fn(column_device_view const& d_dictionary, bool has_nulls = true)
+    : d_dictionary{d_dictionary}, has_nulls{has_nulls}
+  {
+    if (has_nulls) { CUDF_EXPECTS(d_dictionary.nullable(), "unexpected non-nullable column"); }
+  }
+
+  __device__ thrust::pair<KeyType, bool> operator()(size_type idx) const
+  {
+    if (has_nulls && d_dictionary.is_null(idx)) return {KeyType{}, false};
+    auto keys = d_dictionary.child(dictionary_column_view::keys_column_index);
+    return {keys.element<KeyType>(static_cast<size_type>(d_dictionary.element<dictionary32>(idx))),
+            true};
+  };
+
+ private:
+  column_device_view const d_dictionary;
+  bool has_nulls;
+};
+
+/**
+ * @brief Create dictionary iterator that produces key and valid element pair.
+ *
+ * The iterator returns a pair where the `first` value is
+ * `dictionary_column.keys[dictionary_column.indices[i]]`
+ * The `second` pair member is a `bool` which is set to
+ * `dictionary_column.is_valid(i)`.
+ *
+ * @throw cudf::logic_error if `dictionary_column` is not a dictionary column.
+ *
+ * @tparam KeyType The type of the dictionary's key element.
+ *
+ * @param dictionary_column The dictionary device view to iterate.
+ * @param has_nulls Set to `true` if the `dictionary_column` has nulls.
+ * @return Pair iterator with `{value,valid}`
+ */
+template <typename KeyType>
+auto make_dictionary_pair_iterator(column_device_view const& dictionary_column,
+                                   bool has_nulls = true)
+{
+  CUDF_EXPECTS(is_dictionary(dictionary_column.type()),
+               "Dictionary iterator is only for dictionary columns");
+  return cudf::detail::make_counting_transform_iterator(
+    0, dictionary_access_pair_fn<KeyType>{dictionary_column, has_nulls});
+}
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/merge.hpp b/cpp/include/cudf/dictionary/detail/merge.hpp
new file mode 100644
index 0000000..e7ea53c
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/merge.hpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/merge.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+
+/**
+ * @brief Merges two dictionary columns.
+ *
+ * The keys of both dictionary columns are expected to be already matched.
+ * Otherwise, the result is undefined behavior.
+ *
+ * Caller must set the validity mask in the output column.
+ *
+ * @param lcol First column.
+ * @param rcol Second column.
+ * @param row_order Indexes for each column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> merge(dictionary_column_view const& lcol,
+                              dictionary_column_view const& rcol,
+                              cudf::detail::index_vector const& row_order,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/replace.hpp b/cpp/include/cudf/dictionary/detail/replace.hpp
new file mode 100644
index 0000000..0778baa
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/replace.hpp
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+
+/**
+ * @brief Create a new dictionary column by replacing nulls with values
+ * from a second dictionary.
+ *
+ * @throw cudf::logic_error if the keys type of both dictionaries do not match.
+ * @throw cudf::logic_error if the column sizes do not match.
+ *
+ * @param input Column with nulls to replace.
+ * @param replacement Column with values to use for replacing.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column with null rows replaced.
+ */
+std::unique_ptr<column> replace_nulls(dictionary_column_view const& input,
+                                      dictionary_column_view const& replacement,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create a new dictionary column by replacing nulls with a
+ * specified scalar.
+ *
+ * @throw cudf::logic_error if the keys type does not match the replacement type.
+ *
+ * @param input Column with nulls to replace.
+ * @param replacement Value to use for replacing.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column with null rows replaced.
+ */
+std::unique_ptr<column> replace_nulls(dictionary_column_view const& input,
+                                      scalar const& replacement,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/search.hpp b/cpp/include/cudf/dictionary/detail/search.hpp
new file mode 100644
index 0000000..6205930
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/search.hpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+
+/**
+ * @copydoc cudf::dictionary::get_index(dictionary_column_view const&,scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<scalar> get_index(dictionary_column_view const& dictionary,
+                                  scalar const& key,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Get the index for a key if it were added to the given dictionary.
+ *
+ * The actual index is returned if the `key` is already part of the dictionary's key set.
+ *
+ * @code{.pseudo}
+ * d1 = {["a","c","d"],[2,0,1,0]}
+ * idx = get_insert_index(d1,"b")
+ * idx is 1
+ * @endcode{.pseudo}
+ *
+ * @throw cudf::logic_error if `key.type() != dictionary.keys().type()`
+ *
+ * @param dictionary The dictionary to search for the key.
+ * @param key The value to search for in the dictionary keyset.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return Numeric scalar index value of the key within the dictionary
+ */
+std::unique_ptr<scalar> get_insert_index(dictionary_column_view const& dictionary,
+                                         scalar const& key,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/detail/update_keys.hpp b/cpp/include/cudf/dictionary/detail/update_keys.hpp
new file mode 100644
index 0000000..6fd743a
--- /dev/null
+++ b/cpp/include/cudf/dictionary/detail/update_keys.hpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+/**
+ * @copydoc cudf::dictionary::add_keys(dictionary_column_view const&,column_view
+ * const&,mm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> add_keys(dictionary_column_view const& dictionary_column,
+                                 column_view const& new_keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::dictionary::remove_keys(dictionary_column_view const&,column_view
+ * const&,mm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> remove_keys(dictionary_column_view const& dictionary_column,
+                                    column_view const& keys_to_remove,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::dictionary::remove_unused_keys(dictionary_column_view
+ * const&,mm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> remove_unused_keys(dictionary_column_view const& dictionary_column,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::dictionary::set_keys(dictionary_column_view
+ * const&,mm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> set_keys(dictionary_column_view const& dictionary_column,
+                                 column_view const& keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc
+ * cudf::dictionary::match_dictionaries(std::vector<cudf::dictionary_column_view>,mm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::vector<std::unique_ptr<column>> match_dictionaries(
+  cudf::host_span<dictionary_column_view const> input,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create new dictionaries that have keys merged from dictionary columns
+ * found in the provided tables.
+ *
+ * The result includes a vector of new dictionary columns along with a
+ * vector of table_views with corresponding updated column_views.
+ * And any column_views in the input tables that are not dictionary type
+ * are simply copied.
+ *
+ * Merging the dictionary keys also adjusts the indices appropriately in the
+ * output dictionary columns.
+ *
+ * Any null rows are left unchanged.
+ *
+ * @param input Vector of cudf::table_views that include dictionary columns to be matched.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary columns and updated cudf::table_views.
+ */
+std::pair<std::vector<std::unique_ptr<column>>, std::vector<table_view>> match_dictionaries(
+  std::vector<table_view> tables,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/dictionary_column_view.hpp b/cpp/include/cudf/dictionary/dictionary_column_view.hpp
new file mode 100644
index 0000000..9f2bc90
--- /dev/null
+++ b/cpp/include/cudf/dictionary/dictionary_column_view.hpp
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+
+/**
+ * @file
+ * @brief Class definition for cudf::dictionary_column_view
+ */
+
+namespace cudf {
+/**
+ * @addtogroup dictionary_classes
+ * @{
+ */
+
+/**
+ * @brief A wrapper class for operations on a dictionary column.
+ *
+ * A dictionary column contains a set of keys and a column of indices.
+ * The keys are a sorted set of unique values for the column.
+ * The indices represent the corresponding positions of each element's
+ * value in the keys.
+ */
+class dictionary_column_view : private column_view {
+ public:
+  /**
+   * @brief Construct a new dictionary column view object from a column view.
+   *
+   * @param dictionary_column The column view to wrap
+   */
+  dictionary_column_view(column_view const& dictionary_column);
+  dictionary_column_view(dictionary_column_view&&)      = default;  ///< Move constructor
+  dictionary_column_view(dictionary_column_view const&) = default;  ///< Copy constructor
+  ~dictionary_column_view()                             = default;
+
+  /**
+   * @brief Move assignment operator
+   *
+   * @return The reference to this dictionary column
+   */
+  dictionary_column_view& operator=(dictionary_column_view const&) = default;
+
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return The reference to this dictionary column
+   */
+  dictionary_column_view& operator=(dictionary_column_view&&) = default;
+
+  /// Index of the indices column of the dictionary column
+  static constexpr size_type indices_column_index{0};
+  /// Index of the keys column of the dictionary column
+  static constexpr size_type keys_column_index{1};
+
+  using column_view::has_nulls;
+  using column_view::is_empty;
+  using column_view::null_count;
+  using column_view::null_mask;
+  using column_view::offset;
+  using column_view::size;
+
+  /**
+   * @brief Returns the parent column.
+   *
+   * @return The parent column
+   */
+  [[nodiscard]] column_view parent() const noexcept;
+
+  /**
+   * @brief Returns the column of indices
+   *
+   * @return The indices column
+   */
+  [[nodiscard]] column_view indices() const noexcept;
+
+  /**
+   * @brief Returns a column_view combining the indices data
+   * with offset, size, and nulls from the parent.
+   *
+   * @return A sliced indices column view with nulls from the parent
+   */
+  [[nodiscard]] column_view get_indices_annotated() const noexcept;
+
+  /**
+   * @brief Returns the column of keys
+   *
+   * @return  The keys column
+   */
+  [[nodiscard]] column_view keys() const noexcept;
+
+  /**
+   * @brief Returns the cudf::data_type of the keys child column.
+   *
+   * @return The cudf::data_type of the keys child column
+   */
+  [[nodiscard]] data_type keys_type() const noexcept;
+
+  /**
+   * @brief Returns the number of rows in the keys column.
+   *
+   * @return The number of rows in the keys column
+   */
+  [[nodiscard]] size_type keys_size() const noexcept;
+};
+/** @} */  // end of group
+
+//! Dictionary column APIs.
+namespace dictionary {  // defined here for doxygen output
+}
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/dictionary_factories.hpp b/cpp/include/cudf/dictionary/dictionary_factories.hpp
new file mode 100644
index 0000000..821981a
--- /dev/null
+++ b/cpp/include/cudf/dictionary/dictionary_factories.hpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+/**
+ * @addtogroup column_factories Factories
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Construct a dictionary column by copying the provided `keys`
+ * and `indices`.
+ *
+ * It is expected that `keys_column.has_nulls() == false`.
+ * It is assumed the elements in `keys_column` are unique and
+ * are in a strict, total order. Meaning, `keys_column[i]` is ordered before
+ * `keys_column[i+1]` for all `i in [0,n-1)` where `n` is the number of keys.
+ *
+ * The indices values must be in the range [0,keys_column.size()).
+ *
+ * The null_mask and null count for the output column are copied from the indices column.
+ * If element `i` in `indices_column` is null, then element `i` in the returned dictionary column
+ * will also be null.
+ *
+ * ```
+ * k = ["a","c","d"]
+ * i = [1,0,null,2,2]
+ * d = make_dictionary_column(k,i)
+ * d is now {["a","c","d"],[1,0,undefined,2,2]} bitmask={1,1,0,1,1}
+ * ```
+ *
+ * The null_mask and null count for the output column are copied from the indices column.
+ *
+ * @throw cudf::logic_error if keys_column contains nulls
+ * @throw cudf::logic_error if indices_column type is not INT32
+ *
+ * @param keys_column Column of unique, ordered values to use as the new dictionary column's keys.
+ * @param indices_column Indices to use for the new dictionary column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> make_dictionary_column(
+  column_view const& keys_column,
+  column_view const& indices_column,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a dictionary column by taking ownership of the provided keys
+ * and indices columns.
+ *
+ * The keys_column and indices columns must contain no nulls.
+ * It is assumed the elements in `keys_column` are unique and
+ * are in a strict, total order. Meaning, `keys_column[i]` is ordered before
+ * `keys_column[i+1]` for all `i in [0,n-1)` where `n` is the number of keys.
+ *
+ * The indices values must be in the range [0,keys_column.size()).
+ *
+ * @throw cudf::logic_error if keys_column or indices_column contains nulls
+ * @throw cudf::logic_error if indices_column type is not an unsigned integer type
+ *
+ * @param keys_column Column of unique, ordered values to use as the new dictionary column's keys.
+ * @param indices_column Indices to use for the new dictionary column.
+ * @param null_mask Null mask for the output column.
+ * @param null_count Number of nulls for the output column.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> make_dictionary_column(std::unique_ptr<column> keys_column,
+                                               std::unique_ptr<column> indices_column,
+                                               rmm::device_buffer&& null_mask,
+                                               size_type null_count);
+
+/**
+ * @brief Construct a dictionary column by taking ownership of the provided keys
+ * and indices columns.
+ *
+ * The `keys_column` must contain no nulls and is assumed to have elements
+ * that are unique and are in a strict, total order. Meaning, `keys_column[i]`
+ * is ordered before `keys_column[i+1]` for all `i in [0,n-1)` where `n` is the
+ * number of keys.
+ *
+ * The `indices_column` can be any integer type and should contain the null-mask
+ * to be used for the output column.
+ * The indices values must be in the range [0,keys_column.size()).
+ *
+ * @throw cudf::logic_error if keys_column contains nulls
+ *
+ * @param keys_column Column of unique, ordered values to use as the new dictionary column's keys.
+ * @param indices_column Indices values and null-mask to use for the new dictionary column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> make_dictionary_column(
+  std::unique_ptr<column> keys_column,
+  std::unique_ptr<column> indices_column,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/encode.hpp b/cpp/include/cudf/dictionary/encode.hpp
new file mode 100644
index 0000000..959b785
--- /dev/null
+++ b/cpp/include/cudf/dictionary/encode.hpp
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace dictionary {
+/**
+ * @addtogroup dictionary_encode
+ * @{
+ * @file
+ * @brief Dictionary column encode and decode APIs
+ */
+
+/**
+ * @brief Construct a dictionary column by dictionary encoding an existing column
+ *
+ * The output column is a DICTIONARY type with a keys column of non-null, unique values
+ * that are in a strict, total order. Meaning, `keys[i]` is _ordered before
+ * `keys[i+1]` for all `i in [0,n-1)` where `n` is the number of keys.
+
+ * The output column has a child indices column that is of integer type and with
+ * the same size as the input column.
+ *
+ * The null mask and null count are copied from the input column to the output column.
+ *
+ * @throw cudf::logic_error if indices type is not an unsigned integer type
+ * @throw cudf::logic_error if the column to encode is already a DICTIONARY type
+ *
+ * @code{.pseudo}
+ * c = [429, 111, 213, 111, 213, 429, 213]
+ * d = encode(c)
+ * d now has keys [111, 213, 429] and indices [2, 0, 1, 0, 1, 2, 1]
+ * @endcode
+ *
+ * @param column The column to dictionary encode
+ * @param indices_type The integer type to use for the indices
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Returns a dictionary column
+ */
+std::unique_ptr<column> encode(
+  column_view const& column,
+  data_type indices_type              = data_type{type_id::UINT32},
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a column by gathering the keys from the provided
+ * dictionary_column into a new column using the indices from that column.
+ *
+ * @code{.pseudo}
+ * d1 = {["a", "c", "d"], [2, 0, 1, 0]}
+ * s = decode(d1)
+ * s is now ["d", "a", "c", "a"]
+ * @endcode
+ *
+ * @param dictionary_column Existing dictionary column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column with type matching the dictionary_column's keys
+ */
+std::unique_ptr<column> decode(
+  dictionary_column_view const& dictionary_column,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/search.hpp b/cpp/include/cudf/dictionary/search.hpp
new file mode 100644
index 0000000..1b72cf4
--- /dev/null
+++ b/cpp/include/cudf/dictionary/search.hpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace dictionary {
+/**
+ * @addtogroup dictionary_search
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Return the index value for a given key.
+ *
+ * If the key does not exist in the dictionary the returned scalar will have `is_valid()==false`
+ *
+ * @throw cudf::logic_error if `key.type() != dictionary.keys().type()`
+ *
+ * @param dictionary The dictionary to search for the key.
+ * @param key The value to search for in the dictionary keyset.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned scalar's device memory.
+ * @return Numeric scalar index value of the key within the dictionary.
+ */
+std::unique_ptr<scalar> get_index(
+  dictionary_column_view const& dictionary,
+  scalar const& key,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/dictionary/update_keys.hpp b/cpp/include/cudf/dictionary/update_keys.hpp
new file mode 100644
index 0000000..81728e1
--- /dev/null
+++ b/cpp/include/cudf/dictionary/update_keys.hpp
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace dictionary {
+/**
+ * @addtogroup dictionary_update
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Create a new dictionary column by adding the new keys elements
+ * to the existing dictionary_column.
+ *
+ * The indices are updated if any of the new keys are sorted
+ * before any of the existing dictionary elements.
+ *
+ * @code{.pseudo}
+ * d1 = { keys=["a", "c", "d"], indices=[2, 0, 1, 0, 1]}
+ * d2 = add_keys( d1, ["b", "c"] )
+ * d2 is now {keys=["a", "b", "c", "d"], indices=[3, 0, 2, 0, 2]}
+ * @endcode
+ *
+ * The output column will have the same number of rows as the input column.
+ * Null entries from the input column are copied to the output column.
+ * No new null entries are created by this operation.
+ *
+ * @throw cudf_logic_error if the new_keys type does not match the keys type in
+ *        the dictionary_column.
+ * @throw cudf_logic_error if the new_keys contain nulls.
+ *
+ * @param dictionary_column Existing dictionary column.
+ * @param new_keys New keys to incorporate into the dictionary_column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> add_keys(
+  dictionary_column_view const& dictionary_column,
+  column_view const& new_keys,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a new dictionary column by removing the specified keys
+ * from the existing dictionary_column.
+ *
+ * The output column will have the same number of rows as the input column.
+ * Null entries from the input column and copied to the output column.
+ * The indices are updated to the new positions of the remaining keys.
+ * Any indices pointing to removed keys sets that row to null.
+ *
+ * @code{.pseudo}
+ * d1 = {keys=["a", "c", "d"], indices=[2, 0, 1, 0, 2]}
+ * d2 = remove_keys( d1, ["b", "c"] )
+ * d2 is now {keys=["a", "d"], indices=[1, 0, x, 0, 1], valids=[1, 1, 0, 1, 1]}
+ * @endcode
+ * Note that "a" has been removed so output row[2] becomes null.
+ *
+ * @throw cudf_logic_error if the keys_to_remove type does not match the keys type in
+ *        the dictionary_column.
+ * @throw cudf_logic_error if the keys_to_remove contain nulls.
+ *
+ * @param dictionary_column Existing dictionary column.
+ * @param keys_to_remove The keys to remove from the dictionary_column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> remove_keys(
+  dictionary_column_view const& dictionary_column,
+  column_view const& keys_to_remove,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a new dictionary column by removing any keys
+ * that are not referenced by any of the indices.
+ *
+ * The indices are updated to the new position values of the remaining keys.
+ *
+ * @code{.pseudo}
+ * d1 = {["a","c","d"],[2,0,2,0]}
+ * d2 = remove_unused_keys(d1)
+ * d2 is now {["a","d"],[1,0,1,0]}
+ * @endcode
+ *
+ * @param dictionary_column Existing dictionary column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> remove_unused_keys(
+  dictionary_column_view const& dictionary_column,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a new dictionary column by applying only the specified keys
+ * to the existing dictionary_column.
+ *
+ * Any new elements found in the keys parameter are added to the output dictionary.
+ * Any existing keys not in the keys parameter are removed.
+ *
+ * The number of rows in the output column will be the same as the number of rows
+ * in the input column. Existing null entries are copied to the output column.
+ * The indices are updated to reflect the position values of the new keys.
+ * Any indices pointing to removed keys sets those rows to null.
+ *
+ * @code{.pseudo}
+ * d1 = {keys=["a", "b", "c"], indices=[2, 0, 1, 2, 1]}
+ * d2 = set_keys(existing_dict, ["b","c","d"])
+ * d2 is now {keys=["b", "c", "d"], indices=[1, x, 0, 1, 0], valids=[1, 0, 1, 1, 1]}
+ * @endcode
+ *
+ * @throw cudf_logic_error if the keys type does not match the keys type in
+ *        the dictionary_column.
+ * @throw cudf_logic_error if the keys contain nulls.
+ *
+ * @param dictionary_column Existing dictionary column.
+ * @param keys New keys to use for the output column. Must not contain nulls.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary column.
+ */
+std::unique_ptr<column> set_keys(
+  dictionary_column_view const& dictionary_column,
+  column_view const& keys,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create new dictionaries that have keys merged from the input dictionaries.
+ *
+ * This will concatenate the keys for each dictionary and then call `set_keys` on each.
+ * The result is a vector of new dictionaries with a common set of keys.
+ *
+ * @param input Dictionary columns to match keys.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New dictionary columns.
+ */
+std::vector<std::unique_ptr<column>> match_dictionaries(
+  cudf::host_span<dictionary_column_view const> input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/include/cudf/filling.hpp b/cpp/include/cudf/filling.hpp
new file mode 100644
index 0000000..1268f48
--- /dev/null
+++ b/cpp/include/cudf/filling.hpp
@@ -0,0 +1,246 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup transformation_fill
+ * @{
+ * @file
+ * @brief Column APIs for fill, repeat, and sequence
+ */
+
+/**
+ * @brief Fills a range of elements in-place in a column with a scalar value.
+ *
+ * Fills N elements of @p destination starting at @p begin with @p value, where
+ * N = (@p end - @p begin).
+ *
+ * Overwrites the range of elements in @p destination indicated by the indices
+ * [@p begin, @p end) with @p value. Use the out-of-place fill function
+ * returning std::unique_ptr<column> for use cases requiring memory
+ * reallocation.
+ *
+ * @throws cudf::logic_error if memory reallocation is required (e.g. for
+ * variable width types).
+ * @throws cudf::logic_error for invalid range (if @p begin < 0,
+ * @p begin > @p end, or @p end > @p destination.size()).
+ * @throws cudf::logic_error if @p destination and @p value have different
+ * types.
+ * @throws cudf::logic_error if @p value is invalid but @p destination is not
+ * nullable.
+ *
+ * @param destination The preallocated column to fill into
+ * @param begin The starting index of the fill range (inclusive)
+ * @param end The index of the last element in the fill range (exclusive)
+ * @param value The scalar value to fill
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void fill_in_place(mutable_column_view& destination,
+                   size_type begin,
+                   size_type end,
+                   scalar const& value,
+                   rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Fills a range of elements in a column out-of-place with a scalar
+ * value.
+ *
+ * Creates a new column as-if an in-place fill was performed into @p input;
+ * i.e. it is as if a copy of @p input was created first and then the elements
+ * indicated by the indices [@p begin, @p end) were overwritten by @p value.
+ *
+ * @throws cudf::logic_error for invalid range (if @p begin < 0,
+ * @p begin > @p end, or @p end > @p destination.size()).
+ * @throws cudf::logic_error if @p destination and @p value have different
+ * types.
+ *
+ * @param input The input column used to create a new column. The new column
+ * is created by replacing the values of @p input in the specified range with
+ * @p value.
+ * @param begin The starting index of the fill range (inclusive)
+ * @param end The index of the last element in the fill range (exclusive)
+ * @param value The scalar value to fill
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return The result output column
+ */
+std::unique_ptr<column> fill(
+  column_view const& input,
+  size_type begin,
+  size_type end,
+  scalar const& value,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Repeat rows of a Table.
+ *
+ * Creates a new table by repeating the rows of @p input_table. The number of
+ * repetitions of each element is defined by the value at the corresponding
+ * index of @p count
+ * Example:
+ * ```
+ * in = [4,5,6]
+ * count = [1,2,3]
+ * return = [4,5,5,6,6,6]
+ * ```
+ * @p count should not have null values; should not contain negative values;
+ * and the sum of count elements should not overflow the size_type's limit.
+ * The behavior of this function is undefined if @p count has negative values
+ * or the sum overflows.
+ *
+ * @throws cudf::logic_error if the data type of @p count is not size_type.
+ * @throws cudf::logic_error if @p input_table and @p count have different
+ * number of rows.
+ * @throws cudf::logic_error if @p count has null values.
+ *
+ * @param input_table Input table
+ * @param count Non-nullable column of an integral type
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return The result table containing the repetitions
+ */
+std::unique_ptr<table> repeat(
+  table_view const& input_table,
+  column_view const& count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Repeat rows of a Table.
+ *
+ * Creates a new table by repeating @p count times the rows of @p input_table.
+ * Example:
+ * ```
+ * in = [4,5,6]
+ * count = 2
+ * return = [4,4,5,5,6,6]
+ * ```
+ * @throws cudf::logic_error if @p count is negative.
+ * @throws std::overflow_error if @p input_table.num_rows() * @p count overflows size_type.
+ *
+ * @param input_table Input table
+ * @param count Number of repetitions
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return The result table containing the repetitions
+ */
+std::unique_ptr<table> repeat(
+  table_view const& input_table,
+  size_type count,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Fills a column with a sequence of value specified by an initial value and a step.
+ *
+ * Creates a new column and fills with @p size values starting at @p init and
+ * incrementing by @p step, generating the sequence
+ * [ init, init+step, init+2*step, ... init + (size - 1)*step]
+ *
+ * ```
+ * size = 3
+ * init = 0
+ * step = 2
+ * return = [0, 2, 4]
+ * ```
+ * @throws cudf::logic_error if @p init and @p step are not the same type.
+ * @throws cudf::logic_error if scalar types are not numeric.
+ * @throws cudf::logic_error if @p size is < 0.
+ *
+ * @param size Size of the output column
+ * @param init First value in the sequence
+ * @param step Increment value
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return The result column containing the generated sequence
+ */
+std::unique_ptr<column> sequence(
+  size_type size,
+  scalar const& init,
+  scalar const& step,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Fills a column with a sequence of value specified by an initial value and a step of 1.
+ *
+ * Creates a new column and fills with @p size values starting at @p init and
+ * incrementing by 1, generating the sequence
+ * [ init, init+1, init+2, ... init + (size - 1)]
+ *
+ * ```
+ * size = 3
+ * init = 0
+ * return = [0, 1, 2]
+ * ```
+ * @throws cudf::logic_error if @p init is not numeric.
+ * @throws cudf::logic_error if @p size is < 0.
+ *
+ * @param size Size of the output column
+ * @param init First value in the sequence
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return The result column containing the generated sequence
+ */
+std::unique_ptr<column> sequence(
+  size_type size,
+  scalar const& init,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Generate a sequence of timestamps beginning at `init` and incrementing by `months` for
+ * each successive element, i.e., `output[i] = init + i * months` for `i` in `[0, size)`.
+ *
+ * If a given date is invalid, the date is scaled back to the last available day of that month.
+ *
+ * Example:
+ * ```
+ * size = 3
+ * init = 2020-01-31 08:00:00
+ * months = 1
+ * return = [2020-01-31 08:00:00, 2020-02-29 08:00:00, 2020-03-31 08:00:00]
+ * ```
+ *
+ * @throw cudf::logic_error if input datatype is not a TIMESTAMP
+ *
+ * @param size Number of timestamps to generate
+ * @param init The initial timestamp
+ * @param months Months to increment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @return Timestamps column with sequences of months
+ */
+std::unique_ptr<cudf::column> calendrical_month_sequence(
+  size_type size,
+  scalar const& init,
+  size_type months,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/fixed_point/fixed_point.hpp b/cpp/include/cudf/fixed_point/fixed_point.hpp
new file mode 100644
index 0000000..13d8716
--- /dev/null
+++ b/cpp/include/cudf/fixed_point/fixed_point.hpp
@@ -0,0 +1,833 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/fixed_point/temporary.hpp>
+#include <cudf/types.hpp>
+
+#include <cuda/std/limits>
+#include <cuda/std/type_traits>
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <string>
+
+/// `fixed_point` and supporting types
+namespace numeric {
+
+/// The scale type for fixed_point
+enum scale_type : int32_t {};
+
+/**
+ * @brief Scoped enumerator to use when constructing `fixed_point`
+ *
+ * Examples:
+ * ```cpp
+ * using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+ * using binary64  = fixed_point<int64_t, Radix::BASE_2>;
+ * ```
+ */
+enum class Radix : int32_t { BASE_2 = 2, BASE_10 = 10 };
+
+/**
+ * @brief Returns `true` if the representation type is supported by `fixed_point`
+ *
+ * @tparam T The representation type
+ * @return `true` if the type is supported by `fixed_point` implementation
+ */
+template <typename T>
+constexpr inline auto is_supported_representation_type()
+{
+  return cuda::std::is_same_v<T, int32_t> ||  //
+         cuda::std::is_same_v<T, int64_t> ||  //
+         cuda::std::is_same_v<T, __int128_t>;
+}
+
+/**
+ * @brief Returns `true` if the value type is supported for constructing a `fixed_point`
+ *
+ * @tparam T The construction value type
+ * @return `true` if the value type is supported to construct a `fixed_point` type
+ */
+template <typename T>
+constexpr inline auto is_supported_construction_value_type()
+{
+  return cuda::std::is_integral<T>() || cuda::std::is_floating_point_v<T>;
+}
+
+// Helper functions for `fixed_point` type
+namespace detail {
+/**
+ * @brief A function for integer exponentiation by squaring
+ *
+ * https://simple.wikipedia.org/wiki/Exponentiation_by_squaring <br>
+ * Note: this is the iterative equivalent of the recursive definition (faster) <br>
+ * Quick-bench: http://quick-bench.com/Wg7o7HYQC9FW5M0CO0wQAjSwP_Y
+ *
+ * @tparam Rep Representation type for return type
+ * @tparam Base The base to be exponentiated
+ * @param exponent The exponent to be used for exponentiation
+ * @return Result of `Base` to the power of `exponent` of type `Rep`
+ */
+template <typename Rep,
+          Radix Base,
+          typename T,
+          typename cuda::std::enable_if_t<(cuda::std::is_same_v<int32_t, T> &&
+                                           is_supported_representation_type<Rep>())>* = nullptr>
+CUDF_HOST_DEVICE inline Rep ipow(T exponent)
+{
+  cudf_assert(exponent >= 0 && "integer exponentiation with negative exponent is not possible.");
+  if (exponent == 0) { return static_cast<Rep>(1); }
+
+  auto extra  = static_cast<Rep>(1);
+  auto square = static_cast<Rep>(Base);
+  while (exponent > 1) {
+    if (exponent & 1 /* odd */) {
+      extra *= square;
+      exponent -= 1;
+    }
+    exponent /= 2;
+    square *= square;
+  }
+  return square * extra;
+}
+
+/** @brief Function that performs a `right shift` scale "times" on the `val`
+ *
+ * Note: perform this operation when constructing with positive scale
+ *
+ * @tparam Rep Representation type needed for integer exponentiation
+ * @tparam Rad The radix which will act as the base in the exponentiation
+ * @tparam T Type for value `val` being shifted and the return type
+ * @param val The value being shifted
+ * @param scale The amount to shift the value by
+ * @return Shifted value of type T
+ */
+template <typename Rep, Radix Rad, typename T>
+CUDF_HOST_DEVICE inline constexpr T right_shift(T const& val, scale_type const& scale)
+{
+  return val / ipow<Rep, Rad>(static_cast<int32_t>(scale));
+}
+
+/** @brief Function that performs a `left shift` scale "times" on the `val`
+ *
+ * Note: perform this operation when constructing with negative scale
+ *
+ * @tparam Rep Representation type needed for integer exponentiation
+ * @tparam Rad The radix which will act as the base in the exponentiation
+ * @tparam T Type for value `val` being shifted and the return type
+ * @param val The value being shifted
+ * @param scale The amount to shift the value by
+ * @return Shifted value of type T
+ */
+template <typename Rep, Radix Rad, typename T>
+CUDF_HOST_DEVICE inline constexpr T left_shift(T const& val, scale_type const& scale)
+{
+  return val * ipow<Rep, Rad>(static_cast<int32_t>(-scale));
+}
+
+/** @brief Function that performs a `right` or `left shift`
+ * scale "times" on the `val`
+ *
+ * Note: Function will call the correct right or left shift based
+ * on the sign of `val`
+ *
+ * @tparam Rep Representation type needed for integer exponentiation
+ * @tparam Rad The radix which will act as the base in the exponentiation
+ * @tparam T Type for value `val` being shifted and the return type
+ * @param val The value being shifted
+ * @param scale The amount to shift the value by
+ * @return Shifted value of type T
+ */
+template <typename Rep, Radix Rad, typename T>
+CUDF_HOST_DEVICE inline constexpr T shift(T const& val, scale_type const& scale)
+{
+  if (scale == 0) { return val; }
+  if (scale > 0) { return right_shift<Rep, Rad>(val, scale); }
+  return left_shift<Rep, Rad>(val, scale);
+}
+
+}  // namespace detail
+
+/**
+ * @addtogroup fixed_point_classes
+ * @{
+ * @file
+ * @brief Class definition for fixed point data type
+ */
+
+/**
+ * @brief Helper struct for constructing `fixed_point` when value is already shifted
+ *
+ * Example:
+ * ```cpp
+ * using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+ * auto n = decimal32{scaled_integer{1001, 3}}; // n = 1.001
+ * ```
+ *
+ * @tparam Rep The representation type (either `int32_t` or `int64_t`)
+ */
+template <typename Rep,
+          typename cuda::std::enable_if_t<is_supported_representation_type<Rep>()>* = nullptr>
+struct scaled_integer {
+  Rep value;         ///< The value of the fixed point number
+  scale_type scale;  ///< The scale of the value
+  /**
+   * @brief Constructor for `scaled_integer`
+   *
+   * @param v The value of the fixed point number
+   * @param s The scale of the value
+   */
+  CUDF_HOST_DEVICE inline explicit scaled_integer(Rep v, scale_type s) : value{v}, scale{s} {}
+};
+
+/**
+ * @brief A type for representing a number with a fixed amount of precision
+ *
+ * Currently, only binary and decimal `fixed_point` numbers are supported.
+ * Binary operations can only be performed with other `fixed_point` numbers
+ *
+ * @tparam Rep The representation type (either `int32_t` or `int64_t`)
+ * @tparam Rad The radix/base (either `Radix::BASE_2` or `Radix::BASE_10`)
+ */
+template <typename Rep, Radix Rad>
+class fixed_point {
+  Rep _value{};
+  scale_type _scale;
+
+ public:
+  using rep = Rep;  ///< The representation type
+
+  /**
+   * @brief Constructor that will perform shifting to store value appropriately (from floating point
+   * types)
+   *
+   * @tparam T The floating point type that you are constructing from
+   * @param value The value that will be constructed from
+   * @param scale The exponent that is applied to Rad to perform shifting
+   */
+  template <typename T,
+            typename cuda::std::enable_if_t<cuda::std::is_floating_point<T>() &&
+                                            is_supported_representation_type<Rep>()>* = nullptr>
+  CUDF_HOST_DEVICE inline explicit fixed_point(T const& value, scale_type const& scale)
+    : _value{static_cast<Rep>(detail::shift<Rep, Rad>(value, scale))}, _scale{scale}
+  {
+  }
+
+  /**
+   * @brief Constructor that will perform shifting to store value appropriately (from integral
+   * types)
+   *
+   * @tparam T The integral type that you are constructing from
+   * @param value The value that will be constructed from
+   * @param scale The exponent that is applied to Rad to perform shifting
+   */
+  template <typename T,
+            typename cuda::std::enable_if_t<cuda::std::is_integral<T>() &&
+                                            is_supported_representation_type<Rep>()>* = nullptr>
+  CUDF_HOST_DEVICE inline explicit fixed_point(T const& value, scale_type const& scale)
+    // `value` is cast to `Rep` to avoid overflow in cases where
+    // constructing to `Rep` that is wider than `T`
+    : _value{detail::shift<Rep, Rad>(static_cast<Rep>(value), scale)}, _scale{scale}
+  {
+  }
+
+  /**
+   * @brief Constructor that will not perform shifting (assumes value already shifted)
+   *
+   * @param s scaled_integer that contains scale and already shifted value
+   */
+  CUDF_HOST_DEVICE inline explicit fixed_point(scaled_integer<Rep> s)
+    : _value{s.value}, _scale{s.scale}
+  {
+  }
+
+  /**
+   * @brief "Scale-less" constructor that constructs `fixed_point` number with a specified
+   * value and scale of zero
+   *
+   * @tparam T The value type being constructing from
+   * @param value The value that will be constructed from
+   */
+  template <typename T,
+            typename cuda::std::enable_if_t<is_supported_construction_value_type<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline fixed_point(T const& value)
+    : _value{static_cast<Rep>(value)}, _scale{scale_type{0}}
+  {
+  }
+
+  /**
+   * @brief Default constructor that constructs `fixed_point` number with a
+   * value and scale of zero
+   */
+  CUDF_HOST_DEVICE inline fixed_point() : _scale{scale_type{0}} {}
+
+  /**
+   * @brief Explicit conversion operator for casting to floating point types
+   *
+   * @tparam U The floating point type that is being explicitly converted to
+   * @return The `fixed_point` number in base 10 (aka human readable format)
+   */
+  template <typename U,
+            typename cuda::std::enable_if_t<cuda::std::is_floating_point_v<U>>* = nullptr>
+  explicit constexpr operator U() const
+  {
+    return detail::shift<Rep, Rad>(static_cast<U>(_value), scale_type{-_scale});
+  }
+
+  /**
+   * @brief Explicit conversion operator for casting to integral types
+   *
+   * @tparam U The integral type that is being explicitly converted to
+   * @return The `fixed_point` number in base 10 (aka human readable format)
+   */
+  template <typename U, typename cuda::std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  explicit constexpr operator U() const
+  {
+    // Cast to the larger of the two types (of U and Rep) before converting to Rep because in
+    // certain cases casting to U before shifting will result in integer overflow (i.e. if U =
+    // int32_t, Rep = int64_t and _value > 2 billion)
+    auto const value = std::common_type_t<U, Rep>(_value);
+    return static_cast<U>(detail::shift<Rep, Rad>(value, scale_type{-_scale}));
+  }
+
+  /**
+   * @brief Converts the `fixed_point` number to a `scaled_integer`
+   *
+   * @return The `scaled_integer` representation of the `fixed_point` number
+   */
+  CUDF_HOST_DEVICE inline operator scaled_integer<Rep>() const
+  {
+    return scaled_integer<Rep>{_value, _scale};
+  }
+
+  /**
+   * @brief Method that returns the underlying value of the `fixed_point` number
+   *
+   * @return The underlying value of the `fixed_point` number
+   */
+  CUDF_HOST_DEVICE inline rep value() const { return _value; }
+
+  /**
+   * @brief Method that returns the scale of the `fixed_point` number
+   *
+   * @return The scale of the `fixed_point` number
+   */
+  CUDF_HOST_DEVICE inline scale_type scale() const { return _scale; }
+
+  /**
+   * @brief Explicit conversion operator to `bool`
+   *
+   * @return The `fixed_point` value as a boolean (zero is `false`, nonzero is `true`)
+   */
+  CUDF_HOST_DEVICE inline explicit constexpr operator bool() const
+  {
+    return static_cast<bool>(_value);
+  }
+
+  /**
+   * @brief operator +=
+   *
+   * @tparam Rep1 Representation type of the operand `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `rhs`
+   * @param rhs The number being added to `this`
+   * @return The sum
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1>& operator+=(fixed_point<Rep1, Rad1> const& rhs)
+  {
+    *this = *this + rhs;
+    return *this;
+  }
+
+  /**
+   * @brief operator *=
+   *
+   * @tparam Rep1 Representation type of the operand `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `rhs`
+   * @param rhs The number being multiplied to `this`
+   * @return The product
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1>& operator*=(fixed_point<Rep1, Rad1> const& rhs)
+  {
+    *this = *this * rhs;
+    return *this;
+  }
+
+  /**
+   * @brief operator -=
+   *
+   * @tparam Rep1 Representation type of the operand `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `rhs`
+   * @param rhs The number being subtracted from `this`
+   * @return The difference
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1>& operator-=(fixed_point<Rep1, Rad1> const& rhs)
+  {
+    *this = *this - rhs;
+    return *this;
+  }
+
+  /**
+   * @brief operator /=
+   *
+   * @tparam Rep1 Representation type of the operand `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `rhs`
+   * @param rhs The number being divided from `this`
+   * @return The quotient
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1>& operator/=(fixed_point<Rep1, Rad1> const& rhs)
+  {
+    *this = *this / rhs;
+    return *this;
+  }
+
+  /**
+   * @brief operator ++ (post-increment)
+   *
+   * @return The incremented result
+   */
+  CUDF_HOST_DEVICE inline fixed_point<Rep, Rad>& operator++()
+  {
+    *this = *this + fixed_point<Rep, Rad>{1, scale_type{_scale}};
+    return *this;
+  }
+
+  /**
+   * @brief operator + (for adding two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are added.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are added.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return The resulting `fixed_point` sum
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend fixed_point<Rep1, Rad1> operator+(
+    fixed_point<Rep1, Rad1> const& lhs, fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator - (for subtracting two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are subtracted.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are subtracted.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return The resulting `fixed_point` difference
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend fixed_point<Rep1, Rad1> operator-(
+    fixed_point<Rep1, Rad1> const& lhs, fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator * (for multiplying two `fixed_point` numbers)
+   *
+   * `_scale`s are added and `_value`s are multiplied.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return The resulting `fixed_point` product
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend fixed_point<Rep1, Rad1> operator*(
+    fixed_point<Rep1, Rad1> const& lhs, fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator / (for dividing two `fixed_point` numbers)
+   *
+   * `_scale`s are subtracted and `_value`s are divided.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return The resulting `fixed_point` quotient
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend fixed_point<Rep1, Rad1> operator/(
+    fixed_point<Rep1, Rad1> const& lhs, fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator % (for computing the modulo operation of two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, the modulus is computed directly.
+   * If `_scale`s are not equal, the number with larger `_scale` is shifted to the
+   * smaller `_scale`, and then the modulus is computed.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return The resulting `fixed_point` number
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend fixed_point<Rep1, Rad1> operator%(
+    fixed_point<Rep1, Rad1> const& lhs, fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator == (for comparing two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are compared.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are compared.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return true if `lhs` and `rhs` are equal, false if not
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend bool operator==(fixed_point<Rep1, Rad1> const& lhs,
+                                                 fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator != (for comparing two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are compared.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are compared.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return true if `lhs` and `rhs` are not equal, false if not
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend bool operator!=(fixed_point<Rep1, Rad1> const& lhs,
+                                                 fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator <= (for comparing two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are compared.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are compared.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return true if `lhs` less than or equal to `rhs`, false if not
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend bool operator<=(fixed_point<Rep1, Rad1> const& lhs,
+                                                 fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator >= (for comparing two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are compared.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are compared.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return true if `lhs` greater than or equal to `rhs`, false if not
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend bool operator>=(fixed_point<Rep1, Rad1> const& lhs,
+                                                 fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator < (for comparing two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are compared.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are compared.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return true if `lhs` less than `rhs`, false if not
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend bool operator<(fixed_point<Rep1, Rad1> const& lhs,
+                                                fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief operator > (for comparing two `fixed_point` numbers)
+   *
+   * If `_scale`s are equal, `_value`s are compared.
+   * If `_scale`s are not equal, the number with the larger `_scale` is shifted to the
+   * smaller `_scale`, and then the `_value`s are compared.
+   *
+   * @tparam Rep1 Representation type of the operand `lhs` and `rhs`
+   * @tparam Rad1 Radix (base) type of the operand `lhs` and `rhs`
+   * @param lhs The left hand side operand
+   * @param rhs The right hand side operand
+   * @return true if `lhs` greater than `rhs`, false if not
+   */
+  template <typename Rep1, Radix Rad1>
+  CUDF_HOST_DEVICE inline friend bool operator>(fixed_point<Rep1, Rad1> const& lhs,
+                                                fixed_point<Rep1, Rad1> const& rhs);
+
+  /**
+   * @brief Method for creating a `fixed_point` number with a new `scale`
+   *
+   * The `fixed_point` number returned will have the same value, underlying representation and
+   * radix as `this`, the only thing changed is the scale.
+   *
+   * @param scale The `scale` of the returned `fixed_point` number
+   * @return `fixed_point` number with a new `scale`
+   */
+  CUDF_HOST_DEVICE inline fixed_point<Rep, Rad> rescaled(scale_type scale) const
+  {
+    if (scale == _scale) { return *this; }
+    Rep const value = detail::shift<Rep, Rad>(_value, scale_type{scale - _scale});
+    return fixed_point<Rep, Rad>{scaled_integer<Rep>{value, scale}};
+  }
+
+  /**
+   * @brief Returns a string representation of the fixed_point value.
+   */
+  explicit operator std::string() const
+  {
+    if (_scale < 0) {
+      auto const av = detail::abs(_value);
+      Rep const n   = detail::exp10<Rep>(-_scale);
+      Rep const f   = av % n;
+      auto const num_zeros =
+        std::max(0, (-_scale - static_cast<int32_t>(detail::to_string(f).size())));
+      auto const zeros = std::string(num_zeros, '0');
+      auto const sign  = _value < 0 ? std::string("-") : std::string();
+      return sign + detail::to_string(av / n) + std::string(".") + zeros +
+             detail::to_string(av % n);
+    }
+    auto const zeros = std::string(_scale, '0');
+    return detail::to_string(_value) + zeros;
+  }
+};
+
+/**
+ *  @brief Function for identifying integer overflow when adding
+ *
+ * @tparam Rep Type of integer to check for overflow on
+ * @tparam T Types of lhs and rhs (ensures they are the same type)
+ * @param lhs Left hand side of addition
+ * @param rhs Right hand side of addition
+ * @return true if addition causes overflow, false otherwise
+ */
+template <typename Rep, typename T>
+CUDF_HOST_DEVICE inline auto addition_overflow(T lhs, T rhs)
+{
+  return rhs > 0 ? lhs > cuda::std::numeric_limits<Rep>::max() - rhs
+                 : lhs < cuda::std::numeric_limits<Rep>::min() - rhs;
+}
+
+/** @brief Function for identifying integer overflow when subtracting
+ *
+ * @tparam Rep Type of integer to check for overflow on
+ * @tparam T Types of lhs and rhs (ensures they are the same type)
+ * @param lhs Left hand side of subtraction
+ * @param rhs Right hand side of subtraction
+ * @return true if subtraction causes overflow, false otherwise
+ */
+template <typename Rep, typename T>
+CUDF_HOST_DEVICE inline auto subtraction_overflow(T lhs, T rhs)
+{
+  return rhs > 0 ? lhs < cuda::std::numeric_limits<Rep>::min() + rhs
+                 : lhs > cuda::std::numeric_limits<Rep>::max() + rhs;
+}
+
+/** @brief Function for identifying integer overflow when dividing
+ *
+ * @tparam Rep Type of integer to check for overflow on
+ * @tparam T Types of lhs and rhs (ensures they are the same type)
+ * @param lhs Left hand side of division
+ * @param rhs Right hand side of division
+ * @return true if division causes overflow, false otherwise
+ */
+template <typename Rep, typename T>
+CUDF_HOST_DEVICE inline auto division_overflow(T lhs, T rhs)
+{
+  return lhs == cuda::std::numeric_limits<Rep>::min() && rhs == -1;
+}
+
+/** @brief Function for identifying integer overflow when multiplying
+ *
+ * @tparam Rep Type of integer to check for overflow on
+ * @tparam T Types of lhs and rhs (ensures they are the same type)
+ * @param lhs Left hand side of multiplication
+ * @param rhs Right hand side of multiplication
+ * @return true if multiplication causes overflow, false otherwise
+ */
+template <typename Rep, typename T>
+CUDF_HOST_DEVICE inline auto multiplication_overflow(T lhs, T rhs)
+{
+  auto const min = cuda::std::numeric_limits<Rep>::min();
+  auto const max = cuda::std::numeric_limits<Rep>::max();
+  if (rhs > 0) { return lhs > max / rhs || lhs < min / rhs; }
+  if (rhs < -1) { return lhs > min / rhs || lhs < max / rhs; }
+  return rhs == -1 && lhs == min;
+}
+
+// PLUS Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1> operator+(fixed_point<Rep1, Rad1> const& lhs,
+                                                          fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  auto const sum   = lhs.rescaled(scale)._value + rhs.rescaled(scale)._value;
+
+#if defined(__CUDACC_DEBUG__)
+
+  assert(!addition_overflow<Rep1>(lhs.rescaled(scale)._value, rhs.rescaled(scale)._value) &&
+         "fixed_point overflow");
+
+#endif
+
+  return fixed_point<Rep1, Rad1>{scaled_integer<Rep1>{sum, scale}};
+}
+
+// MINUS Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1> operator-(fixed_point<Rep1, Rad1> const& lhs,
+                                                          fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  auto const diff  = lhs.rescaled(scale)._value - rhs.rescaled(scale)._value;
+
+#if defined(__CUDACC_DEBUG__)
+
+  assert(!subtraction_overflow<Rep1>(lhs.rescaled(scale)._value, rhs.rescaled(scale)._value) &&
+         "fixed_point overflow");
+
+#endif
+
+  return fixed_point<Rep1, Rad1>{scaled_integer<Rep1>{diff, scale}};
+}
+
+// MULTIPLIES Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1> operator*(fixed_point<Rep1, Rad1> const& lhs,
+                                                          fixed_point<Rep1, Rad1> const& rhs)
+{
+#if defined(__CUDACC_DEBUG__)
+
+  assert(!multiplication_overflow<Rep1>(lhs._value, rhs._value) && "fixed_point overflow");
+
+#endif
+
+  return fixed_point<Rep1, Rad1>{
+    scaled_integer<Rep1>(lhs._value * rhs._value, scale_type{lhs._scale + rhs._scale})};
+}
+
+// DIVISION Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1> operator/(fixed_point<Rep1, Rad1> const& lhs,
+                                                          fixed_point<Rep1, Rad1> const& rhs)
+{
+#if defined(__CUDACC_DEBUG__)
+
+  assert(!division_overflow<Rep1>(lhs._value, rhs._value) && "fixed_point overflow");
+
+#endif
+
+  return fixed_point<Rep1, Rad1>{
+    scaled_integer<Rep1>(lhs._value / rhs._value, scale_type{lhs._scale - rhs._scale})};
+}
+
+// EQUALITY COMPARISON Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline bool operator==(fixed_point<Rep1, Rad1> const& lhs,
+                                        fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  return lhs.rescaled(scale)._value == rhs.rescaled(scale)._value;
+}
+
+// EQUALITY NOT COMPARISON Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline bool operator!=(fixed_point<Rep1, Rad1> const& lhs,
+                                        fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  return lhs.rescaled(scale)._value != rhs.rescaled(scale)._value;
+}
+
+// LESS THAN OR EQUAL TO Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline bool operator<=(fixed_point<Rep1, Rad1> const& lhs,
+                                        fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  return lhs.rescaled(scale)._value <= rhs.rescaled(scale)._value;
+}
+
+// GREATER THAN OR EQUAL TO Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline bool operator>=(fixed_point<Rep1, Rad1> const& lhs,
+                                        fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  return lhs.rescaled(scale)._value >= rhs.rescaled(scale)._value;
+}
+
+// LESS THAN Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline bool operator<(fixed_point<Rep1, Rad1> const& lhs,
+                                       fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  return lhs.rescaled(scale)._value < rhs.rescaled(scale)._value;
+}
+
+// GREATER THAN Operation
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline bool operator>(fixed_point<Rep1, Rad1> const& lhs,
+                                       fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale = std::min(lhs._scale, rhs._scale);
+  return lhs.rescaled(scale)._value > rhs.rescaled(scale)._value;
+}
+
+// MODULO OPERATION
+template <typename Rep1, Radix Rad1>
+CUDF_HOST_DEVICE inline fixed_point<Rep1, Rad1> operator%(fixed_point<Rep1, Rad1> const& lhs,
+                                                          fixed_point<Rep1, Rad1> const& rhs)
+{
+  auto const scale     = std::min(lhs._scale, rhs._scale);
+  auto const remainder = lhs.rescaled(scale)._value % rhs.rescaled(scale)._value;
+  return fixed_point<Rep1, Rad1>{scaled_integer<Rep1>{remainder, scale}};
+}
+
+using decimal32  = fixed_point<int32_t, Radix::BASE_10>;     ///<  32-bit decimal fixed point
+using decimal64  = fixed_point<int64_t, Radix::BASE_10>;     ///<  64-bit decimal fixed point
+using decimal128 = fixed_point<__int128_t, Radix::BASE_10>;  ///< 128-bit decimal fixed point
+
+/** @} */  // end of group
+}  // namespace numeric
diff --git a/cpp/include/cudf/fixed_point/temporary.hpp b/cpp/include/cudf/fixed_point/temporary.hpp
new file mode 100644
index 0000000..1de7f66
--- /dev/null
+++ b/cpp/include/cudf/fixed_point/temporary.hpp
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+// To avoid https://github.com/NVIDIA/libcudacxx/issues/460
+// in libcudacxx with CTK 12.0/12.1
+#include <cuda_runtime.h>
+
+#include <cudf/types.hpp>
+
+#include <cuda/std/limits>
+#include <cuda/std/type_traits>
+
+#include <algorithm>
+#include <string>
+
+namespace numeric {
+namespace detail {
+
+template <typename T>
+auto to_string(T value) -> std::string
+{
+  if constexpr (cuda::std::is_same_v<T, __int128_t>) {
+    auto s          = std::string{};
+    auto const sign = value < 0;
+    if (sign) {
+      value += 1;  // avoid overflowing if value == _int128_t lowest
+      value *= -1;
+      if (value == cuda::std::numeric_limits<__int128_t>::max())
+        return "-170141183460469231731687303715884105728";
+      value += 1;  // can add back the one, no need to avoid overflow anymore
+    }
+    while (value) {
+      s.push_back("0123456789"[value % 10]);
+      value /= 10;
+    }
+    if (sign) s.push_back('-');
+    std::reverse(s.begin(), s.end());
+    return s;
+  } else {
+    return std::to_string(value);
+  }
+  return std::string{};  // won't ever hit here, need to suppress warning though
+}
+
+template <typename T>
+constexpr auto abs(T value)
+{
+  return value >= 0 ? value : -value;
+}
+
+template <typename T>
+CUDF_HOST_DEVICE inline auto min(T lhs, T rhs)
+{
+  return lhs < rhs ? lhs : rhs;
+}
+
+template <typename T>
+CUDF_HOST_DEVICE inline auto max(T lhs, T rhs)
+{
+  return lhs > rhs ? lhs : rhs;
+}
+
+template <typename BaseType>
+constexpr auto exp10(int32_t exponent)
+{
+  BaseType value = 1;
+  while (exponent > 0)
+    value *= 10, --exponent;
+  return value;
+}
+
+}  // namespace detail
+}  // namespace numeric
diff --git a/cpp/include/cudf/groupby.hpp b/cpp/include/cudf/groupby.hpp
new file mode 100644
index 0000000..1c31e87
--- /dev/null
+++ b/cpp/include/cudf/groupby.hpp
@@ -0,0 +1,422 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <utility>
+#include <vector>
+
+namespace cudf {
+//! `groupby` APIs
+namespace groupby {
+namespace detail {
+namespace sort {
+class sort_groupby_helper;
+
+}  // namespace sort
+}  // namespace detail
+
+/**
+ * @addtogroup aggregation_groupby
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Request for groupby aggregation(s) to perform on a column.
+ *
+ * The group membership of each `value[i]` is determined by the corresponding
+ * row `i` in the original order of `keys` used to construct the
+ * `groupby`. I.e., for each `aggregation`, `values[i]` is aggregated with all
+ * other `values[j]` where rows `i` and `j` in `keys` are equivalent.
+ *
+ * `values.size()` column must equal `keys.num_rows()`.
+ */
+struct aggregation_request {
+  column_view values;                                              ///< The elements to aggregate
+  std::vector<std::unique_ptr<groupby_aggregation>> aggregations;  ///< Desired aggregations
+};
+
+/**
+ * @brief Request for groupby aggregation(s) for scanning a column.
+ *
+ * The group membership of each `value[i]` is determined by the corresponding
+ * row `i` in the original order of `keys` used to construct the
+ * `groupby`. I.e., for each `aggregation`, `values[i]` is aggregated with all
+ * other `values[j]` where rows `i` and `j` in `keys` are equivalent.
+ *
+ * `values.size()` column must equal `keys.num_rows()`.
+ */
+struct scan_request {
+  column_view values;  ///< The elements to aggregate
+  std::vector<std::unique_ptr<groupby_scan_aggregation>> aggregations;  ///< Desired aggregations
+};
+
+/**
+ * @brief The result(s) of an `aggregation_request`
+ *
+ * For every `aggregation_request` given to `groupby::aggregate` an
+ * `aggregation_result` will be returned. The `aggregation_result` holds the
+ * resulting column(s) for each requested aggregation on the `request`s values.
+ */
+struct aggregation_result {
+  /// Columns of results from an `aggregation_request`
+  std::vector<std::unique_ptr<column>> results{};
+};
+
+/**
+ * @brief Groups values by keys and computes aggregations on those groups.
+ */
+class groupby {
+ public:
+  groupby() = delete;
+  ~groupby();
+  groupby(groupby const&)            = delete;
+  groupby(groupby&&)                 = delete;
+  groupby& operator=(groupby const&) = delete;
+  groupby& operator=(groupby&&)      = delete;
+
+  /**
+   * @brief Construct a groupby object with the specified `keys`
+   *
+   * If the `keys` are already sorted, better performance may be achieved by
+   * passing `keys_are_sorted == true` and indicating the  ascending/descending
+   * order of each column and null order in  `column_order` and
+   * `null_precedence`, respectively.
+   *
+   * @note This object does *not* maintain the lifetime of `keys`. It is the
+   * user's responsibility to ensure the `groupby` object does not outlive the
+   * data viewed by the `keys` `table_view`.
+   *
+   * @param keys Table whose rows act as the groupby keys
+   * @param null_handling Indicates whether rows in `keys` that contain
+   * NULL values should be included
+   * @param keys_are_sorted Indicates whether rows in `keys` are already sorted
+   * @param column_order If `keys_are_sorted == YES`, indicates whether each
+   * column is ascending/descending. If empty, assumes all  columns are
+   * ascending. Ignored if `keys_are_sorted == false`.
+   * @param null_precedence If `keys_are_sorted == YES`, indicates the ordering
+   * of null values in each column. Else, ignored. If empty, assumes all columns
+   * use `null_order::AFTER`. Ignored if `keys_are_sorted == false`.
+   */
+  explicit groupby(table_view const& keys,
+                   null_policy null_handling                      = null_policy::EXCLUDE,
+                   sorted keys_are_sorted                         = sorted::NO,
+                   std::vector<order> const& column_order         = {},
+                   std::vector<null_order> const& null_precedence = {});
+
+  /**
+   * @brief Performs grouped aggregations on the specified values.
+   *
+   * The values to aggregate and the aggregations to perform are specified in an
+   * `aggregation_request`. Each request contains a `column_view` of values to
+   * aggregate and a set of `aggregation`s to perform on those elements.
+   *
+   * For each `aggregation` in a request, `values[i]` is aggregated with
+   * all other `values[j]` where rows `i` and `j` in `keys` are equivalent.
+   *
+   * The `size()` of the request column must equal `keys.num_rows()`.
+   *
+   * For every `aggregation_request` an `aggregation_result` will be returned.
+   * The `aggregation_result` holds the resulting column(s) for each requested
+   * aggregation on the `request`s values. The order of the columns in each
+   * result is the same order as was specified in the request.
+   *
+   * The returned `table` contains the group labels for each group, i.e., the
+   * unique rows from `keys`. Element `i` across all aggregation results
+   * belongs to the group at row `i` in the group labels table.
+   *
+   * The order of the rows in the group labels is arbitrary. Furthermore,
+   * successive `groupby::aggregate` calls may return results in different
+   * orders.
+   *
+   * @throws cudf::logic_error If `requests[i].values.size() !=
+   * keys.num_rows()`.
+   *
+   * Example:
+   * ```
+   * Input:
+   * keys:     {1 2 1 3 1}
+   *           {1 2 1 4 1}
+   * request:
+   *   values: {3 1 4 9 2}
+   *   aggregations: {{SUM}, {MIN}}
+   *
+   * result:
+   *
+   * keys:  {3 1 2}
+   *        {4 1 2}
+   * values:
+   *   SUM: {9 9 1}
+   *   MIN: {9 2 1}
+   * ```
+   *
+   * @param requests The set of columns to aggregate and the aggregations to
+   * perform
+   * @param mr Device memory resource used to allocate the returned table and columns' device memory
+   * @return Pair containing the table with each group's unique key and
+   * a vector of aggregation_results for each request in the same order as
+   * specified in `requests`.
+   */
+  std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> aggregate(
+    host_span<aggregation_request const> requests,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @copydoc aggregate(host_span<aggregation_request const>, rmm::mr::device_memory_resource*)
+   *
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   */
+  std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> aggregate(
+    host_span<aggregation_request const> requests,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+  /**
+   * @brief Performs grouped scans on the specified values.
+   *
+   * The values to aggregate and the aggregations to perform are specified in an
+   * `aggregation_request`. Each request contains a `column_view` of values to
+   * aggregate and a set of `aggregation`s to perform on those elements.
+   *
+   * For each `aggregation` in a request, `values[i]` is scan aggregated with
+   * all previous `values[j]` where rows `i` and `j` in `keys` are equivalent.
+   *
+   * The `size()` of the request column must equal `keys.num_rows()`.
+   *
+   * For every `aggregation_request` an `aggregation_result` will be returned.
+   * The `aggregation_result` holds the resulting column(s) for each requested
+   * aggregation on the `request`s values. The order of the columns in each
+   * result is the same order as was specified in the request.
+   *
+   * The returned `table` contains the group labels for each row, i.e., the
+   * `keys` given to groupby object. Element `i` across all aggregation results
+   * belongs to the group at row `i` in the group labels table.
+   *
+   * The order of the rows in the group labels is arbitrary. Furthermore,
+   * successive `groupby::scan` calls may return results in different orders.
+   *
+   * @throws cudf::logic_error If `requests[i].values.size() !=
+   * keys.num_rows()`.
+   *
+   * Example:
+   * ```
+   * Input:
+   * keys:     {1 2 1 3 1}
+   *           {1 2 1 4 1}
+   * request:
+   *   values: {3 1 4 9 2}
+   *   aggregations: {{SUM}, {MIN}}
+   *
+   * result:
+   *
+   * keys:  {3 1 1 1 2}
+   *        {4 1 1 1 2}
+   * values:
+   *   SUM: {9 3 7 9 1}
+   *   MIN: {9 3 3 2 1}
+   * ```
+   *
+   * @param requests The set of columns to scan and the scans to perform
+   * @param mr Device memory resource used to allocate the returned table and columns' device memory
+   * @return Pair containing the table with each group's key and
+   * a vector of aggregation_results for each request in the same order as
+   * specified in `requests`.
+   */
+  std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> scan(
+    host_span<scan_request const> requests,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Performs grouped shifts for specified values.
+   *
+   * In `j`th column, for each group, `i`th element is determined by the `i - offsets[j]`th
+   * element of the group. If `i - offsets[j] < 0 or >= group_size`, the value is determined by
+   * @p fill_values[j].
+   *
+   * @note The first returned table stores the keys passed to the groupby object. Row `i` of the key
+   * table corresponds to the group labels of row `i` in the shifted columns. The key order in
+   * each group matches the input order. The order of each group is arbitrary. The group order
+   * in successive calls to `groupby::shifts` may be different.
+   *
+   * Example:
+   * @code{.pseudo}
+   * keys:    {1 4 1 3 4 4 1}
+   *          {1 2 1 3 2 2 1}
+   * values:  {3 9 1 4 2 5 7}
+   *          {"a" "c" "bb" "ee" "z" "x" "d"}
+   * offset:  {2, -1}
+   * fill_value: {@, @}
+   * result (group order maybe different):
+   *    keys:   {3 1 1 1 4 4 4}
+   *            {3 1 1 1 2 2 2}
+   *    values: {@ @ @ 3 @ @ 9}
+   *            {@ "bb" "d" @ "z" "x" @}
+   *
+   * -------------------------------------------------
+   * keys:    {1 4 1 3 4 4 1}
+   *          {1 2 1 3 2 2 1}
+   * values:  {3 9 1 4 2 5 7}
+   *          {"a" "c" "bb" "ee" "z" "x" "d"}
+   * offset:  {-2, 1}
+   * fill_value: {-1, "42"}
+   * result (group order maybe different):
+   *    keys:   {3 1 1 1 4 4 4}
+   *            {3 1 1 1 2 2 2}
+   *    values: {-1 7 -1 -1 5 -1 -1}
+   *            {"42" "42" "a" "bb" "42" "c" "z"}
+   *
+   * @endcode
+   *
+   * @param values Table whose columns to be shifted
+   * @param offsets The offsets by which to shift the input
+   * @param fill_values Fill values for indeterminable outputs
+   * @param mr Device memory resource used to allocate the returned table and columns' device memory
+   * @return Pair containing the tables with each group's key and the columns shifted
+   *
+   * @throws cudf::logic_error if @p fill_value[i] dtype does not match @p values[i] dtype for
+   * `i`th column
+   */
+  std::pair<std::unique_ptr<table>, std::unique_ptr<table>> shift(
+    table_view const& values,
+    host_span<size_type const> offsets,
+    std::vector<std::reference_wrapper<scalar const>> const& fill_values,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief The grouped data corresponding to a groupby operation on a set of values.
+   *
+   * A `groups` object holds two tables of identical number of rows:
+   * a table of grouped keys and a table of grouped values. In addition, it holds
+   * a vector of integer offsets into the rows of the tables, such that
+   * `offsets[i+1] - offsets[i]` gives the size of group `i`.
+   */
+  struct groups {
+    std::unique_ptr<table> keys;     ///< Table of grouped keys
+    std::vector<size_type> offsets;  ///< Group Offsets
+    std::unique_ptr<table> values;   ///< Table of grouped values
+  };
+
+  /**
+   * @brief Get the grouped keys and values corresponding to a groupby operation on a set of values
+   *
+   * Returns a `groups` object representing the grouped keys and values.
+   * If values is not provided, only a grouping of the keys is performed,
+   * and the `values` of the `groups` object will be `nullptr`.
+   *
+   * @param values Table representing values on which a groupby operation is to be performed
+   * @param mr Device memory resource used to allocate the returned tables's device memory in the
+   * returned groups
+   * @return A `groups` object representing grouped keys and values
+   */
+  groups get_groups(cudf::table_view values             = {},
+                    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Performs grouped replace nulls on @p value
+   *
+   * For each `value[i] == NULL` in group `j`, `value[i]` is replaced with the first non-null value
+   * in group `j` that precedes or follows `value[i]`. If a non-null value is not found in the
+   * specified direction, `value[i]` is left NULL.
+   *
+   * The returned pair contains a column of the sorted keys and the result column. In result column,
+   * values of the same group are in contiguous memory. In each group, the order of values maintain
+   * their original order. The order of groups are not guaranteed.
+   *
+   * Example:
+   * @code{.pseudo}
+   *
+   * //Inputs:
+   * keys:    {3 3 1 3 1 3 4}
+   *          {2 2 1 2 1 2 5}
+   * values:  {3 4 7 @ @ @ @}
+   *          {@ @ @ "x" "tt" @ @}
+   * replace_policies:    {FORWARD, BACKWARD}
+   *
+   * //Outputs (group orders may be different):
+   * keys:    {3 3 3 3 1 1 4}
+   *          {2 2 2 2 1 1 5}
+   * result:  {3 4 4 4 7 7 @}
+   *          {"x" "x" "x" @ "tt" "tt" @}
+   * @endcode
+   *
+   * @param[in] values A table whose column null values will be replaced
+   * @param[in] replace_policies Specify the position of replacement values relative to null values,
+   * one for each column
+   * @param[in] mr Device memory resource used to allocate device memory of the returned column
+   *
+   * @return Pair that contains a table with the sorted keys and the result column
+   */
+  std::pair<std::unique_ptr<table>, std::unique_ptr<table>> replace_nulls(
+    table_view const& values,
+    host_span<cudf::replace_policy const> replace_policies,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+ private:
+  table_view _keys;                                      ///< Keys that determine grouping
+  null_policy _include_null_keys{null_policy::EXCLUDE};  ///< Include rows in keys
+                                                         ///< with NULLs
+  sorted _keys_are_sorted{sorted::NO};                   ///< Whether or not the keys are sorted
+  std::vector<order> _column_order{};                    ///< If keys are sorted, indicates
+                                                         ///< the order of each column
+  std::vector<null_order> _null_precedence{};            ///< If keys are sorted,
+                                                         ///< indicates null order
+                                                         ///< of each column
+  std::unique_ptr<detail::sort::sort_groupby_helper>
+    _helper;  ///< Helper object
+              ///< used by sort based implementation
+
+  /**
+   * @brief Get the sort helper object
+   *
+   * The object is constructed on first invocation and subsequent invocations
+   * of this function return the memoized object.
+   */
+  detail::sort::sort_groupby_helper& helper();
+
+  /**
+   * @brief Dispatches to the appropriate implementation to satisfy the
+   * aggregation requests.
+   */
+  std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> dispatch_aggregation(
+    host_span<aggregation_request const> requests,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr);
+
+  // Sort-based groupby
+  std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> sort_aggregate(
+    host_span<aggregation_request const> requests,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr);
+
+  std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> sort_scan(
+    host_span<scan_request const> requests,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr);
+};
+/** @} */
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/include/cudf/hashing.hpp b/cpp/include/cudf/hashing.hpp
new file mode 100644
index 0000000..72e3271
--- /dev/null
+++ b/cpp/include/cudf/hashing.hpp
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+
+/**
+ * @addtogroup column_hash
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Type of hash value
+ *
+ */
+using hash_value_type = uint32_t;
+
+/**
+ * @brief Identifies the hash function to be used
+ *
+ */
+enum class hash_id {
+  HASH_IDENTITY = 0,   ///< Identity hash function that simply returns the key to be hashed
+  HASH_MURMUR3,        ///< Murmur3 hash function
+  HASH_SPARK_MURMUR3,  ///< Spark Murmur3 hash function
+  HASH_MD5             ///< MD5 hash function
+};
+
+/**
+ * @brief The default seed value for hash functions
+ */
+static constexpr uint32_t DEFAULT_HASH_SEED = 0;
+
+/**
+ * @brief Computes the hash value of each row in the input set of columns.
+ *
+ * @deprecated Since 23.08
+ *
+ * @param input The table of columns to hash
+ * @param hash_function The hash function enum to use
+ * @param seed Optional seed value to use for the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A column where each row is the hash of a column from the input
+ */
+std::unique_ptr<column> hash(
+  table_view const& input,
+  hash_id hash_function               = hash_id::HASH_MURMUR3,
+  uint32_t seed                       = DEFAULT_HASH_SEED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+//! Hash APIs
+namespace hashing {
+
+/**
+ * @brief Computes the MurmurHash3 32-bit hash value of each row in the given table
+ *
+ * This function computes the hash of each column using the `seed` for the first column
+ * and the resulting hash as a seed for the next column and so on.
+ * The result is a uint32 value for each row.
+ *
+ * @param input The table of columns to hash
+ * @param seed Optional seed value to use for the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A column where each row is the hash of a row from the input
+ */
+std::unique_ptr<column> murmurhash3_x86_32(
+  table_view const& input,
+  uint32_t seed                       = DEFAULT_HASH_SEED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the MurmurHash3 64-bit hash value of each row in the given table
+ *
+ * This function takes a 64-bit seed value and returns hash values using the
+ * MurmurHash3_x64_128 algorithm. The hash produces in two uint64 values per row.
+ *
+ * @param input The table of columns to hash
+ * @param seed Optional seed value to use for the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A table of two UINT64 columns
+ */
+std::unique_ptr<table> murmurhash3_x64_128(
+  table_view const& input,
+  uint64_t seed                       = DEFAULT_HASH_SEED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the MurmurHash3 32-bit hash value of each row in the given table
+ *
+ * This function computes the hash similar to MurmurHash3_x86_32 with special processing
+ * to match Spark's implementation results.
+ *
+ * @param input The table of columns to hash
+ * @param seed Optional seed value to use for the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A column where each row is the hash of a row from the input
+ */
+std::unique_ptr<column> spark_murmurhash3_x86_32(
+  table_view const& input,
+  uint32_t seed                       = DEFAULT_HASH_SEED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the MD5 hash value of each row in the given table
+ *
+ * @param input The table of columns to hash
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A column where each row is the hash of a row from the input
+ */
+std::unique_ptr<column> md5(
+  table_view const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the XXHash_64 hash value of each row in the given table
+ *
+ * This function takes a 64-bit seed value and returns a column of type UINT64.
+ *
+ * @param input The table of columns to hash
+ * @param seed Optional seed value to use for the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A column where each row is the hash of a row from the input
+ */
+std::unique_ptr<column> xxhash_64(
+  table_view const& input,
+  uint64_t seed                       = DEFAULT_HASH_SEED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+}  // namespace hashing
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/hashing/detail/default_hash.cuh b/cpp/include/cudf/hashing/detail/default_hash.cuh
new file mode 100644
index 0000000..37e13d8
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/default_hash.cuh
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+
+namespace cudf::hashing::detail {
+
+/**
+ * @brief The default hash algorithm for use within libcudf internal functions
+ *
+ * This is declared here so it may be changed to another algorithm without modifying
+ * all those places that use it. Internal function implementations are encourage to
+ * use the `cudf::hashing::detail::default_hash` where possible.
+ *
+ * @tparam Key The key type for use by the hash class
+ */
+template <typename Key>
+using default_hash = MurmurHash3_x86_32<Key>;
+
+}  // namespace cudf::hashing::detail
diff --git a/cpp/include/cudf/hashing/detail/hash_allocator.cuh b/cpp/include/cudf/hashing/detail/hash_allocator.cuh
new file mode 100644
index 0000000..64a2a85
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/hash_allocator.cuh
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2017-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <new>
+
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+template <class T>
+struct default_allocator {
+  using value_type                    = T;
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource();
+
+  default_allocator() = default;
+
+  template <class U>
+  constexpr default_allocator(default_allocator<U> const&) noexcept
+  {
+  }
+
+  T* allocate(std::size_t n, rmm::cuda_stream_view stream = cudf::get_default_stream()) const
+  {
+    return static_cast<T*>(mr->allocate(n * sizeof(T), stream));
+  }
+
+  void deallocate(T* p,
+                  std::size_t n,
+                  rmm::cuda_stream_view stream = cudf::get_default_stream()) const
+  {
+    mr->deallocate(p, n * sizeof(T), stream);
+  }
+};
+
+template <class T, class U>
+bool operator==(default_allocator<T> const&, default_allocator<U> const&)
+{
+  return true;
+}
+template <class T, class U>
+bool operator!=(default_allocator<T> const&, default_allocator<U> const&)
+{
+  return false;
+}
diff --git a/cpp/include/cudf/hashing/detail/hash_functions.cuh b/cpp/include/cudf/hashing/detail/hash_functions.cuh
new file mode 100644
index 0000000..7a3d199
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/hash_functions.cuh
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2017-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/traits.hpp>
+
+#include <limits>
+
+namespace cudf::hashing::detail {
+
+/**
+ * Normalization of floating point NaNs, passthrough for all other values.
+ */
+template <typename T>
+T __device__ inline normalize_nans(T const& key)
+{
+  if constexpr (cudf::is_floating_point<T>()) {
+    if (std::isnan(key)) { return std::numeric_limits<T>::quiet_NaN(); }
+  }
+  return key;
+}
+
+/**
+ * Normalization of floating point NaNs and zeros, passthrough for all other values.
+ */
+template <typename T>
+T __device__ inline normalize_nans_and_zeros(T const& key)
+{
+  if constexpr (cudf::is_floating_point<T>()) {
+    if (key == T{0.0}) { return T{0.0}; }
+  }
+  return normalize_nans(key);
+}
+
+__device__ inline uint32_t rotate_bits_left(uint32_t x, uint32_t r)
+{
+  // This function is equivalent to (x << r) | (x >> (32 - r))
+  return __funnelshift_l(x, x, r);
+}
+
+__device__ inline uint64_t rotate_bits_left(uint64_t x, uint32_t r)
+{
+  return (x << r) | (x >> (64 - r));
+}
+
+__device__ inline uint32_t rotate_bits_right(uint32_t x, uint32_t r)
+{
+  // This function is equivalent to (x >> r) | (x << (32 - r))
+  return __funnelshift_r(x, x, r);
+}
+
+__device__ inline uint64_t rotate_bits_right(uint64_t x, uint32_t r)
+{
+  return (x >> r) | (x << (64 - r));
+}
+
+}  // namespace cudf::hashing::detail
diff --git a/cpp/include/cudf/hashing/detail/hashing.hpp b/cpp/include/cudf/hashing/detail/hashing.hpp
new file mode 100644
index 0000000..f08d0fb
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/hashing.hpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/hashing.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cstddef>
+#include <functional>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+
+std::unique_ptr<column> murmurhash3_x86_32(table_view const& input,
+                                           uint32_t seed,
+                                           rmm::cuda_stream_view,
+                                           rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<table> murmurhash3_x64_128(table_view const& input,
+                                           uint64_t seed,
+                                           rmm::cuda_stream_view,
+                                           rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<column> spark_murmurhash3_x86_32(table_view const& input,
+                                                 uint32_t seed,
+                                                 rmm::cuda_stream_view,
+                                                 rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<column> md5(table_view const& input,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<column> xxhash_64(table_view const& input,
+                                  uint64_t seed,
+                                  rmm::cuda_stream_view,
+                                  rmm::mr::device_memory_resource* mr);
+
+/* Copyright 2005-2014 Daniel James.
+ *
+ * Use, modification and distribution is subject to the Boost Software
+ * License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
+ * http://www.boost.org/LICENSE_1_0.txt)
+ */
+/**
+ * @brief Combines two hash values into a single hash value.
+ *
+ * Taken from the Boost hash_combine function.
+ * https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html
+ *
+ * @param lhs The first hash value
+ * @param rhs The second hash value
+ * @return Combined hash value
+ */
+constexpr uint32_t hash_combine(uint32_t lhs, uint32_t rhs)
+{
+  return lhs ^ (rhs + 0x9e37'79b9 + (lhs << 6) + (lhs >> 2));
+}
+
+/* Copyright 2005-2014 Daniel James.
+ *
+ * Use, modification and distribution is subject to the Boost Software
+ * License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
+ * http://www.boost.org/LICENSE_1_0.txt)
+ */
+/**
+ * @brief Combines two hash values into a single hash value.
+ *
+ * Adapted from Boost hash_combine function and modified for 64-bit.
+ * https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html
+ *
+ * @param lhs The first hash value
+ * @param rhs The second hash value
+ * @return Combined hash value
+ */
+constexpr std::size_t hash_combine(std::size_t lhs, std::size_t rhs)
+{
+  return lhs ^ (rhs + 0x9e37'79b9'7f4a'7c15 + (lhs << 6) + (lhs >> 2));
+}
+
+}  // namespace detail
+}  // namespace hashing
+}  // namespace cudf
+
+// specialization of std::hash for cudf::data_type
+namespace std {
+template <>
+struct hash<cudf::data_type> {
+  std::size_t operator()(cudf::data_type const& type) const noexcept
+  {
+    return cudf::hashing::detail::hash_combine(
+      std::hash<int32_t>{}(static_cast<int32_t>(type.id())), std::hash<int32_t>{}(type.scale()));
+  }
+};
+}  // namespace std
diff --git a/cpp/include/cudf/hashing/detail/helper_functions.cuh b/cpp/include/cudf/hashing/detail/helper_functions.cuh
new file mode 100644
index 0000000..cd58ec5
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/helper_functions.cuh
@@ -0,0 +1,243 @@
+/*
+ * Copyright (c) 2017-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <thrust/pair.h>
+
+constexpr int64_t DEFAULT_HASH_TABLE_OCCUPANCY = 50;
+
+/**
+ * @brief  Compute requisite size of hash table.
+ *
+ * Computes the number of entries required in a hash table to satisfy
+ * inserting a specified number of keys to achieve the specified hash table
+ * occupancy.
+ *
+ * @param num_keys_to_insert The number of keys that will be inserted
+ * @param desired_occupancy The desired occupancy percentage, e.g., 50 implies a
+ * 50% occupancy
+ * @return size_t The size of the hash table that will satisfy the desired
+ * occupancy for the specified number of insertions
+ */
+inline size_t compute_hash_table_size(cudf::size_type num_keys_to_insert,
+                                      uint32_t desired_occupancy = DEFAULT_HASH_TABLE_OCCUPANCY)
+{
+  assert(desired_occupancy != 0);
+  assert(desired_occupancy <= 100);
+  double const grow_factor{100.0 / desired_occupancy};
+
+  // Calculate size of hash map based on the desired occupancy
+  size_t hash_table_size{static_cast<size_t>(std::ceil(num_keys_to_insert * grow_factor))};
+
+  return hash_table_size;
+}
+
+template <typename pair_type>
+__forceinline__ __device__ pair_type load_pair_vectorized(pair_type const* __restrict__ const ptr)
+{
+  if (sizeof(uint4) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      uint4 vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter = {0, 0, 0, 0};
+    converter.vec_val            = *reinterpret_cast<uint4 const*>(ptr);
+    return converter.pair_val;
+  } else if (sizeof(uint2) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      uint2 vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter = {0, 0};
+    converter.vec_val            = *reinterpret_cast<uint2 const*>(ptr);
+    return converter.pair_val;
+  } else if (sizeof(int) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      int vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter = {0};
+    converter.vec_val            = *reinterpret_cast<int const*>(ptr);
+    return converter.pair_val;
+  } else if (sizeof(short) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      short vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter = {0};
+    converter.vec_val            = *reinterpret_cast<short const*>(ptr);
+    return converter.pair_val;
+  } else {
+    return *ptr;
+  }
+}
+
+template <typename pair_type>
+__forceinline__ __device__ void store_pair_vectorized(pair_type* __restrict__ const ptr,
+                                                      pair_type const val)
+{
+  if (sizeof(uint4) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      uint4 vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter   = {0, 0, 0, 0};
+    converter.pair_val             = val;
+    *reinterpret_cast<uint4*>(ptr) = converter.vec_val;
+  } else if (sizeof(uint2) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      uint2 vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter   = {0, 0};
+    converter.pair_val             = val;
+    *reinterpret_cast<uint2*>(ptr) = converter.vec_val;
+  } else if (sizeof(int) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      int vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter = {0};
+    converter.pair_val           = val;
+    *reinterpret_cast<int*>(ptr) = converter.vec_val;
+  } else if (sizeof(short) == sizeof(pair_type)) {
+    union pair_type2vec_type {
+      short vec_val;
+      pair_type pair_val;
+    };
+    pair_type2vec_type converter   = {0};
+    converter.pair_val             = val;
+    *reinterpret_cast<short*>(ptr) = converter.vec_val;
+  } else {
+    *ptr = val;
+  }
+}
+
+template <typename value_type, typename size_type, typename key_type, typename elem_type>
+__global__ void init_hashtbl(value_type* __restrict__ const hashtbl_values,
+                             size_type const n,
+                             key_type const key_val,
+                             elem_type const elem_val)
+{
+  size_type const idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx < n) {
+    store_pair_vectorized(hashtbl_values + idx, thrust::make_pair(key_val, elem_val));
+  }
+}
+
+template <typename T>
+struct equal_to {
+  using result_type          = bool;
+  using first_argument_type  = T;
+  using second_argument_type = T;
+  __forceinline__ __host__ __device__ constexpr bool operator()(
+    first_argument_type const& lhs, second_argument_type const& rhs) const
+  {
+    return lhs == rhs;
+  }
+};
+
+template <typename Iterator>
+class cycle_iterator_adapter {
+ public:
+  using value_type      = typename std::iterator_traits<Iterator>::value_type;
+  using difference_type = typename std::iterator_traits<Iterator>::difference_type;
+  using pointer         = typename std::iterator_traits<Iterator>::pointer;
+  using reference       = typename std::iterator_traits<Iterator>::reference;
+  using iterator_type   = Iterator;
+
+  cycle_iterator_adapter() = delete;
+
+  __host__ __device__ explicit cycle_iterator_adapter(iterator_type const& begin,
+                                                      iterator_type const& end,
+                                                      iterator_type const& current)
+    : m_begin(begin), m_end(end), m_current(current)
+  {
+  }
+
+  __host__ __device__ cycle_iterator_adapter& operator++()
+  {
+    if (m_end == (m_current + 1))
+      m_current = m_begin;
+    else
+      ++m_current;
+    return *this;
+  }
+
+  __host__ __device__ cycle_iterator_adapter const& operator++() const
+  {
+    if (m_end == (m_current + 1))
+      m_current = m_begin;
+    else
+      ++m_current;
+    return *this;
+  }
+
+  __host__ __device__ cycle_iterator_adapter& operator++(int)
+  {
+    cycle_iterator_adapter<iterator_type> old(m_begin, m_end, m_current);
+    if (m_end == (m_current + 1))
+      m_current = m_begin;
+    else
+      ++m_current;
+    return old;
+  }
+
+  __host__ __device__ cycle_iterator_adapter const& operator++(int) const
+  {
+    cycle_iterator_adapter<iterator_type> old(m_begin, m_end, m_current);
+    if (m_end == (m_current + 1))
+      m_current = m_begin;
+    else
+      ++m_current;
+    return old;
+  }
+
+  __host__ __device__ bool equal(cycle_iterator_adapter<iterator_type> const& other) const
+  {
+    return m_current == other.m_current && m_begin == other.m_begin && m_end == other.m_end;
+  }
+
+  __host__ __device__ reference& operator*() { return *m_current; }
+
+  __host__ __device__ reference const& operator*() const { return *m_current; }
+
+  __host__ __device__ const pointer operator->() const { return m_current.operator->(); }
+
+  __host__ __device__ pointer operator->() { return m_current; }
+
+ private:
+  iterator_type m_current;
+  iterator_type m_begin;
+  iterator_type m_end;
+};
+
+template <class T>
+__host__ __device__ bool operator==(cycle_iterator_adapter<T> const& lhs,
+                                    cycle_iterator_adapter<T> const& rhs)
+{
+  return lhs.equal(rhs);
+}
+
+template <class T>
+__host__ __device__ bool operator!=(cycle_iterator_adapter<T> const& lhs,
+                                    cycle_iterator_adapter<T> const& rhs)
+{
+  return !lhs.equal(rhs);
+}
diff --git a/cpp/include/cudf/hashing/detail/murmurhash3_x64_128.cuh b/cpp/include/cudf/hashing/detail/murmurhash3_x64_128.cuh
new file mode 100644
index 0000000..c986a90
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/murmurhash3_x64_128.cuh
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/hashing/detail/hash_functions.cuh>
+#include <cudf/strings/string_view.cuh>
+
+#include <thrust/pair.h>
+
+namespace cudf::hashing::detail {
+
+// MurmurHash3_x64_128 implementation from
+// https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
+//-----------------------------------------------------------------------------
+// MurmurHash3 was written by Austin Appleby, and is placed in the public
+// domain. The author hereby disclaims copyright to this source code.
+// Note - The x86 and x64 versions do _not_ produce the same results, as the
+// algorithms are optimized for their respective platforms. You can still
+// compile and run any of them on any platform, but your performance with the
+// non-native version will be less than optimal.
+template <typename Key>
+struct MurmurHash3_x64_128 {
+  using result_type = thrust::pair<uint64_t, uint64_t>;
+
+  constexpr MurmurHash3_x64_128() = default;
+  constexpr MurmurHash3_x64_128(uint64_t seed) : m_seed(seed) {}
+
+  __device__ inline uint32_t getblock32(std::byte const* data, cudf::size_type offset) const
+  {
+    // Read a 4-byte value from the data pointer as individual bytes for safe
+    // unaligned access (very likely for string types).
+    auto block = reinterpret_cast<uint8_t const*>(data + offset);
+    return block[0] | (block[1] << 8) | (block[2] << 16) | (block[3] << 24);
+  }
+
+  __device__ inline uint64_t getblock64(std::byte const* data, cudf::size_type offset) const
+  {
+    uint64_t result = getblock32(data, offset + 4);
+    result          = result << 32;
+    return result | getblock32(data, offset);
+  }
+
+  __device__ inline uint64_t fmix64(uint64_t k) const
+  {
+    k ^= k >> 33;
+    k *= 0xff51afd7ed558ccdUL;
+    k ^= k >> 33;
+    k *= 0xc4ceb9fe1a85ec53UL;
+    k ^= k >> 33;
+    return k;
+  }
+
+  result_type __device__ inline operator()(Key const& key) const { return compute(key); }
+
+  template <typename T>
+  result_type __device__ inline compute(T const& key) const
+  {
+    return compute_bytes(reinterpret_cast<std::byte const*>(&key), sizeof(T));
+  }
+
+  result_type __device__ inline compute_remaining_bytes(std::byte const* data,
+                                                        cudf::size_type len,
+                                                        cudf::size_type tail_offset,
+                                                        result_type h) const
+  {
+    // Process remaining bytes that do not fill a 8-byte chunk.
+    uint64_t k1     = 0;
+    uint64_t k2     = 0;
+    auto const tail = reinterpret_cast<uint8_t const*>(data) + tail_offset;
+    switch (len & (BLOCK_SIZE - 1)) {
+      case 15: k2 ^= static_cast<uint64_t>(tail[14]) << 48;
+      case 14: k2 ^= static_cast<uint64_t>(tail[13]) << 40;
+      case 13: k2 ^= static_cast<uint64_t>(tail[12]) << 32;
+      case 12: k2 ^= static_cast<uint64_t>(tail[11]) << 24;
+      case 11: k2 ^= static_cast<uint64_t>(tail[10]) << 16;
+      case 10: k2 ^= static_cast<uint64_t>(tail[9]) << 8;
+      case 9:
+        k2 ^= static_cast<uint64_t>(tail[8]) << 0;
+        k2 *= c2;
+        k2 = rotate_bits_left(k2, 33);
+        k2 *= c1;
+        h.second ^= k2;
+
+      case 8: k1 ^= static_cast<uint64_t>(tail[7]) << 56;
+      case 7: k1 ^= static_cast<uint64_t>(tail[6]) << 48;
+      case 6: k1 ^= static_cast<uint64_t>(tail[5]) << 40;
+      case 5: k1 ^= static_cast<uint64_t>(tail[4]) << 32;
+      case 4: k1 ^= static_cast<uint64_t>(tail[3]) << 24;
+      case 3: k1 ^= static_cast<uint64_t>(tail[2]) << 16;
+      case 2: k1 ^= static_cast<uint64_t>(tail[1]) << 8;
+      case 1:
+        k1 ^= static_cast<uint64_t>(tail[0]) << 0;
+        k1 *= c1;
+        k1 = rotate_bits_left(k1, 31);
+        k1 *= c2;
+        h.first ^= k1;
+    };
+    return h;
+  }
+
+  result_type __device__ compute_bytes(std::byte const* data, cudf::size_type const len) const
+  {
+    auto const nblocks = len / BLOCK_SIZE;
+    uint64_t h1        = m_seed;
+    uint64_t h2        = m_seed;
+
+    // Process all four-byte chunks.
+    for (cudf::size_type i = 0; i < nblocks; i++) {
+      uint64_t k1 = getblock64(data, (i * BLOCK_SIZE));                     // 1st 8 bytes
+      uint64_t k2 = getblock64(data, (i * BLOCK_SIZE) + (BLOCK_SIZE / 2));  // 2nd 8 bytes
+
+      k1 *= c1;
+      k1 = rotate_bits_left(k1, 31);
+      k1 *= c2;
+
+      h1 ^= k1;
+      h1 = rotate_bits_left(h1, 27);
+      h1 += h2;
+      h1 = h1 * 5 + 0x52dce729;
+
+      k2 *= c2;
+      k2 = rotate_bits_left(k2, 33);
+      k2 *= c1;
+
+      h2 ^= k2;
+      h2 = rotate_bits_left(h2, 31);
+      h2 += h1;
+      h2 = h2 * 5 + 0x38495ab5;
+    }
+
+    thrust::tie(h1, h2) = compute_remaining_bytes(data, len, nblocks * BLOCK_SIZE, {h1, h2});
+
+    // Finalize hash.
+    h1 ^= len;
+    h2 ^= len;
+
+    h1 += h2;
+    h2 += h1;
+
+    h1 = fmix64(h1);
+    h2 = fmix64(h2);
+
+    h1 += h2;
+    h2 += h1;
+
+    return {h1, h2};
+  }
+
+ private:
+  uint64_t m_seed{};
+  static constexpr uint32_t BLOCK_SIZE = 16;  // 2 x 64-bit = 16 bytes
+
+  static constexpr uint64_t c1 = 0x87c37b91114253d5UL;
+  static constexpr uint64_t c2 = 0x4cf5ad432745937fUL;
+};
+
+template <>
+MurmurHash3_x64_128<bool>::result_type __device__ inline MurmurHash3_x64_128<bool>::operator()(
+  bool const& key) const
+{
+  return compute<uint8_t>(key);
+}
+
+template <>
+MurmurHash3_x64_128<float>::result_type __device__ inline MurmurHash3_x64_128<float>::operator()(
+  float const& key) const
+{
+  return compute(normalize_nans(key));
+}
+
+template <>
+MurmurHash3_x64_128<double>::result_type __device__ inline MurmurHash3_x64_128<double>::operator()(
+  double const& key) const
+{
+  return compute(normalize_nans(key));
+}
+
+template <>
+MurmurHash3_x64_128<cudf::string_view>::result_type
+  __device__ inline MurmurHash3_x64_128<cudf::string_view>::operator()(
+    cudf::string_view const& key) const
+{
+  auto const data = reinterpret_cast<std::byte const*>(key.data());
+  auto const len  = key.size_bytes();
+  return compute_bytes(data, len);
+}
+
+template <>
+MurmurHash3_x64_128<numeric::decimal32>::result_type
+  __device__ inline MurmurHash3_x64_128<numeric::decimal32>::operator()(
+    numeric::decimal32 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+MurmurHash3_x64_128<numeric::decimal64>::result_type
+  __device__ inline MurmurHash3_x64_128<numeric::decimal64>::operator()(
+    numeric::decimal64 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+MurmurHash3_x64_128<numeric::decimal128>::result_type
+  __device__ inline MurmurHash3_x64_128<numeric::decimal128>::operator()(
+    numeric::decimal128 const& key) const
+{
+  return compute(key.value());
+}
+
+}  // namespace cudf::hashing::detail
diff --git a/cpp/include/cudf/hashing/detail/murmurhash3_x86_32.cuh b/cpp/include/cudf/hashing/detail/murmurhash3_x86_32.cuh
new file mode 100644
index 0000000..6cf0b0f
--- /dev/null
+++ b/cpp/include/cudf/hashing/detail/murmurhash3_x86_32.cuh
@@ -0,0 +1,194 @@
+/*
+ * Copyright (c) 2017-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/hashing/detail/hash_functions.cuh>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cstddef>
+
+namespace cudf::hashing::detail {
+
+// MurmurHash3_x86_32 implementation from
+// https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
+//-----------------------------------------------------------------------------
+// MurmurHash3 was written by Austin Appleby, and is placed in the public
+// domain. The author hereby disclaims copyright to this source code.
+// Note - The x86 and x64 versions do _not_ produce the same results, as the
+// algorithms are optimized for their respective platforms. You can still
+// compile and run any of them on any platform, but your performance with the
+// non-native version will be less than optimal.
+template <typename Key>
+struct MurmurHash3_x86_32 {
+  using result_type = hash_value_type;
+
+  constexpr MurmurHash3_x86_32() = default;
+  constexpr MurmurHash3_x86_32(uint32_t seed) : m_seed(seed) {}
+
+  [[nodiscard]] __device__ inline uint32_t fmix32(uint32_t h) const
+  {
+    h ^= h >> 16;
+    h *= 0x85ebca6b;
+    h ^= h >> 13;
+    h *= 0xc2b2ae35;
+    h ^= h >> 16;
+    return h;
+  }
+
+  [[nodiscard]] __device__ inline uint32_t getblock32(std::byte const* data,
+                                                      cudf::size_type offset) const
+  {
+    // Read a 4-byte value from the data pointer as individual bytes for safe
+    // unaligned access (very likely for string types).
+    auto const block = reinterpret_cast<uint8_t const*>(data + offset);
+    return block[0] | (block[1] << 8) | (block[2] << 16) | (block[3] << 24);
+  }
+
+  [[nodiscard]] result_type __device__ inline operator()(Key const& key) const
+  {
+    return compute(normalize_nans_and_zeros(key));
+  }
+
+  template <typename T>
+  result_type __device__ inline compute(T const& key) const
+  {
+    return compute_bytes(reinterpret_cast<std::byte const*>(&key), sizeof(T));
+  }
+
+  result_type __device__ inline compute_remaining_bytes(std::byte const* data,
+                                                        cudf::size_type len,
+                                                        cudf::size_type tail_offset,
+                                                        result_type h) const
+  {
+    // Process remaining bytes that do not fill a four-byte chunk.
+    uint32_t k1 = 0;
+    switch (len % 4) {
+      case 3: k1 ^= std::to_integer<uint8_t>(data[tail_offset + 2]) << 16; [[fallthrough]];
+      case 2: k1 ^= std::to_integer<uint8_t>(data[tail_offset + 1]) << 8; [[fallthrough]];
+      case 1:
+        k1 ^= std::to_integer<uint8_t>(data[tail_offset]);
+        k1 *= c1;
+        k1 = rotate_bits_left(k1, rot_c1);
+        k1 *= c2;
+        h ^= k1;
+    };
+    return h;
+  }
+
+  result_type __device__ compute_bytes(std::byte const* data, cudf::size_type const len) const
+  {
+    constexpr cudf::size_type BLOCK_SIZE = 4;
+    cudf::size_type const nblocks        = len / BLOCK_SIZE;
+    cudf::size_type const tail_offset    = nblocks * BLOCK_SIZE;
+    result_type h                        = m_seed;
+
+    // Process all four-byte chunks.
+    for (cudf::size_type i = 0; i < nblocks; i++) {
+      uint32_t k1 = getblock32(data, i * BLOCK_SIZE);
+      k1 *= c1;
+      k1 = rotate_bits_left(k1, rot_c1);
+      k1 *= c2;
+      h ^= k1;
+      h = rotate_bits_left(h, rot_c2);
+      h = h * 5 + c3;
+    }
+
+    h = compute_remaining_bytes(data, len, tail_offset, h);
+
+    // Finalize hash.
+    h ^= len;
+    h = fmix32(h);
+    return h;
+  }
+
+ private:
+  uint32_t m_seed{cudf::DEFAULT_HASH_SEED};
+  static constexpr uint32_t c1     = 0xcc9e2d51;
+  static constexpr uint32_t c2     = 0x1b873593;
+  static constexpr uint32_t c3     = 0xe6546b64;
+  static constexpr uint32_t rot_c1 = 15;
+  static constexpr uint32_t rot_c2 = 13;
+};
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<bool>::operator()(bool const& key) const
+{
+  return compute(static_cast<uint8_t>(key));
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<float>::operator()(float const& key) const
+{
+  return compute(normalize_nans_and_zeros(key));
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<double>::operator()(double const& key) const
+{
+  return compute(normalize_nans_and_zeros(key));
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<cudf::string_view>::operator()(
+  cudf::string_view const& key) const
+{
+  auto const data = reinterpret_cast<std::byte const*>(key.data());
+  auto const len  = key.size_bytes();
+  return compute_bytes(data, len);
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<numeric::decimal32>::operator()(
+  numeric::decimal32 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<numeric::decimal64>::operator()(
+  numeric::decimal64 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<numeric::decimal128>::operator()(
+  numeric::decimal128 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<cudf::list_view>::operator()(
+  cudf::list_view const& key) const
+{
+  CUDF_UNREACHABLE("List column hashing is not supported");
+}
+
+template <>
+hash_value_type __device__ inline MurmurHash3_x86_32<cudf::struct_view>::operator()(
+  cudf::struct_view const& key) const
+{
+  CUDF_UNREACHABLE("Direct hashing of struct_view is not supported");
+}
+
+}  // namespace cudf::hashing::detail
diff --git a/cpp/include/cudf/interop.hpp b/cpp/include/cudf/interop.hpp
new file mode 100644
index 0000000..865cc00
--- /dev/null
+++ b/cpp/include/cudf/interop.hpp
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// We disable warning 611 because the `arrow::TableBatchReader` only partially
+// override the `ReadNext` method of `arrow::RecordBatchReader::ReadNext`
+// triggering warning 611-D from nvcc.
+#ifdef __CUDACC__
+#pragma nv_diag_suppress 611
+#pragma nv_diag_suppress 2810
+#endif
+#include <arrow/api.h>
+#ifdef __CUDACC__
+#pragma nv_diag_default 611
+#pragma nv_diag_default 2810
+#endif
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+struct DLManagedTensor;
+
+namespace cudf {
+/**
+ * @addtogroup interop_dlpack
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Convert a DLPack DLTensor into a cudf table
+ *
+ * The `device_type` of the DLTensor must be `kDLCPU`, `kDLCuda`, or
+ * `kDLCUDAHost`, and `device_id` must match the current device. The `ndim`
+ * must be set to 1 or 2. The `dtype` must have 1 lane and the bitsize must
+ * match a supported `cudf::data_type`.
+ *
+ * @note The managed tensor is not deleted by this function.
+ *
+ * @throw cudf::logic_error if the any of the DLTensor fields are unsupported
+ *
+ * @param managed_tensor a 1D or 2D column-major (Fortran order) tensor
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @return Table with a copy of the tensor data
+ */
+std::unique_ptr<table> from_dlpack(
+  DLManagedTensor const* managed_tensor,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Convert a cudf table into a DLPack DLTensor
+ *
+ * All columns must have the same data type and this type must be numeric. The
+ * columns may be nullable, but the null count must be zero. If the input table
+ * is empty or has zero rows, the result will be nullptr.
+ *
+ * @note The `deleter` method of the returned `DLManagedTensor` must be used to
+ * free the memory allocated for the tensor.
+ *
+ * @throw cudf::logic_error if the data types are not equal or not numeric,
+ * or if any of columns have non-zero null count
+ *
+ * @param input Table to convert to DLPack
+ * @param mr Device memory resource used to allocate the returned DLPack tensor's device memory
+ *
+ * @return 1D or 2D DLPack tensor with a copy of the table data, or nullptr
+ */
+DLManagedTensor* to_dlpack(
+  table_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+/**
+ * @addtogroup interop_arrow
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Detailed metadata information for arrow array.
+ *
+ * As of now this contains only name in the hierarchy of children of cudf column,
+ * but in future this can be updated as per requirement.
+ */
+struct column_metadata {
+  std::string name;                            ///< Name of the column
+  std::vector<column_metadata> children_meta;  ///< Metadata of children of the column
+
+  /**
+   * @brief Construct a new column metadata object
+   *
+   * @param _name Name of the column
+   */
+  column_metadata(std::string const& _name) : name(_name) {}
+  column_metadata() = default;
+};
+
+/**
+ * @brief Create `arrow::Table` from cudf table `input`
+ *
+ * Converts the `cudf::table_view` to `arrow::Table` with the provided
+ * metadata `column_names`.
+ *
+ * @throws cudf::logic_error if `column_names` size doesn't match with number of columns.
+ *
+ * @param input table_view that needs to be converted to arrow Table
+ * @param metadata Contains hierarchy of names of columns and children
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param ar_mr arrow memory pool to allocate memory for arrow Table
+ * @return arrow Table generated from `input`
+ */
+std::shared_ptr<arrow::Table> to_arrow(table_view input,
+                                       std::vector<column_metadata> const& metadata = {},
+                                       rmm::cuda_stream_view stream = cudf::get_default_stream(),
+                                       arrow::MemoryPool* ar_mr     = arrow::default_memory_pool());
+
+/**
+ * @brief Create `arrow::Scalar` from cudf scalar `input`
+ *
+ * Converts the `cudf::scalar` to `arrow::Scalar`.
+ *
+ * @param input scalar that needs to be converted to arrow Scalar
+ * @param metadata Contains hierarchy of names of columns and children
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param ar_mr arrow memory pool to allocate memory for arrow Scalar
+ * @return arrow Scalar generated from `input`
+ */
+std::shared_ptr<arrow::Scalar> to_arrow(cudf::scalar const& input,
+                                        column_metadata const& metadata = {},
+                                        rmm::cuda_stream_view stream = cudf::get_default_stream(),
+                                        arrow::MemoryPool* ar_mr = arrow::default_memory_pool());
+/**
+ * @brief Create `cudf::table` from given arrow Table input
+ *
+ * @param input arrow:Table that needs to be converted to `cudf::table`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr    Device memory resource used to allocate `cudf::table`
+ * @return cudf table generated from given arrow Table
+ */
+
+std::unique_ptr<table> from_arrow(
+  arrow::Table const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create `cudf::scalar` from given arrow Scalar input
+ *
+ * @param input `arrow::Scalar` that needs to be converted to `cudf::scalar`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr    Device memory resource used to allocate `cudf::scalar`
+ * @return cudf scalar generated from given arrow Scalar
+ */
+
+std::unique_ptr<cudf::scalar> from_arrow(
+  arrow::Scalar const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/arrow_io_source.hpp b/cpp/include/cudf/io/arrow_io_source.hpp
new file mode 100644
index 0000000..5f79f05
--- /dev/null
+++ b/cpp/include/cudf/io/arrow_io_source.hpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "datasource.hpp"
+
+#include <arrow/filesystem/filesystem.h>
+#include <arrow/io/interfaces.h>
+
+#include <memory>
+#include <string>
+
+namespace cudf::io {
+/**
+ * @addtogroup io_datasources
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Implementation class for reading from an Apache Arrow file. The file
+ * could be a memory-mapped file or other implementation supported by Arrow.
+ */
+class arrow_io_source : public datasource {
+ public:
+  /**
+   * @brief Constructs an object from an Apache Arrow Filesystem URI
+   *
+   * @param arrow_uri Apache Arrow Filesystem URI
+   */
+  explicit arrow_io_source(std::string const& arrow_uri);
+
+  /**
+   * @brief Constructs an object from an `arrow` source object.
+   *
+   * @param file The `arrow` object from which the data is read
+   */
+  explicit arrow_io_source(std::shared_ptr<arrow::io::RandomAccessFile> file) : arrow_file(file) {}
+
+  /**
+   * @brief Returns a buffer with a subset of data from the `arrow` source.
+   *
+   * @param offset The offset in bytes from which to read
+   * @param size The number of bytes to read
+   * @return A buffer with the read data
+   */
+  std::unique_ptr<buffer> host_read(size_t offset, size_t size) override;
+
+  /**
+   * @brief Reads a selected range from the `arrow` source into a preallocated buffer.
+   *
+   * @param[in] offset The offset in bytes from which to read
+   * @param[in] size The number of bytes to read
+   * @param[out] dst The preallocated buffer to read into
+   * @return The number of bytes read
+   */
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override;
+  /**
+   * @brief Returns the size of the data in the `arrow` source.
+   *
+   * @return The size of the data in the `arrow` source
+   */
+  [[nodiscard]] size_t size() const override;
+
+ private:
+  std::shared_ptr<arrow::fs::FileSystem> filesystem;
+  std::shared_ptr<arrow::io::RandomAccessFile> arrow_file;
+};
+
+/** @} */  // end of group
+}  // namespace cudf::io
diff --git a/cpp/include/cudf/io/avro.hpp b/cpp/include/cudf/io/avro.hpp
new file mode 100644
index 0000000..17c168f
--- /dev/null
+++ b/cpp/include/cudf/io/avro.hpp
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "types.hpp"
+
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+/**
+ * @addtogroup io_readers
+ * @{
+ * @file
+ */
+
+class avro_reader_options_builder;
+
+/**
+ * @brief Settings to use for `read_avro()`.
+ */
+class avro_reader_options {
+  source_info _source;
+
+  // Names of column to read; empty is all
+  std::vector<std::string> _columns;
+
+  // Rows to skip from the start;
+  size_type _skip_rows = 0;
+  // Rows to read; -1 is all
+  size_type _num_rows = -1;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src source information used to read avro file
+   */
+  explicit avro_reader_options(source_info const& src) : _source(src) {}
+
+  friend avro_reader_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  avro_reader_options() = default;
+
+  /**
+   * @brief Returns source info.
+   *
+   * @return Source info
+   */
+  [[nodiscard]] source_info const& get_source() const { return _source; }
+
+  /**
+   * @brief Returns names of the columns to be read.
+   *
+   * @return Names of the columns to be read
+   */
+  [[nodiscard]] std::vector<std::string> get_columns() const { return _columns; }
+
+  /**
+   * @brief Returns number of rows to skip from the start.
+   *
+   * @return Number of rows to skip from the start
+   */
+  [[nodiscard]] size_type get_skip_rows() const { return _skip_rows; }
+
+  /**
+   * @brief Returns number of rows to read.
+   *
+   * @return Number of rows to read
+   */
+  [[nodiscard]] size_type get_num_rows() const { return _num_rows; }
+
+  /**
+   * @brief Set names of the column to be read.
+   *
+   * @param col_names Vector of column names
+   */
+  void set_columns(std::vector<std::string> col_names) { _columns = std::move(col_names); }
+
+  /**
+   * @brief Sets number of rows to skip.
+   *
+   * @param val Number of rows to skip from start
+   */
+  void set_skip_rows(size_type val) { _skip_rows = val; }
+
+  /**
+   * @brief Sets number of rows to read.
+   *
+   * @param val Number of rows to read after skip
+   */
+  void set_num_rows(size_type val) { _num_rows = val; }
+
+  /**
+   * @brief create avro_reader_options_builder which will build avro_reader_options.
+   *
+   * @param src source information used to read avro file
+   * @returns builder to build reader options
+   */
+  static avro_reader_options_builder builder(source_info const& src);
+};
+
+/**
+ * @brief Builder to build options for `read_avro()`.
+ */
+class avro_reader_options_builder {
+  avro_reader_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  avro_reader_options_builder() = default;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src The source information used to read avro file
+   */
+  explicit avro_reader_options_builder(source_info const& src) : options(src) {}
+
+  /**
+   * @brief Set names of the column to be read.
+   *
+   * @param col_names Vector of column names
+   * @return this for chaining
+   */
+  avro_reader_options_builder& columns(std::vector<std::string> col_names)
+  {
+    options._columns = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to skip.
+   *
+   * @param val Number of rows to skip from start
+   * @return this for chaining
+   */
+  avro_reader_options_builder& skip_rows(size_type val)
+  {
+    options._skip_rows = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to read.
+   *
+   * @param val Number of rows to read after skip
+   * @return this for chaining
+   */
+  avro_reader_options_builder& num_rows(size_type val)
+  {
+    options._num_rows = val;
+    return *this;
+  }
+
+  /**
+   * @brief move avro_reader_options member once it's built.
+   */
+  operator avro_reader_options&&() { return std::move(options); }
+
+  /**
+   * @brief move avro_reader_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `avro_reader_options` object's r-value reference
+   */
+  avro_reader_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Reads an Avro dataset into a set of columns.
+ *
+ * The following code snippet demonstrates how to read a dataset from a file:
+ * @code
+ *  auto source  = cudf::io::source_info("dataset.avro");
+ *  auto options = cudf::io::avro_reader_options::builder(source);
+ *  auto result  = cudf::io::read_avro(options);
+ * @endcode
+ *
+ * @param options Settings for controlling reading behavior
+ * @param mr Device memory resource used to allocate device memory of the table in the returned
+ * table_with_metadata
+ *
+ * @return The set of columns along with metadata
+ */
+table_with_metadata read_avro(
+  avro_reader_options const& options,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/csv.hpp b/cpp/include/cudf/io/csv.hpp
new file mode 100644
index 0000000..b49a13a
--- /dev/null
+++ b/cpp/include/cudf/io/csv.hpp
@@ -0,0 +1,1725 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/types.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <variant>
+#include <vector>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @addtogroup io_readers
+ * @{
+ * @file
+ */
+
+/**
+ *@brief Builder to build options for `read_csv()`.
+ */
+class csv_reader_options_builder;
+
+/**
+ * @brief Settings to use for `read_csv()`.
+ */
+
+class csv_reader_options {
+  source_info _source;
+
+  // Read settings
+
+  // Specify the compression format of the source or infer from file extension
+  compression_type _compression = compression_type::AUTO;
+  // Bytes to skip from the source start
+  std::size_t _byte_range_offset = 0;
+  // Bytes to read; always reads complete rows
+  std::size_t _byte_range_size = 0;
+  // Names of all the columns; if empty then names are auto-generated
+  std::vector<std::string> _names;
+  // If there is no header or names, prepend this to the column ID as the name
+  std::string _prefix;
+  // Whether to rename duplicate column names
+  bool _mangle_dupe_cols = true;
+
+  // Filter settings
+
+  // Names of columns to read; empty is all columns
+  std::vector<std::string> _use_cols_names;
+  // Indexes of columns to read; empty is all columns
+  std::vector<int> _use_cols_indexes;
+  // Rows to read; -1 is all
+  size_type _nrows = -1;
+  // Rows to skip from the start
+  size_type _skiprows = 0;
+  // Rows to skip from the end
+  size_type _skipfooter = 0;
+  // Header row index
+  size_type _header = 0;
+
+  // Parsing settings
+
+  // Line terminator
+  char _lineterminator = '\n';
+  // Field delimiter
+  char _delimiter = ',';
+  // Numeric data thousands separator; cannot match delimiter
+  char _thousands = '\0';
+  // Decimal point character; cannot match delimiter
+  char _decimal = '.';
+  // Comment line start character
+  char _comment                = '\0';
+  bool _windowslinetermination = false;
+  // Treat whitespace as field delimiter; overrides character delimiter
+  bool _delim_whitespace = false;
+  // Skip whitespace after the delimiter
+  bool _skipinitialspace = false;
+  // Ignore empty lines or parse line values as invalid
+  bool _skip_blank_lines = true;
+  // Treatment of quoting behavior
+  quote_style _quoting = quote_style::MINIMAL;
+  // Quoting character (if `quoting` is true)
+  char _quotechar = '"';
+  // Whether a quote inside a value is double-quoted
+  bool _doublequote = true;
+  // Names of columns to read as datetime
+  std::vector<std::string> _parse_dates_names;
+  // Indexes of columns to read as datetime
+  std::vector<int> _parse_dates_indexes;
+  // Names of columns to parse as hexadecimal
+  std::vector<std::string> _parse_hex_names;
+  // Indexes of columns to parse as hexadecimal
+  std::vector<int> _parse_hex_indexes;
+
+  // Conversion settings
+
+  // Per-column types; disables type inference on those columns
+  std::variant<std::vector<data_type>, std::map<std::string, data_type>> _dtypes;
+  // Additional values to recognize as boolean true values
+  std::vector<std::string> _true_values{"True", "TRUE", "true"};
+  // Additional values to recognize as boolean false values
+  std::vector<std::string> _false_values{"False", "FALSE", "false"};
+  // Additional values to recognize as null values
+  std::vector<std::string> _na_values;
+  // Whether to keep the built-in default NA values
+  bool _keep_default_na = true;
+  // Whether to disable null filter; disabling can improve performance
+  bool _na_filter = true;
+  // Whether to parse dates as DD/MM versus MM/DD
+  bool _dayfirst = false;
+  // Cast timestamp columns to a specific type
+  data_type _timestamp_type{type_id::EMPTY};
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src source information used to read csv file
+   */
+  explicit csv_reader_options(source_info const& src) : _source(src) {}
+
+  friend csv_reader_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  csv_reader_options() = default;
+
+  /**
+   * @brief Creates a `csv_reader_options_builder` which will build `csv_reader_options`.
+   *
+   * @param src Source information to read csv file
+   * @return Builder to build reader options
+   */
+  static csv_reader_options_builder builder(source_info const& src);
+
+  /**
+   * @brief Returns source info.
+   *
+   * @return Source info
+   */
+  [[nodiscard]] source_info const& get_source() const { return _source; }
+
+  /**
+   * @brief Returns compression format of the source.
+   *
+   * @return Compression format of the source
+   */
+  [[nodiscard]] compression_type get_compression() const { return _compression; }
+
+  /**
+   * @brief Returns number of bytes to skip from source start.
+   *
+   * @return Number of bytes to skip from source start
+   */
+  [[nodiscard]] std::size_t get_byte_range_offset() const { return _byte_range_offset; }
+
+  /**
+   * @brief Returns number of bytes to read.
+   *
+   * @return Number of bytes to read
+   */
+  [[nodiscard]] std::size_t get_byte_range_size() const { return _byte_range_size; }
+
+  /**
+   * @brief Returns number of bytes to read with padding.
+   *
+   * @return Number of bytes to read with padding
+   */
+  [[nodiscard]] std::size_t get_byte_range_size_with_padding() const
+  {
+    if (_byte_range_size == 0) {
+      return 0;
+    } else {
+      return _byte_range_size + get_byte_range_padding();
+    }
+  }
+
+  /**
+   * @brief Returns number of bytes to pad when reading.
+   *
+   * @return Number of bytes to pad when reading
+   */
+  [[nodiscard]] std::size_t get_byte_range_padding() const
+  {
+    auto const num_names   = _names.size();
+    auto const num_dtypes  = std::visit([](auto const& dtypes) { return dtypes.size(); }, _dtypes);
+    auto const num_columns = std::max(num_dtypes, num_names);
+
+    auto const max_row_bytes = 16 * 1024;  // 16KB
+    auto const column_bytes  = 64;
+    auto const base_padding  = 1024;  // 1KB
+
+    if (num_columns == 0) {
+      // Use flat size if the number of columns is not known
+      return max_row_bytes;
+    }
+
+    // Expand the size based on the number of columns, if available
+    return base_padding + num_columns * column_bytes;
+  }
+
+  /**
+   * @brief Returns names of the columns.
+   *
+   * @return Names of the columns
+   */
+  [[nodiscard]] std::vector<std::string> const& get_names() const { return _names; }
+
+  /**
+   * @brief Returns prefix to be used for column ID.
+   *
+   * @return Prefix to be used for column ID
+   */
+  [[nodiscard]] std::string get_prefix() const { return _prefix; }
+
+  /**
+   * @brief Whether to rename duplicate column names.
+   *
+   * @return `true` if  duplicate column names are renamed
+   */
+  [[nodiscard]] bool is_enabled_mangle_dupe_cols() const { return _mangle_dupe_cols; }
+
+  /**
+   * @brief Returns names of the columns to be read.
+   *
+   * @return Names of the columns to be read
+   */
+  [[nodiscard]] std::vector<std::string> const& get_use_cols_names() const
+  {
+    return _use_cols_names;
+  }
+
+  /**
+   * @brief Returns indexes of columns to read.
+   *
+   * @return Indexes of columns to read
+   */
+  [[nodiscard]] std::vector<int> const& get_use_cols_indexes() const { return _use_cols_indexes; }
+
+  /**
+   * @brief Returns number of rows to read.
+   *
+   * @return Number of rows to read
+   */
+  [[nodiscard]] size_type get_nrows() const { return _nrows; }
+
+  /**
+   * @brief Returns number of rows to skip from start.
+   *
+   * @return Number of rows to skip from start
+   */
+  [[nodiscard]] size_type get_skiprows() const { return _skiprows; }
+
+  /**
+   * @brief Returns number of rows to skip from end.
+   *
+   * @return Number of rows to skip from end
+   */
+  [[nodiscard]] size_type get_skipfooter() const { return _skipfooter; }
+
+  /**
+   * @brief Returns header row index.
+   *
+   * @return Header row index
+   */
+  [[nodiscard]] size_type get_header() const { return _header; }
+
+  /**
+   * @brief Returns line terminator.
+   *
+   * @return Line terminator
+   */
+  [[nodiscard]] char get_lineterminator() const { return _lineterminator; }
+
+  /**
+   * @brief Returns field delimiter.
+   *
+   * @return Field delimiter
+   */
+  [[nodiscard]] char get_delimiter() const { return _delimiter; }
+
+  /**
+   * @brief Returns numeric data thousands separator.
+   *
+   * @return Numeric data thousands separator
+   */
+  [[nodiscard]] char get_thousands() const { return _thousands; }
+
+  /**
+   * @brief Returns decimal point character.
+   *
+   * @return Decimal point character
+   */
+  [[nodiscard]] char get_decimal() const { return _decimal; }
+
+  /**
+   * @brief Returns comment line start character.
+   *
+   * @return Comment line start character
+   */
+  [[nodiscard]] char get_comment() const { return _comment; }
+
+  /**
+   * @brief Whether to treat `\r\n` as line terminator.
+   *
+   * @return `true` if `\r\n` is treated as line terminator
+   */
+  [[nodiscard]] bool is_enabled_windowslinetermination() const { return _windowslinetermination; }
+
+  /**
+   * @brief Whether to treat whitespace as field delimiter.
+   *
+   * @return `true` if whitespace is treated as field delimiter
+   */
+  [[nodiscard]] bool is_enabled_delim_whitespace() const { return _delim_whitespace; }
+
+  /**
+   * @brief Whether to skip whitespace after the delimiter.
+   *
+   * @return `true` if whitespace is skipped after the delimiter
+   */
+  [[nodiscard]] bool is_enabled_skipinitialspace() const { return _skipinitialspace; }
+
+  /**
+   * @brief Whether to ignore empty lines or parse line values as invalid.
+   *
+   * @return `true` if empty lines or parse line values are ignored as invalid
+   */
+  [[nodiscard]] bool is_enabled_skip_blank_lines() const { return _skip_blank_lines; }
+
+  /**
+   * @brief Returns quoting style.
+   *
+   * @return Quoting style
+   */
+  [[nodiscard]] quote_style get_quoting() const { return _quoting; }
+
+  /**
+   * @brief Returns quoting character.
+   *
+   * @return Quoting character
+   */
+  [[nodiscard]] char get_quotechar() const { return _quotechar; }
+
+  /**
+   * @brief Whether a quote inside a value is double-quoted.
+   *
+   * @return `true` if a quote inside a value is double-quoted
+   */
+  [[nodiscard]] bool is_enabled_doublequote() const { return _doublequote; }
+
+  /**
+   * @brief Returns names of columns to read as datetime.
+   *
+   * @return Names of columns to read as datetime
+   */
+  [[nodiscard]] std::vector<std::string> const& get_parse_dates_names() const
+  {
+    return _parse_dates_names;
+  }
+
+  /**
+   * @brief Returns indexes of columns to read as datetime.
+   *
+   * @return Indexes of columns to read as datetime
+   */
+  [[nodiscard]] std::vector<int> const& get_parse_dates_indexes() const
+  {
+    return _parse_dates_indexes;
+  }
+
+  /**
+   * @brief Returns names of columns to read as hexadecimal.
+   *
+   * @return Names of columns to read as hexadecimal
+   */
+  [[nodiscard]] std::vector<std::string> const& get_parse_hex_names() const
+  {
+    return _parse_hex_names;
+  }
+
+  /**
+   * @brief Returns indexes of columns to read as hexadecimal.
+   *
+   * @return Indexes of columns to read as hexadecimal
+   */
+  [[nodiscard]] std::vector<int> const& get_parse_hex_indexes() const { return _parse_hex_indexes; }
+
+  /**
+   * @brief Returns per-column types.
+   *
+   * @return Per-column types
+   */
+  std::variant<std::vector<data_type>, std::map<std::string, data_type>> const& get_dtypes() const
+  {
+    return _dtypes;
+  }
+
+  /**
+   * @brief Returns additional values to recognize as boolean true values.
+   *
+   * @return Additional values to recognize as boolean true values
+   */
+  std::vector<std::string> const& get_true_values() const { return _true_values; }
+
+  /**
+   * @brief Returns additional values to recognize as boolean false values.
+   *
+   * @return Additional values to recognize as boolean false values
+   */
+  std::vector<std::string> const& get_false_values() const { return _false_values; }
+
+  /**
+   * @brief Returns additional values to recognize as null values.
+   *
+   * @return Additional values to recognize as null values
+   */
+  std::vector<std::string> const& get_na_values() const { return _na_values; }
+
+  /**
+   * @brief Whether to keep the built-in default NA values.
+   *
+   * @return `true` if the built-in default NA values are kept
+   */
+  bool is_enabled_keep_default_na() const { return _keep_default_na; }
+
+  /**
+   * @brief Whether to disable null filter.
+   *
+   * @return `true` if null filter is enabled
+   */
+  bool is_enabled_na_filter() const { return _na_filter; }
+
+  /**
+   * @brief Whether to parse dates as DD/MM versus MM/DD.
+   *
+   * @return True if dates are parsed as DD/MM, false if MM/DD
+   */
+  bool is_enabled_dayfirst() const { return _dayfirst; }
+
+  /**
+   * @brief Returns timestamp_type to which all timestamp columns will be cast.
+   *
+   * @return timestamp_type to which all timestamp columns will be cast
+   */
+  data_type get_timestamp_type() const { return _timestamp_type; }
+
+  /**
+   * @brief Sets compression format of the source.
+   *
+   * @param comp Compression type
+   */
+  void set_compression(compression_type comp) { _compression = comp; }
+
+  /**
+   * @brief Sets number of bytes to skip from source start.
+   *
+   * @param offset Number of bytes of offset
+   */
+  void set_byte_range_offset(std::size_t offset)
+  {
+    if ((offset != 0) and ((_skiprows != 0) or (_skipfooter != 0) or (_nrows != -1))) {
+      CUDF_FAIL(
+        "When there is valid value in skiprows or skipfooter or nrows, offset can't have non-zero "
+        "value");
+    }
+    _byte_range_offset = offset;
+  }
+
+  /**
+   * @brief Sets number of bytes to read.
+   *
+   * @param size Number of bytes to read
+   */
+  void set_byte_range_size(std::size_t size)
+  {
+    if ((size != 0) and ((_skiprows != 0) or (_skipfooter != 0) or (_nrows != -1))) {
+      CUDF_FAIL(
+        "If the value of any of skiprows, skipfooter or nrows is valid, range size cannot be "
+        "non-zero.");
+    }
+    _byte_range_size = size;
+  }
+
+  /**
+   * @brief Sets names of the column.
+   *
+   * @param col_names Vector of column names
+   */
+  void set_names(std::vector<std::string> col_names) { _names = std::move(col_names); }
+
+  /**
+   * @brief Sets prefix to be used for column ID.
+   *
+   * @param pfx String used as prefix in for each column name
+   */
+  void set_prefix(std::string pfx) { _prefix = pfx; }
+
+  /**
+   * @brief Sets whether to rename duplicate column names.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_mangle_dupe_cols(bool val) { _mangle_dupe_cols = val; }
+
+  /**
+   * @brief Sets names of the columns to be read.
+   *
+   * @param col_names Vector of column names that are needed
+   */
+  void set_use_cols_names(std::vector<std::string> col_names)
+  {
+    _use_cols_names = std::move(col_names);
+  }
+
+  /**
+   * @brief Sets indexes of columns to read.
+   *
+   * @param col_indices Vector of column indices that are needed
+   */
+  void set_use_cols_indexes(std::vector<int> col_indices)
+  {
+    _use_cols_indexes = std::move(col_indices);
+  }
+
+  /**
+   * @brief Sets number of rows to read.
+   *
+   * @param nrows Number of rows to read
+   */
+  void set_nrows(size_type nrows)
+  {
+    CUDF_EXPECTS((nrows == 0) or (_skipfooter == 0), "Cannot use both `nrows` and `skipfooter`");
+    if ((nrows != -1) and ((_byte_range_offset != 0) or (_byte_range_size != 0))) {
+      CUDF_FAIL(
+        "nrows can't be a non negative value if range offset and/or range size has been set");
+    }
+
+    _nrows = nrows;
+  }
+
+  /**
+   * @brief Sets number of rows to skip from start.
+   *
+   * @param skiprows Number of rows to skip
+   */
+  void set_skiprows(size_type skiprows)
+  {
+    if ((skiprows != 0) and ((_byte_range_offset != 0) or (_byte_range_size != 0))) {
+      CUDF_FAIL("skiprows must be zero if range offset or range size has been set",
+                std::invalid_argument);
+    }
+    _skiprows = skiprows;
+  }
+
+  /**
+   * @brief Sets number of rows to skip from end.
+   *
+   * @param skipfooter Number of rows to skip
+   */
+  void set_skipfooter(size_type skipfooter)
+  {
+    CUDF_EXPECTS((skipfooter == 0) or (_nrows == -1),
+                 "Cannot use both `nrows` and `skipfooter`",
+                 std::invalid_argument);
+    if ((skipfooter != 0) and ((_byte_range_offset != 0) or (_byte_range_size != 0))) {
+      CUDF_FAIL("skipfooter must be zero if range offset or range size has been set",
+                std::invalid_argument);
+    }
+
+    _skipfooter = skipfooter;
+  }
+
+  /**
+   * @brief Sets header row index.
+   *
+   * @param hdr Index where header row is located
+   */
+  void set_header(size_type hdr) { _header = hdr; }
+
+  /**
+   * @brief Sets line terminator
+   *
+   * @param term A character to indicate line termination
+   */
+  void set_lineterminator(char term) { _lineterminator = term; }
+
+  /**
+   * @brief Sets field delimiter.
+   *
+   * @param delim A character to indicate delimiter
+   */
+  void set_delimiter(char delim) { _delimiter = delim; }
+
+  /**
+   * @brief Sets numeric data thousands separator.
+   *
+   * @param val A character that separates thousands
+   */
+  void set_thousands(char val) { _thousands = val; }
+
+  /**
+   * @brief Sets decimal point character.
+   *
+   * @param val A character that indicates decimal values
+   */
+  void set_decimal(char val) { _decimal = val; }
+
+  /**
+   * @brief Sets comment line start character.
+   *
+   * @param val A character that indicates comment
+   */
+  void set_comment(char val) { _comment = val; }
+
+  /**
+   * @brief Sets whether to treat `\r\n` as line terminator.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_windowslinetermination(bool val) { _windowslinetermination = val; }
+
+  /**
+   * @brief Sets whether to treat whitespace as field delimiter.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_delim_whitespace(bool val) { _delim_whitespace = val; }
+
+  /**
+   * @brief Sets whether to skip whitespace after the delimiter.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_skipinitialspace(bool val) { _skipinitialspace = val; }
+
+  /**
+   * @brief Sets whether to ignore empty lines or parse line values as invalid.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_skip_blank_lines(bool val) { _skip_blank_lines = val; }
+
+  /**
+   * @brief Sets the expected quoting style used in the input CSV data.
+   *
+   * Note: Only the following quoting styles are supported:
+   *   1. MINIMAL: String columns containing special characters like row-delimiters/
+   *               field-delimiter/quotes will be quoted.
+   *   2. NONE: No quoting is done for any columns.
+   *
+   * @param quoting Quoting style used
+   */
+  void set_quoting(quote_style quoting)
+  {
+    CUDF_EXPECTS(quoting == quote_style::MINIMAL || quoting == quote_style::NONE,
+                 "Only MINIMAL and NONE are supported for quoting.");
+    _quoting = quoting;
+  }
+
+  /**
+   * @brief Sets quoting character.
+   *
+   * @param ch A character to indicate quoting
+   */
+  void set_quotechar(char ch) { _quotechar = ch; }
+
+  /**
+   * @brief Sets a quote inside a value is double-quoted.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_doublequote(bool val) { _doublequote = val; }
+
+  /**
+   * @brief Sets names of columns to read as datetime.
+   *
+   * @param col_names Vector of column names to infer as datetime
+   */
+  void set_parse_dates(std::vector<std::string> col_names)
+  {
+    _parse_dates_names = std::move(col_names);
+  }
+
+  /**
+   * @brief Sets indexes of columns to read as datetime.
+   *
+   * @param col_indices Vector of column indices to infer as datetime
+   */
+  void set_parse_dates(std::vector<int> col_indices)
+  {
+    _parse_dates_indexes = std::move(col_indices);
+  }
+
+  /**
+   * @brief Sets names of columns to parse as hexadecimal
+   *
+   * @param col_names Vector of column names to parse as hexadecimal
+   */
+  void set_parse_hex(std::vector<std::string> col_names)
+  {
+    _parse_hex_names = std::move(col_names);
+  }
+
+  /**
+   * @brief Sets indexes of columns to parse as hexadecimal
+   *
+   * @param col_indices Vector of column indices to parse as hexadecimal
+   */
+  void set_parse_hex(std::vector<int> col_indices) { _parse_hex_indexes = std::move(col_indices); }
+
+  /**
+   * @brief Sets per-column types
+   *
+   * @param types Column name -> data type map specifying the columns' target data types
+   */
+  void set_dtypes(std::map<std::string, data_type> types) { _dtypes = std::move(types); }
+
+  /**
+   * @brief Sets per-column types
+   *
+   * @param types Vector specifying the columns' target data types
+   */
+  void set_dtypes(std::vector<data_type> types) { _dtypes = std::move(types); }
+
+  /**
+   * @brief Sets additional values to recognize as boolean true values.
+   *
+   * @param vals Vector of values to be considered to be `true`
+   */
+  void set_true_values(std::vector<std::string> vals)
+  {
+    _true_values.insert(_true_values.end(), vals.begin(), vals.end());
+  }
+
+  /**
+   * @brief Sets additional values to recognize as boolean false values.
+   *
+   * @param vals Vector of values to be considered to be `false`
+   */
+  void set_false_values(std::vector<std::string> vals)
+  {
+    _false_values.insert(_false_values.end(), vals.begin(), vals.end());
+  }
+
+  /**
+   * @brief Sets additional values to recognize as null values.
+   *
+   * @param vals Vector of values to be considered to be null
+   */
+  void set_na_values(std::vector<std::string> vals)
+  {
+    if ((!vals.empty()) and (!_na_filter)) {
+      CUDF_FAIL("Can't set na_values when na_filtering is disabled");
+    }
+
+    _na_values = std::move(vals);
+  }
+
+  /**
+   * @brief Sets whether to keep the built-in default NA values.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_keep_default_na(bool val) { _keep_default_na = val; }
+
+  /**
+   * @brief Sets whether to disable null filter.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_na_filter(bool val)
+  {
+    if (!val) { _na_values.clear(); }
+    _na_filter = val;
+  }
+
+  /**
+   * @brief Sets whether to parse dates as DD/MM versus MM/DD.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_dayfirst(bool val) { _dayfirst = val; }
+
+  /**
+   * @brief Sets timestamp_type to which all timestamp columns will be cast.
+   *
+   * @param type Dtype to which all timestamp column will be cast
+   */
+  void set_timestamp_type(data_type type) { _timestamp_type = type; }
+};
+
+/**
+ * @brief Builder to build options for `read_csv()`.
+ *
+ */
+class csv_reader_options_builder {
+  csv_reader_options options;  ///< Options to be built.
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  csv_reader_options_builder() = default;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src The source information used to read csv file
+   */
+  csv_reader_options_builder(source_info const& src) : options(src) {}
+
+  /**
+   * @brief Sets compression format of the source.
+   *
+   * @param comp Compression type
+   * @return this for chaining
+   */
+  csv_reader_options_builder& compression(compression_type comp)
+  {
+    options._compression = comp;
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of bytes to skip from source start.
+   *
+   * @param offset Number of bytes of offset
+   * @return this for chaining
+   */
+  csv_reader_options_builder& byte_range_offset(std::size_t offset)
+  {
+    options.set_byte_range_offset(offset);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of bytes to read.
+   *
+   * @param size Number of bytes to read
+   * @return this for chaining
+   */
+  csv_reader_options_builder& byte_range_size(std::size_t size)
+  {
+    options.set_byte_range_size(size);
+    return *this;
+  }
+
+  /**
+   * @brief Sets names of the column.
+   *
+   * @param col_names Vector of column names
+   * @return this for chaining
+   */
+  csv_reader_options_builder& names(std::vector<std::string> col_names)
+  {
+    options._names = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets prefix to be used for column ID.
+   *
+   * @param pfx String used as prefix in for each column name
+   * @return this for chaining
+   */
+  csv_reader_options_builder& prefix(std::string pfx)
+  {
+    options._prefix = pfx;
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to rename duplicate column names.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& mangle_dupe_cols(bool val)
+  {
+    options._mangle_dupe_cols = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets names of the columns to be read.
+   *
+   * @param col_names Vector of column names that are needed
+   * @return this for chaining
+   */
+  csv_reader_options_builder& use_cols_names(std::vector<std::string> col_names)
+  {
+    options._use_cols_names = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets indexes of columns to read.
+   *
+   * @param col_indices Vector of column indices that are needed
+   * @return this for chaining
+   */
+  csv_reader_options_builder& use_cols_indexes(std::vector<int> col_indices)
+  {
+    options._use_cols_indexes = std::move(col_indices);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to read.
+   *
+   * @param rows Number of rows to read
+   * @return this for chaining
+   */
+  csv_reader_options_builder& nrows(size_type rows)
+  {
+    options.set_nrows(rows);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to skip from start.
+   *
+   * @param skip Number of rows to skip
+   * @return this for chaining
+   */
+  csv_reader_options_builder& skiprows(size_type skip)
+  {
+    options.set_skiprows(skip);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to skip from end.
+   *
+   * @param skip Number of rows to skip
+   * @return this for chaining
+   */
+  csv_reader_options_builder& skipfooter(size_type skip)
+  {
+    options.set_skipfooter(skip);
+    return *this;
+  }
+
+  /**
+   * @brief Sets header row index.
+   *
+   * @param hdr Index where header row is located
+   * @return this for chaining
+   */
+  csv_reader_options_builder& header(size_type hdr)
+  {
+    options._header = hdr;
+    return *this;
+  }
+
+  /**
+   * @brief Sets line terminator.
+   *
+   * @param term A character to indicate line termination
+   * @return this for chaining
+   */
+  csv_reader_options_builder& lineterminator(char term)
+  {
+    options._lineterminator = term;
+    return *this;
+  }
+
+  /**
+   * @brief Sets field delimiter
+   *
+   * @param delim A character to indicate delimiter
+   * @return this for chaining
+   */
+  csv_reader_options_builder& delimiter(char delim)
+  {
+    options._delimiter = delim;
+    return *this;
+  }
+
+  /**
+   * @brief Sets numeric data thousands separator.
+   *
+   * @param val A character that separates thousands
+   * @return this for chaining
+   */
+  csv_reader_options_builder& thousands(char val)
+  {
+    options._thousands = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets decimal point character.
+   *
+   * @param val A character that indicates decimal values
+   * @return this for chaining
+   */
+  csv_reader_options_builder& decimal(char val)
+  {
+    options._decimal = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets comment line start character.
+   *
+   * @param val A character that indicates comment
+   * @return this for chaining
+   */
+  csv_reader_options_builder& comment(char val)
+  {
+    options._comment = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to treat `\r\n` as line terminator.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& windowslinetermination(bool val)
+  {
+    options._windowslinetermination = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to treat whitespace as field delimiter.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& delim_whitespace(bool val)
+  {
+    options._delim_whitespace = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to skip whitespace after the delimiter.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& skipinitialspace(bool val)
+  {
+    options._skipinitialspace = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to ignore empty lines or parse line values as invalid.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& skip_blank_lines(bool val)
+  {
+    options._skip_blank_lines = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets quoting style.
+   *
+   * @param style Quoting style used
+   * @return this for chaining
+   */
+  csv_reader_options_builder& quoting(quote_style style)
+  {
+    options._quoting = style;
+    return *this;
+  }
+
+  /**
+   * @brief Sets quoting character.
+   *
+   * @param ch A character to indicate quoting
+   * @return this for chaining
+   */
+  csv_reader_options_builder& quotechar(char ch)
+  {
+    options._quotechar = ch;
+    return *this;
+  }
+
+  /**
+   * @brief Sets a quote inside a value is double-quoted.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& doublequote(bool val)
+  {
+    options._doublequote = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets names of columns to read as datetime.
+   *
+   * @param col_names Vector of column names to read as datetime
+   * @return this for chaining
+   */
+  csv_reader_options_builder& parse_dates(std::vector<std::string> col_names)
+  {
+    options._parse_dates_names = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets indexes of columns to read as datetime.
+   *
+   * @param col_indices Vector of column indices to read as datetime
+   * @return this for chaining
+   */
+  csv_reader_options_builder& parse_dates(std::vector<int> col_indices)
+  {
+    options._parse_dates_indexes = std::move(col_indices);
+    return *this;
+  }
+
+  /**
+   * @brief Sets names of columns to parse as hexadecimal.
+   *
+   * @param col_names Vector of column names to parse as hexadecimal
+   * @return this for chaining
+   */
+  csv_reader_options_builder& parse_hex(std::vector<std::string> col_names)
+  {
+    options._parse_hex_names = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets indexes of columns to parse as hexadecimal.
+   *
+   * @param col_indices Vector of column indices to parse as hexadecimal
+   * @return this for chaining
+   */
+  csv_reader_options_builder& parse_hex(std::vector<int> col_indices)
+  {
+    options._parse_hex_indexes = std::move(col_indices);
+    return *this;
+  }
+
+  /**
+   * @brief Sets per-column types.
+   *
+   * @param types Column name -> data type map specifying the columns' target data types
+   * @return this for chaining
+   */
+  csv_reader_options_builder& dtypes(std::map<std::string, data_type> types)
+  {
+    options._dtypes = std::move(types);
+    return *this;
+  }
+
+  /**
+   * @brief Sets per-column types.
+   *
+   * @param types Vector of data types in which the column needs to be read
+   * @return this for chaining
+   */
+  csv_reader_options_builder& dtypes(std::vector<data_type> types)
+  {
+    options._dtypes = std::move(types);
+    return *this;
+  }
+
+  /**
+   * @brief Sets additional values to recognize as boolean true values.
+   *
+   * @param vals Vector of values to be considered to be `true`
+   * @return this for chaining
+   */
+  csv_reader_options_builder& true_values(std::vector<std::string> vals)
+  {
+    options._true_values.insert(options._true_values.end(), vals.begin(), vals.end());
+    return *this;
+  }
+
+  /**
+   * @brief Sets additional values to recognize as boolean false values.
+   *
+   * @param vals Vector of values to be considered to be `false`
+   * @return this for chaining
+   */
+  csv_reader_options_builder& false_values(std::vector<std::string> vals)
+  {
+    options._false_values.insert(options._false_values.end(), vals.begin(), vals.end());
+    return *this;
+  }
+
+  /**
+   * @brief Sets additional values to recognize as null values.
+   *
+   * @param vals Vector of values to be considered to be null
+   * @return this for chaining
+   */
+  csv_reader_options_builder& na_values(std::vector<std::string> vals)
+  {
+    options.set_na_values(std::move(vals));
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to keep the built-in default NA values.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& keep_default_na(bool val)
+  {
+    options.enable_keep_default_na(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to disable null filter.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& na_filter(bool val)
+  {
+    options.enable_na_filter(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether to parse dates as DD/MM versus MM/DD.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_reader_options_builder& dayfirst(bool val)
+  {
+    options._dayfirst = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets timestamp_type to which all timestamp columns will be cast.
+   *
+   * @param type Dtype to which all timestamp column will be cast
+   * @return this for chaining
+   */
+  csv_reader_options_builder& timestamp_type(data_type type)
+  {
+    options._timestamp_type = type;
+    return *this;
+  }
+
+  /**
+   * @brief move csv_reader_options member once it's built.
+   */
+  operator csv_reader_options&&() { return std::move(options); }
+
+  /**
+   * @brief move csv_reader_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `csv_reader_options` object's r-value reference
+   */
+  csv_reader_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Reads a CSV dataset into a set of columns.
+ *
+ * The following code snippet demonstrates how to read a dataset from a file:
+ * @code
+ *  auto source  = cudf::io::source_info("dataset.csv");
+ *  auto options = cudf::io::csv_reader_options::builder(source);
+ *  auto result  = cudf::io::read_csv(options);
+ * @endcode
+ *
+ * @param options Settings for controlling reading behavior
+ * @param mr Device memory resource used to allocate device memory of the table in the returned
+ * table_with_metadata
+ *
+ * @return The set of columns along with metadata
+ */
+table_with_metadata read_csv(
+  csv_reader_options options,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+/**
+ * @addtogroup io_writers
+ * @{
+ * @file
+ */
+
+/**
+ *@brief Builder to build options for `writer_csv()`.
+ */
+class csv_writer_options_builder;
+
+/**
+ * @brief Settings to use for `write_csv()`.
+ */
+class csv_writer_options {
+  // Specify the sink to use for writer output
+  sink_info _sink;
+  // Set of columns to output
+  table_view _table;
+  // string to use for null entries
+  std::string _na_rep = "";
+  // Indicates whether to write headers to csv
+  bool _include_header = true;
+  // maximum number of rows to write in each chunk (limits memory use)
+  size_type _rows_per_chunk = std::numeric_limits<size_type>::max();
+  // character to use for separating lines (default "\n")
+  std::string _line_terminator = "\n";
+  // character to use for separating column values (default ",")
+  char _inter_column_delimiter = ',';
+  // string to use for values != 0 in INT8 types (default 'true')
+  std::string _true_value = std::string{"true"};
+  // string to use for values == 0 in INT8 types (default 'false')
+  std::string _false_value = std::string{"false"};
+  // Names of all columns; if empty, writer will generate column names
+  std::vector<std::string> _names;
+  // Quote style. Currently only MINIMAL and NONE are supported.
+  quote_style _quoting = quote_style::MINIMAL;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit csv_writer_options(sink_info const& sink, table_view const& table)
+    : _sink(sink), _table(table), _rows_per_chunk(table.num_rows())
+  {
+  }
+
+  friend csv_writer_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit csv_writer_options() = default;
+
+  /**
+   * @brief Create builder to create `csv_writer_options`.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   *
+   * @return Builder to build csv_writer_options
+   */
+  static csv_writer_options_builder builder(sink_info const& sink, table_view const& table);
+
+  /**
+   * @brief Returns sink used for writer output.
+   *
+   * @return sink used for writer output
+   */
+  [[nodiscard]] sink_info const& get_sink() const { return _sink; }
+
+  /**
+   * @brief Returns table that would be written to output.
+   *
+   * @return Table that would be written to output
+   */
+  [[nodiscard]] table_view const& get_table() const { return _table; }
+
+  /**
+   * @brief Returns names of the columns.
+   *
+   * @return Names of the columns in the output file
+   */
+  [[nodiscard]] std::vector<std::string> const& get_names() const { return _names; }
+
+  /**
+   * @brief Returns string to used for null entries.
+   *
+   * @return string to used for null entries
+   */
+  [[nodiscard]] std::string get_na_rep() const { return _na_rep; }
+
+  /**
+   * @brief Whether to write headers to csv.
+   *
+   * @return `true` if writing headers to csv
+   */
+  [[nodiscard]] bool is_enabled_include_header() const { return _include_header; }
+
+  /**
+   * @brief Returns maximum number of rows to process for each file write.
+   *
+   * @return Maximum number of rows to process for each file write
+   */
+  [[nodiscard]] size_type get_rows_per_chunk() const { return _rows_per_chunk; }
+
+  /**
+   * @brief Returns character used for separating lines.
+   *
+   * @return Character used for separating lines
+   */
+  [[nodiscard]] std::string get_line_terminator() const { return _line_terminator; }
+
+  /**
+   * @brief Returns character used for separating column values.
+   *
+   * @return Character used for separating column values.
+   */
+  [[nodiscard]] char get_inter_column_delimiter() const { return _inter_column_delimiter; }
+
+  /**
+   * @brief Returns string used for values != 0 in INT8 types.
+   *
+   * @return string used for values != 0 in INT8 types
+   */
+  [[nodiscard]] std::string get_true_value() const { return _true_value; }
+
+  /**
+   * @brief Returns string used for values == 0 in INT8 types.
+   *
+   * @return string used for values == 0 in INT8 types
+   */
+  [[nodiscard]] std::string get_false_value() const { return _false_value; }
+
+  /**
+   * @brief Returns the quote style for the writer.
+   *
+   * Note: Only MINIMAL and NONE are supported.
+   *   1. MINIMAL: String columns containing special characters like row-delimiters
+   *               field-delimiter/quotes will be quoted.
+   *   2. NONE: No quoting is done for any columns.
+   *
+   * @return quote_style The quote style for the writer
+   */
+  [[nodiscard]] quote_style get_quoting() const { return _quoting; }
+
+  // Setter
+  /**
+   * @brief Sets optional associated column names.
+   *
+   @param names Associated column names
+   */
+  void set_names(std::vector<std::string> names) { _names = std::move(names); }
+
+  /**
+   * @brief Sets string to used for null entries.
+   *
+   * @param val String to represent null value
+   */
+  void set_na_rep(std::string val) { _na_rep = val; }
+
+  /**
+   * @brief Enables/Disables headers being written to csv.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_include_header(bool val) { _include_header = val; }
+
+  /**
+   * @brief Sets maximum number of rows to process for each file write.
+   *
+   * @param val Number of rows per chunk
+   */
+  void set_rows_per_chunk(size_type val) { _rows_per_chunk = val; }
+
+  /**
+   * @brief Sets character used for separating lines.
+   *
+   * @param term Character to represent line termination
+   */
+  void set_line_terminator(std::string term) { _line_terminator = term; }
+
+  /**
+   * @brief Sets character used for separating column values.
+   *
+   * @param delim Character to delimit column values
+   */
+  void set_inter_column_delimiter(char delim) { _inter_column_delimiter = delim; }
+
+  /**
+   * @brief Sets string used for values != 0 in INT8 types.
+   *
+   * @param val String to represent values != 0 in INT8 types
+   */
+  void set_true_value(std::string val) { _true_value = val; }
+
+  /**
+   * @brief Sets string used for values == 0 in INT8 types.
+   *
+   * @param val String to represent values == 0 in INT8 types
+   */
+  void set_false_value(std::string val) { _false_value = val; }
+
+  /**
+   * @brief (Re)sets the table being written.
+   *
+   * @param table Table to be written
+   */
+  void set_table(table_view const& table) { _table = table; }
+
+  /**
+   * @brief Sets the quote style for the writer.
+   *
+   * Note: Only the following quote styles are supported:
+   *   1. MINIMAL: String columns containing special characters like row-delimiters/
+   *               field-delimiter/quotes will be quoted.
+   *   2. NONE: No quoting is done for any columns.
+   *
+   * @param quoting The new quote_style for the writer.
+   */
+  void set_quoting(quote_style quoting)
+  {
+    CUDF_EXPECTS(quoting == quote_style::MINIMAL || quoting == quote_style::NONE,
+                 "Only MINIMAL and NONE are supported for quoting.");
+    _quoting = quoting;
+  }
+};
+
+/**
+ * @brief Builder to build options for `writer_csv()`
+ */
+class csv_writer_options_builder {
+  csv_writer_options options;  ///< Options to be built.
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit csv_writer_options_builder() = default;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit csv_writer_options_builder(sink_info const& sink, table_view const& table)
+    : options{sink, table}
+  {
+  }
+
+  /**
+   * @brief Sets optional column names.
+   *
+   * @param names Column names
+   * @return this for chaining
+   */
+  csv_writer_options_builder& names(std::vector<std::string> names)
+  {
+    options._names = names;
+    return *this;
+  }
+
+  /**
+   * @brief Sets string to used for null entries.
+   *
+   * @param val String to represent null value
+   * @return this for chaining
+   */
+  csv_writer_options_builder& na_rep(std::string val)
+  {
+    options._na_rep = val;
+    return *this;
+  };
+
+  /**
+   * @brief Enables/Disables headers being written to csv.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  csv_writer_options_builder& include_header(bool val)
+  {
+    options._include_header = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets maximum number of rows to process for each file write.
+   *
+   * @param val Number of rows per chunk
+   * @return this for chaining
+   */
+  csv_writer_options_builder& rows_per_chunk(int val)
+  {
+    options._rows_per_chunk = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets character used for separating lines.
+   *
+   * @param term Character to represent line termination
+   * @return this for chaining
+   */
+  csv_writer_options_builder& line_terminator(std::string term)
+  {
+    options._line_terminator = term;
+    return *this;
+  }
+
+  /**
+   * @brief Sets character used for separating column values.
+   *
+   * @param delim Character to delimit column values
+   * @return this for chaining
+   */
+  csv_writer_options_builder& inter_column_delimiter(char delim)
+  {
+    options._inter_column_delimiter = delim;
+    return *this;
+  }
+
+  /**
+   * @brief Sets string used for values != 0 in INT8 types.
+   *
+   * @param val String to represent values != 0 in INT8 types
+   * @return this for chaining
+   */
+  csv_writer_options_builder& true_value(std::string val)
+  {
+    options._true_value = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets string used for values == 0 in INT8 types.
+   *
+   * @param val String to represent values == 0 in INT8 types
+   * @return this for chaining
+   */
+  csv_writer_options_builder& false_value(std::string val)
+  {
+    options._false_value = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets the quote style for the writer.
+   *
+   * Only MINIMAL and NONE are supported.
+   *
+   * @param quoting The new quote style for the writer.
+   * @return this for chaining
+   */
+  csv_writer_options_builder& quoting(quote_style quoting)
+  {
+    options.set_quoting(quoting);
+    return *this;
+  }
+
+  /**
+   * @brief move `csv_writer_options` member once it's built.
+   */
+  operator csv_writer_options&&() { return std::move(options); }
+
+  /**
+   * @brief move `csv_writer_options` member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `csv_writer_options` object's r-value reference
+   */
+  csv_writer_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Writes a set of columns to CSV format.
+ *
+ * The following code snippet demonstrates how to write columns to a file:
+ * @code
+ *  auto destination = cudf::io::sink_info("dataset.csv");
+ *  auto options     = cudf::io::csv_writer_options(destination, table->view())
+ *    .na_rep(na)
+ *    .include_header(include_header)
+ *    .rows_per_chunk(rows_per_chunk);
+ *
+ *  cudf::io::write_csv(options);
+ * @endcode
+ *
+ * @param options Settings for controlling writing behavior
+ * @param mr Device memory resource to use for device memory allocation
+ */
+void write_csv(csv_writer_options const& options,
+               rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/data_sink.hpp b/cpp/include/cudf/io/data_sink.hpp
new file mode 100644
index 0000000..69d8a38
--- /dev/null
+++ b/cpp/include/cudf/io/data_sink.hpp
@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <algorithm>
+#include <future>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace cudf {
+//! IO interfaces
+namespace io {
+
+/**
+ * @addtogroup io_datasinks
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Interface class for storing the output data from the writers
+ */
+class data_sink {
+ public:
+  /**
+   * @brief Create a sink from a file path
+   *
+   * @param[in] filepath Path to the file to use
+   * @return Constructed data_sink object
+   */
+  static std::unique_ptr<data_sink> create(std::string const& filepath);
+
+  /**
+   * @brief Create a sink from a std::vector
+   *
+   * @param[in,out] buffer Pointer to the output vector
+   * @return Constructed data_sink object
+   */
+  static std::unique_ptr<data_sink> create(std::vector<char>* buffer);
+
+  /**
+   * @brief Create a void sink (one that does no actual io)
+   *
+   * A useful code path for benchmarking, to eliminate physical
+   * hardware randomness from profiling.
+   *
+   * @return Constructed data_sink object
+   */
+  static std::unique_ptr<data_sink> create();
+
+  /**
+   * @brief Create a wrapped custom user data sink
+   *
+   * @param[in] user_sink User-provided data sink (typically custom class)
+   *
+   * The data sink returned here is not the one passed by the user. It is an internal
+   * class that wraps the user pointer.  The principle is to allow the user to declare
+   * a custom sink instance and use it across multiple write() calls.
+   *
+   * @return Constructed data_sink object
+   */
+  static std::unique_ptr<data_sink> create(cudf::io::data_sink* const user_sink);
+
+  /**
+   * @brief Creates a vector of data sinks, one per element in the input vector.
+   *
+   * @param[in] args vector of parameters
+   * @return Constructed vector of data sinks
+   */
+  template <typename T>
+  static std::vector<std::unique_ptr<data_sink>> create(std::vector<T> const& args)
+  {
+    std::vector<std::unique_ptr<data_sink>> sinks;
+    sinks.reserve(args.size());
+    std::transform(args.cbegin(), args.cend(), std::back_inserter(sinks), [](auto const& arg) {
+      return data_sink::create(arg);
+    });
+    return sinks;
+  }
+
+  /**
+   * @brief Base class destructor
+   */
+  virtual ~data_sink(){};
+
+  /**
+   * @pure @brief Append the buffer content to the sink
+   *
+   * @param[in] data Pointer to the buffer to be written into the sink object
+   * @param[in] size Number of bytes to write
+   */
+  virtual void host_write(void const* data, size_t size) = 0;
+
+  /**
+   * @brief Whether or not this sink supports writing from gpu memory addresses.
+   *
+   * Internal to some of the file format writers, we have code that does things like
+   *
+   * tmp_buffer = alloc_temp_buffer();
+   * cudaMemcpy(tmp_buffer, device_buffer, size);
+   * sink->write(tmp_buffer, size);
+   *
+   * In the case where the sink type is itself a memory buffered write, this ends up
+   * being effectively a second memcpy.  So a useful optimization for a "smart"
+   * custom data_sink is to do it's own internal management of the movement
+   * of data between cpu and gpu; turning the internals of the writer into simply
+   *
+   * sink->device_write(device_buffer, size)
+   *
+   * If this function returns true, the data_sink will receive calls to device_write()
+   * instead of write() when possible.  However, it is still possible to receive
+   * write() calls as well.
+   *
+   * @return If this writer supports device_write() calls
+   */
+  [[nodiscard]] virtual bool supports_device_write() const { return false; }
+
+  /**
+   * @brief Estimates whether a direct device write would be more optimal for the given size.
+   *
+   * @param size Number of bytes to write
+   * @return whether the device write is expected to be more performant for the given size
+   */
+  [[nodiscard]] virtual bool is_device_write_preferred(size_t size) const
+  {
+    return supports_device_write();
+  }
+
+  /**
+   * @brief Append the buffer content to the sink from a gpu address
+   *
+   * For optimal performance, should only be called when `is_device_write_preferred` returns `true`.
+   * Data sink implementations that don't support direct device writes don't need to override
+   * this function.
+   *
+   * @throws cudf::logic_error the object does not support direct device writes, i.e.
+   * `supports_device_write` returns `false`.
+   *
+   * @param gpu_data Pointer to the buffer to be written into the sink object
+   * @param size Number of bytes to write
+   * @param stream CUDA stream to use
+   */
+  virtual void device_write(void const* gpu_data, size_t size, rmm::cuda_stream_view stream)
+  {
+    CUDF_FAIL("data_sink classes that support device_write must override it.");
+  }
+
+  /**
+   * @brief Asynchronously append the buffer content to the sink from a gpu address
+   *
+   * For optimal performance, should only be called when `is_device_write_preferred` returns `true`.
+   * Data sink implementations that don't support direct device writes don't need to override
+   * this function.
+   *
+   * `gpu_data` must not be freed until this call is synchronized.
+   * @code{.pseudo}
+   * auto result = device_write_async(gpu_data, size, stream);
+   * result.wait(); // OR result.get()
+   * @endcode
+   *
+   * @throws cudf::logic_error the object does not support direct device writes, i.e.
+   * `supports_device_write` returns `false`.
+   * @throws cudf::logic_error
+   *
+   * @param gpu_data Pointer to the buffer to be written into the sink object
+   * @param size Number of bytes to write
+   * @param stream CUDA stream to use
+   * @return a future that can be used to synchronize the call
+   */
+  virtual std::future<void> device_write_async(void const* gpu_data,
+                                               size_t size,
+                                               rmm::cuda_stream_view stream)
+  {
+    CUDF_FAIL("data_sink classes that support device_write_async must override it.");
+  }
+
+  /**
+   * @pure @brief Flush the data written into the sink
+   */
+  virtual void flush() = 0;
+
+  /**
+   * @pure @brief Returns the total number of bytes written into this sink
+   *
+   * @return Total number of bytes written into this sink
+   */
+  virtual size_t bytes_written() = 0;
+};
+
+/** @} */  // end of group
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/datasource.hpp b/cpp/include/cudf/io/datasource.hpp
new file mode 100644
index 0000000..28263d4
--- /dev/null
+++ b/cpp/include/cudf/io/datasource.hpp
@@ -0,0 +1,379 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <future>
+#include <memory>
+
+namespace cudf {
+//! IO interfaces
+namespace io {
+
+/**
+ * @addtogroup io_datasources
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Interface class for providing input data to the readers.
+ */
+class datasource {
+ public:
+  template <typename Container>
+  class owning_buffer;  // forward declaration
+  /**
+   * @brief Interface class for buffers that the datasource returns to the caller.
+   *
+   * Provides a basic interface to return the data address and size.
+   */
+  class buffer {
+   public:
+    /**
+     * @pure @brief Returns the buffer size in bytes.
+     *
+     * @return Buffer size in bytes
+     */
+    [[nodiscard]] virtual size_t size() const = 0;
+
+    /**
+     * @pure @brief Returns the address of the data in the buffer.
+     *
+     * @return Address of the data in the buffer
+     */
+    [[nodiscard]] virtual uint8_t const* data() const = 0;
+
+    /**
+     * @brief Base class destructor
+     */
+    virtual ~buffer() {}
+
+    /**
+     * @brief Factory to construct a datasource buffer object from a container.
+     *
+     * @tparam Container Type of the container to construct the buffer from
+     * @param data_owner The container to construct the buffer from (ownership is transferred)
+     * @return Constructed buffer object
+     */
+    template <typename Container>
+    static std::unique_ptr<buffer> create(Container&& data_owner)
+    {
+      return std::make_unique<owning_buffer<Container>>(std::move(data_owner));
+    }
+  };
+
+  /**
+   * @brief Creates a source from a file path.
+   *
+   * @param[in] filepath Path to the file to use
+   * @param[in] offset Bytes from the start of the file (the default is zero)
+   * @param[in] size Bytes from the offset; use zero for entire file (the default is zero)
+   * @return Constructed datasource object
+   */
+  static std::unique_ptr<datasource> create(std::string const& filepath,
+                                            size_t offset = 0,
+                                            size_t size   = 0);
+
+  /**
+   * @brief Creates a source from a host memory buffer.
+   *
+   # @deprecated Since 23.04
+   *
+   * @param[in] buffer Host buffer object
+   * @return Constructed datasource object
+   */
+  static std::unique_ptr<datasource> create(host_buffer const& buffer);
+
+  /**
+   * @brief Creates a source from a host memory buffer.
+   *
+   * @param[in] buffer Host buffer object
+   * @return Constructed datasource object
+   */
+  static std::unique_ptr<datasource> create(cudf::host_span<std::byte const> buffer);
+
+  /**
+   * @brief Creates a source from a device memory buffer.
+   *
+   * @param buffer Device buffer object
+   * @return Constructed datasource object
+   */
+  static std::unique_ptr<datasource> create(cudf::device_span<std::byte const> buffer);
+
+  /**
+   * @brief Creates a source from an user implemented datasource object.
+   *
+   * @param[in] source Non-owning pointer to the datasource object
+   * @return Constructed datasource object
+   */
+  static std::unique_ptr<datasource> create(datasource* source);
+
+  /**
+   * @brief Creates a vector of datasources, one per element in the input vector.
+   *
+   * @param[in] args vector of parameters
+   * @return Constructed vector of datasource objects
+   */
+  template <typename T>
+  static std::vector<std::unique_ptr<datasource>> create(std::vector<T> const& args)
+  {
+    std::vector<std::unique_ptr<datasource>> sources;
+    sources.reserve(args.size());
+    std::transform(args.cbegin(), args.cend(), std::back_inserter(sources), [](auto const& arg) {
+      return datasource::create(arg);
+    });
+    return sources;
+  }
+
+  /**
+   * @brief Base class destructor
+   */
+  virtual ~datasource(){};
+
+  /**
+   * @brief Returns a buffer with a subset of data from the source.
+   *
+   * @param[in] offset Bytes from the start
+   * @param[in] size Bytes to read
+   *
+   * @return The data buffer (can be smaller than size)
+   */
+  virtual std::unique_ptr<datasource::buffer> host_read(size_t offset, size_t size) = 0;
+
+  /**
+   * @brief Reads a selected range into a preallocated buffer.
+   *
+   * @param[in] offset Bytes from the start
+   * @param[in] size Bytes to read
+   * @param[in] dst Address of the existing host memory
+   *
+   * @return The number of bytes read (can be smaller than size)
+   */
+  virtual size_t host_read(size_t offset, size_t size, uint8_t* dst) = 0;
+
+  /**
+   * @brief Whether or not this source supports reading directly into device memory.
+   *
+   * If this function returns true, the datasource will receive calls to device_read() instead of
+   * host_read() when the reader processes the data on the device. Most readers will still make
+   * host_read() calls, for the parts of input that are processed on the host (e.g. metadata).
+   *
+   * Data source implementations that don't support direct device reads don't need to override this
+   * function. The implementations that do should override it to return false.
+   *
+   * @return bool Whether this source supports device_read() calls
+   */
+  [[nodiscard]] virtual bool supports_device_read() const { return false; }
+
+  /**
+   * @brief Estimates whether a direct device read would be more optimal for the given size.
+   *
+   * @param size Number of bytes to read
+   * @return whether the device read is expected to be more performant for the given size
+   */
+  [[nodiscard]] virtual bool is_device_read_preferred(size_t size) const
+  {
+    return supports_device_read();
+  }
+
+  /**
+   * @brief Returns a device buffer with a subset of data from the source.
+   *
+   * For optimal performance, should only be called when `is_device_read_preferred` returns `true`.
+   * Data source implementations that don't support direct device reads don't need to override this
+   * function.
+   *
+   *  @throws cudf::logic_error the object does not support direct device reads, i.e.
+   * `supports_device_read` returns `false`.
+   *
+   * @param offset Number of bytes from the start
+   * @param size Number of bytes to read
+   * @param stream CUDA stream to use
+   *
+   * @return The data buffer in the device memory
+   */
+  virtual std::unique_ptr<datasource::buffer> device_read(size_t offset,
+                                                          size_t size,
+                                                          rmm::cuda_stream_view stream)
+  {
+    CUDF_FAIL("datasource classes that support device_read must override it.");
+  }
+
+  /**
+   * @brief Reads a selected range into a preallocated device buffer
+   *
+   * For optimal performance, should only be called when `is_device_read_preferred` returns `true`.
+   * Data source implementations that don't support direct device reads don't need to override this
+   * function.
+   *
+   *  @throws cudf::logic_error when the object does not support direct device reads, i.e.
+   * `supports_device_read` returns `false`.
+   *
+   * @param offset Number of bytes from the start
+   * @param size Number of bytes to read
+   * @param dst Address of the existing device memory
+   * @param stream CUDA stream to use
+   *
+   * @return The number of bytes read (can be smaller than size)
+   */
+  virtual size_t device_read(size_t offset, size_t size, uint8_t* dst, rmm::cuda_stream_view stream)
+  {
+    CUDF_FAIL("datasource classes that support device_read must override it.");
+  }
+
+  /**
+   * @brief Asynchronously reads a selected range into a preallocated device buffer
+   *
+   * Returns a future value that contains the number of bytes read. Calling `get()` method of the
+   * return value synchronizes this function.
+   *
+   * For optimal performance, should only be called when `is_device_read_preferred` returns `true`.
+   * Data source implementations that don't support direct device reads don't need to override this
+   * function.
+   *
+   *  @throws cudf::logic_error when the object does not support direct device reads, i.e.
+   * `supports_device_read` returns `false`.
+   *
+   * @param offset Number of bytes from the start
+   * @param size Number of bytes to read
+   * @param dst Address of the existing device memory
+   * @param stream CUDA stream to use
+   *
+   * @return The number of bytes read as a future value (can be smaller than size)
+   */
+  virtual std::future<size_t> device_read_async(size_t offset,
+                                                size_t size,
+                                                uint8_t* dst,
+                                                rmm::cuda_stream_view stream)
+  {
+    CUDF_FAIL("datasource classes that support device_read_async must override it.");
+  }
+
+  /**
+   * @brief Returns the size of the data in the source.
+   *
+   * @return The size of the source data in bytes
+   */
+  [[nodiscard]] virtual size_t size() const = 0;
+
+  /**
+   * @brief Returns whether the source contains any data.
+   *
+   * @return True if there is data, False otherwise
+   */
+  [[nodiscard]] virtual bool is_empty() const { return size() == 0; }
+
+  /**
+   * @brief Implementation for non owning buffer where datasource holds buffer until destruction.
+   */
+  class non_owning_buffer : public buffer {
+   public:
+    non_owning_buffer() {}
+
+    /**
+     * @brief Construct a new non owning buffer object
+     *
+     * @param data The data buffer
+     * @param size The size of the data buffer
+     */
+    non_owning_buffer(uint8_t const* data, size_t size) : _data(data), _size(size) {}
+
+    /**
+     * @brief Returns the size of the buffer.
+     *
+     * @return The size of the buffer in bytes
+     */
+    [[nodiscard]] size_t size() const override { return _size; }
+
+    /**
+     * @brief Returns the pointer to the buffer.
+     *
+     * @return Pointer to the buffer
+     */
+    [[nodiscard]] uint8_t const* data() const override { return _data; }
+
+   private:
+    uint8_t const* _data{nullptr};
+    size_t _size{0};
+  };
+
+  /**
+   * @brief Derived implementation of `buffer` that owns the data.
+   *
+   * Can use different container types to hold the data buffer.
+   *
+   * @tparam Container Type of the container object that owns the data
+   */
+  template <typename Container>
+  class owning_buffer : public buffer {
+   public:
+    /**
+     * @brief Moves the input container into the newly created object.
+     *
+     * @param data_owner The container to construct the buffer from (ownership is transferred)
+     */
+    owning_buffer(Container&& data_owner)
+      : _data(std::move(data_owner)), _data_ptr(_data.data()), _size(_data.size())
+    {
+    }
+
+    /**
+     * @brief Moves the input container into the newly created object, and exposes a subspan of the
+     * buffer.
+     *
+     * @param data_owner The container to construct the buffer from (ownership is transferred)
+     * @param data_ptr Pointer to the start of the subspan
+     * @param size The size of the subspan
+     */
+    owning_buffer(Container&& data_owner, uint8_t const* data_ptr, size_t size)
+      : _data(std::move(data_owner)), _data_ptr(data_ptr), _size(size)
+    {
+    }
+
+    /**
+     * @brief Returns the size of the buffer.
+     *
+     * @return The size of the buffer in bytes
+     */
+    [[nodiscard]] size_t size() const override { return _size; }
+
+    /**
+     * @brief Returns the pointer to the data in the buffer.
+     *
+     * @return Pointer to the data in the buffer
+     */
+    [[nodiscard]] uint8_t const* data() const override
+    {
+      return static_cast<uint8_t const*>(_data_ptr);
+    }
+
+   private:
+    Container _data;
+    void const* _data_ptr;
+    size_t _size;
+  };
+};
+
+/** @} */  // end of group
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/detail/avro.hpp b/cpp/include/cudf/io/detail/avro.hpp
new file mode 100644
index 0000000..fede8e6
--- /dev/null
+++ b/cpp/include/cudf/io/detail/avro.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/avro.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace avro {
+
+/**
+ * @brief Reads the entire dataset.
+ *
+ * @param source Input `datasource` object to read the dataset from
+ * @param options Settings for controlling reading behavior
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to use for device memory allocation
+ *
+ * @return The set of columns along with table metadata
+ */
+table_with_metadata read_avro(std::unique_ptr<cudf::io::datasource>&& source,
+                              avro_reader_options const& options,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace avro
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/detail/csv.hpp b/cpp/include/cudf/io/detail/csv.hpp
new file mode 100644
index 0000000..9fdc7a4
--- /dev/null
+++ b/cpp/include/cudf/io/detail/csv.hpp
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/csv.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace csv {
+
+/**
+ * @brief Reads the entire dataset.
+ *
+ * @param sources Input `datasource` object to read the dataset from
+ * @param options Settings for controlling reading behavior
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to use for device memory allocation
+ *
+ * @return The set of columns along with table metadata
+ */
+table_with_metadata read_csv(std::unique_ptr<cudf::io::datasource>&& source,
+                             csv_reader_options const& options,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Write an entire dataset to CSV format.
+ *
+ * @param sink Output sink
+ * @param table The set of columns
+ * @param column_names Column names for the output CSV
+ * @param options Settings for controlling behavior
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource to use for device memory allocation
+ */
+void write_csv(data_sink* sink,
+               table_view const& table,
+               host_span<std::string const> column_names,
+               csv_writer_options const& options,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr);
+
+}  // namespace csv
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/detail/json.hpp b/cpp/include/cudf/io/detail/json.hpp
new file mode 100644
index 0000000..6930a4f
--- /dev/null
+++ b/cpp/include/cudf/io/detail/json.hpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/json.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::io::json::detail {
+
+/**
+ * @brief Reads and returns the entire data set.
+ *
+ * @param sources Input `datasource` objects to read the dataset from
+ * @param options Settings for controlling reading behavior
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to use for device memory allocation
+ *
+ * @return cudf::table object that contains the array of cudf::column.
+ */
+table_with_metadata read_json(host_span<std::unique_ptr<datasource>> sources,
+                              json_reader_options const& options,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Write an entire dataset to JSON format.
+ *
+ * @param sink Output sink
+ * @param table The set of columns
+ * @param options Settings for controlling behavior
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource to use for device memory allocation
+ */
+void write_json(data_sink* sink,
+                table_view const& table,
+                json_writer_options const& options,
+                rmm::cuda_stream_view stream,
+                rmm::mr::device_memory_resource* mr);
+}  // namespace cudf::io::json::detail
diff --git a/cpp/include/cudf/io/detail/orc.hpp b/cpp/include/cudf/io/detail/orc.hpp
new file mode 100644
index 0000000..623f402
--- /dev/null
+++ b/cpp/include/cudf/io/detail/orc.hpp
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/detail/utils.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+
+// Forward declaration
+class orc_reader_options;
+class orc_writer_options;
+class chunked_orc_writer_options;
+
+namespace detail {
+namespace orc {
+
+/**
+ * @brief Class to read ORC dataset data into columns.
+ */
+class reader {
+ private:
+  class impl;
+  std::unique_ptr<impl> _impl;
+
+ public:
+  /**
+   * @brief Constructor from an array of datasources
+   *
+   * @param sources Input `datasource` objects to read the dataset from
+   * @param options Settings for controlling reading behavior
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  explicit reader(std::vector<std::unique_ptr<cudf::io::datasource>>&& sources,
+                  orc_reader_options const& options,
+                  rmm::cuda_stream_view stream,
+                  rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Destructor explicitly declared to avoid inlining in header
+   */
+  ~reader();
+
+  /**
+   * @brief Reads the entire dataset.
+   *
+   * @param options Settings for controlling reading behavior
+   * @return The set of columns along with table metadata
+   */
+  table_with_metadata read(orc_reader_options const& options);
+};
+
+/**
+ * @brief Class to write ORC dataset data into columns.
+ */
+class writer {
+ private:
+  class impl;
+  std::unique_ptr<impl> _impl;
+
+ public:
+  /**
+   * @brief Constructor for output to a file.
+   *
+   * @param sink The data sink to write the data to
+   * @param options Settings for controlling writing behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit writer(std::unique_ptr<cudf::io::data_sink> sink,
+                  orc_writer_options const& options,
+                  single_write_mode mode,
+                  rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Constructor with chunked writer options.
+   *
+   * @param sink The data sink to write the data to
+   * @param options Settings for controlling writing behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit writer(std::unique_ptr<cudf::io::data_sink> sink,
+                  chunked_orc_writer_options const& options,
+                  single_write_mode mode,
+                  rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Destructor explicitly declared to avoid inlining in header
+   */
+  ~writer();
+
+  /**
+   * @brief Writes a single subtable as part of a larger ORC file/table write.
+   *
+   * @param[in] table The table information to be written
+   */
+  void write(table_view const& table);
+
+  /**
+   * @brief Finishes the chunked/streamed write process.
+   */
+  void close();
+};
+}  // namespace orc
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/detail/parquet.hpp b/cpp/include/cudf/io/detail/parquet.hpp
new file mode 100644
index 0000000..074f690
--- /dev/null
+++ b/cpp/include/cudf/io/detail/parquet.hpp
@@ -0,0 +1,254 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file parquet.hpp
+ */
+
+#pragma once
+
+#include <cudf/io/detail/utils.hpp>
+#include <cudf/io/parquet_metadata.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <string>
+#include <vector>
+
+namespace cudf::io {
+
+// Forward declaration
+class parquet_reader_options;
+class parquet_writer_options;
+class chunked_parquet_writer_options;
+
+namespace detail::parquet {
+
+/**
+ * @brief Class to read Parquet dataset data into columns.
+ */
+class reader {
+ protected:
+  class impl;
+  std::unique_ptr<impl> _impl;
+
+  /**
+   * @brief Default constructor, needed for subclassing.
+   */
+  reader();
+
+ public:
+  /**
+   * @brief Constructor from an array of datasources
+   *
+   * @param sources Input `datasource` objects to read the dataset from
+   * @param options Settings for controlling reading behavior
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  explicit reader(std::vector<std::unique_ptr<cudf::io::datasource>>&& sources,
+                  parquet_reader_options const& options,
+                  rmm::cuda_stream_view stream,
+                  rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Destructor explicitly-declared to avoid inlined in header
+   */
+  virtual ~reader();
+
+  /**
+   * @brief Reads the dataset as per given options.
+   *
+   * @param options Settings for controlling reading behavior
+   *
+   * @return The set of columns along with table metadata
+   */
+  table_with_metadata read(parquet_reader_options const& options);
+};
+
+/**
+ * @brief The reader class that supports iterative reading of a given file.
+ *
+ * This class intentionally subclasses the `reader` class with private inheritance to hide the
+ * `reader::read()` API. As such, only chunked reading APIs are supported.
+ */
+class chunked_reader : private reader {
+ public:
+  /**
+   * @brief Constructor from an output size memory limit and an input size memory limit and an array
+   * of data sources with reader options.
+   *
+   * The typical usage should be similar to this:
+   * ```
+   *  do {
+   *    auto const chunk = reader.read_chunk();
+   *    // Process chunk
+   *  } while (reader.has_next());
+   *
+   * ```
+   *
+   * If `chunk_read_limit == 0` (i.e., no output limit), and `pass_read_limit == 0` (no input
+   * temporary memory size limit) a call to `read_chunk()` will read the whole file and return a
+   * table containing all rows.
+   *
+   * The chunk_read_limit parameter controls the size of the output chunks produces.  If the user
+   * specifies 100 MB of data, the reader will attempt to return chunks containing tables that have
+   * a total bytes size (over all columns) of 100 MB or less.  This is a soft limit and the code
+   * will not fail if it cannot satisfy the limit.  It will make a best-effort atttempt only.
+   *
+   * The pass_read_limit parameter controls how much temporary memory is used in the process of
+   * decoding the file.  The primary contributor to this memory usage is the uncompressed size of
+   * the data read out of the file and the decompressed (but not yet decoded) size of the data. The
+   * granularity of a given pass is at the row group level. It will not attempt to read at the sub
+   * row-group level.
+   *
+   * Combined, the way to visualize passes and chunks is as follows:
+   *
+   * @code{.pseudo}
+   * for(each pass){
+   *    for(each output chunk within a pass){
+   *       return a table that fits within the output chunk limit
+   *    }
+   *  }
+   * @endcode
+   *
+   * With a pass_read_limit of `0` you are simply saying you have one pass that reads the entire
+   * file as normal.
+   *
+   * @param chunk_read_limit Limit on total number of bytes to be returned per read,
+   * or `0` if there is no limit
+   * @param pass_read_limit Limit on total amount of memory used for temporary computations during
+   * loading, or `0` if there is no limit
+   * @param sources Input `datasource` objects to read the dataset from
+   * @param options Settings for controlling reading behavior
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  explicit chunked_reader(std::size_t chunk_read_limit,
+                          std::size_t pass_read_limit,
+                          std::vector<std::unique_ptr<cudf::io::datasource>>&& sources,
+                          parquet_reader_options const& options,
+                          rmm::cuda_stream_view stream,
+                          rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Destructor explicitly-declared to avoid inlined in header.
+   *
+   * Since the declaration of the internal `_impl` object does not exist in this header, this
+   * destructor needs to be defined in a separate source file which can access to that object's
+   * declaration.
+   */
+  ~chunked_reader();
+
+  /**
+   * @copydoc cudf::io::chunked_parquet_reader::has_next
+   */
+  [[nodiscard]] bool has_next() const;
+
+  /**
+   * @copydoc cudf::io::chunked_parquet_reader::read_chunk
+   */
+  [[nodiscard]] table_with_metadata read_chunk() const;
+};
+
+/**
+ * @brief Class to write parquet dataset data into columns.
+ */
+class writer {
+ private:
+  class impl;
+  std::unique_ptr<impl> _impl;
+
+ public:
+  /**
+   * @brief Constructor for output to a file.
+   *
+   * @param sinks The data sinks to write the data to
+   * @param options Settings for controlling writing behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit writer(std::vector<std::unique_ptr<data_sink>> sinks,
+                  parquet_writer_options const& options,
+                  single_write_mode mode,
+                  rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Constructor for writer to handle chunked parquet options.
+   *
+   * @param sinks The data sinks to write the data to
+   * @param options Settings for controlling writing behavior for chunked writer
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   *
+   * @return A parquet-compatible blob that contains the data for all rowgroups in the list
+   */
+  explicit writer(std::vector<std::unique_ptr<data_sink>> sinks,
+                  chunked_parquet_writer_options const& options,
+                  single_write_mode mode,
+                  rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Destructor explicitly-declared to avoid inlined in header
+   */
+  ~writer();
+
+  /**
+   * @brief Writes a single subtable as part of a larger parquet file/table write.
+   *
+   * @throws rmm::bad_alloc if there is insufficient space for temporary buffers
+   *
+   * @param[in] table The table information to be written
+   * @param[in] partitions Optional partitions to divide the table into. If specified, must be same
+   * size as number of sinks.
+   */
+  void write(table_view const& table, std::vector<partition_info> const& partitions = {});
+
+  /**
+   * @brief Finishes the chunked/streamed write process.
+   *
+   * @param[in] column_chunks_file_path Column chunks file path to be set in the raw output metadata
+   *
+   * @return A parquet-compatible blob that contains the data for all rowgroups in the list only if
+   * `column_chunks_file_path` is provided, else null.
+   */
+  std::unique_ptr<std::vector<uint8_t>> close(
+    std::vector<std::string> const& column_chunks_file_path = {});
+
+  /**
+   * @brief Merges multiple metadata blobs returned by write_all into a single metadata blob
+   *
+   * @param[in] metadata_list List of input file metadata
+   * @return A parquet-compatible blob that contains the data for all rowgroups in the list
+   */
+  static std::unique_ptr<std::vector<uint8_t>> merge_row_group_metadata(
+    std::vector<std::unique_ptr<std::vector<uint8_t>>> const& metadata_list);
+};
+
+/**
+ * @brief Reads metadata of parquet dataset.
+ *
+ * @param sources Dataset sources to read from
+ *
+ * @return parquet_metadata with parquet schema, number of rows, number of row groups and key-value
+ * metadata.
+ */
+parquet_metadata read_parquet_metadata(host_span<std::unique_ptr<datasource> const> sources);
+}  // namespace detail::parquet
+}  // namespace cudf::io
diff --git a/cpp/include/cudf/io/detail/tokenize_json.hpp b/cpp/include/cudf/io/detail/tokenize_json.hpp
new file mode 100644
index 0000000..b2ea29a
--- /dev/null
+++ b/cpp/include/cudf/io/detail/tokenize_json.hpp
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/json.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf::io::json {
+
+/// Type used to represent the atomic symbol type used within the finite-state machine
+using SymbolT = char;
+
+/// Type used to represent the stack alphabet (i.e.: empty-stack, struct, list)
+using StackSymbolT = char;
+
+/// Type used to index into the symbols within the JSON input
+using SymbolOffsetT = uint32_t;
+
+/// Type large enough to support indexing up to max nesting level (must be signed)
+using StackLevelT = int8_t;
+
+/// Type used to represent a symbol group id of the input alphabet in the pushdown automaton
+using PdaInputSymbolGroupIdT = char;
+
+/// Type used to represent a symbol group id of the stack alphabet in the pushdown automaton
+using PdaStackSymbolGroupIdT = char;
+
+/// Type used to represent a (input-symbol, stack-symbol)-tuple in stack-symbol-major order
+using PdaSymbolGroupIdT = char;
+
+/// Type being emitted by the pushdown automaton transducer
+using PdaTokenT = char;
+
+/// Type used to represent the class of a node (or a node "category") within the tree representation
+using NodeT = char;
+
+/// Type used to index into the nodes within the tree of structs, lists, field names, and value
+/// nodes
+using NodeIndexT = size_type;
+
+/// Type large enough to represent tree depth from [0, max-tree-depth); may be an unsigned type
+using TreeDepthT = StackLevelT;
+
+constexpr NodeIndexT parent_node_sentinel = -1;
+
+/**
+ * @brief Class of a node (or a node "category") within the tree representation
+ */
+enum node_t : NodeT {
+  /// A node representing a struct
+  NC_STRUCT,
+  /// A node representing a list
+  NC_LIST,
+  /// A node representing a field name
+  NC_FN,
+  /// A node representing a string value
+  NC_STR,
+  /// A node representing a numeric or literal value (e.g., true, false, null)
+  NC_VAL,
+  /// A node representing a parser error
+  NC_ERR,
+  /// Total number of node classes
+  NUM_NODE_CLASSES
+};
+
+/**
+ * @brief Tokens emitted while parsing a JSON input
+ */
+enum token_t : PdaTokenT {
+  /// Beginning-of-struct token (on encounter of semantic '{')
+  StructBegin,
+  /// End-of-struct token (on encounter of semantic '}')
+  StructEnd,
+  /// Beginning-of-list token (on encounter of semantic '[')
+  ListBegin,
+  /// End-of-list token (on encounter of semantic ']')
+  ListEnd,
+  // Beginning-of-struct-member token
+  StructMemberBegin,
+  // End-of-struct-member token
+  StructMemberEnd,
+  /// Beginning-of-field-name token (on encounter of first quote)
+  FieldNameBegin,
+  /// End-of-field-name token (on encounter of a field name's second quote)
+  FieldNameEnd,
+  /// Beginning-of-string-value token (on encounter of the string's first quote)
+  StringBegin,
+  /// End-of-string token (on encounter of a string's second quote)
+  StringEnd,
+  /// Beginning-of-value token (first character of literal or numeric)
+  ValueBegin,
+  /// Post-value token (first character after a literal or numeric string)
+  ValueEnd,
+  /// Beginning-of-error token (on first encounter of a parsing error)
+  ErrorBegin,
+  /// Delimiting a JSON line for error recovery
+  LineEnd,
+  /// Total number of tokens
+  NUM_TOKENS
+};
+
+namespace detail {
+
+/**
+ * @brief Parses the given JSON string and emits a sequence of tokens that demarcate relevant
+ * sections from the input.
+ *
+ * @param json_in The JSON input
+ * @param options Parsing options specifying the parsing behaviour
+ * @param stream The CUDA stream to which kernels are dispatched
+ * @param mr Optional, resource with which to allocate
+ * @return Pair of device vectors, where the first vector represents the token types and the second
+ * vector represents the index within the input corresponding to each token
+ */
+std::pair<rmm::device_uvector<PdaTokenT>, rmm::device_uvector<SymbolOffsetT>> get_token_stream(
+  device_span<SymbolT const> json_in,
+  cudf::io::json_reader_options const& options,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+
+}  // namespace cudf::io::json
diff --git a/cpp/include/cudf/io/detail/utils.hpp b/cpp/include/cudf/io/detail/utils.hpp
new file mode 100644
index 0000000..7bbda21
--- /dev/null
+++ b/cpp/include/cudf/io/detail/utils.hpp
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+namespace cudf {
+namespace io {
+namespace detail {
+/**
+ * @brief Whether writer writes in chunks or all at once
+ */
+enum class single_write_mode : bool { YES, NO };
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/json.hpp b/cpp/include/cudf/io/json.hpp
new file mode 100644
index 0000000..d408d24
--- /dev/null
+++ b/cpp/include/cudf/io/json.hpp
@@ -0,0 +1,871 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "types.hpp"
+
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <map>
+#include <string>
+#include <variant>
+#include <vector>
+
+namespace cudf {
+namespace io {
+/**
+ * @addtogroup io_readers
+ * @{
+ * @file
+ */
+
+class json_reader_options_builder;
+
+/**
+ * @brief Allows specifying the target types for nested JSON data via json_reader_options'
+ * `set_dtypes` method.
+ */
+struct schema_element {
+  /**
+   * @brief The type that this column should be converted to
+   */
+  data_type type;
+
+  /**
+   * @brief Allows specifying this column's child columns target type
+   */
+  std::map<std::string, schema_element> child_types;
+};
+
+/**
+ * @brief Control the error recovery behavior of the json parser
+ */
+enum class json_recovery_mode_t {
+  FAIL,              ///< Does not recover from an error when encountering an invalid format
+  RECOVER_WITH_NULL  ///< Recovers from an error, replacing invalid records with null
+};
+
+/**
+ * @brief Input arguments to the `read_json` interface.
+ *
+ * Available parameters are closely patterned after PANDAS' `read_json` API.
+ * Not all parameters are supported. If the matching PANDAS' parameter
+ * has a default value of `None`, then a default value of `-1` or `0` may be
+ * used as the equivalent.
+ *
+ * Parameters in PANDAS that are unavailable or in cudf:
+ *
+ * | Name                 | Description                                      |
+ * | -------------------- | ------------------------------------------------ |
+ * | `orient`             | currently fixed-format                           |
+ * | `typ`                | data is always returned as a cudf::table         |
+ * | `convert_axes`       | use column functions for axes operations instead |
+ * | `convert_dates`      | dates are detected automatically                 |
+ * | `keep_default_dates` | dates are detected automatically                 |
+ * | `numpy`              | data is always returned as a cudf::table         |
+ * | `precise_float`      | there is only one converter                      |
+ * | `date_unit`          | only millisecond units are supported             |
+ * | `encoding`           | only ASCII-encoded data is supported             |
+ * | `chunksize`          | use `byte_range_xxx` for chunking instead        |
+ */
+class json_reader_options {
+  source_info _source;
+
+  // Data types of the column; empty to infer dtypes
+  std::variant<std::vector<data_type>,
+               std::map<std::string, data_type>,
+               std::map<std::string, schema_element>>
+    _dtypes;
+  // Specify the compression format of the source or infer from file extension
+  compression_type _compression = compression_type::AUTO;
+
+  // Read the file as a json object per line
+  bool _lines = false;
+
+  // Bytes to skip from the start
+  size_t _byte_range_offset = 0;
+  // Bytes to read; always reads complete rows
+  size_t _byte_range_size = 0;
+
+  // Whether to parse dates as DD/MM versus MM/DD
+  bool _dayfirst = false;
+
+  // Whether to use the legacy reader
+  bool _legacy = false;
+
+  // Whether to keep the quote characters of string values
+  bool _keep_quotes = false;
+
+  // Whether to recover after an invalid JSON line
+  json_recovery_mode_t _recovery_mode = json_recovery_mode_t::FAIL;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src source information used to read parquet file
+   */
+  explicit json_reader_options(source_info const& src) : _source(src) {}
+
+  friend json_reader_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  json_reader_options() = default;
+
+  /**
+   * @brief create json_reader_options_builder which will build json_reader_options.
+   *
+   * @param src source information used to read json file
+   * @returns builder to build the options
+   */
+  static json_reader_options_builder builder(source_info const& src);
+
+  /**
+   * @brief Returns source info.
+   *
+   * @returns Source info
+   */
+  [[nodiscard]] source_info const& get_source() const { return _source; }
+
+  /**
+   * @brief Returns data types of the columns.
+   *
+   * @returns Data types of the columns
+   */
+  std::variant<std::vector<data_type>,
+               std::map<std::string, data_type>,
+               std::map<std::string, schema_element>> const&
+  get_dtypes() const
+  {
+    return _dtypes;
+  }
+
+  /**
+   * @brief Returns compression format of the source.
+   *
+   * @return Compression format of the source
+   */
+  compression_type get_compression() const { return _compression; }
+
+  /**
+   * @brief Returns number of bytes to skip from source start.
+   *
+   * @return Number of bytes to skip from source start
+   */
+  size_t get_byte_range_offset() const { return _byte_range_offset; }
+
+  /**
+   * @brief Returns number of bytes to read.
+   *
+   * @return Number of bytes to read
+   */
+  size_t get_byte_range_size() const { return _byte_range_size; }
+
+  /**
+   * @brief Returns number of bytes to read with padding.
+   *
+   * @return Number of bytes to read with padding
+   */
+  size_t get_byte_range_size_with_padding() const
+  {
+    if (_byte_range_size == 0) {
+      return 0;
+    } else {
+      return _byte_range_size + get_byte_range_padding();
+    }
+  }
+
+  /**
+   * @brief Returns number of bytes to pad when reading.
+   *
+   * @return Number of bytes to pad
+   */
+  size_t get_byte_range_padding() const
+  {
+    auto const num_columns = std::visit([](auto const& dtypes) { return dtypes.size(); }, _dtypes);
+
+    auto const max_row_bytes = 16 * 1024;  // 16KB
+    auto const column_bytes  = 64;
+    auto const base_padding  = 1024;  // 1KB
+
+    if (num_columns == 0) {
+      // Use flat size if the number of columns is not known
+      return max_row_bytes;
+    }
+
+    // Expand the size based on the number of columns, if available
+    return base_padding + num_columns * column_bytes;
+  }
+
+  /**
+   * @brief Whether to read the file as a json object per line.
+   *
+   * @return `true` if reading the file as a json object per line
+   */
+  bool is_enabled_lines() const { return _lines; }
+
+  /**
+   * @brief Whether to parse dates as DD/MM versus MM/DD.
+   *
+   * @returns true if dates are parsed as DD/MM, false if MM/DD
+   */
+  bool is_enabled_dayfirst() const { return _dayfirst; }
+
+  /**
+   * @brief Whether the legacy reader should be used.
+   *
+   * @returns true if the legacy reader will be used, false otherwise
+   */
+  bool is_enabled_legacy() const { return _legacy; }
+
+  /**
+   * @brief Whether the reader should keep quotes of string values.
+   *
+   * @returns true if the reader should keep quotes, false otherwise
+   */
+  bool is_enabled_keep_quotes() const { return _keep_quotes; }
+
+  /**
+   * @brief Queries the JSON reader's behavior on invalid JSON lines.
+   *
+   * @returns An enum that specifies the JSON reader's behavior on invalid JSON lines.
+   */
+  json_recovery_mode_t recovery_mode() const { return _recovery_mode; }
+
+  /**
+   * @brief Set data types for columns to be read.
+   *
+   * @param types Vector of dtypes
+   */
+  void set_dtypes(std::vector<data_type> types) { _dtypes = std::move(types); }
+
+  /**
+   * @brief Set data types for columns to be read.
+   *
+   * @param types Vector dtypes in string format
+   */
+  void set_dtypes(std::map<std::string, data_type> types) { _dtypes = std::move(types); }
+
+  /**
+   * @brief Set data types for a potentially nested column hierarchy.
+   *
+   * @param types Map of column names to schema_element to support arbitrary nesting of data types
+   */
+  void set_dtypes(std::map<std::string, schema_element> types) { _dtypes = std::move(types); }
+
+  /**
+   * @brief Set the compression type.
+   *
+   * @param comp_type The compression type used
+   */
+  void set_compression(compression_type comp_type) { _compression = comp_type; }
+
+  /**
+   * @brief Set number of bytes to skip from source start.
+   *
+   * @param offset Number of bytes of offset
+   */
+  void set_byte_range_offset(size_type offset) { _byte_range_offset = offset; }
+
+  /**
+   * @brief Set number of bytes to read.
+   *
+   * @param size Number of bytes to read
+   */
+  void set_byte_range_size(size_type size) { _byte_range_size = size; }
+
+  /**
+   * @brief Set whether to read the file as a json object per line.
+   *
+   * @param val Boolean value to enable/disable the option to read each line as a json object
+   */
+  void enable_lines(bool val) { _lines = val; }
+
+  /**
+   * @brief Set whether to parse dates as DD/MM versus MM/DD.
+   *
+   * @param val Boolean value to enable/disable day first parsing format
+   */
+  void enable_dayfirst(bool val) { _dayfirst = val; }
+
+  /**
+   * @brief Set whether to use the legacy reader.
+   *
+   * @param val Boolean value to enable/disable the legacy reader
+   */
+  void enable_legacy(bool val) { _legacy = val; }
+
+  /**
+   * @brief Set whether the reader should keep quotes of string values.
+   *
+   * @param val Boolean value to indicate whether the reader should keep quotes
+   * of string values
+   */
+  void enable_keep_quotes(bool val) { _keep_quotes = val; }
+
+  /**
+   * @brief Specifies the JSON reader's behavior on invalid JSON lines.
+   *
+   * @param val An enum value to indicate the JSON reader's behavior on invalid JSON lines.
+   */
+  void set_recovery_mode(json_recovery_mode_t val) { _recovery_mode = val; }
+};
+
+/**
+ * @brief Builds settings to use for `read_json()`.
+ */
+class json_reader_options_builder {
+  json_reader_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit json_reader_options_builder() = default;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src The source information used to read avro file
+   */
+  explicit json_reader_options_builder(source_info const& src) : options(src) {}
+
+  /**
+   * @brief Set data types for columns to be read.
+   *
+   * @param types Vector of dtypes
+   * @return this for chaining
+   */
+  json_reader_options_builder& dtypes(std::vector<data_type> types)
+  {
+    options._dtypes = std::move(types);
+    return *this;
+  }
+
+  /**
+   * @brief Set data types for columns to be read.
+   *
+   * @param types Column name -> dtype map
+   * @return this for chaining
+   */
+  json_reader_options_builder& dtypes(std::map<std::string, data_type> types)
+  {
+    options._dtypes = std::move(types);
+    return *this;
+  }
+
+  /**
+   * @brief Set data types for columns to be read.
+   *
+   * @param types Column name -> schema_element map
+   * @return this for chaining
+   */
+  json_reader_options_builder& dtypes(std::map<std::string, schema_element> types)
+  {
+    options._dtypes = std::move(types);
+    return *this;
+  }
+
+  /**
+   * @brief Set the compression type.
+   *
+   * @param comp_type The compression type used
+   * @return this for chaining
+   */
+  json_reader_options_builder& compression(compression_type comp_type)
+  {
+    options._compression = comp_type;
+    return *this;
+  }
+
+  /**
+   * @brief Set number of bytes to skip from source start.
+   *
+   * @param offset Number of bytes of offset
+   * @return this for chaining
+   */
+  json_reader_options_builder& byte_range_offset(size_type offset)
+  {
+    options._byte_range_offset = offset;
+    return *this;
+  }
+
+  /**
+   * @brief Set number of bytes to read.
+   *
+   * @param size Number of bytes to read
+   * @return this for chaining
+   */
+  json_reader_options_builder& byte_range_size(size_type size)
+  {
+    options._byte_range_size = size;
+    return *this;
+  }
+
+  /**
+   * @brief Set whether to read the file as a json object per line.
+   *
+   * @param val Boolean value to enable/disable the option to read each line as a json object
+   * @return this for chaining
+   */
+  json_reader_options_builder& lines(bool val)
+  {
+    options._lines = val;
+    return *this;
+  }
+
+  /**
+   * @brief Set whether to parse dates as DD/MM versus MM/DD.
+   *
+   * @param val Boolean value to enable/disable day first parsing format
+   * @return this for chaining
+   */
+  json_reader_options_builder& dayfirst(bool val)
+  {
+    options._dayfirst = val;
+    return *this;
+  }
+
+  /**
+   * @brief Set whether to use the legacy reader.
+   *
+   * @param val Boolean value to enable/disable legacy parsing
+   * @return this for chaining
+   */
+  json_reader_options_builder& legacy(bool val)
+  {
+    options._legacy = val;
+    return *this;
+  }
+
+  /**
+   * @brief Set whether the reader should keep quotes of string values.
+   *
+   * @param val Boolean value to indicate whether the reader should keep quotes
+   * of string values
+   * @return this for chaining
+   */
+  json_reader_options_builder& keep_quotes(bool val)
+  {
+    options._keep_quotes = val;
+    return *this;
+  }
+
+  /**
+   * @brief Specifies the JSON reader's behavior on invalid JSON lines.
+   *
+   * @param val An enum value to indicate the JSON reader's behavior on invalid JSON lines.
+   * @return this for chaining
+   */
+  json_reader_options_builder& recovery_mode(json_recovery_mode_t val)
+  {
+    options._recovery_mode = val;
+    return *this;
+  }
+
+  /**
+   * @brief move json_reader_options member once it's built.
+   */
+  operator json_reader_options&&() { return std::move(options); }
+
+  /**
+   * @brief move json_reader_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `json_reader_options` object r-value reference
+   */
+  json_reader_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Reads a JSON dataset into a set of columns.
+ *
+ * The following code snippet demonstrates how to read a dataset from a file:
+ * @code
+ *  auto source  = cudf::io::source_info("dataset.json");
+ *  auto options = cudf::io::read_json_options::builder(source);
+ *  auto result  = cudf::io::read_json(options);
+ * @endcode
+ *
+ * @param options Settings for controlling reading behavior
+ * @param mr Device memory resource used to allocate device memory of the table in the returned
+ * table_with_metadata.
+ *
+ * @return The set of columns along with metadata
+ */
+table_with_metadata read_json(
+  json_reader_options options,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+/**
+ * @addtogroup io_writers
+ * @{
+ * @file
+ */
+
+/**
+ *@brief Builder to build options for `writer_json()`.
+ */
+class json_writer_options_builder;
+
+/**
+ * @brief Settings to use for `write_json()`.
+ */
+class json_writer_options {
+  // Specify the sink to use for writer output
+  sink_info _sink;
+  // Set of columns to output
+  table_view _table;
+  // string to use for null entries
+  std::string _na_rep = "";
+  // Indicates whether to output nulls as 'null' or exclude the field
+  bool _include_nulls = false;
+  // Indicates whether to use JSON lines for records format
+  bool _lines = false;
+  // maximum number of rows to write in each chunk (limits memory use)
+  size_type _rows_per_chunk = std::numeric_limits<size_type>::max();
+  // string to use for values != 0 in INT8 types (default 'true')
+  std::string _true_value = std::string{"true"};
+  // string to use for values == 0 in INT8 types (default 'false')
+  std::string _false_value = std::string{"false"};
+  // Names of all columns; if empty, writer will generate column names
+  std::optional<table_metadata> _metadata;  // Optional column names
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit json_writer_options(sink_info const& sink, table_view const& table)
+    : _sink(sink), _table(table), _rows_per_chunk(table.num_rows())
+  {
+  }
+
+  friend json_writer_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit json_writer_options() = default;
+
+  /**
+   * @brief Create builder to create `json_writer_options`.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   *
+   * @return Builder to build json_writer_options
+   */
+  static json_writer_options_builder builder(sink_info const& sink, table_view const& table);
+
+  /**
+   * @brief Returns sink used for writer output.
+   *
+   * @return sink used for writer output
+   */
+  [[nodiscard]] sink_info const& get_sink() const { return _sink; }
+
+  /**
+   * @brief Returns table that would be written to output.
+   *
+   * @return Table that would be written to output
+   */
+  [[nodiscard]] table_view const& get_table() const { return _table; }
+
+  /**
+   * @brief Returns metadata information.
+   *
+   * @return Metadata information
+   */
+  [[nodiscard]] std::optional<table_metadata> const& get_metadata() const { return _metadata; }
+
+  /**
+   * @brief Returns string to used for null entries.
+   *
+   * @return string to used for null entries
+   */
+  [[nodiscard]] std::string const& get_na_rep() const { return _na_rep; }
+
+  /**
+   * @brief Whether to output nulls as 'null'.
+   *
+   * @return `true` if nulls are output as 'null'
+   */
+  [[nodiscard]] bool is_enabled_include_nulls() const { return _include_nulls; }
+
+  /**
+   * @brief Whether to use JSON lines for records format.
+   *
+   * @return `true` if JSON lines is used for records format
+   */
+  [[nodiscard]] bool is_enabled_lines() const { return _lines; }
+
+  /**
+   * @brief Returns maximum number of rows to process for each file write.
+   *
+   * @return Maximum number of rows to process for each file write
+   */
+  [[nodiscard]] size_type get_rows_per_chunk() const { return _rows_per_chunk; }
+
+  /**
+   * @brief Returns string used for values != 0 in INT8 types.
+   *
+   * @return string used for values != 0 in INT8 types
+   */
+  [[nodiscard]] std::string const& get_true_value() const { return _true_value; }
+
+  /**
+   * @brief Returns string used for values == 0 in INT8 types.
+   *
+   * @return string used for values == 0 in INT8 types
+   */
+  [[nodiscard]] std::string const& get_false_value() const { return _false_value; }
+
+  // Setter
+
+  /**
+   * @brief Sets table to be written to output.
+   *
+   * @param tbl Table for the output
+   */
+  void set_table(table_view tbl) { _table = tbl; }
+
+  /**
+   * @brief Sets metadata.
+   *
+   * @param metadata Associated metadata
+   */
+  void set_metadata(table_metadata metadata) { _metadata = std::move(metadata); }
+
+  /**
+   * @brief Sets string to used for null entries.
+   *
+   * @param val String to represent null value
+   */
+  void set_na_rep(std::string val) { _na_rep = std::move(val); }
+
+  /**
+   * @brief Enables/Disables output of nulls as 'null'.
+   *
+   * @param val Boolean value to enable/disable
+   */
+  void enable_include_nulls(bool val) { _include_nulls = val; }
+
+  /**
+   * @brief Enables/Disables JSON lines for records format.
+   *
+   * @param val Boolean value to enable/disable JSON lines
+   */
+  void enable_lines(bool val) { _lines = val; }
+
+  /**
+   * @brief Sets maximum number of rows to process for each file write.
+   *
+   * @param val Number of rows per chunk
+   */
+  void set_rows_per_chunk(size_type val) { _rows_per_chunk = val; }
+
+  /**
+   * @brief Sets string used for values != 0 in INT8 types.
+   *
+   * @param val String to represent values != 0 in INT8 types
+   */
+  void set_true_value(std::string val) { _true_value = std::move(val); }
+
+  /**
+   * @brief Sets string used for values == 0 in INT8 types.
+   *
+   * @param val String to represent values == 0 in INT8 types
+   */
+  void set_false_value(std::string val) { _false_value = std::move(val); }
+};
+
+/**
+ * @brief Builder to build options for `writer_json()`
+ */
+class json_writer_options_builder {
+  json_writer_options options;  ///< Options to be built.
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit json_writer_options_builder() = default;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit json_writer_options_builder(sink_info const& sink, table_view const& table)
+    : options{sink, table}
+  {
+  }
+
+  /**
+   * @brief Sets table to be written to output.
+   *
+   * @param tbl Table for the output
+   * @return this for chaining
+   */
+  json_writer_options_builder& table(table_view tbl)
+  {
+    options._table = tbl;
+    return *this;
+  }
+
+  /**
+   * @brief Sets optional metadata (with column names).
+   *
+   * @param metadata metadata (with column names)
+   * @return this for chaining
+   */
+  json_writer_options_builder& metadata(table_metadata metadata)
+  {
+    options._metadata = std::move(metadata);
+    return *this;
+  }
+
+  /**
+   * @brief Sets string to used for null entries.
+   *
+   * @param val String to represent null value
+   * @return this for chaining
+   */
+  json_writer_options_builder& na_rep(std::string val)
+  {
+    options._na_rep = std::move(val);
+    return *this;
+  };
+
+  /**
+   * @brief Enables/Disables output of nulls as 'null'.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  json_writer_options_builder& include_nulls(bool val)
+  {
+    options._include_nulls = val;
+    return *this;
+  }
+
+  /**
+   * @brief Enables/Disables JSON lines for records format.
+   *
+   * @param val Boolean value to enable/disable
+   * @return this for chaining
+   */
+  json_writer_options_builder& lines(bool val)
+  {
+    options._lines = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets maximum number of rows to process for each file write.
+   *
+   * @param val Number of rows per chunk
+   * @return this for chaining
+   */
+  json_writer_options_builder& rows_per_chunk(int val)
+  {
+    options._rows_per_chunk = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets string used for values != 0 in INT8 types.
+   *
+   * @param val String to represent values != 0 in INT8 types
+   * @return this for chaining
+   */
+  json_writer_options_builder& true_value(std::string val)
+  {
+    options._true_value = std::move(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets string used for values == 0 in INT8 types.
+   *
+   * @param val String to represent values == 0 in INT8 types
+   * @return this for chaining
+   */
+  json_writer_options_builder& false_value(std::string val)
+  {
+    options._false_value = std::move(val);
+    return *this;
+  }
+
+  /**
+   * @brief move `json_writer_options` member once it's built.
+   */
+  operator json_writer_options&&() { return std::move(options); }
+
+  /**
+   * @brief move `json_writer_options` member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `json_writer_options` object's r-value reference
+   */
+  json_writer_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Writes a set of columns to JSON format.
+ *
+ * The following code snippet demonstrates how to write columns to a file:
+ * @code
+ *  auto destination = cudf::io::sink_info("dataset.json");
+ *  auto options     = cudf::io::json_writer_options(destination, table->view())
+ *    .na_rep(na)
+ *    .lines(lines)
+ *    .rows_per_chunk(rows_per_chunk);
+ *
+ *  cudf::io::write_json(options);
+ * @endcode
+ *
+ * @param options Settings for controlling writing behavior
+ * @param mr Device memory resource to use for device memory allocation
+ */
+void write_json(json_writer_options const& options,
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/orc.hpp b/cpp/include/cudf/io/orc.hpp
new file mode 100644
index 0000000..024f4f2
--- /dev/null
+++ b/cpp/include/cudf/io/orc.hpp
@@ -0,0 +1,1256 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/detail/orc.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <optional>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace cudf {
+namespace io {
+/**
+ * @addtogroup io_readers
+ * @{
+ * @file
+ */
+
+constexpr size_t default_stripe_size_bytes   = 64 * 1024 * 1024;  ///< 64MB default orc stripe size
+constexpr size_type default_stripe_size_rows = 1000000;  ///< 1M rows default orc stripe rows
+constexpr size_type default_row_index_stride = 10000;    ///< 10K rows default orc row index stride
+
+/**
+ * @brief Builds settings to use for `read_orc()`.
+ */
+class orc_reader_options_builder;
+
+/**
+ * @brief Settings to use for `read_orc()`.
+ */
+class orc_reader_options {
+  source_info _source;
+
+  // Names of column to read; `nullopt` is all
+  std::optional<std::vector<std::string>> _columns;
+
+  // List of individual stripes to read (ignored if empty)
+  std::vector<std::vector<size_type>> _stripes;
+  // Rows to skip from the start; ORC stores the number of rows as uint64_t
+  uint64_t _skip_rows = 0;
+  // Rows to read; `nullopt` is all
+  std::optional<size_type> _num_rows;
+
+  // Whether to use row index to speed-up reading
+  bool _use_index = true;
+
+  // Whether to use numpy-compatible dtypes
+  bool _use_np_dtypes = true;
+  // Cast timestamp columns to a specific type
+  data_type _timestamp_type{type_id::EMPTY};
+
+  // Columns that should be read as Decimal128
+  std::vector<std::string> _decimal128_columns;
+
+  friend orc_reader_options_builder;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src source information used to read orc file
+   */
+  explicit orc_reader_options(source_info const& src) : _source(src) {}
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  orc_reader_options() = default;
+
+  /**
+   * @brief Creates `orc_reader_options_builder` which will build `orc_reader_options`.
+   *
+   * @param src Source information to read orc file
+   * @return Builder to build reader options
+   */
+  static orc_reader_options_builder builder(source_info const& src);
+
+  /**
+   * @brief Returns source info.
+   *
+   * @return Source info
+   */
+  [[nodiscard]] source_info const& get_source() const { return _source; }
+
+  /**
+   * @brief Returns names of the columns to read, if set.
+   *
+   * @return Names of the columns to read; `nullopt` if the option is not set
+   */
+  [[nodiscard]] auto const& get_columns() const { return _columns; }
+
+  /**
+   * @brief Returns vector of vectors, stripes to read for each input source
+   *
+   * @return Vector of vectors, stripes to read for each input source
+   */
+  [[nodiscard]] auto const& get_stripes() const { return _stripes; }
+
+  /**
+   * @brief Returns number of rows to skip from the start.
+   *
+   * @return Number of rows to skip from the start
+   */
+  uint64_t get_skip_rows() const { return _skip_rows; }
+
+  /**
+   * @brief Returns number of row to read.
+   *
+   * @return Number of rows to read; `nullopt` if the option hasn't been set (in which case the file
+   * is read until the end)
+   */
+  std::optional<size_type> const& get_num_rows() const { return _num_rows; }
+
+  /**
+   * @brief Whether to use row index to speed-up reading.
+   *
+   * @return `true` if row index is used to speed-up reading
+   */
+  bool is_enabled_use_index() const { return _use_index; }
+
+  /**
+   * @brief Whether to use numpy-compatible dtypes.
+   *
+   * @return `true` if numpy-compatible dtypes are used
+   */
+  bool is_enabled_use_np_dtypes() const { return _use_np_dtypes; }
+
+  /**
+   * @brief Returns timestamp type to which timestamp column will be cast.
+   *
+   * @return Timestamp type to which timestamp column will be cast
+   */
+  data_type get_timestamp_type() const { return _timestamp_type; }
+
+  /**
+   * @brief Returns fully qualified names of columns that should be read as 128-bit Decimal.
+   *
+   * @return Fully qualified names of columns that should be read as 128-bit Decimal
+   */
+  std::vector<std::string> const& get_decimal128_columns() const { return _decimal128_columns; }
+
+  // Setters
+
+  /**
+   * @brief Sets names of the column to read.
+   *
+   * @param col_names Vector of column names
+   */
+  void set_columns(std::vector<std::string> col_names) { _columns = std::move(col_names); }
+
+  /**
+   * @brief Sets list of stripes to read for each input source
+   *
+   * @param stripes Vector of vectors, mapping stripes to read to input sources
+   *
+   * @throw cudf::logic_error if a non-empty vector is passed, and `skip_rows` has been previously
+   * set
+   * @throw cudf::logic_error if a non-empty vector is passed, and `num_rows` has been previously
+   * set
+   */
+  void set_stripes(std::vector<std::vector<size_type>> stripes)
+  {
+    CUDF_EXPECTS(stripes.empty() or (_skip_rows == 0), "Can't set stripes along with skip_rows");
+    CUDF_EXPECTS(stripes.empty() or not _num_rows.has_value(),
+                 "Can't set stripes along with num_rows");
+    _stripes = std::move(stripes);
+  }
+
+  /**
+   * @brief Sets number of rows to skip from the start.
+   *
+   * @param rows Number of rows
+   *
+   * @throw cudf::logic_error if a negative value is passed
+   * @throw cudf::logic_error if stripes have been previously set
+   */
+  void set_skip_rows(uint64_t rows)
+  {
+    CUDF_EXPECTS(rows == 0 or _stripes.empty(), "Can't set both skip_rows along with stripes");
+    _skip_rows = rows;
+  }
+
+  /**
+   * @brief Sets number of row to read.
+   *
+   * @param nrows Number of rows
+   *
+   * @throw cudf::logic_error if a negative value is passed
+   * @throw cudf::logic_error if stripes have been previously set
+   */
+  void set_num_rows(size_type nrows)
+  {
+    CUDF_EXPECTS(nrows >= 0, "num_rows cannot be negative");
+    CUDF_EXPECTS(_stripes.empty(), "Can't set both num_rows and stripes");
+    _num_rows = nrows;
+  }
+
+  /**
+   * @brief Enable/Disable use of row index to speed-up reading.
+   *
+   * @param use Boolean value to enable/disable row index use
+   */
+  void enable_use_index(bool use) { _use_index = use; }
+
+  /**
+   * @brief Enable/Disable use of numpy-compatible dtypes
+   *
+   * @param use Boolean value to enable/disable
+   */
+  void enable_use_np_dtypes(bool use) { _use_np_dtypes = use; }
+
+  /**
+   * @brief Sets timestamp type to which timestamp column will be cast.
+   *
+   * @param type Type of timestamp
+   */
+  void set_timestamp_type(data_type type) { _timestamp_type = type; }
+
+  /**
+   * @brief Set columns that should be read as 128-bit Decimal
+   *
+   * @param val Vector of fully qualified column names
+   */
+  void set_decimal128_columns(std::vector<std::string> val)
+  {
+    _decimal128_columns = std::move(val);
+  }
+};
+
+/**
+ * @brief Builds settings to use for `read_orc()`.
+ */
+class orc_reader_options_builder {
+  orc_reader_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit orc_reader_options_builder() = default;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src The source information used to read orc file
+   */
+  explicit orc_reader_options_builder(source_info const& src) : options{src} {};
+
+  /**
+   * @brief Sets names of the column to read.
+   *
+   * @param col_names Vector of column names
+   * @return this for chaining
+   */
+  orc_reader_options_builder& columns(std::vector<std::string> col_names)
+  {
+    options._columns = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets list of individual stripes to read per source
+   *
+   * @param stripes Vector of vectors, mapping stripes to read to input sources
+   * @return this for chaining
+   */
+  orc_reader_options_builder& stripes(std::vector<std::vector<size_type>> stripes)
+  {
+    options.set_stripes(std::move(stripes));
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to skip from the start.
+   *
+   * @param rows Number of rows
+   * @return this for chaining
+   */
+  orc_reader_options_builder& skip_rows(uint64_t rows)
+  {
+    options.set_skip_rows(rows);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of row to read.
+   *
+   * @param nrows Number of rows
+   * @return this for chaining
+   */
+  orc_reader_options_builder& num_rows(size_type nrows)
+  {
+    options.set_num_rows(nrows);
+    return *this;
+  }
+
+  /**
+   * @brief Enable/Disable use of row index to speed-up reading.
+   *
+   * @param use Boolean value to enable/disable row index use
+   * @return this for chaining
+   */
+  orc_reader_options_builder& use_index(bool use)
+  {
+    options._use_index = use;
+    return *this;
+  }
+
+  /**
+   * @brief Enable/Disable use of numpy-compatible dtypes.
+   *
+   * @param use Boolean value to enable/disable
+   * @return this for chaining
+   */
+  orc_reader_options_builder& use_np_dtypes(bool use)
+  {
+    options._use_np_dtypes = use;
+    return *this;
+  }
+
+  /**
+   * @brief Sets timestamp type to which timestamp column will be cast.
+   *
+   * @param type Type of timestamp
+   * @return this for chaining
+   */
+  orc_reader_options_builder& timestamp_type(data_type type)
+  {
+    options._timestamp_type = type;
+    return *this;
+  }
+
+  /**
+   * @brief Columns that should be read as 128-bit Decimal
+   *
+   * @param val Vector of column names
+   * @return this for chaining
+   */
+  orc_reader_options_builder& decimal128_columns(std::vector<std::string> val)
+  {
+    options._decimal128_columns = std::move(val);
+    return *this;
+  }
+
+  /**
+   * @brief move orc_reader_options member once it's built.
+   */
+  operator orc_reader_options&&() { return std::move(options); }
+
+  /**
+   * @brief move orc_reader_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `orc_reader_options` object's r-value reference
+   */
+  orc_reader_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Reads an ORC dataset into a set of columns.
+ *
+ * The following code snippet demonstrates how to read a dataset from a file:
+ * @code
+ *  auto source  = cudf::io::source_info("dataset.orc");
+ *  auto options = cudf::io::orc_reader_options::builder(source);
+ *  auto result  = cudf::io::read_orc(options);
+ * @endcode
+ *
+ * @param options Settings for controlling reading behavior
+ * @param mr Device memory resource used to allocate device memory of the table in the returned
+ * table_with_metadata.
+ *
+ * @return The set of columns
+ */
+table_with_metadata read_orc(
+  orc_reader_options const& options,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+/**
+ * @addtogroup io_writers
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Builds settings to use for `write_orc()`.
+ */
+class orc_writer_options_builder;
+
+/**
+ * @brief Constants to disambiguate statistics terminology for ORC.
+ *
+ * ORC refers to its finest granularity of row-grouping as "row group",
+ * which corresponds to Parquet "pages".
+ * Similarly, ORC's "stripe" corresponds to a Parquet "row group".
+ * The following constants disambiguate the terminology for the statistics
+ * collected at each level.
+ */
+static constexpr statistics_freq ORC_STATISTICS_STRIPE    = statistics_freq::STATISTICS_ROWGROUP;
+static constexpr statistics_freq ORC_STATISTICS_ROW_GROUP = statistics_freq::STATISTICS_PAGE;
+
+/**
+ * @brief Settings to use for `write_orc()`.
+ */
+class orc_writer_options {
+  // Specify the sink to use for writer output
+  sink_info _sink;
+  // Specify the compression format to use
+  compression_type _compression = compression_type::AUTO;
+  // Specify frequency of statistics collection
+  statistics_freq _stats_freq = ORC_STATISTICS_ROW_GROUP;
+  // Maximum size of each stripe (unless smaller than a single row group)
+  size_t _stripe_size_bytes = default_stripe_size_bytes;
+  // Maximum number of rows in stripe (unless smaller than a single row group)
+  size_type _stripe_size_rows = default_stripe_size_rows;
+  // Row index stride (maximum number of rows in each row group)
+  size_type _row_index_stride = default_row_index_stride;
+  // Set of columns to output
+  table_view _table;
+  // Optional associated metadata
+  std::optional<table_input_metadata> _metadata;
+  // Optional footer key_value_metadata
+  std::map<std::string, std::string> _user_data;
+  // Optional compression statistics
+  std::shared_ptr<writer_compression_statistics> _compression_stats;
+
+  friend orc_writer_options_builder;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit orc_writer_options(sink_info const& sink, table_view const& table)
+    : _sink(sink), _table(table)
+  {
+  }
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit orc_writer_options() = default;
+
+  /**
+   * @brief Create builder to create `orc_writer_options`.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   *
+   * @return Builder to build `orc_writer_options`
+   */
+  static orc_writer_options_builder builder(sink_info const& sink, table_view const& table);
+
+  /**
+   * @brief Returns sink info.
+   *
+   * @return Sink info
+   */
+  [[nodiscard]] sink_info const& get_sink() const { return _sink; }
+
+  /**
+   * @brief Returns compression type.
+   *
+   * @return Compression type
+   */
+  [[nodiscard]] compression_type get_compression() const { return _compression; }
+
+  /**
+   * @brief Whether writing column statistics is enabled/disabled.
+   *
+   * @return `true` if writing column statistics is enabled
+   */
+  [[nodiscard]] bool is_enabled_statistics() const
+  {
+    return _stats_freq != statistics_freq::STATISTICS_NONE;
+  }
+
+  /**
+   * @brief Returns frequency of statistics collection.
+   *
+   * @return Frequency of statistics collection
+   */
+  [[nodiscard]] statistics_freq get_statistics_freq() const { return _stats_freq; }
+
+  /**
+   * @brief Returns maximum stripe size, in bytes.
+   *
+   * @return Maximum stripe size, in bytes
+   */
+  [[nodiscard]] auto get_stripe_size_bytes() const { return _stripe_size_bytes; }
+
+  /**
+   * @brief Returns maximum stripe size, in rows.
+   *
+   * @return Maximum stripe size, in rows
+   */
+  [[nodiscard]] auto get_stripe_size_rows() const { return _stripe_size_rows; }
+
+  /**
+   * @brief Returns the row index stride.
+   *
+   * @return Row index stride
+   */
+  auto get_row_index_stride() const
+  {
+    auto const unaligned_stride = std::min(_row_index_stride, get_stripe_size_rows());
+    return unaligned_stride - unaligned_stride % 8;
+  }
+
+  /**
+   * @brief Returns table to be written to output.
+   *
+   * @return Table to be written to output
+   */
+  [[nodiscard]] table_view get_table() const { return _table; }
+
+  /**
+   * @brief Returns associated metadata.
+   *
+   * @return Associated metadata
+   */
+  [[nodiscard]] auto const& get_metadata() const { return _metadata; }
+
+  /**
+   * @brief Returns Key-Value footer metadata information.
+   *
+   * @return Key-Value footer metadata information
+   */
+  [[nodiscard]] std::map<std::string, std::string> const& get_key_value_metadata() const
+  {
+    return _user_data;
+  }
+
+  /**
+   * @brief Returns a shared pointer to the user-provided compression statistics.
+   *
+   * @return Compression statistics
+   */
+  [[nodiscard]] std::shared_ptr<writer_compression_statistics> get_compression_statistics() const
+  {
+    return _compression_stats;
+  }
+
+  // Setters
+
+  /**
+   * @brief Sets compression type.
+   *
+   * @param comp Compression type
+   */
+  void set_compression(compression_type comp) { _compression = comp; }
+
+  /**
+   * @brief Choose granularity of statistics collection.
+   *
+   * The granularity can be set to:
+   * - cudf::io::STATISTICS_NONE: No statistics are collected.
+   * - cudf::io::ORC_STATISTICS_STRIPE: Statistics are collected for each ORC stripe.
+   * - cudf::io::ORC_STATISTICS_ROWGROUP: Statistics are collected for each ORC row group.
+   *
+   * @param val Frequency of statistics collection
+   */
+  void enable_statistics(statistics_freq val) { _stats_freq = val; }
+
+  /**
+   * @brief Sets the maximum stripe size, in bytes.
+   *
+   * @param size_bytes Maximum stripe size, in bytes to be set
+   *
+   * @throw cudf::logic_error if a value below the minimal size is passed
+   */
+  void set_stripe_size_bytes(size_t size_bytes)
+  {
+    CUDF_EXPECTS(size_bytes >= 64 << 10, "64KB is the minimum stripe size");
+    _stripe_size_bytes = size_bytes;
+  }
+
+  /**
+   * @brief Sets the maximum stripe size, in rows.
+   *
+   * If the stripe size is smaller that the row group size, row group size will be reduced to math
+   * the stripe size.
+   *
+   * @param size_rows Maximum stripe size, in rows to be set
+   *
+   * @throw cudf::logic_error if a value below the minimal number of rows is passed
+   */
+  void set_stripe_size_rows(size_type size_rows)
+  {
+    CUDF_EXPECTS(size_rows >= 512, "Maximum stripe size cannot be smaller than 512");
+    _stripe_size_rows = size_rows;
+  }
+
+  /**
+   * @brief Sets the row index stride.
+   *
+   * Rounded down to a multiple of 8.
+   *
+   * @param stride Row index stride to be set
+   *
+   * @throw cudf::logic_error if a value below the minimal row index stride is passed
+   */
+  void set_row_index_stride(size_type stride)
+  {
+    CUDF_EXPECTS(stride >= 512, "Row index stride cannot be smaller than 512");
+    _row_index_stride = stride;
+  }
+
+  /**
+   * @brief Sets table to be written to output.
+   *
+   * @param tbl Table for the output
+   */
+  void set_table(table_view tbl) { _table = tbl; }
+
+  /**
+   * @brief Sets associated metadata
+   *
+   * @param meta Associated metadata
+   */
+  void set_metadata(table_input_metadata meta) { _metadata = std::move(meta); }
+
+  /**
+   * @brief Sets metadata.
+   *
+   * @param metadata Key-Value footer metadata
+   */
+  void set_key_value_metadata(std::map<std::string, std::string> metadata)
+  {
+    _user_data = std::move(metadata);
+  }
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be updated after writing
+   */
+  void set_compression_statistics(std::shared_ptr<writer_compression_statistics> comp_stats)
+  {
+    _compression_stats = std::move(comp_stats);
+  }
+};
+
+/**
+ * @brief Builds settings to use for `write_orc()`.
+ */
+class orc_writer_options_builder {
+  orc_writer_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  orc_writer_options_builder() = default;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  orc_writer_options_builder(sink_info const& sink, table_view const& table) : options{sink, table}
+  {
+  }
+
+  /**
+   * @brief Sets compression type.
+   *
+   * @param comp The compression type to use
+   * @return this for chaining
+   */
+  orc_writer_options_builder& compression(compression_type comp)
+  {
+    options._compression = comp;
+    return *this;
+  }
+
+  /**
+   * @brief Choose granularity of column statistics to be written
+   *
+   * The granularity can be set to:
+   * - cudf::io::STATISTICS_NONE: No statistics are collected.
+   * - cudf::io::ORC_STATISTICS_STRIPE: Statistics are collected for each ORC stripe.
+   * - cudf::io::ORC_STATISTICS_ROWGROUP: Statistics are collected for each ORC row group.
+   *
+   * @param val Level of statistics collection
+   * @return this for chaining
+   */
+  orc_writer_options_builder& enable_statistics(statistics_freq val)
+  {
+    options._stats_freq = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum stripe size, in bytes.
+   *
+   * @param val maximum stripe size
+   * @return this for chaining
+   */
+  orc_writer_options_builder& stripe_size_bytes(size_t val)
+  {
+    options.set_stripe_size_bytes(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum number of rows in output stripes.
+   *
+   * @param val maximum number or rows
+   * @return this for chaining
+   */
+  orc_writer_options_builder& stripe_size_rows(size_type val)
+  {
+    options.set_stripe_size_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the row index stride.
+   *
+   * @param val new row index stride
+   * @return this for chaining
+   */
+  orc_writer_options_builder& row_index_stride(size_type val)
+  {
+    options.set_row_index_stride(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets table to be written to output.
+   *
+   * @param tbl Table for the output
+   * @return this for chaining
+   */
+  orc_writer_options_builder& table(table_view tbl)
+  {
+    options._table = tbl;
+    return *this;
+  }
+
+  /**
+   * @brief Sets associated metadata.
+   *
+   * @param meta Associated metadata
+   * @return this for chaining
+   */
+  orc_writer_options_builder& metadata(table_input_metadata meta)
+  {
+    options._metadata = std::move(meta);
+    return *this;
+  }
+
+  /**
+   * @brief Sets Key-Value footer metadata.
+   *
+   * @param metadata Key-Value footer metadata
+   * @return this for chaining
+   */
+  orc_writer_options_builder& key_value_metadata(std::map<std::string, std::string> metadata)
+  {
+    options._user_data = std::move(metadata);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be filled once writer is done
+   * @return this for chaining
+   */
+  orc_writer_options_builder& compression_statistics(
+    std::shared_ptr<writer_compression_statistics> const& comp_stats)
+  {
+    options._compression_stats = comp_stats;
+    return *this;
+  }
+
+  /**
+   * @brief move orc_writer_options member once it's built.
+   */
+  operator orc_writer_options&&() { return std::move(options); }
+
+  /**
+   * @brief move orc_writer_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `orc_writer_options` object's r-value reference
+   */
+  orc_writer_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Writes a set of columns to ORC format.
+ *
+ * The following code snippet demonstrates how to write columns to a file:
+ * @code
+ *  auto destination = cudf::io::sink_info("dataset.orc");
+ *  auto options     = cudf::io::orc_writer_options::builder(destination, table->view());
+ *  cudf::io::write_orc(options);
+ * @endcode
+ *
+ * @param options Settings for controlling reading behavior
+ */
+void write_orc(orc_writer_options const& options);
+
+/**
+ * @brief Builds settings to use for `write_orc_chunked()`.
+ */
+class chunked_orc_writer_options_builder;
+
+/**
+ * @brief Settings to use for `write_orc_chunked()`.
+ */
+class chunked_orc_writer_options {
+  // Specify the sink to use for writer output
+  sink_info _sink;
+  // Specify the compression format to use
+  compression_type _compression = compression_type::AUTO;
+  // Specify granularity of statistics collection
+  statistics_freq _stats_freq = ORC_STATISTICS_ROW_GROUP;
+  // Maximum size of each stripe (unless smaller than a single row group)
+  size_t _stripe_size_bytes = default_stripe_size_bytes;
+  // Maximum number of rows in stripe (unless smaller than a single row group)
+  size_type _stripe_size_rows = default_stripe_size_rows;
+  // Row index stride (maximum number of rows in each row group)
+  size_type _row_index_stride = default_row_index_stride;
+  // Optional associated metadata
+  std::optional<table_input_metadata> _metadata;
+  // Optional footer key_value_metadata
+  std::map<std::string, std::string> _user_data;
+  // Optional compression statistics
+  std::shared_ptr<writer_compression_statistics> _compression_stats;
+
+  friend chunked_orc_writer_options_builder;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   */
+  chunked_orc_writer_options(sink_info const& sink) : _sink(sink) {}
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit chunked_orc_writer_options() = default;
+
+  /**
+   * @brief Create builder to create `chunked_orc_writer_options`.
+   *
+   * @param sink The sink used for writer output
+   *
+   * @return Builder to build chunked_orc_writer_options
+   */
+  static chunked_orc_writer_options_builder builder(sink_info const& sink);
+
+  /**
+   * @brief Returns sink info.
+   *
+   * @return Sink info
+   */
+  [[nodiscard]] sink_info const& get_sink() const { return _sink; }
+
+  /**
+   * @brief Returns compression type.
+   *
+   * @return Compression type
+   */
+  [[nodiscard]] compression_type get_compression() const { return _compression; }
+
+  /**
+   * @brief Returns granularity of statistics collection.
+   *
+   * @return Granularity of statistics collection
+   */
+  [[nodiscard]] statistics_freq get_statistics_freq() const { return _stats_freq; }
+
+  /**
+   * @brief Returns maximum stripe size, in bytes.
+   *
+   * @return Maximum stripe size, in bytes
+   */
+  [[nodiscard]] auto get_stripe_size_bytes() const { return _stripe_size_bytes; }
+
+  /**
+   * @brief Returns maximum stripe size, in rows.
+   *
+   * @return Maximum stripe size, in rows
+   */
+  [[nodiscard]] auto get_stripe_size_rows() const { return _stripe_size_rows; }
+
+  /**
+   * @brief Returns the row index stride.
+   *
+   * @return Row index stride
+   */
+  auto get_row_index_stride() const
+  {
+    auto const unaligned_stride = std::min(_row_index_stride, get_stripe_size_rows());
+    return unaligned_stride - unaligned_stride % 8;
+  }
+
+  /**
+   * @brief Returns associated metadata.
+   *
+   * @return Associated metadata
+   */
+  [[nodiscard]] auto const& get_metadata() const { return _metadata; }
+
+  /**
+   * @brief Returns Key-Value footer metadata information.
+   *
+   * @return Key-Value footer metadata information
+   */
+  [[nodiscard]] std::map<std::string, std::string> const& get_key_value_metadata() const
+  {
+    return _user_data;
+  }
+
+  /**
+   * @brief Returns a shared pointer to the user-provided compression statistics.
+   *
+   * @return Compression statistics
+   */
+  [[nodiscard]] std::shared_ptr<writer_compression_statistics> get_compression_statistics() const
+  {
+    return _compression_stats;
+  }
+
+  // Setters
+
+  /**
+   * @brief Sets compression type.
+   *
+   * @param comp The compression type to use
+   */
+  void set_compression(compression_type comp) { _compression = comp; }
+
+  /**
+   * @brief Choose granularity of statistics collection
+   *
+   * The granularity can be set to:
+   * - cudf::io::STATISTICS_NONE: No statistics are collected.
+   * - cudf::io::ORC_STATISTICS_STRIPE: Statistics are collected for each ORC stripe.
+   * - cudf::io::ORC_STATISTICS_ROWGROUP: Statistics are collected for each ORC row group.
+   *
+   * @param val Frequency of statistics collection
+   */
+  void enable_statistics(statistics_freq val) { _stats_freq = val; }
+
+  /**
+   * @brief Sets the maximum stripe size, in bytes.
+   *
+   * @param size_bytes Maximum stripe size, in bytes to be set
+   *
+   * @throw cudf::logic_error if a value below the minimal stripe size is passed
+   */
+  void set_stripe_size_bytes(size_t size_bytes)
+  {
+    CUDF_EXPECTS(size_bytes >= 64 << 10, "64KB is the minimum stripe size");
+    _stripe_size_bytes = size_bytes;
+  }
+
+  /**
+   * @brief Sets the maximum stripe size, in rows.
+   *
+   * If the stripe size is smaller that the row group size, row group size will be reduced to math
+   * the stripe size.
+   *
+   * @param size_rows Maximum stripe size, in rows to be set
+   *
+   * @throw cudf::logic_error if a value below the minimal number of rows in a stripe is passed
+   */
+  void set_stripe_size_rows(size_type size_rows)
+  {
+    CUDF_EXPECTS(size_rows >= 512, "maximum stripe size cannot be smaller than 512");
+    _stripe_size_rows = size_rows;
+  }
+
+  /**
+   * @brief Sets the row index stride.
+   *
+   * Rounded down to a multiple of 8.
+   *
+   * @param stride Row index stride to be set
+   *
+   * @throw cudf::logic_error if a value below the minimal number of rows in a row group is passed
+   */
+  void set_row_index_stride(size_type stride)
+  {
+    CUDF_EXPECTS(stride >= 512, "Row index stride cannot be smaller than 512");
+    _row_index_stride = stride;
+  }
+
+  /**
+   * @brief Sets associated metadata.
+   *
+   * @param meta Associated metadata
+   */
+  void metadata(table_input_metadata meta) { _metadata = std::move(meta); }
+
+  /**
+   * @brief Sets Key-Value footer metadata.
+   *
+   * @param metadata Key-Value footer metadata
+   */
+  void set_key_value_metadata(std::map<std::string, std::string> metadata)
+  {
+    _user_data = std::move(metadata);
+  }
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be updated after writing
+   */
+  void set_compression_statistics(std::shared_ptr<writer_compression_statistics> comp_stats)
+  {
+    _compression_stats = std::move(comp_stats);
+  }
+};
+
+/**
+ * @brief Builds settings to use for `write_orc_chunked()`.
+ */
+class chunked_orc_writer_options_builder {
+  chunked_orc_writer_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  chunked_orc_writer_options_builder() = default;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   */
+  explicit chunked_orc_writer_options_builder(sink_info const& sink) : options{sink} {}
+
+  /**
+   * @brief Sets compression type.
+   *
+   * @param comp The compression type to use
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& compression(compression_type comp)
+  {
+    options._compression = comp;
+    return *this;
+  }
+
+  /**
+   * @brief Choose granularity of statistics collection
+   *
+   * The granularity can be set to:
+   * - cudf::io::STATISTICS_NONE: No statistics are collected.
+   * - cudf::io::ORC_STATISTICS_STRIPE: Statistics are collected for each ORC stripe.
+   * - cudf::io::ORC_STATISTICS_ROWGROUP: Statistics are collected for each ORC row group.
+   *
+   * @param val Frequency of statistics collection
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& enable_statistics(statistics_freq val)
+  {
+    options._stats_freq = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum stripe size, in bytes.
+   *
+   * @param val maximum stripe size
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& stripe_size_bytes(size_t val)
+  {
+    options.set_stripe_size_bytes(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum number of rows in output stripes.
+   *
+   * @param val maximum number or rows
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& stripe_size_rows(size_type val)
+  {
+    options.set_stripe_size_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the row index stride.
+   *
+   * @param val new row index stride
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& row_index_stride(size_type val)
+  {
+    options.set_row_index_stride(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets associated metadata.
+   *
+   * @param meta Associated metadata
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& metadata(table_input_metadata meta)
+  {
+    options._metadata = std::move(meta);
+    return *this;
+  }
+
+  /**
+   * @brief Sets Key-Value footer metadata.
+   *
+   * @param metadata Key-Value footer metadata
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& key_value_metadata(
+    std::map<std::string, std::string> metadata)
+  {
+    options._user_data = std::move(metadata);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be filled once writer is done
+   * @return this for chaining
+   */
+  chunked_orc_writer_options_builder& compression_statistics(
+    std::shared_ptr<writer_compression_statistics> const& comp_stats)
+  {
+    options._compression_stats = comp_stats;
+    return *this;
+  }
+
+  /**
+   * @brief move chunked_orc_writer_options member once it's built.
+   */
+  operator chunked_orc_writer_options&&() { return std::move(options); }
+
+  /**
+   * @brief move chunked_orc_writer_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `chunked_orc_writer_options` object's r-value reference
+   */
+  chunked_orc_writer_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Chunked orc writer class writes an ORC file in a chunked/stream form.
+ *
+ * The intent of the write_orc_chunked_ path is to allow writing of an
+ * arbitrarily large / arbitrary number of rows to an ORC file in multiple passes.
+ *
+ * The following code snippet demonstrates how to write a single ORC file containing
+ * one logical table by writing a series of individual cudf::tables.
+ * @code
+ *  ...
+ *  std::string filepath = "dataset.orc";
+ *  cudf::io::chunked_orc_writer_options options = cudf::io::chunked_orc_writer_options
+ * options::builder(cudf::sink_info(filepath));
+ *  ...
+ *  orc_chunked_writer writer(options)
+ *  writer.write(table0)
+ *  writer.write(table1)
+ *    ...
+ *  writer.close();
+ * @endcode
+ */
+class orc_chunked_writer {
+ public:
+  /**
+   * @brief Default constructor, this should never be used.
+   *        This is added just to satisfy cython.
+   */
+  orc_chunked_writer() = default;
+
+  /**
+   * @brief Constructor with chunked writer options
+   *
+   * @param[in] options options used to write table
+   */
+  orc_chunked_writer(chunked_orc_writer_options const& options);
+
+  /**
+   * @brief Writes table to output.
+   *
+   * @param[in] table Table that needs to be written
+   * @return returns reference of the class object
+   */
+  orc_chunked_writer& write(table_view const& table);
+
+  /**
+   * @brief Finishes the chunked/streamed write process.
+   */
+  void close();
+
+  /// Unique pointer to impl writer class
+  std::unique_ptr<cudf::io::detail::orc::writer> writer;
+};
+
+/** @} */  // end of group
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/orc_metadata.hpp b/cpp/include/cudf/io/orc_metadata.hpp
new file mode 100644
index 0000000..82d5980
--- /dev/null
+++ b/cpp/include/cudf/io/orc_metadata.hpp
@@ -0,0 +1,371 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file orc_metadata.hpp
+ * @brief cuDF-IO freeform API
+ */
+
+#pragma once
+
+#include <cudf/io/orc_types.hpp>
+#include <cudf/io/types.hpp>
+
+#include <optional>
+#include <variant>
+#include <vector>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Holds column names and buffers containing raw file-level and stripe-level statistics.
+ *
+ * The buffers can be parsed using a Protobuf parser. Alternatively, use `parsed_orc_statistics` to
+ * get the statistics parsed into a libcudf representation.
+ *
+ * The `column_names` and `file_stats` members contain one element per column. The `stripes_stats`
+ * contains one element per stripe, where each element contains column statistics for each column.
+ */
+struct raw_orc_statistics {
+  std::vector<std::string> column_names;                ///< Column names
+  std::vector<std::string> file_stats;                  ///< File-level statistics for each column
+  std::vector<std::vector<std::string>> stripes_stats;  ///< Stripe-level statistics for each column
+};
+
+/**
+ * @brief Reads file-level and stripe-level statistics of ORC dataset.
+ *
+ * @ingroup io_readers
+ *
+ * The following code snippet demonstrates how to read statistics of a dataset
+ * from a file:
+ * @code
+ *  auto result = cudf::read_raw_orc_statistics(cudf::source_info("dataset.orc"));
+ * @endcode
+ *
+ * @param src_info Dataset source
+ *
+ * @return Column names and encoded ORC statistics
+ */
+raw_orc_statistics read_raw_orc_statistics(source_info const& src_info);
+
+/**
+ * @brief Monostate type alias for the statistics variant.
+ */
+using no_statistics = std::monostate;
+
+/**
+ * @brief Base class for column statistics that include optional minimum and maximum.
+ *
+ * Includes accessors for the minimum and maximum values.
+ */
+template <typename T>
+struct minmax_statistics {
+  std::optional<T> minimum;  ///< Minimum value
+  std::optional<T> maximum;  ///< Maximum value
+};
+
+/**
+ * @brief Base class for column statistics that include an optional sum.
+ *
+ * Includes accessors for the sum value.
+ */
+template <typename T>
+struct sum_statistics {
+  std::optional<T> sum;  ///< Sum of values in column
+};
+
+/**
+ * @brief Statistics for integral columns.
+ */
+struct integer_statistics : minmax_statistics<int64_t>, sum_statistics<int64_t> {};
+
+/**
+ * @brief Statistics for floating point columns.
+ */
+struct double_statistics : minmax_statistics<double>, sum_statistics<double> {};
+
+/**
+ * @brief Statistics for string columns.
+ *
+ * The `minimum` and `maximum` are the first and last elements, respectively, in lexicographical
+ * order. The `sum` is the total length of elements in the column.
+ * Note: According to ORC specs, the sum should be signed, but pyarrow uses unsigned value
+ */
+struct string_statistics : minmax_statistics<std::string>, sum_statistics<int64_t> {};
+
+/**
+ * @brief Statistics for boolean columns.
+ *
+ * The `count` array contains the count of `true` values.
+ */
+struct bucket_statistics {
+  std::vector<uint64_t> count;  ///< count of `true` values
+};
+
+/**
+ * @brief Statistics for decimal columns.
+ */
+struct decimal_statistics : minmax_statistics<std::string>, sum_statistics<std::string> {};
+
+/**
+ * @brief Statistics for date(time) columns.
+ */
+using date_statistics = minmax_statistics<int32_t>;
+
+/**
+ * @brief Statistics for binary columns.
+ *
+ * The `sum` is the total number of bytes across all elements.
+ */
+using binary_statistics = sum_statistics<int64_t>;
+
+/**
+ * @brief Statistics for timestamp columns.
+ *
+ * The `minimum` and `maximum` min/max elements in the column, as the number of milliseconds since
+ * the UNIX epoch. The `minimum_utc` and `maximum_utc` are the same values adjusted to UTC.
+ */
+struct timestamp_statistics : minmax_statistics<int64_t> {
+  std::optional<int64_t> minimum_utc;    ///< minimum in milliseconds
+  std::optional<int64_t> maximum_utc;    ///< maximum in milliseconds
+  std::optional<int32_t> minimum_nanos;  ///< nanoseconds part of the minimum
+  std::optional<int32_t> maximum_nanos;  ///< nanoseconds part of the maximum
+};
+
+namespace orc {
+// forward declare the type that ProtobufReader uses. The `cudf::io::column_statistics` objects,
+// returned from `read_parsed_orc_statistics`, are constructed from
+// `cudf::io::orc::column_statistics` objects that `ProtobufReader` initializes.
+struct column_statistics;
+}  // namespace orc
+
+/**
+ * @brief Contains per-column ORC statistics.
+ *
+ * All columns can have the `number_of_values` statistics. Depending on the data type, a column can
+ * have additional statistics, accessible through `type_specific_stats` accessor.
+ */
+struct column_statistics {
+  std::optional<uint64_t> number_of_values;  ///< number of statistics
+  std::optional<bool> has_null;              ///< column has any nulls
+  std::variant<no_statistics,
+               integer_statistics,
+               double_statistics,
+               string_statistics,
+               bucket_statistics,
+               decimal_statistics,
+               date_statistics,
+               binary_statistics,
+               timestamp_statistics>
+    type_specific_stats;  ///< type-specific statistics
+
+  /**
+   * @brief Construct a new column statistics object
+   *
+   * @param detail_statistics The statistics to initialize the object with
+   */
+  column_statistics(orc::column_statistics&& detail_statistics);
+};
+
+/**
+ * @brief Holds column names and parsed file-level and stripe-level statistics.
+ *
+ * The `column_names` and `file_stats` members contain one element per column. The `stripes_stats`
+ * member contains one element per stripe, where each element contains column statistics for each
+ * column.
+ */
+struct parsed_orc_statistics {
+  std::vector<std::string> column_names;                      ///< column names
+  std::vector<column_statistics> file_stats;                  ///< file-level statistics
+  std::vector<std::vector<column_statistics>> stripes_stats;  ///< stripe-level statistics
+};
+
+/**
+ * @brief Reads file-level and stripe-level statistics of ORC dataset.
+ *
+ * @ingroup io_readers
+ *
+ * @param src_info Dataset source
+ *
+ * @return Column names and decoded ORC statistics
+ */
+parsed_orc_statistics read_parsed_orc_statistics(source_info const& src_info);
+
+/**
+ * @brief Schema of an ORC column, including the nested columns.
+ */
+struct orc_column_schema {
+ public:
+  /**
+   * @brief constructor
+   *
+   * @param name column name
+   * @param type ORC type
+   * @param children child columns (empty for non-nested types)
+   */
+  orc_column_schema(std::string_view name,
+                    orc::TypeKind type,
+                    std::vector<orc_column_schema> children)
+    : _name{name}, _type_kind{type}, _children{std::move(children)}
+  {
+  }
+
+  /**
+   * @brief Returns ORC column name; can be empty.
+   *
+   * @return Column name
+   */
+  [[nodiscard]] auto name() const { return _name; }
+
+  /**
+   * @brief Returns ORC type of the column.
+   *
+   * @return Column ORC type
+   */
+  [[nodiscard]] auto type_kind() const { return _type_kind; }
+
+  /**
+   * @brief Returns schemas of all child columns.
+   *
+   * @return Children schemas
+   */
+  [[nodiscard]] auto const& children() const& { return _children; }
+
+  /** @copydoc children
+   * Children array is moved out of the object (rvalues only).
+   *
+   */
+  [[nodiscard]] auto children() && { return std::move(_children); }
+
+  /**
+   * @brief Returns schema of the child with the given index.
+   *
+   * @param idx child index
+   *
+   * @return Child schema
+   */
+  [[nodiscard]] auto const& child(int idx) const& { return children().at(idx); }
+
+  /** @copydoc child
+   * Child is moved out of the object (rvalues only).
+   *
+   */
+  [[nodiscard]] auto child(int idx) && { return std::move(children().at(idx)); }
+
+  /**
+   * @brief Returns the number of child columns.
+   *
+   * @return Children count
+   */
+  [[nodiscard]] auto num_children() const { return children().size(); }
+
+ private:
+  std::string _name;
+  orc::TypeKind _type_kind;
+  std::vector<orc_column_schema> _children;
+};
+
+/**
+ * @brief Schema of an ORC file.
+ */
+struct orc_schema {
+ public:
+  /**
+   * @brief constructor
+   *
+   * @param root_column_schema root column
+   */
+  orc_schema(orc_column_schema root_column_schema) : _root{std::move(root_column_schema)} {}
+
+  /**
+   * @brief Returns the schema of the struct column that contains all columns as fields.
+   *
+   * @return Root column schema
+   */
+  [[nodiscard]] auto const& root() const& { return _root; }
+
+  /** @copydoc root
+   * Root column schema is moved out of the object (rvalues only).
+   *
+   */
+  [[nodiscard]] auto root() && { return std::move(_root); }
+
+ private:
+  orc_column_schema _root;
+};
+
+/**
+ * @brief Information about content of an ORC file.
+ */
+class orc_metadata {
+ public:
+  /**
+   * @brief constructor
+   *
+   * @param schema ORC schema
+   * @param num_rows number of rows
+   * @param num_stripes number of stripes
+   */
+  orc_metadata(orc_schema schema, size_type num_rows, size_type num_stripes)
+    : _schema{std::move(schema)}, _num_rows{num_rows}, _num_stripes{num_stripes}
+  {
+  }
+
+  /**
+   * @brief Returns the ORC schema.
+   *
+   * @return ORC schema
+   */
+  [[nodiscard]] auto const& schema() const { return _schema; }
+
+  ///< Number of rows in the root column; can vary for nested columns
+  /**
+   * @brief Returns the number of rows of the root column.
+   *
+   * If a file contains list columns, nested columns can have a different number of rows.
+   *
+   * @return Number of rows
+   */
+  [[nodiscard]] auto num_rows() const { return _num_rows; }
+
+  /**
+   * @brief Returns the number of stripes in the file.
+   *
+   * @return Number of stripes
+   */
+  [[nodiscard]] auto num_stripes() const { return _num_stripes; }
+
+ private:
+  orc_schema _schema;
+  size_type _num_rows;
+  size_type _num_stripes;
+};
+
+/**
+ * @brief Reads metadata of ORC dataset.
+ *
+ * @ingroup io_readers
+ *
+ * @param src_info Dataset source
+ *
+ * @return orc_metadata with ORC schema, number of rows and number of stripes.
+ */
+orc_metadata read_orc_metadata(source_info const& src_info);
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/orc_types.hpp b/cpp/include/cudf/io/orc_types.hpp
new file mode 100644
index 0000000..09cae2e
--- /dev/null
+++ b/cpp/include/cudf/io/orc_types.hpp
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+
+namespace cudf::io::orc {
+
+enum CompressionKind : uint8_t {
+  NONE   = 0,
+  ZLIB   = 1,
+  SNAPPY = 2,
+  LZO    = 3,
+  LZ4    = 4,
+  ZSTD   = 5,
+};
+
+enum TypeKind : int8_t {
+  INVALID_TYPE_KIND = -1,
+  BOOLEAN           = 0,
+  BYTE              = 1,
+  SHORT             = 2,
+  INT               = 3,
+  LONG              = 4,
+  FLOAT             = 5,
+  DOUBLE            = 6,
+  STRING            = 7,
+  BINARY            = 8,
+  TIMESTAMP         = 9,
+  LIST              = 10,
+  MAP               = 11,
+  STRUCT            = 12,
+  UNION             = 13,
+  DECIMAL           = 14,
+  DATE              = 15,
+  VARCHAR           = 16,
+  CHAR              = 17,
+};
+
+enum StreamKind : int8_t {
+  INVALID_STREAM_KIND = -1,
+  PRESENT             = 0,  // boolean stream of whether the next value is non-null
+  DATA                = 1,  // the primary data stream
+  LENGTH              = 2,  // the length of each value for variable length data
+  DICTIONARY_DATA     = 3,  // the dictionary blob
+  DICTIONARY_COUNT    = 4,  // deprecated prior to Hive 0.11
+  SECONDARY           = 5,  // a secondary data stream
+  ROW_INDEX           = 6,  // the index for seeking to particular row groups
+  BLOOM_FILTER        = 7,  // original bloom filters used before ORC-101
+  BLOOM_FILTER_UTF8   = 8,  // bloom filters that consistently use utf8
+};
+
+enum ColumnEncodingKind : int8_t {
+  INVALID_ENCODING_KIND = -1,
+  DIRECT                = 0,  // the encoding is mapped directly to the stream using RLE v1
+  DICTIONARY            = 1,  // the encoding uses a dictionary of unique values using RLE v1
+  DIRECT_V2             = 2,  // the encoding is direct using RLE v2
+  DICTIONARY_V2         = 3,  // the encoding is dictionary-based using RLE v2
+};
+
+enum ProtofType : uint8_t {
+  VARINT      = 0,
+  FIXED64     = 1,
+  FIXEDLEN    = 2,
+  START_GROUP = 3,  // deprecated
+  END_GROUP   = 4,  // deprecated
+  FIXED32     = 5,
+  INVALID_6   = 6,
+  INVALID_7   = 7,
+};
+
+}  // namespace cudf::io::orc
diff --git a/cpp/include/cudf/io/parquet.hpp b/cpp/include/cudf/io/parquet.hpp
new file mode 100644
index 0000000..deaf23d
--- /dev/null
+++ b/cpp/include/cudf/io/parquet.hpp
@@ -0,0 +1,1758 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/io/detail/parquet.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <iostream>
+#include <memory>
+#include <optional>
+#include <string>
+#include <vector>
+
+namespace cudf::io {
+/**
+ * @addtogroup io_readers
+ * @{
+ * @file
+ */
+
+constexpr size_t default_row_group_size_bytes   = 128 * 1024 * 1024;  ///< 128MB per row group
+constexpr size_type default_row_group_size_rows = 1000000;     ///< 1 million rows per row group
+constexpr size_t default_max_page_size_bytes    = 512 * 1024;  ///< 512KB per page
+constexpr size_type default_max_page_size_rows  = 20000;       ///< 20k rows per page
+constexpr int32_t default_column_index_truncate_length = 64;   ///< truncate to 64 bytes
+constexpr size_t default_max_dictionary_size           = 1024 * 1024;  ///< 1MB dictionary size
+constexpr size_type default_max_page_fragment_size     = 5000;  ///< 5000 rows per page fragment
+
+class parquet_reader_options_builder;
+
+/**
+ * @brief Settings for `read_parquet()`.
+ */
+class parquet_reader_options {
+  source_info _source;
+
+  // Path in schema of column to read; `nullopt` is all
+  std::optional<std::vector<std::string>> _columns;
+
+  // List of individual row groups to read (ignored if empty)
+  std::vector<std::vector<size_type>> _row_groups;
+  // Number of rows to skip from the start; Parquet stores the number of rows as int64_t
+  int64_t _skip_rows = 0;
+  // Number of rows to read; `nullopt` is all
+  std::optional<size_type> _num_rows;
+
+  // Predicate filter as AST to filter output rows.
+  std::optional<std::reference_wrapper<ast::expression const>> _filter;
+
+  // Whether to store string data as categorical type
+  bool _convert_strings_to_categories = false;
+  // Whether to use PANDAS metadata to load columns
+  bool _use_pandas_metadata = true;
+  // Cast timestamp columns to a specific type
+  data_type _timestamp_type{type_id::EMPTY};
+
+  std::optional<std::vector<reader_column_schema>> _reader_column_schema;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src source information used to read parquet file
+   */
+  explicit parquet_reader_options(source_info const& src) : _source(src) {}
+
+  friend parquet_reader_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit parquet_reader_options() = default;
+
+  /**
+   * @brief Creates a parquet_reader_options_builder which will build parquet_reader_options.
+   *
+   * @param src Source information to read parquet file
+   * @return Builder to build reader options
+   */
+  static parquet_reader_options_builder builder(source_info const& src);
+
+  /**
+   * @brief Returns source info.
+   *
+   * @return Source info
+   */
+  [[nodiscard]] source_info const& get_source() const { return _source; }
+
+  /**
+   * @brief Returns true/false depending on whether strings should be converted to categories or
+   * not.
+   *
+   * @return `true` if strings should be converted to categories
+   */
+  [[nodiscard]] bool is_enabled_convert_strings_to_categories() const
+  {
+    return _convert_strings_to_categories;
+  }
+
+  /**
+   * @brief Returns true/false depending whether to use pandas metadata or not while reading.
+   *
+   * @return `true` if pandas metadata is used while reading
+   */
+  [[nodiscard]] bool is_enabled_use_pandas_metadata() const { return _use_pandas_metadata; }
+
+  /**
+   * @brief Returns optional tree of metadata.
+   *
+   * @return vector of reader_column_schema objects.
+   */
+  [[nodiscard]] std::optional<std::vector<reader_column_schema>> get_column_schema() const
+  {
+    return _reader_column_schema;
+  }
+
+  /**
+   * @brief Returns number of rows to skip from the start.
+   *
+   * @return Number of rows to skip from the start
+   */
+  [[nodiscard]] int64_t get_skip_rows() const { return _skip_rows; }
+
+  /**
+   * @brief Returns number of rows to read.
+   *
+   * @return Number of rows to read; `nullopt` if the option hasn't been set (in which case the file
+   * is read until the end)
+   */
+  [[nodiscard]] std::optional<size_type> const& get_num_rows() const { return _num_rows; }
+
+  /**
+   * @brief Returns names of column to be read, if set.
+   *
+   * @return Names of column to be read; `nullopt` if the option is not set
+   */
+  [[nodiscard]] auto const& get_columns() const { return _columns; }
+
+  /**
+   * @brief Returns list of individual row groups to be read.
+   *
+   * @return List of individual row groups to be read
+   */
+  [[nodiscard]] auto const& get_row_groups() const { return _row_groups; }
+
+  /**
+   * @brief Returns AST based filter for predicate pushdown.
+   *
+   * @return AST expression to use as filter
+   */
+  [[nodiscard]] auto const& get_filter() const { return _filter; }
+
+  /**
+   * @brief Returns timestamp type used to cast timestamp columns.
+   *
+   * @return Timestamp type used to cast timestamp columns
+   */
+  data_type get_timestamp_type() const { return _timestamp_type; }
+
+  /**
+   * @brief Sets names of the columns to be read.
+   *
+   * @param col_names Vector of column names
+   */
+  void set_columns(std::vector<std::string> col_names) { _columns = std::move(col_names); }
+
+  /**
+   * @brief Sets vector of individual row groups to read.
+   *
+   * @param row_groups Vector of row groups to read
+   */
+  void set_row_groups(std::vector<std::vector<size_type>> row_groups);
+
+  /**
+   * @brief Sets AST based filter for predicate pushdown.
+   *
+   * @param filter AST expression to use as filter
+   */
+  void set_filter(ast::expression const& filter) { _filter = filter; }
+
+  /**
+   * @brief Sets to enable/disable conversion of strings to categories.
+   *
+   * @param val Boolean value to enable/disable conversion of string columns to categories
+   */
+  void enable_convert_strings_to_categories(bool val) { _convert_strings_to_categories = val; }
+
+  /**
+   * @brief Sets to enable/disable use of pandas metadata to read.
+   *
+   * @param val Boolean value whether to use pandas metadata
+   */
+  void enable_use_pandas_metadata(bool val) { _use_pandas_metadata = val; }
+
+  /**
+   * @brief Sets reader column schema.
+   *
+   * @param val Tree of schema nodes to enable/disable conversion of binary to string columns.
+   * Note default is to convert to string columns.
+   */
+  void set_column_schema(std::vector<reader_column_schema> val)
+  {
+    _reader_column_schema = std::move(val);
+  }
+
+  /**
+   * @brief Sets number of rows to skip.
+   *
+   * @param val Number of rows to skip from start
+   */
+  void set_skip_rows(int64_t val);
+
+  /**
+   * @brief Sets number of rows to read.
+   *
+   * @param val Number of rows to read after skip
+   */
+  void set_num_rows(size_type val);
+
+  /**
+   * @brief Sets timestamp_type used to cast timestamp columns.
+   *
+   * @param type The timestamp data_type to which all timestamp columns need to be cast
+   */
+  void set_timestamp_type(data_type type) { _timestamp_type = type; }
+};
+
+/**
+ * @brief Builds parquet_reader_options to use for `read_parquet()`.
+ */
+class parquet_reader_options_builder {
+  parquet_reader_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  parquet_reader_options_builder() = default;
+
+  /**
+   * @brief Constructor from source info.
+   *
+   * @param src The source information used to read parquet file
+   */
+  explicit parquet_reader_options_builder(source_info const& src) : options(src) {}
+
+  /**
+   * @brief Sets names of the columns to be read.
+   *
+   * @param col_names Vector of column names
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& columns(std::vector<std::string> col_names)
+  {
+    options._columns = std::move(col_names);
+    return *this;
+  }
+
+  /**
+   * @brief Sets vector of individual row groups to read.
+   *
+   * @param row_groups Vector of row groups to read
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& row_groups(std::vector<std::vector<size_type>> row_groups)
+  {
+    options.set_row_groups(std::move(row_groups));
+    return *this;
+  }
+
+  /**
+   * @brief Sets vector of individual row groups to read.
+   *
+   * @param filter Vector of row groups to read
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& filter(ast::expression const& filter)
+  {
+    options.set_filter(filter);
+    return *this;
+  }
+
+  /**
+   * @brief Sets enable/disable conversion of strings to categories.
+   *
+   * @param val Boolean value to enable/disable conversion of string columns to categories
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& convert_strings_to_categories(bool val)
+  {
+    options._convert_strings_to_categories = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets to enable/disable use of pandas metadata to read.
+   *
+   * @param val Boolean value whether to use pandas metadata
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& use_pandas_metadata(bool val)
+  {
+    options._use_pandas_metadata = val;
+    return *this;
+  }
+
+  /**
+   * @brief Sets reader metadata.
+   *
+   * @param val Tree of metadata information.
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& set_column_schema(std::vector<reader_column_schema> val)
+  {
+    options._reader_column_schema = std::move(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to skip.
+   *
+   * @param val Number of rows to skip from start
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& skip_rows(int64_t val)
+  {
+    options.set_skip_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets number of rows to read.
+   *
+   * @param val Number of rows to read after skip
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& num_rows(size_type val)
+  {
+    options.set_num_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief timestamp_type used to cast timestamp columns.
+   *
+   * @param type The timestamp data_type to which all timestamp columns need to be cast
+   * @return this for chaining
+   */
+  parquet_reader_options_builder& timestamp_type(data_type type)
+  {
+    options._timestamp_type = type;
+    return *this;
+  }
+
+  /**
+   * @brief move parquet_reader_options member once it's built.
+   */
+  operator parquet_reader_options&&() { return std::move(options); }
+
+  /**
+   * @brief move parquet_reader_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `parquet_reader_options` object's r-value reference
+   */
+  parquet_reader_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Reads a Parquet dataset into a set of columns.
+ *
+ * The following code snippet demonstrates how to read a dataset from a file:
+ * @code
+ *  auto source  = cudf::io::source_info("dataset.parquet");
+ *  auto options = cudf::io::parquet_reader_options::builder(source);
+ *  auto result  = cudf::io::read_parquet(options);
+ * @endcode
+ *
+ * @param options Settings for controlling reading behavior
+ * @param mr Device memory resource used to allocate device memory of the table in the returned
+ * table_with_metadata
+ *
+ * @return The set of columns along with metadata
+ */
+table_with_metadata read_parquet(
+  parquet_reader_options const& options,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief The chunked parquet reader class to read Parquet file iteratively in to a series of
+ * tables, chunk by chunk.
+ *
+ * This class is designed to address the reading issue when reading very large Parquet files such
+ * that the sizes of their column exceed the limit that can be stored in cudf column. By reading the
+ * file content by chunks using this class, each chunk is guaranteed to have its sizes stay within
+ * the given limit.
+ */
+class chunked_parquet_reader {
+ public:
+  /**
+   * @brief Default constructor, this should never be used.
+   *
+   * This is added just to satisfy cython.
+   */
+  chunked_parquet_reader() = default;
+
+  /**
+   * @brief Constructor for chunked reader.
+   *
+   * This constructor requires the same `parquet_reader_option` parameter as in
+   * `cudf::read_parquet()`, and an additional parameter to specify the size byte limit of the
+   * output table for each reading.
+   *
+   * @param chunk_read_limit Limit on total number of bytes to be returned per read,
+   *        or `0` if there is no limit
+   * @param options The options used to read Parquet file
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  chunked_parquet_reader(
+    std::size_t chunk_read_limit,
+    parquet_reader_options const& options,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Constructor for chunked reader.
+   *
+   * This constructor requires the same `parquet_reader_option` parameter as in
+   * `cudf::read_parquet()`, with additional parameters to specify the size byte limit of the
+   * output table for each reading, and a byte limit on the amount of temporary memory to use
+   * when reading. pass_read_limit affects how many row groups we can read at a time by limiting
+   * the amount of memory dedicated to decompression space. pass_read_limit is a hint, not an
+   * absolute limit - if a single row group cannot fit within the limit given, it will still be
+   * loaded.
+   *
+   * @param chunk_read_limit Limit on total number of bytes to be returned per read,
+   * or `0` if there is no limit
+   * @param pass_read_limit Limit on the amount of memory used for reading and decompressing data or
+   * `0` if there is no limit
+   * @param options The options used to read Parquet file
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  chunked_parquet_reader(
+    std::size_t chunk_read_limit,
+    std::size_t pass_read_limit,
+    parquet_reader_options const& options,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Destructor, destroying the internal reader instance.
+   *
+   * Since the declaration of the internal `reader` object does not exist in this header, this
+   * destructor needs to be defined in a separate source file which can access to that object's
+   * declaration.
+   */
+  ~chunked_parquet_reader();
+
+  /**
+   * @brief Check if there is any data in the given file has not yet read.
+   *
+   * @return A boolean value indicating if there is any data left to read
+   */
+  [[nodiscard]] bool has_next() const;
+
+  /**
+   * @brief Read a chunk of rows in the given Parquet file.
+   *
+   * The sequence of returned tables, if concatenated by their order, guarantees to form a complete
+   * dataset as reading the entire given file at once.
+   *
+   * An empty table will be returned if the given file is empty, or all the data in the file has
+   * been read and returned by the previous calls.
+   *
+   * @return An output `cudf::table` along with its metadata
+   */
+  [[nodiscard]] table_with_metadata read_chunk() const;
+
+ private:
+  std::unique_ptr<cudf::io::detail::parquet::chunked_reader> reader;
+};
+
+/** @} */  // end of group
+/**
+ * @addtogroup io_writers
+ * @{
+ * @file
+ */
+
+class parquet_writer_options_builder;
+
+/**
+ * @brief Settings for `write_parquet()`.
+ */
+class parquet_writer_options {
+  // Specify the sink to use for writer output
+  sink_info _sink;
+  // Specify the compression format to use
+  compression_type _compression = compression_type::SNAPPY;
+  // Specify the level of statistics in the output file
+  statistics_freq _stats_level = statistics_freq::STATISTICS_ROWGROUP;
+  // Sets of columns to output
+  table_view _table;
+  // Partitions described as {start_row, num_rows} pairs
+  std::vector<partition_info> _partitions;
+  // Optional associated metadata
+  std::optional<table_input_metadata> _metadata;
+  // Optional footer key_value_metadata
+  std::vector<std::map<std::string, std::string>> _user_data;
+  // Parquet writer can write INT96 or TIMESTAMP_MICROS. Defaults to TIMESTAMP_MICROS.
+  // If true then overrides any per-column setting in _metadata.
+  bool _write_timestamps_as_int96 = false;
+  // Column chunks file paths to be set in the raw output metadata. One per output file
+  std::vector<std::string> _column_chunks_file_paths;
+  // Maximum size of each row group (unless smaller than a single page)
+  size_t _row_group_size_bytes = default_row_group_size_bytes;
+  // Maximum number of rows in row group (unless smaller than a single page)
+  size_type _row_group_size_rows = default_row_group_size_rows;
+  // Maximum size of each page (uncompressed)
+  size_t _max_page_size_bytes = default_max_page_size_bytes;
+  // Maximum number of rows in a page
+  size_type _max_page_size_rows = default_max_page_size_rows;
+  // Maximum size of min or max values in column index
+  int32_t _column_index_truncate_length = default_column_index_truncate_length;
+  // When to use dictionary encoding for data
+  dictionary_policy _dictionary_policy = dictionary_policy::ALWAYS;
+  // Maximum size of column chunk dictionary (in bytes)
+  size_t _max_dictionary_size = default_max_dictionary_size;
+  // Maximum number of rows in a page fragment
+  std::optional<size_type> _max_page_fragment_size;
+  // Optional compression statistics
+  std::shared_ptr<writer_compression_statistics> _compression_stats;
+  // write V2 page headers?
+  bool _v2_page_headers = false;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit parquet_writer_options(sink_info const& sink, table_view const& table)
+    : _sink(sink), _table(table)
+  {
+  }
+
+  friend parquet_writer_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  parquet_writer_options() = default;
+
+  /**
+   * @brief Create builder to create `parquet_writer_options`.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   *
+   * @return Builder to build parquet_writer_options
+   */
+  static parquet_writer_options_builder builder(sink_info const& sink, table_view const& table);
+
+  /**
+   * @brief Create builder to create `parquet_writer_options`.
+   *
+   * @return parquet_writer_options_builder
+   */
+  static parquet_writer_options_builder builder();
+
+  /**
+   * @brief Returns sink info.
+   *
+   * @return Sink info
+   */
+  [[nodiscard]] sink_info const& get_sink() const { return _sink; }
+
+  /**
+   * @brief Returns compression format used.
+   *
+   * @return Compression format
+   */
+  [[nodiscard]] compression_type get_compression() const { return _compression; }
+
+  /**
+   * @brief Returns level of statistics requested in output file.
+   *
+   * @return level of statistics requested in output file
+   */
+  [[nodiscard]] statistics_freq get_stats_level() const { return _stats_level; }
+
+  /**
+   * @brief Returns table_view.
+   *
+   * @return Table view
+   */
+  [[nodiscard]] table_view get_table() const { return _table; }
+
+  /**
+   * @brief Returns partitions.
+   *
+   * @return Partitions
+   */
+  [[nodiscard]] std::vector<partition_info> const& get_partitions() const { return _partitions; }
+
+  /**
+   * @brief Returns associated metadata.
+   *
+   * @return Associated metadata
+   */
+  [[nodiscard]] auto const& get_metadata() const { return _metadata; }
+
+  /**
+   * @brief Returns Key-Value footer metadata information.
+   *
+   * @return Key-Value footer metadata information
+   */
+  std::vector<std::map<std::string, std::string>> const& get_key_value_metadata() const
+  {
+    return _user_data;
+  }
+
+  /**
+   * @brief Returns `true` if timestamps will be written as INT96
+   *
+   * @return `true` if timestamps will be written as INT96
+   */
+  bool is_enabled_int96_timestamps() const { return _write_timestamps_as_int96; }
+
+  /**
+   * @brief Returns Column chunks file paths to be set in the raw output metadata.
+   *
+   * @return Column chunks file paths to be set in the raw output metadata
+   */
+  std::vector<std::string> const& get_column_chunks_file_paths() const
+  {
+    return _column_chunks_file_paths;
+  }
+
+  /**
+   * @brief Returns maximum row group size, in bytes.
+   *
+   * @return Maximum row group size, in bytes
+   */
+  auto get_row_group_size_bytes() const { return _row_group_size_bytes; }
+
+  /**
+   * @brief Returns maximum row group size, in rows.
+   *
+   * @return Maximum row group size, in rows
+   */
+  auto get_row_group_size_rows() const { return _row_group_size_rows; }
+
+  /**
+   * @brief Returns the maximum uncompressed page size, in bytes.
+   *
+   * If set larger than the row group size, then this will return the row group size.
+   *
+   * @return Maximum uncompressed page size, in bytes
+   */
+  auto get_max_page_size_bytes() const
+  {
+    return std::min(_max_page_size_bytes, get_row_group_size_bytes());
+  }
+
+  /**
+   * @brief Returns maximum page size, in rows.
+   *
+   * If set larger than the row group size, then this will return the row group size.
+   *
+   * @return Maximum page size, in rows
+   */
+  auto get_max_page_size_rows() const
+  {
+    return std::min(_max_page_size_rows, get_row_group_size_rows());
+  }
+
+  /**
+   * @brief Returns maximum length of min or max values in column index, in bytes.
+   *
+   * @return length min/max will be truncated to
+   */
+  auto get_column_index_truncate_length() const { return _column_index_truncate_length; }
+
+  /**
+   * @brief Returns policy for dictionary use.
+   *
+   * @return policy for dictionary use
+   */
+  [[nodiscard]] dictionary_policy get_dictionary_policy() const { return _dictionary_policy; }
+
+  /**
+   * @brief Returns maximum dictionary size, in bytes.
+   *
+   * @return Maximum dictionary size, in bytes.
+   */
+  [[nodiscard]] auto get_max_dictionary_size() const { return _max_dictionary_size; }
+
+  /**
+   * @brief Returns maximum page fragment size, in rows.
+   *
+   * @return Maximum page fragment size, in rows.
+   */
+  [[nodiscard]] auto get_max_page_fragment_size() const { return _max_page_fragment_size; }
+
+  /**
+   * @brief Returns a shared pointer to the user-provided compression statistics.
+   *
+   * @return Compression statistics
+   */
+  [[nodiscard]] std::shared_ptr<writer_compression_statistics> get_compression_statistics() const
+  {
+    return _compression_stats;
+  }
+
+  /**
+   * @brief Returns `true` if V2 page headers should be written.
+   *
+   * @return `true` if V2 page headers should be written.
+   */
+  [[nodiscard]] auto is_enabled_write_v2_headers() const { return _v2_page_headers; }
+
+  /**
+   * @brief Sets partitions.
+   *
+   * @param partitions Partitions of input table in {start_row, num_rows} pairs. If specified, must
+   * be same size as number of sinks in sink_info
+   */
+  void set_partitions(std::vector<partition_info> partitions);
+
+  /**
+   * @brief Sets metadata.
+   *
+   * @param metadata Associated metadata
+   */
+  void set_metadata(table_input_metadata metadata) { _metadata = std::move(metadata); }
+
+  /**
+   * @brief Sets metadata.
+   *
+   * @param metadata Key-Value footer metadata
+   */
+  void set_key_value_metadata(std::vector<std::map<std::string, std::string>> metadata);
+
+  /**
+   * @brief Sets the level of statistics.
+   *
+   * @param sf Level of statistics requested in the output file
+   */
+  void set_stats_level(statistics_freq sf) { _stats_level = sf; }
+
+  /**
+   * @brief Sets compression type.
+   *
+   * @param compression The compression type to use
+   */
+  void set_compression(compression_type compression) { _compression = compression; }
+
+  /**
+   * @brief Sets timestamp writing preferences. INT96 timestamps will be written
+   * if `true` and TIMESTAMP_MICROS will be written if `false`.
+   *
+   * @param req Boolean value to enable/disable writing of INT96 timestamps
+   */
+  void enable_int96_timestamps(bool req) { _write_timestamps_as_int96 = req; }
+
+  /**
+   * @brief Sets column chunks file path to be set in the raw output metadata.
+   *
+   * @param file_paths Vector of Strings which indicates file path. Must be same size as number of
+   * data sinks in sink info
+   */
+  void set_column_chunks_file_paths(std::vector<std::string> file_paths);
+
+  /**
+   * @brief Sets the maximum row group size, in bytes.
+   *
+   * @param size_bytes Maximum row group size, in bytes to set
+   */
+  void set_row_group_size_bytes(size_t size_bytes);
+
+  /**
+   * @brief Sets the maximum row group size, in rows.
+   *
+   * @param size_rows Maximum row group size, in rows to set
+   */
+  void set_row_group_size_rows(size_type size_rows);
+
+  /**
+   * @brief Sets the maximum uncompressed page size, in bytes.
+   *
+   * @param size_bytes Maximum uncompressed page size, in bytes to set
+   */
+  void set_max_page_size_bytes(size_t size_bytes);
+
+  /**
+   * @brief Sets the maximum page size, in rows.
+   *
+   * @param size_rows Maximum page size, in rows to set
+   */
+  void set_max_page_size_rows(size_type size_rows);
+
+  /**
+   * @brief Sets the maximum length of min or max values in column index, in bytes.
+   *
+   * @param size_bytes length min/max will be truncated to
+   */
+  void set_column_index_truncate_length(int32_t size_bytes);
+
+  /**
+   * @brief Sets the policy for dictionary use.
+   *
+   * @param policy Policy for dictionary use
+   */
+  void set_dictionary_policy(dictionary_policy policy);
+
+  /**
+   * @brief Sets the maximum dictionary size, in bytes.
+   *
+   * @param size_bytes Maximum dictionary size, in bytes
+   */
+  void set_max_dictionary_size(size_t size_bytes);
+
+  /**
+   * @brief Sets the maximum page fragment size, in rows.
+   *
+   * @param size_rows Maximum page fragment size, in rows.
+   */
+  void set_max_page_fragment_size(size_type size_rows);
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be updated after writing
+   */
+  void set_compression_statistics(std::shared_ptr<writer_compression_statistics> comp_stats)
+  {
+    _compression_stats = std::move(comp_stats);
+  }
+
+  /**
+   * @brief Sets preference for V2 page headers. Write V2 page headers if set to `true`.
+   *
+   * @param val Boolean value to enable/disable writing of V2 page headers.
+   */
+  void enable_write_v2_headers(bool val) { _v2_page_headers = val; }
+};
+
+/**
+ * @brief Class to build `parquet_writer_options`.
+ */
+class parquet_writer_options_builder {
+  parquet_writer_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  explicit parquet_writer_options_builder() = default;
+
+  /**
+   * @brief Constructor from sink and table.
+   *
+   * @param sink The sink used for writer output
+   * @param table Table to be written to output
+   */
+  explicit parquet_writer_options_builder(sink_info const& sink, table_view const& table)
+    : options(sink, table)
+  {
+  }
+
+  /**
+   * @brief Sets partitions in parquet_writer_options.
+   *
+   * @param partitions Partitions of input table in {start_row, num_rows} pairs. If specified, must
+   * be same size as number of sinks in sink_info
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& partitions(std::vector<partition_info> partitions);
+
+  /**
+   * @brief Sets metadata in parquet_writer_options.
+   *
+   * @param metadata Associated metadata
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& metadata(table_input_metadata metadata)
+  {
+    options._metadata = std::move(metadata);
+    return *this;
+  }
+
+  /**
+   * @brief Sets Key-Value footer metadata in parquet_writer_options.
+   *
+   * @param metadata Key-Value footer metadata
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& key_value_metadata(
+    std::vector<std::map<std::string, std::string>> metadata);
+
+  /**
+   * @brief Sets the level of statistics in parquet_writer_options.
+   *
+   * @param sf Level of statistics requested in the output file
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& stats_level(statistics_freq sf)
+  {
+    options._stats_level = sf;
+    return *this;
+  }
+
+  /**
+   * @brief Sets compression type in parquet_writer_options.
+   *
+   * @param compression The compression type to use
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& compression(compression_type compression)
+  {
+    options._compression = compression;
+    return *this;
+  }
+
+  /**
+   * @brief Sets column chunks file path to be set in the raw output metadata.
+   *
+   * @param file_paths Vector of Strings which indicates file path. Must be same size as number of
+   * data sinks
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& column_chunks_file_paths(std::vector<std::string> file_paths);
+
+  /**
+   * @brief Sets the maximum row group size, in bytes.
+   *
+   * @param val maximum row group size
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& row_group_size_bytes(size_t val)
+  {
+    options.set_row_group_size_bytes(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum number of rows in output row groups.
+   *
+   * @param val maximum number or rows
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& row_group_size_rows(size_type val)
+  {
+    options.set_row_group_size_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum uncompressed page size, in bytes.
+   *
+   * Serves as a hint to the writer, and can be exceeded under certain circumstances.
+   * Cannot be larger than the row group size in bytes, and will be adjusted to
+   * match if it is.
+   *
+   * @param val maximum page size
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& max_page_size_bytes(size_t val)
+  {
+    options.set_max_page_size_bytes(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum page size, in rows. Counts only top-level rows, ignoring any nesting.
+   * Cannot be larger than the row group size in rows, and will be adjusted to match if it is.
+   *
+   * @param val maximum rows per page
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& max_page_size_rows(size_type val)
+  {
+    options.set_max_page_size_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the desired maximum size in bytes for min and max values in the column index.
+   *
+   * Values exceeding this limit will be truncated, but modified such that they will still
+   * be valid lower and upper bounds. This only applies to variable length types, such as string.
+   * Maximum values will not be truncated if there is no suitable truncation that results in
+   * a valid upper bound.
+   *
+   * Default value is 64.
+   *
+   * @param val length min/max will be truncated to, with 0 indicating no truncation
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& column_index_truncate_length(int32_t val)
+  {
+    options.set_column_index_truncate_length(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the policy for dictionary use.
+   *
+   * Certain compression algorithms (e.g Zstandard) have limits on how large of a buffer can
+   * be compressed. In some circumstances, the dictionary can grow beyond this limit, which
+   * will prevent the column from being compressed. This setting controls how the writer
+   * should act in these circumstances. A setting of dictionary_policy::ADAPTIVE will disable
+   * dictionary encoding for columns where the dictionary exceeds the limit. A setting of
+   * dictionary_policy::NEVER will disable the use of dictionary encoding globally. A setting of
+   * dictionary_policy::ALWAYS will allow the use of dictionary encoding even if it will result in
+   * the disabling of compression for columns that would otherwise be compressed.
+   *
+   * The default value is dictionary_policy::ALWAYS.
+   *
+   * @param val policy for dictionary use
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& dictionary_policy(enum dictionary_policy val);
+
+  /**
+   * @brief Sets the maximum dictionary size, in bytes.
+   *
+   * Disables dictionary encoding for any column chunk where the dictionary will
+   * exceed this limit.  Only used when the dictionary_policy is set to 'ADAPTIVE'.
+   *
+   * Default value is 1048576 (1MiB).
+   *
+   * @param val maximum dictionary size
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& max_dictionary_size(size_t val);
+
+  /**
+   * @brief Sets the maximum page fragment size, in rows.
+   *
+   * Files with nested schemas or very long strings may need a page fragment size
+   * smaller than the default value of 5000 to ensure a single fragment will not
+   * exceed the desired maximum page size in bytes.
+   *
+   * @param val maximum page fragment size
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& max_page_fragment_size(size_type val);
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be filled once writer is done
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& compression_statistics(
+    std::shared_ptr<writer_compression_statistics> const& comp_stats)
+  {
+    options._compression_stats = comp_stats;
+    return *this;
+  }
+
+  /**
+   * @brief Sets whether int96 timestamps are written or not in parquet_writer_options.
+   *
+   * @param enabled Boolean value to enable/disable int96 timestamps
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& int96_timestamps(bool enabled)
+  {
+    options._write_timestamps_as_int96 = enabled;
+    return *this;
+  }
+
+  /**
+   * @brief Set to true if V2 page headers are to be written.
+   *
+   * @param enabled Boolean value to enable/disable writing of V2 page headers.
+   * @return this for chaining
+   */
+  parquet_writer_options_builder& write_v2_headers(bool enabled);
+
+  /**
+   * @brief move parquet_writer_options member once it's built.
+   */
+  operator parquet_writer_options&&() { return std::move(options); }
+
+  /**
+   * @brief move parquet_writer_options member once it's built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `parquet_writer_options` object's r-value reference
+   */
+  parquet_writer_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief Writes a set of columns to parquet format.
+ *
+ * The following code snippet demonstrates how to write columns to a file:
+ * @code
+ *  auto destination = cudf::io::sink_info("dataset.parquet");
+ *  auto options     = cudf::io::parquet_writer_options::builder(destination, table->view());
+ *  cudf::io::write_parquet(options);
+ * @endcode
+ *
+ * @param options Settings for controlling writing behavior
+ * @return A blob that contains the file metadata (parquet FileMetadata thrift message) if
+ *         requested in parquet_writer_options (empty blob otherwise).
+ */
+
+std::unique_ptr<std::vector<uint8_t>> write_parquet(parquet_writer_options const& options);
+
+/**
+ * @brief Merges multiple raw metadata blobs that were previously created by write_parquet
+ * into a single metadata blob.
+ *
+ * @ingroup io_writers
+ *
+ * @param[in] metadata_list List of input file metadata
+ * @return A parquet-compatible blob that contains the data for all row groups in the list
+ */
+std::unique_ptr<std::vector<uint8_t>> merge_row_group_metadata(
+  std::vector<std::unique_ptr<std::vector<uint8_t>>> const& metadata_list);
+
+class chunked_parquet_writer_options_builder;
+
+/**
+ * @brief Settings for `write_parquet_chunked()`.
+ */
+class chunked_parquet_writer_options {
+  // Specify the sink to use for writer output
+  sink_info _sink;
+  // Specify the compression format to use
+  compression_type _compression = compression_type::AUTO;
+  // Specify the level of statistics in the output file
+  statistics_freq _stats_level = statistics_freq::STATISTICS_ROWGROUP;
+  // Optional associated metadata.
+  std::optional<table_input_metadata> _metadata;
+  // Optional footer key_value_metadata
+  std::vector<std::map<std::string, std::string>> _user_data;
+  // Parquet writer can write INT96 or TIMESTAMP_MICROS. Defaults to TIMESTAMP_MICROS.
+  // If true then overrides any per-column setting in _metadata.
+  bool _write_timestamps_as_int96 = false;
+  // Maximum size of each row group (unless smaller than a single page)
+  size_t _row_group_size_bytes = default_row_group_size_bytes;
+  // Maximum number of rows in row group (unless smaller than a single page)
+  size_type _row_group_size_rows = default_row_group_size_rows;
+  // Maximum size of each page (uncompressed)
+  size_t _max_page_size_bytes = default_max_page_size_bytes;
+  // Maximum number of rows in a page
+  size_type _max_page_size_rows = default_max_page_size_rows;
+  // Maximum size of min or max values in column index
+  int32_t _column_index_truncate_length = default_column_index_truncate_length;
+  // When to use dictionary encoding for data
+  dictionary_policy _dictionary_policy = dictionary_policy::ALWAYS;
+  // Maximum size of column chunk dictionary (in bytes)
+  size_t _max_dictionary_size = default_max_dictionary_size;
+  // Maximum number of rows in a page fragment
+  std::optional<size_type> _max_page_fragment_size;
+  // Optional compression statistics
+  std::shared_ptr<writer_compression_statistics> _compression_stats;
+  // write V2 page headers?
+  bool _v2_page_headers = false;
+
+  /**
+   * @brief Constructor from sink.
+   *
+   * @param sink Sink used for writer output
+   */
+  explicit chunked_parquet_writer_options(sink_info const& sink) : _sink(sink) {}
+
+  friend chunked_parquet_writer_options_builder;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  chunked_parquet_writer_options() = default;
+
+  /**
+   * @brief Returns sink info.
+   *
+   * @return Sink info
+   */
+  [[nodiscard]] sink_info const& get_sink() const { return _sink; }
+
+  /**
+   * @brief Returns compression format used.
+   *
+   * @return Compression format
+   */
+  [[nodiscard]] compression_type get_compression() const { return _compression; }
+
+  /**
+   * @brief Returns level of statistics requested in output file.
+   *
+   * @return Level of statistics requested in output file
+   */
+  [[nodiscard]] statistics_freq get_stats_level() const { return _stats_level; }
+
+  /**
+   * @brief Returns metadata information.
+   *
+   * @return Metadata information
+   */
+  [[nodiscard]] auto const& get_metadata() const { return _metadata; }
+
+  /**
+   * @brief Returns Key-Value footer metadata information.
+   *
+   * @return Key-Value footer metadata information
+   */
+  std::vector<std::map<std::string, std::string>> const& get_key_value_metadata() const
+  {
+    return _user_data;
+  }
+
+  /**
+   * @brief Returns `true` if timestamps will be written as INT96
+   *
+   * @return `true` if timestamps will be written as INT96
+   */
+  bool is_enabled_int96_timestamps() const { return _write_timestamps_as_int96; }
+
+  /**
+   * @brief Returns maximum row group size, in bytes.
+   *
+   * @return Maximum row group size, in bytes
+   */
+  auto get_row_group_size_bytes() const { return _row_group_size_bytes; }
+
+  /**
+   * @brief Returns maximum row group size, in rows.
+   *
+   * @return Maximum row group size, in rows
+   */
+  auto get_row_group_size_rows() const { return _row_group_size_rows; }
+
+  /**
+   * @brief Returns maximum uncompressed page size, in bytes.
+   *
+   * If set larger than the row group size, then this will return the
+   * row group size.
+   *
+   * @return Maximum uncompressed page size, in bytes
+   */
+  auto get_max_page_size_bytes() const
+  {
+    return std::min(_max_page_size_bytes, get_row_group_size_bytes());
+  }
+
+  /**
+   * @brief Returns maximum page size, in rows.
+   *
+   * If set larger than the row group size, then this will return the row group size.
+   *
+   * @return Maximum page size, in rows
+   */
+  auto get_max_page_size_rows() const
+  {
+    return std::min(_max_page_size_rows, get_row_group_size_rows());
+  }
+
+  /**
+   * @brief Returns maximum length of min or max values in column index, in bytes.
+   *
+   * @return length min/max will be truncated to
+   */
+  auto get_column_index_truncate_length() const { return _column_index_truncate_length; }
+
+  /**
+   * @brief Returns policy for dictionary use.
+   *
+   * @return policy for dictionary use
+   */
+  [[nodiscard]] dictionary_policy get_dictionary_policy() const { return _dictionary_policy; }
+
+  /**
+   * @brief Returns maximum dictionary size, in bytes.
+   *
+   * @return Maximum dictionary size, in bytes.
+   */
+  [[nodiscard]] auto get_max_dictionary_size() const { return _max_dictionary_size; }
+
+  /**
+   * @brief Returns maximum page fragment size, in rows.
+   *
+   * @return Maximum page fragment size, in rows.
+   */
+  [[nodiscard]] auto get_max_page_fragment_size() const { return _max_page_fragment_size; }
+
+  /**
+   * @brief Returns a shared pointer to the user-provided compression statistics.
+   *
+   * @return Compression statistics
+   */
+  [[nodiscard]] std::shared_ptr<writer_compression_statistics> get_compression_statistics() const
+  {
+    return _compression_stats;
+  }
+
+  /**
+   * @brief Returns `true` if V2 page headers should be written.
+   *
+   * @return `true` if V2 page headers should be written.
+   */
+  [[nodiscard]] auto is_enabled_write_v2_headers() const { return _v2_page_headers; }
+
+  /**
+   * @brief Sets metadata.
+   *
+   * @param metadata Associated metadata
+   */
+  void set_metadata(table_input_metadata metadata) { _metadata = std::move(metadata); }
+
+  /**
+   * @brief Sets Key-Value footer metadata.
+   *
+   * @param metadata Key-Value footer metadata
+   */
+  void set_key_value_metadata(std::vector<std::map<std::string, std::string>> metadata);
+
+  /**
+   * @brief Sets the level of statistics in parquet_writer_options.
+   *
+   * @param sf Level of statistics requested in the output file
+   */
+  void set_stats_level(statistics_freq sf) { _stats_level = sf; }
+
+  /**
+   * @brief Sets compression type.
+   *
+   * @param compression The compression type to use
+   */
+  void set_compression(compression_type compression) { _compression = compression; }
+
+  /**
+   * @brief Sets timestamp writing preferences.
+   *
+   * INT96 timestamps will be written if `true` and TIMESTAMP_MICROS will be written if `false`.
+   *
+   * @param req Boolean value to enable/disable writing of INT96 timestamps
+   */
+  void enable_int96_timestamps(bool req) { _write_timestamps_as_int96 = req; }
+
+  /**
+   * @brief Sets the maximum row group size, in bytes.
+   *
+   * @param size_bytes Maximum row group size, in bytes to set
+   */
+  void set_row_group_size_bytes(size_t size_bytes);
+
+  /**
+   * @brief Sets the maximum row group size, in rows.
+   *
+   * @param size_rows The maximum row group size, in rows to set
+   */
+  void set_row_group_size_rows(size_type size_rows);
+
+  /**
+   * @brief Sets the maximum uncompressed page size, in bytes.
+   *
+   * @param size_bytes Maximum uncompressed page size, in bytes to set
+   */
+  void set_max_page_size_bytes(size_t size_bytes);
+
+  /**
+   * @brief Sets the maximum page size, in rows.
+   *
+   * @param size_rows The maximum page size, in rows to set
+   */
+  void set_max_page_size_rows(size_type size_rows);
+
+  /**
+   * @brief Sets the maximum length of min or max values in column index, in bytes.
+   *
+   * @param size_bytes length min/max will be truncated to
+   */
+  void set_column_index_truncate_length(int32_t size_bytes);
+
+  /**
+   * @brief Sets the policy for dictionary use.
+   *
+   * @param policy Policy for dictionary use
+   */
+  void set_dictionary_policy(dictionary_policy policy);
+
+  /**
+   * @brief Sets the maximum dictionary size, in bytes.
+   *
+   * @param size_bytes Maximum dictionary size, in bytes
+   */
+  void set_max_dictionary_size(size_t size_bytes);
+
+  /**
+   * @brief Sets the maximum page fragment size, in rows.
+   *
+   * @param size_rows Maximum page fragment size, in rows.
+   */
+  void set_max_page_fragment_size(size_type size_rows);
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be updated after writing
+   */
+  void set_compression_statistics(std::shared_ptr<writer_compression_statistics> comp_stats)
+  {
+    _compression_stats = std::move(comp_stats);
+  }
+
+  /**
+   * @brief Sets preference for V2 page headers. Write V2 page headers if set to `true`.
+   *
+   * @param val Boolean value to enable/disable writing of V2 page headers.
+   */
+  void enable_write_v2_headers(bool val) { _v2_page_headers = val; }
+
+  /**
+   * @brief creates builder to build chunked_parquet_writer_options.
+   *
+   * @param sink sink to use for writer output
+   *
+   * @return Builder to build `chunked_parquet_writer_options`
+   */
+  static chunked_parquet_writer_options_builder builder(sink_info const& sink);
+};
+
+/**
+ * @brief Builds options for chunked_parquet_writer_options.
+ */
+class chunked_parquet_writer_options_builder {
+  chunked_parquet_writer_options options;
+
+ public:
+  /**
+   * @brief Default constructor.
+   *
+   * This has been added since Cython requires a default constructor to create objects on stack.
+   */
+  chunked_parquet_writer_options_builder() = default;
+
+  /**
+   * @brief Constructor from sink.
+   *
+   * @param sink The sink used for writer output
+   */
+  chunked_parquet_writer_options_builder(sink_info const& sink) : options(sink){};
+
+  /**
+   * @brief Sets metadata to chunked_parquet_writer_options.
+   *
+   * @param metadata Associated metadata
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& metadata(table_input_metadata metadata)
+  {
+    options._metadata = std::move(metadata);
+    return *this;
+  }
+
+  /**
+   * @brief Sets Key-Value footer metadata in parquet_writer_options.
+   *
+   * @param metadata Key-Value footer metadata
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& key_value_metadata(
+    std::vector<std::map<std::string, std::string>> metadata);
+
+  /**
+   * @brief Sets the level of statistics in chunked_parquet_writer_options.
+   *
+   * @param sf Level of statistics requested in the output file
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& stats_level(statistics_freq sf)
+  {
+    options._stats_level = sf;
+    return *this;
+  }
+
+  /**
+   * @brief Sets compression type to chunked_parquet_writer_options.
+   *
+   * @param compression The compression type to use
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& compression(compression_type compression)
+  {
+    options._compression = compression;
+    return *this;
+  }
+
+  /**
+   * @brief Set to true if timestamps should be written as
+   * int96 types instead of int64 types. Even though int96 is deprecated and is
+   * not an internal type for cudf, it needs to be written for backwards
+   * compatibility reasons.
+   *
+   * @param enabled Boolean value to enable/disable int96 timestamps
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& int96_timestamps(bool enabled)
+  {
+    options._write_timestamps_as_int96 = enabled;
+    return *this;
+  }
+
+  /**
+   * @brief Set to true if V2 page headers are to be written.
+   *
+   * @param enabled Boolean value to enable/disable writing of V2 page headers.
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& write_v2_headers(bool enabled);
+
+  /**
+   * @brief Sets the maximum row group size, in bytes.
+   *
+   * @param val maximum row group size
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& row_group_size_bytes(size_t val)
+  {
+    options.set_row_group_size_bytes(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum number of rows in output row groups.
+   *
+   * @param val maximum number or rows
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& row_group_size_rows(size_type val)
+  {
+    options.set_row_group_size_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum uncompressed page size, in bytes.
+   *
+   * Serves as a hint to the writer, and can be exceeded under certain circumstances. Cannot be
+   * larger than the row group size in bytes, and will be adjusted to match if it is.
+   *
+   * @param val maximum page size
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& max_page_size_bytes(size_t val)
+  {
+    options.set_max_page_size_bytes(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the maximum page size, in rows. Counts only top-level rows, ignoring any nesting.
+   * Cannot be larger than the row group size in rows, and will be adjusted to match if it is.
+   *
+   * @param val maximum rows per page
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& max_page_size_rows(size_type val)
+  {
+    options.set_max_page_size_rows(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the desired maximum size in bytes for min and max values in the column index.
+   *
+   * Values exceeding this limit will be truncated, but modified such that they will still
+   * be valid lower and upper bounds. This only applies to variable length types, such as string.
+   * Maximum values will not be truncated if there is no suitable truncation that results in
+   * a valid upper bound.
+   *
+   * Default value is 64.
+   *
+   * @param val length min/max will be truncated to, with 0 indicating no truncation
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& column_index_truncate_length(int32_t val)
+  {
+    options.set_column_index_truncate_length(val);
+    return *this;
+  }
+
+  /**
+   * @brief Sets the policy for dictionary use.
+   *
+   * Certain compression algorithms (e.g Zstandard) have limits on how large of a buffer can
+   * be compressed. In some circumstances, the dictionary can grow beyond this limit, which
+   * will prevent the column from being compressed. This setting controls how the writer
+   * should act in these circumstances. A setting of dictionary_policy::ADAPTIVE will disable
+   * dictionary encoding for columns where the dictionary exceeds the limit. A setting of
+   * dictionary_policy::NEVER will disable the use of dictionary encoding globally. A setting of
+   * dictionary_policy::ALWAYS will allow the use of dictionary encoding even if it will result in
+   * the disabling of compression for columns that would otherwise be compressed.
+   *
+   * The default value is dictionary_policy::ALWAYS.
+   *
+   * @param val policy for dictionary use
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& dictionary_policy(enum dictionary_policy val);
+
+  /**
+   * @brief Sets the maximum dictionary size, in bytes.
+   *
+   * Disables dictionary encoding for any column chunk where the dictionary will
+   * exceed this limit.  Only used when the dictionary_policy is set to 'ADAPTIVE'.
+   *
+   * Default value is 1048576 (1MiB).
+   *
+   * @param val maximum dictionary size
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& max_dictionary_size(size_t val);
+
+  /**
+   * @brief Sets the maximum page fragment size, in rows.
+   *
+   * Files with nested schemas or very long strings may need a page fragment size
+   * smaller than the default value of 5000 to ensure a single fragment will not
+   * exceed the desired maximum page size in bytes.
+   *
+   * @param val maximum page fragment size
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& max_page_fragment_size(size_type val);
+
+  /**
+   * @brief Sets the pointer to the output compression statistics.
+   *
+   * @param comp_stats Pointer to compression statistics to be filled once writer is done
+   * @return this for chaining
+   */
+  chunked_parquet_writer_options_builder& compression_statistics(
+    std::shared_ptr<writer_compression_statistics> const& comp_stats)
+  {
+    options._compression_stats = comp_stats;
+    return *this;
+  }
+
+  /**
+   * @brief move chunked_parquet_writer_options member once it's built.
+   */
+  operator chunked_parquet_writer_options&&() { return std::move(options); }
+
+  /**
+   * @brief move chunked_parquet_writer_options member once it's is built.
+   *
+   * This has been added since Cython does not support overloading of conversion operators.
+   *
+   * @return Built `chunked_parquet_writer_options` object's r-value reference
+   */
+  chunked_parquet_writer_options&& build() { return std::move(options); }
+};
+
+/**
+ * @brief chunked parquet writer class to handle options and write tables in chunks.
+ *
+ * The intent of the parquet_chunked_writer is to allow writing of an
+ * arbitrarily large / arbitrary number of rows to a parquet file in multiple passes.
+ *
+ * The following code snippet demonstrates how to write a single parquet file containing
+ * one logical table by writing a series of individual cudf::tables.
+ *
+ * @code
+ *  auto destination = cudf::io::sink_info("dataset.parquet");
+ *  auto options = cudf::io::chunked_parquet_writer_options::builder(destination, table->view());
+ *  auto writer  = cudf::io::parquet_chunked_writer(options);
+ *
+ *  writer.write(table0)
+ *  writer.write(table1)
+ *  writer.close()
+ *  @endcode
+ */
+class parquet_chunked_writer {
+ public:
+  /**
+   * @brief Default constructor, this should never be used.
+   *        This is added just to satisfy cython.
+   */
+  parquet_chunked_writer() = default;
+
+  /**
+   * @brief Constructor with chunked writer options
+   *
+   * @param[in] options options used to write table
+   */
+  parquet_chunked_writer(chunked_parquet_writer_options const& options);
+
+  /**
+   * @brief Writes table to output.
+   *
+   * @param[in] table Table that needs to be written
+   * @param[in] partitions Optional partitions to divide the table into. If specified, must be same
+   * size as number of sinks.
+   *
+   * @throws cudf::logic_error If the number of partitions is not the same as number of sinks
+   * @throws rmm::bad_alloc if there is insufficient space for temporary buffers
+   * @return returns reference of the class object
+   */
+  parquet_chunked_writer& write(table_view const& table,
+                                std::vector<partition_info> const& partitions = {});
+
+  /**
+   * @brief Finishes the chunked/streamed write process.
+   *
+   * @param[in] column_chunks_file_paths Column chunks file path to be set in the raw output
+   * metadata
+   * @return A parquet-compatible blob that contains the data for all rowgroups in the list only if
+   * `column_chunks_file_paths` is provided, else null.
+   */
+  std::unique_ptr<std::vector<uint8_t>> close(
+    std::vector<std::string> const& column_chunks_file_paths = {});
+
+  /// Unique pointer to impl writer class
+  std::unique_ptr<cudf::io::detail::parquet::writer> writer;
+};
+
+/** @} */  // end of group
+
+}  // namespace cudf::io
diff --git a/cpp/include/cudf/io/parquet_metadata.hpp b/cpp/include/cudf/io/parquet_metadata.hpp
new file mode 100644
index 0000000..0c985fc
--- /dev/null
+++ b/cpp/include/cudf/io/parquet_metadata.hpp
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file parquet_metadata.hpp
+ * @brief cuDF-IO freeform API
+ */
+
+#pragma once
+
+#include <cudf/io/types.hpp>
+
+#include <optional>
+#include <string_view>
+#include <variant>
+#include <vector>
+
+namespace cudf {
+namespace io {
+
+namespace parquet {
+/**
+ * @brief Basic data types in Parquet, determines how data is physically stored
+ */
+enum class TypeKind : int8_t {
+  UNDEFINED_TYPE       = -1,  // Undefined for non-leaf nodes
+  BOOLEAN              = 0,
+  INT32                = 1,
+  INT64                = 2,
+  INT96                = 3,  // Deprecated
+  FLOAT                = 4,
+  DOUBLE               = 5,
+  BYTE_ARRAY           = 6,
+  FIXED_LEN_BYTE_ARRAY = 7,
+};
+}  // namespace parquet
+
+/**
+ * @brief Schema of a parquet column, including the nested columns.
+ */
+struct parquet_column_schema {
+ public:
+  /**
+   * @brief constructor
+   *
+   * @param name column name
+   * @param type parquet type
+   * @param children child columns (empty for non-nested types)
+   */
+  parquet_column_schema(std::string_view name,
+                        parquet::TypeKind type,
+                        std::vector<parquet_column_schema> children)
+    : _name{name}, _type_kind{type}, _children{std::move(children)}
+  {
+  }
+
+  /**
+   * @brief Returns parquet column name; can be empty.
+   *
+   * @return Column name
+   */
+  [[nodiscard]] auto name() const { return _name; }
+
+  /**
+   * @brief Returns parquet type of the column.
+   *
+   * @return Column parquet type
+   */
+  [[nodiscard]] auto type_kind() const { return _type_kind; }
+
+  /**
+   * @brief Returns schemas of all child columns.
+   *
+   * @return Children schemas
+   */
+  [[nodiscard]] auto const& children() const& { return _children; }
+
+  /** @copydoc children
+   * Children array is moved out of the object (rvalues only).
+   *
+   */
+  [[nodiscard]] auto children() && { return std::move(_children); }
+
+  /**
+   * @brief Returns schema of the child with the given index.
+   *
+   * @param idx child index
+   *
+   * @return Child schema
+   */
+  [[nodiscard]] auto const& child(int idx) const& { return children().at(idx); }
+
+  /** @copydoc child
+   * Child is moved out of the object (rvalues only).
+   *
+   */
+  [[nodiscard]] auto child(int idx) && { return std::move(children().at(idx)); }
+
+  /**
+   * @brief Returns the number of child columns.
+   *
+   * @return Children count
+   */
+  [[nodiscard]] auto num_children() const { return children().size(); }
+
+ private:
+  std::string _name;
+  // 3 types available: Physical, Converted, Logical.
+  parquet::TypeKind _type_kind;  // Physical
+  std::vector<parquet_column_schema> _children;
+};
+
+/**
+ * @brief Schema of a parquet file.
+ */
+struct parquet_schema {
+ public:
+  /**
+   * @brief constructor
+   *
+   * @param root_column_schema root column
+   */
+  parquet_schema(parquet_column_schema root_column_schema) : _root{std::move(root_column_schema)} {}
+
+  /**
+   * @brief Returns the schema of the struct column that contains all columns as fields.
+   *
+   * @return Root column schema
+   */
+  [[nodiscard]] auto const& root() const& { return _root; }
+
+  /** @copydoc root
+   * Root column schema is moved out of the object (rvalues only).
+   *
+   */
+  [[nodiscard]] auto root() && { return std::move(_root); }
+
+ private:
+  parquet_column_schema _root;
+};
+
+/**
+ * @brief Information about content of a parquet file.
+ */
+class parquet_metadata {
+ public:
+  /// Key-value metadata in the file footer.
+  using key_value_metadata = std::unordered_map<std::string, std::string>;
+
+  /**
+   * @brief constructor
+   *
+   * @param schema parquet schema
+   * @param num_rows number of rows
+   * @param num_rowgroups number of row groups
+   * @param file_metadata key-value metadata in the file footer
+   */
+  parquet_metadata(parquet_schema schema,
+                   int64_t num_rows,
+                   size_type num_rowgroups,
+                   key_value_metadata file_metadata)
+    : _schema{std::move(schema)},
+      _num_rows{num_rows},
+      _num_rowgroups{num_rowgroups},
+      _file_metadata{std::move(file_metadata)}
+  {
+  }
+
+  /**
+   * @brief Returns the parquet schema.
+   *
+   * @return parquet schema
+   */
+  [[nodiscard]] auto const& schema() const { return _schema; }
+
+  /**
+   * @brief Returns the number of rows of the root column.
+   *
+   * If a file contains list columns, nested columns can have a different number of rows.
+   *
+   * @return Number of rows
+   */
+  [[nodiscard]] auto num_rows() const { return _num_rows; }
+
+  /**
+   * @brief Returns the number of rowgroups in the file.
+   *
+   * @return Number of row groups
+   */
+  [[nodiscard]] auto num_rowgroups() const { return _num_rowgroups; }
+  /**
+   * @brief Returns the Key value metadata in the file footer.
+   *
+   * @return Key value metadata as a map
+   */
+  [[nodiscard]] auto const& metadata() const { return _file_metadata; }
+
+ private:
+  parquet_schema _schema;
+  int64_t _num_rows;
+  size_type _num_rowgroups;
+  key_value_metadata _file_metadata;
+};
+
+/**
+ * @brief Reads metadata of parquet dataset.
+ *
+ * @ingroup io_readers
+ *
+ * @param src_info Dataset source
+ *
+ * @return parquet_metadata with parquet schema, number of rows, number of row groups and key-value
+ * metadata.
+ */
+parquet_metadata read_parquet_metadata(source_info const& src_info);
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/text/byte_range_info.hpp b/cpp/include/cudf/io/text/byte_range_info.hpp
new file mode 100644
index 0000000..d20befe
--- /dev/null
+++ b/cpp/include/cudf/io/text/byte_range_info.hpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/error.hpp>
+
+#include <cstdint>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace text {
+
+/**
+ * @brief stores offset and size used to indicate a byte range
+ */
+class byte_range_info {
+ private:
+  int64_t _offset;  ///< offset in bytes
+  int64_t _size;    ///< size in bytes
+
+ public:
+  constexpr byte_range_info() noexcept : _offset(0), _size(0) {}
+  /**
+   * @brief Constructs a byte_range_info object
+   *
+   * @param offset offset in bytes
+   * @param size size in bytes
+   */
+  constexpr byte_range_info(int64_t offset, int64_t size) : _offset(offset), _size(size)
+  {
+    CUDF_EXPECTS(offset >= 0, "offset must be non-negative");
+    CUDF_EXPECTS(size >= 0, "size must be non-negative");
+  }
+
+  /**
+   * @brief Copy constructor
+   *
+   * @param other byte_range_info object to copy
+   */
+  constexpr byte_range_info(byte_range_info const& other) noexcept = default;
+  /**
+   * @brief  Copy assignment operator
+   *
+   * @param other byte_range_info object to copy
+   * @return this object after copying
+   */
+  constexpr byte_range_info& operator=(byte_range_info const& other) noexcept = default;
+
+  /**
+   * @brief Get the offset in bytes
+   *
+   * @return Offset in bytes
+   */
+  [[nodiscard]] constexpr int64_t offset() { return _offset; }
+
+  /**
+   * @brief Get the size in bytes
+   *
+   * @return Size in bytes
+   */
+  [[nodiscard]] constexpr int64_t size() { return _size; }
+
+  /**
+   * @brief Returns whether the span is empty.
+   *
+   * @return true iff the span is empty, i.e. `size() == 0`
+   */
+  [[nodiscard]] constexpr bool empty() { return size() == 0; }
+};
+
+/**
+ * @brief Create a collection of consecutive ranges between [0, total_bytes).
+ *
+ * Each range wil be the same size except if `total_bytes` is not evenly divisible by
+ * `range_count`, in which case the last range size will be the remainder.
+ *
+ * @param total_bytes total number of bytes in all ranges
+ * @param range_count total number of ranges in which to divide bytes
+ * @return Vector of range objects
+ */
+std::vector<byte_range_info> create_byte_range_infos_consecutive(int64_t total_bytes,
+                                                                 int64_t range_count);
+
+/**
+ * @brief Create a byte_range_info which represents as much of a file as possible. Specifically,
+ * `[0, numeric_limit<int64_t>::max())`.
+ *
+ * @return `[0, numeric_limit<int64_t>::max())`
+ */
+byte_range_info create_byte_range_info_max();
+
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/text/data_chunk_source.hpp b/cpp/include/cudf/io/text/data_chunk_source.hpp
new file mode 100644
index 0000000..28204c8
--- /dev/null
+++ b/cpp/include/cudf/io/text/data_chunk_source.hpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_pool.hpp>
+#include <rmm/device_buffer.hpp>
+
+namespace cudf {
+namespace io {
+namespace text {
+
+/**
+ * @brief A contract guaranteeing stream-ordered memory access to the underlying device data.
+ *
+ * This class guarantees access to the underlying data for the stream on which the data was
+ * allocated. Possible implementations may own the device data, or may only have a view over the
+ * data. Any work enqueued to the stream on which this data was allocated is guaranteed to be
+ * performed prior to the destruction of the underlying data, but otherwise no guarantees are made
+ * regarding if or when the underlying data gets destroyed.
+ */
+class device_data_chunk {
+ public:
+  virtual ~device_data_chunk() = default;
+  /**
+   * @pure @brief Returns a pointer to the underlying device data.
+   *
+   * @return A pointer to the underlying device data
+   */
+  [[nodiscard]] virtual char const* data() const = 0;
+  /**
+   * @pure @brief Returns the size of the underlying device data.
+   *
+   * @return The size of the underlying device data
+   */
+  [[nodiscard]] virtual std::size_t size() const = 0;
+  /**
+   * @pure @brief Returns a span over the underlying device data.
+   *
+   * @return A span over the underlying device data
+   */
+  virtual operator device_span<char const>() const = 0;
+};
+
+/**
+ * @brief a reader capable of producing views over device memory.
+ *
+ * The data chunk reader API encapsulates the idea of statefully traversing and loading a data
+ * source. A data source may be a file, a region of device memory, or a region of host memory.
+ * Reading data from these data sources efficiently requires different strategies depending on the
+ * type of data source, type of compression, capabilities of the host and device, the data's
+ * destination. Whole-file decompression should be hidden behind this interface.
+ */
+class data_chunk_reader {
+ public:
+  virtual ~data_chunk_reader() = default;
+  /**
+   * @pure @brief Skips the specified number of bytes in the data source.
+   *
+   * @param size The number of bytes to skip
+   */
+  virtual void skip_bytes(std::size_t size) = 0;
+
+  /**
+   * @pure @brief Get the next chunk of bytes from the data source
+   *
+   * Performs any necessary work to read and prepare the underlying data source for consumption as a
+   * view over device memory. Common implementations may read from a file, copy data from host
+   * memory, allocate temporary memory, perform iterative decompression, or even launch device
+   * kernels.
+   *
+   * @param size number of bytes to read
+   * @param stream stream to associate allocations or perform work required to obtain chunk
+   * @return a chunk of data up to @p size bytes. May return less than @p size bytes if
+   * reader reaches end of underlying data source. Returned data must be accessed in stream order
+   * relative to the specified @p stream
+   */
+  virtual std::unique_ptr<device_data_chunk> get_next_chunk(std::size_t size,
+                                                            rmm::cuda_stream_view stream) = 0;
+};
+
+/**
+ * @brief a data source capable of creating a reader which can produce views of the data source in
+ * device memory.
+ */
+class data_chunk_source {
+ public:
+  virtual ~data_chunk_source() = default;
+
+  /**
+   * @pure @brief Get a reader for the data source.
+   *
+   * @return `data_chunk_reader` object for the data source
+   */
+  [[nodiscard]] virtual std::unique_ptr<data_chunk_reader> create_reader() const = 0;
+};
+
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/text/data_chunk_source_factories.hpp b/cpp/include/cudf/io/text/data_chunk_source_factories.hpp
new file mode 100644
index 0000000..046994d
--- /dev/null
+++ b/cpp/include/cudf/io/text/data_chunk_source_factories.hpp
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/text/data_chunk_source.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <memory>
+#include <string>
+
+namespace cudf::io::text {
+
+/**
+ * @brief Creates a data source capable of producing device-buffered views of a datasource.
+ * @param data the datasource to be exposed as a data chunk source
+ * @return the data chunk source for the provided datasource. It must not outlive the datasource
+ *         used to construct it.
+ */
+std::unique_ptr<data_chunk_source> make_source(datasource& data);
+
+/**
+ * @brief Creates a data source capable of producing device-buffered views of the given string.
+ * @param data the host data to be exposed as a data chunk source. Its lifetime must be at least as
+ *             long as the lifetime of the returned data_chunk_source.
+ * @return the data chunk source for the provided host data. It copies data from the host to the
+ *         device.
+ */
+std::unique_ptr<data_chunk_source> make_source(host_span<char const> data);
+
+/**
+ * @brief Creates a data source capable of producing device-buffered views of the file
+ * @param filename the filename of the file to be exposed as a data chunk source.
+ * @return the data chunk source for the provided filename. It reads data from the file and copies
+ *         it to the device.
+ */
+std::unique_ptr<data_chunk_source> make_source_from_file(std::string_view filename);
+
+/**
+ * @brief Creates a data source capable of producing device-buffered views of a BGZIP compressed
+ *        file.
+ * @param filename the filename of the BGZIP-compressed file to be exposed as a data chunk source.
+ * @return the data chunk source for the provided filename. It reads data from the file and copies
+ *         it to the device, where it will be decompressed.
+ */
+std::unique_ptr<data_chunk_source> make_source_from_bgzip_file(std::string_view filename);
+
+/**
+ * @brief Creates a data source capable of producing device-buffered views of a BGZIP compressed
+ *        file with virtual record offsets.
+ * @param filename the filename of the BGZIP-compressed file to be exposed as a data chunk source.
+ * @param virtual_begin the virtual (Tabix) offset of the first byte to be read. Its upper 48 bits
+ *                      describe the offset into the compressed file, its lower 16 bits describe the
+ *                      block-local offset.
+ * @param virtual_end the virtual (Tabix) offset one past the last byte to be read.
+ * @return the data chunk source for the provided filename. It reads data from the file and copies
+ *         it to the device, where it will be decompressed. The chunk source only returns data
+ *         between the virtual offsets `virtual_begin` and `virtual_end`.
+ */
+std::unique_ptr<data_chunk_source> make_source_from_bgzip_file(std::string_view filename,
+                                                               uint64_t virtual_begin,
+                                                               uint64_t virtual_end);
+
+/**
+ * @brief Creates a data source capable of producing views of the given device string scalar
+ * @param data the device data to be exposed as a data chunk source. Its lifetime must be at least
+ *             as long as the lifetime of the returned data_chunk_source.
+ * @return the data chunk source for the provided host data. It does not create any copies.
+ */
+std::unique_ptr<data_chunk_source> make_source(cudf::string_scalar& data);
+
+}  // namespace cudf::io::text
diff --git a/cpp/include/cudf/io/text/detail/bgzip_utils.hpp b/cpp/include/cudf/io/text/detail/bgzip_utils.hpp
new file mode 100644
index 0000000..627df5f
--- /dev/null
+++ b/cpp/include/cudf/io/text/detail/bgzip_utils.hpp
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <zlib.h>
+
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <algorithm>
+#include <array>
+#include <fstream>
+#include <limits>
+
+namespace cudf::io::text::detail::bgzip {
+
+struct header {
+  int block_size;
+  int extra_length;
+  [[nodiscard]] int data_size() const { return block_size - extra_length - 20; }
+};
+
+struct footer {
+  uint32_t crc;
+  uint32_t decompressed_size;
+};
+
+/**
+ * @brief Reads the full BGZIP header from the given input stream. Afterwards, the stream position
+ *        is at the first data byte.
+ *
+ * @param input_stream The input stream
+ * @return The header storing the compressed size and extra subfield length
+ */
+header read_header(std::istream& input_stream);
+
+/**
+ * @brief Reads the full BGZIP footer from the given input stream. Afterwards, the stream position
+ *        is after the last footer byte.
+ *
+ * @param input_stream The input stream
+ * @return The footer storing uncompressed size and CRC32
+ */
+footer read_footer(std::istream& input_stream);
+
+/**
+ * @brief Writes a header for data of the given compressed size to the given stream.
+ *
+ * @param output_stream The output stream
+ * @param compressed_size The size of the compressed data
+ * @param pre_size_subfields Any GZIP extra subfields (need to be valid) to be placed before the
+ *                           BGZIP block size subfield
+ * @param post_size_subfields Any subfields to be placed after the BGZIP block size subfield
+ */
+void write_header(std::ostream& output_stream,
+                  uint16_t compressed_size,
+                  host_span<char const> pre_size_subfields,
+                  host_span<char const> post_size_subfields);
+
+/**
+ * @brief Writes a footer for the given uncompressed data to the given stream.
+ *
+ * @param output_stream The output stream
+ * @param data The data for which uncompressed size and CRC32 will be computed and written
+ */
+void write_footer(std::ostream& output_stream, host_span<char const> data);
+
+/**
+ * @brief Writes the given data to the given stream as an uncompressed deflate block with BZGIP
+ *        header and footer.
+ *
+ * @param output_stream The output stream
+ * @param data The uncompressed data
+ * @param pre_size_subfields Any GZIP extra subfields (need to be valid) to be placed before the
+ *                           BGZIP block size subfield
+ * @param post_size_subfields Any subfields to be placed after the BGZIP block size subfield
+ */
+void write_uncompressed_block(std::ostream& output_stream,
+                              host_span<char const> data,
+                              host_span<char const> pre_size_subfields  = {},
+                              host_span<char const> post_size_subfields = {});
+
+/**
+ * @brief Writes the given data to the given stream as a compressed deflate block with BZGIP
+ *        header and footer.
+ *
+ * @param output_stream The output stream
+ * @param data The uncompressed data
+ * @param pre_size_subfields Any GZIP extra subfields (need to be valid) to be placed before the
+ *                           BGZIP block size subfield
+ * @param post_size_subfields Any subfields to be placed after the BGZIP block size subfield
+ */
+void write_compressed_block(std::ostream& output_stream,
+                            host_span<char const> data,
+                            host_span<char const> pre_size_subfields  = {},
+                            host_span<char const> post_size_subfields = {});
+
+}  // namespace cudf::io::text::detail::bgzip
diff --git a/cpp/include/cudf/io/text/detail/multistate.hpp b/cpp/include/cudf/io/text/detail/multistate.hpp
new file mode 100644
index 0000000..e4e47d8
--- /dev/null
+++ b/cpp/include/cudf/io/text/detail/multistate.hpp
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+namespace text {
+namespace detail {
+
+/**
+ * @brief Represents up to 7 segments
+ */
+struct multistate {
+ public:
+  /**
+   * @brief The maximum state (head or tail) this multistate can represent
+   */
+
+  static auto constexpr max_segment_value = 15;
+  /**
+   * @brief The maximum number of segments this multistate can represent
+   */
+  static auto constexpr max_segment_count = 7;
+
+  /**
+   * @brief Enqueues a (head, tail] segment to this multistate
+   *
+   * @note: The behavior of this function is undefined if size() => max_segment_count
+   */
+  constexpr void enqueue(uint8_t head, uint8_t tail)
+  {
+    _heads |= (head & 0xFu) << (_size * 4);
+    _tails |= (tail & 0xFu) << (_size * 4);
+    _size++;
+  }
+
+  /**
+   * @brief get's the number of segments this multistate represents
+   */
+  [[nodiscard]] constexpr uint8_t size() const { return _size; }
+
+  /**
+   * @brief get's the highest (____, tail] value this multistate represents
+   */
+  [[nodiscard]] constexpr uint8_t max_tail() const
+  {
+    uint8_t maximum = 0;
+
+    for (uint8_t i = 0; i < _size; i++) {
+      maximum = std::max(maximum, get_tail(i));
+    }
+
+    return maximum;
+  }
+
+  /**
+   * @brief get's the Nth (head, ____] value state this multistate represents
+   */
+  [[nodiscard]] constexpr uint8_t get_head(uint8_t idx) const
+  {
+    return (_heads >> (idx * 4)) & 0xFu;
+  }
+
+  /**
+   * @brief get's the Nth (____, tail] value state this multistate represents
+   */
+  [[nodiscard]] constexpr uint8_t get_tail(uint8_t idx) const
+  {
+    return (_tails >> (idx * 4)) & 0xFu;
+  }
+
+ private:
+  uint8_t _size = 0;
+  uint32_t _heads{};
+  uint32_t _tails{};
+};
+
+/**
+ * @brief associatively inner-joins transition histories.
+ *
+ * Examples:
+ *           <(0, 5]> + <(5, 9]>         = <(0, 9]>
+ *           <(0, 5]> + <(6, 9]>         = <>
+ *   <(0, 1], (0, 2]> + <(2, 3], (1, 4]> = <(0, 4], (0, 3]>
+ *   <(0, 1], (0, 2]> + <(1, 3]>         = <(0, 3]>
+ *
+ * Head and tail value are limited to [0, 1, ..., 16]
+ *
+ * @param lhs past segments
+ * @param rhs future segments
+ * @return full join of past and future segments
+ */
+constexpr multistate operator+(multistate const& lhs, multistate const& rhs)
+{
+  // combine two multistates together by full-joining LHS tails to RHS heads,
+  // and taking the corresponding LHS heads and RHS tails.
+
+  multistate result;
+  for (uint8_t lhs_idx = 0; lhs_idx < lhs.size(); lhs_idx++) {
+    auto tail = lhs.get_tail(lhs_idx);
+    for (uint8_t rhs_idx = 0; rhs_idx < rhs.size(); rhs_idx++) {
+      auto head = rhs.get_head(rhs_idx);
+      if (tail == head) { result.enqueue(lhs.get_head(lhs_idx), rhs.get_tail(rhs_idx)); }
+    }
+  }
+  return result;
+}
+
+}  // namespace detail
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/text/detail/tile_state.hpp b/cpp/include/cudf/io/text/detail/tile_state.hpp
new file mode 100644
index 0000000..6ae399f
--- /dev/null
+++ b/cpp/include/cudf/io/text/detail/tile_state.hpp
@@ -0,0 +1,149 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cub/block/block_scan.cuh>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace io {
+namespace text {
+namespace detail {
+
+enum class scan_tile_status : uint8_t {
+  oob,
+  invalid,
+  partial,
+  inclusive,
+};
+
+template <typename T>
+struct scan_tile_state_view {
+  uint64_t num_tiles;
+  cuda::atomic<scan_tile_status, cuda::thread_scope_device>* tile_status;
+  T* tile_partial;
+  T* tile_inclusive;
+
+  __device__ inline void set_status(cudf::size_type tile_idx, scan_tile_status status)
+  {
+    auto const offset = (tile_idx + num_tiles) % num_tiles;
+    tile_status[offset].store(status, cuda::memory_order_relaxed);
+  }
+
+  __device__ inline void set_partial_prefix(cudf::size_type tile_idx, T value)
+  {
+    auto const offset = (tile_idx + num_tiles) % num_tiles;
+    cub::ThreadStore<cub::STORE_CG>(tile_partial + offset, value);
+    tile_status[offset].store(scan_tile_status::partial);
+  }
+
+  __device__ inline void set_inclusive_prefix(cudf::size_type tile_idx, T value)
+  {
+    auto const offset = (tile_idx + num_tiles) % num_tiles;
+    cub::ThreadStore<cub::STORE_CG>(tile_inclusive + offset, value);
+    tile_status[offset].store(scan_tile_status::inclusive);
+  }
+
+  __device__ inline T get_prefix(cudf::size_type tile_idx, scan_tile_status& status)
+  {
+    auto const offset = (tile_idx + num_tiles) % num_tiles;
+
+    while ((status = tile_status[offset].load(cuda::memory_order_relaxed)) ==
+           scan_tile_status::invalid) {}
+
+    if (status == scan_tile_status::partial) {
+      return cub::ThreadLoad<cub::LOAD_CG>(tile_partial + offset);
+    } else {
+      return cub::ThreadLoad<cub::LOAD_CG>(tile_inclusive + offset);
+    }
+  }
+};
+
+template <typename T>
+struct scan_tile_state {
+  rmm::device_uvector<cuda::atomic<scan_tile_status, cuda::thread_scope_device>> tile_status;
+  rmm::device_uvector<T> tile_state_partial;
+  rmm::device_uvector<T> tile_state_inclusive;
+
+  scan_tile_state(cudf::size_type num_tiles,
+                  rmm::cuda_stream_view stream,
+                  rmm::mr::device_memory_resource* mr)
+    : tile_status(rmm::device_uvector<cuda::atomic<scan_tile_status, cuda::thread_scope_device>>(
+        num_tiles, stream, mr)),
+      tile_state_partial(rmm::device_uvector<T>(num_tiles, stream, mr)),
+      tile_state_inclusive(rmm::device_uvector<T>(num_tiles, stream, mr))
+  {
+  }
+
+  operator scan_tile_state_view<T>()
+  {
+    return scan_tile_state_view<T>{tile_status.size(),
+                                   tile_status.data(),
+                                   tile_state_partial.data(),
+                                   tile_state_inclusive.data()};
+  }
+
+  inline T get_inclusive_prefix(cudf::size_type tile_idx, rmm::cuda_stream_view stream) const
+  {
+    auto const offset = (tile_idx + tile_status.size()) % tile_status.size();
+    return tile_state_inclusive.element(offset, stream);
+  }
+};
+
+template <typename T>
+struct scan_tile_state_callback {
+  __device__ inline scan_tile_state_callback(scan_tile_state_view<T>& tile_state,
+                                             cudf::size_type tile_idx)
+    : _tile_state(tile_state), _tile_idx(tile_idx)
+  {
+  }
+
+  __device__ inline T operator()(T const& block_aggregate)
+  {
+    T exclusive_prefix;
+
+    if (threadIdx.x == 0) {
+      _tile_state.set_partial_prefix(_tile_idx, block_aggregate);
+
+      auto predecessor_idx    = _tile_idx - 1;
+      auto predecessor_status = scan_tile_status::invalid;
+
+      // scan partials to form prefix
+
+      auto window_partial = _tile_state.get_prefix(predecessor_idx, predecessor_status);
+      while (predecessor_status != scan_tile_status::inclusive) {
+        predecessor_idx--;
+        auto predecessor_prefix = _tile_state.get_prefix(predecessor_idx, predecessor_status);
+        window_partial          = predecessor_prefix + window_partial;
+      }
+      exclusive_prefix = window_partial;
+
+      _tile_state.set_inclusive_prefix(_tile_idx, exclusive_prefix + block_aggregate);
+    }
+
+    return exclusive_prefix;
+  }
+
+  scan_tile_state_view<T>& _tile_state;
+  cudf::size_type _tile_idx;
+};
+
+}  // namespace detail
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/text/detail/trie.hpp b/cpp/include/cudf/io/text/detail/trie.hpp
new file mode 100644
index 0000000..7bb2e4e
--- /dev/null
+++ b/cpp/include/cudf/io/text/detail/trie.hpp
@@ -0,0 +1,250 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/text/detail/multistate.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <algorithm>
+#include <queue>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace text {
+namespace detail {
+
+struct trie_node {
+  char token;
+  uint8_t match_length;
+  uint8_t child_begin;
+};
+
+struct trie_device_view {
+  device_span<trie_node const> _nodes;
+
+  /**
+   * @brief create a multistate which contains all partial path matches for the given token.
+   */
+  constexpr multistate transition_init(char c)
+  {
+    auto result = multistate();
+
+    result.enqueue(0, 0);
+
+    for (uint8_t curr = 0; curr < _nodes.size() - 1; curr++) {
+      transition_enqueue_all(c, result, curr, curr);
+    }
+    return result;
+  }
+
+  /**
+   * @brief create a new multistate by transitioning all states in the multistate by the given token
+   *
+   * Eliminates any partial matches that cannot transition using the given token.
+   *
+   * @note always enqueues (0, 0] as the first state of the returned multistate.
+   */
+  constexpr multistate transition(char c, multistate const& states)
+  {
+    auto result = multistate();
+
+    result.enqueue(0, 0);
+
+    for (uint8_t i = 0; i < states.size(); i++) {
+      transition_enqueue_all(c, result, states.get_head(i), states.get_tail(i));
+    }
+
+    return result;
+  }
+
+  /**
+   * @brief returns true if the given index is associated with a matching state.
+   */
+  constexpr bool is_match(uint16_t idx) { return static_cast<bool>(get_match_length(idx)); }
+
+  /**
+   * @brief returns the match length if the given index is associated with a matching state,
+   * otherwise zero.
+   */
+  constexpr uint8_t get_match_length(uint16_t idx) { return _nodes[idx].match_length; }
+
+ private:
+  constexpr void transition_enqueue_all(  //
+    char c,
+    multistate& states,
+    uint8_t head,
+    uint8_t curr)
+  {
+    for (uint32_t tail = _nodes[curr].child_begin; tail < _nodes[curr + 1].child_begin; tail++) {
+      if (_nodes[tail].token == c) {  //
+        states.enqueue(head, tail);
+      }
+    }
+  }
+};
+
+/**
+ * @brief A flat trie contained in device memory.
+ */
+struct trie {
+ private:
+  cudf::size_type _max_duplicate_tokens;
+  rmm::device_uvector<trie_node> _nodes;
+
+  trie(cudf::size_type max_duplicate_tokens, rmm::device_uvector<trie_node>&& nodes)
+    : _max_duplicate_tokens(max_duplicate_tokens), _nodes(std::move(nodes))
+  {
+  }
+
+  /**
+   * @brief Used to build a hierarchical trie which can then be flattened.
+   */
+  struct trie_builder_node {
+    uint8_t match_length;
+    std::unordered_map<char, std::unique_ptr<trie_builder_node>> children;
+
+    /**
+     * @brief Insert the string in to the trie tree, growing the trie as necessary
+     */
+    void insert(std::string s) { insert(s.c_str(), s.size(), 0); }
+
+   private:
+    trie_builder_node& insert(char const* s, uint16_t size, uint8_t depth)
+    {
+      if (size == 0) {
+        match_length = depth;
+        return *this;
+      }
+
+      if (children[*s] == nullptr) { children[*s] = std::make_unique<trie_builder_node>(); }
+
+      return children[*s]->insert(s + 1, size - 1, depth + 1);
+    }
+  };
+
+ public:
+  /**
+   * @brief Gets the number of nodes contained in this trie.
+   */
+  [[nodiscard]] cudf::size_type size() const { return _nodes.size(); }
+
+  /**
+   * @brief A pessimistic count of duplicate tokens in the trie. Used to determine the maximum
+   * possible stack size required to compute matches of this trie in parallel.
+   */
+  [[nodiscard]] cudf::size_type max_duplicate_tokens() const { return _max_duplicate_tokens; }
+
+  /**
+   * @brief Create a trie which represents the given pattern.
+   *
+   * @param pattern The pattern to store in the trie
+   * @param stream The stream to use for allocation and copy
+   * @param mr Memory resource to use for the device memory allocation
+   * @return The trie.
+   */
+  static trie create(std::string const& pattern,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr)
+
+  {
+    return create(std::vector<std::string>{pattern}, stream, mr);
+  }
+
+  /**
+   * @brief Create a trie which represents the given pattern.
+   *
+   * @param pattern The patterns to store in the trie
+   * @param stream The stream to use for allocation and copy
+   * @param mr Memory resource to use for the device memory allocation
+   * @return The trie.
+   */
+  static trie create(std::vector<std::string> const& patterns,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr)
+  {
+    std::vector<char> tokens;
+    std::vector<uint8_t> transitions;
+    std::vector<uint8_t> match_length;
+
+    // create the trie tree
+    auto root = std::make_unique<trie_builder_node>();
+    for (auto& pattern : patterns) {
+      root->insert(pattern);
+    }
+
+    // flatten
+    auto sum = 1;
+    transitions.emplace_back(sum);
+    match_length.emplace_back(root->match_length);
+
+    auto builder_nodes = std::queue<std::unique_ptr<trie_builder_node>>();
+    builder_nodes.push(std::move(root));
+
+    tokens.emplace_back(0);
+
+    while (builder_nodes.size()) {
+      auto layer_size = builder_nodes.size();
+      for (uint32_t i = 0; i < layer_size; i++) {
+        auto node = std::move(builder_nodes.front());
+        builder_nodes.pop();
+        sum += node->children.size();
+        transitions.emplace_back(sum);
+        for (auto& item : node->children) {
+          match_length.emplace_back(item.second->match_length);
+          tokens.emplace_back(item.first);
+          builder_nodes.push(std::move(item.second));
+        }
+      }
+    }
+
+    tokens.emplace_back(0);
+
+    match_length.emplace_back(0);
+
+    std::vector<trie_node> trie_nodes;
+    auto token_counts = std::unordered_map<cudf::size_type, int32_t>();
+
+    for (uint32_t i = 0; i < tokens.size(); i++) {
+      trie_nodes.emplace_back(trie_node{tokens[i], match_length[i], transitions[i]});
+      token_counts[tokens[i]]++;
+    }
+
+    auto most_common_token =
+      std::max_element(token_counts.begin(), token_counts.end(), [](auto const& a, auto const& b) {
+        return a.second < b.second;
+      });
+
+    auto max_duplicate_tokens = most_common_token->second;
+
+    return trie{max_duplicate_tokens,
+                cudf::detail::make_device_uvector_sync(trie_nodes, stream, mr)};
+  }
+
+  [[nodiscard]] trie_device_view view() const { return trie_device_view{_nodes}; }
+};
+
+}  // namespace detail
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/text/multibyte_split.hpp b/cpp/include/cudf/io/text/multibyte_split.hpp
new file mode 100644
index 0000000..a7edc9b
--- /dev/null
+++ b/cpp/include/cudf/io/text/multibyte_split.hpp
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/io/text/byte_range_info.hpp>
+#include <cudf/io/text/data_chunk_source.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <optional>
+
+namespace cudf {
+namespace io {
+namespace text {
+
+/**
+ * @brief Parsing options for multibyte_split.
+ */
+struct parse_options {
+  /**
+   * @brief Only rows starting inside this byte range will be part of the output column.
+   */
+  byte_range_info byte_range = create_byte_range_info_max();
+  /**
+   * @brief Whether delimiters at the end of rows should be stripped from the output column
+   */
+  bool strip_delimiters = false;
+};
+
+/**
+ * @brief Splits the source text into a strings column using a multiple byte delimiter.
+ *
+ * Providing a byte range allows multibyte_split to read a file partially, only returning the
+ * offsets of delimiters which begin within the range. If thinking in terms of "records", where each
+ * delimiter dictates the end of a record, all records which begin within the byte range provided
+ * will be returned, including any record which may begin in the range but end outside of the
+ * range. Records which begin outside of the range will ignored, even if those records end inside
+ * the range.
+ *
+ * @code{.pseudo}
+ * Examples:
+ *  source:     "abc..def..ghi..jkl.."
+ *  delimiter:  ".."
+ *
+ *  byte_range: nullopt
+ *  return:     ["abc..", "def..", "ghi..", jkl..", ""]
+ *
+ *  byte_range: [0, 2)
+ *  return:     ["abc.."]
+ *
+ *  byte_range: [2, 9)
+ *  return:     ["def..", "ghi.."]
+ *
+ *  byte_range: [11, 2)
+ *  return:     []
+ *
+ *  byte_range: [13, 7)
+ *  return:     ["jkl..", ""]
+ * @endcode
+ *
+ * @param source The source string
+ * @param delimiter UTF-8 encoded string for which to find offsets in the source
+ * @param options the parsing options to use (including byte range)
+ * @param mr Memory resource to use for the device memory allocation
+ * @return The strings found by splitting the source by the delimiter within the relevant byte
+ * range.
+ */
+std::unique_ptr<cudf::column> multibyte_split(
+  data_chunk_source const& source,
+  std::string const& delimiter,
+  parse_options options               = {},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+std::unique_ptr<cudf::column> multibyte_split(
+  data_chunk_source const& source,
+  std::string const& delimiter,
+  std::optional<byte_range_info> byte_range,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+std::unique_ptr<cudf::column> multibyte_split(data_chunk_source const& source,
+                                              std::string const& delimiter,
+                                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/io/types.hpp b/cpp/include/cudf/io/types.hpp
new file mode 100644
index 0000000..a97f811
--- /dev/null
+++ b/cpp/include/cudf/io/types.hpp
@@ -0,0 +1,936 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file types.hpp
+ * @brief cuDF-IO API type definitions
+ */
+
+#pragma once
+
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <map>
+#include <memory>
+#include <optional>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace cudf {
+//! IO interfaces
+namespace io {
+class data_sink;
+class datasource;
+}  // namespace io
+}  // namespace cudf
+
+//! cuDF interfaces
+namespace cudf {
+//! IO interfaces
+namespace io {
+/**
+ * @brief Compression algorithms
+ */
+enum class compression_type {
+  NONE,    ///< No compression
+  AUTO,    ///< Automatically detect or select compression format
+  SNAPPY,  ///< Snappy format, using byte-oriented LZ77
+  GZIP,    ///< GZIP format, using DEFLATE algorithm
+  BZIP2,   ///< BZIP2 format, using Burrows-Wheeler transform
+  BROTLI,  ///< BROTLI format, using LZ77 + Huffman + 2nd order context modeling
+  ZIP,     ///< ZIP format, using DEFLATE algorithm
+  XZ,      ///< XZ format, using LZMA(2) algorithm
+  ZLIB,    ///< ZLIB format, using DEFLATE algorithm
+  LZ4,     ///< LZ4 format, using LZ77
+  LZO,     ///< Lempel–Ziv–Oberhumer format
+  ZSTD     ///< Zstandard format
+};
+
+/**
+ * @brief Data source or destination types
+ */
+enum class io_type {
+  FILEPATH,          ///< Input/output is a file path
+  HOST_BUFFER,       ///< Input/output is a buffer in host memory
+  DEVICE_BUFFER,     ///< Input/output is a buffer in device memory
+  VOID,              ///< Input/output is nothing. No work is done. Useful for benchmarking
+  USER_IMPLEMENTED,  ///< Input/output is handled by a custom user class
+};
+
+/**
+ * @brief Behavior when handling quotations in field data
+ */
+enum class quote_style {
+  MINIMAL,     ///< Quote only fields which contain special characters
+  ALL,         ///< Quote all fields
+  NONNUMERIC,  ///< Quote all non-numeric fields
+  NONE         ///< Never quote fields; disable quotation parsing
+};
+
+/**
+ * @brief Column statistics granularity type for parquet/orc writers
+ */
+enum statistics_freq {
+  STATISTICS_NONE     = 0,  ///< No column statistics
+  STATISTICS_ROWGROUP = 1,  ///< Per-Rowgroup column statistics
+  STATISTICS_PAGE     = 2,  ///< Per-page column statistics
+  STATISTICS_COLUMN   = 3,  ///< Full column and offset indices. Implies STATISTICS_ROWGROUP
+};
+
+/**
+ * @brief Statistics about compression performed by a writer.
+ */
+class writer_compression_statistics {
+ public:
+  /**
+   * @brief Default constructor
+   */
+  writer_compression_statistics() = default;
+
+  /**
+   * @brief Constructor with initial values.
+   *
+   * @param num_compressed_bytes The number of bytes that were successfully compressed
+   * @param num_failed_bytes The number of bytes that failed to compress
+   * @param num_skipped_bytes The number of bytes that were skipped during compression
+   * @param num_compressed_output_bytes The number of bytes in the compressed output
+   */
+  writer_compression_statistics(size_t num_compressed_bytes,
+                                size_t num_failed_bytes,
+                                size_t num_skipped_bytes,
+                                size_t num_compressed_output_bytes)
+    : _num_compressed_bytes(num_compressed_bytes),
+      _num_failed_bytes(num_failed_bytes),
+      _num_skipped_bytes(num_skipped_bytes),
+      _num_compressed_output_bytes(num_compressed_output_bytes)
+  {
+  }
+
+  /**
+   * @brief Adds the values from another `writer_compression_statistics` object.
+   *
+   * @param other The other writer_compression_statistics object
+   * @return writer_compression_statistics& Reference to this object
+   */
+  writer_compression_statistics& operator+=(writer_compression_statistics const& other) noexcept
+  {
+    _num_compressed_bytes += other._num_compressed_bytes;
+    _num_failed_bytes += other._num_failed_bytes;
+    _num_skipped_bytes += other._num_skipped_bytes;
+    _num_compressed_output_bytes += other._num_compressed_output_bytes;
+    return *this;
+  }
+
+  /**
+   * @brief Returns the number of bytes in blocks that were successfully compressed.
+   *
+   * This is the number of bytes that were actually compressed, not the size of the compressed
+   * output.
+   *
+   * @return size_t The number of bytes that were successfully compressed
+   */
+  [[nodiscard]] auto num_compressed_bytes() const noexcept { return _num_compressed_bytes; }
+
+  /**
+   * @brief Returns the number of bytes in blocks that failed to compress.
+   *
+   * @return size_t The number of bytes that failed to compress
+   */
+  [[nodiscard]] auto num_failed_bytes() const noexcept { return _num_failed_bytes; }
+
+  /**
+   * @brief Returns the number of bytes in blocks that were skipped during compression.
+   *
+   * @return size_t The number of bytes that were skipped during compression
+   */
+  [[nodiscard]] auto num_skipped_bytes() const noexcept { return _num_skipped_bytes; }
+
+  /**
+   * @brief Returns the total size of compression inputs.
+   *
+   * @return size_t The total size of compression inputs
+   */
+  [[nodiscard]] auto num_total_input_bytes() const noexcept
+  {
+    return num_compressed_bytes() + num_failed_bytes() + num_skipped_bytes();
+  }
+
+  /**
+   * @brief Returns the compression ratio for the successfully compressed blocks.
+   *
+   * Returns nan if there were no successfully compressed blocks.
+   *
+   * @return double The ratio between the size of the compression inputs and the size of the
+   * compressed output.
+   */
+  [[nodiscard]] auto compression_ratio() const noexcept
+  {
+    return static_cast<double>(num_compressed_bytes()) / _num_compressed_output_bytes;
+  }
+
+ private:
+  std::size_t _num_compressed_bytes = 0;  ///< The number of bytes that were successfully compressed
+  std::size_t _num_failed_bytes     = 0;  ///< The number of bytes that failed to compress
+  std::size_t _num_skipped_bytes = 0;  ///< The number of bytes that were skipped during compression
+  std::size_t _num_compressed_output_bytes = 0;  ///< The number of bytes in the compressed output
+};
+
+/**
+ * @brief Control use of dictionary encoding for parquet writer
+ */
+enum dictionary_policy {
+  NEVER,     ///< Never use dictionary encoding
+  ADAPTIVE,  ///< Use dictionary when it will not impact compression
+  ALWAYS     ///< Use dictionary reqardless of impact on compression
+};
+
+/**
+ * @brief Detailed name (and optionally nullability) information for output columns.
+ *
+ * The hierarchy of children matches the hierarchy of children in the output
+ * cudf columns.
+ */
+struct column_name_info {
+  std::string name;                        ///< Column name
+  std::optional<bool> is_nullable;         ///< Column nullability
+  std::vector<column_name_info> children;  ///< Child column names
+
+  /**
+   * @brief Construct a column name info with a name, optional nullabilty, and no children
+   *
+   * @param _name Column name
+   * @param _is_nullable True if column is nullable
+   */
+  column_name_info(std::string const& _name, std::optional<bool> _is_nullable = std::nullopt)
+    : name(_name), is_nullable(_is_nullable)
+  {
+  }
+
+  column_name_info() = default;
+};
+
+/**
+ * @brief Table metadata returned by IO readers.
+ */
+struct table_metadata {
+  std::vector<column_name_info>
+    schema_info;  //!< Detailed name information for the entire output hierarchy
+  std::map<std::string, std::string> user_data;  //!< Format-dependent metadata of the first input
+                                                 //!< file as key-values pairs (deprecated)
+  std::vector<std::unordered_map<std::string, std::string>>
+    per_file_user_data;  //!< Per file format-dependent metadata as key-values pairs
+};
+
+/**
+ * @brief Table with table metadata used by io readers to return the metadata by value
+ */
+struct table_with_metadata {
+  std::unique_ptr<table> tbl;  //!< Table
+  table_metadata metadata;     //!< Table metadata
+};
+
+/**
+ * @brief Non-owning view of a host memory buffer
+ *
+ * @deprecated Since 23.04
+ *
+ * Used to describe buffer input in `source_info` objects.
+ */
+struct host_buffer {
+  // TODO: to be replaced by `host_span`
+  char const* data = nullptr;  //!< Pointer to the buffer
+  size_t size      = 0;        //!< Size of the buffer
+  host_buffer()    = default;
+  /**
+   * @brief Construct a new host buffer object
+   *
+   * @param data Pointer to the buffer
+   * @param size Size of the buffer
+   */
+  host_buffer(char const* data, size_t size) : data(data), size(size) {}
+};
+
+/**
+ * @brief Returns `true` if the type is byte-like, meaning it is reasonable to pass as a pointer to
+ * bytes.
+ *
+ * @tparam T The representation type
+ * @return `true` if the type is considered a byte-like type
+ */
+template <typename T>
+constexpr inline auto is_byte_like_type()
+{
+  using non_cv_T = std::remove_cv_t<T>;
+  return std::is_same_v<non_cv_T, int8_t> || std::is_same_v<non_cv_T, char> ||
+         std::is_same_v<non_cv_T, uint8_t> || std::is_same_v<non_cv_T, unsigned char> ||
+         std::is_same_v<non_cv_T, std::byte>;
+}
+
+/**
+ * @brief Source information for read interfaces
+ */
+struct source_info {
+  source_info() = default;
+
+  /**
+   * @brief Construct a new source info object for multiple files
+   *
+   * @param file_paths Input files paths
+   */
+  explicit source_info(std::vector<std::string> const& file_paths) : _filepaths(file_paths) {}
+
+  /**
+   * @brief Construct a new source info object for a single file
+   *
+   * @param file_path Single input file
+   */
+  explicit source_info(std::string const& file_path) : _filepaths({file_path}) {}
+
+  /**
+   * @brief Construct a new source info object for multiple buffers in host memory
+   *
+   * @deprecated Since 23.04
+   *
+   * @param host_buffers Input buffers in host memory
+   */
+  explicit source_info(std::vector<host_buffer> const& host_buffers) : _type(io_type::HOST_BUFFER)
+  {
+    _host_buffers.reserve(host_buffers.size());
+    std::transform(host_buffers.begin(),
+                   host_buffers.end(),
+                   std::back_inserter(_host_buffers),
+                   [](auto const hb) {
+                     return cudf::host_span<std::byte const>{
+                       reinterpret_cast<std::byte const*>(hb.data), hb.size};
+                   });
+  }
+
+  /**
+   * @brief Construct a new source info object for a single buffer
+   *
+   * @deprecated Since 23.04
+   *
+   * @param host_data Input buffer in host memory
+   * @param size Size of the buffer
+   */
+  explicit source_info(char const* host_data, size_t size)
+    : _type(io_type::HOST_BUFFER),
+      _host_buffers(
+        {cudf::host_span<std::byte const>(reinterpret_cast<std::byte const*>(host_data), size)})
+  {
+  }
+
+  /**
+   * @brief Construct a new source info object for multiple buffers in host memory
+   *
+   * @param host_buffers Input buffers in host memory
+   */
+  template <typename T, CUDF_ENABLE_IF(is_byte_like_type<std::remove_cv_t<T>>())>
+  explicit source_info(cudf::host_span<cudf::host_span<T>> const host_buffers)
+    : _type(io_type::HOST_BUFFER)
+  {
+    if constexpr (not std::is_same_v<std::remove_cv_t<T>, std::byte>) {
+      _host_buffers.reserve(host_buffers.size());
+      std::transform(host_buffers.begin(),
+                     host_buffers.end(),
+                     std::back_inserter(_host_buffers),
+                     [](auto const s) {
+                       return cudf::host_span<std::byte const>{
+                         reinterpret_cast<std::byte const*>(s.data()), s.size()};
+                     });
+    } else {
+      _host_buffers.assign(host_buffers.begin(), host_buffers.end());
+    }
+  }
+
+  /**
+   * @brief Construct a new source info object for a single buffer
+   *
+   * @param host_data Input buffer in host memory
+   */
+  template <typename T, CUDF_ENABLE_IF(is_byte_like_type<std::remove_cv_t<T>>())>
+  explicit source_info(cudf::host_span<T> host_data)
+    : _type(io_type::HOST_BUFFER),
+      _host_buffers{cudf::host_span<std::byte const>(
+        reinterpret_cast<std::byte const*>(host_data.data()), host_data.size())}
+  {
+  }
+
+  /**
+   * @brief Construct a new source info object for multiple buffers in device memory
+   *
+   * @param device_buffers Input buffers in device memory
+   */
+  explicit source_info(cudf::host_span<cudf::device_span<std::byte const>> device_buffers)
+    : _type(io_type::DEVICE_BUFFER), _device_buffers(device_buffers.begin(), device_buffers.end())
+  {
+  }
+
+  /**
+   * @brief Construct a new source info object from a device buffer
+   *
+   * @param d_buffer Input buffer in device memory
+   */
+  explicit source_info(cudf::device_span<std::byte const> d_buffer)
+    : _type(io_type::DEVICE_BUFFER), _device_buffers({{d_buffer}})
+  {
+  }
+
+  /**
+   * @brief Construct a new source info object for multiple user-implemented sources
+   *
+   * @param sources  User-implemented input sources
+   */
+  explicit source_info(std::vector<cudf::io::datasource*> const& sources)
+    : _type(io_type::USER_IMPLEMENTED), _user_sources(sources)
+  {
+  }
+
+  /**
+   * @brief Construct a new source info object for a single user-implemented source
+   *
+   * @param source Single user-implemented Input source
+   */
+  explicit source_info(cudf::io::datasource* source)
+    : _type(io_type::USER_IMPLEMENTED), _user_sources({source})
+  {
+  }
+
+  /**
+   * @brief Get the type of the input
+   *
+   * @return The type of the input
+   */
+  [[nodiscard]] auto type() const { return _type; }
+  /**
+   * @brief Get the filepaths of the input
+   *
+   * @return The filepaths of the input
+   */
+  [[nodiscard]] auto const& filepaths() const { return _filepaths; }
+  /**
+   * @brief Get the host buffers of the input
+   *
+   * @return The host buffers of the input
+   */
+  [[nodiscard]] auto const& host_buffers() const { return _host_buffers; }
+  /**
+   * @brief Get the device buffers of the input
+   *
+   * @return The device buffers of the input
+   */
+  [[nodiscard]] auto const& device_buffers() const { return _device_buffers; }
+  /**
+   * @brief Get the user sources of the input
+   *
+   * @return The user sources of the input
+   */
+  [[nodiscard]] auto const& user_sources() const { return _user_sources; }
+
+ private:
+  io_type _type = io_type::FILEPATH;
+  std::vector<std::string> _filepaths;
+  std::vector<cudf::host_span<std::byte const>> _host_buffers;
+  std::vector<cudf::device_span<std::byte const>> _device_buffers;
+  std::vector<cudf::io::datasource*> _user_sources;
+};
+
+/**
+ * @brief Destination information for write interfaces
+ */
+struct sink_info {
+  sink_info() = default;
+  /**
+   * @brief Construct a new sink info object
+   *
+   * @param num_sinks Number of sinks
+   */
+  sink_info(size_t num_sinks) : _num_sinks(num_sinks) {}
+
+  /**
+   * @brief Construct a new sink info object for multiple files
+   *
+   * @param file_paths Output files paths
+   */
+  explicit sink_info(std::vector<std::string> const& file_paths)
+    : _type(io_type::FILEPATH), _num_sinks(file_paths.size()), _filepaths(file_paths)
+  {
+  }
+
+  /**
+   * @brief Construct a new sink info object for a single file
+   *
+   * @param file_path Single output file path
+   */
+  explicit sink_info(std::string const& file_path)
+    : _type(io_type::FILEPATH), _filepaths({file_path})
+  {
+  }
+
+  /**
+   * @brief Construct a new sink info object for multiple host buffers
+   *
+   * @param buffers Output host buffers
+   */
+  explicit sink_info(std::vector<std::vector<char>*> const& buffers)
+    : _type(io_type::HOST_BUFFER), _num_sinks(buffers.size()), _buffers(buffers)
+  {
+  }
+  /**
+   * @brief Construct a new sink info object for a single host buffer
+   *
+   * @param buffer Single output host buffer
+   */
+  explicit sink_info(std::vector<char>* buffer) : _type(io_type::HOST_BUFFER), _buffers({buffer}) {}
+
+  /**
+   * @brief Construct a new sink info object for multiple user-implemented sinks
+   *
+   * @param user_sinks Output user-implemented sinks
+   */
+  explicit sink_info(std::vector<cudf::io::data_sink*> const& user_sinks)
+    : _type(io_type::USER_IMPLEMENTED), _num_sinks(user_sinks.size()), _user_sinks(user_sinks)
+  {
+  }
+
+  /**
+   * @brief Construct a new sink info object for a single user-implemented sink
+   *
+   * @param user_sink Single output user-implemented sink
+   */
+  explicit sink_info(class cudf::io::data_sink* user_sink)
+    : _type(io_type::USER_IMPLEMENTED), _user_sinks({user_sink})
+  {
+  }
+
+  /**
+   * @brief Get the type of the input
+   *
+   * @return The type of the input
+   */
+  [[nodiscard]] auto type() const { return _type; }
+  /**
+   * @brief Get the number of sinks
+   *
+   * @return The number of sinks
+   */
+  [[nodiscard]] auto num_sinks() const { return _num_sinks; }
+  /**
+   * @brief Get the filepaths of the input
+   *
+   *  @return The filepaths of the input
+   */
+  [[nodiscard]] auto const& filepaths() const { return _filepaths; }
+  /**
+   * @brief Get the host buffers of the input
+   *
+   *  @return The host buffers of the input
+   */
+  [[nodiscard]] auto const& buffers() const { return _buffers; }
+  /**
+   * @brief Get the user sinks of the input
+   *
+   *  @return The user sinks of the input
+   */
+  [[nodiscard]] auto const& user_sinks() const { return _user_sinks; }
+
+ private:
+  io_type _type     = io_type::VOID;
+  size_t _num_sinks = 1;
+  std::vector<std::string> _filepaths;
+  std::vector<std::vector<char>*> _buffers;
+  std::vector<cudf::io::data_sink*> _user_sinks;
+};
+
+class table_input_metadata;
+
+/**
+ * @brief Metadata for a column
+ */
+class column_in_metadata {
+  friend table_input_metadata;
+  std::string _name = "";
+  std::optional<bool> _nullable;
+  bool _list_column_is_map  = false;
+  bool _use_int96_timestamp = false;
+  bool _output_as_binary    = false;
+  std::optional<uint8_t> _decimal_precision;
+  std::optional<int32_t> _parquet_field_id;
+  std::vector<column_in_metadata> children;
+
+ public:
+  column_in_metadata() = default;
+  /**
+   * @brief Construct a new column in metadata object
+   *
+   * @param name Column name
+   */
+  column_in_metadata(std::string_view name) : _name{name} {}
+  /**
+   * @brief Add the children metadata of this column
+   *
+   * @param child The children metadata of this column to add
+   * @return this for chaining
+   */
+  column_in_metadata& add_child(column_in_metadata const& child)
+  {
+    children.push_back(child);
+    return *this;
+  }
+
+  /**
+   * @brief Set the name of this column
+   *
+   * @param name Name of the column
+   * @return this for chaining
+   */
+  column_in_metadata& set_name(std::string const& name) noexcept
+  {
+    _name = name;
+    return *this;
+  }
+
+  /**
+   * @brief Set the nullability of this column
+   *
+   * @param nullable Whether this column is nullable
+   * @return this for chaining
+   */
+  column_in_metadata& set_nullability(bool nullable) noexcept
+  {
+    _nullable = nullable;
+    return *this;
+  }
+
+  /**
+   * @brief Specify that this list column should be encoded as a map in the written file
+   *
+   * The column must have the structure list<struct<key, value>>. This option is invalid otherwise
+   *
+   * @return this for chaining
+   */
+  column_in_metadata& set_list_column_as_map() noexcept
+  {
+    _list_column_is_map = true;
+    return *this;
+  }
+
+  /**
+   * @brief Specifies whether this timestamp column should be encoded using the deprecated int96
+   * physical type. Only valid for the following column types:
+   * timestamp_s, timestamp_ms, timestamp_us, timestamp_ns
+   *
+   * @param req True = use int96 physical type. False = use int64 physical type
+   * @return this for chaining
+   */
+  column_in_metadata& set_int96_timestamps(bool req) noexcept
+  {
+    _use_int96_timestamp = req;
+    return *this;
+  }
+
+  /**
+   * @brief Set the decimal precision of this column. Only valid if this column is a decimal
+   * (fixed-point) type
+   *
+   * @param precision The integer precision to set for this decimal column
+   * @return this for chaining
+   */
+  column_in_metadata& set_decimal_precision(uint8_t precision) noexcept
+  {
+    _decimal_precision = precision;
+    return *this;
+  }
+
+  /**
+   * @brief Set the parquet field id of this column.
+   *
+   * @param field_id The parquet field id to set
+   * @return this for chaining
+   */
+  column_in_metadata& set_parquet_field_id(int32_t field_id) noexcept
+  {
+    _parquet_field_id = field_id;
+    return *this;
+  }
+
+  /**
+   * @brief Specifies whether this column should be written as binary or string data
+   * Only valid for the following column types:
+   * string
+   *
+   * @param binary True = use binary data type. False = use string data type
+   * @return this for chaining
+   */
+  column_in_metadata& set_output_as_binary(bool binary) noexcept
+  {
+    _output_as_binary = binary;
+    return *this;
+  }
+
+  /**
+   * @brief Get reference to a child of this column
+   *
+   * @param i Index of the child to get
+   * @return this for chaining
+   */
+  column_in_metadata& child(size_type i) noexcept { return children[i]; }
+
+  /**
+   * @brief Get const reference to a child of this column
+   *
+   * @param i Index of the child to get
+   * @return this for chaining
+   */
+  [[nodiscard]] column_in_metadata const& child(size_type i) const noexcept { return children[i]; }
+
+  /**
+   * @brief Get the name of this column
+   *
+   * @return The name of this column
+   */
+  [[nodiscard]] std::string get_name() const noexcept { return _name; }
+
+  /**
+   * @brief Get whether nullability has been explicitly set for this column.
+   *
+   * @return Boolean indicating whether nullability has been explicitly set for this column
+   */
+  [[nodiscard]] bool is_nullability_defined() const noexcept { return _nullable.has_value(); }
+
+  /**
+   * @brief Gets the explicitly set nullability for this column.
+   *
+   * @throws If nullability is not explicitly defined for this column.
+   *         Check using `is_nullability_defined()` first.
+   * @return Boolean indicating whether this column is nullable
+   */
+  [[nodiscard]] bool nullable() const { return _nullable.value(); }
+
+  /**
+   * @brief If this is the metadata of a list column, returns whether it is to be encoded as a map.
+   *
+   * @return Boolean indicating whether this column is to be encoded as a map
+   */
+  [[nodiscard]] bool is_map() const noexcept { return _list_column_is_map; }
+
+  /**
+   * @brief Get whether to encode this timestamp column using deprecated int96 physical type
+   *
+   * @return Boolean indicating whether to encode this timestamp column using deprecated int96
+   *         physical type
+   */
+  [[nodiscard]] bool is_enabled_int96_timestamps() const noexcept { return _use_int96_timestamp; }
+
+  /**
+   * @brief Get whether precision has been set for this decimal column
+   *
+   * @return Boolean indicating whether precision has been set for this decimal column
+   */
+  [[nodiscard]] bool is_decimal_precision_set() const noexcept
+  {
+    return _decimal_precision.has_value();
+  }
+
+  /**
+   * @brief Get the decimal precision that was set for this column.
+   *
+   * @throws If decimal precision was not set for this column.
+   *         Check using `is_decimal_precision_set()` first.
+   * @return The decimal precision that was set for this column
+   */
+  [[nodiscard]] uint8_t get_decimal_precision() const { return _decimal_precision.value(); }
+
+  /**
+   * @brief Get whether parquet field id has been set for this column.
+   *
+   * @return Boolean indicating whether parquet field id has been set for this column
+   */
+  [[nodiscard]] bool is_parquet_field_id_set() const noexcept
+  {
+    return _parquet_field_id.has_value();
+  }
+
+  /**
+   * @brief Get the parquet field id that was set for this column.
+   *
+   * @throws If parquet field id was not set for this column.
+   *         Check using `is_parquet_field_id_set()` first.
+   * @return The parquet field id that was set for this column
+   */
+  [[nodiscard]] int32_t get_parquet_field_id() const { return _parquet_field_id.value(); }
+
+  /**
+   * @brief Get the number of children of this column
+   *
+   * @return The number of children of this column
+   */
+  [[nodiscard]] size_type num_children() const noexcept { return children.size(); }
+
+  /**
+   * @brief Get whether to encode this column as binary or string data
+   *
+   * @return Boolean indicating whether to encode this column as binary data
+   */
+  [[nodiscard]] bool is_enabled_output_as_binary() const noexcept { return _output_as_binary; }
+};
+
+/**
+ * @brief Metadata for a table
+ */
+class table_input_metadata {
+ public:
+  table_input_metadata() = default;  // Required by cython
+
+  /**
+   * @brief Construct a new table_input_metadata from a table_view.
+   *
+   * The constructed table_input_metadata has the same structure as the passed table_view
+   *
+   * @param table The table_view to construct metadata for
+   */
+  explicit table_input_metadata(table_view const& table);
+
+  /**
+   * @brief Construct a new table_input_metadata from a table_metadata object.
+   *
+   * The constructed table_input_metadata has the same structure, column names and nullability as
+   * the passed table_metadata.
+   *
+   * @param metadata The table_metadata to construct table_intput_metadata for
+   */
+  explicit table_input_metadata(table_metadata const& metadata);
+
+  std::vector<column_in_metadata> column_metadata;  //!< List of column metadata
+};
+
+/**
+ * @brief Information used while writing partitioned datasets
+ *
+ * This information defines the slice of an input table to write to file. In partitioned dataset
+ * writing, one partition_info struct defines one partition and corresponds to one output file
+ */
+struct partition_info {
+  size_type start_row;  //!< The start row of the partition
+  size_type num_rows;   //!< The number of rows in the partition
+
+  partition_info() = default;
+  /**
+   * @brief Construct a new partition_info
+   *
+   * @param start_row The start row of the partition
+   * @param num_rows The number of rows in the partition
+   */
+  partition_info(size_type start_row, size_type num_rows) : start_row(start_row), num_rows(num_rows)
+  {
+  }
+};
+
+/**
+ * @brief schema element for reader
+ *
+ */
+class reader_column_schema {
+  // Whether to read binary data as a string column
+  bool _convert_binary_to_strings{true};
+
+  std::vector<reader_column_schema> children;
+
+ public:
+  reader_column_schema() = default;
+
+  /**
+   * @brief Construct a new reader column schema object
+   *
+   * @param number_of_children number of child schema objects to default construct
+   */
+  reader_column_schema(size_type number_of_children) { children.resize(number_of_children); }
+
+  /**
+   * @brief Construct a new reader column schema object with a span defining the children
+   *
+   * @param child_span span of child schema objects
+   */
+  reader_column_schema(host_span<reader_column_schema> const& child_span)
+  {
+    children.assign(child_span.begin(), child_span.end());
+  }
+
+  /**
+   * @brief Add the children metadata of this column
+   *
+   * @param child The children metadata of this column to add
+   * @return this for chaining
+   */
+  reader_column_schema& add_child(reader_column_schema const& child)
+  {
+    children.push_back(child);
+    return *this;
+  }
+
+  /**
+   * @brief Get reference to a child of this column
+   *
+   * @param i Index of the child to get
+   * @return this for chaining
+   */
+  [[nodiscard]] reader_column_schema& child(size_type i) { return children[i]; }
+
+  /**
+   * @brief Get const reference to a child of this column
+   *
+   * @param i Index of the child to get
+   * @return this for chaining
+   */
+  [[nodiscard]] reader_column_schema const& child(size_type i) const { return children[i]; }
+
+  /**
+   * @brief Specifies whether this column should be written as binary or string data
+   * Only valid for the following column types:
+   * string, list<int8>
+   *
+   * @param convert_to_string True = convert binary to strings False = return binary
+   * @return this for chaining
+   */
+  reader_column_schema& set_convert_binary_to_strings(bool convert_to_string)
+  {
+    _convert_binary_to_strings = convert_to_string;
+    return *this;
+  }
+
+  /**
+   * @brief Get whether to encode this column as binary or string data
+   *
+   * @return Boolean indicating whether to encode this column as binary data
+   */
+  [[nodiscard]] bool is_enabled_convert_binary_to_strings() const
+  {
+    return _convert_binary_to_strings;
+  }
+
+  /**
+   * @brief Get the number of child objects
+   *
+   * @return number of children
+   */
+  [[nodiscard]] size_t get_num_children() const { return children.size(); }
+};
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/include/cudf/join.hpp b/cpp/include/cudf/join.hpp
new file mode 100644
index 0000000..6c50e1d
--- /dev/null
+++ b/cpp/include/cudf/join.hpp
@@ -0,0 +1,1181 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <optional>
+#include <utility>
+#include <vector>
+
+namespace cudf {
+
+// forward declaration
+namespace hashing::detail {
+template <typename T>
+class MurmurHash3_x86_32;
+}  // namespace hashing::detail
+namespace detail {
+template <typename T>
+class hash_join;
+}  // namespace detail
+
+/**
+ * @addtogroup column_join
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to an
+ * inner join between the specified tables.
+ *
+ * The first returned vector contains the row indices from the left
+ * table that have a match in the right table (in unspecified order).
+ * The corresponding values in the second returned vector are
+ * the matched row indices from the right table.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Result: {{1, 2}, {0, 1}}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Result: {{1}, {0}}
+ * @endcode
+ *
+ * @throw cudf::logic_error if number of elements in `left_keys` or `right_keys`
+ * mismatch.
+ *
+ * @param[in] left_keys The left table
+ * @param[in] right_keys The right table
+ * @param[in] compare_nulls controls whether null join-key values
+ * should match or not.
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing an inner join between two tables with `left_keys` and `right_keys`
+ * as the join keys .
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+inner_join(cudf::table_view const& left_keys,
+           cudf::table_view const& right_keys,
+           null_equality compare_nulls         = null_equality::EQUAL,
+           rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to a
+ * left join between the specified tables.
+ *
+ * The first returned vector contains all the row indices from the left
+ * table (in unspecified order). The corresponding value in the
+ * second returned vector is either (1) the row index of the matched row
+ * from the right table, if there is a match  or  (2) an unspecified
+ * out-of-bounds value.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Result: {{0, 1, 2}, {None, 0, 1}}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Result: {{0, 1, 2}, {None, 0, None}}
+ * @endcode
+ *
+ * @throw cudf::logic_error if number of elements in `left_keys` or `right_keys`
+ * mismatch.
+ *
+ * @param[in] left_keys The left table
+ * @param[in] right_keys The right table
+ * @param[in] compare_nulls controls whether null join-key values
+ * should match or not.
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a left join between two tables with `left_keys` and `right_keys`
+ * as the join keys .
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+left_join(cudf::table_view const& left_keys,
+          cudf::table_view const& right_keys,
+          null_equality compare_nulls         = null_equality::EQUAL,
+          rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to a
+ * full join between the specified tables.
+ *
+ * Taken pairwise, the values from the returned vectors are one of:
+ * (1) row indices corresponding to matching rows from the left and
+ * right tables, (2) a row index and an unspecified out-of-bounds value,
+ * representing a row from one table without a match in the other.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Result: {{0, 1, 2, None}, {None, 0, 1, 2}}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Result: {{0, 1, 2, None, None}, {None, 0, None, 1, 2}}
+ * @endcode
+ *
+ * @throw cudf::logic_error if number of elements in `left_keys` or `right_keys`
+ * mismatch.
+ *
+ * @param[in] left_keys The left table
+ * @param[in] right_keys The right table
+ * @param[in] compare_nulls controls whether null join-key values
+ * should match or not.
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a full join between two tables with `left_keys` and `right_keys`
+ * as the join keys .
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+full_join(cudf::table_view const& left_keys,
+          cudf::table_view const& right_keys,
+          null_equality compare_nulls         = null_equality::EQUAL,
+          rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a vector of row indices corresponding to a left semi-join
+ * between the specified tables.
+ *
+ * The returned vector contains the row indices from the left table
+ * for which there is a matching row in the right table.
+ *
+ * @code{.pseudo}
+ * TableA: {{0, 1, 2}}
+ * TableB: {{1, 2, 3}}
+ * Result: {1, 2}
+ * @endcode
+ *
+ * @param left_keys The left table
+ * @param right_keys The right table
+ * @param compare_nulls Controls whether null join-key values should match or not
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A vector `left_indices` that can be used to construct
+ * the result of performing a left semi join between two tables with
+ * `left_keys` and `right_keys` as the join keys .
+ */
+std::unique_ptr<rmm::device_uvector<size_type>> left_semi_join(
+  cudf::table_view const& left_keys,
+  cudf::table_view const& right_keys,
+  null_equality compare_nulls         = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a vector of row indices corresponding to a left anti join
+ * between the specified tables.
+ *
+ * The returned vector contains the row indices from the left table
+ * for which there is no matching row in the right table.
+ *
+ * @code{.pseudo}
+ * TableA: {{0, 1, 2}}
+ * TableB: {{1, 2, 3}}
+ * Result: {0}
+ * @endcode
+ *
+ * @throw cudf::logic_error if the number of columns in either `left_keys` or `right_keys` is 0
+ *
+ * @param[in] left_keys The left table
+ * @param[in] right_keys The right table
+ * @param[in] compare_nulls controls whether null join-key values
+ * should match or not.
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A column `left_indices` that can be used to construct
+ * the result of performing a left anti join between two tables with
+ * `left_keys` and `right_keys` as the join keys .
+ */
+std::unique_ptr<rmm::device_uvector<size_type>> left_anti_join(
+  cudf::table_view const& left_keys,
+  cudf::table_view const& right_keys,
+  null_equality compare_nulls         = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a cross join on two tables (`left`, `right`)
+ *
+ * The cross join returns the cartesian product of rows from each table.
+ *
+ * @note Warning: This function can easily cause out-of-memory errors. The size of the output is
+ * equal to `left.num_rows() * right.num_rows()`. Use with caution.
+ *
+ * @code{.pseudo}
+ * Left a: {0, 1, 2}
+ * Right b: {3, 4, 5}
+ * Result: { a: {0, 0, 0, 1, 1, 1, 2, 2, 2}, b: {3, 4, 5, 3, 4, 5, 3, 4, 5} }
+ * @endcode
+
+ * @throw cudf::logic_error if the number of columns in either `left` or `right` table is 0
+ *
+ * @param left  The left table
+ * @param right The right table
+ * @param mr    Device memory resource used to allocate the returned table's device memory
+ *
+ * @return     Result of cross joining `left` and `right` tables
+ */
+std::unique_ptr<cudf::table> cross_join(
+  cudf::table_view const& left,
+  cudf::table_view const& right,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief The enum class to specify if any of the input join tables (`build` table and any later
+ * `probe` table) has nulls.
+ *
+ * This is used upon hash_join object construction to specify the existence of nulls in all the
+ * possible input tables. If such null existence is unknown, `YES` should be used as the default
+ * option.
+ */
+enum class nullable_join : bool { YES, NO };
+
+/**
+ * @brief Hash join that builds hash table in creation and probes results in subsequent `*_join`
+ * member functions.
+ *
+ * This class enables the hash join scheme that builds hash table once, and probes as many times as
+ * needed (possibly in parallel).
+ */
+class hash_join {
+ public:
+  using impl_type = typename cudf::detail::hash_join<
+    cudf::hashing::detail::MurmurHash3_x86_32<cudf::hash_value_type>>;  ///< Implementation type
+
+  hash_join() = delete;
+  ~hash_join();
+  hash_join(hash_join const&)            = delete;
+  hash_join(hash_join&&)                 = delete;
+  hash_join& operator=(hash_join const&) = delete;
+  hash_join& operator=(hash_join&&)      = delete;
+
+  /**
+   * @brief Construct a hash join object for subsequent probe calls.
+   *
+   * @note The `hash_join` object must not outlive the table viewed by `build`, else behavior is
+   * undefined.
+   *
+   * @param build The build table, from which the hash table is built
+   * @param compare_nulls Controls whether null join-key values should match or not
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  hash_join(cudf::table_view const& build,
+            null_equality compare_nulls,
+            rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * @copydoc hash_join(cudf::table_view const&, null_equality, rmm::cuda_stream_view)
+   *
+   * @param has_nulls Flag to indicate if there exists any nulls in the `build` table or
+   *        any `probe` table that will be used later for join
+   */
+  hash_join(cudf::table_view const& build,
+            nullable_join has_nulls,
+            null_equality compare_nulls,
+            rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * Returns the row indices that can be used to construct the result of performing
+   * an inner join between two tables. @see cudf::inner_join(). Behavior is undefined if the
+   * provided `output_size` is smaller than the actual output size.
+   *
+   * @param probe The probe table, from which the tuples are probed
+   * @param output_size Optional value which allows users to specify the exact output size
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned table and columns' device
+   * memory.
+   *
+   * @throw cudf::logic_error If the input probe table has nulls while this hash_join object was not
+   * constructed with null check.
+   *
+   * @return A pair of columns [`left_indices`, `right_indices`] that can be used to construct
+   * the result of performing an inner join between two tables with `build` and `probe`
+   * as the join keys .
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  inner_join(cudf::table_view const& probe,
+             std::optional<std::size_t> output_size = {},
+             rmm::cuda_stream_view stream           = cudf::get_default_stream(),
+             rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource()) const;
+
+  /**
+   * Returns the row indices that can be used to construct the result of performing
+   * a left join between two tables. @see cudf::left_join(). Behavior is undefined if the
+   * provided `output_size` is smaller than the actual output size.
+   *
+   * @param probe The probe table, from which the tuples are probed
+   * @param output_size Optional value which allows users to specify the exact output size
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned table and columns' device
+   * memory.
+   *
+   * @throw cudf::logic_error If the input probe table has nulls while this hash_join object was not
+   * constructed with null check.
+   *
+   * @return A pair of columns [`left_indices`, `right_indices`] that can be used to construct
+   * the result of performing a left join between two tables with `build` and `probe`
+   * as the join keys .
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  left_join(cudf::table_view const& probe,
+            std::optional<std::size_t> output_size = {},
+            rmm::cuda_stream_view stream           = cudf::get_default_stream(),
+            rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource()) const;
+
+  /**
+   * Returns the row indices that can be used to construct the result of performing
+   * a full join between two tables. @see cudf::full_join(). Behavior is undefined if the
+   * provided `output_size` is smaller than the actual output size.
+   *
+   * @param probe The probe table, from which the tuples are probed
+   * @param output_size Optional value which allows users to specify the exact output size
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned table and columns' device
+   * memory.
+   *
+   * @throw cudf::logic_error If the input probe table has nulls while this hash_join object was not
+   * constructed with null check.
+   *
+   * @return A pair of columns [`left_indices`, `right_indices`] that can be used to construct
+   * the result of performing a full join between two tables with `build` and `probe`
+   * as the join keys .
+   */
+  std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+            std::unique_ptr<rmm::device_uvector<size_type>>>
+  full_join(cudf::table_view const& probe,
+            std::optional<std::size_t> output_size = {},
+            rmm::cuda_stream_view stream           = cudf::get_default_stream(),
+            rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource()) const;
+
+  /**
+   * Returns the exact number of matches (rows) when performing an inner join with the specified
+   * probe table.
+   *
+   * @param probe The probe table, from which the tuples are probed
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   *
+   * @throw cudf::logic_error If the input probe table has nulls while this hash_join object was not
+   * constructed with null check.
+   *
+   * @return The exact number of output when performing an inner join between two tables with
+   * `build` and `probe` as the join keys .
+   */
+  [[nodiscard]] std::size_t inner_join_size(
+    cudf::table_view const& probe, rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * Returns the exact number of matches (rows) when performing a left join with the specified probe
+   * table.
+   *
+   * @param probe The probe table, from which the tuples are probed
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   *
+   * @throw cudf::logic_error If the input probe table has nulls while this hash_join object was not
+   * constructed with null check.
+   *
+   * @return The exact number of output when performing a left join between two tables with `build`
+   * and `probe` as the join keys .
+   */
+  [[nodiscard]] std::size_t left_join_size(
+    cudf::table_view const& probe, rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * Returns the exact number of matches (rows) when performing a full join with the specified probe
+   * table.
+   *
+   * @param probe The probe table, from which the tuples are probed
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the intermediate table and columns' device
+   * memory.
+   *
+   * @throw cudf::logic_error If the input probe table has nulls while this hash_join object was not
+   * constructed with null check.
+   *
+   * @return The exact number of output when performing a full join between two tables with `build`
+   * and `probe` as the join keys .
+   */
+  std::size_t full_join_size(
+    cudf::table_view const& probe,
+    rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource()) const;
+
+ private:
+  const std::unique_ptr<impl_type const> _impl;
+};
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to all pairs
+ * of rows between the specified tables where the predicate evaluates to true.
+ *
+ * The first returned vector contains the row indices from the left
+ * table that have a match in the right table (in unspecified order).
+ * The corresponding values in the second returned vector are
+ * the matched row indices from the right table.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Expression: Left.Column_0 == Right.Column_0
+ * Result: {{1, 2}, {0, 1}}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Expression: (Left.Column_0 == Right.Column_0) AND (Left.Column_1 == Right.Column_1)
+ * Result: {{1}, {0}}
+ * @endcode
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param output_size Optional value which allows users to specify the exact output size
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a conditional inner join between two tables `left` and `right` .
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_inner_join(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  std::optional<std::size_t> output_size = {},
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to all pairs
+ * of rows between the specified tables where the predicate evaluates to true,
+ * or null matches for rows in left that have no match in right.
+ *
+ * The first returned vector contains all the row indices from the left
+ * table (in unspecified order). The corresponding value in the
+ * second returned vector is either (1) the row index of the matched row
+ * from the right table, if there is a match or (2) an unspecified
+ * out-of-bounds value.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Expression: Left.Column_0 == Right.Column_0
+ * Result: {{0, 1, 2}, {None, 0, 1}}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Expression: (Left.Column_0 == Right.Column_0) AND (Left.Column_1 == Right.Column_1)
+ * Result: {{0, 1, 2}, {None, 0, None}}
+ * @endcode
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param output_size Optional value which allows users to specify the exact output size
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a conditional left join between two tables `left` and `right` .
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_left_join(table_view const& left,
+                      table_view const& right,
+                      ast::expression const& binary_predicate,
+                      std::optional<std::size_t> output_size = {},
+                      rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to all pairs
+ * of rows between the specified tables where the predicate evaluates to true,
+ * or null matches for rows in either table that have no match in the other.
+ *
+ * Taken pairwise, the values from the returned vectors are one of:
+ * (1) row indices corresponding to matching rows from the left and
+ * right tables, (2) a row index and an unspecified out-of-bounds value,
+ * representing a row from one table without a match in the other.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Expression: Left.Column_0 == Right.Column_0
+ * Result: {{0, 1, 2, None}, {None, 0, 1, 2}}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Expression: (Left.Column_0 == Right.Column_0) AND (Left.Column_1 == Right.Column_1)
+ * Result: {{0, 1, 2, None, None}, {None, 0, None, 1, 2}}
+ * @endcode
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a conditional full join between two tables `left` and `right` .
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_full_join(table_view const& left,
+                      table_view const& right,
+                      ast::expression const& binary_predicate,
+                      rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns an index vector corresponding to all rows in the left table
+ * for which there exists some row in the right table where the predicate
+ * evaluates to true.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Expression: Left.Column_0 == Right.Column_0
+ * Result: {1, 2}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Expression: (Left.Column_0 == Right.Column_0) AND (Left.Column_1 == Right.Column_1)
+ * Result: {1}
+ * @endcode
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param output_size Optional value which allows users to specify the exact output size
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A vector `left_indices` that can be used to construct the result of
+ * performing a conditional left semi join between two tables `left` and
+ * `right` .
+ */
+std::unique_ptr<rmm::device_uvector<size_type>> conditional_left_semi_join(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  std::optional<std::size_t> output_size = {},
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns an index vector corresponding to all rows in the left table
+ * for which there does not exist any row in the right table where the
+ * predicate evaluates to true.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @code{.pseudo}
+ * Left: {{0, 1, 2}}
+ * Right: {{1, 2, 3}}
+ * Expression: Left.Column_0 == Right.Column_0
+ * Result: {0}
+ *
+ * Left: {{0, 1, 2}, {3, 4, 5}}
+ * Right: {{1, 2, 3}, {4, 6, 7}}
+ * Expression: (Left.Column_0 == Right.Column_0) AND (Left.Column_1 == Right.Column_1)
+ * Result: {0, 2}
+ * @endcode
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param output_size Optional value which allows users to specify the exact output size
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A vector `left_indices` that can be used to construct the result of
+ * performing a conditional left anti join between two tables `left` and
+ * `right` .
+ */
+std::unique_ptr<rmm::device_uvector<size_type>> conditional_left_anti_join(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  std::optional<std::size_t> output_size = {},
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to all pairs of
+ * rows between the specified tables where the columns of the equality table
+ * are equal and the predicate evaluates to true on the conditional tables.
+ *
+ * The first returned vector contains the row indices from the left
+ * table that have a match in the right table (in unspecified order).
+ * The corresponding values in the second returned vector are
+ * the matched row indices from the right table.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output. It is the user's
+ * responsibility to choose a suitable compare_nulls value AND use appropriate
+ * null-safe operators in the expression.
+ *
+ * If the provided output size or per-row counts are incorrect, behavior is undefined.
+ *
+ * @code{.pseudo}
+ * left_equality: {{0, 1, 2}}
+ * right_equality: {{1, 2, 3}}
+ * left_conditional: {{4, 4, 4}}
+ * right_conditional: {{3, 4, 5}}
+ * Expression: Left.Column_0 > Right.Column_0
+ * Result: {{1}, {0}}
+ * @endcode
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param output_size_data An optional pair of values indicating the exact output size and the
+ * number of matches for each row in the larger of the two input tables, left or right (may be
+ * precomputed using the corresponding mixed_inner_join_size API).
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a mixed inner join between the four input tables.
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_inner_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls = null_equality::EQUAL,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data = {},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to all pairs of
+ * rows between the specified tables where the columns of the equality table
+ * are equal and the predicate evaluates to true on the conditional tables,
+ * or null matches for rows in left that have no match in right.
+ *
+ * The first returned vector contains the row indices from the left
+ * tables that have a match in the right tables (in unspecified order).
+ * The corresponding value in the second returned vector is either (1)
+ * the row index of the matched row from the right tables, or (2) an
+ * unspecified out-of-bounds value.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output. It is the user's
+ * responsibility to choose a suitable compare_nulls value AND use appropriate
+ * null-safe operators in the expression.
+ *
+ * If the provided output size or per-row counts are incorrect, behavior is undefined.
+ *
+ * @code{.pseudo}
+ * left_equality: {{0, 1, 2}}
+ * right_equality: {{1, 2, 3}}
+ * left_conditional: {{4, 4, 4}}
+ * right_conditional: {{3, 4, 5}}
+ * Expression: Left.Column_0 > Right.Column_0
+ * Result: {{0, 1, 2}, {None, 0, None}}
+ * @endcode
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param output_size_data An optional pair of values indicating the exact output size and the
+ * number of matches for each row in the larger of the two input tables, left or right (may be
+ * precomputed using the corresponding mixed_left_join_size API).
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a mixed left join between the four input tables.
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_left_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls = null_equality::EQUAL,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data = {},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a pair of row index vectors corresponding to all pairs of
+ * rows between the specified tables where the columns of the equality table
+ * are equal and the predicate evaluates to true on the conditional tables,
+ * or null matches for rows in either pair of tables that have no matches in
+ * the other pair.
+ *
+ * Taken pairwise, the values from the returned vectors are one of:
+ * (1) row indices corresponding to matching rows from the left and
+ * right tables, (2) a row index and an unspecified out-of-bounds value,
+ * representing a row from one table without a match in the other.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output. It is the user's
+ * responsibility to choose a suitable compare_nulls value AND use appropriate
+ * null-safe operators in the expression.
+ *
+ * If the provided output size or per-row counts are incorrect, behavior is undefined.
+ *
+ * @code{.pseudo}
+ * left_equality: {{0, 1, 2}}
+ * right_equality: {{1, 2, 3}}
+ * left_conditional: {{4, 4, 4}}
+ * right_conditional: {{3, 4, 5}}
+ * Expression: Left.Column_0 > Right.Column_0
+ * Result: {{0, 1, 2, None, None}, {None, 0, None, 1, 2}}
+ * @endcode
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param output_size_data An optional pair of values indicating the exact output size and the
+ * number of matches for each row in the larger of the two input tables, left or right (may be
+ * precomputed using the corresponding mixed_full_join_size API).
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a mixed full join between the four input tables.
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_full_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls = null_equality::EQUAL,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data = {},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns an index vector corresponding to all rows in the left tables
+ * where the columns of the equality table are equal and the predicate
+ * evaluates to true on the conditional tables.
+ *
+ * If the provided predicate returns NULL for a pair of rows (left, right), the
+ * left row is not included in the output. It is the user's responsibility to
+ * choose a suitable compare_nulls value AND use appropriate null-safe
+ * operators in the expression.
+ *
+ * If the provided output size or per-row counts are incorrect, behavior is undefined.
+ *
+ * @code{.pseudo}
+ * left_equality: {{0, 1, 2}}
+ * right_equality: {{1, 2, 3}}
+ * left_conditional: {{4, 4, 4}}
+ * right_conditional: {{3, 4, 5}}
+ * Expression: Left.Column_0 > Right.Column_0
+ * Result: {1}
+ * @endcode
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param output_size_data An optional pair of values indicating the exact output size and the
+ * number of matches for each row in the larger of the two input tables, left or right (may be
+ * precomputed using the corresponding mixed_full_join_size API).
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a mixed full join between the four input tables.
+ */
+std::unique_ptr<rmm::device_uvector<size_type>> mixed_left_semi_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls = null_equality::EQUAL,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data = {},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns an index vector corresponding to all rows in the left tables
+ * for which there is no row in the right tables where the columns of the
+ * equality table are equal and the predicate evaluates to true on the
+ * conditional tables.
+ *
+ * If the provided predicate returns NULL for a pair of rows (left, right), the
+ * left row is not included in the output. It is the user's responsibility to
+ * choose a suitable compare_nulls value AND use appropriate null-safe
+ * operators in the expression.
+ *
+ * If the provided output size or per-row counts are incorrect, behavior is undefined.
+ *
+ * @code{.pseudo}
+ * left_equality: {{0, 1, 2}}
+ * right_equality: {{1, 2, 3}}
+ * left_conditional: {{4, 4, 4}}
+ * right_conditional: {{3, 4, 5}}
+ * Expression: Left.Column_0 > Right.Column_0
+ * Result: {0, 2}
+ * @endcode
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param output_size_data An optional pair of values indicating the exact output size and the
+ * number of matches for each row in the larger of the two input tables, left or right (may be
+ * precomputed using the corresponding mixed_full_join_size API).
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair of vectors [`left_indices`, `right_indices`] that can be used to construct
+ * the result of performing a mixed full join between the four input tables.
+ */
+std::unique_ptr<rmm::device_uvector<size_type>> mixed_left_anti_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls = null_equality::EQUAL,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data = {},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a
+ * mixed inner join between the specified tables where the columns of the
+ * equality table are equal and the predicate evaluates to true on the
+ * conditional tables.
+ *
+ * If the provided predicate returns NULL for a pair of rows (left, right),
+ * that pair is not included in the output. It is the user's responsibility to
+ * choose a suitable compare_nulls value AND use appropriate null-safe
+ * operators in the expression.
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair containing the size that would result from performing the
+ * requested join and the number of matches for each row in one of the two
+ * tables. Which of the two tables is an implementation detail and should not
+ * be relied upon, simply passed to the corresponding `mixed_inner_join` API as
+ * is.
+ */
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_inner_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls         = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a
+ * mixed left join between the specified tables where the columns of the
+ * equality table are equal and the predicate evaluates to true on the
+ * conditional tables.
+ *
+ * If the provided predicate returns NULL for a pair of rows (left, right),
+ * that pair is not included in the output. It is the user's responsibility to
+ * choose a suitable compare_nulls value AND use appropriate null-safe
+ * operators in the expression.
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair containing the size that would result from performing the
+ * requested join and the number of matches for each row in one of the two
+ * tables. Which of the two tables is an implementation detail and should not
+ * be relied upon, simply passed to the corresponding `mixed_left_join` API as
+ * is.
+ */
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_left_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls         = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a mixed
+ * left semi join between the specified tables where the columns of the
+ * equality table are equal and the predicate evaluates to true on the
+ * conditional tables.
+ *
+ * If the provided predicate returns NULL for a pair of rows (left, right),
+ * that pair is not included in the output. It is the user's responsibility to
+ * choose a suitable compare_nulls value AND use appropriate null-safe
+ * operators in the expression.
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair containing the size that would result from performing the
+ * requested join and the number of matches for each row in one of the two
+ * tables. Which of the two tables is an implementation detail and should not
+ * be relied upon, simply passed to the corresponding `mixed_left_join` API as
+ * is.
+ */
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_left_semi_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls         = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a mixed
+ * left anti join between the specified tables.
+ *
+ * If the provided predicate returns NULL for a pair of rows (left, right),
+ * that pair is not included in the output. It is the user's responsibility to
+ * choose a suitable compare_nulls value AND use appropriate null-safe
+ * operators in the expression.
+ *
+ * @throw cudf::logic_error If the binary predicate outputs a non-boolean result.
+ * @throw cudf::logic_error If the number of rows in left_equality and left_conditional do not
+ * match.
+ * @throw cudf::logic_error If the number of rows in right_equality and right_conditional do not
+ * match.
+ *
+ * @param left_equality The left table used for the equality join
+ * @param right_equality The right table used for the equality join
+ * @param left_conditional The left table used for the conditional join
+ * @param right_conditional The right table used for the conditional join
+ * @param binary_predicate The condition on which to join
+ * @param compare_nulls Whether or not null values join to each other or not
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return A pair containing the size that would result from performing the
+ * requested join and the number of matches for each row in one of the two
+ * tables. Which of the two tables is an implementation detail and should not
+ * be relied upon, simply passed to the corresponding `mixed_left_join` API as
+ * is.
+ */
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_left_anti_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls         = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a
+ * conditional inner join between the specified tables where the predicate
+ * evaluates to true.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return The size that would result from performing the requested join
+ */
+std::size_t conditional_inner_join_size(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a
+ * conditional left join between the specified tables where the predicate
+ * evaluates to true.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return The size that would result from performing the requested join
+ */
+std::size_t conditional_left_join_size(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a
+ * conditional left semi join between the specified tables where the predicate
+ * evaluates to true.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return The size that would result from performing the requested join
+ */
+std::size_t conditional_left_semi_join_size(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the exact number of matches (rows) when performing a
+ * conditional left anti join between the specified tables where the predicate
+ * evaluates to true.
+ *
+ * If the provided predicate returns NULL for a pair of rows
+ * (left, right), that pair is not included in the output.
+ *
+ * @throw cudf::logic_error if the binary predicate outputs a non-boolean result.
+ *
+ * @param left The left table
+ * @param right The right table
+ * @param binary_predicate The condition on which to join
+ * @param mr Device memory resource used to allocate the returned table and columns' device memory
+ *
+ * @return The size that would result from performing the requested join
+ */
+std::size_t conditional_left_anti_join_size(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/labeling/label_bins.hpp b/cpp/include/cudf/labeling/label_bins.hpp
new file mode 100644
index 0000000..2776f50
--- /dev/null
+++ b/cpp/include/cudf/labeling/label_bins.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+
+/**
+ * @addtogroup label_bins
+ * @{
+ * @file
+ * @brief APIs for labeling values by bin.
+ */
+
+/**
+ * @brief Enum used to define whether or not bins include their boundary points.
+ */
+enum class inclusive { YES, NO };
+
+/**
+ * @brief Labels elements based on membership in the specified bins.
+ *
+ * A bin `i` is defined by `left_edges[i], right_edges[i]`. Whether the edges are inclusive or
+ * not is determined by `left_inclusive` and `right_inclusive`, respectively.
+ *
+ * A value `input[j]` belongs to bin `i` if `value[j]` is contained in the range `left_edges[i],
+ * right_edges[i]` (with the specified inclusiveness) and `label[j] == i`. If  `input[j]` does not
+ * belong to any bin, then `label[j]` is NULL.
+ *
+ * Notes:
+ *   - If an empty set of edges is provided, all elements in `input` are labeled NULL.
+ *   - NULL elements in `input` belong to no bin and their corresponding label is NULL.
+ *   - NaN elements in `input` belong to no bin and their corresponding label is NULL.
+ *   - Bins must be provided in monotonically increasing order, otherwise behavior is undefined.
+ *   - If two or more bins overlap, behavior is undefined.
+ *
+ * @throws cudf::logic_error if `input.type() == left_edges.type() == right_edges.type()` is
+ * violated.
+ * @throws cudf::logic_error if `left_edges.size() != right_edges.size()`
+ * @throws cudf::logic_error if `left_edges.has_nulls()` or `right_edges.has_nulls()`
+ *
+ * @param input The input elements to label according to the specified bins.
+ * @param left_edges Values of the left edge of each bin.
+ * @param left_inclusive Whether or not the left edge is inclusive.
+ * @param right_edges Value of the right edge of each bin.
+ * @param right_inclusive Whether or not the right edge is inclusive.
+ * @param mr Device memory resource used to allocate the returned column's device.
+ * @return The integer labels of the elements in `input` according to the specified bins.
+ */
+std::unique_ptr<column> label_bins(
+  column_view const& input,
+  column_view const& left_edges,
+  inclusive left_inclusive,
+  column_view const& right_edges,
+  inclusive right_inclusive,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/combine.hpp b/cpp/include/cudf/lists/combine.hpp
new file mode 100644
index 0000000..0bc7682
--- /dev/null
+++ b/cpp/include/cudf/lists/combine.hpp
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+
+//! Lists column APIs
+namespace lists {
+/**
+ * @addtogroup lists_combine
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Flag to specify whether a null list element will be ignored from concatenation, or the
+ * entire concatenation result involving null list elements will be a null element.
+ */
+enum class concatenate_null_policy { IGNORE, NULLIFY_OUTPUT_ROW };
+
+/**
+ * @brief Row-wise concatenating multiple lists columns into a single lists column.
+ *
+ * The output column is generated by concatenating the elements within each row of the input
+ * table. If any row of the input table contains null elements, the concatenation process will
+ * either ignore those null elements, or will simply set the entire resulting row to be a null
+ * element.
+ *
+ * @code{.pseudo}
+ * s1 = [{0, 1}, {2, 3, 4}, {5}, {}, {6, 7}]
+ * s2 = [{8}, {9}, {}, {10, 11, 12}, {13, 14, 15, 16}]
+ * r = lists::concatenate_rows(s1, s2)
+ * r is now [{0, 1, 8}, {2, 3, 4, 9}, {5}, {10, 11, 12}, {6, 7, 13, 14, 15, 16}]
+ * @endcode
+ *
+ * @throws cudf::logic_error if any column of the input table is not a lists column.
+ * @throws cudf::logic_error if all lists columns do not have the same type.
+ *
+ * @param input Table of lists to be concatenated.
+ * @param null_policy The parameter to specify whether a null list element will be ignored from
+ *        concatenation, or any concatenation involving a null element will result in a null list.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return A new column in which each row is a list resulted from concatenating all list elements in
+ *         the corresponding row of the input table.
+ */
+std::unique_ptr<column> concatenate_rows(
+  table_view const& input,
+  concatenate_null_policy null_policy = concatenate_null_policy::IGNORE,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Concatenating multiple lists on the same row of a lists column into a single list.
+ *
+ * Given a lists column where each row in the column is a list of lists of entries, an output lists
+ * column is generated by concatenating all the list elements at the same row together. If any row
+ * contains null list elements, the concatenation process will either ignore those null elements, or
+ * will simply set the entire resulting row to be a null element.
+ *
+ * @code{.pseudo}
+ * l = [ [{1, 2}, {3, 4}, {5}], [{6}, {}, {7, 8, 9}] ]
+ * r = lists::concatenate_list_elements(l);
+ * r is [ {1, 2, 3, 4, 5}, {6, 7, 8, 9} ]
+ * @endcode
+ *
+ * @throws std::invalid_argument if the input column is not at least two-level depth lists column
+ *         (i.e., each row must be a list of lists).
+ *
+ * @param input The lists column containing lists of list elements to concatenate.
+ * @param null_policy The parameter to specify whether a null list element will be ignored from
+ *        concatenation, or any concatenation involving a null element will result in a null list.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return A new column in which each row is a list resulted from concatenating all list elements in
+ *         the corresponding row of the input lists column.
+ */
+std::unique_ptr<column> concatenate_list_elements(
+  column_view const& input,
+  concatenate_null_policy null_policy = concatenate_null_policy::IGNORE,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/contains.hpp b/cpp/include/cudf/lists/contains.hpp
new file mode 100644
index 0000000..21c2ca1
--- /dev/null
+++ b/cpp/include/cudf/lists/contains.hpp
@@ -0,0 +1,174 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace lists {
+/**
+ * @addtogroup lists_contains
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Create a column of `bool` values indicating whether the specified scalar
+ * is an element of each row of a list column.
+ *
+ * The output column has as many elements as the input `lists` column.
+ * Output `column[i]` is set to true if the lists row `lists[i]` contains the value
+ * specified in `search_key`. Otherwise, it is set to false.
+ *
+ * Output `column[i]` is set to null if one or more of the following are true:
+ *   1. The search key `search_key` is null
+ *   2. The list row `lists[i]` is null
+ *
+ * @param lists Lists column whose `n` rows are to be searched
+ * @param search_key The scalar key to be looked up in each list row
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return BOOL8 column of `n` rows with the result of the lookup
+ */
+std::unique_ptr<column> contains(
+  cudf::lists_column_view const& lists,
+  cudf::scalar const& search_key,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a column of `bool` values indicating whether the list rows of the first
+ * column contain the corresponding values in the second column
+ *
+ * The output column has as many elements as the input `lists` column.
+ * Output `column[i]` is set to true if the lists row `lists[i]` contains the value
+ * in `search_keys[i]`. Otherwise, it is set to false.
+ *
+ * Output `column[i]` is set to null if one or more of the following are true:
+ *   1. The row `search_keys[i]` is null
+ *   2. The list row `lists[i]` is null
+ *
+ * @param lists Lists column whose `n` rows are to be searched
+ * @param search_keys Column of elements to be looked up in each list row
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return BOOL8 column of `n` rows with the result of the lookup
+ */
+std::unique_ptr<column> contains(
+  cudf::lists_column_view const& lists,
+  cudf::column_view const& search_keys,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a column of `bool` values indicating whether each row in the `lists` column
+ * contains at least one null element.
+ *
+ * The output column has as many elements as the input `lists` column.
+ * Output `column[i]` is set to null if the row `lists[i]` is null.
+ * Otherwise, `column[i]` is set to a non-null boolean value, depending on whether that list
+ * contains a null element.
+ *
+ * A row with an empty list will always return false.
+ * Nulls inside non-null nested elements (such as lists or structs) are not considered.
+ *
+ * @param lists Lists column whose `n` rows are to be searched
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return BOOL8 column of `n` rows with the result of the lookup
+ */
+std::unique_ptr<column> contains_nulls(
+  cudf::lists_column_view const& lists,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Option to choose whether `index_of()` returns the first or last match
+ * of a search key in a list row
+ */
+enum class duplicate_find_option : int32_t {
+  FIND_FIRST = 0,  ///< Finds first instance of a search key in a list row.
+  FIND_LAST        ///< Finds last instance of a search key in a list row.
+};
+
+/**
+ * @brief Create a column of values indicating the position of a search key
+ * within each list row in the `lists` column
+ *
+ * The output column has as many elements as there are rows in the input `lists` column.
+ * Output `column[i]` contains a 0-based index indicating the position of the search key
+ * in each list, counting from the beginning of the list.
+ * Note:
+ *   1. If the `search_key` is null, all output rows are set to null.
+ *   2. If the row `lists[i]` is null, `output[i]` is also null.
+ *   3. If the row `lists[i]` does not contain the `search_key`, `output[i]` is set to `-1`.
+ *   4. In all other cases, `output[i]` is set to a non-negative `size_type` index.
+ *
+ * If the `find_option` is set to `FIND_FIRST`, the position of the first match for
+ * `search_key` is returned.
+ * If `find_option == FIND_LAST`, the position of the last match in the list row is
+ * returned.
+ *
+ * @throw cudf::data_type_error If `search_keys` type does not match the element type in `lists`
+ *
+ * @param lists Lists column whose `n` rows are to be searched
+ * @param search_key The scalar key to be looked up in each list row
+ * @param find_option Whether to return the position of the first match (`FIND_FIRST`) or
+ * last (`FIND_LAST`)
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return column of `n` rows with the location of the `search_key`
+ */
+std::unique_ptr<column> index_of(
+  cudf::lists_column_view const& lists,
+  cudf::scalar const& search_key,
+  duplicate_find_option find_option   = duplicate_find_option::FIND_FIRST,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a column of values indicating the position of a search key
+ * row within the corresponding list row in the `lists` column
+ *
+ * The output column has as many elements as there are rows in the input `lists` column.
+ * Output `column[i]` contains a 0-based index indicating the position of each search key
+ * row in its corresponding list row, counting from the beginning of the list.
+ * Note:
+ *   1. If `search_keys[i]` is null, `output[i]` is also null.
+ *   2. If the row `lists[i]` is null, `output[i]` is also null.
+ *   3. If the row `lists[i]` does not contain `search_key[i]`, `output[i]` is set to `-1`.
+ *   4. In all other cases, `output[i]` is set to a non-negative `size_type` index.
+ *
+ * If the `find_option` is set to `FIND_FIRST`, the position of the first match for
+ * `search_key` is returned.
+ * If `find_option == FIND_LAST`, the position of the last match in the list row is
+ * returned.
+ *
+ * @throw cudf::logic_error If `search_keys` does not match `lists` in its number of rows
+ * @throw cudf::data_type_error If `search_keys` type does not match the element type in `lists`
+ *
+ * @param lists Lists column whose `n` rows are to be searched
+ * @param search_keys A column of search keys to be looked up in each corresponding row of
+ * `lists`
+ * @param find_option Whether to return the position of the first match (`FIND_FIRST`) or
+ * last (`FIND_LAST`)
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return column of `n` rows with the location of the `search_key`
+ */
+std::unique_ptr<column> index_of(
+  cudf::lists_column_view const& lists,
+  cudf::column_view const& search_keys,
+  duplicate_find_option find_option   = duplicate_find_option::FIND_FIRST,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/count_elements.hpp b/cpp/include/cudf/lists/count_elements.hpp
new file mode 100644
index 0000000..552ba05
--- /dev/null
+++ b/cpp/include/cudf/lists/count_elements.hpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace lists {
+/**
+ * @addtogroup lists_elements
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a numeric column containing the number of rows in
+ * each list element in the given lists column.
+ *
+ * The output column will have the same number of rows as the
+ * input lists column. Each `output[i]` will be `input[i].size()`.
+ *
+ * @code{.pseudo}
+ * l = { {1, 2, 3}, {4}, {5, 6} }
+ * r = count_elements(l)
+ * r is now {3, 1, 2}
+ * @endcode
+ *
+ * Any null input element will result in a corresponding null entry
+ * in the output column.
+ *
+ * @param input Input lists column
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column with the number of elements for each row
+ */
+std::unique_ptr<column> count_elements(
+  lists_column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of lists_elements group
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/combine.hpp b/cpp/include/cudf/lists/detail/combine.hpp
new file mode 100644
index 0000000..4bc45e4
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/combine.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/combine.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+/**
+ * @copydoc cudf::lists::concatenate_rows
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> concatenate_rows(table_view const& input,
+                                         concatenate_null_policy null_policy,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::concatenate_list_elements
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> concatenate_list_elements(column_view const& input,
+                                                  concatenate_null_policy null_policy,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/concatenate.hpp b/cpp/include/cudf/lists/detail/concatenate.hpp
new file mode 100644
index 0000000..a1f149d
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/concatenate.hpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief Returns a single column by concatenating the given vector of
+ * lists columns.
+ *
+ * @code{.pseudo}
+ * s1 = [{0, 1}, {2, 3, 4, 5, 6}]
+ * s2 = [{7, 8, 9}, {10, 11}]
+ * r = concatenate(s1, s2)
+ * r is now [{0, 1}, {2, 3, 4, 5, 6}, {7, 8, 9}, {10, 11}]
+ * @endcode
+ *
+ * @param columns Vector of lists columns to concatenate.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with concatenated results.
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/contains.hpp b/cpp/include/cudf/lists/detail/contains.hpp
new file mode 100644
index 0000000..58ec18c
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/contains.hpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/lists/contains.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @copydoc cudf::lists::index_of(cudf::lists_column_view const&,
+ *                                cudf::scalar const&,
+ *                                duplicate_find_option,
+ *                                rmm::mr::device_memory_resource*)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> index_of(cudf::lists_column_view const& lists,
+                                 cudf::scalar const& search_key,
+                                 cudf::lists::duplicate_find_option find_option,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::index_of(cudf::lists_column_view const&,
+ *                                cudf::column_view const&,
+ *                                duplicate_find_option,
+ *                                rmm::mr::device_memory_resource*)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> index_of(cudf::lists_column_view const& lists,
+                                 cudf::column_view const& search_keys,
+                                 cudf::lists::duplicate_find_option find_option,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::contains(cudf::lists_column_view const&,
+ *                                cudf::scalar const&,
+ *                                rmm::mr::device_memory_resource*)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> contains(cudf::lists_column_view const& lists,
+                                 cudf::scalar const& search_key,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::contains(cudf::lists_column_view const&,
+ *                                cudf::column_view const&,
+ *                                rmm::mr::device_memory_resource*)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> contains(cudf::lists_column_view const& lists,
+                                 cudf::column_view const& search_keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/copying.hpp b/cpp/include/cudf/lists/detail/copying.hpp
new file mode 100644
index 0000000..3760294
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/copying.hpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief Returns a new lists column created from a subset of the
+ * lists column. The subset of lists selected is between start (inclusive)
+ * and end (exclusive).
+ *
+ * @code{.pseudo}
+ * Example:
+ * s1 = {{1, 2, 3}, {4, 5}, {6, 7}, {}, {8, 9}}
+ * s2 = slice( s1, 1, 4)
+ * s2 is {{4, 5}, {6, 7}, {}}
+ * @endcode
+ *
+ * @param lists Lists instance for this operation.
+ * @param start Index to first list to select in the column
+ * @param end One past the index to last list to select in the column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New lists column of size (end - start)
+ */
+std::unique_ptr<cudf::column> copy_slice(lists_column_view const& lists,
+                                         size_type start,
+                                         size_type end,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/dremel.hpp b/cpp/include/cudf/lists/detail/dremel.hpp
new file mode 100644
index 0000000..d36a409
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/dremel.hpp
@@ -0,0 +1,216 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+namespace cudf::detail {
+
+/**
+ * @brief Device view for `dremel_data`.
+ *
+ * @see the `dremel_data` struct for more info.
+ */
+struct dremel_device_view {
+  size_type const* offsets;
+  uint8_t const* rep_levels;
+  uint8_t const* def_levels;
+  size_type const leaf_data_size;
+  uint8_t const max_def_level;
+};
+
+/**
+ * @brief Dremel data that describes one nested type column
+ *
+ * @see get_dremel_data() for more info.
+ */
+struct dremel_data {
+  rmm::device_uvector<size_type> dremel_offsets;
+  rmm::device_uvector<uint8_t> rep_level;
+  rmm::device_uvector<uint8_t> def_level;
+
+  size_type const leaf_data_size;
+  uint8_t const max_def_level;
+
+  operator dremel_device_view() const
+  {
+    return dremel_device_view{
+      dremel_offsets.data(), rep_level.data(), def_level.data(), leaf_data_size, max_def_level};
+  }
+};
+
+/**
+ * @brief Get the dremel offsets and repetition and definition levels for a LIST column
+ *
+ * Dremel is a query system created by Google for ad hoc data analysis. The Dremel engine is
+ * described in depth in the paper "Dremel: Interactive Analysis of Web-Scale
+ * Datasets" (https://research.google/pubs/pub36632/). One of the key components of Dremel
+ * is an encoding that converts record-like data into a columnar store for efficient memory
+ * accesses. The Parquet file format uses Dremel encoding to handle nested data, so libcudf
+ * requires some facilities for working with this encoding. Furthermore, libcudf leverages
+ * Dremel encoding as a means for performing lexicographic comparisons of nested columns.
+ *
+ * Dremel encoding is built around two concepts, the repetition and definition levels.
+ * Since describing them thoroughly is out of scope for this docstring, here are a couple of
+ * blogs that provide useful background:
+ *
+ * http://www.goldsborough.me/distributed-systems/2019/05/18/21-09-00-a_look_at_dremel/
+ * https://akshays-blog.medium.com/wrapping-head-around-repetition-and-definition-levels-in-dremel-powering-bigquery-c1a33c9695da
+ * https://blog.twitter.com/engineering/en_us/a/2013/dremel-made-simple-with-parquet
+ *
+ * The remainder of this documentation assumes familiarity with the Dremel concepts.
+ *
+ * Dremel offsets are the per row offsets into the repetition and definition level arrays for a
+ * column.
+ * Example:
+ * ```
+ * col            = {{1, 2, 3}, { }, {5, 6}}
+ * dremel_offsets = { 0,         3,   4,  6}
+ * rep_level      = { 0, 1, 1,   0,   0, 1}
+ * def_level      = { 1, 1, 1,   0,   1, 1}
+ * ```
+ *
+ * The repetition and definition level values are ideally computed using a recursive call over a
+ * nested structure but in order to better utilize GPU resources, this function calculates them
+ * with a bottom up merge method.
+ *
+ * Given a LIST column of type `List<List<int>>` like so:
+ * ```
+ * col = {
+ *    [],
+ *    [[], [1, 2, 3], [4, 5]],
+ *    [[]]
+ * }
+ * ```
+ * We can represent it in cudf format with two level of offsets like this:
+ * ```
+ * Level 0 offsets = {0, 0, 3, 5, 6}
+ * Level 1 offsets = {0, 0, 3, 5, 5}
+ * Values          = {1, 2, 3, 4, 5}
+ * ```
+ * The desired result of this function is the repetition and definition level values that
+ * correspond to the data values:
+ * ```
+ * col = {[], [[], [1, 2, 3], [4, 5]], [[]]}
+ * def = { 0    1,  2, 2, 2,   2, 2,     1 }
+ * rep = { 0,   0,  0, 2, 2,   1, 2,     0 }
+ * ```
+ *
+ * Since repetition and definition levels arrays contain a value for each empty list, the size of
+ * the rep/def level array can be given by
+ * ```
+ * rep_level.size() = size of leaf column + number of empty lists in level 0
+ *                                        + number of empty lists in level 1 ...
+ * ```
+ *
+ * We start with finding the empty lists in the penultimate level and merging it with the indices
+ * of the leaf level. The values for the merge are the definition and repetition levels
+ * ```
+ * empties at level 1 = {0, 5}
+ * def values at 1    = {1, 1}
+ * rep values at 1    = {1, 1}
+ * indices at leaf    = {0, 1, 2, 3, 4}
+ * def values at leaf = {2, 2, 2, 2, 2}
+ * rep values at leaf = {2, 2, 2, 2, 2}
+ * ```
+ *
+ * merged def values  = {1, 2, 2, 2, 2, 2, 1}
+ * merged rep values  = {1, 2, 2, 2, 2, 2, 1}
+ *
+ * The size of the rep/def values is now larger than the leaf values and the offsets need to be
+ * adjusted in order to point to the correct start indices. We do this with an exclusive scan over
+ * the indices of offsets of empty lists and adding to existing offsets.
+ * ```
+ * Level 1 new offsets = {0, 1, 4, 6, 7}
+ * ```
+ * Repetition values at the beginning of a list need to be decremented. We use the new offsets to
+ * scatter the rep value.
+ * ```
+ * merged rep values  = {1, 2, 2, 2, 2, 2, 1}
+ * scatter (1, new offsets)
+ * new offsets        = {0, 1,       4,    6, 7}
+ * new rep values     = {1, 1, 2, 2, 1, 2, 1}
+ * ```
+ *
+ * Similarly we merge up all the way till level 0 offsets
+ *
+ * STRUCT COLUMNS :
+ * In case of struct columns, we don't have to merge struct levels with their children because a
+ * struct is the same size as its children. e.g. for a column `struct<int, float>`, if the row `i`
+ * is null, then the children columns `int` and `float` are also null at `i`. They also have the
+ * null entry represented in their respective null masks. So for any case of strictly struct based
+ * nesting, we can get the definition levels merely by iterating over the nesting for the same row.
+ *
+ * In case struct and lists are intermixed, the definition levels of all the contiguous struct
+ * levels can be constructed using the aforementioned iterative method. Only when we reach a list
+ * level, we need to do a merge with the subsequent level.
+ *
+ * So, for a column like `struct<list<int>>`, we are going to merge between the levels `struct<list`
+ * and `int`.
+ * For a column like `list<struct<int>>`, we are going to merge between `list` and `struct<int>`.
+ *
+ * In general, one nesting level is the list level and any struct level that precedes it.
+ *
+ * A few more examples to visualize the partitioning of column hierarchy into nesting levels:
+ * (L is list, S is struct, i is integer(leaf data level), angle brackets omitted)
+ * ```
+ * 1. LSi     = L   Si
+ *              - | --
+ *
+ * 2. LLSi    = L   L   Si
+ *              - | - | --
+ *
+ * 3. SSLi    = SSL   i
+ *              --- | -
+ *
+ * 4. LLSLSSi = L   L   SL   SSi
+ *              - | - | -- | ---
+ * ```
+ *
+ * @param input Column of LIST type
+ * @param nullability Pre-determined nullability at each list level. Empty means infer from
+ * `input`
+ * @param output_as_byte_array if `true`, then any nested list level that has a child of type
+ * `uint8_t` will be considered as the last level
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return A struct containing dremel data
+ */
+dremel_data get_dremel_data(column_view input,
+                            std::vector<uint8_t> nullability,
+                            bool output_as_byte_array,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Get Dremel offsets, repetition levels, and modified definition levels to be used for
+ *        lexicographical comparators. The modified definition levels are produced by treating
+ *        each nested column in the input as nullable
+ *
+ * @param input Column of LIST type
+ * @param nullability Pre-determined nullability at each list level. Empty means infer from
+ * `input`
+ * @param output_as_byte_array if `true`, then any nested list level that has a child of type
+ * `uint8_t` will be considered as the last level
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return A struct containing dremel data
+ */
+dremel_data get_comparator_data(column_view input,
+                                std::vector<uint8_t> nullability,
+                                bool output_as_byte_array,
+                                rmm::cuda_stream_view stream);
+}  // namespace cudf::detail
diff --git a/cpp/include/cudf/lists/detail/extract.hpp b/cpp/include/cudf/lists/detail/extract.hpp
new file mode 100644
index 0000000..013f9b4
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/extract.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/lists/extract.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @copydoc cudf::lists::extract_list_element(lists_column_view, size_type,
+ * rmm::mr::device_memory_resource*)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> extract_list_element(lists_column_view lists_column,
+                                             size_type const index,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::extract_list_element(lists_column_view, column_view const&,
+ * rmm::mr::device_memory_resource*)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> extract_list_element(lists_column_view lists_column,
+                                             column_view const& indices,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/gather.cuh b/cpp/include/cudf/lists/detail/gather.cuh
new file mode 100644
index 0000000..18fe707
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/gather.cuh
@@ -0,0 +1,315 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief The information needed to create an iterator to gather level N+1
+ *
+ * @ref make_gather_data
+ */
+struct gather_data {
+  // The offsets column from our parent list (level N)
+  std::unique_ptr<column> offsets;
+  // For each offset in the above offsets column, the original offset value
+  // prior to being gathered.
+  // Example:
+  // If the offsets[3] == 6  (representing row 3 of the new column)
+  // And the original value it was itself gathered from was 15, then
+  // base_offsets[3] == 15
+  rmm::device_uvector<int32_t> base_offsets;
+  // size of the gather map that will be generated from this data
+  size_type gather_map_size;
+};
+
+/**
+ * @copydoc cudf::make_gather_data(cudf::lists_column_view const& source_column,
+ *                                 MapItType gather_map,
+ *                                 size_type gather_map_size,
+ *                                 rmm::cuda_stream_view stream,
+ *                                 rmm::mr::device_memory_resource* mr)
+ *
+ * @param prev_base_offsets The buffer backing the base offsets used in the gather map. We can
+ *                          free this buffer before allocating the new one to keep peak memory
+ *                          usage down.
+ */
+template <bool NullifyOutOfBounds, typename MapItType>
+gather_data make_gather_data(cudf::lists_column_view const& source_column,
+                             MapItType gather_map,
+                             size_type gather_map_size,
+                             rmm::device_uvector<int32_t>&& prev_base_offsets,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  // size of the gather map is the # of output rows
+  size_type output_count = gather_map_size;
+
+  // offsets of the source column
+  int32_t const* src_offsets{source_column.offsets().data<int32_t>() + source_column.offset()};
+  size_type const src_size = source_column.size();
+
+  auto const source_column_nullmask = source_column.null_mask();
+
+  auto sizes_itr = cudf::detail::make_counting_transform_iterator(
+    0,
+    [source_column_nullmask,
+     source_column_offset = source_column.offset(),
+     gather_map,
+     output_count,
+     src_offsets,
+     src_size] __device__(int32_t index) -> int32_t {
+      int32_t offset_index = index < output_count ? gather_map[index] : 0;
+
+      // if this is an invalid index, this will be a NULL list
+      if (NullifyOutOfBounds && ((offset_index < 0) || (offset_index >= src_size))) { return 0; }
+
+      // If the source row is null, the output row size must be 0.
+      if (source_column_nullmask != nullptr &&
+          not cudf::bit_is_set(source_column_nullmask, source_column_offset + offset_index)) {
+        return 0;
+      }
+
+      // the length of this list
+      return src_offsets[offset_index + 1] - src_offsets[offset_index];
+    });
+
+  auto [dst_offsets_c, map_size] =
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + output_count, stream, mr);
+
+  // handle sliced columns
+  size_type const shift =
+    source_column.offset() > 0
+      ? cudf::detail::get_value<size_type>(source_column.offsets(), source_column.offset(), stream)
+      : 0;
+
+  // generate the base offsets
+  rmm::device_uvector<int32_t> base_offsets = rmm::device_uvector<int32_t>(output_count, stream);
+  thrust::transform(
+    rmm::exec_policy_nosync(stream),
+    gather_map,
+    gather_map + output_count,
+    base_offsets.data(),
+    [source_column_nullmask,
+     source_column_offset = source_column.offset(),
+     src_offsets,
+     src_size,
+     shift] __device__(int32_t index) {
+      // if this is an invalid index, this will be a NULL list
+      if (NullifyOutOfBounds && ((index < 0) || (index >= src_size))) { return 0; }
+
+      // If the source row is null, the output row size must be 0.
+      if (source_column_nullmask != nullptr &&
+          not cudf::bit_is_set(source_column_nullmask, source_column_offset + index)) {
+        return 0;
+      }
+
+      return src_offsets[index] - shift;
+    });
+
+  // Retrieve size of the resulting gather map for level N+1 (the last offset)
+  auto const child_gather_map_size = static_cast<size_type>(map_size);
+  return {std::move(dst_offsets_c), std::move(base_offsets), child_gather_map_size};
+}
+
+/**
+ * @brief Generates the data needed to create a `gather_map` for the next level of
+ * recursion in a hierarchy of list columns.
+ *
+ * Gathering from a single level of a list column is similar to gathering from
+ * a string column.  Each row represents a list bounded by offsets.
+ *
+ * @code{.pseudo}
+ * Example:
+ * Level 0 : List<List<int>>
+ *           Size : 3
+ *           Offsets : [0, 2, 5, 10]
+ *
+ * This represents a column with 3 rows.
+ * Row 0 has 2 elements (bounded by offsets 0,2)
+ * Row 1 has 3 elements (bounded by offsets 2,5)
+ * Row 2 has 5 elements (bounded by offsets 5,10)
+ *
+ * If we wanted to gather rows 0 and 2 the offsets for our outgoing column
+ * would be the compacted ranges (0,2) and (5,10). The level 1 column
+ * then looks like
+ *
+ * Level 1 : List<int>
+ *           Size : 2
+ *           Offsets : [0, 2, 7]
+ *
+ * However, we need to then gather one level further, because at the bottom we have
+ * a column of integers.  We cannot gather the elements in the ranges (0, 2) and (2, 7).
+ * Instead, we have to gather elements in the ranges from the Level 0 column (0, 2) and (5, 10).
+ * So our gather_map iterator will need to know these "base" offsets to index properly.
+ * Specifically:
+ *
+ * Offsets        : [0, 2, 7]    The offsets for Level 1
+ * Base Offsets   : [0, 5]       The corresponding base offsets from Level 0
+ *
+ * Using this we can create an iterator that generates the sequence which properly indexes the
+ * final integer values we want to gather.
+ *
+ * [0, 1, 5, 6, 7, 8, 9]
+ * @endcode
+ *
+ * Thinking generally, this means that to produce a gather_map for level N+1, we need to use the
+ * offsets from level N and the "base" offsets from level N-1. So we are always carrying along
+ * one extra buffer of these "base" offsets which keeps our memory usage well controlled.
+ *
+ * @code{.pseudo}
+ * Example:
+ *
+ * "column of lists of lists of ints"
+ * {
+ *   {
+ *      {2, 3}, {4, 5}
+ *   },
+ *   {
+ *      {6, 7, 8}, {9, 10, 11}, {12, 13, 14}
+ *   },
+ *   {
+ *      {15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}
+ *   }
+ * }
+ *
+ * List<List<int32_t>>:
+ * Length : 3
+ * Offsets : 0, 2, 5, 10
+ * Children :
+ *    List<int32_t>:
+ *    Length : 10
+ *    Offsets : 0, 2, 4, 7, 10, 13, 15, 17, 19, 21, 23
+ *       Children :
+ *           2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 17, 18, 17, 18, 17, 18
+ *
+ * Final column, doing gather([0, 2])
+ *
+ * {
+ *   {
+ *      {2, 3}, {4, 5}
+ *   },
+ *   {
+ *      {15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}
+ *   }
+ * }
+ *
+ * List<List<int32_t>>:
+ * Length : 2
+ * Offsets : 0, 2, 7
+ * Children :
+ *    List<int32_t>:
+ *    Length : 7
+ *    Offsets : 0, 2, 4, 6, 8, 10, 12, 14
+ *       Children :
+ *          2, 3, 4, 5, 15, 16, 17, 18, 17, 18, 17, 18, 17, 18
+ * @endcode
+ *
+ * @tparam MapItType Iterator type to access the incoming column.
+ * @tparam NullifyOutOfBounds Nullify values in `gather_map` that are out of bounds
+ * @param source_column View into the column to gather from
+ * @param gather_map Iterator access to the gather map for `source_column` map
+ * @param gather_map_size Size of the gather map.
+ * @param stream CUDA stream on which to execute kernels
+ * @param mr Memory resource to use for all allocations
+ *
+ * @returns The gather_data struct needed to construct the gather map for the
+ *          next level of recursion.
+ */
+template <bool NullifyOutOfBounds, typename MapItType>
+gather_data make_gather_data(cudf::lists_column_view const& source_column,
+                             MapItType gather_map,
+                             size_type gather_map_size,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  return make_gather_data<NullifyOutOfBounds, MapItType>(
+    source_column,
+    gather_map,
+    gather_map_size,
+    rmm::device_uvector<int32_t>{0, stream, mr},
+    stream,
+    mr);
+}
+
+/**
+ * @brief Gather a list column from a hierarchy of list columns.
+ *
+ * The recursion continues from here at least 1 level further.
+ *
+ * @param list View into the list column to gather from
+ * @param gd The gather_data needed to construct a gather map iterator for this level
+ * @param stream CUDA stream on which to execute kernels
+ * @param mr Memory resource to use for all allocations
+ *
+ * @returns column with elements gathered based on `gather_data`
+ */
+std::unique_ptr<column> gather_list_nested(lists_column_view const& list,
+                                           gather_data& gd,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Gather a leaf column from a hierarchy of list columns.
+ *
+ * The recursion terminates here.
+ *
+ * @param column View into the column to gather from
+ * @param gd The gather_data needed to construct a gather map iterator for this level
+ * @param stream CUDA stream on which to execute kernels
+ * @param mr Memory resource to use for all allocations
+ *
+ * @returns column with elements gathered based on `gather_data`
+ */
+std::unique_ptr<column> gather_list_leaf(column_view const& column,
+                                         gather_data const& gd,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::segmented_gather(lists_column_view const& source_column,
+ *                                        lists_column_view const& gather_map_list,
+ *                                        out_of_bounds_policy bounds_policy,
+ *                                        rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream CUDA stream on which to execute kernels
+ */
+std::unique_ptr<column> segmented_gather(lists_column_view const& source_column,
+                                         lists_column_view const& gather_map_list,
+                                         out_of_bounds_policy bounds_policy,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/interleave_columns.hpp b/cpp/include/cudf/lists/detail/interleave_columns.hpp
new file mode 100644
index 0000000..a5cf67c
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/interleave_columns.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief Returns a single column by interleaving rows of the given table of list elements.
+ *
+ * @code{.pseudo}
+ * s1 = [{0, 1}, {2, 3, 4}, {5}, {}, {6, 7}]
+ * s2 = [{8}, {9}, {}, {10, 11, 12}, {13, 14, 15, 16}]
+ * r = lists::interleave_columns(s1, s2)
+ * r is now [{0, 1}, {8}, {2, 3, 4}, {9}, {5}, {}, {}, {10, 11, 12}, {6, 7}, {13, 14, 15, 16}]
+ * @endcode
+ *
+ * @throws cudf::logic_error if any column of the input table is not a lists columns.
+ * @throws cudf::logic_error if any lists column contains nested typed entry.
+ * @throws cudf::logic_error if all lists columns do not have the same entry type.
+ *
+ * @param input Table containing lists columns to interleave.
+ * @param has_null_mask A boolean flag indicating that the input columns have a null mask.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return The interleaved columns as a single column.
+ */
+std::unique_ptr<column> interleave_columns(table_view const& input,
+                                           bool has_null_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/lists_column_factories.hpp b/cpp/include/cudf/lists/detail/lists_column_factories.hpp
new file mode 100644
index 0000000..7b821a0
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/lists_column_factories.hpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief Internal API to construct a lists column from a `list_scalar`, for public
+ * use, use `cudf::make_column_from_scalar`.
+ *
+ * @param[in] value The `list_scalar` to construct from
+ * @param[in] size The number of rows for the output column.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory.
+ */
+std::unique_ptr<cudf::column> make_lists_column_from_scalar(list_scalar const& value,
+                                                            size_type size,
+                                                            rmm::cuda_stream_view stream,
+                                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create an empty lists column.
+ *
+ * A list column requires a child type and so cannot be created with `make_empty_column`.
+ *
+ * @param child_type The type used for the empty child column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> make_empty_lists_column(data_type child_type,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create a lists column with all null rows.
+ *
+ * @param size Size of the output lists column
+ * @param child_type The type used for the empty child column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> make_all_nulls_lists_column(size_type size,
+                                                    data_type child_type,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/reverse.hpp b/cpp/include/cudf/lists/detail/reverse.hpp
new file mode 100644
index 0000000..6e3b952
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/reverse.hpp
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/lists/reverse.hpp>
+
+namespace cudf::lists::detail {
+
+/**
+ * @copydoc cudf::lists::reverse
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<column> reverse(lists_column_view const& input,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::lists::detail
diff --git a/cpp/include/cudf/lists/detail/scatter.cuh b/cpp/include/cudf/lists/detail/scatter.cuh
new file mode 100644
index 0000000..f04b2fd
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/scatter.cuh
@@ -0,0 +1,283 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/detail/scatter_helper.cuh>
+#include <cudf/lists/list_device_view.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_checks.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+
+#include <cinttypes>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+template <typename IndexIterator>
+rmm::device_uvector<unbound_list_view> list_vector_from_column(
+  unbound_list_view::label_type label,
+  cudf::detail::lists_column_device_view const& lists_column,
+  IndexIterator index_begin,
+  IndexIterator index_end,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto n_rows = thrust::distance(index_begin, index_end);
+
+  auto vector = rmm::device_uvector<unbound_list_view>(n_rows, stream, mr);
+
+  thrust::transform(rmm::exec_policy_nosync(stream),
+                    index_begin,
+                    index_end,
+                    vector.begin(),
+                    [label, lists_column] __device__(size_type row_index) {
+                      return unbound_list_view{label, lists_column, row_index};
+                    });
+
+  return vector;
+}
+
+/**
+ * @brief General implementation of scattering into list column
+ *
+ * Scattering `source` into `target` according to `scatter_map`.
+ * The view order of `source` and `target` can be specified by
+ * `source_vector` and `target_vector` respectively.
+ *
+ * @tparam MapIterator must produce index values within the target column.
+ *
+ * @param source_vector A vector of `unbound_list_view` into source column
+ * @param target_vector A vector of `unbound_list_view` into target column
+ * @param scatter_map_begin Start iterator of scatter map
+ * @param scatter_map_end End iterator of scatter map
+ * @param source Source column view
+ * @param target Target column view
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New lists column.
+ */
+template <typename MapIterator>
+std::unique_ptr<column> scatter_impl(rmm::device_uvector<unbound_list_view> const& source_vector,
+                                     rmm::device_uvector<unbound_list_view>& target_vector,
+                                     MapIterator scatter_map_begin,
+                                     MapIterator scatter_map_end,
+                                     column_view const& source,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(column_types_equal(source, target), "Mismatched column types.");
+
+  auto const child_column_type = lists_column_view(target).child().type();
+
+  // Scatter.
+  thrust::scatter(rmm::exec_policy_nosync(stream),
+                  source_vector.begin(),
+                  source_vector.end(),
+                  scatter_map_begin,
+                  target_vector.begin());
+
+  auto const source_lists_column_view =
+    lists_column_view(source);  // Checks that this is a list column.
+  auto const target_lists_column_view =
+    lists_column_view(target);  // Checks that target is a list column.
+
+  auto list_size_begin = thrust::make_transform_iterator(
+    target_vector.begin(), [] __device__(unbound_list_view l) { return l.size(); });
+  auto offsets_column = std::get<0>(cudf::detail::make_offsets_child_column(
+    list_size_begin, list_size_begin + target.size(), stream, mr));
+
+  auto child_column = build_lists_child_column_recursive(child_column_type,
+                                                         target_vector,
+                                                         offsets_column->view(),
+                                                         source_lists_column_view,
+                                                         target_lists_column_view,
+                                                         stream,
+                                                         mr);
+
+  std::vector<std::unique_ptr<column>> children;
+  children.emplace_back(std::move(offsets_column));
+  children.emplace_back(std::move(child_column));
+  auto null_mask =
+    target.has_nulls() ? copy_bitmask(target, stream, mr) : rmm::device_buffer{0, stream, mr};
+
+  // The output column from this function only has null masks copied from the target columns.
+  // That is still not a correct final null mask for the scatter result.
+  // In addition, that null mask may overshadow the non-null rows (lists) scattered from the source
+  // column. Thus, avoid using `cudf::make_lists_column` since it calls `purge_nonempty_nulls`.
+  return std::make_unique<column>(data_type{type_id::LIST},
+                                  target.size(),
+                                  rmm::device_buffer{},
+                                  std::move(null_mask),
+                                  target.null_count(),
+                                  std::move(children));
+}
+
+/**
+ * @brief Scatters lists into a copy of the target column
+ * according to a scatter map.
+ *
+ * The scatter is performed according to the scatter iterator such that row
+ * `scatter_map[i]` of the output column is replaced by the source list-row.
+ * All other rows of the output column equal corresponding rows of the target table.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ *
+ * The caller must update the null mask in the output column.
+ *
+ * @tparam MapIterator must produce index values within the target column.
+ *
+ * @param source Source column view
+ * @param scatter_map_begin Start iterator of scatter map
+ * @param scatter_map_end End iterator of scatter map
+ * @param target Target column view
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New lists column.
+ */
+template <typename MapIterator>
+std::unique_ptr<column> scatter(column_view const& source,
+                                MapIterator scatter_map_begin,
+                                MapIterator scatter_map_end,
+                                column_view const& target,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  auto const num_rows = target.size();
+  if (num_rows == 0) { return cudf::empty_like(target); }
+
+  auto const source_device_view = column_device_view::create(source, stream);
+  auto const scatter_map_size   = thrust::distance(scatter_map_begin, scatter_map_end);
+  auto const source_vector =
+    list_vector_from_column(unbound_list_view::label_type::SOURCE,
+                            cudf::detail::lists_column_device_view(*source_device_view),
+                            thrust::make_counting_iterator<size_type>(0),
+                            thrust::make_counting_iterator<size_type>(scatter_map_size),
+                            stream,
+                            mr);
+
+  auto const target_device_view = column_device_view::create(target, stream);
+  auto target_vector =
+    list_vector_from_column(unbound_list_view::label_type::TARGET,
+                            cudf::detail::lists_column_device_view(*target_device_view),
+                            thrust::make_counting_iterator<size_type>(0),
+                            thrust::make_counting_iterator<size_type>(num_rows),
+                            stream,
+                            mr);
+
+  return scatter_impl(
+    source_vector, target_vector, scatter_map_begin, scatter_map_end, source, target, stream, mr);
+}
+
+/**
+ * @brief Scatters list scalar (a single row) into a copy of the target column
+ * according to a scatter map.
+ *
+ * Returns a copy of the target column where every row specified in the `scatter_map`
+ * is replaced by the row value.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ *
+ * The caller must update the null mask in the output column.
+ *
+ * @tparam MapIterator must produce index values within the target column.
+ *
+ * @param slr Source scalar, specifying row data
+ * @param scatter_map_begin Start iterator of scatter map
+ * @param scatter_map_end End iterator of scatter map
+ * @param target Target column view
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New lists column.
+ */
+template <typename MapIterator>
+std::unique_ptr<column> scatter(scalar const& slr,
+                                MapIterator scatter_map_begin,
+                                MapIterator scatter_map_end,
+                                column_view const& target,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  auto const num_rows = target.size();
+  if (num_rows == 0) { return cudf::empty_like(target); }
+
+  auto lv        = static_cast<list_scalar const*>(&slr);
+  bool slr_valid = slr.is_valid(stream);
+  rmm::device_buffer null_mask =
+    slr_valid ? cudf::detail::create_null_mask(1, mask_state::UNALLOCATED, stream, mr)
+              : cudf::detail::create_null_mask(1, mask_state::ALL_NULL, stream, mr);
+  auto offset_column =
+    make_numeric_column(data_type{type_to_id<size_type>()}, 2, mask_state::UNALLOCATED, stream, mr);
+  thrust::sequence(rmm::exec_policy_nosync(stream),
+                   offset_column->mutable_view().begin<size_type>(),
+                   offset_column->mutable_view().end<size_type>(),
+                   0,
+                   lv->view().size());
+  auto wrapped = column_view(data_type{type_id::LIST},
+                             1,
+                             nullptr,
+                             static_cast<bitmask_type const*>(null_mask.data()),
+                             slr_valid ? 0 : 1,
+                             0,
+                             {offset_column->view(), lv->view()});
+
+  auto const source_device_view = column_device_view::create(wrapped, stream);
+  auto const scatter_map_size   = thrust::distance(scatter_map_begin, scatter_map_end);
+  auto const source_vector =
+    list_vector_from_column(unbound_list_view::label_type::SOURCE,
+                            cudf::detail::lists_column_device_view(*source_device_view),
+                            thrust::make_constant_iterator<size_type>(0),
+                            thrust::make_constant_iterator<size_type>(0) + scatter_map_size,
+                            stream,
+                            mr);
+
+  auto const target_device_view = column_device_view::create(target, stream);
+  auto target_vector =
+    list_vector_from_column(unbound_list_view::label_type::TARGET,
+                            cudf::detail::lists_column_device_view(*target_device_view),
+                            thrust::make_counting_iterator<size_type>(0),
+                            thrust::make_counting_iterator<size_type>(num_rows),
+                            stream,
+                            mr);
+
+  return scatter_impl(
+    source_vector, target_vector, scatter_map_begin, scatter_map_end, wrapped, target, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/scatter_helper.cuh b/cpp/include/cudf/lists/detail/scatter_helper.cuh
new file mode 100644
index 0000000..605f768
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/scatter_helper.cuh
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/list_device_view.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief Holder for a list row's positional information, without
+ *        also holding a reference to the list column.
+ *
+ * Analogous to the list_view, this class is default constructable,
+ * and can thus be stored in rmm::device_uvector. It is used to represent
+ * the results of a `scatter()` operation; a device_uvector may hold
+ * several instances of unbound_list_view, each with a flag indicating
+ * whether it came from the scatter source or target. Each instance
+ * may later be "bound" to the appropriate source/target column, to
+ * reconstruct the list_view.
+ */
+struct unbound_list_view {
+  /**
+   * @brief Flag type, indicating whether this list row originated from
+   *        the source or target column, in `scatter()`.
+   */
+  enum class label_type : bool { SOURCE, TARGET };
+
+  using lists_column_device_view = cudf::detail::lists_column_device_view;
+  using list_device_view         = cudf::list_device_view;
+
+  unbound_list_view()                                    = default;
+  unbound_list_view(unbound_list_view const&)            = default;
+  unbound_list_view(unbound_list_view&&)                 = default;
+  unbound_list_view& operator=(unbound_list_view const&) = default;
+  unbound_list_view& operator=(unbound_list_view&&)      = default;
+
+  /**
+   * @brief __device__ Constructor, for use from `scatter()`.
+   *
+   * @param scatter_source_label Whether the row came from source or target
+   * @param lists_column The actual source/target lists column
+   * @param row_index Index of the row in lists_column that this instance represents
+   */
+  __device__ inline unbound_list_view(label_type scatter_source_label,
+                                      cudf::detail::lists_column_device_view const& lists_column,
+                                      size_type const& row_index)
+    : _label{scatter_source_label}, _row_index{row_index}
+  {
+    _size = list_device_view{lists_column, row_index}.size();
+  }
+
+  /**
+   * @brief __device__ Constructor, for use when constructing the child column
+   *        of a scattered list column
+   *
+   * @param scatter_source_label Whether the row came from source or target
+   * @param row_index Index of the row that this instance represents in the source/target column
+   * @param size The number of elements in this list row
+   */
+  __device__ inline unbound_list_view(label_type scatter_source_label,
+                                      size_type const& row_index,
+                                      size_type const& size)
+    : _label{scatter_source_label}, _row_index{row_index}, _size{size}
+  {
+  }
+
+  /**
+   * @brief Returns number of elements in this list row.
+   */
+  [[nodiscard]] __device__ inline size_type size() const { return _size; }
+
+  /**
+   * @brief Returns whether this row came from the `scatter()` source or target
+   */
+  [[nodiscard]] __device__ inline label_type label() const { return _label; }
+
+  /**
+   * @brief Returns the index in the source/target column
+   */
+  [[nodiscard]] __device__ inline size_type row_index() const { return _row_index; }
+
+  /**
+   * @brief Binds to source/target column (depending on SOURCE/TARGET labels),
+   *        to produce a bound list_view.
+   *
+   * @param scatter_source Source column for the scatter operation
+   * @param scatter_target Target column for the scatter operation
+   * @return A (bound) list_view for the row that this object represents
+   */
+  [[nodiscard]] __device__ inline list_device_view bind_to_column(
+    lists_column_device_view const& scatter_source,
+    lists_column_device_view const& scatter_target) const
+  {
+    return list_device_view(_label == label_type::SOURCE ? scatter_source : scatter_target,
+                            _row_index);
+  }
+
+ private:
+  // Note: Cannot store reference to list column, because of storage in device_uvector.
+  // Only keep track of whether this list row came from the source or target of scatter.
+
+  label_type _label{
+    label_type::SOURCE};   // Whether this list row came from the scatter source or target.
+  size_type _row_index{};  // Row index in the Lists column.
+  size_type _size{};       // Number of elements in *this* list row.
+};
+
+std::unique_ptr<column> build_lists_child_column_recursive(
+  data_type child_column_type,
+  rmm::device_uvector<unbound_list_view> const& list_vector,
+  cudf::column_view const& list_offsets,
+  cudf::lists_column_view const& source_lists_column_view,
+  cudf::lists_column_view const& target_lists_column_view,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/set_operations.hpp b/cpp/include/cudf/lists/detail/set_operations.hpp
new file mode 100644
index 0000000..1411c65
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/set_operations.hpp
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/types.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+namespace cudf::lists::detail {
+
+/**
+ * @copydoc cudf::list::have_overlap
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> have_overlap(lists_column_view const& lhs,
+                                     lists_column_view const& rhs,
+                                     null_equality nulls_equal,
+                                     nan_equality nans_equal,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::list::intersect_distinct
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> intersect_distinct(lists_column_view const& lhs,
+                                           lists_column_view const& rhs,
+                                           null_equality nulls_equal,
+                                           nan_equality nans_equal,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::list::union_distinct
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> union_distinct(lists_column_view const& lhs,
+                                       lists_column_view const& rhs,
+                                       null_equality nulls_equal,
+                                       nan_equality nans_equal,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::list::difference_distinct
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> difference_distinct(lists_column_view const& lhs,
+                                            lists_column_view const& rhs,
+                                            null_equality nulls_equal,
+                                            nan_equality nans_equal,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/** @} */  // end of group
+}  // namespace cudf::lists::detail
diff --git a/cpp/include/cudf/lists/detail/sorting.hpp b/cpp/include/cudf/lists/detail/sorting.hpp
new file mode 100644
index 0000000..c378ca8
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/sorting.hpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @copydoc cudf::lists::sort_lists
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> sort_lists(lists_column_view const& input,
+                                   order column_order,
+                                   null_order null_precedence,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::lists::stable_sort_lists
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> stable_sort_lists(lists_column_view const& input,
+                                          order column_order,
+                                          null_order null_precedence,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/detail/stream_compaction.hpp b/cpp/include/cudf/lists/detail/stream_compaction.hpp
new file mode 100644
index 0000000..7ab9cf9
--- /dev/null
+++ b/cpp/include/cudf/lists/detail/stream_compaction.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+namespace cudf::lists::detail {
+
+/**
+ * @copydoc cudf::lists::apply_boolean_mask(lists_column_view const&, lists_column_view const&,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<column> apply_boolean_mask(lists_column_view const& input,
+                                           lists_column_view const& boolean_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::list::distinct
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> distinct(lists_column_view const& input,
+                                 null_equality nulls_equal,
+                                 nan_equality nans_equal,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::lists::detail
diff --git a/cpp/include/cudf/lists/explode.hpp b/cpp/include/cudf/lists/explode.hpp
new file mode 100644
index 0000000..adf4680
--- /dev/null
+++ b/cpp/include/cudf/lists/explode.hpp
@@ -0,0 +1,209 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup column_reshape
+ * @{
+ * @file
+ * @brief Column APIs for explore list columns
+ */
+
+/**
+ * @brief Explodes a list column's elements.
+ *
+ * Any list is exploded, which means the elements of the list in each row are expanded into new rows
+ * in the output. The corresponding rows for other columns in the input are duplicated. Example:
+ * ```
+ * [[5,10,15], 100],
+ * [[20,25],   200],
+ * [[30],      300],
+ * returns
+ * [5,         100],
+ * [10,        100],
+ * [15,        100],
+ * [20,        200],
+ * [25,        200],
+ * [30,        300],
+ * ```
+ *
+ * Nulls and empty lists propagate in different ways depending on what is null or empty.
+ *```
+ * [[5,null,15], 100],
+ * [null,        200],
+ * [[],          300],
+ * returns
+ * [5,           100],
+ * [null,        100],
+ * [15,          100],
+ * ```
+ * Note that null lists are not included in the resulting table, but nulls inside
+ * lists and empty lists will be represented with a null entry for that column in that row.
+ *
+ * @param input_table Table to explode.
+ * @param explode_column_idx Column index to explode inside the table.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @return A new table with explode_col exploded.
+ */
+std::unique_ptr<table> explode(
+  table_view const& input_table,
+  size_type explode_column_idx,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Explodes a list column's elements and includes a position column.
+ *
+ * Any list is exploded, which means the elements of the list in each row are expanded into new rows
+ * in the output. The corresponding rows for other columns in the input are duplicated. A position
+ * column is added that has the index inside the original list for each row. Example:
+ * ```
+ * [[5,10,15], 100],
+ * [[20,25],   200],
+ * [[30],      300],
+ * returns
+ * [0,   5,     100],
+ * [1,   10,    100],
+ * [2,   15,    100],
+ * [0,   20,    200],
+ * [1,   25,    200],
+ * [0,   30,    300],
+ * ```
+ *
+ * Nulls and empty lists propagate in different ways depending on what is null or empty.
+ *```
+ * [[5,null,15], 100],
+ * [null,        200],
+ * [[],          300],
+ * returns
+ * [0,     5,    100],
+ * [1,  null,    100],
+ * [2,    15,    100],
+ * ```
+ * Note that null lists are not included in the resulting table, but nulls inside
+ * lists and empty lists will be represented with a null entry for that column in that row.
+ *
+ * @param input_table Table to explode.
+ * @param explode_column_idx Column index to explode inside the table.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @return A new table with exploded value and position. The column order of return table is
+ *         [cols before explode_input, explode_position, explode_value, cols after explode_input].
+ */
+std::unique_ptr<table> explode_position(
+  table_view const& input_table,
+  size_type explode_column_idx,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Explodes a list column's elements retaining any null entries or empty lists inside.
+ *
+ * Any list is exploded, which means the elements of the list in each row are expanded into new rows
+ * in the output. The corresponding rows for other columns in the input are duplicated. Example:
+ * ```
+ * [[5,10,15], 100],
+ * [[20,25],   200],
+ * [[30],      300],
+ * returns
+ * [5,         100],
+ * [10,        100],
+ * [15,        100],
+ * [20,        200],
+ * [25,        200],
+ * [30,        300],
+ * ```
+ *
+ * Nulls and empty lists propagate as null entries in the result.
+ *```
+ * [[5,null,15], 100],
+ * [null,        200],
+ * [[],          300],
+ * returns
+ * [5,           100],
+ * [null,        100],
+ * [15,          100],
+ * [null,        200],
+ * [null,        300],
+ * ```
+ *
+ * @param input_table Table to explode.
+ * @param explode_column_idx Column index to explode inside the table.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @return A new table with explode_col exploded.
+ */
+std::unique_ptr<table> explode_outer(
+  table_view const& input_table,
+  size_type explode_column_idx,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Explodes a list column's elements retaining any null entries or empty lists and includes a
+ *position column.
+ *
+ * Any list is exploded, which means the elements of the list in each row are expanded into new rows
+ * in the output. The corresponding rows for other columns in the input are duplicated. A position
+ * column is added that has the index inside the original list for each row. Example:
+ * ```
+ * [[5,10,15], 100],
+ * [[20,25],   200],
+ * [[30],      300],
+ * returns
+ * [0,   5,    100],
+ * [1,  10,    100],
+ * [2,  15,    100],
+ * [0,  20,    200],
+ * [1,  25,    200],
+ * [0,  30,    300],
+ * ```
+ *
+ * Nulls and empty lists propagate as null entries in the result.
+ *```
+ * [[5,null,15], 100],
+ * [null,        200],
+ * [[],          300],
+ * returns
+ * [0,     5,    100],
+ * [1,  null,    100],
+ * [2,    15,    100],
+ * [0,  null,    200],
+ * [0,  null,    300],
+ * ```
+ *
+ * @param input_table Table to explode.
+ * @param explode_column_idx Column index to explode inside the table.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @return A new table with explode_col exploded.
+ */
+std::unique_ptr<table> explode_outer_position(
+  table_view const& input_table,
+  size_type explode_column_idx,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/extract.hpp b/cpp/include/cudf/lists/extract.hpp
new file mode 100644
index 0000000..e923541
--- /dev/null
+++ b/cpp/include/cudf/lists/extract.hpp
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace lists {
+/**
+ * @addtogroup lists_extract
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Create a column where each row is the element at position `index` from the corresponding
+ * sublist in the input `lists_column`.
+ *
+ * Output `column[i]` is set from element `lists_column[i][index]`.
+ * If `index` is larger than the size of the sublist at `lists_column[i]`
+ * then output `column[i] = null`.
+ *
+ * @code{.pseudo}
+ * l = { {1, 2, 3}, {4}, {5, 6} }
+ * r = extract_list_element(l, 1)
+ * r is now {2, null, 6}
+ * @endcode
+ *
+ * The `index` may also be negative in which case the row retrieved is offset
+ * from the end of each sublist.
+ *
+ * @code{.pseudo}
+ * l = { {"a"}, {"b", "c"}, {"d", "e", "f"} }
+ * r = extract_list_element(l, -1)
+ * r is now {"a", "c", "f"}
+ * @endcode
+ *
+ * Any input where `lists_column[i] == null` will produce
+ * output `column[i] = null`. Also, any element where
+ * `lists_column[i][index] == null` will produce
+ * output `column[i] = null`.
+ *
+ * @param lists_column Column to extract elements from.
+ * @param index The row within each sublist to retrieve.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return Column of extracted elements.
+ */
+std::unique_ptr<column> extract_list_element(
+  lists_column_view const& lists_column,
+  size_type index,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a column where each row is a single element from the corresponding sublist
+ * in the input `lists_column`, selected using indices from the `indices` column.
+ *
+ * Output `column[i]` is set from element `lists_column[i][indices[i]]`.
+ * If `indices[i]` is larger than the size of the sublist at `lists_column[i]`
+ * then output `column[i] = null`.
+ * Similarly, if `indices[i]` is `null`, then `column[i] = null`.
+ *
+ * @code{.pseudo}
+ * l = { {1, 2, 3}, {4}, {5, 6} }
+ * r = extract_list_element(l, {0, null, 2})
+ * r is now {1, null, null}
+ * @endcode
+ *
+ * `indices[i]` may also be negative, in which case the row retrieved is offset
+ * from the end of each sublist.
+ *
+ * @code{.pseudo}
+ * l = { {"a"}, {"b", "c"}, {"d", "e", "f"} }
+ * r = extract_list_element(l, {-1, -2, -4})
+ * r is now {"a", "b", null}
+ * @endcode
+ *
+ * Any input where `lists_column[i] == null` produces output `column[i] = null`.
+ * Any input where `lists_column[i][indices[i]] == null` produces output `column[i] = null`.
+ *
+ * @param lists_column Column to extract elements from.
+ * @param indices The column whose rows indicate the element index to be retrieved from each list
+ * row.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return Column of extracted elements.
+ * @throws cudf::logic_error If the sizes of `lists_column` and `indices` do not match.
+ */
+std::unique_ptr<column> extract_list_element(
+  lists_column_view const& lists_column,
+  column_view const& indices,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/filling.hpp b/cpp/include/cudf/lists/filling.hpp
new file mode 100644
index 0000000..059ed5f
--- /dev/null
+++ b/cpp/include/cudf/lists/filling.hpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf::lists {
+/**
+ * @addtogroup lists_filling
+ * @{
+ * @file
+ * @brief Column APIs for individual list sequence
+ */
+
+/**
+ * @brief Create a lists column in which each row contains a sequence of values specified by a tuple
+ * of (`start`, `size`) parameters.
+ *
+ * Create a lists column in which each row is a sequence of values starting from a `start` value,
+ * incrementing by one, and its cardinality is specified by a `size` value. The `start` and `size`
+ * values used to generate each list is taken from the corresponding row of the input @p starts and
+ * @p sizes columns.
+ *
+ *  - @p sizes must be a column of integer types.
+ *  - All the input columns must not have nulls.
+ *  - If any row of the @p sizes column contains negative value, the output is undefined.
+ *
+ * @code{.pseudo}
+ * starts = [0, 1, 2, 3, 4]
+ * sizes  = [0, 2, 2, 1, 3]
+ *
+ * output = [ [], [1, 2], [2, 3], [3], [4, 5, 6] ]
+ * @endcode
+ *
+ * @throws cudf::logic_error if @p sizes column is not of integer types.
+ * @throws cudf::logic_error if any input column has nulls.
+ * @throws cudf::logic_error if @p starts and @p sizes columns do not have the same size.
+ * @throws std::overflow_error if the output column would exceed the column size limit.
+ *
+ * @param starts First values in the result sequences.
+ * @param sizes Numbers of values in the result sequences.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return The result column containing generated sequences.
+ */
+std::unique_ptr<column> sequences(
+  column_view const& starts,
+  column_view const& sizes,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a lists column in which each row contains a sequence of values specified by a tuple
+ * of (`start`, `step`, `size`) parameters.
+ *
+ * Create a lists column in which each row is a sequence of values starting from a `start` value,
+ * incrementing by a `step` value, and its cardinality is specified by a `size` value. The values
+ * `start`, `step`, and `size` used to generate each list is taken from the corresponding row of the
+ * input @p starts, @p steps, and @p sizes columns.
+ *
+ *  - @p sizes must be a column of integer types.
+ *  - @p starts and @p steps columns must have the same type.
+ *  - All the input columns must not have nulls.
+ *  - If any row of the @p sizes column contains negative value, the output is undefined.
+ *
+ * @code{.pseudo}
+ * starts = [0, 1, 2, 3, 4]
+ * steps  = [2, 1, 1, 1, -3]
+ * sizes  = [0, 2, 2, 1, 3]
+ *
+ * output = [ [], [1, 2], [2, 3], [3], [4, 1, -2] ]
+ * @endcode
+ *
+ * @throws cudf::logic_error if @p sizes column is not of integer types.
+ * @throws cudf::logic_error if any input column has nulls.
+ * @throws cudf::logic_error if @p starts and @p steps columns have different types.
+ * @throws cudf::logic_error if @p starts, @p steps, and @p sizes columns do not have the same size.
+ * @throws std::overflow_error if the output column would exceed the column size limit.
+ *
+ * @param starts First values in the result sequences.
+ * @param steps Increment values for the result sequences.
+ * @param sizes Numbers of values in the result sequences.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return The result column containing generated sequences.
+ */
+std::unique_ptr<column> sequences(
+  column_view const& starts,
+  column_view const& steps,
+  column_view const& sizes,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf::lists
diff --git a/cpp/include/cudf/lists/gather.hpp b/cpp/include/cudf/lists/gather.hpp
new file mode 100644
index 0000000..38bed9e
--- /dev/null
+++ b/cpp/include/cudf/lists/gather.hpp
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace lists {
+/**
+ * @addtogroup lists_gather
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Segmented gather of the elements within a list element in each row of a list column.
+ *
+ * `source_column` with any depth and `gather_map_list` with depth 1 are only supported.
+ *
+ * @code{.pseudo}
+ * source_column   : [{"a", "b", "c", "d"}, {"1", "2", "3", "4"}, {"x", "y", "z"}]
+ * gather_map_list : [{0, 1, 3, 2}, {1, 3, 2}, {}]
+ *
+ * result          : [{"a", "b", "d", "c"}, {"2", "4", "3"}, {}]
+ * @endcode
+ *
+ * @throws cudf::logic_error if `gather_map_list` size is not same as `source_column` size.
+ * @throws std::invalid_argument if gather_map contains null values.
+ * @throws cudf::logic_error if gather_map is not list column of an index type.
+ *
+ * If indices in `gather_map_list` are outside the range `[-n, n)`, where `n` is the number of
+ * elements in corresponding row of the source column, the behavior is as follows:
+ *   1. If `bounds_policy` is set to `DONT_CHECK`, the behavior is undefined.
+ *   2. If `bounds_policy` is set to `NULLIFY`, the corresponding element in the list row
+ *      is set to null in the output column.
+ *
+ * @code{.pseudo}
+ * source_column       : [{"a", "b", "c", "d"}, {"1", "2", "3", "4"}, {"x", "y", "z"}]
+ * gather_map_list     : [{0, -1, 4, -5}, {1, 3, 5}, {}]
+ *
+ * result_with_nullify : [{"a", "d", null, null}, {"2", "4", null}, {}]
+ * @endcode
+ *
+ * @param source_column View into the list column to gather from
+ * @param gather_map_list View into a non-nullable list column of integral indices that maps the
+ * element in list of each row in the source columns to rows of lists in the destination columns.
+ * @param bounds_policy Can be `DONT_CHECK` or `NULLIFY`. Selects whether or not to nullify the
+ * output list row's element, when the gather index falls outside the range `[-n, n)`,
+ * where `n` is the number of elements in list row corresponding to the gather-map row.
+ * @param mr Device memory resource to allocate any returned objects
+ * @return column with elements in list of rows gathered based on `gather_map_list`
+ *
+ */
+std::unique_ptr<column> segmented_gather(
+  lists_column_view const& source_column,
+  lists_column_view const& gather_map_list,
+  out_of_bounds_policy bounds_policy  = out_of_bounds_policy::DONT_CHECK,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/list_device_view.cuh b/cpp/include/cudf/lists/list_device_view.cuh
new file mode 100644
index 0000000..07346e7
--- /dev/null
+++ b/cpp/include/cudf/lists/list_device_view.cuh
@@ -0,0 +1,380 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuda_runtime.h>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/lists_column_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+
+namespace cudf {
+
+/**
+ * @brief A non-owning, immutable view of device data that represents
+ * a list of elements of arbitrary type (including further nested lists).
+ */
+class list_device_view {
+  using lists_column_device_view = cudf::detail::lists_column_device_view;
+
+ public:
+  list_device_view() = default;
+
+  /**
+   * @brief Constructs a list_device_view from a list column and index.
+   *
+   * @param lists_column list column device view containing the list to view
+   * @param row_index index of the list row to view
+   */
+  __device__ inline list_device_view(lists_column_device_view const& lists_column,
+                                     size_type const& row_index)
+    : lists_column(lists_column), _row_index(row_index)
+  {
+    column_device_view const& offsets = lists_column.offsets();
+    cudf_assert(row_index >= 0 && row_index < lists_column.size() && row_index < offsets.size() &&
+                "row_index out of bounds");
+
+    begin_offset = offsets.element<size_type>(row_index + lists_column.offset());
+    cudf_assert(begin_offset >= 0 && begin_offset <= lists_column.child().size() &&
+                "begin_offset out of bounds.");
+    _size = offsets.element<size_type>(row_index + 1 + lists_column.offset()) - begin_offset;
+  }
+
+  ~list_device_view() = default;
+
+  /**
+   * @brief Fetches the offset in the list column's child that corresponds to
+   * the element at the specified list index.
+   *
+   * Consider the following lists column:
+   *  [
+   *   [0,1,2],
+   *   [3,4,5],
+   *   [6,7,8]
+   *  ]
+   *
+   * The list's internals would look like:
+   *  offsets: [0, 3, 6, 9]
+   *  child  : [0, 1, 2, 3, 4, 5, 6, 7, 8]
+   *
+   * The second list row (i.e. row_index=1) is [3,4,5].
+   * The third element (i.e. idx=2) of the second list row is 5.
+   *
+   * The offset of this element as stored in the child column (i.e. 5)
+   * may be fetched using this method.
+   *
+   * @param idx The list index of the element to fetch the offset for
+   * @return The offset of the element at the specified list index
+   */
+  [[nodiscard]] __device__ inline size_type element_offset(size_type idx) const
+  {
+    cudf_assert(idx >= 0 && idx < size() && "idx out of bounds");
+    return begin_offset + idx;
+  }
+
+  /**
+   * @brief Fetches the element at the specified index within the list row.
+   *
+   * @tparam T The type of the list's element.
+   * @param idx The index into the list row
+   * @return The element at the specified index of the list row.
+   */
+  template <typename T>
+  __device__ inline T element(size_type idx) const
+  {
+    return lists_column.child().element<T>(element_offset(idx));
+  }
+
+  /**
+   * @brief Checks whether the element is null at the specified index in the list
+   *
+   * @param idx The index into the list row
+   * @return `true` if the element is null at the specified index in the list row
+   */
+  [[nodiscard]] __device__ inline bool is_null(size_type idx) const
+  {
+    cudf_assert(idx >= 0 && idx < size() && "Index out of bounds.");
+    auto element_offset = begin_offset + idx;
+    return lists_column.child().is_null(element_offset);
+  }
+
+  /**
+   * @brief Checks whether this list row is null.
+   *
+   * @return `true` if this list is null
+   */
+  [[nodiscard]] __device__ inline bool is_null() const { return lists_column.is_null(_row_index); }
+
+  /**
+   * @brief Fetches the number of elements in this list row.
+   *
+   * @return The number of elements in this list row
+   */
+  [[nodiscard]] __device__ inline size_type size() const { return _size; }
+
+  /**
+   * @brief Returns the row index of this list in the original lists column.
+   *
+   * @return The row index of this list
+   */
+  [[nodiscard]] __device__ inline size_type row_index() const { return _row_index; }
+
+  /**
+   * @brief Fetches the lists_column_device_view that contains this list.
+   *
+   * @return The lists_column_device_view that contains this list
+   */
+  [[nodiscard]] __device__ inline lists_column_device_view const& get_column() const
+  {
+    return lists_column;
+  }
+
+  template <typename T>
+  struct pair_accessor;
+
+  template <typename T>
+  struct pair_rep_accessor;
+
+  /// const pair iterator for the list
+  template <typename T>
+  using const_pair_iterator =
+    thrust::transform_iterator<pair_accessor<T>, thrust::counting_iterator<cudf::size_type>>;
+
+  /// const pair iterator type for the list
+  template <typename T>
+  using const_pair_rep_iterator =
+    thrust::transform_iterator<pair_rep_accessor<T>, thrust::counting_iterator<cudf::size_type>>;
+
+  /**
+   * @brief Fetcher for a pair iterator to the first element in the list_device_view.
+   *
+   * Dereferencing the returned iterator yields a `thrust::pair<T, bool>`.
+   *
+   * If the element at index `i` is valid, then for `p = iter[i]`,
+   *   1. `p.first` is the value of the element at `i`
+   *   2. `p.second == true`
+   *
+   * If the element at index `i` is null,
+   *   1. `p.first` is undefined
+   *   2. `p.second == false`
+   *
+   * @return A pair iterator to the first element in the list_device_view and whether or not the
+   * element is valid
+   */
+  template <typename T>
+  [[nodiscard]] __device__ inline const_pair_iterator<T> pair_begin() const
+  {
+    return const_pair_iterator<T>{thrust::counting_iterator<size_type>(0), pair_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Fetcher for a pair iterator to one position past the last element in the
+   * list_device_view.
+   *
+   * @return A pair iterator to one past the last element in the list_device_view and whether or not
+   * that element is valid
+   */
+  template <typename T>
+  [[nodiscard]] __device__ inline const_pair_iterator<T> pair_end() const
+  {
+    return const_pair_iterator<T>{thrust::counting_iterator<size_type>(size()),
+                                  pair_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Fetcher for a pair iterator to the first element in the list_device_view.
+   *
+   * Dereferencing the returned iterator yields a `thrust::pair<rep_type, bool>`,
+   * where `rep_type` is `device_storage_type_t<T>`, the type used to store the value
+   * on the device.
+   *
+   * If the element at index `i` is valid, then for `p = iter[i]`,
+   *   1. `p.first` is the value of the element at `i`
+   *   2. `p.second == true`
+   *
+   * If the element at index `i` is null,
+   *   1. `p.first` is undefined
+   *   2. `p.second == false`
+   *
+   * @return A pair iterator to the first element in the list_device_view and whether or not that
+   * element is valid
+   */
+  template <typename T>
+  [[nodiscard]] __device__ inline const_pair_rep_iterator<T> pair_rep_begin() const
+  {
+    return const_pair_rep_iterator<T>{thrust::counting_iterator<size_type>(0),
+                                      pair_rep_accessor<T>{*this}};
+  }
+
+  /**
+   * @brief Fetcher for a pair iterator to one position past the last element in the
+   * list_device_view.
+   *
+   * @return A pair iterator one past the last element in the list_device_view and whether or not
+   * that element is valid
+   */
+  template <typename T>
+  [[nodiscard]] __device__ inline const_pair_rep_iterator<T> pair_rep_end() const
+  {
+    return const_pair_rep_iterator<T>{thrust::counting_iterator<size_type>(size()),
+                                      pair_rep_accessor<T>{*this}};
+  }
+
+ private:
+  lists_column_device_view const& lists_column;
+  size_type _row_index{};  // Row index in the Lists column vector.
+  size_type _size{};       // Number of elements in *this* list row.
+
+  size_type begin_offset;  // Offset in list_column_device_view where this list begins.
+
+  /**
+   * @brief pair accessor for elements in a `list_device_view`
+   *
+   * This unary functor returns a pair of:
+   *   1. data element at a specified index
+   *   2. boolean validity flag for that element
+   *
+   * @tparam T The element-type of the list row
+   */
+  template <typename T>
+  struct pair_accessor {
+    list_device_view const& list;  ///< The list_device_view to access
+
+    /**
+     * @brief constructor
+     *
+     * @param _list The `list_device_view` whose rows are being accessed.
+     */
+    explicit CUDF_HOST_DEVICE inline pair_accessor(list_device_view const& _list) : list{_list} {}
+
+    /**
+     * @brief Accessor for the {data, validity} pair at the specified index
+     *
+     * @param i Index into the list_device_view
+     * @return A pair of data element and its validity flag.
+     */
+    __device__ inline thrust::pair<T, bool> operator()(cudf::size_type i) const
+    {
+      return {list.element<T>(i), !list.is_null(i)};
+    }
+  };
+
+  /**
+   * @brief pair rep accessor for elements in a `list_device_view`
+   *
+   * Returns a `pair<rep_type, bool>`, where `rep_type` = `device_storage_type_t<T>`,
+   * the type used to store the value on the device.
+   *
+   * This unary functor returns a pair of:
+   *   1. rep element at a specified index
+   *   2. boolean validity flag for that element
+   *
+   * @tparam T The element-type of the list row
+   */
+  template <typename T>
+  struct pair_rep_accessor {
+    list_device_view const& list;  ///< The list_device_view whose rows are being accessed
+
+    using rep_type = device_storage_type_t<T>;  ///< The type used to store the value on the device
+
+    /**
+     * @brief constructor
+     *
+     * @param _list The `list_device_view` whose rows are being accessed.
+     */
+    explicit CUDF_HOST_DEVICE inline pair_rep_accessor(list_device_view const& _list) : list{_list}
+    {
+    }
+
+    /**
+     * @brief Accessor for the {rep_data, validity} pair at the specified index
+     *
+     * @param i Index into the list_device_view
+     * @return A pair of data element and its validity flag.
+     */
+    __device__ inline thrust::pair<rep_type, bool> operator()(cudf::size_type i) const
+    {
+      return {get_rep<T>(i), !list.is_null(i)};
+    }
+
+   private:
+    template <typename R, std::enable_if_t<std::is_same_v<R, rep_type>, void>* = nullptr>
+    __device__ inline rep_type get_rep(cudf::size_type i) const
+    {
+      return list.element<R>(i);
+    }
+
+    template <typename R, std::enable_if_t<not std::is_same_v<R, rep_type>, void>* = nullptr>
+    __device__ inline rep_type get_rep(cudf::size_type i) const
+    {
+      return list.element<R>(i).value();
+    }
+  };
+};
+
+/**
+ * @brief Returns the size of the list by row index
+ *
+ */
+struct list_size_functor {
+  detail::lists_column_device_view const d_column;  ///< The list column to access
+  /**
+   * @brief Constructor
+   *
+   * @param d_col The cudf::lists_column_device_view whose rows are being accessed
+   */
+  CUDF_HOST_DEVICE inline list_size_functor(detail::lists_column_device_view const& d_col)
+    : d_column(d_col)
+  {
+  }
+  /**
+   * @brief Returns size of the list by row index
+   *
+   * @param idx row index
+   * @return size of the list
+   */
+  __device__ inline size_type operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) return size_type{0};
+    return d_column.offset_at(idx + 1) - d_column.offset_at(idx);
+  }
+};
+
+/**
+ * @brief Makes an iterator that returns size of the list by row index
+ *
+ * Example:
+ * For a list_column_device_view with 3 rows, `l = {[1, 2, 3], [4, 5], [6, 7, 8, 9]}`,
+ * @code{.cpp}
+ * auto it = make_list_size_iterator(l);
+ * assert(it[0] == 3);
+ * assert(it[1] == 2);
+ * assert(it[2] == 4);
+ * @endcode
+ *
+ * @param c The list_column_device_view to iterate over
+ * @return An iterator that returns the size of the list by row index
+ */
+CUDF_HOST_DEVICE auto inline make_list_size_iterator(detail::lists_column_device_view const& c)
+{
+  return detail::make_counting_transform_iterator(0, list_size_functor{c});
+}
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/list_view.hpp b/cpp/include/cudf/lists/list_view.hpp
new file mode 100644
index 0000000..a3f36a9
--- /dev/null
+++ b/cpp/include/cudf/lists/list_view.hpp
@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+/**
+ * @file list_view.hpp
+ * @brief Class definition for cudf::list_view.
+ */
+
+namespace cudf {
+
+/**
+ * @brief A non-owning, immutable view of device data that represents
+ * a list of elements of arbitrary type (including further nested lists).
+ */
+class list_view {};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/lists_column_device_view.cuh b/cpp/include/cudf/lists/lists_column_device_view.cuh
new file mode 100644
index 0000000..943ccbf
--- /dev/null
+++ b/cpp/include/cudf/lists/lists_column_device_view.cuh
@@ -0,0 +1,120 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cuda_runtime.h>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/types.hpp>
+
+namespace cudf {
+
+namespace detail {
+
+/**
+ * @brief Given a column_device_view, an instance of this class provides a
+ * wrapper on this compound column for list operations.
+ * Analogous to list_column_view.
+ */
+class lists_column_device_view : private column_device_view {
+ public:
+  lists_column_device_view()                                = delete;
+  ~lists_column_device_view()                               = default;
+  lists_column_device_view(lists_column_device_view const&) = default;  ///< Copy constructor
+  lists_column_device_view(lists_column_device_view&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return The reference to this lists column device view
+   */
+  lists_column_device_view& operator=(lists_column_device_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return The reference to this lists column device view
+   */
+  lists_column_device_view& operator=(lists_column_device_view&&) = default;
+
+  /**
+   * @brief Construct a new lists column device view object from a column device view.
+   *
+   * @param underlying_ The column device view to wrap
+   */
+  CUDF_HOST_DEVICE lists_column_device_view(column_device_view const& underlying_)
+    : column_device_view(underlying_)
+  {
+#ifdef __CUDA_ARCH__
+    cudf_assert(underlying_.type().id() == type_id::LIST and
+                "lists_column_device_view only supports lists");
+#else
+    CUDF_EXPECTS(underlying_.type().id() == type_id::LIST,
+                 "lists_column_device_view only supports lists");
+#endif
+  }
+
+  using column_device_view::is_null;
+  using column_device_view::nullable;
+  using column_device_view::offset;
+  using column_device_view::size;
+
+  /**
+   * @brief Fetches the offsets column of the underlying list column.
+   *
+   * @return The offsets column of the underlying list column
+   */
+  [[nodiscard]] __device__ inline column_device_view offsets() const
+  {
+    return column_device_view::child(lists_column_view::offsets_column_index);
+  }
+
+  /**
+   * @brief Fetches the list offset value at a given row index while taking column offset into
+   * account.
+   *
+   * @param idx The row index to fetch the list offset value at
+   * @return The list offset value at a given row index while taking column offset into account
+   */
+  [[nodiscard]] __device__ inline size_type offset_at(size_type idx) const
+  {
+    return offsets().size() > 0 ? offsets().element<size_type>(offset() + idx) : 0;
+  }
+
+  /**
+   * @brief Fetches the child column of the underlying list column.
+   *
+   * @return The child column of the underlying list column
+   */
+  [[nodiscard]] __device__ inline column_device_view child() const
+  {
+    return column_device_view::child(lists_column_view::child_column_index);
+  }
+
+  /**
+   * @brief Fetches the child column of the underlying list column with offset and size applied
+   *
+   * @return The child column sliced relative to the parent's offset and size
+   */
+  [[nodiscard]] __device__ inline column_device_view get_sliced_child() const
+  {
+    auto start = offset_at(0);
+    auto end   = offset_at(size());
+    return child().slice(start, end - start);
+  }
+};
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/lists_column_view.hpp b/cpp/include/cudf/lists/lists_column_view.hpp
new file mode 100644
index 0000000..8c6368e
--- /dev/null
+++ b/cpp/include/cudf/lists/lists_column_view.hpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+/**
+ * @file
+ * @brief Class definition for cudf::lists_column_view
+ */
+
+namespace cudf {
+
+/**
+ * @addtogroup lists_classes
+ * @{
+ */
+
+/**
+ * @brief Given a column-view of lists type, an instance of this class
+ * provides a wrapper on this compound column for list operations.
+ */
+class lists_column_view : private column_view {
+ public:
+  /**
+   * @brief Construct a new lists column view object from a column view.
+   *
+   * @param lists_column The column view to wrap
+   */
+  lists_column_view(column_view const& lists_column);
+  lists_column_view(lists_column_view&&)      = default;  ///< Move constructor
+  lists_column_view(lists_column_view const&) = default;  ///< Copy constructor
+  ~lists_column_view()                        = default;
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return The reference to this lists column
+   */
+  lists_column_view& operator=(lists_column_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return The reference to this lists column
+   */
+  lists_column_view& operator=(lists_column_view&&) = default;
+
+  static constexpr size_type offsets_column_index{0};  ///< The index of the offsets column
+  static constexpr size_type child_column_index{1};    ///< The index of the child column
+
+  using column_view::child_begin;
+  using column_view::child_end;
+  using column_view::has_nulls;
+  using column_view::is_empty;
+  using column_view::null_count;
+  using column_view::null_mask;
+  using column_view::offset;
+  using column_view::size;
+  using offset_iterator = size_type const*;  ///< Iterator type for offsets
+
+  /**
+   * @brief Returns the parent column.
+   *
+   * @return The parent column
+   */
+  [[nodiscard]] column_view parent() const;
+
+  /**
+   * @brief Returns the internal column of offsets
+   *
+   * @throw cudf::logic error if this is an empty column
+   * @return The internal column of offsets
+   */
+  [[nodiscard]] column_view offsets() const;
+
+  /**
+   * @brief Returns the internal child column
+   *
+   * @throw cudf::logic error if this is an empty column
+   * @return The internal child column
+   */
+  [[nodiscard]] column_view child() const;
+
+  /**
+   * @brief Returns the internal child column, applying any offset from the root.
+   *
+   * Slice/split offset values are only stored at the root level of a list column.
+   * So when doing computations on them, we need to apply that offset to
+   * the child columns when recursing.  Most functions operating in a recursive manner
+   * on lists columns should be using `get_sliced_child()` instead of `child()`.
+   *
+   * @throw cudf::logic error if this is an empty column
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return A sliced child column view
+   */
+  [[nodiscard]] column_view get_sliced_child(rmm::cuda_stream_view stream) const;
+
+  /**
+   * @brief Return first offset (accounting for column offset)
+   *
+   * @return Pointer to the first offset
+   */
+  [[nodiscard]] offset_iterator offsets_begin() const noexcept
+  {
+    return offsets().begin<size_type>() + offset();
+  }
+
+  /**
+   * @brief Return pointer to the position that is one past the last offset
+   *
+   * This function return the position that is one past the last offset of the lists column.
+   * Since the current lists column may be a sliced column, this offsets_end() iterator should not
+   * be computed using the size of the offsets() child column, which is also the offsets of the
+   * entire original (non-sliced) lists column.
+   *
+   * @return Pointer to one past the last offset
+   */
+  [[nodiscard]] offset_iterator offsets_end() const noexcept
+  {
+    return offsets_begin() + size() + 1;
+  }
+};
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/reverse.hpp b/cpp/include/cudf/lists/reverse.hpp
new file mode 100644
index 0000000..226d417
--- /dev/null
+++ b/cpp/include/cudf/lists/reverse.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf::lists {
+/**
+ * @addtogroup lists_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Reverse the element order within each list of the input column.
+ *
+ * Any null input row will result in a corresponding null row in the output column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = [ [1, 2, 3], [], null, [4, 5, null] ]
+ * r = reverse(s)
+ * r is now [ [3, 2, 1], [], null, [null, 5, 4] ]
+ * @endcode
+ *
+ * @param input Lists column for this operation
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New lists column with reversed lists
+ */
+std::unique_ptr<column> reverse(
+  lists_column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+
+}  // namespace cudf::lists
diff --git a/cpp/include/cudf/lists/set_operations.hpp b/cpp/include/cudf/lists/set_operations.hpp
new file mode 100644
index 0000000..9d58d0f
--- /dev/null
+++ b/cpp/include/cudf/lists/set_operations.hpp
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+namespace cudf::lists {
+/**
+ * @addtogroup set_operations
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Check if lists at each row of the given lists columns overlap.
+ *
+ * Given two input lists columns, each list row in one column is checked if it has any common
+ * elements with the corresponding row of the other column.
+ *
+ * A null input row in any of the input lists columns will result in a null output row.
+ *
+ * @throw cudf::logic_error if the input lists columns have different sizes.
+ * @throw cudf::logic_error if children of the input lists columns have different data types.
+ *
+ * Example:
+ * @code{.pseudo}
+ * lhs    = { {0, 1, 2}, {1, 2, 3}, null,         {4, null, 5} }
+ * rhs    = { {1, 2, 3}, {4, 5},    {null, 7, 8}, {null, null} }
+ * result = { true, false, null, true }
+ * @endcode
+ *
+ * @param lhs The input lists column for one side
+ * @param rhs The input lists column for the other side
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal, default
+ *        to be `UNEQUAL` which means only non-null elements are checked for overlapping
+ * @param nans_equal Flag to specify whether floating-point NaNs should be considered as equal
+ * @param mr Device memory resource used to allocate the returned object
+ * @return A column of type BOOL containing the check results
+ */
+std::unique_ptr<column> have_overlap(
+  lists_column_view const& lhs,
+  lists_column_view const& rhs,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a lists column of distinct elements common to two input lists columns.
+ *
+ * Given two input lists columns `lhs` and `rhs`, an output lists column is created in a way such
+ * that each of its row `i` contains a list of distinct elements that can be found in both `lhs[i]`
+ * and `rhs[i]`.
+ *
+ * The order of distinct elements in the output rows is unspecified.
+ *
+ * A null input row in any of the input lists columns will result in a null output row.
+ *
+ * @throw cudf::logic_error if the input lists columns have different sizes.
+ * @throw cudf::logic_error if children of the input lists columns have different data types.
+ *
+ * Example:
+ * @code{.pseudo}
+ * lhs    = { {2, 1, 2}, {1, 2, 3}, null,         {4, null, 5} }
+ * rhs    = { {1, 2, 3}, {4, 5},    {null, 7, 8}, {null, null} }
+ * result = { {1, 2}, {}, null, {null} }
+ * @endcode
+ *
+ * @param lhs The input lists column for one side
+ * @param rhs The input lists column for the other side
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether floating-point NaNs should be considered as equal
+ * @param mr Device memory resource used to allocate the returned object
+ * @return A lists column containing the intersection results
+ */
+std::unique_ptr<column> intersect_distinct(
+  lists_column_view const& lhs,
+  lists_column_view const& rhs,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a lists column of distinct elements found in either of two input lists columns.
+ *
+ * Given two input lists columns `lhs` and `rhs`, an output lists column is created in a way such
+ * that each of its row `i` contains a list of distinct elements that can be found in either
+ * `lhs[i]` or `rhs[i]`.
+ *
+ * The order of distinct elements in the output rows is unspecified.
+ *
+ * A null input row in any of the input lists columns will result in a null output row.
+ *
+ * @throw cudf::logic_error if the input lists columns have different sizes.
+ * @throw cudf::logic_error if children of the input lists columns have different data types.
+ *
+ * Example:
+ * @code{.pseudo}
+ * lhs    = { {2, 1, 2}, {1, 2, 3}, null,         {4, null, 5} }
+ * rhs    = { {1, 2, 3}, {4, 5},    {null, 7, 8}, {null, null} }
+ * result = { {1, 2, 3}, {1, 2, 3, 4, 5}, null, {4, null, 5} }
+ * @endcode
+ *
+ * @param lhs The input lists column for one side
+ * @param rhs The input lists column for the other side
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether floating-point NaNs should be considered as equal
+ * @param mr Device memory resource used to allocate the returned object
+ * @return A lists column containing the union results
+ */
+std::unique_ptr<column> union_distinct(
+  lists_column_view const& lhs,
+  lists_column_view const& rhs,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a lists column of distinct elements found only in the left input column.
+ *
+ * Given two input lists columns `lhs` and `rhs`, an output lists column is created in a way such
+ * that each of its row `i` contains a list of distinct elements that can be found in `lhs[i]` but
+ * are not found in `rhs[i]`.
+ *
+ * The order of distinct elements in the output rows is unspecified.
+ *
+ * A null input row in any of the input lists columns will result in a null output row.
+ *
+ * @throw cudf::logic_error if the input lists columns have different sizes.
+ * @throw cudf::logic_error if children of the input lists columns have different data types.
+ *
+ * Example:
+ * @code{.pseudo}
+ * lhs    = { {2, 1, 2}, {1, 2, 3}, null,         {4, null, 5} }
+ * rhs    = { {1, 2, 3}, {4, 5},    {null, 7, 8}, {null, null} }
+ * result = { {}, {1, 2, 3}, null, {4, 5} }
+ * @endcode
+ *
+ * @param lhs The input lists column of elements that may be included
+ * @param rhs The input lists column of elements to exclude
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether floating-point NaNs should be considered as equal
+ * @param mr Device memory resource used to allocate the returned object
+ * @return A lists column containing the difference results
+ */
+std::unique_ptr<column> difference_distinct(
+  lists_column_view const& lhs,
+  lists_column_view const& rhs,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf::lists
diff --git a/cpp/include/cudf/lists/sorting.hpp b/cpp/include/cudf/lists/sorting.hpp
new file mode 100644
index 0000000..c203c45
--- /dev/null
+++ b/cpp/include/cudf/lists/sorting.hpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace lists {
+/**
+ * @addtogroup lists_sort
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Segmented sort of the elements within a list in each row of a list column.
+ *
+ * `source_column` with depth 1 is only supported.
+ *
+ * * @code{.pseudo}
+ * source_column            : [{4, 2, 3, 1}, {1, 2, NULL, 4}, {-10, 10, 0}]
+ *
+ * Ascending,  Null After   : [{1, 2, 3, 4}, {1, 2, 4, NULL}, {-10, 0, 10}]
+ * Ascending,  Null Before  : [{1, 2, 3, 4}, {NULL, 1, 2, 4}, {-10, 0, 10}]
+ * Descending, Null After   : [{4, 3, 2, 1}, {NULL, 4, 2, 1}, {10, 0, -10}]
+ * Descending, Null Before  : [{4, 3, 2, 1}, {4, 2, 1, NULL}, {10, 0, -10}]
+ * @endcode
+ *
+ * @param source_column View of the list column of numeric types to sort
+ * @param column_order The desired sort order
+ * @param null_precedence The desired order of null compared to other elements in the list
+ * @param mr Device memory resource to allocate any returned objects
+ * @return list column with elements in each list sorted.
+ *
+ */
+std::unique_ptr<column> sort_lists(
+  lists_column_view const& source_column,
+  order column_order,
+  null_order null_precedence,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Segmented sort of the elements within a list in each row of a list column using stable
+ * sort.
+ *
+ * @copydoc cudf::lists::sort_lists
+ */
+std::unique_ptr<column> stable_sort_lists(
+  lists_column_view const& source_column,
+  order column_order,
+  null_order null_precedence,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/include/cudf/lists/stream_compaction.hpp b/cpp/include/cudf/lists/stream_compaction.hpp
new file mode 100644
index 0000000..5ddaa99
--- /dev/null
+++ b/cpp/include/cudf/lists/stream_compaction.hpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf::lists {
+
+/**
+ * @addtogroup lists_filtering
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Filters elements in each row of `input` LIST column using `boolean_mask`
+ * LIST of booleans as a mask.
+ *
+ * Given an input `LIST` column and a list-of-bools column, the function produces
+ * a new `LIST` column of the same type as `input`, where each element is copied
+ * from the input row *only* if the corresponding `boolean_mask` is non-null and `true`.
+ *
+ * E.g.
+ * @code{.pseudo}
+ * input        = { {0,1,2}, {3,4}, {5,6,7}, {8,9} };
+ * boolean_mask = { {0,1,1}, {1,0}, {1,1,1}, {0,0} };
+ * results      = { {1,2},   {3},   {5,6,7}, {} };
+ * @endcode
+ *
+ * `input` and `boolean_mask` must have the same number of rows.
+ * The output column has the same number of rows as the input column.
+ * An element is copied to an output row *only* if the corresponding boolean_mask element is `true`.
+ * An output row is invalid only if the input row is invalid.
+ *
+ * @throws cudf::logic_error if `boolean_mask` is not a "lists of bools" column
+ * @throws cudf::logic_error if `input` and `boolean_mask` have different number of rows
+ *
+ * @param input The input list column view to be filtered
+ * @param boolean_mask A nullable list of bools column used to filter `input` elements
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return List column of the same type as `input`, containing filtered list rows
+ */
+std::unique_ptr<column> apply_boolean_mask(
+  lists_column_view const& input,
+  lists_column_view const& boolean_mask,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a new list column without duplicate elements in each list.
+ *
+ * Given a lists column `input`, distinct elements of each list are copied to the corresponding
+ * output list. The order of lists is preserved while the order of elements within each list is not
+ * guaranteed.
+ *
+ * Example:
+ * @code{.pseudo}
+ * input  = { {0, 1, 2, 3, 2}, {3, 1, 2}, null, {4, null, null, 5} }
+ * result = { {0, 1, 2, 3}, {3, 1, 2}, null, {4, null, 5} }
+ * @endcode
+ *
+ * @param input The input lists column
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether floating-point NaNs should be considered as equal
+ * @param mr Device memory resource used to allocate the returned object
+ * @return The resulting lists column containing lists without duplicates
+ */
+std::unique_ptr<column> distinct(
+  lists_column_view const& input,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+}  // namespace cudf::lists
diff --git a/cpp/include/cudf/merge.hpp b/cpp/include/cudf/merge.hpp
new file mode 100644
index 0000000..3d09550
--- /dev/null
+++ b/cpp/include/cudf/merge.hpp
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2018-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+/**
+ * @addtogroup column_merge
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Merge a set of sorted tables.
+ *
+ * Merges sorted tables into one sorted table
+ * containing data from all tables.
+ *
+ * ```
+ * Example 1:
+ * input:
+ * table 1 => col 1 {0, 1, 2, 3}
+ *            col 2 {4, 5, 6, 7}
+ * table 2 => col 1 {1, 2}
+ *            col 2 {8, 9}
+ * table 3 => col 1 {2, 4}
+ *            col 2 {8, 9}
+ * output:
+ * table => col 1 {0, 1, 1, 2, 2, 2, 3, 4}
+ *          col 2 {4, 5, 8, 6, 8, 9, 7, 9}
+ * ```
+ * ```
+ * Example 2:
+ * input:
+ * table 1 => col 0 {1, 0}
+ *            col 1 {'c', 'b'}
+ *            col 2 {RED, GREEN}
+ *
+ *
+ * table 2 => col 0 {1}
+ *            col 1 {'a'}
+ *            col 2 {NULL}
+ *
+ *  with key_cols[] = {0,1}
+ *  and  asc_desc[] = {ASC, ASC};
+ *
+ *  Lex-sorting is on columns {0,1}; hence, lex-sorting of ((L0 x L1) V (R0 x R1)) is:
+ *  (0,'b', GREEN), (1,'a', NULL), (1,'c', RED)
+ *
+ *  (third column, the "color", just "goes along for the ride";
+ *   meaning it is permuted according to the data movements dictated
+ *   by lexicographic ordering of columns 0 and 1)
+ *
+ *   with result columns:
+ *
+ *   Res0 = {0,1,1}
+ *   Res1 = {'b', 'a', 'c'}
+ *   Res2 = {GREEN, NULL, RED}
+ * ```
+ *
+ * @throws cudf::logic_error if tables in `tables_to_merge` have different
+ * number of columns
+ * @throws cudf::logic_error if tables in `tables_to_merge` have columns with
+ * mismatched types
+ * @throws cudf::logic_error if `key_cols` is empty
+ * @throws cudf::logic_error if `key_cols` size is larger than the number of
+ * columns in `tables_to_merge` tables
+ * @throws cudf::logic_error if `key_cols` size and `column_order` size mismatches
+ *
+ * @param[in] tables_to_merge Non-empty list of tables to be merged
+ * @param[in] key_cols Indices of left_cols and right_cols to be used
+ *                     for comparison criteria
+ * @param[in] column_order Sort order types of columns indexed by key_cols
+ * @param[in] null_precedence Array indicating the order of nulls with respect
+ * to non-nulls for the indexing columns (key_cols)
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @returns A table containing sorted data from all input tables
+ */
+std::unique_ptr<cudf::table> merge(
+  std::vector<table_view> const& tables_to_merge,
+  std::vector<cudf::size_type> const& key_cols,
+  std::vector<cudf::order> const& column_order,
+  std::vector<cudf::null_order> const& null_precedence = {},
+  rmm::mr::device_memory_resource* mr                  = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/null_mask.hpp b/cpp/include/cudf/null_mask.hpp
new file mode 100644
index 0000000..672f479
--- /dev/null
+++ b/cpp/include/cudf/null_mask.hpp
@@ -0,0 +1,190 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <vector>
+
+namespace cudf {
+
+/**
+ * @addtogroup column_nullmask
+ * @{
+ * @file
+ * @brief APIs for managing validity bitmasks
+ */
+
+/**
+ * @brief Returns the null count for a null mask of the specified `state`
+ * representing `size` elements.
+ *
+ * @throw std::invalid_argument if state is UNINITIALIZED
+ *
+ * @param state The state of the null mask
+ * @param size The number of elements represented by the mask
+ * @return The count of null elements
+ */
+size_type state_null_count(mask_state state, size_type size);
+
+/**
+ * @brief Computes the required bytes necessary to represent the specified
+ * number of bits with a given padding boundary.
+ *
+ * @note The Arrow specification for the null bitmask requires a 64B padding
+ * boundary.
+ *
+ * @param number_of_bits The number of bits that need to be represented
+ * @param padding_boundary The value returned will be rounded up to a multiple
+ * of this value
+ * @return The necessary number of bytes
+ */
+std::size_t bitmask_allocation_size_bytes(size_type number_of_bits,
+                                          std::size_t padding_boundary = 64);
+
+/**
+ * @brief Returns the number of `bitmask_type` words required to represent the
+ * specified number of bits.
+ *
+ * Unlike `bitmask_allocation_size_bytes`, which returns the number of *bytes*
+ * needed for a bitmask allocation (including padding), this function returns
+ * the *actual* number `bitmask_type` elements necessary to represent
+ * `number_of_bits`. This is useful when one wishes to process all of the bits
+ * in a bitmask and ignore the padding/slack bits.
+ *
+ * @param number_of_bits The number of bits that need to be represented
+ * @return The necessary number of `bitmask_type` elements
+ */
+size_type num_bitmask_words(size_type number_of_bits);
+
+/**
+ * @brief Creates a `device_buffer` for use as a null value indicator bitmask of
+ * a `column`.
+ *
+ * @param size The number of elements to be represented by the mask
+ * @param state The desired state of the mask
+ * @param mr Device memory resource used to allocate the returned device_buffer
+ * @return A `device_buffer` for use as a null bitmask
+ * satisfying the desired size and state
+ */
+rmm::device_buffer create_null_mask(
+  size_type size,
+  mask_state state,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Sets a pre-allocated bitmask buffer to a given state in the range
+ *  `[begin_bit, end_bit)`
+ *
+ * Sets `[begin_bit, end_bit)` bits of bitmask to valid if `valid==true`
+ * or null otherwise.
+ *
+ * @param bitmask Pointer to bitmask (e.g. returned by `column_viewnull_mask()`)
+ * @param begin_bit Index of the first bit to set (inclusive)
+ * @param end_bit Index of the last bit to set (exclusive)
+ * @param valid If true set all entries to valid; otherwise, set all to null
+ */
+void set_null_mask(bitmask_type* bitmask, size_type begin_bit, size_type end_bit, bool valid);
+
+/**
+ * @brief Creates a `device_buffer` from a slice of bitmask defined by a range
+ * of indices `[begin_bit, end_bit)`.
+ *
+ * Returns empty `device_buffer` if `bitmask == nullptr`.
+ *
+ * @throws cudf::logic_error if `begin_bit > end_bit`
+ * @throws cudf::logic_error if `begin_bit < 0`
+ *
+ * @param mask Bitmask residing in device memory whose bits will be copied
+ * @param begin_bit Index of the first bit to be copied (inclusive)
+ * @param end_bit Index of the last bit to be copied (exclusive)
+ * @param mr Device memory resource used to allocate the returned device_buffer
+ * @return A `device_buffer` containing the bits
+ * `[begin_bit, end_bit)` from `mask`.
+ */
+rmm::device_buffer copy_bitmask(
+  bitmask_type const* mask,
+  size_type begin_bit,
+  size_type end_bit,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Copies `view`'s bitmask from the bits
+ * `[view.offset(), view.offset() + view.size())` into a `device_buffer`
+ *
+ * Returns empty `device_buffer` if the column is not nullable
+ *
+ * @param view Column view whose bitmask needs to be copied
+ * @param mr Device memory resource used to allocate the returned device_buffer
+ * @return A `device_buffer` containing the bits
+ * `[view.offset(), view.offset() + view.size())` from `view`'s bitmask.
+ */
+rmm::device_buffer copy_bitmask(
+  column_view const& view,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs bitwise AND of the bitmasks of columns of a table. Returns
+ * a pair of resulting mask and count of unset bits.
+ *
+ * If any of the columns isn't nullable, it is considered all valid.
+ * If no column in the table is nullable, an empty bitmask is returned.
+ *
+ * @param view The table of columns
+ * @param mr Device memory resource used to allocate the returned device_buffer
+ * @return A pair of resulting bitmask and count of unset bits
+ */
+std::pair<rmm::device_buffer, size_type> bitmask_and(
+  table_view const& view,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs bitwise OR of the bitmasks of columns of a table. Returns
+ * a pair of resulting mask and count of unset bits.
+ *
+ * If any of the columns isn't nullable, it is considered all valid.
+ * If no column in the table is nullable, an empty bitmask is returned.
+ *
+ * @param view The table of columns
+ * @param mr Device memory resource used to allocate the returned device_buffer
+ * @return A pair of resulting bitmask and count of unset bits
+ */
+std::pair<rmm::device_buffer, size_type> bitmask_or(
+  table_view const& view,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Given a validity bitmask, counts the number of null elements (unset bits)
+ * in the range `[start, stop)`.
+ *
+ * If `bitmask == nullptr`, all elements are assumed to be valid and the
+ * function returns ``.
+ *
+ * @throws cudf::logic_error if `start > stop`
+ * @throws cudf::logic_error if `start < 0`
+ *
+ * @param bitmask Validity bitmask residing in device memory.
+ * @param start Index of the first bit to count (inclusive).
+ * @param stop Index of the last bit to count (exclusive).
+ * @return The number of null elements in the specified range.
+ */
+cudf::size_type null_count(bitmask_type const* bitmask, size_type start, size_type stop);
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/partitioning.hpp b/cpp/include/cudf/partitioning.hpp
new file mode 100644
index 0000000..2c91bdf
--- /dev/null
+++ b/cpp/include/cudf/partitioning.hpp
@@ -0,0 +1,248 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/hashing.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+/**
+ * @addtogroup reorder_partition
+ * @{
+ * @file
+ * @brief Column partitioning APIs
+ */
+
+/**
+ * @brief Partitions rows of `t` according to the mapping specified by
+ * `partition_map`.
+ *
+ * For each row at `i` in `t`, `partition_map[i]` indicates which partition row
+ * `i` belongs to. `partition` creates a new table by rearranging the rows of
+ * `t` such that rows in the same partition are contiguous. The returned table
+ * is in ascending partition order from `[0, num_partitions)`. The order within
+ * each partition is undefined.
+ *
+ * Returns a `vector<size_type>` of `num_partitions + 1` values that indicate
+ * the starting position of each partition within the returned table, i.e.,
+ * partition `i` starts at `offsets[i]` (inclusive) and ends at `offset[i+1]`
+ * (exclusive). As a result, if value `j` in `[0, num_partitions)` does not
+ * appear in `partition_map`, partition `j` will be empty, i.e.,
+ * `offsets[j+1] - offsets[j] == 0`.
+ *
+ * Values in `partition_map` must be in the range `[0, num_partitions)`,
+ * otherwise behavior is undefined.
+ *
+ * @throw cudf::logic_error when `partition_map` is a non-integer type
+ * @throw cudf::logic_error when `partition_map.has_nulls() == true`
+ * @throw cudf::logic_error when `partition_map.size() != t.num_rows()`
+ *
+ * @param t The table to partition
+ * @param partition_map Non-nullable column of integer values that map each row
+ * in `t` to it's partition.
+ * @param num_partitions The total number of partitions
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Pair containing the reordered table and vector of `num_partitions +
+ * 1` offsets to each partition such that the size of partition `i` is
+ * determined by `offset[i+1] - offset[i]`.
+ */
+std::pair<std::unique_ptr<table>, std::vector<size_type>> partition(
+  table_view const& t,
+  column_view const& partition_map,
+  size_type num_partitions,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Partitions rows from the input table into multiple output tables.
+ *
+ * Partitions rows of `input` into `num_partitions` bins based on the hash
+ * value of the columns specified by `columns_to_hash`. Rows partitioned into
+ * the same bin are grouped consecutively in the output table. Returns a vector
+ * of row offsets to the start of each partition in the output table.
+ *
+ * @throw std::out_of_range if index is `columns_to_hash` is invalid
+ *
+ * @param input The table to partition
+ * @param columns_to_hash Indices of input columns to hash
+ * @param num_partitions The number of partitions to use
+ * @param hash_function Optional hash id that chooses the hash function to use
+ * @param seed Optional seed value to the hash function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @returns An output table and a vector of row offsets to each partition
+ */
+std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
+  table_view const& input,
+  std::vector<size_type> const& columns_to_hash,
+  int num_partitions,
+  hash_id hash_function               = hash_id::HASH_MURMUR3,
+  uint32_t seed                       = DEFAULT_HASH_SEED,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Round-robin partition.
+ *
+ * Returns a new table with rows re-arranged into partition groups and
+ * a vector of row offsets to the start of each partition in the output table.
+ * Rows are assigned partitions based on their row index in the table,
+ * in a round robin fashion.
+ *
+ * @throws cudf::logic_error if `num_partitions <= 1`
+ * @throws cudf::logic_error if `start_partition >= num_partitions`
+ *
+ * A good analogy for the algorithm is dealing out cards:
+ *
+ *  1. The deck of cards is represented as the rows in the table.
+ *  2. The number of partitions is the number of players being dealt cards.
+ *  3. the start_partition indicates which player starts getting cards first.
+ *
+ * The algorithm has two outcomes:
+ *
+ *  1. Another deck of cards formed by stacking each
+ *      player's cards back into a deck again,
+ *      preserving the order of cards dealt to each player,
+ *      starting with player 0.
+ *  2. A vector into the output deck indicating where a player's cards start.
+ *
+ * A player's deck (partition) is the range of cards starting
+ * at the corresponding offset and ending at the next player's
+ * starting offset or the last card in the deck if it's the last player.
+ *
+ * When num_partitions > nrows, we have more players than cards.
+ * We start dealing to the first indicated player and continuing
+ * around the players until we run out of cards before we run out of players.
+ * Players that did not get any cards are represented by
+ * `offset[i] == offset[i+1] or
+ * offset[i] == table.num_rows() if i == num_partitions-1`
+ * meaning there are no cards (rows) in their deck (partition).
+ *
+ * ```
+ * Example 1:
+ * input:
+ * table => col 1 {0, ..., 12}
+ * num_partitions = 3
+ * start_partition = 0
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {0,3,6,9,12,1,4,7,10,2,5,8,11}
+ * partition_offsets => {0,5,9}
+ *
+ * Example 2:
+ * input:
+ * table => col 1 {0, ..., 12}
+ * num_partitions = 3
+ * start_partition = 1
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {2,5,8,11,0,3,6,9,12,1,4,7,10}
+ * partition_offsets => {0,4,9}
+ *
+ * Example 3:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 3
+ * start_partition = 0
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {0,3,6,9,1,4,7,10,2,5,8}
+ * partition_offsets => {0,4,8}
+ *
+ * Example 4:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 3
+ * start_partition = 1
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {2,5,8,0,3,6,9,1,4,7,10}
+ * partition_offsets => {0,3,7}
+ *
+ * Example 5:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 3
+ * start_partition = 2
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {1,4,7,10,2,5,8,0,3,6,9}
+ * partition_offsets => {0,4,7}
+ *
+ * Example 6:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 15 > num_rows = 11
+ * start_partition = 2
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {0,1,2,3,4,5,6,7,8,9,10}
+ * partition_offsets => {0,0,0,1,2,3,4,5,6,7,8,9,10,11,11}
+ *
+ * Example 7:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 15 > num_rows = 11
+ * start_partition = 10
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {5,6,7,8,9,10,0,1,2,3,4}
+ * partition_offsets => {0,1,2,3,4,5,6,6,6,6,6,7,8,9,10}
+ *
+ * Example 8:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 15 > num_rows = 11
+ * start_partition = 14
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {1,2,3,4,5,6,7,8,9,10,0}
+ * partition_offsets => {0,1,2,3,4,5,6,7,8,9,10,10,10,10,10}
+ *
+ * Example 9:
+ * input:
+ * table => col 1 {0, ..., 10}
+ * num_partitions = 11 == num_rows = 11
+ * start_partition = 2
+ *
+ * output: pair<table, partition_offsets>
+ * table => col 1 {9,10,0,1,2,3,4,5,6,7,8}
+ * partition_offsets => {0,1,2,3,4,5,6,7,8,9,10}
+ * ```
+ *
+ * @param[in] input The input table to be round-robin partitioned
+ * @param[in] num_partitions Number of partitions for the table
+ * @param[in] start_partition Index of the 1st partition
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @return A std::pair consisting of a unique_ptr to the partitioned table
+ * and the partition offsets for each partition within the table.
+ */
+std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> round_robin_partition(
+  table_view const& input,
+  cudf::size_type num_partitions,
+  cudf::size_type start_partition     = 0,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/quantiles.hpp b/cpp/include/cudf/quantiles.hpp
new file mode 100644
index 0000000..1f3c26f
--- /dev/null
+++ b/cpp/include/cudf/quantiles.hpp
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+/**
+ * @addtogroup column_quantiles
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Computes quantiles with interpolation.
+ *
+ * Computes the specified quantiles by interpolating values between which they
+ * lie, using the interpolation strategy specified in `interp`.
+ *
+ * @param[in] input           Column from which to compute quantile values
+ * @param[in] q               Specified quantiles in range [0, 1]
+ * @param[in] interp          Strategy used to select between values adjacent to
+ *                            a specified quantile.
+ * @param[in] ordered_indices Column containing the sorted order of `input`.
+ *                            If the column is empty, all `input` values are
+ *                            used in existing order. Indices must be in range
+ *                            [0, `input.size()`), but are not required to be
+ *                            unique. Values not indexed by this column will be
+ *                            ignored.
+ * @param[in] exact           If true, returns doubles.
+ *                            If false, returns same type as input.
+ * @param[in] mr              Device memory resource used to allocate the returned column's device
+ memory
+ * @returns Column of specified quantiles, with nulls for indeterminable values
+ */
+
+std::unique_ptr<column> quantile(
+  column_view const& input,
+  std::vector<double> const& q,
+  interpolation interp                = interpolation::LINEAR,
+  column_view const& ordered_indices  = {},
+  bool exact                          = true,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the rows of the input corresponding to the requested quantiles.
+ *
+ * Quantiles are cut points that divide the range of a dataset into continuous
+ * intervals. e.g: quartiles are the three cut points that divide a dataset into
+ * four equal-sized groups. See https://en.wikipedia.org/wiki/Quantile
+ *
+ * The indices used to gather rows are computed by interpolating between the
+ * index on either side of the desired quantile. Since some columns may be
+ * non-arithmetic, interpolation between rows is limited to non-arithmetic
+ * strategies.
+ *
+ * Non-arithmetic interpolation strategies include HIGHER, LOWER, and NEAREST.
+ *
+ * quantiles `<= 0` correspond to row `0`. (first)
+ * quantiles `>= 1` correspond to row `input.size() - 1`. (last)
+ *
+ * @param input           Table used to compute quantile rows
+ * @param q               Desired quantiles in range [0, 1]
+ * @param interp          Strategy used to select between the two rows on either
+                          side of the desired quantile.
+ * @param is_input_sorted Indicates if the input has been pre-sorted
+ * @param column_order    The desired sort order for each column
+ * @param null_precedence The desired order of null compared to other elements
+ * @param mr              Device memory resource used to allocate the returned table's device memory
+ *
+ * @returns Table of specified quantiles, with nulls for indeterminable values
+ * @throws cudf::logic_error if `interp` is an arithmetic interpolation strategy
+ * @throws cudf::logic_error if `input` is empty
+ */
+std::unique_ptr<table> quantiles(
+  table_view const& input,
+  std::vector<double> const& q,
+  interpolation interp                           = interpolation::NEAREST,
+  cudf::sorted is_input_sorted                   = sorted::NO,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Calculate approximate percentiles on an input tdigest column.
+ *
+ * tdigest (https://arxiv.org/pdf/1902.04023.pdf) columns are produced specifically
+ * by the TDIGEST and MERGE_TDIGEST aggregations.  These columns represent
+ * compressed representations of a very large input data set that can be
+ * queried for quantile information.
+ *
+ * Produces a LIST column where each row `i` represents output from querying the
+ * corresponding tdigest from `input` row `i`. The length of each output list
+ * is the number of percentages specified in `percentages`.
+ *
+ * @param input           tdigest input data. One tdigest per row
+ * @param percentiles     Desired percentiles in range [0, 1]
+ * @param mr              Device memory resource used to allocate the returned column's device
+ * memory
+ *
+ * @throws cudf::logic_error if `input` is not a valid tdigest column.
+ * @throws cudf::logic_error if `percentiles` is not a FLOAT64 column.
+ *
+ * @returns LIST Column containing requested percentile values as FLOAT64
+ */
+std::unique_ptr<column> percentile_approx(
+  tdigest::tdigest_column_view const& input,
+  column_view const& percentiles,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/reduction.hpp b/cpp/include/cudf/reduction.hpp
new file mode 100644
index 0000000..52aebeb
--- /dev/null
+++ b/cpp/include/cudf/reduction.hpp
@@ -0,0 +1,222 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <optional>
+
+namespace cudf {
+/**
+ * @addtogroup aggregation_reduction
+ * @{
+ * @file
+ */
+
+/**
+ *  @brief Enum to describe scan operation type
+ */
+enum class scan_type : bool { INCLUSIVE, EXCLUSIVE };
+
+/**
+ * @brief  Computes the reduction of the values in all rows of a column.
+ *
+ * This function does not detect overflows in reductions. When `output_dtype`
+ * does not match the `col.type()`, their values may be promoted to
+ * `int64_t` or `double` for computing aggregations and then cast to
+ * `output_dtype` before returning.
+ *
+ * Only `min` and `max` ops are supported for reduction of non-arithmetic
+ * types (e.g. timestamp or string).
+ *
+ * Any null values are skipped for the operation.
+ *
+ * If the column is empty or contains all null entries `col.size()==col.null_count()`,
+ * the output scalar value will be `false` for reduction type `any` and `true`
+ * for reduction type `all`. For all other reductions, the output scalar
+ * returns with `is_valid()==false`.
+ *
+ * If the input column is an arithmetic type, the `output_dtype` can be any arithmetic
+ * type. If the input column is a non-arithmetic type (e.g. timestamp or string)
+ * the `output_dtype` must match the `col.type()`. If the reduction type is `any` or
+ * `all`, the `output_dtype` must be type BOOL8.
+ *
+ * If the reduction fails, the output scalar returns with `is_valid()==false`.
+ *
+ * @throw cudf::logic_error if reduction is called for non-arithmetic output
+ * type and operator other than `min` and `max`.
+ * @throw cudf::logic_error if input column data type is not convertible to
+ * `output_dtype`.
+ * @throw cudf::logic_error if `min` or `max` reduction is called and the
+ * output type does not match the input column data type.
+ * @throw cudf::logic_error if `any` or `all` reduction is called and the
+ * output type is not BOOL8.
+ * @throw cudf::logic_error if `mean`, `var`, or `std` reduction is called and
+ * the `output_dtype` is not floating point.
+ *
+ * @param col Input column view
+ * @param agg Aggregation operator applied by the reduction
+ * @param output_dtype The output scalar type
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @returns Output scalar with reduce result
+ */
+std::unique_ptr<scalar> reduce(
+  column_view const& col,
+  reduce_aggregation const& agg,
+  data_type output_dtype,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Computes the reduction of the values in all rows of a column with an initial value
+ *
+ * Only `sum`, `product`, `min`, `max`, `any`, and `all` reductions are supported.
+ *
+ * @throw cudf::logic_error if reduction is not `sum`, `product`, `min`, `max`, `any`, or `all`
+ * and `init` is specified.
+ *
+ * @param col Input column view
+ * @param agg Aggregation operator applied by the reduction
+ * @param output_dtype The output scalar type
+ * @param init The initial value of the reduction
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @returns Output scalar with reduce result
+ */
+std::unique_ptr<scalar> reduce(
+  column_view const& col,
+  reduce_aggregation const& agg,
+  data_type output_dtype,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Compute reduction of each segment in the input column
+ *
+ * This function does not detect overflows in reductions. When `output_dtype`
+ * does not match the `segmented_values.type()`, their values may be promoted to
+ * `int64_t` or `double` for computing aggregations and then cast to
+ * `output_dtype` before returning.
+ *
+ * Null values are treated as identities during reduction.
+ *
+ * If the segment is empty, the row corresponding to the result of the
+ * segment is null.
+ *
+ * If any index in `offsets` is out of bound of `segmented_values`, the behavior
+ * is undefined.
+ *
+ * If the input column has arithmetic type, `output_dtype` can be any arithmetic
+ * type. If the input column has non-arithmetic type, e.g. timestamp, the same
+ * output type must be specified.
+ *
+ * If input is not empty, the result is always nullable.
+ *
+ * @throw cudf::logic_error if reduction is called for non-arithmetic output
+ * type and operator other than `min` and `max`.
+ * @throw cudf::logic_error if input column data type is not convertible to
+ * `output_dtype` type.
+ * @throw cudf::logic_error if `min` or `max` reduction is called and the
+ * `output_dtype` does not match the input column data type.
+ * @throw cudf::logic_error if `any` or `all` reduction is called and the
+ * `output_dtype` is not BOOL8.
+ *
+ * @param segmented_values Column view of segmented inputs
+ * @param offsets Each segment's offset of `segmented_values`. A list of offsets with size
+ * `num_segments + 1`. The size of `i`th segment is `offsets[i+1] - offsets[i]`.
+ * @param agg Aggregation operator applied by the reduction
+ * @param output_dtype  The output column type
+ * @param null_handling If `INCLUDE`, the reduction is valid if all elements in a segment are valid,
+ * otherwise null. If `EXCLUDE`, the reduction is valid if any element in the segment is valid,
+ * otherwise null.
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @returns Output column with results of segmented reduction
+ */
+std::unique_ptr<column> segmented_reduce(
+  column_view const& segmented_values,
+  device_span<size_type const> offsets,
+  segmented_reduce_aggregation const& agg,
+  data_type output_dtype,
+  null_policy null_handling,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Compute reduction of each segment in the input column with an initial value. Only SUM,
+ * PRODUCT, MIN, MAX, ANY, and ALL aggregations are supported.
+ *
+ * @param segmented_values Column view of segmented inputs
+ * @param offsets Each segment's offset of `segmented_values`. A list of offsets with size
+ * `num_segments + 1`. The size of `i`th segment is `offsets[i+1] - offsets[i]`.
+ * @param agg Aggregation operator applied by the reduction
+ * @param output_dtype  The output column type
+ * @param null_handling If `INCLUDE`, the reduction is valid if all elements in a segment are valid,
+ * otherwise null. If `EXCLUDE`, the reduction is valid if any element in the segment is valid,
+ * otherwise null.
+ * @param init The initial value of the reduction
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @returns Output column with results of segmented reduction.
+ */
+std::unique_ptr<column> segmented_reduce(
+  column_view const& segmented_values,
+  device_span<size_type const> offsets,
+  segmented_reduce_aggregation const& agg,
+  data_type output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Computes the scan of a column.
+ *
+ * The null values are skipped for the operation, and if an input element
+ * at `i` is null, then the output element at `i` will also be null.
+ *
+ * @throws cudf::logic_error if column datatype is not numeric type.
+ *
+ * @param[in] input The input column view for the scan
+ * @param[in] agg unique_ptr to aggregation operator applied by the scan
+ * @param[in] inclusive The flag for applying an inclusive scan if scan_type::INCLUSIVE, an
+ * exclusive scan if scan_type::EXCLUSIVE.
+ * @param[in] null_handling Exclude null values when computing the result if null_policy::EXCLUDE.
+ * Include nulls if null_policy::INCLUDE. Any operation with a null results in a null.
+ * @param[in] mr Device memory resource used to allocate the returned scalar's device memory
+ * @returns Scanned output column
+ */
+std::unique_ptr<column> scan(
+  column_view const& input,
+  scan_aggregation const& agg,
+  scan_type inclusive,
+  null_policy null_handling           = null_policy::EXCLUDE,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Determines the minimum and maximum values of a column.
+ *
+ *
+ * @param col column to compute minmax
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A std::pair of scalars with the first scalar being the minimum value and the second
+ * scalar being the maximum value of the input column.
+ */
+std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> minmax(
+  column_view const& col,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/reduction/detail/histogram.hpp b/cpp/include/cudf/reduction/detail/histogram.hpp
new file mode 100644
index 0000000..97c711f
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/histogram.hpp
@@ -0,0 +1,57 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <memory>
+#include <optional>
+
+namespace cudf::reduction::detail {
+
+/**
+ * @brief Compute the frequency for each distinct row in the input table.
+ *
+ * @param input The input table to compute histogram
+ * @param partial_counts An optional column containing count for each row
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate memory of the returned objects
+ * @return A pair of array contains the (stable-order) indices of the distinct rows in the input
+ * table, and their corresponding distinct counts
+ */
+[[nodiscard]] std::pair<std::unique_ptr<rmm::device_uvector<size_type>>, std::unique_ptr<column>>
+compute_row_frequencies(table_view const& input,
+                        std::optional<column_view> const& partial_counts,
+                        rmm::cuda_stream_view stream,
+                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Create an empty histogram column.
+ *
+ * A histogram column is a structs column `STRUCT<T, int64_t>` where T is type of the input
+ * values.
+ *
+ * @returns An empty histogram column
+ */
+[[nodiscard]] std::unique_ptr<column> make_empty_histogram_like(column_view const& values);
+
+}  // namespace cudf::reduction::detail
diff --git a/cpp/include/cudf/reduction/detail/reduction.cuh b/cpp/include/cudf/reduction/detail/reduction.cuh
new file mode 100644
index 0000000..1620635
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/reduction.cuh
@@ -0,0 +1,235 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "reduction_operators.cuh"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <cub/device/device_reduce.cuh>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/iterator_traits.h>
+
+#include <optional>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+/**
+ * @brief Compute the specified simple reduction over the input range of elements.
+ *
+ * @param[in] d_in      the begin iterator
+ * @param[in] num_items the number of items
+ * @param[in] op        the reduction operator
+ * @param[in] init      Optional initial value of the reduction
+ * @param[in] stream    CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr        Device memory resource used to allocate the returned scalar's device
+ * memory
+ * @returns   Output scalar in device memory
+ *
+ * @tparam Op               the reduction operator with device binary operator
+ * @tparam InputIterator    the input column iterator
+ * @tparam OutputType       the output type of reduction
+ */
+template <typename Op,
+          typename InputIterator,
+          typename OutputType = typename thrust::iterator_value<InputIterator>::type,
+          std::enable_if_t<is_fixed_width<OutputType>() &&
+                           not cudf::is_fixed_point<OutputType>()>* = nullptr>
+std::unique_ptr<scalar> reduce(InputIterator d_in,
+                               cudf::size_type num_items,
+                               op::simple_op<Op> op,
+                               std::optional<OutputType> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto const binary_op     = op.get_binary_op();
+  auto const initial_value = init.value_or(op.template get_identity<OutputType>());
+  auto dev_result          = rmm::device_scalar<OutputType>{initial_value, stream, mr};
+
+  // Allocate temporary storage
+  rmm::device_buffer d_temp_storage;
+  size_t temp_storage_bytes = 0;
+  cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                            temp_storage_bytes,
+                            d_in,
+                            dev_result.data(),
+                            num_items,
+                            binary_op,
+                            initial_value,
+                            stream.value());
+  d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+
+  // Run reduction
+  cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                            temp_storage_bytes,
+                            d_in,
+                            dev_result.data(),
+                            num_items,
+                            binary_op,
+                            initial_value,
+                            stream.value());
+
+  // only for string_view, data is copied
+  auto s = new cudf::scalar_type_t<OutputType>(std::move(dev_result), true, stream, mr);
+  return std::unique_ptr<scalar>(s);
+}
+
+template <typename Op,
+          typename InputIterator,
+          typename OutputType = typename thrust::iterator_value<InputIterator>::type,
+          std::enable_if_t<is_fixed_point<OutputType>()>* = nullptr>
+std::unique_ptr<scalar> reduce(InputIterator d_in,
+                               cudf::size_type num_items,
+                               op::simple_op<Op> op,
+                               std::optional<OutputType> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL(
+    "This function should never be called. fixed_point reduce should always go through the reduce "
+    "for the corresponding device_storage_type_t");
+}
+
+// @brief string_view specialization of simple reduction
+template <typename Op,
+          typename InputIterator,
+          typename OutputType = typename thrust::iterator_value<InputIterator>::type,
+          std::enable_if_t<std::is_same_v<OutputType, string_view>>* = nullptr>
+std::unique_ptr<scalar> reduce(InputIterator d_in,
+                               cudf::size_type num_items,
+                               op::simple_op<Op> op,
+                               std::optional<OutputType> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto const binary_op     = op.get_binary_op();
+  auto const initial_value = init.value_or(op.template get_identity<OutputType>());
+  auto dev_result          = rmm::device_scalar<OutputType>{initial_value, stream};
+
+  // Allocate temporary storage
+  rmm::device_buffer d_temp_storage;
+  size_t temp_storage_bytes = 0;
+  cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                            temp_storage_bytes,
+                            d_in,
+                            dev_result.data(),
+                            num_items,
+                            binary_op,
+                            initial_value,
+                            stream.value());
+  d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+
+  // Run reduction
+  cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                            temp_storage_bytes,
+                            d_in,
+                            dev_result.data(),
+                            num_items,
+                            binary_op,
+                            initial_value,
+                            stream.value());
+
+  using ScalarType = cudf::scalar_type_t<OutputType>;
+  auto s = new ScalarType(dev_result, true, stream, mr);  // only for string_view, data is copied
+  return std::unique_ptr<scalar>(s);
+}
+
+/**
+ * @brief compute reduction by the compound operator (reduce and transform)
+ *
+ * @param[in] d_in        the begin iterator
+ * @param[in] num_items   the number of items
+ * @param[in] op          the reduction operator
+ * @param[in] valid_count Number of valid items
+ * @param[in] ddof        Delta degrees of freedom used for standard deviation and variance
+ * @param[in] init        Optional initial value of the reduction
+ * @param[in] stream      CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr          Device memory resource used to allocate the returned scalar's device
+ * memory
+ * @returns   Output scalar in device memory
+ *
+ * The reduction operator must have `intermediate::compute_result()` method.
+ * This method performs reduction using binary operator `Op::Op` and transforms the
+ * result to `OutputType` using `compute_result()` transform method.
+ *
+ * @tparam Op               the reduction operator with device binary operator
+ * @tparam InputIterator    the input column iterator
+ * @tparam OutputType       the output type of reduction
+ */
+template <typename Op,
+          typename InputIterator,
+          typename OutputType,
+          typename IntermediateType = typename thrust::iterator_value<InputIterator>::type>
+std::unique_ptr<scalar> reduce(InputIterator d_in,
+                               cudf::size_type num_items,
+                               op::compound_op<Op> op,
+                               cudf::size_type valid_count,
+                               cudf::size_type ddof,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto const binary_op     = op.get_binary_op();
+  auto const initial_value = op.template get_identity<IntermediateType>();
+
+  rmm::device_scalar<IntermediateType> intermediate_result{initial_value, stream};
+
+  // Allocate temporary storage
+  rmm::device_buffer d_temp_storage;
+  size_t temp_storage_bytes = 0;
+  cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                            temp_storage_bytes,
+                            d_in,
+                            intermediate_result.data(),
+                            num_items,
+                            binary_op,
+                            initial_value,
+                            stream.value());
+  d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+
+  // Run reduction
+  cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                            temp_storage_bytes,
+                            d_in,
+                            intermediate_result.data(),
+                            num_items,
+                            binary_op,
+                            initial_value,
+                            stream.value());
+
+  // compute the result value from intermediate value in device
+  using ScalarType = cudf::scalar_type_t<OutputType>;
+  auto result      = new ScalarType(OutputType{0}, true, stream, mr);
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     intermediate_result.data(),
+                     1,
+                     [dres = result->data(), op, valid_count, ddof] __device__(auto i) {
+                       *dres = op.template compute_result<OutputType>(i, valid_count, ddof);
+                     });
+  return std::unique_ptr<scalar>(result);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/include/cudf/reduction/detail/reduction.hpp b/cpp/include/cudf/reduction/detail/reduction.hpp
new file mode 100644
index 0000000..4cbfb82
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/reduction.hpp
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/types.hpp>
+
+#include <optional>
+
+namespace cudf::reduction::detail {
+
+/**
+ * @copydoc cudf::reduce(column_view const&, reduce_aggregation const&, data_type,
+ * std::optional<std::reference_wrapper<scalar const>>, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<scalar> reduce(column_view const& col,
+                               reduce_aggregation const& agg,
+                               data_type output_dtype,
+                               std::optional<std::reference_wrapper<scalar const>> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::reduction::detail
diff --git a/cpp/include/cudf/reduction/detail/reduction_functions.hpp b/cpp/include/cudf/reduction/detail/reduction_functions.hpp
new file mode 100644
index 0000000..704332c
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/reduction_functions.hpp
@@ -0,0 +1,356 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+/**
+ * @brief Computes sum of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`
+ * @throw cudf::logic_error if `output_dtype` is not an arithmetic type
+ *
+ * @param col input column to compute sum
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param init initial value of the sum
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Sum as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> sum(column_view const& col,
+                            data_type const output_dtype,
+                            std::optional<std::reference_wrapper<scalar const>> init,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes minimum of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is convertible to `output_dtype`
+ *
+ * @param col input column to compute minimum
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param init initial value of the minimum
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Minimum element as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> min(column_view const& col,
+                            data_type const output_dtype,
+                            std::optional<std::reference_wrapper<scalar const>> init,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes maximum of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is convertible to `output_dtype`
+ *
+ * @param col input column to compute maximum
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param init initial value of the maximum
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Maximum element as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> max(column_view const& col,
+                            data_type const output_dtype,
+                            std::optional<std::reference_wrapper<scalar const>> init,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes any of elements in input column is true when typecasted to bool
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to bool
+ * @throw cudf::logic_error if `output_dtype` is not bool
+ *
+ * @param col input column to compute any
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param init initial value of the any
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return bool scalar if any of elements is true when typecasted to bool
+ */
+std::unique_ptr<scalar> any(column_view const& col,
+                            data_type const output_dtype,
+                            std::optional<std::reference_wrapper<scalar const>> init,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes all of elements in input column is true when typecasted to bool
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to bool
+ * @throw cudf::logic_error if `output_dtype` is not bool
+ *
+ * @param col input column to compute all
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param init initial value of the all
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return bool scalar if all of elements is true when typecasted to bool
+ */
+std::unique_ptr<scalar> all(column_view const& col,
+                            data_type const output_dtype,
+                            std::optional<std::reference_wrapper<scalar const>> init,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Compute frequency for each unique element in the input column.
+ *
+ * The result histogram is stored in structs column having two children. The first child contains
+ * unique elements from the input, and the second child contains their corresponding frequencies.
+ *
+ * @param input The column to compute histogram
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return A list_scalar storing a structs column as the result histogram
+ */
+std::unique_ptr<scalar> histogram(column_view const& input,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Merge multiple histograms together.
+ *
+ * @param input The input given as multiple histograms concatenated together
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return A list_scalar storing the result histogram
+ */
+std::unique_ptr<scalar> merge_histogram(column_view const& input,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes product of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`
+ * @throw cudf::logic_error if `output_dtype` is not an arithmetic type
+ *
+ * @param col input column to compute product
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param init initial value of the product
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Product as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> product(column_view const& col,
+                                data_type const output_dtype,
+                                std::optional<std::reference_wrapper<scalar const>> init,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes sum of squares of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`
+ * @throw cudf::logic_error if `output_dtype` is not an arithmetic type
+ *
+ * @param col input column to compute sum of squares
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Sum of squares as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> sum_of_squares(column_view const& col,
+                                       data_type const output_dtype,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes mean of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not floating point type
+ *
+ * @param col input column to compute mean
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Mean as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> mean(column_view const& col,
+                             data_type const output_dtype,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes variance of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not floating point type
+ *
+ * @param col input column to compute variance
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param ddof Delta degrees of freedom. The divisor used is N - ddof, where N represents the number
+ * of elements.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Variance as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> variance(column_view const& col,
+                                 data_type const output_dtype,
+                                 size_type ddof,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes standard deviation of elements in input column
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not floating point type
+ *
+ * @param col input column to compute standard deviation
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param ddof Delta degrees of freedom. The divisor used is N - ddof, where N represents the number
+ * of elements.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Standard deviation as scalar of type `output_dtype`
+ */
+std::unique_ptr<scalar> standard_deviation(column_view const& col,
+                                           data_type const output_dtype,
+                                           size_type ddof,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Returns nth element in input column
+ *
+ * A negative value `n` is interpreted as `n+count`, where `count` is the number of valid
+ * elements in the input column if `null_handling` is `null_policy::EXCLUDE`, else `col.size()`.
+ *
+ * If all elements in input column are null, output scalar is null.
+ *
+ * @warning This function is expensive (invokes a kernel launch). So, it is not
+ * recommended to be used in performance sensitive code or inside a loop.
+ * It takes O(`col.size()`) time and space complexity for nullable column with
+ * `null_policy::EXCLUDE` as input.
+ *
+ * @throw cudf::logic_error if n falls outside the range `[-count, count)` where `count` is the
+ * number of valid * elements in the input column if `null_handling` is `null_policy::EXCLUDE`,
+ * else `col.size()`.
+ *
+ * @param col input column to get nth element from
+ * @param n index of element to get
+ * @param null_handling Indicates if null values will be counted while indexing
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return nth element as scalar
+ */
+std::unique_ptr<scalar> nth_element(column_view const& col,
+                                    size_type n,
+                                    null_policy null_handling,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Collect input column into a (list) scalar
+ *
+ * @param col input column to collect from
+ * @param null_handling Indicates if null values will be counted while collecting
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return collected list as scalar
+ */
+std::unique_ptr<scalar> collect_list(column_view const& col,
+                                     null_policy null_handling,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Merge a bunch of list scalars into single list scalar
+ *
+ * @param col input list column representing numbers of list scalars to be merged
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return merged list as scalar
+ */
+std::unique_ptr<scalar> merge_lists(lists_column_view const& col,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Collect input column into a (list) scalar without duplicated elements
+ *
+ * @param col input column to collect from
+ * @param null_handling Indicates if null values will be counted while collecting
+ * @param nulls_equal Indicates if null values will be considered as equal values
+ * @param nans_equal Indicates if nan values will be considered as equal values
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return collected list with unique elements as scalar
+ */
+std::unique_ptr<scalar> collect_set(column_view const& col,
+                                    null_policy null_handling,
+                                    null_equality nulls_equal,
+                                    nan_equality nans_equal,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Merge a bunch of list scalars into single list scalar then drop duplicated elements
+ *
+ * @param col input list column representing numbers of list scalars to be merged
+ * @param nulls_equal Indicates if null values will be considered as equal values
+ * @param nans_equal Indicates if nan values will be considered as equal values
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return collected list with unique elements as scalar
+ */
+std::unique_ptr<scalar> merge_sets(lists_column_view const& col,
+                                   null_equality nulls_equal,
+                                   nan_equality nans_equal,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/include/cudf/reduction/detail/reduction_operators.cuh b/cpp/include/cudf/reduction/detail/reduction_operators.cuh
new file mode 100644
index 0000000..a747f7b
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/reduction_operators.cuh
@@ -0,0 +1,295 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/detail/utilities/transform_unary_functions.cuh>
+#include <cudf/types.hpp>  //for CUDF_HOST_DEVICE
+
+#include <cmath>
+#include <thrust/functional.h>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+// intermediate data structure to compute `var`, `std`
+template <typename ResultType>
+struct var_std {
+  ResultType value;          /// the value
+  ResultType value_squared;  /// the value of squared
+
+  CUDF_HOST_DEVICE inline var_std(ResultType _value = 0, ResultType _value_squared = 0)
+    : value(_value), value_squared(_value_squared){};
+
+  using this_t = var_std<ResultType>;
+
+  CUDF_HOST_DEVICE inline this_t operator+(this_t const& rhs) const
+  {
+    return this_t((this->value + rhs.value), (this->value_squared + rhs.value_squared));
+  };
+};
+
+// transformer for `struct var_std` in order to compute `var`, `std`
+template <typename ResultType>
+struct transformer_var_std {
+  using OutputType = var_std<ResultType>;
+
+  CUDF_HOST_DEVICE inline OutputType operator()(ResultType const& value)
+  {
+    return OutputType(value, value * value);
+  };
+};
+
+// ------------------------------------------------------------------------
+// Definitions of device struct for reduction operation
+// all `op::xxx` must have `op` and `transformer`
+// `op`  is used to compute the reduction at device
+// `transformer` is used to convert elements for computing the reduction at device.
+// By default `transformer` is static type conversion to ResultType.
+// In some cases, it could be square or abs or complex operations
+namespace op {
+/**
+ * @brief  Simple reduction operator CRTP Base class
+ *
+ * @tparam Derived operator with simple_op interface
+ */
+template <typename Derived>
+struct simple_op {
+  /**
+   * @brief Get binary operator functor for reduction
+   *
+   * @return binary operator functor object
+   */
+  auto get_binary_op()
+  {
+    using binary_op = typename Derived::op;
+    return binary_op{};
+  }
+
+  /**
+   * @brief Get transformer functor for transforming input column
+   * which inturn is used by reduction binary operator
+   *
+   * @tparam ResultType output type for element transformer
+   *
+   * @return element transformer functor object
+   */
+  template <typename ResultType>
+  auto get_element_transformer()
+  {
+    using element_transformer = typename Derived::transformer<ResultType>;
+    return element_transformer{};
+  }
+
+  /**
+   * @brief Get transformer functor for transforming input column pair iterator
+   * which is used by reduction binary operator
+   *
+   * @tparam ResultType output type for element transformer
+   *
+   * @return element transformer functor object
+   */
+  template <typename ResultType>
+  auto get_null_replacing_element_transformer()
+  {
+    using element_transformer = typename Derived::transformer<ResultType>;
+    return null_replacing_transformer<ResultType, element_transformer>{get_identity<ResultType>(),
+                                                                       element_transformer{}};
+  }
+
+  /**
+   * @brief get identity value of type `T` for binary reduction operator
+   *
+   * @tparam T data type of identity value
+   *
+   * @return identity value
+   */
+  template <typename T>
+  constexpr T get_identity()
+  {
+    return Derived::op::template identity<T>();
+  }
+};
+
+// `sum`, `product`, `sum_of_squares`, `min`, `max` are used at simple_reduction
+// interface is defined by CRTP class simple_op
+
+// operator for `sum`
+struct sum : public simple_op<sum> {
+  using op = cudf::DeviceSum;
+
+  template <typename ResultType>
+  using transformer = thrust::identity<ResultType>;
+};
+
+// operator for `product`
+struct product : public simple_op<product> {
+  using op = cudf::DeviceProduct;
+
+  template <typename ResultType>
+  using transformer = thrust::identity<ResultType>;
+};
+
+// operator for `sum_of_squares`
+struct sum_of_squares : public simple_op<sum_of_squares> {
+  using op = cudf::DeviceSum;
+
+  template <typename ResultType>
+  using transformer = cudf::transformer_squared<ResultType>;
+};
+
+// operator for `min`
+struct min : public simple_op<min> {
+  using op = cudf::DeviceMin;
+
+  template <typename ResultType>
+  using transformer = thrust::identity<ResultType>;
+};
+
+// operator for `max`
+struct max : public simple_op<max> {
+  using op = cudf::DeviceMax;
+
+  template <typename ResultType>
+  using transformer = thrust::identity<ResultType>;
+};
+
+/**
+ * @brief  Compound reduction operator CRTP Base class
+ * This template class defines the interface for compound operators
+ * In addition to interface defined by simple_op CRTP, this class defines
+ * interface for final result transformation.
+ *
+ * @tparam Derived compound operators derived from compound_op
+ */
+template <typename Derived>
+struct compound_op : public simple_op<Derived> {
+  /**
+   * @copydoc simple_op<Derived>::template get_null_replacing_element_transformer<ResultType>()
+   */
+  template <typename ResultType>
+  auto get_null_replacing_element_transformer()
+  {
+    using element_transformer = typename Derived::transformer<ResultType>;
+    using OutputType          = typename Derived::intermediate<ResultType>::IntermediateType;
+    return null_replacing_transformer<OutputType, element_transformer>{
+      simple_op<Derived>::template get_identity<OutputType>(), element_transformer{}};
+  }
+  /**
+   * @brief  computes the transformed result from result of simple operator.
+   *
+   * @tparam ResultType output type of compound reduction operator
+   * @tparam IntermediateType output type of simple reduction operator
+   * @param input output of simple reduction as input for result transformation
+   * @param count validity count
+   * @param ddof  `ddof` parameter used by variance and standard deviation
+   *
+   * @return transformed output result of compound operator
+   */
+  template <typename ResultType, typename IntermediateType>
+  CUDF_HOST_DEVICE inline static ResultType compute_result(IntermediateType const& input,
+                                                           cudf::size_type const& count,
+                                                           cudf::size_type const& ddof)
+  {
+    // Enforced interface
+    return Derived::template intermediate<ResultType>::compute_result(input, count, ddof);
+  }
+};
+
+// `mean`, `variance`, `standard_deviation` are used at compound_reduction
+// compound_reduction requires intermediate::IntermediateType and
+// intermediate::compute_result IntermediateType is the intermediate data
+// structure type of a single reduction call, it is also used as OutputType of
+// cudf::reduction::detail::reduce at compound_reduction. compute_result
+// computes the final ResultType from the IntermediateType.
+// intermediate::compute_result method is enforced by CRTP base class compound_op
+
+// operator for `mean`
+struct mean : public compound_op<mean> {
+  using op = cudf::DeviceSum;
+
+  template <typename ResultType>
+  using transformer = thrust::identity<ResultType>;
+
+  template <typename ResultType>
+  struct intermediate {
+    using IntermediateType = ResultType;  // sum value
+
+    // compute `mean` from intermediate type `IntermediateType`
+    CUDF_HOST_DEVICE inline static ResultType compute_result(IntermediateType const& input,
+                                                             cudf::size_type const& count,
+                                                             cudf::size_type const& ddof)
+    {
+      return (input / count);
+    };
+  };
+};
+
+// operator for `variance`
+struct variance : public compound_op<variance> {
+  using op = cudf::DeviceSum;
+
+  template <typename ResultType>
+  using transformer = cudf::reduction::detail::transformer_var_std<ResultType>;
+
+  template <typename ResultType>
+  struct intermediate {
+    using IntermediateType = var_std<ResultType>;  // with sum of value, and sum of squared value
+
+    // compute `variance` from intermediate type `IntermediateType`
+    CUDF_HOST_DEVICE inline static ResultType compute_result(IntermediateType const& input,
+                                                             cudf::size_type const& count,
+                                                             cudf::size_type const& ddof)
+    {
+      ResultType mean     = input.value / count;
+      ResultType asum     = input.value_squared;
+      cudf::size_type div = count - ddof;
+      ResultType var      = asum / div - ((mean * mean) * count) / div;
+
+      return var;
+    };
+  };
+};
+
+// operator for `standard deviation`
+struct standard_deviation : public compound_op<standard_deviation> {
+  using op = cudf::DeviceSum;
+
+  template <typename ResultType>
+  using transformer = cudf::reduction::detail::transformer_var_std<ResultType>;
+
+  template <typename ResultType>
+  struct intermediate {
+    using IntermediateType = var_std<ResultType>;  // with sum of value, and sum of squared value
+
+    // compute `standard deviation` from intermediate type `IntermediateType`
+    CUDF_HOST_DEVICE inline static ResultType compute_result(IntermediateType const& input,
+                                                             cudf::size_type const& count,
+                                                             cudf::size_type const& ddof)
+    {
+      using intermediateOp = variance::template intermediate<ResultType>;
+      ResultType var       = intermediateOp::compute_result(input, count, ddof);
+
+      return static_cast<ResultType>(std::sqrt(var));
+    };
+  };
+};
+}  // namespace op
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/include/cudf/reduction/detail/segmented_reduction.cuh b/cpp/include/cudf/reduction/detail/segmented_reduction.cuh
new file mode 100644
index 0000000..5c2eaf8
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/segmented_reduction.cuh
@@ -0,0 +1,198 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "reduction_operators.cuh"
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <cub/device/device_segmented_reduce.cuh>
+
+#include <thrust/iterator/iterator_traits.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+/**
+ * @brief Compute the specified simple reduction over each of the segments in the
+ * input range of elements
+ *
+ * @tparam InputIterator    Input iterator type
+ * @tparam OffsetIterator   Offset iterator type
+ * @tparam OutputIterator   Output iterator type
+ * @tparam BinaryOp         Binary operator used for reduce
+ * @tparam OutputType       The output type derived from the OutputIterator
+ *
+ * @param d_in           Input data iterator
+ * @param d_offset_begin Begin iterator to segment indices
+ * @param d_offset_end   End iterator to segment indices
+ * @param d_out          Output data iterator
+ * @param binary_op      The reduction operator
+ * @param initial_value  Initial value of the reduction
+ * @param stream         CUDA stream used for device memory operations and kernel launches
+ *
+ */
+template <typename InputIterator,
+          typename OffsetIterator,
+          typename OutputIterator,
+          typename BinaryOp,
+          typename OutputType = typename thrust::iterator_value<OutputIterator>::type,
+          typename std::enable_if_t<is_fixed_width<OutputType>() &&
+                                    !cudf::is_fixed_point<OutputType>()>* = nullptr>
+void segmented_reduce(InputIterator d_in,
+                      OffsetIterator d_offset_begin,
+                      OffsetIterator d_offset_end,
+                      OutputIterator d_out,
+                      BinaryOp binary_op,
+                      OutputType initial_value,
+                      rmm::cuda_stream_view stream)
+{
+  auto const num_segments = static_cast<size_type>(std::distance(d_offset_begin, d_offset_end)) - 1;
+
+  // Allocate temporary storage
+  size_t temp_storage_bytes = 0;
+  cub::DeviceSegmentedReduce::Reduce(nullptr,
+                                     temp_storage_bytes,
+                                     d_in,
+                                     d_out,
+                                     num_segments,
+                                     d_offset_begin,
+                                     d_offset_begin + 1,
+                                     binary_op,
+                                     initial_value,
+                                     stream.value());
+  auto d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+
+  // Run reduction
+  cub::DeviceSegmentedReduce::Reduce(d_temp_storage.data(),
+                                     temp_storage_bytes,
+                                     d_in,
+                                     d_out,
+                                     num_segments,
+                                     d_offset_begin,
+                                     d_offset_begin + 1,
+                                     binary_op,
+                                     initial_value,
+                                     stream.value());
+}
+
+template <typename InputIterator,
+          typename OffsetIterator,
+          typename OutputIterator,
+          typename BinaryOp,
+          typename OutputType = typename thrust::iterator_value<OutputIterator>::type,
+          typename std::enable_if_t<!(is_fixed_width<OutputType>() &&
+                                      !cudf::is_fixed_point<OutputType>())>* = nullptr>
+void segmented_reduce(InputIterator,
+                      OffsetIterator,
+                      OffsetIterator,
+                      OutputIterator,
+                      BinaryOp,
+                      OutputType,
+                      rmm::cuda_stream_view)
+{
+  CUDF_FAIL(
+    "Unsupported data types called on segmented_reduce. Only numeric and chrono types are "
+    "supported.");
+}
+
+/**
+ * @brief Compute reduction by the compound operator (reduce and transform)
+ *
+ * The reduction operator must have an `intermediate::compute_result()` method.
+ * This method performs reduction using binary operator `Op::Op` and calculates the
+ * result to `OutputType` using `compute_result()` through the transform method.
+ *
+ * @tparam Op              Reduction operator
+ * @tparam InputIterator   Input iterator type
+ * @tparam OffsetIterator  Offsets iterator type
+ * @tparam OutputIterator  Output iterator type
+ *
+ * @param d_in           Input data iterator
+ * @param d_offset_begin Begin iterator to segment indices
+ * @param d_offset_end   End iterator to segment indices
+ * @param d_out          Output data iterator
+ * @param op             The reduction operator
+ * @param ddof           Delta degrees of freedom used for standard deviation and variance
+ * @param d_valid_counts Number of valid values per segment
+ * @param stream         CUDA stream used for device memory operations and kernel launches
+ */
+template <typename Op, typename InputIterator, typename OffsetIterator, typename OutputIterator>
+void segmented_reduce(InputIterator d_in,
+                      OffsetIterator d_offset_begin,
+                      OffsetIterator d_offset_end,
+                      OutputIterator d_out,
+                      op::compound_op<Op> op,
+                      size_type ddof,
+                      size_type* d_valid_counts,
+                      rmm::cuda_stream_view stream)
+{
+  using OutputType       = typename thrust::iterator_value<OutputIterator>::type;
+  using IntermediateType = typename thrust::iterator_value<InputIterator>::type;
+  auto num_segments      = static_cast<size_type>(std::distance(d_offset_begin, d_offset_end)) - 1;
+  auto const binary_op   = op.get_binary_op();
+  auto const initial_value = op.template get_identity<IntermediateType>();
+
+  rmm::device_uvector<IntermediateType> intermediate_result{static_cast<std::size_t>(num_segments),
+                                                            stream};
+
+  // Allocate temporary storage
+  size_t temp_storage_bytes = 0;
+  cub::DeviceSegmentedReduce::Reduce(nullptr,
+                                     temp_storage_bytes,
+                                     d_in,
+                                     intermediate_result.data(),
+                                     num_segments,
+                                     d_offset_begin,
+                                     d_offset_begin + 1,
+                                     binary_op,
+                                     initial_value,
+                                     stream.value());
+  auto d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+
+  // Run reduction
+  cub::DeviceSegmentedReduce::Reduce(d_temp_storage.data(),
+                                     temp_storage_bytes,
+                                     d_in,
+                                     intermediate_result.data(),
+                                     num_segments,
+                                     d_offset_begin,
+                                     d_offset_begin + 1,
+                                     binary_op,
+                                     initial_value,
+                                     stream.value());
+
+  // compute the result value from intermediate value in device
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(num_segments),
+    d_out,
+    [ir = intermediate_result.data(), op, d_valid_counts, ddof] __device__(auto idx) {
+      auto const count = d_valid_counts[idx];
+      return count > 0 ? op.template compute_result<OutputType>(ir[idx], count, ddof)
+                       : OutputType{0};
+    });
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/include/cudf/reduction/detail/segmented_reduction_functions.hpp b/cpp/include/cudf/reduction/detail/segmented_reduction_functions.hpp
new file mode 100644
index 0000000..3902a72
--- /dev/null
+++ b/cpp/include/cudf/reduction/detail/segmented_reduction_functions.hpp
@@ -0,0 +1,358 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+/**
+ * @brief Compute sum of each segment in the input column
+ *
+ * If an input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`.
+ * @throw cudf::logic_error if `output_dtype` is not an arithmetic type.
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param init Initial value of each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Sums of segments as type `output_dtype`
+ */
+std::unique_ptr<column> segmented_sum(column_view const& col,
+                                      device_span<size_type const> offsets,
+                                      data_type const output_dtype,
+                                      null_policy null_handling,
+                                      std::optional<std::reference_wrapper<scalar const>> init,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes product of each segment in the input column
+ *
+ * If an input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`.
+ * @throw cudf::logic_error if `output_dtype` is not an arithmetic type.
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param init Initial value of each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Product of segments as type `output_dtype`
+ */
+std::unique_ptr<column> segmented_product(column_view const& col,
+                                          device_span<size_type const> offsets,
+                                          data_type const output_dtype,
+                                          null_policy null_handling,
+                                          std::optional<std::reference_wrapper<scalar const>> init,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Compute minimum of each segment in the input column
+ *
+ * If an input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`.
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param init Initial value of each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Minimums of segments as type `output_dtype`
+ */
+std::unique_ptr<column> segmented_min(column_view const& col,
+                                      device_span<size_type const> offsets,
+                                      data_type const output_dtype,
+                                      null_policy null_handling,
+                                      std::optional<std::reference_wrapper<scalar const>> init,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Compute maximum of each segment in the input column
+ *
+ * If an input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to `output_dtype`.
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param init Initial value of each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Maximums of segments as type `output_dtype`
+ */
+std::unique_ptr<column> segmented_max(column_view const& col,
+                                      device_span<size_type const> offsets,
+                                      data_type const output_dtype,
+                                      null_policy null_handling,
+                                      std::optional<std::reference_wrapper<scalar const>> init,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Compute if any of the values in the segment are true when typecasted to bool
+ *
+ * If an input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to bool.
+ * @throw cudf::logic_error if `output_dtype` is not BOOL8.
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param init Initial value of each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of type BOOL8 for the results of the segments
+ */
+std::unique_ptr<column> segmented_any(column_view const& col,
+                                      device_span<size_type const> offsets,
+                                      data_type const output_dtype,
+                                      null_policy null_handling,
+                                      std::optional<std::reference_wrapper<scalar const>> init,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Compute if all of the values in the segment are true when typecasted to bool
+ *
+ * If an input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not convertible to bool.
+ * @throw cudf::logic_error if `output_dtype` is not BOOL8.
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param init Initial value of each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of BOOL8 for the results of the segments
+ */
+std::unique_ptr<column> segmented_all(column_view const& col,
+                                      device_span<size_type const> offsets,
+                                      data_type const output_dtype,
+                                      null_policy null_handling,
+                                      std::optional<std::reference_wrapper<scalar const>> init,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes mean of elements of segments in the input column
+ *
+ * If input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not floating point type
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of `output_dtype` for the reduction results of the segments
+ */
+std::unique_ptr<column> segmented_mean(column_view const& col,
+                                       device_span<size_type const> offsets,
+                                       data_type const output_dtype,
+                                       null_policy null_handling,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes sum of squares of elements of segments in the input column
+ *
+ * If input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not an arithmetic type
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of `output_dtype` for the reduction results of the segments
+ */
+std::unique_ptr<column> segmented_sum_of_squares(column_view const& col,
+                                                 device_span<size_type const> offsets,
+                                                 data_type const output_dtype,
+                                                 null_policy null_handling,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes the standard deviation of elements of segments in the input column
+ *
+ * If input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not floating point type
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param ddof Delta degrees of freedom.
+ *             The divisor used is N - ddof, where N the number of elements in each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of `output_dtype` for the reduction results of the segments
+ */
+std::unique_ptr<column> segmented_standard_deviation(column_view const& col,
+                                                     device_span<size_type const> offsets,
+                                                     data_type const output_dtype,
+                                                     null_policy null_handling,
+                                                     size_type ddof,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes the variance of elements of segments in the input column
+ *
+ * If input segment is empty, the segment result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, all elements in a segment must be valid
+ * for the reduced value to be valid.
+ * If `null_handling==null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @throw cudf::logic_error if input column type is not arithmetic type
+ * @throw cudf::logic_error if `output_dtype` is not floating point type
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param output_dtype Data type of the output column
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param ddof Delta degrees of freedom.
+ *             The divisor used is N - ddof, where N the number of elements in each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of `output_dtype` for the reduction results of the segments
+ */
+std::unique_ptr<column> segmented_variance(column_view const& col,
+                                           device_span<size_type const> offsets,
+                                           data_type const output_dtype,
+                                           null_policy null_handling,
+                                           size_type ddof,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Counts the number of unique values within each segment of a column
+ *
+ * Unique entries are counted by comparing adjacent values so the column segments
+ * are expected to be sorted before calling this function otherwise the results
+ * are undefined.
+ *
+ * If any input segment is empty, that segment's result is null.
+ *
+ * If `null_handling==null_policy::INCLUDE`, the segment count is the number of
+ * unique values +1 which includes all the null entries in that segment.
+ * If `null_handling==null_policy::EXCLUDE`, the segment count does not include nulls.
+ *
+ * @throw cudf::logic_error if input column type is a nested type
+ *
+ * @param col Input column data
+ * @param offsets Indices to identify segment boundaries within input `col`
+ * @param null_handling Specifies how null elements are processed for each segment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of unique counts per segment
+ */
+std::unique_ptr<column> segmented_nunique(column_view const& col,
+                                          device_span<size_type const> offsets,
+                                          null_policy null_handling,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/include/cudf/replace.hpp b/cpp/include/cudf/replace.hpp
new file mode 100644
index 0000000..3405dc8
--- /dev/null
+++ b/cpp/include/cudf/replace.hpp
@@ -0,0 +1,310 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup transformation_replace
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Policy to specify the position of replacement values relative to null rows
+ *
+ * `PRECEDING` means the replacement value is the first non-null value preceding the null row.
+ * `FOLLOWING` means the replacement value is the first non-null value following the null row.
+ */
+enum class replace_policy : bool { PRECEDING, FOLLOWING };
+
+/**
+ * @brief Replaces all null values in a column with corresponding values of another column
+ *
+ * If `input[i]` is NULL, then `output[i]` will contain `replacement[i]`.
+ * `input` and `replacement` must be of the same type and size.
+ *
+ * @param[in] input A column whose null values will be replaced
+ * @param[in] replacement A cudf::column whose values will replace null values in input
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns A copy of `input` with the null values replaced with corresponding values from
+ * `replacement`.
+ */
+std::unique_ptr<column> replace_nulls(
+  column_view const& input,
+  column_view const& replacement,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces all null values in a column with a scalar.
+ *
+ * If `input[i]` is NULL, then `output[i]` will contain `replacement`.
+ * `input` and `replacement` must have the same type.
+ *
+ * @param[in] input A column whose null values will be replaced
+ * @param[in] replacement Scalar used to replace null values in `input`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns Copy of `input` with null values replaced by `replacement`
+ */
+std::unique_ptr<column> replace_nulls(
+  column_view const& input,
+  scalar const& replacement,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces all null values in a column with the first non-null value that precedes/follows.
+ *
+ * If `input[i]` is NULL, then `output[i]` will contain the first non-null value that precedes or
+ * follows the null value, based on `replace_policy`.
+ *
+ * @param[in] input A column whose null values will be replaced
+ * @param[in] replace_policy Specify the position of replacement values relative to null values
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @returns Copy of `input` with null values replaced based on `replace_policy`
+ */
+std::unique_ptr<column> replace_nulls(
+  column_view const& input,
+  replace_policy const& replace_policy,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces all NaN values in a column with corresponding values from another column
+ *
+ * If `input[i]` is NaN, then `output[i]` will contain `replacement[i]`.
+ * @code{.pseudo}
+ * input        = {1.0, NaN, 4.0}
+ * replacement  = {3.0, 9.0, 7.0}
+ * output       = {1.0, 9.0, 4.0}
+ * @endcode
+ *
+ * @note Nulls are not considered as NaN
+ *
+ * @throws cudf::logic_error If `input` and `replacement` are of different type or size.
+ * @throws cudf::logic_error If `input` or `replacement` are not of floating-point dtype.
+ *
+ * @param input A column whose NaN values will be replaced
+ * @param replacement A cudf::column whose values will replace NaN values in input
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A copy of `input` with the NaN values replaced with corresponding values from
+ * `replacement`.
+ */
+std::unique_ptr<column> replace_nans(
+  column_view const& input,
+  column_view const& replacement,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces all NaN values in a column with a scalar
+ *
+ * If `input[i]` is NaN, then `output[i]` will contain `replacement`.
+ * @code{.pseudo}
+ * input        = {1.0, NaN, 4.0}
+ * replacement  = 7.0
+ * output       = {1.0, 7.0, 4.0}
+ * @endcode
+ *
+ * @note Nulls are not considered as NaN
+ *
+ * @throws cudf::logic_error If `input` and `replacement` are of different type.
+ * @throws cudf::logic_error If `input` or `replacement` are not of floating-point dtype.
+ *
+ * @param input A column whose NaN values will be replaced
+ * @param replacement A cudf::scalar whose value will replace NaN values in input
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A copy of `input` with the NaN values replaced by `replacement`
+ */
+std::unique_ptr<column> replace_nans(
+  column_view const& input,
+  scalar const& replacement,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Return a copy of `input_col` replacing any `values_to_replace[i]`
+ * found with `replacement_values[i]`.
+ *
+ * @param input_col The column to find and replace values in
+ * @param values_to_replace The values to replace
+ * @param replacement_values The values to replace with
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns Copy of `input_col` with specified values replaced
+ */
+std::unique_ptr<column> find_and_replace_all(
+  column_view const& input_col,
+  column_view const& values_to_replace,
+  column_view const& replacement_values,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces values less than `lo` in `input` with `lo_replace`,
+ * and values greater than `hi` with `hi_replace`.
+ *
+ * if `lo` is invalid, then lo will not be considered while
+ * evaluating the input (Essentially considered minimum value of that type).
+ * if `hi` is invalid, then hi will not be considered while
+ * evaluating the input (Essentially considered maximum value of that type).
+ *
+ * @note: If `lo` is valid then `lo_replace` should be valid
+ *        If `hi` is valid then `hi_replace` should be valid
+ *
+ * ```
+ * Example:
+ *    input: {1, 2, 3, NULL, 5, 6, 7}
+ *
+ *    valid lo and hi
+ *    lo: 3, hi: 5, lo_replace : 0, hi_replace : 16
+ *    output:{0, 0, 3, NULL, 5, 16, 16}
+ *
+ *    invalid lo
+ *    lo: NULL, hi: 5, lo_replace : 0, hi_replace : 16
+ *    output:{1, 2, 3, NULL, 5, 16, 16}
+ *
+ *    invalid hi
+ *    lo: 3, hi: NULL, lo_replace : 0, hi_replace : 16
+ *    output:{0, 0, 3, NULL, 5, 6, 7}
+ * ```
+ *
+ * @throws cudf::logic_error if `lo.type() != hi.type()`
+ * @throws cudf::logic_error if `lo_replace.type() != hi_replace.type()`
+ * @throws cudf::logic_error if `lo.type() != lo_replace.type()`
+ * @throws cudf::logic_error if `lo.type() != input.type()`
+ *
+ * @param[in] input Column whose elements will be clamped
+ * @param[in] lo Minimum clamp value. All elements less than `lo` will be replaced by `lo_replace`
+ * Ignored if null.
+ * @param[in] lo_replace All elements less than `lo` will be replaced by `lo_replace`
+ * @param[in] hi Maximum clamp value. All elements greater than `hi` will be replaced by
+ * `hi_replace`. Ignored if null.
+ * @param[in] hi_replace All elements greater than `hi` will be replaced by `hi_replace`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @return Returns a clamped column as per `lo` and `hi` boundaries
+ */
+std::unique_ptr<column> clamp(
+  column_view const& input,
+  scalar const& lo,
+  scalar const& lo_replace,
+  scalar const& hi,
+  scalar const& hi_replace,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces values less than `lo` in `input` with `lo`,
+ * and values greater than `hi` with `hi`.
+ *
+ * if `lo` is invalid, then lo will not be considered while
+ * evaluating the input (Essentially considered minimum value of that type).
+ * if `hi` is invalid, then hi will not be considered while
+ * evaluating the input (Essentially considered maximum value of that type).
+ *
+ * ```
+ * Example:
+ *    input: {1, 2, 3, NULL, 5, 6, 7}
+ *
+ *    valid lo and hi
+ *    lo: 3, hi: 5
+ *    output:{3, 3, 3, NULL, 5, 5, 5}
+ *
+ *    invalid lo
+ *    lo: NULL, hi:5
+ *    output:{1, 2, 3, NULL, 5, 5, 5}
+ *
+ *    invalid hi
+ *    lo: 3, hi:NULL
+ *    output:{3, 3, 3, NULL, 5, 6, 7}
+ * ```
+ *
+ * @throws cudf::logic_error if `lo.type() != hi.type()`
+ * @throws cudf::logic_error if `lo.type() != input.type()`
+ *
+ * @param[in] input Column whose elements will be clamped
+ * @param[in] lo Minimum clamp value. All elements less than `lo` will be replaced by `lo` Ignored
+ * if null.
+ * @param[in] hi Maximum clamp value. All elements greater than `hi` will be replaced by `hi`
+ * Ignored if null.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr Device memory resource used to allocate device memory of the returned column
+ *
+ * @return Returns a clamped column as per `lo` and `hi` boundaries
+ */
+std::unique_ptr<column> clamp(
+  column_view const& input,
+  scalar const& lo,
+  scalar const& hi,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Copies from a column of floating-point elements and replaces `-NaN` and `-0.0` with `+NaN`
+ * and `+0.0`, respectively.
+ *
+ * Converts floating point values from @p input using the following rules:
+ *        Convert  -NaN  -> NaN
+ *        Convert  -0.0  -> 0.0
+ *
+ * @throws cudf::logic_error if column does not have floating point data type.
+ * @param[in] input column_view of floating-point elements to copy and normalize
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr device_memory_resource allocator for allocating output data
+ *
+ * @returns new column with the modified data
+ */
+std::unique_ptr<column> normalize_nans_and_zeros(
+  column_view const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Modifies a column of floating-point elements to replace all `-NaN` and `-0.0` with `+NaN`
+ * and `+0.0`, respectively.
+ *
+ * Converts floating point values from @p in_out using the following rules:
+ *        Convert  -NaN  -> NaN
+ *        Convert  -0.0  -> 0.0
+ *
+ * @throws cudf::logic_error if column does not have floating point data type.
+ * @param[in, out] in_out of floating-point elements to normalize
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void normalize_nans_and_zeros(mutable_column_view& in_out,
+                              rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/reshape.hpp b/cpp/include/cudf/reshape.hpp
new file mode 100644
index 0000000..42cfb89
--- /dev/null
+++ b/cpp/include/cudf/reshape.hpp
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup column_reshape
+ * @{
+ * @file
+ * @brief Column APIs for interleave and tile
+ */
+
+/**
+ * @brief Interleave columns of a table into a single column.
+ *
+ * Converts the column major table `input` into a row major column.
+ * Example:
+ * ```
+ * in     = [[A1, A2, A3], [B1, B2, B3]]
+ * return = [A1, B1, A2, B2, A3, B3]
+ * ```
+ *
+ * @throws cudf::logic_error if input contains no columns.
+ * @throws cudf::logic_error if input columns dtypes are not identical.
+ *
+ * @param[in] input Table containing columns to interleave
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @return The interleaved columns as a single column
+ */
+std::unique_ptr<column> interleave_columns(
+  table_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Repeats the rows from `input` table `count` times to form a new table.
+ *
+ * `output.num_columns() == input.num_columns()`
+ * `output.num_rows() == input.num_rows() * count`
+ *
+ * ```
+ * input  = [[8, 4, 7], [5, 2, 3]]
+ * count  = 2
+ * return = [[8, 4, 7, 8, 4, 7], [5, 2, 3, 5, 2, 3]]
+ * ```
+ *
+ * @param[in] input Table containing rows to be repeated
+ * @param[in] count Number of times to tile "rows". Must be non-negative
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @return The table containing the tiled "rows"
+ */
+std::unique_ptr<table> tile(
+  table_view const& input,
+  size_type count,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Configures whether byte casting flips endianness
+ */
+enum class flip_endianness : bool { NO, YES };
+
+/**
+ * @brief Converts a column's elements to lists of bytes
+ *
+ * ```
+ * input<int32>  = [8675, 309]
+ * configuration = flip_endianness::YES
+ * return        = [[0x00, 0x00, 0x21, 0xe3], [0x00, 0x00, 0x01, 0x35]]
+ * ```
+ *
+ * @param input_column Column to be converted to lists of bytes
+ * @param endian_configuration Whether to retain or flip the endianness of the elements
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @return The column containing the lists of bytes
+ */
+std::unique_ptr<column> byte_cast(
+  column_view const& input_column,
+  flip_endianness endian_configuration,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/rolling.hpp b/cpp/include/cudf/rolling.hpp
new file mode 100644
index 0000000..ec93c70
--- /dev/null
+++ b/cpp/include/cudf/rolling.hpp
@@ -0,0 +1,597 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/rolling/range_window_bounds.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup aggregation_rolling
+ * @{
+ * @file
+ */
+
+/**
+ * @brief  Applies a fixed-size rolling window function to the values in a column.
+ *
+ * This function aggregates values in a window around each element i of the input column, and
+ * invalidates the bit mask for element i if there are not enough observations. The window size is
+ * static (the same for each element). This matches Pandas' API for DataFrame.rolling with a few
+ * notable differences:
+ * - instead of the center flag it uses a two-part window to allow for more flexible windows.
+ *   The total window size = `preceding_window + following_window`. Element `i` uses elements
+ *   `[i-preceding_window+1, i+following_window]` to do the window computation.
+ * - instead of storing NA/NaN for output rows that do not meet the minimum number of observations
+ *   this function updates the valid bitmask of the column to indicate which elements are valid.
+ *
+ * Notes on return column types:
+ * - The returned column for count aggregation always has `INT32` type.
+ * - The returned column for VARIANCE/STD aggregations always has `FLOAT64` type.
+ * - All other operators return a column of the same type as the input. Therefore
+ *   it is suggested to convert integer column types (especially low-precision integers)
+ *   to `FLOAT32` or `FLOAT64` before doing a rolling `MEAN`.
+ *
+ * @param[in] input The input column
+ * @param[in] preceding_window The static rolling window size in the backward direction
+ * @param[in] following_window The static rolling window size in the forward direction
+ * @param[in] min_periods Minimum number of observations in window required to have a value,
+ *                        otherwise element `i` is null.
+ * @param[in] agg The rolling window aggregation type (SUM, MAX, MIN, etc.)
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns   A nullable output column containing the rolling window results
+ */
+std::unique_ptr<column> rolling_window(
+  column_view const& input,
+  size_type preceding_window,
+  size_type following_window,
+  size_type min_periods,
+  rolling_aggregation const& agg,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  @copybrief rolling_window
+ * @details  @copydetails rolling_window(
+ *            column_view const& input,
+ *            size_type preceding_window,
+ *            size_type following_window,
+ *            size_type min_periods,
+ *            rolling_aggregation const& agg,
+ *            rmm::mr::device_memory_resource* mr)
+ *
+ * @param default_outputs A column of per-row default values to be returned instead
+ *                        of nulls. Used for LEAD()/LAG(), if the row offset crosses
+ *                        the boundaries of the column.
+ */
+std::unique_ptr<column> rolling_window(
+  column_view const& input,
+  column_view const& default_outputs,
+  size_type preceding_window,
+  size_type following_window,
+  size_type min_periods,
+  rolling_aggregation const& agg,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Abstraction for window boundary sizes
+ */
+struct window_bounds {
+ public:
+  /**
+   * @brief Construct bounded window boundary.
+   *
+   * @param value Finite window boundary (in days or rows)
+   * @returns A window boundary
+   */
+  static window_bounds get(size_type value) { return window_bounds(false, value); }
+
+  /**
+   * @brief Construct unbounded window boundary.
+   *
+   * @return window_bounds
+   */
+  static window_bounds unbounded()
+  {
+    return window_bounds(true, std::numeric_limits<cudf::size_type>::max());
+  }
+
+  /**
+   * Whether the window_bounds is unbounded.
+   *
+   * @return true if the window bounds is unbounded.
+   * @return false if the window bounds has a finite row boundary.
+   */
+  [[nodiscard]] bool is_unbounded() const { return _is_unbounded; }
+
+  /**
+   * @brief Gets the row-boundary for this window_bounds.
+   *
+   * @return the row boundary value (in days or rows)
+   */
+  [[nodiscard]] size_type value() const { return _value; }
+
+ private:
+  explicit window_bounds(bool is_unbounded_, size_type value_ = 0)
+    : _is_unbounded{is_unbounded_}, _value{value_}
+  {
+  }
+
+  bool const _is_unbounded;  ///< Whether the window boundary is unbounded
+  size_type const _value;    ///< Finite window boundary value (in days or rows)
+};
+
+/**
+ * @brief  Applies a grouping-aware, fixed-size rolling window function to the values in a column.
+ *
+ * Like `rolling_window()`, this function aggregates values in a window around each
+ * element of a specified `input` column. It differs from `rolling_window()` in that elements of the
+ * `input` column are grouped into distinct groups (e.g. the result of a groupby). The window
+ * aggregation cannot cross the group boundaries. For a row `i` of `input`, the group is determined
+ * from the corresponding (i.e. i-th) values of the columns under `group_keys`.
+ *
+ * Note: This method requires that the rows are presorted by the `group_key` values.
+ *
+ * @code{.pseudo}
+ * Example: Consider a user-sales dataset, where the rows look as follows:
+ * { "user_id", sales_amt, day }
+ *
+ * The `grouped_rolling_window()` method enables windowing queries such as grouping a dataset by
+ * `user_id`, and summing up the `sales_amt` column over a window of 3 rows (2 preceding (including
+ * current row), 1 row following).
+ *
+ * In this example,
+ *    1. `group_keys == [ user_id ]`
+ *    2. `input == sales_amt`
+ * The data are grouped by `user_id`, and ordered by `day`-string. The aggregation
+ * (SUM) is then calculated for a window of 3 values around (and including) each row.
+ *
+ * For the following input:
+ *
+ *  [ // user,  sales_amt
+ *    { "user1",   10      },
+ *    { "user2",   20      },
+ *    { "user1",   20      },
+ *    { "user1",   10      },
+ *    { "user2",   30      },
+ *    { "user2",   80      },
+ *    { "user1",   50      },
+ *    { "user1",   60      },
+ *    { "user2",   40      }
+ *  ]
+ *
+ * Partitioning (grouping) by `user_id` yields the following `sales_amt` vector
+ * (with 2 groups, one for each distinct `user_id`):
+ *
+ *    [ 10,  20,  10,  50,  60,  20,  30,  80,  40 ]
+ *      <-------user1-------->|<------user2------->
+ *
+ * The SUM aggregation is applied with 1 preceding and 1 following
+ * row, with a minimum of 1 period. The aggregation window is thus 3 rows wide,
+ * yielding the following column:
+ *
+ *    [ 30, 40,  80, 120, 110,  50, 130, 150, 120 ]
+ *
+ * Note: The SUMs calculated at the group boundaries (i.e. indices 0, 4, 5, and 8)
+ * consider only 2 values each, in spite of the window-size being 3.
+ * Each aggregation operation cannot cross group boundaries.
+ * @endcode
+ *
+ * The returned column for `op == COUNT` always has `INT32` type. All other operators return a
+ * column of the same type as the input. Therefore it is suggested to convert integer column types
+ * (especially low-precision integers) to `FLOAT32` or `FLOAT64` before doing a rolling `MEAN`.
+ *
+ * Note: `preceding_window` and `following_window` could well have negative values. This yields
+ * windows where the current row might not be included at all. For instance, consider a window
+ * defined as (preceding=3, following=-1). This produces a window from 2 (i.e. 3-1) rows preceding
+ * the current row, and 1 row *preceding* the current row. For the example above, the window for
+ * row#3 is:
+ *
+ *    [ 10,  20,  10,  50,  60,  20,  30,  80,  40 ]
+ *      <--window-->   ^
+ *                     |
+ *               current_row
+ *
+ * Similarly, `preceding` could have a negative value, indicating that the window begins at a
+ * position after the current row.  It differs slightly from the semantics for `following`, because
+ * `preceding` includes the current row. Therefore:
+ *   1. preceding=1  => Window starts at the current row.
+ *   2. preceding=0  => Window starts at 1 past the current row.
+ *   3. preceding=-1 => Window starts at 2 past the current row. Etc.
+ *
+ * @param[in] group_keys The (pre-sorted) grouping columns
+ * @param[in] input The input column (to be aggregated)
+ * @param[in] preceding_window The static rolling window size in the backward direction (for
+ * positive values), or forward direction (for negative values)
+ * @param[in] following_window The static rolling window size in the forward direction (for positive
+ * values), or backward direction (for negative values)
+ * @param[in] min_periods Minimum number of observations in window required to have a value,
+ *                        otherwise element `i` is null.
+ * @param[in] aggr The rolling window aggregation type (SUM, MAX, MIN, etc.)
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns   A nullable output column containing the rolling window results
+ */
+std::unique_ptr<column> grouped_rolling_window(
+  table_view const& group_keys,
+  column_view const& input,
+  size_type preceding_window,
+  size_type following_window,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  @copybrief grouped_rolling_window
+ * @details @copydetails grouped_rolling_window(
+ *            table_view const& group_keys,
+ *            column_view const& input,
+ *            size_type preceding_window,
+ *            size_type following_window,
+ *            size_type min_periods,
+ *            rolling_aggregation const& aggr,
+ *            rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<column> grouped_rolling_window(
+  table_view const& group_keys,
+  column_view const& input,
+  window_bounds preceding_window,
+  window_bounds following_window,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  @copybrief grouped_rolling_window
+ * @details @copydetails grouped_rolling_window(
+ *            table_view const& group_keys,
+ *            column_view const& input,
+ *            size_type preceding_window,
+ *            size_type following_window,
+ *            size_type min_periods,
+ *            rolling_aggregation const& aggr,
+ *            rmm::mr::device_memory_resource* mr)
+ *
+ * @param default_outputs A column of per-row default values to be returned instead
+ *                        of nulls. Used for LEAD()/LAG(), if the row offset crosses
+ *                        the boundaries of the column or group.
+ */
+std::unique_ptr<column> grouped_rolling_window(
+  table_view const& group_keys,
+  column_view const& input,
+  column_view const& default_outputs,
+  size_type preceding_window,
+  size_type following_window,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  @copybrief grouped_rolling_window
+ * @details @copydetails grouped_rolling_window(
+ *            table_view const& group_keys,
+ *            column_view const& input,
+ *            column_view const& default_outputs,
+ *            size_type preceding_window,
+ *            size_type following_window,
+ *            size_type min_periods,
+ *            rolling_aggregation const& aggr,
+ *            rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<column> grouped_rolling_window(
+  table_view const& group_keys,
+  column_view const& input,
+  column_view const& default_outputs,
+  window_bounds preceding_window,
+  window_bounds following_window,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Applies a grouping-aware, timestamp-based rolling window function to the values in a
+ *         column.
+ *
+ * Like `rolling_window()`, this function aggregates values in a window around each
+ * element of a specified `input` column. It differs from `rolling_window()` in two respects:
+ *   1. The elements of the `input` column are grouped into distinct groups (e.g. the result of a
+ *      groupby), determined by the corresponding values of the columns under `group_keys`. The
+ *      window-aggregation cannot cross the group boundaries.
+ *   2. Within a group, the aggregation window is calculated based on a time interval (e.g. number
+ *      of days preceding/following the current row). The timestamps for the input data are
+ *      specified by the `timestamp_column` argument.
+ *
+ * Note: This method requires that the rows are presorted by the group keys and timestamp values.
+ *
+ * @code{.pseudo}
+ * Example: Consider a user-sales dataset, where the rows look as follows:
+ *  { "user_id", sales_amt, date }
+ *
+ * This method enables windowing queries such as grouping a dataset by `user_id`, sorting by
+ * increasing `date`, and summing up the `sales_amt` column over a window of 3 days (1 preceding
+ *day, the current day, and 1 following day).
+ *
+ * In this example,
+ *    1. `group_keys == [ user_id ]`
+ *    2. `timestamp_column == date`
+ *    3. `input == sales_amt`
+ * The data are grouped by `user_id`, and ordered by `date`. The aggregation
+ * (SUM) is then calculated for a window of 3 days around (and including) each row.
+ *
+ * For the following input:
+ *
+ *  [ // user,  sales_amt,  YYYYMMDD (date)
+ *    { "user1",   10,      20200101    },
+ *    { "user2",   20,      20200101    },
+ *    { "user1",   20,      20200102    },
+ *    { "user1",   10,      20200103    },
+ *    { "user2",   30,      20200101    },
+ *    { "user2",   80,      20200102    },
+ *    { "user1",   50,      20200107    },
+ *    { "user1",   60,      20200107    },
+ *    { "user2",   40,      20200104    }
+ *  ]
+ *
+ * Partitioning (grouping) by `user_id`, and ordering by `date` yields the following `sales_amt`
+ * vector (with 2 groups, one for each distinct `user_id`):
+ *
+ * Date :(202001-)  [ 01,  02,  03,  07,  07,    01,   01,   02,  04 ]
+ * Input:           [ 10,  20,  10,  50,  60,    20,   30,   80,  40 ]
+ *                    <-------user1-------->|<---------user2--------->
+ *
+ * The SUM aggregation is applied, with 1 day preceding, and 1 day following, with a minimum of 1
+ * period. The aggregation window is thus 3 *days* wide, yielding the following output column:
+ *
+ *  Results:        [ 30,  40,  30,  110, 110,  130,  130,  130,  40 ]
+ *
+ * @endcode
+ *
+ * Note: The number of rows participating in each window might vary, based on the index within the
+ * group, datestamp, and `min_periods`. Apropos:
+ *  1. results[0] considers 2 values, because it is at the beginning of its group, and has no
+ *     preceding values.
+ *  2. results[5] considers 3 values, despite being at the beginning of its group. It must include 2
+ *     following values, based on its datestamp.
+ *
+ * Each aggregation operation cannot cross group boundaries.
+ *
+ * The returned column for `op == COUNT` always has `INT32` type. All other operators return a
+ * column of the same type as the input. Therefore it is suggested to convert integer column types
+ * (especially low-precision integers) to `FLOAT32` or `FLOAT64` before doing a rolling `MEAN`.
+ *
+ * @param[in] group_keys The (pre-sorted) grouping columns
+ * @param[in] timestamp_column The (pre-sorted) timestamps for each row
+ * @param[in] timestamp_order  The order (ASCENDING/DESCENDING) in which the timestamps are sorted
+ * @param[in] input The input column (to be aggregated)
+ * @param[in] preceding_window_in_days The rolling window time-interval in the backward direction
+ * @param[in] following_window_in_days The rolling window time-interval in the forward direction
+ * @param[in] min_periods Minimum number of observations in window required to have a value,
+ *                        otherwise element `i` is null.
+ * @param[in] aggr The rolling window aggregation type (SUM, MAX, MIN, etc.)
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns   A nullable output column containing the rolling window results
+ */
+std::unique_ptr<column> grouped_time_range_rolling_window(
+  table_view const& group_keys,
+  column_view const& timestamp_column,
+  cudf::order const& timestamp_order,
+  column_view const& input,
+  size_type preceding_window_in_days,
+  size_type following_window_in_days,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Applies a grouping-aware, timestamp-based rolling window function to the values in a
+ *         column,.
+ *
+ * @details @copydetails grouped_time_range_rolling_window(
+ *                table_view const& group_keys,
+ *                column_view const& timestamp_column,
+ *                cudf::order const& timestamp_order,
+ *                column_view const& input,
+ *                size_type preceding_window_in_days,
+ *                size_type following_window_in_days,
+ *                size_type min_periods,
+ *                rolling_aggregation const& aggr,
+ *                rmm::mr::device_memory_resource* mr)
+ *
+ * The `preceding_window_in_days` and `following_window_in_days` are specified as a `window_bounds`
+ * and supports "unbounded" windows, if set to `window_bounds::unbounded()`.
+ */
+std::unique_ptr<column> grouped_time_range_rolling_window(
+  table_view const& group_keys,
+  column_view const& timestamp_column,
+  cudf::order const& timestamp_order,
+  column_view const& input,
+  window_bounds preceding_window_in_days,
+  window_bounds following_window_in_days,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Applies a grouping-aware, value range-based rolling window function to the values in a
+ *         column.
+ *
+ * This function aggregates rows in a window around each element of a specified `input` column.
+ * The window is determined based on the values of an ordered `orderby` column, and on the values
+ * of a `preceding` and `following` scalar representing an inclusive range of orderby column values.
+ *
+ *   1. The elements of the `input` column are grouped into distinct groups (e.g. the result of a
+ *      groupby), determined by the corresponding values of the columns under `group_keys`. The
+ *      window-aggregation cannot cross the group boundaries.
+ *   2. Within a group, with all rows sorted by the `orderby` column, the aggregation window
+ *      for a row at index `i` is determined as follows:
+ *      a) If `orderby` is ASCENDING, aggregation window for row `i` includes all `input` rows at
+ *         index `j` such that:
+ *         @code{.pseudo}
+ *           (orderby[i] - preceding) <= orderby[j] <= orderby[i] + following
+ *         @endcode
+ *      b) If `orderby` is DESCENDING, aggregation window for row `i` includes all `input` rows at
+ *         index `j` such that:
+ *         @code{.pseudo}
+ *           (orderby[i] + preceding) >= orderby[j] >= orderby[i] - following
+ *         @endcode
+ *
+ * Note: This method requires that the rows are presorted by the group keys and orderby column
+ * values.
+ *
+ * The window intervals are specified as scalar values appropriate for the orderby column.
+ * Currently, only the following combinations of `orderby` column type and range types
+ * are supported:
+ *   1. If `orderby` column is a TIMESTAMP, the `preceding`/`following` windows are specified
+ *      in terms of `DURATION` scalars of the same resolution.
+ *      E.g. For `orderby` column of type `TIMESTAMP_SECONDS`, the intervals may only be
+ *      `DURATION_SECONDS`. Durations of higher resolution (e.g. `DURATION_NANOSECONDS`)
+ *      or lower (e.g. `DURATION_DAYS`) cannot be used.
+ *   2. If the `orderby` column is an integral type (e.g. `INT32`), the `preceding`/`following`
+ *      should be the exact same type (`INT32`).
+ *
+ * @code{.pseudo}
+ * Example: Consider a motor-racing statistics dataset, containing the following columns:
+ *   1. driver_name:   (STRING) Name of the car driver
+ *   2. num_overtakes: (INT32)  Number of times the driver overtook another car in a lap
+ *   3. lap_number:    (INT32)  The number of the lap
+ *
+ * The `group_range_rolling_window()` function allows one to calculate the total number of overtakes
+ * each driver made within any 3 lap window of each entry:
+ *   1. Group/partition the dataset by `driver_id` (This is the group_keys argument.)
+ *   2. Sort each group by the `lap_number` (i.e. This is the orderby_column.)
+ *   3. Calculate the SUM(num_overtakes) over a window (preceding=1, following=1)
+ *
+ * For the following input:
+ *
+ *  [ // driver_name,  num_overtakes,  lap_number
+ *    {   "bottas",        1,            1        },
+ *    {   "hamilton",      2,            1        },
+ *    {   "bottas",        2,            2        },
+ *    {   "bottas",        1,            3        },
+ *    {   "hamilton",      3,            1        },
+ *    {   "hamilton",      8,            2        },
+ *    {   "bottas",        5,            7        },
+ *    {   "bottas",        6,            8        },
+ *    {   "hamilton",      4,            4        }
+ *  ]
+ *
+ * Partitioning (grouping) by `driver_name`, and ordering by `lap_number` yields the following
+ * `num_overtakes` vector (with 2 groups, one for each distinct `driver_name`):
+ *
+ * lap_number:      [ 1,  2,  3,  7,  8,   1,  1,   2,  4 ]
+ * num_overtakes:   [ 1,  2,  1,  5,  6,   2,  3,   8,  4 ]
+ *                    <-----bottas------>|<----hamilton--->
+ *
+ * The SUM aggregation is applied, with 1 preceding, and 1 following, with a minimum of 1
+ * period. The aggregation window is thus 3 (laps) wide, yielding the following output column:
+ *
+ *  Results:        [ 3,  4,  3,  11, 11,  13, 13,  13,  4 ]
+ *
+ * @endcode
+ *
+ * Note: The number of rows participating in each window might vary, based on the index within the
+ * group, datestamp, and `min_periods`. Apropos:
+ *  1. results[0] considers 2 values, because it is at the beginning of its group, and has no
+ *     preceding values.
+ *  2. results[5] considers 3 values, despite being at the beginning of its group. It must include 2
+ *     following values, based on its orderby_column value.
+ *
+ * Each aggregation operation cannot cross group boundaries.
+ *
+ * The type of the returned column depends on the input column type `T`, and the aggregation:
+ *   1. COUNT   returns `INT32` columns
+ *   2. MIN/MAX returns `T` columns
+ *   3. SUM     returns the promoted type for T. Sum on `INT32` yields `INT64`.
+ *   4. MEAN    returns FLOAT64 columns
+ *   5. COLLECT returns columns of type `LIST<T>`.
+ *
+ * LEAD/LAG/ROW_NUMBER are undefined for range queries.
+ *
+ * @param[in] group_keys The (pre-sorted) grouping columns
+ * @param[in] orderby_column The (pre-sorted) order-by column, for range comparisons
+ * @param[in] order  The order (ASCENDING/DESCENDING) in which the order-by column is sorted
+ * @param[in] input The input column (to be aggregated)
+ * @param[in] preceding The interval value in the backward direction
+ * @param[in] following The interval value in the forward direction
+ * @param[in] min_periods Minimum number of observations in window required to have a value,
+ *                        otherwise element `i` is null.
+ * @param[in] aggr The rolling window aggregation type (SUM, MAX, MIN, etc.)
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns   A nullable output column containing the rolling window results
+ */
+std::unique_ptr<column> grouped_range_rolling_window(
+  table_view const& group_keys,
+  column_view const& orderby_column,
+  cudf::order const& order,
+  column_view const& input,
+  range_window_bounds const& preceding,
+  range_window_bounds const& following,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Applies a variable-size rolling window function to the values in a column.
+ *
+ * This function aggregates values in a window around each element i of the input column, and
+ * invalidates the bit mask for element i if there are not enough observations. The window size is
+ * dynamic (varying for each element). This matches Pandas' API for DataFrame.rolling with a few
+ * notable differences:
+ * - instead of the center flag it uses a two-part window to allow for more flexible windows.
+ *   The total window size = `preceding_window + following_window`. Element `i` uses elements
+ *   `[i-preceding_window+1, i+following_window]` to do the window computation.
+ * - instead of storing NA/NaN for output rows that do not meet the minimum number of observations
+ *   this function updates the valid bitmask of the column to indicate which elements are valid.
+ * - support for dynamic rolling windows, i.e. window size can be specified for each element using
+ *   an additional array.
+ *
+ * The returned column for count aggregation always has INT32 type. All other operators return a
+ * column of the same type as the input. Therefore it is suggested to convert integer column types
+ * (especially low-precision integers) to `FLOAT32` or `FLOAT64` before doing a rolling `MEAN`.
+ *
+ * @throws cudf::logic_error if window column type is not INT32
+ *
+ * @param[in] input The input column
+ * @param[in] preceding_window A non-nullable column of INT32 window sizes in the forward direction.
+ *                             `preceding_window[i]` specifies preceding window size for
+ *                             element `i`.
+ * @param[in] following_window A non-nullable column of INT32 window sizes in the backward
+ *                             direction. `following_window[i]` specifies following window size
+ *                             for element `i`.
+ * @param[in] min_periods Minimum number of observations in window required to have a value,
+ *                        otherwise element `i` is null.
+ * @param[in] agg The rolling window aggregation type (sum, max, min, etc.)
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns   A nullable output column containing the rolling window results
+ */
+std::unique_ptr<column> rolling_window(
+  column_view const& input,
+  column_view const& preceding_window,
+  column_view const& following_window,
+  size_type min_periods,
+  rolling_aggregation const& agg,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/rolling/range_window_bounds.hpp b/cpp/include/cudf/rolling/range_window_bounds.hpp
new file mode 100644
index 0000000..c5b0c21
--- /dev/null
+++ b/cpp/include/cudf/rolling/range_window_bounds.hpp
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+
+namespace cudf {
+
+/**
+ * @brief Abstraction for window boundary sizes, to be used with
+ *        `grouped_range_rolling_window()`.
+ *
+ * Similar to `window_bounds` in `grouped_rolling_window()`, `range_window_bounds`
+ * represents window boundaries for use with `grouped_range_rolling_window()`.
+ * A window may be specified as one of the following:
+ *   1. A fixed-width numeric scalar value. E.g.
+ *      a) A `DURATION_DAYS` scalar, for use with a `TIMESTAMP_DAYS` orderby column
+ *      b) An `INT32` scalar, for use with an `INT32` orderby column
+ *   2. "unbounded", indicating that the bounds stretch to the first/last
+ *      row in the group.
+ *   3. "current row", indicating that the bounds end at the first/last
+ *      row in the group that match the value of the current row.
+ */
+struct range_window_bounds {
+ public:
+  /**
+   * @brief The type of range_window_bounds.
+   */
+  enum class extent_type : int32_t {
+    CURRENT_ROW = 0,  /// Bounds defined as the first/last row that matches the current row.
+    BOUNDED,          /// Bounds defined as the first/last row that falls within
+                      /// a specified range from the current row.
+    UNBOUNDED         /// Bounds stretching to the first/last row in the entire group.
+  };
+
+  /**
+   * @brief Factory method to construct a bounded window boundary.
+   *
+   * @param boundary Finite window boundary
+   * @return A bounded window boundary object
+   */
+  static range_window_bounds get(scalar const& boundary);
+
+  /**
+   * @brief Factory method to construct a window boundary
+   *  limited to the value of the current row
+   *
+   * @param type The datatype of the window boundary
+   * @return  A "current row" window boundary object
+   */
+  static range_window_bounds current_row(data_type type);
+
+  /**
+   * @brief Whether or not the window is bounded to the current row
+   *
+   * @return true If window is bounded to the current row
+   * @return false If window is not bounded to the current row
+   */
+  [[nodiscard]] bool is_current_row() const { return _extent == extent_type::CURRENT_ROW; }
+
+  /**
+   * @brief Factory method to construct an unbounded window boundary.
+   *
+   * @param type The datatype of the window boundary
+   * @return  An unbounded window boundary object
+   */
+  static range_window_bounds unbounded(data_type type);
+
+  /**
+   * @brief Whether or not the window is unbounded
+   *
+   * @return true If window is unbounded
+   * @return false If window is of finite bounds
+   */
+  [[nodiscard]] bool is_unbounded() const { return _extent == extent_type::UNBOUNDED; }
+
+  /**
+   * @brief Returns the underlying scalar value for the bounds
+   *
+   * @return  The underlying scalar value for the bounds
+   */
+  [[nodiscard]] scalar const& range_scalar() const { return *_range_scalar; }
+
+  range_window_bounds(range_window_bounds const&) = default;  ///< Copy constructor
+  range_window_bounds() = default;  // Required for use as return types from dispatch functors.
+
+ private:
+  const extent_type _extent{extent_type::UNBOUNDED};
+  std::shared_ptr<scalar> _range_scalar{nullptr};  // To enable copy construction/assignment.
+
+  range_window_bounds(extent_type extent_, std::unique_ptr<scalar> range_scalar_);
+};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/round.hpp b/cpp/include/cudf/round.hpp
new file mode 100644
index 0000000..030d3d4
--- /dev/null
+++ b/cpp/include/cudf/round.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+
+/**
+ * @addtogroup transformation_unaryops
+ * @{
+ * @file
+ * @brief Column APIs for round
+ */
+
+/**
+ * @brief Different rounding methods for `cudf::round`
+ *
+ * Info on HALF_UP   rounding: https://en.wikipedia.org/wiki/Rounding#Round_half_up
+ * Info on HALF_EVEN rounding: https://en.wikipedia.org/wiki/Rounding#Round_half_to_even
+ */
+enum class rounding_method : int32_t { HALF_UP, HALF_EVEN };
+
+/**
+ * @brief Rounds all the values in a column to the specified number of decimal places.
+ *
+ * `cudf::round` currently supports HALF_UP and HALF_EVEN rounding for integer, floating point and
+ * `decimal32` and `decimal64` numbers. For `decimal32` and `decimal64` numbers, negated
+ * `numeric::scale` is equivalent to `decimal_places`.
+ *
+ * Example:
+ * ```
+ * using namespace cudf;
+ *
+ * column_view a; // contains { 1.729, 17.29, 172.9, 1729 };
+ *
+ * auto result1 = round(a);     // { 2,   17,   173,   1729 }
+ * auto result2 = round(a, 1);  // { 1.7, 17.3, 172.9, 1729 }
+ * auto result3 = round(a, -1); // { 0,   20,   170,   1730 }
+ *
+ * column_view b; // contains { 1.5, 2.5, 1.35, 1.45, 15, 25 };
+ *
+ * auto result4 = round(b,  0, rounding_method::HALF_EVEN); // { 2,   2,   1,   1,   15, 25};
+ * auto result5 = round(b,  1, rounding_method::HALF_EVEN); // { 1.5, 2.5, 1.4, 1.4, 15, 25};
+ * auto result6 = round(b, -1, rounding_method::HALF_EVEN); // { 0,   0,   0,   0,   20, 20};
+ * ```
+ *
+ * @param input          Column of values to be rounded
+ * @param decimal_places Number of decimal places to round to (default 0). If negative, this
+ * specifies the number of positions to the left of the decimal point.
+ * @param method         Rounding method
+ * @param mr             Device memory resource used to allocate the returned column's device memory
+ *
+ * @return Column with each of the values rounded
+ */
+std::unique_ptr<column> round(
+  column_view const& input,
+  int32_t decimal_places              = 0,
+  rounding_method method              = rounding_method::HALF_UP,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/scalar/scalar.hpp b/cpp/include/cudf/scalar/scalar.hpp
new file mode 100644
index 0000000..af5e6d6
--- /dev/null
+++ b/cpp/include/cudf/scalar/scalar.hpp
@@ -0,0 +1,893 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+/**
+ * @file
+ * @brief Class definitions for cudf::scalar
+ */
+
+namespace cudf {
+/**
+ * @addtogroup scalar_classes
+ * @{
+ */
+
+/**
+ * @brief An owning class to represent a singular value.
+ *
+ * A scalar is a singular value of any of the supported datatypes in cudf.
+ * Classes derived from this class are used to represent a scalar. Objects of
+ * derived classes should be upcasted to this class while passing to an
+ * external libcudf API.
+ */
+class scalar {
+ public:
+  virtual ~scalar()                      = default;
+  scalar& operator=(scalar const& other) = delete;
+  scalar& operator=(scalar&& other)      = delete;
+
+  /**
+   * @brief Returns the scalar's logical value type.
+   *
+   * @return The scalar's logical value type
+   */
+  [[nodiscard]] data_type type() const noexcept;
+
+  /**
+   * @brief Updates the validity of the value.
+   *
+   * @param is_valid true: set the value to valid. false: set it to null.
+   * @param stream CUDA stream used for device memory operations.
+   */
+  void set_valid_async(bool is_valid, rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * @brief Indicates whether the scalar contains a valid value.
+   *
+   * @note Using the value when `is_valid() == false` is undefined behavior. In addition, this
+   * function does a stream synchronization.
+   *
+   * @param stream CUDA stream used for device memory operations.
+   * @return true Value is valid
+   * @return false Value is invalid/null
+   */
+  [[nodiscard]] bool is_valid(rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * @brief Returns a raw pointer to the validity bool in device memory.
+   *
+   * @return Raw pointer to the validity bool in device memory
+   */
+  bool* validity_data();
+
+  /**
+   * @brief Return a const raw pointer to the validity bool in device memory.
+   *
+   * @return Raw pointer to the validity bool in device memory
+   */
+  [[nodiscard]] bool const* validity_data() const;
+
+ protected:
+  data_type _type{type_id::EMPTY};     ///< Logical type of value in the scalar
+  rmm::device_scalar<bool> _is_valid;  ///< Device bool signifying validity
+
+  scalar() = delete;
+
+  /**
+   * @brief Move constructor for scalar.
+   * @param other The other scalar to move from.
+   */
+  scalar(scalar&& other) = default;
+
+  /**
+   * @brief Construct a new scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  scalar(scalar const& other,
+         rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+         rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new scalar object.
+   *
+   * @note Do not use this constructor directly. Instead, use a factory method
+   * like make_numeric_scalar or make_string_scalar
+   *
+   * @param type Data type of the scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  scalar(data_type type,
+         bool is_valid                       = false,
+         rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+         rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+};
+
+namespace detail {
+/**
+ * @brief An owning class to represent a fixed-width type value in device memory.
+ *
+ * @tparam T the data type of the fixed-width type value.
+ */
+template <typename T>
+class fixed_width_scalar : public scalar {
+  static_assert(is_fixed_width<T>(), "Unexpected non-fixed-width type.");
+
+ public:
+  using value_type = T;  ///< Type of the value held by the scalar.
+
+  ~fixed_width_scalar() override = default;
+
+  /**
+   * @brief Move constructor for fixed_width_scalar.
+   * @param other The other fixed_width_scalar to move from.
+   */
+  fixed_width_scalar(fixed_width_scalar&& other) = default;
+
+  fixed_width_scalar& operator=(fixed_width_scalar const& other) = delete;
+  fixed_width_scalar& operator=(fixed_width_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new fixed-width scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_width_scalar(fixed_width_scalar const& other,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Set the value of the scalar.
+   *
+   * @param value New value of scalar.
+   * @param stream CUDA stream used for device memory operations.
+   */
+  void set_value(T value, rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  /**
+   * @brief Explicit conversion operator to get the value of the scalar on the host.
+   */
+  explicit operator value_type() const;
+
+  /**
+   * @brief Get the value of the scalar.
+   *
+   * @param stream CUDA stream used for device memory operations.
+   * @return Value of the scalar
+   */
+  T value(rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * @brief Returns a raw pointer to the value in device memory.
+   * @return A raw pointer to the value in device memory
+   */
+  T* data();
+
+  /**
+   * @brief Returns a const raw pointer to the value in device memory.
+   * @return A const raw pointer to the value in device memory
+   */
+  T const* data() const;
+
+ protected:
+  rmm::device_scalar<T> _data;  ///< device memory containing the value
+
+  fixed_width_scalar() = delete;
+
+  /**
+   * @brief Construct a new fixed width scalar object.
+   *
+   * @param value The initial value of the scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_width_scalar(T value,
+                     bool is_valid                       = true,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new fixed width scalar object from existing device memory.
+   *
+   * @param data The scalar's data in device memory.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_width_scalar(rmm::device_scalar<T>&& data,
+                     bool is_valid                       = true,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+};
+
+}  // namespace detail
+
+/**
+ * @brief An owning class to represent a numerical value in device memory.
+ *
+ * @tparam T the data type of the numerical value.
+ */
+template <typename T>
+class numeric_scalar : public detail::fixed_width_scalar<T> {
+  static_assert(is_numeric<T>(), "Unexpected non-numeric type.");
+
+ public:
+  numeric_scalar()  = delete;
+  ~numeric_scalar() = default;
+
+  /**
+   * @brief Move constructor for numeric_scalar.
+   * @param other The other numeric_scalar to move from.
+   */
+  numeric_scalar(numeric_scalar&& other) = default;
+
+  numeric_scalar& operator=(numeric_scalar const& other) = delete;
+  numeric_scalar& operator=(numeric_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new numeric scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  numeric_scalar(numeric_scalar const& other,
+                 rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                 rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new numeric scalar object.
+   *
+   * @param value The initial value of the scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  numeric_scalar(T value,
+                 bool is_valid                       = true,
+                 rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                 rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new numeric scalar object from existing device memory.
+   *
+   * @param data The scalar's data in device memory.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  numeric_scalar(rmm::device_scalar<T>&& data,
+                 bool is_valid                       = true,
+                 rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                 rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+};
+
+/**
+ * @brief An owning class to represent a fixed_point number in device memory.
+ *
+ * @tparam T the data type of the fixed_point number.
+ */
+template <typename T>
+class fixed_point_scalar : public scalar {
+  static_assert(is_fixed_point<T>(), "Unexpected non-fixed_point type.");
+
+ public:
+  using rep_type   = typename T::rep;  ///< The representation type of the fixed_point number.
+  using value_type = T;                ///< The value type of the fixed_point number.
+
+  fixed_point_scalar()           = delete;
+  ~fixed_point_scalar() override = default;
+
+  /**
+   * @brief Move constructor for fixed_point_scalar.
+   * @param other The other fixed_point_scalar to move from.
+   */
+  fixed_point_scalar(fixed_point_scalar&& other) = default;
+
+  fixed_point_scalar& operator=(fixed_point_scalar const& other) = delete;
+  fixed_point_scalar& operator=(fixed_point_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new fixed_point scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_point_scalar(fixed_point_scalar const& other,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new fixed_point scalar object from already shifted value and scale.
+   *
+   * @param value The initial shifted value of the fixed_point scalar.
+   * @param scale The scale of the fixed_point scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_point_scalar(rep_type value,
+                     numeric::scale_type scale,
+                     bool is_valid                       = true,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new fixed_point scalar object from a value and default 0-scale.
+   *
+   * @param value The initial value of the fixed_point scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_point_scalar(rep_type value,
+                     bool is_valid                       = true,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new fixed_point scalar object from a fixed_point number.
+   *
+   * @param value The fixed_point number from which the fixed_point scalar will be initialized.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_point_scalar(T value,
+                     bool is_valid                       = true,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new fixed_point scalar object from existing device memory.
+   *
+   * @param data The scalar's data in device memory.
+   * @param scale The scale of the fixed_point scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  fixed_point_scalar(rmm::device_scalar<rep_type>&& data,
+                     numeric::scale_type scale,
+                     bool is_valid                       = true,
+                     rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                     rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Get the value of the scalar.
+   *
+   * @param stream CUDA stream used for device memory operations.
+   * @return The value of the scalar
+   */
+  rep_type value(rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * @brief Get the decimal32, decimal64 or decimal128.
+   *
+   * @param stream CUDA stream used for device memory operations.
+   * @return The decimal32, decimal64 or decimal128 value
+   */
+  T fixed_point_value(rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * @brief Explicit conversion operator to get the value of the scalar on the host.
+   */
+  explicit operator value_type() const;
+
+  /**
+   * @brief Returns a raw pointer to the value in device memory.
+   * @return A raw pointer to the value in device memory
+   */
+  rep_type* data();
+
+  /**
+   * @brief Returns a const raw pointer to the value in device memory.
+   * @return a const raw pointer to the value in device memory
+   */
+  rep_type const* data() const;
+
+ protected:
+  rmm::device_scalar<rep_type> _data;  ///< device memory containing the value
+};
+
+/**
+ * @brief An owning class to represent a string in device memory.
+ */
+class string_scalar : public scalar {
+ public:
+  using value_type = cudf::string_view;  ///< The value type of the string scalar.
+
+  string_scalar()           = delete;
+  ~string_scalar() override = default;
+
+  /**
+   * @brief Move constructor for string_scalar.
+   * @param other The other string_scalar to move from.
+   */
+  string_scalar(string_scalar&& other) = default;
+
+  // string_scalar(string_scalar const& other) = delete;
+  string_scalar& operator=(string_scalar const& other) = delete;
+  string_scalar& operator=(string_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new string scalar object by deep copying another string_scalar.
+   *
+   * @param other The other string_scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  string_scalar(string_scalar const& other,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new string scalar object.
+   *
+   * @throws std::overflow_error If the size of the input string exceeds cudf::size_type
+   *
+   * @param string The value of the string.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  string_scalar(std::string const& string,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new string scalar object from string_view.
+   *
+   * Note that this function copies the data pointed by string_view.
+   *
+   * @param source The string_view pointing the string value to copy.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  string_scalar(value_type const& source,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new string scalar object from string_view in device memory.
+   *
+   * Note that this function copies the data pointed by string_view.
+   *
+   * @param data The device_scalar of string_view pointing to the string value to copy.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  string_scalar(rmm::device_scalar<value_type>& data,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new string scalar object by moving an existing string data buffer.
+   *
+   * Note that this constructor moves the existing buffer into the internal data buffer;
+   * no copy is performed.
+   *
+   * @param data The existing buffer to take over.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  string_scalar(rmm::device_buffer&& data,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Explicit conversion operator to get the value of the scalar in a host std::string.
+   */
+  explicit operator std::string() const;
+
+  /**
+   * @brief Get the value of the scalar in a host std::string.
+   *
+   * @param stream CUDA stream used for device memory operations.
+   * @return The value of the scalar in a host std::string
+   */
+  [[nodiscard]] std::string to_string(
+    rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * @brief Get the value of the scalar as a string_view.
+   *
+   * @param stream CUDA stream used for device memory operations.
+   * @return The value of the scalar as a string_view
+   */
+  [[nodiscard]] value_type value(rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+
+  /**
+   * @brief Returns the size of the string in bytes.
+   * @return The size of the string in bytes
+   */
+  [[nodiscard]] size_type size() const;
+
+  /**
+   * @brief Returns a raw pointer to the string in device memory.
+   * @return a raw pointer to the string in device memory
+   */
+  [[nodiscard]] char const* data() const;
+
+ protected:
+  rmm::device_buffer _data{};  ///< device memory containing the string
+};
+
+/**
+ * @brief An owning class to represent a timestamp/duration value in device memory.
+ *
+ * @tparam T the data type of the timestamp/duration value.
+ * @see cudf/wrappers/timestamps.hpp, cudf/wrappers/durations.hpp for a list of allowed types.
+ */
+template <typename T>
+class chrono_scalar : public detail::fixed_width_scalar<T> {
+  static_assert(is_chrono<T>(), "Unexpected non-chrono type");
+
+ public:
+  chrono_scalar()  = delete;
+  ~chrono_scalar() = default;
+
+  /**
+   * @brief Move constructor for chrono_scalar.
+   * @param other The other chrono_scalar to move from.
+   */
+  chrono_scalar(chrono_scalar&& other) = default;
+
+  chrono_scalar& operator=(chrono_scalar const& other) = delete;
+  chrono_scalar& operator=(chrono_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new chrono scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  chrono_scalar(chrono_scalar const& other,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new chrono scalar object.
+   *
+   * @param value The initial value of the scalar.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  chrono_scalar(T value,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new chrono scalar object from existing device memory.
+   *
+   * @param data The scalar's data in device memory.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  chrono_scalar(rmm::device_scalar<T>&& data,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+};
+
+/**
+ * @brief An owning class to represent a timestamp value in device memory.
+ *
+ * @tparam T the data type of the timestamp value.
+ * @see cudf/wrappers/timestamps.hpp for a list of allowed types.
+ */
+template <typename T>
+class timestamp_scalar : public chrono_scalar<T> {
+ public:
+  static_assert(is_timestamp<T>(), "Unexpected non-timestamp type");
+  using chrono_scalar<T>::chrono_scalar;
+  using rep_type = typename T::rep;  ///< The underlying representation type of the timestamp.
+
+  timestamp_scalar() = delete;
+
+  /**
+   * @brief Move constructor for timestamp_scalar.
+   * @param other The other timestamp_scalar to move from.
+   */
+  timestamp_scalar(timestamp_scalar&& other) = default;
+
+  /**
+   * @brief Construct a new timestamp scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  timestamp_scalar(timestamp_scalar const& other,
+                   rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                   rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new timestamp scalar object from a duration that is
+   * convertible to T::duration
+   *
+   * @param value Duration representing number of ticks since the UNIX epoch or another duration
+   *        that is convertible to timestamps duration.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  template <typename Duration2>
+  timestamp_scalar(Duration2 const& value,
+                   bool is_valid,
+                   rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                   rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Returns the duration in number of ticks since the UNIX epoch.
+   * @return The duration in number of ticks since the UNIX epoch
+   */
+  rep_type ticks_since_epoch();
+};
+
+/**
+ * @brief An owning class to represent a duration value in device memory.
+ *
+ * @tparam T the data type of the duration value.
+ * @see cudf/wrappers/durations.hpp for a list of allowed types.
+ */
+template <typename T>
+class duration_scalar : public chrono_scalar<T> {
+ public:
+  static_assert(is_duration<T>(), "Unexpected non-duration type");
+  using chrono_scalar<T>::chrono_scalar;
+  using rep_type = typename T::rep;  ///< The duration's underlying representation type.
+
+  duration_scalar() = delete;
+
+  /**
+   * @brief Move constructor for duration_scalar.
+   * @param other The other duration_scalar to move from.
+   */
+  duration_scalar(duration_scalar&& other) = default;
+
+  /**
+   * @brief Construct a new duration scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  duration_scalar(duration_scalar const& other,
+                  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new duration scalar object from tick counts.
+   *
+   * @param value Integer representing number of ticks since the UNIX epoch.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  duration_scalar(rep_type value,
+                  bool is_valid,
+                  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Returns the duration in number of ticks.
+   * @return The duration in number of ticks
+   */
+  rep_type count();
+};
+
+/**
+ * @brief An owning class to represent a list value in device memory.
+ */
+class list_scalar : public scalar {
+ public:
+  list_scalar()           = delete;
+  ~list_scalar() override = default;
+
+  /**
+   * @brief Move constructor for list_scalar.
+   * @param other The other list_scalar to move from.
+   */
+  list_scalar(list_scalar&& other) = default;
+
+  list_scalar& operator=(list_scalar const& other) = delete;
+  list_scalar& operator=(list_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new list scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  list_scalar(list_scalar const& other,
+              rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+              rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new list scalar object from column_view.
+   *
+   * The input column_view is copied.
+   *
+   * @param data The column data to copy.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  list_scalar(cudf::column_view const& data,
+              bool is_valid                       = true,
+              rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+              rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new list scalar object from existing column.
+   *
+   * @param data The column to take ownership of.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  list_scalar(cudf::column&& data,
+              bool is_valid                       = true,
+              rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+              rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Returns a non-owning, immutable view to underlying device data.
+   * @return A non-owning, immutable view to underlying device data
+   */
+  [[nodiscard]] column_view view() const;
+
+ private:
+  cudf::column _data;
+};
+
+/**
+ * @brief An owning class to represent a struct value in device memory.
+ */
+class struct_scalar : public scalar {
+ public:
+  struct_scalar()           = delete;
+  ~struct_scalar() override = default;
+
+  /**
+   * @brief Move constructor for struct_scalar.
+   * @param other The other struct_scalar to move from.
+   */
+  struct_scalar(struct_scalar&& other)                 = default;
+  struct_scalar& operator=(struct_scalar const& other) = delete;
+  struct_scalar& operator=(struct_scalar&& other)      = delete;
+
+  /**
+   * @brief Construct a new struct scalar object by deep copying another.
+   *
+   * @param other The scalar to copy.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  struct_scalar(struct_scalar const& other,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new struct scalar object from table_view.
+   *
+   * The input table_view is deep-copied.
+   *
+   * @param data The table data to copy.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  struct_scalar(table_view const& data,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new struct scalar object from a host_span of column_views.
+   *
+   * The input column_views are deep-copied.
+   *
+   * @param data The column_views to copy.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  struct_scalar(host_span<column_view const> data,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new struct scalar object from an existing table in device memory.
+   *
+   * Note that this constructor moves the existing table data into the internal table data;
+   * no copies are performed.
+   *
+   * @param data The existing table data to take over.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   */
+  struct_scalar(table&& data,
+                bool is_valid                       = true,
+                rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Returns a non-owning, immutable view to underlying device data.
+   * @return A non-owning, immutable view to underlying device data
+   */
+  [[nodiscard]] table_view view() const;
+
+ private:
+  table _data;
+
+  /**
+   * @brief Check if all the input columns constructing this struct scalar have valid size.
+   */
+  void assert_valid_size();
+
+  /**
+   * @brief Initialize the internal table data for struct scalar.
+   *
+   * @param data The existing table data to take over.
+   * @param is_valid Whether the value held by the scalar is valid.
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource to use for device memory allocation.
+   * @return The table after initialization
+   */
+  static table init_data(table&& data,
+                         bool is_valid,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr);
+};
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/scalar/scalar_device_view.cuh b/cpp/include/cudf/scalar/scalar_device_view.cuh
new file mode 100644
index 0000000..846da0b
--- /dev/null
+++ b/cpp/include/cudf/scalar/scalar_device_view.cuh
@@ -0,0 +1,443 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/types.hpp>
+
+/**
+ * @file scalar_device_view.cuh
+ * @brief Scalar device view class definitions
+ */
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief A non-owning view of scalar from device that is trivially copyable
+ * and usable in CUDA device code.
+ */
+class scalar_device_view_base {
+ public:
+  ~scalar_device_view_base() = default;
+
+  /**
+   * @brief Returns the value type
+   *
+   * @returns The value type
+   */
+  [[nodiscard]] __host__ __device__ data_type type() const noexcept { return _type; }
+
+  /**
+   * @brief Returns whether the scalar holds a valid value (i.e., not null).
+   *
+   * @return true The element is valid
+   * @return false The element is null
+   */
+  [[nodiscard]] __device__ bool is_valid() const noexcept { return *_is_valid; }
+
+  /**
+   * @brief Updates the validity of the value
+   *
+   * @param is_valid true: set the value to valid. false: set it to null
+   */
+  __device__ void set_valid(bool is_valid) noexcept { *_is_valid = is_valid; }
+
+ protected:
+  data_type _type{type_id::EMPTY};  ///< Value data type
+  bool* _is_valid{};                ///< Pointer to device memory containing
+                                    ///< boolean representing validity of the value.
+
+  /**
+   * @brief Construct a new scalar device view base object  from a device pointer
+   * and a validity boolean.
+   *
+   * @param type The data type of the scalar
+   * @param is_valid Pointer to device memory containing boolean representing
+   * validity of the scalar.
+   */
+  scalar_device_view_base(data_type type, bool* is_valid) : _type(type), _is_valid(is_valid) {}
+
+  scalar_device_view_base() = default;
+};
+
+/**
+ * @brief A type-erased scalar_device_view where the value is a fixed width type
+ */
+class fixed_width_scalar_device_view_base : public detail::scalar_device_view_base {
+ public:
+  /**
+   * @brief Returns reference to stored value.
+   *
+   * @tparam T The desired type
+   * @returns Reference to stored value
+   */
+  template <typename T>
+  __device__ T& value() noexcept
+  {
+    return *data<T>();
+  }
+
+  /**
+   * @brief Returns const reference to stored value.
+   *
+   * @tparam T The desired type
+   * @returns Const reference to stored value
+   */
+  template <typename T>
+  __device__ T const& value() const noexcept
+  {
+    return *data<T>();
+  }
+
+  /**
+   * @brief Stores the value in scalar
+   *
+   * @tparam T The desired type
+   * @param value The value to store in scalar
+   */
+  template <typename T>
+  __device__ void set_value(T value)
+  {
+    *static_cast<T*>(_data) = value;
+  }
+
+  /**
+   * @brief Returns a raw pointer to the value in device memory
+   *
+   * @tparam T The desired type
+   * @returns Raw pointer to the value in device memory
+   */
+  template <typename T>
+  __device__ T* data() noexcept
+  {
+    return static_cast<T*>(_data);
+  }
+
+  /**
+   * @brief Returns a const raw pointer to the value in device memory
+   *
+   * @tparam T The desired type
+   * @returns Const raw pointer to the value in device memory
+   */
+  template <typename T>
+  __device__ T const* data() const noexcept
+  {
+    return static_cast<T const*>(_data);
+  }
+
+ protected:
+  void* _data{};  ///< Pointer to device memory containing the value
+
+  /**
+   * @brief Construct a new fixed width scalar device view object
+   *
+   * This constructor should not be used directly. get_scalar_device_view
+   * should be used to get the view of an existing scalar
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  fixed_width_scalar_device_view_base(data_type type, void* data, bool* is_valid)
+    : detail::scalar_device_view_base(type, is_valid), _data(data)
+  {
+  }
+};
+
+/**
+ * @brief A type of scalar_device_view where the value is a fixed width type
+ */
+template <typename T>
+class fixed_width_scalar_device_view : public detail::fixed_width_scalar_device_view_base {
+ public:
+  using value_type = T;  ///< The value type of the scalar
+
+  /**
+   * @brief Returns reference to stored value.
+   *
+   * @returns Reference to stored value
+   */
+  __device__ T& value() noexcept { return fixed_width_scalar_device_view_base::value<T>(); }
+
+  /**
+   * @brief Returns const reference to stored value.
+   *
+   * @returns Const reference to stored value
+   */
+  __device__ T const& value() const noexcept
+  {
+    return fixed_width_scalar_device_view_base::value<T>();
+  }
+
+  /**
+   * @brief Stores the value in scalar
+   *
+   * @param value The value to store in scalar
+   */
+  __device__ void set_value(T value) { fixed_width_scalar_device_view_base::set_value<T>(value); }
+
+  /**
+   * @brief Returns a raw pointer to the value in device memory
+   *
+   * @returns Raw pointer to the value in device memory
+   */
+  __device__ T* data() noexcept { return fixed_width_scalar_device_view_base::data<T>(); }
+
+  /**
+   * @brief Returns a const raw pointer to the value in device memory
+   *
+   * @returns Const raw pointer to the value in device memory
+   */
+  __device__ T const* data() const noexcept
+  {
+    return fixed_width_scalar_device_view_base::data<T>();
+  }
+
+ protected:
+  /**
+   * @brief Construct a new fixed width scalar device view object
+   *
+   * This constructor should not be used directly. get_scalar_device_view
+   * should be used to get the view of an existing scalar
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  fixed_width_scalar_device_view(data_type type, T* data, bool* is_valid)
+    : detail::fixed_width_scalar_device_view_base(type, data, is_valid)
+  {
+  }
+};
+
+}  // namespace detail
+
+/**
+ * @brief A type of scalar_device_view that stores a pointer to a numerical value
+ */
+template <typename T>
+class numeric_scalar_device_view : public detail::fixed_width_scalar_device_view<T> {
+ public:
+  /**
+   * @brief Construct a new numeric scalar device view object from data and validity pointers.
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  numeric_scalar_device_view(data_type type, T* data, bool* is_valid)
+    : detail::fixed_width_scalar_device_view<T>(type, data, is_valid)
+  {
+  }
+};
+
+/**
+ * @brief A type of scalar_device_view that stores a pointer to a fixed_point value
+ */
+template <typename T>
+class fixed_point_scalar_device_view : public detail::scalar_device_view_base {
+ public:
+  using rep_type = typename T::rep;  ///< The representation type of the fixed_point value
+
+  /**
+   * @brief Construct a new fixed point scalar device view object from data and validity pointers.
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  fixed_point_scalar_device_view(data_type type, rep_type* data, bool* is_valid)
+    : detail::scalar_device_view_base(type, is_valid), _data(data)
+  {
+  }
+
+  /**
+   * @brief Stores the value in scalar
+   *
+   * @param value The value to store in scalar
+   */
+  __device__ void set_value(rep_type value) { *_data = value; }
+
+  /**
+   * @brief Get the value of the scalar, as a `rep_type`.
+   *
+   * @returns The value of the scalar, as a `rep_type`
+   */
+  __device__ rep_type const& rep() const noexcept { return *_data; }
+
+ private:
+  rep_type* _data{};
+};
+
+/**
+ * @brief A type of scalar_device_view that stores a pointer to a string value
+ */
+class string_scalar_device_view : public detail::scalar_device_view_base {
+ public:
+  using ValueType = cudf::string_view;  ///< The value type of the string scalar
+
+  /**
+   * @brief Construct a new string scalar device view object from string data, size and validity
+   * pointers.
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the string data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   * @param size The pointer to the size of the string in device memory
+   */
+  string_scalar_device_view(data_type type, char const* data, bool* is_valid, size_type size)
+    : detail::scalar_device_view_base(type, is_valid), _data(data), _size(size)
+  {
+  }
+
+  /**
+   * @brief Returns string_view of the value of this scalar.
+   *
+   * @returns string_view of the value of this scalar
+   */
+  [[nodiscard]] __device__ ValueType value() const noexcept
+  {
+    return ValueType{this->data(), _size};
+  }
+
+  /**
+   * @brief Returns a raw pointer to the value in device memory
+   *
+   * @returns Raw pointer to the value in device memory
+   */
+  [[nodiscard]] __device__ char const* data() const noexcept
+  {
+    return static_cast<char const*>(_data);
+  }
+
+  /**
+   * @brief Returns the size of the string in bytes.
+   *
+   * @returns The size of the string in bytes
+   */
+  [[nodiscard]] __device__ size_type size() const noexcept { return _size; }
+
+ private:
+  char const* _data{};  ///< Pointer to device memory containing the value
+  size_type _size;      ///< Size of the string in bytes
+};
+
+/**
+ * @brief A type of scalar_device_view that stores a pointer to a timestamp value
+ */
+template <typename T>
+class timestamp_scalar_device_view : public detail::fixed_width_scalar_device_view<T> {
+ public:
+  /**
+   * @brief Construct a new timestamp scalar device view object
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  timestamp_scalar_device_view(data_type type, T* data, bool* is_valid)
+    : detail::fixed_width_scalar_device_view<T>(type, data, is_valid)
+  {
+  }
+};
+
+/**
+ * @brief A type of scalar_device_view that stores a pointer to a duration value
+ */
+template <typename T>
+class duration_scalar_device_view : public detail::fixed_width_scalar_device_view<T> {
+ public:
+  /**
+   * @brief Construct a new duration scalar device view object from data and validity pointers.
+   *
+   * @param type The data type of the value
+   * @param data The pointer to the data in device memory
+   * @param is_valid The pointer to the bool in device memory that indicates the
+   * validity of the stored value
+   */
+  duration_scalar_device_view(data_type type, T* data, bool* is_valid)
+    : detail::fixed_width_scalar_device_view<T>(type, data, is_valid)
+  {
+  }
+};
+
+/**
+ * @brief Get the device view of a numeric_scalar
+ *
+ * @param s The numeric_scalar to get the device view of
+ * @return A device view of a numeric_scalar
+ */
+template <typename T>
+auto get_scalar_device_view(numeric_scalar<T>& s)
+{
+  return numeric_scalar_device_view<T>(s.type(), s.data(), s.validity_data());
+}
+
+/**
+ * @brief Get the device view of a string_scalar
+ *
+ * @param s The string_scalar to get the device view of
+ * @return A device view of a string_scalar
+ */
+inline auto get_scalar_device_view(string_scalar& s)
+{
+  return string_scalar_device_view(s.type(), s.data(), s.validity_data(), s.size());
+}
+
+/**
+ * @brief Get the device view of a timestamp_scalar
+ *
+ * @param s The timestamp_scalar to get the device view of
+ * @return A device view of a timestamp_scalar
+ */
+template <typename T>
+auto get_scalar_device_view(timestamp_scalar<T>& s)
+{
+  return timestamp_scalar_device_view<T>(s.type(), s.data(), s.validity_data());
+}
+
+/**
+ * @brief Get the device view of a duration_scalar
+ *
+ * @param s The duration_scalar to get the device view of
+ * @return A device view of a duration_scalar
+ */
+template <typename T>
+auto get_scalar_device_view(duration_scalar<T>& s)
+{
+  return duration_scalar_device_view<T>(s.type(), s.data(), s.validity_data());
+}
+
+/**
+ * @brief Get the device view of a fixed_point_scalar
+ *
+ * @param s The fixed_point_scalar to get the device view of
+ * @return The device view of the fixed_point_scalar
+ */
+template <typename T>
+auto get_scalar_device_view(fixed_point_scalar<T>& s)
+{
+  return fixed_point_scalar_device_view<T>(s.type(), s.data(), s.validity_data());
+}
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/scalar/scalar_factories.hpp b/cpp/include/cudf/scalar/scalar_factories.hpp
new file mode 100644
index 0000000..78b6c4f
--- /dev/null
+++ b/cpp/include/cudf/scalar/scalar_factories.hpp
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+/**
+ * @addtogroup scalar_factories
+ * @{
+ * @file
+ * @brief Scalar factory APIs
+ */
+
+/**
+ * @brief Construct scalar with uninitialized storage to hold a value of the
+ * specified numeric `data_type`.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a numeric type
+ *
+ * @param type The desired numeric element type
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns An uninitialized numeric scalar
+ */
+std::unique_ptr<scalar> make_numeric_scalar(
+  data_type type,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct scalar with uninitialized storage to hold a value of the
+ * specified timestamp `data_type`.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a timestamp type
+ *
+ * @param type The desired timestamp element type
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @return An uninitialized timestamp scalar
+ */
+std::unique_ptr<scalar> make_timestamp_scalar(
+  data_type type,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct scalar with uninitialized storage to hold a value of the
+ * specified duration `data_type`.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a duration type
+ *
+ * @param type The desired duration element type
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @return An uninitialized duration scalar
+ */
+std::unique_ptr<scalar> make_duration_scalar(
+  data_type type,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct scalar with uninitialized storage to hold a value of the
+ * specified fixed-width `data_type`.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ * @throws cudf::logic_error if `type` is not a fixed-width type
+ *
+ * @param type The desired fixed-width element type
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @return An uninitialized fixed-width scalar
+ */
+std::unique_ptr<scalar> make_fixed_width_scalar(
+  data_type type,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct STRING type scalar given a `std::string`.
+ * The size of the `std::string` must not exceed the maximum size of size_type.
+ * The string characters are expected to be UTF-8 encoded sequence of char bytes.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ *
+ * @param string The `std::string` to copy to device
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A string scalar with the contents of `string`
+ */
+std::unique_ptr<scalar> make_string_scalar(
+  std::string const& string,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Constructs default constructed scalar of type `type`
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ *
+ * @param type The desired element type
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A scalar of type `type`
+ */
+std::unique_ptr<scalar> make_default_constructed_scalar(
+  data_type type,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates an empty (invalid) scalar of the same type as the `input` column_view.
+ *
+ * @throw cudf::logic_error if the `input` column is struct type and empty
+ *
+ * @param input Immutable view of input column to emulate
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A scalar of type of `input` column
+ */
+std::unique_ptr<scalar> make_empty_scalar_like(
+  column_view const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct scalar using the given value of fixed width type
+ *
+ * @tparam T Datatype of the value to be represented by the scalar
+ * @param value The value to store in the scalar object
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A scalar of type `T`
+ */
+template <typename T>
+std::unique_ptr<scalar> make_fixed_width_scalar(
+  T value,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  return std::make_unique<scalar_type_t<T>>(value, true, stream, mr);
+}
+
+/**
+ * @brief Construct scalar using the given value of fixed_point type
+ *
+ * @tparam T Datatype of the value to be represented by the scalar
+ * @param value The value to store in the scalar object
+ * @param scale The scale of the fixed point value
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A scalar of type `T`
+ */
+template <typename T>
+std::unique_ptr<scalar> make_fixed_point_scalar(
+  typename T::rep value,
+  numeric::scale_type scale,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  return std::make_unique<scalar_type_t<T>>(value, scale, true, stream, mr);
+}
+
+/**
+ * @brief Construct scalar using the given column of elements
+ *
+ * @param elements Elements of the list
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A list scalar
+ */
+std::unique_ptr<scalar> make_list_scalar(
+  column_view elements,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a struct scalar using the given table_view.
+ *
+ * The columns must have 1 row.
+ *
+ * @param data The columnar data to store in the scalar object
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A struct scalar
+ */
+std::unique_ptr<scalar> make_struct_scalar(
+  table_view const& data,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Construct a struct scalar using the given span of column views.
+ *
+ * The columns must have 1 row.
+ *
+ * @param data The columnar data to store in the scalar object
+ * @param stream CUDA stream used for device memory operations.
+ * @param mr Device memory resource used to allocate the scalar's `data` and `is_valid` bool.
+ * @returns A struct scalar
+ */
+std::unique_ptr<scalar> make_struct_scalar(
+  host_span<column_view const> data,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/search.hpp b/cpp/include/cudf/search.hpp
new file mode 100644
index 0000000..49acce6
--- /dev/null
+++ b/cpp/include/cudf/search.hpp
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <vector>
+
+namespace cudf {
+/**
+ * @addtogroup column_search
+ * @{
+ * @file
+ * @brief Column APIs for lower_bound, upper_bound, and contains
+ */
+
+/**
+ * @brief Find smallest indices in a sorted table where values should be inserted to maintain order.
+ *
+ * For each row in `needles`, find the first index in `haystack` where inserting the row still
+ * maintains its sort order.
+ *
+ * @code{.pseudo}
+ * Example:
+ *
+ *  Single column:
+ *      idx        0   1   2   3   4
+ *   haystack = { 10, 20, 20, 30, 50 }
+ *   needles  = { 20 }
+ *   result   = {  1 }
+ *
+ *  Multi Column:
+ *      idx          0    1    2    3    4
+ *   haystack = {{  10,  20,  20,  20,  20 },
+ *               { 5.0,  .5,  .5,  .7,  .7 },
+ *               {  90,  77,  78,  61,  61 }}
+ *   needles  = {{ 20 },
+ *               { .7 },
+ *               { 61 }}
+ *   result   = {   3 }
+ * @endcode
+ *
+ * @param haystack The table containing search space
+ * @param needles Values for which to find the insert locations in the search space
+ * @param column_order Vector of column sort order
+ * @param null_precedence Vector of null_precedence enums needles
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A non-nullable column of elements containing the insertion points
+ */
+std::unique_ptr<column> lower_bound(
+  table_view const& haystack,
+  table_view const& needles,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Find largest indices in a sorted table where values should be inserted to maintain order.
+ *
+ * For each row in `needles`, find the last index in `haystack` where inserting the row still
+ * maintains its sort order.
+ *
+ * @code{.pseudo}
+ * Example:
+ *
+ *  Single Column:
+ *      idx        0   1   2   3   4
+ *   haystack = { 10, 20, 20, 30, 50 }
+ *   needles  = { 20 }
+ *   result   = {  3 }
+ *
+ *  Multi Column:
+ *      idx          0    1    2    3    4
+ *   haystack = {{  10,  20,  20,  20,  20 },
+ *               { 5.0,  .5,  .5,  .7,  .7 },
+ *               {  90,  77,  78,  61,  61 }}
+ *   needles  = {{ 20 },
+ *               { .7 },
+ *               { 61 }}
+ *   result =     { 5 }
+ * @endcode
+ *
+ * @param haystack The table containing search space
+ * @param needles Values for which to find the insert locations in the search space
+ * @param column_order Vector of column sort order
+ * @param null_precedence Vector of null_precedence enums needles
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A non-nullable column of elements containing the insertion points
+ */
+std::unique_ptr<column> upper_bound(
+  table_view const& haystack,
+  table_view const& needles,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Check if the given `needle` value exists in the `haystack` column.
+ *
+ * @throws cudf::logic_error If `haystack.type() != needle.type()`.
+ *
+ * @code{.pseudo}
+ *  Single Column:
+ *   idx           0   1   2   3   4
+ *   haystack = { 10, 20, 20, 30, 50 }
+ *   needle   = { 20 }
+ *   result   = true
+ * @endcode
+ *
+ * @param haystack The column containing search space
+ * @param needle A scalar value to check for existence in the search space
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return true if the given `needle` value exists in the `haystack` column
+ */
+bool contains(column_view const& haystack,
+              scalar const& needle,
+              rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Check if the given `needles` values exists in the `haystack` column.
+ *
+ * The new column will have type BOOL and have the same size and null mask as the input `needles`
+ * column. That is, any null row in the `needles` column will result in a nul row in the output
+ * column.
+ *
+ * @throws cudf::logic_error If `haystack.type() != needles.type()`
+ *
+ * @code{.pseudo}
+ *   haystack = { 10, 20, 30, 40, 50 }
+ *   needles  = { 20, 40, 60, 80 }
+ *   result   = { true, true, false, false }
+ * @endcode
+ *
+ * @param haystack The column containing search space
+ * @param needles A column of values to check for existence in the search space
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A BOOL column indicating if each element in `needles` exists in the search space
+ */
+std::unique_ptr<column> contains(
+  column_view const& haystack,
+  column_view const& needles,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/sorting.hpp b/cpp/include/cudf/sorting.hpp
new file mode 100644
index 0000000..e4e803b
--- /dev/null
+++ b/cpp/include/cudf/sorting.hpp
@@ -0,0 +1,355 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+
+/**
+ * @addtogroup column_sort
+ * @{
+ * @file
+ * @brief Column APIs for sort and rank
+ */
+
+/**
+ * @brief Computes the row indices that would produce `input` in a lexicographical sorted order.
+ *
+ * @param input The table to sort
+ * @param column_order The desired sort order for each column. Size must be
+ * equal to `input.num_columns()` or empty. If empty, all columns will be sorted
+ * in ascending order.
+ * @param null_precedence The desired order of null compared to other elements
+ * for each column. Size must be equal to `input.num_columns()` or empty.
+ * If empty, all columns will be sorted in `null_order::BEFORE`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A non-nullable column of elements containing the permuted row indices of
+ * `input` if it were sorted
+ */
+std::unique_ptr<column> sorted_order(
+  table_view const& input,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the row indices that would produce `input` in a stable
+ * lexicographical sorted order.
+ *
+ * The order of equivalent elements is guaranteed to be preserved.
+ *
+ * @copydoc cudf::sorted_order
+ */
+std::unique_ptr<column> stable_sorted_order(
+  table_view const& input,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Checks whether the rows of a `table` are sorted in a lexicographical
+ *        order.
+ *
+ * @param table             Table whose rows need to be compared for ordering
+ * @param column_order      The expected sort order for each column. Size
+ *                          must be equal to `in.num_columns()` or empty. If
+ *                          empty, it is expected all columns are in
+ *                          ascending order.
+ * @param null_precedence   The desired order of null compared to other
+ *                          elements for each column. Size must be equal to
+ *                          `input.num_columns()` or empty. If empty,
+ *                          `null_order::BEFORE` is assumed for all columns.
+ *
+ * @param stream            CUDA stream used for device memory operations and kernel launches
+ * @returns                 true if sorted as expected, false if not
+ */
+bool is_sorted(cudf::table_view const& table,
+               std::vector<order> const& column_order,
+               std::vector<null_order> const& null_precedence,
+               rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Performs a lexicographic sort of the rows of a table
+ *
+ * @param input The table to sort
+ * @param column_order The desired order for each column. Size must be
+ * equal to `input.num_columns()` or empty. If empty, all columns are sorted in
+ * ascending order.
+ * @param null_precedence The desired order of a null element compared to other
+ * elements for each column in `input`. Size must be equal to
+ * `input.num_columns()` or empty. If empty, all columns will be sorted with
+ * `null_order::BEFORE`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return New table containing the desired sorted order of `input`
+ */
+std::unique_ptr<table> sort(
+  table_view const& input,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a key-value sort.
+ *
+ * Creates a new table that reorders the rows of `values` according to the
+ * lexicographic ordering of the rows of `keys`.
+ *
+ * @throws cudf::logic_error if `values.num_rows() != keys.num_rows()`.
+ *
+ * @param values The table to reorder
+ * @param keys The table that determines the ordering
+ * @param column_order The desired order for each column in `keys`. Size must be
+ * equal to `keys.num_columns()` or empty. If empty, all columns are sorted in
+ * ascending order.
+ * @param null_precedence The desired order of a null element compared to other
+ * elements for each column in `keys`. Size must be equal to
+ * `keys.num_columns()` or empty. If empty, all columns will be sorted with
+ * `null_order::BEFORE`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return The reordering of `values` determined by the lexicographic order of
+ * the rows of `keys`.
+ */
+std::unique_ptr<table> sort_by_key(
+  table_view const& values,
+  table_view const& keys,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a key-value stable sort.
+ *
+ * Creates a new table that reorders the rows of `values` according to the
+ * lexicographic ordering of the rows of `keys`.
+ *
+ * The order of equivalent elements is guaranteed to be preserved.
+ *
+ * @throws cudf::logic_error if `values.num_rows() != keys.num_rows()`.
+ *
+ * @param values The table to reorder
+ * @param keys The table that determines the ordering
+ * @param column_order The desired order for each column in `keys`. Size must be
+ * equal to `keys.num_columns()` or empty. If empty, all columns are sorted in
+ * ascending order.
+ * @param null_precedence The desired order of a null element compared to other
+ * elements for each column in `keys`. Size must be equal to
+ * `keys.num_columns()` or empty. If empty, all columns will be sorted with
+ * `null_order::BEFORE`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return The reordering of `values` determined by the lexicographic order of
+ * the rows of `keys`.
+ */
+std::unique_ptr<table> stable_sort_by_key(
+  table_view const& values,
+  table_view const& keys,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Computes the ranks of input column in sorted order.
+ *
+ * Rank indicate the position of each element in the sorted column and rank
+ * value starts from 1.
+ *
+ * @code{.pseudo}
+ * input = { 3, 4, 5, 4, 1, 2}
+ * Result for different rank_method are
+ * FIRST    = {3, 4, 6, 5, 1, 2}
+ * AVERAGE  = {3, 4.5, 6, 4.5, 1, 2}
+ * MIN      = {3, 4, 6, 4, 1, 2}
+ * MAX      = {3, 5, 6, 5, 1, 2}
+ * DENSE    = {3, 4, 5, 4, 1, 2}
+ * @endcode
+ *
+ * @param input The column to rank
+ * @param method The ranking method used for tie breaking (same values)
+ * @param column_order The desired sort order for ranking
+ * @param null_handling  flag to include nulls during ranking. If nulls are not
+ * included, corresponding rank will be null.
+ * @param null_precedence The desired order of null compared to other elements
+ * for column
+ * @param percentage flag to convert ranks to percentage in range (0,1]
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A column of containing the rank of the each element of the column of `input`. The output
+ * column type will be `size_type`column by default or else `double` when
+ * `method=rank_method::AVERAGE` or `percentage=True`
+ */
+std::unique_ptr<column> rank(
+  column_view const& input,
+  rank_method method,
+  order column_order,
+  null_policy null_handling,
+  null_order null_precedence,
+  bool percentage,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns sorted order after sorting each segment in the table.
+ *
+ * If segment_offsets contains values larger than the number of rows, the behavior is undefined.
+ * @throws cudf::logic_error if `segment_offsets` is not `size_type` column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * keys = { {9, 8, 7, 6, 5, 4, 3, 2, 1, 0} }
+ * offsets = {0, 3, 7, 10}
+ * result = cudf::segmented_sorted_order(keys, offsets);
+ * result is { 2,1,0, 6,5,4,3, 9,8,7 }
+ * @endcode
+ *
+ * If segment_offsets is empty or contains a single index, no values are sorted
+ * and the result is a sequence of integers from 0 to keys.size()-1.
+ *
+ * The segment_offsets are not required to include all indices. Any indices
+ * outside the specified segments will not be sorted.
+ *
+ * @code{.pseudo}
+ * Example: (offsets do not cover all indices)
+ * keys = { {9, 8, 7, 6, 5, 4, 3, 2, 1, 0} }
+ * offsets = {3, 7}
+ * result = cudf::segmented_sorted_order(keys, offsets);
+ * result is { 0,1,2, 6,5,4,3, 7,8,9 }
+ * @endcode
+ *
+ * @param keys The table that determines the ordering of elements in each segment
+ * @param segment_offsets The column of `size_type` type containing start offset index for each
+ * contiguous segment.
+ * @param column_order The desired order for each column in `keys`. Size must be
+ * equal to `keys.num_columns()` or empty. If empty, all columns are sorted in
+ * ascending order.
+ * @param null_precedence The desired order of a null element compared to other
+ * elements for each column in `keys`. Size must be equal to
+ * `keys.num_columns()` or empty. If empty, all columns will be sorted with
+ * `null_order::BEFORE`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to allocate any returned objects
+ * @return sorted order of the segment sorted table
+ *
+ */
+std::unique_ptr<column> segmented_sorted_order(
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns sorted order after stably sorting each segment in the table.
+ *
+ * @copydoc cudf::segmented_sorted_order
+ */
+std::unique_ptr<column> stable_segmented_sorted_order(
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a lexicographic segmented sort of a table
+ *
+ * If segment_offsets contains values larger than the number of rows, the behavior is undefined.
+ * @throws cudf::logic_error if `values.num_rows() != keys.num_rows()`.
+ * @throws cudf::logic_error if `segment_offsets` is not `size_type` column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * keys = { {9, 8, 7, 6, 5, 4, 3, 2, 1, 0} }
+ * values = { {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'} }
+ * offsets = {0, 3, 7, 10}
+ * result = cudf::segmented_sort_by_key(keys, values, offsets);
+ * result is { 'c','b','a', 'g','f','e','d', 'j','i','h' }
+ * @endcode
+ *
+ * If segment_offsets is empty or contains a single index, no values are sorted
+ * and the result is a copy of the values.
+ *
+ * The segment_offsets are not required to include all indices. Any indices
+ * outside the specified segments will not be sorted.
+ *
+ * @code{.pseudo}
+ * Example: (offsets do not cover all indices)
+ * keys = { {9, 8, 7, 6, 5, 4, 3, 2, 1, 0} }
+ * values = { {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'} }
+ * offsets = {3, 7}
+ * result = cudf::segmented_sort_by_key(keys, values, offsets);
+ * result is { 'a','b','c', 'g','f','e','d', 'h','i','j' }
+ * @endcode
+ *
+ * @param values The table to reorder
+ * @param keys The table that determines the ordering of elements in each segment
+ * @param segment_offsets The column of `size_type` type containing start offset index for each
+ * contiguous segment.
+ * @param column_order The desired order for each column in `keys`. Size must be
+ * equal to `keys.num_columns()` or empty. If empty, all columns are sorted in
+ * ascending order.
+ * @param null_precedence The desired order of a null element compared to other
+ * elements for each column in `keys`. Size must be equal to
+ * `keys.num_columns()` or empty. If empty, all columns will be sorted with
+ * `null_order::BEFORE`.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to allocate any returned objects
+ * @return table with elements in each segment sorted
+ *
+ */
+std::unique_ptr<table> segmented_sort_by_key(
+  table_view const& values,
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Performs a stably lexicographic segmented sort of a table
+ *
+ * @copydoc cudf::segmented_sort_by_key
+ */
+std::unique_ptr<table> stable_segmented_sort_by_key(
+  table_view const& values,
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order         = {},
+  std::vector<null_order> const& null_precedence = {},
+  rmm::cuda_stream_view stream                   = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr            = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/stream_compaction.hpp b/cpp/include/cudf/stream_compaction.hpp
new file mode 100644
index 0000000..984e303
--- /dev/null
+++ b/cpp/include/cudf/stream_compaction.hpp
@@ -0,0 +1,378 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+/**
+ * @addtogroup reorder_compact
+ * @{
+ * @file
+ * @brief Column APIs for filtering rows
+ */
+
+/**
+ * @brief Filters a table to remove null elements with threshold count.
+ *
+ * Filters the rows of the `input` considering specified columns indicated in
+ * `keys` for validity / null values.
+ *
+ * Given an input table_view, row `i` from the input columns is copied to
+ * the output if the same row `i` of @p keys has at least @p keep_threshold
+ * non-null fields.
+ *
+ * This operation is stable: the input order is preserved in the output.
+ *
+ * Any non-nullable column in the input is treated as all non-null.
+ *
+ * @code{.pseudo}
+ *          input   {col1: {1, 2,    3,    null},
+ *                   col2: {4, 5,    null, null},
+ *                   col3: {7, null, null, null}}
+ *          keys = {0, 1, 2} // All columns
+ *          keep_threshold = 2
+ *
+ *          output {col1: {1, 2}
+ *                  col2: {4, 5}
+ *                  col3: {7, null}}
+ * @endcode
+ *
+ * @note if @p input.num_rows() is zero, or @p keys is empty or has no nulls,
+ * there is no error, and an empty `table` is returned
+ *
+ * @param[in] input The input `table_view` to filter
+ * @param[in] keys  vector of indices representing key columns from `input`
+ * @param[in] keep_threshold The minimum number of non-null fields in a row
+ *                           required to keep the row.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return Table containing all rows of the `input` with at least @p
+ * keep_threshold non-null fields in @p keys.
+ */
+std::unique_ptr<table> drop_nulls(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  cudf::size_type keep_threshold,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Filters a table to remove null elements.
+ *
+ * Filters the rows of the `input` considering specified columns indicated in
+ * `keys` for validity / null values.
+ *
+ * @code{.pseudo}
+ *          input   {col1: {1, 2,    3,    null},
+ *                   col2: {4, 5,    null, null},
+ *                   col3: {7, null, null, null}}
+ *          keys = {0, 1, 2} //All columns
+ *
+ *          output {col1: {1}
+ *                  col2: {4}
+ *                  col3: {7}}
+ * @endcode
+ *
+ * Same as drop_nulls but defaults keep_threshold to the number of columns in
+ * @p keys.
+ *
+ * @param[in] input The input `table_view` to filter
+ * @param[in] keys  vector of indices representing key columns from `input`
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return Table containing all rows of the `input` without nulls in the columns
+ * of @p keys.
+ */
+std::unique_ptr<table> drop_nulls(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Filters a table to remove NANs with threshold count.
+ *
+ * Filters the rows of the `input` considering specified columns indicated in
+ * `keys` for NANs. These key columns must be of floating-point type.
+ *
+ * Given an input table_view, row `i` from the input columns is copied to
+ * the output if the same row `i` of @p keys has at least @p keep_threshold
+ * non-NAN elements.
+ *
+ * This operation is stable: the input order is preserved in the output.
+ *
+ * @code{.pseudo}
+ *          input   {col1: {1.0, 2.0, 3.0, NAN},
+ *                   col2: {4.0, null, NAN, NAN},
+ *                   col3: {7.0, NAN, NAN, NAN}}
+ *          keys = {0, 1, 2} // All columns
+ *          keep_threshold = 2
+ *
+ *          output {col1: {1.0, 2.0}
+ *                  col2: {4.0, null}
+ *                  col3: {7.0, NAN}}
+ * @endcode
+ *
+ * @note if @p input.num_rows() is zero, or @p keys is empty,
+ * there is no error, and an empty `table` is returned
+ *
+ * @throws cudf::logic_error if The `keys` columns are not floating-point type.
+ *
+ * @param[in] input The input `table_view` to filter
+ * @param[in] keys  vector of indices representing key columns from `input`
+ * @param[in] keep_threshold The minimum number of non-NAN elements in a row
+ *                           required to keep the row.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return Table containing all rows of the `input` with at least @p
+ * keep_threshold non-NAN elements in @p keys.
+ */
+std::unique_ptr<table> drop_nans(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  cudf::size_type keep_threshold,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Filters a table to remove NANs.
+ *
+ * Filters the rows of the `input` considering specified columns indicated in
+ * `keys` for NANs. These key columns must be of floating-point type.
+ *
+ * @code{.pseudo}
+ *          input   {col1: {1.0, 2.0, 3.0, NAN},
+ *                   col2: {4.0, null, NAN, NAN},
+ *                   col3: {null, NAN, NAN, NAN}}
+ *          keys = {0, 1, 2} // All columns
+ *          keep_threshold = 2
+ *
+ *          output {col1: {1.0}
+ *                  col2: {4.0}
+ *                  col3: {null}}
+ * @endcode
+ *
+ * Same as drop_nans but defaults keep_threshold to the number of columns in
+ * @p keys.
+ *
+ * @param[in] input The input `table_view` to filter
+ * @param[in] keys  vector of indices representing key columns from `input`
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return Table containing all rows of the `input` without NANs in the columns
+ * of @p keys.
+ */
+std::unique_ptr<table> drop_nans(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Filters `input` using `boolean_mask` of boolean values as a mask.
+ *
+ * Given an input `table_view` and a mask `column_view`, an element `i` from
+ * each column_view of the `input` is copied to the corresponding output column
+ * if the corresponding element `i` in the mask is non-null and `true`.
+ * This operation is stable: the input order is preserved.
+ *
+ * @note if @p input.num_rows() is zero, there is no error, and an empty table
+ * is returned.
+ *
+ * @throws cudf::logic_error if `input.num_rows() != boolean_mask.size()`.
+ * @throws cudf::logic_error if `boolean_mask` is not `type_id::BOOL8` type.
+ *
+ * @param[in] input The input table_view to filter
+ * @param[in] boolean_mask A nullable column_view of type type_id::BOOL8 used
+ * as a mask to filter the `input`.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ * @return Table containing copy of all rows of @p input passing
+ * the filter defined by @p boolean_mask.
+ */
+std::unique_ptr<table> apply_boolean_mask(
+  table_view const& input,
+  column_view const& boolean_mask,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Choices for drop_duplicates API for retainment of duplicate rows
+ */
+enum class duplicate_keep_option {
+  KEEP_ANY = 0,  ///< Keep an unspecified occurrence
+  KEEP_FIRST,    ///< Keep first occurrence
+  KEEP_LAST,     ///< Keep last occurrence
+  KEEP_NONE      ///< Keep no (remove all) occurrences of duplicates
+};
+
+/**
+ * @brief Create a new table with consecutive duplicate rows removed.
+ *
+ * Given an `input` table_view, each row is copied to the output table to create a set of distinct
+ * rows. If there are duplicate rows, which row is copied depends on the `keep` parameter.
+ *
+ * The order of rows in the output table remains the same as in the input.
+ *
+ * A row is distinct if there are no equivalent rows in the table. A row is unique if there is no
+ * adjacent equivalent row. That is, keeping distinct rows removes all duplicates in the
+ * table/column, while keeping unique rows only removes duplicates from consecutive groupings.
+ *
+ * Performance hint: if the input is pre-sorted, `cudf::unique` can produce an equivalent result
+ * (i.e., same set of output rows) but with less running time than `cudf::distinct`.
+ *
+ * @throws cudf::logic_error if the `keys` column indices are out of bounds in the `input` table.
+ *
+ * @param[in] input           input table_view to copy only unique rows
+ * @param[in] keys            vector of indices representing key columns from `input`
+ * @param[in] keep            keep any, first, last, or none of the found duplicates
+ * @param[in] nulls_equal     flag to denote nulls are equal if null_equality::EQUAL, nulls are not
+ *                            equal if null_equality::UNEQUAL
+ * @param[in] mr              Device memory resource used to allocate the returned table's device
+ *                            memory
+ *
+ * @return Table with unique rows from each sequence of equivalent rows as specified by `keep`
+ */
+std::unique_ptr<table> unique(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  duplicate_keep_option keep,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a new table without duplicate rows.
+ *
+ * Given an `input` table_view, each row is copied to the output table to create a set of distinct
+ * rows. If there are duplicate rows, which row is copied depends on the `keep` parameter.
+ *
+ * The order of rows in the output table is not specified.
+ *
+ * Performance hint: if the input is pre-sorted, `cudf::unique` can produce an equivalent result
+ * (i.e., same set of output rows) but with less running time than `cudf::distinct`.
+ *
+ * @param input The input table
+ * @param keys Vector of indices indicating key columns in the `input` table
+ * @param keep Copy any, first, last, or none of the found duplicates
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether NaN elements should be considered as equal
+ * @param mr Device memory resource used to allocate the returned table
+ * @return Table with distinct rows in an unspecified order
+ */
+std::unique_ptr<table> distinct(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  duplicate_keep_option keep          = duplicate_keep_option::KEEP_ANY,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Create a new table without duplicate rows, preserving input order.
+ *
+ * Given an `input` table_view, each row is copied to the output table to create a set of distinct
+ * rows. The input row order is preserved. If there are duplicate rows, which row is copied depends
+ * on the `keep` parameter.
+ *
+ * This API produces the same output rows as `cudf::distinct`, but with input order preserved.
+ *
+ * Note that when `keep` is `KEEP_ANY`, the choice of which duplicate row to keep is arbitrary, but
+ * the returned table will retain the input order. That is, if the key column contained `1, 2, 1`
+ * with another values column `3, 4, 5`, the result could contain values `3, 4` or `4, 5` but not
+ * `4, 3` or `5, 4`.
+ *
+ * @param input The input table
+ * @param keys Vector of indices indicating key columns in the `input` table
+ * @param keep Copy any, first, last, or none of the found duplicates
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether NaN elements should be considered as equal
+ * @param mr Device memory resource used to allocate the returned table
+ * @return Table with distinct rows, preserving input order
+ */
+std::unique_ptr<table> stable_distinct(
+  table_view const& input,
+  std::vector<size_type> const& keys,
+  duplicate_keep_option keep          = duplicate_keep_option::KEEP_ANY,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  nan_equality nans_equal             = nan_equality::ALL_EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Count the number of consecutive groups of equivalent rows in a column.
+ *
+ * If `null_handling` is null_policy::EXCLUDE and `nan_handling` is  nan_policy::NAN_IS_NULL, both
+ * `NaN` and `null` values are ignored. If `null_handling` is null_policy::EXCLUDE and
+ * `nan_handling` is nan_policy::NAN_IS_VALID, only `null` is ignored, `NaN` is considered in count.
+ *
+ * `null`s are handled as equal.
+ *
+ * @param[in] input The column_view whose consecutive groups of equivalent rows will be counted
+ * @param[in] null_handling flag to include or ignore `null` while counting
+ * @param[in] nan_handling flag to consider `NaN==null` or not
+ *
+ * @return number of consecutive groups of equivalent rows in the column
+ */
+cudf::size_type unique_count(column_view const& input,
+                             null_policy null_handling,
+                             nan_policy nan_handling);
+
+/**
+ * @brief Count the number of consecutive groups of equivalent rows in a table.
+ *
+ * @param[in] input Table whose consecutive groups of equivalent rows will be counted
+ * @param[in] nulls_equal flag to denote if null elements should be considered equal
+ *            nulls are not equal if null_equality::UNEQUAL.
+ *
+ * @return number of consecutive groups of equivalent rows in the column
+ */
+cudf::size_type unique_count(table_view const& input,
+                             null_equality nulls_equal = null_equality::EQUAL);
+
+/**
+ * @brief Count the distinct elements in the column_view.
+ *
+ * If `nulls_equal == nulls_equal::UNEQUAL`, all `null`s are distinct.
+ *
+ * Given an input column_view, number of distinct elements in this column_view is returned.
+ *
+ * If `null_handling` is null_policy::EXCLUDE and `nan_handling` is  nan_policy::NAN_IS_NULL, both
+ * `NaN` and `null` values are ignored. If `null_handling` is null_policy::EXCLUDE and
+ * `nan_handling` is nan_policy::NAN_IS_VALID, only `null` is ignored, `NaN` is considered in
+ * distinct count.
+ *
+ * `null`s are handled as equal.
+ *
+ * @param[in] input The column_view whose distinct elements will be counted
+ * @param[in] null_handling flag to include or ignore `null` while counting
+ * @param[in] nan_handling flag to consider `NaN==null` or not
+ *
+ * @return number of distinct rows in the table
+ */
+cudf::size_type distinct_count(column_view const& input,
+                               null_policy null_handling,
+                               nan_policy nan_handling);
+
+/**
+ * @brief Count the distinct rows in a table.
+ *
+ * @param[in] input Table whose distinct rows will be counted
+ * @param[in] nulls_equal flag to denote if null elements should be considered equal.
+ *            nulls are not equal if null_equality::UNEQUAL.
+ *
+ * @return number of distinct rows in the table
+ */
+cudf::size_type distinct_count(table_view const& input,
+                               null_equality nulls_equal = null_equality::EQUAL);
+
+/** @} */
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/attributes.hpp b/cpp/include/cudf/strings/attributes.hpp
new file mode 100644
index 0000000..85086e4
--- /dev/null
+++ b/cpp/include/cudf/strings/attributes.hpp
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+
+//! Strings column APIs
+namespace strings {
+/**
+ * @addtogroup strings_apis
+ * @{
+ * @file strings/attributes.hpp
+ * @brief Read attributes of strings column
+ */
+
+/**
+ * @brief Returns a column containing character lengths
+ * of each string in the given column
+ *
+ * The output column will have the same number of rows as the
+ * specified strings column. Each row value will be the number of
+ * characters in the corresponding string.
+ *
+ * Any null string will result in a null entry for that row in the output column.
+ *
+ * @param input Strings instance for this operation
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column with lengths for each string
+ */
+std::unique_ptr<column> count_characters(
+  strings_column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column containing byte lengths
+ * of each string in the given column
+ *
+ * The output column will have the same number of rows as the
+ * specified strings column. Each row value will be the number of
+ * bytes in the corresponding string.
+ *
+ * Any null string will result in a null entry for that row in the output column.
+ *
+ * @param input Strings instance for this operation
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column with the number of bytes for each string
+ */
+std::unique_ptr<column> count_bytes(
+  strings_column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a numeric column with code point values (integers) for each
+ * character of each string
+ *
+ * A code point is the integer value representation of a character.
+ * For example, the code point value for the character 'A' in UTF-8 is 65.
+ *
+ * The size of the output column will be the total number of characters in the
+ * strings column.
+ *
+ * Any null string is ignored. No null entries will appear in the output column.
+ *
+ * @param input Strings instance for this operation
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New INT32 column with code point integer values for each character
+ */
+std::unique_ptr<column> code_points(
+  strings_column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of strings_apis group
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/capitalize.hpp b/cpp/include/cudf/strings/capitalize.hpp
new file mode 100644
index 0000000..57375e9
--- /dev/null
+++ b/cpp/include/cudf/strings/capitalize.hpp
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/char_types/char_types.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_case
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a column of capitalized strings.
+ *
+ * If the `delimiters` is an empty string, then only the first character of each
+ * row is capitalized. Otherwise, a non-delimiter character is capitalized after
+ * any delimiter character is found.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input = ["tesT1", "a Test", "Another Test", "a\tb"];
+ * output = capitalize(input)
+ * output is ["Test1", "A test", "Another test", "A\tb"]
+ * output = capitalize(input, " ")
+ * output is ["Test1", "A Test", "Another Test", "A\tb"]
+ * output = capitalize(input, " \t")
+ * output is ["Test1", "A Test", "Another Test", "A\tB"]
+ * @endcode
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @throw cudf::logic_error if `delimiter.is_valid()` is `false`.
+ *
+ * @param input String column
+ * @param delimiters Characters for identifying words to capitalize
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of strings capitalized from the input column
+ */
+std::unique_ptr<column> capitalize(
+  strings_column_view const& input,
+  string_scalar const& delimiters     = string_scalar("", true, cudf::get_default_stream()),
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Modifies first character of each word to upper-case and lower-cases the rest.
+ *
+ * A word here is a sequence of characters of `sequence_type` delimited by
+ * any characters not part of the `sequence_type` character set.
+ *
+ * This function returns a column of strings where, for each string row in the input,
+ * the first character of each word is converted to upper-case,
+ * while all the remaining characters in a word are converted to lower-case.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input = ["   teST1", "a Test", " Another test ", "n2vidia"];
+ * output = title(input)
+ * output is ["   Test1", "A Test", " Another Test ", "N2Vidia"]
+ * output = title(input,ALPHANUM)
+ * output is ["   Test1", "A Test", " Another Test ", "N2vidia"]
+ * @endcode
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @param input String column
+ * @param sequence_type The character type that is used when identifying words
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of titled strings
+ */
+std::unique_ptr<column> title(
+  strings_column_view const& input,
+  string_character_types sequence_type = string_character_types::ALPHA,
+  rmm::cuda_stream_view stream         = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Checks if the strings in the input column are title formatted.
+ *
+ * The first character of each word should be upper-case while all other
+ * characters should be lower-case. A word is a sequence of upper-case
+ * and lower-case characters.
+ *
+ * This function returns a column of booleans indicating true if the string in
+ * the input row is in title format and false if not.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input = ["   Test1", "A Test", " Another test ", "N2Vidia Corp", "!Abc"];
+ * output = is_title(input)
+ * output is [true, true, false, true, true]
+ * @endcode
+ *
+ * Any null string entries result in corresponding null output column entries.
+ *
+ * @param input String column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of type BOOL8
+ */
+std::unique_ptr<column> is_title(
+  strings_column_view const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/case.hpp b/cpp/include/cudf/strings/case.hpp
new file mode 100644
index 0000000..9419168
--- /dev/null
+++ b/cpp/include/cudf/strings/case.hpp
@@ -0,0 +1,91 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_case
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Converts a column of strings to lower case.
+ *
+ * Only upper case alphabetical characters are converted. All other characters are copied.
+ * Case conversion may result in strings that are longer or shorter than the
+ * original string in bytes.
+ *
+ * Any null entries create null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of strings with characters converted.
+ */
+std::unique_ptr<column> to_lower(
+  strings_column_view const& strings,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Converts a column of strings to upper case.
+ *
+ * Only lower case alphabetical characters are converted. All other characters are copied.
+ * Case conversion may result in strings that are longer or shorter than the
+ * original string in bytes.
+ *
+ * Any null entries create null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of strings with characters converted.
+ */
+std::unique_ptr<column> to_upper(
+  strings_column_view const& strings,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of strings converting lower case characters to
+ * upper case and vice versa.
+ *
+ * Only upper or lower case alphabetical characters are converted. All other characters are copied.
+ * Case conversion may result in strings that are longer or shorter than the
+ * original string in bytes.
+ *
+ * Any null entries create null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of strings with characters converted.
+ */
+std::unique_ptr<column> swapcase(
+  strings_column_view const& strings,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/char_types/char_cases.hpp b/cpp/include/cudf/strings/char_types/char_cases.hpp
new file mode 100644
index 0000000..9eb63f7
--- /dev/null
+++ b/cpp/include/cudf/strings/char_types/char_cases.hpp
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Regenerates the special case mapping tables used to handle non-trivial unicode
+ * character case conversions.
+ *
+ * 'special' cased characters are those defined as not having trivial single->single character
+ * mappings when having upper(), lower() or titlecase() operations applied.  Typically this is
+ * for cases where a single character maps to multiple, but there are also cases of
+ * non-reversible mappings, where:  codepoint != lower(upper(code_point)).
+ */
+void generate_special_mapping_hash_table();
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/char_types/char_types.hpp b/cpp/include/cudf/strings/char_types/char_types.hpp
new file mode 100644
index 0000000..8b6c434
--- /dev/null
+++ b/cpp/include/cudf/strings/char_types/char_types.hpp
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/char_types/char_types_enum.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_types
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a boolean column identifying strings entries in which all
+ * characters are of the type specified.
+ *
+ * The output row entry will be set to false if the corresponding string element
+ * is empty or has at least one character not of the specified type. If all
+ * characters fit the type then true is set in that output row entry.
+ *
+ * To ignore all but specific types, set the `verify_types` to those types
+ * which should be checked. Otherwise, the default `ALL_TYPES` will verify all
+ * characters match `types`.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['ab', 'a b', 'a7', 'a B']
+ * b1 = s.all_characters_of_type(s,LOWER)
+ * b1 is [true, false, false, false]
+ * b2 = s.all_characters_of_type(s,LOWER,LOWER|UPPER)
+ * b2 is [true, true, true, false]
+ * @endcode
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param types The character types to check in each string.
+ * @param verify_types Only verify against these character types.
+ *                     Default `ALL_TYPES` means return `true`
+ *                     iff all characters match `types`.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of boolean results for each string.
+ */
+std::unique_ptr<column> all_characters_of_type(
+  strings_column_view const& strings,
+  string_character_types types,
+  string_character_types verify_types = string_character_types::ALL_TYPES,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Filter specific character types from a column of strings.
+ *
+ * To remove all characters of a specific type, set that type in
+ * `types_to_remove` and set `types_to_keep` to `ALL_TYPES`.
+ *
+ * To filter out characters NOT of a select type, specify `ALL_TYPES` for
+ * `types_to_remove` and which types to not remove in `types_to_keep`.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['ab', 'a b', 'a7bb', 'A7B234']
+ * s1 = s.filter_characters_of_type(s,NUMERIC,"",ALL_TYPES)
+ * s1 is ['ab', 'a b', 'abb', 'AB']
+ * s2 = s.filter_characters_of_type(s,ALL_TYPES,"-",LOWER)
+ * s2 is ['ab', 'a-b', 'a-bb', '------']
+ * @endcode
+ *
+ * In `s1` all NUMERIC types have been removed.
+ * In `s2` all non-LOWER types have been replaced.
+ *
+ * One but not both parameters `types_to_remove` and `types_to_keep` must
+ * be set to `ALL_TYPES`.
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @throw cudf::logic_error if neither or both `types_to_remove` and
+ *        `types_to_keep` are set to `ALL_TYPES`.
+ *
+ * @param strings Strings instance for this operation.
+ * @param types_to_remove The character types to check in each string.
+ *        Use `ALL_TYPES` here to specify `types_to_keep` instead.
+ * @param replacement The replacement character to use when removing characters.
+ * @param types_to_keep Default `ALL_TYPES` means all characters of
+ *        `types_to_remove` will be filtered.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of boolean results for each string.
+ */
+std::unique_ptr<column> filter_characters_of_type(
+  strings_column_view const& strings,
+  string_character_types types_to_remove,
+  string_scalar const& replacement     = string_scalar(""),
+  string_character_types types_to_keep = string_character_types::ALL_TYPES,
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/char_types/char_types_enum.hpp b/cpp/include/cudf/strings/char_types/char_types_enum.hpp
new file mode 100644
index 0000000..8d248cb
--- /dev/null
+++ b/cpp/include/cudf/strings/char_types/char_types_enum.hpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+#include <type_traits>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_types
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Character type values.
+ * These types can be or'd to check for any combination of types.
+ *
+ * This cannot be turned into an enum class because or'd entries can
+ * result in values that are not in the class. For example,
+ * combining NUMERIC|SPACE is a valid, reasonable combination but
+ * does not match to any explicitly named enumerator.
+ */
+enum string_character_types : uint32_t {
+  DECIMAL    = 1 << 0,                             ///< all decimal characters
+  NUMERIC    = 1 << 1,                             ///< all numeric characters
+  DIGIT      = 1 << 2,                             ///< all digit characters
+  ALPHA      = 1 << 3,                             ///< all alphabetic characters
+  SPACE      = 1 << 4,                             ///< all space characters
+  UPPER      = 1 << 5,                             ///< all upper case characters
+  LOWER      = 1 << 6,                             ///< all lower case characters
+  ALPHANUM   = DECIMAL | NUMERIC | DIGIT | ALPHA,  ///< all alphanumeric characters
+  CASE_TYPES = UPPER | LOWER,                      ///< all case-able characters
+  ALL_TYPES  = ALPHANUM | CASE_TYPES | SPACE       ///< all character types
+};
+
+/**
+ * @brief OR operator for combining string_character_types
+ *
+ * @param lhs left-hand side of OR operation
+ * @param rhs right-hand side of OR operation
+ * @return combined string_character_types
+ */
+constexpr string_character_types operator|(string_character_types lhs, string_character_types rhs)
+{
+  return static_cast<string_character_types>(
+    static_cast<std::underlying_type_t<string_character_types>>(lhs) |
+    static_cast<std::underlying_type_t<string_character_types>>(rhs));
+}
+
+/**
+ * @brief Compound assignment OR operator for combining string_character_types
+ *
+ * @param lhs left-hand side of OR operation
+ * @param rhs right-hand side of OR operation
+ * @return Reference to `lhs` after combining `lhs` and `rhs`
+ */
+constexpr string_character_types& operator|=(string_character_types& lhs,
+                                             string_character_types rhs)
+{
+  lhs = static_cast<string_character_types>(
+    static_cast<std::underlying_type_t<string_character_types>>(lhs) |
+    static_cast<std::underlying_type_t<string_character_types>>(rhs));
+  return lhs;
+}
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/combine.hpp b/cpp/include/cudf/strings/combine.hpp
new file mode 100644
index 0000000..71f65ac
--- /dev/null
+++ b/cpp/include/cudf/strings/combine.hpp
@@ -0,0 +1,328 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_combine
+ * @{
+ * @file strings/combine.hpp
+ * @brief Strings APIs for concatenate and join
+ */
+
+/**
+ * @brief Setting for specifying how separators are added with
+ * null strings elements.
+ */
+enum class separator_on_nulls {
+  YES,  ///< Always add separators between elements
+  NO    ///< Do not add separators if an element is null
+};
+
+/**
+ * @brief Setting for specifying what will be output from `join_list_elements` when an input list
+ * is empty.
+ */
+enum class output_if_empty_list {
+  EMPTY_STRING,  ///< Empty list will result in empty string
+  NULL_ELEMENT   ///< Empty list will result in a null
+};
+
+/**
+ * @brief Concatenates all strings in the column into one new string delimited
+ * by an optional separator string.
+ *
+ * This returns a column with one string. Any null entries are ignored unless
+ * the @p narep parameter specifies a replacement string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['aa', null, '', 'zz' ]
+ * r = join_strings(s,':','_')
+ * r is ['aa:_::zz']
+ * @endcode
+ *
+ * @throw cudf::logic_error if separator is not valid.
+ *
+ * @param strings Strings for this operation.
+ * @param separator String that should inserted between each string.
+ *        Default is an empty string.
+ * @param narep String that should represent any null strings found.
+ *        Default of invalid-scalar will ignore any null entries.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column containing one string.
+ */
+std::unique_ptr<column> join_strings(
+  strings_column_view const& strings,
+  string_scalar const& separator      = string_scalar(""),
+  string_scalar const& narep          = string_scalar("", false),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Concatenates a list of strings columns using separators for each row
+ * and returns the result as a strings column.
+ *
+ * Each new string is created by concatenating the strings from the same
+ * row delimited by the row separator provided for that row. The following rules
+ * are applicable:
+ *
+ * - If row separator for a given row is null, output column for that row is null, unless
+ *   there is a valid @p separator_narep
+ * - The separator is applied between two output row values if the @p separate_nulls
+ *   is `YES` or only between valid rows if @p separate_nulls is `NO`.
+ * - If @p separator_narep and @p col_narep are both valid, the output column is always
+ *   non nullable
+ *
+ * @code{.pseudo}
+ * Example:
+ * c0   = ['aa', null, '',  'ee',  null, 'ff']
+ * c1   = [null, 'cc', 'dd', null, null, 'gg']
+ * c2   = ['bb', '',   null, null, null, 'hh']
+ * sep  = ['::', '%%', '^^', '!',  '*',  null]
+ * out = concatenate({c0, c1, c2}, sep)
+ * // all rows have at least one null or sep[i]==null
+ * out is [null, null, null, null, null, null]
+ *
+ * sep_rep = '+'
+ * out = concatenate({c0, c1, c2}, sep, sep_rep)
+ * // all rows with at least one null output as null
+ * out is [null, null, null, null, null, 'ff+gg+hh']
+ *
+ * col_narep = '-'
+ * sep_na = non-valid scalar
+ * out = concatenate({c0, c1, c2}, sep, sep_na, col_narep)
+ * // only the null entry in the sep column produces a null row
+ * out is ['aa::-::bb', '-%%cc%%', '^^dd^^-', 'ee!-!-', '-*-*-', null]
+ *
+ * col_narep = ''
+ * out = concatenate({c0, c1, c2}, sep, sep_rep, col_narep, separator_on_nulls:NO)
+ * // parameter suppresses separator for null rows
+ * out is ['aa::bb', 'cc%%', '^^dd', 'ee', '', 'ff+gg+hh']
+ * @endcode
+ *
+ * @throw cudf::logic_error if no input columns are specified - table view is empty
+ * @throw cudf::logic_error if input columns are not all strings columns.
+ * @throw cudf::logic_error if the number of rows from @p separators and @p strings_columns
+ *                          do not match
+ *
+ * @param strings_columns List of strings columns to concatenate.
+ * @param separators Strings column that provides the separator for a given row
+ * @param separator_narep String that should be used in place of a null separator for a given
+ *        row. Default of invalid-scalar means no row separator value replacements.
+ *        Default is an invalid string.
+ * @param col_narep String that should be used in place of any null strings
+ *        found in any column. Default of invalid-scalar means no null column value replacements.
+ *        Default is an invalid string.
+ * @param separate_nulls If YES, then the separator is included for null rows
+ *        if `col_narep` is valid.
+ * @param mr Resource for allocating device memory.
+ * @return New column with concatenated results.
+ */
+std::unique_ptr<column> concatenate(
+  table_view const& strings_columns,
+  strings_column_view const& separators,
+  string_scalar const& separator_narep = string_scalar("", false),
+  string_scalar const& col_narep       = string_scalar("", false),
+  separator_on_nulls separate_nulls    = separator_on_nulls::YES,
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Row-wise concatenates the given list of strings columns and
+ * returns a single strings column result.
+ *
+ * Each new string is created by concatenating the strings from the same
+ * row delimited by the separator provided.
+ *
+ * Any row with a null entry will result in the corresponding output
+ * row to be null entry unless a narep string is specified to be used
+ * in its place.
+ *
+ * If @p separate_nulls is set to `NO` and @p narep is valid then
+ * separators are not added to the output between null elements.
+ * Otherwise, separators are always added if @p narep is valid.
+ *
+ * More than one column must be specified in the input @p strings_columns
+ * table.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s1 = ['aa', null, '', 'dd']
+ * s2 = ['', 'bb', 'cc', null]
+ * out = concatenate({s1, s2})
+ * out is ['aa', null, 'cc', null]
+ *
+ * out = concatenate({s1, s2}, ':', '_')
+ * out is ['aa:', '_:bb', ':cc', 'dd:_']
+ *
+ * out = concatenate({s1, s2}, ':', '', separator_on_nulls::NO)
+ * out is ['aa:', 'bb', ':cc', 'dd']
+ * @endcode
+ *
+ * @throw cudf::logic_error if input columns are not all strings columns.
+ * @throw cudf::logic_error if separator is not valid.
+ * @throw cudf::logic_error if only one column is specified
+ *
+ * @param strings_columns List of string columns to concatenate.
+ * @param separator String that should inserted between each string from each row.
+ *        Default is an empty string.
+ * @param narep String that should be used in place of any null strings
+ *        found in any column. Default of invalid-scalar means any null entry in any column will
+ *        produces a null result for that row.
+ * @param separate_nulls If YES, then the separator is included for null rows if `narep` is valid.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with concatenated results.
+ */
+std::unique_ptr<column> concatenate(
+  table_view const& strings_columns,
+  string_scalar const& separator      = string_scalar(""),
+  string_scalar const& narep          = string_scalar("", false),
+  separator_on_nulls separate_nulls   = separator_on_nulls::YES,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Given a lists column of strings (each row is a list of strings), concatenates the strings
+ * within each row and returns a single strings column result.
+ *
+ * Each new string is created by concatenating the strings from the same row (same list element)
+ * delimited by the row separator provided in the @p separators strings column.
+ *
+ * A null list row will always result in a null string in the output row. Any non-null list row
+ * having a null element will result in the corresponding output row to be null unless a valid
+ * @p string_narep scalar is provided to be used in its place. Any null row in the @p separators
+ * column will also result in a null output row unless a valid @p separator_narep scalar is provided
+ * to be used in place of the null separators.
+ *
+ * If @p separate_nulls is set to `NO` and @p string_narep is valid then separators are not added to
+ * the output between null elements. Otherwise, separators are always added if @p string_narep is
+ * valid.
+ *
+ * If @p empty_list_policy is set to `EMPTY_STRING`, any row that is an empty list will result in
+ * an empty output string. Otherwise, the output will be a null.
+ *
+ * In the special case when the input list row contains all null elements, the output will be the
+ * same as in case of empty input list regardless of @p string_narep and @p separate_nulls values.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = [ ['aa', 'bb', 'cc'], null, ['', 'dd'], ['ee', null], ['ff', 'gg'] ]
+ * sep  = ['::', '%%',  '!',  '*',  null]
+ *
+ * out = join_list_elements(s, sep)
+ * out is ['aa::bb::cc', null, '!dd', null, null]
+ *
+ * out = join_list_elements(s, sep, ':', '_')
+ * out is ['aa::bb::cc', null,  '!dd', 'ee*_', 'ff:gg']
+ *
+ * out = join_list_elements(s, sep, ':', '', separator_on_nulls::NO)
+ * out is ['aa::bb::cc', null,  '!dd', 'ee', 'ff:gg']
+ * @endcode
+ *
+ * @throw cudf::logic_error if input column is not lists of strings column.
+ * @throw cudf::logic_error if the number of rows from `separators` and `lists_strings_column` do
+ *        not match
+ *
+ * @param lists_strings_column Column containing lists of strings to concatenate.
+ * @param separators Strings column that provides separators for concatenation.
+ * @param separator_narep String that should be used to replace null separator, default is an
+ *        invalid-scalar denoting that rows containing null separator will result in null string in
+ *        the corresponding output rows.
+ * @param string_narep String that should be used to replace null strings in any non-null list row,
+ *        default is an invalid-scalar denoting that list rows containing null strings will result
+ *        in null string in the corresponding output rows.
+ * @param separate_nulls If YES, then the separator is included for null rows if `narep` is valid.
+ * @param empty_list_policy if set to EMPTY_STRING, any input row that is an empty list will
+ *        result in an empty string. Otherwise, it will result in a null.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with concatenated results.
+ */
+std::unique_ptr<column> join_list_elements(
+  lists_column_view const& lists_strings_column,
+  strings_column_view const& separators,
+  string_scalar const& separator_narep   = string_scalar("", false),
+  string_scalar const& string_narep      = string_scalar("", false),
+  separator_on_nulls separate_nulls      = separator_on_nulls::YES,
+  output_if_empty_list empty_list_policy = output_if_empty_list::EMPTY_STRING,
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Given a lists column of strings (each row is a list of strings), concatenates the strings
+ * within each row and returns a single strings column result.
+ *
+ * Each new string is created by concatenating the strings from the same row (same list element)
+ * delimited by the @p separator provided.
+ *
+ * A null list row will always result in a null string in the output row. Any non-null list row
+ * having a null element will result in the corresponding output row to be null unless a
+ * @p narep string is specified to be used in its place.
+ *
+ * If @p separate_nulls is set to `NO` and @p narep is valid then separators are not added to the
+ * output between null elements. Otherwise, separators are always added if @p narep is valid.
+ *
+ * If @p empty_list_policy is set to `EMPTY_STRING`, any row that is an empty list will result in
+ * an empty output string. Otherwise, the output will be a null.
+ *
+ * In the special case when the input list row contains all null elements, the output will be the
+ * same as in case of empty input list regardless of @p narep and @p separate_nulls values.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = [ ['aa', 'bb', 'cc'], null, ['', 'dd'], ['ee', null], ['ff'] ]
+ *
+ * out = join_list_elements(s)
+ * out is ['aabbcc', null, 'dd', null, 'ff']
+ *
+ * out = join_list_elements(s, ':', '_')
+ * out is ['aa:bb:cc', null,  ':dd', 'ee:_', 'ff']
+ *
+ * out = join_list_elements(s, ':', '', separator_on_nulls::NO)
+ * out is ['aa:bb:cc', null,  ':dd', 'ee', 'ff']
+ * @endcode
+ *
+ * @throw cudf::logic_error if input column is not lists of strings column.
+ * @throw cudf::logic_error if separator is not valid.
+ *
+ * @param lists_strings_column Column containing lists of strings to concatenate.
+ * @param separator String that should inserted between strings of each list row, default is an
+ *        empty string.
+ * @param narep String that should be used to replace null strings in any non-null list row, default
+ *        is an invalid-scalar denoting that list rows containing null strings will result in null
+ *        string in the corresponding output rows.
+ * @param separate_nulls If YES, then the separator is included for null rows if `narep` is valid.
+ * @param empty_list_policy if set to EMPTY_STRING, any input row that is an empty list will result
+ *        in an empty string. Otherwise, it will result in a null.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with concatenated results.
+ */
+std::unique_ptr<column> join_list_elements(
+  lists_column_view const& lists_strings_column,
+  string_scalar const& separator         = string_scalar(""),
+  string_scalar const& narep             = string_scalar("", false),
+  separator_on_nulls separate_nulls      = separator_on_nulls::YES,
+  output_if_empty_list empty_list_policy = output_if_empty_list::EMPTY_STRING,
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/contains.hpp b/cpp/include/cudf/strings/contains.hpp
new file mode 100644
index 0000000..23c77cb
--- /dev/null
+++ b/cpp/include/cudf/strings/contains.hpp
@@ -0,0 +1,201 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/regex/flags.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+
+struct regex_program;
+
+/**
+ * @addtogroup strings_contains
+ * @{
+ * @file strings/contains.hpp
+ * @brief Strings APIs for regex contains, count, matches
+ */
+
+/**
+ * @brief Returns a boolean column identifying rows which
+ * match the given regex_program object
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abc", "123", "def456"]
+ * p = regex_program::create("\\d+")
+ * r = contains_re(s, p)
+ * r is now [false, true, true]
+ * @endcode
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column of boolean results for each string
+ */
+std::unique_ptr<column> contains_re(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying rows which
+ * matching the given regex_program object but only at the beginning the string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abc", "123", "def456"]
+ * p = regex_program::create("\\d+")
+ * r = matches_re(s, p)
+ * r is now [false, true, false]
+ * @endcode
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column of boolean results for each string
+ */
+std::unique_ptr<column> matches_re(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the number of times the given regex_program's pattern
+ * matches in each string
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abc", "123", "def45"]
+ * p = regex_program::create("\\d")
+ * r = count_re(s, p)
+ * r is now [0, 3, 2]
+ * @endcode
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column of match counts for each string
+ */
+std::unique_ptr<column> count_re(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying rows which
+ * match the given like pattern.
+ *
+ * The like pattern expects only 2 wildcard special characters:
+ * - `%` zero or more of any character
+ * - `_` any single character
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["azaa", "ababaabba", "aaxa"]
+ * r = like(s, "%a_aa%")
+ * r is now [1, 1, 0]
+ * r = like(s, "a__a")
+ * r is now [1, 0, 1]
+ * @endcode
+ *
+ * Specify an escape character to include either `%` or `_` in the search.
+ * The `escape_character` is expected to be either 0 or 1 characters.
+ * If more than one character is specified only the first character is used.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abc_def", "abc1def", "abc_"]
+ * r = like(s, "abc/_d%", "/")
+ * r is now [1, 0, 0]
+ * @endcode
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @throw cudf::logic_error if `pattern` or `escape_character` is invalid
+ *
+ * @param input Strings instance for this operation
+ * @param pattern Like pattern to match within each string
+ * @param escape_character Optional character specifies the escape prefix;
+ *                         default is no escape character
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New boolean column
+ */
+std::unique_ptr<column> like(
+  strings_column_view const& input,
+  string_scalar const& pattern,
+  string_scalar const& escape_character = string_scalar(""),
+  rmm::mr::device_memory_resource* mr   = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying rows which
+ * match the corresponding like pattern in the given patterns
+ *
+ * The like pattern expects only 2 wildcard special characters:
+ * - `%` zero or more of any character
+ * - `_` any single character
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["azaa", "ababaabba", "aaxa"]
+ * p = ["%a", "b%", "__x_"]
+ * r = like(s, p)
+ * r is now [1, 0, 1]
+ * @endcode
+ *
+ * Specify an escape character to include either `%` or `_` in the search.
+ * The `escape_character` is expected to be either 0 or 1 characters.
+ * If more than one character is specified only the first character is used.
+ * The escape character is applied to all patterns.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @throw cudf::logic_error if `patterns` contains nulls or `escape_character` is invalid
+ * @throw cudf::logic_error if `patterns.size() != input.size()`
+ *
+ * @param input Strings instance for this operation
+ * @param patterns Like patterns to match within each corresponding string
+ * @param escape_character Optional character specifies the escape prefix;
+ *                         default is no escape character
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New boolean column
+ */
+std::unique_ptr<column> like(
+  strings_column_view const& input,
+  strings_column_view const& patterns,
+  string_scalar const& escape_character = string_scalar(""),
+  rmm::mr::device_memory_resource* mr   = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_booleans.hpp b/cpp/include/cudf/strings/convert/convert_booleans.hpp
new file mode 100644
index 0000000..ab63503
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_booleans.hpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new BOOL8 column by parsing boolean values from the strings
+ * in the provided strings column.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param true_string String to expect for true. Non-matching strings are false.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New BOOL8 column converted from strings.
+ */
+std::unique_ptr<column> to_booleans(
+  strings_column_view const& strings,
+  string_scalar const& true_string    = string_scalar("true"),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting the boolean values from the
+ * provided column into strings.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * @throw cudf::logic_error if the input column is not BOOL8 type.
+ *
+ * @param booleans Boolean column to convert.
+ * @param true_string String to use for true in the output column.
+ * @param false_string String to use for false in the output column.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> from_booleans(
+  column_view const& booleans,
+  string_scalar const& true_string    = string_scalar("true"),
+  string_scalar const& false_string   = string_scalar("false"),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_datetime.hpp b/cpp/include/cudf/strings/convert/convert_datetime.hpp
new file mode 100644
index 0000000..fa729d2
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_datetime.hpp
@@ -0,0 +1,251 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new timestamp column converting a strings column into
+ * timestamps using the provided format pattern.
+ *
+ * The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z"
+ *
+ * | Specifier | Description |
+ * | :-------: | ----------- |
+ * | \%d | Day of the month: 01-31 |
+ * | \%m | Month of the year: 01-12 |
+ * | \%y | Year without century: 00-99. [0,68] maps to [2000,2068] and [69,99] maps to [1969,1999] |
+ * | \%Y | Year with century: 0001-9999 |
+ * | \%H | 24-hour of the day: 00-23 |
+ * | \%I | 12-hour of the day: 01-12 |
+ * | \%M | Minute of the hour: 00-59 |
+ * | \%S | Second of the minute: 00-59. Leap second is not supported. |
+ * | \%f | 6-digit microsecond: 000000-999999 |
+ * | \%z | UTC offset with format ±HHMM Example +0500 |
+ * | \%j | Day of the year: 001-366 |
+ * | \%p | Only 'AM', 'PM' or 'am', 'pm' are recognized |
+ * | \%W | Week of the year with Monday as the first day of the week: 00-53 |
+ * | \%w | Day of week: 0-6 = Sunday-Saturday |
+ * | \%U | Week of the year with Sunday as the first day of the week: 00-53 |
+ * | \%u | Day of week: 1-7 = Monday-Sunday |
+ *
+ * Other specifiers are not currently supported.
+ *
+ * Invalid formats are not checked. If the string contains unexpected
+ * or insufficient characters, that output row entry's timestamp value is undefined.
+ *
+ * Any null string entry will result in a corresponding null row in the output column.
+ *
+ * The resulting time units are specified by the `timestamp_type` parameter.
+ * The time units are independent of the number of digits parsed by the "%f" specifier.
+ * The "%f" supports a precision value to read the numeric digits. Specify the
+ * precision with a single integer value (1-9) as follows:
+ * use "%3f" for milliseconds, "%6f" for microseconds and "%9f" for nanoseconds.
+ *
+ * Although leap second is not supported for "%S", no checking is performed on the value.
+ * The cudf::strings::is_timestamp can be used to verify the valid range of values.
+ *
+ * If "%W"/"%w" (or "%U/%u") and "%m"/"%d" are both specified, the "%W"/%U and "%w"/%u values
+ * take precedent when computing the date part of the timestamp result.
+ *
+ * @throw cudf::logic_error if timestamp_type is not a timestamp type.
+ *
+ * @param strings Strings instance for this operation.
+ * @param timestamp_type The timestamp type used for creating the output column.
+ * @param format String specifying the timestamp format in strings.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New datetime column.
+ */
+std::unique_ptr<column> to_timestamps(
+  strings_column_view const& strings,
+  data_type timestamp_type,
+  std::string_view format,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Verifies the given strings column can be parsed to timestamps using the provided format
+ * pattern.
+ *
+ * The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z"
+ *
+ * | Specifier | Description |
+ * | :-------: | ----------- |
+ * | \%d | Day of the month: 01-31 |
+ * | \%m | Month of the year: 01-12 |
+ * | \%y | Year without century: 00-99. [0,68] maps to [2000,2068] and [69,99] maps to [1969,1999] |
+ * | \%Y | Year with century: 0001-9999 |
+ * | \%H | 24-hour of the day: 00-23 |
+ * | \%I | 12-hour of the day: 01-12 |
+ * | \%M | Minute of the hour: 00-59|
+ * | \%S | Second of the minute: 00-59. Leap second is not supported. |
+ * | \%f | 6-digit microsecond: 000000-999999 |
+ * | \%z | UTC offset with format ±HHMM Example +0500 |
+ * | \%j | Day of the year: 001-366 |
+ * | \%p | Only 'AM', 'PM' or 'am', 'pm' are recognized |
+ * | \%W | Week of the year with Monday as the first day of the week: 00-53 |
+ * | \%w | Day of week: 0-6 = Sunday-Saturday |
+ * | \%U | Week of the year with Sunday as the first day of the week: 00-53 |
+ * | \%u | Day of week: 1-7 = Monday-Sunday |
+ *
+ * Other specifiers are not currently supported.
+ * The "%f" supports a precision value to read the numeric digits. Specify the
+ * precision with a single integer value (1-9) as follows:
+ * use "%3f" for milliseconds, "%6f" for microseconds and "%9f" for nanoseconds.
+ *
+ * Any null string entry will result in a corresponding null row in the output column.
+ *
+ * This will return a column of type BOOL8 where a `true` row indicates the corresponding
+ * input string can be parsed correctly with the given format.
+ *
+ * @param strings Strings instance for this operation.
+ * @param format String specifying the timestamp format in strings.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New BOOL8 column.
+ */
+std::unique_ptr<column> is_timestamp(
+  strings_column_view const& strings,
+  std::string_view format,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting a timestamp column into
+ * strings using the provided format pattern.
+ *
+ * The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z,%Z"
+ *
+ * | Specifier | Description |
+ * | :-------: | ----------- |
+ * | \%d | Day of the month: 01-31 |
+ * | \%m | Month of the year: 01-12 |
+ * | \%y | Year without century: 00-99 |
+ * | \%Y | Year with century: 0001-9999 |
+ * | \%H | 24-hour of the day: 00-23 |
+ * | \%I | 12-hour of the day: 01-12 |
+ * | \%M | Minute of the hour: 00-59|
+ * | \%S | Second of the minute: 00-59 |
+ * | \%f | 6-digit microsecond: 000000-999999 |
+ * | \%z | Always outputs "+0000" |
+ * | \%Z | Always outputs "UTC" |
+ * | \%j | Day of the year: 001-366 |
+ * | \%u | ISO weekday where Monday is 1 and Sunday is 7 |
+ * | \%w | Weekday where Sunday is 0 and Saturday is 6 |
+ * | \%U | Week of the year with Sunday as the first day: 00-53 |
+ * | \%W | Week of the year with Monday as the first day: 00-53 |
+ * | \%V | Week of the year per ISO-8601 format: 01-53 |
+ * | \%G | Year based on the ISO-8601 weeks: 0000-9999 |
+ * | \%p | AM/PM from `timestamp_names::am_str/pm_str` |
+ * | \%a | Weekday abbreviation from the `names` parameter |
+ * | \%A | Weekday from the `names` parameter |
+ * | \%b | Month name abbreviation from the `names` parameter |
+ * | \%B | Month name from the `names` parameter |
+ *
+ * Additional descriptions can be found here:
+ * https://en.cppreference.com/w/cpp/chrono/system_clock/formatter
+ *
+ * No checking is done for invalid formats or invalid timestamp values.
+ * All timestamps values are formatted to UTC.
+ *
+ * Any null input entry will result in a corresponding null entry in the output column.
+ *
+ * The time units of the input column do not influence the number of digits written by
+ * the "%f" specifier. The "%f" supports a precision value to write out numeric digits
+ * for the subsecond value. Specify the precision with a single integer value (1-9)
+ * between the "%" and the "f" as follows: use "%3f" for milliseconds, use "%6f" for
+ * microseconds and use "%9f" for nanoseconds. If the precision is higher than the
+ * units, then zeroes are padded to the right of the subsecond value. If the precision
+ * is lower than the units, the subsecond value may be truncated.
+ *
+ * If the "%a", "%A", "%b", "%B" specifiers are included in the format, the caller
+ * should provide the format names in the `names` strings column using the following
+ * as a guide:
+ *
+ * @code{.pseudo}
+ * ["AM", "PM",                             // specify the AM/PM strings
+ *  "Sunday", "Monday", ..., "Saturday",    // Weekday full names
+ *  "Sun", "Mon", ..., "Sat",               // Weekday abbreviated names
+ *  "January", "February", ..., "December", // Month full names
+ *  "Jan", "Feb", ..., "Dec"]               // Month abbreviated names
+ * @endcode
+ *
+ * The result is undefined if the format names are not provided for these specifiers.
+ *
+ * These format names can be retrieved for specific locales using the `nl_langinfo`
+ * functions from C++ `clocale` (std) library or the Python `locale` library.
+ *
+ * The following code is an example of retrieving these strings from the locale
+ * using c++ std functions:
+ *
+ * @code{.cpp}
+ * #include <clocale>
+ * #include <langinfo.h>
+ *
+ * // note: install language pack on Ubuntu using 'apt-get install language-pack-de'
+ * {
+ *   // set to a German language locale for date settings
+ *   std::setlocale(LC_TIME, "de_DE.UTF-8");
+ *
+ *   std::vector<std::string> names({nl_langinfo(AM_STR), nl_langinfo(PM_STR),
+ *     nl_langinfo(DAY_1), nl_langinfo(DAY_2), nl_langinfo(DAY_3), nl_langinfo(DAY_4),
+ *      nl_langinfo(DAY_5), nl_langinfo(DAY_6), nl_langinfo(DAY_7),
+ *     nl_langinfo(ABDAY_1), nl_langinfo(ABDAY_2), nl_langinfo(ABDAY_3), nl_langinfo(ABDAY_4),
+ *      nl_langinfo(ABDAY_5), nl_langinfo(ABDAY_6), nl_langinfo(ABDAY_7),
+ *     nl_langinfo(MON_1), nl_langinfo(MON_2), nl_langinfo(MON_3), nl_langinfo(MON_4),
+ *      nl_langinfo(MON_5), nl_langinfo(MON_6), nl_langinfo(MON_7), nl_langinfo(MON_8),
+ *      nl_langinfo(MON_9), nl_langinfo(MON_10), nl_langinfo(MON_11), nl_langinfo(MON_12),
+ *     nl_langinfo(ABMON_1), nl_langinfo(ABMON_2), nl_langinfo(ABMON_3), nl_langinfo(ABMON_4),
+ *      nl_langinfo(ABMON_5), nl_langinfo(ABMON_6), nl_langinfo(ABMON_7), nl_langinfo(ABMON_8),
+ *      nl_langinfo(ABMON_9), nl_langinfo(ABMON_10), nl_langinfo(ABMON_11), nl_langinfo(ABMON_12)});
+ *
+ *   std::setlocale(LC_TIME,""); // reset to default locale
+ * }
+ * @endcode
+ *
+ * @throw cudf::logic_error if `timestamps` column parameter is not a timestamp type.
+ * @throw cudf::logic_error if the `format` string is empty
+ * @throw cudf::logic_error if `names.size()` is an invalid size. Must be 0 or 40 strings.
+ *
+ * @param timestamps Timestamp values to convert.
+ * @param format The string specifying output format.
+ *        Default format is "%Y-%m-%dT%H:%M:%SZ".
+ * @param names The string names to use for weekdays ("%a", "%A") and months ("%b", "%B")
+ *        Default is an empty `strings_column_view`.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with formatted timestamps.
+ */
+std::unique_ptr<column> from_timestamps(
+  column_view const& timestamps,
+  std::string_view format             = "%Y-%m-%dT%H:%M:%SZ",
+  strings_column_view const& names    = strings_column_view(column_view{
+    data_type{type_id::STRING}, 0, nullptr, nullptr, 0}),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_durations.hpp b/cpp/include/cudf/strings/convert/convert_durations.hpp
new file mode 100644
index 0000000..e915ec2
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_durations.hpp
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new duration column converting a strings column into
+ * durations using the provided format pattern.
+ *
+ * The format pattern can include the following specifiers:
+ * "%%,%n,%t,%D,%H,%I,%M,%S,%p,%R,%T,%r,%OH,%OI,%OM,%OS"
+ *
+ * | Specifier | Description | Range |
+ * | :-------: | ----------- | ---------------- |
+ * | %% | A literal % character | % |
+ * | \%n | A newline character | \\n |
+ * | \%t | A horizontal tab character | \\t |
+ * | \%D | Days | -2,147,483,648 to 2,147,483,647 |
+ * | \%H | 24-hour of the day | 00 to 23 |
+ * | \%I | 12-hour of the day | 00 to 11 |
+ * | \%M | Minute of the hour | 00 to 59 |
+ * | \%S | Second of the minute | 00 to 59.999999999 |
+ * | \%OH | same as %H but without sign | 00 to 23 |
+ * | \%OI | same as %I but without sign | 00 to 11 |
+ * | \%OM | same as %M but without sign | 00 to 59 |
+ * | \%OS | same as %S but without sign | 00 to 59 |
+ * | \%p | AM/PM designations associated with a 12-hour clock | 'AM' or 'PM' |
+ * | \%R | Equivalent to "%H:%M" |  |
+ * | \%T | Equivalent to "%H:%M:%S" |  |
+ * | \%r | Equivalent to "%OI:%OM:%OS %p" |  |
+ *
+ * Other specifiers are not currently supported.
+ *
+ * Invalid formats are not checked. If the string contains unexpected
+ * or insufficient characters, that output row entry's duration value is undefined.
+ *
+ * Any null string entry will result in a corresponding null row in the output column.
+ *
+ * The resulting time units are specified by the `duration_type` parameter.
+ *
+ * @throw cudf::logic_error if duration_type is not a duration type.
+ *
+ * @param strings Strings instance for this operation.
+ * @param duration_type The duration type used for creating the output column.
+ * @param format String specifying the duration format in strings.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New duration column.
+ */
+std::unique_ptr<column> to_durations(
+  strings_column_view const& strings,
+  data_type duration_type,
+  std::string_view format,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting a duration column into
+ * strings using the provided format pattern.
+ *
+ * The format pattern can include the following specifiers:
+ * "%%,%n,%t,%D,%H,%I,%M,%S,%p,%R,%T,%r,%OH,%OI,%OM,%OS"
+ *
+ * | Specifier | Description | Range |
+ * | :-------: | ----------- | ---------------- |
+ * | %% | A literal % character | % |
+ * | \%n | A newline character | \\n |
+ * | \%t | A horizontal tab character | \\t |
+ * | \%D | Days | -2,147,483,648 to 2,147,483,647 |
+ * | \%H | 24-hour of the day | 00 to 23 |
+ * | \%I | 12-hour of the day | 00 to 11 |
+ * | \%M | Minute of the hour | 00 to 59 |
+ * | \%S | Second of the minute | 00 to 59.999999999 |
+ * | \%OH | same as %H but without sign | 00 to 23 |
+ * | \%OI | same as %I but without sign | 00 to 11 |
+ * | \%OM | same as %M but without sign | 00 to 59 |
+ * | \%OS | same as %S but without sign | 00 to 59 |
+ * | \%p | AM/PM designations associated with a 12-hour clock | 'AM' or 'PM' |
+ * | \%R | Equivalent to "%H:%M" |  |
+ * | \%T | Equivalent to "%H:%M:%S" |  |
+ * | \%r | Equivalent to "%OI:%OM:%OS %p" |  |
+ *
+ * No checking is done for invalid formats or invalid duration values. Formatting sticks to
+ * specifications of `std::formatter<std::chrono::duration>` as much as possible.
+ *
+ * Any null input entry will result in a corresponding null entry in the output column.
+ *
+ * The time units of the input column influence the number of digits in decimal of seconds.
+ * It uses 3 digits for milliseconds, 6 digits for microseconds and 9 digits for nanoseconds.
+ * If duration value is negative, only one negative sign is written to output string. The specifiers
+ * with signs are "%H,%I,%M,%S,%R,%T".
+ *
+ * @throw cudf::logic_error if `durations` column parameter is not a duration type.
+ *
+ * @param durations Duration values to convert.
+ * @param format The string specifying output format.
+ *        Default format is ""%d days %H:%M:%S".
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with formatted durations.
+ */
+std::unique_ptr<column> from_durations(
+  column_view const& durations,
+  std::string_view format             = "%D days %H:%M:%S",
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_fixed_point.hpp b/cpp/include/cudf/strings/convert/convert_fixed_point.hpp
new file mode 100644
index 0000000..3852dc8
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_fixed_point.hpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new fixed-point column parsing decimal values from the
+ * provided strings column.
+ *
+ * Any null entries result in corresponding null entries in the output column.
+ *
+ * The expected format is `[sign][integer][.][fraction]`, where the sign is either
+ * not present, `-` or `+`, The decimal point `[.]` may or may not be present, and
+ * `integer` and `fraction` are comprised of zero or more digits in [0-9].
+ * An invalid data format results in undefined behavior in the corresponding
+ * output row result.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123', '-876', '543.2', '-0.12']
+ * datatype = {DECIMAL32, scale=-2}
+ * fp = to_fixed_point(s, datatype)
+ * fp is [123400, -87600, 54320, -12]
+ * @endcode
+ *
+ * Overflow of the resulting value type is not checked.
+ * The scale in the `output_type` is used for setting the integer component.
+ *
+ * @throw cudf::logic_error if `output_type` is not a fixed-point decimal type.
+ *
+ * @param input Strings instance for this operation.
+ * @param output_type Type of fixed-point column to return including the scale value.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of `output_type`.
+ */
+std::unique_ptr<column> to_fixed_point(
+  strings_column_view const& input,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting the fixed-point values
+ * into a strings column.
+ *
+ * Any null entries result in corresponding null entries in the output column.
+ *
+ * For each value, a string is created in base-10 decimal.
+ * Negative numbers include a '-' prefix in the output string.
+ * The column's scale value is used to place the decimal point.
+ * A negative scale value may add padded zeros after the decimal point.
+ *
+ * @code{.pseudo}
+ * Example:
+ * fp is [110, 222, 3330, -440, -1] with scale = -2
+ * s = from_fixed_point(fp)
+ * s is now ['1.10', '2.22', '33.30', '-4.40', '-0.01']
+ * @endcode
+ *
+ * @throw cudf::logic_error if the `input` column is not a fixed-point decimal type.
+ *
+ * @param input Fixed-point column to convert.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> from_fixed_point(
+  column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying strings in which all
+ * characters are valid for conversion to fixed-point.
+ *
+ * The sign and the exponent is optional. The decimal point may only appear once.
+ * Also, the integer component must fit within the size limits of the
+ * underlying fixed-point storage type. The value of the integer component
+ * is based on the scale of the `decimal_type` provided.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123', '-456', '', '1.2.3', '+17E30', '12.34', '.789', '-0.005]
+ * b = is_fixed_point(s)
+ * b is [true, true, false, false, true, true, true, true]
+ * @endcode
+ *
+ * Any null entries result in corresponding null entries in the output column.
+ *
+ * @throw cudf::logic_error if the `decimal_type` is not a fixed-point decimal type.
+ *
+ * @param input Strings instance for this operation.
+ * @param decimal_type Fixed-point type (with scale) used only for checking overflow.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of boolean results for each string.
+ */
+std::unique_ptr<column> is_fixed_point(
+  strings_column_view const& input,
+  data_type decimal_type              = data_type{type_id::DECIMAL64},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_floats.hpp b/cpp/include/cudf/strings/convert/convert_floats.hpp
new file mode 100644
index 0000000..38a84fc
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_floats.hpp
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new numeric column by parsing float values from each string
+ * in the provided strings column.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * Only characters [0-9] plus a prefix '-' and '+' and decimal '.' are recognized.
+ * Additionally, scientific notation is also supported (e.g. "-1.78e+5").
+ *
+ * @throw cudf::logic_error if output_type is not float type.
+ *
+ * @param strings Strings instance for this operation.
+ * @param output_type Type of float numeric column to return.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with floats converted from strings.
+ */
+std::unique_ptr<column> to_floats(
+  strings_column_view const& strings,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting the float values from the
+ * provided column into strings.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * For each float, a string is created in base-10 decimal.
+ * Negative numbers will include a '-' prefix.
+ * Numbers producing more than 10 significant digits will produce a string that
+ * includes scientific notation (e.g. "-1.78e+15").
+ *
+ * @throw cudf::logic_error if floats column is not float type.
+ *
+ * @param floats Numeric column to convert.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with floats as strings.
+ */
+std::unique_ptr<column> from_floats(
+  column_view const& floats,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying strings in which all
+ * characters are valid for conversion to floats.
+ *
+ * The output row entry will be set to `true` if the corresponding string element
+ * has at least one character in [-+0-9eE.].
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123', '-456', '', 'A', '+7', '8.9' '3.7e+5']
+ * b = s.is_float(s)
+ * b is [true, true, false, false, true, true, true]
+ * @endcode
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of boolean results for each string.
+ */
+std::unique_ptr<column> is_float(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_integers.hpp b/cpp/include/cudf/strings/convert/convert_integers.hpp
new file mode 100644
index 0000000..44213b8
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_integers.hpp
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new integer numeric column parsing integer values from the
+ * provided strings column.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * Only characters [0-9] plus a prefix '-' and '+' are recognized.
+ * When any other character is encountered, the parsing ends for that string
+ * and the current digits are converted into an integer.
+ *
+ * Overflow of the resulting integer type is not checked.
+ * Each string is converted using an int64 type and then cast to the
+ * target integer type before storing it into the output column.
+ * If the resulting integer type is too small to hold the value,
+ * the stored value will be undefined.
+ *
+ * @throw cudf::logic_error if output_type is not integral type.
+ *
+ * @param strings Strings instance for this operation.
+ * @param output_type Type of integer numeric column to return.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with integers converted from strings.
+ */
+std::unique_ptr<column> to_integers(
+  strings_column_view const& strings,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting the integer values from the
+ * provided column into strings.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * For each integer, a string is created in base-10 decimal.
+ * Negative numbers will include a '-' prefix.
+ *
+ * @throw cudf::logic_error if integers column is not integral type.
+ *
+ * @param integers Numeric column to convert.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with integers as strings.
+ */
+std::unique_ptr<column> from_integers(
+  column_view const& integers,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying strings in which all
+ * characters are valid for conversion to integers.
+ *
+ * The output row entry will be set to `true` if the corresponding string element
+ * have all characters in [-+0-9]. The optional sign character must only be in the first
+ * position. Notice that the integer value is not checked to be within its storage limits.
+ * For strict integer type check, use the other `is_integer()` API which accepts `data_type`
+ * argument.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123', '-456', '', 'A', '+7']
+ * b = s.is_integer(s)
+ * b is [true, true, false, false, true]
+ * @endcode
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @param strings  Strings instance for this operation.
+ * @param mr       Device memory resource used to allocate the returned column's device memory.
+ * @return         New column of boolean results for each string.
+ */
+std::unique_ptr<column> is_integer(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying strings in which all
+ * characters are valid for conversion to integers.
+ *
+ * The output row entry will be set to `true` if the corresponding string element
+ * has all characters in [-+0-9]. The optional sign character must only be in the first
+ * position. Also, the integer component must fit within the size limits of the underlying
+ * storage type, which is provided by the int_type parameter.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123456', '-456', '', 'A', '+7']
+ *
+ * output1 = s.is_integer(s, data_type{type_id::INT32})
+ * output1 is [true, true, false, false, true]
+ *
+ * output2 = s.is_integer(s, data_type{type_id::INT8})
+ * output2 is [false, false, false, false, true]
+ * @endcode
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @param strings  Strings instance for this operation.
+ * @param int_type Integer type used for checking underflow and overflow.
+ * @param mr       Device memory resource used to allocate the returned column's device memory.
+ * @return         New column of boolean results for each string.
+ */
+std::unique_ptr<column> is_integer(
+  strings_column_view const& strings,
+  data_type int_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new integer numeric column parsing hexadecimal values from the
+ * provided strings column.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * Only characters [0-9] and [A-F] are recognized.
+ * When any other character is encountered, the parsing ends for that string.
+ * No interpretation is made on the sign of the integer.
+ *
+ * Overflow of the resulting integer type is not checked.
+ * Each string is converted using an int64 type and then cast to the
+ * target integer type before storing it into the output column.
+ * If the resulting integer type is too small to hold the value,
+ * the stored value will be undefined.
+ *
+ * @throw cudf::logic_error if output_type is not integral type.
+ *
+ * @param strings Strings instance for this operation.
+ * @param output_type Type of integer numeric column to return.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with integers converted from strings.
+ */
+std::unique_ptr<column> hex_to_integers(
+  strings_column_view const& strings,
+  data_type output_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying strings in which all
+ * characters are valid for conversion to integers from hex.
+ *
+ * The output row entry will be set to `true` if the corresponding string element
+ * has at least one character in [0-9A-Za-z]. Also, the string may start
+ * with '0x'.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123', '-456', '', 'AGE', '+17EA', '0x9EF' '123ABC']
+ * b = is_hex(s)
+ * b is [true, false, false, false, false, true, true]
+ * @endcode
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of boolean results for each string.
+ */
+std::unique_ptr<column> is_hex(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column converting integer columns to hexadecimal
+ * characters.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * The output character set is '0'-'9' and 'A'-'F'. The output string width will
+ * be a multiple of 2 depending on the size of the integer type. A single leading
+ * zero is applied to the first non-zero output byte if it less than 0x10.
+ *
+ * @code{.pseudo}
+ * Example:
+ * input = [123, -1, 0, 27, 342718233] // int32 type input column
+ * s = integers_to_hex(input)
+ * s is [ '04D2', 'FFFFFFFF', '00', '1B', '146D7719']
+ * @endcode
+ *
+ * The example above shows an `INT32` type column where each integer is 4 bytes.
+ * Leading zeros are suppressed unless filling out a complete byte as in
+ * `123 -> '04D2'` instead of `000004D2` or `4D2`.
+ *
+ * @throw cudf::logic_error if the input column is not integral type.
+ *
+ * @param input Integer column to convert to hex.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with hexadecimal characters.
+ */
+std::unique_ptr<column> integers_to_hex(
+  column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_ipv4.hpp b/cpp/include/cudf/strings/convert/convert_ipv4.hpp
new file mode 100644
index 0000000..22272af
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_ipv4.hpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Converts IPv4 addresses into integers.
+ *
+ * The IPv4 format is 1-3 character digits [0-9] between 3 dots
+ * (e.g. 123.45.67.890). Each section can have a value between [0-255].
+ *
+ * The four sets of digits are converted to integers and placed in 8-bit fields inside
+ * the resulting integer.
+ * ```
+ *   i0.i1.i2.i3 -> (i0 << 24) | (i1 << 16) | (i2 << 8) | (i3)
+ * ```
+ *
+ * No checking is done on the format. If a string is not in IPv4 format, the resulting
+ * integer is undefined.
+ *
+ * The resulting 32-bit integer is placed in an int64_t to avoid setting the sign-bit
+ * in an int32_t type. This could be changed if cudf supported a UINT32 type in the future.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New INT64 column converted from strings.
+ */
+std::unique_ptr<column> ipv4_to_integers(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Converts integers into IPv4 addresses as strings.
+ *
+ * The IPv4 format is 1-3 character digits [0-9] between 3 dots
+ * (e.g. 123.45.67.890). Each section can have a value between [0-255].
+ *
+ * Each input integer is dissected into four integers by dividing the input into 8-bit sections.
+ * These sub-integers are then converted into [0-9] characters and placed between '.' characters.
+ *
+ * No checking is done on the input integer value. Only the lower 32-bits are used.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * @throw cudf::logic_error if the input column is not INT64 type.
+ *
+ * @param integers Integer (INT64) column to convert.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> integers_to_ipv4(
+  column_view const& integers,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a boolean column identifying strings in which all
+ * characters are valid for conversion to integers from IPv4 format.
+ *
+ * The output row entry will be set to `true` if the corresponding string element
+ * has the following format `xxx.xxx.xxx.xxx` where `xxx` is integer digits
+ * between 0-255.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['123.255.0.7', '127.0.0.1', '', '1.2.34' '123.456.789.10']
+ * b = s.is_ipv4(s)
+ * b is [true, true, false, false, true]
+ * @endcode
+ *
+ * Any null row results in a null entry for that row in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of boolean results for each string.
+ */
+std::unique_ptr<column> is_ipv4(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_lists.hpp b/cpp/include/cudf/strings/convert/convert_lists.hpp
new file mode 100644
index 0000000..7ab1bf4
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_lists.hpp
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Convert a list column of strings into a formatted strings column.
+ *
+ * The `separators` column should contain 3 strings elements in the following order:
+ * - element separator (default is comma `,`)
+ * - left-hand enclosure (default is `[`)
+ * - right-hand enclosure (default is `]`)
+ *
+ * @code{.pseudo}
+ * l1 = { [[a,b,c], [d,e]], [[f,g], [h]] }
+ * s1 = format_list_column(l1)
+ * s1 is now ["[[a,b,c],[d,e]]", "[[f,g],[h]]"]
+ *
+ * l2 = { [[a,b,c], [d,e]], [NULL], [[f,g], NULL, [h]] }
+ * s2 = format_list_column(l1, '-', [':', '{', '}'])
+ * s2 is now ["{{a:b:c}:{d:e}}", "{-}", "{{f:g}:-:{h}}"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if the input column is not a LIST type with a STRING child.
+ *
+ * @param input Lists column to format.
+ * @param na_rep Replacement string for null elements.
+ * @param separators Strings to use for enclosing list components and separating elements.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> format_list_column(
+  lists_column_view const& input,
+  string_scalar const& na_rep           = string_scalar("NULL"),
+  strings_column_view const& separators = strings_column_view(column_view{
+    data_type{type_id::STRING}, 0, nullptr, nullptr, 0}),
+  rmm::mr::device_memory_resource* mr   = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/convert/convert_urls.hpp b/cpp/include/cudf/strings/convert/convert_urls.hpp
new file mode 100644
index 0000000..7f29a0d
--- /dev/null
+++ b/cpp/include/cudf/strings/convert/convert_urls.hpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_convert
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Decodes each string using URL encoding.
+ *
+ * Converts mostly non-ascii characters and control characters into UTF-8 hex code-points
+ * prefixed with '%'. For example, the space character must be converted to characters '%20' where
+ * the '20' indicates the hex value for space in UTF-8. Likewise, multi-byte characters are
+ * converted to multiple hex characters. For example, the é character is converted to characters
+ * '%C3%A9' where 'C3A9' is the UTF-8 bytes 0xC3A9 for this character.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> url_encode(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Encodes each string using URL encoding.
+ *
+ * Converts all character sequences starting with '%' into character code-points
+ * interpreting the 2 following characters as hex values to create the code-point.
+ * For example, the sequence '%20' is converted into byte (0x20) which is a single
+ * space character. Another example converts '%C3%A9' into 2 sequential bytes
+ * (0xc3 and 0xa9 respectively) which is the é character. Overall, 3 characters
+ * are converted into one char byte whenever a '%%' (single percent) character
+ * is encountered in the string.
+ *
+ * Any null entries will result in corresponding null entries in the output column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> url_decode(
+  strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/char_tables.hpp b/cpp/include/cudf/strings/detail/char_tables.hpp
new file mode 100644
index 0000000..0901076
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/char_tables.hpp
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+// Type for the character flags table.
+using character_flags_table_type = std::uint8_t;
+
+/**
+ * @brief Returns pointer to device memory that contains the static
+ * characters flags table. On first call, this will copy the table into
+ * device memory and is guaranteed to be thread-safe.
+ *
+ * This table is used to check the type of character like
+ * alphanumeric, decimal, etc.
+ *
+ * @return Device memory pointer to character flags table.
+ */
+character_flags_table_type const* get_character_flags_table();
+
+// utilities to dissect a character-table flag
+constexpr uint8_t IS_DECIMAL(uint8_t x) { return ((x) & (1 << 0)); }
+constexpr uint8_t IS_NUMERIC(uint8_t x) { return ((x) & (1 << 1)); }
+constexpr uint8_t IS_DIGIT(uint8_t x) { return ((x) & (1 << 2)); }
+constexpr uint8_t IS_ALPHA(uint8_t x) { return ((x) & (1 << 3)); }
+constexpr uint8_t IS_SPACE(uint8_t x) { return ((x) & (1 << 4)); }
+constexpr uint8_t IS_UPPER(uint8_t x) { return ((x) & (1 << 5)); }
+constexpr uint8_t IS_LOWER(uint8_t x) { return ((x) & (1 << 6)); }
+constexpr uint8_t IS_SPECIAL(uint8_t x) { return ((x) & (1 << 7)); }
+constexpr uint8_t IS_ALPHANUM(uint8_t x) { return ((x) & (0x0F)); }
+constexpr uint8_t IS_UPPER_OR_LOWER(uint8_t x) { return ((x) & ((1 << 5) | (1 << 6))); }
+constexpr uint8_t ALL_FLAGS = 0xFF;
+
+// Type for the character cases table.
+using character_cases_table_type = uint16_t;
+
+/**
+ * @brief Returns pointer to device memory that contains the static
+ * characters case table. On first call, this will copy the table into
+ * device memory and is guaranteed to be thread-safe.
+ *
+ * This table is used to map upper and lower case characters with
+ * their counterpart.
+ *
+ * @return Device memory pointer to character cases table.
+ */
+character_cases_table_type const* get_character_cases_table();
+
+/**
+ * @brief Case mapping structure for special characters.
+ *
+ * This is used for special mapping of a small set of characters that do not
+ * fit in the character-cases-table.
+ *
+ * @see cpp/src/strings/char_types/char_cases.h
+ */
+struct special_case_mapping {
+  uint16_t num_upper_chars;
+  uint16_t upper[3];
+  uint16_t num_lower_chars;
+  uint16_t lower[3];
+};
+
+/**
+ * @brief Returns pointer to device memory that contains the special
+ * case mapping table. On first call, this will copy the table into
+ * device memory and is guaranteed to be thread-safe.
+ *
+ * This table is used to handle special case character mappings that
+ * don't trivially work with the normal character cases table.
+ *
+ * @return Device memory pointer to the special case mapping table
+ */
+const struct special_case_mapping* get_special_case_mapping_table();
+
+/**
+ * @brief Get the special mapping table index for a given code-point.
+ *
+ * @see cpp/src/strings/char_types/char_cases.h
+ */
+constexpr uint16_t get_special_case_hash_index(uint32_t code_point)
+{
+  constexpr uint16_t special_case_prime = 499;  // computed from generate_special_mapping_hash_table
+  return static_cast<uint16_t>(code_point % special_case_prime);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/combine.hpp b/cpp/include/cudf/strings/detail/combine.hpp
new file mode 100644
index 0000000..3b8ed0f
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/combine.hpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @copydoc concatenate(table_view const&,string_scalar const&,string_scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> concatenate(table_view const& strings_columns,
+                                    string_scalar const& separator,
+                                    string_scalar const& narep,
+                                    separator_on_nulls separate_nulls,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc join_strings(table_view const&,string_scalar const&,string_scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> join_strings(strings_column_view const& strings,
+                                     string_scalar const& separator,
+                                     string_scalar const& narep,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc join_list_elements(table_view const&,string_scalar const&,string_scalar
+ * const&,separator_on_nulls,output_if_empty_list,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> join_list_elements(lists_column_view const& lists_strings_column,
+                                           string_scalar const& separator,
+                                           string_scalar const& narep,
+                                           separator_on_nulls separate_nulls,
+                                           output_if_empty_list empty_list_policy,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/concatenate.hpp b/cpp/include/cudf/strings/detail/concatenate.hpp
new file mode 100644
index 0000000..511e240
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/concatenate.hpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Returns a single column by vertically concatenating the given vector of
+ * strings columns.
+ *
+ * ```
+ * s1 = ['aa', 'bb', 'cc']
+ * s2 = ['dd', 'ee']
+ * r = concatenate_vertically([s1,s2])
+ * r is now ['aa', 'bb', 'cc', 'dd', 'ee']
+ * ```
+ *
+ * @param columns List of string columns to concatenate.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with concatenated results.
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/convert/fixed_point.cuh b/cpp/include/cudf/strings/detail/convert/fixed_point.cuh
new file mode 100644
index 0000000..dd55cae
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/convert/fixed_point.cuh
@@ -0,0 +1,166 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/fixed_point/temporary.hpp>
+
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+#include <cuda/std/type_traits>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Return the integer component of a decimal string.
+ *
+ * This reads everything up to the exponent 'e' notation.
+ * The return includes the integer digits and any exponent offset.
+ *
+ * @tparam UnsignedDecimalType The unsigned version of the desired decimal type.
+ *                             Use the `std::make_unsigned_t` to create the
+ *                             unsigned type from the storage type.
+ *
+ * @param[in,out] iter Start of characters to parse
+ * @param[in] end End of characters to parse
+ * @return Integer component and exponent offset.
+ */
+template <typename UnsignedDecimalType>
+__device__ inline thrust::pair<UnsignedDecimalType, int32_t> parse_integer(
+  char const*& iter, char const* iter_end, char const decimal_pt_char = '.')
+{
+  // highest value where another decimal digit cannot be appended without an overflow;
+  // this preserves the most digits when scaling the final result for this type
+  constexpr UnsignedDecimalType decimal_max =
+    (std::numeric_limits<UnsignedDecimalType>::max() - 9L) / 10L;
+
+  __uint128_t value  = 0;  // for checking overflow
+  int32_t exp_offset = 0;
+  bool decimal_found = false;
+
+  while (iter < iter_end) {
+    auto const ch = *iter++;
+    if (ch == decimal_pt_char && !decimal_found) {
+      decimal_found = true;
+      continue;
+    }
+    if (ch < '0' || ch > '9') {
+      --iter;
+      break;
+    }
+    if (value > decimal_max) {
+      exp_offset += static_cast<int32_t>(!decimal_found);
+    } else {
+      value = (value * 10) + static_cast<UnsignedDecimalType>(ch - '0');
+      exp_offset -= static_cast<int32_t>(decimal_found);
+    }
+  }
+  return {value, exp_offset};
+}
+
+/**
+ * @brief Return the exponent of a decimal string.
+ *
+ * This should only be called after the exponent 'e' notation was detected.
+ * The return is the exponent (base-10) integer and can only be
+ * invalid if `check_only == true` and invalid characters are found or the
+ * exponent overflows an int32.
+ *
+ * @tparam check_only Set to true to verify the characters are valid and the
+ *         exponent value in the decimal string does not overflow int32
+ * @param[in,out] iter Start of characters to parse
+ *                     (points to the character after the 'E' or 'e')
+ * @param[in] end End of characters to parse
+ * @return Integer value of the exponent
+ */
+template <bool check_only = false>
+__device__ thrust::optional<int32_t> parse_exponent(char const* iter, char const* iter_end)
+{
+  constexpr uint32_t exponent_max = static_cast<uint32_t>(std::numeric_limits<int32_t>::max());
+
+  // get optional exponent sign
+  int32_t const exp_sign = [&iter] {
+    auto const ch = *iter;
+    if (ch != '-' && ch != '+') { return 1; }
+    ++iter;
+    return (ch == '-' ? -1 : 1);
+  }();
+
+  // parse exponent integer
+  int32_t exp_ten = 0;
+  while (iter < iter_end) {
+    auto const ch = *iter++;
+    if (ch < '0' || ch > '9') {
+      if (check_only) { return thrust::nullopt; }
+      break;
+    }
+
+    uint32_t exp_check = static_cast<uint32_t>(exp_ten * 10) + static_cast<uint32_t>(ch - '0');
+    if (check_only && (exp_check > exponent_max)) { return thrust::nullopt; }  // check overflow
+    exp_ten = static_cast<int32_t>(exp_check);
+  }
+
+  return exp_ten * exp_sign;
+}
+
+/**
+ * @brief Converts the string in the range [iter, iter_end) into a decimal.
+ *
+ * @tparam DecimalType The decimal type to be returned
+ * @param iter The beginning of the string
+ * @param iter_end The end of the characters to parse
+ * @param scale The scale to be applied
+ * @return
+ */
+template <typename DecimalType>
+__device__ DecimalType parse_decimal(char const* iter, char const* iter_end, int32_t scale)
+{
+  auto const sign = [&] {
+    if (iter_end <= iter) { return 0; }
+    if (*iter == '-') { return -1; }
+    if (*iter == '+') { return 1; }
+    return 0;
+  }();
+
+  // if string begins with a sign, continue with next character
+  if (sign != 0) ++iter;
+
+  using UnsignedDecimalType = cuda::std::make_unsigned_t<DecimalType>;
+  auto [value, exp_offset]  = parse_integer<UnsignedDecimalType>(iter, iter_end);
+  if (value == 0) { return DecimalType{0}; }
+
+  // check for exponent
+  int32_t exp_ten = 0;
+  if ((iter < iter_end) && (*iter == 'e' || *iter == 'E')) {
+    ++iter;
+    if (iter < iter_end) { exp_ten = parse_exponent<false>(iter, iter_end).value(); }
+  }
+  exp_ten += exp_offset;
+
+  // shift the output value based on the exp_ten and the scale values
+  auto const shift_adjust =
+    abs(scale - exp_ten) > cuda::std::numeric_limits<UnsignedDecimalType>::digits10
+      ? cuda::std::numeric_limits<UnsignedDecimalType>::max()
+      : numeric::detail::exp10<UnsignedDecimalType>(abs(scale - exp_ten));
+  value = exp_ten < scale ? value / shift_adjust : value * shift_adjust;
+
+  return static_cast<DecimalType>(value) * (sign == 0 ? 1 : sign);
+}
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/convert/fixed_point_to_string.cuh b/cpp/include/cudf/strings/detail/convert/fixed_point_to_string.cuh
new file mode 100644
index 0000000..0ee26ec
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/convert/fixed_point_to_string.cuh
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/detail/convert/int_to_string.cuh>
+
+namespace cudf::strings::detail {
+
+/**
+ * @brief Returns the number of digits in the given fixed point number.
+ *
+ * @param value The value of the fixed point number
+ * @param scale The scale of the fixed point number
+ * @return int32_t The number of digits required to represent the fixed point number
+ */
+__device__ inline int32_t fixed_point_string_size(__int128_t const& value, int32_t scale)
+{
+  if (scale >= 0) return count_digits(value) + scale;
+
+  auto const abs_value = numeric::detail::abs(value);
+  auto const exp_ten   = numeric::detail::exp10<__int128_t>(-scale);
+  auto const fraction  = count_digits(abs_value % exp_ten);
+  auto const num_zeros = std::max(0, (-scale - fraction));
+  return static_cast<int32_t>(value < 0) +    // sign if negative
+         count_digits(abs_value / exp_ten) +  // integer
+         1 +                                  // decimal point
+         num_zeros +                          // zeros padding
+         fraction;                            // size of fraction
+}
+
+/**
+ * @brief Converts the given fixed point number to a string.
+ *
+ * Caller is responsible for ensuring that the output buffer is large enough. The required output
+ * buffer size can be obtained by calling `fixed_point_string_size`.
+ *
+ * @param value The value of the fixed point number
+ * @param scale The scale of the fixed point number
+ * @param out_ptr The pointer to the output string
+ */
+__device__ inline void fixed_point_to_string(__int128_t const& value, int32_t scale, char* out_ptr)
+{
+  if (scale >= 0) {
+    out_ptr += integer_to_string(value, out_ptr);
+    thrust::generate_n(thrust::seq, out_ptr, scale, []() { return '0'; });  // add zeros
+    return;
+  }
+
+  // scale < 0
+  // write format:   [-]integer.fraction
+  // where integer  = abs(value) / (10^abs(scale))
+  //       fraction = abs(value) % (10^abs(scale))
+  if (value < 0) *out_ptr++ = '-';  // add sign
+  auto const abs_value = numeric::detail::abs(value);
+  auto const exp_ten   = numeric::detail::exp10<__int128_t>(-scale);
+  auto const num_zeros = std::max(0, (-scale - count_digits(abs_value % exp_ten)));
+
+  out_ptr += integer_to_string(abs_value / exp_ten, out_ptr);  // add the integer part
+  *out_ptr++ = '.';                                            // add decimal point
+
+  thrust::generate_n(thrust::seq, out_ptr, num_zeros, []() { return '0'; });  // add zeros
+  out_ptr += num_zeros;
+
+  integer_to_string(abs_value % exp_ten, out_ptr);  // add the fraction part
+}
+
+}  // namespace cudf::strings::detail
diff --git a/cpp/include/cudf/strings/detail/convert/int_to_string.cuh b/cpp/include/cudf/strings/detail/convert/int_to_string.cuh
new file mode 100644
index 0000000..f6e6a10
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/convert/int_to_string.cuh
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Converts an integer into string
+ *
+ * @tparam IntegerType integer type to convert from
+ * @param value integer value to convert
+ * @param d_buffer character buffer to store the converted string
+ */
+template <typename IntegerType>
+__device__ inline size_type integer_to_string(IntegerType value, char* d_buffer)
+{
+  if (value == 0) {
+    *d_buffer = '0';
+    return 1;
+  }
+  bool const is_negative = cuda::std::is_signed<IntegerType>() ? (value < 0) : false;
+
+  constexpr IntegerType base = 10;
+  // largest 64-bit integer is 20 digits; largest 128-bit integer is 39 digits
+  constexpr int MAX_DIGITS = cuda::std::numeric_limits<IntegerType>::digits10 + 1;
+  char digits[MAX_DIGITS];  // place-holder for digit chars
+  int digits_idx = 0;
+  while (value != 0) {
+    assert(digits_idx < MAX_DIGITS);
+    digits[digits_idx++] = '0' + cudf::util::absolute_value(value % base);
+    // next digit
+    value = value / base;
+  }
+  size_type const bytes = digits_idx + static_cast<size_type>(is_negative);
+
+  char* ptr = d_buffer;
+  if (is_negative) *ptr++ = '-';
+  // digits are backwards, reverse the string into the output
+  while (digits_idx-- > 0)
+    *ptr++ = digits[digits_idx];
+  return bytes;
+}
+
+/**
+ * @brief Counts number of digits in a integer value including '-' sign
+ *
+ * @tparam IntegerType integer type of input value
+ * @param value input value to count the digits of
+ * @return size_type number of digits in input value
+ */
+template <typename IntegerType>
+constexpr size_type count_digits(IntegerType value)
+{
+  if (value == 0) return 1;
+  bool const is_negative = cuda::std::is_signed<IntegerType>() ? (value < 0) : false;
+  // abs(std::numeric_limits<IntegerType>::min()) is negative;
+  // for all integer types, the max() and min() values have the same number of digits
+  value = (value == cuda::std::numeric_limits<IntegerType>::min())
+            ? cuda::std::numeric_limits<IntegerType>::max()
+            : cudf::util::absolute_value(value);
+
+  auto const digits = [value] {
+    // largest 8-byte  unsigned value is 18446744073709551615 (20 digits)
+    // largest 16-byte unsigned value is 340282366920938463463374607431768211455 (39 digits)
+    auto constexpr max_digits = cuda::std::numeric_limits<IntegerType>::digits10 + 1;
+
+    size_type digits = 1;
+    __int128_t pow10 = 10;
+    for (; digits < max_digits; ++digits, pow10 *= 10)
+      if (value < pow10) break;
+    return digits;
+  }();
+
+  return digits + static_cast<size_type>(is_negative);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/convert/is_float.cuh b/cpp/include/cudf/strings/detail/convert/is_float.cuh
new file mode 100644
index 0000000..5b09da9
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/convert/is_float.cuh
@@ -0,0 +1,120 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Returns true if input contains the not-a-number string.
+ *
+ * The following are valid for this function: "NAN" and "NaN"
+ * @param d_str input string
+ * @return true if input is as valid NaN string.
+ */
+inline __device__ bool is_nan_str(string_view const& d_str)
+{
+  auto const ptr = d_str.data();
+  return (d_str.size_bytes() == 3) && (ptr[0] == 'N' || ptr[0] == 'n') &&
+         (ptr[1] == 'A' || ptr[1] == 'a') && (ptr[2] == 'N' || ptr[2] == 'n');
+}
+
+/**
+ * @brief Returns true if input contains the infinity string.
+ *
+ * The following are valid for this function: "INF", "INFINITY", and "Inf"
+ * @param d_str input string
+ * @return true if input is as valid Inf string.
+ */
+inline __device__ bool is_inf_str(string_view const& d_str)
+{
+  auto const ptr  = d_str.data();
+  auto const size = d_str.size_bytes();
+
+  if (size != 3 && size != 8) return false;
+
+  auto const prefix_valid = (ptr[0] == 'I' || ptr[0] == 'i') && (ptr[1] == 'N' || ptr[1] == 'n') &&
+                            (ptr[2] == 'F' || ptr[2] == 'f');
+
+  return prefix_valid &&
+         ((size == 3) || ((ptr[3] == 'I' || ptr[3] == 'i') && (ptr[4] == 'N' || ptr[4] == 'n') &&
+                          (ptr[5] == 'I' || ptr[5] == 'i') && (ptr[6] == 'T' || ptr[6] == 't') &&
+                          (ptr[7] == 'Y' || ptr[7] == 'y')));
+}
+
+/**
+ * @brief Returns `true` if all characters in the string
+ * are valid for conversion to a float type.
+ *
+ * Valid characters are in [-+0-9eE.]. The sign character (+/-)
+ * is optional but if present must be the first character.
+ * The sign character may also optionally appear right after the 'e' or 'E'
+ * if the string is formatted with scientific notation.
+ * The decimal character can appear only once and never after the
+ * 'e' or 'E' character.
+ * An empty string returns `false`.
+ * No bounds checking is performed to verify if the value would fit
+ * within a specific float type.
+ * The following strings are also allowed and will return true:
+ *  "NaN", "NAN", "Inf", "INF", "INFINITY"
+ *
+ * @param d_str String to check.
+ * @return true if string has valid float characters
+ */
+inline __device__ bool is_float(string_view const& d_str)
+{
+  if (d_str.empty()) return false;
+  bool decimal_found  = false;
+  bool exponent_found = false;
+  size_type bytes     = d_str.size_bytes();
+  char const* data    = d_str.data();
+  // sign character allowed at the beginning of the string
+  size_type ch_idx = (*data == '-' || *data == '+') ? 1 : 0;
+
+  bool result = ch_idx < bytes;
+  // check for nan and infinity strings
+  if (result && data[ch_idx] > '9') {
+    auto const inf_nan = string_view(data + ch_idx, bytes - ch_idx);
+    if (is_nan_str(inf_nan) || is_inf_str(inf_nan)) return true;
+  }
+
+  // check for float chars [0-9] and a single decimal '.'
+  // and scientific notation [eE][+-][0-9]
+  for (; ch_idx < bytes; ++ch_idx) {
+    auto chr = data[ch_idx];
+    if (chr >= '0' && chr <= '9') continue;
+    if (!decimal_found && chr == '.') {
+      decimal_found = true;  // no more decimals
+      continue;
+    }
+    if (!exponent_found && (chr == 'e' || chr == 'E')) {
+      if (ch_idx + 1 < bytes) chr = data[ch_idx + 1];
+      if (chr == '-' || chr == '+') ++ch_idx;
+      decimal_found  = true;  // no decimal allowed in exponent
+      exponent_found = true;  // no more exponents
+      continue;
+    }
+    return false;
+  }
+  return result;
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/convert/string_to_float.cuh b/cpp/include/cudf/strings/detail/convert/string_to_float.cuh
new file mode 100644
index 0000000..ab93475
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/convert/string_to_float.cuh
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/strings/detail/convert/is_float.cuh>
+#include <cudf/strings/string_view.cuh>
+
+#include <cmath>
+#include <limits>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief This function converts the given string into a
+ * floating point double value.
+ *
+ * This will also map strings containing "NaN", "Inf", etc.
+ * to the appropriate float values.
+ *
+ * This function will also handle scientific notation format.
+ */
+__device__ inline double stod(string_view const& d_str)
+{
+  char const* in_ptr = d_str.data();
+  char const* end    = in_ptr + d_str.size_bytes();
+  if (end == in_ptr) return 0.0;
+  double sign{1.0};
+  if (*in_ptr == '-' || *in_ptr == '+') {
+    sign = (*in_ptr == '-' ? -1 : 1);
+    ++in_ptr;
+  }
+
+#ifndef CUDF_JIT_UDF
+  constexpr double infinity      = std::numeric_limits<double>::infinity();
+  constexpr uint64_t max_holding = (std::numeric_limits<uint64_t>::max() - 9L) / 10L;
+#else
+  constexpr double infinity      = (1.0 / 0.0);
+  constexpr uint64_t max_holding = (18446744073709551615UL - 9UL) / 10UL;
+#endif
+
+  // special strings: NaN, Inf
+  if ((in_ptr < end) && *in_ptr > '9') {
+    auto const inf_nan = string_view(in_ptr, static_cast<size_type>(end - in_ptr));
+    if (is_nan_str(inf_nan)) return nan("");
+    if (is_inf_str(inf_nan)) return sign * infinity;
+  }
+
+  // Parse and store the mantissa as much as we can,
+  // until we are about to exceed the limit of uint64_t
+  uint64_t digits = 0;
+  int exp_off     = 0;
+  bool decimal    = false;
+  while (in_ptr < end) {
+    char ch = *in_ptr;
+    if (ch == '.') {
+      decimal = true;
+      ++in_ptr;
+      continue;
+    }
+    if (ch < '0' || ch > '9') break;
+    if (digits > max_holding)
+      exp_off += (int)!decimal;
+    else {
+      digits = (digits * 10L) + static_cast<uint64_t>(ch - '0');
+      if (digits > max_holding) {
+        digits = digits / 10L;
+        exp_off += (int)!decimal;
+      } else
+        exp_off -= (int)decimal;
+    }
+    ++in_ptr;
+  }
+  if (digits == 0) return sign * static_cast<double>(0);
+
+  // check for exponent char
+  int exp_ten  = 0;
+  int exp_sign = 1;
+  if (in_ptr < end) {
+    char ch = *in_ptr++;
+    if (ch == 'e' || ch == 'E') {
+      if (in_ptr < end) {
+        ch = *in_ptr;
+        if (ch == '-' || ch == '+') {
+          exp_sign = (ch == '-' ? -1 : 1);
+          ++in_ptr;
+        }
+        while (in_ptr < end) {
+          ch = *in_ptr++;
+          if (ch < '0' || ch > '9') break;
+          exp_ten = (exp_ten * 10) + (int)(ch - '0');
+        }
+      }
+    }
+  }
+
+  int const num_digits = static_cast<int>(log10(static_cast<double>(digits))) + 1;
+  exp_ten *= exp_sign;
+  exp_ten += exp_off;
+  exp_ten += num_digits - 1;
+  if (exp_ten > std::numeric_limits<double>::max_exponent10) {
+    return sign > 0 ? infinity : -infinity;
+  }
+
+  double base = sign * static_cast<double>(digits);
+
+  exp_ten += 1 - num_digits;
+  // If 10^exp_ten would result in a subnormal value, the base and
+  // exponent should be adjusted so that 10^exp_ten is a normal value
+  auto const subnormal_shift = std::numeric_limits<double>::min_exponent10 - exp_ten;
+  if (subnormal_shift > 0) {
+    // Handle subnormal values. Ensure that both base and exponent are
+    // normal values before computing their product.
+    base = base / exp10(static_cast<double>(num_digits - 1 + subnormal_shift));
+    exp_ten += num_digits - 1;  // adjust exponent
+    auto const exponent = exp10(static_cast<double>(exp_ten + subnormal_shift));
+    return base * exponent;
+  }
+
+  double const exponent = exp10(static_cast<double>(std::abs(exp_ten)));
+  return exp_ten < 0 ? base / exponent : base * exponent;
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/convert/string_to_int.cuh b/cpp/include/cudf/strings/detail/convert/string_to_int.cuh
new file mode 100644
index 0000000..8bbaea9
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/convert/string_to_int.cuh
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Converts a single string into an integer.
+ *
+ * The '+' and '-' are allowed but only at the beginning of the string.
+ * The string is expected to contain base-10 [0-9] characters only.
+ * Any other character will end the parse.
+ * Overflow of the int64 type is not detected.
+ */
+__device__ inline int64_t string_to_integer(string_view const& d_str)
+{
+  int64_t value   = 0;
+  size_type bytes = d_str.size_bytes();
+  if (bytes == 0) return value;
+  char const* ptr = d_str.data();
+  int sign        = 1;
+  if (*ptr == '-' || *ptr == '+') {
+    sign = (*ptr == '-' ? -1 : 1);
+    ++ptr;
+    --bytes;
+  }
+  for (size_type idx = 0; idx < bytes; ++idx) {
+    char chr = *ptr++;
+    if (chr < '0' || chr > '9') break;
+    value = (value * 10) + static_cast<int64_t>(chr - '0');
+  }
+  return value * static_cast<int64_t>(sign);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/converters.hpp b/cpp/include/cudf/strings/detail/converters.hpp
new file mode 100644
index 0000000..3337815
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/converters.hpp
@@ -0,0 +1,157 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @copydoc to_integers(strings_column_view const&,data_type,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> to_integers(strings_column_view const& strings,
+                                    data_type output_type,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc from_integers(strings_column_view const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> from_integers(column_view const& integers,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc to_floats(strings_column_view const&,data_type,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> to_floats(strings_column_view const& strings,
+                                  data_type output_type,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc from_floats(strings_column_view const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> from_floats(column_view const& floats,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc to_booleans(strings_column_view const&,string_scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> to_booleans(strings_column_view const& strings,
+                                    string_scalar const& true_string,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc from_booleans(strings_column_view const&,string_scalar const&,string_scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> from_booleans(column_view const& booleans,
+                                      string_scalar const& true_string,
+                                      string_scalar const& false_string,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc to_timestamps(strings_column_view const&,data_type,std::string_view,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<cudf::column> to_timestamps(strings_column_view const& strings,
+                                            data_type timestamp_type,
+                                            std::string_view format,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc from_timestamps(strings_column_view const&,std::string_view,
+ * strings_column_view const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> from_timestamps(column_view const& timestamps,
+                                        std::string_view format,
+                                        strings_column_view const& names,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc to_durations(strings_column_view const&,data_type,std::string_view,
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> to_durations(strings_column_view const& strings,
+                                     data_type duration_type,
+                                     std::string_view format,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc from_durations(strings_column_view const&,std::string_view.
+ * rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> from_durations(column_view const& durations,
+                                       std::string_view format,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc to_fixed_point(strings_column_view const&,data_type,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> to_fixed_point(strings_column_view const& strings,
+                                       data_type output_type,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc from_fixed_point(strings_column_view const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> from_fixed_point(column_view const& integers,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/copy_if_else.cuh b/cpp/include/cudf/strings/detail/copy_if_else.cuh
new file mode 100644
index 0000000..b553b49
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/copy_if_else.cuh
@@ -0,0 +1,116 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Returns a new strings column using the specified Filter to select
+ * strings from the lhs iterator or the rhs iterator.
+ *
+ * ```
+ * output[i] = filter_fn(i) ? lhs(i) : rhs(i)
+ * ```
+ *
+ * @tparam StringIterLeft A random access iterator whose value_type is
+ * `thrust::optional<string_view>` where the `optional` has a value iff the element is valid.
+ * @tparam StringIterRight A random access iterator whose value_type is
+ * `thrust::optional<string_view>` where the `optional` has a value iff the element is valid.
+ * @tparam Filter Functor that takes an index and returns a boolean.
+ *
+ * @param lhs_begin Start of first set of data. Used when `filter_fn` returns true.
+ * @param lhs_end End of first set of data.
+ * @param rhs_begin Strings of second set of data. Used when `filter_fn` returns false.
+ * @param filter_fn Called to determine which iterator to use for a specific row.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+template <typename StringIterLeft, typename StringIterRight, typename Filter>
+std::unique_ptr<cudf::column> copy_if_else(StringIterLeft lhs_begin,
+                                           StringIterLeft lhs_end,
+                                           StringIterRight rhs_begin,
+                                           Filter filter_fn,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto strings_count = std::distance(lhs_begin, lhs_end);
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  // create null mask
+  auto valid_mask = cudf::detail::valid_if(
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(strings_count),
+    [lhs_begin, rhs_begin, filter_fn] __device__(size_type idx) {
+      return filter_fn(idx) ? lhs_begin[idx].has_value() : rhs_begin[idx].has_value();
+    },
+    stream,
+    mr);
+  size_type null_count = valid_mask.second;
+  auto null_mask       = (null_count > 0) ? std::move(valid_mask.first) : rmm::device_buffer{};
+
+  // build offsets column
+  auto offsets_transformer = [lhs_begin, rhs_begin, filter_fn] __device__(size_type idx) {
+    auto const result = filter_fn(idx) ? lhs_begin[idx] : rhs_begin[idx];
+    return result.has_value() ? result->size_bytes() : 0;
+  };
+
+  auto offsets_transformer_itr = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<size_type>(0), offsets_transformer);
+  auto [offsets_column, bytes] = cudf::detail::make_offsets_child_column(
+    offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  auto d_offsets = offsets_column->view().template data<int32_t>();
+
+  // build chars column
+  auto chars_column = create_chars_child_column(bytes, stream, mr);
+  auto d_chars      = chars_column->mutable_view().template data<char>();
+  // fill in chars
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    strings_count,
+    [lhs_begin, rhs_begin, filter_fn, d_offsets, d_chars] __device__(size_type idx) {
+      auto const result = filter_fn(idx) ? lhs_begin[idx] : rhs_begin[idx];
+      if (!result.has_value()) return;
+      auto const d_str = *result;
+      memcpy(d_chars + d_offsets[idx], d_str.data(), d_str.size_bytes());
+    });
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/copy_range.cuh b/cpp/include/cudf/strings/detail/copy_range.cuh
new file mode 100644
index 0000000..5da3add
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/copy_range.cuh
@@ -0,0 +1,216 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace {
+template <bool source_has_nulls,
+          bool target_has_nulls,
+          typename SourceValueIterator,
+          typename SourceValidityIterator>
+struct compute_element_size {
+  SourceValueIterator source_value_begin;
+  SourceValidityIterator source_validity_begin;
+  cudf::column_device_view d_target;
+  cudf::size_type target_begin;
+  cudf::size_type target_end;
+
+  __device__ cudf::size_type operator()(cudf::size_type idx)
+  {
+    if (idx >= target_begin && idx < target_end) {
+      if (source_has_nulls) {
+        return *(source_validity_begin + (idx - target_begin))
+                 ? (*(source_value_begin + (idx - target_begin))).size_bytes()
+                 : 0;
+      } else {
+        return (*(source_value_begin + (idx - target_begin))).size_bytes();
+      }
+    } else {
+      if (target_has_nulls) {
+        return d_target.is_valid_nocheck(idx)
+                 ? d_target.element<cudf::string_view>(idx).size_bytes()
+                 : 0;
+      } else {
+        return d_target.element<cudf::string_view>(idx).size_bytes();
+      }
+    }
+  }
+};
+
+}  // namespace
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Internal API to copy a range of string elements out-of-place from
+ * source iterators to a target column.
+ *
+ * Creates a new column as if an in-place copy was performed into @p target.
+ * The elements indicated by the indices [@p target_begin, @p target_end) were
+ * replaced with the elements retrieved from source iterators;
+ * *(@p source_value_begin + idx) if *(@p source_validity_begin + idx) is true,
+ * invalidate otherwise (where idx = [0, @p target_end - @p target_begin)).
+ * Elements outside the range are copied from @p target into the new target
+ * column to return.
+ *
+ * @throws cudf::logic_error for invalid range (if @p target_begin < 0,
+ * target_begin >= @p target.size(), or @p target_end > @p target.size()).
+ *
+ * @tparam SourceValueIterator Iterator for retrieving source values
+ * @tparam SourceValidityIterator Iterator for retrieving source validities
+ * @param source_value_begin Start of source value iterator
+ * @param source_validity_begin Start of source validity iterator
+ * @param target The strings column to copy from outside the range.
+ * @param target_begin The starting index of the target range (inclusive)
+ * @param target_end The index of the last element in the target range
+ * (exclusive)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return std::unique_ptr<column> The result target column
+ */
+template <typename SourceValueIterator, typename SourceValidityIterator>
+std::unique_ptr<column> copy_range(SourceValueIterator source_value_begin,
+                                   SourceValidityIterator source_validity_begin,
+                                   strings_column_view const& target,
+                                   size_type target_begin,
+                                   size_type target_end,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(
+    (target_begin >= 0) && (target_begin < target.size()) && (target_end <= target.size()),
+    "Range is out of bounds.");
+
+  if (target_end == target_begin) {
+    return std::make_unique<column>(target.parent(), stream, mr);
+  } else {
+    auto p_target_device_view = column_device_view::create(target.parent(), stream);
+    auto d_target             = *p_target_device_view;
+
+    // create resulting null mask
+
+    std::pair<rmm::device_buffer, size_type> valid_mask{};
+    if (target.has_nulls()) {  // check validities for both source & target
+      valid_mask = cudf::detail::valid_if(
+        thrust::make_counting_iterator<size_type>(0),
+        thrust::make_counting_iterator<size_type>(target.size()),
+        [source_validity_begin, d_target, target_begin, target_end] __device__(size_type idx) {
+          return (idx >= target_begin && idx < target_end)
+                   ? *(source_validity_begin + (idx - target_begin))
+                   : d_target.is_valid_nocheck(idx);
+        },
+        stream,
+        mr);
+    } else {  // check validities for source only
+      valid_mask = cudf::detail::valid_if(
+        thrust::make_counting_iterator<size_type>(0),
+        thrust::make_counting_iterator<size_type>(target.size()),
+        [source_validity_begin, d_target, target_begin, target_end] __device__(size_type idx) {
+          return (idx >= target_begin && idx < target_end)
+                   ? *(source_validity_begin + (idx - target_begin))
+                   : true;
+        },
+        stream,
+        mr);
+    }
+
+    auto null_count = valid_mask.second;
+    rmm::device_buffer null_mask{0, stream, mr};
+    if (target.parent().nullable() || null_count > 0) { null_mask = std::move(valid_mask.first); }
+
+    // build offsets column
+
+    std::unique_ptr<column> p_offsets_column{nullptr};
+    size_type chars_bytes = 0;
+    if (target.has_nulls()) {  // check validities for both source & target
+      auto string_size_begin = thrust::make_transform_iterator(
+        thrust::make_counting_iterator(0),
+        compute_element_size<true, true, SourceValueIterator, SourceValidityIterator>{
+          source_value_begin, source_validity_begin, d_target, target_begin, target_end});
+
+      std::tie(p_offsets_column, chars_bytes) = cudf::detail::make_offsets_child_column(
+        string_size_begin, string_size_begin + target.size(), stream, mr);
+    } else if (null_count > 0) {  // check validities for source only
+      auto string_size_begin = thrust::make_transform_iterator(
+        thrust::make_counting_iterator(0),
+        compute_element_size<true, false, SourceValueIterator, SourceValidityIterator>{
+          source_value_begin, source_validity_begin, d_target, target_begin, target_end});
+
+      std::tie(p_offsets_column, chars_bytes) = cudf::detail::make_offsets_child_column(
+        string_size_begin, string_size_begin + target.size(), stream, mr);
+    } else {  // no need to check validities
+      auto string_size_begin = thrust::make_transform_iterator(
+        thrust::make_counting_iterator(0),
+        compute_element_size<false, false, SourceValueIterator, SourceValidityIterator>{
+          source_value_begin, source_validity_begin, d_target, target_begin, target_end});
+
+      std::tie(p_offsets_column, chars_bytes) = cudf::detail::make_offsets_child_column(
+        string_size_begin, string_size_begin + target.size(), stream, mr);
+    }
+
+    // create the chars column
+
+    auto p_offsets =
+      thrust::device_pointer_cast(p_offsets_column->view().template data<size_type>());
+    auto p_chars_column = strings::detail::create_chars_child_column(chars_bytes, stream, mr);
+
+    // copy to the chars column
+
+    auto p_chars = (p_chars_column->mutable_view()).template data<char>();
+    thrust::for_each(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator(0),
+                     thrust::make_counting_iterator(target.size()),
+                     [source_value_begin,
+                      source_validity_begin,
+                      d_target,
+                      target_begin,
+                      target_end,
+                      p_offsets,
+                      p_chars] __device__(size_type idx) {
+                       if (p_offsets[idx + 1] - p_offsets[idx] > 0) {
+                         const auto source = (idx >= target_begin && idx < target_end)
+                                               ? *(source_value_begin + (idx - target_begin))
+                                               : d_target.element<string_view>(idx);
+                         memcpy(p_chars + p_offsets[idx], source.data(), source.size_bytes());
+                       }
+                     });
+
+    return make_strings_column(target.size(),
+                               std::move(p_offsets_column),
+                               std::move(p_chars_column),
+                               null_count,
+                               std::move(null_mask));
+  }
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/copying.hpp b/cpp/include/cudf/strings/detail/copying.hpp
new file mode 100644
index 0000000..7e82ad4
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/copying.hpp
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Returns a new strings column created from a subset of
+ * of the strings column.
+ *
+ * The subset of strings selected is between
+ * start (inclusive) and end (exclusive).
+ *
+ * @code{.pseudo}
+ * Example:
+ * s1 = ["a", "b", "c", "d", "e", "f"]
+ * s2 = copy_slice( s1, 2 )
+ * s2 is ["c", "d", "e", "f"]
+ * s2 = copy_slice( s1, 1, 3 )
+ * s2 is ["b", "c"]
+ * @endcode
+ *
+ * @param strings Strings instance for this operation.
+ * @param start Index to first string to select in the column (inclusive).
+ * @param end Index to last string to select in the column (exclusive).
+ *            Default -1 indicates the last element.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column of size (end-start)/step.
+ */
+std::unique_ptr<cudf::column> copy_slice(strings_column_view const& strings,
+                                         size_type start,
+                                         size_type end,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Returns a new strings column created by shifting the rows by a specified offset.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a", "b", "c", "d", "e", "f"]
+ * r1 = shift(s, 2, "_")
+ * r1 is now ["_", "_", "a", "b", "c", "d"]
+ * r2 = shift(s, -2, "_")
+ * r2 is now ["c", "d", "e", "f", "_", "_"]
+ * @endcode
+ *
+ * The caller should set the validity mask in the output column.
+ *
+ * @param input Strings instance for this operation.
+ * @param offset The offset by which to shift the input.
+ * @param fill_value Fill value for indeterminable outputs.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> shift(strings_column_view const& input,
+                              size_type offset,
+                              scalar const& fill_value,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/fill.hpp b/cpp/include/cudf/strings/detail/fill.hpp
new file mode 100644
index 0000000..43e3f61
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/fill.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Returns a strings column replacing a range of rows
+ * with the specified string.
+ *
+ * If the value parameter is invalid, the specified rows are filled with
+ * null entries.
+ *
+ * @throw cudf::logic_error if [begin,end) is outside the range of the input column.
+ *
+ * @param strings Strings column to fill.
+ * @param begin First row index to include the new string.
+ * @param end Last row index (exclusive).
+ * @param value String to use when filling the range.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> fill(strings_column_view const& strings,
+                             size_type begin,
+                             size_type end,
+                             string_scalar const& value,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/gather.cuh b/cpp/include/cudf/strings/detail/gather.cuh
new file mode 100644
index 0000000..7cd2338
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/gather.cuh
@@ -0,0 +1,361 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+// Helper function for loading 16B from a potentially unaligned memory location to registers.
+__forceinline__ __device__ uint4 load_uint4(char const* ptr)
+{
+  auto const offset       = reinterpret_cast<std::uintptr_t>(ptr) % 4;
+  auto const* aligned_ptr = reinterpret_cast<unsigned int const*>(ptr - offset);
+  auto const shift        = offset * 8;
+
+  uint4 regs = {aligned_ptr[0], aligned_ptr[1], aligned_ptr[2], aligned_ptr[3]};
+  uint tail  = 0;
+  if (shift) tail = aligned_ptr[4];
+
+  regs.x = __funnelshift_r(regs.x, regs.y, shift);
+  regs.y = __funnelshift_r(regs.y, regs.z, shift);
+  regs.z = __funnelshift_r(regs.z, regs.w, shift);
+  regs.w = __funnelshift_r(regs.w, tail, shift);
+
+  return regs;
+}
+
+/**
+ * @brief Gather characters from the input iterator, with string parallel strategy.
+ *
+ * This strategy assigns strings to warps so that each warp can cooperatively copy from the input
+ * location of the string to the corresponding output location. Large datatype (uint4) is used for
+ * stores. This strategy is best suited for large strings.
+ *
+ * @tparam StringIterator Iterator should produce `string_view` objects.
+ * @tparam MapIterator Iterator for retrieving integer indices of the `StringIterator`.
+ *
+ * @param strings_begin Start of the iterator to retrieve `string_view` instances.
+ * @param out_chars Output buffer for gathered characters.
+ * @param out_offsets The offset values associated with the output buffer.
+ * @param string_indices Start of index iterator.
+ * @param total_out_strings Number of output strings to be gathered.
+ */
+template <typename StringIterator, typename MapIterator>
+__global__ void gather_chars_fn_string_parallel(StringIterator strings_begin,
+                                                char* out_chars,
+                                                cudf::device_span<int32_t const> const out_offsets,
+                                                MapIterator string_indices,
+                                                size_type total_out_strings)
+{
+  constexpr size_t out_datatype_size = sizeof(uint4);
+  constexpr size_t in_datatype_size  = sizeof(uint);
+
+  int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  int global_warp_id   = global_thread_id / cudf::detail::warp_size;
+  int warp_lane        = global_thread_id % cudf::detail::warp_size;
+  int nwarps           = gridDim.x * blockDim.x / cudf::detail::warp_size;
+
+  auto const alignment_offset = reinterpret_cast<std::uintptr_t>(out_chars) % out_datatype_size;
+  uint4* out_chars_aligned    = reinterpret_cast<uint4*>(out_chars - alignment_offset);
+
+  for (size_type istring = global_warp_id; istring < total_out_strings; istring += nwarps) {
+    auto const out_start = out_offsets[istring];
+    auto const out_end   = out_offsets[istring + 1];
+
+    // This check is necessary because string_indices[istring] may be out of bound.
+    if (out_start == out_end) continue;
+
+    char const* in_start = strings_begin[string_indices[istring]].data();
+
+    // Both `out_start_aligned` and `out_end_aligned` are indices into `out_chars`.
+    // `out_start_aligned` is the first 16B aligned memory location after `out_start + 4`.
+    // `out_end_aligned` is the last 16B aligned memory location before `out_end - 4`. Characters
+    // between `[out_start_aligned, out_end_aligned)` will be copied using uint4.
+    // `out_start + 4` and `out_end - 4` are used instead of `out_start` and `out_end` to avoid
+    // `load_uint4` reading beyond string boundaries.
+    int32_t out_start_aligned =
+      (out_start + in_datatype_size + alignment_offset + out_datatype_size - 1) /
+        out_datatype_size * out_datatype_size -
+      alignment_offset;
+    int32_t out_end_aligned =
+      (out_end - in_datatype_size + alignment_offset) / out_datatype_size * out_datatype_size -
+      alignment_offset;
+
+    for (size_type ichar = out_start_aligned + warp_lane * out_datatype_size;
+         ichar < out_end_aligned;
+         ichar += cudf::detail::warp_size * out_datatype_size) {
+      *(out_chars_aligned + (ichar + alignment_offset) / out_datatype_size) =
+        load_uint4(in_start + ichar - out_start);
+    }
+
+    // Tail logic: copy characters of the current string outside `[out_start_aligned,
+    // out_end_aligned)`.
+    if (out_end_aligned <= out_start_aligned) {
+      // In this case, `[out_start_aligned, out_end_aligned)` is an empty set, and we copy the
+      // entire string.
+      for (int32_t ichar = out_start + warp_lane; ichar < out_end;
+           ichar += cudf::detail::warp_size) {
+        out_chars[ichar] = in_start[ichar - out_start];
+      }
+    } else {
+      // Copy characters in range `[out_start, out_start_aligned)`.
+      if (out_start + warp_lane < out_start_aligned) {
+        out_chars[out_start + warp_lane] = in_start[warp_lane];
+      }
+      // Copy characters in range `[out_end_aligned, out_end)`.
+      int32_t ichar = out_end_aligned + warp_lane;
+      if (ichar < out_end) { out_chars[ichar] = in_start[ichar - out_start]; }
+    }
+  }
+}
+
+/**
+ * @brief Gather characters from the input iterator, with char parallel strategy.
+ *
+ * This strategy assigns characters to threads, and uses binary search for getting the string
+ * index. To improve the binary search performance, fixed number of strings per threadblock is
+ * used. This strategy is best suited for small strings.
+ *
+ * @tparam StringIterator Iterator should produce `string_view` objects.
+ * @tparam MapIterator Iterator for retrieving integer indices of the `StringIterator`.
+ *
+ * @param strings_begin Start of the iterator to retrieve `string_view` instances.
+ * @param out_chars Output buffer for gathered characters.
+ * @param out_offsets The offset values associated with the output buffer.
+ * @param string_indices Start of index iterator.
+ * @param total_out_strings Number of output strings to be gathered.
+ */
+template <int strings_per_threadblock, typename StringIterator, typename MapIterator>
+__global__ void gather_chars_fn_char_parallel(StringIterator strings_begin,
+                                              char* out_chars,
+                                              cudf::device_span<int32_t const> const out_offsets,
+                                              MapIterator string_indices,
+                                              size_type total_out_strings)
+{
+  __shared__ int32_t out_offsets_threadblock[strings_per_threadblock + 1];
+
+  // Current thread block will process output strings starting at `begin_out_string_idx`.
+  size_type begin_out_string_idx = blockIdx.x * strings_per_threadblock;
+
+  // Number of strings to be processed by the current threadblock.
+  size_type strings_current_threadblock =
+    min(strings_per_threadblock, total_out_strings - begin_out_string_idx);
+
+  if (strings_current_threadblock <= 0) return;
+
+  // Collectively load offsets of strings processed by the current thread block.
+  for (size_type idx = threadIdx.x; idx <= strings_current_threadblock; idx += blockDim.x) {
+    out_offsets_threadblock[idx] = out_offsets[idx + begin_out_string_idx];
+  }
+  __syncthreads();
+
+  for (int32_t out_ibyte = threadIdx.x + out_offsets_threadblock[0];
+       out_ibyte < out_offsets_threadblock[strings_current_threadblock];
+       out_ibyte += blockDim.x) {
+    // binary search for the string index corresponding to out_ibyte
+    auto const string_idx_iter =
+      thrust::prev(thrust::upper_bound(thrust::seq,
+                                       out_offsets_threadblock,
+                                       out_offsets_threadblock + strings_current_threadblock,
+                                       out_ibyte));
+    size_type string_idx = thrust::distance(out_offsets_threadblock, string_idx_iter);
+
+    // calculate which character to load within the string
+    int32_t icharacter = out_ibyte - out_offsets_threadblock[string_idx];
+
+    size_type in_string_idx = string_indices[begin_out_string_idx + string_idx];
+    out_chars[out_ibyte]    = strings_begin[in_string_idx].data()[icharacter];
+  }
+}
+
+/**
+ * @brief Returns a new chars column using the specified indices to select
+ * strings from the input iterator.
+ *
+ * This uses a character-parallel gather CUDA kernel that performs very
+ * well on a strings column with long strings (e.g. average > 64 bytes).
+ *
+ * @tparam StringIterator Iterator should produce `string_view` objects.
+ * @tparam MapIterator Iterator for retrieving integer indices of the `StringIterator`.
+ *
+ * @param strings_begin Start of the iterator to retrieve `string_view` instances.
+ * @param map_begin Start of index iterator.
+ * @param map_end End of index iterator.
+ * @param offsets The offset values to be associated with the output chars column.
+ * @param chars_bytes The total number of bytes for the output chars column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New chars column fit for a strings column.
+ */
+template <typename StringIterator, typename MapIterator>
+std::unique_ptr<cudf::column> gather_chars(StringIterator strings_begin,
+                                           MapIterator map_begin,
+                                           MapIterator map_end,
+                                           cudf::device_span<int32_t const> const offsets,
+                                           size_type chars_bytes,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto const output_count = std::distance(map_begin, map_end);
+  if (output_count == 0) return make_empty_column(type_id::INT8);
+
+  auto chars_column  = create_chars_child_column(chars_bytes, stream, mr);
+  auto const d_chars = chars_column->mutable_view().template data<char>();
+
+  constexpr int warps_per_threadblock = 4;
+  // String parallel strategy will be used if average string length is above this threshold.
+  // Otherwise, char parallel strategy will be used.
+  constexpr size_type string_parallel_threshold = 32;
+
+  size_type average_string_length = chars_bytes / output_count;
+
+  if (average_string_length > string_parallel_threshold) {
+    constexpr int max_threadblocks = 65536;
+    gather_chars_fn_string_parallel<<<
+      min((static_cast<int>(output_count) + warps_per_threadblock - 1) / warps_per_threadblock,
+          max_threadblocks),
+      warps_per_threadblock * cudf::detail::warp_size,
+      0,
+      stream.value()>>>(strings_begin, d_chars, offsets, map_begin, output_count);
+  } else {
+    constexpr int strings_per_threadblock = 32;
+    gather_chars_fn_char_parallel<strings_per_threadblock>
+      <<<(output_count + strings_per_threadblock - 1) / strings_per_threadblock,
+         warps_per_threadblock * cudf::detail::warp_size,
+         0,
+         stream.value()>>>(strings_begin, d_chars, offsets, map_begin, output_count);
+  }
+
+  return chars_column;
+}
+
+/**
+ * @brief Returns a new strings column using the specified indices to select
+ * elements from the `strings` column.
+ *
+ * Caller must update the validity mask in the output column.
+ *
+ * ```
+ * s1 = ["a", "b", "c", "d", "e", "f"]
+ * map = [0, 2]
+ * s2 = gather<true>( s1, map.begin(), map.end() )
+ * s2 is ["a", "c"]
+ * ```
+ *
+ * @tparam NullifyOutOfBounds If true, indices outside the column's range are nullified.
+ * @tparam MapIterator Iterator for retrieving integer indices of the column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param begin Start of index iterator.
+ * @param end End of index iterator.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column containing the gathered strings.
+ */
+template <bool NullifyOutOfBounds, typename MapIterator>
+std::unique_ptr<cudf::column> gather(strings_column_view const& strings,
+                                     MapIterator begin,
+                                     MapIterator end,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  auto const output_count = std::distance(begin, end);
+  if (output_count == 0) return make_empty_column(type_id::STRING);
+
+  // build offsets column
+  auto const d_strings    = column_device_view::create(strings.parent(), stream);
+  auto const d_in_offsets = !strings.is_empty() ? strings.offsets_begin() : nullptr;
+
+  auto offsets_itr = thrust::make_transform_iterator(
+    begin, [d_strings = *d_strings, d_in_offsets] __device__(size_type idx) {
+      if (NullifyOutOfBounds && (idx < 0 || idx >= d_strings.size())) { return 0; }
+      if (not d_strings.is_valid(idx)) { return 0; }
+      return d_in_offsets[idx + 1] - d_in_offsets[idx];
+    });
+  auto [out_offsets_column, total_bytes] =
+    cudf::detail::make_offsets_child_column(offsets_itr, offsets_itr + output_count, stream, mr);
+
+  // build chars column
+  auto const offsets_view = out_offsets_column->view();
+  auto out_chars_column   = gather_chars(
+    d_strings->begin<string_view>(), begin, end, offsets_view, total_bytes, stream, mr);
+
+  return make_strings_column(output_count,
+                             std::move(out_offsets_column),
+                             std::move(out_chars_column),
+                             0,  // caller sets these
+                             rmm::device_buffer{});
+}
+
+/**
+ * @brief Returns a new strings column using the specified indices to select
+ * elements from the `strings` column.
+ *
+ * Caller must update the validity mask in the output column.
+ *
+ * ```
+ * s1 = ["a", "b", "c", "d", "e", "f"]
+ * map = [0, 2]
+ * s2 = gather( s1, map.begin(), map.end(), true )
+ * s2 is ["a", "c"]
+ * ```
+ *
+ * @tparam MapIterator Iterator for retrieving integer indices of the column.
+ *
+ * @param strings Strings instance for this operation.
+ * @param begin Start of index iterator.
+ * @param end End of index iterator.
+ * @param nullify_out_of_bounds If true, indices outside the column's range are nullified.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column containing the gathered strings.
+ */
+template <typename MapIterator>
+std::unique_ptr<cudf::column> gather(strings_column_view const& strings,
+                                     MapIterator begin,
+                                     MapIterator end,
+                                     bool nullify_out_of_bounds,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  if (nullify_out_of_bounds) return gather<true>(strings, begin, end, stream, mr);
+  return gather<false>(strings, begin, end, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/json.hpp b/cpp/include/cudf/strings/detail/json.hpp
new file mode 100644
index 0000000..0fb06d3
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/json.hpp
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/json.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @copydoc cudf::strings::get_json_object
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::column> get_json_object(cudf::strings_column_view const& col,
+                                              cudf::string_scalar const& json_path,
+                                              cudf::strings::get_json_object_options options,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/merge.cuh b/cpp/include/cudf/strings/detail/merge.cuh
new file mode 100644
index 0000000..965e89c
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/merge.cuh
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/merge.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Merges two strings columns.
+ *
+ * Caller must set the validity mask in the output column.
+ *
+ * @tparam row_order_iterator This must be an iterator for type thrust::tuple<side,size_type>.
+ *
+ * @param lhs First column.
+ * @param rhs Second column.
+ * @param row_order Indexes for each column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+template <typename index_type, typename row_order_iterator>
+std::unique_ptr<column> merge(strings_column_view const& lhs,
+                              strings_column_view const& rhs,
+                              row_order_iterator begin,
+                              row_order_iterator end,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  using cudf::detail::side;
+  size_type strings_count = static_cast<size_type>(std::distance(begin, end));
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  auto lhs_column = column_device_view::create(lhs.parent(), stream);
+  auto d_lhs      = *lhs_column;
+  auto rhs_column = column_device_view::create(rhs.parent(), stream);
+  auto d_rhs      = *rhs_column;
+
+  // caller will set the null mask
+  rmm::device_buffer null_mask{0, stream, mr};
+  size_type null_count = lhs.null_count() + rhs.null_count();
+  if (null_count > 0)
+    null_mask = cudf::detail::create_null_mask(strings_count, mask_state::ALL_VALID, stream, mr);
+
+  // build offsets column
+  auto offsets_transformer = [d_lhs, d_rhs] __device__(auto index_pair) {
+    auto const [side, index] = index_pair;
+    if (side == side::LEFT ? d_lhs.is_null(index) : d_rhs.is_null(index)) return 0;
+    auto d_str =
+      side == side::LEFT ? d_lhs.element<string_view>(index) : d_rhs.element<string_view>(index);
+    return d_str.size_bytes();
+  };
+  auto offsets_transformer_itr = thrust::make_transform_iterator(begin, offsets_transformer);
+  auto [offsets_column, bytes] = cudf::detail::make_offsets_child_column(
+    offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  auto d_offsets = offsets_column->view().template data<int32_t>();
+
+  // create the chars column
+  auto chars_column = strings::detail::create_chars_child_column(bytes, stream, mr);
+  // merge the strings
+  auto d_chars = chars_column->mutable_view().template data<char>();
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     strings_count,
+                     [d_lhs, d_rhs, begin, d_offsets, d_chars] __device__(size_type idx) {
+                       auto const [side, index] = begin[idx];
+                       if (side == side::LEFT ? d_lhs.is_null(index) : d_rhs.is_null(index)) return;
+                       auto d_str = side == side::LEFT ? d_lhs.element<string_view>(index)
+                                                       : d_rhs.element<string_view>(index);
+                       memcpy(d_chars + d_offsets[idx], d_str.data(), d_str.size_bytes());
+                     });
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/pad_impl.cuh b/cpp/include/cudf/strings/detail/pad_impl.cuh
new file mode 100644
index 0000000..648c240
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/pad_impl.cuh
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/side_type.hpp>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Return the size in bytes of padding d_str to width characters using a fill character
+ * with byte length of fill_char_size
+ *
+ * Pad does not perform truncation. That is, If `d_str.length() > width` then `d_str.size_bytes()`
+ * is returned.
+ *
+ * @param d_str String to pad
+ * @param width Number of characters for the padded string result
+ * @param fill_char_size Size of the fill character in bytes
+ * @return The number of bytes required for the pad
+ */
+__device__ size_type compute_padded_size(string_view d_str,
+                                         size_type width,
+                                         size_type fill_char_size)
+{
+  auto const length = d_str.length();
+  auto bytes        = d_str.size_bytes();
+  if (width > length)                            // no truncating;
+    bytes += fill_char_size * (width - length);  // add padding
+  return bytes;
+}
+
+/**
+ * @brief Pad d_str with fill_char into output up to width characters
+ *
+ * Pad does not perform truncation. That is, If `d_str.length() > width` then
+ * then d_str is copied into output.
+ *
+ * @tparam side Specifies where fill_char is added to d_str
+ * @param d_str String to pad
+ * @param width Number of characters for the padded string result
+ * @param fill_char Size of the fill character in bytes
+ * @param output Device memory to copy the padded string into
+ */
+template <side_type side = side_type::RIGHT>
+__device__ void pad_impl(cudf::string_view d_str,
+                         cudf::size_type width,
+                         cudf::char_utf8 fill_char,
+                         char* output)
+{
+  auto length = d_str.length();
+  if constexpr (side == side_type::LEFT) {
+    while (length++ < width) {
+      output += from_char_utf8(fill_char, output);
+    }
+    copy_string(output, d_str);
+  }
+  if constexpr (side == side_type::RIGHT) {
+    output = copy_string(output, d_str);
+    while (length++ < width) {
+      output += from_char_utf8(fill_char, output);
+    }
+  }
+  if constexpr (side == side_type::BOTH) {
+    auto const pad_size = width - length;
+    // an odd width will right-justify
+    auto right_pad = (width % 2) ? pad_size / 2 : (pad_size - pad_size / 2);
+    auto left_pad  = pad_size - right_pad;  // e.g. width=7: "++foxx+"; width=6: "+fox++"
+    while (left_pad-- > 0) {
+      output += from_char_utf8(fill_char, output);
+    }
+    output = copy_string(output, d_str);
+    while (right_pad-- > 0) {
+      output += from_char_utf8(fill_char, output);
+    }
+  }
+}
+
+/**
+ * @brief Prepend d_str with '0' into output up to width characters
+ *
+ * Pad does not perform truncation. That is, If `d_str.length() > width` then
+ * then d_str is copied into output.
+ *
+ * If d_str starts with a sign character ('-' or '+') then '0' padding
+ * starts after the sign.
+ *
+ * @param d_str String to pad
+ * @param width Number of characters for the padded string result
+ * @param output Device memory to copy the padded string into
+ */
+__device__ void zfill_impl(cudf::string_view d_str, cudf::size_type width, char* output)
+{
+  auto length = d_str.length();
+  auto in_ptr = d_str.data();
+  // if the string starts with a sign, output the sign first
+  if (!d_str.empty() && (*in_ptr == '-' || *in_ptr == '+')) {
+    *output++ = *in_ptr++;
+    d_str     = cudf::string_view{in_ptr, d_str.size_bytes() - 1};
+  }
+  while (length++ < width)
+    *output++ = '0';  // prepend zero char
+  copy_string(output, d_str);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/replace.hpp b/cpp/include/cudf/strings/detail/replace.hpp
new file mode 100644
index 0000000..aa6fb2f
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/replace.hpp
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief The type of algorithm to use for a replace operation.
+ */
+enum class replace_algorithm {
+  AUTO,          ///< Automatically choose the algorithm based on heuristics
+  ROW_PARALLEL,  ///< Row-level parallelism
+  CHAR_PARALLEL  ///< Character-level parallelism
+};
+
+/**
+ * @copydoc cudf::strings::replace(strings_column_view const&, string_scalar const&,
+ * string_scalar const&, int32_t, rmm::mr::device_memory_resource*)
+ *
+ * @tparam    alg    Replacement algorithm to use
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <replace_algorithm alg = replace_algorithm::AUTO>
+std::unique_ptr<column> replace(strings_column_view const& strings,
+                                string_scalar const& target,
+                                string_scalar const& repl,
+                                int32_t maxrepl,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::strings::replace_slice(strings_column_view const&, string_scalar const&,
+ * size_type. size_type, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace_slice(strings_column_view const& strings,
+                                      string_scalar const& repl,
+                                      size_type start,
+                                      size_type stop,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::strings::replace(strings_column_view const&, strings_column_view const&,
+ * strings_column_view const&, rmm::mr::device_memory_resource*)
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> replace(strings_column_view const& strings,
+                                strings_column_view const& targets,
+                                strings_column_view const& repls,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Replaces any null string entries with the given string.
+ *
+ * This returns a strings column with no null entries.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", nullptr, "goodbye"]
+ * r = replace_nulls(s,"**")
+ * r is now ["hello", "**", "goodbye"]
+ * @endcode
+ *
+ * @param strings Strings column for this operation.
+ * @param repl Replacement string for null entries. Default is empty string.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace_nulls(strings_column_view const& strings,
+                                      string_scalar const& repl,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/scatter.cuh b/cpp/include/cudf/strings/detail/scatter.cuh
new file mode 100644
index 0000000..55dd5bd
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/scatter.cuh
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scatter.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Scatters strings into a copy of the target column
+ * according to a scatter map.
+ *
+ * The scatter is performed according to the scatter iterator such that row
+ * `scatter_map[i]` of the output column is replaced by the source string.
+ * All other rows of the output column equal corresponding rows of the target table.
+ *
+ * If the same index appears more than once in the scatter map, the result is
+ * undefined.
+ *
+ * The caller must update the null mask in the output column.
+ *
+ * @tparam SourceIterator must produce string_view objects
+ * @tparam MapIterator must produce index values within the target column.
+ *
+ * @param source The iterator of source strings to scatter into the output column.
+ * @param scatter_map Iterator of indices into the output column.
+ * @param target The set of columns into which values from the source column
+ *        are to be scattered.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings column.
+ */
+template <typename SourceIterator, typename MapIterator>
+std::unique_ptr<column> scatter(SourceIterator begin,
+                                SourceIterator end,
+                                MapIterator scatter_map,
+                                strings_column_view const& target,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  if (target.is_empty()) return make_empty_column(type_id::STRING);
+
+  // create vector of string_view's to scatter into
+  rmm::device_uvector<string_view> target_vector =
+    create_string_vector_from_column(target, stream, rmm::mr::get_current_device_resource());
+
+  // this ensures empty strings are not mapped to nulls in the make_strings_column function
+  auto const size = thrust::distance(begin, end);
+  auto itr        = thrust::make_transform_iterator(
+    begin, [] __device__(string_view const sv) { return sv.empty() ? string_view{} : sv; });
+
+  // do the scatter
+  thrust::scatter(
+    rmm::exec_policy_nosync(stream), itr, itr + size, scatter_map, target_vector.begin());
+
+  // build the output column
+  auto sv_span = cudf::device_span<string_view const>(target_vector);
+  return make_strings_column(sv_span, string_view{nullptr, 0}, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/split_utils.cuh b/cpp/include/cudf/strings/detail/split_utils.cuh
new file mode 100644
index 0000000..a95a9ee
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/split_utils.cuh
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+constexpr bool is_whitespace(char_utf8 ch) { return ch <= ' '; }
+
+/**
+ * @brief Count tokens delimited by whitespace
+ *
+ * @param d_str String to tokenize
+ * @param max_tokens Maximum number of tokens to count
+ * @return Number of tokens delimited by whitespace
+ */
+__device__ inline size_type count_tokens_whitespace(
+  string_view d_str, size_type const max_tokens = std::numeric_limits<size_type>::max())
+{
+  auto token_count = size_type{0};
+  auto spaces      = true;
+  auto itr         = d_str.data();
+  auto const end   = itr + d_str.size_bytes();
+  while (itr < end && token_count < max_tokens) {
+    cudf::char_utf8 ch   = 0;
+    auto const chr_width = cudf::strings::detail::to_char_utf8(itr, ch);
+    if (spaces == is_whitespace(ch)) {
+      itr += chr_width;
+    } else {
+      token_count += static_cast<size_type>(spaces);
+      spaces = !spaces;
+    }
+  }
+  return token_count;
+}
+
+// JIT has trouble including thrust/pair.h
+struct position_pair {
+  size_type first;
+  size_type second;
+};
+
+/**
+ * @brief Instantiated for each string to manage navigating tokens from
+ * the beginning or the end of that string.
+ */
+struct whitespace_string_tokenizer {
+  /**
+   * @brief Identifies the position range of the next token in the given
+   * string at the specified iterator position.
+   *
+   * Tokens are delimited by one or more whitespace characters.
+   *
+   * @return true if a token has been found
+   */
+  __device__ bool next_token()
+  {
+    if (start_position >= d_str.size_bytes()) { return false; }
+    auto const src_ptr = d_str.data();
+    if (current_position != 0) {
+      current_position += cudf::strings::detail::bytes_in_char_utf8(src_ptr[current_position]);
+      start_position = current_position;
+    }
+    if (start_position >= d_str.size_bytes()) { return false; }
+    // continue search for the next token
+    end_position = d_str.size_bytes();
+    while (current_position < d_str.size_bytes()) {
+      cudf::char_utf8 ch   = 0;
+      auto const chr_width = cudf::strings::detail::to_char_utf8(src_ptr + current_position, ch);
+      if (spaces == is_whitespace(ch)) {
+        current_position += chr_width;
+        if (spaces) {
+          start_position = current_position;
+        } else {
+          end_position = current_position;
+        }
+        continue;
+      }
+      spaces = !spaces;
+      if (spaces) {
+        end_position = current_position;
+        break;
+      }
+      current_position += chr_width;
+    }
+    return start_position < end_position;
+  }
+
+  /**
+   * @brief Identifies the position range of the previous token in the given
+   * string at the specified iterator position.
+   *
+   * Tokens are delimited by one or more whitespace characters.
+   *
+   * @return true if a token has been found
+   */
+  __device__ bool prev_token()
+  {
+    end_position = start_position - 1;
+    --itr;
+    if (end_position <= 0) return false;
+    // continue search for the next token
+    start_position = 0;
+    for (; itr >= d_str.begin(); --itr) {
+      if (spaces == (*itr <= ' ')) {
+        if (spaces)
+          end_position = itr.byte_offset();
+        else
+          start_position = itr.byte_offset();
+        continue;
+      }
+      spaces = !spaces;
+      if (spaces) {
+        start_position = (itr + 1).byte_offset();
+        break;
+      }
+    }
+    return start_position < end_position;
+  }
+
+  __device__ position_pair get_token() const { return position_pair{start_position, end_position}; }
+
+  __device__ whitespace_string_tokenizer(string_view const& d_str, bool reverse = false)
+    : d_str{d_str},
+      spaces(true),
+      start_position{reverse ? d_str.size_bytes() + 1 : 0},
+      end_position{d_str.size_bytes()},
+      itr{reverse ? d_str.end() : d_str.begin()},
+      current_position{0}
+  {
+  }
+
+ private:
+  string_view const d_str;
+  bool spaces;  // true if current position is whitespace
+  cudf::string_view::const_iterator itr;
+  size_type start_position;
+  size_type end_position;
+  size_type current_position;
+};
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/strings_children.cuh b/cpp/include/cudf/strings/detail/strings_children.cuh
new file mode 100644
index 0000000..5f8a2a3
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/strings_children.cuh
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <stdexcept>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Creates child offsets and chars columns by applying the template function that
+ * can be used for computing the output size of each string as well as create the output
+ *
+ * @throws std::overflow_error if the output strings column exceeds the column size limit
+ *
+ * @tparam SizeAndExecuteFunction Function must accept an index and return a size.
+ *         It must also have members d_offsets and d_chars which are set to
+ *         memory containing the offsets and chars columns during write.
+ *
+ * @param size_and_exec_fn This is called twice. Once for the output size of each string
+ *        and once again to fill in the memory pointed to by d_chars.
+ * @param exec_size Number of rows for executing the `size_and_exec_fn` function.
+ * @param strings_count Number of strings.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned columns' device memory.
+ * @return offsets child column and chars child column for a strings column
+ */
+template <typename SizeAndExecuteFunction>
+auto make_strings_children(SizeAndExecuteFunction size_and_exec_fn,
+                           size_type exec_size,
+                           size_type strings_count,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr)
+{
+  auto offsets_column = make_numeric_column(
+    data_type{type_to_id<size_type>()}, strings_count + 1, mask_state::UNALLOCATED, stream, mr);
+  auto offsets_view          = offsets_column->mutable_view();
+  auto d_offsets             = offsets_view.template data<int32_t>();
+  size_and_exec_fn.d_offsets = d_offsets;
+
+  // This is called twice -- once for offsets and once for chars.
+  // Reducing the number of places size_and_exec_fn is inlined speeds up compile time.
+  auto for_each_fn = [exec_size, stream](SizeAndExecuteFunction& size_and_exec_fn) {
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       exec_size,
+                       size_and_exec_fn);
+  };
+
+  // Compute the output sizes
+  for_each_fn(size_and_exec_fn);
+
+  // Convert the sizes to offsets
+  auto const bytes =
+    cudf::detail::sizes_to_offsets(d_offsets, d_offsets + strings_count + 1, d_offsets, stream);
+  CUDF_EXPECTS(bytes <= std::numeric_limits<size_type>::max(),
+               "Size of output exceeds the column size limit",
+               std::overflow_error);
+
+  // Now build the chars column
+  std::unique_ptr<column> chars_column =
+    create_chars_child_column(static_cast<size_type>(bytes), stream, mr);
+
+  // Execute the function fn again to fill the chars column.
+  // Note that if the output chars column has zero size, the function fn should not be called to
+  // avoid accidentally overwriting the offsets.
+  if (bytes > 0) {
+    size_and_exec_fn.d_chars = chars_column->mutable_view().template data<char>();
+    for_each_fn(size_and_exec_fn);
+  }
+
+  return std::pair(std::move(offsets_column), std::move(chars_column));
+}
+
+/**
+ * @brief Creates child offsets and chars columns by applying the template function that
+ * can be used for computing the output size of each string as well as create the output.
+ *
+ * @tparam SizeAndExecuteFunction Function must accept an index and return a size.
+ *         It must also have members d_offsets and d_chars which are set to
+ *         memory containing the offsets and chars columns during write.
+ *
+ * @param size_and_exec_fn This is called twice. Once for the output size of each string
+ *        and once again to fill in the memory pointed to by d_chars.
+ * @param strings_count Number of strings.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned columns' device memory.
+ * @return offsets child column and chars child column for a strings column
+ */
+template <typename SizeAndExecuteFunction>
+auto make_strings_children(SizeAndExecuteFunction size_and_exec_fn,
+                           size_type strings_count,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr)
+{
+  return make_strings_children(size_and_exec_fn, strings_count, strings_count, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/strings_column_factories.cuh b/cpp/include/cudf/strings/detail/strings_column_factories.cuh
new file mode 100644
index 0000000..7e608cd
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/strings_column_factories.cuh
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/strings/detail/gather.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Basic type expected for iterators passed to `make_strings_column` that represent string
+ * data in device memory.
+ */
+using string_index_pair = thrust::pair<char const*, size_type>;
+
+/**
+ * @brief Average string byte-length threshold for deciding character-level
+ * vs. row-level parallel algorithm.
+ *
+ * This value was determined by running the factory_benchmark against different
+ * string lengths and observing the point where the performance is faster for
+ * long strings.
+ */
+constexpr size_type FACTORY_BYTES_PER_ROW_THRESHOLD = 64;
+
+/**
+ * @brief Create a strings-type column from iterators of pointer/size pairs
+ *
+ * @tparam IndexPairIterator iterator over type `pair<char const*,size_type>` values
+ *
+ * @param begin First string row (inclusive)
+ * @param end Last string row (exclusive)
+ * @param stream CUDA stream used for device memory operations
+ * @param mr  Device memory resource used to allocate the returned column's device memory
+ * @return New strings column
+ */
+template <typename IndexPairIterator>
+std::unique_ptr<column> make_strings_column(IndexPairIterator begin,
+                                            IndexPairIterator end,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  size_type strings_count = thrust::distance(begin, end);
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  // build offsets column from the strings sizes
+  auto offsets_transformer = [] __device__(string_index_pair item) -> size_type {
+    return (item.first != nullptr ? static_cast<size_type>(item.second) : size_type{0});
+  };
+  auto offsets_transformer_itr = thrust::make_transform_iterator(begin, offsets_transformer);
+  auto [offsets_column, bytes] = cudf::detail::make_offsets_child_column(
+    offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  auto offsets_view = offsets_column->view();
+
+  // create null mask
+  auto validator = [] __device__(string_index_pair const item) { return item.first != nullptr; };
+  auto new_nulls = cudf::detail::valid_if(begin, end, validator, stream, mr);
+  auto const null_count = new_nulls.second;
+  auto null_mask =
+    (null_count > 0) ? std::move(new_nulls.first) : rmm::device_buffer{0, stream, mr};
+
+  // build chars column
+  std::unique_ptr<column> chars_column =
+    [offsets_view, bytes = bytes, begin, strings_count, null_count, stream, mr] {
+      auto const avg_bytes_per_row = bytes / std::max(strings_count - null_count, 1);
+      // use a character-parallel kernel for long string lengths
+      if (avg_bytes_per_row > FACTORY_BYTES_PER_ROW_THRESHOLD) {
+        auto const d_data = offsets_view.template data<size_type>();
+        auto const d_offsets =
+          device_span<size_type const>{d_data, static_cast<std::size_t>(offsets_view.size())};
+        auto const str_begin = thrust::make_transform_iterator(begin, [] __device__(auto ip) {
+          return string_view{ip.first, ip.second};
+        });
+
+        return gather_chars(str_begin,
+                            thrust::make_counting_iterator<size_type>(0),
+                            thrust::make_counting_iterator<size_type>(strings_count),
+                            d_offsets,
+                            bytes,
+                            stream,
+                            mr);
+      } else {
+        // this approach is 2-3x faster for a large number of smaller string lengths
+        auto chars_column = create_chars_child_column(bytes, stream, mr);
+        auto d_chars      = chars_column->mutable_view().template data<char>();
+        auto copy_chars   = [d_chars] __device__(auto item) {
+          string_index_pair const str = thrust::get<0>(item);
+          size_type const offset      = thrust::get<1>(item);
+          if (str.first != nullptr) memcpy(d_chars + offset, str.first, str.second);
+        };
+        thrust::for_each_n(rmm::exec_policy(stream),
+                           thrust::make_zip_iterator(
+                             thrust::make_tuple(begin, offsets_view.template begin<int32_t>())),
+                           strings_count,
+                           copy_chars);
+        return chars_column;
+      }
+    }();
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+/**
+ * @brief Create a strings-type column from iterators to chars, offsets, and bitmask.
+ *
+ * @tparam CharIterator iterator over character bytes (int8)
+ * @tparam OffsetIterator iterator over offset values (size_type)
+ *
+ * @param chars_begin First character byte (inclusive)
+ * @param chars_end Last character byte (exclusive)
+ * @param offset_begin First offset value (inclusive)
+ * @param offset_end Last offset value (exclusive)
+ * @param null_count Number of null rows
+ * @param null_mask The validity bitmask in Arrow format
+ * @param stream CUDA stream used for device memory operations
+ * @param mr  Device memory resource used to allocate the returned column's device memory
+ * @return New strings column
+ */
+template <typename CharIterator, typename OffsetIterator>
+std::unique_ptr<column> make_strings_column(CharIterator chars_begin,
+                                            CharIterator chars_end,
+                                            OffsetIterator offsets_begin,
+                                            OffsetIterator offsets_end,
+                                            size_type null_count,
+                                            rmm::device_buffer&& null_mask,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  size_type strings_count = thrust::distance(offsets_begin, offsets_end) - 1;
+  size_type bytes         = std::distance(chars_begin, chars_end) * sizeof(char);
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(bytes >= 0, "invalid offsets data");
+
+  // build offsets column -- this is the number of strings + 1
+  auto offsets_column = make_numeric_column(
+    data_type{type_to_id<size_type>()}, strings_count + 1, mask_state::UNALLOCATED, stream, mr);
+  auto offsets_view = offsets_column->mutable_view();
+  thrust::transform(rmm::exec_policy(stream),
+                    offsets_begin,
+                    offsets_end,
+                    offsets_view.data<int32_t>(),
+                    [] __device__(auto offset) { return static_cast<int32_t>(offset); });
+
+  // build chars column
+  auto chars_column = strings::detail::create_chars_child_column(bytes, stream, mr);
+  auto chars_view   = chars_column->mutable_view();
+  thrust::copy(rmm::exec_policy(stream), chars_begin, chars_end, chars_view.data<char>());
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/strip.cuh b/cpp/include/cudf/strings/detail/strip.cuh
new file mode 100644
index 0000000..264ea0c
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/strip.cuh
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/strings/side_type.hpp>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Strips a specified character from the either or both ends of a string
+ *
+ * @param d_str Input string to strip
+ * @param d_to_strip String containing the character to strip;
+ *                   only the first character is used
+ * @param side Which ends of the input string to strip from
+ * @return New string excluding the stripped ends
+ */
+__device__ cudf::string_view strip(cudf::string_view const d_str,
+                                   cudf::string_view const d_to_strip,
+                                   side_type side = side_type::BOTH)
+{
+  if (d_str.empty()) { return cudf::string_view{}; }  // sanitize empty return
+
+  auto is_strip_character = [d_to_strip](char_utf8 chr) -> bool {
+    if (d_to_strip.empty()) return chr <= ' ';  // whitespace check
+    for (auto c : d_to_strip) {
+      if (c == chr) return true;
+    }
+    return false;
+  };
+
+  auto const left_offset = [&] {
+    if (side != side_type::LEFT && side != side_type::BOTH) return 0;
+    for (auto itr = d_str.begin(); itr < d_str.end(); ++itr) {
+      if (!is_strip_character(*itr)) return itr.byte_offset();
+    }
+    return d_str.size_bytes();
+  }();
+
+  auto const right_offset = [&] {
+    if (side != side_type::RIGHT && side != side_type::BOTH) return d_str.size_bytes();
+    for (auto itr = d_str.end(); itr > d_str.begin(); --itr) {
+      if (!is_strip_character(*(itr - 1))) return itr.byte_offset();
+    }
+    return 0;
+  }();
+
+  auto const bytes = (right_offset > left_offset) ? right_offset - left_offset : 0;
+  return cudf::string_view{d_str.data() + left_offset, bytes};
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/utf8.hpp b/cpp/include/cudf/strings/detail/utf8.hpp
new file mode 100644
index 0000000..e045725
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/utf8.hpp
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+
+/**
+ * @file
+ * @brief Standalone string functions.
+ */
+
+namespace cudf {
+
+using char_utf8 = uint32_t;  ///< UTF-8 characters are 1-4 bytes
+
+namespace strings {
+namespace detail {
+
+/**
+ * @brief This will return true if passed a continuation byte of a UTF-8 character.
+ *
+ * @param chr Any single byte from a valid UTF-8 character
+ * @return true if this is not the first byte of the character
+ */
+constexpr bool is_utf8_continuation_char(unsigned char chr)
+{
+  // The (0xC0 & 0x80) bit pattern identifies a continuation byte of a character.
+  return (chr & 0xC0) == 0x80;
+}
+
+/**
+ * @brief This will return true if passed the first byte of a UTF-8 character.
+ *
+ * @param chr Any single byte from a valid UTF-8 character
+ * @return true if this the first byte of the character
+ */
+constexpr bool is_begin_utf8_char(unsigned char chr) { return not is_utf8_continuation_char(chr); }
+
+/**
+ * @brief This will return true if the passed in byte could be the start of
+ * a valid UTF-8 character.
+ *
+ * This differs from is_begin_utf8_char(uint8_t) in that byte may not be valid
+ * UTF-8, so a more rigorous check is performed.
+ *
+ * @param byte The byte to be tested
+ * @return true if this can be the first byte of a character
+ */
+constexpr bool is_valid_begin_utf8_char(uint8_t byte)
+{
+  // to be the first byte of a valid (up to 4 byte) UTF-8 char, byte must be one of:
+  //  0b0vvvvvvv a 1 byte character
+  //  0b110vvvvv start of a 2 byte character
+  //  0b1110vvvv start of a 3 byte character
+  //  0b11110vvv start of a 4 byte character
+  return (byte & 0x80) == 0 || (byte & 0xE0) == 0xC0 || (byte & 0xF0) == 0xE0 ||
+         (byte & 0xF8) == 0xF0;
+}
+
+/**
+ * @brief Returns the number of bytes in the specified character.
+ *
+ * @param character Single character
+ * @return Number of bytes
+ */
+constexpr size_type bytes_in_char_utf8(char_utf8 character)
+{
+  return 1 + static_cast<size_type>((character & 0x0000'FF00u) > 0) +
+         static_cast<size_type>((character & 0x00FF'0000u) > 0) +
+         static_cast<size_type>((character & 0xFF00'0000u) > 0);
+}
+
+/**
+ * @brief Returns the number of bytes used to represent the provided byte.
+ *
+ * This could be 0 to 4 bytes. 0 is returned for intermediate bytes within a
+ * single character. For example, for the two-byte 0xC3A8 single character,
+ * the first byte would return 2 and the second byte would return 0.
+ *
+ * @param byte Byte from an encoded character.
+ * @return Number of bytes.
+ */
+constexpr size_type bytes_in_utf8_byte(uint8_t byte)
+{
+  return 1 + static_cast<size_type>((byte & 0xF0) == 0xF0)  // 4-byte character prefix
+         + static_cast<size_type>((byte & 0xE0) == 0xE0)    // 3-byte character prefix
+         + static_cast<size_type>((byte & 0xC0) == 0xC0)    // 2-byte character prefix
+         - static_cast<size_type>((byte & 0xC0) == 0x80);   // intermediate byte
+}
+
+/**
+ * @brief Convert a char array into a char_utf8 value.
+ *
+ * @param str String containing encoded char bytes.
+ * @param[out] character Single char_utf8 value.
+ * @return The number of bytes in the character
+ */
+constexpr size_type to_char_utf8(char const* str, char_utf8& character)
+{
+  size_type const chr_width = bytes_in_utf8_byte(static_cast<uint8_t>(*str));
+
+  character = static_cast<char_utf8>(*str++) & 0xFF;
+  if (chr_width > 1) {
+    character = character << 8;
+    character |= (static_cast<char_utf8>(*str++) & 0xFF);  // << 8;
+    if (chr_width > 2) {
+      character = character << 8;
+      character |= (static_cast<char_utf8>(*str++) & 0xFF);  // << 16;
+      if (chr_width > 3) {
+        character = character << 8;
+        character |= (static_cast<char_utf8>(*str++) & 0xFF);  // << 24;
+      }
+    }
+  }
+  return chr_width;
+}
+
+/**
+ * @brief Place a char_utf8 value into a char array.
+ *
+ * @param character Single character
+ * @param[out] str Output array.
+ * @return The number of bytes in the character
+ */
+constexpr inline size_type from_char_utf8(char_utf8 character, char* str)
+{
+  size_type const chr_width = bytes_in_char_utf8(character);
+  for (size_type idx = 0; idx < chr_width; ++idx) {
+    str[chr_width - idx - 1] = static_cast<char>(character) & 0xFF;
+    character                = character >> 8;
+  }
+  return chr_width;
+}
+
+/**
+ * @brief Converts a single UTF-8 character into a code-point value that
+ * can be used for lookup in the character flags or the character case tables.
+ *
+ * @param utf8_char Single UTF-8 character to convert.
+ * @return Code-point for the UTF-8 character.
+ */
+constexpr uint32_t utf8_to_codepoint(cudf::char_utf8 utf8_char)
+{
+  uint32_t unchr = 0;
+  if (utf8_char < 0x0000'0080)  // single-byte pass thru
+    unchr = utf8_char;
+  else if (utf8_char < 0x0000'E000)  // two bytes
+  {
+    unchr = (utf8_char & 0x1F00) >> 2;  // shift and
+    unchr |= (utf8_char & 0x003F);      // unmask
+  } else if (utf8_char < 0x00F0'0000)   // three bytes
+  {
+    unchr = (utf8_char & 0x0F'0000) >> 4;   // get upper 4 bits
+    unchr |= (utf8_char & 0x00'3F00) >> 2;  // shift and
+    unchr |= (utf8_char & 0x00'003F);       // unmask
+  } else if (utf8_char <= 0xF800'0000u)     // four bytes
+  {
+    unchr = (utf8_char & 0x0300'0000) >> 6;   // upper 3 bits
+    unchr |= (utf8_char & 0x003F'0000) >> 4;  // next 6 bits
+    unchr |= (utf8_char & 0x0000'3F00) >> 2;  // next 6 bits
+    unchr |= (utf8_char & 0x0000'003F);       // unmask
+  }
+  return unchr;
+}
+
+/**
+ * @brief Converts a character code-point value into a UTF-8 character.
+ *
+ * @param unchr Character code-point to convert.
+ * @return Single UTF-8 character.
+ */
+constexpr cudf::char_utf8 codepoint_to_utf8(uint32_t unchr)
+{
+  cudf::char_utf8 utf8 = 0;
+  if (unchr < 0x0000'0080)  // single byte utf8
+    utf8 = unchr;
+  else if (unchr < 0x0000'0800)  // double byte utf8
+  {
+    utf8 = (unchr << 2) & 0x1F00;  // shift bits for
+    utf8 |= (unchr & 0x3F);        // utf8 encoding
+    utf8 |= 0x0000'C080;
+  } else if (unchr < 0x0001'0000)  // triple byte utf8
+  {
+    utf8 = (unchr << 4) & 0x0F'0000;   // upper 4 bits
+    utf8 |= (unchr << 2) & 0x00'3F00;  // next 6 bits
+    utf8 |= (unchr & 0x3F);            // last 6 bits
+    utf8 |= 0x00E0'8080;
+  } else if (unchr < 0x0011'0000)  // quadruple byte utf8
+  {
+    utf8 = (unchr << 6) & 0x0700'0000;   // upper 3 bits
+    utf8 |= (unchr << 4) & 0x003F'0000;  // next 6 bits
+    utf8 |= (unchr << 2) & 0x0000'3F00;  // next 6 bits
+    utf8 |= (unchr & 0x3F);              // last 6 bits
+    utf8 |= 0xF080'8080u;
+  }
+  return utf8;
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/utilities.cuh b/cpp/include/cudf/strings/detail/utilities.cuh
new file mode 100644
index 0000000..5c719cd
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/utilities.cuh
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+
+#include <mutex>
+#include <unordered_map>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Copies input string data into a buffer and increments the pointer by the number of bytes
+ * copied.
+ *
+ * @param buffer Device buffer to copy to
+ * @param input Data to copy from
+ * @param bytes Number of bytes to copy
+ * @return Pointer to the end of the output buffer after the copy
+ */
+__device__ inline char* copy_and_increment(char* buffer, char const* input, size_type bytes)
+{
+  // this can be slightly faster than memcpy
+  thrust::copy_n(thrust::seq, input, bytes, buffer);
+  return buffer + bytes;
+}
+
+/**
+ * @brief Copies input string data into a buffer and increments the pointer by the number of bytes
+ * copied.
+ *
+ * @param buffer Device buffer to copy to.
+ * @param d_string String to copy.
+ * @return Pointer to the end of the output buffer after the copy.
+ */
+__device__ inline char* copy_string(char* buffer, string_view const& d_string)
+{
+  return copy_and_increment(buffer, d_string.data(), d_string.size_bytes());
+}
+
+// This template is a thin wrapper around per-context singleton objects.
+// It maintains a single object for each CUDA context.
+template <typename TableType>
+class per_context_cache {
+ public:
+  // Find an object cached for a current CUDA context.
+  // If there is no object available in the cache, it calls the initializer
+  // `init` to create a new one and cache it for later uses.
+  template <typename Initializer>
+  TableType* find_or_initialize(Initializer const& init)
+  {
+    int device_id;
+    CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+
+    auto finder = cache_.find(device_id);
+    if (finder == cache_.end()) {
+      TableType* result = init();
+      cache_[device_id] = result;
+      return result;
+    } else
+      return finder->second;
+  }
+
+ private:
+  std::unordered_map<int, TableType*> cache_;
+};
+
+// This template is a thread-safe version of per_context_cache.
+template <typename TableType>
+class thread_safe_per_context_cache : public per_context_cache<TableType> {
+ public:
+  template <typename Initializer>
+  TableType* find_or_initialize(Initializer const& init)
+  {
+    std::lock_guard<std::mutex> guard(mutex);
+    return per_context_cache<TableType>::find_or_initialize(init);
+  }
+
+ private:
+  std::mutex mutex;
+};
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/detail/utilities.hpp b/cpp/include/cudf/strings/detail/utilities.hpp
new file mode 100644
index 0000000..41a2654
--- /dev/null
+++ b/cpp/include/cudf/strings/detail/utilities.hpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Create a chars column to be a child of a strings column.
+ *
+ * This will return the properly sized column to be filled in by the caller.
+ *
+ * @param bytes Number of bytes for the chars column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return The chars child column for a strings column.
+ */
+std::unique_ptr<column> create_chars_child_column(size_type bytes,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Creates a string_view vector from a strings column.
+ *
+ * @param strings Strings column instance.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned vector's device memory.
+ * @return Device vector of string_views
+ */
+rmm::device_uvector<string_view> create_string_vector_from_column(
+  cudf::strings_column_view const strings,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/extract.hpp b/cpp/include/cudf/strings/extract.hpp
new file mode 100644
index 0000000..586cb1f
--- /dev/null
+++ b/cpp/include/cudf/strings/extract.hpp
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/regex/flags.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+
+struct regex_program;
+
+/**
+ * @addtogroup strings_extract
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a table of strings columns where each column corresponds to the matching
+ * group specified in the given regex_program object
+ *
+ * All the strings for the first group will go in the first output column; the second group
+ * go in the second column and so on. Null entries are added to the columns in row `i` if
+ * the string at row `i` does not match.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a1", "b2", "c3"]
+ * p = regex_program::create("([ab])(\\d)")
+ * r = extract(s, p)
+ * r is now [ ["a", "b", null],
+ *            ["1", "2", null] ]
+ * @endcode
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return Columns of strings extracted from the input column
+ */
+std::unique_ptr<table> extract(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a lists column of strings where each string column row corresponds to the
+ * matching group specified in the given regex_program object
+ *
+ * All the matching groups for the first row will go in the first row output column; the second
+ * row results will go into the second row output column and so on.
+ *
+ * A null output row will result if the corresponding input string row does not match or
+ * that input row is null.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a1 b4", "b2", "c3 a5", "b", null]
+ * p = regex_program::create("([ab])(\\d)")
+ * r = extract_all_record(s, p)
+ * r is now [ ["a", "1", "b", "4"],
+ *            ["b", "2"],
+ *            ["a", "5"],
+ *            null,
+ *            null ]
+ * @endcode
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param mr Device memory resource used to allocate any returned device memory
+ * @return Lists column containing strings extracted from the input column
+ */
+std::unique_ptr<column> extract_all_record(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/find.hpp b/cpp/include/cudf/strings/find.hpp
new file mode 100644
index 0000000..c1aa8b2
--- /dev/null
+++ b/cpp/include/cudf/strings/find.hpp
@@ -0,0 +1,264 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_find
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a column of character position values where the target
+ * string is first found in each string of the provided column.
+ *
+ * If `target` is not found, -1 is returned for that row entry in the output column.
+ *
+ * The target string is searched within each string in the character
+ * position range [start,stop). If the stop parameter is -1, then the
+ * end of each string becomes the final position to include in the search.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @throw cudf::logic_error if start position is greater than stop position.
+ *
+ * @param input Strings instance for this operation
+ * @param target UTF-8 encoded string to search for in each string
+ * @param start First character position to include in the search
+ * @param stop Last position (exclusive) to include in the search.
+ *             Default of -1 will search to the end of the string.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New integer column with character position values
+ */
+std::unique_ptr<column> find(
+  strings_column_view const& input,
+  string_scalar const& target,
+  size_type start                     = 0,
+  size_type stop                      = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of character position values where the target
+ * string is first found searching from the end of each string.
+ *
+ * If `target` is not found, -1 is returned for that entry.
+ *
+ * The target string is searched within each string in the character
+ * position range [start,stop). If the stop parameter is -1, then the
+ * end of each string becomes the final position to include in the search.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @throw cudf::logic_error if start position is greater than stop position.
+ *
+ * @param input Strings instance for this operation
+ * @param target UTF-8 encoded string to search for in each string
+ * @param start First position to include in the search
+ * @param stop Last position (exclusive) to include in the search.
+ *             Default of -1 will search starting at the end of the string.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New integer column with character position values
+ */
+std::unique_ptr<column> rfind(
+  strings_column_view const& input,
+  string_scalar const& target,
+  size_type start                     = 0,
+  size_type stop                      = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of character position values where the target
+ * string is first found in the corresponding string of the provided column
+ *
+ * The output of row `i` is the character position of the target string for row `i`
+ * within input string of row `i` starting at the character position `start`.
+ * If the target is not found within the input string, -1 is returned for that
+ * row entry in the output column.
+ *
+ * Any null input or target entries return corresponding null output column entries.
+ *
+ * @throw cudf::logic_error if `input.size() != target.size()`
+ *
+ * @param input Strings to search against
+ * @param target Strings to search for in `input`
+ * @param start First character position to include in the search
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New integer column with character position values
+ */
+std::unique_ptr<column> find(
+  strings_column_view const& input,
+  strings_column_view const& target,
+  size_type start                     = 0,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of boolean values for each string where true indicates
+ * the target string was found within that string in the provided column.
+ *
+ * If the `target` is not found for a string, false is returned for that entry in the output column.
+ * If `target` is an empty string, true is returned for all non-null entries in the output column.
+ *
+ * Any null string entries return corresponding null entries in the output columns.
+ *
+ * @param input Strings instance for this operation
+ * @param target UTF-8 encoded string to search for in each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New BOOL8 column
+ */
+std::unique_ptr<column> contains(
+  strings_column_view const& input,
+  string_scalar const& target,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of boolean values for each string where true indicates
+ * the corresponding target string was found within that string in the provided column.
+ *
+ * The 'output[i] = true` if string `targets[i]` is found inside `input[i]` otherwise
+ * `output[i] = false`.
+ * If `target[i]` is an empty string, true is returned for `output[i]`.
+ * If `target[i]` is null, false is returned for `output[i]`.
+ *
+ * Any null string entries return corresponding null entries in the output columns.
+ *
+ * @throw cudf::logic_error if `strings.size() != targets.size()`.
+ *
+ * @param input Strings instance for this operation
+ * @param targets Strings column of targets to check row-wise in `strings`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New BOOL8 column
+ */
+std::unique_ptr<column> contains(
+  strings_column_view const& input,
+  strings_column_view const& targets,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of boolean values for each string where true indicates
+ * the target string was found at the beginning of that string in the provided column.
+ *
+ * If `target` is not found at the beginning of a string, false is set for
+ * that row entry in the output column.
+ * If `target` is an empty string, true is returned for all non-null entries in the output column.
+ *
+ * Any null string entries return corresponding null entries in the output columns.
+ *
+ * @param input Strings instance for this operation
+ * @param target UTF-8 encoded string to search for in each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New type_id::BOOL8 column.
+ */
+std::unique_ptr<column> starts_with(
+  strings_column_view const& input,
+  string_scalar const& target,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of boolean values for each string where true indicates
+ * corresponding string in target column was found at the beginning of that string in
+ * the provided column.
+ *
+ * If `targets[i]` is not found at the beginning of a string in `strings[i]`, false is set for
+ * that row entry in the output column.
+ * If `targets[i]` is an empty string, true is returned for corresponding entry in the
+ * output column.
+ *
+ * Any null string entries in `targets` return corresponding null entries in the output columns.
+ *
+ * @throw cudf::logic_error if `strings.size() != targets.size()`.
+ *
+ * @param input Strings instance for this operation
+ * @param targets Strings instance for this operation
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New BOOL8 column
+ */
+std::unique_ptr<column> starts_with(
+  strings_column_view const& input,
+  strings_column_view const& targets,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of boolean values for each string where true indicates
+ * the target string was found at the end of that string in the provided column.
+ *
+ * If `target` is not found at the end of a string, false is set for
+ * that row entry in the output column.
+ * If `target` is an empty string, true is returned for all non-null entries in the output column.
+ *
+ * Any null string entries return corresponding null entries in the output columns.
+ *
+ * @param input Strings instance for this operation
+ * @param target UTF-8 encoded string to search for in each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New BOOL8 column
+ */
+std::unique_ptr<column> ends_with(
+  strings_column_view const& input,
+  string_scalar const& target,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a column of boolean values for each string where true indicates
+ * corresponding string in target column was found at the end of that string in
+ * the provided column.
+ *
+ * If `targets[i]` is not found at the end of a string in `strings[i]`, false is set for
+ * that row entry in the output column.
+ * If `targets[i]` is an empty string, true is returned for the corresponding entry in the
+ * output column.
+ *
+ * Any null string entries in `targets` return corresponding null entries in the output columns.
+ *
+ * @throw cudf::logic_error if `strings.size() != targets.size()`.
+ *
+ * @param input Strings instance for this operation
+ * @param targets Strings instance for this operation
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New BOOL8 column
+ */
+std::unique_ptr<column> ends_with(
+  strings_column_view const& input,
+  strings_column_view const& targets,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/find_multiple.hpp b/cpp/include/cudf/strings/find_multiple.hpp
new file mode 100644
index 0000000..06b851c
--- /dev/null
+++ b/cpp/include/cudf/strings/find_multiple.hpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_find
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a lists column with character position values where each
+ * of the target strings are found in each string.
+ *
+ * The size of the output column is `input.size()`.
+ * Each row of the output column is of size `targets.size()`.
+ *
+ * `output[i,j]` contains the position of `targets[j]` in `input[i]`
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abc", "def"]
+ * t = ["a", "c", "e"]
+ * r = find_multiple(s, t)
+ * r is now {[ 0, 2,-1],   // for "abc": "a" at pos 0, "c" at pos 2, "e" not found
+ *           [-1,-1, 1 ]}  // for "def": "a" and "b" not found, "e" at  pos 1
+ * @endcode
+ *
+ * @throw cudf::logic_error if `targets` is empty or contains nulls
+ *
+ * @param input Strings instance for this operation
+ * @param targets Strings to search for in each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Lists column with character position values
+ */
+std::unique_ptr<column> find_multiple(
+  strings_column_view const& input,
+  strings_column_view const& targets,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/findall.hpp b/cpp/include/cudf/strings/findall.hpp
new file mode 100644
index 0000000..379b962
--- /dev/null
+++ b/cpp/include/cudf/strings/findall.hpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/regex/flags.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+
+struct regex_program;
+
+/**
+ * @addtogroup strings_contains
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a lists column of strings for each matching occurrence using
+ * the regex_program pattern within each string
+ *
+ * Each output row includes all the substrings within the corresponding input row
+ * that match the given pattern. If no matches are found, the output row is empty.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["bunny", "rabbit", "hare", "dog"]
+ * p = regex_program::create("[ab]")
+ * r = findall(s, p)
+ * r is now a lists column like:
+ *  [ ["b"]
+ *    ["a","b","b"]
+ *    ["a"]
+ *    [] ]
+ * @endcode
+ *
+ * A null output row occurs if the corresponding input row is null.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param input Strings instance for this operation
+ * @param prog Regex program instance
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New lists column of strings
+ */
+std::unique_ptr<column> findall(
+  strings_column_view const& input,
+  regex_program const& prog,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/json.hpp b/cpp/include/cudf/strings/json.hpp
new file mode 100644
index 0000000..8fabee6
--- /dev/null
+++ b/cpp/include/cudf/strings/json.hpp
@@ -0,0 +1,174 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/optional.h>
+
+namespace cudf {
+namespace strings {
+
+/**
+ * @addtogroup strings_json
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Settings for `get_json_object()`.
+ */
+class get_json_object_options {
+  // allow single quotes to represent strings in JSON
+  bool allow_single_quotes = false;
+
+  // individual string values are returned with quotes stripped.
+  bool strip_quotes_from_single_strings = true;
+
+  // Whether to return nulls when an object does not contain the requested field.
+  bool missing_fields_as_nulls = false;
+
+ public:
+  /**
+   * @brief Default constructor.
+   */
+  explicit get_json_object_options() = default;
+
+  /**
+   * @brief Returns true/false depending on whether single-quotes for representing strings
+   * are allowed.
+   *
+   * @return true if single-quotes are allowed, false otherwise.
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE inline bool get_allow_single_quotes() const
+  {
+    return allow_single_quotes;
+  }
+
+  /**
+   * @brief Returns true/false depending on whether individually returned string values have
+   * their quotes stripped.
+   *
+   * When set to true, if the return value for a given row is an individual string
+   * (not an object, or an array of strings), strip the quotes from the string and return only the
+   * contents of the string itself.  Example:
+   *
+   * @code{.pseudo}
+   *
+   * With strip_quotes_from_single_strings OFF:
+   * Input  = {"a" : "b"}
+   * Query  = $.a
+   * Output = "b"
+   *
+   * With strip_quotes_from_single_strings ON:
+   * Input  = {"a" : "b"}
+   * Query  = $.a
+   * Output = b
+   *
+   * @endcode
+   *
+   * @return true if individually returned string values have their quotes stripped.
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE inline bool get_strip_quotes_from_single_strings() const
+  {
+    return strip_quotes_from_single_strings;
+  }
+
+  /**
+   * @brief Whether a field not contained by an object is to be interpreted as null.
+   *
+   * When set to true, if an object is queried for a field it does not contain, a null is returned.
+   *
+   * @code{.pseudo}
+   *
+   * With missing_fields_as_nulls OFF:
+   * Input  = {"a" : [{"x": "1", "y": "2"}, {"x": "3"}]}
+   * Query  = $.a[*].y
+   * Output = ["2"]
+   *
+   * With missing_fields_as_nulls ON:
+   * Input  = {"a" : [{"x": "1", "y": "2"}, {"x": "3"}]}
+   * Query  = $.a[*].y
+   * Output = ["2", null]
+   *
+   * @endcode
+   *
+   * @return true if missing fields are interpreted as null.
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE inline bool get_missing_fields_as_nulls() const
+  {
+    return missing_fields_as_nulls;
+  }
+
+  /**
+   * @brief Set whether single-quotes for strings are allowed.
+   *
+   * @param _allow_single_quotes bool indicating desired behavior.
+   */
+  void set_allow_single_quotes(bool _allow_single_quotes)
+  {
+    allow_single_quotes = _allow_single_quotes;
+  }
+
+  /**
+   * @brief Set whether individually returned string values have their quotes stripped.
+   *
+   * @param _strip_quotes_from_single_strings bool indicating desired behavior.
+   */
+  void set_strip_quotes_from_single_strings(bool _strip_quotes_from_single_strings)
+  {
+    strip_quotes_from_single_strings = _strip_quotes_from_single_strings;
+  }
+
+  /**
+   * @brief Set whether missing fields are interpreted as null.
+   *
+   * @param _missing_fields_as_nulls bool indicating desired behavior.
+   */
+  void set_missing_fields_as_nulls(bool _missing_fields_as_nulls)
+  {
+    missing_fields_as_nulls = _missing_fields_as_nulls;
+  }
+};
+
+/**
+ * @brief Apply a JSONPath string to all rows in an input strings column.
+ *
+ * Applies a JSONPath string to an incoming strings column where each row in the column
+ * is a valid json string.  The output is returned by row as a strings column.
+ *
+ * https://tools.ietf.org/id/draft-goessner-dispatch-jsonpath-00.html
+ * Implements only the operators: $ . [] *
+ *
+ * @param col The input strings column. Each row must contain a valid json string
+ * @param json_path The JSONPath string to be applied to each row
+ * @param options Options for controlling the behavior of the function
+ * @param mr Resource for allocating device memory.
+ * @return New strings column containing the retrieved json object strings
+ *
+ * @throw std::invalid_argument if provided an invalid operator or an empty name
+ */
+std::unique_ptr<cudf::column> get_json_object(
+  cudf::strings_column_view const& col,
+  cudf::string_scalar const& json_path,
+  get_json_object_options options     = get_json_object_options{},
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/padding.hpp b/cpp/include/cudf/strings/padding.hpp
new file mode 100644
index 0000000..7699159
--- /dev/null
+++ b/cpp/include/cudf/strings/padding.hpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/side_type.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Add padding to each string using a provided character.
+ *
+ * If the string is already `width` or more characters, no padding is performed.
+ * Also, no strings are truncated.
+ *
+ * Null string entries result in corresponding null entries in the output column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['aa','bbb','cccc','ddddd']
+ * r = pad(s,4)
+ * r is now ['aa  ','bbb ','cccc','ddddd']
+ * @endcode
+ *
+ * @param input Strings instance for this operation
+ * @param width The minimum number of characters for each string
+ * @param side Where to place the padding characters;
+ *        Default is pad right (left justify)
+ * @param fill_char Single UTF-8 character to use for padding;
+ *        Default is the space character
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column with padded strings
+ */
+std::unique_ptr<column> pad(
+  strings_column_view const& input,
+  size_type width,
+  side_type side                      = side_type::RIGHT,
+  std::string_view fill_char          = " ",
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Add '0' as padding to the left of each string.
+ *
+ * This is equivalent to `pad(width,left,'0')` but preserves the sign character
+ * if it appears in the first position.
+ *
+ * If the string is already width or more characters, no padding is performed.
+ * No strings are truncated.
+ *
+ * Null rows in the input result in corresponding null rows in the output column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ['1234','-9876','+0.34','-342567', '2+2']
+ * r = zfill(s,6)
+ * r is now ['001234','-09876','+00.34','-342567', '0002+2']
+ * @endcode
+ *
+ * @param input Strings instance for this operation.
+ * @param width The minimum number of characters for each string.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column of strings.
+ */
+std::unique_ptr<column> zfill(
+  strings_column_view const& input,
+  size_type width,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/regex/flags.hpp b/cpp/include/cudf/strings/regex/flags.hpp
new file mode 100644
index 0000000..44ca684
--- /dev/null
+++ b/cpp/include/cudf/strings/regex/flags.hpp
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+
+namespace cudf {
+namespace strings {
+
+/**
+ * @addtogroup strings_regex
+ * @{
+ */
+
+/**
+ * @brief Regex flags.
+ *
+ * These types can be or'd to combine them.
+ * The values are chosen to leave room for future flags
+ * and to match the Python flag values.
+ */
+enum regex_flags : uint32_t {
+  DEFAULT   = 0,   ///< default
+  MULTILINE = 8,   ///< the '^' and '$' honor new-line characters
+  DOTALL    = 16,  ///< the '.' matching includes new-line characters
+  ASCII     = 256  ///< use only ASCII when matching built-in character classes
+};
+
+/**
+ * @brief Returns true if the given flags contain MULTILINE.
+ *
+ * @param f Regex flags to check
+ * @return true if `f` includes MULTILINE
+ */
+constexpr bool is_multiline(regex_flags const f)
+{
+  return (f & regex_flags::MULTILINE) == regex_flags::MULTILINE;
+}
+
+/**
+ * @brief Returns true if the given flags contain DOTALL.
+ *
+ * @param f Regex flags to check
+ * @return true if `f` includes DOTALL
+ */
+constexpr bool is_dotall(regex_flags const f)
+{
+  return (f & regex_flags::DOTALL) == regex_flags::DOTALL;
+}
+
+/**
+ * @brief Returns true if the given flags contain ASCII.
+ *
+ * @param f Regex flags to check
+ * @return true if `f` includes ASCII
+ */
+constexpr bool is_ascii(regex_flags const f)
+{
+  return (f & regex_flags::ASCII) == regex_flags::ASCII;
+}
+
+/**
+ * @brief Capture groups setting
+ *
+ * For processing a regex pattern containing capture groups.
+ * These can be used to optimize the generated regex instructions
+ * where the capture groups do not require extracting the groups.
+ */
+enum class capture_groups : uint32_t {
+  EXTRACT,     ///< Capture groups processed normally for extract
+  NON_CAPTURE  ///< Convert all capture groups to non-capture groups
+};
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/regex/regex_program.hpp b/cpp/include/cudf/strings/regex/regex_program.hpp
new file mode 100644
index 0000000..bdf541f
--- /dev/null
+++ b/cpp/include/cudf/strings/regex/regex_program.hpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/regex/flags.hpp>
+#include <cudf/types.hpp>
+
+#include <memory>
+#include <string>
+
+namespace cudf {
+namespace strings {
+
+/**
+ * @addtogroup strings_regex
+ * @{
+ */
+
+/**
+ * @brief Regex program class
+ *
+ * Create an instance from a regex pattern and use it to call the appropriate
+ * strings APIs. An instance can be reused.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns and APIs that support regex.
+ */
+struct regex_program {
+  struct regex_program_impl;
+
+  /**
+   * @brief Create a program from a pattern
+   *
+   * @throw cudf::logic_error If pattern is invalid or contains unsupported features
+   *
+   * @param pattern Regex pattern
+   * @param flags Regex flags for interpreting special characters in the pattern
+   * @param capture Controls how capture groups in the pattern are used
+   * @return Instance of this object
+   */
+  static std::unique_ptr<regex_program> create(std::string_view pattern,
+                                               regex_flags flags      = regex_flags::DEFAULT,
+                                               capture_groups capture = capture_groups::EXTRACT);
+
+  /**
+   * @brief Move constructor
+   *
+   * @param other Object to move from
+   */
+  regex_program(regex_program&& other);
+
+  /**
+   * @brief Move operator assignment
+   *
+   * @param other Object to move from
+   * @return this object
+   */
+  regex_program& operator=(regex_program&& other);
+
+  /**
+   * @brief Return the pattern used to create this instance
+   *
+   * @return regex pattern as a string
+   */
+  std::string pattern() const;
+
+  /**
+   * @brief Return the regex_flags used to create this instance
+   *
+   * @return regex flags setting
+   */
+  regex_flags flags() const;
+
+  /**
+   * @brief Return the capture_groups used to create this instance
+   *
+   * @return capture groups setting
+   */
+  capture_groups capture() const;
+
+  /**
+   * @brief Return the number of instructions in this instance
+   *
+   * @return Number of instructions
+   */
+  int32_t instructions_count() const;
+
+  /**
+   * @brief Return the number of capture groups in this instance
+   *
+   * @return Number of groups
+   */
+  int32_t groups_count() const;
+
+  /**
+   * @brief Return the size of the working memory for the regex execution
+   *
+   * @param num_strings Number of strings for computation
+   * @return Size of the working memory in bytes
+   */
+  std::size_t compute_working_memory_size(int32_t num_strings) const;
+
+  ~regex_program();
+
+ private:
+  regex_program() = delete;
+
+  std::string _pattern;
+  regex_flags _flags;
+  capture_groups _capture;
+
+  std::unique_ptr<regex_program_impl> _impl;
+
+  /**
+   * @brief Constructor
+   *
+   * Called by create()
+   */
+  regex_program(std::string_view pattern, regex_flags flags, capture_groups capture);
+
+  friend struct regex_device_builder;
+};
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/repeat_strings.hpp b/cpp/include/cudf/strings/repeat_strings.hpp
new file mode 100644
index 0000000..2b6575f
--- /dev/null
+++ b/cpp/include/cudf/strings/repeat_strings.hpp
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_copy
+ * @{
+ * @file
+ * @brief Strings APIs for copying strings.
+ */
+
+/**
+ * @brief Repeat the given string scalar a given number of times
+ *
+ * An output string scalar is generated by repeating the input string by a number of times given by
+ * the `repeat_times` parameter.
+ *
+ * In special cases:
+ *  - If `repeat_times` is not a positive value, an empty (valid) string scalar will be returned.
+ *  - An invalid input scalar will always result in an invalid output scalar regardless of the
+ *    value of `repeat_times` parameter.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s   = '123XYZ-'
+ * out = repeat_strings(s, 3)
+ * out is '123XYZ-123XYZ-123XYZ-'
+ * @endcode
+ *
+ * @throw std::overflow_error if the size of the output string scalar exceeds the maximum value that
+ *        can be stored by the scalar: `input.size() * repeat_times > max of size_type`
+ *
+ * @param input The scalar containing the string to repeat
+ * @param repeat_times The number of times the input string is repeated
+ * @param mr Device memory resource used to allocate the returned string scalar
+ * @return New string scalar in which the input string is repeated
+ */
+std::unique_ptr<string_scalar> repeat_string(
+  string_scalar const& input,
+  size_type repeat_times,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Repeat each string in the given strings column a given number of times
+ *
+ * An output strings column is generated by repeating each string from the input strings column by
+ * the number of times given by the `repeat_times` parameter.
+ *
+ * In special cases:
+ *  - If `repeat_times` is not a positive number, a non-null input string will always result in
+ *    an empty output string.
+ *  - A null input string will always result in a null output string regardless of the value of the
+ *    `repeat_times` parameter.
+ *
+ * @code{.pseudo}
+ * Example:
+ * strs = ['aa', null, '', 'bbc']
+ * out  = repeat_strings(strs, 3)
+ * out is ['aaaaaa', null, '', 'bbcbbcbbc']
+ * @endcode
+ *
+ * @param input The column containing strings to repeat
+ * @param repeat_times The number of times each input string is repeated
+ * @param mr Device memory resource used to allocate the returned strings column
+ * @return New column containing the repeated strings
+ */
+std::unique_ptr<column> repeat_strings(
+  strings_column_view const& input,
+  size_type repeat_times,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Repeat each string in the given strings column by the numbers of times given in another
+ * numeric column
+ *
+ * An output strings column is generated by repeating each of the input string by a number of times
+ * given by the corresponding row in a `repeat_times` numeric column.
+ *
+ * In special cases:
+ *  - Any null row (from either the input strings column or the `repeat_times` column) will always
+ *    result in a null output string.
+ *  - If any value in the `repeat_times` column is not a positive number and its corresponding input
+ *    string is not null, the output string will be an empty string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * strs         = ['aa', null, '', 'bbc-']
+ * repeat_times = [ 1,   2,     3,  4   ]
+ * out          = repeat_strings(strs, repeat_times)
+ * out is ['aa', null, '', 'bbc-bbc-bbc-bbc-']
+ * @endcode
+ *
+ * @throw cudf::logic_error if the input `repeat_times` is not an integer type
+ * @throw cudf::logic_error if the input columns have different sizes.
+ *
+ * @param input The column containing strings to repeat
+ * @param repeat_times The column containing numbers of times that the corresponding input strings
+ *                     are repeated
+ * @param mr Device memory resource used to allocate the returned strings column
+ * @return New column containing the repeated strings.
+ */
+std::unique_ptr<column> repeat_strings(
+  strings_column_view const& input,
+  column_view const& repeat_times,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/replace.hpp b/cpp/include/cudf/strings/replace.hpp
new file mode 100644
index 0000000..22818f7
--- /dev/null
+++ b/cpp/include/cudf/strings/replace.hpp
@@ -0,0 +1,158 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_replace
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Replaces target string within each string with the specified
+ * replacement string.
+ *
+ * This function searches each string in the column for the target string.
+ * If found, the target string is replaced by the repl string within the
+ * input string. If not found, the output entry is just a copy of the
+ * corresponding input string.
+ *
+ * Specifying an empty string for repl will essentially remove the target
+ * string if found in each string.
+ *
+ * Null string entries will return null output string entries.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "goodbye"]
+ * r1 = replace(s,"o","OOO")
+ * r1 is now ["hellOOO","gOOOOOOdbye"]
+ * r2 = replace(s,"oo","")
+ * r2 is now ["hello","gdbye"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if target is an empty string.
+ *
+ * @param strings Strings column for this operation.
+ * @param target String to search for within each string.
+ * @param repl Replacement string if target is found.
+ * @param maxrepl Maximum times to replace if target appears multiple times in the input string.
+ *        Default of -1 specifies replace all occurrences of target in each string.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace(
+  strings_column_view const& strings,
+  string_scalar const& target,
+  string_scalar const& repl,
+  int32_t maxrepl                     = -1,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief This function replaces each string in the column with the provided
+ * repl string within the [start,stop) character position range.
+ *
+ * Null string entries will return null output string entries.
+ *
+ * Position values are 0-based meaning position 0 is the first character
+ * of each string.
+ *
+ * This function can be used to insert a string into specific position
+ * by specifying the same position value for start and stop. The repl
+ * string can be appended to each string by specifying -1 for both
+ * start and stop.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abcdefghij","0123456789"]
+ * r = s.replace_slice(s,2,5,"z")
+ * r is now ["abzfghij", "01z56789"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if start is greater than stop.
+ *
+ * @param strings Strings column for this operation.
+ * @param repl Replacement string for specified positions found.
+ *        Default is empty string.
+ * @param start Start position where repl will be added.
+ *        Default is 0, first character position.
+ * @param stop End position (exclusive) to use for replacement.
+ *        Default of -1 specifies the end of each string.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace_slice(
+  strings_column_view const& strings,
+  string_scalar const& repl           = string_scalar(""),
+  size_type start                     = 0,
+  size_type stop                      = -1,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Replaces substrings matching a list of targets with the corresponding
+ * replacement strings.
+ *
+ * For each string in strings, the list of targets is searched within that string.
+ * If a target string is found, it is replaced by the corresponding entry in the repls column.
+ * All occurrences found in each string are replaced.
+ *
+ * This does not use regex to match targets in the string.
+ *
+ * Null string entries will return null output string entries.
+ *
+ * The repls argument can optionally contain a single string. In this case, all
+ * matching target substrings will be replaced by that single string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "goodbye"]
+ * tgts = ["e","o"]
+ * repls = ["EE","OO"]
+ * r1 = replace(s,tgts,repls)
+ * r1 is now ["hEEllO", "gOOOOdbyEE"]
+ * tgts = ["e","oo"]
+ * repls = ["33",""]
+ * r2 = replace(s,tgts,repls)
+ * r2 is now ["h33llo", "gdby33"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if targets and repls are different sizes except
+ * if repls is a single string.
+ * @throw cudf::logic_error if targets or repls contain null entries.
+ *
+ * @param strings Strings column for this operation.
+ * @param targets Strings to search for in each string.
+ * @param repls Corresponding replacement strings for target strings.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace(
+  strings_column_view const& strings,
+  strings_column_view const& targets,
+  strings_column_view const& repls,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/replace_re.hpp b/cpp/include/cudf/strings/replace_re.hpp
new file mode 100644
index 0000000..bc66598
--- /dev/null
+++ b/cpp/include/cudf/strings/replace_re.hpp
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/regex/flags.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace strings {
+
+struct regex_program;
+
+/**
+ * @addtogroup strings_replace
+ * @{
+ * @file
+ */
+
+/**
+ * @brief For each string, replaces any character sequence matching the given regex
+ * with the provided replacement string.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param replacement The string used to replace the matched sequence in each string.
+ *        Default is an empty string.
+ * @param max_replace_count The maximum number of times to replace the matched pattern
+ *        within each string. Default replaces every substring that is matched.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings column
+ */
+std::unique_ptr<column> replace_re(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  string_scalar const& replacement           = string_scalar(""),
+  std::optional<size_type> max_replace_count = std::nullopt,
+  rmm::mr::device_memory_resource* mr        = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief For each string, replaces any character sequence matching the given patterns
+ * with the corresponding string in the `replacements` column.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param strings Strings instance for this operation.
+ * @param patterns The regular expression patterns to search within each string.
+ * @param replacements The strings used for replacement.
+ * @param flags Regex flags for interpreting special characters in the patterns.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace_re(
+  strings_column_view const& strings,
+  std::vector<std::string> const& patterns,
+  strings_column_view const& replacements,
+  regex_flags const flags             = regex_flags::DEFAULT,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief For each string, replaces any character sequence matching the given regex
+ * using the replacement template for back-references.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @throw cudf::logic_error if capture index values in `replacement` are not in range 0-99, and also
+ * if the index exceeds the group count specified in the pattern
+ *
+ * @param strings Strings instance for this operation
+ * @param prog Regex program instance
+ * @param replacement The replacement template for creating the output string
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings column
+ */
+std::unique_ptr<column> replace_with_backrefs(
+  strings_column_view const& strings,
+  regex_program const& prog,
+  std::string_view replacement,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/reverse.hpp b/cpp/include/cudf/strings/reverse.hpp
new file mode 100644
index 0000000..26fb36a
--- /dev/null
+++ b/cpp/include/cudf/strings/reverse.hpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Reverses the characters within each string
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["abcdef", "12345", "", "A"]
+ * r = reverse(s)
+ * r is now ["fedcba", "54321", "", "A"]
+ * @endcode
+ *
+ * @param input Strings column for this operation
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings column
+ */
+std::unique_ptr<column> reverse(
+  strings_column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/side_type.hpp b/cpp/include/cudf/strings/side_type.hpp
new file mode 100644
index 0000000..5905e08
--- /dev/null
+++ b/cpp/include/cudf/strings/side_type.hpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Direction identifier for cudf::strings::strip and cudf::strings::pad functions.
+ */
+enum class side_type {
+  LEFT,   ///< strip/pad characters from the beginning of the string
+  RIGHT,  ///< strip/pad characters from the end of the string
+  BOTH    ///< strip/pad characters from the beginning and end of the string
+};
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/slice.hpp b/cpp/include/cudf/strings/slice.hpp
new file mode 100644
index 0000000..5f2c717
--- /dev/null
+++ b/cpp/include/cudf/strings/slice.hpp
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_slice
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new strings column that contains substrings of the
+ * strings in the provided column.
+ *
+ * The character positions to retrieve in each string are `[start,stop)`.
+ * If the start position is outside a string's length, an empty
+ * string is returned for that entry. If the stop position is past the
+ * end of a string's length, the end of the string is used for
+ * stop position for that string.
+ *
+ * Null string entries will return null output string entries.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "goodbye"]
+ * r = slice_strings(s,2,6)
+ * r is now ["llo","odby"]
+ * r2 = slice_strings(s,2,5,2)
+ * r2 is now ["lo","ob"]
+ * @endcode
+ *
+ * @param strings Strings column for this operation.
+ * @param start First character position to begin the substring.
+ * @param stop Last character position (exclusive) to end the substring.
+ * @param step Distance between input characters retrieved.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with sorted elements of this instance.
+ */
+std::unique_ptr<column> slice_strings(
+  strings_column_view const& strings,
+  numeric_scalar<size_type> const& start = numeric_scalar<size_type>(0, false),
+  numeric_scalar<size_type> const& stop  = numeric_scalar<size_type>(0, false),
+  numeric_scalar<size_type> const& step  = numeric_scalar<size_type>(1),
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a new strings column that contains substrings of the
+ * strings in the provided column using unique ranges for each string.
+ *
+ * The character positions to retrieve in each string are specified in
+ * the `starts` and `stops` integer columns.
+ * If a start position is outside a string's length, an empty
+ * string is returned for that entry. If a stop position is past the
+ * end of a string's length, the end of the string is used for
+ * stop position for that string. Any stop position value set to -1 will
+ * indicate to use the end of the string as the stop position for that
+ * string.
+ *
+ * Null string entries will return null output string entries.
+ *
+ * The starts and stops column must both be the same integer type and
+ * must be the same size as the strings column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "goodbye"]
+ * starts = [ 1, 2 ]
+ * stops = [ 5, 4 ]
+ * r = slice_strings(s,starts,stops)
+ * r is now ["ello","od"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if starts or stops is a different size than the strings column.
+ * @throw cudf::logic_error if starts and stops are not same integer type.
+ * @throw cudf::logic_error if starts or stops contains nulls.
+ *
+ * @param strings Strings column for this operation.
+ * @param starts First character positions to begin the substring.
+ * @param stops Last character (exclusive) positions to end the substring.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with sorted elements of this instance.
+ */
+std::unique_ptr<column> slice_strings(
+  strings_column_view const& strings,
+  column_view const& starts,
+  column_view const& stops,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/split/partition.hpp b/cpp/include/cudf/strings/split/partition.hpp
new file mode 100644
index 0000000..52ffb73
--- /dev/null
+++ b/cpp/include/cudf/strings/split/partition.hpp
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_split
+ * @{
+ * @file strings/split/partition.hpp
+ * @brief Strings partition APIs
+ */
+
+/**
+ * @brief Returns a set of 3 columns by splitting each string using the
+ * specified delimiter.
+ *
+ * The number of rows in the output columns will be the same as the
+ * input column. The first column will contain the first tokens of
+ * each string as a result of the split. The second column will contain
+ * the delimiter. The third column will contain the remaining characters
+ * of each string after the delimiter.
+ *
+ * Any null string entries return corresponding null output columns.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["ab_cd","def_g_h"]
+ * r = partition(s,"_")
+ * r[0] is ["ab","def"]
+ * r[1] is ["_","_"]
+ * r[2] is ["cd","g_h"]
+ * @endcode
+ *
+ * @param strings Strings instance for this operation.
+ * @param delimiter UTF-8 encoded string indicating where to split each string.
+ *        Default of empty string indicates split on whitespace.
+ * @param mr Device memory resource used to allocate the returned table's device memory.
+ * @return New table of strings columns.
+ */
+std::unique_ptr<table> partition(
+  strings_column_view const& strings,
+  string_scalar const& delimiter      = string_scalar(""),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a set of 3 columns by splitting each string using the
+ * specified delimiter starting from the end of each string.
+ *
+ * The number of rows in the output columns will be the same as the
+ * input column. The first column will contain the characters of
+ * each string before the last delimiter found. The second column will contain
+ * the delimiter. The third column will contain the remaining characters
+ * of each string after the delimiter.
+ *
+ * Any null string entries return corresponding null output columns.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["ab_cd","def_g_h"]
+ * r = rpartition(s,"_")
+ * r[0] is ["ab","def_g"]
+ * r[1] is ["_","_"]
+ * r[2] is ["cd","h"]
+ * @endcode
+ *
+ * @param strings Strings instance for this operation.
+ * @param delimiter UTF-8 encoded string indicating where to split each string.
+ *        Default of empty string indicates split on whitespace.
+ * @param mr Device memory resource used to allocate the returned table's device memory.
+ * @return New strings columns.
+ */
+std::unique_ptr<table> rpartition(
+  strings_column_view const& strings,
+  string_scalar const& delimiter      = string_scalar(""),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/split/split.hpp b/cpp/include/cudf/strings/split/split.hpp
new file mode 100644
index 0000000..701950e
--- /dev/null
+++ b/cpp/include/cudf/strings/split/split.hpp
@@ -0,0 +1,247 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_split
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a list of columns by splitting each string using the
+ * specified delimiter.
+ *
+ * The number of rows in the output columns will be the same as the
+ * input column. The first column will contain the first tokens of
+ * each string as a result of the split. Subsequent columns contain
+ * the next token strings. Null entries are added for a row where
+ * split results have been exhausted. The total number of columns
+ * will equal the maximum number of splits encountered on any string
+ * in the input column.
+ *
+ * Any null string entries return corresponding null output columns.
+ *
+ * @param strings_column Strings instance for this operation
+ * @param delimiter UTF-8 encoded string indicating the split points in each string;
+ *        Default of empty string indicates split on whitespace.
+ * @param maxsplit Maximum number of splits to perform;
+ *        Default of -1 indicates all possible splits on each string.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return New table of strings columns
+ */
+std::unique_ptr<table> split(
+  strings_column_view const& strings_column,
+  string_scalar const& delimiter      = string_scalar(""),
+  size_type maxsplit                  = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a list of columns by splitting each string using the
+ * specified delimiter starting from the end of each string.
+ *
+ * The number of rows in the output columns will be the same as the
+ * input column. The first column will contain the first tokens encountered
+ * in each string as a result of the split. Subsequent columns contain
+ * the next token strings. Null entries are added for a row where
+ * split results have been exhausted. The total number of columns
+ * will equal the maximum number of splits encountered on any string
+ * in the input column.
+ *
+ * Any null string entries return corresponding null output columns.
+ *
+ * @param strings_column Strings instance for this operation
+ * @param delimiter UTF-8 encoded string indicating the split points in each string;
+ *        Default of empty string indicates split on whitespace.
+ * @param maxsplit Maximum number of splits to perform;
+ *        Default of -1 indicates all possible splits on each string.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return New strings columns.
+ */
+std::unique_ptr<table> rsplit(
+  strings_column_view const& strings_column,
+  string_scalar const& delimiter      = string_scalar(""),
+  size_type maxsplit                  = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Splits individual strings elements into a list of strings.
+ *
+ * Each element generates an array of strings that are stored in an output
+ * lists column.
+ *
+ * The number of elements in the output column will be the same as the number of
+ * elements in the input column. Each individual list item will contain the
+ * new strings for that row. The resulting number of strings in each row can vary
+ * from 0 to `maxsplit + 1`.
+ *
+ * The `delimiter` is searched within each string from beginning to end
+ * and splitting stops when either `maxsplit` or the end of the string is reached.
+ *
+ * If a delimiter is not whitespace and occurs adjacent to another delimiter,
+ * an empty string is produced for that split occurrence. Likewise, a non-whitespace
+ * delimiter produces an empty string if it appears at the beginning or the end
+ * of a string.
+ *
+ * @code{.pseudo}
+ * s = ["a_bc_def_g", "a__bc", "_ab_cd", "ab_cd_"]
+ * s1 = split_record(s, "_")
+ * s1 is a lists column of strings:
+ *     [ ["a", "bc", "def", "g"],
+ *       ["a", "", "bc"],
+ *       ["", "ab", "cd"],
+ *       ["ab", "cd", ""] ]
+ * s2 = split_record(s, "_", 1)
+ * s2 is a lists column of strings:
+ *     [ ["a", "bc_def_g"],
+ *       ["a", "_bc"],
+ *       ["", "ab_cd"],
+ *       ["ab", "cd_"] ]
+ * @endcode
+ *
+ * A whitespace delimiter produces no empty strings.
+ * @code{.pseudo}
+ * s = ["a bc def", "a  bc", " ab cd", "ab cd "]
+ * s1 = split_record(s, "")
+ * s1 is a lists column of strings:
+ *     [ ["a", "bc", "def"],
+ *       ["a", "bc"],
+ *       ["ab", "cd"],
+ *       ["ab", "cd"] ]
+ * s2 = split_record(s, "", 1)
+ * s2 is a lists column of strings:
+ *     [ ["a", "bc def"],
+ *       ["a", "bc"],
+ *       ["ab", "cd"],
+ *       ["ab", "cd "] ]
+ * @endcode
+ *
+ * A null string element will result in a null list item for that row.
+ *
+ * @throw cudf:logic_error if `delimiter` is invalid.
+ *
+ * @param strings A column of string elements to be split
+ * @param delimiter The string to identify split points in each string;
+ *        Default of empty string indicates split on whitespace.
+ * @param maxsplit Maximum number of splits to perform;
+ *        Default of -1 indicates all possible splits on each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned result's device memory
+ * @return Lists column of strings;
+ *         Each row of the lists column holds splits from a single row
+ *         element of the input column.
+ */
+std::unique_ptr<column> split_record(
+  strings_column_view const& strings,
+  string_scalar const& delimiter      = string_scalar(""),
+  size_type maxsplit                  = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Splits individual strings elements into a list of strings starting
+ * from the end of each string.
+ *
+ * Each element generates an array of strings that are stored in an output
+ * lists column.
+ *
+ * The number of elements in the output column will be the same as the number of
+ * elements in the input column. Each individual list item will contain the
+ * new strings for that row. The resulting number of strings in each row can vary
+ * from 0 to `maxsplit + 1`.
+ *
+ * The `delimiter` is searched from end to beginning within each string
+ * and splitting stops when either `maxsplit` or the beginning of the string
+ * is reached.
+ *
+ * If a delimiter is not whitespace and occurs adjacent to another delimiter,
+ * an empty string is produced for that split occurrence. Likewise, a non-whitespace
+ * delimiter produces an empty string if it appears at the beginning or the end
+ * of a string.
+ *
+ * Note that `rsplit_record` and `split_record` produce equivalent results for
+ * the default `maxsplit` value.
+ *
+ * @code{.pseudo}
+ * s = ["a_bc_def_g", "a__bc", "_ab_cd", "ab_cd_"]
+ * s1 = rsplit_record(s, "_")
+ * s1 is a lists column of strings:
+ *     [ ["a", "bc", "def", "g"],
+ *       ["a", "", "bc"],
+ *       ["", "ab", "cd"],
+ *       ["ab", "cd", ""] ]
+ * s2 = rsplit_record(s, "_", 1)
+ * s2 is a lists column of strings:
+ *     [ ["a_bc_def", "g"],
+ *       ["a_", "bc"],
+ *       ["_ab", "cd"],
+ *       ["ab_cd", ""] ]
+ * @endcode
+ *
+ * A whitespace delimiter produces no empty strings.
+ * @code{.pseudo}
+ * s = ["a bc def", "a  bc", " ab cd", "ab cd "]
+ * s1 = rsplit_record(s, "")
+ * s1 is a lists column of strings:
+ *     [ ["a", "bc", "def"],
+ *       ["a", "bc"],
+ *       ["ab", "cd"],
+ *       ["ab", "cd"] ]
+ * s2 = rsplit_record(s, "", 1)
+ * s2 is a lists column of strings:
+ *     [ ["a bc", "def"],
+ *       ["a", "bc"],
+ *       [" ab", "cd"],
+ *       ["ab", "cd"] ]
+ * @endcode
+ *
+ * A null string element will result in a null list item for that row.
+ *
+ * @throw cudf:logic_error if `delimiter` is invalid.
+ *
+ * @param strings A column of string elements to be split
+ * @param delimiter The string to identify split points in each string;
+ *        Default of empty string indicates split on whitespace.
+ * @param maxsplit Maximum number of splits to perform;
+ *        Default of -1 indicates all possible splits on each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned result's device memory
+ * @return Lists column of strings;
+ *         Each row of the lists column holds splits from a single row
+ *         element of the input column.
+ */
+std::unique_ptr<column> rsplit_record(
+  strings_column_view const& strings,
+  string_scalar const& delimiter      = string_scalar(""),
+  size_type maxsplit                  = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/split/split_re.hpp b/cpp/include/cudf/strings/split/split_re.hpp
new file mode 100644
index 0000000..14fcfae
--- /dev/null
+++ b/cpp/include/cudf/strings/split/split_re.hpp
@@ -0,0 +1,257 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+
+struct regex_program;
+
+/**
+ * @addtogroup strings_split
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Splits strings elements into a table of strings columns
+ * using a regex_program's pattern to delimit each string
+ *
+ * Each element generates a vector of strings that are stored in corresponding
+ * rows in the output table -- `table[col,row] = token[col] of strings[row]`
+ * where `token` is a substring between delimiters.
+ *
+ * The number of rows in the output table will be the same as the number of
+ * elements in the input column. The resulting number of columns will be the
+ * maximum number of tokens found in any input row.
+ *
+ * The `pattern` is used to identify the delimiters within a string
+ * and splitting stops when either `maxsplit` or the end of the string is reached.
+ *
+ * An empty input string will produce a corresponding empty string in the
+ * corresponding row of the first column.
+ * A null row will produce corresponding null rows in the output table.
+ *
+ * The regex_program's regex_flags are ignored.
+ *
+ * @code{.pseudo}
+ * s = ["a_bc def_g", "a__bc", "_ab cd", "ab_cd "]
+ * p1 = regex_program::create("[_ ]")
+ * s1 = split_re(s, p1)
+ * s1 is a table of strings columns:
+ *     [ ["a", "a", "", "ab"],
+ *       ["bc", "", "ab", "cd"],
+ *       ["def", "bc", "cd", ""],
+ *       ["g", null, null, null] ]
+ * p2 = regex_program::create("[ _]")
+ * s2 = split_re(s, p2, 1)
+ * s2 is a table of strings columns:
+ *     [ ["a", "a", "", "ab"],
+ *       ["bc def_g", "_bc", "ab cd", "cd "] ]
+ * @endcode
+ *
+ * @throw cudf::logic_error if `pattern` is empty.
+ *
+ * @param input A column of string elements to be split
+ * @param prog Regex program instance
+ * @param maxsplit Maximum number of splits to perform.
+ *        Default of -1 indicates all possible splits on each string.
+ * @param mr Device memory resource used to allocate the returned result's device memory
+ * @return A table of columns of strings
+ */
+std::unique_ptr<table> split_re(
+  strings_column_view const& input,
+  regex_program const& prog,
+  size_type maxsplit                  = -1,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Splits strings elements into a table of strings columns using a
+ * regex_program's pattern to delimit each string starting from the end of the string
+ *
+ * Each element generates a vector of strings that are stored in corresponding
+ * rows in the output table -- `table[col,row] = token[col] of string[row]`
+ * where `token` is the substring between each delimiter.
+ *
+ * The number of rows in the output table will be the same as the number of
+ * elements in the input column. The resulting number of columns will be the
+ * maximum number of tokens found in any input row.
+ *
+ * Splitting occurs by traversing starting from the end of the input string.
+ * The `pattern` is used to identify the delimiters within a string
+ * and splitting stops when either `maxsplit` or the beginning of the string
+ * is reached.
+ *
+ * An empty input string will produce a corresponding empty string in the
+ * corresponding row of the first column.
+ * A null row will produce corresponding null rows in the output table.
+ *
+ * The regex_program's regex_flags are ignored.
+ *
+ * @code{.pseudo}
+ * s = ["a_bc def_g", "a__bc", "_ab cd", "ab_cd "]
+ * p1 = regex_program::create("[_ ]")
+ * s1 = rsplit_re(s, p1)
+ * s1 is a table of strings columns:
+ *     [ ["a", "a", "", "ab"],
+ *       ["bc", "", "ab", "cd"],
+ *       ["def", "bc", "cd", ""],
+ *       ["g", null, null, null] ]
+ * p2 = regex_program::create("[ _]")
+ * s2 = rsplit_re(s, p2, 1)
+ * s2 is a table of strings columns:
+ *     [ ["a_bc def", "a_", "_ab", "ab"],
+ *       ["g", "bc", "cd", "cd "] ]
+ * @endcode
+ *
+ * @throw cudf::logic_error if `pattern` is empty.
+ *
+ * @param input A column of string elements to be split.
+ * @param prog Regex program instance
+ * @param maxsplit Maximum number of splits to perform.
+ *        Default of -1 indicates all possible splits on each string.
+ * @param mr Device memory resource used to allocate the returned result's device memory.
+ * @return A table of columns of strings.
+ */
+std::unique_ptr<table> rsplit_re(
+  strings_column_view const& input,
+  regex_program const& prog,
+  size_type maxsplit                  = -1,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Splits strings elements into a list column of strings
+ * using the given regex_program to delimit each string
+ *
+ * Each element generates an array of strings that are stored in an output
+ * lists column -- `list[row] = [token1, token2, ...] found in input[row]`
+ * where `token` is a substring between delimiters.
+ *
+ * The number of elements in the output column will be the same as the number of
+ * elements in the input column. Each individual list item will contain the
+ * new strings for that row. The resulting number of strings in each row can vary
+ * from 0 to `maxsplit + 1`.
+ *
+ * The `pattern` is used to identify the delimiters within a string
+ * and splitting stops when either `maxsplit` or the end of the string is reached.
+ *
+ * An empty input string will produce a corresponding empty list item output row.
+ * A null row will produce a corresponding null output row.
+ *
+ * The regex_program's regex_flags are ignored.
+ *
+ * @code{.pseudo}
+ * s = ["a_bc def_g", "a__bc", "_ab cd", "ab_cd "]
+ * p1 = regex_program::create("[_ ]")
+ * s1 = split_record_re(s, p1)
+ * s1 is a lists column of strings:
+ *     [ ["a", "bc", "def", "g"],
+ *       ["a", "", "bc"],
+ *       ["", "ab", "cd"],
+ *       ["ab", "cd", ""] ]
+ * p2 = regex_program::create("[ _]")
+ * s2 = split_record_re(s, p2, 1)
+ * s2 is a lists column of strings:
+ *     [ ["a", "bc def_g"],
+ *       ["a", "_bc"],
+ *       ["", "ab cd"],
+ *       ["ab", "cd "] ]
+ * @endcode
+ *
+ * @throw cudf::logic_error if `pattern` is empty.
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @param input A column of string elements to be split
+ * @param prog Regex program instance
+ * @param maxsplit Maximum number of splits to perform.
+ *        Default of -1 indicates all possible splits on each string.
+ * @param mr Device memory resource used to allocate the returned result's device memory
+ * @return Lists column of strings.
+ */
+std::unique_ptr<column> split_record_re(
+  strings_column_view const& input,
+  regex_program const& prog,
+  size_type maxsplit                  = -1,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Splits strings elements into a list column of strings using the given
+ * regex_program to delimit each string starting from the end of the string
+ *
+ * Each element generates a vector of strings that are stored in an output
+ * lists column -- `list[row] = [token1, token2, ...] found in input[row]`
+ * where `token` is a substring between delimiters.
+ *
+ * The number of elements in the output column will be the same as the number of
+ * elements in the input column. Each individual list item will contain the
+ * new strings for that row. The resulting number of strings in each row can vary
+ * from 0 to `maxsplit + 1`.
+ *
+ * Splitting occurs by traversing starting from the end of the input string.
+ * The `pattern` is used to identify the separation points within a string
+ * and splitting stops when either `maxsplit` or the beginning of the string
+ * is reached.
+ *
+ * An empty input string will produce a corresponding empty list item output row.
+ * A null row will produce a corresponding null output row.
+ *
+ * The regex_program's regex_flags are ignored.
+ *
+ * @code{.pseudo}
+ * s = ["a_bc def_g", "a__bc", "_ab cd", "ab_cd "]
+ * p1 = regex_program::create("[_ ]")
+ * s1 = rsplit_record_re(s, p1)
+ * s1 is a lists column of strings:
+ *     [ ["a", "bc", "def", "g"],
+ *       ["a", "", "bc"],
+ *       ["", "ab", "cd"],
+ *       ["ab", "cd", ""] ]
+ * p2 = regex_program::create("[ _]")
+ * s2 = rsplit_record_re(s, p2, 1)
+ * s2 is a lists column of strings:
+ *     [ ["a_bc def", "g"],
+ *       ["a_", "bc"],
+ *       ["_ab", "cd"],
+ *       ["ab_cd", ""] ]
+ * @endcode
+ *
+ * See the @ref md_regex "Regex Features" page for details on patterns supported by this API.
+ *
+ * @throw cudf::logic_error if `pattern` is empty.
+ *
+ * @param input A column of string elements to be split
+ * @param prog Regex program instance
+ * @param maxsplit Maximum number of splits to perform.
+ *        Default of -1 indicates all possible splits on each string.
+ * @param mr Device memory resource used to allocate the returned result's device memory
+ * @return Lists column of strings
+ */
+std::unique_ptr<column> rsplit_record_re(
+  strings_column_view const& input,
+  regex_program const& prog,
+  size_type maxsplit                  = -1,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/string_view.cuh b/cpp/include/cudf/strings/string_view.cuh
new file mode 100644
index 0000000..74df1ea
--- /dev/null
+++ b/cpp/include/cudf/strings/string_view.cuh
@@ -0,0 +1,451 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.hpp>
+
+#ifndef __CUDA_ARCH__
+#include <cudf/utilities/error.hpp>
+#endif
+
+// This is defined when including this header in a https://github.com/NVIDIA/jitify
+// or jitify2 source file. The jitify cannot include thrust headers at this time.
+#ifndef CUDF_JIT_UDF
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#endif
+
+#include <algorithm>
+
+// This file should only include device code logic.
+// Host-only or host/device code should be defined in the string_view.hpp header file.
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief Return the number of UTF-8 characters in this provided char array.
+ *
+ * @param str String with encoded char bytes.
+ * @param bytes Number of bytes in str.
+ * @return The number of characters in the array.
+ */
+__device__ inline size_type characters_in_string(char const* str, size_type bytes)
+{
+  if ((str == nullptr) || (bytes == 0)) return 0;
+  auto ptr = reinterpret_cast<uint8_t const*>(str);
+#ifndef CUDF_JIT_UDF
+  return thrust::count_if(
+    thrust::seq, ptr, ptr + bytes, [](uint8_t chr) { return is_begin_utf8_char(chr); });
+#else
+  size_type chars = 0;
+  auto const end  = ptr + bytes;
+  while (ptr < end) {
+    chars += is_begin_utf8_char(*ptr++);
+  }
+  return chars;
+#endif
+}
+
+/**
+ * @brief Count the bytes to a specified character position
+ *
+ * Returns the number of bytes and any left over position value.
+ * The returned position is > 0 if the given position would read past
+ * the end of the input string.
+ *
+ * @param d_str Input string to count bytes within
+ * @param pos Character position to count to
+ * @return The number of bytes and the left over non-counted position value
+ */
+__device__ inline std::pair<size_type, size_type> bytes_to_character_position(string_view d_str,
+                                                                              size_type pos)
+{
+  size_type bytes    = 0;
+  auto ptr           = d_str.data();
+  auto const end_ptr = ptr + d_str.size_bytes();
+  while ((pos > 0) && (ptr < end_ptr)) {
+    auto const width = strings::detail::bytes_in_utf8_byte(static_cast<uint8_t>(*ptr));
+    if (width) { --pos; }
+    bytes += width;
+    ++ptr;
+  }
+  return {bytes, pos};
+}
+
+/**
+ * @brief string value for sentinel which is used in min, max reduction
+ * operators
+ *
+ * This sentinel string value is the highest possible valid UTF-8 encoded
+ * character. This serves as identity value for maximum operator on string
+ * values. Also, this char pointer serves as valid device pointer of identity
+ * value for minimum operator on string values.
+ */
+static __constant__ char max_string_sentinel[5]{"\xF7\xBF\xBF\xBF"};
+}  // namespace detail
+}  // namespace strings
+
+/**
+ * @brief Return minimum value associated with the string type
+ *
+ * This function is needed to be host callable because it is called by a host
+ * callable function DeviceMax::identity<string_view>()
+ *
+ * @return An empty string
+ */
+CUDF_HOST_DEVICE inline string_view string_view::min() { return string_view(); }
+
+/**
+ * @brief Return maximum value associated with the string type
+ *
+ * This function is needed to be host callable because it is called by a host
+ * callable function DeviceMin::identity<string_view>()
+ *
+ * @return A string value which represents the highest possible valid UTF-8 encoded
+ * character.
+ */
+CUDF_HOST_DEVICE inline string_view string_view::max()
+{
+  char const* psentinel{nullptr};
+#if defined(__CUDA_ARCH__)
+  psentinel = &cudf::strings::detail::max_string_sentinel[0];
+#else
+  CUDF_CUDA_TRY(
+    cudaGetSymbolAddress((void**)&psentinel, cudf::strings::detail::max_string_sentinel));
+#endif
+  return string_view(psentinel, 4);
+}
+
+__device__ inline size_type string_view::length() const
+{
+  if (_length == UNKNOWN_STRING_LENGTH)
+    _length = strings::detail::characters_in_string(_data, _bytes);
+  return _length;
+}
+
+// @cond
+// this custom iterator knows about UTF8 encoding
+__device__ inline string_view::const_iterator::const_iterator(string_view const& str, size_type pos)
+  : p{str.data()}, bytes{str.size_bytes()}, char_pos{pos}, byte_pos{str.byte_offset(pos)}
+{
+}
+
+__device__ inline string_view::const_iterator::const_iterator(string_view const& str,
+                                                              size_type pos,
+                                                              size_type offset)
+  : p{str.data()}, bytes{str.size_bytes()}, char_pos{pos}, byte_pos{offset}
+{
+}
+
+__device__ inline string_view::const_iterator& string_view::const_iterator::operator++()
+{
+  if (byte_pos < bytes)
+    byte_pos += strings::detail::bytes_in_utf8_byte(static_cast<uint8_t>(p[byte_pos]));
+  ++char_pos;
+  return *this;
+}
+
+__device__ inline string_view::const_iterator string_view::const_iterator::operator++(int)
+{
+  string_view::const_iterator tmp(*this);
+  operator++();
+  return tmp;
+}
+
+__device__ inline string_view::const_iterator string_view::const_iterator::operator+(
+  string_view::const_iterator::difference_type offset) const
+{
+  const_iterator tmp(*this);
+  size_type adjust = abs(offset);
+  while (adjust-- > 0)
+    offset > 0 ? ++tmp : --tmp;
+  return tmp;
+}
+
+__device__ inline string_view::const_iterator& string_view::const_iterator::operator+=(
+  string_view::const_iterator::difference_type offset)
+{
+  size_type adjust = abs(offset);
+  while (adjust-- > 0)
+    offset > 0 ? operator++() : operator--();
+  return *this;
+}
+
+__device__ inline string_view::const_iterator& string_view::const_iterator::operator--()
+{
+  if (byte_pos > 0)
+    while (strings::detail::bytes_in_utf8_byte(static_cast<uint8_t>(p[--byte_pos])) == 0)
+      ;
+  --char_pos;
+  return *this;
+}
+
+__device__ inline string_view::const_iterator string_view::const_iterator::operator--(int)
+{
+  string_view::const_iterator tmp(*this);
+  operator--();
+  return tmp;
+}
+
+__device__ inline string_view::const_iterator& string_view::const_iterator::operator-=(
+  string_view::const_iterator::difference_type offset)
+{
+  size_type adjust = abs(offset);
+  while (adjust-- > 0)
+    offset > 0 ? operator--() : operator++();
+  return *this;
+}
+
+__device__ inline string_view::const_iterator string_view::const_iterator::operator-(
+  string_view::const_iterator::difference_type offset) const
+{
+  const_iterator tmp(*this);
+  size_type adjust = abs(offset);
+  while (adjust-- > 0)
+    offset > 0 ? --tmp : ++tmp;
+  return tmp;
+}
+
+__device__ inline string_view::const_iterator& string_view::const_iterator::move_to(
+  size_type new_pos)
+{
+  *this += (new_pos - char_pos);  // more efficient than recounting from the start
+  return *this;
+}
+
+__device__ inline bool string_view::const_iterator::operator==(
+  string_view::const_iterator const& rhs) const
+{
+  return (p == rhs.p) && (char_pos == rhs.char_pos);
+}
+
+__device__ inline bool string_view::const_iterator::operator!=(
+  string_view::const_iterator const& rhs) const
+{
+  return (p != rhs.p) || (char_pos != rhs.char_pos);
+}
+
+__device__ inline bool string_view::const_iterator::operator<(
+  string_view::const_iterator const& rhs) const
+{
+  return (p == rhs.p) && (char_pos < rhs.char_pos);
+}
+
+__device__ inline bool string_view::const_iterator::operator<=(
+  string_view::const_iterator const& rhs) const
+{
+  return (p == rhs.p) && (char_pos <= rhs.char_pos);
+}
+
+__device__ inline bool string_view::const_iterator::operator>(
+  string_view::const_iterator const& rhs) const
+{
+  return (p == rhs.p) && (char_pos > rhs.char_pos);
+}
+
+__device__ inline bool string_view::const_iterator::operator>=(
+  string_view::const_iterator const& rhs) const
+{
+  return (p == rhs.p) && (char_pos >= rhs.char_pos);
+}
+
+__device__ inline char_utf8 string_view::const_iterator::operator*() const
+{
+  char_utf8 chr = 0;
+  strings::detail::to_char_utf8(p + byte_offset(), chr);
+  return chr;
+}
+
+__device__ inline size_type string_view::const_iterator::position() const { return char_pos; }
+
+__device__ inline size_type string_view::const_iterator::byte_offset() const { return byte_pos; }
+
+__device__ inline string_view::const_iterator string_view::begin() const
+{
+  return const_iterator(*this, 0, 0);
+}
+
+__device__ inline string_view::const_iterator string_view::end() const
+{
+  return const_iterator(*this, length(), size_bytes());
+}
+// @endcond
+
+__device__ inline char_utf8 string_view::operator[](size_type pos) const
+{
+  size_type offset = byte_offset(pos);
+  if (offset >= _bytes) return 0;
+  char_utf8 chr = 0;
+  strings::detail::to_char_utf8(data() + offset, chr);
+  return chr;
+}
+
+__device__ inline size_type string_view::byte_offset(size_type pos) const
+{
+  if (length() == size_bytes()) return pos;
+  return std::get<0>(strings::detail::bytes_to_character_position(*this, pos));
+}
+
+__device__ inline int string_view::compare(string_view const& in) const
+{
+  return compare(in.data(), in.size_bytes());
+}
+
+__device__ inline int string_view::compare(char const* data, size_type bytes) const
+{
+  size_type const len1 = size_bytes();
+  auto const* ptr1     = reinterpret_cast<unsigned char const*>(this->data());
+  auto const* ptr2     = reinterpret_cast<unsigned char const*>(data);
+  if ((ptr1 == ptr2) && (bytes == len1)) return 0;
+  size_type idx = 0;
+  for (; (idx < len1) && (idx < bytes); ++idx) {
+    if (*ptr1 != *ptr2) return static_cast<int32_t>(*ptr1) - static_cast<int32_t>(*ptr2);
+    ++ptr1;
+    ++ptr2;
+  }
+  if (idx < len1) return 1;
+  if (idx < bytes) return -1;
+  return 0;
+}
+
+__device__ inline bool string_view::operator==(string_view const& rhs) const
+{
+  return (size_bytes() == rhs.size_bytes()) && (compare(rhs) == 0);
+}
+
+__device__ inline bool string_view::operator!=(string_view const& rhs) const
+{
+  return compare(rhs) != 0;
+}
+
+__device__ inline bool string_view::operator<(string_view const& rhs) const
+{
+  return compare(rhs) < 0;
+}
+
+__device__ inline bool string_view::operator>(string_view const& rhs) const
+{
+  return compare(rhs) > 0;
+}
+
+__device__ inline bool string_view::operator<=(string_view const& rhs) const
+{
+  int rc = compare(rhs);
+  return (rc == 0) || (rc < 0);
+}
+
+__device__ inline bool string_view::operator>=(string_view const& rhs) const
+{
+  int rc = compare(rhs);
+  return (rc == 0) || (rc > 0);
+}
+
+__device__ inline size_type string_view::find(string_view const& str,
+                                              size_type pos,
+                                              size_type count) const
+{
+  return find(str.data(), str.size_bytes(), pos, count);
+}
+
+template <bool forward>
+__device__ inline size_type string_view::find_impl(char const* str,
+                                                   size_type bytes,
+                                                   size_type pos,
+                                                   size_type count) const
+{
+  auto const nchars = length();
+  if (!str || pos < 0 || pos > nchars) return npos;
+  if (count < 0) count = nchars;
+
+  // use iterator to help reduce character/byte counting
+  auto itr        = begin() + pos;
+  auto const spos = itr.byte_offset();
+  auto const epos = ((pos + count) < nchars) ? (itr + count).byte_offset() : size_bytes();
+
+  auto const find_length = (epos - spos) - bytes + 1;
+
+  auto ptr = data() + (forward ? spos : (epos - bytes));
+  for (size_type idx = 0; idx < find_length; ++idx) {
+    bool match = true;
+    for (size_type jdx = 0; match && (jdx < bytes); ++jdx) {
+      match = (ptr[jdx] == str[jdx]);
+    }
+    if (match) { return forward ? pos : character_offset(epos - bytes - idx); }
+    // use pos to record the current find position
+    pos += strings::detail::is_begin_utf8_char(*ptr);
+    forward ? ++ptr : --ptr;
+  }
+  return npos;
+}
+
+__device__ inline size_type string_view::find(char const* str,
+                                              size_type bytes,
+                                              size_type pos,
+                                              size_type count) const
+{
+  return find_impl<true>(str, bytes, pos, count);
+}
+
+__device__ inline size_type string_view::find(char_utf8 chr, size_type pos, size_type count) const
+{
+  char str[sizeof(char_utf8)];
+  size_type chwidth = strings::detail::from_char_utf8(chr, str);
+  return find(str, chwidth, pos, count);
+}
+
+__device__ inline size_type string_view::rfind(string_view const& str,
+                                               size_type pos,
+                                               size_type count) const
+{
+  return rfind(str.data(), str.size_bytes(), pos, count);
+}
+
+__device__ inline size_type string_view::rfind(char const* str,
+                                               size_type bytes,
+                                               size_type pos,
+                                               size_type count) const
+{
+  return find_impl<false>(str, bytes, pos, count);
+}
+
+__device__ inline size_type string_view::rfind(char_utf8 chr, size_type pos, size_type count) const
+{
+  char str[sizeof(char_utf8)];
+  size_type chwidth = strings::detail::from_char_utf8(chr, str);
+  return rfind(str, chwidth, pos, count);
+}
+
+// parameters are character position values
+__device__ inline string_view string_view::substr(size_type pos, size_type count) const
+{
+  if (pos < 0 || pos >= length()) { return string_view{}; }
+  auto const itr  = begin() + pos;
+  auto const spos = itr.byte_offset();
+  auto const epos = count >= 0 ? (itr + count).byte_offset() : size_bytes();
+  return string_view(data() + spos, epos - spos);
+}
+
+__device__ inline size_type string_view::character_offset(size_type bytepos) const
+{
+  if (length() == size_bytes()) return bytepos;
+  return strings::detail::characters_in_string(data(), bytepos);
+}
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/string_view.hpp b/cpp/include/cudf/strings/string_view.hpp
new file mode 100644
index 0000000..afc7e02
--- /dev/null
+++ b/cpp/include/cudf/strings/string_view.hpp
@@ -0,0 +1,409 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <cuda_runtime.h>
+
+#include <iterator>
+
+/**
+ * @file
+ * @brief Class definition for cudf::string_view.
+ */
+
+namespace cudf {
+
+using char_utf8 = uint32_t;  ///< UTF-8 characters are 1-4 bytes
+
+/**
+ * @brief A non-owning, immutable view of device data that is a variable length
+ * char array representing a UTF-8 string.
+ *
+ * @ingroup strings_classes
+ *
+ * The caller must maintain the device memory for the lifetime of this instance.
+ *
+ * This may be used to wrap a device pointer and size but any member function
+ * that requires accessing the device memory must be called from a kernel.
+ */
+class string_view {
+ public:
+  /**
+   * @brief Return the number of bytes in this string
+   *
+   * @return The number of bytes in this string
+   */
+  CUDF_HOST_DEVICE [[nodiscard]] inline size_type size_bytes() const { return _bytes; }
+  /**
+   * @brief Return the number of characters in this string
+   *
+   * @return The number of characters in this string
+   */
+  __device__ [[nodiscard]] inline size_type length() const;
+  /**
+   * @brief Return a pointer to the internal device array
+   *
+   * @return A pointer to the internal device array
+   */
+  CUDF_HOST_DEVICE [[nodiscard]] inline char const* data() const { return _data; }
+
+  /**
+   * @brief Return true if string has no characters
+   *
+   * @return true if string has no characters
+   */
+  CUDF_HOST_DEVICE [[nodiscard]] inline bool empty() const { return size_bytes() == 0; }
+
+  /**
+   * @brief Handy iterator for navigating through encoded characters.
+   */
+  class const_iterator {
+    /// @cond
+   public:
+    using difference_type   = ptrdiff_t;
+    using value_type        = char_utf8;
+    using reference         = char_utf8&;
+    using pointer           = char_utf8*;
+    using iterator_category = std::input_iterator_tag;
+    __device__ inline const_iterator(string_view const& str, size_type pos);
+    const_iterator(const_iterator const& mit)        = default;
+    const_iterator(const_iterator&& mit)             = default;
+    const_iterator& operator=(const_iterator const&) = default;
+    const_iterator& operator=(const_iterator&&)      = default;
+    __device__ inline const_iterator& operator++();
+    __device__ inline const_iterator operator++(int);
+    __device__ inline const_iterator& operator+=(difference_type);
+    __device__ inline const_iterator operator+(difference_type) const;
+    __device__ inline const_iterator& operator--();
+    __device__ inline const_iterator operator--(int);
+    __device__ inline const_iterator& operator-=(difference_type);
+    __device__ inline const_iterator operator-(difference_type) const;
+    __device__ inline const_iterator& move_to(size_type);
+    __device__ inline bool operator==(const_iterator const&) const;
+    __device__ inline bool operator!=(const_iterator const&) const;
+    __device__ inline bool operator<(const_iterator const&) const;
+    __device__ inline bool operator<=(const_iterator const&) const;
+    __device__ inline bool operator>(const_iterator const&) const;
+    __device__ inline bool operator>=(const_iterator const&) const;
+    __device__ inline char_utf8 operator*() const;
+    [[nodiscard]] __device__ inline size_type position() const;
+    [[nodiscard]] __device__ inline size_type byte_offset() const;
+
+   private:
+    friend class string_view;
+    char const* p{};
+    size_type bytes{};
+    size_type char_pos{};
+    size_type byte_pos{};
+    __device__ inline const_iterator(string_view const& str, size_type pos, size_type offset);
+    /// @endcond
+  };
+
+  /**
+   * @brief Return new iterator pointing to the beginning of this string
+   *
+   * @return new iterator pointing to the beginning of this string
+   */
+  __device__ [[nodiscard]] inline const_iterator begin() const;
+  /**
+   * @brief Return new iterator pointing past the end of this string
+   *
+   * @return new iterator pointing past the end of this string
+   */
+  __device__ [[nodiscard]] inline const_iterator end() const;
+
+  /**
+   * @brief Return single UTF-8 character at the given character position
+   *
+   * @param pos Character position
+   * @return UTF-8 character at the given character position
+   */
+  __device__ inline char_utf8 operator[](size_type pos) const;
+  /**
+   * @brief Return the byte offset from data() for a given character position
+   *
+   * @param pos Character position
+   * @return Byte offset from data() for a given character position
+   */
+  __device__ [[nodiscard]] inline size_type byte_offset(size_type pos) const;
+
+  /**
+   * @brief Comparing target string with this string. Each character is compared
+   * as a UTF-8 code-point value.
+   *
+   * @param str Target string to compare with this string.
+   * @return 0  If they compare equal.
+   *         <0 Either the value of the first character of this string that does
+   *            not match is lower in the arg string, or all compared characters
+   *            match but the arg string is shorter.
+   *         >0 Either the value of the first character of this string that does
+   *            not match is greater in the arg string, or all compared characters
+   *            match but the arg string is longer.
+   */
+  __device__ [[nodiscard]] inline int compare(string_view const& str) const;
+  /**
+   * @brief Comparing target string with this string. Each character is compared
+   * as a UTF-8 code-point value.
+   *
+   * @param str Target string to compare with this string.
+   * @param bytes Number of bytes in str.
+   * @return 0  If they compare equal.
+   *         <0 Either the value of the first character of this string that does
+   *            not match is lower in the arg string, or all compared characters
+   *            match but the arg string is shorter.
+   *         >0 Either the value of the first character of this string that does
+   *            not match is greater in the arg string, or all compared characters
+   *            match but the arg string is longer.
+   */
+  __device__ inline int compare(char const* str, size_type bytes) const;
+
+  /**
+   * @brief Returns true if rhs matches this string exactly.
+   *
+   * @param rhs Target string to compare with this string.
+   * @return true if rhs matches this string exactly
+   */
+  __device__ inline bool operator==(string_view const& rhs) const;
+  /**
+   * @brief Returns true if rhs does not match this string.
+   *
+   * @param rhs Target string to compare with this string.
+   * @return true if rhs does not match this string
+   */
+  __device__ inline bool operator!=(string_view const& rhs) const;
+  /**
+   * @brief Returns true if this string is ordered before rhs.
+   *
+   * @param rhs Target string to compare with this string.
+   * @return true if this string is ordered before rhs
+   */
+  __device__ inline bool operator<(string_view const& rhs) const;
+  /**
+   * @brief Returns true if rhs is ordered before this string.
+   *
+   * @param rhs Target string to compare with this string.
+   * @return true if rhs is ordered before this string
+   */
+  __device__ inline bool operator>(string_view const& rhs) const;
+  /**
+   * @brief Returns true if this string matches or is ordered before rhs.
+   *
+   * @param rhs Target string to compare with this string.
+   * @return true if this string matches or is ordered before rhs
+   */
+  __device__ inline bool operator<=(string_view const& rhs) const;
+  /**
+   * @brief Returns true if rhs matches or is ordered before this string.
+   *
+   * @param rhs Target string to compare with this string.
+   * @return true if rhs matches or is ordered before this string
+   */
+  __device__ inline bool operator>=(string_view const& rhs) const;
+
+  /**
+   * @brief Returns the character position of the first occurrence where the
+   * argument str is found in this string within the character range [pos,pos+n).
+   *
+   * @param str Target string to search within this string.
+   * @param pos Character position to start search within this string.
+   * @param count Number of characters from pos to include in the search.
+   *              Specify -1 to indicate to the end of the string.
+   * @return npos if str is not found in this string.
+   */
+  __device__ [[nodiscard]] inline size_type find(string_view const& str,
+                                                 size_type pos   = 0,
+                                                 size_type count = -1) const;
+  /**
+   * @brief Returns the character position of the first occurrence where the
+   * array str is found in this string within the character range [pos,pos+n).
+   *
+   * @param str Target array to search within this string.
+   * @param bytes Number of bytes in str.
+   * @param pos Character position to start search within this string.
+   * @param count Number of characters from pos to include in the search.
+   *              Specify -1 to indicate to the end of the string.
+   * @return npos if arg string is not found in this string.
+   */
+  __device__ inline size_type find(char const* str,
+                                   size_type bytes,
+                                   size_type pos   = 0,
+                                   size_type count = -1) const;
+  /**
+   * @brief Returns the character position of the first occurrence where
+   * character is found in this string within the character range [pos,pos+n).
+   *
+   * @param character Single encoded character.
+   * @param pos Character position to start search within this string.
+   * @param count Number of characters from pos to include in the search.
+   *              Specify -1 to indicate to the end of the string.
+   * @return npos if arg string is not found in this string.
+   */
+  __device__ [[nodiscard]] inline size_type find(char_utf8 character,
+                                                 size_type pos   = 0,
+                                                 size_type count = -1) const;
+  /**
+   * @brief Returns the character position of the last occurrence where the
+   * argument str is found in this string within the character range [pos,pos+n).
+   *
+   * @param str Target string to search within this string.
+   * @param pos Character position to start search within this string.
+   * @param count Number of characters from pos to include in the search.
+   *              Specify -1 to indicate to the end of the string.
+   * @return npos if arg string is not found in this string.
+   */
+  __device__ [[nodiscard]] inline size_type rfind(string_view const& str,
+                                                  size_type pos   = 0,
+                                                  size_type count = -1) const;
+  /**
+   * @brief Returns the character position of the last occurrence where the
+   * array str is found in this string within the character range [pos,pos+n).
+   *
+   * @param str Target string to search with this string.
+   * @param bytes Number of bytes in str.
+   * @param pos Character position to start search within this string.
+   * @param count Number of characters from pos to include in the search.
+   *              Specify -1 to indicate to the end of the string.
+   * @return npos if arg string is not found in this string.
+   */
+  __device__ inline size_type rfind(char const* str,
+                                    size_type bytes,
+                                    size_type pos   = 0,
+                                    size_type count = -1) const;
+  /**
+   * @brief Returns the character position of the last occurrence where
+   * character is found in this string within the character range [pos,pos+n).
+   *
+   * @param character Single encoded character.
+   * @param pos Character position to start search within this string.
+   * @param count Number of characters from pos to include in the search.
+   *              Specify -1 to indicate to the end of the string.
+   * @return npos if arg string is not found in this string.
+   */
+  __device__ [[nodiscard]] inline size_type rfind(char_utf8 character,
+                                                  size_type pos   = 0,
+                                                  size_type count = -1) const;
+
+  /**
+   * @brief Return a sub-string of this string. The original string and device
+   * memory must still be maintained for the lifetime of the returned instance.
+   *
+   * @param start Character position to start the sub-string.
+   * @param length Number of characters from start to include in the sub-string.
+   * @return New instance pointing to a subset of the characters within this instance.
+   */
+  __device__ [[nodiscard]] inline string_view substr(size_type start, size_type length) const;
+
+  /**
+   * @brief Return minimum value associated with the string type
+   *
+   * This function is needed to be host callable because it is called by a host
+   * callable function DeviceMax::identity<string_view>()
+   *
+   * @return An empty string
+   */
+  CUDF_HOST_DEVICE inline static string_view min();
+
+  /**
+   * @brief Return maximum value associated with the string type
+   *
+   * This function is needed to be host callable because it is called by a host
+   * callable function DeviceMin::identity<string_view>()
+   *
+   * @return A string value which represents the highest possible valid UTF-8 encoded
+   * character.
+   */
+  CUDF_HOST_DEVICE inline static string_view max();
+
+  /**
+   * @brief Default constructor represents an empty string.
+   */
+  CUDF_HOST_DEVICE inline string_view() : _data("") {}
+
+  /**
+   * @brief Create instance from existing device char array.
+   *
+   * @param data Device char array encoded in UTF8.
+   * @param bytes Number of bytes in data array.
+   */
+  CUDF_HOST_DEVICE inline string_view(char const* data, size_type bytes)
+    : _data(data), _bytes(bytes), _length(UNKNOWN_STRING_LENGTH)
+  {
+  }
+
+  string_view(string_view const&) = default;  ///< Copy constructor
+  string_view(string_view&&)      = default;  ///< Move constructor
+  ~string_view()                  = default;
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this instance
+   */
+  string_view& operator=(string_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this instance (after transferring ownership)
+   */
+  string_view& operator=(string_view&&) = default;
+
+  /**
+   * @brief No-position value.
+   *
+   * Used when specifying or returning an invalid or unknown character position value.
+   */
+  static inline cudf::size_type const npos{-1};
+
+ private:
+  char const* _data{};          ///< Pointer to device memory contain char array for this string
+  size_type _bytes{};           ///< Number of bytes in _data for this string
+  mutable size_type _length{};  ///< Number of characters in this string (computed)
+
+  /**
+   * @brief The string length is initialized to this value as a place-holder
+   *
+   * The number of characters in a string is computed on-demand.
+   */
+  static inline cudf::size_type const UNKNOWN_STRING_LENGTH{-1};
+
+  /**
+   * @brief Return the character position of the given byte offset.
+   *
+   * @param bytepos Byte position from start of _data.
+   * @return The character position for the specified byte.
+   */
+  __device__ [[nodiscard]] inline size_type character_offset(size_type bytepos) const;
+
+  /**
+   * @brief Common internal implementation for string_view::find and string_view::rfind.
+   *
+   * @tparam forward True for find and false for rfind
+   *
+   * @param str Target string to search with this string
+   * @param bytes Number of bytes in str
+   * @param pos Character position to start search within this string
+   * @param count Number of characters from pos to include in the search
+   * @return npos if str is not found in this string
+   */
+  template <bool forward>
+  __device__ inline size_type find_impl(char const* str,
+                                        size_type bytes,
+                                        size_type pos,
+                                        size_type count) const;
+};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/strings_column_view.hpp b/cpp/include/cudf/strings/strings_column_view.hpp
new file mode 100644
index 0000000..f1aa8e4
--- /dev/null
+++ b/cpp/include/cudf/strings/strings_column_view.hpp
@@ -0,0 +1,153 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+
+/**
+ * @file
+ * @brief Class definition for cudf::strings_column_view
+ */
+
+namespace cudf {
+
+/**
+ * @addtogroup strings_classes
+ * @{
+ */
+
+/**
+ * @brief Given a column-view of strings type, an instance of this class
+ * provides a wrapper on this compound column for strings operations.
+ */
+class strings_column_view : private column_view {
+ public:
+  /**
+   * @brief Construct a new strings column view object from a column view.s
+   *
+   * @param strings_column The column view to wrap.
+   */
+  strings_column_view(column_view strings_column);
+  strings_column_view(strings_column_view&&)      = default;  ///< Move constructor
+  strings_column_view(strings_column_view const&) = default;  ///< Copy constructor
+  ~strings_column_view()                          = default;
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this instance
+   */
+  strings_column_view& operator=(strings_column_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this instance (after transferring ownership)
+   */
+  strings_column_view& operator=(strings_column_view&&) = default;
+
+  static constexpr size_type offsets_column_index{0};  ///< Child index of the offsets column
+  static constexpr size_type chars_column_index{1};    ///< Child index of the characters column
+
+  using column_view::has_nulls;
+  using column_view::is_empty;
+  using column_view::null_count;
+  using column_view::null_mask;
+  using column_view::offset;
+  using column_view::size;
+
+  using offset_iterator = size_type const*;  ///< offsets iterator type
+  using chars_iterator  = char const*;       ///< character iterator type
+
+  /**
+   * @brief Returns the parent column.
+   *
+   * @return The parents column
+   */
+  [[nodiscard]] column_view parent() const;
+
+  /**
+   * @brief Returns the internal column of offsets
+   *
+   * @throw cudf::logic error if this is an empty column
+   * @return The offsets column
+   */
+  [[nodiscard]] column_view offsets() const;
+
+  /**
+   * @brief Return an iterator for the offsets child column.
+   *
+   * This automatically applies the offset of the parent.
+   *
+   * @return Iterator pointing to the first offset value.
+   */
+  [[nodiscard]] offset_iterator offsets_begin() const;
+
+  /**
+   * @brief Return an end iterator for the offsets child column.
+   *
+   * This automatically applies the offset of the parent.
+   *
+   * @return Iterator pointing 1 past the last offset value.
+   */
+  [[nodiscard]] offset_iterator offsets_end() const;
+
+  /**
+   * @brief Returns the internal column of chars
+   *
+   * @throw cudf::logic error if this is an empty column
+   * @return The chars column
+   */
+  [[nodiscard]] column_view chars() const;
+
+  /**
+   * @brief Returns the number of bytes in the chars child column.
+   *
+   * This accounts for empty columns but does not reflect a sliced parent column
+   * view  (i.e.: non-zero offset or reduced row count).
+   *
+   * @return Number of bytes in the chars child column
+   */
+  [[nodiscard]] size_type chars_size() const noexcept;
+
+  /**
+   * @brief Return an iterator for the chars child column.
+   *
+   * This does not apply the offset of the parent.
+   * The offsets child must be used to properly address the char bytes.
+   *
+   * For example, to access the first character of string `i` (accounting for
+   * a sliced column offset) use: `chars_begin()[offsets_begin()[i]]`.
+   *
+   * @return Iterator pointing to the first char byte.
+   */
+  [[nodiscard]] chars_iterator chars_begin() const;
+
+  /**
+   * @brief Return an end iterator for the offsets child column.
+   *
+   * This does not apply the offset of the parent.
+   * The offsets child must be used to properly address the char bytes.
+   *
+   * @return Iterator pointing 1 past the last char byte.
+   */
+  [[nodiscard]] chars_iterator chars_end() const;
+};
+
+//! Strings column APIs.
+namespace strings {
+}  // namespace strings
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/strip.hpp b/cpp/include/cudf/strings/strip.hpp
new file mode 100644
index 0000000..adf3b29
--- /dev/null
+++ b/cpp/include/cudf/strings/strip.hpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/side_type.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Removes the specified characters from the beginning or end
+ * (or both) of each string.
+ *
+ * The to_strip parameter can contain one or more characters.
+ * All characters in `to_strip` are removed from the input strings.
+ *
+ * If `to_strip` is the empty string, whitespace characters are removed.
+ * Whitespace is considered the space character plus control characters
+ * like tab and line feed.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = [" aaa ", "_bbbb ", "__cccc  ", "ddd", " ee _ff gg_"]
+ * r = strip(s,both," _")
+ * r is now ["aaa", "bbbb", "cccc", "ddd", "ee _ff gg"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if `to_strip` is invalid.
+ *
+ * @param input Strings column for this operation
+ * @param side Indicates characters are to be stripped from the beginning, end, or both of each
+ *        string; Default is both
+ * @param to_strip UTF-8 encoded characters to strip from each string;
+ *        Default is empty string which indicates strip whitespace characters
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column.
+ */
+std::unique_ptr<column> strip(
+  strings_column_view const& input,
+  side_type side                      = side_type::BOTH,
+  string_scalar const& to_strip       = string_scalar(""),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/translate.hpp b/cpp/include/cudf/strings/translate.hpp
new file mode 100644
index 0000000..0cbf6b2
--- /dev/null
+++ b/cpp/include/cudf/strings/translate.hpp
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <vector>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Translates individual characters within each string.
+ *
+ * This can also be used to remove a character by specifying 0 for the corresponding table entry.
+ *
+ * Null string entries result in null entries in the output column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["aa","bbb","cccc","abcd"]
+ * t = [['a','A'],['b',''],['d':'Q']]
+ * r = translate(s,t)
+ * r is now ["AA", "", "cccc", "AcQ"]
+ * @endcode
+ *
+ * @param strings Strings instance for this operation.
+ * @param chars_table Table of UTF-8 character mappings.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with padded strings.
+ */
+std::unique_ptr<column> translate(
+  strings_column_view const& strings,
+  std::vector<std::pair<char_utf8, char_utf8>> const& chars_table,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Removes or keeps the specified character ranges in cudf::strings::filter_characters
+ */
+enum class filter_type : bool {
+  KEEP,   ///< All characters but those specified are removed
+  REMOVE  ///< Only the specified characters are removed
+};
+
+/**
+ * @brief Removes ranges of characters from each string in a strings column.
+ *
+ * This can also be used to keep only the specified character ranges
+ * and remove all others from each string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["aeiou", "AEIOU", "0123456789", "bcdOPQ5"]
+ * f = [{'M','Z'}, {'a','l'}, {'4','6'}]
+ * r1 = filter_characters(s, f)
+ * r1 is now ["aei", "OU", "456", "bcdOPQ5"]
+ * r2 = filter_characters(s, f, REMOVE)
+ * r2 is now ["ou", "AEI", "0123789", ""]
+ * r3 = filter_characters(s, f, KEEP, "*")
+ * r3 is now ["aei**", "***OU", "****456***", "bcdOPQ5"]
+ * @endcode
+ *
+ * Null string entries result in null entries in the output column.
+ *
+ * @throw cudf::logic_error if `replacement` is invalid
+ *
+ * @param strings Strings instance for this operation.
+ * @param characters_to_filter Table of character ranges to filter on.
+ * @param keep_characters If true, the `characters_to_filter` are retained and all other characters
+ * are removed.
+ * @param replacement Optional replacement string for each character removed.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New column with filtered strings.
+ */
+std::unique_ptr<column> filter_characters(
+  strings_column_view const& strings,
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> characters_to_filter,
+  filter_type keep_characters         = filter_type::KEEP,
+  string_scalar const& replacement    = string_scalar(""),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/strings/wrap.hpp b/cpp/include/cudf/strings/wrap.hpp
new file mode 100644
index 0000000..8d2d43c
--- /dev/null
+++ b/cpp/include/cudf/strings/wrap.hpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace strings {
+/**
+ * @addtogroup strings_modify
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Wraps strings onto multiple lines shorter than `width` by replacing appropriate white
+ * space with new-line characters (ASCII 0x0A).
+ *
+ * For each string row in the input column longer than `width`, the corresponding output string row
+ * will have newline characters inserted so that each line is no more than `width characters`.
+ * Attempts to use existing white space locations to split the strings, but may split
+ * non-white-space sequences if necessary.
+ *
+ * Any null string entries return corresponding null output column entries.
+ *
+ * Example 1:
+ * ```
+ * width = 3
+ * input_string_tbl = [ "12345", "thesé", nullptr, "ARE THE", "tést strings", "" ];
+ *
+ * wrapped_string_tbl = wrap(input_string_tbl, width)
+ * wrapped_string_tbl = [ "12345", "thesé", nullptr, "ARE\nTHE", "tést\nstrings", "" ]
+ * ```
+ *
+ * Example 2:
+ * ```
+ * width = 12;
+ * input_string_tbl = ["the quick brown fox jumped over the lazy brown dog", "hello, world"]
+ *
+ * wrapped_string_tbl = wrap(input_string_tbl, width)
+ * wrapped_string_tbl = ["the quick\nbrown fox\njumped over\nthe lazy\nbrown dog", "hello, world"]
+ * ```
+ *
+ * @param[in] strings String column.
+ * @param[in] width Maximum character width of a line within each string.
+ * @param[in] mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of wrapped strings.
+ */
+std::unique_ptr<column> wrap(
+  strings_column_view const& strings,
+  size_type width,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of doxygen group
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/include/cudf/structs/detail/concatenate.hpp b/cpp/include/cudf/structs/detail/concatenate.hpp
new file mode 100644
index 0000000..82ccca1
--- /dev/null
+++ b/cpp/include/cudf/structs/detail/concatenate.hpp
@@ -0,0 +1,57 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+namespace cudf {
+namespace structs {
+namespace detail {
+
+/**
+ * @brief Returns a single column by concatenating the given vector of structs columns.
+ *
+ * @code{.pseudo}
+ * s1 = [ col0 : {0, 1}
+ *        col1 : {2, 3, 4, 5, 6}
+ *        col2 : {"abc", "def", "ghi"} ]
+ *
+ * s2 = [ col0 : {7, 8}
+ *        col1 : {-4, -5, -6}
+ *        col2 : {"uvw", "xyz"} ]
+ *
+ * r = concatenate({s1, s2})
+ *
+ * r is now [ col0: {0, 1, 7, 8}
+ *            col1: {2, 3, 4, 5, 6, -4, -5, -6}
+ *            col2: {"abc", "def", "ghi", "uvw", "xyz"} ]
+ * @endcode
+ *
+ * @param columns Vector of structs columns to concatenate.
+ * @param stream  CUDA stream used for device memory operations and kernel launches.
+ * @param mr      Device memory resource used to allocate the returned column's device memory.
+ * @return        New column with concatenated results.
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace structs
+}  // namespace cudf
diff --git a/cpp/include/cudf/structs/struct_view.hpp b/cpp/include/cudf/structs/struct_view.hpp
new file mode 100644
index 0000000..7548370
--- /dev/null
+++ b/cpp/include/cudf/structs/struct_view.hpp
@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+/**
+ * @file
+ * @brief Class definition for cudf::struct_view.
+ */
+
+namespace cudf {
+
+/**
+ * @brief A non-owning, immutable view of device data that represents
+ * a struct with fields of arbitrary types (including primitives, lists,
+ * and other structs)
+ */
+class struct_view {};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/structs/structs_column_device_view.cuh b/cpp/include/cudf/structs/structs_column_device_view.cuh
new file mode 100644
index 0000000..7580582
--- /dev/null
+++ b/cpp/include/cudf/structs/structs_column_device_view.cuh
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/types.hpp>
+
+namespace cudf {
+
+namespace detail {
+
+/**
+ * @brief Given a column_device_view, an instance of this class provides a
+ * wrapper on this compound column for struct operations.
+ * Analogous to struct_column_view.
+ */
+class structs_column_device_view : private column_device_view {
+ public:
+  structs_column_device_view()                                  = delete;
+  ~structs_column_device_view()                                 = default;
+  structs_column_device_view(structs_column_device_view const&) = default;  ///< Copy constructor
+  structs_column_device_view(structs_column_device_view&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return The reference to this structs column
+   */
+  structs_column_device_view& operator=(structs_column_device_view const&) = default;
+
+  /**
+   * @brief Move assignment operator
+   *
+   * @return The reference to this structs column
+   */
+  structs_column_device_view& operator=(structs_column_device_view&&) = default;
+
+  /**
+   * @brief Construct a new structs column device view object from a column device view.
+   *
+   * @param underlying_ The column device view to wrap
+   */
+  CUDF_HOST_DEVICE structs_column_device_view(column_device_view const& underlying_)
+    : column_device_view(underlying_)
+  {
+#ifdef __CUDA_ARCH__
+    cudf_assert(underlying_.type().id() == type_id::STRUCT and
+                "structs_column_device_view only supports structs");
+#else
+    CUDF_EXPECTS(underlying_.type().id() == type_id::STRUCT,
+                 "structs_column_device_view only supports structs");
+#endif
+  }
+
+  using column_device_view::child;
+  using column_device_view::is_null;
+  using column_device_view::nullable;
+  using column_device_view::offset;
+  using column_device_view::size;
+
+  /**
+   * @brief Fetches the child column of the underlying struct column.
+   *
+   * @param idx The index of the child column to fetch
+   * @return The child column sliced relative to the parent's offset and size
+   */
+  [[nodiscard]] __device__ inline column_device_view get_sliced_child(size_type idx) const
+  {
+    return child(idx).slice(offset(), size());
+  }
+};
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/structs/structs_column_view.hpp b/cpp/include/cudf/structs/structs_column_view.hpp
new file mode 100644
index 0000000..6a9f289
--- /dev/null
+++ b/cpp/include/cudf/structs/structs_column_view.hpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+/**
+ * @file
+ * @brief Class definition for cudf::structs_column_view.
+ */
+
+namespace cudf {
+
+/**
+ * @addtogroup structs_classes
+ * @{
+ */
+
+/**
+ * @brief Given a column view of struct type, an instance of this class
+ * provides a wrapper on this compound column for struct operations.
+ */
+class structs_column_view : public column_view {
+ public:
+  // Foundation members:
+  structs_column_view(structs_column_view const&) = default;  ///< Copy constructor
+  structs_column_view(structs_column_view&&)      = default;  ///< Move constructor
+  ~structs_column_view()                          = default;
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return The reference to this structs column
+   */
+  structs_column_view& operator=(structs_column_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return The reference to this structs column
+   */
+  structs_column_view& operator=(structs_column_view&&) = default;
+
+  /**
+   * @brief Construct a new structs column view object from a column view.
+   *
+   * @param col The column view to wrap
+   */
+  explicit structs_column_view(column_view const& col);
+
+  /**
+   * @brief Returns the parent column.
+   *
+   * @return The parent column
+   */
+  [[nodiscard]] column_view parent() const;
+
+  using column_view::child_begin;
+  using column_view::child_end;
+  using column_view::has_nulls;
+  using column_view::null_count;
+  using column_view::null_mask;
+  using column_view::num_children;
+  using column_view::offset;
+  using column_view::size;
+
+  /**
+   * @brief Returns the internal child column, applying any offset from the root.
+   *
+   * Slice/split offset values are only stored at the root level of a struct column.
+   * So when doing computations on them, we need to apply that offset to
+   * the child columns when recursing.  Most functions operating in a recursive manner
+   * on struct columns should be using `get_sliced_child()` instead of `child()`.
+   *
+   * @throw cudf::logic error if this is an empty column
+   *
+   * @param index The index of the child column to return
+   * @param stream The stream on which to perform the operation. Uses the default CUDF
+   *        stream if none is specified.
+   * @return The child column sliced relative to the parent's offset and size
+   */
+  [[nodiscard]] column_view get_sliced_child(
+    int index, rmm::cuda_stream_view stream = cudf::get_default_stream()) const;
+};         // class structs_column_view;
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/table/experimental/row_operators.cuh b/cpp/include/cudf/table/experimental/row_operators.cuh
new file mode 100644
index 0000000..6b024d9
--- /dev/null
+++ b/cpp/include/cudf/table/experimental/row_operators.cuh
@@ -0,0 +1,1963 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/algorithm.cuh>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/hashing/detail/default_hash.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/lists/detail/dremel.hpp>
+#include <cudf/lists/list_device_view.cuh>
+#include <cudf/lists/lists_column_device_view.cuh>
+#include <cudf/sorting.hpp>
+#include <cudf/structs/structs_column_device_view.cuh>
+#include <cudf/table/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/detail/use_default.h>
+#include <thrust/equal.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/iterator_adaptor.h>
+#include <thrust/iterator/iterator_categories.h>
+#include <thrust/iterator/iterator_facade.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/swap.h>
+#include <thrust/transform_reduce.h>
+
+#include <cuda/std/tuple>
+#include <cuda/std/utility>
+
+#include <limits>
+#include <memory>
+#include <optional>
+#include <utility>
+
+namespace cudf {
+
+namespace experimental {
+
+/**
+ * @brief A map from cudf::type_id to cudf type that excludes LIST and STRUCT types.
+ *
+ * To be used with type_dispatcher in place of the default map, when it is required that STRUCT and
+ * LIST map to void. This is useful when we want to avoid recursion in a functor. For example, in
+ * element_comparator, we have a specialization for STRUCT but the type_dispatcher in it is only
+ * used to dispatch to the same functor for non-nested types. Even when we're guaranteed to not have
+ * non-nested types at that point, the compiler doesn't know this and would try to create recursive
+ * code which is very slow.
+ *
+ * Usage:
+ * @code
+ * type_dispatcher<dispatch_nested_to_void>(data_type(), functor{});
+ * @endcode
+ */
+template <cudf::type_id t>
+struct dispatch_void_if_nested {
+  /// The type to dispatch to if the type is nested
+  using type = std::conditional_t<t == type_id::STRUCT or t == type_id::LIST, void, id_to_type<t>>;
+};
+
+namespace row {
+
+enum class lhs_index_type : size_type {};
+enum class rhs_index_type : size_type {};
+
+/**
+ * @brief A counting iterator that uses strongly typed indices bound to tables.
+ *
+ * Performing lexicographic or equality comparisons between values in two
+ * tables requires the use of strongly typed indices. The strong index types
+ * `lhs_index_type` and `rhs_index_type` ensure that index values are bound to
+ * the correct table, regardless of the order in which these indices are
+ * provided to the call operator. This struct and its type aliases
+ * `lhs_iterator` and `rhs_iterator` provide an interface similar to a counting
+ * iterator, with strongly typed values to represent the table indices.
+ *
+ * @tparam Index The strong index type
+ */
+template <typename Index, typename Underlying = std::underlying_type_t<Index>>
+struct strong_index_iterator : public thrust::iterator_facade<strong_index_iterator<Index>,
+                                                              Index,
+                                                              thrust::use_default,
+                                                              thrust::random_access_traversal_tag,
+                                                              Index,
+                                                              Underlying> {
+  using super_t =
+    thrust::iterator_adaptor<strong_index_iterator<Index>, Index>;  ///< The base class
+
+  /**
+   * @brief Constructs a strong index iterator
+   *
+   * @param n The beginning index
+   */
+  explicit constexpr strong_index_iterator(Underlying n) : begin{n} {}
+
+  friend class thrust::iterator_core_access;  ///< Allow access to the base class
+
+ private:
+  __device__ constexpr void increment() { ++begin; }
+  __device__ constexpr void decrement() { --begin; }
+
+  __device__ constexpr void advance(Underlying n) { begin += n; }
+
+  __device__ constexpr bool equal(strong_index_iterator<Index> const& other) const noexcept
+  {
+    return begin == other.begin;
+  }
+
+  __device__ constexpr Index dereference() const noexcept { return static_cast<Index>(begin); }
+
+  __device__ constexpr Underlying distance_to(
+    strong_index_iterator<Index> const& other) const noexcept
+  {
+    return other.begin - begin;
+  }
+
+  Underlying begin{};
+};
+
+/**
+ * @brief Iterator representing indices into a left-side table.
+ */
+using lhs_iterator = strong_index_iterator<lhs_index_type>;
+
+/**
+ * @brief Iterator representing indices into a right-side table.
+ */
+using rhs_iterator = strong_index_iterator<rhs_index_type>;
+
+namespace lexicographic {
+
+/**
+ * @brief Computes a weak ordering of two values with special sorting behavior.
+ *
+ * This relational comparator functor compares physical values rather than logical
+ * elements like lists, strings, or structs. It evaluates `NaN` as not less than, equal to, or
+ * greater than other values and is IEEE-754 compliant.
+ */
+struct physical_element_comparator {
+  /**
+   * @brief Operator for relational comparisons.
+   *
+   * @param lhs First element
+   * @param rhs Second element
+   * @return Relation between elements
+   */
+  template <typename Element>
+  __device__ constexpr weak_ordering operator()(Element const lhs, Element const rhs) const noexcept
+  {
+    return detail::compare_elements(lhs, rhs);
+  }
+};
+
+/**
+ * @brief Relational comparator functor that compares physical values rather than logical
+ * elements like lists, strings, or structs. It evaluates `NaN` as equivalent to other `NaN`s and
+ * greater than all other values.
+ */
+struct sorting_physical_element_comparator {
+  /**
+   * @brief Operator for relational comparison of non-floating point values.
+   *
+   * @param lhs First element
+   * @param rhs Second element
+   * @return Relation between elements
+   */
+  template <typename Element, CUDF_ENABLE_IF(not std::is_floating_point_v<Element>)>
+  __device__ constexpr weak_ordering operator()(Element const lhs, Element const rhs) const noexcept
+  {
+    return detail::compare_elements(lhs, rhs);
+  }
+
+  /**
+   * @brief Operator for relational comparison of floating point values.
+   *
+   * @param lhs First element
+   * @param rhs Second element
+   * @return Relation between elements
+   */
+  template <typename Element, CUDF_ENABLE_IF(std::is_floating_point_v<Element>)>
+  __device__ constexpr weak_ordering operator()(Element const lhs, Element const rhs) const noexcept
+  {
+    if (isnan(lhs)) {
+      return isnan(rhs) ? weak_ordering::EQUIVALENT : weak_ordering::GREATER;
+    } else if (isnan(rhs)) {
+      return weak_ordering::LESS;
+    }
+
+    return detail::compare_elements(lhs, rhs);
+  }
+};
+
+using optional_dremel_view = thrust::optional<detail::dremel_device_view const>;
+
+// The has_nested_columns template parameter of the device_row_comparator is
+// necessary to help the compiler optimize our code. Without it, the list and
+// struct view specializations are present in the code paths used for primitive
+// types, and the compiler fails to inline this nearly as well resulting in a
+// significant performance drop.  As a result, there is some minor tension in
+// the current design between the presence of this parameter and the way that
+// the Dremel data is passed around, first as a
+// std::optional<device_span<dremel_device_view>> in the
+// preprocessed_table/device_row_comparator (which is always valid when
+// has_nested_columns and is otherwise invalid) that is then unpacked to a
+// thrust::optional<dremel_device_view> at the element_comparator level (which
+// is always valid for a list column and otherwise invalid).  We cannot use an
+// additional template parameter for the element_comparator on a per-column
+// basis because we cannot conditionally define dremel_device_view member
+// variables without jumping through extra hoops with inheritance, so the
+// thrust::optional<dremel_device_view> member must be an optional rather than
+// a raw dremel_device_view.
+/**
+ * @brief Computes the lexicographic comparison between 2 rows.
+ *
+ * Lexicographic ordering is determined by:
+ * - Two rows are compared element by element.
+ * - The first mismatching element defines which row is lexicographically less
+ * or greater than the other.
+ * - If the rows are compared without mismatched elements, the rows are equivalent
+ *
+ *
+ * Lexicographic ordering is exactly equivalent to doing an alphabetical sort of
+ * two words, for example, `aac` would be *less* than (or precede) `abb`. The
+ * second letter in both words is the first non-equal letter, and `a < b`, thus
+ * `aac < abb`.
+ *
+ * @note The operator overloads in sub-class `element_comparator` are templated via the
+ *        `type_dispatcher` to help select an overload instance for each column in a table.
+ *        So, `cudf::is_nested<Element>` will return `true` if the table has nested-type columns,
+ *        but it will be a runtime error if template parameter `has_nested_columns != true`.
+ *
+ * @tparam has_nested_columns compile-time optimization for primitive types.
+ *         This template parameter is to be used by the developer by querying
+ *         `cudf::detail::has_nested_columns(input)`. `true` compiles operator
+ *         overloads for nested types, while `false` only compiles operator
+ *         overloads for primitive types.
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ * @tparam PhysicalElementComparator A relational comparator functor that compares individual values
+ * rather than logical elements, defaults to `NaN` aware relational comparator that evaluates `NaN`
+ * as greater than all other values.
+ */
+template <bool has_nested_columns,
+          typename Nullate,
+          typename PhysicalElementComparator = sorting_physical_element_comparator>
+class device_row_comparator {
+  friend class self_comparator;       ///< Allow self_comparator to access private members
+  friend class two_table_comparator;  ///< Allow two_table_comparator to access private members
+
+  /**
+   * @brief Construct a function object for performing a lexicographic
+   * comparison between the rows of two tables.
+   *
+   * @param check_nulls Indicates if any input column contains nulls.
+   * @param lhs The first table
+   * @param rhs The second table (may be the same table as `lhs`)
+   * @param depth Optional, device array the same length as a row that contains starting depths of
+   * columns if they're nested, and 0 otherwise.
+   * @param column_order Optional, device array the same length as a row that indicates the desired
+   * ascending/descending order of each column in a row. If `nullopt`, it is assumed all columns are
+   * sorted in ascending order.
+   * @param null_precedence Optional, device array the same length as a row and indicates how null
+   * values compare to all other for every column. If `nullopt`, then null precedence would be
+   * `null_order::BEFORE` for all columns.
+   * @param comparator Physical element relational comparison functor.
+   */
+  device_row_comparator(Nullate check_nulls,
+                        table_device_view lhs,
+                        table_device_view rhs,
+                        device_span<detail::dremel_device_view const> l_dremel_device_views,
+                        device_span<detail::dremel_device_view const> r_dremel_device_views,
+                        std::optional<device_span<int const>> depth                  = std::nullopt,
+                        std::optional<device_span<order const>> column_order         = std::nullopt,
+                        std::optional<device_span<null_order const>> null_precedence = std::nullopt,
+                        PhysicalElementComparator comparator                         = {}) noexcept
+    : _lhs{lhs},
+      _rhs{rhs},
+      _l_dremel(l_dremel_device_views),
+      _r_dremel(r_dremel_device_views),
+      _check_nulls{check_nulls},
+      _depth{depth},
+      _column_order{column_order},
+      _null_precedence{null_precedence},
+      _comparator{comparator}
+  {
+  }
+
+  /**
+   * @brief Performs a relational comparison between two elements in two columns.
+   */
+  class element_comparator {
+   public:
+    /**
+     * @brief Construct type-dispatched function object for performing a
+     * relational comparison between two elements.
+     *
+     * @note `lhs` and `rhs` may be the same.
+     *
+     * @param check_nulls Indicates if either input column contains nulls.
+     * @param lhs The column containing the first element
+     * @param rhs The column containing the second element (may be the same as lhs)
+     * @param null_precedence Indicates how null values are ordered with other values
+     * @param depth The depth of the column if part of a nested column @see
+     * preprocessed_table::depths
+     * @param comparator Physical element relational comparison functor.
+     */
+    __device__ element_comparator(Nullate check_nulls,
+                                  column_device_view lhs,
+                                  column_device_view rhs,
+                                  null_order null_precedence                = null_order::BEFORE,
+                                  int depth                                 = 0,
+                                  PhysicalElementComparator comparator      = {},
+                                  optional_dremel_view l_dremel_device_view = {},
+                                  optional_dremel_view r_dremel_device_view = {})
+      : _lhs{lhs},
+        _rhs{rhs},
+        _check_nulls{check_nulls},
+        _null_precedence{null_precedence},
+        _depth{depth},
+        _l_dremel_device_view{l_dremel_device_view},
+        _r_dremel_device_view{r_dremel_device_view},
+        _comparator{comparator}
+    {
+    }
+
+    /**
+     * @brief Performs a relational comparison between the specified elements
+     *
+     * @param lhs_element_index The index of the first element
+     * @param rhs_element_index The index of the second element
+     * @return Indicates the relationship between the elements in the `lhs` and `rhs` columns, along
+     * with the depth at which a null value was encountered.
+     */
+    template <typename Element,
+              CUDF_ENABLE_IF(cudf::is_relationally_comparable<Element, Element>())>
+    __device__ cuda::std::pair<weak_ordering, int> operator()(
+      size_type const lhs_element_index, size_type const rhs_element_index) const noexcept
+    {
+      if (_check_nulls) {
+        bool const lhs_is_null{_lhs.is_null(lhs_element_index)};
+        bool const rhs_is_null{_rhs.is_null(rhs_element_index)};
+
+        if (lhs_is_null or rhs_is_null) {  // at least one is null
+          return cuda::std::pair(null_compare(lhs_is_null, rhs_is_null, _null_precedence), _depth);
+        }
+      }
+
+      return cuda::std::pair(_comparator(_lhs.element<Element>(lhs_element_index),
+                                         _rhs.element<Element>(rhs_element_index)),
+                             std::numeric_limits<int>::max());
+    }
+
+    template <typename Element,
+              CUDF_ENABLE_IF(not cudf::is_relationally_comparable<Element, Element>() and
+                             (not has_nested_columns or not cudf::is_nested<Element>()))>
+    __device__ cuda::std::pair<weak_ordering, int> operator()(size_type const,
+                                                              size_type const) const noexcept
+    {
+      CUDF_UNREACHABLE("Attempted to compare elements of uncomparable types.");
+    }
+
+    template <typename Element,
+              CUDF_ENABLE_IF(has_nested_columns and std::is_same_v<Element, cudf::struct_view>)>
+    __device__ cuda::std::pair<weak_ordering, int> operator()(
+      size_type const lhs_element_index, size_type const rhs_element_index) const noexcept
+    {
+      column_device_view lcol = _lhs;
+      column_device_view rcol = _rhs;
+      int depth               = _depth;
+      while (lcol.type().id() == type_id::STRUCT) {
+        bool const lhs_is_null{lcol.is_null(lhs_element_index)};
+        bool const rhs_is_null{rcol.is_null(rhs_element_index)};
+
+        if (lhs_is_null or rhs_is_null) {  // at least one is null
+          weak_ordering state = null_compare(lhs_is_null, rhs_is_null, _null_precedence);
+          return cuda::std::pair(state, depth);
+        }
+
+        if (lcol.num_child_columns() == 0) {
+          return cuda::std::pair(weak_ordering::EQUIVALENT, std::numeric_limits<int>::max());
+        }
+
+        // Non-empty structs have been modified to only have 1 child when using this.
+        lcol = detail::structs_column_device_view(lcol).get_sliced_child(0);
+        rcol = detail::structs_column_device_view(rcol).get_sliced_child(0);
+        ++depth;
+      }
+
+      return cudf::type_dispatcher<dispatch_void_if_nested>(
+        lcol.type(),
+        element_comparator{_check_nulls, lcol, rcol, _null_precedence, depth, _comparator},
+        lhs_element_index,
+        rhs_element_index);
+    }
+
+    template <typename Element,
+              CUDF_ENABLE_IF(has_nested_columns and std::is_same_v<Element, cudf::list_view>)>
+    __device__ cuda::std::pair<weak_ordering, int> operator()(size_type lhs_element_index,
+                                                              size_type rhs_element_index)
+    {
+      // only order top-NULLs according to null_order
+      auto const is_l_row_null = _lhs.is_null(lhs_element_index);
+      auto const is_r_row_null = _rhs.is_null(rhs_element_index);
+      if (is_l_row_null || is_r_row_null) {
+        return cuda::std::pair(null_compare(is_l_row_null, is_r_row_null, _null_precedence),
+                               _depth);
+      }
+
+      // These are all the values from the Dremel encoding.
+      auto const l_max_def_level = _l_dremel_device_view->max_def_level;
+      auto const r_max_def_level = _r_dremel_device_view->max_def_level;
+      auto const l_def_levels    = _l_dremel_device_view->def_levels;
+      auto const r_def_levels    = _r_dremel_device_view->def_levels;
+      auto const l_rep_levels    = _l_dremel_device_view->rep_levels;
+      auto const r_rep_levels    = _r_dremel_device_view->rep_levels;
+
+      // Traverse the nested list hierarchy to get a column device view
+      // pointing to the underlying child data.
+      column_device_view lcol = _lhs.slice(lhs_element_index, 1);
+      column_device_view rcol = _rhs.slice(rhs_element_index, 1);
+
+      while (lcol.type().id() == type_id::LIST) {
+        lcol = detail::lists_column_device_view(lcol).get_sliced_child();
+        rcol = detail::lists_column_device_view(rcol).get_sliced_child();
+      }
+
+      // These start and end values indicate the start and end points of all
+      // the elements of the lists in the current list element
+      // (`[lhs|rhs]_element_index`) that we are comparing.
+      auto const l_offsets = _l_dremel_device_view->offsets;
+      auto const r_offsets = _r_dremel_device_view->offsets;
+      auto l_start         = l_offsets[lhs_element_index];
+      auto l_end           = l_offsets[lhs_element_index + 1];
+      auto r_start         = r_offsets[rhs_element_index];
+      auto r_end           = r_offsets[rhs_element_index + 1];
+
+      // This comparator will be used to compare leaf (non-nested) data types.
+      auto comparator =
+        element_comparator{_check_nulls, lcol, rcol, _null_precedence, _depth, _comparator};
+
+      // Loop over each element in the encoding. Note that this includes nulls
+      // and empty lists, so not every index corresponds to an actual element
+      // in the child column. The element_index is used to keep track of the current
+      // child element that we're actually comparing.
+      for (int l_dremel_index = l_start, r_dremel_index = r_start, element_index = 0;
+           l_dremel_index < l_end and r_dremel_index < r_end;
+           ++l_dremel_index, ++r_dremel_index) {
+        auto const l_rep_level = l_rep_levels[l_dremel_index];
+        auto const r_rep_level = r_rep_levels[r_dremel_index];
+
+        // early exit for smaller sub-list
+        if (l_rep_level != r_rep_level) {
+          // the lower repetition level is a smaller sub-list
+          return l_rep_level < r_rep_level ? cuda::std::pair(weak_ordering::LESS, _depth)
+                                           : cuda::std::pair(weak_ordering::GREATER, _depth);
+        }
+
+        // only compare if left and right are at same nesting level
+        auto const l_def_level = l_def_levels[l_dremel_index];
+        auto const r_def_level = r_def_levels[r_dremel_index];
+
+        // either left or right are empty or NULLs of arbitrary nesting
+        if (l_def_level < l_max_def_level || r_def_level < r_max_def_level) {
+          // in the fully unraveled version of the list column, only the
+          // most nested NULLs and leafs are present
+          // In this rare condition that we get to the most nested NULL, we increment
+          // element_index because either both rows have a deeply nested NULL at the
+          // same position, and we'll "continue" in our iteration, or we will early
+          // exit if only one of the rows has a deeply nested NULL
+          if ((lcol.nullable() and l_def_levels[l_dremel_index] == l_max_def_level - 1) or
+              (rcol.nullable() and r_def_levels[r_dremel_index] == r_max_def_level - 1)) {
+            ++element_index;
+          }
+          if (l_def_level == r_def_level) { continue; }
+          // We require [] < [NULL] < [leaf] for nested nulls.
+          // The null_precedence only affects top level nulls.
+          return l_def_level < r_def_level ? cuda::std::pair(weak_ordering::LESS, _depth)
+                                           : cuda::std::pair(weak_ordering::GREATER, _depth);
+        }
+
+        // finally, compare leaf to leaf
+        weak_ordering state{weak_ordering::EQUIVALENT};
+        int last_null_depth                    = _depth;
+        cuda::std::tie(state, last_null_depth) = cudf::type_dispatcher<dispatch_void_if_nested>(
+          lcol.type(), comparator, element_index, element_index);
+        if (state != weak_ordering::EQUIVALENT) { return cuda::std::pair(state, _depth); }
+        ++element_index;
+      }
+
+      // If we have reached this stage, we know that definition levels,
+      // repetition levels, and actual elements are identical in both list
+      // columns up to the `min(l_end - l_start, r_end - r_start)` element of
+      // the Dremel encoding. However, two lists can only compare equivalent if
+      // they are of the same length. Otherwise, the shorter of the two is less
+      // than the longer. This final check determines the appropriate resulting
+      // ordering by checking how many total elements each list is composed of.
+      return cuda::std::pair(detail::compare_elements(l_end - l_start, r_end - r_start), _depth);
+    }
+
+   private:
+    column_device_view const _lhs;
+    column_device_view const _rhs;
+    Nullate const _check_nulls;
+    null_order const _null_precedence;
+    int const _depth;
+    optional_dremel_view _l_dremel_device_view;
+    optional_dremel_view _r_dremel_device_view;
+    PhysicalElementComparator const _comparator;
+  };
+
+ public:
+  /**
+   * @brief Checks whether the row at `lhs_index` in the `lhs` table compares
+   * lexicographically less, greater, or equivalent to the row at `rhs_index` in the `rhs` table.
+   *
+   * @param lhs_index The index of the row in the `lhs` table to examine
+   * @param rhs_index The index of the row in the `rhs` table to examine
+   * @return weak ordering comparison of the row in the `lhs` table relative to the row in the `rhs`
+   * table
+   */
+  __device__ constexpr weak_ordering operator()(size_type const lhs_index,
+                                                size_type const rhs_index) const noexcept
+  {
+    int last_null_depth = std::numeric_limits<int>::max();
+    size_type list_column_index{-1};
+    for (size_type i = 0; i < _lhs.num_columns(); ++i) {
+      if (_lhs.column(i).type().id() == type_id::LIST) { ++list_column_index; }
+
+      int const depth = _depth.has_value() ? (*_depth)[i] : 0;
+      if (depth > last_null_depth) { continue; }
+
+      bool const ascending =
+        _column_order.has_value() ? (*_column_order)[i] == order::ASCENDING : true;
+
+      null_order const null_precedence =
+        _null_precedence.has_value() ? (*_null_precedence)[i] : null_order::BEFORE;
+
+      // TODO: At what point do we verify that the columns of lhs and rhs are
+      // all of the same types? I assume that it's already happened before
+      // here, otherwise the current code would be failing.
+      auto const [l_dremel_i, r_dremel_i] =
+        _lhs.column(i).type().id() == type_id::LIST
+          ? std::make_tuple(optional_dremel_view(_l_dremel[list_column_index]),
+                            optional_dremel_view(_r_dremel[list_column_index]))
+          : std::make_tuple(optional_dremel_view{}, optional_dremel_view{});
+
+      auto element_comp = element_comparator{_check_nulls,
+                                             _lhs.column(i),
+                                             _rhs.column(i),
+                                             null_precedence,
+                                             depth,
+                                             _comparator,
+                                             l_dremel_i,
+                                             r_dremel_i};
+
+      weak_ordering state;
+      cuda::std::tie(state, last_null_depth) =
+        cudf::type_dispatcher(_lhs.column(i).type(), element_comp, lhs_index, rhs_index);
+
+      if (state == weak_ordering::EQUIVALENT) { continue; }
+
+      return ascending
+               ? state
+               : (state == weak_ordering::GREATER ? weak_ordering::LESS : weak_ordering::GREATER);
+    }
+    return weak_ordering::EQUIVALENT;
+  }
+
+ private:
+  table_device_view const _lhs;
+  table_device_view const _rhs;
+  device_span<detail::dremel_device_view const> const _l_dremel;
+  device_span<detail::dremel_device_view const> const _r_dremel;
+  Nullate const _check_nulls;
+  std::optional<device_span<int const>> const _depth;
+  std::optional<device_span<order const>> const _column_order;
+  std::optional<device_span<null_order const>> const _null_precedence;
+  PhysicalElementComparator const _comparator;
+};  // class device_row_comparator
+
+/**
+ * @brief Wraps and interprets the result of templated Comparator that returns a weak_ordering.
+ * Returns true if the weak_ordering matches any of the templated values.
+ *
+ * Note that this should never be used with only `weak_ordering::EQUIVALENT`.
+ * An equality comparator should be used instead for optimal performance.
+ *
+ * @tparam Comparator generic comparator that returns a weak_ordering.
+ * @tparam values weak_ordering parameter pack of orderings to interpret as true
+ */
+template <typename Comparator, weak_ordering... values>
+struct weak_ordering_comparator_impl {
+  static_assert(not((weak_ordering::EQUIVALENT == values) && ...),
+                "weak_ordering_comparator should not be used for pure equality comparisons. The "
+                "`row_equality_comparator` should be used instead");
+
+  template <typename LhsType, typename RhsType>
+  __device__ constexpr bool operator()(LhsType const lhs_index,
+                                       RhsType const rhs_index) const noexcept
+  {
+    weak_ordering const result = comparator(lhs_index, rhs_index);
+    return ((result == values) || ...);
+  }
+  Comparator const comparator;
+};
+
+/**
+ * @brief Wraps and interprets the result of device_row_comparator, true if the result is
+ * weak_ordering::LESS meaning one row is lexicographically *less* than another row.
+ *
+ * @tparam Comparator generic comparator that returns a weak_ordering
+ */
+template <typename Comparator>
+struct less_comparator : weak_ordering_comparator_impl<Comparator, weak_ordering::LESS> {
+  /**
+   * @brief Constructs a less_comparator
+   *
+   * @param comparator The comparator to wrap
+   */
+  less_comparator(Comparator const& comparator)
+    : weak_ordering_comparator_impl<Comparator, weak_ordering::LESS>{comparator}
+  {
+  }
+};
+
+/**
+ * @brief Wraps and interprets the result of device_row_comparator, true if the result is
+ * weak_ordering::LESS or weak_ordering::EQUIVALENT meaning one row is lexicographically *less* than
+ * or *equivalent* to another row.
+ *
+ * @tparam Comparator generic comparator that returns a weak_ordering
+ */
+template <typename Comparator>
+struct less_equivalent_comparator
+  : weak_ordering_comparator_impl<Comparator, weak_ordering::LESS, weak_ordering::EQUIVALENT> {
+  /**
+   * @brief Constructs a less_equivalent_comparator
+   *
+   * @param comparator The comparator to wrap
+   */
+  less_equivalent_comparator(Comparator const& comparator)
+    : weak_ordering_comparator_impl<Comparator, weak_ordering::LESS, weak_ordering::EQUIVALENT>{
+        comparator}
+  {
+  }
+};
+
+/**
+ * @brief Preprocessed table for use with lexicographical comparison
+ *
+ */
+struct preprocessed_table {
+  /// Type of table device view owner for the preprocessed table.
+  using table_device_view_owner =
+    std::invoke_result_t<decltype(table_device_view::create), table_view, rmm::cuda_stream_view>;
+
+  /**
+   * @brief Preprocess table for use with lexicographical comparison
+   *
+   * Sets up the table for use with lexicographical comparison. The resulting preprocessed table can
+   * be passed to the constructor of `lexicographic::self_comparator` or
+   * `lexicographic::two_table_comparator` to avoid preprocessing again.
+   *
+   * Note that the output of this factory function should not be used in `two_table_comparator` if
+   * the input table contains lists-of-structs. In such cases, please use the overload
+   * `preprocessed_table::create(table_view const&, table_view const&,...)` to preprocess both input
+   * tables at the same time.
+   *
+   * @param table The table to preprocess
+   * @param column_order Optional, host array the same length as a row that indicates the desired
+   *        ascending/descending order of each column in a row. If empty, it is assumed all columns
+   *        are sorted in ascending order.
+   * @param null_precedence Optional, an array having the same length as the number of columns in
+   *        the input tables that indicates how null values compare to all other. If it is empty,
+   *        the order `null_order::BEFORE` will be used for all columns.
+   * @param stream The stream to launch kernels and h->d copies on while preprocessing
+   * @return A shared pointer to a preprocessed table
+   */
+  static std::shared_ptr<preprocessed_table> create(table_view const& table,
+                                                    host_span<order const> column_order,
+                                                    host_span<null_order const> null_precedence,
+                                                    rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Preprocess tables for use with lexicographical comparison
+   *
+   * Sets up the tables for use with lexicographical comparison. The resulting preprocessed tables
+   * can be passed to the constructor of `lexicographic::self_comparator` or
+   * `lexicographic::two_table_comparator` to avoid preprocessing again.
+   *
+   * This factory function performs some extra operations to guarantee that its output can be used
+   * in `two_table_comparator` for all cases.
+   *
+   * @param lhs The lhs table to preprocess
+   * @param rhs The rhs table to preprocess
+   * @param column_order Optional, host array the same length as a row that indicates the desired
+   *        ascending/descending order of each column in a row. If empty, it is assumed all columns
+   *        are sorted in ascending order.
+   * @param null_precedence Optional, an array having the same length as the number of columns in
+   *        the input tables that indicates how null values compare to all other. If it is empty,
+   *        the order `null_order::BEFORE` will be used for all columns.
+   * @param stream The stream to launch kernels and h->d copies on while preprocessing
+   * @return A pair of shared pointers to the preprocessed tables
+   */
+  static std::pair<std::shared_ptr<preprocessed_table>, std::shared_ptr<preprocessed_table>> create(
+    table_view const& lhs,
+    table_view const& rhs,
+    host_span<order const> column_order,
+    host_span<null_order const> null_precedence,
+    rmm::cuda_stream_view stream);
+
+ private:
+  friend class self_comparator;       ///< Allow self_comparator to access private members
+  friend class two_table_comparator;  ///< Allow two_table_comparator to access private members
+
+  /**
+   * @brief Create the output preprocessed table from intermediate preprocessing results
+   *
+   * @param preprocessed_input The table resulted from preprocessing
+   * @param verticalized_col_depths The depths of each column resulting from decomposing struct
+   *        columns in the original input table
+   * @param transformed_columns Store the intermediate columns generated from transforming
+   *        nested children columns into integers columns using `cudf::rank()`
+   * @param column_order Optional, host array the same length as a row that indicates the desired
+   *        ascending/descending order of each column in a row. If empty, it is assumed all columns
+   *        are sorted in ascending order.
+   * @param null_precedence Optional, an array having the same length as the number of columns in
+   *        the input tables that indicates how null values compare to all other. If it is empty,
+   *        the order `null_order::BEFORE` will be used for all columns.
+   * @param has_ranked_children Flag indicating if the input table was preprocessed to transform
+   *        any nested child column into an integer column using `cudf::rank`
+   * @param stream The stream to launch kernels and h->d copies on while preprocessing
+   * @return A shared pointer to a preprocessed table
+   */
+  static std::shared_ptr<preprocessed_table> create(
+    table_view const& preprocessed_input,
+    std::vector<int>&& verticalized_col_depths,
+    std::vector<std::unique_ptr<column>>&& transformed_columns,
+    host_span<order const> column_order,
+    host_span<null_order const> null_precedence,
+    bool has_ranked_children,
+    rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Construct a preprocessed table for use with lexicographical comparison
+   *
+   * Sets up the table for use with lexicographical comparison. The resulting preprocessed table can
+   * be passed to the constructor of `lexicographic::self_comparator` to avoid preprocessing again.
+   *
+   * @param table The table to preprocess
+   * @param column_order Optional, device array the same length as a row that indicates the desired
+   *        ascending/descending order of each column in a row. If empty, it is assumed all columns
+   *        are sorted in ascending order.
+   * @param null_precedence Optional, device array the same length as a row and indicates how null
+   *        values compare to all other for every column. If it is nullptr, then null precedence
+   *        would be `null_order::BEFORE` for all columns.
+   * @param depths The depths of each column resulting from decomposing struct columns.
+   * @param dremel_data The dremel data for each list column. The length of this object is the
+   *        number of list columns in the table.
+   * @param dremel_device_views Device views into the dremel_data structs contained in the
+   *        `dremel_data` parameter. For columns that are not list columns, this uvector will should
+   *        contain an empty `dremel_device_view`. As such, this uvector has as many elements as
+   *        there are columns in the table (unlike the `dremel_data` parameter, which is only as
+   *        long as the number of list columns).
+   * @param transformed_columns Store the intermediate columns generated from transforming
+   *        nested children columns into integers columns using `cudf::rank()`
+   * @param has_ranked_children Flag indicating if the input table was preprocessed to transform
+   *        any lists-of-structs column having floating-point children using `cudf::rank`
+   */
+  preprocessed_table(table_device_view_owner&& table,
+                     rmm::device_uvector<order>&& column_order,
+                     rmm::device_uvector<null_order>&& null_precedence,
+                     rmm::device_uvector<size_type>&& depths,
+                     std::vector<detail::dremel_data>&& dremel_data,
+                     rmm::device_uvector<detail::dremel_device_view>&& dremel_device_views,
+                     std::vector<std::unique_ptr<column>>&& transformed_columns,
+                     bool has_ranked_children);
+
+  preprocessed_table(table_device_view_owner&& table,
+                     rmm::device_uvector<order>&& column_order,
+                     rmm::device_uvector<null_order>&& null_precedence,
+                     rmm::device_uvector<size_type>&& depths,
+                     std::vector<std::unique_ptr<column>>&& transformed_columns,
+                     bool has_ranked_children);
+
+  /**
+   * @brief Implicit conversion operator to a `table_device_view` of the preprocessed table.
+   *
+   * @return table_device_view
+   */
+  operator table_device_view() { return *_t; }
+
+  /**
+   * @brief Get a device array containing the desired order of each column in the preprocessed table
+   *
+   * @return Device array containing respective column orders. If no explicit column orders were
+   * specified during the creation of this object then this will be `nullopt`.
+   */
+  [[nodiscard]] std::optional<device_span<order const>> column_order() const
+  {
+    return _column_order.size() ? std::optional<device_span<order const>>(_column_order)
+                                : std::nullopt;
+  }
+
+  /**
+   * @brief Get a device array containing the desired null precedence of each column in the
+   * preprocessed table
+   *
+   * @return Device array containing respective column null precedence. If no explicit column null
+   * precedences were specified during the creation of this object then this will be `nullopt`.
+   */
+  [[nodiscard]] std::optional<device_span<null_order const>> null_precedence() const
+  {
+    return _null_precedence.size() ? std::optional<device_span<null_order const>>(_null_precedence)
+                                   : std::nullopt;
+  }
+
+  /**
+   * @brief Get a device array containing the depth of each column in the preprocessed table
+   *
+   * @see struct_linearize()
+   *
+   * @return std::optional<device_span<int const>> Device array containing respective column depths.
+   * If there are no nested columns in the table then this will be `nullopt`.
+   */
+  [[nodiscard]] std::optional<device_span<int const>> depths() const
+  {
+    return _depths.size() ? std::optional<device_span<int const>>(_depths) : std::nullopt;
+  }
+
+  [[nodiscard]] device_span<detail::dremel_device_view const> dremel_device_views() const
+  {
+    if (_dremel_device_views.has_value()) {
+      return device_span<detail::dremel_device_view const>(*_dremel_device_views);
+    } else {
+      return {};
+    }
+  }
+
+  template <typename PhysicalElementComparator>
+  void check_physical_element_comparator()
+  {
+    if constexpr (!std::is_same_v<PhysicalElementComparator, sorting_physical_element_comparator>) {
+      CUDF_EXPECTS(!_has_ranked_children,
+                   "The input table has nested type children and they were transformed using a "
+                   "different type of physical element comparator.");
+    }
+  }
+
+ private:
+  table_device_view_owner const _t;
+  rmm::device_uvector<order> const _column_order;
+  rmm::device_uvector<null_order> const _null_precedence;
+  rmm::device_uvector<size_type> const _depths;
+
+  // Dremel encoding of list columns used for the comparison algorithm
+  std::optional<std::vector<detail::dremel_data>> _dremel_data;
+  std::optional<rmm::device_uvector<detail::dremel_device_view>> _dremel_device_views;
+
+  // Intermediate columns generated from transforming nested children columns into
+  // integers columns using `cudf::rank()`, need to be kept alive.
+  std::vector<std::unique_ptr<column>> _transformed_columns;
+
+  // Flag to record if the input table was preprocessed to transform any nested children column(s)
+  // into integer column(s) using `cudf::rank`.
+  bool const _has_ranked_children;
+};
+
+/**
+ * @brief An owning object that can be used to lexicographically compare two rows of the same table
+ *
+ * This class can take a table_view and preprocess certain columns to allow for lexicographical
+ * comparison. The preprocessed table and temporary data required for the comparison are created and
+ * owned by this class.
+ *
+ * Alternatively, `self_comparator` can be constructed from an existing
+ * `shared_ptr<preprocessed_table>` when sharing the same table among multiple comparators.
+ *
+ * This class can then provide a functor object that can used on the device.
+ * The object of this class must outlive the usage of the device functor.
+ */
+class self_comparator {
+ public:
+  /**
+   * @brief Construct an owning object for performing a lexicographic comparison between two rows of
+   * the same table.
+   *
+   * @param t The table to compare
+   * @param column_order Optional, host array the same length as a row that indicates the desired
+   *        ascending/descending order of each column in a row. If empty, it is assumed all columns
+   *        are sorted in ascending order.
+   * @param null_precedence Optional, device array the same length as a row and indicates how null
+   *        values compare to all other for every column. If empty, then null precedence would be
+   *        `null_order::BEFORE` for all columns.
+   * @param stream The stream to construct this object on. Not the stream that will be used for
+   *        comparisons using this object.
+   */
+  self_comparator(table_view const& t,
+                  host_span<order const> column_order         = {},
+                  host_span<null_order const> null_precedence = {},
+                  rmm::cuda_stream_view stream                = cudf::get_default_stream())
+    : d_t{preprocessed_table::create(t, column_order, null_precedence, stream)}
+  {
+  }
+
+  /**
+   * @brief Construct an owning object for performing a lexicographic comparison between two rows of
+   * the same preprocessed table.
+   *
+   * This constructor allows independently constructing a `preprocessed_table` and sharing it among
+   * multiple comparators.
+   *
+   * @param t A table preprocessed for lexicographic comparison
+   */
+  self_comparator(std::shared_ptr<preprocessed_table> t) : d_t{std::move(t)} {}
+
+  /**
+   * @brief Return the binary operator for comparing rows in the table.
+   *
+   * Returns a binary callable, `F`, with signature `bool F(size_type, size_type)`.
+   *
+   * `F(i,j)` returns true if and only if row `i` compares lexicographically less than row `j`.
+   *
+   * @note The operator overloads in sub-class `element_comparator` are templated via the
+   *       `type_dispatcher` to help select an overload instance for each column in a table.
+   *       So, `cudf::is_nested<Element>` will return `true` if the table has nested-type columns,
+   *       but it will be a runtime error if template parameter `has_nested_columns != true`.
+   *
+   * @tparam has_nested_columns compile-time optimization for primitive types.
+   *         This template parameter is to be used by the developer by querying
+   *         `cudf::detail::has_nested_columns(input)`. `true` compiles operator
+   *         overloads for nested types, while `false` only compiles operator
+   *         overloads for primitive types.
+   * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+   * @tparam PhysicalElementComparator A relational comparator functor that compares individual
+   *         values rather than logical elements, defaults to `NaN` aware relational comparator
+   *         that evaluates `NaN` as greater than all other values.
+   * @throw cudf::logic_error if the input table was preprocessed to transform any nested children
+   *        columns into integer columns but `PhysicalElementComparator` is not
+   *        `sorting_physical_element_comparator`.
+   * @param nullate Indicates if any input column contains nulls.
+   * @param comparator Physical element relational comparison functor.
+   * @return A binary callable object.
+   */
+  template <bool has_nested_columns,
+            typename Nullate,
+            typename PhysicalElementComparator = sorting_physical_element_comparator>
+  auto less(Nullate nullate = {}, PhysicalElementComparator comparator = {}) const
+  {
+    d_t->check_physical_element_comparator<PhysicalElementComparator>();
+
+    return less_comparator{
+      device_row_comparator<has_nested_columns, Nullate, PhysicalElementComparator>{
+        nullate,
+        *d_t,
+        *d_t,
+        d_t->dremel_device_views(),
+        d_t->dremel_device_views(),
+        d_t->depths(),
+        d_t->column_order(),
+        d_t->null_precedence(),
+        comparator}};
+  }
+
+  /// @copydoc less()
+  template <bool has_nested_columns,
+            typename Nullate,
+            typename PhysicalElementComparator = sorting_physical_element_comparator>
+  auto less_equivalent(Nullate nullate = {}, PhysicalElementComparator comparator = {}) const
+  {
+    d_t->check_physical_element_comparator<PhysicalElementComparator>();
+
+    return less_equivalent_comparator{
+      device_row_comparator<has_nested_columns, Nullate, PhysicalElementComparator>{
+        nullate,
+        *d_t,
+        *d_t,
+        d_t->dremel_device_views(),
+        d_t->dremel_device_views(),
+        d_t->depths(),
+        d_t->column_order(),
+        d_t->null_precedence(),
+        comparator}};
+  }
+
+ private:
+  std::shared_ptr<preprocessed_table> d_t;
+};
+
+// @cond
+template <typename Comparator>
+struct strong_index_comparator_adapter {
+  strong_index_comparator_adapter(Comparator const& comparator) : comparator{comparator} {}
+
+  __device__ constexpr weak_ordering operator()(lhs_index_type const lhs_index,
+                                                rhs_index_type const rhs_index) const noexcept
+  {
+    return comparator(static_cast<cudf::size_type>(lhs_index),
+                      static_cast<cudf::size_type>(rhs_index));
+  }
+
+  __device__ constexpr weak_ordering operator()(rhs_index_type const rhs_index,
+                                                lhs_index_type const lhs_index) const noexcept
+  {
+    auto const left_right_ordering =
+      comparator(static_cast<cudf::size_type>(lhs_index), static_cast<cudf::size_type>(rhs_index));
+
+    // Invert less/greater values to reflect right to left ordering
+    if (left_right_ordering == weak_ordering::LESS) {
+      return weak_ordering::GREATER;
+    } else if (left_right_ordering == weak_ordering::GREATER) {
+      return weak_ordering::LESS;
+    }
+    return weak_ordering::EQUIVALENT;
+  }
+
+  Comparator const comparator;
+};
+// @endcond
+
+/**
+ * @brief An owning object that can be used to lexicographically compare rows of two different
+ * tables
+ *
+ * This class takes two table_views and preprocesses certain columns to allow for lexicographical
+ * comparison. The preprocessed table and temporary data required for the comparison are created and
+ * owned by this class.
+ *
+ * Alternatively, `two_table_comparator` can be constructed from two existing
+ * `shared_ptr<preprocessed_table>`s when sharing the same tables among multiple comparators.
+ *
+ * This class can then provide a functor object that can used on the device.
+ * The object of this class must outlive the usage of the device functor.
+ */
+class two_table_comparator {
+ public:
+  /**
+   * @brief Construct an owning object for performing a lexicographic comparison between rows of
+   * two different tables.
+   *
+   * The left and right table are expected to have the same number of columns
+   * and data types for each column.
+   *
+   * @param left The left table to compare
+   * @param right The right table to compare
+   * @param column_order Optional, host array the same length as a row that indicates the desired
+   *        ascending/descending order of each column in a row. If empty, it is assumed all columns
+   *        are sorted in ascending order.
+   * @param null_precedence Optional, device array the same length as a row and indicates how null
+   *        values compare to all other for every column. If empty, then null precedence would be
+   *        `null_order::BEFORE` for all columns.
+   * @param stream The stream to construct this object on. Not the stream that will be used for
+   *        comparisons using this object.
+   */
+  two_table_comparator(table_view const& left,
+                       table_view const& right,
+                       host_span<order const> column_order         = {},
+                       host_span<null_order const> null_precedence = {},
+                       rmm::cuda_stream_view stream                = cudf::get_default_stream());
+
+  /**
+   * @brief Construct an owning object for performing a lexicographic comparison between two rows of
+   * the same preprocessed table.
+   *
+   * This constructor allows independently constructing a `preprocessed_table` and sharing it among
+   * multiple comparators.
+   *
+   * The preprocessed_table(s) should have been pre-generated together using the factory function
+   * `preprocessed_table::create(table_view const&, table_view const&)`. Otherwise, the comparison
+   * results between two tables may be incorrect.
+   *
+   * @param left A table preprocessed for lexicographic comparison
+   * @param right A table preprocessed for lexicographic comparison
+   */
+  two_table_comparator(std::shared_ptr<preprocessed_table> left,
+                       std::shared_ptr<preprocessed_table> right)
+    : d_left_table{std::move(left)}, d_right_table{std::move(right)}
+  {
+  }
+
+  /**
+   * @brief Return the binary operator for comparing rows in the table.
+   *
+   * Returns a binary callable, `F`, with signatures
+   * `bool F(lhs_index_type, rhs_index_type)` and
+   * `bool F(rhs_index_type, lhs_index_type)`.
+   *
+   * `F(lhs_index_type i, rhs_index_type j)` returns true if and only if row
+   * `i` of the left table compares lexicographically less than row `j` of the
+   * right table.
+   *
+   * Similarly, `F(rhs_index_type i, lhs_index_type j)` returns true if and
+   * only if row `i` of the right table compares lexicographically less than row
+   * `j` of the left table.
+   *
+   * @note The operator overloads in sub-class `element_comparator` are templated via the
+   *       `type_dispatcher` to help select an overload instance for each column in a table.
+   *       So, `cudf::is_nested<Element>` will return `true` if the table has nested-type columns,
+   *       but it will be a runtime error if template parameter `has_nested_columns != true`.
+   *
+   * @tparam has_nested_columns compile-time optimization for primitive types.
+   *         This template parameter is to be used by the developer by querying
+   *         `cudf::detail::has_nested_columns(input)`. `true` compiles operator
+   *         overloads for nested types, while `false` only compiles operator
+   *         overloads for primitive types.
+   * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+   * @tparam PhysicalElementComparator A relational comparator functor that compares individual
+   *         values rather than logical elements, defaults to `NaN` aware relational comparator
+   *         that evaluates `NaN` as greater than all other values.
+   * @throw cudf::logic_error if the input tables were preprocessed to transform any nested children
+   *        columns into integer columns but `PhysicalElementComparator` is not
+   *        `sorting_physical_element_comparator`.
+   * @param nullate Indicates if any input column contains nulls.
+   * @param comparator Physical element relational comparison functor.
+   * @return A binary callable object.
+   */
+  template <bool has_nested_columns,
+            typename Nullate,
+            typename PhysicalElementComparator = sorting_physical_element_comparator>
+  auto less(Nullate nullate = {}, PhysicalElementComparator comparator = {}) const
+  {
+    d_left_table->check_physical_element_comparator<PhysicalElementComparator>();
+    d_right_table->check_physical_element_comparator<PhysicalElementComparator>();
+
+    return less_comparator{strong_index_comparator_adapter{
+      device_row_comparator<has_nested_columns, Nullate, PhysicalElementComparator>{
+        nullate,
+        *d_left_table,
+        *d_right_table,
+        d_left_table->dremel_device_views(),
+        d_right_table->dremel_device_views(),
+        d_left_table->depths(),
+        d_left_table->column_order(),
+        d_left_table->null_precedence(),
+        comparator}}};
+  }
+
+  /// @copydoc less()
+  template <bool has_nested_columns,
+            typename Nullate,
+            typename PhysicalElementComparator = sorting_physical_element_comparator>
+  auto less_equivalent(Nullate nullate = {}, PhysicalElementComparator comparator = {}) const
+  {
+    d_left_table->check_physical_element_comparator<PhysicalElementComparator>();
+    d_right_table->check_physical_element_comparator<PhysicalElementComparator>();
+
+    return less_equivalent_comparator{strong_index_comparator_adapter{
+      device_row_comparator<has_nested_columns, Nullate, PhysicalElementComparator>{
+        nullate,
+        *d_left_table,
+        *d_right_table,
+        d_left_table->dremel_device_views(),
+        d_right_table->dremel_device_views(),
+        d_left_table->depths(),
+        d_left_table->column_order(),
+        d_left_table->null_precedence(),
+        comparator}}};
+  }
+
+ private:
+  std::shared_ptr<preprocessed_table> d_left_table;
+  std::shared_ptr<preprocessed_table> d_right_table;
+};
+
+}  // namespace lexicographic
+
+namespace hash {
+class row_hasher;
+}  // namespace hash
+
+namespace equality {
+
+/**
+ * @brief Equality comparator functor that compares physical values rather than logical
+ * elements like lists, strings, or structs. It evaluates `NaN` not equal to all other values for
+ * IEEE-754 compliance.
+ */
+struct physical_equality_comparator {
+  /**
+   * @brief Operator for equality comparisons.
+   *
+   * Note that `NaN != NaN`, following IEEE-754.
+   *
+   * @param lhs First element
+   * @param rhs Second element
+   * @return `true` if `lhs == rhs` else `false`
+   */
+  template <typename Element>
+  __device__ constexpr bool operator()(Element const lhs, Element const rhs) const noexcept
+  {
+    return lhs == rhs;
+  }
+};
+
+/**
+ * @brief Equality comparator functor that compares physical values rather than logical
+ * elements like lists, strings, or structs. It evaluates `NaN` as equal to other `NaN`s.
+ */
+struct nan_equal_physical_equality_comparator {
+  /**
+   * @brief Operator for equality comparison of non-floating point values.
+   *
+   * @param lhs First element
+   * @param rhs Second element
+   * @return `true` if `lhs == rhs` else `false`
+   */
+  template <typename Element, CUDF_ENABLE_IF(not std::is_floating_point_v<Element>)>
+  __device__ constexpr bool operator()(Element const lhs, Element const rhs) const noexcept
+  {
+    return lhs == rhs;
+  }
+
+  /**
+   * @brief Operator for equality comparison of floating point values.
+   *
+   * Note that `NaN == NaN`.
+   *
+   * @param lhs First element
+   * @param rhs Second element
+   * @return `true` if `lhs` == `rhs` else `false`
+   */
+  template <typename Element, CUDF_ENABLE_IF(std::is_floating_point_v<Element>)>
+  __device__ constexpr bool operator()(Element const lhs, Element const rhs) const noexcept
+  {
+    return isnan(lhs) and isnan(rhs) ? true : lhs == rhs;
+  }
+};
+
+/**
+ * @brief Computes the equality comparison between 2 rows.
+ *
+ * Equality is determined by comparing rows element by element. The first mismatching element
+ * returns false, representing unequal rows. If the rows are compared without mismatched elements,
+ * the rows are equal.
+ *
+ * @note The operator overloads in sub-class `element_comparator` are templated via the
+ *        `type_dispatcher` to help select an overload instance for each column in a table.
+ *        So, `cudf::is_nested<Element>` will return `true` if the table has nested-type columns,
+ *        but it will be a runtime error if template parameter `has_nested_columns != true`.
+ *
+ * @tparam has_nested_columns compile-time optimization for primitive types.
+ *         This template parameter is to be used by the developer by querying
+ *         `cudf::detail::has_nested_columns(input)`. `true` compiles operator
+ *         overloads for nested types, while `false` only compiles operator
+ *         overloads for primitive types.
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ * @tparam PhysicalEqualityComparator A equality comparator functor that compares individual values
+ * rather than logical elements, defaults to a comparator for which `NaN == NaN`.
+ */
+template <bool has_nested_columns,
+          typename Nullate,
+          typename PhysicalEqualityComparator = nan_equal_physical_equality_comparator>
+class device_row_comparator {
+  friend class self_comparator;       ///< Allow self_comparator to access private members
+  friend class two_table_comparator;  ///< Allow two_table_comparator to access private members
+
+ public:
+  /**
+   * @brief Checks whether the row at `lhs_index` in the `lhs` table is equal to the row at
+   * `rhs_index` in the `rhs` table.
+   *
+   * @param lhs_index The index of the row in the `lhs` table to examine
+   * @param rhs_index The index of the row in the `rhs` table to examine
+   * @return `true` if row from the `lhs` table is equal to the row in the `rhs` table
+   */
+  __device__ constexpr bool operator()(size_type const lhs_index,
+                                       size_type const rhs_index) const noexcept
+  {
+    auto equal_elements = [=](column_device_view l, column_device_view r) {
+      return cudf::type_dispatcher(
+        l.type(),
+        element_comparator{check_nulls, l, r, nulls_are_equal, comparator},
+        lhs_index,
+        rhs_index);
+    };
+
+    return thrust::equal(thrust::seq, lhs.begin(), lhs.end(), rhs.begin(), equal_elements);
+  }
+
+ private:
+  /**
+   * @brief Construct a function object for performing equality comparison between the rows of two
+   * tables.
+   *
+   * @param check_nulls Indicates if any input column contains nulls.
+   * @param lhs The first table
+   * @param rhs The second table (may be the same table as `lhs`)
+   * @param nulls_are_equal Indicates if two null elements are treated as equivalent
+   * @param comparator Physical element equality comparison functor.
+   */
+  device_row_comparator(Nullate check_nulls,
+                        table_device_view lhs,
+                        table_device_view rhs,
+                        null_equality nulls_are_equal         = null_equality::EQUAL,
+                        PhysicalEqualityComparator comparator = {}) noexcept
+    : lhs{lhs},
+      rhs{rhs},
+      check_nulls{check_nulls},
+      nulls_are_equal{nulls_are_equal},
+      comparator{comparator}
+  {
+  }
+
+  /**
+   * @brief Performs an equality comparison between two elements in two columns.
+   */
+  class element_comparator {
+   public:
+    /**
+     * @brief Construct type-dispatched function object for comparing equality
+     * between two elements.
+     *
+     * @note `lhs` and `rhs` may be the same.
+     *
+     * @param check_nulls Indicates if either input column contains nulls.
+     * @param lhs The column containing the first element
+     * @param rhs The column containing the second element (may be the same as lhs)
+     * @param nulls_are_equal Indicates if two null elements are treated as equivalent
+     * @param comparator Physical element equality comparison functor.
+     */
+    __device__ element_comparator(Nullate check_nulls,
+                                  column_device_view lhs,
+                                  column_device_view rhs,
+                                  null_equality nulls_are_equal         = null_equality::EQUAL,
+                                  PhysicalEqualityComparator comparator = {}) noexcept
+      : lhs{lhs},
+        rhs{rhs},
+        check_nulls{check_nulls},
+        nulls_are_equal{nulls_are_equal},
+        comparator{comparator}
+    {
+    }
+
+    /**
+     * @brief Compares the specified elements for equality.
+     *
+     * @param lhs_element_index The index of the first element
+     * @param rhs_element_index The index of the second element
+     * @return True if lhs and rhs are equal or if both lhs and rhs are null and nulls are
+     * considered equal (`nulls_are_equal` == `null_equality::EQUAL`)
+     */
+    template <typename Element, CUDF_ENABLE_IF(cudf::is_equality_comparable<Element, Element>())>
+    __device__ bool operator()(size_type const lhs_element_index,
+                               size_type const rhs_element_index) const noexcept
+    {
+      if (check_nulls) {
+        bool const lhs_is_null{lhs.is_null(lhs_element_index)};
+        bool const rhs_is_null{rhs.is_null(rhs_element_index)};
+        if (lhs_is_null and rhs_is_null) {
+          return nulls_are_equal == null_equality::EQUAL;
+        } else if (lhs_is_null != rhs_is_null) {
+          return false;
+        }
+      }
+
+      return comparator(lhs.element<Element>(lhs_element_index),
+                        rhs.element<Element>(rhs_element_index));
+    }
+
+    template <typename Element,
+              CUDF_ENABLE_IF(not cudf::is_equality_comparable<Element, Element>() and
+                             (not has_nested_columns or not cudf::is_nested<Element>())),
+              typename... Args>
+    __device__ bool operator()(Args...)
+    {
+      CUDF_UNREACHABLE("Attempted to compare elements of uncomparable types.");
+    }
+
+    template <typename Element, CUDF_ENABLE_IF(has_nested_columns and cudf::is_nested<Element>())>
+    __device__ bool operator()(size_type const lhs_element_index,
+                               size_type const rhs_element_index) const noexcept
+    {
+      column_device_view lcol = lhs.slice(lhs_element_index, 1);
+      column_device_view rcol = rhs.slice(rhs_element_index, 1);
+      while (lcol.type().id() == type_id::STRUCT || lcol.type().id() == type_id::LIST) {
+        if (check_nulls) {
+          auto lvalid = detail::make_validity_iterator<true>(lcol);
+          auto rvalid = detail::make_validity_iterator<true>(rcol);
+          if (nulls_are_equal == null_equality::UNEQUAL) {
+            if (thrust::any_of(
+                  thrust::seq, lvalid, lvalid + lcol.size(), thrust::logical_not<bool>()) or
+                thrust::any_of(
+                  thrust::seq, rvalid, rvalid + rcol.size(), thrust::logical_not<bool>())) {
+              return false;
+            }
+          } else {
+            if (not thrust::equal(thrust::seq, lvalid, lvalid + lcol.size(), rvalid)) {
+              return false;
+            }
+          }
+        }
+        if (lcol.type().id() == type_id::STRUCT) {
+          if (lcol.num_child_columns() == 0) { return true; }
+          // Non-empty structs are assumed to be decomposed and contain only one child
+          lcol = detail::structs_column_device_view(lcol).get_sliced_child(0);
+          rcol = detail::structs_column_device_view(rcol).get_sliced_child(0);
+        } else if (lcol.type().id() == type_id::LIST) {
+          auto l_list_col = detail::lists_column_device_view(lcol);
+          auto r_list_col = detail::lists_column_device_view(rcol);
+
+          auto lsizes = make_list_size_iterator(l_list_col);
+          auto rsizes = make_list_size_iterator(r_list_col);
+          if (not thrust::equal(thrust::seq, lsizes, lsizes + lcol.size(), rsizes)) {
+            return false;
+          }
+
+          lcol = l_list_col.get_sliced_child();
+          rcol = r_list_col.get_sliced_child();
+          if (lcol.size() != rcol.size()) { return false; }
+        }
+      }
+
+      auto comp = column_comparator{
+        element_comparator{check_nulls, lcol, rcol, nulls_are_equal, comparator}, lcol.size()};
+      return type_dispatcher<dispatch_void_if_nested>(lcol.type(), comp);
+    }
+
+   private:
+    /**
+     * @brief Serially compare two columns for equality.
+     *
+     * When we want to get the equivalence of two columns by serially comparing all elements in
+     * one column with the corresponding elements in the other column, this saves us from type
+     * dispatching for each individual element in the range
+     */
+    struct column_comparator {
+      element_comparator const comp;
+      size_type const size;
+
+      /**
+       * @brief Serially compare two columns for equality.
+       *
+       * @return True if ALL elements compare equal, false otherwise
+       */
+      template <typename Element, CUDF_ENABLE_IF(cudf::is_equality_comparable<Element, Element>())>
+      __device__ bool operator()() const noexcept
+      {
+        return thrust::all_of(thrust::seq,
+                              thrust::make_counting_iterator(0),
+                              thrust::make_counting_iterator(0) + size,
+                              [=](auto i) { return comp.template operator()<Element>(i, i); });
+      }
+
+      template <typename Element,
+                CUDF_ENABLE_IF(not cudf::is_equality_comparable<Element, Element>()),
+                typename... Args>
+      __device__ bool operator()(Args...) const noexcept
+      {
+        CUDF_UNREACHABLE("Attempted to compare elements of uncomparable types.");
+      }
+    };
+
+    column_device_view const lhs;
+    column_device_view const rhs;
+    Nullate const check_nulls;
+    null_equality const nulls_are_equal;
+    PhysicalEqualityComparator const comparator;
+  };
+
+  table_device_view const lhs;
+  table_device_view const rhs;
+  Nullate const check_nulls;
+  null_equality const nulls_are_equal;
+  PhysicalEqualityComparator const comparator;
+};
+
+/**
+ * @brief Preprocessed table for use with row equality comparison or row hashing
+ *
+ */
+struct preprocessed_table {
+  /**
+   * @brief Factory to construct preprocessed_table for use with
+   * row equality comparison or row hashing
+   *
+   * Sets up the table for use with row equality comparison or row hashing. The resulting
+   * preprocessed table can be passed to the constructor of `equality::self_comparator` to
+   * avoid preprocessing again.
+   *
+   * @param table The table to preprocess
+   * @param stream The cuda stream to use while preprocessing.
+   * @return A preprocessed table as shared pointer
+   */
+  static std::shared_ptr<preprocessed_table> create(table_view const& table,
+                                                    rmm::cuda_stream_view stream);
+
+ private:
+  friend class self_comparator;       ///< Allow self_comparator to access private members
+  friend class two_table_comparator;  ///< Allow two_table_comparator to access private members
+  friend class hash::row_hasher;      ///< Allow row_hasher to access private members
+
+  using table_device_view_owner =
+    std::invoke_result_t<decltype(table_device_view::create), table_view, rmm::cuda_stream_view>;
+
+  preprocessed_table(table_device_view_owner&& table,
+                     std::vector<rmm::device_buffer>&& null_buffers,
+                     std::vector<std::unique_ptr<column>>&& tmp_columns)
+    : _t(std::move(table)),
+      _null_buffers(std::move(null_buffers)),
+      _tmp_columns(std::move(tmp_columns))
+  {
+  }
+
+  /**
+   * @brief Implicit conversion operator to a `table_device_view` of the preprocessed table.
+   *
+   * @return table_device_view
+   */
+  operator table_device_view() { return *_t; }
+
+  table_device_view_owner _t;
+  std::vector<rmm::device_buffer> _null_buffers;
+  std::vector<std::unique_ptr<column>> _tmp_columns;
+};
+
+/**
+ * @brief Comparator for performing equality comparisons between two rows of the same table.
+ *
+ */
+class self_comparator {
+ public:
+  /**
+   * @brief Construct an owning object for performing equality comparisons between two rows of the
+   * same table.
+   *
+   * @param t The table to compare
+   * @param stream The stream to construct this object on. Not the stream that will be used for
+   * comparisons using this object.
+   */
+  self_comparator(table_view const& t, rmm::cuda_stream_view stream)
+    : d_t(preprocessed_table::create(t, stream))
+  {
+  }
+
+  /**
+   * @brief Construct an owning object for performing equality comparisons between two rows of the
+   * same table.
+   *
+   * This constructor allows independently constructing a `preprocessed_table` and sharing it among
+   * multiple comparators.
+   *
+   * @param t A table preprocessed for equality comparison
+   */
+  self_comparator(std::shared_ptr<preprocessed_table> t) : d_t{std::move(t)} {}
+
+  /**
+   * @brief Get the comparison operator to use on the device
+   *
+   * Returns a binary callable, `F`, with signature `bool F(size_type, size_type)`.
+   *
+   * `F(i,j)` returns true if and only if row `i` compares equal to row `j`.
+   *
+   * @note The operator overloads in sub-class `element_comparator` are templated via the
+   *        `type_dispatcher` to help select an overload instance for each column in a table.
+   *        So, `cudf::is_nested<Element>` will return `true` if the table has nested-type columns,
+   *        but it will be a runtime error if template parameter `has_nested_columns != true`.
+   *
+   * @tparam has_nested_columns compile-time optimization for primitive types.
+   *         This template parameter is to be used by the developer by querying
+   *         `cudf::detail::has_nested_columns(input)`. `true` compiles operator
+   *         overloads for nested types, while `false` only compiles operator
+   *         overloads for primitive types.
+   * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+   * @tparam PhysicalEqualityComparator A equality comparator functor that compares individual
+   * values rather than logical elements, defaults to a comparator for which `NaN == NaN`.
+   * @param nullate Indicates if any input column contains nulls.
+   * @param nulls_are_equal Indicates if nulls are equal.
+   * @param comparator Physical element equality comparison functor.
+   * @return A binary callable object
+   */
+  template <bool has_nested_columns,
+            typename Nullate,
+            typename PhysicalEqualityComparator = nan_equal_physical_equality_comparator>
+  auto equal_to(Nullate nullate                       = {},
+                null_equality nulls_are_equal         = null_equality::EQUAL,
+                PhysicalEqualityComparator comparator = {}) const noexcept
+  {
+    return device_row_comparator<has_nested_columns, Nullate, PhysicalEqualityComparator>{
+      nullate, *d_t, *d_t, nulls_are_equal, comparator};
+  }
+
+ private:
+  std::shared_ptr<preprocessed_table> d_t;
+};
+
+// @cond
+template <typename Comparator>
+struct strong_index_comparator_adapter {
+  strong_index_comparator_adapter(Comparator const& comparator) : comparator{comparator} {}
+
+  __device__ constexpr bool operator()(lhs_index_type const lhs_index,
+                                       rhs_index_type const rhs_index) const noexcept
+  {
+    return comparator(static_cast<cudf::size_type>(lhs_index),
+                      static_cast<cudf::size_type>(rhs_index));
+  }
+
+  __device__ constexpr bool operator()(rhs_index_type const rhs_index,
+                                       lhs_index_type const lhs_index) const noexcept
+  {
+    return this->operator()(lhs_index, rhs_index);
+  }
+
+  Comparator const comparator;
+};
+// @endcond
+
+/**
+ * @brief An owning object that can be used to equality compare rows of two different tables.
+ *
+ * This class takes two table_views and preprocesses certain columns to allow for equality
+ * comparison. The preprocessed table and temporary data required for the comparison are created and
+ * owned by this class.
+ *
+ * Alternatively, `two_table_comparator` can be constructed from two existing
+ * `shared_ptr<preprocessed_table>`s when sharing the same tables among multiple comparators.
+ *
+ * This class can then provide a functor object that can used on the device.
+ * The object of this class must outlive the usage of the device functor.
+ */
+class two_table_comparator {
+ public:
+  /**
+   * @brief Construct an owning object for performing equality comparisons between two rows from two
+   * tables.
+   *
+   * The left and right table are expected to have the same number of columns and data types for
+   * each column.
+   *
+   * @param left The left table to compare.
+   * @param right The right table to compare.
+   * @param stream The stream to construct this object on. Not the stream that will be used for
+   * comparisons using this object.
+   */
+  two_table_comparator(table_view const& left,
+                       table_view const& right,
+                       rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Construct an owning object for performing equality comparisons between two rows from two
+   * tables.
+   *
+   * This constructor allows independently constructing a `preprocessed_table` and sharing it among
+   * multiple comparators.
+   *
+   * @param left The left table preprocessed for equality comparison.
+   * @param right The right table preprocessed for equality comparison.
+   */
+  two_table_comparator(std::shared_ptr<preprocessed_table> left,
+                       std::shared_ptr<preprocessed_table> right)
+    : d_left_table{std::move(left)}, d_right_table{std::move(right)}
+  {
+  }
+
+  /**
+   * @brief Return the binary operator for comparing rows in the table.
+   *
+   * Returns a binary callable, `F`, with signatures `bool F(lhs_index_type, rhs_index_type)` and
+   * `bool F(rhs_index_type, lhs_index_type)`.
+   *
+   * `F(lhs_index_type i, rhs_index_type j)` returns true if and only if row `i` of the left table
+   * compares equal to row `j` of the right table.
+   *
+   * Similarly, `F(rhs_index_type i, lhs_index_type j)` returns true if and only if row `i` of the
+   * right table compares equal to row `j` of the left table.
+   *
+   * @note The operator overloads in sub-class `element_comparator` are templated via the
+   *        `type_dispatcher` to help select an overload instance for each column in a table.
+   *        So, `cudf::is_nested<Element>` will return `true` if the table has nested-type columns,
+   *        but it will be a runtime error if template parameter `has_nested_columns != true`.
+   *
+   * @tparam has_nested_columns compile-time optimization for primitive types.
+   *         This template parameter is to be used by the developer by querying
+   *         `cudf::detail::has_nested_columns(input)`. `true` compiles operator
+   *         overloads for nested types, while `false` only compiles operator
+   *         overloads for primitive types.
+   * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+   * @tparam PhysicalEqualityComparator A equality comparator functor that compares individual
+   * values rather than logical elements, defaults to a `NaN == NaN` equality comparator.
+   * @param nullate Indicates if any input column contains nulls.
+   * @param nulls_are_equal Indicates if nulls are equal.
+   * @param comparator Physical element equality comparison functor.
+   * @return A binary callable object
+   */
+  template <bool has_nested_columns,
+            typename Nullate,
+            typename PhysicalEqualityComparator = nan_equal_physical_equality_comparator>
+  auto equal_to(Nullate nullate                       = {},
+                null_equality nulls_are_equal         = null_equality::EQUAL,
+                PhysicalEqualityComparator comparator = {}) const noexcept
+  {
+    return strong_index_comparator_adapter{
+      device_row_comparator<has_nested_columns, Nullate, PhysicalEqualityComparator>(
+        nullate, *d_left_table, *d_right_table, nulls_are_equal, comparator)};
+  }
+
+ private:
+  std::shared_ptr<preprocessed_table> d_left_table;
+  std::shared_ptr<preprocessed_table> d_right_table;
+};
+
+}  // namespace equality
+
+namespace hash {
+
+/**
+ * @brief Computes the hash value of an element in the given column.
+ *
+ * @tparam hash_function Hash functor to use for hashing elements.
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ */
+template <template <typename> class hash_function, typename Nullate>
+class element_hasher {
+ public:
+  /**
+   * @brief Constructs an element_hasher object.
+   *
+   * @param nulls Indicates whether to check for nulls
+   * @param seed  The seed to use for the hash function
+   * @param null_hash The hash value to use for nulls
+   */
+  __device__ element_hasher(
+    Nullate nulls,
+    uint32_t seed             = DEFAULT_HASH_SEED,
+    hash_value_type null_hash = std::numeric_limits<hash_value_type>::max()) noexcept
+    : _check_nulls(nulls), _seed(seed), _null_hash(null_hash)
+  {
+  }
+
+  /**
+   * @brief Returns the hash value of the given element.
+   *
+   * @tparam T The type of the element to hash
+   * @param col The column to hash
+   * @param row_index The index of the row to hash
+   * @return The hash value of the given element
+   */
+  template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  __device__ hash_value_type operator()(column_device_view const& col,
+                                        size_type row_index) const noexcept
+  {
+    if (_check_nulls && col.is_null(row_index)) { return _null_hash; }
+    return hash_function<T>{_seed}(col.element<T>(row_index));
+  }
+
+  /**
+   * @brief Returns the hash value of the given element.
+   *
+   * @tparam T The type of the element to hash
+   * @param col The column to hash
+   * @param row_index The index of the row to hash
+   * @return The hash value of the given element
+   */
+  template <typename T, CUDF_ENABLE_IF(not column_device_view::has_element_accessor<T>())>
+  __device__ hash_value_type operator()(column_device_view const& col,
+                                        size_type row_index) const noexcept
+  {
+    CUDF_UNREACHABLE("Unsupported type in hash.");
+  }
+
+  Nullate _check_nulls;        ///< Whether to check for nulls
+  uint32_t _seed;              ///< The seed to use for hashing
+  hash_value_type _null_hash;  ///< Hash value to use for null elements
+};
+
+/**
+ * @brief Computes the hash value of a row in the given table.
+ *
+ * @tparam hash_function Hash functor to use for hashing elements.
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ */
+template <template <typename> class hash_function, typename Nullate>
+class device_row_hasher {
+  friend class row_hasher;  ///< Allow row_hasher to access private members.
+
+ public:
+  /**
+   * @brief Return the hash value of a row in the given table.
+   *
+   * @param row_index The row index to compute the hash value of
+   * @return The hash value of the row
+   */
+  __device__ auto operator()(size_type row_index) const noexcept
+  {
+    auto it = thrust::make_transform_iterator(_table.begin(), [=](auto const& column) {
+      return cudf::type_dispatcher<dispatch_storage_type>(
+        column.type(),
+        element_hasher_adapter<hash_function>{_check_nulls, _seed},
+        column,
+        row_index);
+    });
+
+    // Hash each element and combine all the hash values together
+    return detail::accumulate(it, it + _table.num_columns(), _seed, [](auto hash, auto h) {
+      return cudf::hashing::detail::hash_combine(hash, h);
+    });
+  }
+
+ private:
+  /**
+   * @brief Computes the hash value of an element in the given column.
+   *
+   * When the column is non-nested, this is a simple wrapper around the element_hasher.
+   * When the column is nested, this uses the element_hasher to hash the shape and values of the
+   * column.
+   */
+  template <template <typename> class hash_fn>
+  class element_hasher_adapter {
+    static constexpr hash_value_type NULL_HASH     = std::numeric_limits<hash_value_type>::max();
+    static constexpr hash_value_type NON_NULL_HASH = 0;
+
+   public:
+    __device__ element_hasher_adapter(Nullate check_nulls, uint32_t seed) noexcept
+      : _element_hasher(check_nulls, seed), _check_nulls(check_nulls)
+    {
+    }
+
+    template <typename T, CUDF_ENABLE_IF(not cudf::is_nested<T>())>
+    __device__ hash_value_type operator()(column_device_view const& col,
+                                          size_type row_index) const noexcept
+    {
+      return _element_hasher.template operator()<T>(col, row_index);
+    }
+
+    template <typename T, CUDF_ENABLE_IF(cudf::is_nested<T>())>
+    __device__ hash_value_type operator()(column_device_view const& col,
+                                          size_type row_index) const noexcept
+    {
+      auto hash                   = hash_value_type{0};
+      column_device_view curr_col = col.slice(row_index, 1);
+      while (curr_col.type().id() == type_id::STRUCT || curr_col.type().id() == type_id::LIST) {
+        if (_check_nulls) {
+          auto validity_it = detail::make_validity_iterator<true>(curr_col);
+          hash             = detail::accumulate(
+            validity_it, validity_it + curr_col.size(), hash, [](auto hash, auto is_valid) {
+              return cudf::hashing::detail::hash_combine(hash,
+                                                         is_valid ? NON_NULL_HASH : NULL_HASH);
+            });
+        }
+        if (curr_col.type().id() == type_id::STRUCT) {
+          if (curr_col.num_child_columns() == 0) { return hash; }
+          // Non-empty structs are assumed to be decomposed and contain only one child
+          curr_col = detail::structs_column_device_view(curr_col).get_sliced_child(0);
+        } else if (curr_col.type().id() == type_id::LIST) {
+          auto list_col   = detail::lists_column_device_view(curr_col);
+          auto list_sizes = make_list_size_iterator(list_col);
+          hash            = detail::accumulate(
+            list_sizes, list_sizes + list_col.size(), hash, [](auto hash, auto size) {
+              return cudf::hashing::detail::hash_combine(hash, hash_fn<size_type>{}(size));
+            });
+          curr_col = list_col.get_sliced_child();
+        }
+      }
+      for (int i = 0; i < curr_col.size(); ++i) {
+        hash = cudf::hashing::detail::hash_combine(
+          hash,
+          type_dispatcher<dispatch_void_if_nested>(curr_col.type(), _element_hasher, curr_col, i));
+      }
+      return hash;
+    }
+
+    element_hasher<hash_fn, Nullate> const _element_hasher;
+    Nullate const _check_nulls;
+  };
+
+  CUDF_HOST_DEVICE device_row_hasher(Nullate check_nulls,
+                                     table_device_view t,
+                                     uint32_t seed = DEFAULT_HASH_SEED) noexcept
+    : _check_nulls{check_nulls}, _table{t}, _seed(seed)
+  {
+  }
+
+  Nullate const _check_nulls;
+  table_device_view const _table;
+  uint32_t const _seed;
+};
+
+// Inject row::equality::preprocessed_table into the row::hash namespace
+// As a result, row::equality::preprocessed_table and row::hash::preprocessed table are the same
+// type and are interchangeable.
+using preprocessed_table = row::equality::preprocessed_table;
+
+/**
+ * @brief Computes the hash value of a row in the given table.
+ *
+ */
+class row_hasher {
+ public:
+  /**
+   * @brief Construct an owning object for hashing the rows of a table
+   *
+   * @param t The table containing rows to hash
+   * @param stream The stream to construct this object on. Not the stream that will be used for
+   * comparisons using this object.
+   */
+  row_hasher(table_view const& t, rmm::cuda_stream_view stream)
+    : d_t(preprocessed_table::create(t, stream))
+  {
+  }
+
+  /**
+   * @brief Construct an owning object for hashing the rows of a table from an existing
+   * preprocessed_table
+   *
+   * This constructor allows independently constructing a `preprocessed_table` and sharing it among
+   * multiple `row_hasher` and `equality::self_comparator` objects.
+   *
+   * @param t A table preprocessed for hashing or equality.
+   */
+  row_hasher(std::shared_ptr<preprocessed_table> t) : d_t{std::move(t)} {}
+
+  /**
+   * @brief Get the hash operator to use on the device
+   *
+   * Returns a unary callable, `F`, with signature `hash_function::hash_value_type F(size_type)`.
+   *
+   * `F(i)` returns the hash of row i.
+   *
+   * @tparam Nullate A cudf::nullate type describing whether to check for nulls
+   * @param nullate Indicates if any input column contains nulls
+   * @param seed The seed to use for the hash function
+   * @return A hash operator to use on the device
+   */
+  template <template <typename> class hash_function = cudf::hashing::detail::default_hash,
+            template <template <typename> class, typename>
+            class DeviceRowHasher = device_row_hasher,
+            typename Nullate>
+  DeviceRowHasher<hash_function, Nullate> device_hasher(Nullate nullate = {},
+                                                        uint32_t seed   = DEFAULT_HASH_SEED) const
+  {
+    return DeviceRowHasher<hash_function, Nullate>(nullate, *d_t, seed);
+  }
+
+ private:
+  std::shared_ptr<preprocessed_table> d_t;
+};
+
+}  // namespace hash
+
+}  // namespace row
+
+}  // namespace experimental
+}  // namespace cudf
diff --git a/cpp/include/cudf/table/row_operators.cuh b/cpp/include/cudf/table/row_operators.cuh
new file mode 100644
index 0000000..4806f96
--- /dev/null
+++ b/cpp/include/cudf/table/row_operators.cuh
@@ -0,0 +1,639 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/hashing/detail/hash_functions.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/equal.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/swap.h>
+#include <thrust/transform_reduce.h>
+
+#include <limits>
+
+namespace cudf {
+
+/**
+ * @brief Result type of the `element_relational_comparator` function object.
+ *
+ * Indicates how two elements `a` and `b` compare with one and another.
+ *
+ * Equivalence is defined as `not (a<b) and not (b<a)`. Elements that are
+ * EQUIVALENT may not necessarily be *equal*.
+ */
+enum class weak_ordering {
+  LESS,        ///< Indicates `a` is less than (ordered before) `b`
+  EQUIVALENT,  ///< Indicates `a` is ordered neither before nor after `b`
+  GREATER      ///< Indicates `a` is greater than (ordered after) `b`
+};
+
+namespace detail {
+/**
+ * @brief Compare the elements ordering with respect to `lhs`.
+ *
+ * @param lhs first element
+ * @param rhs second element
+ * @return Indicates the relationship between the elements in
+ * the `lhs` and `rhs` columns.
+ */
+template <typename Element>
+__device__ weak_ordering compare_elements(Element lhs, Element rhs)
+{
+  if (lhs < rhs) {
+    return weak_ordering::LESS;
+  } else if (rhs < lhs) {
+    return weak_ordering::GREATER;
+  }
+  return weak_ordering::EQUIVALENT;
+}
+}  // namespace detail
+
+/**
+ * @brief A specialization for floating-point `Element` type relational comparison
+ * to derive the order of the elements with respect to `lhs`.
+ *
+ * This specialization handles `nan` in the following order:
+ * `[-Inf, -ve, 0, -0, +ve, +Inf, NaN, NaN, null] (for null_order::AFTER)`
+ * `[null, -Inf, -ve, 0, -0, +ve, +Inf, NaN, NaN] (for null_order::BEFORE)`
+ *
+ */
+template <typename Element, std::enable_if_t<std::is_floating_point_v<Element>>* = nullptr>
+__device__ weak_ordering relational_compare(Element lhs, Element rhs)
+{
+  if (isnan(lhs) and isnan(rhs)) {
+    return weak_ordering::EQUIVALENT;
+  } else if (isnan(rhs)) {
+    return weak_ordering::LESS;
+  } else if (isnan(lhs)) {
+    return weak_ordering::GREATER;
+  }
+
+  return detail::compare_elements(lhs, rhs);
+}
+
+/**
+ * @brief Compare the nulls according to null order.
+ *
+ * @param lhs_is_null boolean representing if lhs is null
+ * @param rhs_is_null boolean representing if lhs is null
+ * @param null_precedence null order
+ * @return Indicates the relationship between null in lhs and rhs columns.
+ */
+inline __device__ auto null_compare(bool lhs_is_null, bool rhs_is_null, null_order null_precedence)
+{
+  if (lhs_is_null and rhs_is_null) {  // null <? null
+    return weak_ordering::EQUIVALENT;
+  } else if (lhs_is_null) {  // null <? x
+    return (null_precedence == null_order::BEFORE) ? weak_ordering::LESS : weak_ordering::GREATER;
+  } else if (rhs_is_null) {  // x <? null
+    return (null_precedence == null_order::AFTER) ? weak_ordering::LESS : weak_ordering::GREATER;
+  }
+  return weak_ordering::EQUIVALENT;
+}
+
+/**
+ * @brief A specialization for non-floating-point `Element` type relational
+ * comparison to derive the order of the elements with respect to `lhs`.
+ *
+ * @param lhs The first element
+ * @param rhs The second element
+ * @return Indicates the relationship between the elements in the `lhs` and `rhs` columns
+ */
+template <typename Element, std::enable_if_t<not std::is_floating_point_v<Element>>* = nullptr>
+__device__ weak_ordering relational_compare(Element lhs, Element rhs)
+{
+  return detail::compare_elements(lhs, rhs);
+}
+
+/**
+ * @brief A specialization for floating-point `Element` type to check if
+ * `lhs` is equivalent to `rhs`. `nan == nan`.
+ *
+ * @param lhs first element
+ * @param rhs second element
+ * @return `true` if `lhs` == `rhs` else `false`.
+ */
+template <typename Element, std::enable_if_t<std::is_floating_point_v<Element>>* = nullptr>
+__device__ bool equality_compare(Element lhs, Element rhs)
+{
+  if (isnan(lhs) and isnan(rhs)) { return true; }
+  return lhs == rhs;
+}
+
+/**
+ * @brief A specialization for non-floating-point `Element` type to check if
+ * `lhs` is equivalent to `rhs`.
+ *
+ * @param lhs first element
+ * @param rhs second element
+ * @return `true` if `lhs` == `rhs` else `false`.
+ */
+template <typename Element, std::enable_if_t<not std::is_floating_point_v<Element>>* = nullptr>
+__device__ bool equality_compare(Element const lhs, Element const rhs)
+{
+  return lhs == rhs;
+}
+
+/**
+ * @brief Performs an equality comparison between two elements in two columns.
+ *
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <typename Nullate>
+class element_equality_comparator {
+ public:
+  /**
+   * @brief Construct type-dispatched function object for comparing equality
+   * between two elements.
+   *
+   * @note `lhs` and `rhs` may be the same.
+   *
+   * @param has_nulls Indicates if either input column contains nulls.
+   * @param lhs The column containing the first element
+   * @param rhs The column containing the second element (may be the same as lhs)
+   * @param nulls_are_equal Indicates if two null elements are treated as equivalent
+   */
+  __host__ __device__
+  element_equality_comparator(Nullate has_nulls,
+                              column_device_view lhs,
+                              column_device_view rhs,
+                              null_equality nulls_are_equal = null_equality::EQUAL)
+    : lhs{lhs}, rhs{rhs}, nulls{has_nulls}, nulls_are_equal{nulls_are_equal}
+  {
+  }
+
+  /**
+   * @brief Compares the specified elements for equality.
+   *
+   * @param lhs_element_index The index of the first element
+   * @param rhs_element_index The index of the second element
+   * @return True if both lhs and rhs element are both nulls and `nulls_are_equal` is true, or equal
+   */
+  template <typename Element,
+            std::enable_if_t<cudf::is_equality_comparable<Element, Element>()>* = nullptr>
+  __device__ bool operator()(size_type lhs_element_index,
+                             size_type rhs_element_index) const noexcept
+  {
+    if (nulls) {
+      bool const lhs_is_null{lhs.is_null(lhs_element_index)};
+      bool const rhs_is_null{rhs.is_null(rhs_element_index)};
+      if (lhs_is_null and rhs_is_null) {
+        return nulls_are_equal == null_equality::EQUAL;
+      } else if (lhs_is_null != rhs_is_null) {
+        return false;
+      }
+    }
+
+    return equality_compare(lhs.element<Element>(lhs_element_index),
+                            rhs.element<Element>(rhs_element_index));
+  }
+
+  // @cond
+  template <typename Element,
+            std::enable_if_t<not cudf::is_equality_comparable<Element, Element>()>* = nullptr>
+  __device__ bool operator()(size_type lhs_element_index, size_type rhs_element_index)
+  {
+    CUDF_UNREACHABLE("Attempted to compare elements of uncomparable types.");
+  }
+  // @endcond
+
+ private:
+  column_device_view lhs;
+  column_device_view rhs;
+  Nullate nulls;
+  null_equality nulls_are_equal;
+};
+
+/**
+ * @brief Performs a relational comparison between two elements in two tables.
+ *
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls
+ */
+template <typename Nullate>
+class row_equality_comparator {
+ public:
+  /**
+   * @brief Construct a new row equality comparator object
+   *
+   * @param has_nulls Indicates if either input column contains nulls
+   * @param lhs The column containing the first element
+   * @param rhs The column containing the second element (may be the same as lhs)
+   * @param nulls_are_equal Indicates if two null elements are treated as equivalent
+   */
+  row_equality_comparator(Nullate has_nulls,
+                          table_device_view lhs,
+                          table_device_view rhs,
+                          null_equality nulls_are_equal = null_equality::EQUAL)
+    : lhs{lhs}, rhs{rhs}, nulls{has_nulls}, nulls_are_equal{nulls_are_equal}
+  {
+    CUDF_EXPECTS(lhs.num_columns() == rhs.num_columns(), "Mismatched number of columns.");
+  }
+
+  /**
+   * @brief Compares the specified rows for equality.
+   *
+   * @param lhs_row_index The index of the first row to compare (in the lhs table)
+   * @param rhs_row_index The index of the second row to compare (in the rhs table)
+   * @return true if both rows are equal, otherwise false
+   */
+  __device__ bool operator()(size_type lhs_row_index, size_type rhs_row_index) const noexcept
+  {
+    auto equal_elements = [=](column_device_view l, column_device_view r) {
+      return cudf::type_dispatcher(l.type(),
+                                   element_equality_comparator{nulls, l, r, nulls_are_equal},
+                                   lhs_row_index,
+                                   rhs_row_index);
+    };
+
+    return thrust::equal(thrust::seq, lhs.begin(), lhs.end(), rhs.begin(), equal_elements);
+  }
+
+ private:
+  table_device_view lhs;
+  table_device_view rhs;
+  Nullate nulls;
+  null_equality nulls_are_equal;
+};
+
+/**
+ * @brief Performs a relational comparison between two elements in two columns.
+ *
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <typename Nullate>
+class element_relational_comparator {
+ public:
+  /**
+   * @brief Construct type-dispatched function object for performing a
+   * relational comparison between two elements.
+   *
+   * @note `lhs` and `rhs` may be the same.
+   *
+   * @param lhs The column containing the first element
+   * @param rhs The column containing the second element (may be the same as lhs)
+   * @param has_nulls Indicates if either input column contains nulls.
+   * @param null_precedence Indicates how null values are ordered with other values
+   */
+  __host__ __device__ element_relational_comparator(Nullate has_nulls,
+                                                    column_device_view lhs,
+                                                    column_device_view rhs,
+                                                    null_order null_precedence)
+    : lhs{lhs}, rhs{rhs}, nulls{has_nulls}, null_precedence{null_precedence}
+  {
+  }
+
+  /**
+   * @brief Construct type-dispatched function object for performing a relational comparison between
+   * two elements in two columns.
+   *
+   * @param has_nulls Indicates if either input column contains nulls
+   * @param lhs The column containing the first element
+   * @param rhs The column containing the second element (may be the same as lhs)
+   */
+  __host__ __device__ element_relational_comparator(Nullate has_nulls,
+                                                    column_device_view lhs,
+                                                    column_device_view rhs)
+    : lhs{lhs}, rhs{rhs}, nulls{has_nulls}
+  {
+  }
+
+  /**
+   * @brief Performs a relational comparison between the specified elements
+   *
+   * @param lhs_element_index The index of the first element
+   * @param rhs_element_index The index of the second element
+   * @return Indicates the relationship between the elements in
+   * the `lhs` and `rhs` columns.
+   */
+  template <typename Element,
+            std::enable_if_t<cudf::is_relationally_comparable<Element, Element>()>* = nullptr>
+  __device__ weak_ordering operator()(size_type lhs_element_index,
+                                      size_type rhs_element_index) const noexcept
+  {
+    if (nulls) {
+      bool const lhs_is_null{lhs.is_null(lhs_element_index)};
+      bool const rhs_is_null{rhs.is_null(rhs_element_index)};
+
+      if (lhs_is_null or rhs_is_null) {  // at least one is null
+        return null_compare(lhs_is_null, rhs_is_null, null_precedence);
+      }
+    }
+
+    return relational_compare(lhs.element<Element>(lhs_element_index),
+                              rhs.element<Element>(rhs_element_index));
+  }
+
+  // @cond
+  template <typename Element,
+            std::enable_if_t<not cudf::is_relationally_comparable<Element, Element>()>* = nullptr>
+  __device__ weak_ordering operator()(size_type lhs_element_index, size_type rhs_element_index)
+  {
+    CUDF_UNREACHABLE("Attempted to compare elements of uncomparable types.");
+  }
+  // @endcond
+
+ private:
+  column_device_view lhs;
+  column_device_view rhs;
+  Nullate nulls;
+  null_order null_precedence{};
+};
+
+/**
+ * @brief Computes whether one row is lexicographically *less* than another row.
+ *
+ * Lexicographic ordering is determined by:
+ * - Two rows are compared element by element.
+ * - The first mismatching element defines which row is lexicographically less
+ * or greater than the other.
+ *
+ * Lexicographic ordering is exactly equivalent to doing an alphabetical sort of
+ * two words, for example, `aac` would be *less* than (or precede) `abb`. The
+ * second letter in both words is the first non-equal letter, and `a < b`, thus
+ * `aac < abb`.
+ *
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <typename Nullate>
+class row_lexicographic_comparator {
+ public:
+  /**
+   * @brief Construct a function object for performing a lexicographic
+   * comparison between the rows of two tables.
+   *
+   * Behavior is undefined if called with incomparable column types.
+   *
+   * @throws cudf::logic_error if `lhs.num_columns() != rhs.num_columns()`
+   *
+   * @param has_nulls Indicates if either input table contains columns with nulls.
+   * @param lhs The first table
+   * @param rhs The second table (may be the same table as `lhs`)
+   * @param column_order Optional, device array the same length as a row that
+   * indicates the desired ascending/descending order of each column in a row.
+   * If `nullptr`, it is assumed all columns are sorted in ascending order.
+   * @param null_precedence Optional, device array the same length as a row
+   * and indicates how null values compare to all other for every column. If
+   * it is nullptr, then null precedence would be `null_order::BEFORE` for all
+   * columns.
+   */
+  row_lexicographic_comparator(Nullate has_nulls,
+                               table_device_view lhs,
+                               table_device_view rhs,
+                               order const* column_order         = nullptr,
+                               null_order const* null_precedence = nullptr)
+    : _lhs{lhs},
+      _rhs{rhs},
+      _nulls{has_nulls},
+      _column_order{column_order},
+      _null_precedence{null_precedence}
+  {
+    CUDF_EXPECTS(_lhs.num_columns() == _rhs.num_columns(), "Mismatched number of columns.");
+  }
+
+  /**
+   * @brief Checks whether the row at `lhs_index` in the `lhs` table compares
+   * lexicographically less than the row at `rhs_index` in the `rhs` table.
+   *
+   * @param lhs_index The index of the row in the `lhs` table to examine
+   * @param rhs_index The index of the row in the `rhs` table to examine
+   * @return `true` if row from the `lhs` table compares less than row in the
+   * `rhs` table
+   */
+  __device__ bool operator()(size_type lhs_index, size_type rhs_index) const noexcept
+  {
+    for (size_type i = 0; i < _lhs.num_columns(); ++i) {
+      bool ascending = (_column_order == nullptr) or (_column_order[i] == order::ASCENDING);
+
+      null_order null_precedence =
+        _null_precedence == nullptr ? null_order::BEFORE : _null_precedence[i];
+
+      auto comparator =
+        element_relational_comparator{_nulls, _lhs.column(i), _rhs.column(i), null_precedence};
+
+      weak_ordering state =
+        cudf::type_dispatcher(_lhs.column(i).type(), comparator, lhs_index, rhs_index);
+
+      if (state == weak_ordering::EQUIVALENT) { continue; }
+
+      return state == (ascending ? weak_ordering::LESS : weak_ordering::GREATER);
+    }
+    return false;
+  }
+
+ private:
+  table_device_view _lhs;
+  table_device_view _rhs;
+  Nullate _nulls{};
+  null_order const* _null_precedence{};
+  order const* _column_order{};
+};  // class row_lexicographic_comparator
+
+/**
+ * @brief Computes the hash value of an element in the given column.
+ *
+ * @tparam hash_function Hash functor to use for hashing elements.
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <template <typename> class hash_function, typename Nullate>
+class element_hasher {
+ public:
+  /**
+   * @brief Returns the hash value of the given element in the given column.
+   *
+   * @tparam T The type of the element to hash
+   * @param col The column to hash
+   * @param row_index The index of the row to hash
+   * @return The hash value of the given element
+   */
+  template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  __device__ hash_value_type operator()(column_device_view col, size_type row_index) const
+  {
+    if (has_nulls && col.is_null(row_index)) { return std::numeric_limits<hash_value_type>::max(); }
+    return hash_function<T>{}(col.element<T>(row_index));
+  }
+
+  /**
+   * @brief Returns the hash value of the given element in the given column.
+   *
+   * @tparam T The type of the element to hash
+   * @param col The column to hash
+   * @param row_index The index of the row to hash
+   * @return The hash value of the given element
+   */
+  template <typename T, CUDF_ENABLE_IF(not column_device_view::has_element_accessor<T>())>
+  __device__ hash_value_type operator()(column_device_view col, size_type row_index) const
+  {
+    CUDF_UNREACHABLE("Unsupported type in hash.");
+  }
+
+  Nullate has_nulls;  ///< A cudf::nullate type describing how to check for nulls.
+};
+
+/**
+ * @brief Function object for computing the hash value of a row in a column.
+ *
+ * @tparam hash_function Hash functor to use for hashing elements
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls
+ */
+template <template <typename> class hash_function, typename Nullate>
+class element_hasher_with_seed {
+ public:
+  /**
+   * @brief Constructs a function object for hashing an element in the given column
+   *
+   * @param has_nulls Indicates if either input column contains nulls
+   * @param seed The seed to use for the hash function
+   */
+  __device__ element_hasher_with_seed(Nullate has_nulls, uint32_t seed)
+    : _seed{seed}, _has_nulls{has_nulls}
+  {
+  }
+
+  /**
+   * @brief Constructs a function object for hashing an element in the given column
+   *
+   * @param has_nulls Indicates if either input column contains nulls
+   * @param seed The seed to use for the hash function
+   * @param null_hash The hash value to use for null elements
+   */
+  __device__ element_hasher_with_seed(Nullate has_nulls, uint32_t seed, hash_value_type null_hash)
+    : _seed{seed}, _null_hash{null_hash}, _has_nulls{has_nulls}
+  {
+  }
+
+  /**
+   * @brief Returns the hash value of the given element in the given column.
+   *
+   * @tparam T The type of the element to hash
+   * @param col The column to hash
+   * @param row_index The index of the row to hash
+   * @return The hash value of the given element
+   */
+  template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+  __device__ hash_value_type operator()(column_device_view col, size_type row_index) const
+  {
+    if (_has_nulls && col.is_null(row_index)) { return _null_hash; }
+    return hash_function<T>{_seed}(col.element<T>(row_index));
+  }
+
+  /**
+   * @brief Returns the hash value of the given element in the given column.
+   *
+   * @tparam T The type of the element to hash
+   * @param col The column to hash
+   * @param row_index The index of the row to hash
+   * @return The hash value of the given element
+   */
+  template <typename T, CUDF_ENABLE_IF(not column_device_view::has_element_accessor<T>())>
+  __device__ hash_value_type operator()(column_device_view col, size_type row_index) const
+  {
+    CUDF_UNREACHABLE("Unsupported type in hash.");
+  }
+
+ private:
+  uint32_t _seed{DEFAULT_HASH_SEED};
+  hash_value_type _null_hash{std::numeric_limits<hash_value_type>::max()};
+  Nullate _has_nulls;
+};
+
+/**
+ * @brief Computes the hash value of a row in the given table.
+ *
+ * @tparam hash_function Hash functor to use for hashing elements.
+ * @tparam Nullate A cudf::nullate type describing how to check for nulls.
+ */
+template <template <typename> class hash_function, typename Nullate>
+class row_hasher {
+ public:
+  row_hasher() = delete;
+
+  /**
+   * @brief Constructs a row_hasher object.
+   *
+   * @param has_nulls Indicates if either input table contains nulls
+   * @param t A table_device_view to hash
+   */
+  CUDF_HOST_DEVICE row_hasher(Nullate has_nulls, table_device_view t)
+    : _table{t}, _has_nulls{has_nulls}
+  {
+  }
+  /**
+   * @brief Constructs a row_hasher object with a seed value.
+   *
+   * @param has_nulls Indicates if either input table contains nulls
+   * @param t A table_device_view to hash
+   * @param seed A seed value to use for hashing
+   */
+  CUDF_HOST_DEVICE row_hasher(Nullate has_nulls, table_device_view t, uint32_t seed)
+    : _table{t}, _seed(seed), _has_nulls{has_nulls}
+  {
+  }
+
+  /**
+   * @brief Computes the hash value of the row at `row_index` in the `table`
+   *
+   * @param row_index The index of the row in the `table` to hash
+   * @return The hash value of the row at `row_index` in the `table`
+   */
+  __device__ auto operator()(size_type row_index) const
+  {
+    // Hash the first column w/ the seed
+    auto const initial_hash = cudf::hashing::detail::hash_combine(
+      hash_value_type{0},
+      type_dispatcher<dispatch_storage_type>(
+        _table.column(0).type(),
+        element_hasher_with_seed<hash_function, Nullate>{_has_nulls, _seed},
+        _table.column(0),
+        row_index));
+
+    // Hashes an element in a column
+    auto hasher = [=](size_type column_index) {
+      return cudf::type_dispatcher<dispatch_storage_type>(
+        _table.column(column_index).type(),
+        element_hasher<hash_function, Nullate>{_has_nulls},
+        _table.column(column_index),
+        row_index);
+    };
+
+    // Hash each element and combine all the hash values together
+    return thrust::transform_reduce(
+      thrust::seq,
+      // note that this starts at 1 and not 0 now since we already hashed the first column
+      thrust::make_counting_iterator(1),
+      thrust::make_counting_iterator(_table.num_columns()),
+      hasher,
+      initial_hash,
+      [](hash_value_type lhs, hash_value_type rhs) {
+        return cudf::hashing::detail::hash_combine(lhs, rhs);
+      });
+  }
+
+ private:
+  table_device_view _table;
+  Nullate _has_nulls;
+  uint32_t _seed{DEFAULT_HASH_SEED};
+};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/table/table.hpp b/cpp/include/cudf/table/table.hpp
new file mode 100644
index 0000000..cfcae2c
--- /dev/null
+++ b/cpp/include/cudf/table/table.hpp
@@ -0,0 +1,190 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <vector>
+
+/**
+ * @file
+ * @brief Class definition for cudf::table
+ */
+
+namespace cudf {
+
+/**
+ * @brief A set of cudf::column's of the same size.
+ *
+ * @ingroup table_classes
+ */
+class table {
+ public:
+  table()                        = default;
+  ~table()                       = default;
+  table(table&&)                 = default;  ///< Move constructor
+  table& operator=(table const&) = delete;
+  table& operator=(table&&)      = delete;
+
+  /**
+   * @brief Construct a new table by copying the contents of another table.
+   *
+   * @param other The table to copy
+   */
+  table(table const& other);
+
+  /**
+   * @brief Moves the contents from a vector of `unique_ptr`s to columns to
+   * construct a new table.
+   *
+   * @param columns The vector of `unique_ptr`s to columns whose contents will
+   * be moved into the new table.
+   */
+  table(std::vector<std::unique_ptr<column>>&& columns);
+
+  /**
+   * @brief Copy the contents of a `table_view` to construct a new `table`.
+   *
+   * @param view The view whose contents will be copied to create a new `table`
+   * @param stream CUDA stream used for device memory operations.
+   * @param mr Device memory resource used for allocating the device memory for the new columns
+   */
+  table(table_view view,
+        rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+        rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Returns the number of columns in the table
+   *
+   * @return The number of columns in the table
+   */
+  [[nodiscard]] size_type num_columns() const noexcept { return _columns.size(); }
+
+  /**
+   * @brief Returns the number of rows
+   *
+   * @return  The number of rows
+   */
+  [[nodiscard]] size_type num_rows() const noexcept { return _num_rows; }
+
+  /**
+   * @brief Returns an immutable, non-owning `table_view` of the contents of
+   *this `table`.
+   *
+   * @return An immutable, non-owning `table_view` of the contents of this `table`
+   */
+  [[nodiscard]] table_view view() const;
+
+  /**
+   * @brief Conversion operator to an immutable, non-owning `table_view` of the
+   * contents of this `table`.
+   */
+  operator table_view() const { return this->view(); };
+
+  /**
+   * @brief Returns a mutable, non-owning `mutable_table_view` of the contents
+   * of this `table`.
+   *
+   * @return A mutable, non-owning `mutable_table_view` of the contents of this `table`
+   */
+  mutable_table_view mutable_view();
+
+  /**
+   * @brief Conversion operator to a mutable, non-owning `mutable_table_view` of
+   *the contents of this `table`.
+   */
+  operator mutable_table_view() { return this->mutable_view(); };
+
+  /**
+   * @brief Releases ownership of the `column`s by returning a vector of
+   * `unique_ptr`s to the constituent columns.
+   *
+   * After `release()`, `num_columns() == 0` and `num_rows() == 0`
+   *
+   * @returns A vector of `unique_ptr`s to the constituent columns
+   */
+  std::vector<std::unique_ptr<column>> release();
+
+  /**
+   * @brief Returns a table_view built from a range of column indices.
+   *
+   * @throws std::out_of_range
+   * If any index is outside [0, num_columns())
+   *
+   * @param begin Beginning of the range
+   * @param end Ending of the range
+   * @return A table_view consisting of columns from the original table
+   * specified by the elements of `column_indices`
+   */
+
+  template <typename InputIterator>
+  table_view select(InputIterator begin, InputIterator end) const
+  {
+    std::vector<column_view> columns(std::distance(begin, end));
+    std::transform(
+      begin, end, columns.begin(), [this](auto index) { return _columns.at(index)->view(); });
+    return table_view(columns);
+  }
+
+  /**
+   * @brief Returns a table_view with set of specified columns.
+   *
+   * @throws std::out_of_range
+   * If any element in `column_indices` is outside [0, num_columns())
+   *
+   * @param column_indices Indices of columns in the table
+   * @return A table_view consisting of columns from the original table
+   * specified by the elements of `column_indices`
+   */
+  [[nodiscard]] table_view select(std::vector<cudf::size_type> const& column_indices) const
+  {
+    return select(column_indices.begin(), column_indices.end());
+  };
+
+  /**
+   * @brief Returns a reference to the specified column
+   *
+   * @throws std::out_of_range
+   * If i is out of the range [0, num_columns)
+   *
+   * @param column_index Index of the desired column
+   * @return A reference to the desired column
+   */
+  column& get_column(cudf::size_type column_index) { return *(_columns.at(column_index)); }
+
+  /**
+   * @brief Returns a const reference to the specified column
+   *
+   * @throws std::out_of_range
+   * If i is out of the range [0, num_columns)
+   *
+   * @param i Index of the desired column
+   * @return A const reference to the desired column
+   */
+  [[nodiscard]] column const& get_column(cudf::size_type i) const { return *(_columns.at(i)); }
+
+ private:
+  std::vector<std::unique_ptr<column>> _columns{};
+  size_type _num_rows{};
+};
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/table/table_device_view.cuh b/cpp/include/cudf/table/table_device_view.cuh
new file mode 100644
index 0000000..511013b
--- /dev/null
+++ b/cpp/include/cudf/table/table_device_view.cuh
@@ -0,0 +1,274 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <cassert>
+#include <memory>
+#include <numeric>
+
+/**
+ * @file table_device_view.cuh
+ * @brief Table device view class definitions
+ */
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Base class for a device table of `ColumnDeviceView`s
+ *
+ * This class should not be used directly. Instead:
+ * - `table_device_view` should be used for a table of columns of type `column_device_view`
+ * - `mutable_table_device_view` should be used for a table of columns of type
+ * `mutable_column_device_view`
+ *
+ * All public constructors and member functions of `table_device_view_base` are
+ * available in both `table_device_view` and `mutable_table_device_view`.
+ *
+ * @tparam ColumnDeviceView The type of column device view the table contains:
+ *                          expects column_device_view or mutable_column_device_view
+ * @tparam HostTableView The type of table view used to create the table device view:
+ *                       expects table_view or mutable_table_view
+ */
+template <typename ColumnDeviceView, typename HostTableView>
+class table_device_view_base {
+ public:
+  table_device_view_base()                              = delete;
+  ~table_device_view_base()                             = default;
+  table_device_view_base(table_device_view_base const&) = default;  ///< Copy constructor
+  table_device_view_base(table_device_view_base&&)      = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  table_device_view_base& operator=(table_device_view_base const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  table_device_view_base& operator=(table_device_view_base&&) = default;
+
+  /**
+   * @brief Returns an iterator to the first view in the `table`.
+   *
+   * @return An iterator to the first view in the `table`
+   */
+  __device__ ColumnDeviceView* begin() const noexcept { return _columns; }
+
+  /**
+   * @brief Returns an iterator one past the last column view in the `table`.
+   *
+   * `end()` acts as a place holder. Attempting to dereference it results in
+   * undefined behavior.
+   *
+   * @return An iterator to one past the last column view in the `table`
+   */
+  __device__ ColumnDeviceView* end() const noexcept { return _columns + _num_columns; }
+
+  /**
+   * @brief Returns a reference to the view of the specified column
+   *
+   * @param column_index The index of the desired column
+   * @return A reference to the desired column
+   */
+  __device__ ColumnDeviceView const& column(size_type column_index) const noexcept
+  {
+    assert(column_index >= 0);
+    assert(column_index < _num_columns);
+    return _columns[column_index];
+  }
+
+  /**
+   * @brief Returns a reference to the view of the specified column
+   *
+   * @param column_index The index of the desired column
+   * @return A reference to the desired column
+   */
+  __device__ ColumnDeviceView& column(size_type column_index) noexcept
+  {
+    assert(column_index >= 0);
+    assert(column_index < _num_columns);
+    return _columns[column_index];
+  }
+
+  /**
+   * @brief Returns the number of columns
+   *
+   * @return The number of columns
+   */
+  [[nodiscard]] __host__ __device__ size_type num_columns() const noexcept { return _num_columns; }
+
+  /**
+   * @brief Returns the number of rows
+   *
+   * @return The number of rows
+   */
+  [[nodiscard]] __host__ __device__ size_type num_rows() const noexcept { return _num_rows; }
+
+  /**
+   * @brief Destroy the `table_device_view` object.
+   *
+   * @note Does not free the table data, simply frees the device memory
+   * allocated to hold the constituent column views.
+   */
+  void destroy();
+
+ private:
+  ColumnDeviceView* _columns{};  ///< Array of view objects in device memory
+  size_type _num_rows{};
+  size_type _num_columns{};
+
+ protected:
+  /**
+   * @brief Construct a new table device view base object from host table_view
+   *
+   * @param source_view The host table_view to create table device view from
+   * @param stream The CUDA stream to use for device memory allocation
+   */
+  table_device_view_base(HostTableView source_view, rmm::cuda_stream_view stream);
+
+  /// Pointer to device memory holding the descendant storage
+  rmm::device_buffer* _descendant_storage{};
+};
+}  // namespace detail
+
+/**
+ * @brief Table device view that is usable in device memory
+ */
+class table_device_view : public detail::table_device_view_base<column_device_view, table_view> {
+ public:
+  /**
+   * @brief Factory to construct a table device view that is usable in device memory.
+   *
+   * Allocates and copies views of `source_view`'s children to device memory to
+   * make them accessible in device code.
+   *
+   * Returns a `std::unique_ptr<table_device_view>` with a custom deleter to
+   * free the device memory allocated for the children.
+   *
+   * @param source_view The table view whose contents will be copied to create a new table
+   * @param stream CUDA stream used for device memory operations
+   * @return A `unique_ptr` to a `table_device_view` that makes the data from `source_view`
+   * available in device memory
+   */
+  static auto create(table_view source_view,
+                     rmm::cuda_stream_view stream = cudf::get_default_stream())
+  {
+    auto deleter = [](table_device_view* t) { t->destroy(); };
+    return std::unique_ptr<table_device_view, decltype(deleter)>{
+      new table_device_view(source_view, stream), deleter};
+  }
+
+ private:
+  table_device_view(table_view source_view, rmm::cuda_stream_view stream)
+    : detail::table_device_view_base<column_device_view, table_view>(source_view, stream)
+  {
+  }
+};
+
+/**
+ * @brief Mutable table device view that is usable in device memory
+ *
+ * Elements of the table can be modified in device memory.
+ */
+class mutable_table_device_view
+  : public detail::table_device_view_base<mutable_column_device_view, mutable_table_view> {
+ public:
+  /**
+   * @brief Factory to construct a mutable table device view that is usable in device memory.
+   *
+   * Allocates and copies views of `source_view`'s children to device memory to
+   * make them accessible in device code.
+   *
+   * Returns a `std::unique_ptr<mutable_table_device_view>` with a custom deleter to
+   * free the device memory allocated for the children.
+   *
+   * @param source_view The table view whose contents will be copied to create a new table
+   * @param stream CUDA stream used for device memory operations
+   * @return A `unique_ptr` to a `mutable_table_device_view` that makes the data from `source_view`
+   * available in device memory
+   */
+  static auto create(mutable_table_view source_view,
+                     rmm::cuda_stream_view stream = cudf::get_default_stream())
+  {
+    auto deleter = [](mutable_table_device_view* t) { t->destroy(); };
+    return std::unique_ptr<mutable_table_device_view, decltype(deleter)>{
+      new mutable_table_device_view(source_view, stream), deleter};
+  }
+
+ private:
+  mutable_table_device_view(mutable_table_view source_view, rmm::cuda_stream_view stream)
+    : detail::table_device_view_base<mutable_column_device_view, mutable_table_view>(source_view,
+                                                                                     stream)
+  {
+  }
+};
+
+/**
+ * @brief Copies the contents of a table_view to a column device view in contiguous device memory
+ *
+ * @tparam ColumnDeviceView The column device view type to copy to
+ * @tparam HostTableView The type of the table_view to copy from
+ * @param source_view The table_view to copy from
+ * @param stream The stream to use for device memory allocation
+ * @return tuple of device_buffer and @p ColumnDeviceView device pointer
+ */
+template <typename ColumnDeviceView, typename HostTableView>
+auto contiguous_copy_column_device_views(HostTableView source_view, rmm::cuda_stream_view stream)
+{
+  // First calculate the size of memory needed to hold the
+  // table's ColumnDeviceViews. This is done by calling extent()
+  // for each of the table's ColumnViews columns.
+  std::size_t views_size_bytes = std::accumulate(
+    source_view.begin(), source_view.end(), std::size_t{0}, [](std::size_t init, auto col) {
+      return init + ColumnDeviceView::extent(col);
+    });
+  // pad the allocation for aligning the first pointer
+  auto padded_views_size_bytes = views_size_bytes + std::size_t{alignof(ColumnDeviceView) - 1};
+  // A buffer of CPU memory is allocated to hold the ColumnDeviceView
+  // objects. Once filled, the CPU memory is then copied to device memory
+  // and the pointer is set in the d_columns member.
+  std::vector<int8_t> h_buffer(padded_views_size_bytes);
+  // Each ColumnDeviceView instance may have child objects which may
+  // require setting some internal device pointers before being copied
+  // from CPU to device.
+  // Allocate the device memory to be used in the result.
+  // We need this pointer in order to pass it down when creating the
+  // ColumnDeviceViews so the column can set the pointer(s) for any
+  // of its child objects.
+  // align both h_ptr, d_ptr
+  auto descendant_storage = std::make_unique<rmm::device_buffer>(padded_views_size_bytes, stream);
+  void* h_ptr             = detail::align_ptr_for_type<ColumnDeviceView>(h_buffer.data());
+  void* d_ptr    = detail::align_ptr_for_type<ColumnDeviceView>(descendant_storage->data());
+  auto d_columns = detail::child_columns_to_device_array<ColumnDeviceView>(
+    source_view.begin(), source_view.end(), h_ptr, d_ptr);
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(d_ptr, h_ptr, views_size_bytes, cudaMemcpyDefault, stream.value()));
+  stream.synchronize();
+  return std::make_tuple(std::move(descendant_storage), d_columns);
+}
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/table/table_view.hpp b/cpp/include/cudf/table/table_view.hpp
new file mode 100644
index 0000000..b90b2da
--- /dev/null
+++ b/cpp/include/cudf/table/table_view.hpp
@@ -0,0 +1,399 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <algorithm>
+#include <vector>
+
+/**
+ * @file
+ * @brief Class definitions for `(mutable)_table_view`
+ *
+ * A `(mutable_)table_view` is a set of `(mutable_)column_view`s of equal
+ * size.
+ *
+ * A `(mutable_)table_view` is non-owning and trivially copyable and should be
+ * passed by value.
+ */
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Base class for a table of `ColumnView`s
+ *
+ * This class should not be used directly. Instead:
+ * - `table_view` should be used for a table of columns of type `column_view`
+ * - `mutable_table_view` should be used for a table of columns of type `mutable_column_view`
+ *
+ * All public constructors and member functions of `table_view_base` are
+ * available in both `table_view` and `mutable_table_view`.
+ *
+ * @tparam ColumnView The type of column view the table contains:
+ *                    expects column_view or mutable_column_view
+ */
+template <typename ColumnView>
+class table_view_base {
+  static_assert(std::is_same_v<ColumnView, column_view> or
+                  std::is_same_v<ColumnView, mutable_column_view>,
+                "table_view_base can only be instantiated with column_view or "
+                "column_view_base.");
+
+ private:
+  std::vector<ColumnView> _columns{};  ///< ColumnViews to columns of equal size
+  size_type _num_rows{};               ///< The number of elements in every column
+
+ public:
+  using iterator       = decltype(std::begin(_columns));   ///< Iterator type for the table
+  using const_iterator = decltype(std::cbegin(_columns));  ///< const iterator type for the table
+
+  /**
+   * @brief Construct a table from a vector of column views
+   *
+   * @note Because a `std::vector` is constructible from a
+   * `std::initializer_list`, this constructor also supports the following
+   * usage:
+   * ```
+   * column_view c0, c1, c2;
+   * ...
+   * table_view t{{c0,c1,c2}}; // Creates a `table` from c0, c1, c2
+   * ```
+   *
+   * @throws cudf::logic_error If all views do not have the same size
+   *
+   * @param cols The vector of columns to construct the table from
+   */
+  explicit table_view_base(std::vector<ColumnView> const& cols);
+
+  /**
+   * @brief Returns an iterator to the first view in the `table`.
+   *
+   * @return An iterator to the first column_view
+   */
+  iterator begin() noexcept { return std::begin(_columns); }
+
+  /**
+   * @brief Returns an iterator to the first view in the `table`.
+   *
+   * @return An iterator to the first view in the `table`
+   */
+  [[nodiscard]] const_iterator begin() const noexcept { return std::begin(_columns); }
+
+  /**
+   * @brief Returns an iterator one past the last column view in the `table`.
+   *
+   * `end()` acts as a place holder. Attempting to dereference it results in
+   * undefined behavior.
+   *
+   * @return An iterator to one past the last column view in the `table`
+   */
+  iterator end() noexcept { return std::end(_columns); }
+
+  /**
+   * @brief Returns an iterator one past the last column view in the `table`.
+   *
+   * `end()` acts as a place holder. Attempting to dereference it results in
+   * undefined behavior.
+   *
+   * @return An iterator to one past the last column view in the `table`
+   */
+  [[nodiscard]] const_iterator end() const noexcept { return std::end(_columns); }
+
+  /**
+   * @brief Returns a reference to the view of the specified column
+   *
+   * @throws std::out_of_range
+   * If `column_index` is out of the range [0, num_columns)
+   *
+   * @param column_index The index of the desired column
+   * @return A reference to the desired column
+   */
+  ColumnView const& column(size_type column_index) const;
+
+  /**
+   * @brief Returns the number of columns
+   *
+   * @return The number of columns
+   */
+  [[nodiscard]] size_type num_columns() const noexcept { return _columns.size(); }
+
+  /**
+   * @brief Returns the number of rows
+   *
+   * @return The number of rows
+   */
+  [[nodiscard]] size_type num_rows() const noexcept { return _num_rows; }
+
+  /**
+   * @brief Returns true if `num_columns()` returns zero, or false otherwise
+   *
+   * @return True if `num_columns()` returns zero, or false otherwise
+   */
+  [[nodiscard]] size_type is_empty() const noexcept { return num_columns() == 0; }
+
+  table_view_base() = default;
+
+  ~table_view_base() = default;
+
+  table_view_base(table_view_base const&) = default;  ///< Copy constructor
+
+  table_view_base(table_view_base&&) = default;  ///< Move constructor
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return Reference to this object
+   */
+  table_view_base& operator=(table_view_base const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object (after transferring ownership)
+   */
+  table_view_base& operator=(table_view_base&&) = default;
+};
+
+/**
+ * @brief Determine if any nested columns exist in a given table.
+ *
+ * @param table The input table
+ * @return Whether nested columns exist in the input table
+ */
+bool has_nested_columns(table_view const& table);
+}  // namespace detail
+
+/**
+ * @brief A set of cudf::column_view's of the same size.
+ *
+ * @ingroup table_classes
+ *
+ * All public member functions and constructors are inherited from
+ * `table_view_base<column_view>`.
+ */
+class table_view : public detail::table_view_base<column_view> {
+  using detail::table_view_base<column_view>::table_view_base;
+
+ public:
+  using ColumnView = column_view;  ///< The type of column view the table contains
+
+  table_view() = default;
+
+  /**
+   * @brief Construct a table from a vector of table views
+   *
+   * @note Because a `std::vector` is constructible from a
+   * `std::initializer_list`, this constructor also supports the following
+   * usage:
+   * ```
+   * table_view t0, t1, t2;
+   * ...
+   * table_view t{{t0,t1,t2}}; // Creates a `table` from the columns of
+   * t0, t1, t2
+   * ```
+   *
+   * @throws cudf::logic_error
+   * If number of rows mismatch
+   *
+   * @param views The vector of table views to construct the table from
+   */
+  table_view(std::vector<table_view> const& views);
+
+  /**
+   * @brief Returns a table_view built from a range of column indices.
+   *
+   * @throws std::out_of_range
+   * If any index is outside [0, num_columns())
+   *
+   * @param begin Beginning of the range
+   * @param end Ending of the range
+   * @return A table_view consisting of columns from the original table
+   * specified by the elements of `column_indices`
+   */
+  template <typename InputIterator>
+  table_view select(InputIterator begin, InputIterator end) const
+  {
+    std::vector<column_view> columns(std::distance(begin, end));
+    std::transform(begin, end, columns.begin(), [this](auto index) { return this->column(index); });
+    return table_view(columns);
+  }
+
+  /**
+   * @brief Returns a table_view with set of specified columns.
+   *
+   * @throws std::out_of_range
+   * If any element in `column_indices` is outside [0, num_columns())
+   *
+   * @param column_indices Indices of columns in the table
+   * @return A table_view consisting of columns from the original table
+   * specified by the elements of `column_indices`
+   */
+  [[nodiscard]] table_view select(std::vector<size_type> const& column_indices) const;
+};
+
+/**
+ * @brief A set of `mutable_column_view`s of the same size.
+ *
+ * @ingroup table_classes
+ *
+ * All public member functions and constructors are inherited from
+ * `table_view_base<mutable_column_view>`.
+ */
+class mutable_table_view : public detail::table_view_base<mutable_column_view> {
+  using detail::table_view_base<mutable_column_view>::table_view_base;
+
+ public:
+  using ColumnView = mutable_column_view;  ///< The type of column views in the table
+
+  mutable_table_view() = default;
+
+  /**
+   * @brief Returns column at specified index
+   *
+   * @param column_index The index of the desired column
+   * @return A mutable column view reference to the desired column
+   */
+  [[nodiscard]] mutable_column_view& column(size_type column_index) const
+  {
+    return const_cast<mutable_column_view&>(table_view_base::column(column_index));
+  }
+  /**
+   * @brief Creates an immutable `table_view` of the columns
+   */
+  operator table_view();
+
+  /**
+   * @brief Construct a table from a vector of table views
+   *
+   * @note Because a `std::vector` is constructible from a
+   * `std::initializer_list`, this constructor also supports the following
+   * usage:
+   * ```
+   * table_view t0, t1, t2;
+   * ...
+   * table_view t{{t0,t1,t2}}; // Creates a `table` from the columns of
+   * t0, t1, t2
+   * ```
+   *
+   * @throws cudf::logic_error
+   * If number of rows mismatch
+   *
+   * @param views The vector of table views to construct the table from
+   */
+  mutable_table_view(std::vector<mutable_table_view> const& views);
+};
+
+/**
+ * @brief Returns True if any of the columns in the table is nullable. (not entire hierarchy)
+ *
+ * @param view The table to check for nullability
+ * @return True if any of the columns in the table is nullable, false otherwise
+ */
+inline bool nullable(table_view const& view)
+{
+  return std::any_of(view.begin(), view.end(), [](auto const& col) { return col.nullable(); });
+}
+
+/**
+ * @brief Returns True if the table has nulls in any of its columns.
+ *
+ * This checks for nulls in the columns and but not in any of the columns' children.
+ *
+ * @param view The table to check for nulls
+ * @return True if the table has nulls in any of its columns, false otherwise
+ */
+inline bool has_nulls(table_view const& view)
+{
+  return std::any_of(view.begin(), view.end(), [](auto const& col) { return col.has_nulls(); });
+}
+
+/**
+ * @brief Returns True if the table has nulls in any of its columns hierarchy
+ *
+ * @param input The table to check for nulls
+ * @return True if the table has nulls in any of its columns hierarchy, false otherwise
+ */
+inline bool has_nested_nulls(table_view const& input)
+{
+  return std::any_of(input.begin(), input.end(), [](auto const& col) {
+    return col.has_nulls() ||
+           std::any_of(col.child_begin(), col.child_end(), [](auto const& child_col) {
+             return has_nested_nulls(table_view{{child_col}});
+           });
+  });
+}
+
+/**
+ * @brief The function to collect all nullable columns at all nested levels in a given table.
+ *
+ * @param table The input table
+ * @return A vector containing all nullable columns in the input table
+ */
+std::vector<column_view> get_nullable_columns(table_view const& table);
+
+/**
+ * @brief Checks if two `table_view`s have columns of same types
+ *
+ * @param lhs left-side table_view operand
+ * @param rhs right-side table_view operand
+ * @return boolean comparison result
+ */
+inline bool have_same_types(table_view const& lhs, table_view const& rhs)
+{
+  return std::equal(
+    lhs.begin(),
+    lhs.end(),
+    rhs.begin(),
+    rhs.end(),
+    [](column_view const& lcol, column_view const& rcol) { return (lcol.type() == rcol.type()); });
+}
+
+/**
+ * @brief Copy column_views from a table_view into another table_view according to
+ * a column indices map.
+ *
+ * The output table view, `out_table` is a copy of the `target` table_view but with
+ * elements updated according to `out_table[map[i]] = source[i]` where `i` is
+ * `[0,source.size())`
+ *
+ * @param source Table of new columns to scatter into the output table view.
+ * @param map The indices where each new_column should be copied into the output.
+ * @param target Table to receive the updated column views.
+ * @return New table_view.
+ */
+table_view scatter_columns(table_view const& source,
+                           std::vector<size_type> const& map,
+                           table_view const& target);
+
+namespace detail {
+/**
+ * @brief Indicates whether respective columns in input tables are relationally comparable.
+ *
+ * @param lhs The first table
+ * @param rhs The second table (may be the same table as `lhs`)
+ * @return true all of respective columns on `lhs` and 'rhs` tables are comparable.
+ * @return false any of respective columns on `lhs` and 'rhs` tables are not comparable.
+ */
+template <typename TableView>
+bool is_relationally_comparable(TableView const& lhs, TableView const& rhs);
+// @cond
+extern template bool is_relationally_comparable<table_view>(table_view const& lhs,
+                                                            table_view const& rhs);
+extern template bool is_relationally_comparable<mutable_table_view>(mutable_table_view const& lhs,
+                                                                    mutable_table_view const& rhs);
+// @endcond
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/tdigest/tdigest_column_view.hpp b/cpp/include/cudf/tdigest/tdigest_column_view.hpp
new file mode 100644
index 0000000..f2f493c
--- /dev/null
+++ b/cpp/include/cudf/tdigest/tdigest_column_view.hpp
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+namespace cudf {
+namespace tdigest {
+
+/**
+ * @brief Given a column_view containing tdigest data, an instance of this class
+ * provides a wrapper on the compound column for tdigest operations.
+ *
+ * A tdigest is a "compressed" set of input scalars represented as a sorted
+ * set of centroids (https://arxiv.org/pdf/1902.04023.pdf).
+ * This data can be queried for quantile information. Each row in a tdigest
+ * column represents an entire tdigest.
+ *
+ * The column has the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    }
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ */
+class tdigest_column_view : private column_view {
+ public:
+  tdigest_column_view(column_view const&);  ///< Construct tdigest_column_view from a column_view
+  tdigest_column_view(tdigest_column_view&&)      = default;  ///< Move constructor
+  tdigest_column_view(tdigest_column_view const&) = default;  ///< Copy constructor
+  ~tdigest_column_view()                          = default;
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return this object after copying the contents of the other object (copy)
+   */
+  tdigest_column_view& operator=(tdigest_column_view const&) = default;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return this object after moving the contents of the other object (transfer ownership)
+   */
+  tdigest_column_view& operator=(tdigest_column_view&&) = default;
+
+  using column_view::size;
+  using offset_iterator = size_type const*;  ///< Iterator over offsets
+
+  // mean and weight column indices within tdigest inner struct columns
+  static constexpr size_type mean_column_index{0};    ///< Mean column index
+  static constexpr size_type weight_column_index{1};  ///< Weight column index
+
+  // min and max column indices within tdigest outer struct columns
+  static constexpr size_type centroid_column_index{0};  ///< Centroid column index
+  static constexpr size_type min_column_index{1};       ///< Min column index
+  static constexpr size_type max_column_index{2};       ///< Max column index
+
+  /**
+   * @brief Returns the parent column.
+   *
+   * @return The parent column
+   */
+  [[nodiscard]] column_view parent() const;
+
+  /**
+   * @brief Returns the column of centroids
+   *
+   * @return The list column of centroids
+   */
+  [[nodiscard]] lists_column_view centroids() const;
+
+  /**
+   * @brief Returns the internal column of mean values
+   *
+   * @return The internal column of mean values
+   */
+  [[nodiscard]] column_view means() const;
+
+  /**
+   * @brief Returns the internal column of weight values
+   *
+   * @return The internal column of weight values
+   */
+  [[nodiscard]] column_view weights() const;
+
+  /**
+   * @brief Returns the first min value for the column. Each row corresponds
+   * to the minimum value for the accompanying digest.
+   *
+   * @return const pointer to the first min value for the column
+   */
+  [[nodiscard]] double const* min_begin() const;
+
+  /**
+   * @brief Returns the first max value for the column. Each row corresponds
+   * to the maximum value for the accompanying digest.
+   *
+   * @return const pointer to the first max value for the column
+   */
+  [[nodiscard]] double const* max_begin() const;
+};
+
+}  // namespace tdigest
+}  // namespace cudf
diff --git a/cpp/include/cudf/timezone.hpp b/cpp/include/cudf/timezone.hpp
new file mode 100644
index 0000000..56678c7
--- /dev/null
+++ b/cpp/include/cudf/timezone.hpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+#include <optional>
+#include <string>
+
+namespace cudf {
+class table;
+
+// Cycle in which the time offsets repeat in Gregorian calendar
+static constexpr int32_t solar_cycle_years = 400;
+// Number of future entries in the timezone transition table:
+// Two entries per year, over the length of the Gregorian calendar's solar cycle
+static constexpr uint32_t solar_cycle_entry_count = 2 * solar_cycle_years;
+
+/**
+ * @brief Creates a transition table to convert ORC timestamps to UTC.
+ *
+ * Uses system's TZif files. Assumes little-endian platform when parsing these files.
+ * The transition table starts with the entries from the TZif file. For timestamps after the file's
+ * last transition, the table includes entries that form a `solar_cycle_years`-year cycle (future
+ * entries). This portion of the table has `solar_cycle_entry_count` elements, as it assumes two
+ * transitions per year from Daylight Saving Time. If the timezone does not have DST, the table will
+ * still include the future entries, which will all have the same offset.
+ *
+ * @param tzif_dir The directory where the TZif files are located
+ * @param timezone_name standard timezone name (for example, "America/Los_Angeles")
+ * @param mr Device memory resource used to allocate the returned table's device memory.
+ *
+ * @return The transition table for the given timezone
+ */
+std::unique_ptr<table> make_timezone_transition_table(
+  std::optional<std::string_view> tzif_dir,
+  std::string_view timezone_name,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/transform.hpp b/cpp/include/cudf/transform.hpp
new file mode 100644
index 0000000..412fe17
--- /dev/null
+++ b/cpp/include/cudf/transform.hpp
@@ -0,0 +1,228 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup transformation_transform
+ * @{
+ * @file
+ * @brief Column APIs for transforming rows
+ */
+
+/**
+ * @brief Creates a new column by applying a unary function against every
+ * element of an input column.
+ *
+ * Computes:
+ * `out[i] = F(in[i])`
+ *
+ * The output null mask is the same is the input null mask so if input[i] is
+ * null then output[i] is also null
+ *
+ * @param input         An immutable view of the input column to transform
+ * @param unary_udf     The PTX/CUDA string of the unary function to apply
+ * @param output_type   The output type that is compatible with the output type in the UDF
+ * @param is_ptx        true: the UDF is treated as PTX code; false: the UDF is treated as CUDA code
+ * @param mr            Device memory resource used to allocate the returned column's device memory
+ * @return              The column resulting from applying the unary function to
+ *                      every element of the input
+ */
+std::unique_ptr<column> transform(
+  column_view const& input,
+  std::string const& unary_udf,
+  data_type output_type,
+  bool is_ptx,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a null_mask from `input` by converting `NaN` to null and
+ * preserving existing null values and also returns new null_count.
+ *
+ * @throws cudf::logic_error if `input.type()` is a non-floating type
+ *
+ * @param input         An immutable view of the input column of floating-point type
+ * @param mr            Device memory resource used to allocate the returned bitmask
+ * @return A pair containing a `device_buffer` with the new bitmask and it's
+ * null count obtained by replacing `NaN` in `input` with null.
+ */
+std::pair<std::unique_ptr<rmm::device_buffer>, size_type> nans_to_nulls(
+  column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Compute a new column by evaluating an expression tree on a table.
+ *
+ * This evaluates an expression over a table to produce a new column. Also called an n-ary
+ * transform.
+ *
+ * @throws cudf::logic_error if passed an expression operating on table_reference::RIGHT.
+ *
+ * @param table The table used for expression evaluation
+ * @param expr The root of the expression tree
+ * @param mr Device memory resource
+ * @return Output column
+ */
+std::unique_ptr<column> compute_column(
+  table_view const& table,
+  ast::expression const& expr,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a bitmask from a column of boolean elements.
+ *
+ * If element `i` in `input` is `true`, bit `i` in the resulting mask is set (`1`). Else,
+ * if element `i` is `false` or null, bit `i` is unset (`0`).
+ *
+ *
+ * @throws cudf::logic_error if `input.type()` is a non-boolean type
+ *
+ * @param input        Boolean elements to convert to a bitmask
+ * @param mr           Device memory resource used to allocate the returned bitmask
+ * @return A pair containing a `device_buffer` with the new bitmask and it's
+ * null count obtained from input considering `true` represent `valid`/`1` and
+ * `false` represent `invalid`/`0`.
+ */
+std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type> bools_to_mask(
+  column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Encode the rows of the given table as integers
+ *
+ * The encoded values are integers in the range [0, n), where `n`
+ * is the number of distinct rows in the input table.
+ * The result table is such that `keys[result[i]] == input[i]`,
+ * where `keys` is a table containing the distinct rows  in `input` in
+ * sorted ascending order. Nulls, if any, are sorted to the end of
+ * the `keys` table.
+ *
+ * Examples:
+ * @code{.pseudo}
+ * input: [{'a', 'b', 'b', 'a'}]
+ * output: [{'a', 'b'}], {0, 1, 1, 0}
+ *
+ * input: [{1, 3, 1, 2, 9}, {1, 2, 1, 3, 5}]
+ * output: [{1, 2, 3, 9}, {1, 3, 2, 5}], {0, 2, 0, 1, 3}
+ * @endcode
+ *
+ * @param input Table containing values to be encoded
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return A pair containing the distinct row of the input table in sorter order,
+ * and a column of integer indices representing the encoded rows.
+ */
+std::pair<std::unique_ptr<cudf::table>, std::unique_ptr<cudf::column>> encode(
+  cudf::table_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Encodes `input` by generating a new column for each value in `categories` indicating the
+ * presence of that value in `input`.
+ *
+ * The resulting per-category columns are returned concatenated as a single column viewed by a
+ * `table_view`.
+ *
+ * The `i`th row of the `j`th column in the output table equals 1
+ * if `input[i] == categories[j]`, and 0 otherwise.
+ *
+ * The `i`th row of the `j`th column in the output table equals 1
+ * if input[i] == categories[j], and 0 otherwise.
+ *
+ * Examples:
+ * @code{.pseudo}
+ * input: [{'a', 'c', null, 'c', 'b'}]
+ * categories: ['c', null]
+ * output: [{0, 1, 0, 1, 0}, {0, 0, 1, 0, 0}]
+ * @endcode
+ *
+ * @throws cudf::logic_error if input and categories are of different types.
+ *
+ * @param input Column containing values to be encoded
+ * @param categories Column containing categories
+ * @param mr Device memory resource used to allocate the returned table's device memory
+ * @return A pair containing the owner to all encoded data and a table view into the data
+ */
+std::pair<std::unique_ptr<column>, table_view> one_hot_encode(
+  column_view const& input,
+  column_view const& categories,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a boolean column from given bitmask.
+ *
+ * Returns a `bool` for each bit in `[begin_bit, end_bit)`. If bit `i` in least-significant bit
+ * numbering is set (1), then element `i` in the output is `true`, otherwise `false`.
+ *
+ * @throws cudf::logic_error if `bitmask` is null and end_bit-begin_bit > 0
+ * @throws cudf::logic_error if begin_bit > end_bit
+ *
+ * Examples:
+ * @code{.pseudo}
+ * input: {0b10101010}
+ * output: [{false, true, false, true, false, true, false, true}]
+ * @endcode
+ *
+ * @param bitmask A device pointer to the bitmask which needs to be converted
+ * @param begin_bit position of the bit from which the conversion should start
+ * @param end_bit position of the bit before which the conversion should stop
+ * @param mr Device memory resource used to allocate the returned columns' device memory
+ * @return A boolean column representing the given mask from [begin_bit, end_bit)
+ */
+std::unique_ptr<column> mask_to_bools(
+  bitmask_type const* bitmask,
+  size_type begin_bit,
+  size_type end_bit,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns an approximate cumulative size in bits of all columns in the `table_view` for
+ * each row.
+ *
+ * This function counts bits instead of bytes to account for the null mask which only has one
+ * bit per row.
+ *
+ * Each row in the returned column is the sum of the per-row size for each column in
+ * the table.
+ *
+ * In some cases, this is an inexact approximation. Specifically, columns of lists and strings
+ * require N+1 offsets to represent N rows. It is up to the caller to calculate the small
+ * additional overhead of the terminating offset for any group of rows being considered.
+ *
+ * This function returns the per-row sizes as the columns are currently formed. This can
+ * end up being larger than the number you would get by gathering the rows. Specifically,
+ * the push-down of struct column validity masks can nullify rows that contain data for
+ * string or list columns. In these cases, the size returned is conservative:
+ *
+ * row_bit_count(column(x)) >= row_bit_count(gather(column(x)))
+ *
+ * @param t The table view to perform the computation on
+ * @param mr Device memory resource used to allocate the returned columns' device memory
+ * @return A 32-bit integer column containing the per-row bit counts
+ */
+std::unique_ptr<column> row_bit_count(
+  table_view const& t,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/transpose.hpp b/cpp/include/cudf/transpose.hpp
new file mode 100644
index 0000000..e5d083a
--- /dev/null
+++ b/cpp/include/cudf/transpose.hpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+/**
+ * @addtogroup reshape_transpose
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Transposes a table.
+ *
+ * Stores output in a contiguous column, exposing the transposed table as
+ * a `table_view`.
+ *
+ * @throw cudf::logic_error if column types are non-homogeneous
+ * @throw cudf::logic_error if column types are non-fixed-width
+ *
+ * @param[in] input A table (M cols x N rows) to be transposed
+ * @param[in] mr Device memory resource used to allocate the device memory of returned value
+ * @return          The transposed input (N cols x M rows) as a `column` and
+ *                  `table_view`, representing the owner and transposed table,
+ *                  respectively.
+ */
+std::pair<std::unique_ptr<column>, table_view> transpose(
+  table_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/types.hpp b/cpp/include/cudf/types.hpp
new file mode 100644
index 0000000..addab16
--- /dev/null
+++ b/cpp/include/cudf/types.hpp
@@ -0,0 +1,333 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#ifdef __CUDACC__
+#define CUDF_HOST_DEVICE __host__ __device__
+#else
+#define CUDF_HOST_DEVICE
+#endif
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <iterator>
+
+/**
+ * @file
+ * @brief Type declarations for libcudf.
+ */
+
+// Forward declarations
+/// @cond
+namespace rmm {
+class device_buffer;
+/// @endcond
+
+}  // namespace rmm
+
+namespace cudf {
+// Forward declaration
+class column;
+class column_view;
+class mutable_column_view;
+class string_view;
+class list_view;
+class struct_view;
+
+class scalar;
+
+// clang-format off
+class list_scalar;
+class struct_scalar;
+class string_scalar;
+template <typename T> class numeric_scalar;
+template <typename T> class fixed_point_scalar;
+template <typename T> class timestamp_scalar;
+template <typename T> class duration_scalar;
+
+class string_scalar_device_view;
+template <typename T> class numeric_scalar_device_view;
+template <typename T> class fixed_point_scalar_device_view;
+template <typename T> class timestamp_scalar_device_view;
+template <typename T> class duration_scalar_device_view;
+// clang-format on
+
+class table;
+class table_view;
+class mutable_table_view;
+
+/**
+ * @addtogroup utility_types
+ * @{
+ * @file
+ */
+
+using size_type         = int32_t;   ///< Row index type for columns and tables
+using bitmask_type      = uint32_t;  ///< Bitmask type stored as 32-bit unsigned integer
+using valid_type        = uint8_t;   ///< Valid type in host memory
+using thread_index_type = int64_t;   ///< Thread index type in kernels
+
+/**
+ * @brief Similar to `std::distance` but returns `cudf::size_type` and performs `static_cast`
+ *
+ * @tparam T Iterator type
+ * @param f "first" iterator
+ * @param l "last" iterator
+ * @return The distance between first and last
+ */
+template <typename T>
+size_type distance(T f, T l)
+{
+  return static_cast<size_type>(std::distance(f, l));
+}
+
+/**
+ * @brief Indicates the order in which elements should be sorted.
+ */
+enum class order : bool {
+  ASCENDING,  ///< Elements ordered from small to large
+  DESCENDING  ///< Elements ordered from large to small
+};
+
+/**
+ * @brief Enum to specify whether to include nulls or exclude nulls
+ */
+enum class null_policy : bool {
+  EXCLUDE,  ///< exclude null elements
+  INCLUDE   ///< include null elements
+};
+
+/**
+ * @brief Enum to treat NaN floating point value as null or non-null element
+ */
+enum class nan_policy : bool {
+  NAN_IS_NULL,  ///< treat nans as null elements
+  NAN_IS_VALID  ///< treat nans as valid elements (non-null)
+};
+
+/**
+ * @brief Enum to consider different elements (of floating point types) holding NaN value as equal
+ * or unequal
+ */
+enum class nan_equality /*unspecified*/ {
+  ALL_EQUAL,  ///< All NaNs compare equal, regardless of sign
+  UNEQUAL     ///< All NaNs compare unequal (IEEE754 behavior)
+};
+
+/**
+ * @brief Enum to consider two nulls as equal or unequal
+ */
+enum class null_equality : bool {
+  EQUAL,   ///< nulls compare equal
+  UNEQUAL  ///< nulls compare unequal
+};
+
+/**
+ * @brief Indicates how null values compare against all other values.
+ */
+enum class null_order : bool {
+  AFTER,  ///< NULL values ordered *after* all other values
+  BEFORE  ///< NULL values ordered *before* all other values
+};
+
+/**
+ * @brief Indicates whether a collection of values is known to be sorted.
+ */
+enum class sorted : bool { NO, YES };
+
+/**
+ * @brief Indicates how a collection of values has been ordered.
+ */
+struct order_info {
+  sorted is_sorted;          ///< Indicates whether the collection is sorted
+  order ordering;            ///< Indicates the order in which the values are sorted
+  null_order null_ordering;  ///< Indicates how null values compare against all other values
+};
+
+/**
+ * @brief Controls the allocation/initialization of a null mask.
+ */
+enum class mask_state : int32_t {
+  UNALLOCATED,    ///< Null mask not allocated, (all elements are valid)
+  UNINITIALIZED,  ///< Null mask allocated, but not initialized
+  ALL_VALID,      ///< Null mask allocated, initialized to all elements valid
+  ALL_NULL        ///< Null mask allocated, initialized to all elements NULL
+};
+
+/**
+ * @brief Interpolation method to use when the desired quantile lies between
+ * two data points i and j
+ */
+enum class interpolation : int32_t {
+  LINEAR,    ///< Linear interpolation between i and j
+  LOWER,     ///< Lower data point (i)
+  HIGHER,    ///< Higher data point (j)
+  MIDPOINT,  ///< (i + j)/2
+  NEAREST    ///< i or j, whichever is nearest
+};
+
+/**
+ * @brief Identifies a column's logical element type
+ */
+enum class type_id : int32_t {
+  EMPTY,                   ///< Always null with no underlying data
+  INT8,                    ///< 1 byte signed integer
+  INT16,                   ///< 2 byte signed integer
+  INT32,                   ///< 4 byte signed integer
+  INT64,                   ///< 8 byte signed integer
+  UINT8,                   ///< 1 byte unsigned integer
+  UINT16,                  ///< 2 byte unsigned integer
+  UINT32,                  ///< 4 byte unsigned integer
+  UINT64,                  ///< 8 byte unsigned integer
+  FLOAT32,                 ///< 4 byte floating point
+  FLOAT64,                 ///< 8 byte floating point
+  BOOL8,                   ///< Boolean using one byte per value, 0 == false, else true
+  TIMESTAMP_DAYS,          ///< point in time in days since Unix Epoch in int32
+  TIMESTAMP_SECONDS,       ///< point in time in seconds since Unix Epoch in int64
+  TIMESTAMP_MILLISECONDS,  ///< point in time in milliseconds since Unix Epoch in int64
+  TIMESTAMP_MICROSECONDS,  ///< point in time in microseconds since Unix Epoch in int64
+  TIMESTAMP_NANOSECONDS,   ///< point in time in nanoseconds since Unix Epoch in int64
+  DURATION_DAYS,           ///< time interval of days in int32
+  DURATION_SECONDS,        ///< time interval of seconds in int64
+  DURATION_MILLISECONDS,   ///< time interval of milliseconds in int64
+  DURATION_MICROSECONDS,   ///< time interval of microseconds in int64
+  DURATION_NANOSECONDS,    ///< time interval of nanoseconds in int64
+  DICTIONARY32,            ///< Dictionary type using int32 indices
+  STRING,                  ///< String elements
+  LIST,                    ///< List elements
+  DECIMAL32,               ///< Fixed-point type with int32_t
+  DECIMAL64,               ///< Fixed-point type with int64_t
+  DECIMAL128,              ///< Fixed-point type with __int128_t
+  STRUCT,                  ///< Struct elements
+  // `NUM_TYPE_IDS` must be last!
+  NUM_TYPE_IDS  ///< Total number of type ids
+};
+
+/**
+ * @brief Indicator for the logical data type of an element in a column.
+ *
+ * Simple types can be entirely described by their `id()`, but some types
+ * require additional metadata to fully describe elements of that type.
+ */
+class data_type {
+ public:
+  data_type()                 = default;
+  ~data_type()                = default;
+  data_type(data_type const&) = default;  ///< Copy constructor
+  data_type(data_type&&)      = default;  ///< Move constructor
+
+  /**
+   * @brief Copy assignment operator for data_type
+   *
+   * @return Reference to this object
+   */
+  data_type& operator=(data_type const&) = default;
+
+  /**
+   * @brief Move assignment operator for data_type
+   *
+   * @return Reference to this object
+   */
+  data_type& operator=(data_type&&) = default;
+
+  /**
+   * @brief Construct a new `data_type` object
+   *
+   * @param id The type's identifier
+   */
+  explicit constexpr data_type(type_id id) : _id{id} {}
+
+  /**
+   * @brief Construct a new `data_type` object for `numeric::fixed_point`
+   *
+   * @param id The `fixed_point`'s identifier
+   * @param scale The `fixed_point`'s scale (see `fixed_point::_scale`)
+   */
+  explicit data_type(type_id id, int32_t scale) : _id{id}, _fixed_point_scale{scale}
+  {
+    assert(id == type_id::DECIMAL32 || id == type_id::DECIMAL64 || id == type_id::DECIMAL128);
+  }
+
+  /**
+   * @brief Returns the type identifier
+   *
+   * @return The type identifier
+   */
+  [[nodiscard]] constexpr type_id id() const noexcept { return _id; }
+
+  /**
+   * @brief Returns the scale (for fixed_point types)
+   *
+   * @return The scale
+   */
+  [[nodiscard]] constexpr int32_t scale() const noexcept { return _fixed_point_scale; }
+
+ private:
+  type_id _id{type_id::EMPTY};
+
+  // Below is additional type specific metadata. Currently, only _fixed_point_scale is stored.
+
+  int32_t _fixed_point_scale{};  // numeric::scale_type not available here, use int32_t
+};
+
+/**
+ * @brief Compares two `data_type` objects for equality.
+ *
+ * // TODO Define exactly what it means for two `data_type`s to be equal. e.g.,
+ * are two timestamps with different resolutions equal? How about decimals with
+ * different scale/precision?
+ *
+ * @param lhs The first `data_type` to compare
+ * @param rhs The second `data_type` to compare
+ * @return true `lhs` is equal to `rhs`
+ * @return false `lhs` is not equal to `rhs`
+ */
+constexpr bool operator==(data_type const& lhs, data_type const& rhs)
+{
+  // use std::tie in the future, breaks JITIFY currently
+  return lhs.id() == rhs.id() && lhs.scale() == rhs.scale();
+}
+
+/**
+ * @brief Compares two `data_type` objects for inequality.
+ *
+ * // TODO Define exactly what it means for two `data_type`s to be equal. e.g.,
+ * are two timestamps with different resolutions equal? How about decimals with
+ * different scale/precision?
+ *
+ * @param lhs The first `data_type` to compare
+ * @param rhs The second `data_type` to compare
+ * @return true `lhs` is not equal to `rhs`
+ * @return false `lhs` is equal to `rhs`
+ */
+inline bool operator!=(data_type const& lhs, data_type const& rhs) { return !(lhs == rhs); }
+
+/**
+ * @brief Returns the size in bytes of elements of the specified `data_type`
+ *
+ * @note Only fixed-width types are supported
+ *
+ * @throws cudf::logic_error if `is_fixed_width(element_type) == false`
+ *
+ * @param t The `data_type` to get the size of
+ * @return Size in bytes of an element of the specified `data_type`
+ */
+std::size_t size_of(data_type t);
+
+/** @} */
+}  // namespace cudf
diff --git a/cpp/include/cudf/unary.hpp b/cpp/include/cudf/unary.hpp
new file mode 100644
index 0000000..1130c41
--- /dev/null
+++ b/cpp/include/cudf/unary.hpp
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2018-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+/**
+ * @addtogroup transformation_unaryops
+ * @{
+ * @file
+ * @brief Column APIs for unary ops
+ */
+
+/**
+ * @brief Types of unary operations that can be performed on data.
+ */
+enum class unary_operator : int32_t {
+  SIN,         ///< Trigonometric sine
+  COS,         ///< Trigonometric cosine
+  TAN,         ///< Trigonometric tangent
+  ARCSIN,      ///< Trigonometric sine inverse
+  ARCCOS,      ///< Trigonometric cosine inverse
+  ARCTAN,      ///< Trigonometric tangent inverse
+  SINH,        ///< Hyperbolic sine
+  COSH,        ///< Hyperbolic cosine
+  TANH,        ///< Hyperbolic tangent
+  ARCSINH,     ///< Hyperbolic sine inverse
+  ARCCOSH,     ///< Hyperbolic cosine inverse
+  ARCTANH,     ///< Hyperbolic tangent inverse
+  EXP,         ///< Exponential (base e, Euler number)
+  LOG,         ///< Natural Logarithm (base e)
+  SQRT,        ///< Square-root (x^0.5)
+  CBRT,        ///< Cube-root (x^(1.0/3))
+  CEIL,        ///< Smallest integer value not less than arg
+  FLOOR,       ///< largest integer value not greater than arg
+  ABS,         ///< Absolute value
+  RINT,        ///< Rounds the floating-point argument arg to an integer value
+  BIT_INVERT,  ///< Bitwise Not (~)
+  NOT,         ///< Logical Not (!)
+};
+
+/**
+ * @brief Performs unary op on all values in column
+ *
+ * Note: For `decimal32` and `decimal64`, only `ABS`, `CEIL` and `FLOOR` are supported.
+ *
+ * @param input A `column_view` as input
+ * @param op operation to perform
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns Column of same size as `input` containing result of the operation
+ */
+std::unique_ptr<cudf::column> unary_operation(
+  cudf::column_view const& input,
+  cudf::unary_operator op,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a column of `type_id::BOOL8` elements where for every element in `input` `true`
+ * indicates the value is null and `false` indicates the value is valid.
+ *
+ * @param input A `column_view` as input
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A non-nullable column of `type_id::BOOL8` elements with `true`
+ * representing `null` values.
+ */
+std::unique_ptr<cudf::column> is_null(
+  cudf::column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a column of `type_id::BOOL8` elements where for every element in `input` `true`
+ * indicates the value is valid and `false` indicates the value is null.
+ *
+ * @param input A `column_view` as input
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A non-nullable column of `type_id::BOOL8` elements with `false`
+ * representing `null` values.
+ */
+std::unique_ptr<cudf::column> is_valid(
+  cudf::column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief  Casts data from dtype specified in input to dtype specified in output.
+ *
+ * Supports only fixed-width types.
+ *
+ * @param input Input column
+ * @param out_type Desired datatype of output column
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns Column of same size as `input` containing result of the cast operation
+ * @throw cudf::logic_error if `out_type` is not a fixed-width type
+ */
+std::unique_ptr<column> cast(
+  column_view const& input,
+  data_type out_type,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a column of `type_id::BOOL8` elements indicating the presence of `NaN` values
+ * in a column of floating point values.
+ * The output element at row `i` is `true` if the element in `input` at row i is `NAN`, else `false`
+ *
+ * @throws cudf::logic_error if `input` is a non-floating point type
+ *
+ * @param input A column of floating-point elements
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A non-nullable column of `type_id::BOOL8` elements with `true` representing `NAN` values
+ */
+std::unique_ptr<column> is_nan(
+  cudf::column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a column of `type_id::BOOL8` elements indicating the absence of `NaN` values
+ * in a column of floating point values.
+ * The output element at row `i` is `false` if the element in `input` at row i is `NAN`, else `true`
+ *
+ * @throws cudf::logic_error if `input` is a non-floating point type
+ *
+ * @param input A column of floating-point elements
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A non-nullable column of `type_id::BOOL8` elements with `false` representing `NAN`
+ * values
+ */
+std::unique_ptr<column> is_not_nan(
+  cudf::column_view const& input,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/bit.hpp b/cpp/include/cudf/utilities/bit.hpp
new file mode 100644
index 0000000..11a797e
--- /dev/null
+++ b/cpp/include/cudf/utilities/bit.hpp
@@ -0,0 +1,218 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cassert>
+#include <cuda/std/climits>
+#include <cudf/types.hpp>
+
+/**
+ * @file bit.hpp
+ * @brief Utilities for bit and bitmask operations.
+ */
+
+namespace cudf {
+namespace detail {
+// @cond
+// Work around a bug in NVRTC that fails to compile assert() in constexpr
+// functions (fixed after CUDA 11.0)
+#if defined __GNUC__
+#define LIKELY(EXPR) __builtin_expect(!!(EXPR), 1)
+#else
+#define LIKELY(EXPR) (!!(EXPR))
+#endif
+
+#ifdef NDEBUG
+#define constexpr_assert(CHECK) static_cast<void>(0)
+#else
+#define constexpr_assert(CHECK) (LIKELY(CHECK) ? void(0) : [] { assert(!#CHECK); }())
+#endif
+// @endcond
+
+/**
+ * @brief Returns the number of bits the given type can hold.
+ *
+ * @tparam T The type to query
+ * @return `sizeof(T)` in bits
+ */
+template <typename T>
+constexpr CUDF_HOST_DEVICE inline std::size_t size_in_bits()
+{
+  static_assert(CHAR_BIT == 8, "Size of a byte must be 8 bits.");
+  return sizeof(T) * CHAR_BIT;
+}
+}  // namespace detail
+
+/**
+ * @addtogroup utility_bitmask
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns the index of the word containing the specified bit.
+ *
+ * @param bit_index The index of the bit to query
+ * @return The index of the word containing the specified bit
+ */
+constexpr CUDF_HOST_DEVICE inline size_type word_index(size_type bit_index)
+{
+  return bit_index / detail::size_in_bits<bitmask_type>();
+}
+
+/**
+ * @brief Returns the position within a word of the specified bit.
+ *
+ * @param bit_index The index of the bit to query
+ * @return The position within a word of the specified bit
+ */
+constexpr CUDF_HOST_DEVICE inline size_type intra_word_index(size_type bit_index)
+{
+  return bit_index % detail::size_in_bits<bitmask_type>();
+}
+
+/**
+ * @brief Sets the specified bit to `1`
+ *
+ * This function is not thread-safe, i.e., attempting to update bits within the
+ * same word concurrently from multiple threads results in undefined behavior.
+ *
+ * @param bitmask The bitmask containing the bit to set
+ * @param bit_index Index of the bit to set
+ */
+CUDF_HOST_DEVICE inline void set_bit_unsafe(bitmask_type* bitmask, size_type bit_index)
+{
+  assert(nullptr != bitmask);
+  bitmask[word_index(bit_index)] |= (bitmask_type{1} << intra_word_index(bit_index));
+}
+
+/**
+ * @brief Sets the specified bit to `0`
+ *
+ * This function is not thread-safe, i.e., attempting to update bits within the
+ * same word concurrently from multiple threads results in undefined behavior.
+ *
+ * @param bitmask The bitmask containing the bit to clear
+ * @param bit_index The index of the bit to clear
+ */
+CUDF_HOST_DEVICE inline void clear_bit_unsafe(bitmask_type* bitmask, size_type bit_index)
+{
+  assert(nullptr != bitmask);
+  bitmask[word_index(bit_index)] &= ~(bitmask_type{1} << intra_word_index(bit_index));
+}
+
+/**
+ * @brief Indicates whether the specified bit is set to `1`
+ *
+ * @param bitmask The bitmask containing the bit to clear
+ * @param bit_index Index of the bit to test
+ * @return true The specified bit is `1`
+ * @return false  The specified bit is `0`
+ */
+CUDF_HOST_DEVICE inline bool bit_is_set(bitmask_type const* bitmask, size_type bit_index)
+{
+  assert(nullptr != bitmask);
+  return bitmask[word_index(bit_index)] & (bitmask_type{1} << intra_word_index(bit_index));
+}
+
+/**
+ * @brief optional-like interface to check if a specified bit of a bitmask is set.
+ *
+ * @param bitmask The bitmask containing the bit to clear
+ * @param bit_index Index of the bit to test
+ * @param default_value Value to return if `bitmask` is nullptr
+ * @return true The specified bit is `1`
+ * @return false  The specified bit is `0`
+ * @return `default_value` if `bitmask` is nullptr
+ */
+CUDF_HOST_DEVICE inline bool bit_value_or(bitmask_type const* bitmask,
+                                          size_type bit_index,
+                                          bool default_value)
+{
+  return bitmask != nullptr ? bit_is_set(bitmask, bit_index) : default_value;
+}
+
+/**
+ * @brief Returns a bitmask word with the `n` least significant bits set.
+ *
+ * Behavior is undefined if `n < 0` or if `n >= size_in_bits<bitmask_type>()`
+ *
+ * @param n The number of least significant bits to set
+ * @return A bitmask word with `n` least significant bits set
+ */
+constexpr CUDF_HOST_DEVICE inline bitmask_type set_least_significant_bits(size_type n)
+{
+  constexpr_assert(0 <= n && n < static_cast<size_type>(detail::size_in_bits<bitmask_type>()));
+  return ((bitmask_type{1} << n) - 1);
+}
+
+/**
+ * @brief Returns a bitmask word with the `n` most significant bits set.
+ *
+ * Behavior is undefined if `n < 0` or if `n >= size_in_bits<bitmask_type>()`
+ *
+ * @param n The number of most significant bits to set
+ * @return A bitmask word with `n` most significant bits set
+ */
+constexpr CUDF_HOST_DEVICE inline bitmask_type set_most_significant_bits(size_type n)
+{
+  constexpr size_type word_size{detail::size_in_bits<bitmask_type>()};
+  constexpr_assert(0 <= n && n < word_size);
+  return ~((bitmask_type{1} << (word_size - n)) - 1);
+}
+
+#ifdef __CUDACC__
+
+/**
+ * @brief Sets the specified bit to `1`
+ *
+ * @note This operation requires a global atomic operation. Therefore, it is
+ * not recommended to use this function in performance critical regions. When
+ * possible, it is more efficient to compute and update an entire word at
+ * once using `set_word`.
+ *
+ * This function is thread-safe.
+ *
+ * @param bitmask The bitmask containing the bit to set
+ * @param bit_index  Index of the bit to set
+ */
+__device__ inline void set_bit(bitmask_type* bitmask, size_type bit_index)
+{
+  assert(nullptr != bitmask);
+  atomicOr(&bitmask[word_index(bit_index)], (bitmask_type{1} << intra_word_index(bit_index)));
+}
+
+/**
+ * @brief Sets the specified bit to `0`
+ *
+ * @note This operation requires a global atomic operation. Therefore, it is
+ * not recommended to use this function in performance critical regions. When
+ * possible, it is more efficient to compute and update an entire element at
+ * once using `set_element`.
+
+ * This function is thread-safe.
+ *
+ * @param bit_index  Index of the bit to clear
+ */
+__device__ inline void clear_bit(bitmask_type* bitmask, size_type bit_index)
+{
+  assert(nullptr != bitmask);
+  atomicAnd(&bitmask[word_index(bit_index)], ~(bitmask_type{1} << intra_word_index(bit_index)));
+}
+#endif
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/default_stream.hpp b/cpp/include/cudf/utilities/default_stream.hpp
new file mode 100644
index 0000000..1eec3b9
--- /dev/null
+++ b/cpp/include/cudf/utilities/default_stream.hpp
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+
+/**
+ * @brief Get the current default stream
+ *
+ * @return The current default stream.
+ */
+rmm::cuda_stream_view const get_default_stream();
+
+/**
+ * @brief Check if per-thread default stream is enabled.
+ *
+ * @return true if PTDS is enabled, false otherwise.
+ */
+bool is_ptds_enabled();
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/error.hpp b/cpp/include/cudf/utilities/error.hpp
new file mode 100644
index 0000000..afb9275
--- /dev/null
+++ b/cpp/include/cudf/utilities/error.hpp
@@ -0,0 +1,284 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/utilities/stacktrace.hpp>
+
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace cudf {
+/**
+ * @addtogroup utility_error
+ * @{
+ * @file
+ */
+
+/**
+ * @brief The struct to store the current stacktrace upon its construction.
+ */
+struct stacktrace_recorder {
+  stacktrace_recorder()
+    // Exclude the current stackframe, as it is this constructor.
+    : _stacktrace{cudf::detail::get_stacktrace(cudf::detail::capture_last_stackframe::NO)}
+  {
+  }
+
+ public:
+  /**
+   * @brief Get the stored stacktrace captured during object construction.
+   *
+   * @return The pointer to a null-terminated string storing the output stacktrace
+   */
+  char const* stacktrace() const { return _stacktrace.c_str(); }
+
+ protected:
+  std::string const _stacktrace;  //!< The whole stacktrace stored as one string.
+};
+
+/**
+ * @brief Exception thrown when logical precondition is violated.
+ *
+ * This exception should not be thrown directly and is instead thrown by the
+ * CUDF_EXPECTS macro.
+ */
+struct logic_error : public std::logic_error, public stacktrace_recorder {
+  /**
+   * @brief Constructs a logic_error with the error message.
+   *
+   * @param message Message to be associated with the exception
+   */
+  logic_error(char const* const message) : std::logic_error(message) {}
+
+  /**
+   * @brief Construct a new logic error object with error message
+   *
+   * @param message Message to be associated with the exception
+   */
+  logic_error(std::string const& message) : std::logic_error(message) {}
+
+  // TODO Add an error code member? This would be useful for translating an
+  // exception to an error code in a pure-C API
+};
+/**
+ * @brief Exception thrown when a CUDA error is encountered.
+ *
+ */
+struct cuda_error : public std::runtime_error, public stacktrace_recorder {
+  /**
+   * @brief Construct a new cuda error object with error message and code.
+   *
+   * @param message Error message
+   * @param error CUDA error code
+   */
+  cuda_error(std::string const& message, cudaError_t const& error)
+    : std::runtime_error(message), _cudaError(error)
+  {
+  }
+
+ public:
+  /**
+   * @brief Returns the CUDA error code associated with the exception.
+   *
+   * @return CUDA error code
+   */
+  cudaError_t error_code() const { return _cudaError; }
+
+ protected:
+  cudaError_t _cudaError;  //!< CUDA error code
+};
+
+struct fatal_cuda_error : public cuda_error {
+  using cuda_error::cuda_error;  // Inherit constructors
+};
+
+/**
+ * @brief Exception thrown when an operation is attempted on an unsupported dtype.
+ *
+ * This exception should be thrown when an operation is attempted on an
+ * unsupported data_type. This exception should not be thrown directly and is
+ * instead thrown by the CUDF_EXPECTS or CUDF_FAIL macros.
+ */
+struct data_type_error : public std::invalid_argument, public stacktrace_recorder {
+  /**
+   * @brief Constructs a data_type_error with the error message.
+   *
+   * @param message Message to be associated with the exception
+   */
+  data_type_error(char const* const message) : std::invalid_argument(message) {}
+
+  /**
+   * @brief Construct a new data_type_error object with error message
+   *
+   * @param message Message to be associated with the exception
+   */
+  data_type_error(std::string const& message) : std::invalid_argument(message) {}
+};
+/** @} */
+
+}  // namespace cudf
+
+#define STRINGIFY_DETAIL(x) #x                   ///< Stringify a macro argument
+#define CUDF_STRINGIFY(x)   STRINGIFY_DETAIL(x)  ///< Stringify a macro argument
+
+/**
+ * @addtogroup utility_error
+ * @{
+ */
+
+/**
+ * @brief Macro for checking (pre-)conditions that throws an exception when
+ * a condition is violated.
+ *
+ * Defaults to throwing `cudf::logic_error`, but a custom exception may also be
+ * specified.
+ *
+ * Example usage:
+ * ```
+ * // throws cudf::logic_error
+ * CUDF_EXPECTS(p != nullptr, "Unexpected null pointer");
+ *
+ * // throws std::runtime_error
+ * CUDF_EXPECTS(p != nullptr, "Unexpected nullptr", std::runtime_error);
+ * ```
+ * @param ... This macro accepts either two or three arguments:
+ *   - The first argument must be an expression that evaluates to true or
+ *     false, and is the condition being checked.
+ *   - The second argument is a string literal used to construct the `what` of
+ *     the exception.
+ *   - When given, the third argument is the exception to be thrown. When not
+ *     specified, defaults to `cudf::logic_error`.
+ * @throw `_exception_type` if the condition evaluates to 0 (false).
+ */
+#define CUDF_EXPECTS(...)                                             \
+  GET_CUDF_EXPECTS_MACRO(__VA_ARGS__, CUDF_EXPECTS_3, CUDF_EXPECTS_2) \
+  (__VA_ARGS__)
+
+/// @cond
+
+#define GET_CUDF_EXPECTS_MACRO(_1, _2, _3, NAME, ...) NAME
+
+#define CUDF_EXPECTS_3(_condition, _reason, _exception_type)                    \
+  do {                                                                          \
+    static_assert(std::is_base_of_v<std::exception, _exception_type>);          \
+    (_condition) ? static_cast<void>(0)                                         \
+                 : throw _exception_type /*NOLINT(bugprone-macro-parentheses)*/ \
+      {"CUDF failure at: " __FILE__ ":" CUDF_STRINGIFY(__LINE__) ": " _reason}; \
+  } while (0)
+
+#define CUDF_EXPECTS_2(_condition, _reason) CUDF_EXPECTS_3(_condition, _reason, cudf::logic_error)
+
+/// @endcond
+
+/**
+ * @brief Indicates that an erroneous code path has been taken.
+ *
+ * Example usage:
+ * ```c++
+ * // Throws `cudf::logic_error`
+ * CUDF_FAIL("Unsupported code path");
+ *
+ * // Throws `std::runtime_error`
+ * CUDF_FAIL("Unsupported code path", std::runtime_error);
+ * ```
+ *
+ * @param ... This macro accepts either one or two arguments:
+ *   - The first argument is a string literal used to construct the `what` of
+ *     the exception.
+ *   - When given, the second argument is the exception to be thrown. When not
+ *     specified, defaults to `cudf::logic_error`.
+ * @throw `_exception_type` if the condition evaluates to 0 (false).
+ */
+#define CUDF_FAIL(...)                                       \
+  GET_CUDF_FAIL_MACRO(__VA_ARGS__, CUDF_FAIL_2, CUDF_FAIL_1) \
+  (__VA_ARGS__)
+
+/// @cond
+
+#define GET_CUDF_FAIL_MACRO(_1, _2, NAME, ...) NAME
+
+#define CUDF_FAIL_2(_what, _exception_type)      \
+  /*NOLINTNEXTLINE(bugprone-macro-parentheses)*/ \
+  throw _exception_type { "CUDF failure at:" __FILE__ ":" CUDF_STRINGIFY(__LINE__) ": " _what }
+
+#define CUDF_FAIL_1(_what) CUDF_FAIL_2(_what, cudf::logic_error)
+
+/// @endcond
+
+namespace cudf {
+namespace detail {
+// @cond
+inline void throw_cuda_error(cudaError_t error, char const* file, unsigned int line)
+{
+  // Calls cudaGetLastError to clear the error status. It is nearly certain that a fatal error
+  // occurred if it still returns the same error after a cleanup.
+  cudaGetLastError();
+  auto const last = cudaFree(0);
+  auto const msg  = std::string{"CUDA error encountered at: " + std::string{file} + ":" +
+                               std::to_string(line) + ": " + std::to_string(error) + " " +
+                               cudaGetErrorName(error) + " " + cudaGetErrorString(error)};
+  // Call cudaDeviceSynchronize to ensure `last` did not result from an asynchronous error.
+  // between two calls.
+  if (error == last && last == cudaDeviceSynchronize()) {
+    throw fatal_cuda_error{"Fatal " + msg, error};
+  } else {
+    throw cuda_error{msg, error};
+  }
+}
+// @endcond
+}  // namespace detail
+}  // namespace cudf
+
+/**
+ * @brief Error checking macro for CUDA runtime API functions.
+ *
+ * Invokes a CUDA runtime API function call, if the call does not return
+ * cudaSuccess, invokes cudaGetLastError() to clear the error and throws an
+ * exception detailing the CUDA error that occurred
+ */
+#define CUDF_CUDA_TRY(call)                                                                    \
+  do {                                                                                         \
+    cudaError_t const status = (call);                                                         \
+    if (cudaSuccess != status) { cudf::detail::throw_cuda_error(status, __FILE__, __LINE__); } \
+  } while (0);
+
+/**
+ * @brief Debug macro to check for CUDA errors
+ *
+ * In a non-release build, this macro will synchronize the specified stream
+ * before error checking. In both release and non-release builds, this macro
+ * checks for any pending CUDA errors from previous calls. If an error is
+ * reported, an exception is thrown detailing the CUDA error that occurred.
+ *
+ * The intent of this macro is to provide a mechanism for synchronous and
+ * deterministic execution for debugging asynchronous CUDA execution. It should
+ * be used after any asynchronous CUDA call, e.g., cudaMemcpyAsync, or an
+ * asynchronous kernel launch.
+ */
+#ifndef NDEBUG
+#define CUDF_CHECK_CUDA(stream)                   \
+  do {                                            \
+    CUDF_CUDA_TRY(cudaStreamSynchronize(stream)); \
+    CUDF_CUDA_TRY(cudaPeekAtLastError());         \
+  } while (0);
+#else
+#define CUDF_CHECK_CUDA(stream) CUDF_CUDA_TRY(cudaPeekAtLastError());
+#endif
+/** @} */
diff --git a/cpp/include/cudf/utilities/logger.hpp b/cpp/include/cudf/utilities/logger.hpp
new file mode 100644
index 0000000..a39df06
--- /dev/null
+++ b/cpp/include/cudf/utilities/logger.hpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <spdlog/spdlog.h>
+
+namespace cudf {
+
+/**
+ * @brief Returns the global logger.
+ *
+ * This is a global instance of a spdlog logger. It can be used to configure logging behavior in
+ * libcudf.
+ *
+ * Examples:
+ * @code{.cpp}
+ * // Turn off logging at runtime
+ * cudf::logger().set_level(spdlog::level::off);
+ * // Add a stdout sink to the logger
+ * cudf::logger().sinks().push_back(std::make_shared<spdlog::sinks::stdout_sink_mt>());
+ * // Replace the default sink
+ * cudf::logger().sinks() ={std::make_shared<spdlog::sinks::stderr_sink_mt>()};
+ * @endcode
+ *
+ * Note: Changes to the sinks are not thread safe and should only be done during global
+ * initialization.
+ *
+ * @return spdlog::logger& The logger.
+ */
+spdlog::logger& logger();
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/span.hpp b/cpp/include/cudf/utilities/span.hpp
new file mode 100644
index 0000000..3e5f6e3
--- /dev/null
+++ b/cpp/include/cudf/utilities/span.hpp
@@ -0,0 +1,491 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/device_vector.hpp>
+
+#include <thrust/detail/raw_pointer_cast.h>
+#include <thrust/device_vector.h>
+#include <thrust/host_vector.h>
+#include <thrust/memory.h>
+
+#include <cstddef>
+#include <limits>
+#include <type_traits>
+
+namespace cudf {
+
+/// A constant used to differentiate std::span of static and dynamic extent
+constexpr std::size_t dynamic_extent = std::numeric_limits<std::size_t>::max();
+
+namespace detail {
+
+/**
+ * @brief C++20 std::span with reduced feature set.
+ *
+ */
+template <typename T, std::size_t Extent, typename Derived>
+class span_base {
+  static_assert(Extent == dynamic_extent, "Only dynamic extent is supported");
+
+ public:
+  using element_type    = T;                  ///< The type of the elements in the span
+  using value_type      = std::remove_cv<T>;  ///< Stored value type
+  using size_type       = std::size_t;        ///< The type used for the size of the span
+  using difference_type = std::ptrdiff_t;     ///< std::ptrdiff_t
+  using pointer         = T*;                 ///< The type of the pointer returned by data()
+  using iterator        = T*;                 ///< The type of the iterator returned by begin()
+  using const_pointer   = T const*;           ///< The type of the pointer returned by data() const
+  using reference       = T&;  ///< The type of the reference returned by operator[](size_type)
+  using const_reference =
+    T const&;  ///< The type of the reference returned by operator[](size_type) const
+
+  static constexpr std::size_t extent = Extent;  ///< The extent of the span
+
+  constexpr span_base() noexcept {}
+  /**
+   * @brief Constructs a span from a pointer and a size.
+   *
+   * @param data Pointer to the first element in the span.
+   * @param size The number of elements in the span.
+   */
+  constexpr span_base(pointer data, size_type size) : _data(data), _size(size) {}
+  // constexpr span_base(pointer begin, pointer end) : _data(begin), _size(end - begin) {}
+  constexpr span_base(span_base const&) noexcept = default;  ///< Copy constructor
+  /**
+   * @brief Copy assignment operator.
+   *
+   * @return Reference to this span.
+   */
+  constexpr span_base& operator=(span_base const&) noexcept = default;
+
+  // not noexcept due to undefined behavior when size = 0
+  /**
+   * @brief Returns a reference to the first element in the span.
+   *
+   * Calling front on an empty span results in undefined behavior.
+   *
+   * @return Reference to the first element in the span
+   */
+  constexpr reference front() const { return _data[0]; }
+  // not noexcept due to undefined behavior when size = 0
+  /**
+   * @brief Returns a reference to the last element in the span.
+   *
+   * Calling last on an empty span results in undefined behavior.
+   *
+   * @return Reference to the last element in the span
+   */
+  constexpr reference back() const { return _data[_size - 1]; }
+  // not noexcept due to undefined behavior when idx < 0 || idx >= size
+  /**
+   * @brief Returns a reference to the idx-th element of the sequence.
+   *
+   * The behavior is undefined if idx is out of range (i.e., if it is greater than or equal to
+   * size()).
+   *
+   * @param idx the index of the element to access
+   * @return A reference to the idx-th element of the sequence, i.e., `data()[idx]`
+   */
+  constexpr reference operator[](size_type idx) const { return _data[idx]; }
+
+  /**
+   * @brief Returns an iterator to the first element of the span.
+   *
+   * If the span is empty, the returned iterator will be equal to end().
+   *
+   * @return An iterator to the first element of the span
+   */
+  constexpr iterator begin() const noexcept { return _data; }
+  /**
+   * @brief Returns an iterator to the element following the last element of the span.
+   *
+   * This element acts as a placeholder; attempting to access it results in undefined behavior.
+   *
+   * @return An iterator to the element following the last element of the span
+   */
+  constexpr iterator end() const noexcept { return _data + _size; }
+  /**
+   * @brief Returns a pointer to the beginning of the sequence.
+   *
+   * @return A pointer to the first element of the span
+   */
+  constexpr pointer data() const noexcept { return _data; }
+
+  /**
+   * @brief Returns the number of elements in the span.
+   *
+   * @return The number of elements in the span
+   */
+  [[nodiscard]] constexpr size_type size() const noexcept { return _size; }
+  /**
+   * @brief Returns the size of the sequence in bytes.
+   *
+   * @return The size of the sequence in bytes
+   */
+  [[nodiscard]] constexpr size_type size_bytes() const noexcept { return sizeof(T) * _size; }
+  /**
+   * @brief Checks if the span is empty.
+   *
+   * @return True if the span is empty, false otherwise
+   */
+  [[nodiscard]] constexpr bool empty() const noexcept { return _size == 0; }
+
+  /**
+   * @brief Obtains a subspan consisting of the first N elements of the sequence
+   *
+   * @param count Number of elements from the beginning of this span to put in the subspan.
+   * @return A subspan of the first N elements of the sequence
+   */
+  constexpr Derived first(size_type count) const noexcept { return Derived(_data, count); }
+
+  /**
+   * @brief Obtains a subspan consisting of the last N elements of the sequence
+   *
+   * @param count Number of elements from the end of this span to put in the subspan
+   * @return A subspan of the last N elements of the sequence
+   */
+  constexpr Derived last(size_type count) const noexcept
+  {
+    return Derived(_data + _size - count, count);
+  }
+
+  /**
+   * @brief Obtains a span that is a view over the `count` elements of this span starting at offset
+   *
+   * @param offset The offset of the first element in the subspan
+   * @param count The number of elements in the subspan
+   * @return A subspan of the sequence, of requested count and offset
+   */
+  constexpr Derived subspan(size_type offset, size_type count) const noexcept
+  {
+    return Derived(_data + offset, count);
+  }
+
+ private:
+  pointer _data{nullptr};
+  size_type _size{0};
+};
+
+}  // namespace detail
+
+// ===== host_span =================================================================================
+
+template <typename T>
+struct is_host_span_supported_container : std::false_type {};
+
+template <typename T, typename Alloc>
+struct is_host_span_supported_container<  //
+  std::vector<T, Alloc>> : std::true_type {};
+
+template <typename T, typename Alloc>
+struct is_host_span_supported_container<  //
+  thrust::host_vector<T, Alloc>> : std::true_type {};
+
+template <typename T, typename Alloc>
+struct is_host_span_supported_container<  //
+  std::basic_string<T, std::char_traits<T>, Alloc>> : std::true_type {};
+
+/**
+ * @brief C++20 std::span with reduced feature set.
+ *
+ */
+template <typename T, std::size_t Extent = cudf::dynamic_extent>
+struct host_span : public cudf::detail::span_base<T, Extent, host_span<T, Extent>> {
+  using base = cudf::detail::span_base<T, Extent, host_span<T, Extent>>;  ///< Base type
+  using base::base;
+
+  constexpr host_span() noexcept : base() {}  // required to compile on centos
+
+  /// Constructor from container
+  /// @param in The container to construct the span from
+  template <
+    typename C,
+    // Only supported containers of types convertible to T
+    std::enable_if_t<is_host_span_supported_container<C>::value &&
+                     std::is_convertible_v<std::remove_pointer_t<decltype(thrust::raw_pointer_cast(
+                                             std::declval<C&>().data()))> (*)[],
+                                           T (*)[]>>* = nullptr>
+  constexpr host_span(C& in) : base(thrust::raw_pointer_cast(in.data()), in.size())
+  {
+  }
+
+  /// Constructor from const container
+  /// @param in The container to construct the span from
+  template <
+    typename C,
+    // Only supported containers of types convertible to T
+    std::enable_if_t<is_host_span_supported_container<C>::value &&
+                     std::is_convertible_v<std::remove_pointer_t<decltype(thrust::raw_pointer_cast(
+                                             std::declval<C&>().data()))> (*)[],
+                                           T (*)[]>>* = nullptr>
+  constexpr host_span(C const& in) : base(thrust::raw_pointer_cast(in.data()), in.size())
+  {
+  }
+
+  // Copy construction to support const conversion
+  /// @param other The span to copy
+  template <typename OtherT,
+            std::size_t OtherExtent,
+            std::enable_if_t<(Extent == OtherExtent || Extent == dynamic_extent) &&
+                               std::is_convertible_v<OtherT (*)[], T (*)[]>,
+                             void>* = nullptr>
+  constexpr host_span(host_span<OtherT, OtherExtent> const& other) noexcept
+    : base(other.data(), other.size())
+  {
+  }
+};
+
+// ===== device_span ===============================================================================
+
+template <typename T>
+struct is_device_span_supported_container : std::false_type {};
+
+template <typename T, typename Alloc>
+struct is_device_span_supported_container<  //
+  thrust::device_vector<T, Alloc>> : std::true_type {};
+
+template <typename T>
+struct is_device_span_supported_container<  //
+  rmm::device_vector<T>> : std::true_type {};
+
+template <typename T>
+struct is_device_span_supported_container<  //
+  rmm::device_uvector<T>> : std::true_type {};
+
+/**
+ * @brief Device version of C++20 std::span with reduced feature set.
+ *
+ */
+template <typename T, std::size_t Extent = cudf::dynamic_extent>
+struct device_span : public cudf::detail::span_base<T, Extent, device_span<T, Extent>> {
+  using base = cudf::detail::span_base<T, Extent, device_span<T, Extent>>;  ///< Base type
+  using base::base;
+
+  constexpr device_span() noexcept : base() {}  // required to compile on centos
+
+  /// Constructor from container
+  /// @param in The container to construct the span from
+  template <
+    typename C,
+    // Only supported containers of types convertible to T
+    std::enable_if_t<is_device_span_supported_container<C>::value &&
+                     std::is_convertible_v<std::remove_pointer_t<decltype(thrust::raw_pointer_cast(
+                                             std::declval<C&>().data()))> (*)[],
+                                           T (*)[]>>* = nullptr>
+  constexpr device_span(C& in) : base(thrust::raw_pointer_cast(in.data()), in.size())
+  {
+  }
+
+  /// Constructor from const container
+  /// @param in The container to construct the span from
+  template <
+    typename C,
+    // Only supported containers of types convertible to T
+    std::enable_if_t<is_device_span_supported_container<C>::value &&
+                     std::is_convertible_v<std::remove_pointer_t<decltype(thrust::raw_pointer_cast(
+                                             std::declval<C&>().data()))> (*)[],
+                                           T (*)[]>>* = nullptr>
+  constexpr device_span(C const& in) : base(thrust::raw_pointer_cast(in.data()), in.size())
+  {
+  }
+
+  // Copy construction to support const conversion
+  /// @param other The span to copy
+  template <typename OtherT,
+            std::size_t OtherExtent,
+            std::enable_if_t<(Extent == OtherExtent || Extent == dynamic_extent) &&
+                               std::is_convertible_v<OtherT (*)[], T (*)[]>,
+                             void>* = nullptr>
+  constexpr device_span(device_span<OtherT, OtherExtent> const& other) noexcept
+    : base(other.data(), other.size())
+  {
+  }
+};
+
+namespace detail {
+
+/**
+ * @brief Generic class for row-major 2D spans. Not compliant with STL container semantics/syntax.
+ *
+ * The index operator returns the corresponding row.
+ */
+template <typename T, template <typename, std::size_t> typename RowType>
+class base_2dspan {
+ public:
+  using size_type =
+    std::pair<size_t, size_t>;  ///< Type used to represent the dimension of the span
+
+  constexpr base_2dspan() noexcept = default;
+  /**
+   * @brief Constructor a 2D span
+   *
+   * @param data Pointer to the data
+   * @param rows Number of rows
+   * @param columns Number of columns
+   */
+  constexpr base_2dspan(T* data, size_t rows, size_t columns) noexcept
+    : _data{data}, _size{rows, columns}
+  {
+  }
+  /**
+   * @brief Constructor a 2D span
+   *
+   * @param data Pointer to the data
+   * @param size Size of the 2D span as pair
+   */
+  base_2dspan(T* data, size_type size) noexcept : _data{data}, _size{size} {}
+
+  /**
+   * @brief Returns a pointer to the beginning of the sequence.
+   *
+   * @return A pointer to the first element of the span
+   */
+  constexpr auto data() const noexcept { return _data; }
+  /**
+   * @brief Returns the size in the span as pair.
+   *
+   * @return pair representing rows and columns size of the span
+   */
+  constexpr auto size() const noexcept { return _size; }
+  /**
+   * @brief Returns the number of elements in the span.
+   *
+   * @return Number of elements in the span
+   */
+  constexpr auto count() const noexcept { return size().first * size().second; }
+  /**
+   * @brief Checks if the span is empty.
+   *
+   * @return True if the span is empty, false otherwise
+   */
+  [[nodiscard]] constexpr bool is_empty() const noexcept { return count() == 0; }
+
+  /**
+   * @brief Returns flattened index of the element at the specified 2D position.
+   *
+   * @param row The row index
+   * @param column The column index
+   * @param size The size of the 2D span as pair
+   * @return The flattened index of the element at the specified 2D position
+   */
+  static constexpr size_t flatten_index(size_t row, size_t column, size_type size) noexcept
+  {
+    return row * size.second + column;
+  }
+
+  /**
+   * @brief Returns a reference to the row-th element of the sequence.
+   *
+   * The behavior is undefined if row is out of range (i.e., if it is greater than or equal to
+   * size()).
+   *
+   * @param row the index of the element to access
+   * @return A reference to the row-th element of the sequence, i.e., `data()[row]`
+   */
+  constexpr RowType<T, dynamic_extent> operator[](size_t row) const
+  {
+    return {this->data() + flatten_index(row, 0, this->size()), this->size().second};
+  }
+
+  /**
+   * @brief Returns a reference to the first element in the span.
+   *
+   * Calling front() on an empty span results in undefined behavior.
+   *
+   * @return Reference to the first element in the span
+   */
+  [[nodiscard]] constexpr RowType<T, dynamic_extent> front() const { return (*this)[0]; }
+  /**
+   * @brief Returns a reference to the last element in the span.
+   *
+   * Calling back() on an empty span results in undefined behavior.
+   *
+   * @return Reference to the last element in the span
+   */
+  [[nodiscard]] constexpr RowType<T, dynamic_extent> back() const
+  {
+    return (*this)[size().first - 1];
+  }
+
+  /**
+   * @brief Obtains a 2D span that is a view over the `num_rows` rows of this span starting at
+   * `first_row`
+   *
+   * @param first_row The first row in the subspan
+   * @param num_rows The number of rows in the subspan
+   * @return A subspan of the sequence, of requested starting `first_row` and `num_rows`
+   */
+  constexpr base_2dspan subspan(size_t first_row, size_t num_rows) const noexcept
+  {
+    return base_2dspan(
+      _data + flatten_index(first_row, 0, this->size()), num_rows, this->size().second);
+  }
+
+  /**
+   * @brief Returns a flattened span of the 2D span.
+   *
+   * @return A flattened span of the 2D span
+   */
+  constexpr RowType<T, dynamic_extent> flat_view()
+  {
+    return {this->data(), this->size().first * this->size().second};
+  }
+
+  /**
+   * @brief Construct a 2D span from another 2D span of convertible type
+   *
+   * @tparam OtherT Type of the other 2D span
+   * @tparam OtherRowType Type of the row of the other 2D span
+   * @param other The other 2D span
+   */
+  template <typename OtherT,
+            template <typename, size_t>
+            typename OtherRowType,
+            std::enable_if_t<std::is_convertible_v<OtherRowType<OtherT, dynamic_extent>,
+                                                   RowType<T, dynamic_extent>>,
+                             void>* = nullptr>
+  constexpr base_2dspan(base_2dspan<OtherT, OtherRowType> const& other) noexcept
+    : _data{other.data()}, _size{other.size()}
+  {
+  }
+
+ protected:
+  T* _data = nullptr;     ///< pointer to the first element
+  size_type _size{0, 0};  ///< rows, columns
+};
+
+/**
+ * @brief Alias for the 2D span for host data.
+ *
+ * Index operator returns rows as `host_span`.
+ */
+template <class T>
+using host_2dspan = base_2dspan<T, host_span>;
+
+/**
+ * @brief Alias for the 2D span for device data.
+ *
+ * Index operator returns rows as `device_span`.
+ */
+template <class T>
+using device_2dspan = base_2dspan<T, device_span>;
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/traits.cuh b/cpp/include/cudf/utilities/traits.cuh
new file mode 100644
index 0000000..43587ff
--- /dev/null
+++ b/cpp/include/cudf/utilities/traits.cuh
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cuda/std/atomic>
+
+namespace cudf {
+
+/**
+ * @addtogroup utility_types
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Indicates whether the type `T` has support for atomics
+ *
+ * @tparam T     The type to verify
+ * @return true  `T` has support for atomics
+ * @return false `T` no support for atomics
+ */
+template <typename T>
+constexpr inline bool has_atomic_support()
+{
+  return cuda::std::atomic<T>::is_always_lock_free;
+}
+
+struct has_atomic_support_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return has_atomic_support<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` has support for atomics
+ *
+ * @param type   The `data_type` to verify
+ * @return true  `type` has support for atomics
+ * @return false `type` no support for atomics
+ */
+constexpr inline bool has_atomic_support(data_type type)
+{
+  return cudf::type_dispatcher(type, has_atomic_support_impl{});
+}
+
+/** @} */
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/traits.hpp b/cpp/include/cudf/utilities/traits.hpp
new file mode 100644
index 0000000..51f5d9d
--- /dev/null
+++ b/cpp/include/cudf/utilities/traits.hpp
@@ -0,0 +1,598 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/types.hpp>
+#include <cudf/wrappers/dictionary.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cuda/std/type_traits>
+
+namespace cudf {
+
+/**
+ * @addtogroup utility_types
+ * @{
+ * @file
+ */
+
+/// Utility metafunction that maps a sequence of any types to the type void.
+template <typename...>
+using void_t = void;
+
+/**
+ * @brief Convenience macro for SFINAE as an unnamed template parameter.
+ *
+ * Example:
+ * \code{cpp}
+ * // This function will participate in overload resolution only if T is an integral type
+ * template <typename T, CUDF_ENABLE_IF(std::is_integral_v<T> )>
+ * void foo();
+ * \endcode
+ *
+ */
+#define CUDF_ENABLE_IF(...) std::enable_if_t<(__VA_ARGS__)>* = nullptr
+
+/// Checks if two types are comparable using less operator (i.e. <).
+template <typename L, typename R>
+using less_comparable = decltype(std::declval<L>() < std::declval<R>());
+
+/// Checks if two types are comparable using greater operator (i.e. >).
+template <typename L, typename R>
+using greater_comparable = decltype(std::declval<L>() > std::declval<R>());
+
+/// Checks if two types are comparable using equality operator (i.e. ==).
+template <typename L, typename R>
+using equality_comparable = decltype(std::declval<L>() == std::declval<R>());
+
+namespace detail {
+template <typename L, typename R, typename = void>
+struct is_relationally_comparable_impl : std::false_type {};
+
+template <typename L, typename R>
+struct is_relationally_comparable_impl<L,
+                                       R,
+                                       void_t<less_comparable<L, R>, greater_comparable<L, R>>>
+  : std::true_type {};
+
+template <typename L, typename R, typename = void>
+struct is_equality_comparable_impl : std::false_type {};
+
+template <typename L, typename R>
+struct is_equality_comparable_impl<L, R, void_t<equality_comparable<L, R>>> : std::true_type {};
+
+// has common type
+template <typename AlwaysVoid, typename... Ts>
+struct has_common_type_impl : std::false_type {};
+
+template <typename... Ts>
+struct has_common_type_impl<void_t<std::common_type_t<Ts...>>, Ts...> : std::true_type {};
+}  // namespace detail
+
+/// Checks if types have a common type
+template <typename... Ts>
+using has_common_type = typename detail::has_common_type_impl<void, Ts...>::type;
+
+/// Helper variable template for has_common_type<>::value
+template <typename... Ts>
+constexpr inline bool has_common_type_v = detail::has_common_type_impl<void, Ts...>::value;
+
+/// Checks if a type is a timestamp type.
+template <typename T>
+using is_timestamp_t = cuda::std::disjunction<std::is_same<cudf::timestamp_D, T>,
+                                              std::is_same<cudf::timestamp_s, T>,
+                                              std::is_same<cudf::timestamp_ms, T>,
+                                              std::is_same<cudf::timestamp_us, T>,
+                                              std::is_same<cudf::timestamp_ns, T>>;
+
+/// Checks if a type is a duration type.
+template <typename T>
+using is_duration_t = cuda::std::disjunction<std::is_same<cudf::duration_D, T>,
+                                             std::is_same<cudf::duration_s, T>,
+                                             std::is_same<cudf::duration_ms, T>,
+                                             std::is_same<cudf::duration_us, T>,
+                                             std::is_same<cudf::duration_ns, T>>;
+
+/**
+ * @brief Indicates whether objects of types `L` and `R` can be relationally
+ *compared.
+ *
+ * Given two objects `L l`, and `R r`, returns true if `l < r` and `l > r` are
+ * well-formed expressions.
+ *
+ * @tparam L Type of the first object
+ * @tparam R Type of the second object
+ * @return true Objects of types `L` and `R` can be relationally be compared
+ * @return false Objects of types `L` and `R` cannot be compared
+ */
+template <typename L, typename R>
+constexpr inline bool is_relationally_comparable()
+{
+  return detail::is_relationally_comparable_impl<L, R>::value;
+}
+
+/**
+ * @brief Checks whether `data_type` `type` supports relational comparisons.
+ *
+ * @param type Data_type for comparison.
+ * @return true If `type` supports relational comparisons.
+ * @return false If `type` does not support relational comparisons.
+ */
+bool is_relationally_comparable(data_type type);
+
+/**
+ * @brief Indicates whether objects of types `L` and `R` can be compared
+ * for equality.
+ *
+ * Given two objects `L l`, and `R r`, returns true if `l == r` is a
+ * well-formed expression.
+ *
+ * @tparam L Type of the first object
+ * @tparam R Type of the second object
+ * @return true Objects of types `L` and `R` can be compared for equality
+ * @return false Objects of types `L` and `R` cannot be compared
+ */
+template <typename L, typename R>
+constexpr inline bool is_equality_comparable()
+{
+  return detail::is_equality_comparable_impl<L, R>::value;
+}
+
+/**
+ * @brief Checks whether `data_type` `type` supports equality comparisons.
+ *
+ * @param type Data_type for comparison.
+ * @return true If `type` supports equality comparisons.
+ * @return false If `type` does not support equality comparisons.
+ */
+bool is_equality_comparable(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a numeric type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is numeric
+ * @return false  `T` is not numeric
+ */
+template <typename T>
+constexpr inline bool is_numeric()
+{
+  return cuda::std::is_arithmetic<T>();
+}
+
+/**
+ * @brief Indicates whether `type` is a numeric `data_type`.
+ *
+ * "Numeric" types are fundamental integral/floating point types such as `INT*`
+ * or `FLOAT*`. Types that wrap a numeric type are not considered numeric, e.g.,
+ *`TIMESTAMP`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is numeric
+ * @return false `type` is not numeric
+ */
+bool is_numeric(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a index type.
+ *
+ * A type `T` is considered an index type if it is valid to use
+ * elements of type `T` to index into a column. I.e.,
+ * index types are integral types such as 'INT*' apart from 'bool'.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is index type
+ * @return false  `T` is not index type
+ */
+template <typename T>
+constexpr inline bool is_index_type()
+{
+  return std::is_integral_v<T> and not std::is_same_v<T, bool>;
+}
+
+/**
+ * @brief Indicates whether the type `type` is a index type.
+ *
+ * A type `T` is considered an index type if it is valid to use
+ * elements of type `T` to index into a column. I.e.,
+ * index types are integral types such as 'INT*' apart from 'bool'.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is index type
+ * @return false `type` is not index type
+ */
+bool is_index_type(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a unsigned numeric type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is unsigned numeric
+ * @return false  `T` is signed numeric
+ */
+template <typename T>
+constexpr inline bool is_unsigned()
+{
+  return std::is_unsigned_v<T>;
+}
+
+/**
+ * @brief Indicates whether `type` is a unsigned numeric `data_type`.
+ *
+ * "Unsigned Numeric" types are fundamental integral types such as `UINT*`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is unsigned numeric
+ * @return false `type` is signed numeric
+ */
+bool is_unsigned(data_type type);
+
+/**
+ * @brief Indicates whether the `Iterator` value type is unsigned.
+ *
+ * @tparam Iterator  The type to verify
+ * @return true if the iterator's value type is unsigned
+ */
+template <typename Iterator>
+constexpr inline bool is_signed_iterator()
+{
+  return std::is_signed_v<typename std::iterator_traits<Iterator>::value_type>;
+}
+
+/**
+ * @brief Indicates whether the type `T` is an integral type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is integral
+ * @return false  `T` is not integral
+ */
+template <typename T>
+constexpr inline bool is_integral()
+{
+  return cuda::std::is_integral_v<T>;
+}
+
+/**
+ * @brief Indicates whether `type` is a integral `data_type`.
+ *
+ * "Integral" types are fundamental integer types such as `INT*` and `UINT*`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is integral
+ * @return false `type` is integral
+ */
+bool is_integral(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a floating point type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is floating point
+ * @return false  `T` is not floating point
+ */
+template <typename T>
+constexpr inline bool is_floating_point()
+{
+  return std::is_floating_point_v<T>;
+}
+
+/**
+ * @brief Indicates whether `type` is a floating point `data_type`.
+ *
+ * "Floating point" types are fundamental floating point types such as `FLOAT*`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is floating point
+ * @return false `type` is not floating point
+ */
+bool is_floating_point(data_type type);
+
+/**
+ * @brief Indicates whether `T` is a std::byte type.
+ *
+ * @tparam T The type to verify
+ * @return true `type` is std::byte
+ * @return false `type` is not std::byte
+ */
+template <typename T>
+constexpr inline bool is_byte()
+{
+  return std::is_same_v<std::remove_cv_t<T>, std::byte>;
+}
+
+/**
+ * @brief Indicates whether `T` is a Boolean type.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is Boolean
+ * @return false `type` is not Boolean
+ */
+template <typename T>
+constexpr inline bool is_boolean()
+{
+  return std::is_same_v<T, bool>;
+}
+
+/**
+ * @brief Indicates whether `type` is a Boolean `data_type`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a Boolean
+ * @return false `type` is not a Boolean
+ */
+bool is_boolean(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a timestamp type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is a timestamp
+ * @return false  `T` is not a timestamp
+ */
+template <typename T>
+constexpr inline bool is_timestamp()
+{
+  return is_timestamp_t<T>::value;
+}
+
+/**
+ * @brief Indicates whether `type` is a timestamp `data_type`.
+ *
+ * "Timestamp" types are int32_t or int64_t durations since the unix epoch.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a timestamp
+ * @return false `type` is not a timestamp
+ */
+bool is_timestamp(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a fixed-point type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is a fixed-point type
+ * @return false  `T` is not a fixed-point type
+ */
+template <typename T>
+constexpr inline bool is_fixed_point()
+{
+  return std::is_same_v<numeric::decimal32, T> || std::is_same_v<numeric::decimal64, T> ||
+         std::is_same_v<numeric::decimal128, T>;
+}
+
+/**
+ * @brief Indicates whether `type` is a fixed point `data_type`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a fixed point type
+ * @return false `type` is not a fixed point type
+ */
+bool is_fixed_point(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a duration type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is a duration
+ * @return false  `T` is not a duration
+ */
+template <typename T>
+constexpr inline bool is_duration()
+{
+  return is_duration_t<T>::value;
+}
+
+/**
+ * @brief Indicates whether `type` is a duration `data_type`.
+ *
+ * "Duration" types are int32_t or int64_t tick counts representing a time interval.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a duration
+ * @return false `type` is not a duration
+ */
+bool is_duration(data_type type);
+
+/**
+ * @brief Indicates whether the type `T` is a chrono type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is a duration or a timestamp type
+ * @return false  `T` is neither a duration nor a timestamp type
+ */
+template <typename T>
+constexpr inline bool is_chrono()
+{
+  return is_duration<T>() || is_timestamp<T>();
+}
+
+/**
+ * @brief Indicates whether `type` is a chrono `data_type`.
+ *
+ * Chrono types include cudf timestamp types, which represent a point in time, and cudf
+ * duration types that represent a time interval.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a chrono type
+ * @return false `type` is not a chrono type
+ */
+bool is_chrono(data_type type);
+
+/**
+ * @brief Indicates whether `T` is layout compatible with its "representation" type.
+ *
+ * For example, in a column, a `decimal32` is concretely represented by a single `int32_t`, but the
+ * `decimal32` type itself contains both the integer representation and the scale. Therefore,
+ * `decimal32` is _not_ layout compatible with `int32_t`.
+ *
+ * As further example, `duration_ns` is distinct from its concrete `int64_t` representation type,
+ * but they are layout compatible.
+ *
+ * @return true if `T` is layout compatible with its "representation" type
+ */
+template <typename T>
+constexpr bool is_rep_layout_compatible()
+{
+  return cudf::is_numeric<T>() or cudf::is_chrono<T>() or cudf::is_boolean<T>() or
+         cudf::is_byte<T>();
+}
+
+/**
+ * @brief Indicates whether the type `T` is a dictionary type.
+ *
+ * @tparam T  The type to verify
+ * @return true `T` is a dictionary-type
+ * @return false  `T` is not dictionary-type
+ */
+template <typename T>
+constexpr inline bool is_dictionary()
+{
+  return std::is_same_v<dictionary32, T>;
+}
+
+/**
+ * @brief Indicates whether `type` is a dictionary `data_type`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a dictionary type
+ * @return false `type` is not a dictionary type
+ */
+bool is_dictionary(data_type type);
+
+/**
+ * @brief Indicates whether elements of type `T` are fixed-width.
+ *
+ * Elements of a fixed-width type all have the same size in bytes.
+ *
+ * @tparam T The C++ type to verify
+ * @return true `T` corresponds to a fixed-width element type
+ * @return false `T` corresponds to a variable-width element type
+ */
+template <typename T>
+constexpr inline bool is_fixed_width()
+{
+  // TODO Add fixed width wrapper types
+  // Is a category fixed width?
+  return cudf::is_numeric<T>() || cudf::is_chrono<T>() || cudf::is_fixed_point<T>();
+}
+
+/**
+ * @brief Indicates whether elements of `type` are fixed-width.
+ *
+ * Elements of a fixed-width type all have the same size in bytes.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is fixed-width
+ * @return false  `type` is variable-width
+ */
+bool is_fixed_width(data_type type);
+
+class string_view;
+
+/**
+ * @brief Indicates whether the type `T` is a compound type.
+ *
+ * `column`s with "compound" elements are logically a single column of elements,
+ * but may be concretely implemented with two or more `column`s. For example, a
+ * `STRING` column could contain a `column` of offsets and a child `column` of
+ * characters.
+ *
+ * @tparam T The type to verify
+ * @return true `T` corresponds to a "compound" type
+ * @return false `T` corresponds to a "simple" type
+ */
+template <typename T>
+constexpr inline bool is_compound()
+{
+  return std::is_same_v<T, cudf::string_view> or std::is_same_v<T, cudf::dictionary32> or
+         std::is_same_v<T, cudf::list_view> or std::is_same_v<T, cudf::struct_view>;
+}
+
+/**
+ * @brief Indicates whether elements of `type` are compound.
+ *
+ * `column`s with "compound" elements are logically a single column of elements,
+ * but may be concretely implemented with two or more `column`s. For example, a
+ * `STRING` column could contain a `column` of offsets and a child `column` of
+ * characters.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a compound type
+ * @return false `type` is a simple type
+ */
+bool is_compound(data_type type);
+
+/**
+ * @brief Indicates whether `T` is a nested type.
+ *
+ * "Nested" types are distinct from compound types in that they
+ * can have an arbitrarily deep list of descendants of the same
+ * type. Strings are not a nested type, but lists are.
+ *
+ * @param T The type to verify
+ * @return true T is a nested type
+ * @return false T is not a nested type
+ */
+template <typename T>
+constexpr inline bool is_nested()
+{
+  return std::is_same_v<T, cudf::list_view> || std::is_same_v<T, cudf::struct_view>;
+}
+
+/**
+ * @brief Indicates whether `type` is a nested type
+ *
+ * "Nested" types are distinct from compound types in that they
+ * can have an arbitrarily deep list of descendants of the same
+ * type. Strings are not a nested type, but lists are.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a nested type
+ * @return false `type` is not a nested type
+ */
+bool is_nested(data_type type);
+
+/**
+ * @brief Indicates whether `from` is bit-castable to `to`.
+ *
+ * This casting is based on std::bit_cast. Data types that have the same size and are trivially
+ * copyable are eligible for this casting.
+ *
+ * See `cudf::bit_cast()` which returns a zero-copy `column_view` when casting between
+ * bit-castable types.
+ *
+ * @param from The `data_type` to convert from
+ * @param to The `data_type` to convert to
+ * @return `true` if the types are castable
+ */
+bool is_bit_castable(data_type from, data_type to);
+
+template <typename From, typename To>
+struct is_convertible : std::is_convertible<From, To> {};
+
+// This will ensure that timestamps can be promoted to a higher precision. Presently, they can't
+// do that due to nvcc/gcc compiler issues
+template <typename Duration1, typename Duration2>
+struct is_convertible<cudf::detail::timestamp<Duration1>, cudf::detail::timestamp<Duration2>>
+  : std::is_convertible<typename cudf::detail::time_point<Duration1>::duration,
+                        typename cudf::detail::time_point<Duration2>::duration> {};
+
+/** @} */
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/type_checks.hpp b/cpp/include/cudf/utilities/type_checks.hpp
new file mode 100644
index 0000000..b925fc8
--- /dev/null
+++ b/cpp/include/cudf/utilities/type_checks.hpp
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+
+namespace cudf {
+
+/**
+ * @brief Compares the type of two `column_view`s
+ *
+ * This function returns true if the type of `lhs` equals that of `rhs`.
+ * - For fixed point types, the scale is compared.
+ * - For dictionary types, the type of the keys are compared if both are
+ *   non-empty columns.
+ * - For lists types, the type of child columns are compared recursively.
+ * - For struct types, the type of each field are compared in order.
+ * - For all other types, the `id` of `data_type` is compared.
+ *
+ * @param lhs The first `column_view` to compare
+ * @param rhs The second `column_view` to compare
+ * @return true if column types match
+ */
+bool column_types_equal(column_view const& lhs, column_view const& rhs);
+
+/**
+ * @brief Compare the type IDs of two `column_view`s
+ * This function returns true if the type of `lhs` equals that of `rhs`.
+ * - For fixed point types, the scale is ignored.
+ *
+ * @param lhs The first `column_view` to compare
+ * @param rhs The second `column_view` to compare
+ * @return true if column types match
+ */
+bool column_types_equivalent(column_view const& lhs, column_view const& rhs);
+
+}  // namespace cudf
diff --git a/cpp/include/cudf/utilities/type_dispatcher.hpp b/cpp/include/cudf/utilities/type_dispatcher.hpp
new file mode 100644
index 0000000..a80cd15
--- /dev/null
+++ b/cpp/include/cudf/utilities/type_dispatcher.hpp
@@ -0,0 +1,615 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/wrappers/dictionary.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <string>
+
+/**
+ * @file
+ * @brief Defines the mapping between `cudf::type_id` runtime type information
+ * and concrete C++ types.
+ */
+namespace cudf {
+/**
+ * @addtogroup utility_dispatcher
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Maps a C++ type to its corresponding `cudf::type_id`
+ *
+ * When explicitly passed a template argument of a given type, returns the
+ * appropriate `type_id` enum for the specified C++ type.
+ *
+ * For example:
+ *
+ * ```
+ * return cudf::type_to_id<int32_t>();        // Returns INT32
+ * ```
+ *
+ * @tparam T The type to map to a `cudf::type_id`
+ * @return The `cudf::type_id` corresponding to the specified type
+ */
+template <typename T>
+inline constexpr type_id type_to_id()
+{
+  return type_id::EMPTY;
+};
+
+/**
+ * @brief Maps a `cudf::type_id` types to its corresponding C++ type name string
+ *
+ */
+struct type_to_name_impl {
+  /**
+   * @brief Maps a `cudf::type_id` types to its corresponding C++ type name string
+   *
+   * @return The C++ type name as string
+   */
+  template <typename T>
+  inline std::string operator()()
+  {
+    return "void";
+  }
+};
+
+template <cudf::type_id t>
+struct id_to_type_impl {
+  using type = void;
+};
+/**
+ * @brief Maps a `cudf::type_id` to its corresponding concrete C++ type
+ *
+ * Example:
+ * ```
+ * static_assert(std::is_same<int32_t, id_to_type<id_type::INT32>);
+ * ```
+ * @tparam t The `cudf::type_id` to map
+ */
+template <cudf::type_id Id>
+using id_to_type = typename id_to_type_impl<Id>::type;
+
+/**
+ * @brief "Returns" the corresponding type that is stored on the device when using `cudf::column`
+ *
+ * For `decimal32`,  the storage type is an `int32_t`.
+ * For `decimal64`,  the storage type is an `int64_t`.
+ * For `decimal128`, the storage type is an `__int128_t`.
+ *
+ * Use this "type function" with the `using` type alias:
+ * @code
+ * using Type = device_storage_type_t<Element>;
+ * @endcode
+ *
+ * @tparam T The literal type that is stored on the host
+ */
+// clang-format off
+template <typename T>
+using device_storage_type_t =
+  std::conditional_t<std::is_same_v<numeric::decimal32,  T>, int32_t,
+  std::conditional_t<std::is_same_v<numeric::decimal64,  T>, int64_t,
+  std::conditional_t<std::is_same_v<numeric::decimal128, T>, __int128_t, T>>>;
+// clang-format on
+
+/**
+ * @brief Checks if `fixed_point`-like types have template type `T` matching the column's
+ * stored type id
+ *
+ * @tparam     T The type that is stored on the device
+ * @param id   The `data_type::id` of the column
+ * @return     `true` If T matches the stored column `type_id`
+ * @return     `false` If T does not match the stored column `type_id`
+ */
+template <typename T>
+constexpr bool type_id_matches_device_storage_type(type_id id)
+{
+  return (id == type_id::DECIMAL32 && std::is_same_v<T, int32_t>) ||
+         (id == type_id::DECIMAL64 && std::is_same_v<T, int64_t>) ||
+         (id == type_id::DECIMAL128 && std::is_same_v<T, __int128_t>) || id == type_to_id<T>();
+}
+
+/**
+ * @brief Macro used to define a mapping between a concrete C++ type and a
+ *`cudf::type_id` enum.
+
+ * @param Type The concrete C++ type
+ * @param Id The `cudf::type_id` enum
+ */
+#ifndef CUDF_TYPE_MAPPING
+#define CUDF_TYPE_MAPPING(Type, Id)                        \
+  template <>                                              \
+  constexpr inline type_id type_to_id<Type>()              \
+  {                                                        \
+    return Id;                                             \
+  }                                                        \
+  template <>                                              \
+  inline std::string type_to_name_impl::operator()<Type>() \
+  {                                                        \
+    return CUDF_STRINGIFY(Type);                           \
+  }                                                        \
+  template <>                                              \
+  struct id_to_type_impl<Id> {                             \
+    using type = Type;                                     \
+  };
+#endif
+
+// Defines all of the mappings between C++ types and their corresponding `cudf::type_id` values.
+CUDF_TYPE_MAPPING(int8_t, type_id::INT8)
+CUDF_TYPE_MAPPING(int16_t, type_id::INT16)
+CUDF_TYPE_MAPPING(int32_t, type_id::INT32)
+CUDF_TYPE_MAPPING(int64_t, type_id::INT64)
+CUDF_TYPE_MAPPING(uint8_t, type_id::UINT8)
+CUDF_TYPE_MAPPING(uint16_t, type_id::UINT16)
+CUDF_TYPE_MAPPING(uint32_t, type_id::UINT32)
+CUDF_TYPE_MAPPING(uint64_t, type_id::UINT64)
+CUDF_TYPE_MAPPING(float, type_id::FLOAT32)
+CUDF_TYPE_MAPPING(double, type_id::FLOAT64)
+CUDF_TYPE_MAPPING(bool, type_id::BOOL8)
+CUDF_TYPE_MAPPING(cudf::timestamp_D, type_id::TIMESTAMP_DAYS)
+CUDF_TYPE_MAPPING(cudf::timestamp_s, type_id::TIMESTAMP_SECONDS)
+CUDF_TYPE_MAPPING(cudf::timestamp_ms, type_id::TIMESTAMP_MILLISECONDS)
+CUDF_TYPE_MAPPING(cudf::timestamp_us, type_id::TIMESTAMP_MICROSECONDS)
+CUDF_TYPE_MAPPING(cudf::timestamp_ns, type_id::TIMESTAMP_NANOSECONDS)
+CUDF_TYPE_MAPPING(cudf::duration_D, type_id::DURATION_DAYS)
+CUDF_TYPE_MAPPING(cudf::duration_s, type_id::DURATION_SECONDS)
+CUDF_TYPE_MAPPING(cudf::duration_ms, type_id::DURATION_MILLISECONDS)
+CUDF_TYPE_MAPPING(cudf::duration_us, type_id::DURATION_MICROSECONDS)
+CUDF_TYPE_MAPPING(cudf::duration_ns, type_id::DURATION_NANOSECONDS)
+CUDF_TYPE_MAPPING(cudf::dictionary32, type_id::DICTIONARY32)
+CUDF_TYPE_MAPPING(cudf::string_view, type_id::STRING)
+CUDF_TYPE_MAPPING(cudf::list_view, type_id::LIST)
+CUDF_TYPE_MAPPING(numeric::decimal32, type_id::DECIMAL32)
+CUDF_TYPE_MAPPING(numeric::decimal64, type_id::DECIMAL64)
+CUDF_TYPE_MAPPING(numeric::decimal128, type_id::DECIMAL128)
+CUDF_TYPE_MAPPING(cudf::struct_view, type_id::STRUCT)
+
+/**
+ * @brief Use this specialization on `type_dispatcher` whenever you only need to operate on the
+ * underlying stored type.
+ *
+ * For example, `cudf::sort` in sort.cu uses `cudf::type_dispatcher<dispatch_storage_type>(...)`.
+ * `cudf::gather` in gather.cuh also uses `cudf::type_dispatcher<dispatch_storage_type>(...)`.
+ * However, reductions needs both `data_type` and underlying type, so cannot use this.
+ */
+template <cudf::type_id Id>
+struct dispatch_storage_type {
+  using type = device_storage_type_t<id_to_type<Id>>;  ///< The underlying type
+};
+
+template <typename T>
+struct type_to_scalar_type_impl {
+  using ScalarType = cudf::scalar;
+};
+
+/**
+ * @brief Macro used to define scalar type and scalar device type for
+ * `cudf::numeric_scalar` template class for numeric C++ types.
+ *
+ * @param Type The numeric C++ type
+ */
+#ifndef MAP_NUMERIC_SCALAR
+#define MAP_NUMERIC_SCALAR(Type)                                     \
+  template <>                                                        \
+  struct type_to_scalar_type_impl<Type> {                            \
+    using ScalarType       = cudf::numeric_scalar<Type>;             \
+    using ScalarDeviceType = cudf::numeric_scalar_device_view<Type>; \
+  };
+#endif
+
+MAP_NUMERIC_SCALAR(int8_t)
+MAP_NUMERIC_SCALAR(int16_t)
+MAP_NUMERIC_SCALAR(int32_t)
+MAP_NUMERIC_SCALAR(int64_t)
+MAP_NUMERIC_SCALAR(__int128_t)
+MAP_NUMERIC_SCALAR(uint8_t)
+MAP_NUMERIC_SCALAR(uint16_t)
+MAP_NUMERIC_SCALAR(uint32_t)
+MAP_NUMERIC_SCALAR(uint64_t)
+MAP_NUMERIC_SCALAR(float)
+MAP_NUMERIC_SCALAR(double)
+MAP_NUMERIC_SCALAR(bool)
+
+template <>
+struct type_to_scalar_type_impl<std::string> {
+  using ScalarType       = cudf::string_scalar;
+  using ScalarDeviceType = cudf::string_scalar_device_view;
+};
+
+template <>
+struct type_to_scalar_type_impl<cudf::string_view> {
+  using ScalarType       = cudf::string_scalar;
+  using ScalarDeviceType = cudf::string_scalar_device_view;
+};
+
+template <>
+struct type_to_scalar_type_impl<numeric::decimal32> {
+  using ScalarType       = cudf::fixed_point_scalar<numeric::decimal32>;
+  using ScalarDeviceType = cudf::fixed_point_scalar_device_view<numeric::decimal32>;
+};
+
+template <>
+struct type_to_scalar_type_impl<numeric::decimal64> {
+  using ScalarType       = cudf::fixed_point_scalar<numeric::decimal64>;
+  using ScalarDeviceType = cudf::fixed_point_scalar_device_view<numeric::decimal64>;
+};
+
+template <>
+struct type_to_scalar_type_impl<numeric::decimal128> {
+  using ScalarType       = cudf::fixed_point_scalar<numeric::decimal128>;
+  using ScalarDeviceType = cudf::fixed_point_scalar_device_view<numeric::decimal128>;
+};
+
+template <>  // TODO: this is a temporary solution for make_pair_iterator
+struct type_to_scalar_type_impl<cudf::dictionary32> {
+  using ScalarType       = cudf::numeric_scalar<int32_t>;
+  using ScalarDeviceType = cudf::numeric_scalar_device_view<int32_t>;
+};
+
+template <>  // TODO: this is to get compilation working. list scalars will be implemented at a
+             // later time.
+struct type_to_scalar_type_impl<cudf::list_view> {
+  using ScalarType = cudf::list_scalar;
+  // using ScalarDeviceType = cudf::list_scalar_device_view;
+};
+
+template <>  // TODO: Ditto, likewise.
+struct type_to_scalar_type_impl<cudf::struct_view> {
+  using ScalarType = cudf::struct_scalar;
+  // using ScalarDeviceType = cudf::struct_scalar_device_view; // CALEB: TODO!
+};
+
+/**
+ * @brief Macro used to define scalar type and scalar device type for
+ * `cudf::timestamp_scalar` template class for timestamp C++ types.
+ *
+ * @param Type The timestamp C++ type
+ */
+#ifndef MAP_TIMESTAMP_SCALAR
+#define MAP_TIMESTAMP_SCALAR(Type)                                     \
+  template <>                                                          \
+  struct type_to_scalar_type_impl<Type> {                              \
+    using ScalarType       = cudf::timestamp_scalar<Type>;             \
+    using ScalarDeviceType = cudf::timestamp_scalar_device_view<Type>; \
+  };
+#endif
+
+MAP_TIMESTAMP_SCALAR(timestamp_D)
+MAP_TIMESTAMP_SCALAR(timestamp_s)
+MAP_TIMESTAMP_SCALAR(timestamp_ms)
+MAP_TIMESTAMP_SCALAR(timestamp_us)
+MAP_TIMESTAMP_SCALAR(timestamp_ns)
+
+/**
+ * @brief Macro used to define scalar type and scalar device type for
+ * `cudf::duration_scalar` template class for duration C++ types.
+ *
+ * @param Type The duration C++ type
+ */
+#ifndef MAP_DURATION_SCALAR
+#define MAP_DURATION_SCALAR(Type)                                     \
+  template <>                                                         \
+  struct type_to_scalar_type_impl<Type> {                             \
+    using ScalarType       = cudf::duration_scalar<Type>;             \
+    using ScalarDeviceType = cudf::duration_scalar_device_view<Type>; \
+  };
+#endif
+
+MAP_DURATION_SCALAR(duration_D)
+MAP_DURATION_SCALAR(duration_s)
+MAP_DURATION_SCALAR(duration_ms)
+MAP_DURATION_SCALAR(duration_us)
+MAP_DURATION_SCALAR(duration_ns)
+
+/**
+ * @brief Maps a C++ type to the scalar type required to hold its value
+ *
+ * @tparam T The concrete C++ type to map
+ */
+template <typename T>
+using scalar_type_t = typename type_to_scalar_type_impl<T>::ScalarType;
+
+/**
+ * @brief Maps a C++ type to the scalar device type required to hold its value
+ *
+ * @tparam T The concrete C++ type to map
+ */
+template <typename T>
+using scalar_device_type_t = typename type_to_scalar_type_impl<T>::ScalarDeviceType;
+
+/**
+ * @brief Invokes an `operator()` template with the type instantiation based on
+ * the specified `cudf::data_type`'s `id()`.
+ *
+ * Example usage with a functor that returns the size of the dispatched type:
+ *
+ * @code
+ * struct size_of_functor{
+ *  template <typename T>
+ *  int operator()(){
+ *    return sizeof(T);
+ *  }
+ * };
+ * cudf::data_type t{INT32};
+ * cudf::type_dispatcher(t, size_of_functor{});  // returns 4
+ * @endcode
+ *
+ * The `type_dispatcher` uses `cudf::type_to_id<t>` to provide a default mapping
+ * of `cudf::type_id`s to dispatched C++ types. However, this mapping may be
+ * customized by explicitly specifying a user-defined trait struct for the
+ * `IdTypeMap`. For example, to always dispatch `int32_t`
+ *
+ * @code
+ * template<cudf::type_id t> struct always_int{ using type = int32_t; }
+ *
+ * // This will always invoke operator()<int32_t>
+ * cudf::type_dispatcher<always_int>(data_type, f);
+ * @endcode
+ *
+ * It is sometimes necessary to customize the dispatched functor's
+ * `operator()` for different types.  This can be done in several ways.
+ *
+ * The first method is to use explicit template specialization. This is useful
+ * for specializing behavior for single types. For example, a functor that
+ * prints `int32_t` or `double` when invoked with either of those types, else it
+ * prints `unhandled type`:
+ *
+ * @code
+ * struct type_printer {
+ *   template <typename ColumnType>
+ *   void operator()() { std::cout << "unhandled type\n"; }
+ * };
+ *
+ * // Due to a bug in g++, explicit member function specializations need to be
+ * // defined outside of the class definition
+ * template <>
+ * void type_printer::operator()<int32_t>() { std::cout << "int32_t\n"; }
+ *
+ * template <>
+ * void type_printer::operator()<double>() { std::cout << "double\n"; }
+ * @endcode
+ *
+ * A second method is to use SFINAE with `std::enable_if_t`. This is useful for
+ * specializing for a set of types that share some property. For example, a
+ * functor that prints `integral` or `floating point` for integral or floating
+ * point types:
+ *
+ * @code
+ * struct integral_or_floating_point {
+ *   template <typename ColumnType,
+ *             std::enable_if_t<not std::is_integral_v<ColumnType>  and
+ *                              not std::is_floating_point_v<ColumnType> >* = nullptr>
+ *   void operator()() {
+ *     std::cout << "neither integral nor floating point\n "; }
+ *
+ *   template <typename ColumnType,
+ *             std::enable_if_t<std::is_integral_v<ColumnType> >* = nullptr>
+ *   void operator()() { std::cout << "integral\n"; }
+ *
+ *   template <typename ColumnType,
+ *             std::enable_if_t<std::is_floating_point_v<ColumnType> >* = nullptr>
+ *   void operator()() { std::cout << "floating point\n"; }
+ * };
+ * @endcode
+ *
+ * For more info on SFINAE and `std::enable_if`, see
+ * https://eli.thegreenplace.net/2014/sfinae-and-enable_if/
+ *
+ * The return type for all template instantiations of the functor's "operator()"
+ * lambda must be the same, else there will be a compiler error as you would be
+ * trying to return different types from the same function.
+ *
+ * @tparam id_to_type_impl Maps a `cudf::type_id` its dispatched C++ type
+ * @tparam Functor The callable object's type
+ * @tparam Ts Variadic parameter pack type
+ * @param dtype The `cudf::data_type` whose `id()` determines which template
+ * instantiation is invoked
+ * @param f The callable whose `operator()` template is invoked
+ * @param args Parameter pack of arguments forwarded to the `operator()`
+ * invocation
+ * @return Whatever is returned by the callable's `operator()`
+ */
+// This pragma disables a compiler warning that complains about the valid usage
+// of calling a __host__ functor from this function which is __host__ __device__
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+template <template <cudf::type_id> typename IdTypeMap = id_to_type_impl,
+          typename Functor,
+          typename... Ts>
+CUDF_HOST_DEVICE __forceinline__ constexpr decltype(auto) type_dispatcher(cudf::data_type dtype,
+                                                                          Functor f,
+                                                                          Ts&&... args)
+{
+  switch (dtype.id()) {
+    case type_id::INT8:
+      return f.template operator()<typename IdTypeMap<type_id::INT8>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::INT16:
+      return f.template operator()<typename IdTypeMap<type_id::INT16>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::INT32:
+      return f.template operator()<typename IdTypeMap<type_id::INT32>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::INT64:
+      return f.template operator()<typename IdTypeMap<type_id::INT64>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::UINT8:
+      return f.template operator()<typename IdTypeMap<type_id::UINT8>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::UINT16:
+      return f.template operator()<typename IdTypeMap<type_id::UINT16>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::UINT32:
+      return f.template operator()<typename IdTypeMap<type_id::UINT32>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::UINT64:
+      return f.template operator()<typename IdTypeMap<type_id::UINT64>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::FLOAT32:
+      return f.template operator()<typename IdTypeMap<type_id::FLOAT32>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::FLOAT64:
+      return f.template operator()<typename IdTypeMap<type_id::FLOAT64>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::BOOL8:
+      return f.template operator()<typename IdTypeMap<type_id::BOOL8>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_DAYS:
+      return f.template operator()<typename IdTypeMap<type_id::TIMESTAMP_DAYS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_SECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::TIMESTAMP_SECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_MILLISECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::TIMESTAMP_MILLISECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_MICROSECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::TIMESTAMP_MICROSECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::TIMESTAMP_NANOSECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::TIMESTAMP_NANOSECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DURATION_DAYS:
+      return f.template operator()<typename IdTypeMap<type_id::DURATION_DAYS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DURATION_SECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::DURATION_SECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DURATION_MILLISECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::DURATION_MILLISECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DURATION_MICROSECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::DURATION_MICROSECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DURATION_NANOSECONDS:
+      return f.template operator()<typename IdTypeMap<type_id::DURATION_NANOSECONDS>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DICTIONARY32:
+      return f.template operator()<typename IdTypeMap<type_id::DICTIONARY32>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::STRING:
+      return f.template operator()<typename IdTypeMap<type_id::STRING>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::LIST:
+      return f.template operator()<typename IdTypeMap<type_id::LIST>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DECIMAL32:
+      return f.template operator()<typename IdTypeMap<type_id::DECIMAL32>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DECIMAL64:
+      return f.template operator()<typename IdTypeMap<type_id::DECIMAL64>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::DECIMAL128:
+      return f.template operator()<typename IdTypeMap<type_id::DECIMAL128>::type>(
+        std::forward<Ts>(args)...);
+    case type_id::STRUCT:
+      return f.template operator()<typename IdTypeMap<type_id::STRUCT>::type>(
+        std::forward<Ts>(args)...);
+    default: {
+#ifndef __CUDA_ARCH__
+      CUDF_FAIL("Invalid type_id.");
+#else
+      CUDF_UNREACHABLE("Invalid type_id.");
+#endif
+    }
+  }
+}
+
+// @cond
+namespace detail {
+template <typename T1>
+struct double_type_dispatcher_second_type {
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+  template <typename T2, typename F, typename... Ts>
+  CUDF_HOST_DEVICE __forceinline__ decltype(auto) operator()(F&& f, Ts&&... args) const
+  {
+    return f.template operator()<T1, T2>(std::forward<Ts>(args)...);
+  }
+};
+
+template <template <cudf::type_id> typename IdTypeMap>
+struct double_type_dispatcher_first_type {
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+  template <typename T1, typename F, typename... Ts>
+  CUDF_HOST_DEVICE __forceinline__ decltype(auto) operator()(cudf::data_type type2,
+                                                             F&& f,
+                                                             Ts&&... args) const
+  {
+    return type_dispatcher<IdTypeMap>(type2,
+                                      detail::double_type_dispatcher_second_type<T1>{},
+                                      std::forward<F>(f),
+                                      std::forward<Ts>(args)...);
+  }
+};
+}  // namespace detail
+// @endcond
+
+/**
+ * @brief Dispatches two type template parameters to a callable.
+ *
+ * This function expects a callable `f` with an `operator()` template accepting
+ * two typename template parameters `T1` and `T2`.
+ *
+ * @param type1 The `data_type` used to dispatch a type for the first template
+ * parameter of the callable `F`
+ * @param type2 The `data_type` used to dispatch a type for the second template
+ * parameter of the callable `F`
+ * @param f The callable whose `operator()` template is invoked
+ * @param args Parameter pack forwarded to the `operator()` invocation `F`.
+ *
+ * @return The result of invoking `f.template operator<T1, T2>(args)`
+ */
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+template <template <cudf::type_id> typename IdTypeMap = id_to_type_impl, typename F, typename... Ts>
+CUDF_HOST_DEVICE __forceinline__ constexpr decltype(auto) double_type_dispatcher(
+  cudf::data_type type1, cudf::data_type type2, F&& f, Ts&&... args)
+{
+  return type_dispatcher<IdTypeMap>(type1,
+                                    detail::double_type_dispatcher_first_type<IdTypeMap>{},
+                                    type2,
+                                    std::forward<F>(f),
+                                    std::forward<Ts>(args)...);
+}
+
+/**
+ * @brief Return a name for a given type.
+ *
+ * The returned type names are intended for error messages and are not
+ * guaranteed to be stable.
+ *
+ * @param type The `data_type`
+ * @return Name of the type
+ */
+std::string type_to_name(data_type type);
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/wrappers/dictionary.hpp b/cpp/include/cudf/wrappers/dictionary.hpp
new file mode 100644
index 0000000..329f1fa
--- /dev/null
+++ b/cpp/include/cudf/wrappers/dictionary.hpp
@@ -0,0 +1,219 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda_runtime.h>
+#include <cudf/types.hpp>
+
+#include <limits>
+
+/**
+ * @file
+ * @brief Concrete type definition for dictionary columns.
+ */
+
+namespace cudf {
+/**
+ * @addtogroup dictionary_classes
+ * @{
+ * @file
+ */
+
+/**
+ * @brief A strongly typed wrapper for indices in a DICTIONARY type column.
+ *
+ * IndexType will be integer types like int32_t.
+ *
+ * For example, `dictionary32` is a strongly typed wrapper around an `int32_t`
+ * value that holds the offset into the dictionary keys for a specific element.
+ *
+ * This wrapper provides common conversion and comparison operations for
+ * the IndexType.
+ */
+template <typename IndexType>
+struct dictionary_wrapper {
+  using value_type = IndexType;  ///< The underlying type of the dictionary
+
+  dictionary_wrapper()                          = default;
+  ~dictionary_wrapper()                         = default;
+  dictionary_wrapper(dictionary_wrapper&&)      = default;  ///< Move constructor
+  dictionary_wrapper(dictionary_wrapper const&) = default;  ///< Copy constructor
+
+  /**
+   * @brief Move assignment operator
+   *
+   * @return The reference to this dictionary wrapper object
+   */
+  dictionary_wrapper& operator=(dictionary_wrapper&&) = default;
+
+  /**
+   * @brief Copy assignment operator
+   *
+   * @return The reference to this dictionary wrapper object
+   */
+  dictionary_wrapper& operator=(dictionary_wrapper const&) = default;
+
+  /**
+   * @brief Construct dictionary_wrapper from a value
+   *
+   * @param v The value to construct the dictionary_wrapper from
+   */
+  CUDF_HOST_DEVICE inline constexpr explicit dictionary_wrapper(value_type v) : _value{v} {}
+
+  /**
+   * @brief Conversion operator
+   *
+   * @return The value of this dictionary wrapper
+   */
+  CUDF_HOST_DEVICE inline explicit operator value_type() const { return _value; }
+
+  /**
+   * @brief Simple accessor
+   *
+   * @return The value of this dictionary wrapper
+   */
+  CUDF_HOST_DEVICE inline value_type value() const { return _value; }
+
+  /**
+   * @brief Returns the maximum value of the value type.
+   *
+   * @return The maximum value of the value type
+   */
+  static CUDF_HOST_DEVICE inline constexpr value_type max_value()
+  {
+    return std::numeric_limits<value_type>::max();
+  }
+
+  /**
+   * @brief Returns the minimum value of the value type.
+   *
+   * @return The minimum value of the value type
+   */
+  static CUDF_HOST_DEVICE inline constexpr value_type min_value()
+  {
+    return std::numeric_limits<value_type>::min();
+  }
+
+  /**
+   * @brief Returns the lowest value of the value type.
+   *
+   * @return The lowest value of the value type
+   */
+  static CUDF_HOST_DEVICE inline constexpr value_type lowest_value()
+  {
+    return std::numeric_limits<value_type>::lowest();
+  }
+
+ private:
+  value_type _value;
+};
+
+// comparison operators
+/**
+ * @brief Wqual to operator for dictionary_wrapper
+ *
+ * @tparam Integer Index type
+ * @param lhs Left hand side of comparison
+ * @param rhs Right hand side of comparison
+ * @return Returns true if lhs is equal to rhs, false otherwise
+ */
+template <typename Integer>
+CUDF_HOST_DEVICE inline bool operator==(dictionary_wrapper<Integer> const& lhs,
+                                        dictionary_wrapper<Integer> const& rhs)
+{
+  return lhs.value() == rhs.value();
+}
+
+/**
+ * @brief Not equal to operator for dictionary_wrapper
+ *
+ * @tparam Integer Index type
+ * @param lhs Left hand side of comparison
+ * @param rhs Right hand side of comparison
+ * @return Returns true if lhs is not equal to rhs, false otherwise
+ */
+template <typename Integer>
+CUDF_HOST_DEVICE inline bool operator!=(dictionary_wrapper<Integer> const& lhs,
+                                        dictionary_wrapper<Integer> const& rhs)
+{
+  return lhs.value() != rhs.value();
+}
+
+/**
+ * @brief Less than or equal to operator for dictionary_wrapper
+ *
+ * @tparam Integer Index type
+ * @param lhs Left hand side of comparison
+ * @param rhs Right hand side of comparison
+ * @return Returns true if lhs is less than or equal to rhs, false otherwise
+ */
+template <typename Integer>
+CUDF_HOST_DEVICE inline bool operator<=(dictionary_wrapper<Integer> const& lhs,
+                                        dictionary_wrapper<Integer> const& rhs)
+{
+  return lhs.value() <= rhs.value();
+}
+
+/**
+ * @brief Greater than or equal to operator for dictionary_wrapper
+ *
+ * @tparam Integer Index type
+ * @param lhs Left hand side of comparison
+ * @param rhs Right hand side of comparison
+ * @return Returns true if lhs is greater than or equal to rhs, false otherwise
+ */
+template <typename Integer>
+CUDF_HOST_DEVICE inline bool operator>=(dictionary_wrapper<Integer> const& lhs,
+                                        dictionary_wrapper<Integer> const& rhs)
+{
+  return lhs.value() >= rhs.value();
+}
+
+/**
+ * @brief Less than operator for dictionary_wrapper
+ *
+ * @tparam Integer Index type
+ * @param lhs Left hand side of comparison
+ * @param rhs Right hand side of comparison
+ * @return Returns true if lhs is less than rhs, false otherwise
+ */
+template <typename Integer>
+CUDF_HOST_DEVICE inline constexpr bool operator<(dictionary_wrapper<Integer> const& lhs,
+                                                 dictionary_wrapper<Integer> const& rhs)
+{
+  return lhs.value() < rhs.value();
+}
+
+/**
+ * @brief Greater than operator for dictionary_wrapper
+ *
+ * @tparam Integer Index type
+ * @param lhs Left hand side of comparison
+ * @param rhs Right hand side of comparison
+ * @return Returns true if lhs is greater than rhs, false otherwise
+ */
+template <typename Integer>
+CUDF_HOST_DEVICE inline bool operator>(dictionary_wrapper<Integer> const& lhs,
+                                       dictionary_wrapper<Integer> const& rhs)
+{
+  return lhs.value() > rhs.value();
+}
+
+using dictionary32 = dictionary_wrapper<int32_t>;  ///< 32-bit integer indexed dictionary wrapper
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/wrappers/durations.hpp b/cpp/include/cudf/wrappers/durations.hpp
new file mode 100644
index 0000000..62aa22c
--- /dev/null
+++ b/cpp/include/cudf/wrappers/durations.hpp
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda/std/chrono>
+
+namespace cudf {
+
+/**
+ * @addtogroup timestamp_classes Timestamp
+ * @{
+ * @file durations.hpp
+ * @brief Concrete type definitions for int32_t and int64_t durations in varying resolutions.
+ */
+
+/**
+ * @brief Type alias representing an int32_t duration of days.
+ */
+using duration_D = cuda::std::chrono::duration<int32_t, cuda::std::chrono::days::period>;
+/**
+ * @brief Type alias representing an int32_t duration of hours.
+ */
+using duration_h = cuda::std::chrono::duration<int32_t, cuda::std::chrono::hours::period>;
+/**
+ * @brief Type alias representing an int32_t duration of minutes.
+ */
+using duration_m = cuda::std::chrono::duration<int32_t, cuda::std::chrono::minutes::period>;
+/**
+ * @brief Type alias representing an int64_t duration of seconds.
+ */
+using duration_s = cuda::std::chrono::duration<int64_t, cuda::std::chrono::seconds::period>;
+/**
+ * @brief Type alias representing an int64_t duration of milliseconds.
+ */
+using duration_ms = cuda::std::chrono::duration<int64_t, cuda::std::chrono::milliseconds::period>;
+/**
+ * @brief Type alias representing an int64_t duration of microseconds.
+ */
+using duration_us = cuda::std::chrono::duration<int64_t, cuda::std::chrono::microseconds::period>;
+/**
+ * @brief Type alias representing an int64_t duration of nanoseconds.
+ */
+using duration_ns = cuda::std::chrono::duration<int64_t, cuda::std::chrono::nanoseconds::period>;
+
+static_assert(sizeof(duration_D) == sizeof(typename duration_D::rep), "");
+static_assert(sizeof(duration_h) == sizeof(typename duration_h::rep), "");
+static_assert(sizeof(duration_m) == sizeof(typename duration_m::rep), "");
+static_assert(sizeof(duration_s) == sizeof(typename duration_s::rep), "");
+static_assert(sizeof(duration_ms) == sizeof(typename duration_ms::rep), "");
+static_assert(sizeof(duration_us) == sizeof(typename duration_us::rep), "");
+static_assert(sizeof(duration_ns) == sizeof(typename duration_ns::rep), "");
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf/wrappers/timestamps.hpp b/cpp/include/cudf/wrappers/timestamps.hpp
new file mode 100644
index 0000000..0341ac6
--- /dev/null
+++ b/cpp/include/cudf/wrappers/timestamps.hpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/wrappers/durations.hpp>
+
+/**
+ * @file timestamps.hpp
+ * @brief Concrete type definitions for int32_t and int64_t timestamps in
+ * varying resolutions as durations since the UNIX epoch.
+ */
+namespace cudf {
+namespace detail {
+// TODO: Use chrono::utc_clock when available in libcu++?
+template <class Duration>
+using time_point = cuda::std::chrono::sys_time<Duration>;  ///< Time point type
+
+/**
+ * @brief A wrapper around a column of time_point in varying resolutions
+ *
+ * @tparam Duration The underlying duration type
+ */
+template <class Duration>
+using timestamp = time_point<Duration>;
+}  // namespace detail
+
+/**
+ * @addtogroup timestamp_classes
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Type alias representing a cudf::duration_D (int32_t) since the unix epoch.
+ */
+using timestamp_D = detail::timestamp<cudf::duration_D>;
+/**
+ * @brief Type alias representing a cudf::duration_h (int32_t) since the unix epoch.
+ */
+using timestamp_h = detail::timestamp<cudf::duration_h>;
+/**
+ * @brief Type alias representing a cudf::duration_m (int32_t) since the unix epoch.
+ */
+using timestamp_m = detail::timestamp<cudf::duration_m>;
+/**
+ * @brief Type alias representing a cudf::duration_s (int64_t) since the unix epoch.
+ */
+using timestamp_s = detail::timestamp<cudf::duration_s>;
+/**
+ * @brief Type alias representing a cudf::duration_ms (int64_t) since the unix epoch.
+ */
+using timestamp_ms = detail::timestamp<cudf::duration_ms>;
+/**
+ * @brief Type alias representing a cudf::duration_us (int64_t) since the unix epoch.
+ */
+using timestamp_us = detail::timestamp<cudf::duration_us>;
+/**
+ * @brief Type alias representing a cudf::duration_ns (int64_t) since the unix epoch.
+ */
+using timestamp_ns = detail::timestamp<cudf::duration_ns>;
+
+static_assert(sizeof(timestamp_D) == sizeof(typename timestamp_D::rep), "");
+static_assert(sizeof(timestamp_h) == sizeof(typename timestamp_h::rep), "");
+static_assert(sizeof(timestamp_m) == sizeof(typename timestamp_m::rep), "");
+static_assert(sizeof(timestamp_s) == sizeof(typename timestamp_s::rep), "");
+static_assert(sizeof(timestamp_ms) == sizeof(typename timestamp_ms::rep), "");
+static_assert(sizeof(timestamp_us) == sizeof(typename timestamp_us::rep), "");
+static_assert(sizeof(timestamp_ns) == sizeof(typename timestamp_ns::rep), "");
+
+/** @} */  // end of group
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/base_fixture.hpp b/cpp/include/cudf_test/base_fixture.hpp
new file mode 100644
index 0000000..06aabbe
--- /dev/null
+++ b/cpp/include/cudf_test/base_fixture.hpp
@@ -0,0 +1,399 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <random>
+
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/cxxopts.hpp>
+#include <cudf_test/default_stream.hpp>
+#include <cudf_test/file_utilities.hpp>
+#include <cudf_test/stream_checking_resource_adaptor.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/arena_memory_resource.hpp>
+#include <rmm/mr/device/binning_memory_resource.hpp>
+#include <rmm/mr/device/cuda_async_memory_resource.hpp>
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <rmm/mr/device/owning_wrapper.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+namespace cudf {
+namespace test {
+/**
+ * @brief Base test fixture class from which all libcudf tests should inherit.
+ *
+ * Example:
+ * ```
+ * class MyTestFixture : public cudf::test::BaseFixture {};
+ * ```
+ */
+class BaseFixture : public ::testing::Test {
+  rmm::mr::device_memory_resource* _mr{rmm::mr::get_current_device_resource()};
+
+ public:
+  /**
+   * @brief Returns pointer to `device_memory_resource` that should be used for
+   * all tests inheriting from this fixture
+   * @return pointer to memory resource
+   */
+  rmm::mr::device_memory_resource* mr() { return _mr; }
+};
+
+/**
+ * @brief Base test fixture that takes a parameter.
+ *
+ * Example:
+ * ```
+ * class MyIntTestFixture : public cudf::test::BaseFixtureWithParam<int> {};
+ * ```
+ */
+template <typename T>
+class BaseFixtureWithParam : public ::testing::TestWithParam<T> {
+  rmm::mr::device_memory_resource* _mr{rmm::mr::get_current_device_resource()};
+
+ public:
+  /**
+   * @brief Returns pointer to `device_memory_resource` that should be used for
+   * all tests inheriting from this fixture
+   * @return pointer to memory resource
+   */
+  rmm::mr::device_memory_resource* mr() const { return _mr; }
+};
+
+template <typename T, typename Enable = void>
+struct uniform_distribution_impl {};
+template <typename T>
+struct uniform_distribution_impl<T, std::enable_if_t<std::is_integral_v<T>>> {
+  using type = std::uniform_int_distribution<T>;
+};
+
+template <>
+struct uniform_distribution_impl<bool> {
+  using type = std::bernoulli_distribution;
+};
+
+template <typename T>
+struct uniform_distribution_impl<T, std::enable_if_t<std::is_floating_point_v<T>>> {
+  using type = std::uniform_real_distribution<T>;
+};
+
+template <typename T>
+struct uniform_distribution_impl<
+  T,
+  std::enable_if_t<cudf::is_chrono<T>() or cudf::is_fixed_point<T>()>> {
+  using type = std::uniform_int_distribution<typename T::rep>;
+};
+
+template <typename T>
+using uniform_distribution_t = typename uniform_distribution_impl<T>::type;
+
+namespace detail {
+
+/**
+ * @brief Returns an incrementing seed value for use with UniformRandomGenerator.
+ *
+ *  The intent behind this is to handle the following case:
+ *
+ * auto lhs = make_random_wrapped_column<TypeLhs>(10000);
+ * auto rhs = make_random_wrapped_column<TypeRhs>(10000);
+ *
+ * Previously, the binops test framework had a persistent UniformRandomGenerator
+ * that would produce unique values across two calls to make_random_wrapped_column()
+ * like this.  However that code has been changed and each call to make_random_wrapped_column()
+ * now uses a local UniformRandomGenerator object.  If we didn't generate an incrementing seed
+ * for each one, every call to make_random_wrapped_column() would return the same values. This
+ * fixes that case and also leaves results across multiple test runs deterministic.
+ */
+uint64_t random_generator_incrementing_seed();
+
+}  // namespace detail
+
+/**
+ * @brief Provides uniform random number generation.
+ *
+ * It is often useful in testing to have a convenient source of random numbers.
+ * This class is intended to serve as a base class for test fixtures to provide
+ * random number generation. `UniformRandomGenerator::generate()` will generate
+ * the next random number in the sequence.
+ *
+ * Example:
+ * ```c++
+ * UniformRandomGenerator g(0,100);
+ * g.generate(); // Returns a random number in the range [0,100]
+ * ```
+ *
+ * @tparam T The type of values that will be generated.
+ */
+template <typename T = cudf::size_type, typename Engine = std::default_random_engine>
+class UniformRandomGenerator {
+ public:
+  using uniform_distribution = uniform_distribution_t<T>;  ///< The uniform distribution type for T.
+
+  UniformRandomGenerator() : rng{std::mt19937_64{detail::random_generator_incrementing_seed()}()} {}
+
+  /**
+   * @brief Construct a new Uniform Random Generator to generate uniformly
+   * random numbers in the range `[upper,lower]`
+   *
+   * @param lower Lower bound of the range
+   * @param upper Upper bound of the desired range
+   * @param seed  seed to initialize generator with
+   */
+  template <typename TL                                                          = T,
+            std::enable_if_t<cudf::is_numeric<TL>() && !cudf::is_boolean<TL>()>* = nullptr>
+  UniformRandomGenerator(T lower,
+                         T upper,
+                         uint64_t seed = detail::random_generator_incrementing_seed())
+    : dist{lower, upper}, rng{std::mt19937_64{seed}()}
+  {
+  }
+
+  /**
+   * @brief Construct a new Uniform Random Generator to generate uniformly random booleans
+   *
+   * @param lower ignored
+   * @param upper ignored
+   * @param seed  seed to initialize generator with
+   */
+  template <typename TL = T, std::enable_if_t<cudf::is_boolean<TL>()>* = nullptr>
+  UniformRandomGenerator(T lower,
+                         T upper,
+                         uint64_t seed = detail::random_generator_incrementing_seed())
+    : dist{0.5}, rng{std::mt19937_64{seed}()}
+  {
+  }
+
+  /**
+   * @brief Construct a new Uniform Random Generator to generate uniformly
+   * random numbers in the range `[upper,lower]`
+   *
+   * @param lower Lower bound of the range
+   * @param upper Upper bound of the desired range
+   * @param seed  seed to initialize generator with
+   */
+  template <typename TL                                                            = T,
+            std::enable_if_t<cudf::is_chrono<TL>() or cudf::is_fixed_point<TL>()>* = nullptr>
+  UniformRandomGenerator(typename TL::rep lower,
+                         typename TL::rep upper,
+                         uint64_t seed = detail::random_generator_incrementing_seed())
+    : dist{lower, upper}, rng{std::mt19937_64{seed}()}
+  {
+  }
+
+  /**
+   * @brief Returns the next random number.
+   *
+   * @return generated random number
+   */
+  template <typename TL = T, std::enable_if_t<!cudf::is_timestamp<TL>()>* = nullptr>
+  T generate()
+  {
+    return T{dist(rng)};
+  }
+
+  /**
+   * @brief Returns the next random number.
+   * @return generated random number
+   */
+  template <typename TL = T, std::enable_if_t<cudf::is_timestamp<TL>()>* = nullptr>
+  T generate()
+  {
+    return T{typename T::duration{dist(rng)}};
+  }
+
+ private:
+  uniform_distribution dist{};  ///< Distribution
+  Engine rng;                   ///< Random generator
+};
+
+/**
+ * @brief Provides temporary directory for temporary test files.
+ *
+ * Example:
+ * ```c++
+ * ::testing::Environment* const temp_env =
+ *    ::testing::AddGlobalTestEnvironment(new TempDirTestEnvironment);
+ * ```
+ */
+class TempDirTestEnvironment : public ::testing::Environment {
+  temp_directory const tmpdir{"gtest"};
+
+ public:
+  /**
+   * @brief Get directory path to use for temporary files
+   *
+   * @return std::string The temporary directory path
+   */
+  std::string get_temp_dir() { return tmpdir.path(); }
+
+  /**
+   * @brief Get a temporary filepath to use for the specified filename
+   *
+   * @param filename name of the file to be placed in temporary directory.
+   * @return std::string The temporary filepath
+   */
+  std::string get_temp_filepath(std::string filename) { return tmpdir.path() + filename; }
+};
+
+/// MR factory functions
+inline auto make_cuda() { return std::make_shared<rmm::mr::cuda_memory_resource>(); }
+
+inline auto make_async() { return std::make_shared<rmm::mr::cuda_async_memory_resource>(); }
+
+inline auto make_managed() { return std::make_shared<rmm::mr::managed_memory_resource>(); }
+
+inline auto make_pool()
+{
+  auto const [free, total] = rmm::detail::available_device_memory();
+  auto min_alloc =
+    rmm::detail::align_down(std::min(free, total / 10), rmm::detail::CUDA_ALLOCATION_ALIGNMENT);
+  return rmm::mr::make_owning_wrapper<rmm::mr::pool_memory_resource>(make_cuda(), min_alloc);
+}
+
+inline auto make_arena()
+{
+  return rmm::mr::make_owning_wrapper<rmm::mr::arena_memory_resource>(make_cuda());
+}
+
+inline auto make_binning()
+{
+  auto pool = make_pool();
+  // Add a binning_memory_resource with fixed-size bins of sizes 256, 512, 1024, 2048 and 4096KiB
+  // Larger allocations will use the pool resource
+  auto mr = rmm::mr::make_owning_wrapper<rmm::mr::binning_memory_resource>(pool, 18, 22);
+  return mr;
+}
+
+/**
+ * @brief Creates a memory resource for the unit test environment
+ * given the name of the allocation mode.
+ *
+ * The returned resource instance must be kept alive for the duration of
+ * the tests. Attaching the resource to a TestEnvironment causes
+ * issues since the environment objects are not destroyed until
+ * after the runtime is shutdown.
+ *
+ * @throw cudf::logic_error if the `allocation_mode` is unsupported.
+ *
+ * @param allocation_mode String identifies which resource type.
+ *        Accepted types are "pool", "cuda", and "managed" only.
+ * @return Memory resource instance
+ */
+inline std::shared_ptr<rmm::mr::device_memory_resource> create_memory_resource(
+  std::string const& allocation_mode)
+{
+  if (allocation_mode == "binning") return make_binning();
+  if (allocation_mode == "cuda") return make_cuda();
+  if (allocation_mode == "async") return make_async();
+  if (allocation_mode == "pool") return make_pool();
+  if (allocation_mode == "arena") return make_arena();
+  if (allocation_mode == "managed") return make_managed();
+  CUDF_FAIL("Invalid RMM allocation mode: " + allocation_mode);
+}
+
+}  // namespace test
+}  // namespace cudf
+
+/**
+ * @brief Parses the cuDF test command line options.
+ *
+ * Currently only supports 'rmm_mode' string parameter, which set the rmm
+ * allocation mode. The default value of the parameter is 'pool'.
+ * Environment variable 'CUDF_TEST_RMM_MODE' can also be used to set the rmm
+ * allocation mode. If both are set, the value of 'rmm_mode' string parameter
+ * takes precedence.
+ *
+ * @return Parsing results in the form of unordered map
+ */
+inline auto parse_cudf_test_opts(int argc, char** argv)
+{
+  try {
+    cxxopts::Options options(argv[0], " - cuDF tests command line options");
+    char const* env_rmm_mode = std::getenv("GTEST_CUDF_RMM_MODE");  // Overridden by CLI options
+    char const* env_stream_mode =
+      std::getenv("GTEST_CUDF_STREAM_MODE");  // Overridden by CLI options
+    char const* env_stream_error_mode =
+      std::getenv("GTEST_CUDF_STREAM_ERROR_MODE");  // Overridden by CLI options
+    auto default_rmm_mode          = env_rmm_mode ? env_rmm_mode : "pool";
+    auto default_stream_mode       = env_stream_mode ? env_stream_mode : "default";
+    auto default_stream_error_mode = env_stream_error_mode ? env_stream_error_mode : "error";
+    options.allow_unrecognised_options().add_options()(
+      "rmm_mode",
+      "RMM allocation mode",
+      cxxopts::value<std::string>()->default_value(default_rmm_mode));
+    // `new_cudf_default` means that cudf::get_default_stream has been patched,
+    // so we raise errors anywhere that a CUDA default stream is observed
+    // instead of cudf::get_default_stream(). This corresponds to compiling
+    // identify_stream_usage with STREAM_MODE_TESTING=OFF (must do both at the
+    // same time).
+    // `new_testing_default` means that cudf::test::get_default_stream has been
+    // patched, so we raise errors anywhere that _any_ other stream is
+    // observed. This corresponds to compiling identify_stream_usage with
+    // STREAM_MODE_TESTING=ON (must do both at the same time).
+    options.allow_unrecognised_options().add_options()(
+      "stream_mode",
+      "Whether to use a non-default stream",
+      cxxopts::value<std::string>()->default_value(default_stream_mode));
+    options.allow_unrecognised_options().add_options()(
+      "stream_error_mode",
+      "Whether to error or print to stdout when a non-default stream is observed and stream_mode "
+      "is not \"default\"",
+      cxxopts::value<std::string>()->default_value(default_stream_error_mode));
+    return options.parse(argc, argv);
+  } catch (cxxopts::OptionException const& e) {
+    CUDF_FAIL("Error parsing command line options");
+  }
+}
+
+/**
+ * @brief Macro that defines main function for gtest programs that use rmm
+ *
+ * Should be included in every test program that uses rmm allocators since
+ * it maintains the lifespan of the rmm default memory resource.
+ * This `main` function is a wrapper around the google test generated `main`,
+ * maintaining the original functionality. In addition, this custom `main`
+ * function parses the command line to customize test behavior, like the
+ * allocation mode used for creating the default memory resource.
+ */
+#define CUDF_TEST_PROGRAM_MAIN()                                                              \
+  int main(int argc, char** argv)                                                             \
+  {                                                                                           \
+    ::testing::InitGoogleTest(&argc, argv);                                                   \
+    auto const cmd_opts = parse_cudf_test_opts(argc, argv);                                   \
+    auto const rmm_mode = cmd_opts["rmm_mode"].as<std::string>();                             \
+    auto resource       = cudf::test::create_memory_resource(rmm_mode);                       \
+    rmm::mr::set_current_device_resource(resource.get());                                     \
+                                                                                              \
+    auto const stream_mode = cmd_opts["stream_mode"].as<std::string>();                       \
+    if ((stream_mode == "new_cudf_default") || (stream_mode == "new_testing_default")) {      \
+      auto const stream_error_mode       = cmd_opts["stream_error_mode"].as<std::string>();   \
+      auto const error_on_invalid_stream = (stream_error_mode == "error");                    \
+      auto const check_default_stream    = (stream_mode == "new_cudf_default");               \
+      auto adaptor                       = make_stream_checking_resource_adaptor(             \
+        resource.get(), error_on_invalid_stream, check_default_stream); \
+      rmm::mr::set_current_device_resource(&adaptor);                                         \
+      return RUN_ALL_TESTS();                                                                 \
+    }                                                                                         \
+                                                                                              \
+    return RUN_ALL_TESTS();                                                                   \
+  }
diff --git a/cpp/include/cudf_test/column_utilities.hpp b/cpp/include/cudf_test/column_utilities.hpp
new file mode 100644
index 0000000..059bd10
--- /dev/null
+++ b/cpp/include/cudf_test/column_utilities.hpp
@@ -0,0 +1,310 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf::test {
+
+/**
+ * @brief Verbosity level of output from column and table comparison functions.
+ */
+enum class debug_output_level {
+  FIRST_ERROR = 0,  // print first error only
+  ALL_ERRORS,       // print all errors
+  QUIET             // no debug output
+};
+
+constexpr size_type default_ulp = 4;
+
+namespace detail {
+
+/**
+ * @brief Verifies the property equality of two columns.
+ *
+ * @note This function should not be used directly. Use `CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL`
+ * instead.
+ *
+ * @param lhs The first column
+ * @param rhs The second column
+ * @param verbosity Level of debug output verbosity
+ *
+ * @returns True if the column properties are equal, false otherwise
+ */
+bool expect_column_properties_equal(cudf::column_view const& lhs,
+                                    cudf::column_view const& rhs,
+                                    debug_output_level verbosity = debug_output_level::FIRST_ERROR);
+
+/**
+ * @brief Verifies the property equivalence of two columns.
+ *
+ * If the columns don't have nulls, then the nullability equality is relaxed.
+ * i.e. the two columns are considered equivalent even if one has a null mask
+ * and the other doesn't.
+ *
+ * @note This function should not be used directly. Use
+ * `CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT` instead.
+ *
+ * @param lhs The first column
+ * @param rhs The second column
+ * @param verbosity Level of debug output verbosity
+ *
+ * @returns True if the column properties are equivalent, false otherwise
+ */
+bool expect_column_properties_equivalent(
+  cudf::column_view const& lhs,
+  cudf::column_view const& rhs,
+  debug_output_level verbosity = debug_output_level::FIRST_ERROR);
+
+/**
+ * @brief Verifies the element-wise equality of two columns.
+ *
+ * Treats null elements as equivalent.
+ *
+ * @note This function should not be used directly. Use
+ * `CUDF_TEST_EXPECT_COLUMNS_EQUAL` instead.
+ *
+ * @param lhs The first column
+ * @param rhs The second column
+ * @param verbosity Level of debug output verbosity
+ *
+ * @returns True if the columns (and their properties) are equal, false otherwise
+ */
+bool expect_columns_equal(cudf::column_view const& lhs,
+                          cudf::column_view const& rhs,
+                          debug_output_level verbosity = debug_output_level::FIRST_ERROR);
+
+/**
+ * @brief Verifies the element-wise equivalence of two columns.
+ *
+ * Uses machine epsilon to compare floating point types.
+ * Treats null elements as equivalent.
+ *
+ * @note This function should not be used directly. Use `CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT`
+ * instead.
+ *
+ * @param lhs The first column
+ * @param rhs The second column
+ * @param verbosity Level of debug output verbosity
+ * @param fp_ulps # of ulps of tolerance to allow when comparing
+ * floating point values
+ *
+ * @returns True if the columns (and their properties) are equivalent, false otherwise
+ */
+bool expect_columns_equivalent(cudf::column_view const& lhs,
+                               cudf::column_view const& rhs,
+                               debug_output_level verbosity = debug_output_level::FIRST_ERROR,
+                               size_type fp_ulps            = cudf::test::default_ulp);
+
+/**
+ * @brief Verifies the bitwise equality of two device memory buffers.
+ *
+ * @note This function should not be used directly. Use `CUDF_TEST_EXPECT_EQUAL_BUFFERS` instead.
+ *
+ * @param lhs The first buffer
+ * @param rhs The second buffer
+ * @param size_bytes The number of bytes to check for equality
+ */
+void expect_equal_buffers(void const* lhs, void const* rhs, std::size_t size_bytes);
+
+}  // namespace detail
+
+/**
+ * @brief Verifies the given column is empty
+ *
+ * @param col The column to check
+ */
+void expect_column_empty(cudf::column_view const& col);
+
+/**
+ * @brief Formats a column view as a string
+ *
+ * @param col The column view
+ * @param delimiter The delimiter to put between strings
+ */
+std::string to_string(cudf::column_view const& col, std::string const& delimiter);
+
+/**
+ * @brief Formats a null mask as a string
+ *
+ * @param null_mask The null mask buffer
+ * @param null_mask_size Size of the null mask (in rows)
+ */
+std::string to_string(std::vector<bitmask_type> const& null_mask, size_type null_mask_size);
+
+/**
+ * @brief Convert column values to a host vector of strings
+ *
+ * @param col The column view
+ */
+std::vector<std::string> to_strings(cudf::column_view const& col);
+
+/**
+ * @brief Print a column view to an ostream
+ *
+ * @param os        The output stream
+ * @param col       The column view
+ */
+void print(cudf::column_view const& col,
+           std::ostream& os             = std::cout,
+           std::string const& delimiter = ",");
+
+/**
+ * @brief Copy the null bitmask from a column view to a host vector
+ *
+ * @param c      The column view
+ * @returns      Vector of bitmask_type elements
+ */
+std::vector<bitmask_type> bitmask_to_host(cudf::column_view const& c);
+
+/**
+ * @brief Validates bitmask situated in host as per `number_of_elements`
+ *
+ * This takes care of padded bits
+ *
+ * @param        expected_mask A vector representing expected mask
+ * @param        got_mask A vector representing mask obtained from column
+ * @param        number_of_elements number of elements the mask represent
+ *
+ * @returns      true if both vector match till the `number_of_elements`
+ */
+bool validate_host_masks(std::vector<bitmask_type> const& expected_mask,
+                         std::vector<bitmask_type> const& got_mask_begin,
+                         size_type number_of_elements);
+
+/**
+ * @brief Copies the data and bitmask of a `column_view` to the host.
+ *
+ * @tparam T The data type of the elements of the `column_view`
+ * @param c the `column_view` to copy from
+ * @return std::pair<thrust::host_vector<T>, std::vector<bitmask_type>> first is the
+ *  `column_view`'s data, and second is the column's bitmask.
+ */
+template <typename T, std::enable_if_t<not cudf::is_fixed_point<T>()>* = nullptr>
+std::pair<thrust::host_vector<T>, std::vector<bitmask_type>> to_host(column_view c)
+{
+  thrust::host_vector<T> host_data(c.size());
+  CUDF_CUDA_TRY(cudaMemcpy(host_data.data(), c.data<T>(), c.size() * sizeof(T), cudaMemcpyDefault));
+  return {host_data, bitmask_to_host(c)};
+}
+
+/**
+ * @brief Copies the data and bitmask of a `column_view` to the host.
+ *
+ * This is the specialization for `fixed_point` that performs construction of a `fixed_point` from
+ * the underlying `rep` type that is stored on the device.
+ *
+ * @tparam T The data type of the elements of the `column_view`
+ * @param c the `column_view` to copy from
+ * @return std::pair<thrust::host_vector<T>, std::vector<bitmask_type>> first is the
+ *  `column_view`'s data, and second is the column's bitmask.
+ */
+template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+std::pair<thrust::host_vector<T>, std::vector<bitmask_type>> to_host(column_view c)
+{
+  using namespace numeric;
+  using Rep = typename T::rep;
+
+  auto host_rep_types = thrust::host_vector<Rep>(c.size());
+
+  CUDF_CUDA_TRY(
+    cudaMemcpy(host_rep_types.data(), c.begin<Rep>(), c.size() * sizeof(Rep), cudaMemcpyDefault));
+
+  auto to_fp = [&](Rep val) { return T{scaled_integer<Rep>{val, scale_type{c.type().scale()}}}; };
+  auto begin = thrust::make_transform_iterator(std::cbegin(host_rep_types), to_fp);
+  auto const host_fixed_points = thrust::host_vector<T>(begin, begin + c.size());
+
+  return {host_fixed_points, bitmask_to_host(c)};
+}
+
+/**
+ * @brief Copies the data and bitmask of a `column_view` of strings
+ * column to the host.
+ *
+ * @throw cudf::logic_error if c is not strings column.
+ *
+ * @param c the `column_view` of strings to copy from
+ * @return std::pair first is `std::vector` of `std::string`
+ * and second is the column's bitmask.
+ */
+template <>
+inline std::pair<thrust::host_vector<std::string>, std::vector<bitmask_type>> to_host(column_view c)
+{
+  thrust::host_vector<std::string> host_data(c.size());
+  if (c.size() > c.null_count()) {
+    auto const scv     = strings_column_view(c);
+    auto const h_chars = cudf::detail::make_std_vector_sync<char>(
+      cudf::device_span<char const>(scv.chars().data<char>(), scv.chars().size()),
+      cudf::get_default_stream());
+    auto const h_offsets = cudf::detail::make_std_vector_sync(
+      cudf::device_span<cudf::size_type const>(scv.offsets().data<cudf::size_type>() + scv.offset(),
+                                               scv.size() + 1),
+      cudf::get_default_stream());
+
+    // build std::string vector from chars and offsets
+    std::transform(
+      std::begin(h_offsets),
+      std::end(h_offsets) - 1,
+      std::begin(h_offsets) + 1,
+      host_data.begin(),
+      [&](auto start, auto end) { return std::string(h_chars.data() + start, end - start); });
+  }
+  return {std::move(host_data), bitmask_to_host(c)};
+}
+
+}  // namespace cudf::test
+
+// Macros for showing line of failure.
+#define CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(lhs, rhs)        \
+  do {                                                            \
+    SCOPED_TRACE(" <--  line of failure\n");                      \
+    cudf::test::detail::expect_column_properties_equal(lhs, rhs); \
+  } while (0)
+
+#define CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT(lhs, rhs)        \
+  do {                                                                 \
+    SCOPED_TRACE(" <--  line of failure\n");                           \
+    cudf::test::detail::expect_column_properties_equivalent(lhs, rhs); \
+  } while (0)
+
+#define CUDF_TEST_EXPECT_COLUMNS_EQUAL(lhs, rhs...)     \
+  do {                                                  \
+    SCOPED_TRACE(" <--  line of failure\n");            \
+    cudf::test::detail::expect_columns_equal(lhs, rhs); \
+  } while (0)
+
+#define CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lhs, rhs...)     \
+  do {                                                       \
+    SCOPED_TRACE(" <--  line of failure\n");                 \
+    cudf::test::detail::expect_columns_equivalent(lhs, rhs); \
+  } while (0)
+
+#define CUDF_TEST_EXPECT_EQUAL_BUFFERS(lhs, rhs, size_bytes)        \
+  do {                                                              \
+    SCOPED_TRACE(" <--  line of failure\n");                        \
+    cudf::test::detail::expect_equal_buffers(lhs, rhs, size_bytes); \
+  } while (0)
diff --git a/cpp/include/cudf_test/column_wrapper.hpp b/cpp/include/cudf_test/column_wrapper.hpp
new file mode 100644
index 0000000..c0932b8
--- /dev/null
+++ b/cpp/include/cudf_test/column_wrapper.hpp
@@ -0,0 +1,1910 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/default_stream.hpp>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/functional.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+#include <iterator>
+#include <memory>
+#include <numeric>
+
+namespace cudf {
+namespace test {
+namespace detail {
+/**
+ * @brief Base class for a wrapper around a `cudf::column`.
+ *
+ * Classes that derive from `column_wrapper` may be passed directly into any
+ * API expecting a `column_view` or `mutable_column_view`.
+ *
+ * `column_wrapper` should not be instantiated directly.
+ */
+class column_wrapper {
+ public:
+  /**
+   * @brief Implicit conversion operator to `column_view`.
+   *
+   * Allows passing in a `column_wrapper` (or any class deriving from
+   * `column_wrapper`) to be passed into any API expecting a `column_view`
+   * parameter.
+   */
+  operator column_view() const { return wrapped->view(); }
+
+  /**
+   * @brief Implicit conversion operator to `mutable_column_view`.
+   *
+   * Allows passing in a `column_wrapper` (or any class deriving from
+   * `column_wrapper`) to be passed into any API expecting a
+   * `mutable_column_view` parameter.
+   */
+  operator mutable_column_view() { return wrapped->mutable_view(); }
+
+  /**
+   * @brief Releases internal unique_ptr to wrapped column
+   *
+   * @return unique_ptr to wrapped column
+   */
+  std::unique_ptr<cudf::column> release() { return std::move(wrapped); }
+
+ protected:
+  std::unique_ptr<cudf::column> wrapped{};  ///< The wrapped column
+};
+
+/**
+ * @brief Convert between source and target types when they differ and where possible.
+ */
+template <typename From, typename To>
+struct fixed_width_type_converter {
+  /**
+   * @brief No conversion necessary: Same type, simply copy element to output.
+   *
+   * @tparam FromT Source type
+   * @tparam ToT Target type
+   * @param element Source value
+   * @return The converted target value, same as source value
+   */
+  template <typename FromT                                      = From,
+            typename ToT                                        = To,
+            std::enable_if_t<std::is_same_v<FromT, ToT>, void>* = nullptr>
+  constexpr ToT operator()(FromT element) const
+  {
+    return element;
+  }
+
+  /**
+   * @brief Convert types if possible, otherwise construct target from source.
+   *
+   * @tparam FromT Source type
+   * @tparam ToT Target type
+   * @param element Source value
+   * @return The converted target value
+   */
+  template <
+    typename FromT          = From,
+    typename ToT            = To,
+    std::enable_if_t<!std::is_same_v<FromT, ToT> && (cudf::is_convertible<FromT, ToT>::value ||
+                                                     std::is_constructible_v<ToT, FromT>),
+                     void>* = nullptr>
+  constexpr ToT operator()(FromT element) const
+  {
+    return static_cast<ToT>(element);
+  }
+
+  /**
+   * @brief Convert integral values to timestamps
+   *
+   * @tparam FromT Source type
+   * @tparam ToT Target type
+   * @param element Source value
+   * @return The converted target `timestamp` value
+   */
+  template <
+    typename FromT                                                                  = From,
+    typename ToT                                                                    = To,
+    std::enable_if_t<std::is_integral_v<FromT> && cudf::is_timestamp<ToT>(), void>* = nullptr>
+  constexpr ToT operator()(FromT element) const
+  {
+    return ToT{typename ToT::duration{element}};
+  }
+};
+
+/**
+ * @brief Creates a `device_buffer` containing the elements in the range `[begin,end)`.
+ *
+ * @tparam ElementTo The element type that is being created (non-`fixed_point`)
+ * @tparam ElementFrom The element type used to create elements of type `ElementTo`
+ * @tparam InputIterator Iterator type for `begin` and `end`
+ * @param begin Beginning of the sequence of elements
+ * @param end End of the sequence of elements
+ * @return rmm::device_buffer Buffer containing all elements in the range `[begin,end)`
+ */
+template <typename ElementTo,
+          typename ElementFrom,
+          typename InputIterator,
+          std::enable_if_t<not cudf::is_fixed_point<ElementTo>()>* = nullptr>
+rmm::device_buffer make_elements(InputIterator begin, InputIterator end)
+{
+  static_assert(cudf::is_fixed_width<ElementTo>(), "Unexpected non-fixed width type.");
+  auto transformer     = fixed_width_type_converter<ElementFrom, ElementTo>{};
+  auto transform_begin = thrust::make_transform_iterator(begin, transformer);
+  auto const size      = cudf::distance(begin, end);
+  auto const elements  = thrust::host_vector<ElementTo>(transform_begin, transform_begin + size);
+  return rmm::device_buffer{
+    elements.data(), size * sizeof(ElementTo), cudf::test::get_default_stream()};
+}
+
+/**
+ * @brief Creates a `device_buffer` containing the elements in the range `[begin,end)`.
+ *
+ * @tparam ElementTo The element type that is being created (`fixed_point` specialization)
+ * @tparam ElementFrom The element type used to create elements of type `ElementTo`
+ * (non-`fixed-point`)
+ * @tparam InputIterator Iterator type for `begin` and `end`
+ * @param begin Beginning of the sequence of elements
+ * @param end End of the sequence of elements
+ * @return rmm::device_buffer Buffer containing all elements in the range `[begin,end)`
+ */
+template <typename ElementTo,
+          typename ElementFrom,
+          typename InputIterator,
+          std::enable_if_t<not cudf::is_fixed_point<ElementFrom>() and
+                           cudf::is_fixed_point<ElementTo>()>* = nullptr>
+rmm::device_buffer make_elements(InputIterator begin, InputIterator end)
+{
+  using RepType        = typename ElementTo::rep;
+  auto transformer     = fixed_width_type_converter<ElementFrom, RepType>{};
+  auto transform_begin = thrust::make_transform_iterator(begin, transformer);
+  auto const size      = cudf::distance(begin, end);
+  auto const elements  = thrust::host_vector<RepType>(transform_begin, transform_begin + size);
+  return rmm::device_buffer{
+    elements.data(), size * sizeof(RepType), cudf::test::get_default_stream()};
+}
+
+/**
+ * @brief Creates a `device_buffer` containing the elements in the range `[begin,end)`.
+ *
+ * @tparam ElementTo The element type that is being created (`fixed_point` specialization)
+ * @tparam ElementFrom The element type used to create elements of type `ElementTo` (`fixed_point`)
+ * @tparam InputIterator Iterator type for `begin` and `end`
+ * @param begin Beginning of the sequence of elements
+ * @param end End of the sequence of elements
+ * @return rmm::device_buffer Buffer containing all elements in the range `[begin,end)`
+ */
+template <typename ElementTo,
+          typename ElementFrom,
+          typename InputIterator,
+          std::enable_if_t<cudf::is_fixed_point<ElementFrom>() and
+                           cudf::is_fixed_point<ElementTo>()>* = nullptr>
+rmm::device_buffer make_elements(InputIterator begin, InputIterator end)
+{
+  using namespace numeric;
+  using RepType = typename ElementTo::rep;
+
+  auto to_rep            = [](ElementTo fp) { return fp.value(); };
+  auto transformer_begin = thrust::make_transform_iterator(begin, to_rep);
+  auto const size        = cudf::distance(begin, end);
+  auto const elements = thrust::host_vector<RepType>(transformer_begin, transformer_begin + size);
+  return rmm::device_buffer{
+    elements.data(), size * sizeof(RepType), cudf::test::get_default_stream()};
+}
+
+/**
+ * @brief Create a `std::vector` containing a validity indicator bitmask using
+ * the range `[begin,end)` interpreted as booleans to indicate the state of
+ * each bit.
+ *
+ * If `*(begin + i) == true`, then bit `i` is set to 1, else it is zero.
+ *
+ * @tparam ValidityIterator
+ * @param begin The beginning of the validity indicator sequence
+ * @param end The end of the validity indicator sequence
+ * @return std::vector Contains a bitmask where bits are set for every
+ * element in `[begin,end)` that evaluated to `true`.
+ */
+template <typename ValidityIterator>
+std::pair<std::vector<bitmask_type>, cudf::size_type> make_null_mask_vector(ValidityIterator begin,
+                                                                            ValidityIterator end)
+{
+  auto const size      = cudf::distance(begin, end);
+  auto const num_words = cudf::bitmask_allocation_size_bytes(size) / sizeof(bitmask_type);
+
+  auto null_mask  = std::vector<bitmask_type>(num_words, 0);
+  auto null_count = cudf::size_type{0};
+  for (auto i = 0; i < size; ++i) {
+    if (*(begin + i)) {
+      set_bit_unsafe(null_mask.data(), i);
+    } else {
+      ++null_count;
+    }
+  }
+
+  return {std::move(null_mask), null_count};
+}
+
+/**
+ * @brief Create a `device_buffer` containing a validity indicator bitmask using
+ * the range `[begin,end)` interpreted as booleans to indicate the state of
+ *each bit.
+ *
+ * If `*(begin + i) == true`, then bit `i` is set to 1, else it is zero.
+ *
+ * @tparam ValidityIterator
+ * @param begin The beginning of the validity indicator sequence
+ * @param end The end of the validity indicator sequence
+ * @return rmm::device_buffer Contains a bitmask where bits are set for every
+ * element in `[begin,end)` that evaluated to `true`.
+ */
+template <typename ValidityIterator>
+std::pair<rmm::device_buffer, cudf::size_type> make_null_mask(ValidityIterator begin,
+                                                              ValidityIterator end)
+{
+  auto [null_mask, null_count] = make_null_mask_vector(begin, end);
+  auto d_mask                  = rmm::device_buffer{null_mask.data(),
+                                   cudf::bitmask_allocation_size_bytes(cudf::distance(begin, end)),
+                                   cudf::test::get_default_stream()};
+  return {std::move(d_mask), null_count};
+}
+
+/**
+ * @brief Given the range `[begin,end)`, converts each value to a string and
+ * then creates a packed vector of characters for each string and a vector of
+ * offsets indicating the starting position of each string.
+ *
+ * @tparam StringsIterator A `std::string` must be constructible from
+ * dereferencing a `StringsIterator`.
+ * @tparam ValidityIterator Dereferencing a ValidityIterator must be
+ * convertible to `bool`
+ * @param begin The beginning of the sequence of values to convert to strings
+ * @param end The end of the sequence of values to convert to strings
+ * @param v The beginning of the validity indicator sequence
+ * @return std::pair containing the vector of chars and offsets
+ */
+template <typename StringsIterator, typename ValidityIterator>
+auto make_chars_and_offsets(StringsIterator begin, StringsIterator end, ValidityIterator v)
+{
+  std::vector<char> chars{};
+  std::vector<cudf::size_type> offsets(1, 0);
+  for (auto str = begin; str < end; ++str) {
+    std::string tmp = (*v++) ? std::string(*str) : std::string{};
+    chars.insert(chars.end(), std::cbegin(tmp), std::cend(tmp));
+    offsets.push_back(offsets.back() + tmp.length());
+  }
+  return std::pair(std::move(chars), std::move(offsets));
+};
+}  // namespace detail
+
+/**
+ * @brief `column_wrapper` derived class for wrapping columns of fixed-width
+ * elements.
+ *
+ * @tparam ElementTo The fixed-width element type that is created
+ * @tparam SourceElementT The fixed-width element type that is used to create elements of type
+ * `ElementTo`
+ */
+template <typename ElementTo, typename SourceElementT = ElementTo>
+class fixed_width_column_wrapper : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Default constructor initializes an empty column with proper dtype
+   */
+  fixed_width_column_wrapper() : column_wrapper{}
+  {
+    std::vector<ElementTo> empty;
+    wrapped.reset(
+      new cudf::column{cudf::data_type{cudf::type_to_id<ElementTo>()},
+                       0,
+                       detail::make_elements<ElementTo, SourceElementT>(empty.begin(), empty.end()),
+                       rmm::device_buffer{},
+                       0});
+  }
+
+  /**
+   * @brief Construct a non-nullable column of the fixed-width elements in the
+   * range `[begin,end)`.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable column of INT32 elements with 5 elements: {0, 2, 4, 6, 8}
+   * auto elements = make_counting_transform_iterator(0, [](auto i){return i*2;});
+   * fixed_width_column_wrapper<int32_t> w(elements, elements + 5);
+   * @endcode
+   *
+   * Note: similar to `std::vector`, this "range" constructor should be used
+   *       with parentheses `()` and not braces `{}`. The latter should only
+   *       be used for the `initializer_list` constructors
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end The end of the sequence of elements
+   */
+  template <typename InputIterator>
+  fixed_width_column_wrapper(InputIterator begin, InputIterator end) : column_wrapper{}
+  {
+    auto const size = cudf::distance(begin, end);
+    wrapped.reset(new cudf::column{cudf::data_type{cudf::type_to_id<ElementTo>()},
+                                   size,
+                                   detail::make_elements<ElementTo, SourceElementT>(begin, end),
+                                   rmm::device_buffer{},
+                                   0});
+  }
+
+  /**
+   * @brief Construct a nullable column of the fixed-width elements in the range
+   * `[begin,end)` using the range `[v, v + distance(begin,end))` interpreted
+   * as booleans to indicate the validity of each element.
+   *
+   * If `v[i] == true`, element `i` is valid, else it is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable column of INT32 elements with 5 elements: {null, 1, null, 3, null}
+   * auto elements = make_counting_transform_iterator(0, [](auto i){return i;});
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;})
+   * fixed_width_column_wrapper<int32_t> w(elements, elements + 5, validity);
+   * @endcode
+   *
+   * Note: similar to `std::vector`, this "range" constructor should be used
+   *       with parentheses `()` and not braces `{}`. The latter should only
+   *       be used for the `initializer_list` constructors
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end The end of the sequence of elements
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename InputIterator, typename ValidityIterator>
+  fixed_width_column_wrapper(InputIterator begin, InputIterator end, ValidityIterator v)
+    : column_wrapper{}
+  {
+    auto const size              = cudf::distance(begin, end);
+    auto [null_mask, null_count] = detail::make_null_mask(v, v + size);
+    wrapped.reset(new cudf::column{cudf::data_type{cudf::type_to_id<ElementTo>()},
+                                   size,
+                                   detail::make_elements<ElementTo, SourceElementT>(begin, end),
+                                   std::move(null_mask),
+                                   null_count});
+  }
+
+  /**
+   * @brief Construct a non-nullable column of fixed-width elements from an
+   * initializer list.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable INT32 column with 4 elements: {1, 2, 3, 4}
+   * fixed_width_column_wrapper<int32_t> w{{1, 2, 3, 4}};
+   * @endcode
+   *
+   * @param elements The list of elements
+   */
+  template <typename ElementFrom>
+  fixed_width_column_wrapper(std::initializer_list<ElementFrom> elements)
+    : fixed_width_column_wrapper(std::cbegin(elements), std::cend(elements))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column from a list of fixed-width elements
+   * using another list to indicate the validity of each element.
+   *
+   * The validity of each element is determined by an `initializer_list` of
+   * booleans where `true` indicates the element is valid, and `false` indicates
+   * the element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable INT32 column with 4 elements: {1, NULL, 3, NULL}
+   * fixed_width_column_wrapper<int32_t> w{ {1,2,3,4}, {1, 0, 1, 0}};
+   * @endcode
+   *
+   * @param elements The list of elements
+   * @param validity The list of validity indicator booleans
+   */
+  template <typename ElementFrom>
+  fixed_width_column_wrapper(std::initializer_list<ElementFrom> elements,
+                             std::initializer_list<bool> validity)
+    : fixed_width_column_wrapper(std::cbegin(elements), std::cend(elements), std::cbegin(validity))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column from a list of fixed-width elements and
+   * the range `[v, v + element_list.size())` interpreted as booleans to
+   * indicate the validity of each element.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable INT32 column with 4 elements: {NULL, 1, NULL, 3}
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;})
+   * fixed_width_column_wrapper<int32_t> w{ {1,2,3,4}, validity}
+   * @endcode
+   *
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be
+   * convertible to `bool`
+   * @param element_list The list of elements
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename ValidityIterator, typename ElementFrom>
+  fixed_width_column_wrapper(std::initializer_list<ElementFrom> element_list, ValidityIterator v)
+    : fixed_width_column_wrapper(std::cbegin(element_list), std::cend(element_list), v)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column of the fixed-width elements in the range
+   * `[begin,end)` using a validity initializer list to indicate the validity of each element.
+   *
+   * The validity of each element is determined by an `initializer_list` of
+   * booleans where `true` indicates the element is valid, and `false` indicates
+   * the element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable column of INT32 elements with 5 elements: {null, 1, null, 3, null}
+   * fixed_width_column_wrapper<int32_t> w(elements, elements + 5, {0, 1, 0, 1, 0});
+   * @endcode
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end The end of the sequence of elements
+   * @param validity The list of validity indicator booleans
+   */
+  template <typename InputIterator>
+  fixed_width_column_wrapper(InputIterator begin,
+                             InputIterator end,
+                             std::initializer_list<bool> const& validity)
+    : fixed_width_column_wrapper(begin, end, std::cbegin(validity))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column from a list of pairs of fixed-width
+   * elements and validity booleans of each element.
+   *
+   * The validity of each element is determined by the boolean element in the pair
+   * where `true` indicates the element is valid, and `false` indicates the
+   * element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable INT32 column with 4 elements: {1, NULL, 3, NULL}
+   * using p = std::pair<int32_t, bool>;
+   * fixed_width_column_wrapper<int32_t> w( p{1, true}, p{2, false}, p{3, true}, p{4, false} );
+   * @endcode
+   *
+   * @param elements The list of pairs of element and validity booleans
+   */
+  template <typename ElementFrom>
+  fixed_width_column_wrapper(std::initializer_list<std::pair<ElementFrom, bool>> elements)
+  {
+    auto begin =
+      thrust::make_transform_iterator(elements.begin(), [](auto const& e) { return e.first; });
+    auto end = begin + elements.size();
+    auto v =
+      thrust::make_transform_iterator(elements.begin(), [](auto const& e) { return e.second; });
+    wrapped = fixed_width_column_wrapper<ElementTo, ElementFrom>(begin, end, v).release();
+  }
+};
+
+/**
+ * @brief A wrapper for a column of fixed-width elements.
+ *
+ * @tparam Rep The type of the column
+ */
+template <typename Rep>
+class fixed_point_column_wrapper : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Construct a non-nullable column of the decimal elements in the range `[begin,end)`.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable column of DECIMAL32 elements with 5 elements: {0, 2, 4, 6, 8}
+   * auto elements = make_counting_transform_iterator(0, [](auto i) { return i * 2;});
+   * auto w = fixed_point_column_wrapper<int32_t>(elements, elements + 5, scale_type{0});
+   * @endcode
+   *
+   * @tparam FixedPointRepIterator Iterator for fixed_point::rep
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end   The end of the sequence of elements
+   * @param scale The scale of the elements in the column
+   */
+  template <typename FixedPointRepIterator>
+  fixed_point_column_wrapper(FixedPointRepIterator begin,
+                             FixedPointRepIterator end,
+                             numeric::scale_type scale)
+    : column_wrapper{}
+  {
+    CUDF_EXPECTS(numeric::is_supported_representation_type<Rep>(), "not valid representation type");
+
+    auto const size      = cudf::distance(begin, end);
+    auto const elements  = thrust::host_vector<Rep>(begin, end);
+    auto const id        = type_to_id<numeric::fixed_point<Rep, numeric::Radix::BASE_10>>();
+    auto const data_type = cudf::data_type{id, static_cast<int32_t>(scale)};
+
+    wrapped.reset(new cudf::column{
+      data_type,
+      size,
+      rmm::device_buffer{elements.data(), size * sizeof(Rep), cudf::test::get_default_stream()},
+      rmm::device_buffer{},
+      0});
+  }
+
+  /**
+   * @brief Construct a non-nullable column of decimal elements from an initializer list.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable `decimal32` column with 4 elements: {42.0, 4.2, 0.4}
+   * auto const col = fixed_point_column_wrapper<int32_t>{{420, 42, 4}, scale_type{-1}};
+   * @endcode
+   *
+   * @param values The initializer list of already shifted values
+   * @param scale  The scale of the elements in the column
+   */
+  fixed_point_column_wrapper(std::initializer_list<Rep> values, numeric::scale_type scale)
+    : fixed_point_column_wrapper(std::cbegin(values), std::cend(values), scale)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column of the fixed-point elements from a range.
+   *
+   * Constructs a nullable column of the fixed-point elements in the range `[begin,end)` using the
+   * range `[v, v + distance(begin,end))` interpreted as Booleans to indicate the validity of each
+   * element.
+   *
+   * If `v[i] == true`, element `i` is valid, else it is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable column of DECIMAL32 elements with 5 elements: {null, 100, null, 300,
+   * null}
+   * auto elements = make_counting_transform_iterator(0, [](auto i){ return i; });
+   * auto validity = make_counting_transform_iterator(0, [](auto i){ return i % 2; });
+   * fixed_point_column_wrapper<int32_t> w(elements, elements + 5, validity, scale_type{2});
+   * @endcode
+   *
+   * Note: similar to `std::vector`, this "range" constructor should be used
+   *       with parentheses `()` and not braces `{}`. The latter should only
+   *       be used for the `initializer_list` constructors
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end   The end of the sequence of elements
+   * @param v     The beginning of the sequence of validity indicators
+   * @param scale The scale of the elements in the column
+   */
+  template <typename FixedPointRepIterator, typename ValidityIterator>
+  fixed_point_column_wrapper(FixedPointRepIterator begin,
+                             FixedPointRepIterator end,
+                             ValidityIterator v,
+                             numeric::scale_type scale)
+    : column_wrapper{}
+  {
+    CUDF_EXPECTS(numeric::is_supported_representation_type<Rep>(), "not valid representation type");
+
+    auto const size              = cudf::distance(begin, end);
+    auto const elements          = thrust::host_vector<Rep>(begin, end);
+    auto const id                = type_to_id<numeric::fixed_point<Rep, numeric::Radix::BASE_10>>();
+    auto const data_type         = cudf::data_type{id, static_cast<int32_t>(scale)};
+    auto [null_mask, null_count] = detail::make_null_mask(v, v + size);
+    wrapped.reset(new cudf::column{
+      data_type,
+      size,
+      rmm::device_buffer{elements.data(), size * sizeof(Rep), cudf::test::get_default_stream()},
+      std::move(null_mask),
+      null_count});
+  }
+
+  /**
+   * @brief Construct a nullable column from an initializer list of decimal elements using another
+   * list to indicate the validity of each element.
+   *
+   * The validity of each element is determined by an `initializer_list` of booleans where `true`
+   * indicates the element is valid, and `false` indicates the element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable INT32 column with 4 elements: {1, null, 3, null}
+   * fixed_width_column_wrapper<int32_t> w{ {1,2,3,4}, {1, 0, 1, 0}, scale_type{0}};
+   * @endcode
+   *
+   * @param elements The initializer list of elements
+   * @param validity The initializer list of validity indicator booleans
+   * @param scale    The scale of the elements in the column
+   */
+  fixed_point_column_wrapper(std::initializer_list<Rep> elements,
+                             std::initializer_list<bool> validity,
+                             numeric::scale_type scale)
+    : fixed_point_column_wrapper(
+        std::cbegin(elements), std::cend(elements), std::cbegin(validity), scale)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column from an initializer list of decimal elements and the
+   * range `[v, v + element_list.size())` interpreted as booleans to indicate the validity of each
+   * element.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable INT32 column with 4 elements: {null, 1, null, 3}
+   * auto validity = make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+   * auto w        = fixed_width_column_wrapper<int32_t>{ {1,2,3,4}, validity, scale_type{0}};
+   * @endcode
+   *
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be convertible to `bool`
+   *
+   * @param element_list The initializer list of elements
+   * @param v            The beginning of the sequence of validity indicators
+   * @param scale        The scale of the elements in the column
+   */
+  template <typename ValidityIterator>
+  fixed_point_column_wrapper(std::initializer_list<Rep> element_list,
+                             ValidityIterator v,
+                             numeric::scale_type scale)
+    : fixed_point_column_wrapper(std::cbegin(element_list), std::cend(element_list), v, scale)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column of the decimal elements in the range `[begin,end)` using a
+   * validity initializer list to indicate the validity of each element.
+   *
+   * The validity of each element is determined by an `initializer_list` of booleans where `true`
+   * indicates the element is valid, and `false` indicates the element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable column of DECIMAL32 elements with 5 elements: {null, 1, null, 3, null}
+   * fixed_point_column_wrapper<int32_t> w(elements, elements + 5, {0, 1, 0, 1, 0}, scale_type{0});
+   * @endcode
+   *
+   * @tparam FixedPointRepIterator Iterator for fixed_point::rep
+   *
+   * @param begin    The beginning of the sequence of elements
+   * @param end      The end of the sequence of elements
+   * @param validity The initializer list of validity indicator booleans
+   * @param scale    The scale of the elements in the column
+   */
+  template <typename FixedPointRepIterator>
+  fixed_point_column_wrapper(FixedPointRepIterator begin,
+                             FixedPointRepIterator end,
+                             std::initializer_list<bool> const& validity,
+                             numeric::scale_type scale)
+    : fixed_point_column_wrapper(begin, end, std::cbegin(validity), scale)
+  {
+  }
+};
+
+/**
+ * @brief `column_wrapper` derived class for wrapping columns of strings.
+ */
+class strings_column_wrapper : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Default constructor initializes an empty column of strings
+   */
+  strings_column_wrapper() : strings_column_wrapper(std::initializer_list<std::string>{}) {}
+
+  /**
+   * @brief Construct a non-nullable column of strings from the range
+   * `[begin,end)`.
+   *
+   * Values in the sequence `[begin,end)` will each be converted to
+   *`std::string` and a column will be created containing all of the strings.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable STRING column with 7 string elements:
+   * // {"", "this", "is", "a", "column", "of", "strings"}
+   * std::vector<std::string> strings{"", "this", "is", "a", "column", "of", "strings"};
+   * strings_column_wrapper s(strings.begin(), strings.end());
+   * @endcode
+   *
+   * @tparam StringsIterator A `std::string` must be constructible from
+   * dereferencing a `StringsIterator`.
+   * @param begin The beginning of the sequence
+   * @param end The end of the sequence
+   */
+  template <typename StringsIterator>
+  strings_column_wrapper(StringsIterator begin, StringsIterator end) : column_wrapper{}
+  {
+    auto all_valid        = thrust::make_constant_iterator(true);
+    auto [chars, offsets] = detail::make_chars_and_offsets(begin, end, all_valid);
+    auto d_chars          = cudf::detail::make_device_uvector_sync(
+      chars, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+    auto d_offsets = cudf::detail::make_device_uvector_sync(
+      offsets, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+    wrapped =
+      cudf::make_strings_column(d_chars, d_offsets, {}, 0, cudf::test::get_default_stream());
+  }
+
+  /**
+   * @brief Construct a nullable column of strings from the range
+   * `[begin,end)` using the range `[v, v + distance(begin,end))` interpreted
+   * as booleans to indicate the validity of each string.
+   *
+   * Values in the sequence `[begin,end)` will each be converted to
+   *`std::string` and a column will be created containing all of the strings.
+   *
+   * If `v[i] == true`, string `i` is valid, else it is null. If a string
+   * `*(begin+i)` is null, it's value is ignored and treated as an empty string.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable STRING column with 7 string elements:
+   * // {NULL, "this", NULL, "a", NULL, "of", NULL}
+   * std::vector<std::string> strings{"", "this", "is", "a", "column", "of", "strings"};
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * strings_column_wrapper s(strings.begin(), strings.end(), validity);
+   * @endcode
+   *
+   * @tparam StringsIterator A `std::string` must be constructible from
+   * dereferencing a `StringsIterator`.
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be convertible to `bool`
+   *
+   * @param begin The beginning of the sequence
+   * @param end The end of the sequence
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename StringsIterator, typename ValidityIterator>
+  strings_column_wrapper(StringsIterator begin, StringsIterator end, ValidityIterator v)
+    : column_wrapper{}
+  {
+    size_type num_strings        = std::distance(begin, end);
+    auto [chars, offsets]        = detail::make_chars_and_offsets(begin, end, v);
+    auto [null_mask, null_count] = detail::make_null_mask_vector(v, v + num_strings);
+    auto d_chars                 = cudf::detail::make_device_uvector_sync(
+      chars, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+    auto d_offsets = cudf::detail::make_device_uvector_sync(
+      offsets, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+    auto d_bitmask = cudf::detail::make_device_uvector_sync(
+      null_mask, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+    wrapped = cudf::make_strings_column(d_chars, d_offsets, d_bitmask, null_count);
+  }
+
+  /**
+   * @brief Construct a non-nullable column of strings from a list of strings.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable STRING column with 7 string elements:
+   * // {"", "this", "is", "a", "column", "of", "strings"}
+   * strings_column_wrapper s({"", "this", "is", "a", "column", "of", "strings"});
+   * @endcode
+   *
+   * @param strings The list of strings
+   */
+  strings_column_wrapper(std::initializer_list<std::string> strings)
+    : strings_column_wrapper(std::cbegin(strings), std::cend(strings))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column of strings from a list of strings and
+   * the range `[v, v + strings.size())` interpreted as booleans to indicate the
+   * validity of each string.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable STRING column with 7 string elements:
+   * // {NULL, "this", NULL, "a", NULL, "of", NULL}
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * strings_column_wrapper s({"", "this", "is", "a", "column", "of", "strings"}, validity);
+   * @endcode
+   *
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be
+   * convertible to `bool`
+   * @param strings The list of strings
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename ValidityIterator>
+  strings_column_wrapper(std::initializer_list<std::string> strings, ValidityIterator v)
+    : strings_column_wrapper(std::cbegin(strings), std::cend(strings), v)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column of strings from a list of strings and
+   * a list of booleans to indicate the validity of each string.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable STRING column with 7 string elements:
+   * // {NULL, "this", NULL, "a", NULL, "of", NULL}
+   * strings_column_wrapper s({"", "this", "is", "a", "column", "of", "strings"},
+   *                          {0,1,0,1,0,1,0});
+   * @endcode
+   *
+   * @param strings The list of strings
+   * @param validity The list of validity indicator booleans
+   */
+  strings_column_wrapper(std::initializer_list<std::string> strings,
+                         std::initializer_list<bool> validity)
+    : strings_column_wrapper(std::cbegin(strings), std::cend(strings), std::cbegin(validity))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable column from a list of pairs of strings
+   * and validity booleans of each string.
+   *
+   * The validity of each string is determined by the boolean element in the pair
+   * where `true` indicates the string is valid, and `false` indicates the
+   * string is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable STRING column with 7 string elements:
+   * // {NULL, "this", NULL, "a", NULL, "of", NULL}
+   * using p = std::pair<std::string, bool>;
+   * strings_column_wrapper s( p{"", false}, p{"this", true}, p{"is", false},
+   *                           p{"a", true}, p{"column", false}, p{"of", true},
+   *                           p{"strings", false} );
+   * @endcode
+   *
+   * @param strings The list of pairs of strings and validity booleans
+   */
+  strings_column_wrapper(std::initializer_list<std::pair<std::string, bool>> strings)
+  {
+    auto begin =
+      thrust::make_transform_iterator(strings.begin(), [](auto const& s) { return s.first; });
+    auto end = begin + strings.size();
+    auto v =
+      thrust::make_transform_iterator(strings.begin(), [](auto const& s) { return s.second; });
+    wrapped = strings_column_wrapper(begin, end, v).release();
+  }
+};
+
+/**
+ * @brief `column_wrapper` derived class for wrapping dictionary columns.
+ *
+ * This class handles fixed-width type keys.
+ *
+ * @tparam KeyElementTo Specify a fixed-width type for the key values of the dictionary
+ * @tparam SourceElementTo For converting fixed-width values to the KeyElementTo
+ */
+template <typename KeyElementTo, typename SourceElementT = KeyElementTo>
+class dictionary_column_wrapper : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Cast to dictionary_column_view
+   */
+  operator dictionary_column_view() const { return cudf::dictionary_column_view{wrapped->view()}; }
+
+  /**
+   * @brief Default constructor initializes an empty column with dictionary type.
+   */
+  dictionary_column_wrapper() : column_wrapper{}
+  {
+    wrapped = cudf::make_empty_column(cudf::type_id::DICTIONARY32);
+  }
+
+  /**
+   * @brief Construct a non-nullable dictionary column of the fixed-width elements in the
+   * range `[begin,end)`.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable dictionary column of INT32 elements with 5 elements
+   * std::vector<int32_t> elements{0, 2, 2, 6, 6};
+   * dictionary_column_wrapper<int32_t> w(element.begin(), elements.end());
+   * // keys = {0, 2, 6}, indices = {0, 1, 1, 2, 2}
+   * @endcode
+   *
+   * @note Similar to `std::vector`, this "range" constructor should be used
+   *       with parentheses `()` and not braces `{}`. The latter should only
+   *       be used for the `initializer_list` constructors.
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end The end of the sequence of elements
+   */
+  template <typename InputIterator>
+  dictionary_column_wrapper(InputIterator begin, InputIterator end) : column_wrapper{}
+  {
+    wrapped =
+      cudf::dictionary::encode(fixed_width_column_wrapper<KeyElementTo, SourceElementT>(begin, end),
+                               cudf::data_type{type_id::UINT32},
+                               cudf::test::get_default_stream());
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column of the fixed-width elements in the range
+   * `[begin,end)` using the range `[v, v + distance(begin,end))` interpreted
+   * as booleans to indicate the validity of each element.
+   *
+   * If `v[i] == true`, element `i` is valid, else it is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable dictionary column with 5 elements and a validity iterator.
+   * std::vector<int32_t> elements{0, 2, 0, 6, 0};
+   * // Validity iterator here sets even rows to null.
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;})
+   * dictionary_column_wrapper<int32_t> w(elements, elements + 5, validity);
+   * // keys = {2, 6}, indices = {NULL, 0, NULL, 1, NULL}
+   * @endcode
+   *
+   * @note Similar to `std::vector`, this "range" constructor should be used
+   *       with parentheses `()` and not braces `{}`. The latter should only
+   *       be used for the `initializer_list` constructors.
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end The end of the sequence of elements
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename InputIterator, typename ValidityIterator>
+  dictionary_column_wrapper(InputIterator begin, InputIterator end, ValidityIterator v)
+    : column_wrapper{}
+  {
+    wrapped = cudf::dictionary::encode(
+      fixed_width_column_wrapper<KeyElementTo, SourceElementT>(begin, end, v),
+      cudf::data_type{type_id::UINT32},
+      cudf::test::get_default_stream());
+  }
+
+  /**
+   * @brief Construct a non-nullable dictionary column of fixed-width elements from an
+   * initializer list.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable dictionary column with 4 elements.
+   * dictionary_column_wrapper<int32_t> w{{1, 2, 3, 1}};
+   * // keys = {1, 2, 3}, indices = {0, 1, 2, 0}
+   * @endcode
+   *
+   * @param elements The list of elements
+   */
+  template <typename ElementFrom>
+  dictionary_column_wrapper(std::initializer_list<ElementFrom> elements)
+    : dictionary_column_wrapper(std::cbegin(elements), std::cend(elements))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column from a list of fixed-width elements
+   * using another list to indicate the validity of each element.
+   *
+   * The validity of each element is determined by an `initializer_list` of
+   * booleans where `true` indicates the element is valid, and `false` indicates
+   * the element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable dictionary column with 4 elements and validity initializer.
+   * dictionary_column_wrapper<int32_t> w{ {1, 0, 3, 0}, {1, 0, 1, 0}};
+   * // keys = {1, 3}, indices = {0, NULL, 1, NULL}
+   * @endcode
+   *
+   * @param elements The list of elements
+   * @param validity The list of validity indicator booleans
+   */
+  template <typename ElementFrom>
+  dictionary_column_wrapper(std::initializer_list<ElementFrom> elements,
+                            std::initializer_list<bool> validity)
+    : dictionary_column_wrapper(std::cbegin(elements), std::cend(elements), std::cbegin(validity))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column from a list of fixed-width elements and
+   * the range `[v, v + element_list.size())` interpreted as booleans to
+   * indicate the validity of each element.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable dictionary column with 6 elements and a validity iterator.
+   * // This validity iterator sets even rows to null.
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;})
+   * dictionary_column_wrapper<int32_t> w{ {0, 4, 0, 4, 0, 5}, validity}
+   * // keys = {4, 5}, indices = {NULL, 0, NULL, 0, NULL, 1}
+   * @endcode
+   *
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be convertible to `bool`
+   * @param element_list The list of elements
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename ValidityIterator, typename ElementFrom>
+  dictionary_column_wrapper(std::initializer_list<ElementFrom> element_list, ValidityIterator v)
+    : dictionary_column_wrapper(std::cbegin(element_list), std::cend(element_list), v)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column of the fixed-width elements in the range
+   * `[begin,end)` using a validity initializer list to indicate the validity of each element.
+   *
+   * The validity of each element is determined by an `initializer_list` of
+   * booleans where `true` indicates the element is valid, and `false` indicates
+   * the element is null.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable column of dictionary elements with 5 elements and validity initializer.
+   * std::vector<int32_t> elements{0, 2, 2, 6, 6};
+   * dictionary_width_column_wrapper<int32_t> w(elements, elements + 5, {0, 1, 0, 1, 0});
+   * // keys = {2, 6}, indices = {NULL, 0, NULL, 1, NULL}
+   * @endcode
+   *
+   * @param begin The beginning of the sequence of elements
+   * @param end The end of the sequence of elements
+   * @param validity The list of validity indicator booleans
+   */
+  template <typename InputIterator>
+  dictionary_column_wrapper(InputIterator begin,
+                            InputIterator end,
+                            std::initializer_list<bool> const& validity)
+    : dictionary_column_wrapper(begin, end, std::cbegin(validity))
+  {
+  }
+};
+
+/**
+ * @brief `column_wrapper` derived class for wrapping a dictionary column with string keys.
+ *
+ * This is a specialization of the `dictionary_column_wrapper` class for strings.
+ */
+template <>
+class dictionary_column_wrapper<std::string> : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Cast to dictionary_column_view
+   *
+   */
+  operator dictionary_column_view() const { return cudf::dictionary_column_view{wrapped->view()}; }
+
+  /**
+   * @brief Access keys column view
+   *
+   * @return column_view to keys column
+   */
+  column_view keys() const { return cudf::dictionary_column_view{wrapped->view()}.keys(); }
+
+  /**
+   * @brief Access indices column view
+   *
+   * @return column_view to indices column
+   */
+  column_view indices() const { return cudf::dictionary_column_view{wrapped->view()}.indices(); }
+
+  /**
+   * @brief Default constructor initializes an empty dictionary column of strings
+   */
+  dictionary_column_wrapper() : dictionary_column_wrapper(std::initializer_list<std::string>{}) {}
+
+  /**
+   * @brief Construct a non-nullable dictionary column of strings from the range
+   * `[begin,end)`.
+   *
+   * Values in the sequence `[begin,end)` will each be converted to
+   *`std::string` and a dictionary column will be created by encoding the strings.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable dictionary column with 7 string elements
+   * std::vector<std::string> strings{"", "aaa", "bbb", "aaa", "bbb, "ccc", "bbb"};
+   * dictionary_column_wrapper<std::string> d(strings.begin(), strings.end());
+   * // keys = {"","aaa","bbb","ccc"}, indices = {0, 1, 2, 1, 2, 3, 2}
+   * @endcode
+   *
+   * @tparam StringsIterator A `std::string` must be constructible from
+   *                         dereferencing a `StringsIterator`.
+   * @param begin The beginning of the sequence
+   * @param end The end of the sequence
+   */
+  template <typename StringsIterator>
+  dictionary_column_wrapper(StringsIterator begin, StringsIterator end) : column_wrapper{}
+  {
+    wrapped = cudf::dictionary::encode(strings_column_wrapper(begin, end),
+                                       cudf::data_type{type_id::UINT32},
+                                       cudf::test::get_default_stream());
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column of strings from the range
+   * `[begin,end)` using the range `[v, v + distance(begin,end))` interpreted
+   * as booleans to indicate the validity of each string.
+   *
+   * Values in the sequence `[begin,end)` will each be converted to
+   * `std::string` and a dictionary column will be created by encoding the strings.
+   *
+   * If `v[i] == true`, string `i` is valid, else it is treated as null row.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable dictionary column with 7 strings elements and validity iterator.
+   * std::vector<std::string> strings{"", "aaa", "", "aaa", "", "bbb", ""};
+   * // Validity iterator sets even rows to null.
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * dictionary_column_wrapper<std::string> d(strings.begin(), strings.end(), validity);
+   * // keys = {"aaa", "bbb"}, indices = {NULL, 0, NULL, 0, NULL, 1, NULL}
+   * @endcode
+   *
+   * @tparam StringsIterator A `std::string` must be constructible from
+   *                         dereferencing a `StringsIterator`.
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be
+   *                          convertible to `bool`
+   * @param begin The beginning of the sequence
+   * @param end The end of the sequence
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename StringsIterator, typename ValidityIterator>
+  dictionary_column_wrapper(StringsIterator begin, StringsIterator end, ValidityIterator v)
+    : column_wrapper{}
+  {
+    wrapped = cudf::dictionary::encode(strings_column_wrapper(begin, end, v),
+                                       cudf::data_type{type_id::UINT32},
+                                       cudf::test::get_default_stream());
+  }
+
+  /**
+   * @brief Construct a non-nullable dictionary column of strings from a list of strings.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a non-nullable dictionary column with 7 string elements.
+   * dictionary_column_wrapper<std::string> d({"", "bb", "a", "bb", "a", "ccc", "a"});
+   * // keys = {"","a","bb","ccc"}, indices = {0, 2, 1, 2, 1, 3, 1}
+   * @endcode
+   *
+   * @param strings The list of strings
+   */
+  dictionary_column_wrapper(std::initializer_list<std::string> strings)
+    : dictionary_column_wrapper(std::cbegin(strings), std::cend(strings))
+  {
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column of strings from a list of strings and
+   * the range `[v, v + strings.size())` interpreted as booleans to indicate the
+   * validity of each string.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable dictionary column with 7 string elements and a validity iterator.
+   * // Validity iterator here sets even rows to null.
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * dictionary_column_wrapper<std::string> d({"", "bb", "", "bb", "", "a", ""}, validity);
+   * // keys = {"a", "bb"}, indices = {NULL, 1, NULL, 1, NULL, 0, NULL}
+   * @endcode
+   *
+   * @tparam ValidityIterator Dereferencing a ValidityIterator must be convertible to `bool`
+   * @param strings The list of strings
+   * @param v The beginning of the sequence of validity indicators
+   */
+  template <typename ValidityIterator>
+  dictionary_column_wrapper(std::initializer_list<std::string> strings, ValidityIterator v)
+    : dictionary_column_wrapper(std::cbegin(strings), std::cend(strings), v)
+  {
+  }
+
+  /**
+   * @brief Construct a nullable dictionary column of strings from a list of strings and
+   * a list of booleans to indicate the validity of each string.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a nullable STRING column with 7 string elements and validity initializer.
+   * dictionary_column_wrapper<std::string> ({"", "a", "", "bb", "", "ccc", ""},
+   *                                         {0,  1,   0,  1,    0,  1,     0});
+   * // keys = {"a", "bb", "ccc"}, indices = {NULL, 0, NULL, 1, NULL, 2, NULL}
+   * @endcode
+   *
+   * @param strings The list of strings
+   * @param validity The list of validity indicator booleans
+   */
+  dictionary_column_wrapper(std::initializer_list<std::string> strings,
+                            std::initializer_list<bool> validity)
+    : dictionary_column_wrapper(std::cbegin(strings), std::cend(strings), std::cbegin(validity))
+  {
+  }
+};
+
+/**
+ * @brief `column_wrapper` derived class for wrapping columns of lists.
+ *
+ * Important note : due to the way initializer lists work, there is a
+ * non-obvious behavioral difference when declaring nested empty lists
+ * in different situations.  Specifically,
+ *
+ * - When compiled inside of a templated class function (such as a TYPED_TEST
+ *   cudf test wrapper), nested empty lists behave as they read, semantically.
+ *
+ * @code{.pseudo}
+ *   lists_column_wrapper<int> col{ {LCW{}} }
+ *   This yields a List<List<int>> column containing 1 row : a list
+ *   containing an empty list.
+ * @endcode
+ *
+ * - When compiled under other situations (a global function, or a non
+ *   templated class function), the behavior is different.
+ *
+ * @code{.pseudo}
+ *   lists_column_wrapper<int> col{ {LCW{}} }
+ *   This yields a List<int> column containing 1 row that is an empty
+ *   list.
+ * @endcode
+ *
+ * This only effects the initial nesting of the empty list. In summary, the
+ * correct way to declare an "Empty List" in the two cases are:
+ *
+ * @code{.pseudo}
+ *   // situation 1 (cudf TYPED_TEST case)
+ *   LCW{}
+ *   // situation 2 (cudf TEST_F case)
+ *   {LCW{}}
+ * @endcode
+ */
+template <typename T, typename SourceElementT = T>
+class lists_column_wrapper : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Construct a lists column containing a single list of fixed-width
+   * type from an initializer list of values.
+   *
+   * Example:
+   * @code{.cpp}
+   * Creates a LIST column with 1 list composed of 2 total integers
+   * [{0, 1}]
+   * lists_column_wrapper l{0, 1};
+   * @endcode
+   *
+   * @param elements The list of elements
+   */
+  template <typename Element = T, std::enable_if_t<cudf::is_fixed_width<Element>()>* = nullptr>
+  lists_column_wrapper(std::initializer_list<SourceElementT> elements) : column_wrapper{}
+  {
+    build_from_non_nested(
+      std::move(cudf::test::fixed_width_column_wrapper<T, SourceElementT>(elements).release()));
+  }
+
+  /**
+   * @brief  Construct a lists column containing a single list of fixed-width
+   * type from an iterator range.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 1 list composed of 5 total integers
+   * auto elements = make_counting_transform_iterator(0, [](auto i){return i*2;});
+   * // [{0, 1, 2, 3, 4}]
+   * lists_column_wrapper l(elements, elements+5);
+   * @endcode
+   *
+   * @param begin Beginning of the sequence
+   * @param end End of the sequence
+   */
+  template <typename Element = T,
+            typename InputIterator,
+            std::enable_if_t<cudf::is_fixed_width<Element>()>* = nullptr>
+  lists_column_wrapper(InputIterator begin, InputIterator end) : column_wrapper{}
+  {
+    build_from_non_nested(
+      std::move(cudf::test::fixed_width_column_wrapper<T, SourceElementT>(begin, end).release()));
+  }
+
+  /**
+   * @brief Construct a lists column containing a single list of fixed-width
+   * type from an initializer list of values and a validity iterator.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 1 lists composed of 2 total integers
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * // [{0, NULL}]
+   * lists_column_wrapper l{{0, 1}, validity};
+   * @endcode
+   *
+   * @param elements The list of elements
+   * @param v The validity iterator
+   */
+  template <typename Element = T,
+            typename ValidityIterator,
+            std::enable_if_t<cudf::is_fixed_width<Element>()>* = nullptr>
+  lists_column_wrapper(std::initializer_list<SourceElementT> elements, ValidityIterator v)
+    : column_wrapper{}
+  {
+    build_from_non_nested(
+      std::move(cudf::test::fixed_width_column_wrapper<T, SourceElementT>(elements, v).release()));
+  }
+
+  /**
+   * @brief Construct a lists column containing a single list of fixed-width
+   * type from an iterator range and a validity iterator.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 1 lists composed of 5 total integers
+   * auto elements = make_counting_transform_iterator(0, [](auto i){return i*2;});
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * // [{0, NULL, 2, NULL, 4}]
+   * lists_column_wrapper l(elements, elements+5, validity);
+   * @endcode
+   *
+   * @param begin Beginning of the sequence
+   * @param end End of the sequence
+   * @param v The validity iterator
+   */
+  template <typename Element = T,
+            typename InputIterator,
+            typename ValidityIterator,
+            std::enable_if_t<cudf::is_fixed_width<Element>()>* = nullptr>
+  lists_column_wrapper(InputIterator begin, InputIterator end, ValidityIterator v)
+    : column_wrapper{}
+  {
+    build_from_non_nested(std::move(
+      cudf::test::fixed_width_column_wrapper<T, SourceElementT>(begin, end, v).release()));
+  }
+
+  /**
+   * @brief Construct a lists column containing a single list of strings
+   * from an initializer list of values.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 1 list composed of 2 total strings
+   * // [{"abc", "def"}]
+   * lists_column_wrapper l{"abc", "def"};
+   * @endcode
+   *
+   * @param elements The list of elements
+   */
+  template <typename Element                                              = T,
+            std::enable_if_t<std::is_same_v<Element, cudf::string_view>>* = nullptr>
+  lists_column_wrapper(std::initializer_list<std::string> elements) : column_wrapper{}
+  {
+    build_from_non_nested(
+      std::move(cudf::test::strings_column_wrapper(elements.begin(), elements.end()).release()));
+  }
+
+  /**
+   * @brief Construct a lists column containing a single list of strings
+   * from an initializer list of values and a validity iterator.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 1 list composed of 2 total strings
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * // [{"abc", NULL}]
+   * lists_column_wrapper l{{"abc", "def"}, validity};
+   * @endcode
+   *
+   * @param elements The list of elements
+   * @param v The validity iterator
+   */
+  template <typename Element = T,
+            typename ValidityIterator,
+            std::enable_if_t<std::is_same_v<Element, cudf::string_view>>* = nullptr>
+  lists_column_wrapper(std::initializer_list<std::string> elements, ValidityIterator v)
+    : column_wrapper{}
+  {
+    build_from_non_nested(
+      std::move(cudf::test::strings_column_wrapper(elements.begin(), elements.end(), v).release()));
+  }
+
+  /**
+   * @brief Construct a lists column of nested lists from an initializer list of values.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 3 lists
+   * // [{0, 1}, {2, 3}, {4, 5}]
+   * lists_column_wrapper l{ {0, 1}, {2, 3}, {4, 5} };
+   * @endcode
+   *
+   * Automatically handles nesting
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST of LIST columns with 2 lists on the top level and
+   * // 4 below
+   * // [ {{0, 1}, {2, 3}}, {{4, 5}, {6, 7}} ]
+   * lists_column_wrapper l{ {{0, 1}, {2, 3}}, {{4, 5}, {6, 7}} };
+   * @endcode
+   *
+   * @param elements The list of elements
+   */
+  lists_column_wrapper(std::initializer_list<lists_column_wrapper<T, SourceElementT>> elements)
+    : column_wrapper{}
+  {
+    std::vector<bool> valids;
+    build_from_nested(elements, valids);
+  }
+
+  /**
+   * @brief Construct am empty lists column
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates an empty LIST column
+   * // []
+   * lists_column_wrapper l{};
+   * @endcode
+   *
+   */
+  lists_column_wrapper() : column_wrapper{}
+  {
+    build_from_non_nested(make_empty_column(cudf::type_to_id<T>()));
+  }
+
+  /**
+   * @brief Construct a lists column of nested lists from an initializer list of values
+   * and a validity iterator.
+   *
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST column with 3 lists
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * // [{0, 1}, NULL, {4, 5}]
+   * lists_column_wrapper l{ {{0, 1}, {2, 3}, {4, 5}, validity} };
+   * @endcode
+   *
+   * Automatically handles nesting
+   * Example:
+   * @code{.cpp}
+   * // Creates a LIST of LIST columns with 2 lists on the top level and
+   * // 4 below
+   * auto validity = make_counting_transform_iterator(0, [](auto i){return i%2;});
+   * // [ {{0, 1}, NULL}, {{4, 5}, NULL} ]
+   * lists_column_wrapper l{ {{{0, 1}, {2, 3}}, validity}, {{{4, 5}, {6, 7}}, validity} };
+   * @endcode
+   *
+   * @param elements The list of elements
+   * @param v The validity iterator
+   */
+  template <typename ValidityIterator>
+  lists_column_wrapper(std::initializer_list<lists_column_wrapper<T, SourceElementT>> elements,
+                       ValidityIterator v)
+    : column_wrapper{}
+  {
+    std::vector<bool> validity;
+    std::transform(elements.begin(),
+                   elements.end(),
+                   v,
+                   std::back_inserter(validity),
+                   [](lists_column_wrapper const& l, bool valid) { return valid; });
+    build_from_nested(elements, validity);
+  }
+
+  /**
+   * @brief Construct a list column containing a single empty, optionally null row.
+   *
+   * @param valid Whether or not the empty row is also null
+   * @return A list column containing a single empty row
+   */
+  static lists_column_wrapper<T> make_one_empty_row_column(bool valid = true)
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets{0, 0};
+    cudf::test::fixed_width_column_wrapper<int> values{};
+    return lists_column_wrapper<T>(
+      1,
+      offsets.release(),
+      values.release(),
+      valid ? 0 : 1,
+      valid ? rmm::device_buffer{} : cudf::create_null_mask(1, cudf::mask_state::ALL_NULL));
+  }
+
+ private:
+  /**
+   * @brief Construct a list column from constituent parts.
+   *
+   * @param num_rows The number of lists the column represents
+   * @param offsets The column of offset values for this column
+   * @param values The column of values bounded by the offsets
+   * @param null_count The number of null list entries
+   * @param null_mask The bits specifying the null lists in device memory
+   */
+  lists_column_wrapper(size_type num_rows,
+                       std::unique_ptr<cudf::column>&& offsets,
+                       std::unique_ptr<cudf::column>&& values,
+                       size_type null_count,
+                       rmm::device_buffer&& null_mask)
+  {
+    // construct the list column
+    wrapped = make_lists_column(
+      num_rows, std::move(offsets), std::move(values), null_count, std::move(null_mask));
+  }
+
+  /**
+   * @brief Initialize as a nested list column composed of other list columns.
+   *
+   * This function handles a special case.  For convenience of declaration, we want to treat these
+   * two cases as equivalent
+   *
+   * List<int>      = { 0, 1 }
+   * List<int>      = { {0, 1} }
+   *
+   * while at the same time, allowing further nesting
+   * List<List<int> = { {{0, 1}} }
+   *
+   * @param c Input column to be wrapped
+   *
+   */
+  void build_from_nested(std::initializer_list<lists_column_wrapper<T, SourceElementT>> elements,
+                         std::vector<bool> const& v)
+  {
+    auto valids = cudf::detail::make_counting_transform_iterator(
+      0, [&v](auto i) { return v.empty() ? true : v[i]; });
+
+    // compute the expected hierarchy and depth
+    auto const hierarchy_and_depth =
+      std::accumulate(elements.begin(),
+                      elements.end(),
+                      std::pair<column_view, int32_t>{{}, -1},
+                      [](auto acc, lists_column_wrapper const& lcw) {
+                        return lcw.depth > acc.second ? std::pair(lcw.get_view(), lcw.depth) : acc;
+                      });
+    column_view expected_hierarchy = hierarchy_and_depth.first;
+    int32_t const expected_depth   = hierarchy_and_depth.second;
+
+    // preprocess columns so that every column_view in 'cols' is an equivalent hierarchy
+    auto [cols, stubs] = preprocess_columns(elements, expected_hierarchy, expected_depth);
+
+    // generate offsets
+    size_type count = 0;
+    std::vector<size_type> offsetv;
+    std::transform(cols.cbegin(),
+                   cols.cend(),
+                   valids,
+                   std::back_inserter(offsetv),
+                   [&](cudf::column_view const& col, bool valid) {
+                     // nulls are represented as a repeated offset
+                     size_type ret = count;
+                     if (valid) { count += col.size(); }
+                     return ret;
+                   });
+    // add the final offset
+    offsetv.push_back(count);
+    auto offsets =
+      cudf::test::fixed_width_column_wrapper<size_type>(offsetv.begin(), offsetv.end()).release();
+
+    // concatenate them together, skipping children that are null.
+    std::vector<column_view> children;
+    thrust::copy_if(
+      std::cbegin(cols), std::cend(cols), valids, std::back_inserter(children), thrust::identity{});
+
+    auto data = children.empty() ? cudf::empty_like(expected_hierarchy)
+                                 : cudf::concatenate(children,
+                                                     cudf::test::get_default_stream(),
+                                                     rmm::mr::get_current_device_resource());
+
+    // increment depth
+    depth = expected_depth + 1;
+
+    auto [null_mask, null_count] = [&] {
+      if (v.size() <= 0) return std::make_pair(rmm::device_buffer{}, cudf::size_type{0});
+      return cudf::test::detail::make_null_mask(v.begin(), v.end());
+    }();
+
+    // construct the list column
+    wrapped = make_lists_column(
+      cols.size(), std::move(offsets), std::move(data), null_count, std::move(null_mask));
+  }
+
+  /**
+   * @brief Initialize as a "root" list column from a non-list input column.  Root columns
+   * will be "unwrapped" when used in the nesting (list of lists) case.
+   *
+   * @param c Input column to be wrapped
+   *
+   */
+  void build_from_non_nested(std::unique_ptr<column> c)
+  {
+    CUDF_EXPECTS(c->type().id() == type_id::EMPTY || !cudf::is_nested(c->type()),
+                 "Unexpected type");
+
+    std::vector<size_type> offsetv;
+    if (c->size() > 0) {
+      offsetv.push_back(0);
+      offsetv.push_back(c->size());
+    }
+    auto offsets =
+      cudf::test::fixed_width_column_wrapper<size_type>(offsetv.begin(), offsetv.end()).release();
+
+    // construct the list column. mark this as a root
+    root  = true;
+    depth = 0;
+
+    size_type num_elements = offsets->size() == 0 ? 0 : offsets->size() - 1;
+    wrapped =
+      make_lists_column(num_elements, std::move(offsets), std::move(c), 0, rmm::device_buffer{});
+  }
+
+  /**
+   * @brief Given an input column that may be an "incomplete hierarchy" due to being empty
+   * at a level before the leaf, normalize it so that it matches the expected hierarchy of
+   * sibling columns.
+   *
+   * cudf functions that handle lists expect that all columns are fully formed hierarchies,
+   * even if they are empty somewhere in the middle of the hierarchy.
+   * If we had the following lists_column_wrapper<int> declaration:
+   *
+   * @code{.pseudo}
+   * [ {{{1, 2, 3}}}, {} ]
+   * Row 0 in this case is a List<List<List<int>>>, where row 1 appears to be just a List<>.
+   * @endcode
+   *
+   * These two columns will end up getting passed to cudf::concatenate() to merge. But
+   * concatenate() will throw an exception because row 1 will appear to have a child type
+   * of nothing, while row 0 will appear to have a child type of List<List<int>>.
+   * To handle this cleanly, we want to "normalize" row 1 so that it appears as a
+   * List<List<List<int>>> column even though it has 0 elements at the top level.
+   *
+   * This function also detects the case where the user has constructed a truly invalid
+   * pair of columns, such as
+   *
+   * @code{.pseudo}
+   * [ {{{1, 2, 3}}}, {4, 5} ]
+   * Row 0 in this case is a List<List<List<int>>>, and row 1 is a concrete List<int> with
+   * elements. This is purely an invalid way of constructing a lists column.
+   * @endcode
+   *
+   * @param col Input column to be normalized
+   * @param expected_hierarchy Input column which represents the expected hierarchy
+   *
+   * @return A new column representing a normalized copy of col
+   */
+  std::unique_ptr<column> normalize_column(column_view const& col,
+                                           column_view const& expected_hierarchy)
+  {
+    // if are at the bottom of the short column, it must be empty
+    if (col.type().id() != type_id::LIST) {
+      CUDF_EXPECTS(col.is_empty(), "Encountered mismatched column!");
+
+      auto remainder = empty_like(expected_hierarchy);
+      return remainder;
+    }
+
+    lists_column_view lcv(col);
+    return make_lists_column(col.size(),
+                             std::make_unique<column>(lcv.offsets()),
+                             normalize_column(lists_column_view(col).child(),
+                                              lists_column_view(expected_hierarchy).child()),
+                             col.null_count(),
+                             copy_bitmask(col));
+  }
+
+  std::pair<std::vector<column_view>, std::vector<std::unique_ptr<column>>> preprocess_columns(
+    std::initializer_list<lists_column_wrapper<T, SourceElementT>> const& elements,
+    column_view& expected_hierarchy,
+    int expected_depth)
+  {
+    std::vector<std::unique_ptr<column>> stubs;
+    std::vector<column_view> cols;
+
+    // preprocess the incoming lists.
+    // - unwrap any "root" lists
+    // - handle incomplete hierarchies
+    std::transform(elements.begin(),
+                   elements.end(),
+                   std::back_inserter(cols),
+                   [&](lists_column_wrapper const& l) -> column_view {
+                     // depth mismatch.  attempt to normalize the short column.
+                     // this function will also catch if this is a legitimately broken
+                     // set of input
+                     if (l.depth < expected_depth) {
+                       if (l.root) {
+                         // this exception distinguishes between the following two cases:
+                         //
+                         // { {{{1, 2, 3}}}, {} }
+                         // In this case, row 0 is a List<List<List<int>>>, whereas row 1 is
+                         // just a List<> which is an apparent mismatch.  However, because row 1
+                         // is empty we will allow that to semantically mean
+                         // "a List<List<List<int>>> that's empty at the top level"
+                         //
+                         // { {{{1, 2, 3}}}, {4, 5, 6} }
+                         // In this case, row 1 is a concrete List<int> with actual values.
+                         // There is no way to rectify the differences so we will treat it as a
+                         // true column mismatch.
+                         CUDF_EXPECTS(l.wrapped->size() == 0, "Mismatch in column types!");
+                         stubs.push_back(empty_like(expected_hierarchy));
+                       } else {
+                         stubs.push_back(normalize_column(l.get_view(), expected_hierarchy));
+                       }
+                       return *(stubs.back());
+                     }
+                     // the empty hierarchy case
+                     return l.get_view();
+                   });
+
+    return {std::move(cols), std::move(stubs)};
+  }
+
+  column_view get_view() const { return root ? lists_column_view(*wrapped).child() : *wrapped; }
+
+  int depth = 0;
+  bool root = false;
+};
+
+/**
+ * @brief `column_wrapper` derived class for wrapping columns of structs.
+ */
+class structs_column_wrapper : public detail::column_wrapper {
+ public:
+  /**
+   * @brief Constructs a struct column from the specified list of pre-constructed child columns.
+   *
+   * The child columns are "adopted" by the struct column constructed here.
+   *
+   * Example usage:
+   * @code{.cpp}
+   * // The following constructs a column for struct< int, string >.
+   * auto child_int_col = fixed_width_column_wrapper<int32_t>{ 1, 2, 3, 4, 5 }.release();
+   * auto child_string_col = string_column_wrapper {"All", "the", "leaves", "are",
+   * "brown"}.release();
+   *
+   * std::vector<std::unique_ptr<column>> child_columns;
+   * child_columns.push_back(std::move(child_int_col));
+   * child_columns.push_back(std::move(child_string_col));
+   *
+   * struct_column_wrapper struct_column_wrapper{
+   *  child_cols,
+   *  {1,0,1,0,1} // Validity.
+   * };
+   *
+   * auto struct_col {struct_column_wrapper.release()};
+   * @endcode
+   *
+   * @param child_columns The vector of pre-constructed child columns
+   * @param validity The vector of bools representing the column validity values
+   */
+  structs_column_wrapper(std::vector<std::unique_ptr<cudf::column>>&& child_columns,
+                         std::vector<bool> const& validity = {})
+  {
+    init(std::move(child_columns), validity);
+  }
+
+  /**
+   * @brief Constructs a struct column from the list of column wrappers for child columns.
+   *
+   * Example usage:
+   * @code{.cpp}
+   * // The following constructs a column for struct< int, string >.
+   * fixed_width_column_wrapper<int32_t> child_int_col_wrapper{ 1, 2, 3, 4, 5 };
+   * string_column_wrapper child_string_col_wrapper {"All", "the", "leaves", "are", "brown"};
+   *
+   * struct_column_wrapper struct_column_wrapper{
+   *  {child_int_col_wrapper, child_string_col_wrapper}
+   *  {1,0,1,0,1} // Validity.
+   * };
+   *
+   * auto struct_col {struct_column_wrapper.release()};
+   * @endcode
+   *
+   * @param child_column_wrappers The list of child column wrappers
+   * @param validity The vector of bools representing the column validity values
+   */
+  structs_column_wrapper(
+    std::initializer_list<std::reference_wrapper<detail::column_wrapper>> child_column_wrappers,
+    std::vector<bool> const& validity = {})
+  {
+    std::vector<std::unique_ptr<cudf::column>> child_columns;
+    child_columns.reserve(child_column_wrappers.size());
+    std::transform(child_column_wrappers.begin(),
+                   child_column_wrappers.end(),
+                   std::back_inserter(child_columns),
+                   [&](auto const& column_wrapper) {
+                     return std::make_unique<cudf::column>(column_wrapper.get());
+                   });
+    init(std::move(child_columns), validity);
+  }
+
+  /**
+   * @brief Constructs a struct column from the list of column wrappers for child columns.
+   *
+   * Example usage:
+   * @code{.cpp}
+   * // The following constructs a column for struct< int, string >.
+   * fixed_width_column_wrapper<int32_t> child_int_col_wrapper{ 1, 2, 3, 4, 5 };
+   * string_column_wrapper child_string_col_wrapper {"All", "the", "leaves", "are", "brown"};
+   *
+   * struct_column_wrapper struct_column_wrapper{
+   *  {child_int_col_wrapper, child_string_col_wrapper}
+   *  cudf::detail::make_counting_transform_iterator(0, [](auto i){ return i%2; }) // Validity.
+   * };
+   *
+   * auto struct_col {struct_column_wrapper.release()};
+   * @endcode
+   *
+   * @param child_column_wrappers The list of child column wrappers
+   * @param validity_iter Iterator returning the per-row validity bool
+   */
+  template <typename V>
+  structs_column_wrapper(
+    std::initializer_list<std::reference_wrapper<detail::column_wrapper>> child_column_wrappers,
+    V validity_iter)
+  {
+    std::vector<std::unique_ptr<cudf::column>> child_columns;
+    child_columns.reserve(child_column_wrappers.size());
+    std::transform(child_column_wrappers.begin(),
+                   child_column_wrappers.end(),
+                   std::back_inserter(child_columns),
+                   [&](auto const& column_wrapper) {
+                     return std::make_unique<cudf::column>(column_wrapper.get());
+                   });
+    init(std::move(child_columns), validity_iter);
+  }
+
+ private:
+  void init(std::vector<std::unique_ptr<cudf::column>>&& child_columns,
+            std::vector<bool> const& validity)
+  {
+    size_type num_rows = child_columns.empty() ? 0 : child_columns[0]->size();
+
+    CUDF_EXPECTS(std::all_of(child_columns.begin(),
+                             child_columns.end(),
+                             [&](auto const& p_column) { return p_column->size() == num_rows; }),
+                 "All struct member columns must have the same row count.");
+
+    CUDF_EXPECTS(validity.size() <= 0 || static_cast<size_type>(validity.size()) == num_rows,
+                 "Validity buffer must have as many elements as rows in the struct column.");
+
+    auto [null_mask, null_count] = [&] {
+      if (validity.size() <= 0) return std::make_pair(rmm::device_buffer{}, cudf::size_type{0});
+      return cudf::test::detail::make_null_mask(validity.begin(), validity.end());
+    }();
+
+    wrapped = cudf::make_structs_column(
+      num_rows, std::move(child_columns), null_count, std::move(null_mask));
+  }
+
+  template <typename V>
+  void init(std::vector<std::unique_ptr<cudf::column>>&& child_columns, V validity_iterator)
+  {
+    size_type const num_rows = child_columns.empty() ? 0 : child_columns[0]->size();
+
+    CUDF_EXPECTS(std::all_of(child_columns.begin(),
+                             child_columns.end(),
+                             [&](auto const& p_column) { return p_column->size() == num_rows; }),
+                 "All struct member columns must have the same row count.");
+
+    std::vector<bool> validity(num_rows);
+    std::copy(validity_iterator, validity_iterator + num_rows, validity.begin());
+
+    init(std::move(child_columns), validity);
+  }
+};
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/cudf_gtest.hpp b/cpp/include/cudf_test/cudf_gtest.hpp
new file mode 100644
index 0000000..fa76204
--- /dev/null
+++ b/cpp/include/cudf_test/cudf_gtest.hpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#ifdef GTEST_INCLUDE_GTEST_GTEST_H_
+#error "Don't include gtest/gtest.h directly, include cudf_gtest.hpp instead"
+#endif
+
+/**
+ * @file cudf_gtest.hpp
+ * @brief Work around for GTests( <=v1.10 ) emulation of variadic templates in
+ * @verbatim ::Testing::Types @endverbatim
+ *
+ * @note Instead of including `gtest/gtest.h`, all libcudf test files should
+ * include `cudf_gtest.hpp` instead.
+ *
+ * Removes the 50 type limit in a type-parameterized test list.
+ *
+ * Uses macros to rename GTests's emulated variadic template types and then
+ * redefines them properly.
+ */
+
+// @cond
+#if __has_include(<gtest/internal/gtest-type-util.h.pump>)
+// gtest doesn't provide a version header so we need to
+// use a file existence trick.
+// gtest-type-util.h.pump only exists in versions < 1.11
+#define Types      Types_NOT_USED
+#define Types0     Types0_NOT_USED
+#define TypeList   TypeList_NOT_USED
+#define Templates  Templates_NOT_USED
+#define Templates0 Templates0_NOT_USED
+#include <gtest/internal/gtest-type-util.h>
+#undef Types
+#undef Types0
+#undef TypeList
+#undef Templates
+#undef Templates0
+
+namespace testing {
+template <class... TYPES>
+struct Types {
+  using type = Types;
+};
+
+template <class T, class... TYPES>
+struct Types<T, TYPES...> {
+  using Head = T;
+  using Tail = Types<TYPES...>;
+
+  using type = Types;
+};
+
+namespace internal {
+using Types0 = Types<>;
+
+template <GTEST_TEMPLATE_... TYPES>
+struct Templates {};
+
+template <GTEST_TEMPLATE_ HEAD, GTEST_TEMPLATE_... TAIL>
+struct Templates<HEAD, TAIL...> {
+  using Head = internal::TemplateSel<HEAD>;
+  using Tail = Templates<TAIL...>;
+
+  using type = Templates;
+};
+
+using Templates0 = Templates<>;
+
+template <typename T>
+struct TypeList {
+  using type = Types<T>;
+};
+
+template <class... TYPES>
+struct TypeList<Types<TYPES...>> {
+  using type = Types<TYPES...>;
+};
+
+}  // namespace internal
+}  // namespace testing
+#endif  // gtest < 1.11
+// @endcond
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+
+/**
+ * @brief test macro to be expects `expr` to return cudaSuccess
+ *
+ * This will stop the test process on failure.
+ *
+ * @param expr expression to be tested
+ */
+#define ASSERT_CUDA_SUCCEEDED(expr) ASSERT_EQ(cudaSuccess, expr)
+/**
+ * @brief test macro to be expects `expr` to return cudaSuccess
+ *
+ * @param expr expression to be tested
+ */
+#define EXPECT_CUDA_SUCCEEDED(expr) EXPECT_EQ(cudaSuccess, expr)
+
+/**
+ * @brief test macro to be expected as no exception.
+ *
+ * The testing is same with EXPECT_NO_THROW() in gtest.
+ * It also outputs captured error message, useful for debugging.
+ *
+ * @param statement The statement to be tested
+ */
+#define CUDF_EXPECT_NO_THROW(statement)                                                       \
+  try {                                                                                       \
+    statement;                                                                                \
+  } catch (std::exception & e) {                                                              \
+    FAIL() << "statement:" << #statement << std::endl << "reason: " << e.what() << std::endl; \
+  }
+
+/**
+ * @brief test macro comparing for equality of \p lhs and \p rhs for the first \p size elements.
+ */
+#define CUDF_TEST_EXPECT_VECTOR_EQUAL(lhs, rhs, size)          \
+  do {                                                         \
+    for (decltype(size) i = 0; i < size; i++)                  \
+      EXPECT_EQ(lhs[i], rhs[i]) << "Mismatch at index #" << i; \
+  } while (0)
diff --git a/cpp/include/cudf_test/cxxopts.hpp b/cpp/include/cudf_test/cxxopts.hpp
new file mode 100644
index 0000000..248fc75
--- /dev/null
+++ b/cpp/include/cudf_test/cxxopts.hpp
@@ -0,0 +1,1504 @@
+/*
+Copyright (c) 2014, 2015, 2016, 2017 Jarryd Beck
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+*/
+
+#ifndef CXXOPTS_HPP_INCLUDED
+#define CXXOPTS_HPP_INCLUDED
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+
+#include <cctype>
+#include <cstring>
+#include <exception>
+#include <iostream>
+#include <limits>
+#include <map>
+#include <memory>
+#include <regex>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#ifndef CXXOPTS_VECTOR_DELIMITER
+#define CXXOPTS_VECTOR_DELIMITER ','
+#endif
+
+#define CXXOPTS__VERSION_MAJOR 2
+#define CXXOPTS__VERSION_MINOR 2
+#define CXXOPTS__VERSION_PATCH 0
+
+namespace cxxopts {
+static constexpr struct {
+  uint8_t major, minor, patch;
+} version = {CXXOPTS__VERSION_MAJOR, CXXOPTS__VERSION_MINOR, CXXOPTS__VERSION_PATCH};
+}  // namespace cxxopts
+
+// when we ask cxxopts to use Unicode, help strings are processed using ICU,
+// which results in the correct lengths being computed for strings when they
+// are formatted for the help output
+// it is necessary to make sure that <unicode/unistr.h> can be found by the
+// compiler, and that icu-uc is linked in to the binary.
+
+#ifdef CXXOPTS_USE_UNICODE
+#include <unicode/unistr.h>
+
+namespace cxxopts {
+typedef icu::UnicodeString String;
+
+inline String toLocalString(std::string s) { return icu::UnicodeString::fromUTF8(std::move(s)); }
+
+class UnicodeStringIterator : public std::iterator<std::forward_iterator_tag, int32_t> {
+ public:
+  UnicodeStringIterator(icu::UnicodeString const* string, int32_t pos) : s(string), i(pos) {}
+
+  value_type operator*() const { return s->char32At(i); }
+
+  bool operator==(UnicodeStringIterator const& rhs) const { return s == rhs.s && i == rhs.i; }
+
+  bool operator!=(UnicodeStringIterator const& rhs) const { return !(*this == rhs); }
+
+  UnicodeStringIterator& operator++()
+  {
+    ++i;
+    return *this;
+  }
+
+  UnicodeStringIterator operator+(int32_t v) { return UnicodeStringIterator(s, i + v); }
+
+ private:
+  icu::UnicodeString const* s;
+  int32_t i;
+};
+
+inline String& stringAppend(String& s, String a) { return s.append(std::move(a)); }
+
+inline String& stringAppend(String& s, int n, UChar32 c)
+{
+  for (int i = 0; i != n; ++i) {
+    s.append(c);
+  }
+
+  return s;
+}
+
+template <typename Iterator>
+String& stringAppend(String& s, Iterator begin, Iterator end)
+{
+  while (begin != end) {
+    s.append(*begin);
+    ++begin;
+  }
+
+  return s;
+}
+
+inline size_t stringLength(String const& s) { return s.length(); }
+
+inline std::string toUTF8String(String const& s)
+{
+  std::string result;
+  s.toUTF8String(result);
+
+  return result;
+}
+
+inline bool empty(String const& s) { return s.isEmpty(); }
+}  // namespace cxxopts
+
+namespace std {
+inline cxxopts::UnicodeStringIterator begin(icu::UnicodeString const& s)
+{
+  return cxxopts::UnicodeStringIterator(&s, 0);
+}
+
+inline cxxopts::UnicodeStringIterator end(icu::UnicodeString const& s)
+{
+  return cxxopts::UnicodeStringIterator(&s, s.length());
+}
+}  // namespace std
+
+// ifdef CXXOPTS_USE_UNICODE
+#else
+
+namespace cxxopts {
+typedef std::string String;
+
+template <typename T>
+T toLocalString(T&& t)
+{
+  return std::forward<T>(t);
+}
+
+inline size_t stringLength(String const& s) { return s.length(); }
+
+inline String& stringAppend(String& s, String a) { return s.append(std::move(a)); }
+
+inline String& stringAppend(String& s, size_t n, char c) { return s.append(n, c); }
+
+template <typename Iterator>
+String& stringAppend(String& s, Iterator begin, Iterator end)
+{
+  return s.append(begin, end);
+}
+
+template <typename T>
+std::string toUTF8String(T&& t)
+{
+  return std::forward<T>(t);
+}
+
+inline bool empty(std::string const& s) { return s.empty(); }
+}  // namespace cxxopts
+
+// ifdef CXXOPTS_USE_UNICODE
+#endif
+
+namespace cxxopts {
+namespace {
+#ifdef _WIN32
+const std::string LQUOTE("\'");
+const std::string RQUOTE("\'");
+#else
+const std::string LQUOTE("‘");
+const std::string RQUOTE("’");
+#endif
+}  // namespace
+
+class Value : public std::enable_shared_from_this<Value> {
+ public:
+  virtual ~Value() = default;
+
+  virtual std::shared_ptr<Value> clone() const = 0;
+
+  virtual void parse(std::string const& text) const = 0;
+
+  virtual void parse() const = 0;
+
+  virtual bool has_default() const = 0;
+
+  virtual bool is_container() const = 0;
+
+  virtual bool has_implicit() const = 0;
+
+  virtual std::string get_default_value() const = 0;
+
+  virtual std::string get_implicit_value() const = 0;
+
+  virtual std::shared_ptr<Value> default_value(std::string const& value) = 0;
+
+  virtual std::shared_ptr<Value> implicit_value(std::string const& value) = 0;
+
+  virtual std::shared_ptr<Value> no_implicit_value() = 0;
+
+  virtual bool is_boolean() const = 0;
+};
+
+class OptionException : public std::exception {
+ public:
+  OptionException(std::string const& message) : m_message(message) {}
+
+  virtual char const* what() const noexcept { return m_message.c_str(); }
+
+ private:
+  std::string m_message;
+};
+
+class OptionSpecException : public OptionException {
+ public:
+  OptionSpecException(std::string const& message) : OptionException(message) {}
+};
+
+class OptionParseException : public OptionException {
+ public:
+  OptionParseException(std::string const& message) : OptionException(message) {}
+};
+
+class option_exists_error : public OptionSpecException {
+ public:
+  option_exists_error(std::string const& option)
+    : OptionSpecException("Option " + LQUOTE + option + RQUOTE + " already exists")
+  {
+  }
+};
+
+class invalid_option_format_error : public OptionSpecException {
+ public:
+  invalid_option_format_error(std::string const& format)
+    : OptionSpecException("Invalid option format " + LQUOTE + format + RQUOTE)
+  {
+  }
+};
+
+class option_syntax_exception : public OptionParseException {
+ public:
+  option_syntax_exception(std::string const& text)
+    : OptionParseException("Argument " + LQUOTE + text + RQUOTE +
+                           " starts with a - but has incorrect syntax")
+  {
+  }
+};
+
+class option_not_exists_exception : public OptionParseException {
+ public:
+  option_not_exists_exception(std::string const& option)
+    : OptionParseException("Option " + LQUOTE + option + RQUOTE + " does not exist")
+  {
+  }
+};
+
+class missing_argument_exception : public OptionParseException {
+ public:
+  missing_argument_exception(std::string const& option)
+    : OptionParseException("Option " + LQUOTE + option + RQUOTE + " is missing an argument")
+  {
+  }
+};
+
+class option_requires_argument_exception : public OptionParseException {
+ public:
+  option_requires_argument_exception(std::string const& option)
+    : OptionParseException("Option " + LQUOTE + option + RQUOTE + " requires an argument")
+  {
+  }
+};
+
+class option_not_has_argument_exception : public OptionParseException {
+ public:
+  option_not_has_argument_exception(std::string const& option, std::string const& arg)
+    : OptionParseException("Option " + LQUOTE + option + RQUOTE +
+                           " does not take an argument, but argument " + LQUOTE + arg + RQUOTE +
+                           " given")
+  {
+  }
+};
+
+class option_not_present_exception : public OptionParseException {
+ public:
+  option_not_present_exception(std::string const& option)
+    : OptionParseException("Option " + LQUOTE + option + RQUOTE + " not present")
+  {
+  }
+};
+
+class argument_incorrect_type : public OptionParseException {
+ public:
+  argument_incorrect_type(std::string const& arg)
+    : OptionParseException("Argument " + LQUOTE + arg + RQUOTE + " failed to parse")
+  {
+  }
+};
+
+class option_required_exception : public OptionParseException {
+ public:
+  option_required_exception(std::string const& option)
+    : OptionParseException("Option " + LQUOTE + option + RQUOTE + " is required but not present")
+  {
+  }
+};
+
+template <typename T>
+void throw_or_mimic(std::string const& text)
+{
+  static_assert(std::is_base_of<std::exception, T>::value,
+                "throw_or_mimic only works on std::exception and "
+                "deriving classes");
+
+#ifndef CXXOPTS_NO_EXCEPTIONS
+  // If CXXOPTS_NO_EXCEPTIONS is not defined, just throw
+  throw T{text};
+#else
+  // Otherwise manually instantiate the exception, print what() to stderr,
+  // and abort
+  T exception{text};
+  std::cerr << exception.what() << std::endl;
+  std::cerr << "Aborting (exceptions disabled)..." << std::endl;
+  std::abort();
+#endif
+}
+
+namespace values {
+namespace {
+std::basic_regex<char> integer_pattern("(-)?(0x)?([0-9a-zA-Z]+)|((0x)?0)");
+std::basic_regex<char> truthy_pattern("(t|T)(rue)?|1");
+std::basic_regex<char> falsy_pattern("(f|F)(alse)?|0");
+}  // namespace
+
+namespace detail {
+template <typename T, bool B>
+struct SignedCheck;
+
+template <typename T>
+struct SignedCheck<T, true> {
+  template <typename U>
+  void operator()(bool negative, U u, std::string const& text)
+  {
+    if (negative) {
+      if (u > static_cast<U>((std::numeric_limits<T>::min)())) {
+        throw_or_mimic<argument_incorrect_type>(text);
+      }
+    } else {
+      if (u > static_cast<U>((std::numeric_limits<T>::max)())) {
+        throw_or_mimic<argument_incorrect_type>(text);
+      }
+    }
+  }
+};
+
+template <typename T>
+struct SignedCheck<T, false> {
+  template <typename U>
+  void operator()(bool, U, std::string const&)
+  {
+  }
+};
+
+template <typename T, typename U>
+void check_signed_range(bool negative, U value, std::string const& text)
+{
+  SignedCheck<T, std::numeric_limits<T>::is_signed>()(negative, value, text);
+}
+}  // namespace detail
+
+template <typename R, typename T>
+R checked_negate(T&& t, std::string const&, std::true_type)
+{
+  // if we got to here, then `t` is a positive number that fits into
+  // `R`. So to avoid MSVC C4146, we first cast it to `R`.
+  // See https://github.com/jarro2783/cxxopts/issues/62 for more details.
+  return static_cast<R>(-static_cast<R>(t - 1) - 1);
+}
+
+template <typename R, typename T>
+T checked_negate(T&& t, std::string const& text, std::false_type)
+{
+  throw_or_mimic<argument_incorrect_type>(text);
+  return t;
+}
+
+template <typename T>
+void integer_parser(std::string const& text, T& value)
+{
+  std::smatch match;
+  std::regex_match(text, match, integer_pattern);
+
+  if (match.length() == 0) { throw_or_mimic<argument_incorrect_type>(text); }
+
+  if (match.length(4) > 0) {
+    value = 0;
+    return;
+  }
+
+  using US = typename std::make_unsigned<T>::type;
+
+  constexpr bool is_signed = std::numeric_limits<T>::is_signed;
+  bool const negative      = match.length(1) > 0;
+  const uint8_t base       = match.length(2) > 0 ? 16 : 10;
+
+  auto value_match = match[3];
+
+  US result = 0;
+
+  for (auto iter = value_match.first; iter != value_match.second; ++iter) {
+    US digit = 0;
+
+    if (*iter >= '0' && *iter <= '9') {
+      digit = static_cast<US>(*iter - '0');
+    } else if (base == 16 && *iter >= 'a' && *iter <= 'f') {
+      digit = static_cast<US>(*iter - 'a' + 10);
+    } else if (base == 16 && *iter >= 'A' && *iter <= 'F') {
+      digit = static_cast<US>(*iter - 'A' + 10);
+    } else {
+      throw_or_mimic<argument_incorrect_type>(text);
+    }
+
+    const US next = static_cast<US>(result * base + digit);
+    if (result > next) { throw_or_mimic<argument_incorrect_type>(text); }
+
+    result = next;
+  }
+
+  detail::check_signed_range<T>(negative, result, text);
+
+  if (negative) {
+    value = checked_negate<T>(result, text, std::integral_constant<bool, is_signed>());
+  } else {
+    value = static_cast<T>(result);
+  }
+}
+
+template <typename T>
+void stringstream_parser(std::string const& text, T& value)
+{
+  std::stringstream in(text);
+  in >> value;
+  if (!in) { throw_or_mimic<argument_incorrect_type>(text); }
+}
+
+inline void parse_value(std::string const& text, uint8_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, int8_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, uint16_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, int16_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, uint32_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, int32_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, uint64_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, int64_t& value) { integer_parser(text, value); }
+
+inline void parse_value(std::string const& text, bool& value)
+{
+  std::smatch result;
+  std::regex_match(text, result, truthy_pattern);
+
+  if (!result.empty()) {
+    value = true;
+    return;
+  }
+
+  std::regex_match(text, result, falsy_pattern);
+  if (!result.empty()) {
+    value = false;
+    return;
+  }
+
+  throw_or_mimic<argument_incorrect_type>(text);
+}
+
+inline void parse_value(std::string const& text, std::string& value) { value = text; }
+
+// The fallback parser. It uses the stringstream parser to parse all types
+// that have not been overloaded explicitly.  It has to be placed in the
+// source code before all other more specialized templates.
+template <typename T>
+void parse_value(std::string const& text, T& value)
+{
+  stringstream_parser(text, value);
+}
+
+template <typename T>
+void parse_value(std::string const& text, std::vector<T>& value)
+{
+  std::stringstream in(text);
+  std::string token;
+  while (in.eof() == false && std::getline(in, token, CXXOPTS_VECTOR_DELIMITER)) {
+    T v;
+    parse_value(token, v);
+    value.emplace_back(std::move(v));
+  }
+}
+
+inline void parse_value(std::string const& text, char& c)
+{
+  if (text.length() != 1) { throw_or_mimic<argument_incorrect_type>(text); }
+
+  c = text[0];
+}
+
+template <typename T>
+struct type_is_container {
+  static constexpr bool value = false;
+};
+
+template <typename T>
+struct type_is_container<std::vector<T>> {
+  static constexpr bool value = true;
+};
+
+template <typename T>
+class abstract_value : public Value {
+  using Self = abstract_value<T>;
+
+ public:
+  abstract_value() : m_result(std::make_shared<T>()), m_store(m_result.get()) {}
+
+  abstract_value(T* t) : m_store(t) {}
+
+  virtual ~abstract_value() = default;
+
+  abstract_value(abstract_value const& rhs)
+  {
+    if (rhs.m_result) {
+      m_result = std::make_shared<T>();
+      m_store  = m_result.get();
+    } else {
+      m_store = rhs.m_store;
+    }
+
+    m_default        = rhs.m_default;
+    m_implicit       = rhs.m_implicit;
+    m_default_value  = rhs.m_default_value;
+    m_implicit_value = rhs.m_implicit_value;
+  }
+
+  void parse(std::string const& text) const { parse_value(text, *m_store); }
+
+  bool is_container() const { return type_is_container<T>::value; }
+
+  void parse() const { parse_value(m_default_value, *m_store); }
+
+  bool has_default() const { return m_default; }
+
+  bool has_implicit() const { return m_implicit; }
+
+  std::shared_ptr<Value> default_value(std::string const& value)
+  {
+    m_default       = true;
+    m_default_value = value;
+    return shared_from_this();
+  }
+
+  std::shared_ptr<Value> implicit_value(std::string const& value)
+  {
+    m_implicit       = true;
+    m_implicit_value = value;
+    return shared_from_this();
+  }
+
+  std::shared_ptr<Value> no_implicit_value()
+  {
+    m_implicit = false;
+    return shared_from_this();
+  }
+
+  std::string get_default_value() const { return m_default_value; }
+
+  std::string get_implicit_value() const { return m_implicit_value; }
+
+  bool is_boolean() const { return std::is_same_v<T, bool>; }
+
+  T const& get() const
+  {
+    if (m_store == nullptr) {
+      return *m_result;
+    } else {
+      return *m_store;
+    }
+  }
+
+ protected:
+  std::shared_ptr<T> m_result;
+  T* m_store;
+
+  bool m_default  = false;
+  bool m_implicit = false;
+
+  std::string m_default_value;
+  std::string m_implicit_value;
+};
+
+template <typename T>
+class standard_value : public abstract_value<T> {
+ public:
+  using abstract_value<T>::abstract_value;
+
+  std::shared_ptr<Value> clone() const { return std::make_shared<standard_value<T>>(*this); }
+};
+
+template <>
+class standard_value<bool> : public abstract_value<bool> {
+ public:
+  ~standard_value() = default;
+
+  standard_value() { set_default_and_implicit(); }
+
+  standard_value(bool* b) : abstract_value(b) { set_default_and_implicit(); }
+
+  std::shared_ptr<Value> clone() const { return std::make_shared<standard_value<bool>>(*this); }
+
+ private:
+  void set_default_and_implicit()
+  {
+    m_default        = true;
+    m_default_value  = "false";
+    m_implicit       = true;
+    m_implicit_value = "true";
+  }
+};
+}  // namespace values
+
+template <typename T>
+std::shared_ptr<Value> value()
+{
+  return std::make_shared<values::standard_value<T>>();
+}
+
+template <typename T>
+std::shared_ptr<Value> value(T& t)
+{
+  return std::make_shared<values::standard_value<T>>(&t);
+}
+
+class OptionAdder;
+
+class OptionDetails {
+ public:
+  OptionDetails(std::string const& short_,
+                std::string const& long_,
+                String const& desc,
+                std::shared_ptr<Value const> val)
+    : m_short(short_), m_long(long_), m_desc(desc), m_value(val), m_count(0)
+  {
+  }
+
+  OptionDetails(OptionDetails const& rhs) : m_desc(rhs.m_desc), m_count(rhs.m_count)
+  {
+    m_value = rhs.m_value->clone();
+  }
+
+  OptionDetails(OptionDetails&& rhs) = default;
+
+  String const& description() const { return m_desc; }
+
+  Value const& value() const { return *m_value; }
+
+  std::shared_ptr<Value> make_storage() const { return m_value->clone(); }
+
+  std::string const& short_name() const { return m_short; }
+
+  std::string const& long_name() const { return m_long; }
+
+ private:
+  std::string m_short;
+  std::string m_long;
+  String m_desc;
+  std::shared_ptr<Value const> m_value;
+  int m_count;
+};
+
+struct HelpOptionDetails {
+  std::string s;
+  std::string l;
+  String desc;
+  bool has_default;
+  std::string default_value;
+  bool has_implicit;
+  std::string implicit_value;
+  std::string arg_help;
+  bool is_container;
+  bool is_boolean;
+};
+
+struct HelpGroupDetails {
+  std::string name;
+  std::string description;
+  std::vector<HelpOptionDetails> options;
+};
+
+class OptionValue {
+ public:
+  void parse(std::shared_ptr<OptionDetails const> details, std::string const& text)
+  {
+    ensure_value(details);
+    ++m_count;
+    m_value->parse(text);
+  }
+
+  void parse_default(std::shared_ptr<OptionDetails const> details)
+  {
+    ensure_value(details);
+    m_default = true;
+    m_value->parse();
+  }
+
+  size_t count() const noexcept { return m_count; }
+
+  // TODO: maybe default options should count towards the number of arguments
+  bool has_default() const noexcept { return m_default; }
+
+  template <typename T>
+  T const& as() const
+  {
+    if (m_value == nullptr) { throw_or_mimic<std::domain_error>("No value"); }
+
+#ifdef CXXOPTS_NO_RTTI
+    return static_cast<values::standard_value<T> const&>(*m_value).get();
+#else
+    return dynamic_cast<values::standard_value<T> const&>(*m_value).get();
+#endif
+  }
+
+ private:
+  void ensure_value(std::shared_ptr<OptionDetails const> details)
+  {
+    if (m_value == nullptr) { m_value = details->make_storage(); }
+  }
+
+  std::shared_ptr<Value> m_value;
+  size_t m_count = 0;
+  bool m_default = false;
+};
+
+class KeyValue {
+ public:
+  KeyValue(std::string key_, std::string value_)
+    : m_key(std::move(key_)), m_value(std::move(value_))
+  {
+  }
+
+  std::string const& key() const { return m_key; }
+
+  std::string const& value() const { return m_value; }
+
+  template <typename T>
+  T as() const
+  {
+    T result;
+    values::parse_value(m_value, result);
+    return result;
+  }
+
+ private:
+  std::string m_key;
+  std::string m_value;
+};
+
+class ParseResult {
+ public:
+  ParseResult(
+    const std::shared_ptr<std::unordered_map<std::string, std::shared_ptr<OptionDetails>>>,
+    std::vector<std::string>,
+    bool allow_unrecognised,
+    int&,
+    char**&);
+
+  size_t count(std::string const& o) const
+  {
+    auto iter = m_options->find(o);
+    if (iter == m_options->end()) { return 0; }
+
+    auto riter = m_results.find(iter->second);
+
+    return riter->second.count();
+  }
+
+  OptionValue const& operator[](std::string const& option) const
+  {
+    auto iter = m_options->find(option);
+
+    if (iter == m_options->end()) { throw_or_mimic<option_not_present_exception>(option); }
+
+    auto riter = m_results.find(iter->second);
+
+    return riter->second;
+  }
+
+  std::vector<KeyValue> const& arguments() const { return m_sequential; }
+
+ private:
+  void parse(int& argc, char**& argv);
+
+  void add_to_option(std::string const& option, std::string const& arg);
+
+  bool consume_positional(std::string a);
+
+  void parse_option(std::shared_ptr<OptionDetails> value,
+                    std::string const& name,
+                    std::string const& arg = "");
+
+  void parse_default(std::shared_ptr<OptionDetails> details);
+
+  void checked_parse_arg(int argc,
+                         char* argv[],
+                         int& current,
+                         std::shared_ptr<OptionDetails> value,
+                         std::string const& name);
+
+  const std::shared_ptr<std::unordered_map<std::string, std::shared_ptr<OptionDetails>>> m_options;
+  std::vector<std::string> m_positional;
+  std::vector<std::string>::iterator m_next_positional;
+  std::unordered_set<std::string> m_positional_set;
+  std::unordered_map<std::shared_ptr<OptionDetails>, OptionValue> m_results;
+
+  bool m_allow_unrecognised;
+
+  std::vector<KeyValue> m_sequential;
+};
+
+struct Option {
+  Option(std::string const& opts,
+         std::string const& desc,
+         std::shared_ptr<Value const> const& value = ::cxxopts::value<bool>(),
+         std::string const& arg_help               = "")
+    : opts_(opts), desc_(desc), value_(value), arg_help_(arg_help)
+  {
+  }
+
+  std::string opts_;
+  std::string desc_;
+  std::shared_ptr<Value const> value_;
+  std::string arg_help_;
+};
+
+class Options {
+  typedef std::unordered_map<std::string, std::shared_ptr<OptionDetails>> OptionMap;
+
+ public:
+  Options(std::string program, std::string help_string = "")
+    : m_program(std::move(program)),
+      m_help_string(toLocalString(std::move(help_string))),
+      m_custom_help("[OPTION...]"),
+      m_positional_help("positional parameters"),
+      m_show_positional(false),
+      m_allow_unrecognised(false),
+      m_options(std::make_shared<OptionMap>()),
+      m_next_positional(m_positional.end())
+  {
+  }
+
+  Options& positional_help(std::string help_text)
+  {
+    m_positional_help = std::move(help_text);
+    return *this;
+  }
+
+  Options& custom_help(std::string help_text)
+  {
+    m_custom_help = std::move(help_text);
+    return *this;
+  }
+
+  Options& show_positional_help()
+  {
+    m_show_positional = true;
+    return *this;
+  }
+
+  Options& allow_unrecognised_options()
+  {
+    m_allow_unrecognised = true;
+    return *this;
+  }
+
+  ParseResult parse(int& argc, char**& argv);
+
+  OptionAdder add_options(std::string group = "");
+
+  void add_options(std::string const& group, std::initializer_list<Option> options);
+
+  void add_option(std::string const& group, Option const& option);
+
+  void add_option(std::string const& group,
+                  std::string const& s,
+                  std::string const& l,
+                  std::string desc,
+                  std::shared_ptr<Value const> value,
+                  std::string arg_help);
+
+  // parse positional arguments into the given option
+  void parse_positional(std::string option);
+
+  void parse_positional(std::vector<std::string> options);
+
+  void parse_positional(std::initializer_list<std::string> options);
+
+  template <typename Iterator>
+  void parse_positional(Iterator begin, Iterator end)
+  {
+    parse_positional(std::vector<std::string>{begin, end});
+  }
+
+  std::string help(std::vector<std::string> const& groups = {}) const;
+
+  const std::vector<std::string> groups() const;
+
+  HelpGroupDetails const& group_help(std::string const& group) const;
+
+ private:
+  void add_one_option(std::string const& option, std::shared_ptr<OptionDetails> details);
+
+  String help_one_group(std::string const& group) const;
+
+  void generate_group_help(String& result, std::vector<std::string> const& groups) const;
+
+  void generate_all_groups_help(String& result) const;
+
+  std::string m_program;
+  String m_help_string;
+  std::string m_custom_help;
+  std::string m_positional_help;
+  bool m_show_positional;
+  bool m_allow_unrecognised;
+
+  std::shared_ptr<OptionMap> m_options;
+  std::vector<std::string> m_positional;
+  std::vector<std::string>::iterator m_next_positional;
+  std::unordered_set<std::string> m_positional_set;
+
+  // mapping from groups to help options
+  std::map<std::string, HelpGroupDetails> m_help;
+};
+
+class OptionAdder {
+ public:
+  OptionAdder(Options& options, std::string group) : m_options(options), m_group(std::move(group))
+  {
+  }
+
+  OptionAdder& operator()(std::string const& opts,
+                          std::string const& desc,
+                          std::shared_ptr<Value const> value = ::cxxopts::value<bool>(),
+                          std::string arg_help               = "");
+
+ private:
+  Options& m_options;
+  std::string m_group;
+};
+
+namespace {
+constexpr int OPTION_LONGEST  = 30;
+constexpr int OPTION_DESC_GAP = 2;
+
+std::basic_regex<char> option_matcher("--([[:alnum:]][-_[:alnum:]]+)(=(.*))?|-([[:alnum:]]+)");
+
+std::basic_regex<char> option_specifier("(([[:alnum:]]),)?[ ]*([[:alnum:]][-_[:alnum:]]*)?");
+
+String format_option(HelpOptionDetails const& o)
+{
+  auto& s = o.s;
+  auto& l = o.l;
+
+  String result = "  ";
+
+  if (s.size() > 0) {
+    result += "-" + toLocalString(s) + ",";
+  } else {
+    result += "   ";
+  }
+
+  if (l.size() > 0) { result += " --" + toLocalString(l); }
+
+  auto arg = o.arg_help.size() > 0 ? toLocalString(o.arg_help) : "arg";
+
+  if (!o.is_boolean) {
+    if (o.has_implicit) {
+      result += " [=" + arg + "(=" + toLocalString(o.implicit_value) + ")]";
+    } else {
+      result += " " + arg;
+    }
+  }
+
+  return result;
+}
+
+String format_description(HelpOptionDetails const& o, size_t start, size_t width)
+{
+  auto desc = o.desc;
+
+  if (o.has_default && (!o.is_boolean || o.default_value != "false")) {
+    if (o.default_value != "") {
+      desc += toLocalString(" (default: " + o.default_value + ")");
+    } else {
+      desc += toLocalString(" (default: \"\")");
+    }
+  }
+
+  String result;
+
+  auto current   = std::begin(desc);
+  auto startLine = current;
+  auto lastSpace = current;
+
+  auto size = size_t{};
+
+  while (current != std::end(desc)) {
+    if (*current == ' ') { lastSpace = current; }
+
+    if (*current == '\n') {
+      startLine = current + 1;
+      lastSpace = startLine;
+    } else if (size > width) {
+      if (lastSpace == startLine) {
+        stringAppend(result, startLine, current + 1);
+        stringAppend(result, "\n");
+        stringAppend(result, start, ' ');
+        startLine = current + 1;
+        lastSpace = startLine;
+      } else {
+        stringAppend(result, startLine, lastSpace);
+        stringAppend(result, "\n");
+        stringAppend(result, start, ' ');
+        startLine = lastSpace + 1;
+        lastSpace = startLine;
+      }
+      size = 0;
+    } else {
+      ++size;
+    }
+
+    ++current;
+  }
+
+  // append whatever is left
+  stringAppend(result, startLine, current);
+
+  return result;
+}
+}  // namespace
+
+inline ParseResult::ParseResult(
+  const std::shared_ptr<std::unordered_map<std::string, std::shared_ptr<OptionDetails>>> options,
+  std::vector<std::string> positional,
+  bool allow_unrecognised,
+  int& argc,
+  char**& argv)
+  : m_options(options),
+    m_positional(std::move(positional)),
+    m_next_positional(m_positional.begin()),
+    m_allow_unrecognised(allow_unrecognised)
+{
+  parse(argc, argv);
+}
+
+inline void Options::add_options(std::string const& group, std::initializer_list<Option> options)
+{
+  OptionAdder option_adder(*this, group);
+  for (auto const& option : options) {
+    option_adder(option.opts_, option.desc_, option.value_, option.arg_help_);
+  }
+}
+
+inline OptionAdder Options::add_options(std::string group)
+{
+  return OptionAdder(*this, std::move(group));
+}
+
+inline OptionAdder& OptionAdder::operator()(std::string const& opts,
+                                            std::string const& desc,
+                                            std::shared_ptr<Value const> value,
+                                            std::string arg_help)
+{
+  std::match_results<char const*> result;
+  std::regex_match(opts.c_str(), result, option_specifier);
+
+  if (result.empty()) { throw_or_mimic<invalid_option_format_error>(opts); }
+
+  auto const& short_match = result[2];
+  auto const& long_match  = result[3];
+
+  if (!short_match.length() && !long_match.length()) {
+    throw_or_mimic<invalid_option_format_error>(opts);
+  } else if (long_match.length() == 1 && short_match.length()) {
+    throw_or_mimic<invalid_option_format_error>(opts);
+  }
+
+  auto option_names = [](std::sub_match<char const*> const& short_,
+                         std::sub_match<char const*> const& long_) {
+    if (long_.length() == 1) {
+      return std::make_tuple(long_.str(), short_.str());
+    } else {
+      return std::make_tuple(short_.str(), long_.str());
+    }
+  }(short_match, long_match);
+
+  m_options.add_option(m_group,
+                       std::get<0>(option_names),
+                       std::get<1>(option_names),
+                       desc,
+                       value,
+                       std::move(arg_help));
+
+  return *this;
+}
+
+inline void ParseResult::parse_default(std::shared_ptr<OptionDetails> details)
+{
+  m_results[details].parse_default(details);
+}
+
+inline void ParseResult::parse_option(std::shared_ptr<OptionDetails> value,
+                                      std::string const& /*name*/,
+                                      std::string const& arg)
+{
+  auto& result = m_results[value];
+  result.parse(value, arg);
+
+  m_sequential.emplace_back(value->long_name(), arg);
+}
+
+inline void ParseResult::checked_parse_arg(int argc,
+                                           char* argv[],
+                                           int& current,
+                                           std::shared_ptr<OptionDetails> value,
+                                           std::string const& name)
+{
+  if (current + 1 >= argc) {
+    if (value->value().has_implicit()) {
+      parse_option(value, name, value->value().get_implicit_value());
+    } else {
+      throw_or_mimic<missing_argument_exception>(name);
+    }
+  } else {
+    if (value->value().has_implicit()) {
+      parse_option(value, name, value->value().get_implicit_value());
+    } else {
+      parse_option(value, name, argv[current + 1]);
+      ++current;
+    }
+  }
+}
+
+inline void ParseResult::add_to_option(std::string const& option, std::string const& arg)
+{
+  auto iter = m_options->find(option);
+
+  if (iter == m_options->end()) { throw_or_mimic<option_not_exists_exception>(option); }
+
+  parse_option(iter->second, option, arg);
+}
+
+inline bool ParseResult::consume_positional(std::string a)
+{
+  while (m_next_positional != m_positional.end()) {
+    auto iter = m_options->find(*m_next_positional);
+    if (iter != m_options->end()) {
+      auto& result = m_results[iter->second];
+      if (!iter->second->value().is_container()) {
+        if (result.count() == 0) {
+          add_to_option(*m_next_positional, a);
+          ++m_next_positional;
+          return true;
+        } else {
+          ++m_next_positional;
+          continue;
+        }
+      } else {
+        add_to_option(*m_next_positional, a);
+        return true;
+      }
+    } else {
+      throw_or_mimic<option_not_exists_exception>(*m_next_positional);
+    }
+  }
+
+  return false;
+}
+
+inline void Options::parse_positional(std::string option)
+{
+  parse_positional(std::vector<std::string>{std::move(option)});
+}
+
+inline void Options::parse_positional(std::vector<std::string> options)
+{
+  m_positional      = std::move(options);
+  m_next_positional = m_positional.begin();
+
+  m_positional_set.insert(m_positional.begin(), m_positional.end());
+}
+
+inline void Options::parse_positional(std::initializer_list<std::string> options)
+{
+  parse_positional(std::vector<std::string>(std::move(options)));
+}
+
+inline ParseResult Options::parse(int& argc, char**& argv)
+{
+  ParseResult result(m_options, m_positional, m_allow_unrecognised, argc, argv);
+  return result;
+}
+
+inline void ParseResult::parse(int& argc, char**& argv)
+{
+  int current = 1;
+
+  int nextKeep = 1;
+
+  bool consume_remaining = false;
+
+  while (current != argc) {
+    if (strcmp(argv[current], "--") == 0) {
+      consume_remaining = true;
+      ++current;
+      break;
+    }
+
+    std::match_results<char const*> result;
+    std::regex_match(argv[current], result, option_matcher);
+
+    if (result.empty()) {
+      // not a flag
+
+      // but if it starts with a `-`, then it's an error
+      if (argv[current][0] == '-' && argv[current][1] != '\0') {
+        if (!m_allow_unrecognised) { throw_or_mimic<option_syntax_exception>(argv[current]); }
+      }
+
+      // if true is returned here then it was consumed, otherwise it is
+      // ignored
+      if (consume_positional(argv[current])) {
+      } else {
+        argv[nextKeep] = argv[current];
+        ++nextKeep;
+      }
+      // if we return from here then it was parsed successfully, so continue
+    } else {
+      // short or long option?
+      if (result[4].length() != 0) {
+        std::string const& s = result[4];
+
+        for (std::size_t i = 0; i != s.size(); ++i) {
+          std::string name(1, s[i]);
+          auto iter = m_options->find(name);
+
+          if (iter == m_options->end()) {
+            if (m_allow_unrecognised) {
+              continue;
+            } else {
+              // error
+              throw_or_mimic<option_not_exists_exception>(name);
+            }
+          }
+
+          auto value = iter->second;
+
+          if (i + 1 == s.size()) {
+            // it must be the last argument
+            checked_parse_arg(argc, argv, current, value, name);
+          } else if (value->value().has_implicit()) {
+            parse_option(value, name, value->value().get_implicit_value());
+          } else {
+            // error
+            throw_or_mimic<option_requires_argument_exception>(name);
+          }
+        }
+      } else if (result[1].length() != 0) {
+        std::string const& name = result[1];
+
+        auto iter = m_options->find(name);
+
+        if (iter == m_options->end()) {
+          if (m_allow_unrecognised) {
+            // keep unrecognised options in argument list, skip to next argument
+            argv[nextKeep] = argv[current];
+            ++nextKeep;
+            ++current;
+            continue;
+          } else {
+            // error
+            throw_or_mimic<option_not_exists_exception>(name);
+          }
+        }
+
+        auto opt = iter->second;
+
+        // equals provided for long option?
+        if (result[2].length() != 0) {
+          // parse the option given
+
+          parse_option(opt, name, result[3]);
+        } else {
+          // parse the next argument
+          checked_parse_arg(argc, argv, current, opt, name);
+        }
+      }
+    }
+
+    ++current;
+  }
+
+  for (auto& opt : *m_options) {
+    auto& detail = opt.second;
+    auto& value  = detail->value();
+
+    auto& store = m_results[detail];
+
+    if (value.has_default() && !store.count() && !store.has_default()) { parse_default(detail); }
+  }
+
+  if (consume_remaining) {
+    while (current < argc) {
+      if (!consume_positional(argv[current])) { break; }
+      ++current;
+    }
+
+    // adjust argv for any that couldn't be swallowed
+    while (current != argc) {
+      argv[nextKeep] = argv[current];
+      ++nextKeep;
+      ++current;
+    }
+  }
+
+  argc = nextKeep;
+}
+
+inline void Options::add_option(std::string const& group, Option const& option)
+{
+  add_options(group, {option});
+}
+
+inline void Options::add_option(std::string const& group,
+                                std::string const& s,
+                                std::string const& l,
+                                std::string desc,
+                                std::shared_ptr<Value const> value,
+                                std::string arg_help)
+{
+  auto stringDesc = toLocalString(std::move(desc));
+  auto option     = std::make_shared<OptionDetails>(s, l, stringDesc, value);
+
+  if (s.size() > 0) { add_one_option(s, option); }
+
+  if (l.size() > 0) { add_one_option(l, option); }
+
+  // add the help details
+  auto& options = m_help[group];
+
+  options.options.emplace_back(HelpOptionDetails{s,
+                                                 l,
+                                                 stringDesc,
+                                                 value->has_default(),
+                                                 value->get_default_value(),
+                                                 value->has_implicit(),
+                                                 value->get_implicit_value(),
+                                                 std::move(arg_help),
+                                                 value->is_container(),
+                                                 value->is_boolean()});
+}
+
+inline void Options::add_one_option(std::string const& option,
+                                    std::shared_ptr<OptionDetails> details)
+{
+  auto in = m_options->emplace(option, details);
+
+  if (!in.second) { throw_or_mimic<option_exists_error>(option); }
+}
+
+inline String Options::help_one_group(std::string const& g) const
+{
+  typedef std::vector<std::pair<String, String>> OptionHelp;
+
+  auto group = m_help.find(g);
+  if (group == m_help.end()) { return ""; }
+
+  OptionHelp format;
+
+  size_t longest = 0;
+
+  String result;
+
+  if (!g.empty()) { result += toLocalString(" " + g + " options:\n"); }
+
+  for (auto const& o : group->second.options) {
+    if (m_positional_set.find(o.l) != m_positional_set.end() && !m_show_positional) { continue; }
+
+    auto s  = format_option(o);
+    longest = (std::max)(longest, stringLength(s));
+    format.push_back(std::make_pair(s, String()));
+  }
+
+  longest = (std::min)(longest, static_cast<size_t>(OPTION_LONGEST));
+
+  // widest allowed description
+  auto allowed = size_t{76} - longest - OPTION_DESC_GAP;
+
+  auto fiter = format.begin();
+  for (auto const& o : group->second.options) {
+    if (m_positional_set.find(o.l) != m_positional_set.end() && !m_show_positional) { continue; }
+
+    auto d = format_description(o, longest + OPTION_DESC_GAP, allowed);
+
+    result += fiter->first;
+    if (stringLength(fiter->first) > longest) {
+      result += '\n';
+      result += toLocalString(std::string(longest + OPTION_DESC_GAP, ' '));
+    } else {
+      result +=
+        toLocalString(std::string(longest + OPTION_DESC_GAP - stringLength(fiter->first), ' '));
+    }
+    result += d;
+    result += '\n';
+
+    ++fiter;
+  }
+
+  return result;
+}
+
+inline void Options::generate_group_help(String& result,
+                                         std::vector<std::string> const& print_groups) const
+{
+  for (size_t i = 0; i != print_groups.size(); ++i) {
+    String const& group_help_text = help_one_group(print_groups[i]);
+    if (empty(group_help_text)) { continue; }
+    result += group_help_text;
+    if (i < print_groups.size() - 1) { result += '\n'; }
+  }
+}
+
+inline void Options::generate_all_groups_help(String& result) const
+{
+  std::vector<std::string> all_groups;
+  all_groups.reserve(m_help.size());
+
+  for (auto& group : m_help) {
+    all_groups.push_back(group.first);
+  }
+
+  generate_group_help(result, all_groups);
+}
+
+inline std::string Options::help(std::vector<std::string> const& help_groups) const
+{
+  String result =
+    m_help_string + "\nUsage:\n  " + toLocalString(m_program) + " " + toLocalString(m_custom_help);
+
+  if (m_positional.size() > 0 && m_positional_help.size() > 0) {
+    result += " " + toLocalString(m_positional_help);
+  }
+
+  result += "\n\n";
+
+  if (help_groups.empty()) {
+    generate_all_groups_help(result);
+  } else {
+    generate_group_help(result, help_groups);
+  }
+
+  return toUTF8String(result);
+}
+
+inline const std::vector<std::string> Options::groups() const
+{
+  std::vector<std::string> g;
+
+  std::transform(
+    m_help.begin(),
+    m_help.end(),
+    std::back_inserter(g),
+    [](const std::map<std::string, HelpGroupDetails>::value_type& pair) { return pair.first; });
+
+  return g;
+}
+
+inline HelpGroupDetails const& Options::group_help(std::string const& group) const
+{
+  return m_help.at(group);
+}
+
+}  // namespace cxxopts
+
+#endif  // DOXYGEN_SHOULD_SKIP_THIS
+#endif  // CXXOPTS_HPP_INCLUDED
diff --git a/cpp/include/cudf_test/default_stream.hpp b/cpp/include/cudf_test/default_stream.hpp
new file mode 100644
index 0000000..1da97d7
--- /dev/null
+++ b/cpp/include/cudf_test/default_stream.hpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace test {
+
+/**
+ * @brief Get the default stream to use for tests.
+ *
+ * The standard behavior of this function is to return cudf's default stream
+ * (cudf::get_default_stream). This function is primarily provided as an
+ * overload target for preload libraries (via LD_PRELOAD) so that the default
+ * stream used for tests may be modified for tracking purposes. All tests of
+ * public APIs that accept streams should pass `cudf::test::get_default_stream`
+ * as the stream argument so that a preload library changing the behavior of
+ * this function will trigger those tests to run on a different stream than
+ * `cudf::get_default_stream`.
+ *
+ * @return The default stream to use for tests.
+ */
+rmm::cuda_stream_view const get_default_stream();
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/detail/column_utilities.hpp b/cpp/include/cudf_test/detail/column_utilities.hpp
new file mode 100644
index 0000000..f8270f6
--- /dev/null
+++ b/cpp/include/cudf_test/detail/column_utilities.hpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/null_mask.hpp>
+
+namespace cudf {
+namespace test {
+namespace detail {
+
+/**
+ * @brief Formats a column view as a string
+ *
+ * @param col The column view
+ * @param delimiter The delimiter to put between strings
+ * @param indent Indentation for all output. See detail::to_strings for detailed
+ * explanation.
+ */
+std::string to_string(cudf::column_view const& col,
+                      std::string const& delimiter,
+                      std::string const& indent = "");
+
+/**
+ * @brief Formats a null mask as a string
+ *
+ * @param null_mask The null mask buffer
+ * @param null_mask_size Size of the null mask (in rows)
+ * @param indent Indentation for all output. See detail::to_strings for detailed
+ * explanation.
+ */
+std::string to_string(std::vector<bitmask_type> const& null_mask,
+                      size_type null_mask_size,
+                      std::string const& indent = "");
+
+/**
+ * @brief Convert column values to a host vector of strings
+ *
+ * Supports indentation of all output.  For example, if the displayed output of your column
+ * would be
+ *
+ * @code{.pseudo}
+ * "1,2,3,4,5"
+ * @endcode
+ * and the `indent` parameter was "   ", that indentation would be prepended to
+ * result in the output
+ * @code{.pseudo}
+ * "   1,2,3,4,5"
+ * @endcode
+ *
+ * The can be useful for displaying complex types. An example use case would be for
+ * displaying the nesting of a LIST type column (via recursion).
+ *
+ *  List<List<int>>:
+ *  Length : 3
+ *  Offsets : 0, 2, 5, 6
+ *  Children :
+ *     List<int>:
+ *     Length : 6
+ *     Offsets : 0, 2, 4, 7, 8, 9, 11
+ *     Children :
+ *        1, 2, 3, 4, 5, 6, 7, 0, 8, 9, 10
+ *
+ * @param col The column view
+ * @param indent Indentation for all output
+ */
+std::vector<std::string> to_strings(cudf::column_view const& col, std::string const& indent = "");
+
+}  // namespace detail
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/file_utilities.hpp b/cpp/include/cudf_test/file_utilities.hpp
new file mode 100644
index 0000000..fa3daae
--- /dev/null
+++ b/cpp/include/cudf_test/file_utilities.hpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdio>
+#include <cstdlib>
+#include <filesystem>
+#include <string>
+
+#include <ftw.h>
+
+#include <cudf/utilities/error.hpp>
+
+/**
+ * @brief RAII class for creating a temporary directory.
+ *
+ */
+class temp_directory {
+  std::string _path;
+
+ public:
+  /**
+   * @brief Construct a new temp directory object
+   *
+   * @param base_name The base name of the temporary directory
+   */
+  temp_directory(std::string const& base_name)
+  {
+    std::string dir_template{std::filesystem::temp_directory_path().string()};
+    if (auto env_p = std::getenv("WORKSPACE")) dir_template = env_p;
+
+    dir_template += "/" + base_name + ".XXXXXX";
+    auto const tmpdirptr = mkdtemp(const_cast<char*>(dir_template.data()));
+    CUDF_EXPECTS(tmpdirptr != nullptr, "Temporary directory creation failure: " + dir_template);
+
+    _path = dir_template + "/";
+  }
+
+  temp_directory& operator=(temp_directory const&) = delete;
+  temp_directory(temp_directory const&)            = delete;
+  /**
+   * @brief Move assignment operator
+   *
+   * @return Reference to this object
+   */
+  temp_directory& operator=(temp_directory&&) = default;
+  temp_directory(temp_directory&&)            = default;  ///< Move constructor
+
+  ~temp_directory() { std::filesystem::remove_all(std::filesystem::path{_path}); }
+
+  /**
+   * @brief Returns the path of the temporary directory
+   *
+   * @return string path of the temporary directory
+   */
+  [[nodiscard]] std::string const& path() const { return _path; }
+};
diff --git a/cpp/include/cudf_test/io_metadata_utilities.hpp b/cpp/include/cudf_test/io_metadata_utilities.hpp
new file mode 100644
index 0000000..6fd1a52
--- /dev/null
+++ b/cpp/include/cudf_test/io_metadata_utilities.hpp
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/io/types.hpp>
+
+namespace cudf::test {
+
+void expect_metadata_equal(cudf::io::table_input_metadata in_meta,
+                           cudf::io::table_metadata out_meta);
+
+/**
+ * @brief Ensures that the metadata of two tables matches for the root columns as well as for all
+ * descendents (recursively)
+ */
+void expect_metadata_equal(cudf::io::table_metadata lhs_meta, cudf::io::table_metadata rhs_meta);
+
+}  // namespace cudf::test
diff --git a/cpp/include/cudf_test/iterator_utilities.hpp b/cpp/include/cudf_test/iterator_utilities.hpp
new file mode 100644
index 0000000..10f6e77
--- /dev/null
+++ b/cpp/include/cudf_test/iterator_utilities.hpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <iterator>
+
+namespace cudf {
+namespace test {
+namespace iterators {
+/**
+ * @brief Bool iterator for marking (possibly multiple) null elements in a column_wrapper.
+ *
+ * The returned iterator yields `false` (to mark `null`) at all the specified indices,
+ * and yields `true` (to mark valid rows) for all other indices. E.g.
+ *
+ * @code
+ * auto indices = std::vector<size_type>{8,9};
+ * auto iter = nulls_at(indices.cbegin(), indices.end());
+ * iter[6] == true;  // i.e. Valid row at index 6.
+ * iter[7] == true;  // i.e. Valid row at index 7.
+ * iter[8] == false; // i.e. Invalid row at index 8.
+ * iter[9] == false; // i.e. Invalid row at index 9.
+ * @endcode
+ *
+ * @tparam Iter Iterator type
+ * @param index_start Iterator to start of indices for which the validity iterator
+ *                    must return `false` (i.e. null)
+ * @param index_end   Iterator to end of indices for the validity iterator
+ * @return auto Validity iterator
+ */
+template <typename Iter>
+[[maybe_unused]] static auto nulls_at(Iter index_start, Iter index_end)
+{
+  using index_type = typename std::iterator_traits<Iter>::value_type;
+
+  return cudf::detail::make_counting_transform_iterator(
+    0, [indices = std::vector<index_type>{index_start, index_end}](auto i) {
+      return std::find(indices.cbegin(), indices.cend(), i) == indices.cend();
+    });
+}
+
+/**
+ * @brief Bool iterator for marking (possibly multiple) null elements in a column_wrapper.
+ *
+ * The returned iterator yields `false` (to mark `null`) at all the specified indices,
+ * and yields `true` (to mark valid rows) for all other indices. E.g.
+ *
+ * @code
+ * auto iter = nulls_at({8,9});
+ * iter[6] == true;  // i.e. Valid row at index 6.
+ * iter[7] == true;  // i.e. Valid row at index 7.
+ * iter[8] == false; // i.e. Invalid row at index 8.
+ * iter[9] == false; // i.e. Invalid row at index 9.
+ * @endcode
+ *
+ * @param indices The indices for which the validity iterator must return `false` (i.e. null)
+ * @return auto Validity iterator
+ */
+[[maybe_unused]] static auto nulls_at(std::vector<cudf::size_type> const& indices)
+{
+  return nulls_at(indices.cbegin(), indices.cend());
+}
+
+/**
+ * @brief Bool iterator for marking a single null element in a column_wrapper
+ *
+ * The returned iterator yields `false` (to mark `null`) at the specified index,
+ * and yields `true` (to mark valid rows) for all other indices. E.g.
+ *
+ * @code
+ * auto iter = null_at(8);
+ * iter[7] == true;  // i.e. Valid row at index 7.
+ * iter[8] == false; // i.e. Invalid row at index 8.
+ * @endcode
+ *
+ * @param index The index for which the validity iterator must return `false` (i.e. null)
+ * @return auto Validity iterator
+ */
+[[maybe_unused]] static auto null_at(cudf::size_type index)
+{
+  return nulls_at(std::vector<cudf::size_type>{index});
+}
+
+/**
+ * @brief Bool iterator for marking all elements are null
+ *
+ * @return auto Validity iterator which always yields `false`
+ */
+[[maybe_unused]] static auto all_nulls() { return thrust::make_constant_iterator(false); }
+
+/**
+ * @brief Bool iterator for marking all elements are valid (non-null)
+ *
+ * @return auto Validity iterator which always yields `true`
+ */
+[[maybe_unused]] static auto no_nulls() { return thrust::make_constant_iterator(true); }
+
+/**
+ * @brief Bool iterator for marking null elements from pointers of data
+ *
+ * The returned iterator yields `false` (to mark `null`) at the indices corresponding to the
+ * pointers having `nullptr` values and `true` for the remaining indices.
+ *
+ * @note The input vector is referenced by the transform iterator, so the
+ * lifespan must be just as long as the iterator.
+ *
+ * @tparam T the data type
+ * @param ptrs The data pointers for which the validity iterator is computed
+ * @return auto Validity iterator
+ */
+template <class T>
+[[maybe_unused]] static auto nulls_from_nullptrs(std::vector<T const*> const& ptrs)
+{
+  return thrust::make_transform_iterator(ptrs.begin(), [](auto ptr) { return ptr != nullptr; });
+}
+
+}  // namespace iterators
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/print_utilities.cuh b/cpp/include/cudf_test/print_utilities.cuh
new file mode 100644
index 0000000..37ffcd4
--- /dev/null
+++ b/cpp/include/cudf_test/print_utilities.cuh
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <type_traits>
+
+namespace cudf::test::print {
+
+constexpr int32_t hex_tag = 0;
+
+template <int32_t TagT, typename T>
+struct TaggedType {
+  T v;
+};
+
+template <typename T>
+using hex_t = TaggedType<hex_tag, T>;
+
+/**
+ * @brief Function object to transform a built-in type to a tagged type (e.g., in order to print
+ * values from an iterator returning uint32_t as hex values)
+ *
+ * @tparam TaggedTypeT A TaggedType template specialisation
+ */
+template <typename TaggedTypeT>
+struct ToTaggedType {
+  template <typename T>
+  CUDF_HOST_DEVICE TaggedTypeT operator()(T const& v) const
+  {
+    return TaggedTypeT{v};
+  }
+};
+
+/**
+ * @brief Returns an iterator that causes the values from \p it to be printed as hex values.
+ *
+ * @tparam InItT A random-access input iterator type
+ * @param it A random-access input iterator t
+ * @return
+ */
+template <typename InItT>
+auto hex(InItT it)
+{
+  using value_t  = typename std::iterator_traits<InItT>::value_type;
+  using tagged_t = hex_t<value_t>;
+  return thrust::make_transform_iterator(it, ToTaggedType<tagged_t>{});
+}
+
+template <typename T, CUDF_ENABLE_IF(std::is_integral_v<T>&& std::is_signed_v<T>)>
+CUDF_HOST_DEVICE void print_value(int32_t width, T arg)
+{
+  printf("%*d", width, arg);
+}
+
+template <typename T, CUDF_ENABLE_IF(std::is_integral_v<T>&& std::is_unsigned_v<T>)>
+CUDF_HOST_DEVICE void print_value(int32_t width, T arg)
+{
+  printf("%*d", width, arg);
+}
+
+CUDF_HOST_DEVICE void print_value(int32_t width, char arg) { printf("%*c", width, arg); }
+
+template <typename T>
+CUDF_HOST_DEVICE void print_value(int32_t width, hex_t<T> arg)
+{
+  printf("%*X", width, arg.v);
+}
+
+namespace detail {
+template <typename T>
+CUDF_HOST_DEVICE void print_values(int32_t width, char delimiter, T arg)
+{
+  print_value(width, arg);
+}
+
+template <typename T, typename... Ts>
+CUDF_HOST_DEVICE void print_values(int32_t width, char delimiter, T arg, Ts... args)
+{
+  print_value(width, arg);
+  if (delimiter) printf("%c", delimiter);
+  print_values(width, delimiter, args...);
+}
+
+template <typename... Ts>
+__global__ void print_array_kernel(std::size_t count, int32_t width, char delimiter, Ts... args)
+{
+  if (threadIdx.x == 0 && blockIdx.x == 0) {
+    for (std::size_t i = 0; i < count; i++) {
+      printf("%6lu: ", i);
+      print_values(width, delimiter, args[i]...);
+      printf("\n");
+    }
+  }
+}
+}  // namespace detail
+
+/**
+ * @brief Prints \p count elements from each of the given device-accessible iterators.
+ *
+ * @param count The number of items to print from each device-accessible iterator
+ * @param stream The cuda stream to which the printing kernel shall be dispatched
+ * @param args List of iterators to be printed
+ */
+template <typename... Ts>
+void print_array(std::size_t count, rmm::cuda_stream_view stream, Ts... args)
+{
+  // The width to pad printed numbers to
+  constexpr int32_t width = 6;
+
+  // Delimiter used for separating values from subsequent iterators
+  constexpr char delimiter = ',';
+
+  // TODO we want this to compile to nothing dependnig on compiler flag, rather than runtime
+  if (std::getenv("CUDA_DBG_DUMP") != nullptr) {
+    detail::print_array_kernel<<<1, 1, 0, stream.value()>>>(count, width, delimiter, args...);
+  }
+}
+
+}  // namespace cudf::test::print
diff --git a/cpp/include/cudf_test/stream_checking_resource_adaptor.hpp b/cpp/include/cudf_test/stream_checking_resource_adaptor.hpp
new file mode 100644
index 0000000..e184004
--- /dev/null
+++ b/cpp/include/cudf_test/stream_checking_resource_adaptor.hpp
@@ -0,0 +1,202 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf_test/default_stream.hpp>
+
+#include <cudf/detail/utilities/stacktrace.hpp>
+
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <iostream>
+
+/**
+ * @brief Resource that verifies that the default stream is not used in any allocation.
+ *
+ * @tparam Upstream Type of the upstream resource used for
+ * allocation/deallocation.
+ */
+template <typename Upstream>
+class stream_checking_resource_adaptor final : public rmm::mr::device_memory_resource {
+ public:
+  /**
+   * @brief Construct a new adaptor.
+   *
+   * @throws `cudf::logic_error` if `upstream == nullptr`
+   *
+   * @param upstream The resource used for allocating/deallocating device memory
+   */
+  stream_checking_resource_adaptor(Upstream* upstream,
+                                   bool error_on_invalid_stream,
+                                   bool check_default_stream)
+    : upstream_{upstream},
+      error_on_invalid_stream_{error_on_invalid_stream},
+      check_default_stream_{check_default_stream}
+  {
+    CUDF_EXPECTS(nullptr != upstream, "Unexpected null upstream resource pointer.");
+  }
+
+  stream_checking_resource_adaptor()                                                   = delete;
+  ~stream_checking_resource_adaptor() override                                         = default;
+  stream_checking_resource_adaptor(stream_checking_resource_adaptor const&)            = delete;
+  stream_checking_resource_adaptor& operator=(stream_checking_resource_adaptor const&) = delete;
+  stream_checking_resource_adaptor(stream_checking_resource_adaptor&&) noexcept        = default;
+  stream_checking_resource_adaptor& operator=(stream_checking_resource_adaptor&&) noexcept =
+    default;
+
+  /**
+   * @brief Return pointer to the upstream resource.
+   *
+   * @return Pointer to the upstream resource.
+   */
+  Upstream* get_upstream() const noexcept { return upstream_; }
+
+  /**
+   * @brief Checks whether the upstream resource supports streams.
+   *
+   * @return Whether or not the upstream resource supports streams
+   */
+  bool supports_streams() const noexcept override { return upstream_->supports_streams(); }
+
+  /**
+   * @brief Query whether the resource supports the get_mem_info API.
+   *
+   * @return Whether or not the upstream resource supports get_mem_info
+   */
+  bool supports_get_mem_info() const noexcept override
+  {
+    return upstream_->supports_get_mem_info();
+  }
+
+ private:
+  /**
+   * @brief Allocates memory of size at least `bytes` using the upstream
+   * resource as long as it fits inside the allocation limit.
+   *
+   * The returned pointer has at least 256B alignment.
+   *
+   * @throws `rmm::bad_alloc` if the requested allocation could not be fulfilled
+   * by the upstream resource.
+   * @throws `cudf::logic_error` if attempted on a default stream
+   *
+   * @param bytes The size, in bytes, of the allocation
+   * @param stream Stream on which to perform the allocation
+   * @return Pointer to the newly allocated memory
+   */
+  void* do_allocate(std::size_t bytes, rmm::cuda_stream_view stream) override
+  {
+    verify_stream(stream);
+    return upstream_->allocate(bytes, stream);
+  }
+
+  /**
+   * @brief Free allocation of size `bytes` pointed to by `ptr`
+   *
+   * @throws `cudf::logic_error` if attempted on a default stream
+   *
+   * @param ptr Pointer to be deallocated
+   * @param bytes Size of the allocation
+   * @param stream Stream on which to perform the deallocation
+   */
+  void do_deallocate(void* ptr, std::size_t bytes, rmm::cuda_stream_view stream) override
+  {
+    verify_stream(stream);
+    upstream_->deallocate(ptr, bytes, stream);
+  }
+
+  /**
+   * @brief Compare the upstream resource to another.
+   *
+   * @param other The other resource to compare to
+   * @return Whether or not the two resources are equivalent
+   */
+  bool do_is_equal(device_memory_resource const& other) const noexcept override
+  {
+    if (this == &other) { return true; }
+    auto cast = dynamic_cast<stream_checking_resource_adaptor<Upstream> const*>(&other);
+    return cast != nullptr ? upstream_->is_equal(*cast->get_upstream())
+                           : upstream_->is_equal(other);
+  }
+
+  /**
+   * @brief Get free and available memory from upstream resource.
+   *
+   * @throws `rmm::cuda_error` if unable to retrieve memory info.
+   * @throws `cudf::logic_error` if attempted on a default stream
+   *
+   * @param stream Stream on which to get the mem info.
+   * @return std::pair with available and free memory for resource
+   */
+  std::pair<std::size_t, std::size_t> do_get_mem_info(rmm::cuda_stream_view stream) const override
+  {
+    verify_stream(stream);
+    return upstream_->get_mem_info(stream);
+  }
+
+  /**
+   * @brief Throw an error if the provided stream is invalid.
+   *
+   * A stream is invalid if:
+   * - check_default_stream_ is true and this function is passed one of CUDA's
+   *   default stream specifiers, or
+   * - check_default_stream_ is false and this function is passed any stream
+   *   other than the result of cudf::test::get_default_stream().
+   *
+   * @throws `std::runtime_error` if provided an invalid stream
+   */
+  void verify_stream(rmm::cuda_stream_view const stream) const
+  {
+    auto cstream{stream.value()};
+    auto const invalid_stream =
+      check_default_stream_ ? ((cstream == cudaStreamDefault) || (cstream == cudaStreamLegacy) ||
+                               (cstream == cudaStreamPerThread))
+                            : (cstream != cudf::test::get_default_stream().value());
+
+    if (invalid_stream) {
+      // Exclude the current function from stacktrace.
+      std::cout << cudf::detail::get_stacktrace(cudf::detail::capture_last_stackframe::NO)
+                << std::endl;
+
+      if (error_on_invalid_stream_) {
+        throw std::runtime_error("Attempted to perform an operation on an unexpected stream!");
+      } else {
+        std::cout << "Attempted to perform an operation on an unexpected stream!" << std::endl;
+      }
+    }
+  }
+
+  Upstream* upstream_;            // the upstream resource used for satisfying allocation requests
+  bool error_on_invalid_stream_;  // If true, throw an exception when the wrong stream is detected.
+                                  // If false, simply print to stdout.
+  bool check_default_stream_;  // If true, throw an exception when the default stream is observed.
+                               // If false, throw an exception when anything other than
+                               // cudf::test::get_default_stream() is observed.
+};
+
+/**
+ * @brief Convenience factory to return a `stream_checking_resource_adaptor` around the
+ * upstream resource `upstream`.
+ *
+ * @tparam Upstream Type of the upstream `device_memory_resource`.
+ * @param upstream Pointer to the upstream resource
+ */
+template <typename Upstream>
+stream_checking_resource_adaptor<Upstream> make_stream_checking_resource_adaptor(
+  Upstream* upstream, bool error_on_invalid_stream, bool check_default_stream)
+{
+  return stream_checking_resource_adaptor<Upstream>{
+    upstream, error_on_invalid_stream, check_default_stream};
+}
diff --git a/cpp/include/cudf_test/table_utilities.hpp b/cpp/include/cudf_test/table_utilities.hpp
new file mode 100644
index 0000000..79229df
--- /dev/null
+++ b/cpp/include/cudf_test/table_utilities.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+namespace cudf::test::detail {
+/**
+ * @brief Verifies the property equality of two tables.
+ *
+ * @note This function should not be used directly. Use `CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL`
+ * instead.
+ *
+ * @param lhs The first table
+ * @param rhs The second table
+ */
+void expect_table_properties_equal(cudf::table_view lhs, cudf::table_view rhs);
+
+/**
+ * @brief Verifies the equality of two tables.
+ *
+ * Treats null elements as equivalent.
+ *
+ * @note This function should not be used directly. Use `CUDF_TEST_EXPECT_TABLES_EQUAL` instead.
+ *
+ * @param lhs The first table
+ * @param rhs The second table
+ */
+void expect_tables_equal(cudf::table_view lhs, cudf::table_view rhs);
+
+/**
+ * @brief Verifies the equivalency of two tables.
+ *
+ * Treats null elements as equivalent.  Columns that have nullability but no nulls,
+ * and columns that are not nullable are considered equivalent.
+ *
+ * @note This function should not be used directly. Use `CUDF_TEST_EXPECT_TABLES_EQUIVALENT`
+ * instead.
+ *
+ * @param lhs The first table
+ * @param rhs The second table
+ */
+void expect_tables_equivalent(cudf::table_view lhs, cudf::table_view rhs);
+
+}  // namespace cudf::test::detail
+
+// Macros for showing line of failure.
+#define CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(lhs, rhs)        \
+  do {                                                           \
+    SCOPED_TRACE(" <--  line of failure\n");                     \
+    cudf::test::detail::expect_table_properties_equal(lhs, rhs); \
+  } while (0)
+
+#define CUDF_TEST_EXPECT_TABLES_EQUAL(lhs, rhs)        \
+  do {                                                 \
+    SCOPED_TRACE(" <--  line of failure\n");           \
+    cudf::test::detail::expect_tables_equal(lhs, rhs); \
+  } while (0)
+
+#define CUDF_TEST_EXPECT_TABLES_EQUIVALENT(lhs, rhs)        \
+  do {                                                      \
+    SCOPED_TRACE(" <--  line of failure\n");                \
+    cudf::test::detail::expect_tables_equivalent(lhs, rhs); \
+  } while (0)
diff --git a/cpp/include/cudf_test/tdigest_utilities.cuh b/cpp/include/cudf_test/tdigest_utilities.cuh
new file mode 100644
index 0000000..742cd76
--- /dev/null
+++ b/cpp/include/cudf_test/tdigest_utilities.cuh
@@ -0,0 +1,586 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/extrema.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+// for use with groupby and reduction aggregation tests.
+
+namespace cudf {
+namespace test {
+
+using expected_value = thrust::tuple<size_type, double, double>;
+
+/**
+ * @brief Device functor to compute min of a sequence of values serially.
+ */
+template <typename T>
+struct column_min {
+  /**
+   * @brief Computes the min of a sequence of values serially.
+   *
+   * @param vals The sequence of values to compute the min of
+   * @return The min value
+   */
+  __device__ double operator()(device_span<T const> vals)
+  {
+    return static_cast<double>(*thrust::min_element(thrust::seq, vals.begin(), vals.end()));
+  }
+};
+
+/**
+ * @brief Device functor to compute max of a sequence of values serially.
+ */
+template <typename T>
+struct column_max {
+  /**
+   * @brief Computes the max of a sequence of values serially.
+   *
+   * @param vals The sequence of values to compute the max of
+   * @return The max value
+   */
+  __device__ double operator()(device_span<T const> vals)
+  {
+    return static_cast<double>(*thrust::max_element(thrust::seq, vals.begin(), vals.end()));
+  }
+};
+
+/**
+ * @brief Functor to generate a tdigest.
+ */
+struct tdigest_gen {
+  // @cond
+  template <
+    typename T,
+    typename Func,
+    typename std::enable_if_t<cudf::is_numeric<T>() || cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(Func op, column_view const& values, int delta)
+  {
+    return op(values, delta);
+  }
+
+  template <
+    typename T,
+    typename Func,
+    typename std::enable_if_t<!cudf::is_numeric<T>() && !cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(Func op, column_view const& values, int delta)
+  {
+    CUDF_FAIL("Invalid tdigest test type");
+  }
+  // @endcond
+};
+
+template <typename T>
+inline T frand()
+{
+  return static_cast<T>(rand()) / static_cast<T>(RAND_MAX);
+}
+
+template <typename T>
+inline T rand_range(T min, T max)
+{
+  return min + static_cast<T>(frand<T>() * (max - min));
+}
+
+inline std::unique_ptr<column> generate_typed_percentile_distribution(
+  std::vector<double> const& buckets,
+  std::vector<int> const& sizes,
+  data_type t,
+  bool sorted = false)
+{
+  srand(0);
+
+  std::vector<double> values;
+  size_t total_size = std::reduce(sizes.begin(), sizes.end(), 0);
+  values.reserve(total_size);
+  for (size_t idx = 0; idx < sizes.size(); idx++) {
+    double min = idx == 0 ? 0.0f : buckets[idx - 1];
+    double max = buckets[idx];
+
+    for (int v_idx = 0; v_idx < sizes[idx]; v_idx++) {
+      values.push_back(rand_range(min, max));
+    }
+  }
+
+  if (sorted) { std::sort(values.begin(), values.end()); }
+
+  cudf::test::fixed_width_column_wrapper<double> src(values.begin(), values.end());
+  return cudf::cast(src, t);
+}
+
+// "standardized" means the parameters sent into generate_typed_percentile_distribution. the intent
+// is to provide a standardized set of inputs for use with tdigest generation tests and
+// percentile_approx tests. std::vector<double>
+// buckets{10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0}; std::vector<int>
+// sizes{50000, 50000, 50000, 50000, 50000, 100000, 100000, 100000, 100000, 100000};
+inline std::unique_ptr<column> generate_standardized_percentile_distribution(
+  data_type t = data_type{type_id::FLOAT64}, bool sorted = false)
+{
+  std::vector<double> buckets{10.0f, 20.0f, 30.0f, 40.0f, 50.0f, 60.0f, 70.0f, 80.0, 90.0f, 100.0f};
+  std::vector<int> b_sizes{
+    50000, 50000, 50000, 50000, 50000, 100000, 100000, 100000, 100000, 100000};
+  return generate_typed_percentile_distribution(buckets, b_sizes, t, sorted);
+}
+
+/**
+ * @brief Compare a tdigest column against a sampling of expected values.
+ */
+void tdigest_sample_compare(cudf::tdigest::tdigest_column_view const& tdv,
+                            std::vector<expected_value> const& h_expected);
+
+/**
+ * @brief Compare the min/max values of a tdigest against inputs.
+ */
+template <typename T>
+void tdigest_minmax_compare(cudf::tdigest::tdigest_column_view const& tdv,
+                            column_view const& input_values)
+{
+  // verify min/max
+  thrust::host_vector<device_span<T const>> h_spans;
+  h_spans.push_back({input_values.begin<T>(), static_cast<size_t>(input_values.size())});
+  auto spans = cudf::detail::make_device_uvector_async(
+    h_spans, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto expected_min = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, spans.size(), mask_state::UNALLOCATED);
+  thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                    spans.begin(),
+                    spans.end(),
+                    expected_min->mutable_view().template begin<double>(),
+                    column_min<T>{});
+  column_view result_min(data_type{type_id::FLOAT64}, tdv.size(), tdv.min_begin(), nullptr, 0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result_min, *expected_min);
+
+  auto expected_max = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, spans.size(), mask_state::UNALLOCATED);
+  thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                    spans.begin(),
+                    spans.end(),
+                    expected_max->mutable_view().template begin<double>(),
+                    column_max<T>{});
+  column_view result_max(data_type{type_id::FLOAT64}, tdv.size(), tdv.max_begin(), nullptr, 0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result_max, *expected_max);
+}
+
+/// Expected values for tdigest tests
+struct expected_tdigest {
+  // @cond
+  column_view mean;
+  column_view weight;
+  double min, max;
+  // @endcond
+};
+
+/**
+ * @brief Create an expected tdigest column given component inputs.
+ */
+std::unique_ptr<column> make_expected_tdigest_column(std::vector<expected_tdigest> const& groups);
+
+// shared test for groupby/reduction.
+template <typename T, typename Func>
+void tdigest_simple_aggregation(Func op)
+{
+  // create a tdigest that has far fewer values in it than the delta value. this should result
+  // in every value remaining uncompressed
+  cudf::test::fixed_width_column_wrapper<T> values{126, 15, 1, 99, 67};
+  int const delta = 1000;
+  auto result     = cudf::type_dispatcher(
+    static_cast<column_view>(values).type(), tdigest_gen{}, op, values, delta);
+
+  cudf::test::fixed_width_column_wrapper<T> raw_mean({1, 15, 67, 99, 126});
+  cudf::test::fixed_width_column_wrapper<double> weight{1, 1, 1, 1, 1};
+  auto mean        = cudf::cast(raw_mean, data_type{type_id::FLOAT64});
+  double const min = 1;
+  double const max = 126;
+  auto expected    = make_expected_tdigest_column({{*mean,
+                                                    weight,
+                                                    static_cast<double>(static_cast<T>(min)),
+                                                    static_cast<double>(static_cast<T>(max))}});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
+
+// shared test for groupby/reduction.
+template <typename T, typename Func>
+void tdigest_simple_with_nulls_aggregation(Func op)
+{
+  // create a tdigest that has far fewer values in it than the delta value. this should result
+  // in every value remaining uncompressed
+  cudf::test::fixed_width_column_wrapper<T> values{{122, 15, 1, 99, 67, 101, 100, 84, 44, 2},
+                                                   {1, 0, 1, 0, 1, 0, 1, 0, 1, 0}};
+  int const delta = 1000;
+  auto result     = cudf::type_dispatcher(
+    static_cast<column_view>(values).type(), tdigest_gen{}, op, values, delta);
+
+  cudf::test::fixed_width_column_wrapper<T> raw_mean({1, 44, 67, 100, 122});
+  cudf::test::fixed_width_column_wrapper<double> weight{1, 1, 1, 1, 1};
+  auto mean        = cudf::cast(raw_mean, data_type{type_id::FLOAT64});
+  double const min = 1;
+  double const max = 122;
+  auto expected    = make_expected_tdigest_column({{*mean,
+                                                    weight,
+                                                    static_cast<double>(static_cast<T>(min)),
+                                                    static_cast<double>(static_cast<T>(max))}});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
+
+// shared test for groupby/reduction.
+template <typename T, typename Func>
+void tdigest_simple_all_nulls_aggregation(Func op)
+{
+  // create a tdigest that has far fewer values in it than the delta value. this should result
+  // in every value remaining uncompressed
+  cudf::test::fixed_width_column_wrapper<T> values{{122, 15, 1, 99, 67, 101, 100, 84, 44, 2},
+                                                   {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
+  int const delta = 1000;
+  auto result     = cudf::type_dispatcher(
+    static_cast<column_view>(values).type(), tdigest_gen{}, op, values, delta);
+
+  // NOTE: an empty tdigest column still has 1 row.
+  auto expected = cudf::tdigest::detail::make_empty_tdigest_column(
+    cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
+
+// shared test for groupby/reduction.
+template <typename Func>
+void tdigest_simple_large_input_double_aggregation(Func op)
+{
+  // these tests are being done explicitly because of the way we have to precompute the correct
+  // answers. since the input values generated by the generate_distribution() function below are
+  // cast to specific types -before- being sent into the aggregation, I can't (safely) just use the
+  // expected values that you get when using doubles all the way through.  so I have to pregenerate
+  // the correct answers for each type by hand. so, we'll choose a reasonable subset (double,
+  // decimal, int, bool)
+
+  auto values = generate_standardized_percentile_distribution(data_type{type_id::FLOAT64});
+
+  // compare against a sample of known/expected values (which themselves were verified against the
+  // Arrow implementation)
+
+  // delta 1000
+  {
+    int const delta = 1000;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 0.00040692343794663995, 7},
+                                         {10, 0.16234555627091204477, 153},
+                                         {59, 5.12764811246045937310, 858},
+                                         {250, 62.54581814492237157310, 2356},
+                                         {368, 87.85834376680742252574, 1735},
+                                         {409, 94.07685720279611985006, 1272},
+                                         {491, 99.94197663121231300920, 130},
+                                         {500, 99.99969880795092080916, 2}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *values);
+  }
+
+  // delta 100
+  {
+    int const delta = 100;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 0.07265722021410986331, 739},
+                                         {7, 8.19766194442652640362, 10693},
+                                         {16, 36.82277869518204482802, 20276},
+                                         {29, 72.95424834129075009059, 22623},
+                                         {38, 90.61229683516096145013, 15581},
+                                         {46, 99.07283498858802772702, 5142},
+                                         {50, 99.99970905482754801596, 1}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *values);
+  }
+
+  // delta 10
+  {
+    int const delta = 10;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 7.15508346777729631327, 71618},
+                                         {1, 33.04971680740474226923, 187499},
+                                         {2, 62.50566666553867634093, 231762},
+                                         {3, 83.46216572053654658703, 187500},
+                                         {4, 96.42204425201593664951, 71620},
+                                         {5, 99.99970905482754801596, 1}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *values);
+  }
+}
+
+// shared test for groupby/reduction.
+template <typename Func>
+void tdigest_simple_large_input_int_aggregation(Func op)
+{
+  // these tests are being done explicitly because of the way we have to precompute the correct
+  // answers. since the input values generated by the generate_distribution() function below are
+  // cast to specific types -before- being sent into the aggregation, I can't (safely) just use the
+  // expected values that you get when using doubles all the way through.  so I have to pregenerate
+  // the correct answers for each type by hand. so, we'll choose a reasonable subset (double,
+  // decimal, int, bool)
+
+  auto values = generate_standardized_percentile_distribution(data_type{type_id::INT32});
+
+  // compare against a sample of known/expected values (which themselves were verified against the
+  // Arrow implementation)
+
+  // delta 1000
+  {
+    int const delta = 1000;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 0, 7},
+                                         {14, 0, 212},
+                                         {26, 0.83247422680412408447, 388},
+                                         {44, 2, 648},
+                                         {45, 2.42598187311178170589, 662},
+                                         {342, 82.75190258751908345403, 1971},
+                                         {383, 90, 1577},
+                                         {417, 94.88376068376066996279, 1170},
+                                         {418, 95, 1157},
+                                         {479, 99, 307},
+                                         {500, 99, 2}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<int>(tdv, *values);
+  }
+
+  // delta 100
+  {
+    int const delta = 100;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 0, 739},
+                                         {7, 7.71486018890863167741, 10693},
+                                         {16, 36.32491615703294485229, 20276},
+                                         {29, 72.44392874508245938614, 22623},
+                                         {38, 90.14209614273795523332, 15581},
+                                         {46, 98.64041229093737683797, 5142},
+                                         {50, 99, 1}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<int>(tdv, *values);
+  }
+
+  // delta 10
+  {
+    int const delta = 10;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 6.66025300902007799664, 71618},
+                                         {1, 32.54912826201739051157, 187499},
+                                         {2, 62.00734805533262772315, 231762},
+                                         {3, 82.96355733333332693746, 187500},
+                                         {4, 95.91280368612116546956, 71620},
+                                         {5, 99, 1}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<int>(tdv, *values);
+  }
+}
+
+// shared test for groupby/reduction.
+template <typename Func>
+void tdigest_simple_large_input_decimal_aggregation(Func op)
+{
+  // these tests are being done explicitly because of the way we have to precompute the correct
+  // answers. since the input values generated by the generate_distribution() function below are
+  // cast to specific types -before- being sent into the aggregation, I can't (safely) just use the
+  // expected values that you get when using doubles all the way through.  so I have to pregenerate
+  // the correct answers for each type by hand. so, we'll choose a reasonable subset (double,
+  // decimal, int, bool)
+
+  auto values = generate_standardized_percentile_distribution(data_type{type_id::DECIMAL32, -4});
+  auto cast_values = cudf::cast(*values, data_type{type_id::FLOAT64});
+
+  // compare against a sample of known/expected values (which themselves were verified against the
+  // Arrow implementation)
+
+  // delta 1000
+  {
+    int const delta = 1000;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 0.00035714285714285709, 7},
+                                         {10, 0.16229738562091505782, 153},
+                                         {59, 5.12759696969697031932, 858},
+                                         {250, 62.54576854838715860296, 2356},
+                                         {368, 87.85829446685879418055, 1735},
+                                         {409, 94.07680636792450457051, 1272},
+                                         {491, 99.94192461538463589932, 130},
+                                         {500, 99.99965000000000259206, 2}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *cast_values);
+  }
+
+  // delta 100
+  {
+    int const delta = 100;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 0.07260811907983763525, 739},
+                                         {7, 8.19761183016926864298, 10693},
+                                         {16, 36.82272891595975750079, 20276},
+                                         {29, 72.95419827167043536065, 22623},
+                                         {38, 90.61224673640975879607, 15581},
+                                         {46, 99.07278498638662256326, 5142},
+                                         {50, 99.99970000000000425189, 1}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *cast_values);
+  }
+
+  // delta 10
+  {
+    int const delta = 10;
+    auto result = cudf::type_dispatcher(values->view().type(), tdigest_gen{}, op, *values, delta);
+    std::vector<expected_value> expected{{0, 7.15503361864335740705, 71618},
+                                         {1, 33.04966679715625588187, 187499},
+                                         {2, 62.50561666407782013266, 231762},
+                                         {3, 83.46211575573336460820, 187500},
+                                         {4, 96.42199425300195514410, 71620},
+                                         {5, 99.99970000000000425189, 1}};
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *cast_values);
+  }
+}
+
+// Note: there is no need to test different types here as the internals of a tdigest are always
+// the same regardless of input.
+template <typename Func, typename MergeFunc>
+void tdigest_merge_simple(Func op, MergeFunc merge_op)
+{
+  auto values = generate_standardized_percentile_distribution(data_type{type_id::FLOAT64});
+  CUDF_EXPECTS(values->size() == 750000, "Unexpected distribution size");
+
+  auto split_values = cudf::split(*values, {250000, 500000});
+
+  int const delta = 1000;
+
+  // generate separate digests
+  std::vector<std::unique_ptr<column>> parts;
+  auto iter = thrust::make_counting_iterator(0);
+  std::transform(
+    iter, iter + split_values.size(), std::back_inserter(parts), [&split_values, delta, op](int i) {
+      return op(split_values[i], delta);
+    });
+  std::vector<column_view> part_views;
+  std::transform(parts.begin(),
+                 parts.end(),
+                 std::back_inserter(part_views),
+                 [](std::unique_ptr<column> const& col) { return col->view(); });
+
+  // merge delta = 1000
+  {
+    int const merge_delta = 1000;
+
+    // merge them
+    auto merge_input = cudf::concatenate(part_views);
+    auto result      = merge_op(*merge_input, merge_delta);
+    cudf::tdigest::tdigest_column_view tdv(*result);
+
+    // verify centroids
+    std::vector<expected_value> expected{{0, 0.00013945158577498588, 2},
+                                         {10, 0.04804393446447510763, 50},
+                                         {59, 1.68846964439246893797, 284},
+                                         {250, 33.36323141295877547918, 1479},
+                                         {368, 65.36307727957283475462, 2292},
+                                         {409, 73.95399208218296394080, 1784},
+                                         {490, 87.67566167909056673579, 1570},
+                                         {491, 87.83119717763385381204, 1570},
+                                         {500, 89.24891838334393412424, 1555},
+                                         {578, 95.87182997389099625707, 583},
+                                         {625, 98.20470345147104751504, 405},
+                                         {700, 99.96818381983835877236, 56},
+                                         {711, 99.99970905482754801596, 1}};
+    tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    tdigest_minmax_compare<double>(tdv, *values);
+  }
+}
+
+// shared test for groupby/reduction.
+template <typename MergeFunc>
+void tdigest_merge_empty(MergeFunc merge_op)
+{
+  // 3 empty tdigests all in the same group
+  auto a = cudf::tdigest::detail::make_empty_tdigest_column(cudf::get_default_stream(),
+                                                            rmm::mr::get_current_device_resource());
+  auto b = cudf::tdigest::detail::make_empty_tdigest_column(cudf::get_default_stream(),
+                                                            rmm::mr::get_current_device_resource());
+  auto c = cudf::tdigest::detail::make_empty_tdigest_column(cudf::get_default_stream(),
+                                                            rmm::mr::get_current_device_resource());
+  std::vector<column_view> cols;
+  cols.push_back(*a);
+  cols.push_back(*b);
+  cols.push_back(*c);
+  auto values = cudf::concatenate(cols);
+
+  auto const delta = 1000;
+  auto result      = merge_op(*values, delta);
+
+  auto expected = cudf::tdigest::detail::make_empty_tdigest_column(
+    cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *result);
+}
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/timestamp_utilities.cuh b/cpp/include/cudf_test/timestamp_utilities.cuh
new file mode 100644
index 0000000..6cab8b9
--- /dev/null
+++ b/cpp/include/cudf_test/timestamp_utilities.cuh
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/logical.h>
+#include <thrust/sequence.h>
+
+namespace cudf {
+namespace test {
+using time_point_ms =
+  cuda::std::chrono::time_point<cuda::std::chrono::system_clock, cuda::std::chrono::milliseconds>;
+
+/**
+ * @brief Creates a `fixed_width_column_wrapper` with ascending timestamps in the
+ * range `[start, stop)`.
+ *
+ * The period is inferred from `count` and difference between `start`
+ * and `stop`.
+ *
+ * @tparam Rep The arithmetic type representing the number of ticks
+ * @tparam Period A cuda::std::ratio representing the tick period (i.e. the
+ *number of seconds per tick)
+ * @param count The number of timestamps to create
+ * @param start The first timestamp as a cuda::std::chrono::time_point
+ * @param stop The last timestamp as a cuda::std::chrono::time_point
+ */
+template <typename T, bool nullable = false>
+inline cudf::test::fixed_width_column_wrapper<T, int64_t> generate_timestamps(int32_t count,
+                                                                              time_point_ms start,
+                                                                              time_point_ms stop)
+{
+  using Rep        = typename T::rep;
+  using Period     = typename T::period;
+  using ToDuration = cuda::std::chrono::duration<Rep, Period>;
+
+  auto lhs = start.time_since_epoch().count();
+  auto rhs = stop.time_since_epoch().count();
+
+  auto const min   = std::min(lhs, rhs);
+  auto const max   = std::max(lhs, rhs);
+  auto const range = max - min;
+  auto iter        = cudf::detail::make_counting_transform_iterator(0, [=](auto i) {
+    return cuda::std::chrono::floor<ToDuration>(
+             cuda::std::chrono::milliseconds(min + (range / count) * i))
+      .count();
+  });
+
+  if (nullable) {
+    auto mask =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+    return cudf::test::fixed_width_column_wrapper<T, int64_t>(iter, iter + count, mask);
+  } else {
+    // This needs to be in an else to quash `statement_not_reachable` warnings
+    return cudf::test::fixed_width_column_wrapper<T, int64_t>(iter, iter + count);
+  }
+}
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/type_list_utilities.hpp b/cpp/include/cudf_test/type_list_utilities.hpp
new file mode 100644
index 0000000..b069a34
--- /dev/null
+++ b/cpp/include/cudf_test/type_list_utilities.hpp
@@ -0,0 +1,630 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "cudf_gtest.hpp"
+
+/**
+ * @file type_list_utilities.hpp
+ * @brief Utilities for creating type lists for typed tests in Google Test
+ *
+ * A "type list" is a list of types passed to a Google Test type-parameterized
+ * test suite. The set of tests in the suite will be invoked once for each type
+ * in the list. Normally, this is done by using the `testing::Types` `struct`
+ * provided by GTest. For example,
+ *
+ * ```
+ * using TestTypes = ::testing::Types<int, char, float>;
+ *
+ * template <class T>
+ * class TestFixture : ::testing::Test { };
+ *
+ * TYPED_TEST_SUITE(TestFixture, TestTypes);
+ *
+ * TYPED_TEST(TestFixture, mytest){
+ *   using Type0 = GetType<TypeParam,0>; // the first type element
+ * }
+ * ```
+ *
+ * The test `mytest` will be invoked 3 times, once for each of the types `int,
+ * char, float`.
+ *
+ * Instead of using \::testing::Types directly, we provide
+ * `cudf::test::Types`. This is a drop in replacement for GTest's
+ * \::testing::Types. In lieu of including `gtest/gtest.h`, include
+ * `cudf_gtest.hpp` to ensure `cudf::test::Types` is used.
+ *
+ * Using the utilities in this file, you can compose complex type lists.
+ *
+ * For example, `CrossProduct` may be used to compute the cross-product of two or
+ * more type lists:
+ *
+ * ```
+ * using TestTypes = CrossProduct<Types<int,float>,Types<char, void*>>;
+ * // TestTypes ==  Types< <int,char> <int,void*> <float,char> <float,void*> >
+ * ```
+ * RemoveIf can be used to remove some parameters that match a given predicate:
+ *
+ * ```
+ * using TestTypes = RemoveIf<AllSame, CrossProduct<Types<int,char>,
+ *Types<int,char>>>;
+ * // TestTypes ==  Types< <int,char>,<char,int> >
+ * ```
+ *
+ * @note WARNING: Abusing and overusing these utilities can lead to dramatically
+ * increased compile-times. Use responsibly.
+ */
+
+namespace cudf {
+namespace test {
+// Utilities for creating parameters for typed tests on GoogleTest
+//
+// Types is used to define type list, it's just an alias to ::testing::Types:
+// using Types = util::Types<int,char,float>;
+//
+
+// Types -----------------------------------------
+using ::testing::Types;
+
+// @cond
+template <class T, int D>
+struct GetTypeImpl {
+  static_assert(D == 0, "Out of bounds");
+  using type = T;
+};
+
+template <class... T, int D>
+struct GetTypeImpl<Types<T...>, D> {
+  static_assert(D < sizeof...(T), "Out of bounds");
+
+  using raw_type = decltype(std::get<D>(std::declval<std::tuple<T...>>()));
+  using type     = std::decay_t<raw_type>;
+};
+template <class T, class... ARGS>
+struct GetTypeImpl<Types<T, ARGS...>, 0> {
+  using type = T;
+};
+// @endcond
+
+/**
+ * @brief Gives the specified type from a type list
+ *
+ * Example:
+ * ```
+ * using T = GetType< Types<int, float, char, void*>, 2>
+ * // T == char
+ * ```
+ *
+ * @tparam TUPLE The type list
+ * @tparam D Index of the desired type
+ */
+template <class TUPLE, int D>
+using GetType = typename GetTypeImpl<TUPLE, D>::type;
+
+// GetSize -------------------------------
+// @cond
+template <class TUPLE>
+struct GetSizeImpl;
+
+template <class... TYPES>
+struct GetSizeImpl<Types<TYPES...>> {
+  static constexpr auto value = sizeof...(TYPES);
+};
+// @endcond
+
+/**
+ * @brief Returns the size (number of elements) in a type list
+ *
+ * Example:
+ * ```
+ * GetSize< Types<int, float, double, void*> == 4
+ * ```
+ */
+template <class TUPLE>
+constexpr auto GetSize = GetSizeImpl<TUPLE>::value;
+
+// Concat -----------------------------------------
+// @cond
+namespace detail {
+template <class A, class B>
+struct Concat2;
+
+template <class... T, class... U>
+struct Concat2<Types<T...>, Types<U...>> {
+  using type = Types<T..., U...>;
+};
+}  // namespace detail
+
+template <class... T>
+struct ConcatImpl;
+
+template <class HEAD1, class HEAD2, class... TAIL>
+struct ConcatImpl<HEAD1, HEAD2, TAIL...> {
+  using type = typename ConcatImpl<typename detail::Concat2<HEAD1, HEAD2>::type, TAIL...>::type;
+};
+
+template <class A>
+struct ConcatImpl<A> {
+  using type = A;
+};
+
+template <class... A>
+struct ConcatImpl<Types<A...>> {
+  using type = Types<A...>;
+};
+
+template <>
+struct ConcatImpl<> {
+  using type = Types<>;
+};
+// @endcond
+
+/**
+ * @brief Concatenates compile-time lists of types into a single type list.
+ *
+ * Example:
+ * ```
+ * using MyTypes = Concat< Types<int, float>, Types<char, double>>
+ * // MyTypes == Types<int, float, char, double>;
+ * ```
+ */
+template <class... T>
+using Concat = typename ConcatImpl<T...>::type;
+
+// Flatten -----------------------------------------
+// @cond
+template <class T>
+struct FlattenImpl;
+
+template <>
+struct FlattenImpl<Types<>> {
+  using type = Types<>;
+};
+
+template <class HEAD, class... TAIL>
+struct FlattenImpl<Types<HEAD, TAIL...>> {
+  using type = Concat<Types<HEAD>, typename FlattenImpl<Types<TAIL...>>::type>;
+};
+
+template <class... HEAD, class... TAIL>
+struct FlattenImpl<Types<Types<HEAD...>, TAIL...>> {
+  using type = typename FlattenImpl<Types<HEAD..., TAIL...>>::type;
+};
+// @endcond
+
+/**
+ * @brief Flattens nested compile-time lists of types into a single list of
+ *types.
+ *
+ * Example:
+ * ```
+ * // Flatten< Types< int, Types< double, Types<char> > > == Types<int, double,
+ *char> static_assert(std::is_same<Flatten<Types<Types<int, Types<double>>,
+ *float>>, Types<int, double, float>>::value, "");
+ * ```
+ */
+template <class T>
+using Flatten = typename FlattenImpl<T>::type;
+
+// CrossProduct -----------------------------------------
+// @cond
+namespace detail {
+// prepend T in TUPLE
+template <class T, class TUPLE>
+struct Prepend1;
+
+template <class T, class... ARGS>
+struct Prepend1<T, Types<ARGS...>> {
+  using type = Flatten<Types<T, ARGS...>>;
+};
+
+template <class T, class TUPLES>
+struct Prepend;
+
+// Prepend T in all TUPLES
+template <class T, class... TUPLES>
+struct Prepend<T, Types<TUPLES...>> {
+  using type = Types<typename Prepend1<T, TUPLES>::type...>;
+};
+
+// skip empty tuples
+template <class T, class... TUPLES>
+struct Prepend<T, Types<Types<>, TUPLES...>> : Prepend<T, Types<TUPLES...>> {};
+}  // namespace detail
+
+template <class... ARGS>
+struct CrossProductImpl;
+
+template <>
+struct CrossProductImpl<> {
+  using type = Types<>;
+};
+
+template <class... ARGS>
+struct CrossProductImpl<Types<ARGS...>> {
+  using type = Types<Types<ARGS>...>;
+};
+
+template <class... AARGS, class... TAIL>
+struct CrossProductImpl<Types<AARGS...>, TAIL...> {
+  using type =
+    Concat<typename detail::Prepend<AARGS, typename CrossProductImpl<TAIL...>::type>::type...>;
+};
+
+// to make it easy for the user when there's only one element to be joined
+template <class T, class... TAIL>
+struct CrossProductImpl<T, TAIL...> : CrossProductImpl<Types<T>, TAIL...> {};
+// @endcond
+
+/**
+ * @brief Creates a new type list from the cross product (cartesian product) of
+ * two type lists.
+ *
+ * @note This should be used with caution, as it can easily lead to a large
+ * number of typed test cases. For example, computing the `CrossProduct` of two type
+ * lists of size `n` and `m`, the resulting list will have `n*m` types.
+ *
+ * Example:
+ * ```
+ * using Types = CrossProduct<Types<int,float>, Types<char, double>>;
+ * // Types == Types< Types<int, char>, Types<int, double>, Types<float, char>,
+ * Types<float, double> >
+ * ```
+ */
+template <class... ARGS>
+using CrossProduct = typename CrossProductImpl<ARGS...>::type;
+
+// AllSame -----------------------------------------
+// @cond
+namespace detail {
+template <class... ITEMS>
+struct AllSame : std::false_type {};
+
+// degenerate case
+template <class A>
+struct AllSame<A> : std::true_type {};
+
+template <class A>
+struct AllSame<A, A> : std::true_type {};
+
+template <class HEAD, class... TAIL>
+struct AllSame<HEAD, HEAD, TAIL...> : AllSame<HEAD, TAIL...> {};
+
+template <class... ITEMS>
+struct AllSame<Types<ITEMS...>> : AllSame<ITEMS...> {};
+
+}  // namespace detail
+// @endcond
+
+/**
+ * @brief Indicates if all types in a list are identical.
+ *
+ * This is useful as a predicate for `RemoveIf`.
+ *
+ * Example:
+ * ```
+ * // AllSame::Call<Types<int, int, int>> == true_type
+ * // AllSame::Call<Types<float, bool>> == false_type
+ *
+ * // Used as a predicate
+ * RemoveIf<AllSame, Types<Types<int, int, int>>> ==  Types<>
+ * RemoveIf<AllSame, Types<Types<int, float, int>>> ==  Types<Types<int, float,
+ *int>>
+ * ```
+ */
+struct AllSame {
+  /**
+   * @brief Invoked as predicate for RemoveIf
+   *
+   * @tparam ITEMS The type to check if they are all same
+   */
+  template <class... ITEMS>
+  using Call = detail::AllSame<ITEMS...>;
+};
+
+// Exists ---------------------------------
+// @cond
+// Do a linear search to find NEEDLE in HAYSACK
+template <class NEEDLE, class HAYSACK>
+struct ExistsImpl;
+
+// end case, no more types to check
+template <class NEEDLE>
+struct ExistsImpl<NEEDLE, Types<>> : std::false_type {};
+
+// next one matches
+template <class NEEDLE, class... TAIL>
+struct ExistsImpl<NEEDLE, Types<NEEDLE, TAIL...>> : std::true_type {};
+
+// next one doesn't match
+template <class NEEDLE, class HEAD, class... TAIL>
+struct ExistsImpl<NEEDLE, Types<HEAD, TAIL...>> : ExistsImpl<NEEDLE, Types<TAIL...>> {};
+// @endcond
+
+/**
+ * @brief Indicates if a type exists within a type list.
+ *
+ * Example:
+ * ```
+ * // Exists<int, Types<float, double, int>> == true_type
+ * // Exists<char, Types<int, float, void*>> == false_type
+ * ```
+ * @tparam NEEDLE The type to search for
+ * @tparam HAYSACK The list to search in
+ */
+template <class NEEDLE, class HAYSACK>
+constexpr bool Exists = ExistsImpl<NEEDLE, HAYSACK>::value;
+
+// ContainedIn -----------------------------------------
+/**
+ * @brief Indicates if a type exists within a type list.
+ *
+ * Used as a predicate for RemoveIf
+ *
+ * Example:
+ * ```
+ * ContainedIn<Types<Types<int, char>>>::Call<Types<int, char>>::value ==
+ *true_type ContainedIn<Types<Types<int, char>>>::Call<Types<int, float>>::value
+ *== false_type
+ *
+ * // Used as a predicate
+ * using MyTypes = RemoveIf<ContainedIn<Types<Types<char, char>>>,
+ *                          Types<Types<char, char>, Types<float,int>>>;
+ * // MyTypes == Types<float, int>
+ *
+ * ```
+ *
+ * @tparam HAYSACK The type list to search
+ */
+template <class HAYSACK>
+struct ContainedIn {
+  /**
+   * @brief Invoked as predicate for RemoveIf
+   *
+   * @tparam NEEDLE The type to search for
+   */
+  template <class NEEDLE>
+  using Call = ExistsImpl<NEEDLE, HAYSACK>;
+};
+
+// RemoveIf -----------------------------------------
+// @cond
+template <class PRED, class TUPLE>
+struct RemoveIfImpl;
+
+template <class PRED>
+struct RemoveIfImpl<PRED, Types<>> {
+  using type = Types<>;
+};
+
+template <class PRED, class HEAD, class... TAIL>
+struct RemoveIfImpl<PRED, Types<HEAD, TAIL...>> {
+  using type =
+    Concat<typename std::conditional<PRED::template Call<HEAD>::value, Types<>, Types<HEAD>>::type,
+           typename RemoveIfImpl<PRED, Types<TAIL...>>::type>;
+};
+// @endcond
+
+/**
+ * @brief Removes types from a type list that satisfy a predicate
+ *
+ * Possible predicates: `AllSame`, `ContainedIn`
+ *
+ * Example:
+ * ```
+ * RemoveIf<AllSame, Types<Types<int, int, int>>> ==  Types<>
+ * RemoveIf<AllSame, Types<Types<int, float, int>>> ==  Types<Types<int, float,
+ *int>>
+ *
+ * using MyTypes = RemoveIf<ContainedIn<Types<Types<char, char>>>,
+ *                          Types<Types<char, char>, Types<float,int>>>;
+ * // MyTypes == Types<float, int>
+ * ```
+ *
+ * @tparam PRED The predicate
+ * @tparam TUPLE The list of types on which to apply the predicate
+ */
+template <class PRED, class TUPLE>
+using RemoveIf = typename RemoveIfImpl<PRED, TUPLE>::type;
+
+// Transform --------------------------------
+// @cond
+template <class XFORM, class TYPES>
+struct TransformImpl;
+
+template <class XFORM, class... ITEMS>
+struct TransformImpl<XFORM, Types<ITEMS...>> {
+  using type = Types<typename XFORM::template Call<ITEMS>...>;
+};
+// @endcond
+
+/**
+ * @brief Applies a transformation to every type in a type list
+ *
+ * Possible transformations: Repeat
+ *
+ * Example:
+ * ```
+ * // Repeat transformation repeats each type for a specified count
+ * using MyTypes = Transform<Repeat<2>, Types<int, float>>;
+ * // MyTypes == Types< Types<int, int>, Types<float, float>>);
+ * ```
+ *
+ * @tparam XFORM The transformation to apply
+ * @tparam TYPES The list of types to transform
+ */
+template <class XFORM, class TYPES>
+using Transform = typename TransformImpl<XFORM, TYPES>::type;
+
+// Repeat --------------------------------
+// @cond
+namespace detail {
+template <class T, int N, class RES>
+struct Repeat;
+
+template <class T, int N, class... ITEMS>
+struct Repeat<T, N, Types<ITEMS...>> {
+  using type = typename Repeat<T, N - 1, Types<T, ITEMS...>>::type;
+};
+
+template <class T, class... ITEMS>
+struct Repeat<T, 0, Types<ITEMS...>> {
+  using type = Types<ITEMS...>;
+};
+}  // namespace detail
+// @endcond
+
+/**
+ * @brief Transformation that repeats a type for a specified count.
+ *
+ * Used in Transform.
+ *
+ * Example:
+ * ```
+ * // Repeat transformation repeats each type for a specified count
+ * using MyTypes = Transform<Repeat<2>, Types<int, float>>;
+ * // MyTypes == Types< Types<int, int>, Types<float, float>>);
+ * ```
+ *
+ * @tparam N The number of times to repeat the type
+ */
+template <int N>
+struct Repeat {
+  /**
+   * @brief Invoked as predicate for Transform
+   *
+   * @tparam T The type to repeat
+   */
+  template <class T>
+  using Call = typename detail::Repeat<T, N, Types<>>::type;
+};
+
+// Append --------------------------------
+// @cond
+template <class TYPES, class... ITEMS>
+struct AppendImpl;
+
+template <class... HEAD, class... TAIL>
+struct AppendImpl<Types<HEAD...>, TAIL...> {
+  using type = Types<HEAD..., TAIL...>;
+};
+// @endcond
+
+/**
+ * @brief Appends types to a type list
+ *
+ * Example:
+ * ```
+ * using MyTypes = Append<Types<int>, float, char>;
+ * MyTypes ==  Types<int, float, char>;
+ * ```
+ *
+ * @tparam TYPES The type list to append to
+ * @tparam ITEMS The types to append
+ */
+template <class TYPES, class... ITEMS>
+using Append = typename AppendImpl<TYPES, ITEMS...>::type;
+
+// Remove -------------------------------------------
+// remove items from tuple given by their indices
+// @cond
+namespace detail {
+template <class TUPLE, int CUR, int... IDXs>
+struct Remove;
+
+// nothing else to do?
+template <class... ITEMS, int CUR>
+struct Remove<Types<ITEMS...>, CUR> {
+  using type = Types<ITEMS...>;
+};
+
+// index match current item?
+template <class HEAD, class... TAIL, int CUR, int... IDXTAIL>
+struct Remove<Types<HEAD, TAIL...>, CUR, CUR, IDXTAIL...> {
+  // remove it, and recurse into the remaining items
+  using type = typename Remove<Types<TAIL...>, CUR + 1, IDXTAIL...>::type;
+};
+
+// index doesn't match current item?
+template <class HEAD, class... TAIL, int CUR, int IDXHEAD, int... IDXTAIL>
+struct Remove<Types<HEAD, TAIL...>, CUR, IDXHEAD, IDXTAIL...> {
+  static_assert(sizeof...(TAIL) + 1 > IDXHEAD - CUR, "Index out of bounds");
+
+  // add current item to output and recurse into the remaining items
+  using type =
+    Concat<Types<HEAD>, typename Remove<Types<TAIL...>, CUR + 1, IDXHEAD, IDXTAIL...>::type>;
+};
+}  // namespace detail
+
+template <class TUPLE, int... IDXs>
+struct RemoveImpl {
+  using type = typename detail::Remove<TUPLE, 0, IDXs...>::type;
+};
+// @endcond
+
+/**
+ * @brief Removes types at specified indices from a type list.
+ *
+ * @tparam TUPLE Type list to remove types from
+ * @tparam IDXs Indices of types to remove
+ */
+template <class TUPLE, int... IDXs>
+using Remove = typename RemoveImpl<TUPLE, IDXs...>::type;
+
+// Unique --------------------------------
+// @cond
+namespace detail {
+template <class... ITEMS>
+struct Unique;
+
+template <>
+struct Unique<> {
+  using type = Types<>;
+};
+
+template <class HEAD, class... TAIL>
+struct Unique<HEAD, TAIL...> {
+  using type = Concat<std::conditional_t<Exists<HEAD, Types<TAIL...>>, Types<>, Types<HEAD>>,
+                      typename Unique<TAIL...>::type>;
+};
+}  // namespace detail
+
+template <class TYPES>
+struct UniqueImpl;
+
+template <class... ITEMS>
+struct UniqueImpl<Types<ITEMS...>> {
+  using type = typename detail::Unique<ITEMS...>::type;
+};
+// @endcond
+
+/**
+ * @brief Removes duplicate types from a type list
+ *
+ * Example:
+ * ```
+ * using MyTypes = Unique<Types<int, float, int, float>>;
+ * MyTypes == Types<int, float>)
+ * ```
+ *
+ * @tparam TYPES The type list from which to remove duplicates
+ */
+template <class TYPES>
+using Unique = typename UniqueImpl<TYPES>::type;
+
+}  // namespace test
+
+}  // namespace cudf
diff --git a/cpp/include/cudf_test/type_lists.hpp b/cpp/include/cudf_test/type_lists.hpp
new file mode 100644
index 0000000..2404cf0
--- /dev/null
+++ b/cpp/include/cudf_test/type_lists.hpp
@@ -0,0 +1,435 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+
+#include <thrust/host_vector.h>
+
+#include <array>
+#include <tuple>
+#include <type_traits>
+
+/**
+ * @file type_lists.hpp
+ * @brief Provides centralized type lists for use in Google Test
+ * type-parameterized tests.
+ *
+ * These lists should be used for consistency across tests as well as
+ * future-proofing against the addition of any new types in the future.
+ */
+namespace cudf {
+namespace test {
+namespace detail {
+template <typename TYPES, std::size_t... Indices>
+constexpr std::array<cudf::type_id, sizeof...(Indices)> types_to_ids_impl(
+  std::index_sequence<Indices...>)
+{
+  return {{cudf::type_to_id<GetType<TYPES, Indices>>()...}};
+}
+
+/**
+ * @brief Converts a `Types` list of types into a `std::array` of the
+ * corresponding `cudf::type_id`s for each type in the list
+ *
+ * Example:
+ * ```
+ * auto array = types_to_ids<Types<int32_t, float>>();
+ * array == {type_id::INT32, type_id::FLOAT};
+ * ```
+ *
+ * @tparam TYPES List of types to convert to `type_id`s
+ * @return `std::array` of `type_id`s corresponding to each type in `TYPES`
+ */
+template <typename TYPES>
+constexpr auto types_to_ids()
+{
+  constexpr auto N = GetSize<TYPES>;
+  return types_to_ids_impl<TYPES>(std::make_index_sequence<N>());
+}
+
+}  // namespace detail
+
+/**
+ * @brief Convert numeric values of type T to numeric vector of type TypeParam.
+ *
+ * This will also convert negative values to positive values if the output type is unsigned.
+ *
+ * @param init_list Values used to create the output vector
+ * @return Vector of TypeParam with the values specified
+ */
+template <typename TypeParam, typename T>
+std::enable_if_t<cudf::is_fixed_width<TypeParam>() && !cudf::is_timestamp_t<TypeParam>::value,
+                 thrust::host_vector<TypeParam>>
+make_type_param_vector(std::initializer_list<T> const& init_list)
+{
+  std::vector<T> input{init_list};
+  std::vector<TypeParam> vec(init_list.size());
+  std::transform(
+    std::cbegin(input), std::cend(input), std::begin(vec), [](auto const& e) -> TypeParam {
+      if constexpr (std::is_unsigned_v<TypeParam>) { return static_cast<TypeParam>(std::abs(e)); }
+      return static_cast<TypeParam>(e);
+    });
+  return vec;
+}
+
+/**
+ * @brief Convert numeric values of type T to timestamp vector
+ *
+ * @param init_list Values used to create the output vector
+ * @return Vector of TypeParam with the values specified
+ */
+template <typename TypeParam, typename T>
+std::enable_if_t<cudf::is_timestamp_t<TypeParam>::value, thrust::host_vector<TypeParam>>
+make_type_param_vector(std::initializer_list<T> const& init_list)
+{
+  thrust::host_vector<TypeParam> vec(init_list.size());
+  std::transform(std::cbegin(init_list), std::cend(init_list), std::begin(vec), [](auto const& e) {
+    return TypeParam{typename TypeParam::duration{e}};
+  });
+  return vec;
+}
+
+/**
+ * @brief Convert numeric values of type T to vector of std::string
+ *
+ * @param init_list Values used to create the output vector
+ * @return Vector of TypeParam with the values specified
+ */
+
+template <typename TypeParam, typename T>
+std::enable_if_t<std::is_same_v<TypeParam, std::string>, thrust::host_vector<std::string>>
+make_type_param_vector(std::initializer_list<T> const& init_list)
+{
+  thrust::host_vector<std::string> vec(init_list.size());
+  std::transform(std::cbegin(init_list), std::cend(init_list), std::begin(vec), [](auto const& e) {
+    return std::to_string(e);
+  });
+  return vec;
+}
+
+/**
+ * @brief Convert the numeric value of type T to a fixed width type of type TypeParam.
+ *
+ * This function is necessary because some types (such as timestamp types) are not directly
+ * constructible from numeric types. This function is offered as a convenience to allow
+ * implicitly constructing such objects from numeric values.
+ *
+ * @param init_value Value used to initialize the fixed width type
+ * @return A fixed width type - [u]int32/float/duration etc. of type TypeParam with the
+ *         value specified
+ */
+template <typename TypeParam, typename T>
+std::enable_if_t<cudf::is_fixed_width<TypeParam>() && !cudf::is_timestamp_t<TypeParam>::value,
+                 TypeParam>
+make_type_param_scalar(T const init_value)
+{
+  return static_cast<TypeParam>(init_value);
+}
+
+/**
+ * @brief Convert the timestamp value of type T to a fixed width type of type TypeParam.
+ *
+ * This function is necessary because some types (such as timestamp types) are not directly
+ * constructible from timestamp types. This function is offered as a convenience to allow
+ * implicitly constructing such objects from timestamp values.
+ *
+ * @param init_value Value used to initialize the fixed width type
+ * @return A fixed width type - TimeStamp of type TypeParam with the
+ *         value specified
+ */
+template <typename TypeParam, typename T>
+std::enable_if_t<cudf::is_timestamp_t<TypeParam>::value, TypeParam> make_type_param_scalar(
+  T const init_value)
+{
+  return TypeParam{typename TypeParam::duration(init_value)};
+}
+
+/**
+ * @brief Convert the numeric value of type T to a string type.
+ *
+ * This function converts the numeric value of type T to its string representation.
+ *
+ * @param init_value Value to convert to a string
+ * @return string representation of the value
+ */
+template <typename TypeParam, typename T>
+std::enable_if_t<std::is_same_v<TypeParam, std::string>, TypeParam> make_type_param_scalar(
+  T const init_value)
+{
+  return std::to_string(init_value);
+}
+
+/**
+ * @brief Type list for all integral types except type bool.
+ */
+using IntegralTypesNotBool =
+  cudf::test::Types<int8_t, int16_t, int32_t, int64_t, uint8_t, uint16_t, uint32_t, uint64_t>;
+
+/**
+ * @brief Type list for all integral types.
+ */
+using IntegralTypes = Concat<IntegralTypesNotBool, cudf::test::Types<bool>>;
+
+/**
+ * @brief Provides a list of all floating point types supported in libcudf for
+ * use in a GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all floating point types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::FloatingPointTypes);
+ * ```
+ */
+using FloatingPointTypes = cudf::test::Types<float, double>;
+
+/**
+ * @brief Provides a list of all numeric types supported in libcudf for use in a
+ * GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all numeric types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::NumericTypes);
+ * ```
+ */
+using NumericTypes = Concat<IntegralTypes, FloatingPointTypes>;
+
+/**
+ * @brief Provides a list of all timestamp types supported in libcudf for use
+ * in a GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all timestamp types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::TimestampTypes);
+ * ```
+ */
+using TimestampTypes =
+  cudf::test::Types<timestamp_D, timestamp_s, timestamp_ms, timestamp_us, timestamp_ns>;
+
+/**
+ * @brief Provides a list of all duration types supported in libcudf for use
+ * in a GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all duration types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::DurationTypes);
+ * ```
+ */
+using DurationTypes =
+  cudf::test::Types<duration_D, duration_s, duration_ms, duration_us, duration_ns>;
+
+/**
+ * @brief Provides a list of all chrono types supported in libcudf for use in a GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all chrono types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::ChronoTypes);
+ * ```
+ */
+using ChronoTypes = Concat<TimestampTypes, DurationTypes>;
+
+/**
+ * @brief Provides a list of all string types supported in libcudf for use in a
+ * GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all string types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::StringTypes);
+ * ```
+ */
+using StringTypes = cudf::test::Types<string_view>;
+
+/**
+ * @brief Provides a list of all list types supported in libcudf for use in a
+ * GTest typed test.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all list types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::ListTypes);
+ * ```
+ */
+using ListTypes = cudf::test::Types<list_view>;
+
+/**
+ * @brief Provides a list of all fixed-point element types for use in GTest
+ * typed tests.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all fixed-width types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::FixedPointTypes);
+ * ```
+ */
+using FixedPointTypes =
+  cudf::test::Types<numeric::decimal32, numeric::decimal64, numeric::decimal128>;
+
+/**
+ * @brief Provides a list of all fixed-width element types for use in GTest
+ * typed tests.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all fixed-width types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::FixedWidthTypes);
+ * ```
+ */
+using FixedWidthTypes = Concat<NumericTypes, ChronoTypes, FixedPointTypes>;
+
+/**
+ * @brief Provides a list of all fixed-width element types except for the
+ * fixed-point types for use in GTest typed tests.
+ *
+ * Certain tests written for fixed-width types don't work for fixed-point as
+ * fixed-point types aren't constructible from other fixed-width types
+ * because a scale needs to be specified.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all fixed-width types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::FixedWidthTypesWithoutFixedPoint);
+ * ```
+ */
+using FixedWidthTypesWithoutFixedPoint = Concat<NumericTypes, ChronoTypes>;
+
+/**
+ * @brief Provides a list of all fixed-width element types except for the
+ * chrono types for use in GTest typed tests.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all fixed-width types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::FixedWidthTypesWithoutChrono);
+ * ```
+ */
+using FixedWidthTypesWithoutChrono = Concat<NumericTypes, FixedPointTypes>;
+
+/**
+ * @brief Provides a list of sortable types for use in GTest typed tests.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all sortable types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::ComparableTypes);
+ * ```
+ */
+using ComparableTypes = Concat<NumericTypes, ChronoTypes, StringTypes>;
+
+/**
+ * @brief Provides a list of all compound types for use in GTest typed tests.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all compound types in libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::CompoundTypes);
+ * ```
+ */
+using CompoundTypes =
+  cudf::test::Types<cudf::string_view, cudf::dictionary32, cudf::list_view, cudf::struct_view>;
+
+/**
+ * @brief Provides a list of all types supported in libcudf for use in a GTest
+ * typed test.
+ *
+ * @note Currently does not provide any of the "wrapped" types, e.g.,
+ * category, etc.
+ *
+ * Example:
+ * ```
+ * // Invokes all typed fixture tests for all types supported by libcudf
+ * TYPED_TEST_SUITE(MyTypedFixture, cudf::test::AllTypes);
+ * ```
+ */
+using AllTypes = Concat<NumericTypes, ChronoTypes, FixedPointTypes>;
+
+/**
+ * @brief `std::array` of all `cudf::type_id`s
+ *
+ * This can be used for iterating over `type_id`s for custom testing, or used in
+ * GTest value-parameterized tests.
+ */
+static constexpr auto all_type_ids{detail::types_to_ids<AllTypes>()};
+
+/**
+ * @brief `std::array` of all numeric `cudf::type_id`s
+ *
+ * This can be used for iterating over `type_id`s for custom testing, or used in
+ * GTest value-parameterized tests.
+ */
+static constexpr auto numeric_type_ids{detail::types_to_ids<NumericTypes>()};
+
+/**
+ * @brief `std::array` of all timestamp `cudf::type_id`s
+ *
+ * This can be used for iterating over `type_id`s for custom testing, or used in
+ * GTest value-parameterized tests.
+ */
+static constexpr std::array<cudf::type_id, 5> timestamp_type_ids{
+  detail::types_to_ids<TimestampTypes>()};
+
+/**
+ * @brief `std::array` of all duration `cudf::type_id`s
+ *
+ * This can be used for iterating over `type_id`s for custom testing, or used in
+ * GTest value-parameterized tests.
+ */
+static constexpr std::array<cudf::type_id, 5> duration_type_ids{
+  detail::types_to_ids<DurationTypes>()};
+
+/**
+ * @brief `std::array` of all non-numeric `cudf::type_id`s
+ *
+ * This can be used for iterating over `type_id`s for custom testing, or used in
+ * GTest value-parameterized tests.
+ */
+static constexpr std::array<cudf::type_id, 12> non_numeric_type_ids{
+  cudf::type_id::EMPTY,
+  cudf::type_id::TIMESTAMP_DAYS,
+  cudf::type_id::TIMESTAMP_SECONDS,
+  cudf::type_id::TIMESTAMP_MILLISECONDS,
+  cudf::type_id::TIMESTAMP_MICROSECONDS,
+  cudf::type_id::TIMESTAMP_NANOSECONDS,
+  cudf::type_id::DURATION_DAYS,
+  cudf::type_id::DURATION_SECONDS,
+  cudf::type_id::DURATION_MILLISECONDS,
+  cudf::type_id::DURATION_MICROSECONDS,
+  cudf::type_id::DURATION_NANOSECONDS,
+  cudf::type_id::STRING};
+
+/**
+ * @brief `std::array` of all non-fixed-width `cudf::type_id`s
+ *
+ * This can be used for iterating over `type_id`s for custom testing, or used in
+ * GTest value-parameterized tests.
+ */
+static constexpr std::array<cudf::type_id, 2> non_fixed_width_type_ids{cudf::type_id::EMPTY,
+                                                                       cudf::type_id::STRING};
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/include/doxygen_groups.h b/cpp/include/doxygen_groups.h
new file mode 100644
index 0000000..4da2807
--- /dev/null
+++ b/cpp/include/doxygen_groups.h
@@ -0,0 +1,184 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file
+ * @brief Doxygen group definitions
+ */
+
+// This header is only processed by doxygen and does
+// not need to be included in any source file.
+// Below are the main groups that doxygen uses to build
+// the Modules page in the specified order.
+//
+// To add a new API to an existing group, just use the
+// @ingroup tag to the API's doxygen comment.
+// Add a new group by first specifying in the hierarchy below.
+
+/**
+ * @defgroup cudf_classes Classes
+ * @{
+ *   @defgroup column_classes Column
+ *   @{
+ *      @defgroup column_factories Factories
+ *      @defgroup strings_classes Strings
+ *      @defgroup dictionary_classes Dictionary
+ *      @defgroup timestamp_classes Timestamp
+ *      @defgroup lists_classes Lists
+ *      @defgroup structs_classes Structs
+ *   @}
+ *   @defgroup table_classes Table
+ *   @defgroup scalar_classes Scalar
+ *   @{
+ *      @defgroup scalar_factories Factories
+ *   @}
+ *   @defgroup fixed_point_classes Fixed Point
+ * @}
+ * @defgroup column_apis Column and Table
+ * @{
+ *   @defgroup column_copy Copying
+ *   @{
+ *     @defgroup copy_concatenate Concatenating
+ *     @defgroup copy_gather Gathering
+ *     @{
+ *        @file cudf/copying.hpp
+ *     @}
+ *     @defgroup copy_scatter Scattering
+ *     @{
+ *        @file cudf/copying.hpp
+ *     @}
+ *     @defgroup copy_slice Slicing
+ *     @{
+ *        @file cudf/copying.hpp
+ *     @}
+ *     @defgroup copy_split Splitting
+ *     @{
+ *        @file cudf/contiguous_split.hpp
+ *        @file cudf/copying.hpp
+ *     @}
+ *     @defgroup copy_shift Shifting
+ *     @{
+ *        @file cudf/copying.hpp
+ *     @}
+ *   @}
+ *   @defgroup column_nullmask Bitmask Operations
+ *   @defgroup column_sort Sorting
+ *   @defgroup column_search Searching
+ *   @defgroup column_hash Hashing
+ *   @defgroup column_merge Merging
+ *   @defgroup column_join Joining
+ *   @defgroup column_quantiles Quantiles
+ *   @defgroup column_aggregation Aggregation
+ *   @{
+ *     @defgroup aggregation_factories Aggregation Factories
+ *     @defgroup aggregation_reduction Reduction
+ *     @defgroup aggregation_groupby GroupBy
+ *     @defgroup aggregation_rolling Rolling Window
+ *   @}
+ *   @defgroup column_transformation Transformation
+ *   @{
+ *     @defgroup transformation_unaryops Unary Operations
+ *     @defgroup transformation_binaryops Binary Operations
+ *     @defgroup transformation_transform Transform
+ *     @defgroup transformation_replace Replacing
+ *     @defgroup transformation_fill Filling
+ *   @}
+ *   @defgroup column_reshape Reshaping
+ *   @{
+ *     @defgroup reshape_transpose Transpose
+ *   @}
+ *   @defgroup column_reorder Reordering
+ *   @{
+ *     @defgroup reorder_partition Partitioning
+ *     @defgroup reorder_compact Stream Compaction
+ *   @}
+ *   @defgroup column_interop Interop
+ *   @{
+ *     @defgroup interop_dlpack DLPack
+ *     @defgroup interop_arrow Arrow
+ *   @}
+ * @}
+ * @defgroup datetime_apis DateTime
+ * @{
+ *   @defgroup datetime_extract Extracting
+ *   @defgroup datetime_compute Compute Day
+ * @}
+ * @defgroup strings_apis Strings
+ * @{
+ *   @defgroup strings_case Case
+ *   @defgroup strings_types Character Types
+ *   @defgroup strings_combine Combining
+ *   @defgroup strings_contains Searching
+ *   @defgroup strings_convert Converting
+ *   @defgroup strings_copy Copying
+ *   @defgroup strings_slice Slicing
+ *   @defgroup strings_find Finding
+ *   @defgroup strings_modify Modifying
+ *   @defgroup strings_replace Replacing
+ *   @defgroup strings_split Splitting
+ *   @defgroup strings_extract Extracting
+ *   @defgroup strings_json JSON
+ *   @defgroup strings_regex Regex
+ * @}
+ * @defgroup dictionary_apis Dictionary
+ * @{
+ *   @defgroup dictionary_encode Encoding
+ *   @defgroup dictionary_search Searching
+ *   @defgroup dictionary_update Updating Keys
+ * @}
+ * @defgroup io_apis IO
+ * @{
+ *   @defgroup io_readers Readers
+ *   @defgroup io_writers Writers
+ *   @defgroup io_datasources Data Sources
+ *   @defgroup io_datasinks Data Sinks
+ * @}
+ * @defgroup lists_apis Lists
+ * @{
+ *   @defgroup lists_combine Combining
+ *   @defgroup lists_modify Modifying
+ *   @defgroup lists_extract Extracting
+ *   @defgroup lists_filling Filling
+ *   @defgroup lists_contains Searching
+ *   @defgroup lists_gather Gathering
+ *   @defgroup lists_elements Counting
+ *   @defgroup lists_filtering Filtering
+ *   @defgroup lists_sort Sorting
+ *   @defgroup set_operations Set Operations
+ * @}
+ * @defgroup nvtext_apis NVText
+ * @{
+ *   @defgroup nvtext_ngrams NGrams
+ *   @defgroup nvtext_normalize Normalizing
+ *   @defgroup nvtext_stemmer Stemming
+ *   @defgroup nvtext_edit_distance Edit Distance
+ *   @defgroup nvtext_tokenize Tokenizing
+ *   @defgroup nvtext_replace Replacing
+ *   @defgroup nvtext_minhash MinHashing
+ *   @defgroup nvtext_jaccard Jaccard Index
+ * @}
+ * @defgroup utility_apis Utilities
+ * @{
+ *   @defgroup utility_types Types
+ *   @defgroup utility_dispatcher Type Dispatcher
+ *   @defgroup utility_bitmask Bitmask
+ *   @defgroup utility_error Exception
+ * @}
+ * @defgroup labeling_apis Labeling
+ * @{
+ *   @defgroup label_bins Bin Labeling
+ * @}
+ */
diff --git a/cpp/include/nvtext/bpe_tokenize.hpp b/cpp/include/nvtext/bpe_tokenize.hpp
new file mode 100644
index 0000000..c67f4bd
--- /dev/null
+++ b/cpp/include/nvtext/bpe_tokenize.hpp
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+namespace nvtext {
+
+/**
+ * @addtogroup nvtext_tokenize
+ * @{
+ * @file
+ */
+
+/**
+ * @brief The table of merge pairs for the BPE encoder.
+ *
+ * To create an instance, call @ref nvtext::load_merge_pairs_file
+ */
+struct bpe_merge_pairs {
+  struct bpe_merge_pairs_impl;
+  std::unique_ptr<bpe_merge_pairs_impl> impl{};  ///< Implementation of the BPE merge pairs table.
+
+  /**
+   * @brief Construct a new bpe merge pairs object
+   *
+   * @param input The input file containing the BPE merge pairs
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the device memory
+   */
+  bpe_merge_pairs(std::unique_ptr<cudf::column>&& input,
+                  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  /**
+   * @brief Construct a new bpe merge pairs object
+   *
+   * @param input The input column of strings
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the device memory
+   */
+  bpe_merge_pairs(cudf::strings_column_view const& input,
+                  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+  ~bpe_merge_pairs();
+};
+
+/**
+ * @brief Create a nvtext::bpe_merge_pairs from an input file.
+ *
+ * The file should contain a pair of strings per line separated by
+ * a single space.
+ *
+ * Example:
+ * @code{.txt}
+ * e n
+ * i t
+ * i s
+ * e s
+ * en t
+ * c e
+ * es t
+ * en ce
+ * T h
+ * Th is
+ * t est
+ * s ent
+ * ...
+ * @endcode
+ *
+ * The pairs are expected to be ordered in the file by their rank
+ * relative to each other. A pair earlier in the file has priority over
+ * any pairs below it.
+ *
+ * @param filename_merges Local file path of pairs encoded in UTF-8.
+ * @param mr Memory resource to allocate any returned objects.
+ * @return A nvtext::bpe_merge_pairs object
+ */
+std::unique_ptr<bpe_merge_pairs> load_merge_pairs_file(
+  std::string const& filename_merges,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Byte pair encode the input strings.
+ *
+ * This will split each string on whitespace, perform the encoding,
+ * and then build the output column using the given `separator`.
+ *
+ * The encoding algorithm rebuilds each string by matching substrings
+ * in the `merge_pairs` table and iteratively removing the minimum ranked pair
+ * until no pairs are left. Then, a space is inserted between the remaining
+ * pairs before the result is joined to make the output string.
+ *
+ * @code{.pseudo}
+ * mps = load_merges_file("merges.txt") // see doxygen for example contents
+ * input = ["test sentence", "thisis test"]
+ * result = byte_pair_encoding(input, mps)
+ * result is now ["test sent ence", "this is test"]
+ * @endcode
+ *
+ * @throw cudf::logic_error if `merge_pairs` is empty
+ * @throw cudf::logic_error if `separator` is invalid
+ *
+ * @param input Strings to encode.
+ * @param merges_pairs Created by a call to @ref nvtext::load_merge_pairs_file.
+ * @param separator String used to build the output after encoding.
+ *                  Default is a space.
+ * @param mr Memory resource to allocate any returned objects.
+ * @return An encoded column of strings.
+ */
+std::unique_ptr<cudf::column> byte_pair_encoding(
+  cudf::strings_column_view const& input,
+  bpe_merge_pairs const& merges_pairs,
+  cudf::string_scalar const& separator = cudf::string_scalar(" "),
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/detail/generate_ngrams.hpp b/cpp/include/nvtext/detail/generate_ngrams.hpp
new file mode 100644
index 0000000..8351241
--- /dev/null
+++ b/cpp/include/nvtext/detail/generate_ngrams.hpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <nvtext/generate_ngrams.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace nvtext {
+namespace detail {
+
+/**
+ * @copydoc hash_character_ngrams(cudf::strings_column_view const&,
+ * cudf::size_type, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for allocating/copying device memory and launching kernels
+ */
+std::unique_ptr<cudf::column> hash_character_ngrams(cudf::strings_column_view const& strings,
+                                                    cudf::size_type ngrams,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/detail/load_hash_file.hpp b/cpp/include/nvtext/detail/load_hash_file.hpp
new file mode 100644
index 0000000..9f4640f
--- /dev/null
+++ b/cpp/include/nvtext/detail/load_hash_file.hpp
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <nvtext/subword_tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cstdint>
+#include <cstring>
+
+namespace nvtext {
+namespace detail {
+
+/**
+ * @brief Load the hashed vocabulary file into device memory.
+ *
+ * The object here can be used to call the subword_tokenize without
+ * incurring the cost of loading the same file each time.
+ *
+ * @param filename_hashed_vocabulary A path to the preprocessed vocab.txt file.
+ *        Note that this is the file AFTER python/perfect_hash.py has been used
+ *        for preprocessing.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Memory resource to allocate any returned objects.
+ * @return vocabulary hash-table elements
+ */
+std::unique_ptr<hashed_vocabulary> load_vocabulary_file(
+  std::string const& filename_hashed_vocabulary,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/detail/tokenize.hpp b/cpp/include/nvtext/detail/tokenize.hpp
new file mode 100644
index 0000000..80a6edc
--- /dev/null
+++ b/cpp/include/nvtext/detail/tokenize.hpp
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace nvtext {
+namespace detail {
+/**
+ * @copydoc nvtext::tokenize(strings_column_view const&,string_scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::column> tokenize(cudf::strings_column_view const& strings,
+                                       cudf::string_scalar const& delimiter,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc nvtext::tokenize(strings_column_view const&,strings_column_view
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::column> tokenize(cudf::strings_column_view const& strings,
+                                       cudf::strings_column_view const& delimiters,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc nvtext::count_tokens(strings_column_view const&, string_scalar
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::column> count_tokens(cudf::strings_column_view const& strings,
+                                           cudf::string_scalar const& delimiter,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc nvtext::count_tokens(strings_column_view const&,strings_column_view
+ * const&,rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::column> count_tokens(cudf::strings_column_view const& strings,
+                                           cudf::strings_column_view const& delimiters,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/edit_distance.hpp b/cpp/include/nvtext/edit_distance.hpp
new file mode 100644
index 0000000..953ecf7
--- /dev/null
+++ b/cpp/include/nvtext/edit_distance.hpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+//! NVText APIs
+namespace nvtext {
+/**
+ * @addtogroup nvtext_edit_distance
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Compute the edit distance between individual strings in two strings columns.
+ *
+ * The `output[i]` is the edit distance between `strings[i]` and `targets[i]`.
+ * This edit distance calculation uses the Levenshtein algorithm as documented here:
+ * https://www.cuelogic.com/blog/the-levenshtein-algorithm
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "", "world"]
+ * t = ["hallo", "goodbye", "world"]
+ * d = edit_distance(s, t)
+ * d is now [1, 7, 0]
+ * @endcode
+ *
+ * Any null entries for either `strings` or `targets` is ignored and the edit distance
+ * is computed as though the null entry is an empty string.
+ *
+ * The `targets.size()` must equal `strings.size()` unless `targets.size()==1`.
+ * In this case, all `strings` will be computed against the single `targets[0]` string.
+ *
+ * @throw cudf::logic_error if `targets.size() != strings.size()` and
+ *                          if `targets.size() != 1`
+ *
+ * @param strings Strings column of input strings
+ * @param targets Strings to compute edit distance against `strings`
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of with replaced strings.
+ */
+std::unique_ptr<cudf::column> edit_distance(
+  cudf::strings_column_view const& strings,
+  cudf::strings_column_view const& targets,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Compute the edit distance between all the strings in the input column.
+ *
+ * This uses the Levenshtein algorithm to calculate the edit distance between
+ * two strings as documented here: https://www.cuelogic.com/blog/the-levenshtein-algorithm
+ *
+ * The output is essentially a `strings.size() x strings.size()` square matrix of integers.
+ * All values at diagonal `row == col` are 0 since the edit distance between two identical
+ * strings is zero. All values above the diagonal are reflected below since the edit distance
+ * calculation is also commutative.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "hallo", "hella"]
+ * d = edit_distance_matrix(s)
+ * d is now [[0, 1, 1],
+ *           [1, 0, 2]
+ *           [1, 2, 0]]
+ * @endcode
+ *
+ * Null entries for `strings` are ignored and the edit distance
+ * is computed as though the null entry is an empty string.
+ *
+ * The output is a lists column of size `strings.size()` and where each list item
+ * is `strings.size()` elements.
+ *
+ * @throw cudf::logic_error if `strings.size() == 1`
+ *
+ * @param strings Strings column of input strings
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New lists column of edit distance values.
+ */
+std::unique_ptr<cudf::column> edit_distance_matrix(
+  cudf::strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/generate_ngrams.hpp b/cpp/include/nvtext/generate_ngrams.hpp
new file mode 100644
index 0000000..46f2c0e
--- /dev/null
+++ b/cpp/include/nvtext/generate_ngrams.hpp
@@ -0,0 +1,131 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+namespace nvtext {
+/**
+ * @addtogroup nvtext_ngrams
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a single column of strings by generating ngrams from
+ * a strings column.
+ *
+ * An ngram is a grouping of 2 or more strings with a separator. For example,
+ * generating bigrams groups all adjacent pairs of strings.
+ *
+ * ```
+ * ["a", "bb", "ccc"] would generate bigrams as ["a_bb", "bb_ccc"]
+ * and trigrams as ["a_bb_ccc"]
+ * ```
+ *
+ * The size of the output column will be the total number of ngrams generated from
+ * the input strings column.
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * @throw cudf::logic_error if `ngrams < 2`
+ * @throw cudf::logic_error if `separator` is invalid
+ * @throw cudf::logic_error if there are not enough strings to generate any ngrams
+ *
+ * @param input Strings column to tokenize and produce ngrams from
+ * @param ngrams The ngram number to generate
+ * @param separator The string to use for separating ngram tokens
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings columns of tokens
+ */
+std::unique_ptr<cudf::column> generate_ngrams(
+  cudf::strings_column_view const& input,
+  cudf::size_type ngrams,
+  cudf::string_scalar const& separator,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Generates ngrams of characters within each string.
+ *
+ * Each character of a string used to build ngrams.
+ * Ngrams are not created across strings.
+ *
+ * ```
+ * ["ab", "cde", "fgh"] would generate bigrams as ["ab", "cd", "de", "fg", "gh"]
+ * ```
+ *
+ * The size of the output column will be the total number of ngrams generated from
+ * the input strings column.
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * @throw cudf::logic_error if `ngrams < 2`
+ * @throw cudf::logic_error if there are not enough characters to generate any ngrams
+ *
+ * @param input Strings column to produce ngrams from
+ * @param ngrams The ngram number to generate.
+ *               Default is 2 = bigram.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings columns of tokens
+ */
+std::unique_ptr<cudf::column> generate_character_ngrams(
+  cudf::strings_column_view const& input,
+  cudf::size_type ngrams              = 2,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Hashes ngrams of characters within each string
+ *
+ * Each character of a string used to build the ngrams and ngrams are not
+ * produced across adjacent strings rows.
+ *
+ * ```
+ * "abcdefg" would generate ngrams=5 as ["abcde", "bcdef" "cdefg"]
+ * ```
+ *
+ * The ngrams for each string are hashed and returned in a list column where
+ * the offsets specify rows of hash values for each string.
+ *
+ * The size of the child column will be the total number of ngrams generated from
+ * the input strings column.
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * The hash algorithm uses MurmurHash32 on each ngram.
+ *
+ * @throw cudf::logic_error if `ngrams < 2`
+ * @throw cudf::logic_error if there are not enough characters to generate any ngrams
+ *
+ * @param input Strings column to produce ngrams from
+ * @param ngrams The ngram number to generate. Default is 5.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return A lists column of hash values
+ */
+std::unique_ptr<cudf::column> hash_character_ngrams(
+  cudf::strings_column_view const& input,
+  cudf::size_type ngrams              = 5,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/jaccard.hpp b/cpp/include/nvtext/jaccard.hpp
new file mode 100644
index 0000000..19d6c11
--- /dev/null
+++ b/cpp/include/nvtext/jaccard.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+namespace nvtext {
+/**
+ * @addtogroup nvtext_jaccard
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Computes the Jaccard similarity between individual rows
+ * in two strings columns
+ *
+ * The similarity is calculated between strings in corresponding rows
+ * such that `output[row] = J(input1[row],input2[row])`.
+ *
+ * The Jaccard index formula is https://en.wikipedia.org/wiki/Jaccard_index
+ * ```
+ *  J = |A ∩ B| / |A ∪ B|
+ *  where |A ∩ B| is number of common values between A and B
+ *  and |x| is the number of unique values in x.
+ * ```
+ *
+ * The computation here compares strings columns by treating each string as text (i.e. sentences,
+ * paragraphs, articles) instead of individual words or tokens to be compared directly. The
+ * algorithm applies a sliding window (size specified by the `width` parameter) to each string to
+ * form the set of tokens to compare within each row of the two input columns.
+ *
+ * These substrings are essentially character ngrams and used as part of the union and intersect
+ * calculations for that row. For efficiency, the substrings are hashed using the default
+ * MurmurHash32 to identify uniqueness within each row. Once the union and intersect sizes for the
+ * row are resolved, the Jaccard index is computed using the above formula and returned as a float32
+ * value.
+ *
+ * @code{.pseudo}
+ * input1 = ["the fuzzy dog", "little piggy", "funny bunny", "chatty parrot"]
+ * input2 = ["the fuzzy cat", "bitty piggy", "funny bunny", "silent partner"]
+ * r = jaccard_index(input1, input2)
+ * r is now [0.5, 0.15384616, 1.0, 0]
+ * @endcode
+ *
+ * If either input column's row is null, the output for that row will also be null.
+ *
+ * @throw std::invalid_argument if the `width < 2` or `input1.size() != input2.size()`
+ *
+ * @param input1 Strings column to compare with `input2`
+ * @param input2 Strings column to compare with `input1`
+ * @param width The character width used for apply substrings
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Index calculation values
+ */
+std::unique_ptr<cudf::column> jaccard_index(
+  cudf::strings_column_view const& input1,
+  cudf::strings_column_view const& input2,
+  cudf::size_type width,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/minhash.hpp b/cpp/include/nvtext/minhash.hpp
new file mode 100644
index 0000000..47c625b
--- /dev/null
+++ b/cpp/include/nvtext/minhash.hpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+namespace nvtext {
+/**
+ * @addtogroup nvtext_minhash
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns the minhash value for each string
+ *
+ * Hash values are computed from substrings of each string and the
+ * minimum hash value is returned for each string.
+ *
+ * Any null row entries result in corresponding null output rows.
+ *
+ * This function uses MurmurHash3_x86_32 for the hash algorithm.
+ *
+ * @throw std::invalid_argument if the width < 2
+ *
+ * @param input Strings column to compute minhash
+ * @param seed  Seed value used for the hash algorithm
+ * @param width The character width used for apply substrings;
+ *              Default is 4 characters.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Minhash values for each string in input
+ */
+std::unique_ptr<cudf::column> minhash(
+  cudf::strings_column_view const& input,
+  cudf::numeric_scalar<uint32_t> seed = 0,
+  cudf::size_type width               = 4,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the minhash values for each string per seed
+ *
+ * Hash values are computed from substrings of each string and the
+ * minimum hash value is returned for each string for each seed.
+ * Each row of the list column are seed results for the corresponding
+ * string. The order of the elements in each row match the order of
+ * the seeds provided in the `seeds` parameter.
+ *
+ * This function uses MurmurHash3_x86_32 for the hash algorithm.
+ *
+ * Any null row entries result in corresponding null output rows.
+ *
+ * @throw std::invalid_argument if the width < 2
+ * @throw std::invalid_argument if seeds is empty
+ * @throw std::overflow_error if `seeds * input.size()` exceeds the column size limit
+ *
+ * @param input Strings column to compute minhash
+ * @param seeds Seed values used for the hash algorithm
+ * @param width The character width used for apply substrings;
+ *              Default is 4 characters.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return List column of minhash values for each string per seed
+ */
+std::unique_ptr<cudf::column> minhash(
+  cudf::strings_column_view const& input,
+  cudf::device_span<uint32_t const> seeds,
+  cudf::size_type width               = 4,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the minhash value for each string
+ *
+ * Hash values are computed from substrings of each string and the
+ * minimum hash value is returned for each string.
+ *
+ * Any null row entries result in corresponding null output rows.
+ *
+ * This function uses MurmurHash3_x64_128 for the hash algorithm.
+ * The hash function returns 2 uint64 values but only the first value
+ * is used with the minhash calculation.
+ *
+ * @throw std::invalid_argument if the width < 2
+ *
+ * @param input Strings column to compute minhash
+ * @param seed  Seed value used for the hash algorithm
+ * @param width The character width used for apply substrings;
+ *              Default is 4 characters.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Minhash values as UINT64 for each string in input
+ */
+std::unique_ptr<cudf::column> minhash64(
+  cudf::strings_column_view const& input,
+  cudf::numeric_scalar<uint64_t> seed = 0,
+  cudf::size_type width               = 4,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the minhash values for each string per seed
+ *
+ * Hash values are computed from substrings of each string and the
+ * minimum hash value is returned for each string for each seed.
+ * Each row of the list column are seed results for the corresponding
+ * string. The order of the elements in each row match the order of
+ * the seeds provided in the `seeds` parameter.
+ *
+ * This function uses MurmurHash3_x64_128 for the hash algorithm.
+ *
+ * Any null row entries result in corresponding null output rows.
+ *
+ * @throw std::invalid_argument if the width < 2
+ * @throw std::invalid_argument if seeds is empty
+ * @throw std::overflow_error if `seeds * input.size()` exceeds the column size limit
+ *
+ * @param input Strings column to compute minhash
+ * @param seeds Seed values used for the hash algorithm
+ * @param width The character width used for apply substrings;
+ *              Default is 4 characters.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return List column of minhash values for each string per seed
+ */
+std::unique_ptr<cudf::column> minhash64(
+  cudf::strings_column_view const& input,
+  cudf::device_span<uint64_t const> seeds,
+  cudf::size_type width               = 4,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/ngrams_tokenize.hpp b/cpp/include/nvtext/ngrams_tokenize.hpp
new file mode 100644
index 0000000..9d76ef8
--- /dev/null
+++ b/cpp/include/nvtext/ngrams_tokenize.hpp
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+namespace nvtext {
+/**
+ * @addtogroup nvtext_ngrams
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a single column of strings by tokenizing the input strings
+ * column and then producing ngrams of each string.
+ *
+ * An ngram is a grouping of 2 or more tokens with a separator. For example,
+ * generating bigrams groups all adjacent pairs of tokens for a string.
+ *
+ * ```
+ * ["a bb ccc"] can be tokenized to ["a", "bb", "ccc"]
+ * bigrams would generate ["a_bb", "bb_ccc"] and trigrams would generate ["a_bb_ccc"]
+ * ```
+ *
+ * The `delimiter` is used for tokenizing and may be zero or more characters.
+ * If the `delimiter` is empty, whitespace (character code-point <= ' ') is used
+ * for identifying tokens.
+ *
+ * Once tokens are identified, ngrams are produced by joining the tokens
+ * with the specified separator. The generated ngrams use the tokens for each
+ * string and not across strings in adjacent rows.
+ * Any input string that contains fewer tokens than the specified ngrams value is
+ * skipped and will not contribute to the output. Therefore, a bigram of a single
+ * token is ignored as well as a trigram of 2 or less tokens.
+ *
+ * Tokens are found by locating delimiter(s) starting at the beginning of each string.
+ * As each string is tokenized, the ngrams are generated using input column row order
+ * to build the output column. That is, ngrams created in input row[i] will be placed in
+ * the output column directly before ngrams created in input row[i+1].
+ *
+ * The size of the output column will be the total number of ngrams generated from
+ * the input strings column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a b c", "d e", "f g h i", "j"]
+ * t = ngrams_tokenize(s, 2, " ", "_")
+ * t is now ["a_b", "b_c", "d_e", "f_g", "g_h", "h_i"]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * @param input Strings column to tokenize and produce ngrams from
+ * @param ngrams The ngram number to generate
+ * @param delimiter UTF-8 characters used to separate each string into tokens.
+ *                  An empty string will separate tokens using whitespace.
+ * @param separator The string to use for separating ngram tokens
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings columns of tokens
+ */
+std::unique_ptr<cudf::column> ngrams_tokenize(
+  cudf::strings_column_view const& input,
+  cudf::size_type ngrams,
+  cudf::string_scalar const& delimiter,
+  cudf::string_scalar const& separator,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/normalize.hpp b/cpp/include/nvtext/normalize.hpp
new file mode 100644
index 0000000..1be25b4
--- /dev/null
+++ b/cpp/include/nvtext/normalize.hpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+//! NVText APIs
+namespace nvtext {
+/**
+ * @addtogroup nvtext_normalize
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a new strings column by normalizing the whitespace in each
+ * string in the input column.
+ *
+ * Normalizing a string replaces any number of whitespace character
+ * (character code-point <= ' ') runs with a single space ' ' and
+ * trims whitespace from the beginning and end of the string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a b", "  c  d\n", "e \t f "]
+ * t = normalize_spaces(s)
+ * t is now ["a b","c d","e f"]
+ * @endcode
+ *
+ * A null input element at row `i` produces a corresponding null entry
+ * for row `i` in the output column.
+ *
+ * @param strings Strings column to normalize.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of normalized strings.
+ */
+std::unique_ptr<cudf::column> normalize_spaces(
+  cudf::strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Normalizes strings characters for tokenizing.
+ *
+ * This uses the normalizer that is built into the nvtext::subword_tokenize function
+ * which includes:
+ *
+ * - adding padding around punctuation (unicode category starts with "P")
+ *   as well as certain ASCII symbols like "^" and "$"
+ * - adding padding around the [CJK Unicode block
+ * characters](https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block))
+ * - changing whitespace (e.g. `"\t", "\n", "\r"`) to just space `" "`
+ * - removing control characters (unicode categories "Cc" and "Cf")
+ *
+ * The padding process here adds a single space before and after the character.
+ * Details on _unicode category_ can be found here:
+ * https://unicodebook.readthedocs.io/unicode.html#categories
+ *
+ * If `do_lower_case = true`, lower-casing also removes the accents. The
+ * accents cannot be removed from upper-case characters without lower-casing
+ * and lower-casing cannot be performed without also removing accents.
+ * However, if the accented character is already lower-case, then only the
+ * accent is removed.
+ *
+ * @code{.pseudo}
+ * s = ["éâîô\teaio", "ĂĆĖÑÜ", "ACENU", "$24.08", "[a,bb]"]
+ * s1 = normalize_characters(s,true)
+ * s1 is now ["eaio eaio", "acenu", "acenu", " $ 24 . 08", " [ a , bb ] "]
+ * s2 = normalize_characters(s,false)
+ * s2 is now ["éâîô eaio", "ĂĆĖÑÜ", "ACENU", " $ 24 . 08", " [ a , bb ] "]
+ * @endcode
+ *
+ * A null input element at row `i` produces a corresponding null entry
+ * for row `i` in the output column.
+ *
+ * This function requires about 16x the number of character bytes in the input
+ * strings column as working memory.
+ *
+ * @param strings The input strings to normalize.
+ * @param do_lower_case If true, upper-case characters are converted to
+ *        lower-case and accents are stripped from those characters.
+ *        If false, accented and upper-case characters are not transformed.
+ * @param mr Memory resource to allocate any returned objects.
+ * @return Normalized strings column
+ */
+std::unique_ptr<cudf::column> normalize_characters(
+  cudf::strings_column_view const& strings,
+  bool do_lower_case,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/replace.hpp b/cpp/include/nvtext/replace.hpp
new file mode 100644
index 0000000..0dde7f1
--- /dev/null
+++ b/cpp/include/nvtext/replace.hpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+//! NVText APIs
+namespace nvtext {
+/**
+ * @addtogroup nvtext_replace
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Replaces specified tokens with corresponding replacement strings.
+ *
+ * Tokens are identified in each string and if any match the specified `targets`
+ * strings, they are replaced with corresponding `replacements` string such that
+ * if `targets[i]` is found, then it is replaced by `replacements[i]`.
+ *
+ * The `delimiter` may be zero or more characters. If the `delimiter` is empty,
+ * whitespace (character code-point <= ' ') is used for identifying tokens.
+ * Also, any consecutive delimiters found in a string are ignored.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["this is me", "theme music"]
+ * tgt = ["is", "me"]
+ * rpl = ["+", "_"]
+ * result = replace_tokens(s,tgt,rpl)
+ * result is now ["this + _", "theme music"]
+ * @endcode
+ *
+ * A null input element at row `i` produces a corresponding null entry
+ * for row `i` in the output column.
+ *
+ * An empty string is allowed for a replacement string but the delimiters
+ * will not be removed.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["this is me", "theme music"]
+ * tgt = ["me", "this"]
+ * rpl = ["", ""]
+ * result = replace_tokens(s,tgt,rpl)
+ * result is now [" is ", "theme music"]
+ * @endcode
+ *
+ * Note the first string in `result` still retains the space delimiters.
+ *
+ * The `replacements.size()` must equal `targets.size()` unless `replacements.size()==1`.
+ * In this case, all matching `targets` strings will be replaced with the
+ * single `replacements[0]` string.
+ *
+ * @throw cudf::logic_error if `targets.size() != replacements.size()` and
+ *                          if `replacements.size() != 1`
+ * @throw cudf::logic_error if targets or replacements contain nulls
+ * @throw cudf::logic_error if delimiter is invalid
+ *
+ * @param strings Strings column to replace.
+ * @param targets Strings to compare against tokens found in `strings`
+ * @param replacements Replacement strings for each string in `targets`
+ * @param delimiter Characters used to separate each string into tokens.
+ *                  The default of empty string will identify tokens using whitespace.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of with replaced strings.
+ */
+std::unique_ptr<cudf::column> replace_tokens(
+  cudf::strings_column_view const& strings,
+  cudf::strings_column_view const& targets,
+  cudf::strings_column_view const& replacements,
+  cudf::string_scalar const& delimiter = cudf::string_scalar{""},
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Removes tokens whose lengths are less than a specified number of characters.
+ *
+ * Tokens identified in each string are removed from the corresponding output string.
+ * The removed tokens can be replaced by specifying a `replacement` string as well.
+ *
+ * The `delimiter` may be zero or more characters. If the `delimiter` is empty,
+ * whitespace (character code-point <= ' ') is used for identifying tokens.
+ * Also, any consecutive delimiters found in a string are ignored.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["this is me", "theme music"]
+ * result = filter_tokens(s,3)
+ * result is now ["this  ", "theme music"]
+ * @endcode
+ *
+ * Note the first string in `result` still retains the space delimiters.
+ *
+ * Example with a `replacement` string.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["this is me", "theme music"]
+ * result = filter_tokens(s,5,"---")
+ * result is now ["--- --- ---", "theme music"]
+ * @endcode
+ *
+ * The `replacement` string is allowed to be shorter than min_token_length.
+ *
+ * @throw cudf::logic_error if `delimiter` or `replacement` is invalid
+ *
+ * @param strings Strings column to replace.
+ * @param min_token_length The minimum number of characters to retain a token in the output string.
+ * @param replacement Optional replacement string to be used in place of removed tokens.
+ * @param delimiter Characters used to separate each string into tokens.
+ *                  The default of empty string will identify tokens using whitespace.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of with replaced strings.
+ */
+std::unique_ptr<cudf::column> filter_tokens(
+  cudf::strings_column_view const& strings,
+  cudf::size_type min_token_length,
+  cudf::string_scalar const& replacement = cudf::string_scalar{""},
+  cudf::string_scalar const& delimiter   = cudf::string_scalar{""},
+  rmm::mr::device_memory_resource* mr    = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/stemmer.hpp b/cpp/include/nvtext/stemmer.hpp
new file mode 100644
index 0000000..0a57f89
--- /dev/null
+++ b/cpp/include/nvtext/stemmer.hpp
@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+namespace nvtext {
+/**
+ * @addtogroup nvtext_stemmer
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Used for specifying letter type to check.
+ */
+enum class letter_type {
+  CONSONANT,  ///< Letter is a consonant
+  VOWEL       ///< Letter is not a consonant
+};
+
+/**
+ * @brief Returns boolean column indicating if `character_index` of the input strings
+ * is a consonant or vowel.
+ *
+ * Determining consonants and vowels is described in the following
+ * paper: https://tartarus.org/martin/PorterStemmer/def.txt
+ *
+ * Each string in the input column is expected to contain a single, lower-cased
+ * word (or subword) with no punctuation and no whitespace otherwise the
+ * measure value for that string is undefined.
+ *
+ * Also, the algorithm only works with English words.
+ *
+ * @code{.pseudo}
+ * Example:
+ * st = ["trouble", "toy", "sygyzy"]
+ * b1 = is_letter(st, VOWEL, 1)
+ * b1 is now [false, true, true]
+ * @endcode
+ *
+ * A negative index value will check the character starting from the end
+ * of each string. That is, for `character_index < 0` the letter checked for string
+ * `strings[i]` is at position `strings[i].length + index`.
+ *
+ * @code{.pseudo}
+ * Example:
+ * st = ["trouble", "toy", "sygyzy"]
+ * b2 = is_letter(st, CONSONANT, -1) // last letter checked in each string
+ * b2 is now [false, true, false]
+ * @endcode
+ *
+ * A null input element at row `i` produces a corresponding null entry
+ * for row `i` in the output column.
+ *
+ * @param strings Strings column of words to measure.
+ * @param ltype Specify letter type to check.
+ * @param character_index The character position to check in each string.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New BOOL column.
+ */
+std::unique_ptr<cudf::column> is_letter(
+  cudf::strings_column_view const& strings,
+  letter_type ltype,
+  cudf::size_type character_index,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns boolean column indicating if character at `indices[i]` of `strings[i]`
+ * is a consonant or vowel.
+ *
+ * Determining consonants and vowels is described in the following
+ * paper: https://tartarus.org/martin/PorterStemmer/def.txt
+ *
+ * Each string in the input column is expected to contain a single, lower-cased
+ * word (or subword) with no punctuation and no whitespace otherwise the
+ * measure value for that string is undefined.
+ *
+ * Also, the algorithm only works with English words.
+ *
+ * @code{.pseudo}
+ * Example:
+ * st = ["trouble", "toy", "sygyzy"]
+ * ix = [3, 1, 4]
+ * b1 = is_letter(st, VOWEL, ix)
+ * b1 is now [true, true, false]
+ * @endcode
+ *
+ * A negative index value will check the character starting from the end
+ * of each string. That is, for `character_index < 0` the letter checked for string
+ * `strings[i]` is at position `strings[i].length + indices[i]`.
+ *
+ * @code{.pseudo}
+ * Example:
+ * st = ["trouble", "toy", "sygyzy"]
+ * ix = [3, -2, 4] // 2nd to last character in st[1] is checked
+ * b2 = is_letter(st, CONSONANT, ix)
+ * b2 is now [false, false, true]
+ * @endcode
+ *
+ * A null input element at row `i` produces a corresponding null entry
+ * for row `i` in the output column.
+ *
+ * @throw cudf::logic_error if `indices.size() != strings.size()`
+ * @throw cudf::logic_error if `indices` contain nulls.
+ *
+ * @param strings Strings column of words to measure.
+ * @param ltype Specify letter type to check.
+ * @param indices The character positions to check in each string.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New BOOL column.
+ */
+std::unique_ptr<cudf::column> is_letter(
+  cudf::strings_column_view const& strings,
+  letter_type ltype,
+  cudf::column_view const& indices,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the Porter Stemmer measurements of a strings column.
+ *
+ * Porter stemming is used to normalize words by removing plural and tense endings
+ * from words in English. The stemming measurement involves counting consonant/vowel
+ * patterns within a string.
+ * Reference paper: https://tartarus.org/martin/PorterStemmer/def.txt
+ *
+ * Each string in the input column is expected to contain a single, lower-cased
+ * word (or subword) with no punctuation and no whitespace otherwise the
+ * measure value for that string is undefined.
+ *
+ * Also, the algorithm only works with English words.
+ *
+ * @code{.pseudo}
+ * Example:
+ * st = ["tr", "troubles", "trouble"]
+ * m = porter_stemmer_measure(st)
+ * m is now [0,2,1]
+ * @endcode
+ *
+ * A null input element at row `i` produces a corresponding null entry
+ * for row `i` in the output column.
+ *
+ * @param strings Strings column of words to measure.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New INT32 column of measure values.
+ */
+std::unique_ptr<cudf::column> porter_stemmer_measure(
+  cudf::strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/subword_tokenize.hpp b/cpp/include/nvtext/subword_tokenize.hpp
new file mode 100644
index 0000000..72a899d
--- /dev/null
+++ b/cpp/include/nvtext/subword_tokenize.hpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+namespace nvtext {
+
+/**
+ * @addtogroup nvtext_tokenize
+ * @{
+ * @file
+ */
+
+/**
+ * @brief The vocabulary data for use with the subword_tokenize function.
+ */
+struct hashed_vocabulary {
+  uint16_t first_token_id{};            ///< The first token id in the vocabulary
+  uint16_t separator_token_id{};        ///< The separator token id in the vocabulary
+  uint16_t unknown_token_id{};          ///< The unknown token id in the vocabulary
+  uint32_t outer_hash_a{};              ///< The a parameter for the outer hash
+  uint32_t outer_hash_b{};              ///< The b parameter for the outer hash
+  uint16_t num_bins{};                  ///< Number of bins
+  std::unique_ptr<cudf::column> table;  ///< uint64 column, the flattened hash table with key, value
+                                        ///< pairs packed in 64-bits
+  std::unique_ptr<cudf::column> bin_coefficients;  ///< uint64 column, containing the hashing
+                                                   ///< parameters for each hash bin on the GPU
+  std::unique_ptr<cudf::column> bin_offsets;  ///< uint16 column, containing the start index of each
+                                              ///< bin in the flattened hash table
+  std::unique_ptr<cudf::column>
+    cp_metadata;  ///< uint32 column, The code point metadata table to use for normalization
+  std::unique_ptr<cudf::column>
+    aux_cp_table;  ///< uint64 column, The auxiliary code point table to use for normalization
+};
+
+/**
+ * @brief Load the hashed vocabulary file into device memory.
+ *
+ * The object here can be used to call the subword_tokenize without
+ * incurring the cost of loading the same file each time.
+ *
+ * @throw cudf::logic_error if the `filename_hashed_vocabulary` could not be opened.
+ *
+ * @param filename_hashed_vocabulary A path to the preprocessed vocab.txt file.
+ *        Note that this is the file AFTER python/perfect_hash.py has been used
+ *        for preprocessing.
+ * @param mr Memory resource to allocate any returned objects.
+ * @return vocabulary hash-table elements
+ */
+std::unique_ptr<hashed_vocabulary> load_vocabulary_file(
+  std::string const& filename_hashed_vocabulary,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Result object for the subword_tokenize functions.
+ */
+struct tokenizer_result {
+  /**
+   * @brief The number of rows for the output token-ids.
+   */
+  uint32_t nrows_tensor{};
+  /**
+   * @brief The number of token-ids in each row.
+   */
+  uint32_t sequence_length{};
+  /**
+   * @brief A vector of token-ids for each row.
+   *
+   * The data is a flat matrix (nrows_tensor x sequence_length) of token-ids.
+   * This column is of type UINT32 with no null entries.
+   */
+  std::unique_ptr<cudf::column> tensor_token_ids;
+  /**
+   * @brief This mask identifies which tensor-token-ids are valid.
+   *
+   * This column is of type UINT32 with no null entries.
+   */
+  std::unique_ptr<cudf::column> tensor_attention_mask;
+  /**
+   * @brief The metadata for each tensor row.
+   *
+   * There are three elements per tensor row [row-id, start_pos, stop_pos])
+   * This column is of type UINT32 with no null entries.
+   */
+  std::unique_ptr<cudf::column> tensor_metadata;
+};
+
+/**
+ * @brief Creates a tokenizer that cleans the text, splits it into tokens and
+ *        returns token-ids from an input vocabulary.
+ *
+ * The strings are first normalized by converting to lower-case, removing
+ * punctuation, replacing a select set of multi-byte characters and
+ * whitespace characters.
+ *
+ * The strings are then tokenized by using whitespace as a delimiter.
+ * Consecutive delimiters are ignored. Each token is then assigned
+ * a 4-byte token-id mapped from the provided vocabulary table.
+ *
+ * Essentially each string is converted into one or more vectors of token-ids
+ * in the output column. The total number of these vectors times `max_sequence_length`
+ * is the size of the `tensor_token_ids` output column. For `do_truncate==true`:
+ * ```
+ * size of tensor_token_ids = max_sequence_length * strings.size()
+ * size of tensor_attention_mask = max_sequence_length * strings.size()
+ * size of tensor_metadata = 3 * strings.size()
+ * ```
+ *
+ * For `do_truncate==false` the number of rows per output string depends on the
+ * number of tokens resolved and the `stride` value which may repeat tokens
+ * in subsequent overflow rows.
+ *
+ * This function requires about 21x the number of character bytes in the input
+ * strings column as working memory.
+ *
+ * @throw cudf::logic_error if `stride > max_sequence_length`
+ * @throw std::overflow_error if `max_sequence_length * max_rows_tensor`
+ *        exceeds the column size limit
+ *
+ * @param strings The input strings to tokenize.
+ * @param vocabulary_table The vocabulary table pre-loaded into this object.
+ * @param max_sequence_length Limit of the number of token-ids per row in final tensor
+ *        for each string.
+ * @param stride Each row in the output token-ids will replicate `max_sequence_length - stride`
+ *        the token-ids from the previous row, unless it is the first string.
+ * @param do_lower_case If true, the tokenizer will convert uppercase characters in the
+ *        input stream to lower-case and strip accents from those characters.
+ *        If false, accented and uppercase characters are not transformed.
+ * @param do_truncate If true, the tokenizer will discard all the token-ids after
+ *        `max_sequence_length` for each input string. If false, it will use a new row
+ *        in the output token-ids to continue generating the output.
+ * @param mr Memory resource to allocate any returned objects.
+ * @return token-ids, attention-mask, and metadata
+ */
+tokenizer_result subword_tokenize(
+  cudf::strings_column_view const& strings,
+  hashed_vocabulary const& vocabulary_table,
+  uint32_t max_sequence_length,
+  uint32_t stride,
+  bool do_lower_case,
+  bool do_truncate,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace nvtext
diff --git a/cpp/include/nvtext/tokenize.hpp b/cpp/include/nvtext/tokenize.hpp
new file mode 100644
index 0000000..44f8f44
--- /dev/null
+++ b/cpp/include/nvtext/tokenize.hpp
@@ -0,0 +1,297 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+namespace nvtext {
+/**
+ * @addtogroup nvtext_tokenize
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Returns a single column of strings by tokenizing the input strings
+ * column using the provided characters as delimiters.
+ *
+ * The `delimiter` may be zero or more characters. If the `delimiter` is empty,
+ * whitespace (character code-point <= ' ') is used for identifying tokens.
+ * Also, any consecutive delimiters found in a string are ignored.
+ * This means only non-empty tokens are returned.
+ *
+ * Tokens are found by locating delimiter(s) starting at the beginning of each string.
+ * As each string is tokenized, the tokens are appended using input column row order
+ * to build the output column. That is, tokens found in input row[i] will be placed in
+ * the output column directly before tokens found in input row[i+1].
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a", "b c", "d  e f "]
+ * t = tokenize(s)
+ * t is now ["a", "b", "c", "d", "e", "f"]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * @param strings Strings column tokenize.
+ * @param delimiter UTF-8 characters used to separate each string into tokens.
+ *                  The default of empty string will separate tokens using whitespace.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of tokens.
+ */
+std::unique_ptr<cudf::column> tokenize(
+  cudf::strings_column_view const& strings,
+  cudf::string_scalar const& delimiter = cudf::string_scalar{""},
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a single column of strings by tokenizing the input strings
+ * column using multiple strings as delimiters.
+ *
+ * Tokens are found by locating delimiter(s) starting at the beginning of each string.
+ * Any consecutive delimiters found in a string are ignored.
+ * This means only non-empty tokens are returned.
+ *
+ * As each string is tokenized, the tokens are appended using input column row order
+ * to build the output column. That is, tokens found in input row[i] will be placed in
+ * the output column directly before tokens found in input row[i+1].
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a", "b c", "d.e:f;"]
+ * d = [".", ":", ";"]
+ * t = tokenize(s,d)
+ * t is now ["a", "b c", "d", "e", "f"]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * @throw cudf::logic_error if the delimiters column is empty or contains nulls.
+ *
+ * @param strings Strings column to tokenize.
+ * @param delimiters Strings used to separate individual strings into tokens.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of tokens.
+ */
+std::unique_ptr<cudf::column> tokenize(
+  cudf::strings_column_view const& strings,
+  cudf::strings_column_view const& delimiters,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the number of tokens in each string of a strings column.
+ *
+ * The `delimiter` may be zero or more characters. If the `delimiter` is empty,
+ * whitespace (character code-point <= ' ') is used for identifying tokens.
+ * Also, any consecutive delimiters found in a string are ignored.
+ * This means that only empty strings or null rows will result in a token count of 0.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a", "b c", " ", "d e f"]
+ * t = count_tokens(s)
+ * t is now [1, 2, 0, 3]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ * The number of tokens for a null element is set to 0 in the output column.
+ *
+ * @param strings Strings column to use for this operation
+ * @param delimiter Strings used to separate each string into tokens;
+ *                  The default of empty string will separate tokens using whitespace.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column of token counts
+ */
+std::unique_ptr<cudf::column> count_tokens(
+  cudf::strings_column_view const& strings,
+  cudf::string_scalar const& delimiter = cudf::string_scalar{""},
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the number of tokens in each string of a strings column
+ * by using multiple strings delimiters to identify tokens in each string.
+ *
+ * Also, any consecutive delimiters found in a string are ignored.
+ * This means that only empty strings or null rows will result in a token count of 0.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["a", "b c", "d.e:f;"]
+ * d = [".", ":", ";"]
+ * t = count_tokens(s,d)
+ * t is now [1, 1, 3]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ * The number of tokens for a null element is set to 0 in the output column.
+ *
+ * @throw cudf::logic_error if the delimiters column is empty or contains nulls
+ *
+ * @param strings Strings column to use for this operation
+ * @param delimiters Strings used to separate each string into tokens
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column of token counts
+ */
+std::unique_ptr<cudf::column> count_tokens(
+  cudf::strings_column_view const& strings,
+  cudf::strings_column_view const& delimiters,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns a single column of strings by converting each character to a string.
+ *
+ * Each string is converted to multiple strings -- one for each character.
+ * Note that a character maybe more than one byte.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello world", null, "goodbye"]
+ * t = character_tokenize(s)
+ * t is now ["h","e","l","l","o"," ","w","o","r","l","d","g","o","o","d","b","y","e"]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ *
+ * @param strings Strings column to tokenize.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of tokens.
+ */
+std::unique_ptr<cudf::column> character_tokenize(
+  cudf::strings_column_view const& strings,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Creates a strings column from a strings column of tokens and an
+ * associated column of row ids.
+ *
+ * Multiple tokens from the input column may be combined into a single row (string)
+ * in the output column. The tokens are concatenated along with the `separator` string
+ * in the order in which they appear in the `row_indices` column.
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello", "world", "one", "two", "three"]
+ * r = [0, 0, 1, 1, 1]
+ * s1 = detokenize(s,r)
+ * s1 is now ["hello world", "one two three"]
+ * r = [0, 2, 1, 1, 0]
+ * s2 = detokenize(s,r)
+ * s2 is now ["hello three", "one two", "world"]
+ * @endcode
+ *
+ * All null row entries are ignored and the output contains all valid rows.
+ * The values in `row_indices` are expected to have positive, sequential
+ * values without any missing row indices otherwise the output is undefined.
+ *
+ * @throw cudf::logic_error is `separator` is invalid
+ * @throw cudf::logic_error if `row_indices.size() != strings.size()`
+ * @throw cudf::logic_error if `row_indices` contains nulls
+ *
+ * @param strings Strings column to detokenize.
+ * @param row_indices The relative output row index assigned for each token in the input column.
+ * @param separator String to append after concatenating each token to the proper output row.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings columns of tokens.
+ */
+std::unique_ptr<cudf::column> detokenize(
+  cudf::strings_column_view const& strings,
+  cudf::column_view const& row_indices,
+  cudf::string_scalar const& separator = cudf::string_scalar(" "),
+  rmm::mr::device_memory_resource* mr  = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Vocabulary object to be used with nvtext::tokenize_with_vocabulary
+ *
+ * Use nvtext::load_vocabulary to create this object.
+ */
+struct tokenize_vocabulary {
+  /**
+   * @brief Vocabulary object constructor
+   *
+   * Token ids are the row indices within the vocabulary column.
+   * Each vocabulary entry is expected to be unique otherwise the behavior is undefined.
+   *
+   * @throw cudf::logic_error if `vocabulary` contains nulls or is empty
+   *
+   * @param input Strings for the vocabulary
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   */
+  tokenize_vocabulary(cudf::strings_column_view const& input,
+                      rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+                      rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+  ~tokenize_vocabulary();
+
+  struct tokenize_vocabulary_impl;
+  tokenize_vocabulary_impl* _impl{};
+};
+
+/**
+ * @brief Create a tokenize_vocabulary object from a strings column
+ *
+ * Token ids are the row indices within the vocabulary column.
+ * Each vocabulary entry is expected to be unique otherwise the behavior is undefined.
+ *
+ * @throw cudf::logic_error if `vocabulary` contains nulls or is empty
+ *
+ * @param input Strings for the vocabulary
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Object to be used with nvtext::tokenize_with_vocabulary
+ */
+std::unique_ptr<tokenize_vocabulary> load_vocabulary(
+  cudf::strings_column_view const& input,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/**
+ * @brief Returns the token ids for the input string by looking up each delimited
+ * token in the given vocabulary
+ *
+ * @code{.pseudo}
+ * Example:
+ * s = ["hello world", "hello there", "there there world", "watch out world"]
+ * v = load_vocabulary(["hello", "there", "world"])
+ * r = tokenize_with_vocabulary(s,v)
+ * r is now [[0,2], [0,1], [1,1,2], [-1,-1,2]]
+ * @endcode
+ *
+ * Any null row entry results in a corresponding null entry in the output
+ *
+ * @throw cudf::logic_error if `delimiter` is invalid
+ *
+ * @param input Strings column to tokenize
+ * @param vocabulary Used to lookup tokens within
+ * @param delimiter Used to identify tokens within `input`
+ * @param default_id The token id to be used for tokens not found in the `vocabulary`;
+ *                   Default is -1
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Lists column of token ids
+ */
+std::unique_ptr<cudf::column> tokenize_with_vocabulary(
+  cudf::strings_column_view const& input,
+  tokenize_vocabulary const& vocabulary,
+  cudf::string_scalar const& delimiter,
+  cudf::size_type default_id          = -1,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of tokenize group
+}  // namespace nvtext
diff --git a/cpp/libcudf_kafka/CMakeLists.txt b/cpp/libcudf_kafka/CMakeLists.txt
new file mode 100644
index 0000000..33bd04f
--- /dev/null
+++ b/cpp/libcudf_kafka/CMakeLists.txt
@@ -0,0 +1,111 @@
+# =============================================================================
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+include(../../fetch_rapids.cmake)
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-cuda)
+include(rapids-export)
+include(rapids-find)
+
+project(
+  CUDA_KAFKA
+  VERSION 23.10.00
+  LANGUAGES CXX
+)
+
+# Set a default build type if none was specified
+rapids_cmake_build_type(Release)
+
+# ##################################################################################################
+# * conda environment -----------------------------------------------------------------------------
+rapids_cmake_support_conda_env(conda_env MODIFY_PREFIX_PATH)
+
+# ##################################################################################################
+# * Build options
+option(BUILD_TESTS "Build tests for libcudf_kafka" ON)
+
+message(VERBOSE "CUDF_KAFKA: Build gtests: ${BUILD_TESTS}")
+
+# ##################################################################################################
+# * Dependencies
+
+# add third party dependencies using CPM
+rapids_cpm_init()
+include(cmake/thirdparty/get_cudf.cmake)
+include(cmake/thirdparty/get_rdkafka.cmake)
+
+# # GTests if enabled
+if(BUILD_TESTS)
+  # GoogleTest
+  include(../cmake/thirdparty/get_gtest.cmake)
+
+  # include CTest module -- automatically calls enable_testing()
+  include(CTest)
+  add_subdirectory(tests)
+endif()
+
+# ##################################################################################################
+# * library target --------------------------------------------------------------------------------
+add_library(cudf_kafka SHARED src/kafka_consumer.cpp src/kafka_callback.cpp)
+
+# ##################################################################################################
+# * include paths ---------------------------------------------------------------------------------
+target_include_directories(
+  cudf_kafka PUBLIC "$<BUILD_INTERFACE:${CUDA_KAFKA_SOURCE_DIR}/include>"
+                    "$<INSTALL_INTERFACE:include>"
+)
+
+# ##################################################################################################
+# * library paths ---------------------------------------------------------------------------------
+target_link_libraries(cudf_kafka PUBLIC cudf::cudf RDKAFKA::RDKAFKA)
+
+# Add Conda library, and include paths if specified
+if(TARGET conda_env)
+  target_link_libraries(cudf_kafka PRIVATE conda_env)
+endif()
+
+set_target_properties(
+  cudf_kafka
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             INSTALL_RPATH "\$ORIGIN" # set target compile options
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+)
+
+# ##################################################################################################
+# * cudf_kafka Install ----------------------------------------------------------------------------
+rapids_cmake_install_lib_dir(lib_dir)
+install(
+  TARGETS cudf_kafka
+  DESTINATION ${lib_dir}
+  EXPORT cudf_kafka-exports
+)
+
+install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/include DESTINATION include)
+
+rapids_export(
+  INSTALL cudf_kafka
+  EXPORT_SET cudf_kafka-exports
+  GLOBAL_TARGETS cudf_kafka
+  NAMESPACE cudf_kafka::
+)
+
+rapids_export(
+  BUILD cudf_kafka
+  EXPORT_SET cudf_kafka-exports
+  GLOBAL_TARGETS cudf_kafka
+  NAMESPACE cudf_kafka::
+)
diff --git a/cpp/libcudf_kafka/cmake/thirdparty/get_cudf.cmake b/cpp/libcudf_kafka/cmake/thirdparty/get_cudf.cmake
new file mode 100644
index 0000000..aa4c5b6
--- /dev/null
+++ b/cpp/libcudf_kafka/cmake/thirdparty/get_cudf.cmake
@@ -0,0 +1,55 @@
+# =============================================================================
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds cudf and sets any additional necessary environment variables.
+function(find_and_configure_cudf VERSION)
+  rapids_cmake_parse_version(MAJOR_MINOR ${VERSION} major_minor)
+  rapids_cpm_find(
+    cudf ${VERSION}
+    BUILD_EXPORT_SET cudf_kafka-exports
+    INSTALL_EXPORT_SET cudf_kafka-exports
+    CPM_ARGS
+    GIT_REPOSITORY https://github.com/rapidsai/cudf.git
+    GIT_TAG branch-${major_minor}
+    GIT_SHALLOW TRUE SOURCE_SUBDIR cpp
+    OPTIONS "BUILD_TESTS OFF" "BUILD_BENCHMARKS OFF"
+  )
+  # If after loading cudf we now have the CMAKE_CUDA_COMPILER variable we know that we need to
+  # re-enable the cuda language
+  if(CMAKE_CUDA_COMPILER)
+    set(cudf_REQUIRES_CUDA
+        TRUE
+        PARENT_SCOPE
+    )
+  endif()
+endfunction()
+
+set(CUDA_KAFKA_MIN_VERSION_cudf
+    "${CUDA_KAFKA_VERSION_MAJOR}.${CUDA_KAFKA_VERSION_MINOR}.${CUDA_KAFKA_VERSION_PATCH}"
+)
+find_and_configure_cudf(${CUDA_KAFKA_MIN_VERSION_cudf})
+
+if(cudf_REQUIRES_CUDA)
+  rapids_cuda_init_architectures(CUDA_KAFKA)
+
+  # Since we are building cudf as part of ourselves we need to enable the CUDA language in the
+  # top-most scope
+  enable_language(CUDA)
+
+  # Since CUDA_KAFKA only enables CUDA optionally we need to manually include the file that
+  # rapids_cuda_init_architectures relies on `project` calling
+  if(DEFINED CMAKE_PROJECT_CUDA_KAFKA_INCLUDE)
+    include("${CMAKE_PROJECT_CUDA_KAFKA_INCLUDE}")
+  endif()
+endif()
diff --git a/cpp/libcudf_kafka/cmake/thirdparty/get_rdkafka.cmake b/cpp/libcudf_kafka/cmake/thirdparty/get_rdkafka.cmake
new file mode 100644
index 0000000..5c3c9f0
--- /dev/null
+++ b/cpp/libcudf_kafka/cmake/thirdparty/get_rdkafka.cmake
@@ -0,0 +1,41 @@
+# =============================================================================
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# This function finds rdkafka and sets any additional necessary environment variables.
+function(get_RDKafka)
+  rapids_find_generate_module(
+    RDKAFKA
+    HEADER_NAMES rdkafkacpp.h
+    INCLUDE_SUFFIXES librdkafka
+    LIBRARY_NAMES rdkafka++
+    BUILD_EXPORT_SET cudf_kafka-exports
+    INSTALL_EXPORT_SET cudf_kafka-exports
+  )
+
+  if(DEFINED ENV{RDKAFKA_ROOT})
+    # Since this is inside a function the modification of CMAKE_PREFIX_PATH won't leak to other
+    # callers/users
+    list(APPEND CMAKE_PREFIX_PATH "$ENV{RDKAFKA_ROOT}")
+    list(APPEND CMAKE_PREFIX_PATH "$ENV{RDKAFKA_ROOT}/build")
+  endif()
+
+  rapids_find_package(
+    RDKAFKA REQUIRED
+    BUILD_EXPORT_SET cudf_kafka-exports
+    INSTALL_EXPORT_SET cudf_kafka-exports
+  )
+
+endfunction()
+
+get_RDKafka()
diff --git a/cpp/libcudf_kafka/include/cudf_kafka/kafka_callback.hpp b/cpp/libcudf_kafka/include/cudf_kafka/kafka_callback.hpp
new file mode 100644
index 0000000..0bde7e4
--- /dev/null
+++ b/cpp/libcudf_kafka/include/cudf_kafka/kafka_callback.hpp
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/io/datasource.hpp>
+
+#include <librdkafka/rdkafkacpp.h>
+
+#include <map>
+#include <memory>
+#include <string>
+
+namespace cudf {
+namespace io {
+namespace external {
+namespace kafka {
+
+/**
+ * @brief Python Callback function wrapper type used for Kafka OAuth events
+ *
+ * The KafkaConsumer calls the `kafka_oauth_callback_wrapper_type` when the existing
+ * oauth token is considered expired by the KafkaConsumer. Typically that
+ * means this will be invoked a single time when the KafkaConsumer is created
+ * to get the initial token and then intermediately as the token becomes
+ * expired.
+ *
+ * The callback function signature is:
+ *     `std::map<std::string, std::string> kafka_oauth_callback_wrapper_type(void*)`
+ *
+ * The callback function returns a std::map<std::string, std::string>,
+ * where the std::map consists of the Oauth token and its
+ * linux epoch expiration time. Generally the token and expiration
+ * time is retrieved from an external service by the callback.
+ * Ex: [token, token_expiration_in_epoch]
+ */
+using kafka_oauth_callback_wrapper_type = std::map<std::string, std::string> (*)(void*);
+using python_callable_type              = void*;
+
+/**
+ * @brief Callback to retrieve OAuth token from external source. Invoked when
+ * token refresh is required.
+ */
+class python_oauth_refresh_callback : public RdKafka::OAuthBearerTokenRefreshCb {
+ public:
+  /**
+   * @brief Construct a new python oauth refresh callback object
+   *
+   * @param callback_wrapper Cython wrapper that will
+   *                 be used to invoke the `python_callable`. This wrapper serves the purpose
+   *                 of preventing us from having to link against the Python development library
+   *                 in libcudf_kafka.
+   * @param python_callable pointer to a Python `functools.partial` object
+   */
+  python_oauth_refresh_callback(kafka_oauth_callback_wrapper_type callback_wrapper,
+                                python_callable_type python_callable);
+
+  /**
+   * @brief Invoke the Python callback function to get the OAuth token and its expiration time
+   *
+   * @param handle
+   * @param oauthbearer_config pointer to the OAuthBearerConfig object
+   */
+  void oauthbearer_token_refresh_cb(RdKafka::Handle* handle, std::string const& oauthbearer_config);
+
+ private:
+  kafka_oauth_callback_wrapper_type callback_wrapper_;
+  python_callable_type python_callable_;
+};
+
+}  // namespace kafka
+}  // namespace external
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/libcudf_kafka/include/cudf_kafka/kafka_consumer.hpp b/cpp/libcudf_kafka/include/cudf_kafka/kafka_consumer.hpp
new file mode 100644
index 0000000..dc2c62d
--- /dev/null
+++ b/cpp/libcudf_kafka/include/cudf_kafka/kafka_consumer.hpp
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "kafka_callback.hpp"
+
+#include <cudf/io/datasource.hpp>
+
+#include <librdkafka/rdkafkacpp.h>
+
+#include <algorithm>
+#include <chrono>
+#include <map>
+#include <memory>
+#include <string>
+
+namespace cudf {
+namespace io {
+namespace external {
+namespace kafka {
+
+/**
+ * @brief libcudf datasource for Apache Kafka
+ *
+ * @ingroup io_datasources
+ */
+class kafka_consumer : public cudf::io::datasource {
+ public:
+  /**
+   * @brief Creates an instance of the Kafka consumer object that is in a semi-ready state.
+   *
+   * A consumer in a semi-ready state does not have all required parameters to make successful
+   * consumer interactions with the Kafka broker. However in the semi-ready state Kafka metadata
+   * operations are still possible. This is useful for clients who plan to only use those metadata
+   * operations. This is useful when the need for delayed partition and topic assignment
+   * is not known ahead of time and needs to be delayed to as late as possible.
+   * Documentation for librdkafka configurations can be found at
+   * https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
+   *
+   * @param configs key/value pairs of librdkafka configurations that will be
+   *                passed to the librdkafka client
+   * @param python_callable `python_callable_type` pointer to a Python functools.partial object
+   * @param callable_wrapper `kafka_oauth_callback_wrapper_type` Cython wrapper that will
+   *                 be used to invoke the `python_callable`. This wrapper serves the purpose
+   *                 of preventing us from having to link against the Python development library
+   *                 in libcudf_kafka.
+   */
+  kafka_consumer(std::map<std::string, std::string> configs,
+                 python_callable_type python_callable,
+                 kafka_oauth_callback_wrapper_type callable_wrapper);
+
+  /**
+   * @brief Instantiate a Kafka consumer object. Documentation for librdkafka configurations can be
+   * found at https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
+   *
+   * @param configs key/value pairs of librdkafka configurations that will be
+   *                passed to the librdkafka client
+   * @param python_callable `python_callable_type` pointer to a Python functools.partial object
+   * @param callable_wrapper `kafka_oauth_callback_wrapper_type` Cython wrapper that will
+   *                 be used to invoke the `python_callable`. This wrapper serves the purpose
+   *                 of preventing us from having to link against the Python development library
+   *                 in libcudf_kafka.
+   * @param topic_name name of the Kafka topic to consume from
+   * @param partition partition index to consume from between `0` and `TOPIC_NUM_PARTITIONS - 1`
+   * inclusive
+   * @param start_offset seek position for the specified TOPPAR (Topic/Partition combo)
+   * @param end_offset position in the specified TOPPAR to read to
+   * @param batch_timeout maximum (millisecond) read time allowed. If end_offset is not reached
+   * before batch_timeout, a smaller subset will be returned
+   * @param delimiter optional delimiter to insert into the output between kafka messages, Ex: "\n"
+   */
+  kafka_consumer(std::map<std::string, std::string> configs,
+                 python_callable_type python_callable,
+                 kafka_oauth_callback_wrapper_type callable_wrapper,
+                 std::string const& topic_name,
+                 int partition,
+                 int64_t start_offset,
+                 int64_t end_offset,
+                 int batch_timeout,
+                 std::string const& delimiter);
+
+  /**
+   * @brief Returns a buffer with a subset of data from Kafka Topic
+   *
+   * @param[in] offset Bytes from the start
+   * @param[in] size Bytes to read
+   *
+   * @return The data buffer
+   */
+  std::unique_ptr<cudf::io::datasource::buffer> host_read(size_t offset, size_t size) override;
+
+  /**
+   * @brief Returns the size of the data in Kafka buffer
+   *
+   * @return size_t The size of the source data in bytes
+   */
+  size_t size() const override;
+
+  /**
+   * @brief Reads a selected range into a preallocated buffer.
+   *
+   * @param[in] offset Bytes from the start
+   * @param[in] size Bytes to read
+   * @param[in] dst Address of the existing host memory
+   *
+   * @return The number of bytes read (can be smaller than size)
+   */
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override;
+
+  /**
+   * @brief Commits an offset to a specified Kafka Topic/Partition instance
+   *
+   * @throws cudf::logic_error on failure to commit the partition offset
+   *
+   * @param[in] topic Name of the Kafka topic that the offset should be set for
+   * @param[in] partition Partition on the specified topic that should be used
+   * @param[in] offset Offset that should be set for the topic/partition pair
+   *
+   */
+  void commit_offset(std::string const& topic, int partition, int64_t offset);
+
+  /**
+   * @brief Retrieve the watermark offset values for a topic/partition
+   *
+   * @param[in] topic Name of the Kafka topic that the watermark should be retrieved for
+   * @param[in] partition Partition on the specified topic which should be used
+   * @param[in] timeout Max milliseconds to wait on a response from the Kafka broker
+   * @param[in] cached If True uses the last retrieved value from the Kafka broker, if False
+   *            the latest value will be retrieved from the Kafka broker by making a network
+   *            request.
+   * @return The watermark offset value for the specified topic/partition
+   */
+  std::map<std::string, int64_t> get_watermark_offset(std::string const& topic,
+                                                      int partition,
+                                                      int timeout,
+                                                      bool cached);
+
+  /**
+   * @brief Retrieve the current Kafka client configurations
+   *
+   * @return Map<string, string> of key/value pairs of the current client configurations
+   */
+  std::map<std::string, std::string> current_configs();
+
+  /**
+   * @brief Get the latest offset that was successfully committed to the Kafka broker
+   *
+   * @param[in] topic Topic name for the topic/partition pair
+   * @param[in] partition Partition number of the topic/partition pair
+   *
+   * @return Latest offset for the specified topic/partition pair
+   */
+  int64_t get_committed_offset(std::string const& topic, int partition);
+
+  /**
+   * @brief Query the Kafka broker for the list of Topic partitions for a Topic. If no topic is
+   * specified then the partitions for all Topics in the broker will be retrieved.
+   *
+   * @param[in] specific_topic The name of the topic for which to retrieve partitions. If empty then
+   * the partitions for all topics will be retrieved.
+   *
+   * @return Map of Kafka topic names with their corresponding list of topic partition values.
+   */
+  std::map<std::string, std::vector<int32_t>> list_topics(std::string specific_topic);
+
+  /**
+   * @brief Close the underlying socket connection to Kafka and clean up system resources
+   *
+   * @throws cudf::logic_error on failure to close the connection
+   * @param timeout Max milliseconds to wait on a response
+   */
+  void close(int timeout);
+
+  /**
+   * @brief Stop all active consumption and remove consumer subscriptions to topic/partition
+   * instances
+   *
+   * @throws cudf::logic_error on failure to unsubscribe from the active partition assignments.
+   */
+  void unsubscribe();
+
+  virtual ~kafka_consumer(){};
+
+ private:
+  std::unique_ptr<RdKafka::Conf> kafka_conf;  // RDKafka configuration object
+  std::unique_ptr<RdKafka::KafkaConsumer> consumer;
+
+  std::map<std::string, std::string> configs;
+  python_callable_type python_callable_;
+  kafka_oauth_callback_wrapper_type callable_wrapper_;
+
+  std::string topic_name;
+  int partition;
+  int64_t start_offset;
+  int64_t end_offset;
+  int batch_timeout;
+  int default_timeout = 10000;  // milliseconds
+  std::string delimiter;
+
+  std::string buffer;
+
+ private:
+  RdKafka::ErrorCode update_consumer_topic_partition_assignment(std::string const& topic,
+                                                                int partition,
+                                                                int64_t offset);
+
+  /**
+   * Convenience method for getting "now()" in Kafka's standard format
+   */
+  int64_t now();
+
+  void consume_to_buffer();
+};
+
+}  // namespace kafka
+}  // namespace external
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/libcudf_kafka/src/kafka_callback.cpp b/cpp/libcudf_kafka/src/kafka_callback.cpp
new file mode 100644
index 0000000..79a4064
--- /dev/null
+++ b/cpp/libcudf_kafka/src/kafka_callback.cpp
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf_kafka/kafka_callback.hpp>
+
+#include <librdkafka/rdkafkacpp.h>
+
+namespace cudf {
+namespace io {
+namespace external {
+namespace kafka {
+
+python_oauth_refresh_callback::python_oauth_refresh_callback(
+  kafka_oauth_callback_wrapper_type callback_wrapper, python_callable_type python_callable)
+  : callback_wrapper_(callback_wrapper), python_callable_(python_callable){};
+
+void python_oauth_refresh_callback::oauthbearer_token_refresh_cb(
+  RdKafka::Handle* handle, std::string const& oauthbearer_config)
+{
+  std::map<std::string, std::string> resp = callback_wrapper_(python_callable_);
+
+  // Build parameters to pass to librdkafka
+  std::string token         = resp["token"];
+  int64_t token_lifetime_ms = std::stoll(resp["token_expiration_in_epoch"]);
+  std::list<std::string> extensions;  // currently not supported
+  std::string errstr;
+  CUDF_EXPECTS(
+    RdKafka::ErrorCode::ERR_NO_ERROR ==
+      handle->oauthbearer_set_token(token, token_lifetime_ms, "kafka", extensions, errstr),
+    "Error occurred while setting the oauthbearer token");
+}
+
+}  // namespace kafka
+}  // namespace external
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/libcudf_kafka/src/kafka_consumer.cpp b/cpp/libcudf_kafka/src/kafka_consumer.cpp
new file mode 100644
index 0000000..3abdf20
--- /dev/null
+++ b/cpp/libcudf_kafka/src/kafka_consumer.cpp
@@ -0,0 +1,268 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf_kafka/kafka_consumer.hpp>
+
+#include <librdkafka/rdkafkacpp.h>
+
+#include <chrono>
+#include <memory>
+
+namespace cudf {
+namespace io {
+namespace external {
+namespace kafka {
+
+kafka_consumer::kafka_consumer(std::map<std::string, std::string> configs,
+                               python_callable_type python_callable,
+                               kafka_oauth_callback_wrapper_type callable_wrapper)
+  : configs(configs),
+    python_callable_(python_callable),
+    callable_wrapper_(callable_wrapper),
+    kafka_conf(RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL))
+{
+  for (auto const& key_value : configs) {
+    std::string error_string;
+    CUDF_EXPECTS(RdKafka::Conf::ConfResult::CONF_OK ==
+                   kafka_conf->set(key_value.first, key_value.second, error_string),
+                 "Invalid Kafka configuration");
+  }
+
+  if (python_callable_ != nullptr) {
+    std::string error_string;
+    python_oauth_refresh_callback cb(callable_wrapper_, python_callable_);
+    CUDF_EXPECTS(RdKafka::Conf::ConfResult::CONF_OK ==
+                   kafka_conf->set("oauthbearer_token_refresh_cb", &cb, error_string),
+                 "Failed to set Kafka oauth callback");
+  }
+
+  // Kafka 0.9 > requires group.id in the configuration
+  std::string conf_val;
+  CUDF_EXPECTS(RdKafka::Conf::ConfResult::CONF_OK == kafka_conf->get("group.id", conf_val),
+               "Kafka group.id must be configured");
+
+  std::string errstr;
+  consumer = std::unique_ptr<RdKafka::KafkaConsumer>(
+    RdKafka::KafkaConsumer::create(kafka_conf.get(), errstr));
+}
+
+kafka_consumer::kafka_consumer(std::map<std::string, std::string> configs,
+                               python_callable_type python_callable,
+                               kafka_oauth_callback_wrapper_type callback_wrapper,
+                               std::string const& topic_name,
+                               int partition,
+                               int64_t start_offset,
+                               int64_t end_offset,
+                               int batch_timeout,
+                               std::string const& delimiter)
+  : configs(configs),
+    python_callable_(python_callable),
+    callable_wrapper_(callback_wrapper),
+    topic_name(topic_name),
+    partition(partition),
+    start_offset(start_offset),
+    end_offset(end_offset),
+    batch_timeout(batch_timeout),
+    delimiter(delimiter),
+    kafka_conf(RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL))
+{
+  for (auto const& key_value : configs) {
+    std::string error_string;
+    CUDF_EXPECTS(RdKafka::Conf::ConfResult::CONF_OK ==
+                   kafka_conf->set(key_value.first, key_value.second, error_string),
+                 "Invalid Kafka configuration");
+  }
+
+  if (python_callable_ != nullptr) {
+    std::string error_string;
+    python_oauth_refresh_callback cb(callable_wrapper_, python_callable_);
+    CUDF_EXPECTS(RdKafka::Conf::ConfResult::CONF_OK ==
+                   kafka_conf->set("oauthbearer_token_refresh_cb", &cb, error_string),
+                 "Failed to set Kafka oauth callback");
+  }
+
+  // Kafka 0.9 > requires group.id in the configuration
+  std::string conf_val;
+  CUDF_EXPECTS(RdKafka::Conf::ConfResult::CONF_OK == kafka_conf->get("group.id", conf_val),
+               "Kafka group.id must be configured");
+
+  std::string errstr;
+  consumer = std::unique_ptr<RdKafka::KafkaConsumer>(
+    RdKafka::KafkaConsumer::create(kafka_conf.get(), errstr));
+
+  // Pre fill the local buffer with messages so the datasource->size() invocation
+  // will return a valid size.
+  consume_to_buffer();
+}
+
+std::unique_ptr<cudf::io::datasource::buffer> kafka_consumer::host_read(size_t offset, size_t size)
+{
+  if (offset > buffer.size()) { return 0; }
+  size = std::min(size, buffer.size() - offset);
+  return std::make_unique<non_owning_buffer>((uint8_t*)buffer.data() + offset, size);
+}
+
+size_t kafka_consumer::host_read(size_t offset, size_t size, uint8_t* dst)
+{
+  if (offset > buffer.size()) { return 0; }
+  auto const read_size = std::min(size, buffer.size() - offset);
+  memcpy(dst, buffer.data() + offset, size);
+  return read_size;
+}
+
+size_t kafka_consumer::size() const { return buffer.size(); }
+
+/**
+ * Change the TOPPAR assignment for this consumer instance
+ */
+RdKafka::ErrorCode kafka_consumer::update_consumer_topic_partition_assignment(
+  std::string const& topic, int partition, int64_t offset)
+{
+  std::vector<RdKafka::TopicPartition*> topic_partitions;
+  topic_partitions.push_back(RdKafka::TopicPartition::create(topic, partition, offset));
+  return consumer.get()->assign(topic_partitions);
+}
+
+void kafka_consumer::consume_to_buffer()
+{
+  update_consumer_topic_partition_assignment(topic_name, partition, start_offset);
+
+  int64_t messages_read = 0;
+  auto end = std::chrono::steady_clock::now() + std::chrono::milliseconds(batch_timeout);
+
+  while (messages_read < end_offset - start_offset && end > std::chrono::steady_clock::now()) {
+    std::unique_ptr<RdKafka::Message> msg{
+      consumer->consume((end - std::chrono::steady_clock::now()).count())};
+
+    if (msg->err() == RdKafka::ErrorCode::ERR_NO_ERROR) {
+      buffer.append(static_cast<char*>(msg->payload()));
+      buffer.append(delimiter);
+      messages_read++;
+    } else if (msg->err() == RdKafka::ErrorCode::ERR__PARTITION_EOF) {
+      // If there are no more messages return
+      break;
+    }
+  }
+}
+
+std::map<std::string, std::string> kafka_consumer::current_configs()
+{
+  std::map<std::string, std::string> configs;
+  std::list<std::string>* dump = kafka_conf->dump();
+  for (auto it = dump->begin(); it != dump->end(); std::advance(it, 2))
+    configs.insert({*it, *std::next(it)});
+  return configs;
+}
+
+int64_t kafka_consumer::get_committed_offset(std::string const& topic, int partition)
+{
+  std::vector<RdKafka::TopicPartition*> toppar_list;
+  toppar_list.push_back(RdKafka::TopicPartition::create(topic, partition));
+
+  // Query Kafka to populate the TopicPartitions with the desired offsets
+  CUDF_EXPECTS(RdKafka::ERR_NO_ERROR == consumer->committed(toppar_list, default_timeout),
+               "Failed retrieve Kafka committed offsets");
+
+  int64_t offset = toppar_list[0]->offset();
+  return offset > 0 ? offset : -1001;
+}
+
+std::map<std::string, std::vector<int32_t>> kafka_consumer::list_topics(std::string specific_topic)
+{
+  auto const metadata = [&]() {
+    std::string errstr;
+    auto spec_topic = std::unique_ptr<RdKafka::Topic>(
+      RdKafka::Topic::create(consumer.get(), specific_topic, nullptr, errstr));
+
+    RdKafka::Metadata* md;
+    CUDF_EXPECTS(
+      RdKafka::ERR_NO_ERROR ==
+        consumer->metadata(spec_topic == nullptr, spec_topic.get(), &md, default_timeout),
+      "Failed to list_topics in Kafka broker");
+    return std::unique_ptr<RdKafka::Metadata>{md};
+  }();
+  std::map<std::string, std::vector<int32_t>> topic_parts;
+
+  for (auto const& topic : *(metadata->topics())) {
+    auto& part_ids    = topic_parts[topic->topic()];
+    auto const& parts = *(topic->partitions());
+    std::transform(
+      parts.cbegin(), parts.cend(), std::back_inserter(part_ids), [](auto const& part) {
+        return part->id();
+      });
+  }
+
+  return topic_parts;
+}
+
+std::map<std::string, int64_t> kafka_consumer::get_watermark_offset(std::string const& topic,
+                                                                    int partition,
+                                                                    int timeout,
+                                                                    bool cached)
+{
+  int64_t low;
+  int64_t high;
+  std::map<std::string, int64_t> results;
+  RdKafka::ErrorCode err;
+
+  if (cached) {
+    err = consumer->get_watermark_offsets(topic, partition, &low, &high);
+  } else {
+    err = consumer->query_watermark_offsets(topic, partition, &low, &high, timeout);
+  }
+
+  if (err != RdKafka::ErrorCode::ERR_NO_ERROR) {
+    if (err == RdKafka::ErrorCode::ERR__PARTITION_EOF) {
+      results.insert(std::pair<std::string, int64_t>("low", low));
+      results.insert(std::pair<std::string, int64_t>("high", high));
+    } else {
+      CUDF_FAIL("Error retrieving Kafka watermark offset from broker");
+    }
+  } else {
+    results.insert(std::pair<std::string, int64_t>("low", low));
+    results.insert(std::pair<std::string, int64_t>("high", high));
+  }
+
+  return results;
+}
+
+void kafka_consumer::commit_offset(std::string const& topic, int partition, int64_t offset)
+{
+  std::vector<RdKafka::TopicPartition*> partitions_;
+  RdKafka::TopicPartition* toppar = RdKafka::TopicPartition::create(topic, partition, offset);
+  CUDF_EXPECTS(toppar != nullptr, "RdKafka failed to create TopicPartition");
+  toppar->set_offset(offset);
+  partitions_.push_back(toppar);
+  CUDF_EXPECTS(RdKafka::ERR_NO_ERROR == consumer->commitSync(partitions_),
+               "Failed to commit consumer offsets");
+}
+
+void kafka_consumer::unsubscribe()
+{
+  CUDF_EXPECTS(RdKafka::ErrorCode::ERR_NO_ERROR == consumer.get()->unassign(),
+               "Failed to unsubscribe from Kafka Consumer");
+}
+
+void kafka_consumer::close(int timeout)
+{
+  CUDF_EXPECTS(RdKafka::ERR_NO_ERROR == consumer->close(), "Failed to close Kafka consumer");
+  consumer.reset(nullptr);
+  kafka_conf.reset(nullptr);
+}
+
+}  // namespace kafka
+}  // namespace external
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/libcudf_kafka/tests/CMakeLists.txt b/cpp/libcudf_kafka/tests/CMakeLists.txt
new file mode 100644
index 0000000..68a5327
--- /dev/null
+++ b/cpp/libcudf_kafka/tests/CMakeLists.txt
@@ -0,0 +1,49 @@
+# =============================================================================
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# ##################################################################################################
+# enable testing ################################################################################
+# ##################################################################################################
+enable_testing()
+
+include(rapids-test)
+rapids_test_init()
+
+# This function takes in a test name and test source and handles setting all of the associated
+# properties and linking to build the test
+function(ConfigureTest test_name)
+  add_executable(${test_name} ${ARGN})
+  set_target_properties(
+    ${test_name}
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUDA_KAFKA_BINARY_DIR}/gtests>"
+               INSTALL_RPATH "\$ORIGIN/../../../lib"
+  )
+  target_link_libraries(
+    ${test_name} PRIVATE GTest::gmock GTest::gmock_main GTest::gtest_main cudf_kafka
+  )
+  rapids_test_add(
+    NAME ${test_name}
+    COMMAND ${test_name}
+    GPUS 1
+    PERCENT 25
+    INSTALL_COMPONENT_SET testing
+  )
+endfunction()
+
+# ##################################################################################################
+# * Kafka host tests
+# ----------------------------------------------------------------------------------
+ConfigureTest(KAFKA_HOST_TEST kafka_consumer_tests.cpp)
+
+rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcudf_kafka)
diff --git a/cpp/libcudf_kafka/tests/kafka_consumer_tests.cpp b/cpp/libcudf_kafka/tests/kafka_consumer_tests.cpp
new file mode 100644
index 0000000..53d87e0
--- /dev/null
+++ b/cpp/libcudf_kafka/tests/kafka_consumer_tests.cpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_kafka/kafka_consumer.hpp>
+#include <gtest/gtest.h>
+#include <map>
+#include <memory>
+#include <string>
+
+#include <cudf/io/csv.hpp>
+#include <cudf/io/datasource.hpp>
+
+namespace kafka = cudf::io::external::kafka;
+
+struct KafkaDatasourceTest : public ::testing::Test {};
+
+TEST_F(KafkaDatasourceTest, MissingGroupID)
+{
+  // group.id is a required configuration.
+  std::map<std::string, std::string> kafka_configs;
+  kafka_configs["bootstrap.servers"] = "localhost:9092";
+
+  kafka::python_callable_type python_callable;
+  kafka::kafka_oauth_callback_wrapper_type callback_wrapper;
+
+  EXPECT_THROW(
+    kafka::kafka_consumer kc(
+      kafka_configs, python_callable, callback_wrapper, "csv-topic", 0, 0, 3, 5000, "\n"),
+    cudf::logic_error);
+}
+
+TEST_F(KafkaDatasourceTest, InvalidConfigValues)
+{
+  // Give a made up configuration value
+  std::map<std::string, std::string> kafka_configs;
+  kafka_configs["completely_made_up_config"] = "wrong";
+
+  kafka::python_callable_type python_callable;
+  kafka::kafka_oauth_callback_wrapper_type callback_wrapper;
+
+  EXPECT_THROW(
+    kafka::kafka_consumer kc(
+      kafka_configs, python_callable, callback_wrapper, "csv-topic", 0, 0, 3, 5000, "\n"),
+    cudf::logic_error);
+
+  // Give a good config property with a bad value
+  kafka_configs.clear();
+  kafka_configs["message.max.bytes"] = "this should be a number not text";
+
+  EXPECT_THROW(
+    kafka::kafka_consumer kc(
+      kafka_configs, python_callable, callback_wrapper, "csv-topic", 0, 0, 3, 5000, "\n"),
+    cudf::logic_error);
+}
diff --git a/cpp/scripts/gdb-pretty-printers.py b/cpp/scripts/gdb-pretty-printers.py
new file mode 100644
index 0000000..ebb56a8
--- /dev/null
+++ b/cpp/scripts/gdb-pretty-printers.py
@@ -0,0 +1,84 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import gdb
+
+global_locals = locals()
+if not all(
+    name in global_locals
+    for name in (
+        "HostIterator",
+        "DeviceIterator",
+        "is_template_type_not_alias",
+        "template_match",
+    )
+):
+    raise NameError(
+        "This file expects the RMM pretty-printers to be loaded already. "
+        "Either load them manually, or use the generated load-pretty-printers "
+        "script in the build directory"
+    )
+
+
+class CudfHostSpanPrinter(gdb.printing.PrettyPrinter):
+    """Print a cudf::host_span"""
+
+    def __init__(self, val):
+        self.val = val
+        self.pointer = val["_data"]
+        self.size = int(val["_size"])
+
+    def children(self):
+        return HostIterator(self.pointer, self.size)
+
+    def to_string(self):
+        return f"{self.val.type} of length {self.size} at {hex(self.pointer)}"
+
+    def display_hint(self):
+        return "array"
+
+
+class CudfDeviceSpanPrinter(gdb.printing.PrettyPrinter):
+    """Print a cudf::device_span"""
+
+    def __init__(self, val):
+        self.val = val
+        self.pointer = val["_data"]
+        self.size = int(val["_size"])
+
+    def children(self):
+        return DeviceIterator(self.pointer, self.size)
+
+    def to_string(self):
+        return f"{self.val.type} of length {self.size} at {hex(self.pointer)}"
+
+    def display_hint(self):
+        return "array"
+
+
+def lookup_cudf_type(val):
+    if not str(val.type.unqualified()).startswith("cudf::"):
+        return None
+    suffix = str(val.type.unqualified())[6:]
+    if not is_template_type_not_alias(suffix):
+        return None
+    if template_match(suffix, "host_span"):
+        return CudfHostSpanPrinter(val)
+    if template_match(suffix, "device_span"):
+        return CudfDeviceSpanPrinter(val)
+    return None
+
+
+gdb.pretty_printers.append(lookup_cudf_type)
diff --git a/cpp/scripts/load-pretty-printers.in b/cpp/scripts/load-pretty-printers.in
new file mode 100644
index 0000000..4c00384
--- /dev/null
+++ b/cpp/scripts/load-pretty-printers.in
@@ -0,0 +1,3 @@
+source @Thrust_SOURCE_DIR@/scripts/gdb-pretty-printers.py
+source @rmm_SOURCE_DIR@/scripts/gdb-pretty-printers.py
+source @PROJECT_SOURCE_DIR@/scripts/gdb-pretty-printers.py
diff --git a/cpp/scripts/run-clang-tidy.py b/cpp/scripts/run-clang-tidy.py
new file mode 100644
index 0000000..e5e57db
--- /dev/null
+++ b/cpp/scripts/run-clang-tidy.py
@@ -0,0 +1,253 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import re
+import os
+import subprocess
+import argparse
+import json
+import multiprocessing as mp
+import shutil
+
+
+EXPECTED_VERSION = "16.0.6"
+VERSION_REGEX = re.compile(r"  LLVM version ([0-9.]+)")
+GPU_ARCH_REGEX = re.compile(r"sm_(\d+)")
+SPACES = re.compile(r"\s+")
+SEPARATOR = "-" * 16
+
+
+def parse_args():
+    argparser = argparse.ArgumentParser("Runs clang-tidy on a project")
+    argparser.add_argument("-cdb", type=str,
+                           # TODO This is a hack, needs to be fixed
+                           default="cpp/build/cuda-11.5.0/clang-tidy/release/compile_commands.clangd.json",
+                           help="Path to cmake-generated compilation database"
+                           " file. It is always found inside the root of the "
+                           "cmake build folder. So make sure that `cmake` has "
+                           "been run once before running this script!")
+    argparser.add_argument("-exe", type=str, default="clang-tidy",
+                           help="Path to clang-tidy exe")
+    argparser.add_argument("-ignore", type=str, default="[.]cu$|examples/kmeans/",
+                           help="Regex used to ignore files from checking")
+    argparser.add_argument("-select", type=str, default=None,
+                           help="Regex used to select files for checking")
+    argparser.add_argument("-j", type=int, default=-1,
+                           help="Number of parallel jobs to launch.")
+    args = argparser.parse_args()
+    if args.j <= 0:
+        args.j = mp.cpu_count()
+    args.ignore_compiled = re.compile(args.ignore) if args.ignore else None
+    args.select_compiled = re.compile(args.select) if args.select else None
+    ret = subprocess.check_output("%s --version" % args.exe, shell=True)
+    ret = ret.decode("utf-8")
+    version = VERSION_REGEX.search(ret)
+    if version is None:
+        raise Exception("Failed to figure out clang-tidy version!")
+    version = version.group(1)
+    if version != EXPECTED_VERSION:
+        raise Exception("clang-tidy exe must be v%s found '%s'" % \
+                        (EXPECTED_VERSION, version))
+    if not os.path.exists(args.cdb):
+        raise Exception("Compilation database '%s' missing" % args.cdb)
+    return args
+
+
+def get_all_commands(cdb):
+    with open(cdb) as fp:
+        return json.load(fp)
+
+
+def get_gpu_archs(command):
+    archs = []
+    for loc in range(len(command)):
+        if command[loc] != "-gencode":
+            continue
+        arch_flag = command[loc + 1]
+        match = GPU_ARCH_REGEX.search(arch_flag)
+        if match is not None:
+            archs.append("--cuda-gpu-arch=sm_%s" % match.group(1))
+    return archs
+
+
+def get_index(arr, item):
+    try:
+        return arr.index(item)
+    except:
+        return -1
+
+
+def remove_item(arr, item):
+    loc = get_index(arr, item)
+    if loc >= 0:
+        del arr[loc]
+    return loc
+
+
+def remove_item_plus_one(arr, item):
+    loc = get_index(arr, item)
+    if loc >= 0:
+        del arr[loc + 1]
+        del arr[loc]
+    return loc
+
+
+def get_clang_includes(exe):
+    dir = os.getenv("CONDA_PREFIX")
+    if dir is None:
+        ret = subprocess.check_output("which %s 2>&1" % exe, shell=True)
+        ret = ret.decode("utf-8")
+        dir = os.path.dirname(os.path.dirname(ret))
+    header = os.path.join(dir, "include", "ClangHeaders")
+    return ["-I", header]
+
+
+def get_tidy_args(cmd, exe):
+    command, file = cmd["command"], cmd["file"]
+    is_cuda = file.endswith(".cu")
+    command = re.split(SPACES, command)
+    # compiler is always clang++!
+    command[0] = "clang++"
+    # remove compilation and output targets from the original command
+    remove_item_plus_one(command, "-c")
+    remove_item_plus_one(command, "-o")
+    if is_cuda:
+        # replace nvcc's "-gencode ..." with clang's "--cuda-gpu-arch ..."
+        archs = get_gpu_archs(command)
+        command.extend(archs)
+        while True:
+            loc = remove_item_plus_one(command, "-gencode")
+            if loc < 0:
+                break
+        # "-x cuda" is the right usage in clang
+        loc = get_index(command, "-x")
+        if loc >= 0:
+            command[loc + 1] = "cuda"
+        remove_item_plus_one(command, "-ccbin")
+        remove_item(command, "--expt-extended-lambda")
+        remove_item(command, "--diag_suppress=unrecognized_gcc_pragma")
+    command.extend(get_clang_includes(exe))
+    return command, is_cuda
+
+
+def run_clang_tidy_command(tidy_cmd):
+    cmd = " ".join(tidy_cmd)
+    result = subprocess.run(cmd, check=False, shell=True,
+                            stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+    status = result.returncode == 0
+    if status:
+        out = ""
+    else:
+        out = "CMD: " + cmd
+    out += result.stdout.decode("utf-8").rstrip()
+    return status, out
+
+
+def run_clang_tidy(cmd, args):
+    command, is_cuda = get_tidy_args(cmd, args.exe)
+    tidy_cmd = [args.exe,
+                "-header-filter='.*cudf/cpp/(src|include|bench|comms).*'",
+                cmd["file"], "--", ]
+    tidy_cmd.extend(command)
+    status = True
+    out = ""
+    if is_cuda:
+        tidy_cmd.append("--cuda-device-only")
+        tidy_cmd.append(cmd["file"])
+        ret, out1 = run_clang_tidy_command(tidy_cmd)
+        out += out1
+        out += "%s" % SEPARATOR
+        if not ret:
+            status = ret
+        tidy_cmd[-2] = "--cuda-host-only"
+        ret, out1 = run_clang_tidy_command(tidy_cmd)
+        if not ret:
+            status = ret
+        out += out1
+    else:
+        tidy_cmd.append(cmd["file"])
+        ret, out1 = run_clang_tidy_command(tidy_cmd)
+        if not ret:
+            status = ret
+        out += out1
+    return status, out, cmd["file"]
+
+
+# yikes! global var :(
+results = []
+def collect_result(result):
+    global results
+    results.append(result)
+
+
+def print_result(passed, stdout, file):
+    status_str = "PASSED" if passed else "FAILED"
+    print(f"{SEPARATOR} File:{file} {status_str} {SEPARATOR}")
+    if stdout:
+        print(stdout)
+        print(f"{SEPARATOR} File:{file} ENDS {SEPARATOR}")
+
+
+def print_results():
+    global results
+    status = True
+    for passed, stdout, file in results:
+        print_result(passed, stdout, file)
+        if not passed:
+            status = False
+    return status
+
+
+def run_tidy_for_all_files(args, all_files):
+    pool = None if args.j == 1 else mp.Pool(args.j)
+    # actual tidy checker
+    for cmd in all_files:
+        # skip files that we don't want to look at
+        if args.ignore_compiled is not None and \
+           re.search(args.ignore_compiled, cmd["file"]) is not None:
+            continue
+        if args.select_compiled is not None and \
+           re.search(args.select_compiled, cmd["file"]) is None:
+            continue
+        if pool is not None:
+            pool.apply_async(run_clang_tidy, args=(cmd, args),
+                             callback=collect_result)
+        else:
+            passed, stdout, file = run_clang_tidy(cmd, args)
+            collect_result((passed, stdout, file))
+    if pool is not None:
+        pool.close()
+        pool.join()
+    return print_results()
+
+
+def main():
+    args = parse_args()
+    # Attempt to making sure that we run this script from root of repo always
+    if not os.path.exists(".git"):
+        raise Exception("This needs to always be run from the root of repo")
+    # Check whether clang-tidy exists
+    # print(args)
+    if "exe" not in args and shutil.which("clang-tidy") is not None:
+        print("clang-tidy not found. Exiting...")
+        return
+    all_files = get_all_commands(args.cdb)
+    status = run_tidy_for_all_files(args, all_files)
+    if not status:
+        raise Exception("clang-tidy failed! Refer to the errors above.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/cpp/scripts/run-cmake-format.sh b/cpp/scripts/run-cmake-format.sh
new file mode 100755
index 0000000..f3e2177
--- /dev/null
+++ b/cpp/scripts/run-cmake-format.sh
@@ -0,0 +1,84 @@
+#!/bin/bash
+
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+# This script is a wrapper for cmakelang that may be used with pre-commit. The
+# wrapping is necessary because RAPIDS libraries split configuration for
+# cmakelang linters between a local config file and a second config file that's
+# shared across all of RAPIDS via rapids-cmake. In order to keep it up to date
+# this file is only maintained in one place (the rapids-cmake repo) and
+# pulled down during builds. We need a way to invoke CMake linting commands
+# without causing pre-commit failures (which could block local commits or CI),
+# while also being sufficiently flexible to allow users to maintain the config
+# file independently of a build directory.
+#
+# This script provides the minimal functionality to enable those use cases. It
+# searches in a number of predefined locations for the rapids-cmake config file
+# and exits gracefully if the file is not found. If a user wishes to specify a
+# config file at a nonstandard location, they may do so by setting the
+# environment variable RAPIDS_CMAKE_FORMAT_FILE.
+#
+# This script can be invoked directly anywhere within the project repository.
+# Alternatively, it may be invoked as a pre-commit hook via
+# `pre-commit run (cmake-format)|(cmake-lint)`.
+#
+# Usage:
+# bash run-cmake-format.sh {cmake-format,cmake-lint} infile [infile ...]
+
+status=0
+if [ -z ${CUDF_ROOT:+PLACEHOLDER} ]; then
+    CUDF_BUILD_DIR=$(git rev-parse --show-toplevel 2>&1)/cpp/build
+    status=$?
+else
+    CUDF_BUILD_DIR=${CUDF_ROOT}
+fi
+
+if ! [ ${status} -eq 0 ]; then
+    if [[ ${CUDF_BUILD_DIR} == *"not a git repository"* ]]; then
+        echo "This script must be run inside the cudf repository, or the CUDF_ROOT environment variable must be set."
+    else
+        echo "Script failed with unknown error attempting to determine project root:"
+        echo ${CUDF_BUILD_DIR}
+    fi
+    exit 1
+fi
+
+DEFAULT_FORMAT_FILE_LOCATIONS=(
+  "${CUDF_BUILD_DIR:-${HOME}}/_deps/rapids-cmake-src/cmake-format-rapids-cmake.json"
+  "cpp/libcudf_kafka/build/_deps/rapids-cmake-src/cmake-format-rapids-cmake.json"
+)
+
+if [ -z ${RAPIDS_CMAKE_FORMAT_FILE:+PLACEHOLDER} ]; then
+    for file_path in ${DEFAULT_FORMAT_FILE_LOCATIONS[@]}; do
+        if [ -f ${file_path} ]; then
+            RAPIDS_CMAKE_FORMAT_FILE=${file_path}
+            break
+        fi
+    done
+fi
+
+if [ -z ${RAPIDS_CMAKE_FORMAT_FILE:+PLACEHOLDER} ]; then
+  echo "The rapids-cmake cmake-format configuration file was not found at any of the default search locations: "
+  echo ""
+  ( IFS=$'\n'; echo "${DEFAULT_FORMAT_FILE_LOCATIONS[*]}" )
+  echo ""
+  echo "Try setting the environment variable RAPIDS_CMAKE_FORMAT_FILE to the path to the config file."
+  exit 0
+else
+  echo "Using format file ${RAPIDS_CMAKE_FORMAT_FILE}"
+fi
+
+if [[ $1 == "cmake-format" ]]; then
+  cmake-format -i --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- ${@:2}
+elif [[ $1 == "cmake-lint" ]]; then
+  # Since the pre-commit hook is verbose, we have to be careful to only
+  # present cmake-lint's output (which is quite verbose) if we actually
+  # observe a failure.
+  OUTPUT=$(cmake-lint --config-files cpp/cmake/config.json ${RAPIDS_CMAKE_FORMAT_FILE} -- ${@:2})
+  status=$?
+
+  if ! [ ${status} -eq 0 ]; then
+    echo "${OUTPUT}"
+  fi
+  exit ${status}
+fi
diff --git a/cpp/scripts/sort_ninja_log.py b/cpp/scripts/sort_ninja_log.py
new file mode 100755
index 0000000..3fe503f
--- /dev/null
+++ b/cpp/scripts/sort_ninja_log.py
@@ -0,0 +1,404 @@
+#
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+#
+import argparse
+import os
+import sys
+import xml.etree.ElementTree as ET
+from pathlib import Path
+from xml.dom import minidom
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "log_file", type=str, default=".ninja_log", help=".ninja_log file"
+)
+parser.add_argument(
+    "--fmt",
+    type=str,
+    default="csv",
+    choices=["csv", "xml", "html"],
+    help="output format (to stdout)",
+)
+parser.add_argument(
+    "--msg",
+    type=str,
+    default=None,
+    help="optional text file to include at the top of the html output",
+)
+parser.add_argument(
+    "--cmp_log",
+    type=str,
+    default=None,
+    help="optional baseline ninja_log to compare results",
+)
+args = parser.parse_args()
+
+log_file = args.log_file
+output_fmt = args.fmt
+cmp_file = args.cmp_log
+
+# build a map of the log entries
+def build_log_map(log_file):
+    entries = {}
+    log_path = os.path.dirname(os.path.abspath(log_file))
+    with open(log_file) as log:
+        last = 0
+        files = {}
+        for line in log:
+            entry = line.split()
+            if len(entry) > 4:
+                obj_file = entry[3]
+                file_size = (
+                    os.path.getsize(os.path.join(log_path, obj_file))
+                    if os.path.exists(obj_file)
+                    else 0
+                )
+                start = int(entry[0])
+                end = int(entry[1])
+                # logic based on ninjatracing
+                if end < last:
+                    files = {}
+                last = end
+                files.setdefault(entry[4], (entry[3], start, end, file_size))
+
+        # build entries from files dict
+        for entry in files.values():
+            entries[entry[0]] = (entry[1], entry[2], entry[3])
+
+    return entries
+
+
+# output results in XML format
+def output_xml(entries, sorted_list, args):
+    root = ET.Element("testsuites")
+    testsuite = ET.Element(
+        "testsuite",
+        attrib={
+            "name": "build-time",
+            "tests": str(len(sorted_list)),
+            "failures": str(0),
+            "errors": str(0),
+        },
+    )
+    root.append(testsuite)
+    for name in sorted_list:
+        entry = entries[name]
+        build_time = float(entry[1] - entry[0]) / 1000
+        item = ET.Element(
+            "testcase",
+            attrib={
+                "classname": "BuildTime",
+                "name": name,
+                "time": str(build_time),
+            },
+        )
+        testsuite.append(item)
+
+    tree = ET.ElementTree(root)
+    xmlstr = minidom.parseString(ET.tostring(root)).toprettyxml(indent="   ")
+    print(xmlstr)
+
+
+# utility converts a millisecond value to a column width in pixels
+def time_to_width(value, end):
+    # map a value from (0,end) to (0,1000)
+    r = (float(value) / float(end)) * 1000.0
+    return int(r)
+
+
+# assign each entry to a thread by analyzing the start/end times and
+# slotting them into thread buckets where they fit
+def assign_entries_to_threads(entries):
+    # first sort the entries' keys by end timestamp
+    sorted_keys = sorted(
+        list(entries.keys()), key=lambda k: entries[k][1], reverse=True
+    )
+
+    # build the chart data by assigning entries to threads
+    results = {}
+    threads = []
+    for name in sorted_keys:
+        entry = entries[name]
+
+        # assign this entry by finding the first available thread identified
+        # by the thread's current start time greater than the entry's end time
+        tid = -1
+        for t in range(len(threads)):
+            if threads[t] >= entry[1]:
+                threads[t] = entry[0]
+                tid = t
+                break
+
+        # if no current thread found, create a new one with this entry
+        if tid < 0:
+            threads.append(entry[0])
+            tid = len(threads) - 1
+
+        # add entry name to the array associated with this tid
+        if tid not in results.keys():
+            results[tid] = []
+        results[tid].append(name)
+
+    # first entry has the last end time
+    end_time = entries[sorted_keys[0]][1]
+
+    # return the threaded entries and the last end time
+    return (results, end_time)
+
+
+# format the build-time
+def format_build_time(input_time):
+    build_time = abs(input_time)
+    build_time_str = str(build_time) + " ms"
+    if build_time > 120000:  # 2 minutes
+        minutes = int(build_time / 60000)
+        seconds = int(((build_time / 60000) - minutes) * 60)
+        build_time_str = "{:d}:{:02d} min".format(minutes, seconds)
+    elif build_time > 1000:
+        build_time_str = "{:.3f} s".format(build_time / 1000)
+    if input_time < 0:
+        build_time_str = "-" + build_time_str
+    return build_time_str
+
+
+# format file size
+def format_file_size(input_size):
+    file_size = abs(input_size)
+    file_size_str = ""
+    if file_size > 1000000:
+        file_size_str = "{:.3f} MB".format(file_size / 1000000)
+    elif file_size > 1000:
+        file_size_str = "{:.3f} KB".format(file_size / 1000)
+    elif file_size > 0:
+        file_size_str = str(file_size) + " bytes"
+    if input_size < 0:
+        file_size_str = "-" + file_size_str
+    return file_size_str
+
+
+# Output chart results in HTML format
+# Builds a standalone html file with no javascript or styles
+def output_html(entries, sorted_list, cmp_entries, args):
+    print("<html><head><title>Build Metrics Report</title>")
+    print("</head><body>")
+    if args.msg is not None:
+        msg_file = Path(args.msg)
+        if msg_file.is_file():
+            msg = msg_file.read_text()
+            print("<p>", msg, "</p>")
+
+    # map entries to threads
+    # the end_time is used to scale all the entries to a fixed output width
+    threads, end_time = assign_entries_to_threads(entries)
+
+    # color ranges for build times
+    summary = {"red": 0, "yellow": 0, "green": 0, "white": 0}
+    red = "bgcolor='#FFBBD0'"
+    yellow = "bgcolor='#FFFF80'"
+    green = "bgcolor='#AAFFBD'"
+    white = "bgcolor='#FFFFFF'"
+
+    # create the build-time chart
+    print("<table id='chart' width='1000px' bgcolor='#BBBBBB'>")
+    for tid in range(len(threads)):
+        names = threads[tid]
+        # sort the names for this thread by start time
+        names = sorted(names, key=lambda k: entries[k][0])
+
+        # use the last entry's end time as the total row size
+        # (this is an estimate and does not have to be exact)
+        last_entry = entries[names[len(names) - 1]]
+        last_time = time_to_width(last_entry[1], end_time)
+        print(
+            "<tr><td><table width='",
+            last_time,
+            "px' border='0' cellspacing='1' cellpadding='0'><tr>",
+            sep="",
+        )
+
+        prev_end = 0  # used for spacing between entries
+
+        # write out each entry for this thread as a column for a single row
+        for name in names:
+            entry = entries[name]
+            start = entry[0]
+            end = entry[1]
+
+            # this handles minor gaps between end of the
+            # previous entry and the start of the next
+            if prev_end > 0 and start > prev_end:
+                size = time_to_width(start - prev_end, end_time)
+                print("<td width='", size, "px'></td>")
+            # adjust for the cellspacing
+            prev_end = end + int(end_time / 500)
+
+            build_time = end - start
+            build_time_str = format_build_time(build_time)
+
+            # assign color and accumulate legend values
+            color = white
+            if build_time > 300000:  # 5 minutes
+                color = red
+                summary["red"] += 1
+            elif build_time > 120000:  # 2 minutes
+                color = yellow
+                summary["yellow"] += 1
+            elif build_time > 1000:  # 1 second
+                color = green
+                summary["green"] += 1
+            else:
+                summary["white"] += 1
+
+            # compute the pixel width based on build-time
+            size = max(time_to_width(build_time, end_time), 2)
+            # output the column for this entry
+            print("<td height='20px' width='", size, "px' ", sep="", end="")
+            # title text is shown as hover-text by most browsers
+            print(color, "title='", end="")
+            print(name, "\n", build_time_str, "' ", sep="", end="")
+            # centers the name if it fits in the box
+            print("align='center' nowrap>", end="")
+            # use a slightly smaller, fixed-width font
+            print("<font size='-2' face='courier'>", end="")
+
+            # add the file-name if it fits, otherwise, truncate the name
+            file_name = os.path.basename(name)
+            if len(file_name) + 3 > size / 7:
+                abbr_size = int(size / 7) - 3
+                if abbr_size > 1:
+                    print(file_name[:abbr_size], "...", sep="", end="")
+            else:
+                print(file_name, end="")
+            # done with this entry
+            print("</font></td>")
+            # update the entry with just the computed output info
+            entries[name] = (build_time, color, entry[2])
+
+        # add a filler column at the end of each row
+        print("<td width='*'></td></tr></table></td></tr>")
+
+    # done with the chart
+    print("</table><br/>")
+
+    # output detail table in build-time descending order
+    print("<table id='detail' bgcolor='#EEEEEE'>")
+    print(
+        "<tr><th>File</th>", "<th>Compile time</th>", "<th>Size</th>", sep=""
+    )
+    if cmp_entries:
+        print("<th>t-cmp</th>", sep="")
+    print("</tr>")
+
+    for name in sorted_list:
+        entry = entries[name]
+        build_time = entry[0]
+        color = entry[1]
+        file_size = entry[2]
+
+        build_time_str = format_build_time(build_time)
+        file_size_str = format_file_size(file_size)
+
+        # output entry row
+        print("<tr ", color, "><td>", name, "</td>", sep="", end="")
+        print("<td align='right'>", build_time_str, "</td>", sep="", end="")
+        print("<td align='right'>", file_size_str, "</td>", sep="", end="")
+        # output diff column
+        cmp_entry = (
+            cmp_entries[name] if cmp_entries and name in cmp_entries else None
+        )
+        if cmp_entry:
+            diff_time = build_time - (cmp_entry[1] - cmp_entry[0])
+            diff_time_str = format_build_time(diff_time)
+            diff_color = white
+            diff_percent = int((diff_time / build_time) * 100)
+            if build_time > 60000:
+                if diff_percent > 20:
+                    diff_color = red
+                    diff_time_str = "<b>" + diff_time_str + "</b>"
+                elif diff_percent < -20:
+                    diff_color = green
+                    diff_time_str = "<b>" + diff_time_str + "</b>"
+                elif diff_percent > 0:
+                    diff_color = yellow
+            print(
+                "<td align='right' ",
+                diff_color,
+                ">",
+                diff_time_str,
+                "</td>",
+                sep="",
+                end="",
+            )
+        print("</tr>")
+
+    print("</table><br/>")
+
+    # include summary table with color legend
+    print("<table id='legend' border='2' bgcolor='#EEEEEE'>")
+    print("<tr><td", red, ">time &gt; 5 minutes</td>")
+    print("<td align='right'>", summary["red"], "</td></tr>")
+    print("<tr><td", yellow, ">2 minutes &lt; time &lt; 5 minutes</td>")
+    print("<td align='right'>", summary["yellow"], "</td></tr>")
+    print("<tr><td", green, ">1 second &lt; time &lt; 2 minutes</td>")
+    print("<td align='right'>", summary["green"], "</td></tr>")
+    print("<tr><td", white, ">time &lt; 1 second</td>")
+    print("<td align='right'>", summary["white"], "</td></tr>")
+    print("</table>")
+
+    if cmp_entries:
+        print("<table id='legend' border='2' bgcolor='#EEEEEE'>")
+        print("<tr><td", red, ">time increase &gt; 20%</td></tr>")
+        print("<tr><td", yellow, ">time increase &gt; 0</td></tr>")
+        print("<tr><td", green, ">time decrease &gt; 20%</td></tr>")
+        print(
+            "<tr><td",
+            white,
+            ">time change &lt; 20%% or build time &lt; 1 minute</td></tr>",
+        )
+        print("</table>")
+
+    print("</body></html>")
+
+
+# output results in CSV format
+def output_csv(entries, sorted_list, cmp_entries, args):
+    print("time,size,file", end="")
+    if cmp_entries:
+        print(",diff", end="")
+    print()
+    for name in sorted_list:
+        entry = entries[name]
+        build_time = entry[1] - entry[0]
+        file_size = entry[2]
+        cmp_entry = (
+            cmp_entries[name] if cmp_entries and name in cmp_entries else None
+        )
+        print(build_time, file_size, name, sep=",", end="")
+        if cmp_entry:
+            diff_time = build_time - (cmp_entry[1] - cmp_entry[0])
+            print(",", diff_time, sep="", end="")
+        print()
+
+
+# parse log file into map
+entries = build_log_map(log_file)
+if len(entries) == 0:
+    print("Could not parse", log_file)
+    exit()
+
+# sort the entries by build-time (descending order)
+sorted_list = sorted(
+    list(entries.keys()),
+    key=lambda k: entries[k][1] - entries[k][0],
+    reverse=True,
+)
+
+# load the comparison build log if available
+cmp_entries = build_log_map(cmp_file) if cmp_file else None
+
+if output_fmt == "xml":
+    output_xml(entries, sorted_list, args)
+elif output_fmt == "html":
+    output_html(entries, sorted_list, cmp_entries, args)
+else:
+    output_csv(entries, sorted_list, cmp_entries, args)
diff --git a/cpp/src/aggregation/aggregation.cpp b/cpp/src/aggregation/aggregation.cpp
new file mode 100644
index 0000000..b3f2a77
--- /dev/null
+++ b/cpp/src/aggregation/aggregation.cpp
@@ -0,0 +1,888 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <memory>
+
+namespace cudf {
+
+namespace detail {
+
+// simple_aggregations_collector ----------------------------------------
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, aggregation const& agg)
+{
+  std::vector<std::unique_ptr<aggregation>> aggs;
+  aggs.push_back(agg.clone());
+  return aggs;
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, sum_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, product_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, min_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, max_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, count_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, histogram_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, any_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, all_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, sum_of_squares_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, mean_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, m2_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, var_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, std_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, median_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, quantile_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, argmax_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, argmin_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, nunique_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, nth_element_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, row_number_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, rank_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, collect_list_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, collect_set_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, lead_lag_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, udf_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, merge_lists_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, merge_sets_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, merge_m2_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, merge_histogram_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, covariance_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, correlation_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, tdigest_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+std::vector<std::unique_ptr<aggregation>> simple_aggregations_collector::visit(
+  data_type col_type, merge_tdigest_aggregation const& agg)
+{
+  return visit(col_type, static_cast<aggregation const&>(agg));
+}
+
+// aggregation_finalizer ----------------------------------------
+
+void aggregation_finalizer::visit(aggregation const& agg) {}
+
+void aggregation_finalizer::visit(sum_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(product_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(min_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(max_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(count_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+void aggregation_finalizer::visit(histogram_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(any_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(all_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(sum_of_squares_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(mean_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(m2_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(var_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(std_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(median_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(quantile_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(argmax_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(argmin_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(nunique_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(nth_element_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(row_number_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(rank_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(collect_list_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(collect_set_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(lead_lag_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(udf_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(merge_lists_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(merge_sets_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(merge_m2_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(merge_histogram_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(covariance_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(correlation_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(tdigest_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+void aggregation_finalizer::visit(merge_tdigest_aggregation const& agg)
+{
+  visit(static_cast<aggregation const&>(agg));
+}
+
+}  // namespace detail
+
+std::vector<std::unique_ptr<aggregation>> aggregation::get_simple_aggregations(
+  data_type col_type, cudf::detail::simple_aggregations_collector& collector) const
+{
+  return collector.visit(col_type, *this);
+}
+
+/// Factory to create a SUM aggregation
+template <typename Base>
+std::unique_ptr<Base> make_sum_aggregation()
+{
+  return std::make_unique<detail::sum_aggregation>();
+}
+template std::unique_ptr<aggregation> make_sum_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_sum_aggregation<rolling_aggregation>();
+template std::unique_ptr<groupby_aggregation> make_sum_aggregation<groupby_aggregation>();
+template std::unique_ptr<groupby_scan_aggregation> make_sum_aggregation<groupby_scan_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_sum_aggregation<reduce_aggregation>();
+template std::unique_ptr<scan_aggregation> make_sum_aggregation<scan_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_sum_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a PRODUCT aggregation
+template <typename Base>
+std::unique_ptr<Base> make_product_aggregation()
+{
+  return std::make_unique<detail::product_aggregation>();
+}
+template std::unique_ptr<aggregation> make_product_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation> make_product_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_product_aggregation<reduce_aggregation>();
+template std::unique_ptr<scan_aggregation> make_product_aggregation<scan_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_product_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a MIN aggregation
+template <typename Base>
+std::unique_ptr<Base> make_min_aggregation()
+{
+  return std::make_unique<detail::min_aggregation>();
+}
+template std::unique_ptr<aggregation> make_min_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_min_aggregation<rolling_aggregation>();
+template std::unique_ptr<groupby_aggregation> make_min_aggregation<groupby_aggregation>();
+template std::unique_ptr<groupby_scan_aggregation> make_min_aggregation<groupby_scan_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_min_aggregation<reduce_aggregation>();
+template std::unique_ptr<scan_aggregation> make_min_aggregation<scan_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_min_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a MAX aggregation
+template <typename Base>
+std::unique_ptr<Base> make_max_aggregation()
+{
+  return std::make_unique<detail::max_aggregation>();
+}
+template std::unique_ptr<aggregation> make_max_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_max_aggregation<rolling_aggregation>();
+template std::unique_ptr<groupby_aggregation> make_max_aggregation<groupby_aggregation>();
+template std::unique_ptr<groupby_scan_aggregation> make_max_aggregation<groupby_scan_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_max_aggregation<reduce_aggregation>();
+template std::unique_ptr<scan_aggregation> make_max_aggregation<scan_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_max_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a COUNT aggregation
+template <typename Base>
+std::unique_ptr<Base> make_count_aggregation(null_policy null_handling)
+{
+  auto kind =
+    (null_handling == null_policy::INCLUDE) ? aggregation::COUNT_ALL : aggregation::COUNT_VALID;
+  return std::make_unique<detail::count_aggregation>(kind);
+}
+template std::unique_ptr<aggregation> make_count_aggregation<aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<rolling_aggregation> make_count_aggregation<rolling_aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<groupby_aggregation> make_count_aggregation<groupby_aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<groupby_scan_aggregation> make_count_aggregation<groupby_scan_aggregation>(
+  null_policy null_handling);
+
+/// Factory to create a HISTOGRAM aggregation
+template <typename Base>
+std::unique_ptr<Base> make_histogram_aggregation()
+{
+  return std::make_unique<detail::histogram_aggregation>();
+}
+template std::unique_ptr<aggregation> make_histogram_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation> make_histogram_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_histogram_aggregation<reduce_aggregation>();
+
+/// Factory to create a ANY aggregation
+template <typename Base>
+std::unique_ptr<Base> make_any_aggregation()
+{
+  return std::make_unique<detail::any_aggregation>();
+}
+template std::unique_ptr<aggregation> make_any_aggregation<aggregation>();
+template std::unique_ptr<reduce_aggregation> make_any_aggregation<reduce_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_any_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a ALL aggregation
+template <typename Base>
+std::unique_ptr<Base> make_all_aggregation()
+{
+  return std::make_unique<detail::all_aggregation>();
+}
+template std::unique_ptr<aggregation> make_all_aggregation<aggregation>();
+template std::unique_ptr<reduce_aggregation> make_all_aggregation<reduce_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_all_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a SUM_OF_SQUARES aggregation
+template <typename Base>
+std::unique_ptr<Base> make_sum_of_squares_aggregation()
+{
+  return std::make_unique<detail::sum_of_squares_aggregation>();
+}
+template std::unique_ptr<aggregation> make_sum_of_squares_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation>
+make_sum_of_squares_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_sum_of_squares_aggregation<reduce_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_sum_of_squares_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a MEAN aggregation
+template <typename Base>
+std::unique_ptr<Base> make_mean_aggregation()
+{
+  return std::make_unique<detail::mean_aggregation>();
+}
+template std::unique_ptr<aggregation> make_mean_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_mean_aggregation<rolling_aggregation>();
+template std::unique_ptr<groupby_aggregation> make_mean_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_mean_aggregation<reduce_aggregation>();
+template std::unique_ptr<segmented_reduce_aggregation>
+make_mean_aggregation<segmented_reduce_aggregation>();
+
+/// Factory to create a M2 aggregation
+template <typename Base>
+std::unique_ptr<Base> make_m2_aggregation()
+{
+  return std::make_unique<detail::m2_aggregation>();
+}
+template std::unique_ptr<aggregation> make_m2_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation> make_m2_aggregation<groupby_aggregation>();
+
+/// Factory to create a VARIANCE aggregation
+template <typename Base>
+std::unique_ptr<Base> make_variance_aggregation(size_type ddof)
+{
+  return std::make_unique<detail::var_aggregation>(ddof);
+}
+template std::unique_ptr<aggregation> make_variance_aggregation<aggregation>(size_type ddof);
+template std::unique_ptr<rolling_aggregation> make_variance_aggregation<rolling_aggregation>(
+  size_type ddof);
+template std::unique_ptr<groupby_aggregation> make_variance_aggregation<groupby_aggregation>(
+  size_type ddof);
+template std::unique_ptr<reduce_aggregation> make_variance_aggregation<reduce_aggregation>(
+  size_type ddof);
+template std::unique_ptr<segmented_reduce_aggregation>
+make_variance_aggregation<segmented_reduce_aggregation>(size_type ddof);
+
+/// Factory to create a STD aggregation
+template <typename Base>
+std::unique_ptr<Base> make_std_aggregation(size_type ddof)
+{
+  return std::make_unique<detail::std_aggregation>(ddof);
+}
+template std::unique_ptr<aggregation> make_std_aggregation<aggregation>(size_type ddof);
+template std::unique_ptr<rolling_aggregation> make_std_aggregation<rolling_aggregation>(
+  size_type ddof);
+template std::unique_ptr<groupby_aggregation> make_std_aggregation<groupby_aggregation>(
+  size_type ddof);
+template std::unique_ptr<reduce_aggregation> make_std_aggregation<reduce_aggregation>(
+  size_type ddof);
+template std::unique_ptr<segmented_reduce_aggregation>
+make_std_aggregation<segmented_reduce_aggregation>(size_type ddof);
+
+/// Factory to create a MEDIAN aggregation
+template <typename Base>
+std::unique_ptr<Base> make_median_aggregation()
+{
+  return std::make_unique<detail::median_aggregation>();
+}
+template std::unique_ptr<aggregation> make_median_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation> make_median_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_median_aggregation<reduce_aggregation>();
+
+/// Factory to create a QUANTILE aggregation
+template <typename Base>
+std::unique_ptr<Base> make_quantile_aggregation(std::vector<double> const& quantiles,
+                                                interpolation interp)
+{
+  return std::make_unique<detail::quantile_aggregation>(quantiles, interp);
+}
+template std::unique_ptr<aggregation> make_quantile_aggregation<aggregation>(
+  std::vector<double> const& quantiles, interpolation interp);
+template std::unique_ptr<groupby_aggregation> make_quantile_aggregation<groupby_aggregation>(
+  std::vector<double> const& quantiles, interpolation interp);
+template std::unique_ptr<reduce_aggregation> make_quantile_aggregation<reduce_aggregation>(
+  std::vector<double> const& quantiles, interpolation interp);
+
+/// Factory to create an ARGMAX aggregation
+template <typename Base>
+std::unique_ptr<Base> make_argmax_aggregation()
+{
+  return std::make_unique<detail::argmax_aggregation>();
+}
+template std::unique_ptr<aggregation> make_argmax_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_argmax_aggregation<rolling_aggregation>();
+template std::unique_ptr<groupby_aggregation> make_argmax_aggregation<groupby_aggregation>();
+
+/// Factory to create an ARGMIN aggregation
+template <typename Base>
+std::unique_ptr<Base> make_argmin_aggregation()
+{
+  return std::make_unique<detail::argmin_aggregation>();
+}
+template std::unique_ptr<aggregation> make_argmin_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_argmin_aggregation<rolling_aggregation>();
+template std::unique_ptr<groupby_aggregation> make_argmin_aggregation<groupby_aggregation>();
+
+/// Factory to create an NUNIQUE aggregation
+template <typename Base>
+std::unique_ptr<Base> make_nunique_aggregation(null_policy null_handling)
+{
+  return std::make_unique<detail::nunique_aggregation>(null_handling);
+}
+template std::unique_ptr<aggregation> make_nunique_aggregation<aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<groupby_aggregation> make_nunique_aggregation<groupby_aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<reduce_aggregation> make_nunique_aggregation<reduce_aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<segmented_reduce_aggregation>
+make_nunique_aggregation<segmented_reduce_aggregation>(null_policy null_handling);
+
+/// Factory to create an NTH_ELEMENT aggregation
+template <typename Base>
+std::unique_ptr<Base> make_nth_element_aggregation(size_type n, null_policy null_handling)
+{
+  return std::make_unique<detail::nth_element_aggregation>(n, null_handling);
+}
+template std::unique_ptr<aggregation> make_nth_element_aggregation<aggregation>(
+  size_type n, null_policy null_handling);
+template std::unique_ptr<groupby_aggregation> make_nth_element_aggregation<groupby_aggregation>(
+  size_type n, null_policy null_handling);
+template std::unique_ptr<reduce_aggregation> make_nth_element_aggregation<reduce_aggregation>(
+  size_type n, null_policy null_handling);
+template std::unique_ptr<rolling_aggregation> make_nth_element_aggregation<rolling_aggregation>(
+  size_type n, null_policy null_handling);
+
+/// Factory to create a ROW_NUMBER aggregation
+template <typename Base>
+std::unique_ptr<Base> make_row_number_aggregation()
+{
+  return std::make_unique<detail::row_number_aggregation>();
+}
+template std::unique_ptr<aggregation> make_row_number_aggregation<aggregation>();
+template std::unique_ptr<rolling_aggregation> make_row_number_aggregation<rolling_aggregation>();
+
+/// Factory to create a RANK aggregation
+template <typename Base>
+std::unique_ptr<Base> make_rank_aggregation(rank_method method,
+                                            order column_order,
+                                            null_policy null_handling,
+                                            null_order null_precedence,
+                                            rank_percentage percentage)
+{
+  return std::make_unique<detail::rank_aggregation>(
+    method, column_order, null_handling, null_precedence, percentage);
+}
+template std::unique_ptr<aggregation> make_rank_aggregation<aggregation>(
+  rank_method method,
+  order column_order,
+  null_policy null_handling,
+  null_order null_precedence,
+  rank_percentage percentage);
+template std::unique_ptr<groupby_scan_aggregation> make_rank_aggregation<groupby_scan_aggregation>(
+  rank_method method,
+  order column_order,
+  null_policy null_handling,
+  null_order null_precedence,
+  rank_percentage percentage);
+template std::unique_ptr<scan_aggregation> make_rank_aggregation<scan_aggregation>(
+  rank_method method,
+  order column_order,
+  null_policy null_handling,
+  null_order null_precedence,
+  rank_percentage percentage);
+
+/// Factory to create a COLLECT_LIST aggregation
+template <typename Base>
+std::unique_ptr<Base> make_collect_list_aggregation(null_policy null_handling)
+{
+  return std::make_unique<detail::collect_list_aggregation>(null_handling);
+}
+template std::unique_ptr<aggregation> make_collect_list_aggregation<aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<rolling_aggregation> make_collect_list_aggregation<rolling_aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<groupby_aggregation> make_collect_list_aggregation<groupby_aggregation>(
+  null_policy null_handling);
+template std::unique_ptr<reduce_aggregation> make_collect_list_aggregation<reduce_aggregation>(
+  null_policy null_handling);
+
+/// Factory to create a COLLECT_SET aggregation
+template <typename Base>
+std::unique_ptr<Base> make_collect_set_aggregation(null_policy null_handling,
+                                                   null_equality nulls_equal,
+                                                   nan_equality nans_equal)
+{
+  return std::make_unique<detail::collect_set_aggregation>(null_handling, nulls_equal, nans_equal);
+}
+template std::unique_ptr<aggregation> make_collect_set_aggregation<aggregation>(
+  null_policy null_handling, null_equality nulls_equal, nan_equality nans_equal);
+template std::unique_ptr<rolling_aggregation> make_collect_set_aggregation<rolling_aggregation>(
+  null_policy null_handling, null_equality nulls_equal, nan_equality nans_equal);
+template std::unique_ptr<groupby_aggregation> make_collect_set_aggregation<groupby_aggregation>(
+  null_policy null_handling, null_equality nulls_equal, nan_equality nans_equal);
+template std::unique_ptr<reduce_aggregation> make_collect_set_aggregation<reduce_aggregation>(
+  null_policy null_handling, null_equality nulls_equal, nan_equality nans_equal);
+
+/// Factory to create a LAG aggregation
+template <typename Base>
+std::unique_ptr<Base> make_lag_aggregation(size_type offset)
+{
+  return std::make_unique<detail::lead_lag_aggregation>(aggregation::LAG, offset);
+}
+template std::unique_ptr<aggregation> make_lag_aggregation<aggregation>(size_type offset);
+template std::unique_ptr<rolling_aggregation> make_lag_aggregation<rolling_aggregation>(
+  size_type offset);
+
+/// Factory to create a LEAD aggregation
+template <typename Base>
+std::unique_ptr<Base> make_lead_aggregation(size_type offset)
+{
+  return std::make_unique<detail::lead_lag_aggregation>(aggregation::LEAD, offset);
+}
+template std::unique_ptr<aggregation> make_lead_aggregation<aggregation>(size_type offset);
+template std::unique_ptr<rolling_aggregation> make_lead_aggregation<rolling_aggregation>(
+  size_type offset);
+
+/// Factory to create a UDF aggregation
+template <typename Base>
+std::unique_ptr<Base> make_udf_aggregation(udf_type type,
+                                           std::string const& user_defined_aggregator,
+                                           data_type output_type)
+{
+  auto* a =
+    new detail::udf_aggregation{type == udf_type::PTX ? aggregation::PTX : aggregation::CUDA,
+                                user_defined_aggregator,
+                                output_type};
+  return std::unique_ptr<detail::udf_aggregation>(a);
+}
+template std::unique_ptr<aggregation> make_udf_aggregation<aggregation>(
+  udf_type type, std::string const& user_defined_aggregator, data_type output_type);
+template std::unique_ptr<rolling_aggregation> make_udf_aggregation<rolling_aggregation>(
+  udf_type type, std::string const& user_defined_aggregator, data_type output_type);
+
+/// Factory to create a MERGE_LISTS aggregation
+template <typename Base>
+std::unique_ptr<Base> make_merge_lists_aggregation()
+{
+  return std::make_unique<detail::merge_lists_aggregation>();
+}
+template std::unique_ptr<aggregation> make_merge_lists_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation> make_merge_lists_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_merge_lists_aggregation<reduce_aggregation>();
+
+/// Factory to create a MERGE_SETS aggregation
+template <typename Base>
+std::unique_ptr<Base> make_merge_sets_aggregation(null_equality nulls_equal,
+                                                  nan_equality nans_equal)
+{
+  return std::make_unique<detail::merge_sets_aggregation>(nulls_equal, nans_equal);
+}
+template std::unique_ptr<aggregation> make_merge_sets_aggregation<aggregation>(null_equality,
+                                                                               nan_equality);
+template std::unique_ptr<groupby_aggregation> make_merge_sets_aggregation<groupby_aggregation>(
+  null_equality, nan_equality);
+template std::unique_ptr<reduce_aggregation> make_merge_sets_aggregation<reduce_aggregation>(
+  null_equality, nan_equality);
+
+/// Factory to create a MERGE_M2 aggregation
+template <typename Base>
+std::unique_ptr<Base> make_merge_m2_aggregation()
+{
+  return std::make_unique<detail::merge_m2_aggregation>();
+}
+template std::unique_ptr<aggregation> make_merge_m2_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation> make_merge_m2_aggregation<groupby_aggregation>();
+
+/// Factory to create a MERGE_HISTOGRAM aggregation
+template <typename Base>
+std::unique_ptr<Base> make_merge_histogram_aggregation()
+{
+  return std::make_unique<detail::merge_histogram_aggregation>();
+}
+template std::unique_ptr<aggregation> make_merge_histogram_aggregation<aggregation>();
+template std::unique_ptr<groupby_aggregation>
+make_merge_histogram_aggregation<groupby_aggregation>();
+template std::unique_ptr<reduce_aggregation> make_merge_histogram_aggregation<reduce_aggregation>();
+
+/// Factory to create a COVARIANCE aggregation
+template <typename Base>
+std::unique_ptr<Base> make_covariance_aggregation(size_type min_periods, size_type ddof)
+{
+  return std::make_unique<detail::covariance_aggregation>(min_periods, ddof);
+}
+template std::unique_ptr<aggregation> make_covariance_aggregation<aggregation>(
+  size_type min_periods, size_type ddof);
+template std::unique_ptr<groupby_aggregation> make_covariance_aggregation<groupby_aggregation>(
+  size_type min_periods, size_type ddof);
+
+/// Factory to create a CORRELATION aggregation
+template <typename Base>
+std::unique_ptr<Base> make_correlation_aggregation(correlation_type type, size_type min_periods)
+{
+  return std::make_unique<detail::correlation_aggregation>(type, min_periods);
+}
+template std::unique_ptr<aggregation> make_correlation_aggregation<aggregation>(
+  correlation_type type, size_type min_periods);
+template std::unique_ptr<groupby_aggregation> make_correlation_aggregation<groupby_aggregation>(
+  correlation_type type, size_type min_periods);
+
+template <typename Base>
+std::unique_ptr<Base> make_tdigest_aggregation(int max_centroids)
+{
+  return std::make_unique<detail::tdigest_aggregation>(max_centroids);
+}
+template std::unique_ptr<aggregation> make_tdigest_aggregation<aggregation>(int max_centroids);
+template std::unique_ptr<groupby_aggregation> make_tdigest_aggregation<groupby_aggregation>(
+  int max_centroids);
+template std::unique_ptr<reduce_aggregation> make_tdigest_aggregation<reduce_aggregation>(
+  int max_centroids);
+
+template <typename Base>
+std::unique_ptr<Base> make_merge_tdigest_aggregation(int max_centroids)
+{
+  return std::make_unique<detail::merge_tdigest_aggregation>(max_centroids);
+}
+template std::unique_ptr<aggregation> make_merge_tdigest_aggregation<aggregation>(
+  int max_centroids);
+template std::unique_ptr<groupby_aggregation> make_merge_tdigest_aggregation<groupby_aggregation>(
+  int max_centroids);
+template std::unique_ptr<reduce_aggregation> make_merge_tdigest_aggregation<reduce_aggregation>(
+  int max_centroids);
+
+namespace detail {
+namespace {
+struct target_type_functor {
+  data_type type;
+  template <typename Source, aggregation::Kind k>
+  constexpr data_type operator()() const noexcept
+  {
+    using Type    = target_type_t<Source, k>;
+    auto const id = type_to_id<Type>();
+    return cudf::is_fixed_point<Type>() ? data_type{id, type.scale()} : data_type{id};
+  }
+};
+
+struct is_valid_aggregation_impl {
+  template <typename Source, aggregation::Kind k>
+  constexpr bool operator()() const noexcept
+  {
+    return is_valid_aggregation<Source, k>();
+  }
+};
+}  // namespace
+
+// Return target data_type for the given source_type and aggregation
+data_type target_type(data_type source, aggregation::Kind k)
+{
+  return dispatch_type_and_aggregation(source, k, target_type_functor{source});
+}
+
+// Verifies the aggregation `k` is valid on the type `source`
+bool is_valid_aggregation(data_type source, aggregation::Kind k)
+{
+  return dispatch_type_and_aggregation(source, k, is_valid_aggregation_impl{});
+}
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/aggregation/aggregation.cu b/cpp/src/aggregation/aggregation.cu
new file mode 100644
index 0000000..02998b8
--- /dev/null
+++ b/cpp/src/aggregation/aggregation.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/aggregation/aggregation.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+void initialize_with_identity(mutable_table_view& table,
+                              std::vector<aggregation::Kind> const& aggs,
+                              rmm::cuda_stream_view stream)
+{
+  // TODO: Initialize all the columns in a single kernel instead of invoking one
+  // kernel per column
+  for (size_type i = 0; i < table.num_columns(); ++i) {
+    auto col = table.column(i);
+    dispatch_type_and_aggregation(col.type(), aggs[i], identity_initializer{}, col, stream);
+  }
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/aggregation/result_cache.cpp b/cpp/src/aggregation/result_cache.cpp
new file mode 100644
index 0000000..ea6894b
--- /dev/null
+++ b/cpp/src/aggregation/result_cache.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/aggregation/result_cache.hpp>
+
+namespace cudf {
+namespace detail {
+
+bool result_cache::has_result(column_view const& input, aggregation const& agg) const
+{
+  return _cache.count({input, agg});
+}
+
+void result_cache::add_result(column_view const& input,
+                              aggregation const& agg,
+                              std::unique_ptr<column>&& col)
+{
+  // We can't guarantee that agg will outlive the cache, so we need to take ownership of a copy.
+  // To allow lookup by reference, make the key a reference and keep the owner in the value pair.
+  auto owned_agg  = agg.clone();
+  auto const& key = *owned_agg;
+  // try_emplace doesn't update/insert if already present
+  _cache.try_emplace({input, key}, std::move(owned_agg), std::move(col));
+}
+
+column_view result_cache::get_result(column_view const& input, aggregation const& agg) const
+{
+  auto result_it = _cache.find({input, agg});
+  CUDF_EXPECTS(result_it != _cache.end(), "Result does not exist in cache");
+  return result_it->second.second->view();
+}
+
+std::unique_ptr<column> result_cache::release_result(column_view const& input,
+                                                     aggregation const& agg)
+{
+  auto node = _cache.extract({input, agg});
+  CUDF_EXPECTS(not node.empty(), "Result does not exist in cache");
+  return std::move(node.mapped().second);
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/ast/expression_parser.cpp b/cpp/src/ast/expression_parser.cpp
new file mode 100644
index 0000000..3b650d7
--- /dev/null
+++ b/cpp/src/ast/expression_parser.cpp
@@ -0,0 +1,240 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/detail/operators.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+#include <functional>
+#include <iterator>
+
+namespace cudf {
+
+namespace ast {
+
+namespace detail {
+
+device_data_reference::device_data_reference(device_data_reference_type reference_type,
+                                             cudf::data_type data_type,
+                                             cudf::size_type data_index,
+                                             table_reference table_source)
+  : reference_type(reference_type),
+    data_type(data_type),
+    data_index(data_index),
+    table_source(table_source)
+{
+}
+
+device_data_reference::device_data_reference(device_data_reference_type reference_type,
+                                             cudf::data_type data_type,
+                                             cudf::size_type data_index)
+  : reference_type(reference_type),
+    data_type(data_type),
+    data_index(data_index),
+    table_source(table_reference::LEFT)
+{
+}
+
+cudf::size_type expression_parser::intermediate_counter::take()
+{
+  auto const first_missing = find_first_missing();
+  used_values.insert(used_values.cbegin() + first_missing, first_missing);
+  max_used = std::max(max_used, first_missing + 1);
+  return first_missing;
+}
+
+void expression_parser::intermediate_counter::give(cudf::size_type value)
+{
+  // TODO: add comment
+  auto const lower_bound = std::lower_bound(used_values.cbegin(), used_values.cend(), value);
+  if ((lower_bound != used_values.cend()) && (*lower_bound == value))
+    used_values.erase(lower_bound);
+}
+
+cudf::size_type expression_parser::intermediate_counter::find_first_missing() const
+{
+  if (used_values.empty() || (used_values.front() != 0)) { return 0; }
+  // Search for the first non-contiguous pair of elements.
+  auto diff_not_one = [](auto a, auto b) { return a != b - 1; };
+  auto it           = std::adjacent_find(used_values.cbegin(), used_values.cend(), diff_not_one);
+  return it != used_values.cend()
+           ? *it + 1              // A missing value was found and is returned.
+           : used_values.size();  // No missing elements. Return the next element in the sequence.
+}
+
+cudf::size_type expression_parser::visit(literal const& expr)
+{
+  if (_expression_count == 0) {
+    // Handle the trivial case of a literal as the entire expression.
+    return visit(operation(ast_operator::IDENTITY, expr));
+  } else {
+    _expression_count++;                                           // Increment the expression index
+    auto const data_type     = expr.get_data_type();               // Resolve expression type
+    auto device_view         = expr.get_value();                   // Construct a scalar device view
+    auto const literal_index = cudf::size_type(_literals.size());  // Push literal
+    _literals.push_back(device_view);
+    auto const source = detail::device_data_reference(detail::device_data_reference_type::LITERAL,
+                                                      data_type,
+                                                      literal_index);  // Push data reference
+    return add_data_reference(source);
+  }
+}
+
+cudf::size_type expression_parser::visit(column_reference const& expr)
+{
+  if (_expression_count == 0) {
+    // Handle the trivial case of a column reference as the entire expression.
+    return visit(operation(ast_operator::IDENTITY, expr));
+  } else {
+    // Increment the expression index
+    _expression_count++;
+    // Resolve expression type
+    cudf::data_type data_type;
+    if (expr.get_table_source() == table_reference::LEFT) {
+      data_type = expr.get_data_type(_left);
+    } else {
+      if (_right.has_value()) {
+        data_type = expr.get_data_type(*_right);
+      } else {
+        CUDF_FAIL(
+          "Your expression contains a reference to the RIGHT table even though it will only be "
+          "evaluated on a single table (by convention, the LEFT table).");
+      }
+    }
+    // Push data reference
+    auto const source = detail::device_data_reference(detail::device_data_reference_type::COLUMN,
+                                                      data_type,
+                                                      expr.get_column_index(),
+                                                      expr.get_table_source());
+    return add_data_reference(source);
+  }
+}
+
+cudf::size_type expression_parser::visit(operation const& expr)
+{
+  // Increment the expression index
+  auto const expression_index = _expression_count++;
+  // Visit children (operands) of this expression
+  auto const operand_data_ref_indices = visit_operands(expr.get_operands());
+  // Resolve operand types
+  auto data_ref = [this](auto const& index) { return _data_references[index].data_type; };
+  auto begin    = thrust::make_transform_iterator(operand_data_ref_indices.cbegin(), data_ref);
+  auto end      = begin + operand_data_ref_indices.size();
+  auto const operand_types = std::vector<cudf::data_type>(begin, end);
+
+  // Validate types of operand data references match
+  if (std::adjacent_find(operand_types.cbegin(), operand_types.cend(), std::not_equal_to<>()) !=
+      operand_types.cend()) {
+    CUDF_FAIL("An AST expression was provided non-matching operand types.");
+  }
+
+  // Give back intermediate storage locations that are consumed by this operation
+  std::for_each(
+    operand_data_ref_indices.cbegin(),
+    operand_data_ref_indices.cend(),
+    [this](auto const& data_reference_index) {
+      auto const operand_source = _data_references[data_reference_index];
+      if (operand_source.reference_type == detail::device_data_reference_type::INTERMEDIATE) {
+        auto const intermediate_index = operand_source.data_index;
+        _intermediate_counter.give(intermediate_index);
+      }
+    });
+  // Resolve expression type
+  auto const op        = expr.get_operator();
+  auto const data_type = cudf::ast::detail::ast_operator_return_type(op, operand_types);
+  _operators.push_back(op);
+  // Push data reference
+  auto const output = [&]() {
+    if (expression_index == 0) {
+      // This expression is the root. Output should be directed to the output column.
+      return detail::device_data_reference(
+        detail::device_data_reference_type::COLUMN, data_type, 0, table_reference::OUTPUT);
+    } else {
+      // This expression is not the root. Output is an intermediate value.
+      // Ensure that the output type is fixed width and fits in the intermediate storage.
+      if (!cudf::is_fixed_width(data_type)) {
+        CUDF_FAIL(
+          "The output data type is not a fixed-width type but must be stored in an intermediate.");
+      } else if (cudf::size_of(data_type) > (_has_nulls ? sizeof(IntermediateDataType<true>)
+                                                        : sizeof(IntermediateDataType<false>))) {
+        CUDF_FAIL("The output data type is too large to be stored in an intermediate.");
+      }
+      return detail::device_data_reference(
+        detail::device_data_reference_type::INTERMEDIATE, data_type, _intermediate_counter.take());
+    }
+  }();
+  auto const index = add_data_reference(output);
+  // Insert source indices from all operands (sources) and this operator (destination)
+  _operator_source_indices.insert(_operator_source_indices.end(),
+                                  operand_data_ref_indices.cbegin(),
+                                  operand_data_ref_indices.cend());
+  _operator_source_indices.push_back(index);
+  return index;
+}
+
+// TODO: Eliminate column name references from expression_parser because
+// 2 code paths diverge in supporting column name references:
+// 1. column name references are specific to cuIO
+// 2. column name references are not supported in the libcudf table operations such as join,
+// transform.
+cudf::size_type expression_parser::visit(column_name_reference const& expr)
+{
+  CUDF_FAIL("Column name references are not supported in the AST expression parser.");
+}
+
+cudf::data_type expression_parser::output_type() const
+{
+  return _data_references.empty() ? cudf::data_type(cudf::type_id::EMPTY)
+                                  : _data_references.back().data_type;
+}
+
+std::vector<cudf::size_type> expression_parser::visit_operands(
+  std::vector<std::reference_wrapper<expression const>> operands)
+{
+  auto operand_data_reference_indices = std::vector<cudf::size_type>();
+  for (auto const& operand : operands) {
+    auto const operand_data_reference_index = operand.get().accept(*this);
+    operand_data_reference_indices.push_back(operand_data_reference_index);
+  }
+  return operand_data_reference_indices;
+}
+
+cudf::size_type expression_parser::add_data_reference(detail::device_data_reference data_ref)
+{
+  // If an equivalent data reference already exists, return its index. Otherwise add this data
+  // reference and return the new index.
+  auto const it = std::find(_data_references.cbegin(), _data_references.cend(), data_ref);
+  if (it != _data_references.cend()) {
+    return std::distance(_data_references.cbegin(), it);
+  } else {
+    _data_references.push_back(data_ref);
+    return _data_references.size() - 1;
+  }
+}
+
+}  // namespace detail
+
+}  // namespace ast
+
+}  // namespace cudf
diff --git a/cpp/src/ast/expressions.cpp b/cpp/src/ast/expressions.cpp
new file mode 100644
index 0000000..b45b9d0
--- /dev/null
+++ b/cpp/src/ast/expressions.cpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/detail/expression_transformer.hpp>
+#include <cudf/ast/detail/operators.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+namespace cudf {
+namespace ast {
+
+operation::operation(ast_operator op, expression const& input) : op(op), operands({input})
+{
+  if (cudf::ast::detail::ast_operator_arity(op) != 1) {
+    CUDF_FAIL("The provided operator is not a unary operator.");
+  }
+}
+
+operation::operation(ast_operator op, expression const& left, expression const& right)
+  : op(op), operands({left, right})
+{
+  if (cudf::ast::detail::ast_operator_arity(op) != 2) {
+    CUDF_FAIL("The provided operator is not a binary operator.");
+  }
+}
+
+cudf::size_type literal::accept(detail::expression_parser& visitor) const
+{
+  return visitor.visit(*this);
+}
+cudf::size_type column_reference::accept(detail::expression_parser& visitor) const
+{
+  return visitor.visit(*this);
+}
+cudf::size_type operation::accept(detail::expression_parser& visitor) const
+{
+  return visitor.visit(*this);
+}
+cudf::size_type column_name_reference::accept(detail::expression_parser& visitor) const
+{
+  return visitor.visit(*this);
+}
+
+auto literal::accept(detail::expression_transformer& visitor) const
+  -> decltype(visitor.visit(*this))
+{
+  return visitor.visit(*this);
+}
+auto column_reference::accept(detail::expression_transformer& visitor) const
+  -> decltype(visitor.visit(*this))
+{
+  return visitor.visit(*this);
+}
+auto operation::accept(detail::expression_transformer& visitor) const
+  -> decltype(visitor.visit(*this))
+{
+  return visitor.visit(*this);
+}
+auto column_name_reference::accept(detail::expression_transformer& visitor) const
+  -> decltype(visitor.visit(*this))
+{
+  return visitor.visit(*this);
+}
+}  // namespace ast
+
+}  // namespace cudf
diff --git a/cpp/src/binaryop/binaryop.cpp b/cpp/src/binaryop/binaryop.cpp
new file mode 100644
index 0000000..ef07de8
--- /dev/null
+++ b/cpp/src/binaryop/binaryop.cpp
@@ -0,0 +1,442 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compiled/binary_ops.hpp"
+
+#include <jit_preprocessed_files/binaryop/jit/kernel.cu.jit.hpp>
+
+#include <jit/cache.hpp>
+#include <jit/parser.hpp>
+#include <jit/util.hpp>
+
+#include <cudf/binaryop.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/binaryop.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <string>
+
+#include <thrust/optional.h>
+
+namespace cudf {
+namespace binops {
+
+/**
+ * @brief Computes output valid mask for op between a column and a scalar
+ */
+std::pair<rmm::device_buffer, size_type> scalar_col_valid_mask_and(
+  column_view const& col,
+  scalar const& s,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (col.is_empty()) return std::pair(rmm::device_buffer{0, stream, mr}, 0);
+
+  if (not s.is_valid(stream)) {
+    return std::pair(cudf::detail::create_null_mask(col.size(), mask_state::ALL_NULL, stream, mr),
+                     col.size());
+  } else if (s.is_valid(stream) and col.nullable()) {
+    return std::pair(cudf::detail::copy_bitmask(col, stream, mr), col.null_count());
+  } else {
+    return std::pair(rmm::device_buffer{0, stream, mr}, 0);
+  }
+}
+
+/**
+ * @brief Does the binop need to know if an operand is null/invalid to perform special
+ * processing?
+ */
+inline bool is_null_dependent(binary_operator op)
+{
+  return op == binary_operator::NULL_EQUALS || op == binary_operator::NULL_MIN ||
+         op == binary_operator::NULL_MAX || op == binary_operator::NULL_LOGICAL_AND ||
+         op == binary_operator::NULL_LOGICAL_OR;
+}
+
+/**
+ * @brief Returns `true` if `binary_operator` `op` is a basic arithmetic binary operation
+ */
+bool is_basic_arithmetic_binop(binary_operator op)
+{
+  return op == binary_operator::ADD or       // operator +
+         op == binary_operator::SUB or       // operator -
+         op == binary_operator::MUL or       // operator *
+         op == binary_operator::DIV or       // operator / using common type of lhs and rhs
+         op == binary_operator::NULL_MIN or  // 2 null = null, 1 null = value, else min
+         op == binary_operator::NULL_MAX or  // 2 null = null, 1 null = value, else max
+         op == binary_operator::MOD or       // operator %
+         op == binary_operator::PMOD or      // positive modulo operator
+         op == binary_operator::PYMOD;  // operator % but following Python's negative sign rules
+}
+
+/**
+ * @brief Returns `true` if `binary_operator` `op` is a comparison binary operation
+ */
+bool is_comparison_binop(binary_operator op)
+{
+  return op == binary_operator::EQUAL or          // operator ==
+         op == binary_operator::NOT_EQUAL or      // operator !=
+         op == binary_operator::LESS or           // operator <
+         op == binary_operator::GREATER or        // operator >
+         op == binary_operator::LESS_EQUAL or     // operator <=
+         op == binary_operator::GREATER_EQUAL or  // operator >=
+         op == binary_operator::NULL_EQUALS;      // 2 null = true; 1 null = false; else ==
+}
+
+/**
+ * @brief Returns `true` if `binary_operator` `op` is supported by `fixed_point`
+ */
+bool is_supported_fixed_point_binop(binary_operator op)
+{
+  return is_basic_arithmetic_binop(op) or is_comparison_binop(op);
+}
+
+/**
+ * @brief Helper predicate function that identifies if `op` requires scales to be the same
+ *
+ * @param op `binary_operator`
+ * @return true `op` requires scales of lhs and rhs to be the same
+ * @return false `op` does not require scales of lhs and rhs to be the same
+ */
+bool is_same_scale_necessary(binary_operator op)
+{
+  return op != binary_operator::MUL && op != binary_operator::DIV;
+}
+
+namespace jit {
+void binary_operation(mutable_column_view& out,
+                      column_view const& lhs,
+                      column_view const& rhs,
+                      std::string const& ptx,
+                      rmm::cuda_stream_view stream)
+{
+  std::string const output_type_name = cudf::type_to_name(out.type());
+
+  std::string cuda_source =
+    cudf::jit::parse_single_function_ptx(ptx, "GENERIC_BINARY_OP", output_type_name);
+
+  std::string kernel_name = jitify2::reflection::Template("cudf::binops::jit::kernel_v_v")
+                              .instantiate(output_type_name,  // list of template arguments
+                                           cudf::type_to_name(lhs.type()),
+                                           cudf::type_to_name(rhs.type()),
+                                           std::string("cudf::binops::jit::UserDefinedOp"));
+
+  cudf::jit::get_program_cache(*binaryop_jit_kernel_cu_jit)
+    .get_kernel(kernel_name, {}, {{"binaryop/jit/operation-udf.hpp", cuda_source}}, {"-arch=sm_."})
+    ->configure_1d_max_occupancy(0, 0, 0, stream.value())
+    ->launch(out.size(),
+             cudf::jit::get_data_ptr(out),
+             cudf::jit::get_data_ptr(lhs),
+             cudf::jit::get_data_ptr(rhs));
+}
+}  // namespace jit
+
+// Compiled Binary operation
+namespace compiled {
+
+template <typename Lhs, typename Rhs>
+void fixed_point_binary_operation_validation(binary_operator op,
+                                             Lhs lhs,
+                                             Rhs rhs,
+                                             thrust::optional<cudf::data_type> output_type = {})
+{
+  CUDF_EXPECTS((is_fixed_point(lhs) or is_fixed_point(rhs)),
+               "One of the inputs must have fixed_point data_type.");
+  CUDF_EXPECTS(binops::is_supported_fixed_point_binop(op),
+               "Unsupported fixed_point binary operation");
+  if (output_type.has_value() and binops::is_comparison_binop(op))
+    CUDF_EXPECTS(output_type == cudf::data_type{type_id::BOOL8},
+                 "Comparison operations require boolean output type.");
+}
+
+/**
+ * @copydoc cudf::binary_operation(column_view const&, column_view const&,
+ * binary_operator, data_type, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename LhsType, typename RhsType>
+std::unique_ptr<column> binary_operation(LhsType const& lhs,
+                                         RhsType const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if constexpr (std::is_same_v<LhsType, column_view> and std::is_same_v<RhsType, column_view>)
+    CUDF_EXPECTS(lhs.size() == rhs.size(), "Column sizes don't match");
+
+  if (lhs.type().id() == type_id::STRING and rhs.type().id() == type_id::STRING and
+      output_type.id() == type_id::STRING and
+      (op == binary_operator::NULL_MAX or op == binary_operator::NULL_MIN))
+    return cudf::binops::compiled::string_null_min_max(lhs, rhs, op, output_type, stream, mr);
+
+  if (not cudf::binops::compiled::is_supported_operation(output_type, lhs.type(), rhs.type(), op))
+    CUDF_FAIL("Unsupported operator for these types", cudf::data_type_error);
+
+  if (cudf::is_fixed_point(lhs.type()) or cudf::is_fixed_point(rhs.type())) {
+    cudf::binops::compiled::fixed_point_binary_operation_validation(
+      op, lhs.type(), rhs.type(), output_type);
+  }
+
+  auto out = make_fixed_width_column_for_output(lhs, rhs, op, output_type, stream, mr);
+
+  if constexpr (std::is_same_v<LhsType, column_view>)
+    if (lhs.is_empty()) return out;
+  if constexpr (std::is_same_v<RhsType, column_view>)
+    if (rhs.is_empty()) return out;
+
+  auto out_view = out->mutable_view();
+  cudf::binops::compiled::binary_operation(out_view, lhs, rhs, op, stream);
+  // TODO: consider having the binary_operation count nulls instead
+  out->set_null_count(cudf::detail::null_count(out_view.null_mask(), 0, out->size(), stream));
+  return out;
+}
+}  // namespace compiled
+}  // namespace binops
+
+namespace detail {
+
+// There are 3 overloads of each of the following functions:
+// - `make_fixed_width_column_for_output`
+// - `binary_operation`
+
+// The overloads are overloaded on the first two parameters of each function:
+// - scalar      const& lhs, column_view const& rhs,
+// - column_view const& lhs, scalar      const& rhs
+// - column_view const& lhs, column_view const& rhs,
+
+/**
+ * @brief Helper function for making output column for binary operation
+ *
+ * @param lhs Left-hand side `scalar` used in the binary operation
+ * @param rhs Right-hand side `column_view` used in the binary operation
+ * @param op `binary_operator` to be used to combine `lhs` and `rhs`
+ * @param output_type `data_type` of the output column
+ * @param stream CUDA stream used for device memory operations
+ * @param mr Device memory resource to use for device memory allocation
+ * @return std::unique_ptr<column> Output column used for binary operation
+ */
+std::unique_ptr<column> make_fixed_width_column_for_output(scalar const& lhs,
+                                                           column_view const& rhs,
+                                                           binary_operator op,
+                                                           data_type output_type,
+                                                           rmm::cuda_stream_view stream,
+                                                           rmm::mr::device_memory_resource* mr)
+{
+  if (binops::is_null_dependent(op)) {
+    return make_fixed_width_column(output_type, rhs.size(), mask_state::ALL_VALID, stream, mr);
+  } else {
+    auto [new_mask, new_null_count] = binops::scalar_col_valid_mask_and(rhs, lhs, stream, mr);
+    return make_fixed_width_column(
+      output_type, rhs.size(), std::move(new_mask), new_null_count, stream, mr);
+  }
+};
+
+/**
+ * @brief Helper function for making output column for binary operation
+ *
+ * @param lhs Left-hand side `column_view` used in the binary operation
+ * @param rhs Right-hand side `scalar` used in the binary operation
+ * @param op `binary_operator` to be used to combine `lhs` and `rhs`
+ * @param output_type `data_type` of the output column
+ * @param stream CUDA stream used for device memory operations
+ * @param mr Device memory resource to use for device memory allocation
+ * @return std::unique_ptr<column> Output column used for binary operation
+ */
+std::unique_ptr<column> make_fixed_width_column_for_output(column_view const& lhs,
+                                                           scalar const& rhs,
+                                                           binary_operator op,
+                                                           data_type output_type,
+                                                           rmm::cuda_stream_view stream,
+                                                           rmm::mr::device_memory_resource* mr)
+{
+  if (binops::is_null_dependent(op)) {
+    return make_fixed_width_column(output_type, lhs.size(), mask_state::ALL_VALID, stream, mr);
+  } else {
+    auto [new_mask, new_null_count] = binops::scalar_col_valid_mask_and(lhs, rhs, stream, mr);
+    return make_fixed_width_column(
+      output_type, lhs.size(), std::move(new_mask), new_null_count, stream, mr);
+  }
+};
+
+/**
+ * @brief Helper function for making output column for binary operation
+ *
+ * @param lhs Left-hand side `column_view` used in the binary operation
+ * @param rhs Right-hand side `column_view` used in the binary operation
+ * @param op `binary_operator` to be used to combine `lhs` and `rhs`
+ * @param output_type `data_type` of the output column
+ * @param stream CUDA stream used for device memory operations
+ * @param mr Device memory resource to use for device memory allocation
+ * @return std::unique_ptr<column> Output column used for binary operation
+ */
+std::unique_ptr<column> make_fixed_width_column_for_output(column_view const& lhs,
+                                                           column_view const& rhs,
+                                                           binary_operator op,
+                                                           data_type output_type,
+                                                           rmm::cuda_stream_view stream,
+                                                           rmm::mr::device_memory_resource* mr)
+{
+  if (binops::is_null_dependent(op)) {
+    return make_fixed_width_column(output_type, rhs.size(), mask_state::ALL_VALID, stream, mr);
+  } else {
+    auto [new_mask, null_count] = cudf::detail::bitmask_and(table_view({lhs, rhs}), stream, mr);
+    return make_fixed_width_column(
+      output_type, lhs.size(), std::move(new_mask), null_count, stream, mr);
+  }
+};
+
+std::unique_ptr<column> binary_operation(scalar const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  return binops::compiled::binary_operation<scalar, column_view>(
+    lhs, rhs, op, output_type, stream, mr);
+}
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         scalar const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  return binops::compiled::binary_operation<column_view, scalar>(
+    lhs, rhs, op, output_type, stream, mr);
+}
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  return binops::compiled::binary_operation<column_view, column_view>(
+    lhs, rhs, op, output_type, stream, mr);
+}
+
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         std::string const& ptx,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  // Check for datatype
+  auto is_type_supported_ptx = [](data_type type) -> bool {
+    return is_fixed_width(type) and not is_fixed_point(type) and
+           type.id() != type_id::INT8;  // Numba PTX doesn't support int8
+  };
+
+  CUDF_EXPECTS(is_type_supported_ptx(lhs.type()), "Invalid/Unsupported lhs datatype");
+  CUDF_EXPECTS(is_type_supported_ptx(rhs.type()), "Invalid/Unsupported rhs datatype");
+  CUDF_EXPECTS(is_type_supported_ptx(output_type), "Invalid/Unsupported output datatype");
+
+  CUDF_EXPECTS((lhs.size() == rhs.size()), "Column sizes don't match");
+
+  auto [new_mask, null_count] = bitmask_and(table_view({lhs, rhs}), stream, mr);
+  auto out =
+    make_fixed_width_column(output_type, lhs.size(), std::move(new_mask), null_count, stream, mr);
+
+  // Check for 0 sized data
+  if (lhs.is_empty() or rhs.is_empty()) return out;
+
+  auto out_view = out->mutable_view();
+  binops::jit::binary_operation(out_view, lhs, rhs, ptx, stream);
+  out->set_null_count(cudf::detail::null_count(out_view.null_mask(), 0, out->size(), stream));
+  return out;
+}
+}  // namespace detail
+
+int32_t binary_operation_fixed_point_scale(binary_operator op,
+                                           int32_t left_scale,
+                                           int32_t right_scale)
+{
+  CUDF_EXPECTS(binops::is_supported_fixed_point_binop(op),
+               "Unsupported fixed_point binary operation.");
+  if (op == binary_operator::MUL) return left_scale + right_scale;
+  if (op == binary_operator::DIV) return left_scale - right_scale;
+  return std::min(left_scale, right_scale);
+}
+
+cudf::data_type binary_operation_fixed_point_output_type(binary_operator op,
+                                                         cudf::data_type const& lhs,
+                                                         cudf::data_type const& rhs)
+{
+  cudf::binops::compiled::fixed_point_binary_operation_validation(op, lhs, rhs);
+
+  auto const scale = binary_operation_fixed_point_scale(op, lhs.scale(), rhs.scale());
+  return cudf::data_type{lhs.id(), scale};
+}
+
+std::unique_ptr<column> binary_operation(scalar const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::binary_operation(lhs, rhs, op, output_type, cudf::get_default_stream(), mr);
+}
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         scalar const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::binary_operation(lhs, rhs, op, output_type, cudf::get_default_stream(), mr);
+}
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::binary_operation(lhs, rhs, op, output_type, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         std::string const& ptx,
+                                         data_type output_type,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::binary_operation(lhs, rhs, ptx, output_type, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/binaryop/compiled/ATan2.cu b/cpp/src/binaryop/compiled/ATan2.cu
new file mode 100644
index 0000000..f43a469
--- /dev/null
+++ b/cpp/src/binaryop/compiled/ATan2.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::ATan2>(mutable_column_view&,
+                                          column_view const&,
+                                          column_view const&,
+                                          bool is_lhs_scalar,
+                                          bool is_rhs_scalar,
+                                          rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Add.cu b/cpp/src/binaryop/compiled/Add.cu
new file mode 100644
index 0000000..1dbfa5b
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Add.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Add>(mutable_column_view&,
+                                        column_view const&,
+                                        column_view const&,
+                                        bool is_lhs_scalar,
+                                        bool is_rhs_scalar,
+                                        rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/BitwiseAnd.cu b/cpp/src/binaryop/compiled/BitwiseAnd.cu
new file mode 100644
index 0000000..cfabb14
--- /dev/null
+++ b/cpp/src/binaryop/compiled/BitwiseAnd.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::BitwiseAnd>(mutable_column_view&,
+                                               column_view const&,
+                                               column_view const&,
+                                               bool is_lhs_scalar,
+                                               bool is_rhs_scalar,
+                                               rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/BitwiseOr.cu b/cpp/src/binaryop/compiled/BitwiseOr.cu
new file mode 100644
index 0000000..01ef118
--- /dev/null
+++ b/cpp/src/binaryop/compiled/BitwiseOr.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::BitwiseOr>(mutable_column_view&,
+                                              column_view const&,
+                                              column_view const&,
+                                              bool is_lhs_scalar,
+                                              bool is_rhs_scalar,
+                                              rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/BitwiseXor.cu b/cpp/src/binaryop/compiled/BitwiseXor.cu
new file mode 100644
index 0000000..44f74ba
--- /dev/null
+++ b/cpp/src/binaryop/compiled/BitwiseXor.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::BitwiseXor>(mutable_column_view&,
+                                               column_view const&,
+                                               column_view const&,
+                                               bool is_lhs_scalar,
+                                               bool is_rhs_scalar,
+                                               rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Div.cu b/cpp/src/binaryop/compiled/Div.cu
new file mode 100644
index 0000000..f377778
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Div.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Div>(mutable_column_view&,
+                                        column_view const&,
+                                        column_view const&,
+                                        bool is_lhs_scalar,
+                                        bool is_rhs_scalar,
+                                        rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/FloorDiv.cu b/cpp/src/binaryop/compiled/FloorDiv.cu
new file mode 100644
index 0000000..f9cd323
--- /dev/null
+++ b/cpp/src/binaryop/compiled/FloorDiv.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::FloorDiv>(mutable_column_view&,
+                                             column_view const&,
+                                             column_view const&,
+                                             bool is_lhs_scalar,
+                                             bool is_rhs_scalar,
+                                             rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Greater.cu b/cpp/src/binaryop/compiled/Greater.cu
new file mode 100644
index 0000000..db06cc4
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Greater.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Greater>(mutable_column_view&,
+                                            column_view const&,
+                                            column_view const&,
+                                            bool is_lhs_scalar,
+                                            bool is_rhs_scalar,
+                                            rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/GreaterEqual.cu b/cpp/src/binaryop/compiled/GreaterEqual.cu
new file mode 100644
index 0000000..c239e1e
--- /dev/null
+++ b/cpp/src/binaryop/compiled/GreaterEqual.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::GreaterEqual>(mutable_column_view&,
+                                                 column_view const&,
+                                                 column_view const&,
+                                                 bool is_lhs_scalar,
+                                                 bool is_rhs_scalar,
+                                                 rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/IntPow.cu b/cpp/src/binaryop/compiled/IntPow.cu
new file mode 100644
index 0000000..468feaa
--- /dev/null
+++ b/cpp/src/binaryop/compiled/IntPow.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::IntPow>(mutable_column_view&,
+                                           column_view const&,
+                                           column_view const&,
+                                           bool is_lhs_scalar,
+                                           bool is_rhs_scalar,
+                                           rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Less.cu b/cpp/src/binaryop/compiled/Less.cu
new file mode 100644
index 0000000..e866371
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Less.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Less>(mutable_column_view&,
+                                         column_view const&,
+                                         column_view const&,
+                                         bool is_lhs_scalar,
+                                         bool is_rhs_scalar,
+                                         rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/LessEqual.cu b/cpp/src/binaryop/compiled/LessEqual.cu
new file mode 100644
index 0000000..d2f88fa
--- /dev/null
+++ b/cpp/src/binaryop/compiled/LessEqual.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::LessEqual>(mutable_column_view&,
+                                              column_view const&,
+                                              column_view const&,
+                                              bool is_lhs_scalar,
+                                              bool is_rhs_scalar,
+                                              rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/LogBase.cu b/cpp/src/binaryop/compiled/LogBase.cu
new file mode 100644
index 0000000..8a2162c
--- /dev/null
+++ b/cpp/src/binaryop/compiled/LogBase.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::LogBase>(mutable_column_view&,
+                                            column_view const&,
+                                            column_view const&,
+                                            bool is_lhs_scalar,
+                                            bool is_rhs_scalar,
+                                            rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/LogicalAnd.cu b/cpp/src/binaryop/compiled/LogicalAnd.cu
new file mode 100644
index 0000000..64e5c1a
--- /dev/null
+++ b/cpp/src/binaryop/compiled/LogicalAnd.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::LogicalAnd>(mutable_column_view&,
+                                               column_view const&,
+                                               column_view const&,
+                                               bool is_lhs_scalar,
+                                               bool is_rhs_scalar,
+                                               rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/LogicalOr.cu b/cpp/src/binaryop/compiled/LogicalOr.cu
new file mode 100644
index 0000000..a4b64cc
--- /dev/null
+++ b/cpp/src/binaryop/compiled/LogicalOr.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::LogicalOr>(mutable_column_view&,
+                                              column_view const&,
+                                              column_view const&,
+                                              bool is_lhs_scalar,
+                                              bool is_rhs_scalar,
+                                              rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Mod.cu b/cpp/src/binaryop/compiled/Mod.cu
new file mode 100644
index 0000000..fcdd01b
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Mod.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Mod>(mutable_column_view&,
+                                        column_view const&,
+                                        column_view const&,
+                                        bool is_lhs_scalar,
+                                        bool is_rhs_scalar,
+                                        rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Mul.cu b/cpp/src/binaryop/compiled/Mul.cu
new file mode 100644
index 0000000..de6506d
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Mul.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Mul>(mutable_column_view&,
+                                        column_view const&,
+                                        column_view const&,
+                                        bool is_lhs_scalar,
+                                        bool is_rhs_scalar,
+                                        rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/NullEquals.cu b/cpp/src/binaryop/compiled/NullEquals.cu
new file mode 100644
index 0000000..f4780c1
--- /dev/null
+++ b/cpp/src/binaryop/compiled/NullEquals.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::NullEquals>(mutable_column_view&,
+                                               column_view const&,
+                                               column_view const&,
+                                               bool is_lhs_scalar,
+                                               bool is_rhs_scalar,
+                                               rmm::cuda_stream_view);
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/compiled/NullLogicalAnd.cu b/cpp/src/binaryop/compiled/NullLogicalAnd.cu
new file mode 100644
index 0000000..55e71a5
--- /dev/null
+++ b/cpp/src/binaryop/compiled/NullLogicalAnd.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::NullLogicalAnd>(mutable_column_view&,
+                                                   column_view const&,
+                                                   column_view const&,
+                                                   bool is_lhs_scalar,
+                                                   bool is_rhs_scalar,
+                                                   rmm::cuda_stream_view);
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/compiled/NullLogicalOr.cu b/cpp/src/binaryop/compiled/NullLogicalOr.cu
new file mode 100644
index 0000000..ee3b27c
--- /dev/null
+++ b/cpp/src/binaryop/compiled/NullLogicalOr.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::NullLogicalOr>(mutable_column_view&,
+                                                  column_view const&,
+                                                  column_view const&,
+                                                  bool is_lhs_scalar,
+                                                  bool is_rhs_scalar,
+                                                  rmm::cuda_stream_view);
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/compiled/NullMax.cu b/cpp/src/binaryop/compiled/NullMax.cu
new file mode 100644
index 0000000..6fae253
--- /dev/null
+++ b/cpp/src/binaryop/compiled/NullMax.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::NullMax>(mutable_column_view&,
+                                            column_view const&,
+                                            column_view const&,
+                                            bool is_lhs_scalar,
+                                            bool is_rhs_scalar,
+                                            rmm::cuda_stream_view);
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/compiled/NullMin.cu b/cpp/src/binaryop/compiled/NullMin.cu
new file mode 100644
index 0000000..cb7fdb4
--- /dev/null
+++ b/cpp/src/binaryop/compiled/NullMin.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::NullMin>(mutable_column_view&,
+                                            column_view const&,
+                                            column_view const&,
+                                            bool is_lhs_scalar,
+                                            bool is_rhs_scalar,
+                                            rmm::cuda_stream_view);
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/compiled/PMod.cu b/cpp/src/binaryop/compiled/PMod.cu
new file mode 100644
index 0000000..63b1f1f
--- /dev/null
+++ b/cpp/src/binaryop/compiled/PMod.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::PMod>(mutable_column_view&,
+                                         column_view const&,
+                                         column_view const&,
+                                         bool is_lhs_scalar,
+                                         bool is_rhs_scalar,
+                                         rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Pow.cu b/cpp/src/binaryop/compiled/Pow.cu
new file mode 100644
index 0000000..435e1ac
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Pow.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Pow>(mutable_column_view&,
+                                        column_view const&,
+                                        column_view const&,
+                                        bool is_lhs_scalar,
+                                        bool is_rhs_scalar,
+                                        rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/PyMod.cu b/cpp/src/binaryop/compiled/PyMod.cu
new file mode 100644
index 0000000..1e21359
--- /dev/null
+++ b/cpp/src/binaryop/compiled/PyMod.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::PyMod>(mutable_column_view&,
+                                          column_view const&,
+                                          column_view const&,
+                                          bool is_lhs_scalar,
+                                          bool is_rhs_scalar,
+                                          rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/ShiftLeft.cu b/cpp/src/binaryop/compiled/ShiftLeft.cu
new file mode 100644
index 0000000..797821a
--- /dev/null
+++ b/cpp/src/binaryop/compiled/ShiftLeft.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::ShiftLeft>(mutable_column_view&,
+                                              column_view const&,
+                                              column_view const&,
+                                              bool is_lhs_scalar,
+                                              bool is_rhs_scalar,
+                                              rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/ShiftRight.cu b/cpp/src/binaryop/compiled/ShiftRight.cu
new file mode 100644
index 0000000..8a2566f
--- /dev/null
+++ b/cpp/src/binaryop/compiled/ShiftRight.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::ShiftRight>(mutable_column_view&,
+                                               column_view const&,
+                                               column_view const&,
+                                               bool is_lhs_scalar,
+                                               bool is_rhs_scalar,
+                                               rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/ShiftRightUnsigned.cu b/cpp/src/binaryop/compiled/ShiftRightUnsigned.cu
new file mode 100644
index 0000000..827029b
--- /dev/null
+++ b/cpp/src/binaryop/compiled/ShiftRightUnsigned.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::ShiftRightUnsigned>(mutable_column_view&,
+                                                       column_view const&,
+                                                       column_view const&,
+                                                       bool is_lhs_scalar,
+                                                       bool is_rhs_scalar,
+                                                       rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/Sub.cu b/cpp/src/binaryop/compiled/Sub.cu
new file mode 100644
index 0000000..3022294
--- /dev/null
+++ b/cpp/src/binaryop/compiled/Sub.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::Sub>(mutable_column_view&,
+                                        column_view const&,
+                                        column_view const&,
+                                        bool is_lhs_scalar,
+                                        bool is_rhs_scalar,
+                                        rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/TrueDiv.cu b/cpp/src/binaryop/compiled/TrueDiv.cu
new file mode 100644
index 0000000..4d0fc2d
--- /dev/null
+++ b/cpp/src/binaryop/compiled/TrueDiv.cu
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+template void apply_binary_op<ops::TrueDiv>(mutable_column_view&,
+                                            column_view const&,
+                                            column_view const&,
+                                            bool is_lhs_scalar,
+                                            bool is_rhs_scalar,
+                                            rmm::cuda_stream_view);
+}
diff --git a/cpp/src/binaryop/compiled/binary_ops.cu b/cpp/src/binaryop/compiled/binary_ops.cu
new file mode 100644
index 0000000..1f7f342
--- /dev/null
+++ b/cpp/src/binaryop/compiled/binary_ops.cu
@@ -0,0 +1,469 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.hpp"
+#include "operation.cuh"
+#include "struct_binary_ops.cuh"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace binops {
+namespace compiled {
+
+namespace {
+/**
+ * @brief Converts scalar to column_view with single element.
+ *
+ * @return pair with column_view and column containing any auxiliary data to create column_view from
+ * scalar
+ */
+struct scalar_as_column_view {
+  using return_type = typename std::pair<column_view, std::unique_ptr<column>>;
+  template <typename T, CUDF_ENABLE_IF(is_fixed_width<T>())>
+  return_type operator()(scalar const& s, rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+  {
+    auto& h_scalar_type_view = static_cast<cudf::scalar_type_t<T>&>(const_cast<scalar&>(s));
+    auto col_v               = column_view(s.type(),
+                             1,
+                             h_scalar_type_view.data(),
+                             reinterpret_cast<bitmask_type const*>(s.validity_data()),
+                             !s.is_valid());
+    return std::pair{col_v, std::unique_ptr<column>(nullptr)};
+  }
+  template <typename T, CUDF_ENABLE_IF(!is_fixed_width<T>())>
+  return_type operator()(scalar const&, rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Unsupported type");
+  }
+};
+// specialization for cudf::string_view
+template <>
+scalar_as_column_view::return_type scalar_as_column_view::operator()<cudf::string_view>(
+  scalar const& s, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  using T                  = cudf::string_view;
+  auto& h_scalar_type_view = static_cast<cudf::scalar_type_t<T>&>(const_cast<scalar&>(s));
+
+  // build offsets column from the string size
+  auto offsets_transformer_itr =
+    thrust::make_constant_iterator<size_type>(h_scalar_type_view.size());
+  auto offsets_column = std::get<0>(cudf::detail::make_offsets_child_column(
+    offsets_transformer_itr, offsets_transformer_itr + 1, stream, mr));
+
+  auto chars_column_v = column_view(
+    data_type{type_id::INT8}, h_scalar_type_view.size(), h_scalar_type_view.data(), nullptr, 0);
+  // Construct string column_view
+  auto col_v = column_view(s.type(),
+                           1,
+                           nullptr,
+                           reinterpret_cast<bitmask_type const*>(s.validity_data()),
+                           static_cast<size_type>(!s.is_valid(stream)),
+                           0,
+                           {offsets_column->view(), chars_column_v});
+  return std::pair{col_v, std::move(offsets_column)};
+}
+
+// specializing for struct column
+template <>
+scalar_as_column_view::return_type scalar_as_column_view::operator()<cudf::struct_view>(
+  scalar const& s, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  auto col = make_column_from_scalar(s, 1, stream, mr);
+  return std::pair{col->view(), std::move(col)};
+}
+
+/**
+ * @brief Converts scalar to column_view with single element.
+ *
+ * @param scal    scalar to convert
+ * @param stream  CUDA stream used for device memory operations and kernel launches.
+ * @param mr      Device memory resource used to allocate the returned column's device memory
+ * @return        pair with column_view and column containing any auxiliary data to create
+ * column_view from scalar
+ */
+auto scalar_to_column_view(
+  scalar const& scal,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  return type_dispatcher(scal.type(), scalar_as_column_view{}, scal, stream, mr);
+}
+
+// This functor does the actual comparison between string column value and a scalar string
+// or between two string column values using a comparator
+template <typename LhsDeviceViewT, typename RhsDeviceViewT, typename OutT, typename CompareFunc>
+struct compare_functor {
+  LhsDeviceViewT const lhs_dev_view_;  // Scalar or a column device view - lhs
+  RhsDeviceViewT const rhs_dev_view_;  // Scalar or a column device view - rhs
+  CompareFunc const cfunc_;            // Comparison function
+
+  compare_functor(LhsDeviceViewT const& lhs_dev_view,
+                  RhsDeviceViewT const& rhs_dev_view,
+                  CompareFunc cf)
+    : lhs_dev_view_(lhs_dev_view), rhs_dev_view_(rhs_dev_view), cfunc_(cf)
+  {
+  }
+
+  // This is used to compare a scalar and a column value
+  template <typename LhsViewT = LhsDeviceViewT, typename RhsViewT = RhsDeviceViewT>
+  __device__ inline std::enable_if_t<std::is_same_v<LhsViewT, column_device_view> &&
+                                       !std::is_same_v<RhsViewT, column_device_view>,
+                                     OutT>
+  operator()(cudf::size_type i) const
+  {
+    return cfunc_(lhs_dev_view_.is_valid(i),
+                  rhs_dev_view_.is_valid(),
+                  lhs_dev_view_.is_valid(i) ? lhs_dev_view_.template element<cudf::string_view>(i)
+                                            : cudf::string_view{},
+                  rhs_dev_view_.is_valid() ? rhs_dev_view_.value() : cudf::string_view{});
+  }
+
+  // This is used to compare a scalar and a column value
+  template <typename LhsViewT = LhsDeviceViewT, typename RhsViewT = RhsDeviceViewT>
+  __device__ inline std::enable_if_t<!std::is_same_v<LhsViewT, column_device_view> &&
+                                       std::is_same_v<RhsViewT, column_device_view>,
+                                     OutT>
+  operator()(cudf::size_type i) const
+  {
+    return cfunc_(lhs_dev_view_.is_valid(),
+                  rhs_dev_view_.is_valid(i),
+                  lhs_dev_view_.is_valid() ? lhs_dev_view_.value() : cudf::string_view{},
+                  rhs_dev_view_.is_valid(i) ? rhs_dev_view_.template element<cudf::string_view>(i)
+                                            : cudf::string_view{});
+  }
+
+  // This is used to compare 2 column values
+  template <typename LhsViewT = LhsDeviceViewT, typename RhsViewT = RhsDeviceViewT>
+  __device__ inline std::enable_if_t<std::is_same_v<LhsViewT, column_device_view> &&
+                                       std::is_same_v<RhsViewT, column_device_view>,
+                                     OutT>
+  operator()(cudf::size_type i) const
+  {
+    return cfunc_(lhs_dev_view_.is_valid(i),
+                  rhs_dev_view_.is_valid(i),
+                  lhs_dev_view_.is_valid(i) ? lhs_dev_view_.template element<cudf::string_view>(i)
+                                            : cudf::string_view{},
+                  rhs_dev_view_.is_valid(i) ? rhs_dev_view_.template element<cudf::string_view>(i)
+                                            : cudf::string_view{});
+  }
+};
+
+// This functor performs null aware binop between two columns or a column and a scalar by
+// iterating over them on the device
+struct null_considering_binop {
+  [[nodiscard]] auto get_device_view(cudf::scalar const& scalar_item) const
+  {
+    return get_scalar_device_view(
+      static_cast<cudf::scalar_type_t<cudf::string_view>&>(const_cast<scalar&>(scalar_item)));
+  }
+
+  [[nodiscard]] auto get_device_view(column_device_view const& col_item) const { return col_item; }
+
+  template <typename LhsViewT, typename RhsViewT, typename OutT, typename CompareFunc>
+  void populate_out_col(LhsViewT const& lhsv,
+                        RhsViewT const& rhsv,
+                        cudf::size_type col_size,
+                        rmm::cuda_stream_view stream,
+                        CompareFunc cfunc,
+                        OutT* out_col) const
+  {
+    // Create binop functor instance
+    compare_functor<LhsViewT, RhsViewT, OutT, CompareFunc> binop_func{lhsv, rhsv, cfunc};
+
+    // Execute it on every element
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(col_size),
+                      out_col,
+                      binop_func);
+  }
+
+  // This is invoked to perform comparison between cudf string types
+  template <typename LhsT, typename RhsT>
+  std::unique_ptr<column> operator()(LhsT const& lhs,
+                                     RhsT const& rhs,
+                                     binary_operator op,
+                                     data_type output_type,
+                                     cudf::size_type col_size,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    // Create device views for inputs
+    auto const lhs_dev_view = get_device_view(lhs);
+    auto const rhs_dev_view = get_device_view(rhs);
+    // Validate input
+    CUDF_EXPECTS(output_type.id() == lhs.type().id(),
+                 "Output column type should match input column type");
+
+    // Shallow copy of the resultant strings
+    rmm::device_uvector<cudf::string_view> out_col_strings(col_size, stream);
+
+    // Invalid output column strings - null rows
+    cudf::string_view const invalid_str{nullptr, 0};
+
+    // Create a compare function lambda
+    auto minmax_func =
+      [op, invalid_str] __device__(
+        bool lhs_valid, bool rhs_valid, cudf::string_view lhs_value, cudf::string_view rhs_value) {
+        if (!lhs_valid && !rhs_valid)
+          return invalid_str;
+        else if (lhs_valid && rhs_valid) {
+          return (op == binary_operator::NULL_MAX)
+                   ? thrust::maximum<cudf::string_view>()(lhs_value, rhs_value)
+                   : thrust::minimum<cudf::string_view>()(lhs_value, rhs_value);
+        } else if (lhs_valid)
+          return lhs_value;
+        else
+          return rhs_value;
+      };
+
+    // Populate output column
+    populate_out_col(
+      lhs_dev_view, rhs_dev_view, col_size, stream, minmax_func, out_col_strings.data());
+
+    // Create an output column with the resultant strings
+    return cudf::make_strings_column(out_col_strings, invalid_str, stream, mr);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> string_null_min_max(scalar const& lhs,
+                                            column_view const& rhs,
+                                            binary_operator op,
+                                            data_type output_type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  // hard-coded to only work with cudf::string_view so we don't explode compile times
+  CUDF_EXPECTS(lhs.type().id() == cudf::type_id::STRING, "Invalid/Unsupported lhs datatype");
+  CUDF_EXPECTS(rhs.type().id() == cudf::type_id::STRING, "Invalid/Unsupported rhs datatype");
+  CUDF_EXPECTS(op == binary_operator::NULL_MAX or op == binary_operator::NULL_MIN,
+               "Unsupported binary operation");
+  if (rhs.is_empty()) return cudf::make_empty_column(output_type);
+  auto rhs_device_view = cudf::column_device_view::create(rhs, stream);
+  return null_considering_binop{}(lhs, *rhs_device_view, op, output_type, rhs.size(), stream, mr);
+}
+
+std::unique_ptr<column> string_null_min_max(column_view const& lhs,
+                                            scalar const& rhs,
+                                            binary_operator op,
+                                            data_type output_type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  // hard-coded to only work with cudf::string_view so we don't explode compile times
+  CUDF_EXPECTS(lhs.type().id() == cudf::type_id::STRING, "Invalid/Unsupported lhs datatype");
+  CUDF_EXPECTS(rhs.type().id() == cudf::type_id::STRING, "Invalid/Unsupported rhs datatype");
+  CUDF_EXPECTS(op == binary_operator::NULL_MAX or op == binary_operator::NULL_MIN,
+               "Unsupported binary operation");
+  if (lhs.is_empty()) return cudf::make_empty_column(output_type);
+  auto lhs_device_view = cudf::column_device_view::create(lhs, stream);
+  return null_considering_binop{}(*lhs_device_view, rhs, op, output_type, lhs.size(), stream, mr);
+}
+
+std::unique_ptr<column> string_null_min_max(column_view const& lhs,
+                                            column_view const& rhs,
+                                            binary_operator op,
+                                            data_type output_type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  // hard-coded to only work with cudf::string_view so we don't explode compile times
+  CUDF_EXPECTS(lhs.type().id() == cudf::type_id::STRING, "Invalid/Unsupported lhs datatype");
+  CUDF_EXPECTS(rhs.type().id() == cudf::type_id::STRING, "Invalid/Unsupported rhs datatype");
+  CUDF_EXPECTS(op == binary_operator::NULL_MAX or op == binary_operator::NULL_MIN,
+               "Unsupported binary operation");
+  CUDF_EXPECTS(lhs.size() == rhs.size(), "Column sizes do not match");
+  if (lhs.is_empty()) return cudf::make_empty_column(output_type);
+  auto lhs_device_view = cudf::column_device_view::create(lhs, stream);
+  auto rhs_device_view = cudf::column_device_view::create(rhs, stream);
+  return null_considering_binop{}(
+    *lhs_device_view, *rhs_device_view, op, output_type, lhs.size(), stream, mr);
+}
+
+void operator_dispatcher(mutable_column_view& out,
+                         column_view const& lhs,
+                         column_view const& rhs,
+                         bool is_lhs_scalar,
+                         bool is_rhs_scalar,
+                         binary_operator op,
+                         rmm::cuda_stream_view stream)
+{
+  // clang-format off
+switch (op) {
+case binary_operator::ADD:                  apply_binary_op<ops::Add>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::SUB:                  apply_binary_op<ops::Sub>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::MUL:                  apply_binary_op<ops::Mul>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::DIV:                  apply_binary_op<ops::Div>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::TRUE_DIV:             apply_binary_op<ops::TrueDiv>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::FLOOR_DIV:            apply_binary_op<ops::FloorDiv>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::MOD:                  apply_binary_op<ops::Mod>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::PYMOD:                apply_binary_op<ops::PyMod>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::POW:                  apply_binary_op<ops::Pow>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::INT_POW:               apply_binary_op<ops::IntPow>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::EQUAL:
+case binary_operator::NOT_EQUAL:
+if(out.type().id() != type_id::BOOL8) CUDF_FAIL("Output type of Comparison operator should be bool type");
+dispatch_equality_op(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, op, stream); break;
+case binary_operator::LESS:                 apply_binary_op<ops::Less>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::GREATER:              apply_binary_op<ops::Greater>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::LESS_EQUAL:           apply_binary_op<ops::LessEqual>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::GREATER_EQUAL:        apply_binary_op<ops::GreaterEqual>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::BITWISE_AND:          apply_binary_op<ops::BitwiseAnd>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::BITWISE_OR:           apply_binary_op<ops::BitwiseOr>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::BITWISE_XOR:          apply_binary_op<ops::BitwiseXor>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::LOGICAL_AND:          apply_binary_op<ops::LogicalAnd>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::LOGICAL_OR:           apply_binary_op<ops::LogicalOr>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+/*
+case binary_operator::GENERIC_BINARY:      // Cannot be compiled, should be called by jit::binary_operation
+*/
+case binary_operator::SHIFT_LEFT:           apply_binary_op<ops::ShiftLeft>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::SHIFT_RIGHT:          apply_binary_op<ops::ShiftRight>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::SHIFT_RIGHT_UNSIGNED: apply_binary_op<ops::ShiftRightUnsigned>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::LOG_BASE:             apply_binary_op<ops::LogBase>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::ATAN2:                apply_binary_op<ops::ATan2>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::PMOD:                 apply_binary_op<ops::PMod>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::NULL_EQUALS:          apply_binary_op<ops::NullEquals>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::NULL_MAX:             apply_binary_op<ops::NullMax>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::NULL_MIN:             apply_binary_op<ops::NullMin>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::NULL_LOGICAL_AND:     apply_binary_op<ops::NullLogicalAnd>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+case binary_operator::NULL_LOGICAL_OR:      apply_binary_op<ops::NullLogicalOr>(out, lhs, rhs, is_lhs_scalar, is_rhs_scalar, stream); break;
+default:;
+}
+  // clang-format on
+}
+
+// vector_vector
+void binary_operation(mutable_column_view& out,
+                      column_view const& lhs,
+                      column_view const& rhs,
+                      binary_operator op,
+                      rmm::cuda_stream_view stream)
+{
+  operator_dispatcher(out, lhs, rhs, false, false, op, stream);
+}
+// scalar_vector
+void binary_operation(mutable_column_view& out,
+                      scalar const& lhs,
+                      column_view const& rhs,
+                      binary_operator op,
+                      rmm::cuda_stream_view stream)
+{
+  auto [lhsv, aux] = scalar_to_column_view(lhs, stream);
+  operator_dispatcher(out, lhsv, rhs, true, false, op, stream);
+}
+// vector_scalar
+void binary_operation(mutable_column_view& out,
+                      column_view const& lhs,
+                      scalar const& rhs,
+                      binary_operator op,
+                      rmm::cuda_stream_view stream)
+{
+  auto [rhsv, aux] = scalar_to_column_view(rhs, stream);
+  operator_dispatcher(out, lhs, rhsv, false, true, op, stream);
+}
+
+namespace detail {
+void apply_sorting_struct_binary_op(mutable_column_view& out,
+                                    column_view const& lhs,
+                                    column_view const& rhs,
+                                    bool is_lhs_scalar,
+                                    bool is_rhs_scalar,
+                                    binary_operator op,
+                                    rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(lhs.type().id() == type_id::STRUCT && rhs.type().id() == type_id::STRUCT,
+               "Both columns must be struct columns");
+  CUDF_EXPECTS(!cudf::structs::detail::is_or_has_nested_lists(lhs) and
+                 !cudf::structs::detail::is_or_has_nested_lists(rhs),
+               "List type is not supported");
+  // Struct child column type and structure mismatches are caught within the two_table_comparator
+  switch (op) {
+    case binary_operator::EQUAL: [[fallthrough]];
+    case binary_operator::NULL_EQUALS: [[fallthrough]];
+    case binary_operator::NOT_EQUAL:
+      detail::apply_struct_equality_op(
+        out,
+        lhs,
+        rhs,
+        is_lhs_scalar,
+        is_rhs_scalar,
+        op,
+        cudf::experimental::row::equality::nan_equal_physical_equality_comparator{},
+        stream);
+      break;
+    case binary_operator::LESS:
+      detail::apply_struct_binary_op<ops::Less>(
+        out,
+        lhs,
+        rhs,
+        is_lhs_scalar,
+        is_rhs_scalar,
+        cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+        stream);
+      break;
+    case binary_operator::GREATER:
+      detail::apply_struct_binary_op<ops::Greater>(
+        out,
+        lhs,
+        rhs,
+        is_lhs_scalar,
+        is_rhs_scalar,
+        cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+        stream);
+      break;
+    case binary_operator::LESS_EQUAL:
+      detail::apply_struct_binary_op<ops::LessEqual>(
+        out,
+        lhs,
+        rhs,
+        is_lhs_scalar,
+        is_rhs_scalar,
+        cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+        stream);
+      break;
+    case binary_operator::GREATER_EQUAL:
+      detail::apply_struct_binary_op<ops::GreaterEqual>(
+        out,
+        lhs,
+        rhs,
+        is_lhs_scalar,
+        is_rhs_scalar,
+        cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+        stream);
+      break;
+    default: CUDF_FAIL("Unsupported operator for structs");
+  }
+}
+}  // namespace detail
+}  // namespace compiled
+}  // namespace binops
+}  // namespace cudf
diff --git a/cpp/src/binaryop/compiled/binary_ops.cuh b/cpp/src/binaryop/compiled/binary_ops.cuh
new file mode 100644
index 0000000..9a50eb0
--- /dev/null
+++ b/cpp/src/binaryop/compiled/binary_ops.cuh
@@ -0,0 +1,307 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "binary_ops.hpp"
+#include "operation.cuh"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace binops {
+namespace compiled {
+
+template <typename BinaryOperator, typename TypeLhs, typename TypeRhs>
+constexpr bool is_bool_result()
+{
+  using ReturnType = std::invoke_result_t<BinaryOperator, TypeLhs, TypeRhs>;
+  return std::is_same_v<bool, ReturnType>;
+}
+
+/**
+ * @brief Type casts each element of the column to `CastType`
+ *
+ */
+template <typename CastType>
+struct type_casted_accessor {
+  template <typename Element>
+  __device__ inline CastType operator()(cudf::size_type i,
+                                        column_device_view const& col,
+                                        bool is_scalar) const
+  {
+    if constexpr (column_device_view::has_element_accessor<Element>() and
+                  std::is_convertible_v<Element, CastType>)
+      return static_cast<CastType>(col.element<Element>(is_scalar ? 0 : i));
+    return {};
+  }
+};
+
+/**
+ * @brief Type casts value to column type and stores in `i`th row of the column
+ *
+ */
+template <typename FromType>
+struct typed_casted_writer {
+  template <typename Element>
+  __device__ inline void operator()(cudf::size_type i,
+                                    mutable_column_device_view const& col,
+                                    FromType val) const
+  {
+    if constexpr (mutable_column_device_view::has_element_accessor<Element>() and
+                  std::is_constructible_v<Element, FromType>) {
+      col.element<Element>(i) = static_cast<Element>(val);
+    } else if constexpr (is_fixed_point<Element>() and
+                         (is_fixed_point<FromType>() or
+                          std::is_constructible_v<Element, FromType>)) {
+      if constexpr (is_fixed_point<FromType>())
+        col.data<Element::rep>()[i] = val.rescaled(numeric::scale_type{col.type().scale()}).value();
+      else
+        col.data<Element::rep>()[i] = Element{val, numeric::scale_type{col.type().scale()}}.value();
+    }
+  }
+};
+
+// Functors to launch only defined operations.
+
+/**
+ * @brief Functor to launch only defined operations with common type.
+ *
+ * @tparam BinaryOperator binary operator functor
+ */
+template <typename BinaryOperator>
+struct ops_wrapper {
+  mutable_column_device_view& out;
+  column_device_view const& lhs;
+  column_device_view const& rhs;
+  bool const& is_lhs_scalar;
+  bool const& is_rhs_scalar;
+  template <typename TypeCommon>
+  __device__ void operator()(size_type i)
+  {
+    if constexpr (std::is_invocable_v<BinaryOperator, TypeCommon, TypeCommon>) {
+      TypeCommon x =
+        type_dispatcher(lhs.type(), type_casted_accessor<TypeCommon>{}, i, lhs, is_lhs_scalar);
+      TypeCommon y =
+        type_dispatcher(rhs.type(), type_casted_accessor<TypeCommon>{}, i, rhs, is_rhs_scalar);
+      auto result = [&]() {
+        if constexpr (std::is_same_v<BinaryOperator, ops::NullEquals> or
+                      std::is_same_v<BinaryOperator, ops::NullLogicalAnd> or
+                      std::is_same_v<BinaryOperator, ops::NullLogicalOr> or
+                      std::is_same_v<BinaryOperator, ops::NullMax> or
+                      std::is_same_v<BinaryOperator, ops::NullMin>) {
+          bool output_valid = false;
+          auto result       = BinaryOperator{}.template operator()<TypeCommon, TypeCommon>(
+            x,
+            y,
+            lhs.is_valid(is_lhs_scalar ? 0 : i),
+            rhs.is_valid(is_rhs_scalar ? 0 : i),
+            output_valid);
+          if (out.nullable() && !output_valid) out.set_null(i);
+          return result;
+        } else {
+          return BinaryOperator{}.template operator()<TypeCommon, TypeCommon>(x, y);
+        }
+        // To suppress nvcc warning
+        return std::invoke_result_t<BinaryOperator, TypeCommon, TypeCommon>{};
+      }();
+      if constexpr (is_bool_result<BinaryOperator, TypeCommon, TypeCommon>())
+        out.element<decltype(result)>(i) = result;
+      else
+        type_dispatcher(out.type(), typed_casted_writer<decltype(result)>{}, i, out, result);
+    }
+    (void)i;
+  }
+};
+
+/**
+ * @brief Functor to launch only defined operations without common type.
+ *
+ * @tparam BinaryOperator binary operator functor
+ */
+template <typename BinaryOperator>
+struct ops2_wrapper {
+  mutable_column_device_view& out;
+  column_device_view const& lhs;
+  column_device_view const& rhs;
+  bool const& is_lhs_scalar;
+  bool const& is_rhs_scalar;
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ void operator()(size_type i)
+  {
+    if constexpr (!has_common_type_v<TypeLhs, TypeRhs> and
+                  std::is_invocable_v<BinaryOperator, TypeLhs, TypeRhs>) {
+      TypeLhs x   = lhs.element<TypeLhs>(is_lhs_scalar ? 0 : i);
+      TypeRhs y   = rhs.element<TypeRhs>(is_rhs_scalar ? 0 : i);
+      auto result = [&]() {
+        if constexpr (std::is_same_v<BinaryOperator, ops::NullEquals> or
+                      std::is_same_v<BinaryOperator, ops::NullLogicalAnd> or
+                      std::is_same_v<BinaryOperator, ops::NullLogicalOr> or
+                      std::is_same_v<BinaryOperator, ops::NullMax> or
+                      std::is_same_v<BinaryOperator, ops::NullMin>) {
+          bool output_valid = false;
+          auto result       = BinaryOperator{}.template operator()<TypeLhs, TypeRhs>(
+            x,
+            y,
+            lhs.is_valid(is_lhs_scalar ? 0 : i),
+            rhs.is_valid(is_rhs_scalar ? 0 : i),
+            output_valid);
+          if (out.nullable() && !output_valid) out.set_null(i);
+          return result;
+        } else {
+          return BinaryOperator{}.template operator()<TypeLhs, TypeRhs>(x, y);
+        }
+        // To suppress nvcc warning
+        return std::invoke_result_t<BinaryOperator, TypeLhs, TypeRhs>{};
+      }();
+      if constexpr (is_bool_result<BinaryOperator, TypeLhs, TypeRhs>())
+        out.element<decltype(result)>(i) = result;
+      else
+        type_dispatcher(out.type(), typed_casted_writer<decltype(result)>{}, i, out, result);
+    }
+    (void)i;
+  }
+};
+
+/**
+ * @brief Functor which does single type dispatcher in device code
+ *
+ * single type dispatcher for lhs and rhs with common types.
+ *
+ * @tparam BinaryOperator binary operator functor
+ */
+template <class BinaryOperator>
+struct binary_op_device_dispatcher {
+  data_type common_data_type;
+  mutable_column_device_view out;
+  column_device_view lhs;
+  column_device_view rhs;
+  bool is_lhs_scalar;
+  bool is_rhs_scalar;
+
+  __forceinline__ __device__ void operator()(size_type i)
+  {
+    type_dispatcher(common_data_type,
+                    ops_wrapper<BinaryOperator>{out, lhs, rhs, is_lhs_scalar, is_rhs_scalar},
+                    i);
+  }
+};
+
+/**
+ * @brief Functor which does double type dispatcher in device code
+ *
+ * double type dispatcher for lhs and rhs without common types.
+ *
+ * @tparam BinaryOperator binary operator functor
+ */
+template <class BinaryOperator>
+struct binary_op_double_device_dispatcher {
+  mutable_column_device_view out;
+  column_device_view lhs;
+  column_device_view rhs;
+  bool is_lhs_scalar;
+  bool is_rhs_scalar;
+
+  __forceinline__ __device__ void operator()(size_type i)
+  {
+    double_type_dispatcher(
+      lhs.type(),
+      rhs.type(),
+      ops2_wrapper<BinaryOperator>{out, lhs, rhs, is_lhs_scalar, is_rhs_scalar},
+      i);
+  }
+};
+
+/**
+ * @brief Simplified for_each kernel
+ *
+ * @param size number of elements to process.
+ * @param f Functor object to call for each element.
+ */
+template <typename Functor>
+__global__ void for_each_kernel(cudf::size_type size, Functor f)
+{
+  int tid    = threadIdx.x;
+  int blkid  = blockIdx.x;
+  int blksz  = blockDim.x;
+  int gridsz = gridDim.x;
+
+  int start = tid + blkid * blksz;
+  int step  = blksz * gridsz;
+
+#pragma unroll
+  for (cudf::size_type i = start; i < size; i += step) {
+    f(i);
+  }
+}
+
+/**
+ * @brief Launches Simplified for_each kernel with maximum occupancy grid dimensions.
+ *
+ * @tparam Functor
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param size number of elements to process.
+ * @param f Functor object to call for each element.
+ */
+template <typename Functor>
+void for_each(rmm::cuda_stream_view stream, cudf::size_type size, Functor f)
+{
+  int block_size;
+  int min_grid_size;
+  CUDF_CUDA_TRY(
+    cudaOccupancyMaxPotentialBlockSize(&min_grid_size, &block_size, for_each_kernel<decltype(f)>));
+  // 2 elements per thread.
+  int const grid_size = util::div_rounding_up_safe(size, 2 * block_size);
+  for_each_kernel<<<grid_size, block_size, 0, stream.value()>>>(size, std::forward<Functor&&>(f));
+}
+
+template <class BinaryOperator>
+void apply_binary_op(mutable_column_view& out,
+                     column_view const& lhs,
+                     column_view const& rhs,
+                     bool is_lhs_scalar,
+                     bool is_rhs_scalar,
+                     rmm::cuda_stream_view stream)
+{
+  auto common_dtype = get_common_type(out.type(), lhs.type(), rhs.type());
+
+  auto lhsd = column_device_view::create(lhs, stream);
+  auto rhsd = column_device_view::create(rhs, stream);
+  auto outd = mutable_column_device_view::create(out, stream);
+  // Create binop functor instance
+  if (common_dtype) {
+    // Execute it on every element
+    for_each(stream,
+             out.size(),
+             binary_op_device_dispatcher<BinaryOperator>{
+               *common_dtype, *outd, *lhsd, *rhsd, is_lhs_scalar, is_rhs_scalar});
+  } else {
+    // Execute it on every element
+    for_each(stream,
+             out.size(),
+             binary_op_double_device_dispatcher<BinaryOperator>{
+               *outd, *lhsd, *rhsd, is_lhs_scalar, is_rhs_scalar});
+  }
+}
+
+}  // namespace compiled
+}  // namespace binops
+}  // namespace cudf
diff --git a/cpp/src/binaryop/compiled/binary_ops.hpp b/cpp/src/binaryop/compiled/binary_ops.hpp
new file mode 100644
index 0000000..47fd50c
--- /dev/null
+++ b/cpp/src/binaryop/compiled/binary_ops.hpp
@@ -0,0 +1,218 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/binaryop.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+
+namespace cudf {
+// Forward declarations
+class column_device_view;
+class mutable_column_device_view;
+
+namespace binops {
+namespace compiled {
+
+std::unique_ptr<column> string_null_min_max(scalar const& lhs,
+                                            column_view const& rhs,
+                                            binary_operator op,
+                                            data_type output_type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<column> string_null_min_max(column_view const& lhs,
+                                            scalar const& rhs,
+                                            binary_operator op,
+                                            data_type output_type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+std::unique_ptr<column> string_null_min_max(column_view const& lhs,
+                                            column_view const& rhs,
+                                            binary_operator op,
+                                            data_type output_type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Performs a binary operation between a string scalar and a string
+ * column.
+ *
+ * The output contains the result of op(lhs, rhs[i]) for all 0 <= i < rhs.size()
+ * The scalar is the left operand and the column elements are the right operand.
+ * This distinction is significant in case of non-commutative binary operations
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands
+ *
+ * @param lhs         The left operand string scalar
+ * @param rhs         The right operand string column
+ * @param op          The binary operator
+ * @param output_type The desired data type of the output column
+ * @param stream      CUDA stream used for device memory operations and kernel launches.
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return std::unique_ptr<column> Output column
+ */
+std::unique_ptr<column> binary_operation(scalar const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Performs a binary operation between a string column and a string
+ * scalar.
+ *
+ * The output contains the result of op(lhs[i], rhs) for all 0 <= i < lhs.size()
+ * The column elements are the left operand and the scalar is the right operand.
+ * This distinction is significant in case of non-commutative binary operations
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands
+ *
+ * @param lhs         The left operand string column
+ * @param rhs         The right operand string scalar
+ * @param op          The binary operator
+ * @param output_type The desired data type of the output column
+ * @param stream      CUDA stream used for device memory operations and kernel launches.
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return std::unique_ptr<column> Output column
+ */
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         scalar const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Performs a binary operation between two string columns.
+ *
+ * @note The sizes of @p lhs and @p rhs should be the same
+ *
+ * The output contains the result of op(lhs[i], rhs[i]) for all 0 <= i < lhs.size()
+ *
+ * Regardless of the operator, the validity of the output value is the logical
+ * AND of the validity of the two operands
+ *
+ * @param lhs         The left operand string column
+ * @param rhs         The right operand string column
+ * @param op          The binary operator enum
+ * @param output_type The desired data type of the output column
+ * @param stream      CUDA stream used for device memory operations and kernel launches.
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return std::unique_ptr<column> Output column
+ */
+std::unique_ptr<column> binary_operation(column_view const& lhs,
+                                         column_view const& rhs,
+                                         binary_operator op,
+                                         data_type output_type,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+void binary_operation(mutable_column_view& out,
+                      scalar const& lhs,
+                      column_view const& rhs,
+                      binary_operator op,
+                      rmm::cuda_stream_view stream);
+void binary_operation(mutable_column_view& out,
+                      column_view const& lhs,
+                      scalar const& rhs,
+                      binary_operator op,
+                      rmm::cuda_stream_view stream);
+void binary_operation(mutable_column_view& out,
+                      column_view const& lhs,
+                      column_view const& rhs,
+                      binary_operator op,
+                      rmm::cuda_stream_view stream);
+
+// Defined in util.cpp
+/**
+ * @brief Get the common type among all input types.
+ *
+ * @param out type 1
+ * @param lhs type 2
+ * @param rhs type 3
+ * @return common type among @p out, @p lhs, @p rhs.
+ */
+std::optional<data_type> get_common_type(data_type out, data_type lhs, data_type rhs);
+/**
+ * @brief Check if input binary operation is supported for the given input and output types.
+ *
+ * @param out output type of the binary operation
+ * @param lhs first operand type of the binary operation
+ * @param rhs second operand type of the binary operation
+ * @param op binary operator enum.
+ * @return true if given binary operator supports given input and output types.
+ */
+bool is_supported_operation(data_type out, data_type lhs, data_type rhs, binary_operator op);
+
+// Defined in individual .cu files.
+/**
+ * @brief Deploys single type or double type dispatcher that runs binary operation on each element
+ * of @p lhs and @p rhs columns.
+ *
+ * This template is instantiated for each binary operator.
+ *
+ * @tparam BinaryOperator Binary operator functor
+ * @param out mutable view of output column
+ * @param lhs view of left operand column
+ * @param rhs view of right operand column
+ * @param is_lhs_scalar true if @p lhs is a single element column representing a scalar
+ * @param is_rhs_scalar true if @p rhs is a single element column representing a scalar
+ * @param stream CUDA stream used for device memory operations
+ */
+template <class BinaryOperator>
+void apply_binary_op(mutable_column_view& out,
+                     column_view const& lhs,
+                     column_view const& rhs,
+                     bool is_lhs_scalar,
+                     bool is_rhs_scalar,
+                     rmm::cuda_stream_view stream);
+/**
+ * @brief Deploys single type or double type dispatcher that runs equality operation on each element
+ * of @p lhs and @p rhs columns.
+ *
+ * Comparison operators are EQUAL, NOT_EQUAL, NULL_EQUALS.
+ * @p out type is boolean.
+ *
+ * This template is instantiated for each binary operator.
+ *
+ * @param out mutable view of output column
+ * @param lhs view of left operand column
+ * @param rhs view of right operand column
+ * @param is_lhs_scalar true if @p lhs is a single element column representing a scalar
+ * @param is_rhs_scalar true if @p rhs is a single element column representing a scalar
+ * @param op comparison binary operator
+ * @param stream CUDA stream used for device memory operations
+ */
+void dispatch_equality_op(mutable_column_view& out,
+                          column_view const& lhs,
+                          column_view const& rhs,
+                          bool is_lhs_scalar,
+                          bool is_rhs_scalar,
+                          binary_operator op,
+                          rmm::cuda_stream_view stream);
+}  // namespace compiled
+}  // namespace binops
+}  // namespace cudf
diff --git a/cpp/src/binaryop/compiled/equality_ops.cu b/cpp/src/binaryop/compiled/equality_ops.cu
new file mode 100644
index 0000000..041fca7
--- /dev/null
+++ b/cpp/src/binaryop/compiled/equality_ops.cu
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "binary_ops.cuh"
+
+namespace cudf::binops::compiled {
+void dispatch_equality_op(mutable_column_view& out,
+                          column_view const& lhs,
+                          column_view const& rhs,
+                          bool is_lhs_scalar,
+                          bool is_rhs_scalar,
+                          binary_operator op,
+                          rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(op == binary_operator::EQUAL || op == binary_operator::NOT_EQUAL,
+               "Unsupported operator for these types",
+               cudf::data_type_error);
+  auto common_dtype = get_common_type(out.type(), lhs.type(), rhs.type());
+  auto outd         = mutable_column_device_view::create(out, stream);
+  auto lhsd         = column_device_view::create(lhs, stream);
+  auto rhsd         = column_device_view::create(rhs, stream);
+  if (common_dtype) {
+    if (op == binary_operator::EQUAL) {
+      for_each(stream,
+               out.size(),
+               binary_op_device_dispatcher<ops::Equal>{
+                 *common_dtype, *outd, *lhsd, *rhsd, is_lhs_scalar, is_rhs_scalar});
+    } else if (op == binary_operator::NOT_EQUAL) {
+      for_each(stream,
+               out.size(),
+               binary_op_device_dispatcher<ops::NotEqual>{
+                 *common_dtype, *outd, *lhsd, *rhsd, is_lhs_scalar, is_rhs_scalar});
+    }
+  } else {
+    if (op == binary_operator::EQUAL) {
+      for_each(stream,
+               out.size(),
+               binary_op_double_device_dispatcher<ops::Equal>{
+                 *outd, *lhsd, *rhsd, is_lhs_scalar, is_rhs_scalar});
+    } else if (op == binary_operator::NOT_EQUAL) {
+      for_each(stream,
+               out.size(),
+               binary_op_double_device_dispatcher<ops::NotEqual>{
+                 *outd, *lhsd, *rhsd, is_lhs_scalar, is_rhs_scalar});
+    }
+  }
+}
+
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/compiled/operation.cuh b/cpp/src/binaryop/compiled/operation.cuh
new file mode 100644
index 0000000..214803d
--- /dev/null
+++ b/cpp/src/binaryop/compiled/operation.cuh
@@ -0,0 +1,527 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/traits.hpp>
+
+#include <cmath>
+
+namespace cudf {
+namespace binops {
+namespace compiled {
+
+// All binary operations
+namespace ops {
+
+struct Add {
+  template <typename T1, typename T2>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> decltype(lhs + rhs)
+  {
+    return lhs + rhs;
+  }
+};
+
+struct Sub {
+  template <typename T1, typename T2>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> decltype(lhs - rhs)
+  {
+    return lhs - rhs;
+  }
+};
+
+struct Mul {
+  template <typename TypeLhs, typename TypeRhs>
+  static constexpr inline bool is_supported()
+  {
+    return has_common_type_v<TypeLhs, TypeRhs> or
+           // FIXME: without the following line, compilation error
+           // _deps/libcudacxx-src/include/cuda/std/detail/libcxx/include/chrono(917): error:
+           // identifier "cuda::std::__3::ratio<(long)86400000000l, (long)1l> ::num" is undefined in
+           // device code
+           (is_duration<TypeLhs>() and std::is_integral<TypeRhs>()) or
+           (std::is_integral<TypeLhs>() and is_duration<TypeRhs>()) or
+           (is_fixed_point<TypeLhs>() and is_numeric<TypeRhs>()) or
+           (is_numeric<TypeLhs>() and is_fixed_point<TypeRhs>());
+  }
+  template <typename T1, typename T2, std::enable_if_t<is_supported<T1, T2>()>* = nullptr>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> decltype(lhs * rhs)
+  {
+    return lhs * rhs;
+  }
+};
+
+struct Div {
+  template <typename TypeLhs, typename TypeRhs>
+  static constexpr inline bool is_supported()
+  {
+    return has_common_type_v<TypeLhs, TypeRhs> or
+           // FIXME: without this, compilation error on chrono:917
+           (is_duration<TypeLhs>() and (std::is_integral<TypeRhs>() or is_duration<TypeRhs>())) or
+           (is_fixed_point<TypeLhs>() and is_numeric<TypeRhs>()) or
+           (is_numeric<TypeLhs>() and is_fixed_point<TypeRhs>());
+  }
+  template <typename T1, typename T2, std::enable_if_t<is_supported<T1, T2>()>* = nullptr>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> decltype(lhs / rhs)
+  {
+    return lhs / rhs;
+  }
+};
+
+struct TrueDiv {
+  template <typename T1, typename T2>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs)
+    -> decltype((static_cast<double>(lhs) / static_cast<double>(rhs)))
+  {
+    return (static_cast<double>(lhs) / static_cast<double>(rhs));
+  }
+};
+
+struct FloorDiv {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_integral_v<std::common_type_t<TypeLhs, TypeRhs>> and
+                              std::is_signed_v<std::common_type_t<TypeLhs, TypeRhs>>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x / y)
+  {
+    auto const quotient          = x / y;
+    auto const nonzero_remainder = (x % y) != 0;
+    auto const mixed_sign        = (x ^ y) < 0;
+    return quotient - mixed_sign * nonzero_remainder;
+  }
+
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_integral_v<std::common_type_t<TypeLhs, TypeRhs>> and
+                              !std::is_signed_v<std::common_type_t<TypeLhs, TypeRhs>>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x / y)
+  {
+    return x / y;
+  }
+
+  template <
+    typename TypeLhs,
+    typename TypeRhs,
+    std::enable_if_t<(std::is_same_v<std::common_type_t<TypeLhs, TypeRhs>, float>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> float
+  {
+    return floorf(x / y);
+  }
+
+  template <
+    typename TypeLhs,
+    typename TypeRhs,
+    std::enable_if_t<(std::is_same_v<std::common_type_t<TypeLhs, TypeRhs>, double>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> double
+  {
+    return floor(x / y);
+  }
+};
+
+struct Mod {
+  template <typename TypeLhs, typename TypeRhs>
+  static constexpr inline bool is_supported()
+  {
+    return has_common_type_v<TypeLhs, TypeRhs> or
+           // FIXME: without this, compilation error
+           //_deps/libcudacxx-src/include/cuda/std/detail/libcxx/include/chrono(1337):
+           // error : expression must have integral or unscoped enum type
+           (is_duration<TypeLhs>() and (std::is_integral<TypeRhs>() or is_duration<TypeRhs>()));
+  }
+  template <typename T1, typename T2, std::enable_if_t<is_supported<T1, T2>()>* = nullptr>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> decltype(lhs % rhs)
+  {
+    return lhs % rhs;
+  }
+  template <typename T1,
+            typename T2,
+            std::enable_if_t<(std::is_same_v<float, std::common_type_t<T1, T2>>)>* = nullptr>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> float
+  {
+    return fmodf(static_cast<float>(lhs), static_cast<float>(rhs));
+  }
+  template <typename T1,
+            typename T2,
+            std::enable_if_t<(std::is_same_v<double, std::common_type_t<T1, T2>>)>* = nullptr>
+  __device__ inline auto operator()(T1 const& lhs, T2 const& rhs) -> double
+  {
+    return fmod(static_cast<double>(lhs), static_cast<double>(rhs));
+  }
+};
+
+struct PMod {
+  // Ideally, these two specializations - one for integral types and one for non integral
+  // types shouldn't be required, as std::fmod should promote integral types automatically
+  // to double and call the std::fmod overload for doubles. Sadly, doing this in jitified
+  // code does not work - it is having trouble deciding between float/double overloads
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_integral_v<std::common_type_t<TypeLhs, TypeRhs>>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y)
+  {
+    using common_t = std::common_type_t<TypeLhs, TypeRhs>;
+    common_t xconv = static_cast<common_t>(x);
+    common_t yconv = static_cast<common_t>(y);
+    auto rem       = xconv % yconv;
+    if constexpr (std::is_signed_v<decltype(rem)>)
+      if (rem < 0) rem = (rem + yconv) % yconv;
+    return rem;
+  }
+
+  template <
+    typename TypeLhs,
+    typename TypeRhs,
+    std::enable_if_t<(std::is_floating_point_v<std::common_type_t<TypeLhs, TypeRhs>>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y)
+  {
+    using common_t = std::common_type_t<TypeLhs, TypeRhs>;
+    common_t xconv = static_cast<common_t>(x);
+    common_t yconv = static_cast<common_t>(y);
+    auto rem       = std::fmod(xconv, yconv);
+    if (rem < 0) rem = std::fmod(rem + yconv, yconv);
+    return rem;
+  }
+
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<cudf::is_fixed_point<TypeLhs>() and
+                             std::is_same_v<TypeLhs, TypeRhs>>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y)
+  {
+    auto const remainder = x % y;
+    return remainder.value() < 0 ? (remainder + y) % y : remainder;
+  }
+};
+
+struct PyMod {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_integral_v<std::common_type_t<TypeLhs, TypeRhs>> or
+                              (cudf::is_fixed_point<TypeLhs>() and
+                               std::is_same_v<TypeLhs, TypeRhs>))>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(((x % y) + y) % y)
+  {
+    return ((x % y) + y) % y;
+  }
+
+  template <
+    typename TypeLhs,
+    typename TypeRhs,
+    std::enable_if_t<(std::is_floating_point_v<std::common_type_t<TypeLhs, TypeRhs>>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> double
+  {
+    auto x1 = static_cast<double>(x);
+    auto y1 = static_cast<double>(y);
+    return fmod(fmod(x1, y1) + y1, y1);
+  }
+
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(is_duration<TypeLhs>())>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(((x % y) + y) % y)
+  {
+    return ((x % y) + y) % y;
+  }
+};
+
+struct Pow {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_convertible_v<TypeLhs, double> and
+                              std::is_convertible_v<TypeRhs, double>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> double
+  {
+    return pow(static_cast<double>(x), static_cast<double>(y));
+  }
+};
+
+struct IntPow {
+  template <
+    typename TypeLhs,
+    typename TypeRhs,
+    std::enable_if_t<(std::is_integral_v<TypeLhs> and std::is_integral_v<TypeRhs>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> TypeLhs
+  {
+    if constexpr (std::is_signed_v<TypeRhs>) {
+      if (y < 0) {
+        // Integer exponentiation with negative exponent is not possible.
+        return 0;
+      }
+    }
+    if (y == 0) { return 1; }
+    if (x == 0) { return 0; }
+    TypeLhs extra = 1;
+    while (y > 1) {
+      if (y & 1) {
+        // The exponent is odd, so multiply by one factor of x.
+        extra *= x;
+        y -= 1;
+      }
+      // The exponent is even, so square x and divide the exponent y by 2.
+      y /= 2;
+      x *= x;
+    }
+    return x * extra;
+  }
+};
+
+struct LogBase {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_convertible_v<TypeLhs, double> and
+                              std::is_convertible_v<TypeRhs, double>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> double
+  {
+    return (std::log(static_cast<double>(x)) / std::log(static_cast<double>(y)));
+  }
+};
+
+struct ATan2 {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            std::enable_if_t<(std::is_convertible_v<TypeLhs, double> and
+                              std::is_convertible_v<TypeRhs, double>)>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> double
+  {
+    return std::atan2(static_cast<double>(x), static_cast<double>(y));
+  }
+};
+
+struct ShiftLeft {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x << y)
+  {
+    return (x << y);
+  }
+};
+
+struct ShiftRight {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x >> y)
+  {
+    return (x >> y);
+  }
+};
+
+struct ShiftRightUnsigned {
+  template <
+    typename TypeLhs,
+    typename TypeRhs,
+    std::enable_if_t<(std::is_integral_v<TypeLhs> and not is_boolean<TypeLhs>())>* = nullptr>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y)
+    -> decltype(static_cast<std::make_unsigned_t<TypeLhs>>(x) >> y)
+  {
+    return (static_cast<std::make_unsigned_t<TypeLhs>>(x) >> y);
+  }
+};
+
+struct BitwiseAnd {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x & y)
+  {
+    return (x & y);
+  }
+};
+
+struct BitwiseOr {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x | y)
+  {
+    return (x | y);
+  }
+};
+
+struct BitwiseXor {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x ^ y)
+  {
+    return (x ^ y);
+  }
+};
+
+struct LogicalAnd {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x && y)
+  {
+    return (x && y);
+  }
+};
+
+struct LogicalOr {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x || y)
+  {
+    return (x || y);
+  }
+};
+
+struct Equal {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x == y)
+  {
+    return (x == y);
+  }
+};
+
+struct NotEqual {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x != y)
+  {
+    return (x != y);
+  }
+};
+
+struct Less {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x < y)
+  {
+    return (x < y);
+  }
+};
+
+struct Greater {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x > y)
+  {
+    return (x > y);
+  }
+};
+
+struct LessEqual {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x <= y)
+  {
+    return (x <= y);
+  }
+};
+
+struct GreaterEqual {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x >= y)
+  {
+    return (x >= y);
+  }
+};
+
+struct NullEquals {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(
+    TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) -> decltype(x == y)
+  {
+    output_valid = true;
+    if (!lhs_valid && !rhs_valid) return true;
+    if (lhs_valid && rhs_valid) return x == y;
+    return false;
+  }
+  // To allow std::is_invocable_v = true
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x == y);
+};
+
+struct NullMax {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            typename common_t = std::common_type_t<TypeLhs, TypeRhs>>
+  __device__ inline auto operator()(
+    TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid)
+    -> decltype(static_cast<common_t>(static_cast<common_t>(x) > static_cast<common_t>(y) ? x : y))
+  {
+    output_valid      = true;
+    auto const x_conv = static_cast<common_t>(x);
+    auto const y_conv = static_cast<common_t>(y);
+    if (!lhs_valid && !rhs_valid) {
+      output_valid = false;
+      return common_t{};
+    } else if (lhs_valid && rhs_valid) {
+      return (x_conv > y_conv) ? x_conv : y_conv;
+    } else if (lhs_valid)
+      return x_conv;
+    else
+      return y_conv;
+  }
+  // To allow std::is_invocable_v = true
+  template <typename TypeLhs,
+            typename TypeRhs,
+            typename common_t = std::common_type_t<TypeLhs, TypeRhs>>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y)
+    -> decltype(static_cast<common_t>(static_cast<common_t>(x) > static_cast<common_t>(y) ? x : y));
+};
+
+struct NullMin {
+  template <typename TypeLhs,
+            typename TypeRhs,
+            typename common_t = std::common_type_t<TypeLhs, TypeRhs>>
+  __device__ inline auto operator()(
+    TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid)
+    -> decltype(static_cast<common_t>(static_cast<common_t>(x) < static_cast<common_t>(y) ? x : y))
+  {
+    output_valid      = true;
+    auto const x_conv = static_cast<common_t>(x);
+    auto const y_conv = static_cast<common_t>(y);
+    if (!lhs_valid && !rhs_valid) {
+      output_valid = false;
+      return common_t{};
+    } else if (lhs_valid && rhs_valid) {
+      return (x_conv < y_conv) ? x_conv : y_conv;
+    } else if (lhs_valid)
+      return x_conv;
+    else
+      return y_conv;
+  }
+  // To allow std::is_invocable_v = true
+  template <typename TypeLhs,
+            typename TypeRhs,
+            typename common_t = std::common_type_t<TypeLhs, TypeRhs>>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y)
+    -> decltype(static_cast<common_t>(static_cast<common_t>(x) < static_cast<common_t>(y) ? x : y));
+};
+
+struct NullLogicalAnd {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(
+    TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) -> decltype(x && y)
+  {
+    bool lhs_false  = lhs_valid && !x;
+    bool rhs_false  = rhs_valid && !y;
+    bool both_valid = lhs_valid && rhs_valid;
+    output_valid    = lhs_false || rhs_false || both_valid;
+    return both_valid && !lhs_false && !rhs_false;
+  }
+  // To allow std::is_invocable_v = true
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x && y);
+};
+
+struct NullLogicalOr {
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(
+    TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) -> decltype(x || y)
+  {
+    bool lhs_true   = lhs_valid && x;
+    bool rhs_true   = rhs_valid && y;
+    bool both_valid = lhs_valid && rhs_valid;
+    output_valid    = lhs_true || rhs_true || both_valid;
+    return lhs_true || rhs_true;
+  }
+  // To allow std::is_invocable_v = true
+  template <typename TypeLhs, typename TypeRhs>
+  __device__ inline auto operator()(TypeLhs x, TypeRhs y) -> decltype(x || y);
+};
+
+}  // namespace ops
+}  // namespace compiled
+}  // namespace binops
+}  // namespace cudf
diff --git a/cpp/src/binaryop/compiled/struct_binary_ops.cuh b/cpp/src/binaryop/compiled/struct_binary_ops.cuh
new file mode 100644
index 0000000..2299df5
--- /dev/null
+++ b/cpp/src/binaryop/compiled/struct_binary_ops.cuh
@@ -0,0 +1,192 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "binary_ops.hpp"
+#include "operation.cuh"
+
+#include <cudf/binaryop.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/tabulate.h>
+
+namespace cudf::binops::compiled::detail {
+template <class T, class... Ts>
+inline constexpr bool is_any_v = std::disjunction<std::is_same<T, Ts>...>::value;
+
+template <typename OptionalIterator, typename DeviceComparator>
+struct device_comparison_functor {
+  // Explicit constructor definition required to avoid a "no instance of constructor" compilation
+  // error
+  device_comparison_functor(OptionalIterator const optional_iter,
+                            bool const is_lhs_scalar,
+                            bool const is_rhs_scalar,
+                            DeviceComparator const& comparator)
+    : _optional_iter(optional_iter),
+      _is_lhs_scalar(is_lhs_scalar),
+      _is_rhs_scalar(is_rhs_scalar),
+      _comparator(comparator)
+  {
+  }
+
+  bool __device__ operator()(size_type i)
+  {
+    return _optional_iter[i].has_value() &&
+           _comparator(cudf::experimental::row::lhs_index_type{_is_lhs_scalar ? 0 : i},
+                       cudf::experimental::row::rhs_index_type{_is_rhs_scalar ? 0 : i});
+  }
+
+  OptionalIterator const _optional_iter;
+  bool const _is_lhs_scalar;
+  bool const _is_rhs_scalar;
+  DeviceComparator const _comparator;
+};
+
+template <class BinaryOperator,
+          typename PhysicalElementComparator =
+            cudf::experimental::row::lexicographic::sorting_physical_element_comparator>
+void apply_struct_binary_op(mutable_column_view& out,
+                            column_view const& lhs,
+                            column_view const& rhs,
+                            bool is_lhs_scalar,
+                            bool is_rhs_scalar,
+                            PhysicalElementComparator comparator,
+                            rmm::cuda_stream_view stream)
+{
+  auto const compare_orders = std::vector<order>(
+    lhs.size(),
+    is_any_v<BinaryOperator, ops::Greater, ops::GreaterEqual> ? order::DESCENDING
+                                                              : order::ASCENDING);
+  auto const tlhs             = table_view{{lhs}};
+  auto const trhs             = table_view{{rhs}};
+  auto const table_comparator = cudf::experimental::row::lexicographic::two_table_comparator{
+    tlhs, trhs, compare_orders, {}, stream};
+  auto outd = column_device_view::create(out, stream);
+  auto optional_iter =
+    cudf::detail::make_optional_iterator<bool>(*outd, nullate::DYNAMIC{out.has_nulls()});
+  auto const comparator_nulls = nullate::DYNAMIC{has_nested_nulls(tlhs) || has_nested_nulls(trhs)};
+
+  auto tabulate_device_operator = [&](auto device_comparator) {
+    thrust::tabulate(
+      rmm::exec_policy(stream),
+      out.begin<bool>(),
+      out.end<bool>(),
+      device_comparison_functor{optional_iter, is_lhs_scalar, is_rhs_scalar, device_comparator});
+  };
+  if (cudf::detail::has_nested_columns(tlhs) || cudf::detail::has_nested_columns(trhs)) {
+    is_any_v<BinaryOperator, ops::LessEqual, ops::GreaterEqual>
+      ? tabulate_device_operator(
+          table_comparator.less_equivalent<true>(comparator_nulls, comparator))
+      : tabulate_device_operator(table_comparator.less<true>(comparator_nulls, comparator));
+  } else {
+    is_any_v<BinaryOperator, ops::LessEqual, ops::GreaterEqual>
+      ? tabulate_device_operator(
+          table_comparator.less_equivalent<false>(comparator_nulls, comparator))
+      : tabulate_device_operator(table_comparator.less<false>(comparator_nulls, comparator));
+  }
+}
+
+template <typename OptionalIteratorType, typename DeviceComparatorType>
+struct struct_equality_functor {
+  struct_equality_functor(OptionalIteratorType optional_iter,
+                          DeviceComparatorType device_comparator,
+                          bool is_lhs_scalar,
+                          bool is_rhs_scalar,
+                          bool preserve_output)
+    : _optional_iter(optional_iter),
+      _device_comparator(device_comparator),
+      _is_lhs_scalar(is_lhs_scalar),
+      _is_rhs_scalar(is_rhs_scalar),
+      _preserve_output(preserve_output)
+  {
+  }
+
+  auto __device__ operator()(size_type i) const noexcept
+  {
+    auto const lhs = cudf::experimental::row::lhs_index_type{_is_lhs_scalar ? 0 : i};
+    auto const rhs = cudf::experimental::row::rhs_index_type{_is_rhs_scalar ? 0 : i};
+    return _optional_iter[i].has_value() and (_device_comparator(lhs, rhs) == _preserve_output);
+  }
+
+ private:
+  OptionalIteratorType _optional_iter;
+  DeviceComparatorType _device_comparator;
+  bool _is_lhs_scalar;
+  bool _is_rhs_scalar;
+  bool _preserve_output;
+};
+
+template <typename PhysicalEqualityComparator =
+            cudf::experimental::row::equality::physical_equality_comparator>
+void apply_struct_equality_op(mutable_column_view& out,
+                              column_view const& lhs,
+                              column_view const& rhs,
+                              bool is_lhs_scalar,
+                              bool is_rhs_scalar,
+                              binary_operator op,
+                              PhysicalEqualityComparator comparator,
+                              rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(op == binary_operator::EQUAL || op == binary_operator::NOT_EQUAL ||
+                 op == binary_operator::NULL_EQUALS,
+               "Unsupported operator for these types",
+               cudf::data_type_error);
+
+  auto tlhs = table_view{{lhs}};
+  auto trhs = table_view{{rhs}};
+  auto table_comparator =
+    cudf::experimental::row::equality::two_table_comparator{tlhs, trhs, stream};
+
+  auto outd = column_device_view::create(out, stream);
+  auto optional_iter =
+    cudf::detail::make_optional_iterator<bool>(*outd, nullate::DYNAMIC{out.has_nulls()});
+
+  auto const comparator_helper = [&](auto const device_comparator) {
+    thrust::tabulate(rmm::exec_policy(stream),
+                     out.begin<bool>(),
+                     out.end<bool>(),
+                     struct_equality_functor<decltype(optional_iter), decltype(device_comparator)>(
+                       optional_iter,
+                       device_comparator,
+                       is_lhs_scalar,
+                       is_rhs_scalar,
+                       op != binary_operator::NOT_EQUAL));
+  };
+
+  if (cudf::detail::has_nested_columns(tlhs) or cudf::detail::has_nested_columns(trhs)) {
+    auto device_comparator = table_comparator.equal_to<true>(
+      nullate::DYNAMIC{has_nested_nulls(tlhs) || has_nested_nulls(trhs)},
+      null_equality::EQUAL,
+      comparator);
+
+    comparator_helper(device_comparator);
+  } else {
+    auto device_comparator = table_comparator.equal_to<false>(
+      nullate::DYNAMIC{has_nested_nulls(tlhs) || has_nested_nulls(trhs)},
+      null_equality::EQUAL,
+      comparator);
+
+    comparator_helper(device_comparator);
+  }
+}
+}  // namespace cudf::binops::compiled::detail
diff --git a/cpp/src/binaryop/compiled/util.cpp b/cpp/src/binaryop/compiled/util.cpp
new file mode 100644
index 0000000..1ef521d
--- /dev/null
+++ b/cpp/src/binaryop/compiled/util.cpp
@@ -0,0 +1,221 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "operation.cuh"
+
+#include <cudf/binaryop.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <optional>
+
+namespace cudf::binops::compiled {
+
+namespace {
+
+struct common_type_functor {
+  template <typename TypeLhs, typename TypeRhs>
+  std::optional<data_type> operator()() const
+  {
+    if constexpr (cudf::has_common_type_v<TypeLhs, TypeRhs>) {
+      using TypeCommon = std::common_type_t<TypeLhs, TypeRhs>;
+      return data_type{type_to_id<TypeCommon>()};
+    }
+
+    // A compiler bug may cause a compilation error when using empty
+    // initializer list to construct an std::optional object containing no
+    // `data_type` value. Therefore, we explicitly return `std::nullopt`
+    // instead.
+    return std::nullopt;
+  }
+};
+
+struct has_mutable_element_accessor_functor {
+  template <typename T>
+  bool operator()() const
+  {
+    return mutable_column_device_view::has_element_accessor<T>();
+  }
+};
+
+bool has_mutable_element_accessor(data_type t)
+{
+  return type_dispatcher(t, has_mutable_element_accessor_functor{});
+}
+
+template <typename InputType>
+struct is_constructible_functor {
+  template <typename TargetType>
+  bool operator()() const
+  {
+    return std::is_constructible_v<TargetType, InputType>;
+  }
+};
+
+template <typename InputType>
+bool is_constructible(data_type target_type)
+{
+  return type_dispatcher(target_type, is_constructible_functor<InputType>{});
+}
+
+/**
+ * @brief Functor that return true if BinaryOperator supports given input and output types.
+ *
+ * @tparam BinaryOperator binary operator functor
+ */
+template <typename BinaryOperator>
+struct is_binary_operation_supported {
+  // For types where Out type is fixed. (e.g. comparison types)
+  template <typename TypeLhs, typename TypeRhs>
+  inline constexpr bool operator()() const
+  {
+    if constexpr (column_device_view::has_element_accessor<TypeLhs>() and
+                  column_device_view::has_element_accessor<TypeRhs>()) {
+      if constexpr (has_common_type_v<TypeLhs, TypeRhs>) {
+        using common_t = std::common_type_t<TypeLhs, TypeRhs>;
+        return std::is_invocable_v<BinaryOperator, common_t, common_t>;
+      } else {
+        return std::is_invocable_v<BinaryOperator, TypeLhs, TypeRhs>;
+      }
+    } else {
+      return false;
+    }
+  }
+
+  template <typename TypeLhs, typename TypeRhs>
+  inline constexpr bool operator()(data_type out_type) const
+  {
+    if constexpr (column_device_view::has_element_accessor<TypeLhs>() and
+                  column_device_view::has_element_accessor<TypeRhs>()) {
+      if (has_mutable_element_accessor(out_type) or is_fixed_point(out_type)) {
+        if constexpr (has_common_type_v<TypeLhs, TypeRhs>) {
+          using common_t = std::common_type_t<TypeLhs, TypeRhs>;
+          if constexpr (std::is_invocable_v<BinaryOperator, common_t, common_t>) {
+            using ReturnType = std::invoke_result_t<BinaryOperator, common_t, common_t>;
+            return is_constructible<ReturnType>(out_type) or
+                   (is_fixed_point<ReturnType>() and is_fixed_point(out_type));
+          }
+        } else if constexpr (std::is_invocable_v<BinaryOperator, TypeLhs, TypeRhs>) {
+          using ReturnType = std::invoke_result_t<BinaryOperator, TypeLhs, TypeRhs>;
+          return is_constructible<ReturnType>(out_type);
+        }
+      }
+    }
+    return false;
+  }
+};
+
+struct is_supported_operation_functor {
+  template <typename TypeLhs, typename TypeRhs>
+  struct nested_support_functor {
+    template <typename BinaryOperator>
+    inline constexpr bool call(data_type out_type) const
+    {
+      return is_binary_operation_supported<BinaryOperator>{}.template operator()<TypeLhs, TypeRhs>(
+        out_type);
+    }
+    inline constexpr bool operator()(binary_operator op, data_type out_type) const
+    {
+      switch (op) {
+        // clang-format off
+        case binary_operator::ADD:                  return call<ops::Add>(out_type);
+        case binary_operator::SUB:                  return call<ops::Sub>(out_type);
+        case binary_operator::MUL:                  return call<ops::Mul>(out_type);
+        case binary_operator::DIV:                  return call<ops::Div>(out_type);
+        case binary_operator::TRUE_DIV:             return call<ops::TrueDiv>(out_type);
+        case binary_operator::FLOOR_DIV:            return call<ops::FloorDiv>(out_type);
+        case binary_operator::MOD:                  return call<ops::Mod>(out_type);
+        case binary_operator::PYMOD:                return call<ops::PyMod>(out_type);
+        case binary_operator::POW:                  return call<ops::Pow>(out_type);
+        case binary_operator::INT_POW:              return call<ops::IntPow>(out_type);
+        case binary_operator::BITWISE_AND:          return call<ops::BitwiseAnd>(out_type);
+        case binary_operator::BITWISE_OR:           return call<ops::BitwiseOr>(out_type);
+        case binary_operator::BITWISE_XOR:          return call<ops::BitwiseXor>(out_type);
+        case binary_operator::SHIFT_LEFT:           return call<ops::ShiftLeft>(out_type);
+        case binary_operator::SHIFT_RIGHT:          return call<ops::ShiftRight>(out_type);
+        case binary_operator::SHIFT_RIGHT_UNSIGNED: return call<ops::ShiftRightUnsigned>(out_type);
+        case binary_operator::LOG_BASE:             return call<ops::LogBase>(out_type);
+        case binary_operator::ATAN2:                return call<ops::ATan2>(out_type);
+        case binary_operator::PMOD:                 return call<ops::PMod>(out_type);
+        case binary_operator::NULL_MAX:             return call<ops::NullMax>(out_type);
+        case binary_operator::NULL_MIN:             return call<ops::NullMin>(out_type);
+        /*
+        case binary_operator::GENERIC_BINARY:       // defined in jit only.
+        */
+        default:                                    return false;
+          // clang-format on
+      }
+    }
+  };
+
+  template <typename BinaryOperator, typename TypeLhs, typename TypeRhs>
+  inline constexpr bool bool_op(data_type out) const
+  {
+    return out.id() == type_id::BOOL8 and
+           is_binary_operation_supported<BinaryOperator>{}.template operator()<TypeLhs, TypeRhs>();
+  }
+  template <typename TypeLhs, typename TypeRhs>
+  inline constexpr bool operator()(data_type out, binary_operator op) const
+  {
+    switch (op) {
+      // output type should be bool type.
+      case binary_operator::LOGICAL_AND: return bool_op<ops::LogicalAnd, TypeLhs, TypeRhs>(out);
+      case binary_operator::LOGICAL_OR: return bool_op<ops::LogicalOr, TypeLhs, TypeRhs>(out);
+      case binary_operator::EQUAL: return bool_op<ops::Equal, TypeLhs, TypeRhs>(out);
+      case binary_operator::NOT_EQUAL: return bool_op<ops::NotEqual, TypeLhs, TypeRhs>(out);
+      case binary_operator::LESS: return bool_op<ops::Less, TypeLhs, TypeRhs>(out);
+      case binary_operator::GREATER: return bool_op<ops::Greater, TypeLhs, TypeRhs>(out);
+      case binary_operator::LESS_EQUAL: return bool_op<ops::LessEqual, TypeLhs, TypeRhs>(out);
+      case binary_operator::GREATER_EQUAL: return bool_op<ops::GreaterEqual, TypeLhs, TypeRhs>(out);
+      case binary_operator::NULL_EQUALS: return bool_op<ops::NullEquals, TypeLhs, TypeRhs>(out);
+      case binary_operator::NULL_LOGICAL_AND:
+        return bool_op<ops::NullLogicalAnd, TypeLhs, TypeRhs>(out);
+      case binary_operator::NULL_LOGICAL_OR:
+        return bool_op<ops::NullLogicalOr, TypeLhs, TypeRhs>(out);
+      default: return nested_support_functor<TypeLhs, TypeRhs>{}(op, out);
+    }
+    return false;
+  }
+};
+
+}  // namespace
+
+std::optional<data_type> get_common_type(data_type out, data_type lhs, data_type rhs)
+{
+  // Compute the common type of (out, lhs, rhs) if it exists, or the common
+  // type of (lhs, rhs) if it exists, else return a null optional.
+  // We can avoid a triple type dispatch by using the definition of
+  // std::common_type to compute this with double type dispatches.
+  // Specifically, std::common_type_t<TypeOut, TypeLhs, TypeRhs> is the same as
+  // std::common_type_t<std::common_type_t<TypeOut, TypeLhs>, TypeRhs>.
+  auto common_type = double_type_dispatcher(out, lhs, common_type_functor{});
+  if (common_type.has_value()) {
+    common_type = double_type_dispatcher(common_type.value(), rhs, common_type_functor{});
+  }
+  // If no common type of (out, lhs, rhs) exists, fall back to the common type
+  // of (lhs, rhs).
+  if (!common_type.has_value()) {
+    common_type = double_type_dispatcher(lhs, rhs, common_type_functor{});
+  }
+  return common_type;
+}
+
+bool is_supported_operation(data_type out, data_type lhs, data_type rhs, binary_operator op)
+{
+  return double_type_dispatcher(lhs, rhs, is_supported_operation_functor{}, out, op);
+}
+}  // namespace cudf::binops::compiled
diff --git a/cpp/src/binaryop/jit/kernel.cu b/cpp/src/binaryop/jit/kernel.cu
new file mode 100644
index 0000000..c9cc61a
--- /dev/null
+++ b/cpp/src/binaryop/jit/kernel.cu
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *     Copyright 2018 Rommel Quintanilla <rommel@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <binaryop/jit/operation-udf.hpp>
+#include <cuda/std/type_traits>
+
+namespace cudf {
+namespace binops {
+namespace jit {
+
+struct UserDefinedOp {
+  template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+  static TypeOut operate(TypeLhs x, TypeRhs y)
+  {
+    TypeOut output;
+    using TypeCommon = typename cuda::std::common_type<TypeOut, TypeLhs, TypeRhs>::type;
+    GENERIC_BINARY_OP(&output, static_cast<TypeCommon>(x), static_cast<TypeCommon>(y));
+    return output;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs, typename TypeOpe>
+__global__ void kernel_v_v(cudf::size_type size,
+                           TypeOut* out_data,
+                           TypeLhs* lhs_data,
+                           TypeRhs* rhs_data)
+{
+  int tid    = threadIdx.x;
+  int blkid  = blockIdx.x;
+  int blksz  = blockDim.x;
+  int gridsz = gridDim.x;
+
+  int start = tid + blkid * blksz;
+  int step  = blksz * gridsz;
+
+  for (cudf::size_type i = start; i < size; i += step) {
+    out_data[i] = TypeOpe::template operate<TypeOut, TypeLhs, TypeRhs>(lhs_data[i], rhs_data[i]);
+  }
+}
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs, typename TypeOpe>
+__global__ void kernel_v_v_with_validity(cudf::size_type size,
+                                         TypeOut* out_data,
+                                         TypeLhs* lhs_data,
+                                         TypeRhs* rhs_data,
+                                         cudf::bitmask_type* output_mask,
+                                         cudf::bitmask_type const* lhs_mask,
+                                         cudf::size_type lhs_offset,
+                                         cudf::bitmask_type const* rhs_mask,
+                                         cudf::size_type rhs_offset)
+{
+  int tid    = threadIdx.x;
+  int blkid  = blockIdx.x;
+  int blksz  = blockDim.x;
+  int gridsz = gridDim.x;
+
+  int start = tid + blkid * blksz;
+  int step  = blksz * gridsz;
+
+  for (cudf::size_type i = start; i < size; i += step) {
+    bool output_valid = false;
+    out_data[i]       = TypeOpe::template operate<TypeOut, TypeLhs, TypeRhs>(
+      lhs_data[i],
+      rhs_data[i],
+      lhs_mask ? cudf::bit_is_set(lhs_mask, lhs_offset + i) : true,
+      rhs_mask ? cudf::bit_is_set(rhs_mask, rhs_offset + i) : true,
+      output_valid);
+    if (output_mask && !output_valid) cudf::clear_bit(output_mask, i);
+  }
+}
+
+}  // namespace jit
+}  // namespace binops
+}  // namespace cudf
diff --git a/cpp/src/binaryop/jit/operation-udf.hpp b/cpp/src/binaryop/jit/operation-udf.hpp
new file mode 100644
index 0000000..eaab211
--- /dev/null
+++ b/cpp/src/binaryop/jit/operation-udf.hpp
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// This file serves as a placeholder for user defined functions, so jitify can choose to override it
+// at runtime.
diff --git a/cpp/src/bitmask/is_element_valid.cpp b/cpp/src/bitmask/is_element_valid.cpp
new file mode 100644
index 0000000..e0f0ccd
--- /dev/null
+++ b/cpp/src/bitmask/is_element_valid.cpp
@@ -0,0 +1,47 @@
+
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+bool is_element_valid_sync(column_view const& col_view,
+                           size_type element_index,
+                           rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(element_index >= 0 and element_index < col_view.size(), "invalid index.");
+  if (!col_view.nullable()) { return true; }
+
+  bitmask_type word;
+  // null_mask() returns device ptr to bitmask without offset
+  size_type index = element_index + col_view.offset();
+  CUDF_CUDA_TRY(cudaMemcpyAsync(&word,
+                                col_view.null_mask() + word_index(index),
+                                sizeof(bitmask_type),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  stream.synchronize();
+  return static_cast<bool>(word & (bitmask_type{1} << intra_word_index(index)));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/bitmask/null_mask.cu b/cpp/src/bitmask/null_mask.cu
new file mode 100644
index 0000000..5a0d3e4
--- /dev/null
+++ b/cpp/src/bitmask/null_mask.cu
@@ -0,0 +1,543 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/null_mask.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/device_ptr.h>
+#include <thrust/extrema.h>
+
+#include <cub/cub.cuh>
+
+#include <algorithm>
+#include <numeric>
+#include <type_traits>
+
+namespace cudf {
+size_type state_null_count(mask_state state, size_type size)
+{
+  switch (state) {
+    case mask_state::UNALLOCATED: return 0;
+    case mask_state::ALL_NULL: return size;
+    case mask_state::ALL_VALID: return 0;
+    default: CUDF_FAIL("Invalid null mask state.", std::invalid_argument);
+  }
+}
+
+// Computes required allocation size of a bitmask
+std::size_t bitmask_allocation_size_bytes(size_type number_of_bits, std::size_t padding_boundary)
+{
+  CUDF_EXPECTS(padding_boundary > 0, "Invalid padding boundary");
+  auto necessary_bytes = cudf::util::div_rounding_up_safe<size_type>(number_of_bits, CHAR_BIT);
+
+  auto padded_bytes = padding_boundary * cudf::util::div_rounding_up_safe<size_type>(
+                                           necessary_bytes, padding_boundary);
+  return padded_bytes;
+}
+
+// Computes number of *actual* bitmask_type elements needed
+size_type num_bitmask_words(size_type number_of_bits)
+{
+  return cudf::util::div_rounding_up_safe<size_type>(number_of_bits,
+                                                     detail::size_in_bits<bitmask_type>());
+}
+
+namespace detail {
+
+// Create a device_buffer for a null mask
+rmm::device_buffer create_null_mask(size_type size,
+                                    mask_state state,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  size_type mask_size{0};
+
+  if (state != mask_state::UNALLOCATED) { mask_size = bitmask_allocation_size_bytes(size); }
+
+  rmm::device_buffer mask(mask_size, stream, mr);
+
+  if (state != mask_state::UNINITIALIZED) {
+    uint8_t fill_value = (state == mask_state::ALL_VALID) ? 0xff : 0x00;
+    CUDF_CUDA_TRY(cudaMemsetAsync(
+      static_cast<bitmask_type*>(mask.data()), fill_value, mask_size, stream.value()));
+  }
+
+  return mask;
+}
+
+namespace {
+__global__ void set_null_mask_kernel(bitmask_type* __restrict__ destination,
+                                     size_type begin_bit,
+                                     size_type end_bit,
+                                     bool valid,
+                                     size_type number_of_mask_words)
+{
+  auto x                            = destination + word_index(begin_bit);
+  thread_index_type const last_word = word_index(end_bit) - word_index(begin_bit);
+  bitmask_type fill_value           = valid ? 0xffff'ffff : 0;
+
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+
+  for (thread_index_type destination_word_index = grid_1d::global_thread_id();
+       destination_word_index < number_of_mask_words;
+       destination_word_index += stride) {
+    if (destination_word_index == 0 || destination_word_index == last_word) {
+      bitmask_type mask = ~bitmask_type{0};
+      if (destination_word_index == 0) {
+        mask = ~(set_least_significant_bits(intra_word_index(begin_bit)));
+      }
+      if (destination_word_index == last_word) {
+        mask = mask & set_least_significant_bits(intra_word_index(end_bit));
+      }
+      x[destination_word_index] =
+        valid ? x[destination_word_index] | mask : x[destination_word_index] & ~mask;
+    } else {
+      x[destination_word_index] = fill_value;
+    }
+  }
+}
+}  // namespace
+
+// Set pre-allocated null mask of given bit range [begin_bit, end_bit) to valid, if valid==true,
+// or null, otherwise;
+void set_null_mask(bitmask_type* bitmask,
+                   size_type begin_bit,
+                   size_type end_bit,
+                   bool valid,
+                   rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(begin_bit >= 0, "Invalid range.");
+  CUDF_EXPECTS(begin_bit <= end_bit, "Invalid bit range.");
+  if (begin_bit == end_bit) return;
+  if (bitmask != nullptr) {
+    auto number_of_mask_words =
+      num_bitmask_words(end_bit) - begin_bit / detail::size_in_bits<bitmask_type>();
+    cudf::detail::grid_1d config(number_of_mask_words, 256);
+    set_null_mask_kernel<<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+      static_cast<bitmask_type*>(bitmask), begin_bit, end_bit, valid, number_of_mask_words);
+    CUDF_CHECK_CUDA(stream.value());
+  }
+}
+
+}  // namespace detail
+
+// Create a device_buffer for a null mask
+rmm::device_buffer create_null_mask(size_type size,
+                                    mask_state state,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  return detail::create_null_mask(size, state, cudf::get_default_stream(), mr);
+}
+
+// Set pre-allocated null mask of given bit range [begin_bit, end_bit) to valid, if valid==true,
+// or null, otherwise;
+void set_null_mask(bitmask_type* bitmask, size_type begin_bit, size_type end_bit, bool valid)
+{
+  return detail::set_null_mask(bitmask, begin_bit, end_bit, valid, cudf::get_default_stream());
+}
+
+namespace detail {
+
+namespace {
+/**
+ * @brief Copies the bits starting at the specified offset from a source
+ * bitmask into the destination bitmask.
+ *
+ * Bit `i` in `destination` will be equal to bit `i + offset` from `source`.
+ *
+ * @param destination The mask to copy into
+ * @param source The mask to copy from
+ * @param source_begin_bit The offset into `source` from which to begin the copy
+ * @param source_end_bit   The offset into `source` till which copying is done
+ * @param number_of_mask_words The number of `cudf::bitmask_type` words to copy
+ */
+// TODO: Also make binops test that uses offset in column_view
+__global__ void copy_offset_bitmask(bitmask_type* __restrict__ destination,
+                                    bitmask_type const* __restrict__ source,
+                                    size_type source_begin_bit,
+                                    size_type source_end_bit,
+                                    size_type number_of_mask_words)
+{
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+  for (thread_index_type destination_word_index = grid_1d::global_thread_id();
+       destination_word_index < number_of_mask_words;
+       destination_word_index += stride) {
+    destination[destination_word_index] = detail::get_mask_offset_word(
+      source, destination_word_index, source_begin_bit, source_end_bit);
+  }
+}
+
+}  // namespace
+
+// Create a bitmask from a specific range
+rmm::device_buffer copy_bitmask(bitmask_type const* mask,
+                                size_type begin_bit,
+                                size_type end_bit,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(begin_bit >= 0, "Invalid range.");
+  CUDF_EXPECTS(begin_bit <= end_bit, "Invalid bit range.");
+  rmm::device_buffer dest_mask{};
+  auto num_bytes = bitmask_allocation_size_bytes(end_bit - begin_bit);
+  if ((mask == nullptr) || (num_bytes == 0)) { return dest_mask; }
+  if (begin_bit == 0) {
+    dest_mask = rmm::device_buffer{static_cast<void const*>(mask), num_bytes, stream, mr};
+  } else {
+    auto number_of_mask_words = num_bitmask_words(end_bit - begin_bit);
+    dest_mask                 = rmm::device_buffer{num_bytes, stream, mr};
+    cudf::detail::grid_1d config(number_of_mask_words, 256);
+    copy_offset_bitmask<<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+      static_cast<bitmask_type*>(dest_mask.data()), mask, begin_bit, end_bit, number_of_mask_words);
+    CUDF_CHECK_CUDA(stream.value());
+  }
+  return dest_mask;
+}
+
+// Create a bitmask from a column view
+rmm::device_buffer copy_bitmask(column_view const& view,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  rmm::device_buffer null_mask{0, stream, mr};
+  if (view.nullable()) {
+    null_mask =
+      copy_bitmask(view.null_mask(), view.offset(), view.offset() + view.size(), stream, mr);
+  }
+  return null_mask;
+}
+
+namespace {
+/**
+ * @brief Counts the number of non-zero bits in a bitmask in the range
+ * `[first_bit_index, last_bit_index]`.
+ *
+ * Expects `0 <= first_bit_index <= last_bit_index`.
+ *
+ * @param[in] bitmask The bitmask whose non-zero bits will be counted.
+ * @param[in] first_bit_index The index (inclusive) of the first bit to count
+ * @param[in] last_bit_index The index (inclusive) of the last bit to count
+ * @param[out] global_count The number of non-zero bits in the specified range
+ */
+template <size_type block_size>
+__global__ void count_set_bits_kernel(bitmask_type const* bitmask,
+                                      size_type first_bit_index,
+                                      size_type last_bit_index,
+                                      size_type* global_count)
+{
+  constexpr auto const word_size{detail::size_in_bits<bitmask_type>()};
+
+  auto const first_word_index{word_index(first_bit_index)};
+  auto const last_word_index{word_index(last_bit_index)};
+  thread_index_type const tid         = grid_1d::global_thread_id();
+  thread_index_type const stride      = grid_1d::grid_stride();
+  thread_index_type thread_word_index = tid + first_word_index;
+  size_type thread_count{0};
+
+  // First, just count the bits in all words
+  while (thread_word_index <= last_word_index) {
+    thread_count += __popc(bitmask[thread_word_index]);
+    thread_word_index += stride;
+  }
+
+  // Subtract any slack bits counted from the first and last word
+  // Two threads handle this -- one for first word, one for last
+  if (tid < 2) {
+    bool const first{tid == 0};
+    bool const last{not first};
+
+    size_type bit_index  = (first) ? first_bit_index : last_bit_index;
+    size_type word_index = (first) ? first_word_index : last_word_index;
+
+    size_type num_slack_bits = bit_index % word_size;
+    if (last) { num_slack_bits = word_size - num_slack_bits - 1; }
+
+    if (num_slack_bits > 0) {
+      bitmask_type word = bitmask[word_index];
+      auto slack_mask   = (first) ? set_least_significant_bits(num_slack_bits)
+                                  : set_most_significant_bits(num_slack_bits);
+
+      thread_count -= __popc(word & slack_mask);
+    }
+  }
+
+  using BlockReduce = cub::BlockReduce<size_type, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  size_type block_count{BlockReduce(temp_storage).Sum(thread_count)};
+
+  if (threadIdx.x == 0) { atomicAdd(global_count, block_count); }
+}
+
+}  // namespace
+
+// Count non-zero bits in the specified range
+cudf::size_type count_set_bits(bitmask_type const* bitmask,
+                               size_type start,
+                               size_type stop,
+                               rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(bitmask != nullptr, "Invalid bitmask.");
+  CUDF_EXPECTS(start >= 0, "Invalid range.");
+  CUDF_EXPECTS(start <= stop, "Invalid bit range.");
+
+  auto const num_bits_to_count = stop - start;
+  if (num_bits_to_count == 0) { return 0; }
+
+  auto const num_words = num_bitmask_words(num_bits_to_count);
+
+  constexpr size_type block_size{256};
+
+  cudf::detail::grid_1d grid(num_words, block_size);
+
+  rmm::device_scalar<size_type> non_zero_count(0, stream);
+
+  count_set_bits_kernel<block_size>
+    <<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+      bitmask, start, stop - 1, non_zero_count.data());
+
+  return non_zero_count.value(stream);
+}
+
+// Count zero bits in the specified range
+cudf::size_type count_unset_bits(bitmask_type const* bitmask,
+                                 size_type start,
+                                 size_type stop,
+                                 rmm::cuda_stream_view stream)
+{
+  auto const num_set_bits   = detail::count_set_bits(bitmask, start, stop, stream);
+  auto const total_num_bits = (stop - start);
+  return total_num_bits - num_set_bits;
+}
+
+// Count valid elements in the specified range of a validity bitmask
+cudf::size_type valid_count(bitmask_type const* bitmask,
+                            size_type start,
+                            size_type stop,
+                            rmm::cuda_stream_view stream)
+{
+  if (bitmask == nullptr) {
+    CUDF_EXPECTS(start >= 0, "Invalid range.");
+    CUDF_EXPECTS(start <= stop, "Invalid bit range.");
+    auto const total_num_bits = (stop - start);
+    return total_num_bits;
+  }
+
+  return detail::count_set_bits(bitmask, start, stop, stream);
+}
+
+// Count null elements in the specified range of a validity bitmask
+cudf::size_type null_count(bitmask_type const* bitmask,
+                           size_type start,
+                           size_type stop,
+                           rmm::cuda_stream_view stream)
+{
+  if (bitmask == nullptr) {
+    CUDF_EXPECTS(start >= 0, "Invalid range.");
+    CUDF_EXPECTS(start <= stop, "Invalid bit range.");
+    return 0;
+  }
+
+  return detail::count_unset_bits(bitmask, start, stop, stream);
+}
+
+// Count non-zero bits in the specified ranges of a bitmask
+std::vector<size_type> segmented_count_set_bits(bitmask_type const* bitmask,
+                                                host_span<size_type const> indices,
+                                                rmm::cuda_stream_view stream)
+{
+  return detail::segmented_count_set_bits(bitmask, indices.begin(), indices.end(), stream);
+}
+
+// Count zero bits in the specified ranges of a bitmask
+std::vector<size_type> segmented_count_unset_bits(bitmask_type const* bitmask,
+                                                  host_span<size_type const> indices,
+                                                  rmm::cuda_stream_view stream)
+{
+  return detail::segmented_count_unset_bits(bitmask, indices.begin(), indices.end(), stream);
+}
+
+// Count valid elements in the specified ranges of a validity bitmask
+std::vector<size_type> segmented_valid_count(bitmask_type const* bitmask,
+                                             host_span<size_type const> indices,
+                                             rmm::cuda_stream_view stream)
+{
+  return detail::segmented_valid_count(bitmask, indices.begin(), indices.end(), stream);
+}
+
+// Count null elements in the specified ranges of a validity bitmask
+std::vector<size_type> segmented_null_count(bitmask_type const* bitmask,
+                                            host_span<size_type const> indices,
+                                            rmm::cuda_stream_view stream)
+{
+  return detail::segmented_null_count(bitmask, indices.begin(), indices.end(), stream);
+}
+
+// Inplace Bitwise AND of the masks
+cudf::size_type inplace_bitmask_and(device_span<bitmask_type> dest_mask,
+                                    host_span<bitmask_type const* const> masks,
+                                    host_span<size_type const> begin_bits,
+                                    size_type mask_size,
+                                    rmm::cuda_stream_view stream)
+{
+  return inplace_bitmask_binop(
+    [] __device__(bitmask_type left, bitmask_type right) { return left & right; },
+    dest_mask,
+    masks,
+    begin_bits,
+    mask_size,
+    stream);
+}
+
+// Bitwise AND of the masks
+std::pair<rmm::device_buffer, size_type> bitmask_and(host_span<bitmask_type const* const> masks,
+                                                     host_span<size_type const> begin_bits,
+                                                     size_type mask_size,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  return bitmask_binop(
+    [] __device__(bitmask_type left, bitmask_type right) { return left & right; },
+    masks,
+    begin_bits,
+    mask_size,
+    stream,
+    mr);
+}
+
+// Returns the bitwise AND of the null masks of all columns in the table view
+std::pair<rmm::device_buffer, size_type> bitmask_and(table_view const& view,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  rmm::device_buffer null_mask{0, stream, mr};
+  if (view.num_rows() == 0 or view.num_columns() == 0) {
+    return std::pair(std::move(null_mask), 0);
+  }
+
+  std::vector<bitmask_type const*> masks;
+  std::vector<size_type> offsets;
+  for (auto&& col : view) {
+    if (col.nullable()) {
+      masks.push_back(col.null_mask());
+      offsets.push_back(col.offset());
+    }
+  }
+
+  if (masks.size() > 0) {
+    return cudf::detail::bitmask_binop(
+      [] __device__(bitmask_type left, bitmask_type right) { return left & right; },
+      masks,
+      offsets,
+      view.num_rows(),
+      stream,
+      mr);
+  }
+
+  return std::pair(std::move(null_mask), 0);
+}
+
+// Returns the bitwise OR of the null masks of all columns in the table view
+std::pair<rmm::device_buffer, size_type> bitmask_or(table_view const& view,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  rmm::device_buffer null_mask{0, stream, mr};
+  if (view.num_rows() == 0 or view.num_columns() == 0) {
+    return std::pair(std::move(null_mask), 0);
+  }
+
+  std::vector<bitmask_type const*> masks;
+  std::vector<size_type> offsets;
+  for (auto&& col : view) {
+    if (col.nullable()) {
+      masks.push_back(col.null_mask());
+      offsets.push_back(col.offset());
+    }
+  }
+
+  if (static_cast<size_type>(masks.size()) == view.num_columns()) {
+    return cudf::detail::bitmask_binop(
+      [] __device__(bitmask_type left, bitmask_type right) { return left | right; },
+      masks,
+      offsets,
+      view.num_rows(),
+      stream,
+      mr);
+  }
+
+  return std::pair(std::move(null_mask), 0);
+}
+
+}  // namespace detail
+
+// Create a bitmask from a specific range
+rmm::device_buffer copy_bitmask(bitmask_type const* mask,
+                                size_type begin_bit,
+                                size_type end_bit,
+                                rmm::mr::device_memory_resource* mr)
+{
+  return detail::copy_bitmask(mask, begin_bit, end_bit, cudf::get_default_stream(), mr);
+}
+
+// Create a bitmask from a column view
+rmm::device_buffer copy_bitmask(column_view const& view, rmm::mr::device_memory_resource* mr)
+{
+  return detail::copy_bitmask(view, cudf::get_default_stream(), mr);
+}
+
+std::pair<rmm::device_buffer, size_type> bitmask_and(table_view const& view,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  return detail::bitmask_and(view, cudf::get_default_stream(), mr);
+}
+
+std::pair<rmm::device_buffer, size_type> bitmask_or(table_view const& view,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  return detail::bitmask_or(view, cudf::get_default_stream(), mr);
+}
+
+// Count non-zero bits in the specified range
+cudf::size_type null_count(bitmask_type const* bitmask, size_type start, size_type stop)
+{
+  return detail::null_count(bitmask, start, stop, cudf::get_default_stream());
+}
+
+}  // namespace cudf
diff --git a/cpp/src/column/column.cu b/cpp/src/column/column.cu
new file mode 100644
index 0000000..8276dbe
--- /dev/null
+++ b/cpp/src/column/column.cu
@@ -0,0 +1,264 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/lists/detail/copying.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/strings/detail/copying.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <algorithm>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+namespace cudf {
+
+// Copy ctor w/ optional stream/mr
+column::column(column const& other,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr)
+  : _type{other._type},
+    _size{other._size},
+    _data{other._data, stream, mr},
+    _null_mask{other._null_mask, stream, mr},
+    _null_count{other._null_count}
+{
+  _children.reserve(other.num_children());
+  for (auto const& c : other._children) {
+    _children.emplace_back(std::make_unique<column>(*c, stream, mr));
+  }
+}
+
+// Move constructor
+column::column(column&& other) noexcept
+  : _type{other._type},
+    _size{other._size},
+    _data{std::move(other._data)},
+    _null_mask{std::move(other._null_mask)},
+    _null_count{other._null_count},
+    _children{std::move(other._children)}
+{
+  other._size       = 0;
+  other._null_count = 0;
+  other._type       = data_type{type_id::EMPTY};
+}
+
+// Release contents
+column::contents column::release() noexcept
+{
+  _size       = 0;
+  _null_count = 0;
+  _type       = data_type{type_id::EMPTY};
+  return column::contents{std::make_unique<rmm::device_buffer>(std::move(_data)),
+                          std::make_unique<rmm::device_buffer>(std::move(_null_mask)),
+                          std::move(_children)};
+}
+
+// Create immutable view
+column_view column::view() const
+{
+  // Create views of children
+  std::vector<column_view> child_views;
+  child_views.reserve(_children.size());
+  for (auto const& c : _children) {
+    child_views.emplace_back(*c);
+  }
+
+  return column_view{type(),
+                     size(),
+                     _data.data(),
+                     static_cast<bitmask_type const*>(_null_mask.data()),
+                     null_count(),
+                     0,
+                     child_views};
+}
+
+// Create mutable view
+mutable_column_view column::mutable_view()
+{
+  CUDF_FUNC_RANGE();
+
+  // create views of children
+  std::vector<mutable_column_view> child_views;
+  child_views.reserve(_children.size());
+  for (auto const& c : _children) {
+    child_views.emplace_back(*c);
+  }
+
+  return mutable_column_view{type(),
+                             size(),
+                             _data.data(),
+                             static_cast<bitmask_type*>(_null_mask.data()),
+                             _null_count,
+                             0,
+                             child_views};
+}
+
+void column::set_null_mask(rmm::device_buffer&& new_null_mask, size_type new_null_count)
+{
+  if (new_null_count > 0) {
+    CUDF_EXPECTS(new_null_mask.size() >= cudf::bitmask_allocation_size_bytes(this->size()),
+                 "Column with null values must be nullable and the null mask \
+                  buffer size should match the size of the column.");
+  }
+  _null_mask  = std::move(new_null_mask);  // move
+  _null_count = new_null_count;
+}
+
+void column::set_null_mask(rmm::device_buffer const& new_null_mask,
+                           size_type new_null_count,
+                           rmm::cuda_stream_view stream)
+{
+  if (new_null_count > 0) {
+    CUDF_EXPECTS(new_null_mask.size() >= cudf::bitmask_allocation_size_bytes(this->size()),
+                 "Column with null values must be nullable and the null mask \
+                  buffer size should match the size of the column.");
+  }
+  _null_mask  = rmm::device_buffer{new_null_mask, stream};  // copy
+  _null_count = new_null_count;
+}
+
+void column::set_null_count(size_type new_null_count)
+{
+  if (new_null_count > 0) { CUDF_EXPECTS(nullable(), "Invalid null count."); }
+  _null_count = new_null_count;
+}
+
+namespace {
+struct create_column_from_view {
+  cudf::column_view view;
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+  rmm::mr::device_memory_resource* mr;
+
+  template <typename ColumnType,
+            std::enable_if_t<std::is_same_v<ColumnType, cudf::string_view>>* = nullptr>
+  std::unique_ptr<column> operator()()
+  {
+    cudf::strings_column_view sview(view);
+    return cudf::strings::detail::copy_slice(sview, 0, view.size(), stream, mr);
+  }
+
+  template <typename ColumnType,
+            std::enable_if_t<std::is_same_v<ColumnType, cudf::dictionary32>>* = nullptr>
+  std::unique_ptr<column> operator()()
+  {
+    std::vector<std::unique_ptr<column>> children;
+    if (view.num_children()) {
+      cudf::dictionary_column_view dict_view(view);
+      auto indices_view = column_view(dict_view.indices().type(),
+                                      dict_view.size(),
+                                      dict_view.indices().head(),
+                                      nullptr,
+                                      0,
+                                      dict_view.offset());
+      children.emplace_back(std::make_unique<column>(indices_view, stream, mr));
+      children.emplace_back(std::make_unique<column>(dict_view.keys(), stream, mr));
+    }
+    return std::make_unique<column>(view.type(),
+                                    view.size(),
+                                    rmm::device_buffer{0, stream, mr},
+                                    cudf::detail::copy_bitmask(view, stream, mr),
+                                    view.null_count(),
+                                    std::move(children));
+  }
+
+  template <typename ColumnType, std::enable_if_t<cudf::is_fixed_width<ColumnType>()>* = nullptr>
+  std::unique_ptr<column> operator()()
+  {
+    auto op       = [&](auto const& child) { return std::make_unique<column>(child, stream, mr); };
+    auto begin    = thrust::make_transform_iterator(view.child_begin(), op);
+    auto children = std::vector<std::unique_ptr<column>>(begin, begin + view.num_children());
+
+    return std::make_unique<column>(
+      view.type(),
+      view.size(),
+      rmm::device_buffer{
+        static_cast<char const*>(view.head()) + (view.offset() * cudf::size_of(view.type())),
+        view.size() * cudf::size_of(view.type()),
+        stream,
+        mr},
+      cudf::detail::copy_bitmask(view, stream, mr),
+      view.null_count(),
+      std::move(children));
+  }
+
+  template <typename ColumnType,
+            std::enable_if_t<std::is_same_v<ColumnType, cudf::list_view>>* = nullptr>
+  std::unique_ptr<column> operator()()
+  {
+    auto lists_view = lists_column_view(view);
+    return cudf::lists::detail::copy_slice(lists_view, 0, view.size(), stream, mr);
+  }
+
+  template <typename ColumnType,
+            std::enable_if_t<std::is_same_v<ColumnType, cudf::struct_view>>* = nullptr>
+  std::unique_ptr<column> operator()()
+  {
+    if (view.is_empty()) { return cudf::empty_like(view); }
+
+    std::vector<std::unique_ptr<column>> children;
+    children.reserve(view.num_children());
+    auto begin = view.offset();
+    auto end   = begin + view.size();
+
+    std::transform(view.child_begin(),
+                   view.child_end(),
+                   std::back_inserter(children),
+                   [begin, end, stream = this->stream, mr = this->mr](auto child) {
+                     return std::make_unique<column>(
+                       cudf::detail::slice(child, begin, end, stream), stream, mr);
+                   });
+
+    auto num_rows = view.size();
+
+    return make_structs_column(num_rows,
+                               std::move(children),
+                               view.null_count(),
+                               cudf::detail::copy_bitmask(view.null_mask(), begin, end, stream, mr),
+                               stream,
+                               mr);
+  }
+};
+}  // anonymous namespace
+
+// Copy from a view
+column::column(column_view view, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+  :  // Move is needed here because the dereference operator of unique_ptr returns
+     // an lvalue reference, which would otherwise dispatch to the copy constructor
+    column{std::move(*type_dispatcher(view.type(), create_column_from_view{view, stream, mr}))}
+{
+}
+
+}  // namespace cudf
diff --git a/cpp/src/column/column_device_view.cu b/cpp/src/column/column_device_view.cu
new file mode 100644
index 0000000..fc24452
--- /dev/null
+++ b/cpp/src/column/column_device_view.cu
@@ -0,0 +1,176 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <numeric>
+
+namespace cudf {
+// Trivially copy all members but the children
+column_device_view::column_device_view(column_view source)
+  : detail::column_device_view_base{source.type(),
+                                    source.size(),
+                                    source.head(),
+                                    source.null_mask(),
+                                    source.offset()},
+    _num_children{source.num_children()}
+{
+}
+
+// Free device memory allocated for children
+void column_device_view::destroy() { delete this; }
+
+namespace {
+// helper function for column_device_view::create and mutable_column_device::create methods
+template <typename ColumnView, typename ColumnDeviceView>
+std::unique_ptr<ColumnDeviceView, std::function<void(ColumnDeviceView*)>>
+create_device_view_from_view(ColumnView const& source, rmm::cuda_stream_view stream)
+{
+  size_type num_children = source.num_children();
+  // First calculate the size of memory needed to hold the child columns. This is done by calling
+  // extent() for each of the children.
+  auto get_extent = cudf::detail::make_counting_transform_iterator(
+    0, [&source](auto i) { return ColumnDeviceView::extent(source.child(i)); });
+
+  // pad the allocation for aligning the first pointer
+  auto const descendant_storage_bytes = std::accumulate(
+    get_extent, get_extent + num_children, std::size_t{alignof(ColumnDeviceView) - 1});
+
+  // A buffer of CPU memory is allocated to hold the ColumnDeviceView
+  // objects. Once filled, the CPU memory is copied to device memory
+  // and then set into the d_children member pointer.
+  std::vector<char> staging_buffer(descendant_storage_bytes);
+
+  // Each ColumnDeviceView instance may have child objects that
+  // require setting some internal device pointers before being copied
+  // from CPU to device.
+  rmm::device_buffer* const descendant_storage =
+    new rmm::device_buffer(descendant_storage_bytes, stream);
+
+  auto deleter = [descendant_storage](ColumnDeviceView* v) {
+    v->destroy();
+    delete descendant_storage;
+  };
+
+  std::unique_ptr<ColumnDeviceView, decltype(deleter)> result{
+    new ColumnDeviceView(source, staging_buffer.data(), descendant_storage->data()), deleter};
+
+  // copy the CPU memory with all the children into device memory
+  CUDF_CUDA_TRY(cudaMemcpyAsync(descendant_storage->data(),
+                                staging_buffer.data(),
+                                descendant_storage->size(),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  stream.synchronize();
+
+  return result;
+}
+
+}  // namespace
+
+// Place any child objects in host memory (h_ptr) and use the device
+// memory ptr (d_ptr) to set any child object pointers.
+column_device_view::column_device_view(column_view source, void* h_ptr, void* d_ptr)
+  : detail::column_device_view_base{source.type(),
+                                    source.size(),
+                                    source.head(),
+                                    source.null_mask(),
+                                    source.offset()},
+    _num_children{source.num_children()}
+{
+  d_children = detail::child_columns_to_device_array<column_device_view>(
+    source.child_begin(), source.child_end(), h_ptr, d_ptr);
+}
+
+// Construct a unique_ptr that invokes `destroy()` as it's deleter
+std::unique_ptr<column_device_view, std::function<void(column_device_view*)>>
+column_device_view::create(column_view source, rmm::cuda_stream_view stream)
+{
+  size_type num_children = source.num_children();
+  if (num_children == 0) {
+    // Can't use make_unique since the ctor is protected
+    return std::unique_ptr<column_device_view>(new column_device_view(source));
+  }
+
+  return create_device_view_from_view<column_view, column_device_view>(source, stream);
+}
+
+std::size_t column_device_view::extent(column_view const& source)
+{
+  auto get_extent = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0), [&source](auto i) { return extent(source.child(i)); });
+
+  return std::accumulate(
+    get_extent, get_extent + source.num_children(), sizeof(column_device_view));
+}
+
+// For use with inplace-new to pre-fill memory to be copied to device
+mutable_column_device_view::mutable_column_device_view(mutable_column_view source)
+  : detail::column_device_view_base{source.type(),
+                                    source.size(),
+                                    source.head(),
+                                    source.null_mask(),
+                                    source.offset()},
+    _num_children{source.num_children()}
+{
+}
+
+mutable_column_device_view::mutable_column_device_view(mutable_column_view source,
+                                                       void* h_ptr,
+                                                       void* d_ptr)
+  : detail::column_device_view_base{source.type(),
+                                    source.size(),
+                                    source.head(),
+                                    source.null_mask(),
+                                    source.offset()},
+    _num_children{source.num_children()}
+{
+  d_children = detail::child_columns_to_device_array<mutable_column_device_view>(
+    source.child_begin(), source.child_end(), h_ptr, d_ptr);
+}
+
+// Handle freeing children
+void mutable_column_device_view::destroy() { delete this; }
+
+// Construct a unique_ptr that invokes `destroy()` as it's deleter
+std::unique_ptr<mutable_column_device_view, std::function<void(mutable_column_device_view*)>>
+mutable_column_device_view::create(mutable_column_view source, rmm::cuda_stream_view stream)
+{
+  return source.num_children() == 0
+           ? std::unique_ptr<mutable_column_device_view>(new mutable_column_device_view(source))
+           : create_device_view_from_view<mutable_column_view, mutable_column_device_view>(source,
+                                                                                           stream);
+}
+
+std::size_t mutable_column_device_view::extent(mutable_column_view source)
+{
+  auto get_extent = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0), [&source](auto i) { return extent(source.child(i)); });
+
+  return std::accumulate(
+    get_extent, get_extent + source.num_children(), sizeof(mutable_column_device_view));
+}
+
+}  // namespace cudf
diff --git a/cpp/src/column/column_factories.cpp b/cpp/src/column/column_factories.cpp
new file mode 100644
index 0000000..3fcc67a
--- /dev/null
+++ b/cpp/src/column/column_factories.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/fill.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/detail/fill.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+
+namespace cudf {
+namespace {
+struct size_of_helper {
+  cudf::data_type type;
+  template <typename T, std::enable_if_t<not is_fixed_width<T>()>* = nullptr>
+  constexpr int operator()() const
+  {
+    CUDF_FAIL("Invalid, non fixed-width element type.");
+    return 0;
+  }
+
+  template <typename T, std::enable_if_t<is_fixed_width<T>() && not is_fixed_point<T>()>* = nullptr>
+  constexpr int operator()() const noexcept
+  {
+    return sizeof(T);
+  }
+
+  template <typename T, std::enable_if_t<is_fixed_point<T>()>* = nullptr>
+  constexpr int operator()() const noexcept
+  {
+    // Only want the sizeof fixed_point::Rep as fixed_point::scale is stored in data_type
+    return sizeof(typename T::rep);
+  }
+};
+}  // namespace
+
+std::size_t size_of(data_type element_type)
+{
+  CUDF_EXPECTS(is_fixed_width(element_type), "Invalid element type.");
+  return cudf::type_dispatcher(element_type, size_of_helper{element_type});
+}
+
+// Empty column of specified type
+std::unique_ptr<column> make_empty_column(data_type type)
+{
+  CUDF_EXPECTS(type.id() == type_id::EMPTY || !cudf::is_nested(type),
+               "make_empty_column is invalid to call on nested types");
+  return std::make_unique<column>(type, 0, rmm::device_buffer{}, rmm::device_buffer{}, 0);
+}
+
+// Empty column of specified type id
+std::unique_ptr<column> make_empty_column(type_id id) { return make_empty_column(data_type{id}); }
+
+// Allocate storage for a specified number of numeric elements
+std::unique_ptr<column> make_numeric_column(data_type type,
+                                            size_type size,
+                                            mask_state state,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(is_numeric(type), "Invalid, non-numeric type.");
+  CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+
+  return std::make_unique<column>(
+    type,
+    size,
+    rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+    detail::create_null_mask(size, state, stream, mr),
+    state == mask_state::UNINITIALIZED ? 0 : state_null_count(state, size),
+    std::vector<std::unique_ptr<column>>{});
+}
+
+// Allocate storage for a specified number of numeric elements
+std::unique_ptr<column> make_fixed_point_column(data_type type,
+                                                size_type size,
+                                                mask_state state,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(is_fixed_point(type), "Invalid, non-fixed_point type.");
+  CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+
+  return std::make_unique<column>(
+    type,
+    size,
+    rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+    detail::create_null_mask(size, state, stream, mr),
+    state == mask_state::UNINITIALIZED ? 0 : state_null_count(state, size),
+    std::vector<std::unique_ptr<column>>{});
+}
+
+// Allocate storage for a specified number of timestamp elements
+std::unique_ptr<column> make_timestamp_column(data_type type,
+                                              size_type size,
+                                              mask_state state,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(is_timestamp(type), "Invalid, non-timestamp type.");
+  CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+
+  return std::make_unique<column>(
+    type,
+    size,
+    rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+    detail::create_null_mask(size, state, stream, mr),
+    state == mask_state::UNINITIALIZED ? 0 : state_null_count(state, size),
+    std::vector<std::unique_ptr<column>>{});
+}
+
+// Allocate storage for a specified number of duration elements
+std::unique_ptr<column> make_duration_column(data_type type,
+                                             size_type size,
+                                             mask_state state,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(is_duration(type), "Invalid, non-duration type.");
+  CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+
+  return std::make_unique<column>(
+    type,
+    size,
+    rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+    detail::create_null_mask(size, state, stream, mr),
+    state == mask_state::UNINITIALIZED ? 0 : state_null_count(state, size),
+    std::vector<std::unique_ptr<column>>{});
+}
+
+// Allocate storage for a specified number of fixed width elements
+std::unique_ptr<column> make_fixed_width_column(data_type type,
+                                                size_type size,
+                                                mask_state state,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(is_fixed_width(type), "Invalid, non-fixed-width type.");
+
+  // clang-format off
+  if      (is_timestamp  (type)) return make_timestamp_column  (type, size, state, stream, mr);
+  else if (is_duration   (type)) return make_duration_column   (type, size, state, stream, mr);
+  else if (is_fixed_point(type)) return make_fixed_point_column(type, size, state, stream, mr);
+  else                           return make_numeric_column    (type, size, state, stream, mr);
+  // clang-format on
+}
+
+std::unique_ptr<column> make_dictionary_from_scalar(scalar const& s,
+                                                    size_type size,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  if (size == 0) return make_empty_column(type_id::DICTIONARY32);
+  CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+  CUDF_EXPECTS(s.is_valid(stream), "cannot create a dictionary with a null key");
+  return make_dictionary_column(
+    make_column_from_scalar(s, 1, stream, mr),
+    make_column_from_scalar(numeric_scalar<uint32_t>(0), size, stream, mr),
+    rmm::device_buffer{0, stream, mr},
+    0);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/column/column_factories.cu b/cpp/src/column/column_factories.cu
new file mode 100644
index 0000000..0e65a13
--- /dev/null
+++ b/cpp/src/column/column_factories.cu
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/fill.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/fill.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+
+namespace cudf {
+
+namespace {
+
+struct column_from_scalar_dispatch {
+  template <typename T>
+  std::unique_ptr<cudf::column> operator()(scalar const& value,
+                                           size_type size,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr) const
+  {
+    if (size == 0) return make_empty_column(value.type());
+    if (!value.is_valid(stream))
+      return make_fixed_width_column(value.type(), size, mask_state::ALL_NULL, stream, mr);
+    auto output_column =
+      make_fixed_width_column(value.type(), size, mask_state::UNALLOCATED, stream, mr);
+    auto view = output_column->mutable_view();
+    detail::fill_in_place(view, 0, size, value, stream);
+    return output_column;
+  }
+};
+
+template <>
+std::unique_ptr<cudf::column> column_from_scalar_dispatch::operator()<cudf::string_view>(
+  scalar const& value,
+  size_type size,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  if (size == 0) return make_empty_column(value.type());
+
+  // Since we are setting every row to the scalar, the fill() never needs to access
+  // any of the children in the strings column which would otherwise cause an exception.
+  column_view sc{value.type(), size, nullptr, nullptr, 0};
+  auto& sv = static_cast<scalar_type_t<cudf::string_view> const&>(value);
+
+  // fill the column with the scalar
+  auto output = strings::detail::fill(strings_column_view(sc), 0, size, sv, stream, mr);
+
+  return output;
+}
+
+template <>
+std::unique_ptr<cudf::column> column_from_scalar_dispatch::operator()<cudf::dictionary32>(
+  scalar const&, size_type, rmm::cuda_stream_view, rmm::mr::device_memory_resource*) const
+{
+  CUDF_FAIL("dictionary not supported when creating from scalar");
+}
+
+template <>
+std::unique_ptr<cudf::column> column_from_scalar_dispatch::operator()<cudf::list_view>(
+  scalar const& value,
+  size_type size,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  auto lv = static_cast<list_scalar const*>(&value);
+  return lists::detail::make_lists_column_from_scalar(*lv, size, stream, mr);
+}
+
+template <>
+std::unique_ptr<cudf::column> column_from_scalar_dispatch::operator()<cudf::struct_view>(
+  scalar const& value,
+  size_type size,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  if (size == 0) CUDF_FAIL("0-length struct column is unsupported.");
+  auto& ss  = static_cast<scalar_type_t<cudf::struct_view> const&>(value);
+  auto iter = thrust::make_constant_iterator(0);
+
+  auto children =
+    detail::gather(ss.view(), iter, iter + size, out_of_bounds_policy::NULLIFY, stream, mr);
+  auto const is_valid = ss.is_valid(stream);
+  return make_structs_column(size,
+                             std::move(children->release()),
+                             is_valid ? 0 : size,
+                             is_valid
+                               ? rmm::device_buffer{}
+                               : detail::create_null_mask(size, mask_state::ALL_NULL, stream, mr),
+                             stream,
+                             mr);
+}
+
+}  // anonymous namespace
+
+std::unique_ptr<column> make_column_from_scalar(scalar const& s,
+                                                size_type size,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(s.type(), column_from_scalar_dispatch{}, s, size, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/column/column_view.cpp b/cpp/src/column/column_view.cpp
new file mode 100644
index 0000000..75722ed
--- /dev/null
+++ b/cpp/src/column/column_view.cpp
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+#include <exception>
+#include <numeric>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+column_view_base::column_view_base(data_type type,
+                                   size_type size,
+                                   void const* data,
+                                   bitmask_type const* null_mask,
+                                   size_type null_count,
+                                   size_type offset)
+  : _type{type},
+    _size{size},
+    _data{data},
+    _null_mask{null_mask},
+    _null_count{null_count},
+    _offset{offset}
+{
+  CUDF_EXPECTS(size >= 0, "Column size cannot be negative.");
+
+  if (type.id() == type_id::EMPTY) {
+    _null_count = size;
+    CUDF_EXPECTS(nullptr == data, "EMPTY column should have no data.");
+    CUDF_EXPECTS(nullptr == null_mask, "EMPTY column should have no null mask.");
+  } else if (is_compound(type)) {
+    CUDF_EXPECTS(nullptr == data, "Compound (parent) columns cannot have data");
+  } else if (size > 0) {
+    CUDF_EXPECTS(nullptr != data, "Null data pointer.");
+  }
+
+  CUDF_EXPECTS(offset >= 0, "Invalid offset.");
+
+  if ((null_count > 0) and (type.id() != type_id::EMPTY)) {
+    CUDF_EXPECTS(nullptr != null_mask, "Invalid null mask for non-zero null count.");
+  }
+}
+
+size_type column_view_base::null_count(size_type begin, size_type end) const
+{
+  CUDF_EXPECTS((begin >= 0) && (end <= size()) && (begin <= end), "Range is out of bounds.");
+  return (null_count() == 0)
+           ? 0
+           : cudf::detail::null_count(
+               null_mask(), offset() + begin, offset() + end, cudf::get_default_stream());
+}
+
+// Struct to use custom hash combine and fold expression
+struct HashValue {
+  std::size_t hash;
+  explicit HashValue(std::size_t h) : hash{h} {}
+  HashValue operator^(HashValue const& other) const
+  {
+    return HashValue{cudf::hashing::detail::hash_combine(hash, other.hash)};
+  }
+};
+
+template <typename... Ts>
+constexpr auto hash(Ts&&... ts)
+{
+  return (... ^ HashValue(std::hash<Ts>{}(ts))).hash;
+}
+
+std::size_t shallow_hash_impl(column_view const& c, bool is_parent_empty = false)
+{
+  std::size_t const init = (is_parent_empty or c.is_empty())
+                             ? hash(c.type(), 0)
+                             : hash(c.type(), c.size(), c.head(), c.null_mask(), c.offset());
+  return std::accumulate(c.child_begin(),
+                         c.child_end(),
+                         init,
+                         [&c, is_parent_empty](std::size_t hash, auto const& child) {
+                           return cudf::hashing::detail::hash_combine(
+                             hash, shallow_hash_impl(child, c.is_empty() or is_parent_empty));
+                         });
+}
+
+std::size_t shallow_hash(column_view const& input) { return shallow_hash_impl(input); }
+
+bool shallow_equivalent_impl(column_view const& lhs,
+                             column_view const& rhs,
+                             bool is_parent_empty = false)
+{
+  bool const is_empty = (lhs.is_empty() and rhs.is_empty()) or is_parent_empty;
+  return (lhs.type() == rhs.type()) and
+         (is_empty or ((lhs.size() == rhs.size()) and (lhs.head() == rhs.head()) and
+                       (lhs.null_mask() == rhs.null_mask()) and (lhs.offset() == rhs.offset()))) and
+         std::equal(lhs.child_begin(),
+                    lhs.child_end(),
+                    rhs.child_begin(),
+                    rhs.child_end(),
+                    [is_empty](auto const& lhs_child, auto const& rhs_child) {
+                      return shallow_equivalent_impl(lhs_child, rhs_child, is_empty);
+                    });
+}
+bool is_shallow_equivalent(column_view const& lhs, column_view const& rhs)
+{
+  return shallow_equivalent_impl(lhs, rhs);
+}
+}  // namespace detail
+
+// Immutable view constructor
+column_view::column_view(data_type type,
+                         size_type size,
+                         void const* data,
+                         bitmask_type const* null_mask,
+                         size_type null_count,
+                         size_type offset,
+                         std::vector<column_view> const& children)
+  : detail::column_view_base{type, size, data, null_mask, null_count, offset}, _children{children}
+{
+  if (type.id() == type_id::EMPTY) {
+    CUDF_EXPECTS(num_children() == 0, "EMPTY column cannot have children.");
+  }
+}
+
+// Mutable view constructor
+mutable_column_view::mutable_column_view(data_type type,
+                                         size_type size,
+                                         void* data,
+                                         bitmask_type* null_mask,
+                                         size_type null_count,
+                                         size_type offset,
+                                         std::vector<mutable_column_view> const& children)
+  : detail::column_view_base{type, size, data, null_mask, null_count, offset},
+    mutable_children{children}
+{
+  if (type.id() == type_id::EMPTY) {
+    CUDF_EXPECTS(num_children() == 0, "EMPTY column cannot have children.");
+  }
+}
+
+// Update the null count
+void mutable_column_view::set_null_count(size_type new_null_count)
+{
+  if (new_null_count > 0) { CUDF_EXPECTS(nullable(), "Invalid null count."); }
+  _null_count = new_null_count;
+}
+
+// Conversion from mutable to immutable
+mutable_column_view::operator column_view() const
+{
+  // Convert children to immutable views
+  std::vector<column_view> child_views(num_children());
+  std::copy(std::cbegin(mutable_children), std::cend(mutable_children), std::begin(child_views));
+  return column_view{_type, _size, _data, _null_mask, _null_count, _offset, std::move(child_views)};
+}
+
+size_type count_descendants(column_view parent)
+{
+  auto descendants = [](auto const& child) { return count_descendants(child); };
+  auto begin       = thrust::make_transform_iterator(parent.child_begin(), descendants);
+  return std::accumulate(begin, begin + parent.num_children(), size_type{parent.num_children()});
+}
+
+column_view bit_cast(column_view const& input, data_type type)
+{
+  CUDF_EXPECTS(is_bit_castable(input._type, type), "types are not bit-castable");
+  return column_view{type,
+                     input._size,
+                     input._data,
+                     input._null_mask,
+                     input._null_count,
+                     input._offset,
+                     input._children};
+}
+
+mutable_column_view bit_cast(mutable_column_view const& input, data_type type)
+{
+  CUDF_EXPECTS(is_bit_castable(input._type, type), "types are not bit-castable");
+  return mutable_column_view{type,
+                             input._size,
+                             const_cast<void*>(input._data),
+                             const_cast<cudf::bitmask_type*>(input._null_mask),
+                             input._null_count,
+                             input._offset,
+                             input.mutable_children};
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/concatenate.cu b/cpp/src/copying/concatenate.cu
new file mode 100644
index 0000000..d08c302
--- /dev/null
+++ b/cpp/src/copying/concatenate.cu
@@ -0,0 +1,603 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/concatenate_masks.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/detail/concatenate.hpp>
+#include <cudf/lists/detail/concatenate.hpp>
+#include <cudf/strings/detail/concatenate.hpp>
+#include <cudf/structs/detail/concatenate.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform_scan.h>
+
+#include <algorithm>
+#include <numeric>
+#include <utility>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+// From benchmark data, the fused kernel optimization appears to perform better
+// when there are more than a trivial number of columns, or when the null mask
+// can also be computed at the same time
+constexpr bool use_fused_kernel_heuristic(bool const has_nulls, size_t const num_columns)
+{
+  return has_nulls || num_columns > 4;
+}
+
+auto create_device_views(host_span<column_view const> views, rmm::cuda_stream_view stream)
+{
+  // Create device views for each input view
+  using CDViewPtr         = decltype(column_device_view::create(std::declval<column_view>(),
+                                                        std::declval<rmm::cuda_stream_view>()));
+  auto device_view_owners = std::vector<CDViewPtr>(views.size());
+  std::transform(views.begin(), views.end(), device_view_owners.begin(), [stream](auto const& col) {
+    return column_device_view::create(col, stream);
+  });
+
+  // Assemble contiguous array of device views
+  auto device_views = thrust::host_vector<column_device_view>();
+  device_views.reserve(views.size());
+  std::transform(device_view_owners.cbegin(),
+                 device_view_owners.cend(),
+                 std::back_inserter(device_views),
+                 [](auto const& col) { return *col; });
+
+  auto d_views =
+    make_device_uvector_async(device_views, stream, rmm::mr::get_current_device_resource());
+
+  // Compute the partition offsets
+  auto offsets = thrust::host_vector<size_t>(views.size() + 1);
+  thrust::transform_inclusive_scan(
+    thrust::host,
+    device_views.cbegin(),
+    device_views.cend(),
+    std::next(offsets.begin()),
+    [](auto const& col) { return col.size(); },
+    thrust::plus{});
+  auto d_offsets =
+    make_device_uvector_async(offsets, stream, rmm::mr::get_current_device_resource());
+  auto const output_size = offsets.back();
+
+  return std::make_tuple(
+    std::move(device_view_owners), std::move(d_views), std::move(d_offsets), output_size);
+}
+
+/**
+ * @brief Concatenates the null mask bits of all the column device views in the
+ * `views` array to the destination bitmask.
+ *
+ * @tparam block_size Block size for using with single_lane_block_sum_reduce
+ *
+ * @param views Array of column_device_view
+ * @param output_offsets Prefix sum of sizes of elements of `views`
+ * @param number_of_views Size of `views` array
+ * @param dest_mask The output buffer to copy null masks into
+ * @param number_of_mask_bits The total number of null masks bits that are being copied
+ * @param out_valid_count To hold the total number of valid bits set
+ */
+template <size_type block_size>
+__global__ void concatenate_masks_kernel(column_device_view const* views,
+                                         size_t const* output_offsets,
+                                         size_type number_of_views,
+                                         bitmask_type* dest_mask,
+                                         size_type number_of_mask_bits,
+                                         size_type* out_valid_count)
+{
+  auto tidx         = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+  auto active_mask  = __ballot_sync(0xFFFF'FFFFu, tidx < number_of_mask_bits);
+
+  size_type warp_valid_count = 0;
+
+  while (tidx < number_of_mask_bits) {
+    auto const mask_index = static_cast<cudf::size_type>(tidx);
+    size_type const source_view_index =
+      thrust::upper_bound(
+        thrust::seq, output_offsets, output_offsets + number_of_views, mask_index) -
+      output_offsets - 1;
+    bool bit_is_set = true;
+    if (source_view_index < number_of_views) {
+      size_type const column_element_index = mask_index - output_offsets[source_view_index];
+      bit_is_set = views[source_view_index].is_valid(column_element_index);
+    }
+    bitmask_type const new_word = __ballot_sync(active_mask, bit_is_set);
+
+    if (threadIdx.x % detail::warp_size == 0) {
+      dest_mask[word_index(mask_index)] = new_word;
+      warp_valid_count += __popc(new_word);
+    }
+
+    tidx += stride;
+    active_mask = __ballot_sync(active_mask, tidx < number_of_mask_bits);
+  }
+
+  using detail::single_lane_block_sum_reduce;
+  auto const block_valid_count = single_lane_block_sum_reduce<block_size, 0>(warp_valid_count);
+  if (threadIdx.x == 0) { atomicAdd(out_valid_count, block_valid_count); }
+}
+}  // namespace
+
+size_type concatenate_masks(device_span<column_device_view const> d_views,
+                            device_span<size_t const> d_offsets,
+                            bitmask_type* dest_mask,
+                            size_type output_size,
+                            rmm::cuda_stream_view stream)
+{
+  rmm::device_scalar<size_type> d_valid_count(0, stream);
+  constexpr size_type block_size{256};
+  cudf::detail::grid_1d config(output_size, block_size);
+  concatenate_masks_kernel<block_size>
+    <<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+      d_views.data(),
+      d_offsets.data(),
+      static_cast<size_type>(d_views.size()),
+      dest_mask,
+      output_size,
+      d_valid_count.data());
+  return output_size - d_valid_count.value(stream);
+}
+
+size_type concatenate_masks(host_span<column_view const> views,
+                            bitmask_type* dest_mask,
+                            rmm::cuda_stream_view stream)
+{
+  // Preprocess and upload inputs to device memory
+  auto const device_views = create_device_views(views, stream);
+  auto const& d_views     = std::get<1>(device_views);
+  auto const& d_offsets   = std::get<2>(device_views);
+  auto const output_size  = std::get<3>(device_views);
+
+  return concatenate_masks(d_views, d_offsets, dest_mask, output_size, stream);
+}
+
+namespace {
+template <typename T, size_type block_size, bool Nullable>
+__global__ void fused_concatenate_kernel(column_device_view const* input_views,
+                                         size_t const* input_offsets,
+                                         size_type num_input_views,
+                                         mutable_column_device_view output_view,
+                                         size_type* out_valid_count)
+{
+  auto const output_size = output_view.size();
+  auto* output_data      = output_view.data<T>();
+
+  auto output_index          = cudf::detail::grid_1d::global_thread_id();
+  auto const stride          = cudf::detail::grid_1d::grid_stride();
+  size_type warp_valid_count = 0;
+
+  unsigned active_mask;
+  if (Nullable) { active_mask = __ballot_sync(0xFFFF'FFFFu, output_index < output_size); }
+  while (output_index < output_size) {
+    // Lookup input index by searching for output index in offsets
+    auto const offset_it            = thrust::prev(thrust::upper_bound(
+      thrust::seq, input_offsets, input_offsets + num_input_views, output_index));
+    size_type const partition_index = offset_it - input_offsets;
+
+    // Copy input data to output
+    auto const offset_index   = output_index - *offset_it;
+    auto const& input_view    = input_views[partition_index];
+    auto const* input_data    = input_view.data<T>();
+    output_data[output_index] = input_data[offset_index];
+
+    if (Nullable) {
+      bool const bit_is_set       = input_view.is_valid(offset_index);
+      bitmask_type const new_word = __ballot_sync(active_mask, bit_is_set);
+
+      // First thread writes bitmask word
+      if (threadIdx.x % detail::warp_size == 0) {
+        output_view.null_mask()[word_index(output_index)] = new_word;
+      }
+
+      warp_valid_count += __popc(new_word);
+    }
+
+    output_index += stride;
+    if (Nullable) { active_mask = __ballot_sync(active_mask, output_index < output_size); }
+  }
+
+  if (Nullable) {
+    using detail::single_lane_block_sum_reduce;
+    auto block_valid_count = single_lane_block_sum_reduce<block_size, 0>(warp_valid_count);
+    if (threadIdx.x == 0) { atomicAdd(out_valid_count, block_valid_count); }
+  }
+}
+
+template <typename T>
+std::unique_ptr<column> fused_concatenate(host_span<column_view const> views,
+                                          bool const has_nulls,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  using mask_policy = cudf::mask_allocation_policy;
+
+  // Preprocess and upload inputs to device memory
+  auto const device_views = create_device_views(views, stream);
+  auto const& d_views     = std::get<1>(device_views);
+  auto const& d_offsets   = std::get<2>(device_views);
+  auto const output_size  = std::get<3>(device_views);
+
+  CUDF_EXPECTS(output_size <= static_cast<std::size_t>(std::numeric_limits<size_type>::max()),
+               "Total number of concatenated rows exceeds the column size limit",
+               std::overflow_error);
+
+  // Allocate output
+  auto const policy = has_nulls ? mask_policy::ALWAYS : mask_policy::NEVER;
+  auto out_col      = detail::allocate_like(views.front(), output_size, policy, stream, mr);
+  auto out_view     = out_col->mutable_view();
+  auto d_out_view   = mutable_column_device_view::create(out_view, stream);
+
+  rmm::device_scalar<size_type> d_valid_count(0, stream);
+
+  // Launch kernel
+  constexpr size_type block_size{256};
+  cudf::detail::grid_1d config(output_size, block_size);
+  auto const kernel = has_nulls ? fused_concatenate_kernel<T, block_size, true>
+                                : fused_concatenate_kernel<T, block_size, false>;
+  kernel<<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+    d_views.data(),
+    d_offsets.data(),
+    static_cast<size_type>(d_views.size()),
+    *d_out_view,
+    d_valid_count.data());
+
+  if (has_nulls) {
+    out_col->set_null_count(output_size - d_valid_count.value(stream));
+  } else {
+    out_col->set_null_count(0);  // prevent null count from being materialized
+  }
+
+  return out_col;
+}
+
+template <typename T>
+std::unique_ptr<column> for_each_concatenate(host_span<column_view const> views,
+                                             bool const has_nulls,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  size_type const total_element_count =
+    std::accumulate(views.begin(), views.end(), 0, [](auto accumulator, auto const& v) {
+      return accumulator + v.size();
+    });
+
+  using mask_policy = cudf::mask_allocation_policy;
+  auto const policy = has_nulls ? mask_policy::ALWAYS : mask_policy::NEVER;
+  auto col = cudf::detail::allocate_like(views.front(), total_element_count, policy, stream, mr);
+
+  auto m_view = col->mutable_view();
+
+  auto count = 0;
+  for (auto& v : views) {
+    thrust::copy(rmm::exec_policy(stream), v.begin<T>(), v.end<T>(), m_view.begin<T>() + count);
+    count += v.size();
+  }
+
+  // If concatenated column is nullable, proceed to calculate it
+  if (has_nulls) {
+    col->set_null_count(
+      cudf::detail::concatenate_masks(views, (col->mutable_view()).null_mask(), stream));
+  } else {
+    col->set_null_count(0);  // prevent null count from being materialized
+  }
+
+  return col;
+}
+
+struct concatenate_dispatch {
+  host_span<column_view const> views;
+  rmm::cuda_stream_view stream;
+  rmm::mr::device_memory_resource* mr;
+
+  // fixed width
+  template <typename T>
+  std::unique_ptr<column> operator()()
+  {
+    bool const has_nulls =
+      std::any_of(views.begin(), views.end(), [](auto const& col) { return col.has_nulls(); });
+
+    // Use a heuristic to guess when the fused kernel will be faster
+    if (use_fused_kernel_heuristic(has_nulls, views.size())) {
+      return fused_concatenate<T>(views, has_nulls, stream, mr);
+    } else {
+      return for_each_concatenate<T>(views, has_nulls, stream, mr);
+    }
+  }
+};
+
+template <>
+std::unique_ptr<column> concatenate_dispatch::operator()<cudf::dictionary32>()
+{
+  return cudf::dictionary::detail::concatenate(views, stream, mr);
+}
+
+template <>
+std::unique_ptr<column> concatenate_dispatch::operator()<cudf::string_view>()
+{
+  return cudf::strings::detail::concatenate(views, stream, mr);
+}
+
+template <>
+std::unique_ptr<column> concatenate_dispatch::operator()<cudf::list_view>()
+{
+  return cudf::lists::detail::concatenate(views, stream, mr);
+}
+
+template <>
+std::unique_ptr<column> concatenate_dispatch::operator()<cudf::struct_view>()
+{
+  return cudf::structs::detail::concatenate(views, stream, mr);
+}
+
+void bounds_and_type_check(host_span<column_view const> cols, rmm::cuda_stream_view stream);
+
+/**
+ * @brief Functor for traversing child columns and recursively verifying concatenation
+ * bounds and types.
+ */
+class traverse_children {
+ public:
+  // nothing to do for simple types.
+  template <typename T>
+  void operator()(host_span<column_view const>, rmm::cuda_stream_view)
+  {
+  }
+
+ private:
+  // verify length of concatenated offsets.
+  void check_offsets_size(host_span<column_view const> cols)
+  {
+    // offsets.  we can't just add up the total sizes of all offset child columns because each one
+    // has an extra value, regardless of the # of parent rows.  So we have to add up the total # of
+    // rows in the base column and add 1 at the end
+    size_t const total_offset_count =
+      std::accumulate(cols.begin(),
+                      cols.end(),
+                      std::size_t{},
+                      [](size_t a, auto const& b) -> size_t { return a + b.size(); }) +
+      1;
+    CUDF_EXPECTS(total_offset_count <= static_cast<size_t>(std::numeric_limits<size_type>::max()),
+                 "Total number of concatenated offsets exceeds the column size limit",
+                 std::overflow_error);
+  }
+};
+
+template <>
+void traverse_children::operator()<cudf::string_view>(host_span<column_view const> cols,
+                                                      rmm::cuda_stream_view stream)
+{
+  // verify offsets
+  check_offsets_size(cols);
+
+  // chars
+  size_t const total_char_count = std::accumulate(
+    cols.begin(), cols.end(), std::size_t{}, [stream](size_t a, auto const& b) -> size_t {
+      strings_column_view scv(b);
+      return a + (scv.is_empty() ? 0
+                  // if the column is unsliced, skip the offset retrieval.
+                  : scv.offset() > 0
+                    ? cudf::detail::get_value<size_type>(
+                        scv.offsets(), scv.offset() + scv.size(), stream) -
+                        cudf::detail::get_value<size_type>(scv.offsets(), scv.offset(), stream)
+                  // if the offset() is 0, it can still be sliced to a shorter length. in this case
+                  // we only need to read a single offset. otherwise just return the full length
+                  // (chars_size())
+                  : scv.size() + 1 == scv.offsets().size()
+                    ? scv.chars_size()
+                    : cudf::detail::get_value<size_type>(scv.offsets(), scv.size(), stream));
+    });
+  CUDF_EXPECTS(total_char_count <= static_cast<size_t>(std::numeric_limits<size_type>::max()),
+               "Total number of concatenated chars exceeds the column size limit",
+               std::overflow_error);
+}
+
+template <>
+void traverse_children::operator()<cudf::struct_view>(host_span<column_view const> cols,
+                                                      rmm::cuda_stream_view stream)
+{
+  // march each child
+  auto child_iter         = thrust::make_counting_iterator(0);
+  auto const num_children = cols.front().num_children();
+  std::vector<column_view> nth_children;
+  nth_children.reserve(cols.size());
+  std::for_each(child_iter, child_iter + num_children, [&](auto child_index) {
+    std::transform(cols.begin(),
+                   cols.end(),
+                   std::back_inserter(nth_children),
+                   [child_index, stream](column_view const& col) {
+                     structs_column_view scv(col);
+                     return scv.get_sliced_child(child_index, stream);
+                   });
+
+    bounds_and_type_check(nth_children, stream);
+    nth_children.clear();
+  });
+}
+
+template <>
+void traverse_children::operator()<cudf::list_view>(host_span<column_view const> cols,
+                                                    rmm::cuda_stream_view stream)
+{
+  // verify offsets
+  check_offsets_size(cols);
+
+  // recurse into the child columns
+  std::vector<column_view> nth_children;
+  nth_children.reserve(cols.size());
+  std::transform(
+    cols.begin(), cols.end(), std::back_inserter(nth_children), [stream](column_view const& col) {
+      lists_column_view lcv(col);
+      return lcv.get_sliced_child(stream);
+    });
+  bounds_and_type_check(nth_children, stream);
+}
+
+/**
+ * @brief Verifies that the sum of the sizes of all the columns to be concatenated
+ * will not exceed the max value of size_type, and verifies all column types match
+ *
+ * @param columns_to_concat Span of columns to check
+ *
+ * @throws cudf::logic_error if the total length of the concatenated columns would
+ * exceed the max value of size_type
+ *
+ * @throws cudf::logic_error if all of the input column types don't match
+ */
+void bounds_and_type_check(host_span<column_view const> cols, rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(std::all_of(cols.begin(),
+                           cols.end(),
+                           [expected_type = cols.front().type()](auto const& c) {
+                             return c.type() == expected_type;
+                           }),
+               "Type mismatch in columns to concatenate.");
+
+  // total size of all concatenated rows
+  size_t const total_row_count =
+    std::accumulate(cols.begin(), cols.end(), std::size_t{}, [](size_t a, auto const& b) {
+      return a + static_cast<size_t>(b.size());
+    });
+  CUDF_EXPECTS(total_row_count <= static_cast<size_t>(std::numeric_limits<size_type>::max()),
+               "Total number of concatenated rows exceeds the column size limit",
+               std::overflow_error);
+
+  // traverse children
+  cudf::type_dispatcher(cols.front().type(), traverse_children{}, cols, stream);
+}
+
+}  // anonymous namespace
+
+// Concatenates the elements from a vector of column_views
+std::unique_ptr<column> concatenate(host_span<column_view const> columns_to_concat,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(not columns_to_concat.empty(), "Unexpected empty list of columns to concatenate.");
+
+  // verify all types match and that we won't overflow size_type in output size
+  bounds_and_type_check(columns_to_concat, stream);
+
+  if (std::all_of(columns_to_concat.begin(), columns_to_concat.end(), [](column_view const& c) {
+        return c.is_empty();
+      })) {
+    return empty_like(columns_to_concat.front());
+  }
+
+  return type_dispatcher<dispatch_storage_type>(
+    columns_to_concat.front().type(), concatenate_dispatch{columns_to_concat, stream, mr});
+}
+
+std::unique_ptr<table> concatenate(host_span<table_view const> tables_to_concat,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (tables_to_concat.empty()) { return std::make_unique<table>(); }
+
+  table_view const first_table = tables_to_concat.front();
+  CUDF_EXPECTS(std::all_of(tables_to_concat.begin(),
+                           tables_to_concat.end(),
+                           [&first_table](auto const& t) {
+                             return t.num_columns() == first_table.num_columns();
+                           }),
+               "Mismatch in table columns to concatenate.");
+
+  std::vector<std::unique_ptr<column>> concat_columns;
+  for (size_type i = 0; i < first_table.num_columns(); ++i) {
+    std::vector<column_view> cols;
+    std::transform(tables_to_concat.begin(),
+                   tables_to_concat.end(),
+                   std::back_inserter(cols),
+                   [i](auto const& t) { return t.column(i); });
+
+    // verify all types match and that we won't overflow size_type in output size
+    bounds_and_type_check(cols, stream);
+    concat_columns.emplace_back(detail::concatenate(cols, stream, mr));
+  }
+  return std::make_unique<table>(std::move(concat_columns));
+}
+
+rmm::device_buffer concatenate_masks(host_span<column_view const> views,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  bool const has_nulls =
+    std::any_of(views.begin(), views.end(), [](column_view const col) { return col.has_nulls(); });
+  if (has_nulls) {
+    size_type const total_element_count =
+      std::accumulate(views.begin(), views.end(), 0, [](auto accumulator, auto const& v) {
+        return accumulator + v.size();
+      });
+
+    rmm::device_buffer null_mask =
+      create_null_mask(total_element_count, mask_state::UNINITIALIZED, mr);
+
+    detail::concatenate_masks(views, static_cast<bitmask_type*>(null_mask.data()), stream);
+
+    return null_mask;
+  }
+  // no nulls, so return an empty device buffer
+  return rmm::device_buffer{0, stream, mr};
+}
+
+}  // namespace detail
+
+rmm::device_buffer concatenate_masks(host_span<column_view const> views,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate_masks(views, stream, mr);
+}
+
+// Concatenates the elements from a vector of column_views
+std::unique_ptr<column> concatenate(host_span<column_view const> columns_to_concat,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate(columns_to_concat, stream, mr);
+}
+
+std::unique_ptr<table> concatenate(host_span<table_view const> tables_to_concat,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate(tables_to_concat, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/contiguous_split.cu b/cpp/src/copying/contiguous_split.cu
new file mode 100644
index 0000000..5ea56a0
--- /dev/null
+++ b/cpp/src/copying/contiguous_split.cu
@@ -0,0 +1,2086 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/contiguous_split.hpp>
+#include <cudf/detail/contiguous_split.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/iterator_categories.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <cstddef>
+#include <numeric>
+
+namespace cudf {
+namespace {
+
+// Align all column size allocations to this boundary so that all output column buffers
+// start at that alignment.
+static constexpr std::size_t split_align = 64;
+
+// The size that contiguous split uses internally as the GPU unit of work.
+// The number of `desired_batch_size` batches equals the number of CUDA blocks
+// that will be used for the main kernel launch (`copy_partitions`).
+static constexpr std::size_t desired_batch_size = 1 * 1024 * 1024;
+
+/**
+ * @brief Struct which contains information on a source buffer.
+ *
+ * The definition of "buffer" used throughout this module is a component piece of a
+ * cudf column. So for example, a fixed-width column with validity would have 2 associated
+ * buffers : the data itself and the validity buffer.  contiguous_split operates by breaking
+ * each column up into it's individual components and copying each one as a separate kernel
+ * block.
+ */
+struct src_buf_info {
+  src_buf_info(cudf::type_id _type,
+               int const* _offsets,
+               int _offset_stack_pos,
+               int _parent_offsets_index,
+               bool _is_validity,
+               size_type _column_offset)
+    : type(_type),
+      offsets(_offsets),
+      offset_stack_pos(_offset_stack_pos),
+      parent_offsets_index(_parent_offsets_index),
+      is_validity(_is_validity),
+      column_offset(_column_offset)
+  {
+  }
+
+  cudf::type_id type;
+  int const* offsets;        // a pointer to device memory offsets if I am an offset buffer
+  int offset_stack_pos;      // position in the offset stack buffer
+  int parent_offsets_index;  // immediate parent that has offsets, or -1 if none
+  bool is_validity;          // if I am a validity buffer
+  size_type column_offset;   // offset in the case of a sliced column
+};
+
+/**
+ * @brief Struct which contains information on a destination buffer.
+ *
+ * Similar to src_buf_info, dst_buf_info contains information on a destination buffer we
+ * are going to copy to.  If we have N input buffers (which come from X columns), and
+ * M partitions, then we have N*M destination buffers.
+ */
+struct dst_buf_info {
+  // constant across all copy commands for this buffer
+  std::size_t buf_size;  // total size of buffer, including padding
+  int num_elements;      // # of elements to be copied
+  int element_size;      // size of each element in bytes
+  int num_rows;  // # of rows to be copied(which may be different from num_elements in the case of
+                 // validity or offset buffers)
+
+  int src_element_index;   // element index to start reading from my associated source buffer
+  std::size_t dst_offset;  // my offset into the per-partition allocation
+  int value_shift;         // amount to shift values down by (for offset buffers)
+  int bit_shift;           // # of bits to shift right by (for validity buffers)
+  size_type valid_count;   // validity count for this block of work
+
+  int src_buf_index;  // source buffer index
+  int dst_buf_index;  // destination buffer index
+};
+
+/**
+ * @brief Copy a single buffer of column data, shifting values (for offset columns),
+ * and validity (for validity buffers) as necessary.
+ *
+ * Copies a single partition of a source column buffer to a destination buffer. Shifts
+ * element values by value_shift in the case of a buffer of offsets (value_shift will
+ * only ever be > 0 in that case).  Shifts elements bitwise by bit_shift in the case of
+ * a validity buffer (bit_shift will only ever be > 0 in that case).  This function assumes
+ * value_shift and bit_shift will never be > 0 at the same time.
+ *
+ * This function expects:
+ * - src may be a misaligned address
+ * - dst must be an aligned address
+ *
+ * This function always does the ALU work related to value_shift and bit_shift because it is
+ * entirely memory-bandwidth bound.
+ *
+ * @param dst Destination buffer
+ * @param src Source buffer
+ * @param t Thread index
+ * @param num_elements Number of elements to copy
+ * @param element_size Size of each element in bytes
+ * @param src_element_index Element index to start copying at
+ * @param stride Size of the kernel block
+ * @param value_shift Shift incoming 4-byte offset values down by this amount
+ * @param bit_shift Shift incoming data right by this many bits
+ * @param num_rows Number of rows being copied
+ * @param valid_count Optional pointer to a value to store count of set bits
+ */
+template <int block_size>
+__device__ void copy_buffer(uint8_t* __restrict__ dst,
+                            uint8_t const* __restrict__ src,
+                            int t,
+                            std::size_t num_elements,
+                            std::size_t element_size,
+                            std::size_t src_element_index,
+                            uint32_t stride,
+                            int value_shift,
+                            int bit_shift,
+                            std::size_t num_rows,
+                            size_type* valid_count)
+{
+  src += (src_element_index * element_size);
+
+  size_type thread_valid_count = 0;
+
+  // handle misalignment. read 16 bytes in 4 byte reads. write in a single 16 byte store.
+  std::size_t const num_bytes = num_elements * element_size;
+  // how many bytes we're misaligned from 4-byte alignment
+  uint32_t const ofs = reinterpret_cast<uintptr_t>(src) % 4;
+  std::size_t pos    = t * 16;
+  stride *= 16;
+  while (pos + 20 <= num_bytes) {
+    // read from the nearest aligned address.
+    uint32_t const* in32 = reinterpret_cast<uint32_t const*>((src + pos) - ofs);
+    uint4 v              = uint4{in32[0], in32[1], in32[2], in32[3]};
+    if (ofs || bit_shift) {
+      v.x = __funnelshift_r(v.x, v.y, ofs * 8 + bit_shift);
+      v.y = __funnelshift_r(v.y, v.z, ofs * 8 + bit_shift);
+      v.z = __funnelshift_r(v.z, v.w, ofs * 8 + bit_shift);
+      v.w = __funnelshift_r(v.w, in32[4], ofs * 8 + bit_shift);
+    }
+    v.x -= value_shift;
+    v.y -= value_shift;
+    v.z -= value_shift;
+    v.w -= value_shift;
+    reinterpret_cast<uint4*>(dst)[pos / 16] = v;
+    if (valid_count) {
+      thread_valid_count += (__popc(v.x) + __popc(v.y) + __popc(v.z) + __popc(v.w));
+    }
+    pos += stride;
+  }
+
+  // copy trailing bytes
+  if (t == 0) {
+    std::size_t remainder;
+    if (num_bytes < 16) {
+      remainder = num_bytes;
+    } else {
+      std::size_t const last_bracket = (num_bytes / 16) * 16;
+      remainder                      = num_bytes - last_bracket;
+      if (remainder < 4) {
+        // we had less than 20 bytes for the last possible 16 byte copy, so copy 16 + the extra
+        remainder += 16;
+      }
+    }
+
+    // if we're performing a value shift (offsets), or a bit shift (validity) the # of bytes and
+    // alignment must be a multiple of 4. value shifting and bit shifting are mutually exclusive
+    // and will never both be true at the same time.
+    if (value_shift || bit_shift) {
+      std::size_t idx = (num_bytes - remainder) / 4;
+      uint32_t v = remainder > 0 ? (reinterpret_cast<uint32_t const*>(src)[idx] - value_shift) : 0;
+
+      constexpr size_type rows_per_element = 32;
+      auto const have_trailing_bits = ((num_elements * rows_per_element) - num_rows) < bit_shift;
+      while (remainder) {
+        // if we're at the very last word of a validity copy, we do not always need to read the next
+        // word to get the final trailing bits.
+        auto const read_trailing_bits = bit_shift > 0 && remainder == 4 && have_trailing_bits;
+        uint32_t const next           = (read_trailing_bits || remainder > 4)
+                                          ? (reinterpret_cast<uint32_t const*>(src)[idx + 1] - value_shift)
+                                          : 0;
+
+        uint32_t const val = (v >> bit_shift) | (next << (32 - bit_shift));
+        if (valid_count) { thread_valid_count += __popc(val); }
+        reinterpret_cast<uint32_t*>(dst)[idx] = val;
+        v                                     = next;
+        idx++;
+        remainder -= 4;
+      }
+    } else {
+      while (remainder) {
+        std::size_t const idx = num_bytes - remainder--;
+        uint32_t const val    = reinterpret_cast<uint8_t const*>(src)[idx];
+        if (valid_count) { thread_valid_count += __popc(val); }
+        reinterpret_cast<uint8_t*>(dst)[idx] = val;
+      }
+    }
+  }
+
+  if (valid_count) {
+    if (num_bytes == 0) {
+      if (!t) { *valid_count = 0; }
+    } else {
+      using BlockReduce = cub::BlockReduce<size_type, block_size>;
+      __shared__ typename BlockReduce::TempStorage temp_storage;
+      size_type block_valid_count{BlockReduce(temp_storage).Sum(thread_valid_count)};
+      if (!t) {
+        // we may have copied more bits than there are actual rows in the output.
+        // so we need to subtract off the count of any bits that shouldn't have been
+        // considered during the copy step.
+        std::size_t const max_row    = (num_bytes * 8);
+        std::size_t const slack_bits = max_row > num_rows ? max_row - num_rows : 0;
+        auto const slack_mask        = set_most_significant_bits(slack_bits);
+        if (slack_mask > 0) {
+          uint32_t const last_word = reinterpret_cast<uint32_t*>(dst + (num_bytes - 4))[0];
+          block_valid_count -= __popc(last_word & slack_mask);
+        }
+        *valid_count = block_valid_count;
+      }
+    }
+  }
+}
+
+/**
+ * @brief Kernel which copies data from multiple source buffers to multiple
+ * destination buffers.
+ *
+ * When doing a contiguous_split on X columns comprising N total internal buffers
+ * with M splits, we end up having to copy N*M source/destination buffer pairs.
+ * These logical copies are further subdivided to distribute the amount of work
+ * to be done as evenly as possible across the multiprocessors on the device.
+ * This kernel is arranged such that each block copies 1 source/destination pair.
+ *
+ * @param index_to_buffer A function that given a `buf_index` returns the destination buffer
+ * @param src_bufs Input source buffers
+ * @param buf_info Information on the range of values to be copied for each destination buffer
+ */
+template <int block_size, typename IndexToDstBuf>
+__global__ void copy_partitions(IndexToDstBuf index_to_buffer,
+                                uint8_t const** src_bufs,
+                                dst_buf_info* buf_info)
+{
+  auto const buf_index     = blockIdx.x;
+  auto const src_buf_index = buf_info[buf_index].src_buf_index;
+
+  // copy, shifting offsets and validity bits as needed
+  copy_buffer<block_size>(
+    index_to_buffer(buf_index) + buf_info[buf_index].dst_offset,
+    src_bufs[src_buf_index],
+    threadIdx.x,
+    buf_info[buf_index].num_elements,
+    buf_info[buf_index].element_size,
+    buf_info[buf_index].src_element_index,
+    blockDim.x,
+    buf_info[buf_index].value_shift,
+    buf_info[buf_index].bit_shift,
+    buf_info[buf_index].num_rows,
+    buf_info[buf_index].valid_count > 0 ? &buf_info[buf_index].valid_count : nullptr);
+}
+
+// The block of functions below are all related:
+//
+// compute_offset_stack_size()
+// setup_src_buf_data()
+// count_src_bufs()
+// setup_source_buf_info()
+// build_output_columns()
+//
+// Critically, they all traverse the hierarchy of source columns and their children
+// in a specific order to guarantee they produce various outputs in a consistent
+// way.  For example, setup_src_buf_info() produces a series of information
+// structs that must appear in the same order that setup_src_buf_data() produces
+// buffers.
+//
+// So please be careful if you change the way in which these functions and
+// functors traverse the hierarchy.
+
+/**
+ * @brief Returns whether or not the specified type is a column that contains offsets.
+ */
+bool is_offset_type(type_id id) { return (id == type_id::STRING or id == type_id::LIST); }
+
+/**
+ * @brief Compute total device memory stack size needed to process nested
+ * offsets per-output buffer.
+ *
+ * When determining the range of rows to be copied for each output buffer
+ * we have to recursively apply the stack of offsets from our parent columns
+ * (lists or strings).  We want to do this computation on the gpu because offsets
+ * are stored in device memory.  However we don't want to do recursion on the gpu, so
+ * each destination buffer gets a "stack" of space to work with equal in size to
+ * it's offset nesting depth.  This function computes the total size of all of those
+ * stacks.
+ *
+ * This function is called recursively in the case of nested types.
+ *
+ * @param begin Beginning of input columns
+ * @param end End of input columns
+ * @param offset_depth Current offset nesting depth
+ *
+ * @returns Total offset stack size needed for this range of columns
+ */
+template <typename InputIter>
+std::size_t compute_offset_stack_size(InputIter begin, InputIter end, int offset_depth = 0)
+{
+  return std::accumulate(begin, end, 0, [offset_depth](auto stack_size, column_view const& col) {
+    auto const num_buffers = 1 + (col.nullable() ? 1 : 0);
+    return stack_size + (offset_depth * num_buffers) +
+           compute_offset_stack_size(
+             col.child_begin(), col.child_end(), offset_depth + is_offset_type(col.type().id()));
+  });
+}
+
+/**
+ * @brief Retrieve all buffers for a range of source columns.
+ *
+ * Retrieve the individual buffers that make up a range of input columns.
+ *
+ * This function is called recursively in the case of nested types.
+ *
+ * @param begin Beginning of input columns
+ * @param end End of input columns
+ * @param out_buf Iterator into output buffer infos
+ *
+ * @returns next output buffer iterator
+ */
+template <typename InputIter, typename OutputIter>
+OutputIter setup_src_buf_data(InputIter begin, InputIter end, OutputIter out_buf)
+{
+  std::for_each(begin, end, [&out_buf](column_view const& col) {
+    if (col.nullable()) {
+      *out_buf = reinterpret_cast<uint8_t const*>(col.null_mask());
+      out_buf++;
+    }
+    // NOTE: we're always returning the base pointer here.  column-level offset is accounted
+    // for later. Also, for some column types (string, list, struct) this pointer will be null
+    // because there is no associated data with the root column.
+    *out_buf = col.head<uint8_t>();
+    out_buf++;
+
+    out_buf = setup_src_buf_data(col.child_begin(), col.child_end(), out_buf);
+  });
+  return out_buf;
+}
+
+/**
+ * @brief Count the total number of source buffers we will be copying
+ * from.
+ *
+ * This count includes buffers for all input columns. For example a
+ * fixed-width column with validity would be 2 buffers (data, validity).
+ * A string column with validity would be 3 buffers (chars, offsets, validity).
+ *
+ * This function is called recursively in the case of nested types.
+ *
+ * @param begin Beginning of input columns
+ * @param end End of input columns
+ *
+ * @returns total number of source buffers for this range of columns
+ */
+template <typename InputIter>
+size_type count_src_bufs(InputIter begin, InputIter end)
+{
+  auto buf_iter = thrust::make_transform_iterator(begin, [](column_view const& col) {
+    auto const children_counts = count_src_bufs(col.child_begin(), col.child_end());
+    return 1 + (col.nullable() ? 1 : 0) + children_counts;
+  });
+  return std::accumulate(buf_iter, buf_iter + std::distance(begin, end), 0);
+}
+
+/**
+ * @brief Computes source buffer information for the copy kernel.
+ *
+ * For each input column to be split we need to know several pieces of information
+ * in the copy kernel.  This function traverses the input columns and prepares this
+ * information for the gpu.
+ *
+ * This function is called recursively in the case of nested types.
+ *
+ * @param begin Beginning of input columns
+ * @param end End of input columns
+ * @param head Beginning of source buffer info array
+ * @param current Current source buffer info to be written to
+ * @param offset_stack_pos Integer representing our current offset nesting depth
+ * (how many list or string levels deep we are)
+ * @param parent_offset_index Index into src_buf_info output array indicating our nearest
+ * containing list parent. -1 if we have no list parent
+ * @param offset_depth Current offset nesting depth (how many list levels deep we are)
+ *
+ * @returns next src_buf_output after processing this range of input columns
+ */
+// setup source buf info
+template <typename InputIter>
+std::pair<src_buf_info*, size_type> setup_source_buf_info(InputIter begin,
+                                                          InputIter end,
+                                                          src_buf_info* head,
+                                                          src_buf_info* current,
+                                                          rmm::cuda_stream_view stream,
+                                                          int offset_stack_pos    = 0,
+                                                          int parent_offset_index = -1,
+                                                          int offset_depth        = 0);
+
+/**
+ * @brief Functor that builds source buffer information based on input columns.
+ *
+ * Called by setup_source_buf_info to build information for a single source column.  This function
+ * will recursively call setup_source_buf_info in the case of nested types.
+ */
+struct buf_info_functor {
+  src_buf_info* head;
+
+  template <typename T>
+  std::pair<src_buf_info*, size_type> operator()(column_view const& col,
+                                                 src_buf_info* current,
+                                                 int offset_stack_pos,
+                                                 int parent_offset_index,
+                                                 int offset_depth,
+                                                 rmm::cuda_stream_view)
+  {
+    if (col.nullable()) {
+      std::tie(current, offset_stack_pos) =
+        add_null_buffer(col, current, offset_stack_pos, parent_offset_index, offset_depth);
+    }
+
+    // info for the data buffer
+    *current = src_buf_info(
+      col.type().id(), nullptr, offset_stack_pos, parent_offset_index, false, col.offset());
+
+    return {current + 1, offset_stack_pos + offset_depth};
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<std::is_same_v<T, cudf::dictionary32>, std::pair<src_buf_info*, size_type>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type");
+  }
+
+ private:
+  std::pair<src_buf_info*, size_type> add_null_buffer(column_view const& col,
+                                                      src_buf_info* current,
+                                                      int offset_stack_pos,
+                                                      int parent_offset_index,
+                                                      int offset_depth)
+  {
+    // info for the validity buffer
+    *current = src_buf_info(
+      type_id::INT32, nullptr, offset_stack_pos, parent_offset_index, true, col.offset());
+
+    return {current + 1, offset_stack_pos + offset_depth};
+  }
+};
+
+template <>
+std::pair<src_buf_info*, size_type> buf_info_functor::operator()<cudf::string_view>(
+  column_view const& col,
+  src_buf_info* current,
+  int offset_stack_pos,
+  int parent_offset_index,
+  int offset_depth,
+  rmm::cuda_stream_view)
+{
+  if (col.nullable()) {
+    std::tie(current, offset_stack_pos) =
+      add_null_buffer(col, current, offset_stack_pos, parent_offset_index, offset_depth);
+  }
+
+  // string columns hold no actual data, but we need to keep a record
+  // of it so we know it's size when we are constructing the output columns
+  *current = src_buf_info(
+    type_id::STRING, nullptr, offset_stack_pos, parent_offset_index, false, col.offset());
+  current++;
+  offset_stack_pos += offset_depth;
+
+  // string columns don't necessarily have children
+  if (col.num_children() > 0) {
+    CUDF_EXPECTS(col.num_children() == 2, "Encountered malformed string column");
+    strings_column_view scv(col);
+
+    // info for the offsets buffer
+    auto offset_col = current;
+    CUDF_EXPECTS(not scv.offsets().nullable(), "Encountered nullable string offsets column");
+    *current = src_buf_info(type_id::INT32,
+                            // note: offsets can be null in the case where the string column
+                            // has been created with empty_like().
+                            scv.offsets().begin<cudf::id_to_type<type_id::INT32>>(),
+                            offset_stack_pos,
+                            parent_offset_index,
+                            false,
+                            col.offset());
+
+    current++;
+    offset_stack_pos += offset_depth;
+
+    // since we are crossing an offset boundary, calculate our new depth and parent offset index.
+    offset_depth++;
+    parent_offset_index = offset_col - head;
+
+    // prevent appending buf_info for non-existent chars buffer
+    CUDF_EXPECTS(not scv.chars().nullable(), "Encountered nullable string chars column");
+
+    // info for the chars buffer
+    *current = src_buf_info(
+      type_id::INT8, nullptr, offset_stack_pos, parent_offset_index, false, col.offset());
+    current++;
+    offset_stack_pos += offset_depth;
+  }
+
+  return {current, offset_stack_pos};
+}
+
+template <>
+std::pair<src_buf_info*, size_type> buf_info_functor::operator()<cudf::list_view>(
+  column_view const& col,
+  src_buf_info* current,
+  int offset_stack_pos,
+  int parent_offset_index,
+  int offset_depth,
+  rmm::cuda_stream_view stream)
+{
+  lists_column_view lcv(col);
+
+  if (col.nullable()) {
+    std::tie(current, offset_stack_pos) =
+      add_null_buffer(col, current, offset_stack_pos, parent_offset_index, offset_depth);
+  }
+
+  // list columns hold no actual data, but we need to keep a record
+  // of it so we know it's size when we are constructing the output columns
+  *current = src_buf_info(
+    type_id::LIST, nullptr, offset_stack_pos, parent_offset_index, false, col.offset());
+  current++;
+  offset_stack_pos += offset_depth;
+
+  CUDF_EXPECTS(col.num_children() == 2, "Encountered malformed list column");
+
+  // info for the offsets buffer
+  auto offset_col = current;
+  *current        = src_buf_info(type_id::INT32,
+                          // note: offsets can be null in the case where the lists column
+                          // has been created with empty_like().
+                          lcv.offsets().begin<cudf::id_to_type<type_id::INT32>>(),
+                          offset_stack_pos,
+                          parent_offset_index,
+                          false,
+                          col.offset());
+  current++;
+  offset_stack_pos += offset_depth;
+
+  // since we are crossing an offset boundary, calculate our new depth and parent offset index.
+  offset_depth++;
+  parent_offset_index = offset_col - head;
+
+  return setup_source_buf_info(col.child_begin() + 1,
+                               col.child_end(),
+                               head,
+                               current,
+                               stream,
+                               offset_stack_pos,
+                               parent_offset_index,
+                               offset_depth);
+}
+
+template <>
+std::pair<src_buf_info*, size_type> buf_info_functor::operator()<cudf::struct_view>(
+  column_view const& col,
+  src_buf_info* current,
+  int offset_stack_pos,
+  int parent_offset_index,
+  int offset_depth,
+  rmm::cuda_stream_view stream)
+{
+  if (col.nullable()) {
+    std::tie(current, offset_stack_pos) =
+      add_null_buffer(col, current, offset_stack_pos, parent_offset_index, offset_depth);
+  }
+
+  // struct columns hold no actual data, but we need to keep a record
+  // of it so we know it's size when we are constructing the output columns
+  *current = src_buf_info(
+    type_id::STRUCT, nullptr, offset_stack_pos, parent_offset_index, false, col.offset());
+  current++;
+  offset_stack_pos += offset_depth;
+
+  // recurse on children
+  cudf::structs_column_view scv(col);
+  std::vector<column_view> sliced_children;
+  sliced_children.reserve(scv.num_children());
+  std::transform(
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(scv.num_children()),
+    std::back_inserter(sliced_children),
+    [&scv, &stream](size_type child_index) { return scv.get_sliced_child(child_index, stream); });
+  return setup_source_buf_info(sliced_children.begin(),
+                               sliced_children.end(),
+                               head,
+                               current,
+                               stream,
+                               offset_stack_pos,
+                               parent_offset_index,
+                               offset_depth);
+}
+
+template <typename InputIter>
+std::pair<src_buf_info*, size_type> setup_source_buf_info(InputIter begin,
+                                                          InputIter end,
+                                                          src_buf_info* head,
+                                                          src_buf_info* current,
+                                                          rmm::cuda_stream_view stream,
+                                                          int offset_stack_pos,
+                                                          int parent_offset_index,
+                                                          int offset_depth)
+{
+  std::for_each(begin, end, [&](column_view const& col) {
+    std::tie(current, offset_stack_pos) = cudf::type_dispatcher(col.type(),
+                                                                buf_info_functor{head},
+                                                                col,
+                                                                current,
+                                                                offset_stack_pos,
+                                                                parent_offset_index,
+                                                                offset_depth,
+                                                                stream);
+  });
+  return {current, offset_stack_pos};
+}
+
+/**
+ * @brief Given a column, processed split buffers, and a metadata builder, populate
+ * the metadata for this column in the builder, and return a tuple of:
+ * column size, data offset, bitmask offset and null count.
+ *
+ * @param src column_view to create metadata from
+ * @param current_info dst_buf_info pointer reference, pointing to this column's buffer info
+ *                     This is a pointer reference because it is updated by this function as the
+ *                     columns's validity and data buffers are visited
+ * @param mb A metadata_builder instance to update with the column's packed metadata
+ * @param use_src_null_count True for the chunked_pack case where current_info has invalid null
+ *                           count information. The null count should be taken
+ *                           from `src` because this case is restricted to a single partition
+ *                           (no splits)
+ * @returns a std::tuple containing:
+ *          column size, data offset, bitmask offset, and null count
+ */
+template <typename BufInfo>
+std::tuple<size_type, int64_t, int64_t, size_type> build_output_column_metadata(
+  column_view const& src,
+  BufInfo& current_info,
+  detail::metadata_builder& mb,
+  bool use_src_null_count)
+{
+  auto [bitmask_offset, null_count] = [&]() {
+    if (src.nullable()) {
+      // offsets in the existing serialized_column metadata are int64_t
+      // that's the reason for the casting in this code.
+      int64_t const bitmask_offset =
+        current_info->num_elements == 0
+          ? -1  // this means that the bitmask buffer pointer should be nullptr
+          : static_cast<int64_t>(current_info->dst_offset);
+
+      // use_src_null_count is used for the chunked contig split case, where we have
+      // no splits: the null_count is just the source column's null_count
+      size_type const null_count = use_src_null_count
+                                     ? src.null_count()
+                                     : (current_info->num_elements == 0
+                                          ? 0
+                                          : (current_info->num_rows - current_info->valid_count));
+
+      ++current_info;
+      return std::pair(bitmask_offset, null_count);
+    }
+    return std::pair(static_cast<int64_t>(-1), 0);
+  }();
+
+  // size/data pointer for the column
+  auto const col_size       = static_cast<size_type>(current_info->num_elements);
+  int64_t const data_offset = src.num_children() > 0 || col_size == 0 || src.head() == nullptr
+                                ? -1
+                                : static_cast<int64_t>(current_info->dst_offset);
+
+  mb.add_column_info_to_meta(
+    src.type(), col_size, null_count, data_offset, bitmask_offset, src.num_children());
+
+  ++current_info;
+  return {col_size, data_offset, bitmask_offset, null_count};
+}
+
+/**
+ * @brief Given a set of input columns and processed split buffers, produce
+ * output columns.
+ *
+ * After performing the split we are left with 1 large buffer per incoming split
+ * partition.  We need to traverse this buffer and distribute the individual
+ * subpieces that represent individual columns and children to produce the final
+ * output columns.
+ *
+ * This function is called recursively in the case of nested types.
+ *
+ * @param begin Beginning of input columns
+ * @param end End of input columns
+ * @param info_begin Iterator of dst_buf_info structs containing information about each
+ * copied buffer
+ * @param out_begin Output iterator of column views
+ * @param base_ptr Pointer to the base address of copied data for the working partition
+ *
+ * @returns new dst_buf_info iterator after processing this range of input columns
+ */
+template <typename InputIter, typename BufInfo, typename Output>
+BufInfo build_output_columns(InputIter begin,
+                             InputIter end,
+                             BufInfo info_begin,
+                             Output out_begin,
+                             uint8_t const* const base_ptr,
+                             detail::metadata_builder& mb)
+{
+  auto current_info = info_begin;
+  std::transform(begin, end, out_begin, [&current_info, base_ptr, &mb](column_view const& src) {
+    auto [col_size, data_offset, bitmask_offset, null_count] =
+      build_output_column_metadata<BufInfo>(src, current_info, mb, false);
+
+    auto const bitmask_ptr =
+      base_ptr != nullptr && bitmask_offset != -1
+        ? reinterpret_cast<bitmask_type const*>(base_ptr + static_cast<uint64_t>(bitmask_offset))
+        : nullptr;
+
+    // size/data pointer for the column
+    uint8_t const* data_ptr = base_ptr != nullptr && data_offset != -1
+                                ? base_ptr + static_cast<uint64_t>(data_offset)
+                                : nullptr;
+
+    // children
+    auto children = std::vector<column_view>{};
+    children.reserve(src.num_children());
+
+    current_info = build_output_columns(
+      src.child_begin(), src.child_end(), current_info, std::back_inserter(children), base_ptr, mb);
+
+    return column_view{
+      src.type(), col_size, data_ptr, bitmask_ptr, null_count, 0, std::move(children)};
+  });
+
+  return current_info;
+}
+
+/**
+ * @brief Given a set of input columns, processed split buffers, and a metadata_builder,
+ * append column metadata using the builder.
+ *
+ * After performing the split we are left with 1 large buffer per incoming split
+ * partition.  We need to traverse this buffer and distribute the individual
+ * subpieces that represent individual columns and children to produce the final
+ * output columns.
+ *
+ * This function is called recursively in the case of nested types.
+ *
+ * @param begin Beginning of input columns
+ * @param end End of input columns
+ * @param info_begin Iterator of dst_buf_info structs containing information about each
+ * copied buffer
+ * @param mb packed column metadata builder
+ *
+ * @returns new dst_buf_info iterator after processing this range of input columns
+ */
+template <typename InputIter, typename BufInfo>
+BufInfo populate_metadata(InputIter begin,
+                          InputIter end,
+                          BufInfo info_begin,
+                          detail::metadata_builder& mb)
+{
+  auto current_info = info_begin;
+  std::for_each(begin, end, [&current_info, &mb](column_view const& src) {
+    build_output_column_metadata<BufInfo>(src, current_info, mb, true);
+
+    // children
+    current_info = populate_metadata(src.child_begin(), src.child_end(), current_info, mb);
+  });
+
+  return current_info;
+}
+
+/**
+ * @brief Functor that retrieves the size of a destination buffer
+ */
+struct buf_size_functor {
+  dst_buf_info const* ci;
+  std::size_t operator() __device__(int index) { return ci[index].buf_size; }
+};
+
+/**
+ * @brief Functor that retrieves the split "key" for a given output
+ * buffer index.
+ *
+ * The key is simply the partition index.
+ */
+struct split_key_functor {
+  int const num_src_bufs;
+  int operator() __device__(int buf_index) const { return buf_index / num_src_bufs; }
+};
+
+/**
+ * @brief Output iterator for writing values to the dst_offset field of the
+ * dst_buf_info struct
+ */
+struct dst_offset_output_iterator {
+  dst_buf_info* c;
+  using value_type        = std::size_t;
+  using difference_type   = std::size_t;
+  using pointer           = std::size_t*;
+  using reference         = std::size_t&;
+  using iterator_category = thrust::output_device_iterator_tag;
+
+  dst_offset_output_iterator operator+ __host__ __device__(int i) { return {c + i}; }
+
+  void operator++ __host__ __device__() { c++; }
+
+  reference operator[] __device__(int i) { return dereference(c + i); }
+  reference operator* __device__() { return dereference(c); }
+
+ private:
+  reference __device__ dereference(dst_buf_info* c) { return c->dst_offset; }
+};
+
+/**
+ * @brief Output iterator for writing values to the valid_count field of the
+ * dst_buf_info struct
+ */
+struct dst_valid_count_output_iterator {
+  dst_buf_info* c;
+  using value_type        = size_type;
+  using difference_type   = size_type;
+  using pointer           = size_type*;
+  using reference         = size_type&;
+  using iterator_category = thrust::output_device_iterator_tag;
+
+  dst_valid_count_output_iterator operator+ __host__ __device__(int i)
+  {
+    return dst_valid_count_output_iterator{c + i};
+  }
+
+  void operator++ __host__ __device__() { c++; }
+
+  reference operator[] __device__(int i) { return dereference(c + i); }
+  reference operator* __device__() { return dereference(c); }
+
+ private:
+  reference __device__ dereference(dst_buf_info* c) { return c->valid_count; }
+};
+
+/**
+ * @brief Functor for computing size of data elements for a given cudf type.
+ *
+ * Note: columns types which themselves inherently have no data (strings, lists,
+ * structs) return 0.
+ */
+struct size_of_helper {
+  template <typename T>
+  constexpr std::enable_if_t<not is_fixed_width<T>(), int> __device__ operator()() const
+  {
+    return 0;
+  }
+
+  template <typename T>
+  constexpr std::enable_if_t<is_fixed_width<T>(), int> __device__ operator()() const noexcept
+  {
+    return sizeof(cudf::device_storage_type_t<T>);
+  }
+};
+
+/**
+ * @brief Functor for returning the number of batches an input buffer is being
+ * subdivided into during the repartitioning step.
+ *
+ * Note: columns types which themselves inherently have no data (strings, lists,
+ * structs) return 0.
+ */
+struct num_batches_func {
+  thrust::pair<std::size_t, std::size_t> const* const batches;
+  __device__ std::size_t operator()(size_type i) const { return thrust::get<0>(batches[i]); }
+};
+
+/**
+ * @brief Get the size in bytes of a batch described by `dst_buf_info`.
+ */
+struct batch_byte_size_function {
+  size_type const num_batches;
+  dst_buf_info const* const infos;
+  __device__ std::size_t operator()(size_type i) const
+  {
+    if (i == num_batches) { return 0; }
+    auto const& buf = *(infos + i);
+    std::size_t const bytes =
+      static_cast<std::size_t>(buf.num_elements) * static_cast<std::size_t>(buf.element_size);
+    return util::round_up_unsafe(bytes, split_align);
+  }
+};
+
+/**
+ * @brief Get the input buffer index given the output buffer index.
+ */
+struct out_to_in_index_function {
+  size_type const* const batch_offsets;
+  int const num_bufs;
+  __device__ int operator()(size_type i) const
+  {
+    return static_cast<size_type>(
+             thrust::upper_bound(thrust::seq, batch_offsets, batch_offsets + num_bufs + 1, i) -
+             batch_offsets) -
+           1;
+  }
+};
+
+// packed block of memory 1: split indices and src_buf_info structs
+struct packed_split_indices_and_src_buf_info {
+  packed_split_indices_and_src_buf_info(cudf::table_view const& input,
+                                        std::vector<size_type> const& splits,
+                                        std::size_t num_partitions,
+                                        cudf::size_type num_src_bufs,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* temp_mr)
+    : indices_size(
+        cudf::util::round_up_safe((num_partitions + 1) * sizeof(size_type), split_align)),
+      src_buf_info_size(
+        cudf::util::round_up_safe(num_src_bufs * sizeof(src_buf_info), split_align)),
+      // host-side
+      h_indices_and_source_info(indices_size + src_buf_info_size),
+      h_indices{reinterpret_cast<size_type*>(h_indices_and_source_info.data())},
+      h_src_buf_info{
+        reinterpret_cast<src_buf_info*>(h_indices_and_source_info.data() + indices_size)}
+  {
+    // compute splits -> indices.
+    // these are row numbers per split
+    h_indices[0]              = 0;
+    h_indices[num_partitions] = input.column(0).size();
+    std::copy(splits.begin(), splits.end(), std::next(h_indices));
+
+    // setup source buf info
+    setup_source_buf_info(input.begin(), input.end(), h_src_buf_info, h_src_buf_info, stream);
+
+    offset_stack_partition_size = compute_offset_stack_size(input.begin(), input.end());
+    offset_stack_size           = offset_stack_partition_size * num_partitions * sizeof(size_type);
+    // device-side
+    // gpu-only : stack space needed for nested list offset calculation
+    d_indices_and_source_info =
+      rmm::device_buffer(indices_size + src_buf_info_size + offset_stack_size, stream, temp_mr);
+    d_indices      = reinterpret_cast<size_type*>(d_indices_and_source_info.data());
+    d_src_buf_info = reinterpret_cast<src_buf_info*>(
+      reinterpret_cast<uint8_t*>(d_indices_and_source_info.data()) + indices_size);
+    d_offset_stack =
+      reinterpret_cast<size_type*>(reinterpret_cast<uint8_t*>(d_indices_and_source_info.data()) +
+                                   indices_size + src_buf_info_size);
+
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      d_indices, h_indices, indices_size + src_buf_info_size, cudaMemcpyDefault, stream.value()));
+  }
+
+  size_type const indices_size;
+  std::size_t const src_buf_info_size;
+  std::size_t offset_stack_size;
+
+  std::vector<uint8_t> h_indices_and_source_info;
+  rmm::device_buffer d_indices_and_source_info;
+
+  size_type* const h_indices;
+  src_buf_info* const h_src_buf_info;
+
+  int offset_stack_partition_size;
+  size_type* d_indices;
+  src_buf_info* d_src_buf_info;
+  size_type* d_offset_stack;
+};
+
+// packed block of memory 2: partition buffer sizes and dst_buf_info structs
+struct packed_partition_buf_size_and_dst_buf_info {
+  packed_partition_buf_size_and_dst_buf_info(std::size_t num_partitions,
+                                             std::size_t num_bufs,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* temp_mr)
+    : stream(stream),
+      buf_sizes_size{cudf::util::round_up_safe(num_partitions * sizeof(std::size_t), split_align)},
+      dst_buf_info_size{cudf::util::round_up_safe(num_bufs * sizeof(dst_buf_info), split_align)},
+      // host-side
+      h_buf_sizes_and_dst_info(buf_sizes_size + dst_buf_info_size),
+      h_buf_sizes{reinterpret_cast<std::size_t*>(h_buf_sizes_and_dst_info.data())},
+      h_dst_buf_info{
+        reinterpret_cast<dst_buf_info*>(h_buf_sizes_and_dst_info.data() + buf_sizes_size)},
+      // device-side
+      d_buf_sizes_and_dst_info(buf_sizes_size + dst_buf_info_size, stream, temp_mr),
+      d_buf_sizes{reinterpret_cast<std::size_t*>(d_buf_sizes_and_dst_info.data())},
+      // destination buffer info
+      d_dst_buf_info{reinterpret_cast<dst_buf_info*>(
+        static_cast<uint8_t*>(d_buf_sizes_and_dst_info.data()) + buf_sizes_size)}
+  {
+  }
+
+  void copy_to_host()
+  {
+    // DtoH buf sizes and col info back to the host
+    CUDF_CUDA_TRY(cudaMemcpyAsync(h_buf_sizes,
+                                  d_buf_sizes,
+                                  buf_sizes_size + dst_buf_info_size,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+  }
+
+  rmm::cuda_stream_view const stream;
+
+  // buffer sizes and destination info (used in batched copies)
+  std::size_t const buf_sizes_size;
+  std::size_t const dst_buf_info_size;
+
+  std::vector<uint8_t> h_buf_sizes_and_dst_info;
+  std::size_t* const h_buf_sizes;
+  dst_buf_info* const h_dst_buf_info;
+
+  rmm::device_buffer d_buf_sizes_and_dst_info;
+  std::size_t* const d_buf_sizes;
+  dst_buf_info* const d_dst_buf_info;
+};
+
+// Packed block of memory 3:
+// Pointers to source and destination buffers (and stack space on the
+// gpu for offset computation)
+struct packed_src_and_dst_pointers {
+  packed_src_and_dst_pointers(cudf::table_view const& input,
+                              std::size_t num_partitions,
+                              cudf::size_type num_src_bufs,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* temp_mr)
+    : stream(stream),
+      src_bufs_size{cudf::util::round_up_safe(num_src_bufs * sizeof(uint8_t*), split_align)},
+      dst_bufs_size{cudf::util::round_up_safe(num_partitions * sizeof(uint8_t*), split_align)},
+      // host-side
+      h_src_and_dst_buffers(src_bufs_size + dst_bufs_size),
+      h_src_bufs{reinterpret_cast<uint8_t const**>(h_src_and_dst_buffers.data())},
+      h_dst_bufs{reinterpret_cast<uint8_t**>(h_src_and_dst_buffers.data() + src_bufs_size)},
+      // device-side
+      d_src_and_dst_buffers{rmm::device_buffer(src_bufs_size + dst_bufs_size, stream, temp_mr)},
+      d_src_bufs{reinterpret_cast<uint8_t const**>(d_src_and_dst_buffers.data())},
+      d_dst_bufs{reinterpret_cast<uint8_t**>(
+        reinterpret_cast<uint8_t*>(d_src_and_dst_buffers.data()) + src_bufs_size)}
+  {
+    // setup src buffers
+    setup_src_buf_data(input.begin(), input.end(), h_src_bufs);
+  }
+
+  void copy_to_device()
+  {
+    CUDF_CUDA_TRY(cudaMemcpyAsync(d_src_and_dst_buffers.data(),
+                                  h_src_and_dst_buffers.data(),
+                                  src_bufs_size + dst_bufs_size,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+  }
+
+  rmm::cuda_stream_view const stream;
+  std::size_t const src_bufs_size;
+  std::size_t const dst_bufs_size;
+
+  std::vector<uint8_t> h_src_and_dst_buffers;
+  uint8_t const** const h_src_bufs;
+  uint8_t** const h_dst_bufs;
+
+  rmm::device_buffer d_src_and_dst_buffers;
+  uint8_t const** const d_src_bufs;
+  uint8_t** const d_dst_bufs;
+};
+
+/**
+ * @brief Create an instance of `packed_src_and_dst_pointers` populating destination
+ * partitition buffers (if any) from `out_buffers`. In the chunked_pack case
+ * `out_buffers` is empty, and the destination pointer is provided separately
+ * to the `copy_partitions` kernel.
+ *
+ * @param input source table view
+ * @param num_partitions the number of partitions (1 meaning no splits)
+ * @param num_src_bufs number of buffers for the source columns including children
+ * @param out_buffers the destination buffers per partition if in the non-chunked case
+ * @param stream Optional CUDA stream on which to execute kernels
+ * @param temp_mr A memory resource for temporary and scratch space
+ *
+ * @returns new unique pointer to packed_src_and_dst_pointers
+ */
+std::unique_ptr<packed_src_and_dst_pointers> setup_src_and_dst_pointers(
+  cudf::table_view const& input,
+  std::size_t num_partitions,
+  cudf::size_type num_src_bufs,
+  std::vector<rmm::device_buffer>& out_buffers,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* temp_mr)
+{
+  auto src_and_dst_pointers = std::make_unique<packed_src_and_dst_pointers>(
+    input, num_partitions, num_src_bufs, stream, temp_mr);
+
+  std::transform(
+    out_buffers.begin(), out_buffers.end(), src_and_dst_pointers->h_dst_bufs, [](auto& buf) {
+      return static_cast<uint8_t*>(buf.data());
+    });
+
+  // copy the struct to device memory to access from the kernel
+  src_and_dst_pointers->copy_to_device();
+
+  return src_and_dst_pointers;
+}
+
+/**
+ * @brief Create an instance of `packed_partition_buf_size_and_dst_buf_info` containing
+ * the partition-level dst_buf_info structs for each partition and column buffer.
+ *
+ * @param input source table view
+ * @param splits the numeric value (in rows) for each split, empty for 1 partition
+ * @param num_partitions the number of partitions create (1 meaning no splits)
+ * @param num_src_bufs number of buffers for the source columns including children
+ * @param num_bufs num_src_bufs times the number of partitions
+ * @param stream Optional CUDA stream on which to execute kernels
+ * @param temp_mr A memory resource for temporary and scratch space
+ *
+ * @returns new unique pointer to `packed_partition_buf_size_and_dst_buf_info`
+ */
+std::unique_ptr<packed_partition_buf_size_and_dst_buf_info> compute_splits(
+  cudf::table_view const& input,
+  std::vector<size_type> const& splits,
+  std::size_t num_partitions,
+  cudf::size_type num_src_bufs,
+  std::size_t num_bufs,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* temp_mr)
+{
+  auto partition_buf_size_and_dst_buf_info =
+    std::make_unique<packed_partition_buf_size_and_dst_buf_info>(
+      num_partitions, num_bufs, stream, temp_mr);
+
+  auto const d_dst_buf_info = partition_buf_size_and_dst_buf_info->d_dst_buf_info;
+  auto const d_buf_sizes    = partition_buf_size_and_dst_buf_info->d_buf_sizes;
+
+  auto const split_indices_and_src_buf_info = packed_split_indices_and_src_buf_info(
+    input, splits, num_partitions, num_src_bufs, stream, temp_mr);
+
+  auto const d_src_buf_info = split_indices_and_src_buf_info.d_src_buf_info;
+  auto const offset_stack_partition_size =
+    split_indices_and_src_buf_info.offset_stack_partition_size;
+  auto const d_offset_stack = split_indices_and_src_buf_info.d_offset_stack;
+  auto const d_indices      = split_indices_and_src_buf_info.d_indices;
+
+  // compute sizes of each column in each partition, including alignment.
+  thrust::transform(
+    rmm::exec_policy(stream, temp_mr),
+    thrust::make_counting_iterator<std::size_t>(0),
+    thrust::make_counting_iterator<std::size_t>(num_bufs),
+    d_dst_buf_info,
+    [d_src_buf_info,
+     offset_stack_partition_size,
+     d_offset_stack,
+     d_indices,
+     num_src_bufs] __device__(std::size_t t) {
+      int const split_index   = t / num_src_bufs;
+      int const src_buf_index = t % num_src_bufs;
+      auto const& src_info    = d_src_buf_info[src_buf_index];
+
+      // apply nested offsets (lists and string columns).
+      //
+      // We can't just use the incoming row indices to figure out where to read from in a
+      // nested list situation.  We have to apply offsets every time we cross a boundary
+      // (list or string).  This loop applies those offsets so that our incoming row_index_start
+      // and row_index_end get transformed to our final values.
+      //
+      int const stack_pos = src_info.offset_stack_pos + (split_index * offset_stack_partition_size);
+      size_type* offset_stack  = &d_offset_stack[stack_pos];
+      int parent_offsets_index = src_info.parent_offsets_index;
+      int stack_size           = 0;
+      int root_column_offset   = src_info.column_offset;
+      while (parent_offsets_index >= 0) {
+        offset_stack[stack_size++] = parent_offsets_index;
+        root_column_offset         = d_src_buf_info[parent_offsets_index].column_offset;
+        parent_offsets_index       = d_src_buf_info[parent_offsets_index].parent_offsets_index;
+      }
+      // make sure to include the -column- offset on the root column in our calculation.
+      int row_start = d_indices[split_index] + root_column_offset;
+      int row_end   = d_indices[split_index + 1] + root_column_offset;
+      while (stack_size > 0) {
+        stack_size--;
+        auto const offsets = d_src_buf_info[offset_stack[stack_size]].offsets;
+        // this case can happen when you have empty string or list columns constructed with
+        // empty_like()
+        if (offsets != nullptr) {
+          row_start = offsets[row_start];
+          row_end   = offsets[row_end];
+        }
+      }
+
+      // final element indices and row count
+      int const out_element_index = src_info.is_validity ? row_start / 32 : row_start;
+      int const num_rows          = row_end - row_start;
+      // if I am an offsets column, all my values need to be shifted
+      int const value_shift = src_info.offsets == nullptr ? 0 : src_info.offsets[row_start];
+      // if I am a validity column, we may need to shift bits
+      int const bit_shift = src_info.is_validity ? row_start % 32 : 0;
+      // # of rows isn't necessarily the same as # of elements to be copied.
+      auto const num_elements = [&]() {
+        if (src_info.offsets != nullptr && num_rows > 0) {
+          return num_rows + 1;
+        } else if (src_info.is_validity) {
+          return (num_rows + 31) / 32;
+        }
+        return num_rows;
+      }();
+      int const element_size = cudf::type_dispatcher(data_type{src_info.type}, size_of_helper{});
+      std::size_t const bytes =
+        static_cast<std::size_t>(num_elements) * static_cast<std::size_t>(element_size);
+
+      return dst_buf_info{util::round_up_unsafe(bytes, split_align),
+                          num_elements,
+                          element_size,
+                          num_rows,
+                          out_element_index,
+                          0,
+                          value_shift,
+                          bit_shift,
+                          src_info.is_validity ? 1 : 0,
+                          src_buf_index,
+                          split_index};
+    });
+
+  // compute total size of each partition
+  // key is the split index
+  {
+    auto const keys = cudf::detail::make_counting_transform_iterator(
+      0, split_key_functor{static_cast<int>(num_src_bufs)});
+    auto values =
+      cudf::detail::make_counting_transform_iterator(0, buf_size_functor{d_dst_buf_info});
+
+    thrust::reduce_by_key(rmm::exec_policy(stream, temp_mr),
+                          keys,
+                          keys + num_bufs,
+                          values,
+                          thrust::make_discard_iterator(),
+                          d_buf_sizes);
+  }
+
+  // compute start offset for each output buffer for each split
+  {
+    auto const keys = cudf::detail::make_counting_transform_iterator(
+      0, split_key_functor{static_cast<int>(num_src_bufs)});
+    auto values =
+      cudf::detail::make_counting_transform_iterator(0, buf_size_functor{d_dst_buf_info});
+
+    thrust::exclusive_scan_by_key(rmm::exec_policy(stream, temp_mr),
+                                  keys,
+                                  keys + num_bufs,
+                                  values,
+                                  dst_offset_output_iterator{d_dst_buf_info},
+                                  std::size_t{0});
+  }
+
+  partition_buf_size_and_dst_buf_info->copy_to_host();
+
+  stream.synchronize();
+
+  return partition_buf_size_and_dst_buf_info;
+}
+
+/**
+ * @brief Struct containing information about the actual batches we will send to the
+ * `copy_partitions` kernel and the number of iterations we need to carry out this copy.
+ *
+ * For the non-chunked contiguous_split case, this contains the batched dst_buf_infos and the
+ * number of iterations is going to be 1 since the non-chunked case is single pass.
+ *
+ * For the chunked_pack case, this also contains the batched dst_buf_infos for all
+ * iterations in addition to helping keep the state about what batches have been copied so far
+ * and what are the sizes (in bytes) of each iteration.
+ */
+struct chunk_iteration_state {
+  chunk_iteration_state(rmm::device_uvector<dst_buf_info> _d_batched_dst_buf_info,
+                        rmm::device_uvector<size_type> _d_batch_offsets,
+                        std::vector<std::size_t>&& _h_num_buffs_per_iteration,
+                        std::vector<std::size_t>&& _h_size_of_buffs_per_iteration,
+                        std::size_t total_size)
+    : num_iterations(_h_num_buffs_per_iteration.size()),
+      current_iteration{0},
+      starting_batch{0},
+      d_batched_dst_buf_info(std::move(_d_batched_dst_buf_info)),
+      d_batch_offsets(std::move(_d_batch_offsets)),
+      h_num_buffs_per_iteration(std::move(_h_num_buffs_per_iteration)),
+      h_size_of_buffs_per_iteration(std::move(_h_size_of_buffs_per_iteration)),
+      total_size(total_size)
+  {
+  }
+
+  static std::unique_ptr<chunk_iteration_state> create(
+    rmm::device_uvector<thrust::pair<std::size_t, std::size_t>> const& batches,
+    int num_bufs,
+    dst_buf_info* d_orig_dst_buf_info,
+    std::size_t const* const h_buf_sizes,
+    std::size_t num_partitions,
+    std::size_t user_buffer_size,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* temp_mr);
+
+  /**
+   * @brief As of the time of the call, return the starting 1MB batch index, and the
+   * number of batches to copy.
+   *
+   * @return the current iteration's starting_batch and batch count as a pair
+   */
+  std::pair<std::size_t, std::size_t> get_current_starting_index_and_buff_count() const
+  {
+    CUDF_EXPECTS(current_iteration < num_iterations,
+                 "current_iteration cannot exceed num_iterations");
+    auto count_for_current = h_num_buffs_per_iteration[current_iteration];
+    return {starting_batch, count_for_current};
+  }
+
+  /**
+   * @brief Advance the iteration state if there are iterations left, updating the
+   * starting batch and returning the amount of bytes were copied in the iteration
+   * we just finished.
+   * @throws cudf::logic_error If the state was at the last iteration before entering
+   * this function.
+   * @return size in bytes that were copied in the finished iteration
+   */
+  std::size_t advance_iteration()
+  {
+    CUDF_EXPECTS(current_iteration < num_iterations,
+                 "current_iteration cannot exceed num_iterations");
+    std::size_t bytes_copied = h_size_of_buffs_per_iteration[current_iteration];
+    starting_batch += h_num_buffs_per_iteration[current_iteration];
+    ++current_iteration;
+    return bytes_copied;
+  }
+
+  /**
+   * Returns true if there are iterations left.
+   */
+  bool has_more_copies() const { return current_iteration < num_iterations; }
+
+  rmm::device_uvector<dst_buf_info> d_batched_dst_buf_info;  ///< dst_buf_info per 1MB batch
+  rmm::device_uvector<size_type> const d_batch_offsets;  ///< Offset within a batch per dst_buf_info
+  std::size_t const total_size;                          ///< The aggregate size of all iterations
+  int const num_iterations;                              ///< The total number of iterations
+  int current_iteration;  ///< Marks the current iteration being worked on
+
+ private:
+  std::size_t starting_batch;  ///< Starting batch index for the current iteration
+  std::vector<std::size_t> const h_num_buffs_per_iteration;  ///< The count of batches per iteration
+  std::vector<std::size_t> const
+    h_size_of_buffs_per_iteration;  ///< The size in bytes per iteration
+};
+
+std::unique_ptr<chunk_iteration_state> chunk_iteration_state::create(
+  rmm::device_uvector<thrust::pair<std::size_t, std::size_t>> const& batches,
+  int num_bufs,
+  dst_buf_info* d_orig_dst_buf_info,
+  std::size_t const* const h_buf_sizes,
+  std::size_t num_partitions,
+  std::size_t user_buffer_size,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* temp_mr)
+{
+  rmm::device_uvector<size_type> d_batch_offsets(num_bufs + 1, stream, temp_mr);
+
+  auto const buf_count_iter = cudf::detail::make_counting_transform_iterator(
+    0, [num_bufs, num_batches = num_batches_func{batches.begin()}] __device__(size_type i) {
+      return i == num_bufs ? 0 : num_batches(i);
+    });
+
+  thrust::exclusive_scan(rmm::exec_policy(stream, temp_mr),
+                         buf_count_iter,
+                         buf_count_iter + num_bufs + 1,
+                         d_batch_offsets.begin(),
+                         0);
+
+  auto const num_batches_iter =
+    cudf::detail::make_counting_transform_iterator(0, num_batches_func{batches.begin()});
+  size_type const num_batches = thrust::reduce(
+    rmm::exec_policy(stream, temp_mr), num_batches_iter, num_batches_iter + batches.size());
+
+  auto out_to_in_index = out_to_in_index_function{d_batch_offsets.begin(), num_bufs};
+
+  auto const iter = thrust::make_counting_iterator(0);
+
+  // load up the batches as d_dst_buf_info
+  rmm::device_uvector<dst_buf_info> d_batched_dst_buf_info(num_batches, stream, temp_mr);
+
+  thrust::for_each(
+    rmm::exec_policy(stream, temp_mr),
+    iter,
+    iter + num_batches,
+    [d_orig_dst_buf_info,
+     d_batched_dst_buf_info = d_batched_dst_buf_info.begin(),
+     batches                = batches.begin(),
+     d_batch_offsets        = d_batch_offsets.begin(),
+     out_to_in_index] __device__(size_type i) {
+      size_type const in_buf_index = out_to_in_index(i);
+      size_type const batch_index  = i - d_batch_offsets[in_buf_index];
+      auto const batch_size        = thrust::get<1>(batches[in_buf_index]);
+      dst_buf_info const& in       = d_orig_dst_buf_info[in_buf_index];
+
+      // adjust info
+      dst_buf_info& out = d_batched_dst_buf_info[i];
+      out.element_size  = in.element_size;
+      out.value_shift   = in.value_shift;
+      out.bit_shift     = in.bit_shift;
+      out.valid_count =
+        in.valid_count;  // valid count will be set to 1 if this is a validity buffer
+      out.src_buf_index = in.src_buf_index;
+      out.dst_buf_index = in.dst_buf_index;
+
+      size_type const elements_per_batch =
+        out.element_size == 0 ? 0 : batch_size / out.element_size;
+      out.num_elements = ((batch_index + 1) * elements_per_batch) > in.num_elements
+                           ? in.num_elements - (batch_index * elements_per_batch)
+                           : elements_per_batch;
+
+      size_type const rows_per_batch =
+        // if this is a validity buffer, each element is a bitmask_type, which
+        // corresponds to 32 rows.
+        out.valid_count > 0
+          ? elements_per_batch * static_cast<size_type>(cudf::detail::size_in_bits<bitmask_type>())
+          : elements_per_batch;
+      out.num_rows = ((batch_index + 1) * rows_per_batch) > in.num_rows
+                       ? in.num_rows - (batch_index * rows_per_batch)
+                       : rows_per_batch;
+
+      out.src_element_index = in.src_element_index + (batch_index * elements_per_batch);
+      out.dst_offset        = in.dst_offset + (batch_index * batch_size);
+
+      // out.bytes and out.buf_size are unneeded here because they are only used to
+      // calculate real output buffer sizes. the data we are generating here is
+      // purely intermediate for the purposes of doing more uniform copying of data
+      // underneath the final structure of the output
+    });
+
+  /**
+   * In the chunked case, this is the code that fixes up the offsets of each batch
+   * and prepares each iteration. Given the batches computed before, it figures
+   * out the number of batches that will fit in an iteration of `user_buffer_size`.
+   *
+   * Specifically, offsets for batches are reset to the 0th byte when a new iteration
+   * of `user_buffer_size` bytes is needed.
+   */
+  if (user_buffer_size != 0) {
+    // copy the batch offsets back to host
+    std::vector<std::size_t> h_offsets(num_batches + 1);
+    {
+      rmm::device_uvector<std::size_t> offsets(h_offsets.size(), stream, temp_mr);
+      auto const batch_byte_size_iter = cudf::detail::make_counting_transform_iterator(
+        0, batch_byte_size_function{num_batches, d_batched_dst_buf_info.begin()});
+
+      thrust::exclusive_scan(rmm::exec_policy(stream, temp_mr),
+                             batch_byte_size_iter,
+                             batch_byte_size_iter + num_batches + 1,
+                             offsets.begin());
+
+      CUDF_CUDA_TRY(cudaMemcpyAsync(h_offsets.data(),
+                                    offsets.data(),
+                                    sizeof(std::size_t) * offsets.size(),
+                                    cudaMemcpyDefault,
+                                    stream.value()));
+
+      // the next part is working on the CPU, so we want to synchronize here
+      stream.synchronize();
+    }
+
+    std::vector<std::size_t> num_batches_per_iteration;
+    std::vector<std::size_t> size_of_batches_per_iteration;
+    std::vector<std::size_t> accum_size_per_iteration;
+    std::size_t accum_size = 0;
+    {
+      auto current_offset_it = h_offsets.begin();
+      // figure out how many iterations we need, while fitting batches to iterations
+      // with no more than user_buffer_size bytes worth of batches
+      while (current_offset_it != h_offsets.end()) {
+        // next_iteration_it points to the batch right above the boundary (the batch
+        // that didn't fit).
+        auto next_iteration_it =
+          std::lower_bound(current_offset_it,
+                           h_offsets.end(),
+                           // We add the cumulative size + 1 because we want to find what would fit
+                           // within a buffer of user_buffer_size (up to user_buffer_size).
+                           // Since h_offsets is a prefix scan, we add the size we accumulated so
+                           // far so we are looking for the next user_buffer_sized boundary.
+                           user_buffer_size + accum_size + 1);
+
+        // we subtract 1 from the number of batch here because next_iteration_it points
+        // to the batch that didn't fit, so it's one off.
+        auto batches_in_iter = std::distance(current_offset_it, next_iteration_it) - 1;
+
+        // to get the amount of bytes in this iteration we get the prefix scan size
+        // and subtract the cumulative size so far, leaving the bytes belonging to this
+        // iteration
+        auto iter_size_bytes = *(current_offset_it + batches_in_iter) - accum_size;
+        accum_size += iter_size_bytes;
+
+        num_batches_per_iteration.push_back(batches_in_iter);
+        size_of_batches_per_iteration.push_back(iter_size_bytes);
+        accum_size_per_iteration.push_back(accum_size);
+
+        if (next_iteration_it == h_offsets.end()) { break; }
+
+        current_offset_it += batches_in_iter;
+      }
+    }
+
+    // apply changed offset
+    {
+      auto d_accum_size_per_iteration =
+        cudf::detail::make_device_uvector_async(accum_size_per_iteration, stream, temp_mr);
+
+      // we want to update the offset of batches for every iteration, except the first one (because
+      // offsets in the first iteration are all 0 based)
+      auto num_batches_in_first_iteration = num_batches_per_iteration[0];
+      auto const iter     = thrust::make_counting_iterator(num_batches_in_first_iteration);
+      auto num_iterations = accum_size_per_iteration.size();
+      thrust::for_each(
+        rmm::exec_policy(stream, temp_mr),
+        iter,
+        iter + num_batches - num_batches_in_first_iteration,
+        [num_iterations,
+         d_batched_dst_buf_info     = d_batched_dst_buf_info.begin(),
+         d_accum_size_per_iteration = d_accum_size_per_iteration.begin()] __device__(size_type i) {
+          auto prior_iteration_size =
+            thrust::upper_bound(thrust::seq,
+                                d_accum_size_per_iteration,
+                                d_accum_size_per_iteration + num_iterations,
+                                d_batched_dst_buf_info[i].dst_offset) -
+            1;
+          d_batched_dst_buf_info[i].dst_offset -= *prior_iteration_size;
+        });
+    }
+    return std::make_unique<chunk_iteration_state>(std::move(d_batched_dst_buf_info),
+                                                   std::move(d_batch_offsets),
+                                                   std::move(num_batches_per_iteration),
+                                                   std::move(size_of_batches_per_iteration),
+                                                   accum_size);
+
+  } else {
+    // we instantiate an "iteration state" for the regular single pass contiguous_split
+    // consisting of 1 iteration with all of the batches and totalling `total_size` bytes.
+    auto const total_size = std::reduce(h_buf_sizes, h_buf_sizes + num_partitions);
+
+    // 1 iteration with the whole size
+    return std::make_unique<chunk_iteration_state>(
+      std::move(d_batched_dst_buf_info),
+      std::move(d_batch_offsets),
+      std::move(std::vector<std::size_t>{static_cast<std::size_t>(num_batches)}),
+      std::move(std::vector<std::size_t>{total_size}),
+      total_size);
+  }
+}
+
+/**
+ * @brief Create an instance of `chunk_iteration_state` containing 1MB batches of work
+ * that are further grouped into chunks or iterations.
+ *
+ * This function handles both the `chunked_pack` case: when `user_buffer_size` is non-zero,
+ * and the single-shot `contiguous_split` case.
+ *
+ * @param num_bufs num_src_bufs times the number of partitions
+ * @param d_dst_buf_info dst_buf_info per partition produced in `compute_splits`
+ * @param h_buf_sizes size in bytes of a partition (accessible from host)
+ * @param num_partitions the number of partitions (1 meaning no splits)
+ * @param user_buffer_size if non-zero, it is the size in bytes that 1MB batches should be
+ *        grouped in, as different iterations.
+ * @param stream Optional CUDA stream on which to execute kernels
+ * @param temp_mr A memory resource for temporary and scratch space
+ *
+ * @returns new unique pointer to `chunk_iteration_state`
+ */
+std::unique_ptr<chunk_iteration_state> compute_batches(int num_bufs,
+                                                       dst_buf_info* const d_dst_buf_info,
+                                                       std::size_t const* const h_buf_sizes,
+                                                       std::size_t num_partitions,
+                                                       std::size_t user_buffer_size,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* temp_mr)
+{
+  // Since we parallelize at one block per copy, performance is vulnerable to situations where we
+  // have small numbers of copies to do (a combination of small numbers of splits and/or columns),
+  // so we will take the actual set of outgoing source/destination buffers and further partition
+  // them into much smaller batches in order to drive up the number of blocks and overall
+  // occupancy.
+  rmm::device_uvector<thrust::pair<std::size_t, std::size_t>> batches(num_bufs, stream, temp_mr);
+  thrust::transform(
+    rmm::exec_policy(stream, temp_mr),
+    d_dst_buf_info,
+    d_dst_buf_info + num_bufs,
+    batches.begin(),
+    [desired_batch_size = desired_batch_size] __device__(
+      dst_buf_info const& buf) -> thrust::pair<std::size_t, std::size_t> {
+      // Total bytes for this incoming partition
+      std::size_t const bytes =
+        static_cast<std::size_t>(buf.num_elements) * static_cast<std::size_t>(buf.element_size);
+
+      // This clause handles nested data types (e.g. list or string) that store no data in the row
+      // columns, only in their children.
+      if (bytes == 0) { return {1, 0}; }
+
+      // The number of batches we want to subdivide this buffer into
+      std::size_t const num_batches = std::max(
+        std::size_t{1}, util::round_up_unsafe(bytes, desired_batch_size) / desired_batch_size);
+
+      // NOTE: leaving batch size as a separate parameter for future tuning
+      // possibilities, even though in the current implementation it will be a
+      // constant.
+      return {num_batches, desired_batch_size};
+    });
+
+  return chunk_iteration_state::create(batches,
+                                       num_bufs,
+                                       d_dst_buf_info,
+                                       h_buf_sizes,
+                                       num_partitions,
+                                       user_buffer_size,
+                                       stream,
+                                       temp_mr);
+}
+
+void copy_data(int num_batches_to_copy,
+               int starting_batch,
+               uint8_t const** d_src_bufs,
+               uint8_t** d_dst_bufs,
+               rmm::device_uvector<dst_buf_info>& d_dst_buf_info,
+               uint8_t* user_buffer,
+               rmm::cuda_stream_view stream)
+{
+  constexpr size_type block_size = 256;
+  if (user_buffer != nullptr) {
+    auto index_to_buffer = [user_buffer] __device__(unsigned int) { return user_buffer; };
+    copy_partitions<block_size><<<num_batches_to_copy, block_size, 0, stream.value()>>>(
+      index_to_buffer, d_src_bufs, d_dst_buf_info.data() + starting_batch);
+  } else {
+    auto index_to_buffer = [d_dst_bufs,
+                            dst_buf_info = d_dst_buf_info.data(),
+                            user_buffer] __device__(unsigned int buf_index) {
+      auto const dst_buf_index = dst_buf_info[buf_index].dst_buf_index;
+      return d_dst_bufs[dst_buf_index];
+    };
+    copy_partitions<block_size><<<num_batches_to_copy, block_size, 0, stream.value()>>>(
+      index_to_buffer, d_src_bufs, d_dst_buf_info.data() + starting_batch);
+  }
+}
+
+/**
+ * @brief Function that checks an input table_view and splits for specific edge cases.
+ *
+ * It will return true if the input is "empty" (no rows or columns), which means
+ * special handling has to happen in the calling code.
+ *
+ * @param input table_view of source table to be split
+ * @param splits the splits specified by the user, or an empty vector if no splits
+ * @returns true if the input is empty, false otherwise
+ */
+bool check_inputs(cudf::table_view const& input, std::vector<size_type> const& splits)
+{
+  if (input.num_columns() == 0) { return true; }
+  if (splits.size() > 0) {
+    CUDF_EXPECTS(splits.back() <= input.column(0).size(),
+                 "splits can't exceed size of input columns");
+  }
+  size_type begin = 0;
+  for (auto end : splits) {
+    CUDF_EXPECTS(begin >= 0, "Starting index cannot be negative.");
+    CUDF_EXPECTS(end >= begin, "End index cannot be smaller than the starting index.");
+    CUDF_EXPECTS(end <= input.column(0).size(), "Slice range out of bounds.");
+    begin = end;
+  }
+  return input.column(0).size() == 0;
+}
+
+};  // anonymous namespace
+
+namespace detail {
+
+/**
+ * @brief A helper struct containing the state of contiguous_split, whether the caller
+ * is using the single-pass contiguous_split or chunked_pack.
+ *
+ * It exposes an iterator-like pattern where contiguous_split_state::has_next()
+ * returns true when there is work to be done, and false otherwise.
+ *
+ * contiguous_split_state::contiguous_split() performs a single-pass contiguous_split
+ * and is valid iff contiguous_split_state is instantiated with 0 for the user_buffer_size.
+ *
+ * contiguous_split_state::contiguous_split_chunk(device_span) is only valid when
+ * user_buffer_size > 0. It should be called as long as has_next() returns true. The
+ * device_span passed to contiguous_split_chunk must be allocated in stream `stream` by
+ * the user.
+ *
+ * None of the methods are thread safe.
+ */
+struct contiguous_split_state {
+  contiguous_split_state(cudf::table_view const& input,
+                         std::size_t user_buffer_size,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr,
+                         rmm::mr::device_memory_resource* temp_mr)
+    : contiguous_split_state(input, {}, user_buffer_size, stream, mr, temp_mr)
+  {
+  }
+
+  contiguous_split_state(cudf::table_view const& input,
+                         std::vector<size_type> const& splits,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr,
+                         rmm::mr::device_memory_resource* temp_mr)
+    : contiguous_split_state(input, splits, 0, stream, mr, temp_mr)
+  {
+  }
+
+  bool has_next() const { return !is_empty && chunk_iter_state->has_more_copies(); }
+
+  std::size_t get_total_contiguous_size() const
+  {
+    return is_empty ? 0 : chunk_iter_state->total_size;
+  }
+
+  std::vector<packed_table> contiguous_split()
+  {
+    CUDF_EXPECTS(user_buffer_size == 0, "Cannot contiguous split with a user buffer");
+    if (is_empty || input.num_columns() == 0) { return make_packed_tables(); }
+
+    auto const num_batches_total =
+      std::get<1>(chunk_iter_state->get_current_starting_index_and_buff_count());
+
+    // perform the copy.
+    copy_data(num_batches_total,
+              0 /* starting at buffer for single-shot 0*/,
+              src_and_dst_pointers->d_src_bufs,
+              src_and_dst_pointers->d_dst_bufs,
+              chunk_iter_state->d_batched_dst_buf_info,
+              nullptr,
+              stream);
+
+    // these "orig" dst_buf_info pointers describe the prior-to-batching destination
+    // buffers per partition
+    auto d_orig_dst_buf_info = partition_buf_size_and_dst_buf_info->d_dst_buf_info;
+    auto h_orig_dst_buf_info = partition_buf_size_and_dst_buf_info->h_dst_buf_info;
+
+    // postprocess valid_counts: apply the valid counts computed by copy_data for each
+    // batch back to the original dst_buf_infos
+    auto const keys = cudf::detail::make_counting_transform_iterator(
+      0, out_to_in_index_function{chunk_iter_state->d_batch_offsets.begin(), (int)num_bufs});
+
+    auto values = thrust::make_transform_iterator(
+      chunk_iter_state->d_batched_dst_buf_info.begin(),
+      [] __device__(dst_buf_info const& info) { return info.valid_count; });
+
+    thrust::reduce_by_key(rmm::exec_policy(stream, temp_mr),
+                          keys,
+                          keys + num_batches_total,
+                          values,
+                          thrust::make_discard_iterator(),
+                          dst_valid_count_output_iterator{d_orig_dst_buf_info});
+
+    CUDF_CUDA_TRY(cudaMemcpyAsync(h_orig_dst_buf_info,
+                                  d_orig_dst_buf_info,
+                                  partition_buf_size_and_dst_buf_info->dst_buf_info_size,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+
+    stream.synchronize();
+
+    // not necessary for the non-chunked case, but it makes it so further calls to has_next
+    // return false, just in case
+    chunk_iter_state->advance_iteration();
+
+    return make_packed_tables();
+  }
+
+  cudf::size_type contiguous_split_chunk(cudf::device_span<uint8_t> const& user_buffer)
+  {
+    CUDF_FUNC_RANGE();
+    CUDF_EXPECTS(
+      user_buffer.size() == user_buffer_size,
+      "Cannot use a device span smaller than the output buffer size configured at instantiation!");
+    CUDF_EXPECTS(has_next(), "Cannot call contiguous_split_chunk with has_next() == false!");
+
+    auto [starting_batch, num_batches_to_copy] =
+      chunk_iter_state->get_current_starting_index_and_buff_count();
+
+    // perform the copy.
+    copy_data(num_batches_to_copy,
+              starting_batch,
+              src_and_dst_pointers->d_src_bufs,
+              src_and_dst_pointers->d_dst_bufs,
+              chunk_iter_state->d_batched_dst_buf_info,
+              user_buffer.data(),
+              stream);
+
+    // We do not need to post-process null counts since the null count info is
+    // taken from the source table in the contiguous_split_chunk case (no splits)
+    return chunk_iter_state->advance_iteration();
+  }
+
+  std::unique_ptr<std::vector<uint8_t>> build_packed_column_metadata()
+  {
+    CUDF_EXPECTS(num_partitions == 1, "build_packed_column_metadata supported only without splits");
+
+    if (input.num_columns() == 0) { return std::unique_ptr<std::vector<uint8_t>>(); }
+
+    if (is_empty) {
+      // this is a bit ugly, but it was done to re-use make_empty_packed_table between the
+      // regular contiguous_split and chunked_pack cases.
+      auto empty_packed_tables = std::move(make_empty_packed_table().front());
+      return std::move(empty_packed_tables.data.metadata);
+    }
+
+    auto& h_dst_buf_info  = partition_buf_size_and_dst_buf_info->h_dst_buf_info;
+    auto cur_dst_buf_info = h_dst_buf_info;
+    detail::metadata_builder mb{input.num_columns()};
+
+    populate_metadata(input.begin(), input.end(), cur_dst_buf_info, mb);
+
+    return std::make_unique<std::vector<uint8_t>>(std::move(mb.build()));
+  }
+
+ private:
+  contiguous_split_state(cudf::table_view const& input,
+                         std::vector<size_type> const& splits,
+                         std::size_t user_buffer_size,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr,
+                         rmm::mr::device_memory_resource* temp_mr)
+    : input(input),
+      user_buffer_size(user_buffer_size),
+      stream(stream),
+      mr(mr),
+      temp_mr(temp_mr),
+      is_empty{check_inputs(input, splits)},
+      num_partitions{splits.size() + 1},
+      num_src_bufs{count_src_bufs(input.begin(), input.end())},
+      num_bufs{num_src_bufs * num_partitions}
+  {
+    // if the table we are about to contig split is empty, we have special
+    // handling where metadata is produced and a 0-byte contiguous buffer
+    // is the result.
+    if (is_empty) { return; }
+
+    // First pass over the source tables to generate a `dst_buf_info` per split and column buffer
+    // (`num_bufs`). After this, contiguous_split uses `dst_buf_info` to further subdivide the work
+    // into 1MB batches in `compute_batches`
+    partition_buf_size_and_dst_buf_info = std::move(
+      compute_splits(input, splits, num_partitions, num_src_bufs, num_bufs, stream, temp_mr));
+
+    // Second pass: uses `dst_buf_info` to break down the work into 1MB batches.
+    chunk_iter_state = compute_batches(num_bufs,
+                                       partition_buf_size_and_dst_buf_info->d_dst_buf_info,
+                                       partition_buf_size_and_dst_buf_info->h_buf_sizes,
+                                       num_partitions,
+                                       user_buffer_size,
+                                       stream,
+                                       temp_mr);
+
+    // allocate output partition buffers, in the non-chunked case
+    if (user_buffer_size == 0) {
+      out_buffers.reserve(num_partitions);
+      auto h_buf_sizes = partition_buf_size_and_dst_buf_info->h_buf_sizes;
+      std::transform(h_buf_sizes,
+                     h_buf_sizes + num_partitions,
+                     std::back_inserter(out_buffers),
+                     [stream = stream, mr = mr](std::size_t bytes) {
+                       return rmm::device_buffer{bytes, stream, mr};
+                     });
+    }
+
+    src_and_dst_pointers = std::move(setup_src_and_dst_pointers(
+      input, num_partitions, num_src_bufs, out_buffers, stream, temp_mr));
+  }
+
+  std::vector<packed_table> make_packed_tables()
+  {
+    if (input.num_columns() == 0) { return std::vector<packed_table>(); }
+    if (is_empty) { return make_empty_packed_table(); }
+    std::vector<packed_table> result;
+    result.reserve(num_partitions);
+    std::vector<column_view> cols;
+    cols.reserve(input.num_columns());
+
+    auto& h_dst_buf_info = partition_buf_size_and_dst_buf_info->h_dst_buf_info;
+    auto& h_dst_bufs     = src_and_dst_pointers->h_dst_bufs;
+
+    auto cur_dst_buf_info = h_dst_buf_info;
+    detail::metadata_builder mb(input.num_columns());
+
+    for (std::size_t idx = 0; idx < num_partitions; idx++) {
+      // traverse the buffers and build the columns.
+      cur_dst_buf_info = build_output_columns(input.begin(),
+                                              input.end(),
+                                              cur_dst_buf_info,
+                                              std::back_inserter(cols),
+                                              h_dst_bufs[idx],
+                                              mb);
+
+      // pack the columns
+      result.emplace_back(packed_table{
+        cudf::table_view{cols},
+        packed_columns{std::make_unique<std::vector<uint8_t>>(mb.build()),
+                       std::make_unique<rmm::device_buffer>(std::move(out_buffers[idx]))}});
+
+      cols.clear();
+      mb.clear();
+    }
+
+    return result;
+  }
+
+  std::vector<packed_table> make_empty_packed_table()
+  {
+    // sanitize the inputs (to handle corner cases like sliced tables)
+    std::vector<cudf::column_view> empty_column_views;
+    empty_column_views.reserve(input.num_columns());
+    std::transform(input.begin(),
+                   input.end(),
+                   std::back_inserter(empty_column_views),
+                   [](column_view const& col) { return cudf::empty_like(col)->view(); });
+
+    table_view empty_inputs(empty_column_views);
+
+    // build the empty results
+    std::vector<packed_table> result;
+    result.reserve(num_partitions);
+    auto const iter = thrust::make_counting_iterator(0);
+    std::transform(iter,
+                   iter + num_partitions,
+                   std::back_inserter(result),
+                   [&empty_inputs](int partition_index) {
+                     return packed_table{empty_inputs,
+                                         packed_columns{std::make_unique<std::vector<uint8_t>>(
+                                                          pack_metadata(empty_inputs, nullptr, 0)),
+                                                        std::make_unique<rmm::device_buffer>()}};
+                   });
+
+    return result;
+  }
+
+  cudf::table_view const input;        ///< The input table_view to operate on
+  std::size_t const user_buffer_size;  ///< The size of the user buffer for the chunked_pack case
+  rmm::cuda_stream_view const stream;
+  rmm::mr::device_memory_resource* const mr;  ///< The memory resource for any data returned
+
+  // this resource defaults to `mr` for the contiguous_split case, but it can be useful for the
+  // `chunked_pack` case to allocate scratch/temp memory in a pool
+  rmm::mr::device_memory_resource* const temp_mr;  ///< The memory resource for scratch/temp space
+
+  // whether the table was empty to begin with (0 rows or 0 columns) and should be metadata-only
+  bool const is_empty;  ///< True if the source table has 0 rows or 0 columns
+
+  // This can be 1 if `contiguous_split` is just packing and not splitting
+  std::size_t const num_partitions;  ///< The number of partitions to produce
+
+  size_type const num_src_bufs;  ///< Number of source buffers including children
+
+  std::size_t const num_bufs;  ///< Number of source buffers including children * number of splits
+
+  std::unique_ptr<packed_partition_buf_size_and_dst_buf_info>
+    partition_buf_size_and_dst_buf_info;  ///< Per-partition buffer size and destination buffer info
+
+  std::unique_ptr<packed_src_and_dst_pointers>
+    src_and_dst_pointers;  ///< Src. and dst. pointers for `copy_partition`
+
+  //
+  // State around the chunked pattern
+  //
+
+  // chunked_pack will have 1 or more "chunks" to iterate on, defined in chunk_iter_state
+  // contiguous_split will have a single "chunk" in chunk_iter_state, so no iteration.
+  std::unique_ptr<chunk_iteration_state>
+    chunk_iter_state;  ///< State object for chunk iteration state
+
+  // Two API usages are allowed:
+  //  - `chunked_pack`: for this mode, the user will provide a buffer that must be at least 1MB.
+  //    The behavior is "chunked" in that it will contiguously copy up until the user specified
+  //    `user_buffer_size` limit, exposing a next() call for the user to invoke. Note that in this
+  //    mode, no partitioning occurs, hence the name "pack".
+  //
+  //  - `contiguous_split` (default): when the user doesn't provide their own buffer,
+  //    `contiguous_split` will allocate a buffer per partition and will place contiguous results in
+  //    each buffer.
+  //
+  std::vector<rmm::device_buffer>
+    out_buffers;  ///< Buffers allocated for a regular `contiguous_split`
+};
+
+std::vector<packed_table> contiguous_split(cudf::table_view const& input,
+                                           std::vector<size_type> const& splits,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  // `temp_mr` is the same as `mr` for contiguous_split as it allocates all
+  // of its memory from the default memory resource in cuDF
+  auto temp_mr = mr;
+  auto state   = contiguous_split_state(input, splits, stream, mr, temp_mr);
+  return state.contiguous_split();
+}
+
+};  // namespace detail
+
+std::vector<packed_table> contiguous_split(cudf::table_view const& input,
+                                           std::vector<size_type> const& splits,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contiguous_split(input, splits, cudf::get_default_stream(), mr);
+}
+
+chunked_pack::chunked_pack(cudf::table_view const& input,
+                           std::size_t user_buffer_size,
+                           rmm::mr::device_memory_resource* temp_mr)
+{
+  CUDF_EXPECTS(user_buffer_size >= desired_batch_size,
+               "The output buffer size must be at least 1MB in size");
+  // We pass `nullptr` for the first `mr` in `contiguous_split_state` to indicate
+  // that it does not allocate any user-bound data for the `chunked_pack` case.
+  state = std::make_unique<detail::contiguous_split_state>(
+    input, user_buffer_size, cudf::get_default_stream(), nullptr, temp_mr);
+}
+
+// required for the unique_ptr to work with a incomplete type (contiguous_split_state)
+chunked_pack::~chunked_pack() = default;
+
+std::size_t chunked_pack::get_total_contiguous_size() const
+{
+  return state->get_total_contiguous_size();
+}
+
+bool chunked_pack::has_next() const { return state->has_next(); }
+
+std::size_t chunked_pack::next(cudf::device_span<uint8_t> const& user_buffer)
+{
+  return state->contiguous_split_chunk(user_buffer);
+}
+
+std::unique_ptr<std::vector<uint8_t>> chunked_pack::build_metadata() const
+{
+  return state->build_packed_column_metadata();
+}
+
+std::unique_ptr<chunked_pack> chunked_pack::create(cudf::table_view const& input,
+                                                   std::size_t user_buffer_size,
+                                                   rmm::mr::device_memory_resource* temp_mr)
+{
+  return std::make_unique<chunked_pack>(input, user_buffer_size, temp_mr);
+}
+
+};  // namespace cudf
diff --git a/cpp/src/copying/copy.cpp b/cpp/src/copying/copy.cpp
new file mode 100644
index 0000000..490a1cc
--- /dev/null
+++ b/cpp/src/copying/copy.cpp
@@ -0,0 +1,195 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+inline mask_state should_allocate_mask(mask_allocation_policy mask_alloc, bool mask_exists)
+{
+  if ((mask_alloc == mask_allocation_policy::ALWAYS) ||
+      (mask_alloc == mask_allocation_policy::RETAIN && mask_exists)) {
+    return mask_state::UNINITIALIZED;
+  } else {
+    return mask_state::UNALLOCATED;
+  }
+}
+
+/**
+ * @brief Functor to produce an empty column of the same type as the
+ * input scalar.
+ *
+ * In the case of nested types, full column hierarchy is preserved.
+ */
+template <typename T>
+struct scalar_empty_like_functor_impl {
+  std::unique_ptr<column> operator()(scalar const& input)
+  {
+    return cudf::make_empty_column(input.type());
+  }
+};
+
+template <>
+struct scalar_empty_like_functor_impl<cudf::list_view> {
+  std::unique_ptr<column> operator()(scalar const& input)
+  {
+    auto ls = static_cast<list_scalar const*>(&input);
+
+    // TODO:  add a manual constructor for lists_column_view.
+    column_view offsets{cudf::data_type{cudf::type_id::INT32}, 0, nullptr, nullptr, 0};
+    std::vector<column_view> children;
+    children.push_back(offsets);
+    children.push_back(ls->view());
+    column_view lcv{cudf::data_type{cudf::type_id::LIST}, 0, nullptr, nullptr, 0, 0, children};
+
+    return empty_like(lcv);
+  }
+};
+
+template <>
+struct scalar_empty_like_functor_impl<cudf::struct_view> {
+  std::unique_ptr<column> operator()(scalar const& input)
+  {
+    auto ss = static_cast<struct_scalar const*>(&input);
+
+    // TODO: add a manual constructor for structs_column_view
+    // TODO: add cudf::get_element() support for structs
+    cudf::table_view tbl = ss->view();
+    std::vector<column_view> children(tbl.begin(), tbl.end());
+    column_view scv{cudf::data_type{cudf::type_id::STRUCT}, 0, nullptr, nullptr, 0, 0, children};
+
+    return empty_like(scv);
+  }
+};
+
+template <>
+struct scalar_empty_like_functor_impl<cudf::dictionary32> {
+  std::unique_ptr<column> operator()(scalar const& input)
+  {
+    CUDF_FAIL("Dictionary scalars not supported");
+  }
+};
+
+struct scalar_empty_like_functor {
+  template <typename T>
+  std::unique_ptr<column> operator()(scalar const& input)
+  {
+    scalar_empty_like_functor_impl<T> func;
+    return func(input);
+  }
+};
+
+}  // namespace
+
+/*
+ * Creates an uninitialized new column of the specified size and same type as
+ * the `input`. Supports only fixed-width types.
+ */
+std::unique_ptr<column> allocate_like(column_view const& input,
+                                      size_type size,
+                                      mask_allocation_policy mask_alloc,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(is_fixed_width(input.type()), "Expects only fixed-width type column");
+  mask_state allocate_mask = should_allocate_mask(mask_alloc, input.nullable());
+
+  return std::make_unique<column>(input.type(),
+                                  size,
+                                  rmm::device_buffer(size * size_of(input.type()), stream, mr),
+                                  detail::create_null_mask(size, allocate_mask, stream, mr),
+                                  0);
+}
+
+}  // namespace detail
+
+/*
+ * Initializes and returns an empty column of the same type as the `input`.
+ */
+std::unique_ptr<column> empty_like(column_view const& input)
+{
+  CUDF_FUNC_RANGE();
+
+  std::vector<std::unique_ptr<column>> children;
+  std::transform(input.child_begin(),
+                 input.child_end(),
+                 std::back_inserter(children),
+                 [](column_view const& col) { return empty_like(col); });
+
+  return std::make_unique<cudf::column>(
+    input.type(), 0, rmm::device_buffer{}, rmm::device_buffer{}, 0, std::move(children));
+}
+
+/*
+ * Initializes and returns an empty column of the same type as the `input`.
+ */
+std::unique_ptr<column> empty_like(scalar const& input)
+{
+  CUDF_FUNC_RANGE();
+  return type_dispatcher(input.type(), detail::scalar_empty_like_functor{}, input);
+};
+
+/*
+ * Creates a table of empty columns with the same types as the `input_table`
+ */
+std::unique_ptr<table> empty_like(table_view const& input_table)
+{
+  CUDF_FUNC_RANGE();
+  std::vector<std::unique_ptr<column>> columns(input_table.num_columns());
+  std::transform(input_table.begin(), input_table.end(), columns.begin(), [&](column_view in_col) {
+    return empty_like(in_col);
+  });
+  return std::make_unique<table>(std::move(columns));
+}
+
+std::unique_ptr<column> allocate_like(column_view const& input,
+                                      mask_allocation_policy mask_alloc,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::allocate_like(input, input.size(), mask_alloc, stream, mr);
+}
+
+std::unique_ptr<column> allocate_like(column_view const& input,
+                                      size_type size,
+                                      mask_allocation_policy mask_alloc,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::allocate_like(input, size, mask_alloc, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/copy.cu b/cpp/src/copying/copy.cu
new file mode 100644
index 0000000..6b7fae3
--- /dev/null
+++ b/cpp/src/copying/copy.cu
@@ -0,0 +1,451 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/copy_if_else.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/scatter.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/copy_if_else.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+template <typename T, typename Enable = void>
+struct copy_if_else_functor_impl {
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type for copy_if_else.");
+  }
+};
+
+/**
+ * @brief Functor to fetch a device-view for the specified scalar/column_view.
+ */
+struct get_iterable_device_view {
+  template <typename T, CUDF_ENABLE_IF(std::is_same_v<T, cudf::column_view>)>
+  auto operator()(T const& input, rmm::cuda_stream_view stream)
+  {
+    return cudf::column_device_view::create(input, stream);
+  }
+
+  template <typename T, CUDF_ENABLE_IF(std::is_same_v<T, cudf::scalar>)>
+  auto operator()(T const& input, rmm::cuda_stream_view)
+  {
+    return &input;
+  }
+};
+
+template <typename T>
+struct copy_if_else_functor_impl<T, std::enable_if_t<is_rep_layout_compatible<T>()>> {
+  template <typename Left, typename Right, typename Filter>
+  std::unique_ptr<column> operator()(Left const& lhs_h,
+                                     Right const& rhs_h,
+                                     size_type size,
+                                     bool left_nullable,
+                                     bool right_nullable,
+                                     Filter filter,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto p_lhs      = get_iterable_device_view{}(lhs_h, stream);
+    auto p_rhs      = get_iterable_device_view{}(rhs_h, stream);
+    auto const& lhs = *p_lhs;
+    auto const& rhs = *p_rhs;
+
+    auto lhs_iter = cudf::detail::make_optional_iterator<T>(lhs, nullate::DYNAMIC{left_nullable});
+    auto rhs_iter = cudf::detail::make_optional_iterator<T>(rhs, nullate::DYNAMIC{right_nullable});
+    return detail::copy_if_else(left_nullable || right_nullable,
+                                lhs_iter,
+                                lhs_iter + size,
+                                rhs_iter,
+                                filter,
+                                lhs.type(),
+                                stream,
+                                mr);
+  }
+};
+
+/**
+ * @brief Specialization of copy_if_else_functor for string_views.
+ */
+template <>
+struct copy_if_else_functor_impl<string_view> {
+  template <typename Left, typename Right, typename Filter>
+  std::unique_ptr<column> operator()(Left const& lhs_h,
+                                     Right const& rhs_h,
+                                     size_type size,
+                                     bool left_nullable,
+                                     bool right_nullable,
+                                     Filter filter,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    using T = string_view;
+
+    auto p_lhs      = get_iterable_device_view{}(lhs_h, stream);
+    auto p_rhs      = get_iterable_device_view{}(rhs_h, stream);
+    auto const& lhs = *p_lhs;
+    auto const& rhs = *p_rhs;
+
+    auto lhs_iter = cudf::detail::make_optional_iterator<T>(lhs, nullate::DYNAMIC{left_nullable});
+    auto rhs_iter = cudf::detail::make_optional_iterator<T>(rhs, nullate::DYNAMIC{right_nullable});
+    return strings::detail::copy_if_else(lhs_iter, lhs_iter + size, rhs_iter, filter, stream, mr);
+  }
+};
+
+/**
+ * @brief Adapter to negate predicates.
+ */
+template <typename Predicate>
+class logical_not {
+ public:
+  explicit logical_not(Predicate predicate) : _pred{predicate} {}
+  bool __device__ operator()(size_type i) const { return not _pred(i); }
+
+ private:
+  Predicate _pred;
+};
+
+/**
+ * @brief Implementation of copy_if_else() with gather()/scatter().
+ *
+ * Handles nested-typed column views. Uses the iterator `is_left` to decide what row to pick for
+ * the output column.
+ *
+ * Uses `rhs` as the destination for scatter. First gathers indices of rows to copy from lhs.
+ *
+ * @tparam Filter Bool iterator producing `true` for indices of output rows to copy from `lhs` and
+ * `false` for indices of output rows to copy from `rhs`
+ * @param lhs Left-hand side input column view
+ * @param rhs Right-hand side input column view
+ * @param size The size of the output column, inputs rows are iterated from 0 to `size - 1`
+ * @param is_left Predicate for picking rows from `lhs` on `true` or `rhs` on `false`
+ * @param stream The stream on which to perform the allocation
+ * @param mr The resource used to allocate the device storage
+ * @return Column with rows populated according to the `is_left` predicate
+ */
+template <typename Filter>
+std::unique_ptr<column> scatter_gather_based_if_else(cudf::column_view const& lhs,
+                                                     cudf::column_view const& rhs,
+                                                     size_type size,
+                                                     Filter is_left,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  auto gather_map = rmm::device_uvector<size_type>{static_cast<std::size_t>(size), stream};
+  auto const gather_map_end = thrust::copy_if(rmm::exec_policy(stream),
+                                              thrust::make_counting_iterator(size_type{0}),
+                                              thrust::make_counting_iterator(size_type{size}),
+                                              gather_map.begin(),
+                                              is_left);
+
+  gather_map.resize(thrust::distance(gather_map.begin(), gather_map_end), stream);
+
+  auto const scatter_src_lhs = cudf::detail::gather(table_view{std::vector<column_view>{lhs}},
+                                                    gather_map,
+                                                    out_of_bounds_policy::DONT_CHECK,
+                                                    negative_index_policy::NOT_ALLOWED,
+                                                    stream,
+                                                    rmm::mr::get_current_device_resource());
+
+  auto result = cudf::detail::scatter(
+    table_view{std::vector<column_view>{scatter_src_lhs->get_column(0).view()}},
+    gather_map,
+    table_view{std::vector<column_view>{rhs}},
+    stream,
+    mr);
+
+  return std::move(result->release()[0]);
+}
+
+template <typename Filter>
+std::unique_ptr<column> scatter_gather_based_if_else(cudf::scalar const& lhs,
+                                                     cudf::column_view const& rhs,
+                                                     size_type size,
+                                                     Filter is_left,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  auto scatter_map = rmm::device_uvector<size_type>{static_cast<std::size_t>(size), stream};
+  auto const scatter_map_end = thrust::copy_if(rmm::exec_policy(stream),
+                                               thrust::make_counting_iterator(size_type{0}),
+                                               thrust::make_counting_iterator(size_type{size}),
+                                               scatter_map.begin(),
+                                               is_left);
+
+  auto const scatter_map_size  = std::distance(scatter_map.begin(), scatter_map_end);
+  auto scatter_source          = std::vector<std::reference_wrapper<scalar const>>{std::ref(lhs)};
+  auto scatter_map_column_view = cudf::column_view{cudf::data_type{cudf::type_id::INT32},
+                                                   static_cast<cudf::size_type>(scatter_map_size),
+                                                   scatter_map.begin(),
+                                                   nullptr,
+                                                   0};
+
+  auto result = cudf::detail::scatter(
+    scatter_source, scatter_map_column_view, table_view{std::vector<column_view>{rhs}}, stream, mr);
+
+  return std::move(result->release()[0]);
+}
+
+template <typename Filter>
+std::unique_ptr<column> scatter_gather_based_if_else(cudf::column_view const& lhs,
+                                                     cudf::scalar const& rhs,
+                                                     size_type size,
+                                                     Filter is_left,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  return scatter_gather_based_if_else(rhs, lhs, size, logical_not{is_left}, stream, mr);
+}
+
+template <typename Filter>
+std::unique_ptr<column> scatter_gather_based_if_else(cudf::scalar const& lhs,
+                                                     cudf::scalar const& rhs,
+                                                     size_type size,
+                                                     Filter is_left,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  auto rhs_col = cudf::make_column_from_scalar(rhs, size, stream, mr);
+  return scatter_gather_based_if_else(lhs, rhs_col->view(), size, is_left, stream, mr);
+}
+
+template <>
+struct copy_if_else_functor_impl<struct_view> {
+  template <typename Left, typename Right, typename Filter>
+  std::unique_ptr<column> operator()(Left const& lhs,
+                                     Right const& rhs,
+                                     size_type size,
+                                     bool,
+                                     bool,
+                                     Filter filter,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return scatter_gather_based_if_else(lhs, rhs, size, filter, stream, mr);
+  }
+};
+
+template <>
+struct copy_if_else_functor_impl<list_view> {
+  template <typename Left, typename Right, typename Filter>
+  std::unique_ptr<column> operator()(Left const& lhs,
+                                     Right const& rhs,
+                                     size_type size,
+                                     bool,
+                                     bool,
+                                     Filter filter,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return scatter_gather_based_if_else(lhs, rhs, size, filter, stream, mr);
+  }
+};
+
+template <>
+struct copy_if_else_functor_impl<dictionary32> {
+  template <typename Left, typename Right, typename Filter>
+  std::unique_ptr<column> operator()(Left const& lhs,
+                                     Right const& rhs,
+                                     size_type size,
+                                     bool,
+                                     bool,
+                                     Filter filter,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return scatter_gather_based_if_else(lhs, rhs, size, filter, stream, mr);
+  }
+};
+
+/**
+ * @brief Functor called by the `type_dispatcher` to invoke copy_if_else on combinations
+ *        of column_view and scalar
+ */
+struct copy_if_else_functor {
+  template <typename T, typename Left, typename Right, typename Filter>
+  std::unique_ptr<column> operator()(Left const& lhs,
+                                     Right const& rhs,
+                                     size_type size,
+                                     bool left_nullable,
+                                     bool right_nullable,
+                                     Filter filter,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    copy_if_else_functor_impl<T> copier{};
+    return copier(lhs, rhs, size, left_nullable, right_nullable, filter, stream, mr);
+  }
+};
+
+// wrap up boolean_mask into a filter lambda
+template <typename Left, typename Right>
+std::unique_ptr<column> copy_if_else(Left const& lhs,
+                                     Right const& rhs,
+                                     bool left_nullable,
+                                     bool right_nullable,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(boolean_mask.type() == data_type(type_id::BOOL8),
+               "Boolean mask column must be of type type_id::BOOL8");
+
+  if (boolean_mask.is_empty()) { return cudf::empty_like(lhs); }
+
+  auto bool_mask_device_p             = column_device_view::create(boolean_mask, stream);
+  column_device_view bool_mask_device = *bool_mask_device_p;
+
+  auto const has_nulls = boolean_mask.has_nulls();
+  auto filter          = [bool_mask_device, has_nulls] __device__(cudf::size_type i) {
+    return (!has_nulls || bool_mask_device.is_valid_nocheck(i)) and
+           bool_mask_device.element<bool>(i);
+  };
+
+  // always dispatch on dictionary-type if either input is a dictionary
+  auto dispatch_type = cudf::is_dictionary(rhs.type()) ? rhs.type() : lhs.type();
+
+  return cudf::type_dispatcher<dispatch_storage_type>(dispatch_type,
+                                                      copy_if_else_functor{},
+                                                      lhs,
+                                                      rhs,
+                                                      boolean_mask.size(),
+                                                      left_nullable,
+                                                      right_nullable,
+                                                      filter,
+                                                      stream,
+                                                      mr);
+}
+
+};  // namespace
+
+std::unique_ptr<column> copy_if_else(column_view const& lhs,
+                                     column_view const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(boolean_mask.size() == lhs.size(),
+               "Boolean mask column must be the same size as lhs and rhs columns");
+  CUDF_EXPECTS(lhs.size() == rhs.size(), "Both columns must be of the size");
+  CUDF_EXPECTS(lhs.type() == rhs.type(), "Both inputs must be of the same type");
+
+  return copy_if_else(lhs, rhs, lhs.has_nulls(), rhs.has_nulls(), boolean_mask, stream, mr);
+}
+
+std::unique_ptr<column> copy_if_else(scalar const& lhs,
+                                     column_view const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(boolean_mask.size() == rhs.size(),
+               "Boolean mask column must be the same size as rhs column");
+
+  auto rhs_type =
+    cudf::is_dictionary(rhs.type()) ? cudf::dictionary_column_view(rhs).keys_type() : rhs.type();
+  CUDF_EXPECTS(lhs.type() == rhs_type, "Both inputs must be of the same type");
+
+  return copy_if_else(lhs, rhs, !lhs.is_valid(stream), rhs.has_nulls(), boolean_mask, stream, mr);
+}
+
+std::unique_ptr<column> copy_if_else(column_view const& lhs,
+                                     scalar const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(boolean_mask.size() == lhs.size(),
+               "Boolean mask column must be the same size as lhs column");
+
+  auto lhs_type =
+    cudf::is_dictionary(lhs.type()) ? cudf::dictionary_column_view(lhs).keys_type() : lhs.type();
+  CUDF_EXPECTS(lhs_type == rhs.type(), "Both inputs must be of the same type");
+
+  return copy_if_else(lhs, rhs, lhs.has_nulls(), !rhs.is_valid(stream), boolean_mask, stream, mr);
+}
+
+std::unique_ptr<column> copy_if_else(scalar const& lhs,
+                                     scalar const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(lhs.type() == rhs.type(), "Both inputs must be of the same type");
+  return copy_if_else(
+    lhs, rhs, !lhs.is_valid(stream), !rhs.is_valid(stream), boolean_mask, stream, mr);
+}
+
+};  // namespace detail
+
+std::unique_ptr<column> copy_if_else(column_view const& lhs,
+                                     column_view const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::copy_if_else(lhs, rhs, boolean_mask, stream, mr);
+}
+
+std::unique_ptr<column> copy_if_else(scalar const& lhs,
+                                     column_view const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::copy_if_else(lhs, rhs, boolean_mask, stream, mr);
+}
+
+std::unique_ptr<column> copy_if_else(column_view const& lhs,
+                                     scalar const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::copy_if_else(lhs, rhs, boolean_mask, stream, mr);
+}
+
+std::unique_ptr<column> copy_if_else(scalar const& lhs,
+                                     scalar const& rhs,
+                                     column_view const& boolean_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::copy_if_else(lhs, rhs, boolean_mask, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/copy_range.cu b/cpp/src/copying/copy_range.cu
new file mode 100644
index 0000000..af25385
--- /dev/null
+++ b/cpp/src/copying/copy_range.cu
@@ -0,0 +1,295 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy_range.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/strings/detail/copy_range.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+
+#include <memory>
+
+namespace {
+template <typename T>
+void in_place_copy_range(cudf::column_view const& source,
+                         cudf::mutable_column_view& target,
+                         cudf::size_type source_begin,
+                         cudf::size_type source_end,
+                         cudf::size_type target_begin,
+                         rmm::cuda_stream_view stream)
+{
+  auto p_source_device_view = cudf::column_device_view::create(source, stream);
+  if (source.has_nulls()) {
+    cudf::detail::copy_range(
+      cudf::detail::make_null_replacement_iterator<T>(*p_source_device_view, T()) + source_begin,
+      cudf::detail::make_validity_iterator(*p_source_device_view) + source_begin,
+      target,
+      target_begin,
+      target_begin + (source_end - source_begin),
+      stream);
+  } else {
+    cudf::detail::copy_range(p_source_device_view->begin<T>() + source_begin,
+                             thrust::make_constant_iterator(true),  // dummy
+                             target,
+                             target_begin,
+                             target_begin + (source_end - source_begin),
+                             stream);
+  }
+}
+
+struct in_place_copy_range_dispatch {
+  cudf::column_view const& source;
+  cudf::mutable_column_view& target;
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_rep_layout_compatible<T>())>
+  void operator()(cudf::size_type source_begin,
+                  cudf::size_type source_end,
+                  cudf::size_type target_begin,
+                  rmm::cuda_stream_view stream)
+  {
+    in_place_copy_range<T>(source, target, source_begin, source_end, target_begin, stream);
+  }
+
+  template <typename T, typename... Args>
+  void operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type for in-place copy.");
+  }
+};
+
+struct out_of_place_copy_range_dispatch {
+  cudf::column_view const& source;
+  cudf::column_view const& target;
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_rep_layout_compatible<T>())>
+  std::unique_ptr<cudf::column> operator()(
+    cudf::size_type source_begin,
+    cudf::size_type source_end,
+    cudf::size_type target_begin,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+  {
+    auto p_ret = std::make_unique<cudf::column>(target, stream, mr);
+    if ((!p_ret->nullable()) && source.has_nulls(source_begin, source_end)) {
+      p_ret->set_null_mask(
+        cudf::detail::create_null_mask(p_ret->size(), cudf::mask_state::ALL_VALID, stream, mr), 0);
+    }
+
+    if (source_end != source_begin) {  // otherwise no-op
+      auto ret_view = p_ret->mutable_view();
+      in_place_copy_range<T>(source, ret_view, source_begin, source_end, target_begin, stream);
+      p_ret->set_null_count(ret_view.null_count());
+    }
+
+    return p_ret;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_rep_layout_compatible<T>(), std::unique_ptr<cudf::column>>
+  operator()(Args...)
+  {
+    CUDF_FAIL("Unsupported type for out of place copy.");
+  }
+};
+
+template <>
+std::unique_ptr<cudf::column> out_of_place_copy_range_dispatch::operator()<cudf::string_view>(
+  cudf::size_type source_begin,
+  cudf::size_type source_end,
+  cudf::size_type target_begin,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto target_end           = target_begin + (source_end - source_begin);
+  auto p_source_device_view = cudf::column_device_view::create(source, stream);
+  if (source.has_nulls()) {
+    return cudf::strings::detail::copy_range(
+      cudf::detail::make_null_replacement_iterator<cudf::string_view>(*p_source_device_view,
+                                                                      cudf::string_view()) +
+        source_begin,
+      cudf::detail::make_validity_iterator(*p_source_device_view) + source_begin,
+      cudf::strings_column_view(target),
+      target_begin,
+      target_end,
+      stream,
+      mr);
+  } else {
+    return cudf::strings::detail::copy_range(
+      p_source_device_view->begin<cudf::string_view>() + source_begin,
+      thrust::make_constant_iterator(true),
+      cudf::strings_column_view(target),
+      target_begin,
+      target_end,
+      stream,
+      mr);
+  }
+}
+
+template <>
+std::unique_ptr<cudf::column> out_of_place_copy_range_dispatch::operator()<cudf::dictionary32>(
+  cudf::size_type source_begin,
+  cudf::size_type source_end,
+  cudf::size_type target_begin,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // check the keys in the source and target
+  cudf::dictionary_column_view const dict_source(source);
+  cudf::dictionary_column_view const dict_target(target);
+  CUDF_EXPECTS(dict_source.keys().type() == dict_target.keys().type(),
+               "dictionary keys must be the same type");
+
+  // combine keys so both dictionaries have the same set
+  auto target_matched =
+    cudf::dictionary::detail::add_keys(dict_target, dict_source.keys(), stream, mr);
+  auto const target_view = cudf::dictionary_column_view(target_matched->view());
+  auto source_matched    = cudf::dictionary::detail::set_keys(
+    dict_source, target_view.keys(), stream, rmm::mr::get_current_device_resource());
+  auto const source_view = cudf::dictionary_column_view(source_matched->view());
+
+  // build the new indices by calling in_place_copy_range on just the indices
+  auto const source_indices = source_view.get_indices_annotated();
+  auto target_contents      = target_matched->release();
+  auto target_indices(std::move(target_contents.children.front()));
+  cudf::mutable_column_view new_indices(
+    target_indices->type(),
+    dict_target.size(),
+    target_indices->mutable_view().head(),
+    static_cast<cudf::bitmask_type*>(target_contents.null_mask->data()),
+    dict_target.null_count());
+  cudf::type_dispatcher(new_indices.type(),
+                        in_place_copy_range_dispatch{source_indices, new_indices},
+                        source_begin,
+                        source_end,
+                        target_begin,
+                        stream);
+  auto null_count = new_indices.null_count();
+  auto indices_column =
+    std::make_unique<cudf::column>(new_indices.type(),
+                                   new_indices.size(),
+                                   std::move(*(target_indices->release().data.release())),
+                                   rmm::device_buffer{0, stream, mr},
+                                   0);
+
+  // take the keys from the matched column allocated using mr
+  auto keys_column(std::move(target_contents.children.back()));
+
+  // create column with keys_column and indices_column
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                std::move(*(target_contents.null_mask.release())),
+                                null_count);
+}
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+void copy_range_in_place(column_view const& source,
+                         mutable_column_view& target,
+                         size_type source_begin,
+                         size_type source_end,
+                         size_type target_begin,
+                         rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(cudf::is_fixed_width(target.type()),
+               "In-place copy_range does not support variable-sized types.");
+  CUDF_EXPECTS((source_begin >= 0) && (source_end <= source.size()) &&
+                 (source_begin <= source_end) && (target_begin >= 0) &&
+                 (target_begin <= target.size() - (source_end - source_begin)),
+               "Range is out of bounds.");
+  CUDF_EXPECTS(target.type() == source.type(), "Data type mismatch.");
+  CUDF_EXPECTS(target.nullable() || not source.has_nulls(),
+               "target should be nullable if source has null values.");
+
+  if (source_end != source_begin) {  // otherwise no-op
+    cudf::type_dispatcher<dispatch_storage_type>(target.type(),
+                                                 in_place_copy_range_dispatch{source, target},
+                                                 source_begin,
+                                                 source_end,
+                                                 target_begin,
+                                                 stream);
+  }
+}
+
+std::unique_ptr<column> copy_range(column_view const& source,
+                                   column_view const& target,
+                                   size_type source_begin,
+                                   size_type source_end,
+                                   size_type target_begin,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS((source_begin >= 0) && (source_end <= source.size()) &&
+                 (source_begin <= source_end) && (target_begin >= 0) &&
+                 (target_begin <= target.size() - (source_end - source_begin)),
+               "Range is out of bounds.");
+  CUDF_EXPECTS(target.type() == source.type(), "Data type mismatch.");
+
+  return cudf::type_dispatcher<dispatch_storage_type>(
+    target.type(),
+    out_of_place_copy_range_dispatch{source, target},
+    source_begin,
+    source_end,
+    target_begin,
+    stream,
+    mr);
+}
+
+}  // namespace detail
+
+void copy_range_in_place(column_view const& source,
+                         mutable_column_view& target,
+                         size_type source_begin,
+                         size_type source_end,
+                         size_type target_begin,
+                         rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::copy_range_in_place(
+    source, target, source_begin, source_end, target_begin, stream);
+}
+
+std::unique_ptr<column> copy_range(column_view const& source,
+                                   column_view const& target,
+                                   size_type source_begin,
+                                   size_type source_end,
+                                   size_type target_begin,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::copy_range(source, target, source_begin, source_end, target_begin, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/gather.cu b/cpp/src/copying/gather.cu
new file mode 100644
index 0000000..267c715
--- /dev/null
+++ b/cpp/src/copying/gather.cu
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace detail {
+
+std::unique_ptr<table> gather(table_view const& source_table,
+                              column_view const& gather_map,
+                              out_of_bounds_policy bounds_policy,
+                              negative_index_policy neg_indices,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(not gather_map.has_nulls(), "gather_map contains nulls");
+
+  // create index type normalizing iterator for the gather_map
+  auto map_begin = indexalator_factory::make_input_iterator(gather_map);
+  auto map_end   = map_begin + gather_map.size();
+
+  if (neg_indices == negative_index_policy::ALLOWED) {
+    cudf::size_type n_rows = source_table.num_rows();
+    auto idx_converter = [n_rows] __device__(size_type in) { return in < 0 ? in + n_rows : in; };
+    return gather(source_table,
+                  thrust::make_transform_iterator(map_begin, idx_converter),
+                  thrust::make_transform_iterator(map_end, idx_converter),
+                  bounds_policy,
+                  stream,
+                  mr);
+  }
+  return gather(source_table, map_begin, map_end, bounds_policy, stream, mr);
+}
+
+std::unique_ptr<table> gather(table_view const& source_table,
+                              device_span<size_type const> const gather_map,
+                              out_of_bounds_policy bounds_policy,
+                              negative_index_policy neg_indices,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(gather_map.size() <= static_cast<size_t>(std::numeric_limits<size_type>::max()),
+               "gather map size exceeds the column size limit",
+               std::overflow_error);
+  auto map_col = column_view(data_type{type_to_id<size_type>()},
+                             static_cast<size_type>(gather_map.size()),
+                             gather_map.data(),
+                             nullptr,
+                             0);
+  return gather(source_table, map_col, bounds_policy, neg_indices, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> gather(table_view const& source_table,
+                              column_view const& gather_map,
+                              out_of_bounds_policy bounds_policy,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  auto index_policy = is_unsigned(gather_map.type()) ? detail::negative_index_policy::NOT_ALLOWED
+                                                     : detail::negative_index_policy::ALLOWED;
+
+  return detail::gather(source_table, gather_map, bounds_policy, index_policy, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/get_element.cu b/cpp/src/copying/get_element.cu
new file mode 100644
index 0000000..a3f9be0
--- /dev/null
+++ b/cpp/src/copying/get_element.cu
@@ -0,0 +1,211 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/is_element_valid.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/lists/detail/copying.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+struct get_element_functor {
+  template <typename T, std::enable_if_t<is_fixed_width<T>() && !is_fixed_point<T>()>* p = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     size_type index,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto s = make_fixed_width_scalar(data_type(type_to_id<T>()), stream, mr);
+
+    using ScalarType = cudf::scalar_type_t<T>;
+    auto typed_s     = static_cast<ScalarType*>(s.get());
+
+    auto device_s   = get_scalar_device_view(*typed_s);
+    auto device_col = column_device_view::create(input, stream);
+
+    device_single_thread(
+      [device_s, d_col = *device_col, index] __device__() mutable {
+        device_s.set_value(d_col.element<T>(index));
+        device_s.set_valid(d_col.is_valid(index));
+      },
+      stream);
+    return s;
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, string_view>>* p = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     size_type index,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto device_col = column_device_view::create(input, stream);
+
+    rmm::device_scalar<string_view> temp_data(stream, mr);
+    rmm::device_scalar<bool> temp_valid(stream, mr);
+
+    device_single_thread(
+      [buffer   = temp_data.data(),
+       validity = temp_valid.data(),
+       d_col    = *device_col,
+       index] __device__() mutable {
+        *buffer   = d_col.element<string_view>(index);
+        *validity = d_col.is_valid(index);
+      },
+      stream);
+
+    return std::make_unique<string_scalar>(temp_data, temp_valid.value(stream), stream, mr);
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, dictionary32>>* p = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     size_type index,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto dict_view    = dictionary_column_view(input);
+    auto indices_iter = detail::indexalator_factory::make_input_iterator(dict_view.indices());
+    numeric_scalar<size_type> key_index_scalar{index, true, stream};
+    auto d_key_index = get_scalar_device_view(key_index_scalar);
+    auto d_col       = column_device_view::create(input, stream);
+
+    // retrieve the indices value at index
+    device_single_thread(
+      [d_key_index, d_col = *d_col, indices_iter, index] __device__() mutable {
+        d_key_index.set_value(indices_iter[index]);
+        d_key_index.set_valid(d_col.is_valid(index));
+      },
+      stream);
+
+    if (!key_index_scalar.is_valid(stream)) {
+      auto null_result = make_default_constructed_scalar(dict_view.keys().type(), stream, mr);
+      null_result->set_valid_async(false, stream);
+      return null_result;
+    }
+
+    // retrieve the key element using the key-index
+    return type_dispatcher(dict_view.keys().type(),
+                           get_element_functor{},
+                           dict_view.keys(),
+                           key_index_scalar.value(stream),
+                           stream,
+                           mr);
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, list_view>>* p = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     size_type index,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    bool valid               = is_element_valid_sync(input, index, stream);
+    auto const child_col_idx = lists_column_view::child_column_index;
+
+    if (valid) {
+      lists_column_view lcv(input);
+      // Make a copy of the row
+      auto row_slice_contents =
+        lists::detail::copy_slice(lcv, index, index + 1, stream, mr)->release();
+      // Construct scalar with row data
+      return std::make_unique<list_scalar>(
+        std::move(*row_slice_contents.children[child_col_idx]), valid, stream, mr);
+    } else {
+      auto empty_row_contents = empty_like(input)->release();
+      return std::make_unique<list_scalar>(
+        std::move(*empty_row_contents.children[child_col_idx]), valid, stream, mr);
+    }
+  }
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* p = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     size_type index,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    using Type = typename T::rep;
+
+    auto device_col = column_device_view::create(input, stream);
+
+    rmm::device_scalar<Type> temp_data(stream, mr);
+    rmm::device_scalar<bool> temp_valid(stream, mr);
+
+    device_single_thread(
+      [buffer   = temp_data.data(),
+       validity = temp_valid.data(),
+       d_col    = *device_col,
+       index] __device__() mutable {
+        *buffer   = d_col.element<Type>(index);
+        *validity = d_col.is_valid(index);
+      },
+      stream);
+
+    return std::make_unique<fixed_point_scalar<T>>(std::move(temp_data),
+                                                   numeric::scale_type{input.type().scale()},
+                                                   temp_valid.value(stream),
+                                                   stream,
+                                                   mr);
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, struct_view>>* p = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     size_type index,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    bool valid = is_element_valid_sync(input, index, stream);
+    auto row_contents =
+      std::make_unique<column>(slice(input, index, index + 1, stream), stream, mr)->release();
+    auto scalar_contents = table(std::move(row_contents.children));
+    return std::make_unique<struct_scalar>(std::move(scalar_contents), valid, stream, mr);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<scalar> get_element(column_view const& input,
+                                    size_type index,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(index >= 0 and index < input.size(), "Index out of bounds");
+  return type_dispatcher(input.type(), get_element_functor{}, input, index, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<scalar> get_element(column_view const& input,
+                                    size_type index,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::get_element(input, index, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/pack.cpp b/cpp/src/copying/pack.cpp
new file mode 100644
index 0000000..e4de4a4
--- /dev/null
+++ b/cpp/src/copying/pack.cpp
@@ -0,0 +1,304 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/contiguous_split.hpp>
+#include <cudf/detail/contiguous_split.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief The data that is stored as anonymous bytes in the `packed_columns` metadata
+ * field.
+ *
+ * The metadata field of the `packed_columns` struct is simply an array of these.
+ * This struct is exposed here because it is needed by both contiguous_split, pack
+ * and unpack.
+ */
+struct serialized_column {
+  serialized_column() = default;
+
+  serialized_column(data_type _type,
+                    size_type _size,
+                    size_type _null_count,
+                    int64_t _data_offset,
+                    int64_t _null_mask_offset,
+                    size_type _num_children)
+    : type(_type),
+      size(_size),
+      null_count(_null_count),
+      data_offset(_data_offset),
+      null_mask_offset(_null_mask_offset),
+      num_children(_num_children),
+      pad(0)
+  {
+  }
+
+  data_type type;
+  size_type size;
+  size_type null_count;
+  int64_t data_offset;       // offset into contiguous data buffer, or -1 if column data is null
+  int64_t null_mask_offset;  // offset into contiguous data buffer, or -1 if column data is null
+  size_type num_children;
+  // Explicitly pad to avoid uninitialized padding bits, allowing `serialized_column` to be bit-wise
+  // comparable
+  int pad;
+};
+
+/**
+ * @brief Deserialize a single column into a column_view
+ *
+ * Deserializes a single column (it's children are assumed to be already deserialized)
+ * non-recursively.
+ *
+ * @param serial_column Serialized column information
+ * @param children Children for the column
+ * @param base_ptr Base pointer for the entire contiguous buffer from which all columns
+ * were serialized into
+ * @return Fully formed column_view
+ */
+column_view deserialize_column(serialized_column serial_column,
+                               std::vector<column_view> const& children,
+                               uint8_t const* base_ptr)
+{
+  auto const data_ptr =
+    serial_column.data_offset != -1 ? base_ptr + serial_column.data_offset : nullptr;
+  auto const null_mask_ptr =
+    serial_column.null_mask_offset != -1
+      ? reinterpret_cast<bitmask_type const*>(base_ptr + serial_column.null_mask_offset)
+      : nullptr;
+
+  return column_view(serial_column.type,
+                     serial_column.size,
+                     data_ptr,
+                     null_mask_ptr,
+                     serial_column.null_count,
+                     0,
+                     children);
+}
+
+/**
+ * @brief Build and add metadata for a column and all of it's children, recursively
+ *
+ *
+ * @param mb metadata_builder instance
+ * @param col Column to build metadata for
+ * @param base_ptr Base pointer for the entire contiguous buffer from which all columns
+ * were serialized into
+ * @param data_size Size of the incoming buffer
+ */
+void build_column_metadata(metadata_builder& mb,
+                           column_view const& col,
+                           uint8_t const* base_ptr,
+                           size_t data_size)
+{
+  uint8_t const* data_ptr = col.size() == 0 || !col.head<uint8_t>() ? nullptr : col.head<uint8_t>();
+  if (data_ptr != nullptr) {
+    CUDF_EXPECTS(data_ptr >= base_ptr && data_ptr < base_ptr + data_size,
+                 "Encountered column data outside the range of input buffer");
+  }
+  int64_t const data_offset = data_ptr ? data_ptr - base_ptr : -1;
+
+  uint8_t const* null_mask_ptr = col.size() == 0 || !col.nullable()
+                                   ? nullptr
+                                   : reinterpret_cast<uint8_t const*>(col.null_mask());
+  if (null_mask_ptr != nullptr) {
+    CUDF_EXPECTS(null_mask_ptr >= base_ptr && null_mask_ptr < base_ptr + data_size,
+                 "Encountered column null mask outside the range of input buffer");
+  }
+  int64_t const null_mask_offset = null_mask_ptr ? null_mask_ptr - base_ptr : -1;
+
+  // add metadata
+  mb.add_column_info_to_meta(
+    col.type(), col.size(), col.null_count(), data_offset, null_mask_offset, col.num_children());
+
+  std::for_each(
+    col.child_begin(), col.child_end(), [&mb, &base_ptr, &data_size](column_view const& col) {
+      build_column_metadata(mb, col, base_ptr, data_size);
+    });
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::detail::pack
+ */
+packed_columns pack(cudf::table_view const& input,
+                    rmm::cuda_stream_view stream,
+                    rmm::mr::device_memory_resource* mr)
+{
+  // do a contiguous_split with no splits to get the memory for the table
+  // arranged as we want it
+  auto contig_split_result = cudf::detail::contiguous_split(input, {}, stream, mr);
+  return contig_split_result.empty() ? packed_columns{} : std::move(contig_split_result[0].data);
+}
+
+std::vector<uint8_t> pack_metadata(table_view const& table,
+                                   uint8_t const* contiguous_buffer,
+                                   size_t buffer_size,
+                                   metadata_builder& builder)
+{
+  std::for_each(
+    table.begin(), table.end(), [&builder, contiguous_buffer, buffer_size](column_view const& col) {
+      build_column_metadata(builder, col, contiguous_buffer, buffer_size);
+    });
+
+  return builder.build();
+}
+
+class metadata_builder_impl {
+ public:
+  metadata_builder_impl(size_type const num_root_columns) { metadata.reserve(num_root_columns); }
+
+  void add_column_info_to_meta(data_type const col_type,
+                               size_type const col_size,
+                               size_type const col_null_count,
+                               int64_t const data_offset,
+                               int64_t const null_mask_offset,
+                               size_type const num_children)
+  {
+    metadata.emplace_back(
+      col_type, col_size, col_null_count, data_offset, null_mask_offset, num_children);
+  }
+
+  std::vector<uint8_t> build() const
+  {
+    auto output = std::vector<uint8_t>(metadata.size() * sizeof(detail::serialized_column));
+    std::memcpy(output.data(), metadata.data(), output.size());
+    return output;
+  }
+
+  void clear()
+  {
+    // Clear all, except the first metadata entry storing the number of top level columns that
+    // was added upon object construction.
+    metadata.resize(1);
+  }
+
+ private:
+  std::vector<detail::serialized_column> metadata;
+};
+
+/**
+ * @copydoc cudf::detail::unpack
+ */
+table_view unpack(uint8_t const* metadata, uint8_t const* gpu_data)
+{
+  // gpu data can be null if everything is empty but the metadata must always be valid
+  CUDF_EXPECTS(metadata != nullptr, "Encountered invalid packed column input");
+  auto serialized_columns = reinterpret_cast<serialized_column const*>(metadata);
+  uint8_t const* base_ptr = gpu_data;
+  // first entry is a stub where size == the total # of top level columns (see pack_metadata above)
+  auto const num_columns = serialized_columns[0].size;
+  size_t current_index   = 1;
+
+  std::function<std::vector<column_view>(size_type)> get_columns;
+  get_columns = [&serialized_columns, &current_index, base_ptr, &get_columns](size_t num_columns) {
+    std::vector<column_view> cols;
+    for (size_t i = 0; i < num_columns; i++) {
+      auto serial_column = serialized_columns[current_index];
+      current_index++;
+
+      std::vector<column_view> children = get_columns(serial_column.num_children);
+
+      cols.emplace_back(deserialize_column(serial_column, children, base_ptr));
+    }
+
+    return cols;
+  };
+
+  return table_view{get_columns(num_columns)};
+}
+
+metadata_builder::metadata_builder(size_type const num_root_columns)
+  : impl(std::make_unique<metadata_builder_impl>(num_root_columns +
+                                                 1 /*one more extra metadata entry as below*/))
+{
+  // first metadata entry is a stub indicating how many total (top level) columns
+  // there are
+  impl->add_column_info_to_meta(data_type{type_id::EMPTY}, num_root_columns, 0, -1, -1, 0);
+}
+
+metadata_builder::~metadata_builder() = default;
+
+void metadata_builder::add_column_info_to_meta(data_type const col_type,
+                                               size_type const col_size,
+                                               size_type const col_null_count,
+                                               int64_t const data_offset,
+                                               int64_t const null_mask_offset,
+                                               size_type const num_children)
+{
+  impl->add_column_info_to_meta(
+    col_type, col_size, col_null_count, data_offset, null_mask_offset, num_children);
+}
+
+std::vector<uint8_t> metadata_builder::build() const { return impl->build(); }
+
+void metadata_builder::clear() { return impl->clear(); }
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::pack
+ */
+packed_columns pack(cudf::table_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::pack(input, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc cudf::pack_metadata
+ */
+std::vector<uint8_t> pack_metadata(table_view const& table,
+                                   uint8_t const* contiguous_buffer,
+                                   size_t buffer_size)
+{
+  CUDF_FUNC_RANGE();
+  if (table.is_empty()) { return std::vector<uint8_t>{}; }
+
+  auto builder = cudf::detail::metadata_builder(table.num_columns());
+  return detail::pack_metadata(table, contiguous_buffer, buffer_size, builder);
+}
+
+/**
+ * @copydoc cudf::unpack
+ */
+table_view unpack(packed_columns const& input)
+{
+  CUDF_FUNC_RANGE();
+  return input.metadata->size() == 0
+           ? table_view{}
+           : detail::unpack(input.metadata->data(),
+                            reinterpret_cast<uint8_t const*>(input.gpu_data->data()));
+}
+
+/**
+ * @copydoc cudf::unpack(uint8_t const*, uint8_t const* )
+ */
+table_view unpack(uint8_t const* metadata, uint8_t const* gpu_data)
+{
+  CUDF_FUNC_RANGE();
+  return detail::unpack(metadata, gpu_data);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/purge_nonempty_nulls.cu b/cpp/src/copying/purge_nonempty_nulls.cu
new file mode 100644
index 0000000..b578f31
--- /dev/null
+++ b/cpp/src/copying/purge_nonempty_nulls.cu
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/count.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+
+using cudf::type_id;
+
+namespace {
+
+/// Check if nonempty-null checks can be skipped for a given type.
+bool type_may_have_nonempty_nulls(cudf::type_id const& type)
+{
+  return type == type_id::STRING || type == type_id::LIST || type == type_id::STRUCT;
+}
+
+/// Check if the (STRING/LIST) column has any null rows with non-zero length.
+bool has_nonempty_null_rows(cudf::column_view const& input, rmm::cuda_stream_view stream)
+{
+  if (not input.has_nulls()) { return false; }  // No nulls => no dirty rows.
+
+  if ((input.size() == input.null_count()) && (input.num_children() == 0)) { return false; }
+
+  // Cross-reference nullmask and offsets.
+  auto const type         = input.type().id();
+  auto const offsets      = (type == type_id::STRING) ? (strings_column_view{input}).offsets_begin()
+                                                      : (lists_column_view{input}).offsets_begin();
+  auto const d_input      = cudf::column_device_view::create(input, stream);
+  auto const is_dirty_row = [d_input = *d_input, offsets] __device__(size_type const& row_idx) {
+    return d_input.is_null_nocheck(row_idx) && (offsets[row_idx] != offsets[row_idx + 1]);
+  };
+
+  auto const row_begin = thrust::counting_iterator<cudf::size_type>(0);
+  auto const row_end   = row_begin + input.size();
+  return thrust::count_if(rmm::exec_policy(stream), row_begin, row_end, is_dirty_row) > 0;
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::detail::has_nonempty_nulls
+ */
+bool has_nonempty_nulls(cudf::column_view const& input, rmm::cuda_stream_view stream)
+{
+  auto const type = input.type().id();
+
+  if (not type_may_have_nonempty_nulls(type)) { return false; }
+
+  // For types with variable-length rows, check if any rows are "dirty".
+  // A dirty row is a null row with non-zero length.
+  if ((type == type_id::STRING || type == type_id::LIST) && has_nonempty_null_rows(input, stream)) {
+    return true;
+  }
+
+  // For complex types, check if child columns need purging.
+  if ((type == type_id::STRUCT || type == type_id::LIST) &&
+      std::any_of(input.child_begin(), input.child_end(), [stream](auto const& child) {
+        return cudf::detail::has_nonempty_nulls(child, stream);
+      })) {
+    return true;
+  }
+
+  return false;
+}
+
+std::unique_ptr<column> purge_nonempty_nulls(column_view const& input,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  // If not compound types (LIST/STRING/STRUCT/DICTIONARY) then just copy the input into output.
+  if (!cudf::is_compound(input.type())) { return std::make_unique<column>(input, stream, mr); }
+
+  // Implement via identity gather.
+  auto gathered_table = cudf::detail::gather(table_view{{input}},
+                                             thrust::make_counting_iterator(0),
+                                             thrust::make_counting_iterator(input.size()),
+                                             out_of_bounds_policy::DONT_CHECK,
+                                             stream,
+                                             mr);
+  return std::move(gathered_table->release().front());
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::may_have_nonempty_nulls
+ */
+bool may_have_nonempty_nulls(column_view const& input)
+{
+  auto const type = input.type().id();
+
+  if (not detail::type_may_have_nonempty_nulls(type)) { return false; }
+
+  if ((type == type_id::STRING || type == type_id::LIST) && input.has_nulls()) { return true; }
+
+  if ((type == type_id::STRUCT || type == type_id::LIST) &&
+      std::any_of(input.child_begin(), input.child_end(), may_have_nonempty_nulls)) {
+    return true;
+  }
+
+  return false;
+}
+
+/**
+ * @copydoc cudf::has_nonempty_nulls
+ */
+bool has_nonempty_nulls(column_view const& input, rmm::cuda_stream_view stream)
+{
+  return detail::has_nonempty_nulls(input, stream);
+}
+
+/**
+ * @copydoc cudf::purge_nonempty_nulls(column_view const&, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<cudf::column> purge_nonempty_nulls(column_view const& input,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  return detail::purge_nonempty_nulls(input, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/reverse.cu b/cpp/src/copying/reverse.cu
new file mode 100644
index 0000000..fbbbc56
--- /dev/null
+++ b/cpp/src/copying/reverse.cu
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_output_iterator.h>
+#include <thrust/scan.h>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<table> reverse(table_view const& source_table,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  size_type num_rows = source_table.num_rows();
+  auto elements =
+    make_counting_transform_iterator(0, [num_rows] __device__(auto i) { return num_rows - i - 1; });
+  auto elements_end = elements + source_table.num_rows();
+
+  return gather(source_table, elements, elements_end, out_of_bounds_policy::DONT_CHECK, stream, mr);
+}
+
+std::unique_ptr<column> reverse(column_view const& source_column,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  return std::move(
+    cudf::detail::reverse(table_view({source_column}), stream, mr)->release().front());
+}
+}  // namespace detail
+
+std::unique_ptr<table> reverse(table_view const& source_table,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::reverse(source_table, stream, mr);
+}
+
+std::unique_ptr<column> reverse(column_view const& source_column,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::reverse(source_column, stream, mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/copying/sample.cu b/cpp/src/copying/sample.cu
new file mode 100644
index 0000000..f3d8d62
--- /dev/null
+++ b/cpp/src/copying/sample.cu
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/random.h>
+#include <thrust/random/uniform_int_distribution.h>
+#include <thrust/shuffle.h>
+
+namespace cudf {
+namespace detail {
+
+std::unique_ptr<table> sample(table_view const& input,
+                              size_type const n,
+                              sample_with_replacement replacement,
+                              int64_t const seed,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(n >= 0, "expected number of samples should be non-negative");
+  auto const num_rows = input.num_rows();
+
+  if ((n > num_rows) and (replacement == sample_with_replacement::FALSE)) {
+    CUDF_FAIL("If n > number of rows, then multiple sampling of the same row should be allowed");
+  }
+
+  if (n == 0) return cudf::empty_like(input);
+
+  if (replacement == sample_with_replacement::TRUE) {
+    auto RandomGen = [seed, num_rows] __device__(auto i) {
+      thrust::default_random_engine rng(seed);
+      thrust::uniform_int_distribution<size_type> dist{0, num_rows - 1};
+      rng.discard(i);
+      return dist(rng);
+    };
+
+    auto begin = cudf::detail::make_counting_transform_iterator(0, RandomGen);
+
+    return detail::gather(input, begin, begin + n, out_of_bounds_policy::DONT_CHECK, stream, mr);
+  } else {
+    auto gather_map =
+      make_numeric_column(data_type{type_id::INT32}, num_rows, mask_state::UNALLOCATED, stream);
+    auto gather_map_mutable_view = gather_map->mutable_view();
+    // Shuffle all the row indices
+    thrust::shuffle_copy(rmm::exec_policy(stream),
+                         thrust::counting_iterator<size_type>(0),
+                         thrust::counting_iterator<size_type>(num_rows),
+                         gather_map_mutable_view.begin<size_type>(),
+                         thrust::default_random_engine(seed));
+
+    auto gather_map_view = (n == num_rows)
+                             ? gather_map->view()
+                             : cudf::detail::slice(gather_map->view(), {0, n}, stream)[0];
+    return detail::gather(input,
+                          gather_map_view.begin<size_type>(),
+                          gather_map_view.end<size_type>(),
+                          out_of_bounds_policy::DONT_CHECK,
+                          stream,
+                          mr);
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> sample(table_view const& input,
+                              size_type const n,
+                              sample_with_replacement replacement,
+                              int64_t const seed,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sample(input, n, replacement, seed, stream, mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/copying/scatter.cu b/cpp/src/copying/scatter.cu
new file mode 100644
index 0000000..11c27fc
--- /dev/null
+++ b/cpp/src/copying/scatter.cu
@@ -0,0 +1,536 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/scatter.cuh>
+#include <cudf/detail/scatter.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/detail/scatter.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/count.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+template <bool mark_true, typename MapIterator>
+__global__ void marking_bitmask_kernel(mutable_column_device_view destination,
+                                       MapIterator scatter_map,
+                                       size_type num_scatter_rows)
+{
+  auto row          = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+
+  while (row < num_scatter_rows) {
+    size_type const output_row = scatter_map[row];
+
+    if (mark_true) {
+      destination.set_valid(output_row);
+    } else {
+      destination.set_null(output_row);
+    }
+
+    row += stride;
+  }
+}
+
+template <typename MapIterator>
+void scatter_scalar_bitmask_inplace(std::reference_wrapper<scalar const> const& source,
+                                    MapIterator scatter_map,
+                                    size_type num_scatter_rows,
+                                    column& target,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  constexpr size_type block_size = 256;
+  size_type const grid_size      = grid_1d(num_scatter_rows, block_size).num_blocks;
+
+  auto const source_is_valid = source.get().is_valid(stream);
+  if (target.nullable() or not source_is_valid) {
+    if (not target.nullable()) {
+      // Target must have a null mask if the source is not valid
+      auto mask = detail::create_null_mask(target.size(), mask_state::ALL_VALID, stream, mr);
+      target.set_null_mask(std::move(mask), 0);
+    }
+
+    auto target_view = mutable_column_device_view::create(target, stream);
+
+    auto bitmask_kernel = source_is_valid ? marking_bitmask_kernel<true, decltype(scatter_map)>
+                                          : marking_bitmask_kernel<false, decltype(scatter_map)>;
+    bitmask_kernel<<<grid_size, block_size, 0, stream.value()>>>(
+      *target_view, scatter_map, num_scatter_rows);
+
+    target.set_null_count(
+      cudf::detail::null_count(target.view().null_mask(), 0, target.size(), stream));
+  }
+}
+
+template <typename Element, typename MapIterator>
+struct column_scalar_scatterer_impl {
+  std::unique_ptr<column> operator()(std::reference_wrapper<scalar const> const& source,
+                                     MapIterator scatter_iter,
+                                     size_type scatter_rows,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    CUDF_EXPECTS(source.get().type() == target.type(), "scalar and column types must match");
+
+    // make a copy of data and null mask from source
+    auto result      = std::make_unique<column>(target, stream, mr);
+    auto result_view = result->mutable_view();
+
+    // Use permutation iterator with constant index to dereference scalar data
+    auto scalar_impl = static_cast<scalar_type_t<Element> const*>(&source.get());
+    auto scalar_iter =
+      thrust::make_permutation_iterator(scalar_impl->data(), thrust::make_constant_iterator(0));
+
+    thrust::scatter(rmm::exec_policy_nosync(stream),
+                    scalar_iter,
+                    scalar_iter + scatter_rows,
+                    scatter_iter,
+                    result_view.begin<Element>());
+
+    scatter_scalar_bitmask_inplace(source, scatter_iter, scatter_rows, *result, stream, mr);
+    return result;
+  }
+};
+
+template <typename MapIterator>
+struct column_scalar_scatterer_impl<string_view, MapIterator> {
+  std::unique_ptr<column> operator()(std::reference_wrapper<scalar const> const& source,
+                                     MapIterator scatter_iter,
+                                     size_type scatter_rows,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    CUDF_EXPECTS(source.get().type() == target.type(), "scalar and column types must match");
+
+    auto const scalar_impl = static_cast<string_scalar const*>(&source.get());
+    auto const source_view = string_view(scalar_impl->data(), scalar_impl->size());
+    auto const begin       = thrust::make_constant_iterator(source_view);
+    auto const end         = begin + scatter_rows;
+    auto result            = strings::detail::scatter(begin, end, scatter_iter, target, stream, mr);
+
+    scatter_scalar_bitmask_inplace(source, scatter_iter, scatter_rows, *result, stream, mr);
+    return result;
+  }
+};
+
+template <typename MapIterator>
+struct column_scalar_scatterer_impl<list_view, MapIterator> {
+  std::unique_ptr<column> operator()(std::reference_wrapper<scalar const> const& source,
+                                     MapIterator scatter_iter,
+                                     size_type scatter_rows,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto result =
+      lists::detail::scatter(source, scatter_iter, scatter_iter + scatter_rows, target, stream, mr);
+
+    scatter_scalar_bitmask_inplace(source, scatter_iter, scatter_rows, *result, stream, mr);
+    return result;
+  }
+};
+
+template <typename MapIterator>
+struct column_scalar_scatterer_impl<dictionary32, MapIterator> {
+  std::unique_ptr<column> operator()(std::reference_wrapper<scalar const> const& source,
+                                     MapIterator scatter_iter,
+                                     size_type scatter_rows,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto dict_target =
+      dictionary::detail::add_keys(dictionary_column_view(target),
+                                   make_column_from_scalar(source.get(), 1, stream)->view(),
+                                   stream,
+                                   mr);
+    auto dict_view    = dictionary_column_view(dict_target->view());
+    auto scalar_index = dictionary::detail::get_index(
+      dict_view, source.get(), stream, rmm::mr::get_current_device_resource());
+    auto scalar_iter = thrust::make_permutation_iterator(
+      indexalator_factory::make_input_iterator(*scalar_index), thrust::make_constant_iterator(0));
+    auto new_indices = std::make_unique<column>(dict_view.get_indices_annotated(), stream, mr);
+    auto target_iter = indexalator_factory::make_output_iterator(new_indices->mutable_view());
+
+    thrust::scatter(rmm::exec_policy_nosync(stream),
+                    scalar_iter,
+                    scalar_iter + scatter_rows,
+                    scatter_iter,
+                    target_iter);
+
+    // build the dictionary indices column from the result
+    auto const indices_type = new_indices->type();
+    auto const output_size  = new_indices->size();
+    auto const null_count   = new_indices->null_count();
+    auto contents           = new_indices->release();
+    auto indices_column     = std::make_unique<column>(indices_type,
+                                                   static_cast<size_type>(output_size),
+                                                   std::move(*(contents.data.release())),
+                                                   rmm::device_buffer{},
+                                                   0);
+    // use the keys from the matched column
+    std::unique_ptr<column> keys_column(std::move(dict_target->release().children.back()));
+    // create the output column
+    auto result = make_dictionary_column(std::move(keys_column),
+                                         std::move(indices_column),
+                                         std::move(*(contents.null_mask.release())),
+                                         null_count);
+
+    scatter_scalar_bitmask_inplace(source, scatter_iter, scatter_rows, *result, stream, mr);
+    return result;
+  }
+};
+
+template <typename MapIterator>
+struct column_scalar_scatterer {
+  template <typename Element>
+  std::unique_ptr<column> operator()(std::reference_wrapper<scalar const> const& source,
+                                     MapIterator scatter_iter,
+                                     size_type scatter_rows,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    column_scalar_scatterer_impl<Element, MapIterator> scatterer{};
+    return scatterer(source, scatter_iter, scatter_rows, target, stream, mr);
+  }
+};
+
+template <typename MapIterator>
+struct column_scalar_scatterer_impl<struct_view, MapIterator> {
+  std::unique_ptr<column> operator()(std::reference_wrapper<scalar const> const& source,
+                                     MapIterator scatter_iter,
+                                     size_type scatter_rows,
+                                     column_view const& target,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    // For each field of `source`, copy construct a scalar from the field
+    // and dispatch to the corresponding scalar scatterer
+
+    auto typed_s             = static_cast<struct_scalar const*>(&source.get());
+    size_type const n_fields = typed_s->view().num_columns();
+    CUDF_EXPECTS(n_fields == target.num_children(), "Mismatched number of fields.");
+
+    auto scatter_functor   = column_scalar_scatterer<decltype(scatter_iter)>{};
+    auto fields_iter_begin = make_counting_transform_iterator(0, [&](auto const& i) {
+      auto row_slr = detail::get_element(
+        typed_s->view().column(i), 0, stream, rmm::mr::get_current_device_resource());
+      return type_dispatcher<dispatch_storage_type>(row_slr->type(),
+                                                    scatter_functor,
+                                                    *row_slr,
+                                                    scatter_iter,
+                                                    scatter_rows,
+                                                    target.child(i),
+                                                    stream,
+                                                    mr);
+    });
+    std::vector<std::unique_ptr<column>> fields(fields_iter_begin, fields_iter_begin + n_fields);
+
+    // Compute null mask
+    rmm::device_buffer null_mask =
+      target.nullable() ? copy_bitmask(target, stream, mr)
+                        : create_null_mask(target.size(), mask_state::UNALLOCATED, stream, mr);
+    column null_mask_stub(data_type{type_id::STRUCT},
+                          target.size(),
+                          rmm::device_buffer{},
+                          std::move(null_mask),
+                          target.null_count());
+    scatter_scalar_bitmask_inplace(source, scatter_iter, scatter_rows, null_mask_stub, stream, mr);
+    size_type null_count = null_mask_stub.null_count();
+    auto contents        = null_mask_stub.release();
+
+    // Null mask pushdown inside factory method
+    return make_structs_column(
+      target.size(), std::move(fields), null_count, std::move(*contents.null_mask), stream, mr);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<table> scatter(table_view const& source,
+                               column_view const& scatter_map,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(source.num_columns() == target.num_columns(),
+               "Number of columns in source and target not equal");
+  CUDF_EXPECTS(scatter_map.size() <= source.num_rows(),
+               "Size of scatter map must be equal to or less than source rows");
+  CUDF_EXPECTS(std::equal(source.begin(),
+                          source.end(),
+                          target.begin(),
+                          [](auto const& col1, auto const& col2) {
+                            return col1.type().id() == col2.type().id();
+                          }),
+               "Column types do not match between source and target");
+  CUDF_EXPECTS(not scatter_map.has_nulls(), "Scatter map contains nulls");
+
+  if (scatter_map.is_empty()) { return std::make_unique<table>(target, stream, mr); }
+
+  // create index type normalizing iterator for the scatter_map
+  auto map_begin = indexalator_factory::make_input_iterator(scatter_map);
+  auto map_end   = map_begin + scatter_map.size();
+  return detail::scatter(source, map_begin, map_end, target, stream, mr);
+}
+
+std::unique_ptr<table> scatter(table_view const& source,
+                               device_span<size_type const> const scatter_map,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(scatter_map.size() <= static_cast<size_t>(std::numeric_limits<size_type>::max()),
+               "scatter map size exceeds the column size limit",
+               std::overflow_error);
+  auto map_col = column_view(data_type{type_to_id<size_type>()},
+                             static_cast<size_type>(scatter_map.size()),
+                             scatter_map.data(),
+                             nullptr,
+                             0);
+  return detail::scatter(source, map_col, target, stream, mr);
+}
+
+std::unique_ptr<table> scatter(std::vector<std::reference_wrapper<scalar const>> const& source,
+                               column_view const& indices,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(source.size() == static_cast<size_t>(target.num_columns()),
+               "Number of columns in source and target not equal");
+  CUDF_EXPECTS(not indices.has_nulls(), "indices contains nulls");
+
+  if (indices.is_empty()) { return std::make_unique<table>(target, stream, mr); }
+
+  // Create normalizing iterator for indices column
+  auto map_begin = indexalator_factory::make_input_iterator(indices);
+
+  // Optionally check map index values are within the number of target rows.
+  auto const n_rows = target.num_rows();
+
+  // Transform negative indices to index + target size
+  auto scatter_rows = indices.size();
+  // note: the intermediate ((in % n_rows) + n_rows) will overflow a size_type for any value of `in`
+  // > (2^31)/2, but the end result after the final (% n_rows) will fit. so we'll do the computation
+  // using a signed 64 bit value.
+  auto scatter_iter = thrust::make_transform_iterator(
+    map_begin, [n_rows = static_cast<int64_t>(n_rows)] __device__(size_type in) -> size_type {
+      return ((static_cast<int64_t>(in) % n_rows) + n_rows) % n_rows;
+    });
+
+  // Dispatch over data type per column
+  auto result          = std::vector<std::unique_ptr<column>>(target.num_columns());
+  auto scatter_functor = column_scalar_scatterer<decltype(scatter_iter)>{};
+  std::transform(source.begin(),
+                 source.end(),
+                 target.begin(),
+                 result.begin(),
+                 [=](auto const& source_scalar, auto const& target_col) {
+                   return type_dispatcher<dispatch_storage_type>(target_col.type(),
+                                                                 scatter_functor,
+                                                                 source_scalar,
+                                                                 scatter_iter,
+                                                                 scatter_rows,
+                                                                 target_col,
+                                                                 stream,
+                                                                 mr);
+                 });
+
+  return std::make_unique<table>(std::move(result));
+}
+
+std::unique_ptr<column> boolean_mask_scatter(column_view const& input,
+                                             column_view const& target,
+                                             column_view const& boolean_mask,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  auto indices = cudf::make_numeric_column(
+    data_type{type_id::INT32}, target.size(), mask_state::UNALLOCATED, stream);
+  auto mutable_indices = indices->mutable_view();
+
+  thrust::sequence(rmm::exec_policy_nosync(stream),
+                   mutable_indices.begin<size_type>(),
+                   mutable_indices.end<size_type>(),
+                   0);
+
+  // The scatter map is actually a table with only one column, which is scatter map.
+  auto scatter_map = detail::apply_boolean_mask(
+    table_view{{indices->view()}}, boolean_mask, stream, rmm::mr::get_current_device_resource());
+  auto output_table = detail::scatter(
+    table_view{{input}}, scatter_map->get_column(0).view(), table_view{{target}}, stream, mr);
+
+  // There is only one column in output_table
+  return std::make_unique<column>(std::move(output_table->get_column(0)));
+}
+
+std::unique_ptr<column> boolean_mask_scatter(scalar const& input,
+                                             column_view const& target,
+                                             column_view const& boolean_mask,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  return detail::copy_if_else(input, target, boolean_mask, stream, mr);
+}
+
+std::unique_ptr<table> boolean_mask_scatter(table_view const& input,
+                                            table_view const& target,
+                                            column_view const& boolean_mask,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.num_columns() == target.num_columns(),
+               "Mismatch in number of input columns and target columns");
+  CUDF_EXPECTS(boolean_mask.size() == target.num_rows(),
+               "Boolean mask size and number of target rows mismatch");
+  CUDF_EXPECTS(boolean_mask.type().id() == type_id::BOOL8, "Mask must be of Boolean type");
+  // Count valid pair of input and columns as per type at each column index i
+  CUDF_EXPECTS(
+    std::all_of(thrust::counting_iterator<size_type>(0),
+                thrust::counting_iterator<size_type>(target.num_columns()),
+                [&input, &target](auto index) {
+                  return ((input.column(index).type().id()) == (target.column(index).type().id()));
+                }),
+    "Type mismatch in input column and target column");
+
+  if (target.num_rows() != 0) {
+    std::vector<std::unique_ptr<column>> out_columns(target.num_columns());
+    std::transform(
+      input.begin(),
+      input.end(),
+      target.begin(),
+      out_columns.begin(),
+      [&boolean_mask, mr, stream](auto const& input_column, auto const& target_column) {
+        return boolean_mask_scatter(input_column, target_column, boolean_mask, stream, mr);
+      });
+
+    return std::make_unique<table>(std::move(out_columns));
+  } else {
+    return empty_like(target);
+  }
+}
+
+std::unique_ptr<table> boolean_mask_scatter(
+  std::vector<std::reference_wrapper<scalar const>> const& input,
+  table_view const& target,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(static_cast<size_type>(input.size()) == target.num_columns(),
+               "Mismatch in number of scalars and target columns");
+  CUDF_EXPECTS(boolean_mask.size() == target.num_rows(),
+               "Boolean mask size and number of target rows mismatch");
+  CUDF_EXPECTS(boolean_mask.type().id() == type_id::BOOL8, "Mask must be of Boolean type");
+
+  // Count valid pair of input and columns as per type at each column/scalar index i
+  CUDF_EXPECTS(
+    std::all_of(thrust::counting_iterator<size_type>(0),
+                thrust::counting_iterator<size_type>(target.num_columns()),
+                [&input, &target](auto index) {
+                  return (input[index].get().type().id() == target.column(index).type().id());
+                }),
+    "Type mismatch in input scalar and target column");
+
+  if (target.num_rows() != 0) {
+    std::vector<std::unique_ptr<column>> out_columns(target.num_columns());
+    std::transform(input.begin(),
+                   input.end(),
+                   target.begin(),
+                   out_columns.begin(),
+                   [&boolean_mask, mr, stream](auto const& scalar, auto const& target_column) {
+                     return boolean_mask_scatter(
+                       scalar.get(), target_column, boolean_mask, stream, mr);
+                   });
+
+    return std::make_unique<table>(std::move(out_columns));
+  } else {
+    return empty_like(target);
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> scatter(table_view const& source,
+                               column_view const& scatter_map,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::scatter(source, scatter_map, target, stream, mr);
+}
+
+std::unique_ptr<table> scatter(std::vector<std::reference_wrapper<scalar const>> const& source,
+                               column_view const& indices,
+                               table_view const& target,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::scatter(source, indices, target, stream, mr);
+}
+
+std::unique_ptr<table> boolean_mask_scatter(table_view const& input,
+                                            table_view const& target,
+                                            column_view const& boolean_mask,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::boolean_mask_scatter(input, target, boolean_mask, stream, mr);
+}
+
+std::unique_ptr<table> boolean_mask_scatter(
+  std::vector<std::reference_wrapper<scalar const>> const& input,
+  table_view const& target,
+  column_view const& boolean_mask,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::boolean_mask_scatter(input, target, boolean_mask, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/segmented_shift.cu b/cpp/src/copying/segmented_shift.cu
new file mode 100644
index 0000000..dd2733c
--- /dev/null
+++ b/cpp/src/copying/segmented_shift.cu
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/copy_if_else.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/copy_if_else.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Common filter function to convert index values into copy-if-else left/right result.
+ *
+ * The offset position is used to identify which segment to copy from.
+ */
+struct segmented_shift_filter {
+  device_span<size_type const> const segment_offsets;
+  size_type const offset;
+
+  __device__ bool operator()(size_type const i) const
+  {
+    auto const segment_bound_idx =
+      thrust::upper_bound(thrust::seq, segment_offsets.begin(), segment_offsets.end(), i) -
+      (offset > 0);
+    auto const left_idx  = *segment_bound_idx + (offset < 0 ? offset : 0);
+    auto const right_idx = *segment_bound_idx + (offset > 0 ? offset : 0);
+    return not(left_idx <= i and i < right_idx);
+  };
+};
+
+template <typename T, typename Enable = void>
+struct segmented_shift_functor {
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type for segmented_shift.");
+  }
+};
+
+/**
+ * @brief Segmented shift specialization for representation layout compatible types.
+ */
+template <typename T>
+struct segmented_shift_functor<T, std::enable_if_t<is_rep_layout_compatible<T>()>> {
+  std::unique_ptr<column> operator()(column_view const& segmented_values,
+                                     device_span<size_type const> segment_offsets,
+                                     size_type offset,
+                                     scalar const& fill_value,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto values_device_view = column_device_view::create(segmented_values, stream);
+    bool nullable           = not fill_value.is_valid(stream) or segmented_values.nullable();
+    auto input_iterator     = cudf::detail::make_optional_iterator<T>(
+                            *values_device_view, nullate::DYNAMIC{segmented_values.has_nulls()}) -
+                          offset;
+    auto fill_iterator = cudf::detail::make_optional_iterator<T>(fill_value, nullate::YES{});
+    return copy_if_else(nullable,
+                        input_iterator,
+                        input_iterator + segmented_values.size(),
+                        fill_iterator,
+                        segmented_shift_filter{segment_offsets, offset},
+                        segmented_values.type(),
+                        stream,
+                        mr);
+  }
+};
+
+/**
+ * @brief Segmented shift specialization for `string_view`.
+ */
+template <>
+struct segmented_shift_functor<string_view> {
+  std::unique_ptr<column> operator()(column_view const& segmented_values,
+                                     device_span<size_type const> segment_offsets,
+                                     size_type offset,
+                                     scalar const& fill_value,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto values_device_view = column_device_view::create(segmented_values, stream);
+    auto input_iterator     = make_optional_iterator<cudf::string_view>(
+                            *values_device_view, nullate::DYNAMIC{segmented_values.has_nulls()}) -
+                          offset;
+    auto fill_iterator = make_optional_iterator<cudf::string_view>(fill_value, nullate::YES{});
+    return strings::detail::copy_if_else(input_iterator,
+                                         input_iterator + segmented_values.size(),
+                                         fill_iterator,
+                                         segmented_shift_filter{segment_offsets, offset},
+                                         stream,
+                                         mr);
+  }
+};
+
+/**
+ * @brief Functor to instantiate the specializations for segmented shift and
+ * forward arguments.
+ */
+struct segmented_shift_functor_forwarder {
+  template <typename T>
+  std::unique_ptr<column> operator()(column_view const& segmented_values,
+                                     device_span<size_type const> segment_offsets,
+                                     size_type offset,
+                                     scalar const& fill_value,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    segmented_shift_functor<T> shifter;
+    return shifter(segmented_values, segment_offsets, offset, fill_value, stream, mr);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> segmented_shift(column_view const& segmented_values,
+                                        device_span<size_type const> segment_offsets,
+                                        size_type offset,
+                                        scalar const& fill_value,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  if (segmented_values.is_empty()) { return empty_like(segmented_values); }
+  if (offset == 0) { return std::make_unique<column>(segmented_values, stream, mr); };
+
+  return type_dispatcher<dispatch_storage_type>(segmented_values.type(),
+                                                segmented_shift_functor_forwarder{},
+                                                segmented_values,
+                                                segment_offsets,
+                                                offset,
+                                                fill_value,
+                                                stream,
+                                                mr);
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/copying/shift.cu b/cpp/src/copying/shift.cu
new file mode 100644
index 0000000..89d6551
--- /dev/null
+++ b/cpp/src/copying/shift.cu
@@ -0,0 +1,180 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/copying.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <iterator>
+#include <memory>
+
+namespace cudf {
+namespace {
+inline bool __device__ out_of_bounds(size_type size, size_type idx)
+{
+  return idx < 0 || idx >= size;
+}
+
+std::pair<rmm::device_buffer, size_type> create_null_mask(column_device_view const& input,
+                                                          size_type offset,
+                                                          scalar const& fill_value,
+                                                          rmm::cuda_stream_view stream,
+                                                          rmm::mr::device_memory_resource* mr)
+{
+  auto const size = input.size();
+  auto func_validity =
+    [size, offset, fill = fill_value.validity_data(), input] __device__(size_type idx) {
+      auto src_idx = idx - offset;
+      return out_of_bounds(size, src_idx) ? *fill : input.is_valid(src_idx);
+    };
+  return detail::valid_if(thrust::make_counting_iterator<size_type>(0),
+                          thrust::make_counting_iterator<size_type>(size),
+                          func_validity,
+                          stream,
+                          mr);
+}
+
+struct shift_functor {
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_fixed_width<T>() and not std::is_same_v<cudf::string_view, T>,
+                   std::unique_ptr<column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("shift only supports fixed-width or string types.");
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<std::is_same_v<cudf::string_view, T>, std::unique_ptr<column>> operator()(
+    column_view const& input,
+    size_type offset,
+    scalar const& fill_value,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    auto output = cudf::strings::detail::shift(
+      cudf::strings_column_view(input), offset, fill_value, stream, mr);
+
+    if (input.nullable() || not fill_value.is_valid(stream)) {
+      auto const d_input           = column_device_view::create(input, stream);
+      auto [null_mask, null_count] = create_null_mask(*d_input, offset, fill_value, stream, mr);
+      output->set_null_mask(std::move(null_mask), null_count);
+    }
+
+    return output;
+  }
+
+  template <typename T>
+  std::enable_if_t<cudf::is_fixed_width<T>(), std::unique_ptr<column>> operator()(
+    column_view const& input,
+    size_type offset,
+    scalar const& fill_value,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    using ScalarType = cudf::scalar_type_t<T>;
+    auto& scalar     = static_cast<ScalarType const&>(fill_value);
+
+    auto device_input = column_device_view::create(input, stream);
+    auto output =
+      detail::allocate_like(input, input.size(), mask_allocation_policy::NEVER, stream, mr);
+    auto device_output = mutable_column_device_view::create(*output, stream);
+
+    auto const scalar_is_valid = scalar.is_valid(stream);
+
+    if (input.nullable() || not scalar_is_valid) {
+      auto [null_mask, null_count] =
+        create_null_mask(*device_input, offset, fill_value, stream, mr);
+      output->set_null_mask(std::move(null_mask), null_count);
+    }
+
+    auto const size  = input.size();
+    auto index_begin = thrust::make_counting_iterator<size_type>(0);
+    auto index_end   = thrust::make_counting_iterator<size_type>(size);
+    auto data        = device_output->data<T>();
+
+    // avoid assigning elements we know to be invalid.
+    if (not scalar_is_valid) {
+      if (std::abs(offset) > size) { return output; }
+      if (offset > 0) {
+        index_begin = thrust::make_counting_iterator<size_type>(offset);
+        data        = data + offset;
+      } else if (offset < 0) {
+        index_end = thrust::make_counting_iterator<size_type>(size + offset);
+      }
+    }
+
+    auto func_value =
+      [size, offset, fill = scalar.data(), input = *device_input] __device__(size_type idx) {
+        auto src_idx = idx - offset;
+        return out_of_bounds(size, src_idx) ? *fill : input.element<T>(src_idx);
+      };
+
+    thrust::transform(rmm::exec_policy(stream), index_begin, index_end, data, func_value);
+
+    return output;
+  }
+};
+
+}  // anonymous namespace
+
+namespace detail {
+
+std::unique_ptr<column> shift(column_view const& input,
+                              size_type offset,
+                              scalar const& fill_value,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type() == fill_value.type(),
+               "shift requires each fill value type to match the corresponding column type.");
+
+  if (input.is_empty()) { return empty_like(input); }
+
+  return type_dispatcher<dispatch_storage_type>(
+    input.type(), shift_functor{}, input, offset, fill_value, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> shift(column_view const& input,
+                              size_type offset,
+                              scalar const& fill_value,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::shift(input, offset, fill_value, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/copying/slice.cu b/cpp/src/copying/slice.cu
new file mode 100644
index 0000000..7c524dd
--- /dev/null
+++ b/cpp/src/copying/slice.cu
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace detail {
+
+template <typename ColumnView>
+ColumnView slice(ColumnView const& input,
+                 size_type begin,
+                 size_type end,
+                 rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(begin >= 0, "Invalid beginning of range.");
+  CUDF_EXPECTS(end >= begin, "Invalid end of range.");
+  CUDF_EXPECTS(end <= input.size(), "Slice range out of bounds.");
+
+  std::vector<ColumnView> children{};
+  children.reserve(input.num_children());
+  for (size_type index = 0; index < input.num_children(); index++) {
+    children.emplace_back(input.child(index));
+  }
+
+  return ColumnView(
+    input.type(),
+    end - begin,
+    input.head(),
+    input.null_mask(),
+    input.null_count() ? cudf::detail::null_count(input.null_mask(), begin, end, stream) : 0,
+    input.offset() + begin,
+    children);
+}
+
+template column_view slice<column_view>(column_view const&,
+                                        size_type,
+                                        size_type,
+                                        rmm::cuda_stream_view);
+template mutable_column_view slice<mutable_column_view>(mutable_column_view const&,
+                                                        size_type,
+                                                        size_type,
+                                                        rmm::cuda_stream_view);
+
+std::vector<column_view> slice(column_view const& input,
+                               host_span<size_type const> indices,
+                               rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(indices.size() % 2 == 0, "indices size must be even");
+
+  if (indices.empty()) return {};
+
+  // need to shift incoming indices by the column offset to generate the correct bit ranges
+  // to count
+  auto indices_iter = cudf::detail::make_counting_transform_iterator(
+    0, [offset = input.offset(), &indices](size_type index) { return indices[index] + offset; });
+  auto null_counts = cudf::detail::segmented_null_count(
+    input.null_mask(), indices_iter, indices_iter + indices.size(), stream);
+
+  auto const children = std::vector<column_view>(input.child_begin(), input.child_end());
+
+  auto op = [&](auto i) {
+    auto begin = indices[2 * i];
+    auto end   = indices[2 * i + 1];
+    CUDF_EXPECTS(begin >= 0, "Starting index cannot be negative.");
+    CUDF_EXPECTS(end >= begin, "End index cannot be smaller than the starting index.");
+    CUDF_EXPECTS(end <= input.size(), "Slice range out of bounds.");
+    return column_view{input.type(),
+                       end - begin,
+                       input.head(),
+                       input.null_mask(),
+                       null_counts[i],
+                       input.offset() + begin,
+                       children};
+  };
+  auto begin = cudf::detail::make_counting_transform_iterator(0, op);
+  return std::vector<column_view>{begin, begin + indices.size() / 2};
+}
+
+std::vector<table_view> slice(table_view const& input,
+                              host_span<size_type const> indices,
+                              rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(indices.size() % 2 == 0, "indices size must be even");
+  if (indices.empty()) { return {}; }
+
+  // 2d arrangement of column_views that represent the outgoing table_views sliced_table[i][j]
+  // where i is the i'th column of the j'th table_view
+  auto op = [&indices, &stream](auto const& c) { return cudf::detail::slice(c, indices, stream); };
+  auto f  = thrust::make_transform_iterator(input.begin(), op);
+
+  auto sliced_table = std::vector<std::vector<cudf::column_view>>(f, f + input.num_columns());
+  sliced_table.reserve(indices.size() + 1);
+
+  std::vector<cudf::table_view> result{};
+  // distribute columns into outgoing table_views
+  size_t num_output_tables = indices.size() / 2;
+  for (size_t i = 0; i < num_output_tables; i++) {
+    std::vector<cudf::column_view> table_columns;
+    for (size_type j = 0; j < input.num_columns(); j++) {
+      table_columns.emplace_back(sliced_table[j][i]);
+    }
+    result.emplace_back(table_view{table_columns});
+  }
+
+  return result;
+}
+
+std::vector<column_view> slice(column_view const& input,
+                               std::initializer_list<size_type> indices,
+                               rmm::cuda_stream_view stream)
+{
+  return detail::slice(input, host_span<size_type const>(indices.begin(), indices.size()), stream);
+}
+
+std::vector<table_view> slice(table_view const& input,
+                              std::initializer_list<size_type> indices,
+                              rmm::cuda_stream_view stream)
+{
+  return detail::slice(input, host_span<size_type const>(indices.begin(), indices.size()), stream);
+};
+
+}  // namespace detail
+
+std::vector<column_view> slice(column_view const& input,
+                               host_span<size_type const> indices,
+                               rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::slice(input, indices, stream);
+}
+
+std::vector<table_view> slice(table_view const& input,
+                              host_span<size_type const> indices,
+                              rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::slice(input, indices, stream);
+};
+
+std::vector<column_view> slice(column_view const& input,
+                               std::initializer_list<size_type> indices,
+                               rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::slice(input, indices, stream);
+}
+
+std::vector<table_view> slice(table_view const& input,
+                              std::initializer_list<size_type> indices,
+                              rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::slice(input, indices, stream);
+};
+
+}  // namespace cudf
diff --git a/cpp/src/copying/split.cpp b/cpp/src/copying/split.cpp
new file mode 100644
index 0000000..1621bcd
--- /dev/null
+++ b/cpp/src/copying/split.cpp
@@ -0,0 +1,116 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <algorithm>
+
+namespace cudf {
+namespace detail {
+namespace {
+template <typename T>
+std::vector<T> split(T const& input,
+                     size_type column_size,
+                     host_span<size_type const> splits,
+                     rmm::cuda_stream_view stream)
+{
+  if (splits.empty() or column_size == 0) { return std::vector<T>{input}; }
+  CUDF_EXPECTS(splits.back() <= column_size, "splits can't exceed size of input columns");
+
+  // If the size is not zero, the split will always start at `0`
+  std::vector<size_type> indices{0};
+  std::for_each(splits.begin(), splits.end(), [&indices](auto split) {
+    indices.push_back(split);  // This for end
+    indices.push_back(split);  // This for the start
+  });
+
+  indices.push_back(column_size);  // This to include rest of the elements
+
+  return detail::slice(input, indices, stream);
+}
+
+};  // anonymous namespace
+
+std::vector<cudf::column_view> split(cudf::column_view const& input,
+                                     host_span<size_type const> splits,
+                                     rmm::cuda_stream_view stream)
+{
+  return split(input, input.size(), splits, stream);
+}
+
+std::vector<cudf::table_view> split(cudf::table_view const& input,
+                                    host_span<size_type const> splits,
+                                    rmm::cuda_stream_view stream)
+{
+  if (input.num_columns() == 0) { return {}; }
+  return split(input, input.column(0).size(), splits, stream);
+}
+
+std::vector<column_view> split(column_view const& input,
+                               std::initializer_list<size_type> splits,
+                               rmm::cuda_stream_view stream)
+{
+  return detail::split(input, host_span<size_type const>(splits.begin(), splits.size()), stream);
+}
+
+std::vector<table_view> split(table_view const& input,
+                              std::initializer_list<size_type> splits,
+                              rmm::cuda_stream_view stream)
+{
+  return detail::split(input, host_span<size_type const>(splits.begin(), splits.size()), stream);
+}
+
+}  // namespace detail
+
+std::vector<cudf::column_view> split(cudf::column_view const& input,
+                                     host_span<size_type const> splits,
+                                     rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split(input, splits, stream);
+}
+
+std::vector<cudf::table_view> split(cudf::table_view const& input,
+                                    host_span<size_type const> splits,
+                                    rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split(input, splits, stream);
+}
+
+std::vector<column_view> split(column_view const& input,
+                               std::initializer_list<size_type> splits,
+                               rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split(input, splits, stream);
+}
+
+std::vector<table_view> split(table_view const& input,
+                              std::initializer_list<size_type> splits,
+                              rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split(input, splits, stream);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/datetime/datetime_ops.cu b/cpp/src/datetime/datetime_ops.cu
new file mode 100644
index 0000000..371663c
--- /dev/null
+++ b/cpp/src/datetime/datetime_ops.cu
@@ -0,0 +1,725 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/datetime.hpp>
+#include <cudf/detail/datetime.hpp>
+#include <cudf/detail/datetime_ops.cuh>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf/wrappers/durations.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace datetime {
+namespace detail {
+enum class datetime_component {
+  INVALID = 0,
+  YEAR,
+  MONTH,
+  DAY,
+  WEEKDAY,
+  HOUR,
+  MINUTE,
+  SECOND,
+  MILLISECOND,
+  MICROSECOND,
+  NANOSECOND
+};
+
+enum class rounding_function {
+  CEIL,   ///< Rounds up to the next integer multiple of the provided frequency
+  FLOOR,  ///< Rounds down to the next integer multiple of the provided frequency
+  ROUND   ///< Rounds to the nearest integer multiple of the provided frequency
+};
+
+template <datetime_component Component>
+struct extract_component_operator {
+  template <typename Timestamp>
+  __device__ inline int16_t operator()(Timestamp const ts) const
+  {
+    using namespace cuda::std::chrono;
+
+    auto days_since_epoch = floor<days>(ts);
+
+    auto time_since_midnight = ts - days_since_epoch;
+
+    if (time_since_midnight.count() < 0) { time_since_midnight += days(1); }
+
+    auto const hrs_  = [&] { return duration_cast<hours>(time_since_midnight); };
+    auto const mins_ = [&] { return duration_cast<minutes>(time_since_midnight) - hrs_(); };
+    auto const secs_ = [&] {
+      return duration_cast<seconds>(time_since_midnight) - hrs_() - mins_();
+    };
+    auto const millisecs_ = [&] {
+      return duration_cast<milliseconds>(time_since_midnight) - hrs_() - mins_() - secs_();
+    };
+    auto const microsecs_ = [&] {
+      return duration_cast<microseconds>(time_since_midnight) - hrs_() - mins_() - secs_() -
+             millisecs_();
+    };
+    auto const nanosecs_ = [&] {
+      return duration_cast<nanoseconds>(time_since_midnight) - hrs_() - mins_() - secs_() -
+             millisecs_() - microsecs_();
+    };
+
+    switch (Component) {
+      case datetime_component::YEAR:
+        return static_cast<int>(year_month_day(days_since_epoch).year());
+      case datetime_component::MONTH:
+        return static_cast<unsigned>(year_month_day(days_since_epoch).month());
+      case datetime_component::DAY:
+        return static_cast<unsigned>(year_month_day(days_since_epoch).day());
+      case datetime_component::WEEKDAY:
+        return year_month_weekday(days_since_epoch).weekday().iso_encoding();
+      case datetime_component::HOUR: return hrs_().count();
+      case datetime_component::MINUTE: return mins_().count();
+      case datetime_component::SECOND: return secs_().count();
+      case datetime_component::MILLISECOND: return millisecs_().count();
+      case datetime_component::MICROSECOND: return microsecs_().count();
+      case datetime_component::NANOSECOND: return nanosecs_().count();
+      default: return 0;
+    }
+  }
+};
+
+// This functor takes the rounding type as runtime info and dispatches to the ceil/floor/round
+// function.
+template <typename DurationType>
+struct RoundFunctor {
+  template <typename Timestamp>
+  __device__ inline auto operator()(rounding_function round_kind, Timestamp dt)
+  {
+    switch (round_kind) {
+      case rounding_function::CEIL: return cuda::std::chrono::ceil<DurationType>(dt);
+      case rounding_function::FLOOR: return cuda::std::chrono::floor<DurationType>(dt);
+      case rounding_function::ROUND: return cuda::std::chrono::round<DurationType>(dt);
+      default: CUDF_UNREACHABLE("Unsupported rounding kind.");
+    }
+  }
+};
+
+struct RoundingDispatcher {
+  rounding_function round_kind;
+  rounding_frequency component;
+
+  RoundingDispatcher(rounding_function round_kind, rounding_frequency component)
+    : round_kind(round_kind), component(component)
+  {
+  }
+
+  template <typename Timestamp>
+  __device__ inline Timestamp operator()(Timestamp const ts) const
+  {
+    switch (component) {
+      case rounding_frequency::DAY:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_D>{}(round_kind, ts));
+      case rounding_frequency::HOUR:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_h>{}(round_kind, ts));
+      case rounding_frequency::MINUTE:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_m>{}(round_kind, ts));
+      case rounding_frequency::SECOND:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_s>{}(round_kind, ts));
+      case rounding_frequency::MILLISECOND:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_ms>{}(round_kind, ts));
+      case rounding_frequency::MICROSECOND:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_us>{}(round_kind, ts));
+      case rounding_frequency::NANOSECOND:
+        return time_point_cast<typename Timestamp::duration>(
+          RoundFunctor<duration_ns>{}(round_kind, ts));
+      default: CUDF_UNREACHABLE("Unsupported datetime rounding resolution.");
+    }
+  }
+};
+
+// Number of days until month indexed by leap year and month (0-based index)
+static __device__ int16_t const days_until_month[2][13] = {
+  {0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365},  // For non leap years
+  {0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366}   // For leap years
+};
+
+// Round up the date to the last day of the month and return the
+// date only (without the time component)
+struct extract_last_day_of_month {
+  template <typename Timestamp>
+  __device__ inline timestamp_D operator()(Timestamp const ts) const
+  {
+    using namespace cuda::std::chrono;
+    year_month_day const ymd(floor<days>(ts));
+    auto const ymdl = year_month_day_last{ymd.year() / ymd.month() / last};
+    return timestamp_D{sys_days{ymdl}};
+  }
+};
+
+// Extract the number of days of the month
+// A similar operator to `extract_last_day_of_month`, except this returns
+// an integer while the other returns a timestamp.
+struct days_in_month_op {
+  template <typename Timestamp>
+  __device__ inline int16_t operator()(Timestamp const ts) const
+  {
+    using namespace cuda::std::chrono;
+    auto const date = year_month_day(floor<days>(ts));
+    auto const ymdl = year_month_day_last(date.year() / date.month() / last);
+    return static_cast<int16_t>(unsigned{ymdl.day()});
+  }
+};
+
+// Extract the day number of the year present in the timestamp
+struct extract_day_num_of_year {
+  template <typename Timestamp>
+  __device__ inline int16_t operator()(Timestamp const ts) const
+  {
+    using namespace cuda::std::chrono;
+
+    // Only has the days - time component is chopped off, which is what we want
+    auto const days_since_epoch = floor<days>(ts);
+    auto const date             = year_month_day(days_since_epoch);
+
+    return days_until_month[date.year().is_leap()][unsigned{date.month()} - 1] +
+           unsigned{date.day()};
+  }
+};
+
+// Extract the quarter to which the timestamp belongs to
+struct extract_quarter_op {
+  template <typename Timestamp>
+  __device__ inline int16_t operator()(Timestamp const ts) const
+  {
+    using namespace cuda::std::chrono;
+
+    // Only has the days - time component is chopped off, which is what we want
+    auto const days_since_epoch = floor<days>(ts);
+    auto const date             = year_month_day(days_since_epoch);
+    auto const month            = unsigned{date.month()};
+
+    // (x + y - 1) / y = ceil(x/y), where x and y are unsigned. x = month, y = 3
+    return (month + 2) / 3;
+  }
+};
+
+// Returns true if the year is a leap year
+struct is_leap_year_op {
+  template <typename Timestamp>
+  __device__ inline bool operator()(Timestamp const ts) const
+  {
+    using namespace cuda::std::chrono;
+    auto const days_since_epoch = floor<days>(ts);
+    auto const date             = year_month_day(days_since_epoch);
+    return date.year().is_leap();
+  }
+};
+
+// Specific function for applying ceil/floor/round date ops
+struct dispatch_round {
+  template <typename Timestamp>
+  std::enable_if_t<cudf::is_timestamp<Timestamp>(), std::unique_ptr<cudf::column>> operator()(
+    rounding_function round_kind,
+    rounding_frequency component,
+    cudf::column_view const& column,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto size            = column.size();
+    auto output_col_type = data_type{cudf::type_to_id<Timestamp>()};
+
+    // Return an empty column if source column is empty
+    if (size == 0) return make_empty_column(output_col_type);
+
+    auto output = make_fixed_width_column(output_col_type,
+                                          size,
+                                          cudf::detail::copy_bitmask(column, stream, mr),
+                                          column.null_count(),
+                                          stream,
+                                          mr);
+
+    thrust::transform(rmm::exec_policy(stream),
+                      column.begin<Timestamp>(),
+                      column.end<Timestamp>(),
+                      output->mutable_view().begin<Timestamp>(),
+                      RoundingDispatcher{round_kind, component});
+
+    output->set_null_count(column.null_count());
+
+    return output;
+  }
+
+  template <typename Timestamp, typename... Args>
+  std::enable_if_t<!cudf::is_timestamp<Timestamp>(), std::unique_ptr<cudf::column>> operator()(
+    Args&&...)
+  {
+    CUDF_FAIL("Must be cudf::timestamp");
+  }
+};
+
+// Apply the functor for every element/row in the input column to create the output column
+template <typename TransformFunctor, typename OutputColT>
+struct launch_functor {
+  column_view input;
+  mutable_column_view output;
+
+  launch_functor(column_view inp, mutable_column_view out) : input(inp), output(out) {}
+
+  template <typename Element>
+  std::enable_if_t<!cudf::is_timestamp_t<Element>::value, void> operator()(
+    rmm::cuda_stream_view stream) const
+  {
+    CUDF_FAIL("Cannot extract datetime component from non-timestamp column.");
+  }
+
+  template <typename Timestamp>
+  std::enable_if_t<cudf::is_timestamp_t<Timestamp>::value, void> operator()(
+    rmm::cuda_stream_view stream) const
+  {
+    thrust::transform(rmm::exec_policy(stream),
+                      input.begin<Timestamp>(),
+                      input.end<Timestamp>(),
+                      output.begin<OutputColT>(),
+                      TransformFunctor{});
+  }
+};
+
+// Create an output column by applying the functor to every element from the input column
+template <typename TransformFunctor, cudf::type_id OutputColCudfT>
+std::unique_ptr<column> apply_datetime_op(column_view const& column,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_timestamp(column.type()), "Column type should be timestamp");
+  auto size            = column.size();
+  auto output_col_type = data_type{OutputColCudfT};
+
+  // Return an empty column if source column is empty
+  if (size == 0) return make_empty_column(output_col_type);
+
+  auto output = make_fixed_width_column(output_col_type,
+                                        size,
+                                        cudf::detail::copy_bitmask(column, stream, mr),
+                                        column.null_count(),
+                                        stream,
+                                        mr);
+  auto launch = launch_functor<TransformFunctor, cudf::id_to_type<OutputColCudfT>>{
+    column, static_cast<mutable_column_view>(*output)};
+
+  type_dispatcher(column.type(), launch, stream);
+
+  return output;
+}
+
+struct add_calendrical_months_functor {
+  template <typename Element, typename... Args>
+  std::enable_if_t<!cudf::is_timestamp_t<Element>::value, std::unique_ptr<column>> operator()(
+    Args&&...) const
+  {
+    CUDF_FAIL("Cannot extract datetime component from non-timestamp column.");
+  }
+
+  template <typename Timestamp, typename MonthIterator>
+  std::enable_if_t<cudf::is_timestamp_t<Timestamp>::value, std::unique_ptr<column>> operator()(
+    column_view timestamp_column,
+    MonthIterator months_begin,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto size            = timestamp_column.size();
+    auto output_col_type = timestamp_column.type();
+
+    // Return an empty column if source column is empty
+    if (size == 0) return make_empty_column(output_col_type);
+
+    // The nullmask of `output` cannot be determined without information from
+    // the `months` type (column or scalar). Therefore, it is initialized as
+    // `UNALLOCATED` and assigned at a later stage.
+    auto output =
+      make_fixed_width_column(output_col_type, size, mask_state::UNALLOCATED, stream, mr);
+    auto output_mview = output->mutable_view();
+
+    thrust::transform(rmm::exec_policy(stream),
+                      timestamp_column.begin<Timestamp>(),
+                      timestamp_column.end<Timestamp>(),
+                      months_begin,
+                      output->mutable_view().begin<Timestamp>(),
+                      [] __device__(auto& timestamp, auto& months) {
+                        return add_calendrical_months_with_scale_back(
+                          timestamp, cuda::std::chrono::months{months});
+                      });
+    return output;
+  }
+};
+
+std::unique_ptr<column> add_calendrical_months(column_view const& timestamp_column,
+                                               column_view const& months_column,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_timestamp(timestamp_column.type()), "Column type should be timestamp");
+  CUDF_EXPECTS(
+    months_column.type().id() == type_id::INT16 or months_column.type().id() == type_id::INT32,
+    "Months column type should be INT16 or INT32.");
+  CUDF_EXPECTS(timestamp_column.size() == months_column.size(),
+               "Timestamp and months column should be of the same size");
+
+  auto const months_begin_iter =
+    cudf::detail::indexalator_factory::make_input_iterator(months_column);
+  auto output = type_dispatcher(timestamp_column.type(),
+                                add_calendrical_months_functor{},
+                                timestamp_column,
+                                months_begin_iter,
+                                stream,
+                                mr);
+
+  auto [output_null_mask, null_count] =
+    cudf::detail::bitmask_and(table_view{{timestamp_column, months_column}}, stream, mr);
+  output->set_null_mask(std::move(output_null_mask), null_count);
+  return output;
+}
+
+std::unique_ptr<column> add_calendrical_months(column_view const& timestamp_column,
+                                               scalar const& months,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_timestamp(timestamp_column.type()), "Column type should be timestamp");
+  CUDF_EXPECTS(months.type().id() == type_id::INT16 or months.type().id() == type_id::INT32,
+               "Months type should be INT16 or INT32");
+
+  if (months.is_valid(stream)) {
+    auto const months_begin_iter = thrust::make_permutation_iterator(
+      cudf::detail::indexalator_factory::make_input_iterator(months),
+      thrust::make_constant_iterator(0));
+    auto output = type_dispatcher(timestamp_column.type(),
+                                  add_calendrical_months_functor{},
+                                  timestamp_column,
+                                  months_begin_iter,
+                                  stream,
+                                  mr);
+    output->set_null_mask(cudf::detail::copy_bitmask(timestamp_column, stream, mr),
+                          timestamp_column.null_count());
+    return output;
+  } else {
+    return make_timestamp_column(
+      timestamp_column.type(), timestamp_column.size(), mask_state::ALL_NULL, stream, mr);
+  }
+}
+
+std::unique_ptr<column> round_general(rounding_function round_kind,
+                                      rounding_frequency component,
+                                      column_view const& column,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  return cudf::type_dispatcher(
+    column.type(), dispatch_round{}, round_kind, component, column, stream, mr);
+}
+
+std::unique_ptr<column> extract_year(column_view const& column,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::YEAR>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_month(column_view const& column,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::MONTH>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_day(column_view const& column,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::DAY>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_weekday(column_view const& column,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::WEEKDAY>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_hour(column_view const& column,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::HOUR>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_minute(column_view const& column,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::MINUTE>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_second(column_view const& column,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::SECOND>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_millisecond_fraction(column_view const& column,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::MILLISECOND>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_microsecond_fraction(column_view const& column,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::MICROSECOND>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_nanosecond_fraction(column_view const& column,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<
+    detail::extract_component_operator<detail::datetime_component::NANOSECOND>,
+    cudf::type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> last_day_of_month(column_view const& column,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<detail::extract_last_day_of_month,
+                                   cudf::type_id::TIMESTAMP_DAYS>(column, stream, mr);
+}
+
+std::unique_ptr<column> day_of_year(column_view const& column,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  return detail::apply_datetime_op<detail::extract_day_num_of_year, cudf::type_id::INT16>(
+    column, stream, mr);
+}
+
+std::unique_ptr<column> is_leap_year(column_view const& column,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  return apply_datetime_op<is_leap_year_op, type_id::BOOL8>(column, stream, mr);
+}
+
+std::unique_ptr<column> days_in_month(column_view const& column,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  return apply_datetime_op<days_in_month_op, type_id::INT16>(column, stream, mr);
+}
+
+std::unique_ptr<column> extract_quarter(column_view const& column,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  return apply_datetime_op<extract_quarter_op, type_id::INT16>(column, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> ceil_datetimes(column_view const& column,
+                                       rounding_frequency freq,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::round_general(
+    detail::rounding_function::CEIL, freq, column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> floor_datetimes(column_view const& column,
+                                        rounding_frequency freq,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::round_general(
+    detail::rounding_function::FLOOR, freq, column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> round_datetimes(column_view const& column,
+                                        rounding_frequency freq,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::round_general(
+    detail::rounding_function::ROUND, freq, column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_year(column_view const& column, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_year(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_month(column_view const& column,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_month(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_day(column_view const& column, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_day(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_weekday(column_view const& column,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_weekday(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_hour(column_view const& column, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_hour(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_minute(column_view const& column,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_minute(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_second(column_view const& column,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_second(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_millisecond_fraction(column_view const& column,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_millisecond_fraction(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_microsecond_fraction(column_view const& column,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_microsecond_fraction(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_nanosecond_fraction(column_view const& column,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_nanosecond_fraction(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> last_day_of_month(column_view const& column,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::last_day_of_month(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> day_of_year(column_view const& column, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::day_of_year(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> add_calendrical_months(cudf::column_view const& timestamp_column,
+                                                     cudf::column_view const& months_column,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::add_calendrical_months(
+    timestamp_column, months_column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> add_calendrical_months(cudf::column_view const& timestamp_column,
+                                                     cudf::scalar const& months,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::add_calendrical_months(timestamp_column, months, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> is_leap_year(column_view const& column, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_leap_year(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> days_in_month(column_view const& column,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::days_in_month(column, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> extract_quarter(column_view const& column,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_quarter(column, cudf::get_default_stream(), mr);
+}
+
+}  // namespace datetime
+}  // namespace cudf
diff --git a/cpp/src/datetime/timezone.cpp b/cpp/src/datetime/timezone.cpp
new file mode 100644
index 0000000..1b40a99
--- /dev/null
+++ b/cpp/src/datetime/timezone.cpp
@@ -0,0 +1,515 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/detail/timezone.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/table/table.hpp>
+
+#include <algorithm>
+#include <filesystem>
+#include <fstream>
+
+namespace cudf {
+
+namespace {
+
+constexpr uint32_t tzif_magic           = ('T' << 0) | ('Z' << 8) | ('i' << 16) | ('f' << 24);
+std::string const tzif_system_directory = "/usr/share/zoneinfo/";
+
+#pragma pack(push, 1)
+/**
+ * @brief 32-bit TZif header
+ */
+struct timezone_file_header {
+  uint32_t magic;          ///< "TZif"
+  uint8_t version;         ///< 0:version1, '2':version2, '3':version3
+  uint8_t reserved15[15];  ///< unused, reserved for future use
+  uint32_t isutccnt;       ///< number of UTC/local indicators contained in the body
+  uint32_t isstdcnt;       ///< number of standard/wall indicators contained in the body
+  uint32_t leapcnt;        ///< number of leap second records contained in the body
+  uint32_t timecnt;        ///< number of transition times contained in the body
+  uint32_t typecnt;  ///< number of local time type Records contained in the body - MUST NOT be zero
+  uint32_t charcnt;  ///< total number of octets used by the set of time zone designations contained
+                     ///< in the body
+};
+
+struct localtime_type_record_s {
+  int32_t utcoff;    // number of seconds to be added to UTC in order to determine local time
+  uint8_t isdst;     // 0:standard time, 1:Daylight Savings Time (DST)
+  uint8_t desigidx;  // index into the series of time zone designation characters
+};
+
+struct dst_transition_s {
+  char type;  // Transition type ('J','M' or day)
+  int month;  // Month of transition
+  int week;   // Week of transition
+  int day;    // Day of transition
+  int time;   // Time of day (seconds)
+};
+#pragma pack(pop)
+
+struct timezone_file {
+  timezone_file_header header;
+  bool is_header_from_64bit = false;
+
+  std::vector<int64_t> transition_times;
+  std::vector<uint8_t> ttime_idx;
+  std::vector<localtime_type_record_s> ttype;
+  std::vector<char> posix_tz_string;
+
+  [[nodiscard]] auto timecnt() const { return header.timecnt; }
+  [[nodiscard]] auto typecnt() const { return header.typecnt; }
+
+  // Based on https://tools.ietf.org/id/draft-murchison-tzdist-tzif-00.html
+  static constexpr auto leap_second_rec_size(bool is_64bit) noexcept
+  {
+    return (is_64bit ? sizeof(uint64_t) : sizeof(uint32_t)) + sizeof(uint32_t);
+  }
+  static constexpr auto file_content_size_32(timezone_file_header const& header) noexcept
+  {
+    return header.timecnt * sizeof(uint32_t) +                 // transition times
+           header.timecnt * sizeof(uint8_t) +                  // transition time index
+           header.typecnt * sizeof(localtime_type_record_s) +  // local time type records
+           header.charcnt * sizeof(uint8_t) +                  // time zone designations
+           header.leapcnt * leap_second_rec_size(false) +      // leap second records
+           header.isstdcnt * sizeof(uint8_t) +                 // standard/wall indicators
+           header.isutccnt * sizeof(uint8_t);                  // UTC/local indicators
+  }
+
+  /**
+   * @brief Used because little-endian platform in assumed.
+   */
+  void header_to_little_endian()
+  {
+    header.isutccnt = __builtin_bswap32(header.isutccnt);
+    header.isstdcnt = __builtin_bswap32(header.isstdcnt);
+    header.leapcnt  = __builtin_bswap32(header.leapcnt);
+    header.timecnt  = __builtin_bswap32(header.timecnt);
+    header.typecnt  = __builtin_bswap32(header.typecnt);
+    header.charcnt  = __builtin_bswap32(header.charcnt);
+  }
+
+  void read_header(std::ifstream& input_file, size_t file_size)
+  {
+    input_file.read(reinterpret_cast<char*>(&header), sizeof(header));
+    CUDF_EXPECTS(!input_file.fail() && header.magic == tzif_magic,
+                 "Error reading time zones file header.");
+    header_to_little_endian();
+
+    // Check for 64-bit header
+    if (header.version != 0) {
+      if (file_content_size_32(header) + sizeof(header) < file_size) {
+        // skip the 32-bit content
+        input_file.seekg(file_content_size_32(header), std::ios_base::cur);
+        // read the 64-bit header
+        input_file.read(reinterpret_cast<char*>(&header), sizeof(header));
+        header_to_little_endian();
+        is_header_from_64bit = true;
+      }
+    }
+    CUDF_EXPECTS(
+      header.typecnt > 0 && header.typecnt <= file_size / sizeof(localtime_type_record_s),
+      "Invalid number of time types in timezone file.");
+    CUDF_EXPECTS(header.timecnt <= file_size,
+                 "Number of transition times is larger than the file size.");
+  }
+
+  timezone_file(std::optional<std::string_view> tzif_dir, std::string_view timezone_name)
+  {
+    using std::ios_base;
+
+    // Open the input file
+    auto const tz_filename =
+      std::filesystem::path{tzif_dir.value_or(tzif_system_directory)} / timezone_name;
+    std::ifstream fin;
+    fin.open(tz_filename, ios_base::in | ios_base::binary | ios_base::ate);
+    CUDF_EXPECTS(fin, "Failed to open the timezone file.");
+    auto const file_size = fin.tellg();
+    fin.seekg(0);
+
+    read_header(fin, file_size);
+
+    // Read transition times (convert from 32-bit to 64-bit if necessary)
+    transition_times.resize(timecnt());
+    if (is_header_from_64bit) {
+      fin.read(reinterpret_cast<char*>(transition_times.data()),
+               transition_times.size() * sizeof(int64_t));
+      for (auto& tt : transition_times) {
+        tt = __builtin_bswap64(tt);
+      }
+    } else {
+      std::vector<int32_t> tt32(timecnt());
+      fin.read(reinterpret_cast<char*>(tt32.data()), tt32.size() * sizeof(int32_t));
+      std::transform(
+        tt32.cbegin(), tt32.cend(), std::back_inserter(transition_times), [](auto& tt) {
+          return __builtin_bswap32(tt);
+        });
+    }
+    ttime_idx.resize(timecnt());
+    fin.read(reinterpret_cast<char*>(ttime_idx.data()), timecnt() * sizeof(uint8_t));
+
+    // Read time types
+    ttype.resize(typecnt());
+    fin.read(reinterpret_cast<char*>(ttype.data()), typecnt() * sizeof(localtime_type_record_s));
+    CUDF_EXPECTS(!fin.fail(), "Failed to read time types from the time zone file.");
+    for (uint32_t i = 0; i < typecnt(); i++) {
+      ttype[i].utcoff = __builtin_bswap32(ttype[i].utcoff);
+    }
+
+    // Read posix TZ string
+    fin.seekg(header.charcnt + header.leapcnt * leap_second_rec_size(is_header_from_64bit) +
+                header.isstdcnt + header.isutccnt,
+              ios_base::cur);
+    auto const file_pos = fin.tellg();
+    if (file_size - file_pos > 1) {
+      posix_tz_string.resize(file_size - file_pos);
+      fin.read(posix_tz_string.data(), file_size - file_pos);
+    }
+  }
+};
+
+/**
+ * @brief Posix TZ parser
+ */
+template <class Container>
+class posix_parser {
+ public:
+  posix_parser(Container const& tz_string) : cur{tz_string.begin()}, end{tz_string.end()} {}
+
+  /**
+   * @brief Advances the parser past a name from the posix TZ string.
+   */
+  void skip_name();
+
+  /**
+   * @brief Parses a number from the posix TZ string.
+   *
+   * @return Parsed number
+   */
+  int64_t parse_number();
+
+  /**
+   * @brief Parses a UTC offset from the posix TZ string.
+   *
+   * @return Parsed offset
+   */
+  int32_t parse_offset();
+
+  /**
+   * @brief Parses a DST transition time from the posix TZ string.
+   *
+   * @return Parsed transition time
+   */
+  dst_transition_s parse_transition();
+
+  /**
+   * @brief Returns the remaining number of characters in the input.
+   */
+  auto remaining_char_cnt() const { return end - cur; }
+
+  /**
+   * @brief Returns the next character in the input.
+   */
+  [[nodiscard]] char next_character() const { return *cur; }
+
+ private:
+  typename Container::const_iterator cur;
+  typename Container::const_iterator const end;
+};
+
+/**
+ * @brief Skips the next name token.
+ *
+ * Name can be a string of letters, such as EST, or an arbitrary string surrounded by angle
+ * brackets, such as <UTC-05>
+ */
+template <class Container>
+void posix_parser<Container>::skip_name()
+{
+  cur = std::find_if(cur, end, [](auto c) {
+    return std::isdigit(c) || c == '-' || c == ',' || c == '+' || c == '<';
+  });
+
+  if (*cur == '<') cur = std::next(std::find(cur, end, '>'));
+}
+
+template <class Container>
+int64_t posix_parser<Container>::parse_number()
+{
+  int64_t v = 0;
+  while (cur < end) {
+    auto const c = *cur - '0';
+    if (c > 9 || c < 0) { break; }
+    v = v * 10 + c;
+    ++cur;
+  }
+  return v;
+}
+
+template <class Container>
+int32_t posix_parser<Container>::parse_offset()
+{
+  CUDF_EXPECTS(cur < end, "Unexpected end of input stream");
+
+  auto const sign = *cur;
+  cur += (sign == '-' || sign == '+');
+
+  auto const hours   = parse_number();
+  auto scale         = 60 * 60;
+  auto total_seconds = hours * scale;
+
+  // Parse minutes and seconds, if present
+  while (cur < end && scale > 1 && *cur == ':') {
+    // Skip the ':' character
+    ++cur;
+    // Scale becomes 60, for minutes, and then 1, for seconds
+    scale /= 60;
+    total_seconds += parse_number() * scale;
+  }
+
+  return (sign == '-') ? -total_seconds : total_seconds;
+}
+
+template <class Container>
+dst_transition_s posix_parser<Container>::parse_transition()
+{
+  CUDF_EXPECTS(cur < end, "Unexpected end of input stream");
+
+  // Transition at 2AM by default
+  int32_t time = 2 * 60 * 60;
+  if (cur + 2 <= end && *cur == ',') {
+    char const type = cur[1];
+    int month       = 0;
+    int week        = 0;
+    int day         = 0;
+    cur += (type == 'M' || type == 'J') ? 2 : 1;
+    if (type == 'M') {
+      month = parse_number();
+      if (cur < end && *cur == '.') {
+        ++cur;
+        week = parse_number();
+        if (cur < end && *cur == '.') {
+          ++cur;
+          day = parse_number();
+        }
+      }
+    } else {
+      day = parse_number();
+    }
+    if (cur < end && *cur == '/') {
+      ++cur;
+      time = parse_offset();
+    }
+    return {type, month, week, day, time};
+  }
+  return {0, 0, 0, 0, time};
+}
+
+/**
+ * @brief Returns the number of days in a month.
+ */
+static int days_in_month(int month, bool is_leap_year)
+{
+  CUDF_EXPECTS(month > 0 && month <= 12, "Invalid month");
+
+  if (month == 2) return 28 + is_leap_year;
+  return 30 + ((0b1010110101010 >> month) & 1);
+}
+
+/**
+ * @brief Converts a daylight saving transition time to a number of seconds.
+ *
+ * @param trans transition day information
+ * @param year year of transition
+ *
+ * @return transition time in seconds from the beginning of the year
+ */
+static int64_t get_transition_time(dst_transition_s const& trans, int year)
+{
+  auto day = trans.day;
+
+  auto const is_leap = cuda::std::chrono::year{year}.is_leap();
+
+  if (trans.type == 'M') {
+    auto const month = std::min(std::max(trans.month, 1), 12);
+    auto week        = std::min(std::max(trans.week, 1), 52);
+
+    // Year-to-year day adjustment
+    auto const adjusted_month = (month + 9) % 12 + 1;
+    auto const adjusted_year  = year - (month <= 2);
+    auto day_of_week =
+      ((26 * adjusted_month - 2) / 10 + 1 + (adjusted_year % 100) + (adjusted_year % 100) / 4 +
+       (adjusted_year / 400) - 2 * (adjusted_year / 100)) %
+      7;
+    if (day_of_week < 0) { day_of_week += 7; }
+    day = (day - day_of_week + 7) % 7;
+
+    // Add weeks
+    while (week > 1 && day + 7 < days_in_month(month, is_leap)) {
+      week--;
+      day += 7;
+    }
+    // Add months
+    for (int m = 1; m < month; m++) {
+      day += days_in_month(m, is_leap);
+    }
+  } else if (trans.type == 'J') {
+    // Account for 29th of February on leap years
+    day += (day > 31 + 29 && is_leap);
+  }
+
+  return trans.time + cuda::std::chrono::duration_cast<duration_s>(duration_D{day}).count();
+}
+
+}  // namespace
+
+std::unique_ptr<table> make_timezone_transition_table(std::optional<std::string_view> tzif_dir,
+                                                      std::string_view timezone_name,
+                                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::make_timezone_transition_table(
+    tzif_dir, timezone_name, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+
+std::unique_ptr<table> make_timezone_transition_table(std::optional<std::string_view> tzif_dir,
+                                                      std::string_view timezone_name,
+                                                      rmm::cuda_stream_view stream,
+                                                      rmm::mr::device_memory_resource* mr)
+{
+  if (timezone_name == "UTC" || timezone_name.empty()) {
+    // Return an empty table for UTC
+    return std::make_unique<cudf::table>();
+  }
+
+  timezone_file const tzf(tzif_dir, timezone_name);
+
+  std::vector<timestamp_s::rep> transition_times(1);
+  std::vector<duration_s::rep> offsets(1);
+  // One ancient rule entry, one per TZ file entry, 2 entries per year in the future cycle
+  transition_times.reserve(1 + tzf.timecnt() + solar_cycle_entry_count);
+  offsets.reserve(1 + tzf.timecnt() + solar_cycle_entry_count);
+  size_t earliest_std_idx = 0;
+  for (size_t t = 0; t < tzf.timecnt(); t++) {
+    auto const ttime = tzf.transition_times[t];
+    auto const idx   = tzf.ttime_idx[t];
+    CUDF_EXPECTS(idx < tzf.typecnt(), "Out-of-range type index");
+    auto const utcoff = tzf.ttype[idx].utcoff;
+    transition_times.push_back(ttime);
+    offsets.push_back(utcoff);
+    if (!earliest_std_idx && !tzf.ttype[idx].isdst) {
+      earliest_std_idx = transition_times.size() - 1;
+    }
+  }
+
+  if (tzf.timecnt() != 0) {
+    if (!earliest_std_idx) { earliest_std_idx = 1; }
+    transition_times[0] = transition_times[earliest_std_idx];
+    offsets[0]          = offsets[earliest_std_idx];
+  } else {
+    if (tzf.typecnt() == 0 || tzf.ttype[0].utcoff == 0) {
+      // No transitions, offset is zero; Table would be a no-op.
+      // Return an empty table to speed up parsing.
+      return std::make_unique<cudf::table>();
+    }
+    // No transitions to use for the time/offset - use the first offset and apply to all timestamps
+    transition_times[0] = std::numeric_limits<int64_t>::max();
+    offsets[0]          = tzf.ttype[0].utcoff;
+  }
+
+  // Generate entries for times after the last transition
+  auto future_std_offset = offsets[tzf.timecnt()];
+  auto future_dst_offset = future_std_offset;
+  dst_transition_s dst_start{};
+  dst_transition_s dst_end{};
+  if (!tzf.posix_tz_string.empty()) {
+    posix_parser<decltype(tzf.posix_tz_string)> parser(tzf.posix_tz_string);
+    parser.skip_name();
+    future_std_offset = -parser.parse_offset();
+    if (parser.remaining_char_cnt() > 1) {
+      // Parse Daylight Saving Time information
+      parser.skip_name();
+      if (parser.remaining_char_cnt() > 0 && parser.next_character() != ',') {
+        future_dst_offset = -parser.parse_offset();
+      } else {
+        future_dst_offset = future_std_offset + 60 * 60;
+      }
+      dst_start = parser.parse_transition();
+      dst_end   = parser.parse_transition();
+    } else {
+      future_dst_offset = future_std_offset;
+    }
+  }
+
+  // Add entries to fill the transition cycle
+  int64_t year_timestamp = 0;
+  for (int32_t year = 1970; year < 1970 + solar_cycle_years; ++year) {
+    auto const dst_start_time = get_transition_time(dst_start, year);
+    auto const dst_end_time   = get_transition_time(dst_end, year);
+
+    // Two entries per year, since there are two transitions
+    transition_times.push_back(year_timestamp + dst_start_time - future_std_offset);
+    offsets.push_back(future_dst_offset);
+    transition_times.push_back(year_timestamp + dst_end_time - future_dst_offset);
+    offsets.push_back(future_std_offset);
+
+    // Swap the newly added transitions if in descending order
+    if (transition_times.rbegin()[1] > transition_times.rbegin()[0]) {
+      std::swap(transition_times.rbegin()[0], transition_times.rbegin()[1]);
+      std::swap(offsets.rbegin()[0], offsets.rbegin()[1]);
+    }
+
+    year_timestamp += cuda::std::chrono::duration_cast<duration_s>(
+                        duration_D{365 + cuda::std::chrono::year{year}.is_leap()})
+                        .count();
+  }
+
+  CUDF_EXPECTS(transition_times.size() == offsets.size(),
+               "Error reading TZif file for timezone " + std::string{timezone_name});
+
+  std::vector<timestamp_s> ttimes_typed;
+  ttimes_typed.reserve(transition_times.size());
+  std::transform(transition_times.cbegin(),
+                 transition_times.cend(),
+                 std::back_inserter(ttimes_typed),
+                 [](auto ts) { return timestamp_s{duration_s{ts}}; });
+  std::vector<duration_s> offsets_typed;
+  offsets_typed.reserve(offsets.size());
+  std::transform(offsets.cbegin(), offsets.cend(), std::back_inserter(offsets_typed), [](auto ts) {
+    return duration_s{ts};
+  });
+
+  auto d_ttimes  = cudf::detail::make_device_uvector_async(ttimes_typed, stream, mr);
+  auto d_offsets = cudf::detail::make_device_uvector_async(offsets_typed, stream, mr);
+
+  std::vector<std::unique_ptr<column>> tz_table_columns;
+  tz_table_columns.emplace_back(
+    std::make_unique<cudf::column>(std::move(d_ttimes), rmm::device_buffer{}, 0));
+  tz_table_columns.emplace_back(
+    std::make_unique<cudf::column>(std::move(d_offsets), rmm::device_buffer{}, 0));
+
+  // Need to finish copies before transition_times and offsets go out of scope
+  stream.synchronize();
+
+  return std::make_unique<cudf::table>(std::move(tz_table_columns));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/dictionary/add_keys.cu b/cpp/src/dictionary/add_keys.cu
new file mode 100644
index 0000000..3973100
--- /dev/null
+++ b/cpp/src/dictionary/add_keys.cu
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+/**
+ * @brief Create a new dictionary column by adding the new keys elements
+ * to the existing dictionary_column.
+ *
+ * ```
+ * Example:
+ * d1 = {[a, b, c, d, f], {4, 0, 3, 1, 2, 2, 2, 4, 0}}
+ * d2 = add_keys( d1, [d, b, e] )
+ * d2 is now {[a, b, c, d, e, f], [5, 0, 3, 1, 2, 2, 2, 5, 0]}
+ * ```
+ */
+std::unique_ptr<column> add_keys(dictionary_column_view const& dictionary_column,
+                                 column_view const& new_keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!new_keys.has_nulls(), "Keys must not have nulls");
+  auto old_keys = dictionary_column.keys();  // [a,b,c,d,f]
+  CUDF_EXPECTS(new_keys.type() == old_keys.type(), "Keys must be the same type");
+  // first, concatenate the keys together
+  // [a,b,c,d,f] + [d,b,e] = [a,b,c,d,f,d,b,e]
+  auto combined_keys = cudf::detail::concatenate(
+    std::vector<column_view>{old_keys, new_keys}, stream, rmm::mr::get_current_device_resource());
+
+  // Drop duplicates from the combined keys, then sort the result.
+  // sort(distinct([a,b,c,d,f,d,b,e])) = [a,b,c,d,e,f]
+  auto table_keys = cudf::detail::distinct(table_view{{combined_keys->view()}},
+                                           std::vector<size_type>{0},  // only one key column
+                                           duplicate_keep_option::KEEP_ANY,
+                                           null_equality::EQUAL,
+                                           nan_equality::ALL_EQUAL,
+                                           stream,
+                                           mr);
+  std::vector<order> column_order{order::ASCENDING};
+  std::vector<null_order> null_precedence{null_order::AFTER};  // should be no nulls here
+  auto sorted_keys =
+    cudf::detail::sort(table_keys->view(), column_order, null_precedence, stream, mr)->release();
+
+  std::unique_ptr<column> keys_column(std::move(sorted_keys.front()));
+  // create a map for the indices
+  // lower_bound([a,b,c,d,e,f],[a,b,c,d,f]) = [0,1,2,3,5]
+  auto map_indices = cudf::detail::lower_bound(table_view{{keys_column->view()}},
+                                               table_view{{old_keys}},
+                                               column_order,
+                                               null_precedence,
+                                               stream,
+                                               mr);
+  // now create the indices column -- map old values to the new ones
+  // gather([4,0,3,1,2,2,2,4,0],[0,1,2,3,5]) = [5,0,3,1,2,2,2,5,0]
+  column_view indices_view(dictionary_column.indices().type(),
+                           dictionary_column.size(),
+                           dictionary_column.indices().head(),
+                           nullptr,
+                           0,
+                           dictionary_column.offset());
+  // the result may contain nulls if the input contains nulls
+  // and the corresponding index is therefore invalid/undefined
+  auto table_indices = cudf::detail::gather(table_view{{map_indices->view()}},
+                                            indices_view,
+                                            cudf::out_of_bounds_policy::NULLIFY,
+                                            cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                            stream,
+                                            mr)
+                         ->release();
+  // The output of lower_bound is INT32 but we need to convert to unsigned indices.
+  auto const indices_type = get_indices_type_for_size(keys_column->size());
+  auto indices_column     = [&] {
+    column_view gather_result = table_indices.front()->view();
+    auto const indices_size   = gather_result.size();
+    // we can just use the lower-bound/gather data directly for UINT32 case
+    if (indices_type.id() == type_id::UINT32) {
+      auto contents = table_indices.front()->release();
+      return std::make_unique<column>(data_type{type_id::UINT32},
+                                      indices_size,
+                                      std::move(*(contents.data.release())),
+                                      rmm::device_buffer{0, stream, mr},
+                                      0);
+    }
+    // otherwise we need to convert the gather result
+    column_view cast_view(gather_result.type(), indices_size, gather_result.head(), nullptr, 0);
+    return cudf::detail::cast(cast_view, indices_type, stream, mr);
+  }();
+
+  // create new dictionary column with keys_column and indices_column
+  // null mask has not changed
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                cudf::detail::copy_bitmask(dictionary_column.parent(), stream, mr),
+                                dictionary_column.null_count());
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> add_keys(dictionary_column_view const& dictionary_column,
+                                 column_view const& keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::add_keys(dictionary_column, keys, stream, mr);
+}
+
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/decode.cu b/cpp/src/dictionary/decode.cu
new file mode 100644
index 0000000..fdf546b
--- /dev/null
+++ b/cpp/src/dictionary/decode.cu
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+/**
+ * @brief Decode a column from a dictionary.
+ */
+std::unique_ptr<column> decode(dictionary_column_view const& source,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  if (source.is_empty()) return make_empty_column(type_id::EMPTY);
+
+  column_view indices{source.indices().type(),
+                      source.size(),
+                      source.indices().head(),
+                      nullptr,  // no nulls for gather indices
+                      0,
+                      source.offset()};
+  // use gather to create the output column -- use ignore_out_of_bounds=true
+  auto table_column = cudf::detail::gather(table_view{{source.keys()}},
+                                           indices,
+                                           cudf::out_of_bounds_policy::NULLIFY,
+                                           cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                           stream,
+                                           mr)
+                        ->release();
+  auto output_column = std::unique_ptr<column>(std::move(table_column.front()));
+
+  // apply any nulls to the output column
+  output_column->set_null_mask(cudf::detail::copy_bitmask(source.parent(), stream, mr),
+                               source.null_count());
+
+  return output_column;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> decode(dictionary_column_view const& source,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::decode(source, stream, mr);
+}
+
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/detail/concatenate.cu b/cpp/src/dictionary/detail/concatenate.cu
new file mode 100644
index 0000000..121b5bc
--- /dev/null
+++ b/cpp/src/dictionary/detail/concatenate.cu
@@ -0,0 +1,298 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/detail/concatenate.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+#include <algorithm>
+#include <vector>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+namespace {
+
+/**
+ * @brief Keys and indices offsets values.
+ *
+ * The first value is the keys offsets and the second values is the indices offsets.
+ * These are offsets to the beginning of each input column after concatenating.
+ */
+using offsets_pair = thrust::pair<size_type, size_type>;
+
+/**
+ * @brief Utility for calculating the offsets for the concatenated child columns
+ *        of the output dictionary column.
+ */
+struct compute_children_offsets_fn {
+  /**
+   * @brief Create the utility functor.
+   *
+   * The columns vector is converted into vector of column_view pointers so they
+   * can be used in thrust::transform_exclusive_scan without causing the
+   * compiler warning/error: "host/device function calling host function".
+   *
+   * @param columns The input dictionary columns.
+   */
+  compute_children_offsets_fn(host_span<column_view const> columns) : columns_ptrs{columns.size()}
+  {
+    std::transform(
+      columns.begin(), columns.end(), columns_ptrs.begin(), [](auto& cv) { return &cv; });
+  }
+
+  /**
+   * @brief Return the first keys().type of the dictionary columns.
+   */
+  data_type get_keys_type()
+  {
+    auto const view(*std::find_if(
+      columns_ptrs.begin(), columns_ptrs.end(), [](auto pcv) { return pcv->size() > 0; }));
+    return dictionary_column_view(*view).keys().type();
+  }
+
+  /**
+   * @brief Create the offsets pair for the concatenated columns.
+   *
+   * Both vectors have the length of the number of input columns.
+   * The sizes of each child (keys and indices) of the individual columns
+   * are used to create the offsets.
+   *
+   * @param stream Stream used for allocating the output rmm::device_uvector.
+   * @return Vector of offsets_pair objects for keys and indices.
+   */
+  rmm::device_uvector<offsets_pair> create_children_offsets(rmm::cuda_stream_view stream)
+  {
+    std::vector<offsets_pair> offsets(columns_ptrs.size());
+    thrust::transform_exclusive_scan(
+      thrust::host,
+      columns_ptrs.begin(),
+      columns_ptrs.end(),
+      offsets.begin(),
+      [](auto pcv) {
+        dictionary_column_view view(*pcv);
+        return offsets_pair{view.keys_size(), view.size()};
+      },
+      offsets_pair{0, 0},
+      [](auto lhs, auto rhs) {
+        return offsets_pair{lhs.first + rhs.first, lhs.second + rhs.second};
+      });
+    return cudf::detail::make_device_uvector_sync(
+      offsets, stream, rmm::mr::get_current_device_resource());
+  }
+
+ private:
+  std::vector<column_view const*> columns_ptrs;  ///< pointer version of input column_view vector
+};
+
+/**
+ * @brief Type-dispatch functor for remapping the old indices to new values based
+ * on the new key-set.
+ *
+ * The dispatch is based on the key type.
+ * The output column is the updated indices child for the new dictionary column.
+ */
+struct dispatch_compute_indices {
+  template <typename Element>
+  std::enable_if_t<cudf::is_relationally_comparable<Element, Element>(), std::unique_ptr<column>>
+  operator()(column_view const& all_keys,
+             column_view const& all_indices,
+             column_view const& new_keys,
+             offsets_pair const* d_offsets,
+             size_type const* d_map_to_keys,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr)
+  {
+    auto keys_view     = column_device_view::create(all_keys, stream);
+    auto indices_view  = column_device_view::create(all_indices, stream);
+    auto d_all_indices = *indices_view;
+
+    auto indices_itr = cudf::detail::indexalator_factory::make_input_iterator(all_indices);
+    // map the concatenated indices to the concatenated keys
+    auto all_itr = thrust::make_permutation_iterator(
+      keys_view->begin<Element>(),
+      thrust::make_transform_iterator(
+        thrust::make_counting_iterator<size_type>(0),
+        [d_offsets, d_map_to_keys, d_all_indices, indices_itr] __device__(size_type idx) {
+          if (d_all_indices.is_null(idx)) return 0;
+          return indices_itr[idx] + d_offsets[d_map_to_keys[idx]].first;
+        }));
+
+    auto new_keys_view = column_device_view::create(new_keys, stream);
+
+    auto begin = new_keys_view->begin<Element>();
+    auto end   = new_keys_view->end<Element>();
+
+    // create the indices output column
+    auto result = make_numeric_column(
+      all_indices.type(), all_indices.size(), mask_state::UNALLOCATED, stream, mr);
+    auto result_itr =
+      cudf::detail::indexalator_factory::make_output_iterator(result->mutable_view());
+    // new indices values are computed by matching the concatenated keys to the new key set
+
+#ifdef NDEBUG
+    thrust::lower_bound(rmm::exec_policy(stream),
+                        begin,
+                        end,
+                        all_itr,
+                        all_itr + all_indices.size(),
+                        result_itr,
+                        thrust::less<Element>());
+#else
+    // There is a problem with thrust::lower_bound and the output_indexalator.
+    // https://github.com/NVIDIA/thrust/issues/1452; thrust team created nvbug 3322776
+    // This is a workaround.
+    thrust::transform(rmm::exec_policy(stream),
+                      all_itr,
+                      all_itr + all_indices.size(),
+                      result_itr,
+                      [begin, end] __device__(auto key) {
+                        auto itr = thrust::lower_bound(thrust::seq, begin, end, key);
+                        return static_cast<size_type>(thrust::distance(begin, itr));
+                      });
+#endif
+    return result;
+  }
+
+  template <typename Element, typename... Args>
+  std::enable_if_t<!cudf::is_relationally_comparable<Element, Element>(), std::unique_ptr<column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("dictionary concatenate not supported for this column type");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  // exception here is the same behavior as in cudf::concatenate
+  CUDF_EXPECTS(not columns.empty(), "Unexpected empty list of columns to concatenate.");
+
+  // concatenate the keys (and check the keys match)
+  compute_children_offsets_fn child_offsets_fn{columns};
+  auto keys_type = child_offsets_fn.get_keys_type();
+  std::vector<column_view> keys_views(columns.size());
+  std::transform(columns.begin(), columns.end(), keys_views.begin(), [keys_type](auto cv) {
+    auto dict_view = dictionary_column_view(cv);
+    // empty column may not have keys so we create an empty column_view place-holder
+    if (dict_view.is_empty()) return column_view{keys_type, 0, nullptr, nullptr, 0};
+    auto keys = dict_view.keys();
+    CUDF_EXPECTS(keys.type() == keys_type, "key types of all dictionary columns must match");
+    return keys;
+  });
+  auto all_keys =
+    cudf::detail::concatenate(keys_views, stream, rmm::mr::get_current_device_resource());
+
+  // sort keys and remove duplicates;
+  // this becomes the keys child for the output dictionary column
+  auto table_keys  = cudf::detail::distinct(table_view{{all_keys->view()}},
+                                           std::vector<size_type>{0},
+                                           duplicate_keep_option::KEEP_ANY,
+                                           null_equality::EQUAL,
+                                           nan_equality::ALL_EQUAL,
+                                           stream,
+                                           mr);
+  auto sorted_keys = cudf::detail::sort(table_keys->view(),
+                                        std::vector<order>{order::ASCENDING},
+                                        std::vector<null_order>{null_order::BEFORE},
+                                        stream,
+                                        mr)
+                       ->release();
+  std::unique_ptr<column> keys_column(std::move(sorted_keys.front()));
+
+  // next, concatenate the indices
+  std::vector<column_view> indices_views(columns.size());
+  std::transform(columns.begin(), columns.end(), indices_views.begin(), [](auto cv) {
+    auto dict_view = dictionary_column_view(cv);
+    if (dict_view.is_empty()) {
+      return column_view{data_type{type_id::UINT32}, 0, nullptr, nullptr, 0};
+    }
+    return dict_view.get_indices_annotated();  // nicely includes validity mask and view offset
+  });
+  auto all_indices        = cudf::detail::concatenate(indices_views, stream, mr);
+  auto const indices_size = all_indices->size();
+
+  // build a vector of values to map the old indices to the concatenated keys
+  auto children_offsets = child_offsets_fn.create_children_offsets(stream);
+  rmm::device_uvector<size_type> map_to_keys(indices_size, stream);
+  auto indices_itr =
+    cudf::detail::make_counting_transform_iterator(1, [] __device__(size_type idx) {
+      return offsets_pair{0, idx};
+    });
+  // the indices offsets (pair.second) are for building the map
+  thrust::lower_bound(
+    rmm::exec_policy(stream),
+    children_offsets.begin() + 1,
+    children_offsets.end(),
+    indices_itr,
+    indices_itr + indices_size,
+    map_to_keys.begin(),
+    [] __device__(auto const& lhs, auto const& rhs) { return lhs.second < rhs.second; });
+
+  // now recompute the indices values for the new keys_column;
+  // the keys offsets (pair.first) are for mapping to the input keys
+  auto indices_column = type_dispatcher(keys_type,
+                                        dispatch_compute_indices{},
+                                        all_keys->view(),     // old keys
+                                        all_indices->view(),  // old indices
+                                        keys_column->view(),  // new keys
+                                        children_offsets.data(),
+                                        map_to_keys.data(),
+                                        stream,
+                                        mr);
+
+  // remove the bitmask from the all_indices
+  auto null_count = all_indices->null_count();  // get before release()
+  auto contents   = all_indices->release();     // all_indices will now be empty
+
+  // finally, frankenstein that dictionary column together
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                std::move(*(contents.null_mask.release())),
+                                null_count);
+}
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/detail/merge.cu b/cpp/src/dictionary/detail/merge.cu
new file mode 100644
index 0000000..2fe2168
--- /dev/null
+++ b/cpp/src/dictionary/detail/merge.cu
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/detail/merge.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+
+std::unique_ptr<column> merge(dictionary_column_view const& lcol,
+                              dictionary_column_view const& rcol,
+                              cudf::detail::index_vector const& row_order,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  auto const lcol_iter = cudf::detail::indexalator_factory::make_input_iterator(lcol.indices());
+  auto const rcol_iter = cudf::detail::indexalator_factory::make_input_iterator(rcol.indices());
+
+  // create output indices column
+  auto const merged_size  = lcol.size() + rcol.size();
+  auto const indices_type = get_indices_type_for_size(merged_size);
+  auto indices_column =
+    make_fixed_width_column(indices_type, merged_size, cudf::mask_state::UNALLOCATED, stream, mr);
+  auto output_iter =
+    cudf::detail::indexalator_factory::make_output_iterator(indices_column->mutable_view());
+
+  // merge the input indices columns into the output column
+  thrust::transform(rmm::exec_policy(stream),
+                    row_order.begin(),
+                    row_order.end(),
+                    output_iter,
+                    [lcol_iter, rcol_iter] __device__(auto const& index_pair) {
+                      auto const [side, index] = index_pair;
+                      return side == cudf::detail::side::LEFT ? lcol_iter[index] : rcol_iter[index];
+                    });
+
+  // build dictionary; the validity mask is updated by the caller
+  return make_dictionary_column(
+    std::make_unique<column>(lcol.keys(), stream, mr),
+    std::move(indices_column),
+    cudf::detail::create_null_mask(
+      lcol.has_nulls() || rcol.has_nulls() ? static_cast<size_t>(merged_size) : 0,
+      mask_state::UNINITIALIZED,
+      stream,
+      mr),
+    lcol.null_count() + rcol.null_count());
+}
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/dictionary_column_view.cpp b/cpp/src/dictionary/dictionary_column_view.cpp
new file mode 100644
index 0000000..4906e5b
--- /dev/null
+++ b/cpp/src/dictionary/dictionary_column_view.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/error.hpp>
+
+namespace cudf {
+//
+dictionary_column_view::dictionary_column_view(column_view const& dictionary_column)
+  : column_view(dictionary_column)
+{
+  CUDF_EXPECTS(type().id() == type_id::DICTIONARY32,
+               "dictionary_column_view only supports DICTIONARY type");
+  if (size() > 0) CUDF_EXPECTS(num_children() == 2, "dictionary column has no children");
+}
+
+column_view dictionary_column_view::parent() const noexcept
+{
+  return static_cast<column_view>(*this);
+}
+
+column_view dictionary_column_view::indices() const noexcept { return child(0); }
+
+column_view dictionary_column_view::get_indices_annotated() const noexcept
+{
+  return column_view(
+    indices().type(), size(), indices().head(), null_mask(), null_count(), offset());
+}
+
+column_view dictionary_column_view::keys() const noexcept { return child(1); }
+
+size_type dictionary_column_view::keys_size() const noexcept
+{
+  return (size() == 0) ? 0 : keys().size();
+}
+
+data_type dictionary_column_view::keys_type() const noexcept
+{
+  return (size() == 0) ? data_type{type_id::EMPTY} : keys().type();
+}
+
+}  // namespace cudf
diff --git a/cpp/src/dictionary/dictionary_factories.cu b/cpp/src/dictionary/dictionary_factories.cu
new file mode 100644
index 0000000..f70423a
--- /dev/null
+++ b/cpp/src/dictionary/dictionary_factories.cu
@@ -0,0 +1,151 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace {
+struct dispatch_create_indices {
+  template <typename IndexType, std::enable_if_t<is_index_type<IndexType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& indices,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(std::is_unsigned<IndexType>(), "indices must be an unsigned type");
+    column_view indices_view{
+      indices.type(), indices.size(), indices.data<IndexType>(), nullptr, 0, indices.offset()};
+    return std::make_unique<column>(indices_view, stream, mr);
+  }
+  template <typename IndexType, std::enable_if_t<!is_index_type<IndexType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("indices must be an integer type.");
+  }
+};
+}  // namespace
+
+std::unique_ptr<column> make_dictionary_column(column_view const& keys_column,
+                                               column_view const& indices_column,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!keys_column.has_nulls(), "keys column must not have nulls");
+  if (keys_column.is_empty()) return make_empty_column(type_id::DICTIONARY32);
+
+  auto keys_copy = std::make_unique<column>(keys_column, stream, mr);
+  auto indices_copy =
+    type_dispatcher(indices_column.type(), dispatch_create_indices{}, indices_column, stream, mr);
+  rmm::device_buffer null_mask{0, stream, mr};
+  auto null_count = indices_column.null_count();
+  if (null_count) null_mask = detail::copy_bitmask(indices_column, stream, mr);
+
+  std::vector<std::unique_ptr<column>> children;
+  children.emplace_back(std::move(indices_copy));
+  children.emplace_back(std::move(keys_copy));
+  return std::make_unique<column>(data_type{type_id::DICTIONARY32},
+                                  indices_column.size(),
+                                  rmm::device_buffer{0, stream, mr},
+                                  std::move(null_mask),
+                                  null_count,
+                                  std::move(children));
+}
+
+std::unique_ptr<column> make_dictionary_column(std::unique_ptr<column> keys_column,
+                                               std::unique_ptr<column> indices_column,
+                                               rmm::device_buffer&& null_mask,
+                                               size_type null_count)
+{
+  CUDF_EXPECTS(!keys_column->has_nulls(), "keys column must not have nulls");
+  CUDF_EXPECTS(!indices_column->has_nulls(), "indices column must not have nulls");
+  CUDF_EXPECTS(is_unsigned(indices_column->type()), "indices must be type unsigned integer");
+
+  auto count = indices_column->size();
+  std::vector<std::unique_ptr<column>> children;
+  children.emplace_back(std::move(indices_column));
+  children.emplace_back(std::move(keys_column));
+  return std::make_unique<column>(data_type{type_id::DICTIONARY32},
+                                  count,
+                                  rmm::device_buffer{},
+                                  std::move(null_mask),
+                                  null_count,
+                                  std::move(children));
+}
+
+namespace {
+
+/**
+ * @brief This functor maps signed type_ids to unsigned counterparts.
+ */
+struct make_unsigned_fn {
+  template <typename T, std::enable_if_t<is_index_type<T>()>* = nullptr>
+  constexpr cudf::type_id operator()()
+  {
+    return cudf::type_to_id<std::make_unsigned_t<T>>();
+  }
+  template <typename T, std::enable_if_t<not is_index_type<T>()>* = nullptr>
+  constexpr cudf::type_id operator()()
+  {
+    return cudf::type_to_id<T>();
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> make_dictionary_column(std::unique_ptr<column> keys,
+                                               std::unique_ptr<column> indices,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!keys->has_nulls(), "keys column must not have nulls");
+
+  // signed integer data can be used directly in the unsigned indices column
+  auto const indices_type = cudf::type_dispatcher(indices->type(), make_unsigned_fn{});
+  auto const indices_size = indices->size();        // these need to be saved
+  auto const null_count   = indices->null_count();  // before calling release()
+  auto contents           = indices->release();
+  // compute the indices type using the size of the key set
+  auto const new_type = dictionary::detail::get_indices_type_for_size(keys->size());
+
+  // create the dictionary indices: convert to unsigned and remove nulls
+  auto indices_column = [&] {
+    // If the types match, then just commandeer the column's data buffer.
+    if (new_type.id() == indices_type) {
+      return std::make_unique<column>(
+        new_type, indices_size, std::move(*(contents.data.release())), rmm::device_buffer{}, 0);
+    }
+    // If the new type does not match, then convert the data.
+    cudf::column_view cast_view{
+      cudf::data_type{indices_type}, indices_size, contents.data->data(), nullptr, 0};
+    return cudf::detail::cast(cast_view, new_type, stream, mr);
+  }();
+
+  return make_dictionary_column(std::move(keys),
+                                std::move(indices_column),
+                                std::move(*(contents.null_mask.release())),
+                                null_count);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/dictionary/encode.cu b/cpp/src/dictionary/encode.cu
new file mode 100644
index 0000000..c92b57f
--- /dev/null
+++ b/cpp/src/dictionary/encode.cu
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+/**
+ * @copydoc cudf::dictionary::encode
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> encode(column_view const& input_column,
+                               data_type indices_type,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_unsigned(indices_type), "indices must be type unsigned integer");
+  CUDF_EXPECTS(input_column.type().id() != type_id::DICTIONARY32,
+               "cannot encode a dictionary from a dictionary");
+
+  auto codified       = cudf::detail::encode(cudf::table_view({input_column}), stream, mr);
+  auto keys_table     = std::move(codified.first);
+  auto indices_column = std::move(codified.second);
+  auto keys_column    = std::move(keys_table->release().front());
+
+  if (keys_column->has_nulls()) {
+    keys_column = std::make_unique<column>(
+      cudf::detail::slice(
+        keys_column->view(), std::vector<size_type>{0, keys_column->size() - 1}, stream)
+        .front(),
+      stream,
+      mr);
+    keys_column->set_null_mask(rmm::device_buffer{0, stream, mr}, 0);  // remove the null-mask
+  }
+
+  // the encode() returns INT32 for indices
+  if (indices_column->type().id() != indices_type.id())
+    indices_column = cudf::detail::cast(indices_column->view(), indices_type, stream, mr);
+
+  // create column with keys_column and indices_column
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                cudf::detail::copy_bitmask(input_column, stream, mr),
+                                input_column.null_count());
+}
+
+/**
+ * @copydoc cudf::dictionary::detail::get_indices_type_for_size
+ */
+data_type get_indices_type_for_size(size_type keys_size)
+{
+  if (keys_size <= std::numeric_limits<uint8_t>::max()) return data_type{type_id::UINT8};
+  if (keys_size <= std::numeric_limits<uint16_t>::max()) return data_type{type_id::UINT16};
+  return data_type{type_id::UINT32};
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> encode(column_view const& input_column,
+                               data_type indices_type,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::encode(input_column, indices_type, stream, mr);
+}
+
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/remove_keys.cu b/cpp/src/dictionary/remove_keys.cu
new file mode 100644
index 0000000..86b70f1
--- /dev/null
+++ b/cpp/src/dictionary/remove_keys.cu
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+namespace {
+
+/**
+ * @brief Return a new dictionary by removing identified keys from the provided dictionary.
+ *
+ * This is a common utility for `remove_keys` and `remove_unused_keys` detail functions.
+ * It will create a new dictionary with the remaining keys and create new indices values
+ * to go with these new keys.
+ *
+ * @tparam KeysKeeper Function bool(size_type) that takes keys position index
+ *                    and returns true if that key is to be used in the output dictionary.
+ * @param dictionary_column The column to use for creating the new dictionary.
+ * @param keys_to_keep_fn Called to determine which keys in `dictionary_column` to keep.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ */
+template <typename KeysKeeper>
+std::unique_ptr<column> remove_keys_fn(dictionary_column_view const& dictionary_column,
+                                       KeysKeeper keys_to_keep_fn,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto const keys_view    = dictionary_column.keys();
+  auto const indices_type = dictionary_column.indices().type();
+  auto const max_size     = dictionary_column.size();
+
+  // create/init indices map array
+  auto map_indices =
+    make_fixed_width_column(indices_type, keys_view.size(), mask_state::UNALLOCATED, stream);
+  auto map_itr =
+    cudf::detail::indexalator_factory::make_output_iterator(map_indices->mutable_view());
+  // init to max to identify new nulls
+  thrust::fill(rmm::exec_policy(stream),
+               map_itr,
+               map_itr + keys_view.size(),
+               max_size);  // all valid indices are less than this value
+
+  // build keys column and indices map
+  std::unique_ptr<column> keys_column = [&] {
+    // create keys positions column to identify original key positions after removing they keys
+    auto keys_positions = [&] {
+      auto positions = make_fixed_width_column(
+        indices_type, keys_view.size(), cudf::mask_state::UNALLOCATED, stream);
+      auto itr = cudf::detail::indexalator_factory::make_output_iterator(positions->mutable_view());
+      thrust::sequence(rmm::exec_policy(stream), itr, itr + keys_view.size());
+      return positions;
+    }();
+    // copy the non-removed keys ( keys_to_keep_fn(idx)==true )
+    auto table_keys =
+      cudf::detail::copy_if(
+        table_view{{keys_view, keys_positions->view()}}, keys_to_keep_fn, stream, mr)
+        ->release();
+    auto const filtered_view = table_keys[1]->view();
+    auto filtered_itr = cudf::detail::indexalator_factory::make_input_iterator(filtered_view);
+    auto positions_itr =
+      cudf::detail::indexalator_factory::make_input_iterator(keys_positions->view());
+    // build indices mapper
+    // Example scatter([0,1,2][0,2,4][max,max,max,max,max]) => [0,max,1,max,2]
+    thrust::scatter(rmm::exec_policy(stream),
+                    positions_itr,
+                    positions_itr + filtered_view.size(),
+                    filtered_itr,
+                    map_itr);
+    return std::move(table_keys.front());
+  }();
+
+  // create non-nullable indices view with offset applied -- this is used as a gather map
+  column_view indices_view(dictionary_column.indices().type(),
+                           dictionary_column.size(),
+                           dictionary_column.indices().head(),
+                           nullptr,
+                           0,
+                           dictionary_column.offset());
+  // create new indices column
+  // Example: gather([0,max,1,max,2],[4,0,3,1,2,2,2,4,0]) => [2,0,max,max,1,1,1,2,0]
+  auto table_indices = cudf::detail::gather(table_view{{map_indices->view()}},
+                                            indices_view,
+                                            cudf::out_of_bounds_policy::NULLIFY,
+                                            cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                            stream,
+                                            mr)
+                         ->release();
+  std::unique_ptr<column> indices_column(std::move(table_indices.front()));
+  indices_column->set_null_mask(rmm::device_buffer{}, 0);
+
+  // compute new nulls -- merge the existing nulls with the newly created ones (value<0)
+  auto const offset = dictionary_column.offset();
+  auto d_null_mask  = dictionary_column.null_mask();
+  auto indices_itr = cudf::detail::indexalator_factory::make_input_iterator(indices_column->view());
+  auto new_nulls   = cudf::detail::valid_if(
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(dictionary_column.size()),
+    [offset, d_null_mask, indices_itr, max_size] __device__(size_type idx) {
+      if (d_null_mask && !bit_is_set(d_null_mask, idx + offset)) return false;
+      return (indices_itr[idx] < max_size);  // new nulls have max values
+    },
+    stream,
+    mr);
+  rmm::device_buffer new_null_mask =
+    (new_nulls.second > 0) ? std::move(new_nulls.first) : rmm::device_buffer{0, stream, mr};
+
+  // create column with keys_column and indices_column
+  return make_dictionary_column(
+    std::move(keys_column), std::move(indices_column), std::move(new_null_mask), new_nulls.second);
+}
+
+}  // namespace
+
+std::unique_ptr<column> remove_keys(dictionary_column_view const& dictionary_column,
+                                    column_view const& keys_to_remove,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!keys_to_remove.has_nulls(), "keys_to_remove must not have nulls");
+  auto const keys_view = dictionary_column.keys();
+  CUDF_EXPECTS(keys_view.type() == keys_to_remove.type(), "keys types must match");
+
+  // locate keys to remove by searching the keys column
+  auto const matches = cudf::detail::contains(keys_to_remove, keys_view, stream, mr);
+  auto d_matches     = matches->view().data<bool>();
+  // call common utility method to keep the keys not matched to keys_to_remove
+  auto key_matcher = [d_matches] __device__(size_type idx) { return !d_matches[idx]; };
+  return remove_keys_fn(dictionary_column, key_matcher, stream, mr);
+}
+
+std::unique_ptr<column> remove_unused_keys(dictionary_column_view const& dictionary_column,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  // locate the keys to remove
+  auto const keys_size     = dictionary_column.keys_size();
+  column_view indices_view = dictionary_column.get_indices_annotated();
+
+  // search the indices values with key indices to look for any holes
+  auto const matches = [&] {
+    // build keys index to verify against indices values
+    rmm::device_uvector<uint32_t> keys_positions(keys_size, stream);
+    thrust::sequence(rmm::exec_policy(stream), keys_positions.begin(), keys_positions.end());
+    // wrap the indices for comparison in contains()
+    column_view keys_positions_view(
+      data_type{type_id::UINT32}, keys_size, keys_positions.data(), nullptr, 0);
+    return cudf::detail::contains(indices_view, keys_positions_view, stream, mr);
+  }();
+  auto d_matches = matches->view().data<bool>();
+
+  // call common utility method to keep the keys that match
+  auto key_matcher = [d_matches] __device__(size_type idx) { return d_matches[idx]; };
+  return remove_keys_fn(dictionary_column, key_matcher, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> remove_keys(dictionary_column_view const& dictionary_column,
+                                    column_view const& keys_to_remove,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::remove_keys(dictionary_column, keys_to_remove, stream, mr);
+}
+
+std::unique_ptr<column> remove_unused_keys(dictionary_column_view const& dictionary_column,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::remove_unused_keys(dictionary_column, stream, mr);
+}
+
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/replace.cu b/cpp/src/dictionary/replace.cu
new file mode 100644
index 0000000..7069993
--- /dev/null
+++ b/cpp/src/dictionary/replace.cu
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/copy_if_else.cuh>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/detail/replace.hpp>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+namespace {
+
+/**
+ * @brief This utility uses `copy_if_else` to replace null entries using the input bitmask as a
+ * predicate.
+ *
+ * The predicate identifies which column row to copy from and the bitmask specifies which rows
+ * are null. Since the `copy_if_else` accepts iterators, we also supply it with pair-iterators
+ * created from indexalators and the validity masks.
+ *
+ * @tparam ReplacementItr must be a pair iterator of (index,valid).
+ *
+ * @param input lhs for `copy_if_else`
+ * @param replacement_iter rhs for `copy_if_else`
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return Always returns column of type INT32 (size_type)
+ */
+template <typename ReplacementIter>
+std::unique_ptr<column> replace_indices(column_view const& input,
+                                        ReplacementIter replacement_iter,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  auto const input_view = column_device_view::create(input, stream);
+  auto const d_input    = *input_view;
+  auto predicate        = [d_input] __device__(auto i) { return d_input.is_valid(i); };
+
+  auto input_iterator = cudf::detail::indexalator_factory::make_input_optional_iterator(input);
+
+  return cudf::detail::copy_if_else(true,
+                                    input_iterator,
+                                    input_iterator + input.size(),
+                                    replacement_iter,
+                                    predicate,
+                                    data_type{type_to_id<size_type>()},
+                                    stream,
+                                    mr);
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::dictionary::detail::replace_nulls(cudf::column_view const&,cudf::column_view
+ * const& rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<column> replace_nulls(dictionary_column_view const& input,
+                                      dictionary_column_view const& replacement,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return cudf::empty_like(input.parent()); }
+  if (!input.has_nulls()) { return std::make_unique<cudf::column>(input.parent(), stream, mr); }
+  CUDF_EXPECTS(input.keys().type() == replacement.keys().type(), "keys must match");
+  CUDF_EXPECTS(replacement.size() == input.size(), "column sizes must match");
+
+  // first combine the keys so both input dictionaries have the same set
+  auto matched =
+    match_dictionaries(std::vector<dictionary_column_view>({input, replacement}), stream, mr);
+
+  // now build the new indices by doing replace-null using the updated input indices
+  auto const input_indices =
+    dictionary_column_view(matched.front()->view()).get_indices_annotated();
+  auto const repl_indices = dictionary_column_view(matched.back()->view()).get_indices_annotated();
+
+  auto new_indices =
+    replace_indices(input_indices,
+                    cudf::detail::indexalator_factory::make_input_optional_iterator(repl_indices),
+                    stream,
+                    mr);
+
+  return make_dictionary_column(
+    std::move(matched.front()->release().children.back()), std::move(new_indices), stream, mr);
+}
+
+/**
+ * @copydoc cudf::dictionary::detail::replace_nulls(cudf::column_view const&,cudf::scalar
+ * const&, rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<column> replace_nulls(dictionary_column_view const& input,
+                                      scalar const& replacement,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return cudf::empty_like(input.parent()); }
+  if (!input.has_nulls() || !replacement.is_valid(stream)) {
+    return std::make_unique<cudf::column>(input.parent(), stream, mr);
+  }
+  CUDF_EXPECTS(input.keys().type() == replacement.type(), "keys must match scalar type");
+
+  // first add the replacement to the keys so only the indices need to be processed
+  auto input_matched = dictionary::detail::add_keys(
+    input, make_column_from_scalar(replacement, 1, stream)->view(), stream, mr);
+  auto const input_view = dictionary_column_view(input_matched->view());
+  auto const scalar_index =
+    get_index(input_view, replacement, stream, rmm::mr::get_current_device_resource());
+
+  // now build the new indices by doing replace-null on the updated indices
+  auto const input_indices = input_view.get_indices_annotated();
+  auto new_indices =
+    replace_indices(input_indices,
+                    cudf::detail::indexalator_factory::make_input_optional_iterator(*scalar_index),
+                    stream,
+                    mr);
+  new_indices->set_null_mask(rmm::device_buffer{0, stream, mr}, 0);
+
+  return make_dictionary_column(
+    std::move(input_matched->release().children.back()), std::move(new_indices), stream, mr);
+}
+
+}  // namespace detail
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/search.cu b/cpp/src/dictionary/search.cu
new file mode 100644
index 0000000..e35aded
--- /dev/null
+++ b/cpp/src/dictionary/search.cu
@@ -0,0 +1,185 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/search.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+
+namespace {
+
+struct dispatch_scalar_index {
+  template <typename IndexType, std::enable_if_t<is_index_type<IndexType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(size_type index,
+                                     bool is_valid,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return std::make_unique<numeric_scalar<IndexType>>(index, is_valid, stream, mr);
+  }
+  template <typename IndexType,
+            typename... Args,
+            std::enable_if_t<not is_index_type<IndexType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(Args&&...)
+  {
+    CUDF_FAIL("indices must be an integral type");
+  }
+};
+
+/**
+ * @brief Find index of a given key within a dictionary's keys column.
+ *
+ * The index is the position within the keys column where the given key (scalar) is found.
+ * The keys column is sorted and unique so only one value is expected.
+ * The result is an integer scalar identifying the index value.
+ * If the key is not found, the resulting scalar has `is_valid()=false`.
+ */
+struct find_index_fn {
+  template <typename Element,
+            std::enable_if_t<not std::is_same_v<Element, dictionary32> and
+                             not std::is_same_v<Element, list_view> and
+                             not std::is_same_v<Element, struct_view>>* = nullptr>
+  std::unique_ptr<scalar> operator()(dictionary_column_view const& input,
+                                     scalar const& key,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    if (!key.is_valid(stream))
+      return type_dispatcher(input.indices().type(), dispatch_scalar_index{}, 0, false, stream, mr);
+    CUDF_EXPECTS(input.keys().type() == key.type(),
+                 "search key type must match dictionary keys type");
+
+    using ScalarType = cudf::scalar_type_t<Element>;
+    auto find_key    = static_cast<ScalarType const&>(key).value(stream);
+    auto keys_view   = column_device_view::create(input.keys(), stream);
+    auto iter        = thrust::equal_range(
+      rmm::exec_policy(stream), keys_view->begin<Element>(), keys_view->end<Element>(), find_key);
+    return type_dispatcher(input.indices().type(),
+                           dispatch_scalar_index{},
+                           thrust::distance(keys_view->begin<Element>(), iter.first),
+                           (thrust::distance(iter.first, iter.second) > 0),
+                           stream,
+                           mr);
+  }
+
+  template <
+    typename Element,
+    std::enable_if_t<std::is_same_v<Element, dictionary32> or std::is_same_v<Element, list_view> or
+                     std::is_same_v<Element, struct_view>>* = nullptr>
+  std::unique_ptr<scalar> operator()(dictionary_column_view const&,
+                                     scalar const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL(
+      "dictionary, list_view, and struct_view columns cannot be the keys column of a dictionary");
+  }
+};
+
+struct find_insert_index_fn {
+  template <typename Element,
+            std::enable_if_t<not std::is_same_v<Element, dictionary32> and
+                             not std::is_same_v<Element, list_view> and
+                             not std::is_same_v<Element, struct_view>>* = nullptr>
+  std::unique_ptr<scalar> operator()(dictionary_column_view const& input,
+                                     scalar const& key,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    if (!key.is_valid(stream))
+      return type_dispatcher(input.indices().type(), dispatch_scalar_index{}, 0, false, stream, mr);
+    CUDF_EXPECTS(input.keys().type() == key.type(),
+                 "search key type must match dictionary keys type");
+
+    using ScalarType = cudf::scalar_type_t<Element>;
+    auto find_key    = static_cast<ScalarType const&>(key).value(stream);
+    auto keys_view   = column_device_view::create(input.keys(), stream);
+    auto iter        = thrust::lower_bound(
+      rmm::exec_policy(stream), keys_view->begin<Element>(), keys_view->end<Element>(), find_key);
+    return type_dispatcher(input.indices().type(),
+                           dispatch_scalar_index{},
+                           thrust::distance(keys_view->begin<Element>(), iter),
+                           true,
+                           stream,
+                           mr);
+  }
+
+  template <
+    typename Element,
+    std::enable_if_t<std::is_same_v<Element, dictionary32> or std::is_same_v<Element, list_view> or
+                     std::is_same_v<Element, struct_view>>* = nullptr>
+  std::unique_ptr<scalar> operator()(dictionary_column_view const&,
+                                     scalar const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("dictionary, list_view, and struct_view columns cannot be the keys for a dictionary");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<scalar> get_index(dictionary_column_view const& dictionary,
+                                  scalar const& key,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  if (dictionary.is_empty())
+    return std::make_unique<numeric_scalar<uint32_t>>(0, false, stream, mr);
+  return type_dispatcher<dispatch_storage_type>(
+    dictionary.keys().type(), find_index_fn(), dictionary, key, stream, mr);
+}
+
+std::unique_ptr<scalar> get_insert_index(dictionary_column_view const& dictionary,
+                                         scalar const& key,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if (dictionary.is_empty())
+    return std::make_unique<numeric_scalar<uint32_t>>(0, false, stream, mr);
+  return type_dispatcher<dispatch_storage_type>(
+    dictionary.keys().type(), find_insert_index_fn(), dictionary, key, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<scalar> get_index(dictionary_column_view const& dictionary,
+                                  scalar const& key,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::get_index(dictionary, key, stream, mr);
+}
+
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/dictionary/set_keys.cu b/cpp/src/dictionary/set_keys.cu
new file mode 100644
index 0000000..b49cf78
--- /dev/null
+++ b/cpp/src/dictionary/set_keys.cu
@@ -0,0 +1,261 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <iterator>
+
+namespace cudf {
+namespace dictionary {
+namespace detail {
+namespace {
+
+/**
+ * @brief Type-dispatch functor for remapping the old indices to new values based on the new
+ * key-set.
+ *
+ * The dispatch is based on the key type.
+ * The output column is the new indices column for the new dictionary column.
+ */
+struct dispatch_compute_indices {
+  template <typename Element>
+  std::enable_if_t<cudf::is_relationally_comparable<Element, Element>(), std::unique_ptr<column>>
+  operator()(dictionary_column_view const& input,
+             column_view const& new_keys,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr)
+  {
+    auto dictionary_view = column_device_view::create(input.parent(), stream);
+    auto dictionary_itr  = make_dictionary_iterator<Element>(*dictionary_view);
+    auto new_keys_view   = column_device_view::create(new_keys, stream);
+
+    auto begin = new_keys_view->begin<Element>();
+    auto end   = new_keys_view->end<Element>();
+
+    // create output indices column
+    auto result = make_numeric_column(get_indices_type_for_size(new_keys.size()),
+                                      input.size(),
+                                      mask_state::UNALLOCATED,
+                                      stream,
+                                      mr);
+    auto result_itr =
+      cudf::detail::indexalator_factory::make_output_iterator(result->mutable_view());
+
+#ifdef NDEBUG
+    thrust::lower_bound(rmm::exec_policy(stream),
+                        begin,
+                        end,
+                        dictionary_itr,
+                        dictionary_itr + input.size(),
+                        result_itr,
+                        thrust::less<Element>());
+#else
+    // There is a problem with thrust::lower_bound and the output_indexalator
+    // https://github.com/NVIDIA/thrust/issues/1452; thrust team created nvbug 3322776
+    // This is a workaround.
+    thrust::transform(rmm::exec_policy(stream),
+                      dictionary_itr,
+                      dictionary_itr + input.size(),
+                      result_itr,
+                      [begin, end] __device__(auto key) {
+                        auto itr = thrust::lower_bound(thrust::seq, begin, end, key);
+                        return static_cast<size_type>(thrust::distance(begin, itr));
+                      });
+#endif
+    result->set_null_count(0);
+
+    return result;
+  }
+
+  template <typename Element, typename... Args>
+  std::enable_if_t<!cudf::is_relationally_comparable<Element, Element>(), std::unique_ptr<column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("dictionary set_keys not supported for this column type");
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<column> set_keys(dictionary_column_view const& dictionary_column,
+                                 column_view const& new_keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!new_keys.has_nulls(), "keys parameter must not have nulls");
+  auto keys = dictionary_column.keys();
+  CUDF_EXPECTS(keys.type() == new_keys.type(), "keys types must match");
+
+  // copy the keys -- use cudf::distinct to make sure there are no duplicates,
+  // then sort the results.
+  auto distinct_keys = cudf::detail::distinct(table_view{{new_keys}},
+                                              std::vector<size_type>{0},
+                                              duplicate_keep_option::KEEP_ANY,
+                                              null_equality::EQUAL,
+                                              nan_equality::ALL_EQUAL,
+                                              stream,
+                                              mr);
+  auto sorted_keys   = cudf::detail::sort(distinct_keys->view(),
+                                        std::vector<order>{order::ASCENDING},
+                                        std::vector<null_order>{null_order::BEFORE},
+                                        stream,
+                                        mr)
+                       ->release();
+  std::unique_ptr<column> keys_column(std::move(sorted_keys.front()));
+
+  // compute the new nulls
+  auto matches   = cudf::detail::contains(keys_column->view(), keys, stream, mr);
+  auto d_matches = matches->view().data<bool>();
+  auto indices_itr =
+    cudf::detail::indexalator_factory::make_input_iterator(dictionary_column.indices());
+  auto d_null_mask = dictionary_column.null_mask();
+  auto new_nulls   = cudf::detail::valid_if(
+    thrust::make_counting_iterator<size_type>(dictionary_column.offset()),
+    thrust::make_counting_iterator<size_type>(dictionary_column.offset() +
+                                              dictionary_column.size()),
+    [d_null_mask, indices_itr, d_matches] __device__(size_type idx) {
+      if (d_null_mask && !bit_is_set(d_null_mask, idx)) return false;
+      return d_matches[indices_itr[idx]];
+    },
+    stream,
+    mr);
+
+  // compute the new indices
+  auto indices_column = type_dispatcher(keys_column->type(),
+                                        dispatch_compute_indices{},
+                                        dictionary_column,
+                                        keys_column->view(),
+                                        stream,
+                                        mr);
+
+  // create column with keys_column and indices_column
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                std::move(new_nulls.first),
+                                new_nulls.second);
+}
+
+std::vector<std::unique_ptr<column>> match_dictionaries(
+  cudf::host_span<dictionary_column_view const> input,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  std::vector<column_view> keys(input.size());
+  std::transform(input.begin(), input.end(), keys.begin(), [](auto& col) { return col.keys(); });
+  auto new_keys  = cudf::detail::concatenate(keys, stream, rmm::mr::get_current_device_resource());
+  auto keys_view = new_keys->view();
+  std::vector<std::unique_ptr<column>> result(input.size());
+  std::transform(input.begin(), input.end(), result.begin(), [keys_view, mr, stream](auto& col) {
+    return set_keys(col, keys_view, stream, mr);
+  });
+  return result;
+}
+
+std::pair<std::vector<std::unique_ptr<column>>, std::vector<table_view>> match_dictionaries(
+  std::vector<table_view> tables, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  // Make a copy of all the column views from each table_view
+  std::vector<std::vector<column_view>> updated_columns;
+  std::transform(tables.begin(), tables.end(), std::back_inserter(updated_columns), [](auto& t) {
+    return std::vector<column_view>(t.begin(), t.end());
+  });
+
+  // Each column in a table must match in type.
+  // Once a dictionary column is found, all the corresponding column_views in the
+  // other table_views are matched. The matched column_views then replace the originals.
+  std::vector<std::unique_ptr<column>> dictionary_columns;
+  auto first_table = tables.front();
+  for (size_type col_idx = 0; col_idx < first_table.num_columns(); ++col_idx) {
+    auto col = first_table.column(col_idx);
+    if (col.type().id() == type_id::DICTIONARY32) {
+      std::vector<dictionary_column_view> dict_views;  // hold all column_views at col_idx
+      std::transform(
+        tables.begin(), tables.end(), std::back_inserter(dict_views), [col_idx](auto& t) {
+          return dictionary_column_view(t.column(col_idx));
+        });
+      // now match the keys in these dictionary columns
+      auto dict_cols = dictionary::detail::match_dictionaries(dict_views, stream, mr);
+      // replace the updated_columns vector entries for the set of columns at col_idx
+      auto dict_col_idx = 0;
+      for (auto& v : updated_columns)
+        v[col_idx] = dict_cols[dict_col_idx++]->view();
+      // move the updated dictionary columns into the main output vector
+      std::move(dict_cols.begin(), dict_cols.end(), std::back_inserter(dictionary_columns));
+    }
+  }
+  // All the new column_views are in now updated_columns.
+
+  // Rebuild the table_views from the column_views.
+  std::vector<table_view> updated_tables;
+  std::transform(updated_columns.begin(),
+                 updated_columns.end(),
+                 std::back_inserter(updated_tables),
+                 [](auto& v) { return table_view{v}; });
+
+  // Return the new dictionary columns and table_views
+  return {std::move(dictionary_columns), std::move(updated_tables)};
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> set_keys(dictionary_column_view const& dictionary_column,
+                                 column_view const& keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::set_keys(dictionary_column, keys, stream, mr);
+}
+
+std::vector<std::unique_ptr<column>> match_dictionaries(
+  cudf::host_span<dictionary_column_view const> input,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::match_dictionaries(input, stream, mr);
+}
+
+}  // namespace dictionary
+}  // namespace cudf
diff --git a/cpp/src/filling/calendrical_month_sequence.cu b/cpp/src/filling/calendrical_month_sequence.cu
new file mode 100644
index 0000000..80badb7
--- /dev/null
+++ b/cpp/src/filling/calendrical_month_sequence.cu
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/calendrical_month_sequence.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<cudf::column> calendrical_month_sequence(size_type size,
+                                                         scalar const& init,
+                                                         size_type months,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(
+    init.type(), calendrical_month_sequence_functor{}, size, init, months, stream, mr);
+}
+}  // namespace detail
+
+std::unique_ptr<cudf::column> calendrical_month_sequence(size_type size,
+                                                         scalar const& init,
+                                                         size_type months,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::calendrical_month_sequence(size, init, months, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/filling/fill.cu b/cpp/src/filling/fill.cu
new file mode 100644
index 0000000..3d84db1
--- /dev/null
+++ b/cpp/src/filling/fill.cu
@@ -0,0 +1,267 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy_range.cuh>
+#include <cudf/detail/fill.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/detail/fill.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+
+#include <memory>
+
+namespace {
+template <typename T>
+void in_place_fill(cudf::mutable_column_view& destination,
+                   cudf::size_type begin,
+                   cudf::size_type end,
+                   cudf::scalar const& value,
+                   rmm::cuda_stream_view stream)
+{
+  using ScalarType = cudf::scalar_type_t<T>;
+  auto p_scalar    = static_cast<ScalarType const*>(&value);
+  T fill_value     = p_scalar->value(stream);
+  bool is_valid    = p_scalar->is_valid(stream);
+  cudf::detail::copy_range(thrust::make_constant_iterator(fill_value),
+                           thrust::make_constant_iterator(is_valid),
+                           destination,
+                           begin,
+                           end,
+                           stream);
+}
+
+struct in_place_fill_range_dispatch {
+  cudf::scalar const& value;
+  cudf::mutable_column_view& destination;
+
+  template <typename T>
+  std::enable_if_t<cudf::is_fixed_width<T>() && not cudf::is_fixed_point<T>(), void> operator()(
+    cudf::size_type begin, cudf::size_type end, rmm::cuda_stream_view stream)
+  {
+    in_place_fill<T>(destination, begin, end, value, stream);
+  }
+
+  template <typename T>
+  std::enable_if_t<cudf::is_fixed_point<T>(), void> operator()(cudf::size_type begin,
+                                                               cudf::size_type end,
+                                                               rmm::cuda_stream_view stream)
+  {
+    auto unscaled = static_cast<cudf::fixed_point_scalar<T> const&>(value).value(stream);
+    using RepType = typename T::rep;
+    auto s        = cudf::numeric_scalar<RepType>(unscaled, value.is_valid(stream));
+    in_place_fill<RepType>(destination, begin, end, s, stream);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_fixed_width<T>(), void> operator()(Args&&...)
+  {
+    CUDF_FAIL("in-place fill does not work for variable width types.");
+  }
+};
+
+struct out_of_place_fill_range_dispatch {
+  cudf::scalar const& value;
+  cudf::column_view const& input;
+
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_rep_layout_compatible<T>() and not cudf::is_fixed_point<T>(),
+                   std::unique_ptr<cudf::column>>
+  operator()(Args...)
+  {
+    CUDF_FAIL("Unsupported type in fill.");
+  }
+
+  template <typename T,
+            CUDF_ENABLE_IF(cudf::is_rep_layout_compatible<T>() or cudf::is_fixed_point<T>())>
+  std::unique_ptr<cudf::column> operator()(cudf::size_type begin,
+                                           cudf::size_type end,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(input.type() == value.type(), "Data type mismatch.");
+    auto p_ret = std::make_unique<cudf::column>(input, stream, mr);
+
+    if (end != begin) {  // otherwise no fill
+      if (!p_ret->nullable() && !value.is_valid(stream)) {
+        p_ret->set_null_mask(
+          cudf::detail::create_null_mask(p_ret->size(), cudf::mask_state::ALL_VALID, stream, mr),
+          0);
+      }
+
+      auto ret_view    = p_ret->mutable_view();
+      using DeviceType = cudf::device_storage_type_t<T>;
+      in_place_fill<DeviceType>(ret_view, begin, end, value, stream);
+      p_ret->set_null_count(ret_view.null_count());
+    }
+
+    return p_ret;
+  }
+};
+
+template <>
+std::unique_ptr<cudf::column> out_of_place_fill_range_dispatch::operator()<cudf::string_view>(
+  cudf::size_type begin,
+  cudf::size_type end,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type() == value.type(), "Data type mismatch.");
+  using ScalarType = cudf::scalar_type_t<cudf::string_view>;
+  auto p_scalar    = static_cast<ScalarType const*>(&value);
+  return cudf::strings::detail::fill(
+    cudf::strings_column_view(input), begin, end, *p_scalar, stream, mr);
+}
+
+template <>
+std::unique_ptr<cudf::column> out_of_place_fill_range_dispatch::operator()<cudf::dictionary32>(
+  cudf::size_type begin,
+  cudf::size_type end,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return std::make_unique<cudf::column>(input, stream, mr);
+  cudf::dictionary_column_view const target(input);
+  CUDF_EXPECTS(target.keys().type() == value.type(), "Data type mismatch.");
+
+  // if the scalar is invalid, then just copy the column and fill the null mask
+  if (!value.is_valid(stream)) {
+    auto result = std::make_unique<cudf::column>(input, stream, mr);
+    auto mview  = result->mutable_view();
+    cudf::detail::set_null_mask(mview.null_mask(), begin, end, false, stream);
+    result->set_null_count(input.null_count() + (end - begin));
+    return result;
+  }
+
+  // add the scalar to get the output dictionary key-set
+  auto scalar_column = cudf::make_column_from_scalar(value, 1, stream);
+  auto target_matched =
+    cudf::dictionary::detail::add_keys(target, scalar_column->view(), stream, mr);
+  cudf::column_view const target_indices =
+    cudf::dictionary_column_view(target_matched->view()).get_indices_annotated();
+
+  // get the index of the key just added
+  auto index_of_value = cudf::dictionary::detail::get_index(
+    target_matched->view(), value, stream, rmm::mr::get_current_device_resource());
+  // now call fill using just the indices column and the new index
+  auto new_indices =
+    cudf::type_dispatcher(target_indices.type(),
+                          out_of_place_fill_range_dispatch{*index_of_value, target_indices},
+                          begin,
+                          end,
+                          stream,
+                          mr);
+  auto const indices_type = new_indices->type();
+  auto const output_size  = new_indices->size();        // record these
+  auto const null_count   = new_indices->null_count();  // before the release()
+  auto contents           = new_indices->release();
+  // create the new indices column from the result
+  auto indices_column = std::make_unique<cudf::column>(indices_type,
+                                                       static_cast<cudf::size_type>(output_size),
+                                                       std::move(*(contents.data.release())),
+                                                       rmm::device_buffer{0, stream, mr},
+                                                       0);
+
+  // take the keys from matched column
+  std::unique_ptr<cudf::column> keys_column(std::move(target_matched->release().children.back()));
+
+  // create column with keys_column and indices_column
+  return cudf::make_dictionary_column(std::move(keys_column),
+                                      std::move(indices_column),
+                                      std::move(*(contents.null_mask.release())),
+                                      null_count);
+}
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+void fill_in_place(mutable_column_view& destination,
+                   size_type begin,
+                   size_type end,
+                   scalar const& value,
+                   rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(cudf::is_fixed_width(destination.type()),
+               "In-place fill does not support variable-sized types.");
+  CUDF_EXPECTS((begin >= 0) && (end <= destination.size()) && (begin <= end),
+               "Range is out of bounds.");
+  CUDF_EXPECTS(destination.nullable() || value.is_valid(stream),
+               "destination should be nullable or value should be non-null.");
+  CUDF_EXPECTS(destination.type() == value.type(), "Data type mismatch.");
+
+  if (end != begin) {  // otherwise no-op
+    cudf::type_dispatcher(
+      destination.type(), in_place_fill_range_dispatch{value, destination}, begin, end, stream);
+  }
+
+  return;
+}
+
+std::unique_ptr<column> fill(column_view const& input,
+                             size_type begin,
+                             size_type end,
+                             scalar const& value,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS((begin >= 0) && (end <= input.size()) && (begin <= end), "Range is out of bounds.");
+
+  return cudf::type_dispatcher(
+    input.type(), out_of_place_fill_range_dispatch{value, input}, begin, end, stream, mr);
+}
+
+}  // namespace detail
+
+void fill_in_place(mutable_column_view& destination,
+                   size_type begin,
+                   size_type end,
+                   scalar const& value,
+                   rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::fill_in_place(destination, begin, end, value, stream);
+}
+
+std::unique_ptr<column> fill(column_view const& input,
+                             size_type begin,
+                             size_type end,
+                             scalar const& value,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::fill(input, begin, end, value, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/filling/repeat.cu b/cpp/src/filling/repeat.cu
new file mode 100644
index 0000000..677d9a0
--- /dev/null
+++ b/cpp/src/filling/repeat.cu
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/repeat.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_output_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/sort.h>
+
+#include <limits>
+#include <memory>
+
+namespace {
+struct count_accessor {
+  cudf::scalar const* p_scalar = nullptr;
+
+  template <typename T>
+  std::enable_if_t<std::is_integral_v<T>, cudf::size_type> operator()(rmm::cuda_stream_view stream)
+  {
+    using ScalarType = cudf::scalar_type_t<T>;
+#if 1
+    // TODO: temporary till cudf::scalar's value() function is marked as const
+    auto p_count = const_cast<ScalarType*>(static_cast<ScalarType const*>(this->p_scalar));
+#else
+    auto p_count = static_cast<ScalarType const*>(this->p_scalar);
+#endif
+    auto count = p_count->value(stream);
+    // static_cast is necessary due to bool
+    CUDF_EXPECTS(static_cast<int64_t>(count) <= std::numeric_limits<cudf::size_type>::max(),
+                 "count should not exceed the column size limit",
+                 std::overflow_error);
+    return static_cast<cudf::size_type>(count);
+  }
+
+  template <typename T>
+  std::enable_if_t<not std::is_integral_v<T>, cudf::size_type> operator()(rmm::cuda_stream_view)
+  {
+    CUDF_FAIL("count value should be a integral type.");
+  }
+};
+
+struct count_checker {
+  cudf::column_view const& count;
+
+  template <typename T>
+  std::enable_if_t<std::is_integral_v<T>, void> operator()(rmm::cuda_stream_view stream)
+  {
+    // static_cast is necessary due to bool
+    if (static_cast<int64_t>(std::numeric_limits<T>::max()) >
+        std::numeric_limits<cudf::size_type>::max()) {
+      auto max = thrust::reduce(
+        rmm::exec_policy(stream), count.begin<T>(), count.end<T>(), 0, thrust::maximum<T>());
+      CUDF_EXPECTS(max <= std::numeric_limits<cudf::size_type>::max(),
+                   "count exceeds the column size limit",
+                   std::overflow_error);
+    }
+  }
+
+  template <typename T>
+  std::enable_if_t<not std::is_integral_v<T>, void> operator()(rmm::cuda_stream_view)
+  {
+    CUDF_FAIL("count value type should be integral.");
+  }
+};
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<table> repeat(table_view const& input_table,
+                              column_view const& count,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input_table.num_rows() == count.size(), "in and count must have equal size");
+  CUDF_EXPECTS(not count.has_nulls(), "count cannot contain nulls");
+
+  if (input_table.num_rows() == 0) { return cudf::empty_like(input_table); }
+
+  auto count_iter = cudf::detail::indexalator_factory::make_input_iterator(count);
+
+  rmm::device_uvector<cudf::size_type> offsets(count.size(), stream);
+  thrust::inclusive_scan(
+    rmm::exec_policy(stream), count_iter, count_iter + count.size(), offsets.begin());
+
+  size_type output_size{offsets.back_element(stream)};
+  rmm::device_uvector<size_type> indices(output_size, stream);
+  thrust::upper_bound(rmm::exec_policy(stream),
+                      offsets.begin(),
+                      offsets.end(),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(output_size),
+                      indices.begin());
+
+  return gather(
+    input_table, indices.begin(), indices.end(), out_of_bounds_policy::DONT_CHECK, stream, mr);
+}
+
+std::unique_ptr<table> repeat(table_view const& input_table,
+                              size_type count,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  if ((input_table.num_rows() == 0) || (count == 0)) { return cudf::empty_like(input_table); }
+
+  CUDF_EXPECTS(count >= 0, "count value should be non-negative");
+  CUDF_EXPECTS(input_table.num_rows() <= std::numeric_limits<size_type>::max() / count,
+               "The resulting table exceeds the column size limit",
+               std::overflow_error);
+
+  auto output_size = input_table.num_rows() * count;
+  auto map_begin   = cudf::detail::make_counting_transform_iterator(
+    0, [count] __device__(auto i) { return i / count; });
+  auto map_end = map_begin + output_size;
+
+  return gather(input_table, map_begin, map_end, out_of_bounds_policy::DONT_CHECK, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> repeat(table_view const& input_table,
+                              column_view const& count,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::repeat(input_table, count, stream, mr);
+}
+
+std::unique_ptr<table> repeat(table_view const& input_table,
+                              size_type count,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::repeat(input_table, count, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/filling/sequence.cu b/cpp/src/filling/sequence.cu
new file mode 100644
index 0000000..99a17f8
--- /dev/null
+++ b/cpp/src/filling/sequence.cu
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/sequence.h>
+#include <thrust/tabulate.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+// This functor only exists here because using a lambda directly in the tabulate() call generates
+// the cryptic
+// __T289 link error.  This seems to be related to lambda usage within functions using SFINAE.
+template <typename T>
+struct tabulator {
+  cudf::numeric_scalar_device_view<T> const n_init;
+  cudf::numeric_scalar_device_view<T> const n_step;
+
+  T __device__ operator()(cudf::size_type i)
+  {
+    return n_init.value() + (static_cast<T>(i) * n_step.value());
+  }
+};
+
+template <typename T>
+struct const_tabulator {
+  cudf::numeric_scalar_device_view<T> const n_init;
+
+  T __device__ operator()(cudf::size_type i) { return n_init.value() + static_cast<T>(i); }
+};
+
+/**
+ * @brief Functor called by the `type_dispatcher` to generate the sequence specified
+ * by init and step.
+ */
+struct sequence_functor {
+  template <typename T,
+            std::enable_if_t<cudf::is_numeric<T>() and not cudf::is_boolean<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(size_type size,
+                                     scalar const& init,
+                                     scalar const& step,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto result = make_fixed_width_column(init.type(), size, mask_state::UNALLOCATED, stream, mr);
+    auto result_device_view = mutable_column_device_view::create(*result, stream);
+
+    auto n_init =
+      get_scalar_device_view(static_cast<cudf::scalar_type_t<T>&>(const_cast<scalar&>(init)));
+    auto n_step =
+      get_scalar_device_view(static_cast<cudf::scalar_type_t<T>&>(const_cast<scalar&>(step)));
+
+    // not using thrust::sequence because it requires init and step to be passed as
+    // constants, not iterators. to do that we would have to retrieve the scalar values off the gpu,
+    // which is undesirable from a performance perspective.
+    thrust::tabulate(rmm::exec_policy(stream),
+                     result_device_view->begin<T>(),
+                     result_device_view->end<T>(),
+                     tabulator<T>{n_init, n_step});
+
+    return result;
+  }
+
+  template <typename T,
+            std::enable_if_t<cudf::is_numeric<T>() and not cudf::is_boolean<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(size_type size,
+                                     scalar const& init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto result = make_fixed_width_column(init.type(), size, mask_state::UNALLOCATED, stream, mr);
+    auto result_device_view = mutable_column_device_view::create(*result, stream);
+
+    auto n_init =
+      get_scalar_device_view(static_cast<cudf::scalar_type_t<T>&>(const_cast<scalar&>(init)));
+
+    // not using thrust::sequence because it requires init and step to be passed as
+    // constants, not iterators. to do that we would have to retrieve the scalar values off the gpu,
+    // which is undesirable from a performance perspective.
+    thrust::tabulate(rmm::exec_policy(stream),
+                     result_device_view->begin<T>(),
+                     result_device_view->end<T>(),
+                     const_tabulator<T>{n_init});
+
+    return result;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_numeric<T>() or cudf::is_boolean<T>(), std::unique_ptr<column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported sequence scalar type");
+  }
+};
+
+}  // anonymous namespace
+
+std::unique_ptr<column> sequence(size_type size,
+                                 scalar const& init,
+                                 scalar const& step,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(init.type() == step.type(), "init and step must be of the same type.");
+  CUDF_EXPECTS(size >= 0, "size must be >= 0");
+  CUDF_EXPECTS(is_numeric(init.type()), "Input scalar types must be numeric");
+
+  return type_dispatcher(init.type(), sequence_functor{}, size, init, step, stream, mr);
+}
+
+std::unique_ptr<column> sequence(size_type size,
+                                 scalar const& init,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(size >= 0, "size must be >= 0");
+  CUDF_EXPECTS(is_numeric(init.type()), "init scalar type must be numeric");
+
+  return type_dispatcher(init.type(), sequence_functor{}, size, init, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> sequence(size_type size,
+                                 scalar const& init,
+                                 scalar const& step,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sequence(size, init, step, stream, mr);
+}
+
+std::unique_ptr<column> sequence(size_type size,
+                                 scalar const& init,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sequence(size, init, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/groupby/common/utils.hpp b/cpp/src/groupby/common/utils.hpp
new file mode 100644
index 0000000..09b85c7
--- /dev/null
+++ b/cpp/src/groupby/common/utils.hpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/aggregation/result_cache.hpp>
+#include <cudf/detail/groupby.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+
+template <typename RequestType>
+inline std::vector<aggregation_result> extract_results(host_span<RequestType const> requests,
+                                                       cudf::detail::result_cache& cache,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  std::vector<aggregation_result> results(requests.size());
+  std::unordered_map<std::pair<column_view, std::reference_wrapper<aggregation const>>,
+                     column_view,
+                     cudf::detail::pair_column_aggregation_hash,
+                     cudf::detail::pair_column_aggregation_equal_to>
+    repeated_result;
+  for (size_t i = 0; i < requests.size(); i++) {
+    for (auto&& agg : requests[i].aggregations) {
+      if (cache.has_result(requests[i].values, *agg)) {
+        results[i].results.emplace_back(cache.release_result(requests[i].values, *agg));
+        repeated_result[{requests[i].values, *agg}] = results[i].results.back()->view();
+      } else {
+        auto it = repeated_result.find({requests[i].values, *agg});
+        if (it != repeated_result.end()) {
+          results[i].results.emplace_back(std::make_unique<column>(it->second, stream, mr));
+        } else {
+          CUDF_FAIL("Cannot extract result from the cache");
+        }
+      }
+    }
+  }
+  return results;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/groupby.cu b/cpp/src/groupby/groupby.cu
new file mode 100644
index 0000000..e3c021e
--- /dev/null
+++ b/cpp/src/groupby/groupby.cu
@@ -0,0 +1,339 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby.hpp>
+#include <cudf/detail/groupby/group_replace_nulls.hpp>
+#include <cudf/detail/groupby/sort_helper.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/reduction/detail/histogram.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <memory>
+#include <utility>
+
+namespace cudf {
+namespace groupby {
+// Constructor
+groupby::groupby(table_view const& keys,
+                 null_policy include_null_keys,
+                 sorted keys_are_sorted,
+                 std::vector<order> const& column_order,
+                 std::vector<null_order> const& null_precedence)
+  : _keys{keys},
+    _include_null_keys{include_null_keys},
+    _keys_are_sorted{keys_are_sorted},
+    _column_order{column_order},
+    _null_precedence{null_precedence}
+{
+}
+
+// Select hash vs. sort groupby implementation
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby::dispatch_aggregation(
+  host_span<aggregation_request const> requests,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // If sort groupby has been called once on this groupby object, then
+  // always use sort groupby from now on. Because once keys are sorted,
+  // all the aggs that can be done by hash groupby are efficiently done by
+  // sort groupby as well.
+  // Only use hash groupby if the keys aren't sorted and all requests can be
+  // satisfied with a hash implementation
+  if (_keys_are_sorted == sorted::NO and not _helper and
+      detail::hash::can_use_hash_groupby(requests)) {
+    return detail::hash::groupby(_keys, requests, _include_null_keys, stream, mr);
+  } else {
+    return sort_aggregate(requests, stream, mr);
+  }
+}
+
+// Destructor
+// Needs to be in source file because sort_groupby_helper was forward declared
+groupby::~groupby() = default;
+
+namespace {
+
+/**
+ * @brief Factory to construct empty result columns.
+ *
+ * Adds special handling for COLLECT_LIST/COLLECT_SET, because:
+ * 1. `make_empty_column()` does not support construction of nested columns.
+ * 2. Empty lists need empty child columns, to persist type information.
+ * Adds special handling for RANK, because it needs to return double type column when rank_method is
+ * AVERAGE or percentage is true.
+ */
+struct empty_column_constructor {
+  column_view values;
+  aggregation const& agg;
+
+  template <typename ValuesType, aggregation::Kind k>
+  std::unique_ptr<cudf::column> operator()() const
+  {
+    using namespace cudf;
+    using namespace cudf::detail;
+
+    if constexpr (k == aggregation::Kind::COLLECT_LIST || k == aggregation::Kind::COLLECT_SET) {
+      return make_lists_column(
+        0, make_empty_column(type_to_id<size_type>()), empty_like(values), 0, {});
+    }
+
+    if constexpr (k == aggregation::Kind::HISTOGRAM) {
+      return make_lists_column(0,
+                               make_empty_column(type_to_id<size_type>()),
+                               cudf::reduction::detail::make_empty_histogram_like(values),
+                               0,
+                               {});
+    }
+    if constexpr (k == aggregation::Kind::MERGE_HISTOGRAM) { return empty_like(values); }
+
+    if constexpr (k == aggregation::Kind::RANK) {
+      auto const& rank_agg = dynamic_cast<cudf::detail::rank_aggregation const&>(agg);
+      if (rank_agg._method == cudf::rank_method::AVERAGE or
+          rank_agg._percentage != rank_percentage::NONE)
+        return make_empty_column(type_to_id<double>());
+      return make_empty_column(target_type(values.type(), k));
+    }
+
+    // If `values` is LIST typed, and the aggregation results match the type,
+    // construct empty results based on `values`.
+    // Most generally, this applies if input type matches output type.
+    //
+    // Note: `target_type_t` is not recursive, and `ValuesType` does not consider children.
+    //       It is important that `COLLECT_LIST` and `COLLECT_SET` are handled before this
+    //       point, because `COLLECT_LIST(LIST)` produces `LIST<LIST>`, but `target_type_t`
+    //       wouldn't know the difference.
+    if constexpr (std::is_same_v<target_type_t<ValuesType, k>, ValuesType>) {
+      return empty_like(values);
+    }
+
+    return make_empty_column(target_type(values.type(), k));
+  }
+};
+
+/// Make an empty table with appropriate types for requested aggs
+template <typename RequestType>
+auto empty_results(host_span<RequestType const> requests)
+{
+  std::vector<aggregation_result> empty_results;
+
+  std::transform(
+    requests.begin(), requests.end(), std::back_inserter(empty_results), [](auto const& request) {
+      std::vector<std::unique_ptr<column>> results;
+
+      std::transform(
+        request.aggregations.begin(),
+        request.aggregations.end(),
+        std::back_inserter(results),
+        [&request](auto const& agg) {
+          return cudf::detail::dispatch_type_and_aggregation(
+            request.values.type(), agg->kind, empty_column_constructor{request.values, *agg});
+        });
+
+      return aggregation_result{std::move(results)};
+    });
+
+  return empty_results;
+}
+
+/// Verifies the agg requested on the request's values is valid
+template <typename RequestType>
+void verify_valid_requests(host_span<RequestType const> requests)
+{
+  CUDF_EXPECTS(
+    std::all_of(
+      requests.begin(),
+      requests.end(),
+      [](auto const& request) {
+        return std::all_of(
+          request.aggregations.begin(), request.aggregations.end(), [&request](auto const& agg) {
+            auto values_type = cudf::is_dictionary(request.values.type())
+                                 ? cudf::dictionary_column_view(request.values).keys().type()
+                                 : request.values.type();
+            return cudf::detail::is_valid_aggregation(values_type, agg->kind);
+          });
+      }),
+    "Invalid type/aggregation combination.");
+}
+
+}  // namespace
+
+// Compute aggregation requests
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby::aggregate(
+  host_span<aggregation_request const> requests, rmm::mr::device_memory_resource* mr)
+{
+  return aggregate(requests, cudf::get_default_stream(), mr);
+}
+
+// Compute aggregation requests
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby::aggregate(
+  host_span<aggregation_request const> requests,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(
+    std::all_of(requests.begin(),
+                requests.end(),
+                [this](auto const& request) { return request.values.size() == _keys.num_rows(); }),
+    "Size mismatch between request values and groupby keys.");
+
+  verify_valid_requests(requests);
+
+  if (_keys.num_rows() == 0) { return {empty_like(_keys), empty_results(requests)}; }
+
+  return dispatch_aggregation(requests, stream, mr);
+}
+
+// Compute scan requests
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby::scan(
+  host_span<scan_request const> requests, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(
+    std::all_of(requests.begin(),
+                requests.end(),
+                [this](auto const& request) { return request.values.size() == _keys.num_rows(); }),
+    "Size mismatch between request values and groupby keys.");
+
+  verify_valid_requests(requests);
+
+  if (_keys.num_rows() == 0) { return std::pair(empty_like(_keys), empty_results(requests)); }
+
+  return sort_scan(requests, cudf::get_default_stream(), mr);
+}
+
+groupby::groups groupby::get_groups(table_view values, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto const stream = cudf::get_default_stream();
+  auto grouped_keys = helper().sorted_keys(stream, mr);
+
+  auto const& group_offsets       = helper().group_offsets(stream);
+  auto const group_offsets_vector = cudf::detail::make_std_vector_sync(group_offsets, stream);
+
+  if (not values.is_empty()) {
+    auto grouped_values = cudf::detail::gather(values,
+                                               helper().key_sort_order(stream),
+                                               cudf::out_of_bounds_policy::DONT_CHECK,
+                                               cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                               stream,
+                                               mr);
+    return groupby::groups{
+      std::move(grouped_keys), std::move(group_offsets_vector), std::move(grouped_values)};
+  } else {
+    return groupby::groups{std::move(grouped_keys), std::move(group_offsets_vector)};
+  }
+}
+
+std::pair<std::unique_ptr<table>, std::unique_ptr<table>> groupby::replace_nulls(
+  table_view const& values,
+  host_span<cudf::replace_policy const> replace_policies,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(_keys.num_rows() == values.num_rows(),
+               "Size mismatch between group labels and value.");
+  CUDF_EXPECTS(static_cast<cudf::size_type>(replace_policies.size()) == values.num_columns(),
+               "Size mismatch between num_columns and replace_policies.");
+
+  if (values.is_empty()) { return std::pair(empty_like(_keys), empty_like(values)); }
+  auto const stream = cudf::get_default_stream();
+
+  auto const& group_labels = helper().group_labels(stream);
+  std::vector<std::unique_ptr<column>> results;
+  results.reserve(values.num_columns());
+  std::transform(
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(values.num_columns()),
+    std::back_inserter(results),
+    [&](auto i) {
+      bool nullable       = values.column(i).nullable();
+      auto final_mr       = nullable ? rmm::mr::get_current_device_resource() : mr;
+      auto grouped_values = helper().grouped_values(values.column(i), stream, final_mr);
+      return nullable ? detail::group_replace_nulls(
+                          *grouped_values, group_labels, replace_policies[i], stream, mr)
+                      : std::move(grouped_values);
+    });
+
+  return std::pair(std::move(helper().sorted_keys(stream, mr)),
+                   std::make_unique<table>(std::move(results)));
+}
+
+// Get the sort helper object
+detail::sort::sort_groupby_helper& groupby::helper()
+{
+  if (_helper) return *_helper;
+  _helper = std::make_unique<detail::sort::sort_groupby_helper>(
+    _keys, _include_null_keys, _keys_are_sorted, _null_precedence);
+  return *_helper;
+};
+
+std::pair<std::unique_ptr<table>, std::unique_ptr<table>> groupby::shift(
+  table_view const& values,
+  host_span<size_type const> offsets,
+  std::vector<std::reference_wrapper<scalar const>> const& fill_values,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(values.num_columns() == static_cast<size_type>(fill_values.size()),
+               "Mismatch number of fill_values and columns.");
+  CUDF_EXPECTS(
+    std::all_of(thrust::make_counting_iterator(0),
+                thrust::make_counting_iterator(values.num_columns()),
+                [&](auto i) { return values.column(i).type() == fill_values[i].get().type(); }),
+    "values and fill_value should have the same type.");
+
+  auto stream = cudf::get_default_stream();
+  std::vector<std::unique_ptr<column>> results;
+  auto const& group_offsets = helper().group_offsets(stream);
+  std::transform(
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(values.num_columns()),
+    std::back_inserter(results),
+    [&](size_type i) {
+      auto grouped_values =
+        helper().grouped_values(values.column(i), stream, rmm::mr::get_current_device_resource());
+      return cudf::detail::segmented_shift(
+        grouped_values->view(), group_offsets, offsets[i], fill_values[i].get(), stream, mr);
+    });
+
+  return std::pair(helper().sorted_keys(stream, mr),
+                   std::make_unique<cudf::table>(std::move(results)));
+}
+
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/hash/groupby.cu b/cpp/src/groupby/hash/groupby.cu
new file mode 100644
index 0000000..5068328
--- /dev/null
+++ b/cpp/src/groupby/hash/groupby.cu
@@ -0,0 +1,682 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/common/utils.hpp>
+#include <groupby/hash/groupby_kernels.cuh>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.cuh>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/aggregation/result_cache.hpp>
+#include <cudf/detail/binaryop.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/replace.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/detail/utilities/algorithm.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/hashing/detail/default_hash.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.cuh>
+#include <cudf/utilities/traits.hpp>
+#include <hash/concurrent_unordered_map.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <memory>
+#include <unordered_set>
+#include <utility>
+
+#include <cuda/std/atomic>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace hash {
+namespace {
+
+// TODO: replace it with `cuco::static_map`
+// https://github.com/rapidsai/cudf/issues/10401
+template <typename ComparatorType>
+using map_type = concurrent_unordered_map<
+  cudf::size_type,
+  cudf::size_type,
+  cudf::experimental::row::hash::device_row_hasher<cudf::hashing::detail::default_hash,
+                                                   cudf::nullate::DYNAMIC>,
+  ComparatorType>;
+
+/**
+ * @brief List of aggregation operations that can be computed with a hash-based
+ * implementation.
+ */
+constexpr std::array<aggregation::Kind, 12> hash_aggregations{aggregation::SUM,
+                                                              aggregation::PRODUCT,
+                                                              aggregation::MIN,
+                                                              aggregation::MAX,
+                                                              aggregation::COUNT_VALID,
+                                                              aggregation::COUNT_ALL,
+                                                              aggregation::ARGMIN,
+                                                              aggregation::ARGMAX,
+                                                              aggregation::SUM_OF_SQUARES,
+                                                              aggregation::MEAN,
+                                                              aggregation::STD,
+                                                              aggregation::VARIANCE};
+
+// Could be hash: SUM, PRODUCT, MIN, MAX, COUNT_VALID, COUNT_ALL, ANY, ALL,
+// Compound: MEAN(SUM, COUNT_VALID), VARIANCE, STD(MEAN (SUM, COUNT_VALID), COUNT_VALID),
+// ARGMAX, ARGMIN
+
+// TODO replace with std::find in C++20 onwards.
+template <class T, size_t N>
+constexpr bool array_contains(std::array<T, N> const& haystack, T needle)
+{
+  for (auto const& val : haystack) {
+    if (val == needle) return true;
+  }
+  return false;
+}
+
+/**
+ * @brief Indicates whether the specified aggregation operation can be computed
+ * with a hash-based implementation.
+ *
+ * @param t The aggregation operation to verify
+ * @return true `t` is valid for a hash based groupby
+ * @return false `t` is invalid for a hash based groupby
+ */
+bool constexpr is_hash_aggregation(aggregation::Kind t)
+{
+  return array_contains(hash_aggregations, t);
+}
+
+class groupby_simple_aggregations_collector final
+  : public cudf::detail::simple_aggregations_collector {
+ public:
+  using cudf::detail::simple_aggregations_collector::visit;
+
+  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                  cudf::detail::min_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(col_type.id() == type_id::STRING ? make_argmin_aggregation()
+                                                    : make_min_aggregation());
+    return aggs;
+  }
+
+  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                  cudf::detail::max_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(col_type.id() == type_id::STRING ? make_argmax_aggregation()
+                                                    : make_max_aggregation());
+    return aggs;
+  }
+
+  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                  cudf::detail::mean_aggregation const&) override
+  {
+    (void)col_type;
+    CUDF_EXPECTS(is_fixed_width(col_type), "MEAN aggregation expects fixed width type");
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(make_sum_aggregation());
+    // COUNT_VALID
+    aggs.push_back(make_count_aggregation());
+
+    return aggs;
+  }
+
+  std::vector<std::unique_ptr<aggregation>> visit(data_type,
+                                                  cudf::detail::var_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(make_sum_aggregation());
+    // COUNT_VALID
+    aggs.push_back(make_count_aggregation());
+
+    return aggs;
+  }
+
+  std::vector<std::unique_ptr<aggregation>> visit(data_type,
+                                                  cudf::detail::std_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(make_sum_aggregation());
+    // COUNT_VALID
+    aggs.push_back(make_count_aggregation());
+
+    return aggs;
+  }
+
+  std::vector<std::unique_ptr<aggregation>> visit(
+    data_type, cudf::detail::correlation_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(make_sum_aggregation());
+    // COUNT_VALID
+    aggs.push_back(make_count_aggregation());
+
+    return aggs;
+  }
+};
+
+template <typename ComparatorType>
+class hash_compound_agg_finalizer final : public cudf::detail::aggregation_finalizer {
+  column_view col;
+  data_type result_type;
+  cudf::detail::result_cache* sparse_results;
+  cudf::detail::result_cache* dense_results;
+  device_span<size_type const> gather_map;
+  map_type<ComparatorType> const& map;
+  bitmask_type const* __restrict__ row_bitmask;
+  rmm::cuda_stream_view stream;
+  rmm::mr::device_memory_resource* mr;
+
+ public:
+  using cudf::detail::aggregation_finalizer::visit;
+
+  hash_compound_agg_finalizer(column_view col,
+                              cudf::detail::result_cache* sparse_results,
+                              cudf::detail::result_cache* dense_results,
+                              device_span<size_type const> gather_map,
+                              map_type<ComparatorType> const& map,
+                              bitmask_type const* row_bitmask,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+    : col(col),
+      sparse_results(sparse_results),
+      dense_results(dense_results),
+      gather_map(gather_map),
+      map(map),
+      row_bitmask(row_bitmask),
+      stream(stream),
+      mr(mr)
+  {
+    result_type = cudf::is_dictionary(col.type()) ? cudf::dictionary_column_view(col).keys().type()
+                                                  : col.type();
+  }
+
+  auto to_dense_agg_result(cudf::aggregation const& agg)
+  {
+    auto s                  = sparse_results->get_result(col, agg);
+    auto dense_result_table = cudf::detail::gather(table_view({std::move(s)}),
+                                                   gather_map,
+                                                   out_of_bounds_policy::DONT_CHECK,
+                                                   cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                                   stream,
+                                                   mr);
+    return std::move(dense_result_table->release()[0]);
+  }
+
+  // Enables conversion of ARGMIN/ARGMAX into MIN/MAX
+  auto gather_argminmax(aggregation const& agg)
+  {
+    auto arg_result = to_dense_agg_result(agg);
+    // We make a view of ARG(MIN/MAX) result without a null mask and gather
+    // using this map. The values in data buffer of ARG(MIN/MAX) result
+    // corresponding to null values was initialized to ARG(MIN/MAX)_SENTINEL
+    // which is an out of bounds index value (-1) and causes the gathered
+    // value to be null.
+    column_view null_removed_map(
+      data_type(type_to_id<size_type>()),
+      arg_result->size(),
+      static_cast<void const*>(arg_result->view().template data<size_type>()),
+      nullptr,
+      0);
+    auto gather_argminmax =
+      cudf::detail::gather(table_view({col}),
+                           null_removed_map,
+                           arg_result->nullable() ? cudf::out_of_bounds_policy::NULLIFY
+                                                  : cudf::out_of_bounds_policy::DONT_CHECK,
+                           cudf::detail::negative_index_policy::NOT_ALLOWED,
+                           stream,
+                           mr);
+    return std::move(gather_argminmax->release()[0]);
+  }
+
+  // Declare overloads for each kind of aggregation to dispatch
+  void visit(cudf::aggregation const& agg) override
+  {
+    if (dense_results->has_result(col, agg)) return;
+    dense_results->add_result(col, agg, to_dense_agg_result(agg));
+  }
+
+  void visit(cudf::detail::min_aggregation const& agg) override
+  {
+    if (dense_results->has_result(col, agg)) return;
+    if (result_type.id() == type_id::STRING) {
+      auto transformed_agg = make_argmin_aggregation();
+      dense_results->add_result(col, agg, gather_argminmax(*transformed_agg));
+    } else {
+      dense_results->add_result(col, agg, to_dense_agg_result(agg));
+    }
+  }
+
+  void visit(cudf::detail::max_aggregation const& agg) override
+  {
+    if (dense_results->has_result(col, agg)) return;
+
+    if (result_type.id() == type_id::STRING) {
+      auto transformed_agg = make_argmax_aggregation();
+      dense_results->add_result(col, agg, gather_argminmax(*transformed_agg));
+    } else {
+      dense_results->add_result(col, agg, to_dense_agg_result(agg));
+    }
+  }
+
+  void visit(cudf::detail::mean_aggregation const& agg) override
+  {
+    if (dense_results->has_result(col, agg)) return;
+
+    auto sum_agg   = make_sum_aggregation();
+    auto count_agg = make_count_aggregation();
+    this->visit(*sum_agg);
+    this->visit(*count_agg);
+    column_view sum_result   = dense_results->get_result(col, *sum_agg);
+    column_view count_result = dense_results->get_result(col, *count_agg);
+
+    auto result =
+      cudf::detail::binary_operation(sum_result,
+                                     count_result,
+                                     binary_operator::DIV,
+                                     cudf::detail::target_type(result_type, aggregation::MEAN),
+                                     stream,
+                                     mr);
+    dense_results->add_result(col, agg, std::move(result));
+  }
+
+  void visit(cudf::detail::var_aggregation const& agg) override
+  {
+    if (dense_results->has_result(col, agg)) return;
+
+    auto sum_agg   = make_sum_aggregation();
+    auto count_agg = make_count_aggregation();
+    this->visit(*sum_agg);
+    this->visit(*count_agg);
+    column_view sum_result   = sparse_results->get_result(col, *sum_agg);
+    column_view count_result = sparse_results->get_result(col, *count_agg);
+
+    auto values_view = column_device_view::create(col, stream);
+    auto sum_view    = column_device_view::create(sum_result, stream);
+    auto count_view  = column_device_view::create(count_result, stream);
+
+    auto var_result = make_fixed_width_column(
+      cudf::detail::target_type(result_type, agg.kind), col.size(), mask_state::ALL_NULL, stream);
+    auto var_result_view = mutable_column_device_view::create(var_result->mutable_view(), stream);
+    mutable_table_view var_table_view{{var_result->mutable_view()}};
+    cudf::detail::initialize_with_identity(var_table_view, {agg.kind}, stream);
+
+    thrust::for_each_n(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator(0),
+      col.size(),
+      ::cudf::detail::var_hash_functor<map_type<ComparatorType>>{
+        map, row_bitmask, *var_result_view, *values_view, *sum_view, *count_view, agg._ddof});
+    sparse_results->add_result(col, agg, std::move(var_result));
+    dense_results->add_result(col, agg, to_dense_agg_result(agg));
+  }
+
+  void visit(cudf::detail::std_aggregation const& agg) override
+  {
+    if (dense_results->has_result(col, agg)) return;
+    auto var_agg = make_variance_aggregation(agg._ddof);
+    this->visit(*dynamic_cast<cudf::detail::var_aggregation*>(var_agg.get()));
+    column_view variance = dense_results->get_result(col, *var_agg);
+
+    auto result = cudf::detail::unary_operation(variance, unary_operator::SQRT, stream, mr);
+    dense_results->add_result(col, agg, std::move(result));
+  }
+};
+// flatten aggs to filter in single pass aggs
+std::tuple<table_view, std::vector<aggregation::Kind>, std::vector<std::unique_ptr<aggregation>>>
+flatten_single_pass_aggs(host_span<aggregation_request const> requests)
+{
+  std::vector<column_view> columns;
+  std::vector<std::unique_ptr<aggregation>> aggs;
+  std::vector<aggregation::Kind> agg_kinds;
+
+  for (auto const& request : requests) {
+    auto const& agg_v = request.aggregations;
+
+    std::unordered_set<aggregation::Kind> agg_kinds_set;
+    auto insert_agg = [&](column_view const& request_values, std::unique_ptr<aggregation>&& agg) {
+      if (agg_kinds_set.insert(agg->kind).second) {
+        agg_kinds.push_back(agg->kind);
+        aggs.push_back(std::move(agg));
+        columns.push_back(request_values);
+      }
+    };
+
+    auto values_type = cudf::is_dictionary(request.values.type())
+                         ? cudf::dictionary_column_view(request.values).keys().type()
+                         : request.values.type();
+    for (auto&& agg : agg_v) {
+      groupby_simple_aggregations_collector collector;
+
+      for (auto& agg_s : agg->get_simple_aggregations(values_type, collector)) {
+        insert_agg(request.values, std::move(agg_s));
+      }
+    }
+  }
+
+  return std::make_tuple(table_view(columns), std::move(agg_kinds), std::move(aggs));
+}
+
+/**
+ * @brief Gather sparse results into dense using `gather_map` and add to
+ * `dense_cache`
+ *
+ * @see groupby_null_templated()
+ */
+template <typename ComparatorType>
+void sparse_to_dense_results(table_view const& keys,
+                             host_span<aggregation_request const> requests,
+                             cudf::detail::result_cache* sparse_results,
+                             cudf::detail::result_cache* dense_results,
+                             device_span<size_type const> gather_map,
+                             map_type<ComparatorType> const& map,
+                             bool keys_have_nulls,
+                             null_policy include_null_keys,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  auto row_bitmask = bitmask_and(keys, stream, rmm::mr::get_current_device_resource()).first;
+  bool skip_key_rows_with_nulls = keys_have_nulls and include_null_keys == null_policy::EXCLUDE;
+  bitmask_type const* row_bitmask_ptr =
+    skip_key_rows_with_nulls ? static_cast<bitmask_type*>(row_bitmask.data()) : nullptr;
+
+  for (auto const& request : requests) {
+    auto const& agg_v = request.aggregations;
+    auto const& col   = request.values;
+
+    // Given an aggregation, this will get the result from sparse_results and
+    // convert and return dense, compacted result
+    auto finalizer = hash_compound_agg_finalizer(
+      col, sparse_results, dense_results, gather_map, map, row_bitmask_ptr, stream, mr);
+    for (auto&& agg : agg_v) {
+      agg->finalize(finalizer);
+    }
+  }
+}
+
+// make table that will hold sparse results
+auto create_sparse_results_table(table_view const& flattened_values,
+                                 std::vector<aggregation::Kind> aggs,
+                                 rmm::cuda_stream_view stream)
+{
+  // TODO single allocation - room for performance improvement
+  std::vector<std::unique_ptr<column>> sparse_columns;
+  std::transform(
+    flattened_values.begin(),
+    flattened_values.end(),
+    aggs.begin(),
+    std::back_inserter(sparse_columns),
+    [stream](auto const& col, auto const& agg) {
+      bool nullable =
+        (agg == aggregation::COUNT_VALID or agg == aggregation::COUNT_ALL)
+          ? false
+          : (col.has_nulls() or agg == aggregation::VARIANCE or agg == aggregation::STD);
+      auto mask_flag = (nullable) ? mask_state::ALL_NULL : mask_state::UNALLOCATED;
+
+      auto col_type = cudf::is_dictionary(col.type())
+                        ? cudf::dictionary_column_view(col).keys().type()
+                        : col.type();
+
+      return make_fixed_width_column(
+        cudf::detail::target_type(col_type, agg), col.size(), mask_flag, stream);
+    });
+
+  table sparse_table(std::move(sparse_columns));
+  mutable_table_view table_view = sparse_table.mutable_view();
+  cudf::detail::initialize_with_identity(table_view, aggs, stream);
+  return sparse_table;
+}
+
+/**
+ * @brief Computes all aggregations from `requests` that require a single pass
+ * over the data and stores the results in `sparse_results`
+ */
+template <typename ComparatorType>
+void compute_single_pass_aggs(table_view const& keys,
+                              host_span<aggregation_request const> requests,
+                              cudf::detail::result_cache* sparse_results,
+                              map_type<ComparatorType>& map,
+                              bool keys_have_nulls,
+                              null_policy include_null_keys,
+                              rmm::cuda_stream_view stream)
+{
+  // flatten the aggs to a table that can be operated on by aggregate_row
+  auto const [flattened_values, agg_kinds, aggs] = flatten_single_pass_aggs(requests);
+
+  // make table that will hold sparse results
+  table sparse_table = create_sparse_results_table(flattened_values, agg_kinds, stream);
+  // prepare to launch kernel to do the actual aggregation
+  auto d_sparse_table = mutable_table_device_view::create(sparse_table, stream);
+  auto d_values       = table_device_view::create(flattened_values, stream);
+  auto const d_aggs   = cudf::detail::make_device_uvector_async(
+    agg_kinds, stream, rmm::mr::get_current_device_resource());
+  auto const skip_key_rows_with_nulls =
+    keys_have_nulls and include_null_keys == null_policy::EXCLUDE;
+
+  auto row_bitmask =
+    skip_key_rows_with_nulls
+      ? cudf::detail::bitmask_and(keys, stream, rmm::mr::get_current_device_resource()).first
+      : rmm::device_buffer{};
+
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator(0),
+                     keys.num_rows(),
+                     hash::compute_single_pass_aggs_fn<map_type<ComparatorType>>{
+                       map,
+                       *d_values,
+                       *d_sparse_table,
+                       d_aggs.data(),
+                       static_cast<bitmask_type*>(row_bitmask.data()),
+                       skip_key_rows_with_nulls});
+  // Add results back to sparse_results cache
+  auto sparse_result_cols = sparse_table.release();
+  for (size_t i = 0; i < aggs.size(); i++) {
+    // Note that the cache will make a copy of this temporary aggregation
+    sparse_results->add_result(
+      flattened_values.column(i), *aggs[i], std::move(sparse_result_cols[i]));
+  }
+}
+
+/**
+ * @brief Computes and returns a device vector containing all populated keys in
+ * `map`.
+ */
+template <typename ComparatorType>
+rmm::device_uvector<size_type> extract_populated_keys(map_type<ComparatorType> const& map,
+                                                      size_type num_keys,
+                                                      rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<size_type> populated_keys(num_keys, stream);
+
+  auto const get_key = [] __device__(auto const& element) { return element.first; };  // first = key
+  auto const key_used = [unused = map.get_unused_key()] __device__(auto key) {
+    return key != unused;
+  };
+  auto const key_itr = thrust::make_transform_iterator(map.data(), get_key);
+  auto const end_it  = cudf::detail::copy_if_safe(
+    key_itr, key_itr + map.capacity(), populated_keys.begin(), key_used, stream);
+
+  populated_keys.resize(std::distance(populated_keys.begin(), end_it), stream);
+  return populated_keys;
+}
+
+/**
+ * @brief Computes groupby using hash table.
+ *
+ * First, we create a hash table that stores the indices of unique rows in
+ * `keys`. The upper limit on the number of values in this map is the number
+ * of rows in `keys`.
+ *
+ * To store the results of aggregations, we create temporary sparse columns
+ * which have the same size as input value columns. Using the hash map, we
+ * determine the location within the sparse column to write the result of the
+ * aggregation into.
+ *
+ * The sparse column results of all aggregations are stored into the cache
+ * `sparse_results`. This enables the use of previously calculated results in
+ * other aggregations.
+ *
+ * All the aggregations which can be computed in a single pass are computed
+ * first, in a combined kernel. Then using these results, aggregations that
+ * require multiple passes, will be computed.
+ *
+ * Finally, using the hash map, we generate a vector of indices of populated
+ * values in sparse result columns. Then, for each aggregation originally
+ * requested in `requests`, we gather sparse results into a column of dense
+ * results using the aforementioned index vector. Dense results are stored into
+ * the in/out parameter `cache`.
+ */
+std::unique_ptr<table> groupby(table_view const& keys,
+                               host_span<aggregation_request const> requests,
+                               cudf::detail::result_cache* cache,
+                               bool const keys_have_nulls,
+                               null_policy const include_null_keys,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto const num_keys            = keys.num_rows();
+  auto const null_keys_are_equal = null_equality::EQUAL;
+  auto const has_null            = nullate::DYNAMIC{cudf::has_nested_nulls(keys)};
+
+  auto preprocessed_keys = cudf::experimental::row::hash::preprocessed_table::create(keys, stream);
+  auto const comparator  = cudf::experimental::row::equality::self_comparator{preprocessed_keys};
+  auto const row_hash    = cudf::experimental::row::hash::row_hasher{std::move(preprocessed_keys)};
+  auto const d_row_hash  = row_hash.device_hasher(has_null);
+
+  size_type constexpr unused_key{std::numeric_limits<size_type>::max()};
+  size_type constexpr unused_value{std::numeric_limits<size_type>::max()};
+
+  // Cache of sparse results where the location of aggregate value in each
+  // column is indexed by the hash map
+  cudf::detail::result_cache sparse_results(requests.size());
+
+  auto const comparator_helper = [&](auto const d_key_equal) {
+    using allocator_type = typename map_type<decltype(d_key_equal)>::allocator_type;
+
+    auto const map = map_type<decltype(d_key_equal)>::create(compute_hash_table_size(num_keys),
+                                                             stream,
+                                                             unused_key,
+                                                             unused_value,
+                                                             d_row_hash,
+                                                             d_key_equal,
+                                                             allocator_type());
+    // Compute all single pass aggs first
+    compute_single_pass_aggs(
+      keys, requests, &sparse_results, *map, keys_have_nulls, include_null_keys, stream);
+
+    // Extract the populated indices from the hash map and create a gather map.
+    // Gathering using this map from sparse results will give dense results.
+    auto gather_map = extract_populated_keys(*map, keys.num_rows(), stream);
+
+    // Compact all results from sparse_results and insert into cache
+    sparse_to_dense_results(keys,
+                            requests,
+                            &sparse_results,
+                            cache,
+                            gather_map,
+                            *map,
+                            keys_have_nulls,
+                            include_null_keys,
+                            stream,
+                            mr);
+
+    return cudf::detail::gather(keys,
+                                gather_map,
+                                out_of_bounds_policy::DONT_CHECK,
+                                cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                stream,
+                                mr);
+  };
+
+  if (cudf::detail::has_nested_columns(keys)) {
+    auto const d_key_equal = comparator.equal_to<true>(has_null, null_keys_are_equal);
+    return comparator_helper(d_key_equal);
+  } else {
+    auto const d_key_equal = comparator.equal_to<false>(has_null, null_keys_are_equal);
+    return comparator_helper(d_key_equal);
+  }
+}
+
+}  // namespace
+
+/**
+ * @brief Indicates if a set of aggregation requests can be satisfied with a
+ * hash-based groupby implementation.
+ *
+ * @param requests The set of columns to aggregate and the aggregations to
+ * perform
+ * @return true A hash-based groupby should be used
+ * @return false A hash-based groupby should not be used
+ */
+bool can_use_hash_groupby(host_span<aggregation_request const> requests)
+{
+  return std::all_of(requests.begin(), requests.end(), [](aggregation_request const& r) {
+    auto const v_type = is_dictionary(r.values.type())
+                          ? cudf::dictionary_column_view(r.values).keys().type()
+                          : r.values.type();
+
+    // Currently, input values (not keys) of STRUCT and LIST types are not supported in any of
+    // hash-based aggregations. For those situations, we fallback to sort-based aggregations.
+    if (v_type.id() == type_id::STRUCT or v_type.id() == type_id::LIST) { return false; }
+
+    return std::all_of(r.aggregations.begin(), r.aggregations.end(), [v_type](auto const& a) {
+      return cudf::has_atomic_support(cudf::detail::target_type(v_type, a->kind)) and
+             is_hash_aggregation(a->kind);
+    });
+  });
+}
+
+// Hash-based groupby
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby(
+  table_view const& keys,
+  host_span<aggregation_request const> requests,
+  null_policy include_null_keys,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  cudf::detail::result_cache cache(requests.size());
+
+  std::unique_ptr<table> unique_keys =
+    groupby(keys, requests, &cache, cudf::has_nulls(keys), include_null_keys, stream, mr);
+
+  return std::pair(std::move(unique_keys), extract_results(requests, cache, stream, mr));
+}
+}  // namespace hash
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/hash/groupby_kernels.cuh b/cpp/src/groupby/hash/groupby_kernels.cuh
new file mode 100644
index 0000000..eedb072
--- /dev/null
+++ b/cpp/src/groupby/hash/groupby_kernels.cuh
@@ -0,0 +1,119 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "multi_pass_kernels.cuh"
+#include <cudf/detail/aggregation/aggregation.cuh>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/utilities/bit.hpp>
+
+#include <thrust/pair.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace hash {
+/**
+ * @brief Compute single-pass aggregations and store results into a sparse
+ * `output_values` table, and populate `map` with indices of unique keys
+ *
+ * The hash map is built by inserting every row `i` from the `keys` and
+ * `values` tables as a single (key,value) pair. When the pair is inserted, if
+ * the key was not already present in the map, then the corresponding value is
+ * simply copied to the output. If the key was already present in the map,
+ * then the inserted `values` row is aggregated with the existing row. This
+ * aggregation is done for every element `j` in the row by applying aggregation
+ * operation `j` between the new and existing element.
+ *
+ * Instead of storing the entire rows from `input_keys` and `input_values` in
+ * the hashmap, we instead store the row indices. For example, when inserting
+ * row at index `i` from `input_keys` into the hash map, the value `i` is what
+ * gets stored for the hash map's "key". It is assumed the `map` was constructed
+ * with a custom comparator that uses these row indices to check for equality
+ * between key rows. For example, comparing two keys `k0` and `k1` will compare
+ * the two rows `input_keys[k0] ?= input_keys[k1]`
+ *
+ * Likewise, we store the row indices for the hash maps "values". These indices
+ * index into the `output_values` table. For a given key `k` (which is an index
+ * into `input_keys`), the corresponding value `v` indexes into `output_values`
+ * and stores the result of aggregating rows from `input_values` from rows of
+ * `input_keys` equivalent to the row at `k`.
+ *
+ * The exact size of the result is not known a priori, but can be upper bounded
+ * by the number of rows in `input_keys` & `input_values`. Therefore, it is
+ * assumed `output_values` has sufficient storage for an equivalent number of
+ * rows. In this way, after all rows are aggregated, `output_values` will likely
+ * be "sparse", meaning that not all rows contain the result of an aggregation.
+ *
+ * @tparam Map The type of the hash map
+ */
+template <typename Map>
+struct compute_single_pass_aggs_fn {
+  Map map;
+  table_device_view input_values;
+  mutable_table_device_view output_values;
+  aggregation::Kind const* __restrict__ aggs;
+  bitmask_type const* __restrict__ row_bitmask;
+  bool skip_rows_with_nulls;
+
+  /**
+   * @brief Construct a new compute_single_pass_aggs_fn functor object
+   *
+   * @param map Hash map object to insert key,value pairs into.
+   * @param input_values The table whose rows will be aggregated in the values
+   * of the hash map
+   * @param output_values Table that stores the results of aggregating rows of
+   * `input_values`.
+   * @param aggs The set of aggregation operations to perform across the
+   * columns of the `input_values` rows
+   * @param row_bitmask Bitmask where bit `i` indicates the presence of a null
+   * value in row `i` of input keys. Only used if `skip_rows_with_nulls` is `true`
+   * @param skip_rows_with_nulls Indicates if rows in `input_keys` containing
+   * null values should be skipped. It `true`, it is assumed `row_bitmask` is a
+   * bitmask where bit `i` indicates the presence of a null value in row `i`.
+   */
+  compute_single_pass_aggs_fn(Map map,
+                              table_device_view input_values,
+                              mutable_table_device_view output_values,
+                              aggregation::Kind const* aggs,
+                              bitmask_type const* row_bitmask,
+                              bool skip_rows_with_nulls)
+    : map(map),
+      input_values(input_values),
+      output_values(output_values),
+      aggs(aggs),
+      row_bitmask(row_bitmask),
+      skip_rows_with_nulls(skip_rows_with_nulls)
+  {
+  }
+
+  __device__ void operator()(size_type i)
+  {
+    if (not skip_rows_with_nulls or cudf::bit_is_set(row_bitmask, i)) {
+      auto result = map.insert(thrust::make_pair(i, i));
+
+      cudf::detail::aggregate_row<true, true>(
+        output_values, result.first->second, input_values, i, aggs);
+    }
+  }
+};
+
+}  // namespace hash
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/hash/multi_pass_kernels.cuh b/cpp/src/groupby/hash/multi_pass_kernels.cuh
new file mode 100644
index 0000000..4bc7363
--- /dev/null
+++ b/cpp/src/groupby/hash/multi_pass_kernels.cuh
@@ -0,0 +1,116 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cuda/atomic>
+
+#include <cmath>
+
+namespace cudf {
+namespace detail {
+
+template <typename Map, bool target_has_nulls = true, bool source_has_nulls = true>
+struct var_hash_functor {
+  Map const map;
+  bitmask_type const* __restrict__ row_bitmask;
+  mutable_column_device_view target;
+  column_device_view source;
+  column_device_view sum;
+  column_device_view count;
+  size_type ddof;
+  var_hash_functor(Map const map,
+                   bitmask_type const* row_bitmask,
+                   mutable_column_device_view target,
+                   column_device_view source,
+                   column_device_view sum,
+                   column_device_view count,
+                   size_type ddof)
+    : map(map),
+      row_bitmask(row_bitmask),
+      target(target),
+      source(source),
+      sum(sum),
+      count(count),
+      ddof(ddof)
+  {
+  }
+
+  template <typename Source>
+  constexpr static bool is_supported()
+  {
+    return is_numeric<Source>() && !is_fixed_point<Source>();
+  }
+
+  template <typename Source>
+  __device__ std::enable_if_t<!is_supported<Source>()> operator()(column_device_view const& source,
+                                                                  size_type source_index,
+                                                                  size_type target_index) noexcept
+  {
+    CUDF_UNREACHABLE("Invalid source type for std, var aggregation combination.");
+  }
+
+  template <typename Source>
+  __device__ std::enable_if_t<is_supported<Source>()> operator()(column_device_view const& source,
+                                                                 size_type source_index,
+                                                                 size_type target_index) noexcept
+  {
+    using Target    = target_type_t<Source, aggregation::VARIANCE>;
+    using SumType   = target_type_t<Source, aggregation::SUM>;
+    using CountType = target_type_t<Source, aggregation::COUNT_VALID>;
+
+    if (source_has_nulls and source.is_null(source_index)) return;
+    CountType group_size = count.element<CountType>(target_index);
+    if (group_size == 0 or group_size - ddof <= 0) return;
+
+    auto x        = static_cast<Target>(source.element<Source>(source_index));
+    auto mean     = static_cast<Target>(sum.element<SumType>(target_index)) / group_size;
+    Target result = (x - mean) * (x - mean) / (group_size - ddof);
+    cuda::atomic_ref<Target, cuda::thread_scope_device> ref{target.element<Target>(target_index)};
+    ref.fetch_add(result, cuda::std::memory_order_relaxed);
+    // STD sqrt is applied in finalize()
+
+    if (target_has_nulls and target.is_null(target_index)) { target.set_valid(target_index); }
+  }
+  __device__ inline void operator()(size_type source_index)
+  {
+    if (row_bitmask == nullptr or cudf::bit_is_set(row_bitmask, source_index)) {
+      auto result       = map.find(source_index);
+      auto target_index = result->second;
+
+      auto col         = source;
+      auto source_type = source.type();
+      if (source_type.id() == type_id::DICTIONARY32) {
+        col          = source.child(cudf::dictionary_column_view::keys_column_index);
+        source_type  = col.type();
+        source_index = static_cast<size_type>(source.element<dictionary32>(source_index));
+      }
+
+      type_dispatcher(source_type, *this, col, source_index, target_index);
+    }
+  }
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/aggregate.cpp b/cpp/src/groupby/sort/aggregate.cpp
new file mode 100644
index 0000000..10c271f
--- /dev/null
+++ b/cpp/src/groupby/sort/aggregate.cpp
@@ -0,0 +1,818 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/common/utils.hpp>
+#include <groupby/sort/functors.hpp>
+#include <groupby/sort/group_reductions.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/aggregation/result_cache.hpp>
+#include <cudf/detail/binaryop.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby/sort_helper.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/lists/detail/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+#include <unordered_map>
+#include <utility>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+/**
+ * @brief Functor to dispatch aggregation with
+ *
+ * This functor is to be used with `aggregation_dispatcher` to compute the
+ * appropriate aggregation. If the values on which to run the aggregation are
+ * unchanged, then this functor should be re-used. This is because it stores
+ * memoised sorted and/or grouped values and re-using will save on computation
+ * of these values.
+ */
+struct aggregate_result_functor final : store_result_functor {
+  using store_result_functor::store_result_functor;
+  template <aggregation::Kind k>
+  void operator()(aggregation const& agg)
+  {
+    CUDF_FAIL("Unsupported aggregation.");
+  }
+};
+
+template <>
+void aggregate_result_functor::operator()<aggregation::COUNT_VALID>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    get_grouped_values().nullable()
+      ? detail::group_count_valid(
+          get_grouped_values(), helper.group_labels(stream), helper.num_groups(stream), stream, mr)
+      : detail::group_count_all(
+          helper.group_offsets(stream), helper.num_groups(stream), stream, mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::COUNT_ALL>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_count_all(helper.group_offsets(stream), helper.num_groups(stream), stream, mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::HISTOGRAM>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_histogram(
+      get_grouped_values(), helper.group_labels(stream), helper.num_groups(stream), stream, mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::SUM>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_sum(
+      get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::PRODUCT>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_product(
+      get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::ARGMAX>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(values,
+                   agg,
+                   detail::group_argmax(get_grouped_values(),
+                                        helper.num_groups(stream),
+                                        helper.group_labels(stream),
+                                        helper.key_sort_order(stream),
+                                        stream,
+                                        mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::ARGMIN>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(values,
+                   agg,
+                   detail::group_argmin(get_grouped_values(),
+                                        helper.num_groups(stream),
+                                        helper.group_labels(stream),
+                                        helper.key_sort_order(stream),
+                                        stream,
+                                        mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::MIN>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto result = [&]() {
+    auto values_type = cudf::is_dictionary(values.type())
+                         ? dictionary_column_view(values).keys().type()
+                         : values.type();
+    if (cudf::is_fixed_width(values_type)) {
+      return detail::group_min(
+        get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr);
+    } else {
+      auto argmin_agg = make_argmin_aggregation();
+      operator()<aggregation::ARGMIN>(*argmin_agg);
+      column_view argmin_result = cache.get_result(values, *argmin_agg);
+
+      // We make a view of ARGMIN result without a null mask and gather using
+      // this mask. The values in data buffer of ARGMIN result corresponding
+      // to null values was initialized to ARGMIN_SENTINEL which is an out of
+      // bounds index value and causes the gathered value to be null.
+      column_view null_removed_map(
+        data_type(type_to_id<size_type>()),
+        argmin_result.size(),
+        static_cast<void const*>(argmin_result.template data<size_type>()),
+        nullptr,
+        0);
+      auto transformed_result =
+        cudf::detail::gather(table_view({values}),
+                             null_removed_map,
+                             argmin_result.nullable() ? cudf::out_of_bounds_policy::NULLIFY
+                                                      : cudf::out_of_bounds_policy::DONT_CHECK,
+                             cudf::detail::negative_index_policy::NOT_ALLOWED,
+                             stream,
+                             mr);
+      return std::move(transformed_result->release()[0]);
+    }
+  }();
+
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::MAX>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto result = [&]() {
+    auto values_type = cudf::is_dictionary(values.type())
+                         ? dictionary_column_view(values).keys().type()
+                         : values.type();
+    if (cudf::is_fixed_width(values_type)) {
+      return detail::group_max(
+        get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr);
+    } else {
+      auto argmax_agg = make_argmax_aggregation();
+      operator()<aggregation::ARGMAX>(*argmax_agg);
+      column_view argmax_result = cache.get_result(values, *argmax_agg);
+
+      // We make a view of ARGMAX result without a null mask and gather using
+      // this mask. The values in data buffer of ARGMAX result corresponding
+      // to null values was initialized to ARGMAX_SENTINEL which is an out of
+      // bounds index value and causes the gathered value to be null.
+      column_view null_removed_map(
+        data_type(type_to_id<size_type>()),
+        argmax_result.size(),
+        static_cast<void const*>(argmax_result.template data<size_type>()),
+        nullptr,
+        0);
+      auto transformed_result =
+        cudf::detail::gather(table_view({values}),
+                             null_removed_map,
+                             argmax_result.nullable() ? cudf::out_of_bounds_policy::NULLIFY
+                                                      : cudf::out_of_bounds_policy::DONT_CHECK,
+                             cudf::detail::negative_index_policy::NOT_ALLOWED,
+                             stream,
+                             mr);
+      return std::move(transformed_result->release()[0]);
+    }
+  }();
+
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::MEAN>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto sum_agg   = make_sum_aggregation();
+  auto count_agg = make_count_aggregation();
+  operator()<aggregation::SUM>(*sum_agg);
+  operator()<aggregation::COUNT_VALID>(*count_agg);
+  column_view sum_result   = cache.get_result(values, *sum_agg);
+  column_view count_result = cache.get_result(values, *count_agg);
+
+  // TODO (dm): Special case for timestamp. Add target_type_impl for it.
+  //            Blocked until we support operator+ on timestamps
+  auto col_type = cudf::is_dictionary(values.type())
+                    ? cudf::dictionary_column_view(values).keys().type()
+                    : values.type();
+  auto result =
+    cudf::detail::binary_operation(sum_result,
+                                   count_result,
+                                   binary_operator::DIV,
+                                   cudf::detail::target_type(col_type, aggregation::MEAN),
+                                   stream,
+                                   mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::M2>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto const mean_agg = make_mean_aggregation();
+  operator()<aggregation::MEAN>(*mean_agg);
+  auto const mean_result = cache.get_result(values, *mean_agg);
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_m2(get_grouped_values(), mean_result, helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::VARIANCE>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto& var_agg  = dynamic_cast<cudf::detail::var_aggregation const&>(agg);
+  auto mean_agg  = make_mean_aggregation();
+  auto count_agg = make_count_aggregation();
+  operator()<aggregation::MEAN>(*mean_agg);
+  operator()<aggregation::COUNT_VALID>(*count_agg);
+  column_view mean_result = cache.get_result(values, *mean_agg);
+  column_view group_sizes = cache.get_result(values, *count_agg);
+
+  auto result = detail::group_var(get_grouped_values(),
+                                  mean_result,
+                                  group_sizes,
+                                  helper.group_labels(stream),
+                                  var_agg._ddof,
+                                  stream,
+                                  mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::STD>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto& std_agg = dynamic_cast<cudf::detail::std_aggregation const&>(agg);
+  auto var_agg  = make_variance_aggregation(std_agg._ddof);
+  operator()<aggregation::VARIANCE>(*var_agg);
+  column_view var_result = cache.get_result(values, *var_agg);
+
+  auto result = cudf::detail::unary_operation(var_result, unary_operator::SQRT, stream, mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::QUANTILE>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto count_agg = make_count_aggregation();
+  operator()<aggregation::COUNT_VALID>(*count_agg);
+  column_view group_sizes = cache.get_result(values, *count_agg);
+  auto& quantile_agg      = dynamic_cast<cudf::detail::quantile_aggregation const&>(agg);
+
+  auto result = detail::group_quantiles(get_sorted_values(),
+                                        group_sizes,
+                                        helper.group_offsets(stream),
+                                        helper.num_groups(stream),
+                                        quantile_agg._quantiles,
+                                        quantile_agg._interpolation,
+                                        stream,
+                                        mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::MEDIAN>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto count_agg = make_count_aggregation();
+  operator()<aggregation::COUNT_VALID>(*count_agg);
+  column_view group_sizes = cache.get_result(values, *count_agg);
+
+  auto result = detail::group_quantiles(get_sorted_values(),
+                                        group_sizes,
+                                        helper.group_offsets(stream),
+                                        helper.num_groups(stream),
+                                        {0.5},
+                                        interpolation::LINEAR,
+                                        stream,
+                                        mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::NUNIQUE>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto& nunique_agg = dynamic_cast<cudf::detail::nunique_aggregation const&>(agg);
+
+  auto result = detail::group_nunique(get_sorted_values(),
+                                      helper.group_labels(stream),
+                                      helper.num_groups(stream),
+                                      helper.group_offsets(stream),
+                                      nunique_agg._null_handling,
+                                      stream,
+                                      mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::NTH_ELEMENT>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  auto& nth_element_agg = dynamic_cast<cudf::detail::nth_element_aggregation const&>(agg);
+
+  auto count_agg = make_count_aggregation(nth_element_agg._null_handling);
+  if (count_agg->kind == aggregation::COUNT_VALID) {
+    operator()<aggregation::COUNT_VALID>(*count_agg);
+  } else if (count_agg->kind == aggregation::COUNT_ALL) {
+    operator()<aggregation::COUNT_ALL>(*count_agg);
+  } else {
+    CUDF_FAIL("Wrong count aggregation kind");
+  }
+  column_view group_sizes = cache.get_result(values, *count_agg);
+
+  cache.add_result(values,
+                   agg,
+                   detail::group_nth_element(get_grouped_values(),
+                                             group_sizes,
+                                             helper.group_labels(stream),
+                                             helper.group_offsets(stream),
+                                             helper.num_groups(stream),
+                                             nth_element_agg._n,
+                                             nth_element_agg._null_handling,
+                                             stream,
+                                             mr));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::COLLECT_LIST>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  auto const null_handling =
+    dynamic_cast<cudf::detail::collect_list_aggregation const&>(agg)._null_handling;
+  auto result = detail::group_collect(get_grouped_values(),
+                                      helper.group_offsets(stream),
+                                      helper.num_groups(stream),
+                                      null_handling,
+                                      stream,
+                                      mr);
+  cache.add_result(values, agg, std::move(result));
+}
+
+template <>
+void aggregate_result_functor::operator()<aggregation::COLLECT_SET>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  auto const null_handling =
+    dynamic_cast<cudf::detail::collect_set_aggregation const&>(agg)._null_handling;
+  auto const collect_result = detail::group_collect(get_grouped_values(),
+                                                    helper.group_offsets(stream),
+                                                    helper.num_groups(stream),
+                                                    null_handling,
+                                                    stream,
+                                                    rmm::mr::get_current_device_resource());
+  auto const nulls_equal =
+    dynamic_cast<cudf::detail::collect_set_aggregation const&>(agg)._nulls_equal;
+  auto const nans_equal =
+    dynamic_cast<cudf::detail::collect_set_aggregation const&>(agg)._nans_equal;
+  cache.add_result(
+    values,
+    agg,
+    lists::detail::distinct(
+      lists_column_view{collect_result->view()}, nulls_equal, nans_equal, stream, mr));
+}
+
+/**
+ * @brief Perform merging for the lists that correspond to the same key value.
+ *
+ * This aggregation is similar to `COLLECT_LIST` with the following differences:
+ *  - It requires the input values to be a non-nullable lists column, and
+ *  - The values (lists) corresponding to the same key will not result in a list of lists as output
+ *    from `COLLECT_LIST`. Instead, those lists will result in a list generated by merging them
+ *    together.
+ *
+ * In practice, this aggregation is used to merge the partial results of multiple (distributed)
+ * groupby `COLLECT_LIST` aggregations into a final `COLLECT_LIST` result. Those distributed
+ * aggregations were executed on different values columns partitioned from the original values
+ * column, then their results were (vertically) concatenated before given as the values column for
+ * this aggregation.
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::MERGE_LISTS>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_merge_lists(
+      get_grouped_values(), helper.group_offsets(stream), helper.num_groups(stream), stream, mr));
+}
+
+/**
+ * @brief Perform merging for the lists corresponding to the same key value, then dropping duplicate
+ * list entries.
+ *
+ * This aggregation is similar to `COLLECT_SET` with the following differences:
+ *  - It requires the input values to be a non-nullable lists column, and
+ *  - The values (lists) corresponding to the same key will result in a list generated by merging
+ *    them together then dropping duplicate entries.
+ *
+ * In practice, this aggregation is used to merge the partial results of multiple (distributed)
+ * groupby `COLLECT_LIST` or `COLLECT_SET` aggregations into a final `COLLECT_SET` result. Those
+ * distributed aggregations were executed on different values columns partitioned from the original
+ * values column, then their results were (vertically) concatenated before given as the values
+ * column for this aggregation.
+ *
+ * Firstly, this aggregation performs `MERGE_LISTS` to concatenate the input lists (corresponding to
+ * the same key) into intermediate lists, then it calls `lists::distinct` on them to
+ * remove duplicate list entries. As such, the input (partial results) to this aggregation should be
+ * generated by (distributed) `COLLECT_LIST` aggregations, not `COLLECT_SET`, to avoid unnecessarily
+ * removing duplicate entries for the partial results.
+ *
+ * Since duplicate list entries will be removed, the parameters `null_equality` and `nan_equality`
+ * are needed for calling `lists::distinct`.
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::MERGE_SETS>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  auto const merged_result   = detail::group_merge_lists(get_grouped_values(),
+                                                       helper.group_offsets(stream),
+                                                       helper.num_groups(stream),
+                                                       stream,
+                                                       rmm::mr::get_current_device_resource());
+  auto const& merge_sets_agg = dynamic_cast<cudf::detail::merge_sets_aggregation const&>(agg);
+  cache.add_result(values,
+                   agg,
+                   lists::detail::distinct(lists_column_view{merged_result->view()},
+                                           merge_sets_agg._nulls_equal,
+                                           merge_sets_agg._nans_equal,
+                                           stream,
+                                           mr));
+}
+
+/**
+ * @brief Perform merging for the M2 values that correspond to the same key value.
+ *
+ * The partial results input to this aggregation is a structs column with children are columns
+ * generated by three other groupby aggregations: `COUNT_VALID`, `MEAN`, and `M2` that were
+ * performed on partitioned datasets. After distributedly computed, the results output from these
+ * aggregations are (vertically) concatenated before assembling into a structs column given as the
+ * values column for this aggregation.
+ *
+ * For recursive merging of `M2` values, the aggregations values of all input (`COUNT_VALID`,
+ * `MEAN`, and `M2`) are all merged and stored in the output of this aggregation. As such, the
+ * output will be a structs column containing children columns of merged `COUNT_VALID`, `MEAN`, and
+ * `M2` values.
+ *
+ * The values of M2 are merged following the parallel algorithm described here:
+ * https://www.wikiwand.com/en/Algorithms_for_calculating_variance#/Parallel_algorithm
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::MERGE_M2>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_merge_m2(
+      get_grouped_values(), helper.group_offsets(stream), helper.num_groups(stream), stream, mr));
+}
+
+/**
+ * @brief Perform merging for multiple histograms that correspond to the same key value.
+ *
+ * The partial results input to this aggregation is a structs column that is concatenated from
+ * multiple outputs of HISTOGRAM aggregations.
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::MERGE_HISTOGRAM>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  cache.add_result(
+    values,
+    agg,
+    detail::group_merge_histogram(
+      get_grouped_values(), helper.group_offsets(stream), helper.num_groups(stream), stream, mr));
+}
+
+/**
+ * @brief Creates column views with only valid elements in both input column views
+ *
+ * @param column_0 The first column
+ * @param column_1 The second column
+ * @return tuple with new null mask (if null masks of input differ) and new column views
+ */
+auto column_view_with_common_nulls(column_view const& column_0, column_view const& column_1)
+{
+  auto [new_nullmask, null_count] = cudf::bitmask_and(table_view{{column_0, column_1}});
+  if (null_count == 0) { return std::make_tuple(std::move(new_nullmask), column_0, column_1); }
+  auto column_view_with_new_nullmask = [](auto const& col, void* nullmask, auto null_count) {
+    return column_view(col.type(),
+                       col.size(),
+                       col.head(),
+                       static_cast<cudf::bitmask_type const*>(nullmask),
+                       null_count,
+                       col.offset(),
+                       std::vector(col.child_begin(), col.child_end()));
+  };
+  auto new_column_0 = null_count == column_0.null_count()
+                        ? column_0
+                        : column_view_with_new_nullmask(column_0, new_nullmask.data(), null_count);
+  auto new_column_1 = null_count == column_1.null_count()
+                        ? column_1
+                        : column_view_with_new_nullmask(column_1, new_nullmask.data(), null_count);
+  return std::make_tuple(std::move(new_nullmask), new_column_0, new_column_1);
+}
+
+/**
+ * @brief Perform covariance between two child columns of non-nullable struct column.
+ *
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::COVARIANCE>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+  CUDF_EXPECTS(values.type().id() == type_id::STRUCT,
+               "Input to `groupby covariance` must be a structs column.");
+  CUDF_EXPECTS(values.num_children() == 2,
+               "Input to `groupby covariance` must be a structs column having 2 children columns.");
+
+  auto const& cov_agg = dynamic_cast<cudf::detail::covariance_aggregation const&>(agg);
+  // Covariance only for valid values in both columns.
+  // in non-identical null mask cases, this prevents caching of the results - STD, MEAN, COUNT.
+  auto [_, values_child0, values_child1] =
+    column_view_with_common_nulls(values.child(0), values.child(1));
+
+  auto mean_agg = make_mean_aggregation();
+  aggregate_result_functor(values_child0, helper, cache, stream, mr).operator()<aggregation::MEAN>(*mean_agg);
+  aggregate_result_functor(values_child1, helper, cache, stream, mr).operator()<aggregation::MEAN>(*mean_agg);
+
+  auto const mean0 = cache.get_result(values_child0, *mean_agg);
+  auto const mean1 = cache.get_result(values_child1, *mean_agg);
+  auto count_agg   = make_count_aggregation();
+  auto const count = cache.get_result(values_child0, *count_agg);
+
+  cache.add_result(values,
+                   agg,
+                   detail::group_covariance(get_grouped_values().child(0),
+                                            get_grouped_values().child(1),
+                                            helper.group_labels(stream),
+                                            helper.num_groups(stream),
+                                            count,
+                                            mean0,
+                                            mean1,
+                                            cov_agg._min_periods,
+                                            cov_agg._ddof,
+                                            stream,
+                                            mr));
+}
+
+/**
+ * @brief Perform correlation between two child columns of non-nullable struct column.
+ *
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::CORRELATION>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+  CUDF_EXPECTS(values.type().id() == type_id::STRUCT,
+               "Input to `groupby correlation` must be a structs column.");
+  CUDF_EXPECTS(
+    values.num_children() == 2,
+    "Input to `groupby correlation` must be a structs column having 2 children columns.");
+  CUDF_EXPECTS(not values.nullable(),
+               "Input to `groupby correlation` must be a non-nullable structs column.");
+
+  auto const& corr_agg = dynamic_cast<cudf::detail::correlation_aggregation const&>(agg);
+  CUDF_EXPECTS(corr_agg._type == correlation_type::PEARSON,
+               "Only Pearson correlation is supported.");
+
+  // Correlation only for valid values in both columns.
+  // in non-identical null mask cases, this prevents caching of the results - STD, MEAN, COUNT
+  auto [_, values_child0, values_child1] =
+    column_view_with_common_nulls(values.child(0), values.child(1));
+
+  auto std_agg = make_std_aggregation();
+  aggregate_result_functor(values_child0, helper, cache, stream, mr).operator()<aggregation::STD>(*std_agg);
+  aggregate_result_functor(values_child1, helper, cache, stream, mr).operator()<aggregation::STD>(*std_agg);
+
+  // Compute covariance here to avoid repeated computation of mean & count
+  auto cov_agg = make_covariance_aggregation(corr_agg._min_periods);
+  if (not cache.has_result(values, *cov_agg)) {
+    auto mean_agg    = make_mean_aggregation();
+    auto const mean0 = cache.get_result(values_child0, *mean_agg);
+    auto const mean1 = cache.get_result(values_child1, *mean_agg);
+    auto count_agg   = make_count_aggregation();
+    auto const count = cache.get_result(values_child0, *count_agg);
+
+    auto const& cov_agg_obj = dynamic_cast<cudf::detail::covariance_aggregation const&>(*cov_agg);
+    cache.add_result(values,
+                     *cov_agg,
+                     detail::group_covariance(get_grouped_values().child(0),
+                                              get_grouped_values().child(1),
+                                              helper.group_labels(stream),
+                                              helper.num_groups(stream),
+                                              count,
+                                              mean0,
+                                              mean1,
+                                              cov_agg_obj._min_periods,
+                                              cov_agg_obj._ddof,
+                                              stream,
+                                              mr));
+  }
+
+  auto const stddev0    = cache.get_result(values_child0, *std_agg);
+  auto const stddev1    = cache.get_result(values_child1, *std_agg);
+  auto const covariance = cache.get_result(values, *cov_agg);
+  cache.add_result(
+    values, agg, detail::group_correlation(covariance, stddev0, stddev1, stream, mr));
+}
+
+/**
+ * @brief Generate a tdigest column from a grouped set of numeric input values.
+ *
+ * The tdigest column produced is of the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * Each output row is a single tdigest.  The length of the row is the "size" of the
+ * tdigest, each element of which represents a weighted centroid (mean, weight).
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::TDIGEST>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  auto const max_centroids =
+    dynamic_cast<cudf::detail::tdigest_aggregation const&>(agg).max_centroids;
+
+  auto count_agg = make_count_aggregation();
+  operator()<aggregation::COUNT_VALID>(*count_agg);
+  column_view valid_counts = cache.get_result(values, *count_agg);
+
+  cache.add_result(values,
+                   agg,
+                   cudf::tdigest::detail::group_tdigest(
+                     get_sorted_values(),
+                     helper.group_offsets(stream),
+                     helper.group_labels(stream),
+                     {valid_counts.begin<size_type>(), static_cast<size_t>(valid_counts.size())},
+                     helper.num_groups(stream),
+                     max_centroids,
+                     stream,
+                     mr));
+}
+
+/**
+ * @brief Generate a merged tdigest column from a grouped set of input tdigest columns.
+ *
+ * The tdigest column produced is of the following structure:
+ *
+ * struct {
+ *   // centroids for the digest
+ *   list {
+ *    struct {
+ *      double    // mean
+ *      double    // weight
+ *    },
+ *    ...
+ *   }
+ *   // these are from the input stream, not the centroids. they are used
+ *   // during the percentile_approx computation near the beginning or
+ *   // end of the quantiles
+ *   double       // min
+ *   double       // max
+ * }
+ *
+ * Each output row is a single tdigest.  The length of the row is the "size" of the
+ * tdigest, each element of which represents a weighted centroid (mean, weight).
+ */
+template <>
+void aggregate_result_functor::operator()<aggregation::MERGE_TDIGEST>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) { return; }
+
+  auto const max_centroids =
+    dynamic_cast<cudf::detail::merge_tdigest_aggregation const&>(agg).max_centroids;
+  cache.add_result(values,
+                   agg,
+                   cudf::tdigest::detail::group_merge_tdigest(get_grouped_values(),
+                                                              helper.group_offsets(stream),
+                                                              helper.group_labels(stream),
+                                                              helper.num_groups(stream),
+                                                              max_centroids,
+                                                              stream,
+                                                              mr));
+}
+
+}  // namespace detail
+
+// Sort-based groupby
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby::sort_aggregate(
+  host_span<aggregation_request const> requests,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // We're going to start by creating a cache of results so that aggs that
+  // depend on other aggs will not have to be recalculated. e.g. mean depends on
+  // sum and count. std depends on mean and count
+  cudf::detail::result_cache cache(requests.size());
+
+  for (auto const& request : requests) {
+    auto store_functor =
+      detail::aggregate_result_functor(request.values, helper(), cache, stream, mr);
+    for (auto const& agg : request.aggregations) {
+      // TODO (dm): single pass compute all supported reductions
+      cudf::detail::aggregation_dispatcher(agg->kind, store_functor, *agg);
+    }
+  }
+
+  auto results = detail::extract_results(requests, cache, stream, mr);
+
+  return std::pair(helper().unique_keys(stream, mr), std::move(results));
+}
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/common_utils.cuh b/cpp/src/groupby/sort/common_utils.cuh
new file mode 100644
index 0000000..fe5d7c3
--- /dev/null
+++ b/cpp/src/groupby/sort/common_utils.cuh
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+
+namespace cudf::groupby::detail {
+/**
+ * @brief Functor to compare two rows of a table in given permutation order
+ *
+ * This is useful to identify unique elements in a sorted order table, when the permutation order is
+ * the sorted order of the table.
+ */
+template <typename ComparatorT, typename Iterator>
+struct permuted_row_equality_comparator {
+  /**
+   * @brief Constructs a permuted comparator object which compares two rows of the table in given
+   * permutation order
+   *
+   * @param comparator Equality comparator
+   * @param permutation The permutation map that specifies the effective ordering of
+   * `t`. Must be the same size as `t.num_rows()`
+   */
+  permuted_row_equality_comparator(ComparatorT const& comparator, Iterator const permutation)
+    : _comparator{comparator}, _permutation{permutation}
+  {
+  }
+
+  /**
+   * @brief Returns true if the two rows at the specified indices in the permuted
+   * order are equivalent.
+   *
+   * For example, comparing rows `i` and `j` is equivalent to comparing
+   * rows `permutation[i]` and `permutation[j]` in the original table.
+   *
+   * @param lhs The index of the first row
+   * @param rhs The index of the second row
+   * @returns true if the two specified rows in the permuted order are equivalent
+   */
+  __device__ bool operator()(cudf::size_type lhs, cudf::size_type rhs) const
+  {
+    return _comparator(_permutation[lhs], _permutation[rhs]);
+  };
+
+ private:
+  ComparatorT const _comparator;
+  Iterator const _permutation;
+};
+}  // namespace cudf::groupby::detail
diff --git a/cpp/src/groupby/sort/functors.hpp b/cpp/src/groupby/sort/functors.hpp
new file mode 100644
index 0000000..be36956
--- /dev/null
+++ b/cpp/src/groupby/sort/functors.hpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/result_cache.hpp>
+#include <cudf/detail/groupby/sort_helper.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+/**
+ * @brief Functor to dispatch aggregation with
+ *
+ * This functor is to be used with `aggregation_dispatcher` to compute the
+ * appropriate aggregation. If the values on which to run the aggregation are
+ * unchanged, then this functor should be re-used. This is because it stores
+ * memoised sorted and/or grouped values and re-using will save on computation
+ * of these values.
+ */
+struct store_result_functor {
+  store_result_functor(column_view const& values,
+                       sort::sort_groupby_helper& helper,
+                       cudf::detail::result_cache& cache,
+                       rmm::cuda_stream_view stream,
+                       rmm::mr::device_memory_resource* mr,
+                       sorted keys_are_sorted = sorted::NO)
+    : helper(helper),
+      cache(cache),
+      values(values),
+      stream(stream),
+      mr(mr),
+      keys_are_sorted(keys_are_sorted)
+  {
+  }
+
+ protected:
+  /**
+   * @brief Check if the groupby keys are presorted
+   */
+  [[nodiscard]] bool is_presorted() const { return keys_are_sorted == sorted::YES; }
+
+  /**
+   * @brief Get the grouped values
+   *
+   * Computes the grouped values from @p values on first invocation and returns
+   * the stored result on subsequent invocation
+   */
+  column_view get_grouped_values()
+  {
+    if (is_presorted()) { return values; }
+
+    // TODO (dm): After implementing single pass multi-agg, explore making a
+    //            cache of all grouped value columns rather than one at a time
+    if (grouped_values)
+      return grouped_values->view();
+    else if (sorted_values)
+      // In scan, it wouldn't be ok to return sorted values when asked for grouped values.
+      // It's overridden in scan implementation.
+      return sorted_values->view();
+    else
+      return (grouped_values = helper.grouped_values(values, stream, mr))->view();
+  };
+
+  /**
+   * @brief Get the grouped and sorted values
+   *
+   * Computes the grouped and sorted (within each group) values from @p values
+   * on first invocation and returns the stored result on subsequent invocation
+   */
+  column_view get_sorted_values()
+  {
+    return sorted_values ? sorted_values->view()
+                         : (sorted_values = helper.sorted_values(values, stream, mr))->view();
+  };
+
+ protected:
+  sort::sort_groupby_helper& helper;  ///< Sort helper
+  cudf::detail::result_cache& cache;  ///< cache of results to store into
+  column_view const& values;          ///< Column of values to group and aggregate
+
+  rmm::cuda_stream_view stream;         ///< CUDA stream on which to execute kernels
+  rmm::mr::device_memory_resource* mr;  ///< Memory resource to allocate space for results
+
+  sorted keys_are_sorted;                  ///< Whether the keys are sorted
+  std::unique_ptr<column> sorted_values;   ///< Memoised grouped and sorted values
+  std::unique_ptr<column> grouped_values;  ///< Memoised grouped values
+};
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_argmax.cu b/cpp/src/groupby/sort/group_argmax.cu
new file mode 100644
index 0000000..466171e
--- /dev/null
+++ b/cpp/src/groupby/sort/group_argmax.cu
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_single_pass_reduction_util.cuh>
+
+#include <cudf/detail/gather.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/gather.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_argmax(column_view const& values,
+                                     size_type num_groups,
+                                     cudf::device_span<size_type const> group_labels,
+                                     column_view const& key_sort_order,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  auto indices = type_dispatcher(values.type(),
+                                 group_reduction_dispatcher<aggregation::ARGMAX>{},
+                                 values,
+                                 num_groups,
+                                 group_labels,
+                                 stream,
+                                 mr);
+
+  // The functor returns the index of maximum in the sorted values.
+  // We need the index of maximum in the original unsorted values.
+  // So use indices to gather the sort order used to sort `values`.
+  // Gather map cannot be null so we make a view with the mask removed.
+  // The values in data buffer of indices corresponding to null values was
+  // initialized to ARGMAX_SENTINEL. Using gather_if.
+  // This can't use gather because nulls in gathered column will not store ARGMAX_SENTINEL.
+  auto indices_view = indices->mutable_view();
+  thrust::gather_if(rmm::exec_policy(stream),
+                    indices_view.begin<size_type>(),    // map first
+                    indices_view.end<size_type>(),      // map last
+                    indices_view.begin<size_type>(),    // stencil
+                    key_sort_order.begin<size_type>(),  // input
+                    indices_view.begin<size_type>(),    // result
+                    [] __device__(auto i) { return (i != cudf::detail::ARGMAX_SENTINEL); });
+  return indices;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_argmin.cu b/cpp/src/groupby/sort/group_argmin.cu
new file mode 100644
index 0000000..4f7b2b7
--- /dev/null
+++ b/cpp/src/groupby/sort/group_argmin.cu
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_single_pass_reduction_util.cuh>
+
+#include <cudf/detail/gather.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/gather.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_argmin(column_view const& values,
+                                     size_type num_groups,
+                                     cudf::device_span<size_type const> group_labels,
+                                     column_view const& key_sort_order,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  auto indices = type_dispatcher(values.type(),
+                                 group_reduction_dispatcher<aggregation::ARGMIN>{},
+                                 values,
+                                 num_groups,
+                                 group_labels,
+                                 stream,
+                                 mr);
+
+  // The functor returns the index of minimum in the sorted values.
+  // We need the index of minimum in the original unsorted values.
+  // So use indices to gather the sort order used to sort `values`.
+  // The values in data buffer of indices corresponding to null values was
+  // initialized to ARGMIN_SENTINEL. Using gather_if.
+  // This can't use gather because nulls in gathered column will not store ARGMIN_SENTINEL.
+  auto indices_view = indices->mutable_view();
+  thrust::gather_if(rmm::exec_policy(stream),
+                    indices_view.begin<size_type>(),    // map first
+                    indices_view.end<size_type>(),      // map last
+                    indices_view.begin<size_type>(),    // stencil
+                    key_sort_order.begin<size_type>(),  // input
+                    indices_view.begin<size_type>(),    // result
+                    [] __device__(auto i) { return (i != cudf::detail::ARGMIN_SENTINEL); });
+
+  return indices;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_collect.cu b/cpp/src/groupby/sort/group_collect.cu
new file mode 100644
index 0000000..f95ad72
--- /dev/null
+++ b/cpp/src/groupby/sort/group_collect.cu
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <memory>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+/**
+ * @brief Purge null entries in grouped values, and adjust group offsets.
+ *
+ * @param values Grouped values to be purged
+ * @param offsets Offsets of groups' starting points
+ * @param num_groups Number of groups
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Pair of null-eliminated grouped values and corresponding offsets
+ */
+std::pair<std::unique_ptr<column>, std::unique_ptr<column>> purge_null_entries(
+  column_view const& values,
+  column_view const& offsets,
+  size_type num_groups,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto values_device_view = column_device_view::create(values, stream);
+
+  auto not_null_pred = [d_value = *values_device_view] __device__(auto i) {
+    return d_value.is_valid_nocheck(i);
+  };
+
+  // Purge null entries in grouped values.
+  auto null_purged_entries =
+    cudf::detail::copy_if(table_view{{values}}, not_null_pred, stream, mr)->release();
+
+  auto null_purged_values = std::move(null_purged_entries.front());
+  null_purged_values->set_null_mask(rmm::device_buffer{0, stream, mr}, 0);
+
+  // Recalculate offsets after null entries are purged.
+  rmm::device_uvector<size_type> null_purged_sizes(num_groups, stream);
+
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(num_groups),
+    null_purged_sizes.begin(),
+    [d_offsets = offsets.template begin<size_type>(), not_null_pred] __device__(auto i) {
+      return thrust::count_if(thrust::seq,
+                              thrust::make_counting_iterator<size_type>(d_offsets[i]),
+                              thrust::make_counting_iterator<size_type>(d_offsets[i + 1]),
+                              not_null_pred);
+    });
+
+  auto null_purged_offsets = std::get<0>(cudf::detail::make_offsets_child_column(
+    null_purged_sizes.cbegin(), null_purged_sizes.cend(), stream, mr));
+
+  return std::pair(std::move(null_purged_values), std::move(null_purged_offsets));
+}
+
+std::unique_ptr<column> group_collect(column_view const& values,
+                                      cudf::device_span<size_type const> group_offsets,
+                                      size_type num_groups,
+                                      null_policy null_handling,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  auto [child_column,
+        offsets_column] = [null_handling, num_groups, &values, &group_offsets, stream, mr] {
+    auto offsets_column = make_numeric_column(
+      data_type(type_to_id<size_type>()), num_groups + 1, mask_state::UNALLOCATED, stream, mr);
+
+    thrust::copy(rmm::exec_policy(stream),
+                 group_offsets.begin(),
+                 group_offsets.end(),
+                 offsets_column->mutable_view().template begin<size_type>());
+
+    // If column of grouped values contains null elements, and null_policy == EXCLUDE,
+    // those elements must be filtered out, and offsets recomputed.
+    if (null_handling == null_policy::EXCLUDE && values.has_nulls()) {
+      return cudf::groupby::detail::purge_null_entries(
+        values, offsets_column->view(), num_groups, stream, mr);
+    } else {
+      return std::pair(std::make_unique<cudf::column>(values, stream, mr),
+                       std::move(offsets_column));
+    }
+  }();
+
+  return make_lists_column(num_groups,
+                           std::move(offsets_column),
+                           std::move(child_column),
+                           0,
+                           rmm::device_buffer{0, stream, mr},
+                           stream,
+                           mr);
+}
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_correlation.cu b/cpp/src/groupby/sort/group_correlation.cu
new file mode 100644
index 0000000..887e82e
--- /dev/null
+++ b/cpp/src/groupby/sort/group_correlation.cu
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_reductions.hpp>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <type_traits>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+
+template <typename T>
+constexpr bool is_double_convertible()
+{
+  return std::is_convertible_v<T, double> || std::is_constructible_v<double, T>;
+}
+
+struct is_double_convertible_impl {
+  template <typename T>
+  bool operator()()
+  {
+    return is_double_convertible<T>();
+  }
+};
+
+/**
+ * @brief Typecasts each element of the column to `CastType`
+ */
+template <typename CastType>
+struct type_casted_accessor {
+  template <typename Element>
+  __device__ inline CastType operator()(cudf::size_type i, column_device_view const& col) const
+  {
+    if constexpr (column_device_view::has_element_accessor<Element>() and
+                  std::is_convertible_v<Element, CastType>)
+      return static_cast<CastType>(col.element<Element>(i));
+    (void)i;
+    (void)col;
+    return {};
+  }
+};
+
+template <typename ResultType>
+struct covariance_transform {
+  column_device_view const d_values_0, d_values_1;
+  ResultType const *d_means_0, *d_means_1;
+  size_type const* d_group_sizes;
+  size_type const* d_group_labels;
+  size_type ddof{1};  // TODO update based on bias.
+
+  __device__ static ResultType value(column_device_view const& view, size_type i)
+  {
+    bool const is_dict = view.type().id() == type_id::DICTIONARY32;
+    i                  = is_dict ? static_cast<size_type>(view.element<dictionary32>(i)) : i;
+    auto values_col    = is_dict ? view.child(dictionary_column_view::keys_column_index) : view;
+    return type_dispatcher(values_col.type(), type_casted_accessor<ResultType>{}, i, values_col);
+  }
+
+  __device__ ResultType operator()(size_type i)
+  {
+    if (d_values_0.is_null(i) or d_values_1.is_null(i)) return 0.0;
+
+    // This has to be device dispatch because x and y type may differ
+    auto const x = value(d_values_0, i);
+    auto const y = value(d_values_1, i);
+
+    size_type const group_idx  = d_group_labels[i];
+    size_type const group_size = d_group_sizes[group_idx];
+
+    // prevent divide by zero error
+    if (group_size == 0 or group_size - ddof <= 0) return 0.0;
+
+    ResultType const xmean = d_means_0[group_idx];
+    ResultType const ymean = d_means_1[group_idx];
+    return (x - xmean) * (y - ymean) / (group_size - ddof);
+  }
+};
+}  // namespace
+
+std::unique_ptr<column> group_covariance(column_view const& values_0,
+                                         column_view const& values_1,
+                                         cudf::device_span<size_type const> group_labels,
+                                         size_type num_groups,
+                                         column_view const& count,
+                                         column_view const& mean_0,
+                                         column_view const& mean_1,
+                                         size_type min_periods,
+                                         size_type ddof,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  using result_type = id_to_type<type_id::FLOAT64>;
+  static_assert(
+    std::is_same_v<cudf::detail::target_type_t<result_type, aggregation::Kind::CORRELATION>,
+                   result_type>);
+
+  // check if each child type can be converted to float64.
+  auto get_base_type = [](auto const& col) {
+    return (col.type().id() == type_id::DICTIONARY32
+              ? col.child(dictionary_column_view::keys_column_index)
+              : col)
+      .type();
+  };
+  bool const is_convertible =
+    type_dispatcher(get_base_type(values_0), is_double_convertible_impl{}) and
+    type_dispatcher(get_base_type(values_1), is_double_convertible_impl{});
+
+  CUDF_EXPECTS(is_convertible,
+               "Input to `group_correlation` must be columns of type convertible to float64.");
+
+  auto mean0_ptr = mean_0.begin<result_type>();
+  auto mean1_ptr = mean_1.begin<result_type>();
+
+  auto d_values_0 = column_device_view::create(values_0, stream);
+  auto d_values_1 = column_device_view::create(values_1, stream);
+  covariance_transform<result_type> covariance_transform_op{*d_values_0,
+                                                            *d_values_1,
+                                                            mean0_ptr,
+                                                            mean1_ptr,
+                                                            count.data<size_type>(),
+                                                            group_labels.begin(),
+                                                            ddof};
+
+  auto result = make_numeric_column(
+    data_type(type_to_id<result_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+  auto d_result = result->mutable_view().begin<result_type>();
+
+  auto corr_iter =
+    thrust::make_transform_iterator(thrust::make_counting_iterator(0), covariance_transform_op);
+
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        group_labels.begin(),
+                        group_labels.end(),
+                        corr_iter,
+                        thrust::make_discard_iterator(),
+                        d_result);
+
+  auto is_null = [ddof, min_periods] __device__(size_type group_size) {
+    return not(group_size == 0 or group_size - ddof <= 0 or group_size < min_periods);
+  };
+  auto [new_nullmask, null_count] =
+    cudf::detail::valid_if(count.begin<size_type>(), count.end<size_type>(), is_null, stream, mr);
+  if (null_count != 0) { result->set_null_mask(std::move(new_nullmask), null_count); }
+  return result;
+}
+
+std::unique_ptr<column> group_correlation(column_view const& covariance,
+                                          column_view const& stddev_0,
+                                          column_view const& stddev_1,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  using result_type = id_to_type<type_id::FLOAT64>;
+  CUDF_EXPECTS(covariance.type().id() == type_id::FLOAT64, "Covariance result must be FLOAT64");
+  auto stddev0_ptr = stddev_0.begin<result_type>();
+  auto stddev1_ptr = stddev_1.begin<result_type>();
+  auto stddev_iter = thrust::make_zip_iterator(thrust::make_tuple(stddev0_ptr, stddev1_ptr));
+  auto result      = make_numeric_column(covariance.type(),
+                                    covariance.size(),
+                                    cudf::detail::copy_bitmask(covariance, stream, mr),
+                                    covariance.null_count(),
+                                    stream,
+                                    mr);
+  auto d_result    = result->mutable_view().begin<result_type>();
+  thrust::transform(rmm::exec_policy(stream),
+                    covariance.begin<result_type>(),
+                    covariance.end<result_type>(),
+                    stddev_iter,
+                    d_result,
+                    [] __device__(auto const covariance, auto const stddev) {
+                      return covariance / thrust::get<0>(stddev) / thrust::get<1>(stddev);
+                    });
+
+  result->set_null_count(covariance.null_count());
+
+  return result;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_count.cu b/cpp/src/groupby/sort/group_count.cu
new file mode 100644
index 0000000..e727403
--- /dev/null
+++ b/cpp/src/groupby/sort/group_count.cu
@@ -0,0 +1,98 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/adjacent_difference.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_count_valid(column_view const& values,
+                                          cudf::device_span<size_type const> group_labels,
+                                          size_type num_groups,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(num_groups >= 0, "number of groups cannot be negative");
+  CUDF_EXPECTS(static_cast<size_t>(values.size()) == group_labels.size(),
+               "Size of values column should be same as that of group labels");
+
+  auto result = make_numeric_column(
+    data_type(type_to_id<size_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+
+  if (num_groups == 0) { return result; }
+
+  if (values.nullable()) {
+    auto values_view = column_device_view::create(values, stream);
+
+    // make_validity_iterator returns a boolean iterator that sums to 1 (1+1=1)
+    // so we need to transform it to cast it to an integer type
+    auto bitmask_iterator =
+      thrust::make_transform_iterator(cudf::detail::make_validity_iterator(*values_view),
+                                      [] __device__(auto b) { return static_cast<size_type>(b); });
+
+    thrust::reduce_by_key(rmm::exec_policy(stream),
+                          group_labels.begin(),
+                          group_labels.end(),
+                          bitmask_iterator,
+                          thrust::make_discard_iterator(),
+                          result->mutable_view().begin<size_type>());
+  } else {
+    thrust::reduce_by_key(rmm::exec_policy(stream),
+                          group_labels.begin(),
+                          group_labels.end(),
+                          thrust::make_constant_iterator(1),
+                          thrust::make_discard_iterator(),
+                          result->mutable_view().begin<size_type>());
+  }
+
+  return result;
+}
+
+std::unique_ptr<column> group_count_all(cudf::device_span<size_type const> group_offsets,
+                                        size_type num_groups,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(num_groups >= 0, "number of groups cannot be negative");
+
+  auto result = make_numeric_column(
+    data_type(type_to_id<size_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+
+  if (num_groups == 0) { return result; }
+
+  thrust::adjacent_difference(rmm::exec_policy(stream),
+                              group_offsets.begin() + 1,
+                              group_offsets.end(),
+                              result->mutable_view().begin<size_type>());
+  return result;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_count_scan.cu b/cpp/src/groupby/sort/group_count_scan.cu
new file mode 100644
index 0000000..0caef47
--- /dev/null
+++ b/cpp/src/groupby/sort/group_count_scan.cu
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/scan.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> count_scan(cudf::device_span<size_type const> group_labels,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  std::unique_ptr<column> result = make_fixed_width_column(
+    data_type{type_id::INT32}, group_labels.size(), mask_state::UNALLOCATED, stream, mr);
+
+  if (group_labels.empty()) { return result; }
+
+  auto resultview = result->mutable_view();
+  // aggregation::COUNT_ALL
+  thrust::exclusive_scan_by_key(rmm::exec_policy(stream),
+                                group_labels.begin(),
+                                group_labels.end(),
+                                thrust::make_constant_iterator<size_type>(1),
+                                resultview.begin<size_type>());
+  return result;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_histogram.cu b/cpp/src/groupby/sort/group_histogram.cu
new file mode 100644
index 0000000..bb70037
--- /dev/null
+++ b/cpp/src/groupby/sort/group_histogram.cu
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <lists/utilities.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/labeling/label_segments.cuh>
+#include <cudf/reduction/detail/histogram.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/gather.h>
+
+namespace cudf::groupby::detail {
+
+namespace {
+
+std::unique_ptr<column> build_histogram(column_view const& values,
+                                        cudf::device_span<size_type const> group_labels,
+                                        std::optional<column_view> const& partial_counts,
+                                        size_type num_groups,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(static_cast<size_t>(values.size()) == group_labels.size(),
+               "Size of values column should be the same as that of group labels.",
+               std::invalid_argument);
+
+  // Attach group labels to the input values.
+  auto const labels_cv      = column_view{data_type{type_to_id<size_type>()},
+                                     static_cast<size_type>(group_labels.size()),
+                                     group_labels.data(),
+                                     nullptr,
+                                     0};
+  auto const labeled_values = table_view{{labels_cv, values}};
+
+  // Build histogram for the labeled values.
+  auto [distinct_indices, distinct_counts] =
+    cudf::reduction::detail::compute_row_frequencies(labeled_values, partial_counts, stream, mr);
+
+  // Gather the distinct rows for the output histogram.
+  auto out_table = cudf::detail::gather(labeled_values,
+                                        *distinct_indices,
+                                        out_of_bounds_policy::DONT_CHECK,
+                                        cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                        stream,
+                                        mr);
+
+  // Build offsets for the output lists column containing output histograms.
+  // Each list will be a histogram corresponding to one value group.
+  auto out_offsets = cudf::lists::detail::reconstruct_offsets(
+    out_table->get_column(0).view(), num_groups, stream, mr);
+
+  std::vector<std::unique_ptr<column>> struct_children;
+  struct_children.emplace_back(std::move(out_table->release().back()));
+  struct_children.emplace_back(std::move(distinct_counts));
+  auto out_structs = make_structs_column(static_cast<size_type>(distinct_indices->size()),
+                                         std::move(struct_children),
+                                         0,
+                                         {},
+                                         stream,
+                                         mr);
+
+  return make_lists_column(
+    num_groups, std::move(out_offsets), std::move(out_structs), 0, {}, stream, mr);
+}
+
+}  // namespace
+
+std::unique_ptr<column> group_histogram(column_view const& values,
+                                        cudf::device_span<size_type const> group_labels,
+                                        size_type num_groups,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  // Empty group should be handled before reaching here.
+  CUDF_EXPECTS(num_groups > 0, "Group should not be empty.", std::invalid_argument);
+
+  return build_histogram(values, group_labels, std::nullopt, num_groups, stream, mr);
+}
+
+std::unique_ptr<column> group_merge_histogram(column_view const& values,
+                                              cudf::device_span<size_type const> group_offsets,
+                                              size_type num_groups,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  // Empty group should be handled before reaching here.
+  CUDF_EXPECTS(num_groups > 0, "Group should not be empty.", std::invalid_argument);
+
+  // The input must be a lists column without nulls.
+  CUDF_EXPECTS(!values.has_nulls(), "The input column must not have nulls.", std::invalid_argument);
+  CUDF_EXPECTS(values.type().id() == type_id::LIST,
+               "The input of MERGE_HISTOGRAM aggregation must be a lists column.",
+               std::invalid_argument);
+
+  // Child of the input lists column must be a structs column without nulls,
+  // and its second child is a columns of integer type having no nulls.
+  auto const lists_cv     = lists_column_view{values};
+  auto const histogram_cv = lists_cv.get_sliced_child(stream);
+  CUDF_EXPECTS(!histogram_cv.has_nulls(),
+               "Child of the input lists column must not have nulls.",
+               std::invalid_argument);
+  CUDF_EXPECTS(histogram_cv.type().id() == type_id::STRUCT && histogram_cv.num_children() == 2,
+               "The input column has invalid histograms structure.",
+               std::invalid_argument);
+  CUDF_EXPECTS(
+    cudf::is_integral(histogram_cv.child(1).type()) && !histogram_cv.child(1).has_nulls(),
+    "The input column has invalid histograms structure.",
+    std::invalid_argument);
+
+  // Concatenate the histograms corresponding to the same key values.
+  // That is equivalent to creating a new lists column (view) from the input lists column
+  // with new offsets gathered as below.
+  auto new_offsets = rmm::device_uvector<size_type>(num_groups + 1, stream);
+  thrust::gather(rmm::exec_policy(stream),
+                 group_offsets.begin(),
+                 group_offsets.end(),
+                 lists_cv.offsets_begin(),
+                 new_offsets.begin());
+
+  // Generate labels for the new lists.
+  auto key_labels = rmm::device_uvector<size_type>(histogram_cv.size(), stream);
+  cudf::detail::label_segments(
+    new_offsets.begin(), new_offsets.end(), key_labels.begin(), key_labels.end(), stream);
+
+  auto const structs_cv   = structs_column_view{histogram_cv};
+  auto const input_values = structs_cv.get_sliced_child(0, stream);
+  auto const input_counts = structs_cv.get_sliced_child(1, stream);
+
+  return build_histogram(input_values, key_labels, input_counts, num_groups, stream, mr);
+}
+
+}  // namespace cudf::groupby::detail
diff --git a/cpp/src/groupby/sort/group_m2.cu b/cpp/src/groupby/sort/group_m2.cu
new file mode 100644
index 0000000..70b0510
--- /dev/null
+++ b/cpp/src/groupby/sort/group_m2.cu
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+
+template <typename ResultType, typename Iterator>
+struct m2_transform {
+  column_device_view const d_values;
+  Iterator const values_iter;
+  ResultType const* d_means;
+  size_type const* d_group_labels;
+
+  __device__ ResultType operator()(size_type const idx) const noexcept
+  {
+    if (d_values.is_null(idx)) { return 0.0; }
+
+    auto const x         = static_cast<ResultType>(values_iter[idx]);
+    auto const group_idx = d_group_labels[idx];
+    auto const mean      = d_means[group_idx];
+    auto const diff      = x - mean;
+    return diff * diff;
+  }
+};
+
+template <typename ResultType, typename Iterator>
+void compute_m2_fn(column_device_view const& values,
+                   Iterator values_iter,
+                   cudf::device_span<size_type const> group_labels,
+                   ResultType const* d_means,
+                   ResultType* d_result,
+                   rmm::cuda_stream_view stream)
+{
+  auto m2_fn = m2_transform<ResultType, decltype(values_iter)>{
+    values, values_iter, d_means, group_labels.data()};
+  auto const itr = thrust::counting_iterator<size_type>(0);
+  // Using a temporary buffer for intermediate transform results instead of
+  // using the transform-iterator directly in thrust::reduce_by_key
+  // improves compile-time significantly.
+  auto m2_vals = rmm::device_uvector<ResultType>(values.size(), stream);
+  thrust::transform(rmm::exec_policy(stream), itr, itr + values.size(), m2_vals.begin(), m2_fn);
+
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        group_labels.begin(),
+                        group_labels.end(),
+                        m2_vals.begin(),
+                        thrust::make_discard_iterator(),
+                        d_result);
+}
+
+struct m2_functor {
+  template <typename T>
+  std::enable_if_t<std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(
+    column_view const& values,
+    column_view const& group_means,
+    cudf::device_span<size_type const> group_labels,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    using result_type = cudf::detail::target_type_t<T, aggregation::Kind::M2>;
+    auto result       = make_numeric_column(data_type(type_to_id<result_type>()),
+                                      group_means.size(),
+                                      mask_state::UNALLOCATED,
+                                      stream,
+                                      mr);
+
+    auto const values_dv_ptr = column_device_view::create(values, stream);
+    auto const d_values      = *values_dv_ptr;
+    auto const d_means       = group_means.data<result_type>();
+    auto const d_result      = result->mutable_view().data<result_type>();
+
+    if (!cudf::is_dictionary(values.type())) {
+      auto const values_iter = d_values.begin<T>();
+      compute_m2_fn(d_values, values_iter, group_labels, d_means, d_result, stream);
+    } else {
+      auto const values_iter =
+        cudf::dictionary::detail::make_dictionary_iterator<T>(*values_dv_ptr);
+      compute_m2_fn(d_values, values_iter, group_labels, d_means, d_result, stream);
+    }
+
+    // M2 column values should have the same bitmask as means's.
+    if (group_means.nullable()) {
+      result->set_null_mask(cudf::detail::copy_bitmask(group_means, stream, mr),
+                            group_means.null_count());
+    }
+
+    return result;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Only numeric types are supported in M2 groupby aggregation");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> group_m2(column_view const& values,
+                                 column_view const& group_means,
+                                 cudf::device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+
+  return type_dispatcher(values_type, m2_functor{}, values, group_means, group_labels, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_max.cu b/cpp/src/groupby/sort/group_max.cu
new file mode 100644
index 0000000..5da1526
--- /dev/null
+++ b/cpp/src/groupby/sort/group_max.cu
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_single_pass_reduction_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_max(column_view const& values,
+                                  size_type num_groups,
+                                  cudf::device_span<size_type const> group_labels,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+  return type_dispatcher(values_type,
+                         group_reduction_dispatcher<aggregation::MAX>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_max_scan.cu b/cpp/src/groupby/sort/group_max_scan.cu
new file mode 100644
index 0000000..1551dc0
--- /dev/null
+++ b/cpp/src/groupby/sort/group_max_scan.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_scan_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> max_scan(column_view const& values,
+                                 size_type num_groups,
+                                 cudf::device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(values.type(),
+                         group_scan_dispatcher<aggregation::MAX>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_merge_lists.cu b/cpp/src/groupby/sort/group_merge_lists.cu
new file mode 100644
index 0000000..2c72128
--- /dev/null
+++ b/cpp/src/groupby/sort/group_merge_lists.cu
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/gather.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_merge_lists(column_view const& values,
+                                          cudf::device_span<size_type const> group_offsets,
+                                          size_type num_groups,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(values.type().id() == type_id::LIST,
+               "Input to `group_merge_lists` must be a lists column.");
+  CUDF_EXPECTS(!values.nullable(),
+               "Input to `group_merge_lists` must be a non-nullable lists column.");
+
+  auto offsets_column = make_numeric_column(
+    data_type(type_to_id<size_type>()), num_groups + 1, mask_state::UNALLOCATED, stream, mr);
+
+  // Generate offsets of the output lists column by gathering from the provided group offsets and
+  // the input list offsets.
+  //
+  // For example:
+  //   values        = [[2, 1], [], [4, -1, -2], [], [<NA>, 4, <NA>]]
+  //   list_offsets  =  [0,     2,   2,           5,   5              8]
+  //   group_offsets = [0,                        3,                  5]
+  //
+  //   then, the output offsets_column is [0, 5, 8].
+  //
+  thrust::gather(rmm::exec_policy(stream),
+                 group_offsets.begin(),
+                 group_offsets.end(),
+                 lists_column_view(values).offsets_begin(),
+                 offsets_column->mutable_view().template begin<size_type>());
+
+  // The child column of the output lists column is just copied from the input column.
+  auto child_column =
+    std::make_unique<column>(lists_column_view(values).get_sliced_child(stream), stream, mr);
+
+  return make_lists_column(num_groups,
+                           std::move(offsets_column),
+                           std::move(child_column),
+                           0,
+                           rmm::device_buffer{},
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_merge_m2.cu b/cpp/src/groupby/sort/group_merge_m2.cu
new file mode 100644
index 0000000..a580c9d
--- /dev/null
+++ b/cpp/src/groupby/sort/group_merge_m2.cu
@@ -0,0 +1,201 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+/**
+ * @brief Struct to store partial results for merging.
+ */
+template <class result_type>
+struct partial_result {
+  size_type count;
+  result_type mean;
+  result_type M2;
+};
+
+/**
+ * @brief Functor to accumulate (merge) all partial results corresponding to the same key into a
+ * final result storing in a member variable. It performs merging for the partial results of
+ * `COUNT_VALID`, `MEAN`, and `M2` at the same time.
+ */
+template <class result_type>
+struct accumulate_fn {
+  partial_result<result_type> merge_vals;
+
+  void __device__ operator()(partial_result<result_type> const& partial_vals) noexcept
+  {
+    if (partial_vals.count == 0) { return; }
+
+    auto const n_ab  = merge_vals.count + partial_vals.count;
+    auto const delta = partial_vals.mean - merge_vals.mean;
+    merge_vals.M2 += partial_vals.M2 + (delta * delta) *
+                                         static_cast<result_type>(merge_vals.count) *
+                                         static_cast<result_type>(partial_vals.count) / n_ab;
+    merge_vals.mean =
+      (merge_vals.mean * merge_vals.count + partial_vals.mean * partial_vals.count) / n_ab;
+    merge_vals.count = n_ab;
+  }
+};
+
+/**
+ * @brief Functor to merge partial results of `COUNT_VALID`, `MEAN`, and `M2` aggregations
+ * for a given group (key) index.
+ */
+template <class result_type>
+struct merge_fn {
+  size_type const* const d_offsets;
+  size_type const* const d_counts;
+  result_type const* const d_means;
+  result_type const* const d_M2s;
+
+  auto __device__ operator()(size_type const group_idx) noexcept
+  {
+    auto const start_idx = d_offsets[group_idx], end_idx = d_offsets[group_idx + 1];
+
+    // This case should never happen, because all groups are non-empty as the results of
+    // aggregation. Here we just to make sure we cover this case.
+    if (start_idx == end_idx) {
+      return thrust::make_tuple(size_type{0}, result_type{0}, result_type{0}, int8_t{0});
+    }
+
+    // If `(n = d_counts[idx]) > 0` then `d_means[idx] != null` and `d_M2s[idx] != null`.
+    // Otherwise (`n == 0`), these value (mean and M2) will always be nulls.
+    // In such cases, reading `mean` and `M2` from memory will return garbage values.
+    // By setting these values to zero when `n == 0`, we can safely merge the all-zero tuple without
+    // affecting the final result.
+    auto get_partial_result = [&] __device__(size_type idx) {
+      {
+        auto const n = d_counts[idx];
+        return n > 0 ? partial_result<result_type>{n, d_means[idx], d_M2s[idx]}
+                     : partial_result<result_type>{size_type{0}, result_type{0}, result_type{0}};
+      };
+    };
+
+    // Firstly, store tuple(count, mean, M2) of the first partial result in an accumulator.
+    auto accumulator = accumulate_fn<result_type>{get_partial_result(start_idx)};
+
+    // Then, accumulate (merge) the remaining partial results into that accumulator.
+    for (auto idx = start_idx + 1; idx < end_idx; ++idx) {
+      accumulator(get_partial_result(idx));
+    }
+
+    // Get the final result after merging.
+    auto const& merge_vals = accumulator.merge_vals;
+
+    // If there are all nulls in the partial results (i.e., sum of all valid counts is
+    // zero), then the output is a null.
+    auto const is_valid = int8_t{merge_vals.count > 0};
+
+    return thrust::make_tuple(merge_vals.count, merge_vals.mean, merge_vals.M2, is_valid);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> group_merge_m2(column_view const& values,
+                                       cudf::device_span<size_type const> group_offsets,
+                                       size_type num_groups,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(values.type().id() == type_id::STRUCT,
+               "Input to `group_merge_m2` must be a structs column.");
+  CUDF_EXPECTS(values.num_children() == 3,
+               "Input to `group_merge_m2` must be a structs column having 3 children columns.");
+
+  using result_type = id_to_type<type_id::FLOAT64>;
+  static_assert(
+    std::is_same_v<cudf::detail::target_type_t<result_type, aggregation::Kind::M2>, result_type>);
+  CUDF_EXPECTS(values.child(0).type().id() == type_id::INT32 &&
+                 values.child(1).type().id() == type_to_id<result_type>() &&
+                 values.child(2).type().id() == type_to_id<result_type>(),
+               "Input to `group_merge_m2` must be a structs column having children columns "
+               "containing tuples of (M2_value, mean, valid_count).");
+
+  auto result_counts = make_numeric_column(
+    data_type(type_to_id<size_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+  auto result_means = make_numeric_column(
+    data_type(type_to_id<result_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+  auto result_M2s = make_numeric_column(
+    data_type(type_to_id<result_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+  auto validities = rmm::device_uvector<int8_t>(num_groups, stream);
+
+  // Perform merging for all the aggregations. Their output (and their validity data) are written
+  // out concurrently through an output zip iterator.
+  using iterator_tuple  = thrust::tuple<size_type*, result_type*, result_type*, int8_t*>;
+  using output_iterator = thrust::zip_iterator<iterator_tuple>;
+  auto const out_iter =
+    output_iterator{thrust::make_tuple(result_counts->mutable_view().template data<size_type>(),
+                                       result_means->mutable_view().template data<result_type>(),
+                                       result_M2s->mutable_view().template data<result_type>(),
+                                       validities.begin())};
+
+  auto const count_valid = values.child(0);
+  auto const mean_values = values.child(1);
+  auto const M2_values   = values.child(2);
+  auto const iter        = thrust::make_counting_iterator<size_type>(0);
+
+  auto const fn = merge_fn<result_type>{group_offsets.begin(),
+                                        count_valid.template begin<size_type>(),
+                                        mean_values.template begin<result_type>(),
+                                        M2_values.template begin<result_type>()};
+  thrust::transform(rmm::exec_policy(stream), iter, iter + num_groups, out_iter, fn);
+
+  // Generate bitmask for the output.
+  // Only mean and M2 values can be nullable. Count column must be non-nullable.
+  auto [null_mask, null_count] =
+    cudf::detail::valid_if(validities.begin(), validities.end(), thrust::identity{}, stream, mr);
+  if (null_count > 0) {
+    result_means->set_null_mask(null_mask, null_count, stream);   // copy null_mask
+    result_M2s->set_null_mask(std::move(null_mask), null_count);  // take over null_mask
+  }
+
+  // Output is a structs column containing the merged values of `COUNT_VALID`, `MEAN`, and `M2`.
+  std::vector<std::unique_ptr<column>> out_columns;
+  out_columns.emplace_back(std::move(result_counts));
+  out_columns.emplace_back(std::move(result_means));
+  out_columns.emplace_back(std::move(result_M2s));
+  auto result = cudf::make_structs_column(
+    num_groups, std::move(out_columns), 0, rmm::device_buffer{0, stream, mr}, stream, mr);
+
+  return result;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_min.cu b/cpp/src/groupby/sort/group_min.cu
new file mode 100644
index 0000000..c42a0b9
--- /dev/null
+++ b/cpp/src/groupby/sort/group_min.cu
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_single_pass_reduction_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_min(column_view const& values,
+                                  size_type num_groups,
+                                  cudf::device_span<size_type const> group_labels,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+  return type_dispatcher(values_type,
+                         group_reduction_dispatcher<aggregation::MIN>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_min_scan.cu b/cpp/src/groupby/sort/group_min_scan.cu
new file mode 100644
index 0000000..daaeb6b
--- /dev/null
+++ b/cpp/src/groupby/sort/group_min_scan.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_scan_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> min_scan(column_view const& values,
+                                 size_type num_groups,
+                                 cudf::device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(values.type(),
+                         group_scan_dispatcher<aggregation::MIN>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_nth_element.cu b/cpp/src/groupby/sort/group_nth_element.cu
new file mode 100644
index 0000000..58d76a8
--- /dev/null
+++ b/cpp/src/groupby/sort/group_nth_element.cu
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+#include <thrust/iterator/discard_iterator.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/transform.h>
+#include <thrust/uninitialized_fill.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_nth_element(column_view const& values,
+                                          column_view const& group_sizes,
+                                          cudf::device_span<size_type const> group_labels,
+                                          cudf::device_span<size_type const> group_offsets,
+                                          size_type num_groups,
+                                          size_type n,
+                                          null_policy null_handling,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(static_cast<size_t>(values.size()) == group_labels.size(),
+               "Size of values column should be same as that of group labels");
+
+  if (num_groups == 0) { return empty_like(values); }
+
+  auto nth_index = rmm::device_uvector<size_type>(num_groups, stream);
+  // TODO: replace with async version
+  thrust::uninitialized_fill_n(
+    rmm::exec_policy(stream), nth_index.begin(), num_groups, values.size());
+
+  // nulls_policy::INCLUDE (equivalent to pandas nth(dropna=None) but return nulls for n
+  if (null_handling == null_policy::INCLUDE || !values.has_nulls()) {
+    // Returns index of nth value.
+    thrust::transform_if(
+      rmm::exec_policy(stream),
+      group_sizes.begin<size_type>(),
+      group_sizes.end<size_type>(),
+      group_offsets.begin(),
+      group_sizes.begin<size_type>(),  // stencil
+      nth_index.begin(),
+      [n] __device__(auto group_size, auto group_offset) {
+        return group_offset + ((n < 0) ? group_size + n : n);
+      },
+      [n] __device__(auto group_size) {  // nth within group
+        return (n < 0) ? group_size >= (-n) : group_size > n;
+      });
+  } else {  // skip nulls (equivalent to pandas nth(dropna='any'))
+    // Returns index of nth value.
+    auto values_view = column_device_view::create(values, stream);
+    auto bitmask_iterator =
+      thrust::make_transform_iterator(cudf::detail::make_validity_iterator(*values_view),
+                                      [] __device__(auto b) { return static_cast<size_type>(b); });
+    rmm::device_uvector<size_type> intra_group_index(values.size(), stream);
+    // intra group index for valids only.
+    thrust::exclusive_scan_by_key(rmm::exec_policy(stream),
+                                  group_labels.begin(),
+                                  group_labels.end(),
+                                  bitmask_iterator,
+                                  intra_group_index.begin());
+    // group_size to recalculate n if n<0
+    rmm::device_uvector<size_type> group_count = [&] {
+      if (n < 0) {
+        rmm::device_uvector<size_type> group_count(num_groups, stream);
+        thrust::reduce_by_key(rmm::exec_policy(stream),
+                              group_labels.begin(),
+                              group_labels.end(),
+                              bitmask_iterator,
+                              thrust::make_discard_iterator(),
+                              group_count.begin());
+        return group_count;
+      } else {
+        return rmm::device_uvector<size_type>(0, stream);
+      }
+    }();
+    // gather the valid index == n
+    thrust::scatter_if(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       thrust::make_counting_iterator<size_type>(values.size()),
+                       group_labels.begin(),                          // map
+                       thrust::make_counting_iterator<size_type>(0),  // stencil
+                       nth_index.begin(),
+                       [n,
+                        bitmask_iterator,
+                        group_size        = group_count.begin(),
+                        group_labels      = group_labels.begin(),
+                        intra_group_index = intra_group_index.begin()] __device__(auto i) -> bool {
+                         auto nth = ((n < 0) ? group_size[group_labels[i]] + n : n);
+                         return (bitmask_iterator[i] && intra_group_index[i] == nth);
+                       });
+  }
+
+  auto output_table = cudf::detail::gather(table_view{{values}},
+                                           nth_index,
+                                           out_of_bounds_policy::NULLIFY,
+                                           cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                           stream,
+                                           mr);
+  if (!output_table->get_column(0).has_nulls()) output_table->get_column(0).set_null_mask({}, 0);
+  return std::make_unique<column>(std::move(output_table->get_column(0)));
+}
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_nunique.cu b/cpp/src/groupby/sort/group_nunique.cu
new file mode 100644
index 0000000..1a5f169
--- /dev/null
+++ b/cpp/src/groupby/sort/group_nunique.cu
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+
+template <bool has_nested_columns, typename Nullate>
+struct is_unique_iterator_fn {
+  using comparator_type =
+    typename cudf::experimental::row::equality::device_row_comparator<has_nested_columns, Nullate>;
+
+  Nullate nulls;
+  column_device_view const v;
+  comparator_type equal;
+  null_policy null_handling;
+  size_type const* group_offsets;
+  size_type const* group_labels;
+
+  is_unique_iterator_fn(Nullate nulls,
+                        column_device_view const& v,
+                        comparator_type const& equal,
+                        null_policy null_handling,
+                        size_type const* group_offsets,
+                        size_type const* group_labels)
+    : nulls{nulls},
+      v{v},
+      equal{equal},
+      null_handling{null_handling},
+      group_offsets{group_offsets},
+      group_labels{group_labels}
+  {
+  }
+
+  __device__ size_type operator()(size_type i) const
+  {
+    auto const is_input_countable =
+      !nulls || (null_handling == null_policy::INCLUDE || v.is_valid_nocheck(i));
+    auto const is_unique =
+      is_input_countable && (group_offsets[group_labels[i]] == i ||  // first element or
+                             (not equal(i, i - 1)));                 // new unique value in sorted
+    return static_cast<size_type>(is_unique);
+  }
+};
+}  // namespace
+
+std::unique_ptr<column> group_nunique(column_view const& values,
+                                      cudf::device_span<size_type const> group_labels,
+                                      size_type const num_groups,
+                                      cudf::device_span<size_type const> group_offsets,
+                                      null_policy null_handling,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(num_groups >= 0, "number of groups cannot be negative");
+  CUDF_EXPECTS(static_cast<size_t>(values.size()) == group_labels.size(),
+               "Size of values column should be same as that of group labels");
+
+  auto result = make_numeric_column(
+    data_type(type_to_id<size_type>()), num_groups, mask_state::UNALLOCATED, stream, mr);
+
+  if (num_groups == 0) { return result; }
+
+  auto const values_view = table_view{{values}};
+  auto const comparator  = cudf::experimental::row::equality::self_comparator{values_view, stream};
+
+  auto const d_values_view = column_device_view::create(values, stream);
+
+  auto d_result = rmm::device_uvector<size_type>(group_labels.size(), stream);
+
+  auto const comparator_helper = [&](auto const d_equal) {
+    auto fn = is_unique_iterator_fn{nullate::DYNAMIC{values.has_nulls()},
+                                    *d_values_view,
+                                    d_equal,
+                                    null_handling,
+                                    group_offsets.data(),
+                                    group_labels.data()};
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(values.size()),
+                      d_result.begin(),
+                      fn);
+  };
+
+  if (cudf::detail::has_nested_columns(values_view)) {
+    auto const d_equal = comparator.equal_to<true>(
+      cudf::nullate::DYNAMIC{cudf::has_nested_nulls(values_view)}, null_equality::EQUAL);
+    comparator_helper(d_equal);
+  } else {
+    auto const d_equal = comparator.equal_to<false>(
+      cudf::nullate::DYNAMIC{cudf::has_nested_nulls(values_view)}, null_equality::EQUAL);
+    comparator_helper(d_equal);
+  }
+
+  // calling this with a vector instead of a transform iterator is 10x faster to compile;
+  // it also helps that we are only calling it once for both conditions
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        group_labels.begin(),
+                        group_labels.end(),
+                        d_result.begin(),
+                        thrust::make_discard_iterator(),
+                        result->mutable_view().begin<size_type>());
+
+  return result;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_product.cu b/cpp/src/groupby/sort/group_product.cu
new file mode 100644
index 0000000..74f5cbe
--- /dev/null
+++ b/cpp/src/groupby/sort/group_product.cu
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <groupby/sort/group_single_pass_reduction_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_product(column_view const& values,
+                                      size_type num_groups,
+                                      cudf::device_span<size_type const> group_labels,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+  return type_dispatcher(values_type,
+                         group_reduction_dispatcher<aggregation::PRODUCT>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_quantiles.cu b/cpp/src/groupby/sort/group_quantiles.cu
new file mode 100644
index 0000000..a9edcfe
--- /dev/null
+++ b/cpp/src/groupby/sort/group_quantiles.cu
@@ -0,0 +1,180 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "group_reductions.hpp"
+#include <quantiles/quantiles_util.hpp>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+
+template <typename ResultType, typename Iterator>
+struct calculate_quantile_fn {
+  Iterator values_iter;
+  column_device_view d_group_size;
+  mutable_column_device_view d_result;
+  size_type const* d_group_offset;
+  double const* d_quantiles;
+  size_type num_quantiles;
+  interpolation interpolation;
+
+  __device__ void operator()(size_type i)
+  {
+    size_type segment_size = d_group_size.element<size_type>(i);
+
+    auto d_itr = values_iter + d_group_offset[i];
+    thrust::transform(thrust::seq,
+                      d_quantiles,
+                      d_quantiles + num_quantiles,
+                      d_result.data<ResultType>() + i * num_quantiles,
+                      [d_itr, segment_size, interpolation = interpolation](auto q) {
+                        return cudf::detail::select_quantile_data<ResultType>(
+                          d_itr, segment_size, q, interpolation);
+                      });
+
+    size_type offset = i * num_quantiles;
+    thrust::for_each_n(thrust::seq,
+                       thrust::make_counting_iterator(0),
+                       num_quantiles,
+                       [d_result = d_result, segment_size, offset](size_type j) {
+                         if (segment_size == 0)
+                           d_result.set_null(offset + j);
+                         else
+                           d_result.set_valid(offset + j);
+                       });
+  }
+};
+
+struct quantiles_functor {
+  template <typename T>
+  std::enable_if_t<std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(
+    column_view const& values,
+    column_view const& group_sizes,
+    cudf::device_span<size_type const> group_offsets,
+    size_type const num_groups,
+    device_span<double const> quantile,
+    interpolation interpolation,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    using ResultType = cudf::detail::target_type_t<T, aggregation::QUANTILE>;
+
+    auto result = make_numeric_column(data_type(type_to_id<ResultType>()),
+                                      group_sizes.size() * quantile.size(),
+                                      mask_state::UNINITIALIZED,
+                                      stream,
+                                      mr);
+    // TODO (dm): Support for no-materialize index indirection values
+    // TODO (dm): Future optimization: add column order to aggregation request
+    //            so that sorting isn't required. Then add support for pre-sorted
+
+    // prepare args to be used by lambda below
+    auto values_view     = column_device_view::create(values, stream);
+    auto group_size_view = column_device_view::create(group_sizes, stream);
+    auto result_view     = mutable_column_device_view::create(result->mutable_view(), stream);
+
+    // For each group, calculate quantile
+    if (!cudf::is_dictionary(values.type())) {
+      auto values_iter = values_view->begin<T>();
+      thrust::for_each_n(rmm::exec_policy(stream),
+                         thrust::make_counting_iterator(0),
+                         num_groups,
+                         calculate_quantile_fn<ResultType, decltype(values_iter)>{
+                           values_iter,
+                           *group_size_view,
+                           *result_view,
+                           group_offsets.data(),
+                           quantile.data(),
+                           static_cast<size_type>(quantile.size()),
+                           interpolation});
+    } else {
+      auto values_iter = cudf::dictionary::detail::make_dictionary_iterator<T>(*values_view);
+      thrust::for_each_n(rmm::exec_policy(stream),
+                         thrust::make_counting_iterator(0),
+                         num_groups,
+                         calculate_quantile_fn<ResultType, decltype(values_iter)>{
+                           values_iter,
+                           *group_size_view,
+                           *result_view,
+                           group_offsets.data(),
+                           quantile.data(),
+                           static_cast<size_type>(quantile.size()),
+                           interpolation});
+    }
+
+    return result;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Only arithmetic types are supported in quantiles");
+  }
+};
+
+}  // namespace
+
+// TODO: add optional check for is_sorted. Use context.flag_sorted
+std::unique_ptr<column> group_quantiles(column_view const& values,
+                                        column_view const& group_sizes,
+                                        cudf::device_span<size_type const> group_offsets,
+                                        size_type const num_groups,
+                                        std::vector<double> const& quantiles,
+                                        interpolation interp,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  auto dv_quantiles = cudf::detail::make_device_uvector_async(
+    quantiles, stream, rmm::mr::get_current_device_resource());
+
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+
+  return type_dispatcher(values_type,
+                         quantiles_functor{},
+                         values,
+                         group_sizes,
+                         group_offsets,
+                         num_groups,
+                         dv_quantiles,
+                         interp,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_rank_scan.cu b/cpp/src/groupby/sort/group_rank_scan.cu
new file mode 100644
index 0000000..5cf7844
--- /dev/null
+++ b/cpp/src/groupby/sort/group_rank_scan.cu
@@ -0,0 +1,330 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common_utils.cuh"
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+
+template <bool forward, typename permuted_equal_t, typename value_resolver>
+struct unique_identifier {
+  unique_identifier(size_type const* labels,
+                    size_type const* offsets,
+                    permuted_equal_t permuted_equal,
+                    value_resolver resolver)
+    : _labels(labels), _offsets(offsets), _permuted_equal(permuted_equal), _resolver(resolver)
+  {
+  }
+
+  auto __device__ operator()(size_type row_index) const noexcept
+  {
+    auto const group_start = _offsets[_labels[row_index]];
+    if constexpr (forward) {
+      // First value of equal values is 1.
+      return _resolver(row_index == group_start || !_permuted_equal(row_index, row_index - 1),
+                       row_index - group_start);
+    } else {
+      auto const group_end = _offsets[_labels[row_index] + 1];
+      // Last value of equal values is 1.
+      return _resolver(row_index + 1 == group_end || !_permuted_equal(row_index, row_index + 1),
+                       row_index - group_start);
+    }
+  }
+
+ private:
+  size_type const* _labels;
+  size_type const* _offsets;
+  permuted_equal_t _permuted_equal;
+  value_resolver _resolver;
+};
+
+/**
+ * @brief generate grouped row ranks or dense ranks using a row comparison then scan the results
+ *
+ * @tparam forward true if the rank scan computation should use forward iterator traversal (default)
+ * else reverse iterator traversal
+ * @tparam value_resolver flag value resolver function with boolean first and row number arguments
+ * @tparam scan_operator scan function ran on the flag values
+ * @param grouped_values input column to generate ranks for
+ * @param value_order column of type INT32 that contains the order of the values in the
+ * grouped_values column
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param group_offsets group index offsets with group ID indices
+ * @param resolver flag value resolver
+ * @param scan_op scan operation ran on the flag results
+ * @param has_nulls true if nulls are included in the `grouped_values` column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return std::unique_ptr<column> rank values
+ */
+template <bool forward, typename value_resolver, typename scan_operator>
+std::unique_ptr<column> rank_generator(column_view const& grouped_values,
+                                       column_view const& value_order,
+                                       device_span<size_type const> group_labels,
+                                       device_span<size_type const> group_offsets,
+                                       value_resolver resolver,
+                                       scan_operator scan_op,
+                                       bool has_nulls,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto const grouped_values_view = table_view{{grouped_values}};
+  auto const comparator =
+    cudf::experimental::row::equality::self_comparator{grouped_values_view, stream};
+
+  auto ranks = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, grouped_values.size(), mask_state::UNALLOCATED, stream, mr);
+  auto mutable_ranks = ranks->mutable_view();
+
+  auto const comparator_helper = [&](auto const d_equal) {
+    auto const permuted_equal =
+      permuted_row_equality_comparator(d_equal, value_order.begin<size_type>());
+
+    thrust::tabulate(rmm::exec_policy(stream),
+                     mutable_ranks.begin<size_type>(),
+                     mutable_ranks.end<size_type>(),
+                     unique_identifier<forward, decltype(permuted_equal), value_resolver>(
+                       group_labels.begin(), group_offsets.begin(), permuted_equal, resolver));
+  };
+
+  if (cudf::detail::has_nested_columns(grouped_values_view)) {
+    auto const d_equal =
+      comparator.equal_to<true>(cudf::nullate::DYNAMIC{has_nulls}, null_equality::EQUAL);
+    comparator_helper(d_equal);
+  } else {
+    auto const d_equal =
+      comparator.equal_to<false>(cudf::nullate::DYNAMIC{has_nulls}, null_equality::EQUAL);
+    comparator_helper(d_equal);
+  }
+
+  auto [group_labels_begin, mutable_rank_begin] = [&]() {
+    if constexpr (forward) {
+      return thrust::pair{group_labels.begin(), mutable_ranks.begin<size_type>()};
+    } else {
+      return thrust::pair{thrust::reverse_iterator(group_labels.end()),
+                          thrust::reverse_iterator(mutable_ranks.end<size_type>())};
+    }
+  }();
+  thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                group_labels_begin,
+                                group_labels_begin + group_labels.size(),
+                                mutable_rank_begin,
+                                mutable_rank_begin,
+                                thrust::equal_to{},
+                                scan_op);
+  return ranks;
+}
+}  // namespace
+
+std::unique_ptr<column> min_rank_scan(column_view const& grouped_values,
+                                      column_view const& value_order,
+                                      device_span<size_type const> group_labels,
+                                      device_span<size_type const> group_offsets,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  return rank_generator<true>(
+    grouped_values,
+    value_order,
+    group_labels,
+    group_offsets,
+    [] __device__(bool unequal, auto row_index_in_group) {
+      return unequal ? row_index_in_group + 1 : 0;
+    },
+    DeviceMax{},
+    has_nested_nulls(table_view{{grouped_values}}),
+    stream,
+    mr);
+}
+
+std::unique_ptr<column> max_rank_scan(column_view const& grouped_values,
+                                      column_view const& value_order,
+                                      device_span<size_type const> group_labels,
+                                      device_span<size_type const> group_offsets,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  return rank_generator<false>(
+    grouped_values,
+    value_order,
+    group_labels,
+    group_offsets,
+    [] __device__(bool unequal, auto row_index_in_group) {
+      return unequal ? row_index_in_group + 1 : std::numeric_limits<size_type>::max();
+    },
+    DeviceMin{},
+    has_nested_nulls(table_view{{grouped_values}}),
+    stream,
+    mr);
+}
+
+std::unique_ptr<column> first_rank_scan(column_view const& grouped_values,
+                                        column_view const&,
+                                        device_span<size_type const> group_labels,
+                                        device_span<size_type const> group_offsets,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  auto ranks = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, group_labels.size(), mask_state::UNALLOCATED, stream, mr);
+  auto mutable_ranks = ranks->mutable_view();
+  thrust::tabulate(rmm::exec_policy(stream),
+                   mutable_ranks.begin<size_type>(),
+                   mutable_ranks.end<size_type>(),
+                   [labels  = group_labels.begin(),
+                    offsets = group_offsets.begin()] __device__(size_type row_index) {
+                     auto group_start = offsets[labels[row_index]];
+                     return row_index - group_start + 1;
+                   });
+  return ranks;
+}
+
+std::unique_ptr<column> average_rank_scan(column_view const& grouped_values,
+                                          column_view const& value_order,
+                                          device_span<size_type const> group_labels,
+                                          device_span<size_type const> group_offsets,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  auto max_rank = max_rank_scan(grouped_values,
+                                value_order,
+                                group_labels,
+                                group_offsets,
+                                stream,
+                                rmm::mr::get_current_device_resource());
+  auto min_rank = min_rank_scan(grouped_values,
+                                value_order,
+                                group_labels,
+                                group_offsets,
+                                stream,
+                                rmm::mr::get_current_device_resource());
+  auto ranks    = make_fixed_width_column(
+    data_type{type_to_id<double>()}, group_labels.size(), mask_state::UNALLOCATED, stream, mr);
+  auto mutable_ranks = ranks->mutable_view();
+  thrust::transform(rmm::exec_policy(stream),
+                    max_rank->view().begin<size_type>(),
+                    max_rank->view().end<size_type>(),
+                    min_rank->view().begin<size_type>(),
+                    mutable_ranks.begin<double>(),
+                    [] __device__(auto max_rank, auto min_rank) -> double {
+                      return min_rank + (max_rank - min_rank) / 2.0;
+                    });
+  return ranks;
+}
+
+std::unique_ptr<column> dense_rank_scan(column_view const& grouped_values,
+                                        column_view const& value_order,
+                                        device_span<size_type const> group_labels,
+                                        device_span<size_type const> group_offsets,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  return rank_generator<true>(
+    grouped_values,
+    value_order,
+    group_labels,
+    group_offsets,
+    [] __device__(bool const unequal, size_type const) { return unequal ? 1 : 0; },
+    DeviceSum{},
+    has_nested_nulls(table_view{{grouped_values}}),
+    stream,
+    mr);
+}
+
+std::unique_ptr<column> group_rank_to_percentage(rank_method const method,
+                                                 rank_percentage const percentage,
+                                                 column_view const& rank,
+                                                 column_view const& count,
+                                                 device_span<size_type const> group_labels,
+                                                 device_span<size_type const> group_offsets,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(percentage != rank_percentage::NONE, "Percentage cannot be NONE");
+  auto ranks = make_fixed_width_column(
+    data_type{type_to_id<double>()}, group_labels.size(), mask_state::UNALLOCATED, stream, mr);
+  auto mutable_ranks = ranks->mutable_view();
+
+  auto one_normalized = [] __device__(auto const rank, auto const group_size) {
+    return group_size == 1 ? 0.0 : ((rank - 1.0) / (group_size - 1));
+  };
+  if (method == rank_method::DENSE) {
+    thrust::tabulate(rmm::exec_policy(stream),
+                     mutable_ranks.begin<double>(),
+                     mutable_ranks.end<double>(),
+                     [percentage,
+                      one_normalized,
+                      is_double = rank.type().id() == type_id::FLOAT64,
+                      dcount    = count.begin<size_type>(),
+                      labels    = group_labels.begin(),
+                      offsets   = group_offsets.begin(),
+                      d_rank    = rank.begin<double>(),
+                      s_rank = rank.begin<size_type>()] __device__(size_type row_index) -> double {
+                       double const r   = is_double ? d_rank[row_index] : s_rank[row_index];
+                       auto const count = dcount[labels[row_index]];
+                       size_type const last_rank_index = offsets[labels[row_index]] + count - 1;
+                       auto const last_rank            = s_rank[last_rank_index];
+                       return percentage == rank_percentage::ZERO_NORMALIZED
+                                ? r / last_rank
+                                : one_normalized(r, last_rank);
+                     });
+  } else {
+    thrust::tabulate(rmm::exec_policy(stream),
+                     mutable_ranks.begin<double>(),
+                     mutable_ranks.end<double>(),
+                     [percentage,
+                      one_normalized,
+                      is_double = rank.type().id() == type_id::FLOAT64,
+                      dcount    = count.begin<size_type>(),
+                      labels    = group_labels.begin(),
+                      d_rank    = rank.begin<double>(),
+                      s_rank = rank.begin<size_type>()] __device__(size_type row_index) -> double {
+                       double const r   = is_double ? d_rank[row_index] : s_rank[row_index];
+                       auto const count = dcount[labels[row_index]];
+                       return percentage == rank_percentage::ZERO_NORMALIZED
+                                ? r / count
+                                : one_normalized(r, count);
+                     });
+  }
+
+  ranks->set_null_count(0);
+  return ranks;
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_reductions.hpp b/cpp/src/groupby/sort/group_reductions.hpp
new file mode 100644
index 0000000..3aa79f2
--- /dev/null
+++ b/cpp/src/groupby/sort/group_reductions.hpp
@@ -0,0 +1,543 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+/** @internal @file Internal API in this file are mostly segmented reduction operations on column,
+ * which are used in sort-based groupby aggregations.
+ *
+ */
+namespace cudf {
+namespace groupby {
+namespace detail {
+/**
+ * @brief Internal API to calculate groupwise sum
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups   = 4
+ *
+ * group_sum    = [7, -3, 4, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get sum of
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_sum(column_view const& values,
+                                  size_type num_groups,
+                                  cudf::device_span<size_type const> group_labels,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise product
+ *
+ * @code{.pseudo}
+ * values        = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels  = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups    = 4
+ *
+ * group_product = [6, 2, 4, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get product of
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_product(column_view const& values,
+                                      size_type num_groups,
+                                      cudf::device_span<size_type const> group_labels,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise minimum value
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups   = 4
+ *
+ * group_min    = [1, -2, 4, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get minimum from
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_min(column_view const& values,
+                                  size_type num_groups,
+                                  cudf::device_span<size_type const> group_labels,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise maximum value
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups   = 4
+ *
+ * group_max    = [4, -1, 4, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get maximum from
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_max(column_view const& values,
+                                  size_type num_groups,
+                                  cudf::device_span<size_type const> group_labels,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate group-wise indices of maximum values.
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups   = 4
+ *
+ * group_max    = [2, 0, 0, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get maximum value's index from
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param key_sort_order Indices indicating sort order of groupby keys
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_argmax(column_view const& values,
+                                     size_type num_groups,
+                                     cudf::device_span<size_type const> group_labels,
+                                     column_view const& key_sort_order,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate group-wise indices of minimum values.
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups   = 4
+ *
+ * group_max    = [1, 1, 0, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get minimum value's index from
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param key_sort_order Indices indicating sort order of groupby keys
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_argmin(column_view const& values,
+                                     size_type num_groups,
+                                     cudf::device_span<size_type const> group_labels,
+                                     column_view const& key_sort_order,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate number of non-null values in each group of
+ *  @p values
+ *
+ * @code{.pseudo}
+ * values            = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels      = [0, 0, 0,  1,  1,    2, 2,    3]
+ * num_groups        = 4
+ *
+ * group_count_valid = [3, 2, 1, 0]
+ * @endcode
+ *
+ * @param values Grouped values to get valid count of
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param num_groups Number of groups ( unique values in @p group_labels )
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_count_valid(column_view const& values,
+                                          cudf::device_span<size_type const> group_labels,
+                                          size_type num_groups,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate number of values in each group of @p values
+ *
+ * @code{.pseudo}
+ * group_offsets = [0, 3, 5, 7, 8]
+ * num_groups    = 4
+ *
+ * group_count_all = [3, 2, 2, 1]
+ * @endcode
+ *
+ * @param group_offsets Offsets of groups' starting points within @p values
+ * @param num_groups Number of groups ( unique values in @p group_labels )
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_count_all(cudf::device_span<size_type const> group_offsets,
+                                        size_type num_groups,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+/**
+ * @brief Internal API to compute histogram for each group in @p values.
+ *
+ * The returned column is a lists column, each list corresponds to one input group and stores the
+ * histogram of the distinct elements in that group in the form of `STRUCT<value, count>`.
+ *
+ * Note that the order of distinct elements in each output list is not specified.
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 1, 3, 5, 2, 2, 3, 1, 4]
+ * group_labels = [0, 0, 0, 1, 1, 1, 1, 1, 2, 2]
+ * num_groups   = 3
+ *
+ * output = [[<1, 2>, <2, 1>], [<2, 2>, <3, 2>, <5, 1>], [<1, 1>, <4, 1>]]
+ * @endcode
+ *
+ * @param values Grouped values to compute histogram
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param num_groups Number of groups
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_histogram(column_view const& values,
+                                        cudf::device_span<size_type const> group_labels,
+                                        size_type num_groups,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate sum of squares of differences from means.
+ *
+ * If there are only nulls in the group, the output value of that group will be null.
+ *
+ * @code{.pseudo}
+ * values        = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels  = [0, 0, 0,  1,  1,    2, 2,    3]
+ * group_means   = [2.333333, -1.5, 4.0, <NA>]
+ * group_m2(...) = [4.666666,  1.0, 0.0, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to compute M2 values
+ * @param group_means Pre-computed groupwise MEAN
+ * @param group_labels ID of group corresponding value in @p values belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_m2(column_view const& values,
+                                 column_view const& group_means,
+                                 cudf::device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise variance
+ *
+ * @code{.pseudo}
+ * values       = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * group_means  = [2.333333, -1.5, 4.0, <NA>]
+ * group_sizes  = [3, 2, 2, 1]
+ * ddof         = 1
+ *
+ * group_var    = [2.333333, 0.5, <NA>, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get variance of
+ * @param group_means Pre-calculated groupwise MEAN
+ * @param group_sizes Number of valid elements per group
+ * @param group_labels ID of group corresponding value in @p values belongs to
+ * @param ddof Delta degrees of freedom. The divisor used in calculation of
+ *             `var` is `N - ddof`, where `N` is the group size.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_var(column_view const& values,
+                                  column_view const& group_means,
+                                  column_view const& group_sizes,
+                                  cudf::device_span<size_type const> group_labels,
+                                  size_type ddof,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise quantiles
+ *
+ * @code{.pseudo}
+ * values       = [1, 2, 4, -2, -1, <NA>, 4, <NA>]
+ * group_labels = [0, 0, 0,  1,  1,    2, 2,    3]
+ * group_sizes  = [3, 2, 2, 1]
+ * num_groups   = 4
+ * quantiles    = [0.25, 0.5]
+ *
+ * group_quantiles = [1.5, 2, -1.75, -1.5,  4,  4, <NA>, <NA>]
+ * @endcode
+ *
+ * @param values Grouped and sorted (within group) values to get quantiles from
+ * @param group_sizes Number of valid elements per group
+ * @param group_offsets Offsets of groups' starting points within @p values
+ * @param quantiles List of quantiles q where q lies in [0,1]
+ * @param interp Method to use when desired value lies between data points
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_quantiles(column_view const& values,
+                                        column_view const& group_sizes,
+                                        cudf::device_span<size_type const> group_offsets,
+                                        size_type const num_groups,
+                                        std::vector<double> const& quantiles,
+                                        interpolation interp,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate number of unique values in each group of
+ *  @p values
+ *
+ * @code{.pseudo}
+ * values        = [2, 4, 4, -1, -2, <NA>, 4, <NA>]
+ * group_labels  = [0, 0, 0,  1,  1,    2, 2,    3]
+ * group_offsets = [0,        3,        5,       7, 8]
+ * num_groups    = 4
+ *
+ * group_nunique(null_policy::EXCLUDE) = [2, 2, 1, 0]
+ * group_nunique(null_policy::INCLUDE) = [2, 2, 2, 1]
+ * @endcode
+ *
+ * @param values Grouped and sorted (within group) values to get unique count of
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param num_groups Number of groups ( unique values in @p group_labels )
+ * @param group_offsets Offsets of groups' starting points within @p values
+ * @param null_handling Exclude nulls while counting if null_policy::EXCLUDE,
+ *  Include nulls if null_policy::INCLUDE.
+ *  Nulls are treated equal.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_nunique(column_view const& values,
+                                      cudf::device_span<size_type const> group_labels,
+                                      size_type const num_groups,
+                                      cudf::device_span<size_type const> group_offsets,
+                                      null_policy null_handling,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate nth values in each group of  @p values
+ *
+ * @code{.pseudo}
+ * values        = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_sizes   = [3,        2,        2,       1]
+ * group_labels  = [0, 0, 0,  1,  1,    2, 2,    3]
+ * group_offsets = [0,        3,        5,       7, 8]
+ * num_groups    = 4
+ *
+ * group_nth_element(n=0, null_policy::EXCLUDE) = [2, -1, 4, <NA>]
+ * group_nth_element(n=0, null_policy::INCLUDE) = [2, -1, <NA>, <NA>]
+ * @endcode
+ *
+ * @param values Grouped values to get nth value of
+ * @param group_sizes Number of elements per group
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param group_offsets Offsets of groups' starting points within @p values
+ * @param num_groups Number of groups ( unique values in @p group_labels )
+ * @param n nth element to choose from each group of @p values
+ * @param null_handling Exclude nulls while counting if null_policy::EXCLUDE,
+ *  Include nulls if null_policy::INCLUDE.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_nth_element(column_view const& values,
+                                          column_view const& group_sizes,
+                                          cudf::device_span<size_type const> group_labels,
+                                          cudf::device_span<size_type const> group_offsets,
+                                          size_type num_groups,
+                                          size_type n,
+                                          null_policy null_handling,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+/**
+ * @brief Internal API to collect grouped values into a lists column
+ *
+ * @code{.pseudo}
+ * values        = [2, 1, 4, -1, -2, <NA>, 4, <NA>]
+ * group_offsets = [0,        3,        5,       7, 8]
+ * num_groups    = 4
+ *
+ * group_collect(...) = [[2, 1, 4], [-1, -2], [<NA>, 4], [<NA>]]
+ * @endcode
+ *
+ * @param values Grouped values to collect.
+ * @param group_offsets Offsets of groups' starting points within @p values.
+ * @param num_groups Number of groups.
+ * @param null_handling Exclude nulls while counting if null_policy::EXCLUDE,
+ *        include nulls if null_policy::INCLUDE.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ */
+std::unique_ptr<column> group_collect(column_view const& values,
+                                      cudf::device_span<size_type const> group_offsets,
+                                      size_type num_groups,
+                                      null_policy null_handling,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to merge grouped lists into one list.
+ *
+ * @code{.pseudo}
+ * values        = [[2, 1], [], [4, -1, -2], [], [<NA>, 4, <NA>]]
+ * group_offsets = [0,                        3,                  5]
+ * num_groups    = 2
+ *
+ * group_merge_lists(...) = [[2, 1, 4, -1, -2], [<NA>, 4, <NA>]]
+ * @endcode
+ *
+ * @param values Grouped values (lists column) to collect.
+ * @param group_offsets Offsets of groups' starting points within @p values.
+ * @param num_groups Number of groups.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ */
+std::unique_ptr<column> group_merge_lists(column_view const& values,
+                                          cudf::device_span<size_type const> group_offsets,
+                                          size_type num_groups,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to merge grouped M2 values corresponding to the same key.
+ *
+ * The values of M2 are merged following the parallel algorithm described here:
+ * `https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm`
+ *
+ * Merging M2 values require accessing to partial M2 values, means, and valid counts. Thus, the
+ * input to this aggregation need to be a structs column containing tuples of 3 values
+ * `(valid_count, mean, M2)`.
+ *
+ * This aggregation not only merges the partial results of `M2` but also merged all the partial
+ * results of input aggregations (`COUNT_VALID`, `MEAN`, and `M2`). As such, the output will be a
+ * structs column containing children columns of merged `COUNT_VALID`, `MEAN`, and `M2` values.
+ *
+ * @param values Grouped values (tuples of values `(valid_count, mean, M2)`) to merge.
+ * @param group_offsets Offsets of groups' starting points within @p values.
+ * @param num_groups Number of groups.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_merge_m2(column_view const& values,
+                                       cudf::device_span<size_type const> group_offsets,
+                                       size_type num_groups,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to merge multiple output of HISTOGRAM aggregation.
+ *
+ * The input values column should be given as a lists column in the form of
+ * `LIST<STRUCT<value, count>>`.
+ * After merging, the order of distinct elements in each output list is not specified.
+ *
+ * @code{.pseudo}
+ * values        = [ [<1, 2>, <2, 1>], [<2, 2>], [<3, 2>, <2, 1>], [<1, 1>, <2, 1>] ]
+ * group_offsets = [ 0,                          2,                                 4]
+ * num_groups    = 2
+ *
+ * output = [[<1, 2>, <2, 3>], [<1, 1>, <2, 2>, <3, 2>]]]
+ * @endcode
+ *
+ * @param values Grouped values to get valid count of
+ * @param group_offsets Offsets of groups' starting points within @p values
+ * @param num_groups Number of groups
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_merge_histogram(column_view const& values,
+                                              cudf::device_span<size_type const> group_offsets,
+                                              size_type num_groups,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to find covariance of child columns of a non-nullable struct column.
+ *
+ * @param values_0 The first grouped values column to compute covariance
+ * @param values_1 The second grouped values column to compute covariance
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param num_groups Number of groups.
+ * @param count The count of valid rows of the grouped values of both columns
+ * @param mean_0 The mean of the first grouped values column
+ * @param mean_1 The mean of the second grouped values column
+ * @param min_periods The minimum number of non-null rows required to consider the covariance
+ * @param ddof The delta degrees of freedom used in the calculation of the variance
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_covariance(column_view const& values_0,
+                                         column_view const& values_1,
+                                         cudf::device_span<size_type const> group_labels,
+                                         size_type num_groups,
+                                         column_view const& count,
+                                         column_view const& mean_0,
+                                         column_view const& mean_1,
+                                         size_type min_periods,
+                                         size_type ddof,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to find correlation from covariance and standard deviation.
+ *
+ * @param covariance The covariance of two grouped values columns
+ * @param stddev_0 The standard deviation of the first grouped values column
+ * @param stddev_1 The standard deviation of the second grouped values column
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> group_correlation(column_view const& covariance,
+                                          column_view const& stddev_0,
+                                          column_view const& stddev_1,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_replace_nulls.cu b/cpp/src/groupby/sort/group_replace_nulls.cu
new file mode 100644
index 0000000..4955716
--- /dev/null
+++ b/cpp/src/groupby/sort/group_replace_nulls.cu
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby/group_replace_nulls.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/replace/nulls.cuh>
+#include <cudf/replace.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/tuple.h>
+
+#include <utility>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+
+std::unique_ptr<column> group_replace_nulls(cudf::column_view const& grouped_value,
+                                            device_span<size_type const> group_labels,
+                                            cudf::replace_policy replace_policy,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  cudf::size_type size = grouped_value.size();
+
+  auto device_in = cudf::column_device_view::create(grouped_value, stream);
+  auto index     = thrust::make_counting_iterator<cudf::size_type>(0);
+  auto valid_it  = cudf::detail::make_validity_iterator(*device_in);
+  auto in_begin  = thrust::make_zip_iterator(thrust::make_tuple(index, valid_it));
+
+  rmm::device_uvector<cudf::size_type> gather_map(size, stream);
+  auto gm_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(gather_map.begin(), thrust::make_discard_iterator()));
+
+  auto func = cudf::detail::replace_policy_functor();
+  thrust::equal_to<cudf::size_type> eq;
+  if (replace_policy == cudf::replace_policy::PRECEDING) {
+    thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                  group_labels.begin(),
+                                  group_labels.begin() + size,
+                                  in_begin,
+                                  gm_begin,
+                                  eq,
+                                  func);
+  } else {
+    auto gl_rbegin = thrust::make_reverse_iterator(group_labels.begin() + size);
+    auto in_rbegin = thrust::make_reverse_iterator(in_begin + size);
+    auto gm_rbegin = thrust::make_reverse_iterator(gm_begin + size);
+    thrust::inclusive_scan_by_key(
+      rmm::exec_policy(stream), gl_rbegin, gl_rbegin + size, in_rbegin, gm_rbegin, eq, func);
+  }
+
+  auto output = cudf::detail::gather(cudf::table_view({grouped_value}),
+                                     gather_map,
+                                     cudf::out_of_bounds_policy::DONT_CHECK,
+                                     cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                     stream,
+                                     mr);
+
+  return std::move(output->release()[0]);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_scan.hpp b/cpp/src/groupby/sort/group_scan.hpp
new file mode 100644
index 0000000..dc0eb69
--- /dev/null
+++ b/cpp/src/groupby/sort/group_scan.hpp
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+/**
+ * @brief Internal API to calculate groupwise cumulative sum
+ *
+ * @param values Grouped values to get sum of
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> sum_scan(column_view const& values,
+                                 size_type num_groups,
+                                 device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise cumulative minimum value
+ *
+ * @param values Grouped values to get minimum from
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> min_scan(column_view const& values,
+                                 size_type num_groups,
+                                 device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise cumulative maximum value
+ *
+ * @param values Grouped values to get maximum from
+ * @param num_groups Number of groups
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+std::unique_ptr<column> max_scan(column_view const& values,
+                                 size_type num_groups,
+                                 device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate cumulative number of values in each group
+ *
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of type INT32 of count values
+ */
+std::unique_ptr<column> count_scan(device_span<size_type const> group_labels,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise min rank value
+ *
+ * @param grouped_values column or struct column that rows within a group are sorted by
+ * @param value_order column of type INT32 that contains the order of the values in the
+ * grouped_values column
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param group_offsets group index offsets with group ID indices
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of type size_type of rank values
+ */
+std::unique_ptr<column> min_rank_scan(column_view const& grouped_values,
+                                      column_view const& value_order,
+                                      device_span<size_type const> group_labels,
+                                      device_span<size_type const> group_offsets,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise max rank value
+ *
+ * @details  @copydetails min_rank_scan(column_view const& grouped_values,
+ *                                      column_view const& value_order,
+ *                                      device_span<size_type const> group_labels,
+ *                                      device_span<size_type const> group_offsets,
+ *                                      rmm::cuda_stream_view stream,
+ *                                      rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<column> max_rank_scan(column_view const& grouped_values,
+                                      column_view const& value_order,
+                                      device_span<size_type const> group_labels,
+                                      device_span<size_type const> group_offsets,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise first rank value
+ *
+ * @details  @copydetails min_rank_scan(column_view const& grouped_values,
+ *                                      column_view const& value_order,
+ *                                      device_span<size_type const> group_labels,
+ *                                      device_span<size_type const> group_offsets,
+ *                                      rmm::cuda_stream_view stream,
+ *                                      rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<column> first_rank_scan(column_view const& grouped_values,
+                                        column_view const& value_order,
+                                        device_span<size_type const> group_labels,
+                                        device_span<size_type const> group_offsets,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise average rank value
+ *
+ * @details  @copydetails min_rank_scan(column_view const& grouped_values,
+ *                                      column_view const& value_order,
+ *                                      device_span<size_type const> group_labels,
+ *                                      device_span<size_type const> group_offsets,
+ *                                      rmm::cuda_stream_view stream,
+ *                                      rmm::mr::device_memory_resource* mr)
+ */
+std::unique_ptr<column> average_rank_scan(column_view const& grouped_values,
+                                          column_view const& value_order,
+                                          device_span<size_type const> group_labels,
+                                          device_span<size_type const> group_offsets,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Internal API to calculate groupwise dense rank value
+ *
+ * @param grouped_values column or struct column that rows within a group are sorted by
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param group_offsets group index offsets with group ID indices
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of type size_type of dense rank values
+ */
+std::unique_ptr<column> dense_rank_scan(column_view const& grouped_values,
+                                        column_view const& value_order,
+                                        device_span<size_type const> group_labels,
+                                        device_span<size_type const> group_offsets,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Convert groupwise rank to groupwise percentage rank
+ *
+ * @param method rank method
+ * @param percentage enum to denote the type of conversion ranks to percentage in range (0,1]
+ * @param rank Groupwise rank column
+ * @param count Groupwise count column
+ * @param group_labels ID of group that the corresponding value belongs to
+ * @param group_offsets group index offsets with group ID indices
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Column of type double of rank values
+
+ */
+std::unique_ptr<column> group_rank_to_percentage(rank_method const method,
+                                                 rank_percentage const percentage,
+                                                 column_view const& rank,
+                                                 column_view const& count,
+                                                 device_span<size_type const> group_labels,
+                                                 device_span<size_type const> group_offsets,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_scan_util.cuh b/cpp/src/groupby/sort/group_scan_util.cuh
new file mode 100644
index 0000000..3ed5394
--- /dev/null
+++ b/cpp/src/groupby/sort/group_scan_util.cuh
@@ -0,0 +1,246 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <reductions/nested_type_minmax_util.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scan.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+// Error case when no other overload or specialization is available
+template <aggregation::Kind K, typename T, typename Enable = void>
+struct group_scan_functor {
+  template <typename... Args>
+  static std::unique_ptr<column> invoke(Args&&...)
+  {
+    CUDF_FAIL("Unsupported groupby scan type-agg combination.");
+  }
+};
+
+template <aggregation::Kind K>
+struct group_scan_dispatcher {
+  template <typename T>
+  std::unique_ptr<column> operator()(column_view const& values,
+                                     size_type num_groups,
+                                     cudf::device_span<cudf::size_type const> group_labels,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return group_scan_functor<K, T>::invoke(values, num_groups, group_labels, stream, mr);
+  }
+};
+
+/**
+ * @brief Check if the given aggregation K with data type T is supported in groupby scan.
+ */
+template <aggregation::Kind K, typename T>
+static constexpr bool is_group_scan_supported()
+{
+  if (K == aggregation::SUM)
+    return cudf::is_numeric<T>() || cudf::is_duration<T>() || cudf::is_fixed_point<T>();
+  else if (K == aggregation::MIN or K == aggregation::MAX)
+    return not cudf::is_dictionary<T>() and
+           (is_relationally_comparable<T, T>() or std::is_same_v<T, cudf::struct_view>);
+  else
+    return false;
+}
+
+template <aggregation::Kind K, typename T>
+struct group_scan_functor<K, T, std::enable_if_t<is_group_scan_supported<K, T>()>> {
+  static std::unique_ptr<column> invoke(column_view const& values,
+                                        size_type num_groups,
+                                        cudf::device_span<cudf::size_type const> group_labels,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    using DeviceType       = device_storage_type_t<T>;
+    using OpType           = cudf::detail::corresponding_operator_t<K>;
+    using ResultType       = cudf::detail::target_type_t<T, K>;
+    using ResultDeviceType = device_storage_type_t<ResultType>;
+
+    auto result_type = is_fixed_point<T>()
+                         ? data_type{type_to_id<ResultType>(), values.type().scale()}
+                         : data_type{type_to_id<ResultType>()};
+
+    std::unique_ptr<column> result =
+      make_fixed_width_column(result_type, values.size(), mask_state::UNALLOCATED, stream, mr);
+
+    if (values.is_empty()) { return result; }
+
+    auto result_table = mutable_table_view({*result});
+    cudf::detail::initialize_with_identity(result_table, {K}, stream);
+
+    auto result_view = mutable_column_device_view::create(result->mutable_view(), stream);
+    auto values_view = column_device_view::create(values, stream);
+
+    // Perform segmented scan.
+    auto const do_scan = [&](auto const& inp_iter, auto const& out_iter, auto const& binop) {
+      thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                    group_labels.begin(),
+                                    group_labels.end(),
+                                    inp_iter,
+                                    out_iter,
+                                    thrust::equal_to{},
+                                    binop);
+    };
+
+    if (values.has_nulls()) {
+      auto input = thrust::make_transform_iterator(
+        make_null_replacement_iterator(*values_view, OpType::template identity<DeviceType>()),
+        thrust::identity<ResultDeviceType>{});
+      do_scan(input, result_view->begin<ResultDeviceType>(), OpType{});
+      result->set_null_mask(cudf::detail::copy_bitmask(values, stream, mr), values.null_count());
+    } else {
+      auto input = thrust::make_transform_iterator(values_view->begin<DeviceType>(),
+                                                   thrust::identity<ResultDeviceType>{});
+      do_scan(input, result_view->begin<ResultDeviceType>(), OpType{});
+    }
+    return result;
+  }
+};
+
+template <aggregation::Kind K>
+struct group_scan_functor<K,
+                          cudf::string_view,
+                          std::enable_if_t<is_group_scan_supported<K, cudf::string_view>()>> {
+  static std::unique_ptr<column> invoke(column_view const& values,
+                                        size_type num_groups,
+                                        cudf::device_span<cudf::size_type const> group_labels,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    using OpType = cudf::detail::corresponding_operator_t<K>;
+
+    if (values.is_empty()) { return cudf::make_empty_column(cudf::type_id::STRING); }
+
+    // create an empty output vector we can fill with string_view instances
+    auto results_vector = rmm::device_uvector<string_view>(values.size(), stream);
+
+    auto values_view = column_device_view::create(values, stream);
+
+    // Perform segmented scan.
+    auto const do_scan = [&](auto const& inp_iter, auto const& out_iter, auto const& binop) {
+      thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                    group_labels.begin(),
+                                    group_labels.end(),
+                                    inp_iter,
+                                    out_iter,
+                                    thrust::equal_to{},
+                                    binop);
+    };
+
+    if (values.has_nulls()) {
+      auto input = make_null_replacement_iterator(
+        *values_view, OpType::template identity<string_view>(), values.has_nulls());
+      do_scan(input, results_vector.begin(), OpType{});
+    } else {
+      do_scan(values_view->begin<string_view>(), results_vector.begin(), OpType{});
+    }
+
+    // turn the string_view vector into a strings column
+    auto results = make_strings_column(results_vector, string_view{}, stream, mr);
+    if (values.has_nulls())
+      results->set_null_mask(cudf::detail::copy_bitmask(values, stream, mr), values.null_count());
+    return results;
+  }
+};
+
+template <aggregation::Kind K>
+struct group_scan_functor<K,
+                          cudf::struct_view,
+                          std::enable_if_t<is_group_scan_supported<K, cudf::struct_view>()>> {
+  static std::unique_ptr<column> invoke(column_view const& values,
+                                        size_type num_groups,
+                                        cudf::device_span<cudf::size_type const> group_labels,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    if (values.is_empty()) { return cudf::empty_like(values); }
+
+    // Create a gather map containing indices of the prefix min/max elements within each group.
+    auto gather_map = rmm::device_uvector<size_type>(values.size(), stream);
+
+    auto const binop_generator =
+      cudf::reduction::detail::comparison_binop_generator::create<K>(values, stream);
+    thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                  group_labels.begin(),
+                                  group_labels.end(),
+                                  thrust::make_counting_iterator<size_type>(0),
+                                  gather_map.begin(),
+                                  thrust::equal_to{},
+                                  binop_generator.binop());
+
+    //
+    // Gather the children elements of the prefix min/max struct elements first.
+    //
+    // Typically, we should use `get_sliced_child` for each child column to properly handle the
+    // input if it is a sliced view. However, since the input to this function is just generated
+    // from groupby internal APIs which is never a sliced view, we just use `child_begin` and
+    // `child_end` iterators for simplicity.
+    auto scanned_children =
+      cudf::detail::gather(
+        table_view(std::vector<column_view>{values.child_begin(), values.child_end()}),
+        gather_map,
+        cudf::out_of_bounds_policy::DONT_CHECK,
+        cudf::detail::negative_index_policy::NOT_ALLOWED,
+        stream,
+        mr)
+        ->release();
+
+    // After gathering the children elements, we need to push down nulls from the root structs
+    // column to them.
+    if (values.has_nulls()) {
+      for (std::unique_ptr<column>& child : scanned_children) {
+        child = structs::detail::superimpose_nulls(
+          values.null_mask(), values.null_count(), std::move(child), stream, mr);
+      }
+    }
+
+    return make_structs_column(values.size(),
+                               std::move(scanned_children),
+                               values.null_count(),
+                               cudf::detail::copy_bitmask(values, stream, mr),
+                               stream,
+                               mr);
+  }
+};
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_single_pass_reduction_util.cuh b/cpp/src/groupby/sort/group_single_pass_reduction_util.cuh
new file mode 100644
index 0000000..3454314
--- /dev/null
+++ b/cpp/src/groupby/sort/group_single_pass_reduction_util.cuh
@@ -0,0 +1,268 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <reductions/nested_type_minmax_util.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/element_argminmax.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+
+/**
+ * @brief Value accessor for column which supports dictionary column too.
+ *
+ * This is similar to `value_accessor` in `column_device_view.cuh` but with support of dictionary
+ * type.
+ *
+ * @tparam T Type of the underlying column. For dictionary column, type of the key column.
+ */
+template <typename T>
+struct value_accessor {
+  column_device_view const col;
+  bool const is_dict;
+
+  value_accessor(column_device_view const& col) : col(col), is_dict(cudf::is_dictionary(col.type()))
+  {
+  }
+
+  __device__ T value(size_type i) const
+  {
+    if (is_dict) {
+      auto keys = col.child(dictionary_column_view::keys_column_index);
+      return keys.element<T>(static_cast<size_type>(col.element<dictionary32>(i)));
+    } else {
+      return col.element<T>(i);
+    }
+  }
+
+  __device__ auto operator()(size_type i) const { return value(i); }
+};
+
+/**
+ * @brief Null replaced value accessor for column which supports dictionary column too.
+ * For null value, returns null `init` value
+ *
+ * @tparam SourceType Type of the underlying column. For dictionary column, type of the key column.
+ * @tparam TargetType Type that is used for computation.
+ */
+template <typename SourceType, typename TargetType>
+struct null_replaced_value_accessor : value_accessor<SourceType> {
+  using super_t = value_accessor<SourceType>;
+
+  TargetType const init;
+  bool const has_nulls;
+
+  null_replaced_value_accessor(column_device_view const& col,
+                               TargetType const& init,
+                               bool const has_nulls)
+    : super_t(col), init(init), has_nulls(has_nulls)
+  {
+  }
+
+  __device__ TargetType operator()(size_type i) const
+  {
+    return has_nulls && super_t::col.is_null_nocheck(i)
+             ? init
+             : static_cast<TargetType>(super_t::value(i));
+  }
+};
+
+// Error case when no other overload or specialization is available
+template <aggregation::Kind K, typename T, typename Enable = void>
+struct group_reduction_functor {
+  template <typename... Args>
+  static std::unique_ptr<column> invoke(Args&&...)
+  {
+    CUDF_FAIL("Unsupported groupby reduction type-agg combination.");
+  }
+};
+
+template <aggregation::Kind K>
+struct group_reduction_dispatcher {
+  template <typename T>
+  std::unique_ptr<column> operator()(column_view const& values,
+                                     size_type num_groups,
+                                     cudf::device_span<cudf::size_type const> group_labels,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return group_reduction_functor<K, T>::invoke(values, num_groups, group_labels, stream, mr);
+  }
+};
+
+/**
+ * @brief Check if the given aggregation K with data type T is supported in groupby reduction.
+ */
+template <aggregation::Kind K, typename T>
+static constexpr bool is_group_reduction_supported()
+{
+  switch (K) {
+    case aggregation::SUM:
+      return cudf::is_numeric<T>() || cudf::is_duration<T>() || cudf::is_fixed_point<T>();
+    case aggregation::PRODUCT: return cudf::detail::is_product_supported<T>();
+    case aggregation::MIN:
+    case aggregation::MAX: return cudf::is_fixed_width<T>() and is_relationally_comparable<T, T>();
+    case aggregation::ARGMIN:
+    case aggregation::ARGMAX: return is_relationally_comparable<T, T>() or cudf::is_nested<T>();
+    default: return false;
+  }
+}
+
+template <aggregation::Kind K, typename T>
+struct group_reduction_functor<
+  K,
+  T,
+  std::enable_if_t<is_group_reduction_supported<K, T>() && !cudf::is_nested<T>()>> {
+  static std::unique_ptr<column> invoke(column_view const& values,
+                                        size_type num_groups,
+                                        cudf::device_span<cudf::size_type const> group_labels,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+
+  {
+    using SourceDType = device_storage_type_t<T>;
+    using ResultType  = cudf::detail::target_type_t<T, K>;
+    using ResultDType = device_storage_type_t<ResultType>;
+
+    auto result_type = is_fixed_point<ResultType>()
+                         ? data_type{type_to_id<ResultType>(), values.type().scale()}
+                         : data_type{type_to_id<ResultType>()};
+
+    std::unique_ptr<column> result =
+      make_fixed_width_column(result_type, num_groups, mask_state::UNALLOCATED, stream, mr);
+
+    if (values.is_empty()) { return result; }
+
+    // Perform segmented reduction.
+    auto const do_reduction = [&](auto const& inp_iter, auto const& out_iter, auto const& binop) {
+      thrust::reduce_by_key(rmm::exec_policy(stream),
+                            group_labels.data(),
+                            group_labels.data() + group_labels.size(),
+                            inp_iter,
+                            thrust::make_discard_iterator(),
+                            out_iter,
+                            thrust::equal_to{},
+                            binop);
+    };
+
+    auto const d_values_ptr = column_device_view::create(values, stream);
+    auto const result_begin = result->mutable_view().template begin<ResultDType>();
+
+    if constexpr (K == aggregation::ARGMAX || K == aggregation::ARGMIN) {
+      auto const count_iter = thrust::make_counting_iterator<ResultType>(0);
+      auto const binop      = cudf::detail::element_argminmax_fn<T>{
+        *d_values_ptr, values.has_nulls(), K == aggregation::ARGMIN};
+      do_reduction(count_iter, result_begin, binop);
+    } else {
+      using OpType    = cudf::detail::corresponding_operator_t<K>;
+      auto init       = OpType::template identity<ResultDType>();
+      auto inp_values = cudf::detail::make_counting_transform_iterator(
+        0,
+        null_replaced_value_accessor<SourceDType, ResultDType>{
+          *d_values_ptr, init, values.has_nulls()});
+      do_reduction(inp_values, result_begin, OpType{});
+    }
+
+    if (values.has_nulls()) {
+      rmm::device_uvector<bool> validity(num_groups, stream);
+      do_reduction(cudf::detail::make_validity_iterator(*d_values_ptr),
+                   validity.begin(),
+                   thrust::logical_or{});
+
+      auto [null_mask, null_count] =
+        cudf::detail::valid_if(validity.begin(), validity.end(), thrust::identity{}, stream, mr);
+      result->set_null_mask(std::move(null_mask), null_count);
+    }
+    return result;
+  }
+};
+
+template <aggregation::Kind K, typename T>
+struct group_reduction_functor<
+  K,
+  T,
+  std::enable_if_t<is_group_reduction_supported<K, T>() && cudf::is_nested<T>()>> {
+  static std::unique_ptr<column> invoke(column_view const& values,
+                                        size_type num_groups,
+                                        cudf::device_span<cudf::size_type const> group_labels,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    // This is be expected to be size_type.
+    using ResultType = cudf::detail::target_type_t<T, K>;
+
+    auto result = make_fixed_width_column(
+      data_type{type_to_id<ResultType>()}, num_groups, mask_state::UNALLOCATED, stream, mr);
+
+    if (values.is_empty()) { return result; }
+
+    // Perform segmented reduction to find ARGMIN/ARGMAX.
+    auto const do_reduction = [&](auto const& inp_iter, auto const& out_iter, auto const& binop) {
+      thrust::reduce_by_key(rmm::exec_policy(stream),
+                            group_labels.data(),
+                            group_labels.data() + group_labels.size(),
+                            inp_iter,
+                            thrust::make_discard_iterator(),
+                            out_iter,
+                            thrust::equal_to{},
+                            binop);
+    };
+
+    auto const count_iter   = thrust::make_counting_iterator<ResultType>(0);
+    auto const result_begin = result->mutable_view().template begin<ResultType>();
+    auto const binop_generator =
+      cudf::reduction::detail::comparison_binop_generator::create<K>(values, stream);
+    do_reduction(count_iter, result_begin, binop_generator.binop());
+
+    if (values.has_nulls()) {
+      // Generate bitmask for the output by segmented reduction of the input bitmask.
+      auto const d_values_ptr = column_device_view::create(values, stream);
+      auto validity           = rmm::device_uvector<bool>(num_groups, stream);
+      do_reduction(cudf::detail::make_validity_iterator(*d_values_ptr),
+                   validity.begin(),
+                   thrust::logical_or{});
+
+      auto [null_mask, null_count] =
+        cudf::detail::valid_if(validity.begin(), validity.end(), thrust::identity{}, stream, mr);
+      result->set_null_mask(std::move(null_mask), null_count);
+    }
+
+    return result;
+  }
+};
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_std.cu b/cpp/src/groupby/sort/group_std.cu
new file mode 100644
index 0000000..30b6f67
--- /dev/null
+++ b/cpp/src/groupby/sort/group_std.cu
@@ -0,0 +1,190 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "group_reductions.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace {
+
+template <typename ResultType, typename Iterator>
+struct var_transform {
+  column_device_view const d_values;
+  Iterator values_iter;
+  ResultType const* d_means;
+  size_type const* d_group_sizes;
+  size_type const* d_group_labels;
+  size_type ddof;
+
+  __device__ ResultType operator()(size_type i) const
+  {
+    if (d_values.is_null(i)) return 0.0;
+
+    auto x = static_cast<ResultType>(values_iter[i]);
+
+    size_type group_idx  = d_group_labels[i];
+    size_type group_size = d_group_sizes[group_idx];
+
+    // prevent divide by zero error
+    if (group_size == 0 or group_size - ddof <= 0) return 0.0;
+
+    ResultType mean = d_means[group_idx];
+    return (x - mean) * (x - mean) / (group_size - ddof);
+  }
+};
+
+template <typename ResultType, typename Iterator>
+void reduce_by_key_fn(column_device_view const& values,
+                      Iterator values_iter,
+                      cudf::device_span<size_type const> group_labels,
+                      ResultType const* d_means,
+                      size_type const* d_group_sizes,
+                      size_type ddof,
+                      ResultType* d_result,
+                      rmm::cuda_stream_view stream)
+{
+  auto var_fn = var_transform<ResultType, decltype(values_iter)>{
+    values, values_iter, d_means, d_group_sizes, group_labels.data(), ddof};
+  auto const itr = thrust::make_counting_iterator<size_type>(0);
+  // Using a temporary buffer for intermediate transform results instead of
+  // using the transform-iterator directly in thrust::reduce_by_key
+  // improves compile-time significantly.
+  auto vars = rmm::device_uvector<ResultType>(values.size(), stream);
+  thrust::transform(rmm::exec_policy(stream), itr, itr + values.size(), vars.begin(), var_fn);
+
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        group_labels.begin(),
+                        group_labels.end(),
+                        vars.begin(),
+                        thrust::make_discard_iterator(),
+                        d_result);
+}
+
+struct var_functor {
+  template <typename T>
+  std::enable_if_t<std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(
+    column_view const& values,
+    column_view const& group_means,
+    column_view const& group_sizes,
+    cudf::device_span<size_type const> group_labels,
+    size_type ddof,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    using ResultType = cudf::detail::target_type_t<T, aggregation::Kind::VARIANCE>;
+
+    std::unique_ptr<column> result = make_numeric_column(data_type(type_to_id<ResultType>()),
+                                                         group_sizes.size(),
+                                                         mask_state::UNINITIALIZED,
+                                                         stream,
+                                                         mr);
+
+    auto values_view = column_device_view::create(values, stream);
+    auto d_values    = *values_view;
+
+    auto d_means       = group_means.data<ResultType>();
+    auto d_group_sizes = group_sizes.data<size_type>();
+    auto d_result      = result->mutable_view().data<ResultType>();
+
+    if (!cudf::is_dictionary(values.type())) {
+      auto values_iter = d_values.begin<T>();
+      reduce_by_key_fn(
+        d_values, values_iter, group_labels, d_means, d_group_sizes, ddof, d_result, stream);
+    } else {
+      auto values_iter = cudf::dictionary::detail::make_dictionary_iterator<T>(*values_view);
+      reduce_by_key_fn(
+        d_values, values_iter, group_labels, d_means, d_group_sizes, ddof, d_result, stream);
+    }
+
+    // set nulls
+    auto result_view  = mutable_column_device_view::create(*result, stream);
+    auto null_count   = rmm::device_scalar<cudf::size_type>(0, stream, mr);
+    auto d_null_count = null_count.data();
+    thrust::for_each_n(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator(0),
+      group_sizes.size(),
+      [d_result = *result_view, d_group_sizes, ddof, d_null_count] __device__(size_type i) {
+        size_type group_size = d_group_sizes[i];
+        if (group_size == 0 or group_size - ddof <= 0) {
+          d_result.set_null(i);
+          // Assuming that typical data does not have too many nulls this
+          // atomic shouldn't serialize the code too much. The alternatives
+          // would be 1) writing a more complex kernel using cub/shmem to
+          // increase parallelism, or 2) calling `cudf::count_nulls` after the
+          // fact. (1) is more work than it's worth without benchmarking, and
+          // this approach should outperform (2) unless large amounts of the
+          // data is null.
+          atomicAdd(d_null_count, 1);
+        } else {
+          d_result.set_valid(i);
+        }
+      });
+
+    result->set_null_count(null_count.value(stream));
+    return result;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Only numeric types are supported in std/variance");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> group_var(column_view const& values,
+                                  column_view const& group_means,
+                                  column_view const& group_sizes,
+                                  cudf::device_span<size_type const> group_labels,
+                                  size_type ddof,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+
+  return type_dispatcher(
+    values_type, var_functor{}, values, group_means, group_sizes, group_labels, ddof, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_sum.cu b/cpp/src/groupby/sort/group_sum.cu
new file mode 100644
index 0000000..e3c2ce7
--- /dev/null
+++ b/cpp/src/groupby/sort/group_sum.cu
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <groupby/sort/group_single_pass_reduction_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> group_sum(column_view const& values,
+                                  size_type num_groups,
+                                  cudf::device_span<size_type const> group_labels,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto values_type = cudf::is_dictionary(values.type())
+                       ? dictionary_column_view(values).keys().type()
+                       : values.type();
+  return type_dispatcher(values_type,
+                         group_reduction_dispatcher<aggregation::SUM>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/group_sum_scan.cu b/cpp/src/groupby/sort/group_sum_scan.cu
new file mode 100644
index 0000000..632fde3
--- /dev/null
+++ b/cpp/src/groupby/sort/group_sum_scan.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/sort/group_scan_util.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+std::unique_ptr<column> sum_scan(column_view const& values,
+                                 size_type num_groups,
+                                 cudf::device_span<size_type const> group_labels,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(values.type(),
+                         group_scan_dispatcher<aggregation::SUM>{},
+                         values,
+                         num_groups,
+                         group_labels,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/scan.cpp b/cpp/src/groupby/sort/scan.cpp
new file mode 100644
index 0000000..3212098
--- /dev/null
+++ b/cpp/src/groupby/sort/scan.cpp
@@ -0,0 +1,219 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <groupby/common/utils.hpp>
+#include <groupby/sort/functors.hpp>
+#include <groupby/sort/group_reductions.hpp>
+#include <groupby/sort/group_scan.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/aggregation/result_cache.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/scatter.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+/**
+ * @brief Functor to dispatch aggregation with
+ *
+ * This functor is to be used with `aggregation_dispatcher` to compute the
+ * appropriate aggregation. If the values on which to run the aggregation are
+ * unchanged, then this functor should be re-used. This is because it stores
+ * memoised sorted and/or grouped values and re-using will save on computation
+ * of these values.
+ */
+struct scan_result_functor final : store_result_functor {
+  using store_result_functor::store_result_functor;
+  template <aggregation::Kind k>
+  void operator()(aggregation const& agg)
+  {
+    CUDF_FAIL("Unsupported groupby scan aggregation");
+  }
+
+ private:
+  column_view get_grouped_values()
+  {
+    // early exit if presorted
+    if (is_presorted()) { return values; }
+
+    // TODO (dm): After implementing single pass multi-agg, explore making a
+    //            cache of all grouped value columns rather than one at a time
+    if (grouped_values)
+      return grouped_values->view();
+    else
+      return (grouped_values = helper.grouped_values(values, stream, mr))->view();
+  };
+};
+
+template <>
+void scan_result_functor::operator()<aggregation::SUM>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::sum_scan(
+      get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void scan_result_functor::operator()<aggregation::MIN>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::min_scan(
+      get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void scan_result_functor::operator()<aggregation::MAX>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(
+    values,
+    agg,
+    detail::max_scan(
+      get_grouped_values(), helper.num_groups(stream), helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void scan_result_functor::operator()<aggregation::COUNT_ALL>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  cache.add_result(values, agg, detail::count_scan(helper.group_labels(stream), stream, mr));
+}
+
+template <>
+void scan_result_functor::operator()<aggregation::RANK>(aggregation const& agg)
+{
+  if (cache.has_result(values, agg)) return;
+
+  CUDF_EXPECTS(!cudf::structs::detail::is_or_has_nested_lists(values),
+               "Unsupported list type in grouped rank scan.");
+  auto const& rank_agg         = dynamic_cast<cudf::detail::rank_aggregation const&>(agg);
+  auto const& group_labels     = helper.group_labels(stream);
+  auto const group_labels_view = column_view(cudf::device_span<size_type const>(group_labels));
+  auto const gather_map        = [&]() {
+    if (is_presorted()) {  // assumes both keys and values are sorted, Spark does this.
+      return cudf::detail::sequence(group_labels.size(),
+                                    *cudf::make_fixed_width_scalar(size_type{0}, stream),
+                                    stream,
+                                    rmm::mr::get_current_device_resource());
+    } else {
+      auto sort_order = (rank_agg._method == rank_method::FIRST ? cudf::detail::stable_sorted_order
+                                                                       : cudf::detail::sorted_order);
+      return sort_order(table_view({group_labels_view, get_grouped_values()}),
+                               {order::ASCENDING, rank_agg._column_order},
+                               {null_order::AFTER, rank_agg._null_precedence},
+                        stream,
+                        rmm::mr::get_current_device_resource());
+    }
+  }();
+
+  auto rank_scan = [&]() {
+    switch (rank_agg._method) {
+      case rank_method::FIRST: return detail::first_rank_scan;
+      case rank_method::AVERAGE: return detail::average_rank_scan;
+      case rank_method::DENSE: return detail::dense_rank_scan;
+      case rank_method::MIN: return detail::min_rank_scan;
+      case rank_method::MAX: return detail::max_rank_scan;
+      default: CUDF_FAIL("Unsupported rank method in groupby scan");
+    }
+  }();
+  auto result = rank_scan(get_grouped_values(),
+                          *gather_map,
+                          helper.group_labels(stream),
+                          helper.group_offsets(stream),
+                          stream,
+                          rmm::mr::get_current_device_resource());
+  if (rank_agg._percentage != rank_percentage::NONE) {
+    auto count = get_grouped_values().nullable() and rank_agg._null_handling == null_policy::EXCLUDE
+                   ? detail::group_count_valid(get_grouped_values(),
+                                               helper.group_labels(stream),
+                                               helper.num_groups(stream),
+                                               stream,
+                                               rmm::mr::get_current_device_resource())
+                   : detail::group_count_all(helper.group_offsets(stream),
+                                             helper.num_groups(stream),
+                                             stream,
+                                             rmm::mr::get_current_device_resource());
+    result     = detail::group_rank_to_percentage(rank_agg._method,
+                                              rank_agg._percentage,
+                                              *result,
+                                              *count,
+                                              helper.group_labels(stream),
+                                              helper.group_offsets(stream),
+                                              stream,
+                                              mr);
+  }
+  result = std::move(
+    cudf::detail::scatter(table_view{{*result}}, *gather_map, table_view{{*result}}, stream, mr)
+      ->release()[0]);
+  if (rank_agg._null_handling == null_policy::EXCLUDE) {
+    auto const values = get_grouped_values();
+    result->set_null_mask(cudf::detail::copy_bitmask(values, stream, mr), values.null_count());
+  }
+  cache.add_result(values, agg, std::move(result));
+}
+}  // namespace detail
+
+// Sort-based groupby
+std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby::sort_scan(
+  host_span<scan_request const> requests,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // We're going to start by creating a cache of results so that aggs that
+  // depend on other aggs will not have to be recalculated. e.g. mean depends on
+  // sum and count. std depends on mean and count
+  cudf::detail::result_cache cache(requests.size());
+
+  for (auto const& request : requests) {
+    auto store_functor =
+      detail::scan_result_functor(request.values, helper(), cache, stream, mr, _keys_are_sorted);
+    for (auto const& aggregation : request.aggregations) {
+      // TODO (dm): single pass compute all supported reductions
+      cudf::detail::aggregation_dispatcher(aggregation->kind, store_functor, *aggregation);
+    }
+  }
+
+  auto results = detail::extract_results(requests, cache, stream, mr);
+
+  return std::pair(helper().sorted_keys(stream, mr), std::move(results));
+}
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/groupby/sort/sort_helper.cu b/cpp/src/groupby/sort/sort_helper.cu
new file mode 100644
index 0000000..4c87c09
--- /dev/null
+++ b/cpp/src/groupby/sort/sort_helper.cu
@@ -0,0 +1,319 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "common_utils.cuh"
+
+#include <stream_compaction/stream_compaction_common.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby/sort_helper.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/labeling/label_segments.cuh>
+#include <cudf/detail/scatter.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/unique.h>
+
+#include <algorithm>
+#include <numeric>
+#include <tuple>
+
+namespace cudf {
+namespace groupby {
+namespace detail {
+namespace sort {
+
+sort_groupby_helper::sort_groupby_helper(table_view const& keys,
+                                         null_policy include_null_keys,
+                                         sorted keys_pre_sorted,
+                                         std::vector<null_order> const& null_precedence)
+  : _keys(keys),
+    _num_keys(-1),
+    _keys_pre_sorted(keys_pre_sorted),
+    _include_null_keys(include_null_keys),
+    _null_precedence(null_precedence)
+{
+  // Cannot depend on caller's sorting if the column contains nulls,
+  // and null values are to be excluded.
+  // Re-sort the data, to filter out nulls more easily.
+  if (keys_pre_sorted == sorted::YES and include_null_keys == null_policy::EXCLUDE and
+      has_nulls(keys)) {
+    _keys_pre_sorted = sorted::NO;
+  }
+};
+
+size_type sort_groupby_helper::num_keys(rmm::cuda_stream_view stream)
+{
+  if (_num_keys > -1) return _num_keys;
+
+  if (_include_null_keys == null_policy::EXCLUDE and has_nulls(_keys)) {
+    // The number of rows w/o null values `n` is indicated by number of valid bits
+    // in the row bitmask. When `_include_null_keys == NO`, then only rows `[0, n)`
+    // in the sorted keys are considered for grouping.
+    _num_keys = keys_bitmask_column(stream).size() - keys_bitmask_column(stream).null_count();
+  } else {
+    _num_keys = _keys.num_rows();
+  }
+
+  return _num_keys;
+}
+
+column_view sort_groupby_helper::key_sort_order(rmm::cuda_stream_view stream)
+{
+  auto sliced_key_sorted_order = [stream, this]() {
+    return cudf::detail::slice(this->_key_sorted_order->view(), 0, this->num_keys(stream), stream);
+  };
+
+  if (_key_sorted_order) { return sliced_key_sorted_order(); }
+
+  if (_keys_pre_sorted == sorted::YES) {
+    _key_sorted_order = cudf::detail::sequence(_keys.num_rows(),
+                                               numeric_scalar<size_type>(0),
+                                               numeric_scalar<size_type>(1),
+                                               stream,
+                                               rmm::mr::get_current_device_resource());
+    return sliced_key_sorted_order();
+  }
+
+  if (_include_null_keys == null_policy::INCLUDE || !cudf::has_nulls(_keys)) {  // SQL style
+    auto const precedence = _null_precedence.empty()
+                              ? std::vector(_keys.num_columns(), null_order::AFTER)
+                              : _null_precedence;
+    _key_sorted_order     = cudf::detail::stable_sorted_order(
+      _keys, {}, precedence, stream, rmm::mr::get_current_device_resource());
+  } else {  // Pandas style
+    // Temporarily prepend the keys table with a column that indicates the
+    // presence of a null value within a row. This allows moving all rows that
+    // contain a null value to the end of the sorted order.
+
+    auto const augmented_keys = table_view({table_view({keys_bitmask_column(stream)}), _keys});
+    auto const precedence     = [&]() {
+      auto precedence = _null_precedence.empty()
+                              ? std::vector<null_order>(_keys.num_columns(), null_order::AFTER)
+                              : _null_precedence;
+      precedence.insert(precedence.begin(), null_order::AFTER);
+      return precedence;
+    }();
+
+    _key_sorted_order = cudf::detail::stable_sorted_order(
+      augmented_keys, {}, precedence, stream, rmm::mr::get_current_device_resource());
+
+    // All rows with one or more null values are at the end of the resulting sorted order.
+  }
+
+  return sliced_key_sorted_order();
+}
+
+sort_groupby_helper::index_vector const& sort_groupby_helper::group_offsets(
+  rmm::cuda_stream_view stream)
+{
+  if (_group_offsets) return *_group_offsets;
+
+  auto const size = num_keys(stream);
+  // Create a temporary variable and only set _group_offsets right before the return.
+  // This way, a 2nd (parallel) call to this will not be given a partially created object.
+  auto group_offsets = std::make_unique<index_vector>(size + 1, stream);
+
+  auto const comparator = cudf::experimental::row::equality::self_comparator{_keys, stream};
+
+  auto const sorted_order = key_sort_order(stream).data<size_type>();
+  decltype(group_offsets->begin()) result_end;
+
+  if (cudf::detail::has_nested_columns(_keys)) {
+    auto const d_key_equal = comparator.equal_to<true>(
+      cudf::nullate::DYNAMIC{cudf::has_nested_nulls(_keys)}, null_equality::EQUAL);
+    // Using a temporary buffer for intermediate transform results from the iterator containing
+    // the comparator speeds up compile-time significantly without much degradation in
+    // runtime performance over using the comparator directly in thrust::unique_copy.
+    auto result       = rmm::device_uvector<bool>(size, stream);
+    auto const itr    = thrust::make_counting_iterator<size_type>(0);
+    auto const row_eq = permuted_row_equality_comparator(d_key_equal, sorted_order);
+    auto const ufn    = cudf::detail::unique_copy_fn<decltype(itr), decltype(row_eq)>{
+      itr, duplicate_keep_option::KEEP_FIRST, row_eq, size - 1};
+    thrust::transform(rmm::exec_policy(stream), itr, itr + size, result.begin(), ufn);
+    result_end = thrust::copy_if(rmm::exec_policy(stream),
+                                 itr,
+                                 itr + size,
+                                 result.begin(),
+                                 group_offsets->begin(),
+                                 thrust::identity<bool>{});
+  } else {
+    auto const d_key_equal = comparator.equal_to<false>(
+      cudf::nullate::DYNAMIC{cudf::has_nested_nulls(_keys)}, null_equality::EQUAL);
+    result_end = thrust::unique_copy(rmm::exec_policy(stream),
+                                     thrust::counting_iterator<size_type>(0),
+                                     thrust::counting_iterator<size_type>(size),
+                                     group_offsets->begin(),
+                                     permuted_row_equality_comparator(d_key_equal, sorted_order));
+  }
+
+  auto const num_groups = thrust::distance(group_offsets->begin(), result_end);
+  group_offsets->set_element_async(num_groups, size, stream);
+  group_offsets->resize(num_groups + 1, stream);
+
+  _group_offsets = std::move(group_offsets);
+  return *_group_offsets;
+}
+
+sort_groupby_helper::index_vector const& sort_groupby_helper::group_labels(
+  rmm::cuda_stream_view stream)
+{
+  if (_group_labels) return *_group_labels;
+
+  // Create a temporary variable and only set _group_labels right before the return.
+  // This way, a 2nd (parallel) call to this will not be given a partially created object.
+  auto group_labels = std::make_unique<index_vector>(num_keys(stream), stream);
+
+  if (num_keys(stream)) {
+    auto const& offsets = group_offsets(stream);
+    cudf::detail::label_segments(
+      offsets.begin(), offsets.end(), group_labels->begin(), group_labels->end(), stream);
+  }
+
+  _group_labels = std::move(group_labels);
+  return *_group_labels;
+}
+
+column_view sort_groupby_helper::unsorted_keys_labels(rmm::cuda_stream_view stream)
+{
+  if (_unsorted_keys_labels) return _unsorted_keys_labels->view();
+
+  column_ptr temp_labels = make_numeric_column(
+    data_type(type_to_id<size_type>()), _keys.num_rows(), mask_state::ALL_NULL, stream);
+
+  auto group_labels_view = cudf::column_view(data_type(type_to_id<size_type>()),
+                                             group_labels(stream).size(),
+                                             group_labels(stream).data(),
+                                             nullptr,
+                                             0);
+
+  auto scatter_map = key_sort_order(stream);
+
+  std::unique_ptr<table> t_unsorted_keys_labels =
+    cudf::detail::scatter(table_view({group_labels_view}),
+                          scatter_map,
+                          table_view({temp_labels->view()}),
+                          stream,
+                          rmm::mr::get_current_device_resource());
+
+  _unsorted_keys_labels = std::move(t_unsorted_keys_labels->release()[0]);
+
+  return _unsorted_keys_labels->view();
+}
+
+column_view sort_groupby_helper::keys_bitmask_column(rmm::cuda_stream_view stream)
+{
+  if (_keys_bitmask_column) return _keys_bitmask_column->view();
+
+  auto [row_bitmask, null_count] =
+    cudf::detail::bitmask_and(_keys, stream, rmm::mr::get_current_device_resource());
+
+  auto const zero = numeric_scalar<int8_t>(0);
+  // Create a temporary variable and only set _keys_bitmask_column right before the return.
+  // This way, a 2nd (parallel) call to this will not be given a partially created object.
+  auto keys_bitmask_column = cudf::detail::sequence(
+    _keys.num_rows(), zero, zero, stream, rmm::mr::get_current_device_resource());
+  keys_bitmask_column->set_null_mask(std::move(row_bitmask), null_count);
+
+  _keys_bitmask_column = std::move(keys_bitmask_column);
+  return _keys_bitmask_column->view();
+}
+
+sort_groupby_helper::column_ptr sort_groupby_helper::sorted_values(
+  column_view const& values, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  column_ptr values_sort_order =
+    cudf::detail::stable_sorted_order(table_view({unsorted_keys_labels(stream), values}),
+                                      {},
+                                      std::vector<null_order>(2, null_order::AFTER),
+                                      stream,
+                                      mr);
+
+  // Zero-copy slice this sort order so that its new size is num_keys()
+  column_view gather_map =
+    cudf::detail::slice(values_sort_order->view(), 0, num_keys(stream), stream);
+
+  auto sorted_values_table = cudf::detail::gather(table_view({values}),
+                                                  gather_map,
+                                                  cudf::out_of_bounds_policy::DONT_CHECK,
+                                                  cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                                  stream,
+                                                  mr);
+
+  return std::move(sorted_values_table->release()[0]);
+}
+
+sort_groupby_helper::column_ptr sort_groupby_helper::grouped_values(
+  column_view const& values, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  auto gather_map = key_sort_order(stream);
+
+  auto grouped_values_table = cudf::detail::gather(table_view({values}),
+                                                   gather_map,
+                                                   cudf::out_of_bounds_policy::DONT_CHECK,
+                                                   cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                                   stream,
+                                                   mr);
+
+  return std::move(grouped_values_table->release()[0]);
+}
+
+std::unique_ptr<table> sort_groupby_helper::unique_keys(rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  auto idx_data = key_sort_order(stream).data<size_type>();
+
+  auto gather_map_it = thrust::make_transform_iterator(
+    group_offsets(stream).begin(), [idx_data] __device__(size_type i) { return idx_data[i]; });
+
+  return cudf::detail::gather(_keys,
+                              gather_map_it,
+                              gather_map_it + num_groups(stream),
+                              out_of_bounds_policy::DONT_CHECK,
+                              stream,
+                              mr);
+}
+
+std::unique_ptr<table> sort_groupby_helper::sorted_keys(rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  return cudf::detail::gather(_keys,
+                              key_sort_order(stream),
+                              cudf::out_of_bounds_policy::DONT_CHECK,
+                              cudf::detail::negative_index_policy::NOT_ALLOWED,
+                              stream,
+                              mr);
+}
+
+}  // namespace sort
+}  // namespace detail
+}  // namespace groupby
+}  // namespace cudf
diff --git a/cpp/src/hash/concurrent_unordered_map.cuh b/cpp/src/hash/concurrent_unordered_map.cuh
new file mode 100644
index 0000000..d773c27
--- /dev/null
+++ b/cpp/src/hash/concurrent_unordered_map.cuh
@@ -0,0 +1,558 @@
+/*
+ * Copyright (c) 2017-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <hash/managed.cuh>
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/hashing/detail/default_hash.cuh>
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/pair.h>
+
+#include <iostream>
+#include <iterator>
+#include <limits>
+#include <type_traits>
+
+#include <cuda/atomic>
+
+namespace {
+template <std::size_t N>
+struct packed {
+  using type = void;
+};
+template <>
+struct packed<sizeof(uint64_t)> {
+  using type = uint64_t;
+};
+template <>
+struct packed<sizeof(uint32_t)> {
+  using type = uint32_t;
+};
+template <typename pair_type>
+using packed_t = typename packed<sizeof(pair_type)>::type;
+
+/**
+ * @brief Indicates if a pair type can be packed.
+ *
+ * When the size of the key,value pair being inserted into the hash table is
+ * equal in size to a type where atomicCAS is natively supported, it is more
+ * efficient to "pack" the pair and insert it with a single atomicCAS.
+ *
+ * Only integral key and value types may be packed because we use
+ * bitwise equality comparison, which may not be valid for non-integral
+ * types.
+ *
+ * Also, the `pair_type` must not contain any padding bits otherwise
+ * accessing the packed value would be undefined.
+ *
+ * @tparam pair_type The pair type that will be packed
+ * @return true If the pair type can be packed
+ * @return false  If the pair type cannot be packed
+ */
+template <typename pair_type,
+          typename key_type   = typename pair_type::first_type,
+          typename value_type = typename pair_type::second_type>
+constexpr bool is_packable()
+{
+  return std::is_integral_v<key_type> and std::is_integral_v<value_type> and
+         not std::is_void_v<packed_t<pair_type>> and
+         std::has_unique_object_representations_v<pair_type>;
+}
+
+/**
+ * @brief Allows viewing a pair in a packed representation
+ *
+ * Used as an optimization for inserting when a pair can be inserted with a
+ * single atomicCAS
+ */
+template <typename pair_type, typename Enable = void>
+union pair_packer;
+
+template <typename pair_type>
+union pair_packer<pair_type, std::enable_if_t<is_packable<pair_type>()>> {
+  using packed_type = packed_t<pair_type>;
+  packed_type packed;
+  pair_type pair;
+
+  __device__ pair_packer(pair_type _pair) : pair{_pair} {}
+
+  __device__ pair_packer(packed_type _packed) : packed{_packed} {}
+};
+}  // namespace
+
+/**
+ * Supports concurrent insert, but not concurrent insert and find.
+ *
+ * @note The user is responsible for the following stream semantics:
+ * - Either the same stream should be used to create the map as is used by the kernels that access
+ * it, or
+ * - the stream used to create the map should be synchronized before it is accessed from a different
+ * stream or from host code.
+ *
+ * TODO:
+ *  - add constructor that takes pointer to hash_table to avoid allocations
+ */
+template <typename Key,
+          typename Element,
+          typename Hasher    = cudf::hashing::detail::default_hash<Key>,
+          typename Equality  = equal_to<Key>,
+          typename Allocator = default_allocator<thrust::pair<Key, Element>>>
+class concurrent_unordered_map {
+ public:
+  using size_type      = size_t;
+  using hasher         = Hasher;
+  using key_equal      = Equality;
+  using allocator_type = Allocator;
+  using key_type       = Key;
+  using mapped_type    = Element;
+  using value_type     = thrust::pair<Key, Element>;
+  using iterator       = cycle_iterator_adapter<value_type*>;
+  using const_iterator = cycle_iterator_adapter<value_type*> const;
+
+ public:
+  /**
+   * @brief Factory to construct a new concurrent unordered map.
+   *
+   * Returns a `std::unique_ptr` to a new concurrent unordered map object. The
+   * map is non-owning and trivially copyable and should be passed by value into
+   * kernels. The `unique_ptr` contains a custom deleter that will free the
+   * map's contents.
+   *
+   * @note The implementation of this unordered_map uses sentinel values to
+   * indicate an entry in the hash table that is empty, i.e., if a hash bucket
+   * is empty, the pair residing there will be equal to (unused_key,
+   * unused_element). As a result, attempting to insert a key equal to
+   *`unused_key` results in undefined behavior.
+   *
+   * @note All allocations, kernels and copies in the constructor take place
+   * on stream but the constructor does not synchronize the stream. It is the user's
+   * responsibility to synchronize or use the same stream to access the map.
+   *
+   * @param capacity The maximum number of pairs the map may hold
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param unused_element The sentinel value to use for an empty value
+   * @param unused_key The sentinel value to use for an empty key
+   * @param hash_function The hash function to use for hashing keys
+   * @param equal The equality comparison function for comparing if two keys are
+   * equal
+   * @param allocator The allocator to use for allocation the hash table's
+   * storage
+   */
+  static auto create(size_type capacity,
+                     rmm::cuda_stream_view stream,
+                     mapped_type const unused_element = std::numeric_limits<mapped_type>::max(),
+                     key_type const unused_key        = std::numeric_limits<key_type>::max(),
+                     Hasher const& hash_function      = hasher(),
+                     Equality const& equal            = key_equal(),
+                     allocator_type const& allocator  = allocator_type())
+  {
+    CUDF_FUNC_RANGE();
+    using Self = concurrent_unordered_map<Key, Element, Hasher, Equality, Allocator>;
+
+    // Note: need `(*p).destroy` instead of `p->destroy` here
+    // due to compiler bug: https://github.com/rapidsai/cudf/pull/5692
+    auto deleter = [stream](Self* p) { (*p).destroy(stream); };
+
+    return std::unique_ptr<Self, std::function<void(Self*)>>{
+      new Self(capacity, unused_element, unused_key, hash_function, equal, allocator, stream),
+      deleter};
+  }
+
+  /**
+   * @brief Returns an iterator to the first element in the map
+   *
+   * @note `__device__` code that calls this function should either run in the
+   * same stream as `create()`, or the accessing stream either be running on the
+   * same stream as create(), or the accessing stream should be appropriately
+   * synchronized with the creating stream.
+   *
+   * @returns iterator to the first element in the map.
+   */
+  __device__ iterator begin()
+  {
+    return iterator(m_hashtbl_values, m_hashtbl_values + m_capacity, m_hashtbl_values);
+  }
+
+  /**
+   * @brief Returns a constant iterator to the first element in the map
+   *
+   * @note `__device__` code that calls this function should either run in the
+   * same stream as `create()`, or the accessing stream either be running on the
+   * same stream as create(), or the accessing stream should be appropriately
+   * synchronized with the creating stream.
+   *
+   * @returns constant iterator to the first element in the map.
+   */
+  __device__ const_iterator begin() const
+  {
+    return const_iterator(m_hashtbl_values, m_hashtbl_values + m_capacity, m_hashtbl_values);
+  }
+
+  /**
+   * @brief Returns an iterator to the one past the last element in the map
+   *
+   * @note `__device__` code that calls this function should either run in the
+   * same stream as `create()`, or the accessing stream either be running on the
+   * same stream as create(), or the accessing stream should be appropriately
+   * synchronized with the creating stream.
+   *
+   * @returns iterator to the one past the last element in the map.
+   */
+  __device__ iterator end()
+  {
+    return iterator(m_hashtbl_values, m_hashtbl_values + m_capacity, m_hashtbl_values + m_capacity);
+  }
+
+  /**
+   * @brief Returns a constant iterator to the one past the last element in the map
+   *
+   * @note When called in a device code, user should make sure that it should
+   * either be running on the same stream as create(), or the accessing stream
+   * should be appropriately synchronized with the creating stream.
+   *
+   * @returns constant iterator to the one past the last element in the map.
+   */
+  __device__ const_iterator end() const
+  {
+    return const_iterator(
+      m_hashtbl_values, m_hashtbl_values + m_capacity, m_hashtbl_values + m_capacity);
+  }
+  __host__ __device__ value_type* data() const { return m_hashtbl_values; }
+
+  __host__ __device__ key_type get_unused_key() const { return m_unused_key; }
+
+  __host__ __device__ mapped_type get_unused_element() const { return m_unused_element; }
+
+  [[nodiscard]] __host__ __device__ size_type capacity() const { return m_capacity; }
+
+ private:
+  /**
+   * @brief Enumeration of the possible results of attempting to insert into
+   *a hash bucket
+   */
+  enum class insert_result {
+    CONTINUE,  ///< Insert did not succeed, continue trying to insert
+               ///< (collision)
+    SUCCESS,   ///< New pair inserted successfully
+    DUPLICATE  ///< Insert did not succeed, key is already present
+  };
+
+  /**
+   * @brief Specialization for value types that can be packed.
+   *
+   * When the size of the key,value pair being inserted is equal in size to
+   *a type where atomicCAS is natively supported, this optimization path
+   *will insert the pair in a single atomicCAS operation.
+   */
+  template <typename pair_type = value_type>
+  __device__ std::enable_if_t<is_packable<pair_type>(), insert_result> attempt_insert(
+    value_type* const __restrict__ insert_location, value_type const& insert_pair)
+  {
+    pair_packer<pair_type> expected{thrust::make_pair(m_unused_key, m_unused_element)};
+    pair_packer<pair_type> desired{insert_pair};
+
+    using packed_type = typename pair_packer<pair_type>::packed_type;
+
+    auto* insert_ptr = reinterpret_cast<packed_type*>(insert_location);
+    cuda::atomic_ref<packed_type, cuda::thread_scope_device> ref{*insert_ptr};
+    auto const success =
+      ref.compare_exchange_strong(expected.packed, desired.packed, cuda::std::memory_order_relaxed);
+
+    if (success) {
+      return insert_result::SUCCESS;
+    } else if (m_equal(expected.pair.first, insert_pair.first)) {
+      return insert_result::DUPLICATE;
+    }
+    return insert_result::CONTINUE;
+  }
+
+  /**
+   * @brief Attempts to insert a key,value pair at the specified hash bucket.
+   *
+   * @param[in] insert_location Pointer to hash bucket to attempt insert
+   * @param[in] insert_pair The pair to insert
+   * @return Enum indicating result of insert attempt.
+   */
+  template <typename pair_type = value_type>
+  __device__ std::enable_if_t<not is_packable<pair_type>(), insert_result> attempt_insert(
+    value_type* const __restrict__ insert_location, value_type const& insert_pair)
+  {
+    auto expected = m_unused_key;
+    cuda::atomic_ref<key_type, cuda::thread_scope_device> ref{insert_location->first};
+    auto const key_success =
+      ref.compare_exchange_strong(expected, insert_pair.first, cuda::std::memory_order_relaxed);
+
+    // Hash bucket empty
+    if (key_success) {
+      insert_location->second = insert_pair.second;
+      return insert_result::SUCCESS;
+    }
+    // Key already exists
+    else if (m_equal(expected, insert_pair.first)) {
+      return insert_result::DUPLICATE;
+    }
+
+    return insert_result::CONTINUE;
+  }
+
+ public:
+  /**
+   * @brief Attempts to insert a key, value pair into the map.
+   *
+   * Returns an iterator, boolean pair.
+   *
+   * If the new key already present in the map, the iterator points to
+   * the location of the existing key and the boolean is `false` indicating
+   * that the insert did not succeed.
+   *
+   * If the new key was not present, the iterator points to the location
+   * where the insert occurred and the boolean is `true` indicating that the
+   *insert succeeded.
+   *
+   * @param insert_pair The key and value pair to insert
+   * @return Iterator, Boolean pair. Iterator is to the location of the
+   *newly inserted pair, or the existing pair that prevented the insert.
+   *Boolean indicates insert success.
+   */
+  __device__ thrust::pair<iterator, bool> insert(value_type const& insert_pair)
+  {
+    size_type const key_hash{m_hf(insert_pair.first)};
+    size_type index{key_hash % m_capacity};
+
+    insert_result status{insert_result::CONTINUE};
+
+    value_type* current_bucket{nullptr};
+
+    while (status == insert_result::CONTINUE) {
+      current_bucket = &m_hashtbl_values[index];
+      status         = attempt_insert(current_bucket, insert_pair);
+      index          = (index + 1) % m_capacity;
+    }
+
+    bool const insert_success = status == insert_result::SUCCESS;
+
+    return thrust::make_pair(
+      iterator(m_hashtbl_values, m_hashtbl_values + m_capacity, current_bucket), insert_success);
+  }
+
+  /**
+   * @brief Searches the map for the specified key.
+   *
+   * @note `find` is not threadsafe with `insert`. I.e., it is not safe to
+   *do concurrent `insert` and `find` operations.
+   *
+   * @param k The key to search for
+   * @return An iterator to the key if it exists, else map.end()
+   */
+  __device__ const_iterator find(key_type const& k) const
+  {
+    size_type const key_hash = m_hf(k);
+    size_type index          = key_hash % m_capacity;
+
+    value_type* current_bucket = &m_hashtbl_values[index];
+
+    while (true) {
+      key_type const existing_key = current_bucket->first;
+
+      if (m_unused_key == existing_key) { return this->end(); }
+
+      if (m_equal(k, existing_key)) {
+        return const_iterator(m_hashtbl_values, m_hashtbl_values + m_capacity, current_bucket);
+      }
+
+      index          = (index + 1) % m_capacity;
+      current_bucket = &m_hashtbl_values[index];
+    }
+  }
+
+  /**
+   * @brief Searches the map for the specified key.
+   *
+   * This version of the find function specifies a hashing function and an
+   * equality comparison.  This allows the caller to use different functions
+   * for insert and find (for example, when you want to insert keys from
+   * one table and use find to match keys from a different table with the
+   * keys from the first table).
+   *
+   * @note `find` is not threadsafe with `insert`. I.e., it is not safe to
+   * do concurrent `insert` and `find` operations.
+   *
+   * @tparam find_hasher     Type of hashing function
+   * @tparam find_key_equal  Type of equality comparison
+   *
+   * @param k         The key to search for
+   * @param f_hash    The hashing function to use to hash this key
+   * @param f_equal   The equality function to use to compare this key with the
+   *                  contents of the hash table
+   * @return An iterator to the key if it exists, else map.end()
+   */
+  template <typename find_hasher, typename find_key_equal>
+  __device__ const_iterator find(key_type const& k,
+                                 find_hasher f_hash,
+                                 find_key_equal f_equal) const
+  {
+    size_type const key_hash = f_hash(k);
+    size_type index          = key_hash % m_capacity;
+
+    value_type* current_bucket = &m_hashtbl_values[index];
+
+    while (true) {
+      key_type const existing_key = current_bucket->first;
+
+      if (m_unused_key == existing_key) { return this->end(); }
+
+      if (f_equal(k, existing_key)) {
+        return const_iterator(m_hashtbl_values, m_hashtbl_values + m_capacity, current_bucket);
+      }
+
+      index          = (index + 1) % m_capacity;
+      current_bucket = &m_hashtbl_values[index];
+    }
+  }
+
+  void assign_async(concurrent_unordered_map const& other, rmm::cuda_stream_view stream)
+  {
+    if (other.m_capacity <= m_capacity) {
+      m_capacity = other.m_capacity;
+    } else {
+      m_allocator.deallocate(m_hashtbl_values, m_capacity, stream);
+      m_capacity = other.m_capacity;
+      m_capacity = other.m_capacity;
+
+      m_hashtbl_values = m_allocator.allocate(m_capacity, stream);
+    }
+    CUDF_CUDA_TRY(cudaMemcpyAsync(m_hashtbl_values,
+                                  other.m_hashtbl_values,
+                                  m_capacity * sizeof(value_type),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+  }
+
+  void clear_async(rmm::cuda_stream_view stream)
+  {
+    constexpr int block_size = 128;
+    init_hashtbl<<<((m_capacity - 1) / block_size) + 1, block_size, 0, stream.value()>>>(
+      m_hashtbl_values, m_capacity, m_unused_key, m_unused_element);
+  }
+
+  void print()
+  {
+    for (size_type i = 0; i < m_capacity; ++i) {
+      std::cout << i << ": " << m_hashtbl_values[i].first << "," << m_hashtbl_values[i].second
+                << std::endl;
+    }
+  }
+
+  void prefetch(int const dev_id, rmm::cuda_stream_view stream)
+  {
+    cudaPointerAttributes hashtbl_values_ptr_attributes;
+    cudaError_t status = cudaPointerGetAttributes(&hashtbl_values_ptr_attributes, m_hashtbl_values);
+
+    if (cudaSuccess == status && isPtrManaged(hashtbl_values_ptr_attributes)) {
+      CUDF_CUDA_TRY(cudaMemPrefetchAsync(
+        m_hashtbl_values, m_capacity * sizeof(value_type), dev_id, stream.value()));
+    }
+    CUDF_CUDA_TRY(cudaMemPrefetchAsync(this, sizeof(*this), dev_id, stream.value()));
+  }
+
+  /**
+   * @brief Frees the contents of the map and destroys the map object.
+   *
+   * This function is invoked as the deleter of the `std::unique_ptr` returned
+   * from the `create()` factory function.
+   *
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   */
+  void destroy(rmm::cuda_stream_view stream)
+  {
+    m_allocator.deallocate(m_hashtbl_values, m_capacity, stream);
+    delete this;
+  }
+
+  concurrent_unordered_map()                                           = delete;
+  concurrent_unordered_map(concurrent_unordered_map const&)            = default;
+  concurrent_unordered_map(concurrent_unordered_map&&)                 = default;
+  concurrent_unordered_map& operator=(concurrent_unordered_map const&) = default;
+  concurrent_unordered_map& operator=(concurrent_unordered_map&&)      = default;
+  ~concurrent_unordered_map()                                          = default;
+
+ private:
+  hasher m_hf;
+  key_equal m_equal;
+  mapped_type m_unused_element;
+  key_type m_unused_key;
+  allocator_type m_allocator;
+  size_type m_capacity;
+  value_type* m_hashtbl_values;
+
+  /**
+   * @brief Private constructor used by `create` factory function.
+   *
+   * @param capacity The desired m_capacity of the hash table
+   * @param unused_element The sentinel value to use for an empty value
+   * @param unused_key The sentinel value to use for an empty key
+   * @param hash_function The hash function to use for hashing keys
+   * @param equal The equality comparison function for comparing if two keys
+   *are equal
+   * @param allocator The allocator to use for allocation the hash table's
+   * storage
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   */
+  concurrent_unordered_map(size_type capacity,
+                           mapped_type const unused_element,
+                           key_type const unused_key,
+                           Hasher const& hash_function,
+                           Equality const& equal,
+                           allocator_type const& allocator,
+                           rmm::cuda_stream_view stream)
+    : m_hf(hash_function),
+      m_equal(equal),
+      m_allocator(allocator),
+      m_capacity(capacity),
+      m_unused_element(unused_element),
+      m_unused_key(unused_key)
+  {
+    m_hashtbl_values         = m_allocator.allocate(m_capacity, stream);
+    constexpr int block_size = 128;
+    {
+      cudaPointerAttributes hashtbl_values_ptr_attributes;
+      cudaError_t status =
+        cudaPointerGetAttributes(&hashtbl_values_ptr_attributes, m_hashtbl_values);
+
+      if (cudaSuccess == status && isPtrManaged(hashtbl_values_ptr_attributes)) {
+        int dev_id = 0;
+        CUDF_CUDA_TRY(cudaGetDevice(&dev_id));
+        CUDF_CUDA_TRY(cudaMemPrefetchAsync(
+          m_hashtbl_values, m_capacity * sizeof(value_type), dev_id, stream.value()));
+      }
+    }
+
+    if (m_capacity > 0) {
+      init_hashtbl<<<((m_capacity - 1) / block_size) + 1, block_size, 0, stream.value()>>>(
+        m_hashtbl_values, m_capacity, m_unused_key, m_unused_element);
+    }
+
+    CUDF_CHECK_CUDA(stream.value());
+  }
+};
diff --git a/cpp/src/hash/hashing.cu b/cpp/src/hash/hashing.cu
new file mode 100644
index 0000000..68e02ef
--- /dev/null
+++ b/cpp/src/hash/hashing.cu
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+
+std::unique_ptr<column> hash(table_view const& input,
+                             hash_id hash_function,
+                             uint32_t seed,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  switch (hash_function) {
+    case (hash_id::HASH_MURMUR3): return murmurhash3_x86_32(input, seed, stream, mr);
+    case (hash_id::HASH_SPARK_MURMUR3): return spark_murmurhash3_x86_32(input, seed, stream, mr);
+    case (hash_id::HASH_MD5): return md5(input, stream, mr);
+    default: CUDF_FAIL("Unsupported hash function.");
+  }
+}
+
+}  // namespace detail
+}  // namespace hashing
+
+std::unique_ptr<column> hash(table_view const& input,
+                             hash_id hash_function,
+                             uint32_t seed,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return hashing::detail::hash(input, hash_function, seed, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/hash/managed.cuh b/cpp/src/hash/managed.cuh
new file mode 100644
index 0000000..10aeb6e
--- /dev/null
+++ b/cpp/src/hash/managed.cuh
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2017-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <new>
+
+struct managed {
+  static void* operator new(size_t n)
+  {
+    void* ptr          = nullptr;
+    cudaError_t result = cudaMallocManaged(&ptr, n);
+    if (cudaSuccess != result || 0 == ptr) throw std::bad_alloc();
+    return ptr;
+  }
+
+  static void operator delete(void* ptr) noexcept
+  {
+    auto const free_result = cudaFree(ptr);
+    assert(free_result == cudaSuccess);
+  }
+};
+
+inline bool isPtrManaged(cudaPointerAttributes attr)
+{
+#if CUDART_VERSION >= 10000
+  return (attr.type == cudaMemoryTypeManaged);
+#else
+  return attr.isManaged;
+#endif
+}
diff --git a/cpp/src/hash/md5_hash.cu b/cpp/src/hash/md5_hash.cu
new file mode 100644
index 0000000..4e25f9f
--- /dev/null
+++ b/cpp/src/hash/md5_hash.cu
@@ -0,0 +1,384 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/hashing/detail/hash_functions.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <iterator>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+
+namespace {
+
+template <int capacity, typename hash_step_callable>
+struct hash_circular_buffer {
+  uint8_t storage[capacity];
+  uint8_t* cur;
+  int available_space{capacity};
+  hash_step_callable hash_step;
+
+  __device__ inline hash_circular_buffer(hash_step_callable hash_step)
+    : cur{storage}, hash_step{hash_step}
+  {
+  }
+
+  __device__ inline void put(uint8_t const* in, int size)
+  {
+    int copy_start = 0;
+    while (size >= available_space) {
+      // The buffer will be filled by this chunk of data. Copy a chunk of the
+      // data to fill the buffer and trigger a hash step.
+      memcpy(cur, in + copy_start, available_space);
+      hash_step(storage);
+      size -= available_space;
+      copy_start += available_space;
+      cur             = storage;
+      available_space = capacity;
+    }
+    // The buffer will not be filled by the remaining data. That is, `size >= 0
+    // && size < capacity`. We copy the remaining data into the buffer but do
+    // not trigger a hash step.
+    memcpy(cur, in + copy_start, size);
+    cur += size;
+    available_space -= size;
+  }
+
+  __device__ inline void pad(int const space_to_leave)
+  {
+    if (space_to_leave > available_space) {
+      memset(cur, 0x00, available_space);
+      hash_step(storage);
+      cur             = storage;
+      available_space = capacity;
+    }
+    memset(cur, 0x00, available_space - space_to_leave);
+    cur += available_space - space_to_leave;
+    available_space = space_to_leave;
+  }
+
+  __device__ inline uint8_t const& operator[](int idx) const { return storage[idx]; }
+};
+
+// Get a uint8_t pointer to a column element and its size as a pair.
+template <typename Element>
+auto __device__ inline get_element_pointer_and_size(Element const& element)
+{
+  if constexpr (is_fixed_width<Element>() && !is_chrono<Element>()) {
+    return thrust::make_pair(reinterpret_cast<uint8_t const*>(&element), sizeof(Element));
+  } else {
+    CUDF_UNREACHABLE("Unsupported type.");
+  }
+}
+
+template <>
+auto __device__ inline get_element_pointer_and_size(string_view const& element)
+{
+  return thrust::make_pair(reinterpret_cast<uint8_t const*>(element.data()), element.size_bytes());
+}
+
+/**
+ * Modified GPU implementation of
+ * https://johnnylee-sde.github.io/Fast-unsigned-integer-to-hex-string/
+ * Copyright (c) 2015 Barry Clark
+ * Licensed under the MIT license.
+ * See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+ */
+void __device__ inline uint32ToLowercaseHexString(uint32_t num, char* destination)
+{
+  // Transform 0xABCD'1234 => 0x0000'ABCD'0000'1234 => 0x0B0A'0D0C'0201'0403
+  uint64_t x = num;
+  x          = ((x & 0xFFFF'0000u) << 16) | ((x & 0xFFFF));
+  x          = ((x & 0x000F'0000'000Fu) << 8) | ((x & 0x00F0'0000'00F0u) >> 4) |
+      ((x & 0x0F00'0000'0F00u) << 16) | ((x & 0xF000'0000'F000) << 4);
+
+  // Calculate a mask of ascii value offsets for bytes that contain alphabetical hex digits
+  uint64_t offsets = (((x + 0x0606'0606'0606'0606) >> 4) & 0x0101'0101'0101'0101) * 0x27;
+
+  x |= 0x3030'3030'3030'3030;
+  x += offsets;
+  std::memcpy(destination, reinterpret_cast<uint8_t*>(&x), 8);
+}
+
+// The MD5 algorithm and its hash/shift constants are officially specified in
+// RFC 1321. For convenience, these values can also be found on Wikipedia:
+// https://en.wikipedia.org/wiki/MD5
+const __constant__ uint32_t md5_shift_constants[16] = {
+  7, 12, 17, 22, 5, 9, 14, 20, 4, 11, 16, 23, 6, 10, 15, 21};
+
+const __constant__ uint32_t md5_hash_constants[64] = {
+  0xd76aa478, 0xe8c7b756, 0x242070db, 0xc1bdceee, 0xf57c0faf, 0x4787c62a, 0xa8304613, 0xfd469501,
+  0x698098d8, 0x8b44f7af, 0xffff5bb1, 0x895cd7be, 0x6b901122, 0xfd987193, 0xa679438e, 0x49b40821,
+  0xf61e2562, 0xc040b340, 0x265e5a51, 0xe9b6c7aa, 0xd62f105d, 0x02441453, 0xd8a1e681, 0xe7d3fbc8,
+  0x21e1cde6, 0xc33707d6, 0xf4d50d87, 0x455a14ed, 0xa9e3e905, 0xfcefa3f8, 0x676f02d9, 0x8d2a4c8a,
+  0xfffa3942, 0x8771f681, 0x6d9d6122, 0xfde5380c, 0xa4beea44, 0x4bdecfa9, 0xf6bb4b60, 0xbebfbc70,
+  0x289b7ec6, 0xeaa127fa, 0xd4ef3085, 0x04881d05, 0xd9d4d039, 0xe6db99e5, 0x1fa27cf8, 0xc4ac5665,
+  0xf4292244, 0x432aff97, 0xab9423a7, 0xfc93a039, 0x655b59c3, 0x8f0ccc92, 0xffeff47d, 0x85845dd1,
+  0x6fa87e4f, 0xfe2ce6e0, 0xa3014314, 0x4e0811a1, 0xf7537e82, 0xbd3af235, 0x2ad7d2bb, 0xeb86d391,
+};
+
+struct MD5Hasher {
+  static constexpr int message_chunk_size = 64;
+
+  __device__ inline MD5Hasher(char* result_location)
+    : result_location(result_location), buffer(md5_hash_step{hash_values})
+  {
+  }
+
+  __device__ inline ~MD5Hasher()
+  {
+    // On destruction, finalize the message buffer and write out the current
+    // hexadecimal hash value to the result location.
+    // Add a one byte flag 0b10000000 to signal the end of the message.
+    uint8_t constexpr end_of_message = 0x80;
+    // The message length is appended to the end of the last chunk processed.
+    uint64_t const message_length_in_bits = message_length * 8;
+
+    buffer.put(&end_of_message, sizeof(end_of_message));
+    buffer.pad(sizeof(message_length_in_bits));
+    buffer.put(reinterpret_cast<uint8_t const*>(&message_length_in_bits),
+               sizeof(message_length_in_bits));
+
+    for (int i = 0; i < 4; ++i) {
+      uint32ToLowercaseHexString(hash_values[i], result_location + (8 * i));
+    }
+  }
+
+  MD5Hasher(MD5Hasher const&)            = delete;
+  MD5Hasher& operator=(MD5Hasher const&) = delete;
+  MD5Hasher(MD5Hasher&&)                 = delete;
+  MD5Hasher& operator=(MD5Hasher&&)      = delete;
+
+  template <typename Element>
+  void __device__ inline process(Element const& element)
+  {
+    auto const normalized_element  = normalize_nans_and_zeros(element);
+    auto const [element_ptr, size] = get_element_pointer_and_size(normalized_element);
+    buffer.put(element_ptr, size);
+    message_length += size;
+  }
+
+  /**
+   * @brief Core MD5 algorithm implementation. Processes a single 64-byte chunk,
+   * updating the hash value so far. Does not zero out the buffer contents.
+   */
+  struct md5_hash_step {
+    uint32_t (&hash_values)[4];
+
+    void __device__ inline operator()(uint8_t const (&buffer)[message_chunk_size])
+    {
+      uint32_t A = hash_values[0];
+      uint32_t B = hash_values[1];
+      uint32_t C = hash_values[2];
+      uint32_t D = hash_values[3];
+
+      for (int j = 0; j < message_chunk_size; j++) {
+        uint32_t F;
+        uint32_t g;
+        // No default case is needed because j < 64. j / 16 is always 0, 1, 2, or 3.
+        switch (j / 16) {
+          case 0:
+            F = (B & C) | ((~B) & D);
+            g = j;
+            break;
+          case 1:
+            F = (D & B) | ((~D) & C);
+            g = (5 * j + 1) % 16;
+            break;
+          case 2:
+            F = B ^ C ^ D;
+            g = (3 * j + 5) % 16;
+            break;
+          case 3:
+            F = C ^ (B | (~D));
+            g = (7 * j) % 16;
+            break;
+        }
+
+        uint32_t buffer_element_as_int;
+        memcpy(&buffer_element_as_int, &buffer[g * 4], 4);
+        F = F + A + md5_hash_constants[j] + buffer_element_as_int;
+        A = D;
+        D = C;
+        C = B;
+        B = B + rotate_bits_left(F, md5_shift_constants[((j / 16) * 4) + (j % 4)]);
+      }
+
+      hash_values[0] += A;
+      hash_values[1] += B;
+      hash_values[2] += C;
+      hash_values[3] += D;
+    }
+  };
+
+  char* result_location;
+  hash_circular_buffer<message_chunk_size, md5_hash_step> buffer;
+  uint64_t message_length = 0;
+  uint32_t hash_values[4] = {0x67452301, 0xefcdab89, 0x98badcfe, 0x10325476};
+};
+
+template <typename Hasher>
+struct HasherDispatcher {
+  Hasher* hasher;
+  column_device_view const& input_col;
+
+  __device__ inline HasherDispatcher(Hasher* hasher, column_device_view const& input_col)
+    : hasher{hasher}, input_col{input_col}
+  {
+  }
+
+  template <typename Element>
+  void __device__ inline operator()(size_type const row_index) const
+  {
+    if constexpr ((is_fixed_width<Element>() && !is_chrono<Element>()) ||
+                  std::is_same_v<Element, string_view>) {
+      hasher->process(input_col.element<Element>(row_index));
+    } else {
+      (void)row_index;
+      CUDF_UNREACHABLE("Unsupported type for hash function.");
+    }
+  }
+};
+
+template <typename Hasher>
+struct ListHasherDispatcher {
+  Hasher* hasher;
+  column_device_view const& input_col;
+
+  __device__ inline ListHasherDispatcher(Hasher* hasher, column_device_view const& input_col)
+    : hasher{hasher}, input_col{input_col}
+  {
+  }
+
+  template <typename Element>
+  void __device__ inline operator()(size_type const offset_begin, size_type const offset_end) const
+  {
+    if constexpr ((is_fixed_width<Element>() && !is_chrono<Element>()) ||
+                  std::is_same_v<Element, string_view>) {
+      for (size_type i = offset_begin; i < offset_end; i++) {
+        if (input_col.is_valid(i)) { hasher->process(input_col.element<Element>(i)); }
+      }
+    } else {
+      (void)offset_begin;
+      (void)offset_end;
+      CUDF_UNREACHABLE("Unsupported type for hash function.");
+    }
+  }
+};
+
+// MD5 supported leaf data type check
+inline bool md5_leaf_type_check(data_type dt)
+{
+  return (is_fixed_width(dt) && !is_chrono(dt)) || (dt.id() == type_id::STRING);
+}
+
+}  // namespace
+
+std::unique_ptr<column> md5(table_view const& input,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  if (input.num_columns() == 0 || input.num_rows() == 0) {
+    // Return the MD5 hash of a zero-length input.
+    string_scalar const string_128bit("d41d8cd98f00b204e9orig98ecf8427e");
+    return make_column_from_scalar(string_128bit, input.num_rows(), stream, mr);
+  }
+
+  // Accepts string and fixed width columns, or single layer list columns holding those types
+  CUDF_EXPECTS(std::all_of(input.begin(),
+                           input.end(),
+                           [](auto const& col) {
+                             if (col.type().id() == type_id::LIST) {
+                               return md5_leaf_type_check(lists_column_view(col).child().type());
+                             }
+                             return md5_leaf_type_check(col.type());
+                           }),
+               "Unsupported column type for hash function.");
+
+  // Digest size in bytes
+  auto constexpr digest_size = 32;
+  // Result column allocation and creation
+  auto begin = thrust::make_constant_iterator(digest_size);
+  auto [offsets_column, bytes] =
+    cudf::detail::make_offsets_child_column(begin, begin + input.num_rows(), stream, mr);
+
+  auto chars_column = strings::detail::create_chars_child_column(bytes, stream, mr);
+  auto chars_view   = chars_column->mutable_view();
+  auto d_chars      = chars_view.data<char>();
+
+  auto const device_input = table_device_view::create(input, stream);
+
+  // Hash each row, hashing each element sequentially left to right
+  thrust::for_each(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(input.num_rows()),
+    [d_chars, device_input = *device_input] __device__(auto row_index) {
+      MD5Hasher hasher(d_chars + (row_index * digest_size));
+      for (auto const& col : device_input) {
+        if (col.is_valid(row_index)) {
+          if (col.type().id() == type_id::LIST) {
+            auto const data_col = col.child(lists_column_view::child_column_index);
+            auto const offsets  = col.child(lists_column_view::offsets_column_index);
+            if (data_col.type().id() == type_id::LIST) {
+              CUDF_UNREACHABLE("Nested list unsupported");
+            }
+            auto const offset_begin = offsets.element<size_type>(row_index);
+            auto const offset_end   = offsets.element<size_type>(row_index + 1);
+            cudf::type_dispatcher<dispatch_storage_type>(
+              data_col.type(), ListHasherDispatcher(&hasher, data_col), offset_begin, offset_end);
+          } else {
+            cudf::type_dispatcher<dispatch_storage_type>(
+              col.type(), HasherDispatcher(&hasher, col), row_index);
+          }
+        }
+      }
+    });
+
+  return make_strings_column(
+    input.num_rows(), std::move(offsets_column), std::move(chars_column), 0, {});
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> md5(table_view const& input,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::md5(input, stream, mr);
+}
+
+}  // namespace hashing
+}  // namespace cudf
diff --git a/cpp/src/hash/murmurhash3_x64_128.cu b/cpp/src/hash/murmurhash3_x64_128.cu
new file mode 100644
index 0000000..1fc4696
--- /dev/null
+++ b/cpp/src/hash/murmurhash3_x64_128.cu
@@ -0,0 +1,150 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/algorithm.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/hashing/detail/murmurhash3_x64_128.cuh>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+namespace {
+
+using hash_value_type = thrust::pair<uint64_t, uint64_t>;
+
+/**
+ * @brief Computes the hash value of a row in the given table.
+ *
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ */
+template <typename Nullate>
+class murmur_device_row_hasher {
+ public:
+  murmur_device_row_hasher(Nullate nulls,
+                           table_device_view const& t,
+                           uint64_t seed,
+                           uint64_t* d_output1,
+                           uint64_t* d_output2)
+    : _check_nulls(nulls), _input(t), _seed(seed), _output1(d_output1), _output2(d_output2)
+  {
+  }
+
+  /**
+   * @brief Return the hash value of a row in the given table.
+   *
+   * @param row_index The row index to compute the hash value of
+   * @return The hash value of the row
+   */
+  __device__ void operator()(size_type row_index) const noexcept
+  {
+    auto h = cudf::detail::accumulate(
+      _input.begin(),
+      _input.end(),
+      hash_value_type{_seed, 0},
+      [row_index, nulls = this->_check_nulls] __device__(auto hash, auto column) {
+        return cudf::type_dispatcher(
+          column.type(), element_hasher_adapter{}, column, row_index, nulls, hash);
+      });
+    _output1[row_index] = h.first;
+    _output2[row_index] = h.second;
+  }
+
+  /**
+   * @brief Computes the hash value of an element in the given column.
+   */
+  class element_hasher_adapter {
+   public:
+    template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+    __device__ hash_value_type operator()(column_device_view const& col,
+                                          size_type row_index,
+                                          Nullate const _check_nulls,
+                                          hash_value_type const _seed) const noexcept
+    {
+      if (_check_nulls && col.is_null(row_index)) {
+        return {std::numeric_limits<uint64_t>::max(), std::numeric_limits<uint64_t>::max()};
+      }
+      auto const hasher = MurmurHash3_x64_128<T>{_seed.first};
+      return hasher(col.element<T>(row_index));
+    }
+
+    template <typename T, CUDF_ENABLE_IF(not column_device_view::has_element_accessor<T>())>
+    __device__ hash_value_type operator()(column_device_view const&,
+                                          size_type,
+                                          Nullate const,
+                                          hash_value_type const) const noexcept
+    {
+      CUDF_UNREACHABLE("Unsupported type for MurmurHash3_x64_128");
+    }
+  };
+
+  Nullate const _check_nulls;
+  table_device_view const _input;
+  uint64_t const _seed;
+  uint64_t* _output1;
+  uint64_t* _output2;
+};
+
+}  // namespace
+
+std::unique_ptr<table> murmurhash3_x64_128(table_view const& input,
+                                           uint64_t seed,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto output1 = make_numeric_column(
+    data_type(type_id::UINT64), input.num_rows(), mask_state::UNALLOCATED, stream, mr);
+  auto output2 = make_numeric_column(
+    data_type(type_id::UINT64), input.num_rows(), mask_state::UNALLOCATED, stream, mr);
+
+  if (!input.is_empty()) {
+    bool const nullable   = has_nulls(input);
+    auto const input_view = table_device_view::create(input, stream);
+    auto d_output1        = output1->mutable_view().data<uint64_t>();
+    auto d_output2        = output2->mutable_view().data<uint64_t>();
+
+    // Compute the hash value for each row
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::counting_iterator<size_type>(0),
+                       input.num_rows(),
+                       murmur_device_row_hasher(nullable, *input_view, seed, d_output1, d_output2));
+  }
+
+  std::vector<std::unique_ptr<column>> out_columns(2);
+  out_columns.front() = std::move(output1);
+  out_columns.back()  = std::move(output2);
+  return std::make_unique<table>(std::move(out_columns));
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> murmurhash3_x64_128(table_view const& input,
+                                           uint64_t seed,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::murmurhash3_x64_128(input, seed, stream, mr);
+}
+
+}  // namespace hashing
+}  // namespace cudf
diff --git a/cpp/src/hash/murmurhash3_x86_32.cu b/cpp/src/hash/murmurhash3_x86_32.cu
new file mode 100644
index 0000000..a6ab301
--- /dev/null
+++ b/cpp/src/hash/murmurhash3_x86_32.cu
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/tabulate.h>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+
+std::unique_ptr<column> murmurhash3_x86_32(table_view const& input,
+                                           uint32_t seed,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto output = make_numeric_column(data_type(type_to_id<hash_value_type>()),
+                                    input.num_rows(),
+                                    mask_state::UNALLOCATED,
+                                    stream,
+                                    mr);
+
+  // Return early if there's nothing to hash
+  if (input.num_columns() == 0 || input.num_rows() == 0) { return output; }
+
+  bool const nullable   = has_nulls(input);
+  auto const row_hasher = cudf::experimental::row::hash::row_hasher(input, stream);
+  auto output_view      = output->mutable_view();
+
+  // Compute the hash value for each row
+  thrust::tabulate(rmm::exec_policy(stream),
+                   output_view.begin<hash_value_type>(),
+                   output_view.end<hash_value_type>(),
+                   row_hasher.device_hasher<MurmurHash3_x86_32>(nullable, seed));
+
+  return output;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> murmurhash3_x86_32(table_view const& input,
+                                           uint32_t seed,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::murmurhash3_x86_32(input, seed, stream, mr);
+}
+
+}  // namespace hashing
+}  // namespace cudf
diff --git a/cpp/src/hash/spark_murmurhash3_x86_32.cu b/cpp/src/hash/spark_murmurhash3_x86_32.cu
new file mode 100644
index 0000000..c7992b4
--- /dev/null
+++ b/cpp/src/hash/spark_murmurhash3_x86_32.cu
@@ -0,0 +1,442 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/hash_functions.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/tabulate.h>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+
+namespace {
+
+using spark_hash_value_type = int32_t;
+
+template <typename Key, CUDF_ENABLE_IF(not cudf::is_nested<Key>())>
+struct Spark_MurmurHash3_x86_32 {
+  using result_type = spark_hash_value_type;
+
+  constexpr Spark_MurmurHash3_x86_32() = default;
+  constexpr Spark_MurmurHash3_x86_32(uint32_t seed) : m_seed(seed) {}
+
+  [[nodiscard]] __device__ inline uint32_t fmix32(uint32_t h) const
+  {
+    h ^= h >> 16;
+    h *= 0x85ebca6b;
+    h ^= h >> 13;
+    h *= 0xc2b2ae35;
+    h ^= h >> 16;
+    return h;
+  }
+
+  [[nodiscard]] __device__ inline uint32_t getblock32(std::byte const* data,
+                                                      cudf::size_type offset) const
+  {
+    // Read a 4-byte value from the data pointer as individual bytes for safe
+    // unaligned access (very likely for string types).
+    auto block = reinterpret_cast<uint8_t const*>(data + offset);
+    return block[0] | (block[1] << 8) | (block[2] << 16) | (block[3] << 24);
+  }
+
+  [[nodiscard]] result_type __device__ inline operator()(Key const& key) const
+  {
+    return compute(key);
+  }
+
+  template <typename T>
+  result_type __device__ inline compute(T const& key) const
+  {
+    return compute_bytes(reinterpret_cast<std::byte const*>(&key), sizeof(T));
+  }
+
+  result_type __device__ inline compute_remaining_bytes(std::byte const* data,
+                                                        cudf::size_type len,
+                                                        cudf::size_type tail_offset,
+                                                        result_type h) const
+  {
+    // Process remaining bytes that do not fill a four-byte chunk using Spark's approach
+    // (does not conform to normal MurmurHash3).
+    for (auto i = tail_offset; i < len; i++) {
+      // We require a two-step cast to get the k1 value from the byte. First,
+      // we must cast to a signed int8_t. Then, the sign bit is preserved when
+      // casting to uint32_t under 2's complement. Java preserves the sign when
+      // casting byte-to-int, but C++ does not.
+      uint32_t k1 = static_cast<uint32_t>(std::to_integer<int8_t>(data[i]));
+      k1 *= c1;
+      k1 = rotate_bits_left(k1, rot_c1);
+      k1 *= c2;
+      h ^= k1;
+      h = rotate_bits_left(static_cast<uint32_t>(h), rot_c2);
+      h = h * 5 + c3;
+    }
+    return h;
+  }
+
+  result_type __device__ compute_bytes(std::byte const* data, cudf::size_type const len) const
+  {
+    constexpr cudf::size_type BLOCK_SIZE = 4;
+    cudf::size_type const nblocks        = len / BLOCK_SIZE;
+    cudf::size_type const tail_offset    = nblocks * BLOCK_SIZE;
+    result_type h                        = m_seed;
+
+    // Process all four-byte chunks.
+    for (cudf::size_type i = 0; i < nblocks; i++) {
+      uint32_t k1 = getblock32(data, i * BLOCK_SIZE);
+      k1 *= c1;
+      k1 = rotate_bits_left(k1, rot_c1);
+      k1 *= c2;
+      h ^= k1;
+      h = rotate_bits_left(static_cast<uint32_t>(h), rot_c2);
+      h = h * 5 + c3;
+    }
+
+    h = compute_remaining_bytes(data, len, tail_offset, h);
+
+    // Finalize hash.
+    h ^= len;
+    h = fmix32(h);
+    return h;
+  }
+
+ private:
+  uint32_t m_seed{cudf::DEFAULT_HASH_SEED};
+  static constexpr uint32_t c1     = 0xcc9e2d51;
+  static constexpr uint32_t c2     = 0x1b873593;
+  static constexpr uint32_t c3     = 0xe6546b64;
+  static constexpr uint32_t rot_c1 = 15;
+  static constexpr uint32_t rot_c2 = 13;
+};
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<bool>::operator()(
+  bool const& key) const
+{
+  return compute<uint32_t>(key);
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<int8_t>::operator()(
+  int8_t const& key) const
+{
+  return compute<uint32_t>(key);
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<uint8_t>::operator()(
+  uint8_t const& key) const
+{
+  return compute<uint32_t>(key);
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<int16_t>::operator()(
+  int16_t const& key) const
+{
+  return compute<uint32_t>(key);
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<uint16_t>::operator()(
+  uint16_t const& key) const
+{
+  return compute<uint32_t>(key);
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<float>::operator()(
+  float const& key) const
+{
+  return compute<float>(normalize_nans(key));
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<double>::operator()(
+  double const& key) const
+{
+  return compute<double>(normalize_nans(key));
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<cudf::string_view>::operator()(
+  cudf::string_view const& key) const
+{
+  auto const data = reinterpret_cast<std::byte const*>(key.data());
+  auto const len  = key.size_bytes();
+  return compute_bytes(data, len);
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<numeric::decimal32>::operator()(
+  numeric::decimal32 const& key) const
+{
+  return compute<uint64_t>(key.value());
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<numeric::decimal64>::operator()(
+  numeric::decimal64 const& key) const
+{
+  return compute<uint64_t>(key.value());
+}
+
+template <>
+spark_hash_value_type __device__ inline Spark_MurmurHash3_x86_32<numeric::decimal128>::operator()(
+  numeric::decimal128 const& key) const
+{
+  // Generates the Spark MurmurHash3 hash value, mimicking the conversion:
+  // java.math.BigDecimal.valueOf(unscaled_value, _scale).unscaledValue().toByteArray()
+  // https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/hash.scala#L381
+  __int128_t const val               = key.value();
+  constexpr cudf::size_type key_size = sizeof(__int128_t);
+  std::byte const* data              = reinterpret_cast<std::byte const*>(&val);
+
+  // Small negative values start with 0xff..., small positive values start with 0x00...
+  bool const is_negative     = val < 0;
+  std::byte const zero_value = is_negative ? std::byte{0xff} : std::byte{0x00};
+
+  // If the value can be represented with a shorter than 16-byte integer, the
+  // leading bytes of the little-endian value are truncated and are not hashed.
+  auto const reverse_begin = thrust::reverse_iterator(data + key_size);
+  auto const reverse_end   = thrust::reverse_iterator(data);
+  auto const first_nonzero_byte =
+    thrust::find_if_not(thrust::seq, reverse_begin, reverse_end, [zero_value](std::byte const& v) {
+      return v == zero_value;
+    }).base();
+  // Max handles special case of 0 and -1 which would shorten to 0 length otherwise
+  cudf::size_type length =
+    std::max(1, static_cast<cudf::size_type>(thrust::distance(data, first_nonzero_byte)));
+
+  // Preserve the 2's complement sign bit by adding a byte back on if necessary.
+  // e.g. 0x0000ff would shorten to 0x00ff. The 0x00 byte is retained to
+  // preserve the sign bit, rather than leaving an "f" at the front which would
+  // change the sign bit. However, 0x00007f would shorten to 0x7f. No extra byte
+  // is needed because the leftmost bit matches the sign bit. Similarly for
+  // negative values: 0xffff00 --> 0xff00 and 0xffff80 --> 0x80.
+  if ((length < key_size) && (is_negative ^ bool(data[length - 1] & std::byte{0x80}))) { ++length; }
+
+  // Convert to big endian by reversing the range of nonzero bytes. Only those bytes are hashed.
+  __int128_t big_endian_value = 0;
+  auto big_endian_data        = reinterpret_cast<std::byte*>(&big_endian_value);
+  thrust::reverse_copy(thrust::seq, data, data + length, big_endian_data);
+  return compute_bytes(big_endian_data, length);
+}
+
+/**
+ * @brief Computes the hash value of a row in the given table.
+ *
+ * This functor uses Spark conventions for Murmur hashing, which differs from
+ * the Murmur implementation used in the rest of libcudf. These differences
+ * include:
+ * - Serially using the output hash as an input seed for the next item
+ * - Ignorance of null values
+ *
+ * The serial use of hashes as seeds means that data of different nested types
+ * can exhibit hash collisions. For example, a row of an integer column
+ * containing a 1 will have the same hash as a lists column of integers
+ * containing a list of [1] and a struct column of a single integer column
+ * containing a struct of {1}.
+ *
+ * As a consequence of ignoring null values, inputs like [1], [1, null], and
+ * [null, 1] have the same hash (an expected hash collision). This kind of
+ * collision can also occur across a table of nullable columns and with nulls
+ * in structs ({1, null} and {null, 1} have the same hash). The seed value (the
+ * previous element's hash value) is returned as the hash if an element is
+ * null.
+ *
+ * For additional differences such as special tail processing and decimal type
+ * handling, refer to the Spark_MurmurHash3_x86_32 functor.
+ *
+ * @tparam hash_function Hash functor to use for hashing elements. Must be Spark_MurmurHash3_x86_32.
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ */
+template <template <typename> class hash_function, typename Nullate>
+class spark_murmur_device_row_hasher {
+  friend class cudf::experimental::row::hash::row_hasher;  ///< Allow row_hasher to access private
+                                                           ///< members.
+
+ public:
+  /**
+   * @brief Return the hash value of a row in the given table.
+   *
+   * @param row_index The row index to compute the hash value of
+   * @return The hash value of the row
+   */
+  __device__ auto operator()(size_type row_index) const noexcept
+  {
+    return cudf::detail::accumulate(
+      _table.begin(),
+      _table.end(),
+      _seed,
+      [row_index, nulls = this->_check_nulls] __device__(auto hash, auto column) {
+        return cudf::type_dispatcher(
+          column.type(), element_hasher_adapter<hash_function>{nulls, hash}, column, row_index);
+      });
+  }
+
+ private:
+  /**
+   * @brief Computes the hash value of an element in the given column.
+   *
+   * When the column is non-nested, this is a simple wrapper around the element_hasher.
+   * When the column is nested, this uses a seed value to serially compute each
+   * nested element, with the output hash becoming the seed for the next value.
+   * This requires constructing a new hash functor for each nested element,
+   * using the new seed from the previous element's hash. The hash of a null
+   * element is the input seed (the previous element's hash).
+   */
+  template <template <typename> class hash_fn>
+  class element_hasher_adapter {
+   public:
+    __device__ element_hasher_adapter(Nullate check_nulls, uint32_t seed) noexcept
+      : _check_nulls(check_nulls), _seed(seed)
+    {
+    }
+
+    using hash_functor = cudf::experimental::row::hash::element_hasher<hash_fn, Nullate>;
+
+    template <typename T, CUDF_ENABLE_IF(not cudf::is_nested<T>())>
+    __device__ spark_hash_value_type operator()(column_device_view const& col,
+                                                size_type row_index) const noexcept
+    {
+      auto const hasher = hash_functor{_check_nulls, _seed, _seed};
+      return hasher.template operator()<T>(col, row_index);
+    }
+
+    template <typename T, CUDF_ENABLE_IF(cudf::is_nested<T>())>
+    __device__ spark_hash_value_type operator()(column_device_view const& col,
+                                                size_type row_index) const noexcept
+    {
+      column_device_view curr_col = col.slice(row_index, 1);
+      while (curr_col.type().id() == type_id::STRUCT || curr_col.type().id() == type_id::LIST) {
+        if (curr_col.type().id() == type_id::STRUCT) {
+          if (curr_col.num_child_columns() == 0) { return _seed; }
+          // Non-empty structs are assumed to be decomposed and contain only one child
+          curr_col = cudf::detail::structs_column_device_view(curr_col).get_sliced_child(0);
+        } else if (curr_col.type().id() == type_id::LIST) {
+          curr_col = cudf::detail::lists_column_device_view(curr_col).get_sliced_child();
+        }
+      }
+
+      return cudf::detail::accumulate(
+        thrust::counting_iterator(0),
+        thrust::counting_iterator(curr_col.size()),
+        _seed,
+        [curr_col, nulls = this->_check_nulls] __device__(auto hash, auto element_index) {
+          auto const hasher = hash_functor{nulls, hash, hash};
+          return cudf::type_dispatcher<cudf::experimental::dispatch_void_if_nested>(
+            curr_col.type(), hasher, curr_col, element_index);
+        });
+    }
+
+    Nullate const _check_nulls;  ///< Whether to check for nulls
+    uint32_t const _seed;        ///< The seed to use for hashing, also returned for null elements
+  };
+
+  CUDF_HOST_DEVICE spark_murmur_device_row_hasher(Nullate check_nulls,
+                                                  table_device_view t,
+                                                  uint32_t seed = DEFAULT_HASH_SEED) noexcept
+    : _check_nulls{check_nulls}, _table{t}, _seed(seed)
+  {
+    // Error out if passed an unsupported hash_function
+    static_assert(
+      std::is_base_of_v<Spark_MurmurHash3_x86_32<int>, hash_function<int>>,
+      "spark_murmur_device_row_hasher only supports the Spark_MurmurHash3_x86_32 hash function");
+  }
+
+  Nullate const _check_nulls;
+  table_device_view const _table;
+  uint32_t const _seed;
+};
+
+void check_hash_compatibility(table_view const& input)
+{
+  using column_checker_fn_t = std::function<void(column_view const&)>;
+
+  column_checker_fn_t check_column = [&](column_view const& c) {
+    if (c.type().id() == type_id::LIST) {
+      auto const& list_col = lists_column_view(c);
+      CUDF_EXPECTS(list_col.child().type().id() != type_id::STRUCT,
+                   "Cannot compute hash of a table with a LIST of STRUCT columns.");
+      check_column(list_col.child());
+    } else if (c.type().id() == type_id::STRUCT) {
+      for (auto child = c.child_begin(); child != c.child_end(); ++child) {
+        check_column(*child);
+      }
+    }
+  };
+
+  for (column_view const& c : input) {
+    check_column(c);
+  }
+}
+
+}  // namespace
+
+std::unique_ptr<column> spark_murmurhash3_x86_32(table_view const& input,
+                                                 uint32_t seed,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  auto output = make_numeric_column(data_type(type_to_id<spark_hash_value_type>()),
+                                    input.num_rows(),
+                                    mask_state::UNALLOCATED,
+                                    stream,
+                                    mr);
+
+  // Return early if there's nothing to hash
+  if (input.num_columns() == 0 || input.num_rows() == 0) { return output; }
+
+  // Lists of structs are not supported
+  check_hash_compatibility(input);
+
+  bool const nullable   = has_nested_nulls(input);
+  auto const row_hasher = cudf::experimental::row::hash::row_hasher(input, stream);
+  auto output_view      = output->mutable_view();
+
+  // Compute the hash value for each row
+  thrust::tabulate(
+    rmm::exec_policy(stream),
+    output_view.begin<spark_hash_value_type>(),
+    output_view.end<spark_hash_value_type>(),
+    row_hasher.device_hasher<Spark_MurmurHash3_x86_32, spark_murmur_device_row_hasher>(nullable,
+                                                                                       seed));
+
+  return output;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> spark_murmurhash3_x86_32(table_view const& input,
+                                                 uint32_t seed,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::spark_murmurhash3_x86_32(input, seed, stream, mr);
+}
+
+}  // namespace hashing
+}  // namespace cudf
diff --git a/cpp/src/hash/unordered_multiset.cuh b/cpp/src/hash/unordered_multiset.cuh
new file mode 100644
index 0000000..183042f
--- /dev/null
+++ b/cpp/src/hash/unordered_multiset.cuh
@@ -0,0 +1,159 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/default_hash.cuh>
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace detail {
+/*
+ *  Device view of the unordered multiset
+ */
+template <typename Element,
+          typename Hasher   = cudf::hashing::detail::default_hash<Element>,
+          typename Equality = equal_to<Element>>
+class unordered_multiset_device_view {
+ public:
+  unordered_multiset_device_view(size_type hash_size,
+                                 size_type const* hash_begin,
+                                 Element const* hash_data)
+    : hash_size{hash_size}, hash_begin{hash_begin}, hash_data{hash_data}, hasher(), equals()
+  {
+  }
+
+  bool __device__ contains(Element e) const
+  {
+    size_type loc = hasher(e) % (2 * hash_size);
+
+    for (size_type i = hash_begin[loc]; i < hash_begin[loc + 1]; ++i) {
+      if (equals(hash_data[i], e)) return true;
+    }
+
+    return false;
+  }
+
+ private:
+  Hasher hasher;
+  Equality equals;
+  size_type hash_size;
+  size_type const* hash_begin;
+  Element const* hash_data;
+};
+
+/*
+ * Fixed size set on a device.
+ */
+template <typename Element,
+          typename Hasher   = cudf::hashing::detail::default_hash<Element>,
+          typename Equality = equal_to<Element>>
+class unordered_multiset {
+ public:
+  /**
+   * @brief Factory to construct a new unordered_multiset
+   */
+  static unordered_multiset<Element> create(column_view const& col, rmm::cuda_stream_view stream)
+  {
+    auto d_column = column_device_view::create(col, stream);
+    auto d_col    = *d_column;
+
+    auto hash_bins_start = cudf::detail::make_zeroed_device_uvector_async<size_type>(
+      2 * d_col.size() + 1, stream, rmm::mr::get_current_device_resource());
+    auto hash_bins_end = cudf::detail::make_zeroed_device_uvector_async<size_type>(
+      2 * d_col.size() + 1, stream, rmm::mr::get_current_device_resource());
+    auto hash_data = rmm::device_uvector<Element>(d_col.size(), stream);
+
+    Hasher hasher;
+    size_type* d_hash_bins_start = hash_bins_start.data();
+    size_type* d_hash_bins_end   = hash_bins_end.data();
+    Element* d_hash_data         = hash_data.data();
+
+    thrust::for_each(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator<size_type>(0),
+      thrust::make_counting_iterator<size_type>(col.size()),
+      [d_hash_bins_start, d_col, hasher] __device__(size_t idx) {
+        if (!d_col.is_null(idx)) {
+          Element e     = d_col.element<Element>(idx);
+          size_type tmp = hasher(e) % (2 * d_col.size());
+          cuda::atomic_ref<size_type, cuda::thread_scope_device> ref{*(d_hash_bins_start + tmp)};
+          ref.fetch_add(1, cuda::std::memory_order_relaxed);
+        }
+      });
+
+    thrust::exclusive_scan(rmm::exec_policy(stream),
+                           hash_bins_start.begin(),
+                           hash_bins_start.end(),
+                           hash_bins_end.begin());
+
+    thrust::copy(rmm::exec_policy(stream),
+                 hash_bins_end.begin(),
+                 hash_bins_end.end(),
+                 hash_bins_start.begin());
+
+    thrust::for_each(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator<size_type>(0),
+      thrust::make_counting_iterator<size_type>(col.size()),
+      [d_hash_bins_end, d_hash_data, d_col, hasher] __device__(size_t idx) {
+        if (!d_col.is_null(idx)) {
+          Element e     = d_col.element<Element>(idx);
+          size_type tmp = hasher(e) % (2 * d_col.size());
+          cuda::atomic_ref<size_type, cuda::thread_scope_device> ref{*(d_hash_bins_end + tmp)};
+          size_type offset    = ref.fetch_add(1, cuda::std::memory_order_relaxed);
+          d_hash_data[offset] = e;
+        }
+      });
+
+    return unordered_multiset(d_col.size(), std::move(hash_bins_start), std::move(hash_data));
+  }
+
+  unordered_multiset_device_view<Element, Hasher, Equality> to_device() const
+  {
+    return unordered_multiset_device_view<Element, Hasher, Equality>(
+      size, hash_bins.data(), hash_data.data());
+  }
+
+ private:
+  unordered_multiset(size_type size,
+                     rmm::device_uvector<size_type>&& hash_bins,
+                     rmm::device_uvector<Element>&& hash_data)
+    : size{size}, hash_bins{std::move(hash_bins)}, hash_data{std::move(hash_data)}
+  {
+  }
+
+  size_type size;
+  rmm::device_uvector<size_type> hash_bins;
+  rmm::device_uvector<Element> hash_data;
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/hash/xxhash_64.cu b/cpp/src/hash/xxhash_64.cu
new file mode 100644
index 0000000..e17bc13
--- /dev/null
+++ b/cpp/src/hash/xxhash_64.cu
@@ -0,0 +1,337 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/algorithm.cuh>
+#include <cudf/hashing/detail/hash_functions.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/tabulate.h>
+
+namespace cudf {
+namespace hashing {
+namespace detail {
+
+namespace {
+
+using hash_value_type = uint64_t;
+
+template <typename Key>
+struct XXHash_64 {
+  using result_type = hash_value_type;
+
+  constexpr XXHash_64() = default;
+  constexpr XXHash_64(hash_value_type seed) : m_seed(seed) {}
+
+  __device__ inline uint32_t getblock32(std::byte const* data, std::size_t offset) const
+  {
+    // Read a 4-byte value from the data pointer as individual bytes for safe
+    // unaligned access (very likely for string types).
+    auto block = reinterpret_cast<uint8_t const*>(data + offset);
+    return block[0] | (block[1] << 8) | (block[2] << 16) | (block[3] << 24);
+  }
+
+  __device__ inline uint64_t getblock64(std::byte const* data, std::size_t offset) const
+  {
+    uint64_t result = getblock32(data, offset + 4);
+    result          = result << 32;
+    return result | getblock32(data, offset);
+  }
+
+  result_type __device__ inline operator()(Key const& key) const { return compute(key); }
+
+  template <typename T>
+  result_type __device__ inline compute(T const& key) const
+  {
+    auto data = device_span<std::byte const>(reinterpret_cast<std::byte const*>(&key), sizeof(T));
+    return compute_bytes(data);
+  }
+
+  result_type __device__ inline compute_remaining_bytes(device_span<std::byte const>& in,
+                                                        std::size_t offset,
+                                                        result_type h64) const
+  {
+    // remaining data can be processed in 8-byte chunks
+    if ((in.size() % 32) >= 8) {
+      for (; offset <= in.size() - 8; offset += 8) {
+        uint64_t k1 = getblock64(in.data(), offset) * prime2;
+
+        k1 = rotate_bits_left(k1, 31) * prime1;
+        h64 ^= k1;
+        h64 = rotate_bits_left(h64, 27) * prime1 + prime4;
+      }
+    }
+
+    // remaining data can be processed in 4-byte chunks
+    if ((in.size() % 8) >= 4) {
+      for (; offset <= in.size() - 4; offset += 4) {
+        h64 ^= (getblock32(in.data(), offset) & 0xfffffffful) * prime1;
+        h64 = rotate_bits_left(h64, 23) * prime2 + prime3;
+      }
+    }
+
+    // and the rest
+    if (in.size() % 4) {
+      while (offset < in.size()) {
+        h64 ^= (std::to_integer<uint8_t>(in[offset]) & 0xff) * prime5;
+        h64 = rotate_bits_left(h64, 11) * prime1;
+        ++offset;
+      }
+    }
+    return h64;
+  }
+
+  result_type __device__ compute_bytes(device_span<std::byte const>& in) const
+  {
+    uint64_t offset = 0;
+    uint64_t h64;
+    // data can be processed in 32-byte chunks
+    if (in.size() >= 32) {
+      auto limit  = in.size() - 32;
+      uint64_t v1 = m_seed + prime1 + prime2;
+      uint64_t v2 = m_seed + prime2;
+      uint64_t v3 = m_seed;
+      uint64_t v4 = m_seed - prime1;
+
+      do {
+        // pipeline 4*8byte computations
+        v1 += getblock64(in.data(), offset) * prime2;
+        v1 = rotate_bits_left(v1, 31);
+        v1 *= prime1;
+        offset += 8;
+        v2 += getblock64(in.data(), offset) * prime2;
+        v2 = rotate_bits_left(v2, 31);
+        v2 *= prime1;
+        offset += 8;
+        v3 += getblock64(in.data(), offset) * prime2;
+        v3 = rotate_bits_left(v3, 31);
+        v3 *= prime1;
+        offset += 8;
+        v4 += getblock64(in.data(), offset) * prime2;
+        v4 = rotate_bits_left(v4, 31);
+        v4 *= prime1;
+        offset += 8;
+      } while (offset <= limit);
+
+      h64 = rotate_bits_left(v1, 1) + rotate_bits_left(v2, 7) + rotate_bits_left(v3, 12) +
+            rotate_bits_left(v4, 18);
+
+      v1 *= prime2;
+      v1 = rotate_bits_left(v1, 31);
+      v1 *= prime1;
+      h64 ^= v1;
+      h64 = h64 * prime1 + prime4;
+
+      v2 *= prime2;
+      v2 = rotate_bits_left(v2, 31);
+      v2 *= prime1;
+      h64 ^= v2;
+      h64 = h64 * prime1 + prime4;
+
+      v3 *= prime2;
+      v3 = rotate_bits_left(v3, 31);
+      v3 *= prime1;
+      h64 ^= v3;
+      h64 = h64 * prime1 + prime4;
+
+      v4 *= prime2;
+      v4 = rotate_bits_left(v4, 31);
+      v4 *= prime1;
+      h64 ^= v4;
+      h64 = h64 * prime1 + prime4;
+    } else {
+      h64 = m_seed + prime5;
+    }
+
+    h64 += in.size();
+
+    h64 = compute_remaining_bytes(in, offset, h64);
+
+    return finalize(h64);
+  }
+
+  constexpr __host__ __device__ std::uint64_t finalize(std::uint64_t h) const noexcept
+  {
+    h ^= h >> 33;
+    h *= prime2;
+    h ^= h >> 29;
+    h *= prime3;
+    h ^= h >> 32;
+    return h;
+  }
+
+ private:
+  hash_value_type m_seed{};
+  static constexpr uint64_t prime1 = 0x9e3779b185ebca87ul;
+  static constexpr uint64_t prime2 = 0xc2b2ae3d27d4eb4ful;
+  static constexpr uint64_t prime3 = 0x165667b19e3779f9ul;
+  static constexpr uint64_t prime4 = 0x85ebca77c2b2ae63ul;
+  static constexpr uint64_t prime5 = 0x27d4eb2f165667c5ul;
+};
+
+template <>
+hash_value_type __device__ inline XXHash_64<bool>::operator()(bool const& key) const
+{
+  return compute(static_cast<uint8_t>(key));
+}
+
+template <>
+hash_value_type __device__ inline XXHash_64<float>::operator()(float const& key) const
+{
+  return compute(normalize_nans(key));
+}
+
+template <>
+hash_value_type __device__ inline XXHash_64<double>::operator()(double const& key) const
+{
+  return compute(normalize_nans(key));
+}
+
+template <>
+hash_value_type __device__ inline XXHash_64<cudf::string_view>::operator()(
+  cudf::string_view const& key) const
+{
+  auto const len = key.size_bytes();
+  auto data = device_span<std::byte const>(reinterpret_cast<std::byte const*>(key.data()), len);
+  return compute_bytes(data);
+}
+
+template <>
+hash_value_type __device__ inline XXHash_64<numeric::decimal32>::operator()(
+  numeric::decimal32 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+hash_value_type __device__ inline XXHash_64<numeric::decimal64>::operator()(
+  numeric::decimal64 const& key) const
+{
+  return compute(key.value());
+}
+
+template <>
+hash_value_type __device__ inline XXHash_64<numeric::decimal128>::operator()(
+  numeric::decimal128 const& key) const
+{
+  return compute(key.value());
+}
+
+/**
+ * @brief Computes the hash value of a row in the given table.
+ *
+ * @tparam Nullate A cudf::nullate type describing whether to check for nulls.
+ */
+template <typename Nullate>
+class device_row_hasher {
+ public:
+  device_row_hasher(Nullate nulls, table_device_view const& t, hash_value_type seed)
+    : _check_nulls(nulls), _table(t), _seed(seed)
+  {
+  }
+
+  __device__ auto operator()(size_type row_index) const noexcept
+  {
+    return cudf::detail::accumulate(
+      _table.begin(),
+      _table.end(),
+      _seed,
+      [row_index, nulls = _check_nulls] __device__(auto hash, auto column) {
+        return cudf::type_dispatcher(
+          column.type(), element_hasher_adapter{}, column, row_index, nulls, hash);
+      });
+  }
+
+  /**
+   * @brief Computes the hash value of an element in the given column.
+   */
+  class element_hasher_adapter {
+   public:
+    template <typename T, CUDF_ENABLE_IF(column_device_view::has_element_accessor<T>())>
+    __device__ hash_value_type operator()(column_device_view const& col,
+                                          size_type const row_index,
+                                          Nullate const _check_nulls,
+                                          hash_value_type const _seed) const noexcept
+    {
+      if (_check_nulls && col.is_null(row_index)) {
+        return std::numeric_limits<hash_value_type>::max();
+      }
+      auto const hasher = XXHash_64<T>{_seed};
+      return hasher(col.element<T>(row_index));
+    }
+
+    template <typename T, CUDF_ENABLE_IF(not column_device_view::has_element_accessor<T>())>
+    __device__ hash_value_type operator()(column_device_view const&,
+                                          size_type const,
+                                          Nullate const,
+                                          hash_value_type const) const noexcept
+    {
+      CUDF_UNREACHABLE("Unsupported type for XXHash_64");
+    }
+  };
+
+  Nullate const _check_nulls;
+  table_device_view const _table;
+  hash_value_type const _seed;
+};
+
+}  // namespace
+
+std::unique_ptr<column> xxhash_64(table_view const& input,
+                                  uint64_t seed,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto output = make_numeric_column(data_type(type_to_id<hash_value_type>()),
+                                    input.num_rows(),
+                                    mask_state::UNALLOCATED,
+                                    stream,
+                                    mr);
+
+  // Return early if there's nothing to hash
+  if (input.num_columns() == 0 || input.num_rows() == 0) { return output; }
+
+  bool const nullable   = has_nulls(input);
+  auto const input_view = table_device_view::create(input, stream);
+  auto output_view      = output->mutable_view();
+
+  // Compute the hash value for each row
+  thrust::tabulate(rmm::exec_policy(stream),
+                   output_view.begin<hash_value_type>(),
+                   output_view.end<hash_value_type>(),
+                   device_row_hasher(nullable, *input_view, seed));
+
+  return output;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> xxhash_64(table_view const& input,
+                                  uint64_t seed,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::xxhash_64(input, seed, stream, mr);
+}
+
+}  // namespace hashing
+}  // namespace cudf
diff --git a/cpp/src/interop/detail/arrow_allocator.cpp b/cpp/src/interop/detail/arrow_allocator.cpp
new file mode 100644
index 0000000..41fb68a
--- /dev/null
+++ b/cpp/src/interop/detail/arrow_allocator.cpp
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/interop.hpp>
+
+#include <memory>
+#include <sys/mman.h>
+#include <unistd.h>
+
+namespace cudf {
+namespace detail {
+
+/*
+  Enable Transparent Huge Pages (THP) for large (>4MB) allocations.
+  `buf` is returned untouched.
+  Enabling THP can improve performance of device-host memory transfers
+  significantly, see <https://github.com/rapidsai/cudf/pull/13914>.
+*/
+template <typename T>
+T enable_hugepage(T&& buf)
+{
+  if (buf->size() < (1u << 22u)) {  // Smaller than 4 MB
+    return std::move(buf);
+  }
+
+#ifdef MADV_HUGEPAGE
+  const auto pagesize = sysconf(_SC_PAGESIZE);
+  void* addr          = const_cast<uint8_t*>(buf->data());
+  if (addr == nullptr) { return std::move(buf); }
+  auto length{static_cast<std::size_t>(buf->size())};
+  if (std::align(pagesize, pagesize, addr, length)) {
+    // Intentionally not checking for errors that may be returned by older kernel versions;
+    // optimistically tries enabling huge pages.
+    madvise(addr, length, MADV_HUGEPAGE);
+  }
+#endif
+  return std::move(buf);
+}
+
+std::unique_ptr<arrow::Buffer> allocate_arrow_buffer(int64_t const size, arrow::MemoryPool* ar_mr)
+{
+  /*
+  nvcc 11.0 generates Internal Compiler Error during codegen when arrow::AllocateBuffer
+  and `ValueOrDie` are used inside a CUDA compilation unit.
+
+  To work around this issue we compile an allocation shim in C++ and use
+  that from our cuda sources
+  */
+  arrow::Result<std::unique_ptr<arrow::Buffer>> result = arrow::AllocateBuffer(size, ar_mr);
+  CUDF_EXPECTS(result.ok(), "Failed to allocate Arrow buffer");
+  return enable_hugepage(std::move(result).ValueOrDie());
+}
+
+std::shared_ptr<arrow::Buffer> allocate_arrow_bitmap(int64_t const size, arrow::MemoryPool* ar_mr)
+{
+  /*
+  nvcc 11.0 generates Internal Compiler Error during codegen when arrow::AllocateBuffer
+  and `ValueOrDie` are used inside a CUDA compilation unit.
+
+  To work around this issue we compile an allocation shim in C++ and use
+  that from our cuda sources
+  */
+  arrow::Result<std::shared_ptr<arrow::Buffer>> result = arrow::AllocateBitmap(size, ar_mr);
+  CUDF_EXPECTS(result.ok(), "Failed to allocate Arrow bitmap");
+  return enable_hugepage(std::move(result).ValueOrDie());
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/interop/detail/arrow_allocator.hpp b/cpp/src/interop/detail/arrow_allocator.hpp
new file mode 100644
index 0000000..75c1baa
--- /dev/null
+++ b/cpp/src/interop/detail/arrow_allocator.hpp
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/interop.hpp>
+
+namespace cudf {
+namespace detail {
+
+// unique_ptr because that is what AllocateBuffer returns
+std::unique_ptr<arrow::Buffer> allocate_arrow_buffer(int64_t const size, arrow::MemoryPool* ar_mr);
+
+// shared_ptr because that is what AllocateBitmap returns
+std::shared_ptr<arrow::Buffer> allocate_arrow_bitmap(int64_t const size, arrow::MemoryPool* ar_mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/interop/dlpack.cpp b/cpp/src/interop/dlpack.cpp
new file mode 100644
index 0000000..1759c99
--- /dev/null
+++ b/cpp/src/interop/dlpack.cpp
@@ -0,0 +1,313 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/interop.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <dlpack/dlpack.h>
+
+#include <algorithm>
+#include <cudf/utilities/traits.hpp>
+
+namespace cudf {
+namespace {
+struct get_column_data_impl {
+  template <typename T, CUDF_ENABLE_IF(not is_rep_layout_compatible<T>())>
+  void const* operator()(column_view const& col)
+  {
+    CUDF_FAIL("Unsupported type to convert to dlpack.");
+  }
+
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  void const* operator()(column_view const& col)
+  {
+    return col.data<T>();
+  }
+};
+
+template <>
+void const* get_column_data_impl::operator()<string_view>(column_view const& col)
+{
+  return nullptr;
+}
+
+void const* get_column_data(column_view const& col)
+{
+  return type_dispatcher(col.type(), get_column_data_impl{}, col);
+}
+
+data_type DLDataType_to_data_type(DLDataType type)
+{
+  CUDF_EXPECTS(type.lanes == 1, "Unsupported DLPack vector type");
+
+  if (type.code == kDLInt) {
+    switch (type.bits) {
+      case 8: return data_type(type_id::INT8);
+      case 16: return data_type(type_id::INT16);
+      case 32: return data_type(type_id::INT32);
+      case 64: return data_type(type_id::INT64);
+      default: CUDF_FAIL("Unsupported bitsize for kDLInt");
+    }
+  } else if (type.code == kDLUInt) {
+    switch (type.bits) {
+      case 8: return data_type(type_id::UINT8);
+      case 16: return data_type(type_id::UINT16);
+      case 32: return data_type(type_id::UINT32);
+      case 64: return data_type(type_id::UINT64);
+      default: CUDF_FAIL("Unsupported bitsize for kDLUInt");
+    }
+  } else if (type.code == kDLFloat) {
+    switch (type.bits) {
+      case 32: return data_type(type_id::FLOAT32);
+      case 64: return data_type(type_id::FLOAT64);
+      default: CUDF_FAIL("Unsupported bitsize for kDLFloat");
+    }
+  } else {
+    CUDF_FAIL("Invalid DLPack type code");
+  }
+}
+
+struct data_type_to_DLDataType_impl {
+  template <typename T, std::enable_if_t<is_numeric<T>()>* = nullptr>
+  DLDataType operator()()
+  {
+    uint8_t const bits{sizeof(T) * 8};
+    uint16_t const lanes{1};
+    if (std::is_floating_point_v<T>) {
+      return DLDataType{kDLFloat, bits, lanes};
+    } else if (std::is_signed_v<T>) {
+      return DLDataType{kDLInt, bits, lanes};
+    } else {
+      return DLDataType{kDLUInt, bits, lanes};
+    }
+  }
+
+  template <typename T, std::enable_if_t<not is_numeric<T>()>* = nullptr>
+  DLDataType operator()()
+  {
+    CUDF_FAIL("Conversion of non-numeric types to DLPack is unsupported");
+  }
+};
+
+DLDataType data_type_to_DLDataType(data_type type)
+{
+  return type_dispatcher(type, data_type_to_DLDataType_impl{});
+}
+
+// Context object to own memory allocated for DLManagedTensor
+struct dltensor_context {
+  int64_t shape[2];
+  int64_t strides[2];
+  rmm::device_buffer buffer;
+
+  static void deleter(DLManagedTensor* arg)
+  {
+    auto context = static_cast<dltensor_context*>(arg->manager_ctx);
+    delete context;
+    delete arg;
+  }
+};
+
+}  // namespace
+
+namespace detail {
+std::unique_ptr<table> from_dlpack(DLManagedTensor const* managed_tensor,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(nullptr != managed_tensor, "managed_tensor is null");
+  auto const& tensor = managed_tensor->dl_tensor;
+
+  // We can copy from host or device pointers
+  CUDF_EXPECTS(tensor.device.device_type == kDLCPU || tensor.device.device_type == kDLCUDA ||
+                 tensor.device.device_type == kDLCUDAHost,
+               "DLTensor device type must be CPU, CUDA or CUDAHost");
+
+  // Make sure the current device ID matches the Tensor's device ID
+  if (tensor.device.device_type != kDLCPU) {
+    int device_id = 0;
+    CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+    CUDF_EXPECTS(tensor.device.device_id == device_id, "DLTensor device ID must be current device");
+  }
+
+  // We only support 1D and 2D tensors with some restrictions on layout
+  if (tensor.ndim == 1) {
+    // 1D tensors must have dense layout (strides == nullptr <=> dense layout), or have shape (0,)
+    CUDF_EXPECTS(nullptr == tensor.strides || tensor.strides[0] == 1 || tensor.shape[0] == 0,
+                 "from_dlpack of 1D DLTensor only for unit-stride data");
+  } else if (tensor.ndim == 2) {
+    CUDF_EXPECTS(
+      // Empty array is fine. If ncols == 0 then we get an empty dataframe
+      // irrespective of nrows, which is slightly different behaviour from
+      // cudf.DataFrame(np.empty((3, 0))) because there's no way to communicate
+      // the index information out with a table view if no columns exist.
+      (tensor.shape[0] == 0 || tensor.shape[1] == 0)
+        // (N, 1) is fine as long as the 1D array has dense layout
+        || (tensor.shape[1] == 1 && (nullptr == tensor.strides || tensor.strides[0] == 1))
+        // Column major is fine as long as the fastest dimension has dense layout
+        || (nullptr != tensor.strides && tensor.strides[0] == 1 &&
+            tensor.strides[1] >= tensor.shape[0]),
+      "from_dlpack of 2D DLTensor only for column-major unit-stride data");
+  } else {
+    CUDF_FAIL("DLTensor must be 1D or 2D");
+  }
+  CUDF_EXPECTS(tensor.shape[0] >= 0,
+               "DLTensor first dim should be of shape greater than or equal to 0.");
+  CUDF_EXPECTS(tensor.shape[0] <= std::numeric_limits<size_type>::max(),
+               "DLTensor first dim exceeds the column size limit",
+               std::overflow_error);
+  if (tensor.ndim > 1) {
+    CUDF_EXPECTS(tensor.shape[1] >= 0,
+                 "DLTensor second dim should be of shape greater than or equal to 0.");
+    CUDF_EXPECTS(tensor.shape[1] <= std::numeric_limits<size_type>::max(),
+                 "DLTensor second dim exceeds the column size limit",
+                 std::overflow_error);
+  }
+  size_t const num_columns = (tensor.ndim == 2) ? static_cast<size_t>(tensor.shape[1]) : 1;
+
+  // Validate and convert data type to cudf
+  data_type const dtype = DLDataType_to_data_type(tensor.dtype);
+
+  size_t const byte_width = size_of(dtype);
+  auto const num_rows     = static_cast<size_t>(tensor.shape[0]);
+  size_t const bytes      = num_rows * byte_width;
+
+  // For 2D tensors, if the strides pointer is not null, then strides[1] is the
+  // number of elements (not bytes) between the start of each column
+  size_t const col_stride = (tensor.ndim == 2 && nullptr != tensor.strides)
+                              ? byte_width * tensor.strides[1]
+                              : byte_width * num_rows;
+
+  auto tensor_data = reinterpret_cast<uintptr_t>(tensor.data) + tensor.byte_offset;
+
+  // Allocate columns and copy data from tensor
+  std::vector<std::unique_ptr<column>> columns(num_columns);
+  for (auto& col : columns) {
+    col = make_numeric_column(dtype, num_rows, mask_state::UNALLOCATED, stream, mr);
+
+    CUDF_CUDA_TRY(cudaMemcpyAsync(col->mutable_view().head<void>(),
+                                  reinterpret_cast<void*>(tensor_data),
+                                  bytes,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+
+    tensor_data += col_stride;
+  }
+
+  return std::make_unique<table>(std::move(columns));
+}
+
+DLManagedTensor* to_dlpack(table_view const& input,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr)
+{
+  auto const num_rows = input.num_rows();
+  auto const num_cols = input.num_columns();
+  if (num_rows == 0 && num_cols == 0) { return nullptr; }
+
+  // Ensure that type is convertible to DLDataType
+  data_type const type    = input.column(0).type();
+  DLDataType const dltype = data_type_to_DLDataType(type);
+
+  // Ensure all columns are the same type
+  CUDF_EXPECTS(
+    std::all_of(input.begin(), input.end(), [type](auto const& col) { return col.type() == type; }),
+    "All columns required to have same data type");
+
+  // Ensure none of the columns have nulls
+  CUDF_EXPECTS(
+    std::none_of(input.begin(), input.end(), [](auto const& col) { return col.has_nulls(); }),
+    "Input required to have null count zero");
+
+  auto managed_tensor = std::make_unique<DLManagedTensor>();
+  auto context        = std::make_unique<dltensor_context>();
+
+  DLTensor& tensor = managed_tensor->dl_tensor;
+  tensor.dtype     = dltype;
+
+  tensor.ndim     = (num_cols > 1) ? 2 : 1;
+  tensor.shape    = context->shape;
+  tensor.shape[0] = num_rows;
+  if (tensor.ndim > 1) {
+    tensor.shape[1]   = num_cols;
+    tensor.strides    = context->strides;
+    tensor.strides[0] = num_rows > 1 ? 1 : 0;
+    tensor.strides[1] = num_rows;
+  }
+
+  CUDF_CUDA_TRY(cudaGetDevice(&tensor.device.device_id));
+  tensor.device.device_type = kDLCUDA;
+
+  // If there is only one column, then a 1D tensor can just copy the pointer
+  // to the data in the column, and the deleter should not delete the original
+  // data. However, this is inconsistent with the 2D cases where we must do a
+  // copy of each column's data into the dense tensor array. Also, if we don't
+  // copy, then the original column data could be changed, which would change
+  // the contents of the tensor, which might be surprising or cause issues.
+  // Therefore, for now we ALWAYS do a copy of the data. If this becomes
+  // a performance issue we can reevaluate in the future.
+
+  size_t const stride_bytes = num_rows * size_of(type);
+  size_t const total_bytes  = stride_bytes * num_cols;
+
+  context->buffer = rmm::device_buffer(total_bytes, stream, mr);
+  tensor.data     = context->buffer.data();
+
+  auto tensor_data = reinterpret_cast<uintptr_t>(tensor.data);
+  for (auto const& col : input) {
+    CUDF_CUDA_TRY(cudaMemcpyAsync(reinterpret_cast<void*>(tensor_data),
+                                  get_column_data(col),
+                                  stride_bytes,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    tensor_data += stride_bytes;
+  }
+
+  // Defer ownership of managed tensor to caller
+  managed_tensor->deleter     = dltensor_context::deleter;
+  managed_tensor->manager_ctx = context.release();
+
+  // synchronize the stream because after the return the data may be accessed from the host before
+  // the above `cudaMemcpyAsync` calls have completed their copies (especially if pinned host
+  // memory is used).
+  stream.synchronize();
+
+  return managed_tensor.release();
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> from_dlpack(DLManagedTensor const* managed_tensor,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_dlpack(managed_tensor, cudf::get_default_stream(), mr);
+}
+
+DLManagedTensor* to_dlpack(table_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_dlpack(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/interop/from_arrow.cu b/cpp/src/interop/from_arrow.cu
new file mode 100644
index 0000000..e39625c
--- /dev/null
+++ b/cpp/src/interop/from_arrow.cu
@@ -0,0 +1,561 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/interop.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/interop.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/gather.h>
+
+namespace cudf {
+
+namespace detail {
+data_type arrow_to_cudf_type(arrow::DataType const& arrow_type)
+{
+  switch (arrow_type.id()) {
+    case arrow::Type::NA: return data_type(type_id::EMPTY);
+    case arrow::Type::BOOL: return data_type(type_id::BOOL8);
+    case arrow::Type::INT8: return data_type(type_id::INT8);
+    case arrow::Type::INT16: return data_type(type_id::INT16);
+    case arrow::Type::INT32: return data_type(type_id::INT32);
+    case arrow::Type::INT64: return data_type(type_id::INT64);
+    case arrow::Type::UINT8: return data_type(type_id::UINT8);
+    case arrow::Type::UINT16: return data_type(type_id::UINT16);
+    case arrow::Type::UINT32: return data_type(type_id::UINT32);
+    case arrow::Type::UINT64: return data_type(type_id::UINT64);
+    case arrow::Type::FLOAT: return data_type(type_id::FLOAT32);
+    case arrow::Type::DOUBLE: return data_type(type_id::FLOAT64);
+    case arrow::Type::DATE32: return data_type(type_id::TIMESTAMP_DAYS);
+    case arrow::Type::TIMESTAMP: {
+      auto type = static_cast<arrow::TimestampType const*>(&arrow_type);
+      switch (type->unit()) {
+        case arrow::TimeUnit::type::SECOND: return data_type(type_id::TIMESTAMP_SECONDS);
+        case arrow::TimeUnit::type::MILLI: return data_type(type_id::TIMESTAMP_MILLISECONDS);
+        case arrow::TimeUnit::type::MICRO: return data_type(type_id::TIMESTAMP_MICROSECONDS);
+        case arrow::TimeUnit::type::NANO: return data_type(type_id::TIMESTAMP_NANOSECONDS);
+        default: CUDF_FAIL("Unsupported timestamp unit in arrow");
+      }
+    }
+    case arrow::Type::DURATION: {
+      auto type = static_cast<arrow::DurationType const*>(&arrow_type);
+      switch (type->unit()) {
+        case arrow::TimeUnit::type::SECOND: return data_type(type_id::DURATION_SECONDS);
+        case arrow::TimeUnit::type::MILLI: return data_type(type_id::DURATION_MILLISECONDS);
+        case arrow::TimeUnit::type::MICRO: return data_type(type_id::DURATION_MICROSECONDS);
+        case arrow::TimeUnit::type::NANO: return data_type(type_id::DURATION_NANOSECONDS);
+        default: CUDF_FAIL("Unsupported duration unit in arrow");
+      }
+    }
+    case arrow::Type::STRING: return data_type(type_id::STRING);
+    case arrow::Type::DICTIONARY: return data_type(type_id::DICTIONARY32);
+    case arrow::Type::LIST: return data_type(type_id::LIST);
+    case arrow::Type::DECIMAL: {
+      auto const type = static_cast<arrow::Decimal128Type const*>(&arrow_type);
+      return data_type{type_id::DECIMAL128, -type->scale()};
+    }
+    case arrow::Type::STRUCT: return data_type(type_id::STRUCT);
+    default: CUDF_FAIL("Unsupported type_id conversion to cudf");
+  }
+}
+
+namespace {
+/**
+ * @brief Functor to return column for a corresponding arrow array. column
+ * is formed from buffer underneath the arrow array along with any offset and
+ * change in length that array has.
+ */
+struct dispatch_to_cudf_column {
+  /**
+   * @brief Returns mask from an array without any offsets.
+   */
+  std::unique_ptr<rmm::device_buffer> get_mask_buffer(arrow::Array const& array,
+                                                      rmm::cuda_stream_view stream,
+                                                      rmm::mr::device_memory_resource* mr)
+  {
+    if (array.null_bitmap_data() == nullptr) {
+      return std::make_unique<rmm::device_buffer>(0, stream, mr);
+    }
+    auto const null_bitmap_size = array.null_bitmap()->size();
+    auto const allocation_size =
+      bitmask_allocation_size_bytes(static_cast<size_type>(null_bitmap_size * CHAR_BIT));
+    auto mask        = std::make_unique<rmm::device_buffer>(allocation_size, stream, mr);
+    auto mask_buffer = array.null_bitmap();
+    CUDF_CUDA_TRY(cudaMemcpyAsync(mask->data(),
+                                  reinterpret_cast<uint8_t const*>(mask_buffer->address()),
+                                  null_bitmap_size,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    // Zero-initialize trailing padding bytes
+    auto const num_trailing_bytes = allocation_size - null_bitmap_size;
+    if (num_trailing_bytes > 0) {
+      auto trailing_bytes = static_cast<uint8_t*>(mask->data()) + null_bitmap_size;
+      CUDF_CUDA_TRY(cudaMemsetAsync(trailing_bytes, 0, num_trailing_bytes, stream.value()));
+    }
+    return mask;
+  }
+
+  template <typename T, CUDF_ENABLE_IF(not is_rep_layout_compatible<T>())>
+  std::unique_ptr<column> operator()(
+    arrow::Array const&, data_type, bool, rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Unsupported type in from_arrow.");
+  }
+
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  std::unique_ptr<column> operator()(arrow::Array const& array,
+                                     data_type type,
+                                     bool skip_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto data_buffer         = array.data()->buffers[1];
+    size_type const num_rows = array.length();
+    auto const has_nulls     = skip_mask ? false : array.null_bitmap_data() != nullptr;
+    auto col = make_fixed_width_column(type, num_rows, mask_state::UNALLOCATED, stream, mr);
+    auto mutable_column_view = col->mutable_view();
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      mutable_column_view.data<T>(),
+      reinterpret_cast<uint8_t const*>(data_buffer->address()) + array.offset() * sizeof(T),
+      sizeof(T) * num_rows,
+      cudaMemcpyDefault,
+      stream.value()));
+    if (has_nulls) {
+      auto tmp_mask = get_mask_buffer(array, stream, mr);
+
+      // If array is sliced, we have to copy whole mask and then take copy.
+      auto out_mask = (num_rows == static_cast<size_type>(data_buffer->size() / sizeof(T)))
+                        ? std::move(*tmp_mask)
+                        : cudf::detail::copy_bitmask(static_cast<bitmask_type*>(tmp_mask->data()),
+                                                     array.offset(),
+                                                     array.offset() + num_rows,
+                                                     stream,
+                                                     mr);
+
+      col->set_null_mask(std::move(out_mask), array.null_count());
+    }
+
+    return col;
+  }
+};
+
+std::unique_ptr<column> get_empty_type_column(size_type size)
+{
+  // this abomination is required by cuDF Python, which needs to handle
+  // [PyArrow null arrays](https://arrow.apache.org/docs/python/generated/pyarrow.NullArray.html)
+  // of finite length
+  return std::make_unique<column>(
+    data_type(type_id::EMPTY), size, rmm::device_buffer{}, rmm::device_buffer{}, size);
+}
+
+/**
+ * @brief Returns cudf column formed from given arrow array
+ * This has been introduced to take care of compiler error "error: explicit specialization of
+ * function must precede its first use"
+ */
+std::unique_ptr<column> get_column(arrow::Array const& array,
+                                   data_type type,
+                                   bool skip_mask,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+template <>
+std::unique_ptr<column> dispatch_to_cudf_column::operator()<numeric::decimal128>(
+  arrow::Array const& array,
+  data_type type,
+  bool skip_mask,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  using DeviceType = __int128_t;
+
+  auto data_buffer    = array.data()->buffers[1];
+  auto const num_rows = static_cast<size_type>(array.length());
+  auto col = make_fixed_width_column(type, num_rows, mask_state::UNALLOCATED, stream, mr);
+  auto mutable_column_view = col->mutable_view();
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    mutable_column_view.data<DeviceType>(),
+    reinterpret_cast<uint8_t const*>(data_buffer->address()) + array.offset() * sizeof(DeviceType),
+    sizeof(DeviceType) * num_rows,
+    cudaMemcpyDefault,
+    stream.value()));
+
+  auto null_mask = [&] {
+    if (not skip_mask and array.null_bitmap_data()) {
+      auto temp_mask = get_mask_buffer(array, stream, mr);
+      // If array is sliced, we have to copy whole mask and then take copy.
+      return (num_rows == static_cast<size_type>(data_buffer->size() / sizeof(DeviceType)))
+               ? std::move(*temp_mask.release())
+               : cudf::detail::copy_bitmask(static_cast<bitmask_type*>(temp_mask->data()),
+                                            array.offset(),
+                                            array.offset() + num_rows,
+                                            stream,
+                                            mr);
+    }
+    return rmm::device_buffer{};
+  }();
+
+  col->set_null_mask(std::move(null_mask), array.null_count());
+  return col;
+}
+
+template <>
+std::unique_ptr<column> dispatch_to_cudf_column::operator()<bool>(
+  arrow::Array const& array,
+  data_type,
+  bool skip_mask,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto data_buffer = array.data()->buffers[1];
+  // mask-to-bools expects the mask to be bitmask_type aligned/padded
+  auto data = rmm::device_buffer(
+    cudf::bitmask_allocation_size_bytes(data_buffer->size() * CHAR_BIT), stream, mr);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(data.data(),
+                                reinterpret_cast<uint8_t const*>(data_buffer->address()),
+                                data_buffer->size(),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  auto out_col = mask_to_bools(static_cast<bitmask_type*>(data.data()),
+                               array.offset(),
+                               array.offset() + array.length(),
+                               stream,
+                               mr);
+
+  auto const has_nulls = skip_mask ? false : array.null_bitmap_data() != nullptr;
+  if (has_nulls) {
+    auto out_mask =
+      detail::copy_bitmask(static_cast<bitmask_type*>(get_mask_buffer(array, stream, mr)->data()),
+                           array.offset(),
+                           array.offset() + array.length(),
+                           stream,
+                           mr);
+
+    out_col->set_null_mask(std::move(out_mask), array.null_count());
+  }
+
+  return out_col;
+}
+
+template <>
+std::unique_ptr<column> dispatch_to_cudf_column::operator()<cudf::string_view>(
+  arrow::Array const& array,
+  data_type,
+  bool,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (array.length() == 0) { return make_empty_column(type_id::STRING); }
+  auto str_array    = static_cast<arrow::StringArray const*>(&array);
+  auto offset_array = std::make_unique<arrow::Int32Array>(
+    str_array->value_offsets()->size() / sizeof(int32_t), str_array->value_offsets(), nullptr);
+  auto char_array = std::make_unique<arrow::Int8Array>(
+    str_array->value_data()->size(), str_array->value_data(), nullptr);
+
+  auto offsets_column = dispatch_to_cudf_column{}.operator()<int32_t>(
+    *offset_array, data_type(type_id::INT32), true, stream, mr);
+  auto chars_column = dispatch_to_cudf_column{}.operator()<int8_t>(
+    *char_array, data_type(type_id::INT8), true, stream, mr);
+
+  auto const num_rows = offsets_column->size() - 1;
+  auto out_col        = make_strings_column(num_rows,
+                                     std::move(offsets_column),
+                                     std::move(chars_column),
+                                     array.null_count(),
+                                     std::move(*get_mask_buffer(array, stream, mr)));
+
+  return num_rows == array.length()
+           ? std::move(out_col)
+           : std::make_unique<column>(
+               cudf::detail::slice(out_col->view(),
+                                   static_cast<size_type>(array.offset()),
+                                   static_cast<size_type>(array.offset() + array.length()),
+                                   stream),
+               stream,
+               mr);
+}
+
+template <>
+std::unique_ptr<column> dispatch_to_cudf_column::operator()<cudf::dictionary32>(
+  arrow::Array const& array,
+  data_type,
+  bool,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto dict_array  = static_cast<arrow::DictionaryArray const*>(&array);
+  auto dict_type   = arrow_to_cudf_type(*(dict_array->dictionary()->type()));
+  auto keys_column = get_column(*(dict_array->dictionary()), dict_type, true, stream, mr);
+  auto ind_type    = arrow_to_cudf_type(*(dict_array->indices()->type()));
+
+  auto indices_column = get_column(*(dict_array->indices()), ind_type, false, stream, mr);
+  // If index type is not of type uint32_t, then cast it to uint32_t
+  auto const dict_indices_type = data_type{type_id::UINT32};
+  if (indices_column->type().id() != dict_indices_type.id())
+    indices_column = cudf::detail::cast(indices_column->view(), dict_indices_type, stream, mr);
+
+  // Child columns shouldn't have masks and we need the mask in main column
+  auto column_contents = indices_column->release();
+  indices_column       = std::make_unique<column>(dict_indices_type,
+                                            static_cast<size_type>(array.length()),
+                                            std::move(*(column_contents.data)),
+                                            rmm::device_buffer{},
+                                            0);
+
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                std::move(*(column_contents.null_mask)),
+                                array.null_count());
+}
+
+template <>
+std::unique_ptr<column> dispatch_to_cudf_column::operator()<cudf::struct_view>(
+  arrow::Array const& array,
+  data_type,
+  bool,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto struct_array = static_cast<arrow::StructArray const*>(&array);
+  std::vector<std::unique_ptr<column>> child_columns;
+  // Offsets have already been applied to child
+  arrow::ArrayVector array_children = struct_array->fields();
+  std::transform(array_children.cbegin(),
+                 array_children.cend(),
+                 std::back_inserter(child_columns),
+                 [&mr, &stream](auto const& child_array) {
+                   auto type = arrow_to_cudf_type(*(child_array->type()));
+                   return get_column(*child_array, type, false, stream, mr);
+                 });
+
+  auto out_mask = std::move(*(get_mask_buffer(array, stream, mr)));
+  if (struct_array->null_bitmap_data() != nullptr) {
+    out_mask = detail::copy_bitmask(static_cast<bitmask_type*>(out_mask.data()),
+                                    array.offset(),
+                                    array.offset() + array.length(),
+                                    stream,
+                                    mr);
+  }
+
+  return make_structs_column(
+    array.length(), move(child_columns), array.null_count(), std::move(out_mask), stream, mr);
+}
+
+template <>
+std::unique_ptr<column> dispatch_to_cudf_column::operator()<cudf::list_view>(
+  arrow::Array const& array,
+  data_type,
+  bool,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto list_array   = static_cast<arrow::ListArray const*>(&array);
+  auto offset_array = std::make_unique<arrow::Int32Array>(
+    list_array->value_offsets()->size() / sizeof(int32_t), list_array->value_offsets(), nullptr);
+  auto offsets_column = dispatch_to_cudf_column{}.operator()<int32_t>(
+    *offset_array, data_type(type_id::INT32), true, stream, mr);
+
+  auto child_type   = arrow_to_cudf_type(*(list_array->values()->type()));
+  auto child_column = get_column(*(list_array->values()), child_type, false, stream, mr);
+
+  auto const num_rows = offsets_column->size() - 1;
+  auto out_col        = make_lists_column(num_rows,
+                                   std::move(offsets_column),
+                                   std::move(child_column),
+                                   array.null_count(),
+                                   std::move(*get_mask_buffer(array, stream, mr)),
+                                   stream,
+                                   mr);
+
+  return num_rows == array.length()
+           ? std::move(out_col)
+           : std::make_unique<column>(
+               cudf::detail::slice(out_col->view(),
+                                   static_cast<size_type>(array.offset()),
+                                   static_cast<size_type>(array.offset() + array.length()),
+                                   stream),
+               stream,
+               mr);
+}
+
+std::unique_ptr<column> get_column(arrow::Array const& array,
+                                   data_type type,
+                                   bool skip_mask,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  return type.id() != type_id::EMPTY
+           ? type_dispatcher(type, dispatch_to_cudf_column{}, array, type, skip_mask, stream, mr)
+           : get_empty_type_column(array.length());
+}
+
+struct BuilderGenerator {
+  template <typename T,
+            CUDF_ENABLE_IF(!std::is_same_v<T, arrow::ListType> &&
+                           !std::is_same_v<T, arrow::StructType>)>
+  std::shared_ptr<arrow::ArrayBuilder> operator()(std::shared_ptr<arrow::DataType> const& type)
+  {
+    return std::make_shared<typename arrow::TypeTraits<T>::BuilderType>(
+      type, arrow::default_memory_pool());
+  }
+
+  template <typename T,
+            CUDF_ENABLE_IF(std::is_same_v<T, arrow::ListType> ||
+                           std::is_same_v<T, arrow::StructType>)>
+  std::shared_ptr<arrow::ArrayBuilder> operator()(std::shared_ptr<arrow::DataType> const& type)
+  {
+    CUDF_FAIL("Type not supported by BuilderGenerator");
+  }
+};
+
+std::shared_ptr<arrow::ArrayBuilder> make_builder(std::shared_ptr<arrow::DataType> const& type)
+{
+  switch (type->id()) {
+    case arrow::Type::STRUCT: {
+      std::vector<std::shared_ptr<arrow::ArrayBuilder>> field_builders;
+
+      for (auto field : type->fields()) {
+        auto const vt = field->type();
+        if (vt->id() == arrow::Type::STRUCT || vt->id() == arrow::Type::LIST) {
+          field_builders.push_back(make_builder(vt));
+        } else {
+          field_builders.push_back(arrow_type_dispatcher(*vt, BuilderGenerator{}, vt));
+        }
+      }
+      return std::make_shared<arrow::StructBuilder>(
+        type, arrow::default_memory_pool(), field_builders);
+    }
+    case arrow::Type::LIST: {
+      return std::make_shared<arrow::ListBuilder>(arrow::default_memory_pool(),
+                                                  make_builder(type->field(0)->type()));
+    }
+    default: {
+      return arrow_type_dispatcher(*type, BuilderGenerator{}, type);
+    }
+  }
+}
+
+}  // namespace
+
+std::unique_ptr<table> from_arrow(arrow::Table const& input_table,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  if (input_table.num_columns() == 0) { return std::make_unique<table>(); }
+  std::vector<std::unique_ptr<column>> columns;
+  auto chunked_arrays = input_table.columns();
+  std::transform(chunked_arrays.begin(),
+                 chunked_arrays.end(),
+                 std::back_inserter(columns),
+                 [&mr, &stream](auto const& chunked_array) {
+                   std::vector<std::unique_ptr<column>> concat_columns;
+                   auto cudf_type    = arrow_to_cudf_type(*(chunked_array->type()));
+                   auto array_chunks = chunked_array->chunks();
+                   if (cudf_type.id() == type_id::EMPTY) {
+                     return get_empty_type_column(chunked_array->length());
+                   }
+                   std::transform(array_chunks.begin(),
+                                  array_chunks.end(),
+                                  std::back_inserter(concat_columns),
+                                  [&cudf_type, &mr, &stream](auto const& array_chunk) {
+                                    return get_column(*array_chunk, cudf_type, false, stream, mr);
+                                  });
+                   if (concat_columns.empty()) {
+                     return std::make_unique<column>(
+                       cudf_type, 0, rmm::device_buffer{}, rmm::device_buffer{}, 0);
+                   } else if (concat_columns.size() == 1) {
+                     return std::move(concat_columns[0]);
+                   }
+
+                   std::vector<cudf::column_view> column_views;
+                   std::transform(concat_columns.begin(),
+                                  concat_columns.end(),
+                                  std::back_inserter(column_views),
+                                  [](auto const& col) { return col->view(); });
+                   return cudf::detail::concatenate(column_views, stream, mr);
+                 });
+
+  return std::make_unique<table>(std::move(columns));
+}
+
+std::unique_ptr<cudf::scalar> from_arrow(arrow::Scalar const& input,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  // Get a builder for the scalar type
+  auto builder = detail::make_builder(input.type);
+
+  auto status = builder->AppendScalar(input);
+  if (status != arrow::Status::OK()) {
+    if (status.IsNotImplemented()) {
+      // The only known failure case here is for nulls
+      CUDF_FAIL("Cannot create untyped null scalars or nested types with untyped null leaf nodes",
+                std::invalid_argument);
+    }
+    CUDF_FAIL("Arrow ArrayBuilder::AppendScalar failed");
+  }
+
+  auto maybe_array = builder->Finish();
+  if (!maybe_array.ok()) { CUDF_FAIL("Arrow ArrayBuilder::Finish failed"); }
+  auto array = *maybe_array;
+
+  auto field = arrow::field("", input.type);
+
+  auto table = arrow::Table::Make(arrow::schema({field}), {array});
+
+  auto cudf_table = detail::from_arrow(*table, stream, mr);
+
+  auto cv = cudf_table->view().column(0);
+  return get_element(cv, 0, stream);
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> from_arrow(arrow::Table const& input_table,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  return detail::from_arrow(input_table, stream, mr);
+}
+
+std::unique_ptr<cudf::scalar> from_arrow(arrow::Scalar const& input,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  return detail::from_arrow(input, stream, mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/interop/to_arrow.cu b/cpp/src/interop/to_arrow.cu
new file mode 100644
index 0000000..0cd750b
--- /dev/null
+++ b/cpp/src/interop/to_arrow.cu
@@ -0,0 +1,473 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/interop.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/interop.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include "detail/arrow_allocator.hpp"
+
+namespace cudf {
+namespace detail {
+namespace {
+
+/**
+ * @brief Create arrow data buffer from given cudf column
+ */
+template <typename T>
+std::shared_ptr<arrow::Buffer> fetch_data_buffer(column_view input_view,
+                                                 arrow::MemoryPool* ar_mr,
+                                                 rmm::cuda_stream_view stream)
+{
+  int64_t const data_size_in_bytes = sizeof(T) * input_view.size();
+
+  auto data_buffer = allocate_arrow_buffer(data_size_in_bytes, ar_mr);
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(data_buffer->mutable_data(),
+                                input_view.data<T>(),
+                                data_size_in_bytes,
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  return std::move(data_buffer);
+}
+
+/**
+ * @brief Create arrow buffer of mask from given cudf column
+ */
+std::shared_ptr<arrow::Buffer> fetch_mask_buffer(column_view input_view,
+                                                 arrow::MemoryPool* ar_mr,
+                                                 rmm::cuda_stream_view stream)
+{
+  int64_t const mask_size_in_bytes = cudf::bitmask_allocation_size_bytes(input_view.size());
+
+  if (input_view.has_nulls()) {
+    auto mask_buffer = allocate_arrow_bitmap(static_cast<int64_t>(input_view.size()), ar_mr);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      mask_buffer->mutable_data(),
+      (input_view.offset() > 0)
+        ? cudf::detail::copy_bitmask(input_view, stream, rmm::mr::get_current_device_resource())
+            .data()
+        : input_view.null_mask(),
+      mask_size_in_bytes,
+      cudaMemcpyDefault,
+      stream.value()));
+
+    // Resets all padded bits to 0
+    mask_buffer->ZeroPadding();
+
+    return mask_buffer;
+  }
+
+  return nullptr;
+}
+
+/**
+ * @brief Functor to convert cudf column to arrow array
+ */
+struct dispatch_to_arrow {
+  /**
+   * @brief Creates vector Arrays from given cudf column children
+   */
+  std::vector<std::shared_ptr<arrow::Array>> fetch_child_array(
+    column_view input_view,
+    std::vector<column_metadata> const& metadata,
+    arrow::MemoryPool* ar_mr,
+    rmm::cuda_stream_view stream)
+  {
+    std::vector<std::shared_ptr<arrow::Array>> child_arrays;
+    std::transform(
+      input_view.child_begin(),
+      input_view.child_end(),
+      metadata.begin(),
+      std::back_inserter(child_arrays),
+      [&ar_mr, &stream](auto const& child, auto const& meta) {
+        return type_dispatcher(
+          child.type(), dispatch_to_arrow{}, child, child.type().id(), meta, ar_mr, stream);
+      });
+    return child_arrays;
+  }
+
+  template <typename T, CUDF_ENABLE_IF(not is_rep_layout_compatible<T>())>
+  std::shared_ptr<arrow::Array> operator()(
+    column_view, cudf::type_id, column_metadata const&, arrow::MemoryPool*, rmm::cuda_stream_view)
+  {
+    CUDF_FAIL("Unsupported type for to_arrow.");
+  }
+
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  std::shared_ptr<arrow::Array> operator()(column_view input_view,
+                                           cudf::type_id id,
+                                           column_metadata const&,
+                                           arrow::MemoryPool* ar_mr,
+                                           rmm::cuda_stream_view stream)
+  {
+    return to_arrow_array(id,
+                          static_cast<int64_t>(input_view.size()),
+                          fetch_data_buffer<T>(input_view, ar_mr, stream),
+                          fetch_mask_buffer(input_view, ar_mr, stream),
+                          static_cast<int64_t>(input_view.null_count()));
+  }
+};
+
+// Convert decimal types from libcudf to arrow where those types are not
+// directly supported by Arrow. These types must be fit into 128 bits, the
+// smallest decimal resolution supported by Arrow.
+template <typename DeviceType>
+std::shared_ptr<arrow::Array> unsupported_decimals_to_arrow(column_view input,
+                                                            int32_t precision,
+                                                            arrow::MemoryPool* ar_mr,
+                                                            rmm::cuda_stream_view stream)
+{
+  constexpr size_type BIT_WIDTH_RATIO = sizeof(__int128_t) / sizeof(DeviceType);
+
+  rmm::device_uvector<DeviceType> buf(input.size() * BIT_WIDTH_RATIO, stream);
+
+  auto count = thrust::make_counting_iterator(0);
+
+  thrust::for_each(
+    rmm::exec_policy(cudf::get_default_stream()),
+    count,
+    count + input.size(),
+    [in = input.begin<DeviceType>(), out = buf.data(), BIT_WIDTH_RATIO] __device__(auto in_idx) {
+      auto const out_idx = in_idx * BIT_WIDTH_RATIO;
+      // The lowest order bits are the value, the remainder
+      // simply matches the sign bit to satisfy the two's
+      // complement integer representation of negative numbers.
+      out[out_idx] = in[in_idx];
+#pragma unroll BIT_WIDTH_RATIO - 1
+      for (auto i = 1; i < BIT_WIDTH_RATIO; ++i) {
+        out[out_idx + i] = in[in_idx] < 0 ? -1 : 0;
+      }
+    });
+
+  auto const buf_size_in_bytes = buf.size() * sizeof(DeviceType);
+  auto data_buffer             = allocate_arrow_buffer(buf_size_in_bytes, ar_mr);
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    data_buffer->mutable_data(), buf.data(), buf_size_in_bytes, cudaMemcpyDefault, stream.value()));
+
+  auto type    = arrow::decimal(precision, -input.type().scale());
+  auto mask    = fetch_mask_buffer(input, ar_mr, stream);
+  auto buffers = std::vector<std::shared_ptr<arrow::Buffer>>{mask, std::move(data_buffer)};
+  auto data    = std::make_shared<arrow::ArrayData>(type, input.size(), buffers);
+
+  return std::make_shared<arrow::Decimal128Array>(data);
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<numeric::decimal32>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const&,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  return unsupported_decimals_to_arrow<int32_t>(input, 9, ar_mr, stream);
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<numeric::decimal64>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const&,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  return unsupported_decimals_to_arrow<int64_t>(input, 18, ar_mr, stream);
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<numeric::decimal128>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const&,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  using DeviceType = __int128_t;
+
+  rmm::device_uvector<DeviceType> buf(input.size(), stream);
+
+  thrust::copy(rmm::exec_policy(stream),  //
+               input.begin<DeviceType>(),
+               input.end<DeviceType>(),
+               buf.begin());
+
+  auto const buf_size_in_bytes = buf.size() * sizeof(DeviceType);
+  auto data_buffer             = allocate_arrow_buffer(buf_size_in_bytes, ar_mr);
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    data_buffer->mutable_data(), buf.data(), buf_size_in_bytes, cudaMemcpyDefault, stream.value()));
+
+  auto type    = arrow::decimal(18, -input.type().scale());
+  auto mask    = fetch_mask_buffer(input, ar_mr, stream);
+  auto buffers = std::vector<std::shared_ptr<arrow::Buffer>>{mask, std::move(data_buffer)};
+  auto data    = std::make_shared<arrow::ArrayData>(type, input.size(), buffers);
+
+  return std::make_shared<arrow::Decimal128Array>(data);
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<bool>(column_view input,
+                                                                  cudf::type_id id,
+                                                                  column_metadata const&,
+                                                                  arrow::MemoryPool* ar_mr,
+                                                                  rmm::cuda_stream_view stream)
+{
+  auto bitmask = bools_to_mask(input, stream, rmm::mr::get_current_device_resource());
+
+  auto data_buffer = allocate_arrow_buffer(static_cast<int64_t>(bitmask.first->size()), ar_mr);
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(data_buffer->mutable_data(),
+                                bitmask.first->data(),
+                                bitmask.first->size(),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  return to_arrow_array(id,
+                        static_cast<int64_t>(input.size()),
+                        std::move(data_buffer),
+                        fetch_mask_buffer(input, ar_mr, stream),
+                        static_cast<int64_t>(input.null_count()));
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<cudf::string_view>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const&,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  std::unique_ptr<column> tmp_column =
+    ((input.offset() != 0) or
+     ((input.num_children() == 2) and (input.child(0).size() - 1 != input.size())))
+      ? std::make_unique<cudf::column>(input, stream)
+      : nullptr;
+
+  column_view input_view = (tmp_column != nullptr) ? tmp_column->view() : input;
+  auto child_arrays      = fetch_child_array(input_view, {{}, {}}, ar_mr, stream);
+  if (child_arrays.empty()) {
+    // Empty string will have only one value in offset of 4 bytes
+    auto tmp_offset_buffer               = allocate_arrow_buffer(4, ar_mr);
+    auto tmp_data_buffer                 = allocate_arrow_buffer(0, ar_mr);
+    tmp_offset_buffer->mutable_data()[0] = 0;
+
+    return std::make_shared<arrow::StringArray>(
+      0, std::move(tmp_offset_buffer), std::move(tmp_data_buffer));
+  }
+  auto offset_buffer = child_arrays[0]->data()->buffers[1];
+  auto data_buffer   = child_arrays[1]->data()->buffers[1];
+  return std::make_shared<arrow::StringArray>(static_cast<int64_t>(input_view.size()),
+                                              offset_buffer,
+                                              data_buffer,
+                                              fetch_mask_buffer(input_view, ar_mr, stream),
+                                              static_cast<int64_t>(input_view.null_count()));
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<cudf::struct_view>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const& metadata,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(metadata.children_meta.size() == static_cast<std::size_t>(input.num_children()),
+               "Number of field names and number of children doesn't match\n");
+  std::unique_ptr<column> tmp_column = nullptr;
+
+  if (input.offset() != 0) { tmp_column = std::make_unique<cudf::column>(input, stream); }
+
+  column_view input_view = (tmp_column != nullptr) ? tmp_column->view() : input;
+  auto child_arrays      = fetch_child_array(input_view, metadata.children_meta, ar_mr, stream);
+  auto mask              = fetch_mask_buffer(input_view, ar_mr, stream);
+
+  std::vector<std::shared_ptr<arrow::Field>> fields;
+  std::transform(child_arrays.cbegin(),
+                 child_arrays.cend(),
+                 metadata.children_meta.cbegin(),
+                 std::back_inserter(fields),
+                 [](auto const array, auto const meta) {
+                   return std::make_shared<arrow::Field>(
+                     meta.name, array->type(), array->null_count() > 0);
+                 });
+  auto dtype = std::make_shared<arrow::StructType>(fields);
+
+  return std::make_shared<arrow::StructArray>(dtype,
+                                              static_cast<int64_t>(input_view.size()),
+                                              child_arrays,
+                                              mask,
+                                              static_cast<int64_t>(input_view.null_count()));
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<cudf::list_view>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const& metadata,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  std::unique_ptr<column> tmp_column = nullptr;
+  if ((input.offset() != 0) or
+      ((input.num_children() == 2) and (input.child(0).size() - 1 != input.size()))) {
+    tmp_column = std::make_unique<cudf::column>(input, stream);
+  }
+
+  column_view input_view = (tmp_column != nullptr) ? tmp_column->view() : input;
+  auto children_meta =
+    metadata.children_meta.empty() ? std::vector<column_metadata>{{}, {}} : metadata.children_meta;
+  auto child_arrays = fetch_child_array(input_view, children_meta, ar_mr, stream);
+  if (child_arrays.empty()) {
+    return std::make_shared<arrow::ListArray>(arrow::list(arrow::null()), 0, nullptr, nullptr);
+  }
+
+  auto offset_buffer = child_arrays[0]->data()->buffers[1];
+  auto data          = child_arrays[1];
+  return std::make_shared<arrow::ListArray>(arrow::list(data->type()),
+                                            static_cast<int64_t>(input_view.size()),
+                                            offset_buffer,
+                                            data,
+                                            fetch_mask_buffer(input_view, ar_mr, stream),
+                                            static_cast<int64_t>(input_view.null_count()));
+}
+
+template <>
+std::shared_ptr<arrow::Array> dispatch_to_arrow::operator()<cudf::dictionary32>(
+  column_view input,
+  cudf::type_id,
+  column_metadata const& metadata,
+  arrow::MemoryPool* ar_mr,
+  rmm::cuda_stream_view stream)
+{
+  // Arrow dictionary requires indices to be signed integer
+  std::unique_ptr<column> dict_indices =
+    cast(cudf::dictionary_column_view(input).get_indices_annotated(),
+         cudf::data_type{type_id::INT32},
+         stream,
+         rmm::mr::get_current_device_resource());
+  auto indices = dispatch_to_arrow{}.operator()<int32_t>(
+    dict_indices->view(), dict_indices->type().id(), {}, ar_mr, stream);
+  auto dict_keys = cudf::dictionary_column_view(input).keys();
+  auto dictionary =
+    type_dispatcher(dict_keys.type(),
+                    dispatch_to_arrow{},
+                    dict_keys,
+                    dict_keys.type().id(),
+                    metadata.children_meta.empty() ? column_metadata{} : metadata.children_meta[0],
+                    ar_mr,
+                    stream);
+
+  return std::make_shared<arrow::DictionaryArray>(
+    arrow::dictionary(indices->type(), dictionary->type()), indices, dictionary);
+}
+}  // namespace
+
+std::shared_ptr<arrow::Table> to_arrow(table_view input,
+                                       std::vector<column_metadata> const& metadata,
+                                       rmm::cuda_stream_view stream,
+                                       arrow::MemoryPool* ar_mr)
+{
+  CUDF_EXPECTS((metadata.size() == static_cast<std::size_t>(input.num_columns())),
+               "columns' metadata should be equal to number of columns in table");
+
+  std::vector<std::shared_ptr<arrow::Array>> arrays;
+  std::vector<std::shared_ptr<arrow::Field>> fields;
+
+  std::transform(
+    input.begin(),
+    input.end(),
+    metadata.begin(),
+    std::back_inserter(arrays),
+    [&](auto const& c, auto const& meta) {
+      return c.type().id() != type_id::EMPTY
+               ? type_dispatcher(
+                   c.type(), detail::dispatch_to_arrow{}, c, c.type().id(), meta, ar_mr, stream)
+               : std::make_shared<arrow::NullArray>(c.size());
+    });
+
+  std::transform(
+    arrays.begin(),
+    arrays.end(),
+    metadata.begin(),
+    std::back_inserter(fields),
+    [](auto const& array, auto const& meta) { return arrow::field(meta.name, array->type()); });
+
+  auto result = arrow::Table::Make(arrow::schema(fields), arrays);
+
+  // synchronize the stream because after the return the data may be accessed from the host before
+  // the above `cudaMemcpyAsync` calls have completed their copies (especially if pinned host
+  // memory is used).
+  stream.synchronize();
+
+  return result;
+}
+
+std::shared_ptr<arrow::Scalar> to_arrow(cudf::scalar const& input,
+                                        column_metadata const& metadata,
+                                        rmm::cuda_stream_view stream,
+                                        arrow::MemoryPool* ar_mr)
+{
+  auto const column = cudf::make_column_from_scalar(input, 1, stream);
+  cudf::table_view const tv{{column->view()}};
+  auto const arrow_table  = cudf::to_arrow(tv, {metadata}, stream);
+  auto const ac           = arrow_table->column(0);
+  auto const maybe_scalar = ac->GetScalar(0);
+  if (!maybe_scalar.ok()) { CUDF_FAIL("Failed to produce a scalar"); }
+  return maybe_scalar.ValueOrDie();
+}
+}  // namespace detail
+
+std::shared_ptr<arrow::Table> to_arrow(table_view input,
+                                       std::vector<column_metadata> const& metadata,
+                                       rmm::cuda_stream_view stream,
+                                       arrow::MemoryPool* ar_mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_arrow(input, metadata, stream, ar_mr);
+}
+
+std::shared_ptr<arrow::Scalar> to_arrow(cudf::scalar const& input,
+                                        column_metadata const& metadata,
+                                        rmm::cuda_stream_view stream,
+                                        arrow::MemoryPool* ar_mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_arrow(input, metadata, stream, ar_mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/io/avro/avro.cpp b/cpp/src/io/avro/avro.cpp
new file mode 100644
index 0000000..221cdf9
--- /dev/null
+++ b/cpp/src/io/avro/avro.cpp
@@ -0,0 +1,495 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "avro.hpp"
+
+#include <cstring>
+#include <unordered_map>
+
+namespace cudf {
+namespace io {
+namespace avro {
+
+template <>
+uint64_t container::get_encoded()
+{
+  uint64_t val = 0;
+  for (auto len = 0; len < 64; len += 7) {
+    // 64-bit int since shift left is upto 64.
+    uint64_t const byte = get_raw<uint8_t>();
+    val |= (byte & 0x7f) << len;
+    if (byte < 0x80) break;
+  }
+  return val;
+}
+
+template <>
+int64_t container::get_encoded()
+{
+  auto const uval = get_encoded<uint64_t>();
+  return (int64_t)((uval >> 1u) ^ -(int64_t)(uval & 1));
+}
+
+template <>
+std::string container::get_encoded()
+{
+  auto const len = [&] {
+    auto const len = get_encoded<uint64_t>();
+    return (len & 1) || (m_cur >= m_end) ? 0
+                                         : std::min(len >> 1, static_cast<uint64_t>(m_end - m_cur));
+  }();
+  auto const s = reinterpret_cast<char const*>(m_cur);
+  m_cur += len;
+  return std::string(s, len);
+}
+
+/**
+ * @brief AVRO file metadata parser
+ *
+ * @param[out] md parsed avro file metadata
+ * @param[in] max_num_rows maximum number of rows
+ * @param[in] first_row drop blocks below first_row
+ *
+ * @returns true if successful, false if error
+ */
+bool container::parse(file_metadata* md, size_t max_num_rows, size_t first_row)
+{
+  constexpr uint32_t avro_magic = (('O' << 0) | ('b' << 8) | ('j' << 16) | (0x01 << 24));
+
+  uint32_t sig4 = get_raw<uint8_t>();
+  sig4 |= get_raw<uint8_t>() << 8;
+  sig4 |= get_raw<uint8_t>() << 16;
+  sig4 |= get_raw<uint8_t>() << 24;
+  if (sig4 != avro_magic) { return false; }
+  for (;;) {
+    auto num_md_items = static_cast<uint32_t>(get_encoded<int64_t>());
+    if (num_md_items == 0) { break; }
+    for (uint32_t i = 0; i < num_md_items; i++) {
+      auto const key   = get_encoded<std::string>();
+      auto const value = get_encoded<std::string>();
+      if (key == "avro.codec") {
+        md->codec = value;
+      } else if (key == "avro.schema") {
+        schema_parser sp;
+        if (!sp.parse(md->schema, value)) { return false; }
+      } else {
+        // printf("\"%s\" = \"%s\"\n", key.c_str(), value.c_str());
+        md->user_data.emplace(key, value);
+      }
+    }
+  }
+  // Save the first sync markers in the metadata; we compare them to other
+  // sync markers that should be present at the end of a block.  If they
+  // differ, the data should be interpreted as corrupted.
+  md->sync_marker[0] = get_raw<uint64_t>();
+  md->sync_marker[1] = get_raw<uint64_t>();
+
+  // Initialize remaining metadata fields.
+  md->metadata_size  = m_cur - m_base;
+  md->skip_rows      = first_row;
+  md->total_num_rows = 0;
+
+  // Enumerate the blocks in this file.  Each block starts with a count of
+  // objects (rows) in the block (uint64_t), and then the total size in bytes
+  // of the block (uint64_t).  We walk each block and do the following:
+  //    1. Capture the total number of rows present across all blocks.
+  //    2. Add each block to the metadata's list of blocks.
+  //    3. Handle the case where we've been asked to skip or limit rows.
+  //    4. Verify sync markers at the end of each block.
+  //
+  // A row offset is also maintained, and added to each block.  This reflects
+  // the absolute offset that needs to be added to any given row in order to
+  // get the row's index within the destination array.  See `dst_row` in
+  // `avro_decode_row()` for more information.
+  //
+  // N.B. "object" and "row" are used interchangeably here; "object" is
+  //      avro nomenclature, "row" is ours.
+  //
+  // N.B. If we're skipping rows, we ignore blocks (i.e. don't add them to
+  //      md->block_list) that precede the block containing the first row
+  //      we're interested in.
+  //
+
+  // Number of rows in the current block.
+  uint32_t num_rows = 0;
+
+  // Absolute row offset of the current block relative to all blocks selected by
+  // the skip rows/limit rows constraints, if any.  Otherwise, absolute row
+  // offset relative to all blocks.
+  uint32_t row_offset = 0;
+
+  // Maximum block size in bytes encountered whilst processing all blocks
+  // selected by the skip rows/limit rows constraints, if any.  Otherwise,
+  // maximum block size across all blocks.
+  uint32_t max_block_size = 0;
+
+  // Accumulates the total number of rows across all blocks selected by the skip
+  // rows/limit rows constraints, if any.  Otherwise, total number of rows across
+  // all blocks.
+  size_t total_object_count = 0;
+
+  // N.B. The 18 below is (presumably) intended to account for the two 64-bit
+  //      object count and block size integers (16 bytes total), and then an
+  //      additional two bytes to represent the smallest possible row size.
+  while (m_cur + 18 < m_end && total_object_count < max_num_rows) {
+    auto const object_count = static_cast<uint32_t>(get_encoded<int64_t>());
+    auto const block_size   = static_cast<uint32_t>(get_encoded<int64_t>());
+    auto const next_end     = m_cur + block_size + 16;
+    // Abort on terminal conditions.  We keep these as separate lines instead of
+    // combining them into a single if in order to facilitate setting specific
+    // line breakpoints in the debugger.
+    if (block_size <= 0) { return false; }
+    if (object_count <= 0) { return false; }
+    if (next_end > m_end) { return false; }
+
+    // Update our total row count.  This is only captured for information
+    // purposes.
+    md->total_num_rows += object_count;
+
+    if (object_count <= first_row) {
+      // We've been asked to skip rows, and we haven't yet reached our desired
+      // number of rows to skip.  Subtract this block's rows (`object_count`)
+      // from the remaining rows to skip (`first_row`).  Do not add this block
+      // to our block list.
+      first_row -= object_count;
+    } else {
+      // Either we weren't asked to skip rows, or we were, but we've already hit
+      // our target number of rows to skip.  Add this block to our block list.
+      max_block_size = std::max(max_block_size, block_size);
+      total_object_count += object_count;
+      if (!md->block_list.size()) {
+        // This is the first block, so add it to our list with the current value
+        // of `first_row`, which will reflect the number of rows to skip *in
+        // this block*.
+        m_start = m_cur;
+        total_object_count -= first_row;
+        num_rows = total_object_count;
+        CUDF_EXPECTS(row_offset == 0, "Invariant check failed: row_offset != 0");
+        if ((max_num_rows > 0) && (max_num_rows < total_object_count)) { num_rows = max_num_rows; }
+        md->block_list.emplace_back(m_cur - m_base, block_size, row_offset, first_row, num_rows);
+        first_row = 0;
+        row_offset += num_rows;
+      } else {
+        // Not our first block; `first_row` should always be zero here.
+        CUDF_EXPECTS(first_row == 0, "Invariant check failed: first_row != 0");
+
+        num_rows = object_count;
+        if ((max_num_rows > 0) && (max_num_rows < total_object_count)) {
+          num_rows -= (total_object_count - max_num_rows);
+        }
+
+        md->block_list.emplace_back(m_cur - m_base, block_size, row_offset, first_row, num_rows);
+        row_offset += num_rows;
+      }
+    }
+    m_cur += block_size;
+    // Read the next sync markers and ensure they match the first ones we
+    // encountered.  If they don't, we have to assume the data is corrupted,
+    // and thus, we terminate processing immediately.
+    uint64_t const sync_marker[] = {get_raw<uint64_t>(), get_raw<uint64_t>()};
+    bool valid_sync_markers =
+      ((sync_marker[0] == md->sync_marker[0]) && (sync_marker[1] == md->sync_marker[1]));
+    if (!valid_sync_markers) { return false; }
+  }
+  md->max_block_size = max_block_size;
+  // N.B. `total_object_count` has skip_rows applied to it at this point, i.e.
+  //      it represents the number of rows that will be returned *after* rows
+  //      have been skipped (if requested).
+  if ((max_num_rows <= 0) || (max_num_rows > total_object_count)) {
+    md->num_rows = total_object_count;
+  } else {
+    md->num_rows = max_num_rows;
+  }
+  md->total_data_size = m_cur - (m_base + md->metadata_size);
+  CUDF_EXPECTS(m_cur > m_start, "Invariant check failed: `m_cur > m_start` is false.");
+  md->selected_data_size = m_cur - m_start;
+  // Extract columns
+  for (size_t i = 0; i < md->schema.size(); i++) {
+    type_kind_e kind                = md->schema[i].kind;
+    logicaltype_kind_e logical_kind = md->schema[i].logical_kind;
+
+    bool is_supported_kind = ((kind > type_null) && (kind < type_record));
+    if (is_supported_logical_type(logical_kind) || is_supported_kind) {
+      column_desc col;
+      int parent_idx       = md->schema[i].parent_idx;
+      col.schema_data_idx  = (int32_t)i;
+      col.schema_null_idx  = -1;
+      col.parent_union_idx = -1;
+      col.name             = md->schema[i].name;
+      if (parent_idx >= 0) {
+        while (parent_idx >= 0) {
+          if (md->schema[parent_idx].kind == type_union) {
+            std::size_t pos = parent_idx + 1;
+            for (int num_children = md->schema[parent_idx].num_children; num_children > 0;
+                 --num_children) {
+              int skip = 1;
+              if (pos == i) {
+                // parent_idx will always be pointing to our immediate parent
+                // union at this point.
+                col.parent_union_idx = parent_idx;
+              } else if (md->schema[pos].kind == type_null) {
+                col.schema_null_idx = pos;
+                break;
+              }
+              do {
+                skip = skip + md->schema[pos].num_children - 1;
+                pos++;
+              } while (skip != 0);
+            }
+          }
+          // We want to "inherit" the column name from our parent union's
+          // name, as long as we're not dealing with the root (parent_idx == 0)
+          // or array entries.
+          if ((parent_idx != 0 && md->schema[parent_idx].kind != type_array) ||
+              col.name.length() == 0) {
+            if (col.name.length() > 0) { col.name.insert(0, 1, '.'); }
+            col.name.insert(0, md->schema[parent_idx].name);
+          }
+          parent_idx = md->schema[parent_idx].parent_idx;
+        }
+      }
+      md->columns.emplace_back(std::move(col));
+    }
+  }
+  return true;
+}
+
+/**
+ * @brief Parser state
+ */
+enum json_state_e {
+  state_attrname = 0,
+  state_attrcolon,
+  state_attrvalue,
+  state_attrvalue_last,
+  state_nextattr,
+  state_nextsymbol,
+};
+
+enum attrtype_e {
+  attrtype_none = -1,
+  attrtype_type = 0,
+  attrtype_name,
+  attrtype_fields,
+  attrtype_symbols,
+  attrtype_items,
+  attrtype_logicaltype,
+};
+
+/**
+ * @brief AVRO JSON schema parser
+ *
+ * @param[out] schema parsed avro schema
+ * @param[in] json_str avro schema (JSON string)
+ *
+ * @returns true if successful, false if error
+ */
+bool schema_parser::parse(std::vector<schema_entry>& schema, std::string const& json_str)
+{
+  // Empty schema
+  if (json_str == "[]") return true;
+
+  char depthbuf[MAX_SCHEMA_DEPTH];
+  int depth = 0, parent_idx = -1, entry_idx = -1;
+  json_state_e state = state_attrname;
+  std::string str;
+  std::unordered_map<std::string, type_kind_e> const typenames = {
+    {"null", type_null},
+    {"boolean", type_boolean},
+    {"int", type_int},
+    {"long", type_long},
+    {"float", type_float},
+    {"double", type_double},
+    {"bytes", type_bytes},
+    {"string", type_string},
+    {"record", type_record},
+    {"enum", type_enum},
+    {"array", type_array},
+    {"union", type_union},
+    {"fixed", type_fixed},
+    {"decimal", type_decimal},
+    {"date", type_date},
+    {"time-millis", type_time_millis},
+    {"time-micros", type_time_micros},
+    {"timestamp-millis", type_timestamp_millis},
+    {"timestamp-micros", type_timestamp_micros},
+    {"local-timestamp-millis", type_local_timestamp_millis},
+    {"local-timestamp-micros", type_local_timestamp_micros},
+    {"duration", type_duration}};
+  std::unordered_map<std::string, attrtype_e> const attrnames = {
+    {"type", attrtype_type},
+    {"name", attrtype_name},
+    {"fields", attrtype_fields},
+    {"symbols", attrtype_symbols},
+    {"items", attrtype_items},
+    {"logicalType", attrtype_logicaltype}};
+  attrtype_e cur_attr = attrtype_none;
+  m_base              = json_str.c_str();
+  m_cur               = m_base;
+  m_end               = m_base + json_str.length();
+  while (more_data()) {
+    int c = *m_cur++;
+    switch (c) {
+      case '"':
+        str = get_str();
+        // printf("str: \"%s\" (cur_attr=%d, state=%d)\n", str.c_str(), cur_attr, state);
+        if (state == state_attrname && cur_attr == attrtype_none &&
+            typenames.find(str) != typenames.end()) {
+          cur_attr = attrtype_type;
+          state    = state_attrvalue_last;
+        }
+        if (state == state_attrname) {
+          auto t   = attrnames.find(str);
+          cur_attr = (t == attrnames.end()) ? attrtype_none : t->second;
+          state    = state_attrcolon;
+        } else if (state == state_attrvalue || state == state_attrvalue_last) {
+          if (entry_idx < 0) {
+            entry_idx = static_cast<int>(schema.size());
+            schema.emplace_back(type_not_set, parent_idx);
+            if (parent_idx >= 0) { schema[parent_idx].num_children++; }
+          }
+          if (cur_attr == attrtype_type) {
+            auto t = typenames.find(str);
+            if (t == typenames.end()) return false;
+            schema[entry_idx].kind = t->second;
+          } else if (cur_attr == attrtype_logicaltype) {
+            auto t = typenames.find(str);
+            if (t == typenames.end()) return false;
+            schema[entry_idx].logical_kind = static_cast<logicaltype_kind_e>(t->second);
+          } else if (cur_attr == attrtype_name) {
+            if (entry_idx < 0) return false;
+            schema[entry_idx].name = std::move(str);
+          }
+          if (state == state_attrvalue_last) { entry_idx = -1; }
+          state    = state_nextattr;
+          cur_attr = attrtype_none;
+        } else if (state == state_nextsymbol) {
+          if (entry_idx < 0) return false;
+          schema[entry_idx].symbols.emplace_back(std::move(str));
+        }
+        break;
+      case ':':
+        if (state != state_attrcolon) return false;
+        state = state_attrvalue;
+        break;
+      case ',':
+        if (state != state_nextsymbol) {
+          if (state != state_nextattr) return false;
+          state = state_attrname;
+        }
+        break;
+      case '{':
+        if (state == state_attrvalue && cur_attr == attrtype_type) {
+          if (entry_idx < 0) {
+            entry_idx = static_cast<int>(schema.size());
+            schema.emplace_back(type_record, parent_idx);
+            if (parent_idx >= 0) { schema[parent_idx].num_children++; }
+          }
+          cur_attr = attrtype_none;
+          state    = state_attrname;
+        } else if (state == state_attrvalue && cur_attr == attrtype_items && entry_idx >= 0) {
+          // Treat array as a one-field record
+          parent_idx = entry_idx;
+          entry_idx  = -1;
+          cur_attr   = attrtype_none;
+          state      = state_attrname;
+        }
+        if (depth >= MAX_SCHEMA_DEPTH || state != state_attrname) { return false; }
+        depthbuf[depth++] = '{';
+        break;
+      case '}':
+        if (depth == 0 || state != state_nextattr || depthbuf[depth - 1] != '{') return false;
+        --depth;
+        if (entry_idx < 0) {
+          parent_idx = (parent_idx >= 0) ? schema[parent_idx].parent_idx : -1;
+        } else {
+          entry_idx = -1;
+        }
+        break;
+      case '[':
+        if (state == state_attrname && cur_attr == attrtype_none) {
+          cur_attr = attrtype_type;
+          state    = state_attrvalue;
+        }
+        if (depth >= MAX_SCHEMA_DEPTH || state != state_attrvalue) { return false; }
+        depthbuf[depth++] = '[';
+        if (cur_attr == attrtype_symbols) {
+          state = state_nextsymbol;
+          break;
+        } else if (cur_attr == attrtype_type) {
+          if (entry_idx < 0 || schema[entry_idx].kind != type_not_set) {
+            entry_idx = static_cast<int>(schema.size());
+            schema.emplace_back(type_union, parent_idx);
+            if (parent_idx >= 0) { schema[parent_idx].num_children++; }
+          } else {
+            schema[entry_idx].kind = type_union;
+          }
+          parent_idx = entry_idx;
+        } else if (cur_attr != attrtype_fields || entry_idx < 0 ||
+                   schema[entry_idx].kind < type_record) {
+          return false;
+        } else {
+          parent_idx = entry_idx;
+        }
+        entry_idx = -1;
+        cur_attr  = attrtype_none;
+        state     = state_attrname;
+        break;
+      case ']':
+        if (depth == 0 || (state != state_nextattr && state != state_nextsymbol) ||
+            depthbuf[depth - 1] != '[')
+          return false;
+        --depth;
+        if (state == state_nextsymbol) {
+          state = state_nextattr;
+        } else if (parent_idx >= 0) {
+          entry_idx  = parent_idx;
+          parent_idx = schema[parent_idx].parent_idx;
+        }
+        break;
+      case ' ':
+      case '\x09':
+      case '\x0d':
+      case '\x0a':
+        // Ignore spaces, tabs and CRLF
+        break;
+      default: return false;
+    }
+  }
+  // printf("schema (%d entries) = %s\n", (int)schema.size(), m_base);
+  return true;
+}
+
+/**
+ * @brief Parse a string
+ *
+ * @returns parsed string, consuming the terminating quote
+ */
+std::string schema_parser::get_str()
+{
+  std::string s;
+  char const* start = m_cur;
+  char const* cur   = start;
+  while (cur < m_end && *cur++ != '"')
+    ;
+  int32_t len = static_cast<int32_t>(cur - start - 1);
+  m_cur       = cur;
+  return s.assign(start, std::max(len, 0));
+}
+
+}  // namespace avro
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/avro/avro.hpp b/cpp/src/io/avro/avro.hpp
new file mode 100644
index 0000000..f2813a1
--- /dev/null
+++ b/cpp/src/io/avro/avro.hpp
@@ -0,0 +1,184 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "avro_common.hpp"
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
+#include <cstring>
+#include <map>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace avro {
+
+/**
+ * @brief AVRO schema entry
+ */
+struct schema_entry {
+  explicit schema_entry(type_kind_e kind_, int32_t parent_idx_ = -1, int32_t num_children_ = 0)
+    : parent_idx(parent_idx_), num_children(num_children_), kind(kind_)
+  {
+  }
+  int32_t parent_idx   = -1;  // index of parent entry in schema array, negative if no parent
+  int32_t num_children = 0;
+  type_kind_e kind     = type_not_set;
+  logicaltype_kind_e logical_kind = logicaltype_not_set;
+  std::string name                = "";
+  std::vector<std::string> symbols;
+};
+
+/**
+ * @brief AVRO output column
+ */
+struct column_desc {
+  int32_t schema_data_idx  = -1;  // schema index of data column
+  int32_t schema_null_idx  = -1;  // schema index of corresponding null object
+  int32_t parent_union_idx = -1;  // index of this column in parent union (-1 if not a union member)
+  std::string name         = "";
+};
+
+/**
+ * @brief AVRO file metadata struct
+ *
+ * `metadata_size` is the size in bytes of the avro file header.
+ *
+ * `total_data_size` is the size of all data minus `metadata_size`.
+ *
+ * `selected_data_size` is the size of all data minus `metadata_size`, with any
+ * adjustments made to account for the number of rows or rows to skip per the
+ * user's request.  This is the value used to size device-side buffers.
+ *
+ * `num_rows` is the number of rows that will be processed.  If the user has not
+ * requested the number of rows to be limited (i.e. via the `num_rows` param to
+ * `read_avro()`), this number will represent all rows in the file *after* the
+ * `skip_rows` parameter has been taken into consideration (assuming a request
+ * has been made to also skip rows).
+ *
+ * `total_num_rows` is the total number of rows present in the file, across all
+ * blocks.  This may be more than `num_rows` if the user has requested a limit
+ * on the number of rows to return, or if `skip_rows` is active.
+ *
+ * `skip_rows` is the number of rows the user has requested to skip.  Note that
+ * this value may differ from the `block_desc_s.first_row` member, which will
+ * capture the number of rows to skip for a given block.
+ *
+ * `block_list` is a list of all blocks that contain the selected rows.  If no
+ * row filtering has been done via `num_rows` or `skip_rows`; it will contain
+ * all blocks.  Otherwise, it will contain only blocks selected by those
+ * constraints.
+ *
+ * N.B. It is important to note that the coordination of skipping and limiting
+ *      rows is dictated by the `first_row` and `num_rows` members of each block
+ *      in the block list, *not* the `skip_rows` and `num_rows` members of this
+ *      struct.
+ *
+ *      This is because the first row and number of rows to process for each
+ *      block needs to be handled at the individual block level in order to
+ *      correctly support avro multi-block files.
+ *
+ *      See also the `block_desc_s` struct.
+ */
+struct file_metadata {
+  std::map<std::string, std::string> user_data;
+  std::string codec         = "";
+  uint64_t sync_marker[2]   = {0, 0};
+  size_t metadata_size      = 0;
+  size_t total_data_size    = 0;
+  size_t selected_data_size = 0;
+  size_type num_rows        = 0;
+  size_type skip_rows       = 0;
+  size_type total_num_rows  = 0;
+  uint32_t max_block_size   = 0;
+  std::vector<schema_entry> schema;
+  std::vector<block_desc_s> block_list;
+  std::vector<column_desc> columns;
+};
+
+/**
+ * @brief Extract AVRO schema from JSON string
+ */
+class schema_parser {
+ protected:
+  enum { MAX_SCHEMA_DEPTH = 32 };
+
+ public:
+  schema_parser() {}
+  bool parse(std::vector<schema_entry>& schema, std::string const& str);
+
+ protected:
+  [[nodiscard]] bool more_data() const { return (m_cur < m_end); }
+  std::string get_str();
+
+ protected:
+  char const* m_base;
+  char const* m_cur;
+  char const* m_end;
+};
+
+/**
+ * @brief AVRO file container parsing class
+ */
+class container {
+ public:
+  container(uint8_t const* base, size_t len) noexcept
+    : m_base{base}, m_start{base}, m_cur{base}, m_end{base + len}
+  {
+  }
+
+  [[nodiscard]] auto bytecount() const { return m_cur - m_start; }
+
+  template <typename T>
+  T get_raw()
+  {
+    if (m_cur + sizeof(T) > m_end) return T{};
+    T val;
+    memcpy(&val, m_cur, sizeof(T));
+    m_cur += sizeof(T);
+    return val;
+  }
+
+  template <typename T>
+  T get_encoded();
+
+ public:
+  bool parse(file_metadata* md, size_t max_num_rows = 0x7fff'ffff, size_t first_row = 0);
+
+ protected:
+  // Base address of the file data.  This will always point to the file's metadata.
+  uint8_t const* m_base;
+
+  // Start, current, and end pointers for the file.  These pointers refer to the
+  // actual data content of the file, not the metadata.  `m_cur` and `m_start`
+  // will only ever differ if a user has requested `read_avro()` to skip rows;
+  // in this case, `m_start` will be the base address of the block that contains
+  // the first row to be processed.  `m_cur` is updated as the file is parsed,
+  // until either `m_end` is reached, or the number of rows requested by the user
+  // is reached.
+  uint8_t const* m_start;
+  uint8_t const* m_cur;
+  uint8_t const* m_end;
+};
+
+}  // namespace avro
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/avro/avro_common.hpp b/cpp/src/io/avro/avro_common.hpp
new file mode 100644
index 0000000..0058d23
--- /dev/null
+++ b/cpp/src/io/avro/avro_common.hpp
@@ -0,0 +1,168 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <io/utilities/column_buffer.hpp>
+
+#include <cstdint>
+#include <cstdio>
+
+namespace cudf {
+namespace io {
+namespace avro {
+struct block_desc_s {
+  block_desc_s() = default;  // required to compile on ctk-12.2 + aarch64
+
+  explicit constexpr block_desc_s(
+    size_t offset_, uint32_t size_, uint32_t row_offset_, uint32_t first_row_, uint32_t num_rows_)
+    : offset(offset_),
+      size(size_),
+      row_offset(row_offset_),
+      first_row(first_row_),
+      num_rows(num_rows_)
+  {
+  }
+
+  // Offset of this block, in bytes, from the start of the file.
+  size_t offset;
+
+  // Size of this block, in bytes.
+  uint32_t size;
+
+  // The absolute row offset that needs to be added to each row index in order
+  // to derive the offset of the decoded data in the destination array.  E.g.
+  // `const ptrdiff_t dst_row = ((row - first_row) + row_offset)`.  See
+  // `avro_decode_row()` for details.
+  uint32_t row_offset;
+
+  // The index of the first row to be *saved* from this block.  That is, the
+  // number of rows to skip in this block before starting to save values.  If
+  // this is 0, then no rows will be skipped (all rows will be saved).  If a
+  // user has requested `read_avro()` to skip rows, that will materialize as a
+  // non-zero `first_row` value in the appropriate block containing the first
+  // row to be saved.
+  //
+  // N.B. We explicitly use the word "saved" here, not "decoded".  Technically,
+  //      all rows are decoded, one column at a time, as the process of decoding
+  //      a column value is what informs us of the value's size in bytes (in its
+  //      encoded form), and thus, where the next column starts.  However, we
+  //      only *save* these decoded values based on the `first_row`.
+  uint32_t first_row;
+
+  // The number of rows to save from this block.  If a user has requested
+  // `read_avro()` to limit the number of rows to return, this will materialize
+  // as a `num_rows` value less than the total number of rows in the appropriate
+  // block.  Otherwise, `num_rows` will be equal to the total number of rows in
+  // the block, after skipping `first_row` rows (if applicable).
+  //
+  // N.B. Unlike `first_rows`, where all rows and columns are decoded prior to
+  //      reaching the point we've been requested to start *saving* values --
+  //      once the `num_rows` limit has been reached, no further decoding takes
+  //      place.
+  uint32_t num_rows;
+};
+
+enum type_kind_e {
+  type_not_set = -1,
+  // Primitive types
+  type_null = 0,
+  type_boolean,
+  type_int,
+  type_long,
+  type_float,
+  type_double,
+  type_bytes,
+  type_string,
+  // Complex types
+  type_enum,
+  type_record,
+  type_union,
+  type_array,
+  type_fixed,
+  // Logical types
+  type_decimal,
+  type_uuid,
+  type_date,
+  type_time_millis,
+  type_time_micros,
+  type_timestamp_millis,
+  type_timestamp_micros,
+  type_local_timestamp_millis,
+  type_local_timestamp_micros,
+  type_duration,
+};
+
+enum logicaltype_kind_e {
+  logicaltype_not_set = 0,
+  // N.B. We intentionally mirror the logicaltype enum values with their
+  //      equivalent type enum value, as this allows us to cast the type
+  //      value directly to a logical type without an intermediate
+  //      mapping step, and vice versa, e.g.:
+  //
+  //        auto kind = type_date;
+  //        auto logical_kind = static_cast<logical_kind_e>(type_date);
+  //        // logical_kind == logicaltype_kind_e::logicaltype_date
+  //
+  //      And:
+  //
+  //        auto logical_kind = logicaltype_date;
+  //        auto kind = static_cast<type_kind_e>(logical_kind);
+  //        // kind == type_kind_e::type_date
+  //
+  logicaltype_decimal = type_decimal,
+  logicaltype_uuid,
+  logicaltype_date,
+  logicaltype_time_millis,
+  logicaltype_time_micros,
+  logicaltype_timestamp_millis,
+  logicaltype_timestamp_micros,
+  logicaltype_local_timestamp_millis,
+  logicaltype_local_timestamp_micros,
+  logicaltype_duration,
+};
+
+/**
+ * @brief Determines if the supplied logical type is currently supported.
+ *
+ * @param[in] logical_kind Supplies the logicaltype_kind_e enum value.
+ *
+ * @return true if the logical type is supported, false otherwise.
+ */
+inline constexpr bool is_supported_logical_type(logicaltype_kind_e logical_kind)
+{
+  switch (logical_kind) {
+    case logicaltype_date: return true;
+
+    case logicaltype_not_set: [[fallthrough]];
+    case logicaltype_decimal: [[fallthrough]];
+    case logicaltype_uuid: [[fallthrough]];
+    case logicaltype_time_millis: [[fallthrough]];
+    case logicaltype_time_micros: [[fallthrough]];
+    case logicaltype_timestamp_millis: [[fallthrough]];
+    case logicaltype_timestamp_micros: [[fallthrough]];
+    case logicaltype_local_timestamp_millis: [[fallthrough]];
+    case logicaltype_local_timestamp_micros: [[fallthrough]];
+    case logicaltype_duration: [[fallthrough]];
+    default: return false;
+  }
+}
+
+using cudf::io::detail::string_index_pair;
+
+}  // namespace avro
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/avro/avro_gpu.cu b/cpp/src/io/avro/avro_gpu.cu
new file mode 100644
index 0000000..365f6d6
--- /dev/null
+++ b/cpp/src/io/avro/avro_gpu.cu
@@ -0,0 +1,444 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "avro_gpu.hpp"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+using cudf::device_span;
+
+namespace cudf {
+namespace io {
+namespace avro {
+namespace gpu {
+constexpr int num_warps             = 16;
+constexpr int max_shared_schema_len = 1000;
+
+/*
+ * Avro varint encoding - see
+ * https://avro.apache.org/docs/1.2.0/spec.html#binary_encoding
+ */
+static inline int64_t __device__ avro_decode_zigzag_varint(uint8_t const*& cur, uint8_t const* end)
+{
+  uint64_t u = 0;
+  if (cur < end) {
+    u = *cur++;
+    if (u > 0x7f) {
+      uint64_t scale = 128;
+      u &= 0x7f;
+      while (cur < end) {
+        uint32_t c = *cur++;
+        u += (c & 0x7f) * scale;
+        scale <<= 7;
+        if (c < 0x80) break;
+      }
+    }
+  }
+  return (int64_t)((u >> 1u) ^ -(int64_t)(u & 1));
+}
+
+/**
+ * @brief Decode a row of values given an avro schema
+ *
+ * @param[in] schema Schema description
+ * @param[in] schema_g Global schema in device mem
+ * @param[in] schema_len Number of schema entries
+ * @param[in] first_row First row to start saving decoded data
+ * @param[in] row Current row
+ * @param[in] end_row One past the last row to save
+ * @param[in] row_offset Absolute row offset of this row in the
+ *                       destination data.
+ * @param[in] cur Current input data pointer
+ * @param[in] end End of input data
+ * @param[in] global_Dictionary Global dictionary entries
+ * @param[out] skipped_row Whether the row was skipped; set to false
+ *                         if the row was saved (caller should ensure
+ *                         this is initialized to true)
+ *
+ * @return data pointer at the end of the row (start of next row)
+ */
+static uint8_t const* __device__
+avro_decode_row(schemadesc_s const* schema,
+                schemadesc_s* schema_g,
+                uint32_t schema_len,
+                size_t first_row,
+                size_t row,
+                size_t end_row,
+                size_t row_offset,
+                uint8_t const* cur,
+                uint8_t const* end,
+                device_span<string_index_pair const> global_dictionary,
+                bool* skipped_row)
+{
+  // `dst_row` depicts the offset of the decoded row in the destination
+  // `dataptr` array, adjusted for skip rows, if applicable.  For example,
+  // if `row` == 5 and `first_row` == 3, then this is the second row we'll
+  // be storing (5-3).  If `first_row` is greater than `row`, this routine
+  // simply decodes the row and adjusts the returned data pointer, but does
+  // *not* actually store the row in the destination `dataptr` array.  This
+  // is enforced by all writes to the destination memory being guarded in the
+  // following fashion:
+  //    if (dataptr != nullptr && dst_row > 0) {
+  //      static_cast<int32_t*>(dataptr)[dst_row] = static_cast<int32_t>(v);
+  //      *skipped_row = false;
+  //    }
+  // The actual value is calculated by subtracting the first row from this given
+  // row value, and then adding the absolute row offset.  The row offset is
+  // required to ensure we write to the correct destination location when we're
+  // processing multiple blocks, i.e. this block could only have 10 rows, but
+  // it's the 3rd block (where each block has 10 rows), so we need to write to
+  // the 30th row in the destination array.
+  ptrdiff_t const dst_row =
+    (row >= first_row && row < end_row ? static_cast<ptrdiff_t>((row - first_row) + row_offset)
+                                       : -1);
+  // Critical invariant checks: dst_row should be -1 or greater, and
+  // *skipped_row should always be true at this point (we set it to false only
+  // if we write the decoded value to the destination array).
+  if (dst_row < -1) { CUDF_UNREACHABLE("dst_row should be -1 or greater"); }
+  if (*skipped_row != true) { CUDF_UNREACHABLE("skipped_row should be true"); }
+
+  uint32_t array_start = 0, array_repeat_count = 0;
+  int array_children = 0;
+  for (uint32_t i = 0; i < schema_len;) {
+    type_kind_e kind                = schema[i].kind;
+    logicaltype_kind_e logical_kind = schema[i].logical_kind;
+    int skip                        = 0;
+
+    if (is_supported_logical_type(logical_kind)) { kind = static_cast<type_kind_e>(logical_kind); }
+
+    if (kind == type_union) {
+      int skip_after;
+      if (cur >= end) break;
+      skip       = (*cur++) >> 1;  // NOTE: Assumes 1-byte union member
+      skip_after = schema[i].count - skip - 1;
+      ++i;
+      while (skip > 0 && i < schema_len) {
+        if (schema[i].kind >= type_record) { skip += schema[i].count; }
+        ++i;
+        --skip;
+      }
+      if (i >= schema_len || skip_after < 0) break;
+      kind         = schema[i].kind;
+      logical_kind = schema[i].logical_kind;
+      if (is_supported_logical_type(logical_kind)) {
+        kind = static_cast<type_kind_e>(logical_kind);
+      }
+      skip = skip_after;
+    }
+
+    void* dataptr = schema[i].dataptr;
+    switch (kind) {
+      case type_null:
+        if (dataptr != nullptr && dst_row >= 0) {
+          atomicAnd(static_cast<uint32_t*>(dataptr) + (dst_row >> 5), ~(1 << (dst_row & 0x1f)));
+          atomicAdd(&schema_g[i].count, 1);
+          *skipped_row = false;
+        }
+        break;
+
+      case type_int: {
+        int64_t v = avro_decode_zigzag_varint(cur, end);
+        if (dataptr != nullptr && dst_row >= 0) {
+          static_cast<int32_t*>(dataptr)[dst_row] = static_cast<int32_t>(v);
+          *skipped_row                            = false;
+        }
+      } break;
+
+      case type_long: {
+        int64_t v = avro_decode_zigzag_varint(cur, end);
+        if (dataptr != nullptr && dst_row >= 0) {
+          static_cast<int64_t*>(dataptr)[dst_row] = v;
+          *skipped_row                            = false;
+        }
+      } break;
+
+      case type_bytes: [[fallthrough]];
+      case type_string: [[fallthrough]];
+      case type_enum: {
+        int64_t v       = avro_decode_zigzag_varint(cur, end);
+        size_t count    = 0;
+        char const* ptr = nullptr;
+        if (kind == type_enum) {  // dictionary
+          size_t idx = schema[i].count + v;
+          if (idx < global_dictionary.size()) {
+            ptr   = global_dictionary[idx].first;
+            count = global_dictionary[idx].second;
+          }
+        } else if (v >= 0 && cur + v <= end) {  // string or bytes
+          ptr   = reinterpret_cast<char const*>(cur);
+          count = (size_t)v;
+          cur += count;
+        }
+        if (dataptr != nullptr && dst_row >= 0) {
+          static_cast<string_index_pair*>(dataptr)[dst_row].first  = ptr;
+          static_cast<string_index_pair*>(dataptr)[dst_row].second = count;
+          *skipped_row                                             = false;
+        }
+      } break;
+
+      case type_float:
+        if (dataptr != nullptr && dst_row >= 0) {
+          uint32_t v;
+          if (cur + 3 < end) {
+            v = unaligned_load32(cur);
+            cur += 4;
+          } else {
+            v = 0;
+          }
+          static_cast<uint32_t*>(dataptr)[dst_row] = v;
+          *skipped_row                             = false;
+        } else {
+          cur += 4;
+        }
+        break;
+
+      case type_double:
+        if (dataptr != nullptr && dst_row >= 0) {
+          uint64_t v;
+          if (cur + 7 < end) {
+            v = unaligned_load64(cur);
+            cur += 8;
+          } else {
+            v = 0;
+          }
+          static_cast<uint64_t*>(dataptr)[dst_row] = v;
+          *skipped_row                             = false;
+        } else {
+          cur += 8;
+        }
+        break;
+
+      case type_boolean:
+        if (dataptr != nullptr && dst_row >= 0) {
+          uint8_t v                               = (cur < end) ? *cur : 0;
+          static_cast<uint8_t*>(dataptr)[dst_row] = (v) ? 1 : 0;
+          *skipped_row                            = false;
+        }
+        cur++;
+        break;
+
+      case type_array: {
+        int32_t array_block_count = avro_decode_zigzag_varint(cur, end);
+        if (array_block_count < 0) {
+          avro_decode_zigzag_varint(cur, end);  // block size in bytes, ignored
+          array_block_count = -array_block_count;
+        }
+        array_start        = i;
+        array_repeat_count = array_block_count;
+        array_children     = 1;
+        if (array_repeat_count == 0) {
+          skip += schema[i].count;  // Should always be 1
+        }
+      } break;
+
+      case type_duration: {
+        // A duration logical type annotates Avro fixed type of size 12, which
+        // stores three little-endian unsigned integers that represent durations
+        // at different granularities of time. The first stores a number in
+        // months, the second stores a number in days, and the third stores a
+        // number in milliseconds.
+        CUDF_UNREACHABLE("avro type 'duration' not yet implemented");
+      } break;
+
+      // N.B. These aren't handled yet, see the discussion on
+      //      https://github.com/rapidsai/cudf/pull/12788.  The decoding logic
+      //      is correct, though, so there's no harm in having them here.
+      case type_timestamp_millis: [[fallthrough]];
+      case type_timestamp_micros: [[fallthrough]];
+      case type_local_timestamp_millis: [[fallthrough]];
+      case type_local_timestamp_micros: [[fallthrough]];
+      case type_time_millis: [[fallthrough]];
+      case type_time_micros: {
+        // N.B. time-millis is stored as a 32-bit int, however, cudf expects an
+        //      int64 for DURATION_MILLISECONDS.  From our perspective, the fact
+        //      that time-millis comes from a 32-bit int is hidden from us by
+        //      way of the zig-zag varint encoding, so we can safely treat them
+        //      both as int64_t.  Everything else is 64-bit in both avro and
+        //      cudf.
+        CUDF_UNREACHABLE("avro time/timestamp types not yet implemented");
+        //
+        // When we do implement these, the following decoding logic should
+        // be correct:
+        //
+        // int64_t v = avro_decode_zigzag_varint(cur, end);
+        // if (dataptr != nullptr && dst_row >= 0) {
+        //   static_cast<int64_t*>(dataptr)[dst_row] = v;
+        //   *skipped_row = false;
+        // }
+      } break;
+
+      case type_date: {
+        int64_t v = avro_decode_zigzag_varint(cur, end);
+        if (dataptr != nullptr && dst_row >= 0) {
+          static_cast<int32_t*>(dataptr)[dst_row] = static_cast<int32_t>(v);
+          *skipped_row                            = false;
+        }
+      } break;
+    }
+
+    if (array_repeat_count != 0) {
+      array_children--;
+      if (schema[i].kind >= type_record) { array_children += schema[i].count; }
+    }
+    i++;
+    while (skip > 0 && i < schema_len) {
+      if (schema[i].kind >= type_record) { skip += schema[i].count; }
+      ++i;
+      --skip;
+    }
+    // If within an array, check if we reached the last item
+    if (array_repeat_count != 0 && array_children <= 0 && cur < end) {
+      if (!--array_repeat_count) {
+        i = array_start;  // Restart at the array parent
+      } else {
+        i              = array_start + 1;  // Restart after the array parent
+        array_children = schema[array_start].count;
+      }
+    }
+  }
+  return cur;
+}
+
+/**
+ * @brief Decode column data
+ *
+ * @param[in] blocks Data block descriptions
+ * @param[in] schema Schema description
+ * @param[in] global_Dictionary Global dictionary entries
+ * @param[in] avro_data Raw block data
+ * @param[in] schema_len Number of entries in schema
+ * @param[in] min_row_size Minimum size in bytes of a row
+ */
+// blockDim {32,num_warps,1}
+__global__ void __launch_bounds__(num_warps * 32, 2)
+  gpuDecodeAvroColumnData(device_span<block_desc_s const> blocks,
+                          schemadesc_s* schema_g,
+                          device_span<string_index_pair const> global_dictionary,
+                          uint8_t const* avro_data,
+                          uint32_t schema_len,
+                          uint32_t min_row_size)
+{
+  __shared__ __align__(8) schemadesc_s g_shared_schema[max_shared_schema_len];
+  __shared__ __align__(8) block_desc_s blk_g[num_warps];
+
+  schemadesc_s* schema;
+  block_desc_s* const blk = &blk_g[threadIdx.y];
+  uint32_t block_id       = blockIdx.x * num_warps + threadIdx.y;
+
+  // Fetch schema into shared mem if possible
+  if (schema_len <= max_shared_schema_len) {
+    for (int i = threadIdx.y * 32 + threadIdx.x; i < schema_len; i += num_warps * 32) {
+      g_shared_schema[i] = schema_g[i];
+    }
+    __syncthreads();
+    schema = g_shared_schema;
+  } else {
+    schema = schema_g;
+  }
+
+  if (block_id < blocks.size() and threadIdx.x == 0) { *blk = blocks[block_id]; }
+  __syncthreads();
+  if (block_id >= blocks.size()) { return; }
+
+  uint8_t const* cur      = avro_data + blk->offset;
+  uint8_t const* end      = cur + blk->size;
+  size_t first_row        = blk->first_row + blk->row_offset;
+  size_t cur_row          = blk->row_offset;
+  size_t end_row          = first_row + blk->num_rows;
+  uint32_t rows_remaining = blk->num_rows;
+
+  while (cur < end) {
+    uint32_t nrows;
+    uint8_t const* start = cur;
+
+    if (cur + min_row_size * rows_remaining == end) {
+      // We're dealing with predictable fixed-size rows, which means we can
+      // process up to 32 rows (warp-width) at a time.  This will be the case
+      // when we're dealing with fixed-size data, e.g. of floats or doubles,
+      // which are always 4 or 8 bytes respectively.
+      nrows = min(rows_remaining, 32);
+      cur += threadIdx.x * min_row_size;
+    } else {
+      // We're dealing with variable-size data, so only one row can be processed
+      // by one thread at a time.
+      nrows = 1;
+    }
+
+    if (threadIdx.x < nrows) {
+      bool skipped_row = true;
+      cur              = avro_decode_row(schema,
+                            schema_g,
+                            schema_len,
+                            first_row,
+                            cur_row + threadIdx.x,
+                            end_row,
+                            blk->row_offset,
+                            cur,
+                            end,
+                            global_dictionary,
+                            &skipped_row);
+      if (!skipped_row) { rows_remaining -= nrows; }
+    }
+    __syncwarp();
+
+    cur_row += nrows;
+    if (nrows == 1) {
+      // Only lane 0 (i.e. 'threadIdx.x == 0') was active, so we need to
+      // broadcast the new value of 'cur' and 'rows_remaining' to all other
+      // threads in the warp.
+      cur = start + shuffle(static_cast<uint32_t>(cur - start));
+      // rows_remaining is already uint32_t, so we don't need to do the
+      // start + shuffle(this - start) dance like we do above.
+      rows_remaining = shuffle(rows_remaining);
+    } else if (nrows > 1) {
+      cur = start + (nrows * min_row_size);
+    }
+  }
+}
+
+/**
+ * @brief Launches kernel for decoding column data
+ *
+ * @param[in] blocks Data block descriptions
+ * @param[in] schema Schema description
+ * @param[in] global_dictionary Global dictionary entries
+ * @param[in] avro_data Raw block data
+ * @param[in] schema_len Number of entries in schema
+ * @param[in] min_row_size Minimum size in bytes of a row
+ * @param[in] stream CUDA stream to use
+ */
+void DecodeAvroColumnData(device_span<block_desc_s const> blocks,
+                          schemadesc_s* schema,
+                          device_span<string_index_pair const> global_dictionary,
+                          uint8_t const* avro_data,
+                          uint32_t schema_len,
+                          uint32_t min_row_size,
+                          rmm::cuda_stream_view stream)
+{
+  // num_warps warps per threadblock
+  dim3 const dim_block(32, num_warps);
+  // 1 warp per datablock, num_warps datablocks per threadblock
+  dim3 const dim_grid((blocks.size() + num_warps - 1) / num_warps, 1);
+
+  gpuDecodeAvroColumnData<<<dim_grid, dim_block, 0, stream.value()>>>(
+    blocks, schema, global_dictionary, avro_data, schema_len, min_row_size);
+}
+
+}  // namespace gpu
+}  // namespace avro
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/avro/avro_gpu.hpp b/cpp/src/io/avro/avro_gpu.hpp
new file mode 100644
index 0000000..713b855
--- /dev/null
+++ b/cpp/src/io/avro/avro_gpu.hpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "avro_common.hpp"
+
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+namespace avro {
+namespace gpu {
+
+/**
+ * @brief Struct to describe the avro schema
+ */
+struct schemadesc_s {
+  cudf::io::avro::type_kind_e kind;                 // avro type kind
+  cudf::io::avro::logicaltype_kind_e logical_kind;  // avro logicaltype kind
+  uint32_t count;  // for records/unions: number of following child columns, for nulls: global
+                   // null_count, for enums: dictionary ofs
+  void* dataptr;   // Ptr to column data, or null if column not selected
+};
+
+/**
+ * @brief Launches kernel for decoding column data
+ *
+ * @param[in] blocks Data block descriptions
+ * @param[in] schema Schema description
+ * @param[in] global_dictionary Global dictionary entries
+ * @param[in] avro_data Raw block data
+ * @param[in] schema_len Number of entries in schema
+ * @param[in] min_row_size Minimum size in bytes of a row
+ * @param[in] stream CUDA stream to use
+ */
+void DecodeAvroColumnData(cudf::device_span<block_desc_s const> blocks,
+                          schemadesc_s* schema,
+                          cudf::device_span<string_index_pair const> global_dictionary,
+                          uint8_t const* avro_data,
+                          uint32_t schema_len,
+                          uint32_t min_row_size,
+                          rmm::cuda_stream_view stream);
+
+}  // namespace gpu
+}  // namespace avro
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/avro/reader_impl.cu b/cpp/src/io/avro/reader_impl.cu
new file mode 100644
index 0000000..f73e1db
--- /dev/null
+++ b/cpp/src/io/avro/reader_impl.cu
@@ -0,0 +1,624 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "avro.hpp"
+#include "avro_gpu.hpp"
+
+#include <io/comp/gpuinflate.hpp>
+#include <io/utilities/column_buffer.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/detail/avro.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/equal.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/transform_output_iterator.h>
+#include <thrust/tabulate.h>
+
+#include <nvcomp/snappy.h>
+
+#include <memory>
+#include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
+
+using cudf::device_span;
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace avro {
+
+// Import functionality that's independent of legacy code
+using namespace cudf::io::avro;
+using namespace cudf::io;
+
+namespace {
+/**
+ * @brief Function that translates Avro data kind to cuDF type enum
+ */
+type_id to_type_id(avro::schema_entry const* col)
+{
+  avro::type_kind_e kind;
+
+  // N.B. The switch statement seems a bit ridiculous for a single type, but the
+  //      plan is to incrementally add more types to it as support is added for
+  //      them in the future.
+  switch (col->logical_kind) {
+    case avro::logicaltype_date: kind = static_cast<avro::type_kind_e>(col->logical_kind); break;
+    case avro::logicaltype_not_set: [[fallthrough]];
+    default: kind = col->kind; break;
+  }
+
+  switch (kind) {
+    case avro::type_boolean: return type_id::BOOL8;
+    case avro::type_int: return type_id::INT32;
+    case avro::type_long: return type_id::INT64;
+    case avro::type_float: return type_id::FLOAT32;
+    case avro::type_double: return type_id::FLOAT64;
+    case avro::type_bytes: [[fallthrough]];
+    case avro::type_string: return type_id::STRING;
+    case avro::type_date: return type_id::TIMESTAMP_DAYS;
+    case avro::type_timestamp_millis: return type_id::TIMESTAMP_MILLISECONDS;
+    case avro::type_timestamp_micros: return type_id::TIMESTAMP_MICROSECONDS;
+    case avro::type_local_timestamp_millis: return type_id::TIMESTAMP_MILLISECONDS;
+    case avro::type_local_timestamp_micros: return type_id::TIMESTAMP_MICROSECONDS;
+    case avro::type_enum: return (!col->symbols.empty()) ? type_id::STRING : type_id::INT32;
+    // The avro time-millis and time-micros types are closest to Arrow's
+    // TIME32 and TIME64.  They're single-day units, i.e. they won't exceed
+    // 23:59:59.9999 (or .999999 for micros).  There's no equivalent cudf
+    // type for this; type_id::DURATION_MILLISECONDS/MICROSECONDS are close,
+    // but they're not semantically the same.
+    case avro::type_time_millis: [[fallthrough]];
+    case avro::type_time_micros: [[fallthrough]];
+    // There's no cudf equivalent for the avro duration type, which is a fixed
+    // 12 byte value which stores three little-endian unsigned 32-bit integers
+    // representing months, days, and milliseconds, respectively.
+    case avro::type_duration: [[fallthrough]];
+    default: return type_id::EMPTY;
+  }
+}
+
+}  // namespace
+
+/**
+ * @brief A helper wrapper for Avro file metadata. Provides some additional
+ * convenience methods for initializing and accessing the metadata and schema
+ */
+class metadata : public file_metadata {
+ public:
+  explicit metadata(datasource* const src) : source(src) {}
+
+  /**
+   * @brief Initializes the parser and filters down to a subset of rows
+   *
+   * @param[in,out] row_start Starting row of the selection
+   * @param[in,out] row_count Total number of rows selected
+   */
+  void init_and_select_rows(size_type& row_start, size_type& row_count)
+  {
+    auto const buffer = source->host_read(0, source->size());
+    avro::container pod(buffer->data(), buffer->size());
+    CUDF_EXPECTS(pod.parse(this, row_count, row_start), "Cannot parse metadata");
+    row_start = skip_rows;
+    row_count = num_rows;
+  }
+
+  /**
+   * @brief Filters and reduces down to a selection of columns
+   *
+   * @param[in] use_names List of column names to select
+   *
+   * @return List of column names
+   */
+  auto select_columns(std::vector<std::string> use_names)
+  {
+    std::vector<std::pair<int, std::string>> selection;
+
+    auto const num_avro_columns = static_cast<int>(columns.size());
+    if (!use_names.empty()) {
+      int index = 0;
+      for (auto const& use_name : use_names) {
+        for (int i = 0; i < num_avro_columns; ++i, ++index) {
+          if (index >= num_avro_columns) { index = 0; }
+          if (columns[index].name == use_name &&
+              type_id::EMPTY != to_type_id(&schema[columns[index].schema_data_idx])) {
+            selection.emplace_back(index, columns[index].name);
+            index++;
+            break;
+          }
+        }
+      }
+      CUDF_EXPECTS(selection.size() > 0, "Filtered out all columns");
+    } else {
+      for (int i = 0; i < num_avro_columns; ++i) {
+        // Exclude array columns (unsupported)
+        bool column_in_array = false;
+        for (int parent_idx = schema[columns[i].schema_data_idx].parent_idx; parent_idx > 0;
+             parent_idx     = schema[parent_idx].parent_idx) {
+          if (schema[parent_idx].kind == avro::type_array) {
+            column_in_array = true;
+            break;
+          }
+        }
+
+        if (!column_in_array) {
+          auto col_type = to_type_id(&schema[columns[i].schema_data_idx]);
+          CUDF_EXPECTS(col_type != type_id::EMPTY, "Unsupported data type");
+          selection.emplace_back(i, columns[i].name);
+        }
+      }
+    }
+
+    return selection;
+  }
+
+ private:
+  datasource* const source;
+};
+
+rmm::device_buffer decompress_data(datasource& source,
+                                   metadata& meta,
+                                   rmm::device_buffer const& comp_block_data,
+                                   rmm::cuda_stream_view stream)
+{
+  if (meta.codec == "deflate") {
+    auto inflate_in =
+      cudf::detail::hostdevice_vector<device_span<uint8_t const>>(meta.block_list.size(), stream);
+    auto inflate_out =
+      cudf::detail::hostdevice_vector<device_span<uint8_t>>(meta.block_list.size(), stream);
+    auto inflate_stats =
+      cudf::detail::hostdevice_vector<compression_result>(meta.block_list.size(), stream);
+    thrust::fill(rmm::exec_policy(stream),
+                 inflate_stats.d_begin(),
+                 inflate_stats.d_end(),
+                 compression_result{0, compression_status::FAILURE});
+
+    // Guess an initial maximum uncompressed block size. We estimate the compression factor is two
+    // and round up to the next multiple of 4096 bytes.
+    uint32_t const initial_blk_len = meta.max_block_size * 2 + (meta.max_block_size * 2) % 4096;
+    size_t const uncomp_size       = initial_blk_len * meta.block_list.size();
+
+    rmm::device_buffer decomp_block_data(uncomp_size, stream);
+
+    auto const base_offset = meta.block_list[0].offset;
+    for (size_t i = 0, dst_pos = 0; i < meta.block_list.size(); i++) {
+      auto const src_pos = meta.block_list[i].offset - base_offset;
+
+      inflate_in[i]  = {static_cast<uint8_t const*>(comp_block_data.data()) + src_pos,
+                        meta.block_list[i].size};
+      inflate_out[i] = {static_cast<uint8_t*>(decomp_block_data.data()) + dst_pos, initial_blk_len};
+
+      // Update blocks offsets & sizes to refer to uncompressed data
+      meta.block_list[i].offset = dst_pos;
+      meta.block_list[i].size   = static_cast<uint32_t>(inflate_out[i].size());
+      dst_pos += meta.block_list[i].size;
+    }
+    inflate_in.host_to_device_async(stream);
+
+    for (int loop_cnt = 0; loop_cnt < 2; loop_cnt++) {
+      inflate_out.host_to_device_async(stream);
+      gpuinflate(inflate_in, inflate_out, inflate_stats, gzip_header_included::NO, stream);
+      inflate_stats.device_to_host_sync(stream);
+
+      // Check if larger output is required, as it's not known ahead of time
+      if (loop_cnt == 0) {
+        std::vector<size_t> actual_uncomp_sizes;
+        actual_uncomp_sizes.reserve(inflate_out.size());
+        std::transform(inflate_out.begin(),
+                       inflate_out.end(),
+                       inflate_stats.begin(),
+                       std::back_inserter(actual_uncomp_sizes),
+                       [](auto const& inf_out, auto const& inf_stats) {
+                         // If error status is OUTPUT_OVERFLOW, the `bytes_written` field
+                         // actually contains the uncompressed data size
+                         return inf_stats.status == compression_status::OUTPUT_OVERFLOW
+                                  ? std::max(inf_out.size(), inf_stats.bytes_written)
+                                  : inf_out.size();
+                       });
+        auto const total_actual_uncomp_size =
+          std::accumulate(actual_uncomp_sizes.cbegin(), actual_uncomp_sizes.cend(), 0ul);
+        if (total_actual_uncomp_size > uncomp_size) {
+          decomp_block_data.resize(total_actual_uncomp_size, stream);
+          for (size_t i = 0; i < meta.block_list.size(); ++i) {
+            meta.block_list[i].offset =
+              i > 0 ? (meta.block_list[i - 1].size + meta.block_list[i - 1].offset) : 0;
+            meta.block_list[i].size = static_cast<uint32_t>(actual_uncomp_sizes[i]);
+
+            inflate_out[i] = {
+              static_cast<uint8_t*>(decomp_block_data.data()) + meta.block_list[i].offset,
+              meta.block_list[i].size};
+          }
+        } else {
+          break;
+        }
+      }
+    }
+
+    return decomp_block_data;
+  } else if (meta.codec == "snappy") {
+    size_t const num_blocks = meta.block_list.size();
+
+    // comp_block_data contains contents of the avro file starting from the first block, excluding
+    // file header. meta.block_list[i].offset refers to offset of block i in the file, including
+    // file header.
+    // Find ptrs to each compressed block in comp_block_data by removing header offset.
+    cudf::detail::hostdevice_vector<void const*> compressed_data_ptrs(num_blocks, stream);
+    std::transform(meta.block_list.begin(),
+                   meta.block_list.end(),
+                   compressed_data_ptrs.host_ptr(),
+                   [&](auto const& block) {
+                     return static_cast<std::byte const*>(comp_block_data.data()) +
+                            (block.offset - meta.block_list[0].offset);
+                   });
+    compressed_data_ptrs.host_to_device_async(stream);
+
+    cudf::detail::hostdevice_vector<size_t> compressed_data_sizes(num_blocks, stream);
+    std::transform(meta.block_list.begin(),
+                   meta.block_list.end(),
+                   compressed_data_sizes.host_ptr(),
+                   [](auto const& block) { return block.size; });
+    compressed_data_sizes.host_to_device_async(stream);
+
+    cudf::detail::hostdevice_vector<size_t> uncompressed_data_sizes(num_blocks, stream);
+    nvcompStatus_t status =
+      nvcompBatchedSnappyGetDecompressSizeAsync(compressed_data_ptrs.device_ptr(),
+                                                compressed_data_sizes.device_ptr(),
+                                                uncompressed_data_sizes.device_ptr(),
+                                                num_blocks,
+                                                stream.value());
+    CUDF_EXPECTS(status == nvcompStatus_t::nvcompSuccess,
+                 "Unable to get uncompressed sizes for snappy compressed blocks");
+    uncompressed_data_sizes.device_to_host_sync(stream);
+
+    size_t const uncompressed_data_size =
+      std::reduce(uncompressed_data_sizes.begin(), uncompressed_data_sizes.end());
+    size_t const max_uncomp_block_size = std::reduce(
+      uncompressed_data_sizes.begin(), uncompressed_data_sizes.end(), 0, thrust::maximum<size_t>());
+
+    size_t temp_size;
+    status =
+      nvcompBatchedSnappyDecompressGetTempSize(num_blocks, max_uncomp_block_size, &temp_size);
+    CUDF_EXPECTS(status == nvcompStatus_t::nvcompSuccess,
+                 "Unable to get scratch size for snappy decompression");
+
+    rmm::device_buffer scratch(temp_size, stream);
+    rmm::device_buffer decomp_block_data(uncompressed_data_size, stream);
+    rmm::device_uvector<void*> uncompressed_data_ptrs(num_blocks, stream);
+    cudf::detail::hostdevice_vector<size_t> uncompressed_data_offsets(num_blocks, stream);
+
+    std::exclusive_scan(uncompressed_data_sizes.begin(),
+                        uncompressed_data_sizes.end(),
+                        uncompressed_data_offsets.begin(),
+                        0);
+    uncompressed_data_offsets.host_to_device_async(stream);
+
+    thrust::tabulate(rmm::exec_policy(stream),
+                     uncompressed_data_ptrs.begin(),
+                     uncompressed_data_ptrs.end(),
+                     [off  = uncompressed_data_offsets.device_ptr(),
+                      data = static_cast<std::byte*>(decomp_block_data.data())] __device__(int i) {
+                       return data + off[i];
+                     });
+
+    rmm::device_uvector<size_t> actual_uncompressed_data_sizes(num_blocks, stream);
+    rmm::device_uvector<nvcompStatus_t> statuses(num_blocks, stream);
+
+    status = nvcompBatchedSnappyDecompressAsync(compressed_data_ptrs.device_ptr(),
+                                                compressed_data_sizes.device_ptr(),
+                                                uncompressed_data_sizes.device_ptr(),
+                                                actual_uncompressed_data_sizes.data(),
+                                                num_blocks,
+                                                scratch.data(),
+                                                scratch.size(),
+                                                uncompressed_data_ptrs.data(),
+                                                statuses.data(),
+                                                stream);
+    CUDF_EXPECTS(status == nvcompStatus_t::nvcompSuccess, "unable to perform snappy decompression");
+
+    CUDF_EXPECTS(thrust::equal(rmm::exec_policy(stream),
+                               uncompressed_data_sizes.d_begin(),
+                               uncompressed_data_sizes.d_end(),
+                               actual_uncompressed_data_sizes.begin()),
+                 "Mismatch in expected and actual decompressed size during snappy decompression");
+    CUDF_EXPECTS(thrust::equal(rmm::exec_policy(stream),
+                               statuses.begin(),
+                               statuses.end(),
+                               thrust::make_constant_iterator(nvcompStatus_t::nvcompSuccess)),
+                 "Error during snappy decompression");
+
+    // Update blocks offsets & sizes to refer to uncompressed data
+    for (size_t i = 0; i < num_blocks; i++) {
+      meta.block_list[i].offset = uncompressed_data_offsets[i];
+      meta.block_list[i].size   = uncompressed_data_sizes[i];
+    }
+
+    return decomp_block_data;
+  } else {
+    CUDF_FAIL("Unsupported compression codec\n");
+  }
+}
+
+std::vector<column_buffer> decode_data(metadata& meta,
+                                       rmm::device_buffer const& block_data,
+                                       std::vector<std::pair<uint32_t, uint32_t>> const& dict,
+                                       device_span<string_index_pair const> global_dictionary,
+                                       size_t num_rows,
+                                       std::vector<std::pair<int, std::string>> const& selection,
+                                       std::vector<data_type> const& column_types,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto out_buffers = std::vector<column_buffer>();
+
+  for (size_t i = 0; i < column_types.size(); ++i) {
+    auto col_idx     = selection[i].first;
+    bool is_nullable = (meta.columns[col_idx].schema_null_idx >= 0);
+    out_buffers.emplace_back(column_types[i], num_rows, is_nullable, stream, mr);
+  }
+
+  // Build gpu schema
+  auto schema_desc = cudf::detail::hostdevice_vector<gpu::schemadesc_s>(meta.schema.size(), stream);
+
+  uint32_t min_row_data_size = 0;
+  int skip_field_cnt         = 0;
+
+  for (size_t i = 0; i < meta.schema.size(); i++) {
+    type_kind_e kind                = meta.schema[i].kind;
+    logicaltype_kind_e logical_kind = meta.schema[i].logical_kind;
+
+    if (skip_field_cnt != 0) {
+      // Exclude union and array members from min_row_data_size
+      skip_field_cnt += meta.schema[i].num_children - 1;
+    } else {
+      switch (kind) {
+        case type_union:
+        case type_array:
+          skip_field_cnt = meta.schema[i].num_children;
+          // fall through
+        case type_boolean:
+        case type_int:
+        case type_long:
+        case type_bytes:
+        case type_string:
+        case type_enum: min_row_data_size += 1; break;
+        case type_float: min_row_data_size += 4; break;
+        case type_double: min_row_data_size += 8; break;
+        default: break;
+      }
+    }
+    if (kind == type_enum && !meta.schema[i].symbols.size()) { kind = type_int; }
+    schema_desc[i].kind         = kind;
+    schema_desc[i].logical_kind = logical_kind;
+    schema_desc[i].count =
+      (kind == type_enum) ? 0 : static_cast<uint32_t>(meta.schema[i].num_children);
+    schema_desc[i].dataptr = nullptr;
+    CUDF_EXPECTS(kind != type_union || meta.schema[i].num_children < 2 ||
+                   (meta.schema[i].num_children == 2 &&
+                    (meta.schema[i + 1].kind == type_null || meta.schema[i + 2].kind == type_null)),
+                 "Union with non-null type not currently supported");
+  }
+  std::vector<void*> valid_alias(out_buffers.size(), nullptr);
+  for (size_t i = 0; i < out_buffers.size(); i++) {
+    auto const col_idx  = selection[i].first;
+    int schema_data_idx = meta.columns[col_idx].schema_data_idx;
+    int schema_null_idx = meta.columns[col_idx].schema_null_idx;
+
+    schema_desc[schema_data_idx].dataptr = out_buffers[i].data();
+    if (schema_null_idx >= 0) {
+      if (!schema_desc[schema_null_idx].dataptr) {
+        schema_desc[schema_null_idx].dataptr = out_buffers[i].null_mask();
+      } else {
+        valid_alias[i] = schema_desc[schema_null_idx].dataptr;
+      }
+    }
+    if (meta.schema[schema_data_idx].kind == type_enum) {
+      schema_desc[schema_data_idx].count = dict[i].first;
+    }
+    if (out_buffers[i].null_mask_size()) {
+      cudf::detail::set_null_mask(out_buffers[i].null_mask(), 0, num_rows, true, stream);
+    }
+  }
+
+  auto block_list = cudf::detail::make_device_uvector_async(
+    meta.block_list, stream, rmm::mr::get_current_device_resource());
+
+  schema_desc.host_to_device_async(stream);
+
+  gpu::DecodeAvroColumnData(block_list,
+                            schema_desc.device_ptr(),
+                            global_dictionary,
+                            static_cast<uint8_t const*>(block_data.data()),
+                            static_cast<uint32_t>(schema_desc.size()),
+                            min_row_data_size,
+                            stream);
+
+  // Copy valid bits that are shared between columns
+  for (size_t i = 0; i < out_buffers.size(); i++) {
+    if (valid_alias[i] != nullptr) {
+      CUDF_CUDA_TRY(cudaMemcpyAsync(out_buffers[i].null_mask(),
+                                    valid_alias[i],
+                                    out_buffers[i].null_mask_size(),
+                                    cudaMemcpyDefault,
+                                    stream.value()));
+    }
+  }
+  schema_desc.device_to_host_sync(stream);
+
+  for (size_t i = 0; i < out_buffers.size(); i++) {
+    auto const col_idx          = selection[i].first;
+    auto const schema_null_idx  = meta.columns[col_idx].schema_null_idx;
+    out_buffers[i].null_count() = (schema_null_idx >= 0) ? schema_desc[schema_null_idx].count : 0;
+  }
+
+  return out_buffers;
+}
+
+table_with_metadata read_avro(std::unique_ptr<cudf::io::datasource>&& source,
+                              avro_reader_options const& options,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  auto skip_rows = options.get_skip_rows();
+  auto num_rows  = options.get_num_rows();
+  std::vector<std::unique_ptr<column>> out_columns;
+  table_metadata metadata_out;
+
+  // Open the source Avro dataset metadata
+  auto meta = metadata(source.get());
+
+  // Select and read partial metadata / schema within the subset of rows
+  meta.init_and_select_rows(skip_rows, num_rows);
+
+  // Select only columns required by the options
+  auto selected_columns = meta.select_columns(options.get_columns());
+  if (not selected_columns.empty()) {
+    // Get a list of column data types
+    std::vector<data_type> column_types;
+    for (auto const& col : selected_columns) {
+      auto& col_schema = meta.schema[meta.columns[col.first].schema_data_idx];
+
+      auto col_type = to_type_id(&col_schema);
+      CUDF_EXPECTS(col_type != type_id::EMPTY, "Unknown type");
+      column_types.emplace_back(col_type);
+    }
+
+    if (meta.num_rows > 0) {
+      rmm::device_buffer block_data;
+      if (source->is_device_read_preferred(meta.selected_data_size)) {
+        block_data      = rmm::device_buffer{meta.selected_data_size, stream};
+        auto read_bytes = source->device_read(meta.block_list[0].offset,
+                                              meta.selected_data_size,
+                                              static_cast<uint8_t*>(block_data.data()),
+                                              stream);
+        block_data.resize(read_bytes, stream);
+      } else {
+        auto const buffer = source->host_read(meta.block_list[0].offset, meta.selected_data_size);
+        block_data        = rmm::device_buffer{buffer->data(), buffer->size(), stream};
+      }
+
+      if (meta.codec != "" && meta.codec != "null") {
+        auto decomp_block_data = decompress_data(*source, meta, block_data, stream);
+        block_data             = std::move(decomp_block_data);
+      } else {
+        auto dst_ofs = meta.block_list[0].offset;
+        for (size_t i = 0; i < meta.block_list.size(); i++) {
+          meta.block_list[i].offset -= dst_ofs;
+        }
+      }
+
+      size_t total_dictionary_entries = 0;
+      size_t dictionary_data_size     = 0;
+
+      auto dict = std::vector<std::pair<uint32_t, uint32_t>>(column_types.size());
+
+      for (size_t i = 0; i < column_types.size(); ++i) {
+        auto col_idx     = selected_columns[i].first;
+        auto& col_schema = meta.schema[meta.columns[col_idx].schema_data_idx];
+        dict[i].first    = static_cast<uint32_t>(total_dictionary_entries);
+        dict[i].second   = static_cast<uint32_t>(col_schema.symbols.size());
+        total_dictionary_entries += dict[i].second;
+        for (auto const& sym : col_schema.symbols) {
+          dictionary_data_size += sym.length();
+        }
+      }
+
+      auto d_global_dict      = rmm::device_uvector<string_index_pair>(0, stream);
+      auto d_global_dict_data = rmm::device_uvector<char>(0, stream);
+
+      if (total_dictionary_entries > 0) {
+        auto h_global_dict      = std::vector<string_index_pair>(total_dictionary_entries);
+        auto h_global_dict_data = std::vector<char>(dictionary_data_size);
+        size_t dict_pos         = 0;
+
+        for (size_t i = 0; i < column_types.size(); ++i) {
+          auto const col_idx          = selected_columns[i].first;
+          auto const& col_schema      = meta.schema[meta.columns[col_idx].schema_data_idx];
+          auto const col_dict_entries = &(h_global_dict[dict[i].first]);
+          for (size_t j = 0; j < dict[i].second; j++) {
+            auto const& symbols = col_schema.symbols[j];
+
+            auto const data_dst        = h_global_dict_data.data() + dict_pos;
+            auto const len             = symbols.length();
+            col_dict_entries[j].first  = data_dst;
+            col_dict_entries[j].second = len;
+
+            std::copy(symbols.c_str(), symbols.c_str() + len, data_dst);
+            dict_pos += len;
+          }
+        }
+
+        d_global_dict = cudf::detail::make_device_uvector_async(
+          h_global_dict, stream, rmm::mr::get_current_device_resource());
+        d_global_dict_data = cudf::detail::make_device_uvector_async(
+          h_global_dict_data, stream, rmm::mr::get_current_device_resource());
+
+        stream.synchronize();
+      }
+
+      auto out_buffers = decode_data(meta,
+                                     block_data,
+                                     dict,
+                                     d_global_dict,
+                                     num_rows,
+                                     selected_columns,
+                                     column_types,
+                                     stream,
+                                     mr);
+
+      for (size_t i = 0; i < column_types.size(); ++i) {
+        out_columns.emplace_back(make_column(out_buffers[i], nullptr, std::nullopt, stream));
+      }
+    } else {
+      // Create empty columns
+      for (size_t i = 0; i < column_types.size(); ++i) {
+        out_columns.emplace_back(make_empty_column(column_types[i]));
+      }
+    }
+  }
+
+  // Return column names
+  metadata_out.schema_info.reserve(selected_columns.size());
+  std::transform(selected_columns.cbegin(),
+                 selected_columns.cend(),
+                 std::back_inserter(metadata_out.schema_info),
+                 [](auto const& c) { return column_name_info{c.second}; });
+
+  // Return user metadata
+  metadata_out.user_data          = meta.user_data;
+  metadata_out.per_file_user_data = {{meta.user_data.begin(), meta.user_data.end()}};
+
+  return {std::make_unique<table>(std::move(out_columns)), std::move(metadata_out)};
+}
+
+}  // namespace avro
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/brotli_dict.cpp b/cpp/src/io/comp/brotli_dict.cpp
new file mode 100644
index 0000000..1566d1b
--- /dev/null
+++ b/cpp/src/io/comp/brotli_dict.cpp
@@ -0,0 +1,6535 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * Portions of this file are derived from Google's Brotli project at
+ * https://github.com/google/brotli, original license text below.
+ */
+
+/* Copyright 2013 Google Inc. All Rights Reserved.
+
+Distributed under MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+*/
+
+/*
+Copyright(c) 2009, 2010, 2013 - 2016 by the Brotli Authors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions :
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+*/
+
+#include "brotli_dict.hpp"
+
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+static const brotli_dictionary_s g_dictionary = {
+  // size_bits_by_length
+  {0, 0, 0, 0, 10, 10, 11, 11, 10, 10, 10, 10, 10, 9, 9, 8,
+   7, 7, 8, 7, 7,  6,  6,  5,  5,  0,  0,  0,  0,  0, 0, 0},
+
+  // offsets_by_length
+  {0,      0,      0,      0,      0,      4096,   9216,   21504,  35840,  44032,  53248,
+   63488,  74752,  87040,  93696,  100864, 104704, 106752, 108928, 113536, 115968, 118528,
+   119872, 121280, 122016, 122784, 122784, 122784, 122784, 122784, 122784, 122784},
+
+  // dictionary data
+  {116, 105, 109, 101, 100, 111, 119, 110, 108, 105, 102, 101, 108, 101, 102, 116, 98,  97,  99,
+   107, 99,  111, 100, 101, 100, 97,  116, 97,  115, 104, 111, 119, 111, 110, 108, 121, 115, 105,
+   116, 101, 99,  105, 116, 121, 111, 112, 101, 110, 106, 117, 115, 116, 108, 105, 107, 101, 102,
+   114, 101, 101, 119, 111, 114, 107, 116, 101, 120, 116, 121, 101, 97,  114, 111, 118, 101, 114,
+   98,  111, 100, 121, 108, 111, 118, 101, 102, 111, 114, 109, 98,  111, 111, 107, 112, 108, 97,
+   121, 108, 105, 118, 101, 108, 105, 110, 101, 104, 101, 108, 112, 104, 111, 109, 101, 115, 105,
+   100, 101, 109, 111, 114, 101, 119, 111, 114, 100, 108, 111, 110, 103, 116, 104, 101, 109, 118,
+   105, 101, 119, 102, 105, 110, 100, 112, 97,  103, 101, 100, 97,  121, 115, 102, 117, 108, 108,
+   104, 101, 97,  100, 116, 101, 114, 109, 101, 97,  99,  104, 97,  114, 101, 97,  102, 114, 111,
+   109, 116, 114, 117, 101, 109, 97,  114, 107, 97,  98,  108, 101, 117, 112, 111, 110, 104, 105,
+   103, 104, 100, 97,  116, 101, 108, 97,  110, 100, 110, 101, 119, 115, 101, 118, 101, 110, 110,
+   101, 120, 116, 99,  97,  115, 101, 98,  111, 116, 104, 112, 111, 115, 116, 117, 115, 101, 100,
+   109, 97,  100, 101, 104, 97,  110, 100, 104, 101, 114, 101, 119, 104, 97,  116, 110, 97,  109,
+   101, 76,  105, 110, 107, 98,  108, 111, 103, 115, 105, 122, 101, 98,  97,  115, 101, 104, 101,
+   108, 100, 109, 97,  107, 101, 109, 97,  105, 110, 117, 115, 101, 114, 39,  41,  32,  43,  104,
+   111, 108, 100, 101, 110, 100, 115, 119, 105, 116, 104, 78,  101, 119, 115, 114, 101, 97,  100,
+   119, 101, 114, 101, 115, 105, 103, 110, 116, 97,  107, 101, 104, 97,  118, 101, 103, 97,  109,
+   101, 115, 101, 101, 110, 99,  97,  108, 108, 112, 97,  116, 104, 119, 101, 108, 108, 112, 108,
+   117, 115, 109, 101, 110, 117, 102, 105, 108, 109, 112, 97,  114, 116, 106, 111, 105, 110, 116,
+   104, 105, 115, 108, 105, 115, 116, 103, 111, 111, 100, 110, 101, 101, 100, 119, 97,  121, 115,
+   119, 101, 115, 116, 106, 111, 98,  115, 109, 105, 110, 100, 97,  108, 115, 111, 108, 111, 103,
+   111, 114, 105, 99,  104, 117, 115, 101, 115, 108, 97,  115, 116, 116, 101, 97,  109, 97,  114,
+   109, 121, 102, 111, 111, 100, 107, 105, 110, 103, 119, 105, 108, 108, 101, 97,  115, 116, 119,
+   97,  114, 100, 98,  101, 115, 116, 102, 105, 114, 101, 80,  97,  103, 101, 107, 110, 111, 119,
+   97,  119, 97,  121, 46,  112, 110, 103, 109, 111, 118, 101, 116, 104, 97,  110, 108, 111, 97,
+   100, 103, 105, 118, 101, 115, 101, 108, 102, 110, 111, 116, 101, 109, 117, 99,  104, 102, 101,
+   101, 100, 109, 97,  110, 121, 114, 111, 99,  107, 105, 99,  111, 110, 111, 110, 99,  101, 108,
+   111, 111, 107, 104, 105, 100, 101, 100, 105, 101, 100, 72,  111, 109, 101, 114, 117, 108, 101,
+   104, 111, 115, 116, 97,  106, 97,  120, 105, 110, 102, 111, 99,  108, 117, 98,  108, 97,  119,
+   115, 108, 101, 115, 115, 104, 97,  108, 102, 115, 111, 109, 101, 115, 117, 99,  104, 122, 111,
+   110, 101, 49,  48,  48,  37,  111, 110, 101, 115, 99,  97,  114, 101, 84,  105, 109, 101, 114,
+   97,  99,  101, 98,  108, 117, 101, 102, 111, 117, 114, 119, 101, 101, 107, 102, 97,  99,  101,
+   104, 111, 112, 101, 103, 97,  118, 101, 104, 97,  114, 100, 108, 111, 115, 116, 119, 104, 101,
+   110, 112, 97,  114, 107, 107, 101, 112, 116, 112, 97,  115, 115, 115, 104, 105, 112, 114, 111,
+   111, 109, 72,  84,  77,  76,  112, 108, 97,  110, 84,  121, 112, 101, 100, 111, 110, 101, 115,
+   97,  118, 101, 107, 101, 101, 112, 102, 108, 97,  103, 108, 105, 110, 107, 115, 111, 108, 100,
+   102, 105, 118, 101, 116, 111, 111, 107, 114, 97,  116, 101, 116, 111, 119, 110, 106, 117, 109,
+   112, 116, 104, 117, 115, 100, 97,  114, 107, 99,  97,  114, 100, 102, 105, 108, 101, 102, 101,
+   97,  114, 115, 116, 97,  121, 107, 105, 108, 108, 116, 104, 97,  116, 102, 97,  108, 108, 97,
+   117, 116, 111, 101, 118, 101, 114, 46,  99,  111, 109, 116, 97,  108, 107, 115, 104, 111, 112,
+   118, 111, 116, 101, 100, 101, 101, 112, 109, 111, 100, 101, 114, 101, 115, 116, 116, 117, 114,
+   110, 98,  111, 114, 110, 98,  97,  110, 100, 102, 101, 108, 108, 114, 111, 115, 101, 117, 114,
+   108, 40,  115, 107, 105, 110, 114, 111, 108, 101, 99,  111, 109, 101, 97,  99,  116, 115, 97,
+   103, 101, 115, 109, 101, 101, 116, 103, 111, 108, 100, 46,  106, 112, 103, 105, 116, 101, 109,
+   118, 97,  114, 121, 102, 101, 108, 116, 116, 104, 101, 110, 115, 101, 110, 100, 100, 114, 111,
+   112, 86,  105, 101, 119, 99,  111, 112, 121, 49,  46,  48,  34,  60,  47,  97,  62,  115, 116,
+   111, 112, 101, 108, 115, 101, 108, 105, 101, 115, 116, 111, 117, 114, 112, 97,  99,  107, 46,
+   103, 105, 102, 112, 97,  115, 116, 99,  115, 115, 63,  103, 114, 97,  121, 109, 101, 97,  110,
+   38,  103, 116, 59,  114, 105, 100, 101, 115, 104, 111, 116, 108, 97,  116, 101, 115, 97,  105,
+   100, 114, 111, 97,  100, 118, 97,  114, 32,  102, 101, 101, 108, 106, 111, 104, 110, 114, 105,
+   99,  107, 112, 111, 114, 116, 102, 97,  115, 116, 39,  85,  65,  45,  100, 101, 97,  100, 60,
+   47,  98,  62,  112, 111, 111, 114, 98,  105, 108, 108, 116, 121, 112, 101, 85,  46,  83,  46,
+   119, 111, 111, 100, 109, 117, 115, 116, 50,  112, 120, 59,  73,  110, 102, 111, 114, 97,  110,
+   107, 119, 105, 100, 101, 119, 97,  110, 116, 119, 97,  108, 108, 108, 101, 97,  100, 91,  48,
+   93,  59,  112, 97,  117, 108, 119, 97,  118, 101, 115, 117, 114, 101, 36,  40,  39,  35,  119,
+   97,  105, 116, 109, 97,  115, 115, 97,  114, 109, 115, 103, 111, 101, 115, 103, 97,  105, 110,
+   108, 97,  110, 103, 112, 97,  105, 100, 33,  45,  45,  32,  108, 111, 99,  107, 117, 110, 105,
+   116, 114, 111, 111, 116, 119, 97,  108, 107, 102, 105, 114, 109, 119, 105, 102, 101, 120, 109,
+   108, 34,  115, 111, 110, 103, 116, 101, 115, 116, 50,  48,  112, 120, 107, 105, 110, 100, 114,
+   111, 119, 115, 116, 111, 111, 108, 102, 111, 110, 116, 109, 97,  105, 108, 115, 97,  102, 101,
+   115, 116, 97,  114, 109, 97,  112, 115, 99,  111, 114, 101, 114, 97,  105, 110, 102, 108, 111,
+   119, 98,  97,  98,  121, 115, 112, 97,  110, 115, 97,  121, 115, 52,  112, 120, 59,  54,  112,
+   120, 59,  97,  114, 116, 115, 102, 111, 111, 116, 114, 101, 97,  108, 119, 105, 107, 105, 104,
+   101, 97,  116, 115, 116, 101, 112, 116, 114, 105, 112, 111, 114, 103, 47,  108, 97,  107, 101,
+   119, 101, 97,  107, 116, 111, 108, 100, 70,  111, 114, 109, 99,  97,  115, 116, 102, 97,  110,
+   115, 98,  97,  110, 107, 118, 101, 114, 121, 114, 117, 110, 115, 106, 117, 108, 121, 116, 97,
+   115, 107, 49,  112, 120, 59,  103, 111, 97,  108, 103, 114, 101, 119, 115, 108, 111, 119, 101,
+   100, 103, 101, 105, 100, 61,  34,  115, 101, 116, 115, 53,  112, 120, 59,  46,  106, 115, 63,
+   52,  48,  112, 120, 105, 102, 32,  40,  115, 111, 111, 110, 115, 101, 97,  116, 110, 111, 110,
+   101, 116, 117, 98,  101, 122, 101, 114, 111, 115, 101, 110, 116, 114, 101, 101, 100, 102, 97,
+   99,  116, 105, 110, 116, 111, 103, 105, 102, 116, 104, 97,  114, 109, 49,  56,  112, 120, 99,
+   97,  109, 101, 104, 105, 108, 108, 98,  111, 108, 100, 122, 111, 111, 109, 118, 111, 105, 100,
+   101, 97,  115, 121, 114, 105, 110, 103, 102, 105, 108, 108, 112, 101, 97,  107, 105, 110, 105,
+   116, 99,  111, 115, 116, 51,  112, 120, 59,  106, 97,  99,  107, 116, 97,  103, 115, 98,  105,
+   116, 115, 114, 111, 108, 108, 101, 100, 105, 116, 107, 110, 101, 119, 110, 101, 97,  114, 60,
+   33,  45,  45,  103, 114, 111, 119, 74,  83,  79,  78,  100, 117, 116, 121, 78,  97,  109, 101,
+   115, 97,  108, 101, 121, 111, 117, 32,  108, 111, 116, 115, 112, 97,  105, 110, 106, 97,  122,
+   122, 99,  111, 108, 100, 101, 121, 101, 115, 102, 105, 115, 104, 119, 119, 119, 46,  114, 105,
+   115, 107, 116, 97,  98,  115, 112, 114, 101, 118, 49,  48,  112, 120, 114, 105, 115, 101, 50,
+   53,  112, 120, 66,  108, 117, 101, 100, 105, 110, 103, 51,  48,  48,  44,  98,  97,  108, 108,
+   102, 111, 114, 100, 101, 97,  114, 110, 119, 105, 108, 100, 98,  111, 120, 46,  102, 97,  105,
+   114, 108, 97,  99,  107, 118, 101, 114, 115, 112, 97,  105, 114, 106, 117, 110, 101, 116, 101,
+   99,  104, 105, 102, 40,  33,  112, 105, 99,  107, 101, 118, 105, 108, 36,  40,  34,  35,  119,
+   97,  114, 109, 108, 111, 114, 100, 100, 111, 101, 115, 112, 117, 108, 108, 44,  48,  48,  48,
+   105, 100, 101, 97,  100, 114, 97,  119, 104, 117, 103, 101, 115, 112, 111, 116, 102, 117, 110,
+   100, 98,  117, 114, 110, 104, 114, 101, 102, 99,  101, 108, 108, 107, 101, 121, 115, 116, 105,
+   99,  107, 104, 111, 117, 114, 108, 111, 115, 115, 102, 117, 101, 108, 49,  50,  112, 120, 115,
+   117, 105, 116, 100, 101, 97,  108, 82,  83,  83,  34,  97,  103, 101, 100, 103, 114, 101, 121,
+   71,  69,  84,  34,  101, 97,  115, 101, 97,  105, 109, 115, 103, 105, 114, 108, 97,  105, 100,
+   115, 56,  112, 120, 59,  110, 97,  118, 121, 103, 114, 105, 100, 116, 105, 112, 115, 35,  57,
+   57,  57,  119, 97,  114, 115, 108, 97,  100, 121, 99,  97,  114, 115, 41,  59,  32,  125, 112,
+   104, 112, 63,  104, 101, 108, 108, 116, 97,  108, 108, 119, 104, 111, 109, 122, 104, 58,  229,
+   42,  47,  13,  10,  32,  49,  48,  48,  104, 97,  108, 108, 46,  10,  10,  65,  55,  112, 120,
+   59,  112, 117, 115, 104, 99,  104, 97,  116, 48,  112, 120, 59,  99,  114, 101, 119, 42,  47,
+   60,  47,  104, 97,  115, 104, 55,  53,  112, 120, 102, 108, 97,  116, 114, 97,  114, 101, 32,
+   38,  38,  32,  116, 101, 108, 108, 99,  97,  109, 112, 111, 110, 116, 111, 108, 97,  105, 100,
+   109, 105, 115, 115, 115, 107, 105, 112, 116, 101, 110, 116, 102, 105, 110, 101, 109, 97,  108,
+   101, 103, 101, 116, 115, 112, 108, 111, 116, 52,  48,  48,  44,  13,  10,  13,  10,  99,  111,
+   111, 108, 102, 101, 101, 116, 46,  112, 104, 112, 60,  98,  114, 62,  101, 114, 105, 99,  109,
+   111, 115, 116, 103, 117, 105, 100, 98,  101, 108, 108, 100, 101, 115, 99,  104, 97,  105, 114,
+   109, 97,  116, 104, 97,  116, 111, 109, 47,  105, 109, 103, 38,  35,  56,  50,  108, 117, 99,
+   107, 99,  101, 110, 116, 48,  48,  48,  59,  116, 105, 110, 121, 103, 111, 110, 101, 104, 116,
+   109, 108, 115, 101, 108, 108, 100, 114, 117, 103, 70,  82,  69,  69,  110, 111, 100, 101, 110,
+   105, 99,  107, 63,  105, 100, 61,  108, 111, 115, 101, 110, 117, 108, 108, 118, 97,  115, 116,
+   119, 105, 110, 100, 82,  83,  83,  32,  119, 101, 97,  114, 114, 101, 108, 121, 98,  101, 101,
+   110, 115, 97,  109, 101, 100, 117, 107, 101, 110, 97,  115, 97,  99,  97,  112, 101, 119, 105,
+   115, 104, 103, 117, 108, 102, 84,  50,  51,  58,  104, 105, 116, 115, 115, 108, 111, 116, 103,
+   97,  116, 101, 107, 105, 99,  107, 98,  108, 117, 114, 116, 104, 101, 121, 49,  53,  112, 120,
+   39,  39,  41,  59,  41,  59,  34,  62,  109, 115, 105, 101, 119, 105, 110, 115, 98,  105, 114,
+   100, 115, 111, 114, 116, 98,  101, 116, 97,  115, 101, 101, 107, 84,  49,  56,  58,  111, 114,
+   100, 115, 116, 114, 101, 101, 109, 97,  108, 108, 54,  48,  112, 120, 102, 97,  114, 109, 226,
+   128, 153, 115, 98,  111, 121, 115, 91,  48,  93,  46,  39,  41,  59,  34,  80,  79,  83,  84,
+   98,  101, 97,  114, 107, 105, 100, 115, 41,  59,  125, 125, 109, 97,  114, 121, 116, 101, 110,
+   100, 40,  85,  75,  41,  113, 117, 97,  100, 122, 104, 58,  230, 45,  115, 105, 122, 45,  45,
+   45,  45,  112, 114, 111, 112, 39,  41,  59,  13,  108, 105, 102, 116, 84,  49,  57,  58,  118,
+   105, 99,  101, 97,  110, 100, 121, 100, 101, 98,  116, 62,  82,  83,  83,  112, 111, 111, 108,
+   110, 101, 99,  107, 98,  108, 111, 119, 84,  49,  54,  58,  100, 111, 111, 114, 101, 118, 97,
+   108, 84,  49,  55,  58,  108, 101, 116, 115, 102, 97,  105, 108, 111, 114, 97,  108, 112, 111,
+   108, 108, 110, 111, 118, 97,  99,  111, 108, 115, 103, 101, 110, 101, 32,  226, 128, 148, 115,
+   111, 102, 116, 114, 111, 109, 101, 116, 105, 108, 108, 114, 111, 115, 115, 60,  104, 51,  62,
+   112, 111, 117, 114, 102, 97,  100, 101, 112, 105, 110, 107, 60,  116, 114, 62,  109, 105, 110,
+   105, 41,  124, 33,  40,  109, 105, 110, 101, 122, 104, 58,  232, 98,  97,  114, 115, 104, 101,
+   97,  114, 48,  48,  41,  59,  109, 105, 108, 107, 32,  45,  45,  62,  105, 114, 111, 110, 102,
+   114, 101, 100, 100, 105, 115, 107, 119, 101, 110, 116, 115, 111, 105, 108, 112, 117, 116, 115,
+   47,  106, 115, 47,  104, 111, 108, 121, 84,  50,  50,  58,  73,  83,  66,  78,  84,  50,  48,
+   58,  97,  100, 97,  109, 115, 101, 101, 115, 60,  104, 50,  62,  106, 115, 111, 110, 39,  44,
+   32,  39,  99,  111, 110, 116, 84,  50,  49,  58,  32,  82,  83,  83,  108, 111, 111, 112, 97,
+   115, 105, 97,  109, 111, 111, 110, 60,  47,  112, 62,  115, 111, 117, 108, 76,  73,  78,  69,
+   102, 111, 114, 116, 99,  97,  114, 116, 84,  49,  52,  58,  60,  104, 49,  62,  56,  48,  112,
+   120, 33,  45,  45,  60,  57,  112, 120, 59,  84,  48,  52,  58,  109, 105, 107, 101, 58,  52,
+   54,  90,  110, 105, 99,  101, 105, 110, 99,  104, 89,  111, 114, 107, 114, 105, 99,  101, 122,
+   104, 58,  228, 39,  41,  41,  59,  112, 117, 114, 101, 109, 97,  103, 101, 112, 97,  114, 97,
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+   164, 178, 224, 164, 191, 224, 164, 174, 224, 164, 191, 224, 164, 159, 224, 165, 135, 224, 164,
+   161, 100, 101, 115, 99,  114, 105, 112, 116, 105, 111, 110, 34,  32,  99,  111, 110, 116, 101,
+   110, 116, 61,  34,  100, 111, 99,  117, 109, 101, 110, 116, 46,  108, 111, 99,  97,  116, 105,
+   111, 110, 46,  112, 114, 111, 116, 46,  103, 101, 116, 69,  108, 101, 109, 101, 110, 116, 115,
+   66,  121, 84,  97,  103, 78,  97,  109, 101, 40,  60,  33,  68,  79,  67,  84,  89,  80,  69,
+   32,  104, 116, 109, 108, 62,  10,  60,  104, 116, 109, 108, 32,  60,  109, 101, 116, 97,  32,
+   99,  104, 97,  114, 115, 101, 116, 61,  34,  117, 116, 102, 45,  56,  34,  62,  58,  117, 114,
+   108, 34,  32,  99,  111, 110, 116, 101, 110, 116, 61,  34,  104, 116, 116, 112, 58,  47,  47,
+   46,  99,  115, 115, 34,  32,  114, 101, 108, 61,  34,  115, 116, 121, 108, 101, 115, 104, 101,
+   101, 116, 34,  115, 116, 121, 108, 101, 32,  116, 121, 112, 101, 61,  34,  116, 101, 120, 116,
+   47,  99,  115, 115, 34,  62,  116, 121, 112, 101, 61,  34,  116, 101, 120, 116, 47,  99,  115,
+   115, 34,  32,  104, 114, 101, 102, 61,  34,  119, 51,  46,  111, 114, 103, 47,  49,  57,  57,
+   57,  47,  120, 104, 116, 109, 108, 34,  32,  120, 109, 108, 116, 121, 112, 101, 61,  34,  116,
+   101, 120, 116, 47,  106, 97,  118, 97,  115, 99,  114, 105, 112, 116, 34,  32,  109, 101, 116,
+   104, 111, 100, 61,  34,  103, 101, 116, 34,  32,  97,  99,  116, 105, 111, 110, 61,  34,  108,
+   105, 110, 107, 32,  114, 101, 108, 61,  34,  115, 116, 121, 108, 101, 115, 104, 101, 101, 116,
+   34,  32,  32,  61,  32,  100, 111, 99,  117, 109, 101, 110, 116, 46,  103, 101, 116, 69,  108,
+   101, 109, 101, 110, 116, 116, 121, 112, 101, 61,  34,  105, 109, 97,  103, 101, 47,  120, 45,
+   105, 99,  111, 110, 34,  32,  47,  62,  99,  101, 108, 108, 112, 97,  100, 100, 105, 110, 103,
+   61,  34,  48,  34,  32,  99,  101, 108, 108, 115, 112, 46,  99,  115, 115, 34,  32,  116, 121,
+   112, 101, 61,  34,  116, 101, 120, 116, 47,  99,  115, 115, 34,  32,  60,  47,  97,  62,  60,
+   47,  108, 105, 62,  60,  108, 105, 62,  60,  97,  32,  104, 114, 101, 102, 61,  34,  34,  32,
+   119, 105, 100, 116, 104, 61,  34,  49,  34,  32,  104, 101, 105, 103, 104, 116, 61,  34,  49,
+   34,  34,  62,  60,  97,  32,  104, 114, 101, 102, 61,  34,  104, 116, 116, 112, 58,  47,  47,
+   119, 119, 119, 46,  115, 116, 121, 108, 101, 61,  34,  100, 105, 115, 112, 108, 97,  121, 58,
+   110, 111, 110, 101, 59,  34,  62,  97,  108, 116, 101, 114, 110, 97,  116, 101, 34,  32,  116,
+   121, 112, 101, 61,  34,  97,  112, 112, 108, 105, 45,  47,  47,  87,  51,  67,  47,  47,  68,
+   84,  68,  32,  88,  72,  84,  77,  76,  32,  49,  46,  48,  32,  101, 108, 108, 115, 112, 97,
+   99,  105, 110, 103, 61,  34,  48,  34,  32,  99,  101, 108, 108, 112, 97,  100, 32,  116, 121,
+   112, 101, 61,  34,  104, 105, 100, 100, 101, 110, 34,  32,  118, 97,  108, 117, 101, 61,  34,
+   47,  97,  62,  38,  110, 98,  115, 112, 59,  60,  115, 112, 97,  110, 32,  114, 111, 108, 101,
+   61,  34,  115, 10,  60,  105, 110, 112, 117, 116, 32,  116, 121, 112, 101, 61,  34,  104, 105,
+   100, 100, 101, 110, 34,  32,  108, 97,  110, 103, 117, 97,  103, 101, 61,  34,  74,  97,  118,
+   97,  83,  99,  114, 105, 112, 116, 34,  32,  32,  100, 111, 99,  117, 109, 101, 110, 116, 46,
+   103, 101, 116, 69,  108, 101, 109, 101, 110, 116, 115, 66,  103, 61,  34,  48,  34,  32,  99,
+   101, 108, 108, 115, 112, 97,  99,  105, 110, 103, 61,  34,  48,  34,  32,  121, 112, 101, 61,
+   34,  116, 101, 120, 116, 47,  99,  115, 115, 34,  32,  109, 101, 100, 105, 97,  61,  34,  116,
+   121, 112, 101, 61,  39,  116, 101, 120, 116, 47,  106, 97,  118, 97,  115, 99,  114, 105, 112,
+   116, 39,  119, 105, 116, 104, 32,  116, 104, 101, 32,  101, 120, 99,  101, 112, 116, 105, 111,
+   110, 32,  111, 102, 32,  121, 112, 101, 61,  34,  116, 101, 120, 116, 47,  99,  115, 115, 34,
+   32,  114, 101, 108, 61,  34,  115, 116, 32,  104, 101, 105, 103, 104, 116, 61,  34,  49,  34,
+   32,  119, 105, 100, 116, 104, 61,  34,  49,  34,  32,  61,  39,  43,  101, 110, 99,  111, 100,
+   101, 85,  82,  73,  67,  111, 109, 112, 111, 110, 101, 110, 116, 40,  60,  108, 105, 110, 107,
+   32,  114, 101, 108, 61,  34,  97,  108, 116, 101, 114, 110, 97,  116, 101, 34,  32,  10,  98,
+   111, 100, 121, 44,  32,  116, 114, 44,  32,  105, 110, 112, 117, 116, 44,  32,  116, 101, 120,
+   116, 109, 101, 116, 97,  32,  110, 97,  109, 101, 61,  34,  114, 111, 98,  111, 116, 115, 34,
+   32,  99,  111, 110, 109, 101, 116, 104, 111, 100, 61,  34,  112, 111, 115, 116, 34,  32,  97,
+   99,  116, 105, 111, 110, 61,  34,  62,  10,  60,  97,  32,  104, 114, 101, 102, 61,  34,  104,
+   116, 116, 112, 58,  47,  47,  119, 119, 119, 46,  99,  115, 115, 34,  32,  114, 101, 108, 61,
+   34,  115, 116, 121, 108, 101, 115, 104, 101, 101, 116, 34,  32,  60,  47,  100, 105, 118, 62,
+   60,  47,  100, 105, 118, 62,  60,  100, 105, 118, 32,  99,  108, 97,  115, 115, 108, 97,  110,
+   103, 117, 97,  103, 101, 61,  34,  106, 97,  118, 97,  115, 99,  114, 105, 112, 116, 34,  62,
+   97,  114, 105, 97,  45,  104, 105, 100, 100, 101, 110, 61,  34,  116, 114, 117, 101, 34,  62,
+   194, 183, 60,  114, 105, 112, 116, 34,  32,  116, 121, 112, 101, 61,  34,  116, 101, 120, 116,
+   47,  106, 97,  118, 97,  115, 108, 61,  48,  59,  125, 41,  40,  41,  59,  10,  40,  102, 117,
+   110, 99,  116, 105, 111, 110, 40,  41,  123, 98,  97,  99,  107, 103, 114, 111, 117, 110, 100,
+   45,  105, 109, 97,  103, 101, 58,  32,  117, 114, 108, 40,  47,  97,  62,  60,  47,  108, 105,
+   62,  60,  108, 105, 62,  60,  97,  32,  104, 114, 101, 102, 61,  34,  104, 9,   9,   60,  108,
+   105, 62,  60,  97,  32,  104, 114, 101, 102, 61,  34,  104, 116, 116, 112, 58,  47,  47,  97,
+   116, 111, 114, 34,  32,  97,  114, 105, 97,  45,  104, 105, 100, 100, 101, 110, 61,  34,  116,
+   114, 117, 62,  32,  60,  97,  32,  104, 114, 101, 102, 61,  34,  104, 116, 116, 112, 58,  47,
+   47,  119, 119, 119, 46,  108, 97,  110, 103, 117, 97,  103, 101, 61,  34,  106, 97,  118, 97,
+   115, 99,  114, 105, 112, 116, 34,  32,  47,  111, 112, 116, 105, 111, 110, 62,  10,  60,  111,
+   112, 116, 105, 111, 110, 32,  118, 97,  108, 117, 101, 47,  100, 105, 118, 62,  60,  47,  100,
+   105, 118, 62,  60,  100, 105, 118, 32,  99,  108, 97,  115, 115, 61,  114, 97,  116, 111, 114,
+   34,  32,  97,  114, 105, 97,  45,  104, 105, 100, 100, 101, 110, 61,  34,  116, 114, 101, 61,
+   40,  110, 101, 119, 32,  68,  97,  116, 101, 41,  46,  103, 101, 116, 84,  105, 109, 101, 40,
+   41,  112, 111, 114, 116, 117, 103, 117, 195, 170, 115, 32,  40,  100, 111, 32,  66,  114, 97,
+   115, 105, 108, 41,  208, 190, 209, 128, 208, 179, 208, 176, 208, 189, 208, 184, 208, 183, 208,
+   176, 209, 134, 208, 184, 208, 184, 208, 178, 208, 190, 208, 183, 208, 188, 208, 190, 208, 182,
+   208, 189, 208, 190, 209, 129, 209, 130, 209, 140, 208, 190, 208, 177, 209, 128, 208, 176, 208,
+   183, 208, 190, 208, 178, 208, 176, 208, 189, 208, 184, 209, 143, 209, 128, 208, 181, 208, 179,
+   208, 184, 209, 129, 209, 130, 209, 128, 208, 176, 209, 134, 208, 184, 208, 184, 208, 178, 208,
+   190, 208, 183, 208, 188, 208, 190, 208, 182, 208, 189, 208, 190, 209, 129, 209, 130, 208, 184,
+   208, 190, 208, 177, 209, 143, 208, 183, 208, 176, 209, 130, 208, 181, 208, 187, 209, 140, 208,
+   189, 208, 176, 60,  33,  68,  79,  67,  84,  89,  80,  69,  32,  104, 116, 109, 108, 32,  80,
+   85,  66,  76,  73,  67,  32,  34,  110, 116, 45,  84,  121, 112, 101, 34,  32,  99,  111, 110,
+   116, 101, 110, 116, 61,  34,  116, 101, 120, 116, 47,  60,  109, 101, 116, 97,  32,  104, 116,
+   116, 112, 45,  101, 113, 117, 105, 118, 61,  34,  67,  111, 110, 116, 101, 114, 97,  110, 115,
+   105, 116, 105, 111, 110, 97,  108, 47,  47,  69,  78,  34,  32,  34,  104, 116, 116, 112, 58,
+   60,  104, 116, 109, 108, 32,  120, 109, 108, 110, 115, 61,  34,  104, 116, 116, 112, 58,  47,
+   47,  119, 119, 119, 45,  47,  47,  87,  51,  67,  47,  47,  68,  84,  68,  32,  88,  72,  84,
+   77,  76,  32,  49,  46,  48,  32,  84,  68,  84,  68,  47,  120, 104, 116, 109, 108, 49,  45,
+   116, 114, 97,  110, 115, 105, 116, 105, 111, 110, 97,  108, 47,  47,  119, 119, 119, 46,  119,
+   51,  46,  111, 114, 103, 47,  84,  82,  47,  120, 104, 116, 109, 108, 49,  47,  112, 101, 32,
+   61,  32,  39,  116, 101, 120, 116, 47,  106, 97,  118, 97,  115, 99,  114, 105, 112, 116, 39,
+   59,  60,  109, 101, 116, 97,  32,  110, 97,  109, 101, 61,  34,  100, 101, 115, 99,  114, 105,
+   112, 116, 105, 111, 110, 112, 97,  114, 101, 110, 116, 78,  111, 100, 101, 46,  105, 110, 115,
+   101, 114, 116, 66,  101, 102, 111, 114, 101, 60,  105, 110, 112, 117, 116, 32,  116, 121, 112,
+   101, 61,  34,  104, 105, 100, 100, 101, 110, 34,  32,  110, 97,  106, 115, 34,  32,  116, 121,
+   112, 101, 61,  34,  116, 101, 120, 116, 47,  106, 97,  118, 97,  115, 99,  114, 105, 40,  100,
+   111, 99,  117, 109, 101, 110, 116, 41,  46,  114, 101, 97,  100, 121, 40,  102, 117, 110, 99,
+   116, 105, 115, 99,  114, 105, 112, 116, 32,  116, 121, 112, 101, 61,  34,  116, 101, 120, 116,
+   47,  106, 97,  118, 97,  115, 105, 109, 97,  103, 101, 34,  32,  99,  111, 110, 116, 101, 110,
+   116, 61,  34,  104, 116, 116, 112, 58,  47,  47,  85,  65,  45,  67,  111, 109, 112, 97,  116,
+   105, 98,  108, 101, 34,  32,  99,  111, 110, 116, 101, 110, 116, 61,  116, 109, 108, 59,  32,
+   99,  104, 97,  114, 115, 101, 116, 61,  117, 116, 102, 45,  56,  34,  32,  47,  62,  10,  108,
+   105, 110, 107, 32,  114, 101, 108, 61,  34,  115, 104, 111, 114, 116, 99,  117, 116, 32,  105,
+   99,  111, 110, 60,  108, 105, 110, 107, 32,  114, 101, 108, 61,  34,  115, 116, 121, 108, 101,
+   115, 104, 101, 101, 116, 34,  32,  60,  47,  115, 99,  114, 105, 112, 116, 62,  10,  60,  115,
+   99,  114, 105, 112, 116, 32,  116, 121, 112, 101, 61,  61,  32,  100, 111, 99,  117, 109, 101,
+   110, 116, 46,  99,  114, 101, 97,  116, 101, 69,  108, 101, 109, 101, 110, 60,  97,  32,  116,
+   97,  114, 103, 101, 116, 61,  34,  95,  98,  108, 97,  110, 107, 34,  32,  104, 114, 101, 102,
+   61,  32,  100, 111, 99,  117, 109, 101, 110, 116, 46,  103, 101, 116, 69,  108, 101, 109, 101,
+   110, 116, 115, 66,  105, 110, 112, 117, 116, 32,  116, 121, 112, 101, 61,  34,  116, 101, 120,
+   116, 34,  32,  110, 97,  109, 101, 61,  97,  46,  116, 121, 112, 101, 32,  61,  32,  39,  116,
+   101, 120, 116, 47,  106, 97,  118, 97,  115, 99,  114, 105, 110, 112, 117, 116, 32,  116, 121,
+   112, 101, 61,  34,  104, 105, 100, 100, 101, 110, 34,  32,  110, 97,  109, 101, 104, 116, 109,
+   108, 59,  32,  99,  104, 97,  114, 115, 101, 116, 61,  117, 116, 102, 45,  56,  34,  32,  47,
+   62,  100, 116, 100, 34,  62,  10,  60,  104, 116, 109, 108, 32,  120, 109, 108, 110, 115, 61,
+   34,  104, 116, 116, 112, 45,  47,  47,  87,  51,  67,  47,  47,  68,  84,  68,  32,  72,  84,
+   77,  76,  32,  52,  46,  48,  49,  32,  84,  101, 110, 116, 115, 66,  121, 84,  97,  103, 78,
+   97,  109, 101, 40,  39,  115, 99,  114, 105, 112, 116, 39,  41,  105, 110, 112, 117, 116, 32,
+   116, 121, 112, 101, 61,  34,  104, 105, 100, 100, 101, 110, 34,  32,  110, 97,  109, 60,  115,
+   99,  114, 105, 112, 116, 32,  116, 121, 112, 101, 61,  34,  116, 101, 120, 116, 47,  106, 97,
+   118, 97,  115, 34,  32,  115, 116, 121, 108, 101, 61,  34,  100, 105, 115, 112, 108, 97,  121,
+   58,  110, 111, 110, 101, 59,  34,  62,  100, 111, 99,  117, 109, 101, 110, 116, 46,  103, 101,
+   116, 69,  108, 101, 109, 101, 110, 116, 66,  121, 73,  100, 40,  61,  100, 111, 99,  117, 109,
+   101, 110, 116, 46,  99,  114, 101, 97,  116, 101, 69,  108, 101, 109, 101, 110, 116, 40,  39,
+   32,  116, 121, 112, 101, 61,  39,  116, 101, 120, 116, 47,  106, 97,  118, 97,  115, 99,  114,
+   105, 112, 116, 39,  105, 110, 112, 117, 116, 32,  116, 121, 112, 101, 61,  34,  116, 101, 120,
+   116, 34,  32,  110, 97,  109, 101, 61,  34,  100, 46,  103, 101, 116, 69,  108, 101, 109, 101,
+   110, 116, 115, 66,  121, 84,  97,  103, 78,  97,  109, 101, 40,  115, 110, 105, 99,  97,  108,
+   34,  32,  104, 114, 101, 102, 61,  34,  104, 116, 116, 112, 58,  47,  47,  119, 119, 119, 46,
+   67,  47,  47,  68,  84,  68,  32,  72,  84,  77,  76,  32,  52,  46,  48,  49,  32,  84,  114,
+   97,  110, 115, 105, 116, 60,  115, 116, 121, 108, 101, 32,  116, 121, 112, 101, 61,  34,  116,
+   101, 120, 116, 47,  99,  115, 115, 34,  62,  10,  10,  60,  115, 116, 121, 108, 101, 32,  116,
+   121, 112, 101, 61,  34,  116, 101, 120, 116, 47,  99,  115, 115, 34,  62,  105, 111, 110, 97,
+   108, 46,  100, 116, 100, 34,  62,  10,  60,  104, 116, 109, 108, 32,  120, 109, 108, 110, 115,
+   61,  104, 116, 116, 112, 45,  101, 113, 117, 105, 118, 61,  34,  67,  111, 110, 116, 101, 110,
+   116, 45,  84,  121, 112, 101, 100, 105, 110, 103, 61,  34,  48,  34,  32,  99,  101, 108, 108,
+   115, 112, 97,  99,  105, 110, 103, 61,  34,  48,  34,  104, 116, 109, 108, 59,  32,  99,  104,
+   97,  114, 115, 101, 116, 61,  117, 116, 102, 45,  56,  34,  32,  47,  62,  10,  32,  115, 116,
+   121, 108, 101, 61,  34,  100, 105, 115, 112, 108, 97,  121, 58,  110, 111, 110, 101, 59,  34,
+   62,  60,  60,  108, 105, 62,  60,  97,  32,  104, 114, 101, 102, 61,  34,  104, 116, 116, 112,
+   58,  47,  47,  119, 119, 119, 46,  32,  116, 121, 112, 101, 61,  39,  116, 101, 120, 116, 47,
+   106, 97,  118, 97,  115, 99,  114, 105, 112, 116, 39,  62,  208, 180, 208, 181, 209, 143, 209,
+   130, 208, 181, 208, 187, 209, 140, 208, 189, 208, 190, 209, 129, 209, 130, 208, 184, 209, 129,
+   208, 190, 208, 190, 209, 130, 208, 178, 208, 181, 209, 130, 209, 129, 209, 130, 208, 178, 208,
+   184, 208, 184, 208, 191, 209, 128, 208, 190, 208, 184, 208, 183, 208, 178, 208, 190, 208, 180,
+   209, 129, 209, 130, 208, 178, 208, 176, 208, 177, 208, 181, 208, 183, 208, 190, 208, 191, 208,
+   176, 209, 129, 208, 189, 208, 190, 209, 129, 209, 130, 208, 184, 224, 164, 170, 224, 165, 129,
+   224, 164, 184, 224, 165, 141, 224, 164, 164, 224, 164, 191, 224, 164, 149, 224, 164, 190, 224,
+   164, 149, 224, 164, 190, 224, 164, 130, 224, 164, 151, 224, 165, 141, 224, 164, 176, 224, 165,
+   135, 224, 164, 184, 224, 164, 137, 224, 164, 168, 224, 165, 141, 224, 164, 185, 224, 165, 139,
+   224, 164, 130, 224, 164, 168, 224, 165, 135, 224, 164, 181, 224, 164, 191, 224, 164, 167, 224,
+   164, 190, 224, 164, 168, 224, 164, 184, 224, 164, 173, 224, 164, 190, 224, 164, 171, 224, 164,
+   191, 224, 164, 149, 224, 165, 141, 224, 164, 184, 224, 164, 191, 224, 164, 130, 224, 164, 151,
+   224, 164, 184, 224, 165, 129, 224, 164, 176, 224, 164, 149, 224, 165, 141, 224, 164, 183, 224,
+   164, 191, 224, 164, 164, 224, 164, 149, 224, 165, 137, 224, 164, 170, 224, 165, 128, 224, 164,
+   176, 224, 164, 190, 224, 164, 135, 224, 164, 159, 224, 164, 181, 224, 164, 191, 224, 164, 156,
+   224, 165, 141, 224, 164, 158, 224, 164, 190, 224, 164, 170, 224, 164, 168, 224, 164, 149, 224,
+   164, 190, 224, 164, 176, 224, 165, 141, 224, 164, 176, 224, 164, 181, 224, 164, 190, 224, 164,
+   136, 224, 164, 184, 224, 164, 149, 224, 165, 141, 224, 164, 176, 224, 164, 191, 224, 164, 175,
+   224, 164, 164, 224, 164, 190}};
+
+brotli_dictionary_s const* get_brotli_dictionary() { return &g_dictionary; }
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/brotli_dict.hpp b/cpp/src/io/comp/brotli_dict.hpp
new file mode 100644
index 0000000..bbcfd9a
--- /dev/null
+++ b/cpp/src/io/comp/brotli_dict.hpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * Portions of this file are derived from Google's Brotli project at
+ * https://github.com/google/brotli, original license text below.
+ */
+
+/* Copyright 2013 Google Inc. All Rights Reserved.
+
+Distributed under MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+*/
+
+/*
+Copyright(c) 2009, 2010, 2013 - 2016 by the Brotli Authors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions :
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+*/
+
+#pragma once
+
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+struct brotli_dictionary_s {
+  /**
+   * Number of bits to encode index of dictionary word in a bucket.
+   *
+   * Specification: Appendix A. Static Dictionary Data
+   *
+   * Words in a dictionary are bucketed by length.
+   * @c 0 means that there are no words of a given length.
+   * Dictionary consists of words with length of [4..24] bytes.
+   * Values at [0..3] and [25..31] indices should not be addressed.
+   */
+  uint8_t size_bits_by_length[32];
+
+  /* assert(offset[i + 1] == offset[i] + (bits[i] ? (i << bits[i]) : 0)) */
+  uint32_t offsets_by_length[32];
+
+  /* Data array should obey to size_bits_by_length values.
+    Specified size matches default (RFC 7932) dictionary.
+    Its size is also equal to offsets_by_length[31] */
+  uint8_t data[122784];
+};
+
+constexpr int brotli_min_dictionary_word_length = 4;
+constexpr int brotli_max_dictionary_word_length = 24;
+
+brotli_dictionary_s const* get_brotli_dictionary();
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/brotli_tables.hpp b/cpp/src/io/comp/brotli_tables.hpp
new file mode 100644
index 0000000..a788dc4
--- /dev/null
+++ b/cpp/src/io/comp/brotli_tables.hpp
@@ -0,0 +1,2653 @@
+/*
+ * Copyright (c) 2018-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * Portions of this file are derived from Google's Brotli project at
+ * https://github.com/google/brotli, original license text below.
+ */
+
+/* Copyright 2013 Google Inc. All Rights Reserved.
+
+Distributed under MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+*/
+
+/*
+Copyright(c) 2009, 2010, 2013 - 2016 by the Brotli Authors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions :
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+*/
+
+#pragma once
+
+#ifndef CONSTANT
+#define CONSTANT static const
+#endif
+
+constexpr int brotli_num_block_len_symbols = 26;
+constexpr int brotli_num_literal_symbols   = 256;
+constexpr int brotli_num_command_symbols   = 704;
+
+CONSTANT uint8_t kReverseBits[1 << 8] = {
+  0x00, 0x80, 0x40, 0xC0, 0x20, 0xA0, 0x60, 0xE0, 0x10, 0x90, 0x50, 0xD0, 0x30, 0xB0, 0x70, 0xF0,
+  0x08, 0x88, 0x48, 0xC8, 0x28, 0xA8, 0x68, 0xE8, 0x18, 0x98, 0x58, 0xD8, 0x38, 0xB8, 0x78, 0xF8,
+  0x04, 0x84, 0x44, 0xC4, 0x24, 0xA4, 0x64, 0xE4, 0x14, 0x94, 0x54, 0xD4, 0x34, 0xB4, 0x74, 0xF4,
+  0x0C, 0x8C, 0x4C, 0xCC, 0x2C, 0xAC, 0x6C, 0xEC, 0x1C, 0x9C, 0x5C, 0xDC, 0x3C, 0xBC, 0x7C, 0xFC,
+  0x02, 0x82, 0x42, 0xC2, 0x22, 0xA2, 0x62, 0xE2, 0x12, 0x92, 0x52, 0xD2, 0x32, 0xB2, 0x72, 0xF2,
+  0x0A, 0x8A, 0x4A, 0xCA, 0x2A, 0xAA, 0x6A, 0xEA, 0x1A, 0x9A, 0x5A, 0xDA, 0x3A, 0xBA, 0x7A, 0xFA,
+  0x06, 0x86, 0x46, 0xC6, 0x26, 0xA6, 0x66, 0xE6, 0x16, 0x96, 0x56, 0xD6, 0x36, 0xB6, 0x76, 0xF6,
+  0x0E, 0x8E, 0x4E, 0xCE, 0x2E, 0xAE, 0x6E, 0xEE, 0x1E, 0x9E, 0x5E, 0xDE, 0x3E, 0xBE, 0x7E, 0xFE,
+  0x01, 0x81, 0x41, 0xC1, 0x21, 0xA1, 0x61, 0xE1, 0x11, 0x91, 0x51, 0xD1, 0x31, 0xB1, 0x71, 0xF1,
+  0x09, 0x89, 0x49, 0xC9, 0x29, 0xA9, 0x69, 0xE9, 0x19, 0x99, 0x59, 0xD9, 0x39, 0xB9, 0x79, 0xF9,
+  0x05, 0x85, 0x45, 0xC5, 0x25, 0xA5, 0x65, 0xE5, 0x15, 0x95, 0x55, 0xD5, 0x35, 0xB5, 0x75, 0xF5,
+  0x0D, 0x8D, 0x4D, 0xCD, 0x2D, 0xAD, 0x6D, 0xED, 0x1D, 0x9D, 0x5D, 0xDD, 0x3D, 0xBD, 0x7D, 0xFD,
+  0x03, 0x83, 0x43, 0xC3, 0x23, 0xA3, 0x63, 0xE3, 0x13, 0x93, 0x53, 0xD3, 0x33, 0xB3, 0x73, 0xF3,
+  0x0B, 0x8B, 0x4B, 0xCB, 0x2B, 0xAB, 0x6B, 0xEB, 0x1B, 0x9B, 0x5B, 0xDB, 0x3B, 0xBB, 0x7B, 0xFB,
+  0x07, 0x87, 0x47, 0xC7, 0x27, 0xA7, 0x67, 0xE7, 0x17, 0x97, 0x57, 0xD7, 0x37, 0xB7, 0x77, 0xF7,
+  0x0F, 0x8F, 0x4F, 0xCF, 0x2F, 0xAF, 0x6F, 0xEF, 0x1F, 0x9F, 0x5F, 0xDF, 0x3F, 0xBF, 0x7F, 0xFF};
+
+/* Common context lookup table for all context modes. */
+CONSTANT uint8_t kContextLookup[2048] = {
+  /* CONTEXT_LSB6, last byte. */
+  0,
+  1,
+  2,
+  3,
+  4,
+  5,
+  6,
+  7,
+  8,
+  9,
+  10,
+  11,
+  12,
+  13,
+  14,
+  15,
+  16,
+  17,
+  18,
+  19,
+  20,
+  21,
+  22,
+  23,
+  24,
+  25,
+  26,
+  27,
+  28,
+  29,
+  30,
+  31,
+  32,
+  33,
+  34,
+  35,
+  36,
+  37,
+  38,
+  39,
+  40,
+  41,
+  42,
+  43,
+  44,
+  45,
+  46,
+  47,
+  48,
+  49,
+  50,
+  51,
+  52,
+  53,
+  54,
+  55,
+  56,
+  57,
+  58,
+  59,
+  60,
+  61,
+  62,
+  63,
+  0,
+  1,
+  2,
+  3,
+  4,
+  5,
+  6,
+  7,
+  8,
+  9,
+  10,
+  11,
+  12,
+  13,
+  14,
+  15,
+  16,
+  17,
+  18,
+  19,
+  20,
+  21,
+  22,
+  23,
+  24,
+  25,
+  26,
+  27,
+  28,
+  29,
+  30,
+  31,
+  32,
+  33,
+  34,
+  35,
+  36,
+  37,
+  38,
+  39,
+  40,
+  41,
+  42,
+  43,
+  44,
+  45,
+  46,
+  47,
+  48,
+  49,
+  50,
+  51,
+  52,
+  53,
+  54,
+  55,
+  56,
+  57,
+  58,
+  59,
+  60,
+  61,
+  62,
+  63,
+  0,
+  1,
+  2,
+  3,
+  4,
+  5,
+  6,
+  7,
+  8,
+  9,
+  10,
+  11,
+  12,
+  13,
+  14,
+  15,
+  16,
+  17,
+  18,
+  19,
+  20,
+  21,
+  22,
+  23,
+  24,
+  25,
+  26,
+  27,
+  28,
+  29,
+  30,
+  31,
+  32,
+  33,
+  34,
+  35,
+  36,
+  37,
+  38,
+  39,
+  40,
+  41,
+  42,
+  43,
+  44,
+  45,
+  46,
+  47,
+  48,
+  49,
+  50,
+  51,
+  52,
+  53,
+  54,
+  55,
+  56,
+  57,
+  58,
+  59,
+  60,
+  61,
+  62,
+  63,
+  0,
+  1,
+  2,
+  3,
+  4,
+  5,
+  6,
+  7,
+  8,
+  9,
+  10,
+  11,
+  12,
+  13,
+  14,
+  15,
+  16,
+  17,
+  18,
+  19,
+  20,
+  21,
+  22,
+  23,
+  24,
+  25,
+  26,
+  27,
+  28,
+  29,
+  30,
+  31,
+  32,
+  33,
+  34,
+  35,
+  36,
+  37,
+  38,
+  39,
+  40,
+  41,
+  42,
+  43,
+  44,
+  45,
+  46,
+  47,
+  48,
+  49,
+  50,
+  51,
+  52,
+  53,
+  54,
+  55,
+  56,
+  57,
+  58,
+  59,
+  60,
+  61,
+  62,
+  63,
+
+  /* CONTEXT_LSB6, second last byte, */
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+
+  /* CONTEXT_MSB6, last byte. */
+  0,
+  0,
+  0,
+  0,
+  1,
+  1,
+  1,
+  1,
+  2,
+  2,
+  2,
+  2,
+  3,
+  3,
+  3,
+  3,
+  4,
+  4,
+  4,
+  4,
+  5,
+  5,
+  5,
+  5,
+  6,
+  6,
+  6,
+  6,
+  7,
+  7,
+  7,
+  7,
+  8,
+  8,
+  8,
+  8,
+  9,
+  9,
+  9,
+  9,
+  10,
+  10,
+  10,
+  10,
+  11,
+  11,
+  11,
+  11,
+  12,
+  12,
+  12,
+  12,
+  13,
+  13,
+  13,
+  13,
+  14,
+  14,
+  14,
+  14,
+  15,
+  15,
+  15,
+  15,
+  16,
+  16,
+  16,
+  16,
+  17,
+  17,
+  17,
+  17,
+  18,
+  18,
+  18,
+  18,
+  19,
+  19,
+  19,
+  19,
+  20,
+  20,
+  20,
+  20,
+  21,
+  21,
+  21,
+  21,
+  22,
+  22,
+  22,
+  22,
+  23,
+  23,
+  23,
+  23,
+  24,
+  24,
+  24,
+  24,
+  25,
+  25,
+  25,
+  25,
+  26,
+  26,
+  26,
+  26,
+  27,
+  27,
+  27,
+  27,
+  28,
+  28,
+  28,
+  28,
+  29,
+  29,
+  29,
+  29,
+  30,
+  30,
+  30,
+  30,
+  31,
+  31,
+  31,
+  31,
+  32,
+  32,
+  32,
+  32,
+  33,
+  33,
+  33,
+  33,
+  34,
+  34,
+  34,
+  34,
+  35,
+  35,
+  35,
+  35,
+  36,
+  36,
+  36,
+  36,
+  37,
+  37,
+  37,
+  37,
+  38,
+  38,
+  38,
+  38,
+  39,
+  39,
+  39,
+  39,
+  40,
+  40,
+  40,
+  40,
+  41,
+  41,
+  41,
+  41,
+  42,
+  42,
+  42,
+  42,
+  43,
+  43,
+  43,
+  43,
+  44,
+  44,
+  44,
+  44,
+  45,
+  45,
+  45,
+  45,
+  46,
+  46,
+  46,
+  46,
+  47,
+  47,
+  47,
+  47,
+  48,
+  48,
+  48,
+  48,
+  49,
+  49,
+  49,
+  49,
+  50,
+  50,
+  50,
+  50,
+  51,
+  51,
+  51,
+  51,
+  52,
+  52,
+  52,
+  52,
+  53,
+  53,
+  53,
+  53,
+  54,
+  54,
+  54,
+  54,
+  55,
+  55,
+  55,
+  55,
+  56,
+  56,
+  56,
+  56,
+  57,
+  57,
+  57,
+  57,
+  58,
+  58,
+  58,
+  58,
+  59,
+  59,
+  59,
+  59,
+  60,
+  60,
+  60,
+  60,
+  61,
+  61,
+  61,
+  61,
+  62,
+  62,
+  62,
+  62,
+  63,
+  63,
+  63,
+  63,
+
+  /* CONTEXT_MSB6, second last byte, */
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+
+  /* CONTEXT_UTF8, last byte. */
+  /* ASCII range. */
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  4,
+  4,
+  0,
+  0,
+  4,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  8,
+  12,
+  16,
+  12,
+  12,
+  20,
+  12,
+  16,
+  24,
+  28,
+  12,
+  12,
+  32,
+  12,
+  36,
+  12,
+  44,
+  44,
+  44,
+  44,
+  44,
+  44,
+  44,
+  44,
+  44,
+  44,
+  32,
+  32,
+  24,
+  40,
+  28,
+  12,
+  12,
+  48,
+  52,
+  52,
+  52,
+  48,
+  52,
+  52,
+  52,
+  48,
+  52,
+  52,
+  52,
+  52,
+  52,
+  48,
+  52,
+  52,
+  52,
+  52,
+  52,
+  48,
+  52,
+  52,
+  52,
+  52,
+  52,
+  24,
+  12,
+  28,
+  12,
+  12,
+  12,
+  56,
+  60,
+  60,
+  60,
+  56,
+  60,
+  60,
+  60,
+  56,
+  60,
+  60,
+  60,
+  60,
+  60,
+  56,
+  60,
+  60,
+  60,
+  60,
+  60,
+  56,
+  60,
+  60,
+  60,
+  60,
+  60,
+  24,
+  12,
+  28,
+  12,
+  0,
+  /* UTF8 continuation byte range. */
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  0,
+  1,
+  /* UTF8 lead byte range. */
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+  2,
+  3,
+
+  /* CONTEXT_UTF8 second last byte. */
+  /* ASCII range. */
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  1,
+  1,
+  1,
+  1,
+  0,
+  /* UTF8 continuation byte range. */
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  /* UTF8 lead byte range. */
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  0,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+
+  /* CONTEXT_SIGNED, last byte, same as the above values shifted by 3 bits. */
+  0,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  8,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  16,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  24,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  32,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  40,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  48,
+  56,
+
+  /* CONTEXT_SIGNED, second last byte. */
+  0,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  1,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  2,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  3,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  4,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  5,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  6,
+  7,
+};
+
+using CmdLutElement = struct CmdLutElement {
+  uint8_t insert_len_extra_bits;
+  uint8_t copy_len_extra_bits;
+  int8_t distance_code;
+  uint8_t context;
+  uint16_t insert_len_offset;
+  uint16_t copy_len_offset;
+};
+
+CONSTANT CmdLutElement kCmdLut[brotli_num_command_symbols] = {
+  {0x00, 0x00, 0, 0x00, 0x0000, 0x0002},  {0x00, 0x00, 0, 0x01, 0x0000, 0x0003},
+  {0x00, 0x00, 0, 0x02, 0x0000, 0x0004},  {0x00, 0x00, 0, 0x03, 0x0000, 0x0005},
+  {0x00, 0x00, 0, 0x03, 0x0000, 0x0006},  {0x00, 0x00, 0, 0x03, 0x0000, 0x0007},
+  {0x00, 0x00, 0, 0x03, 0x0000, 0x0008},  {0x00, 0x00, 0, 0x03, 0x0000, 0x0009},
+  {0x00, 0x00, 0, 0x00, 0x0001, 0x0002},  {0x00, 0x00, 0, 0x01, 0x0001, 0x0003},
+  {0x00, 0x00, 0, 0x02, 0x0001, 0x0004},  {0x00, 0x00, 0, 0x03, 0x0001, 0x0005},
+  {0x00, 0x00, 0, 0x03, 0x0001, 0x0006},  {0x00, 0x00, 0, 0x03, 0x0001, 0x0007},
+  {0x00, 0x00, 0, 0x03, 0x0001, 0x0008},  {0x00, 0x00, 0, 0x03, 0x0001, 0x0009},
+  {0x00, 0x00, 0, 0x00, 0x0002, 0x0002},  {0x00, 0x00, 0, 0x01, 0x0002, 0x0003},
+  {0x00, 0x00, 0, 0x02, 0x0002, 0x0004},  {0x00, 0x00, 0, 0x03, 0x0002, 0x0005},
+  {0x00, 0x00, 0, 0x03, 0x0002, 0x0006},  {0x00, 0x00, 0, 0x03, 0x0002, 0x0007},
+  {0x00, 0x00, 0, 0x03, 0x0002, 0x0008},  {0x00, 0x00, 0, 0x03, 0x0002, 0x0009},
+  {0x00, 0x00, 0, 0x00, 0x0003, 0x0002},  {0x00, 0x00, 0, 0x01, 0x0003, 0x0003},
+  {0x00, 0x00, 0, 0x02, 0x0003, 0x0004},  {0x00, 0x00, 0, 0x03, 0x0003, 0x0005},
+  {0x00, 0x00, 0, 0x03, 0x0003, 0x0006},  {0x00, 0x00, 0, 0x03, 0x0003, 0x0007},
+  {0x00, 0x00, 0, 0x03, 0x0003, 0x0008},  {0x00, 0x00, 0, 0x03, 0x0003, 0x0009},
+  {0x00, 0x00, 0, 0x00, 0x0004, 0x0002},  {0x00, 0x00, 0, 0x01, 0x0004, 0x0003},
+  {0x00, 0x00, 0, 0x02, 0x0004, 0x0004},  {0x00, 0x00, 0, 0x03, 0x0004, 0x0005},
+  {0x00, 0x00, 0, 0x03, 0x0004, 0x0006},  {0x00, 0x00, 0, 0x03, 0x0004, 0x0007},
+  {0x00, 0x00, 0, 0x03, 0x0004, 0x0008},  {0x00, 0x00, 0, 0x03, 0x0004, 0x0009},
+  {0x00, 0x00, 0, 0x00, 0x0005, 0x0002},  {0x00, 0x00, 0, 0x01, 0x0005, 0x0003},
+  {0x00, 0x00, 0, 0x02, 0x0005, 0x0004},  {0x00, 0x00, 0, 0x03, 0x0005, 0x0005},
+  {0x00, 0x00, 0, 0x03, 0x0005, 0x0006},  {0x00, 0x00, 0, 0x03, 0x0005, 0x0007},
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+  {0x00, 0x04, -1, 0x03, 0x0000, 0x0026}, {0x00, 0x04, -1, 0x03, 0x0000, 0x0036},
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+  {0x00, 0x04, -1, 0x03, 0x0001, 0x0026}, {0x00, 0x04, -1, 0x03, 0x0001, 0x0036},
+  {0x00, 0x01, -1, 0x03, 0x0002, 0x000a}, {0x00, 0x01, -1, 0x03, 0x0002, 0x000c},
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+  {0x00, 0x03, -1, 0x03, 0x0002, 0x0016}, {0x00, 0x03, -1, 0x03, 0x0002, 0x001e},
+  {0x00, 0x04, -1, 0x03, 0x0002, 0x0026}, {0x00, 0x04, -1, 0x03, 0x0002, 0x0036},
+  {0x00, 0x01, -1, 0x03, 0x0003, 0x000a}, {0x00, 0x01, -1, 0x03, 0x0003, 0x000c},
+  {0x00, 0x02, -1, 0x03, 0x0003, 0x000e}, {0x00, 0x02, -1, 0x03, 0x0003, 0x0012},
+  {0x00, 0x03, -1, 0x03, 0x0003, 0x0016}, {0x00, 0x03, -1, 0x03, 0x0003, 0x001e},
+  {0x00, 0x04, -1, 0x03, 0x0003, 0x0026}, {0x00, 0x04, -1, 0x03, 0x0003, 0x0036},
+  {0x00, 0x01, -1, 0x03, 0x0004, 0x000a}, {0x00, 0x01, -1, 0x03, 0x0004, 0x000c},
+  {0x00, 0x02, -1, 0x03, 0x0004, 0x000e}, {0x00, 0x02, -1, 0x03, 0x0004, 0x0012},
+  {0x00, 0x03, -1, 0x03, 0x0004, 0x0016}, {0x00, 0x03, -1, 0x03, 0x0004, 0x001e},
+  {0x00, 0x04, -1, 0x03, 0x0004, 0x0026}, {0x00, 0x04, -1, 0x03, 0x0004, 0x0036},
+  {0x00, 0x01, -1, 0x03, 0x0005, 0x000a}, {0x00, 0x01, -1, 0x03, 0x0005, 0x000c},
+  {0x00, 0x02, -1, 0x03, 0x0005, 0x000e}, {0x00, 0x02, -1, 0x03, 0x0005, 0x0012},
+  {0x00, 0x03, -1, 0x03, 0x0005, 0x0016}, {0x00, 0x03, -1, 0x03, 0x0005, 0x001e},
+  {0x00, 0x04, -1, 0x03, 0x0005, 0x0026}, {0x00, 0x04, -1, 0x03, 0x0005, 0x0036},
+  {0x01, 0x01, -1, 0x03, 0x0006, 0x000a}, {0x01, 0x01, -1, 0x03, 0x0006, 0x000c},
+  {0x01, 0x02, -1, 0x03, 0x0006, 0x000e}, {0x01, 0x02, -1, 0x03, 0x0006, 0x0012},
+  {0x01, 0x03, -1, 0x03, 0x0006, 0x0016}, {0x01, 0x03, -1, 0x03, 0x0006, 0x001e},
+  {0x01, 0x04, -1, 0x03, 0x0006, 0x0026}, {0x01, 0x04, -1, 0x03, 0x0006, 0x0036},
+  {0x01, 0x01, -1, 0x03, 0x0008, 0x000a}, {0x01, 0x01, -1, 0x03, 0x0008, 0x000c},
+  {0x01, 0x02, -1, 0x03, 0x0008, 0x000e}, {0x01, 0x02, -1, 0x03, 0x0008, 0x0012},
+  {0x01, 0x03, -1, 0x03, 0x0008, 0x0016}, {0x01, 0x03, -1, 0x03, 0x0008, 0x001e},
+  {0x01, 0x04, -1, 0x03, 0x0008, 0x0026}, {0x01, 0x04, -1, 0x03, 0x0008, 0x0036},
+  {0x02, 0x00, -1, 0x00, 0x000a, 0x0002}, {0x02, 0x00, -1, 0x01, 0x000a, 0x0003},
+  {0x02, 0x00, -1, 0x02, 0x000a, 0x0004}, {0x02, 0x00, -1, 0x03, 0x000a, 0x0005},
+  {0x02, 0x00, -1, 0x03, 0x000a, 0x0006}, {0x02, 0x00, -1, 0x03, 0x000a, 0x0007},
+  {0x02, 0x00, -1, 0x03, 0x000a, 0x0008}, {0x02, 0x00, -1, 0x03, 0x000a, 0x0009},
+  {0x02, 0x00, -1, 0x00, 0x000e, 0x0002}, {0x02, 0x00, -1, 0x01, 0x000e, 0x0003},
+  {0x02, 0x00, -1, 0x02, 0x000e, 0x0004}, {0x02, 0x00, -1, 0x03, 0x000e, 0x0005},
+  {0x02, 0x00, -1, 0x03, 0x000e, 0x0006}, {0x02, 0x00, -1, 0x03, 0x000e, 0x0007},
+  {0x02, 0x00, -1, 0x03, 0x000e, 0x0008}, {0x02, 0x00, -1, 0x03, 0x000e, 0x0009},
+  {0x03, 0x00, -1, 0x00, 0x0012, 0x0002}, {0x03, 0x00, -1, 0x01, 0x0012, 0x0003},
+  {0x03, 0x00, -1, 0x02, 0x0012, 0x0004}, {0x03, 0x00, -1, 0x03, 0x0012, 0x0005},
+  {0x03, 0x00, -1, 0x03, 0x0012, 0x0006}, {0x03, 0x00, -1, 0x03, 0x0012, 0x0007},
+  {0x03, 0x00, -1, 0x03, 0x0012, 0x0008}, {0x03, 0x00, -1, 0x03, 0x0012, 0x0009},
+  {0x03, 0x00, -1, 0x00, 0x001a, 0x0002}, {0x03, 0x00, -1, 0x01, 0x001a, 0x0003},
+  {0x03, 0x00, -1, 0x02, 0x001a, 0x0004}, {0x03, 0x00, -1, 0x03, 0x001a, 0x0005},
+  {0x03, 0x00, -1, 0x03, 0x001a, 0x0006}, {0x03, 0x00, -1, 0x03, 0x001a, 0x0007},
+  {0x03, 0x00, -1, 0x03, 0x001a, 0x0008}, {0x03, 0x00, -1, 0x03, 0x001a, 0x0009},
+  {0x04, 0x00, -1, 0x00, 0x0022, 0x0002}, {0x04, 0x00, -1, 0x01, 0x0022, 0x0003},
+  {0x04, 0x00, -1, 0x02, 0x0022, 0x0004}, {0x04, 0x00, -1, 0x03, 0x0022, 0x0005},
+  {0x04, 0x00, -1, 0x03, 0x0022, 0x0006}, {0x04, 0x00, -1, 0x03, 0x0022, 0x0007},
+  {0x04, 0x00, -1, 0x03, 0x0022, 0x0008}, {0x04, 0x00, -1, 0x03, 0x0022, 0x0009},
+  {0x04, 0x00, -1, 0x00, 0x0032, 0x0002}, {0x04, 0x00, -1, 0x01, 0x0032, 0x0003},
+  {0x04, 0x00, -1, 0x02, 0x0032, 0x0004}, {0x04, 0x00, -1, 0x03, 0x0032, 0x0005},
+  {0x04, 0x00, -1, 0x03, 0x0032, 0x0006}, {0x04, 0x00, -1, 0x03, 0x0032, 0x0007},
+  {0x04, 0x00, -1, 0x03, 0x0032, 0x0008}, {0x04, 0x00, -1, 0x03, 0x0032, 0x0009},
+  {0x05, 0x00, -1, 0x00, 0x0042, 0x0002}, {0x05, 0x00, -1, 0x01, 0x0042, 0x0003},
+  {0x05, 0x00, -1, 0x02, 0x0042, 0x0004}, {0x05, 0x00, -1, 0x03, 0x0042, 0x0005},
+  {0x05, 0x00, -1, 0x03, 0x0042, 0x0006}, {0x05, 0x00, -1, 0x03, 0x0042, 0x0007},
+  {0x05, 0x00, -1, 0x03, 0x0042, 0x0008}, {0x05, 0x00, -1, 0x03, 0x0042, 0x0009},
+  {0x05, 0x00, -1, 0x00, 0x0062, 0x0002}, {0x05, 0x00, -1, 0x01, 0x0062, 0x0003},
+  {0x05, 0x00, -1, 0x02, 0x0062, 0x0004}, {0x05, 0x00, -1, 0x03, 0x0062, 0x0005},
+  {0x05, 0x00, -1, 0x03, 0x0062, 0x0006}, {0x05, 0x00, -1, 0x03, 0x0062, 0x0007},
+  {0x05, 0x00, -1, 0x03, 0x0062, 0x0008}, {0x05, 0x00, -1, 0x03, 0x0062, 0x0009},
+  {0x02, 0x01, -1, 0x03, 0x000a, 0x000a}, {0x02, 0x01, -1, 0x03, 0x000a, 0x000c},
+  {0x02, 0x02, -1, 0x03, 0x000a, 0x000e}, {0x02, 0x02, -1, 0x03, 0x000a, 0x0012},
+  {0x02, 0x03, -1, 0x03, 0x000a, 0x0016}, {0x02, 0x03, -1, 0x03, 0x000a, 0x001e},
+  {0x02, 0x04, -1, 0x03, 0x000a, 0x0026}, {0x02, 0x04, -1, 0x03, 0x000a, 0x0036},
+  {0x02, 0x01, -1, 0x03, 0x000e, 0x000a}, {0x02, 0x01, -1, 0x03, 0x000e, 0x000c},
+  {0x02, 0x02, -1, 0x03, 0x000e, 0x000e}, {0x02, 0x02, -1, 0x03, 0x000e, 0x0012},
+  {0x02, 0x03, -1, 0x03, 0x000e, 0x0016}, {0x02, 0x03, -1, 0x03, 0x000e, 0x001e},
+  {0x02, 0x04, -1, 0x03, 0x000e, 0x0026}, {0x02, 0x04, -1, 0x03, 0x000e, 0x0036},
+  {0x03, 0x01, -1, 0x03, 0x0012, 0x000a}, {0x03, 0x01, -1, 0x03, 0x0012, 0x000c},
+  {0x03, 0x02, -1, 0x03, 0x0012, 0x000e}, {0x03, 0x02, -1, 0x03, 0x0012, 0x0012},
+  {0x03, 0x03, -1, 0x03, 0x0012, 0x0016}, {0x03, 0x03, -1, 0x03, 0x0012, 0x001e},
+  {0x03, 0x04, -1, 0x03, 0x0012, 0x0026}, {0x03, 0x04, -1, 0x03, 0x0012, 0x0036},
+  {0x03, 0x01, -1, 0x03, 0x001a, 0x000a}, {0x03, 0x01, -1, 0x03, 0x001a, 0x000c},
+  {0x03, 0x02, -1, 0x03, 0x001a, 0x000e}, {0x03, 0x02, -1, 0x03, 0x001a, 0x0012},
+  {0x03, 0x03, -1, 0x03, 0x001a, 0x0016}, {0x03, 0x03, -1, 0x03, 0x001a, 0x001e},
+  {0x03, 0x04, -1, 0x03, 0x001a, 0x0026}, {0x03, 0x04, -1, 0x03, 0x001a, 0x0036},
+  {0x04, 0x01, -1, 0x03, 0x0022, 0x000a}, {0x04, 0x01, -1, 0x03, 0x0022, 0x000c},
+  {0x04, 0x02, -1, 0x03, 0x0022, 0x000e}, {0x04, 0x02, -1, 0x03, 0x0022, 0x0012},
+  {0x04, 0x03, -1, 0x03, 0x0022, 0x0016}, {0x04, 0x03, -1, 0x03, 0x0022, 0x001e},
+  {0x04, 0x04, -1, 0x03, 0x0022, 0x0026}, {0x04, 0x04, -1, 0x03, 0x0022, 0x0036},
+  {0x04, 0x01, -1, 0x03, 0x0032, 0x000a}, {0x04, 0x01, -1, 0x03, 0x0032, 0x000c},
+  {0x04, 0x02, -1, 0x03, 0x0032, 0x000e}, {0x04, 0x02, -1, 0x03, 0x0032, 0x0012},
+  {0x04, 0x03, -1, 0x03, 0x0032, 0x0016}, {0x04, 0x03, -1, 0x03, 0x0032, 0x001e},
+  {0x04, 0x04, -1, 0x03, 0x0032, 0x0026}, {0x04, 0x04, -1, 0x03, 0x0032, 0x0036},
+  {0x05, 0x01, -1, 0x03, 0x0042, 0x000a}, {0x05, 0x01, -1, 0x03, 0x0042, 0x000c},
+  {0x05, 0x02, -1, 0x03, 0x0042, 0x000e}, {0x05, 0x02, -1, 0x03, 0x0042, 0x0012},
+  {0x05, 0x03, -1, 0x03, 0x0042, 0x0016}, {0x05, 0x03, -1, 0x03, 0x0042, 0x001e},
+  {0x05, 0x04, -1, 0x03, 0x0042, 0x0026}, {0x05, 0x04, -1, 0x03, 0x0042, 0x0036},
+  {0x05, 0x01, -1, 0x03, 0x0062, 0x000a}, {0x05, 0x01, -1, 0x03, 0x0062, 0x000c},
+  {0x05, 0x02, -1, 0x03, 0x0062, 0x000e}, {0x05, 0x02, -1, 0x03, 0x0062, 0x0012},
+  {0x05, 0x03, -1, 0x03, 0x0062, 0x0016}, {0x05, 0x03, -1, 0x03, 0x0062, 0x001e},
+  {0x05, 0x04, -1, 0x03, 0x0062, 0x0026}, {0x05, 0x04, -1, 0x03, 0x0062, 0x0036},
+  {0x00, 0x05, -1, 0x03, 0x0000, 0x0046}, {0x00, 0x05, -1, 0x03, 0x0000, 0x0066},
+  {0x00, 0x06, -1, 0x03, 0x0000, 0x0086}, {0x00, 0x07, -1, 0x03, 0x0000, 0x00c6},
+  {0x00, 0x08, -1, 0x03, 0x0000, 0x0146}, {0x00, 0x09, -1, 0x03, 0x0000, 0x0246},
+  {0x00, 0x0a, -1, 0x03, 0x0000, 0x0446}, {0x00, 0x18, -1, 0x03, 0x0000, 0x0846},
+  {0x00, 0x05, -1, 0x03, 0x0001, 0x0046}, {0x00, 0x05, -1, 0x03, 0x0001, 0x0066},
+  {0x00, 0x06, -1, 0x03, 0x0001, 0x0086}, {0x00, 0x07, -1, 0x03, 0x0001, 0x00c6},
+  {0x00, 0x08, -1, 0x03, 0x0001, 0x0146}, {0x00, 0x09, -1, 0x03, 0x0001, 0x0246},
+  {0x00, 0x0a, -1, 0x03, 0x0001, 0x0446}, {0x00, 0x18, -1, 0x03, 0x0001, 0x0846},
+  {0x00, 0x05, -1, 0x03, 0x0002, 0x0046}, {0x00, 0x05, -1, 0x03, 0x0002, 0x0066},
+  {0x00, 0x06, -1, 0x03, 0x0002, 0x0086}, {0x00, 0x07, -1, 0x03, 0x0002, 0x00c6},
+  {0x00, 0x08, -1, 0x03, 0x0002, 0x0146}, {0x00, 0x09, -1, 0x03, 0x0002, 0x0246},
+  {0x00, 0x0a, -1, 0x03, 0x0002, 0x0446}, {0x00, 0x18, -1, 0x03, 0x0002, 0x0846},
+  {0x00, 0x05, -1, 0x03, 0x0003, 0x0046}, {0x00, 0x05, -1, 0x03, 0x0003, 0x0066},
+  {0x00, 0x06, -1, 0x03, 0x0003, 0x0086}, {0x00, 0x07, -1, 0x03, 0x0003, 0x00c6},
+  {0x00, 0x08, -1, 0x03, 0x0003, 0x0146}, {0x00, 0x09, -1, 0x03, 0x0003, 0x0246},
+  {0x00, 0x0a, -1, 0x03, 0x0003, 0x0446}, {0x00, 0x18, -1, 0x03, 0x0003, 0x0846},
+  {0x00, 0x05, -1, 0x03, 0x0004, 0x0046}, {0x00, 0x05, -1, 0x03, 0x0004, 0x0066},
+  {0x00, 0x06, -1, 0x03, 0x0004, 0x0086}, {0x00, 0x07, -1, 0x03, 0x0004, 0x00c6},
+  {0x00, 0x08, -1, 0x03, 0x0004, 0x0146}, {0x00, 0x09, -1, 0x03, 0x0004, 0x0246},
+  {0x00, 0x0a, -1, 0x03, 0x0004, 0x0446}, {0x00, 0x18, -1, 0x03, 0x0004, 0x0846},
+  {0x00, 0x05, -1, 0x03, 0x0005, 0x0046}, {0x00, 0x05, -1, 0x03, 0x0005, 0x0066},
+  {0x00, 0x06, -1, 0x03, 0x0005, 0x0086}, {0x00, 0x07, -1, 0x03, 0x0005, 0x00c6},
+  {0x00, 0x08, -1, 0x03, 0x0005, 0x0146}, {0x00, 0x09, -1, 0x03, 0x0005, 0x0246},
+  {0x00, 0x0a, -1, 0x03, 0x0005, 0x0446}, {0x00, 0x18, -1, 0x03, 0x0005, 0x0846},
+  {0x01, 0x05, -1, 0x03, 0x0006, 0x0046}, {0x01, 0x05, -1, 0x03, 0x0006, 0x0066},
+  {0x01, 0x06, -1, 0x03, 0x0006, 0x0086}, {0x01, 0x07, -1, 0x03, 0x0006, 0x00c6},
+  {0x01, 0x08, -1, 0x03, 0x0006, 0x0146}, {0x01, 0x09, -1, 0x03, 0x0006, 0x0246},
+  {0x01, 0x0a, -1, 0x03, 0x0006, 0x0446}, {0x01, 0x18, -1, 0x03, 0x0006, 0x0846},
+  {0x01, 0x05, -1, 0x03, 0x0008, 0x0046}, {0x01, 0x05, -1, 0x03, 0x0008, 0x0066},
+  {0x01, 0x06, -1, 0x03, 0x0008, 0x0086}, {0x01, 0x07, -1, 0x03, 0x0008, 0x00c6},
+  {0x01, 0x08, -1, 0x03, 0x0008, 0x0146}, {0x01, 0x09, -1, 0x03, 0x0008, 0x0246},
+  {0x01, 0x0a, -1, 0x03, 0x0008, 0x0446}, {0x01, 0x18, -1, 0x03, 0x0008, 0x0846},
+  {0x06, 0x00, -1, 0x00, 0x0082, 0x0002}, {0x06, 0x00, -1, 0x01, 0x0082, 0x0003},
+  {0x06, 0x00, -1, 0x02, 0x0082, 0x0004}, {0x06, 0x00, -1, 0x03, 0x0082, 0x0005},
+  {0x06, 0x00, -1, 0x03, 0x0082, 0x0006}, {0x06, 0x00, -1, 0x03, 0x0082, 0x0007},
+  {0x06, 0x00, -1, 0x03, 0x0082, 0x0008}, {0x06, 0x00, -1, 0x03, 0x0082, 0x0009},
+  {0x07, 0x00, -1, 0x00, 0x00c2, 0x0002}, {0x07, 0x00, -1, 0x01, 0x00c2, 0x0003},
+  {0x07, 0x00, -1, 0x02, 0x00c2, 0x0004}, {0x07, 0x00, -1, 0x03, 0x00c2, 0x0005},
+  {0x07, 0x00, -1, 0x03, 0x00c2, 0x0006}, {0x07, 0x00, -1, 0x03, 0x00c2, 0x0007},
+  {0x07, 0x00, -1, 0x03, 0x00c2, 0x0008}, {0x07, 0x00, -1, 0x03, 0x00c2, 0x0009},
+  {0x08, 0x00, -1, 0x00, 0x0142, 0x0002}, {0x08, 0x00, -1, 0x01, 0x0142, 0x0003},
+  {0x08, 0x00, -1, 0x02, 0x0142, 0x0004}, {0x08, 0x00, -1, 0x03, 0x0142, 0x0005},
+  {0x08, 0x00, -1, 0x03, 0x0142, 0x0006}, {0x08, 0x00, -1, 0x03, 0x0142, 0x0007},
+  {0x08, 0x00, -1, 0x03, 0x0142, 0x0008}, {0x08, 0x00, -1, 0x03, 0x0142, 0x0009},
+  {0x09, 0x00, -1, 0x00, 0x0242, 0x0002}, {0x09, 0x00, -1, 0x01, 0x0242, 0x0003},
+  {0x09, 0x00, -1, 0x02, 0x0242, 0x0004}, {0x09, 0x00, -1, 0x03, 0x0242, 0x0005},
+  {0x09, 0x00, -1, 0x03, 0x0242, 0x0006}, {0x09, 0x00, -1, 0x03, 0x0242, 0x0007},
+  {0x09, 0x00, -1, 0x03, 0x0242, 0x0008}, {0x09, 0x00, -1, 0x03, 0x0242, 0x0009},
+  {0x0a, 0x00, -1, 0x00, 0x0442, 0x0002}, {0x0a, 0x00, -1, 0x01, 0x0442, 0x0003},
+  {0x0a, 0x00, -1, 0x02, 0x0442, 0x0004}, {0x0a, 0x00, -1, 0x03, 0x0442, 0x0005},
+  {0x0a, 0x00, -1, 0x03, 0x0442, 0x0006}, {0x0a, 0x00, -1, 0x03, 0x0442, 0x0007},
+  {0x0a, 0x00, -1, 0x03, 0x0442, 0x0008}, {0x0a, 0x00, -1, 0x03, 0x0442, 0x0009},
+  {0x0c, 0x00, -1, 0x00, 0x0842, 0x0002}, {0x0c, 0x00, -1, 0x01, 0x0842, 0x0003},
+  {0x0c, 0x00, -1, 0x02, 0x0842, 0x0004}, {0x0c, 0x00, -1, 0x03, 0x0842, 0x0005},
+  {0x0c, 0x00, -1, 0x03, 0x0842, 0x0006}, {0x0c, 0x00, -1, 0x03, 0x0842, 0x0007},
+  {0x0c, 0x00, -1, 0x03, 0x0842, 0x0008}, {0x0c, 0x00, -1, 0x03, 0x0842, 0x0009},
+  {0x0e, 0x00, -1, 0x00, 0x1842, 0x0002}, {0x0e, 0x00, -1, 0x01, 0x1842, 0x0003},
+  {0x0e, 0x00, -1, 0x02, 0x1842, 0x0004}, {0x0e, 0x00, -1, 0x03, 0x1842, 0x0005},
+  {0x0e, 0x00, -1, 0x03, 0x1842, 0x0006}, {0x0e, 0x00, -1, 0x03, 0x1842, 0x0007},
+  {0x0e, 0x00, -1, 0x03, 0x1842, 0x0008}, {0x0e, 0x00, -1, 0x03, 0x1842, 0x0009},
+  {0x18, 0x00, -1, 0x00, 0x5842, 0x0002}, {0x18, 0x00, -1, 0x01, 0x5842, 0x0003},
+  {0x18, 0x00, -1, 0x02, 0x5842, 0x0004}, {0x18, 0x00, -1, 0x03, 0x5842, 0x0005},
+  {0x18, 0x00, -1, 0x03, 0x5842, 0x0006}, {0x18, 0x00, -1, 0x03, 0x5842, 0x0007},
+  {0x18, 0x00, -1, 0x03, 0x5842, 0x0008}, {0x18, 0x00, -1, 0x03, 0x5842, 0x0009},
+  {0x02, 0x05, -1, 0x03, 0x000a, 0x0046}, {0x02, 0x05, -1, 0x03, 0x000a, 0x0066},
+  {0x02, 0x06, -1, 0x03, 0x000a, 0x0086}, {0x02, 0x07, -1, 0x03, 0x000a, 0x00c6},
+  {0x02, 0x08, -1, 0x03, 0x000a, 0x0146}, {0x02, 0x09, -1, 0x03, 0x000a, 0x0246},
+  {0x02, 0x0a, -1, 0x03, 0x000a, 0x0446}, {0x02, 0x18, -1, 0x03, 0x000a, 0x0846},
+  {0x02, 0x05, -1, 0x03, 0x000e, 0x0046}, {0x02, 0x05, -1, 0x03, 0x000e, 0x0066},
+  {0x02, 0x06, -1, 0x03, 0x000e, 0x0086}, {0x02, 0x07, -1, 0x03, 0x000e, 0x00c6},
+  {0x02, 0x08, -1, 0x03, 0x000e, 0x0146}, {0x02, 0x09, -1, 0x03, 0x000e, 0x0246},
+  {0x02, 0x0a, -1, 0x03, 0x000e, 0x0446}, {0x02, 0x18, -1, 0x03, 0x000e, 0x0846},
+  {0x03, 0x05, -1, 0x03, 0x0012, 0x0046}, {0x03, 0x05, -1, 0x03, 0x0012, 0x0066},
+  {0x03, 0x06, -1, 0x03, 0x0012, 0x0086}, {0x03, 0x07, -1, 0x03, 0x0012, 0x00c6},
+  {0x03, 0x08, -1, 0x03, 0x0012, 0x0146}, {0x03, 0x09, -1, 0x03, 0x0012, 0x0246},
+  {0x03, 0x0a, -1, 0x03, 0x0012, 0x0446}, {0x03, 0x18, -1, 0x03, 0x0012, 0x0846},
+  {0x03, 0x05, -1, 0x03, 0x001a, 0x0046}, {0x03, 0x05, -1, 0x03, 0x001a, 0x0066},
+  {0x03, 0x06, -1, 0x03, 0x001a, 0x0086}, {0x03, 0x07, -1, 0x03, 0x001a, 0x00c6},
+  {0x03, 0x08, -1, 0x03, 0x001a, 0x0146}, {0x03, 0x09, -1, 0x03, 0x001a, 0x0246},
+  {0x03, 0x0a, -1, 0x03, 0x001a, 0x0446}, {0x03, 0x18, -1, 0x03, 0x001a, 0x0846},
+  {0x04, 0x05, -1, 0x03, 0x0022, 0x0046}, {0x04, 0x05, -1, 0x03, 0x0022, 0x0066},
+  {0x04, 0x06, -1, 0x03, 0x0022, 0x0086}, {0x04, 0x07, -1, 0x03, 0x0022, 0x00c6},
+  {0x04, 0x08, -1, 0x03, 0x0022, 0x0146}, {0x04, 0x09, -1, 0x03, 0x0022, 0x0246},
+  {0x04, 0x0a, -1, 0x03, 0x0022, 0x0446}, {0x04, 0x18, -1, 0x03, 0x0022, 0x0846},
+  {0x04, 0x05, -1, 0x03, 0x0032, 0x0046}, {0x04, 0x05, -1, 0x03, 0x0032, 0x0066},
+  {0x04, 0x06, -1, 0x03, 0x0032, 0x0086}, {0x04, 0x07, -1, 0x03, 0x0032, 0x00c6},
+  {0x04, 0x08, -1, 0x03, 0x0032, 0x0146}, {0x04, 0x09, -1, 0x03, 0x0032, 0x0246},
+  {0x04, 0x0a, -1, 0x03, 0x0032, 0x0446}, {0x04, 0x18, -1, 0x03, 0x0032, 0x0846},
+  {0x05, 0x05, -1, 0x03, 0x0042, 0x0046}, {0x05, 0x05, -1, 0x03, 0x0042, 0x0066},
+  {0x05, 0x06, -1, 0x03, 0x0042, 0x0086}, {0x05, 0x07, -1, 0x03, 0x0042, 0x00c6},
+  {0x05, 0x08, -1, 0x03, 0x0042, 0x0146}, {0x05, 0x09, -1, 0x03, 0x0042, 0x0246},
+  {0x05, 0x0a, -1, 0x03, 0x0042, 0x0446}, {0x05, 0x18, -1, 0x03, 0x0042, 0x0846},
+  {0x05, 0x05, -1, 0x03, 0x0062, 0x0046}, {0x05, 0x05, -1, 0x03, 0x0062, 0x0066},
+  {0x05, 0x06, -1, 0x03, 0x0062, 0x0086}, {0x05, 0x07, -1, 0x03, 0x0062, 0x00c6},
+  {0x05, 0x08, -1, 0x03, 0x0062, 0x0146}, {0x05, 0x09, -1, 0x03, 0x0062, 0x0246},
+  {0x05, 0x0a, -1, 0x03, 0x0062, 0x0446}, {0x05, 0x18, -1, 0x03, 0x0062, 0x0846},
+  {0x06, 0x01, -1, 0x03, 0x0082, 0x000a}, {0x06, 0x01, -1, 0x03, 0x0082, 0x000c},
+  {0x06, 0x02, -1, 0x03, 0x0082, 0x000e}, {0x06, 0x02, -1, 0x03, 0x0082, 0x0012},
+  {0x06, 0x03, -1, 0x03, 0x0082, 0x0016}, {0x06, 0x03, -1, 0x03, 0x0082, 0x001e},
+  {0x06, 0x04, -1, 0x03, 0x0082, 0x0026}, {0x06, 0x04, -1, 0x03, 0x0082, 0x0036},
+  {0x07, 0x01, -1, 0x03, 0x00c2, 0x000a}, {0x07, 0x01, -1, 0x03, 0x00c2, 0x000c},
+  {0x07, 0x02, -1, 0x03, 0x00c2, 0x000e}, {0x07, 0x02, -1, 0x03, 0x00c2, 0x0012},
+  {0x07, 0x03, -1, 0x03, 0x00c2, 0x0016}, {0x07, 0x03, -1, 0x03, 0x00c2, 0x001e},
+  {0x07, 0x04, -1, 0x03, 0x00c2, 0x0026}, {0x07, 0x04, -1, 0x03, 0x00c2, 0x0036},
+  {0x08, 0x01, -1, 0x03, 0x0142, 0x000a}, {0x08, 0x01, -1, 0x03, 0x0142, 0x000c},
+  {0x08, 0x02, -1, 0x03, 0x0142, 0x000e}, {0x08, 0x02, -1, 0x03, 0x0142, 0x0012},
+  {0x08, 0x03, -1, 0x03, 0x0142, 0x0016}, {0x08, 0x03, -1, 0x03, 0x0142, 0x001e},
+  {0x08, 0x04, -1, 0x03, 0x0142, 0x0026}, {0x08, 0x04, -1, 0x03, 0x0142, 0x0036},
+  {0x09, 0x01, -1, 0x03, 0x0242, 0x000a}, {0x09, 0x01, -1, 0x03, 0x0242, 0x000c},
+  {0x09, 0x02, -1, 0x03, 0x0242, 0x000e}, {0x09, 0x02, -1, 0x03, 0x0242, 0x0012},
+  {0x09, 0x03, -1, 0x03, 0x0242, 0x0016}, {0x09, 0x03, -1, 0x03, 0x0242, 0x001e},
+  {0x09, 0x04, -1, 0x03, 0x0242, 0x0026}, {0x09, 0x04, -1, 0x03, 0x0242, 0x0036},
+  {0x0a, 0x01, -1, 0x03, 0x0442, 0x000a}, {0x0a, 0x01, -1, 0x03, 0x0442, 0x000c},
+  {0x0a, 0x02, -1, 0x03, 0x0442, 0x000e}, {0x0a, 0x02, -1, 0x03, 0x0442, 0x0012},
+  {0x0a, 0x03, -1, 0x03, 0x0442, 0x0016}, {0x0a, 0x03, -1, 0x03, 0x0442, 0x001e},
+  {0x0a, 0x04, -1, 0x03, 0x0442, 0x0026}, {0x0a, 0x04, -1, 0x03, 0x0442, 0x0036},
+  {0x0c, 0x01, -1, 0x03, 0x0842, 0x000a}, {0x0c, 0x01, -1, 0x03, 0x0842, 0x000c},
+  {0x0c, 0x02, -1, 0x03, 0x0842, 0x000e}, {0x0c, 0x02, -1, 0x03, 0x0842, 0x0012},
+  {0x0c, 0x03, -1, 0x03, 0x0842, 0x0016}, {0x0c, 0x03, -1, 0x03, 0x0842, 0x001e},
+  {0x0c, 0x04, -1, 0x03, 0x0842, 0x0026}, {0x0c, 0x04, -1, 0x03, 0x0842, 0x0036},
+  {0x0e, 0x01, -1, 0x03, 0x1842, 0x000a}, {0x0e, 0x01, -1, 0x03, 0x1842, 0x000c},
+  {0x0e, 0x02, -1, 0x03, 0x1842, 0x000e}, {0x0e, 0x02, -1, 0x03, 0x1842, 0x0012},
+  {0x0e, 0x03, -1, 0x03, 0x1842, 0x0016}, {0x0e, 0x03, -1, 0x03, 0x1842, 0x001e},
+  {0x0e, 0x04, -1, 0x03, 0x1842, 0x0026}, {0x0e, 0x04, -1, 0x03, 0x1842, 0x0036},
+  {0x18, 0x01, -1, 0x03, 0x5842, 0x000a}, {0x18, 0x01, -1, 0x03, 0x5842, 0x000c},
+  {0x18, 0x02, -1, 0x03, 0x5842, 0x000e}, {0x18, 0x02, -1, 0x03, 0x5842, 0x0012},
+  {0x18, 0x03, -1, 0x03, 0x5842, 0x0016}, {0x18, 0x03, -1, 0x03, 0x5842, 0x001e},
+  {0x18, 0x04, -1, 0x03, 0x5842, 0x0026}, {0x18, 0x04, -1, 0x03, 0x5842, 0x0036},
+  {0x06, 0x05, -1, 0x03, 0x0082, 0x0046}, {0x06, 0x05, -1, 0x03, 0x0082, 0x0066},
+  {0x06, 0x06, -1, 0x03, 0x0082, 0x0086}, {0x06, 0x07, -1, 0x03, 0x0082, 0x00c6},
+  {0x06, 0x08, -1, 0x03, 0x0082, 0x0146}, {0x06, 0x09, -1, 0x03, 0x0082, 0x0246},
+  {0x06, 0x0a, -1, 0x03, 0x0082, 0x0446}, {0x06, 0x18, -1, 0x03, 0x0082, 0x0846},
+  {0x07, 0x05, -1, 0x03, 0x00c2, 0x0046}, {0x07, 0x05, -1, 0x03, 0x00c2, 0x0066},
+  {0x07, 0x06, -1, 0x03, 0x00c2, 0x0086}, {0x07, 0x07, -1, 0x03, 0x00c2, 0x00c6},
+  {0x07, 0x08, -1, 0x03, 0x00c2, 0x0146}, {0x07, 0x09, -1, 0x03, 0x00c2, 0x0246},
+  {0x07, 0x0a, -1, 0x03, 0x00c2, 0x0446}, {0x07, 0x18, -1, 0x03, 0x00c2, 0x0846},
+  {0x08, 0x05, -1, 0x03, 0x0142, 0x0046}, {0x08, 0x05, -1, 0x03, 0x0142, 0x0066},
+  {0x08, 0x06, -1, 0x03, 0x0142, 0x0086}, {0x08, 0x07, -1, 0x03, 0x0142, 0x00c6},
+  {0x08, 0x08, -1, 0x03, 0x0142, 0x0146}, {0x08, 0x09, -1, 0x03, 0x0142, 0x0246},
+  {0x08, 0x0a, -1, 0x03, 0x0142, 0x0446}, {0x08, 0x18, -1, 0x03, 0x0142, 0x0846},
+  {0x09, 0x05, -1, 0x03, 0x0242, 0x0046}, {0x09, 0x05, -1, 0x03, 0x0242, 0x0066},
+  {0x09, 0x06, -1, 0x03, 0x0242, 0x0086}, {0x09, 0x07, -1, 0x03, 0x0242, 0x00c6},
+  {0x09, 0x08, -1, 0x03, 0x0242, 0x0146}, {0x09, 0x09, -1, 0x03, 0x0242, 0x0246},
+  {0x09, 0x0a, -1, 0x03, 0x0242, 0x0446}, {0x09, 0x18, -1, 0x03, 0x0242, 0x0846},
+  {0x0a, 0x05, -1, 0x03, 0x0442, 0x0046}, {0x0a, 0x05, -1, 0x03, 0x0442, 0x0066},
+  {0x0a, 0x06, -1, 0x03, 0x0442, 0x0086}, {0x0a, 0x07, -1, 0x03, 0x0442, 0x00c6},
+  {0x0a, 0x08, -1, 0x03, 0x0442, 0x0146}, {0x0a, 0x09, -1, 0x03, 0x0442, 0x0246},
+  {0x0a, 0x0a, -1, 0x03, 0x0442, 0x0446}, {0x0a, 0x18, -1, 0x03, 0x0442, 0x0846},
+  {0x0c, 0x05, -1, 0x03, 0x0842, 0x0046}, {0x0c, 0x05, -1, 0x03, 0x0842, 0x0066},
+  {0x0c, 0x06, -1, 0x03, 0x0842, 0x0086}, {0x0c, 0x07, -1, 0x03, 0x0842, 0x00c6},
+  {0x0c, 0x08, -1, 0x03, 0x0842, 0x0146}, {0x0c, 0x09, -1, 0x03, 0x0842, 0x0246},
+  {0x0c, 0x0a, -1, 0x03, 0x0842, 0x0446}, {0x0c, 0x18, -1, 0x03, 0x0842, 0x0846},
+  {0x0e, 0x05, -1, 0x03, 0x1842, 0x0046}, {0x0e, 0x05, -1, 0x03, 0x1842, 0x0066},
+  {0x0e, 0x06, -1, 0x03, 0x1842, 0x0086}, {0x0e, 0x07, -1, 0x03, 0x1842, 0x00c6},
+  {0x0e, 0x08, -1, 0x03, 0x1842, 0x0146}, {0x0e, 0x09, -1, 0x03, 0x1842, 0x0246},
+  {0x0e, 0x0a, -1, 0x03, 0x1842, 0x0446}, {0x0e, 0x18, -1, 0x03, 0x1842, 0x0846},
+  {0x18, 0x05, -1, 0x03, 0x5842, 0x0046}, {0x18, 0x05, -1, 0x03, 0x5842, 0x0066},
+  {0x18, 0x06, -1, 0x03, 0x5842, 0x0086}, {0x18, 0x07, -1, 0x03, 0x5842, 0x00c6},
+  {0x18, 0x08, -1, 0x03, 0x5842, 0x0146}, {0x18, 0x09, -1, 0x03, 0x5842, 0x0246},
+  {0x18, 0x0a, -1, 0x03, 0x5842, 0x0446}, {0x18, 0x18, -1, 0x03, 0x5842, 0x0846}};
+
+CONSTANT uint8_t kCodeLengthCodeOrder[18] = {
+  1, 2, 3, 4, 0, 5, 17, 6, 16, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+CONSTANT uint8_t kCodeLengthPrefixLength[16] = {2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 3, 2, 2, 2, 4};
+CONSTANT uint8_t kCodeLengthPrefixValue[16]  = {0, 4, 3, 2, 0, 4, 3, 1, 0, 4, 3, 2, 0, 4, 3, 5};
+
+// Represents the range of values belonging to a prefix code: [offset, offset + 2^nbits)
+CONSTANT uint16_t kBlockLengthPrefixCodeOffset[brotli_num_block_len_symbols] = {
+  1,   5,   9,   13,  17,  25,  33,  41,  49,   65,   81,   97,   113,
+  145, 177, 209, 241, 305, 369, 497, 753, 1265, 2289, 4337, 8433, 16625};
+CONSTANT uint8_t kBlockLengthPrefixCodeBits[brotli_num_block_len_symbols] = {
+  2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 7, 8, 9, 10, 11, 12, 13, 24};
+
+// Maximum possible Huffman table size for an alphabet size of (index * 32),
+// max code length 15 and root table bits 8.
+CONSTANT uint16_t kMaxHuffmanTableSize[] = {
+  256,  402,  436,  468,  500,  534,  566,  598,  630,  662,  694,  726,  758,
+  790,  822,  854,  886,  920,  952,  984,  1016, 1048, 1080, 1112, 1144, 1176,
+  1208, 1240, 1272, 1304, 1336, 1368, 1400, 1432, 1464, 1496, 1528};
+
+constexpr int brotli_huffman_max_size_26  = 396;
+constexpr int brotli_huffman_max_size_258 = 632;
+// Max table size for context map
+constexpr int brotli_huffman_max_size_272 = 646;
+
+enum brotli_transform_type_e {
+  BROTLI_TRANSFORM_IDENTITY        = 0,
+  BROTLI_TRANSFORM_OMIT_LAST_1     = 1,
+  BROTLI_TRANSFORM_OMIT_LAST_2     = 2,
+  BROTLI_TRANSFORM_OMIT_LAST_3     = 3,
+  BROTLI_TRANSFORM_OMIT_LAST_4     = 4,
+  BROTLI_TRANSFORM_OMIT_LAST_5     = 5,
+  BROTLI_TRANSFORM_OMIT_LAST_6     = 6,
+  BROTLI_TRANSFORM_OMIT_LAST_7     = 7,
+  BROTLI_TRANSFORM_OMIT_LAST_8     = 8,
+  BROTLI_TRANSFORM_OMIT_LAST_9     = 9,
+  BROTLI_TRANSFORM_UPPERCASE_FIRST = 10,
+  BROTLI_TRANSFORM_UPPERCASE_ALL   = 11,
+  BROTLI_TRANSFORM_OMIT_FIRST_1    = 12,
+  BROTLI_TRANSFORM_OMIT_FIRST_2    = 13,
+  BROTLI_TRANSFORM_OMIT_FIRST_3    = 14,
+  BROTLI_TRANSFORM_OMIT_FIRST_4    = 15,
+  BROTLI_TRANSFORM_OMIT_FIRST_5    = 16,
+  BROTLI_TRANSFORM_OMIT_FIRST_6    = 17,
+  BROTLI_TRANSFORM_OMIT_FIRST_7    = 18,
+  BROTLI_TRANSFORM_OMIT_FIRST_8    = 19,
+  BROTLI_TRANSFORM_OMIT_FIRST_9    = 20,
+  BROTLI_NUM_TRANSFORM_TYPES  // Counts transforms, not a transform itself.
+};
+
+/* RFC 7932 transforms string data */
+CONSTANT uint8_t kPrefixSuffix[217] = {
+  0x01, 0x20, 0x02, 0x2C, 0x20, 0x08, 0x20, 0x6F, 0x66, 0x20, 0x74, 0x68, 0x65, 0x20, 0x04, 0x20,
+  0x6F, 0x66, 0x20, 0x02, 0x73, 0x20, 0x01, 0x2E, 0x05, 0x20, 0x61, 0x6E, 0x64, 0x20, 0x04, 0x20,
+  0x69, 0x6E, 0x20, 0x01, 0x22, 0x04, 0x20, 0x74, 0x6F, 0x20, 0x02, 0x22, 0x3E, 0x01, 0x0A, 0x02,
+  0x2E, 0x20, 0x01, 0x5D, 0x05, 0x20, 0x66, 0x6F, 0x72, 0x20, 0x03, 0x20, 0x61, 0x20, 0x06, 0x20,
+  0x74, 0x68, 0x61, 0x74, 0x20, 0x01, 0x27, 0x06, 0x20, 0x77, 0x69, 0x74, 0x68, 0x20, 0x06, 0x20,
+  0x66, 0x72, 0x6F, 0x6D, 0x20, 0x04, 0x20, 0x62, 0x79, 0x20, 0x01, 0x28, 0x06, 0x2E, 0x20, 0x54,
+  0x68, 0x65, 0x20, 0x04, 0x20, 0x6F, 0x6E, 0x20, 0x04, 0x20, 0x61, 0x73, 0x20, 0x04, 0x20, 0x69,
+  0x73, 0x20, 0x04, 0x69, 0x6E, 0x67, 0x20, 0x02, 0x0A, 0x09, 0x01, 0x3A, 0x03, 0x65, 0x64, 0x20,
+  0x02, 0x3D, 0x22, 0x04, 0x20, 0x61, 0x74, 0x20, 0x03, 0x6C, 0x79, 0x20, 0x01, 0x2C, 0x02, 0x3D,
+  0x27, 0x05, 0x2E, 0x63, 0x6F, 0x6D, 0x2F, 0x07, 0x2E, 0x20, 0x54, 0x68, 0x69, 0x73, 0x20, 0x05,
+  0x20, 0x6E, 0x6F, 0x74, 0x20, 0x03, 0x65, 0x72, 0x20, 0x03, 0x61, 0x6C, 0x20, 0x04, 0x66, 0x75,
+  0x6C, 0x20, 0x04, 0x69, 0x76, 0x65, 0x20, 0x05, 0x6C, 0x65, 0x73, 0x73, 0x20, 0x04, 0x65, 0x73,
+  0x74, 0x20, 0x04, 0x69, 0x7A, 0x65, 0x20, 0x02, 0xC2, 0xA0, 0x04, 0x6F, 0x75, 0x73, 0x20, 0x05,
+  0x20, 0x74, 0x68, 0x65, 0x20, 0x02, 0x65, 0x20, 0x00};
+
+CONSTANT uint16_t kPrefixSuffixMap[50] = {
+  0x00, 0x02, 0x05, 0x0E, 0x13, 0x16, 0x18, 0x1E, 0x23, 0x25, 0x2A, 0x2D, 0x2F,
+  0x32, 0x34, 0x3A, 0x3E, 0x45, 0x47, 0x4E, 0x55, 0x5A, 0x5C, 0x63, 0x68, 0x6D,
+  0x72, 0x77, 0x7A, 0x7C, 0x80, 0x83, 0x88, 0x8C, 0x8E, 0x91, 0x97, 0x9F, 0xA5,
+  0xA9, 0xAD, 0xB2, 0xB7, 0xBD, 0xC2, 0xC7, 0xCA, 0xCF, 0xD5, 0xD8};
+
+/* RFC 7932 transforms */
+/* Each entry is a [prefix_id, transform, suffix_id] triplet. */
+CONSTANT uint8_t kTransformsData[] = {
+  49, BROTLI_TRANSFORM_IDENTITY,        49, 49, BROTLI_TRANSFORM_IDENTITY,        0,
+  0,  BROTLI_TRANSFORM_IDENTITY,        0,  49, BROTLI_TRANSFORM_OMIT_FIRST_1,    49,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 0,  49, BROTLI_TRANSFORM_IDENTITY,        47,
+  0,  BROTLI_TRANSFORM_IDENTITY,        49, 4,  BROTLI_TRANSFORM_IDENTITY,        0,
+  49, BROTLI_TRANSFORM_IDENTITY,        3,  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 49,
+  49, BROTLI_TRANSFORM_IDENTITY,        6,  49, BROTLI_TRANSFORM_OMIT_FIRST_2,    49,
+  49, BROTLI_TRANSFORM_OMIT_LAST_1,     49, 1,  BROTLI_TRANSFORM_IDENTITY,        0,
+  49, BROTLI_TRANSFORM_IDENTITY,        1,  0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 0,
+  49, BROTLI_TRANSFORM_IDENTITY,        7,  49, BROTLI_TRANSFORM_IDENTITY,        9,
+  48, BROTLI_TRANSFORM_IDENTITY,        0,  49, BROTLI_TRANSFORM_IDENTITY,        8,
+  49, BROTLI_TRANSFORM_IDENTITY,        5,  49, BROTLI_TRANSFORM_IDENTITY,        10,
+  49, BROTLI_TRANSFORM_IDENTITY,        11, 49, BROTLI_TRANSFORM_OMIT_LAST_3,     49,
+  49, BROTLI_TRANSFORM_IDENTITY,        13, 49, BROTLI_TRANSFORM_IDENTITY,        14,
+  49, BROTLI_TRANSFORM_OMIT_FIRST_3,    49, 49, BROTLI_TRANSFORM_OMIT_LAST_2,     49,
+  49, BROTLI_TRANSFORM_IDENTITY,        15, 49, BROTLI_TRANSFORM_IDENTITY,        16,
+  0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 49, 49, BROTLI_TRANSFORM_IDENTITY,        12,
+  5,  BROTLI_TRANSFORM_IDENTITY,        49, 0,  BROTLI_TRANSFORM_IDENTITY,        1,
+  49, BROTLI_TRANSFORM_OMIT_FIRST_4,    49, 49, BROTLI_TRANSFORM_IDENTITY,        18,
+  49, BROTLI_TRANSFORM_IDENTITY,        17, 49, BROTLI_TRANSFORM_IDENTITY,        19,
+  49, BROTLI_TRANSFORM_IDENTITY,        20, 49, BROTLI_TRANSFORM_OMIT_FIRST_5,    49,
+  49, BROTLI_TRANSFORM_OMIT_FIRST_6,    49, 47, BROTLI_TRANSFORM_IDENTITY,        49,
+  49, BROTLI_TRANSFORM_OMIT_LAST_4,     49, 49, BROTLI_TRANSFORM_IDENTITY,        22,
+  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   49, 49, BROTLI_TRANSFORM_IDENTITY,        23,
+  49, BROTLI_TRANSFORM_IDENTITY,        24, 49, BROTLI_TRANSFORM_IDENTITY,        25,
+  49, BROTLI_TRANSFORM_OMIT_LAST_7,     49, 49, BROTLI_TRANSFORM_OMIT_LAST_1,     26,
+  49, BROTLI_TRANSFORM_IDENTITY,        27, 49, BROTLI_TRANSFORM_IDENTITY,        28,
+  0,  BROTLI_TRANSFORM_IDENTITY,        12, 49, BROTLI_TRANSFORM_IDENTITY,        29,
+  49, BROTLI_TRANSFORM_OMIT_FIRST_9,    49, 49, BROTLI_TRANSFORM_OMIT_FIRST_7,    49,
+  49, BROTLI_TRANSFORM_OMIT_LAST_6,     49, 49, BROTLI_TRANSFORM_IDENTITY,        21,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 1,  49, BROTLI_TRANSFORM_OMIT_LAST_8,     49,
+  49, BROTLI_TRANSFORM_IDENTITY,        31, 49, BROTLI_TRANSFORM_IDENTITY,        32,
+  47, BROTLI_TRANSFORM_IDENTITY,        3,  49, BROTLI_TRANSFORM_OMIT_LAST_5,     49,
+  49, BROTLI_TRANSFORM_OMIT_LAST_9,     49, 0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 1,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 8,  5,  BROTLI_TRANSFORM_IDENTITY,        21,
+  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   0,  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 10,
+  49, BROTLI_TRANSFORM_IDENTITY,        30, 0,  BROTLI_TRANSFORM_IDENTITY,        5,
+  35, BROTLI_TRANSFORM_IDENTITY,        49, 47, BROTLI_TRANSFORM_IDENTITY,        2,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 17, 49, BROTLI_TRANSFORM_IDENTITY,        36,
+  49, BROTLI_TRANSFORM_IDENTITY,        33, 5,  BROTLI_TRANSFORM_IDENTITY,        0,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 21, 49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 5,
+  49, BROTLI_TRANSFORM_IDENTITY,        37, 0,  BROTLI_TRANSFORM_IDENTITY,        30,
+  49, BROTLI_TRANSFORM_IDENTITY,        38, 0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   0,
+  49, BROTLI_TRANSFORM_IDENTITY,        39, 0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   49,
+  49, BROTLI_TRANSFORM_IDENTITY,        34, 49, BROTLI_TRANSFORM_UPPERCASE_ALL,   8,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 12, 0,  BROTLI_TRANSFORM_IDENTITY,        21,
+  49, BROTLI_TRANSFORM_IDENTITY,        40, 0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 12,
+  49, BROTLI_TRANSFORM_IDENTITY,        41, 49, BROTLI_TRANSFORM_IDENTITY,        42,
+  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   17, 49, BROTLI_TRANSFORM_IDENTITY,        43,
+  0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 5,  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   10,
+  0,  BROTLI_TRANSFORM_IDENTITY,        34, 49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 33,
+  49, BROTLI_TRANSFORM_IDENTITY,        44, 49, BROTLI_TRANSFORM_UPPERCASE_ALL,   5,
+  45, BROTLI_TRANSFORM_IDENTITY,        49, 0,  BROTLI_TRANSFORM_IDENTITY,        33,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 30, 49, BROTLI_TRANSFORM_UPPERCASE_ALL,   30,
+  49, BROTLI_TRANSFORM_IDENTITY,        46, 49, BROTLI_TRANSFORM_UPPERCASE_ALL,   1,
+  49, BROTLI_TRANSFORM_UPPERCASE_FIRST, 34, 0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 33,
+  0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   30, 0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   1,
+  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   33, 49, BROTLI_TRANSFORM_UPPERCASE_ALL,   21,
+  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   12, 0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   5,
+  49, BROTLI_TRANSFORM_UPPERCASE_ALL,   34, 0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   12,
+  0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 30, 0,  BROTLI_TRANSFORM_UPPERCASE_ALL,   34,
+  0,  BROTLI_TRANSFORM_UPPERCASE_FIRST, 34,
+};
+
+CONSTANT int kNumTransforms = (int)(sizeof(kTransformsData) / (3 * sizeof(kTransformsData[0])));
diff --git a/cpp/src/io/comp/cpu_unbz2.cpp b/cpp/src/io/comp/cpu_unbz2.cpp
new file mode 100644
index 0000000..a116335
--- /dev/null
+++ b/cpp/src/io/comp/cpu_unbz2.cpp
@@ -0,0 +1,601 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * cpu_unbz2.cpp
+ *
+ * Heavily based on libbzip2's decompress.c (original copyright notice below)
+ * Modified version for memory-to-memory decompression.
+ *
+ * bzip2 license information is available at
+ * https://spdx.org/licenses/bzip2-1.0.6.html
+ * https://github.com/asimonov-im/bzip2/blob/master/LICENSE
+ * original source code available at
+ * http://www.sourceware.org/bzip2/
+ */
+
+/*--
+
+Copyright (C) 1996-2002 Julian R Seward.  All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2. The origin of this software must not be misrepresented; you must
+not claim that you wrote the original software.  If you use this
+software in a product, an acknowledgment in the product
+documentation would be appreciated but is not required.
+
+3. Altered source versions must be plainly marked as such, and must
+not be misrepresented as being the original software.
+
+4. The name of the author may not be used to endorse or promote
+products derived from this software without specific prior written
+permission.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Julian Seward, Cambridge, UK.
+jseward@acm.org
+bzip2/libbzip2 version 1.0 of 21 March 2000
+
+This program is based on (at least) the work of:
+Mike Burrows
+David Wheeler
+Peter Fenwick
+Alistair Moffat
+Radford Neal
+Ian H. Witten
+Robert Sedgewick
+Jon L. Bentley
+
+For more information on these sources, see the manual.
+--*/
+
+#include "io_uncomp.hpp"
+#include "unbz2.hpp"
+
+#include <cstdio>
+#include <cstdlib>
+#include <vector>
+
+namespace cudf {
+namespace io {
+
+// Constants for the fast MTF decoder.
+#define MTFA_SIZE 4096
+#define MTFL_SIZE 16
+
+// Header bytes.
+#define BZ_HDR_B 0x42 /* 'B' */
+#define BZ_HDR_Z 0x5a /* 'Z' */
+#define BZ_HDR_h 0x68 /* 'h' */
+#define BZ_HDR_0 0x30 /* '0' */
+
+// Constants for the back end.
+
+#define BZ_MAX_ALPHA_SIZE 258
+#define BZ_MAX_CODE_LEN   23
+
+#define BZ_RUNA 0
+#define BZ_RUNB 1
+
+#define BZ_N_GROUPS 6
+#define BZ_G_SIZE   50
+
+#define BZ_MAX_SELECTORS (2 + (900000 / BZ_G_SIZE))
+
+using huff_s = struct {
+  int32_t minLen;
+  int32_t limit[BZ_MAX_CODE_LEN];
+  int32_t base[BZ_MAX_CODE_LEN];
+  uint16_t perm[BZ_MAX_ALPHA_SIZE];
+};
+
+// Decoder state
+using unbz_state_s = struct {
+  // Input
+  uint8_t const* cur;
+  uint8_t const* end;
+  uint8_t const* base;
+  uint64_t bitbuf;
+  uint32_t bitpos;
+
+  // Output
+  uint8_t* out;
+  uint8_t* outend;
+  uint8_t* outbase;
+
+  // misc administratium
+  uint32_t blockSize100k;
+  int32_t currBlockNo;
+  int32_t save_nblock;
+
+  // for undoing the Burrows-Wheeler transform
+  std::vector<uint32_t> tt;
+  uint32_t origPtr;
+  int32_t nblock_used;
+  int32_t unzftab[256];
+
+  // map of bytes used in block
+  uint8_t seqToUnseq[256];
+
+  // for decoding the MTF values
+  int32_t mtfbase[256 / MTFL_SIZE];
+  uint8_t mtfa[MTFA_SIZE];
+  uint8_t selector[BZ_MAX_SELECTORS];
+  uint8_t len[BZ_MAX_ALPHA_SIZE];
+
+  huff_s ht[BZ_N_GROUPS];
+};
+
+// return next 32 bits
+static inline uint32_t next32bits(unbz_state_s const* s)
+{
+  return (uint32_t)((s->bitbuf << s->bitpos) >> 32);
+}
+
+// return next n bits
+static inline uint32_t showbits(unbz_state_s const* s, uint32_t n)
+{
+  return (uint32_t)((s->bitbuf << s->bitpos) >> (64 - n));
+}
+
+// update bit position, refill bit buffer if necessary
+static void skipbits(unbz_state_s* s, uint32_t n)
+{
+  uint32_t bitpos = s->bitpos + n;
+  if (bitpos >= 32) {
+    uint8_t const* cur = s->cur + 4;
+    uint32_t next32 =
+      (cur + 4 < s->end) ? __builtin_bswap32(*reinterpret_cast<uint32_t const*>(cur + 4)) : 0;
+    s->cur    = cur;
+    s->bitbuf = (s->bitbuf << 32) | next32;
+    bitpos &= 0x1f;
+  }
+  s->bitpos = bitpos;
+}
+
+static inline uint32_t getbits(unbz_state_s* s, uint32_t n)
+{
+  uint32_t bits = showbits(s, n);
+  skipbits(s, n);
+  return bits;
+}
+
+/*---------------------------------------------------*/
+int32_t bz2_decompress_block(unbz_state_s* s)
+{
+  int nInUse;
+
+  int32_t i;
+  int32_t j;
+  int32_t t;
+  int32_t alphaSize;
+  int32_t nGroups;
+  int32_t nSelectors;
+  int32_t EOB;
+  int32_t groupNo;
+  int32_t groupPos;
+  uint32_t nblock, nblockMAX;
+  huff_s const* gSel = nullptr;
+  uint32_t inUse16;
+  uint32_t sig0, sig1;
+
+  // Start-of-block signature
+  sig0 = getbits(s, 24);
+  sig1 = getbits(s, 24);
+  if (sig0 != 0x31'4159 || sig1 != 0x26'5359) { return BZ_DATA_ERROR; }
+
+  s->currBlockNo++;
+
+  skipbits(s, 32);  // block CRC
+
+  if (getbits(s, 1)) return BZ_DATA_ERROR;  // blockRandomized not supported (old bzip versions)
+
+  s->origPtr = getbits(s, 24);
+  if (s->origPtr < 0 || s->origPtr > 10 + 100000 * s->blockSize100k) return BZ_DATA_ERROR;
+
+  // Receive the mapping table
+  inUse16 = getbits(s, 16);
+  nInUse  = 0;
+  for (i = 0; i < 16; i++, inUse16 <<= 1) {
+    if (inUse16 & 0x8000) {
+      uint32_t inUse = getbits(s, 16);
+      for (j = 0; j < 16; j++, inUse <<= 1) {
+        if (inUse & 0x8000) { s->seqToUnseq[nInUse++] = (i << 4) + j; }
+      }
+    }
+  }
+  if (nInUse == 0) return BZ_DATA_ERROR;
+  alphaSize = nInUse + 2;
+
+  // Now the selectors
+  {
+    uint32_t pos;  // BZ_N_GROUPS * 4-bit
+
+    nGroups    = getbits(s, 3);
+    nSelectors = getbits(s, 15);
+    if (nGroups < 2 || nGroups > 6 || nSelectors < 1 || nSelectors > BZ_MAX_SELECTORS)
+      return BZ_DATA_ERROR;
+
+    pos = 0x7654'3210;
+    for (i = 0; i < nSelectors; i++) {
+      uint32_t selectorMtf = 0, mask, tmp;
+      for (int32_t v = next32bits(s); v < 0; v <<= 1) {
+        if (++selectorMtf >= (uint32_t)nGroups) return BZ_DATA_ERROR;
+      }
+      skipbits(s, selectorMtf + 1);
+      // Undo the MTF values for the selectors.
+      tmp            = (pos >> (selectorMtf * 4)) & 0xf;
+      s->selector[i] = tmp;
+      mask           = (1 << ((selectorMtf * 4) + 4)) - 1;
+      pos            = (pos & ~mask) | ((pos << 4) & mask) | tmp;
+    }
+  }
+
+  // Now the coding tables
+  for (t = 0; t < nGroups; t++) {
+    int32_t pp, vec;
+    uint8_t* length = &s->len[0];
+    int32_t curr    = getbits(s, 5);
+    int32_t minLen  = BZ_MAX_CODE_LEN - 1;
+    int32_t maxLen  = 0;
+    huff_s* sel     = &s->ht[t];
+    for (i = 0; i < alphaSize; i++) {
+      for (;;) {
+        uint32_t v = showbits(s, 2);
+        if (curr < 1 || curr > 20) return BZ_DATA_ERROR;
+        if (v < 2) {
+          skipbits(s, 1);
+          break;
+        } else {
+          skipbits(s, 2);
+          curr += 1 - (v & 1) * 2;
+        }
+      }
+      length[i] = curr;
+      if (curr > maxLen) maxLen = curr;
+      if (curr < minLen) minLen = curr;
+    }
+    // Create the Huffman decoding tables for this group
+    pp = 0;
+    for (i = minLen; i <= maxLen; i++)
+      for (j = 0; j < alphaSize; j++)
+        if (length[j] == i) {
+          sel->perm[pp] = j;
+          pp++;
+        };
+
+    for (i = 0; i < BZ_MAX_CODE_LEN; i++) {
+      sel->base[i]  = 0;
+      sel->limit[i] = 0;
+    }
+    for (i = 0; i < alphaSize; i++)
+      sel->base[length[i] + 1]++;
+
+    for (i = 1; i < BZ_MAX_CODE_LEN; i++)
+      sel->base[i] += sel->base[i - 1];
+
+    vec = 0;
+    for (i = minLen; i <= maxLen; i++) {
+      vec += (sel->base[i + 1] - sel->base[i]);
+      sel->limit[i] = vec - 1;
+      vec <<= 1;
+    }
+    for (i = minLen + 1; i <= maxLen; i++)
+      sel->base[i] = ((sel->limit[i - 1] + 1) << 1) - sel->base[i];
+
+    sel->minLen = minLen;
+  }
+
+  // Now the MTF values
+
+  EOB       = nInUse + 1;
+  nblockMAX = 100000 * s->blockSize100k;
+
+  for (i = 0; i <= 255; i++)
+    s->unzftab[i] = 0;
+
+  // MTF init
+  {
+    int32_t kk = MTFA_SIZE - 1;
+    for (int32_t ii = 256 / MTFL_SIZE - 1; ii >= 0; ii--) {
+      for (int32_t jj = MTFL_SIZE - 1; jj >= 0; jj--) {
+        s->mtfa[kk--] = (uint8_t)(ii * MTFL_SIZE + jj);
+      }
+      s->mtfbase[ii] = kk + 1;
+    }
+  }
+  // end MTF init
+
+  nblock   = 0;
+  groupNo  = -1;
+  groupPos = 0;
+
+  for (;;) {
+    uint32_t es = 0;
+    uint32_t N  = 1;
+    uint32_t nextSym, nn, uc;
+    for (;;) {
+      uint32_t next32, zvec;
+      int32_t zn;
+      if (groupPos == 0) {
+        if (++groupNo >= nSelectors) return BZ_DATA_ERROR;
+        groupPos = BZ_G_SIZE;
+        gSel     = &s->ht[s->selector[groupNo]];
+      }
+      groupPos--;
+      next32 = next32bits(s);
+      zn     = gSel->minLen;
+      for (;;) {
+        zvec = next32 >> (32u - (uint32_t)zn);
+        if (zn > 20)  // the longest code
+          return BZ_DATA_ERROR;
+        if (zvec <= (uint32_t)gSel->limit[zn]) break;
+        zn++;
+      }
+      skipbits(s, zn);
+      zvec -= gSel->base[zn];
+      if (zvec >= BZ_MAX_ALPHA_SIZE) return BZ_DATA_ERROR;
+      nextSym = gSel->perm[zvec];
+      if (nextSym > BZ_RUNB) break;
+      es += N << nextSym;
+      N <<= 1;
+    }
+    if (es > 0) {
+      if (nblock + es > nblockMAX) return BZ_DATA_ERROR;
+      uc = s->seqToUnseq[s->mtfa[s->mtfbase[0]]];
+      s->unzftab[uc] += es;
+      do {
+        s->tt[nblock++] = uc;
+      } while (--es);
+    }
+
+    if (nextSym == static_cast<uint32_t>(EOB)) break;
+
+    if (nblock >= nblockMAX) return BZ_DATA_ERROR;
+    nn = nextSym - 1;
+    // uc = MTF ( nextSym-1 )
+    if (nn < MTFL_SIZE) {
+      // avoid general-case expense
+      int32_t pp = s->mtfbase[0];
+      uc         = s->mtfa[pp + nn];
+      while (nn > 3) {
+        int32_t z      = pp + nn;
+        s->mtfa[(z)]   = s->mtfa[(z)-1];
+        s->mtfa[(z)-1] = s->mtfa[(z)-2];
+        s->mtfa[(z)-2] = s->mtfa[(z)-3];
+        s->mtfa[(z)-3] = s->mtfa[(z)-4];
+        nn -= 4;
+      }
+      while (nn > 0) {
+        s->mtfa[(pp + nn)] = s->mtfa[(pp + nn) - 1];
+        nn--;
+      };
+      s->mtfa[pp] = uc;
+    } else {
+      // general case
+      int32_t lno = nn / MTFL_SIZE;
+      int32_t off = nn % MTFL_SIZE;
+      int32_t pp  = s->mtfbase[lno] + off;
+      uc          = s->mtfa[pp];
+      while (pp > s->mtfbase[lno]) {
+        s->mtfa[pp] = s->mtfa[pp - 1];
+        pp--;
+      };
+      s->mtfbase[lno]++;
+      while (lno > 0) {
+        s->mtfbase[lno]--;
+        s->mtfa[s->mtfbase[lno]] = s->mtfa[s->mtfbase[lno - 1] + MTFL_SIZE - 1];
+        lno--;
+      }
+      s->mtfbase[0]--;
+      s->mtfa[s->mtfbase[0]] = uc;
+      if (s->mtfbase[0] == 0) {
+        int kk = MTFA_SIZE - 1;
+        for (int ii = 256 / MTFL_SIZE - 1; ii >= 0; ii--) {
+          for (int jj = MTFL_SIZE - 1; jj >= 0; jj--) {
+            s->mtfa[kk] = s->mtfa[s->mtfbase[ii] + jj];
+            kk--;
+          }
+          s->mtfbase[ii] = kk + 1;
+        }
+      }
+    }
+    uc = s->seqToUnseq[uc];
+    s->unzftab[uc]++;
+    s->tt[nblock++] = uc;
+  }
+
+  // Now we know what nblock is, we can do a better sanity check on s->origPtr.
+  if (s->origPtr < 0 || s->origPtr >= nblock) return BZ_DATA_ERROR;
+
+  // compute the T^(-1) vector
+  {
+    int32_t prev  = s->unzftab[0];
+    s->unzftab[0] = 0;
+    for (i = 1; i < 256; i++) {
+      int32_t tmp   = s->unzftab[i];
+      s->unzftab[i] = prev + s->unzftab[i - 1];
+      prev          = tmp;
+    }
+
+    for (i = 0; i < (int)nblock; i++) {
+      int uc = (s->tt[i] & 0xff);
+      s->tt[s->unzftab[uc]] |= (i << 8);
+      s->unzftab[uc]++;
+    }
+  }
+
+  s->save_nblock = nblock;
+
+  // Verify the end-of-block signature: should be followed by another block or an end-of-stream
+  // signature
+  {
+    uint8_t const* save_cur = s->cur;
+    uint64_t save_bitbuf    = s->bitbuf;
+    uint32_t save_bitpos    = s->bitpos;
+    sig0                    = getbits(s, 24);
+    sig1                    = getbits(s, 24);
+    if (sig0 == 0x31'4159 && sig1 == 0x26'5359) {
+      // Start of another block: restore bitstream location
+      s->cur    = save_cur;
+      s->bitbuf = save_bitbuf;
+      s->bitpos = save_bitpos;
+      return BZ_OK;
+    } else if (sig0 == 0x17'7245 && sig1 == 0x38'5090) {
+      // End-of-stream signature
+      return BZ_STREAM_END;
+    } else {
+      return BZ_DATA_ERROR;
+    }
+  }
+}
+
+static void bzUnRLE(unbz_state_s* s)
+{
+  uint8_t* out    = s->out;
+  uint8_t* outend = s->outend;
+
+  int32_t rle_cnt           = s->save_nblock;
+  int cprev                 = -1;
+  std::vector<uint32_t>& tt = s->tt;
+  uint32_t pos              = tt[s->origPtr] >> 8;
+  int mask                  = ~0;
+
+  s->nblock_used = rle_cnt + 1;
+
+  while (rle_cnt > 0) {
+    int c;
+
+    rle_cnt--;
+    pos = tt[pos];
+    c   = (pos & 0xff);
+    pos >>= 8;
+    if (out < outend) { *out = c; }
+    out++;
+    mask  = (mask * 2 + (c != cprev)) & 7;
+    cprev = c;
+    if (!mask) {
+      int run;
+      if (--rle_cnt < 0) {
+        printf("run split across blocks! (unsupported)\n");
+        break;
+      }
+      pos = tt[pos];
+      run = (pos & 0xff);
+      pos >>= 8;
+      for (int i = 0; i < run; i++) {
+        if (out + i < outend) out[i] = c;
+      }
+      out += run;
+      cprev = -1;
+    }
+  }
+  s->out = out;
+}
+
+int32_t cpu_bz2_uncompress(
+  uint8_t const* source, size_t sourceLen, uint8_t* dest, size_t* destLen, uint64_t* block_start)
+{
+  unbz_state_s s{};
+  uint32_t v;
+  int ret;
+  size_t last_valid_block_in, last_valid_block_out;
+
+  if (dest == nullptr || destLen == nullptr || source == nullptr || sourceLen < 12)
+    return BZ_PARAM_ERROR;
+  s.currBlockNo = 0;
+
+  s.cur  = source;
+  s.base = source;
+  s.end =
+    source + sourceLen - 4;  // We will not read the final combined CRC (last 4 bytes of the file)
+  s.bitbuf = __builtin_bswap64(*reinterpret_cast<uint64_t const*>(source));
+  s.bitpos = 0;
+
+  s.out     = dest;
+  s.outend  = dest + *destLen;
+  s.outbase = dest;
+
+  s.save_nblock = 0;
+
+  v = getbits(&s, 24);
+  if (v != (('B' << 16) | ('Z' << 8) | 'h')) return BZ_DATA_ERROR_MAGIC;
+
+  v = getbits(&s, 8) - '0';
+  if (v < 1 || v > 9) return BZ_DATA_ERROR_MAGIC;
+  s.blockSize100k = v;
+
+  last_valid_block_in  = 0;
+  last_valid_block_out = 0;
+
+  if (block_start) {
+    uint64_t bit_offs = *block_start;
+    if (bit_offs > 32)  // 32-bits are used for the file header (0..32 is considered as first block)
+    {
+      s.cur    = source + (size_t)(bit_offs >> 3);
+      s.bitpos = (uint32_t)(bit_offs & 7);
+      if (s.cur + 8 > s.end) return BZ_PARAM_ERROR;
+      s.bitbuf = __builtin_bswap64(*reinterpret_cast<uint64_t const*>(s.cur));
+    }
+  }
+
+  s.tt.resize(s.blockSize100k * 100000);
+
+  do {
+    last_valid_block_in  = ((s.cur - s.base) << 3) + (s.bitpos);
+    last_valid_block_out = s.out - s.outbase;
+
+    ret = bz2_decompress_block(&s);
+    if (ret == BZ_OK || ret == BZ_STREAM_END) {
+      bzUnRLE(&s);
+      if (s.nblock_used != s.save_nblock + 1 || s.out > s.outend) {
+        ret = (s.out < s.outend) ? BZ_UNEXPECTED_EOF : BZ_OUTBUFF_FULL;
+      }
+    }
+  } while (ret == BZ_OK);
+
+  if (ret == BZ_STREAM_END) {
+    // normal termination
+    last_valid_block_in  = ((s.cur - s.base) << 3) + (s.bitpos);
+    last_valid_block_out = s.out - s.outbase;
+    ret                  = BZ_OK;
+  }
+
+  *destLen = last_valid_block_out;
+  if (block_start) { *block_start = last_valid_block_in; }
+
+  return ret;
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/debrotli.cu b/cpp/src/io/comp/debrotli.cu
new file mode 100644
index 0000000..8bafd05
--- /dev/null
+++ b/cpp/src/io/comp/debrotli.cu
@@ -0,0 +1,2124 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/** @file debrotli.cu
+ *
+ * CUDA-based brotli decompression
+ *
+ * Brotli Compressed Data Format
+ * https://tools.ietf.org/html/rfc7932
+ *
+ * Portions of this file are derived from Google's Brotli project at
+ * https://github.com/google/brotli, original license text below.
+ */
+
+/* Copyright 2013 Google Inc. All Rights Reserved.
+
+Distributed under MIT license.
+See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+*/
+
+/*
+Copyright(c) 2009, 2010, 2013 - 2016 by the Brotli Authors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files(the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions :
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+*/
+
+#include "brotli_dict.hpp"
+#include "gpuinflate.hpp"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+constexpr uint32_t huffman_lookup_table_width      = 8;
+constexpr int8_t brotli_code_length_codes          = 18;
+constexpr uint32_t brotli_num_distance_short_codes = 16;
+constexpr uint32_t brotli_max_allowed_distance     = 0x7FFF'FFFC;
+constexpr int block_size                           = 256;
+
+template <typename T0, typename T1>
+inline __device__ uint16_t huffcode(T0 len, T1 sym)
+{
+  return (uint16_t)(((sym) << 4) + (len));
+}
+
+inline __device__ uint32_t brotli_distance_alphabet_size(uint8_t npostfix,
+                                                         uint32_t ndirect,
+                                                         uint32_t maxnbits)
+{
+  return brotli_num_distance_short_codes + ndirect + (maxnbits << (npostfix + 1));
+}
+
+inline __device__ uint32_t brev8(uint32_t x)
+{
+  return (__brev(x) >> 24u);  // kReverseBits[x]
+}
+
+#define CONSTANT static const __device__ __constant__
+#include "brotli_tables.hpp"
+
+/* typeof(MODE) == ContextType; returns ContextLut */
+__inline__ __device__ int brotli_context_lut(int mode) { return (mode << 9); }
+
+inline __device__ uint8_t brotli_transform_type(int idx) { return kTransformsData[(idx * 3) + 1]; }
+
+inline __device__ uint8_t const* brotli_transform_prefix(int idx)
+{
+  return &kPrefixSuffix[kPrefixSuffixMap[kTransformsData[(idx * 3)]]];
+}
+
+inline __device__ uint8_t const* brotli_transform_suffix(int idx)
+{
+  return &kPrefixSuffix[kPrefixSuffixMap[kTransformsData[(idx * 3) + 2]]];
+}
+
+/* typeof(LUT) == const uint8_t* */
+__inline__ __device__ int brotli_need_context_lut(int mode) { return (mode < (4 << 9)); }
+
+__inline__ __device__ int brotli_context(int p1, int p2, int lut)
+{
+  return kContextLookup[lut + p1] | kContextLookup[lut + p2 + 256];
+}
+
+/**
+ * @brief Various local scratch arrays
+ */
+struct huff_scratch_s {
+  uint16_t code_length_histo[16];
+  uint8_t code_length_code_lengths[brotli_code_length_codes];
+  int8_t offset[6];  // offsets in sorted table for each length
+  uint16_t lenvlctab[32];
+  uint16_t sorted[brotli_code_length_codes];  // symbols sorted by code length
+  int16_t next_symbol[32];
+  uint16_t symbols_lists_array[720];
+};
+
+/**
+ * @brief Contains a collection of Huffman trees with the same alphabet size.
+ * max_symbol is needed due to simple codes since log2(alphabet_size) could be
+ * greater than log2(max_symbol).
+ */
+struct debrotli_huff_tree_group_s {
+  uint16_t alphabet_size;
+  uint16_t max_symbol;
+  uint16_t num_htrees;
+  uint16_t pad;
+  uint16_t* htrees[1];
+};
+
+// Must be able to at least hold worst-case context maps, tree groups and context modes
+constexpr int local_heap_size =
+  (256 * 64 + 256 * 4 + 3 * (sizeof(debrotli_huff_tree_group_s) + 255 * sizeof(uint16_t*)) + 256 +
+   3 * brotli_huffman_max_size_258 * sizeof(uint16_t) +
+   3 * brotli_huffman_max_size_26 * sizeof(uint16_t));
+
+/**
+ * Brotli decoder state
+ */
+struct debrotli_state_s {
+  // Bitstream
+  uint8_t const* cur;
+  uint8_t const* end;
+  uint8_t const* base;
+  uint2 bitbuf;
+  uint32_t bitpos;
+  int32_t error;
+  // Output
+  uint8_t* outbase;
+  uint8_t* out;
+  size_t bytes_left;
+  // Decoded symbols
+  uint8_t window_bits;
+  uint8_t is_last;
+  uint8_t is_uncompressed;
+  uint8_t distance_postfix_bits;
+  uint8_t distance_postfix_mask;
+  uint8_t mtf_upper_bound;
+  uint8_t p1;
+  uint8_t p2;
+  int32_t max_backward_distance;
+  uint32_t num_block_types[3];
+  uint32_t block_length[3];
+  uint32_t num_direct_distance_codes;
+  uint32_t meta_block_len;
+  uint16_t heap_used;
+  uint16_t heap_limit;
+  uint8_t* context_map;
+  uint8_t* dist_context_map;
+  uint8_t* context_modes;
+  uint8_t* fb_base;
+  uint32_t fb_size;
+  uint8_t block_type_rb[6];
+  uint8_t pad[2];
+  int dist_rb_idx;
+  int dist_rb[4];
+  debrotli_huff_tree_group_s* literal_hgroup;
+  debrotli_huff_tree_group_s* insert_copy_hgroup;
+  debrotli_huff_tree_group_s* distance_hgroup;
+  uint16_t* block_type_vlc[3];
+  huff_scratch_s hs;
+  uint32_t mtf[65];
+  __align__(8) char heap[local_heap_size];
+};
+
+inline __device__ uint32_t Log2Floor(uint32_t value) { return 32 - __clz(value); }
+
+/// @brief initializes the bit reader
+__device__ void initbits(debrotli_state_s* s, uint8_t const* base, size_t len, size_t pos = 0)
+{
+  uint8_t const* p  = base + pos;
+  auto prefix_bytes = (uint32_t)(((size_t)p) & 3);
+  p -= prefix_bytes;
+  s->base     = base;
+  s->end      = base + len;
+  s->cur      = p;
+  s->bitbuf.x = (p < s->end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+  p += 4;
+  s->bitbuf.y = (p < s->end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+  s->bitpos   = prefix_bytes * 8;
+}
+
+// return next 32 bits
+inline __device__ uint32_t next32bits(debrotli_state_s const* s)
+{
+  return __funnelshift_rc(s->bitbuf.x, s->bitbuf.y, s->bitpos);
+}
+
+/// return next n bits
+inline __device__ uint32_t showbits(debrotli_state_s const* s, uint32_t n)
+{
+  uint32_t next32 = __funnelshift_rc(s->bitbuf.x, s->bitbuf.y, s->bitpos);
+  return (next32 & ((1 << n) - 1));
+}
+
+inline __device__ void skipbits(debrotli_state_s* s, uint32_t n)
+{
+  uint32_t bitpos = s->bitpos + n;
+  if (bitpos >= 32) {
+    uint8_t const* cur = s->cur + 8;
+    s->bitbuf.x        = s->bitbuf.y;
+    s->bitbuf.y        = (cur < s->end) ? *reinterpret_cast<uint32_t const*>(cur) : 0;
+    s->cur             = cur - 4;
+    bitpos &= 0x1f;
+  }
+  s->bitpos = bitpos;
+}
+
+inline __device__ uint32_t getbits(debrotli_state_s* s, uint32_t n)
+{
+  uint32_t bits = showbits(s, n);
+  skipbits(s, n);
+  return bits;
+}
+
+inline __device__ uint32_t getbits_bytealign(debrotli_state_s* s)
+{
+  auto n        = (uint32_t)((-(int32_t)s->bitpos) & 7);
+  uint32_t bits = showbits(s, n);
+  skipbits(s, n);
+  return bits;
+}
+
+/** @brief Variable-length coding for 8-bit variable (1..11 bits)
+ * encoded with the following variable-length code (as it appears in the
+ * compressed data, where the bits are parsed from right to left,
+ * so 0110111 has the value 12):
+ *
+ * Value    Bit Pattern
+ * -----    -----------
+ * 1                0
+ * 2             0001
+ * 3..4           x0011
+ * 5..8          xx0101
+ * 9..16        xxx0111
+ * 17..32       xxxx1001
+ * 33..64      xxxxx1011
+ * 65..128    xxxxxx1101
+ * 129..256   xxxxxxx1111
+ */
+static __device__ uint32_t getbits_u8vlc(debrotli_state_s* s)
+{
+  uint32_t next32 = next32bits(s);
+  uint32_t v, len;
+  if (!(next32 & 1)) {
+    v   = 0;
+    len = 1;
+  } else {
+    len = (next32 >> 1) & 7;
+    v   = (1 << len) + ((next32 >> 4) & ((1 << len) - 1));
+    len += 4;
+  }
+  skipbits(s, len);
+  return v;
+}
+
+/// Decode a Huffman code with 8-bit initial lookup
+static __device__ uint32_t getvlc(debrotli_state_s* s, uint16_t const* lut)
+{
+  uint32_t next32 = next32bits(s);
+  uint32_t vlc, len;
+  lut += next32 & ((1 << huffman_lookup_table_width) - 1);
+  vlc = lut[0];
+  len = vlc & 0x0f;
+  vlc >>= 4;
+  if (len > huffman_lookup_table_width) {
+    len -= huffman_lookup_table_width;
+    lut += vlc + ((next32 >> huffman_lookup_table_width) & ((1 << len) - 1));
+    vlc = lut[0];
+    len = huffman_lookup_table_width + (vlc & 0xf);
+    vlc >>= 4;
+  }
+  skipbits(s, len);
+  return vlc;
+}
+
+static auto __device__ allocation_size(uint32_t bytes) { return (bytes + 7) & ~7; }
+
+/// Alloc bytes from the local (shared mem) heap
+static __device__ uint8_t* local_alloc(debrotli_state_s* s, uint32_t bytes)
+{
+  int heap_used  = s->heap_used;
+  auto const len = allocation_size(bytes);
+  if (heap_used + len <= s->heap_limit) {
+    auto* ptr    = reinterpret_cast<uint8_t*>(&s->heap[heap_used]);
+    s->heap_used = (uint16_t)(heap_used + len);
+    return ptr;
+  } else {
+    return nullptr;
+  }
+}
+
+/// Shrink the size of the local heap, returns ptr to end (used for stack-like intermediate
+/// allocations at the end of the heap)
+static __device__ uint8_t* local_heap_shrink(debrotli_state_s* s, uint32_t bytes)
+{
+  int heap_used  = s->heap_used;
+  int heap_limit = s->heap_limit;
+  auto const len = allocation_size(bytes);
+  if (heap_limit - len >= heap_used) {
+    heap_limit -= len;
+    s->heap_limit = (uint16_t)heap_limit;
+    return reinterpret_cast<uint8_t*>(&s->heap[heap_limit]);
+  } else {
+    return nullptr;
+  }
+}
+
+static __device__ void local_heap_grow(debrotli_state_s* s, uint32_t bytes)
+{
+  auto const len = allocation_size(bytes);
+  int heap_limit = s->heap_limit + len;
+  s->heap_limit  = (uint16_t)heap_limit;
+}
+
+/// Alloc memory from the fixed-size heap shared between all blocks (thread0-only)
+static __device__ uint8_t* ext_heap_alloc(uint32_t bytes,
+                                          uint8_t* ext_heap_base,
+                                          uint32_t ext_heap_size)
+{
+  uint32_t len              = (bytes + 0xf) & ~0xf;
+  volatile auto* heap_ptr   = reinterpret_cast<volatile uint32_t*>(ext_heap_base);
+  uint32_t first_free_block = ~0;
+  for (;;) {
+    uint32_t blk_next, blk_prev;
+    first_free_block = atomicExch((unsigned int*)heap_ptr, first_free_block);
+    if (first_free_block == ~0 || first_free_block >= ext_heap_size) {
+      // Some other block is holding the heap or there are no free blocks: try again later
+      continue;
+    }
+    if (first_free_block == 0) {
+      // Heap is uninitialized
+      first_free_block = 4 * sizeof(uint32_t);
+      heap_ptr[4]      = ext_heap_size;
+      heap_ptr[5]      = ext_heap_size - first_free_block;
+      __threadfence();
+      continue;
+    }
+    blk_prev = 0;
+    blk_next = first_free_block;
+    do {
+      uint32_t next, blksz;
+      if (((blk_next & 3) != 0) || (blk_next >= ext_heap_size)) {
+        // Corrupted heap
+        atomicExch((unsigned int*)heap_ptr, first_free_block);
+        return nullptr;
+      }
+      next  = heap_ptr[(blk_next >> 2) + 0];
+      blksz = heap_ptr[(blk_next >> 2) + 1];
+      if (blksz >= len) {
+        uint32_t blk_new = blk_next + len;
+        uint32_t sz_new  = blksz - len;
+        if (sz_new >= 16) {
+          // Reduce the size of the current block
+          if (blk_prev == 0)
+            first_free_block = blk_new;
+          else
+            heap_ptr[(blk_prev >> 2) + 0] = blk_new;
+          heap_ptr[(blk_new >> 2) + 0] = next;
+          heap_ptr[(blk_new >> 2) + 1] = sz_new;
+        } else {
+          // Re-use this block
+          if (blk_prev == 0)
+            first_free_block = next;
+          else
+            heap_ptr[(blk_prev >> 2) + 0] = next;
+        }
+        __threadfence();
+        // Restore the list head
+        atomicExch((unsigned int*)heap_ptr, first_free_block);
+        return ext_heap_base + blk_next;
+      } else {
+        blk_prev = blk_next;
+        blk_next = next;
+      }
+    } while (blk_next != 0 && blk_next < ext_heap_size);
+    first_free_block = atomicExch((unsigned int*)heap_ptr, first_free_block);
+    // Reaching here means the heap is full
+    // Just in case we're trying to allocate more than the entire heap
+    if (len > ext_heap_size - 4 * sizeof(uint32_t)) { break; }
+  }
+  return nullptr;
+}
+
+/// Free a memory block (thread0-only)
+static __device__ void ext_heap_free(void* ptr,
+                                     uint32_t bytes,
+                                     uint8_t* ext_heap_base,
+                                     uint32_t ext_heap_size)
+{
+  uint32_t len              = (bytes + 0xf) & ~0xf;
+  volatile auto* heap_ptr   = (volatile uint32_t*)ext_heap_base;
+  uint32_t first_free_block = ~0;
+  auto cur_blk              = static_cast<uint32_t>(static_cast<uint8_t*>(ptr) - ext_heap_base);
+  for (;;) {
+    first_free_block = atomicExch((unsigned int*)heap_ptr, first_free_block);
+    if (first_free_block != ~0) { break; }
+    // Some other block is holding the heap
+  }
+  if (first_free_block >= ext_heap_size) {
+    // Heap is currently empty
+    first_free_block             = cur_blk;
+    heap_ptr[(cur_blk >> 2) + 0] = first_free_block;
+    heap_ptr[(cur_blk >> 2) + 1] = len;
+  } else {
+    uint32_t blk_prev = 0;
+    uint32_t blk_next = first_free_block;
+    for (;;) {
+      uint32_t next  = heap_ptr[(blk_next >> 2) + 0];
+      uint32_t blksz = heap_ptr[(blk_next >> 2) + 1];
+      if (cur_blk + len < blk_next) {
+        // Insert new block
+        heap_ptr[(cur_blk >> 2) + 0] = blk_next;
+        heap_ptr[(cur_blk >> 2) + 1] = len;
+        if (blk_prev == 0) {
+          first_free_block = cur_blk;
+        } else if (blk_prev + heap_ptr[(blk_prev >> 2) + 1] == cur_blk) {
+          // Merge with previous block
+          heap_ptr[(blk_prev >> 2) + 1] = heap_ptr[(blk_prev >> 2) + 1] + len;
+        } else {
+          heap_ptr[(blk_prev >> 2) + 0] = cur_blk;
+        }
+        break;
+      } else if (cur_blk + len == blk_next) {
+        // Merge with next block
+        heap_ptr[(cur_blk >> 2) + 0] = next;
+        heap_ptr[(cur_blk >> 2) + 1] = len + blksz;
+        if (blk_prev == 0) {
+          first_free_block = cur_blk;
+        } else if (blk_prev + heap_ptr[(blk_prev >> 2) + 1] == cur_blk) {
+          // Also merge with previous block
+          heap_ptr[(blk_prev >> 2) + 0] = next;
+          heap_ptr[(blk_prev >> 2) + 1] = heap_ptr[(blk_prev >> 2) + 1] + len + blksz;
+        } else {
+          heap_ptr[(blk_prev >> 2) + 0] = cur_blk;
+        }
+        break;
+      } else if (next < ext_heap_size) {
+        // Move to the next block
+        blk_prev = blk_next;
+        blk_next = next;
+      } else  // Insert this block at the tail
+      {
+        heap_ptr[(cur_blk >> 2) + 0] = next;
+        heap_ptr[(cur_blk >> 2) + 1] = len;
+        if (blk_next + blksz == cur_blk) {
+          // Merge with last block
+          heap_ptr[(blk_next >> 2) + 1] = heap_ptr[(blk_next >> 2) + 1] + len;
+        } else {
+          heap_ptr[(blk_next >> 2) + 0] = cur_blk;
+        }
+        break;
+      }
+    }
+  }
+  __threadfence();
+  atomicExch((unsigned int*)heap_ptr, first_free_block);
+}
+
+static __device__ uint32_t BuildSimpleHuffmanTable(uint16_t* lut,
+                                                   int root_bits,
+                                                   uint16_t* val,
+                                                   uint32_t num_symbols)
+{
+  uint32_t table_size      = 1;
+  uint32_t const goal_size = 1U << root_bits;
+  switch (num_symbols) {
+    case 0: lut[0] = huffcode(0, val[0]); break;
+    case 1:
+      if (val[1] > val[0]) {
+        lut[0] = huffcode(1, val[0]);
+        lut[1] = huffcode(1, val[1]);
+      } else {
+        lut[0] = huffcode(1, val[1]);
+        lut[1] = huffcode(1, val[0]);
+      }
+      table_size = 2;
+      break;
+    case 2:
+      lut[0] = huffcode(1, val[0]);
+      lut[2] = huffcode(1, val[0]);
+      if (val[2] > val[1]) {
+        lut[1] = huffcode(2, val[1]);
+        lut[3] = huffcode(2, val[2]);
+      } else {
+        lut[1] = huffcode(2, val[2]);
+        lut[3] = huffcode(2, val[1]);
+      }
+      table_size = 4;
+      break;
+    case 3: {
+      int i, k;
+      for (i = 0; i < 3; ++i) {
+        for (k = i + 1; k < 4; ++k) {
+          if (val[k] < val[i]) {
+            uint16_t t = val[k];
+            val[k]     = val[i];
+            val[i]     = t;
+          }
+        }
+      }
+      lut[0]     = huffcode(2, val[0]);
+      lut[2]     = huffcode(2, val[1]);
+      lut[1]     = huffcode(2, val[2]);
+      lut[3]     = huffcode(2, val[3]);
+      table_size = 4;
+      break;
+    }
+    case 4: {
+      if (val[3] < val[2]) {
+        uint16_t t = val[3];
+        val[3]     = val[2];
+        val[2]     = t;
+      }
+      lut[0]     = huffcode(1, val[0]);
+      lut[1]     = huffcode(2, val[1]);
+      lut[2]     = huffcode(1, val[0]);
+      lut[3]     = huffcode(3, val[2]);
+      lut[4]     = huffcode(1, val[0]);
+      lut[5]     = huffcode(2, val[1]);
+      lut[6]     = huffcode(1, val[0]);
+      lut[7]     = huffcode(3, val[3]);
+      table_size = 8;
+      break;
+    }
+  }
+  while (table_size != goal_size) {
+    memcpy(&lut[table_size], &lut[0], table_size * sizeof(lut[0]));
+    table_size <<= 1;
+  }
+  return goal_size;
+}
+
+static __device__ void BuildCodeLengthsHuffmanTable(huff_scratch_s* hs)
+{
+  uint32_t code;   // current table entry
+  int symbol;      // symbol index in original or sorted table
+  int key;         // prefix code
+  int key_step;    // prefix code addend
+  int step;        // step size to replicate values in current table
+  int table_size;  // size of current table
+  int bits;
+
+  // Generate offsets into sorted symbol table by code length.
+  symbol = -1;
+  for (bits = 1; bits <= 5; bits++) {
+    symbol += hs->code_length_histo[bits];
+    hs->offset[bits] = (int8_t)symbol;
+  }
+  // Symbols with code length 0 are placed after all other symbols.
+  hs->offset[0] = brotli_code_length_codes - 1;
+  // Sort symbols by length, by symbol order within each length.
+  symbol = brotli_code_length_codes;
+  do {
+    symbol--;
+    hs->sorted[hs->offset[hs->code_length_code_lengths[symbol]]--] = symbol;
+  } while (symbol != 0);
+
+  table_size = 1 << 5;
+
+  // Special case: all symbols but one have 0 code length.
+  if (hs->offset[0] == 0) {
+    code = huffcode(0, hs->sorted[0]);
+    for (key = 0; key < table_size; ++key) {
+      hs->lenvlctab[key] = code;
+    }
+    return;
+  }
+
+  // Fill in table.
+  key      = 0;
+  key_step = 1 << 7;
+  symbol   = 0;
+  bits     = 1;
+  step     = 2;
+  do {
+    for (int bits_count = hs->code_length_histo[bits]; bits_count != 0; --bits_count) {
+      int end     = table_size;
+      code        = huffcode(bits, hs->sorted[symbol++]);
+      uint16_t* p = &hs->lenvlctab[brev8(key)];
+      do {
+        end -= step;
+        p[end] = code;
+      } while (end > 0);
+      key += key_step;
+    }
+    step <<= 1;
+    key_step >>= 1;
+  } while (++bits <= 5);
+}
+
+// Returns the table width of the next 2nd level table. |count| is the histogram
+// of bit lengths for the remaining symbols, |len| is the code length of the
+// next processed symbol.
+static __device__ int NextTableBitSize(uint16_t const* const count, int len, int root_bits)
+{
+  int left = 1 << (len - root_bits);
+  while (len < 15) {
+    left -= count[len];
+    if (left <= 0) break;
+    ++len;
+    left <<= 1;
+  }
+  return len - root_bits;
+}
+
+// Build a huffman lookup table (currently thread0-only)
+static __device__ uint32_t BuildHuffmanTable(uint16_t* root_lut,
+                                             int root_bits,
+                                             uint16_t const* const symbol_lists,
+                                             uint16_t* count)
+{
+  uint32_t code;     // current table entry
+  uint16_t* lut;     // next available space in table
+  int len;           // current code length
+  int symbol;        // symbol index in original or sorted table
+  int key;           // prefix code
+  int key_step;      // prefix code addend
+  int sub_key;       // 2nd level table prefix code
+  int sub_key_step;  // 2nd level table prefix code addend
+  int step;          // step size to replicate values in current table
+  int table_bits;    // key length of current table
+  int table_size;    // size of current table
+  int total_size;    // sum of root table size and 2nd level table sizes
+  int max_length = -1;
+  int bits;
+
+  while (symbol_lists[max_length] == 0xFFFF)
+    max_length--;
+  max_length += 16;
+
+  lut        = root_lut;
+  table_bits = root_bits;
+  table_size = 1 << table_bits;
+  total_size = table_size;
+
+  // Fill in the root table. Reduce the table size to if possible, and create the repetitions by
+  // memcpy.
+  if (table_bits > max_length) {
+    table_bits = max_length;
+    table_size = 1 << table_bits;
+  }
+  key      = 0;
+  key_step = 1 << 7;
+  bits     = 1;
+  step     = 2;
+  do {
+    symbol = bits - 16;
+    for (int bits_count = count[bits]; bits_count != 0; --bits_count) {
+      symbol      = symbol_lists[symbol];
+      code        = huffcode(bits, symbol);
+      uint16_t* p = &lut[brev8(key)];
+      int end     = table_size;
+      do {
+        end -= step;
+        p[end] = code;
+      } while (end > 0);
+      key += key_step;
+    }
+    step <<= 1;
+    key_step >>= 1;
+  } while (++bits <= table_bits);
+
+  // If root_bits != table_bits then replicate to fill the remaining slots.
+  while (total_size != table_size) {
+    memcpy(&lut[table_size], &lut[0], table_size * sizeof(lut[0]));
+    table_size <<= 1;
+  }
+
+  // Fill in 2nd level tables and add pointers to root table.
+  key_step     = (1 << 7) >> (root_bits - 1);
+  sub_key      = (1 << 8);
+  sub_key_step = (1 << 7);
+  for (len = root_bits + 1, step = 2; len <= max_length; ++len) {
+    symbol = len - 16;
+    for (; count[len] != 0; --count[len]) {
+      if (sub_key == (1 << 8)) {
+        lut += table_size;
+        table_bits = NextTableBitSize(count, len, root_bits);
+        table_size = 1 << table_bits;
+        total_size += table_size;
+        sub_key = brev8(key);
+        key += key_step;
+        root_lut[sub_key] =
+          huffcode(table_bits + root_bits, (((size_t)(lut - root_lut)) - sub_key));
+        sub_key = 0;
+      }
+      symbol      = symbol_lists[symbol];
+      code        = huffcode(len - root_bits, symbol);
+      uint16_t* p = &lut[brev8(sub_key)];
+      int end     = table_size;
+      do {
+        end -= step;
+        p[end] = code;
+      } while (end > 0);
+      sub_key += sub_key_step;
+    }
+    step <<= 1;
+    sub_key_step >>= 1;
+  }
+  return (uint32_t)total_size;
+}
+
+/**
+3.4.  Simple Prefix Codes
+
+The first two bits of the compressed representation of each prefix
+code distinguish between simple and complex prefix codes.  If this
+value is 1, then a simple prefix code follows as described in this
+section.  Otherwise, a complex prefix code follows as described in
+Section 3.5.
+
+A simple prefix code can have up to four symbols with non-zero code
+length.  The format of the simple prefix code is as follows:
+
+2 bits: value of 1 indicates a simple prefix code
+2 bits: NSYM - 1, where NSYM = number of symbols coded
+NSYM symbols, each encoded using ALPHABET_BITS bits
+1 bit:  tree-select, present only for NSYM = 4
+
+The value of ALPHABET_BITS depends on the alphabet of the prefix
+code: it is the smallest number of bits that can represent all
+symbols in the alphabet.  For example, for the alphabet of literal
+bytes, ALPHABET_BITS is 8.  The value of each of the NSYM symbols
+above is the value of the ALPHABET_BITS width integer value.  If the
+integer value is greater than or equal to the alphabet size, or the
+value is identical to a previous value, then the stream should be
+rejected as invalid.
+
+Note that the NSYM symbols may not be presented in sorted order.
+Prefix codes of the same bit length must be assigned to the symbols
+in sorted order.
+
+The (non-zero) code lengths of the symbols can be reconstructed as
+follows:
+
+*  if NSYM = 1, the code length for the one symbol is zero -- when
+encoding this symbol in the compressed data stream using this
+prefix code, no actual bits are emitted.  Similarly, when
+decoding a symbol using this prefix code, no bits are read and
+the one symbol is returned.
+
+*  if NSYM = 2, both symbols have code length 1.
+
+*  if NSYM = 3, the code lengths for the symbols are 1, 2, 2 in
+the order they appear in the representation of the simple
+prefix code.
+
+*  if NSYM = 4, the code lengths (in order of symbols decoded)
+depend on the tree-select bit: 2, 2, 2, 2 (tree-select bit 0),
+or 1, 2, 3, 3 (tree-select bit 1).
+
+3.5.  Complex Prefix Codes
+
+A complex prefix code is a canonical prefix code, defined by the
+sequence of code lengths, as discussed in Section 3.2.  For even
+greater compactness, the code length sequences themselves are
+compressed using a prefix code.  The alphabet for code lengths is as
+follows:
+
+0..15: Represent code lengths of 0..15
+16: Copy the previous non-zero code length 3..6 times.
+The next 2 bits indicate repeat length (0 = 3, ... , 3 = 6)
+If this is the first code length, or all previous
+code lengths are zero, a code length of 8 is
+repeated 3..6 times.
+A repeated code length code of 16 modifies the
+repeat count of the previous one as follows:
+repeat count = (4 * (repeat count - 2)) + (3..6 on the next 2 bits)
+Example:  Codes 7, 16 (+2 bits 11), 16 (+2 bits 10)
+will expand to 22 code lengths of 7 (1 + 4 * (6 - 2) + 5)
+17: Repeat a code length of 0 for 3..10 times.
+The next 3 bits indicate repeat length (0 = 3, ... , 7 = 10)
+A repeated code length code of 17 modifies the
+repeat count of the previous one as follows:
+repeat count = (8 * (repeat count - 2)) + (3..10 on the next 3 bits)
+
+Note that a code of 16 that follows an immediately preceding 16
+modifies the previous repeat count, which becomes the new repeat
+count.  The same is true for a 17 following a 17.  A sequence of
+three or more 16 codes in a row or three of more 17 codes in a row is
+possible, modifying the count each time.  Only the final repeat count
+is used.  The modification only applies if the same code follows.  A
+16 repeat does not modify an immediately preceding 17 count nor vice
+versa.
+
+A code length of 0 indicates that the corresponding symbol in the
+alphabet will not occur in the compressed data, and it should not
+participate in the prefix code construction algorithm given earlier.
+A complex prefix code must have at least two non-zero code lengths.
+
+The bit lengths of the prefix code over the code length alphabet are
+compressed with the following variable-length code (as it appears in
+the compressed data, where the bits are parsed from right to left):
+
+Symbol   Code
+------   ----
+0          00
+1        0111
+2         011
+3          10
+4          01
+5        1111
+
+We can now define the format of the complex prefix code as follows:
+
+o  2 bits: HSKIP, the number of skipped code lengths, can have values
+of 0, 2, or 3.  The skipped lengths are taken to be zero.  (An
+HSKIP of 1 indicates a Simple prefix code.)
+
+o  Code lengths for symbols in the code length alphabet given just
+above, in the order: 1, 2, 3, 4, 0, 5, 17, 6, 16, 7, 8, 9, 10, 11,
+12, 13, 14, 15.  If HSKIP is 2, then the code lengths for symbols
+1 and 2 are zero, and the first code length is for symbol 3.  If
+HSKIP is 3, then the code length for symbol 3 is also zero, and
+the first code length is for symbol 4.
+
+The code lengths of code length symbols are between 0 and 5, and
+they are represented with 2..4 bits according to the variable-
+length code above.  A code length of 0 means the corresponding
+code length symbol is not used.
+
+If HSKIP is 2 or 3, a respective number of leading code lengths
+are implicit zeros and are not present in the code length sequence
+above.
+
+If there are at least two non-zero code lengths, any trailing zero
+code lengths are omitted, i.e., the last code length in the
+sequence must be non-zero.  In this case, the sum of (32 >> code
+length) over all the non-zero code lengths must equal to 32.
+
+If the lengths have been read for the entire code length alphabet
+and there was only one non-zero code length, then the prefix code
+has one symbol whose code has zero length.  In this case, that
+symbol results in no bits being emitted by the compressor and no
+bits consumed by the decompressor.  That single symbol is
+immediately returned when this code is decoded.  An example of
+where this occurs is if the entire code to be represented has
+symbols of length 8.  For example, a literal code that represents
+all literal values with equal probability.  In this case the
+single symbol is 16, which repeats the previous length.  The
+previous length is taken to be 8 before any code length code
+lengths are read.
+
+o  Sequence of code length symbols, which is at most the size of the
+alphabet, encoded using the code length prefix code.  Any trailing
+0 or 17 must be omitted, i.e., the last encoded code length symbol
+must be between 1 and 16.  The sum of (32768 >> code length) over
+all the non-zero code lengths in the alphabet, including those
+encoded using repeat code(s) of 16, must be equal to 32768.  If
+the number of times to repeat the previous length or repeat a zero
+length would result in more lengths in total than the number of
+symbols in the alphabet, then the stream should be rejected as
+invalid.
+*/
+
+// Decode Huffman tree (thread0-only)
+static __device__ uint32_t DecodeHuffmanTree(debrotli_state_s* s,
+                                             uint32_t alphabet_size,
+                                             uint32_t max_symbol,
+                                             uint16_t* vlctab)
+{
+  uint32_t prefix_code_type;
+
+  prefix_code_type = getbits(s, 2);
+  if (prefix_code_type == 1) {
+    // Simple prefix Code
+    uint32_t nsym          = getbits(s, 2);
+    uint32_t alphabet_bits = Log2Floor(alphabet_size - 1);
+    for (uint32_t n = 0; n <= nsym; n++) {
+      uint32_t v = getbits(s, alphabet_bits);
+      if (v >= max_symbol) {
+        s->error = -1;
+        return 0;
+      }
+      for (uint32_t i = 0; i < n; i++) {
+        if (v == s->hs.symbols_lists_array[i]) {
+          s->error = -1;  // Duplicate symbol
+          return 0;
+        }
+      }
+      s->hs.symbols_lists_array[n] = (uint16_t)v;
+    }
+    if (nsym == 3) {
+      nsym += getbits(s, 1);  // tree_select;
+    }
+    return BuildSimpleHuffmanTable(
+      vlctab, huffman_lookup_table_width, s->hs.symbols_lists_array, nsym);
+  } else {
+    // Complex prefix code
+    huff_scratch_s* const hs = &s->hs;
+    uint16_t* symbol_lists =
+      &s->hs.symbols_lists_array[16];  // Make small negative indexes addressable
+    uint32_t space = 32, num_codes = 0, i, prev_code_len, symbol, repeat, repeat_code_len;
+
+    memset(&hs->code_length_histo[0], 0, 6 * sizeof(hs->code_length_histo));
+    memset(&hs->code_length_code_lengths[0], 0, sizeof(hs->code_length_code_lengths));
+    for (i = prefix_code_type; i < 18; i++) {
+      uint8_t const code_len_idx = kCodeLengthCodeOrder[i];
+      uint32_t ix, v;
+
+      ix = showbits(s, 4);
+      v  = kCodeLengthPrefixValue[ix];
+      skipbits(s, kCodeLengthPrefixLength[ix]);
+      hs->code_length_code_lengths[code_len_idx] = (uint8_t)v;
+      if (v != 0) {
+        space = space - (32u >> v);
+        ++num_codes;
+        ++hs->code_length_histo[v];
+        if (space - 1u >= 32u) {
+          // space is 0 or wrapped around.
+          break;
+        }
+      }
+    }
+    if (!(num_codes == 1 || space == 0)) {
+      s->error = -1;
+      return 0;
+    }
+    BuildCodeLengthsHuffmanTable(&s->hs);
+    for (i = 0; i <= 15; ++i) {
+      hs->code_length_histo[i]         = 0;
+      hs->next_symbol[i]               = (int16_t)(i - 16);
+      symbol_lists[hs->next_symbol[i]] = 0xFFFF;
+    }
+    symbol          = 0;
+    prev_code_len   = 8;
+    repeat          = 0;
+    repeat_code_len = 0;
+    space           = 32768;
+    while (symbol < max_symbol && space > 0) {
+      uint32_t next32   = next32bits(s);
+      uint32_t code_len = hs->lenvlctab[next32 & 0x1f];
+      uint32_t vlc_len  = code_len & 0xf;  // Use 1..5 bits
+      code_len >>= 4;                      // code_len = 0..17
+      if (code_len < 16) {
+        // Process single decoded symbol code length :
+        // A) reset the repeat variable
+        // B) remember code length(if it is not 0)
+        // C) extend corresponding index - chain
+        // D) reduce the Huffman space
+        // E) update the histogram
+        repeat = 0;
+        if (code_len != 0) {  // code_len == 1..15
+          symbol_lists[hs->next_symbol[code_len]] = (uint16_t)symbol;
+          hs->next_symbol[code_len]               = (int)symbol;
+          prev_code_len                           = code_len;
+          space -= 32768u >> code_len;
+          hs->code_length_histo[code_len]++;
+        }
+        symbol++;
+      } else {
+        // Process repeated symbol code length.
+        // A) Check if it is the extension of previous repeat sequence; if the decoded value is not
+        // 16, then it is a new symbol-skip B) Update repeat variable C) Check if operation is
+        // feasible (fits alphabet) D) For each symbol do the same operations as in single symbol
+        uint32_t extra_bits, repeat_delta, new_len, old_repeat;
+
+        if (code_len == 16) {
+          extra_bits   = 2;
+          repeat_delta = (next32 >> vlc_len) & 3;
+          new_len      = prev_code_len;
+        } else {
+          extra_bits   = 3;
+          repeat_delta = (next32 >> vlc_len) & 7;
+          new_len      = 0;
+        }
+        vlc_len += extra_bits;
+        if (repeat_code_len != new_len) {
+          repeat          = 0;
+          repeat_code_len = new_len;
+        }
+        old_repeat = repeat;
+        if (repeat > 0) { repeat = (repeat - 2) << extra_bits; }
+        repeat += repeat_delta + 3u;
+        repeat_delta = repeat - old_repeat;
+        if (symbol + repeat_delta > max_symbol) {
+          s->error = -1;
+          return 0;
+        }
+        if (repeat_code_len != 0) {
+          uint32_t last = symbol + repeat_delta;
+          int next      = hs->next_symbol[repeat_code_len];
+          do {
+            symbol_lists[next] = (uint16_t)symbol;
+            next               = (int)symbol;
+          } while (++symbol != last);
+          hs->next_symbol[repeat_code_len] = next;
+          space -= repeat_delta << (15 - repeat_code_len);
+          hs->code_length_histo[repeat_code_len] =
+            (uint16_t)(hs->code_length_histo[repeat_code_len] + repeat_delta);
+        } else {
+          symbol += repeat_delta;
+        }
+      }
+      skipbits(s, vlc_len);
+    }
+    if (space != 0) {
+      s->error = -1;
+      return 0;
+    }
+    return BuildHuffmanTable(
+      vlctab, huffman_lookup_table_width, symbol_lists, hs->code_length_histo);
+  }
+}
+
+/**
+9.1.  Format of the Stream Header
+
+The stream header has only the following one field:
+
+1..7 bits: WBITS, a value in the range 10..24, encoded with the
+following variable - length code(as it appears in the
+compressed data, where the bits are parsed from right
+to left) :
+
+Value    Bit Pattern
+---- - ---------- -
+10        0100001
+11        0110001
+12        1000001
+13        1010001
+14        1100001
+15        1110001
+16              0
+17        0000001
+18           0011
+19           0101
+20           0111
+21           1001
+22           1011
+23           1101
+24           1111
+
+Note that bit pattern 0010001 is invalid and must not
+be used.
+
+The size of the sliding window, which is the maximum value of any
+non - dictionary reference backward distance, is given by the following
+formula :
+
+window size = (1 << WBITS) - 16
+*/
+static __device__ void DecodeStreamHeader(debrotli_state_s* s)
+{
+  uint32_t next32 = next32bits(s);
+  uint32_t wbits = 0, len = 0;
+  if ((next32 & 1) == 0) {
+    // 0
+    wbits = 16;
+    len   = 1;
+  } else {
+    uint32_t n = (next32 >> 1) & 7;
+    if (n != 0) {
+      // xxx1
+      wbits = 17 + n;
+      len   = 4;
+    } else {
+      n = (next32 >> 4) & 7;
+      if (n != 1) {
+        wbits = (n) ? 8 + n : 17;  // xxx0001
+        len   = 7;
+      } else {
+        // Large window (not supported) or invalid, bail
+        s->error = -1;
+      }
+    }
+  }
+  s->window_bits           = (uint8_t)wbits;
+  s->max_backward_distance = (1 << s->window_bits) - 16;
+  skipbits(s, len);
+}
+
+/**
+9.2.Format of the Meta - Block Header
+
+A compliant compressed data set has at least one meta - block.Each
+meta - block contains a header with information about the uncompressed
+length of the meta - block, and a bit signaling if the meta - block is
+the last one.The format of the meta - block header is the following :
+
+1 bit : ISLAST, set to 1 if this is the last meta - block
+
+1 bit : ISLASTEMPTY, if set to 1, the meta - block is empty; this
+field is only present if ISLAST bit is set-- if it is 1, then the
+meta - block and the brotli stream ends at that bit, with any remaining
+bits in the last byte of the compressed stream filled with zeros(if the
+fill bits are not zero, then the stream should be rejected as invalid)
+
+2 bits: MNIBBLES, number of nibbles to represent the uncompressed
+length, encoded with the following fixed - length code :
+
+Value    Bit Pattern
+---- - ---------- -
+0             11
+4             00
+5             01
+6             10
+
+If MNIBBLES is 0, the meta - block is empty, i.e., it does not generate
+any uncompressed data.In this case, the rest of the meta - block has
+the following format :
+
+1 bit : reserved, must be zero
+
+2 bits : MSKIPBYTES, number of bytes to represent metadata length
+
+MSKIPBYTES * 8 bits : MSKIPLEN - 1, where MSKIPLEN is the number of
+metadata bytes; this field is only present if MSKIPBYTES is positive;
+otherwise, MSKIPLEN is 0 (if MSKIPBYTES is greater than 1, and the last
+byte is all zeros, then the stream should be rejected as invalid)
+
+0..7 bits: fill bits until the next byte boundary, must be all zeros
+
+MSKIPLEN bytes of metadata, not part of the uncompressed data
+or the sliding window
+
+MNIBBLES * 4 bits: MLEN - 1, where MLEN is the length of the meta -
+block uncompressed data in bytes(if MNIBBLES is greater than 4,
+and the last nibble is all zeros, then the stream should be rejected
+as invalid)
+
+1 bit : ISUNCOMPRESSED, if set to 1, any bits of compressed data up to the
+next byte boundary are ignored, and the rest of the meta - block contains
+MLEN bytes of literal data; this field is only present if the ISLAST bit is
+not set(if the ignored bits are not all zeros, the stream should be rejected
+as invalid)
+*/
+
+static __device__ void DecodeMetaBlockHeader(debrotli_state_s* s)
+{
+  uint32_t next32 = next32bits(s);
+  uint32_t len = 1, is_empty = 0;
+  s->is_last = (uint8_t)(next32 & 1);
+  if (s->is_last) {
+    is_empty = (uint8_t)((next32 >> 1) & 1);
+    len++;
+  }
+  s->meta_block_len  = 0;
+  s->is_uncompressed = 0;
+  if (!is_empty) {
+    uint32_t mnibbles = 4 + ((next32 >> len) & 3);
+    len += 2;
+    if (mnibbles < 7) {
+      s->meta_block_len = 1 + ((next32 >> len) & ((1u << (mnibbles * 4)) - 1));
+      len += mnibbles * 4;
+      if (mnibbles > 4u && s->meta_block_len <= (1u << (mnibbles * 4 - 4))) { s->error = -1; }
+      if (!s->is_last) {
+        s->is_uncompressed = (uint8_t)((next32 >> len) & 1);
+        len++;
+      }
+    } else {
+      uint32_t reserved, mskipbytes, mskiplen;
+      reserved = (next32 >> len) & 1;
+      if (reserved != 0) { s->error = -1; }
+      len += 1;
+      mskipbytes = (next32 >> len) & 3;
+      len += 2;
+      if (mskipbytes > 0) {
+        mskiplen = 1 + (next32 >> len) & ((1u << (mskipbytes * 8)) - 1);
+        if (mskiplen <= ((1u << (mskipbytes * 8)) >> 8)) {
+          s->error = -1;  // Last byte is all zeros
+        }
+        len += mskipbytes * 8;
+      } else {
+        mskiplen = 0;
+      }
+      skipbits(s, len);
+      if (getbits_bytealign(s) != 0) { s->error = 1; }
+      for (len = mskiplen; len >= 32; len -= 32) {
+        skipbits(s, 32);
+      }
+    }
+  }
+  skipbits(s, len);
+}
+
+/**
+1..11 bits: NBLTYPESL, number of literal block types
+
+Prefix code over the block type code alphabet for literal block
+types, appears only if NBLTYPESL >= 2
+
+Prefix code over the block count code alphabet for literal
+block counts, appears only if NBLTYPESL >= 2
+
+Block count code + extra bits for first literal block count,
+appears only if NBLTYPESL >= 2
+
+1..11 bits: NBLTYPESI, number of insert-and-copy block types,
+encoded with the same variable-length code as above
+
+Prefix code over the block type code alphabet for insert-and-
+copy block types, appears only if NBLTYPESI >= 2
+
+Prefix code over the block count code alphabet for insert-and-
+copy block counts, appears only if NBLTYPESI >= 2
+
+Block count code + extra bits for first insert-and-copy block
+count, appears only if NBLTYPESI >= 2
+
+1..11 bits: NBLTYPESD, number of distance block types, encoded
+with the same variable-length code as above
+
+Prefix code over the block type code alphabet for distance
+block types, appears only if NBLTYPESD >= 2
+
+Prefix code over the block count code alphabet for distance
+block counts, appears only if NBLTYPESD >= 2
+
+Block count code + extra bits for first distance block count,
+appears only if NBLTYPESD >= 2
+*/
+
+static __device__ void DecodeHuffmanTables(debrotli_state_s* s)
+{
+  for (int b = 0; b < 3; b++) {
+    uint32_t nbltypes     = 1 + getbits_u8vlc(s);
+    s->num_block_types[b] = nbltypes;
+    if (nbltypes >= 2) {
+      uint32_t alphabet_size = nbltypes + 2, index, nbits, maxtblsz;
+      uint16_t* vlctab;
+      maxtblsz = kMaxHuffmanTableSize[(alphabet_size + 31) >> 5];
+      maxtblsz = (maxtblsz > brotli_huffman_max_size_258) ? brotli_huffman_max_size_258 : maxtblsz;
+      vlctab   = reinterpret_cast<uint16_t*>(
+        local_alloc(s, (brotli_huffman_max_size_26 + maxtblsz) * sizeof(uint16_t)));
+      s->block_type_vlc[b] = vlctab;
+      DecodeHuffmanTree(s, alphabet_size, alphabet_size, vlctab + brotli_huffman_max_size_26);
+      alphabet_size = 26;
+      DecodeHuffmanTree(s, alphabet_size, alphabet_size, vlctab);
+      if (s->error) { break; }
+      index              = getvlc(s, vlctab);
+      nbits              = kBlockLengthPrefixCodeBits[index];  // nbits == 2..24
+      s->block_length[b] = kBlockLengthPrefixCodeOffset[index] + getbits(s, nbits);
+
+    } else {
+      s->block_length[b] = 1 << 24;
+    }
+  }
+  s->block_type_rb[0] = 1;
+  s->block_type_rb[1] = 0;
+  s->block_type_rb[2] = 1;
+  s->block_type_rb[3] = 0;
+  s->block_type_rb[4] = 1;
+  s->block_type_rb[5] = 0;
+}
+
+/** @brief Transform:
+ * 1) initialize list L with values 0, 1,... 255
+ * 2) For each input element X:
+ * 2.1) let Y = L[X]
+ * 2.2) remove X-th element from L
+ * 2.3) prepend Y to L
+ * 2.4) append Y to output
+ *
+ * In most cases max(Y) <= 7, so most of L remains intact.
+ * To reduce the cost of initialization, we reuse L, remember the upper bound
+ * of Y values, and reinitialize only first elements in L.
+ *
+ * Most of input values are 0 and 1. To reduce number of branches, we replace
+ * inner for loop with do-while.
+ */
+static __device__ void InverseMoveToFrontTransform(debrotli_state_s* s, uint8_t* v, uint32_t v_len)
+{
+  // Reinitialize elements that could have been changed.
+  uint32_t i           = 1;
+  uint32_t upper_bound = s->mtf_upper_bound;
+  uint32_t* mtf        = &s->mtf[1];  // Make mtf[-1] addressable.
+  auto* mtf_u8         = reinterpret_cast<uint8_t*>(mtf);
+  uint32_t pattern     = 0x0302'0100;  // Little-endian
+
+  // Initialize list using 4 consequent values pattern.
+  mtf[0] = pattern;
+  do {
+    pattern += 0x0404'0404;  // Advance all 4 values by 4.
+    mtf[i] = pattern;
+    i++;
+  } while (i <= upper_bound);
+
+  // Transform the input.
+  upper_bound = 0;
+  for (i = 0; i < v_len; ++i) {
+    int index     = v[i];
+    uint8_t value = mtf_u8[index];
+    upper_bound |= v[i];
+    v[i]       = value;
+    mtf_u8[-1] = value;
+    do {
+      index--;
+      mtf_u8[index + 1] = mtf_u8[index];
+    } while (index >= 0);
+  }
+  // Remember amount of elements to be reinitialized.
+  s->mtf_upper_bound = upper_bound >> 2;
+}
+
+static __device__ uint32_t DecodeContextMap(debrotli_state_s* s,
+                                            uint8_t* context_map,
+                                            uint32_t context_map_size,
+                                            uint16_t* context_map_vlc)
+{
+  uint32_t num_htrees = getbits_u8vlc(s) + 1;
+  uint32_t bits, context_index, max_run_length_prefix, alphabet_size;
+
+  if (num_htrees <= 1) {
+    memset(context_map, 0, context_map_size);
+    return num_htrees;
+  }
+  bits = showbits(s, 5);
+  if ((bits & 1) != 0) {  // Use RLE for zeros.
+    max_run_length_prefix = (bits >> 1) + 1;
+    skipbits(s, 5);
+  } else {
+    max_run_length_prefix = 0;
+    skipbits(s, 1);
+  }
+  alphabet_size = num_htrees + max_run_length_prefix;
+  DecodeHuffmanTree(s, alphabet_size, alphabet_size, context_map_vlc);
+  if (s->error) { return num_htrees; }
+  context_index = 0;
+  while (context_index < context_map_size) {
+    uint32_t code = getvlc(s, context_map_vlc);
+    if (code == 0) {
+      context_map[context_index++] = 0;
+    } else if (code > max_run_length_prefix) {
+      context_map[context_index++] = (uint8_t)(code - max_run_length_prefix);
+    } else {
+      // RLE sub-stage.
+      uint32_t reps = getbits(s, code) + (1u << code);
+      if (context_index + reps > context_map_size) {
+        s->error = -1;
+        break;
+      }
+      do {
+        context_map[context_index++] = 0;
+      } while (--reps);
+    }
+  }
+  bits = getbits(s, 1);
+  if (bits != 0) { InverseMoveToFrontTransform(s, context_map, context_map_size); }
+  return num_htrees;
+}
+
+static __device__ void DetectTrivialLiteralBlockTypes(debrotli_state_s* s)
+{
+  uint32_t i;
+  for (i = 0; i < s->num_block_types[0]; i++) {
+    uint32_t offset = i << 6;
+    uint32_t error  = 0;
+    uint32_t sample = s->context_map[offset];
+    uint32_t j;
+    for (j = 0; j < (1u << 6); ++j) {
+      error |= s->context_map[offset + j] ^ sample;
+    }
+    if (error == 0) { s->context_modes[i] |= 4u; }
+  }
+}
+
+/**
+
+2 bits: NPOSTFIX, parameter used in the distance coding
+
+4 bits: four most significant bits of NDIRECT, to get the actual
+value of the parameter NDIRECT, left-shift this four-bit
+number by NPOSTFIX bits
+
+NBLTYPESL * 2 bits: context mode for each literal block type
+
+1..11 bits: NTREESL, number of literal prefix trees, encoded with
+the same variable-length code as NBLTYPESL
+
+Literal context map, encoded as described in Section 7.3,
+appears only if NTREESL >= 2; otherwise, the context map has
+only zero values
+
+1..11 bits: NTREESD, number of distance prefix trees, encoded with
+the same variable-length code as NBLTYPESD
+
+Distance context map, encoded as described in Section 7.3,
+appears only if NTREESD >= 2; otherwise, the context map has
+only zero values
+*/
+
+static __device__ debrotli_huff_tree_group_s* HuffmanTreeGroupInit(debrotli_state_s* s,
+                                                                   uint32_t alphabet_size,
+                                                                   uint32_t max_symbol,
+                                                                   uint32_t ntrees)
+{
+  auto* group          = reinterpret_cast<debrotli_huff_tree_group_s*>(local_alloc(
+    s, sizeof(debrotli_huff_tree_group_s) + ntrees * sizeof(uint16_t*) - sizeof(uint16_t*)));
+  group->alphabet_size = (uint16_t)alphabet_size;
+  group->max_symbol    = (uint16_t)max_symbol;
+  group->num_htrees    = (uint16_t)ntrees;
+  group->htrees[0]     = nullptr;
+  return group;
+}
+
+static __device__ void HuffmanTreeGroupAlloc(debrotli_state_s* s, debrotli_huff_tree_group_s* group)
+{
+  if (!group->htrees[0]) {
+    uint32_t alphabet_size  = group->alphabet_size;
+    uint32_t ntrees         = group->num_htrees;
+    uint32_t max_table_size = kMaxHuffmanTableSize[(alphabet_size + 31) >> 5];
+    uint32_t code_size      = sizeof(uint16_t) * ntrees * max_table_size;
+    group->htrees[0]        = reinterpret_cast<uint16_t*>(local_alloc(s, code_size));
+    if (!group->htrees[0]) {
+      if (s->fb_base) { group->htrees[0] = reinterpret_cast<uint16_t*>(s->fb_base + s->fb_size); }
+      s->fb_size += (code_size + 3) & ~3;
+    }
+  }
+}
+
+// Decodes a series of Huffman table using ReadHuffmanCode function.
+static __device__ void HuffmanTreeGroupDecode(debrotli_state_s* s,
+                                              debrotli_huff_tree_group_s* group)
+{
+  uint16_t* next = group->htrees[0];
+
+  for (int htree_index = 0; htree_index < group->num_htrees; htree_index++) {
+    uint32_t table_size = DecodeHuffmanTree(s, group->alphabet_size, group->max_symbol, next);
+    if (s->error) break;
+    group->htrees[htree_index] = next;
+    next += table_size;
+  }
+}
+
+static __device__ void DecodeHuffmanTreeGroups(debrotli_state_s* s,
+                                               uint8_t* fb_heap_base,
+                                               uint32_t fb_heap_size)
+{
+  uint32_t bits, npostfix, ndirect, nbltypesl;
+  uint32_t context_map_size;
+  uint16_t* context_map_vlc;
+  uint32_t num_direct_codes, num_distance_codes, num_literal_htrees, num_dist_htrees;
+
+  // Decode context maps
+  bits                         = getbits(s, 6);
+  npostfix                     = bits & 3;
+  ndirect                      = (bits >> 2) << npostfix;
+  s->distance_postfix_bits     = (uint8_t)npostfix;
+  s->num_direct_distance_codes = brotli_num_distance_short_codes + ndirect;
+  s->distance_postfix_mask     = (1 << npostfix) - 1;
+  nbltypesl                    = s->num_block_types[0];
+  s->context_modes             = local_alloc(s, nbltypesl);
+  for (uint32_t i = 0; i < nbltypesl; i++) {
+    s->context_modes[i] = getbits(s, 2);
+  }
+  context_map_vlc = reinterpret_cast<uint16_t*>(
+    local_heap_shrink(s, brotli_huffman_max_size_272 * sizeof(uint16_t)));
+  context_map_size   = nbltypesl << 6;
+  s->context_map     = local_alloc(s, context_map_size);
+  num_literal_htrees = DecodeContextMap(s, s->context_map, context_map_size, context_map_vlc);
+  if (s->error) return;
+  DetectTrivialLiteralBlockTypes(s);
+  context_map_size    = s->num_block_types[2] << 2;
+  s->dist_context_map = local_alloc(s, context_map_size);
+  num_dist_htrees     = DecodeContextMap(s, s->dist_context_map, context_map_size, context_map_vlc);
+  if (s->error) return;
+  local_heap_grow(s, brotli_huffman_max_size_272 * sizeof(uint16_t));  // free context map vlc
+  num_direct_codes = s->num_direct_distance_codes - brotli_num_distance_short_codes;
+  num_distance_codes =
+    brotli_distance_alphabet_size(s->distance_postfix_bits, num_direct_codes, 24u);
+  s->literal_hgroup = HuffmanTreeGroupInit(
+    s, brotli_num_literal_symbols, brotli_num_literal_symbols, num_literal_htrees);
+  s->insert_copy_hgroup = HuffmanTreeGroupInit(
+    s, brotli_num_command_symbols, brotli_num_command_symbols, s->num_block_types[1]);
+  s->distance_hgroup =
+    HuffmanTreeGroupInit(s, num_distance_codes, num_distance_codes, num_dist_htrees);
+  // Attempt to allocate local memory first, before going to fb
+  s->fb_size = 0;
+  HuffmanTreeGroupAlloc(s, s->literal_hgroup);
+  HuffmanTreeGroupAlloc(s, s->insert_copy_hgroup);
+  HuffmanTreeGroupAlloc(s, s->distance_hgroup);
+  if (s->fb_size != 0) {
+    // Did not fit in local memory -> allocate fb
+    s->fb_base = ext_heap_alloc(s->fb_size, fb_heap_base, fb_heap_size);
+    if (!s->fb_base) {
+      s->error   = -2;
+      s->fb_size = 0;
+      return;
+    }
+    // Repeat allocation falling back to fb
+    s->fb_size = 0;
+    HuffmanTreeGroupAlloc(s, s->literal_hgroup);
+    HuffmanTreeGroupAlloc(s, s->insert_copy_hgroup);
+    HuffmanTreeGroupAlloc(s, s->distance_hgroup);
+  }
+  HuffmanTreeGroupDecode(s, s->literal_hgroup);
+  if (s->error) return;
+  HuffmanTreeGroupDecode(s, s->insert_copy_hgroup);
+  if (s->error) return;
+  HuffmanTreeGroupDecode(s, s->distance_hgroup);
+}
+
+static __device__ int PrepareLiteralDecoding(debrotli_state_s* s, uint8_t const*& context_map_slice)
+{
+  int context_mode;
+  uint32_t block_type     = s->block_type_rb[1];
+  uint32_t context_offset = block_type << 6;
+  context_map_slice       = s->context_map + context_offset;
+  context_mode            = s->context_modes[block_type];
+  return brotli_context_lut(context_mode);
+}
+
+/// Decodes a command or literal and updates block type ring-buffer. Reads 3..54 bits.
+static __device__ uint32_t DecodeBlockTypeAndLength(debrotli_state_s* s, int tree_type)
+{
+  uint32_t max_block_type = s->num_block_types[tree_type];
+  if (max_block_type > 1) {
+    uint16_t const* len_tree  = s->block_type_vlc[tree_type];
+    uint16_t const* type_tree = len_tree + brotli_huffman_max_size_26;
+    uint8_t* ringbuffer       = &s->block_type_rb[tree_type * 2];
+    // Read 0..15 + 3..39 bits.
+    uint32_t block_type = getvlc(s, type_tree);
+    uint32_t block_len  = getvlc(s, len_tree);
+    block_len =
+      kBlockLengthPrefixCodeOffset[block_len] + getbits(s, kBlockLengthPrefixCodeBits[block_len]);
+    if (block_type == 1) {
+      block_type = ringbuffer[1] + 1;
+    } else if (block_type == 0) {
+      block_type = ringbuffer[0];
+    } else {
+      block_type -= 2;
+    }
+    if (block_type >= max_block_type) { block_type -= max_block_type; }
+    ringbuffer[0] = ringbuffer[1];
+    ringbuffer[1] = (uint8_t)block_type;
+    return block_len;
+  } else {
+    return 0;  // Can only get here because of bitstream error
+  }
+}
+
+inline __device__ int ToUpperCase(uint8_t* p)
+{
+  if (p[0] < 0xC0) {
+    if (p[0] >= 'a' && p[0] <= 'z') { p[0] ^= 32; }
+    return 1;
+  }
+  // An overly simplified uppercasing model for UTF-8.
+  if (p[0] < 0xE0) {
+    p[1] ^= 32;
+    return 2;
+  }
+  // An arbitrary transform for three byte characters.
+  p[2] ^= 5;
+  return 3;
+}
+
+static __device__ int TransformDictionaryWord(uint8_t* dst,
+                                              uint8_t const* word,
+                                              int len,
+                                              int transform_idx)
+{
+  int idx               = 0;
+  uint8_t const* prefix = brotli_transform_prefix(transform_idx);
+  uint8_t type          = brotli_transform_type(transform_idx);
+  uint8_t const* suffix = brotli_transform_suffix(transform_idx);
+  {
+    int prefix_len = *prefix++;
+    while (prefix_len--) {
+      dst[idx++] = *prefix++;
+    }
+  }
+  {
+    int const t = type;
+    int i       = 0;
+    if (t <= BROTLI_TRANSFORM_OMIT_LAST_9) {
+      len -= t;
+    } else if (t >= BROTLI_TRANSFORM_OMIT_FIRST_1 && t <= BROTLI_TRANSFORM_OMIT_FIRST_9) {
+      int skip = t - (BROTLI_TRANSFORM_OMIT_FIRST_1 - 1);
+      word += skip;
+      len -= skip;
+    }
+    while (i < len) {
+      dst[idx++] = word[i++];
+    }
+    if (t == BROTLI_TRANSFORM_UPPERCASE_FIRST) {
+      ToUpperCase(&dst[idx - len]);
+    } else if (t == BROTLI_TRANSFORM_UPPERCASE_ALL) {
+      uint8_t* uppercase = &dst[idx - len];
+      while (len > 0) {
+        int step = ToUpperCase(uppercase);
+        uppercase += step;
+        len -= step;
+      }
+    }
+  }
+  {
+    int suffix_len = *suffix++;
+    while (suffix_len--) {
+      dst[idx++] = *suffix++;
+    }
+    return idx;
+  }
+}
+
+/// ProcessCommands, actual decoding: 1 warp, most work done by thread0
+static __device__ void ProcessCommands(debrotli_state_s* s, brotli_dictionary_s const* words, int t)
+{
+  int32_t meta_block_len = s->meta_block_len;
+  uint8_t* out           = s->out;
+  int32_t pos            = 0;
+  int p1                 = s->p1;
+  int p2                 = s->p2;
+  uint16_t const* htree_command;
+  uint8_t const *context_map_slice, *dist_context_map_slice;
+  int dist_rb_idx;
+  uint32_t blen_L, blen_I, blen_D;
+  auto* const dict_scratch = reinterpret_cast<uint8_t*>(
+    &s->hs);  // 24+13 bytes (max length of a dictionary word including prefix & suffix)
+  int context_mode;
+
+  if (!t) {
+    context_mode           = PrepareLiteralDecoding(s, context_map_slice);
+    dist_context_map_slice = s->dist_context_map;
+    htree_command          = s->insert_copy_hgroup->htrees[0];
+    dist_rb_idx            = s->dist_rb_idx;
+    blen_L                 = s->block_length[0];
+    blen_I                 = s->block_length[1];
+    blen_D                 = s->block_length[2];
+  }
+  while (pos < meta_block_len) {
+    uint32_t copy_length;
+    int32_t distance_code;
+
+    if (!t) {
+      if (blen_I == 0) {
+        blen_I        = DecodeBlockTypeAndLength(s, 1);
+        htree_command = s->insert_copy_hgroup->htrees[s->block_type_rb[3]];
+        if (s->cur >= s->end) {
+          s->error = 1;
+          pos      = meta_block_len;
+        }
+      }
+      // Read the insert/copy length in the command.
+      {
+        uint32_t cmd_code        = getvlc(s, htree_command);
+        CmdLutElement v          = kCmdLut[cmd_code];
+        uint8_t distance_context = v.context;
+        uint32_t insert_length   = v.insert_len_offset;
+        int32_t max_distance;
+        distance_code = v.distance_code;
+        if (v.insert_len_extra_bits) { insert_length += getbits(s, v.insert_len_extra_bits); }
+        copy_length = v.copy_len_offset;
+        if (v.copy_len_extra_bits) { copy_length += getbits(s, v.copy_len_extra_bits); }
+        --blen_I;
+        if (insert_length != 0) {
+          if (pos + insert_length > meta_block_len) {
+            s->error = -2;
+            pos      = meta_block_len;
+          }
+          // Read the literals in the command.
+          else
+            do {
+              int len;
+              if (blen_L == 0) {
+                blen_L       = DecodeBlockTypeAndLength(s, 0);
+                context_mode = PrepareLiteralDecoding(s, context_map_slice);
+              }
+              len = min(blen_L, insert_length);
+              insert_length -= len;
+              blen_L -= len;
+              if (brotli_need_context_lut(context_mode)) {
+                debrotli_huff_tree_group_s const* literal_hgroup = s->literal_hgroup;
+                do {
+                  int context = brotli_context(p1, p2, context_mode);
+                  p2          = p1;
+                  p1          = getvlc(s, literal_hgroup->htrees[context_map_slice[context]]);
+                  out[pos++]  = p1;
+                } while (--len);
+              } else {
+                uint16_t const* literal_htree = s->literal_hgroup->htrees[context_map_slice[0]];
+                do {
+                  p2         = p1;
+                  p1         = getvlc(s, literal_htree);
+                  out[pos++] = p1;
+                } while (--len);
+              }
+            } while (insert_length);
+          if (pos == meta_block_len) { copy_length = 0; }
+        }
+        // Non-literal symbol
+        if (pos < meta_block_len) {
+          if (distance_code >= 0) {
+            // Implicit distance case.
+            --dist_rb_idx;
+            distance_code    = s->dist_rb[dist_rb_idx & 3];
+            distance_context = 1;
+          } else {
+            uint16_t const* distance_tree;
+            int distval;
+            // Read distance code in the command, unless it was implicitly zero.
+            if (blen_D == 0) {
+              blen_D                 = DecodeBlockTypeAndLength(s, 2);
+              dist_context_map_slice = s->dist_context_map + (s->block_type_rb[5] << 2);
+            }
+            distance_tree = s->distance_hgroup->htrees[dist_context_map_slice[distance_context]];
+            distance_code = getvlc(s, distance_tree);
+            // Convert the distance code to the actual distance by possibly looking up past
+            // distances from the s->ringbuffer.
+            distance_context = 0;
+            if ((distance_code & ~0xF) == 0) {
+              // Take distance from ring buffer
+              if (distance_code == 0) {
+                --dist_rb_idx;
+                distance_code = s->dist_rb[dist_rb_idx & 3];
+                // Compensate double distance-ring-buffer roll for dictionary items.
+                distance_context = 1;
+              } else {
+                int dist = distance_code << 1;
+                // kDistanceShortCodeIndexOffset has 2-bit values from LSB: 3, 2, 1, 0, 3, 3, 3, 3,
+                // 3, 3, 2, 2, 2, 2, 2, 2
+                uint32_t const kDistanceShortCodeIndexOffset = 0xAAAF'FF1B;
+                // kDistanceShortCodeValueOffset has 2-bit values from LSB: -0, 0,-0, 0,-1, 1,-2,
+                // 2,-3, 3,-1, 1,-2, 2,-3, 3
+                uint32_t const kDistanceShortCodeValueOffset = 0xFA5F'A500;
+                int v         = (dist_rb_idx + (int)(kDistanceShortCodeIndexOffset >> dist)) & 0x3;
+                distance_code = s->dist_rb[v];
+                v             = (int)(kDistanceShortCodeValueOffset >> dist) & 0x3;
+                if ((dist & 0x3) != 0) {
+                  distance_code += v;
+                } else {
+                  distance_code -= v;
+                  if (distance_code <= 0) {
+                    // A huge distance will cause a failure later on. This is a little faster than
+                    // failing here.
+                    distance_code = 0x7FFF'FFFF;
+                  }
+                }
+              }
+            } else {
+              distval = distance_code - (int)s->num_direct_distance_codes;
+              if (distval >= 0) {
+                uint32_t nbits;
+                int postfix;
+                int offset;
+                if (s->distance_postfix_bits == 0) {
+                  nbits  = ((uint32_t)distval >> 1) + 1;
+                  offset = ((2 + (distval & 1)) << nbits) - 4;
+                  distance_code =
+                    (int)s->num_direct_distance_codes + offset + (int)getbits(s, nbits);
+                } else {
+                  // This branch also works well when s->distance_postfix_bits == 0.
+                  uint32_t bits;
+                  postfix = distval & s->distance_postfix_mask;
+                  distval >>= s->distance_postfix_bits;
+                  nbits         = ((uint32_t)distval >> 1) + 1;
+                  bits          = getbits(s, nbits);
+                  offset        = ((2 + (distval & 1)) << nbits) - 4;
+                  distance_code = (int)s->num_direct_distance_codes +
+                                  ((offset + (int)bits) << s->distance_postfix_bits) + postfix;
+                }
+              }
+              distance_code = distance_code - brotli_num_distance_short_codes + 1;
+            }
+            --blen_D;
+          }
+          max_distance = s->max_backward_distance;
+          if (max_distance > (out + pos - s->outbase)) {
+            max_distance = (int32_t)(out + pos - s->outbase);
+          }
+          // Apply copy of LZ77 back-reference, or static dictionary reference if the distance is
+          // larger than the max LZ77 distance
+          if (distance_code > max_distance) {
+            // The maximum allowed distance is brotli_max_allowed_distance = 0x7FFF'FFFC.
+            // With this choice, no signed overflow can occur after decoding
+            // a special distance code (e.g., after adding 3 to the last distance).
+            if (distance_code > brotli_max_allowed_distance ||
+                copy_length < brotli_min_dictionary_word_length ||
+                copy_length > brotli_max_dictionary_word_length) {
+              // printf("distance_code = %d/%d, copy_length = %d\n", distance_code, (int)(out -
+              // s->outbase), copy_length);
+              s->error    = -1;
+              pos         = meta_block_len;
+              copy_length = 0;
+            } else {
+              auto offset            = (int32_t)words->offsets_by_length[copy_length];
+              uint32_t shift         = words->size_bits_by_length[copy_length];
+              uint32_t address       = distance_code - max_distance - 1;
+              int32_t word_idx       = address & ((1 << shift) - 1);
+              uint32_t transform_idx = address >> shift;
+              // Compensate double distance-ring-buffer roll.
+              dist_rb_idx += distance_context;
+              offset += word_idx * copy_length;
+              if (transform_idx == 0) {
+                distance_code = -offset;
+              } else if (transform_idx < kNumTransforms) {
+                copy_length = TransformDictionaryWord(
+                  dict_scratch, &words->data[offset], copy_length, transform_idx);
+                distance_code = 0;
+                if (copy_length == 1) {
+                  // Special case for single byte output
+                  p2          = p1;
+                  p1          = dict_scratch[0];
+                  out[pos++]  = p1;
+                  copy_length = 0;
+                }
+              } else {
+                // printf("transform_idx=%d/%d, distance_code = %d/%d, copy_length = %d\n",
+                // transform_idx, kNumTransforms, distance_code, (int)(out - s->outbase),
+                // copy_length);
+                s->error    = -1;
+                pos         = meta_block_len;
+                copy_length = 0;
+              }
+              if (pos + copy_length > meta_block_len) {
+                s->error    = -1;
+                pos         = meta_block_len;
+                copy_length = 0;
+              }
+            }
+          } else {
+            // Update the recent distances cache.
+            s->dist_rb[dist_rb_idx & 3] = distance_code;
+            ++dist_rb_idx;
+            if (pos + copy_length > meta_block_len) {
+              s->error    = -1;
+              pos         = meta_block_len;
+              copy_length = 0;
+            }
+          }
+        }
+      }
+    }
+    pos         = shuffle(pos);
+    copy_length = shuffle(copy_length);
+    if (copy_length > 0) {
+      uint8_t b;
+      distance_code = shuffle(distance_code);
+      if (distance_code > 0) {
+        // Copy
+        for (uint32_t i = t; i < copy_length; i += 32) {
+          uint8_t const* src =
+            out + pos + ((i >= (uint32_t)distance_code) ? (i % (uint32_t)distance_code) : i) -
+            distance_code;
+          b            = *src;
+          out[pos + i] = b;
+        }
+      } else {
+        // Dictionary
+        uint8_t const* src = (distance_code < 0) ? &words->data[-distance_code] : dict_scratch;
+        if (t < copy_length) {
+          b            = src[t];
+          out[pos + t] = b;
+          if (32 + t < copy_length) {
+            b                 = src[32 + t];
+            out[pos + 32 + t] = b;
+          }
+        }
+      }
+      p1 = shuffle((uint32_t)b, (copy_length - 1) & 0x1f);
+      p2 = shuffle((uint32_t)b, (copy_length - 2) & 0x1f);
+      pos += copy_length;
+    }
+  }
+
+  // Ensure all other threads have observed prior state of p1 & p2 before overwriting
+  __syncwarp();
+
+  if (!t) {
+    s->p1          = (uint8_t)p1;
+    s->p2          = (uint8_t)p2;
+    s->dist_rb_idx = dist_rb_idx;
+  }
+}
+
+/**
+ * @brief Brotli decoding kernel
+ * See https://tools.ietf.org/html/rfc7932
+ *
+ * blockDim = {block_size,1,1}
+ *
+ * @param[in] inputs Source buffer per block
+ * @param[out] outputs Destination buffer per block
+ * @param[out] results Decompressor status per block
+ * @param scratch Intermediate device memory heap space (will be dynamically shared between blocks)
+ * @param scratch_size Size of scratch heap space (smaller sizes may result in serialization between
+ * blocks)
+ */
+__global__ void __launch_bounds__(block_size, 2)
+  gpu_debrotli_kernel(device_span<device_span<uint8_t const> const> inputs,
+                      device_span<device_span<uint8_t> const> outputs,
+                      device_span<compression_result> results,
+                      uint8_t* scratch,
+                      uint32_t scratch_size)
+{
+  __shared__ __align__(16) debrotli_state_s state_g;
+
+  int t                     = threadIdx.x;
+  auto const block_id       = blockIdx.x;
+  debrotli_state_s* const s = &state_g;
+
+  if (block_id >= inputs.size()) { return; }
+  // Thread0: initializes shared state and decode stream header
+  if (!t) {
+    auto const src      = inputs[block_id].data();
+    auto const src_size = inputs[block_id].size();
+    if (src && src_size >= 8) {
+      s->error           = 0;
+      s->out             = outputs[block_id].data();
+      s->outbase         = s->out;
+      s->bytes_left      = outputs[block_id].size();
+      s->mtf_upper_bound = 63;
+      s->dist_rb[0]      = 16;
+      s->dist_rb[1]      = 15;
+      s->dist_rb[2]      = 11;
+      s->dist_rb[3]      = 4;
+      s->dist_rb_idx     = 0;
+      s->p1 = s->p2 = 0;
+      initbits(s, src, src_size);
+      DecodeStreamHeader(s);
+    } else {
+      s->error = 1;
+      s->out = s->outbase = nullptr;
+    }
+  }
+  __syncthreads();
+  if (!s->error) {
+    // Main loop: decode meta-blocks
+    do {
+      __syncthreads();
+      if (!t) {
+        // Thread0: Decode meta-block header
+        DecodeMetaBlockHeader(s);
+        if (!s->error && s->meta_block_len > s->bytes_left) { s->error = 2; }
+      }
+      __syncthreads();
+      if (!s->error && s->meta_block_len != 0) {
+        if (s->is_uncompressed) {
+          // Uncompressed block
+          uint8_t const* src = s->cur + ((s->bitpos + 7) >> 3);
+          uint8_t* dst       = s->out;
+          if (!t) {
+            if (getbits_bytealign(s) != 0) {
+              s->error = -1;
+            } else if (src + s->meta_block_len > s->end) {
+              s->error = 1;
+            } else {
+              initbits(s, s->base, s->end - s->base, src - s->base);
+            }
+          }
+          __syncthreads();
+          if (!s->error) {
+            // Simple block-wide memcpy
+            for (int32_t i = t; i < s->meta_block_len; i += block_size) {
+              dst[i] = src[i];
+            }
+          }
+        } else {
+          // Compressed block
+          if (!t) {
+            // Thread0: Reset local heap, decode huffman tables
+            s->heap_used  = 0;
+            s->heap_limit = (uint16_t)(sizeof(s->heap) / sizeof(s->heap[0]));
+            s->fb_base    = nullptr;
+            s->fb_size    = 0;
+            DecodeHuffmanTables(s);
+            if (!s->error) { DecodeHuffmanTreeGroups(s, scratch, scratch_size); }
+          }
+          __syncthreads();
+          if (!s->error) {
+            // Warp0: Decode compressed block, warps 1..7 are all idle (!)
+            if (t < 32)
+              ProcessCommands(s, reinterpret_cast<brotli_dictionary_s*>(scratch + scratch_size), t);
+            __syncthreads();
+          }
+          // Free any allocated memory
+          if (s->fb_base) {
+            if (!t) { ext_heap_free(s->fb_base, s->fb_size, scratch, scratch_size); }
+            __syncthreads();
+          }
+        }
+        // Update output byte count and position
+        if (!t) {
+          s->bytes_left -= s->meta_block_len;
+          s->out += s->meta_block_len;
+        }
+      }
+      __syncthreads();
+    } while (!s->error && !s->is_last && s->bytes_left != 0);
+  }
+  __syncthreads();
+  // Output decompression status
+  if (!t) {
+    results[block_id].bytes_written = s->out - s->outbase;
+    results[block_id].status =
+      (s->error == 0) ? compression_status::SUCCESS : compression_status::FAILURE;
+    // Return ext heap used by last block (statistics)
+    results[block_id].reserved = s->fb_size;
+  }
+}
+
+/**
+ * @brief Computes the size of temporary memory for Brotli decompression
+ *
+ * In most case, a brotli metablock will require in the order of ~10KB
+ * to ~40KB of scratch space for various lookup tables (mainly context maps
+ * and Huffman lookup tables), as well as temporary scratch space to decode
+ * the header. However, because the syntax allows for a huge number of unique
+ * tables, the theoretical worst case is quite large at ~1.3MB per threadblock,
+ * which would scale with gpu occupancy.
+ *
+ * This is solved by a custom memory allocator that first allocates from a local
+ * heap in shared mem (with the end of the heap being used as a stack for
+ * intermediate small allocations). Once this is exhausted, the 'external'
+ * heap is used, allocating from a single scratch surface shared between all
+ * the threadblocks, such that allocation can't fail, but may cause serialization
+ * between threadblocks should more than one threadblock ever allocate the worst
+ * case size.
+ *
+ * @param[in] max_num_inputs The maximum number of compressed input chunks
+ *
+ * @return The size in bytes of required temporary memory
+ */
+size_t __host__ get_gpu_debrotli_scratch_size(int max_num_inputs)
+{
+  int sm_count = 0;
+  int dev      = 0;
+  uint32_t max_fb_size, min_fb_size, fb_size;
+  CUDF_CUDA_TRY(cudaGetDevice(&dev));
+  if (cudaSuccess == cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev)) {
+    // printf("%d SMs on device %d\n", sm_count, dev);
+    max_num_inputs =
+      min(max_num_inputs, sm_count * 3);  // no more than 3 blocks/sm at most due to 32KB smem use
+    if (max_num_inputs <= 0) {
+      max_num_inputs = sm_count * 2;  // Target 2 blocks/SM by default for scratch mem computation
+    }
+  }
+  max_num_inputs = min(max(max_num_inputs, 1), 512);
+  // Max fb size per block occurs if all huffman tables for all 3 group types fail local_alloc()
+  // with num_htrees=256 (See HuffmanTreeGroupAlloc)
+  max_fb_size = 256 * (630 + 1080 + 920) * 2;  // 1.3MB
+  // Min avg fb size needed per block (typical)
+  min_fb_size = 10 * 1024;  // TODO: Gather some statistics for typical meta-block size
+  // Allocate at least two worst-case metablocks or 1 metablock plus typical size for every other
+  // block
+  fb_size = max(max_fb_size * min(max_num_inputs, 2), max_fb_size + max_num_inputs * min_fb_size);
+  // Add some room for alignment
+  return fb_size + 16 + sizeof(brotli_dictionary_s);
+}
+
+#define DUMP_FB_HEAP 0
+#if DUMP_FB_HEAP
+#include <stdio.h>
+#endif
+
+void gpu_debrotli(device_span<device_span<uint8_t const> const> inputs,
+                  device_span<device_span<uint8_t> const> outputs,
+                  device_span<compression_result> results,
+                  void* scratch,
+                  size_t scratch_size,
+                  rmm::cuda_stream_view stream)
+{
+  auto const count = inputs.size();
+  uint32_t fb_heap_size;
+  auto* scratch_u8 = static_cast<uint8_t*>(scratch);
+  dim3 dim_block(block_size, 1);
+  dim3 dim_grid(count, 1);  // TODO: Check max grid dimensions vs max expected count
+
+  CUDF_EXPECTS(scratch_size >= sizeof(brotli_dictionary_s),
+               "Insufficient scratch space for debrotli");
+  scratch_size = min(scratch_size, static_cast<size_t>(0xffff'ffffu));
+  fb_heap_size = (uint32_t)((scratch_size - sizeof(brotli_dictionary_s)) & ~0xf);
+
+  CUDF_CUDA_TRY(cudaMemsetAsync(scratch_u8, 0, 2 * sizeof(uint32_t), stream.value()));
+  // NOTE: The 128KB dictionary copy can have a relatively large overhead since source isn't
+  // page-locked
+  CUDF_CUDA_TRY(cudaMemcpyAsync(scratch_u8 + fb_heap_size,
+                                get_brotli_dictionary(),
+                                sizeof(brotli_dictionary_s),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  gpu_debrotli_kernel<<<dim_grid, dim_block, 0, stream.value()>>>(
+    inputs, outputs, results, scratch_u8, fb_heap_size);
+#if DUMP_FB_HEAP
+  uint32_t dump[2];
+  uint32_t cur = 0;
+  printf("heap dump (%d bytes)\n", fb_heap_size);
+  while (cur < fb_heap_size && !(cur & 3)) {
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      &dump[0], scratch_u8 + cur, 2 * sizeof(uint32_t), cudaMemcpyDefault, stream.value()));
+    stream.synchronize();
+    printf("@%d: next = %d, size = %d\n", cur, dump[0], dump[1]);
+    cur = (dump[0] > cur) ? dump[0] : 0xffff'ffffu;
+  }
+#endif
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/gpuinflate.cu b/cpp/src/io/comp/gpuinflate.cu
new file mode 100644
index 0000000..8993815
--- /dev/null
+++ b/cpp/src/io/comp/gpuinflate.cu
@@ -0,0 +1,1230 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/** @file gpuinflate.cu
+
+  Derived from zlib's contrib/puff.c, original copyright notice below
+
+*/
+
+/*
+Copyright (C) 2002-2013 Mark Adler, all rights reserved
+version 2.3, 21 Jan 2013
+
+This software is provided 'as-is', without any express or implied
+warranty.  In no event will the author be held liable for any damages
+arising from the use of this software.
+
+Permission is granted to anyone to use this software for any purpose,
+including commercial applications, and to alter it and redistribute it
+freely, subject to the following restrictions:
+
+1. The origin of this software must not be misrepresented; you must not
+claim that you wrote the original software. If you use this software
+in a product, an acknowledgment in the product documentation would be
+appreciated but is not required.
+2. Altered source versions must be plainly marked as such, and must not be
+misrepresented as being the original software.
+3. This notice may not be removed or altered from any source distribution.
+
+Mark Adler    madler@alumni.caltech.edu
+*/
+
+#include "gpuinflate.hpp"
+#include "io_uncomp.hpp"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+
+constexpr int max_bits    = 15;   // maximum bits in a code
+constexpr int max_l_codes = 286;  // maximum number of literal/length codes
+constexpr int max_d_codes = 30;   // maximum number of distance codes
+constexpr int fix_l_codes = 288;  // number of fixed literal/length codes
+
+constexpr int log2_len_lut  = 10;
+constexpr int log2_dist_lut = 8;
+
+/**
+ * @brief Intermediate arrays for building huffman tables
+ */
+struct scratch_arr {
+  int16_t lengths[max_l_codes + max_d_codes];  ///< descriptor code lengths
+  int16_t offs[max_bits + 1];                  ///< offset in symbol table for each length (scratch)
+};
+
+/**
+ * @brief Huffman LUTs for length and distance codes
+ */
+struct lut_arr {
+  int32_t lenlut[1 << log2_len_lut];    ///< LUT for length decoding
+  int32_t distlut[1 << log2_dist_lut];  ///< LUT for fast distance decoding
+};
+
+/// 4 batches of 32 symbols
+constexpr int log2_batch_count = 2;  // 1..5
+constexpr int log2_batch_size  = 5;
+constexpr int batch_count      = (1 << log2_batch_count);
+constexpr int batch_size       = (1 << log2_batch_size);
+
+/**
+ * @brief Inter-warp communication queue
+ */
+struct xwarp_s {
+  int32_t batch_len[batch_count];  //< Length of each batch - <0:end, 0:not ready, >0:symbol count
+  union {
+    uint32_t symqueue[batch_count * batch_size];
+    uint8_t symqueue8[batch_count * batch_size * 4];
+  } u;
+};
+
+#define ENABLE_PREFETCH 1
+
+#if ENABLE_PREFETCH
+constexpr int log2_prefetch_size = 9;  // Must be at least LOG2_BATCH_SIZE+3
+constexpr int prefetch_size      = (1 << log2_prefetch_size);
+
+/// @brief Prefetcher state
+struct prefetch_queue_s {
+  uint8_t const* cur_p;  ///< Prefetch location
+  int run;               ///< prefetcher will exit when run=0
+  uint8_t pref_data[prefetch_size];
+};
+
+template <typename T>
+inline __device__ volatile uint32_t* prefetch_addr32(volatile prefetch_queue_s& q, T* ptr)
+{
+  return reinterpret_cast<volatile uint32_t*>(&q.pref_data[(prefetch_size - 4) & (size_t)(ptr)]);
+}
+
+#endif  // ENABLE_PREFETCH
+
+/**
+ * @brief Inflate decompressor state
+ */
+struct inflate_state_s {
+  // output state
+  uint8_t* out;      ///< output buffer
+  uint8_t* outbase;  ///< start of output buffer
+  uint8_t* outend;   ///< end of output buffer
+  // Input state
+  uint8_t const* cur;  ///< input buffer
+  uint8_t const* end;  ///< end of input buffer
+
+  uint2 bitbuf;     ///< bit buffer (64-bit)
+  uint32_t bitpos;  ///< position in bit buffer
+
+  int32_t err;              ///< Error status
+  int btype;                ///< current block type
+  int blast;                ///< last block
+  uint32_t stored_blk_len;  ///< length of stored (uncompressed) block
+
+  uint16_t first_slow_len;  ///< first code not in fast LUT
+  uint16_t index_slow_len;
+  uint16_t first_slow_dist;
+  uint16_t index_slow_dist;
+
+  volatile xwarp_s x;
+#if ENABLE_PREFETCH
+  volatile prefetch_queue_s pref;
+#endif
+
+  int16_t lencnt[max_bits + 1];
+  int16_t lensym[fix_l_codes];  // Assumes fix_l_codes >= max_l_codes
+  int16_t distcnt[max_bits + 1];
+  int16_t distsym[max_d_codes];
+
+  union {
+    scratch_arr scratch;
+    lut_arr lut;
+  } u;
+};
+
+inline __device__ unsigned int bfe(unsigned int source,
+                                   unsigned int bit_start,
+                                   unsigned int num_bits)
+{
+  unsigned int bits;
+  asm("bfe.u32 %0, %1, %2, %3;" : "=r"(bits) : "r"(source), "r"(bit_start), "r"(num_bits));
+  return bits;
+};
+
+inline __device__ uint32_t showbits(inflate_state_s* s, uint32_t n)
+{
+  uint32_t next32 = __funnelshift_rc(s->bitbuf.x, s->bitbuf.y, s->bitpos);
+  return (next32 & ((1 << n) - 1));
+}
+
+inline __device__ uint32_t nextbits32(inflate_state_s* s)
+{
+  return __funnelshift_rc(s->bitbuf.x, s->bitbuf.y, s->bitpos);
+}
+
+inline __device__ void skipbits(inflate_state_s* s, uint32_t n)
+{
+  uint32_t bitpos = s->bitpos + n;
+  if (bitpos >= 32) {
+    auto cur    = s->cur + 8;
+    s->bitbuf.x = s->bitbuf.y;
+    s->bitbuf.y = (cur < s->end) ? *reinterpret_cast<uint32_t const*>(cur) : 0;
+    s->cur      = cur - 4;
+    bitpos &= 0x1f;
+  }
+  s->bitpos = bitpos;
+}
+
+// TODO: If we require 4-byte alignment of input bitstream & length (padded), reading bits would
+// become quite a bit faster
+__device__ uint32_t getbits(inflate_state_s* s, uint32_t n)
+{
+  uint32_t v = showbits(s, n);
+  skipbits(s, n);
+  return v;
+}
+
+/**
+ * @brief Decode a code from the stream s using huffman table {symbols,counts}.
+ * Return the symbol or a negative value if there is an error.
+ * If all of the lengths are zero, i.e. an empty code, or if the code is
+ * incomplete and an invalid code is received, then -10 is returned after
+ * reading max_bits bits.
+ *
+ * Format notes:
+ *
+ * - The codes as stored in the compressed data are bit-reversed relative to
+ *   a simple integer ordering of codes of the same lengths.  Hence below the
+ *   bits are pulled from the compressed data one at a time and used to
+ *   build the code value reversed from what is in the stream in order to
+ *   permit simple integer comparisons for decoding.  A table-based decoding
+ *   scheme (as used in zlib) does not need to do this reversal.
+ *
+ * - The first code for the shortest length is all zeros.  Subsequent codes of
+ *   the same length are simply integer increments of the previous code.  When
+ *   moving up a length, a zero bit is appended to the code.  For a complete
+ *   code, the last code of the longest length will be all ones.
+ *
+ * - Incomplete codes are handled by this decoder, since they are permitted
+ *   in the deflate format.  See the format notes for fixed() and dynamic().
+ */
+__device__ int decode(inflate_state_s* s, int16_t const* counts, int16_t const* symbols)
+{
+  unsigned int len;    // current number of bits in code
+  unsigned int code;   // len bits being decoded
+  unsigned int first;  // first code of length len
+  unsigned int count;  // number of codes of length len
+  uint32_t next32r = __brev(nextbits32(s));
+
+  first = 0;
+  for (len = 1; len <= max_bits; len++) {
+    code  = (next32r >> (32 - len)) - first;
+    count = counts[len];
+    if (code < count)  // if length len, return symbol
+    {
+      skipbits(s, len);
+      return symbols[code];
+    }
+    symbols += count;  // else update for next length
+    first += count;
+    first <<= 1;
+  }
+  return -10;  // ran out of codes
+}
+
+/**
+ * @brief Given the list of code lengths length[0..n-1] representing a canonical
+ * Huffman code for n symbols, construct the tables required to decode those
+ * codes.  Those tables are the number of codes of each length, and the symbols
+ * sorted by length, retaining their original order within each length.  The
+ * return value is zero for a complete code set, negative for an over-
+ * subscribed code set, and positive for an incomplete code set.  The tables
+ * can be used if the return value is zero or positive, but they cannot be used
+ * if the return value is negative.  If the return value is zero, it is not
+ * possible for decode() using that table to return an error--any stream of
+ * enough bits will resolve to a symbol.  If the return value is positive, then
+ * it is possible for decode() using that table to return an error for received
+ * codes past the end of the incomplete lengths.
+ *
+ * Not used by decode(), but used for error checking, count[0] is the number
+ * of the n symbols not in the code.  So n - count[0] is the number of
+ * codes.  This is useful for checking for incomplete codes that have more than
+ * one symbol, which is an error in a dynamic block.
+ *
+ * Assumption: for all i in 0..n-1, 0 <= length[i] <= max_bits
+ * This is assured by the construction of the length arrays in dynamic() and
+ * fixed() and is not verified by construct().
+ *
+ * Format notes:
+ *
+ * - Permitted and expected examples of incomplete codes are one of the fixed
+ *   codes and any code with a single symbol which in deflate is coded as one
+ *   bit instead of zero bits.  See the format notes for fixed() and dynamic().
+ *
+ * - Within a given code length, the symbols are kept in ascending order for
+ *   the code bits definition.
+ */
+__device__ int construct(
+  inflate_state_s* s, int16_t* counts, int16_t* symbols, int16_t const* length, int n)
+{
+  int symbol;  // current symbol when stepping through length[]
+  int len;     // current length when stepping through counts[]
+  int left;    // number of possible codes left of current length
+  int16_t* offs = s->u.scratch.offs;
+
+  // count number of codes of each length
+  for (len = 0; len <= max_bits; len++)
+    counts[len] = 0;
+  for (symbol = 0; symbol < n; symbol++)
+    (counts[length[symbol]])++;  // assumes lengths are within bounds
+  if (counts[0] == n)            // no codes!
+    return 0;                    // complete, but decode() will fail
+
+  // check for an over-subscribed or incomplete set of lengths
+  left = 1;  // one possible code of zero length
+  for (len = 1; len <= max_bits; len++) {
+    left <<= 1;                 // one more bit, double codes left
+    left -= counts[len];        // deduct count from possible codes
+    if (left < 0) return left;  // over-subscribed--return negative
+  }                             // left > 0 means incomplete
+
+  // generate offsets into symbol table for each length for sorting
+  offs[1] = 0;
+  for (len = 1; len < max_bits; len++)
+    offs[len + 1] = offs[len] + counts[len];
+
+  // put symbols in table sorted by length, by symbol order within each length
+  for (symbol = 0; symbol < n; symbol++)
+    if (length[symbol] != 0) symbols[offs[length[symbol]]++] = symbol;
+
+  // return zero for complete set, positive for incomplete set
+  return left;
+}
+
+/// permutation of code length codes
+static const __device__ __constant__ uint8_t g_code_order[19 + 1] = {
+  16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15, 0xff};
+
+/// Dynamic block (custom huffman tables)
+__device__ int init_dynamic(inflate_state_s* s)
+{
+  int nlen, ndist, ncode; /* number of lengths in descriptor */
+  int index;              /* index of lengths[] */
+  int err;                /* construct() return value */
+  int16_t* lengths = s->u.scratch.lengths;
+
+  // get number of lengths in each table, check lengths
+  nlen  = getbits(s, 5) + 257;
+  ndist = getbits(s, 5) + 1;
+  ncode = getbits(s, 4) + 4;
+  if (nlen > max_l_codes || ndist > max_d_codes) {
+    return -3;  // bad counts
+  }
+  // read code length code lengths (really), missing lengths are zero
+  for (index = 0; index < ncode; index++)
+    lengths[g_code_order[index]] = getbits(s, 3);
+  for (; index < 19; index++)
+    lengths[g_code_order[index]] = 0;
+
+  // build huffman table for code lengths codes (use lencode temporarily)
+  err = construct(s, s->lencnt, s->lensym, lengths, 19);
+  if (err != 0)  // require complete code set here
+    return -4;
+
+  // read length/literal and distance code length tables
+  index = 0;
+  while (index < nlen + ndist) {
+    int symbol = decode(s, s->lencnt, s->lensym);
+    if (symbol < 0) return symbol;  // invalid symbol
+    if (symbol < 16)                // length in 0..15
+      lengths[index++] = symbol;
+    else {                            // repeat instruction
+      int len = 0;                    // last length to repeat, assume repeating zeros
+      if (symbol == 16) {             // repeat last length 3..6 times
+        if (index == 0) return -5;    // no last length!
+        len    = lengths[index - 1];  // last length
+        symbol = 3 + getbits(s, 2);
+      } else if (symbol == 17)  // repeat zero 3..10 times
+        symbol = 3 + getbits(s, 3);
+      else  // == 18, repeat zero 11..138 times
+        symbol = 11 + getbits(s, 7);
+      if (index + symbol > nlen + ndist) return -6;  // too many lengths!
+      while (symbol--)                               // repeat last or zero symbol times
+        lengths[index++] = len;
+    }
+  }
+
+  // check for end-of-block code -- there better be one!
+  if (lengths[256] == 0) return -9;
+
+  // build huffman table for literal/length codes
+  err = construct(s, s->lencnt, s->lensym, lengths, nlen);
+  if (err && (err < 0 || nlen != s->lencnt[0] + s->lencnt[1]))
+    return -7;  // incomplete code ok only for single length 1 code
+
+  // build huffman table for distance codes
+  err = construct(s, s->distcnt, s->distsym, &lengths[nlen], ndist);
+  if (err && (err < 0 || ndist != s->distcnt[0] + s->distcnt[1]))
+    return -8;  // incomplete code ok only for single length 1 code
+
+  return 0;
+}
+
+/**
+ * @brief Initializes a fixed codes block.
+ *
+ * Format notes:
+ *
+ * - This block type can be useful for compressing small amounts of data for
+ *   which the size of the code descriptions in a dynamic block exceeds the
+ *   benefit of custom codes for that block.  For fixed codes, no bits are
+ *   spent on code descriptions.  Instead the code lengths for literal/length
+ *   codes and distance codes are fixed.  The specific lengths for each symbol
+ *   can be seen in the "for" loops below.
+ *
+ * - The literal/length code is complete, but has two symbols that are invalid
+ *   and should result in an error if received.  This cannot be implemented
+ *   simply as an incomplete code since those two symbols are in the "middle"
+ *   of the code.  They are eight bits long and the longest literal/length\
+ *   code is nine bits.  Therefore the code must be constructed with those
+ *   symbols, and the invalid symbols must be detected after decoding.
+ *
+ * - The fixed distance codes also have two invalid symbols that should result
+ *   in an error if received.  Since all of the distance codes are the same
+ *   length, this can be implemented as an incomplete code.  Then the invalid
+ *   codes are detected while decoding.
+ */
+__device__ int init_fixed(inflate_state_s* s)
+{
+  int16_t* lengths = s->u.scratch.lengths;
+  int symbol;
+
+  // literal/length table
+  for (symbol = 0; symbol < 144; symbol++)
+    lengths[symbol] = 8;
+  for (; symbol < 256; symbol++)
+    lengths[symbol] = 9;
+  for (; symbol < 280; symbol++)
+    lengths[symbol] = 7;
+  for (; symbol < fix_l_codes; symbol++)
+    lengths[symbol] = 8;
+  construct(s, s->lencnt, s->lensym, lengths, fix_l_codes);
+
+  // distance table
+  for (symbol = 0; symbol < max_d_codes; symbol++)
+    lengths[symbol] = 5;
+
+  // build huffman table for distance codes
+  construct(s, s->distcnt, s->distsym, lengths, max_d_codes);
+
+  return 0;
+}
+
+/**
+ * @brief Decode literal/length and distance codes until an end-of-block code.
+ *
+ * Format notes:
+ *
+ * - Compressed data that is after the block type if fixed or after the code
+ *   description if dynamic is a combination of literals and length/distance
+ *   pairs terminated by and end-of-block code.  Literals are simply Huffman
+ *   coded bytes.  A length/distance pair is a coded length followed by a
+ *   coded distance to represent a string that occurs earlier in the
+ *   uncompressed data that occurs again at the current location.
+ *
+ * - Literals, lengths, and the end-of-block code are combined into a single
+ *   code of up to 286 symbols.  They are 256 literals (0..255), 29 length
+ *   symbols (257..285), and the end-of-block symbol (256).
+ *
+ * - There are 256 possible lengths (3..258), and so 29 symbols are not enough
+ *   to represent all of those.  Lengths 3..10 and 258 are in fact represented
+ *   by just a length symbol.  Lengths 11..257 are represented as a symbol and
+ *   some number of extra bits that are added as an integer to the base length
+ *   of the length symbol.  The number of extra bits is determined by the base
+ *   length symbol.  These are in the static arrays below, lens[] for the base
+ *   lengths and lext[] for the corresponding number of extra bits.
+ *
+ * - The reason that 258 gets its own symbol is that the longest length is used
+ *   often in highly redundant files.  Note that 258 can also be coded as the
+ *   base value 227 plus the maximum extra value of 31.  While a good deflate
+ *   should never do this, it is not an error, and should be decoded properly.
+ *
+ * - If a length is decoded, including its extra bits if any, then it is
+ *   followed a distance code.  There are up to 30 distance symbols.  Again
+ *   there are many more possible distances (1..32768), so extra bits are added
+ *   to a base value represented by the symbol.  The distances 1..4 get their
+ *   own symbol, but the rest require extra bits.  The base distances and
+ *   corresponding number of extra bits are below in the static arrays dist[]
+ *   and dext[].
+ *
+ * - Literal bytes are simply written to the output.  A length/distance pair is
+ *   an instruction to copy previously uncompressed bytes to the output.  The
+ *   copy is from distance bytes back in the output stream, copying for length
+ *   bytes.
+ *
+ * - Distances pointing before the beginning of the output data are not
+ *   permitted.
+ *
+ * - Overlapped copies, where the length is greater than the distance, are
+ *   allowed and common.  For example, a distance of one and a length of 258
+ *   simply copies the last byte 258 times.  A distance of four and a length of
+ *   twelve copies the last four bytes three times.  A simple forward copy
+ *   ignoring whether the length is greater than the distance or not implements
+ *   this correctly.  You should not use memcpy() since its behavior is not
+ *   defined for overlapped arrays.  You should not use memmove() or bcopy()
+ *   since though their behavior -is- defined for overlapping arrays, it is
+ *   defined to do the wrong thing in this case.
+ */
+
+/// permutation of code length codes
+static const __device__ __constant__ uint16_t g_lens[29] = {  // Size base for length codes 257..285
+  3,  4,  5,  6,  7,  8,  9,  10, 11,  13,  15,  17,  19,  23, 27,
+  31, 35, 43, 51, 59, 67, 83, 99, 115, 131, 163, 195, 227, 258};
+static const __device__ __constant__ uint16_t
+  g_lext[29] = {  // Extra bits for length codes 257..285
+    0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0};
+
+static const __device__ __constant__ uint16_t
+  g_dists[30] = {  // Offset base for distance codes 0..29
+    1,   2,   3,   4,   5,   7,    9,    13,   17,   25,   33,   49,   65,    97,    129,
+    193, 257, 385, 513, 769, 1025, 1537, 2049, 3073, 4097, 6145, 8193, 12289, 16385, 24577};
+static const __device__ __constant__ uint16_t g_dext[30] = {  // Extra bits for distance codes 0..29
+  0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13};
+
+/// @brief Thread 0 only: decode bitstreams and output symbols into the symbol queue
+__device__ void decode_symbols(inflate_state_s* s)
+{
+  uint32_t bitpos = s->bitpos;
+  uint2 bitbuf    = s->bitbuf;
+  auto cur        = s->cur;
+  auto end        = s->end;
+  int32_t batch   = 0;
+  int32_t sym, batch_len;
+
+  do {
+    volatile uint32_t* b = &s->x.u.symqueue[batch * batch_size];
+    // Wait for the next batch entry to be empty
+#if ENABLE_PREFETCH
+    // Wait for prefetcher to fetch a worst-case of 48 bits per symbol
+    while ((*(volatile int32_t*)&s->pref.cur_p - (int32_t)(size_t)cur < batch_size * 6) ||
+           (s->x.batch_len[batch] != 0)) {}
+#else
+    while (s->x.batch_len[batch] != 0) {}
+#endif
+    batch_len = 0;
+#if ENABLE_PREFETCH
+    if (cur + (bitpos >> 3) >= end) {
+      s->err = 1;
+      break;
+    }
+#endif
+    // Inner loop decoding symbols
+    do {
+      uint32_t next32 = __funnelshift_rc(bitbuf.x, bitbuf.y, bitpos);  // nextbits32(s);
+      uint32_t len;
+      sym = s->u.lut.lenlut[next32 & ((1 << log2_len_lut) - 1)];
+      if ((uint32_t)sym < (uint32_t)(0x100 << 5)) {
+        // We can lookup a second symbol if this was a short literal
+        len = sym & 0x1f;
+        sym >>= 5;
+        b[batch_len++] = sym;
+        next32 >>= len;
+        bitpos += len;
+        sym = s->u.lut.lenlut[next32 & ((1 << log2_len_lut) - 1)];
+      }
+      if (sym > 0)  // short symbol
+      {
+        len = sym & 0x1f;
+        sym = ((sym >> 5) & 0x3ff) + ((next32 >> (sym >> 24)) & ((sym >> 16) & 0x1f));
+      } else {
+        // Slow length path
+        uint32_t next32r       = __brev(next32);
+        int16_t const* symbols = &s->lensym[s->index_slow_len];
+        unsigned int first     = s->first_slow_len;
+        int lext;
+#pragma unroll 1
+        for (len = log2_len_lut + 1; len <= max_bits; len++) {
+          unsigned int code  = (next32r >> (32 - len)) - first;
+          unsigned int count = s->lencnt[len];
+          if (code < count)  // if length len, return symbol
+          {
+            sym = symbols[code];
+            break;
+          }
+          symbols += count;  // else update for next length
+          first += count;
+          first <<= 1;
+        }
+        if (len > max_bits) {
+          s->err = -10;
+          sym    = 256;
+          len    = 0;
+        }
+        if (sym > 256) {
+          sym -= 257;
+          lext = g_lext[sym];
+          sym  = 256 + g_lens[sym] + bfe(next32, len, lext);
+          len += lext;
+        }
+      }
+      if (sym > 256) {
+        int dist, dext;
+        // skipbits(s, len) inlined - no limit check
+        bitpos += len;
+        if (bitpos >= 32) {
+          bitbuf.x = bitbuf.y;
+#if ENABLE_PREFETCH
+          bitbuf.y = *prefetch_addr32(s->pref, cur + 8);
+          cur += 4;
+#else
+          cur += 8;
+          bitbuf.y = (cur < end) ? *(uint32_t const*)cur : 0;
+          cur -= 4;
+#endif
+          bitpos &= 0x1f;
+        }
+        // get distance
+        next32 = __funnelshift_rc(bitbuf.x, bitbuf.y, bitpos);  // nextbits32(s);
+        dist   = s->u.lut.distlut[next32 & ((1 << log2_dist_lut) - 1)];
+        if (dist > 0) {
+          len  = dist & 0x1f;
+          dext = bfe(dist, 20, 5);
+          dist = bfe(dist, 5, 15);
+          sym |= (dist + bfe(next32, len, dext)) << 16;
+          len += dext;
+        } else {
+          uint32_t next32r       = __brev(next32);
+          int16_t const* symbols = &s->distsym[s->index_slow_dist];
+          unsigned int first     = s->first_slow_dist;
+#pragma unroll 1
+          for (len = log2_dist_lut + 1; len <= max_bits; len++) {
+            unsigned int code  = (next32r >> (32 - len)) - first;
+            unsigned int count = s->distcnt[len];
+            if (code < count)  // if length len, return symbol
+            {
+              dist = symbols[code];
+              break;
+            }
+            symbols += count;  // else update for next length
+            first += count;
+            first <<= 1;
+          }
+          if (len > max_bits) {
+            s->err = -10;
+            sym    = 256;
+            len    = 0;
+          } else {
+            dext = g_dext[dist];
+            sym |= (g_dists[dist] + bfe(next32, len, dext)) << 16;
+            len += dext;
+          }
+        }
+      }
+      // skipbits(s, len) inlined with added error check for reading past the end of the input
+      // buffer
+      bitpos += len;
+      if (bitpos >= 32) {
+        bitbuf.x = bitbuf.y;
+#if ENABLE_PREFETCH
+        bitbuf.y = *prefetch_addr32(s->pref, cur + 8);
+        cur += 4;
+#else
+        cur += 8;
+        if (cur < end) {
+          bitbuf.y = *(uint32_t const*)cur;
+          cur -= 4;
+        } else {
+          bitbuf.y = 0;
+          cur -= 4;
+          if (cur > end) {
+            s->err = 1;
+            sym    = 256;
+          }
+        }
+#endif
+        bitpos &= 0x1f;
+      }
+      if (sym == 256) break;
+      b[batch_len++] = sym;
+    } while (batch_len < batch_size - 1);
+    s->x.batch_len[batch] = batch_len;
+#if ENABLE_PREFETCH
+    ((volatile inflate_state_s*)s)->cur = cur;
+#endif
+    if (batch_len != 0) batch = (batch + 1) & (batch_count - 1);
+  } while (sym != 256);
+
+  while (s->x.batch_len[batch] != 0) {}
+  s->x.batch_len[batch] = -1;
+  s->bitbuf             = bitbuf;
+  s->bitpos             = bitpos;
+#if !ENABLE_PREFETCH
+  s->cur = cur;
+#endif
+}
+
+/**
+ * @brief Build lookup tables for faster decode
+ * LUT format is symbols*16+length
+ */
+__device__ void init_length_lut(inflate_state_s* s, int t)
+{
+  int32_t* lut = s->u.lut.lenlut;
+
+  for (uint32_t bits = t; bits < (1 << log2_len_lut); bits += blockDim.x) {
+    int16_t const* cnt     = s->lencnt;
+    int16_t const* symbols = s->lensym;
+    int sym                = -10 << 5;
+    unsigned int first     = 0;
+    unsigned int rbits     = __brev(bits) >> (32 - log2_len_lut);
+    for (unsigned int len = 1; len <= log2_len_lut; len++) {
+      unsigned int code  = (rbits >> (log2_len_lut - len)) - first;
+      unsigned int count = cnt[len];
+      if (code < count) {
+        sym = symbols[code];
+        if (sym > 256) {
+          int lext = g_lext[sym - 257];
+          sym = (256 + g_lens[sym - 257]) | (((1 << lext) - 1) << (16 - 5)) | (len << (24 - 5));
+          len += lext;
+        }
+        sym = (sym << 5) | len;
+        break;
+      }
+      symbols += count;  // else update for next length
+      first += count;
+      first <<= 1;
+    }
+    lut[bits] = sym;
+  }
+  if (!t) {
+    unsigned int first = 0;
+    unsigned int index = 0;
+    int16_t const* cnt = s->lencnt;
+    for (unsigned int len = 1; len <= log2_len_lut; len++) {
+      unsigned int count = cnt[len];
+      index += count;
+      first += count;
+      first <<= 1;
+    }
+    s->first_slow_len = first;
+    s->index_slow_len = index;
+  }
+}
+
+/**
+ * @brief Build lookup tables for faster decode of distance symbol
+ * LUT format is symbols*16+length
+ */
+__device__ void init_distance_lut(inflate_state_s* s, int t)
+{
+  int32_t* lut = s->u.lut.distlut;
+
+  for (uint32_t bits = t; bits < (1 << log2_dist_lut); bits += blockDim.x) {
+    int16_t const* cnt     = s->distcnt;
+    int16_t const* symbols = s->distsym;
+    int sym                = 0;
+    unsigned int first     = 0;
+    unsigned int rbits     = __brev(bits) >> (32 - log2_dist_lut);
+    for (unsigned int len = 1; len <= log2_dist_lut; len++) {
+      unsigned int code  = (rbits >> (log2_dist_lut - len)) - first;
+      unsigned int count = cnt[len];
+      if (code < count) {
+        int dist = symbols[code];
+        int dext = g_dext[dist];
+        sym      = g_dists[dist] | (dext << 15);
+        sym      = (sym << 5) | len;
+        break;
+      }
+      symbols += count;  // else update for next length
+      first += count;
+      first <<= 1;
+    }
+    lut[bits] = sym;
+  }
+  if (!t) {
+    unsigned int first = 0;
+    unsigned int index = 0;
+    int16_t const* cnt = s->distcnt;
+    for (unsigned int len = 1; len <= log2_dist_lut; len++) {
+      unsigned int count = cnt[len];
+      index += count;
+      first += count;
+      first <<= 1;
+    }
+    s->first_slow_dist = first;
+    s->index_slow_dist = index;
+  }
+}
+
+/// @brief WARP1: process symbols and output uncompressed stream
+__device__ void process_symbols(inflate_state_s* s, int t)
+{
+  uint8_t* out           = s->out;
+  uint8_t const* outend  = s->outend;
+  uint8_t const* outbase = s->outbase;
+  int batch              = 0;
+
+  do {
+    volatile uint32_t* b = &s->x.u.symqueue[batch * batch_size];
+    int batch_len        = 0;
+    if (t == 0) {
+      while ((batch_len = s->x.batch_len[batch]) == 0) {}
+    }
+    batch_len = shuffle(batch_len);
+    if (batch_len < 0) { break; }
+
+    auto const symt     = (t < batch_len) ? b[t] : 256;
+    auto const lit_mask = ballot(symt >= 256);
+    auto pos            = min((__ffs(lit_mask) - 1) & 0xff, 32);
+
+    if (t == 0) { s->x.batch_len[batch] = 0; }
+
+    if (t < pos && out + t < outend) { out[t] = symt; }
+    out += pos;
+    batch_len -= pos;
+    while (batch_len > 0) {
+      int dist, len, symbol;
+
+      // Process a non-literal symbol
+      symbol = shuffle(symt, pos);
+      len    = max((symbol & 0xffff) - 256, 0);  // max should be unnecessary, but just in case
+      dist   = symbol >> 16;
+      for (int i = t; i < len; i += 32) {
+        uint8_t const* src = out + ((i >= dist) ? (i % dist) : i) - dist;
+        uint8_t b          = (src < outbase) ? 0 : *src;
+        if (out + i < outend) { out[i] = b; }
+      }
+      out += len;
+      pos++;
+      batch_len--;
+      // Process subsequent literals, if any
+      if (!((lit_mask >> pos) & 1)) {
+        len    = min((__ffs(lit_mask >> pos) - 1) & 0xff, batch_len);
+        symbol = shuffle(symt, (pos + t) & 0x1f);
+        if (t < len && out + t < outend) { out[t] = symbol; }
+        out += len;
+        pos += len;
+        batch_len -= len;
+      }
+    }
+    batch = (batch + 1) & (batch_count - 1);
+  } while (true);
+
+  if (t == 0) { s->out = out; }
+}
+
+/**
+ * @brief Initializes a stored block.
+ *
+ * Format notes:
+ *
+ * - After the two-bit stored block type (00), the stored block length and
+ *   stored bytes are byte-aligned for fast copying.  Therefore any leftover
+ *   bits in the byte that has the last bit of the type, as many as seven, are
+ *   discarded.  The value of the discarded bits are not defined and should not
+ *   be checked against any expectation.
+ *
+ * - The second inverted copy of the stored block length does not have to be
+ *   checked, but it's probably a good idea to do so anyway.
+ *
+ * - A stored block can have zero length.  This is sometimes used to byte-align
+ *   subsets of the compressed data for random access or partial recovery.
+ */
+__device__ int init_stored(inflate_state_s* s)
+{
+  uint32_t len, nlen;  // length of stored block
+
+  // Byte align
+  if (s->bitpos & 7) { skipbits(s, 8 - (s->bitpos & 7)); }
+  if (s->cur + (s->bitpos >> 3) >= s->end) {
+    return 2;  // Not enough input
+  }
+  // get length and check against its one's complement
+  len  = getbits(s, 16);
+  nlen = getbits(s, 16);
+  if (len != (nlen ^ 0xffff)) {
+    return -2;  // didn't match complement!
+  }
+  if (s->cur + (s->bitpos >> 3) + len > s->end) {
+    return 2;  // Not enough input
+  }
+  s->stored_blk_len = len;
+
+  // done with a valid stored block
+  return 0;
+}
+
+/// Copy bytes from stored block to destination
+__device__ void copy_stored(inflate_state_s* s, int t)
+{
+  auto len              = s->stored_blk_len;
+  auto cur              = s->cur + s->bitpos / 8;
+  auto out              = s->out;
+  auto outend           = s->outend;
+  auto const slow_bytes = min(len, (int)((16 - reinterpret_cast<size_t>(out)) % 16));
+
+  // Slow copy until output is 16B aligned
+  if (slow_bytes) {
+    for (int i = t; i < slow_bytes; i += blockDim.x) {
+      if (out + i < outend) {
+        out[i] = cur[i];  // Input range has already been validated in init_stored()
+      }
+    }
+    cur += slow_bytes;
+    out += slow_bytes;
+    len -= slow_bytes;
+  }
+  auto fast_bytes = len;
+  if (out < outend) { fast_bytes = (int)min((size_t)fast_bytes, (outend - out)); }
+  fast_bytes &= ~0xf;
+  auto bitpos = ((int)((size_t)cur % 4)) * 8;
+  auto cur4   = cur - (bitpos / 8);
+  if (out < outend) {
+    // Fast copy 16 bytes at a time
+    for (int i = t * 16; i < fast_bytes; i += blockDim.x * 16) {
+      uint4 u;
+      u.x = *reinterpret_cast<uint32_t const*>(cur4 + i + 0 * 4);
+      u.y = *reinterpret_cast<uint32_t const*>(cur4 + i + 1 * 4);
+      u.z = *reinterpret_cast<uint32_t const*>(cur4 + i + 2 * 4);
+      u.w = *reinterpret_cast<uint32_t const*>(cur4 + i + 3 * 4);
+      if (bitpos != 0) {
+        uint32_t v = (bitpos != 0) ? *reinterpret_cast<uint32_t const*>(cur4 + i + 4 * 4) : 0;
+        u.x        = __funnelshift_rc(u.x, u.y, bitpos);
+        u.y        = __funnelshift_rc(u.y, u.z, bitpos);
+        u.z        = __funnelshift_rc(u.z, u.w, bitpos);
+        u.w        = __funnelshift_rc(u.w, v, bitpos);
+      }
+      *reinterpret_cast<uint4*>(out + i) = u;
+    }
+  }
+  cur += fast_bytes;
+  out += fast_bytes;
+  len -= fast_bytes;
+  // Slow copy for remaining bytes
+  for (int i = t; i < len; i += blockDim.x) {
+    if (out + i < outend) {
+      out[i] = cur[i];  // Input range has already been validated in init_stored()
+    }
+  }
+  out += len;
+  __syncthreads();
+  if (t == 0) {
+    // Reset bitstream to end of block
+    auto p            = cur + len;
+    auto prefix_bytes = (uint32_t)(((size_t)p) & 3);
+    p -= prefix_bytes;
+    s->cur      = p;
+    s->bitbuf.x = (p < s->end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+    p += 4;
+    s->bitbuf.y = (p < s->end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+    s->bitpos   = prefix_bytes * 8;
+    s->out      = out;
+  }
+}
+
+#if ENABLE_PREFETCH
+__device__ void init_prefetcher(inflate_state_s* s, int t)
+{
+  if (t == 0) {
+    s->pref.cur_p = s->cur;
+    s->pref.run   = 1;
+  }
+}
+
+__device__ void prefetch_warp(volatile inflate_state_s* s, int t)
+{
+  uint8_t const* cur_p = s->pref.cur_p;
+  uint8_t const* end   = s->end;
+  while (shuffle((t == 0) ? s->pref.run : 0)) {
+    auto cur_lo = (int32_t)(size_t)cur_p;
+    int do_pref =
+      shuffle((t == 0) ? (cur_lo - *(volatile int32_t*)&s->cur < prefetch_size - 32 * 4 - 4) : 0);
+    if (do_pref) {
+      uint8_t const* p             = cur_p + 4 * t;
+      *prefetch_addr32(s->pref, p) = (p < end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+      cur_p += 4 * 32;
+      __threadfence_block();
+      __syncwarp();
+      if (!t) {
+        s->pref.cur_p = cur_p;
+        __threadfence_block();
+      }
+    }
+  }
+}
+#endif  // ENABLE_PREFETCH
+
+/**
+ * @brief Parse GZIP header
+ * See https://tools.ietf.org/html/rfc1952
+ */
+__device__ int parse_gzip_header(uint8_t const* src, size_t src_size)
+{
+  int hdr_len = -1;
+
+  if (src_size >= 18) {
+    uint32_t sig = (src[0] << 16) | (src[1] << 8) | src[2];
+    if (sig == 0x1f'8b08)  // 24-bit GZIP inflate signature {0x1f, 0x8b, 0x08}
+    {
+      uint8_t flags = src[3];
+      hdr_len       = 10;
+      if (flags & GZIPHeaderFlag::fextra)  // Extra fields present
+      {
+        int xlen = src[hdr_len] | (src[hdr_len + 1] << 8);
+        hdr_len += xlen;
+        if (hdr_len >= src_size) return -1;
+      }
+      if (flags & GZIPHeaderFlag::fname)  // Original file name present
+      {
+        // Skip zero-terminated string
+        do {
+          if (hdr_len >= src_size) return -1;
+        } while (src[hdr_len++] != 0);
+      }
+      if (flags & GZIPHeaderFlag::fcomment)  // Comment present
+      {
+        // Skip zero-terminated string
+        do {
+          if (hdr_len >= src_size) return -1;
+        } while (src[hdr_len++] != 0);
+      }
+      if (flags & GZIPHeaderFlag::fhcrc)  // Header CRC present
+      {
+        hdr_len += 2;
+      }
+      if (hdr_len + 8 >= src_size) hdr_len = -1;
+    }
+  }
+  return hdr_len;
+}
+
+/**
+ * @brief INFLATE decompression kernel
+ *
+ * blockDim {block_size,1,1}
+ *
+ * @tparam block_size Thread block dimension for this call
+ * @param inputs Source and destination buffer information per block
+ * @param outputs Destination buffer information per block
+ * @param results Decompression status buffer per block
+ * @param parse_hdr If nonzero, indicates that the compressed bitstream includes a GZIP header
+ */
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  inflate_kernel(device_span<device_span<uint8_t const> const> inputs,
+                 device_span<device_span<uint8_t> const> outputs,
+                 device_span<compression_result> results,
+                 gzip_header_included parse_hdr)
+{
+  __shared__ __align__(16) inflate_state_s state_g;
+
+  int t                  = threadIdx.x;
+  int z                  = blockIdx.x;
+  inflate_state_s* state = &state_g;
+
+  if (!t) {
+    auto p        = inputs[z].data();
+    auto src_size = inputs[z].size();
+    // Parse header if needed
+    state->err = 0;
+    if (parse_hdr == gzip_header_included::YES) {
+      int hdr_len = parse_gzip_header(p, src_size);
+      src_size    = (src_size >= 8) ? src_size - 8 : 0;  // ignore footer
+      if (hdr_len >= 0) {
+        p += hdr_len;
+        src_size -= hdr_len;
+      } else {
+        state->err = hdr_len;
+      }
+    }
+    // Initialize shared state
+    state->out              = outputs[z].data();
+    state->outbase          = state->out;
+    state->outend           = state->out + outputs[z].size();
+    state->end              = p + src_size;
+    auto const prefix_bytes = (uint32_t)(((size_t)p) & 3);
+    p -= prefix_bytes;
+    state->cur      = p;
+    state->bitbuf.x = (p < state->end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+    p += 4;
+    state->bitbuf.y = (p < state->end) ? *reinterpret_cast<uint32_t const*>(p) : 0;
+    state->bitpos   = prefix_bytes * 8;
+  }
+  __syncthreads();
+  // Main loop decoding blocks
+  while (!state->err) {
+    if (!t) {
+      // Thread0: read last flag, block type and custom huffman tables if any
+      if (state->cur + (state->bitpos >> 3) >= state->end)
+        state->err = 2;
+      else {
+        state->blast = getbits(state, 1);
+        state->btype = getbits(state, 2);
+        if (state->btype == 0)
+          state->err = init_stored(state);
+        else if (state->btype == 1)
+          state->err = init_fixed(state);
+        else if (state->btype == 2)
+          state->err = init_dynamic(state);
+        else
+          state->err = -1;  // Invalid block
+      }
+    }
+    __syncthreads();
+    if (!state->err && (state->btype == 1 || state->btype == 2)) {
+      // Initializes lookup tables (block wide)
+      init_length_lut(state, t);
+      init_distance_lut(state, t);
+#if ENABLE_PREFETCH
+      // Initialize prefetcher
+      init_prefetcher(state, t);
+#endif
+      if (t < batch_count) { state->x.batch_len[t] = 0; }
+      __syncthreads();
+      // decode data until end-of-block code
+      if (t < 1 * 32) {
+        // WARP0: decode variable-length symbols
+        if (!t) {
+          // Thread0: decode symbols (single threaded)
+          decode_symbols(state);
+#if ENABLE_PREFETCH
+          state->pref.run = 0;
+#endif
+        }
+      } else if (t < 2 * 32) {
+        // WARP1: perform LZ77 using length and distance codes from WARP0
+        process_symbols(state, t & 0x1f);
+      }
+#if ENABLE_PREFETCH
+      else if (t < 3 * 32) {
+        // WARP2: Prefetcher: prefetch data for WARP0
+        prefetch_warp(state, t & 0x1f);
+      }
+#endif
+      // else WARP3: idle
+    } else if (!state->err && state->btype == 0) {
+      // Uncompressed block (block-wide memcpy)
+      copy_stored(state, t);
+    }
+    if (state->blast) break;
+    __syncthreads();
+  }
+  __syncthreads();
+  // Output decompression status and length
+  if (!t) {
+    if (state->err == 0 && state->cur + ((state->bitpos + 7) >> 3) > state->end) {
+      // Read past the end of the input buffer
+      state->err = 2;
+    } else if (state->err == 0 && state->out > state->outend) {
+      // Output buffer too small
+      state->err = 1;
+    }
+    results[z].bytes_written = state->out - state->outbase;
+    results[z].status        = [&]() {
+      switch (state->err) {
+        case 0: return compression_status::SUCCESS;
+        case 1: return compression_status::OUTPUT_OVERFLOW;
+        default: return compression_status::FAILURE;
+      }
+    }();
+    results[z].reserved = (int)(state->end - state->cur);  // Here mainly for debug purposes
+  }
+}
+
+/**
+ * @brief Copy a group of buffers
+ *
+ * blockDim {1024,1,1}
+ *
+ * @param inputs Source and destination information per block
+ */
+__global__ void __launch_bounds__(1024)
+  copy_uncompressed_kernel(device_span<device_span<uint8_t const> const> inputs,
+                           device_span<device_span<uint8_t> const> outputs)
+{
+  __shared__ uint8_t const* volatile src_g;
+  __shared__ uint8_t* volatile dst_g;
+  __shared__ uint32_t volatile copy_len_g;
+
+  uint32_t t = threadIdx.x;
+  uint32_t z = blockIdx.x;
+  uint8_t const* src;
+  uint8_t* dst;
+  uint32_t len, src_align_bytes, src_align_bits, dst_align_bytes;
+
+  if (!t) {
+    src        = inputs[z].data();
+    dst        = outputs[z].data();
+    len        = static_cast<uint32_t>(min(inputs[z].size(), outputs[z].size()));
+    src_g      = src;
+    dst_g      = dst;
+    copy_len_g = len;
+  }
+  __syncthreads();
+  src = src_g;
+  dst = dst_g;
+  len = copy_len_g;
+  // Align output to 32-bit
+  dst_align_bytes = 3 & -reinterpret_cast<intptr_t>(dst);
+  if (dst_align_bytes != 0) {
+    uint32_t align_len = min(dst_align_bytes, len);
+    if (t < align_len) { dst[t] = src[t]; }
+    src += align_len;
+    dst += align_len;
+    len -= align_len;
+  }
+  src_align_bytes = (uint32_t)(3 & reinterpret_cast<uintptr_t>(src));
+  src_align_bits  = src_align_bytes << 3;
+  while (len >= 32) {
+    auto const* src32 = reinterpret_cast<uint32_t const*>(src - src_align_bytes);
+    uint32_t copy_cnt = min(len >> 2, 1024);
+    if (t < copy_cnt) {
+      uint32_t v = src32[t];
+      if (src_align_bits != 0) { v = __funnelshift_r(v, src32[t + 1], src_align_bits); }
+      reinterpret_cast<uint32_t*>(dst)[t] = v;
+    }
+    src += copy_cnt * 4;
+    dst += copy_cnt * 4;
+    len -= copy_cnt * 4;
+  }
+  if (t < len) { dst[t] = src[t]; }
+}
+
+void gpuinflate(device_span<device_span<uint8_t const> const> inputs,
+                device_span<device_span<uint8_t> const> outputs,
+                device_span<compression_result> results,
+                gzip_header_included parse_hdr,
+                rmm::cuda_stream_view stream)
+{
+  constexpr int block_size = 128;  // Threads per block
+  if (inputs.size() > 0) {
+    inflate_kernel<block_size>
+      <<<inputs.size(), block_size, 0, stream.value()>>>(inputs, outputs, results, parse_hdr);
+  }
+}
+
+void gpu_copy_uncompressed_blocks(device_span<device_span<uint8_t const> const> inputs,
+                                  device_span<device_span<uint8_t> const> outputs,
+                                  rmm::cuda_stream_view stream)
+{
+  if (inputs.size() > 0) {
+    copy_uncompressed_kernel<<<inputs.size(), 1024, 0, stream.value()>>>(inputs, outputs);
+  }
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/gpuinflate.hpp b/cpp/src/io/comp/gpuinflate.hpp
new file mode 100644
index 0000000..5908b77
--- /dev/null
+++ b/cpp/src/io/comp/gpuinflate.hpp
@@ -0,0 +1,168 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Status of a compression/decompression operation.
+ */
+enum class compression_status : uint8_t {
+  SUCCESS,          ///< Successful, output is valid
+  FAILURE,          ///< Failed, output is invalid (e.g. input is unsupported in some way)
+  SKIPPED,          ///< Operation skipped (if conversion, uncompressed data can be used)
+  OUTPUT_OVERFLOW,  ///< Output buffer is too small; operation can succeed with larger output
+};
+
+/**
+ * @brief Descriptor of compression/decompression result.
+ */
+struct compression_result {
+  uint64_t bytes_written;
+  compression_status status;
+  uint32_t reserved;
+};
+
+enum class gzip_header_included { NO, YES };
+
+/**
+ * @brief The value used for padding a data buffer such that its size will be multiple of it.
+ *
+ * Padding is necessary for input/output buffers of several compression/decompression kernels
+ * (inflate_kernel and nvcomp snappy). Such kernels operate on aligned data pointers, which require
+ * padding to the buffers so that the pointers can shift along the address space to satisfy their
+ * alignment requirement.
+ *
+ * In the meantime, it is not entirely clear why such padding is needed. We need to further
+ * investigate and implement a better fix rather than just padding the buffer.
+ * See https://github.com/rapidsai/cudf/issues/13605.
+ */
+constexpr std::size_t BUFFER_PADDING_MULTIPLE{8};
+
+/**
+ * @brief Interface for decompressing GZIP-compressed data
+ *
+ * Multiple, independent chunks of compressed data can be decompressed by using
+ * separate input/output/status for each chunk.
+ *
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[out] results List of output status structures
+ * @param[in] parse_hdr Whether or not to parse GZIP header
+ * @param[in] stream CUDA stream to use
+ */
+void gpuinflate(device_span<device_span<uint8_t const> const> inputs,
+                device_span<device_span<uint8_t> const> outputs,
+                device_span<compression_result> results,
+                gzip_header_included parse_hdr,
+                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Interface for copying uncompressed byte blocks
+ *
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[in] stream CUDA stream to use
+ */
+void gpu_copy_uncompressed_blocks(device_span<device_span<uint8_t const> const> inputs,
+                                  device_span<device_span<uint8_t> const> outputs,
+                                  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Interface for decompressing Snappy-compressed data
+ *
+ * Multiple, independent chunks of compressed data can be decompressed by using
+ * separate input/output/status for each chunk.
+ *
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[out] results List of output status structures
+ * @param[in] stream CUDA stream to use
+ */
+void gpu_unsnap(device_span<device_span<uint8_t const> const> inputs,
+                device_span<device_span<uint8_t> const> outputs,
+                device_span<compression_result> results,
+                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Computes the size of temporary memory for Brotli decompression
+ *
+ * @param[in] max_num_inputs The maximum number of compressed input chunks
+ *
+ * @return The size in bytes of required temporary memory
+ */
+size_t get_gpu_debrotli_scratch_size(int max_num_inputs = 0);
+
+/**
+ * @brief Interface for decompressing Brotli-compressed data
+ *
+ * Multiple, independent chunks of compressed data can be decompressed by using
+ * separate input/output/status pairs for each chunk.
+ *
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[out] results List of output status structures
+ * @param[in] scratch Temporary memory for intermediate work
+ * @param[in] scratch_size Size in bytes of the temporary memory
+ * @param[in] stream CUDA stream to use
+ */
+void gpu_debrotli(device_span<device_span<uint8_t const> const> inputs,
+                  device_span<device_span<uint8_t> const> outputs,
+                  device_span<compression_result> results,
+                  void* scratch,
+                  size_t scratch_size,
+                  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Interface for compressing data with Snappy
+ *
+ * Multiple, independent chunks of compressed data can be compressed by using
+ * separate input/output/status for each chunk.
+ *
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[out] results List of output status structures
+ * @param[in] stream CUDA stream to use
+ */
+void gpu_snap(device_span<device_span<uint8_t const> const> inputs,
+              device_span<device_span<uint8_t> const> outputs,
+              device_span<compression_result> results,
+              rmm::cuda_stream_view stream);
+
+/**
+ * @brief Aggregate results of compression into a single statistics object.
+ *
+ * @param inputs List of uncompressed input buffers
+ * @param results List of compression results
+ * @param stream CUDA stream to use
+ * @return writer_compression_statistics
+ */
+[[nodiscard]] writer_compression_statistics collect_compression_statistics(
+  device_span<device_span<uint8_t const> const> inputs,
+  device_span<compression_result const> results,
+  rmm::cuda_stream_view stream);
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/io_uncomp.hpp b/cpp/src/io/comp/io_uncomp.hpp
new file mode 100644
index 0000000..1c9578f
--- /dev/null
+++ b/cpp/src/io/comp/io_uncomp.hpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2018-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+using cudf::host_span;
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Decompresses a system memory buffer.
+ *
+ * @param compression Type of compression of the input data
+ * @param src Compressed host buffer
+ *
+ * @return Vector containing the Decompressed output
+ */
+std::vector<uint8_t> decompress(compression_type compression, host_span<uint8_t const> src);
+
+size_t decompress(compression_type compression,
+                  host_span<uint8_t const> src,
+                  host_span<uint8_t> dst,
+                  rmm::cuda_stream_view stream);
+
+/**
+ * @brief GZIP header flags
+ * See https://tools.ietf.org/html/rfc1952
+ */
+namespace GZIPHeaderFlag {
+constexpr uint8_t ftext    = 0x01;  // ASCII text hint
+constexpr uint8_t fhcrc    = 0x02;  // Header CRC present
+constexpr uint8_t fextra   = 0x04;  // Extra fields present
+constexpr uint8_t fname    = 0x08;  // Original file name present
+constexpr uint8_t fcomment = 0x10;  // Comment present
+};                                  // namespace GZIPHeaderFlag
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/nvcomp_adapter.cpp b/cpp/src/io/comp/nvcomp_adapter.cpp
new file mode 100644
index 0000000..1a2c90e
--- /dev/null
+++ b/cpp/src/io/comp/nvcomp_adapter.cpp
@@ -0,0 +1,655 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "nvcomp_adapter.hpp"
+#include "nvcomp_adapter.cuh"
+
+#include <cudf/utilities/error.hpp>
+#include <io/utilities/config_utils.hpp>
+
+#include <nvcomp/snappy.h>
+
+#include <mutex>
+
+#define NVCOMP_DEFLATE_HEADER <nvcomp/deflate.h>
+#if __has_include(NVCOMP_DEFLATE_HEADER)
+#include NVCOMP_DEFLATE_HEADER
+#endif
+
+#define NVCOMP_ZSTD_HEADER <nvcomp/zstd.h>
+#if __has_include(NVCOMP_ZSTD_HEADER)
+#include NVCOMP_ZSTD_HEADER
+#endif
+
+#define NVCOMP_HAS_ZSTD_DECOMP(MAJOR, MINOR, PATCH) (MAJOR > 2 or (MAJOR == 2 and MINOR >= 3))
+
+#define NVCOMP_HAS_ZSTD_COMP(MAJOR, MINOR, PATCH) (MAJOR > 2 or (MAJOR == 2 and MINOR >= 4))
+
+#define NVCOMP_HAS_DEFLATE(MAJOR, MINOR, PATCH) (MAJOR > 2 or (MAJOR == 2 and MINOR >= 5))
+
+#define NVCOMP_HAS_DECOMP_TEMPSIZE_EX(MAJOR, MINOR, PATCH) \
+  (MAJOR > 2 or (MAJOR == 2 and MINOR > 3) or (MAJOR == 2 and MINOR == 3 and PATCH >= 1))
+
+#define NVCOMP_HAS_COMP_TEMPSIZE_EX(MAJOR, MINOR, PATCH) (MAJOR > 2 or (MAJOR == 2 and MINOR >= 6))
+
+// ZSTD is stable for nvcomp 2.3.2 or newer
+#define NVCOMP_ZSTD_DECOMP_IS_STABLE(MAJOR, MINOR, PATCH) \
+  (MAJOR > 2 or (MAJOR == 2 and MINOR > 3) or (MAJOR == 2 and MINOR == 3 and PATCH >= 2))
+
+// Issue https://github.com/NVIDIA/spark-rapids/issues/6614 impacts nvCOMP 2.4.0 ZSTD decompression
+// on compute 6.x
+#define NVCOMP_ZSTD_IS_DISABLED_ON_PASCAL(MAJOR, MINOR, PATCH) \
+  (MAJOR == 2 and MINOR == 4 and PATCH == 0)
+
+namespace cudf::io::nvcomp {
+
+// Dispatcher for nvcompBatched<format>DecompressGetTempSizeEx
+template <typename... Args>
+std::optional<nvcompStatus_t> batched_decompress_get_temp_size_ex(compression_type compression,
+                                                                  Args&&... args)
+{
+#if NVCOMP_HAS_DECOMP_TEMPSIZE_EX(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+  switch (compression) {
+    case compression_type::SNAPPY:
+      return nvcompBatchedSnappyDecompressGetTempSizeEx(std::forward<Args>(args)...);
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_DECOMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      return nvcompBatchedZstdDecompressGetTempSizeEx(std::forward<Args>(args)...);
+#else
+      return std::nullopt;
+#endif
+    case compression_type::DEFLATE: [[fallthrough]];
+    default: return std::nullopt;
+  }
+#endif
+  return std::nullopt;
+}
+
+// Dispatcher for nvcompBatched<format>DecompressGetTempSize
+template <typename... Args>
+auto batched_decompress_get_temp_size(compression_type compression, Args&&... args)
+{
+  switch (compression) {
+    case compression_type::SNAPPY:
+      return nvcompBatchedSnappyDecompressGetTempSize(std::forward<Args>(args)...);
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_DECOMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      return nvcompBatchedZstdDecompressGetTempSize(std::forward<Args>(args)...);
+#else
+      CUDF_FAIL("Decompression error: " +
+                nvcomp::is_decompression_disabled(nvcomp::compression_type::ZSTD).value());
+#endif
+    case compression_type::DEFLATE:
+#if NVCOMP_HAS_DEFLATE(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      return nvcompBatchedDeflateDecompressGetTempSize(std::forward<Args>(args)...);
+#else
+      CUDF_FAIL("Decompression error: " +
+                nvcomp::is_decompression_disabled(nvcomp::compression_type::DEFLATE).value());
+#endif
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+// Dispatcher for nvcompBatched<format>DecompressAsync
+template <typename... Args>
+auto batched_decompress_async(compression_type compression, Args&&... args)
+{
+  switch (compression) {
+    case compression_type::SNAPPY:
+      return nvcompBatchedSnappyDecompressAsync(std::forward<Args>(args)...);
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_DECOMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      return nvcompBatchedZstdDecompressAsync(std::forward<Args>(args)...);
+#else
+      CUDF_FAIL("Decompression error: " +
+                nvcomp::is_decompression_disabled(nvcomp::compression_type::ZSTD).value());
+#endif
+    case compression_type::DEFLATE:
+#if NVCOMP_HAS_DEFLATE(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      return nvcompBatchedDeflateDecompressAsync(std::forward<Args>(args)...);
+#else
+      CUDF_FAIL("Decompression error: " +
+                nvcomp::is_decompression_disabled(nvcomp::compression_type::DEFLATE).value());
+#endif
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+std::string compression_type_name(compression_type compression)
+{
+  switch (compression) {
+    case compression_type::SNAPPY: return "Snappy";
+    case compression_type::ZSTD: return "Zstandard";
+    case compression_type::DEFLATE: return "Deflate";
+  }
+  return "compression_type(" + std::to_string(static_cast<int>(compression)) + ")";
+}
+
+size_t batched_decompress_temp_size(compression_type compression,
+                                    size_t num_chunks,
+                                    size_t max_uncomp_chunk_size,
+                                    size_t max_total_uncomp_size)
+{
+  size_t temp_size   = 0;
+  auto nvcomp_status = batched_decompress_get_temp_size_ex(
+    compression, num_chunks, max_uncomp_chunk_size, &temp_size, max_total_uncomp_size);
+
+  if (nvcomp_status.value_or(nvcompStatus_t::nvcompErrorInternal) !=
+      nvcompStatus_t::nvcompSuccess) {
+    nvcomp_status =
+      batched_decompress_get_temp_size(compression, num_chunks, max_uncomp_chunk_size, &temp_size);
+  }
+
+  CUDF_EXPECTS(nvcomp_status == nvcompStatus_t::nvcompSuccess,
+               "Unable to get scratch size for decompression");
+
+  return temp_size;
+}
+
+void batched_decompress(compression_type compression,
+                        device_span<device_span<uint8_t const> const> inputs,
+                        device_span<device_span<uint8_t> const> outputs,
+                        device_span<compression_result> results,
+                        size_t max_uncomp_chunk_size,
+                        size_t max_total_uncomp_size,
+                        rmm::cuda_stream_view stream)
+{
+  auto const num_chunks = inputs.size();
+
+  // cuDF inflate inputs converted to nvcomp inputs
+  auto const nvcomp_args = create_batched_nvcomp_args(inputs, outputs, stream);
+  rmm::device_uvector<size_t> actual_uncompressed_data_sizes(num_chunks, stream);
+  rmm::device_uvector<nvcompStatus_t> nvcomp_statuses(num_chunks, stream);
+  // Temporary space required for decompression
+  auto const temp_size = batched_decompress_temp_size(
+    compression, num_chunks, max_uncomp_chunk_size, max_total_uncomp_size);
+  rmm::device_buffer scratch(temp_size, stream);
+  auto const nvcomp_status = batched_decompress_async(compression,
+                                                      nvcomp_args.input_data_ptrs.data(),
+                                                      nvcomp_args.input_data_sizes.data(),
+                                                      nvcomp_args.output_data_sizes.data(),
+                                                      actual_uncompressed_data_sizes.data(),
+                                                      num_chunks,
+                                                      scratch.data(),
+                                                      scratch.size(),
+                                                      nvcomp_args.output_data_ptrs.data(),
+                                                      nvcomp_statuses.data(),
+                                                      stream.value());
+  CUDF_EXPECTS(nvcomp_status == nvcompStatus_t::nvcompSuccess, "unable to perform decompression");
+
+  update_compression_results(nvcomp_statuses, actual_uncompressed_data_sizes, results, stream);
+}
+
+// Wrapper for nvcompBatched<format>CompressGetTempSize
+auto batched_compress_get_temp_size(compression_type compression,
+                                    size_t batch_size,
+                                    size_t max_uncompressed_chunk_bytes)
+{
+  size_t temp_size             = 0;
+  nvcompStatus_t nvcomp_status = nvcompStatus_t::nvcompSuccess;
+  switch (compression) {
+    case compression_type::SNAPPY:
+      nvcomp_status = nvcompBatchedSnappyCompressGetTempSize(
+        batch_size, max_uncompressed_chunk_bytes, nvcompBatchedSnappyDefaultOpts, &temp_size);
+      break;
+    case compression_type::DEFLATE:
+#if NVCOMP_HAS_DEFLATE(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      nvcomp_status = nvcompBatchedDeflateCompressGetTempSize(
+        batch_size, max_uncompressed_chunk_bytes, nvcompBatchedDeflateDefaultOpts, &temp_size);
+      break;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::DEFLATE).value());
+#endif
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_COMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      nvcomp_status = nvcompBatchedZstdCompressGetTempSize(
+        batch_size, max_uncompressed_chunk_bytes, nvcompBatchedZstdDefaultOpts, &temp_size);
+      break;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::ZSTD).value());
+#endif
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+
+  CUDF_EXPECTS(nvcomp_status == nvcompStatus_t::nvcompSuccess,
+               "Unable to get scratch size for compression");
+  return temp_size;
+}
+
+#if NVCOMP_HAS_COMP_TEMPSIZE_EX(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+// Wrapper for nvcompBatched<format>CompressGetTempSizeEx
+auto batched_compress_get_temp_size_ex(compression_type compression,
+                                       size_t batch_size,
+                                       size_t max_uncompressed_chunk_bytes,
+                                       size_t max_total_uncompressed_bytes)
+{
+  size_t temp_size             = 0;
+  nvcompStatus_t nvcomp_status = nvcompStatus_t::nvcompSuccess;
+  switch (compression) {
+    case compression_type::SNAPPY:
+      nvcomp_status = nvcompBatchedSnappyCompressGetTempSizeEx(batch_size,
+                                                               max_uncompressed_chunk_bytes,
+                                                               nvcompBatchedSnappyDefaultOpts,
+                                                               &temp_size,
+                                                               max_total_uncompressed_bytes);
+      break;
+    case compression_type::DEFLATE:
+      nvcomp_status = nvcompBatchedDeflateCompressGetTempSizeEx(batch_size,
+                                                                max_uncompressed_chunk_bytes,
+                                                                nvcompBatchedDeflateDefaultOpts,
+                                                                &temp_size,
+                                                                max_total_uncompressed_bytes);
+      break;
+    case compression_type::ZSTD:
+      nvcomp_status = nvcompBatchedZstdCompressGetTempSizeEx(batch_size,
+                                                             max_uncompressed_chunk_bytes,
+                                                             nvcompBatchedZstdDefaultOpts,
+                                                             &temp_size,
+                                                             max_total_uncompressed_bytes);
+      break;
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+
+  CUDF_EXPECTS(nvcomp_status == nvcompStatus_t::nvcompSuccess,
+               "Unable to get scratch size for compression");
+  return temp_size;
+}
+#endif
+
+size_t batched_compress_temp_size(compression_type compression,
+                                  size_t num_chunks,
+                                  size_t max_uncomp_chunk_size,
+                                  size_t max_total_uncomp_size)
+{
+#if NVCOMP_HAS_COMP_TEMPSIZE_EX(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+  try {
+    return batched_compress_get_temp_size_ex(
+      compression, num_chunks, max_uncomp_chunk_size, max_total_uncomp_size);
+  } catch (...) {
+    // Ignore errors in the expanded version; fall back to the old API in case of failure
+    CUDF_LOG_WARN(
+      "CompressGetTempSizeEx call failed, falling back to CompressGetTempSize; this may increase "
+      "the memory usage");
+  }
+#endif
+
+  return batched_compress_get_temp_size(compression, num_chunks, max_uncomp_chunk_size);
+}
+
+size_t compress_max_output_chunk_size(compression_type compression,
+                                      uint32_t max_uncompressed_chunk_bytes)
+{
+  auto const capped_uncomp_bytes = std::min<size_t>(
+    compress_max_allowed_chunk_size(compression).value_or(max_uncompressed_chunk_bytes),
+    max_uncompressed_chunk_bytes);
+
+  size_t max_comp_chunk_size = 0;
+  nvcompStatus_t status      = nvcompStatus_t::nvcompSuccess;
+  switch (compression) {
+    case compression_type::SNAPPY:
+      status = nvcompBatchedSnappyCompressGetMaxOutputChunkSize(
+        capped_uncomp_bytes, nvcompBatchedSnappyDefaultOpts, &max_comp_chunk_size);
+      break;
+    case compression_type::DEFLATE:
+#if NVCOMP_HAS_DEFLATE(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      status = nvcompBatchedDeflateCompressGetMaxOutputChunkSize(
+        capped_uncomp_bytes, nvcompBatchedDeflateDefaultOpts, &max_comp_chunk_size);
+      break;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::DEFLATE).value());
+#endif
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_COMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      status = nvcompBatchedZstdCompressGetMaxOutputChunkSize(
+        capped_uncomp_bytes, nvcompBatchedZstdDefaultOpts, &max_comp_chunk_size);
+      break;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::ZSTD).value());
+#endif
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+
+  CUDF_EXPECTS(status == nvcompStatus_t::nvcompSuccess,
+               "failed to get max uncompressed chunk size");
+  return max_comp_chunk_size;
+}
+
+// Dispatcher for nvcompBatched<format>CompressAsync
+static void batched_compress_async(compression_type compression,
+                                   void const* const* device_uncompressed_ptrs,
+                                   size_t const* device_uncompressed_bytes,
+                                   size_t max_uncompressed_chunk_bytes,
+                                   size_t batch_size,
+                                   void* device_temp_ptr,
+                                   size_t temp_bytes,
+                                   void* const* device_compressed_ptrs,
+                                   size_t* device_compressed_bytes,
+                                   rmm::cuda_stream_view stream)
+{
+  nvcompStatus_t nvcomp_status = nvcompStatus_t::nvcompSuccess;
+  switch (compression) {
+    case compression_type::SNAPPY:
+      nvcomp_status = nvcompBatchedSnappyCompressAsync(device_uncompressed_ptrs,
+                                                       device_uncompressed_bytes,
+                                                       max_uncompressed_chunk_bytes,
+                                                       batch_size,
+                                                       device_temp_ptr,
+                                                       temp_bytes,
+                                                       device_compressed_ptrs,
+                                                       device_compressed_bytes,
+                                                       nvcompBatchedSnappyDefaultOpts,
+                                                       stream.value());
+      break;
+    case compression_type::DEFLATE:
+#if NVCOMP_HAS_DEFLATE(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      nvcomp_status = nvcompBatchedDeflateCompressAsync(device_uncompressed_ptrs,
+                                                        device_uncompressed_bytes,
+                                                        max_uncompressed_chunk_bytes,
+                                                        batch_size,
+                                                        device_temp_ptr,
+                                                        temp_bytes,
+                                                        device_compressed_ptrs,
+                                                        device_compressed_bytes,
+                                                        nvcompBatchedDeflateDefaultOpts,
+                                                        stream.value());
+      break;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::DEFLATE).value());
+#endif
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_COMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      nvcomp_status = nvcompBatchedZstdCompressAsync(device_uncompressed_ptrs,
+                                                     device_uncompressed_bytes,
+                                                     max_uncompressed_chunk_bytes,
+                                                     batch_size,
+                                                     device_temp_ptr,
+                                                     temp_bytes,
+                                                     device_compressed_ptrs,
+                                                     device_compressed_bytes,
+                                                     nvcompBatchedZstdDefaultOpts,
+                                                     stream.value());
+      break;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::ZSTD).value());
+#endif
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+  CUDF_EXPECTS(nvcomp_status == nvcompStatus_t::nvcompSuccess, "Error in compression");
+}
+
+bool is_aligned(void const* ptr, std::uintptr_t alignment) noexcept
+{
+  return (reinterpret_cast<std::uintptr_t>(ptr) % alignment) == 0;
+}
+
+void batched_compress(compression_type compression,
+                      device_span<device_span<uint8_t const> const> inputs,
+                      device_span<device_span<uint8_t> const> outputs,
+                      device_span<compression_result> results,
+                      rmm::cuda_stream_view stream)
+{
+  auto const num_chunks = inputs.size();
+
+  auto nvcomp_args = create_batched_nvcomp_args(inputs, outputs, stream);
+
+  skip_unsupported_inputs(
+    nvcomp_args.input_data_sizes, results, compress_max_allowed_chunk_size(compression), stream);
+
+  auto const [max_uncomp_chunk_size, total_uncomp_size] =
+    max_chunk_and_total_input_size(nvcomp_args.input_data_sizes, stream);
+
+  auto const temp_size =
+    batched_compress_temp_size(compression, num_chunks, max_uncomp_chunk_size, total_uncomp_size);
+
+  rmm::device_buffer scratch(temp_size, stream);
+  CUDF_EXPECTS(is_aligned(scratch.data(), 8), "Compression failed, misaligned scratch buffer");
+
+  rmm::device_uvector<size_t> actual_compressed_data_sizes(num_chunks, stream);
+
+  batched_compress_async(compression,
+                         nvcomp_args.input_data_ptrs.data(),
+                         nvcomp_args.input_data_sizes.data(),
+                         max_uncomp_chunk_size,
+                         num_chunks,
+                         scratch.data(),
+                         scratch.size(),
+                         nvcomp_args.output_data_ptrs.data(),
+                         actual_compressed_data_sizes.data(),
+                         stream.value());
+
+  update_compression_results(actual_compressed_data_sizes, results, stream);
+}
+
+feature_status_parameters::feature_status_parameters()
+  : lib_major_version{NVCOMP_MAJOR_VERSION},
+    lib_minor_version{NVCOMP_MINOR_VERSION},
+    lib_patch_version{NVCOMP_PATCH_VERSION},
+    are_all_integrations_enabled{detail::nvcomp_integration::is_all_enabled()},
+    are_stable_integrations_enabled{detail::nvcomp_integration::is_stable_enabled()}
+{
+  int device;
+  CUDF_CUDA_TRY(cudaGetDevice(&device));
+  CUDF_CUDA_TRY(
+    cudaDeviceGetAttribute(&compute_capability_major, cudaDevAttrComputeCapabilityMajor, device));
+}
+
+// Represents all parameters required to determine status of a compression/decompression feature
+using feature_status_inputs = std::pair<compression_type, feature_status_parameters>;
+struct hash_feature_status_inputs {
+  size_t operator()(feature_status_inputs const& fsi) const
+  {
+    // Outside of unit tests, the same `feature_status_parameters` value will always be passed
+    // within a run; for simplicity, only use `compression_type` for the hash
+    return std::hash<compression_type>{}(fsi.first);
+  }
+};
+
+// Hash map type that stores feature status for different combinations of input parameters
+using feature_status_memo_map =
+  std::unordered_map<feature_status_inputs, std::optional<std::string>, hash_feature_status_inputs>;
+
+std::optional<std::string> is_compression_disabled_impl(compression_type compression,
+                                                        feature_status_parameters params)
+{
+  switch (compression) {
+    case compression_type::DEFLATE: {
+      if (not NVCOMP_HAS_DEFLATE(
+            params.lib_major_version, params.lib_minor_version, params.lib_patch_version)) {
+        return "nvCOMP 2.5 or newer is required for Deflate compression";
+      }
+      if (not params.are_all_integrations_enabled) {
+        return "DEFLATE compression is experimental, you can enable it through "
+               "`LIBCUDF_NVCOMP_POLICY` environment variable.";
+      }
+      return std::nullopt;
+    }
+    case compression_type::SNAPPY: {
+      if (not params.are_stable_integrations_enabled) {
+        return "Snappy compression has been disabled through the `LIBCUDF_NVCOMP_POLICY` "
+               "environment variable.";
+      }
+      return std::nullopt;
+    }
+    case compression_type::ZSTD: {
+      if (not NVCOMP_HAS_ZSTD_COMP(
+            params.lib_major_version, params.lib_minor_version, params.lib_patch_version)) {
+        return "nvCOMP 2.4 or newer is required for Zstandard compression";
+      }
+      if (not params.are_stable_integrations_enabled) {
+        return "Zstandard compression is experimental, you can enable it through "
+               "`LIBCUDF_NVCOMP_POLICY` environment variable.";
+      }
+      return std::nullopt;
+    }
+    default: return "Unsupported compression type";
+  }
+  return "Unsupported compression type";
+}
+
+std::optional<std::string> is_compression_disabled(compression_type compression,
+                                                   feature_status_parameters params)
+{
+  static feature_status_memo_map comp_status_reason;
+  static std::mutex memo_map_mutex;
+
+  std::unique_lock memo_map_lock{memo_map_mutex};
+  if (auto mem_res_it = comp_status_reason.find(feature_status_inputs{compression, params});
+      mem_res_it != comp_status_reason.end()) {
+    return mem_res_it->second;
+  }
+
+  // The rest of the function will execute only once per run, the memoized result will be returned
+  // in all subsequent calls with the same compression type
+  auto const reason                         = is_compression_disabled_impl(compression, params);
+  comp_status_reason[{compression, params}] = reason;
+  memo_map_lock.unlock();
+
+  if (reason.has_value()) {
+    CUDF_LOG_INFO("nvCOMP is disabled for {} compression; reason: {}",
+                  compression_type_name(compression),
+                  reason.value());
+  } else {
+    CUDF_LOG_INFO("nvCOMP is enabled for {} compression", compression_type_name(compression));
+  }
+
+  return reason;
+}
+
+std::optional<std::string> is_zstd_decomp_disabled(feature_status_parameters const& params)
+{
+  if (not NVCOMP_HAS_ZSTD_DECOMP(
+        params.lib_major_version, params.lib_minor_version, params.lib_patch_version)) {
+    return "nvCOMP 2.3 or newer is required for Zstandard decompression";
+  }
+
+  if (NVCOMP_ZSTD_DECOMP_IS_STABLE(
+        params.lib_major_version, params.lib_minor_version, params.lib_patch_version)) {
+    if (not params.are_stable_integrations_enabled) {
+      return "Zstandard decompression has been disabled through the `LIBCUDF_NVCOMP_POLICY` "
+             "environment variable.";
+    }
+  } else if (not params.are_all_integrations_enabled) {
+    return "Zstandard decompression is experimental, you can enable it through "
+           "`LIBCUDF_NVCOMP_POLICY` environment variable.";
+  }
+
+  if (NVCOMP_ZSTD_IS_DISABLED_ON_PASCAL(
+        params.lib_major_version, params.lib_minor_version, params.lib_patch_version) and
+      params.compute_capability_major == 6) {
+    return "Zstandard decompression is disabled on Pascal GPUs";
+  }
+  return std::nullopt;
+}
+
+std::optional<std::string> is_decompression_disabled_impl(compression_type compression,
+                                                          feature_status_parameters params)
+{
+  switch (compression) {
+    case compression_type::DEFLATE: {
+      if (not NVCOMP_HAS_DEFLATE(
+            params.lib_major_version, params.lib_minor_version, params.lib_patch_version)) {
+        return "nvCOMP 2.5 or newer is required for Deflate decompression";
+      }
+      if (not params.are_all_integrations_enabled) {
+        return "DEFLATE decompression is experimental, you can enable it through "
+               "`LIBCUDF_NVCOMP_POLICY` environment variable.";
+      }
+      return std::nullopt;
+    }
+    case compression_type::SNAPPY: {
+      if (not params.are_stable_integrations_enabled) {
+        return "Snappy decompression has been disabled through the `LIBCUDF_NVCOMP_POLICY` "
+               "environment variable.";
+      }
+      return std::nullopt;
+    }
+    case compression_type::ZSTD: return is_zstd_decomp_disabled(params);
+    default: return "Unsupported compression type";
+  }
+  return "Unsupported compression type";
+}
+
+std::optional<std::string> is_decompression_disabled(compression_type compression,
+                                                     feature_status_parameters params)
+{
+  static feature_status_memo_map decomp_status_reason;
+  static std::mutex memo_map_mutex;
+
+  std::unique_lock memo_map_lock{memo_map_mutex};
+  if (auto mem_res_it = decomp_status_reason.find(feature_status_inputs{compression, params});
+      mem_res_it != decomp_status_reason.end()) {
+    return mem_res_it->second;
+  }
+
+  // The rest of the function will execute only once per run, the memoized result will be returned
+  // in all subsequent calls with the same compression type
+  auto const reason                           = is_decompression_disabled_impl(compression, params);
+  decomp_status_reason[{compression, params}] = reason;
+  memo_map_lock.unlock();
+
+  if (reason.has_value()) {
+    CUDF_LOG_INFO("nvCOMP is disabled for {} decompression; reason: {}",
+                  compression_type_name(compression),
+                  reason.value());
+  } else {
+    CUDF_LOG_INFO("nvCOMP is enabled for {} decompression", compression_type_name(compression));
+  }
+
+  return reason;
+}
+
+size_t compress_input_alignment_bits(compression_type compression)
+{
+  switch (compression) {
+    case compression_type::DEFLATE: return 0;
+    case compression_type::SNAPPY: return 0;
+    case compression_type::ZSTD: return 2;
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+size_t compress_output_alignment_bits(compression_type compression)
+{
+  switch (compression) {
+    case compression_type::DEFLATE: return 3;
+    case compression_type::SNAPPY: return 0;
+    case compression_type::ZSTD: return 0;
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+std::optional<size_t> compress_max_allowed_chunk_size(compression_type compression)
+{
+  switch (compression) {
+    case compression_type::DEFLATE: return 64 * 1024;
+    case compression_type::SNAPPY: return std::nullopt;
+    case compression_type::ZSTD:
+#if NVCOMP_HAS_ZSTD_COMP(NVCOMP_MAJOR_VERSION, NVCOMP_MINOR_VERSION, NVCOMP_PATCH_VERSION)
+      return nvcompZstdCompressionMaxAllowedChunkSize;
+#else
+      CUDF_FAIL("Compression error: " +
+                nvcomp::is_compression_disabled(nvcomp::compression_type::ZSTD).value());
+#endif
+    default: return std::nullopt;
+  }
+}
+
+}  // namespace cudf::io::nvcomp
diff --git a/cpp/src/io/comp/nvcomp_adapter.cu b/cpp/src/io/comp/nvcomp_adapter.cu
new file mode 100644
index 0000000..794d452
--- /dev/null
+++ b/cpp/src/io/comp/nvcomp_adapter.cu
@@ -0,0 +1,130 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "nvcomp_adapter.cuh"
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf::io::nvcomp {
+
+batched_args create_batched_nvcomp_args(device_span<device_span<uint8_t const> const> inputs,
+                                        device_span<device_span<uint8_t> const> outputs,
+                                        rmm::cuda_stream_view stream)
+{
+  auto const num_comp_chunks = inputs.size();
+  rmm::device_uvector<void const*> input_data_ptrs(num_comp_chunks, stream);
+  rmm::device_uvector<size_t> input_data_sizes(num_comp_chunks, stream);
+  rmm::device_uvector<void*> output_data_ptrs(num_comp_chunks, stream);
+  rmm::device_uvector<size_t> output_data_sizes(num_comp_chunks, stream);
+
+  // Prepare the input vectors
+  auto ins_it = thrust::make_zip_iterator(input_data_ptrs.begin(), input_data_sizes.begin());
+  thrust::transform(
+    rmm::exec_policy(stream), inputs.begin(), inputs.end(), ins_it, [] __device__(auto const& in) {
+      return thrust::make_tuple(in.data(), in.size());
+    });
+
+  // Prepare the output vectors
+  auto outs_it = thrust::make_zip_iterator(output_data_ptrs.begin(), output_data_sizes.begin());
+  thrust::transform(
+    rmm::exec_policy(stream),
+    outputs.begin(),
+    outputs.end(),
+    outs_it,
+    [] __device__(auto const& out) { return thrust::make_tuple(out.data(), out.size()); });
+
+  return {std::move(input_data_ptrs),
+          std::move(input_data_sizes),
+          std::move(output_data_ptrs),
+          std::move(output_data_sizes)};
+}
+
+void update_compression_results(device_span<nvcompStatus_t const> nvcomp_stats,
+                                device_span<size_t const> actual_output_sizes,
+                                device_span<compression_result> results,
+                                rmm::cuda_stream_view stream)
+{
+  thrust::transform_if(
+    rmm::exec_policy(stream),
+    nvcomp_stats.begin(),
+    nvcomp_stats.end(),
+    actual_output_sizes.begin(),
+    results.begin(),
+    results.begin(),
+    [] __device__(auto const& nvcomp_status, auto const& size) {
+      return compression_result{size,
+                                nvcomp_status == nvcompStatus_t::nvcompSuccess
+                                  ? compression_status::SUCCESS
+                                  : compression_status::FAILURE};
+    },
+    [] __device__(auto const& cudf_status) {
+      return cudf_status.status != compression_status::SKIPPED;
+    });
+}
+
+void update_compression_results(device_span<size_t const> actual_output_sizes,
+                                device_span<compression_result> results,
+                                rmm::cuda_stream_view stream)
+{
+  thrust::transform_if(
+    rmm::exec_policy(stream),
+    actual_output_sizes.begin(),
+    actual_output_sizes.end(),
+    results.begin(),
+    results.begin(),
+    [] __device__(auto const& size) { return compression_result{size}; },
+    [] __device__(auto const& results) { return results.status != compression_status::SKIPPED; });
+}
+
+void skip_unsupported_inputs(device_span<size_t> input_sizes,
+                             device_span<compression_result> results,
+                             std::optional<size_t> max_valid_input_size,
+                             rmm::cuda_stream_view stream)
+{
+  if (max_valid_input_size.has_value()) {
+    auto status_size_it = thrust::make_zip_iterator(input_sizes.begin(), results.begin());
+    thrust::transform_if(
+      rmm::exec_policy(stream),
+      results.begin(),
+      results.end(),
+      input_sizes.begin(),
+      status_size_it,
+      [] __device__(auto const& status) {
+        return thrust::pair{0, compression_result{0, compression_status::SKIPPED}};
+      },
+      [max_size = max_valid_input_size.value()] __device__(size_t input_size) {
+        return input_size > max_size;
+      });
+  }
+}
+std::pair<size_t, size_t> max_chunk_and_total_input_size(device_span<size_t const> input_sizes,
+                                                         rmm::cuda_stream_view stream)
+{
+  auto const max = thrust::reduce(rmm::exec_policy(stream),
+                                  input_sizes.begin(),
+                                  input_sizes.end(),
+                                  0ul,
+                                  thrust::maximum<size_t>());
+  auto const sum = thrust::reduce(rmm::exec_policy(stream), input_sizes.begin(), input_sizes.end());
+  return {max, sum};
+}
+
+}  // namespace cudf::io::nvcomp
diff --git a/cpp/src/io/comp/nvcomp_adapter.cuh b/cpp/src/io/comp/nvcomp_adapter.cuh
new file mode 100644
index 0000000..dfc803d
--- /dev/null
+++ b/cpp/src/io/comp/nvcomp_adapter.cuh
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "gpuinflate.hpp"
+
+#include <cudf/utilities/span.hpp>
+
+#include <nvcomp.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <optional>
+
+namespace cudf::io::nvcomp {
+
+struct batched_args {
+  rmm::device_uvector<void const*> input_data_ptrs;
+  rmm::device_uvector<size_t> input_data_sizes;
+  rmm::device_uvector<void*> output_data_ptrs;
+  rmm::device_uvector<size_t> output_data_sizes;
+};
+
+/**
+ * @brief Split lists of src/dst device spans into lists of pointers/sizes.
+ *
+ * @param[in] inputs List of input buffers
+ * @param[in] outputs List of output buffers
+ * @param[in] stream CUDA stream to use
+ */
+batched_args create_batched_nvcomp_args(device_span<device_span<uint8_t const> const> inputs,
+                                        device_span<device_span<uint8_t> const> outputs,
+                                        rmm::cuda_stream_view stream);
+
+/**
+ * @brief Convert nvcomp statuses and output sizes into cuIO compression results.
+ */
+void update_compression_results(device_span<nvcompStatus_t const> nvcomp_stats,
+                                device_span<size_t const> actual_output_sizes,
+                                device_span<compression_result> results,
+                                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Fill the result array based on the actual output sizes.
+ */
+void update_compression_results(device_span<size_t const> actual_output_sizes,
+                                device_span<compression_result> results,
+                                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Mark unsupported input chunks for skipping.
+ */
+void skip_unsupported_inputs(device_span<size_t> input_sizes,
+                             device_span<compression_result> results,
+                             std::optional<size_t> max_valid_input_size,
+                             rmm::cuda_stream_view stream);
+
+/**
+ * @brief Returns the size of the largest input chunk and the total input size.
+ */
+std::pair<size_t, size_t> max_chunk_and_total_input_size(device_span<size_t const> input_sizes,
+                                                         rmm::cuda_stream_view stream);
+
+}  // namespace cudf::io::nvcomp
diff --git a/cpp/src/io/comp/nvcomp_adapter.hpp b/cpp/src/io/comp/nvcomp_adapter.hpp
new file mode 100644
index 0000000..1393b70
--- /dev/null
+++ b/cpp/src/io/comp/nvcomp_adapter.hpp
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "gpuinflate.hpp"
+
+#include <io/utilities/config_utils.hpp>
+
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+
+namespace cudf::io::nvcomp {
+
+enum class compression_type { SNAPPY, ZSTD, DEFLATE };
+
+/**
+ * @brief Set of parameters that impact whether the use nvCOMP features is enabled.
+ */
+struct feature_status_parameters {
+  int lib_major_version;
+  int lib_minor_version;
+  int lib_patch_version;
+  bool are_all_integrations_enabled;
+  bool are_stable_integrations_enabled;
+  int compute_capability_major;
+
+  feature_status_parameters();
+  feature_status_parameters(
+    int major, int minor, int patch, bool all_enabled, bool stable_enabled, int cc_major)
+    : lib_major_version{major},
+      lib_minor_version{minor},
+      lib_patch_version{patch},
+      are_all_integrations_enabled{all_enabled},
+      are_stable_integrations_enabled{stable_enabled},
+      compute_capability_major{cc_major}
+  {
+  }
+};
+
+/**
+ * @brief Equality operator overload. Required to use `feature_status_parameters` as a map key.
+ */
+inline bool operator==(feature_status_parameters const& lhs, feature_status_parameters const& rhs)
+{
+  return lhs.lib_major_version == rhs.lib_major_version and
+         lhs.lib_minor_version == rhs.lib_minor_version and
+         lhs.lib_patch_version == rhs.lib_patch_version and
+         lhs.are_all_integrations_enabled == rhs.are_all_integrations_enabled and
+         lhs.are_stable_integrations_enabled == rhs.are_stable_integrations_enabled and
+         lhs.compute_capability_major == rhs.compute_capability_major;
+}
+
+/**
+ * @brief If a compression type is disabled through nvCOMP, returns the reason as a string.
+ *
+ * Result cab depend on nvCOMP version and environment variables.
+ *
+ * @param compression Compression type
+ * @param params Optional parameters to query status with different configurations
+ * @returns Reason for the feature disablement, `std::nullopt` if the feature is enabled
+ */
+[[nodiscard]] std::optional<std::string> is_compression_disabled(
+  compression_type compression, feature_status_parameters params = feature_status_parameters());
+
+/**
+ * @brief If a decompression type is disabled through nvCOMP, returns the reason as a string.
+ *
+ * Result can depend on nvCOMP version and environment variables.
+ *
+ * @param compression Compression type
+ * @param params Optional parameters to query status with different configurations
+ * @returns Reason for the feature disablement, `std::nullopt` if the feature is enabled
+ */
+[[nodiscard]] std::optional<std::string> is_decompression_disabled(
+  compression_type compression, feature_status_parameters params = feature_status_parameters());
+
+/**
+ * @brief Device batch decompression of given type.
+ *
+ * @param[in] compression Compression type
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[out] results List of output status structures
+ * @param[in] max_uncomp_chunk_size maximum size of uncompressed chunk
+ * @param[in] max_total_uncomp_size maximum total size of uncompressed data
+ * @param[in] stream CUDA stream to use
+ */
+void batched_decompress(compression_type compression,
+                        device_span<device_span<uint8_t const> const> inputs,
+                        device_span<device_span<uint8_t> const> outputs,
+                        device_span<compression_result> results,
+                        size_t max_uncomp_chunk_size,
+                        size_t max_total_uncomp_size,
+                        rmm::cuda_stream_view stream);
+
+/**
+ * @brief Gets the maximum size any chunk could compress to in the batch.
+ *
+ * @param compression Compression type
+ * @param max_uncomp_chunk_size Size of the largest uncompressed chunk in the batch
+ */
+[[nodiscard]] size_t compress_max_output_chunk_size(compression_type compression,
+                                                    uint32_t max_uncomp_chunk_size);
+
+/**
+ * @brief Gets input alignment requirements for the given compression type.
+ *
+ * @param compression Compression type
+ * @returns required alignment, in bits
+ */
+[[nodiscard]] size_t compress_input_alignment_bits(compression_type compression);
+
+/**
+ * @brief Gets output alignment requirements for the given compression type.
+ *
+ * @param compression Compression type
+ * @returns required alignment, in bits
+ */
+[[nodiscard]] size_t compress_output_alignment_bits(compression_type compression);
+
+/**
+ * @brief Maximum size of uncompressed chunks that can be compressed with nvCOMP.
+ *
+ * @param compression Compression type
+ * @returns maximum chunk size
+ */
+[[nodiscard]] std::optional<size_t> compress_max_allowed_chunk_size(compression_type compression);
+
+/**
+ * @brief Device batch compression of given type.
+ *
+ * @param[in] compression Compression type
+ * @param[in] inputs List of input buffers
+ * @param[out] outputs List of output buffers
+ * @param[out] results List of output status structures
+ * @param[in] stream CUDA stream to use
+ */
+void batched_compress(compression_type compression,
+                      device_span<device_span<uint8_t const> const> inputs,
+                      device_span<device_span<uint8_t> const> outputs,
+                      device_span<compression_result> results,
+                      rmm::cuda_stream_view stream);
+
+}  // namespace cudf::io::nvcomp
diff --git a/cpp/src/io/comp/snap.cu b/cpp/src/io/comp/snap.cu
new file mode 100644
index 0000000..0428f4e
--- /dev/null
+++ b/cpp/src/io/comp/snap.cu
@@ -0,0 +1,360 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "gpuinflate.hpp"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+constexpr int hash_bits = 12;
+
+// TBD: Tentatively limits to 2-byte codes to prevent long copy search followed by long literal
+// encoding
+
+/**
+ * @brief snappy compressor state
+ */
+struct snap_state_s {
+  uint8_t const* src;                 ///< Ptr to uncompressed data
+  uint32_t src_len;                   ///< Uncompressed data length
+  uint8_t* dst_base;                  ///< Base ptr to output compressed data
+  uint8_t* dst;                       ///< Current ptr to uncompressed data
+  uint8_t* end;                       ///< End of uncompressed data buffer
+  volatile uint32_t literal_length;   ///< Number of literal bytes
+  volatile uint32_t copy_length;      ///< Number of copy bytes
+  volatile uint32_t copy_distance;    ///< Distance for copy bytes
+  uint16_t hash_map[1 << hash_bits];  ///< Low 16-bit offset from hash
+};
+
+/**
+ * @brief 12-bit hash from four consecutive bytes
+ */
+static inline __device__ uint32_t snap_hash(uint32_t v)
+{
+  return (v * ((1 << 20) + (0x2a00) + (0x6a) + 1)) >> (32 - hash_bits);
+}
+
+/**
+ * @brief Fetches four consecutive bytes
+ */
+static inline __device__ uint32_t fetch4(uint8_t const* src)
+{
+  uint32_t src_align = 3 & reinterpret_cast<uintptr_t>(src);
+  auto const* src32  = reinterpret_cast<uint32_t const*>(src - src_align);
+  uint32_t v         = src32[0];
+  return (src_align) ? __funnelshift_r(v, src32[1], src_align * 8) : v;
+}
+
+/**
+ * @brief Outputs a snappy literal symbol
+ *
+ * @param dst Destination compressed byte stream
+ * @param end End of compressed data buffer
+ * @param src Pointer to literal bytes
+ * @param len_minus1 Number of literal bytes minus 1
+ * @param t Thread in warp
+ *
+ * @return Updated pointer to compressed byte stream
+ */
+static __device__ uint8_t* StoreLiterals(
+  uint8_t* dst, uint8_t* end, uint8_t const* src, uint32_t len_minus1, uint32_t t)
+{
+  if (len_minus1 < 60) {
+    if (!t && dst < end) dst[0] = (len_minus1 << 2);
+    dst += 1;
+  } else if (len_minus1 <= 0xff) {
+    if (!t && dst + 1 < end) {
+      dst[0] = 60 << 2;
+      dst[1] = len_minus1;
+    }
+    dst += 2;
+  } else if (len_minus1 <= 0xffff) {
+    if (!t && dst + 2 < end) {
+      dst[0] = 61 << 2;
+      dst[1] = len_minus1;
+      dst[2] = len_minus1 >> 8;
+    }
+    dst += 3;
+  } else if (len_minus1 <= 0xff'ffff) {
+    if (!t && dst + 3 < end) {
+      dst[0] = 62 << 2;
+      dst[1] = len_minus1;
+      dst[2] = len_minus1 >> 8;
+      dst[3] = len_minus1 >> 16;
+    }
+    dst += 4;
+  } else {
+    if (!t && dst + 4 < end) {
+      dst[0] = 63 << 2;
+      dst[1] = len_minus1;
+      dst[2] = len_minus1 >> 8;
+      dst[3] = len_minus1 >> 16;
+      dst[4] = len_minus1 >> 24;
+    }
+    dst += 5;
+  }
+  for (uint32_t i = t; i <= len_minus1; i += 32) {
+    if (dst + i < end) dst[i] = src[i];
+  }
+  return dst + len_minus1 + 1;
+}
+
+/**
+ * @brief Outputs a snappy copy symbol (assumed to be called by a single thread)
+ *
+ * @param dst Destination compressed byte stream
+ * @param end End of compressed data buffer
+ * @param copy_len Copy length
+ * @param distance Copy distance
+ *
+ * @return Updated pointer to compressed byte stream
+ */
+static __device__ uint8_t* StoreCopy(uint8_t* dst,
+                                     uint8_t* end,
+                                     uint32_t copy_len,
+                                     uint32_t distance)
+{
+  if (copy_len < 12 && distance < 2048) {
+    // xxxxxx01.oooooooo: copy with 3-bit length, 11-bit offset
+    if (dst + 2 <= end) {
+      dst[0] = ((distance & 0x700) >> 3) | ((copy_len - 4) << 2) | 0x01;
+      dst[1] = distance;
+    }
+    return dst + 2;
+  } else {
+    // xxxxxx1x: copy with 6-bit length, 16-bit offset
+    if (dst + 3 <= end) {
+      dst[0] = ((copy_len - 1) << 2) | 0x2;
+      dst[1] = distance;
+      dst[2] = distance >> 8;
+    }
+    return dst + 3;
+  }
+}
+
+/**
+ * @brief Returns mask of any thread in the warp that has a hash value
+ * equal to that of the calling thread
+ */
+static inline __device__ uint32_t HashMatchAny(uint32_t v, uint32_t t)
+{
+#if (__CUDA_ARCH__ >= 700)
+  return __match_any_sync(~0, v);
+#else
+  uint32_t err_map = 0;
+  for (uint32_t i = 0; i < hash_bits; i++, v >>= 1) {
+    uint32_t b       = v & 1;
+    uint32_t match_b = ballot(b);
+    err_map |= match_b ^ -(int32_t)b;
+  }
+  return ~err_map;
+#endif
+}
+
+/**
+ * @brief Finds the first occurrence of a consecutive 4-byte match in the input sequence,
+ * or at most 256 bytes
+ *
+ * @param s Compressor state (copy_length set to 4 if a match is found, zero otherwise)
+ * @param src Uncompressed buffer
+ * @param pos0 Position in uncompressed buffer
+ * @param t thread in warp
+ *
+ * @return Number of bytes before first match (literal length)
+ */
+static __device__ uint32_t FindFourByteMatch(snap_state_s* s,
+                                             uint8_t const* src,
+                                             uint32_t pos0,
+                                             uint32_t t)
+{
+  constexpr int max_literal_length = 256;
+  // Matches encoder limit as described in snappy format description
+  constexpr int max_copy_distance = 32768;
+  uint32_t len                    = s->src_len;
+  uint32_t pos                    = pos0;
+  uint32_t maxpos                 = pos0 + max_literal_length - 31;
+  uint32_t match_mask, literal_cnt;
+  if (t == 0) { s->copy_length = 0; }
+  do {
+    bool valid4               = (pos + t + 4 <= len);
+    uint32_t data32           = (valid4) ? fetch4(src + pos + t) : 0;
+    uint32_t hash             = (valid4) ? snap_hash(data32) : 0;
+    uint32_t local_match      = HashMatchAny(hash, t);
+    uint32_t local_match_lane = 31 - __clz(local_match & ((1 << t) - 1));
+    uint32_t local_match_data = shuffle(data32, min(local_match_lane, t));
+    uint32_t offset, match;
+    if (valid4) {
+      if (local_match_lane < t && local_match_data == data32) {
+        match  = 1;
+        offset = pos + local_match_lane;
+      } else {
+        offset = (pos & ~0xffff) | s->hash_map[hash];
+        if (offset >= pos) { offset = (offset >= 0x1'0000) ? offset - 0x1'0000 : pos; }
+        match =
+          (offset < pos && offset + max_copy_distance >= pos + t && fetch4(src + offset) == data32);
+      }
+    } else {
+      match       = 0;
+      local_match = 0;
+      offset      = pos + t;
+    }
+    match_mask = ballot(match);
+    if (match_mask != 0) {
+      literal_cnt = __ffs(match_mask) - 1;
+      if (t == literal_cnt) {
+        s->copy_distance = pos + t - offset;
+        s->copy_length   = 4;
+      }
+    } else {
+      literal_cnt = 32;
+    }
+    // Update hash up to the first 4 bytes of the copy length
+    local_match &= (0x2 << literal_cnt) - 1;
+    if (t <= literal_cnt && t == 31 - __clz(local_match)) { s->hash_map[hash] = pos + t; }
+    pos += literal_cnt;
+  } while (literal_cnt == 32 && pos < maxpos);
+  return min(pos, len) - pos0;
+}
+
+/// @brief Returns the number of matching bytes for two byte sequences up to 63 bytes
+static __device__ uint32_t Match60(uint8_t const* src1,
+                                   uint8_t const* src2,
+                                   uint32_t len,
+                                   uint32_t t)
+{
+  uint32_t mismatch = ballot(t >= len || src1[t] != src2[t]);
+  if (mismatch == 0) {
+    mismatch = ballot(32 + t >= len || src1[32 + t] != src2[32 + t]);
+    return 31 + __ffs(mismatch);  // mismatch cannot be zero here if len <= 63
+  } else {
+    return __ffs(mismatch) - 1;
+  }
+}
+
+/**
+ * @brief Snappy compression kernel
+ * See http://github.com/google/snappy/blob/master/format_description.txt
+ *
+ * blockDim {128,1,1}
+ *
+ * @param[in] inputs Source/Destination buffer information per block
+ * @param[out] outputs Compression status per block
+ * @param[in] count Number of blocks to compress
+ */
+__global__ void __launch_bounds__(128)
+  snap_kernel(device_span<device_span<uint8_t const> const> inputs,
+              device_span<device_span<uint8_t> const> outputs,
+              device_span<compression_result> results)
+{
+  __shared__ __align__(16) snap_state_s state_g;
+
+  snap_state_s* const s = &state_g;
+  uint32_t t            = threadIdx.x;
+  uint32_t pos;
+  uint8_t const* src;
+
+  if (!t) {
+    auto const src     = inputs[blockIdx.x].data();
+    auto src_len       = static_cast<uint32_t>(inputs[blockIdx.x].size());
+    auto dst           = outputs[blockIdx.x].data();
+    auto const dst_len = static_cast<uint32_t>(outputs[blockIdx.x].size());
+    auto const end     = dst + dst_len;
+    s->src             = src;
+    s->src_len         = src_len;
+    s->dst_base        = dst;
+    s->end             = end;
+    while (src_len > 0x7f) {
+      if (dst < end) { dst[0] = src_len | 0x80; }
+      dst++;
+      src_len >>= 7;
+    }
+    if (dst < end) { dst[0] = src_len; }
+    s->dst            = dst + 1;
+    s->literal_length = 0;
+    s->copy_length    = 0;
+    s->copy_distance  = 0;
+  }
+  for (uint32_t i = t; i < sizeof(s->hash_map) / sizeof(uint32_t); i += 128) {
+    *reinterpret_cast<volatile uint32_t*>(&s->hash_map[i * 2]) = 0;
+  }
+  __syncthreads();
+  src = s->src;
+  pos = 0;
+  while (pos < s->src_len) {
+    uint32_t literal_len = s->literal_length;
+    uint32_t copy_len    = s->copy_length;
+    uint32_t distance    = s->copy_distance;
+    __syncthreads();
+    if (t < 32) {
+      // WARP0: Encode literals and copies
+      uint8_t* dst = s->dst;
+      uint8_t* end = s->end;
+      if (literal_len > 0) {
+        dst = StoreLiterals(dst, end, src + pos, literal_len - 1, t);
+        pos += literal_len;
+      }
+      if (copy_len > 0) {
+        if (t == 0) { dst = StoreCopy(dst, end, copy_len, distance); }
+        pos += copy_len;
+      }
+      __syncwarp();
+      if (t == 0) { s->dst = dst; }
+    } else {
+      pos += literal_len + copy_len;
+      if (t < 32 * 2) {
+        // WARP1: Find a match using 12-bit hashes of 4-byte blocks
+        uint32_t t5 = t & 0x1f;
+        literal_len = FindFourByteMatch(s, src, pos, t5);
+        if (t5 == 0) { s->literal_length = literal_len; }
+        copy_len = s->copy_length;
+        if (copy_len != 0) {
+          uint32_t match_pos = pos + literal_len + copy_len;  // NOTE: copy_len is always 4 here
+          copy_len += Match60(src + match_pos,
+                              src + match_pos - s->copy_distance,
+                              min(s->src_len - match_pos, 64 - copy_len),
+                              t5);
+          if (t5 == 0) { s->copy_length = copy_len; }
+        }
+      }
+    }
+    __syncthreads();
+  }
+  __syncthreads();
+  if (!t) {
+    results[blockIdx.x].bytes_written = s->dst - s->dst_base;
+    results[blockIdx.x].status =
+      (s->dst > s->end) ? compression_status::FAILURE : compression_status::SUCCESS;
+    results[blockIdx.x].reserved = 0;
+  }
+}
+
+void gpu_snap(device_span<device_span<uint8_t const> const> inputs,
+              device_span<device_span<uint8_t> const> outputs,
+              device_span<compression_result> results,
+              rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);  // 4 warps per stream, 1 stream per block
+  dim3 dim_grid(inputs.size(), 1);
+  if (inputs.size() > 0) {
+    snap_kernel<<<dim_grid, dim_block, 0, stream.value()>>>(inputs, outputs, results);
+  }
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/statistics.cu b/cpp/src/io/comp/statistics.cu
new file mode 100644
index 0000000..e0f7e1e
--- /dev/null
+++ b/cpp/src/io/comp/statistics.cu
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "gpuinflate.hpp"
+
+#include <rmm/exec_policy.hpp>
+#include <thrust/transform_reduce.h>
+
+namespace cudf::io {
+
+writer_compression_statistics collect_compression_statistics(
+  device_span<device_span<uint8_t const> const> inputs,
+  device_span<compression_result const> results,
+  rmm::cuda_stream_view stream)
+{
+  // bytes_written on success
+  auto const output_size_successful = thrust::transform_reduce(
+    rmm::exec_policy(stream),
+    results.begin(),
+    results.end(),
+    [] __device__(auto& res) {
+      return res.status == compression_status::SUCCESS ? res.bytes_written : 0;
+    },
+    0ul,
+    thrust::plus<size_t>());
+
+  auto input_size_with_status = [inputs, results, stream](compression_status status) {
+    auto const zipped_begin =
+      thrust::make_zip_iterator(thrust::make_tuple(inputs.begin(), results.begin()));
+    auto const zipped_end = zipped_begin + inputs.size();
+
+    return thrust::transform_reduce(
+      rmm::exec_policy(stream),
+      zipped_begin,
+      zipped_end,
+      [status] __device__(auto tup) {
+        return thrust::get<1>(tup).status == status ? thrust::get<0>(tup).size() : 0;
+      },
+      0ul,
+      thrust::plus<size_t>());
+  };
+
+  return writer_compression_statistics{input_size_with_status(compression_status::SUCCESS),
+                                       input_size_with_status(compression_status::FAILURE),
+                                       input_size_with_status(compression_status::SKIPPED),
+                                       output_size_successful};
+}
+
+}  // namespace cudf::io
diff --git a/cpp/src/io/comp/unbz2.hpp b/cpp/src/io/comp/unbz2.hpp
new file mode 100644
index 0000000..8ee140e
--- /dev/null
+++ b/cpp/src/io/comp/unbz2.hpp
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ *
+ * bzip2 license information is available at
+ * https://spdx.org/licenses/bzip2-1.0.6.html
+ * https://github.com/asimonov-im/bzip2/blob/master/LICENSE
+ * original source code available at
+ * http://www.sourceware.org/bzip2/
+ */
+
+/*--
+
+Copyright (C) 1996-2002 Julian R Seward.  All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+
+2. The origin of this software must not be misrepresented; you must
+not claim that you wrote the original software.  If you use this
+software in a product, an acknowledgment in the product
+documentation would be appreciated but is not required.
+
+3. Altered source versions must be plainly marked as such, and must
+not be misrepresented as being the original software.
+
+4. The name of the author may not be used to endorse or promote
+products derived from this software without specific prior written
+permission.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Julian Seward, Cambridge, UK.
+jseward@acm.org
+bzip2/libbzip2 version 1.0 of 21 March 2000
+
+This program is based on (at least) the work of:
+Mike Burrows
+David Wheeler
+Peter Fenwick
+Alistair Moffat
+Radford Neal
+Ian H. Witten
+Robert Sedgewick
+Jon L. Bentley
+
+For more information on these sources, see the manual.
+--*/
+
+#pragma once
+
+namespace cudf {
+namespace io {
+// If BZ_OUTBUFF_FULL is returned and block_start is non-NULL, dstlen will be updated to point to
+// the end of the last valid block, and block_start will contain the offset in bits of the beginning
+// of the block, so it can be passed in to resume decoding later on.
+#define BZ_OK               0
+#define BZ_RUN_OK           1
+#define BZ_FLUSH_OK         2
+#define BZ_FINISH_OK        3
+#define BZ_STREAM_END       4
+#define BZ_SEQUENCE_ERROR   (-1)
+#define BZ_PARAM_ERROR      (-2)
+#define BZ_MEM_ERROR        (-3)
+#define BZ_DATA_ERROR       (-4)
+#define BZ_DATA_ERROR_MAGIC (-5)
+#define BZ_IO_ERROR         (-6)
+#define BZ_UNEXPECTED_EOF   (-7)
+#define BZ_OUTBUFF_FULL     (-8)
+
+int32_t cpu_bz2_uncompress(uint8_t const* input,
+                           size_t inlen,
+                           uint8_t* dst,
+                           size_t* dstlen,
+                           uint64_t* block_start = nullptr);
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/uncomp.cpp b/cpp/src/io/comp/uncomp.cpp
new file mode 100644
index 0000000..0d2d213
--- /dev/null
+++ b/cpp/src/io/comp/uncomp.cpp
@@ -0,0 +1,562 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "io_uncomp.hpp"
+#include "nvcomp_adapter.hpp"
+#include "unbz2.hpp"  // bz2 uncompress
+
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <cuda_runtime.h>
+
+#include <cstring>  // memset
+
+#include <zlib.h>  // uncompress
+
+using cudf::host_span;
+
+namespace cudf {
+namespace io {
+
+#pragma pack(push, 1)
+
+struct gz_file_header_s {
+  uint8_t id1;        // 0x1f
+  uint8_t id2;        // 0x8b
+  uint8_t comp_mthd;  // compression method (0-7=reserved, 8=deflate)
+  uint8_t flags;      // flags (GZIPHeaderFlag)
+  uint8_t mtime[4];   // If non-zero: modification time (Unix format)
+  uint8_t xflags;     // Extra compressor-specific flags
+  uint8_t os;         // OS id
+};
+
+struct zip_eocd_s  // end of central directory
+{
+  uint32_t sig;            // 0x0605'4b50
+  uint16_t disk_id;        // number of this disk
+  uint16_t start_disk;     // number of the disk with the start of the central directory
+  uint16_t num_entries;    // number of entries in the central dir on this disk
+  uint16_t total_entries;  // total number of entries in the central dir
+  uint32_t cdir_size;      // size of the central directory
+  uint32_t cdir_offset;    // offset of start of central directory with respect to the starting disk
+                         // number uint16_t comment_len;   // comment length (excluded from struct)
+};
+
+struct zip64_eocdl  // end of central dir locator
+{
+  uint32_t sig;         // 0x0706'4b50
+  uint32_t disk_start;  // number of the disk with the start of the zip64 end of central directory
+  uint64_t eocdr_ofs;   // relative offset of the zip64 end of central directory record
+  uint32_t num_disks;   // total number of disks
+};
+
+struct zip_cdfh_s  // central directory file header
+{
+  uint32_t sig;          // 0x0201'4b50
+  uint16_t ver;          // version made by
+  uint16_t min_ver;      // version needed to extract
+  uint16_t gp_flags;     // general purpose bit flag
+  uint16_t comp_method;  // compression method
+  uint16_t file_time;    // last mod file time
+  uint16_t file_date;    // last mod file date
+  uint32_t crc32;        // crc - 32
+  uint32_t comp_size;    // compressed size
+  uint32_t uncomp_size;  // uncompressed size
+  uint16_t fname_len;    // filename length
+  uint16_t extra_len;    // extra field length
+  uint16_t comment_len;  // file comment length
+  uint16_t start_disk;   // disk number start
+  uint16_t int_fattr;    // internal file attributes
+  uint32_t ext_fattr;    // external file attributes
+  uint32_t hdr_ofs;      // relative offset of local header
+};
+
+struct zip_lfh_s {
+  uint32_t sig;          // 0x0403'4b50
+  uint16_t ver;          // version needed to extract
+  uint16_t gp_flags;     // general purpose bit flag
+  uint16_t comp_method;  // compression method
+  uint16_t file_time;    // last mod file time
+  uint16_t file_date;    // last mod file date
+  uint32_t crc32;        // crc - 32
+  uint32_t comp_size;    // compressed size
+  uint32_t uncomp_size;  // uncompressed size
+  uint16_t fname_len;    // filename length
+  uint16_t extra_len;    // extra field length
+};
+
+struct bz2_file_header_s {
+  uint8_t sig[3];  // "BZh"
+  uint8_t blksz;   // block size 1..9 in 100kB units (post-RLE)
+};
+
+#pragma pack(pop)
+
+struct gz_archive_s {
+  gz_file_header_s const* fhdr;
+  uint16_t hcrc16;  // header crc16 if present
+  uint16_t xlen;
+  uint8_t const* fxtra;      // xlen bytes (optional)
+  uint8_t const* fname;      // zero-terminated original filename if present
+  uint8_t const* fcomment;   // zero-terminated comment if present
+  uint8_t const* comp_data;  // compressed data
+  size_t comp_len;           // Compressed data length
+  uint32_t crc32;            // CRC32 of uncompressed data
+  uint32_t isize;            // Input size modulo 2^32
+};
+
+struct zip_archive_s {
+  zip_eocd_s const* eocd;    // end of central directory
+  zip64_eocdl const* eocdl;  // end of central dir locator (optional)
+  zip_cdfh_s const* cdfh;    // start of central directory file headers
+};
+
+bool ParseGZArchive(gz_archive_s* dst, uint8_t const* raw, size_t len)
+{
+  gz_file_header_s const* fhdr;
+
+  if (!dst) return false;
+  memset(dst, 0, sizeof(gz_archive_s));
+  if (len < sizeof(gz_file_header_s) + 8) return false;
+  fhdr = reinterpret_cast<gz_file_header_s const*>(raw);
+  if (fhdr->id1 != 0x1f || fhdr->id2 != 0x8b) return false;
+  dst->fhdr = fhdr;
+  raw += sizeof(gz_file_header_s);
+  len -= sizeof(gz_file_header_s);
+  if (fhdr->flags & GZIPHeaderFlag::fextra) {
+    uint32_t xlen;
+
+    if (len < 2) return false;
+    xlen = raw[0] | (raw[1] << 8);
+    raw += 2;
+    len -= 2;
+    if (len < xlen) return false;
+    dst->xlen  = (uint16_t)xlen;
+    dst->fxtra = raw;
+    raw += xlen;
+    len -= xlen;
+  }
+  if (fhdr->flags & GZIPHeaderFlag::fname) {
+    size_t l = 0;
+    uint8_t c;
+    do {
+      if (l >= len) return false;
+      c = raw[l];
+      l++;
+    } while (c != 0);
+    dst->fname = raw;
+    raw += l;
+    len -= l;
+  }
+  if (fhdr->flags & GZIPHeaderFlag::fcomment) {
+    size_t l = 0;
+    uint8_t c;
+    do {
+      if (l >= len) return false;
+      c = raw[l];
+      l++;
+    } while (c != 0);
+    dst->fcomment = raw;
+    raw += l;
+    len -= l;
+  }
+  if (fhdr->flags & GZIPHeaderFlag::fhcrc) {
+    if (len < 2) return false;
+    dst->hcrc16 = raw[0] | (raw[1] << 8);
+    raw += 2;
+    len -= 2;
+  }
+  if (len < 8) return false;
+  dst->crc32 = raw[len - 8] | (raw[len - 7] << 8) | (raw[len - 6] << 16) | (raw[len - 5] << 24);
+  dst->isize = raw[len - 4] | (raw[len - 3] << 8) | (raw[len - 2] << 16) | (raw[len - 1] << 24);
+  len -= 8;
+  dst->comp_data = raw;
+  dst->comp_len  = len;
+  return (fhdr->comp_mthd == 8 && len > 0);
+}
+
+bool OpenZipArchive(zip_archive_s* dst, uint8_t const* raw, size_t len)
+{
+  memset(dst, 0, sizeof(zip_archive_s));
+  // Find the end of central directory
+  if (len >= sizeof(zip_eocd_s) + 2) {
+    for (ptrdiff_t i = len - sizeof(zip_eocd_s) - 2;
+         i + sizeof(zip_eocd_s) + 2 + 0xffff >= len && i >= 0;
+         i--) {
+      auto const* eocd = reinterpret_cast<zip_eocd_s const*>(raw + i);
+      if (eocd->sig == 0x0605'4b50 &&
+          eocd->disk_id == eocd->start_disk  // multi-file archives not supported
+          && eocd->num_entries == eocd->total_entries &&
+          eocd->cdir_size >= sizeof(zip_cdfh_s) * eocd->num_entries && eocd->cdir_offset < len &&
+          i + *reinterpret_cast<uint16_t const*>(eocd + 1) <= static_cast<ptrdiff_t>(len)) {
+        auto const* cdfh = reinterpret_cast<zip_cdfh_s const*>(raw + eocd->cdir_offset);
+        dst->eocd        = eocd;
+        if (i >= static_cast<ptrdiff_t>(sizeof(zip64_eocdl))) {
+          auto const* eocdl = reinterpret_cast<zip64_eocdl const*>(raw + i - sizeof(zip64_eocdl));
+          if (eocdl->sig == 0x0706'4b50) { dst->eocdl = eocdl; }
+        }
+        // Start of central directory
+        if (cdfh->sig == 0x0201'4b50) { dst->cdfh = cdfh; }
+      }
+    }
+  }
+  return (dst->eocd && dst->cdfh);
+}
+
+int cpu_inflate(uint8_t* uncomp_data, size_t* destLen, uint8_t const* comp_data, size_t comp_len)
+{
+  int zerr;
+  z_stream strm;
+
+  memset(&strm, 0, sizeof(strm));
+  strm.next_in   = const_cast<Bytef*>(reinterpret_cast<Bytef const*>(comp_data));
+  strm.avail_in  = comp_len;
+  strm.total_in  = 0;
+  strm.next_out  = uncomp_data;
+  strm.avail_out = *destLen;
+  strm.total_out = 0;
+  zerr           = inflateInit2(&strm, -15);  // -15 for raw data without GZIP headers
+  if (zerr != 0) {
+    *destLen = 0;
+    return zerr;
+  }
+  zerr     = inflate(&strm, Z_FINISH);
+  *destLen = strm.total_out;
+  inflateEnd(&strm);
+  return (zerr == Z_STREAM_END) ? Z_OK : zerr;
+}
+
+/**
+ * @brief Uncompresses a raw DEFLATE stream to a char vector.
+ * The vector will be grown to match the uncompressed size
+ * Optimized for the case where the initial size is the uncompressed
+ * size truncated to 32-bit, and grows the buffer in 1GB increments.
+ *
+ * @param[out] dst Destination vector
+ * @param[in] comp_data Raw compressed data
+ * @param[in] comp_len Compressed data size
+ */
+void cpu_inflate_vector(std::vector<uint8_t>& dst, uint8_t const* comp_data, size_t comp_len)
+{
+  z_stream strm{};
+  strm.next_in   = const_cast<Bytef*>(reinterpret_cast<Bytef const*>(comp_data));
+  strm.avail_in  = comp_len;
+  strm.total_in  = 0;
+  strm.next_out  = dst.data();
+  strm.avail_out = dst.size();
+  strm.total_out = 0;
+  auto zerr      = inflateInit2(&strm, -15);  // -15 for raw data without GZIP headers
+  CUDF_EXPECTS(zerr == 0, "Error in DEFLATE stream");
+  do {
+    if (strm.avail_out == 0) {
+      dst.resize(strm.total_out + (1 << 30));
+      strm.avail_out = dst.size() - strm.total_out;
+      strm.next_out  = reinterpret_cast<uint8_t*>(dst.data()) + strm.total_out;
+    }
+    zerr = inflate(&strm, Z_SYNC_FLUSH);
+  } while ((zerr == Z_BUF_ERROR || zerr == Z_OK) && strm.avail_out == 0 &&
+           strm.total_out == dst.size());
+  dst.resize(strm.total_out);
+  inflateEnd(&strm);
+  CUDF_EXPECTS(zerr == Z_STREAM_END, "Error in DEFLATE stream");
+}
+
+std::vector<uint8_t> decompress(compression_type compression, host_span<uint8_t const> src)
+{
+  CUDF_EXPECTS(src.data() != nullptr, "Decompression: Source cannot be nullptr");
+  CUDF_EXPECTS(not src.empty(), "Decompression: Source size cannot be 0");
+
+  auto raw                 = src.data();
+  uint8_t const* comp_data = nullptr;
+  size_t comp_len          = 0;
+  size_t uncomp_len        = 0;
+
+  switch (compression) {
+    case compression_type::AUTO:
+    case compression_type::GZIP: {
+      gz_archive_s gz;
+      if (ParseGZArchive(&gz, raw, src.size())) {
+        compression = compression_type::GZIP;
+        comp_data   = gz.comp_data;
+        comp_len    = gz.comp_len;
+        uncomp_len  = gz.isize;
+      }
+      if (compression != compression_type::AUTO) break;
+      [[fallthrough]];
+    }
+    case compression_type::ZIP: {
+      zip_archive_s za;
+      if (OpenZipArchive(&za, raw, src.size())) {
+        size_t cdfh_ofs = 0;
+        for (int i = 0; i < za.eocd->num_entries; i++) {
+          zip_cdfh_s const* cdfh = reinterpret_cast<zip_cdfh_s const*>(
+            reinterpret_cast<uint8_t const*>(za.cdfh) + cdfh_ofs);
+          int cdfh_len = sizeof(zip_cdfh_s) + cdfh->fname_len + cdfh->extra_len + cdfh->comment_len;
+          if (cdfh_ofs + cdfh_len > za.eocd->cdir_size || cdfh->sig != 0x0201'4b50) {
+            // Bad cdir
+            break;
+          }
+          // For now, only accept with non-zero file sizes and DEFLATE
+          if (cdfh->comp_method == 8 && cdfh->comp_size > 0 && cdfh->uncomp_size > 0) {
+            size_t lfh_ofs       = cdfh->hdr_ofs;
+            zip_lfh_s const* lfh = reinterpret_cast<zip_lfh_s const*>(raw + lfh_ofs);
+            if (lfh_ofs + sizeof(zip_lfh_s) <= src.size() && lfh->sig == 0x0403'4b50 &&
+                lfh_ofs + sizeof(zip_lfh_s) + lfh->fname_len + lfh->extra_len <= src.size()) {
+              if (lfh->comp_method == 8 && lfh->comp_size > 0 && lfh->uncomp_size > 0) {
+                size_t file_start = lfh_ofs + sizeof(zip_lfh_s) + lfh->fname_len + lfh->extra_len;
+                size_t file_end   = file_start + lfh->comp_size;
+                if (file_end <= src.size()) {
+                  // Pick the first valid file of non-zero size (only 1 file expected in archive)
+                  compression = compression_type::ZIP;
+                  comp_data   = raw + file_start;
+                  comp_len    = lfh->comp_size;
+                  uncomp_len  = lfh->uncomp_size;
+                  break;
+                }
+              }
+            }
+          }
+          cdfh_ofs += cdfh_len;
+        }
+      }
+    }
+      if (compression != compression_type::AUTO) break;
+      [[fallthrough]];
+    case compression_type::BZIP2:
+      if (src.size() > 4) {
+        bz2_file_header_s const* fhdr = reinterpret_cast<bz2_file_header_s const*>(raw);
+        // Check for BZIP2 file signature "BZh1" to "BZh9"
+        if (fhdr->sig[0] == 'B' && fhdr->sig[1] == 'Z' && fhdr->sig[2] == 'h' &&
+            fhdr->blksz >= '1' && fhdr->blksz <= '9') {
+          compression = compression_type::BZIP2;
+          comp_data   = raw;
+          comp_len    = src.size();
+          uncomp_len  = 0;
+        }
+      }
+      if (compression != compression_type::AUTO) break;
+      [[fallthrough]];
+    default: CUDF_FAIL("Unsupported compressed stream type");
+  }
+
+  CUDF_EXPECTS(comp_data != nullptr and comp_len > 0, "Unsupported compressed stream type");
+
+  if (uncomp_len <= 0) {
+    uncomp_len = comp_len * 4 + 4096;  // In case uncompressed size isn't known in advance, assume
+                                       // ~4:1 compression for initial size
+  }
+
+  if (compression == compression_type::GZIP || compression == compression_type::ZIP) {
+    // INFLATE
+    std::vector<uint8_t> dst(uncomp_len);
+    cpu_inflate_vector(dst, comp_data, comp_len);
+    return dst;
+  }
+  if (compression == compression_type::BZIP2) {
+    size_t src_ofs = 0;
+    size_t dst_ofs = 0;
+    int bz_err     = 0;
+    std::vector<uint8_t> dst(uncomp_len);
+    do {
+      size_t dst_len = uncomp_len - dst_ofs;
+      bz_err = cpu_bz2_uncompress(comp_data, comp_len, dst.data() + dst_ofs, &dst_len, &src_ofs);
+      if (bz_err == BZ_OUTBUFF_FULL) {
+        // TBD: We could infer the compression ratio based on produced/consumed byte counts
+        // in order to minimize realloc events and over-allocation
+        dst_ofs = dst_len;
+        dst_len = uncomp_len + (uncomp_len / 2);
+        dst.resize(dst_len);
+        uncomp_len = dst_len;
+      } else if (bz_err == 0) {
+        uncomp_len = dst_len;
+        dst.resize(uncomp_len);
+      }
+    } while (bz_err == BZ_OUTBUFF_FULL);
+    CUDF_EXPECTS(bz_err == 0, "Decompression: error in stream");
+    return dst;
+  }
+
+  CUDF_FAIL("Unsupported compressed stream type");
+}
+
+/**
+ * @brief ZLIB host decompressor (no header)
+ */
+size_t decompress_zlib(host_span<uint8_t const> src, host_span<uint8_t> dst)
+{
+  size_t uncomp_size = dst.size();
+  CUDF_EXPECTS(0 == cpu_inflate(dst.data(), &uncomp_size, src.data(), src.size()),
+               "ZLIB decompression failed");
+  return uncomp_size;
+}
+
+/**
+ * @brief GZIP host decompressor (includes header)
+ */
+size_t decompress_gzip(host_span<uint8_t const> src, host_span<uint8_t> dst)
+{
+  gz_archive_s gz;
+  auto const parse_succeeded = ParseGZArchive(&gz, src.data(), src.size());
+  CUDF_EXPECTS(parse_succeeded, "Failed to parse GZIP header");
+  return decompress_zlib({gz.comp_data, gz.comp_len}, dst);
+}
+
+/**
+ * @brief SNAPPY host decompressor
+ */
+size_t decompress_snappy(host_span<uint8_t const> src, host_span<uint8_t> dst)
+{
+  CUDF_EXPECTS(not dst.empty() and src.size() >= 1, "invalid Snappy decompress inputs");
+  uint32_t uncompressed_size, bytes_left, dst_pos;
+  auto cur       = src.begin();
+  auto const end = src.end();
+  // Read uncompressed length (varint)
+  {
+    uint32_t l        = 0, c;
+    uncompressed_size = 0;
+    do {
+      c              = *cur++;
+      auto const lo7 = c & 0x7f;
+      if (l >= 28 && c > 0xf) { return 0; }
+      uncompressed_size |= lo7 << l;
+      l += 7;
+    } while (c > 0x7f && cur < end);
+    CUDF_EXPECTS(uncompressed_size != 0 and uncompressed_size <= dst.size() and cur < end,
+                 "Destination buffer too small");
+  }
+  // Decode lz77
+  dst_pos    = 0;
+  bytes_left = uncompressed_size;
+  do {
+    uint32_t blen = *cur++;
+
+    if (blen & 3) {
+      // Copy
+      uint32_t offset;
+      if (blen & 2) {
+        // xxxxxx1x: copy with 6-bit length, 2-byte or 4-byte offset
+        if (cur + 2 > end) break;
+        offset = *reinterpret_cast<uint16_t const*>(cur);
+        cur += 2;
+        if (blen & 1)  // 4-byte offset
+        {
+          if (cur + 2 > end) break;
+          offset |= (*reinterpret_cast<uint16_t const*>(cur)) << 16;
+          cur += 2;
+        }
+        blen = (blen >> 2) + 1;
+      } else {
+        // xxxxxx01.oooooooo: copy with 3-bit length, 11-bit offset
+        if (cur >= end) break;
+        offset = ((blen & 0xe0) << 3) | (*cur++);
+        blen   = ((blen >> 2) & 7) + 4;
+      }
+      if (offset - 1u >= dst_pos || blen > bytes_left) break;
+      bytes_left -= blen;
+      do {
+        dst[dst_pos] = dst[dst_pos - offset];
+        dst_pos++;
+      } while (--blen);
+    } else {
+      // xxxxxx00: literal
+      blen >>= 2;
+      if (blen >= 60) {
+        uint32_t const num_bytes = blen - 59;
+        if (cur + num_bytes >= end) break;
+        blen = cur[0];
+        if (num_bytes > 1) {
+          blen |= cur[1] << 8;
+          if (num_bytes > 2) {
+            blen |= cur[2] << 16;
+            if (num_bytes > 3) { blen |= cur[3] << 24; }
+          }
+        }
+        cur += num_bytes;
+      }
+      blen++;
+      if (cur + blen > end || blen > bytes_left) break;
+      memcpy(dst.data() + dst_pos, cur, blen);
+      cur += blen;
+      dst_pos += blen;
+      bytes_left -= blen;
+    }
+  } while (bytes_left && cur < end);
+  CUDF_EXPECTS(bytes_left == 0, "Snappy Decompression failed");
+  return uncompressed_size;
+}
+
+/**
+ * @brief ZSTD decompressor that uses nvcomp
+ */
+size_t decompress_zstd(host_span<uint8_t const> src,
+                       host_span<uint8_t> dst,
+                       rmm::cuda_stream_view stream)
+{
+  // Init device span of spans (source)
+  auto const d_src =
+    cudf::detail::make_device_uvector_async(src, stream, rmm::mr::get_current_device_resource());
+  auto hd_srcs = cudf::detail::hostdevice_vector<device_span<uint8_t const>>(1, stream);
+  hd_srcs[0]   = d_src;
+  hd_srcs.host_to_device_async(stream);
+
+  // Init device span of spans (temporary destination)
+  auto d_dst   = rmm::device_uvector<uint8_t>(dst.size(), stream);
+  auto hd_dsts = cudf::detail::hostdevice_vector<device_span<uint8_t>>(1, stream);
+  hd_dsts[0]   = d_dst;
+  hd_dsts.host_to_device_async(stream);
+
+  auto hd_stats = cudf::detail::hostdevice_vector<compression_result>(1, stream);
+  hd_stats[0]   = compression_result{0, compression_status::FAILURE};
+  hd_stats.host_to_device_async(stream);
+  auto const max_uncomp_page_size = dst.size();
+  nvcomp::batched_decompress(nvcomp::compression_type::ZSTD,
+                             hd_srcs,
+                             hd_dsts,
+                             hd_stats,
+                             max_uncomp_page_size,
+                             max_uncomp_page_size,
+                             stream);
+
+  hd_stats.device_to_host_sync(stream);
+  CUDF_EXPECTS(hd_stats[0].status == compression_status::SUCCESS, "ZSTD decompression failed");
+
+  // Copy temporary output to `dst`
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    dst.data(), d_dst.data(), hd_stats[0].bytes_written, cudaMemcpyDefault, stream.value()));
+
+  return hd_stats[0].bytes_written;
+}
+
+size_t decompress(compression_type compression,
+                  host_span<uint8_t const> src,
+                  host_span<uint8_t> dst,
+                  rmm::cuda_stream_view stream)
+{
+  switch (compression) {
+    case compression_type::GZIP: return decompress_gzip(src, dst);
+    case compression_type::ZLIB: return decompress_zlib(src, dst);
+    case compression_type::SNAPPY: return decompress_snappy(src, dst);
+    case compression_type::ZSTD: return decompress_zstd(src, dst, stream);
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/comp/unsnap.cu b/cpp/src/io/comp/unsnap.cu
new file mode 100644
index 0000000..504a2fe
--- /dev/null
+++ b/cpp/src/io/comp/unsnap.cu
@@ -0,0 +1,728 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "gpuinflate.hpp"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cub/cub.cuh>
+
+namespace cudf {
+namespace io {
+constexpr int32_t batch_size    = (1 << 5);
+constexpr int32_t batch_count   = (1 << 2);
+constexpr int32_t prefetch_size = (1 << 9);  // 512B, in 32B chunks
+constexpr bool log_cyclecount   = false;
+
+void __device__ busy_wait(size_t cycles)
+{
+  clock_t start = clock();
+  for (;;) {
+    clock_t const now     = clock();
+    clock_t const elapsed = now > start ? now - start : now + (0xffff'ffff - start);
+    if (elapsed >= cycles) return;
+  }
+}
+
+/**
+ * @brief Describes a single LZ77 symbol (single entry in batch)
+ */
+struct unsnap_batch_s {
+  int32_t len;  // 1..64 = Number of bytes
+  uint32_t
+    offset;  // copy distance if greater than zero or negative of literal offset in byte stream
+};
+
+/**
+ * @brief Queue structure used to exchange data between warps
+ */
+struct unsnap_queue_s {
+  unsnap_queue_s() = default;  // required to compile on ctk-12.2 + aarch64
+
+  uint32_t prefetch_wrpos;         ///< Prefetcher write position
+  uint32_t prefetch_rdpos;         ///< Prefetch consumer read position
+  int32_t prefetch_end;            ///< Prefetch enable flag (nonzero stops prefetcher)
+  int32_t batch_len[batch_count];  ///< Length of each batch - <0:end, 0:not ready, >0:symbol count
+  unsnap_batch_s batch[batch_count * batch_size];  ///< LZ77 batch data
+  uint8_t buf[prefetch_size];                      ///< Prefetch buffer
+};
+
+/**
+ * @brief snappy decompression state
+ */
+struct unsnap_state_s {
+  constexpr unsnap_state_s() noexcept {}  // required to compile on ctk-12.2 + aarch64
+
+  uint8_t const* base{};           ///< base ptr of compressed stream
+  uint8_t const* end{};            ///< end of compressed stream
+  uint32_t uncompressed_size{};    ///< uncompressed stream size
+  uint32_t bytes_left{};           ///< remaining bytes to decompress
+  int32_t error{};                 ///< current error status
+  uint32_t tstart{};               ///< start time for perf logging
+  volatile unsnap_queue_s q{};     ///< queue for cross-warp communication
+  device_span<uint8_t const> src;  ///< input for current block
+  device_span<uint8_t> dst;        ///< output for current block
+};
+
+inline __device__ volatile uint8_t& byte_access(unsnap_state_s* s, uint32_t pos)
+{
+  return s->q.buf[pos & (prefetch_size - 1)];
+}
+
+/**
+ * @brief prefetches data for the symbol decoding stage
+ *
+ * @param s decompression state
+ * @param t warp lane id
+ */
+__device__ void snappy_prefetch_bytestream(unsnap_state_s* s, int t)
+{
+  uint8_t const* base = s->base;
+  auto end            = (uint32_t)(s->end - base);
+  auto align_bytes    = (uint32_t)(0x20 - (0x1f & reinterpret_cast<uintptr_t>(base)));
+  int32_t pos         = min(align_bytes, end);
+  int32_t blen;
+  // Start by prefetching up to the next a 32B-aligned location
+  if (t < pos) { s->q.buf[t] = base[t]; }
+  blen = 0;
+  do {
+    __syncwarp();
+    if (!t) {
+      uint32_t minrdpos;
+      s->q.prefetch_wrpos = pos;
+      minrdpos            = pos - min(pos, prefetch_size - 32u);
+      blen                = (int)min(32u, end - pos);
+      for (;;) {
+        uint32_t rdpos = s->q.prefetch_rdpos;
+        if (rdpos >= minrdpos) break;
+        if (s->q.prefetch_end) {
+          blen = 0;
+          break;
+        }
+        busy_wait(20);
+      }
+    }
+    blen = shuffle(blen);
+    if (t < blen) { byte_access(s, pos + t) = base[pos + t]; }
+    pos += blen;
+  } while (blen > 0);
+}
+
+/**
+ * @brief Lookup table for get_len3_mask()
+ *
+ * Indexed by a 10-bit pattern, contains the corresponding 4-bit mask of
+ * 3-byte code lengths in the lower 4 bits, along with the total number of
+ * bytes used for coding the four lengths in the upper 4 bits.
+ * The upper 4-bit value could also be obtained by 8+__popc(mask4)
+ *
+ *   for (uint32_t k = 0; k < 1024; k++)
+ *   {
+ *       for (uint32_t i = 0, v = 0, b = k, n = 0; i < 4; i++)
+ *       {
+ *           v |= (b & 1) << i;
+ *           n += (b & 1) + 2;
+ *           b >>= (b & 1) + 2;
+ *       }
+ *       k_len3lut[k] = v | (n << 4);
+ *   }
+ */
+static const uint8_t __device__ __constant__ k_len3lut[1 << 10] = {
+  0x80, 0x91, 0x80, 0x91, 0x92, 0x91, 0x92, 0x91, 0x80, 0xa3, 0x80, 0xa3, 0x92, 0xa3, 0x92, 0xa3,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xa3, 0x94, 0xa3, 0x92, 0xa3, 0x92, 0xa3,
+  0x80, 0xa5, 0x80, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x80, 0xa3, 0x80, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0x94, 0xa5, 0x94, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x94, 0xa3, 0x94, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0x98, 0x91, 0x98, 0x91, 0x92, 0x91, 0x92, 0x91, 0x98, 0xb7, 0x98, 0xb7, 0x92, 0xb7, 0x92, 0xb7,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xb7, 0x94, 0xb7, 0x92, 0xb7, 0x92, 0xb7,
+  0x98, 0xa5, 0x98, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x98, 0xb7, 0x98, 0xb7, 0xa6, 0xb7, 0xa6, 0xb7,
+  0x94, 0xa5, 0x94, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x94, 0xb7, 0x94, 0xb7, 0xa6, 0xb7, 0xa6, 0xb7,
+  0x80, 0xa9, 0x80, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x80, 0xa3, 0x80, 0xa3, 0xaa, 0xa3, 0xaa, 0xa3,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xa3, 0xac, 0xa3, 0xaa, 0xa3, 0xaa, 0xa3,
+  0x80, 0xa5, 0x80, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x80, 0xa3, 0x80, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0xac, 0xa5, 0xac, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0xac, 0xa3, 0xac, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0x98, 0xa9, 0x98, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x98, 0xb7, 0x98, 0xb7, 0xaa, 0xb7, 0xaa, 0xb7,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xb7, 0xac, 0xb7, 0xaa, 0xb7, 0xaa, 0xb7,
+  0x98, 0xa5, 0x98, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x98, 0xb7, 0x98, 0xb7, 0xa6, 0xb7, 0xa6, 0xb7,
+  0xac, 0xa5, 0xac, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0xac, 0xb7, 0xac, 0xb7, 0xa6, 0xb7, 0xa6, 0xb7,
+  0x80, 0x91, 0x80, 0x91, 0x92, 0x91, 0x92, 0x91, 0x80, 0xbb, 0x80, 0xbb, 0x92, 0xbb, 0x92, 0xbb,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xbb, 0x94, 0xbb, 0x92, 0xbb, 0x92, 0xbb,
+  0x80, 0xbd, 0x80, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x80, 0xbb, 0x80, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0x94, 0xbd, 0x94, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x94, 0xbb, 0x94, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0x98, 0x91, 0x98, 0x91, 0x92, 0x91, 0x92, 0x91, 0x98, 0xb7, 0x98, 0xb7, 0x92, 0xb7, 0x92, 0xb7,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xb7, 0x94, 0xb7, 0x92, 0xb7, 0x92, 0xb7,
+  0x98, 0xbd, 0x98, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x98, 0xb7, 0x98, 0xb7, 0xbe, 0xb7, 0xbe, 0xb7,
+  0x94, 0xbd, 0x94, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x94, 0xb7, 0x94, 0xb7, 0xbe, 0xb7, 0xbe, 0xb7,
+  0x80, 0xa9, 0x80, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x80, 0xbb, 0x80, 0xbb, 0xaa, 0xbb, 0xaa, 0xbb,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xbb, 0xac, 0xbb, 0xaa, 0xbb, 0xaa, 0xbb,
+  0x80, 0xbd, 0x80, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x80, 0xbb, 0x80, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0xac, 0xbd, 0xac, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0xac, 0xbb, 0xac, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0x98, 0xa9, 0x98, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x98, 0xb7, 0x98, 0xb7, 0xaa, 0xb7, 0xaa, 0xb7,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xb7, 0xac, 0xb7, 0xaa, 0xb7, 0xaa, 0xb7,
+  0x98, 0xbd, 0x98, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x98, 0xb7, 0x98, 0xb7, 0xbe, 0xb7, 0xbe, 0xb7,
+  0xac, 0xbd, 0xac, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0xac, 0xb7, 0xac, 0xb7, 0xbe, 0xb7, 0xbe, 0xb7,
+  0x80, 0x91, 0x80, 0x91, 0x92, 0x91, 0x92, 0x91, 0x80, 0xa3, 0x80, 0xa3, 0x92, 0xa3, 0x92, 0xa3,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xa3, 0x94, 0xa3, 0x92, 0xa3, 0x92, 0xa3,
+  0x80, 0xa5, 0x80, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x80, 0xa3, 0x80, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0x94, 0xa5, 0x94, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x94, 0xa3, 0x94, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0x98, 0x91, 0x98, 0x91, 0x92, 0x91, 0x92, 0x91, 0x98, 0xcf, 0x98, 0xcf, 0x92, 0xcf, 0x92, 0xcf,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xcf, 0x94, 0xcf, 0x92, 0xcf, 0x92, 0xcf,
+  0x98, 0xa5, 0x98, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x98, 0xcf, 0x98, 0xcf, 0xa6, 0xcf, 0xa6, 0xcf,
+  0x94, 0xa5, 0x94, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x94, 0xcf, 0x94, 0xcf, 0xa6, 0xcf, 0xa6, 0xcf,
+  0x80, 0xa9, 0x80, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x80, 0xa3, 0x80, 0xa3, 0xaa, 0xa3, 0xaa, 0xa3,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xa3, 0xac, 0xa3, 0xaa, 0xa3, 0xaa, 0xa3,
+  0x80, 0xa5, 0x80, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x80, 0xa3, 0x80, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0xac, 0xa5, 0xac, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0xac, 0xa3, 0xac, 0xa3, 0xa6, 0xa3, 0xa6, 0xa3,
+  0x98, 0xa9, 0x98, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x98, 0xcf, 0x98, 0xcf, 0xaa, 0xcf, 0xaa, 0xcf,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xcf, 0xac, 0xcf, 0xaa, 0xcf, 0xaa, 0xcf,
+  0x98, 0xa5, 0x98, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0x98, 0xcf, 0x98, 0xcf, 0xa6, 0xcf, 0xa6, 0xcf,
+  0xac, 0xa5, 0xac, 0xa5, 0xa6, 0xa5, 0xa6, 0xa5, 0xac, 0xcf, 0xac, 0xcf, 0xa6, 0xcf, 0xa6, 0xcf,
+  0x80, 0x91, 0x80, 0x91, 0x92, 0x91, 0x92, 0x91, 0x80, 0xbb, 0x80, 0xbb, 0x92, 0xbb, 0x92, 0xbb,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xbb, 0x94, 0xbb, 0x92, 0xbb, 0x92, 0xbb,
+  0x80, 0xbd, 0x80, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x80, 0xbb, 0x80, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0x94, 0xbd, 0x94, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x94, 0xbb, 0x94, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0x98, 0x91, 0x98, 0x91, 0x92, 0x91, 0x92, 0x91, 0x98, 0xcf, 0x98, 0xcf, 0x92, 0xcf, 0x92, 0xcf,
+  0x94, 0x91, 0x94, 0x91, 0x92, 0x91, 0x92, 0x91, 0x94, 0xcf, 0x94, 0xcf, 0x92, 0xcf, 0x92, 0xcf,
+  0x98, 0xbd, 0x98, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x98, 0xcf, 0x98, 0xcf, 0xbe, 0xcf, 0xbe, 0xcf,
+  0x94, 0xbd, 0x94, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x94, 0xcf, 0x94, 0xcf, 0xbe, 0xcf, 0xbe, 0xcf,
+  0x80, 0xa9, 0x80, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x80, 0xbb, 0x80, 0xbb, 0xaa, 0xbb, 0xaa, 0xbb,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xbb, 0xac, 0xbb, 0xaa, 0xbb, 0xaa, 0xbb,
+  0x80, 0xbd, 0x80, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x80, 0xbb, 0x80, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0xac, 0xbd, 0xac, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0xac, 0xbb, 0xac, 0xbb, 0xbe, 0xbb, 0xbe, 0xbb,
+  0x98, 0xa9, 0x98, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0x98, 0xcf, 0x98, 0xcf, 0xaa, 0xcf, 0xaa, 0xcf,
+  0xac, 0xa9, 0xac, 0xa9, 0xaa, 0xa9, 0xaa, 0xa9, 0xac, 0xcf, 0xac, 0xcf, 0xaa, 0xcf, 0xaa, 0xcf,
+  0x98, 0xbd, 0x98, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0x98, 0xcf, 0x98, 0xcf, 0xbe, 0xcf, 0xbe, 0xcf,
+  0xac, 0xbd, 0xac, 0xbd, 0xbe, 0xbd, 0xbe, 0xbd, 0xac, 0xcf, 0xac, 0xcf, 0xbe, 0xcf, 0xbe, 0xcf};
+
+/**
+ * @brief Returns a 32-bit mask where 1 means 3-byte code length and 0 means 2-byte
+ * code length, given an input mask of up to 96 bits.
+ *
+ * Implemented by doing 8 consecutive lookups, building the result 4-bit at a time
+ */
+inline __device__ uint32_t get_len3_mask(uint32_t v0, uint32_t v1, uint32_t v2)
+{
+  uint32_t m, v, m4, n;
+  v  = v0;
+  m4 = k_len3lut[v & 0x3ff];
+  m  = m4 & 0xf;
+  n  = m4 >> 4;  // 8..12
+  v  = v0 >> n;
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 4;
+  n += m4 >> 4;  // 16..24
+  v  = __funnelshift_r(v0, v1, n);
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 8;
+  n += m4 >> 4;  // 24..36
+  v >>= (m4 >> 4);
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 12;
+  n  = (n + (m4 >> 4)) & 0x1f;  // (32..48) % 32 = 0..16
+  v1 = __funnelshift_r(v1, v2, n);
+  v2 >>= n;
+  v  = v1;
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 16;
+  n  = m4 >> 4;  // 8..12
+  v  = v1 >> n;
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 20;
+  n += m4 >> 4;  // 16..24
+  v  = __funnelshift_r(v1, v2, n);
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 24;
+  n += m4 >> 4;  // 24..36
+  v >>= (m4 >> 4);
+  m4 = k_len3lut[v & 0x3ff];
+  m |= (m4 & 0xf) << 28;
+  return m;
+}
+
+/**
+ * @brief Returns a 32-bit mask where each 2-bit pair contains the symbol length
+ * minus 2, given two input masks each containing bit0 or bit1 of the corresponding
+ * code length minus 2 for up to 32 bytes
+ */
+inline __device__ uint32_t get_len5_mask(uint32_t v0, uint32_t v1)
+{
+  uint32_t m;
+  m = (v1 & 1) * 2 + (v0 & 1);
+  v0 >>= (m + 2);
+  v1 >>= (m + 1);
+  for (uint32_t i = 1; i < 16; i++) {
+    uint32_t m2 = (v1 & 2) | (v0 & 1);
+    uint32_t n  = m2 + 2;
+    m |= m2 << (i * 2);
+    v0 >>= n;
+    v1 >>= n;
+  }
+  return m;
+}
+
+/**
+ * @brief decode symbols and output LZ77 batches (single-warp)
+ *
+ * @param s decompression state
+ * @param t warp lane id
+ */
+__device__ void snappy_decode_symbols(unsnap_state_s* s, uint32_t t)
+{
+  uint32_t cur        = 0;
+  auto end            = static_cast<uint32_t>(s->end - s->base);
+  uint32_t bytes_left = s->uncompressed_size;
+  uint32_t dst_pos    = 0;
+  int32_t batch       = 0;
+
+  for (;;) {
+    int32_t batch_len;
+    volatile unsnap_batch_s* b;
+
+    // Wait for prefetcher
+    if (t == 0) {
+      s->q.prefetch_rdpos = cur;
+#pragma unroll(1)  // We don't want unrolling here
+      while (s->q.prefetch_wrpos < min(cur + 5 * batch_size, end)) {
+        busy_wait(10);
+      }
+      b = &s->q.batch[batch * batch_size];
+    }
+    // Process small symbols in parallel: for data that does not get good compression,
+    // the stream will consist of a large number of short literals (1-byte or 2-byte)
+    // followed by short repeat runs. This results in many 2-byte or 3-byte symbols
+    // that can all be decoded in parallel once we know the symbol length.
+    {
+      uint32_t v0, v1, v2, len3_mask, cur_t, is_long_sym, short_sym_mask;
+      uint32_t b0;
+      cur            = shuffle(cur);
+      cur_t          = cur + t;
+      b0             = byte_access(s, cur_t);
+      v0             = ballot((b0 == 4) || (b0 & 2));
+      b0             = byte_access(s, cur_t + 32);
+      v1             = ballot((b0 == 4) || (b0 & 2));
+      b0             = byte_access(s, cur_t + 64);
+      v2             = ballot((b0 == 4) || (b0 & 2));
+      len3_mask      = shuffle((t == 0) ? get_len3_mask(v0, v1, v2) : 0);
+      cur_t          = cur + 2 * t + __popc(len3_mask & ((1 << t) - 1));
+      b0             = byte_access(s, cur_t);
+      is_long_sym    = ((b0 & ~4) != 0) && (((b0 + 1) & 2) == 0);
+      short_sym_mask = ballot(is_long_sym);
+      batch_len      = 0;
+      b = reinterpret_cast<volatile unsnap_batch_s*>(shuffle(reinterpret_cast<uintptr_t>(b)));
+      if (!(short_sym_mask & 1)) {
+        batch_len = shuffle((t == 0) ? (short_sym_mask) ? __ffs(short_sym_mask) - 1 : 32 : 0);
+        if (batch_len != 0) {
+          uint32_t blen = 0;
+          int32_t ofs   = 0;
+          if (t < batch_len) {
+            blen        = (b0 & 1) ? ((b0 >> 2) & 7) + 4 : ((b0 >> 2) + 1);
+            ofs         = (b0 & 1)   ? ((b0 & 0xe0) << 3) | byte_access(s, cur_t + 1)
+                          : (b0 & 2) ? byte_access(s, cur_t + 1) | (byte_access(s, cur_t + 2) << 8)
+                                     : -(int32_t)(cur_t + 1);
+            b[t].len    = blen;
+            b[t].offset = ofs;
+            ofs += blen;  // for correct out-of-range detection below
+          }
+          blen           = WarpReducePos32(blen, t);
+          bytes_left     = shuffle(bytes_left);
+          dst_pos        = shuffle(dst_pos);
+          short_sym_mask = __ffs(ballot(blen > bytes_left || ofs > (int32_t)(dst_pos + blen)));
+          if (short_sym_mask != 0) { batch_len = min(batch_len, short_sym_mask - 1); }
+          if (batch_len != 0) {
+            blen = shuffle(blen, batch_len - 1);
+            cur  = shuffle(cur_t, batch_len - 1) + 2 + ((len3_mask >> (batch_len - 1)) & 1);
+            if (t == 0) {
+              dst_pos += blen;
+              bytes_left -= blen;
+            }
+          }
+        }
+      }
+      // Check if the batch was stopped by a 3-byte or 4-byte literal
+      if (batch_len < batch_size - 2 && shuffle(b0 & ~4, batch_len) == 8) {
+        // If so, run a slower version of the above that can also handle 3/4-byte literal sequences
+        uint32_t batch_add;
+        do {
+          uint32_t clen, mask_t;
+          cur_t     = cur + t;
+          b0        = byte_access(s, cur_t);
+          clen      = (b0 & 3) ? (b0 & 2) ? 1 : 0 : (b0 >> 2);  // symbol length minus 2
+          v0        = ballot(clen & 1);
+          v1        = ballot((clen >> 1) & 1);
+          len3_mask = shuffle((t == 0) ? get_len5_mask(v0, v1) : 0);
+          mask_t    = (1 << (2 * t)) - 1;
+          cur_t     = cur + 2 * t + 2 * __popc((len3_mask & 0xaaaa'aaaa) & mask_t) +
+                  __popc((len3_mask & 0x5555'5555) & mask_t);
+          b0          = byte_access(s, cur_t);
+          is_long_sym = ((b0 & 3) ? ((b0 & 3) == 3) : (b0 > 3 * 4)) || (cur_t >= cur + 32) ||
+                        (batch_len + t >= batch_size);
+          batch_add = __ffs(ballot(is_long_sym)) - 1;
+          if (batch_add != 0) {
+            uint32_t blen = 0;
+            int32_t ofs   = 0;
+            if (t < batch_add) {
+              blen                    = (b0 & 1) ? ((b0 >> 2) & 7) + 4 : ((b0 >> 2) + 1);
+              ofs                     = (b0 & 1) ? ((b0 & 0xe0) << 3) | byte_access(s, cur_t + 1)
+                                        : (b0 & 2) ? byte_access(s, cur_t + 1) | (byte_access(s, cur_t + 2) << 8)
+                                                   : -(int32_t)(cur_t + 1);
+              b[batch_len + t].len    = blen;
+              b[batch_len + t].offset = ofs;
+              ofs += blen;  // for correct out-of-range detection below
+            }
+            blen           = WarpReducePos32(blen, t);
+            bytes_left     = shuffle(bytes_left);
+            dst_pos        = shuffle(dst_pos);
+            short_sym_mask = __ffs(ballot(blen > bytes_left || ofs > (int32_t)(dst_pos + blen)));
+            if (short_sym_mask != 0) { batch_add = min(batch_add, short_sym_mask - 1); }
+            if (batch_add != 0) {
+              blen = shuffle(blen, batch_add - 1);
+              cur  = shuffle(cur_t, batch_add - 1) + 2 + ((len3_mask >> ((batch_add - 1) * 2)) & 3);
+              if (t == 0) {
+                dst_pos += blen;
+                bytes_left -= blen;
+              }
+              batch_len += batch_add;
+            }
+          }
+        } while (batch_add >= 6 && batch_len < batch_size - 2);
+      }
+    }
+    if (t == 0) {
+      while (bytes_left > 0 && batch_len < batch_size) {
+        uint32_t blen, offset;
+        uint8_t b0 = byte_access(s, cur);
+        if (b0 & 3) {
+          uint8_t b1 = byte_access(s, cur + 1);
+          if (!(b0 & 2)) {
+            // xxxxxx01.oooooooo: copy with 3-bit length, 11-bit offset
+            offset = ((b0 & 0xe0) << 3) | b1;
+            blen   = ((b0 >> 2) & 7) + 4;
+            cur += 2;
+          } else {
+            // xxxxxx1x: copy with 6-bit length, 2-byte or 4-byte offset
+            offset = b1 | (byte_access(s, cur + 2) << 8);
+            if (b0 & 1)  // 4-byte offset
+            {
+              offset |= (byte_access(s, cur + 3) << 16) | (byte_access(s, cur + 4) << 24);
+              cur += 5;
+            } else {
+              cur += 3;
+            }
+            blen = (b0 >> 2) + 1;
+          }
+          dst_pos += blen;
+          if (offset - 1u >= dst_pos || bytes_left < blen) break;
+          bytes_left -= blen;
+        } else if (b0 < 4 * 4) {
+          // 0000xx00: short literal
+          blen   = (b0 >> 2) + 1;
+          offset = -(int32_t)(cur + 1);
+          cur += 1 + blen;
+          dst_pos += blen;
+          if (bytes_left < blen) break;
+          bytes_left -= blen;
+        } else {
+          // xxxxxx00: literal
+          blen = b0 >> 2;
+          if (blen >= 60) {
+            uint32_t num_bytes = blen - 59;
+            blen               = byte_access(s, cur + 1);
+            if (num_bytes > 1) {
+              blen |= byte_access(s, cur + 2) << 8;
+              if (num_bytes > 2) {
+                blen |= byte_access(s, cur + 3) << 16;
+                if (num_bytes > 3) { blen |= byte_access(s, cur + 4) << 24; }
+              }
+            }
+            cur += num_bytes;
+          }
+          cur += 1;
+          blen += 1;
+          offset = -(int32_t)cur;
+          cur += blen;
+          // Wait for prefetcher
+          s->q.prefetch_rdpos = cur;
+#pragma unroll(1)  // We don't want unrolling here
+          while (s->q.prefetch_wrpos < min(cur + 5 * batch_size, end)) {
+            busy_wait(10);
+          }
+          dst_pos += blen;
+          if (bytes_left < blen) break;
+          bytes_left -= blen;
+        }
+        b[batch_len].len    = blen;
+        b[batch_len].offset = offset;
+        batch_len++;
+      }
+      if (batch_len != 0) {
+        s->q.batch_len[batch] = batch_len;
+        batch                 = (batch + 1) & (batch_count - 1);
+      }
+    }
+    batch_len = shuffle(batch_len);
+    if (t == 0) {
+      while (s->q.batch_len[batch] != 0) {
+        busy_wait(20);
+      }
+    }
+    if (batch_len != batch_size) { break; }
+  }
+  if (!t) {
+    s->q.prefetch_end     = 1;
+    s->q.batch_len[batch] = -1;
+    s->bytes_left         = bytes_left;
+    if (bytes_left != 0) { s->error = -2; }
+  }
+}
+
+/**
+ * @brief process LZ77 symbols and output uncompressed stream
+ *
+ * @param s decompression state
+ * @param t thread id within participating group (lane id)
+ * @param temp_storage temporary storage used by the algorithm
+ *
+ * NOTE: No error checks at this stage (WARP0 responsible for not sending offsets and lengths that
+ *would result in out-of-bounds accesses)
+ */
+template <typename Storage>
+__device__ void snappy_process_symbols(unsnap_state_s* s, int t, Storage& temp_storage)
+{
+  auto const literal_base = s->base;
+  auto out                = s->dst.data();
+  int batch               = 0;
+
+  do {
+    volatile unsnap_batch_s* b = &s->q.batch[batch * batch_size];
+    int32_t batch_len, blen_t, dist_t;
+
+    if (t == 0) {
+      while ((batch_len = s->q.batch_len[batch]) == 0) {
+        busy_wait(20);
+      }
+    } else {
+      batch_len = 0;
+    }
+    batch_len = shuffle(batch_len);
+    if (batch_len <= 0) { break; }
+    if (t < batch_len) {
+      blen_t = b[t].len;
+      dist_t = b[t].offset;
+    } else {
+      blen_t = dist_t = 0;
+    }
+    // Try to combine as many small entries as possible, but try to avoid doing that
+    // if we see a small repeat distance 8 bytes or less
+    if (shuffle(min((uint32_t)dist_t, (uint32_t)shuffle_xor(dist_t, 1))) > 8) {
+      uint32_t n;
+      do {
+        uint32_t bofs      = WarpReducePos32(blen_t, t);
+        uint32_t stop_mask = ballot((uint32_t)dist_t < bofs);
+        uint32_t start_mask =
+          cub::WarpReduce<uint32_t>(temp_storage).Sum((bofs < 32 && t < batch_len) ? 1 << bofs : 0);
+        start_mask = shuffle(start_mask);
+        n          = min(min((uint32_t)__popc(start_mask), (uint32_t)(__ffs(stop_mask) - 1u)),
+                (uint32_t)batch_len);
+        if (n != 0) {
+          uint32_t it  = __popc(start_mask & ((2 << t) - 1));
+          uint32_t tr  = t - shuffle(bofs - blen_t, it);
+          int32_t dist = shuffle(dist_t, it);
+          if (it < n) {
+            uint8_t const* src = (dist > 0) ? (out + t - dist) : (literal_base + tr - dist);
+            out[t]             = *src;
+          }
+          out += shuffle(bofs, n - 1);
+          blen_t = shuffle(blen_t, (n + t) & 0x1f);
+          dist_t = shuffle(dist_t, (n + t) & 0x1f);
+          batch_len -= n;
+        }
+      } while (n >= 4);
+    }
+    for (int i = 0; i < batch_len; i++) {
+      int32_t blen  = shuffle(blen_t, i);
+      int32_t dist  = shuffle(dist_t, i);
+      int32_t blen2 = (i + 1 < batch_len) ? shuffle(blen_t, i + 1) : 32;
+      // Try to combine consecutive small entries if they are independent
+      if ((uint32_t)dist >= (uint32_t)blen && blen + blen2 <= 32) {
+        int32_t dist2 = shuffle(dist_t, i + 1);
+        if ((uint32_t)dist2 >= (uint32_t)(blen + blen2)) {
+          int32_t d;
+          if (t < blen) {
+            d = dist;
+          } else {
+            dist = dist2;
+            d    = (dist2 <= 0) ? dist2 + blen : dist2;
+          }
+          blen += blen2;
+          if (t < blen) {
+            uint8_t const* src = (dist > 0) ? (out - d) : (literal_base - d);
+            out[t]             = src[t];
+          }
+          out += blen;
+          i++;
+          continue;
+        }
+      }
+      if (dist > 0) {
+        // Copy
+        uint8_t b0, b1;
+        if (t < blen) {
+          uint32_t pos       = t;
+          uint8_t const* src = out + ((pos >= dist) ? (pos % dist) : pos) - dist;
+          b0                 = *src;
+        }
+        if (32 + t < blen) {
+          uint32_t pos       = 32 + t;
+          uint8_t const* src = out + ((pos >= dist) ? (pos % dist) : pos) - dist;
+          b1                 = *src;
+        }
+        if (t < blen) { out[t] = b0; }
+        if (32 + t < blen) { out[32 + t] = b1; }
+      } else {
+        // Literal
+        uint8_t b0, b1;
+        dist = -dist;
+        while (blen >= 64) {
+          b0          = literal_base[dist + t];
+          b1          = literal_base[dist + 32 + t];
+          out[t]      = b0;
+          out[32 + t] = b1;
+          dist += 64;
+          out += 64;
+          blen -= 64;
+        }
+        if (t < blen) { b0 = literal_base[dist + t]; }
+        if (32 + t < blen) { b1 = literal_base[dist + 32 + t]; }
+        if (t < blen) { out[t] = b0; }
+        if (32 + t < blen) { out[32 + t] = b1; }
+      }
+      out += blen;
+    }
+    __syncwarp();
+    if (t == 0) { s->q.batch_len[batch] = 0; }
+    batch = (batch + 1) & (batch_count - 1);
+  } while (true);
+}
+
+/**
+ * @brief Snappy decompression kernel
+ * See http://github.com/google/snappy/blob/master/format_description.txt
+ *
+ * blockDim {128,1,1}
+ *
+ * @param[in] inputs Source & destination information per block
+ * @param[out] outputs Decompression status per block
+ */
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  unsnap_kernel(device_span<device_span<uint8_t const> const> inputs,
+                device_span<device_span<uint8_t> const> outputs,
+                device_span<compression_result> results)
+{
+  __shared__ __align__(16) unsnap_state_s state_g;
+  __shared__ cub::WarpReduce<uint32_t>::TempStorage temp_storage;
+  int t             = threadIdx.x;
+  unsnap_state_s* s = &state_g;
+  int strm_id       = blockIdx.x;
+
+  if (t < batch_count) { s->q.batch_len[t] = 0; }
+  __syncthreads();
+  if (!t) {
+    s->src         = inputs[strm_id];
+    s->dst         = outputs[strm_id];
+    auto cur       = s->src.begin();
+    auto const end = s->src.end();
+    s->error       = 0;
+    if (log_cyclecount) { s->tstart = clock(); }
+    if (cur < end) {
+      // Read uncompressed size (varint), limited to 32-bit
+      uint32_t uncompressed_size = *cur++;
+      if (uncompressed_size > 0x7f) {
+        uint32_t c        = (cur < end) ? *cur++ : 0;
+        uncompressed_size = (uncompressed_size & 0x7f) | (c << 7);
+        if (uncompressed_size >= (0x80 << 7)) {
+          c                 = (cur < end) ? *cur++ : 0;
+          uncompressed_size = (uncompressed_size & ((0x7f << 7) | 0x7f)) | (c << 14);
+          if (uncompressed_size >= (0x80 << 14)) {
+            c = (cur < end) ? *cur++ : 0;
+            uncompressed_size =
+              (uncompressed_size & ((0x7f << 14) | (0x7f << 7) | 0x7f)) | (c << 21);
+            if (uncompressed_size >= (0x80 << 21)) {
+              c = (cur < end) ? *cur++ : 0;
+              if (c < 0x8)
+                uncompressed_size =
+                  (uncompressed_size & ((0x7f << 21) | (0x7f << 14) | (0x7f << 7) | 0x7f)) |
+                  (c << 28);
+              else
+                s->error = -1;
+            }
+          }
+        }
+      }
+      s->uncompressed_size = uncompressed_size;
+      s->bytes_left        = uncompressed_size;
+      s->base              = cur;
+      s->end               = end;
+      if ((cur >= end && uncompressed_size != 0) || (uncompressed_size > s->dst.size())) {
+        s->error = -1;
+      }
+    } else {
+      s->error = -1;
+    }
+    s->q.prefetch_end   = 0;
+    s->q.prefetch_wrpos = 0;
+    s->q.prefetch_rdpos = 0;
+  }
+  __syncthreads();
+  if (!s->error) {
+    if (t < 32) {
+      // WARP0: decode lengths and offsets
+      snappy_decode_symbols(s, t);
+    } else if (t < 64) {
+      // WARP1: prefetch byte stream for WARP0
+      snappy_prefetch_bytestream(s, t & 0x1f);
+    } else if (t < 96) {
+      // WARP2: LZ77
+      snappy_process_symbols(s, t & 0x1f, temp_storage);
+    }
+    __syncthreads();
+  }
+  if (!t) {
+    results[strm_id].bytes_written = s->uncompressed_size - s->bytes_left;
+    results[strm_id].status =
+      (s->error == 0) ? compression_status::SUCCESS : compression_status::FAILURE;
+    if (log_cyclecount) {
+      results[strm_id].reserved = clock() - s->tstart;
+    } else {
+      results[strm_id].reserved = 0;
+    }
+  }
+}
+
+void gpu_unsnap(device_span<device_span<uint8_t const> const> inputs,
+                device_span<device_span<uint8_t> const> outputs,
+                device_span<compression_result> results,
+                rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);           // 4 warps per stream, 1 stream per block
+  dim3 dim_grid(inputs.size(), 1);  // TODO: Check max grid dimensions vs max expected count
+
+  unsnap_kernel<128><<<dim_grid, dim_block, 0, stream.value()>>>(inputs, outputs, results);
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/csv_common.hpp b/cpp/src/io/csv/csv_common.hpp
new file mode 100644
index 0000000..7c9c0b0
--- /dev/null
+++ b/cpp/src/io/csv/csv_common.hpp
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+#include <io/utilities/column_type_histogram.hpp>
+
+namespace cudf {
+namespace io {
+namespace csv {
+namespace column_parse {
+/**
+ * @brief Per-column parsing flags used for dtype detection and data conversion
+ */
+enum : uint8_t {
+  disabled       = 0,   ///< data is not read
+  enabled        = 1,   ///< data is read and parsed as usual
+  inferred       = 2,   ///< infer the dtype
+  as_default     = 4,   ///< no special decoding
+  as_hexadecimal = 8,   ///< decode with base-16
+  as_datetime    = 16,  ///< decode as date and/or time
+};
+using flags = uint8_t;
+
+}  // namespace column_parse
+
+}  // namespace csv
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/csv_gpu.cu b/cpp/src/io/csv/csv_gpu.cu
new file mode 100644
index 0000000..248e176
--- /dev/null
+++ b/cpp/src/io/csv/csv_gpu.cu
@@ -0,0 +1,866 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "csv_common.hpp"
+#include "csv_gpu.hpp"
+
+#include <io/utilities/block_utils.cuh>
+#include <io/utilities/parsing_utils.cuh>
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/strings/detail/convert/fixed_point.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <io/utilities/trie.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/detail/copy.h>
+#include <thrust/remove.h>
+#include <thrust/transform.h>
+
+#include <type_traits>
+
+using namespace ::cudf::io;
+
+using cudf::device_span;
+using cudf::detail::grid_1d;
+
+namespace cudf {
+namespace io {
+namespace csv {
+namespace gpu {
+
+/// Block dimension for dtype detection and conversion kernels
+constexpr uint32_t csvparse_block_dim = 128;
+
+/*
+ * @brief Returns true is the input character is a valid digit.
+ * Supports both decimal and hexadecimal digits (uppercase and lowercase).
+ *
+ * @param c Character to check
+ * @param is_hex Whether to check as a hexadecimal
+ *
+ * @return `true` if it is digit-like, `false` otherwise
+ */
+__device__ __inline__ bool is_digit(char c, bool is_hex = false)
+{
+  if (c >= '0' && c <= '9') return true;
+
+  if (is_hex) {
+    if (c >= 'A' && c <= 'F') return true;
+    if (c >= 'a' && c <= 'f') return true;
+  }
+
+  return false;
+}
+
+/*
+ * @brief Checks whether the given character counters indicate a potentially
+ * valid date and/or time field.
+ *
+ * For performance and simplicity, we detect only the most common date
+ * formats. Example formats that are detectable:
+ *
+ *    `2001/02/30`
+ *    `2001-02-30 00:00:00`
+ *    `2/30/2001 T04:05:60.7`
+ *    `2 / 1 / 2011`
+ *    `02/January`
+ *
+ * @param len Number of non special-symbol or numeric characters
+ * @param decimal_count Number of '.' characters
+ * @param colon_count Number of ':' characters
+ * @param dash_count Number of '-' characters
+ * @param slash_count Number of '/' characters
+ *
+ * @return `true` if it is date-like, `false` otherwise
+ */
+__device__ __inline__ bool is_datetime(
+  long len, long decimal_count, long colon_count, long dash_count, long slash_count)
+{
+  // Must not exceed count of longest month (September) plus `T` time indicator
+  if (len > 10) { return false; }
+  // Must not exceed more than one decimals or more than two time separators
+  if (decimal_count > 1 || colon_count > 2) { return false; }
+  // Must have one or two '-' or '/' but not both as date separators
+  if ((dash_count > 0 && dash_count < 3 && slash_count == 0) ||
+      (dash_count == 0 && slash_count > 0 && slash_count < 3)) {
+    return true;
+  }
+
+  return false;
+}
+
+/*
+ * @brief Returns true if the counters indicate a potentially valid float.
+ * False positives are possible because positions are not taken into account.
+ * For example, field "e.123-" would match the pattern.
+ *
+ * @param len Number of non special-symbol or numeric characters
+ * @param digit_count Number of digits characters
+ * @param decimal_count Number of occurrences of the decimal point character
+ * @param thousands_count Number of occurrences of the thousands separator character
+ * @param dash_count Number of '-' characters
+ * @param exponent_count Number of 'e or E' characters
+ *
+ * @return `true` if it is floating point-like, `false` otherwise
+ */
+__device__ __inline__ bool is_floatingpoint(long len,
+                                            long digit_count,
+                                            long decimal_count,
+                                            long thousands_count,
+                                            long dash_count,
+                                            long exponent_count)
+{
+  // Can't have more than one exponent and one decimal point
+  if (decimal_count > 1) return false;
+  if (exponent_count > 1) return false;
+
+  // Without the exponent or a decimal point, this is an integer, not a float
+  if (decimal_count == 0 && exponent_count == 0) return false;
+
+  // Can only have one '-' per component
+  if (dash_count > 1 + exponent_count) return false;
+
+  // If anything other than these characters is present, it's not a float
+  if (digit_count + decimal_count + dash_count + exponent_count + thousands_count != len) {
+    return false;
+  }
+
+  // Needs at least 1 digit, 2 if exponent is present
+  if (digit_count < 1 + exponent_count) return false;
+
+  return true;
+}
+
+/*
+ * @brief CUDA kernel that parses and converts CSV data into cuDF column data.
+ *
+ * Data is processed in one row/record at a time, so the number of total
+ * threads (tid) is equal to the number of rows.
+ *
+ * @param opts A set of parsing options
+ * @param csv_text The entire CSV data to read
+ * @param column_flags Per-column parsing behavior flags
+ * @param row_offsets The start the CSV data of interest
+ * @param d_column_data The count for each column data type
+ */
+__global__ void __launch_bounds__(csvparse_block_dim)
+  data_type_detection(parse_options_view const opts,
+                      device_span<char const> csv_text,
+                      device_span<column_parse::flags const> const column_flags,
+                      device_span<uint64_t const> const row_offsets,
+                      device_span<column_type_histogram> d_column_data)
+{
+  auto const raw_csv = csv_text.data();
+
+  // ThreadIds range per block, so also need the blockId
+  // This is entry into the fields; threadId is an element within `num_records`
+  auto const rec_id      = grid_1d::global_thread_id();
+  auto const rec_id_next = rec_id + 1;
+
+  // we can have more threads than data, make sure we are not past the end of the data
+  if (rec_id_next >= row_offsets.size()) { return; }
+
+  auto field_start   = raw_csv + row_offsets[rec_id];
+  auto const row_end = raw_csv + row_offsets[rec_id_next];
+
+  auto next_field = field_start;
+  int col         = 0;
+  int actual_col  = 0;
+
+  // Going through all the columns of a given record
+  while (col < column_flags.size() && field_start < row_end) {
+    auto next_delimiter = cudf::io::gpu::seek_field_end(field_start, row_end, opts);
+
+    // Checking if this is a column that the user wants --- user can filter columns
+    if (column_flags[col] & column_parse::inferred) {
+      // points to last character in the field
+      auto const field_len = static_cast<size_t>(next_delimiter - field_start);
+      if (serialized_trie_contains(opts.trie_na, {field_start, field_len})) {
+        atomicAdd(&d_column_data[actual_col].null_count, 1);
+      } else if (serialized_trie_contains(opts.trie_true, {field_start, field_len}) ||
+                 serialized_trie_contains(opts.trie_false, {field_start, field_len})) {
+        atomicAdd(&d_column_data[actual_col].bool_count, 1);
+      } else if (cudf::io::is_infinity(field_start, next_delimiter)) {
+        atomicAdd(&d_column_data[actual_col].float_count, 1);
+      } else {
+        long count_number    = 0;
+        long count_decimal   = 0;
+        long count_thousands = 0;
+        long count_slash     = 0;
+        long count_dash      = 0;
+        long count_plus      = 0;
+        long count_colon     = 0;
+        long count_string    = 0;
+        long count_exponent  = 0;
+
+        // Modify field_start & end to ignore whitespace and quotechars
+        // This could possibly result in additional empty fields
+        auto const trimmed_field_range = trim_whitespaces_quotes(field_start, next_delimiter);
+        auto const trimmed_field_len   = trimmed_field_range.second - trimmed_field_range.first;
+
+        for (auto cur = trimmed_field_range.first; cur < trimmed_field_range.second; ++cur) {
+          if (is_digit(*cur)) {
+            count_number++;
+            continue;
+          }
+          if (*cur == opts.decimal) {
+            count_decimal++;
+            continue;
+          }
+          if (*cur == opts.thousands) {
+            count_thousands++;
+            continue;
+          }
+          // Looking for unique characters that will help identify column types.
+          switch (*cur) {
+            case '-': count_dash++; break;
+            case '+': count_plus++; break;
+            case '/': count_slash++; break;
+            case ':': count_colon++; break;
+            case 'e':
+            case 'E':
+              if (cur > trimmed_field_range.first && cur < trimmed_field_range.second - 1)
+                count_exponent++;
+              break;
+            default: count_string++; break;
+          }
+        }
+
+        // Integers have to have the length of the string
+        // Off by one if they start with a minus sign
+        auto const int_req_number_cnt =
+          trimmed_field_len - count_thousands -
+          ((*trimmed_field_range.first == '-' || *trimmed_field_range.first == '+') &&
+           trimmed_field_len > 1);
+
+        if (column_flags[col] & column_parse::as_datetime) {
+          // PANDAS uses `object` dtype if the date is unparseable
+          if (is_datetime(count_string, count_decimal, count_colon, count_dash, count_slash)) {
+            atomicAdd(&d_column_data[actual_col].datetime_count, 1);
+          } else {
+            atomicAdd(&d_column_data[actual_col].string_count, 1);
+          }
+        } else if (count_number == int_req_number_cnt) {
+          auto const is_negative = (*trimmed_field_range.first == '-');
+          auto const data_begin =
+            trimmed_field_range.first + (is_negative || (*trimmed_field_range.first == '+'));
+          cudf::size_type* ptr = cudf::io::gpu::infer_integral_field_counter(
+            data_begin, data_begin + count_number, is_negative, d_column_data[actual_col]);
+          atomicAdd(ptr, 1);
+        } else if (is_floatingpoint(trimmed_field_len,
+                                    count_number,
+                                    count_decimal,
+                                    count_thousands,
+                                    count_dash + count_plus,
+                                    count_exponent)) {
+          atomicAdd(&d_column_data[actual_col].float_count, 1);
+        } else {
+          atomicAdd(&d_column_data[actual_col].string_count, 1);
+        }
+      }
+      actual_col++;
+    }
+    next_field  = next_delimiter + 1;
+    field_start = next_field;
+    col++;
+  }
+}
+
+/**
+ * @brief CUDA kernel that parses and converts CSV data into cuDF column data.
+ *
+ * Data is processed one record at a time
+ *
+ * @param[in] options A set of parsing options
+ * @param[in] data The entire CSV data to read
+ * @param[in] column_flags Per-column parsing behavior flags
+ * @param[in] row_offsets The start the CSV data of interest
+ * @param[in] dtypes The data type of the column
+ * @param[out] columns The output column data
+ * @param[out] valids The bitmaps indicating whether column fields are valid
+ * @param[out] valid_counts The number of valid fields in each column
+ */
+__global__ void __launch_bounds__(csvparse_block_dim)
+  convert_csv_to_cudf(cudf::io::parse_options_view options,
+                      device_span<char const> data,
+                      device_span<column_parse::flags const> column_flags,
+                      device_span<uint64_t const> row_offsets,
+                      device_span<cudf::data_type const> dtypes,
+                      device_span<void* const> columns,
+                      device_span<cudf::bitmask_type* const> valids,
+                      device_span<size_type> valid_counts)
+{
+  auto const raw_csv = data.data();
+  // thread IDs range per block, so also need the block id.
+  // this is entry into the field array - tid is an elements within the num_entries array
+  auto const rec_id      = grid_1d::global_thread_id();
+  auto const rec_id_next = rec_id + 1;
+
+  // we can have more threads than data, make sure we are not past the end of the data
+  if (rec_id_next >= row_offsets.size()) return;
+
+  auto field_start   = raw_csv + row_offsets[rec_id];
+  auto const row_end = raw_csv + row_offsets[rec_id_next];
+
+  auto next_field = field_start;
+  int col         = 0;
+  int actual_col  = 0;
+
+  while (col < column_flags.size() && field_start < row_end) {
+    auto next_delimiter = cudf::io::gpu::seek_field_end(next_field, row_end, options);
+
+    if (column_flags[col] & column_parse::enabled) {
+      // check if the entire field is a NaN string - consistent with pandas
+      auto const is_valid = !serialized_trie_contains(
+        options.trie_na, {field_start, static_cast<size_t>(next_delimiter - field_start)});
+
+      // Modify field_start & end to ignore whitespace and quotechars
+      auto field_end = next_delimiter;
+      if (is_valid && dtypes[actual_col].id() != cudf::type_id::STRING) {
+        auto const trimmed_field =
+          trim_whitespaces_quotes(field_start, field_end, options.quotechar);
+        field_start = trimmed_field.first;
+        field_end   = trimmed_field.second;
+      }
+      if (is_valid) {
+        // Type dispatcher does not handle STRING
+        if (dtypes[actual_col].id() == cudf::type_id::STRING) {
+          auto end = next_delimiter;
+          if (not options.keepquotes) {
+            if ((*field_start == options.quotechar) && (*(end - 1) == options.quotechar)) {
+              ++field_start;
+              --end;
+            }
+          }
+          auto str_list = static_cast<std::pair<char const*, size_t>*>(columns[actual_col]);
+          str_list[rec_id].first  = field_start;
+          str_list[rec_id].second = end - field_start;
+        } else {
+          if (cudf::type_dispatcher(dtypes[actual_col],
+                                    ConvertFunctor{},
+                                    field_start,
+                                    field_end,
+                                    columns[actual_col],
+                                    rec_id,
+                                    dtypes[actual_col],
+                                    options,
+                                    column_flags[col] & column_parse::as_hexadecimal)) {
+            // set the valid bitmap - all bits were set to 0 to start
+            set_bit(valids[actual_col], rec_id);
+            atomicAdd(&valid_counts[actual_col], 1);
+          }
+        }
+      } else if (dtypes[actual_col].id() == cudf::type_id::STRING) {
+        auto str_list           = static_cast<std::pair<char const*, size_t>*>(columns[actual_col]);
+        str_list[rec_id].first  = nullptr;
+        str_list[rec_id].second = 0;
+      }
+      ++actual_col;
+    }
+    next_field  = next_delimiter + 1;
+    field_start = next_field;
+    ++col;
+  }
+}
+
+/*
+ * @brief Merge two packed row contexts (each corresponding to a block of characters)
+ * and return the packed row context corresponding to the merged character block
+ */
+inline __device__ packed_rowctx_t merge_row_contexts(packed_rowctx_t first_ctx,
+                                                     packed_rowctx_t second_ctx)
+{
+  uint32_t id0 = get_row_context(first_ctx, ROW_CTX_NONE) & 3;
+  uint32_t id1 = get_row_context(first_ctx, ROW_CTX_QUOTE) & 3;
+  uint32_t id2 = get_row_context(first_ctx, ROW_CTX_COMMENT) & 3;
+  return (first_ctx & ~pack_row_contexts(3, 3, 3)) +
+         pack_row_contexts(get_row_context(second_ctx, id0),
+                           get_row_context(second_ctx, id1),
+                           get_row_context(second_ctx, id2));
+}
+
+/*
+ * @brief Per-character context:
+ * 1-bit count (0 or 1) per context in the lower 4 bits
+ * 2-bit output context id per input context in bits 8..15
+ */
+constexpr __device__ uint32_t make_char_context(uint32_t id0,
+                                                uint32_t id1,
+                                                uint32_t id2 = ROW_CTX_COMMENT,
+                                                uint32_t c0  = 0,
+                                                uint32_t c1  = 0,
+                                                uint32_t c2  = 0)
+{
+  return (id0 << 8) | (id1 << 10) | (id2 << 12) | (ROW_CTX_EOF << 14) | (c0) | (c1 << 1) |
+         (c2 << 2);
+}
+
+/*
+ * @brief Merge a 1-character context to keep track of bitmasks where new rows occur
+ * Merges a single-character "block" row context at position pos with the current
+ * block's row context (the current block contains 32-pos characters)
+ *
+ * @param ctx Current block context and new rows bitmaps
+ * @param char_ctx state transitions associated with new character
+ * @param pos Position within the current 32-character block
+ *
+ * NOTE: This is probably the most performance-critical piece of the row gathering kernel.
+ * The char_ctx value should be created via make_char_context, and its value should
+ * have been evaluated at compile-time.
+ */
+inline __device__ void merge_char_context(uint4& ctx, uint32_t char_ctx, uint32_t pos)
+{
+  uint32_t id0 = (ctx.w >> 0) & 3;
+  uint32_t id1 = (ctx.w >> 2) & 3;
+  uint32_t id2 = (ctx.w >> 4) & 3;
+  // Set the newrow bit in the bitmap at the corresponding position
+  ctx.x |= ((char_ctx >> id0) & 1) << pos;
+  ctx.y |= ((char_ctx >> id1) & 1) << pos;
+  ctx.z |= ((char_ctx >> id2) & 1) << pos;
+  // Update the output context ids
+  ctx.w = ((char_ctx >> (8 + id0 * 2)) & 0x03) | ((char_ctx >> (6 + id1 * 2)) & 0x0c) |
+          ((char_ctx >> (4 + id2 * 2)) & 0x30) | (ROW_CTX_EOF << 6);
+}
+
+/*
+ * Convert the context-with-row-bitmaps version to a packed row context
+ */
+inline __device__ packed_rowctx_t pack_rowmaps(uint4 ctx_map)
+{
+  return pack_row_contexts(make_row_context(__popc(ctx_map.x), (ctx_map.w >> 0) & 3),
+                           make_row_context(__popc(ctx_map.y), (ctx_map.w >> 2) & 3),
+                           make_row_context(__popc(ctx_map.z), (ctx_map.w >> 4) & 3));
+}
+
+/*
+ * Selects the row bitmap corresponding to the given parser state
+ */
+inline __device__ uint32_t select_rowmap(uint4 ctx_map, uint32_t ctxid)
+{
+  return (ctxid == ROW_CTX_NONE)      ? ctx_map.x
+         : (ctxid == ROW_CTX_QUOTE)   ? ctx_map.y
+         : (ctxid == ROW_CTX_COMMENT) ? ctx_map.z
+                                      : 0;
+}
+
+/**
+ * @brief Single pair-wise 512-wide row context merge transform
+ *
+ * Merge row context blocks and record the merge operation in a context
+ * tree so that the transform is reversible.
+ * The tree is organized such that the left and right children of node n
+ * are located at indices n*2 and n*2+1, the root node starting at index 1
+ *
+ * @tparam lanemask mask to specify source of packed row context
+ * @tparam tmask mask to specify principle thread for merging row context
+ * @tparam base start location for writing into packed row context tree
+ * @tparam level_scale level of the node in the tree
+ * @param[out] ctxtree packed row context tree
+ * @param[in] ctxb packed row context for the current character block
+ * @param t thread id (leaf node id)
+ */
+template <uint32_t lanemask, uint32_t tmask, uint32_t base, uint32_t level_scale>
+inline __device__ void ctx_merge(uint64_t* ctxtree, packed_rowctx_t* ctxb, uint32_t t)
+{
+  uint64_t tmp = shuffle_xor(*ctxb, lanemask);
+  if (!(t & tmask)) {
+    *ctxb                              = merge_row_contexts(*ctxb, tmp);
+    ctxtree[base + (t >> level_scale)] = *ctxb;
+  }
+}
+
+/**
+ * @brief Single 512-wide row context inverse merge transform
+ *
+ * Walks the context tree starting from a root node
+ *
+ * @tparam rmask Mask to specify which threads write input row context
+ * @param[in] base Start read location of the merge transform tree
+ * @param[in] ctxtree Merge transform tree
+ * @param[in] ctx Input context
+ * @param[in] brow4 output row in block *4
+ * @param[in] t thread id (leaf node id)
+ */
+template <uint32_t rmask>
+inline __device__ void ctx_unmerge(
+  uint32_t base, uint64_t* ctxtree, uint32_t* ctx, uint32_t* brow4, uint32_t t)
+{
+  rowctx32_t ctxb_left, ctxb_right, ctxb_sum;
+  ctxb_sum   = get_row_context(ctxtree[base], *ctx);
+  ctxb_left  = get_row_context(ctxtree[(base)*2 + 0], *ctx);
+  ctxb_right = get_row_context(ctxtree[(base)*2 + 1], ctxb_left & 3);
+  if (t & (rmask)) {
+    *brow4 += (ctxb_sum & ~3) - (ctxb_right & ~3);
+    *ctx = ctxb_left & 3;
+  }
+}
+
+/*
+ * @brief 512-wide row context merge transform
+ *
+ * Repeatedly merge row context blocks, keeping track of each merge operation
+ * in a context tree so that the transform is reversible
+ * The tree is organized such that the left and right children of node n
+ * are located at indices n*2 and n*2+1, the root node starting at index 1
+ *
+ * Each node contains the counts and output contexts corresponding to the
+ * possible input contexts.
+ * Each parent node's count is obtained by adding the corresponding counts
+ * from the left child node with the right child node's count selected from
+ * the left child node's output context:
+ *   parent.count[k] = left.count[k] + right.count[left.outctx[k]]
+ *   parent.outctx[k] = right.outctx[left.outctx[k]]
+ *
+ * @param[out] ctxtree packed row context tree
+ * @param[in] ctxb packed row context for the current character block
+ * @param t thread id (leaf node id)
+ */
+static inline __device__ void rowctx_merge_transform(uint64_t ctxtree[1024],
+                                                     packed_rowctx_t ctxb,
+                                                     uint32_t t)
+{
+  ctxtree[512 + t] = ctxb;
+  ctx_merge<1, 0x1, 256, 1>(ctxtree, &ctxb, t);
+  ctx_merge<2, 0x3, 128, 2>(ctxtree, &ctxb, t);
+  ctx_merge<4, 0x7, 64, 3>(ctxtree, &ctxb, t);
+  ctx_merge<8, 0xf, 32, 4>(ctxtree, &ctxb, t);
+  __syncthreads();
+  if (t < 32) {
+    ctxb = ctxtree[32 + t];
+    ctx_merge<1, 0x1, 16, 1>(ctxtree, &ctxb, t);
+    ctx_merge<2, 0x3, 8, 2>(ctxtree, &ctxb, t);
+    ctx_merge<4, 0x7, 4, 3>(ctxtree, &ctxb, t);
+    ctx_merge<8, 0xf, 2, 4>(ctxtree, &ctxb, t);
+    // Final stage
+    uint64_t tmp = shuffle_xor(ctxb, 16);
+    if (t == 0) { ctxtree[1] = merge_row_contexts(ctxb, tmp); }
+  }
+}
+
+/*
+ * @brief 512-wide row context inverse merge transform
+ *
+ * Walks the context tree starting from the root node (index 1) using
+ * the starting context in node index 0.
+ * The return value is the starting row and input context for the given leaf node
+ *
+ * @param[in] ctxtree Merge transform tree
+ * @param[in] t thread id (leaf node id)
+ *
+ * @return Final row context and count (row_position*4 + context_id format)
+ */
+static inline __device__ rowctx32_t rowctx_inverse_merge_transform(uint64_t ctxtree[1024],
+                                                                   uint32_t t)
+{
+  uint32_t ctx     = ctxtree[0] & 3;  // Starting input context
+  rowctx32_t brow4 = 0;               // output row in block *4
+
+  ctx_unmerge<256>(1, ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<128>(2 + (t >> 8), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<64>(4 + (t >> 7), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<32>(8 + (t >> 6), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<16>(16 + (t >> 5), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<8>(32 + (t >> 4), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<4>(64 + (t >> 3), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<2>(128 + (t >> 2), ctxtree, &ctx, &brow4, t);
+  ctx_unmerge<1>(256 + (t >> 1), ctxtree, &ctx, &brow4, t);
+
+  return brow4 + ctx;
+}
+
+/**
+ * @brief Gather row offsets from CSV character data split into 16KB chunks
+ *
+ * This is done in two phases: the first phase returns the possible row counts
+ * per 16K character block for each possible parsing context at the start of the block,
+ * along with the resulting parsing context at the end of the block.
+ * The caller can then compute the actual parsing context at the beginning of each
+ * individual block and total row count.
+ * The second phase outputs the location of each row in the block, using the parsing
+ * context and initial row counter accumulated from the results of the previous phase.
+ * Row parsing context will be updated after phase 2 such that the value contains
+ * the number of rows starting at byte_range_end or beyond.
+ *
+ * @param row_ctx Row parsing context (output of phase 1 or input to phase 2)
+ * @param offsets_out Row offsets (nullptr for phase1, non-null indicates phase 2)
+ * @param data Base pointer of character data (all row offsets are relative to this)
+ * @param chunk_size Total number of characters to parse
+ * @param parse_pos Current parsing position in the file
+ * @param start_offset Position of the start of the character buffer in the file
+ * @param data_size CSV file size
+ * @param byte_range_start Ignore rows starting before this position in the file
+ * @param byte_range_end In phase 2, store the number of rows beyond range in row_ctx
+ * @param skip_rows Number of rows to skip (ignored in phase 1)
+ * @param terminator Line terminator character
+ * @param delimiter Column delimiter character
+ * @param quotechar Quote character
+ * @param escapechar Delimiter escape character
+ * @param commentchar Comment line character (skip rows starting with this character)
+ */
+__global__ void __launch_bounds__(rowofs_block_dim)
+  gather_row_offsets_gpu(uint64_t* row_ctx,
+                         device_span<uint64_t> offsets_out,
+                         device_span<char const> const data,
+                         size_t chunk_size,
+                         size_t parse_pos,
+                         size_t start_offset,
+                         size_t data_size,
+                         size_t byte_range_start,
+                         size_t byte_range_end,
+                         size_t skip_rows,
+                         int terminator,
+                         int delimiter,
+                         int quotechar,
+                         int escapechar,
+                         int commentchar)
+{
+  auto start         = data.begin();
+  using block_reduce = typename cub::BlockReduce<uint32_t, rowofs_block_dim>;
+  __shared__ union {
+    typename block_reduce::TempStorage bk_storage;
+    __align__(8) uint64_t ctxtree[rowofs_block_dim * 2];
+  } temp_storage;
+
+  char const* end = start + (min(parse_pos + chunk_size, data_size) - start_offset);
+  uint32_t t      = threadIdx.x;
+  size_t block_pos =
+    (parse_pos - start_offset) + blockIdx.x * static_cast<size_t>(rowofs_block_bytes) + t * 32;
+  char const* cur = start + block_pos;
+
+  // Initial state is neutral context (no state transitions), zero rows
+  uint4 ctx_map = {
+    .x = 0,
+    .y = 0,
+    .z = 0,
+    .w = (ROW_CTX_NONE << 0) | (ROW_CTX_QUOTE << 2) | (ROW_CTX_COMMENT << 4) | (ROW_CTX_EOF << 6)};
+  int c, c_prev = (cur > start && cur <= end) ? cur[-1] : terminator;
+  // Loop through all 32 bytes and keep a bitmask of row starts for each possible input context
+  for (uint32_t pos = 0; pos < 32; pos++, cur++, c_prev = c) {
+    uint32_t ctx;
+    if (cur < end) {
+      c = cur[0];
+      if (c_prev == terminator) {
+        if (c == commentchar) {
+          // Start of a new comment row
+          ctx = make_char_context(ROW_CTX_COMMENT, ROW_CTX_QUOTE, ROW_CTX_COMMENT, 1, 0, 1);
+        } else if (c == quotechar) {
+          // Quoted string on newrow, or quoted string ending in terminator
+          ctx = make_char_context(ROW_CTX_QUOTE, ROW_CTX_NONE, ROW_CTX_QUOTE, 1, 0, 1);
+        } else {
+          // Start of a new row unless within a quote
+          ctx = make_char_context(ROW_CTX_NONE, ROW_CTX_QUOTE, ROW_CTX_NONE, 1, 0, 1);
+        }
+      } else if (c == quotechar) {
+        if (c_prev == delimiter || c_prev == quotechar) {
+          // Quoted string after delimiter, quoted string ending in delimiter, or double-quote
+          ctx = make_char_context(ROW_CTX_QUOTE, ROW_CTX_NONE);
+        } else {
+          // Closing or ignored quote
+          ctx = make_char_context(ROW_CTX_NONE, ROW_CTX_NONE);
+        }
+      } else {
+        // Neutral character
+        ctx = make_char_context(ROW_CTX_NONE, ROW_CTX_QUOTE);
+      }
+    } else {
+      char const* data_end = start + data_size - start_offset;
+      if (cur <= end && cur == data_end) {
+        // Add a newline at data end (need the extra row offset to infer length of previous row)
+        ctx = make_char_context(ROW_CTX_EOF, ROW_CTX_EOF, ROW_CTX_EOF, 1, 1, 1);
+      } else {
+        // Pass-through context (beyond chunk_size or data_end)
+        ctx = make_char_context(ROW_CTX_NONE, ROW_CTX_QUOTE, ROW_CTX_COMMENT);
+      }
+    }
+    // Merge with current context, keeping track of where new rows occur
+    merge_char_context(ctx_map, ctx, pos);
+  }
+
+  // Eliminate rows that start before byte_range_start
+  if (start_offset + block_pos < byte_range_start) {
+    uint32_t dist_minus1 = min(byte_range_start - (start_offset + block_pos) - 1, UINT64_C(31));
+    uint32_t mask        = 0xffff'fffe << dist_minus1;
+    ctx_map.x &= mask;
+    ctx_map.y &= mask;
+    ctx_map.z &= mask;
+  }
+
+  // Convert the long-form {rowmap,outctx}[inctx] version into packed version
+  // {rowcount,ouctx}[inctx], then merge the row contexts of the 32-character blocks into
+  // a single 16K-character block context
+  rowctx_merge_transform(temp_storage.ctxtree, pack_rowmaps(ctx_map), t);
+
+  // If this is the second phase, get the block's initial parser state and row counter
+  if (offsets_out.data()) {
+    if (t == 0) { temp_storage.ctxtree[0] = row_ctx[blockIdx.x]; }
+    __syncthreads();
+
+    // Walk back the transform tree with the known initial parser state
+    rowctx32_t ctx             = rowctx_inverse_merge_transform(temp_storage.ctxtree, t);
+    uint64_t row               = (temp_storage.ctxtree[0] >> 2) + (ctx >> 2);
+    uint32_t rows_out_of_range = 0;
+    uint32_t rowmap            = select_rowmap(ctx_map, ctx & 3);
+    // Output row positions
+    while (rowmap != 0) {
+      uint32_t pos = __ffs(rowmap);
+      block_pos += pos;
+      if (row >= skip_rows && row - skip_rows < offsets_out.size()) {
+        // Output byte offsets are relative to the base of the input buffer
+        offsets_out[row - skip_rows] = block_pos - 1;
+        rows_out_of_range += (start_offset + block_pos - 1 >= byte_range_end);
+      }
+      row++;
+      rowmap >>= pos;
+    }
+    __syncthreads();
+    // Return the number of rows out of range
+    rows_out_of_range = block_reduce(temp_storage.bk_storage).Sum(rows_out_of_range);
+    if (t == 0) { row_ctx[blockIdx.x] = rows_out_of_range; }
+  } else {
+    // Just store the row counts and output contexts
+    if (t == 0) { row_ctx[blockIdx.x] = temp_storage.ctxtree[1]; }
+  }
+}
+
+size_t __host__ count_blank_rows(cudf::io::parse_options_view const& opts,
+                                 device_span<char const> data,
+                                 device_span<uint64_t const> row_offsets,
+                                 rmm::cuda_stream_view stream)
+{
+  auto const newline  = opts.skipblanklines ? opts.terminator : opts.comment;
+  auto const comment  = opts.comment != '\0' ? opts.comment : newline;
+  auto const carriage = (opts.skipblanklines && opts.terminator == '\n') ? '\r' : comment;
+  return thrust::count_if(
+    rmm::exec_policy(stream),
+    row_offsets.begin(),
+    row_offsets.end(),
+    [data = data, newline, comment, carriage] __device__(uint64_t const pos) {
+      return ((pos != data.size()) &&
+              (data[pos] == newline || data[pos] == comment || data[pos] == carriage));
+    });
+}
+
+device_span<uint64_t> __host__ remove_blank_rows(cudf::io::parse_options_view const& options,
+                                                 device_span<char const> data,
+                                                 device_span<uint64_t> row_offsets,
+                                                 rmm::cuda_stream_view stream)
+{
+  size_t d_size       = data.size();
+  auto const newline  = options.skipblanklines ? options.terminator : options.comment;
+  auto const comment  = options.comment != '\0' ? options.comment : newline;
+  auto const carriage = (options.skipblanklines && options.terminator == '\n') ? '\r' : comment;
+  auto new_end        = thrust::remove_if(
+    rmm::exec_policy(stream),
+    row_offsets.begin(),
+    row_offsets.end(),
+    [data = data, d_size, newline, comment, carriage] __device__(uint64_t const pos) {
+      return ((pos != d_size) &&
+              (data[pos] == newline || data[pos] == comment || data[pos] == carriage));
+    });
+  return row_offsets.subspan(0, new_end - row_offsets.begin());
+}
+
+std::vector<column_type_histogram> detect_column_types(
+  cudf::io::parse_options_view const& options,
+  device_span<char const> const data,
+  device_span<column_parse::flags const> const column_flags,
+  device_span<uint64_t const> const row_starts,
+  size_t const num_active_columns,
+  rmm::cuda_stream_view stream)
+{
+  // Calculate actual block count to use based on records count
+  int const block_size = csvparse_block_dim;
+  int const grid_size  = (row_starts.size() + block_size - 1) / block_size;
+
+  auto d_stats = detail::make_zeroed_device_uvector_async<column_type_histogram>(
+    num_active_columns, stream, rmm::mr::get_current_device_resource());
+
+  data_type_detection<<<grid_size, block_size, 0, stream.value()>>>(
+    options, data, column_flags, row_starts, d_stats);
+
+  return detail::make_std_vector_sync(d_stats, stream);
+}
+
+void decode_row_column_data(cudf::io::parse_options_view const& options,
+                            device_span<char const> data,
+                            device_span<column_parse::flags const> column_flags,
+                            device_span<uint64_t const> row_offsets,
+                            device_span<cudf::data_type const> dtypes,
+                            device_span<void* const> columns,
+                            device_span<cudf::bitmask_type* const> valids,
+                            device_span<size_type> valid_counts,
+                            rmm::cuda_stream_view stream)
+{
+  // Calculate actual block count to use based on records count
+  auto const block_size = csvparse_block_dim;
+  auto const num_rows   = row_offsets.size() - 1;
+  auto const grid_size  = (num_rows + block_size - 1) / block_size;
+
+  convert_csv_to_cudf<<<grid_size, block_size, 0, stream.value()>>>(
+    options, data, column_flags, row_offsets, dtypes, columns, valids, valid_counts);
+}
+
+uint32_t __host__ gather_row_offsets(parse_options_view const& options,
+                                     uint64_t* row_ctx,
+                                     device_span<uint64_t> const offsets_out,
+                                     device_span<char const> const data,
+                                     size_t chunk_size,
+                                     size_t parse_pos,
+                                     size_t start_offset,
+                                     size_t data_size,
+                                     size_t byte_range_start,
+                                     size_t byte_range_end,
+                                     size_t skip_rows,
+                                     rmm::cuda_stream_view stream)
+{
+  uint32_t dim_grid = 1 + (chunk_size / rowofs_block_bytes);
+
+  gather_row_offsets_gpu<<<dim_grid, rowofs_block_dim, 0, stream.value()>>>(
+    row_ctx,
+    offsets_out,
+    data,
+    chunk_size,
+    parse_pos,
+    start_offset,
+    data_size,
+    byte_range_start,
+    byte_range_end,
+    skip_rows,
+    options.terminator,
+    options.delimiter,
+    (options.quotechar) ? options.quotechar : 0x100,
+    /*(options.escapechar) ? options.escapechar :*/ 0x100,
+    (options.comment) ? options.comment : 0x100);
+
+  return dim_grid;
+}
+
+}  // namespace gpu
+}  // namespace csv
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/csv_gpu.hpp b/cpp/src/io/csv/csv_gpu.hpp
new file mode 100644
index 0000000..62bd8f1
--- /dev/null
+++ b/cpp/src/io/csv/csv_gpu.hpp
@@ -0,0 +1,236 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <io/utilities/parsing_utils.cuh>
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+using cudf::device_span;
+
+namespace cudf {
+namespace io {
+namespace csv {
+namespace gpu {
+
+/**
+ * CSV row parsing context
+ * NONE: No special context (normal parsing)
+ * QUOTE: Within a quoted field
+ * COMMENT: Within a comment line (discard every character until terminator)
+ * EOF: End state (EOF reached)
+ */
+enum { ROW_CTX_NONE = 0, ROW_CTX_QUOTE = 1, ROW_CTX_COMMENT = 2, ROW_CTX_EOF = 3 };
+
+constexpr uint32_t rowofs_block_dim = 512;
+/// Character block size for gather_row_offsets
+constexpr uint32_t rowofs_block_bytes = rowofs_block_dim * 32;  // 16KB/threadblock
+
+/**
+ * Row parsing context with row count
+ * Format: row_count * 4 + id, where `row_count` is the number of rows
+ * in a character block, and `id` is the row parser state at the end of the block.
+ */
+using rowctx32_t = uint32_t;
+using rowctx64_t = uint64_t;
+
+/**
+ * Packed row context format
+ *
+ * The 64-bit packed row context format represents the four possible output row context states
+ * from each of the four possible input row context states.
+ * Each rowctx32_t value is truncated to 20-bit (limiting the max number of rows
+ * to 18-bit) and concatenated to form a 80-bit value, whose upper 16 bits are
+ * always zero (EOF input state implies a zero row count) and therefore
+ * stored as 64-bit.
+ */
+using packed_rowctx_t = uint64_t;
+
+/**
+ * @brief return a row context from a {count, id} pair
+ *
+ * The 32-bit row context consists of the 2-bit parser state stored in the lower 2-bits
+ * and a 30-bit row count in the upper 30 bits.
+ */
+inline __host__ __device__ rowctx32_t make_row_context(uint32_t row_count, uint32_t out_ctx)
+{
+  return (row_count << 2) + out_ctx;
+}
+
+/**
+ * @brief pack multiple row contexts together
+ *
+ * Pack four rowctx32_t values, where each value represents the output row context
+ * for one of four possible input contexts when parsing a character block.
+ * Each output state consists of the 2-bit row context state along with a 18-bit row count
+ * value (row count is assumed to be a local count that fits in 18-bit)
+ * The four 20-bit values are concatenated to form a 80-bit value, truncated to 64-bit
+ * since a block starting in a EOF state can only have a zero row count (and the output
+ * state corresponding to an EOF input state can only be EOF, so only the first 3 output
+ * states are included as parameters, and the EOF->EOF state transition is hardcoded)
+ */
+constexpr __host__ __device__ packed_rowctx_t pack_row_contexts(rowctx32_t ctx0,
+                                                                rowctx32_t ctx1,
+                                                                rowctx32_t ctx2)
+{
+  return (ctx0) | (static_cast<uint64_t>(ctx1) << 20) | (static_cast<uint64_t>(ctx2) << 40) |
+         (static_cast<uint64_t>(ROW_CTX_EOF) << 60);
+}
+
+/**
+ * @brief Unpack a row context  (select one of the 4 contexts in packed form)
+ */
+inline __host__ __device__ rowctx32_t get_row_context(packed_rowctx_t packed_ctx, uint32_t ctxid)
+{
+  return static_cast<rowctx32_t>((packed_ctx >> (ctxid * 20)) & ((1 << 20) - 1));
+}
+
+/**
+ * @brief Select the output row context from a given input context and a packed row
+ * context corresponding to a block of characters, and return the new output context with
+ * updated total row count.
+ * The input context is a 64-bit version of the 32-bit single row context as returned
+ * by make_row_context(), so the maximum row count here is a 62-bit value.
+ *
+ * @param sel_ctx input context (2-bit context id, 62-bit row count)
+ * @param packed_ctx row context of character block
+ * @return total_row_count * 4 + output context id
+ */
+inline __host__ __device__ rowctx64_t select_row_context(rowctx64_t sel_ctx,
+                                                         packed_rowctx_t packed_ctx)
+{
+  auto ctxid     = static_cast<uint32_t>(sel_ctx & 3);
+  rowctx32_t ctx = get_row_context(packed_ctx, ctxid);
+  return (sel_ctx & ~3) + ctx;
+}
+
+/**
+ * @brief Launches kernel to gather row offsets
+ *
+ * This is done in two phases: the first phase returns the possible row counts
+ * per 16K character block for each possible parsing context at the start of the block,
+ * along with the resulting parsing context at the end of the block.
+ * The caller can then compute the actual parsing context at the beginning of each
+ * individual block and total row count.
+ * The second phase outputs the location of each row in the block, using the parsing
+ * context and initial row counter resulting from the previous phase.
+ * Row parsing context will be updated after phase 2 such that the value contains
+ * the number of rows starting at byte_range_end or beyond.
+ *
+ * @param options Options that control parsing of individual fields
+ * @param row_ctx Row parsing context (output of phase 1 or input to phase 2)
+ * @param offsets_out Row offsets (nullptr for phase1, non-null indicates phase 2)
+ * @param data Base pointer of character data (all row offsets are relative to this)
+ * @param chunk_size Total number of characters to parse
+ * @param parse_pos Current parsing position in the file
+ * @param start_offset Position of the start of the character buffer in the file
+ * @param data_size CSV file size
+ * @param byte_range_start Ignore rows starting before this position in the file
+ * @param byte_range_end In phase 2, store the number of rows beyond range in row_ctx
+ * @param skip_rows Number of rows to skip (ignored in phase 1)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return Number of row contexts
+ */
+uint32_t gather_row_offsets(cudf::io::parse_options_view const& options,
+                            uint64_t* row_ctx,
+                            device_span<uint64_t> offsets_out,
+                            device_span<char const> data,
+                            size_t chunk_size,
+                            size_t parse_pos,
+                            size_t start_offset,
+                            size_t data_size,
+                            size_t byte_range_start,
+                            size_t byte_range_end,
+                            size_t skip_rows,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * Count the number of blank rows in the given row offset array
+ *
+ * @param options Options that control parsing of individual fields
+ * @param data Character data buffer
+ * @param row_offsets Row offsets in the character data buffer
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+size_t count_blank_rows(cudf::io::parse_options_view const& options,
+                        device_span<char const> data,
+                        device_span<uint64_t const> row_offsets,
+                        rmm::cuda_stream_view stream);
+
+/**
+ * Remove blank rows in the given row offset array
+ *
+ * @param options Options that control parsing of individual fields
+ * @param data Character data buffer
+ * @param row_offsets Row offsets in the character data buffer
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+device_span<uint64_t> remove_blank_rows(cudf::io::parse_options_view const& options,
+                                        device_span<char const> data,
+                                        device_span<uint64_t> row_offsets,
+                                        rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for detecting possible dtype of each column of data
+ *
+ * @param[in] options Options that control individual field data conversion
+ * @param[in] data The row-column data
+ * @param[in] column_flags Flags that control individual column parsing
+ * @param[in] row_offsets List of row data start positions (offsets)
+ * @param[in] stream CUDA stream to use
+ *
+ * @return stats Histogram of each dtypes' occurrence for each column
+ */
+std::vector<column_type_histogram> detect_column_types(
+  cudf::io::parse_options_view const& options,
+  device_span<char const> data,
+  device_span<column_parse::flags const> column_flags,
+  device_span<uint64_t const> row_offsets,
+  size_t const num_active_columns,
+  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for decoding row-column data
+ *
+ * @param[in] options Options that control individual field data conversion
+ * @param[in] data The row-column data
+ * @param[in] column_flags Flags that control individual column parsing
+ * @param[in] row_offsets List of row data start positions (offsets)
+ * @param[in] dtypes List of dtype corresponding to each column
+ * @param[out] columns Device memory output of column data
+ * @param[out] valids Device memory output of column valids bitmap data
+ * @param[out] valid_counts Device memory output of the number of valid fields in each column
+ * @param[in] stream CUDA stream to use
+ */
+void decode_row_column_data(cudf::io::parse_options_view const& options,
+                            device_span<char const> data,
+                            device_span<column_parse::flags const> column_flags,
+                            device_span<uint64_t const> row_offsets,
+                            device_span<cudf::data_type const> dtypes,
+                            device_span<void* const> columns,
+                            device_span<cudf::bitmask_type* const> valids,
+                            device_span<size_type> valid_counts,
+                            rmm::cuda_stream_view stream);
+
+}  // namespace gpu
+}  // namespace csv
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/datetime.cuh b/cpp/src/io/csv/datetime.cuh
new file mode 100644
index 0000000..50d2106
--- /dev/null
+++ b/cpp/src/io/csv/datetime.cuh
@@ -0,0 +1,402 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <io/utilities/parsing_utils.cuh>
+#include <io/utilities/time_utils.cuh>
+
+#include <cudf/fixed_point/fixed_point.hpp>
+
+#include <thrust/equal.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Parses non-negative integral vales.
+ *
+ * This helper function is only intended to handle positive integers. The input
+ * character string is expected to be well-formed.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @return The parsed and converted value
+ */
+template <typename T>
+__inline__ __device__ T to_non_negative_integer(char const* begin, char const* end)
+{
+  T value = 0;
+
+  for (; begin < end; ++begin) {
+    if (*begin >= '0' && *begin <= '9') {
+      value *= 10;
+      value += *begin - '0';
+    }
+  }
+
+  return value;
+}
+
+/**
+ * @brief Extracts the Day, Month, and Year from a string.
+ *
+ * This function takes a string and produces a `year_month_day` representation.
+ * Acceptable formats are a combination of `YYYY`, `M`, `MM`, `D` and `DD` with
+ * `/` or `-` as separators. Data with only year and month (no day) is also valid.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @param dayfirst Flag indicating that first field is the day
+ * @return Extracted year, month and day in `cuda::std::chrono::year_month_day` format
+ */
+__inline__ __device__ cuda::std::chrono::year_month_day extract_date(char const* begin,
+                                                                     char const* end,
+                                                                     bool dayfirst)
+{
+  using namespace cuda::std::chrono;
+
+  char sep = '/';
+
+  auto sep_pos = thrust::find(thrust::seq, begin, end, sep);
+
+  if (sep_pos == end) {
+    sep     = '-';
+    sep_pos = thrust::find(thrust::seq, begin, end, sep);
+  }
+
+  year y;
+  month m;
+  day d;
+
+  //--- is year the first filed?
+  if ((sep_pos - begin) == 4) {
+    y = year{to_non_negative_integer<int32_t>(begin, sep_pos)};  //  year is signed
+
+    // Month
+    auto s2 = sep_pos + 1;
+    sep_pos = thrust::find(thrust::seq, s2, end, sep);
+
+    if (sep_pos == end) {
+      //--- Data is just Year and Month - no day
+      m = month{to_non_negative_integer<uint32_t>(s2, end)};  // month and day are unsigned
+      d = day{1};
+
+    } else {
+      m = month{to_non_negative_integer<uint32_t>(s2, sep_pos)};
+      d = day{to_non_negative_integer<uint32_t>((sep_pos + 1), end)};
+    }
+
+  } else {
+    //--- if the dayfirst flag is set, then restricts the format options
+    if (dayfirst) {
+      d = day{to_non_negative_integer<uint32_t>(begin, sep_pos)};
+
+      auto s2 = sep_pos + 1;
+      sep_pos = thrust::find(thrust::seq, s2, end, sep);
+
+      m = month{to_non_negative_integer<uint32_t>(s2, sep_pos)};
+      y = year{to_non_negative_integer<int32_t>((sep_pos + 1), end)};
+
+    } else {
+      m = month{to_non_negative_integer<uint32_t>(begin, sep_pos)};
+
+      auto s2 = sep_pos + 1;
+      sep_pos = thrust::find(thrust::seq, s2, end, sep);
+
+      if (sep_pos == end) {
+        //--- Data is just Year and Month - no day
+        y = year{to_non_negative_integer<int32_t>(s2, end)};
+        d = day{1};
+
+      } else {
+        d = day{to_non_negative_integer<uint32_t>(s2, sep_pos)};
+        y = year{to_non_negative_integer<int32_t>((sep_pos + 1), end)};
+      }
+    }
+  }
+
+  return year_month_day{y, m, d};
+}
+
+/**
+ * @brief Parses a string to extract the hour, minute, second and millisecond time field
+ * values of a day.
+ *
+ * Incoming format is expected to be `HH:MM:SS.MS`, with the latter second and millisecond fields
+ * optional. Each time field can be a single, double, or triple (in the case of milliseconds)
+ * digits. 12-hr and 24-hr time format is detected via the absence or presence of AM/PM characters
+ * at the end.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @return Extracted hours, minutes, seconds and milliseconds of `chrono::hh_mm_ss` type with a
+ * precision of milliseconds
+ */
+__inline__ __device__ cuda::std::chrono::hh_mm_ss<duration_ms> extract_time_of_day(
+  char const* begin, char const* end)
+{
+  constexpr char sep = ':';
+
+  // Adjust for AM/PM and any whitespace before
+  duration_h d_h{0};
+  auto last = end - 1;
+  if (*last == 'M' || *last == 'm') {
+    if (*(last - 1) == 'P' || *(last - 1) == 'p') { d_h = duration_h{12}; }
+    last = last - 2;
+    while (*last == ' ') {
+      --last;
+    }
+  }
+  end = last + 1;
+
+  // Find hour-minute separator
+  auto const hm_sep = thrust::find(thrust::seq, begin, end, sep);
+  // Extract hours
+  d_h += cudf::duration_h{to_non_negative_integer<int>(begin, hm_sep)};
+
+  duration_m d_m{0};
+  duration_s d_s{0};
+  duration_ms d_ms{0};
+
+  // Find minute-second separator (if present)
+  auto const ms_sep = thrust::find(thrust::seq, hm_sep + 1, end, sep);
+  if (ms_sep == end) {
+    d_m = duration_m{to_non_negative_integer<int32_t>(hm_sep + 1, end)};
+  } else {
+    d_m = duration_m{to_non_negative_integer<int32_t>(hm_sep + 1, ms_sep)};
+
+    // Find second-millisecond separator (if present)
+    auto const sms_sep = thrust::find(thrust::seq, ms_sep + 1, end, '.');
+    if (sms_sep == end) {
+      d_s = duration_s{to_non_negative_integer<int64_t>(ms_sep + 1, end)};
+    } else {
+      d_s  = duration_s{to_non_negative_integer<int64_t>(ms_sep + 1, sms_sep)};
+      d_ms = duration_ms{to_non_negative_integer<int64_t>(sms_sep + 1, end)};
+    }
+  }
+  return cuda::std::chrono::hh_mm_ss<duration_ms>{d_h + d_m + d_s + d_ms};
+}
+
+/**
+ * @brief Checks whether `c` is decimal digit
+ */
+constexpr bool is_digit(char c) { return c >= '0' and c <= '9'; }
+
+/**
+ * @brief Parses a datetime string and computes the corresponding timestamp.
+ *
+ * Acceptable date formats are a combination of `YYYY`, `M`, `MM`, `D` and `DD` with `/` or `-` as
+ * separators. Input with only year and month (no day) is also valid. Character `T` or blank space
+ * is expected to be the separator between date and time of day. Optional time of day information
+ * like hours, minutes, seconds and milliseconds are expected to be `HH:MM:SS.MS`. Each time field
+ * can be a single, double, or triple (in the case of milliseconds) digits. 12-hr and 24-hr time
+ * format is detected via the absence or presence of AM/PM characters at the end.
+ *
+ * @tparam timestamp_type Type of output timestamp
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @param dayfirst Flag to indicate day/month or month/day order
+ * @return Timestamp converted to `timestamp_type`
+ */
+template <typename timestamp_type>
+__inline__ __device__ timestamp_type to_timestamp(char const* begin, char const* end, bool dayfirst)
+{
+  using duration_type = typename timestamp_type::duration;
+
+  auto sep_pos = end;
+
+  // Find end of the date portion
+  int count        = 0;
+  bool digits_only = true;
+  for (auto i = begin; i < end; ++i) {
+    digits_only = digits_only and is_digit(*i);
+    if (*i == 'T') {
+      sep_pos = i;
+      break;
+    } else if (count == 3 && *i == ' ') {
+      sep_pos = i;
+      break;
+    } else if ((*i == '/' || *i == '-') || (count == 2 && *i != ' ')) {
+      count++;
+    }
+  }
+
+  // Exit if the input string is digit-only
+  if (digits_only) {
+    return timestamp_type{
+      duration_type{to_non_negative_integer<typename timestamp_type::rep>(begin, end)}};
+  }
+
+  auto ymd = extract_date(begin, sep_pos, dayfirst);
+  timestamp_type answer{cuda::std::chrono::sys_days{ymd}};
+
+  // Extract time only if separator is present
+  if (sep_pos != end) {
+    auto t = extract_time_of_day(sep_pos + 1, end);
+    answer += cuda::std::chrono::duration_cast<duration_type>(t.to_duration());
+  }
+
+  return answer;
+}
+
+/**
+ * @brief Parses the input string into an integral value of the given type.
+ *
+ * Moves the `begin` iterator past the parsed value.
+ *
+ * @param[in, out] begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @return The parsed and converted value
+ */
+template <typename T>
+__inline__ __device__ T parse_integer(char const** begin, char const* end)
+{
+  bool const is_negative = (**begin == '-');
+  T value                = 0;
+
+  auto cur = *begin + is_negative;
+  while (cur < end) {
+    if (*cur >= '0' && *cur <= '9') {
+      value *= 10;
+      value += *cur - '0';
+    } else
+      break;
+    ++cur;
+  }
+  *begin = cur;
+
+  return is_negative ? -value : value;
+}
+
+/**
+ * @brief Parses the input string into an integral value of the given type if the delimiter is
+ * present.
+ *
+ * Moves the `begin` iterator past the parsed value.
+ *
+ * @param[in, out] begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @param delimiter delimiter character
+ * @return The parsed and converted value, zero is delimiter is not present
+ */
+template <typename T>
+__inline__ __device__ T parse_optional_integer(char const** begin, char const* end, char delimiter)
+{
+  if (**begin != delimiter) { return 0; }
+
+  ++(*begin);
+  return parse_integer<T>(begin, end);
+}
+
+/**
+ * @brief Finds the first element after the leading space characters.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @return Pointer to the first character excluding any leading spaces
+ */
+__inline__ __device__ auto skip_spaces(char const* begin, char const* end)
+{
+  return thrust::find_if(thrust::seq, begin, end, [](auto elem) { return elem != ' '; });
+}
+
+/**
+ * @brief Excludes the prefix from the input range if the string starts with the prefix.
+ *
+ * @tparam N length of the prefix, plus one
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @param prefix String we're searching for at the start of the input range
+ * @return Pointer to the start of the string excluding the prefix
+ */
+template <int N>
+__inline__ __device__ auto skip_if_starts_with(char const* begin,
+                                               char const* end,
+                                               char const (&prefix)[N])
+{
+  static constexpr size_t prefix_len = N - 1;
+  if (end - begin < prefix_len) return begin;
+  return thrust::equal(thrust::seq, begin, begin + prefix_len, prefix) ? begin + prefix_len : begin;
+}
+
+/**
+ * @brief Parses the input string into a duration of `duration_type`.
+ *
+ * The expected format can be one of the following: `DD days`, `DD days +HH:MM:SS.NS`, `DD days
+ * HH:MM::SS.NS`, `HH:MM::SS.NS` and digits-only string. Note `DD` and optional `NS` field can
+ * contain arbitrary number of digits while `HH`, `MM` and `SS` can be single or double digits.
+ *
+ * @tparam duration_type Type of the parsed duration
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @return The parsed duration in `duration_type`
+ */
+template <typename duration_type>
+__inline__ __device__ duration_type to_duration(char const* begin, char const* end)
+{
+  using cuda::std::chrono::duration_cast;
+
+  // %d days [+]%H:%M:%S.n => %d days, %d days [+]%H:%M:%S,  %H:%M:%S.n, %H:%M:%S, %value.
+  constexpr char sep = ':';
+
+  // single pass to parse days, hour, minute, seconds, nanosecond
+  auto cur         = begin;
+  auto const value = parse_integer<int32_t>(&cur, end);
+  cur              = skip_spaces(cur, end);
+  if (std::is_same_v<duration_type, cudf::duration_D> || cur >= end) {
+    return duration_type{static_cast<typename duration_type::rep>(value)};
+  }
+
+  // " days [+]"
+  auto const after_days_sep     = skip_if_starts_with(cur, end, "days");
+  auto const has_days_seperator = (after_days_sep != cur);
+  cur                           = skip_spaces(after_days_sep, end);
+  cur += (*cur == '+');
+
+  duration_D d_d{0};
+  duration_h d_h{0};
+  if (has_days_seperator) {
+    d_d = duration_D{value};
+    d_h = duration_h{parse_integer<int32_t>(&cur, end)};
+  } else {
+    d_h = duration_h{value};
+  }
+
+  duration_m d_m{parse_optional_integer<int32_t>(&cur, end, sep)};
+  duration_s d_s{parse_optional_integer<int64_t>(&cur, end, sep)};
+
+  // Convert all durations to the given type
+  auto output_d = duration_cast<duration_type>(d_d + d_h + d_m + d_s);
+
+  if constexpr (std::is_same_v<duration_type, cudf::duration_s>) { return output_d; }
+
+  auto const d_ns = (*cur != '.') ? duration_ns{0} : [&]() {
+    auto const start_subsecond     = ++cur;
+    auto const unscaled_subseconds = parse_integer<int64_t>(&cur, end);
+    auto const scale               = min(9L, cur - start_subsecond) - 9;
+    auto const rescaled = numeric::decimal64{unscaled_subseconds, numeric::scale_type{scale}};
+    return duration_ns{rescaled.value()};
+  }();
+
+  return output_d + duration_cast<duration_type>(d_ns);
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/durations.cu b/cpp/src/io/csv/durations.cu
new file mode 100644
index 0000000..66143d3
--- /dev/null
+++ b/cpp/src/io/csv/durations.cu
@@ -0,0 +1,235 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/convert/int_to_string.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace csv {
+
+namespace {
+
+// duration components timeparts structure
+struct alignas(4) duration_component {
+  int32_t day;         //-2,147,483,648 to 2,147,483,647
+  int32_t nanosecond;  // 000000000 to 999999999
+  int8_t hour;         // 00 to 23
+  int8_t minute;       // 00 to 59
+  int8_t second;       // 00 to 59
+  bool is_negative;    // true/false
+};
+
+template <typename T>
+__device__ void dissect_duration(T duration, duration_component* timeparts)
+{
+  timeparts->is_negative = (duration < T{0});
+  timeparts->day         = cuda::std::chrono::floor<duration_D>(duration).count();
+
+  if (cuda::std::is_same_v<T, duration_D>) return;
+
+  // adjust for pandas format
+  if (timeparts->is_negative) {
+    duration =
+      cuda::std::chrono::duration_cast<T>(duration % duration_D(1) + cuda::std::chrono::hours(24));
+  }
+  duration_s seconds = cuda::std::chrono::duration_cast<duration_s>(duration);
+  timeparts->hour =
+    (cuda::std::chrono::duration_cast<cuda::std::chrono::hours>(seconds) % duration_D(1)).count();
+  timeparts->minute = (cuda::std::chrono::duration_cast<cuda::std::chrono::minutes>(seconds) %
+                       cuda::std::chrono::hours(1))
+                        .count();
+  timeparts->second = (seconds % cuda::std::chrono::minutes(1)).count();
+  if (not cuda::std::is_same_v<T, duration_s>) {
+    timeparts->nanosecond =
+      (cuda::std::chrono::duration_cast<duration_ns>(duration) % duration_s(1)).count();
+  }
+}
+
+template <typename T>
+struct duration_to_string_size_fn {
+  column_device_view const d_durations;
+
+  __device__ size_type operator()(size_type idx)
+  {
+    if (d_durations.is_null(idx)) return 0;
+    auto duration                = d_durations.element<T>(idx);
+    duration_component timeparts = {0};  // days, hours, minutes, seconds, nanoseconds(9)
+    dissect_duration(duration, &timeparts);
+    // [-] %d days [+]HH:MM:SS.mmmuuunnn
+    return cudf::strings::detail::count_digits(timeparts.day) + 6 + timeparts.is_negative + 18;
+  }
+};
+
+template <typename T>
+struct duration_to_string_fn : public duration_to_string_size_fn<T> {
+  int32_t const* d_offsets;
+  char* d_chars;
+  using duration_to_string_size_fn<T>::d_durations;
+
+  duration_to_string_fn(column_device_view const d_durations,
+                        int32_t const* d_offsets,
+                        char* d_chars)
+    : duration_to_string_size_fn<T>{d_durations}, d_offsets(d_offsets), d_chars(d_chars)
+  {
+  }
+
+  __device__ char* int_to_2digitstr(int8_t value, char* str)
+  {
+    assert(value >= -99 && value <= 99);
+    value  = std::abs(value);
+    str[0] = '0' + value / 10;
+    str[1] = '0' + value % 10;
+    return str + 2;
+  }
+
+  inline __device__ char* day(char* ptr, duration_component const* timeparts)
+  {
+    cudf::strings::detail::integer_to_string(timeparts->day, ptr);
+    return (ptr + cudf::strings::detail::count_digits(timeparts->day));
+  }
+
+  inline __device__ char* hour_24(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(timeparts->hour, ptr);
+  }
+
+  inline __device__ char* minute(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(timeparts->minute, ptr);
+  }
+  inline __device__ char* second(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(timeparts->second, ptr);
+  }
+
+  inline __device__ char* nanosecond(char* ptr, duration_component const* timeparts)
+  {
+    auto value = timeparts->nanosecond;
+    *ptr       = '.';
+    for (int idx = 9; idx > 0; idx--) {
+      *(ptr + idx) = '0' + std::abs(value % 10);
+      value /= 10;
+    }
+    return ptr + 10;
+  }
+
+  inline __device__ char* pandas_format(duration_component const* timeparts, char* ptr)
+  {
+    // if (timeparts->is_negative) *ptr++ = '-';
+    ptr = day(ptr, timeparts);
+    ptr = cudf::strings::detail::copy_and_increment(ptr, " days ", 6);
+    if (timeparts->is_negative) *ptr++ = '+';
+    ptr    = hour_24(ptr, timeparts);
+    *ptr++ = ':';
+    ptr    = minute(ptr, timeparts);
+    *ptr++ = ':';
+    ptr    = second(ptr, timeparts);
+    return nanosecond(ptr, timeparts);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_durations.is_null(idx)) return;
+    auto duration                = d_durations.template element<T>(idx);
+    duration_component timeparts = {0};  // days, hours, minutes, seconds, nanoseconds(9)
+    dissect_duration(duration, &timeparts);
+    // convert to characters
+    pandas_format(&timeparts, d_chars + d_offsets[idx]);
+  }
+};
+
+/**
+ * @brief This dispatch method is for converting durations into strings.
+ *
+ * The template function declaration ensures only duration types are used.
+ */
+struct dispatch_from_durations_fn {
+  template <typename T, std::enable_if_t<cudf::is_duration<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& durations,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    size_type strings_count = durations.size();
+    auto column             = column_device_view::create(durations, stream);
+    auto d_column           = *column;
+
+    // copy null mask
+    rmm::device_buffer null_mask = cudf::detail::copy_bitmask(durations, stream, mr);
+    // build offsets column
+    auto offsets_transformer_itr = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<int32_t>(0), duration_to_string_size_fn<T>{d_column});
+    auto [offsets_column, chars_bytes] = cudf::detail::make_offsets_child_column(
+      offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+    auto offsets_view  = offsets_column->view();
+    auto d_new_offsets = offsets_view.template data<int32_t>();
+
+    // build chars column
+    auto chars_column = strings::detail::create_chars_child_column(chars_bytes, stream, mr);
+    auto chars_view   = chars_column->mutable_view();
+    auto d_chars      = chars_view.template data<char>();
+
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       strings_count,
+                       duration_to_string_fn<T>{d_column, d_new_offsets, d_chars});
+
+    //
+    return make_strings_column(strings_count,
+                               std::move(offsets_column),
+                               std::move(chars_column),
+                               durations.null_count(),
+                               std::move(null_mask));
+  }
+
+  // non-duration types throw an exception
+  template <typename T, std::enable_if_t<not cudf::is_duration<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("Values for from_durations function must be a duration type.");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> pandas_format_durations(column_view const& durations,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = durations.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  return type_dispatcher(durations.type(), dispatch_from_durations_fn{}, durations, stream, mr);
+}
+
+}  // namespace csv
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/durations.hpp b/cpp/src/io/csv/durations.hpp
new file mode 100644
index 0000000..ac92501
--- /dev/null
+++ b/cpp/src/io/csv/durations.hpp
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace csv {
+
+std::unique_ptr<column> pandas_format_durations(column_view const& durations,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr);
+
+}  // namespace csv
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/reader_impl.cu b/cpp/src/io/csv/reader_impl.cu
new file mode 100644
index 0000000..dc28380
--- /dev/null
+++ b/cpp/src/io/csv/reader_impl.cu
@@ -0,0 +1,1008 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file reader_impl.cu
+ * @brief cuDF-IO CSV reader class implementation
+ */
+
+#include "csv_common.hpp"
+#include "csv_gpu.hpp"
+
+#include <io/comp/io_uncomp.hpp>
+#include <io/utilities/column_buffer.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+#include <io/utilities/parsing_utils.cuh>
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/utilities/visitor_overload.hpp>
+#include <cudf/io/csv.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/detail/csv.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <numeric>
+#include <string>
+#include <tuple>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+using std::string;
+using std::vector;
+
+using cudf::device_span;
+using cudf::host_span;
+using cudf::detail::make_device_uvector_async;
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace csv {
+using namespace cudf::io::csv;
+using namespace cudf::io;
+
+namespace {
+
+/**
+ * @brief Offsets of CSV rows in device memory, accessed through a shrinkable span.
+ *
+ * Row offsets are stored this way to avoid reallocation/copies when discarding front or back
+ * elements.
+ */
+class selected_rows_offsets {
+  rmm::device_uvector<uint64_t> all;
+  device_span<uint64_t const> selected;
+
+ public:
+  selected_rows_offsets(rmm::device_uvector<uint64_t>&& data,
+                        device_span<uint64_t const> selected_span)
+    : all{std::move(data)}, selected{selected_span}
+  {
+  }
+  selected_rows_offsets(rmm::cuda_stream_view stream) : all{0, stream}, selected{all} {}
+
+  operator device_span<uint64_t const>() const { return selected; }
+  void shrink(size_t size)
+  {
+    CUDF_EXPECTS(size <= selected.size(), "New size must be smaller");
+    selected = selected.subspan(0, size);
+  }
+  void erase_first_n(size_t n)
+  {
+    CUDF_EXPECTS(n <= selected.size(), "Too many elements to remove");
+    selected = selected.subspan(n, selected.size() - n);
+  }
+  auto size() const { return selected.size(); }
+  auto data() const { return selected.data(); }
+};
+
+/**
+ * @brief Removes the first and Last quote in the string
+ */
+string removeQuotes(string str, char quotechar)
+{
+  // Exclude first and last quotation char
+  size_t const first_quote = str.find(quotechar);
+  if (first_quote != string::npos) { str.erase(first_quote, 1); }
+  size_t const last_quote = str.rfind(quotechar);
+  if (last_quote != string::npos) { str.erase(last_quote, 1); }
+
+  return str;
+}
+
+/**
+ * @brief Parse the first row to set the column names in the raw_csv parameter.
+ * The first row can be either the header row, or the first data row
+ */
+std::vector<std::string> get_column_names(std::vector<char> const& header,
+                                          parse_options_view const& parse_opts,
+                                          int header_row,
+                                          std::string prefix)
+{
+  std::vector<std::string> col_names;
+
+  // If there is only a single character then it would be the terminator
+  if (header.size() <= 1) { return col_names; }
+
+  std::vector<char> first_row = header;
+
+  bool quotation = false;
+  for (size_t pos = 0, prev = 0; pos < first_row.size(); ++pos) {
+    // Flip the quotation flag if current character is a quotechar
+    if (first_row[pos] == parse_opts.quotechar) {
+      quotation = !quotation;
+    }
+    // Check if end of a column/row
+    else if (pos == first_row.size() - 1 ||
+             (!quotation && first_row[pos] == parse_opts.terminator) ||
+             (!quotation && first_row[pos] == parse_opts.delimiter)) {
+      // This is the header, add the column name
+      if (header_row >= 0) {
+        // Include the current character, in case the line is not terminated
+        int col_name_len = pos - prev + 1;
+        // Exclude the delimiter/terminator is present
+        if (first_row[pos] == parse_opts.delimiter || first_row[pos] == parse_opts.terminator) {
+          --col_name_len;
+        }
+        // Also exclude '\r' character at the end of the column name if it's
+        // part of the terminator
+        if (col_name_len > 0 && parse_opts.terminator == '\n' && first_row[pos] == '\n' &&
+            first_row[pos - 1] == '\r') {
+          --col_name_len;
+        }
+
+        string const new_col_name(first_row.data() + prev, col_name_len);
+        col_names.push_back(removeQuotes(new_col_name, parse_opts.quotechar));
+      } else {
+        // This is the first data row, add the automatically generated name
+        col_names.push_back(prefix + std::to_string(col_names.size()));
+      }
+
+      // Stop parsing when we hit the line terminator; relevant when there is
+      // a blank line following the header. In this case, first_row includes
+      // multiple line terminators at the end, as the new recStart belongs to
+      // a line that comes after the blank line(s)
+      if (!quotation && first_row[pos] == parse_opts.terminator) { break; }
+
+      // Skip adjacent delimiters if delim_whitespace is set
+      while (parse_opts.multi_delimiter && pos < first_row.size() &&
+             first_row[pos] == parse_opts.delimiter && first_row[pos + 1] == parse_opts.delimiter) {
+        ++pos;
+      }
+      prev = pos + 1;
+    }
+  }
+
+  return col_names;
+}
+
+template <typename C>
+void erase_except_last(C& container, rmm::cuda_stream_view stream)
+{
+  cudf::detail::device_single_thread(
+    [span = device_span<typename C::value_type>{container}] __device__() mutable {
+      span.front() = span.back();
+    },
+    stream);
+  container.resize(1, stream);
+}
+
+size_t find_first_row_start(char row_terminator, host_span<char const> data)
+{
+  // For now, look for the first terminator (assume the first terminator isn't within a quote)
+  // TODO: Attempt to infer this from the data
+  size_t pos = 0;
+  while (pos < data.size() && data[pos] != row_terminator) {
+    ++pos;
+  }
+  return std::min(pos + 1, data.size());
+}
+
+/**
+ * @brief Finds row positions in the specified input data, and loads the selected data onto GPU.
+ *
+ * This function scans the input data to record the row offsets (relative to the start of the
+ * input data). A row is actually the data/offset between two termination symbols.
+ *
+ * @param data Uncompressed input data in host memory
+ * @param range_begin Only include rows starting after this position
+ * @param range_end Only include rows starting before this position
+ * @param skip_rows Number of rows to skip from the start
+ * @param num_rows Number of rows to read; -1: all remaining data
+ * @param load_whole_file Hint that the entire data will be needed on gpu
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Input data and row offsets in the device memory
+ */
+std::pair<rmm::device_uvector<char>, selected_rows_offsets> load_data_and_gather_row_offsets(
+  csv_reader_options const& reader_opts,
+  parse_options const& parse_opts,
+  std::vector<char>& header,
+  host_span<char const> data,
+  size_t range_begin,
+  size_t range_end,
+  size_t skip_rows,
+  int64_t num_rows,
+  bool load_whole_file,
+  rmm::cuda_stream_view stream)
+{
+  constexpr size_t max_chunk_bytes = 64 * 1024 * 1024;  // 64MB
+  size_t buffer_size               = std::min(max_chunk_bytes, data.size());
+  size_t max_blocks =
+    std::max<size_t>((buffer_size / cudf::io::csv::gpu::rowofs_block_bytes) + 1, 2);
+  cudf::detail::hostdevice_vector<uint64_t> row_ctx(max_blocks, stream);
+  size_t buffer_pos  = std::min(range_begin - std::min(range_begin, sizeof(char)), data.size());
+  size_t pos         = std::min(range_begin, data.size());
+  size_t header_rows = (reader_opts.get_header() >= 0) ? reader_opts.get_header() + 1 : 0;
+  uint64_t ctx       = 0;
+
+  // For compatibility with the previous parser, a row is considered in-range if the
+  // previous row terminator is within the given range
+  range_end += (range_end < data.size());
+
+  // Reserve memory by allocating and then resetting the size
+  rmm::device_uvector<char> d_data{
+    (load_whole_file) ? data.size() : std::min(buffer_size * 2, data.size()), stream};
+  d_data.resize(0, stream);
+  rmm::device_uvector<uint64_t> all_row_offsets{0, stream};
+  do {
+    size_t target_pos = std::min(pos + max_chunk_bytes, data.size());
+    size_t chunk_size = target_pos - pos;
+
+    auto const previous_data_size = d_data.size();
+    d_data.resize(target_pos - buffer_pos, stream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(d_data.begin() + previous_data_size,
+                                  data.begin() + buffer_pos + previous_data_size,
+                                  target_pos - buffer_pos - previous_data_size,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+
+    // Pass 1: Count the potential number of rows in each character block for each
+    // possible parser state at the beginning of the block.
+    uint32_t num_blocks = cudf::io::csv::gpu::gather_row_offsets(parse_opts.view(),
+                                                                 row_ctx.device_ptr(),
+                                                                 device_span<uint64_t>(),
+                                                                 d_data,
+                                                                 chunk_size,
+                                                                 pos,
+                                                                 buffer_pos,
+                                                                 data.size(),
+                                                                 range_begin,
+                                                                 range_end,
+                                                                 skip_rows,
+                                                                 stream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(row_ctx.host_ptr(),
+                                  row_ctx.device_ptr(),
+                                  num_blocks * sizeof(uint64_t),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    stream.synchronize();
+
+    // Sum up the rows in each character block, selecting the row count that
+    // corresponds to the current input context. Also stores the now known input
+    // context per character block that will be needed by the second pass.
+    for (uint32_t i = 0; i < num_blocks; i++) {
+      uint64_t ctx_next = cudf::io::csv::gpu::select_row_context(ctx, row_ctx[i]);
+      row_ctx[i]        = ctx;
+      ctx               = ctx_next;
+    }
+    size_t total_rows = ctx >> 2;
+    if (total_rows > skip_rows) {
+      // At least one row in range in this batch
+      all_row_offsets.resize(total_rows - skip_rows, stream);
+
+      CUDF_CUDA_TRY(cudaMemcpyAsync(row_ctx.device_ptr(),
+                                    row_ctx.host_ptr(),
+                                    num_blocks * sizeof(uint64_t),
+                                    cudaMemcpyDefault,
+                                    stream.value()));
+
+      // Pass 2: Output row offsets
+      cudf::io::csv::gpu::gather_row_offsets(parse_opts.view(),
+                                             row_ctx.device_ptr(),
+                                             all_row_offsets,
+                                             d_data,
+                                             chunk_size,
+                                             pos,
+                                             buffer_pos,
+                                             data.size(),
+                                             range_begin,
+                                             range_end,
+                                             skip_rows,
+                                             stream);
+      // With byte range, we want to keep only one row out of the specified range
+      if (range_end < data.size()) {
+        CUDF_CUDA_TRY(cudaMemcpyAsync(row_ctx.host_ptr(),
+                                      row_ctx.device_ptr(),
+                                      num_blocks * sizeof(uint64_t),
+                                      cudaMemcpyDefault,
+                                      stream.value()));
+        stream.synchronize();
+
+        size_t rows_out_of_range = 0;
+        for (uint32_t i = 0; i < num_blocks; i++) {
+          rows_out_of_range += row_ctx[i];
+        }
+        if (rows_out_of_range != 0) {
+          // Keep one row out of range (used to infer length of previous row)
+          auto new_row_offsets_size =
+            all_row_offsets.size() - std::min(rows_out_of_range - 1, all_row_offsets.size());
+          all_row_offsets.resize(new_row_offsets_size, stream);
+          // Implies we reached the end of the range
+          break;
+        }
+      }
+      // num_rows does not include blank rows
+      if (num_rows >= 0) {
+        if (all_row_offsets.size() > header_rows + static_cast<size_t>(num_rows)) {
+          size_t num_blanks = cudf::io::csv::gpu::count_blank_rows(
+            parse_opts.view(), d_data, all_row_offsets, stream);
+          if (all_row_offsets.size() - num_blanks > header_rows + static_cast<size_t>(num_rows)) {
+            // Got the desired number of rows
+            break;
+          }
+        }
+      }
+    } else {
+      // Discard data (all rows below skip_rows), keeping one character for history
+      size_t discard_bytes = std::max(d_data.size(), sizeof(char)) - sizeof(char);
+      if (discard_bytes != 0) {
+        erase_except_last(d_data, stream);
+        buffer_pos += discard_bytes;
+      }
+    }
+    pos = target_pos;
+  } while (pos < data.size());
+
+  auto const non_blank_row_offsets =
+    io::csv::gpu::remove_blank_rows(parse_opts.view(), d_data, all_row_offsets, stream);
+  auto row_offsets = selected_rows_offsets{std::move(all_row_offsets), non_blank_row_offsets};
+
+  // Remove header rows and extract header
+  size_t const header_row_index = std::max<size_t>(header_rows, 1) - 1;
+  if (header_row_index + 1 < row_offsets.size()) {
+    CUDF_CUDA_TRY(cudaMemcpyAsync(row_ctx.host_ptr(),
+                                  row_offsets.data() + header_row_index,
+                                  2 * sizeof(uint64_t),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    stream.synchronize();
+
+    auto const header_start = buffer_pos + row_ctx[0];
+    auto const header_end   = buffer_pos + row_ctx[1];
+    CUDF_EXPECTS(header_start <= header_end && header_end <= data.size(),
+                 "Invalid csv header location");
+    header.assign(data.begin() + header_start, data.begin() + header_end);
+    if (header_rows > 0) { row_offsets.erase_first_n(header_rows); }
+  }
+  // Apply num_rows limit
+  if (num_rows >= 0 && static_cast<size_t>(num_rows) < row_offsets.size() - 1) {
+    row_offsets.shrink(num_rows + 1);
+  }
+  return {std::move(d_data), std::move(row_offsets)};
+}
+
+std::pair<rmm::device_uvector<char>, selected_rows_offsets> select_data_and_row_offsets(
+  cudf::io::datasource* source,
+  csv_reader_options const& reader_opts,
+  std::vector<char>& header,
+  parse_options const& parse_opts,
+  rmm::cuda_stream_view stream)
+{
+  auto range_offset      = reader_opts.get_byte_range_offset();
+  auto range_size        = reader_opts.get_byte_range_size();
+  auto range_size_padded = reader_opts.get_byte_range_size_with_padding();
+  auto skip_rows         = reader_opts.get_skiprows();
+  auto skip_end_rows     = reader_opts.get_skipfooter();
+  auto num_rows          = reader_opts.get_nrows();
+
+  if (range_offset > 0 || range_size > 0) {
+    CUDF_EXPECTS(reader_opts.get_compression() == compression_type::NONE,
+                 "Reading compressed data using `byte range` is unsupported");
+  }
+
+  // Transfer source data to GPU
+  if (!source->is_empty()) {
+    auto buffer =
+      source->host_read(range_offset, range_size_padded != 0 ? range_size_padded : source->size());
+    auto h_data =
+      host_span<char const>(reinterpret_cast<char const*>(buffer->data()), buffer->size());
+
+    std::vector<uint8_t> h_uncomp_data_owner;
+    if (reader_opts.get_compression() != compression_type::NONE) {
+      h_uncomp_data_owner =
+        decompress(reader_opts.get_compression(), {buffer->data(), buffer->size()});
+      h_data = {reinterpret_cast<char const*>(h_uncomp_data_owner.data()),
+                h_uncomp_data_owner.size()};
+      buffer.reset();
+    }
+
+    // check for and skip UTF-8 BOM
+    uint8_t const UTF8_BOM[] = {0xEF, 0xBB, 0xBF};
+    if (h_data.size() >= sizeof(UTF8_BOM) &&
+        memcmp(h_data.data(), UTF8_BOM, sizeof(UTF8_BOM)) == 0) {
+      h_data = h_data.subspan(sizeof(UTF8_BOM), h_data.size() - sizeof(UTF8_BOM));
+    }
+
+    // None of the parameters for row selection is used, we are parsing the entire file
+    bool const load_whole_file = range_offset == 0 && range_size == 0 && skip_rows <= 0 &&
+                                 skip_end_rows <= 0 && num_rows == -1;
+
+    // With byte range, find the start of the first data row
+    size_t const data_start_offset =
+      (range_offset != 0) ? find_first_row_start(parse_opts.terminator, h_data) : 0;
+
+    // TODO: Allow parsing the header outside the mapped range
+    CUDF_EXPECTS((range_offset == 0 || reader_opts.get_header() < 0),
+                 "byte_range offset with header not supported");
+
+    // Gather row offsets
+    auto data_row_offsets =
+      load_data_and_gather_row_offsets(reader_opts,
+                                       parse_opts,
+                                       header,
+                                       h_data,
+                                       data_start_offset,
+                                       (range_size) ? range_size : h_data.size(),
+                                       (skip_rows > 0) ? skip_rows : 0,
+                                       num_rows,
+                                       load_whole_file,
+                                       stream);
+    auto& row_offsets = data_row_offsets.second;
+    // Exclude the rows that are to be skipped from the end
+    if (skip_end_rows > 0 && static_cast<size_t>(skip_end_rows) < row_offsets.size()) {
+      row_offsets.shrink(row_offsets.size() - skip_end_rows);
+    }
+    return data_row_offsets;
+  }
+  return {rmm::device_uvector<char>{0, stream}, selected_rows_offsets{stream}};
+}
+
+void select_data_types(host_span<data_type const> user_dtypes,
+                       host_span<column_parse::flags> column_flags,
+                       host_span<data_type> column_types)
+{
+  if (user_dtypes.empty()) { return; }
+
+  CUDF_EXPECTS(user_dtypes.size() == 1 || user_dtypes.size() == column_flags.size(),
+               "Specify data types for all columns in file, or use a dictionary/map");
+
+  for (auto col_idx = 0u; col_idx < column_flags.size(); ++col_idx) {
+    if (column_flags[col_idx] & column_parse::enabled) {
+      // If it's a single dtype, assign that dtype to all active columns
+      auto const& dtype     = user_dtypes.size() == 1 ? user_dtypes[0] : user_dtypes[col_idx];
+      column_types[col_idx] = dtype;
+      // Reset the inferred flag, no need to infer the types from the data
+      column_flags[col_idx] &= ~column_parse::inferred;
+    }
+  }
+}
+
+void get_data_types_from_column_names(std::map<std::string, data_type> const& user_dtypes,
+                                      host_span<std::string const> column_names,
+                                      host_span<column_parse::flags> column_flags,
+                                      host_span<data_type> column_types)
+{
+  if (user_dtypes.empty()) { return; }
+  for (auto col_idx = 0u; col_idx < column_flags.size(); ++col_idx) {
+    if (column_flags[col_idx] & column_parse::enabled) {
+      auto const col_type_it = user_dtypes.find(column_names[col_idx]);
+      if (col_type_it != user_dtypes.end()) {
+        // Assign the type from the map
+        column_types[col_idx] = col_type_it->second;
+        // Reset the inferred flag, no need to infer the types from the data
+        column_flags[col_idx] &= ~column_parse::inferred;
+      }
+    }
+  }
+}
+
+void infer_column_types(parse_options const& parse_opts,
+                        host_span<column_parse::flags const> column_flags,
+                        device_span<char const> data,
+                        device_span<uint64_t const> row_offsets,
+                        int32_t num_records,
+                        data_type timestamp_type,
+                        host_span<data_type> column_types,
+                        rmm::cuda_stream_view stream)
+{
+  if (num_records == 0) {
+    for (auto col_idx = 0u; col_idx < column_flags.size(); ++col_idx) {
+      if (column_flags[col_idx] & column_parse::inferred) {
+        column_types[col_idx] = data_type(cudf::type_id::STRING);
+      }
+    }
+    return;
+  }
+
+  auto const num_inferred_columns =
+    std::count_if(column_flags.begin(), column_flags.end(), [](auto& flags) {
+      return flags & column_parse::inferred;
+    });
+  if (num_inferred_columns == 0) { return; }
+
+  auto const column_stats = cudf::io::csv::gpu::detect_column_types(
+    parse_opts.view(),
+    data,
+    make_device_uvector_async(column_flags, stream, rmm::mr::get_current_device_resource()),
+    row_offsets,
+    num_inferred_columns,
+    stream);
+  stream.synchronize();
+
+  auto inf_col_idx = 0;
+  for (auto col_idx = 0u; col_idx < column_flags.size(); ++col_idx) {
+    if (not(column_flags[col_idx] & column_parse::inferred)) { continue; }
+    auto const& stats = column_stats[inf_col_idx++];
+    if (stats.null_count == num_records or stats.total_count() == 0) {
+      // Entire column is NULL; allocate the smallest amount of memory
+      column_types[col_idx] = data_type(cudf::type_id::INT8);
+    } else if (stats.string_count > 0L) {
+      column_types[col_idx] = data_type(cudf::type_id::STRING);
+    } else if (stats.datetime_count > 0L) {
+      column_types[col_idx] = timestamp_type.id() == cudf::type_id::EMPTY
+                                ? data_type(cudf::type_id::TIMESTAMP_NANOSECONDS)
+                                : timestamp_type;
+    } else if (stats.bool_count > 0L) {
+      column_types[col_idx] = data_type(cudf::type_id::BOOL8);
+    } else if (stats.float_count > 0L) {
+      column_types[col_idx] = data_type(cudf::type_id::FLOAT64);
+    } else if (stats.big_int_count == 0) {
+      column_types[col_idx] = data_type(cudf::type_id::INT64);
+    } else if (stats.big_int_count != 0 && stats.negative_small_int_count != 0) {
+      column_types[col_idx] = data_type(cudf::type_id::STRING);
+    } else {
+      // Integers are stored as 64-bit to conform to PANDAS
+      column_types[col_idx] = data_type(cudf::type_id::UINT64);
+    }
+  }
+}
+
+std::vector<column_buffer> decode_data(parse_options const& parse_opts,
+                                       std::vector<column_parse::flags> const& column_flags,
+                                       std::vector<std::string> const& column_names,
+                                       device_span<char const> data,
+                                       device_span<uint64_t const> row_offsets,
+                                       host_span<data_type const> column_types,
+                                       int32_t num_records,
+                                       int32_t num_actual_columns,
+                                       int32_t num_active_columns,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  // Alloc output; columns' data memory is still expected for empty dataframe
+  std::vector<column_buffer> out_buffers;
+  out_buffers.reserve(column_types.size());
+
+  for (int col = 0, active_col = 0; col < num_actual_columns; ++col) {
+    if (column_flags[col] & column_parse::enabled) {
+      auto out_buffer = column_buffer(column_types[active_col], num_records, true, stream, mr);
+
+      out_buffer.name = column_names[col];
+      out_buffers.emplace_back(std::move(out_buffer));
+      active_col++;
+    }
+  }
+
+  thrust::host_vector<void*> h_data(num_active_columns);
+  thrust::host_vector<bitmask_type*> h_valid(num_active_columns);
+
+  for (int i = 0; i < num_active_columns; ++i) {
+    h_data[i]  = out_buffers[i].data();
+    h_valid[i] = out_buffers[i].null_mask();
+  }
+
+  auto d_valid_counts = cudf::detail::make_zeroed_device_uvector_async<size_type>(
+    num_active_columns, stream, rmm::mr::get_current_device_resource());
+
+  cudf::io::csv::gpu::decode_row_column_data(
+    parse_opts.view(),
+    data,
+    make_device_uvector_async(column_flags, stream, rmm::mr::get_current_device_resource()),
+    row_offsets,
+    make_device_uvector_async(column_types, stream, rmm::mr::get_current_device_resource()),
+    make_device_uvector_async(h_data, stream, rmm::mr::get_current_device_resource()),
+    make_device_uvector_async(h_valid, stream, rmm::mr::get_current_device_resource()),
+    d_valid_counts,
+    stream);
+
+  auto const h_valid_counts = cudf::detail::make_std_vector_sync(d_valid_counts, stream);
+  for (int i = 0; i < num_active_columns; ++i) {
+    out_buffers[i].null_count() = num_records - h_valid_counts[i];
+  }
+
+  return out_buffers;
+}
+
+std::vector<data_type> determine_column_types(csv_reader_options const& reader_opts,
+                                              parse_options const& parse_opts,
+                                              host_span<std::string const> column_names,
+                                              device_span<char const> data,
+                                              device_span<uint64_t const> row_offsets,
+                                              int32_t num_records,
+                                              host_span<column_parse::flags> column_flags,
+                                              rmm::cuda_stream_view stream)
+{
+  std::vector<data_type> column_types(column_flags.size());
+
+  std::visit(cudf::detail::visitor_overload{
+               [&](std::vector<data_type> const& user_dtypes) {
+                 return select_data_types(user_dtypes, column_flags, column_types);
+               },
+               [&](std::map<std::string, data_type> const& user_dtypes) {
+                 return get_data_types_from_column_names(
+                   user_dtypes, column_names, column_flags, column_types);
+               }},
+             reader_opts.get_dtypes());
+
+  infer_column_types(parse_opts,
+                     column_flags,
+                     data,
+                     row_offsets,
+                     num_records,
+                     reader_opts.get_timestamp_type(),
+                     column_types,
+                     stream);
+
+  // compact column_types to only include active columns
+  std::vector<data_type> active_col_types;
+  std::copy_if(column_types.cbegin(),
+               column_types.cend(),
+               std::back_inserter(active_col_types),
+               [&column_flags, &types = std::as_const(column_types)](auto& dtype) {
+                 auto const idx = std::distance(types.data(), &dtype);
+                 return column_flags[idx] & column_parse::enabled;
+               });
+
+  return active_col_types;
+}
+
+table_with_metadata read_csv(cudf::io::datasource* source,
+                             csv_reader_options const& reader_opts,
+                             parse_options const& parse_opts,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  std::vector<char> header;
+
+  auto const data_row_offsets =
+    select_data_and_row_offsets(source, reader_opts, header, parse_opts, stream);
+
+  auto const& data        = data_row_offsets.first;
+  auto const& row_offsets = data_row_offsets.second;
+
+  auto const unique_use_cols_indexes = std::set(reader_opts.get_use_cols_indexes().cbegin(),
+                                                reader_opts.get_use_cols_indexes().cend());
+
+  auto const detected_column_names =
+    get_column_names(header, parse_opts.view(), reader_opts.get_header(), reader_opts.get_prefix());
+  auto const opts_have_all_col_names =
+    not reader_opts.get_names().empty() and
+    (
+      // no data to detect (the number of) columns
+      detected_column_names.empty() or
+      // number of user specified names matches what is detected
+      reader_opts.get_names().size() == detected_column_names.size() or
+      // Columns are not selected by indices; read first reader_opts.get_names().size() columns
+      unique_use_cols_indexes.empty());
+  auto column_names = opts_have_all_col_names ? reader_opts.get_names() : detected_column_names;
+
+  auto const num_actual_columns = static_cast<int32_t>(column_names.size());
+  auto num_active_columns       = num_actual_columns;
+  auto column_flags             = std::vector<column_parse::flags>(
+    num_actual_columns, column_parse::enabled | column_parse::inferred);
+
+  // User did not pass column names to override names in the file
+  // Process names from the file to remove empty and duplicated strings
+  if (not opts_have_all_col_names) {
+    std::vector<size_t> col_loop_order(column_names.size());
+    auto unnamed_it = std::copy_if(
+      thrust::make_counting_iterator<size_t>(0),
+      thrust::make_counting_iterator<size_t>(column_names.size()),
+      col_loop_order.begin(),
+      [&column_names](auto col_idx) -> bool { return not column_names[col_idx].empty(); });
+
+    // Rename empty column names to "Unnamed: col_index"
+    std::copy_if(thrust::make_counting_iterator<size_t>(0),
+                 thrust::make_counting_iterator<size_t>(column_names.size()),
+                 unnamed_it,
+                 [&column_names](auto col_idx) -> bool {
+                   auto is_empty = column_names[col_idx].empty();
+                   if (is_empty)
+                     column_names[col_idx] = string("Unnamed: ") + std::to_string(col_idx);
+                   return is_empty;
+                 });
+
+    // Looking for duplicates
+    std::unordered_map<string, int> col_names_counts;
+    if (!reader_opts.is_enabled_mangle_dupe_cols()) {
+      for (auto& col_name : column_names) {
+        if (++col_names_counts[col_name] > 1) {
+          CUDF_LOG_WARN("Multiple columns with name {}; only the first appearance is parsed",
+                        col_name);
+
+          auto const idx    = &col_name - column_names.data();
+          column_flags[idx] = column_parse::disabled;
+        }
+      }
+    } else {
+      // For constant/linear search.
+      std::unordered_multiset<std::string> header(column_names.begin(), column_names.end());
+      for (auto const col_idx : col_loop_order) {
+        auto col       = column_names[col_idx];
+        auto cur_count = col_names_counts[col];
+        if (cur_count > 0) {
+          auto const old_col = col;
+          // Rename duplicates of column X as X.1, X.2, ...; First appearance stays as X
+          while (cur_count > 0) {
+            col_names_counts[old_col] = cur_count + 1;
+            col                       = old_col + "." + std::to_string(cur_count);
+            if (header.find(col) != header.end()) {
+              cur_count++;
+            } else {
+              cur_count = col_names_counts[col];
+            }
+          }
+          if (auto pos = header.find(old_col); pos != header.end()) { header.erase(pos); }
+          header.insert(col);
+          column_names[col_idx] = col;
+        }
+        col_names_counts[col] = cur_count + 1;
+      }
+    }
+
+    // Update the number of columns to be processed, if some might have been removed
+    if (!reader_opts.is_enabled_mangle_dupe_cols()) {
+      num_active_columns = col_names_counts.size();
+    }
+  }
+
+  // User can specify which columns should be parsed
+  auto const unique_use_cols_names = std::unordered_set(reader_opts.get_use_cols_names().cbegin(),
+                                                        reader_opts.get_use_cols_names().cend());
+  auto const is_column_selection_used =
+    not unique_use_cols_names.empty() or not unique_use_cols_indexes.empty();
+
+  // Reset flags and output column count; columns will be reactivated based on the selection options
+  if (is_column_selection_used) {
+    std::fill(column_flags.begin(), column_flags.end(), column_parse::disabled);
+    num_active_columns = 0;
+  }
+
+  // Column selection via column indexes
+  if (not unique_use_cols_indexes.empty()) {
+    // Users can pass names for the selected columns only, if selecting column by their indices
+    auto const are_opts_col_names_used =
+      not reader_opts.get_names().empty() and not opts_have_all_col_names;
+    CUDF_EXPECTS(not are_opts_col_names_used or
+                   reader_opts.get_names().size() == unique_use_cols_indexes.size(),
+                 "Specify names of all columns in the file, or names of all selected columns");
+
+    for (auto const index : unique_use_cols_indexes) {
+      column_flags[index] = column_parse::enabled | column_parse::inferred;
+      if (are_opts_col_names_used) {
+        column_names[index] = reader_opts.get_names()[num_active_columns];
+      }
+      ++num_active_columns;
+    }
+  }
+
+  // Column selection via column names
+  if (not unique_use_cols_names.empty()) {
+    for (auto const& name : unique_use_cols_names) {
+      auto const it = std::find(column_names.cbegin(), column_names.cend(), name);
+      CUDF_EXPECTS(it != column_names.end(), "Nonexistent column selected");
+      auto const col_idx = std::distance(column_names.cbegin(), it);
+      if (column_flags[col_idx] == column_parse::disabled) {
+        column_flags[col_idx] = column_parse::enabled | column_parse::inferred;
+        ++num_active_columns;
+      }
+    }
+  }
+
+  // User can specify which columns should be read as datetime
+  if (!reader_opts.get_parse_dates_indexes().empty() ||
+      !reader_opts.get_parse_dates_names().empty()) {
+    for (auto const index : reader_opts.get_parse_dates_indexes()) {
+      column_flags[index] |= column_parse::as_datetime;
+    }
+
+    for (auto const& name : reader_opts.get_parse_dates_names()) {
+      auto it = std::find(column_names.begin(), column_names.end(), name);
+      if (it != column_names.end()) {
+        column_flags[it - column_names.begin()] |= column_parse::as_datetime;
+      }
+    }
+  }
+
+  // User can specify which columns should be parsed as hexadecimal
+  if (!reader_opts.get_parse_hex_indexes().empty() || !reader_opts.get_parse_hex_names().empty()) {
+    for (auto const index : reader_opts.get_parse_hex_indexes()) {
+      column_flags[index] |= column_parse::as_hexadecimal;
+    }
+
+    for (auto const& name : reader_opts.get_parse_hex_names()) {
+      auto it = std::find(column_names.begin(), column_names.end(), name);
+      if (it != column_names.end()) {
+        column_flags[it - column_names.begin()] |= column_parse::as_hexadecimal;
+      }
+    }
+  }
+
+  // Return empty table rather than exception if nothing to load
+  if (num_active_columns == 0) { return {std::make_unique<table>(), {}}; }
+
+  // Exclude the end-of-data row from number of rows with actual data
+  auto const num_records  = std::max(row_offsets.size(), 1ul) - 1;
+  auto const column_types = determine_column_types(
+    reader_opts, parse_opts, column_names, data, row_offsets, num_records, column_flags, stream);
+
+  auto metadata    = table_metadata{};
+  auto out_columns = std::vector<std::unique_ptr<cudf::column>>();
+  out_columns.reserve(column_types.size());
+  if (num_records != 0) {
+    auto out_buffers = decode_data(  //
+      parse_opts,
+      column_flags,
+      column_names,
+      data,
+      row_offsets,
+      column_types,
+      num_records,
+      num_actual_columns,
+      num_active_columns,
+      stream,
+      mr);
+    for (size_t i = 0; i < column_types.size(); ++i) {
+      metadata.schema_info.emplace_back(out_buffers[i].name);
+      if (column_types[i].id() == type_id::STRING && parse_opts.quotechar != '\0' &&
+          parse_opts.doublequote) {
+        // PANDAS' default behavior of enabling doublequote for two consecutive
+        // quotechars in quoted fields results in reduction to a single quotechar
+        // TODO: Would be much more efficient to perform this operation in-place
+        // during the conversion stage
+        std::string const quotechar(1, parse_opts.quotechar);
+        std::string const dblquotechar(2, parse_opts.quotechar);
+        std::unique_ptr<column> col = cudf::make_strings_column(*out_buffers[i]._strings, stream);
+        out_columns.emplace_back(
+          cudf::strings::detail::replace(col->view(), dblquotechar, quotechar, -1, stream, mr));
+      } else {
+        out_columns.emplace_back(make_column(out_buffers[i], nullptr, std::nullopt, stream));
+      }
+    }
+  } else {
+    // Create empty columns
+    for (size_t i = 0; i < column_types.size(); ++i) {
+      out_columns.emplace_back(make_empty_column(column_types[i]));
+    }
+    // Handle empty metadata
+    for (int col = 0; col < num_actual_columns; ++col) {
+      if (column_flags[col] & column_parse::enabled) {
+        metadata.schema_info.emplace_back(column_names[col]);
+      }
+    }
+  }
+  return {std::make_unique<table>(std::move(out_columns)), std::move(metadata)};
+}
+
+/**
+ * @brief Create a serialized trie for N/A value matching, based on the options.
+ */
+cudf::detail::trie create_na_trie(char quotechar,
+                                  csv_reader_options const& reader_opts,
+                                  rmm::cuda_stream_view stream)
+{
+  // Default values to recognize as null values
+  static std::vector<std::string> const default_na_values{"",
+                                                          "#N/A",
+                                                          "#N/A N/A",
+                                                          "#NA",
+                                                          "-1.#IND",
+                                                          "-1.#QNAN",
+                                                          "-NaN",
+                                                          "-nan",
+                                                          "1.#IND",
+                                                          "1.#QNAN",
+                                                          "<NA>",
+                                                          "N/A",
+                                                          "NA",
+                                                          "NULL",
+                                                          "NaN",
+                                                          "n/a",
+                                                          "nan",
+                                                          "null"};
+
+  if (!reader_opts.is_enabled_na_filter()) { return cudf::detail::trie(0, stream); }
+
+  std::vector<std::string> na_values = reader_opts.get_na_values();
+  if (reader_opts.is_enabled_keep_default_na()) {
+    na_values.insert(na_values.end(), default_na_values.begin(), default_na_values.end());
+  }
+
+  // Pandas treats empty strings as N/A if empty fields are treated as N/A
+  if (std::find(na_values.begin(), na_values.end(), "") != na_values.end()) {
+    na_values.push_back(std::string(2, quotechar));
+  }
+
+  return cudf::detail::create_serialized_trie(na_values, stream);
+}
+
+parse_options make_parse_options(csv_reader_options const& reader_opts,
+                                 rmm::cuda_stream_view stream)
+{
+  auto parse_opts = parse_options{};
+
+  if (reader_opts.is_enabled_delim_whitespace()) {
+    parse_opts.delimiter       = ' ';
+    parse_opts.multi_delimiter = true;
+  } else {
+    parse_opts.delimiter       = reader_opts.get_delimiter();
+    parse_opts.multi_delimiter = false;
+  }
+
+  parse_opts.terminator = reader_opts.get_lineterminator();
+
+  if (reader_opts.get_quotechar() != '\0' && reader_opts.get_quoting() != quote_style::NONE) {
+    parse_opts.quotechar   = reader_opts.get_quotechar();
+    parse_opts.keepquotes  = false;
+    parse_opts.doublequote = reader_opts.is_enabled_doublequote();
+  } else {
+    parse_opts.quotechar   = '\0';
+    parse_opts.keepquotes  = true;
+    parse_opts.doublequote = false;
+  }
+
+  parse_opts.skipblanklines = reader_opts.is_enabled_skip_blank_lines();
+  parse_opts.comment        = reader_opts.get_comment();
+  parse_opts.dayfirst       = reader_opts.is_enabled_dayfirst();
+  parse_opts.decimal        = reader_opts.get_decimal();
+  parse_opts.thousands      = reader_opts.get_thousands();
+
+  CUDF_EXPECTS(parse_opts.decimal != parse_opts.delimiter,
+               "Decimal point cannot be the same as the delimiter");
+  CUDF_EXPECTS(parse_opts.thousands != parse_opts.delimiter,
+               "Thousands separator cannot be the same as the delimiter");
+
+  // Handle user-defined true values, whereby field data is substituted with a
+  // boolean true or numeric `1` value
+  if (not reader_opts.get_true_values().empty()) {
+    parse_opts.trie_true =
+      cudf::detail::create_serialized_trie(reader_opts.get_true_values(), stream);
+  }
+
+  // Handle user-defined false values, whereby field data is substituted with a
+  // boolean false or numeric `0` value
+  if (not reader_opts.get_false_values().empty()) {
+    parse_opts.trie_false =
+      cudf::detail::create_serialized_trie(reader_opts.get_false_values(), stream);
+  }
+
+  // Handle user-defined N/A values, whereby field data is treated as null
+  parse_opts.trie_na = create_na_trie(parse_opts.quotechar, reader_opts, stream);
+
+  return parse_opts;
+}
+
+}  // namespace
+
+table_with_metadata read_csv(std::unique_ptr<cudf::io::datasource>&& source,
+                             csv_reader_options const& options,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  auto parse_options = make_parse_options(options, stream);
+
+  return read_csv(source.get(), options, parse_options, stream, mr);
+}
+
+}  // namespace csv
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/csv/writer_impl.cu b/cpp/src/io/csv/writer_impl.cu
new file mode 100644
index 0000000..8c58630
--- /dev/null
+++ b/cpp/src/io/csv/writer_impl.cu
@@ -0,0 +1,495 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file writer_impl.cu
+ * @brief cuDF-IO CSV writer class implementation
+ */
+
+#include "durations.hpp"
+
+#include "csv_common.hpp"
+#include "csv_gpu.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/detail/csv.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/combine.hpp>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/host_vector.h>
+#include <thrust/logical.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+
+#include <algorithm>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace csv {
+
+using namespace cudf::io::csv;
+using namespace cudf::io;
+
+namespace {
+
+/**
+ * @brief Functor to modify a string column for CSV format.
+ *
+ * If a row contains specific characters, the entire row must be
+ * output in double-quotes. Also, if a double-quote appears it
+ * must be escaped using a 2nd double-quote.
+ */
+struct escape_strings_fn {
+  column_device_view const d_column;
+  string_view const d_delimiter;  // check for column delimiter
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void write_char(char_utf8 chr, char*& d_buffer, size_type& bytes)
+  {
+    if (d_buffer)
+      d_buffer += cudf::strings::detail::from_char_utf8(chr, d_buffer);
+    else
+      bytes += cudf::strings::detail::bytes_in_char_utf8(chr);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    constexpr char_utf8 const quote    = '\"';  // check for quote
+    constexpr char_utf8 const new_line = '\n';  // and for new-line
+
+    auto const d_str = d_column.element<string_view>(idx);
+
+    // if quote, new-line or a column delimiter appear in the string
+    // the entire string must be double-quoted.
+    bool const quote_row = thrust::any_of(
+      thrust::seq, d_str.begin(), d_str.end(), [d_delimiter = d_delimiter](auto chr) {
+        return chr == quote || chr == new_line || chr == d_delimiter[0];
+      });
+
+    char* d_buffer  = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    size_type bytes = 0;
+
+    if (quote_row) write_char(quote, d_buffer, bytes);
+    for (auto chr : d_str) {
+      if (chr == quote) write_char(quote, d_buffer, bytes);
+      write_char(chr, d_buffer, bytes);
+    }
+    if (quote_row) write_char(quote, d_buffer, bytes);
+
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+struct column_to_strings_fn {
+  // compile-time predicate that defines unsupported column types;
+  // based on the conditions used for instantiations of individual
+  // converters in strings/convert/convert_*.hpp;
+  //(this should have been a `variable template`,
+  // instead of a static function, but nvcc (10.0)
+  // fails to compile var-templs);
+  //
+  template <typename column_type>
+  constexpr static bool is_not_handled()
+  {
+    // Note: the case (not std::is_same_v<column_type, bool>)
+    // is already covered by is_integral)
+    //
+    return not((std::is_same_v<column_type, cudf::string_view>) ||
+               (std::is_integral_v<column_type>) || (std::is_floating_point_v<column_type>) ||
+               (cudf::is_fixed_point<column_type>()) || (cudf::is_timestamp<column_type>()) ||
+               (cudf::is_duration<column_type>()));
+  }
+
+  explicit column_to_strings_fn(csv_writer_options const& options,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+    : options_(options), stream_(stream), mr_(mr)
+  {
+  }
+
+  // Note: `null` replacement with `na_rep` deferred to `concatenate()`
+  // instead of column-wise; might be faster
+  //
+  // Note: Cannot pass `stream` to detail::<fname> version of <fname> calls below, because they are
+  // not exposed in header (see, for example, detail::concatenate(tbl_view, separator, na_rep,
+  // stream, mr) is declared and defined in combine.cu); Possible solution: declare `extern`, or
+  // just declare a prototype inside `namespace cudf::strings::detail`;
+
+  // bools:
+  //
+  template <typename column_type>
+  std::enable_if_t<std::is_same_v<column_type, bool>, std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::strings::detail::from_booleans(
+      column, options_.get_true_value(), options_.get_false_value(), stream_, mr_);
+  }
+
+  // strings:
+  //
+  template <typename column_type>
+  std::enable_if_t<std::is_same_v<column_type, cudf::string_view>, std::unique_ptr<column>>
+  operator()(column_view const& column_v) const
+  {
+    if (options_.get_quoting() == cudf::io::quote_style::NONE) {
+      return std::make_unique<column>(column_v, stream_, mr_);
+    }
+
+    // handle special characters: {delimiter, '\n', "} in row:
+    string_scalar delimiter{std::string{options_.get_inter_column_delimiter()}, true, stream_};
+
+    auto d_column = column_device_view::create(column_v, stream_);
+    escape_strings_fn fn{*d_column, delimiter.value(stream_)};
+    auto children = cudf::strings::detail::make_strings_children(fn, column_v.size(), stream_, mr_);
+
+    return make_strings_column(column_v.size(),
+                               std::move(children.first),
+                               std::move(children.second),
+                               column_v.null_count(),
+                               cudf::detail::copy_bitmask(column_v, stream_, mr_));
+  }
+
+  // ints:
+  //
+  template <typename column_type>
+  std::enable_if_t<std::is_integral_v<column_type> && !std::is_same_v<column_type, bool>,
+                   std::unique_ptr<column>>
+  operator()(column_view const& column) const
+  {
+    return cudf::strings::detail::from_integers(column, stream_, mr_);
+  }
+
+  // floats:
+  //
+  template <typename column_type>
+  std::enable_if_t<std::is_floating_point_v<column_type>, std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::strings::detail::from_floats(column, stream_, mr_);
+  }
+
+  // fixed point:
+  //
+  template <typename column_type>
+  std::enable_if_t<cudf::is_fixed_point<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::strings::detail::from_fixed_point(column, stream_, mr_);
+  }
+
+  // timestamps:
+  //
+  template <typename column_type>
+  std::enable_if_t<cudf::is_timestamp<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    std::string format = [&]() {
+      if (std::is_same_v<cudf::timestamp_s, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%SZ"};
+      } else if (std::is_same_v<cudf::timestamp_ms, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%3fZ"};
+      } else if (std::is_same_v<cudf::timestamp_us, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%6fZ"};
+      } else if (std::is_same_v<cudf::timestamp_ns, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%9fZ"};
+      } else {
+        return std::string{"%Y-%m-%d"};
+      }
+    }();
+
+    // handle the cases where delimiter / line-terminator can be
+    // "-" or ":", in which case we need to add quotes to the format
+    //
+    std::string delimiter{options_.get_inter_column_delimiter()};
+    std::string newline{options_.get_line_terminator()};
+
+    constexpr char const* dash{"-"};
+    constexpr char const* colon{":"};
+    if (delimiter == dash || newline == dash || delimiter == colon || newline == colon) {
+      format = "\"" + format + "\"";
+    }
+
+    return cudf::strings::detail::from_timestamps(
+      column,
+      format,
+      strings_column_view(make_empty_column(type_id::STRING)->view()),
+      stream_,
+      mr_);
+  }
+
+  template <typename column_type>
+  std::enable_if_t<cudf::is_duration<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::io::detail::csv::pandas_format_durations(column, stream_, mr_);
+  }
+
+  // unsupported type of column:
+  //
+  template <typename column_type>
+  std::enable_if_t<is_not_handled<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const&) const
+  {
+    CUDF_FAIL("Unsupported column type.");
+  }
+
+ private:
+  csv_writer_options const& options_;
+  rmm::cuda_stream_view stream_;
+  rmm::mr::device_memory_resource* mr_;
+};
+}  // unnamed namespace
+
+// write the header: column names:
+//
+void write_chunked_begin(data_sink* out_sink,
+                         table_view const& table,
+                         host_span<std::string const> user_column_names,
+                         csv_writer_options const& options,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr)
+{
+  if (options.is_enabled_include_header()) {
+    // need to generate column names if names are not provided
+    std::vector<std::string> generated_col_names;
+    if (user_column_names.empty()) {
+      generated_col_names.resize(table.num_columns());
+      thrust::tabulate(generated_col_names.begin(), generated_col_names.end(), [](auto idx) {
+        return std::to_string(idx);
+      });
+    }
+    auto const& column_names = user_column_names.empty() ? generated_col_names : user_column_names;
+    CUDF_EXPECTS(column_names.size() == static_cast<size_t>(table.num_columns()),
+                 "Mismatch between number of column headers and table columns.");
+
+    auto const delimiter  = options.get_inter_column_delimiter();
+    auto const terminator = options.get_line_terminator();
+
+    // process header names:
+    // - if the header name includes the delimiter or terminator character,
+    //   it must be double-quoted
+    // - if the header name includes a double-quote, it must be escaped
+    //   with a 2nd double-quote
+    std::stringstream ss;
+    std::transform(column_names.begin(),
+                   column_names.end(),
+                   std::ostream_iterator<std::string>(ss, std::string{delimiter}.c_str()),
+                   [delimiter, terminator](std::string name) {
+                     char const quote = '\"';
+                     if (name.empty() ||           // no header name
+                         name.front() == quote) {  // header already quoted
+                       return name;
+                     }
+
+                     // escape any quotes
+                     size_t pos = 0;
+                     while ((pos = name.find(quote, pos)) != name.npos) {
+                       name.insert(pos, 1, quote);
+                       pos += 2;
+                     }
+
+                     // check if overall quotes are required
+                     if (std::any_of(name.begin(), name.end(), [&](auto const chr) {
+                           return chr == quote || chr == delimiter || chr == terminator.front();
+                         })) {
+                       name.insert(name.begin(), quote);
+                       name.insert(name.end(), quote);
+                     }
+                     return name;
+                   });
+
+    // add line terminator
+    std::string header = ss.str();
+    if (!header.empty()) {
+      header.erase(header.end() - 1);  // remove extra final delimiter
+    }
+    header.append(terminator);
+
+    out_sink->host_write(header.data(), header.size());
+  }
+}
+
+void write_chunked(data_sink* out_sink,
+                   strings_column_view const& str_column_view,
+                   csv_writer_options const& options,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+{
+  // algorithm outline:
+  //
+  //  for_each(strings_column.begin(), strings_column.end(),
+  //           [sink = out_sink](auto str_row) mutable {
+  //               auto host_buffer = str_row.host_buffer();
+  //               sink->host_write(host_buffer_.data(), host_buffer_.size());
+  //           });//or...sink->device_write(device_buffer,...);
+  //
+  // added line_terminator functionality
+  //
+
+  CUDF_EXPECTS(str_column_view.size() > 0, "Unexpected empty strings column.");
+
+  cudf::string_scalar newline{options.get_line_terminator()};
+  auto p_str_col_w_nl = cudf::strings::detail::join_strings(str_column_view,
+                                                            newline,
+                                                            string_scalar("", false),
+                                                            stream,
+                                                            rmm::mr::get_current_device_resource());
+  strings_column_view strings_column{p_str_col_w_nl->view()};
+
+  auto total_num_bytes      = strings_column.chars_size();
+  char const* ptr_all_bytes = strings_column.chars_begin();
+
+  if (out_sink->is_device_write_preferred(total_num_bytes)) {
+    // Direct write from device memory
+    out_sink->device_write(ptr_all_bytes, total_num_bytes, stream);
+  } else {
+    // copy the bytes to host to write them out
+    thrust::host_vector<char> h_bytes(total_num_bytes);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(h_bytes.data(),
+                                  ptr_all_bytes,
+                                  total_num_bytes * sizeof(char),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    stream.synchronize();
+
+    out_sink->host_write(h_bytes.data(), total_num_bytes);
+  }
+
+  // Needs newline at the end, to separate from next chunk
+  if (out_sink->is_device_write_preferred(newline.size())) {
+    out_sink->device_write(newline.data(), newline.size(), stream);
+  } else {
+    out_sink->host_write(options.get_line_terminator().data(),
+                         options.get_line_terminator().size());
+  }
+}
+
+void write_csv(data_sink* out_sink,
+               table_view const& table,
+               host_span<std::string const> user_column_names,
+               csv_writer_options const& options,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr)
+{
+  // write header: column names separated by delimiter:
+  // (even for tables with no rows)
+  //
+  write_chunked_begin(out_sink, table, user_column_names, options, stream, mr);
+
+  if (table.num_rows() > 0) {
+    // no need to check same-size columns constraint; auto-enforced by table_view
+    auto n_rows_per_chunk = options.get_rows_per_chunk();
+    //
+    // This outputs the CSV in row chunks to save memory.
+    // Maybe we can use the total_rows*count calculation and a memory threshold
+    // instead of an arbitrary chunk count.
+    // The entire CSV chunk must fit in CPU memory before writing it out.
+    //
+    if (n_rows_per_chunk % 8)  // must be divisible by 8
+      n_rows_per_chunk += 8 - (n_rows_per_chunk % 8);
+
+    CUDF_EXPECTS(n_rows_per_chunk >= 8, "write_csv: invalid chunk_rows; must be at least 8");
+
+    auto num_rows = table.num_rows();
+    std::vector<table_view> vector_views;
+
+    if (num_rows <= n_rows_per_chunk) {
+      vector_views.push_back(table);
+    } else {
+      auto const n_chunks = num_rows / n_rows_per_chunk;
+      std::vector<size_type> splits(n_chunks);
+      thrust::tabulate(splits.begin(), splits.end(), [n_rows_per_chunk](auto idx) {
+        return (idx + 1) * n_rows_per_chunk;
+      });
+
+      // split table_view into chunks:
+      vector_views = cudf::detail::split(table, splits, stream);
+    }
+
+    // convert each chunk to CSV:
+    //
+    column_to_strings_fn converter{options, stream, rmm::mr::get_current_device_resource()};
+    for (auto&& sub_view : vector_views) {
+      // Skip if the table has no rows
+      if (sub_view.num_rows() == 0) continue;
+      std::vector<std::unique_ptr<column>> str_column_vec;
+
+      // populate vector of string-converted columns:
+      //
+      std::transform(sub_view.begin(),
+                     sub_view.end(),
+                     std::back_inserter(str_column_vec),
+                     [converter](auto const& current_col) {
+                       return cudf::type_dispatcher(current_col.type(), converter, current_col);
+                     });
+
+      // create string table view from str_column_vec:
+      //
+      auto str_table_ptr  = std::make_unique<cudf::table>(std::move(str_column_vec));
+      auto str_table_view = str_table_ptr->view();
+
+      // concatenate columns in each row into one big string column
+      // (using null representation and delimiter):
+      //
+      std::string delimiter_str{options.get_inter_column_delimiter()};
+      auto str_concat_col = [&] {
+        if (str_table_view.num_columns() > 1)
+          return cudf::strings::detail::concatenate(str_table_view,
+                                                    delimiter_str,
+                                                    options.get_na_rep(),
+                                                    strings::separator_on_nulls::YES,
+                                                    stream,
+                                                    rmm::mr::get_current_device_resource());
+        cudf::string_scalar narep{options.get_na_rep()};
+        return cudf::strings::detail::replace_nulls(
+          str_table_view.column(0), narep, stream, rmm::mr::get_current_device_resource());
+      }();
+
+      write_chunked(out_sink, str_concat_col->view(), options, stream, mr);
+    }
+  }
+}
+
+}  // namespace csv
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/fst/agent_dfa.cuh b/cpp/src/io/fst/agent_dfa.cuh
new file mode 100644
index 0000000..f867a95
--- /dev/null
+++ b/cpp/src/io/fst/agent_dfa.cuh
@@ -0,0 +1,695 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "in_reg_array.cuh"
+
+#include <cub/cub.cuh>
+
+#include <thrust/execution_policy.h>
+#include <thrust/sequence.h>
+
+namespace cudf::io::fst::detail {
+
+/// Type used to enumerate (and index) into the states defined by a DFA
+using StateIndexT = uint32_t;
+
+/**
+ * @brief Implements an associative composition operation for state transition vectors to be used
+ * with a prefix scan.
+ *
+ * Read the following table as follows: c = op(l,r), where op is the composition operator.
+ * For row 0: l maps 0 to 2. r maps 2 to 2. Hence, the result for 0 is 2.
+ * For row 1: l maps 1 to 1. r maps 1 to 2. Hence, the result for 1 is 2.
+ * For row 2: l maps 2 to 0. r maps 0 to 1. Hence, the result for 2 is 1.
+ *
+ *     l   r  = c  (     s->l->r)
+ * 0: [2] [1]  [2] (i.e. 0->2->2)
+ * 1: [1] [2]  [2] (i.e. 1->1->2)
+ * 2: [0] [2]  [1] (i.e. 2->0->1)
+ * @tparam NUM_ITEMS The number of items stored within a vector
+ */
+template <int32_t NUM_ITEMS>
+struct VectorCompositeOp {
+  template <typename VectorT>
+  __host__ __device__ __forceinline__ VectorT operator()(VectorT const& lhs, VectorT const& rhs)
+  {
+    VectorT res{};
+    for (int32_t i = 0; i < NUM_ITEMS; ++i) {
+      res.Set(i, rhs.Get(lhs.Get(i)));
+    }
+    return res;
+  }
+};
+
+/**
+ * @brief A class whose ReadSymbol member function is invoked for each symbol being read from the
+ * input tape. The wrapper class looks up whether a state transition caused by a symbol is supposed
+ * to emit any output symbol (the "transduced" output) and, if so, keeps track of how many symbols
+ * it intends to write out and writing out such symbols to the given output iterators.
+ *
+ * @tparam TransducerTableT The type implementing a transducer table that can be used for looking up
+ * the symbols that are supposed to be emitted on a given state transition.
+ * @tparam TransducedOutItT A Random-access output iterator type to which symbols returned by the
+ * transducer table are assignable.
+ * @tparam TransducedIndexOutItT A Random-access output iterator type to which indexes are written.
+ */
+template <typename TransducerTableT, typename TransducedOutItT, typename TransducedIndexOutItT>
+class DFASimulationCallbackWrapper {
+ public:
+  __host__ __device__ __forceinline__ DFASimulationCallbackWrapper(
+    TransducerTableT transducer_table, TransducedOutItT out_it, TransducedIndexOutItT out_idx_it)
+    : transducer_table(transducer_table), out_it(out_it), out_idx_it(out_idx_it), write(false)
+  {
+  }
+
+  template <typename OffsetT>
+  __host__ __device__ __forceinline__ void Init(OffsetT const& offset)
+  {
+    this->offset = offset;
+    if (!write) out_count = 0;
+  }
+
+  template <typename CharIndexT, typename StateIndexT, typename SymbolIndexT, typename SymbolT>
+  __host__ __device__ __forceinline__ void ReadSymbol(CharIndexT const character_index,
+                                                      StateIndexT const old_state,
+                                                      StateIndexT const new_state,
+                                                      SymbolIndexT const symbol_id,
+                                                      SymbolT const read_symbol)
+  {
+    uint32_t const count = transducer_table(old_state, symbol_id, read_symbol);
+    if (write) {
+#if __CUDA_ARCH__ > 0
+#pragma unroll 1
+#endif
+      for (uint32_t out_char = 0; out_char < count; out_char++) {
+        out_it[out_count + out_char] =
+          transducer_table(old_state, symbol_id, out_char, read_symbol);
+        out_idx_it[out_count + out_char] = offset + character_index;
+      }
+    }
+    out_count += count;
+  }
+
+  __host__ __device__ __forceinline__ void TearDown() {}
+
+ public:
+  TransducerTableT const transducer_table;
+  TransducedOutItT out_it;
+  TransducedIndexOutItT out_idx_it;
+  uint32_t out_count;
+  uint32_t offset;
+  bool write;
+};
+
+/**
+ * @brief Helper class that transitions the state of multiple DFA instances simultaneously whenever
+ * a symbol is read.
+ *
+ * @tparam NUM_INSTANCES The number of DFA instances to keep track of
+ * @tparam TransitionTableT The transition table type used for looking up the new state for a
+ * current_state and a read_symbol.
+ */
+template <int32_t NUM_INSTANCES, typename TransitionTableT>
+class StateVectorTransitionOp {
+ public:
+  __host__ __device__ __forceinline__ StateVectorTransitionOp(
+    TransitionTableT const& transition_table, std::array<StateIndexT, NUM_INSTANCES>& state_vector)
+    : transition_table(transition_table), state_vector(state_vector)
+  {
+  }
+
+  template <typename CharIndexT, typename SymbolIndexT, typename SymbolT>
+  __host__ __device__ __forceinline__ void ReadSymbol(CharIndexT const& character_index,
+                                                      SymbolIndexT const& read_symbol_id,
+                                                      SymbolT const& read_symbol) const
+  {
+    for (int32_t i = 0; i < NUM_INSTANCES; ++i) {
+      state_vector[i] = transition_table(state_vector[i], read_symbol_id);
+    }
+  }
+
+ public:
+  std::array<StateIndexT, NUM_INSTANCES>& state_vector;
+  TransitionTableT const& transition_table;
+};
+
+template <typename CallbackOpT, typename TransitionTableT>
+struct StateTransitionOp {
+  StateIndexT state;
+  TransitionTableT const& transition_table;
+  CallbackOpT& callback_op;
+
+  __host__ __device__ __forceinline__ StateTransitionOp(TransitionTableT const& transition_table,
+                                                        StateIndexT state,
+                                                        CallbackOpT& callback_op)
+    : transition_table(transition_table), state(state), callback_op(callback_op)
+  {
+  }
+
+  template <typename CharIndexT, typename SymbolIndexT, typename SymbolT>
+  __host__ __device__ __forceinline__ void ReadSymbol(CharIndexT const& character_index,
+                                                      SymbolIndexT const& read_symbol_id,
+                                                      SymbolT const& read_symbol)
+  {
+    // Remember what state we were in before we made the transition
+    StateIndexT previous_state = state;
+
+    state = transition_table(state, read_symbol_id);
+    callback_op.ReadSymbol(character_index, previous_state, state, read_symbol_id, read_symbol);
+  }
+};
+
+template <typename AgentDFAPolicy, typename SymbolItT, typename OffsetT>
+struct AgentDFA {
+  using SymbolIndexT = uint32_t;
+  using AliasedLoadT = uint32_t;
+  using CharT        = typename std::iterator_traits<SymbolItT>::value_type;
+
+  //------------------------------------------------------------------------------
+  // DERIVED CONFIGS
+  //------------------------------------------------------------------------------
+  static constexpr uint32_t BLOCK_THREADS    = AgentDFAPolicy::BLOCK_THREADS;
+  static constexpr uint32_t ITEMS_PER_THREAD = AgentDFAPolicy::ITEMS_PER_THREAD;
+
+  // The number of symbols per thread
+  static constexpr uint32_t SYMBOLS_PER_THREAD = ITEMS_PER_THREAD;
+  static constexpr uint32_t SYMBOLS_PER_BLOCK  = BLOCK_THREADS * SYMBOLS_PER_THREAD;
+
+  static constexpr uint32_t MIN_UINTS_PER_BLOCK =
+    CUB_QUOTIENT_CEILING(SYMBOLS_PER_BLOCK, sizeof(AliasedLoadT));
+  static constexpr uint32_t UINTS_PER_THREAD =
+    CUB_QUOTIENT_CEILING(MIN_UINTS_PER_BLOCK, BLOCK_THREADS);
+  static constexpr uint32_t UINTS_PER_BLOCK        = UINTS_PER_THREAD * BLOCK_THREADS;
+  static constexpr uint32_t SYMBOLS_PER_UINT_BLOCK = UINTS_PER_BLOCK * sizeof(AliasedLoadT);
+
+  //------------------------------------------------------------------------------
+  // TYPEDEFS
+  //------------------------------------------------------------------------------
+  struct _TempStorage {
+    // For aliased loading of characters into shared memory
+    union {
+      CharT chars[SYMBOLS_PER_BLOCK];
+      AliasedLoadT uints[UINTS_PER_BLOCK];
+    };
+  };
+
+  struct TempStorage : cub::Uninitialized<_TempStorage> {};
+
+  //------------------------------------------------------------------------------
+  // MEMBER VARIABLES
+  //------------------------------------------------------------------------------
+  _TempStorage& temp_storage;
+
+  //------------------------------------------------------------------------------
+  // CONSTRUCTOR
+  //------------------------------------------------------------------------------
+  __device__ __forceinline__ AgentDFA(TempStorage& temp_storage)
+    : temp_storage(temp_storage.Alias())
+  {
+  }
+
+  template <int32_t NUM_SYMBOLS,
+            typename SymbolMatcherT,
+            typename CallbackOpT,
+            int32_t IS_FULL_BLOCK>
+  __device__ __forceinline__ static void ThreadParse(SymbolMatcherT const& symbol_matcher,
+                                                     CharT const* chars,
+                                                     SymbolIndexT const& max_num_chars,
+                                                     CallbackOpT callback_op,
+                                                     cub::Int2Type<IS_FULL_BLOCK> /*IS_FULL_BLOCK*/)
+  {
+    // Iterate over symbols
+#pragma unroll
+    for (int32_t i = 0; i < NUM_SYMBOLS; ++i) {
+      if (IS_FULL_BLOCK || threadIdx.x * SYMBOLS_PER_THREAD + i < max_num_chars) {
+        auto matched_id = symbol_matcher(chars[i]);
+        callback_op.ReadSymbol(i, matched_id, chars[i]);
+      }
+    }
+  }
+
+  template <int32_t NUM_SYMBOLS,
+            typename SymbolMatcherT,
+            typename StateTransitionOpT,
+            int32_t IS_FULL_BLOCK>
+  __device__ __forceinline__ void GetThreadStateTransitions(
+    SymbolMatcherT const& symbol_matcher,
+    CharT const* chars,
+    SymbolIndexT const& max_num_chars,
+    StateTransitionOpT& state_transition_op,
+    cub::Int2Type<IS_FULL_BLOCK> /*IS_FULL_BLOCK*/)
+  {
+    ThreadParse<NUM_SYMBOLS>(
+      symbol_matcher, chars, max_num_chars, state_transition_op, cub::Int2Type<IS_FULL_BLOCK>());
+  }
+
+  //---------------------------------------------------------------------
+  // LOADING FULL BLOCK OF CHARACTERS, NON-ALIASED
+  //---------------------------------------------------------------------
+  template <typename CharInItT>
+  __device__ __forceinline__ void LoadBlock(CharInItT d_chars,
+                                            OffsetT const block_offset,
+                                            OffsetT const num_total_symbols,
+                                            cub::Int2Type<true> /*IS_FULL_BLOCK*/,
+                                            cub::Int2Type<1> /*ALIGNMENT*/)
+  {
+    CharT thread_chars[SYMBOLS_PER_THREAD];
+
+    CharInItT d_block_symbols = d_chars + block_offset;
+    cub::LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_block_symbols, thread_chars);
+
+#pragma unroll
+    for (int32_t i = 0; i < SYMBOLS_PER_THREAD; ++i) {
+      temp_storage.chars[threadIdx.x + i * BLOCK_THREADS] = thread_chars[i];
+    }
+  }
+
+  //---------------------------------------------------------------------
+  // LOADING PARTIAL BLOCK OF CHARACTERS, NON-ALIASED
+  //---------------------------------------------------------------------
+  template <typename CharInItT>
+  __device__ __forceinline__ void LoadBlock(CharInItT d_chars,
+                                            OffsetT const block_offset,
+                                            OffsetT const num_total_symbols,
+                                            cub::Int2Type<false> /*IS_FULL_BLOCK*/,
+                                            cub::Int2Type<1> /*ALIGNMENT*/)
+  {
+    CharT thread_chars[SYMBOLS_PER_THREAD];
+
+    if (num_total_symbols <= block_offset) return;
+
+    // Last unit to be loaded is IDIV_CEIL(#SYM, SYMBOLS_PER_UNIT)
+    OffsetT num_total_chars = num_total_symbols - block_offset;
+
+    CharInItT d_block_symbols = d_chars + block_offset;
+    cub::LoadDirectStriped<BLOCK_THREADS>(
+      threadIdx.x, d_block_symbols, thread_chars, num_total_chars);
+
+#pragma unroll
+    for (int32_t i = 0; i < SYMBOLS_PER_THREAD; ++i) {
+      temp_storage.chars[threadIdx.x + i * BLOCK_THREADS] = thread_chars[i];
+    }
+  }
+
+  //---------------------------------------------------------------------
+  // LOADING FULL BLOCK OF CHARACTERS, ALIASED
+  //---------------------------------------------------------------------
+  __device__ __forceinline__ void LoadBlock(CharT const* d_chars,
+                                            OffsetT const block_offset,
+                                            OffsetT const num_total_symbols,
+                                            cub::Int2Type<true> /*IS_FULL_BLOCK*/,
+                                            cub::Int2Type<sizeof(AliasedLoadT)> /*ALIGNMENT*/)
+  {
+    AliasedLoadT thread_units[UINTS_PER_THREAD];
+
+    AliasedLoadT const* d_block_symbols =
+      reinterpret_cast<AliasedLoadT const*>(d_chars + block_offset);
+    cub::LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_block_symbols, thread_units);
+
+#pragma unroll
+    for (int32_t i = 0; i < UINTS_PER_THREAD; ++i) {
+      temp_storage.uints[threadIdx.x + i * BLOCK_THREADS] = thread_units[i];
+    }
+  }
+
+  //---------------------------------------------------------------------
+  // LOADING PARTIAL BLOCK OF CHARACTERS, ALIASED
+  //---------------------------------------------------------------------
+  __device__ __forceinline__ void LoadBlock(CharT const* d_chars,
+                                            OffsetT const block_offset,
+                                            OffsetT const num_total_symbols,
+                                            cub::Int2Type<false> /*IS_FULL_BLOCK*/,
+                                            cub::Int2Type<sizeof(AliasedLoadT)> /*ALIGNMENT*/)
+  {
+    AliasedLoadT thread_units[UINTS_PER_THREAD];
+
+    if (num_total_symbols <= block_offset) return;
+
+    // Last unit to be loaded is IDIV_CEIL(#SYM, SYMBOLS_PER_UNIT)
+    OffsetT num_total_units =
+      CUB_QUOTIENT_CEILING(num_total_symbols - block_offset, sizeof(AliasedLoadT));
+
+    AliasedLoadT const* d_block_symbols =
+      reinterpret_cast<AliasedLoadT const*>(d_chars + block_offset);
+    cub::LoadDirectStriped<BLOCK_THREADS>(
+      threadIdx.x, d_block_symbols, thread_units, num_total_units);
+
+#pragma unroll
+    for (int32_t i = 0; i < UINTS_PER_THREAD; ++i) {
+      temp_storage.uints[threadIdx.x + i * BLOCK_THREADS] = thread_units[i];
+    }
+  }
+
+  //---------------------------------------------------------------------
+  // LOADING BLOCK OF CHARACTERS: DISPATCHER
+  //---------------------------------------------------------------------
+  __device__ __forceinline__ void LoadBlock(CharT const* d_chars,
+                                            OffsetT const block_offset,
+                                            OffsetT const num_total_symbols)
+  {
+    // Check if pointer is aligned to four bytes
+    if (((uintptr_t)(void const*)(d_chars + block_offset) % 4) == 0) {
+      if (block_offset + SYMBOLS_PER_UINT_BLOCK < num_total_symbols) {
+        LoadBlock(
+          d_chars, block_offset, num_total_symbols, cub::Int2Type<true>(), cub::Int2Type<4>());
+      } else {
+        LoadBlock(
+          d_chars, block_offset, num_total_symbols, cub::Int2Type<false>(), cub::Int2Type<1>());
+      }
+    } else {
+      if (block_offset + SYMBOLS_PER_UINT_BLOCK < num_total_symbols) {
+        LoadBlock(
+          d_chars, block_offset, num_total_symbols, cub::Int2Type<true>(), cub::Int2Type<1>());
+      } else {
+        LoadBlock(
+          d_chars, block_offset, num_total_symbols, cub::Int2Type<false>(), cub::Int2Type<1>());
+      }
+    }
+  }
+
+  template <typename CharInItT>
+  __device__ __forceinline__ void LoadBlock(CharInItT d_chars,
+                                            OffsetT const block_offset,
+                                            OffsetT const num_total_symbols)
+  {
+    // Check if we are loading a full tile of data
+    if (block_offset + SYMBOLS_PER_UINT_BLOCK < num_total_symbols) {
+      LoadBlock(
+        d_chars, block_offset, num_total_symbols, cub::Int2Type<true>(), cub::Int2Type<1>());
+    } else {
+      LoadBlock(
+        d_chars, block_offset, num_total_symbols, cub::Int2Type<false>(), cub::Int2Type<1>());
+    }
+  }
+
+  template <int32_t NUM_STATES, typename SymbolMatcherT, typename TransitionTableT>
+  __device__ __forceinline__ void GetThreadStateTransitionVector(
+    SymbolMatcherT const& symbol_matcher,
+    TransitionTableT const& transition_table,
+    SymbolItT d_chars,
+    OffsetT const block_offset,
+    OffsetT const num_total_symbols,
+    std::array<StateIndexT, NUM_STATES>& state_vector)
+  {
+    using StateVectorTransitionOpT = StateVectorTransitionOp<NUM_STATES, TransitionTableT>;
+
+    // Start parsing and to transition states
+    StateVectorTransitionOpT transition_op(transition_table, state_vector);
+
+    // Load characters into shared memory
+    LoadBlock(d_chars, block_offset, num_total_symbols);
+
+    // If this is a full block (i.e., all threads can parse all their symbols)
+    OffsetT num_block_chars = num_total_symbols - block_offset;
+    bool is_full_block      = (num_block_chars >= SYMBOLS_PER_BLOCK);
+
+    // Ensure characters have been loaded
+    __syncthreads();
+
+    // Thread's symbols
+    CharT* t_chars = &temp_storage.chars[threadIdx.x * SYMBOLS_PER_THREAD];
+
+    // Parse thread's symbols and transition the state-vector
+    if (is_full_block) {
+      GetThreadStateTransitions<SYMBOLS_PER_THREAD>(
+        symbol_matcher, t_chars, num_block_chars, transition_op, cub::Int2Type<true>());
+    } else {
+      GetThreadStateTransitions<SYMBOLS_PER_THREAD>(
+        symbol_matcher, t_chars, num_block_chars, transition_op, cub::Int2Type<false>());
+    }
+  }
+
+  template <int32_t BYPASS_LOAD,
+            typename SymbolMatcherT,
+            typename TransitionTableT,
+            typename CallbackOpT>
+  __device__ __forceinline__ void GetThreadStateTransitions(
+    SymbolMatcherT const& symbol_matcher,
+    TransitionTableT const& transition_table,
+    SymbolItT d_chars,
+    OffsetT const block_offset,
+    OffsetT const num_total_symbols,
+    StateIndexT& state,
+    CallbackOpT& callback_op,
+    cub::Int2Type<BYPASS_LOAD>)
+  {
+    using StateTransitionOpT = StateTransitionOp<CallbackOpT, TransitionTableT>;
+
+    // Start parsing and to transition states
+    StateTransitionOpT transition_op(transition_table, state, callback_op);
+
+    // Load characters into shared memory
+    if (!BYPASS_LOAD) LoadBlock(d_chars, block_offset, num_total_symbols);
+
+    // If this is a full block (i.e., all threads can parse all their symbols)
+    OffsetT num_block_chars = num_total_symbols - block_offset;
+    bool is_full_block      = (num_block_chars >= SYMBOLS_PER_BLOCK);
+
+    // Ensure characters have been loaded
+    __syncthreads();
+
+    // Thread's symbols
+    CharT* t_chars = &temp_storage.chars[threadIdx.x * SYMBOLS_PER_THREAD];
+
+    // Initialize callback
+    callback_op.Init(block_offset + threadIdx.x * SYMBOLS_PER_THREAD);
+
+    // Parse thread's symbols and transition the state-vector
+    if (is_full_block) {
+      GetThreadStateTransitions<SYMBOLS_PER_THREAD>(
+        symbol_matcher, t_chars, num_block_chars, transition_op, cub::Int2Type<true>());
+    } else {
+      GetThreadStateTransitions<SYMBOLS_PER_THREAD>(
+        symbol_matcher, t_chars, num_block_chars, transition_op, cub::Int2Type<false>());
+    }
+
+    callback_op.TearDown();
+  }
+};
+
+template <bool IS_TRANS_VECTOR_PASS,
+          bool IS_SINGLE_PASS,
+          typename DfaT,
+          typename TileStateT,
+          typename AgentDFAPolicy,
+          typename SymbolItT,
+          typename OffsetT,
+          typename StateVectorT,
+          typename OutOffsetScanTileState,
+          typename TransducedOutItT,
+          typename TransducedIndexOutItT,
+          typename TransducedCountOutItT>
+__launch_bounds__(int32_t(AgentDFAPolicy::BLOCK_THREADS)) __global__
+  void SimulateDFAKernel(DfaT dfa,
+                         SymbolItT d_chars,
+                         OffsetT const num_chars,
+                         StateIndexT seed_state,
+                         StateVectorT* __restrict__ d_thread_state_transition,
+                         TileStateT tile_state,
+                         OutOffsetScanTileState offset_tile_state,
+                         TransducedOutItT transduced_out_it,
+                         TransducedIndexOutItT transduced_out_idx_it,
+                         TransducedCountOutItT d_num_transduced_out_it)
+{
+  using AgentDfaSimT = AgentDFA<AgentDFAPolicy, SymbolItT, OffsetT>;
+
+  static constexpr int32_t NUM_STATES = DfaT::MAX_NUM_STATES;
+
+  constexpr uint32_t BLOCK_THREADS     = AgentDFAPolicy::BLOCK_THREADS;
+  constexpr uint32_t SYMBOLS_PER_BLOCK = AgentDfaSimT::SYMBOLS_PER_BLOCK;
+
+  // Shared memory required by the DFA simulation algorithm
+  __shared__ typename AgentDfaSimT::TempStorage dfa_storage;
+
+  // Shared memory required by the symbol group lookup table
+  __shared__ typename DfaT::SymbolGroupStorageT symbol_matcher_storage;
+
+  // Shared memory required by the transition table
+  __shared__ typename DfaT::TransitionTableStorageT transition_table_storage;
+
+  // Shared memory required by the transducer table
+  __shared__ typename DfaT::TranslationTableStorageT transducer_table_storage;
+
+  // Initialize symbol group lookup table
+  auto symbol_matcher = dfa.InitSymbolGroupLUT(symbol_matcher_storage);
+
+  // Initialize transition table
+  auto transition_table = dfa.InitTransitionTable(transition_table_storage);
+
+  // Initialize transition table
+  auto transducer_table = dfa.InitTranslationTable(transducer_table_storage);
+
+  // Set up DFA
+  AgentDfaSimT agent_dfa(dfa_storage);
+
+  // The state transition vector passed on to the second stage of the algorithm
+  StateVectorT out_state_vector;
+
+  // Stage 1: Compute the state-transition vector
+  if (IS_TRANS_VECTOR_PASS || IS_SINGLE_PASS) {
+    // Keeping track of the state for each of the <NUM_STATES> state machines
+    std::array<StateIndexT, NUM_STATES> state_vector;
+
+    // Initialize the seed state transition vector with the identity vector
+    thrust::sequence(thrust::seq, std::begin(state_vector), std::end(state_vector));
+
+    // Compute the state transition vector
+    agent_dfa.GetThreadStateTransitionVector<NUM_STATES>(symbol_matcher,
+                                                         transition_table,
+                                                         d_chars,
+                                                         blockIdx.x * SYMBOLS_PER_BLOCK,
+                                                         num_chars,
+                                                         state_vector);
+
+    // Initialize the state transition vector passed on to the second stage
+#pragma unroll
+    for (int32_t i = 0; i < NUM_STATES; ++i) {
+      out_state_vector.Set(i, state_vector[i]);
+    }
+
+    // Write out state-transition vector
+    if (!IS_SINGLE_PASS) {
+      d_thread_state_transition[blockIdx.x * BLOCK_THREADS + threadIdx.x] = out_state_vector;
+    }
+  }
+
+  // Stage 2: Perform FSM simulation
+  if ((!IS_TRANS_VECTOR_PASS) || IS_SINGLE_PASS) {
+    StateIndexT state = 0;
+
+    //------------------------------------------------------------------------------
+    // SINGLE-PASS:
+    // -> block-wide inclusive prefix scan on the state transition vector
+    // -> first block/tile: write out block aggregate as the "tile's" inclusive (i.e., the one that
+    // incorporates all preceding blocks/tiles results)
+    //------------------------------------------------------------------------------
+    if (IS_SINGLE_PASS) {
+      uint32_t tile_idx             = blockIdx.x;
+      using StateVectorCompositeOpT = VectorCompositeOp<NUM_STATES>;
+
+      using PrefixCallbackOpT_ =
+        cub::TilePrefixCallbackOp<StateVectorT, StateVectorCompositeOpT, TileStateT>;
+
+      using ItemsBlockScan =
+        cub::BlockScan<StateVectorT, BLOCK_THREADS, cub::BlockScanAlgorithm::BLOCK_SCAN_WARP_SCANS>;
+
+      __shared__ typename ItemsBlockScan::TempStorage scan_temp_storage;
+      __shared__ typename PrefixCallbackOpT_::TempStorage prefix_callback_temp_storage;
+
+      // STATE-TRANSITION IDENTITY VECTOR
+      StateVectorT state_identity_vector;
+      for (int32_t i = 0; i < NUM_STATES; ++i) {
+        state_identity_vector.Set(i, i);
+      }
+      StateVectorCompositeOpT state_vector_scan_op;
+
+      //
+      if (tile_idx == 0) {
+        StateVectorT block_aggregate;
+        ItemsBlockScan(scan_temp_storage)
+          .ExclusiveScan(out_state_vector,
+                         out_state_vector,
+                         state_identity_vector,
+                         state_vector_scan_op,
+                         block_aggregate);
+
+        if (threadIdx.x == 0 /*and not IS_LAST_TILE*/) {
+          tile_state.SetInclusive(0, block_aggregate);
+        }
+      } else {
+        auto prefix_op = PrefixCallbackOpT_(
+          tile_state, prefix_callback_temp_storage, state_vector_scan_op, tile_idx);
+
+        ItemsBlockScan(scan_temp_storage)
+          .ExclusiveScan(out_state_vector, out_state_vector, state_vector_scan_op, prefix_op);
+      }
+      __syncthreads();
+      state = out_state_vector.Get(seed_state);
+    } else {
+      state = d_thread_state_transition[blockIdx.x * BLOCK_THREADS + threadIdx.x].Get(seed_state);
+    }
+
+    // Perform finite-state machine simulation, computing size of transduced output
+    DFASimulationCallbackWrapper<decltype(dfa.InitTranslationTable(transducer_table_storage)),
+                                 TransducedOutItT,
+                                 TransducedIndexOutItT>
+      callback_wrapper(transducer_table, transduced_out_it, transduced_out_idx_it);
+
+    StateIndexT t_start_state = state;
+    agent_dfa.GetThreadStateTransitions(symbol_matcher,
+                                        transition_table,
+                                        d_chars,
+                                        blockIdx.x * SYMBOLS_PER_BLOCK,
+                                        num_chars,
+                                        state,
+                                        callback_wrapper,
+                                        cub::Int2Type<IS_SINGLE_PASS>());
+
+    __syncthreads();
+
+    using OffsetPrefixScanCallbackOpT_ =
+      cub::TilePrefixCallbackOp<OffsetT, cub::Sum, OutOffsetScanTileState>;
+
+    using OutOffsetBlockScan =
+      cub::BlockScan<OffsetT, BLOCK_THREADS, cub::BlockScanAlgorithm::BLOCK_SCAN_WARP_SCANS>;
+
+    __shared__ typename OutOffsetBlockScan::TempStorage scan_temp_storage;
+    __shared__ typename OffsetPrefixScanCallbackOpT_::TempStorage prefix_callback_temp_storage;
+
+    uint32_t tile_idx = blockIdx.x;
+    if (tile_idx == 0) {
+      OffsetT block_aggregate = 0;
+      OutOffsetBlockScan(scan_temp_storage)
+        .ExclusiveScan(callback_wrapper.out_count,
+                       callback_wrapper.out_count,
+                       static_cast<OffsetT>(0),
+                       cub::Sum{},
+                       block_aggregate);
+
+      if (threadIdx.x == 0 /*and not IS_LAST_TILE*/) {
+        offset_tile_state.SetInclusive(0, block_aggregate);
+      }
+
+      if (tile_idx == gridDim.x - 1 && threadIdx.x == 0) {
+        *d_num_transduced_out_it = block_aggregate;
+      }
+    } else {
+      auto prefix_op = OffsetPrefixScanCallbackOpT_(
+        offset_tile_state, prefix_callback_temp_storage, cub::Sum{}, tile_idx);
+
+      OutOffsetBlockScan(scan_temp_storage)
+        .ExclusiveScan(
+          callback_wrapper.out_count, callback_wrapper.out_count, cub::Sum{}, prefix_op);
+
+      if (tile_idx == gridDim.x - 1 && threadIdx.x == 0) {
+        *d_num_transduced_out_it = prefix_op.GetInclusivePrefix();
+      }
+    }
+
+    callback_wrapper.write = true;
+    agent_dfa.GetThreadStateTransitions(symbol_matcher,
+                                        transition_table,
+                                        d_chars,
+                                        blockIdx.x * SYMBOLS_PER_BLOCK,
+                                        num_chars,
+                                        t_start_state,
+                                        callback_wrapper,
+                                        cub::Int2Type<true>());
+  }
+}
+
+}  // namespace cudf::io::fst::detail
diff --git a/cpp/src/io/fst/device_dfa.cuh b/cpp/src/io/fst/device_dfa.cuh
new file mode 100644
index 0000000..7eeff27
--- /dev/null
+++ b/cpp/src/io/fst/device_dfa.cuh
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "dispatch_dfa.cuh"
+
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cstdint>
+
+namespace cudf::io::fst {
+
+/**
+ * @brief Uses a deterministic finite automaton to transduce a sequence of symbols from an input
+ * iterator to a sequence of transduced output symbols.
+ *
+ * @tparam DfaT The DFA specification
+ * @tparam SymbolItT Random-access input iterator type to symbols fed into the FST
+ * @tparam TransducedOutItT Random-access output iterator to which the transduced output will be
+ * written
+ * @tparam TransducedIndexOutItT Random-access output iterator type to which the input symbols'
+ * indexes are written.
+ * @tparam TransducedCountOutItT A single-item output iterator type to which the total number of
+ * output symbols is written
+ * @tparam OffsetT A type large enough to index into either of both: (a) the input symbols and (b)
+ * the output symbols
+ * @param[in] d_temp_storage Device-accessible allocation of temporary storage.  When NULL, the
+ * required allocation size is written to \p temp_storage_bytes and no work is done.
+ * @param[in,out] temp_storage_bytes Reference to size in bytes of \p d_temp_storage allocation
+ * @param[in] dfa The DFA specifying the number of distinct symbol groups, transition table, and
+ * translation table
+ * @param[in] d_chars_in Random-access input iterator to the beginning of the sequence of input
+ * symbols
+ * @param[in] num_chars The total number of input symbols to process
+ * @param[out] transduced_out_it Random-access output iterator to which the transduced output is
+ * written
+ * @param[out] transduced_out_idx_it Random-access output iterator to which, the index i is written
+ * iff the i-th input symbol caused some output to be written
+ * @param[out] d_num_transduced_out_it A single-item output iterator type to which the total number
+ * of output symbols is written
+ * @param[in] seed_state The DFA's starting state. For streaming DFAs this corresponds to the
+ * "end-state" of the previous invocation of the algorithm.
+ * @param[in] stream CUDA stream to launch kernels within. Default is the null-stream.
+ */
+template <typename DfaT,
+          typename SymbolItT,
+          typename TransducedOutItT,
+          typename TransducedIndexOutItT,
+          typename TransducedCountOutItT,
+          typename OffsetT>
+cudaError_t DeviceTransduce(void* d_temp_storage,
+                            size_t& temp_storage_bytes,
+                            DfaT dfa,
+                            SymbolItT d_chars_in,
+                            OffsetT num_chars,
+                            TransducedOutItT transduced_out_it,
+                            TransducedIndexOutItT transduced_out_idx_it,
+                            TransducedCountOutItT d_num_transduced_out_it,
+                            uint32_t seed_state = 0,
+                            cudaStream_t stream = 0)
+{
+  using DispatchDfaT = detail::DispatchFSM<DfaT,
+                                           SymbolItT,
+                                           TransducedOutItT,
+                                           TransducedIndexOutItT,
+                                           TransducedCountOutItT,
+                                           OffsetT>;
+
+  return DispatchDfaT::Dispatch(d_temp_storage,
+                                temp_storage_bytes,
+                                dfa,
+                                seed_state,
+                                d_chars_in,
+                                num_chars,
+                                transduced_out_it,
+                                transduced_out_idx_it,
+                                d_num_transduced_out_it,
+                                stream);
+}
+
+}  // namespace cudf::io::fst
diff --git a/cpp/src/io/fst/dispatch_dfa.cuh b/cpp/src/io/fst/dispatch_dfa.cuh
new file mode 100644
index 0000000..a5c1a4f
--- /dev/null
+++ b/cpp/src/io/fst/dispatch_dfa.cuh
@@ -0,0 +1,456 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "agent_dfa.cuh"
+#include "in_reg_array.cuh"
+
+#include <cub/cub.cuh>
+
+#include <cstdint>
+
+namespace cudf::io::fst::detail {
+
+/**
+ * @brief The tuning policy comprising all the architecture-specific compile-time tuning parameters.
+ *
+ * @tparam _BLOCK_THREADS Number of threads per block
+ * @tparam _ITEMS_PER_THREAD Number of symbols processed by each thread
+ */
+template <int32_t _BLOCK_THREADS, int32_t _ITEMS_PER_THREAD>
+struct AgentDFAPolicy {
+  // The number of threads per block
+  static constexpr int32_t BLOCK_THREADS = _BLOCK_THREADS;
+
+  // The number of symbols processed by each thread
+  static constexpr int32_t ITEMS_PER_THREAD = _ITEMS_PER_THREAD;
+};
+
+/**
+ * @brief The list of architecture-specific tuning policies. Yet TBD.
+ */
+struct DeviceFSMPolicy {
+  //------------------------------------------------------------------------------
+  // Architecture-specific tuning policies
+  //------------------------------------------------------------------------------
+  struct Policy900 : cub::ChainedPolicy<900, Policy900, Policy900> {
+    enum {
+      BLOCK_THREADS    = 128,
+      ITEMS_PER_THREAD = 32,
+    };
+
+    using AgentDFAPolicy = AgentDFAPolicy<BLOCK_THREADS, ITEMS_PER_THREAD>;
+  };
+
+  // Top-of-list of the tuning policy "chain"
+  using MaxPolicy = Policy900;
+};
+
+/**
+ * @brief Kernel for initializing single-pass prefix scan tile states
+ *
+ * @param items_state The tile state
+ * @param num_tiles The number of tiles to be initialized
+ * @return
+ */
+template <typename TileState>
+__global__ void initialization_pass_kernel(TileState items_state, uint32_t num_tiles)
+{
+  items_state.InitializeStatus(num_tiles);
+}
+
+template <typename DfaT,
+          typename SymbolItT,
+          typename TransducedOutItT,
+          typename TransducedIndexOutItT,
+          typename TransducedCountOutItT,
+          typename OffsetT>
+struct DispatchFSM : DeviceFSMPolicy {
+  //------------------------------------------------------------------------------
+  // DEFAULT TYPES
+  //------------------------------------------------------------------------------
+  using StateIndexT  = uint32_t;
+  using BlockOffsetT = uint32_t;
+
+  //------------------------------------------------------------------------------
+  // DERIVED CONFIGS
+  //------------------------------------------------------------------------------
+  // DFA-specific configs
+  static constexpr int32_t MAX_NUM_STATES  = DfaT::MAX_NUM_STATES;
+  static constexpr int32_t MAX_NUM_SYMBOLS = DfaT::MAX_NUM_SYMBOLS;
+
+  // Whether to use a single-pass prefix scan that does all in on
+  static constexpr bool SINGLE_PASS_STV = false;
+
+  // Whether this is a finite-state transform
+  static constexpr bool IS_FST = true;
+
+  //------------------------------------------------------------------------------
+  // TYPEDEFS
+  //------------------------------------------------------------------------------
+  using StateVectorCompositeOpT = VectorCompositeOp<MAX_NUM_STATES>;
+
+  //------------------------------------------------------------------------------
+  // MEMBER VARS
+  //------------------------------------------------------------------------------
+  void* d_temp_storage;
+  size_t& temp_storage_bytes;
+  DfaT dfa;
+  StateIndexT seed_state;
+  SymbolItT d_chars_in;
+  OffsetT num_chars;
+  TransducedOutItT transduced_out_it;
+  TransducedIndexOutItT transduced_out_idx_it;
+  TransducedCountOutItT d_num_transduced_out_it;
+  cudaStream_t stream;
+  int const ptx_version;
+
+  //------------------------------------------------------------------------------
+  // CONSTRUCTOR
+  //------------------------------------------------------------------------------
+  CUB_RUNTIME_FUNCTION __forceinline__ DispatchFSM(void* d_temp_storage,
+                                                   size_t& temp_storage_bytes,
+                                                   DfaT dfa,
+                                                   StateIndexT seed_state,
+                                                   SymbolItT d_chars_in,
+                                                   OffsetT num_chars,
+                                                   TransducedOutItT transduced_out_it,
+                                                   TransducedIndexOutItT transduced_out_idx_it,
+                                                   TransducedCountOutItT d_num_transduced_out_it,
+                                                   cudaStream_t stream,
+                                                   int ptx_version)
+    : d_temp_storage(d_temp_storage),
+      temp_storage_bytes(temp_storage_bytes),
+      dfa(dfa),
+      seed_state(seed_state),
+      d_chars_in(d_chars_in),
+      num_chars(num_chars),
+      transduced_out_it(transduced_out_it),
+      transduced_out_idx_it(transduced_out_idx_it),
+      d_num_transduced_out_it(d_num_transduced_out_it),
+      stream(stream),
+      ptx_version(ptx_version)
+  {
+  }
+
+  //------------------------------------------------------------------------------
+  // DISPATCH INTERFACE
+  //------------------------------------------------------------------------------
+  CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t Dispatch(
+    void* d_temp_storage,
+    size_t& temp_storage_bytes,
+    DfaT dfa,
+    StateIndexT seed_state,
+    SymbolItT d_chars_in,
+    OffsetT num_chars,
+    TransducedOutItT transduced_out_it,
+    TransducedIndexOutItT transduced_out_idx_it,
+    TransducedCountOutItT d_num_transduced_out_it,
+    cudaStream_t stream)
+  {
+    using MaxPolicyT = DispatchFSM::MaxPolicy;
+
+    cudaError_t error;
+
+    // Get PTX version
+    int ptx_version;
+    error = cub::PtxVersion(ptx_version);
+    if (error != cudaSuccess) return error;
+
+    // Create dispatch functor
+    DispatchFSM dispatch(d_temp_storage,
+                         temp_storage_bytes,
+                         dfa,
+                         seed_state,
+                         d_chars_in,
+                         num_chars,
+                         transduced_out_it,
+                         transduced_out_idx_it,
+                         d_num_transduced_out_it,
+                         stream,
+                         ptx_version);
+
+    error = MaxPolicyT::Invoke(ptx_version, dispatch);
+    return error;
+  }
+
+  //------------------------------------------------------------------------------
+  // DFA SIMULATION KERNEL INVOCATION
+  //------------------------------------------------------------------------------
+  template <typename ActivePolicyT,
+            typename DFASimulationKernelT,
+            typename TileStateT,
+            typename FstScanTileStateT,
+            typename StateVectorT>
+  CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t
+  InvokeDFASimulationKernel(DFASimulationKernelT dfa_kernel,
+                            int32_t sm_count,
+                            StateIndexT seed_state,
+                            StateVectorT* d_thread_state_transition,
+                            TileStateT tile_state,
+                            FstScanTileStateT fst_tile_state)
+
+  {
+    cudaError_t error = cudaSuccess;
+    cub::KernelConfig dfa_simulation_config;
+
+    using PolicyT = typename ActivePolicyT::AgentDFAPolicy;
+    if (CubDebug(error = dfa_simulation_config.Init<PolicyT>(dfa_kernel))) return error;
+
+    // Kernel invocation
+    uint32_t grid_size = std::max(
+      1u, CUB_QUOTIENT_CEILING(num_chars, PolicyT::BLOCK_THREADS * PolicyT::ITEMS_PER_THREAD));
+    uint32_t block_threads = dfa_simulation_config.block_threads;
+
+    dfa_kernel<<<grid_size, block_threads, 0, stream>>>(dfa,
+                                                        d_chars_in,
+                                                        num_chars,
+                                                        seed_state,
+                                                        d_thread_state_transition,
+                                                        tile_state,
+                                                        fst_tile_state,
+                                                        transduced_out_it,
+                                                        transduced_out_idx_it,
+                                                        d_num_transduced_out_it);
+
+    // Check for errors
+    if (CubDebug(error = cudaPeekAtLastError())) return error;
+
+    return error;
+  }
+
+  /**
+   * @brief Computes the state-transition vectors
+   */
+  template <typename ActivePolicyT,
+            typename TileStateT,
+            typename FstScanTileStateT,
+            typename StateVectorT>
+  CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t
+  ComputeStateTransitionVector(uint32_t sm_count,
+                               TileStateT tile_state,
+                               FstScanTileStateT fst_tile_state,
+                               StateVectorT* d_thread_state_transition)
+  {
+    StateIndexT seed_state = 0;
+
+    return InvokeDFASimulationKernel<ActivePolicyT>(
+      SimulateDFAKernel<true,
+                        SINGLE_PASS_STV,
+                        DfaT,
+                        TileStateT,
+                        typename ActivePolicyT::AgentDFAPolicy,
+                        SymbolItT,
+                        OffsetT,
+                        StateVectorT,
+                        FstScanTileStateT,
+                        TransducedOutItT,
+                        TransducedIndexOutItT,
+                        TransducedCountOutItT>,
+      sm_count,
+      seed_state,
+      d_thread_state_transition,
+      tile_state,
+      fst_tile_state);
+  }
+
+  /**
+   * @brief Performs the actual DFA simulation.
+   */
+  template <typename ActivePolicyT,
+            typename TileStateT,
+            typename FstScanTileStateT,
+            typename StateVectorT>
+  CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t
+  SimulateDFA(uint32_t sm_count,
+              TileStateT tile_state,
+              FstScanTileStateT fst_tile_state,
+              StateIndexT seed_state,
+              StateVectorT* d_thread_state_transition)
+  {
+    return InvokeDFASimulationKernel<ActivePolicyT>(
+      SimulateDFAKernel<false,
+                        SINGLE_PASS_STV,
+                        DfaT,
+                        TileStateT,
+                        typename ActivePolicyT::AgentDFAPolicy,
+                        SymbolItT,
+                        OffsetT,
+                        StateVectorT,
+                        FstScanTileStateT,
+                        TransducedOutItT,
+                        TransducedIndexOutItT,
+                        TransducedCountOutItT>,
+      sm_count,
+      seed_state,
+      d_thread_state_transition,
+      tile_state,
+      fst_tile_state);
+  }
+
+  //------------------------------------------------------------------------------
+  // POLICY INVOCATION
+  //------------------------------------------------------------------------------
+  template <typename ActivePolicyT>
+  CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t Invoke()
+  {
+    cudaError_t error = cudaSuccess;
+
+    // Get SM count
+    int device_ordinal = -1;
+    int sm_count       = -1;
+
+    // Get current device
+    error = cudaGetDevice(&device_ordinal);
+    if (error != cudaSuccess) return error;
+
+    error = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal);
+    if (error != cudaSuccess) return error;
+
+    //------------------------------------------------------------------------------
+    // DERIVED TYPEDEFS
+    //------------------------------------------------------------------------------
+    // Type used to represent state-transition vectors
+    using StateVectorT = MultiFragmentInRegArray<MAX_NUM_STATES, MAX_NUM_STATES - 1>;
+
+    // Scan tile state used for propagating composed state transition vectors
+    using ScanTileStateT = typename cub::ScanTileState<StateVectorT>;
+
+    // Scan tile state used for propagating transduced output offsets
+    using FstScanTileStateT = typename cub::ScanTileState<OffsetT>;
+
+    // STATE-TRANSITION IDENTITY VECTOR
+    StateVectorT state_identity_vector;
+    for (int32_t i = 0; i < MAX_NUM_STATES; ++i) {
+      state_identity_vector.Set(i, i);
+    }
+    StateVectorCompositeOpT state_vector_scan_op;
+
+    //------------------------------------------------------------------------------
+    // DERIVED CONFIGS
+    //------------------------------------------------------------------------------
+    enum {
+      BLOCK_THREADS         = ActivePolicyT::BLOCK_THREADS,
+      SYMBOLS_PER_THREAD    = ActivePolicyT::ITEMS_PER_THREAD,
+      NUM_SYMBOLS_PER_BLOCK = BLOCK_THREADS * SYMBOLS_PER_THREAD
+    };
+
+    BlockOffsetT num_blocks = std::max(1u, CUB_QUOTIENT_CEILING(num_chars, NUM_SYMBOLS_PER_BLOCK));
+    size_t num_threads      = num_blocks * BLOCK_THREADS;
+
+    //------------------------------------------------------------------------------
+    // TEMPORARY MEMORY REQUIREMENTS
+    //------------------------------------------------------------------------------
+    enum { MEM_STATE_VECTORS = 0, MEM_SCAN, MEM_SINGLE_PASS_STV, MEM_FST_OFFSET, NUM_ALLOCATIONS };
+
+    size_t allocation_sizes[NUM_ALLOCATIONS] = {0};
+    void* allocations[NUM_ALLOCATIONS]       = {0};
+
+    size_t vector_scan_storage_bytes = 0;
+
+    // [MEMORY REQUIREMENTS] STATE-TRANSITION SCAN
+    cub::DeviceScan::ExclusiveScan(nullptr,
+                                   vector_scan_storage_bytes,
+                                   static_cast<StateVectorT*>(allocations[MEM_STATE_VECTORS]),
+                                   static_cast<StateVectorT*>(allocations[MEM_STATE_VECTORS]),
+                                   state_vector_scan_op,
+                                   state_identity_vector,
+                                   num_threads,
+                                   stream);
+
+    allocation_sizes[MEM_STATE_VECTORS] = num_threads * sizeof(StateVectorT);
+    allocation_sizes[MEM_SCAN]          = vector_scan_storage_bytes;
+
+    // Bytes needed for tile status descriptors (fusing state-transition vector + DFA simulation)
+    if constexpr (SINGLE_PASS_STV) {
+      error = ScanTileStateT::AllocationSize(num_blocks, allocation_sizes[MEM_SINGLE_PASS_STV]);
+      if (error != cudaSuccess) return error;
+    }
+
+    // Bytes needed for tile status descriptors (DFA simulation pass for output size computation +
+    // output-generating pass)
+    if constexpr (IS_FST) {
+      error = FstScanTileStateT::AllocationSize(num_blocks, allocation_sizes[MEM_FST_OFFSET]);
+      if (error != cudaSuccess) return error;
+    }
+
+    // Alias the temporary allocations from the single storage blob (or compute the necessary size
+    // of the blob)
+    error =
+      cub::AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes);
+    if (error != cudaSuccess) return error;
+
+    // Return if the caller is simply requesting the size of the storage allocation
+    if (d_temp_storage == NULL) return cudaSuccess;
+
+    // Alias memory for state-transition vectors
+    StateVectorT* d_thread_state_transition =
+      static_cast<StateVectorT*>(allocations[MEM_STATE_VECTORS]);
+
+    //------------------------------------------------------------------------------
+    // INITIALIZE SCAN TILE STATES COMPUTING TRANSDUCED OUTPUT OFFSETS
+    //------------------------------------------------------------------------------
+    FstScanTileStateT fst_offset_tile_state;
+    if constexpr (IS_FST) {
+      // Construct the tile status (aliases memory internally et al.)
+      error = fst_offset_tile_state.Init(
+        num_blocks, allocations[MEM_FST_OFFSET], allocation_sizes[MEM_FST_OFFSET]);
+      if (error != cudaSuccess) return error;
+      constexpr uint32_t FST_INIT_TPB = 256;
+      uint32_t num_fst_init_blocks    = CUB_QUOTIENT_CEILING(num_blocks, FST_INIT_TPB);
+      initialization_pass_kernel<<<num_fst_init_blocks, FST_INIT_TPB, 0, stream>>>(
+        fst_offset_tile_state, num_blocks);
+    }
+
+    //------------------------------------------------------------------------------
+    // COMPUTE STATE-TRANSITION VECTORS
+    //------------------------------------------------------------------------------
+    ScanTileStateT stv_tile_state;
+    if constexpr (SINGLE_PASS_STV) {
+      // Construct the tile status (aliases memory internally et al.)
+      error = stv_tile_state.Init(
+        num_blocks, allocations[MEM_SINGLE_PASS_STV], allocation_sizes[MEM_SINGLE_PASS_STV]);
+      if (error != cudaSuccess) return error;
+      constexpr uint32_t STV_INIT_TPB = 256;
+      uint32_t num_stv_init_blocks    = CUB_QUOTIENT_CEILING(num_blocks, STV_INIT_TPB);
+      initialization_pass_kernel<<<num_stv_init_blocks, STV_INIT_TPB, 0, stream>>>(stv_tile_state,
+                                                                                   num_blocks);
+    } else {
+      // Compute state-transition vectors
+      // TODO tag dispatch or constexpr if depending on single-pass config to avoid superfluous
+      // template instantiations
+      ComputeStateTransitionVector<ActivePolicyT>(
+        sm_count, stv_tile_state, fst_offset_tile_state, d_thread_state_transition);
+
+      // State-transition vector scan computing using the composition operator
+      cub::DeviceScan::ExclusiveScan(allocations[MEM_SCAN],
+                                     allocation_sizes[MEM_SCAN],
+                                     d_thread_state_transition,
+                                     d_thread_state_transition,
+                                     state_vector_scan_op,
+                                     state_identity_vector,
+                                     num_threads,
+                                     stream);
+    }
+
+    //------------------------------------------------------------------------------
+    // SIMULATE DFA
+    //------------------------------------------------------------------------------
+    return SimulateDFA<ActivePolicyT>(
+      sm_count, stv_tile_state, fst_offset_tile_state, seed_state, d_thread_state_transition);
+  }
+};
+}  // namespace cudf::io::fst::detail
diff --git a/cpp/src/io/fst/in_reg_array.cuh b/cpp/src/io/fst/in_reg_array.cuh
new file mode 100644
index 0000000..7f728db
--- /dev/null
+++ b/cpp/src/io/fst/in_reg_array.cuh
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/types.hpp>
+
+#include <cub/cub.cuh>
+
+#include <cstdint>
+
+namespace cudf::io::fst::detail {
+
+/**
+ * @brief A bit-packed array of items that can be backed by registers yet allows to be dynamically
+ * addressed at runtime. The data structure is explained in greater detail in the paper <a
+ * href="http://www.vldb.org/pvldb/vol13/p616-stehle.pdf">ParPaRaw: Massively Parallel Parsing of
+ * Delimiter-Separated Raw Data</a>.
+ *
+ * @tparam NUM_ITEMS The maximum number of items this data structure is supposed to store
+ * @tparam MAX_ITEM_VALUE The maximum value that one item can represent
+ * @tparam BackingFragmentT The data type that is holding the fragments
+ */
+template <uint32_t NUM_ITEMS, uint32_t MAX_ITEM_VALUE, typename BackingFragmentT = uint32_t>
+class MultiFragmentInRegArray {
+ private:
+  /// Minimum number of bits required to represent all values from [0, MAX_ITEM_VALUE]
+  static constexpr uint32_t MIN_BITS_PER_ITEM =
+    (MAX_ITEM_VALUE == 0) ? 1 : cub::Log2<(MAX_ITEM_VALUE + 1)>::VALUE;
+
+  /// Number of bits that each fragment can store
+  static constexpr uint32_t NUM_BITS_PER_FRAGMENT = sizeof(BackingFragmentT) * 8;
+
+  /// The number of bits per fragment per item in the array
+  static constexpr uint32_t AVAIL_BITS_PER_FRAG_ITEM = NUM_BITS_PER_FRAGMENT / NUM_ITEMS;
+
+  /// The number of bits per item per fragment to be a power of two to avoid costly integer
+  /// multiplication
+  static constexpr uint32_t BITS_PER_FRAG_ITEM =
+    0x01U << (cub::Log2<(AVAIL_BITS_PER_FRAG_ITEM + 1)>::VALUE - 1);
+
+  // The total number of fragments required to store all the items
+  static constexpr uint32_t FRAGMENTS_PER_ITEM =
+    cudf::util::div_rounding_up_safe(MIN_BITS_PER_ITEM, BITS_PER_FRAG_ITEM);
+
+  //------------------------------------------------------------------------------
+  // HELPER FUNCTIONS
+  //------------------------------------------------------------------------------
+  /**
+   * @brief Returns the \p num_bits bits starting at \p bit_start
+   */
+  CUDF_HOST_DEVICE [[nodiscard]] uint32_t bfe(uint32_t const& data,
+                                              uint32_t bit_start,
+                                              uint32_t num_bits) const
+  {
+#if CUB_PTX_ARCH > 0
+    return cub::BFE(data, bit_start, num_bits);
+#else
+    uint32_t const MASK = (1 << num_bits) - 1;
+    return (data >> bit_start) & MASK;
+#endif
+  }
+
+  /**
+   * @brief Replaces the \p num_bits bits in \p data starting from \p bit_start with the lower \p
+   * num_bits from \p bits.
+   */
+  CUDF_HOST_DEVICE void bfi(uint32_t& data,
+                            uint32_t bits,
+                            uint32_t bit_start,
+                            uint32_t num_bits) const
+  {
+#if CUB_PTX_ARCH > 0
+    cub::BFI(data, data, bits, bit_start, num_bits);
+#else
+    uint32_t x      = bits << bit_start;
+    uint32_t y      = data;
+    uint32_t MASK_X = ((1 << num_bits) - 1) << bit_start;
+    uint32_t MASK_Y = ~MASK_X;
+    data            = (y & MASK_Y) | (x & MASK_X);
+#endif
+  }
+
+  BackingFragmentT data[FRAGMENTS_PER_ITEM];
+
+  //------------------------------------------------------------------------------
+  // ACCESSORS
+  //------------------------------------------------------------------------------
+ public:
+  CUDF_HOST_DEVICE [[nodiscard]] uint32_t Get(int32_t index) const
+  {
+    uint32_t val = 0;
+
+    for (uint32_t i = 0; i < FRAGMENTS_PER_ITEM; ++i) {
+      val = val | bfe(data[i], index * BITS_PER_FRAG_ITEM, BITS_PER_FRAG_ITEM)
+                    << (i * BITS_PER_FRAG_ITEM);
+    }
+    return val;
+  }
+
+  CUDF_HOST_DEVICE void Set(uint32_t index, uint32_t value)
+  {
+    for (uint32_t i = 0; i < FRAGMENTS_PER_ITEM; ++i) {
+      uint32_t frag_bits = bfe(value, i * BITS_PER_FRAG_ITEM, BITS_PER_FRAG_ITEM);
+      bfi(data[i], frag_bits, index * BITS_PER_FRAG_ITEM, BITS_PER_FRAG_ITEM);
+    }
+  }
+
+  //------------------------------------------------------------------------------
+  // CONSTRUCTORS
+  //------------------------------------------------------------------------------
+  CUDF_HOST_DEVICE MultiFragmentInRegArray()
+  {
+    for (uint32_t i = 0; i < FRAGMENTS_PER_ITEM; ++i) {
+      data[i] = 0;
+    }
+  }
+
+  CUDF_HOST_DEVICE MultiFragmentInRegArray(uint32_t const (&array)[NUM_ITEMS])
+  {
+    for (uint32_t i = 0; i < NUM_ITEMS; ++i) {
+      Set(i, array[i]);
+    }
+  }
+};
+
+}  // namespace cudf::io::fst::detail
diff --git a/cpp/src/io/fst/logical_stack.cuh b/cpp/src/io/fst/logical_stack.cuh
new file mode 100644
index 0000000..c4f9973
--- /dev/null
+++ b/cpp/src/io/fst/logical_stack.cuh
@@ -0,0 +1,473 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf_test/print_utilities.cuh>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/scatter.h>
+
+#include <cub/cub.cuh>
+
+#include <algorithm>
+#include <cstdint>
+#include <type_traits>
+
+namespace cudf::io::fst {
+
+/**
+ * @brief Describes the kind of stack operation.
+ */
+enum class stack_op_type : int8_t {
+  READ  = 0,  ///< Operation reading what is currently on top of the stack
+  PUSH  = 1,  ///< Operation pushing a new item on top of the stack
+  POP   = 2,  ///< Operation popping the item currently on top of the stack
+  RESET = 3   ///< Operation popping all items currently on the stack
+};
+
+namespace detail {
+
+/**
+ * @brief A convenience struct that represents a stack operation as a pair, where the stack_level
+ * represents the stack's level and the value represents the stack symbol.
+ *
+ * @tparam StackLevelT The stack level type sufficient to cover all stack levels. Must be signed
+ * type as any subsequence of stack operations must be able to be covered. E.g., consider the first
+ * 10 operations are all push and the last 10 operations are all pop operations, we need to be able
+ * to represent a partial aggregate of the first ten items, which is '+10', just as well as a
+ * partial aggregate of the last ten items, which is '-10'.
+ * @tparam ValueT The value type that corresponds to the stack symbols (i.e., covers the stack
+ * alphabet).
+ */
+template <typename StackLevelT, typename ValueT>
+struct StackOp {
+  // Must be signed type as any subsequence of stack operations must be able to be covered.
+  static_assert(std::is_signed_v<StackLevelT>, "StackLevelT has to be a signed type");
+
+  StackLevelT stack_level;
+  ValueT value;
+};
+
+/**
+ * @brief Helper class to assist with radix sorting StackOp instances by stack level.
+ *
+ * @tparam BYTE_SIZE The size of the StackOp.
+ */
+template <std::size_t BYTE_SIZE>
+struct StackOpToUnsigned {
+  using UnsignedT = void;
+};
+
+template <>
+struct StackOpToUnsigned<2U> {
+  using UnsignedT = uint16_t;
+};
+
+template <>
+struct StackOpToUnsigned<4U> {
+  using UnsignedT = uint32_t;
+};
+
+template <>
+struct StackOpToUnsigned<8U> {
+  using UnsignedT = uint64_t;
+};
+
+/**
+ * @brief Alias template to retrieve an unsigned bit-representation that can be used for radix
+ * sorting the stack level of a StackOp.
+ *
+ * @tparam StackOpT The StackOp class template instance for which to get an unsigned
+ * bit-representation
+ */
+template <typename StackOpT>
+using UnsignedStackOpType = typename StackOpToUnsigned<sizeof(StackOpT)>::UnsignedT;
+
+/**
+ * @brief Function object class template used for converting a stack symbol to a stack
+ * operation that has a stack level to which an operation applies.
+ *
+ * @tparam StackOpT
+ * @tparam StackSymbolToStackOpTypeT
+ */
+template <typename StackOpT, typename StackSymbolToStackOpTypeT>
+struct StackSymbolToStackOp {
+  template <typename StackSymbolT>
+  constexpr CUDF_HOST_DEVICE StackOpT operator()(StackSymbolT const& stack_symbol) const
+  {
+    stack_op_type stack_op = symbol_to_stack_op_type(stack_symbol);
+    // PUSH => +1, POP => -1, READ => 0
+    int32_t level_delta = (stack_op == stack_op_type::PUSH)  ? 1
+                          : (stack_op == stack_op_type::POP) ? -1
+                                                             : 0;
+    return StackOpT{static_cast<decltype(StackOpT::stack_level)>(level_delta), stack_symbol};
+  }
+
+  /// Function object returning a stack operation type for a given stack symbol
+  StackSymbolToStackOpTypeT symbol_to_stack_op_type;
+};
+
+/**
+ * @brief Binary reduction operator to compute the absolute stack level from relative stack levels
+ * (i.e., +1 for a PUSH, -1 for a POP operation).
+ */
+template <typename StackSymbolToStackOpTypeT>
+struct AddStackLevelFromStackOp {
+  template <typename StackLevelT, typename ValueT>
+  constexpr CUDF_HOST_DEVICE StackOp<StackLevelT, ValueT> operator()(
+    StackOp<StackLevelT, ValueT> const& lhs, StackOp<StackLevelT, ValueT> const& rhs) const
+  {
+    StackLevelT new_level = (symbol_to_stack_op_type(rhs.value) == stack_op_type::RESET)
+                              ? 0
+                              : (lhs.stack_level + rhs.stack_level);
+    return StackOp<StackLevelT, ValueT>{new_level, rhs.value};
+  }
+
+  /// Function object returning a stack operation type for a given stack symbol
+  StackSymbolToStackOpTypeT symbol_to_stack_op_type;
+};
+
+/**
+ * @brief Binary reduction operator that propagates a write operation for a specific stack level to
+ * all reads of that same stack level. That is, if the stack level of LHS compares equal to the
+ * stack level of the RHS and if the RHS is a read and the LHS is a write operation type, then we
+ * return LHS, otherwise we return the RHS.
+ */
+template <typename StackSymbolToStackOpTypeT>
+struct PopulatePopWithPush {
+  template <typename StackLevelT, typename ValueT>
+  constexpr CUDF_HOST_DEVICE StackOp<StackLevelT, ValueT> operator()(
+    StackOp<StackLevelT, ValueT> const& lhs, StackOp<StackLevelT, ValueT> const& rhs) const
+  {
+    // If RHS is a read, then we need to figure out whether we can propagate the value from the LHS
+    bool is_rhs_read = symbol_to_stack_op_type(rhs.value) != stack_op_type::PUSH;
+
+    // Whether LHS is a matching write (i.e., the push operation that is on top of the stack for the
+    // RHS's read)
+    bool is_lhs_matching_write = (lhs.stack_level == rhs.stack_level) &&
+                                 symbol_to_stack_op_type(lhs.value) == stack_op_type::PUSH;
+
+    return (is_rhs_read && is_lhs_matching_write) ? lhs : rhs;
+  }
+
+  /// Function object returning a stack operation type for a given stack symbol
+  StackSymbolToStackOpTypeT symbol_to_stack_op_type;
+};
+
+/**
+ * @brief Binary reduction operator that is used to replace each read_symbol occurrence with the
+ * last non-read_symbol that precedes such read_symbol.
+ */
+template <typename StackSymbolT>
+struct PropagateLastWrite {
+  constexpr CUDF_HOST_DEVICE StackSymbolT operator()(StackSymbolT const& lhs,
+                                                     StackSymbolT const& rhs) const
+  {
+    // If RHS is a yet-to-be-propagated, then we need to check whether we can use the LHS to fill
+    bool is_rhs_read = (rhs == read_symbol);
+
+    // We propagate the write from the LHS if it's a write
+    bool is_lhs_write = (lhs != read_symbol);
+
+    return (is_rhs_read && is_lhs_write) ? lhs : rhs;
+  }
+
+  /// The read_symbol that is supposed to be replaced
+  StackSymbolT read_symbol;
+};
+
+/**
+ * @brief Helper function object class to convert a StackOp to the stack symbol of that
+ * StackOp.
+ */
+struct StackOpToStackSymbol {
+  template <typename StackLevelT, typename ValueT>
+  constexpr CUDF_HOST_DEVICE ValueT operator()(StackOp<StackLevelT, ValueT> const& kv_op) const
+  {
+    return kv_op.value;
+  }
+};
+
+/**
+ * @brief Replaces all operations that apply to stack level '0' with the empty stack symbol
+ */
+template <typename StackOpT>
+struct RemapEmptyStack {
+  constexpr CUDF_HOST_DEVICE StackOpT operator()(StackOpT const& kv_op) const
+  {
+    return kv_op.stack_level == 0 ? empty_stack_symbol : kv_op;
+  }
+  StackOpT empty_stack_symbol;
+};
+
+}  // namespace detail
+
+/**
+ * @brief Takes a sparse representation of a sequence of stack operations that either push something
+ * onto the stack or pop something from the stack and resolves the symbol that is on top of the
+ * stack.
+ *
+ * @tparam StackLevelT Signed integer type that must be sufficient to cover [-max_stack_level,
+ * max_stack_level] for the given sequence of stack operations. Must be signed as it needs to cover
+ * the stack level of any arbitrary subsequence of stack operations.
+ * @tparam StackSymbolItT An input iterator type that provides the sequence of symbols that
+ * represent stack operations
+ * @tparam SymbolPositionT The index that this stack operation is supposed to apply to
+ * @tparam StackSymbolToStackOpTypeT Function object class to transform items from StackSymbolItT to
+ * stack_op_type
+ * @tparam TopOfStackOutItT Output iterator type to which StackSymbolT are being assigned
+ * @tparam StackSymbolT The internal type being used (usually corresponding to StackSymbolItT's
+ * value_type)
+ * @tparam OffsetT Signed or unsigned integer type large enough to index into both the sparse input
+ * sequence and the top-of-stack output sequence
+ *
+ * @param[in] d_symbols Sequence of symbols that represent stack operations. Memory may alias with
+ * \p d_top_of_stack
+ * @param[in,out] d_symbol_positions Sequence of symbol positions (for a sparse representation),
+ * sequence must be ordered in ascending order. Note, the memory of this array is repurposed for
+ * double-buffering.
+ * @param[in] symbol_to_stack_op Function object that returns a stack operation type (push, pop, or
+ * read) for a given symbol from \p d_symbols
+ * @param[out] d_top_of_stack A random access output iterator that will be populated with
+ * what-is-on-top-of-the-stack for the given sequence of stack operations \p d_symbols
+ * @param[in] empty_stack_symbol The symbol that will be written to top_of_stack whenever the stack
+ * was empty
+ * @param[in] read_symbol A symbol that may not be confused for a symbol that would push to the
+ * stack
+ * @param[in] num_symbols_out The number of symbols that are supposed to be filled with
+ * what-is-on-top-of-the-stack
+ * @param[in] stream The cuda stream to which to dispatch the work
+ */
+template <typename StackLevelT,
+          typename StackSymbolItT,
+          typename SymbolPositionT,
+          typename StackSymbolToStackOpTypeT,
+          typename TopOfStackOutItT,
+          typename StackSymbolT>
+void sparse_stack_op_to_top_of_stack(StackSymbolItT d_symbols,
+                                     device_span<SymbolPositionT> d_symbol_positions,
+                                     StackSymbolToStackOpTypeT symbol_to_stack_op,
+                                     TopOfStackOutItT d_top_of_stack,
+                                     StackSymbolT const empty_stack_symbol,
+                                     StackSymbolT const read_symbol,
+                                     std::size_t const num_symbols_out,
+                                     rmm::cuda_stream_view stream)
+{
+  rmm::device_buffer temp_storage{};
+
+  // Type used to hold pairs of (stack_level, value) pairs
+  using StackOpT = detail::StackOp<StackLevelT, StackSymbolT>;
+
+  // The unsigned integer type that we use for radix sorting items of type StackOpT
+  using StackOpUnsignedT = detail::UnsignedStackOpType<StackOpT>;
+  static_assert(!std::is_void<StackOpUnsignedT>(), "unsupported StackOpT size");
+
+  // Transforming sequence of stack symbols to stack operations
+  using StackSymbolToStackOpT = detail::StackSymbolToStackOp<StackOpT, StackSymbolToStackOpTypeT>;
+
+  // TransformInputIterator converting stack symbols to stack operations
+  using TransformInputItT =
+    cub::TransformInputIterator<StackOpT, StackSymbolToStackOpT, StackSymbolItT>;
+
+  auto const num_symbols_in = d_symbol_positions.size();
+
+  // Converting a stack symbol that may either push or pop to a stack operation:
+  // stack_symbol -> ([+1,0,-1], stack_symbol)
+  StackSymbolToStackOpT stack_sym_to_kv_op{symbol_to_stack_op};
+  TransformInputItT stack_symbols_in(d_symbols, stack_sym_to_kv_op);
+
+  // Double-buffer for sorting along the given sequence of symbol positions (the sparse
+  // representation)
+  cub::DoubleBuffer<SymbolPositionT> d_symbol_positions_db{nullptr, nullptr};
+
+  // Double-buffer for sorting the stack operations by the stack level to which such operation
+  // applies
+  cub::DoubleBuffer<StackOpT> d_kv_operations{nullptr, nullptr};
+
+  // A double-buffer that aliases memory from d_kv_operations with unsigned types in order to
+  // be able to perform a radix sort
+  cub::DoubleBuffer<StackOpUnsignedT> d_kv_operations_unsigned{nullptr, nullptr};
+
+  constexpr std::size_t bits_per_byte = 8;
+  constexpr std::size_t begin_bit     = offsetof(StackOpT, stack_level) * bits_per_byte;
+  constexpr std::size_t end_bit       = begin_bit + (sizeof(StackOpT::stack_level) * bits_per_byte);
+
+  // The stack operation that makes sure that reads for stack level '0' will be populated
+  // with the empty_stack_symbol
+  StackOpT const empty_stack{0, empty_stack_symbol};
+
+  cub::TransformInputIterator<StackOpT, detail::RemapEmptyStack<StackOpT>, StackOpT*>
+    kv_ops_scan_in(nullptr, detail::RemapEmptyStack<StackOpT>{empty_stack});
+  StackOpT* kv_ops_scan_out = nullptr;
+
+  std::size_t stack_level_scan_bytes      = 0;
+  std::size_t stack_level_sort_bytes      = 0;
+  std::size_t match_level_scan_bytes      = 0;
+  std::size_t propagate_writes_scan_bytes = 0;
+
+  // Getting temporary storage requirements for the prefix sum of the stack level after each
+  // operation
+  CUDF_CUDA_TRY(cub::DeviceScan::InclusiveScan(
+    nullptr,
+    stack_level_scan_bytes,
+    stack_symbols_in,
+    d_kv_operations.Current(),
+    detail::AddStackLevelFromStackOp<StackSymbolToStackOpTypeT>{symbol_to_stack_op},
+    num_symbols_in,
+    stream));
+
+  // Getting temporary storage requirements for the stable radix sort (sorting by stack level of the
+  // operations)
+  CUDF_CUDA_TRY(cub::DeviceRadixSort::SortPairs(nullptr,
+                                                stack_level_sort_bytes,
+                                                d_kv_operations_unsigned,
+                                                d_symbol_positions_db,
+                                                num_symbols_in,
+                                                begin_bit,
+                                                end_bit,
+                                                stream));
+
+  // Getting temporary storage requirements for the scan to match pop operations with the latest
+  // push of the same level
+  CUDF_CUDA_TRY(cub::DeviceScan::InclusiveScan(
+    nullptr,
+    match_level_scan_bytes,
+    kv_ops_scan_in,
+    kv_ops_scan_out,
+    detail::PopulatePopWithPush<StackSymbolToStackOpTypeT>{symbol_to_stack_op},
+    num_symbols_in,
+    stream));
+
+  // Getting temporary storage requirements for the scan to propagate top-of-stack for spots that
+  // didn't push or pop
+  CUDF_CUDA_TRY(
+    cub::DeviceScan::ExclusiveScan(nullptr,
+                                   propagate_writes_scan_bytes,
+                                   d_top_of_stack,
+                                   d_top_of_stack,
+                                   detail::PropagateLastWrite<StackSymbolT>{read_symbol},
+                                   empty_stack_symbol,
+                                   num_symbols_out,
+                                   stream));
+
+  // Scratch memory required by the algorithms
+  auto total_temp_storage_bytes = std::max({stack_level_scan_bytes,
+                                            stack_level_sort_bytes,
+                                            match_level_scan_bytes,
+                                            propagate_writes_scan_bytes});
+
+  if (temp_storage.size() < total_temp_storage_bytes) {
+    temp_storage.resize(total_temp_storage_bytes, stream);
+  }
+  // Actual device buffer size, as we need to pass in an lvalue-ref to cub algorithms as
+  // temp_storage_bytes
+  total_temp_storage_bytes = temp_storage.size();
+
+  rmm::device_uvector<SymbolPositionT> d_symbol_position_alt{num_symbols_in, stream};
+  rmm::device_uvector<StackOpT> d_kv_ops_current{num_symbols_in, stream};
+  rmm::device_uvector<StackOpT> d_kv_ops_alt{num_symbols_in, stream};
+
+  //------------------------------------------------------------------------------
+  // ALGORITHM
+  //------------------------------------------------------------------------------
+  // Initialize double-buffer for sorting the indexes of the sequence of sparse stack operations
+  d_symbol_positions_db =
+    cub::DoubleBuffer<SymbolPositionT>{d_symbol_positions.data(), d_symbol_position_alt.data()};
+
+  // Initialize double-buffer for sorting the indexes of the sequence of sparse stack operations
+  d_kv_operations = cub::DoubleBuffer<StackOpT>{d_kv_ops_current.data(), d_kv_ops_alt.data()};
+
+  // Compute prefix sum of the stack level after each operation
+  CUDF_CUDA_TRY(cub::DeviceScan::InclusiveScan(
+    temp_storage.data(),
+    total_temp_storage_bytes,
+    stack_symbols_in,
+    d_kv_operations.Current(),
+    detail::AddStackLevelFromStackOp<StackSymbolToStackOpTypeT>{symbol_to_stack_op},
+    num_symbols_in,
+    stream));
+
+  // Stable radix sort, sorting by stack level of the operations
+  d_kv_operations_unsigned = cub::DoubleBuffer<StackOpUnsignedT>{
+    reinterpret_cast<StackOpUnsignedT*>(d_kv_operations.Current()),
+    reinterpret_cast<StackOpUnsignedT*>(d_kv_operations.Alternate())};
+  CUDF_CUDA_TRY(cub::DeviceRadixSort::SortPairs(temp_storage.data(),
+                                                total_temp_storage_bytes,
+                                                d_kv_operations_unsigned,
+                                                d_symbol_positions_db,
+                                                num_symbols_in,
+                                                begin_bit,
+                                                end_bit,
+                                                stream));
+
+  // TransformInputIterator that remaps all operations on stack level 0 to the empty stack symbol
+  kv_ops_scan_in  = {reinterpret_cast<StackOpT*>(d_kv_operations_unsigned.Current()),
+                     detail::RemapEmptyStack<StackOpT>{empty_stack}};
+  kv_ops_scan_out = reinterpret_cast<StackOpT*>(d_kv_operations_unsigned.Alternate());
+
+  // Inclusive scan to match pop operations with the latest push operation of that level
+  CUDF_CUDA_TRY(cub::DeviceScan::InclusiveScan(
+    temp_storage.data(),
+    total_temp_storage_bytes,
+    kv_ops_scan_in,
+    kv_ops_scan_out,
+    detail::PopulatePopWithPush<StackSymbolToStackOpTypeT>{symbol_to_stack_op},
+    num_symbols_in,
+    stream));
+
+  // Fill the output tape with read-symbol
+  thrust::fill(rmm::exec_policy(stream),
+               thrust::device_ptr<StackSymbolT>{d_top_of_stack},
+               thrust::device_ptr<StackSymbolT>{d_top_of_stack + num_symbols_out},
+               read_symbol);
+
+  // Transform the stack operations to the stack symbol they represent
+  cub::TransformInputIterator<StackSymbolT, detail::StackOpToStackSymbol, StackOpT*>
+    kv_op_to_stack_sym_it(kv_ops_scan_out, detail::StackOpToStackSymbol{});
+
+  // Scatter the stack symbols to the output tape (spots that are not scattered to have been
+  // pre-filled with the read-symbol)
+  thrust::scatter(rmm::exec_policy(stream),
+                  kv_op_to_stack_sym_it,
+                  kv_op_to_stack_sym_it + num_symbols_in,
+                  d_symbol_positions_db.Current(),
+                  d_top_of_stack);
+
+  // We perform an exclusive scan in order to fill the items at the very left that may
+  // be reading the empty stack before there's the first push occurrence in the sequence.
+  // Also, we're interested in the top-of-the-stack symbol before the operation was applied.
+  CUDF_CUDA_TRY(
+    cub::DeviceScan::ExclusiveScan(temp_storage.data(),
+                                   total_temp_storage_bytes,
+                                   d_top_of_stack,
+                                   d_top_of_stack,
+                                   detail::PropagateLastWrite<StackSymbolT>{read_symbol},
+                                   empty_stack_symbol,
+                                   num_symbols_out,
+                                   stream));
+}
+
+}  // namespace cudf::io::fst
diff --git a/cpp/src/io/fst/lookup_tables.cuh b/cpp/src/io/fst/lookup_tables.cuh
new file mode 100644
index 0000000..37c9945
--- /dev/null
+++ b/cpp/src/io/fst/lookup_tables.cuh
@@ -0,0 +1,924 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <io/fst/device_dfa.cuh>
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cub/cub.cuh>
+
+#include <cuda/std/iterator>
+
+#include <algorithm>
+#include <cstdint>
+#include <iterator>
+#include <vector>
+
+namespace cudf::io::fst::detail {
+
+/**
+ * @brief Helper function object that delegates a lookup to a given lookup table without mapping any
+ * of the given arguments.
+ */
+struct IdentityOp {
+  template <typename LookUpTableT, typename... Args>
+  __host__ __device__ __forceinline__ auto operator()(LookUpTableT const& lookup_table,
+                                                      Args&&... args) const
+  {
+    return lookup_table.lookup(std::forward<Args>(args)...);
+  }
+};
+
+/**
+ * @brief Class template that can be plugged into the finite-state machine to look up the symbol
+ * group index for a given symbol. Class template does not support multi-symbol lookups (i.e., no
+ * look-ahead). The class uses shared memory for the lookups.
+ *
+ * @tparam SymbolT The symbol type being passed in to lookup the corresponding symbol group id
+ * @tparam PreMapOpT A function object that is invoked with `(lut, symbol)` and must return the
+ * symbol group index of `symbol`.  `lut` is an instance of the lookup table and `symbol` is the
+ * symbol for which to get the symbol group index. If no particular mapping is needed, an instance
+ * of `IdentityOp` can be used.
+ */
+template <typename SymbolT, typename PreMapOpT>
+class SingleSymbolSmemLUT {
+ private:
+  // Type used for representing a symbol group id (i.e., what we return for a given symbol)
+  using SymbolGroupIdT = uint8_t;
+
+  // Number of entries for every lookup (e.g., for 8-bit Symbol this is 256)
+  static constexpr uint32_t NUM_ENTRIES_PER_LUT = 0x01U << (sizeof(SymbolT) * 8U);
+
+  struct _TempStorage {
+    // sym_to_sgid[symbol] -> symbol group index
+    SymbolGroupIdT sym_to_sgid[NUM_ENTRIES_PER_LUT];
+  };
+
+ public:
+  using TempStorage = cub::Uninitialized<_TempStorage>;
+
+  struct KernelParameter {
+    using LookupTableT = SingleSymbolSmemLUT<SymbolT, PreMapOpT>;
+
+    // sym_to_sgid[min(symbol,num_valid_entries)] -> symbol group index
+    uint32_t num_valid_entries;
+
+    // sym_to_sgid[symbol] -> symbol group index
+    SymbolGroupIdT sym_to_sgid[NUM_ENTRIES_PER_LUT];
+
+    // Function object that transforms a symbol to a symbol group id
+    PreMapOpT pre_map_op;
+  };
+
+  /**
+   * @brief Initializes the given \p sgid_init with the symbol group lookups defined by \p
+   * symbol_strings.
+   *
+   * @param symbol_strings Array of strings, where the i-th string holds all symbols
+   * (characters!) that correspond to the i-th symbol group index
+   * @param stream The stream that shall be used to cudaMemcpyAsync the lookup table
+   * @return
+   */
+  template <typename SymbolGroupItT>
+  static KernelParameter InitDeviceSymbolGroupIdLut(SymbolGroupItT const& symbol_strings,
+                                                    PreMapOpT pre_map_op)
+  {
+    KernelParameter init_data{};
+
+    // The symbol group index to be returned if none of the given symbols match
+    SymbolGroupIdT no_match_id = symbol_strings.size();
+
+    // The symbol with the largest value that is mapped to a symbol group id
+    SymbolGroupIdT max_base_match_val = 0;
+
+    // Initialize all entries: by default we return the no-match-id
+    std::fill(&init_data.sym_to_sgid[0], &init_data.sym_to_sgid[NUM_ENTRIES_PER_LUT], no_match_id);
+
+    // Set up lookup table
+    uint32_t sg_id = 0;
+    // Iterate over the symbol groups
+    for (auto const& sg_symbols : symbol_strings) {
+      // Iterate over all symbols that belong to the current symbol group
+      for (auto const& sg_symbol : sg_symbols) {
+        max_base_match_val = std::max(max_base_match_val, static_cast<SymbolGroupIdT>(sg_symbol));
+        init_data.sym_to_sgid[static_cast<int32_t>(sg_symbol)] = sg_id;
+      }
+      sg_id++;
+    }
+
+    // Initialize the out-of-bounds lookup: sym_to_sgid[max_base_match_val+1] -> no_match_id
+    init_data.sym_to_sgid[max_base_match_val + 1] = no_match_id;
+
+    // Alias memory / return memory requirements
+    init_data.num_valid_entries = max_base_match_val + 1;
+    init_data.pre_map_op        = pre_map_op;
+
+    return init_data;
+  }
+
+  _TempStorage& temp_storage;
+  SymbolGroupIdT num_valid_entries;
+  PreMapOpT pre_map_op;
+
+  __device__ __forceinline__ _TempStorage& PrivateStorage()
+  {
+    __shared__ _TempStorage private_storage;
+    return private_storage;
+  }
+
+  /**
+   * @brief Initializes the lookup table, primarily to be invoked from within device code but also
+   * provides host-side implementation for verification.
+   * @note Synchronizes the thread block, if called from device, and, hence, requires all threads
+   * of the thread block to call the constructor
+   */
+  constexpr CUDF_HOST_DEVICE SingleSymbolSmemLUT(KernelParameter const& kernel_param,
+                                                 TempStorage& temp_storage)
+    : temp_storage(temp_storage.Alias()), num_valid_entries(kernel_param.num_valid_entries)
+  {
+    // GPU-side init
+#if CUB_PTX_ARCH > 0
+    for (int32_t i = threadIdx.x; i < kernel_param.num_valid_entries; i += blockDim.x) {
+      this->temp_storage.sym_to_sgid[i] = kernel_param.sym_to_sgid[i];
+    }
+    __syncthreads();
+
+#else
+    // CPU-side init
+    std::copy_n(kernel_param.sym_to_sgid, kernel_param.num_luts, this->temp_storage.sym_to_sgid);
+#endif
+  }
+
+  template <typename SymbolT_>
+  constexpr CUDF_HOST_DEVICE int32_t operator()(SymbolT_ const symbol) const
+  {
+    // Look up the symbol group for given symbol
+    return pre_map_op(*this, symbol);
+  }
+
+  constexpr CUDF_HOST_DEVICE int32_t lookup(SymbolT const symbol) const
+  {
+    // Look up the symbol group for given symbol
+    return temp_storage
+      .sym_to_sgid[min(static_cast<SymbolGroupIdT>(symbol), num_valid_entries - 1U)];
+  }
+};
+
+/**
+ * @brief A simple symbol group lookup wrapper that uses a simple function object to
+ * retrieve the symbol group id for a symbol.
+ *
+ * @tparam SymbolGroupLookupOpT The function object type to return the symbol group for a given
+ * symbol
+ */
+template <typename SymbolGroupLookupOpT>
+class SymbolGroupLookupOp {
+ private:
+  struct _TempStorage {};
+
+ public:
+  using TempStorage = cub::Uninitialized<_TempStorage>;
+
+  struct KernelParameter {
+    // Declare the member type that the DFA is going to instantiate
+    using LookupTableT = SymbolGroupLookupOp<SymbolGroupLookupOpT>;
+    SymbolGroupLookupOpT sgid_lookup_op;
+  };
+
+  static KernelParameter InitDeviceSymbolGroupIdLut(SymbolGroupLookupOpT sgid_lookup_op)
+  {
+    return KernelParameter{sgid_lookup_op};
+  }
+
+ private:
+  _TempStorage& temp_storage;
+  SymbolGroupLookupOpT sgid_lookup_op;
+
+  __device__ __forceinline__ _TempStorage& PrivateStorage()
+  {
+    __shared__ _TempStorage private_storage;
+    return private_storage;
+  }
+
+ public:
+  CUDF_HOST_DEVICE SymbolGroupLookupOp(KernelParameter const& kernel_param,
+                                       TempStorage& temp_storage)
+    : temp_storage(temp_storage.Alias()), sgid_lookup_op(kernel_param.sgid_lookup_op)
+  {
+  }
+
+  template <typename SymbolT_>
+  constexpr CUDF_HOST_DEVICE int32_t operator()(SymbolT_ const symbol) const
+  {
+    // Look up the symbol group for given symbol
+    return sgid_lookup_op(symbol);
+  }
+};
+
+/**
+ * @brief Prepares a simple symbol group lookup wrapper that uses a simple function object to
+ * retrieve the symbol group id for a symbol.
+ *
+ * @tparam FunctorT A function object type that must implement the signature `int32_t
+ * operator()(symbol)`, where `symbol` is a symbol from the input type.
+ * @param sgid_lookup_op A function object that must implement the signature `int32_t
+ * operator()(symbol)`, where `symbol` is a symbol from the input type.
+ * @return The kernel parameter of type SymbolGroupLookupOp::KernelParameter that is used to
+ * initialize a simple symbol group id lookup wrapper
+ */
+template <typename FunctorT>
+auto make_symbol_group_lookup_op(FunctorT sgid_lookup_op)
+{
+  return SymbolGroupLookupOp<FunctorT>::InitDeviceSymbolGroupIdLut(sgid_lookup_op);
+}
+
+/**
+ * @brief Creates a symbol group lookup table of type `SingleSymbolSmemLUT` that uses a two-staged
+ * lookup approach. @p pre_map_op is a function object invoked with `(lut, symbol)` that must return
+ * the symbol group id for the given `symbol`. `lut` is an instance of the lookup table
+ * and `symbol` is a symbol from the input tape. Usually, @p pre_map_op first maps a symbol from
+ * the input tape to an integral that is convertible to `symbol_t`. In a second stage, @p pre_map_op
+ * uses `lut`'s `lookup(mapped_symbol)` that maps that integral to the symbol group id.
+ *
+ * @tparam symbol_t Must be an integral type
+ * @tparam NUM_SYMBOL_GROUPS The number of symbol groups, excluding the catchall symbol group (aka
+ * "other" symbol group)
+ * @tparam pre_map_op_t A unary function object type that returns the symbol group id
+ * @param symbol_strings An array of vectors, where all the symbols in the i-th vector are mapped to
+ * the i-th symbol group
+ * @param pre_map_op A unary function object type that returns the symbol group id for a symbol
+ * @return A symbol group lookup table
+ */
+template <typename symbol_t, std::size_t NUM_SYMBOL_GROUPS, typename pre_map_op_t>
+auto make_symbol_group_lut(
+  std::array<std::vector<symbol_t>, NUM_SYMBOL_GROUPS> const& symbol_strings,
+  pre_map_op_t pre_map_op)
+{
+  using lookup_table_t = SingleSymbolSmemLUT<symbol_t, pre_map_op_t>;
+  return lookup_table_t::InitDeviceSymbolGroupIdLut(symbol_strings, pre_map_op);
+}
+
+/**
+ * @brief Creates a symbol group lookup table of type `SingleSymbolSmemLUT` that uses a two-staged
+ * lookup approach. @p pre_map_op is a function object invoked with `(lut, symbol)` that must return
+ * the symbol group id for the given `symbol`. `lut` is an instance of the lookup table
+ * and `symbol` is a symbol from the input tape. Usually, @p pre_map_op first maps a symbol from
+ * the input tape to an integral that is convertible to `symbol_t`. In a second stage, @p pre_map_op
+ * uses `lut`'s `lookup(mapped_symbol)` that maps that integral to the symbol group id.
+ *
+ * @tparam symbol_t The type returned by @p pre_map_op must be assignable to `char`
+ * @tparam NUM_SYMBOL_GROUPS The number of symbol groups, excluding the catchall symbol group (aka
+ * "other" symbol group)
+ * @tparam pre_map_op_t A unary function object type that returns the symbol group id for a symbol
+ * @param symbol_strings An array of strings, where all the characters in the i-th string are mapped
+ * to the i-th symbol group
+ * @param pre_map_op A unary function object type that returns the symbol group id for a symbol
+ * @return A symbol group lookup table
+ */
+template <std::size_t NUM_SYMBOL_GROUPS, typename pre_map_op_t>
+auto make_symbol_group_lut(std::array<std::string, NUM_SYMBOL_GROUPS> const& symbol_strings,
+                           pre_map_op_t pre_map_op)
+{
+  using symbol_t       = char;
+  using lookup_table_t = SingleSymbolSmemLUT<symbol_t, pre_map_op_t>;
+  return lookup_table_t::InitDeviceSymbolGroupIdLut(symbol_strings, pre_map_op);
+}
+
+/**
+ * @brief Creates a symbol group lookup table that maps a symbol to a symbol group id, requiring the
+ * symbol type from the input tape to be assignable to `symbol_t` and `symbol_t` to be of integral
+ * type.
+ *
+ * @tparam symbol_t The input tape's symbol type must be assignable to this type
+ * @tparam NUM_SYMBOL_GROUPS The number of symbol groups, excluding the catchall symbol group (aka
+ * "other" symbol group)
+ * @param symbol_strings An array of vectors, where all the symbols in the i-th vector are mapped to
+ * the i-th symbol group
+ * @return A symbol group lookup table
+ */
+template <typename symbol_t, std::size_t NUM_SYMBOL_GROUPS>
+auto make_symbol_group_lut(
+  std::array<std::vector<symbol_t>, NUM_SYMBOL_GROUPS> const& symbol_strings)
+{
+  return make_symbol_group_lut(symbol_strings, IdentityOp{});
+}
+
+/**
+ * @brief Creates a symbol group lookup table that maps a symbol to a symbol group id, requiring the
+ * symbol type from the input tape to be assignable to `symbol_t` and `symbol_t` to be of integral
+ * type.
+ *
+ * @tparam symbol_t The input tape's symbol type must be assignable to this type
+ * @tparam NUM_SYMBOL_GROUPS The number of symbol groups, excluding the catchall symbol group (aka
+ * "other" symbol group)
+ * @param symbol_strings An array of strings, where all the characters in the i-th string are mapped
+ * to the i-th symbol group
+ * @return A symbol group lookup table
+ */
+template <std::size_t NUM_SYMBOL_GROUPS>
+auto make_symbol_group_lut(std::array<std::string, NUM_SYMBOL_GROUPS> const& symbol_strings)
+{
+  return make_symbol_group_lut(symbol_strings, IdentityOp{});
+}
+
+/**
+ * @brief Lookup table mapping (old_state, symbol_group_id) transitions to a new target state. The
+ * class uses shared memory for the lookups.
+ *
+ * @tparam MAX_NUM_SYMBOLS The maximum number of symbols being output by a single state transition
+ * @tparam MAX_NUM_STATES The maximum number of states that this lookup table shall support
+ */
+template <int32_t MAX_NUM_SYMBOLS, int32_t MAX_NUM_STATES>
+class TransitionTable {
+ private:
+  // Type used
+  using ItemT = char;
+
+  struct _TempStorage {
+    ItemT transitions[MAX_NUM_STATES * MAX_NUM_SYMBOLS];
+  };
+
+ public:
+  static constexpr int32_t NUM_STATES = MAX_NUM_STATES;
+  using TempStorage                   = cub::Uninitialized<_TempStorage>;
+
+  struct KernelParameter {
+    using LookupTableT = TransitionTable<MAX_NUM_SYMBOLS, MAX_NUM_STATES>;
+
+    ItemT transitions[MAX_NUM_STATES * MAX_NUM_SYMBOLS];
+  };
+
+  template <typename StateIdT>
+  static KernelParameter InitDeviceTransitionTable(
+    std::array<std::array<StateIdT, MAX_NUM_SYMBOLS>, MAX_NUM_STATES> const& translation_table)
+  {
+    KernelParameter init_data{};
+    // translation_table[state][symbol] -> new state
+    for (std::size_t state = 0; state < translation_table.size(); ++state) {
+      for (std::size_t symbol = 0; symbol < translation_table[state].size(); ++symbol) {
+        CUDF_EXPECTS(
+          static_cast<int64_t>(translation_table[state][symbol]) <=
+            std::numeric_limits<ItemT>::max(),
+          "Target state index value exceeds value representable by the transition table's type");
+        init_data.transitions[symbol * MAX_NUM_STATES + state] =
+          static_cast<ItemT>(translation_table[state][symbol]);
+      }
+    }
+
+    return init_data;
+  }
+
+  constexpr CUDF_HOST_DEVICE TransitionTable(KernelParameter const& kernel_param,
+                                             TempStorage& temp_storage)
+    : temp_storage(temp_storage.Alias())
+  {
+#if CUB_PTX_ARCH > 0
+    for (int i = threadIdx.x; i < MAX_NUM_STATES * MAX_NUM_SYMBOLS; i += blockDim.x) {
+      this->temp_storage.transitions[i] = kernel_param.transitions[i];
+    }
+    __syncthreads();
+#else
+    std::copy_n(
+      kernel_param.transitions, MAX_NUM_STATES * MAX_NUM_SYMBOLS, this->temp_storage.transitions);
+#endif
+  }
+
+  /**
+   * @brief Returns a random-access iterator to lookup all the state transitions for one specific
+   * symbol from an arbitrary old_state, i.e., it[old_state] -> new_state.
+   *
+   * @param state_id The DFA's current state index from which we'll transition
+   * @param match_id The symbol group id of the symbol that we just read in
+   * @return
+   */
+  template <typename StateIndexT, typename SymbolIndexT>
+  constexpr CUDF_HOST_DEVICE int32_t operator()(StateIndexT const state_id,
+                                                SymbolIndexT const match_id) const
+  {
+    return temp_storage.transitions[match_id * MAX_NUM_STATES + state_id];
+  }
+
+ private:
+  _TempStorage& temp_storage;
+
+  __device__ __forceinline__ _TempStorage& PrivateStorage()
+  {
+    __shared__ _TempStorage private_storage;
+
+    return private_storage;
+  }
+};
+
+/**
+ * @brief Creates a transition table of type `TransitionTable` that maps `(state_id, match_id)`
+ * pairs to the new target state for the given `(state_id, match_id)`-combination.
+ *
+ * @tparam StateIdT An integral type used to represent state indexes
+ * @tparam MAX_NUM_SYMBOLS The maximum number of symbols being output by a single state transition
+ * @tparam MAX_NUM_STATES The maximum number of states that this lookup table shall support
+ * @param transition_table The transition table
+ * @return A transition table of type `TransitionTable`
+ */
+template <typename StateIdT, std::size_t MAX_NUM_SYMBOLS, std::size_t MAX_NUM_STATES>
+auto make_transition_table(
+  std::array<std::array<StateIdT, MAX_NUM_SYMBOLS>, MAX_NUM_STATES> const& transition_table)
+{
+  using transition_table_t = TransitionTable<MAX_NUM_SYMBOLS, MAX_NUM_STATES>;
+  return transition_table_t::InitDeviceTransitionTable(transition_table);
+}
+
+/**
+ * @brief Compile-time reflection to check if `OpT` type has the `TempStorage` and
+ * `KernelParameter` type members.
+ */
+template <typename OpT, typename = void>
+struct is_complex_op : std::false_type {};
+
+template <typename OpT>
+struct is_complex_op<OpT, std::void_t<typename OpT::TempStorage, typename OpT::KernelParameter>>
+  : std::true_type {};
+
+/**
+ * @brief The device view that is passed to the finite-state transducer algorithm. Each of the
+ * lookup tables can either be a simple function object that defines the `operator()` required for
+ * respective lookup table or a complex class.
+ *
+ * @tparam SymbolGroupIdLookupT
+ * @tparam TransitionTableT
+ * @tparam TranslationTableT
+ * @tparam NUM_STATES
+ */
+template <typename SymbolGroupIdLookupT,
+          typename TransitionTableT,
+          typename TranslationTableT,
+          int32_t NUM_STATES>
+class dfa_device_view {
+ private:
+  // Complex symbol group lookup operators need to declare a `TempStorage` and `KernelParameter`
+  // type member that is passed during device-side initialization.
+  using sgid_lut_init_t = std::conditional_t<is_complex_op<SymbolGroupIdLookupT>::value,
+                                             typename SymbolGroupIdLookupT::KernelParameter,
+                                             SymbolGroupIdLookupT>;
+
+  // Complex transition table lookup operators need to declare a `TempStorage` and
+  // `KernelParameter` type member that is passed during device-side initialization.
+  using transition_table_init_t = std::conditional_t<is_complex_op<TransitionTableT>::value,
+                                                     typename TransitionTableT::KernelParameter,
+                                                     TransitionTableT>;
+
+  // Complex translation table lookup operators need to declare a `TempStorage` and
+  // `KernelParameter` type member that is passed during device-side initialization.
+  using translation_table_init_t = std::conditional_t<is_complex_op<TranslationTableT>::value,
+                                                      typename TranslationTableT::KernelParameter,
+                                                      TranslationTableT>;
+
+ public:
+  // The maximum number of states supported by this DFA instance
+  // This is a value queried by the DFA simulation algorithm
+  static constexpr int32_t MAX_NUM_STATES = NUM_STATES;
+
+  using SymbolGroupStorageT      = std::conditional_t<is_complex_op<SymbolGroupIdLookupT>::value,
+                                                 typename SymbolGroupIdLookupT::TempStorage,
+                                                 typename cub::NullType>;
+  using TransitionTableStorageT  = std::conditional_t<is_complex_op<TransitionTableT>::value,
+                                                     typename TransitionTableT::TempStorage,
+                                                     typename cub::NullType>;
+  using TranslationTableStorageT = std::conditional_t<is_complex_op<TranslationTableT>::value,
+                                                      typename TranslationTableT::TempStorage,
+                                                      typename cub::NullType>;
+
+  __device__ auto InitSymbolGroupLUT(SymbolGroupStorageT& temp_storage)
+  {
+    return SymbolGroupIdLookupT(*d_sgid_lut_init, temp_storage);
+  }
+
+  __device__ auto InitTransitionTable(TransitionTableStorageT& temp_storage)
+  {
+    return TransitionTableT(*d_transition_table_init, temp_storage);
+  }
+
+  __device__ auto InitTranslationTable(TranslationTableStorageT& temp_storage)
+  {
+    return TranslationTableT(*d_translation_table_init, temp_storage);
+  }
+
+  dfa_device_view(sgid_lut_init_t const* d_sgid_lut_init,
+                  transition_table_init_t const* d_transition_table_init,
+                  translation_table_init_t const* d_translation_table_init)
+    : d_sgid_lut_init(d_sgid_lut_init),
+      d_transition_table_init(d_transition_table_init),
+      d_translation_table_init(d_translation_table_init)
+  {
+  }
+
+ private:
+  sgid_lut_init_t const* d_sgid_lut_init;
+  transition_table_init_t const* d_transition_table_init;
+  translation_table_init_t const* d_translation_table_init;
+};
+
+/**
+ * @brief Lookup table mapping (old_state, symbol_group_id) transitions to a sequence of symbols
+ * that the finite-state transducer is supposed to output for each transition. The class uses
+ * shared memory for the lookups.
+ *
+ * @tparam OutSymbolT The symbol type being output
+ * @tparam OutSymbolOffsetT Type sufficiently large to index into the lookup table of output
+ * symbols
+ * @tparam MAX_NUM_SYMBOLS The maximum number of symbols being output by a single state transition
+ * @tparam MAX_NUM_STATES The maximum number of states that this lookup table shall support
+ * @tparam MAX_TABLE_SIZE The maximum number of items in the lookup table of output symbols
+ * be used.
+ */
+template <typename OutSymbolT,
+          typename OutSymbolOffsetT,
+          int32_t MAX_NUM_SYMBOLS,
+          int32_t MAX_NUM_STATES,
+          int32_t MAX_TABLE_SIZE = (MAX_NUM_SYMBOLS * MAX_NUM_STATES)>
+class TransducerLookupTable {
+ private:
+  struct _TempStorage {
+    OutSymbolOffsetT out_offset[MAX_NUM_STATES * MAX_NUM_SYMBOLS + 1];
+    OutSymbolT out_symbols[MAX_TABLE_SIZE];
+  };
+
+ public:
+  using TempStorage = cub::Uninitialized<_TempStorage>;
+
+  struct KernelParameter {
+    using LookupTableT = TransducerLookupTable<OutSymbolT,
+                                               OutSymbolOffsetT,
+                                               MAX_NUM_SYMBOLS,
+                                               MAX_NUM_STATES,
+                                               MAX_TABLE_SIZE>;
+
+    OutSymbolOffsetT d_out_offsets[MAX_NUM_STATES * MAX_NUM_SYMBOLS + 1];
+    OutSymbolT d_out_symbols[MAX_TABLE_SIZE];
+  };
+
+  /**
+   * @brief Initializes the lookup table, primarily to be invoked from within device code but also
+   * provides host-side implementation for verification.
+   * @note Synchronizes the thread block, if called from device, and, hence, requires all threads
+   * of the thread block to call the constructor
+   */
+  static KernelParameter InitDeviceTranslationTable(
+    std::array<std::array<std::vector<OutSymbolT>, MAX_NUM_SYMBOLS>, MAX_NUM_STATES> const&
+      translation_table)
+  {
+    KernelParameter init_data;
+    std::vector<OutSymbolT> out_symbols;
+    out_symbols.reserve(MAX_TABLE_SIZE);
+    std::vector<OutSymbolOffsetT> out_symbol_offsets;
+    out_symbol_offsets.reserve(MAX_NUM_STATES * MAX_NUM_SYMBOLS + 1);
+    out_symbol_offsets.push_back(0);
+
+    // Iterate over the states in the transition table
+    for (auto const& state_trans : translation_table) {
+      uint32_t num_added = 0;
+      // Iterate over the symbols in the transition table
+      for (auto const& symbol_out : state_trans) {
+        // Insert the output symbols for this specific (state, symbol) transition
+        out_symbols.insert(std::end(out_symbols), std::begin(symbol_out), std::end(symbol_out));
+        out_symbol_offsets.push_back(out_symbols.size());
+        num_added++;
+      }
+
+      // Copy the last offset for all symbols (to guarantee a proper lookup for omitted symbols of
+      // this state)
+      if (MAX_NUM_SYMBOLS > num_added) {
+        int32_t count = MAX_NUM_SYMBOLS - num_added;
+        auto begin_it = std::prev(std::end(out_symbol_offsets));
+        std::fill_n(begin_it, count, out_symbol_offsets[0]);
+      }
+    }
+
+    // Check whether runtime-provided table size exceeds the compile-time given max. table size
+    CUDF_EXPECTS(out_symbols.size() <= MAX_TABLE_SIZE, "Unsupported translation table");
+
+    // Prepare host-side data to be copied and passed to the device
+    std::copy(
+      std::cbegin(out_symbol_offsets), std::cend(out_symbol_offsets), init_data.d_out_offsets);
+    std::copy(std::cbegin(out_symbols), std::cend(out_symbols), init_data.d_out_symbols);
+
+    return init_data;
+  }
+
+ private:
+  _TempStorage& temp_storage;
+
+  __device__ __forceinline__ _TempStorage& PrivateStorage()
+  {
+    __shared__ _TempStorage private_storage;
+    return private_storage;
+  }
+
+ public:
+  /**
+   * @brief Initializes the lookup table, primarily to be invoked from within device code but also
+   * provides host-side implementation for verification.
+   * @note Synchronizes the thread block, if called from device, and, hence, requires all threads
+   * of the thread block to call the constructor
+   */
+  CUDF_HOST_DEVICE TransducerLookupTable(KernelParameter const& kernel_param,
+                                         TempStorage& temp_storage)
+    : temp_storage(temp_storage.Alias())
+  {
+    constexpr uint32_t num_offsets = MAX_NUM_STATES * MAX_NUM_SYMBOLS + 1;
+#if CUB_PTX_ARCH > 0
+    for (int i = threadIdx.x; i < num_offsets; i += blockDim.x) {
+      this->temp_storage.out_offset[i] = kernel_param.d_out_offsets[i];
+    }
+    // Make sure all threads in the block can read out_symbol_offsets[num_offsets - 1] from shared
+    // memory
+    __syncthreads();
+    for (int i = threadIdx.x; i < this->temp_storage.out_offset[num_offsets - 1]; i += blockDim.x) {
+      this->temp_storage.out_symbols[i] = kernel_param.d_out_symbols[i];
+    }
+    __syncthreads();
+#else
+    std::copy_n(kernel_param.d_out_offsets, num_offsets, this->temp_storage.out_symbol_offsets);
+    std::copy_n(kernel_param.d_out_symbols,
+                this->temp_storage.out_symbol_offsets,
+                this->temp_storage.out_symbols);
+#endif
+  }
+
+  template <typename StateIndexT, typename SymbolIndexT, typename RelativeOffsetT, typename SymbolT>
+  constexpr CUDF_HOST_DEVICE auto operator()(StateIndexT const state_id,
+                                             SymbolIndexT const match_id,
+                                             RelativeOffsetT const relative_offset,
+                                             SymbolT const /*read_symbol*/) const
+  {
+    auto offset = temp_storage.out_offset[state_id * MAX_NUM_SYMBOLS + match_id] + relative_offset;
+    return temp_storage.out_symbols[offset];
+  }
+
+  template <typename StateIndexT, typename SymbolIndexT, typename SymbolT>
+  constexpr CUDF_HOST_DEVICE OutSymbolOffsetT operator()(StateIndexT const state_id,
+                                                         SymbolIndexT const match_id,
+                                                         SymbolT const /*read_symbol*/) const
+  {
+    return temp_storage.out_offset[state_id * MAX_NUM_SYMBOLS + match_id + 1] -
+           temp_storage.out_offset[state_id * MAX_NUM_SYMBOLS + match_id];
+  }
+};
+
+/**
+ * @brief Creates a translation table that maps (old_state, symbol_group_id) transitions to a
+ * sequence of symbols that the finite-state transducer is supposed to output for each transition.
+ *
+ * @tparam MAX_TABLE_SIZE The maximum number of items in the lookup table of output symbols
+ * be used
+ * @tparam OutSymbolT The symbol type being output
+ * @tparam MAX_NUM_SYMBOLS The maximum number of symbols being output by a single state transition
+ * @tparam MAX_NUM_STATES The maximum number of states that this lookup table shall support
+ * @param translation_table The translation table
+ * @return A translation table of type `TransducerLookupTable`.
+ */
+template <std::size_t MAX_TABLE_SIZE,
+          typename OutSymbolT,
+          std::size_t MAX_NUM_SYMBOLS,
+          std::size_t MAX_NUM_STATES>
+auto make_translation_table(std::array<std::array<std::vector<OutSymbolT>, MAX_NUM_SYMBOLS>,
+                                       MAX_NUM_STATES> const& translation_table)
+{
+  using OutSymbolOffsetT    = int32_t;
+  using translation_table_t = TransducerLookupTable<OutSymbolT,
+                                                    OutSymbolOffsetT,
+                                                    MAX_NUM_SYMBOLS,
+                                                    MAX_NUM_STATES,
+                                                    MAX_TABLE_SIZE>;
+  return translation_table_t::InitDeviceTranslationTable(translation_table);
+}
+
+template <typename TranslationOpT>
+class TranslationOp {
+ private:
+  struct _TempStorage {};
+
+ public:
+  using TempStorage = cub::Uninitialized<_TempStorage>;
+
+  struct KernelParameter {
+    using LookupTableT = TranslationOp<TranslationOpT>;
+    TranslationOpT translation_op;
+  };
+
+  /**
+   * @brief Initializes the lookup table, primarily to be invoked from within device code but also
+   * provides host-side implementation for verification.
+   * @note Synchronizes the thread block, if called from device, and, hence, requires all threads
+   * of the thread block to call the constructor
+   */
+  static KernelParameter InitDeviceTranslationTable(TranslationOpT translation_op)
+  {
+    return KernelParameter{translation_op};
+  }
+
+ private:
+  _TempStorage& temp_storage;
+  TranslationOpT translation_op;
+
+  __device__ __forceinline__ _TempStorage& PrivateStorage()
+  {
+    __shared__ _TempStorage private_storage;
+    return private_storage;
+  }
+
+ public:
+  CUDF_HOST_DEVICE TranslationOp(KernelParameter const& kernel_param, TempStorage& temp_storage)
+    : temp_storage(temp_storage.Alias()), translation_op(kernel_param.translation_op)
+  {
+  }
+
+  template <typename StateIndexT, typename SymbolIndexT, typename RelativeOffsetT, typename SymbolT>
+  constexpr CUDF_HOST_DEVICE auto operator()(StateIndexT const state_id,
+                                             SymbolIndexT const match_id,
+                                             RelativeOffsetT const relative_offset,
+                                             SymbolT const read_symbol) const
+  {
+    return translation_op(*this, state_id, match_id, relative_offset, read_symbol);
+  }
+
+  template <typename StateIndexT, typename SymbolIndexT, typename SymbolT>
+  constexpr CUDF_HOST_DEVICE auto operator()(StateIndexT const state_id,
+                                             SymbolIndexT const match_id,
+                                             SymbolT const read_symbol) const
+  {
+    return translation_op(*this, state_id, match_id, read_symbol);
+  }
+};
+
+/**
+ * @brief Creates a simple translation table that uses a simple function object to retrieve the
+ *
+ * @tparam FunctorT A function object type that must implement two signatures: (1) with `(state_id,
+ * match_id, read_symbol)` and (2) with `(state_id, match_id, relative_offset, read_symbol)`
+ * @param map_op A function object that must implement two signatures: (1) with `(state_id,
+ * match_id, read_symbol)` and (2) with `(state_id, match_id, relative_offset, read_symbol)`.
+ * Invocations of the first signature, (1), must return the number of symbols that are emitted for
+ * the given transition. The second signature, (2), must return the i-th symbol to be emitted for
+ * that transition, where `i` corresponds to `relative_offse`
+ * @return A translation table of type `TranslationO`
+ */
+template <typename FunctorT>
+auto make_translation_functor(FunctorT map_op)
+{
+  return TranslationOp<FunctorT>::InitDeviceTranslationTable(map_op);
+}
+
+/**
+ * @brief Helper class to facilitate the specification and instantiation of a DFA (i.e., the
+ * transition table and its number of states, the mapping of symbols to symbol groups, and the
+ * translation table that specifies which state transitions cause which output to be written).
+ *
+ * @tparam OutSymbolT The symbol type being output by the finite-state transducer
+ * @tparam NUM_SYMBOLS The number of symbol groups amongst which to differentiate including the
+ * wildcard symbol group (one dimension of the transition table)
+ * @tparam NUM_STATES The number of states defined by the DFA (the other dimension of the
+ * transition table)
+ */
+template <typename SymbolGroupIdInitT,
+          typename TransitionTableInitT,
+          typename TranslationTableInitT>
+class Dfa {
+  static constexpr int32_t single_item = 1;
+
+ public:
+  auto get_device_view()
+  {
+    return dfa_device_view<typename SymbolGroupIdInitT::LookupTableT,
+                           typename TransitionTableInitT::LookupTableT,
+                           typename TranslationTableInitT::LookupTableT,
+                           TransitionTableInitT::LookupTableT::NUM_STATES>{
+      &init_data.d_begin()->sgid_lut_init,
+      &init_data.d_begin()->transition_table_init,
+      &init_data.d_begin()->translation_table_init};
+  }
+
+  Dfa(SymbolGroupIdInitT const& sgid_lut_init,
+      TransitionTableInitT const& transition_table_init,
+      TranslationTableInitT const& translation_table_init,
+      rmm::cuda_stream_view stream)
+    : init_data{single_item, stream}
+  {
+    *init_data.host_ptr() = {sgid_lut_init, transition_table_init, translation_table_init};
+    init_data.host_to_device_async(stream);
+  }
+
+  /**
+   * @brief Dispatches the finite-state transducer algorithm to the GPU.
+   *
+   * @tparam SymbolT The atomic symbol type from the input tape
+   * @tparam TransducedOutItT Random-access output iterator to which the transduced output will be
+   * written
+   * @tparam TransducedIndexOutItT Random-access output iterator type to which the input symbols'
+   * indexes are written.
+   * @tparam TransducedCountOutItT A single-item output iterator type to which the total number of
+   * output symbols is written
+   * @tparam OffsetT A type large enough to index into either of both: (a) the input symbols and
+   * (b) the output symbols
+   * @param d_chars Pointer to the input string of symbols
+   * @param num_chars The total number of input symbols to process
+   * @param d_out_it Random-access output iterator to which the transduced output is
+   * written
+   * @param d_out_idx_it Random-access output iterator to which, the index i is written
+   * iff the i-th input symbol caused some output to be written
+   * @param d_num_transduced_out_it A single-item output iterator type to which the total number
+   * of output symbols is written
+   * @param seed_state The DFA's starting state. For streaming DFAs this corresponds to the
+   * "end-state" of the previous invocation of the algorithm.
+   * @param stream CUDA stream to launch kernels within. Default is the null-stream.
+   */
+  template <typename SymbolItT,
+            typename TransducedOutItT,
+            typename TransducedIndexOutItT,
+            typename TransducedCountOutItT,
+            typename OffsetT>
+  void Transduce(SymbolItT d_chars_it,
+                 OffsetT num_chars,
+                 TransducedOutItT d_out_it,
+                 TransducedIndexOutItT d_out_idx_it,
+                 TransducedCountOutItT d_num_transduced_out_it,
+                 uint32_t const seed_state,
+                 rmm::cuda_stream_view stream)
+  {
+    std::size_t temp_storage_bytes = 0;
+    rmm::device_buffer temp_storage{};
+    DeviceTransduce(nullptr,
+                    temp_storage_bytes,
+                    this->get_device_view(),
+                    d_chars_it,
+                    num_chars,
+                    d_out_it,
+                    d_out_idx_it,
+                    d_num_transduced_out_it,
+                    seed_state,
+                    stream);
+
+    if (temp_storage.size() < temp_storage_bytes) {
+      temp_storage.resize(temp_storage_bytes, stream);
+    }
+
+    DeviceTransduce(temp_storage.data(),
+                    temp_storage_bytes,
+                    this->get_device_view(),
+                    d_chars_it,
+                    num_chars,
+                    d_out_it,
+                    d_out_idx_it,
+                    d_num_transduced_out_it,
+                    seed_state,
+                    stream);
+  }
+
+ private:
+  struct host_device_data {
+    SymbolGroupIdInitT sgid_lut_init;
+    TransitionTableInitT transition_table_init;
+    TranslationTableInitT translation_table_init;
+  };
+  cudf::detail::hostdevice_vector<host_device_data> init_data{};
+};
+
+/**
+ * @brief Creates a deterministic finite automaton (DFA) as specified by the triple of (symbol
+ * group, transition, translation)-lookup tables to be used with the finite-state transducer
+ * algorithm.
+ *
+ * @param sgid_lut_init Object used to initialize the symbol group lookup table
+ * @param transition_table_init Object used to initialize the transition table
+ * @param translation_table_init Object used to initialize the translation table
+ * @param stream The stream used to allocate and initialize device-side memory that is used to
+ * initialize the lookup tables
+ * @return A DFA of type `Dfa`.
+ */
+template <typename SymbolGroupIdInitT,
+          typename TransitionTableInitT,
+          typename TranslationTableInitT>
+auto make_fst(SymbolGroupIdInitT const& sgid_lut_init,
+              TransitionTableInitT const& transition_table_init,
+              TranslationTableInitT const& translation_table_init,
+              rmm::cuda_stream_view stream)
+{
+  return Dfa<SymbolGroupIdInitT, TransitionTableInitT, TranslationTableInitT>(
+    sgid_lut_init, transition_table_init, translation_table_init, stream);
+}
+
+}  // namespace cudf::io::fst::detail
diff --git a/cpp/src/io/functions.cpp b/cpp/src/io/functions.cpp
new file mode 100644
index 0000000..392a785
--- /dev/null
+++ b/cpp/src/io/functions.cpp
@@ -0,0 +1,892 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/orc/orc.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/io/avro.hpp>
+#include <cudf/io/csv.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/detail/avro.hpp>
+#include <cudf/io/detail/csv.hpp>
+#include <cudf/io/detail/json.hpp>
+#include <cudf/io/detail/orc.hpp>
+#include <cudf/io/detail/parquet.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/io/orc.hpp>
+#include <cudf/io/orc_metadata.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/io/parquet_metadata.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <algorithm>
+
+namespace cudf {
+namespace io {
+// Returns builder for csv_reader_options
+csv_reader_options_builder csv_reader_options::builder(source_info const& src)
+{
+  return csv_reader_options_builder{src};
+}
+
+// Returns builder for csv_writer_options
+csv_writer_options_builder csv_writer_options::builder(sink_info const& sink,
+                                                       table_view const& table)
+{
+  return csv_writer_options_builder{sink, table};
+}
+
+// Returns builder for orc_reader_options
+orc_reader_options_builder orc_reader_options::builder(source_info const& src)
+{
+  return orc_reader_options_builder{src};
+}
+
+// Returns builder for orc_writer_options
+orc_writer_options_builder orc_writer_options::builder(sink_info const& sink,
+                                                       table_view const& table)
+{
+  return orc_writer_options_builder{sink, table};
+}
+
+// Returns builder for chunked_orc_writer_options
+chunked_orc_writer_options_builder chunked_orc_writer_options::builder(sink_info const& sink)
+{
+  return chunked_orc_writer_options_builder{sink};
+}
+
+// Returns builder for avro_reader_options
+avro_reader_options_builder avro_reader_options::builder(source_info const& src)
+{
+  return avro_reader_options_builder(src);
+}
+
+// Returns builder for json_reader_options
+json_reader_options_builder json_reader_options::builder(source_info const& src)
+{
+  return json_reader_options_builder(src);
+}
+
+// Returns builder for orc_writer_options
+json_writer_options_builder json_writer_options::builder(sink_info const& sink,
+                                                         table_view const& table)
+{
+  return json_writer_options_builder{sink, table};
+}
+
+// Returns builder for parquet_reader_options
+parquet_reader_options_builder parquet_reader_options::builder(source_info const& src)
+{
+  return parquet_reader_options_builder{src};
+}
+
+// Returns builder for parquet_writer_options
+parquet_writer_options_builder parquet_writer_options::builder(sink_info const& sink,
+                                                               table_view const& table)
+{
+  return parquet_writer_options_builder{sink, table};
+}
+
+// Returns builder for parquet_writer_options
+parquet_writer_options_builder parquet_writer_options::builder()
+{
+  return parquet_writer_options_builder();
+}
+
+// Returns builder for chunked_parquet_writer_options
+chunked_parquet_writer_options_builder chunked_parquet_writer_options::builder(
+  sink_info const& sink)
+{
+  return chunked_parquet_writer_options_builder(sink);
+}
+
+namespace {
+
+std::vector<std::unique_ptr<cudf::io::datasource>> make_datasources(source_info const& info,
+                                                                    size_t range_offset = 0,
+                                                                    size_t range_size   = 0)
+{
+  switch (info.type()) {
+    case io_type::FILEPATH: {
+      auto sources = std::vector<std::unique_ptr<cudf::io::datasource>>();
+      for (auto const& filepath : info.filepaths()) {
+        sources.emplace_back(cudf::io::datasource::create(filepath, range_offset, range_size));
+      }
+      return sources;
+    }
+    case io_type::HOST_BUFFER: return cudf::io::datasource::create(info.host_buffers());
+    case io_type::DEVICE_BUFFER: return cudf::io::datasource::create(info.device_buffers());
+    case io_type::USER_IMPLEMENTED: return cudf::io::datasource::create(info.user_sources());
+    default: CUDF_FAIL("Unsupported source type");
+  }
+}
+
+std::vector<std::unique_ptr<data_sink>> make_datasinks(sink_info const& info)
+{
+  switch (info.type()) {
+    case io_type::FILEPATH: return cudf::io::data_sink::create(info.filepaths());
+    case io_type::HOST_BUFFER: return cudf::io::data_sink::create(info.buffers());
+    case io_type::VOID: {
+      std::vector<std::unique_ptr<data_sink>> sinks;
+      for (size_t i = 0; i < info.num_sinks(); ++i) {
+        sinks.push_back(cudf::io::data_sink::create());
+      }
+      return sinks;
+    }
+    case io_type::USER_IMPLEMENTED: return cudf::io::data_sink::create(info.user_sinks());
+    default: CUDF_FAIL("Unsupported sink type");
+  }
+}
+
+}  // namespace
+
+table_with_metadata read_avro(avro_reader_options const& options,
+                              rmm::mr::device_memory_resource* mr)
+{
+  namespace avro = cudf::io::detail::avro;
+
+  CUDF_FUNC_RANGE();
+
+  auto datasources = make_datasources(options.get_source());
+
+  CUDF_EXPECTS(datasources.size() == 1, "Only a single source is currently supported.");
+
+  return avro::read_avro(std::move(datasources[0]), options, cudf::get_default_stream(), mr);
+}
+
+compression_type infer_compression_type(compression_type compression, source_info const& info)
+{
+  if (compression != compression_type::AUTO) { return compression; }
+
+  if (info.type() != io_type::FILEPATH) { return compression_type::NONE; }
+
+  auto filepath = info.filepaths()[0];
+
+  // Attempt to infer from the file extension
+  auto const pos = filepath.find_last_of('.');
+
+  if (pos == std::string::npos) { return {}; }
+
+  auto str_tolower = [](auto const& begin, auto const& end) {
+    std::string out;
+    std::transform(begin, end, std::back_inserter(out), ::tolower);
+    return out;
+  };
+
+  auto const ext = str_tolower(filepath.begin() + pos + 1, filepath.end());
+
+  if (ext == "gz") { return compression_type::GZIP; }
+  if (ext == "zip") { return compression_type::ZIP; }
+  if (ext == "bz2") { return compression_type::BZIP2; }
+  if (ext == "xz") { return compression_type::XZ; }
+
+  return compression_type::NONE;
+}
+
+table_with_metadata read_json(json_reader_options options, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  options.set_compression(infer_compression_type(options.get_compression(), options.get_source()));
+
+  auto datasources = make_datasources(options.get_source(),
+                                      options.get_byte_range_offset(),
+                                      options.get_byte_range_size_with_padding());
+
+  return json::detail::read_json(datasources, options, cudf::get_default_stream(), mr);
+}
+
+void write_json(json_writer_options const& options, rmm::mr::device_memory_resource* mr)
+{
+  auto sinks = make_datasinks(options.get_sink());
+  CUDF_EXPECTS(sinks.size() == 1, "Multiple sinks not supported for JSON writing");
+
+  return json::detail::write_json(  //
+    sinks[0].get(),
+    options.get_table(),
+    options,
+    cudf::get_default_stream(),
+    mr);
+}
+
+table_with_metadata read_csv(csv_reader_options options, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  options.set_compression(infer_compression_type(options.get_compression(), options.get_source()));
+
+  auto datasources = make_datasources(options.get_source(),
+                                      options.get_byte_range_offset(),
+                                      options.get_byte_range_size_with_padding());
+
+  CUDF_EXPECTS(datasources.size() == 1, "Only a single source is currently supported.");
+
+  return cudf::io::detail::csv::read_csv(  //
+    std::move(datasources[0]),
+    options,
+    cudf::get_default_stream(),
+    mr);
+}
+
+// Freeform API wraps the detail writer class API
+void write_csv(csv_writer_options const& options, rmm::mr::device_memory_resource* mr)
+{
+  using namespace cudf::io::detail;
+
+  auto sinks = make_datasinks(options.get_sink());
+  CUDF_EXPECTS(sinks.size() == 1, "Multiple sinks not supported for CSV writing");
+
+  return csv::write_csv(  //
+    sinks[0].get(),
+    options.get_table(),
+    options.get_names(),
+    options,
+    cudf::get_default_stream(),
+    mr);
+}
+
+namespace detail_orc = cudf::io::detail::orc;
+
+raw_orc_statistics read_raw_orc_statistics(source_info const& src_info)
+{
+  auto stream = cudf::get_default_stream();
+  // Get source to read statistics from
+  std::unique_ptr<datasource> source;
+  if (src_info.type() == io_type::FILEPATH) {
+    CUDF_EXPECTS(src_info.filepaths().size() == 1, "Only a single source is currently supported.");
+    source = cudf::io::datasource::create(src_info.filepaths()[0]);
+  } else if (src_info.type() == io_type::HOST_BUFFER) {
+    CUDF_EXPECTS(src_info.host_buffers().size() == 1,
+                 "Only a single source is currently supported.");
+    source = cudf::io::datasource::create(src_info.host_buffers()[0]);
+  } else if (src_info.type() == io_type::DEVICE_BUFFER) {
+    CUDF_EXPECTS(src_info.device_buffers().size() == 1,
+                 "Only a single source is currently supported.");
+    source = cudf::io::datasource::create(src_info.device_buffers()[0]);
+  } else if (src_info.type() == io_type::USER_IMPLEMENTED) {
+    CUDF_EXPECTS(src_info.user_sources().size() == 1,
+                 "Only a single source is currently supported.");
+    source = cudf::io::datasource::create(src_info.user_sources()[0]);
+  } else {
+    CUDF_FAIL("Unsupported source type");
+  }
+
+  orc::metadata metadata(source.get(), stream);
+
+  // Initialize statistics to return
+  raw_orc_statistics result;
+
+  // Get column names
+  for (auto i = 0; i < metadata.get_num_columns(); i++) {
+    result.column_names.push_back(metadata.column_name(i));
+  }
+
+  // Get file-level statistics, statistics of each column of file
+  for (auto const& stats : metadata.ff.statistics) {
+    result.file_stats.push_back(std::string(stats.cbegin(), stats.cend()));
+  }
+
+  // Get stripe-level statistics
+  for (auto const& stripes_stats : metadata.md.stripeStats) {
+    result.stripes_stats.emplace_back();
+    for (auto const& stats : stripes_stats.colStats) {
+      result.stripes_stats.back().push_back(std::string(stats.cbegin(), stats.cend()));
+    }
+  }
+
+  return result;
+}
+
+column_statistics::column_statistics(cudf::io::orc::column_statistics&& cs)
+{
+  number_of_values = cs.number_of_values;
+  has_null         = cs.has_null;
+  if (cs.int_stats) {
+    type_specific_stats = *cs.int_stats;
+  } else if (cs.double_stats) {
+    type_specific_stats = *cs.double_stats;
+  } else if (cs.string_stats) {
+    type_specific_stats = *cs.string_stats;
+  } else if (cs.bucket_stats) {
+    type_specific_stats = *cs.bucket_stats;
+  } else if (cs.decimal_stats) {
+    type_specific_stats = *cs.decimal_stats;
+  } else if (cs.date_stats) {
+    type_specific_stats = *cs.date_stats;
+  } else if (cs.binary_stats) {
+    type_specific_stats = *cs.binary_stats;
+  } else if (cs.timestamp_stats) {
+    type_specific_stats = *cs.timestamp_stats;
+  }
+}
+
+parsed_orc_statistics read_parsed_orc_statistics(source_info const& src_info)
+{
+  auto const raw_stats = read_raw_orc_statistics(src_info);
+
+  parsed_orc_statistics result;
+  result.column_names = raw_stats.column_names;
+
+  auto parse_column_statistics = [](auto const& raw_col_stats) {
+    orc::column_statistics stats_internal;
+    orc::ProtobufReader(reinterpret_cast<uint8_t const*>(raw_col_stats.c_str()),
+                        raw_col_stats.size())
+      .read(stats_internal);
+    return column_statistics(std::move(stats_internal));
+  };
+
+  std::transform(raw_stats.file_stats.cbegin(),
+                 raw_stats.file_stats.cend(),
+                 std::back_inserter(result.file_stats),
+                 parse_column_statistics);
+
+  for (auto const& raw_stripe_stats : raw_stats.stripes_stats) {
+    result.stripes_stats.emplace_back();
+    std::transform(raw_stripe_stats.cbegin(),
+                   raw_stripe_stats.cend(),
+                   std::back_inserter(result.stripes_stats.back()),
+                   parse_column_statistics);
+  }
+
+  return result;
+}
+namespace {
+orc_column_schema make_orc_column_schema(host_span<orc::SchemaType const> orc_schema,
+                                         uint32_t column_id,
+                                         std::string column_name)
+{
+  auto const& orc_col_schema = orc_schema[column_id];
+  std::vector<orc_column_schema> children;
+  children.reserve(orc_col_schema.subtypes.size());
+  std::transform(
+    orc_col_schema.subtypes.cbegin(),
+    orc_col_schema.subtypes.cend(),
+    cudf::detail::make_counting_transform_iterator(0,
+                                                   [&names = orc_col_schema.fieldNames](size_t i) {
+                                                     return i < names.size() ? names[i]
+                                                                             : std::string{};
+                                                   }),
+    std::back_inserter(children),
+    [&](auto& type, auto name) { return make_orc_column_schema(orc_schema, type, name); });
+
+  return {std::move(column_name), orc_schema[column_id].kind, std::move(children)};
+}
+};  // namespace
+
+orc_metadata read_orc_metadata(source_info const& src_info)
+{
+  auto sources = make_datasources(src_info);
+
+  CUDF_EXPECTS(sources.size() == 1, "Only a single source is currently supported.");
+  auto const footer = orc::metadata(sources.front().get(), cudf::detail::default_stream_value).ff;
+
+  return {{make_orc_column_schema(footer.types, 0, "")},
+          static_cast<size_type>(footer.numberOfRows),
+          static_cast<size_type>(footer.stripes.size())};
+}
+
+/**
+ * @copydoc cudf::io::read_orc
+ */
+table_with_metadata read_orc(orc_reader_options const& options, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  auto datasources = make_datasources(options.get_source());
+  auto reader      = std::make_unique<detail_orc::reader>(
+    std::move(datasources), options, cudf::get_default_stream(), mr);
+
+  return reader->read(options);
+}
+
+/**
+ * @copydoc cudf::io::write_orc
+ */
+void write_orc(orc_writer_options const& options)
+{
+  namespace io_detail = cudf::io::detail;
+
+  CUDF_FUNC_RANGE();
+
+  auto sinks = make_datasinks(options.get_sink());
+  CUDF_EXPECTS(sinks.size() == 1, "Multiple sinks not supported for ORC writing");
+
+  auto writer = std::make_unique<detail_orc::writer>(
+    std::move(sinks[0]), options, io_detail::single_write_mode::YES, cudf::get_default_stream());
+
+  writer->write(options.get_table());
+}
+
+/**
+ * @copydoc cudf::io::orc_chunked_writer::orc_chunked_writer
+ */
+orc_chunked_writer::orc_chunked_writer(chunked_orc_writer_options const& options)
+{
+  namespace io_detail = cudf::io::detail;
+
+  auto sinks = make_datasinks(options.get_sink());
+  CUDF_EXPECTS(sinks.size() == 1, "Multiple sinks not supported for ORC writing");
+
+  writer = std::make_unique<detail_orc::writer>(
+    std::move(sinks[0]), options, io_detail::single_write_mode::NO, cudf::get_default_stream());
+}
+
+/**
+ * @copydoc cudf::io::orc_chunked_writer::write
+ */
+orc_chunked_writer& orc_chunked_writer::write(table_view const& table)
+{
+  CUDF_FUNC_RANGE();
+
+  writer->write(table);
+
+  return *this;
+}
+
+/**
+ * @copydoc cudf::io::orc_chunked_writer::close
+ */
+void orc_chunked_writer::close()
+{
+  CUDF_FUNC_RANGE();
+
+  writer->close();
+}
+
+using namespace cudf::io::detail::parquet;
+namespace detail_parquet = cudf::io::detail::parquet;
+
+table_with_metadata read_parquet(parquet_reader_options const& options,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  auto datasources = make_datasources(options.get_source());
+  auto reader      = std::make_unique<detail_parquet::reader>(
+    std::move(datasources), options, cudf::get_default_stream(), mr);
+
+  return reader->read(options);
+}
+
+parquet_metadata read_parquet_metadata(source_info const& src_info)
+{
+  CUDF_FUNC_RANGE();
+
+  auto datasources = make_datasources(src_info);
+  return detail_parquet::read_parquet_metadata(datasources);
+}
+
+/**
+ * @copydoc cudf::io::merge_row_group_metadata
+ */
+std::unique_ptr<std::vector<uint8_t>> merge_row_group_metadata(
+  std::vector<std::unique_ptr<std::vector<uint8_t>>> const& metadata_list)
+{
+  CUDF_FUNC_RANGE();
+  return detail_parquet::writer::merge_row_group_metadata(metadata_list);
+}
+
+table_input_metadata::table_input_metadata(table_view const& table)
+{
+  // Create a metadata hierarchy using `table`
+  std::function<column_in_metadata(column_view const&)> get_children = [&](column_view const& col) {
+    auto col_meta = column_in_metadata{};
+    std::transform(
+      col.child_begin(), col.child_end(), std::back_inserter(col_meta.children), get_children);
+    return col_meta;
+  };
+
+  std::transform(
+    table.begin(), table.end(), std::back_inserter(this->column_metadata), get_children);
+}
+
+table_input_metadata::table_input_metadata(table_metadata const& metadata)
+{
+  auto const& names = metadata.schema_info;
+
+  // Create a metadata hierarchy with naming and nullability using `table_metadata`
+  std::function<column_in_metadata(column_name_info const&)> process_node =
+    [&](column_name_info const& name) {
+      auto col_meta = column_in_metadata{name.name};
+      if (name.is_nullable.has_value()) { col_meta.set_nullability(name.is_nullable.value()); }
+      std::transform(name.children.begin(),
+                     name.children.end(),
+                     std::back_inserter(col_meta.children),
+                     process_node);
+      return col_meta;
+    };
+
+  std::transform(
+    names.begin(), names.end(), std::back_inserter(this->column_metadata), process_node);
+}
+
+/**
+ * @copydoc cudf::io::write_parquet
+ */
+std::unique_ptr<std::vector<uint8_t>> write_parquet(parquet_writer_options const& options)
+{
+  namespace io_detail = cudf::io::detail;
+
+  CUDF_FUNC_RANGE();
+
+  auto sinks  = make_datasinks(options.get_sink());
+  auto writer = std::make_unique<detail_parquet::writer>(
+    std::move(sinks), options, io_detail::single_write_mode::YES, cudf::get_default_stream());
+
+  writer->write(options.get_table(), options.get_partitions());
+
+  return writer->close(options.get_column_chunks_file_paths());
+}
+
+/**
+ * @copydoc cudf::io::chunked_parquet_reader::chunked_parquet_reader
+ */
+chunked_parquet_reader::chunked_parquet_reader(std::size_t chunk_read_limit,
+                                               parquet_reader_options const& options,
+                                               rmm::mr::device_memory_resource* mr)
+  : reader{std::make_unique<detail_parquet::chunked_reader>(chunk_read_limit,
+                                                            0,
+                                                            make_datasources(options.get_source()),
+                                                            options,
+                                                            cudf::get_default_stream(),
+                                                            mr)}
+{
+}
+
+/**
+ * @copydoc cudf::io::chunked_parquet_reader::chunked_parquet_reader
+ */
+chunked_parquet_reader::chunked_parquet_reader(std::size_t chunk_read_limit,
+                                               std::size_t pass_read_limit,
+                                               parquet_reader_options const& options,
+                                               rmm::mr::device_memory_resource* mr)
+  : reader{std::make_unique<detail_parquet::chunked_reader>(chunk_read_limit,
+                                                            pass_read_limit,
+                                                            make_datasources(options.get_source()),
+                                                            options,
+                                                            cudf::get_default_stream(),
+                                                            mr)}
+{
+}
+
+/**
+ * @copydoc cudf::io::chunked_parquet_reader::~chunked_parquet_reader
+ */
+chunked_parquet_reader::~chunked_parquet_reader() = default;
+
+/**
+ * @copydoc cudf::io::chunked_parquet_reader::has_next
+ */
+bool chunked_parquet_reader::has_next() const
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(reader != nullptr, "Reader has not been constructed properly.");
+  return reader->has_next();
+}
+
+/**
+ * @copydoc cudf::io::chunked_parquet_reader::read_chunk
+ */
+table_with_metadata chunked_parquet_reader::read_chunk() const
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(reader != nullptr, "Reader has not been constructed properly.");
+  return reader->read_chunk();
+}
+
+/**
+ * @copydoc cudf::io::parquet_chunked_writer::parquet_chunked_writer
+ */
+parquet_chunked_writer::parquet_chunked_writer(chunked_parquet_writer_options const& options)
+{
+  namespace io_detail = cudf::io::detail;
+
+  auto sinks = make_datasinks(options.get_sink());
+
+  writer = std::make_unique<detail_parquet::writer>(
+    std::move(sinks), options, io_detail::single_write_mode::NO, cudf::get_default_stream());
+}
+
+/**
+ * @copydoc cudf::io::parquet_chunked_writer::write
+ */
+parquet_chunked_writer& parquet_chunked_writer::write(table_view const& table,
+                                                      std::vector<partition_info> const& partitions)
+{
+  CUDF_FUNC_RANGE();
+
+  writer->write(table, partitions);
+
+  return *this;
+}
+
+/**
+ * @copydoc cudf::io::parquet_chunked_writer::close
+ */
+std::unique_ptr<std::vector<uint8_t>> parquet_chunked_writer::close(
+  std::vector<std::string> const& column_chunks_file_path)
+{
+  CUDF_FUNC_RANGE();
+  return writer->close(column_chunks_file_path);
+}
+
+void parquet_reader_options::set_row_groups(std::vector<std::vector<size_type>> row_groups)
+{
+  if ((!row_groups.empty()) and ((_skip_rows != 0) or _num_rows.has_value())) {
+    CUDF_FAIL("row_groups can't be set along with skip_rows and num_rows");
+  }
+
+  _row_groups = std::move(row_groups);
+}
+
+void parquet_reader_options::set_skip_rows(int64_t val)
+{
+  CUDF_EXPECTS(val >= 0, "skip_rows cannot be negative");
+  CUDF_EXPECTS(_row_groups.empty(), "skip_rows can't be set along with a non-empty row_groups");
+
+  _skip_rows = val;
+}
+
+void parquet_reader_options::set_num_rows(size_type val)
+{
+  CUDF_EXPECTS(val >= 0, "num_rows cannot be negative");
+  CUDF_EXPECTS(_row_groups.empty(), "num_rows can't be set along with a non-empty row_groups");
+
+  _num_rows = val;
+}
+
+void parquet_writer_options::set_partitions(std::vector<partition_info> partitions)
+{
+  CUDF_EXPECTS(partitions.size() == _sink.num_sinks(),
+               "Mismatch between number of sinks and number of partitions");
+  _partitions = std::move(partitions);
+}
+
+void parquet_writer_options::set_key_value_metadata(
+  std::vector<std::map<std::string, std::string>> metadata)
+{
+  CUDF_EXPECTS(metadata.size() == _sink.num_sinks(),
+               "Mismatch between number of sinks and number of metadata maps");
+  _user_data = std::move(metadata);
+}
+
+void parquet_writer_options::set_column_chunks_file_paths(std::vector<std::string> file_paths)
+{
+  CUDF_EXPECTS(file_paths.size() == _sink.num_sinks(),
+               "Mismatch between number of sinks and number of chunk paths to set");
+  _column_chunks_file_paths = std::move(file_paths);
+}
+
+void parquet_writer_options::set_row_group_size_bytes(size_t size_bytes)
+{
+  CUDF_EXPECTS(
+    size_bytes >= 1024,
+    "The maximum row group size cannot be smaller than the minimum page size, which is 1KB.");
+  _row_group_size_bytes = size_bytes;
+}
+
+void parquet_writer_options::set_row_group_size_rows(size_type size_rows)
+{
+  CUDF_EXPECTS(size_rows > 0, "The maximum row group row count must be a positive integer.");
+  _row_group_size_rows = size_rows;
+}
+
+void parquet_writer_options::set_max_page_size_bytes(size_t size_bytes)
+{
+  CUDF_EXPECTS(size_bytes >= 1024, "The maximum page size cannot be smaller than 1KB.");
+  CUDF_EXPECTS(size_bytes <= static_cast<size_t>(std::numeric_limits<int32_t>::max()),
+               "The maximum page size cannot exceed 2GB.");
+  _max_page_size_bytes = size_bytes;
+}
+
+void parquet_writer_options::set_max_page_size_rows(size_type size_rows)
+{
+  CUDF_EXPECTS(size_rows > 0, "The maximum page row count must be a positive integer.");
+  _max_page_size_rows = size_rows;
+}
+
+void parquet_writer_options::set_column_index_truncate_length(int32_t size_bytes)
+{
+  CUDF_EXPECTS(size_bytes >= 0, "Column index truncate length cannot be negative.");
+  _column_index_truncate_length = size_bytes;
+}
+
+void parquet_writer_options::set_dictionary_policy(dictionary_policy policy)
+{
+  _dictionary_policy = policy;
+}
+
+void parquet_writer_options::set_max_dictionary_size(size_t size_bytes)
+{
+  CUDF_EXPECTS(size_bytes <= static_cast<size_t>(std::numeric_limits<int32_t>::max()),
+               "The maximum dictionary size cannot exceed 2GB.");
+  _max_dictionary_size = size_bytes;
+}
+
+void parquet_writer_options::set_max_page_fragment_size(size_type size_rows)
+{
+  CUDF_EXPECTS(size_rows > 0, "Page fragment size must be a positive integer.");
+  _max_page_fragment_size = size_rows;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::partitions(
+  std::vector<partition_info> partitions)
+{
+  options.set_partitions(std::move(partitions));
+  return *this;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::key_value_metadata(
+  std::vector<std::map<std::string, std::string>> metadata)
+{
+  options.set_key_value_metadata(std::move(metadata));
+  return *this;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::column_chunks_file_paths(
+  std::vector<std::string> file_paths)
+{
+  options.set_column_chunks_file_paths(std::move(file_paths));
+  return *this;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::dictionary_policy(
+  enum dictionary_policy val)
+{
+  options.set_dictionary_policy(val);
+  return *this;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::max_dictionary_size(size_t val)
+{
+  options.set_max_dictionary_size(val);
+  return *this;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::max_page_fragment_size(
+  size_type val)
+{
+  options.set_max_page_fragment_size(val);
+  return *this;
+}
+
+parquet_writer_options_builder& parquet_writer_options_builder::write_v2_headers(bool enabled)
+{
+  options.enable_write_v2_headers(enabled);
+  return *this;
+}
+
+void chunked_parquet_writer_options::set_key_value_metadata(
+  std::vector<std::map<std::string, std::string>> metadata)
+{
+  CUDF_EXPECTS(metadata.size() == _sink.num_sinks(),
+               "Mismatch between number of sinks and number of metadata maps");
+  _user_data = std::move(metadata);
+}
+
+void chunked_parquet_writer_options::set_row_group_size_bytes(size_t size_bytes)
+{
+  CUDF_EXPECTS(
+    size_bytes >= 1024,
+    "The maximum row group size cannot be smaller than the minimum page size, which is 1KB.");
+  _row_group_size_bytes = size_bytes;
+}
+
+void chunked_parquet_writer_options::set_row_group_size_rows(size_type size_rows)
+{
+  CUDF_EXPECTS(size_rows > 0, "The maximum row group row count must be a positive integer.");
+  _row_group_size_rows = size_rows;
+}
+
+void chunked_parquet_writer_options::set_max_page_size_bytes(size_t size_bytes)
+{
+  CUDF_EXPECTS(size_bytes >= 1024, "The maximum page size cannot be smaller than 1KB.");
+  CUDF_EXPECTS(size_bytes <= static_cast<size_t>(std::numeric_limits<int32_t>::max()),
+               "The maximum page size cannot exceed 2GB.");
+  _max_page_size_bytes = size_bytes;
+}
+
+void chunked_parquet_writer_options::set_max_page_size_rows(size_type size_rows)
+{
+  CUDF_EXPECTS(size_rows > 0, "The maximum page row count must be a positive integer.");
+  _max_page_size_rows = size_rows;
+}
+
+void chunked_parquet_writer_options::set_column_index_truncate_length(int32_t size_bytes)
+{
+  CUDF_EXPECTS(size_bytes >= 0, "Column index truncate length cannot be negative.");
+  _column_index_truncate_length = size_bytes;
+}
+
+void chunked_parquet_writer_options::set_dictionary_policy(dictionary_policy policy)
+{
+  _dictionary_policy = policy;
+}
+
+void chunked_parquet_writer_options::set_max_dictionary_size(size_t size_bytes)
+{
+  CUDF_EXPECTS(size_bytes <= static_cast<size_t>(std::numeric_limits<int32_t>::max()),
+               "The maximum dictionary size cannot exceed 2GB.");
+  _max_dictionary_size = size_bytes;
+}
+
+void chunked_parquet_writer_options::set_max_page_fragment_size(size_type size_rows)
+{
+  CUDF_EXPECTS(size_rows > 0, "Page fragment size must be a positive integer.");
+  _max_page_fragment_size = size_rows;
+}
+
+chunked_parquet_writer_options_builder& chunked_parquet_writer_options_builder::key_value_metadata(
+  std::vector<std::map<std::string, std::string>> metadata)
+{
+  options.set_key_value_metadata(std::move(metadata));
+  return *this;
+}
+
+chunked_parquet_writer_options_builder& chunked_parquet_writer_options_builder::dictionary_policy(
+  enum dictionary_policy val)
+{
+  options.set_dictionary_policy(val);
+  return *this;
+}
+
+chunked_parquet_writer_options_builder& chunked_parquet_writer_options_builder::max_dictionary_size(
+  size_t val)
+{
+  options.set_max_dictionary_size(val);
+  return *this;
+}
+
+chunked_parquet_writer_options_builder& chunked_parquet_writer_options_builder::write_v2_headers(
+  bool enabled)
+{
+  options.enable_write_v2_headers(enabled);
+  return *this;
+}
+
+chunked_parquet_writer_options_builder&
+chunked_parquet_writer_options_builder::max_page_fragment_size(size_type val)
+{
+  options.set_max_page_fragment_size(val);
+  return *this;
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/json/byte_range_info.cu b/cpp/src/io/json/byte_range_info.cu
new file mode 100644
index 0000000..d359e91
--- /dev/null
+++ b/cpp/src/io/json/byte_range_info.cu
@@ -0,0 +1,36 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+#include <thrust/find.h>
+
+namespace cudf::io::json::detail {
+
+// Extract the first character position in the string.
+size_type find_first_delimiter(device_span<char const> d_data,
+                               char const delimiter,
+                               rmm::cuda_stream_view stream)
+{
+  auto const first_delimiter_position =
+    thrust::find(rmm::exec_policy(stream), d_data.begin(), d_data.end(), delimiter);
+  return first_delimiter_position != d_data.end() ? first_delimiter_position - d_data.begin() : -1;
+}
+
+}  // namespace cudf::io::json::detail
diff --git a/cpp/src/io/json/json_column.cu b/cpp/src/io/json/json_column.cu
new file mode 100644
index 0000000..5d7fb9d
--- /dev/null
+++ b/cpp/src/io/json/json_column.cu
@@ -0,0 +1,1037 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_json.hpp"
+#include <io/utilities/parsing_utils.cuh>
+#include <io/utilities/string_parsing.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/utilities/visitor_overload.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/gather.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+#include <thrust/unique.h>
+
+#include <cuda/atomic>
+
+#include <algorithm>
+#include <cstdint>
+
+namespace cudf::io::json::detail {
+
+// DEBUG prints
+auto to_cat = [](auto v) -> std::string {
+  switch (v) {
+    case NC_STRUCT: return " S";
+    case NC_LIST: return " L";
+    case NC_STR: return " \"";
+    case NC_VAL: return " V";
+    case NC_FN: return " F";
+    case NC_ERR: return "ER";
+    default: return "UN";
+  };
+};
+auto to_int    = [](auto v) { return std::to_string(static_cast<int>(v)); };
+auto print_vec = [](auto const& cpu, auto const name, auto converter) {
+  for (auto const& v : cpu)
+    printf("%3s,", converter(v).c_str());
+  std::cout << name << std::endl;
+};
+
+void print_tree(host_span<SymbolT const> input,
+                tree_meta_t const& d_gpu_tree,
+                rmm::cuda_stream_view stream)
+{
+  print_vec(cudf::detail::make_std_vector_async(d_gpu_tree.node_categories, stream),
+            "node_categories",
+            to_cat);
+  print_vec(cudf::detail::make_std_vector_async(d_gpu_tree.parent_node_ids, stream),
+            "parent_node_ids",
+            to_int);
+  print_vec(
+    cudf::detail::make_std_vector_async(d_gpu_tree.node_levels, stream), "node_levels", to_int);
+  auto node_range_begin = cudf::detail::make_std_vector_async(d_gpu_tree.node_range_begin, stream);
+  auto node_range_end   = cudf::detail::make_std_vector_async(d_gpu_tree.node_range_end, stream);
+  print_vec(node_range_begin, "node_range_begin", to_int);
+  print_vec(node_range_end, "node_range_end", to_int);
+  for (int i = 0; i < int(node_range_begin.size()); i++) {
+    printf("%3s ",
+           std::string(input.data() + node_range_begin[i], node_range_end[i] - node_range_begin[i])
+             .c_str());
+  }
+  printf(" (JSON)\n");
+}
+
+/**
+ * @brief Reduces node tree representation to column tree representation.
+ *
+ * @param tree Node tree representation of JSON string
+ * @param original_col_ids Column ids of nodes
+ * @param sorted_col_ids Sorted column ids of nodes
+ * @param ordered_node_ids Node ids of nodes sorted by column ids
+ * @param row_offsets Row offsets of nodes
+ * @param is_array_of_arrays Whether the tree is an array of arrays
+ * @param row_array_parent_col_id Column id of row array, if is_array_of_arrays is true
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return A tuple of column tree representation of JSON string, column ids of columns, and
+ * max row offsets of columns
+ */
+std::tuple<tree_meta_t, rmm::device_uvector<NodeIndexT>, rmm::device_uvector<size_type>>
+reduce_to_column_tree(tree_meta_t& tree,
+                      device_span<NodeIndexT> original_col_ids,
+                      device_span<NodeIndexT> sorted_col_ids,
+                      device_span<NodeIndexT> ordered_node_ids,
+                      device_span<size_type> row_offsets,
+                      bool is_array_of_arrays,
+                      NodeIndexT const row_array_parent_col_id,
+                      rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  // 1. column count for allocation
+  auto const num_columns =
+    thrust::unique_count(rmm::exec_policy(stream), sorted_col_ids.begin(), sorted_col_ids.end());
+
+  // 2. reduce_by_key {col_id}, {row_offset}, max.
+  rmm::device_uvector<NodeIndexT> unique_col_ids(num_columns, stream);
+  rmm::device_uvector<size_type> max_row_offsets(num_columns, stream);
+  auto ordered_row_offsets =
+    thrust::make_permutation_iterator(row_offsets.begin(), ordered_node_ids.begin());
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        sorted_col_ids.begin(),
+                        sorted_col_ids.end(),
+                        ordered_row_offsets,
+                        unique_col_ids.begin(),
+                        max_row_offsets.begin(),
+                        thrust::equal_to<size_type>(),
+                        thrust::maximum<size_type>());
+
+  // 3. reduce_by_key {col_id}, {node_categories} - custom opp (*+v=*, v+v=v, *+#=E)
+  rmm::device_uvector<NodeT> column_categories(num_columns, stream);
+  thrust::reduce_by_key(
+    rmm::exec_policy(stream),
+    sorted_col_ids.begin(),
+    sorted_col_ids.end(),
+    thrust::make_permutation_iterator(tree.node_categories.begin(), ordered_node_ids.begin()),
+    unique_col_ids.begin(),
+    column_categories.begin(),
+    thrust::equal_to<size_type>(),
+    [] __device__(NodeT type_a, NodeT type_b) -> NodeT {
+      auto is_a_leaf = (type_a == NC_VAL || type_a == NC_STR);
+      auto is_b_leaf = (type_b == NC_VAL || type_b == NC_STR);
+      // (v+v=v, *+*=*,  *+v=*, *+#=E, NESTED+VAL=NESTED)
+      // *+*=*, v+v=v
+      if (type_a == type_b) {
+        return type_a;
+      } else if (is_a_leaf) {
+        // *+v=*, N+V=N
+        // STRUCT/LIST + STR/VAL = STRUCT/LIST, STR/VAL + FN = ERR, STR/VAL + STR = STR
+        return type_b == NC_FN ? NC_ERR : (is_b_leaf ? NC_STR : type_b);
+      } else if (is_b_leaf) {
+        return type_a == NC_FN ? NC_ERR : (is_a_leaf ? NC_STR : type_a);
+      }
+      // *+#=E
+      return NC_ERR;
+    });
+
+  // 4. unique_copy parent_node_ids, ranges
+  rmm::device_uvector<TreeDepthT> column_levels(0, stream);  // not required
+  rmm::device_uvector<NodeIndexT> parent_col_ids(num_columns, stream);
+  rmm::device_uvector<SymbolOffsetT> col_range_begin(num_columns, stream);  // Field names
+  rmm::device_uvector<SymbolOffsetT> col_range_end(num_columns, stream);
+  rmm::device_uvector<size_type> unique_node_ids(num_columns, stream);
+  thrust::unique_by_key_copy(rmm::exec_policy(stream),
+                             sorted_col_ids.begin(),
+                             sorted_col_ids.end(),
+                             ordered_node_ids.begin(),
+                             thrust::make_discard_iterator(),
+                             unique_node_ids.begin());
+  thrust::copy_n(
+    rmm::exec_policy(stream),
+    thrust::make_zip_iterator(
+      thrust::make_permutation_iterator(tree.parent_node_ids.begin(), unique_node_ids.begin()),
+      thrust::make_permutation_iterator(tree.node_range_begin.begin(), unique_node_ids.begin()),
+      thrust::make_permutation_iterator(tree.node_range_end.begin(), unique_node_ids.begin())),
+    unique_node_ids.size(),
+    thrust::make_zip_iterator(
+      parent_col_ids.begin(), col_range_begin.begin(), col_range_end.begin()));
+
+  // convert parent_node_ids to parent_col_ids
+  thrust::transform(
+    rmm::exec_policy(stream),
+    parent_col_ids.begin(),
+    parent_col_ids.end(),
+    parent_col_ids.begin(),
+    [col_ids = original_col_ids.begin()] __device__(auto parent_node_id) -> size_type {
+      return parent_node_id == parent_node_sentinel ? parent_node_sentinel
+                                                    : col_ids[parent_node_id];
+    });
+
+  // condition is true if parent is not a list, or sentinel/root
+  // Special case to return true if parent is a list and is_array_of_arrays is true
+  auto is_non_list_parent = [column_categories = column_categories.begin(),
+                             is_array_of_arrays,
+                             row_array_parent_col_id] __device__(auto parent_col_id) -> bool {
+    return !(parent_col_id == parent_node_sentinel ||
+             column_categories[parent_col_id] == NC_LIST &&
+               (!is_array_of_arrays || parent_col_id != row_array_parent_col_id));
+  };
+  // Mixed types in List children go to different columns,
+  // so all immediate children of list column should have same max_row_offsets.
+  //   create list's children max_row_offsets array. (initialize to zero)
+  //   atomicMax on  children max_row_offsets array.
+  //   gather the max_row_offsets from children row offset array.
+  {
+    rmm::device_uvector<NodeIndexT> list_parents_children_max_row_offsets(num_columns, stream);
+    thrust::fill(rmm::exec_policy(stream),
+                 list_parents_children_max_row_offsets.begin(),
+                 list_parents_children_max_row_offsets.end(),
+                 0);
+    thrust::for_each(rmm::exec_policy(stream),
+                     unique_col_ids.begin(),
+                     unique_col_ids.end(),
+                     [column_categories = column_categories.begin(),
+                      parent_col_ids    = parent_col_ids.begin(),
+                      max_row_offsets   = max_row_offsets.begin(),
+                      list_parents_children_max_row_offsets =
+                        list_parents_children_max_row_offsets.begin()] __device__(auto col_id) {
+                       auto parent_col_id = parent_col_ids[col_id];
+                       if (parent_col_id != parent_node_sentinel and
+                           column_categories[parent_col_id] == node_t::NC_LIST) {
+                         cuda::atomic_ref<NodeIndexT, cuda::thread_scope_device> ref{
+                           *(list_parents_children_max_row_offsets + parent_col_id)};
+                         ref.fetch_max(max_row_offsets[col_id], cuda::std::memory_order_relaxed);
+                       }
+                     });
+    thrust::gather_if(
+      rmm::exec_policy(stream),
+      parent_col_ids.begin(),
+      parent_col_ids.end(),
+      parent_col_ids.begin(),
+      list_parents_children_max_row_offsets.begin(),
+      max_row_offsets.begin(),
+      [column_categories = column_categories.begin()] __device__(size_type parent_col_id) {
+        return parent_col_id != parent_node_sentinel and
+               column_categories[parent_col_id] == node_t::NC_LIST;
+      });
+  }
+
+  // copy lists' max_row_offsets to children.
+  // all structs should have same size.
+  thrust::transform_if(
+    rmm::exec_policy(stream),
+    unique_col_ids.begin(),
+    unique_col_ids.end(),
+    max_row_offsets.begin(),
+    [column_categories = column_categories.begin(),
+     is_non_list_parent,
+     parent_col_ids  = parent_col_ids.begin(),
+     max_row_offsets = max_row_offsets.begin()] __device__(size_type col_id) {
+      auto parent_col_id = parent_col_ids[col_id];
+      // condition is true if parent is not a list, or sentinel/root
+      while (is_non_list_parent(parent_col_id)) {
+        col_id        = parent_col_id;
+        parent_col_id = parent_col_ids[parent_col_id];
+      }
+      return max_row_offsets[col_id];
+    },
+    [column_categories = column_categories.begin(),
+     is_non_list_parent,
+     parent_col_ids = parent_col_ids.begin()] __device__(size_type col_id) {
+      auto parent_col_id = parent_col_ids[col_id];
+      // condition is true if parent is not a list, or sentinel/root
+      return is_non_list_parent(parent_col_id);
+    });
+
+  return std::tuple{tree_meta_t{std::move(column_categories),
+                                std::move(parent_col_ids),
+                                std::move(column_levels),
+                                std::move(col_range_begin),
+                                std::move(col_range_end)},
+                    std::move(unique_col_ids),
+                    std::move(max_row_offsets)};
+}
+
+/**
+ * @brief Get the column indices for the values column for array of arrays rows
+ *
+ * @param row_array_children_level The level of the row array's children
+ * @param d_tree The tree metadata
+ * @param col_ids The column ids
+ * @param num_columns The number of columns
+ * @param stream The stream to use
+ * @return The value columns' indices
+ */
+rmm::device_uvector<NodeIndexT> get_values_column_indices(TreeDepthT const row_array_children_level,
+                                                          tree_meta_t const& d_tree,
+                                                          device_span<NodeIndexT> col_ids,
+                                                          size_type const num_columns,
+                                                          rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  auto [level2_nodes, level2_indices] = get_array_children_indices(
+    row_array_children_level, d_tree.node_levels, d_tree.parent_node_ids, stream);
+  auto col_id_location = thrust::make_permutation_iterator(col_ids.begin(), level2_nodes.begin());
+  rmm::device_uvector<NodeIndexT> values_column_indices(num_columns, stream);
+  thrust::scatter(rmm::exec_policy(stream),
+                  level2_indices.begin(),
+                  level2_indices.end(),
+                  col_id_location,
+                  values_column_indices.begin());
+  return values_column_indices;
+}
+
+/**
+ * @brief Copies strings specified by pair of begin, end offsets to host vector of strings.
+ *
+ * @param input String device buffer
+ * @param node_range_begin Begin offset of the strings
+ * @param node_range_end End offset of the strings
+ * @param stream CUDA stream
+ * @return Vector of strings
+ */
+std::vector<std::string> copy_strings_to_host(device_span<SymbolT const> input,
+                                              device_span<SymbolOffsetT const> node_range_begin,
+                                              device_span<SymbolOffsetT const> node_range_end,
+                                              rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  auto const num_strings = node_range_begin.size();
+  rmm::device_uvector<size_type> string_offsets(num_strings, stream);
+  rmm::device_uvector<size_type> string_lengths(num_strings, stream);
+  auto d_offset_pairs = thrust::make_zip_iterator(node_range_begin.begin(), node_range_end.begin());
+  thrust::transform(rmm::exec_policy(stream),
+                    d_offset_pairs,
+                    d_offset_pairs + num_strings,
+                    thrust::make_zip_iterator(string_offsets.begin(), string_lengths.begin()),
+                    [] __device__(auto const& offsets) {
+                      // Note: first character for non-field columns
+                      return thrust::make_tuple(
+                        static_cast<size_type>(thrust::get<0>(offsets)),
+                        static_cast<size_type>(thrust::get<1>(offsets) - thrust::get<0>(offsets)));
+                    });
+
+  cudf::io::parse_options_view options_view{};
+  options_view.quotechar  = '\0';  // no quotes
+  options_view.keepquotes = true;
+  auto d_offset_length_it =
+    thrust::make_zip_iterator(string_offsets.begin(), string_lengths.begin());
+  auto d_column_names = parse_data(input.data(),
+                                   d_offset_length_it,
+                                   num_strings,
+                                   data_type{type_id::STRING},
+                                   rmm::device_buffer{},
+                                   0,
+                                   options_view,
+                                   stream,
+                                   rmm::mr::get_current_device_resource());
+  auto to_host        = [stream](auto const& col) {
+    if (col.is_empty()) return std::vector<std::string>{};
+    auto const scv     = cudf::strings_column_view(col);
+    auto const h_chars = cudf::detail::make_std_vector_sync<char>(
+      cudf::device_span<char const>(scv.chars().data<char>(), scv.chars().size()), stream);
+    auto const h_offsets = cudf::detail::make_std_vector_sync(
+      cudf::device_span<cudf::size_type const>(scv.offsets().data<cudf::size_type>() + scv.offset(),
+                                               scv.size() + 1),
+      stream);
+
+    // build std::string vector from chars and offsets
+    std::vector<std::string> host_data;
+    host_data.reserve(col.size());
+    std::transform(
+      std::begin(h_offsets),
+      std::end(h_offsets) - 1,
+      std::begin(h_offsets) + 1,
+      std::back_inserter(host_data),
+      [&](auto start, auto end) { return std::string(h_chars.data() + start, end - start); });
+    return host_data;
+  };
+  return to_host(d_column_names->view());
+}
+
+/**
+ * @brief Holds member data pointers of `d_json_column`
+ *
+ */
+struct json_column_data {
+  using row_offset_t = json_column::row_offset_t;
+  row_offset_t* string_offsets;
+  row_offset_t* string_lengths;
+  row_offset_t* child_offsets;
+  bitmask_type* validity;
+};
+
+/**
+ * @brief Constructs `d_json_column` from node tree representation
+ * Newly constructed columns are insert into `root`'s children.
+ * `root` must be a list type.
+ *
+ * @param input Input JSON string device data
+ * @param tree Node tree representation of the JSON string
+ * @param col_ids Column ids of the nodes in the tree
+ * @param row_offsets Row offsets of the nodes in the tree
+ * @param root Root node of the `d_json_column` tree
+ * @param is_array_of_arrays Whether the tree is an array of arrays
+ * @param is_enabled_lines Whether the input is a line-delimited JSON
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the device memory
+ * of child_offets and validity members of `d_json_column`
+ */
+void make_device_json_column(device_span<SymbolT const> input,
+                             tree_meta_t& tree,
+                             device_span<NodeIndexT> col_ids,
+                             device_span<size_type> row_offsets,
+                             device_json_column& root,
+                             bool is_array_of_arrays,
+                             bool is_enabled_lines,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto num_nodes = col_ids.size();
+  rmm::device_uvector<NodeIndexT> sorted_col_ids(col_ids.size(), stream);  // make a copy
+  thrust::copy(rmm::exec_policy(stream), col_ids.begin(), col_ids.end(), sorted_col_ids.begin());
+
+  // sort by {col_id} on {node_ids} stable
+  rmm::device_uvector<NodeIndexT> node_ids(col_ids.size(), stream);
+  thrust::sequence(rmm::exec_policy(stream), node_ids.begin(), node_ids.end());
+  thrust::stable_sort_by_key(
+    rmm::exec_policy(stream), sorted_col_ids.begin(), sorted_col_ids.end(), node_ids.begin());
+
+  NodeIndexT const row_array_parent_col_id = [&]() {
+    if (!is_array_of_arrays) return parent_node_sentinel;
+    auto const list_node_index = is_enabled_lines ? 0 : 1;
+    NodeIndexT value;
+    CUDF_CUDA_TRY(cudaMemcpyAsync(&value,
+                                  col_ids.data() + list_node_index,
+                                  sizeof(NodeIndexT),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    stream.synchronize();
+    return value;
+  }();
+
+  // 1. gather column information.
+  auto [d_column_tree, d_unique_col_ids, d_max_row_offsets] =
+    reduce_to_column_tree(tree,
+                          col_ids,
+                          sorted_col_ids,
+                          node_ids,
+                          row_offsets,
+                          is_array_of_arrays,
+                          row_array_parent_col_id,
+                          stream);
+  auto num_columns    = d_unique_col_ids.size();
+  auto unique_col_ids = cudf::detail::make_std_vector_async(d_unique_col_ids, stream);
+  auto column_categories =
+    cudf::detail::make_std_vector_async(d_column_tree.node_categories, stream);
+  auto column_parent_ids =
+    cudf::detail::make_std_vector_async(d_column_tree.parent_node_ids, stream);
+  auto column_range_beg =
+    cudf::detail::make_std_vector_async(d_column_tree.node_range_begin, stream);
+  auto max_row_offsets = cudf::detail::make_std_vector_async(d_max_row_offsets, stream);
+  std::vector<std::string> column_names = copy_strings_to_host(
+    input, d_column_tree.node_range_begin, d_column_tree.node_range_end, stream);
+  // array of arrays column names
+  if (is_array_of_arrays) {
+    TreeDepthT const row_array_children_level = is_enabled_lines ? 1 : 2;
+    auto values_column_indices =
+      get_values_column_indices(row_array_children_level, tree, col_ids, num_columns, stream);
+    auto h_values_column_indices =
+      cudf::detail::make_std_vector_async(values_column_indices, stream);
+    std::transform(unique_col_ids.begin(),
+                   unique_col_ids.end(),
+                   column_names.begin(),
+                   column_names.begin(),
+                   [&h_values_column_indices, &column_parent_ids, row_array_parent_col_id](
+                     auto col_id, auto name) mutable {
+                     return column_parent_ids[col_id] == row_array_parent_col_id
+                              ? std::to_string(h_values_column_indices[col_id])
+                              : name;
+                   });
+  }
+
+  auto to_json_col_type = [](auto category) {
+    switch (category) {
+      case NC_STRUCT: return json_col_t::StructColumn;
+      case NC_LIST: return json_col_t::ListColumn;
+      case NC_STR: [[fallthrough]];
+      case NC_VAL: return json_col_t::StringColumn;
+      default: return json_col_t::Unknown;
+    }
+  };
+  auto init_to_zero = [stream](auto& v) {
+    thrust::uninitialized_fill(rmm::exec_policy(stream), v.begin(), v.end(), 0);
+  };
+
+  auto initialize_json_columns = [&](auto i, auto& col) {
+    if (column_categories[i] == NC_ERR || column_categories[i] == NC_FN) {
+      return;
+    } else if (column_categories[i] == NC_VAL || column_categories[i] == NC_STR) {
+      col.string_offsets.resize(max_row_offsets[i] + 1, stream);
+      col.string_lengths.resize(max_row_offsets[i] + 1, stream);
+      init_to_zero(col.string_offsets);
+      init_to_zero(col.string_lengths);
+    } else if (column_categories[i] == NC_LIST) {
+      col.child_offsets.resize(max_row_offsets[i] + 2, stream);
+      init_to_zero(col.child_offsets);
+    }
+    col.num_rows = max_row_offsets[i] + 1;
+    col.validity =
+      cudf::detail::create_null_mask(col.num_rows, cudf::mask_state::ALL_NULL, stream, mr);
+    col.type = to_json_col_type(column_categories[i]);
+  };
+
+  // 2. generate nested columns tree and its device_memory
+  // reorder unique_col_ids w.r.t. column_range_begin for order of column to be in field order.
+  auto h_range_col_id_it =
+    thrust::make_zip_iterator(column_range_beg.begin(), unique_col_ids.begin());
+  std::sort(h_range_col_id_it, h_range_col_id_it + num_columns, [](auto const& a, auto const& b) {
+    return thrust::get<0>(a) < thrust::get<0>(b);
+  });
+
+  // use hash map because we may skip field name's col_ids
+  std::unordered_map<NodeIndexT, std::reference_wrapper<device_json_column>> columns;
+  // map{parent_col_id, child_col_name}> = child_col_id, used for null value column tracking
+  std::map<std::pair<NodeIndexT, std::string>, NodeIndexT> mapped_columns;
+  // find column_ids which are values, but should be ignored in validity
+  std::vector<uint8_t> ignore_vals(num_columns, 0);
+  columns.try_emplace(parent_node_sentinel, std::ref(root));
+
+  for (auto const this_col_id : unique_col_ids) {
+    if (column_categories[this_col_id] == NC_ERR || column_categories[this_col_id] == NC_FN) {
+      continue;
+    }
+    // Struct, List, String, Value
+    std::string name   = "";
+    auto parent_col_id = column_parent_ids[this_col_id];
+    if (parent_col_id == parent_node_sentinel || column_categories[parent_col_id] == NC_LIST) {
+      if (is_array_of_arrays && parent_col_id == row_array_parent_col_id) {
+        name = column_names[this_col_id];
+      } else {
+        name = list_child_name;
+      }
+    } else if (column_categories[parent_col_id] == NC_FN) {
+      auto field_name_col_id = parent_col_id;
+      parent_col_id          = column_parent_ids[parent_col_id];
+      name                   = column_names[field_name_col_id];
+    } else {
+      CUDF_FAIL("Unexpected parent column category");
+    }
+    // If the child is already found,
+    // replace if this column is a nested column and the existing was a value column
+    // ignore this column if this column is a value column and the existing was a nested column
+    auto it = columns.find(parent_col_id);
+    CUDF_EXPECTS(it != columns.end(), "Parent column not found");
+    auto& parent_col = it->second.get();
+    bool replaced    = false;
+    if (mapped_columns.count({parent_col_id, name}) > 0) {
+      if (column_categories[this_col_id] == NC_VAL || column_categories[this_col_id] == NC_STR) {
+        ignore_vals[this_col_id] = 1;
+        continue;
+      }
+      auto old_col_id = mapped_columns[{parent_col_id, name}];
+      if (column_categories[old_col_id] == NC_VAL || column_categories[old_col_id] == NC_STR) {
+        // remap
+        ignore_vals[old_col_id] = 1;
+        mapped_columns.erase({parent_col_id, name});
+        columns.erase(old_col_id);
+        parent_col.child_columns.erase(name);
+        replaced = true;  // to skip duplicate name in column_order
+      } else {
+        // If this is a nested column but we're trying to insert either (a) a list node into a
+        // struct column or (b) a struct node into a list column, we fail
+        CUDF_EXPECTS(not((column_categories[old_col_id] == NC_LIST and
+                          column_categories[this_col_id] == NC_STRUCT) or
+                         (column_categories[old_col_id] == NC_STRUCT and
+                          column_categories[this_col_id] == NC_LIST)),
+                     "A mix of lists and structs within the same column is not supported");
+      }
+    }
+    CUDF_EXPECTS(parent_col.child_columns.count(name) == 0, "duplicate column name: " + name);
+    // move into parent
+    device_json_column col(stream, mr);
+    initialize_json_columns(this_col_id, col);
+    auto inserted = parent_col.child_columns.try_emplace(name, std::move(col)).second;
+    CUDF_EXPECTS(inserted, "child column insertion failed, duplicate column name in the parent");
+    if (not replaced) parent_col.column_order.push_back(name);
+    columns.try_emplace(this_col_id, std::ref(parent_col.child_columns.at(name)));
+    mapped_columns.try_emplace(std::make_pair(parent_col_id, name), this_col_id);
+  }
+  // restore unique_col_ids order
+  std::sort(h_range_col_id_it, h_range_col_id_it + num_columns, [](auto const& a, auto const& b) {
+    return thrust::get<1>(a) < thrust::get<1>(b);
+  });
+  // move columns data to device.
+  std::vector<json_column_data> columns_data(num_columns);
+  for (auto& [col_id, col_ref] : columns) {
+    if (col_id == parent_node_sentinel) continue;
+    auto& col            = col_ref.get();
+    columns_data[col_id] = json_column_data{col.string_offsets.data(),
+                                            col.string_lengths.data(),
+                                            col.child_offsets.data(),
+                                            static_cast<bitmask_type*>(col.validity.data())};
+  }
+
+  auto d_ignore_vals = cudf::detail::make_device_uvector_async(
+    ignore_vals, stream, rmm::mr::get_current_device_resource());
+  auto d_columns_data = cudf::detail::make_device_uvector_async(
+    columns_data, stream, rmm::mr::get_current_device_resource());
+
+  // 3. scatter string offsets to respective columns, set validity bits
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::counting_iterator<size_type>(0),
+    num_nodes,
+    [node_categories = tree.node_categories.begin(),
+     col_ids         = col_ids.begin(),
+     row_offsets     = row_offsets.begin(),
+     range_begin     = tree.node_range_begin.begin(),
+     range_end       = tree.node_range_end.begin(),
+     d_ignore_vals   = d_ignore_vals.begin(),
+     d_columns_data  = d_columns_data.begin()] __device__(size_type i) {
+      switch (node_categories[i]) {
+        case NC_STRUCT: set_bit(d_columns_data[col_ids[i]].validity, row_offsets[i]); break;
+        case NC_LIST: set_bit(d_columns_data[col_ids[i]].validity, row_offsets[i]); break;
+        case NC_STR: [[fallthrough]];
+        case NC_VAL:
+          if (d_ignore_vals[col_ids[i]]) break;
+          set_bit(d_columns_data[col_ids[i]].validity, row_offsets[i]);
+          d_columns_data[col_ids[i]].string_offsets[row_offsets[i]] = range_begin[i];
+          d_columns_data[col_ids[i]].string_lengths[row_offsets[i]] = range_end[i] - range_begin[i];
+          break;
+        default: break;
+      }
+    });
+
+  // 4. scatter List offset
+  // copy_if only node's whose parent is list, (node_id, parent_col_id)
+  // stable_sort by parent_col_id of {node_id}.
+  // For all unique parent_node_id of (i==0, i-1!=i), write start offset.
+  //                                  (i==last, i+1!=i), write end offset.
+  //    unique_copy_by_key {parent_node_id} {row_offset} to
+  //    col[parent_col_id].child_offsets[row_offset[parent_node_id]]
+
+  auto& parent_col_ids = sorted_col_ids;  // reuse sorted_col_ids
+  auto parent_col_id   = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<size_type>(0),
+    [col_ids         = col_ids.begin(),
+     parent_node_ids = tree.parent_node_ids.begin()] __device__(size_type node_id) {
+      return parent_node_ids[node_id] == parent_node_sentinel ? parent_node_sentinel
+                                                                : col_ids[parent_node_ids[node_id]];
+    });
+  auto const list_children_end = thrust::copy_if(
+    rmm::exec_policy(stream),
+    thrust::make_zip_iterator(thrust::make_counting_iterator<size_type>(0), parent_col_id),
+    thrust::make_zip_iterator(thrust::make_counting_iterator<size_type>(0), parent_col_id) +
+      num_nodes,
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_zip_iterator(node_ids.begin(), parent_col_ids.begin()),
+    [node_categories = tree.node_categories.begin(),
+     parent_node_ids = tree.parent_node_ids.begin()] __device__(size_type node_id) {
+      auto parent_node_id = parent_node_ids[node_id];
+      return parent_node_id != parent_node_sentinel and node_categories[parent_node_id] == NC_LIST;
+    });
+
+  auto const num_list_children =
+    list_children_end - thrust::make_zip_iterator(node_ids.begin(), parent_col_ids.begin());
+  thrust::stable_sort_by_key(rmm::exec_policy(stream),
+                             parent_col_ids.begin(),
+                             parent_col_ids.begin() + num_list_children,
+                             node_ids.begin());
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    num_list_children,
+    [node_ids        = node_ids.begin(),
+     parent_node_ids = tree.parent_node_ids.begin(),
+     parent_col_ids  = parent_col_ids.begin(),
+     row_offsets     = row_offsets.begin(),
+     d_columns_data  = d_columns_data.begin(),
+     num_list_children] __device__(size_type i) {
+      auto const node_id        = node_ids[i];
+      auto const parent_node_id = parent_node_ids[node_id];
+      // scatter to list_offset
+      if (i == 0 or parent_node_ids[node_ids[i - 1]] != parent_node_id) {
+        d_columns_data[parent_col_ids[i]].child_offsets[row_offsets[parent_node_id]] =
+          row_offsets[node_id];
+      }
+      // last value of list child_offset is its size.
+      if (i == num_list_children - 1 or parent_node_ids[node_ids[i + 1]] != parent_node_id) {
+        d_columns_data[parent_col_ids[i]].child_offsets[row_offsets[parent_node_id] + 1] =
+          row_offsets[node_id] + 1;
+      }
+    });
+
+  // 5. scan on offsets.
+  for (auto& [id, col_ref] : columns) {
+    auto& col = col_ref.get();
+    if (col.type == json_col_t::StringColumn) {
+      thrust::inclusive_scan(rmm::exec_policy(stream),
+                             col.string_offsets.begin(),
+                             col.string_offsets.end(),
+                             col.string_offsets.begin(),
+                             thrust::maximum<json_column::row_offset_t>{});
+    } else if (col.type == json_col_t::ListColumn) {
+      thrust::inclusive_scan(rmm::exec_policy(stream),
+                             col.child_offsets.begin(),
+                             col.child_offsets.end(),
+                             col.child_offsets.begin(),
+                             thrust::maximum<json_column::row_offset_t>{});
+    }
+  }
+}
+
+/**
+ * @brief Retrieves the parse_options to be used for type inference and type casting
+ *
+ * @param options The reader options to influence the relevant type inference and type casting
+ * options
+ */
+cudf::io::parse_options parsing_options(cudf::io::json_reader_options const& options,
+                                        rmm::cuda_stream_view stream);
+
+std::pair<std::unique_ptr<column>, std::vector<column_name_info>> device_json_column_to_cudf_column(
+  device_json_column& json_col,
+  device_span<SymbolT const> d_input,
+  cudf::io::parse_options const& options,
+  std::optional<schema_element> schema,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto validity_size_check = [](device_json_column& json_col) {
+    CUDF_EXPECTS(json_col.validity.size() >= bitmask_allocation_size_bytes(json_col.num_rows),
+                 "valid_count is too small");
+  };
+  auto make_validity = [stream, validity_size_check](
+                         device_json_column& json_col) -> std::pair<rmm::device_buffer, size_type> {
+    validity_size_check(json_col);
+    auto null_count = cudf::detail::null_count(
+      static_cast<bitmask_type*>(json_col.validity.data()), 0, json_col.num_rows, stream);
+    // full null_mask is always required for parse_data
+    return {std::move(json_col.validity), null_count};
+    // Note: json_col modified here, moves this memory
+  };
+
+  auto get_child_schema = [schema](auto child_name) -> std::optional<schema_element> {
+    if (schema.has_value()) {
+      auto const result = schema.value().child_types.find(child_name);
+      if (result != std::end(schema.value().child_types)) { return result->second; }
+    }
+    return {};
+  };
+
+  switch (json_col.type) {
+    case json_col_t::StringColumn: {
+      // move string_offsets to GPU and transform to string column
+      auto const col_size      = json_col.string_offsets.size();
+      using char_length_pair_t = thrust::pair<char const*, size_type>;
+      CUDF_EXPECTS(json_col.string_offsets.size() == json_col.string_lengths.size(),
+                   "string offset, string length mismatch");
+      rmm::device_uvector<char_length_pair_t> d_string_data(col_size, stream);
+      // TODO how about directly storing pair<char*, size_t> in json_column?
+      auto offset_length_it =
+        thrust::make_zip_iterator(json_col.string_offsets.begin(), json_col.string_lengths.begin());
+
+      data_type target_type{};
+
+      if (schema.has_value()) {
+#ifdef NJP_DEBUG_PRINT
+        std::cout << "-> explicit type: "
+                  << (schema.has_value() ? std::to_string(static_cast<int>(schema->type.id()))
+                                         : "n/a");
+#endif
+        target_type = schema.value().type;
+      }
+      // Infer column type, if we don't have an explicit type for it
+      else {
+        target_type = cudf::io::detail::infer_data_type(
+          options.json_view(), d_input, offset_length_it, col_size, stream);
+      }
+
+      auto [result_bitmask, null_count] = make_validity(json_col);
+      // Convert strings to the inferred data type
+      auto col = parse_data(d_input.data(),
+                            offset_length_it,
+                            col_size,
+                            target_type,
+                            std::move(result_bitmask),
+                            null_count,
+                            options.view(),
+                            stream,
+                            mr);
+
+      // Reset nullable if we do not have nulls
+      // This is to match the existing JSON reader's behaviour:
+      // - Non-string columns will always be returned as nullable
+      // - String columns will be returned as nullable, iff there's at least one null entry
+      if (target_type.id() == type_id::STRING and col->null_count() == 0) {
+        col->set_null_mask(rmm::device_buffer{0, stream, mr}, 0);
+      }
+
+      // For string columns return ["offsets", "char"] schema
+      if (target_type.id() == type_id::STRING) {
+        return {std::move(col), std::vector<column_name_info>{{"offsets"}, {"chars"}}};
+      }
+      // Non-string leaf-columns (e.g., numeric) do not have child columns in the schema
+      return {std::move(col), std::vector<column_name_info>{}};
+    }
+    case json_col_t::StructColumn: {
+      std::vector<std::unique_ptr<column>> child_columns;
+      std::vector<column_name_info> column_names{};
+      size_type num_rows{json_col.num_rows};
+      // Create children columns
+      for (auto const& col_name : json_col.column_order) {
+        auto const& col = json_col.child_columns.find(col_name);
+        column_names.emplace_back(col->first);
+        auto& child_col            = col->second;
+        auto [child_column, names] = device_json_column_to_cudf_column(
+          child_col, d_input, options, get_child_schema(col_name), stream, mr);
+        CUDF_EXPECTS(num_rows == child_column->size(),
+                     "All children columns must have the same size");
+        child_columns.push_back(std::move(child_column));
+        column_names.back().children = names;
+      }
+      auto [result_bitmask, null_count] = make_validity(json_col);
+      // The null_mask is set after creation of struct column is to skip the superimpose_nulls and
+      // null validation applied in make_structs_column factory, which is not needed for json
+      auto ret_col = make_structs_column(num_rows, std::move(child_columns), 0, {}, stream, mr);
+      ret_col->set_null_mask(std::move(result_bitmask), null_count);
+      return {std::move(ret_col), column_names};
+    }
+    case json_col_t::ListColumn: {
+      size_type num_rows = json_col.child_offsets.size() - 1;
+      std::vector<column_name_info> column_names{};
+      column_names.emplace_back("offsets");
+      column_names.emplace_back(
+        json_col.child_columns.empty() ? list_child_name : json_col.child_columns.begin()->first);
+
+      // Note: json_col modified here, reuse the memory
+      auto offsets_column = std::make_unique<column>(data_type{type_id::INT32},
+                                                     num_rows + 1,
+                                                     json_col.child_offsets.release(),
+                                                     rmm::device_buffer{},
+                                                     0);
+      // Create children column
+      auto [child_column, names] =
+        json_col.child_columns.empty()
+          ? std::pair<std::unique_ptr<column>,
+                      // EMPTY type could not used because gather throws exception on EMPTY type.
+                      std::vector<column_name_info>>{std::make_unique<column>(
+                                                       data_type{type_id::INT8},
+                                                       0,
+                                                       rmm::device_buffer{},
+                                                       rmm::device_buffer{},
+                                                       0),
+                                                     std::vector<column_name_info>{}}
+          : device_json_column_to_cudf_column(
+              json_col.child_columns.begin()->second,
+              d_input,
+              options,
+              get_child_schema(json_col.child_columns.begin()->first),
+              stream,
+              mr);
+      column_names.back().children      = names;
+      auto [result_bitmask, null_count] = make_validity(json_col);
+      auto ret_col                      = make_lists_column(num_rows,
+                                       std::move(offsets_column),
+                                       std::move(child_column),
+                                       0,
+                                       rmm::device_buffer{0, stream, mr},
+                                       stream,
+                                       mr);
+      // The null_mask is set after creation of list column is to skip the purge_nonempty_nulls and
+      // null validation applied in make_lists_column factory, which is not needed for json
+      // parent column cannot be null when its children is non-empty in JSON
+      ret_col->set_null_mask(std::move(result_bitmask), null_count);
+      return {std::move(ret_col), std::move(column_names)};
+    }
+    default: CUDF_FAIL("Unsupported column type"); break;
+  }
+}
+
+table_with_metadata device_parse_nested_json(device_span<SymbolT const> d_input,
+                                             cudf::io::json_reader_options const& options,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  auto gpu_tree = [&]() {
+    // Parse the JSON and get the token stream
+    const auto [tokens_gpu, token_indices_gpu] =
+      get_token_stream(d_input, options, stream, rmm::mr::get_current_device_resource());
+    // gpu tree generation
+    return get_tree_representation(
+      tokens_gpu, token_indices_gpu, stream, rmm::mr::get_current_device_resource());
+  }();  // IILE used to free memory of token data.
+#ifdef NJP_DEBUG_PRINT
+  auto h_input = cudf::detail::make_host_vector_async(d_input, stream);
+  print_tree(h_input, gpu_tree, stream);
+#endif
+
+  bool const is_array_of_arrays = [&]() {
+    std::array<node_t, 2> h_node_categories = {NC_ERR, NC_ERR};
+    auto const size_to_copy                 = std::min(size_t{2}, gpu_tree.node_categories.size());
+    CUDF_CUDA_TRY(cudaMemcpyAsync(h_node_categories.data(),
+                                  gpu_tree.node_categories.data(),
+                                  sizeof(node_t) * size_to_copy,
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    stream.synchronize();
+    if (options.is_enabled_lines()) return h_node_categories[0] == NC_LIST;
+    return h_node_categories[0] == NC_LIST and h_node_categories[1] == NC_LIST;
+  }();
+
+  auto [gpu_col_id, gpu_row_offsets] =
+    records_orient_tree_traversal(d_input,
+                                  gpu_tree,
+                                  is_array_of_arrays,
+                                  options.is_enabled_lines(),
+                                  stream,
+                                  rmm::mr::get_current_device_resource());
+
+  device_json_column root_column(stream, mr);
+  root_column.type = json_col_t::ListColumn;
+  root_column.child_offsets.resize(2, stream);
+  thrust::fill(rmm::exec_policy(stream),
+               root_column.child_offsets.begin(),
+               root_column.child_offsets.end(),
+               0);
+
+  // Get internal JSON column
+  make_device_json_column(d_input,
+                          gpu_tree,
+                          gpu_col_id,
+                          gpu_row_offsets,
+                          root_column,
+                          is_array_of_arrays,
+                          options.is_enabled_lines(),
+                          stream,
+                          mr);
+
+  // data_root refers to the root column of the data represented by the given JSON string
+  auto& data_root =
+    options.is_enabled_lines() ? root_column : root_column.child_columns.begin()->second;
+
+  // Zero row entries
+  if (data_root.type == json_col_t::ListColumn && data_root.child_columns.empty()) {
+    return table_with_metadata{std::make_unique<table>(std::vector<std::unique_ptr<column>>{})};
+  }
+
+  // Verify that we were in fact given a list of structs (or in JSON speech: an array of objects)
+  auto constexpr single_child_col_count = 1;
+  CUDF_EXPECTS(data_root.type == json_col_t::ListColumn and
+                 data_root.child_columns.size() == single_child_col_count and
+                 data_root.child_columns.begin()->second.type ==
+                   (is_array_of_arrays ? json_col_t::ListColumn : json_col_t::StructColumn),
+               "Input needs to be an array of arrays or an array of (nested) objects");
+
+  // Slice off the root list column, which has only a single row that contains all the structs
+  auto& root_struct_col = data_root.child_columns.begin()->second;
+
+  // Initialize meta data to be populated while recursing through the tree of columns
+  std::vector<std::unique_ptr<column>> out_columns;
+  std::vector<column_name_info> out_column_names;
+  auto parse_opt = parsing_options(options, stream);
+
+  // Iterate over the struct's child columns and convert to cudf column
+  size_type column_index = 0;
+  for (auto const& col_name : root_struct_col.column_order) {
+    auto& json_col = root_struct_col.child_columns.find(col_name)->second;
+    // Insert this columns name into the schema
+    out_column_names.emplace_back(col_name);
+
+    std::optional<schema_element> child_schema_element = std::visit(
+      cudf::detail::visitor_overload{
+        [column_index](std::vector<data_type> const& user_dtypes) -> std::optional<schema_element> {
+          return (static_cast<std::size_t>(column_index) < user_dtypes.size())
+                   ? std::optional<schema_element>{{user_dtypes[column_index]}}
+                   : std::optional<schema_element>{};
+        },
+        [col_name](
+          std::map<std::string, data_type> const& user_dtypes) -> std::optional<schema_element> {
+          return (user_dtypes.find(col_name) != std::end(user_dtypes))
+                   ? std::optional<schema_element>{{user_dtypes.find(col_name)->second}}
+                   : std::optional<schema_element>{};
+        },
+        [col_name](std::map<std::string, schema_element> const& user_dtypes)
+          -> std::optional<schema_element> {
+          return (user_dtypes.find(col_name) != std::end(user_dtypes))
+                   ? user_dtypes.find(col_name)->second
+                   : std::optional<schema_element>{};
+        }},
+      options.get_dtypes());
+#ifdef NJP_DEBUG_PRINT
+    auto debug_schema_print = [](auto ret) {
+      std::cout << ", type id: "
+                << (ret.has_value() ? std::to_string(static_cast<int>(ret->type.id())) : "n/a")
+                << ", with " << (ret.has_value() ? ret->child_types.size() : 0) << " children"
+                << "\n";
+    };
+    std::visit(
+      cudf::detail::visitor_overload{[column_index](std::vector<data_type> const&) {
+                                       std::cout << "Column by index: #" << column_index;
+                                     },
+                                     [col_name](std::map<std::string, data_type> const&) {
+                                       std::cout << "Column by flat name: '" << col_name;
+                                     },
+                                     [col_name](std::map<std::string, schema_element> const&) {
+                                       std::cout << "Column by nested name: #" << col_name;
+                                     }},
+      options.get_dtypes());
+    debug_schema_print(child_schema_element);
+#endif
+
+    // Get this JSON column's cudf column and schema info, (modifies json_col)
+    auto [cudf_col, col_name_info] = device_json_column_to_cudf_column(
+      json_col, d_input, parse_opt, child_schema_element, stream, mr);
+    // TODO: RangeIndex as DataFrame.columns names for array of arrays
+    // if (is_array_of_arrays) {
+    //   col_name_info.back().name = "";
+    // }
+
+    out_column_names.back().children = std::move(col_name_info);
+    out_columns.emplace_back(std::move(cudf_col));
+
+    column_index++;
+  }
+
+  return table_with_metadata{std::make_unique<table>(std::move(out_columns)), {out_column_names}};
+}
+
+}  // namespace cudf::io::json::detail
diff --git a/cpp/src/io/json/json_tree.cu b/cpp/src/io/json/json_tree.cu
new file mode 100644
index 0000000..da5b0ee
--- /dev/null
+++ b/cpp/src/io/json/json_tree.cu
@@ -0,0 +1,852 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_json.hpp"
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/scatter.cuh>
+#include <cudf/detail/utilities/algorithm.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/default_hash.cuh>
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <cub/device/device_radix_sort.cuh>
+
+#include <cuco/static_set.cuh>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/fill.h>
+#include <thrust/gather.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_output_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/remove.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+#include <limits>
+
+namespace cudf::io::json {
+namespace detail {
+
+// The node that a token represents
+struct token_to_node {
+  __device__ auto operator()(PdaTokenT const token) -> NodeT
+  {
+    switch (token) {
+      case token_t::StructBegin: return NC_STRUCT;
+      case token_t::ListBegin: return NC_LIST;
+      case token_t::StringBegin: return NC_STR;
+      case token_t::ValueBegin:
+        return NC_STR;  // NC_VAL;
+      // NV_VAL is removed because type inference and
+      // reduce_to_column_tree category collapsing takes care of this.
+      case token_t::FieldNameBegin: return NC_FN;
+      default: return NC_ERR;
+    };
+  }
+};
+
+// Convert token indices to node range for each valid node.
+struct node_ranges {
+  device_span<PdaTokenT const> tokens;
+  device_span<SymbolOffsetT const> token_indices;
+  bool include_quote_char;
+  __device__ auto operator()(size_type i) -> thrust::tuple<SymbolOffsetT, SymbolOffsetT>
+  {
+    // Whether a token expects to be followed by its respective end-of-* token partner
+    auto const is_begin_of_section = [] __device__(PdaTokenT const token) {
+      switch (token) {
+        case token_t::StringBegin:
+        case token_t::ValueBegin:
+        case token_t::FieldNameBegin: return true;
+        default: return false;
+      };
+    };
+    // The end-of-* partner token for a given beginning-of-* token
+    auto const end_of_partner = [] __device__(PdaTokenT const token) {
+      switch (token) {
+        case token_t::StringBegin: return token_t::StringEnd;
+        case token_t::ValueBegin: return token_t::ValueEnd;
+        case token_t::FieldNameBegin: return token_t::FieldNameEnd;
+        default: return token_t::ErrorBegin;
+      };
+    };
+    // Includes quote char for end-of-string token or Skips the quote char for
+    // beginning-of-field-name token
+    auto const get_token_index = [include_quote_char = include_quote_char] __device__(
+                                   PdaTokenT const token, SymbolOffsetT const token_index) {
+      constexpr SymbolOffsetT quote_char_size = 1;
+      switch (token) {
+        // Strip off quote char included for StringBegin
+        case token_t::StringBegin: return token_index + (include_quote_char ? 0 : quote_char_size);
+        // Strip off or Include trailing quote char for string values for StringEnd
+        case token_t::StringEnd: return token_index + (include_quote_char ? quote_char_size : 0);
+        // Strip off quote char included for FieldNameBegin
+        case token_t::FieldNameBegin: return token_index + quote_char_size;
+        default: return token_index;
+      };
+    };
+    PdaTokenT const token = tokens[i];
+    // The section from the original JSON input that this token demarcates
+    SymbolOffsetT range_begin = get_token_index(token, token_indices[i]);
+    SymbolOffsetT range_end   = range_begin + 1;  // non-leaf, non-field nodes ignore this value.
+    if (is_begin_of_section(token)) {
+      if ((i + 1) < tokens.size() && end_of_partner(token) == tokens[i + 1]) {
+        // Update the range_end for this pair of tokens
+        range_end = get_token_index(tokens[i + 1], token_indices[i + 1]);
+      }
+    }
+    return thrust::make_tuple(range_begin, range_end);
+  }
+};
+
+/**
+ * @brief Returns stable sorted keys and its sorted order
+ *
+ * Uses cub stable radix sort. The order is internally generated, hence it saves a copy and memory.
+ * Since the key and order is returned, using double buffer helps to avoid extra copy to user
+ * provided output iterator.
+ *
+ * @tparam IndexType sorted order type
+ * @tparam KeyType key type
+ * @param keys keys to sort
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return Sorted keys and indices producing that sorted order
+ */
+template <typename IndexType = size_t, typename KeyType>
+std::pair<rmm::device_uvector<KeyType>, rmm::device_uvector<IndexType>> stable_sorted_key_order(
+  cudf::device_span<KeyType const> keys, rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+
+  // Determine temporary device storage requirements
+  rmm::device_uvector<KeyType> keys_buffer1(keys.size(), stream);
+  rmm::device_uvector<KeyType> keys_buffer2(keys.size(), stream);
+  rmm::device_uvector<IndexType> order_buffer1(keys.size(), stream);
+  rmm::device_uvector<IndexType> order_buffer2(keys.size(), stream);
+  cub::DoubleBuffer<IndexType> order_buffer(order_buffer1.data(), order_buffer2.data());
+  cub::DoubleBuffer<KeyType> keys_buffer(keys_buffer1.data(), keys_buffer2.data());
+  size_t temp_storage_bytes = 0;
+  cub::DeviceRadixSort::SortPairs(
+    nullptr, temp_storage_bytes, keys_buffer, order_buffer, keys.size());
+  rmm::device_buffer d_temp_storage(temp_storage_bytes, stream);
+
+  thrust::copy(rmm::exec_policy(stream), keys.begin(), keys.end(), keys_buffer1.begin());
+  thrust::sequence(rmm::exec_policy(stream), order_buffer1.begin(), order_buffer1.end());
+
+  cub::DeviceRadixSort::SortPairs(d_temp_storage.data(),
+                                  temp_storage_bytes,
+                                  keys_buffer,
+                                  order_buffer,
+                                  keys.size(),
+                                  0,
+                                  sizeof(KeyType) * 8,
+                                  stream.value());
+
+  return std::pair{keys_buffer.Current() == keys_buffer1.data() ? std::move(keys_buffer1)
+                                                                : std::move(keys_buffer2),
+                   order_buffer.Current() == order_buffer1.data() ? std::move(order_buffer1)
+                                                                  : std::move(order_buffer2)};
+}
+
+/**
+ * @brief Propagate parent node to siblings from first sibling.
+ *
+ * @param node_levels Node levels of each node
+ * @param parent_node_ids parent node ids initialized for first child of each push node,
+ *                       and other siblings are initialized to -1.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+void propagate_parent_to_siblings(cudf::device_span<TreeDepthT const> node_levels,
+                                  cudf::device_span<NodeIndexT> parent_node_ids,
+                                  rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  auto [sorted_node_levels, sorted_order] = stable_sorted_key_order<size_type>(node_levels, stream);
+  // instead of gather, using permutation_iterator, which is ~17% faster
+
+  thrust::inclusive_scan_by_key(
+    rmm::exec_policy(stream),
+    sorted_node_levels.begin(),
+    sorted_node_levels.end(),
+    thrust::make_permutation_iterator(parent_node_ids.begin(), sorted_order.begin()),
+    thrust::make_permutation_iterator(parent_node_ids.begin(), sorted_order.begin()),
+    thrust::equal_to<TreeDepthT>{},
+    thrust::maximum<NodeIndexT>{});
+}
+
+// Generates a tree representation of the given tokens, token_indices.
+tree_meta_t get_tree_representation(device_span<PdaTokenT const> tokens,
+                                    device_span<SymbolOffsetT const> token_indices,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  // Whether a token does represent a node in the tree representation
+  auto const is_node = [] __device__(PdaTokenT const token) -> bool {
+    switch (token) {
+      case token_t::StructBegin:
+      case token_t::ListBegin:
+      case token_t::StringBegin:
+      case token_t::ValueBegin:
+      case token_t::FieldNameBegin:
+      case token_t::ErrorBegin: return true;
+      default: return false;
+    };
+  };
+
+  // Whether the token pops from the parent node stack
+  auto const does_pop = [] __device__(PdaTokenT const token) -> bool {
+    switch (token) {
+      case token_t::StructMemberEnd:
+      case token_t::StructEnd:
+      case token_t::ListEnd: return true;
+      default: return false;
+    };
+  };
+
+  // Whether the token pushes onto the parent node stack
+  auto const does_push = [] __device__(PdaTokenT const token) -> bool {
+    switch (token) {
+      case token_t::FieldNameBegin:
+      case token_t::StructBegin:
+      case token_t::ListBegin: return true;
+      default: return false;
+    };
+  };
+
+  // Look for ErrorBegin and report the point of error.
+  if (auto const error_count =
+        thrust::count(rmm::exec_policy(stream), tokens.begin(), tokens.end(), token_t::ErrorBegin);
+      error_count > 0) {
+    auto const error_location =
+      thrust::find(rmm::exec_policy(stream), tokens.begin(), tokens.end(), token_t::ErrorBegin);
+    SymbolOffsetT error_index;
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(&error_index,
+                      token_indices.data() + thrust::distance(tokens.begin(), error_location),
+                      sizeof(SymbolOffsetT),
+                      cudaMemcpyDefault,
+                      stream.value()));
+    stream.synchronize();
+    CUDF_FAIL("JSON Parser encountered an invalid format at location " +
+              std::to_string(error_index));
+  }
+
+  auto const num_tokens = tokens.size();
+  auto const num_nodes =
+    thrust::count_if(rmm::exec_policy(stream), tokens.begin(), tokens.end(), is_node);
+
+  // Node levels: transform_exclusive_scan, copy_if.
+  rmm::device_uvector<TreeDepthT> node_levels(num_nodes, stream, mr);
+  {
+    rmm::device_uvector<TreeDepthT> token_levels(num_tokens, stream);
+    auto const push_pop_it = thrust::make_transform_iterator(
+      tokens.begin(), [does_push, does_pop] __device__(PdaTokenT const token) -> size_type {
+        return does_push(token) - does_pop(token);
+      });
+    thrust::exclusive_scan(
+      rmm::exec_policy(stream), push_pop_it, push_pop_it + num_tokens, token_levels.begin());
+
+    auto const node_levels_end = cudf::detail::copy_if_safe(token_levels.begin(),
+                                                            token_levels.end(),
+                                                            tokens.begin(),
+                                                            node_levels.begin(),
+                                                            is_node,
+                                                            stream);
+    CUDF_EXPECTS(thrust::distance(node_levels.begin(), node_levels_end) == num_nodes,
+                 "node level count mismatch");
+  }
+
+  // Node parent ids:
+  // previous push node_id transform, stable sort by level, segmented scan with Max, reorder.
+  rmm::device_uvector<NodeIndexT> parent_node_ids(num_nodes, stream, mr);
+  // This block of code is generalized logical stack algorithm. TODO: make this a separate function.
+  {
+    rmm::device_uvector<NodeIndexT> node_token_ids(num_nodes, stream);
+    cudf::detail::copy_if_safe(thrust::make_counting_iterator<NodeIndexT>(0),
+                               thrust::make_counting_iterator<NodeIndexT>(0) + num_tokens,
+                               tokens.begin(),
+                               node_token_ids.begin(),
+                               is_node,
+                               stream);
+
+    // previous push node_id
+    // if previous node is a push, then i-1
+    // if previous node is FE, then i-2 (returns FB's index)
+    // if previous node is SMB and its previous node is a push, then i-2
+    // eg. `{ SMB FB FE VB VE SME` -> `{` index as FB's parent.
+    // else -1
+    auto const first_childs_parent_token_id = [tokens_gpu =
+                                                 tokens.begin()] __device__(auto i) -> NodeIndexT {
+      if (i <= 0) { return -1; }
+      if (tokens_gpu[i - 1] == token_t::StructBegin or tokens_gpu[i - 1] == token_t::ListBegin) {
+        return i - 1;
+      } else if (tokens_gpu[i - 1] == token_t::FieldNameEnd) {
+        return i - 2;
+      } else if (tokens_gpu[i - 1] == token_t::StructMemberBegin and
+                 (tokens_gpu[i - 2] == token_t::StructBegin ||
+                  tokens_gpu[i - 2] == token_t::ListBegin)) {
+        return i - 2;
+      } else {
+        return -1;
+      }
+    };
+
+    thrust::transform(
+      rmm::exec_policy(stream),
+      node_token_ids.begin(),
+      node_token_ids.end(),
+      parent_node_ids.begin(),
+      [node_ids_gpu = node_token_ids.begin(), num_nodes, first_childs_parent_token_id] __device__(
+        NodeIndexT const tid) -> NodeIndexT {
+        auto const pid = first_childs_parent_token_id(tid);
+        return pid < 0
+                 ? parent_node_sentinel
+                 : thrust::lower_bound(thrust::seq, node_ids_gpu, node_ids_gpu + num_nodes, pid) -
+                     node_ids_gpu;
+        // parent_node_sentinel is -1, useful for segmented max operation below
+      });
+  }
+  // Propagate parent node to siblings from first sibling - inplace.
+  propagate_parent_to_siblings(
+    cudf::device_span<TreeDepthT const>{node_levels.data(), node_levels.size()},
+    parent_node_ids,
+    stream);
+
+  // Node categories: copy_if with transform.
+  rmm::device_uvector<NodeT> node_categories(num_nodes, stream, mr);
+  auto const node_categories_it =
+    thrust::make_transform_output_iterator(node_categories.begin(), token_to_node{});
+  auto const node_categories_end =
+    cudf::detail::copy_if_safe(tokens.begin(), tokens.end(), node_categories_it, is_node, stream);
+  CUDF_EXPECTS(node_categories_end - node_categories_it == num_nodes,
+               "node category count mismatch");
+
+  // Node ranges: copy_if with transform.
+  rmm::device_uvector<SymbolOffsetT> node_range_begin(num_nodes, stream, mr);
+  rmm::device_uvector<SymbolOffsetT> node_range_end(num_nodes, stream, mr);
+  auto const node_range_tuple_it =
+    thrust::make_zip_iterator(node_range_begin.begin(), node_range_end.begin());
+  // Whether the tokenizer stage should keep quote characters for string values
+  // If the tokenizer keeps the quote characters, they may be stripped during type casting
+  constexpr bool include_quote_char = true;
+  auto const node_range_out_it      = thrust::make_transform_output_iterator(
+    node_range_tuple_it, node_ranges{tokens, token_indices, include_quote_char});
+
+  auto const node_range_out_end = cudf::detail::copy_if_safe(
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(0) + num_tokens,
+    node_range_out_it,
+    [is_node, tokens_gpu = tokens.begin()] __device__(size_type i) -> bool {
+      return is_node(tokens_gpu[i]);
+    },
+    stream);
+  CUDF_EXPECTS(node_range_out_end - node_range_out_it == num_nodes, "node range count mismatch");
+
+  return {std::move(node_categories),
+          std::move(parent_node_ids),
+          std::move(node_levels),
+          std::move(node_range_begin),
+          std::move(node_range_end)};
+}
+
+/**
+ * @brief Generates unique node_type id for each node.
+ * Field nodes with the same name are assigned the same node_type id.
+ * List, Struct, and String nodes are assigned their category values as node_type ids.
+ *
+ * All inputs and outputs are in node_id order.
+ * @param d_input JSON string in device memory
+ * @param d_tree Tree representation of the JSON
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @return Vector of node_type ids
+ */
+rmm::device_uvector<size_type> hash_node_type_with_field_name(device_span<SymbolT const> d_input,
+                                                              tree_meta_t const& d_tree,
+                                                              rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+
+  auto const num_nodes  = d_tree.node_categories.size();
+  auto const num_fields = thrust::count(rmm::exec_policy(stream),
+                                        d_tree.node_categories.begin(),
+                                        d_tree.node_categories.end(),
+                                        node_t::NC_FN);
+
+  auto const d_hasher = [d_input          = d_input.data(),
+                         node_range_begin = d_tree.node_range_begin.data(),
+                         node_range_end   = d_tree.node_range_end.data()] __device__(auto node_id) {
+    auto const field_name = cudf::string_view(d_input + node_range_begin[node_id],
+                                              node_range_end[node_id] - node_range_begin[node_id]);
+    return cudf::hashing::detail::default_hash<cudf::string_view>{}(field_name);
+  };
+  auto const d_equal = [d_input          = d_input.data(),
+                        node_range_begin = d_tree.node_range_begin.data(),
+                        node_range_end   = d_tree.node_range_end.data()] __device__(auto node_id1,
+                                                                                  auto node_id2) {
+    auto const field_name1 = cudf::string_view(
+      d_input + node_range_begin[node_id1], node_range_end[node_id1] - node_range_begin[node_id1]);
+    auto const field_name2 = cudf::string_view(
+      d_input + node_range_begin[node_id2], node_range_end[node_id2] - node_range_begin[node_id2]);
+    return field_name1 == field_name2;
+  };
+  // key-value pairs: uses node_id itself as node_type. (unique node_id for a field name due to
+  // hashing)
+  auto const iter = thrust::make_counting_iterator<size_type>(0);
+
+  auto const is_field_name_node = [node_categories =
+                                     d_tree.node_categories.data()] __device__(auto node_id) {
+    return node_categories[node_id] == node_t::NC_FN;
+  };
+
+  using hasher_type                             = decltype(d_hasher);
+  constexpr size_type empty_node_index_sentinel = -1;
+  auto key_set =
+    cuco::experimental::static_set{cuco::experimental::extent{compute_hash_table_size(
+                                     num_fields, 40)},  // 40% occupancy in hash map
+                                   cuco::empty_key{empty_node_index_sentinel},
+                                   d_equal,
+                                   cuco::experimental::linear_probing<1, hasher_type>{d_hasher},
+                                   hash_table_allocator_type{default_allocator<char>{}, stream},
+                                   stream.value()};
+  key_set.insert_if_async(iter,
+                          iter + num_nodes,
+                          thrust::counting_iterator<size_type>(0),  // stencil
+                          is_field_name_node,
+                          stream.value());
+
+  auto const get_hash_value =
+    [key_set = key_set.ref(cuco::experimental::op::find)] __device__(auto node_id) -> size_type {
+    auto const it = key_set.find(node_id);
+    return (it == key_set.end()) ? size_type{0} : *it;
+  };
+
+  // convert field nodes to node indices, and other nodes to enum value.
+  rmm::device_uvector<size_type> node_type(num_nodes, stream);
+  thrust::tabulate(rmm::exec_policy(stream),
+                   node_type.begin(),
+                   node_type.end(),
+                   [node_categories = d_tree.node_categories.data(),
+                    is_field_name_node,
+                    get_hash_value] __device__(auto node_id) -> size_type {
+                     if (is_field_name_node(node_id))
+                       return static_cast<size_type>(NUM_NODE_CLASSES) + get_hash_value(node_id);
+                     else
+                       return static_cast<size_type>(node_categories[node_id]);
+                   });
+  return node_type;
+}
+
+std::pair<rmm::device_uvector<NodeIndexT>, rmm::device_uvector<NodeIndexT>>
+get_array_children_indices(TreeDepthT row_array_children_level,
+                           device_span<TreeDepthT const> node_levels,
+                           device_span<NodeIndexT const> parent_node_ids,
+                           rmm::cuda_stream_view stream)
+{
+  // array children level: (level 2 for values, level 1 for values-JSONLines format)
+  // copy nodes id of level 1's children (level 2)
+  // exclusive scan by key (on key their parent_node_id, because we need indices in each row.
+  // parent_node_id for each row will be same).
+  // -> return their indices and their node id
+  auto const num_nodes  = node_levels.size();
+  auto num_level2_nodes = thrust::count(
+    rmm::exec_policy(stream), node_levels.begin(), node_levels.end(), row_array_children_level);
+  rmm::device_uvector<NodeIndexT> level2_nodes(num_level2_nodes, stream);
+  rmm::device_uvector<NodeIndexT> level2_indices(num_level2_nodes, stream);
+  auto const iter = thrust::copy_if(rmm::exec_policy(stream),
+                                    thrust::counting_iterator<NodeIndexT>(0),
+                                    thrust::counting_iterator<NodeIndexT>(num_nodes),
+                                    node_levels.begin(),
+                                    level2_nodes.begin(),
+                                    [row_array_children_level] __device__(auto level) {
+                                      return level == row_array_children_level;
+                                    });
+  auto level2_parent_nodes =
+    thrust::make_permutation_iterator(parent_node_ids.begin(), level2_nodes.cbegin());
+  thrust::exclusive_scan_by_key(rmm::exec_policy(stream),
+                                level2_parent_nodes,
+                                level2_parent_nodes + num_level2_nodes,
+                                thrust::make_constant_iterator(NodeIndexT{1}),
+                                level2_indices.begin());
+  return std::make_pair(std::move(level2_nodes), std::move(level2_indices));
+}
+
+// Two level hashing algorithm
+// 1. Convert node_category+fieldname to node_type. (passed as argument)
+//   a. Create a hashmap to hash field name and assign unique node id as values.
+//   b. Convert the node categories to node types.
+//      Node type is defined as node category enum value if it is not a field node,
+//      otherwise it is the unique node id assigned by the hashmap (value shifted by #NUM_CATEGORY).
+// 2. Set operation on entire path of each node
+//   a. Create a hash map with hash of {node_level, node_type} of its node and the entire parent
+//      until root.
+//   b. While creating hashmap, transform node id to unique node ids that are inserted into the
+//      hash map. This mimics set operation with hash map. This unique node ids are set ids.
+//   c. Return this converted set ids, which are the hash map keys/values, and unique set ids.
+std::pair<rmm::device_uvector<size_type>, rmm::device_uvector<size_type>> hash_node_path(
+  device_span<TreeDepthT const> node_levels,
+  device_span<size_type const> node_type,
+  device_span<NodeIndexT const> parent_node_ids,
+  bool is_array_of_arrays,
+  bool is_enabled_lines,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto const num_nodes = parent_node_ids.size();
+
+  // array of arrays
+  NodeIndexT const row_array_children_level = is_enabled_lines ? 1 : 2;
+  rmm::device_uvector<size_type> list_indices(0, stream);
+  if (is_array_of_arrays) {
+    // For array of arrays, level 2 nodes do not have column name (field name).
+    // So, we need to generate indices for each level 2 node w.r.t to that row, to uniquely
+    // identify each level 2 node as separate column.
+    // Example:
+    // array of structs: [ { a: 1, b: 2}, { a: 3, b: 4} ]
+    //           levels: 0 1 2  3  2  3   1 2  3  2  3
+    // array of arrays:  [ [    1,    2], [    3,    4] ]
+    //           levels: 0 1    2     2   1    2     2
+    // For example, in the above example, we need to generate indices for each level 2 node:
+    // array of arrays:  [ [    1,    2], [    3,    4] ]
+    //          levels:  0 1    2     2   1    2     2
+    //   child indices:         0     1        0     1
+    // These indices uniquely identify each column in each row. This is used during hashing for
+    // level 2 nodes to generate unique column ids, instead of field name for level 2 nodes.
+    auto [level2_nodes, level2_indices] =
+      get_array_children_indices(row_array_children_level, node_levels, parent_node_ids, stream);
+    // memory usage could be reduced by using different data structure (hashmap)
+    // or alternate method to hash it at node_type
+    list_indices.resize(num_nodes, stream);
+    thrust::scatter(rmm::exec_policy(stream),
+                    level2_indices.cbegin(),
+                    level2_indices.cend(),
+                    level2_nodes.cbegin(),
+                    list_indices.begin());
+  }
+
+  // path compression is not used since extra writes make all map operations slow.
+  auto const d_hasher = [node_level      = node_levels.begin(),
+                         node_type       = node_type.begin(),
+                         parent_node_ids = parent_node_ids.begin(),
+                         list_indices    = list_indices.begin(),
+                         is_array_of_arrays,
+                         row_array_children_level] __device__(auto node_id) {
+    auto hash = cudf::hashing::detail::hash_combine(
+      cudf::hashing::detail::default_hash<TreeDepthT>{}(node_level[node_id]),
+      cudf::hashing::detail::default_hash<size_type>{}(node_type[node_id]));
+    node_id = parent_node_ids[node_id];
+    // Each node computes its hash by walking from its node up to the root.
+    while (node_id != parent_node_sentinel) {
+      hash = cudf::hashing::detail::hash_combine(
+        hash, cudf::hashing::detail::default_hash<TreeDepthT>{}(node_level[node_id]));
+      hash = cudf::hashing::detail::hash_combine(
+        hash, cudf::hashing::detail::default_hash<size_type>{}(node_type[node_id]));
+      if (is_array_of_arrays and node_level[node_id] == row_array_children_level)
+        hash = cudf::hashing::detail::hash_combine(hash, list_indices[node_id]);
+      node_id = parent_node_ids[node_id];
+    }
+    return hash;
+  };
+
+  rmm::device_uvector<hash_value_type> node_hash(num_nodes, stream);
+  thrust::tabulate(rmm::exec_policy(stream), node_hash.begin(), node_hash.end(), d_hasher);
+  auto const d_hashed_cache = [node_hash = node_hash.begin()] __device__(auto node_id) {
+    return node_hash[node_id];
+  };
+
+  auto const d_equal = [node_level      = node_levels.begin(),
+                        node_type       = node_type.begin(),
+                        parent_node_ids = parent_node_ids.begin(),
+                        is_array_of_arrays,
+                        row_array_children_level,
+                        list_indices = list_indices.begin(),
+                        d_hashed_cache] __device__(auto node_id1, auto node_id2) {
+    if (node_id1 == node_id2) return true;
+    if (d_hashed_cache(node_id1) != d_hashed_cache(node_id2)) return false;
+    auto const is_equal_level =
+      [node_level, node_type, is_array_of_arrays, row_array_children_level, list_indices](
+        auto node_id1, auto node_id2) {
+        if (node_id1 == node_id2) return true;
+        auto const is_level2_equal = [&]() {
+          if (!is_array_of_arrays) return true;
+          return node_level[node_id1] != row_array_children_level or
+                 list_indices[node_id1] == list_indices[node_id2];
+        }();
+        return node_level[node_id1] == node_level[node_id2] and
+               node_type[node_id1] == node_type[node_id2] and is_level2_equal;
+      };
+    // if both nodes have same node types at all levels, it will check until it has common parent
+    // or root.
+    while (node_id1 != parent_node_sentinel and node_id2 != parent_node_sentinel and
+           node_id1 != node_id2 and is_equal_level(node_id1, node_id2)) {
+      node_id1 = parent_node_ids[node_id1];
+      node_id2 = parent_node_ids[node_id2];
+    }
+    return node_id1 == node_id2;
+  };
+
+  constexpr size_type empty_node_index_sentinel = -1;
+  using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+  using hasher_type               = decltype(d_hashed_cache);
+
+  auto key_set = cuco::experimental::static_set{
+    cuco::experimental::extent{compute_hash_table_size(num_nodes)},
+    cuco::empty_key<cudf::size_type>{empty_node_index_sentinel},
+    d_equal,
+    cuco::experimental::linear_probing<1, hasher_type>{d_hashed_cache},
+    hash_table_allocator_type{default_allocator<char>{}, stream},
+    stream.value()};
+
+  // insert and convert node ids to unique set ids
+  auto nodes_itr         = thrust::make_counting_iterator<size_type>(0);
+  auto const num_columns = key_set.insert(nodes_itr, nodes_itr + num_nodes, stream.value());
+
+  rmm::device_uvector<size_type> unique_keys(num_columns, stream);
+  rmm::device_uvector<size_type> col_id(num_nodes, stream, mr);
+  key_set.find_async(nodes_itr, nodes_itr + num_nodes, col_id.begin(), stream.value());
+  std::ignore = key_set.retrieve_all(unique_keys.begin(), stream.value());
+
+  return {std::move(col_id), std::move(unique_keys)};
+}
+
+/**
+ * @brief Generates column id and parent column id for each node
+ *
+ * 1. Generate col_id:
+ *    a. Set operation on entire path of each node, translate each node id to set id.
+ *       (two level hashing)
+ *    b. gather unique set ids.
+ *    c. sort and use binary search to generate column ids.
+ *    d. Translate parent node ids to parent column ids.
+ *
+ * All inputs and outputs are in node_id order.
+ * @param d_input JSON string in device memory
+ * @param d_tree Tree representation of the JSON
+ * @param is_array_of_arrays Whether the tree is an array of arrays
+ * @param is_enabled_lines Whether the input is a line-delimited JSON
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return column_id, parent_column_id
+ */
+std::pair<rmm::device_uvector<NodeIndexT>, rmm::device_uvector<NodeIndexT>> generate_column_id(
+  device_span<SymbolT const> d_input,
+  tree_meta_t const& d_tree,
+  bool is_array_of_arrays,
+  bool is_enabled_lines,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto const num_nodes = d_tree.node_categories.size();
+
+  // Two level hashing:
+  //   one for field names -> node_type and,
+  //   another for {node_level, node_category} + field hash for the entire path
+  //    which is {node_level, node_type} recursively using parent_node_id
+  auto [col_id, unique_keys] = [&]() {
+    // Convert node_category + field_name to node_type.
+    rmm::device_uvector<size_type> node_type =
+      hash_node_type_with_field_name(d_input, d_tree, stream);
+
+    // hash entire path from node to root.
+    return hash_node_path(d_tree.node_levels,
+                          node_type,
+                          d_tree.parent_node_ids,
+                          is_array_of_arrays,
+                          is_enabled_lines,
+                          stream,
+                          mr);
+  }();
+
+  thrust::sort(rmm::exec_policy(stream), unique_keys.begin(), unique_keys.end());
+  thrust::lower_bound(rmm::exec_policy(stream),
+                      unique_keys.begin(),
+                      unique_keys.end(),
+                      col_id.begin(),
+                      col_id.end(),
+                      col_id.begin());
+
+  rmm::device_uvector<size_type> parent_col_id(num_nodes, stream, mr);
+  thrust::transform(rmm::exec_policy(stream),
+                    d_tree.parent_node_ids.begin(),
+                    d_tree.parent_node_ids.end(),
+                    parent_col_id.begin(),
+                    [col_id = col_id.begin()] __device__(auto node_id) {
+                      return node_id >= 0 ? col_id[node_id] : parent_node_sentinel;
+                    });
+
+  return {std::move(col_id), std::move(parent_col_id)};
+}
+
+/**
+ * @brief Computes row indices of each node in the hierarchy.
+ * 2. Generate row_offset.
+ *   a. Extract only list children
+ *   b. stable_sort by parent_col_id.
+ *   c. scan_by_key {parent_col_id} (done only on nodes who's parent is list)
+ *   d. propagate to non-list leaves from parent list node by recursion
+ *
+ * pre-condition:
+ *  d_tree.node_categories, d_tree.parent_node_ids, parent_col_id are in order of node_id.
+ * post-condition: row_offsets is in order of node_id.
+ *  parent_col_id is moved and reused inside this function.
+ * @param parent_col_id parent node's column id
+ * @param d_tree Tree representation of the JSON string
+ * @param is_array_of_arrays Whether the tree is an array of arrays
+ * @param is_enabled_lines Whether the input is a line-delimited JSON
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return row_offsets
+ */
+rmm::device_uvector<size_type> compute_row_offsets(rmm::device_uvector<NodeIndexT>&& parent_col_id,
+                                                   tree_meta_t const& d_tree,
+                                                   bool is_array_of_arrays,
+                                                   bool is_enabled_lines,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto const num_nodes = d_tree.node_categories.size();
+
+  rmm::device_uvector<size_type> scatter_indices(num_nodes, stream);
+  thrust::sequence(rmm::exec_policy(stream), scatter_indices.begin(), scatter_indices.end());
+
+  // array of arrays
+  NodeIndexT const row_array_parent_level = is_enabled_lines ? 0 : 1;
+  // condition is true if parent is not a list, or sentinel/root
+  // Special case to return true if parent is a list and is_array_of_arrays is true
+  auto is_non_list_parent = [node_categories = d_tree.node_categories.begin(),
+                             node_levels     = d_tree.node_levels.begin(),
+                             is_array_of_arrays,
+                             row_array_parent_level] __device__(auto pnid) {
+    return !(pnid == parent_node_sentinel ||
+             node_categories[pnid] == NC_LIST &&
+               (!is_array_of_arrays || node_levels[pnid] != row_array_parent_level));
+  };
+
+  // Extract only list children. (nodes who's parent is a list/root)
+  auto const list_parent_end =
+    thrust::remove_if(rmm::exec_policy(stream),
+                      thrust::make_zip_iterator(parent_col_id.begin(), scatter_indices.begin()),
+                      thrust::make_zip_iterator(parent_col_id.end(), scatter_indices.end()),
+                      d_tree.parent_node_ids.begin(),
+                      is_non_list_parent);
+  auto const num_list_parent = thrust::distance(
+    thrust::make_zip_iterator(parent_col_id.begin(), scatter_indices.begin()), list_parent_end);
+
+  thrust::stable_sort_by_key(rmm::exec_policy(stream),
+                             parent_col_id.begin(),
+                             parent_col_id.begin() + num_list_parent,
+                             scatter_indices.begin());
+
+  rmm::device_uvector<size_type> row_offsets(num_nodes, stream, mr);
+  // TODO is it possible to generate list child_offsets too here?
+  // write only 1st child offset to parent node id child_offsets?
+  thrust::exclusive_scan_by_key(rmm::exec_policy(stream),
+                                parent_col_id.begin(),
+                                parent_col_id.begin() + num_list_parent,
+                                thrust::make_constant_iterator<size_type>(1),
+                                row_offsets.begin());
+
+  // Using scatter instead of sort.
+  auto& temp_storage = parent_col_id;  // reuse parent_col_id as temp storage
+  thrust::scatter(rmm::exec_policy(stream),
+                  row_offsets.begin(),
+                  row_offsets.begin() + num_list_parent,
+                  scatter_indices.begin(),
+                  temp_storage.begin());
+  row_offsets = std::move(temp_storage);
+
+  // Propagate row offsets to non-list leaves from list's immediate children node by recursion
+  thrust::transform_if(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(num_nodes),
+    row_offsets.begin(),
+    [node_categories = d_tree.node_categories.data(),
+     parent_node_ids = d_tree.parent_node_ids.begin(),
+     row_offsets     = row_offsets.begin(),
+     is_non_list_parent] __device__(size_type node_id) {
+      auto parent_node_id = parent_node_ids[node_id];
+      while (is_non_list_parent(parent_node_id)) {
+        node_id        = parent_node_id;
+        parent_node_id = parent_node_ids[parent_node_id];
+      }
+      return row_offsets[node_id];
+    },
+    [node_categories = d_tree.node_categories.data(),
+     parent_node_ids = d_tree.parent_node_ids.begin(),
+     is_non_list_parent] __device__(size_type node_id) {
+      auto const parent_node_id = parent_node_ids[node_id];
+      return is_non_list_parent(parent_node_id);
+    });
+  return row_offsets;
+}
+
+// This algorithm assigns a unique column id to each node in the tree.
+// The row offset is the row index of the node in that column id.
+// Algorithm:
+// 1. Generate col_id:
+//   a. Set operation on entire path of each node, translate each node id to set id.
+//   b. gather unique set ids.
+//   c. sort and use binary search to generate column ids.
+//   d. Translate parent node ids to parent column ids.
+// 2. Generate row_offset.
+//   a. filter only list children
+//   a. stable_sort by parent_col_id.
+//   b. scan_by_key {parent_col_id} (done only on nodes whose parent is a list)
+//   c. propagate to non-list leaves from parent list node by recursion
+std::tuple<rmm::device_uvector<NodeIndexT>, rmm::device_uvector<size_type>>
+records_orient_tree_traversal(device_span<SymbolT const> d_input,
+                              tree_meta_t const& d_tree,
+                              bool is_array_of_arrays,
+                              bool is_enabled_lines,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto [new_col_id, new_parent_col_id] =
+    generate_column_id(d_input, d_tree, is_array_of_arrays, is_enabled_lines, stream, mr);
+
+  auto row_offsets = compute_row_offsets(
+    std::move(new_parent_col_id), d_tree, is_array_of_arrays, is_enabled_lines, stream, mr);
+  return std::tuple{std::move(new_col_id), std::move(row_offsets)};
+}
+
+}  // namespace detail
+}  // namespace cudf::io::json
diff --git a/cpp/src/io/json/legacy/json_gpu.cu b/cpp/src/io/json/legacy/json_gpu.cu
new file mode 100644
index 0000000..b358cc2
--- /dev/null
+++ b/cpp/src/io/json/legacy/json_gpu.cu
@@ -0,0 +1,616 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "json_gpu.hpp"
+
+#include <io/utilities/column_type_histogram.hpp>
+#include <io/utilities/parsing_utils.cuh>
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <io/utilities/trie.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/detail/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/generate.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/mismatch.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+using cudf::device_span;
+using cudf::detail::grid_1d;
+
+namespace cudf::io::json::detail::legacy {
+
+namespace {
+/**
+ * @brief CUDA Kernel that adjusts the row range to exclude the character outside of the top level
+ * brackets.
+ *
+ * The top level brackets characters are excluded from the resulting range.
+ *
+ * @param[in] begin Pointer to the first character in the row
+ * @param[in] end pointer to the first character after the row
+ */
+__device__ std::pair<char const*, char const*> limit_range_to_brackets(char const* begin,
+                                                                       char const* end)
+{
+  auto const data_begin = thrust::next(thrust::find_if(
+    thrust::seq, begin, end, [] __device__(auto c) { return c == '[' || c == '{'; }));
+  auto const data_end   = thrust::next(thrust::find_if(thrust::seq,
+                                                     thrust::make_reverse_iterator(end),
+                                                     thrust::make_reverse_iterator(data_begin),
+                                                     [](auto c) { return c == ']' || c == '}'; }))
+                          .base();
+  return {data_begin, data_end};
+}
+
+/**
+ * @brief Find the first JSON object key in the range.
+ *
+ * Assumes that begin is not in the middle of a field.
+ *
+ * @param[in] begin Pointer to the first character in the parsing range
+ * @param[in] end pointer to the first character after the parsing range
+ * @param[in] quotechar The character used to denote quotes
+ *
+ * @return Begin and end iterators of the key name; (`end`, `end`) if a key is not found
+ */
+__device__ std::pair<char const*, char const*> get_next_key(char const* begin,
+                                                            char const* end,
+                                                            char quotechar)
+{
+  // Key starts after the first quote
+  auto const key_begin = thrust::find(thrust::seq, begin, end, quotechar) + 1;
+  if (key_begin > end) return {end, end};
+
+  // Key ends after the next unescaped quote
+  auto const key_end_pair = thrust::mismatch(
+    thrust::seq, key_begin, end - 1, key_begin + 1, [quotechar] __device__(auto prev_ch, auto ch) {
+      return !(ch == quotechar && prev_ch != '\\');
+    });
+
+  return {key_begin, key_end_pair.second};
+}
+
+/**
+ * @brief Returns true is the input character is a valid digit.
+ * Supports both decimal and hexadecimal digits (uppercase and lowercase).
+ *
+ * @param c Character to check
+ * @param is_hex Whether to check as a hexadecimal
+ *
+ * @return `true` if it is digit-like, `false` otherwise
+ */
+__device__ __inline__ bool is_digit(char c, bool is_hex = false)
+{
+  if (c >= '0' && c <= '9') return true;
+
+  if (is_hex) {
+    if (c >= 'A' && c <= 'F') return true;
+    if (c >= 'a' && c <= 'f') return true;
+  }
+
+  return false;
+}
+
+/**
+ * @brief Returns true if the counters indicate a potentially valid float.
+ * False positives are possible because positions are not taken into account.
+ * For example, field "e.123-" would match the pattern.
+ */
+__device__ __inline__ bool is_like_float(
+  long len, long digit_cnt, long decimal_cnt, long dash_cnt, long exponent_cnt)
+{
+  // Can't have more than one exponent and one decimal point
+  if (decimal_cnt > 1) return false;
+  if (exponent_cnt > 1) return false;
+  // Without the exponent or a decimal point, this is an integer, not a float
+  if (decimal_cnt == 0 && exponent_cnt == 0) return false;
+
+  // Can only have one '-' per component
+  if (dash_cnt > 1 + exponent_cnt) return false;
+
+  // If anything other than these characters is present, it's not a float
+  if (digit_cnt + decimal_cnt + dash_cnt + exponent_cnt != len) return false;
+
+  // Needs at least 1 digit, 2 if exponent is present
+  if (digit_cnt < 1 + exponent_cnt) return false;
+
+  return true;
+}
+
+/**
+ * @brief Contains information on a JSON file field.
+ */
+struct field_descriptor {
+  cudf::size_type column;
+  char const* value_begin;
+  char const* value_end;
+  bool is_quoted;
+};
+
+/**
+ * @brief Parse the first field in the given range and return its descriptor.
+ *
+ * @param[in] begin Pointer to the first character in the parsing range
+ * @param[in] end pointer to the first character after the parsing range
+ * @param[in] opts The global parsing behavior options
+ * @param[in] field_idx Index of the current field in the input row
+ * @param[in] col_map Pointer to the (column name hash -> column index) map in device memory.
+ * nullptr is passed when the input file does not consist of objects.
+ * @return Descriptor of the parsed field
+ */
+__device__ field_descriptor next_field_descriptor(char const* begin,
+                                                  char const* end,
+                                                  parse_options_view const& opts,
+                                                  cudf::size_type field_idx,
+                                                  col_map_type col_map)
+{
+  auto const desc_pre_trim =
+    col_map.capacity() == 0
+      // No key - column and begin are trivial
+      ? field_descriptor{field_idx,
+                         begin,
+                         cudf::io::gpu::seek_field_end(begin, end, opts, true),
+                         false}
+      : [&]() {
+          auto const key_range = get_next_key(begin, end, opts.quotechar);
+          auto const key_hash  = cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>{}(
+            cudf::string_view(key_range.first, key_range.second - key_range.first));
+          auto const hash_col = col_map.find(key_hash);
+          // Fall back to field index if not found (parsing error)
+          auto const column = (hash_col != col_map.end()) ? (*hash_col).second : field_idx;
+
+          // Skip the colon between the key and the value
+          auto const value_begin = thrust::find(thrust::seq, key_range.second, end, ':') + 1;
+          return field_descriptor{column,
+                                  value_begin,
+                                  cudf::io::gpu::seek_field_end(value_begin, end, opts, true),
+                                  false};
+        }();
+
+  // Modify start & end to ignore whitespace and quotechars
+  auto const trimmed_value_range =
+    trim_whitespaces(desc_pre_trim.value_begin, desc_pre_trim.value_end);
+  bool const is_quoted =
+    thrust::distance(trimmed_value_range.first, trimmed_value_range.second) >= 2 and
+    *trimmed_value_range.first == opts.quotechar and
+    *thrust::prev(trimmed_value_range.second) == opts.quotechar;
+  return {desc_pre_trim.column,
+          trimmed_value_range.first + static_cast<std::ptrdiff_t>(is_quoted),
+          trimmed_value_range.second - static_cast<std::ptrdiff_t>(is_quoted),
+          is_quoted};
+}
+
+/**
+ * @brief Returns the range that contains the data in a given row.
+ *
+ * Excludes the top-level brackets.
+ *
+ * @param[in] data Device span pointing to the JSON data in device memory
+ * @param[in] row_offsets The offset of each row in the input
+ * @param[in] row Index of the row for which the range is returned
+ *
+ * @return The begin and end iterators of the row data.
+ */
+__device__ std::pair<char const*, char const*> get_row_data_range(
+  device_span<char const> const data, device_span<uint64_t const> const row_offsets, size_type row)
+{
+  auto const row_begin = data.begin() + row_offsets[row];
+  auto const row_end =
+    data.begin() + ((row < row_offsets.size() - 1) ? row_offsets[row + 1] : data.size());
+  return limit_range_to_brackets(row_begin, row_end);
+}
+
+/**
+ * @brief CUDA kernel that parses and converts plain text data into cuDF column data.
+ *
+ * Data is processed one record at a time
+ *
+ * @param[in] opts A set of parsing options
+ * @param[in] data The entire data to read
+ * @param[in] row_offsets The offset of each row in the input
+ * @param[in] column_types The data type of each column
+ * @param[in] col_map Pointer to the (column name hash -> column index) map in device memory.
+ * nullptr is passed when the input file does not consist of objects.
+ * @param[out] output_columns The output column data
+ * @param[out] valid_fields The bitmaps indicating whether column fields are valid
+ * @param[out] num_valid_fields The numbers of valid fields in columns
+ */
+__global__ void convert_data_to_columns_kernel(parse_options_view opts,
+                                               device_span<char const> const data,
+                                               device_span<uint64_t const> const row_offsets,
+                                               device_span<data_type const> const column_types,
+                                               col_map_type col_map,
+                                               device_span<void* const> const output_columns,
+                                               device_span<bitmask_type* const> const valid_fields,
+                                               device_span<cudf::size_type> const num_valid_fields)
+{
+  auto const rec_id = grid_1d::global_thread_id();
+  if (rec_id >= row_offsets.size()) return;
+
+  auto const row_data_range = get_row_data_range(data, row_offsets, rec_id);
+
+  auto current = row_data_range.first;
+  for (size_type input_field_index = 0;
+       input_field_index < column_types.size() && current < row_data_range.second;
+       input_field_index++) {
+    auto const desc =
+      next_field_descriptor(current, row_data_range.second, opts, input_field_index, col_map);
+    auto const value_len = static_cast<size_t>(std::max(desc.value_end - desc.value_begin, 0L));
+    auto const is_quoted = static_cast<std::ptrdiff_t>(desc.is_quoted);
+
+    current = desc.value_end + 1;
+
+    using string_index_pair = thrust::pair<char const*, size_type>;
+
+    if (!serialized_trie_contains(opts.trie_na,
+                                  {desc.value_begin - is_quoted, value_len + is_quoted * 2})) {
+      // Type dispatcher does not handle strings
+      if (column_types[desc.column].id() == type_id::STRING) {
+        auto str_list           = static_cast<string_index_pair*>(output_columns[desc.column]);
+        str_list[rec_id].first  = desc.value_begin;
+        str_list[rec_id].second = value_len;
+
+        // set the valid bitmap - all bits were set to 0 to start
+        set_bit(valid_fields[desc.column], rec_id);
+        atomicAdd(&num_valid_fields[desc.column], 1);
+      } else {
+        if (cudf::type_dispatcher(column_types[desc.column],
+                                  ConvertFunctor{},
+                                  desc.value_begin,
+                                  desc.value_end,
+                                  output_columns[desc.column],
+                                  rec_id,
+                                  column_types[desc.column],
+                                  opts,
+                                  false)) {
+          // set the valid bitmap - all bits were set to 0 to start
+          set_bit(valid_fields[desc.column], rec_id);
+          atomicAdd(&num_valid_fields[desc.column], 1);
+        }
+      }
+    } else if (column_types[desc.column].id() == type_id::STRING) {
+      auto str_list           = static_cast<string_index_pair*>(output_columns[desc.column]);
+      str_list[rec_id].first  = nullptr;
+      str_list[rec_id].second = 0;
+    }
+  }
+}
+
+/**
+ * @brief CUDA kernel that processes a buffer of data and determines information about the
+ * column types within.
+ *
+ * Data is processed in one row/record at a time, so the number of total
+ * threads (tid) is equal to the number of rows.
+ *
+ * @param[in] opts A set of parsing options
+ * @param[in] data Input data buffer
+ * @param[in] rec_starts The offset of each row in the input
+ * @param[in] col_map Pointer to the (column name hash -> column index) map in device memory.
+ * nullptr is passed when the input file does not consist of objects.
+ * @param[in] num_columns The number of columns of input data
+ * @param[out] column_infos The count for each column data type
+ */
+__global__ void detect_data_types_kernel(
+  parse_options_view const opts,
+  device_span<char const> const data,
+  device_span<uint64_t const> const row_offsets,
+  col_map_type col_map,
+  int num_columns,
+  device_span<cudf::io::column_type_histogram> const column_infos)
+{
+  auto const rec_id = grid_1d::global_thread_id();
+  if (rec_id >= row_offsets.size()) return;
+
+  auto const are_rows_objects = col_map.capacity() != 0;
+  auto const row_data_range   = get_row_data_range(data, row_offsets, rec_id);
+
+  size_type input_field_index = 0;
+  for (auto current = row_data_range.first;
+       input_field_index < num_columns && current < row_data_range.second;
+       input_field_index++) {
+    auto const desc =
+      next_field_descriptor(current, row_data_range.second, opts, input_field_index, col_map);
+    auto const value_len = static_cast<size_t>(std::max(desc.value_end - desc.value_begin, 0L));
+
+    // Advance to the next field; +1 to skip the delimiter
+    current = desc.value_end + 1;
+
+    // Checking if the field is empty/valid
+    if (serialized_trie_contains(opts.trie_na, {desc.value_begin, value_len})) {
+      // Increase the null count for array rows, where the null count is initialized to zero.
+      if (!are_rows_objects) { atomicAdd(&column_infos[desc.column].null_count, 1); }
+      continue;
+    } else if (are_rows_objects) {
+      // For files with object rows, null count is initialized to row count. The value is decreased
+      // here for every valid field.
+      atomicAdd(&column_infos[desc.column].null_count, -1);
+    }
+    // Don't need counts to detect strings, any field in quotes is deduced to be a string
+    if (desc.is_quoted) {
+      atomicAdd(&column_infos[desc.column].string_count, 1);
+      continue;
+    }
+
+    int digit_count    = 0;
+    int decimal_count  = 0;
+    int slash_count    = 0;
+    int dash_count     = 0;
+    int plus_count     = 0;
+    int colon_count    = 0;
+    int exponent_count = 0;
+    int other_count    = 0;
+
+    bool const maybe_hex =
+      ((value_len > 2 && *desc.value_begin == '0' && *(desc.value_begin + 1) == 'x') ||
+       (value_len > 3 && *desc.value_begin == '-' && *(desc.value_begin + 1) == '0' &&
+        *(desc.value_begin + 2) == 'x'));
+    for (auto pos = desc.value_begin; pos < desc.value_end; ++pos) {
+      if (is_digit(*pos, maybe_hex)) {
+        digit_count++;
+        continue;
+      }
+      // Looking for unique characters that will help identify column types
+      switch (*pos) {
+        case '.': decimal_count++; break;
+        case '-': dash_count++; break;
+        case '+': plus_count++; break;
+        case '/': slash_count++; break;
+        case ':': colon_count++; break;
+        case 'e':
+        case 'E':
+          if (!maybe_hex && pos > desc.value_begin && pos < desc.value_end - 1) exponent_count++;
+          break;
+        default: other_count++; break;
+      }
+    }
+
+    // Integers have to have the length of the string
+    int int_req_number_cnt = value_len;
+    // Off by one if they start with a minus sign
+    if ((*desc.value_begin == '-' || *desc.value_begin == '+') && value_len > 1) {
+      --int_req_number_cnt;
+    }
+    // Off by one if they are a hexadecimal number
+    if (maybe_hex) { --int_req_number_cnt; }
+    if (serialized_trie_contains(opts.trie_true, {desc.value_begin, value_len}) ||
+        serialized_trie_contains(opts.trie_false, {desc.value_begin, value_len})) {
+      atomicAdd(&column_infos[desc.column].bool_count, 1);
+    } else if (digit_count == int_req_number_cnt) {
+      bool is_negative       = (*desc.value_begin == '-');
+      char const* data_begin = desc.value_begin + (is_negative || (*desc.value_begin == '+'));
+      cudf::size_type* ptr   = cudf::io::gpu::infer_integral_field_counter(
+        data_begin, data_begin + digit_count, is_negative, column_infos[desc.column]);
+      atomicAdd(ptr, 1);
+    } else if (is_like_float(
+                 value_len, digit_count, decimal_count, dash_count + plus_count, exponent_count)) {
+      atomicAdd(&column_infos[desc.column].float_count, 1);
+    }
+    // A date-time field cannot have more than 3 non-special characters
+    // A number field cannot have more than one decimal point
+    else if (other_count > 3 || decimal_count > 1) {
+      atomicAdd(&column_infos[desc.column].string_count, 1);
+    } else {
+      // A date field can have either one or two '-' or '\'; A legal combination will only have one
+      // of them To simplify the process of auto column detection, we are not covering all the
+      // date-time formation permutations
+      if ((dash_count > 0 && dash_count <= 2 && slash_count == 0) ||
+          (dash_count == 0 && slash_count > 0 && slash_count <= 2)) {
+        if (colon_count <= 2) {
+          atomicAdd(&column_infos[desc.column].datetime_count, 1);
+        } else {
+          atomicAdd(&column_infos[desc.column].string_count, 1);
+        }
+      } else {
+        // Default field type is string
+        atomicAdd(&column_infos[desc.column].string_count, 1);
+      }
+    }
+  }
+  if (!are_rows_objects) {
+    // For array rows, mark missing fields as null
+    for (; input_field_index < num_columns; ++input_field_index)
+      atomicAdd(&column_infos[input_field_index].null_count, 1);
+  }
+}
+
+/**
+ * @brief Input data range that contains a field in key:value format.
+ */
+struct key_value_range {
+  char const* key_begin;
+  char const* key_end;
+  char const* value_begin;
+  char const* value_end;
+};
+
+/**
+ * @brief Parse the next field in key:value format and return ranges of its parts.
+ */
+__device__ key_value_range get_next_key_value_range(char const* begin,
+                                                    char const* end,
+                                                    parse_options_view const& opts)
+{
+  auto const key_range = get_next_key(begin, end, opts.quotechar);
+
+  // Colon between the key and the value
+  auto const colon = thrust::find(thrust::seq, key_range.second, end, ':');
+  if (colon == end) return {end, end, end};
+
+  // Field value (including delimiters)
+  auto const value_end = cudf::io::gpu::seek_field_end(colon + 1, end, opts, true);
+  return {key_range.first, key_range.second, colon + 1, value_end};
+}
+
+/**
+ * @brief Cuda kernel that collects information about JSON object keys in the file.
+ *
+ * @param[in] options A set of parsing options
+ * @param[in] data Input data buffer
+ * @param[in] row_offsets The offset of each row in the input
+ * @param[out] keys_cnt Number of keys found in the file
+ * @param[out] keys_info optional, information (offset, length, hash) for each found key
+ */
+__global__ void collect_keys_info_kernel(parse_options_view const options,
+                                         device_span<char const> const data,
+                                         device_span<uint64_t const> const row_offsets,
+                                         unsigned long long int* keys_cnt,
+                                         thrust::optional<mutable_table_device_view> keys_info)
+{
+  auto const rec_id = grid_1d::global_thread_id();
+  if (rec_id >= row_offsets.size()) return;
+
+  auto const row_data_range = get_row_data_range(data, row_offsets, rec_id);
+
+  auto advance = [&](char const* begin) {
+    return get_next_key_value_range(begin, row_data_range.second, options);
+  };
+  for (auto field_range = advance(row_data_range.first);
+       field_range.key_begin < row_data_range.second;
+       field_range = advance(field_range.value_end)) {
+    auto const idx = atomicAdd(keys_cnt, 1);
+    if (keys_info.has_value()) {
+      auto const len                              = field_range.key_end - field_range.key_begin;
+      keys_info->column(0).element<uint64_t>(idx) = field_range.key_begin - data.begin();
+      keys_info->column(1).element<uint16_t>(idx) = len;
+      keys_info->column(2).element<uint32_t>(idx) =
+        cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>{}(
+          cudf::string_view(field_range.key_begin, len));
+    }
+  }
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::io::json::detail::legacy::convert_json_to_columns
+ */
+void convert_json_to_columns(parse_options_view const& opts,
+                             device_span<char const> const data,
+                             device_span<uint64_t const> const row_offsets,
+                             device_span<data_type const> const column_types,
+                             col_map_type* col_map,
+                             device_span<void* const> const output_columns,
+                             device_span<bitmask_type* const> const valid_fields,
+                             device_span<cudf::size_type> num_valid_fields,
+                             rmm::cuda_stream_view stream)
+{
+  int block_size;
+  int min_grid_size;
+  CUDF_CUDA_TRY(cudaOccupancyMaxPotentialBlockSize(
+    &min_grid_size, &block_size, convert_data_to_columns_kernel));
+
+  int const grid_size = (row_offsets.size() + block_size - 1) / block_size;
+
+  convert_data_to_columns_kernel<<<grid_size, block_size, 0, stream.value()>>>(opts,
+                                                                               data,
+                                                                               row_offsets,
+                                                                               column_types,
+                                                                               *col_map,
+                                                                               output_columns,
+                                                                               valid_fields,
+                                                                               num_valid_fields);
+
+  CUDF_CHECK_CUDA(stream.value());
+}
+
+/**
+ * @copydoc cudf::io::json::detail::legacy::detect_data_types
+ */
+
+std::vector<cudf::io::column_type_histogram> detect_data_types(
+  parse_options_view const& options,
+  device_span<char const> const data,
+  device_span<uint64_t const> const row_offsets,
+  bool do_set_null_count,
+  int num_columns,
+  col_map_type* col_map,
+  rmm::cuda_stream_view stream)
+{
+  int block_size;
+  int min_grid_size;
+  CUDF_CUDA_TRY(
+    cudaOccupancyMaxPotentialBlockSize(&min_grid_size, &block_size, detect_data_types_kernel));
+
+  auto d_column_infos = [&]() {
+    if (do_set_null_count) {
+      rmm::device_uvector<cudf::io::column_type_histogram> d_column_infos(num_columns, stream);
+      // Set the null count to the row count (all fields assumes to be null).
+      thrust::generate(
+        rmm::exec_policy(stream),
+        d_column_infos.begin(),
+        d_column_infos.end(),
+        [num_records = static_cast<cudf::size_type>(row_offsets.size())] __device__() {
+          return cudf::io::column_type_histogram{num_records};
+        });
+      return d_column_infos;
+    } else {
+      return cudf::detail::make_zeroed_device_uvector_async<cudf::io::column_type_histogram>(
+        num_columns, stream, rmm::mr::get_current_device_resource());
+    }
+  }();
+
+  // Calculate actual block count to use based on records count
+  int const grid_size = (row_offsets.size() + block_size - 1) / block_size;
+
+  detect_data_types_kernel<<<grid_size, block_size, 0, stream.value()>>>(
+    options, data, row_offsets, *col_map, num_columns, d_column_infos);
+
+  return cudf::detail::make_std_vector_sync(d_column_infos, stream);
+}
+
+/**
+ * @copydoc cudf::io::json::detail::legacy::collect_keys_info
+ */
+void collect_keys_info(parse_options_view const& options,
+                       device_span<char const> const data,
+                       device_span<uint64_t const> const row_offsets,
+                       unsigned long long int* keys_cnt,
+                       thrust::optional<mutable_table_device_view> keys_info,
+                       rmm::cuda_stream_view stream)
+{
+  int block_size;
+  int min_grid_size;
+  CUDF_CUDA_TRY(
+    cudaOccupancyMaxPotentialBlockSize(&min_grid_size, &block_size, collect_keys_info_kernel));
+
+  // Calculate actual block count to use based on records count
+  int const grid_size = (row_offsets.size() + block_size - 1) / block_size;
+
+  collect_keys_info_kernel<<<grid_size, block_size, 0, stream.value()>>>(
+    options, data, row_offsets, keys_cnt, keys_info);
+
+  CUDF_CHECK_CUDA(stream.value());
+}
+
+}  // namespace cudf::io::json::detail::legacy
diff --git a/cpp/src/io/json/legacy/json_gpu.hpp b/cpp/src/io/json/legacy/json_gpu.hpp
new file mode 100644
index 0000000..48fe6c6
--- /dev/null
+++ b/cpp/src/io/json/legacy/json_gpu.hpp
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <io/utilities/column_type_histogram.hpp>
+#include <io/utilities/parsing_utils.cuh>
+
+#include <hash/concurrent_unordered_map.cuh>
+
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/optional.h>
+
+using cudf::device_span;
+
+namespace cudf::io::json::detail::legacy {
+
+using col_map_type = concurrent_unordered_map<uint32_t, cudf::size_type>;
+/**
+ * @brief Convert a buffer of input data (text) into raw cuDF column data.
+ *
+ * @param[in] options A set of parsing options
+ * @param[in] data The entire data to read
+ * @param[in] row_offsets The start of each data record
+ * @param[in] dtypes The data type of each column
+ * @param[in] col_map Pointer to the (column name hash -> column index) map in device memory.
+ * nullptr is passed when the input file does not consist of objects.
+ * @param[out] output_columns The output column data
+ * @param[out] valid_fields The bitmaps indicating whether column fields are valid
+ * @param[out] num_valid_fields The numbers of valid fields in columns
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+void convert_json_to_columns(parse_options_view const& options,
+                             device_span<char const> data,
+                             device_span<uint64_t const> row_offsets,
+                             device_span<data_type const> column_types,
+                             col_map_type* col_map,
+                             device_span<void* const> output_columns,
+                             device_span<bitmask_type* const> valid_fields,
+                             device_span<cudf::size_type> num_valid_fields,
+                             rmm::cuda_stream_view stream);
+
+/**
+ * @brief Process a buffer of data and determine information about the column types within.
+ *
+ * @param[in] options A set of parsing options
+ * @param[in] data Input data buffer
+ * @param[in] row_offsets The offset of each row in the input
+ * @param[in] num_columns The number of columns of input data
+ * @param[in] col_map Pointer to the (column name hash -> column index) map in device memory.
+ * nullptr is passed when the input file does not consist of objects.
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @returns The count for each column data type
+ */
+std::vector<cudf::io::column_type_histogram> detect_data_types(
+  parse_options_view const& options,
+  device_span<char const> data,
+  device_span<uint64_t const> row_offsets,
+  bool do_set_null_count,
+  int num_columns,
+  col_map_type* col_map,
+  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Collects information about JSON object keys in the file.
+ *
+ * @param[in] options A set of parsing options
+ * @param[in] data Input data buffer
+ * @param[in] row_offsets The offset of each row in the input
+ * @param[out] keys_cnt Number of keys found in the file
+ * @param[out] keys_info optional, information (offset, length, hash) for each found key
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+void collect_keys_info(parse_options_view const& options,
+                       device_span<char const> data,
+                       device_span<uint64_t const> row_offsets,
+                       unsigned long long int* keys_cnt,
+                       thrust::optional<mutable_table_device_view> keys_info,
+                       rmm::cuda_stream_view stream);
+
+}  // namespace cudf::io::json::detail::legacy
diff --git a/cpp/src/io/json/legacy/read_json.hpp b/cpp/src/io/json/legacy/read_json.hpp
new file mode 100644
index 0000000..e3fa010
--- /dev/null
+++ b/cpp/src/io/json/legacy/read_json.hpp
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/mr/memory_resource.h>
+
+#include <memory>
+#include <vector>
+
+namespace cudf::io::json::detail::legacy {
+
+table_with_metadata read_json(host_span<std::unique_ptr<datasource>> sources,
+                              json_reader_options const& reader_opts,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::io::json::detail::legacy
diff --git a/cpp/src/io/json/legacy/reader_impl.cu b/cpp/src/io/json/legacy/reader_impl.cu
new file mode 100644
index 0000000..1ae7ccf
--- /dev/null
+++ b/cpp/src/io/json/legacy/reader_impl.cu
@@ -0,0 +1,657 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "json_gpu.hpp"
+
+#include <hash/concurrent_unordered_map.cuh>
+
+#include <io/comp/io_uncomp.hpp>
+#include <io/utilities/column_buffer.hpp>
+#include <io/utilities/parsing_utils.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/utilities/visitor_overload.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/detail/json.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+
+using cudf::host_span;
+
+namespace cudf::io::json::detail::legacy {
+
+using col_map_ptr_type = std::unique_ptr<col_map_type, std::function<void(col_map_type*)>>;
+
+/**
+ * @brief Aggregate the table containing keys info by their hash values.
+ *
+ * @param[in] info Table with columns containing key offsets, lengths and hashes, respectively
+ *
+ * @return Table with data aggregated by key hash values
+ */
+std::unique_ptr<table> aggregate_keys_info(std::unique_ptr<table> info)
+{
+  auto const info_view = info->view();
+  std::vector<groupby::aggregation_request> requests;
+  requests.emplace_back(groupby::aggregation_request{info_view.column(0)});
+  requests.back().aggregations.emplace_back(make_min_aggregation<groupby_aggregation>());
+  requests.back().aggregations.emplace_back(make_nth_element_aggregation<groupby_aggregation>(0));
+
+  requests.emplace_back(groupby::aggregation_request{info_view.column(1)});
+  requests.back().aggregations.emplace_back(make_min_aggregation<groupby_aggregation>());
+  requests.back().aggregations.emplace_back(make_nth_element_aggregation<groupby_aggregation>(0));
+
+  // Aggregate by hash values
+  groupby::groupby gb_obj(
+    table_view({info_view.column(2)}), null_policy::EXCLUDE, sorted::NO, {}, {});
+
+  auto result = gb_obj.aggregate(requests);  // TODO: no stream parameter?
+
+  std::vector<std::unique_ptr<column>> out_columns;
+  out_columns.emplace_back(std::move(result.second[0].results[0]));  // offsets
+  out_columns.emplace_back(std::move(result.second[1].results[0]));  // lengths
+  out_columns.emplace_back(std::move(result.first->release()[0]));   // hashes
+  return std::make_unique<table>(std::move(out_columns));
+}
+
+/**
+ * @brief Initializes the (key hash -> column index) hash map.
+ */
+col_map_ptr_type create_col_names_hash_map(column_view column_name_hashes,
+                                           rmm::cuda_stream_view stream)
+{
+  auto key_col_map       = col_map_type::create(column_name_hashes.size(), stream);
+  auto const column_data = column_name_hashes.data<uint32_t>();
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     column_name_hashes.size(),
+                     [map = *key_col_map, column_data] __device__(size_type idx) mutable {
+                       map.insert(thrust::make_pair(column_data[idx], idx));
+                     });
+  return key_col_map;
+}
+
+/**
+ * @brief Create a table whose columns contain the information on JSON objects' keys.
+ *
+ * The columns contain name offsets in the file, name lengths and name hashes, respectively.
+ *
+ * @param[in] options Parsing options (e.g. delimiter and quotation character)
+ * @param[in] data Input JSON device data
+ * @param[in] row_offsets Device array of row start locations in the input buffer
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return std::unique_ptr<table> cudf table with three columns (offsets, lengths, hashes)
+ */
+std::unique_ptr<table> create_json_keys_info_table(parse_options_view const& parse_opts,
+                                                   device_span<char const> const data,
+                                                   device_span<uint64_t const> const row_offsets,
+                                                   rmm::cuda_stream_view stream)
+{
+  // Count keys
+  rmm::device_scalar<unsigned long long int> key_counter(0, stream);
+  collect_keys_info(parse_opts, data, row_offsets, key_counter.data(), {}, stream);
+
+  // Allocate columns to store hash value, length, and offset of each JSON object key in the input
+  auto const num_keys = key_counter.value(stream);
+  std::vector<std::unique_ptr<column>> info_columns;
+  info_columns.emplace_back(
+    make_numeric_column(data_type(type_id::UINT64), num_keys, mask_state::UNALLOCATED, stream));
+  info_columns.emplace_back(
+    make_numeric_column(data_type(type_id::UINT16), num_keys, mask_state::UNALLOCATED, stream));
+  info_columns.emplace_back(
+    make_numeric_column(data_type(type_id::UINT32), num_keys, mask_state::UNALLOCATED, stream));
+  // Create a table out of these columns to pass them around more easily
+  auto info_table           = std::make_unique<table>(std::move(info_columns));
+  auto const info_table_mdv = mutable_table_device_view::create(info_table->mutable_view(), stream);
+
+  // Reset the key counter - now used for indexing
+  key_counter.set_value_to_zero_async(stream);
+  // Fill the allocated columns
+  collect_keys_info(parse_opts, data, row_offsets, key_counter.data(), {*info_table_mdv}, stream);
+  return info_table;
+}
+
+/**
+ * @brief Extract the keys from the JSON file the name offsets/lengths.
+ */
+std::vector<std::string> create_key_strings(char const* h_data,
+                                            table_view sorted_info,
+                                            rmm::cuda_stream_view stream)
+{
+  auto const num_cols = sorted_info.num_rows();
+  std::vector<uint64_t> h_offsets(num_cols);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(h_offsets.data(),
+                                sorted_info.column(0).data<uint64_t>(),
+                                sizeof(uint64_t) * num_cols,
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  std::vector<uint16_t> h_lens(num_cols);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(h_lens.data(),
+                                sorted_info.column(1).data<uint16_t>(),
+                                sizeof(uint16_t) * num_cols,
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  std::vector<std::string> names(num_cols);
+  std::transform(h_offsets.cbegin(),
+                 h_offsets.cend(),
+                 h_lens.cbegin(),
+                 names.begin(),
+                 [&](auto offset, auto len) { return std::string(h_data + offset, len); });
+  return names;
+}
+
+auto sort_keys_info_by_offset(std::unique_ptr<table> info)
+{
+  auto const agg_offset_col_view = info->get_column(0).view();
+  return sort_by_key(info->view(), table_view({agg_offset_col_view}));
+}
+
+/**
+ * @brief Extract JSON object keys from a JSON file.
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return Names of JSON object keys in the file
+ */
+std::pair<std::vector<std::string>, col_map_ptr_type> get_json_object_keys_hashes(
+  parse_options_view const& parse_opts,
+  host_span<char const> h_data,
+  device_span<uint64_t const> rec_starts,
+  device_span<char const> d_data,
+  rmm::cuda_stream_view stream)
+{
+  auto info = create_json_keys_info_table(parse_opts, d_data, rec_starts, stream);
+
+  auto aggregated_info = aggregate_keys_info(std::move(info));
+  auto sorted_info     = sort_keys_info_by_offset(std::move(aggregated_info));
+
+  return {create_key_strings(h_data.data(), sorted_info->view(), stream),
+          create_col_names_hash_map(sorted_info->get_column(2).view(), stream)};
+}
+
+std::vector<uint8_t> ingest_raw_input(host_span<std::unique_ptr<datasource>> sources,
+                                      compression_type compression,
+                                      size_t range_offset,
+                                      size_t range_size,
+                                      size_t range_size_padded)
+{
+  CUDF_FUNC_RANGE();
+  // Iterate through the user defined sources and read the contents into the local buffer
+  size_t total_source_size = 0;
+  for (auto const& source : sources) {
+    total_source_size += source->size();
+  }
+  total_source_size = total_source_size - (range_offset * sources.size());
+
+  auto buffer = std::vector<uint8_t>(total_source_size);
+
+  size_t bytes_read = 0;
+  for (auto const& source : sources) {
+    if (!source->is_empty()) {
+      auto data_size   = (range_size_padded != 0) ? range_size_padded : source->size();
+      auto destination = buffer.data() + bytes_read;
+      bytes_read += source->host_read(range_offset, data_size, destination);
+    }
+  }
+
+  if (compression == compression_type::NONE) {
+    return buffer;
+  } else {
+    return decompress(compression, buffer);
+  }
+}
+
+bool should_load_whole_source(json_reader_options const& reader_opts)
+{
+  return reader_opts.get_byte_range_offset() == 0 and  //
+         reader_opts.get_byte_range_size() == 0;
+}
+
+rmm::device_uvector<uint64_t> find_record_starts(json_reader_options const& reader_opts,
+                                                 host_span<char const> h_data,
+                                                 device_span<char const> d_data,
+                                                 rmm::cuda_stream_view stream)
+{
+  std::vector<char> chars_to_count{'\n'};
+  // Currently, ignoring lineterminations within quotes is handled by recording the records of both,
+  // and then filtering out the records that is a quotechar or a linetermination within a quotechar
+  // pair.
+  // If not starting at an offset, add an extra row to account for the first row in the file
+  cudf::size_type prefilter_count = ((reader_opts.get_byte_range_offset() == 0) ? 1 : 0);
+  if (should_load_whole_source(reader_opts)) {
+    prefilter_count += count_all_from_set(d_data, chars_to_count, stream);
+  } else {
+    prefilter_count += count_all_from_set(h_data, chars_to_count, stream);
+  }
+
+  rmm::device_uvector<uint64_t> rec_starts(prefilter_count, stream);
+
+  auto* find_result_ptr = rec_starts.data();
+  // Manually adding an extra row to account for the first row in the file
+  if (reader_opts.get_byte_range_offset() == 0) {
+    find_result_ptr++;
+    CUDF_CUDA_TRY(cudaMemsetAsync(rec_starts.data(), 0ull, sizeof(uint64_t), stream.value()));
+  }
+
+  std::vector<char> chars_to_find{'\n'};
+  // Passing offset = 1 to return positions AFTER the found character
+  if (should_load_whole_source(reader_opts)) {
+    find_all_from_set(d_data, chars_to_find, 1, find_result_ptr, stream);
+  } else {
+    find_all_from_set(h_data, chars_to_find, 1, find_result_ptr, stream);
+  }
+
+  // Previous call stores the record positions as encountered by all threads
+  // Sort the record positions as subsequent processing may require filtering
+  // certain rows or other processing on specific records
+  thrust::sort(rmm::exec_policy(stream), rec_starts.begin(), rec_starts.end());
+
+  auto filtered_count = prefilter_count;
+
+  // Exclude the ending newline as it does not precede a record start
+  if (h_data.back() == '\n') { filtered_count--; }
+  rec_starts.resize(filtered_count, stream);
+
+  return rec_starts;
+}
+
+/**
+ * @brief Uploads the relevant segment of the input json data onto the GPU.
+ *
+ * Sets the d_data_ data member.
+ * Only rows that need to be parsed are copied, based on the byte range
+ * Also updates the array of record starts to match the device data offset.
+ */
+rmm::device_uvector<char> upload_data_to_device(json_reader_options const& reader_opts,
+                                                host_span<char const> h_data,
+                                                rmm::device_uvector<uint64_t>& rec_starts,
+                                                rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  size_t end_offset = h_data.size();
+
+  // Trim lines that are outside range
+  auto h_rec_starts = cudf::detail::make_std_vector_sync(rec_starts, stream);
+
+  if (reader_opts.get_byte_range_size() != 0) {
+    auto it = h_rec_starts.end() - 1;
+    while (it >= h_rec_starts.begin() && *it > reader_opts.get_byte_range_size()) {
+      end_offset = *it;
+      --it;
+    }
+    h_rec_starts.erase(it + 1, h_rec_starts.end());
+  }
+
+  // Resize to exclude rows outside of the range
+  // Adjust row start positions to account for the data subcopy
+  size_t start_offset = h_rec_starts.front();
+  rec_starts.resize(h_rec_starts.size(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    rec_starts.begin(),
+                    rec_starts.end(),
+                    thrust::make_constant_iterator(start_offset),
+                    rec_starts.begin(),
+                    thrust::minus<uint64_t>());
+
+  size_t const bytes_to_upload = end_offset - start_offset;
+  CUDF_EXPECTS(bytes_to_upload <= h_data.size(),
+               "Error finding the record within the specified byte range.\n");
+
+  // Upload the raw data that is within the rows of interest
+  return cudf::detail::make_device_uvector_async(
+    h_data.subspan(start_offset, bytes_to_upload), stream, rmm::mr::get_current_device_resource());
+}
+
+std::pair<std::vector<std::string>, col_map_ptr_type> get_column_names_and_map(
+  parse_options_view const& parse_opts,
+  host_span<char const> h_data,
+  device_span<uint64_t const> rec_starts,
+  device_span<char const> d_data,
+  rmm::cuda_stream_view stream)
+{
+  // If file only contains one row, use the file size for the row size
+  uint64_t first_row_len = d_data.size();
+  if (rec_starts.size() > 1) {
+    // Set first_row_len to the offset of the second row, if it exists
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      &first_row_len, rec_starts.data() + 1, sizeof(uint64_t), cudaMemcpyDefault, stream.value()));
+  }
+  std::vector<char> first_row(first_row_len);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(first_row.data(),
+                                d_data.data(),
+                                first_row_len * sizeof(char),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  stream.synchronize();
+
+  // Determine the row format between:
+  //   JSON array - [val1, val2, ...] and
+  //   JSON object - {"col1":val1, "col2":val2, ...}
+  // based on the top level opening bracket
+  auto const first_square_bracket = std::find(first_row.begin(), first_row.end(), '[');
+  auto const first_curly_bracket  = std::find(first_row.begin(), first_row.end(), '{');
+  CUDF_EXPECTS(first_curly_bracket != first_row.end() || first_square_bracket != first_row.end(),
+               "Input data is not a valid JSON file.");
+  // If the first opening bracket is '{', assume object format
+  if (first_curly_bracket < first_square_bracket) {
+    // use keys as column names if input rows are objects
+    return get_json_object_keys_hashes(parse_opts, h_data, rec_starts, d_data, stream);
+  } else {
+    int cols_found    = 0;
+    bool quotation    = false;
+    auto column_names = std::vector<std::string>();
+    for (size_t pos = 0; pos < first_row.size(); ++pos) {
+      // Flip the quotation flag if current character is a quotechar
+      if (first_row[pos] == parse_opts.quotechar) {
+        quotation = !quotation;
+      }
+      // Check if end of a column/row
+      else if (pos == first_row.size() - 1 ||
+               (!quotation && first_row[pos] == parse_opts.delimiter)) {
+        column_names.emplace_back(std::to_string(cols_found++));
+      }
+    }
+    return {column_names, col_map_type::create(0, stream)};
+  }
+}
+
+std::vector<data_type> get_data_types(json_reader_options const& reader_opts,
+                                      parse_options_view const& parse_opts,
+                                      std::vector<std::string> const& column_names,
+                                      col_map_type* column_map,
+                                      device_span<uint64_t const> rec_starts,
+                                      device_span<char const> data,
+                                      rmm::cuda_stream_view stream)
+{
+  bool has_to_infer_column_types =
+    std::visit([](auto const& dtypes) { return dtypes.empty(); }, reader_opts.get_dtypes());
+
+  if (!has_to_infer_column_types) {
+    return std::visit(
+      cudf::detail::visitor_overload{
+        [&](std::vector<data_type> const& dtypes) {
+          CUDF_EXPECTS(dtypes.size() == column_names.size(), "Must specify types for all columns");
+          return dtypes;
+        },
+        [&](std::map<std::string, data_type> const& dtypes) {
+          std::vector<data_type> sorted_dtypes;
+          std::transform(std::cbegin(column_names),
+                         std::cend(column_names),
+                         std::back_inserter(sorted_dtypes),
+                         [&](auto const& column_name) {
+                           auto const it = dtypes.find(column_name);
+                           CUDF_EXPECTS(it != dtypes.end(), "Must specify types for all columns");
+                           return it->second;
+                         });
+          return sorted_dtypes;
+        },
+        [&](std::map<std::string, schema_element> const& dtypes) {
+          std::vector<data_type> sorted_dtypes;
+          std::transform(std::cbegin(column_names),
+                         std::cend(column_names),
+                         std::back_inserter(sorted_dtypes),
+                         [&](auto const& column_name) {
+                           auto const it = dtypes.find(column_name);
+                           CUDF_EXPECTS(it != dtypes.end(), "Must specify types for all columns");
+                           return it->second.type;
+                         });
+          return sorted_dtypes;
+        }},
+      reader_opts.get_dtypes());
+  } else {
+    CUDF_EXPECTS(not rec_starts.empty(), "No data available for data type inference.\n");
+    auto const num_columns       = column_names.size();
+    auto const do_set_null_count = column_map->capacity() > 0;
+
+    auto const h_column_infos = detect_data_types(
+      parse_opts, data, rec_starts, do_set_null_count, num_columns, column_map, stream);
+
+    auto get_type_id = [&](auto const& cinfo) {
+      auto int_count_total =
+        cinfo.big_int_count + cinfo.negative_small_int_count + cinfo.positive_small_int_count;
+      if (cinfo.null_count == static_cast<int>(rec_starts.size())) {
+        // Entire column is NULL; allocate the smallest amount of memory
+        return type_id::INT8;
+      } else if (cinfo.string_count > 0) {
+        return type_id::STRING;
+      } else if (cinfo.datetime_count > 0) {
+        return type_id::TIMESTAMP_MILLISECONDS;
+      } else if (cinfo.float_count > 0) {
+        return type_id::FLOAT64;
+      } else if (cinfo.big_int_count == 0 && int_count_total != 0) {
+        return type_id::INT64;
+      } else if (cinfo.big_int_count != 0 && cinfo.negative_small_int_count != 0) {
+        return type_id::STRING;
+      } else if (cinfo.big_int_count != 0) {
+        return type_id::UINT64;
+      } else if (cinfo.bool_count > 0) {
+        return type_id::BOOL8;
+      } else {
+        CUDF_FAIL("Data type detection failed.\n");
+      }
+    };
+
+    std::vector<data_type> dtypes;
+
+    std::transform(std::cbegin(h_column_infos),
+                   std::cend(h_column_infos),
+                   std::back_inserter(dtypes),
+                   [&](auto const& cinfo) { return data_type{get_type_id(cinfo)}; });
+
+    return dtypes;
+  }
+}
+
+table_with_metadata convert_data_to_table(parse_options_view const& parse_opts,
+                                          std::vector<data_type> const& dtypes,
+                                          std::vector<std::string>&& column_names,
+                                          col_map_type* column_map,
+                                          device_span<uint64_t const> rec_starts,
+                                          device_span<char const> data,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  auto const num_columns = dtypes.size();
+  auto const num_records = rec_starts.size();
+
+  // alloc output buffers.
+  std::vector<cudf::io::detail::column_buffer> out_buffers;
+  for (size_t col = 0; col < num_columns; ++col) {
+    out_buffers.emplace_back(dtypes[col], num_records, true, stream, mr);
+  }
+
+  thrust::host_vector<data_type> h_dtypes(num_columns);
+  thrust::host_vector<void*> h_data(num_columns);
+  thrust::host_vector<bitmask_type*> h_valid(num_columns);
+
+  for (size_t i = 0; i < num_columns; ++i) {
+    h_dtypes[i] = dtypes[i];
+    h_data[i]   = out_buffers[i].data();
+    h_valid[i]  = out_buffers[i].null_mask();
+  }
+
+  auto d_dtypes = cudf::detail::make_device_uvector_async<data_type>(
+    h_dtypes, stream, rmm::mr::get_current_device_resource());
+  auto d_data = cudf::detail::make_device_uvector_async<void*>(
+    h_data, stream, rmm::mr::get_current_device_resource());
+  auto d_valid = cudf::detail::make_device_uvector_async<cudf::bitmask_type*>(
+    h_valid, stream, rmm::mr::get_current_device_resource());
+  auto d_valid_counts = cudf::detail::make_zeroed_device_uvector_async<cudf::size_type>(
+    num_columns, stream, rmm::mr::get_current_device_resource());
+
+  convert_json_to_columns(
+    parse_opts, data, rec_starts, d_dtypes, column_map, d_data, d_valid, d_valid_counts, stream);
+
+  stream.synchronize();
+
+  // postprocess columns
+  auto target_chars   = std::vector<char>{'\\', '"', '\\', '\\', '\\', 't', '\\', 'r', '\\', 'b'};
+  auto target_offsets = std::vector<size_type>{0, 2, 4, 6, 8, 10};
+
+  auto repl_chars   = std::vector<char>{'"', '\\', '\t', '\r', '\b'};
+  auto repl_offsets = std::vector<size_type>{0, 1, 2, 3, 4, 5};
+
+  auto target =
+    make_strings_column(cudf::detail::make_device_uvector_async(
+                          target_chars, stream, rmm::mr::get_current_device_resource()),
+                        cudf::detail::make_device_uvector_async(
+                          target_offsets, stream, rmm::mr::get_current_device_resource()),
+                        {},
+                        0,
+                        stream);
+  auto repl = make_strings_column(cudf::detail::make_device_uvector_async(
+                                    repl_chars, stream, rmm::mr::get_current_device_resource()),
+                                  cudf::detail::make_device_uvector_async(
+                                    repl_offsets, stream, rmm::mr::get_current_device_resource()),
+                                  {},
+                                  0,
+                                  stream);
+
+  auto const h_valid_counts = cudf::detail::make_std_vector_sync(d_valid_counts, stream);
+  std::vector<std::unique_ptr<column>> out_columns;
+  for (size_t i = 0; i < num_columns; ++i) {
+    out_buffers[i].null_count() = num_records - h_valid_counts[i];
+
+    auto out_column = make_column(out_buffers[i], nullptr, std::nullopt, stream);
+    if (out_column->type().id() == type_id::STRING) {
+      // Need to remove escape character in case of '\"' and '\\'
+      out_columns.emplace_back(cudf::strings::detail::replace(
+        out_column->view(), target->view(), repl->view(), stream, mr));
+    } else {
+      out_columns.emplace_back(std::move(out_column));
+    }
+  }
+
+  std::vector<column_name_info> column_infos;
+  column_infos.reserve(column_names.size());
+  std::transform(std::make_move_iterator(column_names.begin()),
+                 std::make_move_iterator(column_names.end()),
+                 std::back_inserter(column_infos),
+                 [](auto const& col_name) { return column_name_info{col_name}; });
+
+  // This is to ensure the stream-ordered make_stream_column calls above complete before
+  // the temporary std::vectors are destroyed on exit from this function.
+  stream.synchronize();
+
+  CUDF_EXPECTS(!out_columns.empty(), "No columns created from json input");
+
+  return table_with_metadata{std::make_unique<table>(std::move(out_columns)), {column_infos}};
+}
+
+/**
+ * @brief Read an entire set or a subset of data from the source
+ *
+ * @param[in] options reader options with Number of bytes offset from the start,
+ * Bytes to read; use `0` for all remaining data
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return Table and its metadata
+ */
+table_with_metadata read_json(host_span<std::unique_ptr<datasource>> sources,
+                              json_reader_options const& reader_opts,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(not sources.empty(), "No sources were defined");
+  CUDF_EXPECTS(sources.size() == 1 or reader_opts.get_compression() == compression_type::NONE,
+               "Multiple compressed inputs are not supported");
+  CUDF_EXPECTS(reader_opts.is_enabled_lines(), "Only JSON Lines format is currently supported.\n");
+
+  auto parse_opts = parse_options{',', '\n', '\"', '.'};
+
+  parse_opts.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  parse_opts.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  parse_opts.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  parse_opts.dayfirst = reader_opts.is_enabled_dayfirst();
+
+  auto range_offset      = reader_opts.get_byte_range_offset();
+  auto range_size        = reader_opts.get_byte_range_size();
+  auto range_size_padded = reader_opts.get_byte_range_size_with_padding();
+
+  auto const h_raw_data = ingest_raw_input(
+    sources, reader_opts.get_compression(), range_offset, range_size, range_size_padded);
+  host_span<char const> h_data{reinterpret_cast<char const*>(h_raw_data.data()), h_raw_data.size()};
+
+  CUDF_EXPECTS(not h_data.empty(), "Ingest failed: uncompressed input data has zero size.\n");
+
+  auto d_data = rmm::device_uvector<char>(0, stream);
+
+  if (should_load_whole_source(reader_opts)) {
+    d_data = cudf::detail::make_device_uvector_async(
+      h_data, stream, rmm::mr::get_current_device_resource());
+  }
+
+  auto rec_starts = find_record_starts(reader_opts, h_data, d_data, stream);
+
+  CUDF_EXPECTS(rec_starts.size() > 0, "Error enumerating records.\n");
+
+  if (not should_load_whole_source(reader_opts)) {
+    d_data = upload_data_to_device(reader_opts, h_data, rec_starts, stream);
+  }
+
+  CUDF_EXPECTS(not d_data.is_empty(), "Error uploading input data to the GPU.\n");
+
+  auto column_names_and_map =
+    get_column_names_and_map(parse_opts.view(), h_data, rec_starts, d_data, stream);
+
+  auto column_names = std::get<0>(column_names_and_map);
+  auto column_map   = std::move(std::get<1>(column_names_and_map));
+
+  CUDF_EXPECTS(not column_names.empty(), "Error determining column names.\n");
+
+  auto dtypes = get_data_types(
+    reader_opts, parse_opts.view(), column_names, column_map.get(), rec_starts, d_data, stream);
+
+  CUDF_EXPECTS(not dtypes.empty(), "Error in data type detection.\n");
+
+  return convert_data_to_table(parse_opts.view(),
+                               dtypes,
+                               std::move(column_names),
+                               column_map.get(),
+                               rec_starts,
+                               d_data,
+                               stream,
+                               mr);
+}
+
+}  // namespace cudf::io::json::detail::legacy
diff --git a/cpp/src/io/json/nested_json.hpp b/cpp/src/io/json/nested_json.hpp
new file mode 100644
index 0000000..3bbfc4b
--- /dev/null
+++ b/cpp/src/io/json/nested_json.hpp
@@ -0,0 +1,311 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/detail/tokenize_json.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <map>
+#include <vector>
+
+namespace cudf::io::json {
+
+/**
+ * @brief Struct that encapsulate all information of a columnar tree representation.
+ */
+struct tree_meta_t {
+  rmm::device_uvector<NodeT> node_categories;
+  rmm::device_uvector<NodeIndexT> parent_node_ids;
+  rmm::device_uvector<TreeDepthT> node_levels;
+  rmm::device_uvector<SymbolOffsetT> node_range_begin;
+  rmm::device_uvector<SymbolOffsetT> node_range_end;
+};
+
+/**
+ * @brief A column type
+ */
+enum class json_col_t : char { ListColumn, StructColumn, StringColumn, Unknown };
+
+/**
+ * @brief Enum class to specify whether we just push onto and pop from the stack or whether we also
+ * reset to an empty stack on a newline character.
+ */
+enum class stack_behavior_t : char {
+  /// Opening brackets and braces, [, {, push onto the stack, closing brackets and braces, ], }, pop
+  /// from the stack
+  PushPopWithoutReset,
+
+  /// Opening brackets and braces, [, {, push onto the stack, closing brackets and braces, ], }, pop
+  /// from the stack. Newline characters are considered delimiters and therefore reset to an empty
+  /// stack.
+  ResetOnDelimiter
+};
+
+// Default name for a list's child column
+constexpr auto list_child_name{"element"};
+
+/**
+ * @brief Intermediate representation of data from a nested JSON input
+ */
+struct json_column {
+  // Type used to count number of rows
+  using row_offset_t = size_type;
+
+  // The inferred type of this column (list, struct, or value/string column)
+  json_col_t type = json_col_t::Unknown;
+
+  std::vector<row_offset_t> string_offsets;
+  std::vector<row_offset_t> string_lengths;
+
+  // Row offsets
+  std::vector<row_offset_t> child_offsets;
+
+  // Validity bitmap
+  std::vector<bitmask_type> validity;
+  row_offset_t valid_count = 0;
+
+  // Map of child columns, if applicable.
+  // Following "items" as the default child column's name of a list column
+  // Using the struct's field names
+  std::map<std::string, json_column> child_columns;
+  std::vector<std::string> column_order;
+
+  // Counting the current number of items in this column
+  row_offset_t current_offset = 0;
+
+  json_column()                              = default;
+  json_column(json_column&& other)           = default;
+  json_column& operator=(json_column&&)      = default;
+  json_column(json_column const&)            = delete;
+  json_column& operator=(json_column const&) = delete;
+
+  /**
+   * @brief Fills the rows up to the given \p up_to_row_offset with nulls.
+   *
+   * @param up_to_row_offset The row offset up to which to fill with nulls.
+   */
+  void null_fill(row_offset_t up_to_row_offset);
+
+  /**
+   * @brief Recursively iterates through the tree of columns making sure that all child columns of a
+   * struct column have the same row count, filling missing rows with nulls.
+   *
+   * @param min_row_count The minimum number of rows to be filled.
+   */
+  void level_child_cols_recursively(row_offset_t min_row_count);
+
+  /**
+   * @brief Appends the row at the given index to the column, filling all rows between the column's
+   * current offset and the given \p row_index with null items.
+   *
+   * @param row_index The row index at which to insert the given row
+   * @param row_type The row's type
+   * @param string_offset The string offset within the original JSON input of this item
+   * @param string_end The one-past-the-last-char offset within the original JSON input of this item
+   * @param child_count In case of a list column, this row's number of children is used to compute
+   * the offsets
+   */
+  void append_row(uint32_t row_index,
+                  json_col_t row_type,
+                  uint32_t string_offset,
+                  uint32_t string_end,
+                  uint32_t child_count);
+};
+
+/**
+ * @brief Intermediate representation of data from a nested JSON input, in device memory.
+ * Device memory equivalent of `json_column`.
+ */
+struct device_json_column {
+  // Type used to count number of rows
+  using row_offset_t = size_type;
+
+  // The inferred type of this column (list, struct, or value/string column)
+  json_col_t type = json_col_t::Unknown;
+
+  rmm::device_uvector<row_offset_t> string_offsets;
+  rmm::device_uvector<row_offset_t> string_lengths;
+
+  // Row offsets
+  rmm::device_uvector<row_offset_t> child_offsets;
+
+  // Validity bitmap
+  rmm::device_buffer validity;
+
+  // Map of child columns, if applicable.
+  // Following "element" as the default child column's name of a list column
+  // Using the struct's field names
+  std::map<std::string, device_json_column> child_columns;
+  std::vector<std::string> column_order;
+  // Counting the current number of items in this column
+  row_offset_t num_rows = 0;
+
+  /**
+   * @brief Construct a new d json column object
+   *
+   * @note `mr` is used for allocating the device memory for child_offsets, and validity
+   * since it will moved into cudf::column later.
+   *
+   * @param stream The CUDA stream to which kernels are dispatched
+   * @param mr Optional, resource with which to allocate
+   */
+  device_json_column(rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+    : string_offsets(0, stream),
+      string_lengths(0, stream),
+      child_offsets(0, stream, mr),
+      validity(0, stream, mr)
+  {
+  }
+};
+
+namespace detail {
+
+// TODO: return device_uvector instead of passing pre-allocated memory
+/**
+ * @brief Identifies the stack context for each character from a JSON input. Specifically, we
+ * identify brackets and braces outside of quoted fields (e.g., field names, strings).
+ * At this stage, we do not perform bracket matching, i.e., we do not verify whether a closing
+ * bracket would actually pop a the corresponding opening brace.
+ *
+ * @param[in] json_in The string of input characters
+ * @param[out] d_top_of_stack Will be populated with what-is-on-top-of-the-stack for any given input
+ * character of \p d_json_in, where a '{' represents that the corresponding input character is
+ * within the context of a struct, a '[' represents that it is within the context of an array, and a
+ * '_' symbol that it is at the root of the JSON.
+ * @param[in] stack_behavior Specifies the stack's behavior
+ * @param[in] stream The cuda stream to dispatch GPU kernels to
+ */
+void get_stack_context(device_span<SymbolT const> json_in,
+                       SymbolT* d_top_of_stack,
+                       stack_behavior_t stack_behavior,
+                       rmm::cuda_stream_view stream);
+
+/**
+ * @brief Post-processes a token stream that may contain tokens from invalid lines. Expects that the
+ * token stream begins with a LineEnd token.
+ *
+ * @param tokens The tokens to be post-processed
+ * @param token_indices The tokens' corresponding indices that are post-processed
+ * @param stream The cuda stream to dispatch GPU kernels to
+ * @return Returns the post-processed token stream
+ */
+std::pair<rmm::device_uvector<PdaTokenT>, rmm::device_uvector<SymbolOffsetT>> process_token_stream(
+  device_span<PdaTokenT const> tokens,
+  device_span<SymbolOffsetT const> token_indices,
+  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Parses the given JSON string and generates a tree representation of the given input.
+ *
+ * @param tokens Vector of token types in the json string
+ * @param token_indices The indices within the input string corresponding to each token
+ * @param stream The CUDA stream to which kernels are dispatched
+ * @param mr Optional, resource with which to allocate
+ * @return A tree representation of the input JSON string as vectors of node type, parent index,
+ * level, begin index, and end index in the input JSON string
+ */
+tree_meta_t get_tree_representation(device_span<PdaTokenT const> tokens,
+                                    device_span<SymbolOffsetT const> token_indices,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Traverse the tree representation of the JSON input in records orient format and populate
+ * the output columns indices and row offsets within that column.
+ *
+ * @param d_input The JSON input
+ * @param d_tree A tree representation of the input JSON string as vectors of node type, parent
+ * index, level, begin index, and end index in the input JSON string
+ * @param is_array_of_arrays Whether the tree is an array of arrays
+ * @param is_enabled_lines Whether the input is a line-delimited JSON
+ * @param stream The CUDA stream to which kernels are dispatched
+ * @param mr Optional, resource with which to allocate
+ * @return A tuple of the output column indices and the row offsets within each column for each node
+ */
+std::tuple<rmm::device_uvector<NodeIndexT>, rmm::device_uvector<size_type>>
+records_orient_tree_traversal(device_span<SymbolT const> d_input,
+                              tree_meta_t const& d_tree,
+                              bool is_array_of_arrays,
+                              bool is_enabled_lines,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Searches for and selects nodes at level `row_array_children_level`. For each selected
+ * node, the function outputs the original index of that node (i.e., the nodes index within
+ * `node_levels`) and also generates the child index of that node relative to other children of the
+ * same parent. E.g., the child indices of the following string nodes relative to their respective
+ * list parents are: `[["a", "b", "c"], ["d", "e"]]`: `"a": 0, "b": 1, "c": 2, "d": 0, "e": 1`.
+ *
+ * @param row_array_children_level Level of the nodes to search for
+ * @param node_levels Levels of each node in the tree
+ * @param parent_node_ids Parent node ids of each node in the tree
+ * @param stream The CUDA stream to which kernels are dispatched
+ * @return A pair of device_uvector containing the original node indices and their corresponding
+ * child index
+ */
+std::pair<rmm::device_uvector<NodeIndexT>, rmm::device_uvector<NodeIndexT>>
+get_array_children_indices(TreeDepthT row_array_children_level,
+                           device_span<TreeDepthT const> node_levels,
+                           device_span<NodeIndexT const> parent_node_ids,
+                           rmm::cuda_stream_view stream);
+/**
+ * @brief Reduce node tree into column tree by aggregating each property of column.
+ *
+ * @param tree json node tree to reduce (modified in-place, but restored to original state)
+ * @param col_ids column ids of each node (modified in-place, but restored to original state)
+ * @param row_offsets row offsets of each node (modified in-place, but restored to original state)
+ * @param stream The CUDA stream to which kernels are dispatched
+ * @return A tuple containing the column tree, identifier for each column and the maximum row index
+ * in each column
+ */
+std::tuple<tree_meta_t, rmm::device_uvector<NodeIndexT>, rmm::device_uvector<size_type>>
+reduce_to_column_tree(tree_meta_t& tree,
+                      device_span<NodeIndexT> col_ids,
+                      device_span<size_type> row_offsets,
+                      rmm::cuda_stream_view stream);
+
+/** @copydoc host_parse_nested_json
+ * All processing is done in device memory.
+ *
+ */
+table_with_metadata device_parse_nested_json(device_span<SymbolT const> input,
+                                             cudf::io::json_reader_options const& options,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Parses the given JSON string and generates table from the given input.
+ *
+ * @param input The JSON input
+ * @param options Parsing options specifying the parsing behaviour
+ * @param stream The CUDA stream to which kernels are dispatched
+ * @param mr Optional, resource with which to allocate
+ * @return The data parsed from the given JSON input
+ */
+table_with_metadata host_parse_nested_json(device_span<SymbolT const> input,
+                                           cudf::io::json_reader_options const& options,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+
+}  // namespace cudf::io::json
diff --git a/cpp/src/io/json/nested_json_gpu.cu b/cpp/src/io/json/nested_json_gpu.cu
new file mode 100644
index 0000000..06ac114
--- /dev/null
+++ b/cpp/src/io/json/nested_json_gpu.cu
@@ -0,0 +1,2195 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "nested_json.hpp"
+
+#include <io/fst/logical_stack.cuh>
+#include <io/fst/lookup_tables.cuh>
+#include <io/utilities/parsing_utils.cuh>
+#include <io/utilities/string_parsing.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/utilities/visitor_overload.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/io/detail/tokenize_json.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/device_vector.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <limits>
+#include <stack>
+
+// Debug print flag
+#ifndef NJP_DEBUG_PRINT
+// #define NJP_DEBUG_PRINT
+#endif
+
+namespace {
+
+/**
+ * @brief While parsing the token stream, we use a stack of tree_nodes to maintain all the
+ * information about the data path that is relevant.
+ */
+struct tree_node {
+  // The column that this node is associated with
+  cudf::io::json::json_column* column;
+
+  // The row offset that this node belongs to within the given column
+  uint32_t row_index;
+
+  // Selected child column
+  // E.g., if this is a struct node, and we subsequently encountered the field name "a", then this
+  // point's to the struct's "a" child column
+  cudf::io::json::json_column* current_selected_col = nullptr;
+
+  std::size_t num_children = 0;
+};
+
+/**
+ * @brief Verifies that the JSON input can be handled without corrupted data due to offset
+ * overflows.
+ *
+ * @param input_size The JSON inputs size in bytes
+ */
+void check_input_size(std::size_t input_size)
+{
+  // Transduce() writes symbol offsets that may be as large input_size-1
+  CUDF_EXPECTS(input_size == 0 ||
+                 (input_size - 1) <= std::numeric_limits<cudf::io::json::SymbolOffsetT>::max(),
+               "Given JSON input is too large");
+}
+}  // namespace
+
+namespace cudf::io::json {
+
+// FST to prune tokens of invalid lines for recovering JSON lines format
+namespace token_filter {
+
+// Type used to represent the target state in the transition table
+using StateT = char;
+
+// Type used to represent a symbol group id
+using SymbolGroupT = uint8_t;
+
+/**
+ * @brief Definition of the DFA's states
+ */
+enum class dfa_states : StateT { VALID, INVALID, NUM_STATES };
+
+// Aliases for readability of the transition table
+constexpr auto TT_INV = dfa_states::INVALID;
+constexpr auto TT_VLD = dfa_states::VALID;
+
+/**
+ * @brief Definition of the symbol groups
+ */
+enum class dfa_symbol_group_id : SymbolGroupT {
+  ERROR,             ///< Error token symbol group
+  DELIMITER,         ///< Record / line delimiter symbol group
+  OTHER_SYMBOLS,     ///< Symbol group that implicitly matches all other tokens
+  NUM_SYMBOL_GROUPS  ///< Total number of symbol groups
+};
+
+constexpr auto TT_NUM_STATES     = static_cast<StateT>(dfa_states::NUM_STATES);
+constexpr auto NUM_SYMBOL_GROUPS = static_cast<uint32_t>(dfa_symbol_group_id::NUM_SYMBOL_GROUPS);
+
+// Lookup table to map an input symbol (i.e., a token) to a symbol group
+std::array<std::vector<PdaTokenT>, NUM_SYMBOL_GROUPS - 1> const symbol_groups{{
+  {static_cast<PdaTokenT>(token_t::ErrorBegin)},  // Symbols mapping to ERROR
+  {static_cast<PdaTokenT>(token_t::LineEnd)}      // Symbols mapping to DELIMITER
+}};
+
+/**
+ * @brief Function object to map (token,token_index) tuples to a symbol group.
+ */
+struct UnwrapTokenFromSymbolOp {
+  template <typename SymbolGroupLookupTableT>
+  CUDF_HOST_DEVICE SymbolGroupT operator()(SymbolGroupLookupTableT const& sgid_lut,
+                                           thrust::tuple<PdaTokenT, SymbolOffsetT> symbol) const
+  {
+    PdaTokenT const token_type = thrust::get<0>(symbol);
+    return sgid_lut.lookup(token_type);
+  }
+};
+
+/**
+ * @brief Translation function object that discards line delimiter tokens and tokens belonging to
+ * invalid lines.
+ */
+struct TransduceToken {
+  template <typename TransducerTableT, typename RelativeOffsetT, typename SymbolT>
+  constexpr CUDF_HOST_DEVICE SymbolT operator()(TransducerTableT const&,
+                                                StateT const state_id,
+                                                SymbolGroupT const match_id,
+                                                RelativeOffsetT const relative_offset,
+                                                SymbolT const read_symbol) const
+  {
+    const bool is_end_of_invalid_line =
+      (state_id == static_cast<StateT>(TT_INV) &&
+       match_id == static_cast<SymbolGroupT>(dfa_symbol_group_id::DELIMITER));
+
+    if (is_end_of_invalid_line) {
+      return relative_offset == 0 ? SymbolT{token_t::StructEnd, 0}
+                                  : SymbolT{token_t::StructBegin, 0};
+    } else {
+      return read_symbol;
+    }
+  }
+
+  template <typename TransducerTableT, typename SymbolT>
+  constexpr CUDF_HOST_DEVICE int32_t operator()(TransducerTableT const&,
+                                                StateT const state_id,
+                                                SymbolGroupT const match_id,
+                                                SymbolT const read_symbol) const
+  {
+    // Number of tokens emitted on invalid lines
+    constexpr int32_t num_inv_tokens = 2;
+
+    const bool is_delimiter = match_id == static_cast<SymbolGroupT>(dfa_symbol_group_id::DELIMITER);
+
+    // If state is either invalid or we're entering an invalid state, we discard tokens
+    const bool is_part_of_invalid_line =
+      (match_id != static_cast<SymbolGroupT>(dfa_symbol_group_id::ERROR) &&
+       state_id == static_cast<StateT>(TT_VLD));
+
+    // Indicates whether we transition from an invalid line to a potentially valid line
+    const bool is_end_of_invalid_line = (state_id == static_cast<StateT>(TT_INV) && is_delimiter);
+
+    int32_t const emit_count =
+      is_end_of_invalid_line ? num_inv_tokens : (is_part_of_invalid_line && !is_delimiter ? 1 : 0);
+    return emit_count;
+  }
+};
+
+// Transition table
+std::array<std::array<dfa_states, NUM_SYMBOL_GROUPS>, TT_NUM_STATES> const transition_table{
+  {/* IN_STATE      ERROR   DELIM   OTHER */
+   /* VALID    */ {{TT_INV, TT_VLD, TT_VLD}},
+   /* INVALID  */ {{TT_INV, TT_VLD, TT_INV}}}};
+
+// The DFA's starting state
+constexpr auto start_state = static_cast<StateT>(TT_VLD);
+}  // namespace token_filter
+
+// JSON to stack operator DFA (Deterministic Finite Automata)
+namespace to_stack_op {
+
+// Type used to represent the target state in the transition table
+using StateT = char;
+
+/**
+ * @brief Definition of the DFA's states
+ */
+enum class dfa_states : StateT {
+  // The active state while outside of a string. When encountering an opening bracket or curly
+  // brace, we push it onto the stack. When encountering a closing bracket or brace, we pop from the
+  // stack.
+  TT_OOS = 0U,
+
+  // The active state while within a string (e.g., field name or a string value). We do not push or
+  // pop from the stack while in this state.
+  TT_STR,
+
+  // The active state after encountering an escape symbol (e.g., '\'), while in the TT_STR state.
+  TT_ESC,
+
+  // Total number of states
+  TT_NUM_STATES
+};
+
+// Aliases for readability of the transition table
+constexpr auto TT_OOS = dfa_states::TT_OOS;
+constexpr auto TT_STR = dfa_states::TT_STR;
+constexpr auto TT_ESC = dfa_states::TT_ESC;
+
+/**
+ * @brief Definition of the symbol groups
+ */
+enum class dfa_symbol_group_id : uint8_t {
+  OPENING_BRACE,     ///< Opening brace SG: {
+  OPENING_BRACKET,   ///< Opening bracket SG: [
+  CLOSING_BRACE,     ///< Closing brace SG: }
+  CLOSING_BRACKET,   ///< Closing bracket SG: ]
+  QUOTE_CHAR,        ///< Quote character SG: "
+  ESCAPE_CHAR,       ///< Escape character SG: '\'
+  NEWLINE_CHAR,      ///< Newline character SG: '\n'
+  OTHER_SYMBOLS,     ///< SG implicitly matching all other characters
+  NUM_SYMBOL_GROUPS  ///< Total number of symbol groups
+};
+
+constexpr auto TT_NUM_STATES     = static_cast<StateT>(dfa_states::TT_NUM_STATES);
+constexpr auto NUM_SYMBOL_GROUPS = static_cast<uint32_t>(dfa_symbol_group_id::NUM_SYMBOL_GROUPS);
+
+// The i-th string representing all the characters of a symbol group
+std::array<std::string, NUM_SYMBOL_GROUPS - 1> const symbol_groups{
+  {{"{"}, {"["}, {"}"}, {"]"}, {"\""}, {"\\"}, {"\n"}}};
+
+// Transition table
+std::array<std::array<dfa_states, NUM_SYMBOL_GROUPS>, TT_NUM_STATES> const transition_table{
+  {/* IN_STATE          {       [       }       ]       "       \      \n    OTHER */
+   /* TT_OOS    */ {{TT_OOS, TT_OOS, TT_OOS, TT_OOS, TT_STR, TT_OOS, TT_OOS, TT_OOS}},
+   /* TT_STR    */ {{TT_STR, TT_STR, TT_STR, TT_STR, TT_OOS, TT_ESC, TT_STR, TT_STR}},
+   /* TT_ESC    */ {{TT_STR, TT_STR, TT_STR, TT_STR, TT_STR, TT_STR, TT_STR, TT_STR}}}};
+
+// Translation table (i.e., for each transition, what are the symbols that we output)
+std::array<std::array<std::vector<char>, NUM_SYMBOL_GROUPS>, TT_NUM_STATES> const translation_table{
+  {/* IN_STATE         {      [      }      ]      "      \     \n    OTHER */
+   /* TT_OOS    */ {{{'{'}, {'['}, {'}'}, {']'}, {}, {}, {}, {}}},
+   /* TT_STR    */ {{{}, {}, {}, {}, {}, {}, {}, {}}},
+   /* TT_ESC    */ {{{}, {}, {}, {}, {}, {}, {}, {}}}}};
+
+// Translation table
+std::array<std::array<std::vector<char>, NUM_SYMBOL_GROUPS>, TT_NUM_STATES> const
+  resetting_translation_table{
+    {/* IN_STATE         {      [      }      ]      "      \     \n    OTHER */
+     /* TT_OOS    */ {{{'{'}, {'['}, {'}'}, {']'}, {}, {}, {'\n'}, {}}},
+     /* TT_STR    */ {{{}, {}, {}, {}, {}, {}, {}, {}}},
+     /* TT_ESC    */ {{{}, {}, {}, {}, {}, {}, {}, {}}}}};
+
+// The DFA's starting state
+constexpr auto start_state = static_cast<StateT>(TT_OOS);
+}  // namespace to_stack_op
+
+// JSON tokenizer pushdown automaton
+namespace tokenizer_pda {
+
+// Type used to represent the target state in the transition table
+using StateT = char;
+
+/**
+ * @brief Symbol groups for the input alphabet for the pushdown automaton
+ */
+enum class symbol_group_id : PdaSymbolGroupIdT {
+  /// Opening brace
+  OPENING_BRACE,
+  /// Opening bracket
+  OPENING_BRACKET,
+  /// Closing brace
+  CLOSING_BRACE,
+  /// Closing bracket
+  CLOSING_BRACKET,
+  /// Quote
+  QUOTE,
+  /// Escape
+  ESCAPE,
+  /// Comma
+  COMMA,
+  /// Colon
+  COLON,
+  /// Whitespace
+  WHITE_SPACE,
+  /// Linebreak
+  LINE_BREAK,
+  /// Other (any input symbol not assigned to one of the above symbol groups)
+  OTHER,
+  /// Total number of symbol groups amongst which to differentiate
+  NUM_PDA_INPUT_SGS
+};
+
+/**
+ * @brief Symbols in the stack alphabet
+ */
+enum class stack_symbol_group_id : PdaStackSymbolGroupIdT {
+  /// Symbol representing that we're at the JSON root (nesting level 0)
+  STACK_ROOT,
+
+  /// Symbol representing that we're currently within a list object
+  STACK_LIST,
+
+  /// Symbol representing that we're currently within a struct object
+  STACK_STRUCT,
+
+  /// Total number of symbols in the stack alphabet
+  NUM_STACK_SGS
+};
+constexpr auto NUM_PDA_INPUT_SGS =
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::NUM_PDA_INPUT_SGS);
+constexpr auto NUM_STACK_SGS =
+  static_cast<PdaStackSymbolGroupIdT>(stack_symbol_group_id::NUM_STACK_SGS);
+
+/// Total number of symbol groups to differentiate amongst (stack alphabet * input alphabet)
+constexpr PdaSymbolGroupIdT NUM_PDA_SGIDS = NUM_PDA_INPUT_SGS * NUM_STACK_SGS;
+
+/// Mapping a input symbol to the symbol group id
+static __constant__ PdaSymbolGroupIdT tos_sg_to_pda_sgid[] = {
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::WHITE_SPACE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::LINE_BREAK),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::WHITE_SPACE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::WHITE_SPACE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::QUOTE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::COMMA),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::COLON),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OPENING_BRACKET),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::ESCAPE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::CLOSING_BRACKET),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OPENING_BRACE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::CLOSING_BRACE),
+  static_cast<PdaSymbolGroupIdT>(symbol_group_id::OTHER)};
+
+/**
+ * @brief Maps a (top-of-stack symbol, input symbol)-pair to a symbol group id of the deterministic
+ * visibly pushdown automaton (DVPA)
+ */
+struct PdaSymbolToSymbolGroupId {
+  template <typename SymbolT, typename StackSymbolT>
+  __device__ __forceinline__ PdaSymbolGroupIdT
+  operator()(thrust::tuple<SymbolT, StackSymbolT> symbol_pair) const
+  {
+    // The symbol read from the input
+    auto symbol = thrust::get<0>(symbol_pair);
+
+    // The stack symbol (i.e., what is on top of the stack at the time the input symbol was read)
+    // I.e., whether we're reading in something within a struct, a list, or the JSON root
+    auto stack_symbol = thrust::get<1>(symbol_pair);
+
+    // The stack symbol offset: '_' is the root group (0), '[' is the list group (1), '{' is the
+    // struct group (2)
+    int32_t stack_idx = static_cast<PdaStackSymbolGroupIdT>(
+      (stack_symbol == '_') ? stack_symbol_group_id::STACK_ROOT
+                            : ((stack_symbol == '[') ? stack_symbol_group_id::STACK_LIST
+                                                     : stack_symbol_group_id::STACK_STRUCT));
+
+    // The relative symbol group id of the current input symbol
+    constexpr auto pda_sgid_lookup_size =
+      static_cast<int32_t>(sizeof(tos_sg_to_pda_sgid) / sizeof(tos_sg_to_pda_sgid[0]));
+    PdaSymbolGroupIdT symbol_gid =
+      tos_sg_to_pda_sgid[min(static_cast<int32_t>(symbol), pda_sgid_lookup_size - 1)];
+    return stack_idx * static_cast<PdaSymbolGroupIdT>(symbol_group_id::NUM_PDA_INPUT_SGS) +
+           symbol_gid;
+  }
+};
+
+// The states defined by the pushdown automaton
+enum class pda_state_t : StateT {
+  // Beginning of value
+  PD_BOV,
+  // Beginning of array
+  PD_BOA,
+  // Literal or number
+  PD_LON,
+  // String
+  PD_STR,
+  // After escape char when within string
+  PD_SCE,
+  // After having parsed a value
+  PD_PVL,
+  // Before the next field name
+  PD_BFN,
+  // Field name
+  PD_FLN,
+  // After escape char when within field name
+  PD_FNE,
+  // After a field name inside a struct
+  PD_PFN,
+  // Error state (trap state)
+  PD_ERR,
+  // Total number of PDA states
+  PD_NUM_STATES
+};
+
+enum class json_format_cfg_t {
+  // Format describing regular JSON
+  JSON,
+
+  // Format describing permissive newline-delimited JSON
+  // I.e., newline characters are only treteated as delimiters at the root stack level
+  // E.g., this is treated as a single record:
+  // {"a":
+  //  123}
+  JSON_LINES,
+
+  // Format describing strict newline-delimited JSON
+  // I.e., All newlines are delimiting a record, independent of the context they appear in
+  JSON_LINES_STRICT,
+
+  // Transition table for parsing newline-delimited JSON that recovers from invalid JSON lines
+  // This format also follows `JSON_LINES_STRICT` behaviour
+  JSON_LINES_RECOVER
+
+};
+
+// Aliases for readability of the transition table
+constexpr auto PD_BOV = pda_state_t::PD_BOV;
+constexpr auto PD_BOA = pda_state_t::PD_BOA;
+constexpr auto PD_LON = pda_state_t::PD_LON;
+constexpr auto PD_STR = pda_state_t::PD_STR;
+constexpr auto PD_SCE = pda_state_t::PD_SCE;
+constexpr auto PD_PVL = pda_state_t::PD_PVL;
+constexpr auto PD_BFN = pda_state_t::PD_BFN;
+constexpr auto PD_FLN = pda_state_t::PD_FLN;
+constexpr auto PD_FNE = pda_state_t::PD_FNE;
+constexpr auto PD_PFN = pda_state_t::PD_PFN;
+constexpr auto PD_ERR = pda_state_t::PD_ERR;
+
+constexpr auto PD_NUM_STATES = static_cast<StateT>(pda_state_t::PD_NUM_STATES);
+
+// The starting state of the pushdown automaton
+constexpr auto start_state = static_cast<StateT>(pda_state_t::PD_BOV);
+
+/**
+ * @brief Getting the transition table
+ */
+auto get_transition_table(json_format_cfg_t format)
+{
+  static_assert(static_cast<PdaStackSymbolGroupIdT>(stack_symbol_group_id::STACK_ROOT) == 0);
+  static_assert(static_cast<PdaStackSymbolGroupIdT>(stack_symbol_group_id::STACK_LIST) == 1);
+  static_assert(static_cast<PdaStackSymbolGroupIdT>(stack_symbol_group_id::STACK_STRUCT) == 2);
+
+  std::array<std::array<pda_state_t, NUM_PDA_SGIDS>, PD_NUM_STATES> pda_tt;
+
+  if (format == json_format_cfg_t::JSON || format == json_format_cfg_t::JSON_LINES) {
+    // In case of newline-delimited JSON, multiple newlines are ignored, similar to whitespace.
+    // Thas is, empty lines are ignored
+    // PD_ANL describes the target state after a new line on an empty stack (JSON root level)
+    auto const PD_ANL = (format == json_format_cfg_t::JSON) ? PD_PVL : PD_BOV;
+
+    // First row:  empty stack         ("root" level of the JSON)
+    // Second row: '[' on top of stack (we're parsing a list value)
+    // Third row:  '{' on top of stack (we're parsing a struct value)
+    //  {       [       }       ]       "       \       ,       :     space   newline other
+    pda_tt[static_cast<StateT>(pda_state_t::PD_BOV)] = {
+      PD_BOA, PD_BOA, PD_ERR, PD_ERR, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_BOV, PD_LON,
+      PD_BOA, PD_BOA, PD_ERR, PD_PVL, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_BOV, PD_LON,
+      PD_BOA, PD_BOA, PD_ERR, PD_ERR, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_BOV, PD_LON};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_BOA)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_BOA, PD_BOA, PD_ERR, PD_PVL, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOA, PD_BOA, PD_LON,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_FLN, PD_ERR, PD_ERR, PD_ERR, PD_BOA, PD_BOA, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_LON)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_PVL, PD_LON,
+      PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_BOV, PD_ERR, PD_PVL, PD_PVL, PD_LON,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_ERR, PD_BFN, PD_ERR, PD_PVL, PD_PVL, PD_LON};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_STR)] = {
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_PVL, PD_SCE, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_PVL, PD_SCE, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_PVL, PD_SCE, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_SCE)] = {
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_PVL)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_ANL, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_BOV, PD_ERR, PD_PVL, PD_PVL, PD_ERR,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_ERR, PD_BFN, PD_ERR, PD_PVL, PD_PVL, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_BFN)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_FLN, PD_ERR, PD_ERR, PD_ERR, PD_BFN, PD_BFN, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_FLN)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_PFN, PD_FNE, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_FNE)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_PFN)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_PFN, PD_PFN, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_ERR)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR};
+  }
+  // Transition table for strict JSON lines (including recovery)
+  // Newlines are treated as record delimiters
+  else {
+    // In case of newline-delimited JSON, multiple newlines are ignored, similar to whitespace.
+    // Thas is, empty lines are ignored
+    // PD_ANL describes the target state after a new line after encountering error state
+    auto const PD_ANL = (format == json_format_cfg_t::JSON_LINES_RECOVER) ? PD_BOV : PD_ERR;
+
+    // First row:  empty stack         ("root" level of the JSON)
+    // Second row: '[' on top of stack (we're parsing a list value)
+    // Third row:  '{' on top of stack (we're parsing a struct value)
+    //  {       [       }       ]       "       \       ,       :     space   newline other
+    pda_tt[static_cast<StateT>(pda_state_t::PD_BOV)] = {
+      PD_BOA, PD_BOA, PD_ERR, PD_ERR, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_BOV, PD_LON,
+      PD_BOA, PD_BOA, PD_ERR, PD_PVL, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_BOV, PD_LON,
+      PD_BOA, PD_BOA, PD_ERR, PD_ERR, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_BOV, PD_LON};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_BOA)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_BOA, PD_BOA, PD_ERR, PD_PVL, PD_STR, PD_ERR, PD_ERR, PD_ERR, PD_BOA, PD_BOV, PD_LON,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_FLN, PD_ERR, PD_ERR, PD_ERR, PD_BOA, PD_BOV, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_LON)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_BOV, PD_LON,
+      PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_BOV, PD_ERR, PD_PVL, PD_BOV, PD_LON,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_ERR, PD_BFN, PD_ERR, PD_PVL, PD_BOV, PD_LON};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_STR)] = {
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_PVL, PD_SCE, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_PVL, PD_SCE, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_PVL, PD_SCE, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_SCE)] = {
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR,
+      PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR, PD_STR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_PVL)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_BOV, PD_ERR, PD_PVL, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_ERR, PD_ERR, PD_BFN, PD_ERR, PD_PVL, PD_BOV, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_BFN)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_PVL, PD_ERR, PD_FLN, PD_ERR, PD_ERR, PD_ERR, PD_BFN, PD_BOV, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_FLN)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_PFN, PD_FNE, PD_FLN, PD_FLN, PD_FLN, PD_BOV, PD_FLN};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_FNE)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR,
+      PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN, PD_FLN};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_PFN)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_BOV, PD_PFN, PD_BOV, PD_ERR};
+    pda_tt[static_cast<StateT>(pda_state_t::PD_ERR)] = {
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ANL, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ANL, PD_ERR,
+      PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ERR, PD_ANL, PD_ERR};
+  }
+  return pda_tt;
+}
+
+/**
+ * @brief Getting the translation table
+ */
+auto get_translation_table(bool include_line_delimiter)
+{
+  constexpr auto StructBegin       = token_t::StructBegin;
+  constexpr auto StructEnd         = token_t::StructEnd;
+  constexpr auto ListBegin         = token_t::ListBegin;
+  constexpr auto ListEnd           = token_t::ListEnd;
+  constexpr auto StructMemberBegin = token_t::StructMemberBegin;
+  constexpr auto StructMemberEnd   = token_t::StructMemberEnd;
+  constexpr auto FieldNameBegin    = token_t::FieldNameBegin;
+  constexpr auto FieldNameEnd      = token_t::FieldNameEnd;
+  constexpr auto StringBegin       = token_t::StringBegin;
+  constexpr auto StringEnd         = token_t::StringEnd;
+  constexpr auto ValueBegin        = token_t::ValueBegin;
+  constexpr auto ValueEnd          = token_t::ValueEnd;
+  constexpr auto ErrorBegin        = token_t::ErrorBegin;
+
+  /**
+   * @brief Appends token_t::LineEnd token to the given token sequence, if and only if
+   * `include_line_delimiter` is true.
+   */
+  auto nl_tokens = [include_line_delimiter](std::vector<char> tokens) {
+    if (include_line_delimiter) { tokens.push_back(token_t::LineEnd); }
+    return tokens;
+  };
+
+  std::array<std::array<std::vector<char>, NUM_PDA_SGIDS>, PD_NUM_STATES> pda_tlt;
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_BOV)] = {{                /*ROOT*/
+                                                        {StructBegin},  // OPENING_BRACE
+                                                        {ListBegin},    // OPENING_BRACKET
+                                                        {ErrorBegin},   // CLOSING_BRACE
+                                                        {ErrorBegin},   // CLOSING_BRACKET
+                                                        {StringBegin},  // QUOTE
+                                                        {ErrorBegin},   // ESCAPE
+                                                        {ErrorBegin},   // COMMA
+                                                        {ErrorBegin},   // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {ValueBegin},   // OTHER
+                                                        /*LIST*/
+                                                        {StructBegin},  // OPENING_BRACE
+                                                        {ListBegin},    // OPENING_BRACKET
+                                                        {ErrorBegin},   // CLOSING_BRACE
+                                                        {ListEnd},      // CLOSING_BRACKET
+                                                        {StringBegin},  // QUOTE
+                                                        {ErrorBegin},   // ESCAPE
+                                                        {ErrorBegin},   // COMMA
+                                                        {ErrorBegin},   // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {ValueBegin},   // OTHER
+                                                        /*STRUCT*/
+                                                        {StructBegin},   // OPENING_BRACE
+                                                        {ListBegin},     // OPENING_BRACKET
+                                                        {ErrorBegin},    // CLOSING_BRACE
+                                                        {ErrorBegin},    // CLOSING_BRACKET
+                                                        {StringBegin},   // QUOTE
+                                                        {ErrorBegin},    // ESCAPE
+                                                        {ErrorBegin},    // COMMA
+                                                        {ErrorBegin},    // COLON
+                                                        {},              // WHITE_SPACE
+                                                        nl_tokens({}),   // LINE_BREAK
+                                                        {ValueBegin}}};  // OTHER
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_BOA)] = {
+    {                          /*ROOT*/
+     {ErrorBegin},             // OPENING_BRACE
+     {ErrorBegin},             // OPENING_BRACKET
+     {ErrorBegin},             // CLOSING_BRACE
+     {ErrorBegin},             // CLOSING_BRACKET
+     {ErrorBegin},             // QUOTE
+     {ErrorBegin},             // ESCAPE
+     {ErrorBegin},             // COMMA
+     {ErrorBegin},             // COLON
+     {ErrorBegin},             // WHITE_SPACE
+     nl_tokens({ErrorBegin}),  // LINE_BREAK
+     {ErrorBegin},             // OTHER
+     /*LIST*/
+     {StructBegin},  // OPENING_BRACE
+     {ListBegin},    // OPENING_BRACKET
+     {ErrorBegin},   // CLOSING_BRACE
+     {ListEnd},      // CLOSING_BRACKET
+     {StringBegin},  // QUOTE
+     {ErrorBegin},   // ESCAPE
+     {ErrorBegin},   // COMMA
+     {ErrorBegin},   // COLON
+     {},             // WHITE_SPACE
+     nl_tokens({}),  // LINE_BREAK
+     {ValueBegin},   // OTHER
+     /*STRUCT*/
+     {ErrorBegin},                         // OPENING_BRACE
+     {ErrorBegin},                         // OPENING_BRACKET
+     {StructEnd},                          // CLOSING_BRACE
+     {ErrorBegin},                         // CLOSING_BRACKET
+     {StructMemberBegin, FieldNameBegin},  // QUOTE
+     {ErrorBegin},                         // ESCAPE
+     {ErrorBegin},                         // COMMA
+     {ErrorBegin},                         // COLON
+     {},                                   // WHITE_SPACE
+     nl_tokens({}),                        // LINE_BREAK
+     {ErrorBegin}}};                       // OTHER
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_LON)] = {
+    {                        /*ROOT*/
+     {ErrorBegin},           // OPENING_BRACE
+     {ErrorBegin},           // OPENING_BRACKET
+     {ErrorBegin},           // CLOSING_BRACE
+     {ErrorBegin},           // CLOSING_BRACKET
+     {ErrorBegin},           // QUOTE
+     {ErrorBegin},           // ESCAPE
+     {ErrorBegin},           // COMMA
+     {ErrorBegin},           // COLON
+     {ValueEnd},             // WHITE_SPACE
+     nl_tokens({ValueEnd}),  // LINE_BREAK
+     {},                     // OTHER
+     /*LIST*/
+     {ErrorBegin},           // OPENING_BRACE
+     {ErrorBegin},           // OPENING_BRACKET
+     {ErrorBegin},           // CLOSING_BRACE
+     {ValueEnd, ListEnd},    // CLOSING_BRACKET
+     {ErrorBegin},           // QUOTE
+     {ErrorBegin},           // ESCAPE
+     {ValueEnd},             // COMMA
+     {ErrorBegin},           // COLON
+     {ValueEnd},             // WHITE_SPACE
+     nl_tokens({ValueEnd}),  // LINE_BREAK
+     {},                     // OTHER
+     /*STRUCT*/
+     {ErrorBegin},                            // OPENING_BRACE
+     {ErrorBegin},                            // OPENING_BRACKET
+     {ValueEnd, StructMemberEnd, StructEnd},  // CLOSING_BRACE
+     {ErrorBegin},                            // CLOSING_BRACKET
+     {ErrorBegin},                            // QUOTE
+     {ErrorBegin},                            // ESCAPE
+     {ValueEnd, StructMemberEnd},             // COMMA
+     {ErrorBegin},                            // COLON
+     {ValueEnd},                              // WHITE_SPACE
+     nl_tokens({ValueEnd}),                   // LINE_BREAK
+     {}}};                                    // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_STR)] = {{                /*ROOT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {StringEnd},    // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {},             // OTHER
+                                                        /*LIST*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {StringEnd},    // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {},             // OTHER
+                                                        /*STRUCT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {StringEnd},    // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {}}};           // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_SCE)] = {{                /*ROOT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {},             // OTHER
+                                                        /*LIST*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {},             // OTHER
+                                                        /*STRUCT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {}}};           // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_PVL)] = {
+    {                /*ROOT*/
+     {ErrorBegin},   // OPENING_BRACE
+     {ErrorBegin},   // OPENING_BRACKET
+     {ErrorBegin},   // CLOSING_BRACE
+     {ErrorBegin},   // CLOSING_BRACKET
+     {ErrorBegin},   // QUOTE
+     {ErrorBegin},   // ESCAPE
+     {ErrorBegin},   // COMMA
+     {ErrorBegin},   // COLON
+     {},             // WHITE_SPACE
+     nl_tokens({}),  // LINE_BREAK
+     {ErrorBegin},   // OTHER
+     /*LIST*/
+     {ErrorBegin},   // OPENING_BRACE
+     {ErrorBegin},   // OPENING_BRACKET
+     {ErrorBegin},   // CLOSING_BRACE
+     {ListEnd},      // CLOSING_BRACKET
+     {ErrorBegin},   // QUOTE
+     {ErrorBegin},   // ESCAPE
+     {},             // COMMA
+     {ErrorBegin},   // COLON
+     {},             // WHITE_SPACE
+     nl_tokens({}),  // LINE_BREAK
+     {ErrorBegin},   // OTHER
+     /*STRUCT*/
+     {ErrorBegin},                  // OPENING_BRACE
+     {ErrorBegin},                  // OPENING_BRACKET
+     {StructMemberEnd, StructEnd},  // CLOSING_BRACE
+     {ErrorBegin},                  // CLOSING_BRACKET
+     {ErrorBegin},                  // QUOTE
+     {ErrorBegin},                  // ESCAPE
+     {StructMemberEnd},             // COMMA
+     {ErrorBegin},                  // COLON
+     {},                            // WHITE_SPACE
+     nl_tokens({}),                 // LINE_BREAK
+     {ErrorBegin}}};                // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_BFN)] = {
+    {                          /*ROOT*/
+     {ErrorBegin},             // OPENING_BRACE
+     {ErrorBegin},             // OPENING_BRACKET
+     {ErrorBegin},             // CLOSING_BRACE
+     {ErrorBegin},             // CLOSING_BRACKET
+     {ErrorBegin},             // QUOTE
+     {ErrorBegin},             // ESCAPE
+     {ErrorBegin},             // COMMA
+     {ErrorBegin},             // COLON
+     {ErrorBegin},             // WHITE_SPACE
+     nl_tokens({ErrorBegin}),  // LINE_BREAK
+     {ErrorBegin},             // OTHER
+     /*LIST*/
+     {ErrorBegin},             // OPENING_BRACE
+     {ErrorBegin},             // OPENING_BRACKET
+     {ErrorBegin},             // CLOSING_BRACE
+     {ErrorBegin},             // CLOSING_BRACKET
+     {ErrorBegin},             // QUOTE
+     {ErrorBegin},             // ESCAPE
+     {ErrorBegin},             // COMMA
+     {ErrorBegin},             // COLON
+     {ErrorBegin},             // WHITE_SPACE
+     nl_tokens({ErrorBegin}),  // LINE_BREAK
+     {ErrorBegin},             // OTHER
+     /*STRUCT*/
+     {ErrorBegin},                         // OPENING_BRACE
+     {ErrorBegin},                         // OPENING_BRACKET
+     {StructEnd},                          // CLOSING_BRACE
+     {ErrorBegin},                         // CLOSING_BRACKET
+     {StructMemberBegin, FieldNameBegin},  // QUOTE
+     {ErrorBegin},                         // ESCAPE
+     {ErrorBegin},                         // COMMA
+     {ErrorBegin},                         // COLON
+     {},                                   // WHITE_SPACE
+     nl_tokens({}),                        // LINE_BREAK
+     {ErrorBegin}}};                       // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_FLN)] = {{                          /*ROOT*/
+                                                        {ErrorBegin},             // OPENING_BRACE
+                                                        {ErrorBegin},             // OPENING_BRACKET
+                                                        {ErrorBegin},             // CLOSING_BRACE
+                                                        {ErrorBegin},             // CLOSING_BRACKET
+                                                        {ErrorBegin},             // QUOTE
+                                                        {ErrorBegin},             // ESCAPE
+                                                        {ErrorBegin},             // COMMA
+                                                        {ErrorBegin},             // COLON
+                                                        {ErrorBegin},             // WHITE_SPACE
+                                                        nl_tokens({ErrorBegin}),  // LINE_BREAK
+                                                        {ErrorBegin},             // OTHER
+                                                        /*LIST*/
+                                                        {ErrorBegin},             // OPENING_BRACE
+                                                        {ErrorBegin},             // OPENING_BRACKET
+                                                        {ErrorBegin},             // CLOSING_BRACE
+                                                        {ErrorBegin},             // CLOSING_BRACKET
+                                                        {ErrorBegin},             // QUOTE
+                                                        {ErrorBegin},             // ESCAPE
+                                                        {ErrorBegin},             // COMMA
+                                                        {ErrorBegin},             // COLON
+                                                        {ErrorBegin},             // WHITE_SPACE
+                                                        nl_tokens({ErrorBegin}),  // LINE_BREAK
+                                                        {ErrorBegin},             // OTHER
+                                                        /*STRUCT*/
+                                                        {},              // OPENING_BRACE
+                                                        {},              // OPENING_BRACKET
+                                                        {},              // CLOSING_BRACE
+                                                        {},              // CLOSING_BRACKET
+                                                        {FieldNameEnd},  // QUOTE
+                                                        {},              // ESCAPE
+                                                        {},              // COMMA
+                                                        {},              // COLON
+                                                        {},              // WHITE_SPACE
+                                                        nl_tokens({}),   // LINE_BREAK
+                                                        {}}};            // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_FNE)] = {{                          /*ROOT*/
+                                                        {ErrorBegin},             // OPENING_BRACE
+                                                        {ErrorBegin},             // OPENING_BRACKET
+                                                        {ErrorBegin},             // CLOSING_BRACE
+                                                        {ErrorBegin},             // CLOSING_BRACKET
+                                                        {ErrorBegin},             // QUOTE
+                                                        {ErrorBegin},             // ESCAPE
+                                                        {ErrorBegin},             // COMMA
+                                                        {ErrorBegin},             // COLON
+                                                        {ErrorBegin},             // WHITE_SPACE
+                                                        nl_tokens({ErrorBegin}),  // LINE_BREAK
+                                                        {ErrorBegin},             // OTHER
+                                                        /*LIST*/
+                                                        {ErrorBegin},             // OPENING_BRACE
+                                                        {ErrorBegin},             // OPENING_BRACKET
+                                                        {ErrorBegin},             // CLOSING_BRACE
+                                                        {ErrorBegin},             // CLOSING_BRACKET
+                                                        {ErrorBegin},             // QUOTE
+                                                        {ErrorBegin},             // ESCAPE
+                                                        {ErrorBegin},             // COMMA
+                                                        {ErrorBegin},             // COLON
+                                                        {ErrorBegin},             // WHITE_SPACE
+                                                        nl_tokens({ErrorBegin}),  // LINE_BREAK
+                                                        {ErrorBegin},             // OTHER
+                                                        /*STRUCT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {}}};           // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_PFN)] = {{                          /*ROOT*/
+                                                        {ErrorBegin},             // OPENING_BRACE
+                                                        {ErrorBegin},             // OPENING_BRACKET
+                                                        {ErrorBegin},             // CLOSING_BRACE
+                                                        {ErrorBegin},             // CLOSING_BRACKET
+                                                        {ErrorBegin},             // QUOTE
+                                                        {ErrorBegin},             // ESCAPE
+                                                        {ErrorBegin},             // COMMA
+                                                        {ErrorBegin},             // COLON
+                                                        {ErrorBegin},             // WHITE_SPACE
+                                                        nl_tokens({ErrorBegin}),  // LINE_BREAK
+                                                        {ErrorBegin},             // OTHER
+                                                        /*LIST*/
+                                                        {ErrorBegin},             // OPENING_BRACE
+                                                        {ErrorBegin},             // OPENING_BRACKET
+                                                        {ErrorBegin},             // CLOSING_BRACE
+                                                        {ErrorBegin},             // CLOSING_BRACKET
+                                                        {ErrorBegin},             // QUOTE
+                                                        {ErrorBegin},             // ESCAPE
+                                                        {ErrorBegin},             // COMMA
+                                                        {ErrorBegin},             // COLON
+                                                        {ErrorBegin},             // WHITE_SPACE
+                                                        nl_tokens({ErrorBegin}),  // LINE_BREAK
+                                                        {ErrorBegin},             // OTHER
+                                                        /*STRUCT*/
+                                                        {ErrorBegin},    // OPENING_BRACE
+                                                        {ErrorBegin},    // OPENING_BRACKET
+                                                        {ErrorBegin},    // CLOSING_BRACE
+                                                        {ErrorBegin},    // CLOSING_BRACKET
+                                                        {ErrorBegin},    // QUOTE
+                                                        {ErrorBegin},    // ESCAPE
+                                                        {ErrorBegin},    // COMMA
+                                                        {},              // COLON
+                                                        {},              // WHITE_SPACE
+                                                        nl_tokens({}),   // LINE_BREAK
+                                                        {ErrorBegin}}};  // OTHER
+
+  pda_tlt[static_cast<StateT>(pda_state_t::PD_ERR)] = {{                /*ROOT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {},             // OTHER
+                                                        /*LIST*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {},             // OTHER
+                                                        /*STRUCT*/
+                                                        {},             // OPENING_BRACE
+                                                        {},             // OPENING_BRACKET
+                                                        {},             // CLOSING_BRACE
+                                                        {},             // CLOSING_BRACKET
+                                                        {},             // QUOTE
+                                                        {},             // ESCAPE
+                                                        {},             // COMMA
+                                                        {},             // COLON
+                                                        {},             // WHITE_SPACE
+                                                        nl_tokens({}),  // LINE_BREAK
+                                                        {}}};           // OTHER
+  return pda_tlt;
+}
+
+}  // namespace tokenizer_pda
+
+/**
+ * @brief Function object used to filter for brackets and braces that represent push and pop
+ * operations
+ */
+struct JSONToStackOp {
+  template <typename StackSymbolT>
+  constexpr CUDF_HOST_DEVICE fst::stack_op_type operator()(StackSymbolT const& stack_symbol) const
+  {
+    switch (stack_symbol) {
+      case '{':
+      case '[': return fst::stack_op_type::PUSH;
+      case '}':
+      case ']': return fst::stack_op_type::POP;
+      default: return fst::stack_op_type::READ;
+    }
+  }
+};
+
+/**
+ * @brief Function object used to filter for brackets and braces that represent push and pop
+ * operations
+ */
+struct JSONWithRecoveryToStackOp {
+  template <typename StackSymbolT>
+  constexpr CUDF_HOST_DEVICE fst::stack_op_type operator()(StackSymbolT const& stack_symbol) const
+  {
+    switch (stack_symbol) {
+      case '{':
+      case '[': return fst::stack_op_type::PUSH;
+      case '}':
+      case ']': return fst::stack_op_type::POP;
+      case '\n': return fst::stack_op_type::RESET;
+      default: return fst::stack_op_type::READ;
+    }
+  }
+};
+
+void json_column::null_fill(row_offset_t up_to_row_offset)
+{
+  // Fill all the rows up to up_to_row_offset with "empty"/null rows
+  validity.resize(word_index(up_to_row_offset) + 1);
+  std::fill_n(std::back_inserter(string_offsets),
+              up_to_row_offset - string_offsets.size(),
+              (string_offsets.size() > 0) ? string_offsets.back() : 0);
+  std::fill_n(std::back_inserter(string_lengths), up_to_row_offset - string_lengths.size(), 0);
+  std::fill_n(std::back_inserter(child_offsets),
+              up_to_row_offset + 1 - child_offsets.size(),
+              (child_offsets.size() > 0) ? child_offsets.back() : 0);
+  current_offset = up_to_row_offset;
+}
+
+void json_column::level_child_cols_recursively(row_offset_t min_row_count)
+{
+  // Fill this columns with nulls up to the given row count
+  null_fill(min_row_count);
+
+  // If this is a struct column, we need to level all its child columns
+  if (type == json_col_t::StructColumn) {
+    for (auto it = std::begin(child_columns); it != std::end(child_columns); it++) {
+      it->second.level_child_cols_recursively(min_row_count);
+    }
+  }
+  // If this is a list column, we need to make sure that its child column levels its children
+  else if (type == json_col_t::ListColumn) {
+    auto it = std::begin(child_columns);
+    // Make that child column fill its child columns up to its own row count
+    if (it != std::end(child_columns)) {
+      it->second.level_child_cols_recursively(it->second.current_offset);
+    }
+  }
+};
+
+void json_column::append_row(uint32_t row_index,
+                             json_col_t row_type,
+                             uint32_t string_offset,
+                             uint32_t string_end,
+                             uint32_t child_count)
+{
+  // If, thus far, the column's type couldn't be inferred, we infer it to the given type
+  if (type == json_col_t::Unknown) {
+    type = row_type;
+  }
+  // If, at some point within a column, we encounter a nested type (list or struct),
+  // we change that column's type to that respective nested type and invalidate all previous rows
+  else if (type == json_col_t::StringColumn &&
+           (row_type == json_col_t::ListColumn || row_type == json_col_t::StructColumn)) {
+    // Change the column type
+    type = row_type;
+
+    // Invalidate all previous entries, as they were _not_ of the nested type to which we just
+    // converted
+    std::fill_n(validity.begin(), validity.size(), 0);
+    valid_count = 0U;
+  }
+  // If this is a nested column but we're trying to insert either (a) a list node into a struct
+  // column or (b) a struct node into a list column, we fail
+  CUDF_EXPECTS(not((type == json_col_t::ListColumn and row_type == json_col_t::StructColumn) or
+                   (type == json_col_t::StructColumn and row_type == json_col_t::ListColumn)),
+               "A mix of lists and structs within the same column is not supported");
+
+  // We shouldn't run into this, as we shouldn't be asked to append an "unknown" row type
+  CUDF_EXPECTS(type != json_col_t::Unknown, "Encountered invalid JSON token sequence");
+
+  // Fill all the omitted rows with "empty"/null rows (if needed)
+  null_fill(row_index);
+
+  // Table listing what we intend to use for a given column type and row type combination
+  // col type | row type  => {valid, FAIL, null}
+  // -----------------------------------------------
+  // List     | List      => valid
+  // List     | Struct    => FAIL
+  // List     | String    => null
+  // Struct   | List      => FAIL
+  // Struct   | Struct    => valid
+  // Struct   | String    => null
+  // String   | List      => valid (we switch col type to list, null'ing all previous rows)
+  // String   | Struct    => valid (we switch col type to list, null'ing all previous rows)
+  // String   | String    => valid
+  bool const is_valid = (type == row_type);
+  if (static_cast<size_type>(validity.size()) < word_index(current_offset)) validity.push_back({});
+  if (is_valid) { set_bit_unsafe(&validity.back(), intra_word_index(current_offset)); }
+  valid_count += (is_valid) ? 1U : 0U;
+  string_offsets.push_back(string_offset);
+  string_lengths.push_back(string_end - string_offset);
+  child_offsets.push_back((child_offsets.size() > 0) ? child_offsets.back() + child_count : 0);
+  current_offset++;
+};
+
+namespace detail {
+
+void get_stack_context(device_span<SymbolT const> json_in,
+                       SymbolT* d_top_of_stack,
+                       stack_behavior_t stack_behavior,
+                       rmm::cuda_stream_view stream)
+{
+  check_input_size(json_in.size());
+
+  // Range of encapsulating function that comprises:
+  // -> DFA simulation for filtering out brackets and braces inside of quotes
+  // -> Logical stack to infer the stack context
+  CUDF_FUNC_RANGE();
+
+  // Symbol representing the JSON-root (i.e., we're at nesting level '0')
+  constexpr StackSymbolT root_symbol = '_';
+  // This can be any stack symbol from the stack alphabet that does not push onto stack
+  constexpr StackSymbolT read_symbol = 'x';
+
+  // Number of stack operations in the input (i.e., number of '{', '}', '[', ']' outside of quotes)
+  rmm::device_scalar<SymbolOffsetT> d_num_stack_ops(stream);
+
+  // Sequence of stack symbols and their position in the original input (sparse representation)
+  rmm::device_uvector<StackSymbolT> stack_ops{json_in.size(), stream};
+  rmm::device_uvector<SymbolOffsetT> stack_op_indices{json_in.size(), stream};
+
+  // Prepare finite-state transducer that only selects '{', '}', '[', ']' outside of quotes
+  constexpr auto max_translation_table_size =
+    to_stack_op::NUM_SYMBOL_GROUPS * to_stack_op::TT_NUM_STATES;
+
+  // Translation table specialized on the choice of whether to reset on newlines outside of strings
+  const auto translation_table = (stack_behavior == stack_behavior_t::ResetOnDelimiter)
+                                   ? to_stack_op::resetting_translation_table
+                                   : to_stack_op::translation_table;
+
+  auto json_to_stack_ops_fst = fst::detail::make_fst(
+    fst::detail::make_symbol_group_lut(to_stack_op::symbol_groups),
+    fst::detail::make_transition_table(to_stack_op::transition_table),
+    fst::detail::make_translation_table<max_translation_table_size>(translation_table),
+    stream);
+
+  // "Search" for relevant occurrence of brackets and braces that indicate the beginning/end
+  // of structs/lists
+  json_to_stack_ops_fst.Transduce(json_in.begin(),
+                                  static_cast<SymbolOffsetT>(json_in.size()),
+                                  stack_ops.data(),
+                                  stack_op_indices.data(),
+                                  d_num_stack_ops.data(),
+                                  to_stack_op::start_state,
+                                  stream);
+
+  // Copy back to actual number of stack operations
+  auto const num_stack_ops = d_num_stack_ops.value(stream);
+
+  // Stack operations with indices are converted to top of the stack for each character in the input
+  if (stack_behavior == stack_behavior_t::ResetOnDelimiter) {
+    fst::sparse_stack_op_to_top_of_stack<StackLevelT>(
+      stack_ops.data(),
+      device_span<SymbolOffsetT>{stack_op_indices.data(), num_stack_ops},
+      JSONWithRecoveryToStackOp{},
+      d_top_of_stack,
+      root_symbol,
+      read_symbol,
+      json_in.size(),
+      stream);
+  } else {
+    fst::sparse_stack_op_to_top_of_stack<StackLevelT>(
+      stack_ops.data(),
+      device_span<SymbolOffsetT>{stack_op_indices.data(), num_stack_ops},
+      JSONToStackOp{},
+      d_top_of_stack,
+      root_symbol,
+      read_symbol,
+      json_in.size(),
+      stream);
+  }
+}
+
+std::pair<rmm::device_uvector<PdaTokenT>, rmm::device_uvector<SymbolOffsetT>> process_token_stream(
+  device_span<PdaTokenT const> tokens,
+  device_span<SymbolOffsetT const> token_indices,
+  rmm::cuda_stream_view stream)
+{
+  // Instantiate FST for post-processing the token stream to remove all tokens that belong to an
+  // invalid JSON line
+  token_filter::UnwrapTokenFromSymbolOp sgid_op{};
+  auto filter_fst =
+    fst::detail::make_fst(fst::detail::make_symbol_group_lut(token_filter::symbol_groups, sgid_op),
+                          fst::detail::make_transition_table(token_filter::transition_table),
+                          fst::detail::make_translation_functor(token_filter::TransduceToken{}),
+                          stream);
+
+  auto const mr = rmm::mr::get_current_device_resource();
+  rmm::device_scalar<SymbolOffsetT> d_num_selected_tokens(stream, mr);
+  rmm::device_uvector<PdaTokenT> filtered_tokens_out{tokens.size(), stream, mr};
+  rmm::device_uvector<SymbolOffsetT> filtered_token_indices_out{tokens.size(), stream, mr};
+
+  // The FST is run on the reverse token stream, discarding all tokens between ErrorBegin and the
+  // next LineEnd (LineEnd, inv_token_0, inv_token_1, ..., inv_token_n, ErrorBegin, LineEnd, ...),
+  // emitting a [StructBegin, StructEnd] pair on the end of such an invalid line. In that example,
+  // inv_token_i for i in [0, n] together with the ErrorBegin are removed and replaced with
+  // StructBegin, StructEnd. Also, all LineEnd are removed as well, as these are not relevant after
+  // this stage anymore
+  filter_fst.Transduce(
+    thrust::make_reverse_iterator(thrust::make_zip_iterator(tokens.data(), token_indices.data()) +
+                                  tokens.size()),
+    static_cast<SymbolOffsetT>(tokens.size()),
+    thrust::make_reverse_iterator(
+      thrust::make_zip_iterator(filtered_tokens_out.data(), filtered_token_indices_out.data()) +
+      tokens.size()),
+    thrust::make_discard_iterator(),
+    d_num_selected_tokens.data(),
+    token_filter::start_state,
+    stream);
+
+  auto const num_total_tokens = d_num_selected_tokens.value(stream);
+  rmm::device_uvector<PdaTokenT> tokens_out{num_total_tokens, stream, mr};
+  rmm::device_uvector<SymbolOffsetT> token_indices_out{num_total_tokens, stream, mr};
+  thrust::copy(rmm::exec_policy(stream),
+               filtered_tokens_out.end() - num_total_tokens,
+               filtered_tokens_out.end(),
+               tokens_out.data());
+  thrust::copy(rmm::exec_policy(stream),
+               filtered_token_indices_out.end() - num_total_tokens,
+               filtered_token_indices_out.end(),
+               token_indices_out.data());
+
+  return std::make_pair(std::move(tokens_out), std::move(token_indices_out));
+}
+
+std::pair<rmm::device_uvector<PdaTokenT>, rmm::device_uvector<SymbolOffsetT>> get_token_stream(
+  device_span<SymbolT const> json_in,
+  cudf::io::json_reader_options const& options,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  check_input_size(json_in.size());
+
+  // Range of encapsulating function that parses to internal columnar data representation
+  CUDF_FUNC_RANGE();
+
+  auto const new_line_delimited_json = options.is_enabled_lines();
+
+  // (!new_line_delimited_json)                         => JSON
+  // (new_line_delimited_json and recover_from_error)   => JSON_LINES_RECOVER
+  // (new_line_delimited_json and !recover_from_error)  => JSON_LINES
+  auto format = new_line_delimited_json
+                  ? (options.recovery_mode() == json_recovery_mode_t::RECOVER_WITH_NULL
+                       ? tokenizer_pda::json_format_cfg_t::JSON_LINES_RECOVER
+                       : tokenizer_pda::json_format_cfg_t::JSON_LINES)
+                  : tokenizer_pda::json_format_cfg_t::JSON;
+
+  // Prepare for PDA transducer pass, merging input symbols with stack symbols
+  auto const recover_from_error = (format == tokenizer_pda::json_format_cfg_t::JSON_LINES_RECOVER);
+
+  // Memory holding the top-of-stack stack context for the input
+  rmm::device_uvector<StackSymbolT> stack_symbols{json_in.size(), stream};
+
+  // Identify what is the stack context for each input character (JSON-root, struct, or list)
+  auto const stack_behavior =
+    recover_from_error ? stack_behavior_t::ResetOnDelimiter : stack_behavior_t::PushPopWithoutReset;
+  get_stack_context(json_in, stack_symbols.data(), stack_behavior, stream);
+
+  // Input to the full pushdown automaton finite-state transducer, where a input symbol comprises
+  // the combination of a character from the JSON input together with the stack context for that
+  // character.
+  auto zip_in = thrust::make_zip_iterator(json_in.data(), stack_symbols.data());
+
+  constexpr auto max_translation_table_size =
+    tokenizer_pda::NUM_PDA_SGIDS *
+    static_cast<tokenizer_pda::StateT>(tokenizer_pda::pda_state_t::PD_NUM_STATES);
+  auto json_to_tokens_fst = fst::detail::make_fst(
+    fst::detail::make_symbol_group_lookup_op(tokenizer_pda::PdaSymbolToSymbolGroupId{}),
+    fst::detail::make_transition_table(tokenizer_pda::get_transition_table(format)),
+    fst::detail::make_translation_table<max_translation_table_size>(
+      tokenizer_pda::get_translation_table(recover_from_error)),
+    stream);
+
+  // Perform a PDA-transducer pass
+  // Compute the maximum amount of tokens that can possibly be emitted for a given input size
+  // Worst case ratio of tokens per input char is given for a struct with an empty field name, that
+  // may be arbitrarily deeply nested: {"":_}, where '_' is a placeholder for any JSON value,
+  // possibly another such struct. That is, 6 tokens for 5 chars (plus chars and tokens of '_')
+  std::size_t constexpr min_chars_per_struct  = 5;
+  std::size_t constexpr max_tokens_per_struct = 6;
+  auto const max_token_out_count =
+    cudf::util::div_rounding_up_safe(json_in.size(), min_chars_per_struct) * max_tokens_per_struct;
+  rmm::device_scalar<std::size_t> num_written_tokens{stream};
+  // In case we're recovering on invalid JSON lines, post-processing the token stream requires to
+  // see a JSON-line delimiter as the very first item
+  SymbolOffsetT const delimiter_offset =
+    (format == tokenizer_pda::json_format_cfg_t::JSON_LINES_RECOVER ? 1 : 0);
+  rmm::device_uvector<PdaTokenT> tokens{max_token_out_count + delimiter_offset, stream, mr};
+  rmm::device_uvector<SymbolOffsetT> tokens_indices{
+    max_token_out_count + delimiter_offset, stream, mr};
+
+  json_to_tokens_fst.Transduce(zip_in,
+                               static_cast<SymbolOffsetT>(json_in.size()),
+                               tokens.data() + delimiter_offset,
+                               tokens_indices.data() + delimiter_offset,
+                               num_written_tokens.data(),
+                               tokenizer_pda::start_state,
+                               stream);
+
+  auto const num_total_tokens = num_written_tokens.value(stream) + delimiter_offset;
+  tokens.resize(num_total_tokens, stream);
+  tokens_indices.resize(num_total_tokens, stream);
+
+  if (delimiter_offset == 1) {
+    tokens.set_element(0, token_t::LineEnd, stream);
+    auto [filtered_tokens, filtered_tokens_indices] =
+      process_token_stream(tokens, tokens_indices, stream);
+    tokens         = std::move(filtered_tokens);
+    tokens_indices = std::move(filtered_tokens_indices);
+  }
+
+  CUDF_EXPECTS(num_total_tokens <= max_token_out_count,
+               "Generated token count exceeds the expected token count");
+
+  return std::make_pair(std::move(tokens), std::move(tokens_indices));
+}
+
+/**
+ * @brief Parses the given JSON string and generates a tree representation of the given input.
+ *
+ * @param[in,out] root_column The root column of the hierarchy of columns into which data is parsed
+ * @param[in,out] current_data_path The stack represents the path from the JSON root node to the
+ * first node encountered in \p input
+ * @param[in] input The JSON input in host memory
+ * @param[in] d_input The JSON input in device memory
+ * @param[in] options Parsing options specifying the parsing behaviour
+ * @param[in] include_quote_char Whether to include the original quote chars around string values,
+ * allowing to distinguish string values from numeric and literal values
+ * @param[in] stream The CUDA stream to which kernels are dispatched
+ * @param[in] mr Optional, resource with which to allocate
+ * @return The columnar representation of the data from the given JSON input
+ */
+void make_json_column(json_column& root_column,
+                      std::stack<tree_node>& current_data_path,
+                      host_span<SymbolT const> input,
+                      device_span<SymbolT const> d_input,
+                      cudf::io::json_reader_options const& options,
+                      bool include_quote_char,
+                      rmm::cuda_stream_view stream,
+                      rmm::mr::device_memory_resource* mr)
+{
+  // Range of encapsulating function that parses to internal columnar data representation
+  CUDF_FUNC_RANGE();
+
+  // Parse the JSON and get the token stream
+  auto const [d_tokens_gpu, d_token_indices_gpu] = get_token_stream(d_input, options, stream, mr);
+
+  // Copy the JSON tokens to the host
+  thrust::host_vector<PdaTokenT> tokens =
+    cudf::detail::make_host_vector_async(d_tokens_gpu, stream);
+  thrust::host_vector<SymbolOffsetT> token_indices_gpu =
+    cudf::detail::make_host_vector_async(d_token_indices_gpu, stream);
+
+  // Make sure tokens have been copied to the host
+  stream.synchronize();
+
+  // Whether this token is the valid token to begin the JSON document with
+  auto is_valid_root_token = [](PdaTokenT const token) {
+    switch (token) {
+      case token_t::StructBegin:
+      case token_t::ListBegin:
+      case token_t::StringBegin:
+      case token_t::ValueBegin: return true;
+      default: return false;
+    };
+  };
+
+  // Returns the token's corresponding column type
+  auto token_to_column_type = [](PdaTokenT const token) {
+    switch (token) {
+      case token_t::StructBegin: return json_col_t::StructColumn;
+      case token_t::ListBegin: return json_col_t::ListColumn;
+      case token_t::StringBegin: return json_col_t::StringColumn;
+      case token_t::ValueBegin: return json_col_t::StringColumn;
+      default: return json_col_t::Unknown;
+    };
+  };
+
+  // Depending on whether we want to include the quotes of strings or not, respectively, we:
+  // (a) strip off the beginning quote included in StringBegin and FieldNameBegin or
+  // (b) include of the end quote excluded from in StringEnd and strip off the beginning quote
+  // included FieldNameBegin
+  auto get_token_index = [include_quote_char](PdaTokenT const token,
+                                              SymbolOffsetT const token_index) {
+    constexpr SymbolOffsetT quote_char_size = 1;
+    switch (token) {
+      // Optionally strip off quote char included for StringBegin
+      case token_t::StringBegin: return token_index + (include_quote_char ? 0 : quote_char_size);
+      // Optionally include trailing quote char for string values excluded for StringEnd
+      case token_t::StringEnd: return token_index + (include_quote_char ? quote_char_size : 0);
+      // Strip off quote char included for FieldNameBegin
+      case token_t::FieldNameBegin: return token_index + quote_char_size;
+      default: return token_index;
+    };
+  };
+
+  // The end-of-* partner token for a given beginning-of-* token
+  auto end_of_partner = [](PdaTokenT const token) {
+    switch (token) {
+      case token_t::StringBegin: return token_t::StringEnd;
+      case token_t::ValueBegin: return token_t::ValueEnd;
+      case token_t::FieldNameBegin: return token_t::FieldNameEnd;
+      default: return token_t::ErrorBegin;
+    };
+  };
+
+#ifdef NJP_DEBUG_PRINT
+  auto column_type_string = [](json_col_t column_type) {
+    switch (column_type) {
+      case json_col_t::Unknown: return "Unknown";
+      case json_col_t::ListColumn: return "List";
+      case json_col_t::StructColumn: return "Struct";
+      case json_col_t::StringColumn: return "String";
+      default: return "Unknown";
+    }
+  };
+
+  auto token_to_string = [](PdaTokenT token_type) {
+    switch (token_type) {
+      case token_t::StructBegin: return "StructBegin";
+      case token_t::StructEnd: return "StructEnd";
+      case token_t::ListBegin: return "ListBegin";
+      case token_t::ListEnd: return "ListEnd";
+      case token_t::StructMemberBegin: return "StructMemberBegin";
+      case token_t::StructMemberEnd: return "StructMemberEnd";
+      case token_t::FieldNameBegin: return "FieldNameBegin";
+      case token_t::FieldNameEnd: return "FieldNameEnd";
+      case token_t::StringBegin: return "StringBegin";
+      case token_t::StringEnd: return "StringEnd";
+      case token_t::ValueBegin: return "ValueBegin";
+      case token_t::ValueEnd: return "ValueEnd";
+      case token_t::ErrorBegin: return "ErrorBegin";
+      case token_t::LineEnd: return "LineEnd";
+      default: return "Unknown";
+    }
+  };
+#endif
+
+  /**
+   * @brief Updates the given row in the given column with a new string_end and child_count. In
+   * particular, updating the child count is relevant for list columns.
+   */
+  auto update_row =
+    [](json_column* column, uint32_t row_index, uint32_t string_end, uint32_t child_count) {
+#ifdef NJP_DEBUG_PRINT
+      std::cout << "  -> update_row()\n";
+      std::cout << "  ---> col@" << column << "\n";
+      std::cout << "  ---> row #" << row_index << "\n";
+      std::cout << "  ---> string_lengths = " << (string_end - column->string_offsets[row_index])
+                << "\n";
+      std::cout << "  ---> child_offsets = " << (column->child_offsets[row_index + 1] + child_count)
+                << "\n";
+#endif
+      column->string_lengths[row_index]    = column->child_offsets[row_index + 1] + child_count;
+      column->child_offsets[row_index + 1] = column->child_offsets[row_index + 1] + child_count;
+    };
+
+  /**
+   * @brief Gets the currently selected child column given a \p current_data_path.
+   *
+   * That is, if \p current_data_path top-of-stack is
+   * (a) a struct, the selected child column corresponds to the child column of the last field name
+   * node encountered.
+   * (b) a list, the selected child column corresponds to single child column of
+   * the list column. In this case, the child column may not exist yet.
+   */
+  auto get_selected_column = [](std::stack<tree_node>& current_data_path) {
+    json_column* selected_col = current_data_path.top().current_selected_col;
+
+    // If the node does not have a selected column yet
+    if (selected_col == nullptr) {
+      // We're looking at the child column of a list column
+      if (current_data_path.top().column->type == json_col_t::ListColumn) {
+        CUDF_EXPECTS(current_data_path.top().column->child_columns.size() <= 1,
+                     "Encountered a list column with more than a single child column");
+        // The child column has yet to be created
+        if (current_data_path.top().column->child_columns.empty()) {
+          current_data_path.top().column->child_columns.emplace(std::string{list_child_name},
+                                                                json_column{json_col_t::Unknown});
+          current_data_path.top().column->column_order.push_back(list_child_name);
+        }
+        current_data_path.top().current_selected_col =
+          &current_data_path.top().column->child_columns.begin()->second;
+        selected_col = current_data_path.top().current_selected_col;
+      } else {
+        CUDF_FAIL("Trying to retrieve child column without encountering a field name.");
+      }
+    }
+#ifdef NJP_DEBUG_PRINT
+    std::cout << "  -> get_selected_column()\n";
+    std::cout << "  ---> selected col@" << selected_col << "\n";
+#endif
+    return selected_col;
+  };
+
+  /**
+   * @brief Returns a pointer to the child column with the given \p field_name within the current
+   * struct column.
+   */
+  auto select_column = [](std::stack<tree_node>& current_data_path, std::string const& field_name) {
+#ifdef NJP_DEBUG_PRINT
+    std::cout << "  -> select_column(" << field_name << ")\n";
+#endif
+    // The field name's parent struct node
+    auto& current_struct_node = current_data_path.top();
+
+    // Verify that the field name node is actually a child of a struct
+    CUDF_EXPECTS(current_data_path.top().column->type == json_col_t::StructColumn,
+                 "Invalid JSON token sequence");
+
+    json_column* struct_col  = current_struct_node.column;
+    auto const& child_col_it = struct_col->child_columns.find(field_name);
+
+    // The field name's column exists already, select that as the struct node's currently selected
+    // child column
+    if (child_col_it != struct_col->child_columns.end()) { return &child_col_it->second; }
+
+    // The field name's column does not exist yet, so we have to append the child column to the
+    // struct column
+    struct_col->column_order.push_back(field_name);
+    return &struct_col->child_columns.emplace(field_name, json_column{}).first->second;
+  };
+
+  /**
+   * @brief Gets the row offset at which to insert. I.e., for a child column of a list column, we
+   * just have to append the row to the end. Otherwise we have to propagate the row offset from the
+   * parent struct column.
+   */
+  auto get_target_row_index = [](std::stack<tree_node> const& current_data_path,
+                                 json_column* target_column) {
+#ifdef NJP_DEBUG_PRINT
+    std::cout << " -> target row: "
+              << ((current_data_path.top().column->type == json_col_t::ListColumn)
+                    ? target_column->current_offset
+                    : current_data_path.top().row_index)
+              << "\n";
+#endif
+    return (current_data_path.top().column->type == json_col_t::ListColumn)
+             ? target_column->current_offset
+             : current_data_path.top().row_index;
+  };
+
+  // The offset of the token currently being processed
+  std::size_t offset = 0;
+
+  // Giving names to magic constants
+  constexpr uint32_t zero_child_count = 0;
+
+  CUDF_EXPECTS(tokens.size() == token_indices_gpu.size(),
+               "Unexpected mismatch in number of token types and token indices");
+  CUDF_EXPECTS(tokens.size() > 0, "Empty JSON input not supported");
+
+  // The JSON root may only be a struct, list, string, or value node
+  CUDF_EXPECTS(is_valid_root_token(tokens[offset]), "Invalid beginning of JSON document");
+
+  while (offset < tokens.size()) {
+    // Verify there's at least the JSON root node left on the stack to which we can append data
+    CUDF_EXPECTS(current_data_path.size() > 0, "Invalid JSON structure");
+
+    // Verify that the current node in the tree (which becomes this nodes parent) can have children
+    CUDF_EXPECTS(current_data_path.top().column->type == json_col_t::ListColumn or
+                   current_data_path.top().column->type == json_col_t::StructColumn,
+                 "Invalid JSON structure");
+
+    // The token we're currently parsing
+    auto const& token = tokens[offset];
+
+#ifdef NJP_DEBUG_PRINT
+    std::cout << "[" << token_to_string(token) << "]\n";
+#endif
+
+    // StructBegin token
+    if (token == token_t::StructBegin) {
+      // Get this node's column. That is, the parent node's selected column:
+      // (a) if parent is a list, then this will (create and) return the list's only child column
+      // (b) if parent is a struct, then this will return the column selected by the last field name
+      // encountered.
+      json_column* selected_col = get_selected_column(current_data_path);
+
+      // Get the row offset at which to insert
+      auto const target_row_index = get_target_row_index(current_data_path, selected_col);
+
+      // Increment parent's child count and insert this struct node into the data path
+      current_data_path.top().num_children++;
+      current_data_path.push({selected_col, target_row_index, nullptr, zero_child_count});
+
+      // Add this struct node to the current column
+      selected_col->append_row(target_row_index,
+                               token_to_column_type(tokens[offset]),
+                               get_token_index(tokens[offset], token_indices_gpu[offset]),
+                               get_token_index(tokens[offset], token_indices_gpu[offset]),
+                               zero_child_count);
+    }
+
+    // StructEnd token
+    else if (token == token_t::StructEnd) {
+      // Verify that this node in fact a struct node (i.e., it was part of a struct column)
+      CUDF_EXPECTS(current_data_path.top().column->type == json_col_t::StructColumn,
+                   "Broken invariant while parsing JSON");
+      CUDF_EXPECTS(current_data_path.top().column != nullptr,
+                   "Broken invariant while parsing JSON");
+
+      // Update row to account for string offset
+      update_row(current_data_path.top().column,
+                 current_data_path.top().row_index,
+                 get_token_index(tokens[offset], token_indices_gpu[offset]),
+                 current_data_path.top().num_children);
+
+      // Pop struct from the path stack
+      current_data_path.pop();
+    }
+
+    // ListBegin token
+    else if (token == token_t::ListBegin) {
+      // Get the selected column
+      json_column* selected_col = get_selected_column(current_data_path);
+
+      // Get the row offset at which to insert
+      auto const target_row_index = get_target_row_index(current_data_path, selected_col);
+
+      // Increment parent's child count and insert this struct node into the data path
+      current_data_path.top().num_children++;
+      current_data_path.push({selected_col, target_row_index, nullptr, zero_child_count});
+
+      // Add this struct node to the current column
+      selected_col->append_row(target_row_index,
+                               token_to_column_type(tokens[offset]),
+                               get_token_index(tokens[offset], token_indices_gpu[offset]),
+                               get_token_index(tokens[offset], token_indices_gpu[offset]),
+                               zero_child_count);
+    }
+
+    // ListEnd token
+    else if (token == token_t::ListEnd) {
+      // Verify that this node in fact a list node (i.e., it was part of a list column)
+      CUDF_EXPECTS(current_data_path.top().column->type == json_col_t::ListColumn,
+                   "Broken invariant while parsing JSON");
+      CUDF_EXPECTS(current_data_path.top().column != nullptr,
+                   "Broken invariant while parsing JSON");
+
+      // Update row to account for string offset
+      update_row(current_data_path.top().column,
+                 current_data_path.top().row_index,
+                 get_token_index(tokens[offset], token_indices_gpu[offset]),
+                 current_data_path.top().num_children);
+
+      // Pop list from the path stack
+      current_data_path.pop();
+    }
+
+    // Error token
+    else if (token == token_t::ErrorBegin) {
+#ifdef NJP_DEBUG_PRINT
+      std::cout << "[ErrorBegin]\n";
+      std::cout << "@" << get_token_index(tokens[offset], token_indices_gpu[offset]);
+#endif
+      CUDF_FAIL("Parser encountered an invalid format.");
+    }
+
+    // FieldName, String, or Value (begin, end)-pair
+    else if (token == token_t::FieldNameBegin or token == token_t::StringBegin or
+             token == token_t::ValueBegin) {
+      // Verify that this token has the right successor to build a correct (being, end) token pair
+      CUDF_EXPECTS((offset + 1) < tokens.size(), "Invalid JSON token sequence");
+      CUDF_EXPECTS(tokens[offset + 1] == end_of_partner(token), "Invalid JSON token sequence");
+
+      // The offset to the first symbol from the JSON input associated with the current token
+      auto const& token_begin_offset = get_token_index(tokens[offset], token_indices_gpu[offset]);
+
+      // The offset to one past the last symbol associated with the current token
+      auto const& token_end_offset =
+        get_token_index(tokens[offset + 1], token_indices_gpu[offset + 1]);
+
+      // FieldNameBegin
+      // For the current struct node in the tree, select the child column corresponding to this
+      // field name
+      if (token == token_t::FieldNameBegin) {
+        std::string field_name{input.data() + token_begin_offset,
+                               (token_end_offset - token_begin_offset)};
+        current_data_path.top().current_selected_col = select_column(current_data_path, field_name);
+      }
+      // StringBegin
+      // ValueBegin
+      // As we currently parse to string columns there's no further differentiation
+      else if (token == token_t::StringBegin or token == token_t::ValueBegin) {
+        // Get the selected column
+        json_column* selected_col = get_selected_column(current_data_path);
+
+        // Get the row offset at which to insert
+        auto const target_row_index = get_target_row_index(current_data_path, selected_col);
+
+        current_data_path.top().num_children++;
+
+        selected_col->append_row(target_row_index,
+                                 token_to_column_type(token),
+                                 token_begin_offset,
+                                 token_end_offset,
+                                 zero_child_count);
+      } else {
+        CUDF_FAIL("Unknown JSON token");
+      }
+
+      // As we've also consumed the end-of-* token, we advance the processed token offset by one
+      offset++;
+    }
+
+    offset++;
+  }
+
+  // Make sure all of a struct's child columns have the same length
+  root_column.level_child_cols_recursively(root_column.current_offset);
+}
+
+/**
+ * @brief Retrieves the parse_options to be used for type inference and type casting
+ *
+ * @param options The reader options to influence the relevant type inference and type casting
+ * options
+ * @param stream The CUDA stream to which kernels are dispatched
+ */
+auto parsing_options(cudf::io::json_reader_options const& options, rmm::cuda_stream_view stream)
+{
+  auto parse_opts = cudf::io::parse_options{',', '\n', '\"', '.'};
+
+  parse_opts.dayfirst   = options.is_enabled_dayfirst();
+  parse_opts.keepquotes = options.is_enabled_keep_quotes();
+  parse_opts.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  parse_opts.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  parse_opts.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+  return parse_opts;
+}
+
+std::pair<std::unique_ptr<column>, std::vector<column_name_info>> json_column_to_cudf_column(
+  json_column const& json_col,
+  device_span<SymbolT const> d_input,
+  cudf::io::json_reader_options const& options,
+  std::optional<schema_element> schema,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // Range of orchestrating/encapsulating function
+  CUDF_FUNC_RANGE();
+
+  auto make_validity =
+    [stream, mr](json_column const& json_col) -> std::pair<rmm::device_buffer, size_type> {
+    return {rmm::device_buffer{json_col.validity.data(),
+                               bitmask_allocation_size_bytes(json_col.current_offset),
+                               stream,
+                               mr},
+            json_col.current_offset - json_col.valid_count};
+  };
+
+  auto get_child_schema = [schema](auto child_name) -> std::optional<schema_element> {
+    if (schema.has_value()) {
+      auto const result = schema.value().child_types.find(child_name);
+      if (result != std::end(schema.value().child_types)) { return result->second; }
+    }
+    return {};
+  };
+
+  switch (json_col.type) {
+    case json_col_t::StringColumn: {
+      auto const col_size = json_col.string_offsets.size();
+      CUDF_EXPECTS(json_col.string_offsets.size() == json_col.string_lengths.size(),
+                   "string offset, string length mismatch");
+
+      // Move string_offsets and string_lengths to GPU
+      rmm::device_uvector<json_column::row_offset_t> d_string_offsets =
+        cudf::detail::make_device_uvector_async(
+          json_col.string_offsets, stream, rmm::mr::get_current_device_resource());
+      rmm::device_uvector<json_column::row_offset_t> d_string_lengths =
+        cudf::detail::make_device_uvector_async(
+          json_col.string_lengths, stream, rmm::mr::get_current_device_resource());
+
+      // Prepare iterator that returns (string_offset, string_length)-tuples
+      auto offset_length_it =
+        thrust::make_zip_iterator(d_string_offsets.begin(), d_string_lengths.begin());
+
+      data_type target_type{};
+
+      if (schema.has_value()) {
+#ifdef NJP_DEBUG_PRINT
+        std::cout << "-> explicit type: "
+                  << (schema.has_value() ? std::to_string(static_cast<int>(schema->type.id()))
+                                         : "n/a");
+#endif
+        target_type = schema.value().type;
+      }
+      // Infer column type, if we don't have an explicit type for it
+      else {
+        target_type =
+          cudf::io::detail::infer_data_type(parsing_options(options, stream).json_view(),
+                                            d_input,
+                                            offset_length_it,
+                                            col_size,
+                                            stream);
+      }
+
+      auto [result_bitmask, null_count] = make_validity(json_col);
+
+      // Convert strings to the inferred data type
+      auto col = parse_data(d_input.data(),
+                            offset_length_it,
+                            col_size,
+                            target_type,
+                            std::move(result_bitmask),
+                            null_count,
+                            parsing_options(options, stream).view(),
+                            stream,
+                            mr);
+
+      // Reset nullable if we do not have nulls
+      // This is to match the existing JSON reader's behaviour:
+      // - Non-string columns will always be returned as nullable
+      // - String columns will be returned as nullable, iff there's at least one null entry
+      if (target_type.id() == type_id::STRING and col->null_count() == 0) {
+        col->set_null_mask(rmm::device_buffer{0, stream, mr}, 0);
+      }
+
+      // For string columns return ["offsets", "char"] schema
+      if (target_type.id() == type_id::STRING) {
+        return {std::move(col), std::vector<column_name_info>{{"offsets"}, {"chars"}}};
+      }
+      // Non-string leaf-columns (e.g., numeric) do not have child columns in the schema
+      else {
+        return {std::move(col), std::vector<column_name_info>{}};
+      }
+      break;
+    }
+    case json_col_t::StructColumn: {
+      std::vector<std::unique_ptr<column>> child_columns;
+      std::vector<column_name_info> column_names{};
+      size_type num_rows{json_col.current_offset};
+      // Create children columns
+      for (auto const& col_name : json_col.column_order) {
+        auto const& col = json_col.child_columns.find(col_name);
+        column_names.emplace_back(col->first);
+        auto const& child_col      = col->second;
+        auto [child_column, names] = json_column_to_cudf_column(
+          child_col, d_input, options, get_child_schema(col_name), stream, mr);
+        CUDF_EXPECTS(num_rows == child_column->size(),
+                     "All children columns must have the same size");
+        child_columns.push_back(std::move(child_column));
+        column_names.back().children = names;
+      }
+      auto [result_bitmask, null_count] = make_validity(json_col);
+      return {
+        make_structs_column(
+          num_rows, std::move(child_columns), null_count, std::move(result_bitmask), stream, mr),
+        column_names};
+      break;
+    }
+    case json_col_t::ListColumn: {
+      size_type num_rows = json_col.child_offsets.size();
+      std::vector<column_name_info> column_names{};
+      column_names.emplace_back("offsets");
+      column_names.emplace_back(
+        json_col.child_columns.empty() ? list_child_name : json_col.child_columns.begin()->first);
+
+      rmm::device_uvector<json_column::row_offset_t> d_offsets =
+        cudf::detail::make_device_uvector_async(json_col.child_offsets, stream, mr);
+      auto offsets_column = std::make_unique<column>(
+        data_type{type_id::INT32}, num_rows, d_offsets.release(), rmm::device_buffer{}, 0);
+      // Create children column
+      auto [child_column, names] =
+        json_col.child_columns.empty()
+          ? std::pair<std::unique_ptr<column>,
+                      std::vector<column_name_info>>{std::make_unique<column>(),
+                                                     std::vector<column_name_info>{}}
+          : json_column_to_cudf_column(json_col.child_columns.begin()->second,
+                                       d_input,
+                                       options,
+                                       get_child_schema(json_col.child_columns.begin()->first),
+                                       stream,
+                                       mr);
+      column_names.back().children      = names;
+      auto [result_bitmask, null_count] = make_validity(json_col);
+      return {make_lists_column(num_rows - 1,
+                                std::move(offsets_column),
+                                std::move(child_column),
+                                null_count,
+                                std::move(result_bitmask),
+                                stream,
+                                mr),
+              std::move(column_names)};
+      break;
+    }
+    default: CUDF_FAIL("Unsupported column type, yet to be implemented"); break;
+  }
+
+  return {};
+}
+
+table_with_metadata host_parse_nested_json(device_span<SymbolT const> d_input,
+                                           cudf::io::json_reader_options const& options,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  // Range of orchestrating/encapsulating function
+  CUDF_FUNC_RANGE();
+
+  auto const h_input = cudf::detail::make_std_vector_async(d_input, stream);
+
+  auto const new_line_delimited_json = options.is_enabled_lines();
+
+  // Get internal JSON column
+  json_column root_column{};
+  std::stack<tree_node> data_path{};
+
+  constexpr uint32_t row_offset_zero            = 0;
+  constexpr uint32_t token_begin_offset_zero    = 0;
+  constexpr uint32_t token_end_offset_zero      = 0;
+  constexpr uint32_t node_init_child_count_zero = 0;
+
+  // Whether the tokenizer stage should keep quote characters for string values
+  // If the tokenizer keeps the quote characters, they may be stripped during type casting
+  constexpr bool include_quote_chars = true;
+
+  // We initialize the very root node and root column, which represent the JSON document being
+  // parsed. That root node is a list node and that root column is a list column. The column has the
+  // root node as its only row. The values parsed from the JSON input will be treated as follows:
+  // (1) For JSON lines: we expect to find a list of JSON values that all
+  // will be inserted into this root list column. (2) For regular JSON: we expect to have only a
+  // single value (list, struct, string, number, literal) that will be inserted into this root
+  // column.
+  root_column.append_row(
+    row_offset_zero, json_col_t::ListColumn, token_begin_offset_zero, token_end_offset_zero, 1);
+
+  // Push the root node onto the stack for the data path
+  data_path.push({&root_column, row_offset_zero, nullptr, node_init_child_count_zero});
+
+  make_json_column(
+    root_column, data_path, h_input, d_input, options, include_quote_chars, stream, mr);
+
+  // data_root refers to the root column of the data represented by the given JSON string
+  auto const& data_root =
+    new_line_delimited_json ? root_column : root_column.child_columns.begin()->second;
+
+  // Zero row entries
+  if (data_root.type == json_col_t::ListColumn && data_root.child_columns.empty()) {
+    return table_with_metadata{std::make_unique<table>(std::vector<std::unique_ptr<column>>{})};
+  }
+
+  // Verify that we were in fact given a list of structs (or in JSON speech: an array of objects)
+  auto constexpr single_child_col_count = 1;
+  CUDF_EXPECTS(data_root.type == json_col_t::ListColumn and
+                 data_root.child_columns.size() == single_child_col_count and
+                 data_root.child_columns.begin()->second.type == json_col_t::StructColumn,
+               "Currently the nested JSON parser only supports an array of (nested) objects");
+
+  // Slice off the root list column, which has only a single row that contains all the structs
+  auto const& root_struct_col = data_root.child_columns.begin()->second;
+
+  // Initialize meta data to be populated while recursing through the tree of columns
+  std::vector<std::unique_ptr<column>> out_columns;
+  std::vector<column_name_info> out_column_names;
+
+  // Iterate over the struct's child columns and convert to cudf column
+  size_type column_index = 0;
+  for (auto const& col_name : root_struct_col.column_order) {
+    auto const& json_col = root_struct_col.child_columns.find(col_name)->second;
+    // Insert this columns name into the schema
+    out_column_names.emplace_back(col_name);
+
+    std::optional<schema_element> child_schema_element = std::visit(
+      cudf::detail::visitor_overload{
+        [column_index](std::vector<data_type> const& user_dtypes) -> std::optional<schema_element> {
+          auto ret = (static_cast<std::size_t>(column_index) < user_dtypes.size())
+                       ? std::optional<schema_element>{{user_dtypes[column_index]}}
+                       : std::optional<schema_element>{};
+#ifdef NJP_DEBUG_PRINT
+          std::cout << "Column by index: #" << column_index << ", type id: "
+                    << (ret.has_value() ? std::to_string(static_cast<int>(ret->type.id())) : "n/a")
+                    << ", with " << (ret.has_value() ? ret->child_types.size() : 0) << " children"
+                    << "\n";
+#endif
+          return ret;
+        },
+        [col_name](
+          std::map<std::string, data_type> const& user_dtypes) -> std::optional<schema_element> {
+          auto ret = (user_dtypes.find(col_name) != std::end(user_dtypes))
+                       ? std::optional<schema_element>{{user_dtypes.find(col_name)->second}}
+                       : std::optional<schema_element>{};
+#ifdef NJP_DEBUG_PRINT
+          std::cout << "Column by flat name: '" << col_name << "', type id: "
+                    << (ret.has_value() ? std::to_string(static_cast<int>(ret->type.id())) : "n/a")
+                    << ", with " << (ret.has_value() ? ret->child_types.size() : 0) << " children"
+                    << "\n";
+#endif
+          return ret;
+        },
+        [col_name](std::map<std::string, schema_element> const& user_dtypes)
+          -> std::optional<schema_element> {
+          auto ret = (user_dtypes.find(col_name) != std::end(user_dtypes))
+                       ? user_dtypes.find(col_name)->second
+                       : std::optional<schema_element>{};
+#ifdef NJP_DEBUG_PRINT
+          std::cout << "Column by nested name: #" << col_name << ", type id: "
+                    << (ret.has_value() ? std::to_string(static_cast<int>(ret->type.id())) : "n/a")
+                    << ", with " << (ret.has_value() ? ret->child_types.size() : 0) << " children"
+                    << "\n";
+#endif
+          return ret;
+        }},
+      options.get_dtypes());
+
+    // Get this JSON column's cudf column and schema info
+    auto [cudf_col, col_name_info] =
+      json_column_to_cudf_column(json_col, d_input, options, child_schema_element, stream, mr);
+    out_column_names.back().children = std::move(col_name_info);
+    out_columns.emplace_back(std::move(cudf_col));
+
+    column_index++;
+  }
+
+  return table_with_metadata{std::make_unique<table>(std::move(out_columns)), {out_column_names}};
+}
+
+}  // namespace detail
+}  // namespace cudf::io::json
+
+// Debug print flag
+#undef NJP_DEBUG_PRINT
diff --git a/cpp/src/io/json/read_json.cu b/cpp/src/io/json/read_json.cu
new file mode 100644
index 0000000..080da78
--- /dev/null
+++ b/cpp/src/io/json/read_json.cu
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "read_json.hpp"
+
+#include <io/comp/io_uncomp.hpp>
+#include <io/json/legacy/read_json.hpp>
+#include <io/json/nested_json.hpp>
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/scatter.h>
+
+#include <numeric>
+
+namespace cudf::io::json::detail {
+
+size_t sources_size(host_span<std::unique_ptr<datasource>> const sources,
+                    size_t range_offset,
+                    size_t range_size)
+{
+  return std::accumulate(sources.begin(), sources.end(), 0ul, [=](size_t sum, auto& source) {
+    auto const size = source->size();
+    // TODO take care of 0, 0, or *, 0 case.
+    return sum +
+           (range_size == 0 or range_offset + range_size > size ? size - range_offset : range_size);
+  });
+}
+
+rmm::device_uvector<char> ingest_raw_input(host_span<std::unique_ptr<datasource>> sources,
+                                           compression_type compression,
+                                           size_t range_offset,
+                                           size_t range_size,
+                                           rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  // We append a line delimiter between two files to make sure the last line of file i and the first
+  // line of file i+1 don't end up on the same JSON line, if file i does not already end with a line
+  // delimiter.
+  auto constexpr num_delimiter_chars = 1;
+  auto const num_extra_delimiters    = num_delimiter_chars * (sources.size() - 1);
+
+  // Iterate through the user defined sources and read the contents into the local buffer
+  auto const total_source_size =
+    sources_size(sources, range_offset, range_size) + num_extra_delimiters;
+
+  if (compression == compression_type::NONE) {
+    std::vector<size_type> delimiter_map{};
+    delimiter_map.reserve(sources.size());
+    auto d_buffer     = rmm::device_uvector<char>(total_source_size, stream);
+    size_t bytes_read = 0;
+    std::vector<std::unique_ptr<datasource::buffer>> h_buffers;
+    for (auto const& source : sources) {
+      if (!source->is_empty()) {
+        auto data_size   = (range_size != 0) ? range_size : source->size();
+        auto destination = reinterpret_cast<uint8_t*>(d_buffer.data()) + bytes_read;
+        if (source->is_device_read_preferred(data_size)) {
+          bytes_read += source->device_read(range_offset, data_size, destination, stream);
+        } else {
+          h_buffers.emplace_back(source->host_read(range_offset, data_size));
+          auto const& h_buffer = h_buffers.back();
+          CUDF_CUDA_TRY(cudaMemcpyAsync(
+            destination, h_buffer->data(), h_buffer->size(), cudaMemcpyDefault, stream.value()));
+          bytes_read += h_buffer->size();
+        }
+        delimiter_map.push_back(bytes_read);
+        bytes_read += num_delimiter_chars;
+      }
+    }
+
+    // If this is a multi-file source, we scatter the JSON line delimiters between files
+    if (sources.size() > 1) {
+      static_assert(num_delimiter_chars == 1,
+                    "Currently only single-character delimiters are supported");
+      auto const delimiter_source = thrust::make_constant_iterator('\n');
+      auto const d_delimiter_map  = cudf::detail::make_device_uvector_async(
+        host_span<size_type const>{delimiter_map.data(), delimiter_map.size() - 1},
+        stream,
+        rmm::mr::get_current_device_resource());
+      thrust::scatter(rmm::exec_policy_nosync(stream),
+                      delimiter_source,
+                      delimiter_source + d_delimiter_map.size(),
+                      d_delimiter_map.data(),
+                      d_buffer.data());
+    }
+
+    stream.synchronize();
+    return d_buffer;
+
+  } else {
+    auto buffer = std::vector<uint8_t>(total_source_size);
+    // Single read because only a single compressed source is supported
+    // Reading to host because decompression of a single block is much faster on the CPU
+    sources[0]->host_read(range_offset, total_source_size, buffer.data());
+    auto const uncomp_data = decompress(compression, buffer);
+    return cudf::detail::make_device_uvector_sync(
+      host_span<char const>{reinterpret_cast<char const*>(uncomp_data.data()), uncomp_data.size()},
+      stream,
+      rmm::mr::get_current_device_resource());
+  }
+}
+
+size_type find_first_delimiter_in_chunk(host_span<std::unique_ptr<cudf::io::datasource>> sources,
+                                        json_reader_options const& reader_opts,
+                                        char const delimiter,
+                                        rmm::cuda_stream_view stream)
+{
+  auto const buffer = ingest_raw_input(sources,
+                                       reader_opts.get_compression(),
+                                       reader_opts.get_byte_range_offset(),
+                                       reader_opts.get_byte_range_size(),
+                                       stream);
+  return find_first_delimiter(buffer, delimiter, stream);
+}
+
+bool should_load_whole_source(json_reader_options const& reader_opts)
+{
+  return reader_opts.get_byte_range_offset() == 0 and  //
+         reader_opts.get_byte_range_size() == 0;
+}
+
+/**
+ * @brief Get the byte range between record starts and ends starting from the given range.
+ *
+ * if get_byte_range_offset == 0, then we can skip the first delimiter search
+ * if get_byte_range_offset != 0, then we need to search for the first delimiter in given range.
+ * if not found, skip this chunk, if found, then search for first delimiter in next range until we
+ * find a delimiter. Use this as actual range for parsing.
+ *
+ * @param sources Data sources to read from
+ * @param reader_opts JSON reader options with range offset and range size
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Byte range for parsing
+ */
+auto get_record_range_raw_input(host_span<std::unique_ptr<datasource>> sources,
+                                json_reader_options const& reader_opts,
+                                rmm::cuda_stream_view stream)
+{
+  auto buffer = ingest_raw_input(sources,
+                                 reader_opts.get_compression(),
+                                 reader_opts.get_byte_range_offset(),
+                                 reader_opts.get_byte_range_size(),
+                                 stream);
+  if (should_load_whole_source(reader_opts)) return buffer;
+  auto first_delim_pos =
+    reader_opts.get_byte_range_offset() == 0 ? 0 : find_first_delimiter(buffer, '\n', stream);
+  if (first_delim_pos == -1) {
+    return rmm::device_uvector<char>{0, stream};
+  } else {
+    first_delim_pos = first_delim_pos + reader_opts.get_byte_range_offset();
+    // Find next delimiter
+    decltype(first_delim_pos) next_delim_pos = -1;
+    auto const total_source_size             = sources_size(sources, 0, 0);
+    auto current_offset = reader_opts.get_byte_range_offset() + reader_opts.get_byte_range_size();
+    while (current_offset < total_source_size and next_delim_pos == -1) {
+      buffer         = ingest_raw_input(sources,
+                                reader_opts.get_compression(),
+                                current_offset,
+                                reader_opts.get_byte_range_size(),
+                                stream);
+      next_delim_pos = find_first_delimiter(buffer, '\n', stream);
+      if (next_delim_pos == -1) { current_offset += reader_opts.get_byte_range_size(); }
+    }
+    if (next_delim_pos == -1) {
+      next_delim_pos = total_source_size;
+    } else {
+      next_delim_pos = next_delim_pos + current_offset;
+    }
+    return ingest_raw_input(sources,
+                            reader_opts.get_compression(),
+                            first_delim_pos,
+                            next_delim_pos - first_delim_pos,
+                            stream);
+  }
+}
+
+table_with_metadata read_json(host_span<std::unique_ptr<datasource>> sources,
+                              json_reader_options const& reader_opts,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (reader_opts.is_enabled_legacy()) {
+    return legacy::read_json(sources, reader_opts, stream, mr);
+  }
+
+  if (not should_load_whole_source(reader_opts)) {
+    CUDF_EXPECTS(reader_opts.is_enabled_lines(),
+                 "Specifying a byte range is supported only for JSON Lines");
+    CUDF_EXPECTS(sources.size() == 1,
+                 "Specifying a byte range is supported only for a single source");
+  }
+
+  if (sources.size() > 1) {
+    CUDF_EXPECTS(reader_opts.get_compression() == compression_type::NONE,
+                 "Multiple compressed inputs are not supported");
+    CUDF_EXPECTS(reader_opts.is_enabled_lines(),
+                 "Multiple inputs are supported only for JSON Lines format");
+  }
+
+  auto const buffer = get_record_range_raw_input(sources, reader_opts, stream);
+
+  return device_parse_nested_json(buffer, reader_opts, stream, mr);
+  // For debug purposes, use host_parse_nested_json()
+}
+
+}  // namespace cudf::io::json::detail
diff --git a/cpp/src/io/json/read_json.hpp b/cpp/src/io/json/read_json.hpp
new file mode 100644
index 0000000..db37e7a
--- /dev/null
+++ b/cpp/src/io/json/read_json.hpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <memory>
+
+namespace cudf::io::json::detail {
+
+table_with_metadata read_json(host_span<std::unique_ptr<datasource>> sources,
+                              json_reader_options const& reader_opts,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+size_type find_first_delimiter(device_span<char const> d_data,
+                               char const delimiter,
+                               rmm::cuda_stream_view stream);
+
+size_type find_first_delimiter_in_chunk(host_span<std::unique_ptr<cudf::io::datasource>> sources,
+                                        json_reader_options const& reader_opts,
+                                        char const delimiter,
+                                        rmm::cuda_stream_view stream);
+
+}  // namespace cudf::io::json::detail
diff --git a/cpp/src/io/json/write_json.cu b/cpp/src/io/json/write_json.cu
new file mode 100644
index 0000000..2d363c5
--- /dev/null
+++ b/cpp/src/io/json/write_json.cu
@@ -0,0 +1,912 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file write_json.cu
+ * @brief cuDF-IO JSON writer implementation
+ */
+
+#include <io/csv/durations.hpp>
+#include <io/utilities/parsing_utils.cuh>
+#include <lists/utilities.hpp>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/detail/json.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/combine.hpp>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/gather.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+
+#include <algorithm>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace cudf::io::json::detail {
+
+std::unique_ptr<column> make_column_names_column(host_span<column_name_info const> column_names,
+                                                 size_type num_columns,
+                                                 rmm::cuda_stream_view stream);
+namespace {
+
+/**
+ * @brief Functor to modify a string column for JSON format.
+ *
+ * This will convert escape characters and wrap quotes around strings.
+ */
+struct escape_strings_fn {
+  column_device_view const d_column;
+  bool const append_colon{false};
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void write_char(char_utf8 chr, char*& d_buffer, size_type& bytes)
+  {
+    if (d_buffer)
+      d_buffer += cudf::strings::detail::from_char_utf8(chr, d_buffer);
+    else
+      bytes += cudf::strings::detail::bytes_in_char_utf8(chr);
+  }
+
+  __device__ inline char nibble_to_hex(uint8_t nibble) const
+  {
+    return nibble < 10 ? '0' + nibble : 'a' + nibble - 10;
+  }
+
+  __device__ void write_utf8_codepoint(uint16_t codepoint, char*& d_buffer, size_type& bytes)
+  {
+    if (d_buffer) {
+      d_buffer[0] = '\\';
+      d_buffer[1] = 'u';
+      d_buffer[2] = nibble_to_hex((codepoint >> 12) & 0x0F);
+      d_buffer[3] = nibble_to_hex((codepoint >> 8) & 0x0F);
+      d_buffer[4] = nibble_to_hex((codepoint >> 4) & 0x0F);
+      d_buffer[5] = nibble_to_hex((codepoint)&0x0F);
+      d_buffer += 6;
+    } else {
+      bytes += 6;
+    }
+  }
+
+  __device__ void write_utf16_codepoint(uint32_t codepoint, char*& d_buffer, size_type& bytes)
+  {
+    constexpr uint16_t UTF16_HIGH_SURROGATE_BEGIN = 0xD800;
+    constexpr uint16_t UTF16_LOW_SURROGATE_BEGIN  = 0xDC00;
+    codepoint -= 0x1'0000;
+    uint16_t hex_high = ((codepoint >> 10) & 0x3FF) + UTF16_HIGH_SURROGATE_BEGIN;
+    uint16_t hex_low  = (codepoint & 0x3FF) + UTF16_LOW_SURROGATE_BEGIN;
+    write_utf8_codepoint(hex_high, d_buffer, bytes);
+    write_utf8_codepoint(hex_low, d_buffer, bytes);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const d_str = d_column.element<string_view>(idx);
+
+    // entire string must be double-quoted.
+    constexpr char_utf8 const quote = '\"';  // wrap quotes
+    bool constexpr quote_row        = true;
+
+    char* d_buffer  = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    size_type bytes = 0;
+
+    if (quote_row) write_char(quote, d_buffer, bytes);
+    for (auto utf8_char : d_str) {
+      if (utf8_char > 0x0000'00FF) {
+        // multi-byte char
+        uint32_t codepoint = cudf::strings::detail::utf8_to_codepoint(utf8_char);
+        if (codepoint <= 0x0000'FFFF) {
+          // write \uXXXX utf-8 codepoint
+          write_utf8_codepoint(codepoint, d_buffer, bytes);
+        } else {
+          // write \uXXXX\uXXXX utf-16 surrogate pair
+          // codepoint > 0xFFFF && codepoint <= 0x10FFFF
+          write_utf16_codepoint(codepoint, d_buffer, bytes);
+        }
+        continue;
+      }
+      auto escaped_chars = get_escaped_char(utf8_char);
+      if (escaped_chars.first == '\0') {
+        write_char(escaped_chars.second, d_buffer, bytes);
+      } else {
+        write_char(escaped_chars.first, d_buffer, bytes);
+        write_char(escaped_chars.second, d_buffer, bytes);
+      }
+    }
+    if (quote_row) write_char(quote, d_buffer, bytes);
+    constexpr char_utf8 const colon = ':';  // append colon
+    if (append_colon) write_char(colon, d_buffer, bytes);
+
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+
+  std::unique_ptr<column> get_escaped_strings(column_view const& column_v,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+  {
+    auto children =
+      cudf::strings::detail::make_strings_children(*this, column_v.size(), stream, mr);
+
+    return make_strings_column(column_v.size(),
+                               std::move(children.first),
+                               std::move(children.second),
+                               column_v.null_count(),
+                               cudf::detail::copy_bitmask(column_v, stream, mr));
+  }
+};
+
+// Struct - scatter string_views of each element in a struct column
+struct struct_scatter_strings_fn {
+  table_device_view const tbl;
+  column_device_view const col_names;
+  size_type const strviews_per_column;
+  size_type const num_strviews_per_row;
+  string_view const row_prefix;       // "{"
+  string_view const row_suffix;       // "}" or "}\n" for json-lines
+  string_view const value_separator;  // ","
+  string_view const narep;            // null entry replacement
+  bool const include_nulls;
+  string_view* d_strviews;
+
+  /**
+   * @brief Scatters string_views for each element in a struct column
+   *
+   * @param idx Column-major index of the element to scatter
+   */
+  __device__ void operator()(size_type idx)
+  {
+    auto const row        = idx / tbl.num_columns();
+    auto const col        = idx % tbl.num_columns();
+    auto const d_str_null = tbl.column(col).is_null(row);
+    auto const this_index = row * num_strviews_per_row + col * strviews_per_column + 1;
+    // prefix
+    if (col == 0) d_strviews[this_index - 1] = row_prefix;
+    if (col != 0) d_strviews[this_index - 1] = include_nulls ? value_separator : string_view{};
+    if (!include_nulls && d_str_null) {
+      d_strviews[this_index]     = string_view{};
+      d_strviews[this_index + 1] = string_view{};
+    } else {
+      auto const d_col_name = col_names.element<string_view>(col);
+      auto const d_str = d_str_null ? narep : tbl.column(col).template element<string_view>(row);
+      // column_name: value
+      d_strviews[this_index]     = d_col_name;
+      d_strviews[this_index + 1] = d_str;
+    }
+    // suffix
+    if (col == tbl.num_columns() - 1) { d_strviews[this_index + 2] = row_suffix; }
+  }
+};
+
+struct validity_fn {
+  table_device_view const tbl;
+  __device__ bool operator()(size_type idx) const
+  {
+    auto const row = idx / tbl.num_columns();
+    auto const col = idx % tbl.num_columns();
+    return tbl.column(col).is_valid(row);
+  }
+};
+
+/**
+ * @brief Concatenate the strings from each row of the given table as structs in JSON string
+ *
+ * Each row will be struct with field name as column names and values from each column in the table.
+ *
+ * @param strings_columns Table of strings columns
+ * @param column_names Column of names for each column in the table
+ * @param row_prefix Prepend this string to each row
+ * @param row_suffix  Append this string to each row
+ * @param value_separator Separator between values
+ * @param narep Null-String replacement
+ * @param include_nulls Include null string entries in the output
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource to use for device memory allocation.
+ * @return New strings column of JSON structs in each row
+ */
+std::unique_ptr<column> struct_to_strings(table_view const& strings_columns,
+                                          column_view const& column_names,
+                                          string_view const row_prefix,
+                                          string_view const row_suffix,
+                                          string_view const value_separator,
+                                          string_scalar const& narep,
+                                          bool include_nulls,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(column_names.type().id() == type_id::STRING, "Column names must be of type string");
+  auto const num_columns = strings_columns.num_columns();
+  CUDF_EXPECTS(num_columns == column_names.size(),
+               "Number of column names should be equal to number of columns in the table");
+  auto const strings_count = strings_columns.num_rows();
+  if (strings_count == 0)  // empty begets empty
+    return make_empty_column(type_id::STRING);
+  // check all columns are of type string
+  CUDF_EXPECTS(std::all_of(strings_columns.begin(),
+                           strings_columns.end(),
+                           [](auto const& c) { return c.type().id() == type_id::STRING; }),
+               "All columns must be of type string");
+  auto constexpr strviews_per_column = 3;  // (for each "column_name:", "value",  "separator")
+  auto const num_strviews_per_row    = strings_columns.num_columns() * strviews_per_column + 1;
+  // e.g. {col1: value, col2: value, col3: value} = 1 + 3 + 3 + (3-1) + 1 = 10
+
+  auto tbl_device_view = cudf::table_device_view::create(strings_columns, stream);
+  auto d_column_names  = column_device_view::create(column_names, stream);
+
+  // Note for future: chunk it but maximize parallelism, if memory usage is high.
+  auto const total_strings = num_strviews_per_row * strings_columns.num_rows();
+  auto const total_rows    = strings_columns.num_rows() * strings_columns.num_columns();
+  rmm::device_uvector<string_view> d_strviews(total_strings, stream);
+  struct_scatter_strings_fn scatter_fn{*tbl_device_view,
+                                       *d_column_names,
+                                       strviews_per_column,
+                                       num_strviews_per_row,
+                                       row_prefix,
+                                       row_suffix,
+                                       value_separator,
+                                       narep.value(stream),
+                                       include_nulls,
+                                       d_strviews.begin()};
+  // scatter row_prefix, row_suffix, column_name:, value, value_separator as string_views
+  thrust::for_each(rmm::exec_policy(stream),
+                   thrust::make_counting_iterator<size_type>(0),
+                   thrust::make_counting_iterator<size_type>(total_rows),
+                   scatter_fn);
+  if (!include_nulls) {
+    // if previous column was null, then we skip the value separator
+    rmm::device_uvector<bool> d_str_separator(total_rows, stream);
+    auto row_num = cudf::detail::make_counting_transform_iterator(
+      0, [tbl = *tbl_device_view] __device__(auto idx) -> size_type {
+        return idx / tbl.num_columns();
+      });
+    auto validity_iterator =
+      cudf::detail::make_counting_transform_iterator(0, validity_fn{*tbl_device_view});
+    thrust::exclusive_scan_by_key(rmm::exec_policy(stream),
+                                  row_num,
+                                  row_num + total_rows,
+                                  validity_iterator,
+                                  d_str_separator.begin(),
+                                  false,
+                                  thrust::equal_to<size_type>{},
+                                  thrust::logical_or<bool>{});
+    thrust::for_each(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     thrust::make_counting_iterator<size_type>(total_rows),
+                     [write_separator = d_str_separator.begin(),
+                      d_strviews      = d_strviews.begin(),
+                      value_separator,
+                      tbl = *tbl_device_view,
+                      strviews_per_column,
+                      num_strviews_per_row] __device__(auto idx) {
+                       auto const row = idx / tbl.num_columns();
+                       auto const col = idx % tbl.num_columns();
+                       auto const this_index =
+                         row * num_strviews_per_row + col * strviews_per_column + 1;
+                       if (write_separator[idx] && tbl.column(col).is_valid(row)) {
+                         d_strviews[this_index - 1] = value_separator;
+                       }
+                     });
+  }
+  auto joined_col = make_strings_column(d_strviews, string_view{nullptr, 0}, stream, mr);
+
+  // gather from offset and create a new string column
+  auto old_offsets = strings_column_view(joined_col->view()).offsets();
+  rmm::device_uvector<size_type> row_string_offsets(strings_columns.num_rows() + 1, stream, mr);
+  auto const d_strview_offsets = cudf::detail::make_counting_transform_iterator(
+    0, [num_strviews_per_row] __device__(size_type const i) { return i * num_strviews_per_row; });
+  thrust::gather(rmm::exec_policy(stream),
+                 d_strview_offsets,
+                 d_strview_offsets + row_string_offsets.size(),
+                 old_offsets.begin<size_type>(),
+                 row_string_offsets.begin());
+  return make_strings_column(
+    strings_columns.num_rows(),
+    std::make_unique<cudf::column>(std::move(row_string_offsets), rmm::device_buffer{}, 0),
+    std::move(joined_col->release().children[strings_column_view::chars_column_index]),
+    0,
+    {});
+}
+
+/**
+ * @brief Concatenates a list of strings columns into a single strings column.
+ *
+ * @param lists_strings Column containing lists of strings to concatenate.
+ * @param list_prefix String to place before each list. (typically [)
+ * @param list_suffix String to place after each list. (typically ])
+ * @param element_separator String that should inserted between strings of each list row.
+ * @param element_narep String that should be used in place of any null strings.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New strings column with concatenated results.
+ */
+std::unique_ptr<column> join_list_of_strings(lists_column_view const& lists_strings,
+                                             string_view const list_prefix,
+                                             string_view const list_suffix,
+                                             string_view const element_separator,
+                                             string_view const element_narep,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  /*
+  create string_views of the list elements, and the list separators and list prefix/suffix.
+  then concatenates them all together.
+  gather offset of first string_view of each row as offsets for output string column.
+  Algorithm:
+    calculate #strviews per list using null mask, and list_offsets.
+    scan #strviews to get strviews_offset
+    create label segments.
+    sublist_index = index - offsets[label]
+    strviews_offset[label] + sublist_index = string_view index +1, +2
+    use above 2 to scatter element, element_seperator
+    scatter list_prefix, list_suffix to the right place using list_offsets
+    make_strings_column() and gather offsets, based on strviews_offset.
+  */
+  auto const offsets          = lists_strings.offsets();
+  auto const strings_children = lists_strings.get_sliced_child(stream);
+  auto const num_lists        = lists_strings.size();
+  auto const num_strings      = strings_children.size();
+  auto const num_offsets      = offsets.size();
+
+  rmm::device_uvector<size_type> d_strview_offsets(num_offsets, stream);
+  auto num_strings_per_list = cudf::detail::make_counting_transform_iterator(
+    0, [offsets = offsets.begin<size_type>(), num_offsets] __device__(size_type idx) {
+      if (idx + 1 >= num_offsets) return 0;
+      auto const length = offsets[idx + 1] - offsets[idx];
+      return length == 0 ? 2 : (2 + length + length - 1);
+    });
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         num_strings_per_list,
+                         num_strings_per_list + num_offsets,
+                         d_strview_offsets.begin());
+  auto const total_strings = d_strview_offsets.back_element(stream);
+
+  rmm::device_uvector<string_view> d_strviews(total_strings, stream);
+  // scatter null_list and list_prefix, list_suffix
+  auto col_device_view = cudf::column_device_view::create(lists_strings.parent(), stream);
+  thrust::for_each(rmm::exec_policy(stream),
+                   thrust::make_counting_iterator<size_type>(0),
+                   thrust::make_counting_iterator<size_type>(num_lists),
+                   [col = *col_device_view,
+                    list_prefix,
+                    list_suffix,
+                    d_strview_offsets = d_strview_offsets.begin(),
+                    d_strviews        = d_strviews.begin()] __device__(auto idx) {
+                     if (col.is_null(idx)) {
+                       d_strviews[d_strview_offsets[idx]]     = string_view{};
+                       d_strviews[d_strview_offsets[idx] + 1] = string_view{};
+                     } else {
+                       // [ ]
+                       d_strviews[d_strview_offsets[idx]]         = list_prefix;
+                       d_strviews[d_strview_offsets[idx + 1] - 1] = list_suffix;
+                     }
+                   });
+
+  // scatter string and separator
+  auto labels = cudf::lists::detail::generate_labels(
+    lists_strings, num_strings, stream, rmm::mr::get_current_device_resource());
+  auto d_strings_children = cudf::column_device_view::create(strings_children, stream);
+  thrust::for_each(rmm::exec_policy(stream),
+                   thrust::make_counting_iterator<size_type>(0),
+                   thrust::make_counting_iterator<size_type>(num_strings),
+                   [col                = *col_device_view,
+                    d_strview_offsets  = d_strview_offsets.begin(),
+                    d_strviews         = d_strviews.begin(),
+                    labels             = labels->view().begin<size_type>(),
+                    list_offsets       = offsets.begin<size_type>(),
+                    d_strings_children = *d_strings_children,
+                    element_separator,
+                    element_narep] __device__(auto idx) {
+                     auto const label         = labels[idx];
+                     auto const sublist_index = idx - list_offsets[label];
+                     auto const strview_index = d_strview_offsets[label] + sublist_index * 2 + 1;
+                     // value or na_rep
+                     auto const strview = d_strings_children.element<cudf::string_view>(idx);
+                     d_strviews[strview_index] =
+                       d_strings_children.is_null(idx) ? element_narep : strview;
+                     // separator
+                     if (sublist_index != 0) { d_strviews[strview_index - 1] = element_separator; }
+                   });
+
+  auto joined_col = make_strings_column(d_strviews, string_view{nullptr, 0}, stream, mr);
+
+  // gather from offset and create a new string column
+  auto old_offsets = strings_column_view(joined_col->view()).offsets();
+  rmm::device_uvector<size_type> row_string_offsets(num_offsets, stream, mr);
+  thrust::gather(rmm::exec_policy(stream),
+                 d_strview_offsets.begin(),
+                 d_strview_offsets.end(),
+                 old_offsets.begin<size_type>(),
+                 row_string_offsets.begin());
+  return make_strings_column(
+    num_lists,
+    std::make_unique<cudf::column>(std::move(row_string_offsets), rmm::device_buffer{}, 0),
+    std::move(joined_col->release().children[strings_column_view::chars_column_index]),
+    lists_strings.null_count(),
+    cudf::detail::copy_bitmask(lists_strings.parent(), stream, mr));
+}
+
+/**
+ * @brief Functor to convert a column to string representation for JSON format.
+ */
+struct column_to_strings_fn {
+  /**
+   * @brief Returns true if the specified type is not supported by the JSON writer.
+   */
+  template <typename column_type>
+  constexpr static bool is_not_handled()
+  {
+    // Note: the case (not std::is_same_v<column_type, bool>)  is already covered by is_integral)
+    return not((std::is_same_v<column_type, cudf::string_view>) ||
+               (std::is_integral_v<column_type>) || (std::is_floating_point_v<column_type>) ||
+               (cudf::is_fixed_point<column_type>()) || (cudf::is_timestamp<column_type>()) ||
+               (cudf::is_duration<column_type>()));
+  }
+
+  explicit column_to_strings_fn(json_writer_options const& options,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+    : options_(options),
+      stream_(stream),
+      mr_(mr),
+      narep(options.get_na_rep(), true, stream),
+      struct_value_separator(",", true, stream),
+      struct_row_begin_wrap("{", true, stream),
+      struct_row_end_wrap("}", true, stream),
+      list_value_separator(",", true, stream),
+      list_row_begin_wrap("[", true, stream),
+      list_row_end_wrap("]", true, stream)
+  {
+  }
+
+  // unsupported type of column:
+  template <typename column_type>
+  std::enable_if_t<is_not_handled<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const&) const
+  {
+    CUDF_FAIL("Unsupported column type.");
+  }
+
+  // Note: `null` replacement with `na_rep` deferred to `concatenate()`
+  // instead of column-wise; might be faster.
+
+  // bools:
+  template <typename column_type>
+  std::enable_if_t<std::is_same_v<column_type, bool>, std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::strings::detail::from_booleans(
+      column, options_.get_true_value(), options_.get_false_value(), stream_, mr_);
+  }
+
+  // strings:
+  template <typename column_type>
+  std::enable_if_t<std::is_same_v<column_type, cudf::string_view>, std::unique_ptr<column>>
+  operator()(column_view const& column_v) const
+  {
+    auto d_column = column_device_view::create(column_v, stream_);
+    return escape_strings_fn{*d_column}.get_escaped_strings(column_v, stream_, mr_);
+  }
+
+  // ints:
+  template <typename column_type>
+  std::enable_if_t<std::is_integral_v<column_type> && !std::is_same_v<column_type, bool>,
+                   std::unique_ptr<column>>
+  operator()(column_view const& column) const
+  {
+    return cudf::strings::detail::from_integers(column, stream_, mr_);
+  }
+
+  // floats:
+  template <typename column_type>
+  std::enable_if_t<std::is_floating_point_v<column_type>, std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::strings::detail::from_floats(column, stream_, mr_);
+  }
+
+  // fixed point:
+  template <typename column_type>
+  std::enable_if_t<cudf::is_fixed_point<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    return cudf::strings::detail::from_fixed_point(column, stream_, mr_);
+  }
+
+  // timestamps:
+  template <typename column_type>
+  std::enable_if_t<cudf::is_timestamp<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    std::string format = [&]() {
+      if (std::is_same_v<cudf::timestamp_s, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%SZ"};
+      } else if (std::is_same_v<cudf::timestamp_ms, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%3fZ"};
+      } else if (std::is_same_v<cudf::timestamp_us, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%6fZ"};
+      } else if (std::is_same_v<cudf::timestamp_ns, column_type>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%9fZ"};
+      } else {
+        return std::string{"%Y-%m-%d"};
+      }
+    }();
+
+    // Since format uses ":", we need to add quotes to the format
+    format = "\"" + format + "\"";
+
+    return cudf::strings::detail::from_timestamps(
+      column,
+      format,
+      strings_column_view(make_empty_column(type_id::STRING)->view()),
+      stream_,
+      mr_);
+  }
+
+  template <typename column_type>
+  std::enable_if_t<cudf::is_duration<column_type>(), std::unique_ptr<column>> operator()(
+    column_view const& column) const
+  {
+    auto duration_string = cudf::io::detail::csv::pandas_format_durations(column, stream_, mr_);
+    auto quotes =
+      make_column_from_scalar(string_scalar{"\"", true, stream_}, column.size(), stream_, mr_);
+    return cudf::strings::detail::concatenate(
+      table_view{{quotes->view(), duration_string->view(), quotes->view()}},
+      string_scalar("", true, stream_),
+      string_scalar("", false, stream_),
+      strings::separator_on_nulls::YES,
+      stream_,
+      mr_);
+  }
+
+  // lists:
+  template <typename column_type>
+  std::enable_if_t<std::is_same_v<column_type, cudf::list_view>, std::unique_ptr<column>>
+  operator()(column_view const& column, host_span<column_name_info const> children_names) const
+  {
+    auto child_view            = lists_column_view(column).get_sliced_child(stream_);
+    auto constexpr child_index = lists_column_view::child_column_index;
+
+    auto child_string_with_null = [&]() {
+      if (child_view.type().id() == type_id::STRUCT) {
+        return (*this).template operator()<cudf::struct_view>(
+          child_view,
+          children_names.size() > child_index ? children_names[child_index].children
+                                              : std::vector<column_name_info>{});
+      } else if (child_view.type().id() == type_id::LIST) {
+        return (*this).template operator()<cudf::list_view>(child_view,
+                                                            children_names.size() > child_index
+                                                              ? children_names[child_index].children
+                                                              : std::vector<column_name_info>{});
+      } else {
+        return cudf::type_dispatcher(child_view.type(), *this, child_view);
+      }
+    };
+    auto new_offsets = cudf::lists::detail::get_normalized_offsets(
+      lists_column_view(column), stream_, rmm::mr::get_current_device_resource());
+    auto const list_child_string = make_lists_column(
+      column.size(),
+      std::move(new_offsets),
+      std::move(child_string_with_null()),
+      column.null_count(),
+      cudf::detail::copy_bitmask(column, stream_, rmm::mr::get_current_device_resource()),
+      stream_);
+    return join_list_of_strings(lists_column_view(*list_child_string),
+                                list_row_begin_wrap.value(stream_),
+                                list_row_end_wrap.value(stream_),
+                                list_value_separator.value(stream_),
+                                narep.value(stream_),
+                                stream_,
+                                mr_);
+  }
+
+  // structs:
+  template <typename column_type>
+  std::enable_if_t<std::is_same_v<column_type, cudf::struct_view>, std::unique_ptr<column>>
+  operator()(column_view const& column, host_span<column_name_info const> children_names) const
+  {
+    auto const child_it = cudf::detail::make_counting_transform_iterator(
+      0, [&stream = stream_, structs_view = structs_column_view{column}](auto const child_idx) {
+        return structs_view.get_sliced_child(child_idx, stream);
+      });
+    auto col_string = operator()(child_it,
+                                 child_it + column.num_children(),
+                                 children_names,
+                                 struct_row_end_wrap.value(stream_));
+    col_string->set_null_mask(cudf::detail::copy_bitmask(column, stream_, mr_),
+                              column.null_count());
+    return col_string;
+  }
+
+  // Table:
+  template <typename column_iterator>
+  std::unique_ptr<column> operator()(column_iterator column_begin,
+                                     column_iterator column_end,
+                                     host_span<column_name_info const> children_names,
+                                     cudf::string_view const row_end_wrap_value) const
+  {
+    auto const num_columns = std::distance(column_begin, column_end);
+    auto column_names      = make_column_names_column(children_names, num_columns, stream_);
+    auto column_names_view = column_names->view();
+    std::vector<std::unique_ptr<cudf::column>> str_column_vec;
+
+    // populate vector of string-converted columns:
+    //
+    auto i_col_begin =
+      thrust::make_zip_iterator(thrust::counting_iterator<size_t>(0), column_begin);
+    std::transform(i_col_begin,
+                   i_col_begin + num_columns,
+                   std::back_inserter(str_column_vec),
+                   [this, &children_names](auto const& i_current_col) {
+                     auto const i            = thrust::get<0>(i_current_col);
+                     auto const& current_col = thrust::get<1>(i_current_col);
+                     // Struct needs children's column names
+                     if (current_col.type().id() == type_id::STRUCT) {
+                       return (*this).template operator()<cudf::struct_view>(
+                         current_col,
+                         children_names.size() > i ? children_names[i].children
+                                                   : std::vector<column_name_info>{});
+                     } else if (current_col.type().id() == type_id::LIST) {
+                       return (*this).template operator()<cudf::list_view>(
+                         current_col,
+                         children_names.size() > i ? children_names[i].children
+                                                   : std::vector<column_name_info>{});
+                     } else {
+                       return cudf::type_dispatcher(current_col.type(), *this, current_col);
+                     }
+                   });
+
+    // create string table view from str_column_vec:
+    //
+    auto str_table_ptr  = std::make_unique<cudf::table>(std::move(str_column_vec));
+    auto str_table_view = str_table_ptr->view();
+
+    // concatenate columns in each row into one big string column
+    // (using null representation and delimiter):
+    //
+    return struct_to_strings(str_table_view,
+                             column_names_view,
+                             struct_row_begin_wrap.value(stream_),
+                             row_end_wrap_value,
+                             struct_value_separator.value(stream_),
+                             narep,
+                             options_.is_enabled_include_nulls(),
+                             stream_,
+                             rmm::mr::get_current_device_resource());
+  }
+
+ private:
+  json_writer_options const& options_;
+  rmm::cuda_stream_view stream_;
+  rmm::mr::device_memory_resource* mr_;
+  string_scalar const narep;  // "null"
+  // struct convert constants
+  string_scalar const struct_value_separator;  // ","
+  string_scalar const struct_row_begin_wrap;   // "{"
+  string_scalar const struct_row_end_wrap;     // "}"
+  // list converter constants
+  string_scalar const list_value_separator;  // ","
+  string_scalar const list_row_begin_wrap;   // "["
+  string_scalar const list_row_end_wrap;     // "]"
+};
+
+}  // namespace
+
+std::unique_ptr<column> make_strings_column_from_host(host_span<std::string const> host_strings,
+                                                      rmm::cuda_stream_view stream)
+{
+  std::string const host_chars =
+    std::accumulate(host_strings.begin(), host_strings.end(), std::string(""));
+  auto d_chars = cudf::detail::make_device_uvector_async(
+    host_chars, stream, rmm::mr::get_current_device_resource());
+  std::vector<cudf::size_type> offsets(host_strings.size() + 1, 0);
+  std::transform_inclusive_scan(host_strings.begin(),
+                                host_strings.end(),
+                                offsets.begin() + 1,
+                                std::plus<cudf::size_type>{},
+                                [](auto& str) { return str.size(); });
+  auto d_offsets =
+    cudf::detail::make_device_uvector_sync(offsets, stream, rmm::mr::get_current_device_resource());
+  return cudf::make_strings_column(
+    host_strings.size(), std::move(d_offsets), std::move(d_chars), {}, 0);
+}
+
+std::unique_ptr<column> make_column_names_column(host_span<column_name_info const> column_names,
+                                                 size_type num_columns,
+                                                 rmm::cuda_stream_view stream)
+{
+  std::vector<std::string> unescaped_column_names;
+  if (column_names.empty()) {
+    std::generate_n(std::back_inserter(unescaped_column_names), num_columns, [v = 0]() mutable {
+      return std::to_string(v++);
+    });
+  } else {
+    std::transform(column_names.begin(),
+                   column_names.end(),
+                   std::back_inserter(unescaped_column_names),
+                   [](column_name_info const& name_info) { return name_info.name; });
+  }
+  auto unescaped_string_col = make_strings_column_from_host(unescaped_column_names, stream);
+  auto d_column             = column_device_view::create(*unescaped_string_col, stream);
+  return escape_strings_fn{*d_column, true}.get_escaped_strings(
+    *unescaped_string_col, stream, rmm::mr::get_current_device_resource());
+}
+
+void write_chunked(data_sink* out_sink,
+                   strings_column_view const& str_column_view,
+                   int const skip_last_chars,
+                   json_writer_options const& options,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(str_column_view.size() > 0, "Unexpected empty strings column.");
+
+  auto const total_num_bytes = str_column_view.chars_size() - skip_last_chars;
+  char const* ptr_all_bytes  = str_column_view.chars_begin();
+
+  if (out_sink->is_device_write_preferred(total_num_bytes)) {
+    // Direct write from device memory
+    out_sink->device_write(ptr_all_bytes, total_num_bytes, stream);
+  } else {
+    // copy the bytes to host to write them out
+    auto const h_bytes = cudf::detail::make_host_vector_sync(
+      device_span<char const>(ptr_all_bytes, total_num_bytes), stream);
+
+    out_sink->host_write(h_bytes.data(), total_num_bytes);
+  }
+}
+
+void write_json(data_sink* out_sink,
+                table_view const& table,
+                json_writer_options const& options,
+                rmm::cuda_stream_view stream,
+                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  std::vector<column_name_info> user_column_names = [&]() {
+    auto const& metadata = options.get_metadata();
+    if (metadata.has_value() and not metadata->schema_info.empty()) {
+      return metadata->schema_info;
+    } else {
+      std::vector<column_name_info> names;
+      // generate strings 0 to table.num_columns()
+      std::transform(thrust::make_counting_iterator(0),
+                     thrust::make_counting_iterator(table.num_columns()),
+                     std::back_inserter(names),
+                     [](auto i) { return column_name_info{std::to_string(i)}; });
+      return names;
+    }
+  }();
+  auto const line_terminator = std::string(options.is_enabled_lines() ? "\n" : ",");
+  string_scalar const d_line_terminator_with_row_end{"}" + line_terminator, true, stream};
+  string_scalar const d_line_terminator{line_terminator, true, stream};
+
+  // write header: required for non-record oriented output
+  // header varies depending on orient.
+  // write_chunked_begin(out_sink, table, user_column_names, options, stream, mr);
+  // TODO This should go into the write_chunked_begin function
+  std::string const list_braces{"[]"};
+  string_scalar const d_list_braces{list_braces, true, stream};
+  if (!options.is_enabled_lines()) {
+    if (out_sink->is_device_write_preferred(1)) {
+      out_sink->device_write(d_list_braces.data(), 1, stream);
+    } else {
+      out_sink->host_write(list_braces.data(), 1);
+    }
+  }
+
+  if (table.num_rows() > 0) {
+    auto n_rows_per_chunk = options.get_rows_per_chunk();
+
+    // This outputs the JSON in row chunks to save memory.
+    // Maybe we can use the total_rows*count calculation and a memory threshold
+    // instead of an arbitrary chunk count.
+    // The entire JSON chunk must fit in CPU memory before writing it out.
+    //
+    if (n_rows_per_chunk % 8)  // must be divisible by 8
+      n_rows_per_chunk += 8 - (n_rows_per_chunk % 8);
+
+    CUDF_EXPECTS(n_rows_per_chunk >= 8, "write_json: invalid chunk_rows; must be at least 8");
+
+    auto num_rows = table.num_rows();
+    std::vector<table_view> vector_views;
+
+    if (num_rows <= n_rows_per_chunk) {
+      vector_views.push_back(table);
+    } else {
+      auto const n_chunks = num_rows / n_rows_per_chunk;
+      std::vector<size_type> splits(n_chunks);
+      thrust::tabulate(splits.begin(), splits.end(), [n_rows_per_chunk](auto idx) {
+        return (idx + 1) * n_rows_per_chunk;
+      });
+
+      // split table_view into chunks:
+      vector_views = cudf::detail::split(table, splits, stream);
+    }
+
+    // convert each chunk to JSON:
+    column_to_strings_fn converter{options, stream, rmm::mr::get_current_device_resource()};
+
+    for (auto&& sub_view : vector_views) {
+      // Skip if the table has no rows
+      if (sub_view.num_rows() == 0) continue;
+      std::vector<std::unique_ptr<column>> str_column_vec;
+
+      // struct converter for the table
+      auto str_concat_col = converter(sub_view.begin(),
+                                      sub_view.end(),
+                                      user_column_names,
+                                      d_line_terminator_with_row_end.value(stream));
+
+      // Needs line_terminator at the end, to separate from next chunk
+      bool const include_line_terminator =
+        (&sub_view != &vector_views.back()) or options.is_enabled_lines();
+      auto const skip_last_chars = (include_line_terminator ? 0 : line_terminator.size());
+      write_chunked(out_sink, str_concat_col->view(), skip_last_chars, options, stream, mr);
+    }
+  } else {
+    if (options.is_enabled_lines()) {
+      if (out_sink->is_device_write_preferred(1)) {
+        out_sink->device_write(d_line_terminator.data(), d_line_terminator.size(), stream);
+      } else {
+        out_sink->host_write(line_terminator.data(), line_terminator.size());
+      }
+    }
+  }
+  // TODO write_chunked_end(out_sink, options, stream, mr);
+  if (!options.is_enabled_lines()) {
+    if (out_sink->is_device_write_preferred(1)) {
+      out_sink->device_write(d_list_braces.data() + 1, 1, stream);
+    } else {
+      out_sink->host_write(list_braces.data() + 1, 1);
+    }
+  }
+}
+
+}  // namespace cudf::io::json::detail
diff --git a/cpp/src/io/orc/aggregate_orc_metadata.cpp b/cpp/src/io/orc/aggregate_orc_metadata.cpp
new file mode 100644
index 0000000..2e5eeab
--- /dev/null
+++ b/cpp/src/io/orc/aggregate_orc_metadata.cpp
@@ -0,0 +1,276 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "aggregate_orc_metadata.hpp"
+
+#include <io/utilities/row_selection.hpp>
+
+#include <algorithm>
+#include <numeric>
+#include <optional>
+
+namespace cudf::io::orc::detail {
+
+column_hierarchy::column_hierarchy(nesting_map child_map) : children{std::move(child_map)}
+{
+  // Sort columns by nesting levels
+  std::function<void(size_type, int32_t)> levelize = [&](size_type id, int32_t level) {
+    if (static_cast<int32_t>(levels.size()) == level) levels.emplace_back();
+
+    levels[level].push_back({id, static_cast<int32_t>(children[id].size())});
+
+    for (auto child_id : children[id]) {
+      levelize(child_id, level + 1);
+    }
+  };
+
+  std::for_each(
+    children[0].cbegin(), children[0].cend(), [&](auto col_id) { levelize(col_id, 0); });
+}
+
+namespace {
+
+/**
+ * @brief Goes up to the root to include the column with the given id and its parents.
+ */
+void update_parent_mapping(std::map<size_type, std::vector<size_type>>& selected_columns,
+                           metadata const& metadata,
+                           size_type id)
+{
+  auto current_id = id;
+  while (metadata.column_has_parent(current_id)) {
+    auto parent_id = metadata.parent_id(current_id);
+    if (std::find(selected_columns[parent_id].cbegin(),
+                  selected_columns[parent_id].cend(),
+                  current_id) == selected_columns[parent_id].end()) {
+      selected_columns[parent_id].push_back(current_id);
+    }
+    current_id = parent_id;
+  }
+}
+
+/**
+ * @brief Adds all columns nested under the column with the given id to the nesting map.
+ */
+void add_nested_columns(std::map<size_type, std::vector<size_type>>& selected_columns,
+                        std::vector<SchemaType> const& types,
+                        size_type id)
+{
+  for (auto child_id : types[id].subtypes) {
+    if (std::find(selected_columns[id].cbegin(), selected_columns[id].cend(), child_id) ==
+        selected_columns[id].end()) {
+      selected_columns[id].push_back(child_id);
+    }
+    add_nested_columns(selected_columns, types, child_id);
+  }
+}
+
+/**
+ * @brief Adds the column with the given id to the mapping
+ *
+ * All nested columns and direct ancestors of column `id` are included.
+ * Columns that are not on the direct path are excluded, which may result in pruning.
+ */
+void add_column_to_mapping(std::map<size_type, std::vector<size_type>>& selected_columns,
+                           metadata const& metadata,
+                           size_type id)
+{
+  update_parent_mapping(selected_columns, metadata, id);
+  add_nested_columns(selected_columns, metadata.ff.types, id);
+}
+
+/**
+ * @brief Create a metadata object from each element in the source vector
+ */
+auto metadatas_from_sources(std::vector<std::unique_ptr<datasource>> const& sources,
+                            rmm::cuda_stream_view stream)
+{
+  std::vector<metadata> metadatas;
+  std::transform(
+    sources.cbegin(), sources.cend(), std::back_inserter(metadatas), [stream](auto const& source) {
+      return metadata(source.get(), stream);
+    });
+  return metadatas;
+}
+
+}  // namespace
+
+int64_t aggregate_orc_metadata::calc_num_rows() const
+{
+  return std::accumulate(
+    per_file_metadata.begin(), per_file_metadata.end(), 0l, [](auto const& sum, auto const& pfm) {
+      return sum + pfm.get_total_rows();
+    });
+}
+
+size_type aggregate_orc_metadata::calc_num_stripes() const
+{
+  return std::accumulate(
+    per_file_metadata.begin(), per_file_metadata.end(), 0, [](auto const& sum, auto const& pfm) {
+      return sum + pfm.get_num_stripes();
+    });
+}
+
+aggregate_orc_metadata::aggregate_orc_metadata(
+  std::vector<std::unique_ptr<datasource>> const& sources, rmm::cuda_stream_view stream)
+  : per_file_metadata(metadatas_from_sources(sources, stream)),
+    num_rows(calc_num_rows()),
+    num_stripes(calc_num_stripes())
+{
+  // Verify that the input files have the same number of columns,
+  // as well as matching types, compression, and names
+  for (auto const& pfm : per_file_metadata) {
+    CUDF_EXPECTS(per_file_metadata[0].get_num_columns() == pfm.get_num_columns(),
+                 "All sources must have the same number of columns");
+    CUDF_EXPECTS(per_file_metadata[0].ps.compression == pfm.ps.compression,
+                 "All sources must have the same compression type");
+
+    // Check the types, column names, and decimal scale
+    for (size_t i = 0; i < pfm.ff.types.size(); i++) {
+      CUDF_EXPECTS(pfm.ff.types[i].kind == per_file_metadata[0].ff.types[i].kind,
+                   "Column types across all input sources must be the same");
+      CUDF_EXPECTS(std::equal(pfm.ff.types[i].fieldNames.begin(),
+                              pfm.ff.types[i].fieldNames.end(),
+                              per_file_metadata[0].ff.types[i].fieldNames.begin()),
+                   "All source column names must be the same");
+      CUDF_EXPECTS(
+        pfm.ff.types[i].scale.value_or(0) == per_file_metadata[0].ff.types[i].scale.value_or(0),
+        "All scale values must be the same");
+    }
+  }
+}
+
+std::tuple<int64_t, size_type, std::vector<metadata::stripe_source_mapping>>
+aggregate_orc_metadata::select_stripes(
+  std::vector<std::vector<size_type>> const& user_specified_stripes,
+  uint64_t skip_rows,
+  std::optional<size_type> const& num_rows,
+  rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS((skip_rows == 0 and not num_rows.has_value()) or user_specified_stripes.empty(),
+               "Can't use both the row selection and the stripe selection");
+
+  auto [rows_to_skip, rows_to_read] = [&]() {
+    if (not user_specified_stripes.empty()) { return std::pair<uint64_t, size_type>{0, 0}; }
+    return cudf::io::detail::skip_rows_num_rows_from_options(skip_rows, num_rows, get_num_rows());
+  }();
+
+  std::vector<metadata::stripe_source_mapping> selected_stripes_mapping;
+
+  if (!user_specified_stripes.empty()) {
+    CUDF_EXPECTS(user_specified_stripes.size() == per_file_metadata.size(),
+                 "Must specify stripes for each source");
+
+    // Each vector entry represents a source file; each nested vector represents the
+    // user_defined_stripes to get from that source file
+    for (size_t src_file_idx = 0; src_file_idx < user_specified_stripes.size(); ++src_file_idx) {
+      std::vector<OrcStripeInfo> stripe_infos;
+
+      // Coalesce stripe info at the source file later since that makes downstream processing much
+      // easier in impl::read
+      for (auto const& stripe_idx : user_specified_stripes[src_file_idx]) {
+        CUDF_EXPECTS(
+          stripe_idx >= 0 and stripe_idx < static_cast<decltype(stripe_idx)>(
+                                             per_file_metadata[src_file_idx].ff.stripes.size()),
+          "Invalid stripe index");
+        stripe_infos.push_back(
+          std::pair(&per_file_metadata[src_file_idx].ff.stripes[stripe_idx], nullptr));
+        rows_to_read += per_file_metadata[src_file_idx].ff.stripes[stripe_idx].numberOfRows;
+      }
+      selected_stripes_mapping.push_back({static_cast<int>(src_file_idx), stripe_infos});
+    }
+  } else {
+    uint64_t count             = 0;
+    size_type stripe_skip_rows = 0;
+    // Iterate all source files, each source file has corelating metadata
+    for (size_t src_file_idx = 0;
+         src_file_idx < per_file_metadata.size() && count < rows_to_skip + rows_to_read;
+         ++src_file_idx) {
+      std::vector<OrcStripeInfo> stripe_infos;
+
+      for (size_t stripe_idx = 0; stripe_idx < per_file_metadata[src_file_idx].ff.stripes.size() &&
+                                  count < rows_to_skip + rows_to_read;
+           ++stripe_idx) {
+        count += per_file_metadata[src_file_idx].ff.stripes[stripe_idx].numberOfRows;
+        if (count > rows_to_skip || count == 0) {
+          stripe_infos.push_back(
+            std::pair(&per_file_metadata[src_file_idx].ff.stripes[stripe_idx], nullptr));
+        } else {
+          stripe_skip_rows = count;
+        }
+      }
+
+      selected_stripes_mapping.push_back({static_cast<int>(src_file_idx), stripe_infos});
+    }
+    // Need to remove skipped rows from the stripes which are not selected.
+    rows_to_skip -= stripe_skip_rows;
+  }
+
+  // Read each stripe's stripefooter metadata
+  if (not selected_stripes_mapping.empty()) {
+    for (auto& mapping : selected_stripes_mapping) {
+      // Resize to all stripe_info for the source level
+      per_file_metadata[mapping.source_idx].stripefooters.resize(mapping.stripe_info.size());
+
+      for (size_t i = 0; i < mapping.stripe_info.size(); i++) {
+        auto const stripe         = mapping.stripe_info[i].first;
+        auto const sf_comp_offset = stripe->offset + stripe->indexLength + stripe->dataLength;
+        auto const sf_comp_length = stripe->footerLength;
+        CUDF_EXPECTS(
+          sf_comp_offset + sf_comp_length < per_file_metadata[mapping.source_idx].source->size(),
+          "Invalid stripe information");
+        auto const buffer =
+          per_file_metadata[mapping.source_idx].source->host_read(sf_comp_offset, sf_comp_length);
+        auto sf_data = per_file_metadata[mapping.source_idx].decompressor->decompress_blocks(
+          {buffer->data(), buffer->size()}, stream);
+        ProtobufReader(sf_data.data(), sf_data.size())
+          .read(per_file_metadata[mapping.source_idx].stripefooters[i]);
+        mapping.stripe_info[i].second = &per_file_metadata[mapping.source_idx].stripefooters[i];
+        if (stripe->indexLength == 0) { row_grp_idx_present = false; }
+      }
+    }
+  }
+
+  return {rows_to_skip, rows_to_read, selected_stripes_mapping};
+}
+
+column_hierarchy aggregate_orc_metadata::select_columns(
+  std::optional<std::vector<std::string>> const& column_paths) const
+{
+  auto const& pfm = per_file_metadata[0];
+
+  column_hierarchy::nesting_map selected_columns;
+  if (not column_paths.has_value()) {
+    for (auto const& col_id : pfm.ff.types[0].subtypes) {
+      add_column_to_mapping(selected_columns, pfm, col_id);
+    }
+  } else {
+    for (auto const& path : column_paths.value()) {
+      bool name_found = false;
+      for (auto col_id = 1; col_id < pfm.get_num_columns(); ++col_id) {
+        if (pfm.column_path(col_id) == path) {
+          name_found = true;
+          add_column_to_mapping(selected_columns, pfm, col_id);
+          break;
+        }
+      }
+      CUDF_EXPECTS(name_found, "Unknown column name: " + std::string(path));
+    }
+  }
+  return {std::move(selected_columns)};
+}
+
+}  // namespace cudf::io::orc::detail
diff --git a/cpp/src/io/orc/aggregate_orc_metadata.hpp b/cpp/src/io/orc/aggregate_orc_metadata.hpp
new file mode 100644
index 0000000..587684c
--- /dev/null
+++ b/cpp/src/io/orc/aggregate_orc_metadata.hpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc.hpp"
+
+#include <map>
+#include <optional>
+#include <vector>
+
+namespace cudf::io::orc::detail {
+
+/**
+ * @brief Describes a column hierarchy, which may exclude some input columns.
+ */
+struct column_hierarchy {
+  // Maps column IDs to the IDs of their children columns
+  using nesting_map = std::map<size_type, std::vector<size_type>>;
+  // Children IDs of each column
+  nesting_map children;
+  // Each element contains column at the given nesting level
+  std::vector<std::vector<orc_column_meta>> levels;
+
+  column_hierarchy(nesting_map child_map);
+  auto num_levels() const { return levels.size(); }
+};
+
+/**
+ * @brief In order to support multiple input files/buffers we need to gather
+ * the metadata across all of those input(s). This class provides a place
+ * to aggregate that metadata from all the files.
+ */
+class aggregate_orc_metadata {
+  using OrcStripeInfo = std::pair<StripeInformation const*, StripeFooter const*>;
+
+  /**
+   * @brief Sums up the number of rows of each source
+   */
+  [[nodiscard]] int64_t calc_num_rows() const;
+
+  /**
+   * @brief Number of columns in a ORC file.
+   */
+  [[nodiscard]] size_type calc_num_cols() const;
+
+  /**
+   * @brief Sums up the number of stripes of each source
+   */
+  [[nodiscard]] size_type calc_num_stripes() const;
+
+ public:
+  std::vector<metadata> per_file_metadata;
+  int64_t const num_rows;
+  size_type const num_stripes;
+  bool row_grp_idx_present{true};
+
+  aggregate_orc_metadata(std::vector<std::unique_ptr<datasource>> const& sources,
+                         rmm::cuda_stream_view stream);
+
+  [[nodiscard]] auto const& get_schema(int schema_idx) const
+  {
+    return per_file_metadata[0].ff.types[schema_idx];
+  }
+
+  auto get_col_type(int col_idx) const { return per_file_metadata[0].ff.types[col_idx]; }
+
+  [[nodiscard]] auto get_num_rows() const { return num_rows; }
+
+  auto get_num_cols() const { return per_file_metadata[0].get_num_columns(); }
+
+  [[nodiscard]] auto get_num_stripes() const { return num_stripes; }
+
+  [[nodiscard]] auto const& get_types() const { return per_file_metadata[0].ff.types; }
+
+  [[nodiscard]] int get_row_index_stride() const { return per_file_metadata[0].ff.rowIndexStride; }
+
+  [[nodiscard]] auto is_row_grp_idx_present() const { return row_grp_idx_present; }
+
+  /**
+   * @brief Returns the name of the given column from the given source.
+   */
+  [[nodiscard]] std::string const& column_name(int const source_idx, int const column_id) const
+  {
+    CUDF_EXPECTS(source_idx <= static_cast<int>(per_file_metadata.size()),
+                 "Out of range source_idx provided");
+    return per_file_metadata[source_idx].column_name(column_id);
+  }
+
+  /**
+   * @brief Returns the full name of the given column from the given source.
+   *
+   * Full name includes ancestor columns' names.
+   */
+  [[nodiscard]] std::string const& column_path(int const source_idx, int const column_id) const
+  {
+    CUDF_EXPECTS(source_idx <= static_cast<int>(per_file_metadata.size()),
+                 "Out of range source_idx provided");
+    return per_file_metadata[source_idx].column_path(column_id);
+  }
+
+  /**
+   * @brief Selects the stripes to read, based on the row/stripe selection parameters.
+   *
+   * Stripes are potentially selected from multiple files.
+   */
+  std::tuple<int64_t, size_type, std::vector<metadata::stripe_source_mapping>> select_stripes(
+    std::vector<std::vector<size_type>> const& user_specified_stripes,
+    uint64_t skip_rows,
+    std::optional<size_type> const& num_rows,
+    rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Filters ORC file to a selection of columns, based on their paths in the file.
+   *
+   * Paths are in format "grandparent_col.parent_col.child_col", where the root ORC column is
+   * omitted to match the cuDF table hierarchy.
+   *
+   * @param column_paths List of full column names (i.e. paths) to select from the ORC file;
+   * `nullopt` if user did not select columns to read
+   * @return Columns hierarchy - lists of children columns and sorted columns in each nesting level
+   */
+  column_hierarchy select_columns(
+    std::optional<std::vector<std::string>> const& column_paths) const;
+};
+
+}  // namespace cudf::io::orc::detail
diff --git a/cpp/src/io/orc/dict_enc.cu b/cpp/src/io/orc/dict_enc.cu
new file mode 100644
index 0000000..1d2262a
--- /dev/null
+++ b/cpp/src/io/orc/dict_enc.cu
@@ -0,0 +1,277 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc_gpu.hpp"
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/io/orc_types.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::io::orc::gpu {
+
+/**
+ * @brief Counts the number of characters in each rowgroup of each string column.
+ */
+__global__ void rowgroup_char_counts_kernel(device_2dspan<size_type> char_counts,
+                                            device_span<orc_column_device_view const> orc_columns,
+                                            device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                                            device_span<uint32_t const> str_col_indexes)
+{
+  // Index of the column in the `str_col_indexes` array
+  auto const str_col_idx = blockIdx.y;
+  // Index of the column in the `orc_columns` array
+  auto const col_idx       = str_col_indexes[str_col_idx];
+  auto const row_group_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (row_group_idx >= rowgroup_bounds.size().first) { return; }
+
+  auto const& str_col  = orc_columns[col_idx];
+  auto const start_row = rowgroup_bounds[row_group_idx][col_idx].begin + str_col.offset();
+  auto const num_rows  = rowgroup_bounds[row_group_idx][col_idx].size();
+
+  auto const& offsets = str_col.child(strings_column_view::offsets_column_index);
+  char_counts[str_col_idx][row_group_idx] =
+    (num_rows == 0)
+      ? 0
+      : offsets.element<size_type>(start_row + num_rows) - offsets.element<size_type>(start_row);
+}
+
+void rowgroup_char_counts(device_2dspan<size_type> counts,
+                          device_span<orc_column_device_view const> orc_columns,
+                          device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                          device_span<uint32_t const> str_col_indexes,
+                          rmm::cuda_stream_view stream)
+{
+  if (rowgroup_bounds.count() == 0) { return; }
+
+  auto const num_rowgroups = rowgroup_bounds.size().first;
+  auto const num_str_cols  = str_col_indexes.size();
+  if (num_str_cols == 0) { return; }
+
+  int block_size    = 0;  // suggested thread count to use
+  int min_grid_size = 0;  // minimum block count required
+  CUDF_CUDA_TRY(
+    cudaOccupancyMaxPotentialBlockSize(&min_grid_size, &block_size, rowgroup_char_counts_kernel));
+  auto const grid_size =
+    dim3(cudf::util::div_rounding_up_unsafe<unsigned int>(num_rowgroups, block_size),
+         static_cast<unsigned int>(num_str_cols));
+
+  rowgroup_char_counts_kernel<<<grid_size, block_size, 0, stream.value()>>>(
+    counts, orc_columns, rowgroup_bounds, str_col_indexes);
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  initialize_dictionary_hash_maps_kernel(device_span<stripe_dictionary> dictionaries)
+{
+  auto const dict_map = dictionaries[blockIdx.x].map_slots;
+  auto const t        = threadIdx.x;
+  for (size_type i = 0; i < dict_map.size(); i += block_size) {
+    if (t + i < dict_map.size()) {
+      new (&dict_map[t + i].first) map_type::atomic_key_type{KEY_SENTINEL};
+      new (&dict_map[t + i].second) map_type::atomic_mapped_type{VALUE_SENTINEL};
+    }
+  }
+}
+
+struct equality_functor {
+  column_device_view const& col;
+  __device__ bool operator()(size_type lhs_idx, size_type rhs_idx) const
+  {
+    // We don't call this for nulls so this is fine
+    auto const equal = cudf::experimental::row::equality::nan_equal_physical_equality_comparator{};
+    return equal(col.element<string_view>(lhs_idx), col.element<string_view>(rhs_idx));
+  }
+};
+
+struct hash_functor {
+  column_device_view const& col;
+  __device__ auto operator()(size_type idx) const
+  {
+    return cudf::hashing::detail::MurmurHash3_x86_32<string_view>{}(col.element<string_view>(idx));
+  }
+};
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  populate_dictionary_hash_maps_kernel(device_2dspan<stripe_dictionary> dictionaries,
+                                       device_span<orc_column_device_view const> columns)
+{
+  auto const col_idx    = blockIdx.x;
+  auto const stripe_idx = blockIdx.y;
+  auto const t          = threadIdx.x;
+  auto& dict            = dictionaries[col_idx][stripe_idx];
+  auto const& col       = columns[dict.column_idx];
+
+  // Make a view of the hash map
+  auto hash_map_mutable  = map_type::device_mutable_view(dict.map_slots.data(),
+                                                        dict.map_slots.size(),
+                                                        cuco::empty_key{KEY_SENTINEL},
+                                                        cuco::empty_value{VALUE_SENTINEL});
+  auto const hash_fn     = hash_functor{col};
+  auto const equality_fn = equality_functor{col};
+
+  auto const start_row = dict.start_row;
+  auto const end_row   = dict.start_row + dict.num_rows;
+
+  size_type entry_count{0};
+  size_type char_count{0};
+  // all threads should loop the same number of times
+  for (thread_index_type cur_row = start_row + t; cur_row - t < end_row; cur_row += block_size) {
+    auto const is_valid = cur_row < end_row and col.is_valid(cur_row);
+
+    if (is_valid) {
+      // insert element at cur_row to hash map and count successful insertions
+      auto const is_unique =
+        hash_map_mutable.insert(std::pair(cur_row, cur_row), hash_fn, equality_fn);
+
+      if (is_unique) {
+        ++entry_count;
+        char_count += col.element<string_view>(cur_row).size_bytes();
+      }
+    }
+    // ensure that threads access adjacent rows in each iteration
+    __syncthreads();
+  }
+
+  using block_reduce = cub::BlockReduce<size_type, block_size>;
+  __shared__ typename block_reduce::TempStorage reduce_storage;
+
+  auto const block_entry_count = block_reduce(reduce_storage).Sum(entry_count);
+  __syncthreads();
+  auto const block_char_count = block_reduce(reduce_storage).Sum(char_count);
+
+  if (t == 0) {
+    dict.entry_count = block_entry_count;
+    dict.char_count  = block_char_count;
+  }
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  collect_map_entries_kernel(device_2dspan<stripe_dictionary> dictionaries)
+{
+  auto const col_idx    = blockIdx.x;
+  auto const stripe_idx = blockIdx.y;
+  auto const& dict      = dictionaries[col_idx][stripe_idx];
+
+  if (not dict.is_enabled) { return; }
+
+  auto const t = threadIdx.x;
+  auto map     = map_type::device_view(dict.map_slots.data(),
+                                   dict.map_slots.size(),
+                                   cuco::empty_key{KEY_SENTINEL},
+                                   cuco::empty_value{VALUE_SENTINEL});
+
+  __shared__ cuda::atomic<size_type, cuda::thread_scope_block> counter;
+
+  using cuda::std::memory_order_relaxed;
+  if (t == 0) { new (&counter) cuda::atomic<size_type, cuda::thread_scope_block>{0}; }
+  __syncthreads();
+  for (size_type i = 0; i < dict.map_slots.size(); i += block_size) {
+    if (t + i < dict.map_slots.size()) {
+      auto* slot = reinterpret_cast<map_type::value_type*>(map.begin_slot() + t + i);
+      auto key   = slot->first;
+      if (key != KEY_SENTINEL) {
+        auto loc       = counter.fetch_add(1, memory_order_relaxed);
+        dict.data[loc] = key;
+        slot->second   = loc;
+      }
+    }
+  }
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  get_dictionary_indices_kernel(device_2dspan<stripe_dictionary> dictionaries,
+                                device_span<orc_column_device_view const> columns)
+{
+  auto const col_idx    = blockIdx.x;
+  auto const stripe_idx = blockIdx.y;
+  auto const& dict      = dictionaries[col_idx][stripe_idx];
+  auto const& col       = columns[dict.column_idx];
+
+  if (not dict.is_enabled) { return; }
+
+  auto const t         = threadIdx.x;
+  auto const start_row = dict.start_row;
+  auto const end_row   = dict.start_row + dict.num_rows;
+
+  auto const map = map_type::device_view(dict.map_slots.data(),
+                                         dict.map_slots.size(),
+                                         cuco::empty_key{KEY_SENTINEL},
+                                         cuco::empty_value{VALUE_SENTINEL});
+
+  thread_index_type cur_row = start_row + t;
+  while (cur_row < end_row) {
+    if (col.is_valid(cur_row)) {
+      auto const hash_fn     = hash_functor{col};
+      auto const equality_fn = equality_functor{col};
+      auto const found_slot  = map.find(cur_row, hash_fn, equality_fn);
+      cudf_assert(found_slot != map.end() &&
+                  "Unable to find value in map in dictionary index construction");
+      if (found_slot != map.end()) {
+        // No need for atomic as this is not going to be modified by any other thread
+        auto const val_ptr  = reinterpret_cast<map_type::mapped_type const*>(&found_slot->second);
+        dict.index[cur_row] = *val_ptr;
+      }
+    }
+    cur_row += block_size;
+  }
+}
+
+void initialize_dictionary_hash_maps(device_2dspan<stripe_dictionary> dictionaries,
+                                     rmm::cuda_stream_view stream)
+{
+  if (dictionaries.count() == 0) { return; }
+  constexpr int block_size = 1024;
+  initialize_dictionary_hash_maps_kernel<block_size>
+    <<<dictionaries.count(), block_size, 0, stream.value()>>>(dictionaries.flat_view());
+}
+
+void populate_dictionary_hash_maps(device_2dspan<stripe_dictionary> dictionaries,
+                                   device_span<orc_column_device_view const> columns,
+                                   rmm::cuda_stream_view stream)
+{
+  if (dictionaries.count() == 0) { return; }
+  constexpr int block_size = 256;
+  dim3 const dim_grid(dictionaries.size().first, dictionaries.size().second);
+  populate_dictionary_hash_maps_kernel<block_size>
+    <<<dim_grid, block_size, 0, stream.value()>>>(dictionaries, columns);
+}
+
+void collect_map_entries(device_2dspan<stripe_dictionary> dictionaries,
+                         rmm::cuda_stream_view stream)
+{
+  if (dictionaries.count() == 0) { return; }
+  constexpr int block_size = 1024;
+  dim3 const dim_grid(dictionaries.size().first, dictionaries.size().second);
+  collect_map_entries_kernel<block_size><<<dim_grid, block_size, 0, stream.value()>>>(dictionaries);
+}
+
+void get_dictionary_indices(device_2dspan<stripe_dictionary> dictionaries,
+                            device_span<orc_column_device_view const> columns,
+                            rmm::cuda_stream_view stream)
+{
+  if (dictionaries.count() == 0) { return; }
+  constexpr int block_size = 1024;
+  dim3 const dim_grid(dictionaries.size().first, dictionaries.size().second);
+  get_dictionary_indices_kernel<block_size>
+    <<<dim_grid, block_size, 0, stream.value()>>>(dictionaries, columns);
+}
+
+}  // namespace cudf::io::orc::gpu
diff --git a/cpp/src/io/orc/orc.cpp b/cpp/src/io/orc/orc.cpp
new file mode 100644
index 0000000..bc399b7
--- /dev/null
+++ b/cpp/src/io/orc/orc.cpp
@@ -0,0 +1,530 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc.hpp"
+#include "orc_field_reader.hpp"
+#include "orc_field_writer.hpp"
+
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <thrust/tabulate.h>
+
+#include <string>
+
+namespace cudf::io::orc {
+
+namespace {
+[[nodiscard]] constexpr uint32_t varint_size(uint64_t val)
+{
+  auto len = 1u;
+  while (val > 0x7f) {
+    val >>= 7;
+    ++len;
+  }
+  return len;
+}
+}  // namespace
+
+uint32_t ProtobufReader::read_field_size(uint8_t const* end)
+{
+  auto const size = get<uint32_t>();
+  CUDF_EXPECTS(size <= static_cast<uint32_t>(end - m_cur), "Protobuf parsing out of bounds");
+  return size;
+}
+
+void ProtobufReader::skip_struct_field(int t)
+{
+  switch (t) {
+    case ProtofType::VARINT: get<uint32_t>(); break;
+    case ProtofType::FIXED64: skip_bytes(8); break;
+    case ProtofType::FIXEDLEN: skip_bytes(get<uint32_t>()); break;
+    case ProtofType::FIXED32: skip_bytes(4); break;
+    default: break;
+  }
+}
+
+void ProtobufReader::read(PostScript& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.footerLength),
+                       field_reader(2, s.compression),
+                       field_reader(3, s.compressionBlockSize),
+                       packed_field_reader(4, s.version),
+                       field_reader(5, s.metadataLength),
+                       field_reader(8000, s.magic));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(FileFooter& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.headerLength),
+                       field_reader(2, s.contentLength),
+                       field_reader(3, s.stripes),
+                       field_reader(4, s.types),
+                       field_reader(5, s.metadata),
+                       field_reader(6, s.numberOfRows),
+                       raw_field_reader(7, s.statistics),
+                       field_reader(8, s.rowIndexStride));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(StripeInformation& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.offset),
+                       field_reader(2, s.indexLength),
+                       field_reader(3, s.dataLength),
+                       field_reader(4, s.footerLength),
+                       field_reader(5, s.numberOfRows));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(SchemaType& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.kind),
+                       packed_field_reader(2, s.subtypes),
+                       field_reader(3, s.fieldNames),
+                       field_reader(4, s.maximumLength),
+                       field_reader(5, s.precision),
+                       field_reader(6, s.scale));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(UserMetadataItem& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.name), field_reader(2, s.value));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(StripeFooter& s, size_t maxlen)
+{
+  auto op = std::tuple(
+    field_reader(1, s.streams), field_reader(2, s.columns), field_reader(3, s.writerTimezone));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(Stream& s, size_t maxlen)
+{
+  auto op =
+    std::tuple(field_reader(1, s.kind), field_reader(2, s.column_id), field_reader(3, s.length));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(ColumnEncoding& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.kind), field_reader(2, s.dictionarySize));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(integer_statistics& s, size_t maxlen)
+{
+  auto op =
+    std::tuple(field_reader(1, s.minimum), field_reader(2, s.maximum), field_reader(3, s.sum));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(double_statistics& s, size_t maxlen)
+{
+  auto op =
+    std::tuple(field_reader(1, s.minimum), field_reader(2, s.maximum), field_reader(3, s.sum));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(string_statistics& s, size_t maxlen)
+{
+  auto op =
+    std::tuple(field_reader(1, s.minimum), field_reader(2, s.maximum), field_reader(3, s.sum));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(bucket_statistics& s, size_t maxlen)
+{
+  auto op = std::tuple(packed_field_reader(1, s.count));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(decimal_statistics& s, size_t maxlen)
+{
+  auto op =
+    std::tuple(field_reader(1, s.minimum), field_reader(2, s.maximum), field_reader(3, s.sum));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(date_statistics& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.minimum), field_reader(2, s.maximum));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(binary_statistics& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.sum));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(timestamp_statistics& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.minimum),
+                       field_reader(2, s.maximum),
+                       field_reader(3, s.minimum_utc),
+                       field_reader(4, s.maximum_utc),
+                       field_reader(5, s.minimum_nanos),
+                       field_reader(6, s.maximum_nanos));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(column_statistics& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.number_of_values),
+                       field_reader(2, s.int_stats),
+                       field_reader(3, s.double_stats),
+                       field_reader(4, s.string_stats),
+                       field_reader(5, s.bucket_stats),
+                       field_reader(6, s.decimal_stats),
+                       field_reader(7, s.date_stats),
+                       field_reader(8, s.binary_stats),
+                       field_reader(9, s.timestamp_stats),
+                       field_reader(10, s.has_null));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(StripeStatistics& s, size_t maxlen)
+{
+  auto op = std::tuple(raw_field_reader(1, s.colStats));
+  function_builder(s, maxlen, op);
+}
+
+void ProtobufReader::read(Metadata& s, size_t maxlen)
+{
+  auto op = std::tuple(field_reader(1, s.stripeStats));
+  function_builder(s, maxlen, op);
+}
+
+/**
+ * @brief Add a single rowIndexEntry, negative input values treated as not present
+ */
+void ProtobufWriter::put_row_index_entry(int32_t present_blk,
+                                         int32_t present_ofs,
+                                         int32_t data_blk,
+                                         int32_t data_ofs,
+                                         int32_t data2_blk,
+                                         int32_t data2_ofs,
+                                         TypeKind kind,
+                                         ColStatsBlob const* stats)
+{
+  ProtobufWriter position_writer;
+  auto const positions_size_offset = position_writer.put_uint(
+    encode_field_number(1, ProtofType::FIXEDLEN));  // 1:positions[packed=true]
+  position_writer.put_byte(0xcd);                   // positions size placeholder
+  uint32_t positions_size = 0;
+  if (present_blk >= 0) positions_size += position_writer.put_uint(present_blk);
+  if (present_ofs >= 0) {
+    positions_size += position_writer.put_uint(present_ofs);
+    positions_size += position_writer.put_byte(0);  // run pos = 0
+    positions_size += position_writer.put_byte(0);  // bit pos = 0
+  }
+  if (data_blk >= 0) { positions_size += position_writer.put_uint(data_blk); }
+  if (data_ofs >= 0) {
+    positions_size += position_writer.put_uint(data_ofs);
+    if (kind != STRING && kind != FLOAT && kind != DOUBLE && kind != DECIMAL) {
+      // RLE run pos always zero (assumes RLE aligned with row index boundaries)
+      positions_size += position_writer.put_byte(0);
+      if (kind == BOOLEAN) {
+        // bit position in byte, always zero
+        positions_size += position_writer.put_byte(0);
+      }
+    }
+  }
+  // INT kind can be passed in to bypass 2nd stream index (dictionary length streams)
+  if (kind != INT) {
+    if (data2_blk >= 0) { positions_size += position_writer.put_uint(data2_blk); }
+    if (data2_ofs >= 0) {
+      positions_size += position_writer.put_uint(data2_ofs);
+      // RLE run pos always zero (assumes RLE aligned with row index boundaries)
+      positions_size += position_writer.put_byte(0);
+    }
+  }
+
+  // size of the field 1
+  position_writer.buffer()[positions_size_offset] = static_cast<uint8_t>(positions_size);
+
+  auto const stats_size = (stats == nullptr)
+                            ? 0
+                            : varint_size(encode_field_number<decltype(*stats)>(2)) +
+                                varint_size(stats->size()) + stats->size();
+  auto const entry_size = position_writer.size() + stats_size;
+
+  // 1:RowIndex.entry
+  put_uint(encode_field_number(1, ProtofType::FIXEDLEN));
+  put_uint(entry_size);
+  put_bytes<uint8_t>(position_writer.buffer());
+
+  if (stats != nullptr) {
+    put_uint(encode_field_number<decltype(*stats)>(2));  // 2: statistics
+    // Statistics field contains its length as varint and dtype specific data (encoded on the GPU)
+    put_uint(stats->size());
+    put_bytes<typename ColStatsBlob::value_type>(*stats);
+  }
+}
+
+size_t ProtobufWriter::write(PostScript const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_uint(1, s.footerLength);
+  w.field_uint(2, s.compression);
+  if (s.compression != NONE) { w.field_uint(3, s.compressionBlockSize); }
+  w.field_packed_uint(4, s.version);
+  w.field_uint(5, s.metadataLength);
+  w.field_blob(8000, s.magic);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(FileFooter const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_uint(1, s.headerLength);
+  w.field_uint(2, s.contentLength);
+  w.field_repeated_struct(3, s.stripes);
+  w.field_repeated_struct(4, s.types);
+  w.field_repeated_struct(5, s.metadata);
+  w.field_uint(6, s.numberOfRows);
+  w.field_repeated_struct_blob(7, s.statistics);
+  w.field_uint(8, s.rowIndexStride);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(StripeInformation const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_uint(1, s.offset);
+  w.field_uint(2, s.indexLength);
+  w.field_uint(3, s.dataLength);
+  w.field_uint(4, s.footerLength);
+  w.field_uint(5, s.numberOfRows);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(SchemaType const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_uint(1, s.kind);
+  w.field_packed_uint(2, s.subtypes);
+  w.field_repeated_string(3, s.fieldNames);
+  // w.field_uint(4, s.maximumLength);
+  if (s.precision) w.field_uint(5, *s.precision);
+  if (s.scale) w.field_uint(6, *s.scale);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(UserMetadataItem const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_blob(1, s.name);
+  w.field_blob(2, s.value);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(StripeFooter const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_repeated_struct(1, s.streams);
+  w.field_repeated_struct(2, s.columns);
+  if (s.writerTimezone != "") { w.field_blob(3, s.writerTimezone); }
+  return w.value();
+}
+
+size_t ProtobufWriter::write(Stream const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_uint(1, s.kind);
+  if (s.column_id) w.field_uint(2, *s.column_id);
+  w.field_uint(3, s.length);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(ColumnEncoding const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_uint(1, s.kind);
+  if (s.kind == DICTIONARY || s.kind == DICTIONARY_V2) { w.field_uint(2, s.dictionarySize); }
+  return w.value();
+}
+
+size_t ProtobufWriter::write(StripeStatistics const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_repeated_struct_blob(1, s.colStats);
+  return w.value();
+}
+
+size_t ProtobufWriter::write(Metadata const& s)
+{
+  ProtobufFieldWriter w(this);
+  w.field_repeated_struct(1, s.stripeStats);
+  return w.value();
+}
+
+OrcDecompressor::OrcDecompressor(CompressionKind kind, uint32_t blockSize) : m_blockSize(blockSize)
+{
+  switch (kind) {
+    case NONE:
+      _compression   = compression_type::NONE;
+      m_log2MaxRatio = 0;
+      break;
+    case ZLIB:
+      _compression   = compression_type::ZLIB;
+      m_log2MaxRatio = 11;  // < 2048:1
+      break;
+    case SNAPPY:
+      _compression   = compression_type::SNAPPY;
+      m_log2MaxRatio = 5;  // < 32:1
+      break;
+    case LZO: _compression = compression_type::LZO; break;
+    case LZ4: _compression = compression_type::LZ4; break;
+    case ZSTD:
+      m_log2MaxRatio = 15;
+      _compression   = compression_type::ZSTD;
+      break;
+    default: CUDF_FAIL("Invalid compression type");
+  }
+}
+
+host_span<uint8_t const> OrcDecompressor::decompress_blocks(host_span<uint8_t const> src,
+                                                            rmm::cuda_stream_view stream)
+{
+  // If uncompressed, just pass-through the input
+  if (src.empty() or _compression == compression_type::NONE) { return src; }
+
+  constexpr size_t header_size = 3;
+  CUDF_EXPECTS(src.size() >= header_size, "Total size is less than the 3-byte header");
+
+  // First, scan the input for the number of blocks and worst-case output size
+  size_t max_dst_length = 0;
+  for (size_t i = 0; i + header_size < src.size();) {
+    uint32_t block_len         = src[i] | (src[i + 1] << 8) | (src[i + 2] << 16);
+    auto const is_uncompressed = static_cast<bool>(block_len & 1);
+    i += header_size;
+    block_len >>= 1;
+    if (is_uncompressed) {
+      // Uncompressed block
+      max_dst_length += block_len;
+    } else {
+      max_dst_length += m_blockSize;
+    }
+    i += block_len;
+    CUDF_EXPECTS(i <= src.size() and block_len <= m_blockSize, "Error in decompression");
+  }
+  // Check if we have a single uncompressed block, or no blocks
+  if (max_dst_length < m_blockSize) { return src.subspan(header_size, src.size() - header_size); }
+
+  m_buf.resize(max_dst_length);
+  size_t dst_length = 0;
+  for (size_t i = 0; i + header_size < src.size();) {
+    uint32_t block_len         = src[i] | (src[i + 1] << 8) | (src[i + 2] << 16);
+    auto const is_uncompressed = static_cast<bool>(block_len & 1);
+    i += header_size;
+    block_len >>= 1;
+    if (is_uncompressed) {
+      // Uncompressed block
+      memcpy(m_buf.data() + dst_length, src.data() + i, block_len);
+      dst_length += block_len;
+    } else {
+      // Compressed block
+      dst_length += decompress(
+        _compression, src.subspan(i, block_len), {m_buf.data() + dst_length, m_blockSize}, stream);
+    }
+    i += block_len;
+  }
+
+  m_buf.resize(dst_length);
+  return m_buf;
+}
+
+metadata::metadata(datasource* const src, rmm::cuda_stream_view stream) : source(src)
+{
+  auto const len         = source->size();
+  auto const max_ps_size = std::min(len, static_cast<size_t>(256));
+
+  // Read uncompressed postscript section (max 255 bytes + 1 byte for length)
+  auto buffer            = source->host_read(len - max_ps_size, max_ps_size);
+  size_t const ps_length = buffer->data()[max_ps_size - 1];
+  uint8_t const* ps_data = &buffer->data()[max_ps_size - ps_length - 1];
+  ProtobufReader(ps_data, ps_length).read(ps);
+  CUDF_EXPECTS(ps.footerLength + ps_length < len, "Invalid footer length");
+
+  // If compression is used, the rest of the metadata is compressed
+  // If no compressed is used, the decompressor is simply a pass-through
+  decompressor = std::make_unique<OrcDecompressor>(ps.compression, ps.compressionBlockSize);
+
+  // Read compressed filefooter section
+  buffer             = source->host_read(len - ps_length - 1 - ps.footerLength, ps.footerLength);
+  auto const ff_data = decompressor->decompress_blocks({buffer->data(), buffer->size()}, stream);
+  ProtobufReader(ff_data.data(), ff_data.size()).read(ff);
+  CUDF_EXPECTS(get_num_columns() > 0, "No columns found");
+
+  // Read compressed metadata section
+  buffer =
+    source->host_read(len - ps_length - 1 - ps.footerLength - ps.metadataLength, ps.metadataLength);
+  auto const md_data = decompressor->decompress_blocks({buffer->data(), buffer->size()}, stream);
+  orc::ProtobufReader(md_data.data(), md_data.size()).read(md);
+
+  init_parent_descriptors();
+  init_column_names();
+}
+
+void metadata::init_column_names()
+{
+  column_names.resize(get_num_columns());
+  thrust::tabulate(column_names.begin(), column_names.end(), [&](auto col_id) {
+    if (not column_has_parent(col_id)) return std::string{};
+    auto const& parent_field_names = ff.types[parent_id(col_id)].fieldNames;
+    if (field_index(col_id) < static_cast<size_type>(parent_field_names.size())) {
+      return parent_field_names[field_index(col_id)];
+    }
+
+    // Generate names for list and map child columns
+    if (ff.types[parent_id(col_id)].subtypes.size() == 1) {
+      return std::to_string(lists_column_view::child_column_index);
+    } else {
+      return std::to_string(field_index(col_id));
+    }
+  });
+
+  column_paths.resize(get_num_columns());
+  thrust::tabulate(column_paths.begin(), column_paths.end(), [&](auto col_id) {
+    if (not column_has_parent(col_id)) return std::string{};
+    // Don't include ORC root column name in path
+    return (parent_id(col_id) == 0 ? "" : column_paths[parent_id(col_id)] + ".") +
+           column_names[col_id];
+  });
+}
+
+void metadata::init_parent_descriptors()
+{
+  auto const num_columns = static_cast<size_type>(ff.types.size());
+  parents.resize(num_columns);
+
+  for (size_type col_id = 0; col_id < num_columns; ++col_id) {
+    auto const& subtypes    = ff.types[col_id].subtypes;
+    auto const num_children = static_cast<size_type>(subtypes.size());
+    for (size_type field_idx = 0; field_idx < num_children; ++field_idx) {
+      auto const child_id = static_cast<size_type>(subtypes[field_idx]);
+      CUDF_EXPECTS(child_id > col_id && child_id < num_columns, "Invalid column id");
+      CUDF_EXPECTS(not column_has_parent(child_id), "Same node referenced twice");
+      parents[child_id] = {col_id, field_idx};
+    }
+  }
+}
+
+}  // namespace cudf::io::orc
diff --git a/cpp/src/io/orc/orc.hpp b/cpp/src/io/orc/orc.hpp
new file mode 100644
index 0000000..6f65e38
--- /dev/null
+++ b/cpp/src/io/orc/orc.hpp
@@ -0,0 +1,683 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/orc_metadata.hpp>
+#include <cudf/io/orc_types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <io/comp/io_uncomp.hpp>
+
+#include <thrust/optional.h>
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+#include <memory>
+#include <optional>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace orc {
+
+static constexpr uint32_t block_header_size = 3;
+// Seconds from January 1st, 1970 to January 1st, 2015
+static constexpr int64_t orc_utc_epoch = 1420070400;
+
+struct PostScript {
+  uint64_t footerLength       = 0;     // the length of the footer section in bytes
+  CompressionKind compression = NONE;  // the kind of generic compression used
+  uint32_t compressionBlockSize{};     // the maximum size of each compression chunk
+  std::vector<uint32_t> version;       // the version of the writer [major, minor]
+  uint64_t metadataLength = 0;         // the length of the metadata section in bytes
+  std::string magic       = "";        // the fixed string "ORC"
+};
+
+struct StripeInformation {
+  uint64_t offset       = 0;  // the start of the stripe within the file
+  uint64_t indexLength  = 0;  // the length of the indexes in bytes
+  uint64_t dataLength   = 0;  // the length of the data in bytes
+  uint32_t footerLength = 0;  // the length of the footer in bytes
+  uint32_t numberOfRows = 0;  // the number of rows in the stripe
+};
+
+struct SchemaType {
+  TypeKind kind = INVALID_TYPE_KIND;  // the kind of this type
+  std::vector<uint32_t> subtypes;  // the type ids of any subcolumns for list, map, struct, or union
+  std::vector<std::string> fieldNames;  // the list of field names for struct
+  std::optional<uint32_t>
+    maximumLength;  // the maximum length of the type for varchar or char in UTF-8 characters
+  std::optional<uint32_t> precision;  // the precision for decimal
+  std::optional<uint32_t> scale;      // the scale for decimal
+};
+
+struct UserMetadataItem {
+  std::string name;   // the user defined key
+  std::string value;  // the user defined binary value as string
+};
+
+using ColStatsBlob = std::vector<uint8_t>;  // Column statistics blob
+
+struct FileFooter {
+  uint64_t headerLength  = 0;              // the length of the file header in bytes (always 3)
+  uint64_t contentLength = 0;              // the length of the file header and body in bytes
+  std::vector<StripeInformation> stripes;  // the information about the stripes
+  std::vector<SchemaType> types;           // the schema information
+  std::vector<UserMetadataItem> metadata;  // the user metadata that was added
+  uint64_t numberOfRows = 0;               // the total number of rows in the file
+  std::vector<ColStatsBlob> statistics;    // Column statistics blobs
+  uint32_t rowIndexStride = 0;             // the maximum number of rows in each index entry
+};
+
+struct Stream {
+  StreamKind kind = INVALID_STREAM_KIND;
+  std::optional<uint32_t> column_id;  // ORC column id (different from column index in the table!)
+  uint64_t length = 0;                // the number of bytes in the stream
+
+  // Returns index of the column in the table, if any
+  // Stream of the 'column 0' does not have a corresponding column in the table
+  [[nodiscard]] std::optional<uint32_t> column_index() const noexcept
+  {
+    return column_id.value_or(0) > 0 ? std::optional<uint32_t>{*column_id - 1}
+                                     : std::optional<uint32_t>{};
+  }
+};
+
+struct ColumnEncoding {
+  ColumnEncodingKind kind = INVALID_ENCODING_KIND;
+  uint32_t dictionarySize = 0;  // for dictionary encodings, record the size of the dictionary
+};
+
+struct StripeFooter {
+  std::vector<Stream> streams;          // the location of each stream
+  std::vector<ColumnEncoding> columns;  // the encoding of each column
+  std::string writerTimezone = "";      // time zone of the writer
+};
+
+/**
+ * @brief Contains per-column ORC statistics.
+ *
+ * At most one of the `***_statistics` members has a value.
+ */
+struct column_statistics {
+  std::optional<uint64_t> number_of_values;
+  std::optional<integer_statistics> int_stats;
+  std::optional<double_statistics> double_stats;
+  std::optional<string_statistics> string_stats;
+  std::optional<bucket_statistics> bucket_stats;
+  std::optional<decimal_statistics> decimal_stats;
+  std::optional<date_statistics> date_stats;
+  std::optional<binary_statistics> binary_stats;
+  std::optional<timestamp_statistics> timestamp_stats;
+  std::optional<bool> has_null;
+};
+
+struct StripeStatistics {
+  std::vector<ColStatsBlob> colStats;  // Column statistics blobs
+};
+
+struct Metadata {
+  std::vector<StripeStatistics> stripeStats;
+};
+
+int inline constexpr encode_field_number(int field_number, ProtofType field_type) noexcept
+{
+  return (field_number * 8) + static_cast<int>(field_type);
+}
+
+namespace {
+template <typename base_t,
+          std::enable_if_t<!std::is_arithmetic_v<base_t> and !std::is_enum_v<base_t>>* = nullptr>
+int static constexpr encode_field_number_base(int field_number) noexcept
+{
+  return encode_field_number(field_number, ProtofType::FIXEDLEN);
+}
+
+template <typename base_t,
+          std::enable_if_t<std::is_integral_v<base_t> or std::is_enum_v<base_t>>* = nullptr>
+int static constexpr encode_field_number_base(int field_number) noexcept
+{
+  return encode_field_number(field_number, ProtofType::VARINT);
+}
+
+template <typename base_t, std::enable_if_t<std::is_same_v<base_t, float>>* = nullptr>
+int static constexpr encode_field_number_base(int field_number) noexcept
+{
+  return encode_field_number(field_number, ProtofType::FIXED32);
+}
+
+template <typename base_t, std::enable_if_t<std::is_same_v<base_t, double>>* = nullptr>
+int static constexpr encode_field_number_base(int field_number) noexcept
+{
+  return encode_field_number(field_number, ProtofType::FIXED64);
+}
+};  // namespace
+
+template <typename T,
+          std::enable_if_t<!std::is_class_v<T> or std::is_same_v<T, std::string>>* = nullptr>
+int constexpr encode_field_number(int field_number) noexcept
+{
+  return encode_field_number_base<T>(field_number);
+}
+
+// containers change the field number encoding
+template <typename T,
+          std::enable_if_t<std::is_same_v<T, std::vector<typename T::value_type>>>* = nullptr>
+int constexpr encode_field_number(int field_number) noexcept
+{
+  return encode_field_number_base<T>(field_number);
+}
+
+// optional fields don't change the field number encoding
+template <typename T,
+          std::enable_if_t<std::is_same_v<T, std::optional<typename T::value_type>>>* = nullptr>
+int constexpr encode_field_number(int field_number) noexcept
+{
+  return encode_field_number_base<typename T::value_type>(field_number);
+}
+
+/**
+ * @brief Class for parsing Orc's Protocol Buffers encoded metadata
+ */
+class ProtobufReader {
+ public:
+  ProtobufReader(uint8_t const* base, size_t len) : m_base(base), m_cur(base), m_end(base + len) {}
+
+  template <typename T>
+  void read(T& s)
+  {
+    read(s, m_end - m_cur);
+  }
+  void read(PostScript&, size_t maxlen);
+  void read(FileFooter&, size_t maxlen);
+  void read(StripeInformation&, size_t maxlen);
+  void read(SchemaType&, size_t maxlen);
+  void read(UserMetadataItem&, size_t maxlen);
+  void read(StripeFooter&, size_t maxlen);
+  void read(Stream&, size_t maxlen);
+  void read(ColumnEncoding&, size_t maxlen);
+  void read(integer_statistics&, size_t maxlen);
+  void read(double_statistics&, size_t maxlen);
+  void read(string_statistics&, size_t maxlen);
+  void read(bucket_statistics&, size_t maxlen);
+  void read(decimal_statistics&, size_t maxlen);
+  void read(date_statistics&, size_t maxlen);
+  void read(binary_statistics&, size_t maxlen);
+  void read(timestamp_statistics&, size_t maxlen);
+  void read(column_statistics&, size_t maxlen);
+  void read(StripeStatistics&, size_t maxlen);
+  void read(Metadata&, size_t maxlen);
+
+ private:
+  template <int index>
+  friend class FunctionSwitchImpl;
+
+  void skip_bytes(size_t bytecnt)
+  {
+    bytecnt = std::min(bytecnt, (size_t)(m_end - m_cur));
+    m_cur += bytecnt;
+  }
+
+  template <typename T>
+  T get();
+
+  void skip_struct_field(int t);
+
+  template <typename T, typename... Operator>
+  void function_builder(T& s, size_t maxlen, std::tuple<Operator...>& op);
+
+  uint32_t read_field_size(uint8_t const* end);
+
+  template <typename T, std::enable_if_t<std::is_integral_v<T>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    value = get<T>();
+  }
+
+  template <typename T, std::enable_if_t<std::is_enum_v<T>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    value = static_cast<T>(get<uint32_t>());
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, std::string>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    auto const size = read_field_size(end);
+    value.assign(reinterpret_cast<char const*>(m_cur), size);
+    m_cur += size;
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, std::vector<std::string>>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    auto const size = read_field_size(end);
+    value.emplace_back(reinterpret_cast<char const*>(m_cur), size);
+    m_cur += size;
+  }
+
+  template <typename T,
+            std::enable_if_t<std::is_same_v<T, std::vector<typename T::value_type>> and
+                             !std::is_same_v<std::string, typename T::value_type>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    auto const size = read_field_size(end);
+    value.emplace_back();
+    read(value.back(), size);
+  }
+
+  template <typename T,
+            std::enable_if_t<std::is_same_v<T, std::optional<typename T::value_type>>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    typename T::value_type contained_value;
+    read_field(contained_value, end);
+    value = std::optional<typename T::value_type>{std::move(contained_value)};
+  }
+
+  template <typename T>
+  auto read_field(T& value, uint8_t const* end) -> decltype(read(value, 0))
+  {
+    auto const size = read_field_size(end);
+    read(value, size);
+  }
+
+  template <typename T, std::enable_if_t<std::is_floating_point_v<T>>* = nullptr>
+  void read_field(T& value, uint8_t const* end)
+  {
+    memcpy(&value, m_cur, sizeof(T));
+    m_cur += sizeof(T);
+  }
+
+  template <typename T>
+  void read_packed_field(T& value, uint8_t const* end)
+  {
+    auto const len       = get<uint32_t>();
+    auto const field_end = std::min(m_cur + len, end);
+    while (m_cur < field_end)
+      value.push_back(get<typename T::value_type>());
+  }
+
+  template <typename T>
+  void read_raw_field(T& value, uint8_t const* end)
+  {
+    auto const size = read_field_size(end);
+    value.emplace_back(m_cur, m_cur + size);
+    m_cur += size;
+  }
+
+  template <typename T>
+  struct field_reader {
+    int const encoded_field_number;
+    T& output_value;
+
+    field_reader(int field_number, T& field_value)
+      : encoded_field_number(encode_field_number<T>(field_number)), output_value(field_value)
+    {
+    }
+
+    inline void operator()(ProtobufReader* pbr, uint8_t const* end)
+    {
+      pbr->read_field(output_value, end);
+    }
+  };
+
+  template <typename T>
+  struct packed_field_reader {
+    int const encoded_field_number;
+    T& output_value;
+
+    packed_field_reader(int field_number, T& field_value)
+      : encoded_field_number(encode_field_number<T>(field_number)), output_value(field_value)
+    {
+    }
+
+    inline void operator()(ProtobufReader* pbr, uint8_t const* end)
+    {
+      pbr->read_packed_field(output_value, end);
+    }
+  };
+
+  template <typename T>
+  struct raw_field_reader {
+    int const encoded_field_number;
+    T& output_value;
+
+    raw_field_reader(int field_number, T& field_value)
+      : encoded_field_number(encode_field_number<T>(field_number)), output_value(field_value)
+    {
+    }
+
+    inline void operator()(ProtobufReader* pbr, uint8_t const* end)
+    {
+      pbr->read_raw_field(output_value, end);
+    }
+  };
+
+  uint8_t const* const m_base;
+  uint8_t const* m_cur;
+  uint8_t const* const m_end;
+};
+
+template <>
+inline uint8_t ProtobufReader::get<uint8_t>()
+{
+  return (m_cur < m_end) ? *m_cur++ : 0;
+};
+
+template <>
+inline bool ProtobufReader::get<bool>()
+{
+  return static_cast<bool>(get<uint8_t>());
+};
+
+template <>
+inline uint32_t ProtobufReader::get<uint32_t>()
+{
+  uint32_t v = 0;
+  for (uint32_t l = 0;; l += 7) {
+    uint32_t c = get<uint8_t>();
+    v |= (c & 0x7f) << l;
+    if (c < 0x80) return v;
+  }
+}
+
+template <>
+inline uint64_t ProtobufReader::get<uint64_t>()
+{
+  uint64_t v = 0;
+  for (uint64_t l = 0;; l += 7) {
+    uint64_t c = get<uint8_t>();
+    v |= (c & 0x7f) << l;
+    if (c < 0x80) return v;
+  }
+}
+
+template <typename T>
+auto decode_zigzag(T u)
+{
+  using signed_t = std::make_signed_t<T>;
+  return static_cast<signed_t>((u >> 1u) ^ -static_cast<signed_t>(u & 1));
+}
+
+template <>
+inline int32_t ProtobufReader::get<int32_t>()
+{
+  return decode_zigzag(get<uint32_t>());
+}
+
+template <>
+inline int64_t ProtobufReader::get<int64_t>()
+{
+  return decode_zigzag(get<uint64_t>());
+}
+
+/**
+ * @brief Class for encoding Orc's metadata with Protocol Buffers
+ */
+class ProtobufWriter {
+ public:
+  ProtobufWriter() = default;
+
+  ProtobufWriter(std::size_t bytes) : m_buff(bytes) {}
+
+  uint32_t put_byte(uint8_t v)
+  {
+    m_buff.push_back(v);
+    return 1;
+  }
+
+  template <typename T>
+  uint32_t put_bytes(host_span<T const> values)
+  {
+    static_assert(sizeof(T) == 1);
+    m_buff.reserve(m_buff.size() + values.size());
+    m_buff.insert(m_buff.end(), values.begin(), values.end());
+    return values.size();
+  }
+
+  uint32_t put_uint(uint64_t v)
+  {
+    int l = 1;
+    while (v > 0x7f) {
+      put_byte(static_cast<uint8_t>(v | 0x80));
+      v >>= 7;
+      l++;
+    }
+    put_byte(static_cast<uint8_t>(v));
+    return l;
+  }
+
+  uint32_t put_int(int64_t v)
+  {
+    int64_t s = (v < 0);
+    return put_uint(((v ^ -s) << 1) + s);
+  }
+
+  void put_row_index_entry(int32_t present_blk,
+                           int32_t present_ofs,
+                           int32_t data_blk,
+                           int32_t data_ofs,
+                           int32_t data2_blk,
+                           int32_t data2_ofs,
+                           TypeKind kind,
+                           ColStatsBlob const* stats);
+
+  std::size_t size() const { return m_buff.size(); }
+  uint8_t const* data() { return m_buff.data(); }
+
+  std::vector<uint8_t>& buffer() { return m_buff; }
+  std::vector<uint8_t> release() { return std::move(m_buff); }
+
+ public:
+  size_t write(PostScript const&);
+  size_t write(FileFooter const&);
+  size_t write(StripeInformation const&);
+  size_t write(SchemaType const&);
+  size_t write(UserMetadataItem const&);
+  size_t write(StripeFooter const&);
+  size_t write(Stream const&);
+  size_t write(ColumnEncoding const&);
+  size_t write(StripeStatistics const&);
+  size_t write(Metadata const&);
+
+ protected:
+  std::vector<uint8_t> m_buff;
+  struct ProtobufFieldWriter;
+};
+
+/**
+ * @brief Class for decompressing Orc data blocks using the CPU
+ */
+
+class OrcDecompressor {
+ public:
+  OrcDecompressor(CompressionKind kind, uint32_t blockSize);
+
+  /**
+   * @brief ORC block decompression
+   *
+   * @param src compressed data
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   *
+   * @return decompressed data
+   */
+  host_span<uint8_t const> decompress_blocks(host_span<uint8_t const> src,
+                                             rmm::cuda_stream_view stream);
+  [[nodiscard]] uint32_t GetLog2MaxCompressionRatio() const { return m_log2MaxRatio; }
+  [[nodiscard]] uint32_t GetMaxUncompressedBlockSize(uint32_t block_len) const
+  {
+    return std::min(block_len << m_log2MaxRatio, m_blockSize);
+  }
+  [[nodiscard]] compression_type compression() const { return _compression; }
+  [[nodiscard]] uint32_t GetBlockSize() const { return m_blockSize; }
+
+ protected:
+  compression_type _compression;
+  uint32_t m_log2MaxRatio = 24;  // log2 of maximum compression ratio
+  uint32_t m_blockSize;
+  std::vector<uint8_t> m_buf;
+};
+
+/**
+ * @brief Stores orc id for each column and number of children in that column.
+ *
+ * @code{.pseudo}
+ * Consider following data where a struct has two members and a list column
+ * {"struct": [{"a": 1, "b": 2}, {"a":3, "b":5}], "list":[[1, 2], [2, 3]]}
+ *
+ * `orc_column_meta` for struct column would be
+ * id = 0
+ * num_children = 2
+ *
+ * `orc_column_meta` for list column would be
+ * id = 3
+ * num_children = 1
+ * @endcode
+ *
+ */
+struct orc_column_meta {
+  size_type id;            // orc id for the column
+  size_type num_children;  // number of children at the same level of nesting in case of struct
+};
+
+/**
+ * @brief Stores column's validity map and null count
+ */
+struct column_validity_info {
+  uint32_t* valid_map_base;
+  uint32_t null_count;
+};
+
+/**
+ * @brief A helper class for ORC file metadata. Provides some additional
+ * convenience methods for initializing and accessing metadata.
+ */
+class metadata {
+  using OrcStripeInfo = std::pair<StripeInformation const*, StripeFooter const*>;
+
+ public:
+  struct stripe_source_mapping {
+    int source_idx;
+    std::vector<OrcStripeInfo> stripe_info;
+  };
+
+ public:
+  explicit metadata(datasource* const src, rmm::cuda_stream_view stream);
+
+  [[nodiscard]] size_t get_total_rows() const { return ff.numberOfRows; }
+  [[nodiscard]] int get_num_stripes() const { return ff.stripes.size(); }
+  [[nodiscard]] int get_num_columns() const { return ff.types.size(); }
+  /**
+   * @brief Returns the name of the column with the given ID.
+   *
+   * Name might not be unique in the ORC file, since columns with different parents are allowed to
+   * have the same names.
+   */
+  [[nodiscard]] std::string const& column_name(size_type column_id) const
+  {
+    CUDF_EXPECTS(column_id < get_num_columns(), "Out of range column id provided");
+    return column_names[column_id];
+  }
+  /**
+   * @brief Returns the full name of the column with the given ID - includes the ancestor columns
+   * names.
+   *
+   * Each column in the ORC file has a unique path.
+   */
+  [[nodiscard]] std::string const& column_path(size_type column_id) const
+  {
+    CUDF_EXPECTS(column_id < get_num_columns(), "Out of range column id provided");
+    return column_paths[column_id];
+  }
+  [[nodiscard]] int get_row_index_stride() const { return ff.rowIndexStride; }
+
+  /**
+   * @brief Returns the ID of the parent column of the given column.
+   */
+  [[nodiscard]] size_type parent_id(size_type column_id) const
+  {
+    return parents.at(column_id).value().id;
+  }
+
+  /**
+   * @brief Returns the index the given column has in its parent's children list.
+   */
+  [[nodiscard]] size_type field_index(size_type column_id) const
+  {
+    return parents.at(column_id).value().field_idx;
+  }
+
+  /**
+   * @brief Returns whether the given column has a parent.
+   */
+  [[nodiscard]] size_type column_has_parent(size_type column_id) const
+  {
+    return parents.at(column_id).has_value();
+  }
+
+ public:
+  PostScript ps;
+  FileFooter ff;
+  Metadata md;
+  std::vector<StripeFooter> stripefooters;
+  std::unique_ptr<OrcDecompressor> decompressor;
+  datasource* const source;
+
+ private:
+  struct column_parent {
+    // parent's ID
+    size_type id;
+    // Index of this column in the parent's list of children
+    size_type field_idx;
+    column_parent(size_type parent_id, size_type field_idx) : id{parent_id}, field_idx{field_idx} {}
+  };
+  void init_parent_descriptors();
+  std::vector<std::optional<column_parent>> parents;
+
+  void init_column_names();
+  std::vector<std::string> column_names;
+  std::vector<std::string> column_paths;
+};
+
+/**
+ * @brief `column_device_view` and additional, ORC specific, information on the column.
+ */
+struct orc_column_device_view : public column_device_view {
+  __device__ orc_column_device_view(column_device_view col, thrust::optional<uint32_t> parent_idx)
+    : column_device_view{col}, parent_index{parent_idx}
+  {
+  }
+  thrust::optional<uint32_t> parent_index;
+  bitmask_type const* pushdown_mask = nullptr;
+};
+
+/**
+ * @brief Range of rows within a single rowgroup.
+ */
+struct rowgroup_rows {
+  size_type begin;
+  size_type end;
+  [[nodiscard]] constexpr auto size() const noexcept { return end - begin; }
+};
+
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/orc_field_reader.hpp b/cpp/src/io/orc/orc_field_reader.hpp
new file mode 100644
index 0000000..58f3fff
--- /dev/null
+++ b/cpp/src/io/orc/orc_field_reader.hpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "orc.hpp"
+#include <string>
+
+/**
+ * @file orc_field_reader.hpp
+ * @brief Functors to encapsulate common functionality required to implement
+ * ProtobufReader::read(...) functions
+ */
+
+namespace cudf {
+namespace io {
+namespace orc {
+
+/**
+ * @brief Functor to run an operator for a specified field.
+ *
+ * The purpose of this functor is to replace a switch case. If the field in
+ * the argument is equal to the field specified in any element of the tuple
+ * of operators then it is run with the byte stream and field type arguments.
+ *
+ * If the field does not match any of the functors then skip_struct_field is
+ * called by the ProtobufReader
+ */
+template <int index>
+struct FunctionSwitchImpl {
+  template <typename... Operator>
+  static inline void run(ProtobufReader* pbr,
+                         uint8_t const* end,
+                         int const& encoded_field_number,
+                         std::tuple<Operator...>& ops)
+  {
+    if (encoded_field_number == std::get<index>(ops).encoded_field_number) {
+      std::get<index>(ops)(pbr, end);
+    } else {
+      FunctionSwitchImpl<index - 1>::run(pbr, end, encoded_field_number, ops);
+    }
+  }
+};
+
+template <>
+struct FunctionSwitchImpl<0> {
+  template <typename... Operator>
+  static inline void run(ProtobufReader* pbr,
+                         uint8_t const* end,
+                         int const& encoded_field_number,
+                         std::tuple<Operator...>& ops)
+  {
+    if (encoded_field_number == std::get<0>(ops).encoded_field_number) {
+      std::get<0>(ops)(pbr, end);
+    } else {
+      pbr->skip_struct_field(encoded_field_number & 7);
+    }
+  }
+};
+
+/**
+ * @brief Function to implement ProtobufReader::read based on the tuple of functors provided.
+ *
+ * Bytes are read from the internal metadata stream and field types are matched up against user
+ * supplied reading functors. If they match then the corresponding values are written to references
+ * pointed to by the functors.
+ */
+template <typename T, typename... Operator>
+inline void ProtobufReader::function_builder(T& s, size_t maxlen, std::tuple<Operator...>& op)
+{
+  constexpr int index = std::tuple_size<std::tuple<Operator...>>::value - 1;
+  auto* const end     = std::min(m_cur + maxlen, m_end);
+  while (m_cur < end) {
+    auto const field = get<uint32_t>();
+    FunctionSwitchImpl<index>::run(this, end, field, op);
+  }
+  CUDF_EXPECTS(m_cur <= end, "Current pointer to metadata stream is out of bounds");
+}
+
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/orc_field_writer.hpp b/cpp/src/io/orc/orc_field_writer.hpp
new file mode 100644
index 0000000..4862562
--- /dev/null
+++ b/cpp/src/io/orc/orc_field_writer.hpp
@@ -0,0 +1,134 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "orc.hpp"
+
+#include <numeric>
+#include <string>
+
+/**
+ * @file orc_field_writer.hpp
+ * @brief Struct to encapsulate common functionality required to implement
+ * `ProtobufWriter::write(...)` functions
+ */
+
+namespace cudf {
+namespace io {
+namespace orc {
+
+struct ProtobufWriter::ProtobufFieldWriter {
+  int struct_size;
+  ProtobufWriter* p;
+
+  ProtobufFieldWriter(ProtobufWriter* pbw) : struct_size(0), p(pbw) {}
+
+  /**
+   * @brief Function to write a unsigned integer to the internal buffer
+   */
+  template <typename T>
+  void field_uint(int field, T const& value)
+  {
+    struct_size += p->put_uint(encode_field_number<T>(field));
+    struct_size += p->put_uint(static_cast<uint64_t>(value));
+  }
+
+  /**
+   * @brief Function to write a vector of unsigned integers to the internal
+   * buffer
+   */
+  template <typename T>
+  void field_packed_uint(int field, std::vector<T> const& value)
+  {
+    struct_size += p->put_uint(encode_field_number<std::vector<T>>(field));
+    auto lpos = p->m_buff.size();
+    p->put_byte(0);
+    auto sz = std::accumulate(value.begin(), value.end(), 0, [p = this->p](size_t sum, auto val) {
+      return sum + p->put_uint(val);
+    });
+
+    struct_size += sz + 1;
+    for (; sz > 0x7f; sz >>= 7, struct_size++)
+      p->m_buff.insert(p->m_buff.begin() + (lpos++), static_cast<uint8_t>((sz & 0x7f) | 0x80));
+    (p->m_buff)[lpos] = static_cast<uint8_t>(sz);
+  }
+
+  /**
+   * @brief Function to write a blob to the internal buffer
+   */
+  template <typename T>
+  void field_blob(int field, T const& values)
+  {
+    struct_size += p->put_uint(encode_field_number<T>(field));
+    struct_size += p->put_uint(values.size());
+    struct_size += p->put_bytes<typename T::value_type>(values);
+  }
+
+  /**
+   * @brief Function to write a struct to the internal buffer
+   */
+  template <typename T>
+  void field_struct(int field, T const& value)
+  {
+    struct_size += p->put_uint(encode_field_number(field, ProtofType::FIXEDLEN));
+    auto lpos = p->m_buff.size();
+    p->put_byte(0);
+    auto sz = p->write(value);
+    struct_size += sz + 1;
+    for (; sz > 0x7f; sz >>= 7, struct_size++)
+      p->m_buff.insert(p->m_buff.begin() + (lpos++), static_cast<uint8_t>((sz & 0x7f) | 0x80));
+    (p->m_buff)[lpos] = static_cast<uint8_t>(sz);
+  }
+
+  /**
+   * @brief Function to write a vector of strings to the internal buffer
+   */
+  void field_repeated_string(int field, std::vector<std::string> const& value)
+  {
+    for (auto const& elem : value)
+      field_blob(field, elem);
+  }
+
+  /**
+   * @brief Function to write a vector of structs to the internal buffer
+   */
+  template <typename T>
+  void field_repeated_struct(int field, std::vector<T> const& value)
+  {
+    for (auto const& elem : value)
+      field_struct(field, elem);
+  }
+
+  /**
+   * @brief Function to write a vector of struct blobs to the internal
+   * buffer
+   */
+  template <typename T>
+  void field_repeated_struct_blob(int field, std::vector<T> const& value)
+  {
+    for (auto const& elem : value)
+      field_blob(field, elem);
+  }
+
+  /**
+   * @brief Returns the total length of the buffer written
+   */
+  size_t value() { return struct_size; }
+};
+
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/orc_gpu.hpp b/cpp/src/io/orc/orc_gpu.hpp
new file mode 100644
index 0000000..dba7a9f
--- /dev/null
+++ b/cpp/src/io/orc/orc_gpu.hpp
@@ -0,0 +1,484 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "orc.hpp"
+
+#include <io/comp/gpuinflate.hpp>
+#include <io/statistics/statistics.cuh>
+#include <io/utilities/column_buffer.hpp>
+
+#include <cudf/detail/timezone.cuh>
+#include <cudf/io/orc_types.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cuco/static_map.cuh>
+
+namespace cudf {
+namespace io {
+namespace orc {
+namespace gpu {
+
+using cudf::detail::device_2dspan;
+using cudf::detail::host_2dspan;
+
+auto constexpr KEY_SENTINEL   = size_type{-1};
+auto constexpr VALUE_SENTINEL = size_type{-1};
+
+using map_type = cuco::static_map<size_type, size_type>;
+
+/**
+ * @brief The alias of `map_type::pair_atomic_type` class.
+ *
+ * Declare this struct by trivial subclassing instead of type aliasing so we can have forward
+ * declaration of this struct somewhere else.
+ */
+struct slot_type : public map_type::slot_type {};
+
+struct CompressedStreamInfo {
+  CompressedStreamInfo() = default;
+  explicit constexpr CompressedStreamInfo(uint8_t const* compressed_data_, size_t compressed_size_)
+    : compressed_data(compressed_data_),
+      uncompressed_data(nullptr),
+      compressed_data_size(compressed_size_)
+  {
+  }
+  uint8_t const* compressed_data{};  // [in] base ptr to compressed stream data
+  uint8_t*
+    uncompressed_data{};  // [in] base ptr to uncompressed stream data or NULL if not known yet
+  size_t compressed_data_size{};              // [in] compressed data size for this stream
+  device_span<uint8_t const>* dec_in_ctl{};   // [in] input buffer to decompress
+  device_span<uint8_t>* dec_out_ctl{};        // [in] output buffer to decompress into
+  device_span<compression_result> dec_res{};  // [in] results of decompression
+  device_span<uint8_t const>* copy_in_ctl{};  // [out] input buffer to copy
+  device_span<uint8_t>* copy_out_ctl{};       // [out] output buffer to copy to
+  uint32_t num_compressed_blocks{};           // [in,out] number of entries in decctl(in), number of
+                                              // compressed blocks(out)
+  uint32_t num_uncompressed_blocks{};  // [in,out] number of entries in dec_in_ctl(in), number of
+                                       // uncompressed blocks(out)
+  uint64_t max_uncompressed_size{};    // [out] maximum uncompressed data size of stream
+  uint32_t max_uncompressed_block_size{};  // [out] maximum uncompressed size of any block in stream
+};
+
+enum StreamIndexType {
+  CI_DATA = 0,    // Primary data stream
+  CI_DATA2,       // Secondary/Length stream
+  CI_PRESENT,     // Present stream
+  CI_DICTIONARY,  // Dictionary stream
+  CI_INDEX,       // Index stream
+  CI_NUM_STREAMS
+};
+
+/**
+ * @brief Struct to describe a single entry in the global dictionary
+ */
+struct DictionaryEntry {
+  uint32_t pos;  // Position in data stream
+  uint32_t len;  // Length in data stream
+};
+
+/**
+ * @brief Struct to describe per stripe's column information
+ */
+struct ColumnDesc {
+  uint8_t const* streams[CI_NUM_STREAMS];  // ptr to data stream index
+  uint32_t strm_id[CI_NUM_STREAMS];        // stream ids
+  uint32_t strm_len[CI_NUM_STREAMS];       // stream length
+  uint32_t* valid_map_base;                // base pointer of valid bit map for this column
+  void* column_data_base;                  // base pointer of column data
+  uint32_t start_row;                      // starting row of the stripe
+  uint32_t num_rows;                       // number of rows in stripe
+  uint32_t column_num_rows;                // number of rows in whole column
+  uint32_t num_child_rows;                 // store number of child rows if it's list column
+  uint32_t num_rowgroups;                  // number of rowgroups in the chunk
+  uint32_t dictionary_start;               // start position in global dictionary
+  uint32_t dict_len;                       // length of local dictionary
+  uint32_t null_count;                     // number of null values in this stripe's column
+  uint32_t skip_count;                     // number of non-null values to skip
+  uint32_t rowgroup_id;                    // row group position
+  ColumnEncodingKind encoding_kind;        // column encoding kind
+  TypeKind type_kind;                      // column data type
+  uint8_t dtype_len;          // data type length (for types that can be mapped to different sizes)
+  type_id dtype_id;           // TODO
+  int32_t decimal_scale;      // number of fractional decimal digits for decimal type
+  type_id timestamp_type_id;  // output timestamp type id (type_id::EMPTY by default)
+  column_validity_info parent_validity_info;  // consists of parent column valid_map and null count
+  uint32_t* parent_null_count_prefix_sums;  // per-stripe prefix sums of parent column's null count
+};
+
+/**
+ * @brief Struct to describe a groups of row belonging to a column stripe
+ */
+struct RowGroup {
+  uint32_t chunk_id;        // Column chunk this entry belongs to
+  uint32_t strm_offset[2];  // Index offset for CI_DATA and CI_DATA2 streams
+  uint16_t run_pos[2];      // Run position for CI_DATA and CI_DATA2
+  uint32_t num_rows;        // number of rows in rowgroup
+  uint32_t start_row;       // starting row of the rowgroup
+  uint32_t num_child_rows;  // number of rows of children in rowgroup in case of list type
+};
+
+/**
+ * @brief Struct to describe an encoder data chunk
+ */
+struct EncChunk {
+  uint32_t start_row;                // start row of this chunk
+  uint32_t num_rows;                 // number of rows in this chunk
+  uint32_t null_mask_start_row;      // adjusted to multiple of 8
+  uint32_t null_mask_num_rows;       // adjusted to multiple of 8
+  ColumnEncodingKind encoding_kind;  // column encoding kind
+  TypeKind type_kind;                // column data type
+  uint8_t dtype_len;                 // data type length
+  int32_t scale;                     // scale for decimals or timestamps
+
+  uint32_t* dict_index;  // dictionary index from row index
+  uint32_t* decimal_offsets;
+  orc_column_device_view const* column;
+};
+
+/**
+ * @brief Struct to describe the streams that correspond to a single `EncChunk`.
+ */
+struct encoder_chunk_streams {
+  uint8_t* data_ptrs[CI_NUM_STREAMS];  // encoded output
+  int32_t ids[CI_NUM_STREAMS];         // stream id; -1 if stream is not present
+  uint32_t lengths[CI_NUM_STREAMS];    // in: max length, out: actual length
+};
+
+/**
+ * @brief Struct to describe a column stream within a stripe
+ */
+struct StripeStream {
+  uint8_t* data_ptr;        // encoded and gathered output
+  size_t bfr_offset;        // Offset of this stream in compressed buffer
+  uint32_t stream_size;     // Size of stream in bytes
+  uint32_t first_chunk_id;  // First chunk of the stripe
+  uint32_t num_chunks;      // Number of chunks in the stripe
+  uint32_t column_id;       // column index
+  uint32_t first_block;     // First compressed block
+  uint8_t stream_type;      // Stream index type
+  uint8_t pad[3];
+};
+
+/**
+ * @brief Struct to describe a stripe dictionary
+ */
+struct stripe_dictionary {
+  // input
+  device_span<slot_type> map_slots;  // hash map storage
+  uint32_t column_idx      = 0;      // column index
+  size_type start_row      = 0;      // first row in the stripe
+  size_type start_rowgroup = 0;      // first rowgroup in the stripe
+  size_type num_rows       = 0;      // number of rows in the stripe
+
+  // output
+  device_span<uint32_t> data;     // index of elements in the column to include in the dictionary
+  device_span<uint32_t> index;    // index into the dictionary for each row in the column
+  size_type entry_count = 0;      // number of entries in the dictionary
+  size_type char_count  = 0;      // number of characters in the dictionary
+  bool is_enabled       = false;  // true if dictionary encoding is enabled for this stripe
+};
+
+/**
+ * @brief Initializes the hash maps storage for dictionary encoding to sentinel values.
+ *
+ * @param dictionaries Dictionary descriptors
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void initialize_dictionary_hash_maps(device_2dspan<stripe_dictionary> dictionaries,
+                                     rmm::cuda_stream_view stream);
+
+/**
+ * @brief Populates the hash maps with unique values from the stripe.
+ *
+ * @param dictionaries Dictionary descriptors
+ * @param columns  Pre-order flattened device array of ORC column views
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void populate_dictionary_hash_maps(device_2dspan<stripe_dictionary> dictionaries,
+                                   device_span<orc_column_device_view const> columns,
+                                   rmm::cuda_stream_view stream);
+
+/**
+ * @brief Stores the indices of the hash map entries in the dictionary data buffer.
+ *
+ * @param dictionaries Dictionary descriptors
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void collect_map_entries(device_2dspan<stripe_dictionary> dictionaries,
+                         rmm::cuda_stream_view stream);
+
+/**
+ * @brief Stores the corresponding dictionary indices for each row in the column.
+ *
+ * @param dictionaries Dictionary descriptors
+ * @param columns Pre-order flattened device array of ORC column views
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void get_dictionary_indices(device_2dspan<stripe_dictionary> dictionaries,
+                            device_span<orc_column_device_view const> columns,
+                            rmm::cuda_stream_view stream);
+
+constexpr uint32_t encode_block_size = 512;
+
+/**
+ * @brief Launches kernel for parsing the compressed stripe data
+ *
+ * @param[in] strm_info List of compressed streams
+ * @param[in] num_streams Number of compressed streams
+ * @param[in] compression_block_size maximum size of compressed blocks (up to 16M)
+ * @param[in] log2maxcr log2 of maximum compression ratio (used to infer max uncompressed size from
+ * compressed size)
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void ParseCompressedStripeData(CompressedStreamInfo* strm_info,
+                               int32_t num_streams,
+                               uint32_t compression_block_size,
+                               uint32_t log2maxcr,
+                               rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for re-assembling decompressed blocks into a single contiguous block
+ *
+ * @param[in] strm_info List of compressed streams
+ * @param[in] num_streams Number of compressed streams
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void PostDecompressionReassemble(CompressedStreamInfo* strm_info,
+                                 int32_t num_streams,
+                                 rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for constructing rowgroup from index streams
+ *
+ * @param[out] row_groups RowGroup device array [rowgroup][column]
+ * @param[in] strm_info List of compressed streams (or NULL if uncompressed)
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] num_rowgroups Number of row groups
+ * @param[in] rowidx_stride Row index stride
+ * @param[in] use_base_stride Whether to use base stride obtained from meta or use the computed
+ * value
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void ParseRowGroupIndex(RowGroup* row_groups,
+                        CompressedStreamInfo* strm_info,
+                        ColumnDesc* chunks,
+                        uint32_t num_columns,
+                        uint32_t num_stripes,
+                        uint32_t num_rowgroups,
+                        uint32_t rowidx_stride,
+                        bool use_base_stride,
+                        rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for decoding NULLs and building string dictionary index tables
+ *
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] global_dictionary Global dictionary device array
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] first_row Crop all rows below first_row
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void DecodeNullsAndStringDictionaries(ColumnDesc* chunks,
+                                      DictionaryEntry* global_dictionary,
+                                      uint32_t num_columns,
+                                      uint32_t num_stripes,
+                                      size_t first_row,
+                                      rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for decoding column data
+ *
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] global_dictionary Global dictionary device array
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] first_row Crop all rows below first_row
+ * @param[in] tz_table Timezone translation table
+ * @param[in] tz_len Length of timezone translation table
+ * @param[in] row_groups Optional row index data [rowgroup][column]
+ * @param[in] num_rowgroups Number of row groups in row index data
+ * @param[in] rowidx_stride Row index stride
+ * @param[in] level Current nesting level being processed
+ * @param[out] error_count Number of errors during decode
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void DecodeOrcColumnData(ColumnDesc* chunks,
+                         DictionaryEntry* global_dictionary,
+                         device_2dspan<RowGroup> row_groups,
+                         uint32_t num_columns,
+                         uint32_t num_stripes,
+                         size_t first_row,
+                         table_device_view tz_table,
+                         uint32_t num_rowgroups,
+                         uint32_t rowidx_stride,
+                         size_t level,
+                         size_type* error_count,
+                         rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for encoding column data
+ *
+ * @param[in] chunks  encoder chunk device array [column][rowgroup]
+ * @param[in, out] streams chunk streams device array [column][rowgroup]
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void EncodeOrcColumnData(device_2dspan<EncChunk const> chunks,
+                         device_2dspan<encoder_chunk_streams> streams,
+                         rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for encoding column dictionaries
+ *
+ * @param[in] stripes Stripe dictionaries device array
+ * @param[in] columns Pre-order flattened device array of ORC column views
+ * @param[in] chunks encoder chunk device array [column][rowgroup]
+ * @param[in] num_string_columns Number of string columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in,out] enc_streams chunk streams device array [column][rowgroup]
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void EncodeStripeDictionaries(stripe_dictionary const* stripes,
+                              device_span<orc_column_device_view const> columns,
+                              device_2dspan<EncChunk const> chunks,
+                              uint32_t num_string_columns,
+                              uint32_t num_stripes,
+                              device_2dspan<encoder_chunk_streams> enc_streams,
+                              rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for compacting chunked column data prior to compression
+ *
+ * @param[in,out] strm_desc StripeStream device array [stripe][stream]
+ * @param[in,out] enc_streams chunk streams device array [column][rowgroup]
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void CompactOrcDataStreams(device_2dspan<StripeStream> strm_desc,
+                           device_2dspan<encoder_chunk_streams> enc_streams,
+                           rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel(s) for compressing data streams
+ *
+ * @param[in] compressed_data Output compressed blocks
+ * @param[in] num_compressed_blocks Total number of compressed blocks
+ * @param[in] compression Type of compression
+ * @param[in] comp_blk_size Compression block size
+ * @param[in] max_comp_blk_size Max size of any block after compression
+ * @param[in] comp_block_align Required alignment for compressed blocks
+ * @param[in] collect_statistics Whether to collect compression statistics
+ * @param[in,out] strm_desc StripeStream device array [stripe][stream]
+ * @param[in,out] enc_streams chunk streams device array [column][rowgroup]
+ * @param[out] comp_res Per-block compression status
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return Compression statistics (if requested)
+ */
+std::optional<writer_compression_statistics> CompressOrcDataStreams(
+  device_span<uint8_t> compressed_data,
+  uint32_t num_compressed_blocks,
+  CompressionKind compression,
+  uint32_t comp_blk_size,
+  uint32_t max_comp_blk_size,
+  uint32_t comp_block_align,
+  bool collect_statistics,
+  device_2dspan<StripeStream> strm_desc,
+  device_2dspan<encoder_chunk_streams> enc_streams,
+  device_span<compression_result> comp_res,
+  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Counts the number of characters in each rowgroup of each string column.
+ *
+ * @param counts Output array of character counts [column][rowgroup]
+ * @param orc_columns Pre-order flattened device array of ORC column views
+ * @param rowgroup_bounds Ranges of rows in each rowgroup [rowgroup][column]
+ * @param str_col_indexes Indexes of string columns in orc_columns
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void rowgroup_char_counts(device_2dspan<size_type> counts,
+                          device_span<orc_column_device_view const> orc_columns,
+                          device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                          device_span<uint32_t const> str_col_indexes,
+                          rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernels to initialize statistics collection
+ *
+ * @param[out] groups Statistics groups (rowgroup-level)
+ * @param[in] cols Column descriptors
+ * @param[in] rowgroup_bounds Ranges of rows in each rowgroup [rowgroup][column]
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void orc_init_statistics_groups(statistics_group* groups,
+                                stats_column_desc const* cols,
+                                device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernels to return statistics buffer offsets and sizes
+ *
+ * @param[in,out] groups Statistics merge groups
+ * @param[in] chunks Statistics chunks
+ * @param[in] statistics_count Number of statistics buffers to encode
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void orc_init_statistics_buffersize(statistics_merge_group* groups,
+                                    statistics_chunk const* chunks,
+                                    uint32_t statistics_count,
+                                    rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel to encode statistics in ORC protobuf format
+ *
+ * @param[out] blob_bfr Output buffer for statistics blobs
+ * @param[in,out] groups Statistics merge groups
+ * @param[in,out] chunks Statistics data
+ * @param[in] statistics_count Number of statistics buffers
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void orc_encode_statistics(uint8_t* blob_bfr,
+                           statistics_merge_group* groups,
+                           statistics_chunk const* chunks,
+                           uint32_t statistics_count,
+                           rmm::cuda_stream_view stream);
+
+/**
+ * @brief Number of set bits in pushdown masks, per rowgroup.
+ *
+ * @param[in] orc_columns Pre-order flattened device array of ORC column views
+ * @param[in] rowgroup_bounds Ranges of rows in each rowgroup [rowgroup][column]
+ * @param[out] set_counts Per rowgroup number of set bits
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void reduce_pushdown_masks(device_span<orc_column_device_view const> orc_columns,
+                           device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                           device_2dspan<cudf::size_type> set_counts,
+                           rmm::cuda_stream_view stream);
+
+}  // namespace gpu
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/reader_impl.cu b/cpp/src/io/orc/reader_impl.cu
new file mode 100644
index 0000000..157269c
--- /dev/null
+++ b/cpp/src/io/orc/reader_impl.cu
@@ -0,0 +1,1366 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file reader_impl.cu
+ * @brief cuDF-IO ORC reader class implementation
+ */
+
+#include "orc.hpp"
+#include "orc_gpu.hpp"
+
+#include "reader_impl.hpp"
+
+#include <io/comp/gpuinflate.hpp>
+#include <io/comp/nvcomp_adapter.hpp>
+#include <io/utilities/config_utils.hpp>
+
+#include <cudf/detail/timezone.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <iterator>
+
+namespace cudf::io::detail::orc {
+using namespace cudf::io::orc;
+
+namespace {
+
+/**
+ * @brief Keeps track of orc mapping and child column details.
+ */
+struct reader_column_meta {
+  // Mapping between column id in orc to processing order.
+  std::vector<std::vector<size_type>> orc_col_map;
+
+  // Number of rows in child columns.
+  std::vector<uint32_t> num_child_rows;
+
+  // Consists of parent column valid_map and null count.
+  std::vector<column_validity_info> parent_column_data;
+
+  std::vector<size_type> parent_column_index;
+
+  // Start row of child columns [stripe][column].
+  std::vector<uint32_t> child_start_row;
+
+  // Number of rows of child columns [stripe][column].
+  std::vector<uint32_t> num_child_rows_per_stripe;
+
+  struct row_group_meta {
+    uint32_t num_rows;   // number of rows in a column in a row group
+    uint32_t start_row;  // start row in a column in a row group
+  };
+
+  // Row group metadata [rowgroup][column].
+  std::vector<row_group_meta> rwgrp_meta;
+};
+
+/**
+ * @brief Struct that maps ORC streams to columns
+ */
+struct orc_stream_info {
+  explicit orc_stream_info(uint64_t offset_,
+                           std::size_t dst_pos_,
+                           uint32_t length_,
+                           uint32_t stripe_idx_)
+    : offset(offset_), dst_pos(dst_pos_), length(length_), stripe_idx(stripe_idx_)
+  {
+  }
+  uint64_t offset;      // offset in file
+  std::size_t dst_pos;  // offset in memory relative to start of compressed stripe data
+  std::size_t length;   // length in file
+  uint32_t stripe_idx;  // stripe index
+};
+
+/**
+ * @brief Function that populates column descriptors stream/chunk
+ */
+std::size_t gather_stream_info(std::size_t stripe_index,
+                               orc::StripeInformation const* stripeinfo,
+                               orc::StripeFooter const* stripefooter,
+                               host_span<int const> orc2gdf,
+                               host_span<orc::SchemaType const> types,
+                               bool use_index,
+                               bool apply_struct_map,
+                               std::size_t* num_dictionary_entries,
+                               std::vector<orc_stream_info>& stream_info,
+                               cudf::detail::hostdevice_2dvector<gpu::ColumnDesc>& chunks)
+{
+  uint64_t src_offset = 0;
+  uint64_t dst_offset = 0;
+
+  auto const get_stream_index_type = [](orc::StreamKind kind) {
+    switch (kind) {
+      case orc::DATA: return gpu::CI_DATA;
+      case orc::LENGTH:
+      case orc::SECONDARY: return gpu::CI_DATA2;
+      case orc::DICTIONARY_DATA: return gpu::CI_DICTIONARY;
+      case orc::PRESENT: return gpu::CI_PRESENT;
+      case orc::ROW_INDEX: return gpu::CI_INDEX;
+      default:
+        // Skip this stream as it's not strictly required
+        return gpu::CI_NUM_STREAMS;
+    }
+  };
+
+  for (auto const& stream : stripefooter->streams) {
+    if (!stream.column_id || *stream.column_id >= orc2gdf.size()) {
+      dst_offset += stream.length;
+      continue;
+    }
+
+    auto const column_id = *stream.column_id;
+    auto col             = orc2gdf[column_id];
+
+    if (col == -1 and apply_struct_map) {
+      // A struct-type column has no data itself, but rather child columns
+      // for each of its fields. There is only a PRESENT stream, which
+      // needs to be included for the reader.
+      auto const schema_type = types[column_id];
+      if (not schema_type.subtypes.empty()) {
+        if (schema_type.kind == orc::STRUCT && stream.kind == orc::PRESENT) {
+          for (auto const& idx : schema_type.subtypes) {
+            auto child_idx = (idx < orc2gdf.size()) ? orc2gdf[idx] : -1;
+            if (child_idx >= 0) {
+              col                             = child_idx;
+              auto& chunk                     = chunks[stripe_index][col];
+              chunk.strm_id[gpu::CI_PRESENT]  = stream_info.size();
+              chunk.strm_len[gpu::CI_PRESENT] = stream.length;
+            }
+          }
+        }
+      }
+    }
+    if (col != -1) {
+      if (src_offset >= stripeinfo->indexLength || use_index) {
+        auto& chunk           = chunks[stripe_index][col];
+        auto const index_type = get_stream_index_type(stream.kind);
+        if (index_type < gpu::CI_NUM_STREAMS) {
+          chunk.strm_id[index_type]  = stream_info.size();
+          chunk.strm_len[index_type] = stream.length;
+          // NOTE: skip_count field is temporarily used to track the presence of index streams
+          chunk.skip_count |= 1 << index_type;
+
+          if (index_type == gpu::CI_DICTIONARY) {
+            chunk.dictionary_start = *num_dictionary_entries;
+            chunk.dict_len         = stripefooter->columns[column_id].dictionarySize;
+            *num_dictionary_entries += stripefooter->columns[column_id].dictionarySize;
+          }
+        }
+      }
+      stream_info.emplace_back(
+        stripeinfo->offset + src_offset, dst_offset, stream.length, stripe_index);
+      dst_offset += stream.length;
+    }
+    src_offset += stream.length;
+  }
+
+  return dst_offset;
+}
+
+/**
+ * @brief Decompresses the stripe data, at stream granularity.
+ *
+ * @param decompressor Block decompressor
+ * @param stripe_data List of source stripe column data
+ * @param stream_info List of stream to column mappings
+ * @param chunks Vector of list of column chunk descriptors
+ * @param row_groups Vector of list of row index descriptors
+ * @param num_stripes Number of stripes making up column chunks
+ * @param row_index_stride Distance between each row index
+ * @param use_base_stride Whether to use base stride obtained from meta or use the computed value
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return Device buffer to decompressed page data
+ */
+rmm::device_buffer decompress_stripe_data(
+  OrcDecompressor const& decompressor,
+  host_span<rmm::device_buffer const> stripe_data,
+  host_span<orc_stream_info> stream_info,
+  cudf::detail::hostdevice_2dvector<gpu::ColumnDesc>& chunks,
+  cudf::detail::hostdevice_2dvector<gpu::RowGroup>& row_groups,
+  std::size_t num_stripes,
+  std::size_t row_index_stride,
+  bool use_base_stride,
+  rmm::cuda_stream_view stream)
+{
+  // Parse the columns' compressed info
+  cudf::detail::hostdevice_vector<gpu::CompressedStreamInfo> compinfo(
+    0, stream_info.size(), stream);
+  for (auto const& info : stream_info) {
+    compinfo.push_back(gpu::CompressedStreamInfo(
+      static_cast<uint8_t const*>(stripe_data[info.stripe_idx].data()) + info.dst_pos,
+      info.length));
+  }
+  compinfo.host_to_device_async(stream);
+
+  gpu::ParseCompressedStripeData(compinfo.device_ptr(),
+                                 compinfo.size(),
+                                 decompressor.GetBlockSize(),
+                                 decompressor.GetLog2MaxCompressionRatio(),
+                                 stream);
+  compinfo.device_to_host_sync(stream);
+
+  // Count the exact number of compressed blocks
+  std::size_t num_compressed_blocks   = 0;
+  std::size_t num_uncompressed_blocks = 0;
+  std::size_t total_decomp_size       = 0;
+  for (std::size_t i = 0; i < compinfo.size(); ++i) {
+    num_compressed_blocks += compinfo[i].num_compressed_blocks;
+    num_uncompressed_blocks += compinfo[i].num_uncompressed_blocks;
+    total_decomp_size += compinfo[i].max_uncompressed_size;
+  }
+  CUDF_EXPECTS(
+    not((num_uncompressed_blocks + num_compressed_blocks > 0) and (total_decomp_size == 0)),
+    "Inconsistent info on compression blocks");
+
+  // Buffer needs to be padded.
+  // Required by `gpuDecodeOrcColumnData`.
+  rmm::device_buffer decomp_data(
+    cudf::util::round_up_safe(total_decomp_size, BUFFER_PADDING_MULTIPLE), stream);
+  if (decomp_data.is_empty()) { return decomp_data; }
+
+  rmm::device_uvector<device_span<uint8_t const>> inflate_in(
+    num_compressed_blocks + num_uncompressed_blocks, stream);
+  rmm::device_uvector<device_span<uint8_t>> inflate_out(
+    num_compressed_blocks + num_uncompressed_blocks, stream);
+  rmm::device_uvector<compression_result> inflate_res(num_compressed_blocks, stream);
+  thrust::fill(rmm::exec_policy(stream),
+               inflate_res.begin(),
+               inflate_res.end(),
+               compression_result{0, compression_status::FAILURE});
+
+  // Parse again to populate the decompression input/output buffers
+  std::size_t decomp_offset      = 0;
+  uint32_t max_uncomp_block_size = 0;
+  uint32_t start_pos             = 0;
+  auto start_pos_uncomp          = (uint32_t)num_compressed_blocks;
+  for (std::size_t i = 0; i < compinfo.size(); ++i) {
+    auto dst_base                 = static_cast<uint8_t*>(decomp_data.data());
+    compinfo[i].uncompressed_data = dst_base + decomp_offset;
+    compinfo[i].dec_in_ctl        = inflate_in.data() + start_pos;
+    compinfo[i].dec_out_ctl       = inflate_out.data() + start_pos;
+    compinfo[i].dec_res      = {inflate_res.data() + start_pos, compinfo[i].num_compressed_blocks};
+    compinfo[i].copy_in_ctl  = inflate_in.data() + start_pos_uncomp;
+    compinfo[i].copy_out_ctl = inflate_out.data() + start_pos_uncomp;
+
+    stream_info[i].dst_pos = decomp_offset;
+    decomp_offset += compinfo[i].max_uncompressed_size;
+    start_pos += compinfo[i].num_compressed_blocks;
+    start_pos_uncomp += compinfo[i].num_uncompressed_blocks;
+    max_uncomp_block_size =
+      std::max(max_uncomp_block_size, compinfo[i].max_uncompressed_block_size);
+  }
+  compinfo.host_to_device_async(stream);
+  gpu::ParseCompressedStripeData(compinfo.device_ptr(),
+                                 compinfo.size(),
+                                 decompressor.GetBlockSize(),
+                                 decompressor.GetLog2MaxCompressionRatio(),
+                                 stream);
+
+  // Value for checking whether we decompress successfully.
+  // It doesn't need to be atomic as there is no race condition: we only write `true` if needed.
+  cudf::detail::hostdevice_vector<bool> any_block_failure(1, stream);
+  any_block_failure[0] = false;
+  any_block_failure.host_to_device_async(stream);
+
+  // Dispatch batches of blocks to decompress
+  if (num_compressed_blocks > 0) {
+    device_span<device_span<uint8_t const>> inflate_in_view{inflate_in.data(),
+                                                            num_compressed_blocks};
+    device_span<device_span<uint8_t>> inflate_out_view{inflate_out.data(), num_compressed_blocks};
+    switch (decompressor.compression()) {
+      case compression_type::ZLIB:
+        if (nvcomp::is_decompression_disabled(nvcomp::compression_type::DEFLATE)) {
+          gpuinflate(
+            inflate_in_view, inflate_out_view, inflate_res, gzip_header_included::NO, stream);
+        } else {
+          nvcomp::batched_decompress(nvcomp::compression_type::DEFLATE,
+                                     inflate_in_view,
+                                     inflate_out_view,
+                                     inflate_res,
+                                     max_uncomp_block_size,
+                                     total_decomp_size,
+                                     stream);
+        }
+        break;
+      case compression_type::SNAPPY:
+        if (nvcomp::is_decompression_disabled(nvcomp::compression_type::SNAPPY)) {
+          gpu_unsnap(inflate_in_view, inflate_out_view, inflate_res, stream);
+        } else {
+          nvcomp::batched_decompress(nvcomp::compression_type::SNAPPY,
+                                     inflate_in_view,
+                                     inflate_out_view,
+                                     inflate_res,
+                                     max_uncomp_block_size,
+                                     total_decomp_size,
+                                     stream);
+        }
+        break;
+      case compression_type::ZSTD:
+        if (auto const reason = nvcomp::is_decompression_disabled(nvcomp::compression_type::ZSTD);
+            reason) {
+          CUDF_FAIL("Decompression error: " + reason.value());
+        }
+        nvcomp::batched_decompress(nvcomp::compression_type::ZSTD,
+                                   inflate_in_view,
+                                   inflate_out_view,
+                                   inflate_res,
+                                   max_uncomp_block_size,
+                                   total_decomp_size,
+                                   stream);
+        break;
+      default: CUDF_FAIL("Unexpected decompression dispatch"); break;
+    }
+
+    // Check if any block has been failed to decompress.
+    // Not using `thrust::any` or `thrust::count_if` to defer stream sync.
+    thrust::for_each(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator(std::size_t{0}),
+      thrust::make_counting_iterator(inflate_res.size()),
+      [results           = inflate_res.begin(),
+       any_block_failure = any_block_failure.device_ptr()] __device__(auto const idx) {
+        if (results[idx].status != compression_status::SUCCESS) { *any_block_failure = true; }
+      });
+  }
+
+  if (num_uncompressed_blocks > 0) {
+    device_span<device_span<uint8_t const>> copy_in_view{inflate_in.data() + num_compressed_blocks,
+                                                         num_uncompressed_blocks};
+    device_span<device_span<uint8_t>> copy_out_view{inflate_out.data() + num_compressed_blocks,
+                                                    num_uncompressed_blocks};
+    gpu_copy_uncompressed_blocks(copy_in_view, copy_out_view, stream);
+  }
+
+  // Copy without stream sync, thus need to wait for stream sync below to access.
+  any_block_failure.device_to_host_async(stream);
+
+  gpu::PostDecompressionReassemble(compinfo.device_ptr(), compinfo.size(), stream);
+  compinfo.device_to_host_sync(stream);  // This also sync stream for `any_block_failure`.
+
+  // We can check on host after stream synchronize
+  CUDF_EXPECTS(not any_block_failure[0], "Error during decompression");
+
+  auto const num_columns = chunks.size().second;
+
+  // Update the stream information with the updated uncompressed info
+  // TBD: We could update the value from the information we already
+  // have in stream_info[], but using the gpu results also updates
+  // max_uncompressed_size to the actual uncompressed size, or zero if
+  // decompression failed.
+  for (std::size_t i = 0; i < num_stripes; ++i) {
+    for (std::size_t j = 0; j < num_columns; ++j) {
+      auto& chunk = chunks[i][j];
+      for (int k = 0; k < gpu::CI_NUM_STREAMS; ++k) {
+        if (chunk.strm_len[k] > 0 && chunk.strm_id[k] < compinfo.size()) {
+          chunk.streams[k]  = compinfo[chunk.strm_id[k]].uncompressed_data;
+          chunk.strm_len[k] = compinfo[chunk.strm_id[k]].max_uncompressed_size;
+        }
+      }
+    }
+  }
+
+  if (row_groups.size().first) {
+    chunks.host_to_device_async(stream);
+    row_groups.host_to_device_async(stream);
+    gpu::ParseRowGroupIndex(row_groups.base_device_ptr(),
+                            compinfo.device_ptr(),
+                            chunks.base_device_ptr(),
+                            num_columns,
+                            num_stripes,
+                            row_groups.size().first,
+                            row_index_stride,
+                            use_base_stride,
+                            stream);
+  }
+
+  return decomp_data;
+}
+
+/**
+ * @brief Updates null mask of columns whose parent is a struct column.
+ *
+ * If struct column has null element, that row would be skipped while writing child column in ORC,
+ * so we need to insert the missing null elements in child column. There is another behavior from
+ * pyspark, where if the child column doesn't have any null elements, it will not have present
+ * stream, so in that case parent null mask need to be copied to child column.
+ *
+ * @param chunks Vector of list of column chunk descriptors
+ * @param out_buffers Output columns' device buffers
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource to use for device memory allocation
+ */
+void update_null_mask(cudf::detail::hostdevice_2dvector<gpu::ColumnDesc>& chunks,
+                      host_span<column_buffer> out_buffers,
+                      rmm::cuda_stream_view stream,
+                      rmm::mr::device_memory_resource* mr)
+{
+  auto const num_stripes = chunks.size().first;
+  auto const num_columns = chunks.size().second;
+  bool is_mask_updated   = false;
+
+  for (std::size_t col_idx = 0; col_idx < num_columns; ++col_idx) {
+    if (chunks[0][col_idx].parent_validity_info.valid_map_base != nullptr) {
+      if (not is_mask_updated) {
+        chunks.device_to_host_sync(stream);
+        is_mask_updated = true;
+      }
+
+      auto parent_valid_map_base = chunks[0][col_idx].parent_validity_info.valid_map_base;
+      auto child_valid_map_base  = out_buffers[col_idx].null_mask();
+      auto child_mask_len =
+        chunks[0][col_idx].column_num_rows - chunks[0][col_idx].parent_validity_info.null_count;
+      auto parent_mask_len = chunks[0][col_idx].column_num_rows;
+
+      if (child_valid_map_base != nullptr) {
+        rmm::device_uvector<uint32_t> dst_idx(child_mask_len, stream);
+        // Copy indexes at which the parent has valid value.
+        thrust::copy_if(rmm::exec_policy(stream),
+                        thrust::make_counting_iterator(0),
+                        thrust::make_counting_iterator(0) + parent_mask_len,
+                        dst_idx.begin(),
+                        [parent_valid_map_base] __device__(auto idx) {
+                          return bit_is_set(parent_valid_map_base, idx);
+                        });
+
+        auto merged_null_mask = cudf::detail::create_null_mask(
+          parent_mask_len, mask_state::ALL_NULL, rmm::cuda_stream_view(stream), mr);
+        auto merged_mask      = static_cast<bitmask_type*>(merged_null_mask.data());
+        uint32_t* dst_idx_ptr = dst_idx.data();
+        // Copy child valid bits from child column to valid indexes, this will merge both child
+        // and parent null masks
+        thrust::for_each(rmm::exec_policy(stream),
+                         thrust::make_counting_iterator(0),
+                         thrust::make_counting_iterator(0) + dst_idx.size(),
+                         [child_valid_map_base, dst_idx_ptr, merged_mask] __device__(auto idx) {
+                           if (bit_is_set(child_valid_map_base, idx)) {
+                             cudf::set_bit(merged_mask, dst_idx_ptr[idx]);
+                           };
+                         });
+
+        out_buffers[col_idx].set_null_mask(std::move(merged_null_mask));
+
+      } else {
+        // Since child column doesn't have a mask, copy parent null mask
+        auto mask_size = bitmask_allocation_size_bytes(parent_mask_len);
+        out_buffers[col_idx].set_null_mask(
+          rmm::device_buffer(static_cast<void*>(parent_valid_map_base), mask_size, stream, mr));
+      }
+    }
+  }
+
+  if (is_mask_updated) {
+    // Update chunks with pointers to column data which might have been changed.
+    for (std::size_t stripe_idx = 0; stripe_idx < num_stripes; ++stripe_idx) {
+      for (std::size_t col_idx = 0; col_idx < num_columns; ++col_idx) {
+        auto& chunk          = chunks[stripe_idx][col_idx];
+        chunk.valid_map_base = out_buffers[col_idx].null_mask();
+      }
+    }
+    chunks.host_to_device_sync(stream);
+  }
+}
+
+/**
+ * @brief Converts the stripe column data and outputs to columns.
+ *
+ * @param num_dicts Number of dictionary entries required
+ * @param skip_rows Number of rows to offset from start
+ * @param row_index_stride Distance between each row index
+ * @param level Current nesting level being processed
+ * @param tz_table Local time to UTC conversion table
+ * @param chunks Vector of list of column chunk descriptors
+ * @param row_groups Vector of list of row index descriptors
+ * @param out_buffers Output columns' device buffers
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to use for device memory allocation
+ */
+void decode_stream_data(std::size_t num_dicts,
+                        std::size_t skip_rows,
+                        std::size_t row_index_stride,
+                        std::size_t level,
+                        table_view const& tz_table,
+                        cudf::detail::hostdevice_2dvector<gpu::ColumnDesc>& chunks,
+                        cudf::detail::device_2dspan<gpu::RowGroup> row_groups,
+                        std::vector<column_buffer>& out_buffers,
+                        rmm::cuda_stream_view stream,
+                        rmm::mr::device_memory_resource* mr)
+{
+  auto const num_stripes = chunks.size().first;
+  auto const num_columns = chunks.size().second;
+  thrust::counting_iterator<int> col_idx_it(0);
+  thrust::counting_iterator<int> stripe_idx_it(0);
+
+  // Update chunks with pointers to column data
+  std::for_each(stripe_idx_it, stripe_idx_it + num_stripes, [&](auto stripe_idx) {
+    std::for_each(col_idx_it, col_idx_it + num_columns, [&](auto col_idx) {
+      auto& chunk            = chunks[stripe_idx][col_idx];
+      chunk.column_data_base = out_buffers[col_idx].data();
+      chunk.valid_map_base   = out_buffers[col_idx].null_mask();
+    });
+  });
+
+  // Allocate global dictionary for deserializing
+  rmm::device_uvector<gpu::DictionaryEntry> global_dict(num_dicts, stream);
+
+  chunks.host_to_device_sync(stream);
+  gpu::DecodeNullsAndStringDictionaries(
+    chunks.base_device_ptr(), global_dict.data(), num_columns, num_stripes, skip_rows, stream);
+
+  if (level > 0) {
+    // Update nullmasks for children if parent was a struct and had null mask
+    update_null_mask(chunks, out_buffers, stream, mr);
+  }
+
+  auto const tz_table_dptr = table_device_view::create(tz_table, stream);
+  rmm::device_scalar<size_type> error_count(0, stream);
+  // Update the null map for child columns
+  gpu::DecodeOrcColumnData(chunks.base_device_ptr(),
+                           global_dict.data(),
+                           row_groups,
+                           num_columns,
+                           num_stripes,
+                           skip_rows,
+                           *tz_table_dptr,
+                           row_groups.size().first,
+                           row_index_stride,
+                           level,
+                           error_count.data(),
+                           stream);
+  chunks.device_to_host_async(stream);
+  // `value` synchronizes
+  auto const num_errors = error_count.value(stream);
+  CUDF_EXPECTS(num_errors == 0, "ORC data decode failed");
+
+  std::for_each(col_idx_it + 0, col_idx_it + num_columns, [&](auto col_idx) {
+    out_buffers[col_idx].null_count() =
+      std::accumulate(stripe_idx_it + 0,
+                      stripe_idx_it + num_stripes,
+                      0,
+                      [&](auto null_count, auto const stripe_idx) {
+                        return null_count + chunks[stripe_idx][col_idx].null_count;
+                      });
+  });
+}
+
+/**
+ * @brief Compute the per-stripe prefix sum of null count, for each struct column in the current
+ * layer.
+ */
+void scan_null_counts(cudf::detail::hostdevice_2dvector<gpu::ColumnDesc> const& chunks,
+                      cudf::host_span<rmm::device_uvector<uint32_t>> prefix_sums,
+                      rmm::cuda_stream_view stream)
+{
+  auto const num_stripes = chunks.size().first;
+  if (num_stripes == 0) return;
+
+  auto const num_columns = chunks.size().second;
+  std::vector<thrust::pair<size_type, cudf::device_span<uint32_t>>> prefix_sums_to_update;
+  for (auto col_idx = 0ul; col_idx < num_columns; ++col_idx) {
+    // Null counts sums are only needed for children of struct columns
+    if (chunks[0][col_idx].type_kind == STRUCT) {
+      prefix_sums_to_update.emplace_back(col_idx, prefix_sums[col_idx]);
+    }
+  }
+  auto const d_prefix_sums_to_update = cudf::detail::make_device_uvector_async(
+    prefix_sums_to_update, stream, rmm::mr::get_current_device_resource());
+
+  thrust::for_each(rmm::exec_policy(stream),
+                   d_prefix_sums_to_update.begin(),
+                   d_prefix_sums_to_update.end(),
+                   [chunks = cudf::detail::device_2dspan<gpu::ColumnDesc const>{chunks}] __device__(
+                     auto const& idx_psums) {
+                     auto const col_idx = idx_psums.first;
+                     auto const psums   = idx_psums.second;
+
+                     thrust::transform(
+                       thrust::seq,
+                       thrust::make_counting_iterator(0),
+                       thrust::make_counting_iterator(0) + psums.size(),
+                       psums.begin(),
+                       [&](auto stripe_idx) { return chunks[stripe_idx][col_idx].null_count; });
+
+                     thrust::inclusive_scan(thrust::seq, psums.begin(), psums.end(), psums.begin());
+                   });
+  // `prefix_sums_to_update` goes out of scope, copy has to be done before we return
+  stream.synchronize();
+}
+
+/**
+ * @brief Aggregate child metadata from parent column chunks.
+ */
+void aggregate_child_meta(std::size_t level,
+                          cudf::io::orc::detail::column_hierarchy const& selected_columns,
+                          cudf::detail::host_2dspan<gpu::ColumnDesc> chunks,
+                          cudf::detail::host_2dspan<gpu::RowGroup> row_groups,
+                          host_span<orc_column_meta const> list_col,
+                          host_span<column_buffer> out_buffers,
+                          reader_column_meta& col_meta)
+{
+  auto const num_of_stripes         = chunks.size().first;
+  auto const num_of_rowgroups       = row_groups.size().first;
+  auto const num_child_cols         = selected_columns.levels[level + 1].size();
+  auto const number_of_child_chunks = num_child_cols * num_of_stripes;
+  auto& num_child_rows              = col_meta.num_child_rows;
+  auto& parent_column_data          = col_meta.parent_column_data;
+
+  // Reset the meta to store child column details.
+  num_child_rows.resize(selected_columns.levels[level + 1].size());
+  std::fill(num_child_rows.begin(), num_child_rows.end(), 0);
+  parent_column_data.resize(number_of_child_chunks);
+  col_meta.parent_column_index.resize(number_of_child_chunks);
+  col_meta.child_start_row.resize(number_of_child_chunks);
+  col_meta.num_child_rows_per_stripe.resize(number_of_child_chunks);
+  col_meta.rwgrp_meta.resize(num_of_rowgroups * num_child_cols);
+
+  auto child_start_row = cudf::detail::host_2dspan<uint32_t>(
+    col_meta.child_start_row.data(), num_of_stripes, num_child_cols);
+  auto num_child_rows_per_stripe = cudf::detail::host_2dspan<uint32_t>(
+    col_meta.num_child_rows_per_stripe.data(), num_of_stripes, num_child_cols);
+  auto rwgrp_meta = cudf::detail::host_2dspan<reader_column_meta::row_group_meta>(
+    col_meta.rwgrp_meta.data(), num_of_rowgroups, num_child_cols);
+
+  int index = 0;  // number of child column processed
+
+  // For each parent column, update its child column meta for each stripe.
+  std::for_each(list_col.begin(), list_col.end(), [&](auto const p_col) {
+    auto const parent_col_idx = col_meta.orc_col_map[level][p_col.id];
+    auto start_row            = 0;
+    auto processed_row_groups = 0;
+
+    for (std::size_t stripe_id = 0; stripe_id < num_of_stripes; stripe_id++) {
+      // Aggregate num_rows and start_row from processed parent columns per row groups
+      if (num_of_rowgroups) {
+        auto stripe_num_row_groups = chunks[stripe_id][parent_col_idx].num_rowgroups;
+        auto processed_child_rows  = 0;
+
+        for (std::size_t rowgroup_id = 0; rowgroup_id < stripe_num_row_groups;
+             rowgroup_id++, processed_row_groups++) {
+          auto const child_rows = row_groups[processed_row_groups][parent_col_idx].num_child_rows;
+          for (size_type id = 0; id < p_col.num_children; id++) {
+            auto const child_col_idx                                  = index + id;
+            rwgrp_meta[processed_row_groups][child_col_idx].start_row = processed_child_rows;
+            rwgrp_meta[processed_row_groups][child_col_idx].num_rows  = child_rows;
+          }
+          processed_child_rows += child_rows;
+        }
+      }
+
+      // Aggregate start row, number of rows per chunk and total number of rows in a column
+      auto const child_rows = chunks[stripe_id][parent_col_idx].num_child_rows;
+      for (size_type id = 0; id < p_col.num_children; id++) {
+        auto const child_col_idx = index + id;
+
+        num_child_rows[child_col_idx] += child_rows;
+        num_child_rows_per_stripe[stripe_id][child_col_idx] = child_rows;
+        // start row could be different for each column when there is nesting at each stripe level
+        child_start_row[stripe_id][child_col_idx] = (stripe_id == 0) ? 0 : start_row;
+      }
+      start_row += child_rows;
+    }
+
+    // Parent column null mask and null count would be required for child column
+    // to adjust its nullmask.
+    auto type              = out_buffers[parent_col_idx].type.id();
+    auto parent_null_count = static_cast<uint32_t>(out_buffers[parent_col_idx].null_count());
+    auto parent_valid_map  = out_buffers[parent_col_idx].null_mask();
+    auto num_rows          = out_buffers[parent_col_idx].size;
+
+    for (size_type id = 0; id < p_col.num_children; id++) {
+      auto const child_col_idx                    = index + id;
+      col_meta.parent_column_index[child_col_idx] = parent_col_idx;
+      if (type == type_id::STRUCT) {
+        parent_column_data[child_col_idx] = {parent_valid_map, parent_null_count};
+        // Number of rows in child will remain same as parent in case of struct column
+        num_child_rows[child_col_idx] = num_rows;
+      } else {
+        parent_column_data[child_col_idx] = {nullptr, 0};
+      }
+    }
+    index += p_col.num_children;
+  });
+}
+
+/**
+ * @brief struct to store buffer data and size of list buffer
+ */
+struct list_buffer_data {
+  size_type* data;
+  size_type size;
+};
+
+// Generates offsets for list buffer from number of elements in a row.
+void generate_offsets_for_list(host_span<list_buffer_data> buff_data, rmm::cuda_stream_view stream)
+{
+  for (auto& list_data : buff_data) {
+    thrust::exclusive_scan(rmm::exec_policy_nosync(stream),
+                           list_data.data,
+                           list_data.data + list_data.size,
+                           list_data.data);
+  }
+}
+
+/**
+ * @brief Function that translates ORC data kind to cuDF type enum
+ */
+constexpr type_id to_cudf_type(orc::TypeKind kind,
+                               bool use_np_dtypes,
+                               type_id timestamp_type_id,
+                               type_id decimal_type_id)
+{
+  switch (kind) {
+    case orc::BOOLEAN: return type_id::BOOL8;
+    case orc::BYTE: return type_id::INT8;
+    case orc::SHORT: return type_id::INT16;
+    case orc::INT: return type_id::INT32;
+    case orc::LONG: return type_id::INT64;
+    case orc::FLOAT: return type_id::FLOAT32;
+    case orc::DOUBLE: return type_id::FLOAT64;
+    case orc::STRING:
+    case orc::BINARY:
+    case orc::VARCHAR:
+    case orc::CHAR:
+      // Variable-length types can all be mapped to STRING
+      return type_id::STRING;
+    case orc::TIMESTAMP:
+      return (timestamp_type_id != type_id::EMPTY) ? timestamp_type_id
+                                                   : type_id::TIMESTAMP_NANOSECONDS;
+    case orc::DATE:
+      // There isn't a (DAYS -> np.dtype) mapping
+      return (use_np_dtypes) ? type_id::TIMESTAMP_MILLISECONDS : type_id::TIMESTAMP_DAYS;
+    case orc::DECIMAL: return decimal_type_id;
+    // Need to update once cuDF plans to support map type
+    case orc::MAP:
+    case orc::LIST: return type_id::LIST;
+    case orc::STRUCT: return type_id::STRUCT;
+    default: break;
+  }
+
+  return type_id::EMPTY;
+}
+
+/**
+ * @brief Determines cuDF type of an ORC Decimal column.
+ */
+type_id to_cudf_decimal_type(host_span<std::string const> decimal128_columns,
+                             cudf::io::orc::detail::aggregate_orc_metadata const& metadata,
+                             int column_index)
+{
+  if (metadata.get_col_type(column_index).kind != DECIMAL) { return type_id::EMPTY; }
+
+  if (std::find(decimal128_columns.begin(),
+                decimal128_columns.end(),
+                metadata.column_path(0, column_index)) != decimal128_columns.end()) {
+    return type_id::DECIMAL128;
+  }
+
+  auto const precision = metadata.get_col_type(column_index)
+                           .precision.value_or(cuda::std::numeric_limits<int64_t>::digits10);
+  if (precision <= cuda::std::numeric_limits<int32_t>::digits10) { return type_id::DECIMAL32; }
+  if (precision <= cuda::std::numeric_limits<int64_t>::digits10) { return type_id::DECIMAL64; }
+  return type_id::DECIMAL128;
+}
+
+std::string get_map_child_col_name(std::size_t const idx) { return (idx == 0) ? "key" : "value"; }
+
+/**
+ * @brief Create empty columns and respective schema information from the buffer.
+ */
+std::unique_ptr<column> create_empty_column(
+  size_type orc_col_id,
+  cudf::io::orc::detail::aggregate_orc_metadata const& metadata,
+  host_span<std::string const> decimal128_columns,
+  bool use_np_dtypes,
+  data_type timestamp_type,
+  column_name_info& schema_info,
+  rmm::cuda_stream_view stream)
+{
+  schema_info.name = metadata.column_name(0, orc_col_id);
+  auto const kind  = metadata.get_col_type(orc_col_id).kind;
+  auto const type  = to_cudf_type(kind,
+                                 use_np_dtypes,
+                                 timestamp_type.id(),
+                                 to_cudf_decimal_type(decimal128_columns, metadata, orc_col_id));
+
+  switch (kind) {
+    case orc::LIST: {
+      schema_info.children.emplace_back("offsets");
+      schema_info.children.emplace_back("");
+      return make_lists_column(0,
+                               make_empty_column(type_id::INT32),
+                               create_empty_column(metadata.get_col_type(orc_col_id).subtypes[0],
+                                                   metadata,
+                                                   decimal128_columns,
+                                                   use_np_dtypes,
+                                                   timestamp_type,
+                                                   schema_info.children.back(),
+                                                   stream),
+                               0,
+                               rmm::device_buffer{0, stream},
+                               stream);
+    }
+    case orc::MAP: {
+      schema_info.children.emplace_back("offsets");
+      schema_info.children.emplace_back("struct");
+      auto const child_column_ids = metadata.get_col_type(orc_col_id).subtypes;
+      auto& children_schema       = schema_info.children.back().children;
+      std::vector<std::unique_ptr<column>> child_columns;
+      for (std::size_t idx = 0; idx < metadata.get_col_type(orc_col_id).subtypes.size(); idx++) {
+        children_schema.emplace_back("");
+        child_columns.push_back(create_empty_column(child_column_ids[idx],
+                                                    metadata,
+                                                    decimal128_columns,
+                                                    use_np_dtypes,
+                                                    timestamp_type,
+                                                    schema_info.children.back().children.back(),
+                                                    stream));
+        children_schema[idx].name = get_map_child_col_name(idx);
+      }
+      return make_lists_column(
+        0,
+        make_empty_column(type_id::INT32),
+        make_structs_column(0, std::move(child_columns), 0, rmm::device_buffer{0, stream}, stream),
+        0,
+        rmm::device_buffer{0, stream},
+        stream);
+    }
+
+    case orc::STRUCT: {
+      std::vector<std::unique_ptr<column>> child_columns;
+      for (auto const col : metadata.get_col_type(orc_col_id).subtypes) {
+        schema_info.children.emplace_back("");
+        child_columns.push_back(create_empty_column(col,
+                                                    metadata,
+                                                    decimal128_columns,
+                                                    use_np_dtypes,
+                                                    timestamp_type,
+                                                    schema_info.children.back(),
+                                                    stream));
+      }
+      return make_structs_column(
+        0, std::move(child_columns), 0, rmm::device_buffer{0, stream}, stream);
+    }
+
+    case orc::DECIMAL: {
+      int32_t scale = 0;
+      if (type == type_id::DECIMAL32 or type == type_id::DECIMAL64 or type == type_id::DECIMAL128) {
+        scale = -static_cast<int32_t>(metadata.get_types()[orc_col_id].scale.value_or(0));
+      }
+      return make_empty_column(data_type(type, scale));
+    }
+
+    default: return make_empty_column(type);
+  }
+}
+
+/**
+ * @brief Assemble the buffer with child columns.
+ */
+column_buffer assemble_buffer(size_type orc_col_id,
+                              std::size_t level,
+                              reader_column_meta const& col_meta,
+                              cudf::io::orc::detail::aggregate_orc_metadata const& metadata,
+                              cudf::io::orc::detail::column_hierarchy const& selected_columns,
+                              std::vector<std::vector<column_buffer>>& col_buffers,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  auto const col_id = col_meta.orc_col_map[level][orc_col_id];
+  auto& col_buffer  = col_buffers[level][col_id];
+
+  col_buffer.name = metadata.column_name(0, orc_col_id);
+  auto kind       = metadata.get_col_type(orc_col_id).kind;
+  switch (kind) {
+    case orc::LIST:
+    case orc::STRUCT: {
+      auto const& children_indices = selected_columns.children.at(orc_col_id);
+      for (auto const child_id : children_indices) {
+        col_buffer.children.emplace_back(assemble_buffer(
+          child_id, level + 1, col_meta, metadata, selected_columns, col_buffers, stream, mr));
+      }
+    } break;
+
+    case orc::MAP: {
+      std::vector<column_buffer> child_col_buffers;
+      // Get child buffers
+      auto const& children_indices = selected_columns.children.at(orc_col_id);
+      for (std::size_t idx = 0; idx < children_indices.size(); idx++) {
+        auto const col = children_indices[idx];
+        child_col_buffers.emplace_back(assemble_buffer(
+          col, level + 1, col_meta, metadata, selected_columns, col_buffers, stream, mr));
+        child_col_buffers.back().name = get_map_child_col_name(idx);
+      }
+      // Create a struct buffer
+      auto num_rows = child_col_buffers[0].size;
+      auto struct_buffer =
+        column_buffer(cudf::data_type(type_id::STRUCT), num_rows, false, stream, mr);
+      struct_buffer.children = std::move(child_col_buffers);
+      struct_buffer.name     = "struct";
+
+      col_buffer.children.emplace_back(std::move(struct_buffer));
+    } break;
+
+    default: break;
+  }
+
+  return std::move(col_buffer);
+}
+
+}  // namespace
+
+reader::impl::impl(std::vector<std::unique_ptr<datasource>>&& sources,
+                   orc_reader_options const& options,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+  : _stream(stream),
+    _mr(mr),
+    _sources(std::move(sources)),
+    _metadata{_sources, stream},
+    _selected_columns{_metadata.select_columns(options.get_columns())},
+    _timestamp_type{options.get_timestamp_type()},
+    _use_index{options.is_enabled_use_index()},
+    _use_np_dtypes{options.is_enabled_use_np_dtypes()},
+    _decimal128_columns{options.get_decimal128_columns()},
+    _col_meta{std::make_unique<reader_column_meta>()}
+{
+}
+
+table_with_metadata reader::impl::read(uint64_t skip_rows,
+                                       std::optional<size_type> const& num_rows_opt,
+                                       std::vector<std::vector<size_type>> const& stripes)
+{
+  // Selected columns at different levels of nesting are stored in different elements
+  // of `selected_columns`; thus, size == 1 means no nested columns
+  CUDF_EXPECTS(skip_rows == 0 or _selected_columns.num_levels() == 1,
+               "skip_rows is not supported by nested columns");
+
+  // There are no columns in the table
+  if (_selected_columns.num_levels() == 0) { return {std::make_unique<table>(), table_metadata{}}; }
+
+  std::vector<std::vector<column_buffer>> out_buffers(_selected_columns.num_levels());
+  std::vector<std::unique_ptr<column>> out_columns;
+  table_metadata out_metadata;
+
+  // Copy user data to the output metadata.
+  std::transform(_metadata.per_file_metadata.cbegin(),
+                 _metadata.per_file_metadata.cend(),
+                 std::back_inserter(out_metadata.per_file_user_data),
+                 [](auto& meta) {
+                   std::unordered_map<std::string, std::string> kv_map;
+                   std::transform(meta.ff.metadata.cbegin(),
+                                  meta.ff.metadata.cend(),
+                                  std::inserter(kv_map, kv_map.end()),
+                                  [](auto const& kv) {
+                                    return std::pair{kv.name, kv.value};
+                                  });
+                   return kv_map;
+                 });
+  out_metadata.user_data = {out_metadata.per_file_user_data[0].begin(),
+                            out_metadata.per_file_user_data[0].end()};
+
+  // Select only stripes required (aka row groups)
+  auto const [rows_to_skip, rows_to_read, selected_stripes] =
+    _metadata.select_stripes(stripes, skip_rows, num_rows_opt, _stream);
+
+  // If no rows or stripes to read, return empty columns
+  if (rows_to_read == 0 || selected_stripes.empty()) {
+    std::transform(_selected_columns.levels[0].begin(),
+                   _selected_columns.levels[0].end(),
+                   std::back_inserter(out_columns),
+                   [&](auto const col_meta) {
+                     out_metadata.schema_info.emplace_back("");
+                     return create_empty_column(col_meta.id,
+                                                _metadata,
+                                                _decimal128_columns,
+                                                _use_np_dtypes,
+                                                _timestamp_type,
+                                                out_metadata.schema_info.back(),
+                                                _stream);
+                   });
+    return {std::make_unique<table>(std::move(out_columns)), std::move(out_metadata)};
+  }
+
+  // Set up table for converting timestamp columns from local to UTC time
+  auto const tz_table = [&, &selected_stripes = selected_stripes] {
+    auto const has_timestamp_column = std::any_of(
+      _selected_columns.levels.cbegin(), _selected_columns.levels.cend(), [&](auto const& col_lvl) {
+        return std::any_of(col_lvl.cbegin(), col_lvl.cend(), [&](auto const& col_meta) {
+          return _metadata.get_col_type(col_meta.id).kind == TypeKind::TIMESTAMP;
+        });
+      });
+
+    return has_timestamp_column
+             ? cudf::detail::make_timezone_transition_table(
+                 {}, selected_stripes[0].stripe_info[0].second->writerTimezone, _stream)
+             : std::make_unique<cudf::table>();
+  }();
+
+  std::vector<std::vector<rmm::device_buffer>> lvl_stripe_data(_selected_columns.num_levels());
+  std::vector<std::vector<rmm::device_uvector<uint32_t>>> null_count_prefix_sums;
+
+  // Iterates through levels of nested columns, child column will be one level down
+  // compared to parent column.
+  auto& col_meta = *_col_meta;
+  for (std::size_t level = 0; level < _selected_columns.num_levels(); ++level) {
+    auto& columns_level = _selected_columns.levels[level];
+    // Association between each ORC column and its cudf::column
+    col_meta.orc_col_map.emplace_back(_metadata.get_num_cols(), -1);
+    std::vector<orc_column_meta> nested_col;
+
+    // Get a list of column data types
+    std::vector<data_type> column_types;
+    for (auto& col : columns_level) {
+      auto col_type = to_cudf_type(_metadata.get_col_type(col.id).kind,
+                                   _use_np_dtypes,
+                                   _timestamp_type.id(),
+                                   to_cudf_decimal_type(_decimal128_columns, _metadata, col.id));
+      CUDF_EXPECTS(col_type != type_id::EMPTY, "Unknown type");
+      if (col_type == type_id::DECIMAL32 or col_type == type_id::DECIMAL64 or
+          col_type == type_id::DECIMAL128) {
+        // sign of the scale is changed since cuDF follows c++ libraries like CNL
+        // which uses negative scaling, but liborc and other libraries
+        // follow positive scaling.
+        auto const scale =
+          -static_cast<size_type>(_metadata.get_col_type(col.id).scale.value_or(0));
+        column_types.emplace_back(col_type, scale);
+      } else {
+        column_types.emplace_back(col_type);
+      }
+
+      // Map each ORC column to its column
+      col_meta.orc_col_map[level][col.id] = column_types.size() - 1;
+      if (col_type == type_id::LIST or col_type == type_id::STRUCT) {
+        nested_col.emplace_back(col);
+      }
+    }
+
+    // Get the total number of stripes across all input files.
+    std::size_t total_num_stripes =
+      std::accumulate(selected_stripes.begin(),
+                      selected_stripes.end(),
+                      0,
+                      [](std::size_t sum, auto& stripe_source_mapping) {
+                        return sum + stripe_source_mapping.stripe_info.size();
+                      });
+    auto const num_columns = columns_level.size();
+    cudf::detail::hostdevice_2dvector<gpu::ColumnDesc> chunks(
+      total_num_stripes, num_columns, _stream);
+    memset(chunks.base_host_ptr(), 0, chunks.size_bytes());
+
+    const bool use_index =
+      _use_index &&
+      // Do stripes have row group index
+      _metadata.is_row_grp_idx_present() &&
+      // Only use if we don't have much work with complete columns & stripes
+      // TODO: Consider nrows, gpu, and tune the threshold
+      (rows_to_read > _metadata.get_row_index_stride() && !(_metadata.get_row_index_stride() & 7) &&
+       _metadata.get_row_index_stride() > 0 && num_columns * total_num_stripes < 8 * 128) &&
+      // Only use if first row is aligned to a stripe boundary
+      // TODO: Fix logic to handle unaligned rows
+      (rows_to_skip == 0);
+
+    // Logically view streams as columns
+    std::vector<orc_stream_info> stream_info;
+
+    null_count_prefix_sums.emplace_back();
+    null_count_prefix_sums.back().reserve(_selected_columns.levels[level].size());
+    std::generate_n(std::back_inserter(null_count_prefix_sums.back()),
+                    _selected_columns.levels[level].size(),
+                    [&]() {
+                      return cudf::detail::make_zeroed_device_uvector_async<uint32_t>(
+                        total_num_stripes, _stream, rmm::mr::get_current_device_resource());
+                    });
+
+    // Tracker for eventually deallocating compressed and uncompressed data
+    auto& stripe_data = lvl_stripe_data[level];
+
+    std::size_t stripe_start_row = 0;
+    std::size_t num_dict_entries = 0;
+    std::size_t num_rowgroups    = 0;
+    int stripe_idx               = 0;
+
+    std::vector<std::pair<std::future<std::size_t>, std::size_t>> read_tasks;
+    for (auto const& stripe_source_mapping : selected_stripes) {
+      // Iterate through the source files selected stripes
+      for (auto const& stripe : stripe_source_mapping.stripe_info) {
+        auto const stripe_info   = stripe.first;
+        auto const stripe_footer = stripe.second;
+
+        auto stream_count          = stream_info.size();
+        auto const total_data_size = gather_stream_info(stripe_idx,
+                                                        stripe_info,
+                                                        stripe_footer,
+                                                        col_meta.orc_col_map[level],
+                                                        _metadata.get_types(),
+                                                        use_index,
+                                                        level == 0,
+                                                        &num_dict_entries,
+                                                        stream_info,
+                                                        chunks);
+
+        auto const is_stripe_data_empty = total_data_size == 0;
+        CUDF_EXPECTS(not is_stripe_data_empty or stripe_info->indexLength == 0,
+                     "Invalid index rowgroup stream data");
+
+        // Buffer needs to be padded.
+        // Required by `copy_uncompressed_kernel`.
+        stripe_data.emplace_back(
+          cudf::util::round_up_safe(total_data_size, BUFFER_PADDING_MULTIPLE), _stream);
+        auto dst_base = static_cast<uint8_t*>(stripe_data.back().data());
+
+        // Coalesce consecutive streams into one read
+        while (not is_stripe_data_empty and stream_count < stream_info.size()) {
+          auto const d_dst  = dst_base + stream_info[stream_count].dst_pos;
+          auto const offset = stream_info[stream_count].offset;
+          auto len          = stream_info[stream_count].length;
+          stream_count++;
+
+          while (stream_count < stream_info.size() &&
+                 stream_info[stream_count].offset == offset + len) {
+            len += stream_info[stream_count].length;
+            stream_count++;
+          }
+          if (_metadata.per_file_metadata[stripe_source_mapping.source_idx]
+                .source->is_device_read_preferred(len)) {
+            read_tasks.push_back(
+              std::pair(_metadata.per_file_metadata[stripe_source_mapping.source_idx]
+                          .source->device_read_async(offset, len, d_dst, _stream),
+                        len));
+
+          } else {
+            auto const buffer =
+              _metadata.per_file_metadata[stripe_source_mapping.source_idx].source->host_read(
+                offset, len);
+            CUDF_EXPECTS(buffer->size() == len, "Unexpected discrepancy in bytes read.");
+            CUDF_CUDA_TRY(
+              cudaMemcpyAsync(d_dst, buffer->data(), len, cudaMemcpyDefault, _stream.value()));
+            _stream.synchronize();
+          }
+        }
+
+        auto const num_rows_per_stripe = stripe_info->numberOfRows;
+        auto const rowgroup_id         = num_rowgroups;
+        auto stripe_num_rowgroups      = 0;
+        if (use_index) {
+          stripe_num_rowgroups = (num_rows_per_stripe + _metadata.get_row_index_stride() - 1) /
+                                 _metadata.get_row_index_stride();
+        }
+        // Update chunks to reference streams pointers
+        for (std::size_t col_idx = 0; col_idx < num_columns; col_idx++) {
+          auto& chunk = chunks[stripe_idx][col_idx];
+          // start row, number of rows in a each stripe and total number of rows
+          // may change in lower levels of nesting
+          chunk.start_row = (level == 0)
+                              ? stripe_start_row
+                              : col_meta.child_start_row[stripe_idx * num_columns + col_idx];
+          chunk.num_rows =
+            (level == 0) ? stripe_info->numberOfRows
+                         : col_meta.num_child_rows_per_stripe[stripe_idx * num_columns + col_idx];
+          chunk.column_num_rows = (level == 0) ? rows_to_read : col_meta.num_child_rows[col_idx];
+          chunk.parent_validity_info =
+            (level == 0) ? column_validity_info{} : col_meta.parent_column_data[col_idx];
+          chunk.parent_null_count_prefix_sums =
+            (level == 0)
+              ? nullptr
+              : null_count_prefix_sums[level - 1][col_meta.parent_column_index[col_idx]].data();
+          chunk.encoding_kind = stripe_footer->columns[columns_level[col_idx].id].kind;
+          chunk.type_kind     = _metadata.per_file_metadata[stripe_source_mapping.source_idx]
+                              .ff.types[columns_level[col_idx].id]
+                              .kind;
+          // num_child_rows for a struct column will be same, for other nested types it will be
+          // calculated.
+          chunk.num_child_rows = (chunk.type_kind != orc::STRUCT) ? 0 : chunk.num_rows;
+          chunk.dtype_id       = column_types[col_idx].id();
+          chunk.decimal_scale  = _metadata.per_file_metadata[stripe_source_mapping.source_idx]
+                                  .ff.types[columns_level[col_idx].id]
+                                  .scale.value_or(0);
+
+          chunk.rowgroup_id   = rowgroup_id;
+          chunk.dtype_len     = (column_types[col_idx].id() == type_id::STRING)
+                                  ? sizeof(string_index_pair)
+                                : ((column_types[col_idx].id() == type_id::LIST) or
+                               (column_types[col_idx].id() == type_id::STRUCT))
+                                  ? sizeof(size_type)
+                                  : cudf::size_of(column_types[col_idx]);
+          chunk.num_rowgroups = stripe_num_rowgroups;
+          if (chunk.type_kind == orc::TIMESTAMP) { chunk.timestamp_type_id = _timestamp_type.id(); }
+          if (not is_stripe_data_empty) {
+            for (int k = 0; k < gpu::CI_NUM_STREAMS; k++) {
+              chunk.streams[k] = dst_base + stream_info[chunk.strm_id[k]].dst_pos;
+            }
+          }
+        }
+        stripe_start_row += num_rows_per_stripe;
+        num_rowgroups += stripe_num_rowgroups;
+
+        stripe_idx++;
+      }
+    }
+    for (auto& task : read_tasks) {
+      CUDF_EXPECTS(task.first.get() == task.second, "Unexpected discrepancy in bytes read.");
+    }
+
+    if (stripe_data.empty()) { continue; }
+
+    // Process dataset chunk pages into output columns
+    auto row_groups =
+      cudf::detail::hostdevice_2dvector<gpu::RowGroup>(num_rowgroups, num_columns, _stream);
+    if (level > 0 and row_groups.size().first) {
+      cudf::host_span<gpu::RowGroup> row_groups_span(row_groups.base_host_ptr(),
+                                                     num_rowgroups * num_columns);
+      auto& rw_grp_meta = col_meta.rwgrp_meta;
+
+      // Update start row and num rows per row group
+      std::transform(rw_grp_meta.begin(),
+                     rw_grp_meta.end(),
+                     row_groups_span.begin(),
+                     rw_grp_meta.begin(),
+                     [&](auto meta, auto& row_grp) {
+                       row_grp.num_rows  = meta.num_rows;
+                       row_grp.start_row = meta.start_row;
+                       return meta;
+                     });
+    }
+    // Setup row group descriptors if using indexes
+    if (_metadata.per_file_metadata[0].ps.compression != orc::NONE) {
+      auto decomp_data = decompress_stripe_data(*_metadata.per_file_metadata[0].decompressor,
+                                                stripe_data,
+                                                stream_info,
+                                                chunks,
+                                                row_groups,
+                                                total_num_stripes,
+                                                _metadata.get_row_index_stride(),
+                                                level == 0,
+                                                _stream);
+      stripe_data.clear();
+      stripe_data.push_back(std::move(decomp_data));
+    } else {
+      if (row_groups.size().first) {
+        chunks.host_to_device_async(_stream);
+        row_groups.host_to_device_async(_stream);
+        gpu::ParseRowGroupIndex(row_groups.base_device_ptr(),
+                                nullptr,
+                                chunks.base_device_ptr(),
+                                num_columns,
+                                total_num_stripes,
+                                num_rowgroups,
+                                _metadata.get_row_index_stride(),
+                                level == 0,
+                                _stream);
+      }
+    }
+
+    for (std::size_t i = 0; i < column_types.size(); ++i) {
+      bool is_nullable = false;
+      for (std::size_t j = 0; j < total_num_stripes; ++j) {
+        if (chunks[j][i].strm_len[gpu::CI_PRESENT] != 0) {
+          is_nullable = true;
+          break;
+        }
+      }
+      auto is_list_type = (column_types[i].id() == type_id::LIST);
+      auto n_rows       = (level == 0) ? rows_to_read : col_meta.num_child_rows[i];
+      // For list column, offset column will be always size + 1
+      if (is_list_type) n_rows++;
+      out_buffers[level].emplace_back(column_types[i], n_rows, is_nullable, _stream, _mr);
+    }
+
+    decode_stream_data(num_dict_entries,
+                       rows_to_skip,
+                       _metadata.get_row_index_stride(),
+                       level,
+                       tz_table->view(),
+                       chunks,
+                       row_groups,
+                       out_buffers[level],
+                       _stream,
+                       _mr);
+
+    if (nested_col.size()) {
+      // Extract information to process nested child columns
+      scan_null_counts(chunks, null_count_prefix_sums[level], _stream);
+
+      row_groups.device_to_host_sync(_stream);
+      aggregate_child_meta(
+        level, _selected_columns, chunks, row_groups, nested_col, out_buffers[level], col_meta);
+
+      // ORC stores number of elements at each row, so we need to generate offsets from that
+      std::vector<list_buffer_data> buff_data;
+      std::for_each(
+        out_buffers[level].begin(), out_buffers[level].end(), [&buff_data](auto& out_buffer) {
+          if (out_buffer.type.id() == type_id::LIST) {
+            auto data = static_cast<size_type*>(out_buffer.data());
+            buff_data.emplace_back(list_buffer_data{data, out_buffer.size});
+          }
+        });
+
+      if (not buff_data.empty()) { generate_offsets_for_list(buff_data, _stream); }
+    }
+  }
+
+  // Create columns from buffer with respective schema information.
+  std::transform(
+    _selected_columns.levels[0].begin(),
+    _selected_columns.levels[0].end(),
+    std::back_inserter(out_columns),
+    [&](auto const& orc_col_meta) {
+      out_metadata.schema_info.emplace_back("");
+      auto col_buffer = assemble_buffer(
+        orc_col_meta.id, 0, col_meta, _metadata, _selected_columns, out_buffers, _stream, _mr);
+      return make_column(col_buffer, &out_metadata.schema_info.back(), std::nullopt, _stream);
+    });
+
+  return {std::make_unique<table>(std::move(out_columns)), std::move(out_metadata)};
+}
+
+// Forward to implementation
+reader::reader(std::vector<std::unique_ptr<cudf::io::datasource>>&& sources,
+               orc_reader_options const& options,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr)
+  : _impl{std::make_unique<impl>(std::move(sources), options, stream, mr)}
+{
+}
+
+// Destructor within this translation unit
+reader::~reader() = default;
+
+// Forward to implementation
+table_with_metadata reader::read(orc_reader_options const& options)
+{
+  return _impl->read(options.get_skip_rows(), options.get_num_rows(), options.get_stripes());
+}
+
+}  // namespace cudf::io::detail::orc
diff --git a/cpp/src/io/orc/reader_impl.hpp b/cpp/src/io/orc/reader_impl.hpp
new file mode 100644
index 0000000..7a576d6
--- /dev/null
+++ b/cpp/src/io/orc/reader_impl.hpp
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "aggregate_orc_metadata.hpp"
+#include "orc.hpp"
+#include "orc_gpu.hpp"
+
+#include <io/utilities/column_buffer.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/detail/orc.hpp>
+#include <cudf/io/orc.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace cudf::io::detail::orc {
+using namespace cudf::io::orc;
+
+namespace {
+struct reader_column_meta;
+}
+
+/**
+ * @brief Implementation for ORC reader.
+ */
+class reader::impl {
+ public:
+  /**
+   * @brief Constructor from a dataset source with reader options.
+   *
+   * @param sources Dataset sources
+   * @param options Settings for controlling reading behavior
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  explicit impl(std::vector<std::unique_ptr<datasource>>&& sources,
+                orc_reader_options const& options,
+                rmm::cuda_stream_view stream,
+                rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Read an entire set or a subset of data and returns a set of columns
+   *
+   * @param skip_rows Number of rows to skip from the start
+   * @param num_rows_opt Optional number of rows to read
+   * @param stripes Indices of individual stripes to load if non-empty
+   * @return The set of columns along with metadata
+   */
+  table_with_metadata read(uint64_t skip_rows,
+                           std::optional<size_type> const& num_rows_opt,
+                           std::vector<std::vector<size_type>> const& stripes);
+
+ private:
+  rmm::cuda_stream_view const _stream;
+  rmm::mr::device_memory_resource* const _mr;
+
+  std::vector<std::unique_ptr<datasource>> const _sources;  // Unused but owns data for `_metadata`
+  cudf::io::orc::detail::aggregate_orc_metadata _metadata;
+  cudf::io::orc::detail::column_hierarchy const _selected_columns;  // Need to be after _metadata
+
+  data_type const _timestamp_type;  // Override output timestamp resolution
+  bool const _use_index;            // Enable or disable attempt to use row index for parsing
+  bool const _use_np_dtypes;        // Enable or disable the conversion to numpy-compatible dtypes
+  std::vector<std::string> const _decimal128_columns;   // Control decimals conversion
+  std::unique_ptr<reader_column_meta> const _col_meta;  // Track of orc mapping and child details
+};
+
+}  // namespace cudf::io::detail::orc
diff --git a/cpp/src/io/orc/stats_enc.cu b/cpp/src/io/orc/stats_enc.cu
new file mode 100644
index 0000000..95f1db5
--- /dev/null
+++ b/cpp/src/io/orc/stats_enc.cu
@@ -0,0 +1,484 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc_gpu.hpp"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <cudf/io/orc_types.hpp>
+#include <cudf/strings/detail/convert/fixed_point_to_string.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::io::orc::gpu {
+
+using strings::detail::fixed_point_string_size;
+
+constexpr unsigned int init_threads_per_group = 32;
+constexpr unsigned int init_groups_per_block  = 4;
+constexpr unsigned int init_threads_per_block = init_threads_per_group * init_groups_per_block;
+
+__global__ void __launch_bounds__(init_threads_per_block)
+  gpu_init_statistics_groups(statistics_group* groups,
+                             stats_column_desc const* cols,
+                             device_2dspan<rowgroup_rows const> rowgroup_bounds)
+{
+  __shared__ __align__(4) statistics_group group_g[init_groups_per_block];
+  auto const col_id        = blockIdx.y;
+  auto const chunk_id      = (blockIdx.x * init_groups_per_block) + threadIdx.y;
+  auto const t             = threadIdx.x;
+  auto const num_rowgroups = rowgroup_bounds.size().first;
+  statistics_group* group  = &group_g[threadIdx.y];
+  if (chunk_id < num_rowgroups and t == 0) {
+    group->col                                = &cols[col_id];
+    group->start_row                          = rowgroup_bounds[chunk_id][col_id].begin;
+    group->num_rows                           = rowgroup_bounds[chunk_id][col_id].size();
+    groups[col_id * num_rowgroups + chunk_id] = *group;
+  }
+}
+
+/**
+ * @brief Get the buffer size and offsets of encoded statistics
+ *
+ * @param[in,out] groups Statistics merge groups
+ * @param[in] statistics_count Number of statistics buffers
+ */
+constexpr unsigned int buffersize_reduction_dim = 32;
+constexpr unsigned int block_size        = buffersize_reduction_dim * buffersize_reduction_dim;
+constexpr unsigned int pb_fld_hdrlen     = 1;
+constexpr unsigned int pb_fld_hdrlen32   = 5;
+constexpr unsigned int pb_fldlen_int32   = 5;
+constexpr unsigned int pb_fldlen_int64   = 10;
+constexpr unsigned int pb_fldlen_float64 = 8;
+constexpr unsigned int pb_fldlen_bucket1 = 1 + pb_fldlen_int64;
+// statistics field number + number of values + has null
+constexpr unsigned int pb_fldlen_common =
+  pb_fld_hdrlen + (pb_fld_hdrlen + pb_fldlen_int64) + 2 * pb_fld_hdrlen;
+
+template <unsigned int block_size>
+__global__ void __launch_bounds__(block_size, 1)
+  gpu_init_statistics_buffersize(statistics_merge_group* groups,
+                                 statistics_chunk const* chunks,
+                                 uint32_t statistics_count)
+{
+  using block_scan = cub::BlockScan<uint32_t, block_size, cub::BLOCK_SCAN_WARP_SCANS>;
+  __shared__ typename block_scan::TempStorage temp_storage;
+  volatile uint32_t stats_size = 0;
+  auto t                       = threadIdx.x;
+  __syncthreads();
+  for (thread_index_type start = 0; start < statistics_count; start += block_size) {
+    uint32_t stats_len = 0, stats_pos;
+    auto idx           = start + t;
+    if (idx < statistics_count) {
+      statistics_dtype const dtype = groups[idx].stats_dtype;
+      switch (dtype) {
+        case dtype_bool: stats_len = pb_fldlen_common + pb_fld_hdrlen + pb_fldlen_bucket1; break;
+        case dtype_int8:
+        case dtype_int16:
+        case dtype_int32:
+        case dtype_int64:
+          stats_len = pb_fldlen_common + pb_fld_hdrlen + 3 * (pb_fld_hdrlen + pb_fldlen_int64);
+          break;
+        case dtype_date32:
+          stats_len = pb_fldlen_common + pb_fld_hdrlen + 2 * (pb_fld_hdrlen + pb_fldlen_int64);
+          break;
+        case dtype_timestamp64:
+          stats_len = pb_fldlen_common + pb_fld_hdrlen + 4 * (pb_fld_hdrlen + pb_fldlen_int64) +
+                      2 * (pb_fld_hdrlen + pb_fldlen_int32);
+          break;
+        case dtype_float32:
+        case dtype_float64:
+          stats_len = pb_fldlen_common + pb_fld_hdrlen + 3 * (pb_fld_hdrlen + pb_fldlen_float64);
+          break;
+        case dtype_decimal64:
+        case dtype_decimal128: {
+          auto const scale    = groups[idx].col_dtype.scale();
+          auto const min_size = fixed_point_string_size(chunks[idx].min_value.d128_val, scale);
+          auto const max_size = fixed_point_string_size(chunks[idx].max_value.d128_val, scale);
+          auto const sum_size = fixed_point_string_size(chunks[idx].sum.d128_val, scale);
+          // common + total field length + encoded string lengths + strings
+          stats_len = pb_fldlen_common + pb_fld_hdrlen32 + 3 * (pb_fld_hdrlen + pb_fld_hdrlen32) +
+                      min_size + max_size + sum_size;
+        } break;
+        case dtype_string:
+          stats_len = pb_fldlen_common + pb_fld_hdrlen32 + 3 * (pb_fld_hdrlen + pb_fld_hdrlen32) +
+                      chunks[idx].min_value.str_val.length + chunks[idx].max_value.str_val.length;
+          break;
+        case dtype_none: stats_len = pb_fldlen_common;
+        default: break;
+      }
+    }
+    uint32_t tmp_stats_size;
+    block_scan(temp_storage).ExclusiveSum(stats_len, stats_pos, tmp_stats_size);
+    stats_pos += stats_size;
+    stats_size += tmp_stats_size;
+    if (idx < statistics_count) {
+      groups[idx].start_chunk = stats_pos;
+      groups[idx].num_chunks  = stats_len;
+    }
+    __syncthreads();
+  }
+}
+
+struct stats_state_s {
+  uint8_t* base{};  ///< Output buffer start
+  uint8_t* end{};   ///< Output buffer end
+  statistics_chunk chunk{};
+  statistics_merge_group group{};
+  statistics_dtype stats_dtype{};  //!< Statistics data type for this column
+};
+
+/*
+ * Protobuf encoding - see
+ * https://developers.google.com/protocol-buffers/docs/encoding
+ */
+// Protobuf varint encoding for unsigned int
+__device__ inline uint8_t* pb_encode_uint(uint8_t* p, uint64_t v)
+{
+  while (v > 0x7f) {
+    *p++ = ((uint32_t)v | 0x80);
+    v >>= 7;
+  }
+  *p++ = v;
+  return p;
+}
+
+// Protobuf field encoding for unsigned int
+__device__ inline uint8_t* pb_put_uint(uint8_t* p, uint32_t id, uint64_t v)
+{
+  p[0] = id * 8 + static_cast<ProtofType>(ProtofType::VARINT);  // NOTE: Assumes id < 16
+  return pb_encode_uint(p + 1, v);
+}
+
+// Protobuf field encoding for signed int
+__device__ inline uint8_t* pb_put_int(uint8_t* p, uint32_t id, int64_t v)
+{
+  int64_t s = (v < 0);
+  return pb_put_uint(p, id, (v ^ -s) * 2 + s);
+}
+
+// Protobuf field encoding for 'packed' unsigned int (single value)
+__device__ inline uint8_t* pb_put_packed_uint(uint8_t* p, uint32_t id, uint64_t v)
+{
+  uint8_t* p2 = pb_encode_uint(p + 2, v);
+  p[0]        = id * 8 + ProtofType::FIXEDLEN;
+  p[1]        = static_cast<uint8_t>(p2 - (p + 2));
+  return p2;
+}
+
+// Protobuf field encoding for binary/string
+__device__ inline uint8_t* pb_put_binary(uint8_t* p, uint32_t id, void const* bytes, uint32_t len)
+{
+  p[0] = id * 8 + ProtofType::FIXEDLEN;
+  p    = pb_encode_uint(p + 1, len);
+  memcpy(p, bytes, len);
+  return p + len;
+}
+
+__device__ inline uint8_t* pb_put_decimal(
+  uint8_t* p, uint32_t id, __int128_t value, int32_t scale, int32_t len)
+{
+  p[0] = id * 8 + ProtofType::FIXEDLEN;
+  p    = pb_encode_uint(p + 1, len);
+  strings::detail::fixed_point_to_string(value, scale, reinterpret_cast<char*>(p));
+  return p + len;
+}
+
+// Protobuf field encoding for 64-bit raw encoding (double)
+__device__ inline uint8_t* pb_put_fixed64(uint8_t* p, uint32_t id, void const* raw64)
+{
+  p[0] = id * 8 + ProtofType::FIXED64;
+  memcpy(p + 1, raw64, 8);
+  return p + 9;
+}
+
+// Splits a nanosecond timestamp into milliseconds and nanoseconds
+__device__ std::pair<int64_t, int32_t> split_nanosecond_timestamp(int64_t nano_count)
+{
+  auto const ns           = cuda::std::chrono::nanoseconds(nano_count);
+  auto const ms_floor     = cuda::std::chrono::floor<cuda::std::chrono::milliseconds>(ns);
+  auto const ns_remainder = ns - ms_floor;
+  return {ms_floor.count(), ns_remainder.count()};
+}
+
+/**
+ * @brief Encode statistics in ORC protobuf format
+ *
+ * @param[in,out] groups Statistics merge groups
+ * @param[in,out] chunks Statistics data
+ * @param[in] statistics_count Number of statistics buffers
+ *
+ * ORC statistics format from https://orc.apache.org/specification/ORCv1/
+ *
+ * message ColumnStatistics {
+ *  // the number of values
+ *  optional uint64 numberOfValues = 1;
+ *  // At most one of these has a value for any column
+ *  optional IntegerStatistics intStatistics = 2;
+ *  optional DoubleStatistics doubleStatistics = 3;
+ *  optional StringStatistics stringStatistics = 4;
+ *  optional BucketStatistics bucketStatistics = 5;
+ *  optional DecimalStatistics decimalStatistics = 6;
+ *  optional DateStatistics dateStatistics = 7;
+ *  optional BinaryStatistics binaryStatistics = 8;
+ *  optional TimestampStatistics timestampStatistics = 9;
+ *  optional bool hasNull = 10;
+ * }
+ */
+constexpr unsigned int encode_threads_per_chunk = 32;
+constexpr unsigned int encode_chunks_per_block  = 4;
+constexpr unsigned int encode_threads_per_block =
+  encode_threads_per_chunk * encode_chunks_per_block;
+
+__global__ void __launch_bounds__(encode_threads_per_block)
+  gpu_encode_statistics(uint8_t* blob_bfr,
+                        statistics_merge_group* groups,
+                        statistics_chunk const* chunks,
+                        uint32_t statistics_count)
+{
+  __shared__ __align__(8) stats_state_s state_g[encode_chunks_per_block];
+  auto t                 = threadIdx.x;
+  auto idx               = blockIdx.x * encode_chunks_per_block + threadIdx.y;
+  stats_state_s* const s = &state_g[threadIdx.y];
+
+  // Encode and update actual bfr size
+  if (idx < statistics_count && t == 0) {
+    s->chunk       = chunks[idx];
+    s->group       = groups[idx];
+    s->stats_dtype = s->group.stats_dtype;
+    s->base        = blob_bfr + s->group.start_chunk;
+    s->end         = blob_bfr + s->group.start_chunk + s->group.num_chunks;
+    uint8_t* cur   = pb_put_uint(s->base, 1, s->chunk.non_nulls);
+    cur            = pb_put_uint(cur, 10, s->chunk.null_count != 0);  // hasNull (bool)
+
+    uint8_t* fld_start = cur;
+    switch (s->stats_dtype) {
+      case dtype_int8:
+      case dtype_int16:
+      case dtype_int32:
+      case dtype_int64:
+        // intStatistics = 2
+        // message IntegerStatistics {
+        //  optional sint64 minimum = 1;
+        //  optional sint64 maximum = 2;
+        //  optional sint64 sum = 3;
+        // }
+        if (s->chunk.has_minmax || s->chunk.has_sum) {
+          *cur = 2 * 8 + ProtofType::FIXEDLEN;
+          cur += 2;
+          if (s->chunk.has_minmax) {
+            cur = pb_put_int(cur, 1, s->chunk.min_value.i_val);
+            cur = pb_put_int(cur, 2, s->chunk.max_value.i_val);
+          }
+          if (s->chunk.has_sum) { cur = pb_put_int(cur, 3, s->chunk.sum.i_val); }
+          fld_start[1] = cur - (fld_start + 2);
+        }
+        break;
+      case dtype_float32:
+      case dtype_float64:
+        // doubleStatistics = 3
+        // message DoubleStatistics {
+        //  optional double minimum = 1;
+        //  optional double maximum = 2;
+        //  optional double sum = 3;
+        // }
+        if (s->chunk.has_minmax || s->chunk.has_sum) {
+          *cur = 3 * 8 + ProtofType::FIXEDLEN;
+          cur += 2;
+          if (s->chunk.has_minmax) {
+            cur = pb_put_fixed64(cur, 1, &s->chunk.min_value.fp_val);
+            cur = pb_put_fixed64(cur, 2, &s->chunk.max_value.fp_val);
+          }
+          if (s->chunk.has_sum) { cur = pb_put_fixed64(cur, 3, &s->chunk.sum.fp_val); }
+          fld_start[1] = cur - (fld_start + 2);
+        }
+        break;
+      case dtype_string:
+        // stringStatistics = 4
+        // message StringStatistics {
+        //  optional string minimum = 1;
+        //  optional string maximum = 2;
+        //  optional sint64 sum = 3; // sum will store the total length of all strings
+        // }
+        if (s->chunk.has_minmax || s->chunk.has_sum) {
+          uint32_t sz = 0;
+          if (s->chunk.has_minmax) {
+            sz += (pb_put_uint(cur, 1, s->chunk.min_value.str_val.length) - cur) +
+                  (pb_put_uint(cur, 2, s->chunk.max_value.str_val.length) - cur) +
+                  s->chunk.min_value.str_val.length + s->chunk.max_value.str_val.length;
+          }
+          if (s->chunk.has_sum) { sz += pb_put_int(cur, 3, s->chunk.sum.i_val) - cur; }
+
+          cur[0] = 4 * 8 + ProtofType::FIXEDLEN;
+          cur    = pb_encode_uint(cur + 1, sz);
+
+          if (s->chunk.has_minmax) {
+            cur = pb_put_binary(
+              cur, 1, s->chunk.min_value.str_val.ptr, s->chunk.min_value.str_val.length);
+            cur = pb_put_binary(
+              cur, 2, s->chunk.max_value.str_val.ptr, s->chunk.max_value.str_val.length);
+          }
+          if (s->chunk.has_sum) { cur = pb_put_int(cur, 3, s->chunk.sum.i_val); }
+        }
+        break;
+      case dtype_bool:
+        // bucketStatistics = 5
+        // message BucketStatistics {
+        //  repeated uint64 count = 1 [packed=true];
+        // }
+        if (s->chunk.has_sum) {
+          cur[0] = 5 * 8 + ProtofType::FIXEDLEN;
+          // count is equal to the number of 'true' values, despite what specs say
+          cur          = pb_put_packed_uint(cur + 2, 1, s->chunk.sum.u_val);
+          fld_start[1] = cur - (fld_start + 2);
+        }
+        break;
+      case dtype_decimal64:
+      case dtype_decimal128:
+        // decimalStatistics = 6
+        // message DecimalStatistics {
+        //  optional string minimum = 1;
+        //  optional string maximum = 2;
+        //  optional string sum = 3;
+        // }
+        if (s->chunk.has_minmax or s->chunk.has_sum) {
+          auto const scale = s->group.col_dtype.scale();
+
+          uint32_t sz = 0;
+          auto const min_size =
+            s->chunk.has_minmax ? fixed_point_string_size(s->chunk.min_value.d128_val, scale) : 0;
+          auto const max_size =
+            s->chunk.has_minmax ? fixed_point_string_size(s->chunk.max_value.d128_val, scale) : 0;
+          if (s->chunk.has_minmax) {
+            // encoded string lengths, plus the strings
+            sz += (pb_put_uint(cur, 1, min_size) - cur) + min_size +
+                  (pb_put_uint(cur, 1, max_size) - cur) + max_size;
+          }
+          auto const sum_size =
+            s->chunk.has_sum ? fixed_point_string_size(s->chunk.sum.d128_val, scale) : 0;
+          if (s->chunk.has_sum) { sz += (pb_put_uint(cur, 1, sum_size) - cur) + sum_size; }
+
+          cur[0] = 6 * 8 + ProtofType::FIXEDLEN;
+          cur    = pb_encode_uint(cur + 1, sz);
+
+          if (s->chunk.has_minmax) {
+            cur = pb_put_decimal(cur, 1, s->chunk.min_value.d128_val, scale, min_size);  //  minimum
+            cur = pb_put_decimal(cur, 2, s->chunk.max_value.d128_val, scale, max_size);  // maximum
+          }
+          if (s->chunk.has_sum) {
+            cur = pb_put_decimal(cur, 3, s->chunk.sum.d128_val, scale, sum_size);  // sum
+          }
+        }
+        break;
+      case dtype_date32:
+        // dateStatistics = 7
+        // message DateStatistics { // min,max values saved as days since epoch
+        //  optional sint32 minimum = 1;
+        //  optional sint32 maximum = 2;
+        // }
+        if (s->chunk.has_minmax) {
+          cur[0] = 7 * 8 + ProtofType::FIXEDLEN;
+          cur += 2;
+          cur          = pb_put_int(cur, 1, s->chunk.min_value.i_val);
+          cur          = pb_put_int(cur, 2, s->chunk.max_value.i_val);
+          fld_start[1] = cur - (fld_start + 2);
+        }
+        break;
+      case dtype_timestamp64:
+        // timestampStatistics = 9
+        // message TimestampStatistics {
+        //  optional sint64 minimum = 1; // min,max values saved as milliseconds since epoch
+        //  optional sint64 maximum = 2;
+        //  optional sint64 minimumUtc = 3; // min,max values saved as milliseconds since UNIX epoch
+        //  optional sint64 maximumUtc = 4;
+        //  optional int32 minimumNanos = 5; // lower 6 TS digits for min/max to achieve nanosecond
+        //  precision optional int32 maximumNanos = 6;
+        // }
+        if (s->chunk.has_minmax) {
+          cur[0] = 9 * 8 + ProtofType::FIXEDLEN;
+          cur += 2;
+          auto const [min_ms, min_ns_remainder] =
+            split_nanosecond_timestamp(s->chunk.min_value.i_val);
+          auto const [max_ms, max_ns_remainder] =
+            split_nanosecond_timestamp(s->chunk.max_value.i_val);
+
+          // minimum/maximum are the same as minimumUtc/maximumUtc as we always write files in UTC
+          cur          = pb_put_int(cur, 1, min_ms);            // minimum
+          cur          = pb_put_int(cur, 2, max_ms);            // maximum
+          cur          = pb_put_int(cur, 3, min_ms);            // minimumUtc
+          cur          = pb_put_int(cur, 4, max_ms);            // maximumUtc
+          cur          = pb_put_int(cur, 5, min_ns_remainder);  // minimumNanos
+          cur          = pb_put_int(cur, 6, max_ns_remainder);  // maximumNanos
+          fld_start[1] = cur - (fld_start + 2);
+        }
+        break;
+      default: break;
+    }
+    groups[idx].num_chunks = static_cast<uint32_t>(cur - s->base);
+  }
+}
+
+void orc_init_statistics_groups(statistics_group* groups,
+                                stats_column_desc const* cols,
+                                device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                                rmm::cuda_stream_view stream)
+{
+  dim3 dim_grid((rowgroup_bounds.size().first + init_groups_per_block - 1) / init_groups_per_block,
+                rowgroup_bounds.size().second);
+  dim3 dim_block(init_threads_per_group, init_groups_per_block);
+  gpu_init_statistics_groups<<<dim_grid, dim_block, 0, stream.value()>>>(
+    groups, cols, rowgroup_bounds);
+}
+
+/**
+ * @brief Launches kernels to return statistics buffer offsets and sizes
+ *
+ * @param[in,out] groups Statistics merge groups
+ * @param[in] chunks Statistics chunks
+ * @param[in] statistics_count Number of statistics buffers to encode
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void orc_init_statistics_buffersize(statistics_merge_group* groups,
+                                    statistics_chunk const* chunks,
+                                    uint32_t statistics_count,
+                                    rmm::cuda_stream_view stream)
+{
+  gpu_init_statistics_buffersize<block_size>
+    <<<1, block_size, 0, stream.value()>>>(groups, chunks, statistics_count);
+}
+
+/**
+ * @brief Launches kernel to encode statistics in ORC protobuf format
+ *
+ * @param[out] blob_bfr Output buffer for statistics blobs
+ * @param[in,out] groups Statistics merge groups
+ * @param[in,out] chunks Statistics data
+ * @param[in] statistics_count Number of statistics buffers
+ */
+void orc_encode_statistics(uint8_t* blob_bfr,
+                           statistics_merge_group* groups,
+                           statistics_chunk const* chunks,
+                           uint32_t statistics_count,
+                           rmm::cuda_stream_view stream)
+{
+  unsigned int num_blocks =
+    (statistics_count + encode_chunks_per_block - 1) / encode_chunks_per_block;
+  dim3 dim_block(encode_threads_per_chunk, encode_chunks_per_block);
+  gpu_encode_statistics<<<num_blocks, dim_block, 0, stream.value()>>>(
+    blob_bfr, groups, chunks, statistics_count);
+}
+
+}  // namespace cudf::io::orc::gpu
diff --git a/cpp/src/io/orc/stripe_data.cu b/cpp/src/io/orc/stripe_data.cu
new file mode 100644
index 0000000..3edcd3d
--- /dev/null
+++ b/cpp/src/io/orc/stripe_data.cu
@@ -0,0 +1,1902 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc_gpu.hpp"
+
+#include <cudf/io/orc_types.hpp>
+#include <io/utilities/block_utils.cuh>
+
+#include <cub/cub.cuh>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+namespace orc {
+namespace gpu {
+
+using cudf::io::detail::string_index_pair;
+
+// Must be able to handle 512x 8-byte values. These values are base 128 encoded
+// so 8 byte value is expanded to 10 bytes.
+constexpr int bytestream_buffer_size = 512 * 8 * 2;
+constexpr int bytestream_buffer_mask = (bytestream_buffer_size - 1) >> 2;
+
+// TODO: Should be more efficient with 512 threads per block and circular queue for values
+constexpr int num_warps  = 32;
+constexpr int block_size = 32 * num_warps;
+// Add some margin to look ahead to future rows in case there are many zeroes
+constexpr int row_decoder_buffer_size = block_size + 128;
+inline __device__ uint8_t is_rlev1(uint8_t encoding_mode) { return encoding_mode < DIRECT_V2; }
+
+inline __device__ uint8_t is_dictionary(uint8_t encoding_mode) { return encoding_mode & 1; }
+
+struct orc_bytestream_s {
+  uint8_t const* base;
+  uint32_t pos;
+  uint32_t len;
+  uint32_t fill_pos;
+  uint32_t fill_count;
+  union {
+    uint8_t u8[bytestream_buffer_size];
+    uint32_t u32[bytestream_buffer_size >> 2];
+    uint2 u64[bytestream_buffer_size >> 3];
+  } buf;
+};
+
+struct orc_rlev1_state_s {
+  uint32_t num_runs;
+  uint32_t num_vals;
+  int32_t run_data[num_warps * 12];  // (delta << 24) | (count << 16) | (first_val)
+};
+
+struct orc_rlev2_state_s {
+  uint32_t num_runs;
+  uint32_t num_vals;
+  union {
+    uint32_t u32[num_warps];
+    uint64_t u64[num_warps];
+  } baseval;
+  uint16_t m2_pw_byte3[num_warps];
+  int64_t delta[num_warps];
+  uint16_t runs_loc[block_size];
+};
+
+struct orc_byterle_state_s {
+  uint32_t num_runs;
+  uint32_t num_vals;
+  uint32_t runs_loc[num_warps];
+  uint32_t runs_pos[num_warps];
+};
+
+struct orc_rowdec_state_s {
+  uint32_t nz_count;
+  uint32_t row[row_decoder_buffer_size];  // 0=skip, >0: row position relative to cur_row
+};
+
+struct orc_strdict_state_s {
+  DictionaryEntry* local_dict;
+  uint32_t dict_pos;
+  uint32_t dict_len;
+};
+
+struct orc_datadec_state_s {
+  uint32_t cur_row;         // starting row of current batch
+  uint32_t end_row;         // ending row of this chunk (start_row + num_rows)
+  uint32_t max_vals;        // max # of non-zero values to decode in this batch
+  uint32_t nrows;           // # of rows in current batch (up to block_size)
+  uint32_t buffered_count;  // number of buffered values in the secondary data stream
+  duration_s tz_epoch;      // orc_ut_epoch - ut_offset
+  RowGroup index;
+};
+
+struct orcdec_state_s {
+  ColumnDesc chunk;
+  orc_bytestream_s bs;
+  orc_bytestream_s bs2;
+  int is_string;
+  uint64_t num_child_rows;
+  union {
+    orc_strdict_state_s dict;
+    uint32_t nulls_desc_row;  // number of rows processed for nulls.
+    orc_datadec_state_s data;
+  } top;
+  union {
+    orc_rlev1_state_s rlev1;
+    orc_rlev2_state_s rlev2;
+    orc_byterle_state_s rle8;
+    orc_rowdec_state_s rowdec;
+  } u;
+  union values {
+    uint8_t u8[block_size * 16];
+    uint32_t u32[block_size * 4];
+    int32_t i32[block_size * 4];
+    uint64_t u64[block_size * 2];
+    int64_t i64[block_size * 2];
+    double f64[block_size * 2];
+    __int128_t i128[block_size];
+    __uint128_t u128[block_size];
+  } vals;
+};
+
+/**
+ * @brief Initializes byte stream, modifying length and start position to keep the read pointer
+ * 8-byte aligned.
+ *
+ * Assumes that the address range [start_address & ~7, (start_address + len - 1) | 7]
+ * is valid.
+ *
+ * @param[in,out] bs Byte stream input
+ * @param[in] base Pointer to raw byte stream data
+ * @param[in] len Stream length in bytes
+ */
+static __device__ void bytestream_init(volatile orc_bytestream_s* bs,
+                                       uint8_t const* base,
+                                       uint32_t len)
+{
+  uint32_t pos   = (len > 0) ? static_cast<uint32_t>(7 & reinterpret_cast<size_t>(base)) : 0;
+  bs->base       = base - pos;
+  bs->pos        = pos;
+  bs->len        = (len + pos + 7) & ~7;
+  bs->fill_pos   = 0;
+  bs->fill_count = min(bs->len, bytestream_buffer_size) >> 3;
+}
+
+/**
+ * @brief Increment the read position, returns number of 64-bit slots to fill
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bytes_consumed Number of bytes that were consumed
+ */
+static __device__ void bytestream_flush_bytes(volatile orc_bytestream_s* bs,
+                                              uint32_t bytes_consumed)
+{
+  uint32_t pos     = bs->pos;
+  uint32_t len     = bs->len;
+  uint32_t pos_new = min(pos + bytes_consumed, len);
+  bs->pos          = pos_new;
+  pos              = min(pos + bytestream_buffer_size, len);
+  pos_new          = min(pos_new + bytestream_buffer_size, len);
+  bs->fill_pos     = pos;
+  bs->fill_count   = (pos_new >> 3) - (pos >> 3);
+}
+
+/**
+ * @brief Refill the byte stream buffer
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] t thread id
+ */
+static __device__ void bytestream_fill(orc_bytestream_s* bs, int t)
+{
+  auto const count = bs->fill_count;
+  if (t < count) {
+    auto const pos8 = (bs->fill_pos >> 3) + t;
+    memcpy(&bs->buf.u64[pos8 & ((bytestream_buffer_size >> 3) - 1)],
+           &bs->base[pos8 * sizeof(uint2)],
+           sizeof(uint2));
+  }
+}
+
+/**
+ * @brief Read a byte from the byte stream (byte aligned)
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] pos Position in byte stream
+ * @return byte
+ */
+inline __device__ uint8_t bytestream_readbyte(volatile orc_bytestream_s* bs, int pos)
+{
+  return bs->buf.u8[pos & (bytestream_buffer_size - 1)];
+}
+
+/**
+ * @brief Read 32 bits from a byte stream (little endian, byte aligned)
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] pos Position in byte stream
+ * @result bits
+ */
+inline __device__ uint32_t bytestream_readu32(volatile orc_bytestream_s* bs, int pos)
+{
+  uint32_t a = bs->buf.u32[(pos & (bytestream_buffer_size - 1)) >> 2];
+  uint32_t b = bs->buf.u32[((pos + 4) & (bytestream_buffer_size - 1)) >> 2];
+  return __funnelshift_r(a, b, (pos & 3) * 8);
+}
+
+/**
+ * @brief Read 64 bits from a byte stream (little endian, byte aligned)
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] pos Position in byte stream
+ * @param[in] numbits number of bits
+ * @return bits
+ */
+inline __device__ uint64_t bytestream_readu64(volatile orc_bytestream_s* bs, int pos)
+{
+  uint32_t a    = bs->buf.u32[(pos & (bytestream_buffer_size - 1)) >> 2];
+  uint32_t b    = bs->buf.u32[((pos + 4) & (bytestream_buffer_size - 1)) >> 2];
+  uint32_t c    = bs->buf.u32[((pos + 8) & (bytestream_buffer_size - 1)) >> 2];
+  uint32_t lo32 = __funnelshift_r(a, b, (pos & 3) * 8);
+  uint32_t hi32 = __funnelshift_r(b, c, (pos & 3) * 8);
+  uint64_t v    = hi32;
+  v <<= 32;
+  v |= lo32;
+  return v;
+}
+
+/**
+ * @brief Read up to 32-bits from a byte stream (big endian)
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bitpos Position in byte stream
+ * @param[in] numbits number of bits
+ * @return decoded value
+ */
+inline __device__ uint32_t bytestream_readbits(volatile orc_bytestream_s* bs,
+                                               int bitpos,
+                                               uint32_t numbits)
+{
+  int idx    = bitpos >> 5;
+  uint32_t a = __byte_perm(bs->buf.u32[(idx + 0) & bytestream_buffer_mask], 0, 0x0123);
+  uint32_t b = __byte_perm(bs->buf.u32[(idx + 1) & bytestream_buffer_mask], 0, 0x0123);
+  return __funnelshift_l(b, a, bitpos & 0x1f) >> (32 - numbits);
+}
+
+/**
+ * @brief Read up to 64-bits from a byte stream (big endian)
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bitpos Position in byte stream
+ * @param[in] numbits number of bits
+ * @return decoded value
+ */
+inline __device__ uint64_t bytestream_readbits64(volatile orc_bytestream_s* bs,
+                                                 int bitpos,
+                                                 uint32_t numbits)
+{
+  int idx       = bitpos >> 5;
+  uint32_t a    = __byte_perm(bs->buf.u32[(idx + 0) & bytestream_buffer_mask], 0, 0x0123);
+  uint32_t b    = __byte_perm(bs->buf.u32[(idx + 1) & bytestream_buffer_mask], 0, 0x0123);
+  uint32_t c    = __byte_perm(bs->buf.u32[(idx + 2) & bytestream_buffer_mask], 0, 0x0123);
+  uint32_t hi32 = __funnelshift_l(b, a, bitpos & 0x1f);
+  uint32_t lo32 = __funnelshift_l(c, b, bitpos & 0x1f);
+  uint64_t v    = hi32;
+  v <<= 32;
+  v |= lo32;
+  v >>= (64 - numbits);
+  return v;
+}
+
+/**
+ * @brief Decode a big-endian unsigned 32-bit value
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bitpos Position in byte stream
+ * @param[in] numbits number of bits
+ * @param[out] result decoded value
+ */
+inline __device__ void bytestream_readbe(volatile orc_bytestream_s* bs,
+                                         int bitpos,
+                                         uint32_t numbits,
+                                         uint32_t& result)
+{
+  result = bytestream_readbits(bs, bitpos, numbits);
+}
+
+/**
+ * @brief Decode a big-endian signed 32-bit value
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bitpos Position in byte stream
+ * @param[in] numbits number of bits
+ * @param[out] result decoded value
+ */
+inline __device__ void bytestream_readbe(volatile orc_bytestream_s* bs,
+                                         int bitpos,
+                                         uint32_t numbits,
+                                         int32_t& result)
+{
+  uint32_t u = bytestream_readbits(bs, bitpos, numbits);
+  result     = (int32_t)((u >> 1u) ^ -(int32_t)(u & 1));
+}
+
+/**
+ * @brief Decode a big-endian unsigned 64-bit value
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bitpos Position in byte stream
+ * @param[in] numbits number of bits
+ * @param[out] result decoded value
+ */
+inline __device__ void bytestream_readbe(volatile orc_bytestream_s* bs,
+                                         int bitpos,
+                                         uint32_t numbits,
+                                         uint64_t& result)
+{
+  result = bytestream_readbits64(bs, bitpos, numbits);
+}
+
+/**
+ * @brief Decode a big-endian signed 64-bit value
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] bitpos Position in byte stream
+ * @param[in] numbits number of bits
+ * @param[out] result decoded value
+ */
+inline __device__ void bytestream_readbe(volatile orc_bytestream_s* bs,
+                                         int bitpos,
+                                         uint32_t numbits,
+                                         int64_t& result)
+{
+  uint64_t u = bytestream_readbits64(bs, bitpos, numbits);
+  result     = (int64_t)((u >> 1u) ^ -(int64_t)(u & 1));
+}
+
+/**
+ * @brief Return the length of a base-128 varint
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] pos Position in circular byte stream buffer
+ * @return length of varint in bytes
+ */
+template <class T>
+inline __device__ uint32_t varint_length(volatile orc_bytestream_s* bs, int pos)
+{
+  if (bytestream_readbyte(bs, pos) > 0x7f) {
+    uint32_t next32 = bytestream_readu32(bs, pos + 1);
+    uint32_t zbit   = __ffs((~next32) & 0x8080'8080);
+    if (sizeof(T) <= 4 || zbit) {
+      return 1 + (zbit >> 3);  // up to 5x7 bits
+    } else {
+      next32 = bytestream_readu32(bs, pos + 5);
+      zbit   = __ffs((~next32) & 0x8080'8080);
+      if (zbit) {
+        return 5 + (zbit >> 3);  // up to 9x7 bits
+      } else if ((sizeof(T) <= 8) || (bytestream_readbyte(bs, pos + 9) <= 0x7f)) {
+        return 10;  // up to 70 bits
+      } else {
+        uint64_t next64 = bytestream_readu64(bs, pos + 10);
+        zbit            = __ffsll((~next64) & 0x8080'8080'8080'8080ull);
+        if (zbit) {
+          return 10 + (zbit >> 3);  // Up to 18x7 bits (126)
+        } else {
+          return 19;  // Up to 19x7 bits (133)
+        }
+      }
+    }
+  } else {
+    return 1;
+  }
+}
+
+/**
+ * @brief Decodes a base-128 varint
+ *
+ * @param[in] bs Byte stream input
+ * @param[in] pos Position in circular byte stream buffer
+ * @param[in] result Unpacked value
+ * @return new position in byte stream buffer
+ */
+template <class T>
+inline __device__ int decode_base128_varint(volatile orc_bytestream_s* bs, int pos, T& result)
+{
+  uint32_t v = bytestream_readbyte(bs, pos++);
+  if (v > 0x7f) {
+    uint32_t b = bytestream_readbyte(bs, pos++);
+    v          = (v & 0x7f) | (b << 7);
+    if (b > 0x7f) {
+      b = bytestream_readbyte(bs, pos++);
+      v = (v & 0x3fff) | (b << 14);
+      if (b > 0x7f) {
+        b = bytestream_readbyte(bs, pos++);
+        v = (v & 0x1f'ffff) | (b << 21);
+        if (b > 0x7f) {
+          b = bytestream_readbyte(bs, pos++);
+          v = (v & 0x0fff'ffff) | (b << 28);
+          if constexpr (sizeof(T) > 4) {
+            uint32_t lo = v;
+            uint64_t hi;
+            v = b >> 4;
+            if (b > 0x7f) {
+              b = bytestream_readbyte(bs, pos++);
+              v = (v & 7) | (b << 3);
+              if (b > 0x7f) {
+                b = bytestream_readbyte(bs, pos++);
+                v = (v & 0x3ff) | (b << 10);
+                if (b > 0x7f) {
+                  b = bytestream_readbyte(bs, pos++);
+                  v = (v & 0x1'ffff) | (b << 17);
+                  if (b > 0x7f) {
+                    b = bytestream_readbyte(bs, pos++);
+                    v = (v & 0xff'ffff) | (b << 24);
+                    if (b > 0x7f) {
+                      pos++;  // last bit is redundant (extra byte implies bit63 is 1)
+                    }
+                  }
+                }
+              }
+            }
+            hi = v;
+            hi <<= 32;
+            result = hi | lo;
+            return pos;
+          }
+        }
+      }
+    }
+  }
+  result = v;
+  return pos;
+}
+
+/**
+ * @brief Decodes a signed int128 encoded as base-128 varint (used for decimals)
+ */
+inline __device__ __int128_t decode_varint128(volatile orc_bytestream_s* bs, int pos)
+{
+  auto byte                  = bytestream_readbyte(bs, pos++);
+  __int128_t const sign_mask = -(int32_t)(byte & 1);
+  __int128_t value           = (byte >> 1) & 0x3f;
+  uint32_t bitpos            = 6;
+  while (byte & 0x80 && bitpos < 128) {
+    byte = bytestream_readbyte(bs, pos++);
+    value |= ((__uint128_t)(byte & 0x7f)) << bitpos;
+    bitpos += 7;
+  }
+  return value ^ sign_mask;
+}
+
+/**
+ * @brief Decodes an unsigned 32-bit varint
+ */
+inline __device__ int decode_varint(volatile orc_bytestream_s* bs, int pos, uint32_t& result)
+{
+  uint32_t u;
+  pos    = decode_base128_varint<uint32_t>(bs, pos, u);
+  result = u;
+  return pos;
+}
+
+/**
+ * @brief Decodes an unsigned 64-bit varint
+ */
+inline __device__ int decode_varint(volatile orc_bytestream_s* bs, int pos, uint64_t& result)
+{
+  uint64_t u;
+  pos    = decode_base128_varint<uint64_t>(bs, pos, u);
+  result = u;
+  return pos;
+}
+
+/**
+ * @brief Signed version of 32-bit decode_varint
+ */
+inline __device__ int decode_varint(volatile orc_bytestream_s* bs, int pos, int32_t& result)
+{
+  uint32_t u;
+  pos    = decode_base128_varint<uint32_t>(bs, pos, u);
+  result = (int32_t)((u >> 1u) ^ -(int32_t)(u & 1));
+  return pos;
+}
+
+/**
+ * @brief Signed version of 64-bit decode_varint
+ */
+inline __device__ int decode_varint(volatile orc_bytestream_s* bs, int pos, int64_t& result)
+{
+  uint64_t u;
+  pos    = decode_base128_varint<uint64_t>(bs, pos, u);
+  result = (int64_t)((u >> 1u) ^ -(int64_t)(u & 1));
+  return pos;
+}
+
+/**
+ * @brief In-place conversion from lengths to positions
+ *
+ * @param[in] vals input values
+ * @param[in] numvals number of values
+ * @param[in] t thread id
+ *
+ * @return number of values decoded
+ */
+template <class T>
+inline __device__ void lengths_to_positions(volatile T* vals, uint32_t numvals, unsigned int t)
+{
+  for (uint32_t n = 1; n < numvals; n <<= 1) {
+    __syncthreads();
+    if ((t & n) && (t < numvals)) vals[t] += vals[(t & ~n) | (n - 1)];
+  }
+}
+
+/**
+ * @brief ORC Integer RLEv1 decoding
+ *
+ * @param[in] bs input byte stream
+ * @param[in] rle RLE state
+ * @param[in] vals buffer for output values (uint32_t, int32_t, uint64_t or int64_t)
+ * @param[in] maxvals maximum number of values to decode
+ * @param[in] t thread id
+ *
+ * @return number of values decoded
+ */
+template <class T>
+static __device__ uint32_t Integer_RLEv1(
+  orc_bytestream_s* bs, volatile orc_rlev1_state_s* rle, volatile T* vals, uint32_t maxvals, int t)
+{
+  uint32_t numvals, numruns;
+  if (t == 0) {
+    uint32_t maxpos  = min(bs->len, bs->pos + (bytestream_buffer_size - 8u));
+    uint32_t lastpos = bs->pos;
+    numvals = numruns = 0;
+    // Find the length and start location of each run
+    while (numvals < maxvals && numruns < num_warps * 12) {
+      uint32_t pos = lastpos;
+      uint32_t n   = bytestream_readbyte(bs, pos++);
+      if (n <= 0x7f) {
+        // Run
+        int32_t delta;
+        n = n + 3;
+        if (numvals + n > maxvals) break;
+        delta         = bytestream_readbyte(bs, pos++);
+        vals[numvals] = pos & 0xffff;
+        pos += varint_length<T>(bs, pos);
+        if (pos > maxpos) break;
+        rle->run_data[numruns++] = (delta << 24) | (n << 16) | numvals;
+        numvals += n;
+      } else {
+        // Literals
+        uint32_t i;
+        n = 0x100 - n;
+        if (numvals + n > maxvals) break;
+        i = 0;
+        do {
+          vals[numvals + i] = pos & 0xffff;
+          pos += varint_length<T>(bs, pos);
+        } while (++i < n);
+        if (pos > maxpos) break;
+        numvals += n;
+      }
+      lastpos = pos;
+    }
+    rle->num_runs = numruns;
+    rle->num_vals = numvals;
+    bytestream_flush_bytes(bs, lastpos - bs->pos);
+  }
+  __syncthreads();
+  // Expand the runs
+  numruns = rle->num_runs;
+  if (numruns > 0) {
+    int r  = t >> 5;
+    int tr = t & 0x1f;
+    for (uint32_t run = r; run < numruns; run += num_warps) {
+      int32_t run_data = rle->run_data[run];
+      int n            = (run_data >> 16) & 0xff;
+      int delta        = run_data >> 24;
+      uint32_t base    = run_data & 0x3ff;
+      uint32_t pos     = vals[base] & 0xffff;
+      for (int i = 1 + tr; i < n; i += 32) {
+        vals[base + i] = ((delta * i) << 16) | pos;
+      }
+    }
+    __syncthreads();
+  }
+  numvals = rle->num_vals;
+  // Decode individual 32-bit varints
+  if (t < numvals) {
+    int32_t pos   = vals[t];
+    int32_t delta = pos >> 16;
+    T v;
+    decode_varint(bs, pos, v);
+    vals[t] = v + delta;
+  }
+  __syncthreads();
+  return numvals;
+}
+
+/**
+ * @brief Maps the RLEv2 5-bit length code to 6-bit length
+ */
+static const __device__ __constant__ uint8_t kRLEv2_W[32] = {
+  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
+  17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 30, 32, 40, 48, 56, 64};
+
+/**
+ * @brief Maps the RLEv2 patch size (pw + pgw) to number of bits
+ *
+ * Patch size (in bits) is only allowed to be from the below set. If `pw + pgw == 34` then the size
+ * of the patch in the file is the smallest size in the set that can fit 34 bits i.e.
+ * `ClosestFixedBitsMap[34] == 40`
+ *
+ * @see https://github.com/apache/orc/commit/9faf7f5147a7bc69
+ */
+static const __device__ __constant__ uint8_t ClosestFixedBitsMap[65] = {
+  1,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
+  22, 23, 24, 26, 26, 28, 28, 30, 30, 32, 32, 40, 40, 40, 40, 40, 40, 40, 40, 48, 48, 48,
+  48, 48, 48, 48, 48, 56, 56, 56, 56, 56, 56, 56, 56, 64, 64, 64, 64, 64, 64, 64, 64};
+
+/**
+ * @brief ORC Integer RLEv2 decoding
+ *
+ * @param[in] bs input byte stream
+ * @param[in] rle RLE state
+ * @param[in] vals buffer for output values (uint32_t, int32_t, uint64_t or int64_t)
+ * @param[in] maxvals maximum number of values to decode
+ * @param[in] t thread id
+ * @param[in] has_buffered_values If true, means there are already buffered values
+ *
+ * @return number of values decoded
+ */
+template <class T>
+static __device__ uint32_t Integer_RLEv2(orc_bytestream_s* bs,
+                                         volatile orc_rlev2_state_s* rle,
+                                         volatile T* vals,
+                                         uint32_t maxvals,
+                                         int t,
+                                         bool has_buffered_values = false)
+{
+  if (t == 0) {
+    uint32_t maxpos  = min(bs->len, bs->pos + (bytestream_buffer_size - 8u));
+    uint32_t lastpos = bs->pos;
+    auto numvals     = 0;
+    auto numruns     = 0;
+    // Find the length and start location of each run
+    while (numvals < maxvals) {
+      uint32_t pos   = lastpos;
+      uint32_t byte0 = bytestream_readbyte(bs, pos++);
+      uint32_t n, l;
+      int mode               = byte0 >> 6;
+      rle->runs_loc[numruns] = numvals;
+      vals[numvals]          = lastpos;
+      if (mode == 0) {
+        // 00lllnnn: short repeat encoding
+        l = 1 + ((byte0 >> 3) & 7);  // 1 to 8 bytes
+        n = 3 + (byte0 & 7);         // 3 to 10 values
+      } else {
+        l = kRLEv2_W[(byte0 >> 1) & 0x1f];
+        n = 1 + ((byte0 & 1) << 8) + bytestream_readbyte(bs, pos++);
+        if (mode == 1) {
+          // 01wwwwwn.nnnnnnnn: direct encoding
+          l = (l * n + 7) >> 3;
+        } else if (mode == 2) {
+          // 10wwwwwn.nnnnnnnn.xxxxxxxx.yyyyyyyy: patched base encoding
+          uint32_t byte2      = bytestream_readbyte(bs, pos++);
+          uint32_t byte3      = bytestream_readbyte(bs, pos++);
+          uint32_t bw         = 1 + (byte2 >> 5);        // base value width, 1 to 8 bytes
+          uint32_t pw         = kRLEv2_W[byte2 & 0x1f];  // patch width, 1 to 64 bits
+          uint32_t pgw        = 1 + (byte3 >> 5);        // patch gap width, 1 to 8 bits
+          uint32_t pgw_pw_len = ClosestFixedBitsMap[min(pw + pgw, 64u)];  // ceiled patch width
+          uint32_t pll        = byte3 & 0x1f;                             // patch list length
+          l                   = (l * n + 7) >> 3;
+          l += bw;
+          l += (pll * (pgw_pw_len) + 7) >> 3;
+        } else {
+          // 11wwwwwn.nnnnnnnn.<base>.<delta>: delta encoding
+          uint32_t deltapos = varint_length<T>(bs, pos);
+          deltapos += varint_length<T>(bs, pos + deltapos);
+          l = (l > 1 && n > 2) ? (l * (n - 2) + 7) >> 3 : 0;
+          l += deltapos;
+        }
+      }
+      if ((numvals != 0) and (numvals + n > maxvals)) break;
+      // case where there are buffered values and can't consume a whole chunk
+      // from decoded values, so skip adding any more to buffer, work on buffered values and then
+      // start fresh in next iteration with empty buffer.
+      if ((numvals == 0) and (n > maxvals) and (has_buffered_values)) break;
+
+      pos += l;
+      if (pos > maxpos) break;
+      ((numvals == 0) and (n > maxvals)) ? numvals = maxvals : numvals += n;
+      lastpos = pos;
+      numruns++;
+    }
+    rle->num_vals = numvals;
+    rle->num_runs = numruns;
+    bytestream_flush_bytes(bs, lastpos - bs->pos);
+  }
+  __syncthreads();
+  // Process the runs, 1 warp per run
+  auto const numruns = rle->num_runs;
+  auto const r       = t >> 5;
+  auto const tr      = t & 0x1f;
+  for (uint32_t run = r; run < numruns; run += num_warps) {
+    uint32_t base, pos, w, n;
+    int mode;
+    if (tr == 0) {
+      uint32_t byte0;
+      base  = rle->runs_loc[run];
+      pos   = vals[base];
+      byte0 = bytestream_readbyte(bs, pos++);
+      mode  = byte0 >> 6;
+      if (mode == 0) {
+        T baseval;
+        // 00lllnnn: short repeat encoding
+        w = 8 + (byte0 & 0x38);  // 8 to 64 bits
+        n = 3 + (byte0 & 7);     // 3 to 10 values
+        bytestream_readbe(bs, pos * 8, w, baseval);
+        if constexpr (sizeof(T) <= 4) {
+          rle->baseval.u32[r] = baseval;
+        } else {
+          rle->baseval.u64[r] = baseval;
+        }
+      } else {
+        w = kRLEv2_W[(byte0 >> 1) & 0x1f];
+        n = 1 + ((byte0 & 1) << 8) + bytestream_readbyte(bs, pos++);
+        if (mode > 1) {
+          if (mode == 2) {
+            // Patched base
+            uint32_t byte2 = bytestream_readbyte(bs, pos++);
+            uint32_t byte3 = bytestream_readbyte(bs, pos++);
+            uint32_t bw    = 1 + (byte2 >> 5);        // base value width, 1 to 8 bytes
+            uint32_t pw    = kRLEv2_W[byte2 & 0x1f];  // patch width, 1 to 64 bits
+            if constexpr (sizeof(T) <= 4) {
+              uint32_t baseval;
+              bytestream_readbe(bs, pos * 8, bw * 8, baseval);
+              uint32_t const mask = (1u << (bw * 8 - 1)) - 1;
+              // Negative values are represented with the highest bit set to 1
+              rle->baseval.u32[r] = (std::is_signed_v<T> and baseval > mask)
+                                      ? -static_cast<int32_t>(baseval & mask)
+                                      : baseval;
+            } else {
+              uint64_t baseval;
+              bytestream_readbe(bs, pos * 8, bw * 8, baseval);
+              uint64_t const mask = (1ul << (bw * 8 - 1)) - 1;
+              // Negative values are represented with the highest bit set to 1
+              rle->baseval.u64[r] = (std::is_signed_v<T> and baseval > mask)
+                                      ? -static_cast<int64_t>(baseval & mask)
+                                      : baseval;
+            }
+            rle->m2_pw_byte3[r] = (pw << 8) | byte3;
+            pos += bw;
+          } else {
+            T baseval;
+            int64_t delta;
+            // Delta
+            pos = decode_varint(bs, pos, baseval);
+            if constexpr (sizeof(T) <= 4) {
+              rle->baseval.u32[r] = baseval;
+            } else {
+              rle->baseval.u64[r] = baseval;
+            }
+            pos           = decode_varint(bs, pos, delta);
+            rle->delta[r] = delta;
+          }
+        }
+      }
+    }
+    base = shuffle(base);
+    mode = shuffle(mode);
+    pos  = shuffle(pos);
+    n    = shuffle(n);
+    w    = shuffle(w);
+    __syncwarp();  // Not required, included to fix the racecheck warning
+    for (uint32_t i = tr; i < n; i += 32) {
+      if constexpr (sizeof(T) <= 4) {
+        if (mode == 0) {
+          vals[base + i] = rle->baseval.u32[r];
+        } else if (mode == 1) {
+          T v;
+          bytestream_readbe(bs, pos * 8 + i * w, w, v);
+          vals[base + i] = v;
+        } else if (mode == 2) {
+          uint32_t ofs   = bytestream_readbits(bs, pos * 8 + i * w, w);
+          vals[base + i] = rle->baseval.u32[r] + ofs;
+        } else {
+          int64_t delta = rle->delta[r];
+          if (w > 1 && i > 1) {
+            int32_t delta_s = (delta < 0) ? -1 : 0;
+            vals[base + i] =
+              (bytestream_readbits(bs, pos * 8 + (i - 2) * w, w) ^ delta_s) - delta_s;
+          } else {
+            vals[base + i] = (i == 0) ? 0 : static_cast<uint32_t>(delta);
+          }
+        }
+      } else {
+        if (mode == 0) {
+          vals[base + i] = rle->baseval.u64[r];
+        } else if (mode == 1) {
+          T v;
+          bytestream_readbe(bs, pos * 8 + i * w, w, v);
+          vals[base + i] = v;
+        } else if (mode == 2) {
+          uint64_t ofs   = bytestream_readbits64(bs, pos * 8 + i * w, w);
+          vals[base + i] = rle->baseval.u64[r] + ofs;
+        } else {
+          int64_t delta = rle->delta[r], ofs;
+          if (w > 1 && i > 1) {
+            int64_t delta_s = (delta < 0) ? -1 : 0;
+            ofs = (bytestream_readbits64(bs, pos * 8 + (i - 2) * w, w) ^ delta_s) - delta_s;
+          } else {
+            ofs = (i == 0) ? 0 : delta;
+          }
+          vals[base + i] = ofs;
+        }
+      }
+    }
+    __syncwarp();
+    // Patch values
+    if (mode == 2) {
+      uint32_t pw_byte3 = rle->m2_pw_byte3[r];
+      uint32_t pw       = pw_byte3 >> 8;
+      uint32_t pgw      = 1 + ((pw_byte3 >> 5) & 7);  // patch gap width, 1 to 8 bits
+      uint32_t pll      = pw_byte3 & 0x1f;            // patch list length
+      if (pll != 0) {
+        uint32_t pgw_pw_len = ClosestFixedBitsMap[min(pw + pgw, 64u)];
+        uint64_t patch_pos64 =
+          (tr < pll) ? bytestream_readbits64(
+                         bs, pos * 8 + ((n * w + 7) & ~7) + tr * (pgw_pw_len), pgw_pw_len)
+                     : 0;
+        uint32_t patch_pos;
+        T patch = 1;
+        patch <<= pw;
+        patch = (patch - 1) & (T)patch_pos64;
+        patch <<= w;
+        patch_pos = (uint32_t)(patch_pos64 >> pw);
+        for (uint32_t k = 1; k < pll; k <<= 1) {
+          uint32_t tmp = shuffle(patch_pos, (tr & ~k) | (k - 1));
+          patch_pos += (tr & k) ? tmp : 0;
+        }
+        if (tr < pll && patch_pos < n) { vals[base + patch_pos] += patch; }
+      }
+    }
+    __syncwarp();
+    if (mode == 3) {
+      T baseval;
+      for (uint32_t i = 1; i < n; i <<= 1) {
+        __syncwarp();
+        for (uint32_t j = tr; j < n; j += 32) {
+          if (j & i) vals[base + j] += vals[base + ((j & ~i) | (i - 1))];
+        }
+      }
+      if constexpr (sizeof(T) <= 4)
+        baseval = rle->baseval.u32[r];
+      else
+        baseval = rle->baseval.u64[r];
+      for (uint32_t j = tr; j < n; j += 32) {
+        vals[base + j] += baseval;
+      }
+    }
+    __syncwarp();
+  }
+  __syncthreads();
+  return rle->num_vals;
+}
+
+/**
+ * @brief Reads 32 booleans as a packed 32-bit value
+ *
+ * @param[in] vals 32-bit array of values (little-endian)
+ * @param[in] bitpos bit position
+ *
+ * @return 32-bit value
+ */
+inline __device__ uint32_t rle8_read_bool32(volatile uint32_t* vals, uint32_t bitpos)
+{
+  uint32_t a = vals[(bitpos >> 5) + 0];
+  uint32_t b = vals[(bitpos >> 5) + 1];
+  a          = __byte_perm(a, 0, 0x0123);
+  b          = __byte_perm(b, 0, 0x0123);
+  return __brev(__funnelshift_l(b, a, bitpos));
+}
+
+/**
+ * @brief ORC Byte RLE decoding
+ *
+ * @param[in] bs Input byte stream
+ * @param[in] rle RLE state
+ * @param[in] vals output buffer for decoded 8-bit values
+ * @param[in] maxvals Maximum number of values to decode
+ * @param[in] t thread id
+ *
+ * @return number of values decoded
+ */
+static __device__ uint32_t Byte_RLE(orc_bytestream_s* bs,
+                                    volatile orc_byterle_state_s* rle,
+                                    volatile uint8_t* vals,
+                                    uint32_t maxvals,
+                                    int t)
+{
+  uint32_t numvals, numruns;
+  int r, tr;
+  if (t == 0) {
+    uint32_t maxpos  = min(bs->len, bs->pos + (bytestream_buffer_size - 8u));
+    uint32_t lastpos = bs->pos;
+    numvals = numruns = 0;
+    // Find the length and start location of each run
+    while (numvals < maxvals && numruns < num_warps) {
+      uint32_t pos           = lastpos, n;
+      rle->runs_pos[numruns] = pos;
+      rle->runs_loc[numruns] = numvals;
+      n                      = bytestream_readbyte(bs, pos++);
+      if (n <= 0x7f) {
+        // Run
+        n = n + 3;
+        pos++;
+      } else {
+        // Literals
+        n = 0x100 - n;
+        pos += n;
+      }
+      if ((numvals != 0) and (numvals + n > maxvals)) break;
+      if (pos > maxpos) break;
+      numruns++;
+      ((numvals == 0) and (n > maxvals)) ? numvals = maxvals : numvals += n;
+      lastpos = pos;
+    }
+    rle->num_runs = numruns;
+    rle->num_vals = numvals;
+    bytestream_flush_bytes(bs, lastpos - bs->pos);
+  }
+  __syncthreads();
+  numruns = rle->num_runs;
+  r       = t >> 5;
+  tr      = t & 0x1f;
+  for (int run = r; run < numruns; run += num_warps) {
+    uint32_t pos = rle->runs_pos[run];
+    uint32_t loc = rle->runs_loc[run];
+    uint32_t n   = bytestream_readbyte(bs, pos++);
+    uint32_t literal_mask;
+    if (n <= 0x7f) {
+      literal_mask = 0;
+      n += 3;
+    } else {
+      literal_mask = ~0;
+      n            = 0x100 - n;
+    }
+    for (uint32_t i = tr; i < n; i += 32) {
+      vals[loc + i] = bytestream_readbyte(bs, pos + (i & literal_mask));
+    }
+  }
+  __syncthreads();
+  return rle->num_vals;
+}
+
+static const __device__ __constant__ int64_t kPow5i[28] = {1,
+                                                           5,
+                                                           25,
+                                                           125,
+                                                           625,
+                                                           3125,
+                                                           15625,
+                                                           78125,
+                                                           390625,
+                                                           1953125,
+                                                           9765625,
+                                                           48828125,
+                                                           244140625,
+                                                           1220703125,
+                                                           6103515625ll,
+                                                           30517578125ll,
+                                                           152587890625ll,
+                                                           762939453125ll,
+                                                           3814697265625ll,
+                                                           19073486328125ll,
+                                                           95367431640625ll,
+                                                           476837158203125ll,
+                                                           2384185791015625ll,
+                                                           11920928955078125ll,
+                                                           59604644775390625ll,
+                                                           298023223876953125ll,
+                                                           1490116119384765625ll,
+                                                           7450580596923828125ll};
+
+/**
+ * @brief ORC Decimal decoding (unbounded base-128 varints)
+ *
+ * @param[in] bs Input byte stream
+ * @param[in,out] vals on input: scale from secondary stream, on output: value
+ * @param[in] val_scale Scale of each value
+ * @param[in] col_scale Scale from schema to which value will be adjusted
+ * @param[in] numvals Number of values to decode
+ * @param[in] t thread id
+ *
+ * @return number of values decoded
+ */
+static __device__ int Decode_Decimals(orc_bytestream_s* bs,
+                                      volatile orc_byterle_state_s* scratch,
+                                      volatile orcdec_state_s::values& vals,
+                                      int val_scale,
+                                      int numvals,
+                                      type_id dtype_id,
+                                      int col_scale,
+                                      int t)
+{
+  uint32_t num_vals_read = 0;
+  // Iterates till `numvals` are read or there is nothing to read once the
+  // stream has reached its end, and can't read anything more.
+  while (num_vals_read != numvals) {
+    if (t == 0) {
+      uint32_t maxpos  = min(bs->len, bs->pos + (bytestream_buffer_size - 8u));
+      uint32_t lastpos = bs->pos;
+      uint32_t n;
+      for (n = num_vals_read; n < numvals; n++) {
+        uint32_t pos = lastpos;
+        pos += varint_length<uint4>(bs, pos);
+        if (pos > maxpos) break;
+        vals.i64[2 * n] = lastpos;
+        lastpos         = pos;
+      }
+      scratch->num_vals = n;
+      bytestream_flush_bytes(bs, lastpos - bs->pos);
+    }
+    __syncthreads();
+    uint32_t num_vals_to_read = scratch->num_vals;
+    if (t >= num_vals_read and t < num_vals_to_read) {
+      auto const pos = static_cast<int>(vals.i64[2 * t]);
+      __int128_t v   = decode_varint128(bs, pos);
+
+      auto const scaled_value = [&]() {
+        // Since cuDF column stores just one scale, value needs to be adjusted to col_scale from
+        // val_scale. So the difference of them will be used to add 0s or remove digits.
+        int32_t const scale = (t < numvals) ? col_scale - val_scale : 0;
+        if (scale >= 0) {
+          auto const abs_scale = min(scale, 27);
+          return (v * kPow5i[abs_scale]) << abs_scale;
+        } else {
+          auto const abs_scale = min(-scale, 27);
+          return (v / kPow5i[abs_scale]) >> abs_scale;
+        }
+      }();
+      if (dtype_id == type_id::DECIMAL32) {
+        vals.i32[t] = scaled_value;
+      } else if (dtype_id == type_id::DECIMAL64) {
+        vals.i64[t] = scaled_value;
+      } else {
+        vals.i128[t] = scaled_value;
+      }
+    }
+    // There is nothing to read, so break
+    if (num_vals_read == num_vals_to_read) break;
+
+    // Update number of values read (This contains values of previous iteration)
+    num_vals_read = num_vals_to_read;
+
+    // Have to wait till all threads have copied data
+    __syncthreads();
+    if (num_vals_read != numvals) {
+      bytestream_fill(bs, t);
+      __syncthreads();
+      if (t == 0) {
+        // Needs to be reset since bytestream has been filled
+        bs->fill_count = 0;
+      }
+    }
+    // Adding to get all threads in sync before next read
+    __syncthreads();
+  }
+  return num_vals_read;
+}
+
+/**
+ * @brief Decoding NULLs and builds string dictionary index tables
+ *
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] global_dictionary Global dictionary device array
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] max_num_rows Maximum number of rows to load
+ * @param[in] first_row Crop all rows below first_row
+ */
+// blockDim {block_size,1,1}
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpuDecodeNullsAndStringDictionaries(ColumnDesc* chunks,
+                                      DictionaryEntry* global_dictionary,
+                                      uint32_t num_columns,
+                                      uint32_t num_stripes,
+                                      size_t first_row)
+{
+  __shared__ __align__(16) orcdec_state_s state_g;
+  using warp_reduce  = cub::WarpReduce<uint32_t>;
+  using block_reduce = cub::BlockReduce<uint32_t, block_size>;
+  __shared__ union {
+    typename warp_reduce::TempStorage wr_storage[block_size / 32];
+    typename block_reduce::TempStorage bk_storage;
+  } temp_storage;
+
+  orcdec_state_s* const s = &state_g;
+  bool const is_nulldec   = (blockIdx.y >= num_stripes);
+  uint32_t const column   = blockIdx.x;
+  uint32_t const stripe   = (is_nulldec) ? blockIdx.y - num_stripes : blockIdx.y;
+  uint32_t const chunk_id = stripe * num_columns + column;
+  int t                   = threadIdx.x;
+
+  if (t == 0) s->chunk = chunks[chunk_id];
+  __syncthreads();
+  size_t const max_num_rows = s->chunk.column_num_rows - s->chunk.parent_validity_info.null_count;
+
+  if (is_nulldec) {
+    uint32_t null_count = 0;
+    // Decode NULLs
+    if (t == 0) {
+      s->chunk.skip_count   = 0;
+      s->top.nulls_desc_row = 0;
+      bytestream_init(&s->bs, s->chunk.streams[CI_PRESENT], s->chunk.strm_len[CI_PRESENT]);
+    }
+    __syncthreads();
+    if (s->chunk.strm_len[CI_PRESENT] == 0) {
+      // No present stream: all rows are valid
+      s->vals.u32[t] = ~0;
+    }
+    auto const prev_parent_null_count =
+      (s->chunk.parent_null_count_prefix_sums != nullptr && stripe > 0)
+        ? s->chunk.parent_null_count_prefix_sums[stripe - 1]
+        : 0;
+    auto const parent_null_count =
+      (s->chunk.parent_null_count_prefix_sums != nullptr)
+        ? s->chunk.parent_null_count_prefix_sums[stripe] - prev_parent_null_count
+        : 0;
+    auto const num_elems = s->chunk.num_rows - parent_null_count;
+    while (s->top.nulls_desc_row < num_elems) {
+      uint32_t nrows_max = min(num_elems - s->top.nulls_desc_row, blockDim.x * 32);
+      uint32_t nrows;
+      size_t row_in;
+
+      bytestream_fill(&s->bs, t);
+      __syncthreads();
+      if (s->chunk.strm_len[CI_PRESENT] > 0) {
+        uint32_t nbytes = Byte_RLE(&s->bs, &s->u.rle8, s->vals.u8, (nrows_max + 7) >> 3, t);
+        nrows           = min(nrows_max, nbytes * 8u);
+        if (!nrows) {
+          // Error: mark all remaining rows as null
+          nrows = nrows_max;
+          if (t * 32 < nrows) { s->vals.u32[t] = 0; }
+        }
+      } else {
+        nrows = nrows_max;
+      }
+      __syncthreads();
+
+      row_in = s->chunk.start_row + s->top.nulls_desc_row - prev_parent_null_count;
+      if (row_in + nrows > first_row && row_in < first_row + max_num_rows &&
+          s->chunk.valid_map_base != nullptr) {
+        int64_t dst_row   = row_in - first_row;
+        int64_t dst_pos   = max(dst_row, (int64_t)0);
+        uint32_t startbit = -static_cast<int32_t>(min(dst_row, (int64_t)0));
+        uint32_t nbits    = nrows - min(startbit, nrows);
+        uint32_t* valid   = s->chunk.valid_map_base + (dst_pos >> 5);
+        uint32_t bitpos   = static_cast<uint32_t>(dst_pos) & 0x1f;
+        if ((size_t)(dst_pos + nbits) > max_num_rows) {
+          nbits = static_cast<uint32_t>(max_num_rows - min((size_t)dst_pos, max_num_rows));
+        }
+        // Store bits up to the next 32-bit aligned boundary
+        if (bitpos != 0) {
+          uint32_t n = min(32u - bitpos, nbits);
+          if (t == 0) {
+            uint32_t mask = ((1 << n) - 1) << bitpos;
+            uint32_t bits = (rle8_read_bool32(s->vals.u32, startbit) << bitpos) & mask;
+            atomicAnd(valid, ~mask);
+            atomicOr(valid, bits);
+            null_count += __popc((~bits) & mask);
+          }
+          nbits -= n;
+          startbit += n;
+          valid++;
+        }
+        // Store bits aligned
+        if (t * 32 + 32 <= nbits) {
+          uint32_t bits = rle8_read_bool32(s->vals.u32, startbit + t * 32);
+          valid[t]      = bits;
+          null_count += __popc(~bits);
+        } else if (t * 32 < nbits) {
+          uint32_t n    = nbits - t * 32;
+          uint32_t mask = (1 << n) - 1;
+          uint32_t bits = rle8_read_bool32(s->vals.u32, startbit + t * 32) & mask;
+          atomicAnd(valid + t, ~mask);
+          atomicOr(valid + t, bits);
+          null_count += __popc((~bits) & mask);
+        }
+        __syncthreads();
+      }
+      // We may have some valid values that are not decoded below first_row -> count these in
+      // skip_count, so that subsequent kernel can infer the correct row position
+      if (row_in < first_row && t < 32) {
+        uint32_t skippedrows = min(static_cast<uint32_t>(first_row - row_in), nrows);
+        uint32_t skip_count  = 0;
+        for (thread_index_type i = t * 32; i < skippedrows; i += 32 * 32) {
+          // Need to arrange the bytes to apply mask properly.
+          uint32_t bits = (i + 32 <= skippedrows) ? s->vals.u32[i >> 5]
+                                                  : (__byte_perm(s->vals.u32[i >> 5], 0, 0x0123) &
+                                                     (0xffff'ffffu << (0x20 - skippedrows + i)));
+          skip_count += __popc(bits);
+        }
+        skip_count = warp_reduce(temp_storage.wr_storage[t / 32]).Sum(skip_count);
+        if (t == 0) { s->chunk.skip_count += skip_count; }
+      }
+      __syncthreads();
+      if (t == 0) { s->top.nulls_desc_row += nrows; }
+      __syncthreads();
+    }
+    __syncthreads();
+    // Sum up the valid counts and infer null_count
+    null_count = block_reduce(temp_storage.bk_storage).Sum(null_count);
+    if (t == 0) {
+      chunks[chunk_id].null_count = parent_null_count + null_count;
+      chunks[chunk_id].skip_count = s->chunk.skip_count;
+    }
+  } else {
+    // Decode string dictionary
+    int encoding_kind = s->chunk.encoding_kind;
+    if ((encoding_kind == DICTIONARY || encoding_kind == DICTIONARY_V2) &&
+        (s->chunk.dict_len > 0)) {
+      if (t == 0) {
+        s->top.dict.dict_len   = s->chunk.dict_len;
+        s->top.dict.local_dict = global_dictionary + s->chunk.dictionary_start;  // Local dictionary
+        s->top.dict.dict_pos   = 0;
+        // CI_DATA2 contains the LENGTH stream coding the length of individual dictionary entries
+        bytestream_init(&s->bs, s->chunk.streams[CI_DATA2], s->chunk.strm_len[CI_DATA2]);
+      }
+      __syncthreads();
+      while (s->top.dict.dict_len > 0) {
+        uint32_t numvals        = min(s->top.dict.dict_len, blockDim.x), len;
+        volatile uint32_t* vals = s->vals.u32;
+        bytestream_fill(&s->bs, t);
+        __syncthreads();
+        if (is_rlev1(s->chunk.encoding_kind)) {
+          numvals = Integer_RLEv1(&s->bs, &s->u.rlev1, vals, numvals, t);
+        } else  // RLEv2
+        {
+          numvals = Integer_RLEv2(&s->bs, &s->u.rlev2, vals, numvals, t);
+        }
+        __syncthreads();
+        len = (t < numvals) ? vals[t] : 0;
+        lengths_to_positions(vals, numvals, t);
+        __syncthreads();
+        if (numvals == 0) {
+          // This is an error (ran out of data)
+          numvals = min(s->top.dict.dict_len, blockDim.x);
+          vals[t] = 0;
+        }
+        if (t < numvals) {
+          s->top.dict.local_dict[t] = {s->top.dict.dict_pos + vals[t] - len, len};
+        }
+        __syncthreads();
+        if (t == 0) {
+          s->top.dict.dict_pos += vals[numvals - 1];
+          s->top.dict.dict_len -= numvals;
+          s->top.dict.local_dict += numvals;
+        }
+        __syncthreads();
+      }
+    }
+  }
+}
+
+/**
+ * @brief Decode row positions from valid bits
+ *
+ * @param[in,out] s Column chunk decoder state
+ * @param[in] first_row crop all rows below first rows
+ * @param[in] t thread id
+ * @param[in] temp_storage shared memory storage to perform block reduce
+ */
+template <typename Storage>
+static __device__ void DecodeRowPositions(orcdec_state_s* s,
+                                          size_t first_row,
+                                          int t,
+                                          Storage& temp_storage)
+{
+  using block_reduce = cub::BlockReduce<uint32_t, block_size>;
+
+  if (t == 0) {
+    if (s->chunk.skip_count != 0) {
+      s->u.rowdec.nz_count = min(min(s->chunk.skip_count, s->top.data.max_vals), blockDim.x);
+      s->chunk.skip_count -= s->u.rowdec.nz_count;
+      s->top.data.nrows = s->u.rowdec.nz_count;
+    } else {
+      s->u.rowdec.nz_count = 0;
+    }
+  }
+  __syncthreads();
+  if (t < s->u.rowdec.nz_count) {
+    s->u.rowdec.row[t] = 0;  // Skipped values (below first_row)
+  }
+  while (s->u.rowdec.nz_count < s->top.data.max_vals &&
+         s->top.data.cur_row + s->top.data.nrows < s->top.data.end_row) {
+    uint32_t nrows = min(s->top.data.end_row - (s->top.data.cur_row + s->top.data.nrows),
+                         min((row_decoder_buffer_size - s->u.rowdec.nz_count) * 2, blockDim.x));
+    if (s->chunk.valid_map_base != nullptr) {
+      // We have a present stream
+      uint32_t rmax  = s->top.data.end_row - min((uint32_t)first_row, s->top.data.end_row);
+      auto r         = (uint32_t)(s->top.data.cur_row + s->top.data.nrows + t - first_row);
+      uint32_t valid = (t < nrows && r < rmax)
+                         ? (((uint8_t const*)s->chunk.valid_map_base)[r >> 3] >> (r & 7)) & 1
+                         : 0;
+      volatile auto* row_ofs_plus1 = (volatile uint16_t*)&s->u.rowdec.row[s->u.rowdec.nz_count];
+      uint32_t nz_pos, row_plus1, nz_count = s->u.rowdec.nz_count, last_row;
+      if (t < nrows) { row_ofs_plus1[t] = valid; }
+      lengths_to_positions<uint16_t>(row_ofs_plus1, nrows, t);
+      if (t < nrows) {
+        nz_count += row_ofs_plus1[t];
+        row_plus1 = s->top.data.nrows + t + 1;
+      } else {
+        row_plus1 = 0;
+      }
+      if (t == nrows - 1) { s->u.rowdec.nz_count = min(nz_count, s->top.data.max_vals); }
+      __syncthreads();
+      // TBD: Brute-forcing this, there might be a more efficient way to find the thread with the
+      // last row
+      last_row = (nz_count == s->u.rowdec.nz_count) ? row_plus1 : 0;
+      last_row = block_reduce(temp_storage).Reduce(last_row, cub::Max());
+      nz_pos   = (valid) ? nz_count : 0;
+      if (t == 0) { s->top.data.nrows = last_row; }
+      if (valid && nz_pos - 1 < s->u.rowdec.nz_count) { s->u.rowdec.row[nz_pos - 1] = row_plus1; }
+      __syncthreads();
+    } else {
+      // All values are valid
+      nrows = min(nrows, s->top.data.max_vals - s->u.rowdec.nz_count);
+      if (t < nrows) { s->u.rowdec.row[s->u.rowdec.nz_count + t] = s->top.data.nrows + t + 1; }
+      __syncthreads();
+      if (t == 0) {
+        s->top.data.nrows += nrows;
+        s->u.rowdec.nz_count += nrows;
+      }
+      __syncthreads();
+    }
+  }
+}
+
+/**
+ * @brief Trailing zeroes for decoding timestamp nanoseconds
+ */
+static const __device__ __constant__ uint32_t kTimestampNanoScale[8] = {
+  1, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000};
+
+/**
+ * @brief Decodes column data
+ *
+ * @param[in] chunks ColumnDesc device array
+ * @param[in] global_dictionary Global dictionary device array
+ * @param[in] tz_table Timezone translation table
+ * @param[in] row_groups Optional row index data
+ * @param[in] first_row Crop all rows below first_row
+ * @param[in] rowidx_stride Row index stride
+ * @param[in] level nesting level being processed
+ */
+// blockDim {block_size,1,1}
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpuDecodeOrcColumnData(ColumnDesc* chunks,
+                         DictionaryEntry* global_dictionary,
+                         table_device_view tz_table,
+                         device_2dspan<RowGroup> row_groups,
+                         size_t first_row,
+                         uint32_t rowidx_stride,
+                         size_t level,
+                         size_type* error_count)
+{
+  __shared__ __align__(16) orcdec_state_s state_g;
+  using block_reduce = cub::BlockReduce<uint64_t, block_size>;
+  __shared__ union {
+    typename cub::BlockReduce<uint32_t, block_size>::TempStorage blk_uint32;
+    typename cub::BlockReduce<uint64_t, block_size>::TempStorage blk_uint64;
+  } temp_storage;
+
+  orcdec_state_s* const s = &state_g;
+  uint32_t chunk_id;
+  int t              = threadIdx.x;
+  auto num_rowgroups = row_groups.size().first;
+
+  if (num_rowgroups > 0) {
+    if (t == 0) { s->top.data.index = row_groups[blockIdx.y][blockIdx.x]; }
+    __syncthreads();
+    chunk_id = s->top.data.index.chunk_id;
+  } else {
+    chunk_id = blockIdx.x;
+  }
+  if (t == 0) {
+    s->chunk          = chunks[chunk_id];
+    s->num_child_rows = 0;
+  }
+  __syncthreads();
+  // Struct doesn't have any data in itself, so skip
+  bool const is_valid       = s->chunk.type_kind != STRUCT;
+  size_t const max_num_rows = s->chunk.column_num_rows;
+  if (t == 0 and is_valid) {
+    // If we have an index, seek to the initial run and update row positions
+    if (num_rowgroups > 0) {
+      if (s->top.data.index.strm_offset[0] > s->chunk.strm_len[CI_DATA]) {
+        atomicAdd(error_count, 1);
+      }
+      if (s->top.data.index.strm_offset[1] > s->chunk.strm_len[CI_DATA2]) {
+        atomicAdd(error_count, 1);
+      }
+      uint32_t ofs0 = min(s->top.data.index.strm_offset[0], s->chunk.strm_len[CI_DATA]);
+      uint32_t ofs1 = min(s->top.data.index.strm_offset[1], s->chunk.strm_len[CI_DATA2]);
+      uint32_t rowgroup_rowofs =
+        (level == 0) ? (blockIdx.y - min(s->chunk.rowgroup_id, blockIdx.y)) * rowidx_stride
+                     : s->top.data.index.start_row;
+      ;
+      s->chunk.streams[CI_DATA] += ofs0;
+      s->chunk.strm_len[CI_DATA] -= ofs0;
+      s->chunk.streams[CI_DATA2] += ofs1;
+      s->chunk.strm_len[CI_DATA2] -= ofs1;
+      rowgroup_rowofs = min(rowgroup_rowofs, s->chunk.num_rows);
+      s->chunk.start_row += rowgroup_rowofs;
+      s->chunk.num_rows -= rowgroup_rowofs;
+    }
+    s->is_string = (s->chunk.type_kind == STRING || s->chunk.type_kind == BINARY ||
+                    s->chunk.type_kind == VARCHAR || s->chunk.type_kind == CHAR);
+    s->top.data.cur_row =
+      max(s->chunk.start_row, max((int32_t)(first_row - s->chunk.skip_count), 0));
+    s->top.data.end_row        = s->chunk.start_row + s->chunk.num_rows;
+    s->top.data.buffered_count = 0;
+    if (s->top.data.end_row > first_row + max_num_rows) {
+      s->top.data.end_row = first_row + max_num_rows;
+    }
+    if (num_rowgroups > 0) {
+      s->top.data.end_row =
+        min(s->top.data.end_row, s->chunk.start_row + s->top.data.index.num_rows);
+    }
+    if (!is_dictionary(s->chunk.encoding_kind)) { s->chunk.dictionary_start = 0; }
+
+    static constexpr duration_s d_orc_utc_epoch = duration_s{orc_utc_epoch};
+    s->top.data.tz_epoch = d_orc_utc_epoch - get_ut_offset(tz_table, timestamp_s{d_orc_utc_epoch});
+
+    bytestream_init(&s->bs, s->chunk.streams[CI_DATA], s->chunk.strm_len[CI_DATA]);
+    bytestream_init(&s->bs2, s->chunk.streams[CI_DATA2], s->chunk.strm_len[CI_DATA2]);
+  }
+  __syncthreads();
+
+  while (is_valid && (s->top.data.cur_row < s->top.data.end_row)) {
+    uint32_t list_child_elements = 0;
+    bytestream_fill(&s->bs, t);
+    bytestream_fill(&s->bs2, t);
+    __syncthreads();
+    if (t == 0) {
+      uint32_t max_vals = s->chunk.start_row + s->chunk.num_rows - s->top.data.cur_row;
+      if (num_rowgroups > 0 && (s->is_string || s->chunk.type_kind == TIMESTAMP)) {
+        max_vals +=
+          s->top.data.index.run_pos[is_dictionary(s->chunk.encoding_kind) ? CI_DATA : CI_DATA2];
+      }
+      s->bs.fill_count  = 0;
+      s->bs2.fill_count = 0;
+      s->top.data.nrows = 0;
+      s->top.data.max_vals =
+        min(max_vals, (s->chunk.type_kind == BOOLEAN) ? blockDim.x * 2 : blockDim.x);
+    }
+    __syncthreads();
+    // Decode data streams
+    {
+      uint32_t numvals       = s->top.data.max_vals;
+      uint64_t secondary_val = 0;
+      uint32_t vals_skipped  = 0;
+      if (s->is_string || s->chunk.type_kind == TIMESTAMP) {
+        // For these data types, we have a secondary unsigned 32-bit data stream
+        orc_bytestream_s* bs = (is_dictionary(s->chunk.encoding_kind)) ? &s->bs : &s->bs2;
+        uint32_t ofs         = 0;
+        if (s->chunk.type_kind == TIMESTAMP) {
+          // Restore buffered secondary stream values, if any
+          ofs = s->top.data.buffered_count;
+          if (ofs > 0) {
+            __syncthreads();
+            if (t == 0) { s->top.data.buffered_count = 0; }
+          }
+        }
+        if (numvals > ofs) {
+          if (is_rlev1(s->chunk.encoding_kind)) {
+            if (s->chunk.type_kind == TIMESTAMP)
+              numvals = ofs + Integer_RLEv1(bs, &s->u.rlev1, &s->vals.u64[ofs], numvals - ofs, t);
+            else
+              numvals = ofs + Integer_RLEv1(bs, &s->u.rlev1, &s->vals.u32[ofs], numvals - ofs, t);
+          } else {
+            if (s->chunk.type_kind == TIMESTAMP)
+              numvals =
+                ofs + Integer_RLEv2(bs, &s->u.rlev2, &s->vals.u64[ofs], numvals - ofs, t, ofs > 0);
+            else
+              numvals =
+                ofs + Integer_RLEv2(bs, &s->u.rlev2, &s->vals.u32[ofs], numvals - ofs, t, ofs > 0);
+          }
+          __syncthreads();
+          if (numvals <= ofs && t >= ofs && t < s->top.data.max_vals) { s->vals.u32[t] = 0; }
+        }
+        // If we're using an index, we may have to drop values from the initial run
+        if (num_rowgroups > 0) {
+          int cid          = is_dictionary(s->chunk.encoding_kind) ? CI_DATA : CI_DATA2;
+          uint32_t run_pos = s->top.data.index.run_pos[cid];
+          if (run_pos) {
+            vals_skipped = min(numvals, run_pos);
+            __syncthreads();
+            if (t == 0) { s->top.data.index.run_pos[cid] = 0; }
+            numvals -= vals_skipped;
+            if (t < numvals) {
+              secondary_val = (s->chunk.type_kind == TIMESTAMP) ? s->vals.u64[vals_skipped + t]
+                                                                : s->vals.u32[vals_skipped + t];
+            }
+            __syncthreads();
+            if (t < numvals) {
+              if (s->chunk.type_kind == TIMESTAMP)
+                s->vals.u64[t] = secondary_val;
+              else
+                s->vals.u32[t] = secondary_val;
+            }
+          }
+        }
+        __syncthreads();
+        // For strings with direct encoding, we need to convert the lengths into an offset
+        if (!is_dictionary(s->chunk.encoding_kind)) {
+          if (t < numvals)
+            secondary_val = (s->chunk.type_kind == TIMESTAMP) ? s->vals.u64[t] : s->vals.u32[t];
+          if (s->chunk.type_kind != TIMESTAMP) {
+            lengths_to_positions(s->vals.u32, numvals, t);
+            __syncthreads();
+          }
+        }
+        // Adjust the maximum number of values
+        if (numvals == 0 && vals_skipped == 0) {
+          numvals = s->top.data.max_vals;  // Just so that we don't hang if the stream is corrupted
+        }
+        if (t == 0 && numvals < s->top.data.max_vals) { s->top.data.max_vals = numvals; }
+      }
+      __syncthreads();
+      // Account for skipped values
+      if (num_rowgroups > 0 && !s->is_string) {
+        uint32_t run_pos =
+          (s->chunk.type_kind == DECIMAL || s->chunk.type_kind == LIST || s->chunk.type_kind == MAP)
+            ? s->top.data.index.run_pos[CI_DATA2]
+            : s->top.data.index.run_pos[CI_DATA];
+        numvals =
+          min(numvals + run_pos, (s->chunk.type_kind == BOOLEAN) ? blockDim.x * 2 : blockDim.x);
+      }
+      // Decode the primary data stream
+      if (s->chunk.type_kind == INT || s->chunk.type_kind == DATE || s->chunk.type_kind == SHORT) {
+        // Signed int32 primary data stream
+        if (is_rlev1(s->chunk.encoding_kind)) {
+          numvals = Integer_RLEv1(&s->bs, &s->u.rlev1, s->vals.i32, numvals, t);
+        } else {
+          numvals = Integer_RLEv2(&s->bs, &s->u.rlev2, s->vals.i32, numvals, t);
+        }
+        __syncthreads();
+      } else if (s->chunk.type_kind == LIST or s->chunk.type_kind == MAP) {
+        if (is_rlev1(s->chunk.encoding_kind)) {
+          numvals = Integer_RLEv1<uint64_t>(&s->bs2, &s->u.rlev1, s->vals.u64, numvals, t);
+        } else {
+          numvals = Integer_RLEv2<uint64_t>(&s->bs2, &s->u.rlev2, s->vals.u64, numvals, t);
+        }
+        __syncthreads();
+      } else if (s->chunk.type_kind == BYTE) {
+        numvals = Byte_RLE(&s->bs, &s->u.rle8, s->vals.u8, numvals, t);
+        __syncthreads();
+      } else if (s->chunk.type_kind == BOOLEAN) {
+        int n = ((numvals + 7) >> 3);
+        if (n > s->top.data.buffered_count) {
+          numvals = Byte_RLE(&s->bs,
+                             &s->u.rle8,
+                             &s->vals.u8[s->top.data.buffered_count],
+                             n - s->top.data.buffered_count,
+                             t) +
+                    s->top.data.buffered_count;
+        } else {
+          numvals = s->top.data.buffered_count;
+        }
+        __syncthreads();
+        if (t == 0) {
+          s->top.data.buffered_count = 0;
+          s->top.data.max_vals       = min(s->top.data.max_vals, blockDim.x);
+        }
+        __syncthreads();
+        // If the condition is false, then it means that s->top.data.max_vals is last set of values.
+        // And as numvals is considered to be min(`max_vals+s->top.data.index.run_pos[CI_DATA]`,
+        // blockDim.x*2) we have to return numvals >= s->top.data.index.run_pos[CI_DATA].
+        auto const is_last_set = (s->top.data.max_vals >= s->top.data.index.run_pos[CI_DATA]);
+        auto const max_vals    = (is_last_set ? s->top.data.max_vals + 7 : blockDim.x) / 8;
+        n                      = numvals - max_vals;
+        if (t < n) {
+          secondary_val = s->vals.u8[max_vals + t];
+          if (t == 0) { s->top.data.buffered_count = n; }
+        }
+
+        numvals = min(numvals * 8, is_last_set ? (s->top.data.max_vals + 7) & (~0x7) : blockDim.x);
+
+      } else if (s->chunk.type_kind == LONG || s->chunk.type_kind == TIMESTAMP ||
+                 s->chunk.type_kind == DECIMAL) {
+        orc_bytestream_s* bs = (s->chunk.type_kind == DECIMAL) ? &s->bs2 : &s->bs;
+        if (is_rlev1(s->chunk.encoding_kind)) {
+          numvals = Integer_RLEv1<int64_t>(bs, &s->u.rlev1, s->vals.i64, numvals, t);
+        } else {
+          numvals = Integer_RLEv2<int64_t>(bs, &s->u.rlev2, s->vals.i64, numvals, t);
+        }
+        if (s->chunk.type_kind == DECIMAL) {
+          // If we're using an index, we may have to drop values from the initial run
+          uint32_t skip = 0;
+          int val_scale;
+          if (num_rowgroups > 0) {
+            uint32_t run_pos = s->top.data.index.run_pos[CI_DATA2];
+            if (run_pos) {
+              skip = min(numvals, run_pos);
+              __syncthreads();
+              if (t == 0) { s->top.data.index.run_pos[CI_DATA2] = 0; }
+              numvals -= skip;
+            }
+          }
+          val_scale = (t < numvals) ? (int)s->vals.i64[skip + t] : 0;
+          __syncthreads();
+          numvals = Decode_Decimals(&s->bs,
+                                    &s->u.rle8,
+                                    s->vals,
+                                    val_scale,
+                                    numvals,
+                                    s->chunk.dtype_id,
+                                    s->chunk.decimal_scale,
+                                    t);
+        }
+        __syncthreads();
+      } else if (s->chunk.type_kind == FLOAT) {
+        numvals = min(numvals, (bytestream_buffer_size - 8u) >> 2);
+        if (t < numvals) { s->vals.u32[t] = bytestream_readu32(&s->bs, s->bs.pos + t * 4); }
+        __syncthreads();
+        if (t == 0) { bytestream_flush_bytes(&s->bs, numvals * 4); }
+        __syncthreads();
+      } else if (s->chunk.type_kind == DOUBLE) {
+        numvals = min(numvals, (bytestream_buffer_size - 8u) >> 3);
+        if (t < numvals) { s->vals.u64[t] = bytestream_readu64(&s->bs, s->bs.pos + t * 8); }
+        __syncthreads();
+        if (t == 0) { bytestream_flush_bytes(&s->bs, numvals * 8); }
+        __syncthreads();
+      }
+      __syncthreads();
+      if (numvals == 0 && vals_skipped != 0 && num_rowgroups > 0) {
+        // Special case if the secondary streams produced fewer values than the primary stream's RLE
+        // run, as a result of initial RLE run offset: keep vals_skipped as non-zero to ensure
+        // proper buffered_count/max_vals update below.
+      } else {
+        vals_skipped = 0;
+        if (num_rowgroups > 0) {
+          uint32_t run_pos = (s->chunk.type_kind == LIST or s->chunk.type_kind == MAP)
+                               ? s->top.data.index.run_pos[CI_DATA2]
+                               : s->top.data.index.run_pos[CI_DATA];
+          if (run_pos) {
+            vals_skipped = min(numvals, run_pos);
+            numvals -= vals_skipped;
+            __syncthreads();
+            if (t == 0) {
+              (s->chunk.type_kind == LIST or s->chunk.type_kind == MAP)
+                ? s->top.data.index.run_pos[CI_DATA2] = 0
+                : s->top.data.index.run_pos[CI_DATA]  = 0;
+            }
+          }
+        }
+      }
+      if (t == 0 && numvals + vals_skipped > 0) {
+        auto const max_vals = s->top.data.max_vals;
+        if (max_vals > numvals) {
+          if (s->chunk.type_kind == TIMESTAMP) { s->top.data.buffered_count = max_vals - numvals; }
+          s->top.data.max_vals = numvals;
+        }
+      }
+      __syncthreads();
+      // Use the valid bits to compute non-null row positions until we get a full batch of values to
+      // decode
+      DecodeRowPositions(s, first_row, t, temp_storage.blk_uint32);
+      if (!s->top.data.nrows && !s->u.rowdec.nz_count && !vals_skipped) {
+        // This is a bug (could happen with bitstream errors with a bad run that would produce more
+        // values than the number of remaining rows)
+        return;
+      }
+
+      // Store decoded values to output
+      if (t < min(min(s->top.data.max_vals, s->u.rowdec.nz_count), s->top.data.nrows) &&
+          s->u.rowdec.row[t] != 0 &&
+          s->top.data.cur_row + s->u.rowdec.row[t] - 1 < s->top.data.end_row) {
+        size_t row = s->top.data.cur_row + s->u.rowdec.row[t] - 1 - first_row;
+        if (row < max_num_rows) {
+          void* data_out = s->chunk.column_data_base;
+          switch (s->chunk.type_kind) {
+            case FLOAT:
+            case INT: static_cast<uint32_t*>(data_out)[row] = s->vals.u32[t + vals_skipped]; break;
+            case DOUBLE:
+            case LONG: static_cast<uint64_t*>(data_out)[row] = s->vals.u64[t + vals_skipped]; break;
+            case DECIMAL:
+              if (s->chunk.dtype_id == type_id::DECIMAL32) {
+                static_cast<uint32_t*>(data_out)[row] = s->vals.u32[t + vals_skipped];
+              } else if (s->chunk.dtype_id == type_id::DECIMAL64) {
+                static_cast<uint64_t*>(data_out)[row] = s->vals.u64[t + vals_skipped];
+              } else {
+                // decimal128
+                static_cast<__uint128_t*>(data_out)[row] = s->vals.u128[t + vals_skipped];
+              }
+              break;
+            case MAP:
+            case LIST: {
+              // Since the offsets column in cudf is `size_type`,
+              // If the limit exceeds then value will be 0, which is Fail.
+              cudf_assert(
+                (s->vals.u64[t + vals_skipped] <= std::numeric_limits<size_type>::max()) and
+                "Number of elements is more than what size_type can handle");
+              list_child_elements                   = s->vals.u64[t + vals_skipped];
+              static_cast<uint32_t*>(data_out)[row] = list_child_elements;
+            } break;
+            case SHORT:
+              static_cast<uint16_t*>(data_out)[row] =
+                static_cast<uint16_t>(s->vals.u32[t + vals_skipped]);
+              break;
+            case BYTE: static_cast<uint8_t*>(data_out)[row] = s->vals.u8[t + vals_skipped]; break;
+            case BOOLEAN:
+              static_cast<uint8_t*>(data_out)[row] =
+                (s->vals.u8[(t + vals_skipped) >> 3] >> ((~(t + vals_skipped)) & 7)) & 1;
+              break;
+            case DATE:
+              if (s->chunk.dtype_len == 8) {
+                cudf::duration_D days{s->vals.i32[t + vals_skipped]};
+                // Convert from days to milliseconds
+                static_cast<int64_t*>(data_out)[row] =
+                  cuda::std::chrono::duration_cast<cudf::duration_ms>(days).count();
+              } else {
+                static_cast<uint32_t*>(data_out)[row] = s->vals.u32[t + vals_skipped];
+              }
+              break;
+            case STRING:
+            case BINARY:
+            case VARCHAR:
+            case CHAR: {
+              string_index_pair* strdesc = &static_cast<string_index_pair*>(data_out)[row];
+              void const* ptr            = nullptr;
+              uint32_t count             = 0;
+              if (is_dictionary(s->chunk.encoding_kind)) {
+                auto const dict_idx = s->vals.u32[t + vals_skipped];
+                if (dict_idx < s->chunk.dict_len) {
+                  auto const& g_entry = global_dictionary[s->chunk.dictionary_start + dict_idx];
+
+                  ptr   = s->chunk.streams[CI_DICTIONARY] + g_entry.pos;
+                  count = g_entry.len;
+                }
+              } else {
+                auto const dict_idx =
+                  s->chunk.dictionary_start + s->vals.u32[t + vals_skipped] - secondary_val;
+
+                if (dict_idx + count <= s->chunk.strm_len[CI_DATA]) {
+                  ptr   = s->chunk.streams[CI_DATA] + dict_idx;
+                  count = secondary_val;
+                }
+              }
+              strdesc->first  = static_cast<char const*>(ptr);
+              strdesc->second = count;
+              break;
+            }
+            case TIMESTAMP: {
+              auto seconds = s->top.data.tz_epoch + duration_s{s->vals.i64[t + vals_skipped]};
+              // Convert to UTC
+              seconds += get_ut_offset(tz_table, timestamp_s{seconds});
+
+              duration_ns nanos = duration_ns{(static_cast<int64_t>(secondary_val) >> 3) *
+                                              kTimestampNanoScale[secondary_val & 7]};
+
+              // Adjust seconds only for negative timestamps with positive nanoseconds.
+              // Alternative way to represent negative timestamps is with negative nanoseconds
+              // in which case the adjustment in not needed.
+              // Comparing with 999999 instead of zero to match the apache writer.
+              if (seconds.count() < 0 and nanos.count() > 999999) { seconds -= duration_s{1}; }
+
+              static_cast<int64_t*>(data_out)[row] = [&]() {
+                using cuda::std::chrono::duration_cast;
+                switch (s->chunk.timestamp_type_id) {
+                  case type_id::TIMESTAMP_SECONDS:
+                    return (seconds + duration_cast<duration_s>(nanos)).count();
+                  case type_id::TIMESTAMP_MILLISECONDS:
+                    return (seconds + duration_cast<duration_ms>(nanos)).count();
+                  case type_id::TIMESTAMP_MICROSECONDS:
+                    return (seconds + duration_cast<duration_us>(nanos)).count();
+                  case type_id::TIMESTAMP_NANOSECONDS:
+                  default:
+                    // nanoseconds as output in case of `type_id::EMPTY` and
+                    // `type_id::TIMESTAMP_NANOSECONDS`
+                    return (seconds + nanos).count();
+                }
+              }();
+
+              break;
+            }
+          }
+        }
+      }
+      // Aggregate num of elements for the chunk
+      if (s->chunk.type_kind == LIST or s->chunk.type_kind == MAP) {
+        list_child_elements = block_reduce(temp_storage.blk_uint64).Sum(list_child_elements);
+      }
+      __syncthreads();
+      // Buffer secondary stream values
+      if (s->chunk.type_kind == TIMESTAMP) {
+        int buffer_pos = s->top.data.max_vals;
+        if (t >= buffer_pos && t < buffer_pos + s->top.data.buffered_count) {
+          s->vals.u64[t - buffer_pos] = secondary_val;
+        }
+      } else if (s->chunk.type_kind == BOOLEAN && t < s->top.data.buffered_count) {
+        s->vals.u8[t] = secondary_val;
+      }
+    }
+    __syncthreads();
+    if (t == 0) {
+      s->top.data.cur_row += s->top.data.nrows;
+      if (s->chunk.type_kind == LIST or s->chunk.type_kind == MAP) {
+        s->num_child_rows += list_child_elements;
+      }
+      if (s->is_string && !is_dictionary(s->chunk.encoding_kind) && s->top.data.max_vals > 0) {
+        s->chunk.dictionary_start += s->vals.u32[s->top.data.max_vals - 1];
+      }
+    }
+    __syncthreads();
+  }
+  if (t == 0 and (s->chunk.type_kind == LIST or s->chunk.type_kind == MAP)) {
+    if (num_rowgroups > 0) {
+      row_groups[blockIdx.y][blockIdx.x].num_child_rows = s->num_child_rows;
+    }
+    atomicAdd(&chunks[chunk_id].num_child_rows, s->num_child_rows);
+  }
+}
+
+/**
+ * @brief Launches kernel for decoding NULLs and building string dictionary index tables
+ *
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] global_dictionary Global dictionary device array
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] first_row Crop all rows below first_row
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void __host__ DecodeNullsAndStringDictionaries(ColumnDesc* chunks,
+                                               DictionaryEntry* global_dictionary,
+                                               uint32_t num_columns,
+                                               uint32_t num_stripes,
+                                               size_t first_row,
+                                               rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(block_size, 1);
+  dim3 dim_grid(num_columns, num_stripes * 2);  // 1024 threads per chunk
+  gpuDecodeNullsAndStringDictionaries<block_size><<<dim_grid, dim_block, 0, stream.value()>>>(
+    chunks, global_dictionary, num_columns, num_stripes, first_row);
+}
+
+/**
+ * @brief Launches kernel for decoding column data
+ *
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] global_dictionary Global dictionary device array
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] first_row Crop all rows below first_row
+ * @param[in] tz_table Timezone translation table
+ * @param[in] row_groups Optional row index data [row_group][column]
+ * @param[in] num_rowgroups Number of row groups in row index data
+ * @param[in] rowidx_stride Row index stride
+ * @param[in] level nesting level being processed
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ */
+void __host__ DecodeOrcColumnData(ColumnDesc* chunks,
+                                  DictionaryEntry* global_dictionary,
+                                  device_2dspan<RowGroup> row_groups,
+                                  uint32_t num_columns,
+                                  uint32_t num_stripes,
+                                  size_t first_row,
+                                  table_device_view tz_table,
+                                  uint32_t num_rowgroups,
+                                  uint32_t rowidx_stride,
+                                  size_t level,
+                                  size_type* error_count,
+                                  rmm::cuda_stream_view stream)
+{
+  uint32_t num_chunks = num_columns * num_stripes;
+  dim3 dim_block(block_size, 1);  // 1024 threads per chunk
+  dim3 dim_grid((num_rowgroups > 0) ? num_columns : num_chunks,
+                (num_rowgroups > 0) ? num_rowgroups : 1);
+  gpuDecodeOrcColumnData<block_size><<<dim_grid, dim_block, 0, stream.value()>>>(
+    chunks, global_dictionary, tz_table, row_groups, first_row, rowidx_stride, level, error_count);
+}
+
+}  // namespace gpu
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/stripe_enc.cu b/cpp/src/io/orc/stripe_enc.cu
new file mode 100644
index 0000000..73c41e2
--- /dev/null
+++ b/cpp/src/io/orc/stripe_enc.cu
@@ -0,0 +1,1374 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc_gpu.hpp"
+
+#include <cudf/io/orc_types.hpp>
+#include <io/comp/nvcomp_adapter.hpp>
+#include <io/utilities/block_utils.cuh>
+#include <io/utilities/config_utils.hpp>
+#include <io/utilities/time_utils.cuh>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/bit.hpp>
+
+#include <cub/cub.cuh>
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace io {
+namespace orc {
+namespace gpu {
+
+using cudf::detail::device_2dspan;
+
+constexpr int scratch_buffer_size        = 512 * 4;
+constexpr int compact_streams_block_size = 1024;
+
+// Apache ORC reader does not handle zero-length patch lists for RLEv2 mode2
+// Workaround replaces zero-length patch lists by a dummy zero patch
+constexpr bool zero_pll_war = true;
+
+struct byterle_enc_state_s {
+  uint32_t literal_run;
+  uint32_t repeat_run;
+  volatile uint32_t rpt_map[(512 / 32) + 1];
+};
+
+struct intrle_enc_state_s {
+  uint32_t literal_run;
+  uint32_t delta_run;
+  uint32_t literal_mode;
+  uint32_t literal_w;
+  uint32_t hdr_bytes;
+  uint32_t pl_bytes;
+  volatile uint32_t delta_map[(512 / 32) + 1];
+};
+
+struct strdata_enc_state_s {
+  uint32_t char_count;
+  uint32_t lengths_red[(512 / 32)];
+  char const* str_data[512];
+};
+
+struct orcenc_state_s {
+  uint32_t cur_row;       // Current row in group
+  uint32_t present_rows;  // # of rows in present buffer
+  uint32_t present_out;   // # of rows in present buffer that have been flushed
+  uint32_t nrows;         // # of rows in current batch
+  uint32_t numvals;       // # of non-zero values in current batch (<=nrows)
+  uint32_t numlengths;    // # of non-zero values in DATA2 batch
+  uint32_t nnz;           // Running count of non-null values
+  encoder_chunk_streams stream;
+  EncChunk chunk;
+  uint32_t strm_pos[CI_NUM_STREAMS];
+  uint8_t valid_buf[512];  // valid map bits
+  union {
+    byterle_enc_state_s byterle;
+    intrle_enc_state_s intrle;
+    strdata_enc_state_s strenc;
+    stripe_dictionary const* dict_stripe;
+  } u;
+  union {
+    uint8_t u8[scratch_buffer_size];  // gblock_vminscratch buffer
+    uint32_t u32[scratch_buffer_size / 4];
+  } buf;
+  union {
+    uint8_t u8[2048];
+    uint32_t u32[1024];
+    int32_t i32[1024];
+    uint64_t u64[1024];
+    int64_t i64[1024];
+  } vals;
+  union {
+    uint8_t u8[2048];
+    uint32_t u32[1024];
+    uint64_t u64[1024];
+  } lengths;
+};
+
+static inline __device__ uint32_t zigzag(uint32_t v) { return v; }
+static inline __device__ uint32_t zigzag(int32_t v)
+{
+  int32_t s = (v >> 31);
+  return ((v ^ s) * 2) - s;
+}
+static inline __device__ uint64_t zigzag(uint64_t v) { return v; }
+static inline __device__ uint64_t zigzag(int64_t v)
+{
+  int64_t s = (v < 0) ? 1 : 0;
+  return ((v ^ -s) * 2) + s;
+}
+
+static inline __device__ __uint128_t zigzag(__int128_t v)
+{
+  int64_t s = (v < 0) ? 1 : 0;
+  return ((v ^ -s) * 2) + s;
+}
+
+static inline __device__ uint32_t CountLeadingBytes32(uint32_t v) { return __clz(v) >> 3; }
+static inline __device__ uint32_t CountLeadingBytes64(uint64_t v) { return __clzll(v) >> 3; }
+
+/**
+ * @brief Raw data output
+ *
+ * @tparam cid stream type (strm_pos[cid] will be updated and output stored at
+ * streams[cid]+strm_pos[cid])
+ * @tparam inmask input buffer position mask for circular buffers
+ * @param[in] s encoder state
+ * @param[in] inbuf base input buffer
+ * @param[in] inpos position in input buffer
+ * @param[in] count number of bytes to encode
+ * @param[in] t thread id
+ */
+template <StreamIndexType cid, uint32_t inmask>
+static __device__ void StoreBytes(
+  orcenc_state_s* s, uint8_t const* inbuf, uint32_t inpos, uint32_t count, int t)
+{
+  uint8_t* dst = s->stream.data_ptrs[cid] + s->strm_pos[cid];
+  while (count > 0) {
+    uint32_t n = min(count, 512);
+    if (t < n) { dst[t] = inbuf[(inpos + t) & inmask]; }
+    dst += n;
+    inpos += n;
+    count -= n;
+  }
+  __syncthreads();
+  if (!t) { s->strm_pos[cid] = static_cast<uint32_t>(dst - s->stream.data_ptrs[cid]); }
+}
+
+/**
+ * @brief ByteRLE encoder
+ *
+ * @tparam cid stream type (strm_pos[cid] will be updated and output stored at
+ * streams[cid]+strm_pos[cid])
+ * @tparam inmask input buffer position mask for circular buffers
+ * @param[in] s encoder state
+ * @param[in] inbuf base input buffer
+ * @param[in] inpos position in input buffer
+ * @param[in] numvals max number of values to encode
+ * @param[in] flush encode all remaining values if nonzero
+ * @param[in] t thread id
+ *
+ * @return number of input values encoded
+ */
+template <StreamIndexType cid, uint32_t inmask>
+static __device__ uint32_t ByteRLE(
+  orcenc_state_s* s, uint8_t const* inbuf, uint32_t inpos, uint32_t numvals, uint32_t flush, int t)
+{
+  uint8_t* dst     = s->stream.data_ptrs[cid] + s->strm_pos[cid];
+  uint32_t out_cnt = 0;
+
+  while (numvals > 0) {
+    uint8_t v0       = (t < numvals) ? inbuf[(inpos + t) & inmask] : 0;
+    uint8_t v1       = (t + 1 < numvals) ? inbuf[(inpos + t + 1) & inmask] : 0;
+    uint32_t rpt_map = ballot(t + 1 < numvals && v0 == v1), literal_run, repeat_run,
+             maxvals = min(numvals, 512);
+    if (!(t & 0x1f)) s->u.byterle.rpt_map[t >> 5] = rpt_map;
+    __syncthreads();
+    if (t == 0) {
+      // Find the start of an identical 3-byte sequence
+      // TBD: The two loops below could be eliminated using more ballot+ffs using warp0
+      literal_run = 0;
+      repeat_run  = 0;
+      while (literal_run < maxvals) {
+        uint32_t next = s->u.byterle.rpt_map[(literal_run >> 5) + 1];
+        uint32_t mask = rpt_map & __funnelshift_r(rpt_map, next, 1);
+        if (mask) {
+          uint32_t literal_run_ofs = __ffs(mask) - 1;
+          literal_run += literal_run_ofs;
+          repeat_run = __ffs(~((rpt_map >> literal_run_ofs) >> 1));
+          if (repeat_run + literal_run_ofs == 32) {
+            while (next == ~0) {
+              uint32_t next_idx = ((literal_run + repeat_run) >> 5) + 1;
+              next              = (next_idx < 512 / 32) ? s->u.byterle.rpt_map[next_idx] : 0;
+              repeat_run += 32;
+            }
+            repeat_run += __ffs(~next) - 1;
+          }
+          repeat_run = min(repeat_run + 1, maxvals - min(literal_run, maxvals));
+          if (repeat_run < 3) {
+            literal_run += (flush && literal_run + repeat_run >= numvals) ? repeat_run : 0;
+            repeat_run = 0;
+          }
+          break;
+        }
+        rpt_map = next;
+        literal_run += 32;
+      }
+      if (repeat_run >= 130) {
+        // Limit large runs to multiples of 130
+        repeat_run = (repeat_run >= 3 * 130) ? 3 * 130 : (repeat_run >= 2 * 130) ? 2 * 130 : 130;
+      } else if (literal_run && literal_run + repeat_run == maxvals) {
+        repeat_run = 0;  // Try again at next iteration
+      }
+      s->u.byterle.repeat_run  = repeat_run;
+      s->u.byterle.literal_run = min(literal_run, maxvals);
+    }
+    __syncthreads();
+    literal_run = s->u.byterle.literal_run;
+    if (!flush && literal_run == numvals) {
+      literal_run &= ~0x7f;
+      if (!literal_run) break;
+    }
+    if (literal_run > 0) {
+      uint32_t num_runs = (literal_run + 0x7f) >> 7;
+      if (t < literal_run) {
+        uint32_t run_id = t >> 7;
+        uint32_t run    = min(literal_run - run_id * 128, 128);
+        if (!(t & 0x7f)) dst[run_id + t] = 0x100 - run;
+        dst[run_id + t + 1] = (cid == CI_PRESENT) ? __brev(v0) >> 24 : v0;
+      }
+      dst += num_runs + literal_run;
+      out_cnt += literal_run;
+      numvals -= literal_run;
+      inpos += literal_run;
+    }
+    repeat_run = s->u.byterle.repeat_run;
+    if (repeat_run > 0) {
+      while (repeat_run >= 130) {
+        if (t == literal_run)  // repeat_run follows literal_run
+        {
+          dst[0] = 0x7f;
+          dst[1] = (cid == CI_PRESENT) ? __brev(v0) >> 24 : v0;
+        }
+        dst += 2;
+        out_cnt += 130;
+        numvals -= 130;
+        inpos += 130;
+        repeat_run -= 130;
+      }
+      if (!flush && repeat_run == numvals) {
+        // Wait for more data in case we can continue the run later
+        break;
+      }
+      if (repeat_run >= 3) {
+        if (t == literal_run)  // repeat_run follows literal_run
+        {
+          dst[0] = repeat_run - 3;
+          dst[1] = (cid == CI_PRESENT) ? __brev(v0) >> 24 : v0;
+        }
+        dst += 2;
+        out_cnt += repeat_run;
+        numvals -= repeat_run;
+        inpos += repeat_run;
+      }
+    }
+  }
+  if (!t) { s->strm_pos[cid] = static_cast<uint32_t>(dst - s->stream.data_ptrs[cid]); }
+  return out_cnt;
+}
+
+/**
+ * @brief Maps the symbol size in bytes to RLEv2 5-bit length code
+ */
+static const __device__ __constant__ uint8_t kByteLengthToRLEv2_W[9] = {
+  0, 7, 15, 23, 27, 28, 29, 30, 31};
+
+/**
+ * @brief Encode a varint value, return the number of bytes written
+ */
+static inline __device__ uint32_t StoreVarint(uint8_t* dst, __uint128_t v)
+{
+  uint32_t bytecnt = 0;
+  for (;;) {
+    auto c = static_cast<uint32_t>(v & 0x7f);
+    v >>= 7u;
+    if (v == 0) {
+      dst[bytecnt++] = c;
+      break;
+    } else {
+      dst[bytecnt++] = c + 0x80;
+    }
+  }
+  return bytecnt;
+}
+
+template <class T>
+static inline __device__ void StoreBytesBigEndian(uint8_t* dst, T v, uint32_t w)
+{
+  for (uint32_t i = 0, b = w * 8; i < w; ++i) {
+    b -= 8;
+    dst[i] = static_cast<uint8_t>(v >> b);
+  }
+}
+
+// Combine and store bits for symbol widths less than 8
+static inline __device__ void StoreBitsBigEndian(
+  uint8_t* dst, uint32_t v, uint32_t w, int num_vals, int t)
+{
+  if (t <= (num_vals | 0x1f)) {
+    uint32_t mask;
+    if (w <= 1) {
+      v    = (v << 1) | (shuffle_xor(v, 1) & 0x1);
+      v    = (v << 2) | (shuffle_xor(v, 2) & 0x3);
+      v    = (v << 4) | (shuffle_xor(v, 4) & 0xf);
+      mask = 0x7;
+    } else if (w <= 2) {
+      v    = (v << 2) | (shuffle_xor(v, 1) & 0x3);
+      v    = (v << 4) | (shuffle_xor(v, 2) & 0xf);
+      mask = 0x3;
+    } else  // if (w <= 4)
+    {
+      v    = (v << 4) | (shuffle_xor(v, 1) & 0xf);
+      mask = 0x1;
+    }
+    if (t < num_vals && !(t & mask)) { dst[(t * w) >> 3] = static_cast<uint8_t>(v); }
+  }
+}
+
+/**
+ * @brief Integer RLEv2 encoder
+ *
+ * @tparam cid stream type (strm_pos[cid] will be updated and output stored at
+ * streams[cid]+strm_pos[cid])
+ * @tparam inmask input buffer position mask for circular buffers
+ * @param[in] s encoder state
+ * @param[in] inbuf base input buffer
+ * @param[in] inpos position in input buffer
+ * @param[in] numvals max number of values to encode
+ * @param[in] flush encode all remaining values if nonzero
+ * @param[in] t thread id
+ * @param[in] temp_storage shared memory storage to perform block reduce
+ *
+ * @return number of input values encoded
+ */
+template <StreamIndexType cid,
+          class T,
+          bool is_signed,
+          uint32_t inmask,
+          int block_size,
+          typename Storage>
+static __device__ uint32_t IntegerRLE(
+  orcenc_state_s* s, T const* inbuf, uint32_t inpos, uint32_t numvals, int t, Storage& temp_storage)
+{
+  using block_reduce = cub::BlockReduce<T, block_size>;
+  uint8_t* dst       = s->stream.data_ptrs[cid] + s->strm_pos[cid];
+  uint32_t out_cnt   = 0;
+  __shared__ volatile uint64_t block_vmin;
+
+  while (numvals > 0) {
+    T v0               = (t < numvals) ? inbuf[(inpos + t) & inmask] : 0;
+    T v1               = (t + 1 < numvals) ? inbuf[(inpos + t + 1) & inmask] : 0;
+    T v2               = (t + 2 < numvals) ? inbuf[(inpos + t + 2) & inmask] : 0;
+    uint32_t delta_map = ballot(t + 2 < numvals && v1 - v0 == v2 - v1), maxvals = min(numvals, 512),
+             literal_run, delta_run;
+    if (!(t & 0x1f)) s->u.intrle.delta_map[t >> 5] = delta_map;
+    __syncthreads();
+    if (!t) {
+      // Find the start of the next delta run (2 consecutive values with the same delta)
+      literal_run = delta_run = 0;
+      while (literal_run < maxvals) {
+        if (delta_map != 0) {
+          uint32_t literal_run_ofs = __ffs(delta_map) - 1;
+          literal_run += literal_run_ofs;
+          delta_run = __ffs(~((delta_map >> literal_run_ofs) >> 1));
+          if (literal_run_ofs + delta_run == 32) {
+            for (;;) {
+              uint32_t delta_idx = (literal_run + delta_run) >> 5;
+              delta_map          = (delta_idx < 512 / 32) ? s->u.intrle.delta_map[delta_idx] : 0;
+              if (delta_map != ~0) break;
+              delta_run += 32;
+            }
+            delta_run += __ffs(~delta_map) - 1;
+          }
+          delta_run += 2;
+          break;
+        }
+        literal_run += 32;
+        delta_map = s->u.intrle.delta_map[(literal_run >> 5)];
+      }
+      literal_run             = min(literal_run, maxvals);
+      s->u.intrle.literal_run = literal_run;
+      s->u.intrle.delta_run   = min(delta_run, maxvals - literal_run);
+    }
+    __syncthreads();
+    literal_run = s->u.intrle.literal_run;
+    // Find minimum and maximum values
+    if (literal_run > 0) {
+      // Find min & max
+      T vmin = (t < literal_run) ? v0 : std::numeric_limits<T>::max();
+      T vmax = (t < literal_run) ? v0 : std::numeric_limits<T>::min();
+      uint32_t literal_mode, literal_w;
+      vmin = block_reduce(temp_storage).Reduce(vmin, cub::Min());
+      __syncthreads();
+      vmax = block_reduce(temp_storage).Reduce(vmax, cub::Max());
+      if (t == 0) {
+        uint32_t mode1_w, mode2_w;
+        typename std::make_unsigned<T>::type vrange_mode1, vrange_mode2;
+        block_vmin = static_cast<uint64_t>(vmin);
+        if constexpr (sizeof(T) > 4) {
+          vrange_mode1 = (is_signed) ? max(zigzag(vmin), zigzag(vmax)) : vmax;
+          vrange_mode2 = vmax - vmin;
+          mode1_w      = 8 - min(CountLeadingBytes64(vrange_mode1), 7);
+          mode2_w      = 8 - min(CountLeadingBytes64(vrange_mode2), 7);
+        } else {
+          vrange_mode1 = (is_signed) ? max(zigzag(vmin), zigzag(vmax)) : vmax;
+          vrange_mode2 = vmax - vmin;
+          mode1_w      = 4 - min(CountLeadingBytes32(vrange_mode1), 3);
+          mode2_w      = 4 - min(CountLeadingBytes32(vrange_mode2), 3);
+        }
+        // Decide between mode1 & mode2 (also mode3 for length=2 repeat)
+        if (vrange_mode2 == 0 && mode1_w > 1) {
+          // Should only occur if literal_run==2 (otherwise would have resulted in repeat_run >=
+          // 3)
+          uint32_t bytecnt = 2;
+          dst[0]           = 0xC0 + ((literal_run - 1) >> 8);
+          dst[1]           = (literal_run - 1) & 0xff;
+          bytecnt += StoreVarint(dst + 2, vrange_mode1);
+          dst[bytecnt++]           = 0;  // Zero delta
+          s->u.intrle.literal_mode = 3;
+          s->u.intrle.literal_w    = bytecnt;
+        } else {
+          uint32_t range, w;
+          // Mode 2 base value cannot be bigger than max int64_t, i.e. the first bit has to be 0
+          if (vmin <= std::numeric_limits<int64_t>::max() and mode1_w > mode2_w and
+              (literal_run - 1) * (mode1_w - mode2_w) > 4) {
+            s->u.intrle.literal_mode = 2;
+            w                        = mode2_w;
+            range                    = (uint32_t)vrange_mode2;
+          } else {
+            s->u.intrle.literal_mode = 1;
+            w                        = mode1_w;
+            range                    = (uint32_t)vrange_mode1;
+          }
+          if (w == 1)
+            w = (range >= 16) ? w << 3 : (range >= 4) ? 4 : (range >= 2) ? 2 : 1;
+          else
+            w <<= 3;  // bytes -> bits
+          s->u.intrle.literal_w = w;
+        }
+      }
+      __syncthreads();
+      vmin         = static_cast<T>(block_vmin);
+      literal_mode = s->u.intrle.literal_mode;
+      literal_w    = s->u.intrle.literal_w;
+      if (literal_mode == 1) {
+        // Direct mode
+        if (!t) {
+          dst[0] = 0x40 +
+                   ((literal_w < 8) ? literal_w - 1 : kByteLengthToRLEv2_W[literal_w >> 3]) * 2 +
+                   ((literal_run - 1) >> 8);
+          dst[1] = (literal_run - 1) & 0xff;
+        }
+        dst += 2;
+
+        typename std::make_unsigned<T>::type zzv0 = v0;
+        if (t < literal_run) { zzv0 = zigzag(v0); }
+        if (literal_w < 8) {
+          StoreBitsBigEndian(dst, zzv0, literal_w, literal_run, t);
+        } else if (t < literal_run) {
+          StoreBytesBigEndian(dst + t * (literal_w >> 3), zzv0, (literal_w >> 3));
+        }
+      } else if (literal_mode == 2) {
+        // Patched base mode
+        if (!t) {
+          uint32_t bw, pw = 1, pll, pgw = 1, bv_scale = (is_signed) ? 0 : 1;
+          vmax = (is_signed) ? ((vmin < 0) ? -vmin : vmin) * 2 : vmin;
+          bw   = (sizeof(T) > 4) ? (8 - min(CountLeadingBytes64(vmax << bv_scale), 7))
+                                 : (4 - min(CountLeadingBytes32(vmax << bv_scale), 3));
+          if (zero_pll_war) {
+            // Insert a dummy zero patch
+            pll                                                    = 1;
+            dst[4 + bw + ((literal_run * literal_w + 7) >> 3) + 0] = 0;
+            dst[4 + bw + ((literal_run * literal_w + 7) >> 3) + 1] = 0;
+          } else {
+            pll = 0;
+          }
+          dst[0] = 0x80 +
+                   ((literal_w < 8) ? literal_w - 1 : kByteLengthToRLEv2_W[literal_w >> 3]) * 2 +
+                   ((literal_run - 1) >> 8);
+          dst[1] = (literal_run - 1) & 0xff;
+          dst[2] = ((bw - 1) << 5) | kByteLengthToRLEv2_W[pw];
+          dst[3] = ((pgw - 1) << 5) | pll;
+          if (is_signed) {
+            vmax >>= 1;
+            vmax |= vmin & ((T)1 << (bw * 8 - 1));
+          }
+          StoreBytesBigEndian(dst + 4, vmax, bw);
+          s->u.intrle.hdr_bytes = 4 + bw;
+          s->u.intrle.pl_bytes  = (pll * (pw * 8 + pgw) + 7) >> 3;
+        }
+        __syncthreads();
+        dst += s->u.intrle.hdr_bytes;
+        v0 -= (t < literal_run) ? vmin : 0;
+        if (literal_w < 8)
+          StoreBitsBigEndian(dst, (uint32_t)v0, literal_w, literal_run, t);
+        else if (t < literal_run)
+          StoreBytesBigEndian(dst + t * (literal_w >> 3), v0, (literal_w >> 3));
+        dst += s->u.intrle.pl_bytes;
+      } else {
+        // Delta mode
+        dst += literal_w;
+        literal_w = 0;
+      }
+      dst += (literal_run * literal_w + 7) >> 3;
+      numvals -= literal_run;
+      inpos += literal_run;
+      out_cnt += literal_run;
+      __syncthreads();
+    }
+    delta_run = s->u.intrle.delta_run;
+    if (delta_run > 0) {
+      if (t == literal_run) {
+        int64_t delta       = (int64_t)v1 - (int64_t)v0;
+        uint64_t delta_base = zigzag(v0);
+        if (delta == 0 && delta_run >= 3 && delta_run <= 10) {
+          // Short repeat
+          uint32_t delta_bw = 8 - min(CountLeadingBytes64(delta_base), 7);
+          dst[0]            = ((delta_bw - 1) << 3) + (delta_run - 3);
+          for (uint32_t i = 0, b = delta_bw * 8; i < delta_bw; i++) {
+            b -= 8;
+            dst[1 + i] = static_cast<uint8_t>(delta_base >> b);
+          }
+          s->u.intrle.hdr_bytes = 1 + delta_bw;
+        } else {
+          // Delta
+          uint64_t delta_u = zigzag(delta);
+          uint32_t bytecnt = 2;
+          dst[0]           = 0xC0 + ((delta_run - 1) >> 8);
+          dst[1]           = (delta_run - 1) & 0xff;
+          bytecnt += StoreVarint(dst + bytecnt, delta_base);
+          bytecnt += StoreVarint(dst + bytecnt, delta_u);
+          s->u.intrle.hdr_bytes = bytecnt;
+        }
+      }
+      __syncthreads();
+      dst += s->u.intrle.hdr_bytes;
+      numvals -= delta_run;
+      inpos += delta_run;
+      out_cnt += delta_run;
+    }
+  }
+  if (!t) { s->strm_pos[cid] = static_cast<uint32_t>(dst - s->stream.data_ptrs[cid]); }
+  __syncthreads();
+  return out_cnt;
+}
+
+/**
+ * @brief Store a group of strings as a single concatenated string
+ *
+ * @param[in] dst destination buffer
+ * @param[in] strenc string encoder state
+ * @param[in] len(t) string length (per thread)
+ * @param[in] t thread id
+ */
+static __device__ void StoreStringData(uint8_t* dst,
+                                       strdata_enc_state_s* strenc,
+                                       uint32_t len,
+                                       int t)
+{
+  // Start with summing up all the lengths
+  uint32_t pos = len;
+  uint32_t wt  = t & 0x1f;
+  for (uint32_t n = 1; n < 32; n <<= 1) {
+    uint32_t tmp = shuffle(pos, (wt & ~n) | (n - 1));
+    pos += (wt & n) ? tmp : 0;
+  }
+  if (wt == 0x1f) { strenc->lengths_red[t >> 5] = pos; }
+  dst += pos - len;
+  __syncthreads();
+  if (t < 32) {
+    uint32_t wlen = (wt < 16) ? strenc->lengths_red[wt] : 0;
+    uint32_t wpos = wlen;
+    for (uint32_t n = 1; n < 16; n <<= 1) {
+      uint32_t tmp = shuffle(wpos, (wt & ~n) | (n - 1));
+      wpos += (wt & n) ? tmp : 0;
+    }
+    if (wt < 16) { strenc->lengths_red[wt] = wpos - wlen; }
+    if (wt == 0xf) {
+      strenc->char_count = wpos;  // Update stream position
+    }
+  }
+  __syncthreads();
+  // TBD: Might be more efficient to loop over 4 strings and copy 8 consecutive character at a time
+  // rather than have each thread to a memcpy
+  if (len > 0) { memcpy(dst + strenc->lengths_red[t >> 5], strenc->str_data[t], len); }
+}
+
+/**
+ * @brief In-place conversion from lengths to positions
+ *
+ * @param[in] vals input values
+ * @param[in] numvals number of values
+ * @param[in] t thread id
+ */
+template <class T>
+inline __device__ void lengths_to_positions(volatile T* vals, uint32_t numvals, unsigned int t)
+{
+  for (uint32_t n = 1; n < numvals; n <<= 1) {
+    __syncthreads();
+    if ((t & n) && (t < numvals)) vals[t] += vals[(t & ~n) | (n - 1)];
+  }
+}
+
+template <int block_size, typename Storage>
+static __device__ void encode_null_mask(orcenc_state_s* s,
+                                        bitmask_type const* pushdown_mask,
+                                        Storage& scan_storage,
+                                        int t)
+{
+  if (s->stream.ids[CI_PRESENT] < 0) return;
+
+  auto const column = *s->chunk.column;
+  while (s->present_rows < s->chunk.null_mask_num_rows or s->numvals > 0) {
+    // Number of rows read so far
+    auto present_rows = s->present_rows;
+    // valid_buf capacity is byte per thread in block
+    auto const buf_available_bits = encode_block_size * 8 - s->numvals;
+    // Number of rows for the block to process in this iteration
+    auto const nrows = min(s->chunk.null_mask_num_rows - present_rows, buf_available_bits);
+    // Number of rows for this thread to process in this iteration
+    auto const t_nrows = min(max(static_cast<int32_t>(nrows) - t * 8, 0), 8);
+    auto const row     = s->chunk.null_mask_start_row + present_rows + t * 8;
+
+    auto get_mask_byte = [&](bitmask_type const* mask, size_type offset) -> uint8_t {
+      if (t_nrows == 0) return 0;
+      if (mask == nullptr) return 0xff;
+
+      auto const begin_offset = row + offset;
+      auto const end_offset   = min(begin_offset + 8, offset + column.size());
+      auto const mask_word = cudf::detail::get_mask_offset_word(mask, 0, begin_offset, end_offset);
+      return mask_word & 0xff;
+    };
+
+    uint8_t pd_byte     = (1 << t_nrows) - 1;
+    uint32_t pd_set_cnt = t_nrows;
+    uint32_t offset     = t_nrows != 0 ? t * 8 : nrows;
+    if (pushdown_mask != nullptr) {
+      pd_byte    = get_mask_byte(pushdown_mask, 0) & ((1 << t_nrows) - 1);
+      pd_set_cnt = __popc(pd_byte);
+      // Scan the number of valid bits to get dst offset for each thread
+      cub::BlockScan<uint32_t, block_size>(scan_storage).ExclusiveSum(pd_set_cnt, offset);
+    }
+
+    auto const mask_byte = get_mask_byte(column.null_mask(), column.offset());
+    auto dst_offset      = offset + s->nnz;
+    auto vbuf_bit_idx    = [](int row) {
+      // valid_buf is a circular buffer with validity of 8 rows in each element
+      return row % (encode_block_size * 8);
+    };
+    if (dst_offset % 8 == 0 and pd_set_cnt == 8) {
+      s->valid_buf[vbuf_bit_idx(dst_offset) / 8] = mask_byte;
+    } else {
+      for (auto bit_idx = 0; bit_idx < t_nrows; ++bit_idx) {
+        // skip bits where pushdown mask is not set
+        if (not(pd_byte & (1 << bit_idx))) continue;
+        if (mask_byte & (1 << bit_idx)) {
+          set_bit(reinterpret_cast<uint32_t*>(s->valid_buf), vbuf_bit_idx(dst_offset++));
+        } else {
+          clear_bit(reinterpret_cast<uint32_t*>(s->valid_buf), vbuf_bit_idx(dst_offset++));
+        }
+      }
+    }
+
+    __syncthreads();
+    if (t == block_size - 1) {
+      // Number of loaded rows, available for encode
+      s->numvals += offset + pd_set_cnt;
+      // Number of loaded rows (different from present_rows because of pushdown masks)
+      s->nnz += offset + pd_set_cnt;
+    }
+    present_rows += nrows;
+    if (!t) { s->present_rows = present_rows; }
+    __syncthreads();
+
+    // RLE encode the present stream
+    if (s->numvals > ((present_rows < s->chunk.null_mask_num_rows) ? 130 * 8 : 0)) {
+      auto const flush      = (present_rows < s->chunk.null_mask_num_rows) ? 0 : 7;
+      auto const nbytes_out = (s->numvals + flush) / 8;
+      auto const nrows_encoded =
+        ByteRLE<CI_PRESENT, 0x1ff>(s, s->valid_buf, s->present_out / 8, nbytes_out, flush, t) * 8;
+
+      if (!t) {
+        // Number of rows encoded so far
+        s->present_out += nrows_encoded;
+        s->numvals -= min(s->numvals, nrows_encoded);
+      }
+      __syncthreads();
+    }
+  }
+
+  // reset shared state
+  if (t == 0) { s->nnz = 0; }
+}
+
+/**
+ * @brief Encode column data
+ *
+ * @param[in] chunks encoder chunks device array [column][rowgroup]
+ * @param[in, out] streams chunk streams device array [column][rowgroup]
+ */
+// blockDim {`encode_block_size`,1,1}
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpuEncodeOrcColumnData(device_2dspan<EncChunk const> chunks,
+                         device_2dspan<encoder_chunk_streams> streams)
+{
+  __shared__ __align__(16) orcenc_state_s state_g;
+  __shared__ union {
+    typename cub::BlockScan<uint32_t, block_size>::TempStorage scan_u32;
+    typename cub::BlockReduce<int32_t, block_size>::TempStorage i32;
+    typename cub::BlockReduce<int64_t, block_size>::TempStorage i64;
+    typename cub::BlockReduce<uint32_t, block_size>::TempStorage u32;
+    typename cub::BlockReduce<uint64_t, block_size>::TempStorage u64;
+  } temp_storage;
+
+  orcenc_state_s* const s = &state_g;
+  uint32_t col_id         = blockIdx.x;
+  uint32_t group_id       = blockIdx.y;
+  int t                   = threadIdx.x;
+  if (t == 0) {
+    s->chunk                = chunks[col_id][group_id];
+    s->stream               = streams[col_id][group_id];
+    s->cur_row              = 0;
+    s->present_rows         = 0;
+    s->present_out          = 0;
+    s->numvals              = 0;
+    s->numlengths           = 0;
+    s->nnz                  = 0;
+    s->strm_pos[CI_DATA]    = 0;
+    s->strm_pos[CI_PRESENT] = 0;
+    s->strm_pos[CI_INDEX]   = 0;
+    // Dictionary data is encoded in a separate kernel
+    s->strm_pos[CI_DATA2] =
+      s->chunk.encoding_kind == DICTIONARY_V2 ? s->stream.lengths[CI_DATA2] : 0;
+    s->strm_pos[CI_DICTIONARY] =
+      s->chunk.encoding_kind == DICTIONARY_V2 ? s->stream.lengths[CI_DICTIONARY] : 0;
+  }
+  __syncthreads();
+
+  auto const pushdown_mask = [&]() -> cudf::bitmask_type const* {
+    auto const parent_index = s->chunk.column->parent_index;
+    if (!parent_index.has_value()) return nullptr;
+    return chunks[parent_index.value()][0].column->pushdown_mask;
+  }();
+
+  encode_null_mask<block_size>(s, pushdown_mask, temp_storage.scan_u32, t);
+  __syncthreads();
+
+  auto const column = *s->chunk.column;
+  while (s->cur_row < s->chunk.num_rows || s->numvals + s->numlengths != 0) {
+    // Fetch non-null values
+    auto const length_stream_only = s->chunk.type_kind == LIST or s->chunk.type_kind == MAP;
+    if (not length_stream_only && s->stream.data_ptrs[CI_DATA] == nullptr) {
+      // Pass-through
+      __syncthreads();
+      if (!t) {
+        s->cur_row           = s->chunk.num_rows;
+        s->strm_pos[CI_DATA] = s->chunk.num_rows * s->chunk.dtype_len;
+      }
+    } else if (s->cur_row < s->chunk.num_rows) {
+      uint32_t maxnumvals = (s->chunk.type_kind == BOOLEAN) ? 2048 : 1024;
+      uint32_t nrows =
+        min(min(s->chunk.num_rows - s->cur_row, maxnumvals - max(s->numvals, s->numlengths)),
+            encode_block_size);
+      auto const row = s->chunk.start_row + s->cur_row + t;
+
+      auto const is_value_valid = [&]() {
+        if (t >= nrows) return false;
+        return bit_value_or(pushdown_mask, column.offset() + row, true) and
+               bit_value_or(column.null_mask(), column.offset() + row, true);
+      }();
+      s->buf.u32[t] = is_value_valid ? 1u : 0u;
+
+      // TODO: Could use a faster reduction relying on _popc() for the initial phase
+      lengths_to_positions(s->buf.u32, encode_block_size, t);
+      __syncthreads();
+      if (is_value_valid) {
+        int nz_idx = (s->nnz + s->buf.u32[t] - 1) & (maxnumvals - 1);
+        switch (s->chunk.type_kind) {
+          case INT:
+          case DATE:
+          case FLOAT: s->vals.u32[nz_idx] = column.element<uint32_t>(row); break;
+          case DOUBLE:
+          case LONG: s->vals.u64[nz_idx] = column.element<uint64_t>(row); break;
+          case SHORT: s->vals.u32[nz_idx] = column.element<uint16_t>(row); break;
+          case BOOLEAN:
+          case BYTE: s->vals.u8[nz_idx] = column.element<uint8_t>(row); break;
+          case TIMESTAMP: {
+            int64_t ts          = column.element<int64_t>(row);
+            int32_t ts_scale    = powers_of_ten[9 - min(s->chunk.scale, 9)];
+            int64_t seconds     = ts / ts_scale;
+            int64_t nanos       = (ts - seconds * ts_scale);
+            s->vals.i64[nz_idx] = seconds - orc_utc_epoch;
+            if (nanos != 0) {
+              // Trailing zeroes are encoded in the lower 3-bits
+              uint32_t zeroes = 0;
+              nanos *= powers_of_ten[min(s->chunk.scale, 9)];
+              if (!(nanos % 100)) {
+                nanos /= 100;
+                zeroes = 1;
+                while (zeroes < 7 && !(nanos % 10)) {
+                  nanos /= 10;
+                  zeroes++;
+                }
+              }
+              nanos = (nanos << 3) + zeroes;
+            }
+            s->lengths.u64[nz_idx] = nanos;
+            break;
+          }
+          case STRING:
+            if (s->chunk.encoding_kind == DICTIONARY_V2) {
+              uint32_t dict_idx = s->chunk.dict_index[row];
+              if (dict_idx > 0x7fff'ffffu) {
+                dict_idx = s->chunk.dict_index[dict_idx & 0x7fff'ffffu];
+              }
+              s->vals.u32[nz_idx] = dict_idx;
+            } else {
+              string_view value                       = column.element<string_view>(row);
+              s->u.strenc.str_data[s->buf.u32[t] - 1] = value.data();
+              s->lengths.u32[nz_idx]                  = value.size_bytes();
+            }
+            break;
+            // Reusing the lengths array for the scale stream
+            // Note: can be written in a faster manner, given that all values are equal
+          case DECIMAL: s->lengths.u32[nz_idx] = zigzag(s->chunk.scale); break;
+          case LIST:
+          case MAP: {
+            auto const& offsets = column.child(lists_column_view::offsets_column_index);
+            // Compute list length from the offsets
+            s->lengths.u32[nz_idx] = offsets.element<size_type>(row + 1 + column.offset()) -
+                                     offsets.element<size_type>(row + column.offset());
+          } break;
+          default: break;
+        }
+      }
+      __syncthreads();
+      if (s->chunk.type_kind == STRING && s->chunk.encoding_kind != DICTIONARY_V2) {
+        // Store string data
+        uint32_t nz     = s->buf.u32[511];
+        uint32_t nz_idx = (s->nnz + t) & 0x3ff;
+        uint32_t len    = (t < nz && s->u.strenc.str_data[t]) ? s->lengths.u32[nz_idx] : 0;
+        StoreStringData(s->stream.data_ptrs[CI_DATA] + s->strm_pos[CI_DATA], &s->u.strenc, len, t);
+        if (!t) { s->strm_pos[CI_DATA] += s->u.strenc.char_count; }
+        __syncthreads();
+      } else if (s->chunk.type_kind == BOOLEAN) {
+        // bool8 -> 8x bool1
+        uint32_t nz = s->buf.u32[511];
+        uint8_t n   = ((s->nnz + nz) - (s->nnz & ~7) + 7) >> 3;
+        if (t < n) {
+          uint32_t idx8                              = (s->nnz & ~7) + (t << 3);
+          s->lengths.u8[((s->nnz >> 3) + t) & 0x1ff] = ((s->vals.u8[(idx8 + 0) & 0x7ff] & 1) << 7) |
+                                                       ((s->vals.u8[(idx8 + 1) & 0x7ff] & 1) << 6) |
+                                                       ((s->vals.u8[(idx8 + 2) & 0x7ff] & 1) << 5) |
+                                                       ((s->vals.u8[(idx8 + 3) & 0x7ff] & 1) << 4) |
+                                                       ((s->vals.u8[(idx8 + 4) & 0x7ff] & 1) << 3) |
+                                                       ((s->vals.u8[(idx8 + 5) & 0x7ff] & 1) << 2) |
+                                                       ((s->vals.u8[(idx8 + 6) & 0x7ff] & 1) << 1) |
+                                                       ((s->vals.u8[(idx8 + 7) & 0x7ff] & 1) << 0);
+        }
+        __syncthreads();
+      }
+      if (!t) {
+        uint32_t nz = s->buf.u32[511];
+        s->nnz += nz;
+        s->numvals += nz;
+        s->numlengths += (s->chunk.type_kind == TIMESTAMP || s->chunk.type_kind == DECIMAL ||
+                          s->chunk.type_kind == LIST || s->chunk.type_kind == MAP ||
+                          (s->chunk.type_kind == STRING && s->chunk.encoding_kind != DICTIONARY_V2))
+                           ? nz
+                           : 0;
+        s->cur_row += nrows;
+      }
+      __syncthreads();
+      // Encode values
+      if (s->numvals > 0) {
+        uint32_t flush = (s->cur_row == s->chunk.num_rows) ? 7 : 0, n;
+        switch (s->chunk.type_kind) {
+          case SHORT:
+          case INT:
+          case DATE:
+            n = IntegerRLE<CI_DATA, int32_t, true, 0x3ff, block_size>(
+              s, s->vals.i32, s->nnz - s->numvals, s->numvals, t, temp_storage.i32);
+            break;
+          case LONG:
+          case TIMESTAMP:
+            n = IntegerRLE<CI_DATA, int64_t, true, 0x3ff, block_size>(
+              s, s->vals.i64, s->nnz - s->numvals, s->numvals, t, temp_storage.i64);
+            break;
+          case BYTE:
+            n = ByteRLE<CI_DATA, 0x3ff>(s, s->vals.u8, s->nnz - s->numvals, s->numvals, flush, t);
+            break;
+          case BOOLEAN:
+            n = ByteRLE<CI_DATA, 0x1ff>(s,
+                                        s->lengths.u8,
+                                        (s->nnz - s->numvals + flush) >> 3,
+                                        (s->numvals + flush) >> 3,
+                                        flush,
+                                        t) *
+                8;
+            break;
+          case FLOAT:
+            StoreBytes<CI_DATA, 0xfff>(s, s->vals.u8, (s->nnz - s->numvals) * 4, s->numvals * 4, t);
+            n = s->numvals;
+            break;
+          case DOUBLE:
+            StoreBytes<CI_DATA, 0x1fff>(
+              s, s->vals.u8, (s->nnz - s->numvals) * 8, s->numvals * 8, t);
+            n = s->numvals;
+            break;
+          case STRING:
+            if (s->chunk.encoding_kind == DICTIONARY_V2) {
+              n = IntegerRLE<CI_DATA, uint32_t, false, 0x3ff, block_size>(
+                s, s->vals.u32, s->nnz - s->numvals, s->numvals, t, temp_storage.u32);
+            } else {
+              n = s->numvals;
+            }
+            break;
+          case DECIMAL: {
+            if (is_value_valid) {
+              auto const id = column.type().id();
+              __uint128_t const zz_val =
+                id == type_id::DECIMAL32   ? zigzag(column.element<int32_t>(row))
+                : id == type_id::DECIMAL64 ? zigzag(column.element<int64_t>(row))
+                                           : zigzag(column.element<__int128_t>(row));
+              auto const offset =
+                (row == s->chunk.start_row) ? 0 : s->chunk.decimal_offsets[row - 1];
+              StoreVarint(s->stream.data_ptrs[CI_DATA] + offset, zz_val);
+            }
+            n = s->numvals;
+          } break;
+          default: n = s->numvals; break;
+        }
+        __syncthreads();
+        if (!t) { s->numvals -= min(n, s->numvals); }
+      }
+      // Encode secondary stream values
+      if (s->numlengths > 0) {
+        uint32_t n;
+        switch (s->chunk.type_kind) {
+          case TIMESTAMP:
+            n = IntegerRLE<CI_DATA2, uint64_t, false, 0x3ff, block_size>(
+              s, s->lengths.u64, s->nnz - s->numlengths, s->numlengths, t, temp_storage.u64);
+            break;
+          case DECIMAL:
+          case LIST:
+          case MAP:
+          case STRING:
+            n = IntegerRLE<CI_DATA2, uint32_t, false, 0x3ff, block_size>(
+              s, s->lengths.u32, s->nnz - s->numlengths, s->numlengths, t, temp_storage.u32);
+            break;
+          default: n = s->numlengths; break;
+        }
+        __syncthreads();
+        if (!t) { s->numlengths -= min(n, s->numlengths); }
+      }
+    }
+    __syncthreads();
+  }
+  __syncthreads();
+  if (t <= CI_PRESENT && s->stream.ids[t] >= 0) {
+    // Update actual compressed length
+    // (not needed for decimal data, whose exact size is known before encode)
+    if (!(t == CI_DATA && s->chunk.type_kind == DECIMAL))
+      streams[col_id][group_id].lengths[t] = s->strm_pos[t];
+    if (!s->stream.data_ptrs[t]) {
+      streams[col_id][group_id].data_ptrs[t] =
+        static_cast<uint8_t*>(const_cast<void*>(column.head())) +
+        (column.offset() + s->chunk.start_row) * s->chunk.dtype_len;
+    }
+  }
+}
+
+/**
+ * @brief Encode column dictionaries
+ *
+ * @param[in] stripes Stripe dictionaries device array
+ * @param[in] columns Pre-order flattened device array of ORC column views
+ * @param[in] chunks EncChunk device array [rowgroup][column]
+ * @param[in] num_columns Number of columns
+ */
+// blockDim {512,1,1}
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpuEncodeStringDictionaries(stripe_dictionary const* stripes,
+                              device_span<orc_column_device_view const> columns,
+                              device_2dspan<EncChunk const> chunks,
+                              device_2dspan<encoder_chunk_streams> streams)
+{
+  __shared__ __align__(16) orcenc_state_s state_g;
+  __shared__ typename cub::BlockReduce<uint32_t, block_size>::TempStorage temp_storage;
+
+  orcenc_state_s* const s = &state_g;
+  uint32_t stripe_id      = blockIdx.x;
+  uint32_t cid            = (blockIdx.y) ? CI_DICTIONARY : CI_DATA2;
+  int t                   = threadIdx.x;
+
+  if (t == 0) s->u.dict_stripe = &stripes[stripe_id];
+
+  __syncthreads();
+  auto const strm_ptr = &streams[s->u.dict_stripe->column_idx][s->u.dict_stripe->start_rowgroup];
+  if (t == 0) {
+    s->chunk         = chunks[s->u.dict_stripe->column_idx][s->u.dict_stripe->start_rowgroup];
+    s->stream        = *strm_ptr;
+    s->strm_pos[cid] = 0;
+    s->numlengths    = 0;
+    s->nrows         = s->u.dict_stripe->entry_count;
+    s->cur_row       = 0;
+  }
+  auto const string_column = columns[s->u.dict_stripe->column_idx];
+  auto const dict_data     = s->u.dict_stripe->data;
+  __syncthreads();
+  if (s->chunk.encoding_kind != DICTIONARY_V2) {
+    return;  // This column isn't using dictionary encoding -> bail out
+  }
+
+  while (s->cur_row < s->nrows || s->numlengths != 0) {
+    uint32_t numvals    = min(s->nrows - s->cur_row, min(1024 - s->numlengths, 512));
+    uint32_t string_idx = (t < numvals) ? dict_data[s->cur_row + t] : 0;
+    if (cid == CI_DICTIONARY) {
+      // Encoding string contents
+      char const* ptr = nullptr;
+      uint32_t count  = 0;
+      if (t < numvals) {
+        auto string_val = string_column.element<string_view>(string_idx);
+        ptr             = string_val.data();
+        count           = string_val.size_bytes();
+      }
+      s->u.strenc.str_data[t] = ptr;
+      StoreStringData(s->stream.data_ptrs[CI_DICTIONARY] + s->strm_pos[CI_DICTIONARY],
+                      &s->u.strenc,
+                      (ptr) ? count : 0,
+                      t);
+      if (!t) { s->strm_pos[CI_DICTIONARY] += s->u.strenc.char_count; }
+    } else {
+      // Encoding string lengths
+      uint32_t count =
+        (t < numvals)
+          ? static_cast<uint32_t>(string_column.element<string_view>(string_idx).size_bytes())
+          : 0;
+      uint32_t nz_idx = (s->cur_row + t) & 0x3ff;
+      if (t < numvals) s->lengths.u32[nz_idx] = count;
+      __syncthreads();
+      if (s->numlengths + numvals > 0) {
+        uint32_t n = IntegerRLE<CI_DATA2, uint32_t, false, 0x3ff, block_size>(
+          s, s->lengths.u32, s->cur_row, s->numlengths + numvals, t, temp_storage);
+        __syncthreads();
+        if (!t) {
+          s->numlengths += numvals;
+          s->numlengths -= min(n, s->numlengths);
+        }
+      }
+    }
+    if (t == 0) { s->cur_row += numvals; }
+    __syncthreads();
+  }
+  if (t == 0) { strm_ptr->lengths[cid] = s->strm_pos[cid]; }
+}
+
+/**
+ * @brief Merge chunked column data into a single contiguous stream
+ *
+ * @param[in,out] strm_desc StripeStream device array [stripe][stream]
+ * @param[in,out] streams List of encoder chunk streams [column][rowgroup]
+ */
+// blockDim {compact_streams_block_size,1,1}
+__global__ void __launch_bounds__(compact_streams_block_size)
+  gpuCompactOrcDataStreams(device_2dspan<StripeStream> strm_desc,
+                           device_2dspan<encoder_chunk_streams> streams)
+{
+  __shared__ __align__(16) StripeStream ss;
+
+  auto const stripe_id = blockIdx.x;
+  auto const stream_id = blockIdx.y;
+  auto const t         = threadIdx.x;
+
+  if (t == 0) { ss = strm_desc[stripe_id][stream_id]; }
+  __syncthreads();
+
+  if (ss.data_ptr == nullptr) { return; }
+
+  auto const cid = ss.stream_type;
+  auto dst_ptr   = ss.data_ptr;
+  for (auto group = ss.first_chunk_id; group < ss.first_chunk_id + ss.num_chunks; ++group) {
+    auto const len = streams[ss.column_id][group].lengths[cid];
+    if (len > 0) {
+      auto const src_ptr = streams[ss.column_id][group].data_ptrs[cid];
+      for (uint32_t i = t; i < len; i += blockDim.x) {
+        dst_ptr[i] = src_ptr[i];
+      }
+      __syncthreads();
+    }
+    if (t == 0) { streams[ss.column_id][group].data_ptrs[cid] = dst_ptr; }
+    dst_ptr += len;
+  }
+}
+
+/**
+ * @brief Initializes compression input/output structures
+ *
+ * @param[in] strm_desc StripeStream device array [stripe][stream]
+ * @param[in] chunks EncChunk device array [rowgroup][column]
+ * @param[out] inputs Per-block compression input buffers
+ * @param[out] outputs Per-block compression output buffers
+ * @param[out] results Per-block compression status
+ * @param[in] compressed_bfr Compression output buffer
+ * @param[in] comp_blk_size Compression block size
+ * @param[in] max_comp_blk_size Max size of any block after compression
+ * @param[in] comp_block_align Required alignment for compressed blocks
+ */
+// blockDim {256,1,1}
+__global__ void __launch_bounds__(256)
+  gpuInitCompressionBlocks(device_2dspan<StripeStream const> strm_desc,
+                           device_2dspan<encoder_chunk_streams> streams,  // const?
+                           device_span<device_span<uint8_t const>> inputs,
+                           device_span<device_span<uint8_t>> outputs,
+                           device_span<compression_result> results,
+                           device_span<uint8_t> compressed_bfr,
+                           uint32_t comp_blk_size,
+                           uint32_t max_comp_blk_size,
+                           uint32_t comp_block_align)
+{
+  __shared__ __align__(16) StripeStream ss;
+  __shared__ uint8_t* volatile uncomp_base_g;
+
+  auto const padded_block_header_size = util::round_up_unsafe(block_header_size, comp_block_align);
+  auto const padded_comp_block_size   = util::round_up_unsafe(max_comp_blk_size, comp_block_align);
+
+  auto const stripe_id = blockIdx.x;
+  auto const stream_id = blockIdx.y;
+  uint32_t t           = threadIdx.x;
+  uint32_t num_blocks;
+  uint8_t *src, *dst;
+
+  if (t == 0) {
+    ss            = strm_desc[stripe_id][stream_id];
+    uncomp_base_g = streams[ss.column_id][ss.first_chunk_id].data_ptrs[ss.stream_type];
+  }
+  __syncthreads();
+  src        = uncomp_base_g;
+  dst        = compressed_bfr.data() + ss.bfr_offset;
+  num_blocks = (ss.stream_size > 0) ? (ss.stream_size - 1) / comp_blk_size + 1 : 1;
+  for (uint32_t b = t; b < num_blocks; b += 256) {
+    uint32_t blk_size = min(comp_blk_size, ss.stream_size - min(b * comp_blk_size, ss.stream_size));
+    inputs[ss.first_block + b] = {src + b * comp_blk_size, blk_size};
+    auto const dst_offset =
+      padded_block_header_size + b * (padded_block_header_size + padded_comp_block_size);
+    outputs[ss.first_block + b] = {dst + dst_offset, max_comp_blk_size};
+    results[ss.first_block + b] = {0, compression_status::FAILURE};
+  }
+}
+
+/**
+ * @brief Compacts compressed blocks in a single contiguous stream, and update 3-byte block length
+ *fields
+ *
+ * @param[in,out] strm_desc StripeStream device array [stripe][stream]
+ * @param[in] chunks EncChunk device array [rowgroup][column]
+ * @param[in] inputs Per-block compression input buffers
+ * @param[out] outputs Per-block compression output buffers
+ * @param[out] results Per-block compression status
+ * @param[in] compressed_bfr Compression output buffer
+ * @param[in] comp_blk_size Compression block size
+ * @param[in] max_comp_blk_size Max size of any block after compression
+ */
+// blockDim {1024,1,1}
+__global__ void __launch_bounds__(1024)
+  gpuCompactCompressedBlocks(device_2dspan<StripeStream> strm_desc,
+                             device_span<device_span<uint8_t const> const> inputs,
+                             device_span<device_span<uint8_t> const> outputs,
+                             device_span<compression_result> results,
+                             device_span<uint8_t> compressed_bfr,
+                             uint32_t comp_blk_size,
+                             uint32_t max_comp_blk_size)
+{
+  __shared__ __align__(16) StripeStream ss;
+  __shared__ uint8_t const* volatile comp_src_g;
+  __shared__ uint32_t volatile comp_len_g;
+
+  auto const stripe_id = blockIdx.x;
+  auto const stream_id = blockIdx.y;
+  uint32_t t           = threadIdx.x;
+  uint32_t num_blocks, b, blk_size;
+  uint8_t const* src;
+  uint8_t* dst;
+
+  if (t == 0) ss = strm_desc[stripe_id][stream_id];
+  __syncthreads();
+
+  num_blocks = (ss.stream_size > 0) ? (ss.stream_size - 1) / comp_blk_size + 1 : 0;
+  dst        = compressed_bfr.data() + ss.bfr_offset;
+  b          = 0;
+  do {
+    if (t == 0) {
+      auto const src_len =
+        min(comp_blk_size, ss.stream_size - min(b * comp_blk_size, ss.stream_size));
+      auto dst_len = (results[ss.first_block + b].status == compression_status::SUCCESS)
+                       ? results[ss.first_block + b].bytes_written
+                       : src_len;
+      uint32_t blk_size24{};
+      // Only use the compressed block if it's smaller than the uncompressed
+      // If compression failed, dst_len == src_len, so the uncompressed block will be used
+      if (src_len < dst_len) {
+        // Copy from uncompressed source
+        src                                       = inputs[ss.first_block + b].data();
+        results[ss.first_block + b].bytes_written = src_len;
+        dst_len                                   = src_len;
+        blk_size24                                = dst_len * 2 + 1;
+      } else {
+        // Compressed block
+        src        = outputs[ss.first_block + b].data();
+        blk_size24 = dst_len * 2 + 0;
+      }
+      dst[0]     = static_cast<uint8_t>(blk_size24 >> 0);
+      dst[1]     = static_cast<uint8_t>(blk_size24 >> 8);
+      dst[2]     = static_cast<uint8_t>(blk_size24 >> 16);
+      comp_src_g = src;
+      comp_len_g = dst_len;
+    }
+    __syncthreads();
+    src      = comp_src_g;
+    blk_size = comp_len_g;
+    dst += 3;  // skip over length written by thread0
+    if (src != dst) {
+      for (uint32_t i = 0; i < blk_size; i += 1024) {
+        uint8_t v = (i + t < blk_size) ? src[i + t] : 0;
+        __syncthreads();
+        if (i + t < blk_size) { dst[i + t] = v; }
+      }
+    }
+    dst += blk_size;
+    __syncthreads();
+  } while (++b < num_blocks);
+  // Update stripe stream with the compressed size
+  if (t == 0) {
+    strm_desc[stripe_id][stream_id].stream_size =
+      static_cast<uint32_t>(dst - (compressed_bfr.data() + ss.bfr_offset));
+  }
+}
+
+void EncodeOrcColumnData(device_2dspan<EncChunk const> chunks,
+                         device_2dspan<encoder_chunk_streams> streams,
+                         rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(encode_block_size, 1);  // `encode_block_size` threads per chunk
+  dim3 dim_grid(chunks.size().first, chunks.size().second);
+  gpuEncodeOrcColumnData<encode_block_size>
+    <<<dim_grid, dim_block, 0, stream.value()>>>(chunks, streams);
+}
+
+void EncodeStripeDictionaries(stripe_dictionary const* stripes,
+                              device_span<orc_column_device_view const> columns,
+                              device_2dspan<EncChunk const> chunks,
+                              uint32_t num_string_columns,
+                              uint32_t num_stripes,
+                              device_2dspan<encoder_chunk_streams> enc_streams,
+                              rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(512, 1);  // 512 threads per dictionary
+  dim3 dim_grid(num_string_columns * num_stripes, 2);
+  gpuEncodeStringDictionaries<512>
+    <<<dim_grid, dim_block, 0, stream.value()>>>(stripes, columns, chunks, enc_streams);
+}
+
+void CompactOrcDataStreams(device_2dspan<StripeStream> strm_desc,
+                           device_2dspan<encoder_chunk_streams> enc_streams,
+                           rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(compact_streams_block_size, 1);
+  dim3 dim_grid(strm_desc.size().first, strm_desc.size().second);
+  gpuCompactOrcDataStreams<<<dim_grid, dim_block, 0, stream.value()>>>(strm_desc, enc_streams);
+}
+
+std::optional<writer_compression_statistics> CompressOrcDataStreams(
+  device_span<uint8_t> compressed_data,
+  uint32_t num_compressed_blocks,
+  CompressionKind compression,
+  uint32_t comp_blk_size,
+  uint32_t max_comp_blk_size,
+  uint32_t comp_block_align,
+  bool collect_statistics,
+  device_2dspan<StripeStream> strm_desc,
+  device_2dspan<encoder_chunk_streams> enc_streams,
+  device_span<compression_result> comp_res,
+  rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<device_span<uint8_t const>> comp_in(num_compressed_blocks, stream);
+  rmm::device_uvector<device_span<uint8_t>> comp_out(num_compressed_blocks, stream);
+
+  dim3 dim_block_init(256, 1);
+  dim3 dim_grid(strm_desc.size().first, strm_desc.size().second);
+  gpuInitCompressionBlocks<<<dim_grid, dim_block_init, 0, stream.value()>>>(strm_desc,
+                                                                            enc_streams,
+                                                                            comp_in,
+                                                                            comp_out,
+                                                                            comp_res,
+                                                                            compressed_data,
+                                                                            comp_blk_size,
+                                                                            max_comp_blk_size,
+                                                                            comp_block_align);
+
+  if (compression == SNAPPY) {
+    try {
+      if (nvcomp::is_compression_disabled(nvcomp::compression_type::SNAPPY)) {
+        gpu_snap(comp_in, comp_out, comp_res, stream);
+      } else {
+        nvcomp::batched_compress(
+          nvcomp::compression_type::SNAPPY, comp_in, comp_out, comp_res, stream);
+      }
+    } catch (...) {
+      // There was an error in compressing so set an error status for each block
+      thrust::for_each(
+        rmm::exec_policy(stream),
+        comp_res.begin(),
+        comp_res.end(),
+        [] __device__(compression_result & stat) { stat.status = compression_status::FAILURE; });
+      // Since SNAPPY is the default compression (may not be explicitly requested), fall back to
+      // writing without compression
+      CUDF_LOG_WARN("ORC writer: compression failed, writing uncompressed data");
+    }
+  } else if (compression == ZLIB) {
+    if (auto const reason = nvcomp::is_compression_disabled(nvcomp::compression_type::DEFLATE);
+        reason) {
+      CUDF_FAIL("Compression error: " + reason.value());
+    }
+    nvcomp::batched_compress(
+      nvcomp::compression_type::DEFLATE, comp_in, comp_out, comp_res, stream);
+  } else if (compression == ZSTD) {
+    if (auto const reason = nvcomp::is_compression_disabled(nvcomp::compression_type::ZSTD);
+        reason) {
+      CUDF_FAIL("Compression error: " + reason.value());
+    }
+    nvcomp::batched_compress(nvcomp::compression_type::ZSTD, comp_in, comp_out, comp_res, stream);
+  } else if (compression != NONE) {
+    CUDF_FAIL("Unsupported compression type");
+  }
+
+  dim3 dim_block_compact(1024, 1);
+  gpuCompactCompressedBlocks<<<dim_grid, dim_block_compact, 0, stream.value()>>>(
+    strm_desc, comp_in, comp_out, comp_res, compressed_data, comp_blk_size, max_comp_blk_size);
+
+  if (collect_statistics) {
+    return cudf::io::collect_compression_statistics(comp_in, comp_res, stream);
+  } else {
+    return std::nullopt;
+  }
+}
+
+}  // namespace gpu
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/stripe_init.cu b/cpp/src/io/orc/stripe_init.cu
new file mode 100644
index 0000000..8eeca50
--- /dev/null
+++ b/cpp/src/io/orc/stripe_init.cu
@@ -0,0 +1,612 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "orc_gpu.hpp"
+
+#include <cudf/io/orc_types.hpp>
+#include <io/utilities/block_utils.cuh>
+
+#include <cub/cub.cuh>
+#include <rmm/cuda_stream_view.hpp>
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+
+namespace cudf {
+namespace io {
+namespace orc {
+namespace gpu {
+
+struct comp_in_out {
+  uint8_t const* in_ptr{};
+  size_t in_size{};
+  uint8_t* out_ptr{};
+  size_t out_size{};
+};
+struct compressed_stream_s {
+  CompressedStreamInfo info{};
+  comp_in_out ctl{};
+};
+
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128, 8) gpuParseCompressedStripeData(
+  CompressedStreamInfo* strm_info, int32_t num_streams, uint32_t block_size, uint32_t log2maxcr)
+{
+  __shared__ compressed_stream_s strm_g[4];
+
+  compressed_stream_s* const s = &strm_g[threadIdx.x / 32];
+  int strm_id                  = blockIdx.x * 4 + (threadIdx.x / 32);
+  int lane_id                  = threadIdx.x % 32;
+
+  if (strm_id < num_streams && lane_id == 0) { s->info = strm_info[strm_id]; }
+
+  __syncthreads();
+  if (strm_id < num_streams) {
+    // Walk through the compressed blocks
+    uint8_t const* cur                   = s->info.compressed_data;
+    uint8_t const* end                   = cur + s->info.compressed_data_size;
+    uint8_t* uncompressed                = s->info.uncompressed_data;
+    size_t max_uncompressed_size         = 0;
+    uint32_t max_uncompressed_block_size = 0;
+    uint32_t num_compressed_blocks       = 0;
+    uint32_t num_uncompressed_blocks     = 0;
+    while (cur + block_header_size < end) {
+      uint32_t block_len = shuffle((lane_id == 0) ? cur[0] | (cur[1] << 8) | (cur[2] << 16) : 0);
+      auto const is_uncompressed = static_cast<bool>(block_len & 1);
+      uint32_t uncompressed_size;
+      device_span<uint8_t const>* init_in_ctl = nullptr;
+      device_span<uint8_t>* init_out_ctl      = nullptr;
+      block_len >>= 1;
+      cur += block_header_size;
+      if (block_len > block_size || cur + block_len > end) {
+        // Fatal
+        num_compressed_blocks       = 0;
+        max_uncompressed_size       = 0;
+        max_uncompressed_block_size = 0;
+        break;
+      }
+      // TBD: For some codecs like snappy, it wouldn't be too difficult to get the actual
+      // uncompressed size and avoid waste due to block size alignment For now, rely on the max
+      // compression ratio to limit waste for the most extreme cases (small single-block streams)
+      uncompressed_size = (is_uncompressed)                         ? block_len
+                          : (block_len < (block_size >> log2maxcr)) ? block_len << log2maxcr
+                                                                    : block_size;
+      if (is_uncompressed) {
+        if (uncompressed_size <= 32) {
+          // For short blocks, copy the uncompressed data to output
+          if (uncompressed &&
+              max_uncompressed_size + uncompressed_size <= s->info.max_uncompressed_size &&
+              lane_id < uncompressed_size) {
+            uncompressed[max_uncompressed_size + lane_id] = cur[lane_id];
+          }
+        } else {
+          init_in_ctl =
+            (s->info.copy_in_ctl && num_uncompressed_blocks < s->info.num_uncompressed_blocks)
+              ? &s->info.copy_in_ctl[num_uncompressed_blocks]
+              : nullptr;
+          init_out_ctl =
+            (s->info.copy_out_ctl && num_uncompressed_blocks < s->info.num_uncompressed_blocks)
+              ? &s->info.copy_out_ctl[num_uncompressed_blocks]
+              : nullptr;
+          num_uncompressed_blocks++;
+        }
+      } else {
+        init_in_ctl = (s->info.dec_in_ctl && num_compressed_blocks < s->info.num_compressed_blocks)
+                        ? &s->info.dec_in_ctl[num_compressed_blocks]
+                        : nullptr;
+        init_out_ctl =
+          (s->info.dec_out_ctl && num_compressed_blocks < s->info.num_compressed_blocks)
+            ? &s->info.dec_out_ctl[num_compressed_blocks]
+            : nullptr;
+        num_compressed_blocks++;
+      }
+      if (!lane_id && init_in_ctl) {
+        s->ctl = {cur, block_len, uncompressed + max_uncompressed_size, uncompressed_size};
+      }
+      __syncwarp();
+      if (init_in_ctl && lane_id == 0) {
+        *init_in_ctl  = {s->ctl.in_ptr, s->ctl.in_size};
+        *init_out_ctl = {s->ctl.out_ptr, s->ctl.out_size};
+      }
+      cur += block_len;
+      max_uncompressed_size += uncompressed_size;
+      max_uncompressed_block_size = max(max_uncompressed_block_size, uncompressed_size);
+    }
+    __syncwarp();
+    if (!lane_id) {
+      s->info.num_compressed_blocks       = num_compressed_blocks;
+      s->info.num_uncompressed_blocks     = num_uncompressed_blocks;
+      s->info.max_uncompressed_size       = max_uncompressed_size;
+      s->info.max_uncompressed_block_size = max_uncompressed_block_size;
+    }
+  }
+
+  __syncthreads();
+  if (strm_id < num_streams && lane_id == 0) strm_info[strm_id] = s->info;
+}
+
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128, 8)
+  gpuPostDecompressionReassemble(CompressedStreamInfo* strm_info, int32_t num_streams)
+{
+  __shared__ compressed_stream_s strm_g[4];
+
+  compressed_stream_s* const s = &strm_g[threadIdx.x / 32];
+  int strm_id                  = blockIdx.x * 4 + (threadIdx.x / 32);
+  int lane_id                  = threadIdx.x % 32;
+
+  if (strm_id < num_streams && lane_id == 0) s->info = strm_info[strm_id];
+
+  __syncthreads();
+  if (strm_id < num_streams &&
+      s->info.num_compressed_blocks + s->info.num_uncompressed_blocks > 0 &&
+      s->info.max_uncompressed_size > 0) {
+    // Walk through the compressed blocks
+    uint8_t const* cur              = s->info.compressed_data;
+    uint8_t const* end              = cur + s->info.compressed_data_size;
+    auto dec_out                    = s->info.dec_out_ctl;
+    auto dec_result                 = s->info.dec_res;
+    uint8_t* uncompressed_actual    = s->info.uncompressed_data;
+    uint8_t* uncompressed_estimated = uncompressed_actual;
+    uint32_t num_compressed_blocks  = 0;
+    uint32_t max_compressed_blocks  = s->info.num_compressed_blocks;
+
+    while (cur + block_header_size < end) {
+      uint32_t block_len = shuffle((lane_id == 0) ? cur[0] | (cur[1] << 8) | (cur[2] << 16) : 0);
+      auto const is_uncompressed = static_cast<bool>(block_len & 1);
+      uint32_t uncompressed_size_est, uncompressed_size_actual;
+      block_len >>= 1;
+      cur += block_header_size;
+      if (cur + block_len > end) { break; }
+      if (is_uncompressed) {
+        uncompressed_size_est    = block_len;
+        uncompressed_size_actual = block_len;
+      } else {
+        if (num_compressed_blocks > max_compressed_blocks) { break; }
+        uint32_t const dst_size      = dec_out[num_compressed_blocks].size();
+        uncompressed_size_est        = shuffle((lane_id == 0) ? dst_size : 0);
+        uint32_t const bytes_written = dec_result[num_compressed_blocks].bytes_written;
+        uncompressed_size_actual     = shuffle((lane_id == 0) ? bytes_written : 0);
+      }
+      // In practice, this should never happen with a well-behaved writer, as we would expect the
+      // uncompressed size to always be equal to the compression block size except for the last
+      // block
+      if (uncompressed_actual < uncompressed_estimated) {
+        // warp-level memmove
+        for (int i = lane_id; i < (int)uncompressed_size_actual; i += 32) {
+          uncompressed_actual[i] = uncompressed_estimated[i];
+        }
+      }
+      cur += block_len;
+      num_compressed_blocks += 1 - is_uncompressed;
+      uncompressed_estimated += uncompressed_size_est;
+      uncompressed_actual += uncompressed_size_actual;
+    }
+    // Update info with actual uncompressed size
+    if (!lane_id) {
+      size_t total_uncompressed_size = uncompressed_actual - s->info.uncompressed_data;
+      // Set uncompressed size to zero if there were any errors
+      strm_info[strm_id].max_uncompressed_size =
+        (num_compressed_blocks == s->info.num_compressed_blocks) ? total_uncompressed_size : 0;
+    }
+  }
+}
+
+/**
+ * @brief Shared mem state for gpuParseRowGroupIndex
+ */
+struct rowindex_state_s {
+  ColumnDesc chunk{};
+  uint32_t rowgroup_start{};
+  uint32_t rowgroup_end{};
+  int is_compressed{};
+  uint32_t row_index_entry[3]
+                          [CI_PRESENT]{};  // NOTE: Assumes CI_PRESENT follows CI_DATA and CI_DATA2
+  CompressedStreamInfo strm_info[2]{};
+  RowGroup rowgroups[128]{};
+  uint32_t compressed_offset[128][2]{};
+};
+
+enum row_entry_state_e {
+  NOT_FOUND = 0,
+  GET_LENGTH,
+  SKIP_VARINT,
+  SKIP_FIXEDLEN,
+  STORE_INDEX0,
+  STORE_INDEX1,
+  STORE_INDEX2,
+};
+
+/**
+ * @brief Calculates the order of index streams based on the index types present in the column.
+ *
+ * @param index_types_bitmap The bitmap of index types showing which index streams are present
+ *
+ * @return The order of index streams
+ */
+static auto __device__ index_order_from_index_types(uint32_t index_types_bitmap)
+{
+  constexpr std::array full_order = {CI_PRESENT, CI_DATA, CI_DATA2};
+
+  std::array<uint32_t, full_order.size()> partial_order;
+  thrust::copy_if(thrust::seq,
+                  full_order.cbegin(),
+                  full_order.cend(),
+                  partial_order.begin(),
+                  [index_types_bitmap] __device__(auto index_type) {
+                    // Check if the index type is present
+                    return index_types_bitmap & (1 << index_type);
+                  });
+
+  return partial_order;
+}
+
+/**
+ * @brief Decode a single row group index entry
+ *
+ * @param[in,out] s row group index state
+ * @param[in] start start position in byte stream
+ * @param[in] end end of byte stream
+ * @return bytes consumed
+ */
+static uint32_t __device__ ProtobufParseRowIndexEntry(rowindex_state_s* s,
+                                                      uint8_t const* const start,
+                                                      uint8_t const* const end)
+{
+  constexpr uint32_t pb_rowindexentry_id = ProtofType::FIXEDLEN + 8;
+  auto const stream_order                = index_order_from_index_types(s->chunk.skip_count);
+
+  uint8_t const* cur      = start;
+  row_entry_state_e state = NOT_FOUND;
+  uint32_t length         = 0;
+  uint32_t idx_id         = 0;
+  uint32_t pos_end        = 0;
+  uint32_t ci_id          = CI_NUM_STREAMS;
+  while (cur < end) {
+    uint32_t v = 0;
+    for (uint32_t l = 0; l <= 28; l += 7) {
+      uint32_t c = (cur < end) ? *cur++ : 0;
+      v |= (c & 0x7f) << l;
+      if (c <= 0x7f) break;
+    }
+    switch (state) {
+      case NOT_FOUND:
+        if (v == pb_rowindexentry_id) {
+          state = GET_LENGTH;
+        } else {
+          v &= 7;
+          if (v == ProtofType::FIXED64)
+            cur += 8;
+          else if (v == ProtofType::FIXED32)
+            cur += 4;
+          else if (v == ProtofType::VARINT)
+            state = SKIP_VARINT;
+          else if (v == ProtofType::FIXEDLEN)
+            state = SKIP_FIXEDLEN;
+        }
+        break;
+      case SKIP_VARINT: state = NOT_FOUND; break;
+      case SKIP_FIXEDLEN:
+        cur += v;
+        state = NOT_FOUND;
+        break;
+      case GET_LENGTH:
+        if (length == 0) {
+          length = (uint32_t)(cur + v - start);
+          state = NOT_FOUND;  // Scan for positions (same field id & low-level type as RowIndexEntry
+                              // entry)
+        } else {
+          pos_end = min((uint32_t)(cur + v - start), length);
+          state   = STORE_INDEX0;
+        }
+        break;
+      case STORE_INDEX0:
+        // Start of a new entry; determine the stream index types
+        ci_id = stream_order[idx_id++];
+        if (s->is_compressed) {
+          if (ci_id < CI_PRESENT) s->row_index_entry[0][ci_id] = v;
+          if (cur >= start + pos_end) return length;
+          state = STORE_INDEX1;
+          break;
+        } else {
+          if (ci_id < CI_PRESENT) s->row_index_entry[0][ci_id] = 0;
+          // Fall through to STORE_INDEX1 for uncompressed (always block0)
+        }
+      case STORE_INDEX1:
+        if (ci_id < CI_PRESENT) s->row_index_entry[1][ci_id] = v;
+        if (cur >= start + pos_end) return length;
+        state = (ci_id == CI_DATA && s->chunk.encoding_kind != DICTIONARY &&
+                 s->chunk.encoding_kind != DICTIONARY_V2 &&
+                 (s->chunk.type_kind == STRING || s->chunk.type_kind == BINARY ||
+                  s->chunk.type_kind == VARCHAR || s->chunk.type_kind == CHAR ||
+                  s->chunk.type_kind == DECIMAL || s->chunk.type_kind == FLOAT ||
+                  s->chunk.type_kind == DOUBLE))
+                  ? STORE_INDEX0
+                  : STORE_INDEX2;
+        break;
+      case STORE_INDEX2:
+        if (ci_id < CI_PRESENT) {
+          // Boolean columns have an extra byte to indicate the position of the bit within the byte
+          s->row_index_entry[2][ci_id] = (s->chunk.type_kind == BOOLEAN) ? (v << 3) + *cur : v;
+        }
+        if (ci_id == CI_PRESENT || s->chunk.type_kind == BOOLEAN) cur++;
+        if (cur >= start + pos_end) return length;
+        state = STORE_INDEX0;
+        break;
+    }
+  }
+  return (uint32_t)(end - start);
+}
+
+/**
+ * @brief Decode row group index entries
+ *
+ * @param[in,out] s row group index state
+ * @param[in] num_rowgroups Number of index entries to read
+ */
+static __device__ void gpuReadRowGroupIndexEntries(rowindex_state_s* s, int num_rowgroups)
+{
+  uint8_t const* index_data = s->chunk.streams[CI_INDEX];
+  int index_data_len        = s->chunk.strm_len[CI_INDEX];
+  for (int i = 0; i < num_rowgroups; i++) {
+    s->row_index_entry[0][0] = 0;
+    s->row_index_entry[0][1] = 0;
+    s->row_index_entry[1][0] = 0;
+    s->row_index_entry[1][1] = 0;
+    s->row_index_entry[2][0] = 0;
+    s->row_index_entry[2][1] = 0;
+    if (index_data_len > 0) {
+      int len = ProtobufParseRowIndexEntry(s, index_data, index_data + index_data_len);
+      index_data += len;
+      index_data_len = max(index_data_len - len, 0);
+      for (int j = 0; j < 2; j++) {
+        s->rowgroups[i].strm_offset[j] = s->row_index_entry[1][j];
+        s->rowgroups[i].run_pos[j]     = s->row_index_entry[2][j];
+        s->compressed_offset[i][j]     = s->row_index_entry[0][j];
+      }
+    }
+  }
+  s->chunk.streams[CI_INDEX]  = index_data;
+  s->chunk.strm_len[CI_INDEX] = index_data_len;
+}
+
+/**
+ * @brief Translate block+offset compressed position into an uncompressed offset
+ *
+ * @param[in,out] s row group index state
+ * @param[in] ci_id index to convert (CI_DATA or CI_DATA2)
+ * @param[in] num_rowgroups Number of index entries
+ * @param[in] t thread id
+ */
+static __device__ void gpuMapRowIndexToUncompressed(rowindex_state_s* s,
+                                                    int ci_id,
+                                                    int num_rowgroups,
+                                                    int t)
+{
+  int32_t strm_len = s->chunk.strm_len[ci_id];
+  if (strm_len > 0) {
+    int32_t compressed_offset = (t < num_rowgroups) ? s->compressed_offset[t][ci_id] : 0;
+    if (compressed_offset > 0) {
+      uint8_t const* start   = s->strm_info[ci_id].compressed_data;
+      uint8_t const* cur     = start;
+      uint8_t const* end     = cur + s->strm_info[ci_id].compressed_data_size;
+      auto dec_result        = s->strm_info[ci_id].dec_res.data();
+      uint32_t uncomp_offset = 0;
+      for (;;) {
+        uint32_t block_len;
+
+        if (cur + block_header_size > end || cur + block_header_size >= start + compressed_offset) {
+          break;
+        }
+        block_len = cur[0] | (cur[1] << 8) | (cur[2] << 16);
+        cur += block_header_size;
+        auto const is_uncompressed = static_cast<bool>(block_len & 1);
+        block_len >>= 1;
+        cur += block_len;
+        if (cur > end) { break; }
+        if (is_uncompressed) {
+          uncomp_offset += block_len;
+        } else {
+          uncomp_offset += dec_result->bytes_written;
+          dec_result++;
+        }
+      }
+      s->rowgroups[t].strm_offset[ci_id] += uncomp_offset;
+    }
+  }
+}
+
+/**
+ * @brief Decode index streams
+ *
+ * @param[out] row_groups RowGroup device array [rowgroup][column]
+ * @param[in] strm_info List of compressed streams (or NULL if uncompressed)
+ * @param[in] chunks ColumnDesc device array [stripe][column]
+ * @param[in] num_columns Number of columns
+ * @param[in] num_stripes Number of stripes
+ * @param[in] num_rowgroups Number of row groups
+ * @param[in] rowidx_stride Row index stride
+ * @param[in] use_base_stride Whether to use base stride obtained from meta or use the computed
+ * value
+ */
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128, 8) gpuParseRowGroupIndex(RowGroup* row_groups,
+                                                                CompressedStreamInfo* strm_info,
+                                                                ColumnDesc* chunks,
+                                                                uint32_t num_columns,
+                                                                uint32_t num_stripes,
+                                                                uint32_t num_rowgroups,
+                                                                uint32_t rowidx_stride,
+                                                                bool use_base_stride)
+{
+  __shared__ __align__(16) rowindex_state_s state_g;
+  rowindex_state_s* const s = &state_g;
+  uint32_t chunk_id         = blockIdx.y * num_columns + blockIdx.x;
+  int t                     = threadIdx.x;
+
+  if (t == 0) {
+    s->chunk = chunks[chunk_id];
+    if (strm_info) {
+      if (s->chunk.strm_len[0] > 0) s->strm_info[0] = strm_info[s->chunk.strm_id[0]];
+      if (s->chunk.strm_len[1] > 0) s->strm_info[1] = strm_info[s->chunk.strm_id[1]];
+    }
+
+    uint32_t rowgroups_in_chunk = s->chunk.num_rowgroups;
+    s->rowgroup_start           = s->chunk.rowgroup_id;
+    s->rowgroup_end             = s->rowgroup_start + rowgroups_in_chunk;
+    s->is_compressed            = (strm_info != nullptr);
+  }
+  __syncthreads();
+  while (s->rowgroup_start < s->rowgroup_end) {
+    int num_rowgroups = min(s->rowgroup_end - s->rowgroup_start, 128);
+    int rowgroup_size4, t4, t32;
+
+    s->rowgroups[t].chunk_id = chunk_id;
+    if (t == 0) { gpuReadRowGroupIndexEntries(s, num_rowgroups); }
+    __syncthreads();
+    if (s->is_compressed) {
+      // Convert the block + blk_offset pair into a raw offset into the decompressed stream
+      if (s->chunk.strm_len[CI_DATA] > 0) {
+        gpuMapRowIndexToUncompressed(s, CI_DATA, num_rowgroups, t);
+      }
+      if (s->chunk.strm_len[CI_DATA2] > 0) {
+        gpuMapRowIndexToUncompressed(s, CI_DATA2, num_rowgroups, t);
+      }
+      __syncthreads();
+    }
+    rowgroup_size4 = sizeof(RowGroup) / sizeof(uint32_t);
+    t4             = t & 3;
+    t32            = t >> 2;
+    for (int i = t32; i < num_rowgroups; i += 32) {
+      auto const num_rows =
+        (use_base_stride) ? rowidx_stride
+                          : row_groups[(s->rowgroup_start + i) * num_columns + blockIdx.x].num_rows;
+      auto const start_row =
+        (use_base_stride)
+          ? i * rowidx_stride
+          : row_groups[(s->rowgroup_start + i) * num_columns + blockIdx.x].start_row;
+      for (int j = t4; j < rowgroup_size4; j += 4) {
+        ((uint32_t*)&row_groups[(s->rowgroup_start + i) * num_columns + blockIdx.x])[j] =
+          ((volatile uint32_t*)&s->rowgroups[i])[j];
+      }
+      row_groups[(s->rowgroup_start + i) * num_columns + blockIdx.x].num_rows = num_rows;
+      // Updating in case of struct
+      row_groups[(s->rowgroup_start + i) * num_columns + blockIdx.x].num_child_rows = num_rows;
+      row_groups[(s->rowgroup_start + i) * num_columns + blockIdx.x].start_row      = start_row;
+    }
+    __syncthreads();
+    if (t == 0) { s->rowgroup_start += num_rowgroups; }
+    __syncthreads();
+  }
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpu_reduce_pushdown_masks(device_span<orc_column_device_view const> orc_columns,
+                            device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                            device_2dspan<size_type> set_counts)
+{
+  using BlockReduce = cub::BlockReduce<size_type, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  auto const column_id   = blockIdx.x;
+  auto const rowgroup_id = blockIdx.y;
+  auto const column      = orc_columns[column_id];
+  auto const t           = threadIdx.x;
+
+  auto const use_child_rg = column.type().id() == type_id::LIST;
+  auto const rg           = rowgroup_bounds[rowgroup_id][column_id + (use_child_rg ? 1 : 0)];
+
+  if (column.pushdown_mask == nullptr) {
+    // All elements are valid if the null mask is not present
+    if (t == 0) { set_counts[rowgroup_id][column_id] = rg.size(); }
+    return;
+  };
+
+  size_type count                          = 0;
+  static constexpr size_type bits_per_word = sizeof(bitmask_type) * 8;
+  for (auto row = t * bits_per_word + rg.begin; row < rg.end; row += block_size * bits_per_word) {
+    auto const begin_bit = row;
+    auto const end_bit   = min(static_cast<size_type>(row + bits_per_word), rg.end);
+    auto const mask_len  = end_bit - begin_bit;
+    auto const mask_word =
+      cudf::detail::get_mask_offset_word(column.pushdown_mask, 0, row, end_bit) &
+      ((1 << mask_len) - 1);
+    count += __popc(mask_word);
+  }
+
+  count = BlockReduce(temp_storage).Sum(count);
+  if (t == 0) { set_counts[rowgroup_id][column_id] = count; }
+}
+
+void __host__ ParseCompressedStripeData(CompressedStreamInfo* strm_info,
+                                        int32_t num_streams,
+                                        uint32_t compression_block_size,
+                                        uint32_t log2maxcr,
+                                        rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);
+  dim3 dim_grid((num_streams + 3) >> 2, 1);  // 1 stream per warp, 4 warps per block
+  gpuParseCompressedStripeData<<<dim_grid, dim_block, 0, stream.value()>>>(
+    strm_info, num_streams, compression_block_size, log2maxcr);
+}
+
+void __host__ PostDecompressionReassemble(CompressedStreamInfo* strm_info,
+                                          int32_t num_streams,
+                                          rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);
+  dim3 dim_grid((num_streams + 3) >> 2, 1);  // 1 stream per warp, 4 warps per block
+  gpuPostDecompressionReassemble<<<dim_grid, dim_block, 0, stream.value()>>>(strm_info,
+                                                                             num_streams);
+}
+
+void __host__ ParseRowGroupIndex(RowGroup* row_groups,
+                                 CompressedStreamInfo* strm_info,
+                                 ColumnDesc* chunks,
+                                 uint32_t num_columns,
+                                 uint32_t num_stripes,
+                                 uint32_t num_rowgroups,
+                                 uint32_t rowidx_stride,
+                                 bool use_base_stride,
+                                 rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);
+  dim3 dim_grid(num_columns, num_stripes);  // 1 column chunk per block
+  gpuParseRowGroupIndex<<<dim_grid, dim_block, 0, stream.value()>>>(row_groups,
+                                                                    strm_info,
+                                                                    chunks,
+                                                                    num_columns,
+                                                                    num_stripes,
+                                                                    num_rowgroups,
+                                                                    rowidx_stride,
+                                                                    use_base_stride);
+}
+
+void __host__ reduce_pushdown_masks(device_span<orc_column_device_view const> columns,
+                                    device_2dspan<rowgroup_rows const> rowgroups,
+                                    device_2dspan<cudf::size_type> valid_counts,
+                                    rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);
+  dim3 dim_grid(columns.size(), rowgroups.size().first);  // 1 rowgroup per block
+  gpu_reduce_pushdown_masks<128>
+    <<<dim_grid, dim_block, 0, stream.value()>>>(columns, rowgroups, valid_counts);
+}
+
+}  // namespace gpu
+}  // namespace orc
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/writer_impl.cu b/cpp/src/io/orc/writer_impl.cu
new file mode 100644
index 0000000..3d8bdb4
--- /dev/null
+++ b/cpp/src/io/orc/writer_impl.cu
@@ -0,0 +1,2684 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file writer_impl.cu
+ * @brief cuDF-IO ORC writer class implementation
+ */
+
+#include "writer_impl.hpp"
+
+#include <io/comp/nvcomp_adapter.hpp>
+#include <io/statistics/column_statistics.cuh>
+#include <io/utilities/column_utils.cuh>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/pinned_host_vector.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/extrema.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/memcpy_async.h>
+
+#include <cuda/std/climits>
+#include <cuda/std/limits>
+
+#include <algorithm>
+#include <cstring>
+#include <numeric>
+#include <tuple>
+#include <utility>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace orc {
+using namespace cudf::io::orc;
+using namespace cudf::io;
+
+template <typename T>
+[[nodiscard]] constexpr int varint_size(T val)
+{
+  auto len = 1u;
+  while (val > 0x7f) {
+    val >>= 7;
+    ++len;
+  }
+  return len;
+}
+
+struct row_group_index_info {
+  int32_t pos       = -1;  // Position
+  int32_t blk_pos   = -1;  // Block Position
+  int32_t comp_pos  = -1;  // Compressed Position
+  int32_t comp_size = -1;  // Compressed size
+};
+
+namespace {
+
+/**
+ * @brief Translates ORC compression to nvCOMP compression
+ */
+auto to_nvcomp_compression_type(CompressionKind compression_kind)
+{
+  if (compression_kind == SNAPPY) return nvcomp::compression_type::SNAPPY;
+  if (compression_kind == ZLIB) return nvcomp::compression_type::DEFLATE;
+  if (compression_kind == ZSTD) return nvcomp::compression_type::ZSTD;
+  CUDF_FAIL("Unsupported compression type");
+}
+
+/**
+ * @brief Translates cuDF compression to ORC compression
+ */
+orc::CompressionKind to_orc_compression(compression_type compression)
+{
+  switch (compression) {
+    case compression_type::AUTO:
+    case compression_type::SNAPPY: return orc::CompressionKind::SNAPPY;
+    case compression_type::ZLIB: return orc::CompressionKind::ZLIB;
+    case compression_type::ZSTD: return orc::CompressionKind::ZSTD;
+    case compression_type::NONE: return orc::CompressionKind::NONE;
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+/**
+ * @brief Returns the block size for a given compression kind.
+ */
+constexpr size_t compression_block_size(orc::CompressionKind compression)
+{
+  if (compression == orc::CompressionKind::NONE) { return 0; }
+
+  auto const ncomp_type   = to_nvcomp_compression_type(compression);
+  auto const nvcomp_limit = nvcomp::is_compression_disabled(ncomp_type)
+                              ? std::nullopt
+                              : nvcomp::compress_max_allowed_chunk_size(ncomp_type);
+
+  constexpr size_t max_block_size = 256 * 1024;
+  return std::min(nvcomp_limit.value_or(max_block_size), max_block_size);
+}
+
+/**
+ * @brief Translates cuDF dtype to ORC datatype
+ */
+constexpr orc::TypeKind to_orc_type(cudf::type_id id, bool list_column_as_map)
+{
+  switch (id) {
+    case cudf::type_id::INT8: return TypeKind::BYTE;
+    case cudf::type_id::INT16: return TypeKind::SHORT;
+    case cudf::type_id::INT32: return TypeKind::INT;
+    case cudf::type_id::INT64: return TypeKind::LONG;
+    case cudf::type_id::FLOAT32: return TypeKind::FLOAT;
+    case cudf::type_id::FLOAT64: return TypeKind::DOUBLE;
+    case cudf::type_id::BOOL8: return TypeKind::BOOLEAN;
+    case cudf::type_id::TIMESTAMP_DAYS: return TypeKind::DATE;
+    case cudf::type_id::TIMESTAMP_SECONDS:
+    case cudf::type_id::TIMESTAMP_MICROSECONDS:
+    case cudf::type_id::TIMESTAMP_MILLISECONDS:
+    case cudf::type_id::TIMESTAMP_NANOSECONDS: return TypeKind::TIMESTAMP;
+    case cudf::type_id::STRING: return TypeKind::STRING;
+    case cudf::type_id::DECIMAL32:
+    case cudf::type_id::DECIMAL64:
+    case cudf::type_id::DECIMAL128: return TypeKind::DECIMAL;
+    case cudf::type_id::LIST: return list_column_as_map ? TypeKind::MAP : TypeKind::LIST;
+    case cudf::type_id::STRUCT: return TypeKind::STRUCT;
+    default: return TypeKind::INVALID_TYPE_KIND;
+  }
+}
+
+/**
+ * @brief Translates time unit to nanoscale multiple.
+ */
+constexpr int32_t to_clockscale(cudf::type_id timestamp_id)
+{
+  switch (timestamp_id) {
+    case cudf::type_id::TIMESTAMP_SECONDS: return 9;
+    case cudf::type_id::TIMESTAMP_MILLISECONDS: return 6;
+    case cudf::type_id::TIMESTAMP_MICROSECONDS: return 3;
+    case cudf::type_id::TIMESTAMP_NANOSECONDS:
+    default: return 0;
+  }
+}
+
+/**
+ * @brief Returns the precision of the given decimal type.
+ */
+constexpr auto orc_precision(cudf::type_id decimal_id)
+{
+  using namespace numeric;
+  switch (decimal_id) {
+    case cudf::type_id::DECIMAL32: return cuda::std::numeric_limits<decimal32::rep>::digits10;
+    case cudf::type_id::DECIMAL64: return cuda::std::numeric_limits<decimal64::rep>::digits10;
+    case cudf::type_id::DECIMAL128: return cuda::std::numeric_limits<decimal128::rep>::digits10;
+    default: return 0;
+  }
+}
+
+}  // namespace
+
+/**
+ * @brief Helper class that adds ORC-specific column info
+ */
+class orc_column_view {
+ public:
+  /**
+   * @brief Constructor that extracts out the string position + length pairs
+   * for building dictionaries for string columns
+   */
+  explicit orc_column_view(uint32_t index,
+                           int str_idx,
+                           orc_column_view* parent,
+                           column_view const& col,
+                           column_in_metadata const& metadata)
+    : cudf_column{col},
+      _index{index},
+      _str_idx{str_idx},
+      _is_child{parent != nullptr},
+      _type_width{cudf::is_fixed_width(col.type()) ? cudf::size_of(col.type()) : 0},
+      _type_kind{to_orc_type(col.type().id(), metadata.is_map())},
+      _scale{(_type_kind == TypeKind::DECIMAL) ? -col.type().scale()
+                                               : to_clockscale(col.type().id())},
+      _precision{metadata.is_decimal_precision_set() ? metadata.get_decimal_precision()
+                                                     : orc_precision(col.type().id())},
+      name{metadata.get_name()}
+  {
+    if (metadata.is_nullability_defined()) { nullable_from_metadata = metadata.nullable(); }
+    if (parent != nullptr) {
+      parent->add_child(_index);
+      _parent_index = parent->index();
+    }
+
+    if (_type_kind == TypeKind::MAP) {
+      auto const struct_col = col.child(lists_column_view::child_column_index);
+      CUDF_EXPECTS(struct_col.null_count() == 0,
+                   "struct column of a MAP column should not have null elements");
+      CUDF_EXPECTS(struct_col.num_children() == 2, "MAP column must have two child columns");
+    }
+  }
+
+  void add_child(uint32_t child_idx) { children.emplace_back(child_idx); }
+
+  auto type() const noexcept { return cudf_column.type(); }
+  auto is_string() const noexcept { return cudf_column.type().id() == type_id::STRING; }
+
+  void attach_rowgroup_char_counts(host_span<size_type const> counts)
+  {
+    rowgroup_char_counts = counts;
+  }
+
+  [[nodiscard]] auto rowgroup_char_count(size_type rg_idx) const
+  {
+    return rowgroup_char_counts[rg_idx];
+  }
+
+  [[nodiscard]] auto char_count() const
+  {
+    return std::accumulate(rowgroup_char_counts.begin(), rowgroup_char_counts.end(), size_type{0});
+  }
+
+  [[nodiscard]] auto const& decimal_offsets() const { return d_decimal_offsets; }
+  void attach_decimal_offsets(uint32_t* sizes_ptr) { d_decimal_offsets = sizes_ptr; }
+
+  void attach_stripe_dicts(host_span<gpu::stripe_dictionary const> host_stripe_dicts,
+                           device_span<gpu::stripe_dictionary const> dev_stripe_dicts)
+  {
+    stripe_dicts   = host_stripe_dicts;
+    d_stripe_dicts = dev_stripe_dicts;
+  }
+
+  [[nodiscard]] auto const& host_stripe_dict(size_t stripe) const
+  {
+    CUDF_EXPECTS(is_string(), "Stripe dictionary is only present in string columns.");
+    return stripe_dicts[stripe];
+  }
+
+  [[nodiscard]] auto const& device_stripe_dicts() const noexcept { return d_stripe_dicts; }
+
+  // Index in the table
+  [[nodiscard]] uint32_t index() const noexcept { return _index; }
+  // Index in the table, including only string columns
+  [[nodiscard]] uint32_t str_index() const noexcept { return _str_idx; }
+  // Id in the ORC file
+  [[nodiscard]] auto id() const noexcept { return _index + 1; }
+
+  [[nodiscard]] auto is_child() const noexcept { return _is_child; }
+  auto parent_index() const noexcept { return _parent_index.value(); }
+  auto child_begin() const noexcept { return children.cbegin(); }
+  auto child_end() const noexcept { return children.cend(); }
+  auto num_children() const noexcept { return children.size(); }
+
+  [[nodiscard]] auto type_width() const noexcept { return _type_width; }
+  auto size() const noexcept { return cudf_column.size(); }
+
+  auto null_count() const noexcept { return cudf_column.null_count(); }
+  auto null_mask() const noexcept { return cudf_column.null_mask(); }
+  [[nodiscard]] bool nullable() const noexcept { return null_mask() != nullptr; }
+  auto user_defined_nullable() const noexcept { return nullable_from_metadata; }
+
+  [[nodiscard]] auto scale() const noexcept { return _scale; }
+  [[nodiscard]] auto precision() const noexcept { return _precision; }
+
+  void set_orc_encoding(ColumnEncodingKind e) noexcept { _encoding_kind = e; }
+  [[nodiscard]] auto orc_kind() const noexcept { return _type_kind; }
+  [[nodiscard]] auto orc_encoding() const noexcept { return _encoding_kind; }
+  [[nodiscard]] std::string_view orc_name() const noexcept { return name; }
+
+ private:
+  column_view cudf_column;
+
+  // Identifier within the set of columns
+  uint32_t _index = 0;
+  // Identifier within the set of string columns
+  int _str_idx;
+  bool _is_child = false;
+
+  // ORC-related members
+  TypeKind _type_kind               = INVALID_TYPE_KIND;
+  ColumnEncodingKind _encoding_kind = INVALID_ENCODING_KIND;
+  std::string name;
+
+  size_t _type_width = 0;
+  int32_t _scale     = 0;
+  int32_t _precision = 0;
+
+  host_span<size_type const> rowgroup_char_counts;
+
+  host_span<gpu::stripe_dictionary const> stripe_dicts;
+  device_span<gpu::stripe_dictionary const> d_stripe_dicts;
+
+  // Offsets for encoded decimal elements. Used to enable direct writing of encoded decimal elements
+  // into the output stream.
+  uint32_t* d_decimal_offsets = nullptr;
+
+  std::optional<bool> nullable_from_metadata;
+  std::vector<uint32_t> children;
+  std::optional<uint32_t> _parent_index;
+};
+
+size_type orc_table_view::num_rows() const noexcept
+{
+  return columns.empty() ? 0 : columns.front().size();
+}
+
+namespace {
+struct string_length_functor {
+  __device__ inline size_type operator()(int const i) const
+  {
+    // we translate from 0 -> num_chunks * 2 because each statistic has a min and max
+    // string and we need to calculate lengths for both.
+    if (i >= num_chunks * 2) return 0;
+
+    // min strings are even values, max strings are odd values of i
+    auto const should_copy_min = i % 2 == 0;
+    // index of the chunk
+    auto const idx = i / 2;
+    auto& str_val  = should_copy_min ? stripe_stat_chunks[idx].min_value.str_val
+                                     : stripe_stat_chunks[idx].max_value.str_val;
+    auto const str = stripe_stat_merge[idx].stats_dtype == dtype_string;
+    return str ? str_val.length : 0;
+  }
+
+  int const num_chunks;
+  statistics_chunk const* stripe_stat_chunks;
+  statistics_merge_group const* stripe_stat_merge;
+};
+
+__global__ void copy_string_data(char* string_pool,
+                                 size_type* offsets,
+                                 statistics_chunk* chunks,
+                                 statistics_merge_group const* groups)
+{
+  auto const idx = blockIdx.x / 2;
+  if (groups[idx].stats_dtype == dtype_string) {
+    // min strings are even values, max strings are odd values of i
+    auto const should_copy_min = blockIdx.x % 2 == 0;
+    auto& str_val = should_copy_min ? chunks[idx].min_value.str_val : chunks[idx].max_value.str_val;
+    auto dst      = &string_pool[offsets[blockIdx.x]];
+    auto src      = str_val.ptr;
+
+    for (thread_index_type i = threadIdx.x; i < str_val.length; i += blockDim.x) {
+      dst[i] = src[i];
+    }
+    if (threadIdx.x == 0) { str_val.ptr = dst; }
+  }
+}
+
+}  // namespace
+
+void persisted_statistics::persist(int num_table_rows,
+                                   single_write_mode write_mode,
+                                   intermediate_statistics&& intermediate_stats,
+                                   rmm::cuda_stream_view stream)
+{
+  if (write_mode == single_write_mode::NO) {
+    // persist the strings in the chunks into a string pool and update pointers
+    auto const num_chunks = static_cast<int>(intermediate_stats.stripe_stat_chunks.size());
+    // min offset and max offset + 1 for total size
+    rmm::device_uvector<size_type> offsets((num_chunks * 2) + 1, stream);
+
+    auto iter = cudf::detail::make_counting_transform_iterator(
+      0,
+      string_length_functor{num_chunks,
+                            intermediate_stats.stripe_stat_chunks.data(),
+                            intermediate_stats.stripe_stat_merge.device_ptr()});
+    thrust::exclusive_scan(rmm::exec_policy(stream), iter, iter + offsets.size(), offsets.begin());
+
+    // pull size back to host
+    auto const total_string_pool_size = offsets.element(num_chunks * 2, stream);
+    if (total_string_pool_size > 0) {
+      rmm::device_uvector<char> string_pool(total_string_pool_size, stream);
+
+      // offsets describes where in the string pool each string goes. Going with the simple
+      // approach for now, but it is possible something fancier with breaking up each thread into
+      // copying x bytes instead of a single string is the better method since we are dealing in
+      // min/max strings they almost certainly will not be uniform length.
+      copy_string_data<<<num_chunks * 2, 256, 0, stream.value()>>>(
+        string_pool.data(),
+        offsets.data(),
+        intermediate_stats.stripe_stat_chunks.data(),
+        intermediate_stats.stripe_stat_merge.device_ptr());
+      string_pools.emplace_back(std::move(string_pool));
+    }
+  }
+
+  stripe_stat_chunks.emplace_back(std::move(intermediate_stats.stripe_stat_chunks));
+  stripe_stat_merge.emplace_back(std::move(intermediate_stats.stripe_stat_merge));
+  stats_dtypes = std::move(intermediate_stats.stats_dtypes);
+  col_types    = std::move(intermediate_stats.col_types);
+  num_rows     = num_table_rows;
+}
+
+namespace {
+/**
+ * @brief Gathers stripe information.
+ *
+ * @param columns List of columns
+ * @param rowgroup_bounds Ranges of rows in each rowgroup [rowgroup][column]
+ * @param max_stripe_size Maximum size of each stripe, both in bytes and in rows
+ * @return List of stripe descriptors
+ */
+file_segmentation calculate_segmentation(host_span<orc_column_view const> columns,
+                                         hostdevice_2dvector<rowgroup_rows>&& rowgroup_bounds,
+                                         stripe_size_limits max_stripe_size)
+{
+  std::vector<stripe_rowgroups> infos;
+  auto const num_rowgroups = rowgroup_bounds.size().first;
+  size_t stripe_start      = 0;
+  size_t stripe_bytes      = 0;
+  size_type stripe_rows    = 0;
+  for (size_t rg_idx = 0; rg_idx < num_rowgroups; ++rg_idx) {
+    auto const rowgroup_total_bytes =
+      std::accumulate(columns.begin(), columns.end(), 0ul, [&](size_t total_size, auto const& col) {
+        auto const rows = rowgroup_bounds[rg_idx][col.index()].size();
+        if (col.is_string()) {
+          return total_size + rows + col.rowgroup_char_count(rg_idx);
+        } else {
+          return total_size + col.type_width() * rows;
+        }
+      });
+
+    auto const rowgroup_rows_max =
+      std::max_element(rowgroup_bounds[rg_idx].begin(),
+                       rowgroup_bounds[rg_idx].end(),
+                       [](auto& l, auto& r) { return l.size() < r.size(); })
+        ->size();
+    // Check if adding the current rowgroup to the stripe will make the stripe too large or long
+    if ((rg_idx > stripe_start) && (stripe_bytes + rowgroup_total_bytes > max_stripe_size.bytes ||
+                                    stripe_rows + rowgroup_rows_max > max_stripe_size.rows)) {
+      infos.emplace_back(infos.size(), stripe_start, rg_idx - stripe_start);
+      stripe_start = rg_idx;
+      stripe_bytes = 0;
+      stripe_rows  = 0;
+    }
+
+    stripe_bytes += rowgroup_total_bytes;
+    stripe_rows += rowgroup_rows_max;
+    if (rg_idx + 1 == num_rowgroups) {
+      infos.emplace_back(infos.size(), stripe_start, num_rowgroups - stripe_start);
+    }
+  }
+
+  return {std::move(rowgroup_bounds), std::move(infos)};
+}
+
+/**
+ * @brief Returns the maximum size of RLE encoded values of an integer type.
+ **/
+template <typename T>
+size_t max_varint_size()
+{
+  // varint encodes 7 bits in each byte
+  return cudf::util::div_rounding_up_unsafe(sizeof(T) * 8, 7);
+}
+
+constexpr size_t RLE_stream_size(TypeKind kind, size_t count)
+{
+  using cudf::util::div_rounding_up_unsafe;
+  constexpr auto byte_rle_max_len = 128;
+  switch (kind) {
+    case TypeKind::BOOLEAN:
+      return div_rounding_up_unsafe(count, byte_rle_max_len * 8) * (byte_rle_max_len + 1);
+    case TypeKind::BYTE:
+      return div_rounding_up_unsafe(count, byte_rle_max_len) * (byte_rle_max_len + 1);
+    case TypeKind::SHORT:
+      return div_rounding_up_unsafe(count, gpu::encode_block_size) *
+             (gpu::encode_block_size * max_varint_size<int16_t>() + 2);
+    case TypeKind::FLOAT:
+    case TypeKind::INT:
+    case TypeKind::DATE:
+      return div_rounding_up_unsafe(count, gpu::encode_block_size) *
+             (gpu::encode_block_size * max_varint_size<int32_t>() + 2);
+    case TypeKind::LONG:
+    case TypeKind::DOUBLE:
+      return div_rounding_up_unsafe(count, gpu::encode_block_size) *
+             (gpu::encode_block_size * max_varint_size<int64_t>() + 2);
+    default: CUDF_FAIL("Unsupported ORC type for RLE stream size");
+  }
+}
+
+auto uncomp_block_alignment(CompressionKind compression_kind)
+{
+  if (compression_kind == NONE or
+      nvcomp::is_compression_disabled(to_nvcomp_compression_type(compression_kind))) {
+    return 1u;
+  }
+
+  return 1u << nvcomp::compress_input_alignment_bits(to_nvcomp_compression_type(compression_kind));
+}
+
+auto comp_block_alignment(CompressionKind compression_kind)
+{
+  if (compression_kind == NONE or
+      nvcomp::is_compression_disabled(to_nvcomp_compression_type(compression_kind))) {
+    return 1u;
+  }
+
+  return 1u << nvcomp::compress_output_alignment_bits(to_nvcomp_compression_type(compression_kind));
+}
+
+/**
+ * @brief Builds up per-column streams.
+ *
+ * @param[in,out] columns List of columns
+ * @param[in] segmentation stripe and rowgroup ranges
+ * @param[in] decimal_column_sizes Sizes of encoded decimal columns
+ * @return List of stream descriptors
+ */
+orc_streams create_streams(host_span<orc_column_view> columns,
+                           file_segmentation const& segmentation,
+                           std::map<uint32_t, size_t> const& decimal_column_sizes,
+                           bool enable_dictionary,
+                           CompressionKind compression_kind,
+                           single_write_mode write_mode)
+{
+  // 'column 0' row index stream
+  std::vector<Stream> streams{{ROW_INDEX, 0}};  // TODO: Separate index and data streams?
+  // First n + 1 streams are row index streams
+  streams.reserve(columns.size() + 1);
+  std::transform(columns.begin(), columns.end(), std::back_inserter(streams), [](auto const& col) {
+    return Stream{ROW_INDEX, col.id()};
+  });
+
+  std::vector<int32_t> ids(columns.size() * gpu::CI_NUM_STREAMS, -1);
+  std::vector<TypeKind> types(streams.size(), INVALID_TYPE_KIND);
+
+  for (auto& column : columns) {
+    auto const is_nullable = [&]() -> bool {
+      if (write_mode == single_write_mode::YES) {
+        return column.nullable();
+      } else {
+        // For chunked write, when not provided nullability, we assume the worst case scenario
+        // that all columns are nullable.
+        auto const chunked_nullable = column.user_defined_nullable().value_or(true);
+        CUDF_EXPECTS(chunked_nullable or column.null_count() == 0,
+                     "Mismatch in metadata prescribed nullability and input column. "
+                     "Metadata for input column with nulls cannot prescribe nullability = false");
+        return chunked_nullable;
+      }
+    }();
+
+    auto RLE_column_size = [&](TypeKind type_kind) {
+      return std::accumulate(
+        thrust::make_counting_iterator(0ul),
+        thrust::make_counting_iterator(segmentation.num_rowgroups()),
+        0ul,
+        [&](auto data_size, auto rg_idx) {
+          return data_size +
+                 RLE_stream_size(type_kind, segmentation.rowgroups[rg_idx][column.index()].size());
+        });
+    };
+
+    auto const kind = column.orc_kind();
+
+    auto add_stream =
+      [&](gpu::StreamIndexType index_type, StreamKind kind, TypeKind type_kind, size_t size) {
+        auto const max_alignment_padding = uncomp_block_alignment(compression_kind) - 1;
+        const auto base                  = column.index() * gpu::CI_NUM_STREAMS;
+        ids[base + index_type]           = streams.size();
+        streams.push_back(orc::Stream{
+          kind,
+          column.id(),
+          (size == 0) ? 0 : size + max_alignment_padding * segmentation.num_rowgroups()});
+        types.push_back(type_kind);
+      };
+
+    auto add_RLE_stream = [&](
+                            gpu::StreamIndexType index_type, StreamKind kind, TypeKind type_kind) {
+      add_stream(index_type, kind, type_kind, RLE_column_size(type_kind));
+    };
+
+    if (is_nullable) { add_RLE_stream(gpu::CI_PRESENT, PRESENT, TypeKind::BOOLEAN); }
+    switch (kind) {
+      case TypeKind::BOOLEAN:
+      case TypeKind::BYTE:
+        add_RLE_stream(gpu::CI_DATA, DATA, kind);
+        column.set_orc_encoding(DIRECT);
+        break;
+      case TypeKind::SHORT:
+      case TypeKind::INT:
+      case TypeKind::LONG:
+      case TypeKind::DATE:
+        add_RLE_stream(gpu::CI_DATA, DATA, kind);
+        column.set_orc_encoding(DIRECT_V2);
+        break;
+      case TypeKind::FLOAT:
+      case TypeKind::DOUBLE:
+        // Pass through if no nulls (no RLE encoding for floating point)
+        add_stream(
+          gpu::CI_DATA, DATA, kind, (column.null_count() != 0) ? RLE_column_size(kind) : 0);
+        column.set_orc_encoding(DIRECT);
+        break;
+      case TypeKind::STRING: {
+        bool enable_dict           = enable_dictionary;
+        size_t dict_data_size      = 0;
+        size_t dict_strings        = 0;
+        size_t dict_lengths_div512 = 0;
+        for (auto const& stripe : segmentation.stripes) {
+          auto const sd = column.host_stripe_dict(stripe.id);
+          enable_dict   = (enable_dict && sd.is_enabled);
+          if (enable_dict) {
+            dict_strings += sd.entry_count;
+            dict_lengths_div512 += (sd.entry_count + 0x1ff) >> 9;
+            dict_data_size += sd.char_count;
+          }
+        }
+
+        size_t const direct_data_size = column.char_count();
+        if (enable_dict) {
+          uint32_t dict_bits = 0;
+          for (dict_bits = 1; dict_bits < 32; dict_bits <<= 1) {
+            if (dict_strings <= (1ull << dict_bits)) break;
+          }
+          auto const valid_count = column.size() - column.null_count();
+          dict_data_size += (dict_bits * valid_count + 7) >> 3;
+        }
+
+        // Decide between direct or dictionary encoding
+        if (enable_dict && dict_data_size < direct_data_size) {
+          add_RLE_stream(gpu::CI_DATA, DATA, TypeKind::INT);
+          add_stream(gpu::CI_DATA2, LENGTH, TypeKind::INT, dict_lengths_div512 * (512 * 4 + 2));
+          add_stream(
+            gpu::CI_DICTIONARY, DICTIONARY_DATA, TypeKind::CHAR, std::max(dict_data_size, 1ul));
+          column.set_orc_encoding(DICTIONARY_V2);
+        } else {
+          add_stream(gpu::CI_DATA, DATA, TypeKind::CHAR, std::max<size_t>(direct_data_size, 1));
+          add_RLE_stream(gpu::CI_DATA2, LENGTH, TypeKind::INT);
+          column.set_orc_encoding(DIRECT_V2);
+        }
+        break;
+      }
+      case TypeKind::TIMESTAMP:
+        add_RLE_stream(gpu::CI_DATA, DATA, TypeKind::LONG);
+        add_RLE_stream(gpu::CI_DATA2, SECONDARY, TypeKind::LONG);
+        column.set_orc_encoding(DIRECT_V2);
+        break;
+      case TypeKind::DECIMAL:
+        // varint values (NO RLE)
+        // data_stream_size = decimal_column_sizes.at(column.index());
+        add_stream(gpu::CI_DATA, DATA, TypeKind::DECIMAL, decimal_column_sizes.at(column.index()));
+        // scale stream TODO: compute exact size since all elems are equal
+        add_RLE_stream(gpu::CI_DATA2, SECONDARY, TypeKind::INT);
+        column.set_orc_encoding(DIRECT_V2);
+        break;
+      case TypeKind::LIST:
+      case TypeKind::MAP:
+        // no data stream, only lengths
+        add_RLE_stream(gpu::CI_DATA2, LENGTH, TypeKind::INT);
+        column.set_orc_encoding(DIRECT_V2);
+        break;
+      case TypeKind::STRUCT:
+        // Only has the present stream
+        break;
+      default: CUDF_FAIL("Unsupported ORC type kind");
+    }
+  }
+  return {std::move(streams), std::move(ids), std::move(types)};
+}
+
+std::vector<std::vector<rowgroup_rows>> calculate_aligned_rowgroup_bounds(
+  orc_table_view const& orc_table,
+  file_segmentation const& segmentation,
+  rmm::cuda_stream_view stream)
+{
+  if (segmentation.num_rowgroups() == 0) return {};
+
+  auto d_pd_set_counts_data = rmm::device_uvector<cudf::size_type>(
+    orc_table.num_columns() * segmentation.num_rowgroups(), stream);
+  auto const d_pd_set_counts = device_2dspan<cudf::size_type>{
+    d_pd_set_counts_data.data(), segmentation.num_rowgroups(), orc_table.num_columns()};
+  gpu::reduce_pushdown_masks(orc_table.d_columns, segmentation.rowgroups, d_pd_set_counts, stream);
+
+  auto aligned_rgs = hostdevice_2dvector<rowgroup_rows>(
+    segmentation.num_rowgroups(), orc_table.num_columns(), stream);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(aligned_rgs.base_device_ptr(),
+                                segmentation.rowgroups.base_device_ptr(),
+                                aligned_rgs.count() * sizeof(rowgroup_rows),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  auto const d_stripes = cudf::detail::make_device_uvector_async(
+    segmentation.stripes, stream, rmm::mr::get_current_device_resource());
+
+  // One thread per column, per stripe
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0),
+    orc_table.num_columns() * segmentation.num_stripes(),
+    [columns = device_span<orc_column_device_view const>{orc_table.d_columns},
+     stripes = device_span<stripe_rowgroups const>{d_stripes},
+     d_pd_set_counts,
+     out_rowgroups = device_2dspan<rowgroup_rows>{aligned_rgs}] __device__(auto& idx) {
+      uint32_t const col_idx = idx / stripes.size();
+      // No alignment needed for root columns
+      if (not columns[col_idx].parent_index.has_value()) return;
+
+      auto const stripe_idx     = idx % stripes.size();
+      auto const stripe         = stripes[stripe_idx];
+      auto const parent_col_idx = columns[col_idx].parent_index.value();
+      auto const parent_column  = columns[parent_col_idx];
+      auto const stripe_end     = stripe.first + stripe.size;
+
+      auto seek_last_borrow_rg = [&](auto rg_idx, size_type& bits_to_borrow) {
+        auto curr         = rg_idx + 1;
+        auto curr_rg_size = [&]() {
+          return parent_column.pushdown_mask != nullptr ? d_pd_set_counts[curr][parent_col_idx]
+                                                        : out_rowgroups[curr][col_idx].size();
+        };
+        while (curr < stripe_end and curr_rg_size() <= bits_to_borrow) {
+          // All bits from rowgroup borrowed, make the rowgroup empty
+          out_rowgroups[curr][col_idx].begin = out_rowgroups[curr][col_idx].end;
+          bits_to_borrow -= curr_rg_size();
+          ++curr;
+        }
+        return curr;
+      };
+
+      int previously_borrowed = 0;
+      for (auto rg_idx = stripe.first; rg_idx + 1 < stripe_end; ++rg_idx) {
+        auto& rg = out_rowgroups[rg_idx][col_idx];
+
+        if (parent_column.pushdown_mask == nullptr) {
+          // No pushdown mask, all null mask bits will be encoded
+          // Align on rowgroup size (can be misaligned for list children)
+          if (rg.size() % 8) {
+            auto bits_to_borrow           = 8 - rg.size() % 8;
+            auto const last_borrow_rg_idx = seek_last_borrow_rg(rg_idx, bits_to_borrow);
+            if (last_borrow_rg_idx == stripe_end) {
+              // Didn't find enough bits to borrow, move the rowgroup end to the stripe end
+              rg.end = out_rowgroups[stripe_end - 1][col_idx].end;
+              // Done with this stripe
+              break;
+            }
+            auto& last_borrow_rg = out_rowgroups[last_borrow_rg_idx][col_idx];
+            last_borrow_rg.begin += bits_to_borrow;
+            rg.end = last_borrow_rg.begin;
+            // Skip the rowgroups we emptied in the loop
+            rg_idx = last_borrow_rg_idx - 1;
+          }
+        } else {
+          // pushdown mask present; null mask bits w/ set pushdown mask bits will be encoded
+          // Use the number of set bits in pushdown mask as size
+          auto bits_to_borrow =
+            8 - (d_pd_set_counts[rg_idx][parent_col_idx] - previously_borrowed) % 8;
+          if (bits_to_borrow == 0) {
+            // Didn't borrow any bits for this rowgroup
+            previously_borrowed = 0;
+            continue;
+          }
+
+          // Find rowgroup in which we finish the search for missing bits
+          auto const last_borrow_rg_idx = seek_last_borrow_rg(rg_idx, bits_to_borrow);
+          if (last_borrow_rg_idx == stripe_end) {
+            // Didn't find enough bits to borrow, move the rowgroup end to the stripe end
+            rg.end = out_rowgroups[stripe_end - 1][col_idx].end;
+            // Done with this stripe
+            break;
+          }
+
+          auto& last_borrow_rg = out_rowgroups[last_borrow_rg_idx][col_idx];
+          // First row that does not need to be borrowed
+          auto borrow_end = last_borrow_rg.begin;
+
+          // Adjust the number of bits to borrow in the next iteration
+          previously_borrowed = bits_to_borrow;
+
+          // Find word in which we finish the search for missing bits (guaranteed to be available)
+          while (bits_to_borrow != 0) {
+            auto const mask = cudf::detail::get_mask_offset_word(
+              parent_column.pushdown_mask, 0, borrow_end, borrow_end + 32);
+            auto const valid_in_word = __popc(mask);
+
+            if (valid_in_word > bits_to_borrow) break;
+            bits_to_borrow -= valid_in_word;
+            borrow_end += 32;
+          }
+
+          // Find the last of the missing bits (guaranteed to be available)
+          while (bits_to_borrow != 0) {
+            if (bit_is_set(parent_column.pushdown_mask, borrow_end)) { --bits_to_borrow; };
+            ++borrow_end;
+          }
+
+          last_borrow_rg.begin = borrow_end;
+          rg.end               = borrow_end;
+          // Skip the rowgroups we emptied in the loop
+          rg_idx = last_borrow_rg_idx - 1;
+        }
+      }
+    });
+
+  aligned_rgs.device_to_host_sync(stream);
+
+  std::vector<std::vector<rowgroup_rows>> h_aligned_rgs;
+  h_aligned_rgs.reserve(segmentation.num_rowgroups());
+  std::transform(thrust::make_counting_iterator(0ul),
+                 thrust::make_counting_iterator(segmentation.num_rowgroups()),
+                 std::back_inserter(h_aligned_rgs),
+                 [&](auto idx) -> std::vector<rowgroup_rows> {
+                   return {aligned_rgs[idx].begin(), aligned_rgs[idx].end()};
+                 });
+
+  return h_aligned_rgs;
+}
+
+struct segmented_valid_cnt_input {
+  bitmask_type const* mask;
+  std::vector<size_type> indices;
+};
+
+encoded_data encode_columns(orc_table_view const& orc_table,
+                            encoder_decimal_info&& dec_chunk_sizes,
+                            file_segmentation const& segmentation,
+                            orc_streams const& streams,
+                            uint32_t uncomp_block_align,
+                            rmm::cuda_stream_view stream)
+{
+  auto const num_columns = orc_table.num_columns();
+  hostdevice_2dvector<gpu::EncChunk> chunks(num_columns, segmentation.num_rowgroups(), stream);
+
+  auto const aligned_rowgroups = calculate_aligned_rowgroup_bounds(orc_table, segmentation, stream);
+
+  // Initialize column chunks' descriptions
+  std::map<size_type, segmented_valid_cnt_input> validity_check_inputs;
+
+  for (auto const& column : orc_table.columns) {
+    for (auto const& stripe : segmentation.stripes) {
+      for (auto rg_idx_it = stripe.cbegin(); rg_idx_it < stripe.cend(); ++rg_idx_it) {
+        auto const rg_idx      = *rg_idx_it;
+        auto& ck               = chunks[column.index()][rg_idx];
+        ck.start_row           = segmentation.rowgroups[rg_idx][column.index()].begin;
+        ck.num_rows            = segmentation.rowgroups[rg_idx][column.index()].size();
+        ck.null_mask_start_row = aligned_rowgroups[rg_idx][column.index()].begin;
+        ck.null_mask_num_rows  = aligned_rowgroups[rg_idx][column.index()].size();
+        ck.encoding_kind       = column.orc_encoding();
+        ck.type_kind           = column.orc_kind();
+        if (ck.type_kind == TypeKind::STRING) {
+          ck.dict_index = (ck.encoding_kind == DICTIONARY_V2)
+                            ? column.host_stripe_dict(stripe.id).index.data()
+                            : nullptr;
+          ck.dtype_len  = 1;
+        } else {
+          ck.dtype_len = column.type_width();
+        }
+        ck.scale = column.scale();
+        if (ck.type_kind == TypeKind::DECIMAL) { ck.decimal_offsets = column.decimal_offsets(); }
+      }
+    }
+  }
+  chunks.host_to_device_async(stream);
+  // TODO (future): pass columns separately from chunks (to skip this step)
+  // and remove info from chunks that is common for the entire column
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0ul),
+    chunks.count(),
+    [chunks = device_2dspan<gpu::EncChunk>{chunks},
+     cols = device_span<orc_column_device_view const>{orc_table.d_columns}] __device__(auto& idx) {
+      auto const col_idx             = idx / chunks.size().second;
+      auto const rg_idx              = idx % chunks.size().second;
+      chunks[col_idx][rg_idx].column = &cols[col_idx];
+    });
+
+  auto validity_check_indices = [&](size_t col_idx) {
+    std::vector<size_type> indices;
+    for (auto const& stripe : segmentation.stripes) {
+      for (auto rg_idx_it = stripe.cbegin(); rg_idx_it < stripe.cend() - 1; ++rg_idx_it) {
+        auto const& chunk = chunks[col_idx][*rg_idx_it];
+        indices.push_back(chunk.start_row);
+        indices.push_back(chunk.start_row + chunk.num_rows);
+      }
+    }
+    return indices;
+  };
+  for (auto const& column : orc_table.columns) {
+    if (column.orc_kind() == TypeKind::BOOLEAN && column.nullable()) {
+      validity_check_inputs[column.index()] = {column.null_mask(),
+                                               validity_check_indices(column.index())};
+    }
+  }
+  for (auto& cnt_in : validity_check_inputs) {
+    auto const valid_counts =
+      cudf::detail::segmented_valid_count(cnt_in.second.mask, cnt_in.second.indices, stream);
+    CUDF_EXPECTS(
+      std::none_of(valid_counts.cbegin(),
+                   valid_counts.cend(),
+                   [](auto valid_count) { return valid_count % 8; }),
+      "There's currently a bug in encoding boolean columns. Suggested workaround is to convert "
+      "to int8 type."
+      " Please see https://github.com/rapidsai/cudf/issues/6763 for more information.");
+  }
+
+  hostdevice_2dvector<gpu::encoder_chunk_streams> chunk_streams(
+    num_columns, segmentation.num_rowgroups(), stream);
+  // per-stripe, per-stream owning buffers
+  std::vector<std::vector<rmm::device_uvector<uint8_t>>> encoded_data(segmentation.num_stripes());
+  for (auto const& stripe : segmentation.stripes) {
+    std::generate_n(std::back_inserter(encoded_data[stripe.id]), streams.size(), [stream]() {
+      return rmm::device_uvector<uint8_t>(0, stream);
+    });
+
+    for (size_t col_idx = 0; col_idx < num_columns; col_idx++) {
+      for (int strm_type = 0; strm_type < gpu::CI_NUM_STREAMS; ++strm_type) {
+        auto const& column = orc_table.column(col_idx);
+        auto col_streams   = chunk_streams[col_idx];
+        auto const strm_id = streams.id(col_idx * gpu::CI_NUM_STREAMS + strm_type);
+
+        std::for_each(stripe.cbegin(), stripe.cend(), [&](auto rg_idx) {
+          col_streams[rg_idx].ids[strm_type]     = strm_id;
+          col_streams[rg_idx].lengths[strm_type] = 0;
+        });
+
+        // Calculate rowgroup sizes and stripe size
+        if (strm_id >= 0) {
+          size_t stripe_size = 0;
+          std::for_each(stripe.cbegin(), stripe.cend(), [&](auto rg_idx) {
+            auto const& ck = chunks[col_idx][rg_idx];
+            auto& strm     = col_streams[rg_idx];
+
+            if ((strm_type == gpu::CI_DICTIONARY) ||
+                (strm_type == gpu::CI_DATA2 && ck.encoding_kind == DICTIONARY_V2)) {
+              if (rg_idx == *stripe.cbegin()) {
+                auto const stripe_dict = column.host_stripe_dict(stripe.id);
+                strm.lengths[strm_type] =
+                  (strm_type == gpu::CI_DICTIONARY)
+                    ? stripe_dict.char_count
+                    : (((stripe_dict.entry_count + 0x1ff) >> 9) * (512 * 4 + 2));
+              } else {
+                strm.lengths[strm_type] = 0;
+              }
+            } else if (strm_type == gpu::CI_DATA && ck.type_kind == TypeKind::STRING &&
+                       ck.encoding_kind == DIRECT_V2) {
+              strm.lengths[strm_type] = std::max(column.rowgroup_char_count(rg_idx), 1);
+            } else if (strm_type == gpu::CI_DATA && streams[strm_id].length == 0 &&
+                       (ck.type_kind == DOUBLE || ck.type_kind == FLOAT)) {
+              // Pass-through
+              strm.lengths[strm_type] = ck.num_rows * ck.dtype_len;
+            } else if (ck.type_kind == DECIMAL && strm_type == gpu::CI_DATA) {
+              strm.lengths[strm_type] = dec_chunk_sizes.rg_sizes.at(col_idx)[rg_idx];
+            } else {
+              strm.lengths[strm_type] = RLE_stream_size(streams.type(strm_id), ck.num_rows);
+            }
+            // Allow extra space for alignment
+            stripe_size += strm.lengths[strm_type] + uncomp_block_align - 1;
+          });
+
+          encoded_data[stripe.id][strm_id] = rmm::device_uvector<uint8_t>(stripe_size, stream);
+        }
+
+        // Set offsets
+        for (auto rg_idx_it = stripe.cbegin(); rg_idx_it < stripe.cend(); ++rg_idx_it) {
+          auto const rg_idx = *rg_idx_it;
+          auto const& ck    = chunks[col_idx][rg_idx];
+          auto& strm        = col_streams[rg_idx];
+
+          if (strm_id < 0 or (strm_type == gpu::CI_DATA && streams[strm_id].length == 0 &&
+                              (ck.type_kind == DOUBLE || ck.type_kind == FLOAT))) {
+            strm.data_ptrs[strm_type] = nullptr;
+          } else {
+            if ((strm_type == gpu::CI_DICTIONARY) ||
+                (strm_type == gpu::CI_DATA2 && ck.encoding_kind == DICTIONARY_V2)) {
+              strm.data_ptrs[strm_type] = encoded_data[stripe.id][strm_id].data();
+            } else {
+              strm.data_ptrs[strm_type] = (rg_idx_it == stripe.cbegin())
+                                            ? encoded_data[stripe.id][strm_id].data()
+                                            : (col_streams[rg_idx - 1].data_ptrs[strm_type] +
+                                               col_streams[rg_idx - 1].lengths[strm_type]);
+            }
+          }
+          auto const misalignment =
+            reinterpret_cast<intptr_t>(strm.data_ptrs[strm_type]) % uncomp_block_align;
+          if (misalignment != 0) {
+            strm.data_ptrs[strm_type] += (uncomp_block_align - misalignment);
+          }
+        }
+      }
+    }
+  }
+
+  chunk_streams.host_to_device_async(stream);
+
+  if (orc_table.num_rows() > 0) {
+    if (orc_table.num_string_columns() != 0) {
+      auto d_stripe_dict = orc_table.string_column(0).device_stripe_dicts();
+      gpu::EncodeStripeDictionaries(d_stripe_dict.data(),
+                                    orc_table.d_columns,
+                                    chunks,
+                                    orc_table.num_string_columns(),
+                                    segmentation.num_stripes(),
+                                    chunk_streams,
+                                    stream);
+    }
+
+    gpu::EncodeOrcColumnData(chunks, chunk_streams, stream);
+  }
+  chunk_streams.device_to_host_sync(stream);
+
+  return {std::move(encoded_data), std::move(chunk_streams)};
+}
+
+// TODO: remove StripeInformation from this function and return strm_desc instead
+/**
+ * @brief Returns stripe information after compacting columns' individual data
+ * chunks into contiguous data streams.
+ *
+ * @param[in] num_index_streams Total number of index streams
+ * @param[in] segmentation stripe and rowgroup ranges
+ * @param[in,out] enc_data ORC per-chunk streams of encoded data
+ * @param[in,out] strm_desc List of stream descriptors [stripe][data_stream]
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ * @return The stripes' information
+ */
+std::vector<StripeInformation> gather_stripes(size_t num_index_streams,
+                                              file_segmentation const& segmentation,
+                                              encoded_data* enc_data,
+                                              hostdevice_2dvector<gpu::StripeStream>* strm_desc,
+                                              rmm::cuda_stream_view stream)
+{
+  if (segmentation.num_stripes() == 0) { return {}; }
+
+  // gathered stripes - per-stripe, per-stream (same as encoded_data.data)
+  std::vector<std::vector<rmm::device_uvector<uint8_t>>> gathered_stripes(enc_data->data.size());
+  for (auto& stripe_data : gathered_stripes) {
+    std::generate_n(std::back_inserter(stripe_data), enc_data->data[0].size(), [&]() {
+      return rmm::device_uvector<uint8_t>(0, stream);
+    });
+  }
+  std::vector<StripeInformation> stripes(segmentation.num_stripes());
+  for (auto const& stripe : segmentation.stripes) {
+    for (size_t col_idx = 0; col_idx < enc_data->streams.size().first; col_idx++) {
+      auto const& col_streams = (enc_data->streams)[col_idx];
+      // Assign stream data of column data stream(s)
+      for (int k = 0; k < gpu::CI_INDEX; k++) {
+        auto const stream_id = col_streams[0].ids[k];
+        if (stream_id != -1) {
+          auto const actual_stripe_size = std::accumulate(
+            col_streams.begin() + stripe.first,
+            col_streams.begin() + stripe.first + stripe.size,
+            0ul,
+            [&](auto const& sum, auto const& strm) { return sum + strm.lengths[k]; });
+
+          auto const& allocated_stripe_size = enc_data->data[stripe.id][stream_id].size();
+          CUDF_EXPECTS(allocated_stripe_size >= actual_stripe_size,
+                       "Internal ORC writer error: insufficient allocation size for encoded data");
+          // Allocate buffers of the exact size as encoded data, smaller than the original buffers.
+          // Don't copying the data to exactly sized buffer when only one chunk is present to avoid
+          // performance overhead from the additional copy. When there are multiple chunks, they are
+          // copied anyway, to make them contiguous (i.e. gather them).
+          if (stripe.size > 1 and allocated_stripe_size > actual_stripe_size) {
+            gathered_stripes[stripe.id][stream_id] =
+              rmm::device_uvector<uint8_t>(actual_stripe_size, stream);
+          }
+
+          auto* ss           = &(*strm_desc)[stripe.id][stream_id - num_index_streams];
+          ss->data_ptr       = gathered_stripes[stripe.id][stream_id].data();
+          ss->stream_size    = actual_stripe_size;
+          ss->first_chunk_id = stripe.first;
+          ss->num_chunks     = stripe.size;
+          ss->column_id      = col_idx;
+          ss->stream_type    = k;
+        }
+      }
+    }
+
+    stripes[stripe.id].numberOfRows =
+      stripe.size == 0 ? 0
+                       : segmentation.rowgroups[stripe.first + stripe.size - 1][0].end -
+                           segmentation.rowgroups[stripe.first][0].begin;
+  }
+
+  strm_desc->host_to_device_async(stream);
+  // TODO: use cub::DeviceMemcpy::Batched
+  gpu::CompactOrcDataStreams(*strm_desc, enc_data->streams, stream);
+  strm_desc->device_to_host_async(stream);
+  enc_data->streams.device_to_host_sync(stream);
+
+  // move the gathered stripes to encoded_data.data for lifetime management
+  for (auto stripe_id = 0ul; stripe_id < enc_data->data.size(); ++stripe_id) {
+    for (auto stream_id = 0ul; stream_id < enc_data->data[0].size(); ++stream_id) {
+      if (not gathered_stripes[stripe_id][stream_id].is_empty())
+        enc_data->data[stripe_id][stream_id] = std::move(gathered_stripes[stripe_id][stream_id]);
+    }
+  }
+
+  return stripes;
+}
+
+void set_stat_desc_leaf_cols(device_span<orc_column_device_view const> columns,
+                             device_span<stats_column_desc> stat_desc,
+                             rmm::cuda_stream_view stream)
+{
+  thrust::for_each(rmm::exec_policy(stream),
+                   thrust::make_counting_iterator(0ul),
+                   thrust::make_counting_iterator(stat_desc.size()),
+                   [=] __device__(auto idx) { stat_desc[idx].leaf_column = &columns[idx]; });
+}
+
+cudf::detail::hostdevice_vector<uint8_t> allocate_and_encode_blobs(
+  cudf::detail::hostdevice_vector<statistics_merge_group>& stats_merge_groups,
+  rmm::device_uvector<statistics_chunk>& stat_chunks,
+  int num_stat_blobs,
+  rmm::cuda_stream_view stream)
+{
+  // figure out the buffer size needed for protobuf format
+  gpu::orc_init_statistics_buffersize(
+    stats_merge_groups.device_ptr(), stat_chunks.data(), num_stat_blobs, stream);
+  auto max_blobs = stats_merge_groups.element(num_stat_blobs - 1, stream);
+
+  cudf::detail::hostdevice_vector<uint8_t> blobs(max_blobs.start_chunk + max_blobs.num_chunks,
+                                                 stream);
+  gpu::orc_encode_statistics(blobs.device_ptr(),
+                             stats_merge_groups.device_ptr(),
+                             stat_chunks.data(),
+                             num_stat_blobs,
+                             stream);
+  stats_merge_groups.device_to_host_async(stream);
+  blobs.device_to_host_sync(stream);
+  return blobs;
+}
+
+/**
+ * @brief Returns column statistics in an intermediate format.
+ *
+ * @param statistics_freq Frequency of statistics to be included in the output file
+ * @param orc_table Table information to be written
+ * @param segmentation stripe and rowgroup ranges
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return The statistic information
+ */
+intermediate_statistics gather_statistic_blobs(statistics_freq const stats_freq,
+                                               orc_table_view const& orc_table,
+                                               file_segmentation const& segmentation,
+                                               rmm::cuda_stream_view stream)
+{
+  auto const num_rowgroup_blobs     = segmentation.rowgroups.count();
+  auto const num_stripe_blobs       = segmentation.num_stripes() * orc_table.num_columns();
+  auto const are_statistics_enabled = stats_freq != statistics_freq::STATISTICS_NONE;
+  if (not are_statistics_enabled or num_rowgroup_blobs + num_stripe_blobs == 0) {
+    return intermediate_statistics{stream};
+  }
+
+  cudf::detail::hostdevice_vector<stats_column_desc> stat_desc(orc_table.num_columns(), stream);
+  cudf::detail::hostdevice_vector<statistics_merge_group> rowgroup_merge(num_rowgroup_blobs,
+                                                                         stream);
+  cudf::detail::hostdevice_vector<statistics_merge_group> stripe_merge(num_stripe_blobs, stream);
+  std::vector<statistics_dtype> col_stats_dtypes;
+  std::vector<data_type> col_types;
+  auto rowgroup_stat_merge = rowgroup_merge.host_ptr();
+  auto stripe_stat_merge   = stripe_merge.host_ptr();
+
+  for (auto const& column : orc_table.columns) {
+    stats_column_desc* desc = &stat_desc[column.index()];
+    switch (column.orc_kind()) {
+      case TypeKind::BYTE: desc->stats_dtype = dtype_int8; break;
+      case TypeKind::SHORT: desc->stats_dtype = dtype_int16; break;
+      case TypeKind::INT: desc->stats_dtype = dtype_int32; break;
+      case TypeKind::LONG: desc->stats_dtype = dtype_int64; break;
+      case TypeKind::FLOAT: desc->stats_dtype = dtype_float32; break;
+      case TypeKind::DOUBLE: desc->stats_dtype = dtype_float64; break;
+      case TypeKind::BOOLEAN: desc->stats_dtype = dtype_bool; break;
+      case TypeKind::DATE: desc->stats_dtype = dtype_int32; break;
+      case TypeKind::DECIMAL: desc->stats_dtype = dtype_decimal64; break;
+      case TypeKind::TIMESTAMP: desc->stats_dtype = dtype_timestamp64; break;
+      case TypeKind::STRING: desc->stats_dtype = dtype_string; break;
+      default: desc->stats_dtype = dtype_none; break;
+    }
+    desc->num_rows   = column.size();
+    desc->num_values = column.size();
+    if (desc->stats_dtype == dtype_timestamp64) {
+      // Timestamp statistics are in milliseconds
+      switch (column.scale()) {
+        case 9: desc->ts_scale = 1000; break;
+        case 6: desc->ts_scale = 0; break;
+        case 3: desc->ts_scale = -1000; break;
+        case 0: desc->ts_scale = -1000000; break;
+        default: desc->ts_scale = 0; break;
+      }
+    } else {
+      desc->ts_scale = 0;
+    }
+    col_stats_dtypes.push_back(desc->stats_dtype);
+    col_types.push_back(column.type());
+    for (auto const& stripe : segmentation.stripes) {
+      auto& grp       = stripe_stat_merge[column.index() * segmentation.num_stripes() + stripe.id];
+      grp.col_dtype   = column.type();
+      grp.stats_dtype = desc->stats_dtype;
+      grp.start_chunk =
+        static_cast<uint32_t>(column.index() * segmentation.num_rowgroups() + stripe.first);
+      grp.num_chunks = stripe.size;
+      for (auto rg_idx_it = stripe.cbegin(); rg_idx_it != stripe.cend(); ++rg_idx_it) {
+        auto& rg_grp =
+          rowgroup_stat_merge[column.index() * segmentation.num_rowgroups() + *rg_idx_it];
+        rg_grp.col_dtype   = column.type();
+        rg_grp.stats_dtype = desc->stats_dtype;
+        rg_grp.start_chunk = *rg_idx_it;
+        rg_grp.num_chunks  = 1;
+      }
+    }
+  }
+  stat_desc.host_to_device_async(stream);
+  rowgroup_merge.host_to_device_async(stream);
+  stripe_merge.host_to_device_async(stream);
+  set_stat_desc_leaf_cols(orc_table.d_columns, stat_desc, stream);
+
+  // The rowgroup stat chunks are written out in each stripe. The stripe and file-level chunks are
+  // written in the footer. To prevent persisting the rowgroup stat chunks across multiple write
+  // calls in a chunked write situation, these allocations are split up so stripe data can persist
+  // until the footer is written and rowgroup data can be freed after being written to the stripe.
+  rmm::device_uvector<statistics_chunk> rowgroup_chunks(num_rowgroup_blobs, stream);
+  rmm::device_uvector<statistics_chunk> stripe_chunks(num_stripe_blobs, stream);
+  auto rowgroup_stat_chunks = rowgroup_chunks.data();
+  auto stripe_stat_chunks   = stripe_chunks.data();
+
+  rmm::device_uvector<statistics_group> rowgroup_groups(num_rowgroup_blobs, stream);
+  gpu::orc_init_statistics_groups(
+    rowgroup_groups.data(), stat_desc.device_ptr(), segmentation.rowgroups, stream);
+
+  detail::calculate_group_statistics<detail::io_file_format::ORC>(
+    rowgroup_chunks.data(), rowgroup_groups.data(), num_rowgroup_blobs, stream);
+
+  detail::merge_group_statistics<detail::io_file_format::ORC>(
+    stripe_stat_chunks, rowgroup_stat_chunks, stripe_merge.device_ptr(), num_stripe_blobs, stream);
+
+  // With chunked writes, the orc table can be deallocated between write calls.
+  // This forces our hand to encode row groups and stripes only in this stage and further
+  // we have to persist any data from the table that we need later. The
+  // minimum and maximum string inside the `str_val` structure inside `statistics_val` in
+  // `statistic_chunk` that are copies of the largest and smallest strings in the row group,
+  // or stripe need to be persisted between write calls. We write rowgroup data with each
+  // stripe and then save each stripe's stats until the end where we merge those all together
+  // to get the file-level stats.
+
+  // Skip rowgroup blobs when encoding, if chosen granularity is coarser than "ROW_GROUP".
+  auto const is_granularity_rowgroup = stats_freq == ORC_STATISTICS_ROW_GROUP;
+  // we have to encode the row groups now IF they are being written out
+  auto rowgroup_blobs = [&]() -> std::vector<ColStatsBlob> {
+    if (not is_granularity_rowgroup) { return {}; }
+
+    cudf::detail::hostdevice_vector<uint8_t> blobs =
+      allocate_and_encode_blobs(rowgroup_merge, rowgroup_chunks, num_rowgroup_blobs, stream);
+
+    std::vector<ColStatsBlob> rowgroup_blobs(num_rowgroup_blobs);
+    for (size_t i = 0; i < num_rowgroup_blobs; i++) {
+      auto const stat_begin = blobs.host_ptr(rowgroup_merge[i].start_chunk);
+      auto const stat_end   = stat_begin + rowgroup_merge[i].num_chunks;
+      rowgroup_blobs[i].assign(stat_begin, stat_end);
+    }
+    return rowgroup_blobs;
+  }();
+
+  return {std::move(rowgroup_blobs),
+          std::move(stripe_chunks),
+          std::move(stripe_merge),
+          std::move(col_stats_dtypes),
+          std::move(col_types)};
+}
+
+/**
+ * @brief Returns column statistics encoded in ORC protobuf format stored in the footer.
+ *
+ * @param num_stripes number of stripes in the data
+ * @param incoming_stats intermediate statistics returned from `gather_statistic_blobs`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return The encoded statistic blobs
+ */
+encoded_footer_statistics finish_statistic_blobs(int num_stripes,
+                                                 persisted_statistics& per_chunk_stats,
+                                                 rmm::cuda_stream_view stream)
+{
+  auto stripe_size_iter = thrust::make_transform_iterator(per_chunk_stats.stripe_stat_merge.begin(),
+                                                          [](auto const& i) { return i.size(); });
+
+  auto const num_columns = per_chunk_stats.col_types.size();
+  auto const num_stripe_blobs =
+    thrust::reduce(stripe_size_iter, stripe_size_iter + per_chunk_stats.stripe_stat_merge.size());
+  auto const num_file_blobs = num_columns;
+  auto const num_blobs      = static_cast<int>(num_stripe_blobs + num_file_blobs);
+
+  if (num_stripe_blobs == 0) { return {}; }
+
+  // merge the stripe persisted data and add file data
+  rmm::device_uvector<statistics_chunk> stat_chunks(num_blobs, stream);
+  cudf::detail::hostdevice_vector<statistics_merge_group> stats_merge(num_blobs, stream);
+
+  // we need to merge the stat arrays from the persisted data.
+  // this needs to be done carefully because each array can contain
+  // a different number of stripes and stripes from each column must be
+  // located next to each other. We know the total number of stripes and
+  // we know the size of each array. The number of stripes per column in a chunk array can
+  // be calculated by dividing the number of chunks by the number of columns.
+  // That many chunks need to be copied at a time to the proper destination.
+  size_t num_entries_seen = 0;
+  for (size_t i = 0; i < per_chunk_stats.stripe_stat_chunks.size(); ++i) {
+    auto const stripes_per_col = per_chunk_stats.stripe_stat_chunks[i].size() / num_columns;
+
+    auto const chunk_bytes = stripes_per_col * sizeof(statistics_chunk);
+    auto const merge_bytes = stripes_per_col * sizeof(statistics_merge_group);
+    for (size_t col = 0; col < num_columns; ++col) {
+      CUDF_CUDA_TRY(
+        cudaMemcpyAsync(stat_chunks.data() + (num_stripes * col) + num_entries_seen,
+                        per_chunk_stats.stripe_stat_chunks[i].data() + col * stripes_per_col,
+                        chunk_bytes,
+                        cudaMemcpyDefault,
+                        stream.value()));
+      CUDF_CUDA_TRY(
+        cudaMemcpyAsync(stats_merge.device_ptr() + (num_stripes * col) + num_entries_seen,
+                        per_chunk_stats.stripe_stat_merge[i].device_ptr() + col * stripes_per_col,
+                        merge_bytes,
+                        cudaMemcpyDefault,
+                        stream.value()));
+    }
+    num_entries_seen += stripes_per_col;
+  }
+
+  std::vector<statistics_merge_group> file_stats_merge(num_file_blobs);
+  for (auto i = 0u; i < num_file_blobs; ++i) {
+    auto col_stats         = &file_stats_merge[i];
+    col_stats->col_dtype   = per_chunk_stats.col_types[i];
+    col_stats->stats_dtype = per_chunk_stats.stats_dtypes[i];
+    col_stats->start_chunk = static_cast<uint32_t>(i * num_stripes);
+    col_stats->num_chunks  = static_cast<uint32_t>(num_stripes);
+  }
+
+  auto d_file_stats_merge = stats_merge.device_ptr(num_stripe_blobs);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(d_file_stats_merge,
+                                file_stats_merge.data(),
+                                num_file_blobs * sizeof(statistics_merge_group),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  auto file_stat_chunks = stat_chunks.data() + num_stripe_blobs;
+  detail::merge_group_statistics<detail::io_file_format::ORC>(
+    file_stat_chunks, stat_chunks.data(), d_file_stats_merge, num_file_blobs, stream);
+
+  cudf::detail::hostdevice_vector<uint8_t> blobs =
+    allocate_and_encode_blobs(stats_merge, stat_chunks, num_blobs, stream);
+
+  auto stripe_stat_merge = stats_merge.host_ptr();
+
+  std::vector<ColStatsBlob> stripe_blobs(num_stripe_blobs);
+  for (size_t i = 0; i < num_stripe_blobs; i++) {
+    auto const stat_begin = blobs.host_ptr(stripe_stat_merge[i].start_chunk);
+    auto const stat_end   = stat_begin + stripe_stat_merge[i].num_chunks;
+    stripe_blobs[i].assign(stat_begin, stat_end);
+  }
+
+  std::vector<ColStatsBlob> file_blobs(num_file_blobs);
+  auto file_stat_merge = stats_merge.host_ptr(num_stripe_blobs);
+  for (auto i = 0u; i < num_file_blobs; i++) {
+    auto const stat_begin = blobs.host_ptr(file_stat_merge[i].start_chunk);
+    auto const stat_end   = stat_begin + file_stat_merge[i].num_chunks;
+    file_blobs[i].assign(stat_begin, stat_end);
+  }
+
+  return {std::move(stripe_blobs), std::move(file_blobs)};
+}
+
+/**
+ * @brief Writes the specified column's row index stream.
+ *
+ * @param[in] stripe_id Stripe's identifier
+ * @param[in] stream_id Stream identifier (column id + 1)
+ * @param[in] columns List of columns
+ * @param[in] segmentation stripe and rowgroup ranges
+ * @param[in] enc_streams List of encoder chunk streams [column][rowgroup]
+ * @param[in] strm_desc List of stream descriptors
+ * @param[in] comp_res Output status for compressed streams
+ * @param[in] rg_stats row group level statistics
+ * @param[in,out] stripe Stream's parent stripe
+ * @param[in,out] streams List of all streams
+ * @param[in] compression_kind The compression kind
+ * @param[in] compression_blocksize The block size used for compression
+ * @param[in] out_sink Sink for writing data
+ */
+void write_index_stream(int32_t stripe_id,
+                        int32_t stream_id,
+                        host_span<orc_column_view const> columns,
+                        file_segmentation const& segmentation,
+                        host_2dspan<gpu::encoder_chunk_streams const> enc_streams,
+                        host_2dspan<gpu::StripeStream const> strm_desc,
+                        host_span<compression_result const> comp_res,
+                        host_span<ColStatsBlob const> rg_stats,
+                        StripeInformation* stripe,
+                        orc_streams* streams,
+                        CompressionKind compression_kind,
+                        size_t compression_blocksize,
+                        std::unique_ptr<data_sink> const& out_sink)
+{
+  row_group_index_info present;
+  row_group_index_info data;
+  row_group_index_info data2;
+  auto const column_id = stream_id - 1;
+
+  auto find_record = [=, &strm_desc](gpu::encoder_chunk_streams const& stream,
+                                     gpu::StreamIndexType type) {
+    row_group_index_info record;
+    if (stream.ids[type] > 0) {
+      record.pos = 0;
+      if (compression_kind != NONE) {
+        auto const& ss   = strm_desc[stripe_id][stream.ids[type] - (columns.size() + 1)];
+        record.blk_pos   = ss.first_block;
+        record.comp_pos  = 0;
+        record.comp_size = ss.stream_size;
+      }
+    }
+    return record;
+  };
+  auto scan_record = [=, &comp_res](gpu::encoder_chunk_streams const& stream,
+                                    gpu::StreamIndexType type,
+                                    row_group_index_info& record) {
+    if (record.pos >= 0) {
+      record.pos += stream.lengths[type];
+      while ((record.pos >= 0) && (record.blk_pos >= 0) &&
+             (static_cast<size_t>(record.pos) >= compression_blocksize) &&
+             (record.comp_pos + block_header_size + comp_res[record.blk_pos].bytes_written <
+              static_cast<size_t>(record.comp_size))) {
+        record.pos -= compression_blocksize;
+        record.comp_pos += block_header_size + comp_res[record.blk_pos].bytes_written;
+        record.blk_pos += 1;
+      }
+    }
+  };
+
+  auto kind = TypeKind::STRUCT;
+  // TBD: Not sure we need an empty index stream for column 0
+  if (stream_id != 0) {
+    auto const& strm = enc_streams[column_id][0];
+    present          = find_record(strm, gpu::CI_PRESENT);
+    data             = find_record(strm, gpu::CI_DATA);
+    data2            = find_record(strm, gpu::CI_DATA2);
+
+    // Change string dictionary to int from index point of view
+    kind = columns[column_id].orc_kind();
+    if (kind == TypeKind::STRING && columns[column_id].orc_encoding() == DICTIONARY_V2) {
+      kind = TypeKind::INT;
+    }
+  }
+
+  ProtobufWriter pbw((compression_kind != NONE) ? 3 : 0);
+
+  // Add row index entries
+  auto const& rowgroups_range = segmentation.stripes[stripe_id];
+  std::for_each(rowgroups_range.cbegin(), rowgroups_range.cend(), [&](auto rowgroup) {
+    pbw.put_row_index_entry(present.comp_pos,
+                            present.pos,
+                            data.comp_pos,
+                            data.pos,
+                            data2.comp_pos,
+                            data2.pos,
+                            kind,
+                            (rg_stats.empty() or stream_id == 0)
+                              ? nullptr
+                              : (&rg_stats[column_id * segmentation.num_rowgroups() + rowgroup]));
+
+    if (stream_id != 0) {
+      const auto& strm = enc_streams[column_id][rowgroup];
+      scan_record(strm, gpu::CI_PRESENT, present);
+      scan_record(strm, gpu::CI_DATA, data);
+      scan_record(strm, gpu::CI_DATA2, data2);
+    }
+  });
+
+  (*streams)[stream_id].length = pbw.size();
+  if (compression_kind != NONE) {
+    uint32_t uncomp_ix_len = (uint32_t)((*streams)[stream_id].length - 3) * 2 + 1;
+    pbw.buffer()[0]        = static_cast<uint8_t>(uncomp_ix_len >> 0);
+    pbw.buffer()[1]        = static_cast<uint8_t>(uncomp_ix_len >> 8);
+    pbw.buffer()[2]        = static_cast<uint8_t>(uncomp_ix_len >> 16);
+  }
+  out_sink->host_write(pbw.data(), pbw.size());
+  stripe->indexLength += pbw.size();
+}
+
+/**
+ * @brief Write the specified column's data streams
+ *
+ * @param[in] strm_desc Stream's descriptor
+ * @param[in] enc_stream Chunk's streams
+ * @param[in] compressed_data Compressed stream data
+ * @param[in,out] stream_out Temporary host output buffer
+ * @param[in,out] stripe Stream's parent stripe
+ * @param[in,out] streams List of all streams
+ * @param[in] compression_kind The compression kind
+ * @param[in] out_sink Sink for writing data
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ * @return An std::future that should be synchronized to ensure the writing is complete
+ */
+std::future<void> write_data_stream(gpu::StripeStream const& strm_desc,
+                                    gpu::encoder_chunk_streams const& enc_stream,
+                                    uint8_t const* compressed_data,
+                                    uint8_t* stream_out,
+                                    StripeInformation* stripe,
+                                    orc_streams* streams,
+                                    CompressionKind compression_kind,
+                                    std::unique_ptr<data_sink> const& out_sink,
+                                    rmm::cuda_stream_view stream)
+{
+  auto const length                                        = strm_desc.stream_size;
+  (*streams)[enc_stream.ids[strm_desc.stream_type]].length = length;
+  if (length == 0) {
+    return std::async(std::launch::deferred, [] {});
+  }
+
+  auto const* stream_in = (compression_kind == NONE) ? enc_stream.data_ptrs[strm_desc.stream_type]
+                                                     : (compressed_data + strm_desc.bfr_offset);
+
+  auto write_task = [&]() {
+    if (out_sink->is_device_write_preferred(length)) {
+      return out_sink->device_write_async(stream_in, length, stream);
+    } else {
+      CUDF_CUDA_TRY(
+        cudaMemcpyAsync(stream_out, stream_in, length, cudaMemcpyDefault, stream.value()));
+      stream.synchronize();
+
+      out_sink->host_write(stream_out, length);
+      return std::async(std::launch::deferred, [] {});
+    }
+  }();
+  stripe->dataLength += length;
+  return write_task;
+}
+
+/**
+ * @brief Insert 3-byte uncompressed block headers in a byte vector
+ *
+ * @param compression_kind The compression kind
+ * @param compression_blocksize The block size used for compression
+ * @param v The destitation byte vector to write, which must include initial 3-byte header
+ */
+void add_uncompressed_block_headers(CompressionKind compression_kind,
+                                    size_t compression_blocksize,
+                                    std::vector<uint8_t>& v)
+{
+  if (compression_kind != NONE) {
+    size_t uncomp_len = v.size() - 3, pos = 0, block_len;
+    while (uncomp_len > compression_blocksize) {
+      block_len  = compression_blocksize * 2 + 1;
+      v[pos + 0] = static_cast<uint8_t>(block_len >> 0);
+      v[pos + 1] = static_cast<uint8_t>(block_len >> 8);
+      v[pos + 2] = static_cast<uint8_t>(block_len >> 16);
+      pos += 3 + compression_blocksize;
+      v.insert(v.begin() + pos, 3, 0);
+      uncomp_len -= compression_blocksize;
+    }
+    block_len  = uncomp_len * 2 + 1;
+    v[pos + 0] = static_cast<uint8_t>(block_len >> 0);
+    v[pos + 1] = static_cast<uint8_t>(block_len >> 8);
+    v[pos + 2] = static_cast<uint8_t>(block_len >> 16);
+  }
+}
+
+void pushdown_lists_null_mask(orc_column_view const& col,
+                              device_span<orc_column_device_view> d_columns,
+                              bitmask_type const* parent_pd_mask,
+                              device_span<bitmask_type> out_mask,
+                              rmm::cuda_stream_view stream)
+{
+  // Set all bits - correct unless there's a mismatch between offsets and null mask
+  CUDF_CUDA_TRY(cudaMemsetAsync(static_cast<void*>(out_mask.data()),
+                                255,
+                                out_mask.size() * sizeof(bitmask_type),
+                                stream.value()));
+
+  // Reset bits where a null list element has rows in the child column
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0u),
+    col.size(),
+    [d_columns, col_idx = col.index(), parent_pd_mask, out_mask] __device__(auto& idx) {
+      auto const d_col        = d_columns[col_idx];
+      auto const is_row_valid = d_col.is_valid(idx) and bit_value_or(parent_pd_mask, idx, true);
+      if (not is_row_valid) {
+        auto offsets                = d_col.child(lists_column_view::offsets_column_index);
+        auto const child_rows_begin = offsets.element<size_type>(idx + d_col.offset());
+        auto const child_rows_end   = offsets.element<size_type>(idx + 1 + d_col.offset());
+        for (auto child_row = child_rows_begin; child_row < child_rows_end; ++child_row)
+          clear_bit(out_mask.data(), child_row);
+      }
+    });
+}
+
+/**
+ * @brief All pushdown masks in a table.
+ *
+ * Pushdown masks are applied to child column(s). Only bits of the child column null mask that
+ * correspond to set pushdown mask bits are encoded into the output file. Similarly, rows where
+ * pushdown mask is 0 are treated as invalid and not included in the output.
+ */
+struct pushdown_null_masks {
+  // Owning vector for masks in device memory
+  std::vector<rmm::device_uvector<bitmask_type>> data;
+  // Pointers to pushdown masks in device memory. Can be same for multiple columns.
+  std::vector<bitmask_type const*> masks;
+};
+
+pushdown_null_masks init_pushdown_null_masks(orc_table_view& orc_table,
+                                             rmm::cuda_stream_view stream)
+{
+  std::vector<bitmask_type const*> mask_ptrs;
+  mask_ptrs.reserve(orc_table.num_columns());
+  std::vector<rmm::device_uvector<bitmask_type>> pd_masks;
+  for (auto const& col : orc_table.columns) {
+    // Leaf columns don't need pushdown masks
+    if (col.num_children() == 0) {
+      mask_ptrs.emplace_back(nullptr);
+      continue;
+    }
+    auto const parent_pd_mask = col.is_child() ? mask_ptrs[col.parent_index()] : nullptr;
+    auto const null_mask      = col.null_mask();
+
+    if (null_mask == nullptr and parent_pd_mask == nullptr) {
+      mask_ptrs.emplace_back(nullptr);
+      continue;
+    }
+    if (col.orc_kind() == STRUCT) {
+      if (null_mask != nullptr and parent_pd_mask == nullptr) {
+        // Reuse own null mask
+        mask_ptrs.emplace_back(null_mask);
+      } else if (null_mask == nullptr and parent_pd_mask != nullptr) {
+        // Reuse parent's pushdown mask
+        mask_ptrs.emplace_back(parent_pd_mask);
+      } else {
+        // Both are nullable, allocate new pushdown mask
+        pd_masks.emplace_back(num_bitmask_words(col.size()), stream);
+        mask_ptrs.emplace_back(pd_masks.back().data());
+
+        thrust::transform(rmm::exec_policy(stream),
+                          null_mask,
+                          null_mask + pd_masks.back().size(),
+                          parent_pd_mask,
+                          pd_masks.back().data(),
+                          thrust::bit_and<bitmask_type>());
+      }
+    }
+    if (col.orc_kind() == LIST or col.orc_kind() == MAP) {
+      // Need a new pushdown mask unless both the parent and current column are not nullable
+      auto const child_col = orc_table.column(col.child_begin()[0]);
+      // pushdown mask applies to child column(s); use the child column size
+      pd_masks.emplace_back(num_bitmask_words(child_col.size()), stream);
+      mask_ptrs.emplace_back(pd_masks.back().data());
+      pushdown_lists_null_mask(col, orc_table.d_columns, parent_pd_mask, pd_masks.back(), stream);
+    }
+  }
+
+  // Attach null masks to device column views (async)
+  auto const d_mask_ptrs = cudf::detail::make_device_uvector_async(
+    mask_ptrs, stream, rmm::mr::get_current_device_resource());
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0ul),
+    orc_table.num_columns(),
+    [cols = device_span<orc_column_device_view>{orc_table.d_columns},
+     ptrs = device_span<bitmask_type const* const>{d_mask_ptrs}] __device__(auto& idx) {
+      cols[idx].pushdown_mask = ptrs[idx];
+    });
+
+  return {std::move(pd_masks), std::move(mask_ptrs)};
+}
+
+template <typename T>
+struct device_stack {
+  __device__ device_stack(T* stack_storage, int capacity)
+    : stack(stack_storage), capacity(capacity), size(0)
+  {
+  }
+  __device__ void push(T const& val)
+  {
+    cudf_assert(size < capacity and "Stack overflow");
+    stack[size++] = val;
+  }
+  __device__ T pop()
+  {
+    cudf_assert(size > 0 and "Stack underflow");
+    return stack[--size];
+  }
+  __device__ bool empty() { return size == 0; }
+
+ private:
+  T* stack;
+  int capacity;
+  int size;
+};
+
+orc_table_view make_orc_table_view(table_view const& table,
+                                   table_device_view const& d_table,
+                                   table_input_metadata const& table_meta,
+                                   rmm::cuda_stream_view stream)
+{
+  std::vector<orc_column_view> orc_columns;
+  std::vector<uint32_t> str_col_indexes;
+
+  std::function<void(column_view const&, orc_column_view*, column_in_metadata const&)>
+    append_orc_column =
+      [&](column_view const& col, orc_column_view* parent_col, column_in_metadata const& col_meta) {
+        int const str_idx =
+          (col.type().id() == type_id::STRING) ? static_cast<int>(str_col_indexes.size()) : -1;
+
+        auto const new_col_idx = orc_columns.size();
+        orc_columns.emplace_back(new_col_idx, str_idx, parent_col, col, col_meta);
+        if (orc_columns[new_col_idx].is_string()) { str_col_indexes.push_back(new_col_idx); }
+
+        auto const kind = orc_columns[new_col_idx].orc_kind();
+        if (kind == TypeKind::LIST) {
+          append_orc_column(col.child(lists_column_view::child_column_index),
+                            &orc_columns[new_col_idx],
+                            col_meta.child(lists_column_view::child_column_index));
+        } else if (kind == TypeKind::STRUCT) {
+          for (auto child_idx = 0; child_idx != col.num_children(); ++child_idx) {
+            append_orc_column(
+              col.child(child_idx), &orc_columns[new_col_idx], col_meta.child(child_idx));
+          }
+        } else if (kind == TypeKind::MAP) {
+          // MAP: skip to the list child - include grandchildren columns instead of children
+          auto const real_parent_col   = col.child(lists_column_view::child_column_index);
+          auto const& real_parent_meta = col_meta.child(lists_column_view::child_column_index);
+          CUDF_EXPECTS(real_parent_meta.num_children() == 2,
+                       "Map struct column should have exactly two children");
+          // process MAP key
+          append_orc_column(
+            real_parent_col.child(0), &orc_columns[new_col_idx], real_parent_meta.child(0));
+          // process MAP value
+          append_orc_column(
+            real_parent_col.child(1), &orc_columns[new_col_idx], real_parent_meta.child(1));
+        }
+      };
+
+  for (auto col_idx = 0; col_idx < table.num_columns(); ++col_idx) {
+    append_orc_column(table.column(col_idx), nullptr, table_meta.column_metadata[col_idx]);
+  }
+
+  std::vector<TypeKind> type_kinds;
+  type_kinds.reserve(orc_columns.size());
+  std::transform(
+    orc_columns.cbegin(), orc_columns.cend(), std::back_inserter(type_kinds), [](auto& orc_column) {
+      return orc_column.orc_kind();
+    });
+  auto const d_type_kinds = cudf::detail::make_device_uvector_async(
+    type_kinds, stream, rmm::mr::get_current_device_resource());
+
+  rmm::device_uvector<orc_column_device_view> d_orc_columns(orc_columns.size(), stream);
+  using stack_value_type = thrust::pair<column_device_view const*, thrust::optional<uint32_t>>;
+  rmm::device_uvector<stack_value_type> stack_storage(orc_columns.size(), stream);
+
+  // pre-order append ORC device columns
+  cudf::detail::device_single_thread(
+    [d_orc_cols         = device_span<orc_column_device_view>{d_orc_columns},
+     d_type_kinds       = device_span<TypeKind const>{d_type_kinds},
+     d_table            = d_table,
+     stack_storage      = stack_storage.data(),
+     stack_storage_size = stack_storage.size()] __device__() {
+      device_stack stack(stack_storage, stack_storage_size);
+
+      thrust::for_each(thrust::seq,
+                       thrust::make_reverse_iterator(d_table.end()),
+                       thrust::make_reverse_iterator(d_table.begin()),
+                       [&stack](column_device_view const& c) {
+                         stack.push({&c, thrust::nullopt});
+                       });
+
+      uint32_t idx = 0;
+      while (not stack.empty()) {
+        auto [col, parent] = stack.pop();
+        d_orc_cols[idx]    = orc_column_device_view{*col, parent};
+
+        if (d_type_kinds[idx] == TypeKind::MAP) {
+          // Skip to the list child - do not include the child column, just grandchildren columns
+          col = &col->children()[lists_column_view::child_column_index];
+        }
+
+        if (col->type().id() == type_id::LIST) {
+          stack.push({&col->children()[lists_column_view::child_column_index], idx});
+        } else if (col->type().id() == type_id::STRUCT) {
+          thrust::for_each(thrust::seq,
+                           thrust::make_reverse_iterator(col->children().end()),
+                           thrust::make_reverse_iterator(col->children().begin()),
+                           [&stack, idx](column_device_view const& c) {
+                             stack.push({&c, idx});
+                           });
+        }
+        ++idx;
+      }
+    },
+    stream);
+
+  return {std::move(orc_columns),
+          std::move(d_orc_columns),
+          str_col_indexes,
+          cudf::detail::make_device_uvector_sync(
+            str_col_indexes, stream, rmm::mr::get_current_device_resource())};
+}
+
+hostdevice_2dvector<rowgroup_rows> calculate_rowgroup_bounds(orc_table_view const& orc_table,
+                                                             size_type rowgroup_size,
+                                                             rmm::cuda_stream_view stream)
+{
+  auto const num_rowgroups =
+    cudf::util::div_rounding_up_unsafe<size_t, size_t>(orc_table.num_rows(), rowgroup_size);
+
+  hostdevice_2dvector<rowgroup_rows> rowgroup_bounds(
+    num_rowgroups, orc_table.num_columns(), stream);
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0ul),
+    num_rowgroups,
+    [cols      = device_span<orc_column_device_view const>{orc_table.d_columns},
+     rg_bounds = device_2dspan<rowgroup_rows>{rowgroup_bounds},
+     rowgroup_size] __device__(auto rg_idx) mutable {
+      thrust::transform(
+        thrust::seq, cols.begin(), cols.end(), rg_bounds[rg_idx].begin(), [&](auto const& col) {
+          // Root column
+          if (!col.parent_index.has_value()) {
+            size_type const rows_begin = rg_idx * rowgroup_size;
+            auto const rows_end = thrust::min<size_type>((rg_idx + 1) * rowgroup_size, col.size());
+            return rowgroup_rows{rows_begin, rows_end};
+          } else {
+            // Child column
+            auto const parent_index           = *col.parent_index;
+            orc_column_device_view parent_col = cols[parent_index];
+            auto const parent_rg              = rg_bounds[rg_idx][parent_index];
+            if (parent_col.type().id() != type_id::LIST) {
+              auto const offset_diff = parent_col.offset() - col.offset();
+              return rowgroup_rows{parent_rg.begin + offset_diff, parent_rg.end + offset_diff};
+            }
+
+            auto offsets = parent_col.child(lists_column_view::offsets_column_index);
+            auto const rows_begin =
+              offsets.element<size_type>(parent_rg.begin + parent_col.offset()) - col.offset();
+            auto const rows_end =
+              offsets.element<size_type>(parent_rg.end + parent_col.offset()) - col.offset();
+
+            return rowgroup_rows{rows_begin, rows_end};
+          }
+        });
+    });
+  rowgroup_bounds.device_to_host_sync(stream);
+
+  return rowgroup_bounds;
+}
+
+// returns host vector of per-rowgroup sizes
+encoder_decimal_info decimal_chunk_sizes(orc_table_view& orc_table,
+                                         file_segmentation const& segmentation,
+                                         rmm::cuda_stream_view stream)
+{
+  std::map<uint32_t, rmm::device_uvector<uint32_t>> elem_sizes;
+  // Compute per-element offsets (within each row group) on the device
+  for (auto& orc_col : orc_table.columns) {
+    if (orc_col.orc_kind() == DECIMAL) {
+      auto& current_sizes =
+        elem_sizes.insert({orc_col.index(), rmm::device_uvector<uint32_t>(orc_col.size(), stream)})
+          .first->second;
+      thrust::tabulate(rmm::exec_policy(stream),
+                       current_sizes.begin(),
+                       current_sizes.end(),
+                       [d_cols  = device_span<orc_column_device_view const>{orc_table.d_columns},
+                        col_idx = orc_col.index()] __device__(auto idx) {
+                         auto const& col          = d_cols[col_idx];
+                         auto const pushdown_mask = [&]() -> cudf::bitmask_type const* {
+                           auto const parent_index = d_cols[col_idx].parent_index;
+                           if (!parent_index.has_value()) return nullptr;
+                           return d_cols[parent_index.value()].pushdown_mask;
+                         }();
+
+                         if (col.is_null(idx) or not bit_value_or(pushdown_mask, idx, true))
+                           return 0;
+
+                         __int128_t const element =
+                           col.type().id() == type_id::DECIMAL32   ? col.element<int32_t>(idx)
+                           : col.type().id() == type_id::DECIMAL64 ? col.element<int64_t>(idx)
+                                                                   : col.element<__int128_t>(idx);
+
+                         __int128_t const sign      = (element < 0) ? 1 : 0;
+                         __uint128_t zigzaged_value = ((element ^ -sign) * 2) + sign;
+
+                         return varint_size(zigzaged_value);
+                       });
+
+      // Compute element offsets within each row group
+      thrust::for_each_n(rmm::exec_policy(stream),
+                         thrust::make_counting_iterator(0ul),
+                         segmentation.num_rowgroups(),
+                         [sizes     = device_span<uint32_t>{current_sizes},
+                          rg_bounds = device_2dspan<rowgroup_rows const>{segmentation.rowgroups},
+                          col_idx   = orc_col.index()] __device__(auto rg_idx) {
+                           auto const& range = rg_bounds[rg_idx][col_idx];
+                           thrust::inclusive_scan(thrust::seq,
+                                                  sizes.begin() + range.begin,
+                                                  sizes.begin() + range.end,
+                                                  sizes.begin() + range.begin);
+                         });
+
+      orc_col.attach_decimal_offsets(current_sizes.data());
+    }
+  }
+  if (elem_sizes.empty()) return {};
+
+  // Gather the row group sizes and copy to host
+  auto d_tmp_rowgroup_sizes = rmm::device_uvector<uint32_t>(segmentation.num_rowgroups(), stream);
+  std::map<uint32_t, std::vector<uint32_t>> rg_sizes;
+  for (auto const& [col_idx, esizes] : elem_sizes) {
+    // Copy last elem in each row group - equal to row group size
+    thrust::tabulate(rmm::exec_policy(stream),
+                     d_tmp_rowgroup_sizes.begin(),
+                     d_tmp_rowgroup_sizes.end(),
+                     [src       = esizes.data(),
+                      col_idx   = col_idx,
+                      rg_bounds = device_2dspan<rowgroup_rows const>{
+                        segmentation.rowgroups}] __device__(auto idx) {
+                       return src[rg_bounds[idx][col_idx].end - 1];
+                     });
+
+    rg_sizes[col_idx] = cudf::detail::make_std_vector_async(d_tmp_rowgroup_sizes, stream);
+  }
+
+  return {std::move(elem_sizes), std::move(rg_sizes)};
+}
+
+std::map<uint32_t, size_t> decimal_column_sizes(
+  std::map<uint32_t, std::vector<uint32_t>> const& chunk_sizes)
+{
+  std::map<uint32_t, size_t> column_sizes;
+  std::transform(chunk_sizes.cbegin(),
+                 chunk_sizes.cend(),
+                 std::inserter(column_sizes, column_sizes.end()),
+                 [](auto const& chunk_size) -> std::pair<uint32_t, size_t> {
+                   return {
+                     chunk_size.first,
+                     std::accumulate(chunk_size.second.cbegin(), chunk_size.second.cend(), 0lu)};
+                 });
+  return column_sizes;
+}
+
+size_t max_compression_output_size(CompressionKind compression_kind, uint32_t compression_blocksize)
+{
+  if (compression_kind == NONE) return 0;
+
+  return compress_max_output_chunk_size(to_nvcomp_compression_type(compression_kind),
+                                        compression_blocksize);
+}
+
+std::unique_ptr<table_input_metadata> make_table_meta(table_view const& input)
+{
+  auto table_meta = std::make_unique<table_input_metadata>(input);
+
+  // Fill unnamed columns' names in table_meta
+  std::function<void(column_in_metadata&, std::string)> add_default_name =
+    [&](column_in_metadata& col_meta, std::string default_name) {
+      if (col_meta.get_name().empty()) { col_meta.set_name(default_name); }
+      for (size_type i = 0; i < col_meta.num_children(); ++i) {
+        add_default_name(col_meta.child(i), std::to_string(i));
+      }
+    };
+  for (size_t i = 0; i < table_meta->column_metadata.size(); ++i) {
+    add_default_name(table_meta->column_metadata[i], "_col" + std::to_string(i));
+  }
+
+  return table_meta;
+}
+
+// Computes the number of characters in each rowgroup for each string column and attaches the
+// results to the corresponding orc_column_view. The owning host vector is returned.
+auto set_rowgroup_char_counts(orc_table_view& orc_table,
+                              device_2dspan<rowgroup_rows const> rowgroup_bounds,
+                              rmm::cuda_stream_view stream)
+{
+  auto const num_rowgroups = rowgroup_bounds.size().first;
+  auto const num_str_cols  = orc_table.num_string_columns();
+
+  auto counts         = rmm::device_uvector<size_type>(num_str_cols * num_rowgroups, stream);
+  auto counts_2d_view = device_2dspan<size_type>(counts.data(), num_str_cols, num_rowgroups);
+  gpu::rowgroup_char_counts(counts_2d_view,
+                            orc_table.d_columns,
+                            rowgroup_bounds,
+                            orc_table.d_string_column_indices,
+                            stream);
+
+  auto const h_counts = cudf::detail::make_std_vector_sync(counts, stream);
+
+  for (auto col_idx : orc_table.string_column_indices) {
+    auto& str_column = orc_table.column(col_idx);
+    str_column.attach_rowgroup_char_counts(
+      {h_counts.data() + str_column.str_index() * num_rowgroups, num_rowgroups});
+  }
+
+  return h_counts;
+}
+
+// Holds the stripe dictionary descriptors and dictionary buffers.
+struct stripe_dictionaries {
+  hostdevice_2dvector<gpu::stripe_dictionary> views;       // descriptors [string_column][stripe]
+  std::vector<rmm::device_uvector<uint32_t>> data_owner;   // dictionary data owner, per stripe
+  std::vector<rmm::device_uvector<uint32_t>> index_owner;  // dictionary index owner, per stripe
+
+  // Should be called after encoding is complete to deallocate the dictionary buffers.
+  void on_encode_complete(rmm::cuda_stream_view stream)
+  {
+    data_owner.clear();
+    index_owner.clear();
+
+    for (auto& sd : views.host_view().flat_view()) {
+      sd.data  = {};
+      sd.index = {};
+    }
+    views.host_to_device_async(stream);
+  }
+};
+
+// Build stripe dictionaries for string columns
+stripe_dictionaries build_dictionaries(orc_table_view& orc_table,
+                                       file_segmentation const& segmentation,
+                                       rmm::cuda_stream_view stream)
+{
+  std::vector<std::vector<rmm::device_uvector<gpu::slot_type>>> hash_maps_storage(
+    orc_table.string_column_indices.size());
+  for (auto col_idx : orc_table.string_column_indices) {
+    auto& str_column = orc_table.column(col_idx);
+    for (auto const& stripe : segmentation.stripes) {
+      auto const stripe_num_rows =
+        stripe.size == 0 ? 0
+                         : segmentation.rowgroups[stripe.first + stripe.size - 1][col_idx].end -
+                             segmentation.rowgroups[stripe.first][col_idx].begin;
+      hash_maps_storage[str_column.str_index()].emplace_back(stripe_num_rows * 1.43, stream);
+    }
+  }
+
+  hostdevice_2dvector<gpu::stripe_dictionary> stripe_dicts(
+    orc_table.num_string_columns(), segmentation.num_stripes(), stream);
+  if (stripe_dicts.count() == 0) return {std::move(stripe_dicts), {}, {}};
+
+  // Initialize stripe dictionaries
+  for (auto col_idx : orc_table.string_column_indices) {
+    auto& str_column       = orc_table.column(col_idx);
+    auto const str_col_idx = str_column.str_index();
+    str_column.attach_stripe_dicts(stripe_dicts[str_col_idx],
+                                   stripe_dicts.device_view()[str_col_idx]);
+    for (auto const& stripe : segmentation.stripes) {
+      auto const stripe_idx = stripe.id;
+      auto& sd              = stripe_dicts[str_col_idx][stripe_idx];
+
+      sd.map_slots      = hash_maps_storage[str_col_idx][stripe_idx];
+      sd.column_idx     = col_idx;
+      sd.start_row      = segmentation.rowgroups[stripe.first][col_idx].begin;
+      sd.start_rowgroup = stripe.first;
+      sd.num_rows =
+        segmentation.rowgroups[stripe.first + stripe.size - 1][col_idx].end - sd.start_row;
+
+      sd.entry_count = 0;
+      sd.char_count  = 0;
+    }
+  }
+  stripe_dicts.host_to_device_async(stream);
+
+  gpu::initialize_dictionary_hash_maps(stripe_dicts, stream);
+  gpu::populate_dictionary_hash_maps(stripe_dicts, orc_table.d_columns, stream);
+  // Copy the entry counts and char counts from the device to the host
+  stripe_dicts.device_to_host_sync(stream);
+
+  // Data owners; can be cleared after encode
+  std::vector<rmm::device_uvector<uint32_t>> dict_data_owner;
+  std::vector<rmm::device_uvector<uint32_t>> dict_index_owner;
+  // Make decision about which stripes to encode with dictionary encoding
+  for (auto col_idx : orc_table.string_column_indices) {
+    auto& str_column = orc_table.column(col_idx);
+    bool col_use_dictionary{false};
+    for (auto const& stripe : segmentation.stripes) {
+      auto const stripe_idx        = stripe.id;
+      auto const str_col_idx       = str_column.str_index();
+      auto& sd                     = stripe_dicts[str_col_idx][stripe_idx];
+      auto const direct_char_count = std::accumulate(
+        thrust::make_counting_iterator(stripe.first),
+        thrust::make_counting_iterator(stripe.first + stripe.size),
+        0,
+        [&](auto total, auto const& rg) { return total + str_column.rowgroup_char_count(rg); });
+      // Enable dictionary encoding if the dictionary size is smaller than the direct encode size
+      // The estimate excludes the LENGTH stream size, which is present in both cases
+      sd.is_enabled = [&]() {
+        auto const dict_index_size = varint_size(sd.entry_count);
+        return sd.char_count + dict_index_size * sd.entry_count < direct_char_count;
+      }();
+      if (sd.is_enabled) {
+        dict_data_owner.emplace_back(sd.entry_count, stream);
+        sd.data            = dict_data_owner.back();
+        col_use_dictionary = true;
+      } else {
+        // Clear hash map storage as dictionary encoding is not used for this stripe
+        hash_maps_storage[str_col_idx][stripe_idx] = rmm::device_uvector<gpu::slot_type>(0, stream);
+        sd.map_slots                               = {};
+      }
+    }
+    // If any stripe uses dictionary encoding, allocate index storage for the whole column
+    if (col_use_dictionary) {
+      dict_index_owner.emplace_back(str_column.size(), stream);
+      for (auto& sd : stripe_dicts[str_column.str_index()]) {
+        sd.index = dict_index_owner.back();
+      }
+    }
+  }
+  stripe_dicts.host_to_device_async(stream);
+
+  gpu::collect_map_entries(stripe_dicts, stream);
+  gpu::get_dictionary_indices(stripe_dicts, orc_table.d_columns, stream);
+
+  // Clear map slots; hash map storage is deallocated at the end of this function
+  auto device_dicts_flat = stripe_dicts.device_view().flat_view();
+  thrust::for_each(rmm::exec_policy(stream),
+                   device_dicts_flat.begin(),
+                   device_dicts_flat.end(),
+                   [] __device__(auto& sd) { sd.map_slots = {}; });
+  stripe_dicts.device_to_host_async(stream);
+
+  return {std::move(stripe_dicts), std::move(dict_data_owner), std::move(dict_index_owner)};
+}
+
+/**
+ * @brief Perform the processing steps needed to convert the input table into the output ORC data
+ * for writing, such as compression and ORC encoding.
+ *
+ * @param input The input table
+ * @param table_meta The table metadata
+ * @param max_stripe_size Maximum size of stripes in the output file
+ * @param row_index_stride The row index stride
+ * @param enable_dictionary Whether dictionary is enabled
+ * @param compression_kind The compression kind
+ * @param compression_blocksize The block size used for compression
+ * @param stats_freq Column statistics granularity type for parquet/orc writers
+ * @param collect_compression_stats Flag to indicate if compression statistics should be collected
+ * @param write_mode Flag to indicate if there is only a single table write
+ * @param out_sink Sink for writing data
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return A tuple of the intermediate results containing the processed data
+ */
+auto convert_table_to_orc_data(table_view const& input,
+                               table_input_metadata const& table_meta,
+                               stripe_size_limits max_stripe_size,
+                               size_type row_index_stride,
+                               bool enable_dictionary,
+                               CompressionKind compression_kind,
+                               size_t compression_blocksize,
+                               statistics_freq stats_freq,
+                               bool collect_compression_stats,
+                               single_write_mode write_mode,
+                               data_sink const& out_sink,
+                               rmm::cuda_stream_view stream)
+{
+  auto const input_tview = table_device_view::create(input, stream);
+
+  auto orc_table = make_orc_table_view(input, *input_tview, table_meta, stream);
+
+  // This is unused but it holds memory buffers for later access thus needs to be kept alive.
+  [[maybe_unused]] auto const pd_masks = init_pushdown_null_masks(orc_table, stream);
+
+  auto rowgroup_bounds = calculate_rowgroup_bounds(orc_table, row_index_stride, stream);
+
+  [[maybe_unused]] auto const rg_char_counts_data =
+    set_rowgroup_char_counts(orc_table, rowgroup_bounds, stream);
+
+  // Decide stripe boundaries based on rowgroups and char counts
+  auto segmentation =
+    calculate_segmentation(orc_table.columns, std::move(rowgroup_bounds), max_stripe_size);
+
+  auto stripe_dicts    = build_dictionaries(orc_table, segmentation, stream);
+  auto dec_chunk_sizes = decimal_chunk_sizes(orc_table, segmentation, stream);
+
+  auto const uncompressed_block_align = uncomp_block_alignment(compression_kind);
+  auto const compressed_block_align   = comp_block_alignment(compression_kind);
+
+  auto streams  = create_streams(orc_table.columns,
+                                segmentation,
+                                decimal_column_sizes(dec_chunk_sizes.rg_sizes),
+                                enable_dictionary,
+                                compression_kind,
+                                write_mode);
+  auto enc_data = encode_columns(
+    orc_table, std::move(dec_chunk_sizes), segmentation, streams, uncompressed_block_align, stream);
+
+  stripe_dicts.on_encode_complete(stream);
+
+  auto const num_rows = input.num_rows();
+
+  // Assemble individual disparate column chunks into contiguous data streams
+  size_type const num_index_streams = (orc_table.num_columns() + 1);
+  auto const num_data_streams       = streams.size() - num_index_streams;
+  hostdevice_2dvector<gpu::StripeStream> strm_descs(
+    segmentation.num_stripes(), num_data_streams, stream);
+  auto stripes = gather_stripes(num_index_streams, segmentation, &enc_data, &strm_descs, stream);
+
+  if (num_rows == 0) {
+    return std::tuple{std::move(enc_data),
+                      std::move(segmentation),
+                      std::move(orc_table),
+                      rmm::device_uvector<uint8_t>{0, stream},                // compressed_data
+                      cudf::detail::hostdevice_vector<compression_result>{},  // comp_results
+                      std::move(strm_descs),
+                      intermediate_statistics{stream},
+                      std::optional<writer_compression_statistics>{},
+                      std::move(streams),
+                      std::move(stripes),
+                      std::move(stripe_dicts.views),
+                      cudf::detail::pinned_host_vector<uint8_t>()};
+  }
+
+  // Allocate intermediate output stream buffer
+  size_t compressed_bfr_size   = 0;
+  size_t num_compressed_blocks = 0;
+
+  auto const max_compressed_block_size =
+    max_compression_output_size(compression_kind, compression_blocksize);
+  auto const padded_max_compressed_block_size =
+    util::round_up_unsafe<size_t>(max_compressed_block_size, compressed_block_align);
+  auto const padded_block_header_size =
+    util::round_up_unsafe<size_t>(block_header_size, compressed_block_align);
+
+  auto bounce_buffer = [&]() {
+    size_t max_stream_size = 0;
+    bool all_device_write  = true;
+
+    for (auto& ss : strm_descs.host_view().flat_view()) {
+      if (!out_sink.is_device_write_preferred(ss.stream_size)) { all_device_write = false; }
+      size_t stream_size = ss.stream_size;
+      if (compression_kind != NONE) {
+        ss.first_block = num_compressed_blocks;
+        ss.bfr_offset  = compressed_bfr_size;
+
+        auto num_blocks =
+          std::max<uint32_t>((stream_size + compression_blocksize - 1) / compression_blocksize, 1);
+        stream_size += num_blocks * block_header_size;
+        num_compressed_blocks += num_blocks;
+        compressed_bfr_size +=
+          (padded_block_header_size + padded_max_compressed_block_size) * num_blocks;
+      }
+      max_stream_size = std::max(max_stream_size, stream_size);
+    }
+
+    return cudf::detail::pinned_host_vector<uint8_t>(all_device_write ? 0 : max_stream_size);
+  }();
+
+  // Compress the data streams
+  rmm::device_uvector<uint8_t> compressed_data(compressed_bfr_size, stream);
+  cudf::detail::hostdevice_vector<compression_result> comp_results(num_compressed_blocks, stream);
+  std::optional<writer_compression_statistics> compression_stats;
+  thrust::fill(rmm::exec_policy(stream),
+               comp_results.d_begin(),
+               comp_results.d_end(),
+               compression_result{0, compression_status::FAILURE});
+  if (compression_kind != NONE) {
+    strm_descs.host_to_device_async(stream);
+    compression_stats = gpu::CompressOrcDataStreams(compressed_data,
+                                                    num_compressed_blocks,
+                                                    compression_kind,
+                                                    compression_blocksize,
+                                                    max_compressed_block_size,
+                                                    compressed_block_align,
+                                                    collect_compression_stats,
+                                                    strm_descs,
+                                                    enc_data.streams,
+                                                    comp_results,
+                                                    stream);
+
+    // deallocate encoded data as it is not needed anymore
+    enc_data.data.clear();
+
+    strm_descs.device_to_host_async(stream);
+    comp_results.device_to_host_sync(stream);
+  }
+
+  auto intermediate_stats = gather_statistic_blobs(stats_freq, orc_table, segmentation, stream);
+
+  return std::tuple{std::move(enc_data),
+                    std::move(segmentation),
+                    std::move(orc_table),
+                    std::move(compressed_data),
+                    std::move(comp_results),
+                    std::move(strm_descs),
+                    std::move(intermediate_stats),
+                    std::move(compression_stats),
+                    std::move(streams),
+                    std::move(stripes),
+                    std::move(stripe_dicts.views),
+                    std::move(bounce_buffer)};
+}
+
+}  // namespace
+
+writer::impl::impl(std::unique_ptr<data_sink> sink,
+                   orc_writer_options const& options,
+                   single_write_mode mode,
+                   rmm::cuda_stream_view stream)
+  : _stream(stream),
+    _max_stripe_size{options.get_stripe_size_bytes(), options.get_stripe_size_rows()},
+    _row_index_stride{options.get_row_index_stride()},
+    _compression_kind(to_orc_compression(options.get_compression())),
+    _compression_blocksize(compression_block_size(_compression_kind)),
+    _compression_statistics(options.get_compression_statistics()),
+    _stats_freq(options.get_statistics_freq()),
+    _single_write_mode(mode),
+    _kv_meta(options.get_key_value_metadata()),
+    _out_sink(std::move(sink))
+{
+  if (options.get_metadata()) {
+    _table_meta = std::make_unique<table_input_metadata>(*options.get_metadata());
+  }
+  init_state();
+}
+
+writer::impl::impl(std::unique_ptr<data_sink> sink,
+                   chunked_orc_writer_options const& options,
+                   single_write_mode mode,
+                   rmm::cuda_stream_view stream)
+  : _stream(stream),
+    _max_stripe_size{options.get_stripe_size_bytes(), options.get_stripe_size_rows()},
+    _row_index_stride{options.get_row_index_stride()},
+    _compression_kind(to_orc_compression(options.get_compression())),
+    _compression_blocksize(compression_block_size(_compression_kind)),
+    _compression_statistics(options.get_compression_statistics()),
+    _stats_freq(options.get_statistics_freq()),
+    _single_write_mode(mode),
+    _kv_meta(options.get_key_value_metadata()),
+    _out_sink(std::move(sink))
+{
+  if (options.get_metadata()) {
+    _table_meta = std::make_unique<table_input_metadata>(*options.get_metadata());
+  }
+  init_state();
+}
+
+writer::impl::~impl() { close(); }
+
+void writer::impl::init_state()
+{
+  // Write file header
+  _out_sink->host_write(MAGIC, std::strlen(MAGIC));
+}
+
+void writer::impl::write(table_view const& input)
+{
+  CUDF_EXPECTS(not _closed, "Data has already been flushed to out and closed");
+
+  if (not _table_meta) { _table_meta = make_table_meta(input); }
+
+  // All kinds of memory allocation and data compressions/encoding are performed here.
+  // If any error occurs, such as out-of-memory exception, the internal state of the current writer
+  // is still intact.
+  // Note that `out_sink_` is intentionally passed by const reference to prevent accidentally
+  // writing anything to it.
+  [[maybe_unused]] auto [enc_data,
+                         segmentation,
+                         orc_table,
+                         compressed_data,
+                         comp_results,
+                         strm_descs,
+                         intermediate_stats,
+                         compression_stats,
+                         streams,
+                         stripes,
+                         stripe_dicts, /* unused, but its data will be accessed via pointer later */
+                         bounce_buffer] = [&] {
+    try {
+      return convert_table_to_orc_data(input,
+                                       *_table_meta,
+                                       _max_stripe_size,
+                                       _row_index_stride,
+                                       _enable_dictionary,
+                                       _compression_kind,
+                                       _compression_blocksize,
+                                       _stats_freq,
+                                       _compression_statistics != nullptr,
+                                       _single_write_mode,
+                                       *_out_sink,
+                                       _stream);
+    } catch (...) {  // catch any exception type
+      CUDF_LOG_ERROR(
+        "ORC writer encountered exception during processing. "
+        "No data has been written to the sink.");
+      throw;  // this throws the same exception
+    }
+  }();
+
+  // Compression/encoding were all successful. Now write the intermediate results.
+  write_orc_data_to_sink(enc_data,
+                         segmentation,
+                         orc_table,
+                         compressed_data,
+                         comp_results,
+                         strm_descs,
+                         intermediate_stats.rowgroup_blobs,
+                         streams,
+                         stripes,
+                         bounce_buffer);
+
+  // Update data into the footer. This needs to be called even when num_rows==0.
+  add_table_to_footer_data(orc_table, stripes);
+
+  // Update file-level and compression statistics
+  update_statistics(orc_table.num_rows(), std::move(intermediate_stats), compression_stats);
+}
+
+void writer::impl::update_statistics(
+  size_type num_rows,
+  intermediate_statistics&& intermediate_stats,
+  std::optional<writer_compression_statistics> const& compression_stats)
+{
+  if (intermediate_stats.stripe_stat_chunks.size() > 0) {
+    _persisted_stripe_statistics.persist(
+      num_rows, _single_write_mode, std::move(intermediate_stats), _stream);
+  }
+
+  if (compression_stats.has_value() and _compression_statistics != nullptr) {
+    *_compression_statistics += compression_stats.value();
+  }
+}
+
+void writer::impl::write_orc_data_to_sink(encoded_data const& enc_data,
+                                          file_segmentation const& segmentation,
+                                          orc_table_view const& orc_table,
+                                          device_span<uint8_t const> compressed_data,
+                                          host_span<compression_result const> comp_results,
+                                          host_2dspan<gpu::StripeStream const> strm_descs,
+                                          host_span<ColStatsBlob const> rg_stats,
+                                          orc_streams& streams,
+                                          host_span<StripeInformation> stripes,
+                                          host_span<uint8_t> bounce_buffer)
+{
+  if (orc_table.num_rows() == 0) { return; }
+
+  // Write stripes
+  std::vector<std::future<void>> write_tasks;
+  for (size_t stripe_id = 0; stripe_id < stripes.size(); ++stripe_id) {
+    auto& stripe = stripes[stripe_id];
+
+    stripe.offset = _out_sink->bytes_written();
+
+    // Column (skippable) index streams appear at the start of the stripe
+    size_type const num_index_streams = (orc_table.num_columns() + 1);
+    for (size_type stream_id = 0; stream_id < num_index_streams; ++stream_id) {
+      write_index_stream(stripe_id,
+                         stream_id,
+                         orc_table.columns,
+                         segmentation,
+                         enc_data.streams,
+                         strm_descs,
+                         comp_results,
+                         rg_stats,
+                         &stripe,
+                         &streams,
+                         _compression_kind,
+                         _compression_blocksize,
+                         _out_sink);
+    }
+
+    // Column data consisting one or more separate streams
+    for (auto const& strm_desc : strm_descs[stripe_id]) {
+      write_tasks.push_back(write_data_stream(
+        strm_desc,
+        enc_data.streams[strm_desc.column_id][segmentation.stripes[stripe_id].first],
+        compressed_data.data(),
+        bounce_buffer.data(),
+        &stripe,
+        &streams,
+        _compression_kind,
+        _out_sink,
+        _stream));
+    }
+
+    // Write stripefooter consisting of stream information
+    StripeFooter sf;
+    sf.streams = streams;
+    sf.columns.resize(orc_table.num_columns() + 1);
+    sf.columns[0].kind = DIRECT;
+    for (size_t i = 1; i < sf.columns.size(); ++i) {
+      sf.columns[i].kind = orc_table.column(i - 1).orc_encoding();
+      sf.columns[i].dictionarySize =
+        (sf.columns[i].kind == DICTIONARY_V2)
+          ? orc_table.column(i - 1).host_stripe_dict(stripe_id).entry_count
+          : 0;
+      if (orc_table.column(i - 1).orc_kind() == TIMESTAMP) { sf.writerTimezone = "UTC"; }
+    }
+    ProtobufWriter pbw((_compression_kind != NONE) ? 3 : 0);
+    pbw.write(sf);
+    stripe.footerLength = pbw.size();
+    if (_compression_kind != NONE) {
+      uint32_t uncomp_sf_len = (stripe.footerLength - 3) * 2 + 1;
+      pbw.buffer()[0]        = static_cast<uint8_t>(uncomp_sf_len >> 0);
+      pbw.buffer()[1]        = static_cast<uint8_t>(uncomp_sf_len >> 8);
+      pbw.buffer()[2]        = static_cast<uint8_t>(uncomp_sf_len >> 16);
+    }
+    _out_sink->host_write(pbw.data(), pbw.size());
+  }
+  for (auto const& task : write_tasks) {
+    task.wait();
+  }
+}
+
+void writer::impl::add_table_to_footer_data(orc_table_view const& orc_table,
+                                            std::vector<StripeInformation>& stripes)
+{
+  if (_ffooter.headerLength == 0) {
+    // First call
+    _ffooter.headerLength   = std::strlen(MAGIC);
+    _ffooter.rowIndexStride = _row_index_stride;
+    _ffooter.types.resize(1 + orc_table.num_columns());
+    _ffooter.types[0].kind = STRUCT;
+    for (auto const& column : orc_table.columns) {
+      if (!column.is_child()) {
+        _ffooter.types[0].subtypes.emplace_back(column.id());
+        _ffooter.types[0].fieldNames.emplace_back(column.orc_name());
+      }
+    }
+    for (auto const& column : orc_table.columns) {
+      auto& schema_type = _ffooter.types[column.id()];
+      schema_type.kind  = column.orc_kind();
+      if (column.orc_kind() == DECIMAL) {
+        schema_type.scale     = static_cast<uint32_t>(column.scale());
+        schema_type.precision = column.precision();
+      }
+      std::transform(column.child_begin(),
+                     column.child_end(),
+                     std::back_inserter(schema_type.subtypes),
+                     [&](auto const& child_idx) { return orc_table.column(child_idx).id(); });
+      if (column.orc_kind() == STRUCT) {
+        std::transform(column.child_begin(),
+                       column.child_end(),
+                       std::back_inserter(schema_type.fieldNames),
+                       [&](auto const& child_idx) {
+                         return std::string{orc_table.column(child_idx).orc_name()};
+                       });
+      }
+    }
+  } else {
+    // verify the user isn't passing mismatched tables
+    CUDF_EXPECTS(_ffooter.types.size() == 1 + orc_table.num_columns(),
+                 "Mismatch in table structure between multiple calls to write");
+    CUDF_EXPECTS(
+      std::all_of(orc_table.columns.cbegin(),
+                  orc_table.columns.cend(),
+                  [&](auto const& col) { return _ffooter.types[col.id()].kind == col.orc_kind(); }),
+      "Mismatch in column types between multiple calls to write");
+  }
+  _ffooter.stripes.insert(_ffooter.stripes.end(),
+                          std::make_move_iterator(stripes.begin()),
+                          std::make_move_iterator(stripes.end()));
+  _ffooter.numberOfRows += orc_table.num_rows();
+}
+
+void writer::impl::close()
+{
+  if (_closed) { return; }
+  _closed = true;
+  PostScript ps;
+
+  auto const statistics =
+    finish_statistic_blobs(_ffooter.stripes.size(), _persisted_stripe_statistics, _stream);
+
+  // File-level statistics
+  if (not statistics.file_level.empty()) {
+    ProtobufWriter pbw;
+    pbw.put_uint(encode_field_number<size_type>(1));
+    pbw.put_uint(_persisted_stripe_statistics.num_rows);
+    // First entry contains total number of rows
+    _ffooter.statistics.reserve(_ffooter.types.size());
+    _ffooter.statistics.emplace_back(pbw.release());
+    // Add file stats, stored after stripe stats in `column_stats`
+    _ffooter.statistics.insert(_ffooter.statistics.end(),
+                               std::make_move_iterator(statistics.file_level.begin()),
+                               std::make_move_iterator(statistics.file_level.end()));
+  }
+
+  // Stripe-level statistics
+  if (not statistics.stripe_level.empty()) {
+    _orc_meta.stripeStats.resize(_ffooter.stripes.size());
+    for (size_t stripe_id = 0; stripe_id < _ffooter.stripes.size(); stripe_id++) {
+      _orc_meta.stripeStats[stripe_id].colStats.resize(_ffooter.types.size());
+      ProtobufWriter pbw;
+      pbw.put_uint(encode_field_number<size_type>(1));
+      pbw.put_uint(_ffooter.stripes[stripe_id].numberOfRows);
+      _orc_meta.stripeStats[stripe_id].colStats[0] = pbw.release();
+      for (size_t col_idx = 0; col_idx < _ffooter.types.size() - 1; col_idx++) {
+        size_t idx = _ffooter.stripes.size() * col_idx + stripe_id;
+        _orc_meta.stripeStats[stripe_id].colStats[1 + col_idx] =
+          std::move(statistics.stripe_level[idx]);
+      }
+    }
+  }
+
+  _persisted_stripe_statistics.clear();
+
+  _ffooter.contentLength = _out_sink->bytes_written();
+  std::transform(_kv_meta.begin(),
+                 _kv_meta.end(),
+                 std::back_inserter(_ffooter.metadata),
+                 [&](auto const& udata) {
+                   return UserMetadataItem{udata.first, udata.second};
+                 });
+
+  // Write statistics metadata
+  if (not _orc_meta.stripeStats.empty()) {
+    ProtobufWriter pbw((_compression_kind != NONE) ? 3 : 0);
+    pbw.write(_orc_meta);
+    add_uncompressed_block_headers(_compression_kind, _compression_blocksize, pbw.buffer());
+    ps.metadataLength = pbw.size();
+    _out_sink->host_write(pbw.data(), pbw.size());
+  } else {
+    ps.metadataLength = 0;
+  }
+  ProtobufWriter pbw((_compression_kind != NONE) ? 3 : 0);
+  pbw.write(_ffooter);
+  add_uncompressed_block_headers(_compression_kind, _compression_blocksize, pbw.buffer());
+
+  // Write postscript metadata
+  ps.footerLength         = pbw.size();
+  ps.compression          = _compression_kind;
+  ps.compressionBlockSize = _compression_blocksize;
+  ps.version              = {0, 12};
+  ps.magic                = MAGIC;
+
+  auto const ps_length = static_cast<uint8_t>(pbw.write(ps));
+  pbw.put_byte(ps_length);
+  _out_sink->host_write(pbw.data(), pbw.size());
+  _out_sink->flush();
+}
+
+// Forward to implementation
+writer::writer(std::unique_ptr<data_sink> sink,
+               orc_writer_options const& options,
+               single_write_mode mode,
+               rmm::cuda_stream_view stream)
+  : _impl(std::make_unique<impl>(std::move(sink), options, mode, stream))
+{
+}
+
+// Forward to implementation
+writer::writer(std::unique_ptr<data_sink> sink,
+               chunked_orc_writer_options const& options,
+               single_write_mode mode,
+               rmm::cuda_stream_view stream)
+  : _impl(std::make_unique<impl>(std::move(sink), options, mode, stream))
+{
+}
+
+// Destructor within this translation unit
+writer::~writer() = default;
+
+// Forward to implementation
+void writer::write(table_view const& table) { _impl->write(table); }
+
+// Forward to implementation
+void writer::close() { _impl->close(); }
+
+}  // namespace orc
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/orc/writer_impl.hpp b/cpp/src/io/orc/writer_impl.hpp
new file mode 100644
index 0000000..67c65eb
--- /dev/null
+++ b/cpp/src/io/orc/writer_impl.hpp
@@ -0,0 +1,369 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "orc.hpp"
+#include "orc_gpu.hpp"
+
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/detail/orc.hpp>
+#include <cudf/io/orc.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace orc {
+// Forward internal classes
+class orc_column_view;
+
+using namespace cudf::io::orc;
+using namespace cudf::io;
+using cudf::detail::device_2dspan;
+using cudf::detail::host_2dspan;
+using cudf::detail::hostdevice_2dvector;
+
+/**
+ * Non-owning view of a cuDF table that includes ORC-related information.
+ *
+ * Columns hierarchy is flattened and stored in pre-order.
+ */
+struct orc_table_view {
+  std::vector<orc_column_view> columns;
+  rmm::device_uvector<orc_column_device_view> d_columns;
+  std::vector<uint32_t> string_column_indices;
+  rmm::device_uvector<uint32_t> d_string_column_indices;
+
+  auto num_columns() const noexcept { return columns.size(); }
+  [[nodiscard]] size_type num_rows() const noexcept;
+  auto num_string_columns() const noexcept { return string_column_indices.size(); }
+
+  auto& column(uint32_t idx) { return columns.at(idx); }
+  [[nodiscard]] auto const& column(uint32_t idx) const { return columns.at(idx); }
+
+  auto& string_column(uint32_t idx) { return columns.at(string_column_indices.at(idx)); }
+  [[nodiscard]] auto const& string_column(uint32_t idx) const
+  {
+    return columns.at(string_column_indices.at(idx));
+  }
+};
+
+/**
+ * @brief Indices of rowgroups contained in a stripe.
+ *
+ * Provides a container-like interface to iterate over rowgroup indices.
+ */
+struct stripe_rowgroups {
+  uint32_t id;     // stripe id
+  uint32_t first;  // first rowgroup in the stripe
+  uint32_t size;   // number of rowgroups in the stripe
+  stripe_rowgroups(uint32_t id, uint32_t first, uint32_t size) : id{id}, first{first}, size{size} {}
+  [[nodiscard]] auto cbegin() const { return thrust::make_counting_iterator(first); }
+  [[nodiscard]] auto cend() const { return thrust::make_counting_iterator(first + size); }
+};
+
+/**
+ * @brief Holds the sizes of encoded elements of decimal columns.
+ */
+struct encoder_decimal_info {
+  std::map<uint32_t, rmm::device_uvector<uint32_t>>
+    elem_sizes;                                        ///< Column index -> per-element size map
+  std::map<uint32_t, std::vector<uint32_t>> rg_sizes;  ///< Column index -> per-rowgroup size map
+};
+
+/**
+ * @brief List of per-column ORC streams.
+ *
+ * Provides interface to calculate their offsets.
+ */
+class orc_streams {
+ public:
+  orc_streams(std::vector<Stream> streams, std::vector<int32_t> ids, std::vector<TypeKind> types)
+    : streams{std::move(streams)}, ids{std::move(ids)}, types{std::move(types)}
+  {
+  }
+  Stream const& operator[](int idx) const { return streams[idx]; }
+  Stream& operator[](int idx) { return streams[idx]; }
+  auto id(int idx) const { return ids[idx]; }
+  auto& id(int idx) { return ids[idx]; }
+  auto type(int idx) const { return types[idx]; }
+  auto size() const { return streams.size(); }
+
+  operator std::vector<Stream> const&() const { return streams; }
+
+ private:
+  std::vector<Stream> streams;
+  std::vector<int32_t> ids;
+  std::vector<TypeKind> types;
+};
+/**
+ * @brief Description of how the ORC file is segmented into stripes and rowgroups.
+ */
+struct file_segmentation {
+  hostdevice_2dvector<rowgroup_rows> rowgroups;
+  std::vector<stripe_rowgroups> stripes;
+
+  auto num_rowgroups() const noexcept { return rowgroups.size().first; }
+  auto num_stripes() const noexcept { return stripes.size(); }
+};
+
+/**
+ * @brief ORC per-chunk streams of encoded data.
+ */
+struct encoded_data {
+  std::vector<std::vector<rmm::device_uvector<uint8_t>>> data;  // Owning array of the encoded data
+  hostdevice_2dvector<gpu::encoder_chunk_streams> streams;  // streams of encoded data, per chunk
+};
+
+/**
+ * @brief Dictionary data for string columns and their device views, per column.
+ */
+struct string_dictionaries {
+  std::vector<rmm::device_uvector<uint32_t>> data;
+  std::vector<rmm::device_uvector<uint32_t>> index;
+  rmm::device_uvector<device_span<uint32_t>> d_data_view;
+  rmm::device_uvector<device_span<uint32_t>> d_index_view;
+  // Dictionaries are currently disabled for columns with a rowgroup larger than 2^15
+  thrust::host_vector<bool> dictionary_enabled;
+};
+
+/**
+ * @brief Maximum size of stripes in the output file.
+ */
+struct stripe_size_limits {
+  size_t bytes;
+  size_type rows;
+};
+
+/**
+ * @brief Statistics data stored between calls to write for chunked writes
+ *
+ */
+struct intermediate_statistics {
+  explicit intermediate_statistics(rmm::cuda_stream_view stream) : stripe_stat_chunks(0, stream) {}
+
+  intermediate_statistics(std::vector<ColStatsBlob> rb,
+                          rmm::device_uvector<statistics_chunk> sc,
+                          cudf::detail::hostdevice_vector<statistics_merge_group> smg,
+                          std::vector<statistics_dtype> sdt,
+                          std::vector<data_type> sct)
+    : rowgroup_blobs(std::move(rb)),
+      stripe_stat_chunks(std::move(sc)),
+      stripe_stat_merge(std::move(smg)),
+      stats_dtypes(std::move(sdt)),
+      col_types(std::move(sct))
+  {
+  }
+
+  // blobs for the rowgroups. Not persisted
+  std::vector<ColStatsBlob> rowgroup_blobs;
+
+  rmm::device_uvector<statistics_chunk> stripe_stat_chunks;
+  cudf::detail::hostdevice_vector<statistics_merge_group> stripe_stat_merge;
+  std::vector<statistics_dtype> stats_dtypes;
+  std::vector<data_type> col_types;
+};
+
+/**
+ * @brief used for chunked writes to persist data between calls to write.
+ *
+ */
+struct persisted_statistics {
+  void clear()
+  {
+    stripe_stat_chunks.clear();
+    stripe_stat_merge.clear();
+    string_pools.clear();
+    stats_dtypes.clear();
+    col_types.clear();
+    num_rows = 0;
+  }
+
+  void persist(int num_table_rows,
+               single_write_mode write_mode,
+               intermediate_statistics&& intermediate_stats,
+               rmm::cuda_stream_view stream);
+
+  std::vector<rmm::device_uvector<statistics_chunk>> stripe_stat_chunks;
+  std::vector<cudf::detail::hostdevice_vector<statistics_merge_group>> stripe_stat_merge;
+  std::vector<rmm::device_uvector<char>> string_pools;
+  std::vector<statistics_dtype> stats_dtypes;
+  std::vector<data_type> col_types;
+  int num_rows = 0;
+};
+
+/**
+ * @brief Protobuf encoded statistics created at file close
+ *
+ */
+struct encoded_footer_statistics {
+  std::vector<ColStatsBlob> stripe_level;
+  std::vector<ColStatsBlob> file_level;
+};
+
+/**
+ * @brief Implementation for ORC writer
+ */
+class writer::impl {
+  // ORC datasets start with a 3 byte header
+  static constexpr char const* MAGIC = "ORC";
+
+ public:
+  /**
+   * @brief Constructor with writer options.
+   *
+   * @param sink Output sink
+   * @param options Settings for controlling behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit impl(std::unique_ptr<data_sink> sink,
+                orc_writer_options const& options,
+                single_write_mode mode,
+                rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Constructor with chunked writer options.
+   *
+   * @param sink Output sink
+   * @param options Settings for controlling behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit impl(std::unique_ptr<data_sink> sink,
+                chunked_orc_writer_options const& options,
+                single_write_mode mode,
+                rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Destructor to complete any incomplete write and release resources.
+   */
+  ~impl();
+
+  /**
+   * @brief Begins the chunked/streamed write process.
+   */
+  void init_state();
+
+  /**
+   * @brief Writes a single subtable as part of a larger ORC file/table write.
+   *
+   * @param table The table information to be written
+   */
+  void write(table_view const& table);
+
+  /**
+   * @brief Finishes the chunked/streamed write process.
+   */
+  void close();
+
+ private:
+  /**
+   * @brief Write the intermediate ORC data into the data sink.
+   *
+   * The intermediate data is generated from processing (compressing/encoding) an cuDF input table
+   * by `convert_table_to_orc_data` called in the `write()` function.
+   *
+   * @param[in] enc_data ORC per-chunk streams of encoded data
+   * @param[in] segmentation Description of how the ORC file is segmented into stripes and rowgroups
+   * @param[in] orc_table Non-owning view of a cuDF table that includes ORC-related information
+   * @param[in] compressed_data Compressed stream data
+   * @param[in] comp_results Status of data compression
+   * @param[in] strm_descs List of stream descriptors
+   * @param[in] rg_stats row group level statistics
+   * @param[in,out] streams List of stream descriptors
+   * @param[in,out] stripes List of stripe description
+   * @param[out] bounce_buffer Temporary host output buffer
+   */
+  void write_orc_data_to_sink(encoded_data const& enc_data,
+                              file_segmentation const& segmentation,
+                              orc_table_view const& orc_table,
+                              device_span<uint8_t const> compressed_data,
+                              host_span<compression_result const> comp_results,
+                              host_2dspan<gpu::StripeStream const> strm_descs,
+                              host_span<ColStatsBlob const> rg_stats,
+                              orc_streams& streams,
+                              host_span<StripeInformation> stripes,
+                              host_span<uint8_t> bounce_buffer);
+
+  /**
+   * @brief Add the processed table data into the internal file footer.
+   *
+   * @param orc_table Non-owning view of a cuDF table that includes ORC-related information
+   * @param stripes List of stripe description
+   */
+  void add_table_to_footer_data(orc_table_view const& orc_table,
+                                std::vector<StripeInformation>& stripes);
+
+  /**
+   * @brief Update writer-level statistics with data from the current table.
+   *
+   * @param num_rows Number of rows in the current table
+   * @param single_table_stats Statistics data from the current table
+   * @param compression_stats Compression statistics from the current table
+   */
+  void update_statistics(size_type num_rows,
+                         intermediate_statistics&& single_table_stats,
+                         std::optional<writer_compression_statistics> const& compression_stats);
+
+ private:
+  // CUDA stream.
+  rmm::cuda_stream_view const _stream;
+
+  // Writer options.
+  stripe_size_limits const _max_stripe_size;
+  size_type const _row_index_stride;
+  CompressionKind const _compression_kind;
+  size_t const _compression_blocksize;
+  std::shared_ptr<writer_compression_statistics> _compression_statistics;  // Optional output
+  statistics_freq const _stats_freq;
+  single_write_mode const _single_write_mode;  // Special parameter only used by `write()` to
+                                               // indicate that we are guaranteeing a single table
+                                               // write. This enables some internal optimizations.
+  std::map<std::string, std::string> const _kv_meta;  // Optional user metadata.
+  std::unique_ptr<data_sink> const _out_sink;
+
+  // Debug parameter---currently not yet supported to be user-specified.
+  static bool constexpr _enable_dictionary = true;
+
+  // Internal states, filled during `write()` and written to sink during `write` and `close()`.
+  std::unique_ptr<table_input_metadata> _table_meta;
+  cudf::io::orc::FileFooter _ffooter;
+  cudf::io::orc::Metadata _orc_meta;
+  persisted_statistics _persisted_stripe_statistics;  // Statistics data saved between calls.
+  bool _closed = false;  // To track if the output has been written to sink.
+};
+
+}  // namespace orc
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/chunk_dict.cu b/cpp/src/io/parquet/chunk_dict.cu
new file mode 100644
index 0000000..9ff1869
--- /dev/null
+++ b/cpp/src/io/parquet/chunk_dict.cu
@@ -0,0 +1,303 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "parquet_gpu.cuh"
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/exec_policy.hpp>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+namespace gpu {
+namespace {
+constexpr int DEFAULT_BLOCK_SIZE = 256;
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  initialize_chunk_hash_maps_kernel(device_span<EncColumnChunk> chunks)
+{
+  auto const chunk = chunks[blockIdx.x];
+  auto const t     = threadIdx.x;
+  // fut: Now that per-chunk dict is same size as ck.num_values, try to not use one block per chunk
+  for (thread_index_type i = 0; i < chunk.dict_map_size; i += block_size) {
+    if (t + i < chunk.dict_map_size) {
+      new (&chunk.dict_map_slots[t + i].first) map_type::atomic_key_type{KEY_SENTINEL};
+      new (&chunk.dict_map_slots[t + i].second) map_type::atomic_mapped_type{VALUE_SENTINEL};
+    }
+  }
+}
+
+template <typename T>
+struct equality_functor {
+  column_device_view const& col;
+  __device__ bool operator()(size_type lhs_idx, size_type rhs_idx)
+  {
+    // We don't call this for nulls so this is fine
+    auto const equal = cudf::experimental::row::equality::nan_equal_physical_equality_comparator{};
+    return equal(col.element<T>(lhs_idx), col.element<T>(rhs_idx));
+  }
+};
+
+template <typename T>
+struct hash_functor {
+  column_device_view const& col;
+  __device__ auto operator()(size_type idx) const
+  {
+    return cudf::hashing::detail::MurmurHash3_x86_32<T>{}(col.element<T>(idx));
+  }
+};
+
+struct map_insert_fn {
+  map_type::device_mutable_view& map;
+
+  template <typename T>
+  __device__ bool operator()(column_device_view const& col, size_type i)
+  {
+    if constexpr (column_device_view::has_element_accessor<T>()) {
+      auto hash_fn     = hash_functor<T>{col};
+      auto equality_fn = equality_functor<T>{col};
+      return map.insert(std::pair(i, i), hash_fn, equality_fn);
+    } else {
+      CUDF_UNREACHABLE("Unsupported type to insert in map");
+    }
+  }
+};
+
+struct map_find_fn {
+  map_type::device_view& map;
+
+  template <typename T>
+  __device__ map_type::device_view::iterator operator()(column_device_view const& col, size_type i)
+  {
+    if constexpr (column_device_view::has_element_accessor<T>()) {
+      auto hash_fn     = hash_functor<T>{col};
+      auto equality_fn = equality_functor<T>{col};
+      return map.find(i, hash_fn, equality_fn);
+    } else {
+      CUDF_UNREACHABLE("Unsupported type to find in map");
+    }
+  }
+};
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  populate_chunk_hash_maps_kernel(cudf::detail::device_2dspan<gpu::PageFragment const> frags)
+{
+  auto col_idx = blockIdx.y;
+  auto block_x = blockIdx.x;
+  auto t       = threadIdx.x;
+  auto frag    = frags[col_idx][block_x];
+  auto chunk   = frag.chunk;
+  auto col     = chunk->col_desc;
+
+  if (not chunk->use_dictionary) { return; }
+
+  using block_reduce = cub::BlockReduce<size_type, block_size>;
+  __shared__ typename block_reduce::TempStorage reduce_storage;
+
+  size_type start_row = frag.start_row;
+  size_type end_row   = frag.start_row + frag.num_rows;
+
+  // Find the bounds of values in leaf column to be inserted into the map for current chunk
+  size_type const s_start_value_idx = row_to_value_idx(start_row, *col);
+  size_type const end_value_idx     = row_to_value_idx(end_row, *col);
+
+  column_device_view const& data_col = *col->leaf_column;
+
+  // Make a view of the hash map
+  auto hash_map_mutable = map_type::device_mutable_view(chunk->dict_map_slots,
+                                                        chunk->dict_map_size,
+                                                        cuco::empty_key{KEY_SENTINEL},
+                                                        cuco::empty_value{VALUE_SENTINEL});
+
+  __shared__ size_type total_num_dict_entries;
+  thread_index_type val_idx = s_start_value_idx + t;
+  while (val_idx - block_size < end_value_idx) {
+    auto const is_valid =
+      val_idx < end_value_idx and val_idx < data_col.size() and data_col.is_valid(val_idx);
+
+    // insert element at val_idx to hash map and count successful insertions
+    size_type is_unique      = 0;
+    size_type uniq_elem_size = 0;
+    if (is_valid) {
+      is_unique =
+        type_dispatcher(data_col.type(), map_insert_fn{hash_map_mutable}, data_col, val_idx);
+      uniq_elem_size = [&]() -> size_type {
+        if (not is_unique) { return 0; }
+        switch (col->physical_type) {
+          case Type::INT32: return 4;
+          case Type::INT64: return 8;
+          case Type::INT96: return 12;
+          case Type::FLOAT: return 4;
+          case Type::DOUBLE: return 8;
+          case Type::BYTE_ARRAY: {
+            auto const col_type = data_col.type().id();
+            if (col_type == type_id::STRING) {
+              // Strings are stored as 4 byte length + string bytes
+              return 4 + data_col.element<string_view>(val_idx).size_bytes();
+            } else if (col_type == type_id::LIST) {
+              // Binary is stored as 4 byte length + bytes
+              return 4 + get_element<statistics::byte_array_view>(data_col, val_idx).size_bytes();
+            }
+            CUDF_UNREACHABLE(
+              "Byte array only supports string and list<byte> column types for dictionary "
+              "encoding!");
+          }
+          case Type::FIXED_LEN_BYTE_ARRAY:
+            if (data_col.type().id() == type_id::DECIMAL128) { return sizeof(__int128_t); }
+            CUDF_UNREACHABLE(
+              "Fixed length byte array only supports decimal 128 column types for dictionary "
+              "encoding!");
+          default: CUDF_UNREACHABLE("Unsupported type for dictionary encoding");
+        }
+      }();
+    }
+
+    auto num_unique = block_reduce(reduce_storage).Sum(is_unique);
+    __syncthreads();
+    auto uniq_data_size = block_reduce(reduce_storage).Sum(uniq_elem_size);
+    if (t == 0) {
+      total_num_dict_entries = atomicAdd(&chunk->num_dict_entries, num_unique);
+      total_num_dict_entries += num_unique;
+      atomicAdd(&chunk->uniq_data_size, uniq_data_size);
+    }
+    __syncthreads();
+
+    // Check if the num unique values in chunk has already exceeded max dict size and early exit
+    if (total_num_dict_entries > MAX_DICT_SIZE) { return; }
+
+    val_idx += block_size;
+  }  // while
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  collect_map_entries_kernel(device_span<EncColumnChunk> chunks)
+{
+  auto& chunk = chunks[blockIdx.x];
+  if (not chunk.use_dictionary) { return; }
+
+  auto t   = threadIdx.x;
+  auto map = map_type::device_view(chunk.dict_map_slots,
+                                   chunk.dict_map_size,
+                                   cuco::empty_key{KEY_SENTINEL},
+                                   cuco::empty_value{VALUE_SENTINEL});
+
+  __shared__ cuda::atomic<size_type, cuda::thread_scope_block> counter;
+  using cuda::std::memory_order_relaxed;
+  if (t == 0) { new (&counter) cuda::atomic<size_type, cuda::thread_scope_block>{0}; }
+  __syncthreads();
+  for (size_type i = 0; i < chunk.dict_map_size; i += block_size) {
+    if (t + i < chunk.dict_map_size) {
+      auto* slot = reinterpret_cast<map_type::value_type*>(map.begin_slot() + t + i);
+      auto key   = slot->first;
+      if (key != KEY_SENTINEL) {
+        auto loc = counter.fetch_add(1, memory_order_relaxed);
+        cudf_assert(loc < MAX_DICT_SIZE && "Number of filled slots exceeds max dict size");
+        chunk.dict_data[loc] = key;
+        // If sorting dict page ever becomes a hard requirement, enable the following statement and
+        // add a dict sorting step before storing into the slot's second field.
+        // chunk.dict_data_idx[loc] = t + i;
+        slot->second = loc;
+      }
+    }
+  }
+}
+
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  get_dictionary_indices_kernel(cudf::detail::device_2dspan<gpu::PageFragment const> frags)
+{
+  auto col_idx = blockIdx.y;
+  auto block_x = blockIdx.x;
+  auto t       = threadIdx.x;
+  auto frag    = frags[col_idx][block_x];
+  auto chunk   = frag.chunk;
+  auto col     = chunk->col_desc;
+
+  if (not chunk->use_dictionary) { return; }
+
+  size_type start_row = frag.start_row;
+  size_type end_row   = frag.start_row + frag.num_rows;
+
+  // Find the bounds of values in leaf column to be searched in the map for current chunk
+  auto const s_start_value_idx  = row_to_value_idx(start_row, *col);
+  auto const s_ck_start_val_idx = row_to_value_idx(chunk->start_row, *col);
+  auto const end_value_idx      = row_to_value_idx(end_row, *col);
+
+  column_device_view const& data_col = *col->leaf_column;
+
+  auto map = map_type::device_view(chunk->dict_map_slots,
+                                   chunk->dict_map_size,
+                                   cuco::empty_key{KEY_SENTINEL},
+                                   cuco::empty_value{VALUE_SENTINEL});
+
+  thread_index_type val_idx = s_start_value_idx + t;
+  while (val_idx < end_value_idx) {
+    if (data_col.is_valid(val_idx)) {
+      auto found_slot = type_dispatcher(data_col.type(), map_find_fn{map}, data_col, val_idx);
+      cudf_assert(found_slot != map.end() &&
+                  "Unable to find value in map in dictionary index construction");
+      if (found_slot != map.end()) {
+        // No need for atomic as this is not going to be modified by any other thread
+        auto* val_ptr = reinterpret_cast<map_type::mapped_type*>(&found_slot->second);
+        chunk->dict_index[val_idx - s_ck_start_val_idx] = *val_ptr;
+      }
+    }
+
+    val_idx += block_size;
+  }
+}
+
+void initialize_chunk_hash_maps(device_span<EncColumnChunk> chunks, rmm::cuda_stream_view stream)
+{
+  constexpr int block_size = 1024;
+  initialize_chunk_hash_maps_kernel<block_size>
+    <<<chunks.size(), block_size, 0, stream.value()>>>(chunks);
+}
+
+void populate_chunk_hash_maps(cudf::detail::device_2dspan<gpu::PageFragment const> frags,
+                              rmm::cuda_stream_view stream)
+{
+  dim3 const dim_grid(frags.size().second, frags.size().first);
+  populate_chunk_hash_maps_kernel<DEFAULT_BLOCK_SIZE>
+    <<<dim_grid, DEFAULT_BLOCK_SIZE, 0, stream.value()>>>(frags);
+}
+
+void collect_map_entries(device_span<EncColumnChunk> chunks, rmm::cuda_stream_view stream)
+{
+  constexpr int block_size = 1024;
+  collect_map_entries_kernel<block_size><<<chunks.size(), block_size, 0, stream.value()>>>(chunks);
+}
+
+void get_dictionary_indices(cudf::detail::device_2dspan<gpu::PageFragment const> frags,
+                            rmm::cuda_stream_view stream)
+{
+  dim3 const dim_grid(frags.size().second, frags.size().first);
+  get_dictionary_indices_kernel<DEFAULT_BLOCK_SIZE>
+    <<<dim_grid, DEFAULT_BLOCK_SIZE, 0, stream.value()>>>(frags);
+}
+}  // namespace gpu
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/compact_protocol_reader.cpp b/cpp/src/io/parquet/compact_protocol_reader.cpp
new file mode 100644
index 0000000..5c7b8ca
--- /dev/null
+++ b/cpp/src/io/parquet/compact_protocol_reader.cpp
@@ -0,0 +1,875 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compact_protocol_reader.hpp"
+
+#include <algorithm>
+#include <cstddef>
+#include <functional>
+#include <tuple>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+
+/**
+ * @brief Base class for parquet field functors.
+ *
+ * Holds the field value used by all of the specialized functors.
+ */
+class parquet_field {
+ private:
+  int _field_val;
+
+ protected:
+  parquet_field(int f) : _field_val(f) {}
+
+ public:
+  virtual ~parquet_field() = default;
+  int field() const { return _field_val; }
+};
+
+/**
+ * @brief Abstract base class for list functors.
+ */
+template <typename T>
+class parquet_field_list : public parquet_field {
+ private:
+  using read_func_type = std::function<bool(uint32_t, CompactProtocolReader*)>;
+  FieldType _expected_type;
+  read_func_type _read_value;
+
+ protected:
+  std::vector<T>& val;
+
+  void bind_read_func(read_func_type fn) { _read_value = fn; }
+
+  parquet_field_list(int f, std::vector<T>& v, FieldType t)
+    : parquet_field(f), _expected_type(t), val(v)
+  {
+  }
+
+ public:
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_LIST) { return true; }
+    auto const [t, n] = cpr->get_listh();
+    if (t != _expected_type) { return true; }
+    val.resize(n);
+    for (uint32_t i = 0; i < n; i++) {
+      if (_read_value(i, cpr)) { return true; }
+    }
+    return false;
+  }
+};
+
+/**
+ * @brief Functor to set value to bool read from CompactProtocolReader
+ *
+ * bool doesn't actually encode a value, we just use the field type to indicate true/false
+ *
+ * @return True if field type is not bool
+ */
+class parquet_field_bool : public parquet_field {
+  bool& val;
+
+ public:
+  parquet_field_bool(int f, bool& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_TRUE && field_type != ST_FLD_FALSE) { return true; }
+    val = field_type == ST_FLD_TRUE;
+    return false;
+  }
+};
+
+/**
+ * @brief Functor to read a vector of booleans from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading a
+ * bool fails
+ */
+struct parquet_field_bool_list : public parquet_field_list<bool> {
+  parquet_field_bool_list(int f, std::vector<bool>& v) : parquet_field_list(f, v, ST_FLD_TRUE)
+  {
+    auto const read_value = [this](uint32_t i, CompactProtocolReader* cpr) {
+      auto const current_byte = cpr->getb();
+      if (current_byte != ST_FLD_TRUE && current_byte != ST_FLD_FALSE) { return true; }
+      this->val[i] = current_byte == ST_FLD_TRUE;
+      return false;
+    };
+    bind_read_func(read_value);
+  }
+};
+
+/**
+ * @brief Base type for a functor that reads an integer from CompactProtocolReader
+ *
+ * Assuming signed ints since the parquet spec does not use unsigned ints anywhere.
+ *
+ * @return True if there is a type mismatch
+ */
+template <typename T, int EXPECTED_TYPE>
+class parquet_field_int : public parquet_field {
+  static constexpr bool is_byte = std::is_same_v<T, int8_t>;
+
+  T& val;
+
+ public:
+  parquet_field_int(int f, T& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if constexpr (is_byte) {
+      val = cpr->getb();
+    } else {
+      val = cpr->get_zigzag<T>();
+    }
+    return (field_type != EXPECTED_TYPE);
+  }
+};
+
+using parquet_field_int8  = parquet_field_int<int8_t, ST_FLD_BYTE>;
+using parquet_field_int32 = parquet_field_int<int32_t, ST_FLD_I32>;
+using parquet_field_int64 = parquet_field_int<int64_t, ST_FLD_I64>;
+
+/**
+ * @brief Functor to read a vector of integers from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading an
+ * integer fails
+ */
+template <typename T, FieldType EXPECTED_TYPE>
+struct parquet_field_int_list : public parquet_field_list<T> {
+  parquet_field_int_list(int f, std::vector<T>& v) : parquet_field_list<T>(f, v, EXPECTED_TYPE)
+  {
+    auto const read_value = [this](uint32_t i, CompactProtocolReader* cpr) {
+      this->val[i] = cpr->get_zigzag<T>();
+      return false;
+    };
+    this->bind_read_func(read_value);
+  }
+};
+
+using parquet_field_int64_list = parquet_field_int_list<int64_t, ST_FLD_I64>;
+
+/**
+ * @brief Functor to read a string from CompactProtocolReader
+ *
+ * @return True if field type mismatches or if size of string exceeds bounds
+ * of the CompactProtocolReader
+ */
+class parquet_field_string : public parquet_field {
+  std::string& val;
+
+ public:
+  parquet_field_string(int f, std::string& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_BINARY) { return true; }
+    auto const n = cpr->get_u32();
+    if (n < static_cast<size_t>(cpr->m_end - cpr->m_cur)) {
+      val.assign(reinterpret_cast<char const*>(cpr->m_cur), n);
+      cpr->m_cur += n;
+      return false;
+    } else {
+      return true;
+    }
+  }
+};
+
+/**
+ * @brief Functor to read a vector of strings from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading a
+ * string fails
+ */
+struct parquet_field_string_list : public parquet_field_list<std::string> {
+  parquet_field_string_list(int f, std::vector<std::string>& v)
+    : parquet_field_list(f, v, ST_FLD_BINARY)
+  {
+    auto const read_value = [this](uint32_t i, CompactProtocolReader* cpr) {
+      auto const l = cpr->get_u32();
+      if (l < static_cast<size_t>(cpr->m_end - cpr->m_cur)) {
+        this->val[i].assign(reinterpret_cast<char const*>(cpr->m_cur), l);
+        cpr->m_cur += l;
+      } else {
+        return true;
+      }
+      return false;
+    };
+    bind_read_func(read_value);
+  }
+};
+
+/**
+ * @brief Functor to set value to enum read from CompactProtocolReader
+ *
+ * @return True if field type is not int32
+ */
+template <typename Enum>
+class parquet_field_enum : public parquet_field {
+  Enum& val;
+
+ public:
+  parquet_field_enum(int f, Enum& v) : parquet_field(f), val(v) {}
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    val = static_cast<Enum>(cpr->get_i32());
+    return (field_type != ST_FLD_I32);
+  }
+};
+
+/**
+ * @brief Functor to read a vector of enums from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading an
+ * enum fails
+ */
+template <typename Enum>
+struct parquet_field_enum_list : public parquet_field_list<Enum> {
+  parquet_field_enum_list(int f, std::vector<Enum>& v) : parquet_field_list<Enum>(f, v, ST_FLD_I32)
+  {
+    auto const read_value = [this](uint32_t i, CompactProtocolReader* cpr) {
+      this->val[i] = static_cast<Enum>(cpr->get_i32());
+      return false;
+    };
+    this->bind_read_func(read_value);
+  }
+};
+
+/**
+ * @brief Functor to read a structure from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading a
+ * struct fails
+ */
+template <typename T>
+class parquet_field_struct : public parquet_field {
+  T& val;
+
+ public:
+  parquet_field_struct(int f, T& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    return (field_type != ST_FLD_STRUCT || !(cpr->read(&val)));
+  }
+};
+
+/**
+ * @brief Functor to read optional structures in unions
+ *
+ * @return True if field types mismatch
+ */
+template <typename E, typename T>
+class parquet_field_union_struct : public parquet_field {
+  E& enum_val;
+  thrust::optional<T>& val;  // union structs are always wrapped in std::optional
+
+ public:
+  parquet_field_union_struct(int f, E& ev, thrust::optional<T>& v)
+    : parquet_field(f), enum_val(ev), val(v)
+  {
+  }
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    T v;
+    bool const res = parquet_field_struct<T>(field(), v).operator()(cpr, field_type);
+    if (!res) {
+      val      = v;
+      enum_val = static_cast<E>(field());
+    }
+    return res;
+  }
+};
+
+/**
+ * @brief Functor to read empty structures in unions
+ *
+ * Added to avoid having to define read() functions for empty structs contained in unions.
+ *
+ * @return True if field types mismatch
+ */
+template <typename E>
+class parquet_field_union_enumerator : public parquet_field {
+  E& val;
+
+ public:
+  parquet_field_union_enumerator(int f, E& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_STRUCT) { return true; }
+    cpr->skip_struct_field(field_type);
+    val = static_cast<E>(field());
+    return false;
+  }
+};
+
+/**
+ * @brief Functor to read a vector of structures from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading a
+ * struct fails
+ */
+template <typename T>
+struct parquet_field_struct_list : public parquet_field_list<T> {
+  parquet_field_struct_list(int f, std::vector<T>& v) : parquet_field_list<T>(f, v, ST_FLD_STRUCT)
+  {
+    auto const read_value = [this](uint32_t i, CompactProtocolReader* cpr) {
+      if (not cpr->read(&this->val[i])) { return true; }
+      return false;
+    };
+    this->bind_read_func(read_value);
+  }
+};
+
+// TODO(ets): replace current union handling (which mirrors thrift) to use std::optional fields
+// in a struct
+/**
+ * @brief Functor to read a union member from CompactProtocolReader
+ *
+ * @tparam is_empty True if tparam `T` type is empty type, else false.
+ *
+ * @return True if field types mismatch or if the process of reading a
+ * union member fails
+ */
+template <typename T, bool is_empty = false>
+class ParquetFieldUnionFunctor : public parquet_field {
+  bool& is_set;
+  T& val;
+
+ public:
+  ParquetFieldUnionFunctor(int f, bool& b, T& v) : parquet_field(f), is_set(b), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_STRUCT) {
+      return true;
+    } else {
+      is_set = true;
+      return !cpr->read(&val);
+    }
+  }
+};
+
+template <typename T>
+class ParquetFieldUnionFunctor<T, true> : public parquet_field {
+  bool& is_set;
+  T& val;
+
+ public:
+  ParquetFieldUnionFunctor(int f, bool& b, T& v) : parquet_field(f), is_set(b), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_STRUCT) {
+      return true;
+    } else {
+      is_set = true;
+      cpr->skip_struct_field(field_type);
+      return false;
+    }
+  }
+};
+
+template <typename T>
+ParquetFieldUnionFunctor<T, std::is_empty_v<T>> ParquetFieldUnion(int f, bool& b, T& v)
+{
+  return ParquetFieldUnionFunctor<T, std::is_empty_v<T>>(f, b, v);
+}
+
+/**
+ * @brief Functor to read a binary from CompactProtocolReader
+ *
+ * @return True if field type mismatches or if size of binary exceeds bounds
+ * of the CompactProtocolReader
+ */
+class parquet_field_binary : public parquet_field {
+  std::vector<uint8_t>& val;
+
+ public:
+  parquet_field_binary(int f, std::vector<uint8_t>& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_BINARY) { return true; }
+    auto const n = cpr->get_u32();
+    if (n <= static_cast<size_t>(cpr->m_end - cpr->m_cur)) {
+      val.resize(n);
+      val.assign(cpr->m_cur, cpr->m_cur + n);
+      cpr->m_cur += n;
+      return false;
+    } else {
+      return true;
+    }
+  }
+};
+
+/**
+ * @brief Functor to read a vector of binaries from CompactProtocolReader
+ *
+ * @return True if field types mismatch or if the process of reading a
+ * binary fails
+ */
+struct parquet_field_binary_list : public parquet_field_list<std::vector<uint8_t>> {
+  parquet_field_binary_list(int f, std::vector<std::vector<uint8_t>>& v)
+    : parquet_field_list(f, v, ST_FLD_BINARY)
+  {
+    auto const read_value = [this](uint32_t i, CompactProtocolReader* cpr) {
+      auto const l = cpr->get_u32();
+      if (l <= static_cast<size_t>(cpr->m_end - cpr->m_cur)) {
+        val[i].resize(l);
+        val[i].assign(cpr->m_cur, cpr->m_cur + l);
+        cpr->m_cur += l;
+      } else {
+        return true;
+      }
+      return false;
+    };
+    bind_read_func(read_value);
+  }
+};
+
+/**
+ * @brief Functor to read a struct from CompactProtocolReader
+ *
+ * @return True if field type mismatches
+ */
+class parquet_field_struct_blob : public parquet_field {
+  std::vector<uint8_t>& val;
+
+ public:
+  parquet_field_struct_blob(int f, std::vector<uint8_t>& v) : parquet_field(f), val(v) {}
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    if (field_type != ST_FLD_STRUCT) { return true; }
+    uint8_t const* const start = cpr->m_cur;
+    cpr->skip_struct_field(field_type);
+    if (cpr->m_cur > start) { val.assign(start, cpr->m_cur - 1); }
+    return false;
+  }
+};
+
+/**
+ * @brief functor to wrap functors for optional fields
+ */
+template <typename T, typename FieldFunctor>
+class parquet_field_optional : public parquet_field {
+  thrust::optional<T>& val;
+
+ public:
+  parquet_field_optional(int f, thrust::optional<T>& v) : parquet_field(f), val(v) {}
+
+  inline bool operator()(CompactProtocolReader* cpr, int field_type)
+  {
+    T v;
+    bool const res = FieldFunctor(field(), v).operator()(cpr, field_type);
+    if (!res) { val = v; }
+    return res;
+  }
+};
+
+/**
+ * @brief Skips the number of bytes according to the specified struct type
+ *
+ * @param[in] t Struct type enumeration
+ * @param[in] depth Level of struct nesting
+ *
+ * @return True if the struct type is recognized, false otherwise
+ */
+bool CompactProtocolReader::skip_struct_field(int t, int depth)
+{
+  switch (t) {
+    case ST_FLD_TRUE:
+    case ST_FLD_FALSE: break;
+    case ST_FLD_I16:
+    case ST_FLD_I32:
+    case ST_FLD_I64: get_u64(); break;
+    case ST_FLD_BYTE: skip_bytes(1); break;
+    case ST_FLD_DOUBLE: skip_bytes(8); break;
+    case ST_FLD_BINARY: skip_bytes(get_u32()); break;
+    case ST_FLD_LIST: [[fallthrough]];
+    case ST_FLD_SET: {
+      auto const [t, n] = get_listh();
+      if (depth > 10) { return false; }
+      for (uint32_t i = 0; i < n; i++) {
+        skip_struct_field(t, depth + 1);
+      }
+    } break;
+    case ST_FLD_STRUCT:
+      for (;;) {
+        int const c = getb();
+        t           = c & 0xf;
+        if (c == 0) { break; }               // end of struct
+        if ((c & 0xf0) == 0) { get_i16(); }  // field id is not a delta
+        if (depth > 10) { return false; }
+        skip_struct_field(t, depth + 1);
+      }
+      break;
+    default:
+      // printf("unsupported skip for type %d\n", t);
+      break;
+  }
+  return true;
+}
+
+template <int index>
+struct FunctionSwitchImpl {
+  template <typename... Operator>
+  static inline bool run(CompactProtocolReader* cpr,
+                         int field_type,
+                         int const& field,
+                         std::tuple<Operator...>& ops)
+  {
+    if (field == std::get<index>(ops).field()) {
+      return std::get<index>(ops)(cpr, field_type);
+    } else {
+      return FunctionSwitchImpl<index - 1>::run(cpr, field_type, field, ops);
+    }
+  }
+};
+
+template <>
+struct FunctionSwitchImpl<0> {
+  template <typename... Operator>
+  static inline bool run(CompactProtocolReader* cpr,
+                         int field_type,
+                         int const& field,
+                         std::tuple<Operator...>& ops)
+  {
+    if (field == std::get<0>(ops).field()) {
+      return std::get<0>(ops)(cpr, field_type);
+    } else {
+      cpr->skip_struct_field(field_type);
+      return false;
+    }
+  }
+};
+
+template <typename... Operator>
+inline bool function_builder(CompactProtocolReader* cpr, std::tuple<Operator...>& op)
+{
+  constexpr int index = std::tuple_size<std::tuple<Operator...>>::value - 1;
+  int field           = 0;
+  while (true) {
+    int const current_byte = cpr->getb();
+    if (!current_byte) { break; }
+    int const field_delta    = current_byte >> 4;
+    int const field_type     = current_byte & 0xf;
+    field                    = field_delta ? field + field_delta : cpr->get_i16();
+    bool const exit_function = FunctionSwitchImpl<index>::run(cpr, field_type, field, op);
+    if (exit_function) { return false; }
+  }
+  return true;
+}
+
+bool CompactProtocolReader::read(FileMetaData* f)
+{
+  using optional_list_column_order =
+    parquet_field_optional<std::vector<ColumnOrder>, parquet_field_struct_list<ColumnOrder>>;
+  auto op = std::make_tuple(parquet_field_int32(1, f->version),
+                            parquet_field_struct_list(2, f->schema),
+                            parquet_field_int64(3, f->num_rows),
+                            parquet_field_struct_list(4, f->row_groups),
+                            parquet_field_struct_list(5, f->key_value_metadata),
+                            parquet_field_string(6, f->created_by),
+                            optional_list_column_order(7, f->column_orders));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(SchemaElement* s)
+{
+  auto op = std::make_tuple(parquet_field_enum<Type>(1, s->type),
+                            parquet_field_int32(2, s->type_length),
+                            parquet_field_enum<FieldRepetitionType>(3, s->repetition_type),
+                            parquet_field_string(4, s->name),
+                            parquet_field_int32(5, s->num_children),
+                            parquet_field_enum<ConvertedType>(6, s->converted_type),
+                            parquet_field_int32(7, s->decimal_scale),
+                            parquet_field_int32(8, s->decimal_precision),
+                            parquet_field_optional<int32_t, parquet_field_int32>(9, s->field_id),
+                            parquet_field_struct(10, s->logical_type));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(LogicalType* l)
+{
+  auto op =
+    std::make_tuple(ParquetFieldUnion(1, l->isset.STRING, l->STRING),
+                    ParquetFieldUnion(2, l->isset.MAP, l->MAP),
+                    ParquetFieldUnion(3, l->isset.LIST, l->LIST),
+                    ParquetFieldUnion(4, l->isset.ENUM, l->ENUM),
+                    ParquetFieldUnion(5, l->isset.DECIMAL, l->DECIMAL),  // read the struct
+                    ParquetFieldUnion(6, l->isset.DATE, l->DATE),
+                    ParquetFieldUnion(7, l->isset.TIME, l->TIME),            //  read the struct
+                    ParquetFieldUnion(8, l->isset.TIMESTAMP, l->TIMESTAMP),  //  read the struct
+                    ParquetFieldUnion(10, l->isset.INTEGER, l->INTEGER),     //  read the struct
+                    ParquetFieldUnion(11, l->isset.UNKNOWN, l->UNKNOWN),
+                    ParquetFieldUnion(12, l->isset.JSON, l->JSON),
+                    ParquetFieldUnion(13, l->isset.BSON, l->BSON));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(DecimalType* d)
+{
+  auto op = std::make_tuple(parquet_field_int32(1, d->scale), parquet_field_int32(2, d->precision));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(TimeType* t)
+{
+  auto op =
+    std::make_tuple(parquet_field_bool(1, t->isAdjustedToUTC), parquet_field_struct(2, t->unit));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(TimestampType* t)
+{
+  auto op =
+    std::make_tuple(parquet_field_bool(1, t->isAdjustedToUTC), parquet_field_struct(2, t->unit));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(TimeUnit* u)
+{
+  auto op = std::make_tuple(ParquetFieldUnion(1, u->isset.MILLIS, u->MILLIS),
+                            ParquetFieldUnion(2, u->isset.MICROS, u->MICROS),
+                            ParquetFieldUnion(3, u->isset.NANOS, u->NANOS));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(IntType* i)
+{
+  auto op = std::make_tuple(parquet_field_int8(1, i->bitWidth), parquet_field_bool(2, i->isSigned));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(RowGroup* r)
+{
+  auto op = std::make_tuple(parquet_field_struct_list(1, r->columns),
+                            parquet_field_int64(2, r->total_byte_size),
+                            parquet_field_int64(3, r->num_rows));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(ColumnChunk* c)
+{
+  auto op = std::make_tuple(parquet_field_string(1, c->file_path),
+                            parquet_field_int64(2, c->file_offset),
+                            parquet_field_struct(3, c->meta_data),
+                            parquet_field_int64(4, c->offset_index_offset),
+                            parquet_field_int32(5, c->offset_index_length),
+                            parquet_field_int64(6, c->column_index_offset),
+                            parquet_field_int32(7, c->column_index_length));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(ColumnChunkMetaData* c)
+{
+  auto op = std::make_tuple(parquet_field_enum<Type>(1, c->type),
+                            parquet_field_enum_list(2, c->encodings),
+                            parquet_field_string_list(3, c->path_in_schema),
+                            parquet_field_enum<Compression>(4, c->codec),
+                            parquet_field_int64(5, c->num_values),
+                            parquet_field_int64(6, c->total_uncompressed_size),
+                            parquet_field_int64(7, c->total_compressed_size),
+                            parquet_field_int64(9, c->data_page_offset),
+                            parquet_field_int64(10, c->index_page_offset),
+                            parquet_field_int64(11, c->dictionary_page_offset),
+                            parquet_field_struct(12, c->statistics));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(PageHeader* p)
+{
+  auto op = std::make_tuple(parquet_field_enum<PageType>(1, p->type),
+                            parquet_field_int32(2, p->uncompressed_page_size),
+                            parquet_field_int32(3, p->compressed_page_size),
+                            parquet_field_struct(5, p->data_page_header),
+                            parquet_field_struct(7, p->dictionary_page_header),
+                            parquet_field_struct(8, p->data_page_header_v2));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(DataPageHeader* d)
+{
+  auto op = std::make_tuple(parquet_field_int32(1, d->num_values),
+                            parquet_field_enum<Encoding>(2, d->encoding),
+                            parquet_field_enum<Encoding>(3, d->definition_level_encoding),
+                            parquet_field_enum<Encoding>(4, d->repetition_level_encoding));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(DictionaryPageHeader* d)
+{
+  auto op = std::make_tuple(parquet_field_int32(1, d->num_values),
+                            parquet_field_enum<Encoding>(2, d->encoding));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(DataPageHeaderV2* d)
+{
+  auto op = std::make_tuple(parquet_field_int32(1, d->num_values),
+                            parquet_field_int32(2, d->num_nulls),
+                            parquet_field_int32(3, d->num_rows),
+                            parquet_field_enum<Encoding>(4, d->encoding),
+                            parquet_field_int32(5, d->definition_levels_byte_length),
+                            parquet_field_int32(6, d->repetition_levels_byte_length),
+                            parquet_field_bool(7, d->is_compressed));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(KeyValue* k)
+{
+  auto op = std::make_tuple(parquet_field_string(1, k->key), parquet_field_string(2, k->value));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(PageLocation* p)
+{
+  auto op = std::make_tuple(parquet_field_int64(1, p->offset),
+                            parquet_field_int32(2, p->compressed_page_size),
+                            parquet_field_int64(3, p->first_row_index));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(OffsetIndex* o)
+{
+  auto op = std::make_tuple(parquet_field_struct_list(1, o->page_locations));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(ColumnIndex* c)
+{
+  auto op = std::make_tuple(parquet_field_bool_list(1, c->null_pages),
+                            parquet_field_binary_list(2, c->min_values),
+                            parquet_field_binary_list(3, c->max_values),
+                            parquet_field_enum<BoundaryOrder>(4, c->boundary_order),
+                            parquet_field_int64_list(5, c->null_counts));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(Statistics* s)
+{
+  auto op = std::make_tuple(parquet_field_binary(1, s->max),
+                            parquet_field_binary(2, s->min),
+                            parquet_field_int64(3, s->null_count),
+                            parquet_field_int64(4, s->distinct_count),
+                            parquet_field_binary(5, s->max_value),
+                            parquet_field_binary(6, s->min_value));
+  return function_builder(this, op);
+}
+
+bool CompactProtocolReader::read(ColumnOrder* c)
+{
+  auto op = std::make_tuple(parquet_field_union_enumerator<ColumnOrder::Type>(1, c->type));
+  return function_builder(this, op);
+}
+
+/**
+ * @brief Constructs the schema from the file-level metadata
+ *
+ * @param[in] md File metadata that was previously parsed
+ *
+ * @return True if schema constructed completely, false otherwise
+ */
+bool CompactProtocolReader::InitSchema(FileMetaData* md)
+{
+  if (static_cast<std::size_t>(WalkSchema(md)) != md->schema.size()) { return false; }
+
+  /* Inside FileMetaData, there is a std::vector of RowGroups and each RowGroup contains a
+   * a std::vector of ColumnChunks. Each ColumnChunk has a member ColumnMetaData, which contains
+   * a std::vector of std::strings representing paths. The purpose of the code below is to set the
+   * schema_idx of each column of each row to it corresponding row_group. This is effectively
+   * mapping the columns to the schema.
+   */
+  for (auto& row_group : md->row_groups) {
+    int current_schema_index = 0;
+    for (auto& column : row_group.columns) {
+      int parent = 0;  // root of schema
+      for (auto const& path : column.meta_data.path_in_schema) {
+        auto const it = [&] {
+          // find_if starting at (current_schema_index + 1) and then wrapping
+          auto const schema = [&](auto const& e) {
+            return e.parent_idx == parent && e.name == path;
+          };
+          auto const mid = md->schema.cbegin() + current_schema_index + 1;
+          auto const it  = std::find_if(mid, md->schema.cend(), schema);
+          if (it != md->schema.cend()) { return it; }
+          return std::find_if(md->schema.cbegin(), mid, schema);
+        }();
+        if (it == md->schema.cend()) { return false; }
+        current_schema_index = std::distance(md->schema.cbegin(), it);
+        column.schema_idx    = current_schema_index;
+        parent               = current_schema_index;
+      }
+    }
+  }
+
+  return true;
+}
+
+/**
+ * @brief Populates each node in the schema tree
+ *
+ * @param[out] md File metadata
+ * @param[in] idx Current node index
+ * @param[in] parent_idx Parent node index
+ * @param[in] max_def_level Max definition level
+ * @param[in] max_rep_level Max repetition level
+ *
+ * @return The node index that was populated
+ */
+int CompactProtocolReader::WalkSchema(
+  FileMetaData* md, int idx, int parent_idx, int max_def_level, int max_rep_level)
+{
+  if (idx >= 0 && (size_t)idx < md->schema.size()) {
+    SchemaElement* e = &md->schema[idx];
+    if (e->repetition_type == OPTIONAL) {
+      ++max_def_level;
+    } else if (e->repetition_type == REPEATED) {
+      ++max_def_level;
+      ++max_rep_level;
+    }
+    e->max_definition_level = max_def_level;
+    e->max_repetition_level = max_rep_level;
+    e->parent_idx           = parent_idx;
+
+    parent_idx = idx;
+    ++idx;
+    if (e->num_children > 0) {
+      for (int i = 0; i < e->num_children; i++) {
+        e->children_idx.push_back(idx);
+        int const idx_old = idx;
+        idx               = WalkSchema(md, idx, parent_idx, max_def_level, max_rep_level);
+        if (idx <= idx_old) { break; }  // Error
+      }
+    }
+    return idx;
+  } else {
+    // Error
+    return -1;
+  }
+}
+
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/compact_protocol_reader.hpp b/cpp/src/io/parquet/compact_protocol_reader.hpp
new file mode 100644
index 0000000..619815d
--- /dev/null
+++ b/cpp/src/io/parquet/compact_protocol_reader.hpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "parquet.hpp"
+
+#include <algorithm>
+#include <cstddef>
+#include <optional>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+/**
+ * @brief Class for parsing Parquet's Thrift Compact Protocol encoded metadata
+ *
+ * This class takes in the starting location of the Parquet metadata, and fills
+ * out Thrift-derived structs and a schema tree.
+ *
+ * In a Parquet, the metadata is separated from the data, both conceptually and
+ * physically. There may be multiple data files sharing a common metadata file.
+ *
+ * The parser handles both V1 and V2 Parquet datasets, although not all
+ * compression codecs are supported yet.
+ */
+class CompactProtocolReader {
+ public:
+  explicit CompactProtocolReader(uint8_t const* base = nullptr, size_t len = 0) { init(base, len); }
+  void init(uint8_t const* base, size_t len)
+  {
+    m_base = m_cur = base;
+    m_end          = base + len;
+  }
+  [[nodiscard]] ptrdiff_t bytecount() const noexcept { return m_cur - m_base; }
+  unsigned int getb() noexcept { return (m_cur < m_end) ? *m_cur++ : 0; }
+  void skip_bytes(size_t bytecnt) noexcept
+  {
+    bytecnt = std::min(bytecnt, (size_t)(m_end - m_cur));
+    m_cur += bytecnt;
+  }
+
+  // returns a varint encoded integer
+  template <typename T>
+  T get_varint() noexcept
+  {
+    T v = 0;
+    for (uint32_t l = 0;; l += 7) {
+      T c = getb();
+      v |= (c & 0x7f) << l;
+      if (c < 0x80) { break; }
+    }
+    return v;
+  }
+
+  // returns a zigzag encoded signed integer
+  template <typename T>
+  T get_zigzag() noexcept
+  {
+    using U   = std::make_unsigned_t<T>;
+    U const u = get_varint<U>();
+    return static_cast<T>((u >> 1u) ^ -static_cast<T>(u & 1));
+  }
+
+  // thrift spec says to use zigzag i32 for i16 types
+  int32_t get_i16() noexcept { return get_zigzag<int32_t>(); }
+  int32_t get_i32() noexcept { return get_zigzag<int32_t>(); }
+  int64_t get_i64() noexcept { return get_zigzag<int64_t>(); }
+
+  uint32_t get_u32() noexcept { return get_varint<uint32_t>(); }
+  uint64_t get_u64() noexcept { return get_varint<uint64_t>(); }
+
+  [[nodiscard]] std::pair<uint8_t, uint32_t> get_listh() noexcept
+  {
+    uint32_t const c = getb();
+    uint32_t sz      = c >> 4;
+    uint8_t t        = c & 0xf;
+    if (sz == 0xf) { sz = get_u32(); }
+    return {t, sz};
+  }
+
+  bool skip_struct_field(int t, int depth = 0);
+
+ public:
+  // Generate Thrift structure parsing routines
+  bool read(FileMetaData* f);
+  bool read(SchemaElement* s);
+  bool read(LogicalType* l);
+  bool read(DecimalType* d);
+  bool read(TimeType* t);
+  bool read(TimeUnit* u);
+  bool read(TimestampType* t);
+  bool read(IntType* t);
+  bool read(RowGroup* r);
+  bool read(ColumnChunk* c);
+  bool read(ColumnChunkMetaData* c);
+  bool read(PageHeader* p);
+  bool read(DataPageHeader* d);
+  bool read(DictionaryPageHeader* d);
+  bool read(DataPageHeaderV2* d);
+  bool read(KeyValue* k);
+  bool read(PageLocation* p);
+  bool read(OffsetIndex* o);
+  bool read(ColumnIndex* c);
+  bool read(Statistics* s);
+  bool read(ColumnOrder* c);
+
+ public:
+  static int NumRequiredBits(uint32_t max_level) noexcept
+  {
+    return 32 - CountLeadingZeros32(max_level);
+  }
+  bool InitSchema(FileMetaData* md);
+
+ protected:
+  int WalkSchema(FileMetaData* md,
+                 int idx           = 0,
+                 int parent_idx    = 0,
+                 int max_def_level = 0,
+                 int max_rep_level = 0);
+
+ protected:
+  uint8_t const* m_base = nullptr;
+  uint8_t const* m_cur  = nullptr;
+  uint8_t const* m_end  = nullptr;
+
+  friend class parquet_field_string;
+  friend class parquet_field_string_list;
+  friend class parquet_field_binary;
+  friend class parquet_field_binary_list;
+  friend class parquet_field_struct_blob;
+};
+
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/compact_protocol_writer.cpp b/cpp/src/io/parquet/compact_protocol_writer.cpp
new file mode 100644
index 0000000..60bc898
--- /dev/null
+++ b/cpp/src/io/parquet/compact_protocol_writer.cpp
@@ -0,0 +1,396 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compact_protocol_writer.hpp"
+
+namespace cudf {
+namespace io {
+namespace parquet {
+
+/**
+ * @brief Parquet CompactProtocolWriter class
+ */
+
+size_t CompactProtocolWriter::write(FileMetaData const& f)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_int(1, f.version);
+  c.field_struct_list(2, f.schema);
+  c.field_int(3, f.num_rows);
+  c.field_struct_list(4, f.row_groups);
+  if (not f.key_value_metadata.empty()) { c.field_struct_list(5, f.key_value_metadata); }
+  if (not f.created_by.empty()) { c.field_string(6, f.created_by); }
+  if (f.column_orders.has_value()) { c.field_struct_list(7, f.column_orders.value()); }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(DecimalType const& decimal)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_int(1, decimal.scale);
+  c.field_int(2, decimal.precision);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(TimeUnit const& time_unit)
+{
+  CompactProtocolFieldWriter c(*this);
+  auto const isset = time_unit.isset;
+  if (isset.MILLIS) {
+    c.field_struct(1, time_unit.MILLIS);
+  } else if (isset.MICROS) {
+    c.field_struct(2, time_unit.MICROS);
+  } else if (isset.NANOS) {
+    c.field_struct(3, time_unit.NANOS);
+  }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(TimeType const& time)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_bool(1, time.isAdjustedToUTC);
+  c.field_struct(2, time.unit);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(TimestampType const& timestamp)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_bool(1, timestamp.isAdjustedToUTC);
+  c.field_struct(2, timestamp.unit);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(IntType const& integer)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_int8(1, integer.bitWidth);
+  c.field_bool(2, integer.isSigned);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(LogicalType const& logical_type)
+{
+  CompactProtocolFieldWriter c(*this);
+  auto const isset = logical_type.isset;
+  if (isset.STRING) {
+    c.field_struct(1, logical_type.STRING);
+  } else if (isset.MAP) {
+    c.field_struct(2, logical_type.MAP);
+  } else if (isset.LIST) {
+    c.field_struct(3, logical_type.LIST);
+  } else if (isset.ENUM) {
+    c.field_struct(4, logical_type.ENUM);
+  } else if (isset.DECIMAL) {
+    c.field_struct(5, logical_type.DECIMAL);
+  } else if (isset.DATE) {
+    c.field_struct(6, logical_type.DATE);
+  } else if (isset.TIME) {
+    c.field_struct(7, logical_type.TIME);
+  } else if (isset.TIMESTAMP) {
+    c.field_struct(8, logical_type.TIMESTAMP);
+  } else if (isset.INTEGER) {
+    c.field_struct(10, logical_type.INTEGER);
+  } else if (isset.UNKNOWN) {
+    c.field_struct(11, logical_type.UNKNOWN);
+  } else if (isset.JSON) {
+    c.field_struct(12, logical_type.JSON);
+  } else if (isset.BSON) {
+    c.field_struct(13, logical_type.BSON);
+  }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(SchemaElement const& s)
+{
+  CompactProtocolFieldWriter c(*this);
+  if (s.type != UNDEFINED_TYPE) {
+    c.field_int(1, s.type);
+    if (s.type_length != 0) { c.field_int(2, s.type_length); }
+  }
+  if (s.repetition_type != NO_REPETITION_TYPE) { c.field_int(3, s.repetition_type); }
+  c.field_string(4, s.name);
+
+  if (s.type == UNDEFINED_TYPE) { c.field_int(5, s.num_children); }
+  if (s.converted_type != UNKNOWN) {
+    c.field_int(6, s.converted_type);
+    if (s.converted_type == DECIMAL) {
+      c.field_int(7, s.decimal_scale);
+      c.field_int(8, s.decimal_precision);
+    }
+  }
+  if (s.field_id) { c.field_int(9, s.field_id.value()); }
+  auto const isset = s.logical_type.isset;
+  // TODO: add handling for all logical types
+  // if (isset.STRING or isset.MAP or isset.LIST or isset.ENUM or isset.DECIMAL or isset.DATE or
+  //    isset.TIME or isset.TIMESTAMP or isset.INTEGER or isset.UNKNOWN or isset.JSON or isset.BSON)
+  //    {
+  if (isset.TIMESTAMP or isset.TIME) { c.field_struct(10, s.logical_type); }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(RowGroup const& r)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_struct_list(1, r.columns);
+  c.field_int(2, r.total_byte_size);
+  c.field_int(3, r.num_rows);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(KeyValue const& k)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_string(1, k.key);
+  if (not k.value.empty()) { c.field_string(2, k.value); }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(ColumnChunk const& s)
+{
+  CompactProtocolFieldWriter c(*this);
+  if (not s.file_path.empty()) { c.field_string(1, s.file_path); }
+  c.field_int(2, s.file_offset);
+  c.field_struct(3, s.meta_data);
+  if (s.offset_index_length != 0) {
+    c.field_int(4, s.offset_index_offset);
+    c.field_int(5, s.offset_index_length);
+  }
+  if (s.column_index_length != 0) {
+    c.field_int(6, s.column_index_offset);
+    c.field_int(7, s.column_index_length);
+  }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(ColumnChunkMetaData const& s)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_int(1, s.type);
+  c.field_int_list(2, s.encodings);
+  c.field_string_list(3, s.path_in_schema);
+  c.field_int(4, s.codec);
+  c.field_int(5, s.num_values);
+  c.field_int(6, s.total_uncompressed_size);
+  c.field_int(7, s.total_compressed_size);
+  c.field_int(9, s.data_page_offset);
+  if (s.index_page_offset != 0) { c.field_int(10, s.index_page_offset); }
+  if (s.dictionary_page_offset != 0) { c.field_int(11, s.dictionary_page_offset); }
+  c.field_struct(12, s.statistics);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(Statistics const& s)
+{
+  CompactProtocolFieldWriter c(*this);
+  if (not s.max.empty()) { c.field_binary(1, s.max); }
+  if (not s.min.empty()) { c.field_binary(2, s.min); }
+  if (s.null_count != -1) { c.field_int(3, s.null_count); }
+  if (s.distinct_count != -1) { c.field_int(4, s.distinct_count); }
+  if (not s.max_value.empty()) { c.field_binary(5, s.max_value); }
+  if (not s.min_value.empty()) { c.field_binary(6, s.min_value); }
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(PageLocation const& s)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_int(1, s.offset);
+  c.field_int(2, s.compressed_page_size);
+  c.field_int(3, s.first_row_index);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(OffsetIndex const& s)
+{
+  CompactProtocolFieldWriter c(*this);
+  c.field_struct_list(1, s.page_locations);
+  return c.value();
+}
+
+size_t CompactProtocolWriter::write(ColumnOrder const& co)
+{
+  CompactProtocolFieldWriter c(*this);
+  switch (co) {
+    case ColumnOrder::TYPE_ORDER: c.field_empty_struct(1); break;
+    default: break;
+  }
+  return c.value();
+}
+
+void CompactProtocolFieldWriter::put_byte(uint8_t v) { writer.m_buf.push_back(v); }
+
+void CompactProtocolFieldWriter::put_byte(uint8_t const* raw, uint32_t len)
+{
+  for (uint32_t i = 0; i < len; i++)
+    writer.m_buf.push_back(raw[i]);
+}
+
+uint32_t CompactProtocolFieldWriter::put_uint(uint64_t v)
+{
+  int l = 1;
+  while (v > 0x7f) {
+    put_byte(static_cast<uint8_t>(v | 0x80));
+    v >>= 7;
+    l++;
+  }
+  put_byte(static_cast<uint8_t>(v));
+  return l;
+}
+
+uint32_t CompactProtocolFieldWriter::put_int(int64_t v)
+{
+  int64_t s = (v < 0);
+  return put_uint(((v ^ -s) << 1) + s);
+}
+
+void CompactProtocolFieldWriter::put_field_header(int f, int cur, int t)
+{
+  if (f > cur && f <= cur + 15)
+    put_byte(((f - cur) << 4) | t);
+  else {
+    put_byte(t);
+    put_int(f);
+  }
+}
+
+inline void CompactProtocolFieldWriter::field_bool(int field, bool b)
+{
+  put_field_header(field, current_field_value, b ? ST_FLD_TRUE : ST_FLD_FALSE);
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_int8(int field, int8_t val)
+{
+  put_field_header(field, current_field_value, ST_FLD_BYTE);
+  put_byte(val);
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_int(int field, int32_t val)
+{
+  put_field_header(field, current_field_value, ST_FLD_I32);
+  put_int(val);
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_int(int field, int64_t val)
+{
+  put_field_header(field, current_field_value, ST_FLD_I64);
+  put_int(val);
+  current_field_value = field;
+}
+
+template <typename Enum>
+inline void CompactProtocolFieldWriter::field_int_list(int field, std::vector<Enum> const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_LIST);
+  put_byte((uint8_t)((std::min(val.size(), (size_t)0xfu) << 4) | ST_FLD_I32));
+  if (val.size() >= 0xf) put_uint(val.size());
+  for (auto& v : val) {
+    put_int(static_cast<int32_t>(v));
+  }
+  current_field_value = field;
+}
+
+template <typename T>
+inline void CompactProtocolFieldWriter::field_struct(int field, T const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_STRUCT);
+  if constexpr (not std::is_empty_v<T>) {
+    writer.write(val);  // write the struct if it's not empty
+  } else {
+    put_byte(0);  // otherwise, add a stop field
+  }
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_empty_struct(int field)
+{
+  put_field_header(field, current_field_value, ST_FLD_STRUCT);
+  put_byte(0);  // add a stop field
+  current_field_value = field;
+}
+
+template <typename T>
+inline void CompactProtocolFieldWriter::field_struct_list(int field, std::vector<T> const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_LIST);
+  put_byte((uint8_t)((std::min(val.size(), (size_t)0xfu) << 4) | ST_FLD_STRUCT));
+  if (val.size() >= 0xf) put_uint(val.size());
+  for (auto& v : val) {
+    writer.write(v);
+  }
+  current_field_value = field;
+}
+
+inline size_t CompactProtocolFieldWriter::value()
+{
+  put_byte(0);
+  return writer.m_buf.size() - struct_start_pos;
+}
+
+inline void CompactProtocolFieldWriter::field_struct_blob(int field,
+                                                          std::vector<uint8_t> const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_STRUCT);
+  put_byte(val.data(), static_cast<uint32_t>(val.size()));
+  put_byte(0);
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_binary(int field, std::vector<uint8_t> const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_BINARY);
+  put_uint(val.size());
+  put_byte(val.data(), static_cast<uint32_t>(val.size()));
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_string(int field, std::string const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_BINARY);
+  put_uint(val.size());
+  // FIXME : replace reinterpret_cast
+  put_byte(reinterpret_cast<uint8_t const*>(val.data()), static_cast<uint32_t>(val.size()));
+  current_field_value = field;
+}
+
+inline void CompactProtocolFieldWriter::field_string_list(int field,
+                                                          std::vector<std::string> const& val)
+{
+  put_field_header(field, current_field_value, ST_FLD_LIST);
+  put_byte((uint8_t)((std::min(val.size(), (size_t)0xfu) << 4) | ST_FLD_BINARY));
+  if (val.size() >= 0xf) put_uint(val.size());
+  for (auto& v : val) {
+    put_uint(v.size());
+    // FIXME : replace reinterpret_cast
+    put_byte(reinterpret_cast<uint8_t const*>(v.data()), static_cast<uint32_t>(v.size()));
+  }
+  current_field_value = field;
+}
+
+inline int CompactProtocolFieldWriter::current_field() { return current_field_value; }
+
+inline void CompactProtocolFieldWriter::set_current_field(int const& field)
+{
+  current_field_value = field;
+}
+
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/compact_protocol_writer.hpp b/cpp/src/io/parquet/compact_protocol_writer.hpp
new file mode 100644
index 0000000..26d6652
--- /dev/null
+++ b/cpp/src/io/parquet/compact_protocol_writer.hpp
@@ -0,0 +1,120 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "parquet.hpp"
+#include "parquet_common.hpp"
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+
+/**
+ * @brief Class for parsing Parquet's Thrift Compact Protocol encoded metadata
+ *
+ * This class takes in the Parquet structs and outputs a Thrift-encoded binary blob
+ */
+class CompactProtocolWriter {
+ public:
+  CompactProtocolWriter(std::vector<uint8_t>* output) : m_buf(*output) {}
+
+  size_t write(FileMetaData const&);
+  size_t write(DecimalType const&);
+  size_t write(TimeUnit const&);
+  size_t write(TimeType const&);
+  size_t write(TimestampType const&);
+  size_t write(IntType const&);
+  size_t write(LogicalType const&);
+  size_t write(SchemaElement const&);
+  size_t write(RowGroup const&);
+  size_t write(KeyValue const&);
+  size_t write(ColumnChunk const&);
+  size_t write(ColumnChunkMetaData const&);
+  size_t write(Statistics const&);
+  size_t write(PageLocation const&);
+  size_t write(OffsetIndex const&);
+  size_t write(ColumnOrder const&);
+
+ protected:
+  std::vector<uint8_t>& m_buf;
+  friend class CompactProtocolFieldWriter;
+};
+
+class CompactProtocolFieldWriter {
+  CompactProtocolWriter& writer;
+  size_t struct_start_pos;
+  int current_field_value;
+
+ public:
+  CompactProtocolFieldWriter(CompactProtocolWriter& caller)
+    : writer(caller), struct_start_pos(writer.m_buf.size()), current_field_value(0)
+  {
+  }
+
+  void put_byte(uint8_t v);
+
+  void put_byte(uint8_t const* raw, uint32_t len);
+
+  uint32_t put_uint(uint64_t v);
+
+  uint32_t put_int(int64_t v);
+
+  void put_field_header(int f, int cur, int t);
+
+  inline void field_bool(int field, bool b);
+
+  inline void field_int8(int field, int8_t val);
+
+  inline void field_int(int field, int32_t val);
+
+  inline void field_int(int field, int64_t val);
+
+  template <typename Enum>
+  inline void field_int_list(int field, std::vector<Enum> const& val);
+
+  template <typename T>
+  inline void field_struct(int field, T const& val);
+
+  inline void field_empty_struct(int field);
+
+  template <typename T>
+  inline void field_struct_list(int field, std::vector<T> const& val);
+
+  inline size_t value();
+
+  inline void field_struct_blob(int field, std::vector<uint8_t> const& val);
+
+  inline void field_binary(int field, std::vector<uint8_t> const& val);
+
+  inline void field_string(int field, std::string const& val);
+
+  inline void field_string_list(int field, std::vector<std::string> const& val);
+
+  inline int current_field();
+
+  inline void set_current_field(int const& field);
+};
+
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/decode_preprocess.cu b/cpp/src/io/parquet/decode_preprocess.cu
new file mode 100644
index 0000000..8de3702
--- /dev/null
+++ b/cpp/src/io/parquet/decode_preprocess.cu
@@ -0,0 +1,417 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "page_decode.cuh"
+
+#include <io/utilities/column_buffer.hpp>
+
+#include <cudf/hashing/detail/default_hash.cuh>
+
+#include <rmm/exec_policy.hpp>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+namespace gpu {
+
+namespace {
+
+// # of threads we're decoding with
+constexpr int preprocess_block_size = 512;
+
+// the required number of runs in shared memory we will need to provide the
+// rle_stream object
+constexpr int rle_run_buffer_size = rle_stream_required_run_buffer_size<preprocess_block_size>();
+
+// the size of the rolling batch buffer
+constexpr int rolling_buf_size = LEVEL_DECODE_BUF_SIZE;
+
+using unused_state_buf = page_state_buffers_s<0, 0, 0>;
+
+/**
+ *
+ * This function expects the dictionary position to be at 0 and will traverse
+ * the entire thing.
+ *
+ * Operates on a single warp only. Expects t < 32
+ *
+ * @param s The local page info
+ * @param t Thread index
+ */
+__device__ size_type gpuDecodeTotalPageStringSize(page_state_s* s, int t)
+{
+  size_type target_pos = s->num_input_values;
+  size_type str_len    = 0;
+  if (s->dict_base) {
+    auto const [new_target_pos, len] =
+      gpuDecodeDictionaryIndices<true, unused_state_buf>(s, nullptr, target_pos, t);
+    target_pos = new_target_pos;
+    str_len    = len;
+  } else if ((s->col.data_type & 7) == BYTE_ARRAY) {
+    str_len = gpuInitStringDescriptors<true, unused_state_buf>(s, nullptr, target_pos, t);
+  }
+  if (!t) { *(int32_t volatile*)&s->dict_pos = target_pos; }
+  return str_len;
+}
+
+/**
+ * @brief Update output column sizes for every nesting level based on a batch
+ * of incoming decoded definition and repetition level values.
+ *
+ * If bounds_set is true, computes skipped_values and skipped_leaf_values for the
+ * page to indicate where we need to skip to based on min/max row.
+ *
+ * Operates at the block level.
+ *
+ * @param s The local page info
+ * @param target_value_count The target value count to process up to
+ * @param rep Repetition level buffer
+ * @param def Definition level buffer
+ * @param t Thread index
+ * @param bounds_set A boolean indicating whether or not min/max row bounds have been set
+ */
+template <typename level_t>
+static __device__ void gpuUpdatePageSizes(page_state_s* s,
+                                          int target_value_count,
+                                          level_t const* const rep,
+                                          level_t const* const def,
+                                          int t,
+                                          bool bounds_set)
+{
+  // max nesting depth of the column
+  int const max_depth = s->col.max_nesting_depth;
+
+  constexpr int num_warps      = preprocess_block_size / 32;
+  constexpr int max_batch_size = num_warps * 32;
+
+  using block_reduce = cub::BlockReduce<int, preprocess_block_size>;
+  using block_scan   = cub::BlockScan<int, preprocess_block_size>;
+  __shared__ union {
+    typename block_reduce::TempStorage reduce_storage;
+    typename block_scan::TempStorage scan_storage;
+  } temp_storage;
+
+  // how many input level values we've processed in the page so far
+  int value_count = s->input_value_count;
+  // how many rows we've processed in the page so far
+  int row_count = s->input_row_count;
+  // how many leaf values we've processed in the page so far
+  int leaf_count = s->input_leaf_count;
+  // whether or not we need to continue checking for the first row
+  bool skipped_values_set = s->page.skipped_values >= 0;
+
+  while (value_count < target_value_count) {
+    int const batch_size = min(max_batch_size, target_value_count - value_count);
+
+    // start/end depth
+    int start_depth, end_depth, d;
+    get_nesting_bounds<rolling_buf_size, level_t>(
+      start_depth, end_depth, d, s, rep, def, value_count, value_count + batch_size, t);
+
+    // is this thread within row bounds? in the non skip_rows/num_rows case this will always
+    // be true.
+    int in_row_bounds = 1;
+
+    // if we are in the skip_rows/num_rows case, we need to check against these limits
+    if (bounds_set) {
+      // get absolute thread row index
+      int const is_new_row = start_depth == 0;
+      int thread_row_count, block_row_count;
+      block_scan(temp_storage.scan_storage)
+        .InclusiveSum(is_new_row, thread_row_count, block_row_count);
+      __syncthreads();
+
+      // get absolute thread leaf index
+      int const is_new_leaf = (d >= s->nesting_info[max_depth - 1].max_def_level);
+      int thread_leaf_count, block_leaf_count;
+      block_scan(temp_storage.scan_storage)
+        .InclusiveSum(is_new_leaf, thread_leaf_count, block_leaf_count);
+      __syncthreads();
+
+      // if this thread is in row bounds
+      int const row_index = (thread_row_count + row_count) - 1;
+      in_row_bounds =
+        (row_index >= s->row_index_lower_bound) && (row_index < (s->first_row + s->num_rows));
+
+      // if we have not set skipped values yet, see if we found the first in-bounds row
+      if (!skipped_values_set) {
+        int local_count, global_count;
+        block_scan(temp_storage.scan_storage)
+          .InclusiveSum(in_row_bounds, local_count, global_count);
+        __syncthreads();
+
+        // we found it
+        if (global_count > 0) {
+          // this is the thread that represents the first row.
+          if (local_count == 1 && in_row_bounds) {
+            s->page.skipped_values = value_count + t;
+            s->page.skipped_leaf_values =
+              leaf_count + (is_new_leaf ? thread_leaf_count - 1 : thread_leaf_count);
+          }
+          skipped_values_set = true;
+        }
+      }
+
+      row_count += block_row_count;
+      leaf_count += block_leaf_count;
+    }
+
+    // increment value counts across all nesting depths
+    for (int s_idx = 0; s_idx < max_depth; s_idx++) {
+      int const in_nesting_bounds = (s_idx >= start_depth && s_idx <= end_depth && in_row_bounds);
+      int const count = block_reduce(temp_storage.reduce_storage).Sum(in_nesting_bounds);
+      __syncthreads();
+      if (!t) {
+        PageNestingInfo* pni = &s->page.nesting[s_idx];
+        pni->batch_size += count;
+      }
+    }
+
+    value_count += batch_size;
+  }
+
+  // update final outputs
+  if (!t) {
+    s->input_value_count = value_count;
+
+    // only used in the skip_rows/num_rows case
+    s->input_leaf_count = leaf_count;
+    s->input_row_count  = row_count;
+  }
+}
+
+/**
+ * @brief Kernel for computing per-page column size information for all nesting levels.
+ *
+ * This function will write out the size field for each level of nesting.
+ *
+ * @param pages List of pages
+ * @param chunks List of column chunks
+ * @param min_row Row index to start reading at
+ * @param num_rows Maximum number of rows to read. Pass as INT_MAX to guarantee reading all rows
+ * @param is_base_pass Whether or not this is the base pass.  We first have to compute
+ * the full size information of every page before we come through in a second (trim) pass
+ * to determine what subset of rows in this page we should be reading
+ * @param compute_string_sizes Whether or not we should be computing string sizes
+ * (PageInfo::str_bytes) as part of the pass
+ */
+template <typename level_t>
+__global__ void __launch_bounds__(preprocess_block_size)
+  gpuComputePageSizes(PageInfo* pages,
+                      device_span<ColumnChunkDesc const> chunks,
+                      size_t min_row,
+                      size_t num_rows,
+                      bool is_base_pass,
+                      bool compute_string_sizes)
+{
+  __shared__ __align__(16) page_state_s state_g;
+
+  page_state_s* const s = &state_g;
+  int page_idx          = blockIdx.x;
+  int t                 = threadIdx.x;
+  PageInfo* pp          = &pages[page_idx];
+
+  // whether or not we have repetition levels (lists)
+  bool has_repetition = chunks[pp->chunk_idx].max_level[level_type::REPETITION] > 0;
+
+  // the level stream decoders
+  __shared__ rle_run<level_t> def_runs[rle_run_buffer_size];
+  __shared__ rle_run<level_t> rep_runs[rle_run_buffer_size];
+  rle_stream<level_t, preprocess_block_size> decoders[level_type::NUM_LEVEL_TYPES] = {{def_runs},
+                                                                                      {rep_runs}};
+
+  // setup page info
+  if (!setupLocalPageInfo(s, pp, chunks, min_row, num_rows, all_types_filter{}, false)) { return; }
+
+  // initialize the stream decoders (requires values computed in setupLocalPageInfo)
+  // the size of the rolling batch buffer
+  int const max_batch_size = rolling_buf_size;
+  level_t* rep             = reinterpret_cast<level_t*>(pp->lvl_decode_buf[level_type::REPETITION]);
+  level_t* def             = reinterpret_cast<level_t*>(pp->lvl_decode_buf[level_type::DEFINITION]);
+  decoders[level_type::DEFINITION].init(s->col.level_bits[level_type::DEFINITION],
+                                        s->abs_lvl_start[level_type::DEFINITION],
+                                        s->abs_lvl_end[level_type::DEFINITION],
+                                        max_batch_size,
+                                        def,
+                                        s->page.num_input_values);
+  if (has_repetition) {
+    decoders[level_type::REPETITION].init(s->col.level_bits[level_type::REPETITION],
+                                          s->abs_lvl_start[level_type::REPETITION],
+                                          s->abs_lvl_end[level_type::REPETITION],
+                                          max_batch_size,
+                                          rep,
+                                          s->page.num_input_values);
+  }
+  __syncthreads();
+
+  if (!t) {
+    s->page.skipped_values      = -1;
+    s->page.skipped_leaf_values = 0;
+    s->page.str_bytes           = 0;
+    s->input_row_count          = 0;
+    s->input_value_count        = 0;
+
+    // in the base pass, we're computing the number of rows, make sure we visit absolutely
+    // everything
+    if (is_base_pass) {
+      s->first_row             = 0;
+      s->num_rows              = INT_MAX;
+      s->row_index_lower_bound = -1;
+    }
+  }
+
+  // we only need to preprocess hierarchies with repetition in them (ie, hierarchies
+  // containing lists anywhere within).
+  compute_string_sizes =
+    compute_string_sizes && ((s->col.data_type & 7) == BYTE_ARRAY && s->dtype_len != 4);
+
+  // early out optimizations:
+
+  // - if this is a flat hierarchy (no lists) and is not a string column. in this case we don't need
+  // to do the expensive work of traversing the level data to determine sizes.  we can just compute
+  // it directly.
+  if (!has_repetition && !compute_string_sizes) {
+    int depth = 0;
+    while (depth < s->page.num_output_nesting_levels) {
+      auto const thread_depth = depth + t;
+      if (thread_depth < s->page.num_output_nesting_levels) {
+        if (is_base_pass) { pp->nesting[thread_depth].size = pp->num_input_values; }
+        pp->nesting[thread_depth].batch_size = pp->num_input_values;
+      }
+      depth += blockDim.x;
+    }
+    return;
+  }
+
+  // in the trim pass, for anything with lists, we only need to fully process bounding pages (those
+  // at the beginning or the end of the row bounds)
+  if (!is_base_pass && !is_bounds_page(s, min_row, num_rows, has_repetition)) {
+    int depth = 0;
+    while (depth < s->page.num_output_nesting_levels) {
+      auto const thread_depth = depth + t;
+      if (thread_depth < s->page.num_output_nesting_levels) {
+        // if we are not a bounding page (as checked above) then we are either
+        // returning all rows/values from this page, or 0 of them
+        pp->nesting[thread_depth].batch_size =
+          (s->num_rows == 0 && !is_page_contained(s, min_row, num_rows))
+            ? 0
+            : pp->nesting[thread_depth].size;
+      }
+      depth += blockDim.x;
+    }
+    return;
+  }
+
+  // zero sizes
+  int depth = 0;
+  while (depth < s->page.num_output_nesting_levels) {
+    auto const thread_depth = depth + t;
+    if (thread_depth < s->page.num_output_nesting_levels) {
+      s->page.nesting[thread_depth].batch_size = 0;
+    }
+    depth += blockDim.x;
+  }
+  __syncthreads();
+
+  // the core loop. decode batches of level stream data using rle_stream objects
+  // and pass the results to gpuUpdatePageSizes
+  int processed = 0;
+  while (processed < s->page.num_input_values) {
+    // TODO:  it would not take much more work to make it so that we could run both of these
+    // decodes concurrently. there are a couple of shared variables internally that would have to
+    // get dealt with but that's about it.
+    if (has_repetition) {
+      decoders[level_type::REPETITION].decode_next(t);
+      __syncthreads();
+    }
+    // the # of rep/def levels will always be the same size
+    processed += decoders[level_type::DEFINITION].decode_next(t);
+    __syncthreads();
+
+    // update page sizes
+    gpuUpdatePageSizes<level_t>(s, processed, rep, def, t, !is_base_pass);
+    __syncthreads();
+  }
+
+  // retrieve total string size.
+  // TODO: make this block-based instead of just 1 warp
+  if (compute_string_sizes) {
+    if (t < 32) { s->page.str_bytes = gpuDecodeTotalPageStringSize(s, t); }
+  }
+
+  // update output results:
+  // - real number of rows for the whole page
+  // - nesting sizes for the whole page
+  // - skipped value information for trimmed pages
+  // - string bytes
+  if (is_base_pass) {
+    // nesting level 0 is the root column, so the size is also the # of rows
+    if (!t) { pp->num_rows = s->page.nesting[0].batch_size; }
+
+    // store off this batch size as the "full" size
+    int depth = 0;
+    while (depth < s->page.num_output_nesting_levels) {
+      auto const thread_depth = depth + t;
+      if (thread_depth < s->page.num_output_nesting_levels) {
+        pp->nesting[thread_depth].size = pp->nesting[thread_depth].batch_size;
+      }
+      depth += blockDim.x;
+    }
+  }
+
+  if (!t) {
+    pp->skipped_values      = s->page.skipped_values;
+    pp->skipped_leaf_values = s->page.skipped_leaf_values;
+    pp->str_bytes           = s->page.str_bytes;
+  }
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::io::parquet::gpu::ComputePageSizes
+ */
+void ComputePageSizes(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                      cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                      size_t min_row,
+                      size_t num_rows,
+                      bool compute_num_rows,
+                      bool compute_string_sizes,
+                      int level_type_size,
+                      rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(preprocess_block_size, 1);
+  dim3 dim_grid(pages.size(), 1);  // 1 threadblock per page
+
+  // computes:
+  // PageNestingInfo::size for each level of nesting, for each page.
+  // This computes the size for the entire page, not taking row bounds into account.
+  // If uses_custom_row_bounds is set to true, we have to do a second pass later that "trims"
+  // the starting and ending read values to account for these bounds.
+  if (level_type_size == 1) {
+    gpuComputePageSizes<uint8_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, compute_num_rows, compute_string_sizes);
+  } else {
+    gpuComputePageSizes<uint16_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, compute_num_rows, compute_string_sizes);
+  }
+}
+
+}  // namespace gpu
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/delta_binary.cuh b/cpp/src/io/parquet/delta_binary.cuh
new file mode 100644
index 0000000..2382e4a
--- /dev/null
+++ b/cpp/src/io/parquet/delta_binary.cuh
@@ -0,0 +1,294 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "page_decode.cuh"
+
+namespace cudf::io::parquet::gpu {
+
+// DELTA_XXX encoding support
+//
+// DELTA_BINARY_PACKED is used for INT32 and INT64 data types. Encoding begins with a header
+// containing a block size, number of mini-blocks in each block, total value count, and first
+// value. The first three are ULEB128 variable length ints, and the last is a zigzag ULEB128
+// varint.
+//   -- the block size is a multiple of 128
+//   -- the mini-block count is chosen so that each mini-block will contain a multiple of 32 values
+//   -- the value count includes the first value stored in the header
+//
+// It seems most Parquet encoders will stick with a block size of 128, and 4 mini-blocks of 32
+// elements each. arrow-rs will use a block size of 256 for 64-bit ints.
+//
+// Following the header are the data blocks. Each block is further divided into mini-blocks, with
+// each mini-block having its own encoding bitwidth. Each block begins with a header containing a
+// zigzag ULEB128 encoded minimum delta value, followed by an array of uint8 bitwidths, one entry
+// per mini-block. While encoding, the lowest delta value is subtracted from all the deltas in the
+// block to ensure that all encoded values are positive. The deltas for each mini-block are bit
+// packed using the same encoding as the RLE/Bit-Packing Hybrid encoder.
+//
+// DELTA_BYTE_ARRAY encoding (incremental encoding or front compression), is used for BYTE_ARRAY
+// columns. For each element in a sequence of strings, a prefix length from the preceding string
+// and a suffix is stored. The prefix lengths are DELTA_BINARY_PACKED encoded. The suffixes are
+// encoded with DELTA_LENGTH_BYTE_ARRAY encoding, which is a DELTA_BINARY_PACKED list of suffix
+// lengths, followed by the concatenated suffix data.
+
+// TODO: The delta encodings use ULEB128 integers, but for now we're only
+// using max 64 bits. Need to see what the performance impact is of using
+// __int128_t rather than int64_t.
+using uleb128_t   = uint64_t;
+using zigzag128_t = int64_t;
+
+// we decode one mini-block at a time. max mini-block size seen is 64.
+constexpr int delta_rolling_buf_size = 128;
+
+/**
+ * @brief Read a ULEB128 varint integer
+ *
+ * @param[in,out] cur The current data position, updated after the read
+ * @param[in] end The end data position
+ *
+ * @return The value read
+ */
+inline __device__ uleb128_t get_uleb128(uint8_t const*& cur, uint8_t const* end)
+{
+  uleb128_t v = 0, l = 0, c;
+  while (cur < end) {
+    c = *cur++;
+    v |= (c & 0x7f) << l;
+    l += 7;
+    if ((c & 0x80) == 0) { return v; }
+  }
+  return v;
+}
+
+/**
+ * @brief Read a ULEB128 zig-zag encoded varint integer
+ *
+ * @param[in,out] cur The current data position, updated after the read
+ * @param[in] end The end data position
+ *
+ * @return The value read
+ */
+inline __device__ zigzag128_t get_zz128(uint8_t const*& cur, uint8_t const* end)
+{
+  uleb128_t u = get_uleb128(cur, end);
+  return static_cast<zigzag128_t>((u >> 1u) ^ -static_cast<zigzag128_t>(u & 1));
+}
+
+struct delta_binary_decoder {
+  uint8_t const* block_start;  // start of data, but updated as data is read
+  uint8_t const* block_end;    // end of data
+  uleb128_t block_size;        // usually 128, must be multiple of 128
+  uleb128_t mini_block_count;  // usually 4, chosen such that block_size/mini_block_count is a
+                               // multiple of 32
+  uleb128_t value_count;       // total values encoded in the block
+  zigzag128_t last_value;      // last value decoded, initialized to first_value from header
+
+  uint32_t values_per_mb;      // block_size / mini_block_count, must be multiple of 32
+  uint32_t current_value_idx;  // current value index, initialized to 0 at start of block
+
+  zigzag128_t cur_min_delta;     // min delta for the block
+  uint32_t cur_mb;               // index of the current mini-block within the block
+  uint8_t const* cur_mb_start;   // pointer to the start of the current mini-block data
+  uint8_t const* cur_bitwidths;  // pointer to the bitwidth array in the block
+
+  uleb128_t value[delta_rolling_buf_size];  // circular buffer of delta values
+
+  // returns the number of values encoded in the block data. when all_values is true,
+  // account for the first value in the header. otherwise just count the values encoded
+  // in the mini-block data.
+  constexpr uint32_t num_encoded_values(bool all_values)
+  {
+    return value_count == 0 ? 0 : all_values ? value_count : value_count - 1;
+  }
+
+  // read mini-block header into state object. should only be called from init_binary_block or
+  // setup_next_mini_block. header format is:
+  //
+  // | min delta (int) | bit-width array (1 byte * mini_block_count) |
+  //
+  // on exit db->cur_mb is 0 and db->cur_mb_start points to the first mini-block of data, or
+  // nullptr if out of data.
+  // is_decode indicates whether this is being called from initialization code (false) or
+  // the actual decoding (true)
+  inline __device__ void init_mini_block(bool is_decode)
+  {
+    cur_mb       = 0;
+    cur_mb_start = nullptr;
+
+    if (current_value_idx < num_encoded_values(is_decode)) {
+      auto d_start  = block_start;
+      cur_min_delta = get_zz128(d_start, block_end);
+      cur_bitwidths = d_start;
+
+      d_start += mini_block_count;
+      cur_mb_start = d_start;
+    }
+  }
+
+  // read delta binary header into state object. should be called on thread 0. header format is:
+  //
+  // | block size (uint) | mini-block count (uint) | value count (uint) | first value (int) |
+  //
+  // also initializes the first mini-block before exit
+  inline __device__ void init_binary_block(uint8_t const* d_start, uint8_t const* d_end)
+  {
+    block_end        = d_end;
+    block_size       = get_uleb128(d_start, d_end);
+    mini_block_count = get_uleb128(d_start, d_end);
+    value_count      = get_uleb128(d_start, d_end);
+    last_value       = get_zz128(d_start, d_end);
+
+    current_value_idx = 0;
+    values_per_mb     = block_size / mini_block_count;
+
+    // init the first mini-block
+    block_start = d_start;
+
+    // only call init if there are actually encoded values
+    if (value_count > 1) { init_mini_block(false); }
+  }
+
+  // skip to the start of the next mini-block. should only be called on thread 0.
+  // calls init_binary_block if currently on the last mini-block in a block.
+  // is_decode indicates whether this is being called from initialization code (false) or
+  // the actual decoding (true)
+  inline __device__ void setup_next_mini_block(bool is_decode)
+  {
+    if (current_value_idx >= num_encoded_values(is_decode)) { return; }
+
+    current_value_idx += values_per_mb;
+
+    // just set pointer to start of next mini_block
+    if (cur_mb < mini_block_count - 1) {
+      cur_mb_start += cur_bitwidths[cur_mb] * values_per_mb / 8;
+      cur_mb++;
+    }
+    // out of mini-blocks, start a new block
+    else {
+      block_start = cur_mb_start + cur_bitwidths[cur_mb] * values_per_mb / 8;
+      init_mini_block(is_decode);
+    }
+  }
+
+  // decode the current mini-batch of deltas, and convert to values.
+  // called by all threads in a warp, currently only one warp supported.
+  inline __device__ void calc_mini_block_values(int lane_id)
+  {
+    using cudf::detail::warp_size;
+    if (current_value_idx >= value_count) { return; }
+
+    // need to save first value from header on first pass
+    if (current_value_idx == 0) {
+      if (lane_id == 0) {
+        current_value_idx++;
+        value[0] = last_value;
+      }
+      __syncwarp();
+      if (current_value_idx >= value_count) { return; }
+    }
+
+    uint32_t const mb_bits = cur_bitwidths[cur_mb];
+
+    // need to do in multiple passes if values_per_mb != 32
+    uint32_t const num_pass = values_per_mb / warp_size;
+
+    auto d_start = cur_mb_start;
+
+    for (int i = 0; i < num_pass; i++) {
+      // position at end of the current mini-block since the following calculates
+      // negative indexes
+      d_start += (warp_size * mb_bits) / 8;
+
+      // unpack deltas. modified from version in gpuDecodeDictionaryIndices(), but
+      // that one only unpacks up to bitwidths of 24. simplified some since this
+      // will always do batches of 32.
+      // NOTE: because this needs to handle up to 64 bits, the branching used in the other
+      // implementation has been replaced with a loop. While this uses more registers, the
+      // looping version is just as fast and easier to read. Might need to revisit this when
+      // DELTA_BYTE_ARRAY is implemented.
+      zigzag128_t delta = 0;
+      if (lane_id + current_value_idx < value_count) {
+        int32_t ofs      = (lane_id - warp_size) * mb_bits;
+        uint8_t const* p = d_start + (ofs >> 3);
+        ofs &= 7;
+        if (p < block_end) {
+          uint32_t c = 8 - ofs;  // 0 - 7 bits
+          delta      = (*p++) >> ofs;
+
+          while (c < mb_bits && p < block_end) {
+            delta |= static_cast<zigzag128_t>(*p++) << c;
+            c += 8;
+          }
+          delta &= (static_cast<zigzag128_t>(1) << mb_bits) - 1;
+        }
+      }
+
+      // add min delta to get true delta
+      delta += cur_min_delta;
+
+      // do inclusive scan to get value - first_value at each position
+      __shared__ cub::WarpScan<int64_t>::TempStorage temp_storage;
+      cub::WarpScan<int64_t>(temp_storage).InclusiveSum(delta, delta);
+
+      // now add first value from header or last value from previous block to get true value
+      delta += last_value;
+      int const value_idx =
+        rolling_index<delta_rolling_buf_size>(current_value_idx + warp_size * i + lane_id);
+      value[value_idx] = delta;
+
+      // save value from last lane in warp. this will become the 'first value' added to the
+      // deltas calculated in the next iteration (or invocation).
+      if (lane_id == warp_size - 1) { last_value = delta; }
+      __syncwarp();
+    }
+  }
+
+  // decodes and skips values until the block containing the value after `skip` is reached.
+  // called by all threads in a thread block.
+  inline __device__ void skip_values(int skip)
+  {
+    using cudf::detail::warp_size;
+    int const t       = threadIdx.x;
+    int const lane_id = t % warp_size;
+
+    while (current_value_idx < skip && current_value_idx < num_encoded_values(true)) {
+      if (t < warp_size) {
+        calc_mini_block_values(lane_id);
+        if (lane_id == 0) { setup_next_mini_block(true); }
+      }
+      __syncthreads();
+    }
+  }
+
+  // decodes the current mini block and stores the values obtained. should only be called by
+  // a single warp.
+  inline __device__ void decode_batch()
+  {
+    using cudf::detail::warp_size;
+    int const t       = threadIdx.x;
+    int const lane_id = t % warp_size;
+
+    // unpack deltas and save in db->value
+    calc_mini_block_values(lane_id);
+
+    // set up for next mini-block
+    if (lane_id == 0) { setup_next_mini_block(true); }
+  }
+};
+
+}  // namespace cudf::io::parquet::gpu
diff --git a/cpp/src/io/parquet/page_data.cu b/cpp/src/io/parquet/page_data.cu
new file mode 100644
index 0000000..2308346
--- /dev/null
+++ b/cpp/src/io/parquet/page_data.cu
@@ -0,0 +1,654 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "page_decode.cuh"
+
+#include <io/utilities/column_buffer.hpp>
+
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+
+#include <rmm/exec_policy.hpp>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+namespace gpu {
+
+namespace {
+
+constexpr int decode_block_size = 128;
+constexpr int rolling_buf_size  = decode_block_size * 2;
+
+/**
+ * @brief Output a string descriptor
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[in] dstv Pointer to row output data (string descriptor or 32-bit hash)
+ */
+template <typename state_buf>
+inline __device__ void gpuOutputString(volatile page_state_s* s,
+                                       volatile state_buf* sb,
+                                       int src_pos,
+                                       void* dstv)
+{
+  auto [ptr, len] = gpuGetStringData(s, sb, src_pos);
+  // make sure to only hash `BYTE_ARRAY` when specified with the output type size
+  if (s->dtype_len == 4 and (s->col.data_type & 7) == BYTE_ARRAY) {
+    // Output hash. This hash value is used if the option to convert strings to
+    // categoricals is enabled. The seed value is chosen arbitrarily.
+    uint32_t constexpr hash_seed = 33;
+    cudf::string_view const sv{ptr, static_cast<size_type>(len)};
+    *static_cast<uint32_t*>(dstv) =
+      cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>{hash_seed}(sv);
+  } else {
+    // Output string descriptor
+    auto* dst   = static_cast<string_index_pair*>(dstv);
+    dst->first  = ptr;
+    dst->second = len;
+  }
+}
+
+/**
+ * @brief Output a boolean
+ *
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[in] dst Pointer to row output data
+ */
+template <typename state_buf>
+inline __device__ void gpuOutputBoolean(volatile state_buf* sb, int src_pos, uint8_t* dst)
+{
+  *dst = sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)];
+}
+
+/**
+ * @brief Store a 32-bit data element
+ *
+ * @param[out] dst ptr to output
+ * @param[in] src8 raw input bytes
+ * @param[in] dict_pos byte position in dictionary
+ * @param[in] dict_size size of dictionary
+ */
+inline __device__ void gpuStoreOutput(uint32_t* dst,
+                                      uint8_t const* src8,
+                                      uint32_t dict_pos,
+                                      uint32_t dict_size)
+{
+  uint32_t bytebuf;
+  unsigned int ofs = 3 & reinterpret_cast<size_t>(src8);
+  src8 -= ofs;  // align to 32-bit boundary
+  ofs <<= 3;    // bytes -> bits
+  if (dict_pos < dict_size) {
+    bytebuf = *reinterpret_cast<uint32_t const*>(src8 + dict_pos);
+    if (ofs) {
+      uint32_t bytebufnext = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 4);
+      bytebuf              = __funnelshift_r(bytebuf, bytebufnext, ofs);
+    }
+  } else {
+    bytebuf = 0;
+  }
+  *dst = bytebuf;
+}
+
+/**
+ * @brief Store a 64-bit data element
+ *
+ * @param[out] dst ptr to output
+ * @param[in] src8 raw input bytes
+ * @param[in] dict_pos byte position in dictionary
+ * @param[in] dict_size size of dictionary
+ */
+inline __device__ void gpuStoreOutput(uint2* dst,
+                                      uint8_t const* src8,
+                                      uint32_t dict_pos,
+                                      uint32_t dict_size)
+{
+  uint2 v;
+  unsigned int ofs = 3 & reinterpret_cast<size_t>(src8);
+  src8 -= ofs;  // align to 32-bit boundary
+  ofs <<= 3;    // bytes -> bits
+  if (dict_pos < dict_size) {
+    v.x = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 0);
+    v.y = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 4);
+    if (ofs) {
+      uint32_t next = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 8);
+      v.x           = __funnelshift_r(v.x, v.y, ofs);
+      v.y           = __funnelshift_r(v.y, next, ofs);
+    }
+  } else {
+    v.x = v.y = 0;
+  }
+  *dst = v;
+}
+
+/**
+ * @brief Convert an INT96 Spark timestamp to 64-bit timestamp
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[out] dst Pointer to row output data
+ */
+template <typename state_buf>
+inline __device__ void gpuOutputInt96Timestamp(volatile page_state_s* s,
+                                               volatile state_buf* sb,
+                                               int src_pos,
+                                               int64_t* dst)
+{
+  using cuda::std::chrono::duration_cast;
+
+  uint8_t const* src8;
+  uint32_t dict_pos, dict_size = s->dict_size, ofs;
+
+  if (s->dict_base) {
+    // Dictionary
+    dict_pos =
+      (s->dict_bits > 0) ? sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)] : 0;
+    src8 = s->dict_base;
+  } else {
+    // Plain
+    dict_pos = src_pos;
+    src8     = s->data_start;
+  }
+  dict_pos *= (uint32_t)s->dtype_len_in;
+  ofs = 3 & reinterpret_cast<size_t>(src8);
+  src8 -= ofs;  // align to 32-bit boundary
+  ofs <<= 3;    // bytes -> bits
+
+  if (dict_pos + 4 >= dict_size) {
+    *dst = 0;
+    return;
+  }
+
+  uint3 v;
+  int64_t nanos, days;
+  v.x = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 0);
+  v.y = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 4);
+  v.z = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 8);
+  if (ofs) {
+    uint32_t next = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 12);
+    v.x           = __funnelshift_r(v.x, v.y, ofs);
+    v.y           = __funnelshift_r(v.y, v.z, ofs);
+    v.z           = __funnelshift_r(v.z, next, ofs);
+  }
+  nanos = v.y;
+  nanos <<= 32;
+  nanos |= v.x;
+  // Convert from Julian day at noon to UTC seconds
+  days = static_cast<int32_t>(v.z);
+  cudf::duration_D d_d{
+    days - 2440588};  // TBD: Should be noon instead of midnight, but this matches pyarrow
+
+  *dst = [&]() {
+    switch (s->col.ts_clock_rate) {
+      case 1:  // seconds
+        return duration_cast<duration_s>(d_d).count() +
+               duration_cast<duration_s>(duration_ns{nanos}).count();
+      case 1'000:  // milliseconds
+        return duration_cast<duration_ms>(d_d).count() +
+               duration_cast<duration_ms>(duration_ns{nanos}).count();
+      case 1'000'000:  // microseconds
+        return duration_cast<duration_us>(d_d).count() +
+               duration_cast<duration_us>(duration_ns{nanos}).count();
+      case 1'000'000'000:  // nanoseconds
+      default: return duration_cast<cudf::duration_ns>(d_d).count() + nanos;
+    }
+  }();
+}
+
+/**
+ * @brief Output a 64-bit timestamp
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[in] dst Pointer to row output data
+ */
+template <typename state_buf>
+inline __device__ void gpuOutputInt64Timestamp(volatile page_state_s* s,
+                                               volatile state_buf* sb,
+                                               int src_pos,
+                                               int64_t* dst)
+{
+  uint8_t const* src8;
+  uint32_t dict_pos, dict_size = s->dict_size, ofs;
+  int64_t ts;
+
+  if (s->dict_base) {
+    // Dictionary
+    dict_pos =
+      (s->dict_bits > 0) ? sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)] : 0;
+    src8 = s->dict_base;
+  } else {
+    // Plain
+    dict_pos = src_pos;
+    src8     = s->data_start;
+  }
+  dict_pos *= (uint32_t)s->dtype_len_in;
+  ofs = 3 & reinterpret_cast<size_t>(src8);
+  src8 -= ofs;  // align to 32-bit boundary
+  ofs <<= 3;    // bytes -> bits
+  if (dict_pos + 4 < dict_size) {
+    uint2 v;
+    int64_t val;
+    int32_t ts_scale;
+    v.x = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 0);
+    v.y = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 4);
+    if (ofs) {
+      uint32_t next = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 8);
+      v.x           = __funnelshift_r(v.x, v.y, ofs);
+      v.y           = __funnelshift_r(v.y, next, ofs);
+    }
+    val = v.y;
+    val <<= 32;
+    val |= v.x;
+    // Output to desired clock rate
+    ts_scale = s->ts_scale;
+    if (ts_scale < 0) {
+      // round towards negative infinity
+      int sign = (val < 0);
+      ts       = ((val + sign) / -ts_scale) + sign;
+    } else {
+      ts = val * ts_scale;
+    }
+  } else {
+    ts = 0;
+  }
+  *dst = ts;
+}
+
+/**
+ * @brief Output a byte array as int.
+ *
+ * @param[in] ptr Pointer to the byte array
+ * @param[in] len Byte array length
+ * @param[out] dst Pointer to row output data
+ */
+template <typename T>
+__device__ void gpuOutputByteArrayAsInt(char const* ptr, int32_t len, T* dst)
+{
+  T unscaled = 0;
+  for (auto i = 0; i < len; i++) {
+    uint8_t v = ptr[i];
+    unscaled  = (unscaled << 8) | v;
+  }
+  // Shift the unscaled value up and back down when it isn't all 8 bytes,
+  // which sign extend the value for correctly representing negative numbers.
+  unscaled <<= (sizeof(T) - len) * 8;
+  unscaled >>= (sizeof(T) - len) * 8;
+  *dst = unscaled;
+}
+
+/**
+ * @brief Output a fixed-length byte array as int.
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[in] dst Pointer to row output data
+ */
+template <typename T, typename state_buf>
+__device__ void gpuOutputFixedLenByteArrayAsInt(volatile page_state_s* s,
+                                                volatile state_buf* sb,
+                                                int src_pos,
+                                                T* dst)
+{
+  uint32_t const dtype_len_in = s->dtype_len_in;
+  uint8_t const* data         = s->dict_base ? s->dict_base : s->data_start;
+  uint32_t const pos =
+    (s->dict_base
+       ? ((s->dict_bits > 0) ? sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)] : 0)
+       : src_pos) *
+    dtype_len_in;
+  uint32_t const dict_size = s->dict_size;
+
+  T unscaled = 0;
+  for (unsigned int i = 0; i < dtype_len_in; i++) {
+    uint32_t v = (pos + i < dict_size) ? data[pos + i] : 0;
+    unscaled   = (unscaled << 8) | v;
+  }
+  // Shift the unscaled value up and back down when it isn't all 8 bytes,
+  // which sign extend the value for correctly representing negative numbers.
+  if (dtype_len_in < sizeof(T)) {
+    unscaled <<= (sizeof(T) - dtype_len_in) * 8;
+    unscaled >>= (sizeof(T) - dtype_len_in) * 8;
+  }
+  *dst = unscaled;
+}
+
+/**
+ * @brief Output a small fixed-length value
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[in] dst Pointer to row output data
+ */
+template <typename T, typename state_buf>
+inline __device__ void gpuOutputFast(volatile page_state_s* s,
+                                     volatile state_buf* sb,
+                                     int src_pos,
+                                     T* dst)
+{
+  uint8_t const* dict;
+  uint32_t dict_pos, dict_size = s->dict_size;
+
+  if (s->dict_base) {
+    // Dictionary
+    dict_pos =
+      (s->dict_bits > 0) ? sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)] : 0;
+    dict = s->dict_base;
+  } else {
+    // Plain
+    dict_pos = src_pos;
+    dict     = s->data_start;
+  }
+  dict_pos *= (uint32_t)s->dtype_len_in;
+  gpuStoreOutput(dst, dict, dict_pos, dict_size);
+}
+
+/**
+ * @brief Output a N-byte value
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @param[in] dst8 Pointer to row output data
+ * @param[in] len Length of element
+ */
+template <typename state_buf>
+static __device__ void gpuOutputGeneric(
+  volatile page_state_s* s, volatile state_buf* sb, int src_pos, uint8_t* dst8, int len)
+{
+  uint8_t const* dict;
+  uint32_t dict_pos, dict_size = s->dict_size;
+
+  if (s->dict_base) {
+    // Dictionary
+    dict_pos =
+      (s->dict_bits > 0) ? sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)] : 0;
+    dict = s->dict_base;
+  } else {
+    // Plain
+    dict_pos = src_pos;
+    dict     = s->data_start;
+  }
+  dict_pos *= (uint32_t)s->dtype_len_in;
+  if (len & 3) {
+    // Generic slow path
+    for (unsigned int i = 0; i < len; i++) {
+      dst8[i] = (dict_pos + i < dict_size) ? dict[dict_pos + i] : 0;
+    }
+  } else {
+    // Copy 4 bytes at a time
+    uint8_t const* src8 = dict;
+    unsigned int ofs    = 3 & reinterpret_cast<size_t>(src8);
+    src8 -= ofs;  // align to 32-bit boundary
+    ofs <<= 3;    // bytes -> bits
+    for (unsigned int i = 0; i < len; i += 4) {
+      uint32_t bytebuf;
+      if (dict_pos < dict_size) {
+        bytebuf = *reinterpret_cast<uint32_t const*>(src8 + dict_pos);
+        if (ofs) {
+          uint32_t bytebufnext = *reinterpret_cast<uint32_t const*>(src8 + dict_pos + 4);
+          bytebuf              = __funnelshift_r(bytebuf, bytebufnext, ofs);
+        }
+      } else {
+        bytebuf = 0;
+      }
+      dict_pos += 4;
+      *reinterpret_cast<uint32_t*>(dst8 + i) = bytebuf;
+    }
+  }
+}
+
+/**
+ * @brief Kernel for computing the column data stored in the pages
+ *
+ * This function will write the page data and the page data's validity to the
+ * output specified in the page's column chunk. If necessary, additional
+ * conversion will be performed to translate from the Parquet datatype to
+ * desired output datatype (ex. 32-bit to 16-bit, string to hash).
+ *
+ * @param pages List of pages
+ * @param chunks List of column chunks
+ * @param min_row Row index to start reading at
+ * @param num_rows Maximum number of rows to read
+ * @param error_code Error code to set if an error is encountered
+ */
+template <int lvl_buf_size, typename level_t>
+__global__ void __launch_bounds__(decode_block_size)
+  gpuDecodePageData(PageInfo* pages,
+                    device_span<ColumnChunkDesc const> chunks,
+                    size_t min_row,
+                    size_t num_rows,
+                    int32_t* error_code)
+{
+  __shared__ __align__(16) page_state_s state_g;
+  __shared__ __align__(16)
+    page_state_buffers_s<rolling_buf_size, rolling_buf_size, rolling_buf_size>
+      state_buffers;
+
+  page_state_s* const s = &state_g;
+  auto* const sb        = &state_buffers;
+  int page_idx          = blockIdx.x;
+  int t                 = threadIdx.x;
+  int out_thread0;
+  [[maybe_unused]] null_count_back_copier _{s, t};
+
+  if (!setupLocalPageInfo(
+        s, &pages[page_idx], chunks, min_row, num_rows, mask_filter{KERNEL_MASK_GENERAL}, true)) {
+    return;
+  }
+
+  bool const has_repetition = s->col.max_level[level_type::REPETITION] > 0;
+
+  if (s->dict_base) {
+    out_thread0 = (s->dict_bits > 0) ? 64 : 32;
+  } else {
+    switch (s->col.data_type & 7) {
+      case BOOLEAN: [[fallthrough]];
+      case BYTE_ARRAY: [[fallthrough]];
+      case FIXED_LEN_BYTE_ARRAY: out_thread0 = 64; break;
+      default: out_thread0 = 32;
+    }
+  }
+
+  PageNestingDecodeInfo* nesting_info_base = s->nesting_info;
+
+  __shared__ level_t rep[rolling_buf_size];  // circular buffer of repetition level values
+  __shared__ level_t def[rolling_buf_size];  // circular buffer of definition level values
+
+  // skipped_leaf_values will always be 0 for flat hierarchies.
+  uint32_t skipped_leaf_values = s->page.skipped_leaf_values;
+  while (s->error == 0 &&
+         (s->input_value_count < s->num_input_values || s->src_pos < s->nz_count)) {
+    int target_pos;
+    int src_pos = s->src_pos;
+
+    if (t < out_thread0) {
+      target_pos = min(src_pos + 2 * (decode_block_size - out_thread0),
+                       s->nz_count + (decode_block_size - out_thread0));
+    } else {
+      target_pos = min(s->nz_count, src_pos + decode_block_size - out_thread0);
+      if (out_thread0 > 32) { target_pos = min(target_pos, s->dict_pos); }
+    }
+    __syncthreads();
+    if (t < 32) {
+      // decode repetition and definition levels.
+      // - update validity vectors
+      // - updates offsets (for nested columns)
+      // - produces non-NULL value indices in s->nz_idx for subsequent decoding
+      gpuDecodeLevels<lvl_buf_size, level_t>(s, sb, target_pos, rep, def, t);
+    } else if (t < out_thread0) {
+      // skipped_leaf_values will always be 0 for flat hierarchies.
+      uint32_t src_target_pos = target_pos + skipped_leaf_values;
+
+      // WARP1: Decode dictionary indices, booleans or string positions
+      if (s->dict_base) {
+        src_target_pos = gpuDecodeDictionaryIndices<false>(s, sb, src_target_pos, t & 0x1f).first;
+      } else if ((s->col.data_type & 7) == BOOLEAN) {
+        src_target_pos = gpuDecodeRleBooleans(s, sb, src_target_pos, t & 0x1f);
+      } else if ((s->col.data_type & 7) == BYTE_ARRAY or
+                 (s->col.data_type & 7) == FIXED_LEN_BYTE_ARRAY) {
+        gpuInitStringDescriptors<false>(s, sb, src_target_pos, t & 0x1f);
+      }
+      if (t == 32) { *(volatile int32_t*)&s->dict_pos = src_target_pos; }
+    } else {
+      // WARP1..WARP3: Decode values
+      int const dtype = s->col.data_type & 7;
+      src_pos += t - out_thread0;
+
+      // the position in the output column/buffer
+      int dst_pos = sb->nz_idx[rolling_index<rolling_buf_size>(src_pos)];
+
+      // for the flat hierarchy case we will be reading from the beginning of the value stream,
+      // regardless of the value of first_row. so adjust our destination offset accordingly.
+      // example:
+      // - user has passed skip_rows = 2, so our first_row to output is 2
+      // - the row values we get from nz_idx will be
+      //   0, 1, 2, 3, 4 ....
+      // - by shifting these values by first_row, the sequence becomes
+      //   -1, -2, 0, 1, 2 ...
+      // - so we will end up ignoring the first two input rows, and input rows 2..n will
+      //   get written to the output starting at position 0.
+      //
+      if (!has_repetition) { dst_pos -= s->first_row; }
+
+      // target_pos will always be properly bounded by num_rows, but dst_pos may be negative (values
+      // before first_row) in the flat hierarchy case.
+      if (src_pos < target_pos && dst_pos >= 0) {
+        // src_pos represents the logical row position we want to read from. But in the case of
+        // nested hierarchies, there is no 1:1 mapping of rows to values.  So our true read position
+        // has to take into account the # of values we have to skip in the page to get to the
+        // desired logical row.  For flat hierarchies, skipped_leaf_values will always be 0.
+        uint32_t val_src_pos = src_pos + skipped_leaf_values;
+
+        // nesting level that is storing actual leaf values
+        int leaf_level_index = s->col.max_nesting_depth - 1;
+
+        uint32_t dtype_len = s->dtype_len;
+        void* dst =
+          nesting_info_base[leaf_level_index].data_out + static_cast<size_t>(dst_pos) * dtype_len;
+        if (dtype == BYTE_ARRAY) {
+          if (s->col.converted_type == DECIMAL) {
+            auto const [ptr, len]        = gpuGetStringData(s, sb, val_src_pos);
+            auto const decimal_precision = s->col.decimal_precision;
+            if (decimal_precision <= MAX_DECIMAL32_PRECISION) {
+              gpuOutputByteArrayAsInt(ptr, len, static_cast<int32_t*>(dst));
+            } else if (decimal_precision <= MAX_DECIMAL64_PRECISION) {
+              gpuOutputByteArrayAsInt(ptr, len, static_cast<int64_t*>(dst));
+            } else {
+              gpuOutputByteArrayAsInt(ptr, len, static_cast<__int128_t*>(dst));
+            }
+          } else {
+            gpuOutputString(s, sb, val_src_pos, dst);
+          }
+        } else if (dtype == BOOLEAN) {
+          gpuOutputBoolean(sb, val_src_pos, static_cast<uint8_t*>(dst));
+        } else if (s->col.converted_type == DECIMAL) {
+          switch (dtype) {
+            case INT32: gpuOutputFast(s, sb, val_src_pos, static_cast<uint32_t*>(dst)); break;
+            case INT64: gpuOutputFast(s, sb, val_src_pos, static_cast<uint2*>(dst)); break;
+            default:
+              if (s->dtype_len_in <= sizeof(int32_t)) {
+                gpuOutputFixedLenByteArrayAsInt(s, sb, val_src_pos, static_cast<int32_t*>(dst));
+              } else if (s->dtype_len_in <= sizeof(int64_t)) {
+                gpuOutputFixedLenByteArrayAsInt(s, sb, val_src_pos, static_cast<int64_t*>(dst));
+              } else {
+                gpuOutputFixedLenByteArrayAsInt(s, sb, val_src_pos, static_cast<__int128_t*>(dst));
+              }
+              break;
+          }
+        } else if (dtype == FIXED_LEN_BYTE_ARRAY) {
+          gpuOutputString(s, sb, val_src_pos, dst);
+        } else if (dtype == INT96) {
+          gpuOutputInt96Timestamp(s, sb, val_src_pos, static_cast<int64_t*>(dst));
+        } else if (dtype_len == 8) {
+          if (s->dtype_len_in == 4) {
+            // Reading INT32 TIME_MILLIS into 64-bit DURATION_MILLISECONDS
+            // TIME_MILLIS is the only duration type stored as int32:
+            // https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#deprecated-time-convertedtype
+            gpuOutputFast(s, sb, val_src_pos, static_cast<uint32_t*>(dst));
+          } else if (s->ts_scale) {
+            gpuOutputInt64Timestamp(s, sb, val_src_pos, static_cast<int64_t*>(dst));
+          } else {
+            gpuOutputFast(s, sb, val_src_pos, static_cast<uint2*>(dst));
+          }
+        } else if (dtype_len == 4) {
+          gpuOutputFast(s, sb, val_src_pos, static_cast<uint32_t*>(dst));
+        } else {
+          gpuOutputGeneric(s, sb, val_src_pos, static_cast<uint8_t*>(dst), dtype_len);
+        }
+      }
+
+      if (t == out_thread0) { *(volatile int32_t*)&s->src_pos = target_pos; }
+    }
+    __syncthreads();
+  }
+  if (t == 0 and s->error != 0) {
+    cuda::atomic_ref<int32_t, cuda::thread_scope_device> ref{*error_code};
+    ref.fetch_or(s->error, cuda::std::memory_order_relaxed);
+  }
+}
+
+struct mask_tform {
+  __device__ uint32_t operator()(PageInfo const& p) { return p.kernel_mask; }
+};
+
+}  // anonymous namespace
+
+uint32_t GetAggregatedDecodeKernelMask(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                                       rmm::cuda_stream_view stream)
+{
+  // determine which kernels to invoke
+  auto mask_iter = thrust::make_transform_iterator(pages.d_begin(), mask_tform{});
+  return thrust::reduce(
+    rmm::exec_policy(stream), mask_iter, mask_iter + pages.size(), 0U, thrust::bit_or<uint32_t>{});
+}
+
+/**
+ * @copydoc cudf::io::parquet::gpu::DecodePageData
+ */
+void __host__ DecodePageData(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                             cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                             size_t num_rows,
+                             size_t min_row,
+                             int level_type_size,
+                             int32_t* error_code,
+                             rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(pages.size() > 0, "There is no page to decode");
+
+  dim3 dim_block(decode_block_size, 1);
+  dim3 dim_grid(pages.size(), 1);  // 1 threadblock per page
+
+  if (level_type_size == 1) {
+    gpuDecodePageData<rolling_buf_size, uint8_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, error_code);
+  } else {
+    gpuDecodePageData<rolling_buf_size, uint16_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, error_code);
+  }
+}
+
+}  // namespace gpu
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/page_decode.cuh b/cpp/src/io/parquet/page_decode.cuh
new file mode 100644
index 0000000..cdc2919
--- /dev/null
+++ b/cpp/src/io/parquet/page_decode.cuh
@@ -0,0 +1,1387 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "parquet_gpu.hpp"
+#include "rle_stream.cuh"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <cuda/atomic>
+#include <cuda/std/tuple>
+
+namespace cudf::io::parquet::gpu {
+
+struct page_state_s {
+  constexpr page_state_s() noexcept {}
+  uint8_t const* data_start{};
+  uint8_t const* data_end{};
+  uint8_t const* lvl_end{};
+  uint8_t const* dict_base{};    // ptr to dictionary page data
+  int32_t dict_size{};           // size of dictionary data
+  int32_t first_row{};           // First row in page to output
+  int32_t num_rows{};            // Rows in page to decode (including rows to be skipped)
+  int32_t first_output_value{};  // First value in page to output
+  int32_t num_input_values{};    // total # of input/level values in the page
+  int32_t dtype_len{};           // Output data type length
+  int32_t dtype_len_in{};        // Can be larger than dtype_len if truncating 32-bit into 8-bit
+  int32_t dict_bits{};           // # of bits to store dictionary indices
+  uint32_t dict_run{};
+  int32_t dict_val{};
+  uint32_t initial_rle_run[NUM_LEVEL_TYPES]{};   // [def,rep]
+  int32_t initial_rle_value[NUM_LEVEL_TYPES]{};  // [def,rep]
+  int32_t error{};
+  PageInfo page{};
+  ColumnChunkDesc col{};
+
+  // (leaf) value decoding
+  int32_t nz_count{};  // number of valid entries in nz_idx (write position in circular buffer)
+  int32_t dict_pos{};  // write position of dictionary indices
+  int32_t src_pos{};   // input read position of final output value
+  int32_t ts_scale{};  // timestamp scale: <0: divide by -ts_scale, >0: multiply by ts_scale
+
+  // repetition/definition level decoding
+  int32_t input_value_count{};                  // how many values of the input we've processed
+  int32_t input_row_count{};                    // how many rows of the input we've processed
+  int32_t input_leaf_count{};                   // how many leaf values of the input we've processed
+  uint8_t const* lvl_start[NUM_LEVEL_TYPES]{};  // [def,rep]
+  uint8_t const* abs_lvl_start[NUM_LEVEL_TYPES]{};  // [def,rep]
+  uint8_t const* abs_lvl_end[NUM_LEVEL_TYPES]{};    // [def,rep]
+  int32_t lvl_count[NUM_LEVEL_TYPES]{};             // how many of each of the streams we've decoded
+  int32_t row_index_lower_bound{};                  // lower bound of row indices we should process
+
+  // a shared-memory cache of frequently used data when decoding. The source of this data is
+  // normally stored in global memory which can yield poor performance. So, when possible
+  // we copy that info here prior to decoding
+  PageNestingDecodeInfo nesting_decode_cache[max_cacheable_nesting_decode_info]{};
+  // points to either nesting_decode_cache above when possible, or to the global source otherwise
+  PageNestingDecodeInfo* nesting_info{};
+
+  inline __device__ void set_error_code(decode_error err) volatile
+  {
+    cuda::atomic_ref<int32_t, cuda::thread_scope_block> ref{const_cast<int&>(error)};
+    ref.fetch_or(static_cast<int32_t>(err), cuda::std::memory_order_relaxed);
+  }
+
+  inline __device__ void reset_error_code() volatile
+  {
+    cuda::atomic_ref<int32_t, cuda::thread_scope_block> ref{const_cast<int&>(error)};
+    ref.store(0, cuda::std::memory_order_release);
+  }
+};
+
+// buffers only used in the decode kernel.  separated from page_state_s to keep
+// shared memory usage in other kernels (eg, gpuComputePageSizes) down.
+template <int _nz_buf_size, int _dict_buf_size, int _str_buf_size>
+struct page_state_buffers_s {
+  static constexpr int nz_buf_size   = _nz_buf_size;
+  static constexpr int dict_buf_size = _dict_buf_size;
+  static constexpr int str_buf_size  = _str_buf_size;
+
+  uint32_t nz_idx[nz_buf_size];      // circular buffer of non-null value positions
+  uint32_t dict_idx[dict_buf_size];  // Dictionary index, boolean, or string offset values
+  uint32_t str_len[str_buf_size];    // String length for plain encoding of strings
+};
+
+// Copies null counts back to `nesting_decode` at the end of scope
+struct null_count_back_copier {
+  page_state_s* s;
+  int t;
+  __device__ ~null_count_back_copier()
+  {
+    if (s->nesting_info != nullptr and s->nesting_info == s->nesting_decode_cache) {
+      int depth = 0;
+      while (depth < s->page.num_output_nesting_levels) {
+        int const thread_depth = depth + t;
+        if (thread_depth < s->page.num_output_nesting_levels) {
+          s->page.nesting_decode[thread_depth].null_count =
+            s->nesting_decode_cache[thread_depth].null_count;
+        }
+        depth += blockDim.x;
+      }
+    }
+  }
+};
+
+/**
+ * @brief Test if the given page is in a string column
+ */
+constexpr bool is_string_col(PageInfo const& page, device_span<ColumnChunkDesc const> chunks)
+{
+  if (page.flags & PAGEINFO_FLAGS_DICTIONARY != 0) { return false; }
+  auto const& col = chunks[page.chunk_idx];
+  return is_string_col(col);
+}
+
+/**
+ * @brief Returns whether or not a page spans either the beginning or the end of the
+ * specified row bounds
+ *
+ * @param s The page to be checked
+ * @param start_row The starting row index
+ * @param num_rows The number of rows
+ * @param has_repetition True if the schema has nesting
+ *
+ * @return True if the page spans the beginning or the end of the row bounds
+ */
+inline __device__ bool is_bounds_page(page_state_s* const s,
+                                      size_t start_row,
+                                      size_t num_rows,
+                                      bool has_repetition)
+{
+  size_t const page_begin = s->col.start_row + s->page.chunk_row;
+  size_t const page_end   = page_begin + s->page.num_rows;
+  size_t const begin      = start_row;
+  size_t const end        = start_row + num_rows;
+
+  // for non-nested schemas, rows cannot span pages, so use a more restrictive test
+  return has_repetition
+           ? ((page_begin <= begin && page_end >= begin) || (page_begin <= end && page_end >= end))
+           : ((page_begin < begin && page_end > begin) || (page_begin < end && page_end > end));
+}
+
+/**
+ * @brief Returns whether or not a page is completely contained within the specified
+ * row bounds
+ *
+ * @param s The page to be checked
+ * @param start_row The starting row index
+ * @param num_rows The number of rows
+ *
+ * @return True if the page is completely contained within the row bounds
+ */
+inline __device__ bool is_page_contained(page_state_s* const s, size_t start_row, size_t num_rows)
+{
+  size_t const page_begin = s->col.start_row + s->page.chunk_row;
+  size_t const page_end   = page_begin + s->page.num_rows;
+  size_t const begin      = start_row;
+  size_t const end        = start_row + num_rows;
+
+  return page_begin >= begin && page_end <= end;
+}
+
+/**
+ * @brief Retrieves string information for a string at the specified source position
+ *
+ * @param[in] s Page state input
+ * @param[out] sb Page state buffer output
+ * @param[in] src_pos Source position
+ * @tparam state_buf Typename of the `state_buf` (usually inferred)
+ *
+ * @return A pair containing a pointer to the string and its length
+ */
+template <typename state_buf>
+inline __device__ cuda::std::pair<char const*, size_t> gpuGetStringData(page_state_s volatile* s,
+                                                                        state_buf volatile* sb,
+                                                                        int src_pos)
+{
+  char const* ptr = nullptr;
+  size_t len      = 0;
+
+  if (s->dict_base) {
+    // String dictionary
+    uint32_t dict_pos =
+      (s->dict_bits > 0)
+        ? sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)] * sizeof(string_index_pair)
+        : 0;
+    if (dict_pos < (uint32_t)s->dict_size) {
+      auto const* src = reinterpret_cast<string_index_pair const*>(s->dict_base + dict_pos);
+      ptr             = src->first;
+      len             = src->second;
+    }
+  } else {
+    // Plain encoding
+    uint32_t dict_pos = sb->dict_idx[rolling_index<state_buf::dict_buf_size>(src_pos)];
+    if (dict_pos <= (uint32_t)s->dict_size) {
+      ptr = reinterpret_cast<char const*>(s->data_start + dict_pos);
+      len = sb->str_len[rolling_index<state_buf::str_buf_size>(src_pos)];
+    }
+  }
+
+  return {ptr, len};
+}
+
+/**
+ * @brief Performs RLE decoding of dictionary indexes
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] target_pos Target index position in dict_idx buffer (may exceed this value by up to
+ * 31)
+ * @param[in] t Warp1 thread ID (0..31)
+ * @tparam sizes_only True if only sizes are to be calculated
+ * @tparam state_buf Typename of the `state_buf` (usually inferred)
+ *
+ * @return A pair containing the new output position, and the total length of strings decoded (this
+ * will only be valid on thread 0 and if sizes_only is true). In the event that this function
+ * decodes strings beyond target_pos, the total length of strings returned will include these
+ * additional values.
+ */
+template <bool sizes_only, typename state_buf>
+__device__ cuda::std::pair<int, int> gpuDecodeDictionaryIndices(
+  page_state_s volatile* s, [[maybe_unused]] state_buf volatile* sb, int target_pos, int t)
+{
+  uint8_t const* end = s->data_end;
+  int dict_bits      = s->dict_bits;
+  int pos            = s->dict_pos;
+  int str_len        = 0;
+
+  while (pos < target_pos) {
+    int is_literal, batch_len;
+    if (!t) {
+      uint32_t run       = s->dict_run;
+      uint8_t const* cur = s->data_start;
+      if (run <= 1) {
+        run = (cur < end) ? get_vlq32(cur, end) : 0;
+        if (!(run & 1)) {
+          // Repeated value
+          int bytecnt = (dict_bits + 7) >> 3;
+          if (cur + bytecnt <= end) {
+            int32_t run_val = cur[0];
+            if (bytecnt > 1) {
+              run_val |= cur[1] << 8;
+              if (bytecnt > 2) {
+                run_val |= cur[2] << 16;
+                if (bytecnt > 3) { run_val |= cur[3] << 24; }
+              }
+            }
+            s->dict_val = run_val & ((1 << dict_bits) - 1);
+          }
+          cur += bytecnt;
+        }
+      }
+      if (run & 1) {
+        // Literal batch: must output a multiple of 8, except for the last batch
+        int batch_len_div8;
+        batch_len      = max(min(32, (int)(run >> 1) * 8), 1);
+        batch_len_div8 = (batch_len + 7) >> 3;
+        run -= batch_len_div8 * 2;
+        cur += batch_len_div8 * dict_bits;
+      } else {
+        batch_len = max(min(32, (int)(run >> 1)), 1);
+        run -= batch_len * 2;
+      }
+      s->dict_run   = run;
+      s->data_start = cur;
+      is_literal    = run & 1;
+      __threadfence_block();
+    }
+    __syncwarp();
+    is_literal = shuffle(is_literal);
+    batch_len  = shuffle(batch_len);
+
+    // compute dictionary index.
+    int dict_idx = 0;
+    if (t < batch_len) {
+      dict_idx = s->dict_val;
+      if (is_literal) {
+        int32_t ofs      = (t - ((batch_len + 7) & ~7)) * dict_bits;
+        uint8_t const* p = s->data_start + (ofs >> 3);
+        ofs &= 7;
+        if (p < end) {
+          uint32_t c = 8 - ofs;
+          dict_idx   = (*p++) >> ofs;
+          if (c < dict_bits && p < end) {
+            dict_idx |= (*p++) << c;
+            c += 8;
+            if (c < dict_bits && p < end) {
+              dict_idx |= (*p++) << c;
+              c += 8;
+              if (c < dict_bits && p < end) { dict_idx |= (*p++) << c; }
+            }
+          }
+          dict_idx &= (1 << dict_bits) - 1;
+        }
+      }
+
+      // if we're not computing sizes, store off the dictionary index
+      if constexpr (!sizes_only) {
+        sb->dict_idx[rolling_index<state_buf::dict_buf_size>(pos + t)] = dict_idx;
+      }
+    }
+
+    // if we're computing sizes, add the length(s)
+    if constexpr (sizes_only) {
+      int const len = [&]() {
+        if (t >= batch_len || (pos + t >= target_pos)) { return 0; }
+        uint32_t const dict_pos = (s->dict_bits > 0) ? dict_idx * sizeof(string_index_pair) : 0;
+        if (dict_pos < (uint32_t)s->dict_size) {
+          const auto* src = reinterpret_cast<const string_index_pair*>(s->dict_base + dict_pos);
+          return src->second;
+        }
+        return 0;
+      }();
+
+      using WarpReduce = cub::WarpReduce<size_type>;
+      __shared__ typename WarpReduce::TempStorage temp_storage;
+      // note: str_len will only be valid on thread 0.
+      str_len += WarpReduce(temp_storage).Sum(len);
+    }
+
+    pos += batch_len;
+  }
+  return {pos, str_len};
+}
+
+/**
+ * @brief Performs RLE decoding of dictionary indexes, for when dict_size=1
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] target_pos Target write position
+ * @param[in] t Thread ID
+ * @tparam state_buf Typename of the `state_buf` (usually inferred)
+ *
+ * @return The new output position
+ */
+template <typename state_buf>
+inline __device__ int gpuDecodeRleBooleans(page_state_s volatile* s,
+                                           state_buf volatile* sb,
+                                           int target_pos,
+                                           int t)
+{
+  uint8_t const* end = s->data_end;
+  int64_t pos        = s->dict_pos;
+
+  while (pos < target_pos) {
+    int is_literal, batch_len;
+    if (!t) {
+      uint32_t run       = s->dict_run;
+      uint8_t const* cur = s->data_start;
+      if (run <= 1) {
+        run = (cur < end) ? get_vlq32(cur, end) : 0;
+        if (!(run & 1)) {
+          // Repeated value
+          s->dict_val = (cur < end) ? cur[0] & 1 : 0;
+          cur++;
+        }
+      }
+      if (run & 1) {
+        // Literal batch: must output a multiple of 8, except for the last batch
+        int batch_len_div8;
+        batch_len = max(min(32, (int)(run >> 1) * 8), 1);
+        if (batch_len >= 8) { batch_len &= ~7; }
+        batch_len_div8 = (batch_len + 7) >> 3;
+        run -= batch_len_div8 * 2;
+        cur += batch_len_div8;
+      } else {
+        batch_len = max(min(32, (int)(run >> 1)), 1);
+        run -= batch_len * 2;
+      }
+      s->dict_run   = run;
+      s->data_start = cur;
+      is_literal    = run & 1;
+      __threadfence_block();
+    }
+    __syncwarp();
+    is_literal = shuffle(is_literal);
+    batch_len  = shuffle(batch_len);
+    if (t < batch_len) {
+      int dict_idx;
+      if (is_literal) {
+        int32_t ofs      = t - ((batch_len + 7) & ~7);
+        uint8_t const* p = s->data_start + (ofs >> 3);
+        dict_idx         = (p < end) ? (p[0] >> (ofs & 7u)) & 1 : 0;
+      } else {
+        dict_idx = s->dict_val;
+      }
+      sb->dict_idx[rolling_index<state_buf::dict_buf_size>(pos + t)] = dict_idx;
+    }
+    pos += batch_len;
+  }
+  return pos;
+}
+
+/**
+ * @brief Parses the length and position of strings and returns total length of all strings
+ * processed
+ *
+ * @param[in,out] s Page state input/output
+ * @param[out] sb Page state buffer output
+ * @param[in] target_pos Target output position
+ * @param[in] t Thread ID
+ * @tparam sizes_only True if only sizes are to be calculated
+ * @tparam state_buf Typename of the `state_buf` (usually inferred)
+ *
+ * @return Total length of strings processed
+ */
+template <bool sizes_only, typename state_buf>
+__device__ size_type gpuInitStringDescriptors(page_state_s volatile* s,
+                                              [[maybe_unused]] state_buf volatile* sb,
+                                              int target_pos,
+                                              int t)
+{
+  int pos       = s->dict_pos;
+  int total_len = 0;
+
+  // This step is purely serial
+  if (!t) {
+    uint8_t const* cur = s->data_start;
+    int dict_size      = s->dict_size;
+    int k              = s->dict_val;
+
+    while (pos < target_pos) {
+      int len = 0;
+      if ((s->col.data_type & 7) == FIXED_LEN_BYTE_ARRAY) {
+        if (k < dict_size) { len = s->dtype_len_in; }
+      } else {
+        if (k + 4 <= dict_size) {
+          len = (cur[k]) | (cur[k + 1] << 8) | (cur[k + 2] << 16) | (cur[k + 3] << 24);
+          k += 4;
+          if (k + len > dict_size) { len = 0; }
+        }
+      }
+      if constexpr (!sizes_only) {
+        sb->dict_idx[rolling_index<state_buf::dict_buf_size>(pos)] = k;
+        sb->str_len[rolling_index<state_buf::str_buf_size>(pos)]   = len;
+      }
+      k += len;
+      total_len += len;
+      pos++;
+    }
+    s->dict_val = k;
+    __threadfence_block();
+  }
+
+  return total_len;
+}
+
+/**
+ * @brief Decode values out of a definition or repetition stream
+ *
+ * @param[out] output Level buffer output
+ * @param[in,out] s Page state input/output
+ * @param[in] target_count Target count of stream values on output
+ * @param[in] t Warp0 thread ID (0..31)
+ * @param[in] lvl The level type we are decoding - DEFINITION or REPETITION
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ * @tparam rolling_buf_size Size of the cyclic buffer used to store value data
+ */
+template <typename level_t, int rolling_buf_size>
+__device__ void gpuDecodeStream(
+  level_t* output, page_state_s* s, int32_t target_count, int t, level_type lvl)
+{
+  uint8_t const* cur_def    = s->lvl_start[lvl];
+  uint8_t const* end        = s->lvl_end;
+  uint32_t level_run        = s->initial_rle_run[lvl];
+  int32_t level_val         = s->initial_rle_value[lvl];
+  int level_bits            = s->col.level_bits[lvl];
+  int32_t num_input_values  = s->num_input_values;
+  int32_t value_count       = s->lvl_count[lvl];
+  int32_t batch_coded_count = 0;
+
+  while (s->error == 0 && value_count < target_count && value_count < num_input_values) {
+    int batch_len;
+    if (level_run <= 1) {
+      // Get a new run symbol from the byte stream
+      int sym_len = 0;
+      if (!t) {
+        uint8_t const* cur = cur_def;
+        if (cur < end) { level_run = get_vlq32(cur, end); }
+        if (!(level_run & 1)) {
+          if (cur < end) level_val = cur[0];
+          cur++;
+          if (level_bits > 8) {
+            if (cur < end) level_val |= cur[0] << 8;
+            cur++;
+          }
+        }
+        if (cur > end) {
+          s->set_error_code(decode_error::LEVEL_STREAM_OVERRUN);
+          break;
+        }
+        if (level_run <= 1) {
+          s->set_error_code(decode_error::INVALID_LEVEL_RUN);
+          break;
+        }
+        sym_len = (int32_t)(cur - cur_def);
+        __threadfence_block();
+      }
+      sym_len   = shuffle(sym_len);
+      level_val = shuffle(level_val);
+      level_run = shuffle(level_run);
+      cur_def += sym_len;
+    }
+    if (s->error != 0) { break; }
+
+    batch_len = min(num_input_values - value_count, 32);
+    if (level_run & 1) {
+      // Literal run
+      int batch_len8;
+      batch_len  = min(batch_len, (level_run >> 1) * 8);
+      batch_len8 = (batch_len + 7) >> 3;
+      if (t < batch_len) {
+        int bitpos         = t * level_bits;
+        uint8_t const* cur = cur_def + (bitpos >> 3);
+        bitpos &= 7;
+        if (cur < end) level_val = cur[0];
+        cur++;
+        if (level_bits > 8 - bitpos && cur < end) {
+          level_val |= cur[0] << 8;
+          cur++;
+          if (level_bits > 16 - bitpos && cur < end) level_val |= cur[0] << 16;
+        }
+        level_val = (level_val >> bitpos) & ((1 << level_bits) - 1);
+      }
+      level_run -= batch_len8 * 2;
+      cur_def += batch_len8 * level_bits;
+    } else {
+      // Repeated value
+      batch_len = min(batch_len, level_run >> 1);
+      level_run -= batch_len * 2;
+    }
+    if (t < batch_len) {
+      int idx                                      = value_count + t;
+      output[rolling_index<rolling_buf_size>(idx)] = level_val;
+    }
+    batch_coded_count += batch_len;
+    value_count += batch_len;
+  }
+
+  // update the stream info
+  if (!t) {
+    s->lvl_start[lvl]         = cur_def;
+    s->initial_rle_run[lvl]   = level_run;
+    s->initial_rle_value[lvl] = level_val;
+    s->lvl_count[lvl]         = value_count;
+  }
+}
+
+/**
+ * @brief Store a validity mask containing value_count bits into the output validity buffer of the
+ * page.
+ *
+ * @param[in,out] nesting_info The page/nesting information to store the mask in. The validity map
+ * offset is also updated
+ * @param[in,out] valid_map Pointer to bitmask to store validity information to
+ * @param[in] valid_mask The validity mask to be stored
+ * @param[in] value_count # of bits in the validity mask
+ */
+inline __device__ void store_validity(int valid_map_offset,
+                                      bitmask_type* valid_map,
+                                      uint32_t valid_mask,
+                                      int32_t value_count)
+{
+  int word_offset = valid_map_offset / 32;
+  int bit_offset  = valid_map_offset % 32;
+  // if we fit entirely in the output word
+  if (bit_offset + value_count <= 32) {
+    auto relevant_mask = static_cast<uint32_t>((static_cast<uint64_t>(1) << value_count) - 1);
+
+    if (relevant_mask == ~0) {
+      valid_map[word_offset] = valid_mask;
+    } else {
+      atomicAnd(valid_map + word_offset, ~(relevant_mask << bit_offset));
+      atomicOr(valid_map + word_offset, (valid_mask & relevant_mask) << bit_offset);
+    }
+  }
+  // we're going to spill over into the next word.
+  // note : writing both values here is the lazy/slow way.  we could be writing just
+  // the first word and rolling the remaining bits over into the next call.
+  // however, some basic performance tests shows almost no difference between these two
+  // methods. More detailed performance testing might be worthwhile here.
+  else {
+    uint32_t bits_left = 32 - bit_offset;
+
+    // first word. strip bits_left bits off the beginning and store that
+    uint32_t relevant_mask = ((1 << bits_left) - 1);
+    uint32_t mask_word0    = valid_mask & relevant_mask;
+    atomicAnd(valid_map + word_offset, ~(relevant_mask << bit_offset));
+    atomicOr(valid_map + word_offset, mask_word0 << bit_offset);
+
+    // second word. strip the remainder of the bits off the end and store that
+    relevant_mask       = ((1 << (value_count - bits_left)) - 1);
+    uint32_t mask_word1 = valid_mask & (relevant_mask << bits_left);
+    atomicAnd(valid_map + word_offset + 1, ~(relevant_mask));
+    atomicOr(valid_map + word_offset + 1, mask_word1 >> bits_left);
+  }
+}
+
+/**
+ * @brief Compute the nesting bounds within the hierarchy to add values to, and the definition level
+ * D to which we should considered them null or not.
+ *
+ * @param[out] start_depth The start nesting depth
+ * @param[out] end_depth The end nesting depth (inclusive)
+ * @param[out] d The definition level up to which added values are not-null. if t is out of bounds,
+ * d will be -1
+ * @param[in] s Local page information
+ * @param[in] rep Repetition level buffer
+ * @param[in] def Definition level buffer
+ * @param[in] input_value_count The current count of input level values we have processed
+ * @param[in] target_input_value_count The desired # of input level values we want to process
+ * @param[in] t Thread index
+ * @tparam rolling_buf_size Size of the cyclic buffer used to store value data
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ */
+template <int rolling_buf_size, typename level_t>
+inline __device__ void get_nesting_bounds(int& start_depth,
+                                          int& end_depth,
+                                          int& d,
+                                          page_state_s* s,
+                                          level_t const* const rep,
+                                          level_t const* const def,
+                                          int input_value_count,
+                                          int32_t target_input_value_count,
+                                          int t)
+{
+  start_depth = -1;
+  end_depth   = -1;
+  d           = -1;
+  if (input_value_count + t < target_input_value_count) {
+    int const index = rolling_index<rolling_buf_size>(input_value_count + t);
+    d               = static_cast<int>(def[index]);
+    // if we have repetition (there are list columns involved) we have to
+    // bound what nesting levels we apply values to
+    if (s->col.max_level[level_type::REPETITION] > 0) {
+      int r       = rep[index];
+      start_depth = s->nesting_info[r].start_depth;
+      end_depth   = s->nesting_info[d].end_depth;
+    }
+    // for columns without repetition (even ones involving structs) we always
+    // traverse the entire hierarchy.
+    else {
+      start_depth = 0;
+      end_depth   = s->col.max_nesting_depth - 1;
+    }
+  }
+}
+
+/**
+ * @brief Process a batch of incoming repetition/definition level values and generate
+ *        validity, nested column offsets (where appropriate) and decoding indices.
+ *
+ * @param[in] target_input_value_count The # of repetition/definition levels to process up to
+ * @param[in] s Local page information
+ * @param[out] sb Page state buffer output
+ * @param[in] rep Repetition level buffer
+ * @param[in] def Definition level buffer
+ * @param[in] t Thread index
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ * @tparam state_buf Typename of the `state_buf` (usually inferred)
+ * @tparam rolling_buf_size Size of the cyclic buffer used to store value data
+ */
+template <typename level_t, typename state_buf, int rolling_buf_size>
+__device__ void gpuUpdateValidityOffsetsAndRowIndices(int32_t target_input_value_count,
+                                                      page_state_s* s,
+                                                      state_buf* sb,
+                                                      level_t const* const rep,
+                                                      level_t const* const def,
+                                                      int t)
+{
+  // max nesting depth of the column
+  int const max_depth       = s->col.max_nesting_depth;
+  bool const has_repetition = s->col.max_level[level_type::REPETITION] > 0;
+  // how many (input) values we've processed in the page so far
+  int input_value_count = s->input_value_count;
+  // how many rows we've processed in the page so far
+  int input_row_count = s->input_row_count;
+
+  PageNestingDecodeInfo* nesting_info_base = s->nesting_info;
+
+  // process until we've reached the target
+  while (input_value_count < target_input_value_count) {
+    // determine the nesting bounds for this thread (the range of nesting depths we
+    // will generate new value indices and validity bits for)
+    int start_depth, end_depth, d;
+    get_nesting_bounds<rolling_buf_size, level_t>(
+      start_depth, end_depth, d, s, rep, def, input_value_count, target_input_value_count, t);
+
+    // 4 interesting things to track:
+    // thread_value_count : # of output values from the view of this thread
+    // warp_value_count   : # of output values for the whole warp
+    //
+    // thread_valid_count : # of valid values from the view of this thread
+    // warp_valid_count   : # of valid values for the whole warp
+    uint32_t thread_value_count, warp_value_count;
+    uint32_t thread_valid_count, warp_valid_count;
+
+    // track (page-relative) row index for the thread so we can compare against input bounds
+    // keep track of overall # of rows we've read.
+    int const is_new_row               = start_depth == 0 ? 1 : 0;
+    uint32_t const warp_row_count_mask = ballot(is_new_row);
+    int32_t const thread_row_index =
+      input_row_count + ((__popc(warp_row_count_mask & ((1 << t) - 1)) + is_new_row) - 1);
+    input_row_count += __popc(warp_row_count_mask);
+    // is this thread within read row bounds?
+    int const in_row_bounds = thread_row_index >= s->row_index_lower_bound &&
+                                  thread_row_index < (s->first_row + s->num_rows)
+                                ? 1
+                                : 0;
+
+    // compute warp and thread value counts
+    uint32_t const warp_count_mask =
+      ballot((0 >= start_depth && 0 <= end_depth) && in_row_bounds ? 1 : 0);
+
+    warp_value_count = __popc(warp_count_mask);
+    // Note : ((1 << t) - 1) implies "for all threads before me"
+    thread_value_count = __popc(warp_count_mask & ((1 << t) - 1));
+
+    // walk from 0 to max_depth
+    uint32_t next_thread_value_count, next_warp_value_count;
+    for (int s_idx = 0; s_idx < max_depth; s_idx++) {
+      PageNestingDecodeInfo* nesting_info = &nesting_info_base[s_idx];
+
+      // if we are within the range of nesting levels we should be adding value indices for
+      int const in_nesting_bounds =
+        ((s_idx >= start_depth && s_idx <= end_depth) && in_row_bounds) ? 1 : 0;
+
+      // everything up to the max_def_level is a non-null value
+      uint32_t const is_valid = d >= nesting_info->max_def_level && in_nesting_bounds ? 1 : 0;
+
+      // compute warp and thread valid counts
+      uint32_t const warp_valid_mask =
+        // for flat schemas, a simple ballot_sync gives us the correct count and bit positions
+        // because every value in the input matches to a value in the output
+        !has_repetition
+          ? ballot(is_valid)
+          :
+          // for nested schemas, it's more complicated.  This warp will visit 32 incoming values,
+          // however not all of them will necessarily represent a value at this nesting level. so
+          // the validity bit for thread t might actually represent output value t-6. the correct
+          // position for thread t's bit is cur_value_count. for cuda 11 we could use
+          // __reduce_or_sync(), but until then we have to do a warp reduce.
+          WarpReduceOr32(is_valid << thread_value_count);
+
+      thread_valid_count = __popc(warp_valid_mask & ((1 << thread_value_count) - 1));
+      warp_valid_count   = __popc(warp_valid_mask);
+
+      // if this is the value column emit an index for value decoding
+      if (is_valid && s_idx == max_depth - 1) {
+        int const src_pos = nesting_info->valid_count + thread_valid_count;
+        int const dst_pos = nesting_info->value_count + thread_value_count;
+        // nz_idx is a mapping of src buffer indices to destination buffer indices
+        sb->nz_idx[rolling_index<rolling_buf_size>(src_pos)] = dst_pos;
+      }
+
+      // compute warp and thread value counts for the -next- nesting level. we need to
+      // do this for nested schemas so that we can emit an offset for the -current- nesting
+      // level. more concretely : the offset for the current nesting level == current length of the
+      // next nesting level
+      if (s_idx < max_depth - 1) {
+        uint32_t const next_warp_count_mask =
+          ballot((s_idx + 1 >= start_depth && s_idx + 1 <= end_depth && in_row_bounds) ? 1 : 0);
+        next_warp_value_count   = __popc(next_warp_count_mask);
+        next_thread_value_count = __popc(next_warp_count_mask & ((1 << t) - 1));
+
+        // if we're -not- at a leaf column and we're within nesting/row bounds
+        // and we have a valid data_out pointer, it implies this is a list column, so
+        // emit an offset.
+        if (in_nesting_bounds && nesting_info->data_out != nullptr) {
+          int const idx             = nesting_info->value_count + thread_value_count;
+          cudf::size_type const ofs = nesting_info_base[s_idx + 1].value_count +
+                                      next_thread_value_count +
+                                      nesting_info_base[s_idx + 1].page_start_value;
+          (reinterpret_cast<cudf::size_type*>(nesting_info->data_out))[idx] = ofs;
+        }
+      }
+
+      // nested schemas always read and write to the same bounds (that is, read and write positions
+      // are already pre-bounded by first_row/num_rows). flat schemas will start reading at the
+      // first value, even if that is before first_row, because we cannot trivially jump to
+      // the correct position to start reading. since we are about to write the validity vector here
+      // we need to adjust our computed mask to take into account the write row bounds.
+      int const in_write_row_bounds =
+        !has_repetition
+          ? thread_row_index >= s->first_row && thread_row_index < (s->first_row + s->num_rows)
+          : in_row_bounds;
+      int const first_thread_in_write_range =
+        !has_repetition ? __ffs(ballot(in_write_row_bounds)) - 1 : 0;
+
+      // # of bits to of the validity mask to write out
+      int const warp_valid_mask_bit_count =
+        first_thread_in_write_range < 0 ? 0 : warp_value_count - first_thread_in_write_range;
+
+      // increment count of valid values, count of total values, and update validity mask
+      if (!t) {
+        if (nesting_info->valid_map != nullptr && warp_valid_mask_bit_count > 0) {
+          uint32_t const warp_output_valid_mask = warp_valid_mask >> first_thread_in_write_range;
+          store_validity(nesting_info->valid_map_offset,
+                         nesting_info->valid_map,
+                         warp_output_valid_mask,
+                         warp_valid_mask_bit_count);
+          nesting_info->valid_map_offset += warp_valid_mask_bit_count;
+          nesting_info->null_count += warp_valid_mask_bit_count - __popc(warp_output_valid_mask);
+        }
+        nesting_info->valid_count += warp_valid_count;
+        nesting_info->value_count += warp_value_count;
+      }
+
+      // propagate value counts for the next level
+      warp_value_count   = next_warp_value_count;
+      thread_value_count = next_thread_value_count;
+    }
+
+    input_value_count += min(32, (target_input_value_count - input_value_count));
+    __syncwarp();
+  }
+
+  // update
+  if (!t) {
+    // update valid value count for decoding and total # of values we've processed
+    s->nz_count          = nesting_info_base[max_depth - 1].valid_count;
+    s->input_value_count = input_value_count;
+    s->input_row_count   = input_row_count;
+  }
+}
+
+/**
+ * @brief Process repetition and definition levels up to the target count of leaf values.
+ *
+ * In order to decode actual leaf values from the input stream, we need to generate the
+ * list of non-null value positions (page_state_s::nz_idx). We do this by processing
+ * the repetition and definition level streams.  This process also generates validity information,
+ * and offset column values in the case of nested schemas. Because of the way the streams
+ * are encoded, this function may generate slightly more than target_leaf_count.
+ *
+ * Only runs on 1 warp.
+ *
+ * @param[in] s The local page state
+ * @param[out] sb Page state buffer output
+ * @param[in] target_leaf_count Target count of non-null leaf values to generate indices for
+ * @param[in] rep Repetition level buffer
+ * @param[in] def Definition level buffer
+ * @param[in] t Thread index
+ * @tparam rolling_buf_size Size of the cyclic buffer used to store value data
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ * @tparam state_buf Typename of the `state_buf` (usually inferred)
+ */
+template <int rolling_buf_size, typename level_t, typename state_buf>
+__device__ void gpuDecodeLevels(page_state_s* s,
+                                state_buf* sb,
+                                int32_t target_leaf_count,
+                                level_t* const rep,
+                                level_t* const def,
+                                int t)
+{
+  bool has_repetition = s->col.max_level[level_type::REPETITION] > 0;
+
+  constexpr int batch_size = 32;
+  int cur_leaf_count       = target_leaf_count;
+  while (s->error == 0 && s->nz_count < target_leaf_count &&
+         s->input_value_count < s->num_input_values) {
+    if (has_repetition) {
+      gpuDecodeStream<level_t, rolling_buf_size>(rep, s, cur_leaf_count, t, level_type::REPETITION);
+    }
+    gpuDecodeStream<level_t, rolling_buf_size>(def, s, cur_leaf_count, t, level_type::DEFINITION);
+    __syncwarp();
+
+    // because the rep and def streams are encoded separately, we cannot request an exact
+    // # of values to be decoded at once. we can only process the lowest # of decoded rep/def
+    // levels we get.
+    int actual_leaf_count = has_repetition ? min(s->lvl_count[level_type::REPETITION],
+                                                 s->lvl_count[level_type::DEFINITION])
+                                           : s->lvl_count[level_type::DEFINITION];
+
+    // process what we got back
+    gpuUpdateValidityOffsetsAndRowIndices<level_t, state_buf, rolling_buf_size>(
+      actual_leaf_count, s, sb, rep, def, t);
+    cur_leaf_count = actual_leaf_count + batch_size;
+    __syncwarp();
+  }
+}
+
+/**
+ * @brief Parse the beginning of the level section (definition or repetition),
+ * initializes the initial RLE run & value, and returns the section length
+ *
+ * @param[in,out] s The page state
+ * @param[in] cur The current data position
+ * @param[in] end The end of the data
+ * @param[in] lvl Enum indicating whether this is to initialize repetition or definition level data
+ *
+ * @return The length of the section
+ */
+inline __device__ uint32_t InitLevelSection(page_state_s* s,
+                                            uint8_t const* cur,
+                                            uint8_t const* end,
+                                            level_type lvl)
+{
+  int32_t len;
+  int const level_bits = s->col.level_bits[lvl];
+  auto const encoding  = lvl == level_type::DEFINITION ? s->page.definition_level_encoding
+                                                       : s->page.repetition_level_encoding;
+
+  auto start = cur;
+
+  auto init_rle = [s, lvl, end, level_bits](uint8_t const* cur, uint8_t const* end) {
+    uint32_t const run      = get_vlq32(cur, end);
+    s->initial_rle_run[lvl] = run;
+    if (!(run & 1)) {
+      if (cur < end) {
+        int v = cur[0];
+        cur++;
+        if (level_bits > 8) {
+          v |= ((cur < end) ? cur[0] : 0) << 8;
+          cur++;
+        }
+        s->initial_rle_value[lvl] = v;
+      } else {
+        s->initial_rle_value[lvl] = 0;
+      }
+    }
+    s->lvl_start[lvl] = cur;
+
+    if (cur > end) { s->set_error_code(decode_error::LEVEL_STREAM_OVERRUN); }
+  };
+
+  // this is a little redundant. if level_bits == 0, then nothing should be encoded
+  // for the level, but some V2 files in the wild violate this and encode the data anyway.
+  // thus we will handle V2 headers separately.
+  if ((s->page.flags & PAGEINFO_FLAGS_V2) != 0 && (len = s->page.lvl_bytes[lvl]) != 0) {
+    // V2 only uses RLE encoding so no need to check encoding
+    s->abs_lvl_start[lvl] = cur;
+    init_rle(cur, cur + len);
+  } else if (level_bits == 0) {
+    len                       = 0;
+    s->initial_rle_run[lvl]   = s->page.num_input_values * 2;  // repeated value
+    s->initial_rle_value[lvl] = 0;
+    s->lvl_start[lvl]         = cur;
+    s->abs_lvl_start[lvl]     = cur;
+  } else if (encoding == Encoding::RLE) {  // V1 header with RLE encoding
+    if (cur + 4 < end) {
+      len = (cur[0]) + (cur[1] << 8) + (cur[2] << 16) + (cur[3] << 24);
+      cur += 4;
+      s->abs_lvl_start[lvl] = cur;
+      init_rle(cur, cur + len);
+      // add back the 4 bytes for the length
+      len += 4;
+    } else {
+      len = 0;
+      s->set_error_code(decode_error::LEVEL_STREAM_OVERRUN);
+    }
+  } else if (encoding == Encoding::BIT_PACKED) {
+    len                       = (s->page.num_input_values * level_bits + 7) >> 3;
+    s->initial_rle_run[lvl]   = ((s->page.num_input_values + 7) >> 3) * 2 + 1;  // literal run
+    s->initial_rle_value[lvl] = 0;
+    s->lvl_start[lvl]         = cur;
+    s->abs_lvl_start[lvl]     = cur;
+  } else {
+    len = 0;
+    s->set_error_code(decode_error::UNSUPPORTED_ENCODING);
+  }
+
+  s->abs_lvl_end[lvl] = start + len;
+
+  return static_cast<uint32_t>(len);
+}
+
+/**
+ * @brief Functor for setupLocalPageInfo that always returns true.
+ */
+struct all_types_filter {
+  __device__ inline bool operator()(PageInfo const& page) { return true; }
+};
+
+/**
+ * @brief Functor for setupLocalPageInfo that takes a mask of allowed types.
+ */
+struct mask_filter {
+  int mask;
+  __device__ inline bool operator()(PageInfo const& page) { return (page.kernel_mask & mask) != 0; }
+};
+
+/**
+ * @brief Sets up block-local page state information from the global pages.
+ *
+ * @param[in, out] s The local page state to be filled in
+ * @param[in] p The global page to be copied from
+ * @param[in] chunks The global list of chunks
+ * @param[in] min_row Crop all rows below min_row
+ * @param[in] num_rows Maximum number of rows to read
+ * @param[in] filter Filtering function used to decide which pages to operate on
+ * @param[in] is_decode_step If we are setting up for the decode step (instead of the preprocess)
+ * @tparam Filter Function that takes a PageInfo reference and returns true if the given page should
+ * be operated on Currently only used by gpuComputePageSizes step)
+ * @return True if this page should be processed further
+ */
+template <typename Filter>
+inline __device__ bool setupLocalPageInfo(page_state_s* const s,
+                                          PageInfo const* p,
+                                          device_span<ColumnChunkDesc const> chunks,
+                                          size_t min_row,
+                                          size_t num_rows,
+                                          Filter filter,
+                                          bool is_decode_step)
+{
+  int t = threadIdx.x;
+
+  // Fetch page info
+  if (!t) {
+    s->page         = *p;
+    s->nesting_info = nullptr;
+    s->col          = chunks[s->page.chunk_idx];
+  }
+  __syncthreads();
+
+  // return false if this is a dictionary page or it does not pass the filter condition
+  if ((s->page.flags & PAGEINFO_FLAGS_DICTIONARY) != 0 || !filter(s->page)) { return false; }
+
+  // our starting row (absolute index) is
+  // col.start_row == absolute row index
+  // page.chunk-row == relative row index within the chunk
+  size_t const page_start_row = s->col.start_row + s->page.chunk_row;
+
+  // if we can use the nesting decode cache, set it up now
+  auto const can_use_decode_cache = s->page.nesting_info_size <= max_cacheable_nesting_decode_info;
+  if (can_use_decode_cache) {
+    int depth = 0;
+    while (depth < s->page.nesting_info_size) {
+      int const thread_depth = depth + t;
+      if (thread_depth < s->page.nesting_info_size) {
+        // these values need to be copied over from global
+        s->nesting_decode_cache[thread_depth].max_def_level =
+          s->page.nesting_decode[thread_depth].max_def_level;
+        s->nesting_decode_cache[thread_depth].page_start_value =
+          s->page.nesting_decode[thread_depth].page_start_value;
+        s->nesting_decode_cache[thread_depth].start_depth =
+          s->page.nesting_decode[thread_depth].start_depth;
+        s->nesting_decode_cache[thread_depth].end_depth =
+          s->page.nesting_decode[thread_depth].end_depth;
+      }
+      depth += blockDim.x;
+    }
+  }
+
+  if (!t) {
+    s->nesting_info = can_use_decode_cache ? s->nesting_decode_cache : s->page.nesting_decode;
+
+    // NOTE: s->page.num_rows, s->col.chunk_row, s->first_row and s->num_rows will be
+    // invalid/bogus during first pass of the preprocess step for nested types. this is ok
+    // because we ignore these values in that stage.
+    auto const max_row = min_row + num_rows;
+
+    // if we are totally outside the range of the input, do nothing
+    if ((page_start_row > max_row) || (page_start_row + s->page.num_rows < min_row)) {
+      s->first_row = 0;
+      s->num_rows  = 0;
+    }
+    // otherwise
+    else {
+      s->first_row             = page_start_row >= min_row ? 0 : min_row - page_start_row;
+      auto const max_page_rows = s->page.num_rows - s->first_row;
+      s->num_rows              = (page_start_row + s->first_row) + max_page_rows <= max_row
+                                   ? max_page_rows
+                                   : max_row - (page_start_row + s->first_row);
+    }
+  }
+
+  __syncthreads();
+
+  // zero counts
+  int depth = 0;
+  while (depth < s->page.num_output_nesting_levels) {
+    int const thread_depth = depth + t;
+    if (thread_depth < s->page.num_output_nesting_levels) {
+      s->nesting_info[thread_depth].valid_count = 0;
+      s->nesting_info[thread_depth].value_count = 0;
+      s->nesting_info[thread_depth].null_count  = 0;
+    }
+    depth += blockDim.x;
+  }
+  __syncthreads();
+
+  // if we have no work to do (eg, in a skip_rows/num_rows case) in this page.
+  //
+  // corner case: in the case of lists, we can have pages that contain "0" rows if the current row
+  // starts before this page and ends after this page:
+  //       P0        P1        P2
+  //  |---------|---------|----------|
+  //        ^------------------^
+  //      row start           row end
+  // P1 will contain 0 rows
+  //
+  // NOTE: this check needs to be done after the null counts have been zeroed out
+  bool const has_repetition = s->col.max_level[level_type::REPETITION] > 0;
+  if (is_decode_step && s->num_rows == 0 &&
+      !(has_repetition && (is_bounds_page(s, min_row, num_rows, has_repetition) ||
+                           is_page_contained(s, min_row, num_rows)))) {
+    return false;
+  }
+
+  if (!t) {
+    s->reset_error_code();
+
+    // IMPORTANT : nested schemas can have 0 rows in a page but still have
+    // values. The case is:
+    // - On page N-1, the last row starts, with 2/6 values encoded
+    // - On page N, the remaining 4/6 values are encoded, but there are no new rows.
+    // if (s->page.num_input_values > 0 && s->page.num_rows > 0) {
+    if (s->page.num_input_values > 0) {
+      uint8_t* cur = s->page.page_data;
+      uint8_t* end = cur + s->page.uncompressed_page_size;
+
+      uint32_t dtype_len_out = s->col.data_type >> 3;
+      s->ts_scale            = 0;
+      // Validate data type
+      auto const data_type = s->col.data_type & 7;
+      switch (data_type) {
+        case BOOLEAN:
+          s->dtype_len = 1;  // Boolean are stored as 1 byte on the output
+          break;
+        case INT32: [[fallthrough]];
+        case FLOAT: s->dtype_len = 4; break;
+        case INT64:
+          if (s->col.ts_clock_rate) {
+            int32_t units = 0;
+            // Duration types are not included because no scaling is done when reading
+            if (s->col.converted_type == TIMESTAMP_MILLIS) {
+              units = cudf::timestamp_ms::period::den;
+            } else if (s->col.converted_type == TIMESTAMP_MICROS) {
+              units = cudf::timestamp_us::period::den;
+            } else if (s->col.logical_type.TIMESTAMP.unit.isset.NANOS) {
+              units = cudf::timestamp_ns::period::den;
+            }
+            if (units and units != s->col.ts_clock_rate) {
+              s->ts_scale = (s->col.ts_clock_rate < units) ? -(units / s->col.ts_clock_rate)
+                                                           : (s->col.ts_clock_rate / units);
+            }
+          }
+          [[fallthrough]];
+        case DOUBLE: s->dtype_len = 8; break;
+        case INT96: s->dtype_len = 12; break;
+        case BYTE_ARRAY:
+          if (s->col.converted_type == DECIMAL) {
+            auto const decimal_precision = s->col.decimal_precision;
+            s->dtype_len                 = [decimal_precision]() {
+              if (decimal_precision <= MAX_DECIMAL32_PRECISION) {
+                return sizeof(int32_t);
+              } else if (decimal_precision <= MAX_DECIMAL64_PRECISION) {
+                return sizeof(int64_t);
+              } else {
+                return sizeof(__int128_t);
+              }
+            }();
+          } else {
+            s->dtype_len = sizeof(string_index_pair);
+          }
+          break;
+        default:  // FIXED_LEN_BYTE_ARRAY:
+          s->dtype_len = dtype_len_out;
+          if (s->dtype_len <= 0) { s->set_error_code(decode_error::INVALID_DATA_TYPE); }
+          break;
+      }
+      // Special check for downconversions
+      s->dtype_len_in = s->dtype_len;
+      if (data_type == FIXED_LEN_BYTE_ARRAY) {
+        if (s->col.converted_type == DECIMAL) {
+          s->dtype_len = [dtype_len = s->dtype_len]() {
+            if (dtype_len <= sizeof(int32_t)) {
+              return sizeof(int32_t);
+            } else if (dtype_len <= sizeof(int64_t)) {
+              return sizeof(int64_t);
+            } else {
+              return sizeof(__int128_t);
+            }
+          }();
+        } else {
+          s->dtype_len = sizeof(string_index_pair);
+        }
+      } else if (data_type == INT32) {
+        if (dtype_len_out == 1) {
+          // INT8 output
+          s->dtype_len = 1;
+        } else if (dtype_len_out == 2) {
+          // INT16 output
+          s->dtype_len = 2;
+        } else if (s->col.converted_type == TIME_MILLIS) {
+          // INT64 output
+          s->dtype_len = 8;
+        }
+      } else if (data_type == BYTE_ARRAY && dtype_len_out == 4) {
+        s->dtype_len = 4;  // HASH32 output
+      } else if (data_type == INT96) {
+        s->dtype_len = 8;  // Convert to 64-bit timestamp
+      }
+
+      // during the decoding step we need to offset the global output buffers
+      // for each level of nesting so that we write to the section this page
+      // is responsible for.
+      // - for flat schemas, we can do this directly by using row counts
+      // - for nested schemas, these offsets are computed during the preprocess step
+      //
+      // NOTE: in a chunked read situation, s->col.column_data_base and s->col.valid_map_base
+      // will be aliased to memory that has been freed when we get here in the non-decode step, so
+      // we cannot check against nullptr.  we'll just check a flag directly.
+      if (is_decode_step) {
+        int max_depth = s->col.max_nesting_depth;
+        for (int idx = 0; idx < max_depth; idx++) {
+          PageNestingDecodeInfo* nesting_info = &s->nesting_info[idx];
+
+          size_t output_offset;
+          // schemas without lists
+          if (s->col.max_level[level_type::REPETITION] == 0) {
+            output_offset = page_start_row >= min_row ? page_start_row - min_row : 0;
+          }
+          // for schemas with lists, we've already got the exact value precomputed
+          else {
+            output_offset = nesting_info->page_start_value;
+          }
+
+          if (s->col.column_data_base != nullptr) {
+            nesting_info->data_out = static_cast<uint8_t*>(s->col.column_data_base[idx]);
+            if (s->col.column_string_base != nullptr) {
+              nesting_info->string_out = static_cast<uint8_t*>(s->col.column_string_base[idx]);
+            }
+
+            nesting_info->data_out = static_cast<uint8_t*>(s->col.column_data_base[idx]);
+
+            if (nesting_info->data_out != nullptr) {
+              // anything below max depth with a valid data pointer must be a list, so the
+              // element size is the size of the offset type.
+              uint32_t len = idx < max_depth - 1 ? sizeof(cudf::size_type) : s->dtype_len;
+              // if this is a string column, then dtype_len is a lie. data will be offsets rather
+              // than (ptr,len) tuples.
+              if (is_string_col(s->col)) { len = sizeof(cudf::size_type); }
+              nesting_info->data_out += (output_offset * len);
+            }
+            if (nesting_info->string_out != nullptr) {
+              nesting_info->string_out += s->page.str_offset;
+            }
+            nesting_info->valid_map = s->col.valid_map_base[idx];
+            if (nesting_info->valid_map != nullptr) {
+              nesting_info->valid_map += output_offset >> 5;
+              nesting_info->valid_map_offset = (int32_t)(output_offset & 0x1f);
+            }
+          }
+        }
+      }
+      s->first_output_value = 0;
+
+      // Find the compressed size of repetition levels
+      cur += InitLevelSection(s, cur, end, level_type::REPETITION);
+      // Find the compressed size of definition levels
+      cur += InitLevelSection(s, cur, end, level_type::DEFINITION);
+
+      s->dict_bits = 0;
+      s->dict_base = nullptr;
+      s->dict_size = 0;
+      // NOTE:  if additional encodings are supported in the future, modifications must
+      // be made to is_supported_encoding() in reader_impl_preprocess.cu
+      switch (s->page.encoding) {
+        case Encoding::PLAIN_DICTIONARY:
+        case Encoding::RLE_DICTIONARY:
+          // RLE-packed dictionary indices, first byte indicates index length in bits
+          if (((s->col.data_type & 7) == BYTE_ARRAY) && (s->col.str_dict_index)) {
+            // String dictionary: use index
+            s->dict_base = reinterpret_cast<uint8_t const*>(s->col.str_dict_index);
+            s->dict_size = s->col.page_info[0].num_input_values * sizeof(string_index_pair);
+          } else {
+            s->dict_base =
+              s->col.page_info[0].page_data;  // dictionary is always stored in the first page
+            s->dict_size = s->col.page_info[0].uncompressed_page_size;
+          }
+          s->dict_run  = 0;
+          s->dict_val  = 0;
+          s->dict_bits = (cur < end) ? *cur++ : 0;
+          if (s->dict_bits > 32 || !s->dict_base) {
+            s->set_error_code(decode_error::INVALID_DICT_WIDTH);
+          }
+          break;
+        case Encoding::PLAIN:
+          s->dict_size = static_cast<int32_t>(end - cur);
+          s->dict_val  = 0;
+          if ((s->col.data_type & 7) == BOOLEAN) { s->dict_run = s->dict_size * 2 + 1; }
+          break;
+        case Encoding::RLE: {
+          // first 4 bytes are length of RLE data
+          int const len = (cur[0]) + (cur[1] << 8) + (cur[2] << 16) + (cur[3] << 24);
+          cur += 4;
+          if (cur + len > end) { s->set_error_code(decode_error::DATA_STREAM_OVERRUN); }
+          s->dict_run = 0;
+        } break;
+        case Encoding::DELTA_BINARY_PACKED:
+          // nothing to do, just don't error
+          break;
+        default: {
+          s->set_error_code(decode_error::UNSUPPORTED_ENCODING);
+          break;
+        }
+      }
+      if (cur > end) { s->set_error_code(decode_error::DATA_STREAM_OVERRUN); }
+      s->lvl_end    = cur;
+      s->data_start = cur;
+      s->data_end   = end;
+    } else {
+      s->set_error_code(decode_error::EMPTY_PAGE);
+    }
+
+    s->lvl_count[level_type::REPETITION] = 0;
+    s->lvl_count[level_type::DEFINITION] = 0;
+    s->nz_count                          = 0;
+    s->num_input_values                  = s->page.num_input_values;
+    s->dict_pos                          = 0;
+    s->src_pos                           = 0;
+
+    // for flat hierarchies, we can't know how many leaf values to skip unless we do a full
+    // preprocess of the definition levels (since nulls will have no actual decodable value, there
+    // is no direct correlation between # of rows and # of decodable values).  so we will start
+    // processing at the beginning of the value stream and disregard any indices that start
+    // before the first row.
+    if (s->col.max_level[level_type::REPETITION] == 0) {
+      s->page.skipped_values      = 0;
+      s->page.skipped_leaf_values = 0;
+      s->input_value_count        = 0;
+      s->input_row_count          = 0;
+      s->input_leaf_count         = 0;
+
+      s->row_index_lower_bound = -1;
+    }
+    // for nested hierarchies, we have run a preprocess that lets us skip directly to the values
+    // we need to start decoding at
+    else {
+      // input_row_count translates to "how many rows we have processed so far", so since we are
+      // skipping directly to where we want to start decoding, set it to first_row
+      s->input_row_count = s->first_row;
+
+      // return the lower bound to compare (page-relative) thread row index against. Explanation:
+      // In the case of nested schemas, rows can span page boundaries.  That is to say,
+      // we can encounter the first value for row X on page M, but the last value for page M
+      // might not be the last value for row X. page M+1 (or further) may contain the last value.
+      //
+      // This means that the first values we encounter for a given page (M+1) may not belong to the
+      // row indicated by chunk_row, but to the row before it that spanned page boundaries. If that
+      // previous row is within the overall row bounds, include the values by allowing relative row
+      // index -1
+      int const max_row = (min_row + num_rows) - 1;
+      if (min_row < page_start_row && max_row >= page_start_row - 1) {
+        s->row_index_lower_bound = -1;
+      } else {
+        s->row_index_lower_bound = s->first_row;
+      }
+
+      // if we're in the decoding step, jump directly to the first
+      // value we care about
+      if (is_decode_step) {
+        s->input_value_count = s->page.skipped_values > -1 ? s->page.skipped_values : 0;
+      } else {
+        s->input_value_count = 0;
+        s->input_leaf_count  = 0;
+        s->page.skipped_values =
+          -1;  // magic number to indicate it hasn't been set for use inside UpdatePageSizes
+        s->page.skipped_leaf_values = 0;
+      }
+    }
+
+    __threadfence_block();
+  }
+  __syncthreads();
+
+  return true;
+}
+
+}  // namespace cudf::io::parquet::gpu
diff --git a/cpp/src/io/parquet/page_delta_decode.cu b/cpp/src/io/parquet/page_delta_decode.cu
new file mode 100644
index 0000000..2b78dea
--- /dev/null
+++ b/cpp/src/io/parquet/page_delta_decode.cu
@@ -0,0 +1,187 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "delta_binary.cuh"
+#include "page_string_utils.cuh"
+#include "parquet_gpu.hpp"
+
+#include <cudf/detail/utilities/cuda.cuh>
+
+#include <rmm/exec_policy.hpp>
+#include <thrust/transform_scan.h>
+
+namespace cudf::io::parquet::gpu {
+
+namespace {
+
+// Decode page data that is DELTA_BINARY_PACKED encoded. This encoding is
+// only used for int32 and int64 physical types (and appears to only be used
+// with V2 page headers; see https://www.mail-archive.com/dev@parquet.apache.org/msg11826.html).
+// this kernel only needs 96 threads (3 warps)(for now).
+template <typename level_t>
+__global__ void __launch_bounds__(96)
+  gpuDecodeDeltaBinary(PageInfo* pages,
+                       device_span<ColumnChunkDesc const> chunks,
+                       size_t min_row,
+                       size_t num_rows,
+                       int32_t* error_code)
+{
+  using cudf::detail::warp_size;
+  __shared__ __align__(16) delta_binary_decoder db_state;
+  __shared__ __align__(16) page_state_s state_g;
+  __shared__ __align__(16) page_state_buffers_s<delta_rolling_buf_size, 0, 0> state_buffers;
+
+  page_state_s* const s = &state_g;
+  auto* const sb        = &state_buffers;
+  int const page_idx    = blockIdx.x;
+  int const t           = threadIdx.x;
+  int const lane_id     = t % warp_size;
+  auto* const db        = &db_state;
+  [[maybe_unused]] null_count_back_copier _{s, t};
+
+  if (!setupLocalPageInfo(s,
+                          &pages[page_idx],
+                          chunks,
+                          min_row,
+                          num_rows,
+                          mask_filter{KERNEL_MASK_DELTA_BINARY},
+                          true)) {
+    return;
+  }
+
+  bool const has_repetition = s->col.max_level[level_type::REPETITION] > 0;
+
+  // copying logic from gpuDecodePageData.
+  PageNestingDecodeInfo const* nesting_info_base = s->nesting_info;
+
+  __shared__ level_t rep[delta_rolling_buf_size];  // circular buffer of repetition level values
+  __shared__ level_t def[delta_rolling_buf_size];  // circular buffer of definition level values
+
+  // skipped_leaf_values will always be 0 for flat hierarchies.
+  uint32_t const skipped_leaf_values = s->page.skipped_leaf_values;
+
+  // initialize delta state
+  if (t == 0) { db->init_binary_block(s->data_start, s->data_end); }
+  __syncthreads();
+
+  auto const batch_size = db->values_per_mb;
+
+  // if skipped_leaf_values is non-zero, then we need to decode up to the first mini-block
+  // that has a value we need.
+  if (skipped_leaf_values > 0) { db->skip_values(skipped_leaf_values); }
+
+  while (s->error == 0 &&
+         (s->input_value_count < s->num_input_values || s->src_pos < s->nz_count)) {
+    uint32_t target_pos;
+    uint32_t const src_pos = s->src_pos;
+
+    if (t < 2 * warp_size) {  // warp0..1
+      target_pos = min(src_pos + 2 * batch_size, s->nz_count + batch_size);
+    } else {  // warp2
+      target_pos = min(s->nz_count, src_pos + batch_size);
+    }
+    __syncthreads();
+
+    // warp0 will decode the rep/def levels, warp1 will unpack a mini-batch of deltas.
+    // warp2 waits one cycle for warps 0/1 to produce a batch, and then stuffs values
+    // into the proper location in the output.
+    if (t < warp_size) {
+      // warp 0
+      // decode repetition and definition levels.
+      // - update validity vectors
+      // - updates offsets (for nested columns)
+      // - produces non-NULL value indices in s->nz_idx for subsequent decoding
+      gpuDecodeLevels<delta_rolling_buf_size, level_t>(s, sb, target_pos, rep, def, t);
+    } else if (t < 2 * warp_size) {
+      // warp 1
+      db->decode_batch();
+
+    } else if (src_pos < target_pos) {
+      // warp 2
+      // nesting level that is storing actual leaf values
+      int const leaf_level_index = s->col.max_nesting_depth - 1;
+
+      // process the mini-block in batches of 32
+      for (uint32_t sp = src_pos + lane_id; sp < src_pos + batch_size; sp += 32) {
+        // the position in the output column/buffer
+        int32_t dst_pos = sb->nz_idx[rolling_index<delta_rolling_buf_size>(sp)];
+
+        // handle skip_rows here. flat hierarchies can just skip up to first_row.
+        if (!has_repetition) { dst_pos -= s->first_row; }
+
+        // place value for this thread
+        if (dst_pos >= 0 && sp < target_pos) {
+          void* const dst = nesting_info_base[leaf_level_index].data_out + dst_pos * s->dtype_len;
+          switch (s->dtype_len) {
+            case 1:
+              *static_cast<int8_t*>(dst) =
+                db->value[rolling_index<delta_rolling_buf_size>(sp + skipped_leaf_values)];
+              break;
+            case 2:
+              *static_cast<int16_t*>(dst) =
+                db->value[rolling_index<delta_rolling_buf_size>(sp + skipped_leaf_values)];
+              break;
+            case 4:
+              *static_cast<int32_t*>(dst) =
+                db->value[rolling_index<delta_rolling_buf_size>(sp + skipped_leaf_values)];
+              break;
+            case 8:
+              *static_cast<int64_t*>(dst) =
+                db->value[rolling_index<delta_rolling_buf_size>(sp + skipped_leaf_values)];
+              break;
+          }
+        }
+      }
+
+      if (lane_id == 0) { s->src_pos = src_pos + batch_size; }
+    }
+    __syncthreads();
+  }
+
+  if (t == 0 and s->error != 0) {
+    cuda::atomic_ref<int32_t, cuda::thread_scope_device> ref{*error_code};
+    ref.fetch_or(s->error, cuda::std::memory_order_relaxed);
+  }
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::io::parquet::gpu::DecodeDeltaBinary
+ */
+void __host__ DecodeDeltaBinary(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                                cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                                size_t num_rows,
+                                size_t min_row,
+                                int level_type_size,
+                                int32_t* error_code,
+                                rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(pages.size() > 0, "There is no page to decode");
+
+  dim3 dim_block(96, 1);
+  dim3 dim_grid(pages.size(), 1);  // 1 threadblock per page
+
+  if (level_type_size == 1) {
+    gpuDecodeDeltaBinary<uint8_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, error_code);
+  } else {
+    gpuDecodeDeltaBinary<uint16_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, error_code);
+  }
+}
+
+}  // namespace cudf::io::parquet::gpu
diff --git a/cpp/src/io/parquet/page_enc.cu b/cpp/src/io/parquet/page_enc.cu
new file mode 100644
index 0000000..fe0dbb8
--- /dev/null
+++ b/cpp/src/io/parquet/page_enc.cu
@@ -0,0 +1,2349 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "parquet_gpu.cuh"
+
+#include <io/utilities/block_utils.cuh>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <cub/cub.cuh>
+
+#include <cuda/std/chrono>
+
+#include <thrust/binary_search.h>
+#include <thrust/gather.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/merge.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+namespace gpu {
+
+namespace {
+
+using ::cudf::detail::device_2dspan;
+
+constexpr uint32_t rle_buffer_size = (1 << 9);
+
+// do not truncate statistics
+constexpr int32_t NO_TRUNC_STATS = 0;
+
+// minimum scratch space required for encoding statistics
+constexpr size_t MIN_STATS_SCRATCH_SIZE = sizeof(__int128_t);
+
+// mask to determine lane id
+constexpr uint32_t WARP_MASK = cudf::detail::warp_size - 1;
+
+// currently 64k - 1
+constexpr uint32_t MAX_GRID_Y_SIZE = (1 << 16) - 1;
+
+// space needed for RLE length field
+constexpr int RLE_LENGTH_FIELD_LEN = 4;
+
+struct frag_init_state_s {
+  parquet_column_device_view col;
+  PageFragment frag;
+};
+
+struct page_enc_state_s {
+  uint8_t* cur;          //!< current output ptr
+  uint8_t* rle_out;      //!< current RLE write ptr
+  uint8_t* rle_len_pos;  //!< position to write RLE length (for V2 boolean data)
+  uint32_t rle_run;      //!< current RLE run
+  uint32_t run_val;      //!< current RLE run value
+  uint32_t rle_pos;      //!< RLE encoder positions
+  uint32_t rle_numvals;  //!< RLE input value count
+  uint32_t rle_lit_count;
+  uint32_t rle_rpt_count;
+  uint32_t page_start_val;
+  uint32_t chunk_start_val;
+  volatile uint32_t rpt_map[4];
+  volatile uint32_t scratch_red[32];
+  EncPage page;
+  EncColumnChunk ck;
+  parquet_column_device_view col;
+  uint32_t vals[rle_buffer_size];
+};
+
+/**
+ * @brief Returns the size of the type in the Parquet file.
+ */
+constexpr uint32_t physical_type_len(Type physical_type, type_id id)
+{
+  if (physical_type == FIXED_LEN_BYTE_ARRAY and id == type_id::DECIMAL128) {
+    return sizeof(__int128_t);
+  }
+  switch (physical_type) {
+    case INT96: return 12u;
+    case INT64:
+    case DOUBLE: return sizeof(int64_t);
+    case BOOLEAN: return 1u;
+    default: return sizeof(int32_t);
+  }
+}
+
+constexpr uint32_t max_RLE_page_size(uint8_t value_bit_width, uint32_t num_values)
+{
+  if (value_bit_width == 0) return 0;
+
+  // Run length = 4, max(rle/bitpack header) = 5. bitpacking worst case is one byte every 8 values
+  // (because bitpacked runs are a multiple of 8). Don't need to round up the last term since that
+  // overhead is accounted for in the '5'.
+  // TODO: this formula does not take into account the data for RLE runs. The worst realistic case
+  // is repeated runs of 8 bitpacked, 2 RLE values. In this case, the formula would be
+  //   0.8 * (num_values * bw / 8 + num_values / 8) + 0.2 * (num_values / 2 * (1 + (bw+7)/8))
+  // for bw < 8 the above value will be larger than below, but in testing it seems like for low
+  // bitwidths it's hard to get the pathological 8:2 split.
+  // If the encoder starts printing the data corruption warning, then this will need to be
+  // revisited.
+  return 4 + 5 + util::div_rounding_up_unsafe(num_values * value_bit_width, 8) + (num_values / 8);
+}
+
+// subtract b from a, but return 0 if this would underflow
+constexpr size_t underflow_safe_subtract(size_t a, size_t b)
+{
+  if (b > a) { return 0; }
+  return a - b;
+}
+
+void __device__ init_frag_state(frag_init_state_s* const s,
+                                uint32_t fragment_size,
+                                int part_end_row)
+{
+  // frag.num_rows = fragment_size except for the last fragment in partition which can be
+  // smaller. num_rows is fixed but fragment size could be larger if the data is strings or
+  // nested.
+  s->frag.num_rows           = min(fragment_size, part_end_row - s->frag.start_row);
+  s->frag.num_dict_vals      = 0;
+  s->frag.fragment_data_size = 0;
+  s->frag.dict_data_size     = 0;
+
+  s->frag.start_value_idx  = row_to_value_idx(s->frag.start_row, s->col);
+  auto const end_value_idx = row_to_value_idx(s->frag.start_row + s->frag.num_rows, s->col);
+  s->frag.num_leaf_values  = end_value_idx - s->frag.start_value_idx;
+
+  if (s->col.level_offsets != nullptr) {
+    // For nested schemas, the number of values in a fragment is not directly related to the
+    // number of encoded data elements or the number of rows.  It is simply the number of
+    // repetition/definition values which together encode validity and nesting information.
+    auto const first_level_val_idx = s->col.level_offsets[s->frag.start_row];
+    auto const last_level_val_idx  = s->col.level_offsets[s->frag.start_row + s->frag.num_rows];
+    s->frag.num_values             = last_level_val_idx - first_level_val_idx;
+  } else {
+    s->frag.num_values = s->frag.num_rows;
+  }
+}
+
+template <int block_size>
+void __device__ calculate_frag_size(frag_init_state_s* const s, int t)
+{
+  using block_reduce = cub::BlockReduce<uint32_t, block_size>;
+  __shared__ typename block_reduce::TempStorage reduce_storage;
+
+  auto const physical_type   = s->col.physical_type;
+  auto const leaf_type       = s->col.leaf_column->type().id();
+  auto const dtype_len       = physical_type_len(physical_type, leaf_type);
+  auto const nvals           = s->frag.num_leaf_values;
+  auto const start_value_idx = s->frag.start_value_idx;
+
+  for (uint32_t i = 0; i < nvals; i += block_size) {
+    auto const val_idx  = start_value_idx + i + t;
+    auto const is_valid = i + t < nvals && val_idx < s->col.leaf_column->size() &&
+                          s->col.leaf_column->is_valid(val_idx);
+    uint32_t len;
+    if (is_valid) {
+      len = dtype_len;
+      if (physical_type == BYTE_ARRAY) {
+        switch (leaf_type) {
+          case type_id::STRING: {
+            auto str = s->col.leaf_column->element<string_view>(val_idx);
+            len += str.size_bytes();
+          } break;
+          case type_id::LIST: {
+            auto list_element =
+              get_element<statistics::byte_array_view>(*s->col.leaf_column, val_idx);
+            len += list_element.size_bytes();
+          } break;
+          default: CUDF_UNREACHABLE("Unsupported data type for leaf column");
+        }
+      }
+    } else {
+      len = 0;
+    }
+
+    len = block_reduce(reduce_storage).Sum(len);
+    if (t == 0) { s->frag.fragment_data_size += len; }
+    __syncthreads();
+    // page fragment size must fit in a 32-bit signed integer
+    if (s->frag.fragment_data_size > std::numeric_limits<int32_t>::max()) {
+      CUDF_UNREACHABLE("page fragment size exceeds maximum for i32");
+    }
+  }
+}
+
+Encoding __device__ determine_encoding(PageType page_type,
+                                       Type physical_type,
+                                       bool use_dictionary,
+                                       bool write_v2_headers)
+{
+  // NOTE: For dictionary encoding, parquet v2 recommends using PLAIN in dictionary page and
+  // RLE_DICTIONARY in data page, but parquet v1 uses PLAIN_DICTIONARY in both dictionary and
+  // data pages (actual encoding is identical).
+  switch (page_type) {
+    case PageType::DATA_PAGE: return use_dictionary ? Encoding::PLAIN_DICTIONARY : Encoding::PLAIN;
+    case PageType::DATA_PAGE_V2:
+      // TODO need to work in delta encodings here when they're added
+      return physical_type == BOOLEAN ? Encoding::RLE
+             : use_dictionary         ? Encoding::RLE_DICTIONARY
+                                      : Encoding::PLAIN;
+    case PageType::DICTIONARY_PAGE:
+      return write_v2_headers ? Encoding::PLAIN : Encoding::PLAIN_DICTIONARY;
+    default: CUDF_UNREACHABLE("unsupported page type");
+  }
+}
+
+// operator to use with warp_reduce. stolen from cub::Sum
+struct BitwiseOr {
+  /// Binary OR operator, returns <tt>a | b</tt>
+  template <typename T>
+  __host__ __device__ __forceinline__ T operator()(T const& a, T const& b) const
+  {
+    return a | b;
+  }
+};
+
+}  // anonymous namespace
+
+// blockDim {512,1,1}
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpuInitRowGroupFragments(device_2dspan<PageFragment> frag,
+                           device_span<parquet_column_device_view const> col_desc,
+                           device_span<partition_info const> partitions,
+                           device_span<int const> part_frag_offset,
+                           uint32_t fragment_size)
+{
+  __shared__ __align__(16) frag_init_state_s state_g;
+
+  frag_init_state_s* const s          = &state_g;
+  auto const t                        = threadIdx.x;
+  auto const num_fragments_per_column = frag.size().second;
+
+  if (t == 0) { s->col = col_desc[blockIdx.x]; }
+  __syncthreads();
+
+  for (uint32_t frag_y = blockIdx.y; frag_y < num_fragments_per_column; frag_y += gridDim.y) {
+    if (t == 0) {
+      // Find which partition this fragment came from
+      auto it =
+        thrust::upper_bound(thrust::seq, part_frag_offset.begin(), part_frag_offset.end(), frag_y);
+      int const p            = it - part_frag_offset.begin() - 1;
+      int const part_end_row = partitions[p].start_row + partitions[p].num_rows;
+      s->frag.start_row = (frag_y - part_frag_offset[p]) * fragment_size + partitions[p].start_row;
+      s->frag.chunk     = frag[blockIdx.x][frag_y].chunk;
+      init_frag_state(s, fragment_size, part_end_row);
+    }
+    __syncthreads();
+
+    calculate_frag_size<block_size>(s, t);
+    __syncthreads();
+    if (t == 0) { frag[blockIdx.x][frag_y] = s->frag; }
+  }
+}
+
+// blockDim {512,1,1}
+template <int block_size>
+__global__ void __launch_bounds__(block_size)
+  gpuCalculatePageFragments(device_span<PageFragment> frag,
+                            device_span<size_type const> column_frag_sizes)
+{
+  __shared__ __align__(16) frag_init_state_s state_g;
+
+  EncColumnChunk* const ck_g = frag[blockIdx.x].chunk;
+  frag_init_state_s* const s = &state_g;
+  uint32_t const t           = threadIdx.x;
+  auto const fragment_size   = column_frag_sizes[ck_g->col_desc_id];
+
+  if (t == 0) { s->col = *ck_g->col_desc; }
+  __syncthreads();
+
+  if (t == 0) {
+    int const part_end_row = ck_g->start_row + ck_g->num_rows;
+    s->frag.start_row      = ck_g->start_row + (blockIdx.x - ck_g->first_fragment) * fragment_size;
+    s->frag.chunk          = ck_g;
+    init_frag_state(s, fragment_size, part_end_row);
+  }
+  __syncthreads();
+
+  calculate_frag_size<block_size>(s, t);
+  if (t == 0) { frag[blockIdx.x] = s->frag; }
+}
+
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128)
+  gpuInitFragmentStats(device_span<statistics_group> groups,
+                       device_span<PageFragment const> fragments)
+{
+  uint32_t const lane_id = threadIdx.x & WARP_MASK;
+  uint32_t const frag_id = blockIdx.x * 4 + (threadIdx.x / cudf::detail::warp_size);
+  if (frag_id < fragments.size()) {
+    if (lane_id == 0) {
+      statistics_group g;
+      auto* const ck_g = fragments[frag_id].chunk;
+      g.col            = ck_g->col_desc;
+      g.start_row      = fragments[frag_id].start_value_idx;
+      g.num_rows       = fragments[frag_id].num_leaf_values;
+      g.non_leaf_nulls = fragments[frag_id].num_values - g.num_rows;
+      groups[frag_id]  = g;
+    }
+  }
+}
+
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128)
+  gpuInitPages(device_2dspan<EncColumnChunk> chunks,
+               device_span<gpu::EncPage> pages,
+               device_span<size_type> page_sizes,
+               device_span<size_type> comp_page_sizes,
+               device_span<parquet_column_device_view const> col_desc,
+               statistics_merge_group* page_grstats,
+               statistics_merge_group* chunk_grstats,
+               int32_t num_columns,
+               size_t max_page_size_bytes,
+               size_type max_page_size_rows,
+               uint32_t page_align,
+               bool write_v2_headers)
+{
+  // TODO: All writing seems to be done by thread 0. Could be replaced by thrust foreach
+  __shared__ __align__(8) parquet_column_device_view col_g;
+  __shared__ __align__(8) EncColumnChunk ck_g;
+  __shared__ __align__(8) PageFragment frag_g;
+  __shared__ __align__(8) EncPage page_g;
+  __shared__ __align__(8) statistics_merge_group pagestats_g;
+
+  uint32_t const t          = threadIdx.x;
+  auto const data_page_type = write_v2_headers ? PageType::DATA_PAGE_V2 : PageType::DATA_PAGE;
+
+  if (t == 0) {
+    col_g  = col_desc[blockIdx.x];
+    ck_g   = chunks[blockIdx.y][blockIdx.x];
+    page_g = {};
+  }
+  __syncthreads();
+  if (t < 32) {
+    uint32_t fragments_in_chunk  = 0;
+    uint32_t rows_in_page        = 0;
+    uint32_t values_in_page      = 0;
+    uint32_t leaf_values_in_page = 0;
+    size_t page_size             = 0;
+    uint32_t num_pages           = 0;
+    uint32_t num_rows            = 0;
+    uint32_t page_start          = 0;
+    uint32_t page_offset         = ck_g.ck_stat_size;
+    uint32_t num_dict_entries    = 0;
+    uint32_t comp_page_offset    = ck_g.ck_stat_size;
+    uint32_t page_headers_size   = 0;
+    uint32_t max_page_data_size  = 0;
+    uint32_t cur_row             = ck_g.start_row;
+    uint32_t ck_max_stats_len    = 0;
+    uint32_t max_stats_len       = 0;
+
+    if (!t) {
+      pagestats_g.col_dtype   = col_g.leaf_column->type();
+      pagestats_g.stats_dtype = col_g.stats_dtype;
+      pagestats_g.start_chunk = ck_g.first_fragment;
+      pagestats_g.num_chunks  = 0;
+    }
+    if (ck_g.use_dictionary) {
+      if (!t) {
+        page_g.page_data       = ck_g.uncompressed_bfr + page_offset;
+        page_g.compressed_data = ck_g.compressed_bfr + comp_page_offset;
+        page_g.num_fragments   = 0;
+        page_g.page_type       = PageType::DICTIONARY_PAGE;
+        page_g.chunk           = &chunks[blockIdx.y][blockIdx.x];
+        page_g.chunk_id        = blockIdx.y * num_columns + blockIdx.x;
+        page_g.hdr_size        = 0;
+        page_g.max_hdr_size    = 32;
+        page_g.max_data_size   = ck_g.uniq_data_size;
+        page_g.start_row       = cur_row;
+        page_g.num_rows        = ck_g.num_dict_entries;
+        page_g.num_leaf_values = ck_g.num_dict_entries;
+        page_g.num_values      = ck_g.num_dict_entries;  // TODO: shouldn't matter for dict page
+        page_offset +=
+          util::round_up_unsafe(page_g.max_hdr_size + page_g.max_data_size, page_align);
+        if (not comp_page_sizes.empty()) {
+          comp_page_offset += page_g.max_hdr_size + comp_page_sizes[ck_g.first_page];
+        }
+        page_headers_size += page_g.max_hdr_size;
+        max_page_data_size = max(max_page_data_size, page_g.max_data_size);
+      }
+      __syncwarp();
+      if (t == 0) {
+        if (not pages.empty()) pages[ck_g.first_page] = page_g;
+        if (not page_sizes.empty()) page_sizes[ck_g.first_page] = page_g.max_data_size;
+        if (page_grstats) page_grstats[ck_g.first_page] = pagestats_g;
+      }
+      num_pages = 1;
+    }
+    __syncwarp();
+
+    // page padding needed for RLE encoded boolean data
+    auto const rle_pad =
+      write_v2_headers && col_g.physical_type == BOOLEAN ? RLE_LENGTH_FIELD_LEN : 0;
+
+    // This loop goes over one page fragment at a time and adds it to page.
+    // When page size crosses a particular limit, then it moves on to the next page and then next
+    // page fragment gets added to that one.
+
+    // This doesn't actually deal with data. It's agnostic. It only cares about number of rows and
+    // page size.
+    do {
+      uint32_t minmax_len = 0;
+      __syncwarp();
+      if (num_rows < ck_g.num_rows) {
+        if (t == 0) { frag_g = ck_g.fragments[fragments_in_chunk]; }
+        if (!t && ck_g.stats) {
+          if (col_g.stats_dtype == dtype_string) {
+            minmax_len = max(ck_g.stats[fragments_in_chunk].min_value.str_val.length,
+                             ck_g.stats[fragments_in_chunk].max_value.str_val.length);
+          } else if (col_g.stats_dtype == dtype_byte_array) {
+            minmax_len = max(ck_g.stats[fragments_in_chunk].min_value.byte_val.length,
+                             ck_g.stats[fragments_in_chunk].max_value.byte_val.length);
+          }
+        }
+      } else if (!t) {
+        frag_g.fragment_data_size = 0;
+        frag_g.num_rows           = 0;
+      }
+      __syncwarp();
+      uint32_t fragment_data_size =
+        (ck_g.use_dictionary)
+          ? frag_g.num_leaf_values * util::div_rounding_up_unsafe(ck_g.dict_rle_bits, 8)
+          : frag_g.fragment_data_size;
+
+      // page fragment size must fit in a 32-bit signed integer
+      if (fragment_data_size > std::numeric_limits<int32_t>::max()) {
+        CUDF_UNREACHABLE("page fragment size exceeds maximum for i32");
+      }
+
+      // TODO (dm): this convoluted logic to limit page size needs refactoring
+      size_t this_max_page_size = (values_in_page * 2 >= ck_g.num_values)   ? 256 * 1024
+                                  : (values_in_page * 3 >= ck_g.num_values) ? 384 * 1024
+                                                                            : 512 * 1024;
+
+      // override this_max_page_size if the requested size is smaller
+      this_max_page_size = min(this_max_page_size, max_page_size_bytes);
+
+      // subtract size of rep and def level vectors and RLE length field
+      auto num_vals      = values_in_page + frag_g.num_values;
+      this_max_page_size = underflow_safe_subtract(
+        this_max_page_size,
+        max_RLE_page_size(col_g.num_def_level_bits(), num_vals) +
+          max_RLE_page_size(col_g.num_rep_level_bits(), num_vals) + rle_pad);
+
+      // checks to see when we need to close the current page and start a new one
+      auto const is_last_chunk          = num_rows >= ck_g.num_rows;
+      auto const is_page_bytes_exceeded = page_size + fragment_data_size > this_max_page_size;
+      auto const is_page_rows_exceeded  = rows_in_page + frag_g.num_rows > max_page_size_rows;
+      // only check for limit overflow if there's already at least one fragment for this page
+      auto const is_page_too_big =
+        values_in_page > 0 && (is_page_bytes_exceeded || is_page_rows_exceeded);
+
+      if (is_last_chunk || is_page_too_big) {
+        if (ck_g.use_dictionary) {
+          // Additional byte to store entry bit width
+          page_size = 1 + max_RLE_page_size(ck_g.dict_rle_bits, values_in_page);
+        }
+        if (!t) {
+          page_g.num_fragments = fragments_in_chunk - page_start;
+          page_g.chunk         = &chunks[blockIdx.y][blockIdx.x];
+          page_g.chunk_id      = blockIdx.y * num_columns + blockIdx.x;
+          page_g.page_type     = data_page_type;
+          page_g.hdr_size      = 0;
+          page_g.max_hdr_size  = 32;  // Max size excluding statistics
+          if (ck_g.stats) {
+            uint32_t stats_hdr_len = 16;
+            if (col_g.stats_dtype == dtype_string || col_g.stats_dtype == dtype_byte_array) {
+              stats_hdr_len += 5 * 3 + 2 * max_stats_len;
+            } else {
+              stats_hdr_len += ((col_g.stats_dtype >= dtype_int64) ? 10 : 5) * 3;
+            }
+            page_g.max_hdr_size += stats_hdr_len;
+          }
+          page_g.max_hdr_size = util::round_up_unsafe(page_g.max_hdr_size, page_align);
+          page_g.page_data    = ck_g.uncompressed_bfr + page_offset;
+          if (not comp_page_sizes.empty()) {
+            page_g.compressed_data = ck_g.compressed_bfr + comp_page_offset;
+          }
+          page_g.start_row          = cur_row;
+          page_g.num_rows           = rows_in_page;
+          page_g.num_leaf_values    = leaf_values_in_page;
+          page_g.num_values         = values_in_page;
+          auto const def_level_size = max_RLE_page_size(col_g.num_def_level_bits(), values_in_page);
+          auto const rep_level_size = max_RLE_page_size(col_g.num_rep_level_bits(), values_in_page);
+          auto const max_data_size  = page_size + def_level_size + rep_level_size + rle_pad;
+          // page size must fit in 32-bit signed integer
+          if (max_data_size > std::numeric_limits<int32_t>::max()) {
+            CUDF_UNREACHABLE("page size exceeds maximum for i32");
+          }
+          page_g.max_data_size    = static_cast<uint32_t>(max_data_size);
+          pagestats_g.start_chunk = ck_g.first_fragment + page_start;
+          pagestats_g.num_chunks  = page_g.num_fragments;
+          page_offset +=
+            util::round_up_unsafe(page_g.max_hdr_size + page_g.max_data_size, page_align);
+          if (not comp_page_sizes.empty()) {
+            comp_page_offset += page_g.max_hdr_size + comp_page_sizes[ck_g.first_page + num_pages];
+          }
+          page_headers_size += page_g.max_hdr_size;
+          max_page_data_size = max(max_page_data_size, page_g.max_data_size);
+          cur_row += rows_in_page;
+          ck_max_stats_len = max(ck_max_stats_len, max_stats_len);
+        }
+        __syncwarp();
+        if (t == 0) {
+          if (not pages.empty()) { pages[ck_g.first_page + num_pages] = page_g; }
+          if (not page_sizes.empty()) {
+            page_sizes[ck_g.first_page + num_pages] = page_g.max_data_size;
+          }
+          if (page_grstats) { page_grstats[ck_g.first_page + num_pages] = pagestats_g; }
+        }
+
+        num_pages++;
+        page_size           = 0;
+        rows_in_page        = 0;
+        values_in_page      = 0;
+        leaf_values_in_page = 0;
+        page_start          = fragments_in_chunk;
+        max_stats_len       = 0;
+      }
+      max_stats_len = max(max_stats_len, minmax_len);
+      num_dict_entries += frag_g.num_dict_vals;
+      page_size += fragment_data_size;
+      rows_in_page += frag_g.num_rows;
+      values_in_page += frag_g.num_values;
+      leaf_values_in_page += frag_g.num_leaf_values;
+      num_rows += frag_g.num_rows;
+      fragments_in_chunk++;
+    } while (frag_g.num_rows != 0);
+    __syncwarp();
+    if (!t) {
+      if (ck_g.ck_stat_size == 0 && ck_g.stats) {
+        uint32_t ck_stat_size = util::round_up_unsafe(48 + 2 * ck_max_stats_len, page_align);
+        page_offset += ck_stat_size;
+        comp_page_offset += ck_stat_size;
+        ck_g.ck_stat_size = ck_stat_size;
+      }
+      ck_g.num_pages          = num_pages;
+      ck_g.bfr_size           = page_offset;
+      ck_g.page_headers_size  = page_headers_size;
+      ck_g.max_page_data_size = max_page_data_size;
+      if (not comp_page_sizes.empty()) { ck_g.compressed_size = comp_page_offset; }
+      pagestats_g.start_chunk = ck_g.first_page + ck_g.use_dictionary;  // Exclude dictionary
+      pagestats_g.num_chunks  = num_pages - ck_g.use_dictionary;
+    }
+  }
+  __syncthreads();
+  if (t == 0) {
+    if (not pages.empty()) ck_g.pages = &pages[ck_g.first_page];
+    chunks[blockIdx.y][blockIdx.x] = ck_g;
+    if (chunk_grstats) chunk_grstats[blockIdx.y * num_columns + blockIdx.x] = pagestats_g;
+  }
+}
+
+/**
+ * @brief Mask table representing how many consecutive repeats are needed to code a repeat run
+ *[nbits-1]
+ */
+static __device__ __constant__ uint32_t kRleRunMask[24] = {
+  0x00ff'ffff, 0x0fff, 0x00ff, 0x3f, 0x0f, 0x0f, 0x7, 0x7, 0x3, 0x3, 0x3, 0x3,
+  0x1,         0x1,    0x1,    0x1,  0x1,  0x1,  0x1, 0x1, 0x1, 0x1, 0x1, 0x1};
+
+/**
+ * @brief Variable-length encode an integer
+ */
+inline __device__ uint8_t* VlqEncode(uint8_t* p, uint32_t v)
+{
+  while (v > 0x7f) {
+    *p++ = (v | 0x80);
+    v >>= 7;
+  }
+  *p++ = v;
+  return p;
+}
+
+/**
+ * @brief Pack literal values in output bitstream (1,2,3,4,5,6,8,10,12,16,20 or 24 bits per value)
+ */
+inline __device__ void PackLiteralsShuffle(
+  uint8_t* dst, uint32_t v, uint32_t count, uint32_t w, uint32_t t)
+{
+  constexpr uint32_t MASK2T = 1;  // mask for 2 thread leader
+  constexpr uint32_t MASK4T = 3;  // mask for 4 thread leader
+  constexpr uint32_t MASK8T = 7;  // mask for 8 thread leader
+  uint64_t v64;
+
+  if (t > (count | 0x1f)) { return; }
+
+  switch (w) {
+    case 1:
+      v |= shuffle_xor(v, 1) << 1;  // grab bit 1 from neighbor
+      v |= shuffle_xor(v, 2) << 2;  // grab bits 2-3 from 2 lanes over
+      v |= shuffle_xor(v, 4) << 4;  // grab bits 4-7 from 4 lanes over
+      // sub-warp leader writes the combined bits
+      if (t < count && !(t & MASK8T)) { dst[(t * w) >> 3] = v; }
+      return;
+    case 2:
+      v |= shuffle_xor(v, 1) << 2;
+      v |= shuffle_xor(v, 2) << 4;
+      if (t < count && !(t & MASK4T)) { dst[(t * w) >> 3] = v; }
+      return;
+    case 3:
+      v |= shuffle_xor(v, 1) << 3;
+      v |= shuffle_xor(v, 2) << 6;
+      v |= shuffle_xor(v, 4) << 12;
+      if (t < count && !(t & MASK8T)) {
+        dst[(t >> 3) * 3 + 0] = v;
+        dst[(t >> 3) * 3 + 1] = v >> 8;
+        dst[(t >> 3) * 3 + 2] = v >> 16;
+      }
+      return;
+    case 4:
+      v |= shuffle_xor(v, 1) << 4;
+      if (t < count && !(t & MASK2T)) { dst[(t * w) >> 3] = v; }
+      return;
+    case 5:
+      v |= shuffle_xor(v, 1) << 5;
+      v |= shuffle_xor(v, 2) << 10;
+      v64 = static_cast<uint64_t>(shuffle_xor(v, 4)) << 20 | v;
+      if (t < count && !(t & MASK8T)) {
+        dst[(t >> 3) * 5 + 0] = v64;
+        dst[(t >> 3) * 5 + 1] = v64 >> 8;
+        dst[(t >> 3) * 5 + 2] = v64 >> 16;
+        dst[(t >> 3) * 5 + 3] = v64 >> 24;
+        dst[(t >> 3) * 5 + 4] = v64 >> 32;
+      }
+      return;
+    case 6:
+      v |= shuffle_xor(v, 1) << 6;
+      v |= shuffle_xor(v, 2) << 12;
+      if (t < count && !(t & MASK4T)) {
+        dst[(t >> 2) * 3 + 0] = v;
+        dst[(t >> 2) * 3 + 1] = v >> 8;
+        dst[(t >> 2) * 3 + 2] = v >> 16;
+      }
+      return;
+    case 8:
+      if (t < count) { dst[t] = v; }
+      return;
+    case 10:
+      v |= shuffle_xor(v, 1) << 10;
+      v64 = static_cast<uint64_t>(shuffle_xor(v, 2)) << 20 | v;
+      if (t < count && !(t & MASK4T)) {
+        dst[(t >> 2) * 5 + 0] = v64;
+        dst[(t >> 2) * 5 + 1] = v64 >> 8;
+        dst[(t >> 2) * 5 + 2] = v64 >> 16;
+        dst[(t >> 2) * 5 + 3] = v64 >> 24;
+        dst[(t >> 2) * 5 + 4] = v64 >> 32;
+      }
+      return;
+    case 12:
+      v |= shuffle_xor(v, 1) << 12;
+      if (t < count && !(t & MASK2T)) {
+        dst[(t >> 1) * 3 + 0] = v;
+        dst[(t >> 1) * 3 + 1] = v >> 8;
+        dst[(t >> 1) * 3 + 2] = v >> 16;
+      }
+      return;
+    case 16:
+      if (t < count) {
+        dst[t * 2 + 0] = v;
+        dst[t * 2 + 1] = v >> 8;
+      }
+      return;
+    case 20:
+      v64 = static_cast<uint64_t>(shuffle_xor(v, 1)) << 20 | v;
+      if (t < count && !(t & MASK2T)) {
+        dst[(t >> 1) * 5 + 0] = v64;
+        dst[(t >> 1) * 5 + 1] = v64 >> 8;
+        dst[(t >> 1) * 5 + 2] = v64 >> 16;
+        dst[(t >> 1) * 5 + 3] = v64 >> 24;
+        dst[(t >> 1) * 5 + 4] = v64 >> 32;
+      }
+      return;
+    case 24:
+      if (t < count) {
+        dst[t * 3 + 0] = v;
+        dst[t * 3 + 1] = v >> 8;
+        dst[t * 3 + 2] = v >> 16;
+      }
+      return;
+
+    default: CUDF_UNREACHABLE("Unsupported bit width");
+  }
+}
+
+/**
+ * @brief Pack literals of arbitrary bit-length in output bitstream.
+ */
+inline __device__ void PackLiteralsRoundRobin(
+  uint8_t* dst, uint32_t v, uint32_t count, uint32_t w, uint32_t t)
+{
+  // Scratch space to temporarily write to. Needed because we will use atomics to write 32 bit
+  // words but the destination mem may not be a multiple of 4 bytes.
+  // TODO (dm): This assumes blockdim = 128. Reduce magic numbers.
+  constexpr uint32_t NUM_THREADS  = 128;  // this needs to match gpuEncodePages block_size parameter
+  constexpr uint32_t NUM_BYTES    = (NUM_THREADS * MAX_DICT_BITS) >> 3;
+  constexpr uint32_t SCRATCH_SIZE = NUM_BYTES / sizeof(uint32_t);
+  __shared__ uint32_t scratch[SCRATCH_SIZE];
+  for (uint32_t i = t; i < SCRATCH_SIZE; i += NUM_THREADS) {
+    scratch[i] = 0;
+  }
+  __syncthreads();
+
+  if (t <= count) {
+    // shift symbol left by up to 31 bits
+    uint64_t v64 = v;
+    v64 <<= (t * w) & 0x1f;
+
+    // Copy 64 bit word into two 32 bit words while following C++ strict aliasing rules.
+    uint32_t v32[2];
+    memcpy(&v32, &v64, sizeof(uint64_t));
+
+    // Atomically write result to scratch
+    if (v32[0]) { atomicOr(scratch + ((t * w) >> 5), v32[0]); }
+    if (v32[1]) { atomicOr(scratch + ((t * w) >> 5) + 1, v32[1]); }
+  }
+  __syncthreads();
+
+  // Copy scratch data to final destination
+  auto available_bytes = (count * w + 7) / 8;
+
+  auto scratch_bytes = reinterpret_cast<char*>(&scratch[0]);
+  for (uint32_t i = t; i < available_bytes; i += NUM_THREADS) {
+    dst[i] = scratch_bytes[i];
+  }
+  __syncthreads();
+}
+
+/**
+ * @brief Pack literal values in output bitstream
+ */
+inline __device__ void PackLiterals(
+  uint8_t* dst, uint32_t v, uint32_t count, uint32_t w, uint32_t t)
+{
+  if (w > 24) { CUDF_UNREACHABLE("Unsupported bit width"); }
+  switch (w) {
+    case 1:
+    case 2:
+    case 3:
+    case 4:
+    case 5:
+    case 6:
+    case 8:
+    case 10:
+    case 12:
+    case 16:
+    case 20:
+    case 24:
+      // bit widths that lie on easy boundaries can be handled either directly
+      // (8, 16, 24) or through fast shuffle operations.
+      PackLiteralsShuffle(dst, v, count, w, t);
+      return;
+    default:
+      // bit packing that uses atomics, but can handle arbitrary bit widths up to 24.
+      PackLiteralsRoundRobin(dst, v, count, w, t);
+  }
+}
+
+/**
+ * @brief RLE encoder
+ *
+ * @param[in,out] s Page encode state
+ * @param[in] numvals Total count of input values
+ * @param[in] nbits number of bits per symbol (1..16)
+ * @param[in] flush nonzero if last batch in block
+ * @param[in] t thread id (0..127)
+ */
+static __device__ void RleEncode(
+  page_enc_state_s* s, uint32_t numvals, uint32_t nbits, uint32_t flush, uint32_t t)
+{
+  uint32_t rle_pos = s->rle_pos;
+  uint32_t rle_run = s->rle_run;
+
+  while (rle_pos < numvals || (flush && rle_run)) {
+    uint32_t pos = rle_pos + t;
+    if (rle_run > 0 && !(rle_run & 1)) {
+      // Currently in a long repeat run
+      uint32_t mask = ballot(pos < numvals && s->vals[pos & (rle_buffer_size - 1)] == s->run_val);
+      uint32_t rle_rpt_count, max_rpt_count;
+      if (!(t & 0x1f)) { s->rpt_map[t >> 5] = mask; }
+      __syncthreads();
+      if (t < 32) {
+        uint32_t c32 = ballot(t >= 4 || s->rpt_map[t] != 0xffff'ffffu);
+        if (!t) {
+          uint32_t last_idx = __ffs(c32) - 1;
+          s->rle_rpt_count =
+            last_idx * 32 + ((last_idx < 4) ? __ffs(~s->rpt_map[last_idx]) - 1 : 0);
+        }
+      }
+      __syncthreads();
+      max_rpt_count = min(numvals - rle_pos, 128);
+      rle_rpt_count = s->rle_rpt_count;
+      rle_run += rle_rpt_count << 1;
+      rle_pos += rle_rpt_count;
+      if (rle_rpt_count < max_rpt_count || (flush && rle_pos == numvals)) {
+        if (t == 0) {
+          uint32_t const run_val = s->run_val;
+          uint8_t* dst           = VlqEncode(s->rle_out, rle_run);
+          *dst++                 = run_val;
+          if (nbits > 8) { *dst++ = run_val >> 8; }
+          if (nbits > 16) { *dst++ = run_val >> 16; }
+          s->rle_out = dst;
+        }
+        rle_run = 0;
+      }
+    } else {
+      // New run or in a literal run
+      uint32_t v0      = s->vals[pos & (rle_buffer_size - 1)];
+      uint32_t v1      = s->vals[(pos + 1) & (rle_buffer_size - 1)];
+      uint32_t mask    = ballot(pos + 1 < numvals && v0 == v1);
+      uint32_t maxvals = min(numvals - rle_pos, 128);
+      uint32_t rle_lit_count, rle_rpt_count;
+      if (!(t & 0x1f)) { s->rpt_map[t >> 5] = mask; }
+      __syncthreads();
+      if (t < 32) {
+        // Repeat run can only start on a multiple of 8 values
+        uint32_t idx8        = (t * 8) >> 5;
+        uint32_t pos8        = (t * 8) & 0x1f;
+        uint32_t m0          = (idx8 < 4) ? s->rpt_map[idx8] : 0;
+        uint32_t m1          = (idx8 < 3) ? s->rpt_map[idx8 + 1] : 0;
+        uint32_t needed_mask = kRleRunMask[nbits - 1];
+        mask                 = ballot((__funnelshift_r(m0, m1, pos8) & needed_mask) == needed_mask);
+        if (!t) {
+          uint32_t rle_run_start = (mask != 0) ? min((__ffs(mask) - 1) * 8, maxvals) : maxvals;
+          uint32_t rpt_len       = 0;
+          if (rle_run_start < maxvals) {
+            uint32_t idx_cur = rle_run_start >> 5;
+            uint32_t idx_ofs = rle_run_start & 0x1f;
+            while (idx_cur < 4) {
+              m0   = (idx_cur < 4) ? s->rpt_map[idx_cur] : 0;
+              m1   = (idx_cur < 3) ? s->rpt_map[idx_cur + 1] : 0;
+              mask = ~__funnelshift_r(m0, m1, idx_ofs);
+              if (mask != 0) {
+                rpt_len += __ffs(mask) - 1;
+                break;
+              }
+              rpt_len += 32;
+              idx_cur++;
+            }
+          }
+          s->rle_lit_count = rle_run_start;
+          s->rle_rpt_count = min(rpt_len, maxvals - rle_run_start);
+        }
+      }
+      __syncthreads();
+      rle_lit_count = s->rle_lit_count;
+      rle_rpt_count = s->rle_rpt_count;
+      if (rle_lit_count != 0 || (rle_run != 0 && rle_rpt_count != 0)) {
+        uint32_t lit_div8;
+        bool need_more_data = false;
+        if (!flush && rle_pos + rle_lit_count == numvals) {
+          // Wait for more data
+          rle_lit_count -= min(rle_lit_count, 24);
+          need_more_data = true;
+        }
+        if (rle_lit_count != 0) {
+          lit_div8 = (rle_lit_count + ((flush && rle_pos + rle_lit_count == numvals) ? 7 : 0)) >> 3;
+          if (rle_run + lit_div8 * 2 > 0x7f) {
+            lit_div8      = 0x3f - (rle_run >> 1);  // Limit to fixed 1-byte header (504 literals)
+            rle_rpt_count = 0;                      // Defer repeat run
+          }
+          if (lit_div8 != 0) {
+            uint8_t* dst = s->rle_out + 1 + (rle_run >> 1) * nbits;
+            PackLiterals(dst, (rle_pos + t < numvals) ? v0 : 0, lit_div8 * 8, nbits, t);
+            rle_run = (rle_run + lit_div8 * 2) | 1;
+            rle_pos = min(rle_pos + lit_div8 * 8, numvals);
+          }
+        }
+        if (rle_run >= ((rle_rpt_count != 0 || (flush && rle_pos == numvals)) ? 0x03 : 0x7f)) {
+          __syncthreads();
+          // Complete literal run
+          if (!t) {
+            uint8_t* dst = s->rle_out;
+            dst[0]       = rle_run;  // At most 0x7f
+            dst += 1 + nbits * (rle_run >> 1);
+            s->rle_out = dst;
+          }
+          rle_run = 0;
+        }
+        if (need_more_data) { break; }
+      }
+      // Start a repeat run
+      if (rle_rpt_count != 0) {
+        if (t == s->rle_lit_count) { s->run_val = v0; }
+        rle_run = rle_rpt_count * 2;
+        rle_pos += rle_rpt_count;
+        if (rle_pos + 1 == numvals && !flush) { break; }
+      }
+    }
+    __syncthreads();
+  }
+  __syncthreads();
+  if (!t) {
+    s->rle_run     = rle_run;
+    s->rle_pos     = rle_pos;
+    s->rle_numvals = numvals;
+  }
+}
+
+/**
+ * @brief PLAIN bool encoder
+ *
+ * @param[in,out] s Page encode state
+ * @param[in] numvals Total count of input values
+ * @param[in] flush nonzero if last batch in block
+ * @param[in] t thread id (0..127)
+ */
+static __device__ void PlainBoolEncode(page_enc_state_s* s,
+                                       uint32_t numvals,
+                                       uint32_t flush,
+                                       uint32_t t)
+{
+  uint32_t rle_pos = s->rle_pos;
+  uint8_t* dst     = s->rle_out;
+
+  while (rle_pos < numvals) {
+    uint32_t pos    = rle_pos + t;
+    uint32_t v      = (pos < numvals) ? s->vals[pos & (rle_buffer_size - 1)] : 0;
+    uint32_t n      = min(numvals - rle_pos, 128);
+    uint32_t nbytes = (n + ((flush) ? 7 : 0)) >> 3;
+    if (!nbytes) { break; }
+    v |= shuffle_xor(v, 1) << 1;
+    v |= shuffle_xor(v, 2) << 2;
+    v |= shuffle_xor(v, 4) << 4;
+    if (t < n && !(t & 7)) { dst[t >> 3] = v; }
+    rle_pos = min(rle_pos + nbytes * 8, numvals);
+    dst += nbytes;
+  }
+  __syncthreads();
+  if (!t) {
+    s->rle_pos     = rle_pos;
+    s->rle_numvals = numvals;
+    s->rle_out     = dst;
+  }
+}
+
+/**
+ * @brief Determines the difference between the Proleptic Gregorian Calendar epoch (1970-01-01
+ * 00:00:00 UTC) and the Julian date epoch (-4713-11-24 12:00:00 UTC).
+ *
+ * @return The difference between two epochs in `cuda::std::chrono::duration` format with a period
+ * of hours.
+ */
+constexpr auto julian_calendar_epoch_diff()
+{
+  using namespace cuda::std::chrono;
+  using namespace cuda::std::chrono_literals;
+  return sys_days{January / 1 / 1970} - (sys_days{November / 24 / -4713} + 12h);
+}
+
+/**
+ * @brief Converts number `v` of periods of type `PeriodT` into a pair with nanoseconds since
+ * midnight and number of Julian days. Does not deal with time zones. Used by INT96 code.
+ *
+ * @tparam PeriodT a ratio representing the tick period in duration
+ * @param v count of ticks since epoch
+ * @return A pair of (nanoseconds, days) where nanoseconds is the number of nanoseconds
+ * elapsed in the day and days is the number of days from Julian epoch.
+ */
+template <typename PeriodT>
+__device__ auto julian_days_with_time(int64_t v)
+{
+  using namespace cuda::std::chrono;
+  auto const dur_total             = duration<int64_t, PeriodT>{v};
+  auto const dur_days              = floor<days>(dur_total);
+  auto const dur_time_of_day       = dur_total - dur_days;
+  auto const dur_time_of_day_nanos = duration_cast<nanoseconds>(dur_time_of_day);
+  auto const julian_days           = dur_days + ceil<days>(julian_calendar_epoch_diff());
+  return std::make_pair(dur_time_of_day_nanos, julian_days);
+}
+
+// blockDim(128, 1, 1)
+template <int block_size>
+__global__ void __launch_bounds__(128, 8)
+  gpuEncodePages(device_span<gpu::EncPage> pages,
+                 device_span<device_span<uint8_t const>> comp_in,
+                 device_span<device_span<uint8_t>> comp_out,
+                 device_span<compression_result> comp_results,
+                 bool write_v2_headers)
+{
+  __shared__ __align__(8) page_enc_state_s state_g;
+  using block_reduce = cub::BlockReduce<uint32_t, block_size>;
+  using block_scan   = cub::BlockScan<uint32_t, block_size>;
+  __shared__ union {
+    typename block_reduce::TempStorage reduce_storage;
+    typename block_scan::TempStorage scan_storage;
+  } temp_storage;
+
+  page_enc_state_s* const s = &state_g;
+  auto const t              = threadIdx.x;
+
+  if (t == 0) {
+    state_g = page_enc_state_s{};
+    s->page = pages[blockIdx.x];
+    s->ck   = *s->page.chunk;
+    s->col  = *s->ck.col_desc;
+    s->cur  = s->page.page_data + s->page.max_hdr_size;
+    // init V2 info
+    s->page.def_lvl_bytes = 0;
+    s->page.rep_lvl_bytes = 0;
+    s->page.num_nulls     = 0;
+    s->rle_len_pos        = nullptr;
+  }
+  __syncthreads();
+
+  auto const is_v2 = s->page.page_type == PageType::DATA_PAGE_V2;
+
+  // Encode Repetition and Definition levels
+  if (s->page.page_type != PageType::DICTIONARY_PAGE &&
+      (s->col.num_def_level_bits()) != 0 &&  // This means max definition level is not 0 (nullable)
+      (s->col.num_rep_level_bits()) == 0     // This means there are no repetition levels (non-list)
+  ) {
+    // Calculate definition levels from validity
+    uint32_t def_lvl_bits = s->col.num_def_level_bits();
+    if (def_lvl_bits != 0) {
+      if (!t) {
+        s->rle_run     = 0;
+        s->rle_pos     = 0;
+        s->rle_numvals = 0;
+        s->rle_out     = s->cur;
+        if (not is_v2) {
+          s->rle_out += 4;  // save space for length
+        }
+      }
+      __syncthreads();
+      while (s->rle_numvals < s->page.num_rows) {
+        uint32_t rle_numvals = s->rle_numvals;
+        uint32_t nrows       = min(s->page.num_rows - rle_numvals, 128);
+        auto row             = s->page.start_row + rle_numvals + t;
+        // Definition level encodes validity. Checks the valid map and if it is valid, then sets the
+        // def_lvl accordingly and sets it in s->vals which is then given to RleEncode to encode
+        uint32_t def_lvl = [&]() {
+          bool within_bounds = rle_numvals + t < s->page.num_rows && row < s->col.num_rows;
+          if (not within_bounds) { return 0u; }
+          uint32_t def       = 0;
+          size_type l        = 0;
+          bool is_col_struct = false;
+          auto col           = *s->col.parent_column;
+          do {
+            // If col not nullable then it does not contribute to def levels
+            if (s->col.nullability[l]) {
+              if (col.is_valid(row)) {
+                ++def;
+              } else {
+                // We have found the shallowest level at which this row is null
+                break;
+              }
+            }
+            is_col_struct = (col.type().id() == type_id::STRUCT);
+            if (is_col_struct) {
+              row += col.offset();
+              col = col.child(0);
+              ++l;
+            }
+          } while (is_col_struct);
+          return def;
+        }();
+        s->vals[(rle_numvals + t) & (rle_buffer_size - 1)] = def_lvl;
+        __syncthreads();
+        rle_numvals += nrows;
+        RleEncode(s, rle_numvals, def_lvl_bits, (rle_numvals == s->page.num_rows), t);
+        __syncthreads();
+      }
+      if (t < 32) {
+        uint8_t* const cur       = s->cur;
+        uint8_t* const rle_out   = s->rle_out;
+        uint32_t const rle_bytes = static_cast<uint32_t>(rle_out - cur) - (is_v2 ? 0 : 4);
+        if (is_v2 && t == 0) {
+          s->page.def_lvl_bytes = rle_bytes;
+        } else if (not is_v2 && t < 4) {
+          cur[t] = rle_bytes >> (t * 8);
+        }
+        __syncwarp();
+        if (t == 0) { s->cur = rle_out; }
+      }
+    }
+  } else if (s->page.page_type != PageType::DICTIONARY_PAGE &&
+             s->col.num_rep_level_bits() != 0  // This means there ARE repetition levels (has list)
+  ) {
+    auto encode_levels = [&](uint8_t const* lvl_val_data, uint32_t nbits, uint32_t& lvl_bytes) {
+      // For list types, the repetition and definition levels are pre-calculated. We just need to
+      // encode and write them now.
+      if (!t) {
+        s->rle_run     = 0;
+        s->rle_pos     = 0;
+        s->rle_numvals = 0;
+        s->rle_out     = s->cur;
+        if (not is_v2) {
+          s->rle_out += 4;  // save space for length
+        }
+      }
+      __syncthreads();
+      size_type page_first_val_idx = s->col.level_offsets[s->page.start_row];
+      size_type col_last_val_idx   = s->col.level_offsets[s->col.num_rows];
+      while (s->rle_numvals < s->page.num_values) {
+        uint32_t rle_numvals = s->rle_numvals;
+        uint32_t nvals       = min(s->page.num_values - rle_numvals, 128);
+        uint32_t idx         = page_first_val_idx + rle_numvals + t;
+        uint32_t lvl_val =
+          (rle_numvals + t < s->page.num_values && idx < col_last_val_idx) ? lvl_val_data[idx] : 0;
+        s->vals[(rle_numvals + t) & (rle_buffer_size - 1)] = lvl_val;
+        __syncthreads();
+        rle_numvals += nvals;
+        RleEncode(s, rle_numvals, nbits, (rle_numvals == s->page.num_values), t);
+        __syncthreads();
+      }
+      if (t < 32) {
+        uint8_t* const cur       = s->cur;
+        uint8_t* const rle_out   = s->rle_out;
+        uint32_t const rle_bytes = static_cast<uint32_t>(rle_out - cur) - (is_v2 ? 0 : 4);
+        if (is_v2 && t == 0) {
+          lvl_bytes = rle_bytes;
+        } else if (not is_v2 && t < 4) {
+          cur[t] = rle_bytes >> (t * 8);
+        }
+        __syncwarp();
+        if (t == 0) { s->cur = rle_out; }
+      }
+    };
+    encode_levels(s->col.rep_values, s->col.num_rep_level_bits(), s->page.rep_lvl_bytes);
+    __syncthreads();
+    encode_levels(s->col.def_values, s->col.num_def_level_bits(), s->page.def_lvl_bytes);
+  }
+  // Encode data values
+  __syncthreads();
+  auto const physical_type = s->col.physical_type;
+  auto const type_id       = s->col.leaf_column->type().id();
+  auto const dtype_len_out = physical_type_len(physical_type, type_id);
+  auto const dtype_len_in  = [&]() -> uint32_t {
+    if (physical_type == INT32) { return int32_logical_len(type_id); }
+    if (physical_type == INT96) { return sizeof(int64_t); }
+    return dtype_len_out;
+  }();
+
+  auto const dict_bits = (physical_type == BOOLEAN) ? 1
+                         : (s->ck.use_dictionary and s->page.page_type != PageType::DICTIONARY_PAGE)
+                           ? s->ck.dict_rle_bits
+                           : -1;
+  if (t == 0) {
+    uint8_t* dst   = s->cur;
+    s->rle_run     = 0;
+    s->rle_pos     = 0;
+    s->rle_numvals = 0;
+    s->rle_out     = dst;
+    s->page.encoding =
+      determine_encoding(s->page.page_type, physical_type, s->ck.use_dictionary, write_v2_headers);
+    if (dict_bits >= 0 && physical_type != BOOLEAN) {
+      dst[0]     = dict_bits;
+      s->rle_out = dst + 1;
+    } else if (is_v2 && physical_type == BOOLEAN) {
+      // save space for RLE length. we don't know the total length yet.
+      s->rle_out     = dst + RLE_LENGTH_FIELD_LEN;
+      s->rle_len_pos = dst;
+    }
+    s->page_start_val  = row_to_value_idx(s->page.start_row, s->col);
+    s->chunk_start_val = row_to_value_idx(s->ck.start_row, s->col);
+  }
+  __syncthreads();
+  uint32_t num_valid = 0;
+  for (uint32_t cur_val_idx = 0; cur_val_idx < s->page.num_leaf_values;) {
+    uint32_t nvals = min(s->page.num_leaf_values - cur_val_idx, 128);
+    uint32_t len, pos;
+
+    auto [is_valid, val_idx] = [&]() {
+      uint32_t val_idx;
+      uint32_t is_valid;
+
+      size_type val_idx_in_block = cur_val_idx + t;
+      if (s->page.page_type == PageType::DICTIONARY_PAGE) {
+        val_idx  = val_idx_in_block;
+        is_valid = (val_idx < s->page.num_leaf_values);
+        if (is_valid) { val_idx = s->ck.dict_data[val_idx]; }
+      } else {
+        size_type val_idx_in_leaf_col = s->page_start_val + val_idx_in_block;
+
+        is_valid = (val_idx_in_leaf_col < s->col.leaf_column->size() &&
+                    val_idx_in_block < s->page.num_leaf_values)
+                     ? s->col.leaf_column->is_valid(val_idx_in_leaf_col)
+                     : 0;
+        val_idx =
+          (s->ck.use_dictionary) ? val_idx_in_leaf_col - s->chunk_start_val : val_idx_in_leaf_col;
+      }
+      return std::make_tuple(is_valid, val_idx);
+    }();
+
+    if (is_valid) num_valid++;
+
+    cur_val_idx += nvals;
+    if (dict_bits >= 0) {
+      // Dictionary encoding
+      if (dict_bits > 0) {
+        uint32_t rle_numvals;
+        uint32_t rle_numvals_in_block;
+        block_scan(temp_storage.scan_storage).ExclusiveSum(is_valid, pos, rle_numvals_in_block);
+        rle_numvals = s->rle_numvals;
+        if (is_valid) {
+          uint32_t v;
+          if (physical_type == BOOLEAN) {
+            v = s->col.leaf_column->element<uint8_t>(val_idx);
+          } else {
+            v = s->ck.dict_index[val_idx];
+          }
+          s->vals[(rle_numvals + pos) & (rle_buffer_size - 1)] = v;
+        }
+        rle_numvals += rle_numvals_in_block;
+        __syncthreads();
+        if (!is_v2 && physical_type == BOOLEAN) {
+          PlainBoolEncode(s, rle_numvals, (cur_val_idx == s->page.num_leaf_values), t);
+        } else {
+          RleEncode(s, rle_numvals, dict_bits, (cur_val_idx == s->page.num_leaf_values), t);
+        }
+        __syncthreads();
+      }
+      if (t == 0) { s->cur = s->rle_out; }
+      __syncthreads();
+    } else {
+      // Non-dictionary encoding
+      uint8_t* dst = s->cur;
+
+      if (is_valid) {
+        len = dtype_len_out;
+        if (physical_type == BYTE_ARRAY) {
+          if (type_id == type_id::STRING) {
+            len += s->col.leaf_column->element<string_view>(val_idx).size_bytes();
+          } else if (s->col.output_as_byte_array && type_id == type_id::LIST) {
+            len +=
+              get_element<statistics::byte_array_view>(*s->col.leaf_column, val_idx).size_bytes();
+          }
+        }
+      } else {
+        len = 0;
+      }
+      uint32_t total_len = 0;
+      block_scan(temp_storage.scan_storage).ExclusiveSum(len, pos, total_len);
+      __syncthreads();
+      if (t == 0) { s->cur = dst + total_len; }
+      if (is_valid) {
+        switch (physical_type) {
+          case INT32: [[fallthrough]];
+          case FLOAT: {
+            auto const v = [dtype_len = dtype_len_in,
+                            idx       = val_idx,
+                            col       = s->col.leaf_column,
+                            scale     = s->col.ts_scale == 0 ? 1 : s->col.ts_scale]() -> int32_t {
+              switch (dtype_len) {
+                case 8: return col->element<int64_t>(idx) * scale;
+                case 4: return col->element<int32_t>(idx) * scale;
+                case 2: return col->element<int16_t>(idx) * scale;
+                default: return col->element<int8_t>(idx) * scale;
+              }
+            }();
+
+            dst[pos + 0] = v;
+            dst[pos + 1] = v >> 8;
+            dst[pos + 2] = v >> 16;
+            dst[pos + 3] = v >> 24;
+          } break;
+          case INT64: {
+            int64_t v        = s->col.leaf_column->element<int64_t>(val_idx);
+            int32_t ts_scale = s->col.ts_scale;
+            if (ts_scale != 0) {
+              if (ts_scale < 0) {
+                v /= -ts_scale;
+              } else {
+                v *= ts_scale;
+              }
+            }
+            dst[pos + 0] = v;
+            dst[pos + 1] = v >> 8;
+            dst[pos + 2] = v >> 16;
+            dst[pos + 3] = v >> 24;
+            dst[pos + 4] = v >> 32;
+            dst[pos + 5] = v >> 40;
+            dst[pos + 6] = v >> 48;
+            dst[pos + 7] = v >> 56;
+          } break;
+          case INT96: {
+            int64_t v        = s->col.leaf_column->element<int64_t>(val_idx);
+            int32_t ts_scale = s->col.ts_scale;
+            if (ts_scale != 0) {
+              if (ts_scale < 0) {
+                v /= -ts_scale;
+              } else {
+                v *= ts_scale;
+              }
+            }
+
+            auto const [last_day_nanos, julian_days] = [&] {
+              using namespace cuda::std::chrono;
+              switch (s->col.leaf_column->type().id()) {
+                case type_id::TIMESTAMP_SECONDS:
+                case type_id::TIMESTAMP_MILLISECONDS: {
+                  return julian_days_with_time<cuda::std::milli>(v);
+                } break;
+                case type_id::TIMESTAMP_MICROSECONDS:
+                case type_id::TIMESTAMP_NANOSECONDS: {
+                  return julian_days_with_time<cuda::std::micro>(v);
+                } break;
+              }
+              return julian_days_with_time<cuda::std::nano>(0);
+            }();
+
+            // the 12 bytes of fixed length data.
+            v             = last_day_nanos.count();
+            dst[pos + 0]  = v;
+            dst[pos + 1]  = v >> 8;
+            dst[pos + 2]  = v >> 16;
+            dst[pos + 3]  = v >> 24;
+            dst[pos + 4]  = v >> 32;
+            dst[pos + 5]  = v >> 40;
+            dst[pos + 6]  = v >> 48;
+            dst[pos + 7]  = v >> 56;
+            uint32_t w    = julian_days.count();
+            dst[pos + 8]  = w;
+            dst[pos + 9]  = w >> 8;
+            dst[pos + 10] = w >> 16;
+            dst[pos + 11] = w >> 24;
+          } break;
+
+          case DOUBLE: {
+            auto v = s->col.leaf_column->element<double>(val_idx);
+            memcpy(dst + pos, &v, 8);
+          } break;
+          case BYTE_ARRAY: {
+            auto const bytes = [](cudf::type_id const type_id,
+                                  column_device_view const* leaf_column,
+                                  uint32_t const val_idx) -> void const* {
+              switch (type_id) {
+                case type_id::STRING:
+                  return reinterpret_cast<void const*>(
+                    leaf_column->element<string_view>(val_idx).data());
+                case type_id::LIST:
+                  return reinterpret_cast<void const*>(
+                    get_element<statistics::byte_array_view>(*(leaf_column), val_idx).data());
+                default: CUDF_UNREACHABLE("invalid type id for byte array writing!");
+              }
+            }(type_id, s->col.leaf_column, val_idx);
+            uint32_t v   = len - 4;  // string length
+            dst[pos + 0] = v;
+            dst[pos + 1] = v >> 8;
+            dst[pos + 2] = v >> 16;
+            dst[pos + 3] = v >> 24;
+            if (v != 0) memcpy(dst + pos + 4, bytes, v);
+          } break;
+          case FIXED_LEN_BYTE_ARRAY: {
+            if (type_id == type_id::DECIMAL128) {
+              // When using FIXED_LEN_BYTE_ARRAY for decimals, the rep is encoded in big-endian
+              auto const v = s->col.leaf_column->element<numeric::decimal128>(val_idx).value();
+              auto const v_char_ptr = reinterpret_cast<char const*>(&v);
+              thrust::copy(thrust::seq,
+                           thrust::make_reverse_iterator(v_char_ptr + sizeof(v)),
+                           thrust::make_reverse_iterator(v_char_ptr),
+                           dst + pos);
+            }
+          } break;
+        }
+      }
+      __syncthreads();
+    }
+  }
+
+  uint32_t const valid_count = block_reduce(temp_storage.reduce_storage).Sum(num_valid);
+
+  // save RLE length if necessary
+  if (s->rle_len_pos != nullptr && t < 32) {
+    // size doesn't include the 4 bytes for the length
+    auto const rle_size = static_cast<uint32_t>(s->cur - s->rle_len_pos) - RLE_LENGTH_FIELD_LEN;
+    if (t < RLE_LENGTH_FIELD_LEN) { s->rle_len_pos[t] = rle_size >> (t * 8); }
+    __syncwarp();
+  }
+
+  // V2 does not compress rep and def level data
+  size_t const skip_comp_size = s->page.def_lvl_bytes + s->page.rep_lvl_bytes;
+
+  if (t == 0) {
+    s->page.num_nulls           = s->page.num_values - valid_count;
+    uint8_t* const base         = s->page.page_data + s->page.max_hdr_size;
+    auto const actual_data_size = static_cast<uint32_t>(s->cur - base);
+    if (actual_data_size > s->page.max_data_size) {
+      CUDF_UNREACHABLE("detected possible page data corruption");
+    }
+    s->page.max_data_size = actual_data_size;
+    if (not comp_in.empty()) {
+      comp_in[blockIdx.x]  = {base + skip_comp_size, actual_data_size - skip_comp_size};
+      comp_out[blockIdx.x] = {s->page.compressed_data + s->page.max_hdr_size + skip_comp_size,
+                              0};  // size is unused
+    }
+    pages[blockIdx.x] = s->page;
+    if (not comp_results.empty()) {
+      comp_results[blockIdx.x]   = {0, compression_status::FAILURE};
+      pages[blockIdx.x].comp_res = &comp_results[blockIdx.x];
+    }
+  }
+
+  // copy over uncompressed data
+  if (skip_comp_size != 0 && not comp_in.empty()) {
+    uint8_t const* const src = s->page.page_data + s->page.max_hdr_size;
+    uint8_t* const dst       = s->page.compressed_data + s->page.max_hdr_size;
+    for (int i = t; i < skip_comp_size; i += block_size) {
+      dst[i] = src[i];
+    }
+  }
+}
+
+constexpr int decide_compression_warps_in_block = 4;
+constexpr int decide_compression_block_size =
+  decide_compression_warps_in_block * cudf::detail::warp_size;
+
+// blockDim(decide_compression_block_size, 1, 1)
+__global__ void __launch_bounds__(decide_compression_block_size)
+  gpuDecideCompression(device_span<EncColumnChunk> chunks)
+{
+  __shared__ __align__(8) EncColumnChunk ck_g[decide_compression_warps_in_block];
+  __shared__ __align__(4) unsigned int compression_error[decide_compression_warps_in_block];
+  using warp_reduce = cub::WarpReduce<uint32_t>;
+  __shared__ typename warp_reduce::TempStorage temp_storage[decide_compression_warps_in_block][2];
+
+  auto const lane_id  = threadIdx.x % cudf::detail::warp_size;
+  auto const warp_id  = threadIdx.x / cudf::detail::warp_size;
+  auto const chunk_id = blockIdx.x * decide_compression_warps_in_block + warp_id;
+
+  if (chunk_id >= chunks.size()) { return; }
+
+  if (lane_id == 0) {
+    ck_g[warp_id]              = chunks[chunk_id];
+    compression_error[warp_id] = 0;
+  }
+  __syncwarp();
+
+  uint32_t uncompressed_data_size = 0;
+  uint32_t compressed_data_size   = 0;
+  uint32_t encodings              = 0;
+  auto const num_pages            = ck_g[warp_id].num_pages;
+  for (auto page_id = lane_id; page_id < num_pages; page_id += cudf::detail::warp_size) {
+    auto const& curr_page     = ck_g[warp_id].pages[page_id];
+    auto const page_data_size = curr_page.max_data_size;
+    auto const lvl_bytes      = curr_page.def_lvl_bytes + curr_page.rep_lvl_bytes;
+    uncompressed_data_size += page_data_size;
+    if (auto comp_res = curr_page.comp_res; comp_res != nullptr) {
+      compressed_data_size += comp_res->bytes_written + lvl_bytes;
+      if (comp_res->status != compression_status::SUCCESS) {
+        atomicOr(&compression_error[warp_id], 1);
+      }
+    }
+    // collect encoding info for the chunk metadata
+    encodings |= encoding_to_mask(curr_page.encoding);
+  }
+  uncompressed_data_size = warp_reduce(temp_storage[warp_id][0]).Sum(uncompressed_data_size);
+  compressed_data_size   = warp_reduce(temp_storage[warp_id][1]).Sum(compressed_data_size);
+  __syncwarp();
+  encodings = warp_reduce(temp_storage[warp_id][0]).Reduce(encodings, BitwiseOr{});
+  __syncwarp();
+
+  if (lane_id == 0) {
+    auto const write_compressed = compressed_data_size != 0 and compression_error[warp_id] == 0 and
+                                  compressed_data_size < uncompressed_data_size;
+    chunks[chunk_id].is_compressed = write_compressed;
+    chunks[chunk_id].bfr_size      = uncompressed_data_size;
+    chunks[chunk_id].compressed_size =
+      write_compressed ? compressed_data_size : uncompressed_data_size;
+
+    // if there is repetition or definition level data add RLE encoding
+    auto const rle_bits =
+      ck_g[warp_id].col_desc->num_def_level_bits() + ck_g[warp_id].col_desc->num_rep_level_bits();
+    if (rle_bits > 0) { encodings |= encoding_to_mask(Encoding::RLE); }
+    chunks[chunk_id].encodings = encodings;
+  }
+}
+
+/**
+ * Minimal thrift compact protocol support
+ */
+inline __device__ uint8_t* cpw_put_uint8(uint8_t* p, uint8_t v)
+{
+  *p++ = v;
+  return p;
+}
+
+inline __device__ uint8_t* cpw_put_uint32(uint8_t* p, uint32_t v)
+{
+  while (v > 0x7f) {
+    *p++ = v | 0x80;
+    v >>= 7;
+  }
+  *p++ = v;
+  return p;
+}
+
+inline __device__ uint8_t* cpw_put_uint64(uint8_t* p, uint64_t v)
+{
+  while (v > 0x7f) {
+    *p++ = v | 0x80;
+    v >>= 7;
+  }
+  *p++ = v;
+  return p;
+}
+
+inline __device__ uint8_t* cpw_put_int32(uint8_t* p, int32_t v)
+{
+  int32_t s = (v < 0);
+  return cpw_put_uint32(p, (v ^ -s) * 2 + s);
+}
+
+inline __device__ uint8_t* cpw_put_int64(uint8_t* p, int64_t v)
+{
+  int64_t s = (v < 0);
+  return cpw_put_uint64(p, (v ^ -s) * 2 + s);
+}
+
+inline __device__ uint8_t* cpw_put_fldh(uint8_t* p, int f, int cur, int t)
+{
+  if (f > cur && f <= cur + 15) {
+    *p++ = ((f - cur) << 4) | t;
+    return p;
+  } else {
+    *p++ = t;
+    return cpw_put_int32(p, f);
+  }
+}
+
+class header_encoder {
+  uint8_t* current_header_ptr;
+  int current_field_index;
+
+ public:
+  inline __device__ header_encoder(uint8_t* header_start)
+    : current_header_ptr(header_start), current_field_index(0)
+  {
+  }
+
+  inline __device__ void field_struct_begin(int field)
+  {
+    current_header_ptr =
+      cpw_put_fldh(current_header_ptr, field, current_field_index, ST_FLD_STRUCT);
+    current_field_index = 0;
+  }
+
+  inline __device__ void field_struct_end(int field)
+  {
+    *current_header_ptr++ = 0;
+    current_field_index   = field;
+  }
+
+  inline __device__ void field_list_begin(int field, size_t len, int type)
+  {
+    current_header_ptr = cpw_put_fldh(current_header_ptr, field, current_field_index, ST_FLD_LIST);
+    current_header_ptr = cpw_put_uint8(
+      current_header_ptr, static_cast<uint8_t>((std::min(len, size_t{0xfu}) << 4) | type));
+    if (len >= 0xf) { current_header_ptr = cpw_put_uint32(current_header_ptr, len); }
+    current_field_index = 0;
+  }
+
+  inline __device__ void field_list_end(int field) { current_field_index = field; }
+
+  inline __device__ void put_bool(bool value)
+  {
+    current_header_ptr = cpw_put_uint8(current_header_ptr, value ? ST_FLD_TRUE : ST_FLD_FALSE);
+  }
+
+  inline __device__ void put_binary(void const* value, uint32_t length)
+  {
+    current_header_ptr = cpw_put_uint32(current_header_ptr, length);
+    memcpy(current_header_ptr, value, length);
+    current_header_ptr += length;
+  }
+
+  template <typename T>
+  inline __device__ void put_int64(T value)
+  {
+    current_header_ptr = cpw_put_int64(current_header_ptr, static_cast<int64_t>(value));
+  }
+
+  inline __device__ void field_bool(int field, bool value)
+  {
+    current_header_ptr = cpw_put_fldh(
+      current_header_ptr, field, current_field_index, value ? ST_FLD_TRUE : ST_FLD_FALSE);
+    current_field_index = field;
+  }
+
+  template <typename T>
+  inline __device__ void field_int32(int field, T value)
+  {
+    current_header_ptr  = cpw_put_fldh(current_header_ptr, field, current_field_index, ST_FLD_I32);
+    current_header_ptr  = cpw_put_int32(current_header_ptr, static_cast<int32_t>(value));
+    current_field_index = field;
+  }
+
+  template <typename T>
+  inline __device__ void field_int64(int field, T value)
+  {
+    current_header_ptr  = cpw_put_fldh(current_header_ptr, field, current_field_index, ST_FLD_I64);
+    current_header_ptr  = cpw_put_int64(current_header_ptr, static_cast<int64_t>(value));
+    current_field_index = field;
+  }
+
+  inline __device__ void field_binary(int field, void const* value, uint32_t length)
+  {
+    current_header_ptr =
+      cpw_put_fldh(current_header_ptr, field, current_field_index, ST_FLD_BINARY);
+    current_header_ptr = cpw_put_uint32(current_header_ptr, length);
+    memcpy(current_header_ptr, value, length);
+    current_header_ptr += length;
+    current_field_index = field;
+  }
+
+  inline __device__ void end(uint8_t** header_end, bool termination_flag = true)
+  {
+    if (not termination_flag) { *current_header_ptr++ = 0; }
+    *header_end = current_header_ptr;
+  }
+
+  inline __device__ uint8_t* get_ptr() { return current_header_ptr; }
+
+  inline __device__ void set_ptr(uint8_t* ptr) { current_header_ptr = ptr; }
+};
+
+namespace {
+
+// byteswap 128 bit integer, placing result in dst in network byte order.
+// dst must point to at least 16 bytes of memory.
+__device__ void byte_reverse128(__int128_t v, void* dst)
+{
+  auto const v_char_ptr = reinterpret_cast<unsigned char const*>(&v);
+  auto const d_char_ptr = static_cast<unsigned char*>(dst);
+  thrust::copy(thrust::seq,
+               thrust::make_reverse_iterator(v_char_ptr + sizeof(v)),
+               thrust::make_reverse_iterator(v_char_ptr),
+               d_char_ptr);
+}
+
+/**
+ * @brief Test to see if a span contains all valid UTF-8 characters.
+ *
+ * @param span device_span to test.
+ * @return true if the span contains all valid UTF-8 characters.
+ */
+__device__ bool is_valid_utf8(device_span<unsigned char const> span)
+{
+  auto idx = 0;
+  while (idx < span.size_bytes()) {
+    // UTF-8 character should start with valid beginning bit pattern
+    if (not strings::detail::is_valid_begin_utf8_char(span[idx])) { return false; }
+    // subsequent elements of the character should be continuation chars
+    auto const width = strings::detail::bytes_in_utf8_byte(span[idx++]);
+    for (size_type i = 1; i < width && idx < span.size_bytes(); i++, idx++) {
+      if (not strings::detail::is_utf8_continuation_char(span[idx])) { return false; }
+    }
+  }
+
+  return true;
+}
+
+/**
+ * @brief Increment part of a UTF-8 character.
+ *
+ * Attempt to increment the char pointed to by ptr, which is assumed to be part of a valid UTF-8
+ * character. Returns true if successful, false if the increment caused an overflow, in which case
+ * the data at ptr will be set to the lowest valid UTF-8 bit pattern (start or continuation).
+ * Will halt execution if passed invalid UTF-8.
+ */
+__device__ bool increment_utf8_at(unsigned char* ptr)
+{
+  unsigned char elem = *ptr;
+  // elem is one of (no 5 or 6 byte chars allowed):
+  //  0b0vvvvvvv a 1 byte character
+  //  0b10vvvvvv a continuation byte
+  //  0b110vvvvv start of a 2 byte character
+  //  0b1110vvvv start of a 3 byte character
+  //  0b11110vvv start of a 4 byte character
+
+  // TODO(ets): starting at 4 byte and working down.  Should probably start low and work higher.
+  uint8_t mask  = 0xF8;
+  uint8_t valid = 0xF0;
+
+  while (mask != 0) {
+    if ((elem & mask) == valid) {
+      elem++;
+      if ((elem & mask) != mask) {  // no overflow
+        *ptr = elem;
+        return true;
+      }
+      *ptr = valid;
+      return false;
+    }
+    mask <<= 1;
+    valid <<= 1;
+  }
+
+  // should not reach here since we test for valid UTF-8 higher up the call chain
+  CUDF_UNREACHABLE("Trying to increment non-utf8");
+}
+
+/**
+ * @brief Attempt to truncate a span of UTF-8 characters to at most truncate_length_bytes.
+ *
+ * If is_min is false, then the final character (or characters if there is overflow) will be
+ * incremented so that the resultant UTF-8 will still be a valid maximum. scratch is only used when
+ * is_min is false, and must be at least truncate_length bytes in size. If the span cannot be
+ * truncated, leave it untouched and return the original length.
+ *
+ * @return Pair object containing a pointer to the truncated data and its length.
+ */
+__device__ std::pair<void const*, uint32_t> truncate_utf8(device_span<unsigned char const> span,
+                                                          bool is_min,
+                                                          void* scratch,
+                                                          int32_t truncate_length)
+{
+  // we know at this point that truncate_length < size_bytes, so
+  // there is data at [len]. work backwards until we find
+  // the start of a UTF-8 encoded character, since UTF-8 characters may be multi-byte.
+  auto len = truncate_length;
+  while (not strings::detail::is_begin_utf8_char(span[len]) && len > 0) {
+    len--;
+  }
+
+  if (len != 0) {
+    if (is_min) { return {span.data(), len}; }
+    memcpy(scratch, span.data(), len);
+    // increment last byte, working backwards if the byte overflows
+    auto const ptr = static_cast<unsigned char*>(scratch);
+    for (int32_t i = len - 1; i >= 0; i--) {
+      if (increment_utf8_at(&ptr[i])) {  // true if no overflow
+        return {scratch, len};
+      }
+    }
+    // cannot increment, so fall through
+  }
+
+  // couldn't truncate, return original value
+  return {span.data(), span.size_bytes()};
+}
+
+/**
+ * @brief Attempt to truncate a span of binary data to at most truncate_length bytes.
+ *
+ * If is_min is false, then the final byte (or bytes if there is overflow) will be
+ * incremented so that the resultant binary will still be a valid maximum. scratch is only used when
+ * is_min is false, and must be at least truncate_length bytes in size. If the span cannot be
+ * truncated, leave it untouched and return the original length.
+ *
+ * @return Pair object containing a pointer to the truncated data and its length.
+ */
+__device__ std::pair<void const*, uint32_t> truncate_binary(device_span<uint8_t const> arr,
+                                                            bool is_min,
+                                                            void* scratch,
+                                                            int32_t truncate_length)
+{
+  if (is_min) { return {arr.data(), truncate_length}; }
+  memcpy(scratch, arr.data(), truncate_length);
+  // increment last byte, working backwards if the byte overflows
+  auto const ptr = static_cast<uint8_t*>(scratch);
+  for (int32_t i = truncate_length - 1; i >= 0; i--) {
+    ptr[i]++;
+    if (ptr[i] != 0) {  // no overflow
+      return {scratch, i + 1};
+    }
+  }
+
+  // couldn't truncate, return original value
+  return {arr.data(), arr.size_bytes()};
+}
+
+// TODO (ets): the assumption here is that string columns might have UTF-8 or plain binary,
+// while binary columns are assumed to be binary and will be treated as such.  If this assumption
+// is incorrect, then truncate_byte_array() and truncate_string() should just be combined into
+// a single function.
+/**
+ * @brief Attempt to truncate a UTF-8 string to at most truncate_length bytes.
+ */
+__device__ std::pair<void const*, uint32_t> truncate_string(string_view const& str,
+                                                            bool is_min,
+                                                            void* scratch,
+                                                            int32_t truncate_length)
+{
+  if (truncate_length == NO_TRUNC_STATS or str.size_bytes() <= truncate_length) {
+    return {str.data(), str.size_bytes()};
+  }
+
+  // convert char to unsigned since UTF-8 is just bytes, not chars.  can't use std::byte because
+  // that can't be incremented.
+  auto const span = device_span<unsigned char const>(
+    reinterpret_cast<unsigned char const*>(str.data()), str.size_bytes());
+
+  // if str is all 8-bit chars, or is actually not UTF-8, then we can just use truncate_binary()
+  if (str.size_bytes() != str.length() and is_valid_utf8(span.first(truncate_length))) {
+    return truncate_utf8(span, is_min, scratch, truncate_length);
+  }
+  return truncate_binary(span, is_min, scratch, truncate_length);
+}
+
+/**
+ * @brief Attempt to truncate a binary array to at most truncate_length bytes.
+ */
+__device__ std::pair<void const*, uint32_t> truncate_byte_array(
+  statistics::byte_array_view const& arr, bool is_min, void* scratch, int32_t truncate_length)
+{
+  if (truncate_length == NO_TRUNC_STATS or arr.size_bytes() <= truncate_length) {
+    return {arr.data(), arr.size_bytes()};
+  }
+
+  // convert std::byte to uint8_t since bytes can't be incremented
+  device_span<uint8_t const> const span{reinterpret_cast<uint8_t const*>(arr.data()),
+                                        arr.size_bytes()};
+  return truncate_binary(span, is_min, scratch, truncate_length);
+}
+
+/**
+ * @brief Find a min or max value of the proper form to be included in Parquet statistics
+ * structures.
+ *
+ * Given a statistics_val union and a data type, perform any transformations needed to produce a
+ * valid min or max binary value.  String and byte array types will be truncated if they exceed
+ * truncate_length.
+ */
+__device__ std::pair<void const*, uint32_t> get_extremum(statistics_val const* stats_val,
+                                                         statistics_dtype dtype,
+                                                         void* scratch,
+                                                         bool is_min,
+                                                         int32_t truncate_length)
+{
+  switch (dtype) {
+    case dtype_bool: return {stats_val, sizeof(bool)};
+    case dtype_int8:
+    case dtype_int16:
+    case dtype_int32:
+    case dtype_date32: return {stats_val, sizeof(int32_t)};
+    case dtype_float32: {
+      auto const fp_scratch = static_cast<float*>(scratch);
+      fp_scratch[0]         = stats_val->fp_val;
+      return {scratch, sizeof(float)};
+    }
+    case dtype_int64:
+    case dtype_timestamp64:
+    case dtype_float64: return {stats_val, sizeof(int64_t)};
+    case dtype_decimal64:
+    case dtype_decimal128:
+      byte_reverse128(stats_val->d128_val, scratch);
+      return {scratch, sizeof(__int128_t)};
+    case dtype_string: return truncate_string(stats_val->str_val, is_min, scratch, truncate_length);
+    case dtype_byte_array:
+      return truncate_byte_array(stats_val->byte_val, is_min, scratch, truncate_length);
+    default: CUDF_UNREACHABLE("Invalid statistics data type");
+  }
+}
+
+}  // namespace
+
+__device__ uint8_t* EncodeStatistics(uint8_t* start,
+                                     statistics_chunk const* s,
+                                     statistics_dtype dtype,
+                                     void* scratch)
+{
+  uint8_t* end;
+  header_encoder encoder(start);
+  encoder.field_int64(3, s->null_count);
+  if (s->has_minmax) {
+    auto const [max_ptr, max_size] =
+      get_extremum(&s->max_value, dtype, scratch, false, NO_TRUNC_STATS);
+    encoder.field_binary(5, max_ptr, max_size);
+    auto const [min_ptr, min_size] =
+      get_extremum(&s->min_value, dtype, scratch, true, NO_TRUNC_STATS);
+    encoder.field_binary(6, min_ptr, min_size);
+  }
+  encoder.end(&end);
+  return end;
+}
+
+// blockDim(128, 1, 1)
+__global__ void __launch_bounds__(128)
+  gpuEncodePageHeaders(device_span<EncPage> pages,
+                       device_span<compression_result const> comp_results,
+                       device_span<statistics_chunk const> page_stats,
+                       statistics_chunk const* chunk_stats)
+{
+  // When this whole kernel becomes single thread, the following variables need not be __shared__
+  __shared__ __align__(8) parquet_column_device_view col_g;
+  __shared__ __align__(8) EncColumnChunk ck_g;
+  __shared__ __align__(8) EncPage page_g;
+  __shared__ __align__(8) unsigned char scratch[MIN_STATS_SCRATCH_SIZE];
+
+  auto const t = threadIdx.x;
+
+  if (t == 0) {
+    uint8_t *hdr_start, *hdr_end;
+    uint32_t compressed_page_size, uncompressed_page_size;
+
+    page_g = pages[blockIdx.x];
+    ck_g   = *page_g.chunk;
+    col_g  = *ck_g.col_desc;
+
+    if (chunk_stats && &pages[blockIdx.x] == ck_g.pages) {  // Is this the first page in a chunk?
+      hdr_start = (ck_g.is_compressed) ? ck_g.compressed_bfr : ck_g.uncompressed_bfr;
+      hdr_end =
+        EncodeStatistics(hdr_start, &chunk_stats[page_g.chunk_id], col_g.stats_dtype, scratch);
+      page_g.chunk->ck_stat_size = static_cast<uint32_t>(hdr_end - hdr_start);
+    }
+    uncompressed_page_size = page_g.max_data_size;
+    if (ck_g.is_compressed) {
+      auto const lvl_bytes = page_g.def_lvl_bytes + page_g.rep_lvl_bytes;
+      hdr_start            = page_g.compressed_data;
+      compressed_page_size =
+        static_cast<uint32_t>(comp_results[blockIdx.x].bytes_written) + lvl_bytes;
+      page_g.max_data_size = compressed_page_size;
+    } else {
+      hdr_start            = page_g.page_data;
+      compressed_page_size = uncompressed_page_size;
+    }
+    header_encoder encoder(hdr_start);
+    PageType page_type = page_g.page_type;
+
+    encoder.field_int32(1, page_type);
+    encoder.field_int32(2, uncompressed_page_size);
+    encoder.field_int32(3, compressed_page_size);
+
+    if (page_type == PageType::DATA_PAGE) {
+      // DataPageHeader
+      encoder.field_struct_begin(5);
+      encoder.field_int32(1, page_g.num_values);  // NOTE: num_values != num_rows for list types
+      encoder.field_int32(2, page_g.encoding);    // encoding
+      encoder.field_int32(3, Encoding::RLE);      // definition_level_encoding
+      encoder.field_int32(4, Encoding::RLE);      // repetition_level_encoding
+      // Optionally encode page-level statistics
+      if (not page_stats.empty()) {
+        encoder.field_struct_begin(5);
+        encoder.set_ptr(
+          EncodeStatistics(encoder.get_ptr(), &page_stats[blockIdx.x], col_g.stats_dtype, scratch));
+        encoder.field_struct_end(5);
+      }
+      encoder.field_struct_end(5);
+    } else if (page_type == PageType::DATA_PAGE_V2) {
+      // DataPageHeaderV2
+      encoder.field_struct_begin(8);
+      encoder.field_int32(1, page_g.num_values);
+      encoder.field_int32(2, page_g.num_nulls);
+      encoder.field_int32(3, page_g.num_rows);
+      encoder.field_int32(4, page_g.encoding);
+      encoder.field_int32(5, page_g.def_lvl_bytes);
+      encoder.field_int32(6, page_g.rep_lvl_bytes);
+      encoder.field_bool(7, ck_g.is_compressed);  // TODO can compress at page level now
+      // Optionally encode page-level statistics
+      if (not page_stats.empty()) {
+        encoder.field_struct_begin(8);
+        encoder.set_ptr(
+          EncodeStatistics(encoder.get_ptr(), &page_stats[blockIdx.x], col_g.stats_dtype, scratch));
+        encoder.field_struct_end(8);
+      }
+      encoder.field_struct_end(8);
+    } else {
+      // DictionaryPageHeader
+      encoder.field_struct_begin(7);
+      encoder.field_int32(1, ck_g.num_dict_entries);  // number of values in dictionary
+      encoder.field_int32(2, page_g.encoding);
+      encoder.field_struct_end(7);
+    }
+    encoder.end(&hdr_end, false);
+    page_g.hdr_size = (uint32_t)(hdr_end - hdr_start);
+  }
+  __syncthreads();
+  if (t == 0) pages[blockIdx.x] = page_g;
+}
+
+// blockDim(1024, 1, 1)
+__global__ void __launch_bounds__(1024)
+  gpuGatherPages(device_span<EncColumnChunk> chunks, device_span<gpu::EncPage const> pages)
+{
+  __shared__ __align__(8) EncColumnChunk ck_g;
+  __shared__ __align__(8) EncPage page_g;
+
+  auto const t = threadIdx.x;
+  uint8_t *dst, *dst_base;
+  EncPage const* first_page;
+  uint32_t num_pages, uncompressed_size;
+
+  if (t == 0) ck_g = chunks[blockIdx.x];
+  __syncthreads();
+
+  first_page = ck_g.pages;
+  num_pages  = ck_g.num_pages;
+  dst        = (ck_g.is_compressed) ? ck_g.compressed_bfr : ck_g.uncompressed_bfr;
+  dst += ck_g.ck_stat_size;  // Skip over chunk statistics
+  dst_base          = dst;
+  uncompressed_size = ck_g.bfr_size;
+  for (uint32_t page = 0; page < num_pages; page++) {
+    uint8_t const* src;
+    uint32_t hdr_len, data_len;
+
+    if (t == 0) { page_g = first_page[page]; }
+    __syncthreads();
+
+    src = (ck_g.is_compressed) ? page_g.compressed_data : page_g.page_data;
+    // Copy page header
+    hdr_len = page_g.hdr_size;
+    memcpy_block<1024, true>(dst, src, hdr_len, t);
+    src += page_g.max_hdr_size;
+    dst += hdr_len;
+    // Copy page data
+    uncompressed_size += hdr_len;
+    data_len = page_g.max_data_size;
+    memcpy_block<1024, true>(dst, src, data_len, t);
+    dst += data_len;
+    __syncthreads();
+    if (!t && page == 0 && ck_g.use_dictionary) { ck_g.dictionary_size = hdr_len + data_len; }
+  }
+  if (t == 0) {
+    chunks[blockIdx.x].bfr_size        = uncompressed_size;
+    chunks[blockIdx.x].compressed_size = (dst - dst_base);
+    if (ck_g.use_dictionary) { chunks[blockIdx.x].dictionary_size = ck_g.dictionary_size; }
+  }
+}
+
+namespace {
+
+/**
+ * @brief Tests if statistics are comparable given the column's
+ * physical and converted types
+ */
+__device__ bool is_comparable(Type ptype, ConvertedType ctype)
+{
+  switch (ptype) {
+    case Type::BOOLEAN:
+    case Type::INT32:
+    case Type::INT64:
+    case Type::FLOAT:
+    case Type::DOUBLE:
+    case Type::BYTE_ARRAY: return true;
+    case Type::FIXED_LEN_BYTE_ARRAY:
+      if (ctype == ConvertedType::DECIMAL) { return true; }
+      [[fallthrough]];
+    default: return false;
+  }
+}
+
+/**
+ * @brief Compares two values.
+ * @return -1 if v1 < v2, 0 if v1 == v2, 1 if v1 > v2
+ */
+template <typename T>
+constexpr __device__ int32_t compare(T& v1, T& v2)
+{
+  return (v1 > v2) - (v1 < v2);
+}
+
+/**
+ * @brief Compares two statistics_val structs.
+ * @return < 0 if v1 < v2, 0 if v1 == v2, > 0 if v1 > v2
+ */
+__device__ int32_t compare_values(Type ptype,
+                                  ConvertedType ctype,
+                                  statistics_val const& v1,
+                                  statistics_val const& v2)
+{
+  switch (ptype) {
+    case Type::BOOLEAN: return compare(v1.u_val, v2.u_val);
+    case Type::INT32:
+    case Type::INT64:
+      switch (ctype) {
+        case ConvertedType::UINT_8:
+        case ConvertedType::UINT_16:
+        case ConvertedType::UINT_32:
+        case ConvertedType::UINT_64: return compare(v1.u_val, v2.u_val);
+        default:  // assume everything else is signed
+          return compare(v1.i_val, v2.i_val);
+      }
+    case Type::FLOAT:
+    case Type::DOUBLE: return compare(v1.fp_val, v2.fp_val);
+    case Type::BYTE_ARRAY: return static_cast<string_view>(v1.str_val).compare(v2.str_val);
+    case Type::FIXED_LEN_BYTE_ARRAY:
+      if (ctype == ConvertedType::DECIMAL) { return compare(v1.d128_val, v2.d128_val); }
+  }
+  // calling is_comparable() should prevent reaching here
+  CUDF_UNREACHABLE("Trying to compare non-comparable type");
+  return 0;
+}
+
+/**
+ * @brief Determine if a set of statstistics are in ascending order.
+ */
+__device__ bool is_ascending(statistics_chunk const* s,
+                             Type ptype,
+                             ConvertedType ctype,
+                             uint32_t num_pages)
+{
+  for (uint32_t i = 1; i < num_pages; i++) {
+    if (compare_values(ptype, ctype, s[i - 1].min_value, s[i].min_value) > 0 ||
+        compare_values(ptype, ctype, s[i - 1].max_value, s[i].max_value) > 0) {
+      return false;
+    }
+  }
+  return true;
+}
+
+/**
+ * @brief Determine if a set of statstistics are in descending order.
+ */
+__device__ bool is_descending(statistics_chunk const* s,
+                              Type ptype,
+                              ConvertedType ctype,
+                              uint32_t num_pages)
+{
+  for (uint32_t i = 1; i < num_pages; i++) {
+    if (compare_values(ptype, ctype, s[i - 1].min_value, s[i].min_value) < 0 ||
+        compare_values(ptype, ctype, s[i - 1].max_value, s[i].max_value) < 0) {
+      return false;
+    }
+  }
+  return true;
+}
+
+/**
+ * @brief Determine the ordering of a set of statistics.
+ */
+__device__ int32_t calculate_boundary_order(statistics_chunk const* s,
+                                            Type ptype,
+                                            ConvertedType ctype,
+                                            uint32_t num_pages)
+{
+  if (not is_comparable(ptype, ctype)) { return BoundaryOrder::UNORDERED; }
+  if (is_ascending(s, ptype, ctype, num_pages)) {
+    return BoundaryOrder::ASCENDING;
+  } else if (is_descending(s, ptype, ctype, num_pages)) {
+    return BoundaryOrder::DESCENDING;
+  }
+  return BoundaryOrder::UNORDERED;
+}
+
+// align ptr to an 8-byte boundary. address returned will be <= ptr.
+constexpr __device__ void* align8(void* ptr)
+{
+  // it's ok to round down because we have an extra 7 bytes in the buffer
+  auto algn = 3 & reinterpret_cast<std::uintptr_t>(ptr);
+  return static_cast<char*>(ptr) - algn;
+}
+
+}  // namespace
+
+// blockDim(1, 1, 1)
+__global__ void __launch_bounds__(1)
+  gpuEncodeColumnIndexes(device_span<EncColumnChunk> chunks,
+                         device_span<statistics_chunk const> column_stats,
+                         int32_t column_index_truncate_length)
+{
+  __align__(8) unsigned char s_scratch[MIN_STATS_SCRATCH_SIZE];
+  uint8_t* col_idx_end;
+
+  if (column_stats.empty()) { return; }
+
+  EncColumnChunk* ck_g             = &chunks[blockIdx.x];
+  uint32_t num_pages               = ck_g->num_pages;
+  parquet_column_device_view col_g = *ck_g->col_desc;
+  size_t first_data_page           = ck_g->use_dictionary ? 1 : 0;
+  uint32_t pageidx                 = ck_g->first_page;
+
+  header_encoder encoder(ck_g->column_index_blob);
+
+  // make sure scratch is aligned properly. here column_index_size indicates
+  // how much scratch space is available for this chunk, including space for
+  // truncation scratch + padding for alignment.
+  void* scratch =
+    column_index_truncate_length < MIN_STATS_SCRATCH_SIZE
+      ? s_scratch
+      : align8(ck_g->column_index_blob + ck_g->column_index_size - column_index_truncate_length);
+
+  // null_pages
+  encoder.field_list_begin(1, num_pages - first_data_page, ST_FLD_TRUE);
+  for (uint32_t page = first_data_page; page < num_pages; page++) {
+    encoder.put_bool(column_stats[pageidx + page].non_nulls == 0);
+  }
+  encoder.field_list_end(1);
+  // min_values
+  encoder.field_list_begin(2, num_pages - first_data_page, ST_FLD_BINARY);
+  for (uint32_t page = first_data_page; page < num_pages; page++) {
+    auto const [min_ptr, min_size] = get_extremum(&column_stats[pageidx + page].min_value,
+                                                  col_g.stats_dtype,
+                                                  scratch,
+                                                  true,
+                                                  column_index_truncate_length);
+    encoder.put_binary(min_ptr, min_size);
+  }
+  encoder.field_list_end(2);
+  // max_values
+  encoder.field_list_begin(3, num_pages - first_data_page, ST_FLD_BINARY);
+  for (uint32_t page = first_data_page; page < num_pages; page++) {
+    auto const [max_ptr, max_size] = get_extremum(&column_stats[pageidx + page].max_value,
+                                                  col_g.stats_dtype,
+                                                  scratch,
+                                                  false,
+                                                  column_index_truncate_length);
+    encoder.put_binary(max_ptr, max_size);
+  }
+  encoder.field_list_end(3);
+  // boundary_order
+  encoder.field_int32(4,
+                      calculate_boundary_order(&column_stats[first_data_page + pageidx],
+                                               col_g.physical_type,
+                                               col_g.converted_type,
+                                               num_pages - first_data_page));
+  // null_counts
+  encoder.field_list_begin(5, num_pages - first_data_page, ST_FLD_I64);
+  for (uint32_t page = first_data_page; page < num_pages; page++) {
+    encoder.put_int64(column_stats[pageidx + page].null_count);
+  }
+  encoder.field_list_end(5);
+  encoder.end(&col_idx_end, false);
+
+  // now reset column_index_size to the actual size of the encoded column index blob
+  ck_g->column_index_size = static_cast<uint32_t>(col_idx_end - ck_g->column_index_blob);
+}
+
+void InitRowGroupFragments(device_2dspan<PageFragment> frag,
+                           device_span<parquet_column_device_view const> col_desc,
+                           device_span<partition_info const> partitions,
+                           device_span<int const> part_frag_offset,
+                           uint32_t fragment_size,
+                           rmm::cuda_stream_view stream)
+{
+  auto const num_columns              = frag.size().first;
+  auto const num_fragments_per_column = frag.size().second;
+  auto const grid_y = std::min(static_cast<uint32_t>(num_fragments_per_column), MAX_GRID_Y_SIZE);
+  dim3 const dim_grid(num_columns, grid_y);  // 1 threadblock per fragment
+  gpuInitRowGroupFragments<512><<<dim_grid, 512, 0, stream.value()>>>(
+    frag, col_desc, partitions, part_frag_offset, fragment_size);
+}
+
+void CalculatePageFragments(device_span<PageFragment> frag,
+                            device_span<size_type const> column_frag_sizes,
+                            rmm::cuda_stream_view stream)
+{
+  gpuCalculatePageFragments<512><<<frag.size(), 512, 0, stream.value()>>>(frag, column_frag_sizes);
+}
+
+void InitFragmentStatistics(device_span<statistics_group> groups,
+                            device_span<PageFragment const> fragments,
+                            rmm::cuda_stream_view stream)
+{
+  int const num_fragments = fragments.size();
+  int const dim = util::div_rounding_up_safe(num_fragments, 128 / cudf::detail::warp_size);
+  gpuInitFragmentStats<<<dim, 128, 0, stream.value()>>>(groups, fragments);
+}
+
+void InitEncoderPages(device_2dspan<EncColumnChunk> chunks,
+                      device_span<gpu::EncPage> pages,
+                      device_span<size_type> page_sizes,
+                      device_span<size_type> comp_page_sizes,
+                      device_span<parquet_column_device_view const> col_desc,
+                      int32_t num_columns,
+                      size_t max_page_size_bytes,
+                      size_type max_page_size_rows,
+                      uint32_t page_align,
+                      bool write_v2_headers,
+                      statistics_merge_group* page_grstats,
+                      statistics_merge_group* chunk_grstats,
+                      rmm::cuda_stream_view stream)
+{
+  auto num_rowgroups = chunks.size().first;
+  dim3 dim_grid(num_columns, num_rowgroups);  // 1 threadblock per rowgroup
+  gpuInitPages<<<dim_grid, 128, 0, stream.value()>>>(chunks,
+                                                     pages,
+                                                     page_sizes,
+                                                     comp_page_sizes,
+                                                     col_desc,
+                                                     page_grstats,
+                                                     chunk_grstats,
+                                                     num_columns,
+                                                     max_page_size_bytes,
+                                                     max_page_size_rows,
+                                                     page_align,
+                                                     write_v2_headers);
+}
+
+void EncodePages(device_span<gpu::EncPage> pages,
+                 bool write_v2_headers,
+                 device_span<device_span<uint8_t const>> comp_in,
+                 device_span<device_span<uint8_t>> comp_out,
+                 device_span<compression_result> comp_results,
+                 rmm::cuda_stream_view stream)
+{
+  auto num_pages = pages.size();
+  // A page is part of one column. This is launching 1 block per page. 1 block will exclusively
+  // deal with one datatype.
+  gpuEncodePages<128><<<num_pages, 128, 0, stream.value()>>>(
+    pages, comp_in, comp_out, comp_results, write_v2_headers);
+}
+
+void DecideCompression(device_span<EncColumnChunk> chunks, rmm::cuda_stream_view stream)
+{
+  auto const num_blocks =
+    util::div_rounding_up_safe<int>(chunks.size(), decide_compression_warps_in_block);
+  gpuDecideCompression<<<num_blocks, decide_compression_block_size, 0, stream.value()>>>(chunks);
+}
+
+void EncodePageHeaders(device_span<EncPage> pages,
+                       device_span<compression_result const> comp_results,
+                       device_span<statistics_chunk const> page_stats,
+                       statistics_chunk const* chunk_stats,
+                       rmm::cuda_stream_view stream)
+{
+  // TODO: single thread task. No need for 128 threads/block. Earlier it used to employ rest of the
+  // threads to coop load structs
+  gpuEncodePageHeaders<<<pages.size(), 128, 0, stream.value()>>>(
+    pages, comp_results, page_stats, chunk_stats);
+}
+
+void GatherPages(device_span<EncColumnChunk> chunks,
+                 device_span<gpu::EncPage const> pages,
+                 rmm::cuda_stream_view stream)
+{
+  gpuGatherPages<<<chunks.size(), 1024, 0, stream.value()>>>(chunks, pages);
+}
+
+void EncodeColumnIndexes(device_span<EncColumnChunk> chunks,
+                         device_span<statistics_chunk const> column_stats,
+                         int32_t column_index_truncate_length,
+                         rmm::cuda_stream_view stream)
+{
+  gpuEncodeColumnIndexes<<<chunks.size(), 1, 0, stream.value()>>>(
+    chunks, column_stats, column_index_truncate_length);
+}
+
+}  // namespace gpu
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/page_hdr.cu b/cpp/src/io/parquet/page_hdr.cu
new file mode 100644
index 0000000..6f8b2f5
--- /dev/null
+++ b/cpp/src/io/parquet/page_hdr.cu
@@ -0,0 +1,534 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "parquet_gpu.hpp"
+#include <io/utilities/block_utils.cuh>
+#include <thrust/tuple.h>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+namespace gpu {
+// Minimal thrift implementation for parsing page headers
+// https://github.com/apache/thrift/blob/master/doc/specs/thrift-compact-protocol.md
+
+static const __device__ __constant__ uint8_t g_list2struct[16] = {0,
+                                                                  1,
+                                                                  2,
+                                                                  ST_FLD_BYTE,
+                                                                  ST_FLD_DOUBLE,
+                                                                  5,
+                                                                  ST_FLD_I16,
+                                                                  7,
+                                                                  ST_FLD_I32,
+                                                                  9,
+                                                                  ST_FLD_I64,
+                                                                  ST_FLD_BINARY,
+                                                                  ST_FLD_STRUCT,
+                                                                  ST_FLD_MAP,
+                                                                  ST_FLD_SET,
+                                                                  ST_FLD_LIST};
+
+struct byte_stream_s {
+  uint8_t const* cur{};
+  uint8_t const* end{};
+  uint8_t const* base{};
+  // Parsed symbols
+  PageType page_type{};
+  PageInfo page{};
+  ColumnChunkDesc ck{};
+};
+
+/**
+ * @brief Get current byte from the byte stream
+ *
+ * @param[in] bs Byte stream
+ *
+ * @return Current byte pointed to by the byte stream
+ */
+inline __device__ unsigned int getb(byte_stream_s* bs)
+{
+  return (bs->cur < bs->end) ? *bs->cur++ : 0;
+}
+
+inline __device__ void skip_bytes(byte_stream_s* bs, size_t bytecnt)
+{
+  bytecnt = min(bytecnt, (size_t)(bs->end - bs->cur));
+  bs->cur += bytecnt;
+}
+
+/**
+ * @brief Decode unsigned integer from a byte stream using VarInt encoding
+ *
+ * Concatenate least significant 7 bits of each byte to form a 32 bit
+ * integer. Most significant bit of each byte indicates if more bytes
+ * are to be used to form the number.
+ *
+ * @param[in] bs Byte stream
+ *
+ * @return Decoded 32 bit integer
+ */
+__device__ uint32_t get_u32(byte_stream_s* bs)
+{
+  uint32_t v = 0, l = 0, c;
+  do {
+    c = getb(bs);
+    v |= (c & 0x7f) << l;
+    l += 7;
+  } while (c & 0x80);
+  return v;
+}
+
+/**
+ * @brief Decode signed integer from a byte stream using zigzag encoding
+ *
+ * The number n encountered in a byte stream translates to
+ * -1^(n%2) * ceil(n/2), with the exception of 0 which remains the same.
+ * i.e. 0, 1, 2, 3, 4, 5 etc convert to 0, -1, 1, -2, 2 respectively.
+ *
+ * @param[in] bs Byte stream
+ *
+ * @return Decoded 32 bit integer
+ */
+inline __device__ int32_t get_i32(byte_stream_s* bs)
+{
+  uint32_t u = get_u32(bs);
+  return (int32_t)((u >> 1u) ^ -(int32_t)(u & 1));
+}
+
+__device__ void skip_struct_field(byte_stream_s* bs, int field_type)
+{
+  int struct_depth = 0;
+  int rep_cnt      = 0;
+
+  do {
+    if (rep_cnt != 0) {
+      rep_cnt--;
+    } else if (struct_depth != 0) {
+      unsigned int c;
+      do {
+        c = getb(bs);
+        if (!c) --struct_depth;
+      } while (!c && struct_depth);
+      if (!struct_depth) break;
+      field_type = c & 0xf;
+      if (!(c & 0xf0)) get_i32(bs);
+    }
+    switch (field_type) {
+      case ST_FLD_TRUE:
+      case ST_FLD_FALSE: break;
+      case ST_FLD_I16:
+      case ST_FLD_I32:
+      case ST_FLD_I64: get_u32(bs); break;
+      case ST_FLD_BYTE: skip_bytes(bs, 1); break;
+      case ST_FLD_DOUBLE: skip_bytes(bs, 8); break;
+      case ST_FLD_BINARY: skip_bytes(bs, get_u32(bs)); break;
+      case ST_FLD_LIST:
+      case ST_FLD_SET: {  // NOTE: skipping a list of lists is not handled
+        auto const c = getb(bs);
+        int n        = c >> 4;
+        if (n == 0xf) n = get_u32(bs);
+        field_type = g_list2struct[c & 0xf];
+        if (field_type == ST_FLD_STRUCT)
+          struct_depth += n;
+        else
+          rep_cnt = n;
+      } break;
+      case ST_FLD_STRUCT: struct_depth++; break;
+    }
+  } while (rep_cnt || struct_depth);
+}
+
+/**
+ * @brief Determine which decode kernel to run for the given page.
+ *
+ * @param page The page to decode
+ * @param chunk Column chunk the page belongs to
+ * @return `kernel_mask_bits` value for the given page
+ */
+__device__ uint32_t kernel_mask_for_page(gpu::PageInfo const& page,
+                                         gpu::ColumnChunkDesc const& chunk)
+{
+  if (page.flags & PAGEINFO_FLAGS_DICTIONARY) { return 0; }
+
+  if (page.encoding == Encoding::DELTA_BINARY_PACKED) {
+    return KERNEL_MASK_DELTA_BINARY;
+  } else if (is_string_col(chunk)) {
+    return KERNEL_MASK_STRING;
+  }
+
+  // non-string, non-delta
+  return KERNEL_MASK_GENERAL;
+}
+
+/**
+ * @brief Functor to set value to 32 bit integer read from byte stream
+ *
+ * @return True if field type is not int32
+ */
+struct ParquetFieldInt32 {
+  int field;
+  int32_t& val;
+
+  __device__ ParquetFieldInt32(int f, int32_t& v) : field(f), val(v) {}
+
+  inline __device__ bool operator()(byte_stream_s* bs, int field_type)
+  {
+    val = get_i32(bs);
+    return (field_type != ST_FLD_I32);
+  }
+};
+
+/**
+ * @brief Functor to set value to enum read from byte stream
+ *
+ * @return True if field type is not int32
+ */
+template <typename Enum>
+struct ParquetFieldEnum {
+  int field;
+  Enum& val;
+
+  __device__ ParquetFieldEnum(int f, Enum& v) : field(f), val(v) {}
+
+  inline __device__ bool operator()(byte_stream_s* bs, int field_type)
+  {
+    val = static_cast<Enum>(get_i32(bs));
+    return (field_type != ST_FLD_I32);
+  }
+};
+
+/**
+ * @brief Functor to run operator on byte stream
+ *
+ * @return True if field type is not struct type or if the calling operator
+ * fails
+ */
+template <typename Operator>
+struct ParquetFieldStruct {
+  int field;
+  Operator op;
+
+  __device__ ParquetFieldStruct(int f) : field(f) {}
+
+  inline __device__ bool operator()(byte_stream_s* bs, int field_type)
+  {
+    return ((field_type != ST_FLD_STRUCT) || !op(bs));
+  }
+};
+
+/**
+ * @brief Functor to run an operator
+ *
+ * The purpose of this functor is to replace a switch case. If the field in
+ * the argument is equal to the field specified in any element of the tuple
+ * of operators then it is run with the byte stream and field type arguments.
+ *
+ * If the field does not match any of the functors then skip_struct_field is
+ * called over the byte stream.
+ *
+ * @return Return value of the selected operator or false if no operator
+ * matched the field value
+ */
+template <int index>
+struct FunctionSwitchImpl {
+  template <typename... Operator>
+  static inline __device__ bool run(byte_stream_s* bs,
+                                    int field_type,
+                                    int const& field,
+                                    thrust::tuple<Operator...>& ops)
+  {
+    if (field == thrust::get<index>(ops).field) {
+      return thrust::get<index>(ops)(bs, field_type);
+    } else {
+      return FunctionSwitchImpl<index - 1>::run(bs, field_type, field, ops);
+    }
+  }
+};
+
+template <>
+struct FunctionSwitchImpl<0> {
+  template <typename... Operator>
+  static inline __device__ bool run(byte_stream_s* bs,
+                                    int field_type,
+                                    int const& field,
+                                    thrust::tuple<Operator...>& ops)
+  {
+    if (field == thrust::get<0>(ops).field) {
+      return thrust::get<0>(ops)(bs, field_type);
+    } else {
+      skip_struct_field(bs, field_type);
+      return false;
+    }
+  }
+};
+
+/**
+ * @brief Function to parse page header based on the tuple of functors provided
+ *
+ * Bytes are read from the byte stream and the field delta and field type are
+ * matched up against user supplied reading functors. If they match then the
+ * corresponding values are written to references pointed to by the functors.
+ *
+ * @return Returns false if an unexpected field is encountered while reading
+ * byte stream. Otherwise true is returned.
+ */
+template <typename... Operator>
+inline __device__ bool parse_header(thrust::tuple<Operator...>& op, byte_stream_s* bs)
+{
+  constexpr int index = thrust::tuple_size<thrust::tuple<Operator...>>::value - 1;
+  int field           = 0;
+  while (true) {
+    auto const current_byte = getb(bs);
+    if (!current_byte) break;
+    int const field_delta = current_byte >> 4;
+    int const field_type  = current_byte & 0xf;
+    field                 = field_delta ? field + field_delta : get_i32(bs);
+    bool exit_function    = FunctionSwitchImpl<index>::run(bs, field_type, field, op);
+    if (exit_function) { return false; }
+  }
+  return true;
+}
+
+struct gpuParseDataPageHeader {
+  __device__ bool operator()(byte_stream_s* bs)
+  {
+    auto op = thrust::make_tuple(ParquetFieldInt32(1, bs->page.num_input_values),
+                                 ParquetFieldEnum<Encoding>(2, bs->page.encoding),
+                                 ParquetFieldEnum<Encoding>(3, bs->page.definition_level_encoding),
+                                 ParquetFieldEnum<Encoding>(4, bs->page.repetition_level_encoding));
+    return parse_header(op, bs);
+  }
+};
+
+struct gpuParseDictionaryPageHeader {
+  __device__ bool operator()(byte_stream_s* bs)
+  {
+    auto op = thrust::make_tuple(ParquetFieldInt32(1, bs->page.num_input_values),
+                                 ParquetFieldEnum<Encoding>(2, bs->page.encoding));
+    return parse_header(op, bs);
+  }
+};
+
+struct gpuParseDataPageHeaderV2 {
+  __device__ bool operator()(byte_stream_s* bs)
+  {
+    auto op = thrust::make_tuple(ParquetFieldInt32(1, bs->page.num_input_values),
+                                 ParquetFieldInt32(2, bs->page.num_nulls),
+                                 ParquetFieldInt32(3, bs->page.num_rows),
+                                 ParquetFieldEnum<Encoding>(4, bs->page.encoding),
+                                 ParquetFieldInt32(5, bs->page.lvl_bytes[level_type::DEFINITION]),
+                                 ParquetFieldInt32(6, bs->page.lvl_bytes[level_type::REPETITION]));
+    return parse_header(op, bs);
+  }
+};
+
+struct gpuParsePageHeader {
+  __device__ bool operator()(byte_stream_s* bs)
+  {
+    auto op = thrust::make_tuple(ParquetFieldEnum<PageType>(1, bs->page_type),
+                                 ParquetFieldInt32(2, bs->page.uncompressed_page_size),
+                                 ParquetFieldInt32(3, bs->page.compressed_page_size),
+                                 ParquetFieldStruct<gpuParseDataPageHeader>(5),
+                                 ParquetFieldStruct<gpuParseDictionaryPageHeader>(7),
+                                 ParquetFieldStruct<gpuParseDataPageHeaderV2>(8));
+    return parse_header(op, bs);
+  }
+};
+
+/**
+ * @brief Kernel for outputting page headers from the specified column chunks
+ *
+ * @param[in] chunks List of column chunks
+ * @param[in] num_chunks Number of column chunks
+ */
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128)
+  gpuDecodePageHeaders(ColumnChunkDesc* chunks, int32_t num_chunks)
+{
+  gpuParsePageHeader parse_page_header;
+  __shared__ byte_stream_s bs_g[4];
+
+  int lane_id             = threadIdx.x % 32;
+  int chunk               = (blockIdx.x * 4) + (threadIdx.x / 32);
+  byte_stream_s* const bs = &bs_g[threadIdx.x / 32];
+
+  if (chunk < num_chunks and lane_id == 0) bs->ck = chunks[chunk];
+  __syncthreads();
+
+  if (chunk < num_chunks) {
+    size_t num_values, values_found;
+    uint32_t data_page_count       = 0;
+    uint32_t dictionary_page_count = 0;
+    int32_t max_num_pages;
+    int32_t num_dict_pages = bs->ck.num_dict_pages;
+    PageInfo* page_info;
+
+    if (!lane_id) {
+      bs->base = bs->cur      = bs->ck.compressed_data;
+      bs->end                 = bs->base + bs->ck.compressed_size;
+      bs->page.chunk_idx      = chunk;
+      bs->page.src_col_schema = bs->ck.src_col_schema;
+      // this computation is only valid for flat schemas. for nested schemas,
+      // they will be recomputed in the preprocess step by examining repetition and
+      // definition levels
+      bs->page.chunk_row           = 0;
+      bs->page.num_rows            = 0;
+      bs->page.skipped_values      = -1;
+      bs->page.skipped_leaf_values = 0;
+      bs->page.str_bytes           = 0;
+      bs->page.kernel_mask         = 0;
+    }
+    num_values     = bs->ck.num_values;
+    page_info      = bs->ck.page_info;
+    num_dict_pages = bs->ck.num_dict_pages;
+    max_num_pages  = (page_info) ? bs->ck.max_num_pages : 0;
+    values_found   = 0;
+    __syncwarp();
+    while (values_found < num_values && bs->cur < bs->end) {
+      int index_out = -1;
+
+      if (lane_id == 0) {
+        // this computation is only valid for flat schemas. for nested schemas,
+        // they will be recomputed in the preprocess step by examining repetition and
+        // definition levels
+        bs->page.chunk_row += bs->page.num_rows;
+        bs->page.num_rows = 0;
+        bs->page.flags    = 0;
+        // zero out V2 info
+        bs->page.num_nulls                         = 0;
+        bs->page.lvl_bytes[level_type::DEFINITION] = 0;
+        bs->page.lvl_bytes[level_type::REPETITION] = 0;
+        if (parse_page_header(bs) && bs->page.compressed_page_size >= 0) {
+          switch (bs->page_type) {
+            case PageType::DATA_PAGE:
+              index_out = num_dict_pages + data_page_count;
+              data_page_count++;
+              // this computation is only valid for flat schemas. for nested schemas,
+              // they will be recomputed in the preprocess step by examining repetition and
+              // definition levels
+              bs->page.num_rows = bs->page.num_input_values;
+              values_found += bs->page.num_input_values;
+              break;
+            case PageType::DATA_PAGE_V2:
+              index_out = num_dict_pages + data_page_count;
+              data_page_count++;
+              bs->page.flags |= PAGEINFO_FLAGS_V2;
+              values_found += bs->page.num_input_values;
+              // V2 only uses RLE, so it was removed from the header
+              bs->page.definition_level_encoding = Encoding::RLE;
+              bs->page.repetition_level_encoding = Encoding::RLE;
+              break;
+            case PageType::DICTIONARY_PAGE:
+              index_out = dictionary_page_count;
+              dictionary_page_count++;
+              bs->page.flags |= PAGEINFO_FLAGS_DICTIONARY;
+              break;
+            default: index_out = -1; break;
+          }
+          bs->page.page_data = const_cast<uint8_t*>(bs->cur);
+          bs->cur += bs->page.compressed_page_size;
+          bs->page.kernel_mask = kernel_mask_for_page(bs->page, bs->ck);
+        } else {
+          bs->cur = bs->end;
+        }
+      }
+      index_out = shuffle(index_out);
+      if (index_out >= 0 && index_out < max_num_pages && lane_id == 0)
+        page_info[index_out] = bs->page;
+      num_values = shuffle(num_values);
+      __syncwarp();
+    }
+    if (lane_id == 0) {
+      chunks[chunk].num_data_pages = data_page_count;
+      chunks[chunk].num_dict_pages = dictionary_page_count;
+    }
+  }
+}
+
+/**
+ * @brief Kernel for building dictionary index for the specified column chunks
+ *
+ * This function builds an index to point to each dictionary entry
+ * (string format is 4-byte little-endian string length followed by character
+ * data). The index is a 32-bit integer which contains the offset of each string
+ * relative to the beginning of the dictionary page data.
+ *
+ * @param[in] chunks List of column chunks
+ * @param[in] num_chunks Number of column chunks
+ */
+// blockDim {128,1,1}
+__global__ void __launch_bounds__(128)
+  gpuBuildStringDictionaryIndex(ColumnChunkDesc* chunks, int32_t num_chunks)
+{
+  __shared__ ColumnChunkDesc chunk_g[4];
+
+  int lane_id               = threadIdx.x % 32;
+  int chunk                 = (blockIdx.x * 4) + (threadIdx.x / 32);
+  ColumnChunkDesc* const ck = &chunk_g[threadIdx.x / 32];
+  if (chunk < num_chunks and lane_id == 0) *ck = chunks[chunk];
+  __syncthreads();
+
+  if (chunk >= num_chunks) { return; }
+  if (!lane_id && ck->num_dict_pages > 0 && ck->str_dict_index) {
+    // Data type to describe a string
+    string_index_pair* dict_index = ck->str_dict_index;
+    uint8_t const* dict           = ck->page_info[0].page_data;
+    int dict_size                 = ck->page_info[0].uncompressed_page_size;
+    int num_entries               = ck->page_info[0].num_input_values;
+    int pos = 0, cur = 0;
+    for (int i = 0; i < num_entries; i++) {
+      int len = 0;
+      if (cur + 4 <= dict_size) {
+        len = dict[cur + 0] | (dict[cur + 1] << 8) | (dict[cur + 2] << 16) | (dict[cur + 3] << 24);
+        if (len >= 0 && cur + 4 + len <= dict_size) {
+          pos = cur;
+          cur = cur + 4 + len;
+        } else {
+          cur = dict_size;
+        }
+      }
+      // TODO: Could store 8 entries in shared mem, then do a single warp-wide store
+      dict_index[i].first  = reinterpret_cast<char const*>(dict + pos + 4);
+      dict_index[i].second = len;
+    }
+  }
+}
+
+void __host__ DecodePageHeaders(ColumnChunkDesc* chunks,
+                                int32_t num_chunks,
+                                rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);
+  dim3 dim_grid((num_chunks + 3) >> 2, 1);  // 1 chunk per warp, 4 warps per block
+  gpuDecodePageHeaders<<<dim_grid, dim_block, 0, stream.value()>>>(chunks, num_chunks);
+}
+
+void __host__ BuildStringDictionaryIndex(ColumnChunkDesc* chunks,
+                                         int32_t num_chunks,
+                                         rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(128, 1);
+  dim3 dim_grid((num_chunks + 3) >> 2, 1);  // 1 chunk per warp, 4 warps per block
+  gpuBuildStringDictionaryIndex<<<dim_grid, dim_block, 0, stream.value()>>>(chunks, num_chunks);
+}
+
+}  // namespace gpu
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/page_string_decode.cu b/cpp/src/io/parquet/page_string_decode.cu
new file mode 100644
index 0000000..d79abe4
--- /dev/null
+++ b/cpp/src/io/parquet/page_string_decode.cu
@@ -0,0 +1,808 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "page_decode.cuh"
+#include "page_string_utils.cuh"
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/detail/gather.cuh>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+namespace gpu {
+
+namespace {
+
+constexpr int preprocess_block_size = 512;
+constexpr int decode_block_size     = 128;
+constexpr int rolling_buf_size      = decode_block_size * 2;
+constexpr int preproc_buf_size      = LEVEL_DECODE_BUF_SIZE;
+
+/**
+ * @brief Compute the start and end page value bounds for this page
+ *
+ * This uses definition and repetition level info to determine the number of valid and null
+ * values for the page, taking into account skip_rows/num_rows (if set).
+ *
+ * @param s The local page info
+ * @param min_row Row index to start reading at
+ * @param num_rows Maximum number of rows to read
+ * @param is_bounds_pg True if this page is clipped
+ * @param has_repetition True if the schema is nested
+ * @param decoders Definition and repetition level decoders
+ * @return pair containing start and end value indexes
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ * @tparam rle_buf_size Size of the buffer used when decoding repetition and definition levels
+ */
+template <typename level_t, int rle_buf_size>
+__device__ thrust::pair<int, int> page_bounds(page_state_s* const s,
+                                              size_t min_row,
+                                              size_t num_rows,
+                                              bool is_bounds_pg,
+                                              bool has_repetition,
+                                              rle_stream<level_t, rle_buf_size>* decoders)
+{
+  using block_reduce = cub::BlockReduce<int, preprocess_block_size>;
+  using block_scan   = cub::BlockScan<int, preprocess_block_size>;
+  __shared__ union {
+    typename block_reduce::TempStorage reduce_storage;
+    typename block_scan::TempStorage scan_storage;
+  } temp_storage;
+
+  auto const t = threadIdx.x;
+
+  // decode batches of level stream data using rle_stream objects and use the results to
+  // calculate start and end value positions in the encoded string data.
+  int const max_depth = s->col.max_nesting_depth;
+  int const max_def   = s->nesting_info[max_depth - 1].max_def_level;
+
+  // can skip all this if we know there are no nulls
+  if (max_def == 0 && !is_bounds_pg) {
+    s->page.num_valids = s->num_input_values;
+    s->page.num_nulls  = 0;
+    return {0, s->num_input_values};
+  }
+
+  int start_value = 0;
+  int end_value   = s->page.num_input_values;
+  auto const pp   = &s->page;
+  auto const col  = &s->col;
+
+  // initialize the stream decoders (requires values computed in setupLocalPageInfo)
+  auto const def_decode = reinterpret_cast<level_t*>(pp->lvl_decode_buf[level_type::DEFINITION]);
+  auto const rep_decode = reinterpret_cast<level_t*>(pp->lvl_decode_buf[level_type::REPETITION]);
+  decoders[level_type::DEFINITION].init(s->col.level_bits[level_type::DEFINITION],
+                                        s->abs_lvl_start[level_type::DEFINITION],
+                                        s->abs_lvl_end[level_type::DEFINITION],
+                                        preproc_buf_size,
+                                        def_decode,
+                                        s->page.num_input_values);
+  // only need repetition if this is a bounds page. otherwise all we need is def level info
+  // to count the nulls.
+  if (has_repetition && is_bounds_pg) {
+    decoders[level_type::REPETITION].init(s->col.level_bits[level_type::REPETITION],
+                                          s->abs_lvl_start[level_type::REPETITION],
+                                          s->abs_lvl_end[level_type::REPETITION],
+                                          preproc_buf_size,
+                                          rep_decode,
+                                          s->page.num_input_values);
+  }
+
+  int processed = 0;
+
+  // if this is a bounds page, we need to do extra work to find the start and/or end value index
+  if (is_bounds_pg) {
+    __shared__ int skipped_values;
+    __shared__ int skipped_leaf_values;
+    __shared__ int last_input_value;
+    __shared__ int end_val_idx;
+
+    // need these for skip_rows case
+    auto const page_start_row = col->start_row + pp->chunk_row;
+    auto const max_row        = min_row + num_rows;
+    auto const begin_row      = page_start_row >= min_row ? 0 : min_row - page_start_row;
+    auto const max_page_rows  = pp->num_rows - begin_row;
+    auto const page_rows      = page_start_row + begin_row + max_page_rows <= max_row
+                                  ? max_page_rows
+                                  : max_row - (page_start_row + begin_row);
+    auto end_row              = begin_row + page_rows;
+    int row_fudge             = -1;
+
+    // short circuit for no nulls
+    if (max_def == 0 && !has_repetition) {
+      if (t == 0) {
+        pp->num_nulls  = 0;
+        pp->num_valids = end_row - begin_row;
+      }
+      return {begin_row, end_row};
+    }
+
+    int row_count           = 0;
+    int leaf_count          = 0;
+    bool skipped_values_set = false;
+    bool end_value_set      = false;
+
+    while (processed < s->page.num_input_values) {
+      thread_index_type start_val = processed;
+
+      if (has_repetition) {
+        decoders[level_type::REPETITION].decode_next(t);
+        __syncthreads();
+
+        // special case where page does not begin at a row boundary
+        if (processed == 0 && rep_decode[0] != 0) {
+          if (t == 0) {
+            skipped_values      = 0;
+            skipped_leaf_values = 0;
+          }
+          skipped_values_set = true;
+          end_row++;  // need to finish off the previous row
+          row_fudge = 0;
+        }
+      }
+
+      // the # of rep/def levels will always be the same size
+      processed += decoders[level_type::DEFINITION].decode_next(t);
+      __syncthreads();
+
+      // do something with the level data
+      while (start_val < processed) {
+        auto const idx_t = start_val + t;
+        auto const idx   = rolling_index<preproc_buf_size>(idx_t);
+
+        // get absolute thread row index
+        int is_new_row = idx_t < processed && (!has_repetition || rep_decode[idx] == 0);
+        int thread_row_count, block_row_count;
+        block_scan(temp_storage.scan_storage)
+          .InclusiveSum(is_new_row, thread_row_count, block_row_count);
+        __syncthreads();
+
+        // get absolute thread leaf index
+        int const is_new_leaf = idx_t < processed && (def_decode[idx] >= max_def);
+        int thread_leaf_count, block_leaf_count;
+        block_scan(temp_storage.scan_storage)
+          .InclusiveSum(is_new_leaf, thread_leaf_count, block_leaf_count);
+        __syncthreads();
+
+        // if we have not set skipped values yet, see if we found the first in-bounds row
+        if (!skipped_values_set && row_count + block_row_count > begin_row) {
+          // if this thread is in row bounds
+          int const row_index = thread_row_count + row_count - 1;
+          int const in_row_bounds =
+            idx_t < processed && (row_index >= begin_row) && (row_index < end_row);
+
+          int local_count, global_count;
+          block_scan(temp_storage.scan_storage)
+            .InclusiveSum(in_row_bounds, local_count, global_count);
+          __syncthreads();
+
+          // we found it
+          if (global_count > 0) {
+            // this is the thread that represents the first row. need to test in_row_bounds for
+            // the case where we only want one row and local_count == 1 for many threads.
+            if (local_count == 1 && in_row_bounds) {
+              skipped_values = idx_t;
+              skipped_leaf_values =
+                leaf_count + (is_new_leaf ? thread_leaf_count - 1 : thread_leaf_count);
+            }
+            skipped_values_set = true;
+          }
+        }
+
+        // test if row_count will exceed end_row in this batch
+        if (!end_value_set && row_count + block_row_count >= end_row) {
+          // if this thread exceeds row bounds. row_fudge change depending on whether we've faked
+          // the end row to account for starting a page in the middle of a row.
+          int const row_index          = thread_row_count + row_count + row_fudge;
+          int const exceeds_row_bounds = row_index >= end_row;
+
+          int local_count, global_count;
+          block_scan(temp_storage.scan_storage)
+            .InclusiveSum(exceeds_row_bounds, local_count, global_count);
+          __syncthreads();
+
+          // we found it
+          if (global_count > 0) {
+            // this is the thread that represents the end row.
+            if (local_count == 1) {
+              last_input_value = idx_t;
+              end_val_idx = leaf_count + (is_new_leaf ? thread_leaf_count - 1 : thread_leaf_count);
+            }
+            end_value_set = true;
+            break;
+          }
+        }
+
+        row_count += block_row_count;
+        leaf_count += block_leaf_count;
+
+        start_val += preprocess_block_size;
+      }
+      __syncthreads();
+      if (end_value_set) { break; }
+    }
+
+    start_value = skipped_values_set ? skipped_leaf_values : 0;
+    end_value   = end_value_set ? end_val_idx : leaf_count;
+
+    if (t == 0) {
+      int const v0                = skipped_values_set ? skipped_values : 0;
+      int const vn                = end_value_set ? last_input_value : s->num_input_values;
+      int const total_values      = vn - v0;
+      int const total_leaf_values = end_value - start_value;
+      int const num_nulls         = total_values - total_leaf_values;
+      pp->num_nulls               = num_nulls;
+      pp->num_valids              = total_leaf_values;
+    }
+  }
+  // already filtered out unwanted pages, so need to count all non-null values in this page
+  else {
+    int num_nulls = 0;
+    while (processed < s->page.num_input_values) {
+      thread_index_type start_val = processed;
+      processed += decoders[level_type::DEFINITION].decode_next(t);
+      __syncthreads();
+
+      while (start_val < processed) {
+        auto const idx_t = start_val + t;
+        if (idx_t < processed) {
+          auto const idx = rolling_index<preproc_buf_size>(idx_t);
+          if (def_decode[idx] < max_def) { num_nulls++; }
+        }
+        start_val += preprocess_block_size;
+      }
+      __syncthreads();
+    }
+
+    int const null_count = block_reduce(temp_storage.reduce_storage).Sum(num_nulls);
+
+    if (t == 0) {
+      pp->num_nulls  = null_count;
+      pp->num_valids = pp->num_input_values - null_count;
+    }
+    __syncthreads();
+
+    end_value -= pp->num_nulls;
+  }
+
+  return {start_value, end_value};
+}
+
+/**
+ * @brief Compute string size information for dictionary encoded strings.
+ *
+ * @param data Pointer to the start of the page data stream
+ * @param dict_base Pointer to the start of the dictionary
+ * @param dict_bits The number of bits used to in the dictionary bit packing
+ * @param dict_size Size of the dictionary in bytes
+ * @param data_size Size of the page data in bytes
+ * @param start_value Do not count values that occur before this index
+ * @param end_value Do not count values that occur after this index
+ */
+__device__ size_t totalDictEntriesSize(uint8_t const* data,
+                                       uint8_t const* dict_base,
+                                       int dict_bits,
+                                       int dict_size,
+                                       int data_size,
+                                       int start_value,
+                                       int end_value)
+{
+  int const t              = threadIdx.x;
+  uint8_t const* ptr       = data;
+  uint8_t const* const end = data + data_size;
+  int const bytecnt        = (dict_bits + 7) >> 3;
+  size_t l_str_len         = 0;  // partial sums across threads
+  int pos                  = 0;  // current value index in the data stream
+  int t0                   = 0;  // thread 0 for this batch
+
+  int dict_run = 0;
+  int dict_val = 0;
+
+  while (pos < end_value && ptr <= end) {
+    if (dict_run <= 1) {
+      dict_run = (ptr < end) ? get_vlq32(ptr, end) : 0;
+      if (!(dict_run & 1)) {
+        // Repeated value
+        if (ptr + bytecnt <= end) {
+          int32_t run_val = ptr[0];
+          if (bytecnt > 1) {
+            run_val |= ptr[1] << 8;
+            if (bytecnt > 2) {
+              run_val |= ptr[2] << 16;
+              if (bytecnt > 3) { run_val |= ptr[3] << 24; }
+            }
+          }
+          dict_val = run_val & ((1 << dict_bits) - 1);
+        }
+        ptr += bytecnt;
+      }
+    }
+
+    int batch_len;
+    if (dict_run & 1) {
+      // Literal batch: must output a multiple of 8, except for the last batch
+      int batch_len_div8;
+      batch_len      = max(min(preprocess_block_size, (int)(dict_run >> 1) * 8), 1);
+      batch_len_div8 = (batch_len + 7) >> 3;
+      dict_run -= batch_len_div8 * 2;
+      ptr += batch_len_div8 * dict_bits;
+    } else {
+      batch_len = dict_run >> 1;
+      dict_run  = 0;
+    }
+
+    int const is_literal = dict_run & 1;
+
+    // calculate my thread id for this batch.  way to round-robin the work.
+    int mytid = t - t0;
+    if (mytid < 0) mytid += preprocess_block_size;
+
+    // compute dictionary index.
+    if (is_literal) {
+      int dict_idx = 0;
+      if (mytid < batch_len) {
+        dict_idx         = dict_val;
+        int32_t ofs      = (mytid - ((batch_len + 7) & ~7)) * dict_bits;
+        const uint8_t* p = ptr + (ofs >> 3);
+        ofs &= 7;
+        if (p < end) {
+          uint32_t c = 8 - ofs;
+          dict_idx   = (*p++) >> ofs;
+          if (c < dict_bits && p < end) {
+            dict_idx |= (*p++) << c;
+            c += 8;
+            if (c < dict_bits && p < end) {
+              dict_idx |= (*p++) << c;
+              c += 8;
+              if (c < dict_bits && p < end) { dict_idx |= (*p++) << c; }
+            }
+          }
+          dict_idx &= (1 << dict_bits) - 1;
+        }
+
+        if (pos + mytid < end_value) {
+          uint32_t const dict_pos = (dict_bits > 0) ? dict_idx * sizeof(string_index_pair) : 0;
+          if (pos + mytid >= start_value && dict_pos < (uint32_t)dict_size) {
+            const auto* src = reinterpret_cast<const string_index_pair*>(dict_base + dict_pos);
+            l_str_len += src->second;
+          }
+        }
+      }
+
+      t0 += batch_len;
+    } else {
+      int const start_off =
+        (pos < start_value && pos + batch_len > start_value) ? start_value - pos : 0;
+      batch_len = min(batch_len, end_value - pos);
+      if (mytid == 0) {
+        uint32_t const dict_pos = (dict_bits > 0) ? dict_val * sizeof(string_index_pair) : 0;
+        if (pos + batch_len > start_value && dict_pos < (uint32_t)dict_size) {
+          const auto* src = reinterpret_cast<const string_index_pair*>(dict_base + dict_pos);
+          l_str_len += (batch_len - start_off) * src->second;
+        }
+      }
+
+      t0 += 1;
+    }
+
+    t0 = t0 % preprocess_block_size;
+    pos += batch_len;
+  }
+  __syncthreads();
+
+  using block_reduce = cub::BlockReduce<size_t, preprocess_block_size>;
+  __shared__ typename block_reduce::TempStorage reduce_storage;
+  size_t sum_l = block_reduce(reduce_storage).Sum(l_str_len);
+
+  return sum_l;
+}
+
+/**
+ * @brief Compute string size information for plain encoded strings.
+ *
+ * @param data Pointer to the start of the page data stream
+ * @param data_size Length of data
+ * @param start_value Do not count values that occur before this index
+ * @param end_value Do not count values that occur after this index
+ */
+__device__ size_t totalPlainEntriesSize(uint8_t const* data,
+                                        int data_size,
+                                        int start_value,
+                                        int end_value)
+{
+  int const t      = threadIdx.x;
+  int pos          = 0;
+  size_t total_len = 0;
+
+  // This step is purely serial
+  if (!t) {
+    const uint8_t* cur = data;
+    int k              = 0;
+
+    while (pos < end_value && k < data_size) {
+      int len;
+      if (k + 4 <= data_size) {
+        len = (cur[k]) | (cur[k + 1] << 8) | (cur[k + 2] << 16) | (cur[k + 3] << 24);
+        k += 4;
+        if (k + len > data_size) { len = 0; }
+      } else {
+        len = 0;
+      }
+
+      k += len;
+      if (pos >= start_value) { total_len += len; }
+      pos++;
+    }
+  }
+
+  return total_len;
+}
+
+/**
+ * @brief Kernel for computing string page output size information.
+ *
+ * String columns need accurate data size information to preallocate memory in the column buffer to
+ * store the char data. This calls a kernel to calculate information needed by the string decoding
+ * kernel. On exit, the `str_bytes`, `num_nulls`, and `num_valids` fields of the PageInfo struct
+ * are updated. This call ignores non-string columns.
+ *
+ * @param pages All pages to be decoded
+ * @param chunks All chunks to be decoded
+ * @param min_rows crop all rows below min_row
+ * @param num_rows Maximum number of rows to read
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ */
+template <typename level_t>
+__global__ void __launch_bounds__(preprocess_block_size) gpuComputePageStringSizes(
+  PageInfo* pages, device_span<ColumnChunkDesc const> chunks, size_t min_row, size_t num_rows)
+{
+  __shared__ __align__(16) page_state_s state_g;
+
+  page_state_s* const s = &state_g;
+  int const page_idx    = blockIdx.x;
+  int const t           = threadIdx.x;
+  PageInfo* const pp    = &pages[page_idx];
+
+  // reset str_bytes to 0 in case it's already been calculated
+  if (t == 0) { pp->str_bytes = 0; }
+
+  // whether or not we have repetition levels (lists)
+  bool const has_repetition = chunks[pp->chunk_idx].max_level[level_type::REPETITION] > 0;
+
+  // the required number of runs in shared memory we will need to provide the
+  // rle_stream object
+  constexpr int rle_run_buffer_size = rle_stream_required_run_buffer_size<preprocess_block_size>();
+
+  // the level stream decoders
+  __shared__ rle_run<level_t> def_runs[rle_run_buffer_size];
+  __shared__ rle_run<level_t> rep_runs[rle_run_buffer_size];
+  rle_stream<level_t, preprocess_block_size> decoders[level_type::NUM_LEVEL_TYPES] = {{def_runs},
+                                                                                      {rep_runs}};
+
+  // setup page info
+  if (!setupLocalPageInfo(
+        s, pp, chunks, min_row, num_rows, mask_filter{KERNEL_MASK_STRING}, false)) {
+    return;
+  }
+
+  if (!t) {
+    s->page.num_nulls  = 0;
+    s->page.num_valids = 0;
+    s->page.str_bytes  = 0;
+  }
+  __syncthreads();
+
+  bool const is_bounds_pg = is_bounds_page(s, min_row, num_rows, has_repetition);
+
+  // if we're skipping this page anyway, no need to count it
+  if (!is_bounds_pg && !is_page_contained(s, min_row, num_rows)) { return; }
+
+  // find start/end value indices
+  auto const [start_value, end_value] =
+    page_bounds(s, min_row, num_rows, is_bounds_pg, has_repetition, decoders);
+
+  // need to save num_nulls and num_valids calculated in page_bounds in this page
+  if (t == 0) {
+    pp->num_nulls  = s->page.num_nulls;
+    pp->num_valids = s->page.num_valids;
+  }
+
+  auto const& col  = s->col;
+  size_t str_bytes = 0;
+  // short circuit for FIXED_LEN_BYTE_ARRAY
+  if ((col.data_type & 7) == FIXED_LEN_BYTE_ARRAY) {
+    str_bytes = pp->num_valids * s->dtype_len_in;
+  } else {
+    // now process string info in the range [start_value, end_value)
+    // set up for decoding strings...can be either plain or dictionary
+    uint8_t const* data      = s->data_start;
+    uint8_t const* const end = s->data_end;
+    uint8_t const* dict_base = nullptr;
+    int dict_size            = 0;
+
+    switch (pp->encoding) {
+      case Encoding::PLAIN_DICTIONARY:
+      case Encoding::RLE_DICTIONARY:
+        // RLE-packed dictionary indices, first byte indicates index length in bits
+        if (col.str_dict_index) {
+          // String dictionary: use index
+          dict_base = reinterpret_cast<const uint8_t*>(col.str_dict_index);
+          dict_size = col.page_info[0].num_input_values * sizeof(string_index_pair);
+        } else {
+          dict_base = col.page_info[0].page_data;  // dictionary is always stored in the first page
+          dict_size = col.page_info[0].uncompressed_page_size;
+        }
+
+        // FIXME: need to return an error condition...this won't actually do anything
+        if (s->dict_bits > 32 || !dict_base) { CUDF_UNREACHABLE("invalid dictionary bit size"); }
+
+        str_bytes = totalDictEntriesSize(
+          data, dict_base, s->dict_bits, dict_size, (end - data), start_value, end_value);
+        break;
+      case Encoding::PLAIN:
+        dict_size = static_cast<int32_t>(end - data);
+        str_bytes = is_bounds_pg ? totalPlainEntriesSize(data, dict_size, start_value, end_value)
+                                 : dict_size - sizeof(int) * pp->num_valids;
+        break;
+    }
+  }
+
+  if (t == 0) {
+    // TODO check for overflow
+    pp->str_bytes = str_bytes;
+  }
+}
+
+/**
+ * @brief Kernel for computing the string column data stored in the pages
+ *
+ * This function will write the page data and the page data's validity to the
+ * output specified in the page's column chunk.
+ *
+ * This version uses a single warp to do the string copies.
+ *
+ * @param pages List of pages
+ * @param chunks List of column chunks
+ * @param min_row Row index to start reading at
+ * @param num_rows Maximum number of rows to read
+ * @tparam level_t Type used to store decoded repetition and definition levels
+ */
+template <typename level_t>
+__global__ void __launch_bounds__(decode_block_size)
+  gpuDecodeStringPageData(PageInfo* pages,
+                          device_span<ColumnChunkDesc const> chunks,
+                          size_t min_row,
+                          size_t num_rows,
+                          int32_t* error_code)
+{
+  __shared__ __align__(16) page_state_s state_g;
+  __shared__ __align__(4) size_type last_offset;
+  __shared__ __align__(16)
+    page_state_buffers_s<rolling_buf_size, rolling_buf_size, rolling_buf_size>
+      state_buffers;
+
+  page_state_s* const s = &state_g;
+  auto* const sb        = &state_buffers;
+  int const page_idx    = blockIdx.x;
+  int const t           = threadIdx.x;
+  [[maybe_unused]] null_count_back_copier _{s, t};
+
+  if (!setupLocalPageInfo(
+        s, &pages[page_idx], chunks, min_row, num_rows, mask_filter{KERNEL_MASK_STRING}, true)) {
+    return;
+  }
+
+  bool const has_repetition = s->col.max_level[level_type::REPETITION] > 0;
+
+  // offsets are local to the page
+  if (t == 0) { last_offset = 0; }
+  __syncthreads();
+
+  int const out_thread0                          = s->dict_base && s->dict_bits == 0 ? 32 : 64;
+  int const leaf_level_index                     = s->col.max_nesting_depth - 1;
+  PageNestingDecodeInfo* const nesting_info_base = s->nesting_info;
+
+  __shared__ level_t rep[rolling_buf_size];  // circular buffer of repetition level values
+  __shared__ level_t def[rolling_buf_size];  // circular buffer of definition level values
+
+  // skipped_leaf_values will always be 0 for flat hierarchies.
+  uint32_t skipped_leaf_values = s->page.skipped_leaf_values;
+  while (s->error == 0 &&
+         (s->input_value_count < s->num_input_values || s->src_pos < s->nz_count)) {
+    int target_pos;
+    int src_pos = s->src_pos;
+
+    if (t < out_thread0) {
+      target_pos = min(src_pos + 2 * (decode_block_size - out_thread0),
+                       s->nz_count + (decode_block_size - out_thread0));
+    } else {
+      target_pos = min(s->nz_count, src_pos + decode_block_size - out_thread0);
+      if (out_thread0 > 32) { target_pos = min(target_pos, s->dict_pos); }
+    }
+    __syncthreads();
+    if (t < 32) {
+      // decode repetition and definition levels.
+      // - update validity vectors
+      // - updates offsets (for nested columns)
+      // - produces non-NULL value indices in s->nz_idx for subsequent decoding
+      gpuDecodeLevels<rolling_buf_size, level_t>(s, sb, target_pos, rep, def, t);
+    } else if (t < out_thread0) {
+      // skipped_leaf_values will always be 0 for flat hierarchies.
+      uint32_t src_target_pos = target_pos + skipped_leaf_values;
+
+      // WARP1: Decode dictionary indices, booleans or string positions
+      if (s->dict_base) {
+        src_target_pos = gpuDecodeDictionaryIndices<false>(s, sb, src_target_pos, t & 0x1f).first;
+      } else {
+        gpuInitStringDescriptors<false>(s, sb, src_target_pos, t & 0x1f);
+      }
+      if (t == 32) { *(volatile int32_t*)&s->dict_pos = src_target_pos; }
+    } else {
+      int const me = t - out_thread0;
+
+      // WARP1..WARP3: Decode values
+      src_pos += t - out_thread0;
+
+      // the position in the output column/buffer
+      int dst_pos = sb->nz_idx[rolling_index<rolling_buf_size>(src_pos)];
+
+      // for the flat hierarchy case we will be reading from the beginning of the value stream,
+      // regardless of the value of first_row. so adjust our destination offset accordingly.
+      // example:
+      // - user has passed skip_rows = 2, so our first_row to output is 2
+      // - the row values we get from nz_idx will be
+      //   0, 1, 2, 3, 4 ....
+      // - by shifting these values by first_row, the sequence becomes
+      //   -1, -2, 0, 1, 2 ...
+      // - so we will end up ignoring the first two input rows, and input rows 2..n will
+      //   get written to the output starting at position 0.
+      //
+      if (!has_repetition) { dst_pos -= s->first_row; }
+
+      // need to do this before we branch on src_pos/dst_pos so we don't deadlock
+      // choose a character parallel string copy when the average string is longer than a warp
+      using cudf::detail::warp_size;
+      auto const use_char_ll =
+        s->page.num_valids > 0 && (s->page.str_bytes / s->page.num_valids) >= warp_size;
+
+      if (me < warp_size) {
+        for (int i = 0; i < decode_block_size - out_thread0; i += warp_size) {
+          dst_pos = sb->nz_idx[rolling_index<rolling_buf_size>(src_pos + i)];
+          if (!has_repetition) { dst_pos -= s->first_row; }
+
+          auto [ptr, len] = src_pos + i < target_pos && dst_pos >= 0
+                              ? gpuGetStringData(s, sb, src_pos + skipped_leaf_values + i)
+                              : cuda::std::pair<char const*, size_t>{nullptr, 0};
+
+          __shared__ cub::WarpScan<size_type>::TempStorage temp_storage;
+          size_type offset;
+          cub::WarpScan<size_type>(temp_storage).ExclusiveSum(len, offset);
+          offset += last_offset;
+
+          if (use_char_ll) {
+            __shared__ __align__(8) uint8_t const* pointers[warp_size];
+            __shared__ __align__(4) size_type offsets[warp_size];
+            __shared__ __align__(4) int dsts[warp_size];
+            __shared__ __align__(4) int lengths[warp_size];
+
+            offsets[me]  = offset;
+            pointers[me] = reinterpret_cast<uint8_t const*>(ptr);
+            dsts[me]     = dst_pos;
+            lengths[me]  = len;
+            __syncwarp();
+
+            for (int ss = 0; ss < warp_size && ss + i + s->src_pos < target_pos; ss++) {
+              if (dsts[ss] >= 0) {
+                auto offptr =
+                  reinterpret_cast<int32_t*>(nesting_info_base[leaf_level_index].data_out) +
+                  dsts[ss];
+                *offptr      = lengths[ss];
+                auto str_ptr = nesting_info_base[leaf_level_index].string_out + offsets[ss];
+                ll_strcpy(str_ptr, pointers[ss], lengths[ss], me);
+              }
+            }
+
+          } else {
+            if (src_pos + i < target_pos && dst_pos >= 0) {
+              auto offptr =
+                reinterpret_cast<int32_t*>(nesting_info_base[leaf_level_index].data_out) + dst_pos;
+              *offptr      = len;
+              auto str_ptr = nesting_info_base[leaf_level_index].string_out + offset;
+              memcpy(str_ptr, ptr, len);
+            }
+            __syncwarp();
+          }
+
+          // last thread in warp updates last_offset
+          if (me == warp_size - 1) { last_offset = offset + len; }
+          __syncwarp();
+        }
+      }
+
+      if (t == out_thread0) { *(volatile int32_t*)&s->src_pos = target_pos; }
+    }
+    __syncthreads();
+  }
+
+  // now turn array of lengths into offsets
+  int value_count = nesting_info_base[leaf_level_index].value_count;
+
+  // if no repetition we haven't calculated start/end bounds and instead just skipped
+  // values until we reach first_row. account for that here.
+  if (!has_repetition) { value_count -= s->first_row; }
+
+  auto const offptr = reinterpret_cast<size_type*>(nesting_info_base[leaf_level_index].data_out);
+  block_excl_sum<decode_block_size>(offptr, value_count, s->page.str_offset);
+
+  if (t == 0 and s->error != 0) {
+    cuda::atomic_ref<int32_t, cuda::thread_scope_device> ref{*error_code};
+    ref.fetch_or(s->error, cuda::std::memory_order_relaxed);
+  }
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::io::parquet::gpu::ComputePageStringSizes
+ */
+void ComputePageStringSizes(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                            cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                            size_t min_row,
+                            size_t num_rows,
+                            int level_type_size,
+                            rmm::cuda_stream_view stream)
+{
+  dim3 dim_block(preprocess_block_size, 1);
+  dim3 dim_grid(pages.size(), 1);  // 1 threadblock per page
+  if (level_type_size == 1) {
+    gpuComputePageStringSizes<uint8_t>
+      <<<dim_grid, dim_block, 0, stream.value()>>>(pages.device_ptr(), chunks, min_row, num_rows);
+  } else {
+    gpuComputePageStringSizes<uint16_t>
+      <<<dim_grid, dim_block, 0, stream.value()>>>(pages.device_ptr(), chunks, min_row, num_rows);
+  }
+}
+
+/**
+ * @copydoc cudf::io::parquet::gpu::DecodeStringPageData
+ */
+void __host__ DecodeStringPageData(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                                   cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                                   size_t num_rows,
+                                   size_t min_row,
+                                   int level_type_size,
+                                   int32_t* error_code,
+                                   rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(pages.size() > 0, "There is no page to decode");
+
+  dim3 dim_block(decode_block_size, 1);
+  dim3 dim_grid(pages.size(), 1);  // 1 threadblock per page
+
+  if (level_type_size == 1) {
+    gpuDecodeStringPageData<uint8_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, error_code);
+  } else {
+    gpuDecodeStringPageData<uint16_t><<<dim_grid, dim_block, 0, stream.value()>>>(
+      pages.device_ptr(), chunks, min_row, num_rows, error_code);
+  }
+}
+
+}  // namespace gpu
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/page_string_utils.cuh b/cpp/src/io/parquet/page_string_utils.cuh
new file mode 100644
index 0000000..9395599
--- /dev/null
+++ b/cpp/src/io/parquet/page_string_utils.cuh
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/strings/detail/gather.cuh>
+
+namespace cudf::io::parquet::gpu {
+
+// stole this from cudf/strings/detail/gather.cuh. modified to run on a single string on one warp.
+// copies from src to dst in 16B chunks per thread.
+inline __device__ void wideStrcpy(uint8_t* dst, uint8_t const* src, size_t len, uint32_t lane_id)
+{
+  using cudf::detail::warp_size;
+  using cudf::strings::detail::load_uint4;
+
+  constexpr size_t out_datatype_size = sizeof(uint4);
+  constexpr size_t in_datatype_size  = sizeof(uint);
+
+  auto const alignment_offset = reinterpret_cast<std::uintptr_t>(dst) % out_datatype_size;
+  uint4* out_chars_aligned    = reinterpret_cast<uint4*>(dst - alignment_offset);
+  auto const in_start         = src;
+
+  // Both `out_start_aligned` and `out_end_aligned` are indices into `dst`.
+  // `out_start_aligned` is the first 16B aligned memory location after `dst + 4`.
+  // `out_end_aligned` is the last 16B aligned memory location before `len - 4`. Characters
+  // between `[out_start_aligned, out_end_aligned)` will be copied using uint4.
+  // `dst + 4` and `len - 4` are used instead of `dst` and `len` to avoid
+  // `load_uint4` reading beyond string boundaries.
+  // use signed int since out_end_aligned can be negative.
+  int64_t const out_start_aligned = (in_datatype_size + alignment_offset + out_datatype_size - 1) /
+                                      out_datatype_size * out_datatype_size -
+                                    alignment_offset;
+  int64_t const out_end_aligned =
+    (len - in_datatype_size + alignment_offset) / out_datatype_size * out_datatype_size -
+    alignment_offset;
+
+  for (int64_t ichar = out_start_aligned + lane_id * out_datatype_size; ichar < out_end_aligned;
+       ichar += warp_size * out_datatype_size) {
+    *(out_chars_aligned + (ichar + alignment_offset) / out_datatype_size) =
+      load_uint4((const char*)in_start + ichar);
+  }
+
+  // Tail logic: copy characters of the current string outside
+  // `[out_start_aligned, out_end_aligned)`.
+  if (out_end_aligned <= out_start_aligned) {
+    // In this case, `[out_start_aligned, out_end_aligned)` is an empty set, and we copy the
+    // entire string.
+    for (int64_t ichar = lane_id; ichar < len; ichar += warp_size) {
+      dst[ichar] = in_start[ichar];
+    }
+  } else {
+    // Copy characters in range `[0, out_start_aligned)`.
+    if (lane_id < out_start_aligned) { dst[lane_id] = in_start[lane_id]; }
+    // Copy characters in range `[out_end_aligned, len)`.
+    int64_t ichar = out_end_aligned + lane_id;
+    if (ichar < len) { dst[ichar] = in_start[ichar]; }
+  }
+}
+
+/**
+ * @brief char-parallel string copy.
+ */
+inline __device__ void ll_strcpy(uint8_t* dst, uint8_t const* src, size_t len, uint32_t lane_id)
+{
+  using cudf::detail::warp_size;
+  if (len > 64) {
+    wideStrcpy(dst, src, len, lane_id);
+  } else {
+    for (int i = lane_id; i < len; i += warp_size) {
+      dst[i] = src[i];
+    }
+  }
+}
+
+/**
+ * @brief Perform exclusive scan on an array of any length using a single block of threads.
+ */
+template <int block_size>
+__device__ void block_excl_sum(size_type* arr, size_type length, size_type initial_value)
+{
+  using block_scan = cub::BlockScan<size_type, block_size>;
+  __shared__ typename block_scan::TempStorage scan_storage;
+  int const t = threadIdx.x;
+
+  // do a series of block sums, storing results in arr as we go
+  for (int pos = 0; pos < length; pos += block_size) {
+    int const tidx = pos + t;
+    size_type tval = tidx < length ? arr[tidx] : 0;
+    size_type block_sum;
+    block_scan(scan_storage).ExclusiveScan(tval, tval, initial_value, cub::Sum(), block_sum);
+    if (tidx < length) { arr[tidx] = tval; }
+    initial_value += block_sum;
+  }
+}
+
+}  // namespace cudf::io::parquet::gpu
diff --git a/cpp/src/io/parquet/parquet.hpp b/cpp/src/io/parquet/parquet.hpp
new file mode 100644
index 0000000..c2affc7
--- /dev/null
+++ b/cpp/src/io/parquet/parquet.hpp
@@ -0,0 +1,410 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "parquet_common.hpp"
+
+#include <thrust/optional.h>
+
+#include <cstdint>
+#include <optional>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+constexpr uint32_t parquet_magic = (('P' << 0) | ('A' << 8) | ('R' << 16) | ('1' << 24));
+
+/**
+ * @brief Struct that describes the Parquet file data header
+ */
+struct file_header_s {
+  uint32_t magic;
+};
+
+/**
+ * @brief Struct that describes the Parquet file data postscript
+ */
+struct file_ender_s {
+  uint32_t footer_len;
+  uint32_t magic;
+};
+
+// thrift generated code simplified.
+struct StringType {};
+struct MapType {};
+struct ListType {};
+struct EnumType {};
+struct DecimalType {
+  int32_t scale     = 0;
+  int32_t precision = 0;
+};
+struct DateType {};
+
+struct MilliSeconds {};
+struct MicroSeconds {};
+struct NanoSeconds {};
+using TimeUnit_isset = struct TimeUnit_isset {
+  bool MILLIS{false};
+  bool MICROS{false};
+  bool NANOS{false};
+};
+
+struct TimeUnit {
+  TimeUnit_isset isset;
+  MilliSeconds MILLIS;
+  MicroSeconds MICROS;
+  NanoSeconds NANOS;
+};
+
+struct TimeType {
+  bool isAdjustedToUTC = false;
+  TimeUnit unit;
+};
+struct TimestampType {
+  bool isAdjustedToUTC = false;
+  TimeUnit unit;
+};
+struct IntType {
+  int8_t bitWidth = 0;
+  bool isSigned   = false;
+};
+struct NullType {};
+struct JsonType {};
+struct BsonType {};
+
+// thrift generated code simplified.
+using LogicalType_isset = struct LogicalType_isset {
+  bool STRING{false};
+  bool MAP{false};
+  bool LIST{false};
+  bool ENUM{false};
+  bool DECIMAL{false};
+  bool DATE{false};
+  bool TIME{false};
+  bool TIMESTAMP{false};
+  bool INTEGER{false};
+  bool UNKNOWN{false};
+  bool JSON{false};
+  bool BSON{false};
+};
+
+struct LogicalType {
+  LogicalType_isset isset;
+  StringType STRING;
+  MapType MAP;
+  ListType LIST;
+  EnumType ENUM;
+  DecimalType DECIMAL;
+  DateType DATE;
+  TimeType TIME;
+  TimestampType TIMESTAMP;
+  IntType INTEGER;
+  NullType UNKNOWN;
+  JsonType JSON;
+  BsonType BSON;
+};
+
+/**
+ * Union to specify the order used for the min_value and max_value fields for a column.
+ */
+struct ColumnOrder {
+  enum Type { UNDEFINED, TYPE_ORDER };
+  Type type;
+
+  operator Type() const { return type; }
+};
+
+/**
+ * @brief Struct for describing an element/field in the Parquet format schema
+ *
+ * Parquet is a strongly-typed format so the file layout can be interpreted as
+ * as a schema tree.
+ */
+struct SchemaElement {
+  Type type                    = UNDEFINED_TYPE;
+  ConvertedType converted_type = UNKNOWN;
+  LogicalType logical_type;
+  int32_t type_length =
+    0;  // Byte length of FIXED_LENGTH_BYTE_ARRAY elements, or maximum bit length for other types
+  FieldRepetitionType repetition_type = REQUIRED;
+  std::string name                    = "";
+  int32_t num_children                = 0;
+  int32_t decimal_scale               = 0;
+  int32_t decimal_precision           = 0;
+  thrust::optional<int32_t> field_id  = thrust::nullopt;
+  bool output_as_byte_array           = false;
+
+  // The following fields are filled in later during schema initialization
+  int max_definition_level = 0;
+  int max_repetition_level = 0;
+  int parent_idx           = 0;
+  std::vector<size_t> children_idx;
+
+  bool operator==(SchemaElement const& other) const
+  {
+    return type == other.type && converted_type == other.converted_type &&
+           type_length == other.type_length && repetition_type == other.repetition_type &&
+           name == other.name && num_children == other.num_children &&
+           decimal_scale == other.decimal_scale && decimal_precision == other.decimal_precision &&
+           field_id == other.field_id;
+  }
+
+  // the parquet format is a little squishy when it comes to interpreting
+  // repeated fields. sometimes repeated fields act as "stubs" in the schema
+  // that don't represent a true nesting level.
+  //
+  // this is the case with plain lists:
+  //
+  // optional group my_list (LIST) {
+  //   repeated group element {        <-- not part of the output hierarchy
+  //     required binary str (UTF8);
+  //   };
+  // }
+  //
+  // However, for backwards compatibility reasons, there are a few special cases, namely
+  // List<Struct<>> (which also corresponds to how the map type is specified), where
+  // this does not hold true
+  //
+  // optional group my_list (LIST) {
+  //   repeated group element {        <-- part of the hierarchy because it represents a struct
+  //     required binary str (UTF8);
+  //     required int32 num;
+  //  };
+  // }
+  [[nodiscard]] bool is_stub() const { return repetition_type == REPEATED && num_children == 1; }
+
+  // https://github.com/apache/parquet-cpp/blob/642da05/src/parquet/schema.h#L49-L50
+  // One-level LIST encoding: Only allows required lists with required cells:
+  //   repeated value_type name
+  [[nodiscard]] bool is_one_level_list(SchemaElement const& parent) const
+  {
+    return repetition_type == REPEATED and num_children == 0 and not parent.is_list();
+  }
+
+  // returns true if the element is a list
+  [[nodiscard]] bool is_list() const { return converted_type == LIST; }
+
+  // in parquet terms, a group is a level of nesting in the schema. a group
+  // can be a struct or a list
+  [[nodiscard]] bool is_struct() const
+  {
+    return type == UNDEFINED_TYPE &&
+           // this assumption might be a little weak.
+           ((repetition_type != REPEATED) || (repetition_type == REPEATED && num_children == 2));
+  }
+};
+
+/**
+ * @brief Thrift-derived struct describing column chunk statistics
+ */
+struct Statistics {
+  std::vector<uint8_t> max;        // deprecated max value in signed comparison order
+  std::vector<uint8_t> min;        // deprecated min value in signed comparison order
+  int64_t null_count     = -1;     // count of null values in the column
+  int64_t distinct_count = -1;     // count of distinct values occurring
+  std::vector<uint8_t> max_value;  // max value for column determined by ColumnOrder
+  std::vector<uint8_t> min_value;  // min value for column determined by ColumnOrder
+};
+
+/**
+ * @brief Thrift-derived struct describing a column chunk
+ */
+struct ColumnChunkMetaData {
+  Type type = BOOLEAN;
+  std::vector<Encoding> encodings;
+  std::vector<std::string> path_in_schema;
+  Compression codec  = UNCOMPRESSED;
+  int64_t num_values = 0;
+  int64_t total_uncompressed_size =
+    0;  // total byte size of all uncompressed pages in this column chunk (including the headers)
+  int64_t total_compressed_size =
+    0;  // total byte size of all compressed pages in this column chunk (including the headers)
+  int64_t data_page_offset  = 0;  // Byte offset from beginning of file to first data page
+  int64_t index_page_offset = 0;  // Byte offset from beginning of file to root index page
+  int64_t dictionary_page_offset =
+    0;                    // Byte offset from the beginning of file to first (only) dictionary page
+  Statistics statistics;  // Encoded chunk-level statistics
+};
+
+/**
+ * @brief Thrift-derived struct describing a chunk of data for a particular
+ * column
+ *
+ * Each column chunk lives in a particular row group and are guaranteed to be
+ * contiguous in the file. Any missing or corrupted chunks can be skipped during
+ * reading.
+ */
+struct ColumnChunk {
+  std::string file_path = "";
+  int64_t file_offset   = 0;
+  ColumnChunkMetaData meta_data;
+  int64_t offset_index_offset = 0;  // File offset of ColumnChunk's OffsetIndex
+  int32_t offset_index_length = 0;  // Size of ColumnChunk's OffsetIndex, in bytes
+  int64_t column_index_offset = 0;  // File offset of ColumnChunk's ColumnIndex
+  int32_t column_index_length = 0;  // Size of ColumnChunk's ColumnIndex, in bytes
+
+  // Following fields are derived from other fields
+  int schema_idx = -1;  // Index in flattened schema (derived from path_in_schema)
+};
+
+/**
+ * @brief Thrift-derived struct describing a group of row data
+ *
+ * There may be one or more row groups within a dataset, with each row group
+ * consisting of a column chunk for each column.
+ */
+struct RowGroup {
+  int64_t total_byte_size = 0;
+  std::vector<ColumnChunk> columns;
+  int64_t num_rows = 0;
+};
+
+/**
+ * @brief Thrift-derived struct describing a key-value pair, for user metadata
+ */
+struct KeyValue {
+  std::string key;
+  std::string value;
+};
+
+/**
+ * @brief Thrift-derived struct describing file-level metadata
+ *
+ * The additional information stored in the key_value_metadata can be used
+ * during reading to reconstruct the output data to the exact original dataset
+ * prior to conversion to Parquet.
+ */
+struct FileMetaData {
+  int32_t version = 0;
+  std::vector<SchemaElement> schema;
+  int64_t num_rows = 0;
+  std::vector<RowGroup> row_groups;
+  std::vector<KeyValue> key_value_metadata;
+  std::string created_by = "";
+  thrust::optional<std::vector<ColumnOrder>> column_orders;
+};
+
+/**
+ * @brief Thrift-derived struct describing the header for a data page
+ */
+struct DataPageHeader {
+  int32_t num_values                 = 0;  // Number of values, including NULLs, in this data page.
+  Encoding encoding                  = Encoding::PLAIN;  // Encoding used for this data page
+  Encoding definition_level_encoding = Encoding::PLAIN;  // Encoding used for definition levels
+  Encoding repetition_level_encoding = Encoding::PLAIN;  // Encoding used for repetition levels
+};
+
+/**
+ * @brief Thrift-derived struct describing the header for a V2 data page
+ */
+struct DataPageHeaderV2 {
+  int32_t num_values = 0;  // Number of values, including NULLs, in this data page.
+  int32_t num_nulls  = 0;  // Number of NULL values, in this data page.
+  int32_t num_rows   = 0;  // Number of rows in this data page. which means
+                           // pages change on record boundaries (r = 0)
+  Encoding encoding                     = Encoding::PLAIN;  // Encoding used for this data page
+  int32_t definition_levels_byte_length = 0;                // length of the definition levels
+  int32_t repetition_levels_byte_length = 0;                // length of the repetition levels
+  bool is_compressed                    = true;             // whether the values are compressed.
+};
+
+/**
+ * @brief Thrift-derived struct describing the header for a dictionary page
+ */
+struct DictionaryPageHeader {
+  int32_t num_values = 0;                // Number of values in the dictionary
+  Encoding encoding  = Encoding::PLAIN;  // Encoding using this dictionary page
+};
+
+/**
+ * @brief Thrift-derived struct describing the page header
+ *
+ * Column data are divided into individual chunks, which are subdivided into
+ * pages. Each page has an associated header, describing the page type. There
+ * can be multiple page types interleaved in a column chunk, and each page is
+ * individually compressed and encoded. Any missing or corrupted pages can be
+ * skipped during reading.
+ */
+struct PageHeader {
+  PageType type =
+    PageType::DATA_PAGE;  // the type of the page: indicates which of the *_header fields is set
+  int32_t uncompressed_page_size = 0;  // Uncompressed page size in bytes (not including the header)
+  int32_t compressed_page_size   = 0;  // Compressed page size in bytes (not including the header)
+  DataPageHeader data_page_header;
+  DictionaryPageHeader dictionary_page_header;
+  DataPageHeaderV2 data_page_header_v2;
+};
+
+/**
+ * @brief Thrift-derived struct describing page location information stored
+ * in the offsets index.
+ */
+struct PageLocation {
+  int64_t offset;                // Offset of the page in the file
+  int32_t compressed_page_size;  // Compressed page size in bytes plus the heeader length
+  int64_t first_row_index;  // Index within the column chunk of the first row of the page. reset to
+                            // 0 at the beginning of each column chunk
+};
+
+/**
+ * @brief Thrift-derived struct describing the offset index.
+ */
+struct OffsetIndex {
+  std::vector<PageLocation> page_locations;
+};
+
+/**
+ * @brief Thrift-derived struct describing the column index.
+ */
+struct ColumnIndex {
+  std::vector<bool> null_pages;  // Boolean used to determine if a page contains only null values
+  std::vector<std::vector<uint8_t>> min_values;  // lower bound for values in each page
+  std::vector<std::vector<uint8_t>> max_values;  // upper bound for values in each page
+  BoundaryOrder boundary_order =
+    BoundaryOrder::UNORDERED;        // Indicates if min and max values are ordered
+  std::vector<int64_t> null_counts;  // Optional count of null values per page
+};
+
+// bit space we are reserving in column_buffer::user_data
+constexpr uint32_t PARQUET_COLUMN_BUFFER_SCHEMA_MASK          = (0xff'ffffu);
+constexpr uint32_t PARQUET_COLUMN_BUFFER_FLAG_LIST_TERMINATED = (1 << 24);
+// if this column has a list parent anywhere above it in the hierarchy
+constexpr uint32_t PARQUET_COLUMN_BUFFER_FLAG_HAS_LIST_PARENT = (1 << 25);
+
+/**
+ * @brief Count the number of leading zeros in an unsigned integer
+ */
+static inline int CountLeadingZeros32(uint32_t value)
+{
+#if defined(__clang__) || defined(__GNUC__)
+  if (value == 0) return 32;
+  return static_cast<int>(__builtin_clz(value));
+#else
+  int bitpos = 0;
+  while (value != 0) {
+    value >>= 1;
+    ++bitpos;
+  }
+  return 32 - bitpos;
+#endif
+}
+
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/parquet_common.hpp b/cpp/src/io/parquet/parquet_common.hpp
new file mode 100644
index 0000000..5a1716b
--- /dev/null
+++ b/cpp/src/io/parquet/parquet_common.hpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+namespace parquet {
+// Max decimal precisions according to the parquet spec:
+// https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#decimal
+auto constexpr MAX_DECIMAL32_PRECISION  = 9;
+auto constexpr MAX_DECIMAL64_PRECISION  = 18;
+auto constexpr MAX_DECIMAL128_PRECISION = 38;  // log10(2^(sizeof(int128_t) * 8 - 1) - 1)
+
+/**
+ * @brief Basic data types in Parquet, determines how data is physically stored
+ */
+enum Type : int8_t {
+  UNDEFINED_TYPE       = -1,  // Undefined for non-leaf nodes
+  BOOLEAN              = 0,
+  INT32                = 1,
+  INT64                = 2,
+  INT96                = 3,  // Deprecated
+  FLOAT                = 4,
+  DOUBLE               = 5,
+  BYTE_ARRAY           = 6,
+  FIXED_LEN_BYTE_ARRAY = 7,
+};
+
+/**
+ * @brief High-level data types in Parquet, determines how data is logically interpreted
+ */
+enum ConvertedType {
+  UNKNOWN = -1,  // No type information present
+  UTF8    = 0,   // a BYTE_ARRAY may contain UTF8 encoded chars
+  MAP     = 1,   // a map is converted as an optional field containing a repeated key/value pair
+  MAP_KEY_VALUE = 2,  // a key/value pair is converted into a group of two fields
+  LIST =
+    3,  // a list is converted into an optional field containing a repeated field for its values
+  ENUM    = 4,      // an enum is converted into a binary field
+  DECIMAL = 5,      // A decimal value. 10^(-scale) encoded as 2's complement big endian
+                    // (precision=number of digits, scale=location of decimal point)
+  DATE        = 6,  // A Date, stored as days since Unix epoch, encoded as the INT32 physical type.
+  TIME_MILLIS = 7,  // A time. The total number of milliseconds since midnight.The value is stored
+                    // as an INT32 physical type.
+  TIME_MICROS = 8,  // A time. The total number of microseconds since midnight.  The value is stored
+                    // as an INT64 physical type.
+  TIMESTAMP_MILLIS = 9,   // A date/time combination, recorded as milliseconds since the Unix epoch
+                          // using physical type of INT64.
+  TIMESTAMP_MICROS = 10,  // A date/time combination, microseconds since the Unix epoch as INT64
+  UINT_8           = 11,  // An unsigned integer 8-bit value as INT32
+  UINT_16          = 12,  // An unsigned integer 16-bit value as INT32
+  UINT_32          = 13,  // An unsigned integer 32-bit value as INT32
+  UINT_64          = 14,  // An unsigned integer 64-bit value as INT64
+  INT_8            = 15,  // A signed integer 8-bit value as INT32
+  INT_16           = 16,  // A signed integer 16-bit value as INT32
+  INT_32           = 17,  // A signed integer 32-bit value as INT32
+  INT_64           = 18,  // A signed integer 8-bit value as INT64
+  JSON             = 19,  // A JSON document embedded within a single UTF8 column.
+  BSON             = 20,  // A BSON document embedded within a single BINARY column.
+  INTERVAL = 21,  // This type annotates a time interval stored as a FIXED_LEN_BYTE_ARRAY of length
+                  // 12 for 3 integers {months,days,milliseconds}
+  NA = 25,        // No Type information, For eg, all-nulls.
+};
+
+/**
+ * @brief Encoding types for the actual data stream
+ */
+enum class Encoding : uint8_t {
+  PLAIN                   = 0,
+  GROUP_VAR_INT           = 1,  // Deprecated, never used
+  PLAIN_DICTIONARY        = 2,
+  RLE                     = 3,
+  BIT_PACKED              = 4,  // Deprecated by parquet-format in 2013, superseded by RLE
+  DELTA_BINARY_PACKED     = 5,
+  DELTA_LENGTH_BYTE_ARRAY = 6,
+  DELTA_BYTE_ARRAY        = 7,
+  RLE_DICTIONARY          = 8,
+  BYTE_STREAM_SPLIT       = 9,
+  NUM_ENCODINGS           = 10,
+};
+
+/**
+ * @brief Compression codec used for compressed data pages
+ */
+enum Compression {
+  UNCOMPRESSED = 0,
+  SNAPPY       = 1,
+  GZIP         = 2,
+  LZO          = 3,
+  BROTLI       = 4,  // Added in 2.3.2
+  LZ4          = 5,  // Added in 2.3.2
+  ZSTD         = 6,  // Added in 2.3.2
+};
+
+/**
+ * @brief Compression codec used for compressed data pages
+ */
+enum FieldRepetitionType {
+  NO_REPETITION_TYPE = -1,
+  REQUIRED = 0,  // This field is required (can not be null) and each record has exactly 1 value.
+  OPTIONAL = 1,  // The field is optional (can be null) and each record has 0 or 1 values.
+  REPEATED = 2,  // The field is repeated and can contain 0 or more values
+};
+
+/**
+ * @brief Types of pages
+ */
+enum class PageType : uint8_t {
+  DATA_PAGE       = 0,
+  INDEX_PAGE      = 1,
+  DICTIONARY_PAGE = 2,
+  DATA_PAGE_V2    = 3,
+};
+
+/**
+ * @brief Enum to annotate whether lists of min/max elements inside ColumnIndex
+ * are ordered and if so, in which direction.
+ */
+enum BoundaryOrder {
+  UNORDERED  = 0,
+  ASCENDING  = 1,
+  DESCENDING = 2,
+};
+
+/**
+ * @brief Thrift compact protocol struct field types
+ */
+enum FieldType {
+  ST_FLD_TRUE   = 1,
+  ST_FLD_FALSE  = 2,
+  ST_FLD_BYTE   = 3,
+  ST_FLD_I16    = 4,
+  ST_FLD_I32    = 5,
+  ST_FLD_I64    = 6,
+  ST_FLD_DOUBLE = 7,
+  ST_FLD_BINARY = 8,
+  ST_FLD_LIST   = 9,
+  ST_FLD_SET    = 10,
+  ST_FLD_MAP    = 11,
+  ST_FLD_STRUCT = 12,
+};
+
+}  // namespace parquet
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/parquet_gpu.cuh b/cpp/src/io/parquet/parquet_gpu.cuh
new file mode 100644
index 0000000..dc74bee
--- /dev/null
+++ b/cpp/src/io/parquet/parquet_gpu.cuh
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "parquet_gpu.hpp"
+
+#include <cudf/lists/lists_column_device_view.cuh>
+#include <cudf/types.hpp>
+
+#include <cuco/static_map.cuh>
+
+namespace cudf::io::parquet::gpu {
+
+auto constexpr KEY_SENTINEL   = size_type{-1};
+auto constexpr VALUE_SENTINEL = size_type{-1};
+
+using map_type = cuco::static_map<size_type, size_type>;
+
+/**
+ * @brief The alias of `map_type::pair_atomic_type` class.
+ *
+ * Declare this struct by trivial subclassing instead of type aliasing so we can have forward
+ * declaration of this struct somewhere else.
+ */
+struct slot_type : public map_type::pair_atomic_type {};
+
+/**
+ * @brief Return the byte length of parquet dtypes that are physically represented by INT32
+ */
+inline uint32_t __device__ int32_logical_len(type_id id)
+{
+  switch (id) {
+    case cudf::type_id::INT8: [[fallthrough]];
+    case cudf::type_id::UINT8: return 1;
+    case cudf::type_id::INT16: [[fallthrough]];
+    case cudf::type_id::UINT16: return 2;
+    case cudf::type_id::DURATION_SECONDS: [[fallthrough]];
+    case cudf::type_id::DURATION_MILLISECONDS: return 8;
+    default: return 4;
+  }
+}
+
+/**
+ * @brief Translate the row index of a parent column_device_view into the index of the first value
+ * in the leaf child.
+ * Only works in the context of parquet writer where struct columns are previously modified s.t.
+ * they only have one immediate child.
+ */
+inline size_type __device__ row_to_value_idx(size_type idx,
+                                             parquet_column_device_view const& parquet_col)
+{
+  // with a byte array, we can't go all the way down to the leaf node, but instead we want to leave
+  // the size at the parent level because we are writing out parent row byte arrays.
+  auto col = *parquet_col.parent_column;
+  while (col.type().id() == type_id::LIST or col.type().id() == type_id::STRUCT) {
+    if (col.type().id() == type_id::STRUCT) {
+      idx += col.offset();
+      col = col.child(0);
+    } else {
+      auto list_col = cudf::detail::lists_column_device_view(col);
+      auto child    = list_col.child();
+      if (parquet_col.output_as_byte_array && child.type().id() == type_id::UINT8) { break; }
+      idx = list_col.offset_at(idx);
+      col = child;
+    }
+  }
+  return idx;
+}
+
+}  // namespace cudf::io::parquet::gpu
diff --git a/cpp/src/io/parquet/parquet_gpu.hpp b/cpp/src/io/parquet/parquet_gpu.hpp
new file mode 100644
index 0000000..51c862b
--- /dev/null
+++ b/cpp/src/io/parquet/parquet_gpu.hpp
@@ -0,0 +1,867 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "io/comp/gpuinflate.hpp"
+#include "io/parquet/parquet.hpp"
+#include "io/parquet/parquet_common.hpp"
+#include "io/statistics/statistics.cuh"
+#include "io/utilities/column_buffer.hpp"
+#include "io/utilities/hostdevice_vector.hpp"
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cuda_runtime.h>
+
+#include <vector>
+
+namespace cudf::io::parquet {
+
+using cudf::io::detail::string_index_pair;
+
+// Largest number of bits to use for dictionary keys
+constexpr int MAX_DICT_BITS = 24;
+
+// Total number of unsigned 24 bit values
+constexpr size_type MAX_DICT_SIZE = (1 << MAX_DICT_BITS) - 1;
+
+// level decode buffer size.
+constexpr int LEVEL_DECODE_BUF_SIZE = 2048;
+
+template <int rolling_size>
+constexpr int rolling_index(int index)
+{
+  return index % rolling_size;
+}
+
+/**
+ * @brief Enum for the different types of errors that can occur during decoding.
+ *
+ * These values are used as bitmasks, so they must be powers of 2.
+ */
+enum class decode_error : int32_t {
+  DATA_STREAM_OVERRUN  = 0x1,
+  LEVEL_STREAM_OVERRUN = 0x2,
+  UNSUPPORTED_ENCODING = 0x4,
+  INVALID_LEVEL_RUN    = 0x8,
+  INVALID_DATA_TYPE    = 0x10,
+  EMPTY_PAGE           = 0x20,
+  INVALID_DICT_WIDTH   = 0x40,
+};
+
+/**
+ * @brief Struct representing an input column in the file.
+ */
+struct input_column_info {
+  int schema_idx;
+  std::string name;
+  bool has_repetition;
+  // size == nesting depth. the associated real output
+  // buffer index in the dest column for each level of nesting.
+  std::vector<int> nesting;
+
+  input_column_info(int _schema_idx, std::string _name, bool _has_repetition)
+    : schema_idx(_schema_idx), name(_name), has_repetition(_has_repetition)
+  {
+  }
+
+  auto nesting_depth() const { return nesting.size(); }
+};
+
+namespace gpu {
+
+/**
+ * @brief Enums for the flags in the page header
+ */
+enum {
+  PAGEINFO_FLAGS_DICTIONARY = (1 << 0),  // Indicates a dictionary page
+  PAGEINFO_FLAGS_V2         = (1 << 1),  // V2 page header
+};
+
+/**
+ * @brief Enum for the two encoding streams
+ */
+enum level_type {
+  DEFINITION = 0,
+  REPETITION,
+
+  NUM_LEVEL_TYPES
+};
+
+/**
+ * @brief Enum of mask bits for the PageInfo kernel_mask
+ *
+ * Used to control which decode kernels to run.
+ */
+enum kernel_mask_bits {
+  KERNEL_MASK_GENERAL      = (1 << 0),  // Run catch-all decode kernel
+  KERNEL_MASK_STRING       = (1 << 1),  // Run decode kernel for string data
+  KERNEL_MASK_DELTA_BINARY = (1 << 2)   // Run decode kernel for DELTA_BINARY_PACKED data
+};
+
+/**
+ * @brief Nesting information specifically needed by the decode and preprocessing
+ * kernels.
+ *
+ * This data is kept separate from PageNestingInfo to keep it as small as possible.
+ * It is used in a cached form in shared memory when possible.
+ */
+struct PageNestingDecodeInfo {
+  // set up prior to decoding
+  int32_t max_def_level;
+  // input repetition/definition levels are remapped with these values
+  // into the corresponding real output nesting depths.
+  int32_t start_depth;
+  int32_t end_depth;
+
+  // computed during preprocessing
+  int32_t page_start_value;
+
+  // computed during decoding
+  int32_t null_count;
+
+  // used internally during decoding
+  int32_t valid_map_offset;
+  int32_t valid_count;
+  int32_t value_count;
+  uint8_t* data_out;
+  uint8_t* string_out;
+  bitmask_type* valid_map;
+};
+
+// Use up to 512 bytes of shared memory as a cache for nesting information.
+// As of 1/20/23, this gives us a max nesting depth of 10 (after which it falls back to
+// global memory). This handles all but the most extreme cases.
+constexpr int max_cacheable_nesting_decode_info = (512) / sizeof(PageNestingDecodeInfo);
+
+/**
+ * @brief Nesting information
+ *
+ * This struct serves two purposes:
+ *
+ * - It stores information about output (cudf) columns
+ * - It provides a mapping from input column depth to output column depth via
+ * the start_depth and end_depth fields.
+ *
+ */
+struct PageNestingInfo {
+  // set at initialization (see start_offset_output_iterator in reader_impl_preprocess.cu)
+  cudf::type_id type;  // type of the corresponding cudf output column
+  bool nullable;
+
+  // TODO: these fields might make sense to move into PageNestingDecodeInfo for memory performance
+  // reasons.
+  int32_t size;  // this page/nesting-level's row count contribution to the output column, if fully
+                 // decoded
+  int32_t batch_size;  // the size of the page for this batch
+};
+
+/**
+ * @brief Struct describing a particular page of column chunk data
+ */
+struct PageInfo {
+  uint8_t* page_data;  // Compressed page data before decompression, or uncompressed data after
+                       // decompression
+  int32_t compressed_page_size;    // compressed data size in bytes
+  int32_t uncompressed_page_size;  // uncompressed data size in bytes
+  // for V2 pages, the def and rep level data is not compressed, and lacks the 4-byte length
+  // indicator. instead the lengths for these are stored in the header.
+  int32_t lvl_bytes[level_type::NUM_LEVEL_TYPES];  // length of the rep/def levels (V2 header)
+  // Number of values in this data page or dictionary.
+  // Important : the # of input values does not necessarily
+  // correspond to the number of rows in the output. It just reflects the number
+  // of values in the input stream.
+  // - In the case of a flat schema, it will correspond to the # of output rows
+  // - In the case of a nested schema, you have to decode the repetition and definition
+  //   levels to extract actual column values
+  int32_t num_input_values;
+  int32_t chunk_row;  // starting row of this page relative to the start of the chunk
+  int32_t num_rows;   // number of rows in this page
+  // the next two are calculated in gpuComputePageStringSizes
+  int32_t num_nulls;       // number of null values (V2 header), but recalculated for string cols
+  int32_t num_valids;      // number of non-null values, taking into account skip_rows/num_rows
+  int32_t chunk_idx;       // column chunk this page belongs to
+  int32_t src_col_schema;  // schema index of this column
+  uint8_t flags;           // PAGEINFO_FLAGS_XXX
+  Encoding encoding;       // Encoding for data or dictionary page
+  Encoding definition_level_encoding;  // Encoding used for definition levels (data page)
+  Encoding repetition_level_encoding;  // Encoding used for repetition levels (data page)
+
+  // for nested types, we run a preprocess step in order to determine output
+  // column sizes. Because of this, we can jump directly to the position in the
+  // input data to start decoding instead of reading all of the data and discarding
+  // rows we don't care about.
+  //
+  // NOTE: for flat hierarchies we do not do the preprocess step, so skipped_values and
+  // skipped_leaf_values will always be 0.
+  //
+  // # of values skipped in the repetition/definition level stream
+  int32_t skipped_values;
+  // # of values skipped in the actual data stream.
+  int32_t skipped_leaf_values;
+  // for string columns only, the size of all the chars in the string for
+  // this page. only valid/computed during the base preprocess pass
+  int32_t str_bytes;
+  int32_t str_offset;  // offset into string data for this page
+
+  // nesting information (input/output) for each page. this array contains
+  // input column nesting information, output column nesting information and
+  // mappings between the two. the length of the array, nesting_info_size is
+  // max(num_output_nesting_levels, max_definition_levels + 1)
+  int32_t num_output_nesting_levels;
+  int32_t nesting_info_size;
+  PageNestingInfo* nesting;
+  PageNestingDecodeInfo* nesting_decode;
+
+  // level decode buffers
+  uint8_t* lvl_decode_buf[level_type::NUM_LEVEL_TYPES];
+
+  uint32_t kernel_mask;
+};
+
+/**
+ * @brief Struct describing a particular chunk of column data
+ */
+struct ColumnChunkDesc {
+  constexpr ColumnChunkDesc() noexcept {};
+  explicit ColumnChunkDesc(size_t compressed_size_,
+                           uint8_t* compressed_data_,
+                           size_t num_values_,
+                           uint16_t datatype_,
+                           uint16_t datatype_length_,
+                           size_t start_row_,
+                           uint32_t num_rows_,
+                           int16_t max_definition_level_,
+                           int16_t max_repetition_level_,
+                           int16_t max_nesting_depth_,
+                           uint8_t def_level_bits_,
+                           uint8_t rep_level_bits_,
+                           int8_t codec_,
+                           int8_t converted_type_,
+                           LogicalType logical_type_,
+                           int8_t decimal_precision_,
+                           int32_t ts_clock_rate_,
+                           int32_t src_col_index_,
+                           int32_t src_col_schema_)
+    : compressed_data(compressed_data_),
+      compressed_size(compressed_size_),
+      num_values(num_values_),
+      start_row(start_row_),
+      num_rows(num_rows_),
+      max_level{max_definition_level_, max_repetition_level_},
+      max_nesting_depth{max_nesting_depth_},
+      data_type(datatype_ | (datatype_length_ << 3)),
+      level_bits{def_level_bits_, rep_level_bits_},
+      num_data_pages(0),
+      num_dict_pages(0),
+      max_num_pages(0),
+      page_info(nullptr),
+      str_dict_index(nullptr),
+      valid_map_base{nullptr},
+      column_data_base{nullptr},
+      column_string_base{nullptr},
+      codec(codec_),
+      converted_type(converted_type_),
+      logical_type(logical_type_),
+      decimal_precision(decimal_precision_),
+      ts_clock_rate(ts_clock_rate_),
+      src_col_index(src_col_index_),
+      src_col_schema(src_col_schema_)
+  {
+  }
+
+  uint8_t const* compressed_data{};                  // pointer to compressed column chunk data
+  size_t compressed_size{};                          // total compressed data size for this chunk
+  size_t num_values{};                               // total number of values in this column
+  size_t start_row{};                                // starting row of this chunk
+  uint32_t num_rows{};                               // number of rows in this chunk
+  int16_t max_level[level_type::NUM_LEVEL_TYPES]{};  // max definition/repetition level
+  int16_t max_nesting_depth{};                       // max nesting depth of the output
+  uint16_t data_type{};  // basic column data type, ((type_length << 3) |
+                         // parquet::Type)
+  uint8_t
+    level_bits[level_type::NUM_LEVEL_TYPES]{};  // bits to encode max definition/repetition levels
+  int32_t num_data_pages{};                     // number of data pages
+  int32_t num_dict_pages{};                     // number of dictionary pages
+  int32_t max_num_pages{};                      // size of page_info array
+  PageInfo* page_info{};                        // output page info for up to num_dict_pages +
+                                                // num_data_pages (dictionary pages first)
+  string_index_pair* str_dict_index{};          // index for string dictionary
+  bitmask_type** valid_map_base{};              // base pointers of valid bit map for this column
+  void** column_data_base{};                    // base pointers of column data
+  void** column_string_base{};                  // base pointers of column string data
+  int8_t codec{};                               // compressed codec enum
+  int8_t converted_type{};                      // converted type enum
+  LogicalType logical_type{};                   // logical type
+  int8_t decimal_precision{};                   // Decimal precision
+  int32_t ts_clock_rate{};  // output timestamp clock frequency (0=default, 1000=ms, 1000000000=ns)
+
+  int32_t src_col_index{};   // my input column index
+  int32_t src_col_schema{};  // my schema index in the file
+};
+
+/**
+ * @brief The row_group_info class
+ */
+struct row_group_info {
+  size_type index;  // row group index within a file. aggregate_reader_metadata::get_row_group() is
+                    // called with index and source_index
+  size_t start_row;
+  size_type source_index;  // file index.
+
+  row_group_info() = default;
+
+  row_group_info(size_type index, size_t start_row, size_type source_index)
+    : index{index}, start_row{start_row}, source_index{source_index}
+  {
+  }
+};
+
+/**
+ * @brief Struct to store file-level data that remains constant for
+ * all passes/chunks for the file.
+ */
+struct file_intermediate_data {
+  // all row groups to read
+  std::vector<row_group_info> row_groups{};
+
+  // all chunks from the selected row groups. We may end up reading these chunks progressively
+  // instead of all at once
+  std::vector<gpu::ColumnChunkDesc> chunks{};
+
+  // skip_rows/num_rows values for the entire file. these need to be adjusted per-pass because we
+  // may not be visiting every row group that contains these bounds
+  size_t global_skip_rows;
+  size_t global_num_rows;
+};
+
+/**
+ * @brief Structs to identify the reading row range for each chunk of rows in chunked reading.
+ */
+struct chunk_read_info {
+  size_t skip_rows;
+  size_t num_rows;
+};
+
+/**
+ * @brief Struct to store pass-level data that remains constant for a single pass.
+ */
+struct pass_intermediate_data {
+  std::vector<std::unique_ptr<datasource::buffer>> raw_page_data;
+  rmm::device_buffer decomp_page_data;
+
+  // rowgroup, chunk and page information for the current pass.
+  std::vector<row_group_info> row_groups{};
+  cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc> chunks{};
+  cudf::detail::hostdevice_vector<gpu::PageInfo> pages_info{};
+  cudf::detail::hostdevice_vector<gpu::PageNestingInfo> page_nesting_info{};
+  cudf::detail::hostdevice_vector<gpu::PageNestingDecodeInfo> page_nesting_decode_info{};
+
+  rmm::device_uvector<int32_t> page_keys{0, rmm::cuda_stream_default};
+  rmm::device_uvector<int32_t> page_index{0, rmm::cuda_stream_default};
+  rmm::device_uvector<string_index_pair> str_dict_index{0, rmm::cuda_stream_default};
+
+  std::vector<gpu::chunk_read_info> output_chunk_read_info;
+  std::size_t current_output_chunk{0};
+
+  rmm::device_buffer level_decode_data{};
+  int level_type_size{0};
+
+  // skip_rows and num_rows values for this particular pass. these may be adjusted values from the
+  // global values stored in file_intermediate_data.
+  size_t skip_rows;
+  size_t num_rows;
+};
+
+/**
+ * @brief Struct describing an encoder column
+ */
+struct parquet_column_device_view : stats_column_desc {
+  Type physical_type;            //!< physical data type
+  ConvertedType converted_type;  //!< logical data type
+  uint8_t level_bits;  //!< bits to encode max definition (lower nibble) & repetition (upper nibble)
+                       //!< levels
+  constexpr uint8_t num_def_level_bits() const { return level_bits & 0xf; }
+  constexpr uint8_t num_rep_level_bits() const { return level_bits >> 4; }
+  size_type const* const*
+    nesting_offsets;  //!< If column is a nested type, contains offset array of each nesting level
+
+  size_type const* level_offsets;  //!< Offset array for per-row pre-calculated rep/def level values
+  uint8_t const* rep_values;       //!< Pre-calculated repetition level values
+  uint8_t const* def_values;       //!< Pre-calculated definition level values
+  uint8_t const* nullability;  //!< Array of nullability of each nesting level. e.g. nullable[0] is
+                               //!< nullability of parent_column. May be different from
+                               //!< col.nullable() in case of chunked writing.
+  bool output_as_byte_array;   //!< Indicates this list column is being written as a byte array
+};
+
+struct EncColumnChunk;
+
+/**
+ * @brief Struct describing an encoder page fragment
+ */
+struct PageFragment {
+  uint32_t fragment_data_size;  //!< Size of fragment data in bytes
+  uint32_t dict_data_size;      //!< Size of dictionary for this fragment
+  uint32_t num_values;  //!< Number of values in fragment. Different from num_rows for nested type
+  uint32_t start_value_idx;
+  uint32_t num_leaf_values;  //!< Number of leaf values in fragment. Does not include nulls at
+                             //!< non-leaf level
+  size_type start_row;       //!< First row in fragment
+  uint16_t num_rows;         //!< Number of rows in fragment
+  uint16_t num_dict_vals;    //!< Number of unique dictionary entries
+  EncColumnChunk* chunk;     //!< The chunk that this fragment belongs to
+};
+
+/// Size of hash used for building dictionaries
+constexpr unsigned int kDictHashBits = 16;
+constexpr size_t kDictScratchSize    = (1 << kDictHashBits) * sizeof(uint32_t);
+
+struct EncPage;
+struct slot_type;
+
+// convert Encoding to a mask value
+constexpr uint32_t encoding_to_mask(Encoding encoding)
+{
+  return 1 << static_cast<uint32_t>(encoding);
+}
+
+/**
+ * @brief Struct describing an encoder column chunk
+ */
+struct EncColumnChunk {
+  parquet_column_device_view const* col_desc;  //!< Column description
+  size_type col_desc_id;
+  PageFragment* fragments;        //!< First fragment in chunk
+  uint8_t* uncompressed_bfr;      //!< Uncompressed page data
+  uint8_t* compressed_bfr;        //!< Compressed page data
+  statistics_chunk const* stats;  //!< Fragment statistics
+  uint32_t bfr_size;              //!< Uncompressed buffer size
+  uint32_t compressed_size;       //!< Compressed buffer size
+  uint32_t max_page_data_size;    //!< Max data size (excluding header) of any page in this chunk
+  uint32_t page_headers_size;     //!< Sum of size of all page headers
+  size_type start_row;            //!< First row of chunk
+  uint32_t num_rows;              //!< Number of rows in chunk
+  size_type num_values;     //!< Number of values in chunk. Different from num_rows for nested types
+  uint32_t first_fragment;  //!< First fragment of chunk
+  EncPage* pages;           //!< Ptr to pages that belong to this chunk
+  uint32_t first_page;      //!< First page of chunk
+  uint32_t num_pages;       //!< Number of pages in chunk
+  uint8_t is_compressed;    //!< Nonzero if the chunk uses compression
+  uint32_t dictionary_size;    //!< Size of dictionary page including header
+  uint32_t ck_stat_size;       //!< Size of chunk-level statistics (included in 1st page header)
+  slot_type* dict_map_slots;   //!< Hash map storage for calculating dict encoding for this chunk
+  size_type dict_map_size;     //!< Size of dict_map_slots
+  size_type num_dict_entries;  //!< Total number of entries in dictionary
+  size_type
+    uniq_data_size;  //!< Size of dictionary page (set of all unique values) if dict enc is used
+  size_type plain_data_size;  //!< Size of data in this chunk if plain encoding is used
+  size_type* dict_data;       //!< Dictionary data (unique row indices)
+  size_type* dict_index;  //!< Index of value in dictionary page. column[dict_data[dict_index[row]]]
+  uint8_t dict_rle_bits;  //!< Bit size for encoding dictionary indices
+  bool use_dictionary;    //!< True if the chunk uses dictionary encoding
+  uint8_t* column_index_blob;  //!< Binary blob containing encoded column index for this chunk
+  uint32_t column_index_size;  //!< Size of column index blob
+  uint32_t encodings;          //!< Mask representing the set of encodings used for this chunk
+};
+
+/**
+ * @brief Struct describing an encoder data page
+ */
+struct EncPage {
+  uint8_t* page_data;        //!< Ptr to uncompressed page
+  uint8_t* compressed_data;  //!< Ptr to compressed page
+  uint16_t num_fragments;    //!< Number of fragments in page
+  PageType page_type;        //!< Page type
+  Encoding encoding;         //!< Encoding used for page data
+  EncColumnChunk* chunk;     //!< Chunk that this page belongs to
+  uint32_t chunk_id;         //!< Index in chunk array
+  uint32_t hdr_size;         //!< Size of page header
+  uint32_t max_hdr_size;     //!< Maximum size of page header
+  uint32_t max_data_size;    //!< Maximum size of coded page data (excluding header)
+  uint32_t start_row;        //!< First row of page
+  uint32_t num_rows;         //!< Rows in page
+  uint32_t num_leaf_values;  //!< Values in page. Different from num_rows in case of nested types
+  uint32_t num_values;  //!< Number of def/rep level values in page. Includes null/empty elements in
+                        //!< non-leaf levels
+  uint32_t def_lvl_bytes;        //!< Number of bytes of encoded definition level data (V2 only)
+  uint32_t rep_lvl_bytes;        //!< Number of bytes of encoded repetition level data (V2 only)
+  compression_result* comp_res;  //!< Ptr to compression result
+  uint32_t num_nulls;            //!< Number of null values (V2 only) (down here for alignment)
+};
+
+/**
+ * @brief Test if the given column chunk is in a string column
+ */
+constexpr bool is_string_col(ColumnChunkDesc const& chunk)
+{
+  auto const not_converted_to_decimal = chunk.converted_type != DECIMAL;
+  auto const non_hashed_byte_array =
+    (chunk.data_type & 7) == BYTE_ARRAY and (chunk.data_type >> 3) != 4;
+  auto const fixed_len_byte_array = (chunk.data_type & 7) == FIXED_LEN_BYTE_ARRAY;
+  return not_converted_to_decimal and (non_hashed_byte_array or fixed_len_byte_array);
+}
+
+/**
+ * @brief Launches kernel for parsing the page headers in the column chunks
+ *
+ * @param[in] chunks List of column chunks
+ * @param[in] num_chunks Number of column chunks
+ * @param[in] stream CUDA stream to use
+ */
+void DecodePageHeaders(ColumnChunkDesc* chunks, int32_t num_chunks, rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for building the dictionary index for the column
+ * chunks
+ *
+ * @param[in] chunks List of column chunks
+ * @param[in] num_chunks Number of column chunks
+ * @param[in] stream CUDA stream to use
+ */
+void BuildStringDictionaryIndex(ColumnChunkDesc* chunks,
+                                int32_t num_chunks,
+                                rmm::cuda_stream_view stream);
+
+/**
+ * @brief Get the set of kernels that need to be invoked on these pages as a bitmask.
+ *
+ * This function performs a bitwise OR on all of the individual `kernel_mask` fields on the pages
+ * passed in.
+ *
+ * @param[in] pages List of pages to aggregate
+ * @param[in] stream CUDA stream to use
+ * @return Bitwise OR of all page `kernel_mask` values
+ */
+uint32_t GetAggregatedDecodeKernelMask(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                                       rmm::cuda_stream_view stream);
+
+/**
+ * @brief Compute page output size information.
+ *
+ * When dealing with nested hierarchies (those that contain lists), or when doing a chunked
+ * read, we need to obtain more information up front than we have with just the row counts.
+ *
+ * - We need to determine the sizes of each output cudf column per page
+ * - We need to determine information about where to start decoding the value stream
+ *   if we are using custom user bounds (skip_rows / num_rows)
+ * - We need to determine actual number of top level rows per page
+ * - If we are doing a chunked read, we need to determine the total string size per page
+ *
+ *
+ * @param pages All pages to be decoded
+ * @param chunks All chunks to be decoded
+ * @param min_rows crop all rows below min_row
+ * @param num_rows Maximum number of rows to read
+ * @param compute_num_rows If set to true, the num_rows field in PageInfo will be
+ * computed
+ * @param compute_string_sizes If set to true, the str_bytes field in PageInfo will
+ * be computed
+ * @param level_type_size Size in bytes of the type for level decoding
+ * @param stream CUDA stream to use
+ */
+void ComputePageSizes(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                      cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                      size_t min_row,
+                      size_t num_rows,
+                      bool compute_num_rows,
+                      bool compute_string_sizes,
+                      int level_type_size,
+                      rmm::cuda_stream_view stream);
+
+/**
+ * @brief Compute string page output size information.
+ *
+ * String columns need accurate data size information to preallocate memory in the column buffer to
+ * store the char data. This calls a kernel to calculate information needed by the string decoding
+ * kernel. On exit, the `str_bytes`, `num_nulls`, `num_valids`, and `str_offset` fields of the
+ * PageInfo struct are updated. This call ignores non-string columns.
+ *
+ * @param[in,out] pages All pages to be decoded
+ * @param[in] chunks All chunks to be decoded
+ * @param[in] min_rows crop all rows below min_row
+ * @param[in] num_rows Maximum number of rows to read
+ * @param[in] level_type_size Size in bytes of the type for level decoding
+ * @param[in] stream CUDA stream to use
+ */
+void ComputePageStringSizes(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                            cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                            size_t min_row,
+                            size_t num_rows,
+                            int level_type_size,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for reading the column data stored in the pages
+ *
+ * The page data will be written to the output pointed to in the page's
+ * associated column chunk.
+ *
+ * @param[in,out] pages All pages to be decoded
+ * @param[in] chunks All chunks to be decoded
+ * @param[in] num_rows Total number of rows to read
+ * @param[in] min_row Minimum number of rows to read
+ * @param[in] level_type_size Size in bytes of the type for level decoding
+ * @param[out] error_code Error code for kernel failures
+ * @param[in] stream CUDA stream to use
+ */
+void DecodePageData(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                    cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                    size_t num_rows,
+                    size_t min_row,
+                    int level_type_size,
+                    int32_t* error_code,
+                    rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for reading the string column data stored in the pages
+ *
+ * The page data will be written to the output pointed to in the page's
+ * associated column chunk.
+ *
+ * @param[in,out] pages All pages to be decoded
+ * @param[in] chunks All chunks to be decoded
+ * @param[in] num_rows Total number of rows to read
+ * @param[in] min_row Minimum number of rows to read
+ * @param[in] level_type_size Size in bytes of the type for level decoding
+ * @param[out] error_code Error code for kernel failures
+ * @param[in] stream CUDA stream to use
+ */
+void DecodeStringPageData(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                          cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                          size_t num_rows,
+                          size_t min_row,
+                          int level_type_size,
+                          int32_t* error_code,
+                          rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for reading the DELTA_BINARY_PACKED column data stored in the pages
+ *
+ * The page data will be written to the output pointed to in the page's
+ * associated column chunk.
+ *
+ * @param[in,out] pages All pages to be decoded
+ * @param[in] chunks All chunks to be decoded
+ * @param[in] num_rows Total number of rows to read
+ * @param[in] min_row Minimum number of rows to read
+ * @param[in] level_type_size Size in bytes of the type for level decoding
+ * @param[out] error_code Error code for kernel failures
+ * @param[in] stream CUDA stream to use, default 0
+ */
+void DecodeDeltaBinary(cudf::detail::hostdevice_vector<PageInfo>& pages,
+                       cudf::detail::hostdevice_vector<ColumnChunkDesc> const& chunks,
+                       size_t num_rows,
+                       size_t min_row,
+                       int level_type_size,
+                       int32_t* error_code,
+                       rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for initializing encoder row group fragments
+ *
+ * These fragments are used to calculate row group boundaries.
+ * Based on the number of rows in each fragment, populates the value count, the size of data in the
+ * fragment, the number of unique values, and the data size of unique values.
+ *
+ * @param[out] frag Fragment array [column_id][fragment_id]
+ * @param[in] col_desc Column description array [column_id]
+ * @param[in] partitions Information about partitioning of table
+ * @param[in] first_frag_in_part A Partition's offset into fragment array
+ * @param[in] fragment_size Number of rows per fragment
+ * @param[in] stream CUDA stream to use
+ */
+void InitRowGroupFragments(cudf::detail::device_2dspan<PageFragment> frag,
+                           device_span<parquet_column_device_view const> col_desc,
+                           device_span<partition_info const> partitions,
+                           device_span<int const> first_frag_in_part,
+                           uint32_t fragment_size,
+                           rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for calculating encoder page fragments with variable fragment sizes
+ *
+ * Based on the number of rows in each fragment, populates the value count, the size of data in the
+ * fragment, the number of unique values, and the data size of unique values.
+ *
+ * This assumes an initial call to InitRowGroupFragments has been made.
+ *
+ * @param[out] frag Fragment array [fragment_id]
+ * @param[in] column_frag_sizes Number of rows per fragment per column [column_id]
+ * @param[in] stream CUDA stream to use
+ */
+void CalculatePageFragments(device_span<PageFragment> frag,
+                            device_span<size_type const> column_frag_sizes,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for initializing fragment statistics groups with variable fragment sizes
+ *
+ * @param[out] groups Statistics groups [total_fragments]
+ * @param[in] fragments Page fragments [total_fragments]
+ * @param[in] stream CUDA stream to use
+ */
+void InitFragmentStatistics(device_span<statistics_group> groups,
+                            device_span<PageFragment const> fragments,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Initialize per-chunk hash maps used for dictionary with sentinel values
+ *
+ * @param chunks Flat span of chunks to initialize hash maps for
+ * @param stream CUDA stream to use
+ */
+void initialize_chunk_hash_maps(device_span<EncColumnChunk> chunks, rmm::cuda_stream_view stream);
+
+/**
+ * @brief Insert chunk values into their respective hash maps
+ *
+ * @param frags Column fragments
+ * @param stream CUDA stream to use
+ */
+void populate_chunk_hash_maps(cudf::detail::device_2dspan<gpu::PageFragment const> frags,
+                              rmm::cuda_stream_view stream);
+
+/**
+ * @brief Compact dictionary hash map entries into chunk.dict_data
+ *
+ * @param chunks Flat span of chunks to compact hash maps for
+ * @param stream CUDA stream to use
+ */
+void collect_map_entries(device_span<EncColumnChunk> chunks, rmm::cuda_stream_view stream);
+
+/**
+ * @brief Get the Dictionary Indices for each row
+ *
+ * For each row of a chunk, gets the indices into chunk.dict_data which contains the value otherwise
+ * stored in input column [row]. Stores these indices into chunk.dict_index.
+ *
+ * Since dict_data itself contains indices into the original cudf column, this means that
+ * col[row] == col[dict_data[dict_index[row - chunk.start_row]]]
+ *
+ * @param frags Column fragments
+ * @param stream CUDA stream to use
+ */
+void get_dictionary_indices(cudf::detail::device_2dspan<gpu::PageFragment const> frags,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for initializing encoder data pages
+ *
+ * @param[in,out] chunks Column chunks [rowgroup][column]
+ * @param[out] pages Encode page array (null if just counting pages)
+ * @param[in] col_desc Column description array [column_id]
+ * @param[in] num_rowgroups Number of fragments per column
+ * @param[in] num_columns Number of columns
+ * @param[in] page_grstats Setup for page-level stats
+ * @param[in] page_align Required alignment for uncompressed pages
+ * @param[in] write_v2_headers True if V2 page headers should be written
+ * @param[in] chunk_grstats Setup for chunk-level stats
+ * @param[in] max_page_comp_data_size Calculated maximum compressed data size of pages
+ * @param[in] stream CUDA stream to use
+ */
+void InitEncoderPages(cudf::detail::device_2dspan<EncColumnChunk> chunks,
+                      device_span<gpu::EncPage> pages,
+                      device_span<size_type> page_sizes,
+                      device_span<size_type> comp_page_sizes,
+                      device_span<parquet_column_device_view const> col_desc,
+                      int32_t num_columns,
+                      size_t max_page_size_bytes,
+                      size_type max_page_size_rows,
+                      uint32_t page_align,
+                      bool write_v2_headers,
+                      statistics_merge_group* page_grstats,
+                      statistics_merge_group* chunk_grstats,
+                      rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel for packing column data into parquet pages
+ *
+ * If compression is to be used, `comp_in`, `comp_out`, and `comp_res` will be initialized for
+ * use in subsequent compression operations.
+ *
+ * @param[in,out] pages Device array of EncPages (unordered)
+ * @param[in] write_v2_headers True if V2 page headers should be written
+ * @param[out] comp_in Compressor input buffers
+ * @param[out] comp_out Compressor output buffers
+ * @param[out] comp_res Compressor results
+ * @param[in] stream CUDA stream to use
+ */
+void EncodePages(device_span<EncPage> pages,
+                 bool write_v2_headers,
+                 device_span<device_span<uint8_t const>> comp_in,
+                 device_span<device_span<uint8_t>> comp_out,
+                 device_span<compression_result> comp_res,
+                 rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel to make the compressed vs uncompressed chunk-level decision
+ *
+ * Also calculates the set of page encodings used for each chunk.
+ *
+ * @param[in,out] chunks Column chunks (updated with actual compressed/uncompressed sizes)
+ * @param[in] stream CUDA stream to use
+ */
+void DecideCompression(device_span<EncColumnChunk> chunks, rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel to encode page headers
+ *
+ * @param[in,out] pages Device array of EncPages
+ * @param[in] comp_res Compressor status
+ * @param[in] page_stats Optional page-level statistics to be included in page header
+ * @param[in] chunk_stats Optional chunk-level statistics to be encoded
+ * @param[in] stream CUDA stream to use
+ */
+void EncodePageHeaders(device_span<EncPage> pages,
+                       device_span<compression_result const> comp_res,
+                       device_span<statistics_chunk const> page_stats,
+                       statistics_chunk const* chunk_stats,
+                       rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel to gather pages to a single contiguous block per chunk
+ *
+ * @param[in,out] chunks Column chunks
+ * @param[in] pages Device array of EncPages
+ * @param[in] stream CUDA stream to use
+ */
+void GatherPages(device_span<EncColumnChunk> chunks,
+                 device_span<gpu::EncPage const> pages,
+                 rmm::cuda_stream_view stream);
+
+/**
+ * @brief Launches kernel to calculate ColumnIndex information per chunk
+ *
+ * @param[in,out] chunks Column chunks
+ * @param[in] column_stats Page-level statistics to be encoded
+ * @param[in] column_index_truncate_length Max length of min/max values
+ * @param[in] stream CUDA stream to use
+ */
+void EncodeColumnIndexes(device_span<EncColumnChunk> chunks,
+                         device_span<statistics_chunk const> column_stats,
+                         int32_t column_index_truncate_length,
+                         rmm::cuda_stream_view stream);
+
+}  // namespace gpu
+}  // namespace cudf::io::parquet
diff --git a/cpp/src/io/parquet/predicate_pushdown.cpp b/cpp/src/io/parquet/predicate_pushdown.cpp
new file mode 100644
index 0000000..805d082
--- /dev/null
+++ b/cpp/src/io/parquet/predicate_pushdown.cpp
@@ -0,0 +1,530 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "reader_impl_helpers.hpp"
+
+#include <cudf/ast/detail/expression_transformer.hpp>
+#include <cudf/ast/detail/operators.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <algorithm>
+#include <list>
+#include <numeric>
+#include <optional>
+
+namespace cudf::io::detail::parquet {
+
+namespace {
+/**
+ * @brief Converts statistics in column chunks to 2 device columns - min, max values.
+ *
+ */
+struct stats_caster {
+  size_type total_row_groups;
+  std::vector<metadata> const& per_file_metadata;
+  host_span<std::vector<size_type> const> row_group_indices;
+
+  template <typename ToType, typename FromType>
+  static ToType targetType(FromType const value)
+  {
+    if constexpr (cudf::is_timestamp<ToType>()) {
+      return static_cast<ToType>(
+        typename ToType::duration{static_cast<typename ToType::rep>(value)});
+    } else if constexpr (std::is_same_v<ToType, string_view>) {
+      return ToType{nullptr, 0};
+    } else {
+      return static_cast<ToType>(value);
+    }
+  }
+
+  // uses storage type as T
+  template <typename T, CUDF_ENABLE_IF(cudf::is_dictionary<T>() or cudf::is_nested<T>())>
+  static T convert(uint8_t const* stats_val, size_t stats_size, cudf::io::parquet::Type const type)
+  {
+    CUDF_FAIL("unsupported type for stats casting");
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_boolean<T>())>
+  static T convert(uint8_t const* stats_val, size_t stats_size, cudf::io::parquet::Type const type)
+  {
+    CUDF_EXPECTS(type == BOOLEAN, "Invalid type and stats combination");
+    return targetType<T>(*reinterpret_cast<bool const*>(stats_val));
+  }
+
+  // integral but not boolean, and fixed_point, and chrono.
+  template <typename T,
+            CUDF_ENABLE_IF((cudf::is_integral<T>() and !cudf::is_boolean<T>()) or
+                           cudf::is_fixed_point<T>() or cudf::is_chrono<T>())>
+  static T convert(uint8_t const* stats_val, size_t stats_size, cudf::io::parquet::Type const type)
+  {
+    switch (type) {
+      case INT32: return targetType<T>(*reinterpret_cast<int32_t const*>(stats_val));
+      case INT64: return targetType<T>(*reinterpret_cast<int64_t const*>(stats_val));
+      case INT96:  // Deprecated in parquet specification
+        return targetType<T>(static_cast<__int128_t>(reinterpret_cast<int64_t const*>(stats_val)[0])
+                               << 32 |
+                             reinterpret_cast<int32_t const*>(stats_val)[2]);
+      case BYTE_ARRAY: [[fallthrough]];
+      case FIXED_LEN_BYTE_ARRAY:
+        if (stats_size == sizeof(T)) {
+          // if type size == length of stats_val. then typecast and return.
+          if constexpr (cudf::is_chrono<T>()) {
+            return targetType<T>(*reinterpret_cast<typename T::rep const*>(stats_val));
+          } else {
+            return targetType<T>(*reinterpret_cast<T const*>(stats_val));
+          }
+        }
+        // unsupported type
+      default: CUDF_FAIL("Invalid type and stats combination");
+    }
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_floating_point<T>())>
+  static T convert(uint8_t const* stats_val, size_t stats_size, cudf::io::parquet::Type const type)
+  {
+    switch (type) {
+      case FLOAT: return targetType<T>(*reinterpret_cast<float const*>(stats_val));
+      case DOUBLE: return targetType<T>(*reinterpret_cast<double const*>(stats_val));
+      default: CUDF_FAIL("Invalid type and stats combination");
+    }
+  }
+
+  template <typename T, CUDF_ENABLE_IF(std::is_same_v<T, string_view>)>
+  static T convert(uint8_t const* stats_val, size_t stats_size, cudf::io::parquet::Type const type)
+  {
+    switch (type) {
+      case BYTE_ARRAY: [[fallthrough]];
+      case FIXED_LEN_BYTE_ARRAY:
+        return string_view(reinterpret_cast<char const*>(stats_val), stats_size);
+      default: CUDF_FAIL("Invalid type and stats combination");
+    }
+  }
+
+  // Creates device columns from column statistics (min, max)
+  template <typename T>
+  std::pair<std::unique_ptr<column>, std::unique_ptr<column>> operator()(
+    size_t col_idx,
+    cudf::data_type dtype,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    // List, Struct, Dictionary types are not supported
+    if constexpr (cudf::is_compound<T>() && !std::is_same_v<T, string_view>) {
+      CUDF_FAIL("Compound types do not have statistics");
+    } else {
+      // Local struct to hold host columns
+      struct host_column {
+        // using thrust::host_vector because std::vector<bool> uses bitmap instead of byte per bool.
+        thrust::host_vector<T> val;
+        std::vector<bitmask_type> null_mask;
+        cudf::size_type null_count = 0;
+        host_column(size_type total_row_groups)
+          : val(total_row_groups),
+            null_mask(
+              cudf::util::div_rounding_up_safe<size_type>(
+                cudf::bitmask_allocation_size_bytes(total_row_groups), sizeof(bitmask_type)),
+              ~bitmask_type{0})
+        {
+        }
+
+        void set_index(size_type index, std::vector<uint8_t> const& binary_value, Type const type)
+        {
+          if (!binary_value.empty()) {
+            val[index] = convert<T>(binary_value.data(), binary_value.size(), type);
+          }
+          if (binary_value.empty()) {
+            clear_bit_unsafe(null_mask.data(), index);
+            null_count++;
+          }
+        }
+
+        static auto make_strings_children(host_span<string_view> host_strings,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+        {
+          std::vector<char> chars{};
+          std::vector<cudf::size_type> offsets(1, 0);
+          for (auto const& str : host_strings) {
+            auto tmp =
+              str.empty() ? std::string_view{} : std::string_view(str.data(), str.size_bytes());
+            chars.insert(chars.end(), std::cbegin(tmp), std::cend(tmp));
+            offsets.push_back(offsets.back() + tmp.length());
+          }
+          auto d_chars   = cudf::detail::make_device_uvector_async(chars, stream, mr);
+          auto d_offsets = cudf::detail::make_device_uvector_sync(offsets, stream, mr);
+          return std::tuple{std::move(d_chars), std::move(d_offsets)};
+        }
+
+        auto to_device(cudf::data_type dtype,
+                       rmm::cuda_stream_view stream,
+                       rmm::mr::device_memory_resource* mr)
+        {
+          if constexpr (std::is_same_v<T, string_view>) {
+            auto [d_chars, d_offsets] = make_strings_children(val, stream, mr);
+            return cudf::make_strings_column(
+              val.size(),
+              std::move(d_offsets),
+              std::move(d_chars),
+              rmm::device_buffer{
+                null_mask.data(), cudf::bitmask_allocation_size_bytes(val.size()), stream, mr},
+              null_count);
+          }
+          return std::make_unique<column>(
+            dtype,
+            val.size(),
+            cudf::detail::make_device_uvector_async(val, stream, mr).release(),
+            rmm::device_buffer{
+              null_mask.data(), cudf::bitmask_allocation_size_bytes(val.size()), stream, mr},
+            null_count);
+        }
+      };  // local struct host_column
+      host_column min(total_row_groups);
+      host_column max(total_row_groups);
+
+      size_type stats_idx = 0;
+      for (size_t src_idx = 0; src_idx < row_group_indices.size(); ++src_idx) {
+        for (auto const rg_idx : row_group_indices[src_idx]) {
+          auto const& row_group = per_file_metadata[src_idx].row_groups[rg_idx];
+          auto const& colchunk  = row_group.columns[col_idx];
+          // To support deprecated min, max fields.
+          auto const& min_value = colchunk.meta_data.statistics.min_value.size() > 0
+                                    ? colchunk.meta_data.statistics.min_value
+                                    : colchunk.meta_data.statistics.min;
+          auto const& max_value = colchunk.meta_data.statistics.max_value.size() > 0
+                                    ? colchunk.meta_data.statistics.max_value
+                                    : colchunk.meta_data.statistics.max;
+          // translate binary data to Type then to <T>
+          min.set_index(stats_idx, min_value, colchunk.meta_data.type);
+          max.set_index(stats_idx, max_value, colchunk.meta_data.type);
+          stats_idx++;
+        }
+      };
+      return {min.to_device(dtype, stream, mr), max.to_device(dtype, stream, mr)};
+    }
+  }
+};
+
+/**
+ * @brief Converts AST expression to StatsAST for comparing with column statistics
+ * This is used in row group filtering based on predicate.
+ * statistics min value of a column is referenced by column_index*2
+ * statistics max value of a column is referenced by column_index*2+1
+ *
+ */
+class stats_expression_converter : public ast::detail::expression_transformer {
+ public:
+  stats_expression_converter(ast::expression const& expr, size_type const& num_columns)
+    : _num_columns{num_columns}
+  {
+    expr.accept(*this);
+  }
+
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::literal const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(ast::literal const& expr) override
+  {
+    _stats_expr = std::reference_wrapper<ast::expression const>(expr);
+    return expr;
+  }
+
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::column_reference const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(ast::column_reference const& expr) override
+  {
+    CUDF_EXPECTS(expr.get_table_source() == ast::table_reference::LEFT,
+                 "Statistics AST supports only left table");
+    CUDF_EXPECTS(expr.get_column_index() < _num_columns,
+                 "Column index cannot be more than number of columns in the table");
+    _stats_expr = std::reference_wrapper<ast::expression const>(expr);
+    return expr;
+  }
+
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::column_name_reference const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(
+    ast::column_name_reference const& expr) override
+  {
+    CUDF_FAIL("Column name reference is not supported in statistics AST");
+  }
+
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::operation const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(ast::operation const& expr) override
+  {
+    using cudf::ast::ast_operator;
+    auto const operands = expr.get_operands();
+    auto const op       = expr.get_operator();
+
+    if (auto* v = dynamic_cast<ast::column_reference const*>(&operands[0].get())) {
+      // First operand should be column reference, second should be literal.
+      CUDF_EXPECTS(cudf::ast::detail::ast_operator_arity(op) == 2,
+                   "Only binary operations are supported on column reference");
+      CUDF_EXPECTS(dynamic_cast<ast::literal const*>(&operands[1].get()) != nullptr,
+                   "Second operand of binary operation with column reference must be a literal");
+      v->accept(*this);
+      auto const col_index = v->get_column_index();
+      switch (op) {
+        /* transform to stats conditions. op(col, literal)
+        col1 == val --> vmin <= val && vmax >= val
+        col1 != val --> !(vmin == val && vmax == val)
+        col1 >  val --> vmax > val
+        col1 <  val --> vmin < val
+        col1 >= val --> vmax >= val
+        col1 <= val --> vmin <= val
+        */
+        case ast_operator::EQUAL: {
+          auto const& vmin = _col_ref.emplace_back(col_index * 2);
+          auto const& vmax = _col_ref.emplace_back(col_index * 2 + 1);
+          auto const& op1 =
+            _operators.emplace_back(ast_operator::LESS_EQUAL, vmin, operands[1].get());
+          auto const& op2 =
+            _operators.emplace_back(ast_operator::GREATER_EQUAL, vmax, operands[1].get());
+          _operators.emplace_back(ast::ast_operator::LOGICAL_AND, op1, op2);
+          break;
+        }
+        case ast_operator::NOT_EQUAL: {
+          auto const& vmin = _col_ref.emplace_back(col_index * 2);
+          auto const& vmax = _col_ref.emplace_back(col_index * 2 + 1);
+          auto const& op1  = _operators.emplace_back(ast_operator::NOT_EQUAL, vmin, vmax);
+          auto const& op2 =
+            _operators.emplace_back(ast_operator::NOT_EQUAL, vmax, operands[1].get());
+          _operators.emplace_back(ast_operator::LOGICAL_OR, op1, op2);
+          break;
+        }
+        case ast_operator::LESS: [[fallthrough]];
+        case ast_operator::LESS_EQUAL: {
+          auto const& vmin = _col_ref.emplace_back(col_index * 2);
+          _operators.emplace_back(op, vmin, operands[1].get());
+          break;
+        }
+        case ast_operator::GREATER: [[fallthrough]];
+        case ast_operator::GREATER_EQUAL: {
+          auto const& vmax = _col_ref.emplace_back(col_index * 2 + 1);
+          _operators.emplace_back(op, vmax, operands[1].get());
+          break;
+        }
+        default: CUDF_FAIL("Unsupported operation in Statistics AST");
+      };
+    } else {
+      auto new_operands = visit_operands(operands);
+      if (cudf::ast::detail::ast_operator_arity(op) == 2) {
+        _operators.emplace_back(op, new_operands.front(), new_operands.back());
+      } else if (cudf::ast::detail::ast_operator_arity(op) == 1) {
+        _operators.emplace_back(op, new_operands.front());
+      }
+    }
+    _stats_expr = std::reference_wrapper<ast::expression const>(_operators.back());
+    return std::reference_wrapper<ast::expression const>(_operators.back());
+  }
+
+  /**
+   * @brief Returns the AST to apply on Column chunk statistics.
+   *
+   * @return AST operation expression
+   */
+  [[nodiscard]] std::reference_wrapper<ast::expression const> get_stats_expr() const
+  {
+    return _stats_expr.value().get();
+  }
+
+ private:
+  std::vector<std::reference_wrapper<ast::expression const>> visit_operands(
+    std::vector<std::reference_wrapper<ast::expression const>> operands)
+  {
+    std::vector<std::reference_wrapper<ast::expression const>> transformed_operands;
+    for (auto const& operand : operands) {
+      auto const new_operand = operand.get().accept(*this);
+      transformed_operands.push_back(new_operand);
+    }
+    return transformed_operands;
+  }
+  std::optional<std::reference_wrapper<ast::expression const>> _stats_expr;
+  size_type _num_columns;
+  std::list<ast::column_reference> _col_ref;
+  std::list<ast::operation> _operators;
+};
+}  // namespace
+
+std::optional<std::vector<std::vector<size_type>>> aggregate_reader_metadata::filter_row_groups(
+  host_span<std::vector<size_type> const> row_group_indices,
+  host_span<data_type const> output_dtypes,
+  std::reference_wrapper<ast::expression const> filter,
+  rmm::cuda_stream_view stream) const
+{
+  auto mr = rmm::mr::get_current_device_resource();
+  // Create row group indices.
+  std::vector<std::vector<size_type>> filtered_row_group_indices;
+  std::vector<std::vector<size_type>> all_row_group_indices;
+  host_span<std::vector<size_type> const> input_row_group_indices;
+  if (row_group_indices.empty()) {
+    std::transform(per_file_metadata.cbegin(),
+                   per_file_metadata.cend(),
+                   std::back_inserter(all_row_group_indices),
+                   [](auto const& file_meta) {
+                     std::vector<size_type> rg_idx(file_meta.row_groups.size());
+                     std::iota(rg_idx.begin(), rg_idx.end(), 0);
+                     return rg_idx;
+                   });
+    input_row_group_indices = host_span<std::vector<size_type> const>(all_row_group_indices);
+  } else {
+    input_row_group_indices = row_group_indices;
+  }
+  auto const total_row_groups = std::accumulate(input_row_group_indices.begin(),
+                                                input_row_group_indices.end(),
+                                                0,
+                                                [](size_type sum, auto const& per_file_row_groups) {
+                                                  return sum + per_file_row_groups.size();
+                                                });
+
+  // Converts Column chunk statistics to a table
+  // where min(col[i]) = columns[i*2], max(col[i])=columns[i*2+1]
+  // For each column, it contains #sources * #column_chunks_per_src rows.
+  std::vector<std::unique_ptr<column>> columns;
+  stats_caster stats_col{total_row_groups, per_file_metadata, input_row_group_indices};
+  for (size_t col_idx = 0; col_idx < output_dtypes.size(); col_idx++) {
+    auto const& dtype = output_dtypes[col_idx];
+    // Only comparable types except fixed point are supported.
+    if (cudf::is_compound(dtype) && dtype.id() != cudf::type_id::STRING) {
+      // placeholder only for unsupported types.
+      columns.push_back(cudf::make_numeric_column(
+        data_type{cudf::type_id::BOOL8}, total_row_groups, rmm::device_buffer{}, 0, stream, mr));
+      columns.push_back(cudf::make_numeric_column(
+        data_type{cudf::type_id::BOOL8}, total_row_groups, rmm::device_buffer{}, 0, stream, mr));
+      continue;
+    }
+    auto [min_col, max_col] =
+      cudf::type_dispatcher<dispatch_storage_type>(dtype, stats_col, col_idx, dtype, stream, mr);
+    columns.push_back(std::move(min_col));
+    columns.push_back(std::move(max_col));
+  }
+  auto stats_table = cudf::table(std::move(columns));
+
+  // Converts AST to StatsAST with reference to min, max columns in above `stats_table`.
+  stats_expression_converter stats_expr{filter, static_cast<size_type>(output_dtypes.size())};
+  auto stats_ast     = stats_expr.get_stats_expr();
+  auto predicate_col = cudf::detail::compute_column(stats_table, stats_ast.get(), stream, mr);
+  auto predicate     = predicate_col->view();
+  CUDF_EXPECTS(predicate.type().id() == cudf::type_id::BOOL8,
+               "Filter expression must return a boolean column");
+
+  auto num_bitmasks = num_bitmask_words(predicate.size());
+  std::vector<bitmask_type> host_bitmask(num_bitmasks, ~bitmask_type{0});
+  if (predicate.nullable()) {
+    CUDF_CUDA_TRY(cudaMemcpyAsync(host_bitmask.data(),
+                                  predicate.null_mask(),
+                                  num_bitmasks * sizeof(bitmask_type),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+  }
+  auto validity_it = cudf::detail::make_counting_transform_iterator(
+    0, [bitmask = host_bitmask.data()](auto bit_index) { return bit_is_set(bitmask, bit_index); });
+
+  auto is_row_group_required = cudf::detail::make_std_vector_sync(
+    device_span<uint8_t const>(predicate.data<uint8_t>(), predicate.size()), stream);
+
+  // Return only filtered row groups based on predicate
+  // if all are required or all are nulls, return.
+  if (std::all_of(is_row_group_required.cbegin(),
+                  is_row_group_required.cend(),
+                  [](auto i) { return bool(i); }) or
+      predicate.null_count() == predicate.size()) {
+    return std::nullopt;
+  }
+  size_type is_required_idx = 0;
+  for (size_t src_idx = 0; src_idx < input_row_group_indices.size(); ++src_idx) {
+    std::vector<size_type> filtered_row_groups;
+    for (auto const rg_idx : input_row_group_indices[src_idx]) {
+      if ((!validity_it[is_required_idx]) || is_row_group_required[is_required_idx]) {
+        filtered_row_groups.push_back(rg_idx);
+      }
+      ++is_required_idx;
+    }
+    filtered_row_group_indices.push_back(std::move(filtered_row_groups));
+  }
+  return {std::move(filtered_row_group_indices)};
+}
+
+// convert column named expression to column index reference expression
+std::reference_wrapper<ast::expression const> named_to_reference_converter::visit(
+  ast::literal const& expr)
+{
+  _stats_expr = std::reference_wrapper<ast::expression const>(expr);
+  return expr;
+}
+
+std::reference_wrapper<ast::expression const> named_to_reference_converter::visit(
+  ast::column_reference const& expr)
+{
+  _stats_expr = std::reference_wrapper<ast::expression const>(expr);
+  return expr;
+}
+
+std::reference_wrapper<ast::expression const> named_to_reference_converter::visit(
+  ast::column_name_reference const& expr)
+{
+  // check if column name is in metadata
+  auto col_index_it = column_name_to_index.find(expr.get_column_name());
+  if (col_index_it == column_name_to_index.end()) {
+    CUDF_FAIL("Column name not found in metadata");
+  }
+  auto col_index = col_index_it->second;
+  _col_ref.emplace_back(col_index);
+  _stats_expr = std::reference_wrapper<ast::expression const>(_col_ref.back());
+  return std::reference_wrapper<ast::expression const>(_col_ref.back());
+}
+
+std::reference_wrapper<ast::expression const> named_to_reference_converter::visit(
+  ast::operation const& expr)
+{
+  auto const operands = expr.get_operands();
+  auto op             = expr.get_operator();
+  auto new_operands   = visit_operands(operands);
+  if (cudf::ast::detail::ast_operator_arity(op) == 2) {
+    _operators.emplace_back(op, new_operands.front(), new_operands.back());
+  } else if (cudf::ast::detail::ast_operator_arity(op) == 1) {
+    _operators.emplace_back(op, new_operands.front());
+  }
+  _stats_expr = std::reference_wrapper<ast::expression const>(_operators.back());
+  return std::reference_wrapper<ast::expression const>(_operators.back());
+}
+
+std::vector<std::reference_wrapper<ast::expression const>>
+named_to_reference_converter::visit_operands(
+  std::vector<std::reference_wrapper<ast::expression const>> operands)
+{
+  std::vector<std::reference_wrapper<ast::expression const>> transformed_operands;
+  for (auto const& operand : operands) {
+    auto const new_operand = operand.get().accept(*this);
+    transformed_operands.push_back(new_operand);
+  }
+  return transformed_operands;
+}
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/reader.cpp b/cpp/src/io/parquet/reader.cpp
new file mode 100644
index 0000000..1e87447
--- /dev/null
+++ b/cpp/src/io/parquet/reader.cpp
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "reader_impl.hpp"
+
+namespace cudf::io::detail::parquet {
+
+reader::reader() = default;
+
+reader::reader(std::vector<std::unique_ptr<datasource>>&& sources,
+               parquet_reader_options const& options,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr)
+  : _impl(std::make_unique<impl>(std::move(sources), options, stream, mr))
+{
+}
+
+reader::~reader() = default;
+
+table_with_metadata reader::read(parquet_reader_options const& options)
+{
+  // if the user has specified custom row bounds
+  bool const uses_custom_row_bounds =
+    options.get_num_rows().has_value() || options.get_skip_rows() != 0;
+  return _impl->read(options.get_skip_rows(),
+                     options.get_num_rows(),
+                     uses_custom_row_bounds,
+                     options.get_row_groups(),
+                     options.get_filter());
+}
+
+chunked_reader::chunked_reader(std::size_t chunk_read_limit,
+                               std::size_t pass_read_limit,
+                               std::vector<std::unique_ptr<datasource>>&& sources,
+                               parquet_reader_options const& options,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  _impl = std::make_unique<impl>(
+    chunk_read_limit, pass_read_limit, std::move(sources), options, stream, mr);
+}
+
+chunked_reader::~chunked_reader() = default;
+
+bool chunked_reader::has_next() const { return _impl->has_next(); }
+
+table_with_metadata chunked_reader::read_chunk() const { return _impl->read_chunk(); }
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/reader_impl.cpp b/cpp/src/io/parquet/reader_impl.cpp
new file mode 100644
index 0000000..ea40f29
--- /dev/null
+++ b/cpp/src/io/parquet/reader_impl.cpp
@@ -0,0 +1,574 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "reader_impl.hpp"
+
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/utilities/stream_pool.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <rmm/cuda_stream_pool.hpp>
+
+#include <bitset>
+#include <numeric>
+
+namespace cudf::io::detail::parquet {
+
+void reader::impl::decode_page_data(size_t skip_rows, size_t num_rows)
+{
+  auto& chunks              = _pass_itm_data->chunks;
+  auto& pages               = _pass_itm_data->pages_info;
+  auto& page_nesting        = _pass_itm_data->page_nesting_info;
+  auto& page_nesting_decode = _pass_itm_data->page_nesting_decode_info;
+
+  // Should not reach here if there is no page data.
+  CUDF_EXPECTS(pages.size() > 0, "There is no page to decode");
+
+  size_t const sum_max_depths = std::accumulate(
+    chunks.begin(), chunks.end(), 0, [&](size_t cursum, gpu::ColumnChunkDesc const& chunk) {
+      return cursum + _metadata->get_output_nesting_depth(chunk.src_col_schema);
+    });
+
+  // figure out which kernels to run
+  auto const kernel_mask = GetAggregatedDecodeKernelMask(pages, _stream);
+
+  // Check to see if there are any string columns present. If so, then we need to get size info
+  // for each string page. This size info will be used to pre-allocate memory for the column,
+  // allowing the page decoder to write string data directly to the column buffer, rather than
+  // doing a gather operation later on.
+  // TODO: This step is somewhat redundant if size info has already been calculated (nested schema,
+  // chunked reader).
+  auto const has_strings = (kernel_mask & gpu::KERNEL_MASK_STRING) != 0;
+  std::vector<size_t> col_sizes(_input_columns.size(), 0L);
+  if (has_strings) {
+    gpu::ComputePageStringSizes(
+      pages, chunks, skip_rows, num_rows, _pass_itm_data->level_type_size, _stream);
+
+    col_sizes = calculate_page_string_offsets();
+
+    // check for overflow
+    if (std::any_of(col_sizes.cbegin(), col_sizes.cend(), [](size_t sz) {
+          return sz > std::numeric_limits<size_type>::max();
+        })) {
+      CUDF_FAIL("String column exceeds the column size limit", std::overflow_error);
+    }
+  }
+
+  // In order to reduce the number of allocations of hostdevice_vector, we allocate a single vector
+  // to store all per-chunk pointers to nested data/nullmask. `chunk_offsets[i]` will store the
+  // offset into `chunk_nested_data`/`chunk_nested_valids` for the array of pointers for chunk `i`
+  auto chunk_nested_valids =
+    cudf::detail::hostdevice_vector<bitmask_type*>(sum_max_depths, _stream);
+  auto chunk_nested_data = cudf::detail::hostdevice_vector<void*>(sum_max_depths, _stream);
+  auto chunk_offsets     = std::vector<size_t>();
+  auto chunk_nested_str_data =
+    cudf::detail::hostdevice_vector<void*>(has_strings ? sum_max_depths : 0, _stream);
+
+  // Update chunks with pointers to column data.
+  for (size_t c = 0, page_count = 0, chunk_off = 0; c < chunks.size(); c++) {
+    input_column_info const& input_col = _input_columns[chunks[c].src_col_index];
+    CUDF_EXPECTS(input_col.schema_idx == chunks[c].src_col_schema,
+                 "Column/page schema index mismatch");
+
+    size_t max_depth = _metadata->get_output_nesting_depth(chunks[c].src_col_schema);
+    chunk_offsets.push_back(chunk_off);
+
+    // get a slice of size `nesting depth` from `chunk_nested_valids` to store an array of pointers
+    // to validity data
+    auto valids              = chunk_nested_valids.host_ptr(chunk_off);
+    chunks[c].valid_map_base = chunk_nested_valids.device_ptr(chunk_off);
+
+    // get a slice of size `nesting depth` from `chunk_nested_data` to store an array of pointers to
+    // out data
+    auto data                  = chunk_nested_data.host_ptr(chunk_off);
+    chunks[c].column_data_base = chunk_nested_data.device_ptr(chunk_off);
+
+    auto str_data = has_strings ? chunk_nested_str_data.host_ptr(chunk_off) : nullptr;
+    chunks[c].column_string_base =
+      has_strings ? chunk_nested_str_data.device_ptr(chunk_off) : nullptr;
+
+    chunk_off += max_depth;
+
+    // fill in the arrays on the host.  there are some important considerations to
+    // take into account here for nested columns.  specifically, with structs
+    // there is sharing of output buffers between input columns.  consider this schema
+    //
+    //  required group field_id=1 name {
+    //    required binary field_id=2 firstname (String);
+    //    required binary field_id=3 middlename (String);
+    //    required binary field_id=4 lastname (String);
+    // }
+    //
+    // there are 3 input columns of data here (firstname, middlename, lastname), but
+    // only 1 output column (name).  The structure of the output column buffers looks like
+    // the schema itself
+    //
+    // struct      (name)
+    //     string  (firstname)
+    //     string  (middlename)
+    //     string  (lastname)
+    //
+    // The struct column can contain validity information. the problem is, the decode
+    // step for the input columns will all attempt to decode this validity information
+    // because each one has it's own copy of the repetition/definition levels. but
+    // since this is all happening in parallel it would mean multiple blocks would
+    // be stomping all over the same memory randomly.  to work around this, we set
+    // things up so that only 1 child of any given nesting level fills in the
+    // data (offsets in the case of lists) or validity information for the higher
+    // levels of the hierarchy that are shared.  In this case, it would mean we
+    // would just choose firstname to be the one that decodes the validity for name.
+    //
+    // we do this by only handing out the pointers to the first child we come across.
+    //
+    auto* cols = &_output_buffers;
+    for (size_t idx = 0; idx < max_depth; idx++) {
+      auto& out_buf = (*cols)[input_col.nesting[idx]];
+      cols          = &out_buf.children;
+
+      int owning_schema = out_buf.user_data & PARQUET_COLUMN_BUFFER_SCHEMA_MASK;
+      if (owning_schema == 0 || owning_schema == input_col.schema_idx) {
+        valids[idx] = out_buf.null_mask();
+        data[idx]   = out_buf.data();
+        // only do string buffer for leaf
+        if (out_buf.string_size() == 0 && col_sizes[chunks[c].src_col_index] > 0) {
+          out_buf.create_string_data(col_sizes[chunks[c].src_col_index], _stream);
+        }
+        if (has_strings) { str_data[idx] = out_buf.string_data(); }
+        out_buf.user_data |=
+          static_cast<uint32_t>(input_col.schema_idx) & PARQUET_COLUMN_BUFFER_SCHEMA_MASK;
+      } else {
+        valids[idx] = nullptr;
+        data[idx]   = nullptr;
+      }
+    }
+
+    // column_data_base will always point to leaf data, even for nested types.
+    page_count += chunks[c].max_num_pages;
+  }
+
+  chunks.host_to_device_async(_stream);
+  chunk_nested_valids.host_to_device_async(_stream);
+  chunk_nested_data.host_to_device_async(_stream);
+
+  rmm::device_scalar<int32_t> error_code(0, _stream);
+
+  // get the number of streams we need from the pool and tell them to wait on the H2D copies
+  int const nkernels = std::bitset<32>(kernel_mask).count();
+  auto streams       = cudf::detail::fork_streams(_stream, nkernels);
+
+  auto const level_type_size = _pass_itm_data->level_type_size;
+
+  // launch string decoder
+  int s_idx = 0;
+  if (has_strings) {
+    auto& stream = streams[s_idx++];
+    chunk_nested_str_data.host_to_device_async(stream);
+    gpu::DecodeStringPageData(
+      pages, chunks, num_rows, skip_rows, level_type_size, error_code.data(), stream);
+  }
+
+  // launch delta binary decoder
+  if ((kernel_mask & gpu::KERNEL_MASK_DELTA_BINARY) != 0) {
+    gpu::DecodeDeltaBinary(
+      pages, chunks, num_rows, skip_rows, level_type_size, error_code.data(), streams[s_idx++]);
+  }
+
+  // launch the catch-all page decoder
+  if ((kernel_mask & gpu::KERNEL_MASK_GENERAL) != 0) {
+    gpu::DecodePageData(
+      pages, chunks, num_rows, skip_rows, level_type_size, error_code.data(), streams[s_idx++]);
+  }
+
+  // synchronize the streams
+  cudf::detail::join_streams(streams, _stream);
+
+  pages.device_to_host_async(_stream);
+  page_nesting.device_to_host_async(_stream);
+  page_nesting_decode.device_to_host_async(_stream);
+
+  auto const decode_error = error_code.value(_stream);
+  if (decode_error != 0) {
+    std::stringstream stream;
+    stream << std::hex << decode_error;
+    CUDF_FAIL("Parquet data decode failed with code(s) 0x" + stream.str());
+  }
+
+  // for list columns, add the final offset to every offset buffer.
+  // TODO : make this happen in more efficiently. Maybe use thrust::for_each
+  // on each buffer.
+  // Note : the reason we are doing this here instead of in the decode kernel is
+  // that it is difficult/impossible for a given page to know that it is writing the very
+  // last value that should then be followed by a terminator (because rows can span
+  // page boundaries).
+  for (size_t idx = 0; idx < _input_columns.size(); idx++) {
+    input_column_info const& input_col = _input_columns[idx];
+
+    auto* cols = &_output_buffers;
+    for (size_t l_idx = 0; l_idx < input_col.nesting_depth(); l_idx++) {
+      auto& out_buf = (*cols)[input_col.nesting[l_idx]];
+      cols          = &out_buf.children;
+
+      if (out_buf.type.id() == type_id::LIST &&
+          (out_buf.user_data & PARQUET_COLUMN_BUFFER_FLAG_LIST_TERMINATED) == 0) {
+        CUDF_EXPECTS(l_idx < input_col.nesting_depth() - 1, "Encountered a leaf list column");
+        auto const& child = (*cols)[input_col.nesting[l_idx + 1]];
+
+        // the final offset for a list at level N is the size of it's child
+        int const offset = child.type.id() == type_id::LIST ? child.size - 1 : child.size;
+        CUDF_CUDA_TRY(cudaMemcpyAsync(static_cast<int32_t*>(out_buf.data()) + (out_buf.size - 1),
+                                      &offset,
+                                      sizeof(offset),
+                                      cudaMemcpyDefault,
+                                      _stream.value()));
+        out_buf.user_data |= PARQUET_COLUMN_BUFFER_FLAG_LIST_TERMINATED;
+      } else if (out_buf.type.id() == type_id::STRING) {
+        // need to cap off the string offsets column
+        size_type const sz = static_cast<size_type>(col_sizes[idx]);
+        cudaMemcpyAsync(static_cast<int32_t*>(out_buf.data()) + out_buf.size,
+                        &sz,
+                        sizeof(size_type),
+                        cudaMemcpyDefault,
+                        _stream.value());
+      }
+    }
+  }
+
+  // update null counts in the final column buffers
+  for (size_t idx = 0; idx < pages.size(); idx++) {
+    gpu::PageInfo* pi = &pages[idx];
+    if (pi->flags & gpu::PAGEINFO_FLAGS_DICTIONARY) { continue; }
+    gpu::ColumnChunkDesc* col          = &chunks[pi->chunk_idx];
+    input_column_info const& input_col = _input_columns[col->src_col_index];
+
+    int index                        = pi->nesting_decode - page_nesting_decode.device_ptr();
+    gpu::PageNestingDecodeInfo* pndi = &page_nesting_decode[index];
+
+    auto* cols = &_output_buffers;
+    for (size_t l_idx = 0; l_idx < input_col.nesting_depth(); l_idx++) {
+      auto& out_buf = (*cols)[input_col.nesting[l_idx]];
+      cols          = &out_buf.children;
+
+      // if I wasn't the one who wrote out the validity bits, skip it
+      if (chunk_nested_valids.host_ptr(chunk_offsets[pi->chunk_idx])[l_idx] == nullptr) {
+        continue;
+      }
+      out_buf.null_count() += pndi[l_idx].null_count;
+    }
+  }
+
+  _stream.synchronize();
+}
+
+reader::impl::impl(std::vector<std::unique_ptr<datasource>>&& sources,
+                   parquet_reader_options const& options,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+  : impl(0 /*chunk_read_limit*/,
+         0 /*input_pass_read_limit*/,
+         std::forward<std::vector<std::unique_ptr<cudf::io::datasource>>>(sources),
+         options,
+         stream,
+         mr)
+{
+}
+
+reader::impl::impl(std::size_t chunk_read_limit,
+                   std::size_t pass_read_limit,
+                   std::vector<std::unique_ptr<datasource>>&& sources,
+                   parquet_reader_options const& options,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+  : _stream{stream},
+    _mr{mr},
+    _sources{std::move(sources)},
+    _output_chunk_read_limit{chunk_read_limit},
+    _input_pass_read_limit{pass_read_limit}
+{
+  // Open and parse the source dataset metadata
+  _metadata = std::make_unique<aggregate_reader_metadata>(_sources);
+
+  // Override output timestamp resolution if requested
+  if (options.get_timestamp_type().id() != type_id::EMPTY) {
+    _timestamp_type = options.get_timestamp_type();
+  }
+
+  // Strings may be returned as either string or categorical columns
+  _strings_to_categorical = options.is_enabled_convert_strings_to_categories();
+
+  // Binary columns can be read as binary or strings
+  _reader_column_schema = options.get_column_schema();
+
+  // Select only columns required by the options
+  std::tie(_input_columns, _output_buffers, _output_column_schemas) =
+    _metadata->select_columns(options.get_columns(),
+                              options.is_enabled_use_pandas_metadata(),
+                              _strings_to_categorical,
+                              _timestamp_type.id());
+
+  // Save the states of the output buffers for reuse in `chunk_read()`.
+  for (auto const& buff : _output_buffers) {
+    _output_buffers_template.emplace_back(inline_column_buffer::empty_like(buff));
+  }
+}
+
+void reader::impl::prepare_data(int64_t skip_rows,
+                                std::optional<size_type> const& num_rows,
+                                bool uses_custom_row_bounds,
+                                host_span<std::vector<size_type> const> row_group_indices,
+                                std::optional<std::reference_wrapper<ast::expression const>> filter)
+{
+  // if we have not preprocessed at the whole-file level, do that now
+  if (!_file_preprocessed) {
+    // if filter is not empty, then create output types as vector and pass for filtering.
+    std::vector<data_type> output_types;
+    if (filter.has_value()) {
+      std::transform(_output_buffers.cbegin(),
+                     _output_buffers.cend(),
+                     std::back_inserter(output_types),
+                     [](auto const& col) { return col.type; });
+    }
+    std::tie(
+      _file_itm_data.global_skip_rows, _file_itm_data.global_num_rows, _file_itm_data.row_groups) =
+      _metadata->select_row_groups(
+        row_group_indices, skip_rows, num_rows, output_types, filter, _stream);
+
+    if (_file_itm_data.global_num_rows > 0 && not _file_itm_data.row_groups.empty() &&
+        not _input_columns.empty()) {
+      // fills in chunk information without physically loading or decompressing
+      // the associated data
+      load_global_chunk_info();
+
+      // compute schedule of input reads. Each rowgroup contains 1 chunk per column. For now
+      // we will read an entire row group at a time. However, it is possible to do
+      // sub-rowgroup reads if we made some estimates on individual chunk sizes (tricky) and
+      // changed the high level structure such that we weren't always reading an entire table's
+      // worth of columns at once.
+      compute_input_pass_row_group_info();
+    }
+
+    _file_preprocessed = true;
+  }
+
+  // if we have to start a new pass, do that now
+  if (!_pass_preprocessed) {
+    auto const num_passes = _input_pass_row_group_offsets.size() - 1;
+
+    // always create the pass struct, even if we end up with no passes.
+    // this will also cause the previous pass information to be deleted
+    _pass_itm_data = std::make_unique<cudf::io::parquet::gpu::pass_intermediate_data>();
+
+    if (_file_itm_data.global_num_rows > 0 && not _file_itm_data.row_groups.empty() &&
+        not _input_columns.empty() && _current_input_pass < num_passes) {
+      // setup the pass_intermediate_info for this pass.
+      setup_pass();
+
+      load_and_decompress_data();
+      preprocess_pages(uses_custom_row_bounds, _output_chunk_read_limit);
+
+      if (_output_chunk_read_limit == 0) {  // read the whole file at once
+        CUDF_EXPECTS(_pass_itm_data->output_chunk_read_info.size() == 1,
+                     "Reading the whole file should yield only one chunk.");
+      }
+    }
+
+    _pass_preprocessed = true;
+  }
+}
+
+void reader::impl::populate_metadata(table_metadata& out_metadata)
+{
+  // Return column names
+  out_metadata.schema_info.resize(_output_buffers.size());
+  for (size_t i = 0; i < _output_column_schemas.size(); i++) {
+    auto const& schema                      = _metadata->get_schema(_output_column_schemas[i]);
+    out_metadata.schema_info[i].name        = schema.name;
+    out_metadata.schema_info[i].is_nullable = schema.repetition_type != REQUIRED;
+  }
+
+  // Return user metadata
+  out_metadata.per_file_user_data = _metadata->get_key_value_metadata();
+  out_metadata.user_data          = {out_metadata.per_file_user_data[0].begin(),
+                                     out_metadata.per_file_user_data[0].end()};
+}
+
+table_with_metadata reader::impl::read_chunk_internal(
+  bool uses_custom_row_bounds, std::optional<std::reference_wrapper<ast::expression const>> filter)
+{
+  // If `_output_metadata` has been constructed, just copy it over.
+  auto out_metadata = _output_metadata ? table_metadata{*_output_metadata} : table_metadata{};
+  out_metadata.schema_info.resize(_output_buffers.size());
+
+  // output cudf columns as determined by the top level schema
+  auto out_columns = std::vector<std::unique_ptr<column>>{};
+  out_columns.reserve(_output_buffers.size());
+
+  if (!has_next() || _pass_itm_data->output_chunk_read_info.empty()) {
+    return finalize_output(out_metadata, out_columns, filter);
+  }
+
+  auto const& read_info =
+    _pass_itm_data->output_chunk_read_info[_pass_itm_data->current_output_chunk];
+
+  // Allocate memory buffers for the output columns.
+  allocate_columns(read_info.skip_rows, read_info.num_rows, uses_custom_row_bounds);
+
+  // Parse data into the output buffers.
+  decode_page_data(read_info.skip_rows, read_info.num_rows);
+
+  // Create the final output cudf columns.
+  for (size_t i = 0; i < _output_buffers.size(); ++i) {
+    auto metadata      = _reader_column_schema.has_value()
+                           ? std::make_optional<reader_column_schema>((*_reader_column_schema)[i])
+                           : std::nullopt;
+    auto const& schema = _metadata->get_schema(_output_column_schemas[i]);
+    // FIXED_LEN_BYTE_ARRAY never read as string
+    if (schema.type == FIXED_LEN_BYTE_ARRAY and schema.converted_type != DECIMAL) {
+      metadata = std::make_optional<reader_column_schema>();
+      metadata->set_convert_binary_to_strings(false);
+    }
+    // Only construct `out_metadata` if `_output_metadata` has not been cached.
+    if (!_output_metadata) {
+      column_name_info& col_name = out_metadata.schema_info[i];
+      out_columns.emplace_back(make_column(_output_buffers[i], &col_name, metadata, _stream));
+    } else {
+      out_columns.emplace_back(make_column(_output_buffers[i], nullptr, metadata, _stream));
+    }
+  }
+
+  // Add empty columns if needed. Filter output columns based on filter.
+  return finalize_output(out_metadata, out_columns, filter);
+}
+
+table_with_metadata reader::impl::finalize_output(
+  table_metadata& out_metadata,
+  std::vector<std::unique_ptr<column>>& out_columns,
+  std::optional<std::reference_wrapper<ast::expression const>> filter)
+{
+  // Create empty columns as needed (this can happen if we've ended up with no actual data to read)
+  for (size_t i = out_columns.size(); i < _output_buffers.size(); ++i) {
+    if (!_output_metadata) {
+      column_name_info& col_name = out_metadata.schema_info[i];
+      out_columns.emplace_back(io::detail::empty_like(_output_buffers[i], &col_name, _stream, _mr));
+    } else {
+      out_columns.emplace_back(io::detail::empty_like(_output_buffers[i], nullptr, _stream, _mr));
+    }
+  }
+
+  if (!_output_metadata) {
+    populate_metadata(out_metadata);
+    // Finally, save the output table metadata into `_output_metadata` for reuse next time.
+    _output_metadata = std::make_unique<table_metadata>(out_metadata);
+  }
+
+  // advance chunks/passes as necessary
+  _pass_itm_data->current_output_chunk++;
+  _chunk_count++;
+  if (_pass_itm_data->current_output_chunk >= _pass_itm_data->output_chunk_read_info.size()) {
+    _pass_itm_data->current_output_chunk = 0;
+    _pass_itm_data->output_chunk_read_info.clear();
+
+    _current_input_pass++;
+    _pass_preprocessed = false;
+  }
+
+  if (filter.has_value()) {
+    auto read_table = std::make_unique<table>(std::move(out_columns));
+    auto predicate  = cudf::detail::compute_column(
+      *read_table, filter.value().get(), _stream, rmm::mr::get_current_device_resource());
+    CUDF_EXPECTS(predicate->view().type().id() == type_id::BOOL8,
+                 "Predicate filter should return a boolean");
+    auto output_table = cudf::detail::apply_boolean_mask(*read_table, *predicate, _stream, _mr);
+    return {std::move(output_table), std::move(out_metadata)};
+  }
+  return {std::make_unique<table>(std::move(out_columns)), std::move(out_metadata)};
+}
+
+table_with_metadata reader::impl::read(
+  int64_t skip_rows,
+  std::optional<size_type> const& num_rows,
+  bool uses_custom_row_bounds,
+  host_span<std::vector<size_type> const> row_group_indices,
+  std::optional<std::reference_wrapper<ast::expression const>> filter)
+{
+  CUDF_EXPECTS(_output_chunk_read_limit == 0,
+               "Reading the whole file must not have non-zero byte_limit.");
+  table_metadata metadata;
+  populate_metadata(metadata);
+  auto expr_conv     = named_to_reference_converter(filter, metadata);
+  auto output_filter = expr_conv.get_converted_expr();
+
+  prepare_data(skip_rows, num_rows, uses_custom_row_bounds, row_group_indices, output_filter);
+  return read_chunk_internal(uses_custom_row_bounds, output_filter);
+}
+
+table_with_metadata reader::impl::read_chunk()
+{
+  // Reset the output buffers to their original states (right after reader construction).
+  // Don't need to do it if we read the file all at once.
+  if (_chunk_count > 0) {
+    _output_buffers.resize(0);
+    for (auto const& buff : _output_buffers_template) {
+      _output_buffers.emplace_back(inline_column_buffer::empty_like(buff));
+    }
+  }
+
+  prepare_data(0 /*skip_rows*/,
+               std::nullopt /*num_rows, `nullopt` means unlimited*/,
+               true /*uses_custom_row_bounds*/,
+               {} /*row_group_indices, empty means read all row groups*/,
+               std::nullopt /*filter*/);
+  return read_chunk_internal(true, std::nullopt);
+}
+
+bool reader::impl::has_next()
+{
+  prepare_data(0 /*skip_rows*/,
+               std::nullopt /*num_rows, `nullopt` means unlimited*/,
+               true /*uses_custom_row_bounds*/,
+               {} /*row_group_indices, empty means read all row groups*/,
+               std::nullopt /*filter*/);
+
+  auto const num_input_passes =
+    _input_pass_row_group_offsets.size() == 0 ? 0 : _input_pass_row_group_offsets.size() - 1;
+  return (_pass_itm_data->current_output_chunk < _pass_itm_data->output_chunk_read_info.size()) ||
+         (_current_input_pass < num_input_passes);
+}
+
+namespace {
+parquet_column_schema walk_schema(aggregate_reader_metadata const* mt, int idx)
+{
+  SchemaElement const& sch = mt->get_schema(idx);
+  std::vector<parquet_column_schema> children;
+  for (auto const& child_idx : sch.children_idx) {
+    children.push_back(walk_schema(mt, child_idx));
+  }
+  return parquet_column_schema{
+    sch.name, static_cast<parquet::TypeKind>(sch.type), std::move(children)};
+}
+}  // namespace
+
+parquet_metadata read_parquet_metadata(host_span<std::unique_ptr<datasource> const> sources)
+{
+  // Open and parse the source dataset metadata
+  auto metadata = aggregate_reader_metadata(sources);
+
+  return parquet_metadata{parquet_schema{walk_schema(&metadata, 0)},
+                          metadata.get_num_rows(),
+                          metadata.get_num_row_groups(),
+                          metadata.get_key_value_metadata()[0]};
+}
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/reader_impl.hpp b/cpp/src/io/parquet/reader_impl.hpp
new file mode 100644
index 0000000..9445e4d
--- /dev/null
+++ b/cpp/src/io/parquet/reader_impl.hpp
@@ -0,0 +1,304 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file reader_impl.hpp
+ * @brief cuDF-IO Parquet reader class implementation header
+ */
+
+#pragma once
+
+#include "parquet_gpu.hpp"
+#include "reader_impl_helpers.hpp"
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/detail/parquet.hpp>
+#include <cudf/io/parquet.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <memory>
+#include <optional>
+#include <vector>
+
+namespace cudf::io::detail::parquet {
+
+/**
+ * @brief Implementation for Parquet reader
+ */
+class reader::impl {
+ public:
+  /**
+   * @brief Constructor from an array of dataset sources with reader options.
+   *
+   * By using this constructor, each call to `read()` or `read_chunk()` will perform reading the
+   * entire given file.
+   *
+   * @param sources Dataset sources
+   * @param options Settings for controlling reading behavior
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  explicit impl(std::vector<std::unique_ptr<datasource>>&& sources,
+                parquet_reader_options const& options,
+                rmm::cuda_stream_view stream,
+                rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @brief Read an entire set or a subset of data and returns a set of columns
+   *
+   * @param skip_rows Number of rows to skip from the start
+   * @param num_rows Number of rows to read
+   * @param uses_custom_row_bounds Whether or not num_rows and skip_rows represents user-specific
+   *        bounds
+   * @param row_group_indices Lists of row groups to read, one per source
+   * @param filter Optional AST expression to filter output rows
+   *
+   * @return The set of columns along with metadata
+   */
+  table_with_metadata read(int64_t skip_rows,
+                           std::optional<size_type> const& num_rows,
+                           bool uses_custom_row_bounds,
+                           host_span<std::vector<size_type> const> row_group_indices,
+                           std::optional<std::reference_wrapper<ast::expression const>> filter);
+
+  /**
+   * @brief Constructor from a chunk read limit and an array of dataset sources with reader options.
+   *
+   * By using this constructor, the reader will support iterative (chunked) reading through
+   * `has_next() ` and `read_chunk()`. For example:
+   * ```
+   *  do {
+   *    auto const chunk = reader.read_chunk();
+   *    // Process chunk
+   *  } while (reader.has_next());
+   *
+   * ```
+   *
+   * Reading the whole given file at once through `read()` function is still supported if
+   * `chunk_read_limit == 0` (i.e., no reading limit) and `pass_read_limit == 0` (no temporary
+   * memory limit) In such case, `read_chunk()` will also return rows of the entire file.
+   *
+   * @param chunk_read_limit Limit on total number of bytes to be returned per read,
+   *        or `0` if there is no limit
+   * @param pass_read_limit Limit on memory usage for the purposes of decompression and processing
+   * of input, or `0` if there is no limit.
+   * @param sources Dataset sources
+   * @param options Settings for controlling reading behavior
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource to use for device memory allocation
+   */
+  explicit impl(std::size_t chunk_read_limit,
+                std::size_t pass_read_limit,
+                std::vector<std::unique_ptr<datasource>>&& sources,
+                parquet_reader_options const& options,
+                rmm::cuda_stream_view stream,
+                rmm::mr::device_memory_resource* mr);
+
+  /**
+   * @copydoc cudf::io::chunked_parquet_reader::has_next
+   */
+  bool has_next();
+
+  /**
+   * @copydoc cudf::io::chunked_parquet_reader::read_chunk
+   */
+  table_with_metadata read_chunk();
+
+ private:
+  /**
+   * @brief Perform the necessary data preprocessing for parsing file later on.
+   *
+   * @param skip_rows Number of rows to skip from the start
+   * @param num_rows Number of rows to read, or `std::nullopt` to read all rows
+   * @param uses_custom_row_bounds Whether or not num_rows and skip_rows represents user-specific
+   *        bounds
+   * @param row_group_indices Lists of row groups to read (one per source), or empty if read all
+   * @param filter Optional AST expression to filter row groups based on column chunk statistics
+   */
+  void prepare_data(int64_t skip_rows,
+                    std::optional<size_type> const& num_rows,
+                    bool uses_custom_row_bounds,
+                    host_span<std::vector<size_type> const> row_group_indices,
+                    std::optional<std::reference_wrapper<ast::expression const>> filter);
+
+  void load_global_chunk_info();
+  void compute_input_pass_row_group_info();
+  void setup_pass();
+
+  /**
+   * @brief Create chunk information and start file reads
+   *
+   * @return pair of boolean indicating if compressed chunks were found and a vector of futures for
+   * read completion
+   */
+  std::pair<bool, std::vector<std::future<void>>> read_and_decompress_column_chunks();
+
+  /**
+   * @brief Load and decompress the input file(s) into memory.
+   */
+  void load_and_decompress_data();
+
+  /**
+   * @brief Perform some preprocessing for page data and also compute the split locations
+   * {skip_rows, num_rows} for chunked reading.
+   *
+   * There are several pieces of information we can't compute directly from row counts in
+   * the parquet headers when dealing with nested schemas:
+   * - The total sizes of all output columns at all nesting levels
+   * - The starting output buffer offset for each page, for each nesting level
+   *
+   * For flat schemas, these values are computed during header decoding (see gpuDecodePageHeaders).
+   *
+   * @param uses_custom_row_bounds Whether or not num_rows and skip_rows represents user-specific
+   *        bounds
+   * @param chunk_read_limit Limit on total number of bytes to be returned per read,
+   *        or `0` if there is no limit
+   */
+  void preprocess_pages(bool uses_custom_row_bounds, size_t chunk_read_limit);
+
+  /**
+   * @brief Allocate nesting information storage for all pages and set pointers to it.
+   *
+   * One large contiguous buffer of PageNestingInfo structs is allocated and
+   * distributed among the PageInfo structs.
+   *
+   * Note that this gets called even in the flat schema case so that we have a
+   * consistent place to store common information such as value counts, etc.
+   */
+  void allocate_nesting_info();
+
+  /**
+   * @brief Allocate space for use when decoding definition/repetition levels.
+   *
+   * One large contiguous buffer of data allocated and
+   * distributed among the PageInfo structs.
+   */
+  void allocate_level_decode_space();
+
+  /**
+   * @brief Populate the output table metadata from the parquet file metadata.
+   *
+   * @param out_metadata The output table metadata to add to
+   */
+  void populate_metadata(table_metadata& out_metadata);
+
+  /**
+   * @brief Read a chunk of data and return an output table.
+   *
+   * This function is called internally and expects all preprocessing steps have already been done.
+   *
+   * @param uses_custom_row_bounds Whether or not num_rows and skip_rows represents user-specific
+   *        bounds
+   * @param filter Optional AST expression to filter output rows
+   * @return The output table along with columns' metadata
+   */
+  table_with_metadata read_chunk_internal(
+    bool uses_custom_row_bounds,
+    std::optional<std::reference_wrapper<ast::expression const>> filter);
+
+  /**
+   * @brief Finalize the output table by adding empty columns for the non-selected columns in
+   * schema.
+   *
+   * @param out_metadata The output table metadata
+   * @param out_columns The columns for building the output table
+   * @param filter Optional AST expression to filter output rows
+   * @return The output table along with columns' metadata
+   */
+  table_with_metadata finalize_output(
+    table_metadata& out_metadata,
+    std::vector<std::unique_ptr<column>>& out_columns,
+    std::optional<std::reference_wrapper<ast::expression const>> filter);
+
+  /**
+   * @brief Allocate data buffers for the output columns.
+   *
+   * @param skip_rows Crop all rows below skip_rows
+   * @param num_rows Maximum number of rows to read
+   * @param uses_custom_row_bounds Whether or not num_rows and skip_rows represents user-specific
+   *        bounds
+   */
+  void allocate_columns(size_t skip_rows, size_t num_rows, bool uses_custom_row_bounds);
+
+  /**
+   * @brief Calculate per-page offsets for string data
+   *
+   * @return Vector of total string data sizes for each column
+   */
+  std::vector<size_t> calculate_page_string_offsets();
+
+  /**
+   * @brief Converts the page data and outputs to columns.
+   *
+   * @param skip_rows Minimum number of rows from start
+   * @param num_rows Number of rows to output
+   */
+  void decode_page_data(size_t skip_rows, size_t num_rows);
+
+ private:
+  rmm::cuda_stream_view _stream;
+  rmm::mr::device_memory_resource* _mr = nullptr;
+
+  std::vector<std::unique_ptr<datasource>> _sources;
+  std::unique_ptr<aggregate_reader_metadata> _metadata;
+
+  // input columns to be processed
+  std::vector<input_column_info> _input_columns;
+
+  // Buffers for generating output columns
+  std::vector<inline_column_buffer> _output_buffers;
+
+  // Buffers copied from `_output_buffers` after construction for reuse
+  std::vector<inline_column_buffer> _output_buffers_template;
+
+  // _output_buffers associated schema indices
+  std::vector<int> _output_column_schemas;
+
+  // _output_buffers associated metadata
+  std::unique_ptr<table_metadata> _output_metadata;
+
+  bool _strings_to_categorical = false;
+  std::optional<std::vector<reader_column_schema>> _reader_column_schema;
+  data_type _timestamp_type{type_id::EMPTY};
+
+  // chunked reading happens in 2 parts:
+  //
+  // At the top level there is the "pass" in which we try and limit the
+  // total amount of temporary memory (compressed data, decompressed data) in use
+  // via _input_pass_read_limit.
+  //
+  // Within a pass, we produce one or more chunks of output, whose maximum total
+  // byte size is controlled by _output_chunk_read_limit.
+
+  cudf::io::parquet::gpu::file_intermediate_data _file_itm_data;
+  std::unique_ptr<cudf::io::parquet::gpu::pass_intermediate_data> _pass_itm_data;
+
+  // an array of offsets into _file_itm_data::global_chunks. Each pair of offsets represents
+  // the start/end of the chunks to be loaded for a given pass.
+  std::vector<std::size_t> _input_pass_row_group_offsets{};
+  std::vector<std::size_t> _input_pass_row_count{};
+  std::size_t _current_input_pass{0};
+  std::size_t _chunk_count{0};
+
+  std::size_t _output_chunk_read_limit{0};
+  std::size_t _input_pass_read_limit{0};
+  bool _pass_preprocessed{false};
+  bool _file_preprocessed{false};
+};
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/reader_impl_helpers.cpp b/cpp/src/io/parquet/reader_impl_helpers.cpp
new file mode 100644
index 0000000..fcaa610
--- /dev/null
+++ b/cpp/src/io/parquet/reader_impl_helpers.cpp
@@ -0,0 +1,659 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "reader_impl_helpers.hpp"
+
+#include <io/utilities/row_selection.hpp>
+
+#include <numeric>
+#include <regex>
+
+namespace cudf::io::detail::parquet {
+
+namespace {
+
+ConvertedType logical_type_to_converted_type(LogicalType const& logical)
+{
+  if (logical.isset.STRING) {
+    return parquet::UTF8;
+  } else if (logical.isset.MAP) {
+    return parquet::MAP;
+  } else if (logical.isset.LIST) {
+    return parquet::LIST;
+  } else if (logical.isset.ENUM) {
+    return parquet::ENUM;
+  } else if (logical.isset.DECIMAL) {
+    return parquet::DECIMAL;  // TODO set decimal values
+  } else if (logical.isset.DATE) {
+    return parquet::DATE;
+  } else if (logical.isset.TIME) {
+    if (logical.TIME.unit.isset.MILLIS)
+      return parquet::TIME_MILLIS;
+    else if (logical.TIME.unit.isset.MICROS)
+      return parquet::TIME_MICROS;
+  } else if (logical.isset.TIMESTAMP) {
+    if (logical.TIMESTAMP.unit.isset.MILLIS)
+      return parquet::TIMESTAMP_MILLIS;
+    else if (logical.TIMESTAMP.unit.isset.MICROS)
+      return parquet::TIMESTAMP_MICROS;
+  } else if (logical.isset.INTEGER) {
+    switch (logical.INTEGER.bitWidth) {
+      case 8: return logical.INTEGER.isSigned ? INT_8 : UINT_8;
+      case 16: return logical.INTEGER.isSigned ? INT_16 : UINT_16;
+      case 32: return logical.INTEGER.isSigned ? INT_32 : UINT_32;
+      case 64: return logical.INTEGER.isSigned ? INT_64 : UINT_64;
+      default: break;
+    }
+  } else if (logical.isset.UNKNOWN) {
+    return parquet::NA;
+  } else if (logical.isset.JSON) {
+    return parquet::JSON;
+  } else if (logical.isset.BSON) {
+    return parquet::BSON;
+  }
+  return parquet::UNKNOWN;
+}
+
+}  // namespace
+
+/**
+ * @brief Function that translates Parquet datatype to cuDF type enum
+ */
+type_id to_type_id(SchemaElement const& schema,
+                   bool strings_to_categorical,
+                   type_id timestamp_type_id)
+{
+  parquet::Type const physical            = schema.type;
+  parquet::LogicalType const logical_type = schema.logical_type;
+  parquet::ConvertedType converted_type   = schema.converted_type;
+  int32_t decimal_precision               = schema.decimal_precision;
+
+  // Logical type used for actual data interpretation; the legacy converted type
+  // is superseded by 'logical' type whenever available.
+  auto const inferred_converted_type = logical_type_to_converted_type(logical_type);
+  if (inferred_converted_type != parquet::UNKNOWN) { converted_type = inferred_converted_type; }
+  if (inferred_converted_type == parquet::DECIMAL) {
+    decimal_precision = schema.logical_type.DECIMAL.precision;
+  }
+
+  switch (converted_type) {
+    case parquet::UINT_8: return type_id::UINT8;
+    case parquet::INT_8: return type_id::INT8;
+    case parquet::UINT_16: return type_id::UINT16;
+    case parquet::INT_16: return type_id::INT16;
+    case parquet::UINT_32: return type_id::UINT32;
+    case parquet::UINT_64: return type_id::UINT64;
+    case parquet::DATE: return type_id::TIMESTAMP_DAYS;
+    case parquet::TIME_MILLIS: return type_id::DURATION_MILLISECONDS;
+    case parquet::TIME_MICROS: return type_id::DURATION_MICROSECONDS;
+    case parquet::TIMESTAMP_MILLIS:
+      return (timestamp_type_id != type_id::EMPTY) ? timestamp_type_id
+                                                   : type_id::TIMESTAMP_MILLISECONDS;
+    case parquet::TIMESTAMP_MICROS:
+      return (timestamp_type_id != type_id::EMPTY) ? timestamp_type_id
+                                                   : type_id::TIMESTAMP_MICROSECONDS;
+    case parquet::DECIMAL:
+      if (physical == parquet::INT32) { return type_id::DECIMAL32; }
+      if (physical == parquet::INT64) { return type_id::DECIMAL64; }
+      if (physical == parquet::FIXED_LEN_BYTE_ARRAY) {
+        if (schema.type_length <= static_cast<int32_t>(sizeof(int32_t))) {
+          return type_id::DECIMAL32;
+        }
+        if (schema.type_length <= static_cast<int32_t>(sizeof(int64_t))) {
+          return type_id::DECIMAL64;
+        }
+        if (schema.type_length <= static_cast<int32_t>(sizeof(__int128_t))) {
+          return type_id::DECIMAL128;
+        }
+      }
+      if (physical == parquet::BYTE_ARRAY) {
+        CUDF_EXPECTS(decimal_precision <= MAX_DECIMAL128_PRECISION, "Invalid decimal precision");
+        if (decimal_precision <= MAX_DECIMAL32_PRECISION) {
+          return type_id::DECIMAL32;
+        } else if (decimal_precision <= MAX_DECIMAL64_PRECISION) {
+          return type_id::DECIMAL64;
+        } else {
+          return type_id::DECIMAL128;
+        }
+      }
+      CUDF_FAIL("Invalid representation of decimal type");
+      break;
+
+    // maps are just List<Struct<>>.
+    case parquet::MAP:
+    case parquet::LIST: return type_id::LIST;
+    case parquet::NA: return type_id::STRING;
+    // return type_id::EMPTY; //TODO(kn): enable after Null/Empty column support
+    default: break;
+  }
+
+  if (inferred_converted_type == parquet::UNKNOWN and physical == parquet::INT64 and
+      logical_type.TIMESTAMP.unit.isset.NANOS) {
+    return (timestamp_type_id != type_id::EMPTY) ? timestamp_type_id
+                                                 : type_id::TIMESTAMP_NANOSECONDS;
+  }
+
+  if (inferred_converted_type == parquet::UNKNOWN and physical == parquet::INT64 and
+      logical_type.TIME.unit.isset.NANOS) {
+    return type_id::DURATION_NANOSECONDS;
+  }
+
+  // is it simply a struct?
+  if (schema.is_struct()) { return type_id::STRUCT; }
+
+  // Physical storage type supported by Parquet; controls the on-disk storage
+  // format in combination with the encoding type.
+  switch (physical) {
+    case parquet::BOOLEAN: return type_id::BOOL8;
+    case parquet::INT32: return type_id::INT32;
+    case parquet::INT64: return type_id::INT64;
+    case parquet::FLOAT: return type_id::FLOAT32;
+    case parquet::DOUBLE: return type_id::FLOAT64;
+    case parquet::BYTE_ARRAY:
+    case parquet::FIXED_LEN_BYTE_ARRAY:
+      // Can be mapped to INT32 (32-bit hash) or STRING
+      return strings_to_categorical ? type_id::INT32 : type_id::STRING;
+    case parquet::INT96:
+      return (timestamp_type_id != type_id::EMPTY) ? timestamp_type_id
+                                                   : type_id::TIMESTAMP_NANOSECONDS;
+    default: break;
+  }
+
+  return type_id::EMPTY;
+}
+
+metadata::metadata(datasource* source)
+{
+  constexpr auto header_len = sizeof(file_header_s);
+  constexpr auto ender_len  = sizeof(file_ender_s);
+
+  auto const len           = source->size();
+  auto const header_buffer = source->host_read(0, header_len);
+  auto const header        = reinterpret_cast<file_header_s const*>(header_buffer->data());
+  auto const ender_buffer  = source->host_read(len - ender_len, ender_len);
+  auto const ender         = reinterpret_cast<file_ender_s const*>(ender_buffer->data());
+  CUDF_EXPECTS(len > header_len + ender_len, "Incorrect data source");
+  CUDF_EXPECTS(header->magic == parquet_magic && ender->magic == parquet_magic,
+               "Corrupted header or footer");
+  CUDF_EXPECTS(ender->footer_len != 0 && ender->footer_len <= (len - header_len - ender_len),
+               "Incorrect footer length");
+
+  auto const buffer = source->host_read(len - ender->footer_len - ender_len, ender->footer_len);
+  CompactProtocolReader cp(buffer->data(), ender->footer_len);
+  CUDF_EXPECTS(cp.read(this), "Cannot parse metadata");
+  CUDF_EXPECTS(cp.InitSchema(this), "Cannot initialize schema");
+}
+
+std::vector<metadata> aggregate_reader_metadata::metadatas_from_sources(
+  host_span<std::unique_ptr<datasource> const> sources)
+{
+  std::vector<metadata> metadatas;
+  std::transform(
+    sources.begin(), sources.end(), std::back_inserter(metadatas), [](auto const& source) {
+      return metadata(source.get());
+    });
+  return metadatas;
+}
+
+std::vector<std::unordered_map<std::string, std::string>>
+aggregate_reader_metadata::collect_keyval_metadata() const
+{
+  std::vector<std::unordered_map<std::string, std::string>> kv_maps;
+  std::transform(per_file_metadata.cbegin(),
+                 per_file_metadata.cend(),
+                 std::back_inserter(kv_maps),
+                 [](auto const& pfm) {
+                   std::unordered_map<std::string, std::string> kv_map;
+                   std::transform(pfm.key_value_metadata.cbegin(),
+                                  pfm.key_value_metadata.cend(),
+                                  std::inserter(kv_map, kv_map.end()),
+                                  [](auto const& kv) {
+                                    return std::pair{kv.key, kv.value};
+                                  });
+                   return kv_map;
+                 });
+
+  return kv_maps;
+}
+
+int64_t aggregate_reader_metadata::calc_num_rows() const
+{
+  return std::accumulate(
+    per_file_metadata.cbegin(), per_file_metadata.cend(), 0l, [](auto& sum, auto& pfm) {
+      auto const rowgroup_rows = std::accumulate(
+        pfm.row_groups.cbegin(), pfm.row_groups.cend(), 0l, [](auto& rg_sum, auto& rg) {
+          return rg_sum + rg.num_rows;
+        });
+      CUDF_EXPECTS(pfm.num_rows == 0 || pfm.num_rows == rowgroup_rows,
+                   "Header and row groups disagree about number of rows in file!");
+      return sum + (pfm.num_rows == 0 && rowgroup_rows > 0 ? rowgroup_rows : pfm.num_rows);
+    });
+}
+
+size_type aggregate_reader_metadata::calc_num_row_groups() const
+{
+  return std::accumulate(
+    per_file_metadata.cbegin(), per_file_metadata.cend(), 0, [](auto& sum, auto& pfm) {
+      return sum + pfm.row_groups.size();
+    });
+}
+
+aggregate_reader_metadata::aggregate_reader_metadata(
+  host_span<std::unique_ptr<datasource> const> sources)
+  : per_file_metadata(metadatas_from_sources(sources)),
+    keyval_maps(collect_keyval_metadata()),
+    num_rows(calc_num_rows()),
+    num_row_groups(calc_num_row_groups())
+{
+  if (per_file_metadata.size() > 0) {
+    auto const& first_meta = per_file_metadata.front();
+    auto const num_cols =
+      first_meta.row_groups.size() > 0 ? first_meta.row_groups.front().columns.size() : 0;
+    auto const& schema = first_meta.schema;
+
+    // Verify that the input files have matching numbers of columns and schema.
+    for (auto const& pfm : per_file_metadata) {
+      if (pfm.row_groups.size() > 0) {
+        CUDF_EXPECTS(num_cols == pfm.row_groups.front().columns.size(),
+                     "All sources must have the same number of columns");
+      }
+      CUDF_EXPECTS(schema == pfm.schema, "All sources must have the same schema");
+    }
+  }
+}
+
+RowGroup const& aggregate_reader_metadata::get_row_group(size_type row_group_index,
+                                                         size_type src_idx) const
+{
+  CUDF_EXPECTS(src_idx >= 0 && src_idx < static_cast<size_type>(per_file_metadata.size()),
+               "invalid source index");
+  return per_file_metadata[src_idx].row_groups[row_group_index];
+}
+
+ColumnChunkMetaData const& aggregate_reader_metadata::get_column_metadata(size_type row_group_index,
+                                                                          size_type src_idx,
+                                                                          int schema_idx) const
+{
+  auto col =
+    std::find_if(per_file_metadata[src_idx].row_groups[row_group_index].columns.begin(),
+                 per_file_metadata[src_idx].row_groups[row_group_index].columns.end(),
+                 [schema_idx](ColumnChunk const& col) { return col.schema_idx == schema_idx; });
+  CUDF_EXPECTS(col != std::end(per_file_metadata[src_idx].row_groups[row_group_index].columns),
+               "Found no metadata for schema index");
+  return col->meta_data;
+}
+
+std::string aggregate_reader_metadata::get_pandas_index() const
+{
+  // Assumes that all input files have the same metadata
+  // TODO: verify this assumption
+  auto it = keyval_maps[0].find("pandas");
+  if (it != keyval_maps[0].end()) {
+    // Captures a list of quoted strings found inside square brackets after `"index_columns":`
+    // Inside quotes supports newlines, brackets, escaped quotes, etc.
+    // One-liner regex:
+    // "index_columns"\s*:\s*\[\s*((?:"(?:|(?:.*?(?![^\\]")).?)[^\\]?",?\s*)*)\]
+    // Documented below.
+    std::regex index_columns_expr{
+      R"("index_columns"\s*:\s*\[\s*)"  // match preamble, opening square bracket, whitespace
+      R"(()"                            // Open first capturing group
+      R"((?:")"                         // Open non-capturing group match opening quote
+      R"((?:|(?:.*?(?![^\\]")).?))"     // match empty string or anything between quotes
+      R"([^\\]?")"                      // Match closing non-escaped quote
+      R"(,?\s*)"                        // Match optional comma and whitespace
+      R"()*)"                           // Close non-capturing group and repeat 0 or more times
+      R"())"                            // Close first capturing group
+      R"(\])"                           // Match closing square brackets
+    };
+    std::smatch sm;
+    if (std::regex_search(it->second, sm, index_columns_expr)) { return sm[1].str(); }
+  }
+  return "";
+}
+
+std::vector<std::string> aggregate_reader_metadata::get_pandas_index_names() const
+{
+  std::vector<std::string> names;
+  auto str = get_pandas_index();
+  if (str.length() != 0) {
+    std::regex index_name_expr{R"(\"((?:\\.|[^\"])*)\")"};
+    std::smatch sm;
+    while (std::regex_search(str, sm, index_name_expr)) {
+      if (sm.size() == 2) {  // 2 = whole match, first item
+        if (std::find(names.begin(), names.end(), sm[1].str()) == names.end()) {
+          std::regex esc_quote{R"(\\")"};
+          names.emplace_back(std::regex_replace(sm[1].str(), esc_quote, R"(")"));
+        }
+      }
+      str = sm.suffix();
+    }
+  }
+  return names;
+}
+
+std::tuple<int64_t, size_type, std::vector<gpu::row_group_info>>
+aggregate_reader_metadata::select_row_groups(
+  host_span<std::vector<size_type> const> row_group_indices,
+  int64_t skip_rows_opt,
+  std::optional<size_type> const& num_rows_opt,
+  host_span<data_type const> output_dtypes,
+  std::optional<std::reference_wrapper<ast::expression const>> filter,
+  rmm::cuda_stream_view stream) const
+{
+  std::optional<std::vector<std::vector<size_type>>> filtered_row_group_indices;
+  if (filter.has_value()) {
+    filtered_row_group_indices =
+      filter_row_groups(row_group_indices, output_dtypes, filter.value(), stream);
+    if (filtered_row_group_indices.has_value()) {
+      row_group_indices =
+        host_span<std::vector<size_type> const>(filtered_row_group_indices.value());
+    }
+  }
+  std::vector<gpu::row_group_info> selection;
+  auto [rows_to_skip, rows_to_read] = [&]() {
+    if (not row_group_indices.empty()) { return std::pair<int64_t, size_type>{}; }
+    auto const from_opts = cudf::io::detail::skip_rows_num_rows_from_options(
+      skip_rows_opt, num_rows_opt, get_num_rows());
+    return std::pair{static_cast<int64_t>(from_opts.first), from_opts.second};
+  }();
+
+  if (!row_group_indices.empty()) {
+    CUDF_EXPECTS(row_group_indices.size() == per_file_metadata.size(),
+                 "Must specify row groups for each source");
+
+    for (size_t src_idx = 0; src_idx < row_group_indices.size(); ++src_idx) {
+      for (auto const& rowgroup_idx : row_group_indices[src_idx]) {
+        CUDF_EXPECTS(
+          rowgroup_idx >= 0 &&
+            rowgroup_idx < static_cast<size_type>(per_file_metadata[src_idx].row_groups.size()),
+          "Invalid rowgroup index");
+        selection.emplace_back(rowgroup_idx, rows_to_read, src_idx);
+        rows_to_read += get_row_group(rowgroup_idx, src_idx).num_rows;
+      }
+    }
+  } else {
+    size_type count = 0;
+    for (size_t src_idx = 0; src_idx < per_file_metadata.size(); ++src_idx) {
+      for (size_t rg_idx = 0; rg_idx < per_file_metadata[src_idx].row_groups.size(); ++rg_idx) {
+        auto const chunk_start_row = count;
+        count += get_row_group(rg_idx, src_idx).num_rows;
+        if (count > rows_to_skip || count == 0) {
+          selection.emplace_back(rg_idx, chunk_start_row, src_idx);
+        }
+        if (count >= rows_to_skip + rows_to_read) { break; }
+      }
+    }
+  }
+
+  return {rows_to_skip, rows_to_read, std::move(selection)};
+}
+
+std::tuple<std::vector<input_column_info>,
+           std::vector<inline_column_buffer>,
+           std::vector<size_type>>
+aggregate_reader_metadata::select_columns(std::optional<std::vector<std::string>> const& use_names,
+                                          bool include_index,
+                                          bool strings_to_categorical,
+                                          type_id timestamp_type_id) const
+{
+  auto find_schema_child = [&](SchemaElement const& schema_elem, std::string const& name) {
+    auto const& col_schema_idx =
+      std::find_if(schema_elem.children_idx.cbegin(),
+                   schema_elem.children_idx.cend(),
+                   [&](size_t col_schema_idx) { return get_schema(col_schema_idx).name == name; });
+
+    return (col_schema_idx != schema_elem.children_idx.end())
+             ? static_cast<size_type>(*col_schema_idx)
+             : -1;
+  };
+
+  std::vector<inline_column_buffer> output_columns;
+  std::vector<input_column_info> input_columns;
+  std::vector<int> nesting;
+
+  // Return true if column path is valid. e.g. if the path is {"struct1", "child1"}, then it is
+  // valid if "struct1.child1" exists in this file's schema. If "struct1" exists but "child1" is
+  // not a child of "struct1" then the function will return false for "struct1"
+  std::function<bool(column_name_info const*, int, std::vector<inline_column_buffer>&, bool)>
+    build_column = [&](column_name_info const* col_name_info,
+                       int schema_idx,
+                       std::vector<inline_column_buffer>& out_col_array,
+                       bool has_list_parent) {
+      if (schema_idx < 0) { return false; }
+      auto const& schema_elem = get_schema(schema_idx);
+
+      // if schema_elem is a stub then it does not exist in the column_name_info and column_buffer
+      // hierarchy. So continue on
+      if (schema_elem.is_stub()) {
+        // is this legit?
+        CUDF_EXPECTS(schema_elem.num_children == 1, "Unexpected number of children for stub");
+        auto child_col_name_info = (col_name_info) ? &col_name_info->children[0] : nullptr;
+        return build_column(
+          child_col_name_info, schema_elem.children_idx[0], out_col_array, has_list_parent);
+      }
+
+      // if we're at the root, this is a new output column
+      auto const col_type = schema_elem.is_one_level_list(get_schema(schema_elem.parent_idx))
+                              ? type_id::LIST
+                              : to_type_id(schema_elem, strings_to_categorical, timestamp_type_id);
+      auto const dtype    = to_data_type(col_type, schema_elem);
+
+      inline_column_buffer output_col(dtype, schema_elem.repetition_type == OPTIONAL);
+      if (has_list_parent) { output_col.user_data |= PARQUET_COLUMN_BUFFER_FLAG_HAS_LIST_PARENT; }
+      // store the index of this element if inserted in out_col_array
+      nesting.push_back(static_cast<int>(out_col_array.size()));
+      output_col.name = schema_elem.name;
+
+      // build each child
+      bool path_is_valid = false;
+      if (col_name_info == nullptr or col_name_info->children.empty()) {
+        // add all children of schema_elem.
+        // At this point, we can no longer pass a col_name_info to build_column
+        for (int idx = 0; idx < schema_elem.num_children; idx++) {
+          path_is_valid |= build_column(nullptr,
+                                        schema_elem.children_idx[idx],
+                                        output_col.children,
+                                        has_list_parent || col_type == type_id::LIST);
+        }
+      } else {
+        for (size_t idx = 0; idx < col_name_info->children.size(); idx++) {
+          path_is_valid |=
+            build_column(&col_name_info->children[idx],
+                         find_schema_child(schema_elem, col_name_info->children[idx].name),
+                         output_col.children,
+                         has_list_parent || col_type == type_id::LIST);
+        }
+      }
+
+      // if I have no children, we're at a leaf and I'm an input column (that is, one with actual
+      // data stored) so add me to the list.
+      if (schema_elem.num_children == 0) {
+        input_column_info& input_col = input_columns.emplace_back(
+          input_column_info{schema_idx, schema_elem.name, schema_elem.max_repetition_level > 0});
+
+        // set up child output column for one-level encoding list
+        if (schema_elem.is_one_level_list(get_schema(schema_elem.parent_idx))) {
+          // determine the element data type
+          auto const element_type =
+            to_type_id(schema_elem, strings_to_categorical, timestamp_type_id);
+          auto const element_dtype = to_data_type(element_type, schema_elem);
+
+          inline_column_buffer element_col(element_dtype, schema_elem.repetition_type == OPTIONAL);
+          if (has_list_parent || col_type == type_id::LIST) {
+            element_col.user_data |= PARQUET_COLUMN_BUFFER_FLAG_HAS_LIST_PARENT;
+          }
+          // store the index of this element
+          nesting.push_back(static_cast<int>(output_col.children.size()));
+          // TODO: not sure if we should assign a name or leave it blank
+          element_col.name = "element";
+
+          output_col.children.push_back(std::move(element_col));
+        }
+
+        std::copy(nesting.cbegin(), nesting.cend(), std::back_inserter(input_col.nesting));
+
+        // pop off the extra nesting element.
+        if (schema_elem.is_one_level_list(get_schema(schema_elem.parent_idx))) {
+          nesting.pop_back();
+        }
+
+        path_is_valid = true;  // If we're able to reach leaf then path is valid
+      }
+
+      if (path_is_valid) { out_col_array.push_back(std::move(output_col)); }
+
+      nesting.pop_back();
+      return path_is_valid;
+    };
+
+  std::vector<int> output_column_schemas;
+
+  //
+  // there is not necessarily a 1:1 mapping between input columns and output columns.
+  // For example, parquet does not explicitly store a ColumnChunkDesc for struct columns.
+  // The "structiness" is simply implied by the schema.  For example, this schema:
+  //  required group field_id=1 name {
+  //    required binary field_id=2 firstname (String);
+  //    required binary field_id=3 middlename (String);
+  //    required binary field_id=4 lastname (String);
+  // }
+  // will only contain 3 internal columns of data (firstname, middlename, lastname).  But of
+  // course "name" is ultimately the struct column we want to return.
+  //
+  // "firstname", "middlename" and "lastname" represent the input columns in the file that we
+  // process to produce the final cudf "name" column.
+  //
+  // A user can ask for a single field out of the struct e.g. firstname.
+  // In this case they'll pass a fully qualified name to the schema element like
+  // ["name", "firstname"]
+  //
+  auto const& root = get_schema(0);
+  if (not use_names.has_value()) {
+    for (auto const& schema_idx : root.children_idx) {
+      build_column(nullptr, schema_idx, output_columns, false);
+      output_column_schemas.push_back(schema_idx);
+    }
+  } else {
+    struct path_info {
+      std::string full_path;
+      int schema_idx;
+    };
+
+    // Convert schema into a vector of every possible path
+    std::vector<path_info> all_paths;
+    std::function<void(std::string, int)> add_path = [&](std::string path_till_now,
+                                                         int schema_idx) {
+      auto const& schema_elem = get_schema(schema_idx);
+      std::string curr_path   = path_till_now + schema_elem.name;
+      all_paths.push_back({curr_path, schema_idx});
+      for (auto const& child_idx : schema_elem.children_idx) {
+        add_path(curr_path + ".", child_idx);
+      }
+    };
+    for (auto const& child_idx : get_schema(0).children_idx) {
+      add_path("", child_idx);
+    }
+
+    // Find which of the selected paths are valid and get their schema index
+    std::vector<path_info> valid_selected_paths;
+    for (auto const& selected_path : *use_names) {
+      auto found_path =
+        std::find_if(all_paths.begin(), all_paths.end(), [&](path_info& valid_path) {
+          return valid_path.full_path == selected_path;
+        });
+      if (found_path != all_paths.end()) {
+        valid_selected_paths.push_back({selected_path, found_path->schema_idx});
+      }
+    }
+
+    // Now construct paths as vector of strings for further consumption
+    std::vector<std::vector<std::string>> use_names3;
+    std::transform(valid_selected_paths.cbegin(),
+                   valid_selected_paths.cend(),
+                   std::back_inserter(use_names3),
+                   [&](path_info const& valid_path) {
+                     auto schema_idx = valid_path.schema_idx;
+                     std::vector<std::string> result_path;
+                     do {
+                       SchemaElement const& elem = get_schema(schema_idx);
+                       result_path.push_back(elem.name);
+                       schema_idx = elem.parent_idx;
+                     } while (schema_idx > 0);
+                     return std::vector<std::string>(result_path.rbegin(), result_path.rend());
+                   });
+
+    std::vector<column_name_info> selected_columns;
+    if (include_index) {
+      std::vector<std::string> index_names = get_pandas_index_names();
+      std::transform(index_names.cbegin(),
+                     index_names.cend(),
+                     std::back_inserter(selected_columns),
+                     [](std::string const& name) { return column_name_info(name); });
+    }
+    // Merge the vector use_names into a set of hierarchical column_name_info objects
+    /* This is because if we have columns like this:
+     *     col1
+     *      / \
+     *    s3   f4
+     *   / \
+     * f5   f6
+     *
+     * there may be common paths in use_names like:
+     * {"col1", "s3", "f5"}, {"col1", "f4"}
+     * which means we want the output to contain
+     *     col1
+     *      / \
+     *    s3   f4
+     *   /
+     * f5
+     *
+     * rather than
+     *  col1   col1
+     *   |      |
+     *   s3     f4
+     *   |
+     *   f5
+     */
+    for (auto const& path : use_names3) {
+      auto array_to_find_in = &selected_columns;
+      for (size_t depth = 0; depth < path.size(); ++depth) {
+        // Check if the path exists in our selected_columns and if not, add it.
+        auto const& name_to_find = path[depth];
+        auto found_col           = std::find_if(
+          array_to_find_in->begin(),
+          array_to_find_in->end(),
+          [&name_to_find](column_name_info const& col) { return col.name == name_to_find; });
+        if (found_col == array_to_find_in->end()) {
+          auto& col        = array_to_find_in->emplace_back(name_to_find);
+          array_to_find_in = &col.children;
+        } else {
+          // Path exists. go down further.
+          array_to_find_in = &found_col->children;
+        }
+      }
+    }
+    for (auto& col : selected_columns) {
+      auto const& top_level_col_schema_idx = find_schema_child(root, col.name);
+      bool valid_column = build_column(&col, top_level_col_schema_idx, output_columns, false);
+      if (valid_column) output_column_schemas.push_back(top_level_col_schema_idx);
+    }
+  }
+
+  return std::make_tuple(
+    std::move(input_columns), std::move(output_columns), std::move(output_column_schemas));
+}
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/reader_impl_helpers.hpp b/cpp/src/io/parquet/reader_impl_helpers.hpp
new file mode 100644
index 0000000..61e4f94
--- /dev/null
+++ b/cpp/src/io/parquet/reader_impl_helpers.hpp
@@ -0,0 +1,278 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "compact_protocol_reader.hpp"
+#include "parquet_gpu.hpp"
+
+#include <cudf/ast/detail/expression_transformer.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+
+#include <list>
+#include <tuple>
+#include <vector>
+
+namespace cudf::io::detail::parquet {
+
+using namespace cudf::io::parquet;
+
+/**
+ * @brief Function that translates Parquet datatype to cuDF type enum
+ */
+[[nodiscard]] type_id to_type_id(SchemaElement const& schema,
+                                 bool strings_to_categorical,
+                                 type_id timestamp_type_id);
+
+/**
+ * @brief Converts cuDF type enum to column logical type
+ */
+[[nodiscard]] inline data_type to_data_type(type_id t_id, SchemaElement const& schema)
+{
+  return t_id == type_id::DECIMAL32 || t_id == type_id::DECIMAL64 || t_id == type_id::DECIMAL128
+           ? data_type{t_id, numeric::scale_type{-schema.decimal_scale}}
+           : data_type{t_id};
+}
+
+/**
+ * @brief Class for parsing dataset metadata
+ */
+struct metadata : public FileMetaData {
+  explicit metadata(datasource* source);
+};
+
+class aggregate_reader_metadata {
+  std::vector<metadata> per_file_metadata;
+  std::vector<std::unordered_map<std::string, std::string>> keyval_maps;
+  int64_t num_rows;
+  size_type num_row_groups;
+
+  /**
+   * @brief Create a metadata object from each element in the source vector
+   */
+  static std::vector<metadata> metadatas_from_sources(
+    host_span<std::unique_ptr<datasource> const> sources);
+
+  /**
+   * @brief Collect the keyvalue maps from each per-file metadata object into a vector of maps.
+   */
+  [[nodiscard]] std::vector<std::unordered_map<std::string, std::string>> collect_keyval_metadata()
+    const;
+
+  /**
+   * @brief Sums up the number of rows of each source
+   */
+  [[nodiscard]] int64_t calc_num_rows() const;
+
+  /**
+   * @brief Sums up the number of row groups of each source
+   */
+  [[nodiscard]] size_type calc_num_row_groups() const;
+
+ public:
+  aggregate_reader_metadata(host_span<std::unique_ptr<datasource> const> sources);
+
+  [[nodiscard]] RowGroup const& get_row_group(size_type row_group_index, size_type src_idx) const;
+
+  [[nodiscard]] ColumnChunkMetaData const& get_column_metadata(size_type row_group_index,
+                                                               size_type src_idx,
+                                                               int schema_idx) const;
+
+  [[nodiscard]] auto get_num_rows() const { return num_rows; }
+
+  [[nodiscard]] auto get_num_row_groups() const { return num_row_groups; }
+
+  [[nodiscard]] auto const& get_schema(int schema_idx) const
+  {
+    return per_file_metadata[0].schema[schema_idx];
+  }
+
+  [[nodiscard]] auto const& get_key_value_metadata() const& { return keyval_maps; }
+
+  [[nodiscard]] auto&& get_key_value_metadata() && { return std::move(keyval_maps); }
+  /**
+   * @brief Gets the concrete nesting depth of output cudf columns
+   *
+   * @param schema_index Schema index of the input column
+   *
+   * @return comma-separated index column names in quotes
+   */
+  [[nodiscard]] inline int get_output_nesting_depth(int schema_index) const
+  {
+    auto& pfm = per_file_metadata[0];
+    int depth = 0;
+
+    // walk upwards, skipping repeated fields
+    while (schema_index > 0) {
+      auto const& elm = pfm.schema[schema_index];
+      if (!elm.is_stub()) { depth++; }
+      // schema of one-level encoding list doesn't contain nesting information, so we need to
+      // manually add an extra nesting level
+      if (elm.is_one_level_list(pfm.schema[elm.parent_idx])) { depth++; }
+      schema_index = elm.parent_idx;
+    }
+    return depth;
+  }
+
+  /**
+   * @brief Extracts the pandas "index_columns" section
+   *
+   * PANDAS adds its own metadata to the key_value section when writing out the
+   * dataframe to a file to aid in exact reconstruction. The JSON-formatted
+   * metadata contains the index column(s) and PANDA-specific datatypes.
+   *
+   * @return comma-separated index column names in quotes
+   */
+  [[nodiscard]] std::string get_pandas_index() const;
+
+  /**
+   * @brief Extracts the column name(s) used for the row indexes in a dataframe
+   *
+   * @param names List of column names to load, where index column name(s) will be added
+   */
+  [[nodiscard]] std::vector<std::string> get_pandas_index_names() const;
+
+  /**
+   * @brief Filters the row groups based on predicate filter
+   *
+   * @param row_group_indices Lists of row groups to read, one per source
+   * @param output_dtypes List of output column datatypes
+   * @param filter AST expression to filter row groups based on Column chunk statistics
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return Filtered row group indices, if any is filtered.
+   */
+  [[nodiscard]] std::optional<std::vector<std::vector<size_type>>> filter_row_groups(
+    host_span<std::vector<size_type> const> row_group_indices,
+    host_span<data_type const> output_dtypes,
+    std::reference_wrapper<ast::expression const> filter,
+    rmm::cuda_stream_view stream) const;
+
+  /**
+   * @brief Filters and reduces down to a selection of row groups
+   *
+   * The input `row_start` and `row_count` parameters will be recomputed and output as the valid
+   * values based on the input row group list.
+   *
+   * @param row_group_indices Lists of row groups to read, one per source
+   * @param row_start Starting row of the selection
+   * @param row_count Total number of rows selected
+   * @param output_dtypes List of output column datatypes
+   * @param filter Optional AST expression to filter row groups based on Column chunk statistics
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return A tuple of corrected row_start, row_count and list of row group indexes and its
+   *         starting row
+   */
+  [[nodiscard]] std::tuple<int64_t, size_type, std::vector<gpu::row_group_info>> select_row_groups(
+    host_span<std::vector<size_type> const> row_group_indices,
+    int64_t row_start,
+    std::optional<size_type> const& row_count,
+    host_span<data_type const> output_dtypes,
+    std::optional<std::reference_wrapper<ast::expression const>> filter,
+    rmm::cuda_stream_view stream) const;
+
+  /**
+   * @brief Filters and reduces down to a selection of columns
+   *
+   * @param use_names List of paths of column names to select; `nullopt` if user did not select
+   * columns to read
+   * @param include_index Whether to always include the PANDAS index column(s)
+   * @param strings_to_categorical Type conversion parameter
+   * @param timestamp_type_id Type conversion parameter
+   *
+   * @return input column information, output column information, list of output column schema
+   * indices
+   */
+  [[nodiscard]] std::
+    tuple<std::vector<input_column_info>, std::vector<inline_column_buffer>, std::vector<size_type>>
+    select_columns(std::optional<std::vector<std::string>> const& use_names,
+                   bool include_index,
+                   bool strings_to_categorical,
+                   type_id timestamp_type_id) const;
+};
+
+/**
+ * @brief Converts named columns to index reference columns
+ *
+ */
+class named_to_reference_converter : public ast::detail::expression_transformer {
+ public:
+  named_to_reference_converter(std::optional<std::reference_wrapper<ast::expression const>> expr,
+                               table_metadata const& metadata)
+    : metadata(metadata)
+  {
+    if (!expr.has_value()) return;
+    // create map for column name.
+    std::transform(
+      thrust::make_zip_iterator(metadata.schema_info.cbegin(),
+                                thrust::counting_iterator<size_t>(0)),
+      thrust::make_zip_iterator(metadata.schema_info.cend(),
+                                thrust::counting_iterator(metadata.schema_info.size())),
+      std::inserter(column_name_to_index, column_name_to_index.end()),
+      [](auto const& name_index) {
+        return std::make_pair(thrust::get<0>(name_index).name, thrust::get<1>(name_index));
+      });
+
+    expr.value().get().accept(*this);
+  }
+
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::literal const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(ast::literal const& expr) override;
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::column_reference const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(ast::column_reference const& expr) override;
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::column_name_reference const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(
+    ast::column_name_reference const& expr) override;
+  /**
+   * @copydoc ast::detail::expression_transformer::visit(ast::operation const& )
+   */
+  std::reference_wrapper<ast::expression const> visit(ast::operation const& expr) override;
+
+  /**
+   * @brief Returns the AST to apply on Column chunk statistics.
+   *
+   * @return AST operation expression
+   */
+  [[nodiscard]] std::optional<std::reference_wrapper<ast::expression const>> get_converted_expr()
+    const
+  {
+    return _stats_expr;
+  }
+
+ private:
+  std::vector<std::reference_wrapper<ast::expression const>> visit_operands(
+    std::vector<std::reference_wrapper<ast::expression const>> operands);
+
+  table_metadata const& metadata;
+  std::unordered_map<std::string, size_type> column_name_to_index;
+  std::optional<std::reference_wrapper<ast::expression const>> _stats_expr;
+  // Using std::list or std::deque to avoid reference invalidation
+  std::list<ast::column_reference> _col_ref;
+  std::list<ast::operation> _operators;
+};
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/reader_impl_preprocess.cu b/cpp/src/io/parquet/reader_impl_preprocess.cu
new file mode 100644
index 0000000..c731c46
--- /dev/null
+++ b/cpp/src/io/parquet/reader_impl_preprocess.cu
@@ -0,0 +1,2017 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "reader_impl.hpp"
+
+#include <io/comp/nvcomp_adapter.hpp>
+#include <io/utilities/config_utils.hpp>
+#include <io/utilities/time_utils.cuh>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/fill.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/iterator_categories.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+#include <thrust/unique.h>
+
+#include <numeric>
+
+namespace cudf::io::detail::parquet {
+namespace {
+
+/**
+ * @brief Generate depth remappings for repetition and definition levels.
+ *
+ * When dealing with columns that contain lists, we must examine incoming
+ * repetition and definition level pairs to determine what range of output nesting
+ * is indicated when adding new values.  This function generates the mappings of
+ * the R/D levels to those start/end bounds
+ *
+ * @param remap Maps column schema index to the R/D remapping vectors for that column
+ * @param src_col_schema The column schema to generate the new mapping for
+ * @param md File metadata information
+ */
+void generate_depth_remappings(std::map<int, std::pair<std::vector<int>, std::vector<int>>>& remap,
+                               int src_col_schema,
+                               aggregate_reader_metadata const& md)
+{
+  // already generated for this level
+  if (remap.find(src_col_schema) != remap.end()) { return; }
+  auto schema   = md.get_schema(src_col_schema);
+  int max_depth = md.get_output_nesting_depth(src_col_schema);
+
+  CUDF_EXPECTS(remap.find(src_col_schema) == remap.end(),
+               "Attempting to remap a schema more than once");
+  auto inserted =
+    remap.insert(std::pair<int, std::pair<std::vector<int>, std::vector<int>>>{src_col_schema, {}});
+  auto& depth_remap = inserted.first->second;
+
+  std::vector<int>& rep_depth_remap = (depth_remap.first);
+  rep_depth_remap.resize(schema.max_repetition_level + 1);
+  std::vector<int>& def_depth_remap = (depth_remap.second);
+  def_depth_remap.resize(schema.max_definition_level + 1);
+
+  // the key:
+  // for incoming level values  R/D
+  // add values starting at the shallowest nesting level X has repetition level R
+  // until you reach the deepest nesting level Y that corresponds to the repetition level R1
+  // held by the nesting level that has definition level D
+  //
+  // Example: a 3 level struct with a list at the bottom
+  //
+  //                     R / D   Depth
+  // level0              0 / 1     0
+  //   level1            0 / 2     1
+  //     level2          0 / 3     2
+  //       list          0 / 3     3
+  //         element     1 / 4     4
+  //
+  // incoming R/D : 0, 0  -> add values from depth 0 to 3   (def level 0 always maps to depth 0)
+  // incoming R/D : 0, 1  -> add values from depth 0 to 3
+  // incoming R/D : 0, 2  -> add values from depth 0 to 3
+  // incoming R/D : 1, 4  -> add values from depth 4 to 4
+  //
+  // Note : the -validity- of values is simply checked by comparing the incoming D value against the
+  // D value of the given nesting level (incoming D >= the D for the nesting level == valid,
+  // otherwise NULL).  The tricky part is determining what nesting levels to add values at.
+  //
+  // For schemas with no repetition level (no lists), X is always 0 and Y is always max nesting
+  // depth.
+  //
+
+  // compute "X" from above
+  for (int s_idx = schema.max_repetition_level; s_idx >= 0; s_idx--) {
+    auto find_shallowest = [&](int r) {
+      int shallowest = -1;
+      int cur_depth  = max_depth - 1;
+      int schema_idx = src_col_schema;
+      while (schema_idx > 0) {
+        auto cur_schema = md.get_schema(schema_idx);
+        if (cur_schema.max_repetition_level == r) {
+          // if this is a repeated field, map it one level deeper
+          shallowest = cur_schema.is_stub() ? cur_depth + 1 : cur_depth;
+        }
+        // if it's one-level encoding list
+        else if (cur_schema.is_one_level_list(md.get_schema(cur_schema.parent_idx))) {
+          shallowest = cur_depth - 1;
+        }
+        if (!cur_schema.is_stub()) { cur_depth--; }
+        schema_idx = cur_schema.parent_idx;
+      }
+      return shallowest;
+    };
+    rep_depth_remap[s_idx] = find_shallowest(s_idx);
+  }
+
+  // compute "Y" from above
+  for (int s_idx = schema.max_definition_level; s_idx >= 0; s_idx--) {
+    auto find_deepest = [&](int d) {
+      SchemaElement prev_schema;
+      int schema_idx = src_col_schema;
+      int r1         = 0;
+      while (schema_idx > 0) {
+        SchemaElement cur_schema = md.get_schema(schema_idx);
+        if (cur_schema.max_definition_level == d) {
+          // if this is a repeated field, map it one level deeper
+          r1 = cur_schema.is_stub() ? prev_schema.max_repetition_level
+                                    : cur_schema.max_repetition_level;
+          break;
+        }
+        prev_schema = cur_schema;
+        schema_idx  = cur_schema.parent_idx;
+      }
+
+      // we now know R1 from above. return the deepest nesting level that has the
+      // same repetition level
+      schema_idx = src_col_schema;
+      int depth  = max_depth - 1;
+      while (schema_idx > 0) {
+        SchemaElement cur_schema = md.get_schema(schema_idx);
+        if (cur_schema.max_repetition_level == r1) {
+          // if this is a repeated field, map it one level deeper
+          depth = cur_schema.is_stub() ? depth + 1 : depth;
+          break;
+        }
+        if (!cur_schema.is_stub()) { depth--; }
+        prev_schema = cur_schema;
+        schema_idx  = cur_schema.parent_idx;
+      }
+      return depth;
+    };
+    def_depth_remap[s_idx] = find_deepest(s_idx);
+  }
+}
+
+/**
+ * @brief Return the required number of bits to store a value.
+ */
+template <typename T = uint8_t>
+[[nodiscard]] T required_bits(uint32_t max_level)
+{
+  return static_cast<T>(CompactProtocolReader::NumRequiredBits(max_level));
+}
+
+/**
+ * @brief Converts cuDF units to Parquet units.
+ *
+ * @return A tuple of Parquet type width, Parquet clock rate and Parquet decimal type.
+ */
+[[nodiscard]] std::tuple<int32_t, int32_t, int8_t> conversion_info(type_id column_type_id,
+                                                                   type_id timestamp_type_id,
+                                                                   parquet::Type physical,
+                                                                   int8_t converted,
+                                                                   int32_t length)
+{
+  int32_t type_width = (physical == parquet::FIXED_LEN_BYTE_ARRAY) ? length : 0;
+  int32_t clock_rate = 0;
+  if (column_type_id == type_id::INT8 or column_type_id == type_id::UINT8) {
+    type_width = 1;  // I32 -> I8
+  } else if (column_type_id == type_id::INT16 or column_type_id == type_id::UINT16) {
+    type_width = 2;  // I32 -> I16
+  } else if (column_type_id == type_id::INT32) {
+    type_width = 4;  // str -> hash32
+  } else if (is_chrono(data_type{column_type_id})) {
+    clock_rate = to_clockrate(timestamp_type_id);
+  }
+
+  int8_t converted_type = converted;
+  if (converted_type == parquet::DECIMAL && column_type_id != type_id::FLOAT64 &&
+      not cudf::is_fixed_point(data_type{column_type_id})) {
+    converted_type = parquet::UNKNOWN;  // Not converting to float64 or decimal
+  }
+  return std::make_tuple(type_width, clock_rate, converted_type);
+}
+
+/**
+ * @brief Reads compressed page data to device memory.
+ *
+ * @param sources Dataset sources
+ * @param page_data Buffers to hold compressed page data for each chunk
+ * @param chunks List of column chunk descriptors
+ * @param begin_chunk Index of first column chunk to read
+ * @param end_chunk Index after the last column chunk to read
+ * @param column_chunk_offsets File offset for all chunks
+ * @param chunk_source_map Association between each column chunk and its source
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return A future object for reading synchronization
+ */
+[[nodiscard]] std::future<void> read_column_chunks_async(
+  std::vector<std::unique_ptr<datasource>> const& sources,
+  std::vector<std::unique_ptr<datasource::buffer>>& page_data,
+  cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc>& chunks,
+  size_t begin_chunk,
+  size_t end_chunk,
+  std::vector<size_t> const& column_chunk_offsets,
+  std::vector<size_type> const& chunk_source_map,
+  rmm::cuda_stream_view stream)
+{
+  // Transfer chunk data, coalescing adjacent chunks
+  std::vector<std::future<size_t>> read_tasks;
+  for (size_t chunk = begin_chunk; chunk < end_chunk;) {
+    size_t const io_offset   = column_chunk_offsets[chunk];
+    size_t io_size           = chunks[chunk].compressed_size;
+    size_t next_chunk        = chunk + 1;
+    bool const is_compressed = (chunks[chunk].codec != parquet::Compression::UNCOMPRESSED);
+    while (next_chunk < end_chunk) {
+      size_t const next_offset = column_chunk_offsets[next_chunk];
+      bool const is_next_compressed =
+        (chunks[next_chunk].codec != parquet::Compression::UNCOMPRESSED);
+      if (next_offset != io_offset + io_size || is_next_compressed != is_compressed ||
+          chunk_source_map[chunk] != chunk_source_map[next_chunk]) {
+        // Can't merge if not contiguous or mixing compressed and uncompressed
+        // Not coalescing uncompressed with compressed chunks is so that compressed buffers can be
+        // freed earlier (immediately after decompression stage) to limit peak memory requirements
+        break;
+      }
+      io_size += chunks[next_chunk].compressed_size;
+      next_chunk++;
+    }
+    if (io_size != 0) {
+      auto& source = sources[chunk_source_map[chunk]];
+      if (source->is_device_read_preferred(io_size)) {
+        // Buffer needs to be padded.
+        // Required by `gpuDecodePageData`.
+        auto buffer =
+          rmm::device_buffer(cudf::util::round_up_safe(io_size, BUFFER_PADDING_MULTIPLE), stream);
+        auto fut_read_size = source->device_read_async(
+          io_offset, io_size, static_cast<uint8_t*>(buffer.data()), stream);
+        read_tasks.emplace_back(std::move(fut_read_size));
+        page_data[chunk] = datasource::buffer::create(std::move(buffer));
+      } else {
+        auto const read_buffer = source->host_read(io_offset, io_size);
+        // Buffer needs to be padded.
+        // Required by `gpuDecodePageData`.
+        auto tmp_buffer = rmm::device_buffer(
+          cudf::util::round_up_safe(read_buffer->size(), BUFFER_PADDING_MULTIPLE), stream);
+        CUDF_CUDA_TRY(cudaMemcpyAsync(
+          tmp_buffer.data(), read_buffer->data(), read_buffer->size(), cudaMemcpyDefault, stream));
+        page_data[chunk] = datasource::buffer::create(std::move(tmp_buffer));
+      }
+      auto d_compdata = page_data[chunk]->data();
+      do {
+        chunks[chunk].compressed_data = d_compdata;
+        d_compdata += chunks[chunk].compressed_size;
+      } while (++chunk != next_chunk);
+    } else {
+      chunk = next_chunk;
+    }
+  }
+  auto sync_fn = [](decltype(read_tasks) read_tasks) {
+    for (auto& task : read_tasks) {
+      task.wait();
+    }
+  };
+  return std::async(std::launch::deferred, sync_fn, std::move(read_tasks));
+}
+
+/**
+ * @brief Return the number of total pages from the given column chunks.
+ *
+ * @param chunks List of column chunk descriptors
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return The total number of pages
+ */
+[[nodiscard]] size_t count_page_headers(
+  cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc>& chunks, rmm::cuda_stream_view stream)
+{
+  size_t total_pages = 0;
+
+  chunks.host_to_device_async(stream);
+  gpu::DecodePageHeaders(chunks.device_ptr(), chunks.size(), stream);
+  chunks.device_to_host_sync(stream);
+
+  for (size_t c = 0; c < chunks.size(); c++) {
+    total_pages += chunks[c].num_data_pages + chunks[c].num_dict_pages;
+  }
+
+  return total_pages;
+}
+
+// see setupLocalPageInfo() in page_data.cu for supported page encodings
+constexpr bool is_supported_encoding(Encoding enc)
+{
+  switch (enc) {
+    case Encoding::PLAIN:
+    case Encoding::PLAIN_DICTIONARY:
+    case Encoding::RLE:
+    case Encoding::RLE_DICTIONARY:
+    case Encoding::DELTA_BINARY_PACKED: return true;
+    default: return false;
+  }
+}
+
+/**
+ * @brief Decode the page information from the given column chunks.
+ *
+ * @param chunks List of column chunk descriptors
+ * @param pages List of page information
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @returns The size in bytes of level type data required
+ */
+int decode_page_headers(cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc>& chunks,
+                        cudf::detail::hostdevice_vector<gpu::PageInfo>& pages,
+                        rmm::cuda_stream_view stream)
+{
+  // IMPORTANT : if you change how pages are stored within a chunk (dist pages, then data pages),
+  // please update preprocess_nested_columns to reflect this.
+  for (size_t c = 0, page_count = 0; c < chunks.size(); c++) {
+    chunks[c].max_num_pages = chunks[c].num_data_pages + chunks[c].num_dict_pages;
+    chunks[c].page_info     = pages.device_ptr(page_count);
+    page_count += chunks[c].max_num_pages;
+  }
+
+  chunks.host_to_device_async(stream);
+  gpu::DecodePageHeaders(chunks.device_ptr(), chunks.size(), stream);
+
+  // compute max bytes needed for level data
+  auto level_bit_size =
+    cudf::detail::make_counting_transform_iterator(0, [chunks = chunks.begin()] __device__(int i) {
+      auto c = chunks[i];
+      return static_cast<int>(
+        max(c.level_bits[gpu::level_type::REPETITION], c.level_bits[gpu::level_type::DEFINITION]));
+    });
+  // max level data bit size.
+  int const max_level_bits   = thrust::reduce(rmm::exec_policy(stream),
+                                            level_bit_size,
+                                            level_bit_size + chunks.size(),
+                                            0,
+                                            thrust::maximum<int>());
+  auto const level_type_size = std::max(1, cudf::util::div_rounding_up_safe(max_level_bits, 8));
+
+  pages.device_to_host_sync(stream);
+
+  // validate page encodings
+  CUDF_EXPECTS(std::all_of(pages.begin(),
+                           pages.end(),
+                           [](auto const& page) { return is_supported_encoding(page.encoding); }),
+               "Unsupported page encoding detected");
+
+  return level_type_size;
+}
+
+/**
+ * @brief Decompresses the page data, at page granularity.
+ *
+ * @param chunks List of column chunk descriptors
+ * @param pages List of page information
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return Device buffer to decompressed page data
+ */
+[[nodiscard]] rmm::device_buffer decompress_page_data(
+  cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc>& chunks,
+  cudf::detail::hostdevice_vector<gpu::PageInfo>& pages,
+  rmm::cuda_stream_view stream)
+{
+  auto for_each_codec_page = [&](parquet::Compression codec, std::function<void(size_t)> const& f) {
+    for (size_t c = 0, page_count = 0; c < chunks.size(); c++) {
+      const auto page_stride = chunks[c].max_num_pages;
+      if (chunks[c].codec == codec) {
+        for (int k = 0; k < page_stride; k++) {
+          f(page_count + k);
+        }
+      }
+      page_count += page_stride;
+    }
+  };
+
+  // Brotli scratch memory for decompressing
+  rmm::device_buffer debrotli_scratch;
+
+  // Count the exact number of compressed pages
+  size_t num_comp_pages    = 0;
+  size_t total_decomp_size = 0;
+
+  struct codec_stats {
+    parquet::Compression compression_type = UNCOMPRESSED;
+    size_t num_pages                      = 0;
+    int32_t max_decompressed_size         = 0;
+    size_t total_decomp_size              = 0;
+  };
+
+  std::array codecs{codec_stats{parquet::GZIP},
+                    codec_stats{parquet::SNAPPY},
+                    codec_stats{parquet::BROTLI},
+                    codec_stats{parquet::ZSTD}};
+
+  auto is_codec_supported = [&codecs](int8_t codec) {
+    if (codec == parquet::UNCOMPRESSED) return true;
+    return std::find_if(codecs.begin(), codecs.end(), [codec](auto& cstats) {
+             return codec == cstats.compression_type;
+           }) != codecs.end();
+  };
+  CUDF_EXPECTS(std::all_of(chunks.begin(),
+                           chunks.end(),
+                           [&is_codec_supported](auto const& chunk) {
+                             return is_codec_supported(chunk.codec);
+                           }),
+               "Unsupported compression type");
+
+  for (auto& codec : codecs) {
+    for_each_codec_page(codec.compression_type, [&](size_t page) {
+      auto page_uncomp_size = pages[page].uncompressed_page_size;
+      total_decomp_size += page_uncomp_size;
+      codec.total_decomp_size += page_uncomp_size;
+      codec.max_decompressed_size = std::max(codec.max_decompressed_size, page_uncomp_size);
+      codec.num_pages++;
+      num_comp_pages++;
+    });
+    if (codec.compression_type == parquet::BROTLI && codec.num_pages > 0) {
+      debrotli_scratch.resize(get_gpu_debrotli_scratch_size(codec.num_pages), stream);
+    }
+  }
+
+  // Dispatch batches of pages to decompress for each codec.
+  // Buffer needs to be padded, required by `gpuDecodePageData`.
+  rmm::device_buffer decomp_pages(
+    cudf::util::round_up_safe(total_decomp_size, BUFFER_PADDING_MULTIPLE), stream);
+
+  std::vector<device_span<uint8_t const>> comp_in;
+  comp_in.reserve(num_comp_pages);
+  std::vector<device_span<uint8_t>> comp_out;
+  comp_out.reserve(num_comp_pages);
+
+  // vectors to save v2 def and rep level data, if any
+  std::vector<device_span<uint8_t const>> copy_in;
+  copy_in.reserve(num_comp_pages);
+  std::vector<device_span<uint8_t>> copy_out;
+  copy_out.reserve(num_comp_pages);
+
+  rmm::device_uvector<compression_result> comp_res(num_comp_pages, stream);
+  thrust::fill(rmm::exec_policy(stream),
+               comp_res.begin(),
+               comp_res.end(),
+               compression_result{0, compression_status::FAILURE});
+
+  size_t decomp_offset = 0;
+  int32_t start_pos    = 0;
+  for (auto const& codec : codecs) {
+    if (codec.num_pages == 0) { continue; }
+
+    for_each_codec_page(codec.compression_type, [&](size_t page_idx) {
+      auto const dst_base = static_cast<uint8_t*>(decomp_pages.data()) + decomp_offset;
+      auto& page          = pages[page_idx];
+      // offset will only be non-zero for V2 pages
+      auto const offset =
+        page.lvl_bytes[gpu::level_type::DEFINITION] + page.lvl_bytes[gpu::level_type::REPETITION];
+      // for V2 need to copy def and rep level info into place, and then offset the
+      // input and output buffers. otherwise we'd have to keep both the compressed
+      // and decompressed data.
+      if (offset != 0) {
+        copy_in.emplace_back(page.page_data, offset);
+        copy_out.emplace_back(dst_base, offset);
+      }
+      comp_in.emplace_back(page.page_data + offset,
+                           static_cast<size_t>(page.compressed_page_size - offset));
+      comp_out.emplace_back(dst_base + offset,
+                            static_cast<size_t>(page.uncompressed_page_size - offset));
+      page.page_data = dst_base;
+      decomp_offset += page.uncompressed_page_size;
+    });
+
+    host_span<device_span<uint8_t const> const> comp_in_view{comp_in.data() + start_pos,
+                                                             codec.num_pages};
+    auto const d_comp_in = cudf::detail::make_device_uvector_async(
+      comp_in_view, stream, rmm::mr::get_current_device_resource());
+    host_span<device_span<uint8_t> const> comp_out_view(comp_out.data() + start_pos,
+                                                        codec.num_pages);
+    auto const d_comp_out = cudf::detail::make_device_uvector_async(
+      comp_out_view, stream, rmm::mr::get_current_device_resource());
+    device_span<compression_result> d_comp_res_view(comp_res.data() + start_pos, codec.num_pages);
+
+    switch (codec.compression_type) {
+      case parquet::GZIP:
+        gpuinflate(d_comp_in, d_comp_out, d_comp_res_view, gzip_header_included::YES, stream);
+        break;
+      case parquet::SNAPPY:
+        if (nvcomp_integration::is_stable_enabled()) {
+          nvcomp::batched_decompress(nvcomp::compression_type::SNAPPY,
+                                     d_comp_in,
+                                     d_comp_out,
+                                     d_comp_res_view,
+                                     codec.max_decompressed_size,
+                                     codec.total_decomp_size,
+                                     stream);
+        } else {
+          gpu_unsnap(d_comp_in, d_comp_out, d_comp_res_view, stream);
+        }
+        break;
+      case parquet::ZSTD:
+        nvcomp::batched_decompress(nvcomp::compression_type::ZSTD,
+                                   d_comp_in,
+                                   d_comp_out,
+                                   d_comp_res_view,
+                                   codec.max_decompressed_size,
+                                   codec.total_decomp_size,
+                                   stream);
+        break;
+      case parquet::BROTLI:
+        gpu_debrotli(d_comp_in,
+                     d_comp_out,
+                     d_comp_res_view,
+                     debrotli_scratch.data(),
+                     debrotli_scratch.size(),
+                     stream);
+        break;
+      default: CUDF_FAIL("Unexpected decompression dispatch"); break;
+    }
+    start_pos += codec.num_pages;
+  }
+
+  CUDF_EXPECTS(thrust::all_of(rmm::exec_policy(stream),
+                              comp_res.begin(),
+                              comp_res.end(),
+                              [] __device__(auto const& res) {
+                                return res.status == compression_status::SUCCESS;
+                              }),
+               "Error during decompression");
+
+  // now copy the uncompressed V2 def and rep level data
+  if (not copy_in.empty()) {
+    auto const d_copy_in = cudf::detail::make_device_uvector_async(
+      copy_in, stream, rmm::mr::get_current_device_resource());
+    auto const d_copy_out = cudf::detail::make_device_uvector_async(
+      copy_out, stream, rmm::mr::get_current_device_resource());
+
+    gpu_copy_uncompressed_blocks(d_copy_in, d_copy_out, stream);
+    stream.synchronize();
+  }
+
+  // Update the page information in device memory with the updated value of
+  // page_data; it now points to the uncompressed data buffer
+  pages.host_to_device_async(stream);
+
+  return decomp_pages;
+}
+
+}  // namespace
+
+void reader::impl::allocate_nesting_info()
+{
+  auto const& chunks             = _pass_itm_data->chunks;
+  auto& pages                    = _pass_itm_data->pages_info;
+  auto& page_nesting_info        = _pass_itm_data->page_nesting_info;
+  auto& page_nesting_decode_info = _pass_itm_data->page_nesting_decode_info;
+
+  // compute total # of page_nesting infos needed and allocate space. doing this in one
+  // buffer to keep it to a single gpu allocation
+  size_t const total_page_nesting_infos = std::accumulate(
+    chunks.host_ptr(), chunks.host_ptr() + chunks.size(), 0, [&](int total, auto& chunk) {
+      // the schema of the input column
+      auto const& schema                    = _metadata->get_schema(chunk.src_col_schema);
+      auto const per_page_nesting_info_size = max(
+        schema.max_definition_level + 1, _metadata->get_output_nesting_depth(chunk.src_col_schema));
+      return total + (per_page_nesting_info_size * chunk.num_data_pages);
+    });
+
+  page_nesting_info =
+    cudf::detail::hostdevice_vector<gpu::PageNestingInfo>{total_page_nesting_infos, _stream};
+  page_nesting_decode_info =
+    cudf::detail::hostdevice_vector<gpu::PageNestingDecodeInfo>{total_page_nesting_infos, _stream};
+
+  // update pointers in the PageInfos
+  int target_page_index = 0;
+  int src_info_index    = 0;
+  for (size_t idx = 0; idx < chunks.size(); idx++) {
+    int src_col_schema                    = chunks[idx].src_col_schema;
+    auto& schema                          = _metadata->get_schema(src_col_schema);
+    auto const per_page_nesting_info_size = std::max(
+      schema.max_definition_level + 1, _metadata->get_output_nesting_depth(src_col_schema));
+
+    // skip my dict pages
+    target_page_index += chunks[idx].num_dict_pages;
+    for (int p_idx = 0; p_idx < chunks[idx].num_data_pages; p_idx++) {
+      pages[target_page_index + p_idx].nesting = page_nesting_info.device_ptr() + src_info_index;
+      pages[target_page_index + p_idx].nesting_decode =
+        page_nesting_decode_info.device_ptr() + src_info_index;
+
+      pages[target_page_index + p_idx].nesting_info_size = per_page_nesting_info_size;
+      pages[target_page_index + p_idx].num_output_nesting_levels =
+        _metadata->get_output_nesting_depth(src_col_schema);
+
+      src_info_index += per_page_nesting_info_size;
+    }
+    target_page_index += chunks[idx].num_data_pages;
+  }
+
+  // fill in
+  int nesting_info_index = 0;
+  std::map<int, std::pair<std::vector<int>, std::vector<int>>> depth_remapping;
+  for (size_t idx = 0; idx < chunks.size(); idx++) {
+    int src_col_schema = chunks[idx].src_col_schema;
+
+    // schema of the input column
+    auto& schema = _metadata->get_schema(src_col_schema);
+    // real depth of the output cudf column hierarchy (1 == no nesting, 2 == 1 level, etc)
+    int max_depth = _metadata->get_output_nesting_depth(src_col_schema);
+
+    // # of nesting infos stored per page for this column
+    auto const per_page_nesting_info_size = std::max(schema.max_definition_level + 1, max_depth);
+
+    // if this column has lists, generate depth remapping
+    std::map<int, std::pair<std::vector<int>, std::vector<int>>> depth_remapping;
+    if (schema.max_repetition_level > 0) {
+      generate_depth_remappings(depth_remapping, src_col_schema, *_metadata);
+    }
+
+    // fill in host-side nesting info
+    int schema_idx  = src_col_schema;
+    auto cur_schema = _metadata->get_schema(schema_idx);
+    int cur_depth   = max_depth - 1;
+    while (schema_idx > 0) {
+      // stub columns (basically the inner field of a list scheme element) are not real columns.
+      // we can ignore them for the purposes of output nesting info
+      if (!cur_schema.is_stub()) {
+        // initialize each page within the chunk
+        for (int p_idx = 0; p_idx < chunks[idx].num_data_pages; p_idx++) {
+          gpu::PageNestingInfo* pni =
+            &page_nesting_info[nesting_info_index + (p_idx * per_page_nesting_info_size)];
+
+          gpu::PageNestingDecodeInfo* nesting_info =
+            &page_nesting_decode_info[nesting_info_index + (p_idx * per_page_nesting_info_size)];
+
+          // if we have lists, set our start and end depth remappings
+          if (schema.max_repetition_level > 0) {
+            auto remap = depth_remapping.find(src_col_schema);
+            CUDF_EXPECTS(remap != depth_remapping.end(),
+                         "Could not find depth remapping for schema");
+            std::vector<int> const& rep_depth_remap = (remap->second.first);
+            std::vector<int> const& def_depth_remap = (remap->second.second);
+
+            for (size_t m = 0; m < rep_depth_remap.size(); m++) {
+              nesting_info[m].start_depth = rep_depth_remap[m];
+            }
+            for (size_t m = 0; m < def_depth_remap.size(); m++) {
+              nesting_info[m].end_depth = def_depth_remap[m];
+            }
+          }
+
+          // values indexed by output column index
+          nesting_info[cur_depth].max_def_level = cur_schema.max_definition_level;
+          pni[cur_depth].size                   = 0;
+          pni[cur_depth].type =
+            to_type_id(cur_schema, _strings_to_categorical, _timestamp_type.id());
+          pni[cur_depth].nullable = cur_schema.repetition_type == OPTIONAL;
+        }
+
+        // move up the hierarchy
+        cur_depth--;
+      }
+
+      // next schema
+      schema_idx = cur_schema.parent_idx;
+      cur_schema = _metadata->get_schema(schema_idx);
+    }
+
+    nesting_info_index += (per_page_nesting_info_size * chunks[idx].num_data_pages);
+  }
+
+  // copy nesting info to the device
+  page_nesting_info.host_to_device_async(_stream);
+  page_nesting_decode_info.host_to_device_async(_stream);
+}
+
+void reader::impl::allocate_level_decode_space()
+{
+  auto& pages = _pass_itm_data->pages_info;
+
+  // TODO: this could be made smaller if we ignored dictionary pages and pages with no
+  // repetition data.
+  size_t const per_page_decode_buf_size =
+    LEVEL_DECODE_BUF_SIZE * 2 * _pass_itm_data->level_type_size;
+  auto const decode_buf_size = per_page_decode_buf_size * pages.size();
+  _pass_itm_data->level_decode_data =
+    rmm::device_buffer(decode_buf_size, _stream, rmm::mr::get_current_device_resource());
+
+  // distribute the buffers
+  uint8_t* buf = static_cast<uint8_t*>(_pass_itm_data->level_decode_data.data());
+  for (size_t idx = 0; idx < pages.size(); idx++) {
+    auto& p = pages[idx];
+
+    p.lvl_decode_buf[gpu::level_type::DEFINITION] = buf;
+    buf += (LEVEL_DECODE_BUF_SIZE * _pass_itm_data->level_type_size);
+    p.lvl_decode_buf[gpu::level_type::REPETITION] = buf;
+    buf += (LEVEL_DECODE_BUF_SIZE * _pass_itm_data->level_type_size);
+  }
+}
+
+std::pair<bool, std::vector<std::future<void>>> reader::impl::read_and_decompress_column_chunks()
+{
+  auto const& row_groups_info = _pass_itm_data->row_groups;
+  auto const num_rows         = _pass_itm_data->num_rows;
+
+  auto& raw_page_data = _pass_itm_data->raw_page_data;
+  auto& chunks        = _pass_itm_data->chunks;
+
+  // Descriptors for all the chunks that make up the selected columns
+  auto const num_input_columns = _input_columns.size();
+  auto const num_chunks        = row_groups_info.size() * num_input_columns;
+
+  // Association between each column chunk and its source
+  std::vector<size_type> chunk_source_map(num_chunks);
+
+  // Tracker for eventually deallocating compressed and uncompressed data
+  raw_page_data = std::vector<std::unique_ptr<datasource::buffer>>(num_chunks);
+
+  // Keep track of column chunk file offsets
+  std::vector<size_t> column_chunk_offsets(num_chunks);
+
+  // Initialize column chunk information
+  size_t total_decompressed_size = 0;
+  auto remaining_rows            = num_rows;
+  std::vector<std::future<void>> read_chunk_tasks;
+  size_type chunk_count = 0;
+  for (auto const& rg : row_groups_info) {
+    auto const& row_group       = _metadata->get_row_group(rg.index, rg.source_index);
+    auto const row_group_source = rg.source_index;
+    auto const row_group_rows   = std::min<int>(remaining_rows, row_group.num_rows);
+
+    // generate ColumnChunkDesc objects for everything to be decoded (all input columns)
+    for (size_t i = 0; i < num_input_columns; ++i) {
+      auto const& col = _input_columns[i];
+      // look up metadata
+      auto& col_meta = _metadata->get_column_metadata(rg.index, rg.source_index, col.schema_idx);
+
+      column_chunk_offsets[chunk_count] =
+        (col_meta.dictionary_page_offset != 0)
+          ? std::min(col_meta.data_page_offset, col_meta.dictionary_page_offset)
+          : col_meta.data_page_offset;
+
+      // Map each column chunk to its column index and its source index
+      chunk_source_map[chunk_count] = row_group_source;
+
+      if (col_meta.codec != Compression::UNCOMPRESSED) {
+        total_decompressed_size += col_meta.total_uncompressed_size;
+      }
+
+      chunk_count++;
+    }
+    remaining_rows -= row_group_rows;
+  }
+
+  // Read compressed chunk data to device memory
+  read_chunk_tasks.push_back(read_column_chunks_async(_sources,
+                                                      raw_page_data,
+                                                      chunks,
+                                                      0,
+                                                      chunks.size(),
+                                                      column_chunk_offsets,
+                                                      chunk_source_map,
+                                                      _stream));
+
+  CUDF_EXPECTS(remaining_rows == 0, "All rows data must be read.");
+
+  return {total_decompressed_size > 0, std::move(read_chunk_tasks)};
+}
+
+void reader::impl::load_global_chunk_info()
+{
+  auto const num_rows         = _file_itm_data.global_num_rows;
+  auto const& row_groups_info = _file_itm_data.row_groups;
+  auto& chunks                = _file_itm_data.chunks;
+
+  // Descriptors for all the chunks that make up the selected columns
+  auto const num_input_columns = _input_columns.size();
+  auto const num_chunks        = row_groups_info.size() * num_input_columns;
+
+  // Initialize column chunk information
+  auto remaining_rows = num_rows;
+  for (auto const& rg : row_groups_info) {
+    auto const& row_group      = _metadata->get_row_group(rg.index, rg.source_index);
+    auto const row_group_start = rg.start_row;
+    auto const row_group_rows  = std::min<int>(remaining_rows, row_group.num_rows);
+
+    // generate ColumnChunkDesc objects for everything to be decoded (all input columns)
+    for (size_t i = 0; i < num_input_columns; ++i) {
+      auto col = _input_columns[i];
+      // look up metadata
+      auto& col_meta = _metadata->get_column_metadata(rg.index, rg.source_index, col.schema_idx);
+      auto& schema   = _metadata->get_schema(col.schema_idx);
+
+      auto [type_width, clock_rate, converted_type] =
+        conversion_info(to_type_id(schema, _strings_to_categorical, _timestamp_type.id()),
+                        _timestamp_type.id(),
+                        schema.type,
+                        schema.converted_type,
+                        schema.type_length);
+
+      chunks.push_back(gpu::ColumnChunkDesc(col_meta.total_compressed_size,
+                                            nullptr,
+                                            col_meta.num_values,
+                                            schema.type,
+                                            type_width,
+                                            row_group_start,
+                                            row_group_rows,
+                                            schema.max_definition_level,
+                                            schema.max_repetition_level,
+                                            _metadata->get_output_nesting_depth(col.schema_idx),
+                                            required_bits(schema.max_definition_level),
+                                            required_bits(schema.max_repetition_level),
+                                            col_meta.codec,
+                                            converted_type,
+                                            schema.logical_type,
+                                            schema.decimal_precision,
+                                            clock_rate,
+                                            i,
+                                            col.schema_idx));
+    }
+
+    remaining_rows -= row_group_rows;
+  }
+}
+
+void reader::impl::compute_input_pass_row_group_info()
+{
+  // at this point, row_groups has already been filtered down to just the row groups we need to
+  // handle optional skip_rows/num_rows parameters.
+  auto const& row_groups_info = _file_itm_data.row_groups;
+
+  // if the user hasn't specified an input size limit, read everything in a single pass.
+  if (_input_pass_read_limit == 0) {
+    _input_pass_row_group_offsets.push_back(0);
+    _input_pass_row_group_offsets.push_back(row_groups_info.size());
+    return;
+  }
+
+  // generate passes. make sure to account for the case where a single row group doesn't fit within
+  //
+  std::size_t const read_limit =
+    _input_pass_read_limit > 0 ? _input_pass_read_limit : std::numeric_limits<std::size_t>::max();
+  std::size_t cur_pass_byte_size = 0;
+  std::size_t cur_rg_start       = 0;
+  std::size_t cur_row_count      = 0;
+  _input_pass_row_group_offsets.push_back(0);
+  _input_pass_row_count.push_back(0);
+
+  for (size_t cur_rg_index = 0; cur_rg_index < row_groups_info.size(); cur_rg_index++) {
+    auto const& rgi       = row_groups_info[cur_rg_index];
+    auto const& row_group = _metadata->get_row_group(rgi.index, rgi.source_index);
+
+    // can we add this row group
+    if (cur_pass_byte_size + row_group.total_byte_size >= read_limit) {
+      // A single row group (the current one) is larger than the read limit:
+      // We always need to include at least one row group, so end the pass at the end of the current
+      // row group
+      if (cur_rg_start == cur_rg_index) {
+        _input_pass_row_group_offsets.push_back(cur_rg_index + 1);
+        _input_pass_row_count.push_back(cur_row_count + row_group.num_rows);
+        cur_rg_start       = cur_rg_index + 1;
+        cur_pass_byte_size = 0;
+      }
+      // End the pass at the end of the previous row group
+      else {
+        _input_pass_row_group_offsets.push_back(cur_rg_index);
+        _input_pass_row_count.push_back(cur_row_count);
+        cur_rg_start       = cur_rg_index;
+        cur_pass_byte_size = row_group.total_byte_size;
+      }
+    } else {
+      cur_pass_byte_size += row_group.total_byte_size;
+    }
+    cur_row_count += row_group.num_rows;
+  }
+  // add the last pass if necessary
+  if (_input_pass_row_group_offsets.back() != row_groups_info.size()) {
+    _input_pass_row_group_offsets.push_back(row_groups_info.size());
+    _input_pass_row_count.push_back(cur_row_count);
+  }
+}
+
+void reader::impl::setup_pass()
+{
+  // this will also cause the previous pass information to be deleted
+  _pass_itm_data = std::make_unique<cudf::io::parquet::gpu::pass_intermediate_data>();
+
+  // setup row groups to be loaded for this pass
+  auto const row_group_start = _input_pass_row_group_offsets[_current_input_pass];
+  auto const row_group_end   = _input_pass_row_group_offsets[_current_input_pass + 1];
+  auto const num_row_groups  = row_group_end - row_group_start;
+  _pass_itm_data->row_groups.resize(num_row_groups);
+  std::copy(_file_itm_data.row_groups.begin() + row_group_start,
+            _file_itm_data.row_groups.begin() + row_group_end,
+            _pass_itm_data->row_groups.begin());
+
+  auto const num_passes = _input_pass_row_group_offsets.size() - 1;
+  CUDF_EXPECTS(_current_input_pass < num_passes, "Encountered an invalid read pass index");
+
+  auto const chunks_per_rowgroup = _input_columns.size();
+  auto const num_chunks          = chunks_per_rowgroup * num_row_groups;
+
+  auto chunk_start = _file_itm_data.chunks.begin() + (row_group_start * chunks_per_rowgroup);
+  auto chunk_end   = _file_itm_data.chunks.begin() + (row_group_end * chunks_per_rowgroup);
+
+  _pass_itm_data->chunks =
+    cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc>(num_chunks, _stream);
+  std::copy(chunk_start, chunk_end, _pass_itm_data->chunks.begin());
+
+  // adjust skip_rows and num_rows by what's available in the row groups we are processing
+  if (num_passes == 1) {
+    _pass_itm_data->skip_rows = _file_itm_data.global_skip_rows;
+    _pass_itm_data->num_rows  = _file_itm_data.global_num_rows;
+  } else {
+    auto const global_start_row = _file_itm_data.global_skip_rows;
+    auto const global_end_row   = global_start_row + _file_itm_data.global_num_rows;
+    auto const start_row = std::max(_input_pass_row_count[_current_input_pass], global_start_row);
+    auto const end_row   = std::min(_input_pass_row_count[_current_input_pass + 1], global_end_row);
+
+    // skip_rows is always global in the sense that it is relative to the first row of
+    // everything we will be reading, regardless of what pass we are on.
+    // num_rows is how many rows we are reading this pass.
+    _pass_itm_data->skip_rows = global_start_row + _input_pass_row_count[_current_input_pass];
+    _pass_itm_data->num_rows  = end_row - start_row;
+  }
+}
+
+void reader::impl::load_and_decompress_data()
+{
+  // This function should never be called if `num_rows == 0`.
+  CUDF_EXPECTS(_pass_itm_data->num_rows > 0, "Number of reading rows must not be zero.");
+
+  auto& raw_page_data    = _pass_itm_data->raw_page_data;
+  auto& decomp_page_data = _pass_itm_data->decomp_page_data;
+  auto& chunks           = _pass_itm_data->chunks;
+  auto& pages            = _pass_itm_data->pages_info;
+
+  auto const [has_compressed_data, read_chunks_tasks] = read_and_decompress_column_chunks();
+
+  for (auto& task : read_chunks_tasks) {
+    task.wait();
+  }
+
+  // Process dataset chunk pages into output columns
+  auto const total_pages = count_page_headers(chunks, _stream);
+  if (total_pages <= 0) { return; }
+  pages = cudf::detail::hostdevice_vector<gpu::PageInfo>(total_pages, total_pages, _stream);
+
+  // decoding of column/page information
+  _pass_itm_data->level_type_size = decode_page_headers(chunks, pages, _stream);
+  if (has_compressed_data) {
+    decomp_page_data = decompress_page_data(chunks, pages, _stream);
+    // Free compressed data
+    for (size_t c = 0; c < chunks.size(); c++) {
+      if (chunks[c].codec != parquet::Compression::UNCOMPRESSED) { raw_page_data[c].reset(); }
+    }
+  }
+
+  // build output column info
+  // walk the schema, building out_buffers that mirror what our final cudf columns will look
+  // like. important : there is not necessarily a 1:1 mapping between input columns and output
+  // columns. For example, parquet does not explicitly store a ColumnChunkDesc for struct
+  // columns. The "structiness" is simply implied by the schema.  For example, this schema:
+  //  required group field_id=1 name {
+  //    required binary field_id=2 firstname (String);
+  //    required binary field_id=3 middlename (String);
+  //    required binary field_id=4 lastname (String);
+  // }
+  // will only contain 3 columns of data (firstname, middlename, lastname).  But of course
+  // "name" is a struct column that we want to return, so we have to make sure that we
+  // create it ourselves.
+  // std::vector<output_column_info> output_info = build_output_column_info();
+
+  // the following two allocate functions modify the page data
+  pages.device_to_host_sync(_stream);
+  {
+    // nesting information (sizes, etc) stored -per page-
+    // note : even for flat schemas, we allocate 1 level of "nesting" info
+    allocate_nesting_info();
+
+    // level decode space
+    allocate_level_decode_space();
+  }
+  pages.host_to_device_async(_stream);
+}
+
+namespace {
+
+struct cumulative_row_info {
+  size_t row_count;   // cumulative row count
+  size_t size_bytes;  // cumulative size in bytes
+  int key;            // schema index
+};
+
+#if defined(PREPROCESS_DEBUG)
+void print_pages(cudf::detail::hostdevice_vector<gpu::PageInfo>& pages,
+                 rmm::cuda_stream_view _stream)
+{
+  pages.device_to_host_sync(_stream);
+  for (size_t idx = 0; idx < pages.size(); idx++) {
+    auto const& p = pages[idx];
+    // skip dictionary pages
+    if (p.flags & gpu::PAGEINFO_FLAGS_DICTIONARY) { continue; }
+    printf(
+      "P(%lu, s:%d): chunk_row(%d), num_rows(%d), skipped_values(%d), skipped_leaf_values(%d), "
+      "str_bytes(%d)\n",
+      idx,
+      p.src_col_schema,
+      p.chunk_row,
+      p.num_rows,
+      p.skipped_values,
+      p.skipped_leaf_values,
+      p.str_bytes);
+  }
+}
+
+void print_cumulative_page_info(cudf::detail::hostdevice_vector<gpu::PageInfo>& pages,
+                                rmm::device_uvector<int32_t> const& page_index,
+                                rmm::device_uvector<cumulative_row_info> const& c_info,
+                                rmm::cuda_stream_view stream)
+{
+  pages.device_to_host_sync(stream);
+
+  printf("------------\nCumulative sizes by page\n");
+
+  std::vector<int> schemas(pages.size());
+  std::vector<int> h_page_index(pages.size());
+  CUDF_CUDA_TRY(cudaMemcpy(
+    h_page_index.data(), page_index.data(), sizeof(int) * pages.size(), cudaMemcpyDefault));
+  std::vector<cumulative_row_info> h_cinfo(pages.size());
+  CUDF_CUDA_TRY(cudaMemcpy(
+    h_cinfo.data(), c_info.data(), sizeof(cumulative_row_info) * pages.size(), cudaMemcpyDefault));
+  auto schema_iter = cudf::detail::make_counting_transform_iterator(
+    0, [&](size_type i) { return pages[h_page_index[i]].src_col_schema; });
+  thrust::copy(thrust::seq, schema_iter, schema_iter + pages.size(), schemas.begin());
+  auto last = thrust::unique(thrust::seq, schemas.begin(), schemas.end());
+  schemas.resize(last - schemas.begin());
+  printf("Num schemas: %lu\n", schemas.size());
+
+  for (size_t idx = 0; idx < schemas.size(); idx++) {
+    printf("Schema %d\n", schemas[idx]);
+    for (size_t pidx = 0; pidx < pages.size(); pidx++) {
+      auto const& page = pages[h_page_index[pidx]];
+      if (page.flags & gpu::PAGEINFO_FLAGS_DICTIONARY || page.src_col_schema != schemas[idx]) {
+        continue;
+      }
+      printf("\tP: {%lu, %lu}\n", h_cinfo[pidx].row_count, h_cinfo[pidx].size_bytes);
+    }
+  }
+}
+
+void print_cumulative_row_info(
+  host_span<cumulative_row_info const> sizes,
+  std::string const& label,
+  std::optional<std::vector<gpu::chunk_read_info>> splits = std::nullopt)
+{
+  if (splits.has_value()) {
+    printf("------------\nSplits\n");
+    for (size_t idx = 0; idx < splits->size(); idx++) {
+      printf("{%lu, %lu}\n", splits.value()[idx].skip_rows, splits.value()[idx].num_rows);
+    }
+  }
+
+  printf("------------\nCumulative sizes %s\n", label.c_str());
+  for (size_t idx = 0; idx < sizes.size(); idx++) {
+    printf("{%lu, %lu, %d}", sizes[idx].row_count, sizes[idx].size_bytes, sizes[idx].key);
+    if (splits.has_value()) {
+      // if we have a split at this row count and this is the last instance of this row count
+      auto start = thrust::make_transform_iterator(
+        splits->begin(), [](gpu::chunk_read_info const& i) { return i.skip_rows; });
+      auto end               = start + splits->size();
+      auto split             = std::find(start, end, sizes[idx].row_count);
+      auto const split_index = [&]() -> int {
+        if (split != end &&
+            ((idx == sizes.size() - 1) || (sizes[idx + 1].row_count > sizes[idx].row_count))) {
+          return static_cast<int>(std::distance(start, split));
+        }
+        return idx == 0 ? 0 : -1;
+      }();
+      if (split_index >= 0) {
+        printf(" <-- split {%lu, %lu}",
+               splits.value()[split_index].skip_rows,
+               splits.value()[split_index].num_rows);
+      }
+    }
+    printf("\n");
+  }
+}
+#endif  // PREPROCESS_DEBUG
+
+/**
+ * @brief Functor which reduces two cumulative_row_info structs of the same key.
+ */
+struct cumulative_row_sum {
+  cumulative_row_info operator()
+    __device__(cumulative_row_info const& a, cumulative_row_info const& b) const
+  {
+    return cumulative_row_info{a.row_count + b.row_count, a.size_bytes + b.size_bytes, a.key};
+  }
+};
+
+/**
+ * @brief Functor which computes the total data size for a given type of cudf column.
+ *
+ * In the case of strings, the return size does not include the chars themselves. That
+ * information is tracked separately (see PageInfo::str_bytes).
+ */
+struct row_size_functor {
+  __device__ size_t validity_size(size_t num_rows, bool nullable)
+  {
+    return nullable ? (cudf::util::div_rounding_up_safe(num_rows, size_t{32}) * 4) : 0;
+  }
+
+  template <typename T>
+  __device__ size_t operator()(size_t num_rows, bool nullable)
+  {
+    auto const element_size = sizeof(device_storage_type_t<T>);
+    return (element_size * num_rows) + validity_size(num_rows, nullable);
+  }
+};
+
+template <>
+__device__ size_t row_size_functor::operator()<list_view>(size_t num_rows, bool nullable)
+{
+  auto const offset_size = sizeof(size_type);
+  // NOTE: Adding the + 1 offset here isn't strictly correct.  There will only be 1 extra offset
+  // for the entire column, whereas this is adding an extra offset per page.  So we will get a
+  // small over-estimate of the real size of the order :  # of pages * 4 bytes. It seems better
+  // to overestimate size somewhat than to underestimate it and potentially generate chunks
+  // that are too large.
+  return (offset_size * (num_rows + 1)) + validity_size(num_rows, nullable);
+}
+
+template <>
+__device__ size_t row_size_functor::operator()<struct_view>(size_t num_rows, bool nullable)
+{
+  return validity_size(num_rows, nullable);
+}
+
+template <>
+__device__ size_t row_size_functor::operator()<string_view>(size_t num_rows, bool nullable)
+{
+  // only returns the size of offsets and validity. the size of the actual string chars
+  // is tracked separately.
+  auto const offset_size = sizeof(size_type);
+  // see note about offsets in the list_view template.
+  return (offset_size * (num_rows + 1)) + validity_size(num_rows, nullable);
+}
+
+/**
+ * @brief Functor which computes the total output cudf data size for all of
+ * the data in this page.
+ *
+ * Sums across all nesting levels.
+ */
+struct get_cumulative_row_info {
+  gpu::PageInfo const* const pages;
+
+  __device__ cumulative_row_info operator()(size_type index)
+  {
+    auto const& page = pages[index];
+    if (page.flags & gpu::PAGEINFO_FLAGS_DICTIONARY) {
+      return cumulative_row_info{0, 0, page.src_col_schema};
+    }
+
+    // total nested size, not counting string data
+    auto iter =
+      cudf::detail::make_counting_transform_iterator(0, [page, index] __device__(size_type i) {
+        auto const& pni = page.nesting[i];
+        return cudf::type_dispatcher(
+          data_type{pni.type}, row_size_functor{}, pni.size, pni.nullable);
+      });
+
+    size_t const row_count = static_cast<size_t>(page.nesting[0].size);
+    return {
+      row_count,
+      thrust::reduce(thrust::seq, iter, iter + page.num_output_nesting_levels) + page.str_bytes,
+      page.src_col_schema};
+  }
+};
+
+/**
+ * @brief Functor which computes the effective size of all input columns by page.
+ *
+ * For a given row, we want to find the cost of all pages for all columns involved
+ * in loading up to that row.  The complication here is that not all pages are the
+ * same size between columns. Example:
+ *
+ *              page row counts
+ * Column A:    0 <----> 100 <----> 200
+ * Column B:    0 <---------------> 200 <--------> 400
+                          |
+ * if we decide to split at row 100, we don't really know the actual amount of bytes in column B
+ * at that point.  So we have to proceed as if we are taking the bytes from all 200 rows of that
+ * page. Essentially, a conservative over-estimate of the real size.
+ */
+struct row_total_size {
+  cumulative_row_info const* c_info;
+  size_type const* key_offsets;
+  size_t num_keys;
+
+  __device__ cumulative_row_info operator()(cumulative_row_info const& i)
+  {
+    // sum sizes for each input column at this row
+    size_t sum = 0;
+    for (int idx = 0; idx < num_keys; idx++) {
+      auto const start = key_offsets[idx];
+      auto const end   = key_offsets[idx + 1];
+      auto iter        = cudf::detail::make_counting_transform_iterator(
+        0, [&] __device__(size_type i) { return c_info[i].row_count; });
+      auto const page_index =
+        thrust::lower_bound(thrust::seq, iter + start, iter + end, i.row_count) - iter;
+      sum += c_info[page_index].size_bytes;
+    }
+    return {i.row_count, sum, i.key};
+  }
+};
+
+/**
+ * @brief Given a vector of cumulative {row_count, byte_size} pairs and a chunk read
+ * limit, determine the set of splits.
+ *
+ * @param sizes Vector of cumulative {row_count, byte_size} pairs
+ * @param num_rows Total number of rows to read
+ * @param chunk_read_limit Limit on total number of bytes to be returned per read, for all columns
+ */
+std::vector<gpu::chunk_read_info> find_splits(std::vector<cumulative_row_info> const& sizes,
+                                              size_t num_rows,
+                                              size_t chunk_read_limit)
+{
+  // now we have an array of {row_count, real output bytes}. just walk through it and generate
+  // splits.
+  // TODO: come up with a clever way to do this entirely in parallel. For now, as long as batch
+  // sizes are reasonably large, this shouldn't iterate too many times
+  std::vector<gpu::chunk_read_info> splits;
+  {
+    size_t cur_pos             = 0;
+    size_t cur_cumulative_size = 0;
+    size_t cur_row_count       = 0;
+    auto start = thrust::make_transform_iterator(sizes.begin(), [&](cumulative_row_info const& i) {
+      return i.size_bytes - cur_cumulative_size;
+    });
+    auto end   = start + sizes.size();
+    while (cur_row_count < num_rows) {
+      int64_t split_pos =
+        thrust::lower_bound(thrust::seq, start + cur_pos, end, chunk_read_limit) - start;
+
+      // if we're past the end, or if the returned bucket is > than the chunk_read_limit, move back
+      // one.
+      if (static_cast<size_t>(split_pos) >= sizes.size() ||
+          (sizes[split_pos].size_bytes - cur_cumulative_size > chunk_read_limit)) {
+        split_pos--;
+      }
+
+      // best-try. if we can't find something that'll fit, we have to go bigger. we're doing this in
+      // a loop because all of the cumulative sizes for all the pages are sorted into one big list.
+      // so if we had two columns, both of which had an entry {1000, 10000}, that entry would be in
+      // the list twice. so we have to iterate until we skip past all of them.  The idea is that we
+      // either do this, or we have to call unique() on the input first.
+      while (split_pos < (static_cast<int64_t>(sizes.size()) - 1) &&
+             (split_pos < 0 || sizes[split_pos].row_count == cur_row_count)) {
+        split_pos++;
+      }
+
+      auto const start_row = cur_row_count;
+      cur_row_count        = sizes[split_pos].row_count;
+      splits.push_back(gpu::chunk_read_info{start_row, cur_row_count - start_row});
+      cur_pos             = split_pos;
+      cur_cumulative_size = sizes[split_pos].size_bytes;
+    }
+  }
+  // print_cumulative_row_info(sizes, "adjusted", splits);
+
+  return splits;
+}
+
+/**
+ * @brief Given a set of pages that have had their sizes computed by nesting level and
+ * a limit on total read size, generate a set of {skip_rows, num_rows} pairs representing
+ * a set of reads that will generate output columns of total size <= `chunk_read_limit` bytes.
+ *
+ * @param pages All pages in the file
+ * @param id Additional intermediate information required to process the pages
+ * @param num_rows Total number of rows to read
+ * @param chunk_read_limit Limit on total number of bytes to be returned per read, for all columns
+ * @param stream CUDA stream to use
+ */
+std::vector<gpu::chunk_read_info> compute_splits(
+  cudf::detail::hostdevice_vector<gpu::PageInfo>& pages,
+  gpu::pass_intermediate_data const& id,
+  size_t num_rows,
+  size_t chunk_read_limit,
+  rmm::cuda_stream_view stream)
+{
+  auto const& page_keys  = id.page_keys;
+  auto const& page_index = id.page_index;
+
+  // generate cumulative row counts and sizes
+  rmm::device_uvector<cumulative_row_info> c_info(page_keys.size(), stream);
+  // convert PageInfo to cumulative_row_info
+  auto page_input = thrust::make_transform_iterator(page_index.begin(),
+                                                    get_cumulative_row_info{pages.device_ptr()});
+  thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                page_keys.begin(),
+                                page_keys.end(),
+                                page_input,
+                                c_info.begin(),
+                                thrust::equal_to{},
+                                cumulative_row_sum{});
+  // print_cumulative_page_info(pages, page_index, c_info, stream);
+
+  // sort by row count
+  rmm::device_uvector<cumulative_row_info> c_info_sorted{c_info, stream};
+  thrust::sort(rmm::exec_policy(stream),
+               c_info_sorted.begin(),
+               c_info_sorted.end(),
+               [] __device__(cumulative_row_info const& a, cumulative_row_info const& b) {
+                 return a.row_count < b.row_count;
+               });
+
+  // std::vector<cumulative_row_info> h_c_info_sorted(c_info_sorted.size());
+  // CUDF_CUDA_TRY(cudaMemcpy(h_c_info_sorted.data(),
+  //                          c_info_sorted.data(),
+  //                          sizeof(cumulative_row_info) * c_info_sorted.size(),
+  //                          cudaMemcpyDefault));
+  // print_cumulative_row_info(h_c_info_sorted, "raw");
+
+  // generate key offsets (offsets to the start of each partition of keys). worst case is 1 page per
+  // key
+  rmm::device_uvector<size_type> key_offsets(page_keys.size() + 1, stream);
+  auto const key_offsets_end = thrust::reduce_by_key(rmm::exec_policy(stream),
+                                                     page_keys.begin(),
+                                                     page_keys.end(),
+                                                     thrust::make_constant_iterator(1),
+                                                     thrust::make_discard_iterator(),
+                                                     key_offsets.begin())
+                                 .second;
+  size_t const num_unique_keys = key_offsets_end - key_offsets.begin();
+  thrust::exclusive_scan(
+    rmm::exec_policy(stream), key_offsets.begin(), key_offsets.end(), key_offsets.begin());
+
+  // adjust the cumulative info such that for each row count, the size includes any pages that span
+  // that row count. this is so that if we have this case:
+  //              page row counts
+  // Column A:    0 <----> 100 <----> 200
+  // Column B:    0 <---------------> 200 <--------> 400
+  //                        |
+  // if we decide to split at row 100, we don't really know the actual amount of bytes in column B
+  // at that point.  So we have to proceed as if we are taking the bytes from all 200 rows of that
+  // page.
+  //
+  rmm::device_uvector<cumulative_row_info> aggregated_info(c_info.size(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    c_info_sorted.begin(),
+                    c_info_sorted.end(),
+                    aggregated_info.begin(),
+                    row_total_size{c_info.data(), key_offsets.data(), num_unique_keys});
+
+  // bring back to the cpu
+  std::vector<cumulative_row_info> h_aggregated_info(aggregated_info.size());
+  CUDF_CUDA_TRY(cudaMemcpyAsync(h_aggregated_info.data(),
+                                aggregated_info.data(),
+                                sizeof(cumulative_row_info) * c_info.size(),
+                                cudaMemcpyDefault,
+                                stream.value()));
+  stream.synchronize();
+
+  return find_splits(h_aggregated_info, num_rows, chunk_read_limit);
+}
+
+struct get_page_chunk_idx {
+  __device__ size_type operator()(gpu::PageInfo const& page) { return page.chunk_idx; }
+};
+
+struct get_page_num_rows {
+  __device__ size_type operator()(gpu::PageInfo const& page) { return page.num_rows; }
+};
+
+struct get_page_column_index {
+  gpu::ColumnChunkDesc const* chunks;
+  __device__ size_type operator()(gpu::PageInfo const& page)
+  {
+    return chunks[page.chunk_idx].src_col_index;
+  }
+};
+
+struct input_col_info {
+  int const schema_idx;
+  size_type const nesting_depth;
+};
+
+/**
+ * @brief Converts a 1-dimensional index into page, depth and column indices used in
+ * allocate_columns to compute columns sizes.
+ *
+ * The input index will iterate through pages, nesting depth and column indices in that order.
+ */
+struct reduction_indices {
+  size_t const page_idx;
+  size_type const depth_idx;
+  size_type const col_idx;
+
+  __device__ reduction_indices(size_t index_, size_type max_depth_, size_t num_pages_)
+    : page_idx(index_ % num_pages_),
+      depth_idx((index_ / num_pages_) % max_depth_),
+      col_idx(index_ / (max_depth_ * num_pages_))
+  {
+  }
+};
+
+/**
+ * @brief Returns the size field of a PageInfo struct for a given depth, keyed by schema.
+ */
+struct get_page_nesting_size {
+  input_col_info const* const input_cols;
+  size_type const max_depth;
+  size_t const num_pages;
+  gpu::PageInfo const* const pages;
+  int const* page_indices;
+
+  __device__ size_type operator()(size_t index) const
+  {
+    auto const indices = reduction_indices{index, max_depth, num_pages};
+
+    auto const& page = pages[page_indices[indices.page_idx]];
+    if (page.src_col_schema != input_cols[indices.col_idx].schema_idx ||
+        page.flags & gpu::PAGEINFO_FLAGS_DICTIONARY ||
+        indices.depth_idx >= input_cols[indices.col_idx].nesting_depth) {
+      return 0;
+    }
+
+    return page.nesting[indices.depth_idx].batch_size;
+  }
+};
+
+struct get_reduction_key {
+  size_t const num_pages;
+  __device__ size_t operator()(size_t index) const { return index / num_pages; }
+};
+
+/**
+ * @brief Writes to the chunk_row field of the PageInfo struct.
+ */
+struct chunk_row_output_iter {
+  gpu::PageInfo* p;
+  using value_type        = size_type;
+  using difference_type   = size_type;
+  using pointer           = size_type*;
+  using reference         = size_type&;
+  using iterator_category = thrust::output_device_iterator_tag;
+
+  __host__ __device__ chunk_row_output_iter operator+(int i)
+  {
+    return chunk_row_output_iter{p + i};
+  }
+
+  __host__ __device__ void operator++() { p++; }
+
+  __device__ reference operator[](int i) { return p[i].chunk_row; }
+  __device__ reference operator*() { return p->chunk_row; }
+};
+
+/**
+ * @brief Writes to the page_start_value field of the PageNestingInfo struct, keyed by schema.
+ */
+struct start_offset_output_iterator {
+  gpu::PageInfo const* pages;
+  int const* page_indices;
+  size_t cur_index;
+  input_col_info const* input_cols;
+  size_type max_depth;
+  size_t num_pages;
+  int empty               = 0;
+  using value_type        = size_type;
+  using difference_type   = size_type;
+  using pointer           = size_type*;
+  using reference         = size_type&;
+  using iterator_category = thrust::output_device_iterator_tag;
+
+  constexpr void operator=(start_offset_output_iterator const& other)
+  {
+    pages        = other.pages;
+    page_indices = other.page_indices;
+    cur_index    = other.cur_index;
+    input_cols   = other.input_cols;
+    max_depth    = other.max_depth;
+    num_pages    = other.num_pages;
+  }
+
+  constexpr start_offset_output_iterator operator+(size_t i)
+  {
+    return start_offset_output_iterator{
+      pages, page_indices, cur_index + i, input_cols, max_depth, num_pages};
+  }
+
+  constexpr void operator++() { cur_index++; }
+
+  __device__ reference operator[](size_t i) { return dereference(cur_index + i); }
+  __device__ reference operator*() { return dereference(cur_index); }
+
+ private:
+  __device__ reference dereference(size_t index)
+  {
+    auto const indices = reduction_indices{index, max_depth, num_pages};
+
+    gpu::PageInfo const& p = pages[page_indices[indices.page_idx]];
+    if (p.src_col_schema != input_cols[indices.col_idx].schema_idx ||
+        p.flags & gpu::PAGEINFO_FLAGS_DICTIONARY ||
+        indices.depth_idx >= input_cols[indices.col_idx].nesting_depth) {
+      return empty;
+    }
+    return p.nesting_decode[indices.depth_idx].page_start_value;
+  }
+};
+
+struct flat_column_num_rows {
+  gpu::PageInfo const* pages;
+  gpu::ColumnChunkDesc const* chunks;
+
+  __device__ size_type operator()(size_type pindex) const
+  {
+    gpu::PageInfo const& page = pages[pindex];
+    // ignore dictionary pages and pages belonging to any column containing repetition (lists)
+    if ((page.flags & gpu::PAGEINFO_FLAGS_DICTIONARY) ||
+        (chunks[page.chunk_idx].max_level[gpu::level_type::REPETITION] > 0)) {
+      return 0;
+    }
+    return page.num_rows;
+  }
+};
+
+struct row_counts_nonzero {
+  __device__ bool operator()(size_type count) const { return count > 0; }
+};
+
+struct row_counts_different {
+  size_type const expected;
+  __device__ bool operator()(size_type count) const { return (count != 0) && (count != expected); }
+};
+
+/**
+ * @brief Detect malformed parquet input data.
+ *
+ * We have seen cases where parquet files can be oddly malformed. This function specifically
+ * detects one case in particular:
+ *
+ * - When you have a file containing N rows
+ * - For some reason, the sum total of the number of rows over all pages for a given column
+ *   is != N
+ *
+ * @param pages All pages to be decoded
+ * @param chunks Chunk data
+ * @param page_keys Keys (schema id) associated with each page, sorted by column
+ * @param page_index Page indices for iteration, sorted by column
+ * @param expected_row_count Expected row count, if applicable
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void detect_malformed_pages(cudf::detail::hostdevice_vector<gpu::PageInfo>& pages,
+                            cudf::detail::hostdevice_vector<gpu::ColumnChunkDesc> const& chunks,
+                            device_span<int const> page_keys,
+                            device_span<int const> page_index,
+                            std::optional<size_t> expected_row_count,
+                            rmm::cuda_stream_view stream)
+{
+  // sum row counts for all non-dictionary, non-list columns. other columns will be indicated as 0
+  rmm::device_uvector<size_type> row_counts(pages.size(),
+                                            stream);  // worst case:  num keys == num pages
+  auto const size_iter = thrust::make_transform_iterator(
+    page_index.begin(), flat_column_num_rows{pages.device_ptr(), chunks.device_ptr()});
+  auto const row_counts_begin = row_counts.begin();
+  auto const row_counts_end   = thrust::reduce_by_key(rmm::exec_policy(stream),
+                                                    page_keys.begin(),
+                                                    page_keys.end(),
+                                                    size_iter,
+                                                    thrust::make_discard_iterator(),
+                                                    row_counts_begin)
+                                .second;
+
+  // make sure all non-zero row counts are the same
+  rmm::device_uvector<size_type> compacted_row_counts(pages.size(), stream);
+  auto const compacted_row_counts_begin = compacted_row_counts.begin();
+  auto const compacted_row_counts_end   = thrust::copy_if(rmm::exec_policy(stream),
+                                                        row_counts_begin,
+                                                        row_counts_end,
+                                                        compacted_row_counts_begin,
+                                                        row_counts_nonzero{});
+  if (compacted_row_counts_end != compacted_row_counts_begin) {
+    size_t const found_row_count = static_cast<size_t>(compacted_row_counts.element(0, stream));
+
+    // if we somehow don't match the expected row count from the row groups themselves
+    if (expected_row_count.has_value()) {
+      CUDF_EXPECTS(expected_row_count.value() == found_row_count,
+                   "Encountered malformed parquet page data (unexpected row count in page data)");
+    }
+
+    // all non-zero row counts must be the same
+    auto const chk =
+      thrust::count_if(rmm::exec_policy(stream),
+                       compacted_row_counts_begin,
+                       compacted_row_counts_end,
+                       row_counts_different{static_cast<size_type>(found_row_count)});
+    CUDF_EXPECTS(chk == 0,
+                 "Encountered malformed parquet page data (row count mismatch in page data)");
+  }
+}
+
+struct page_to_string_size {
+  gpu::PageInfo* pages;
+  gpu::ColumnChunkDesc const* chunks;
+
+  __device__ size_t operator()(size_type page_idx) const
+  {
+    auto const page  = pages[page_idx];
+    auto const chunk = chunks[page.chunk_idx];
+
+    if (not is_string_col(chunk) || (page.flags & gpu::PAGEINFO_FLAGS_DICTIONARY) != 0) {
+      return 0;
+    }
+    return pages[page_idx].str_bytes;
+  }
+};
+
+struct page_offset_output_iter {
+  gpu::PageInfo* p;
+  size_type const* index;
+
+  using value_type        = size_type;
+  using difference_type   = size_type;
+  using pointer           = size_type*;
+  using reference         = size_type&;
+  using iterator_category = thrust::output_device_iterator_tag;
+
+  __host__ __device__ page_offset_output_iter operator+(int i)
+  {
+    return page_offset_output_iter{p, index + i};
+  }
+
+  __host__ __device__ void operator++() { index++; }
+
+  __device__ reference operator[](int i) { return p[index[i]].str_offset; }
+  __device__ reference operator*() { return p[*index].str_offset; }
+};
+
+}  // anonymous namespace
+
+void reader::impl::preprocess_pages(bool uses_custom_row_bounds, size_t chunk_read_limit)
+{
+  auto const skip_rows = _pass_itm_data->skip_rows;
+  auto const num_rows  = _pass_itm_data->num_rows;
+  auto& chunks         = _pass_itm_data->chunks;
+  auto& pages          = _pass_itm_data->pages_info;
+
+  // compute page ordering.
+  //
+  // ordering of pages is by input column schema, repeated across row groups.  so
+  // if we had 3 columns, each with 2 pages, and 1 row group, our schema values might look like
+  //
+  // 1, 1, 2, 2, 3, 3
+  //
+  // However, if we had more than one row group, the pattern would be
+  //
+  // 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3
+  // ^ row group 0     |
+  //                   ^ row group 1
+  //
+  // To process pages by key (exclusive_scan_by_key, reduce_by_key, etc), the ordering we actually
+  // want is
+  //
+  // 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3
+  //
+  // We also need to preserve key-relative page ordering, so we need to use a stable sort.
+  rmm::device_uvector<int> page_keys(pages.size(), _stream);
+  rmm::device_uvector<int> page_index(pages.size(), _stream);
+  {
+    thrust::transform(rmm::exec_policy(_stream),
+                      pages.device_ptr(),
+                      pages.device_ptr() + pages.size(),
+                      page_keys.begin(),
+                      get_page_column_index{chunks.device_ptr()});
+
+    thrust::sequence(rmm::exec_policy(_stream), page_index.begin(), page_index.end());
+    thrust::stable_sort_by_key(rmm::exec_policy(_stream),
+                               page_keys.begin(),
+                               page_keys.end(),
+                               page_index.begin(),
+                               thrust::less<int>());
+  }
+
+  // detect malformed columns.
+  // - we have seen some cases in the wild where we have a row group containing N
+  //   rows, but the total number of rows in the pages for column X is != N. while it
+  //   is possible to load this by just capping the number of rows read, we cannot tell
+  //   which rows are invalid so we may be returning bad data. in addition, this mismatch
+  //   confuses the chunked reader
+  detect_malformed_pages(pages,
+                         chunks,
+                         page_keys,
+                         page_index,
+                         uses_custom_row_bounds ? std::nullopt : std::make_optional(num_rows),
+                         _stream);
+
+  // iterate over all input columns and determine if they contain lists so we can further
+  // preprocess them.
+  bool has_lists = false;
+  for (size_t idx = 0; idx < _input_columns.size(); idx++) {
+    auto const& input_col  = _input_columns[idx];
+    size_t const max_depth = input_col.nesting_depth();
+
+    auto* cols = &_output_buffers;
+    for (size_t l_idx = 0; l_idx < max_depth; l_idx++) {
+      auto& out_buf = (*cols)[input_col.nesting[l_idx]];
+      cols          = &out_buf.children;
+
+      // if this has a list parent, we have to get column sizes from the
+      // data computed during gpu::ComputePageSizes
+      if (out_buf.user_data & PARQUET_COLUMN_BUFFER_FLAG_HAS_LIST_PARENT) {
+        has_lists = true;
+        break;
+      }
+    }
+    if (has_lists) { break; }
+  }
+
+  // generate string dict indices if necessary
+  {
+    auto is_dict_chunk = [](gpu::ColumnChunkDesc const& chunk) {
+      return (chunk.data_type & 0x7) == BYTE_ARRAY && chunk.num_dict_pages > 0;
+    };
+
+    // Count the number of string dictionary entries
+    // NOTE: Assumes first page in the chunk is always the dictionary page
+    size_t total_str_dict_indexes = 0;
+    for (size_t c = 0, page_count = 0; c < chunks.size(); c++) {
+      if (is_dict_chunk(chunks[c])) {
+        total_str_dict_indexes += pages[page_count].num_input_values;
+      }
+      page_count += chunks[c].max_num_pages;
+    }
+
+    // Build index for string dictionaries since they can't be indexed
+    // directly due to variable-sized elements
+    _pass_itm_data->str_dict_index =
+      cudf::detail::make_zeroed_device_uvector_async<string_index_pair>(
+        total_str_dict_indexes, _stream, rmm::mr::get_current_device_resource());
+
+    // Update chunks with pointers to string dict indices
+    for (size_t c = 0, page_count = 0, str_ofs = 0; c < chunks.size(); c++) {
+      input_column_info const& input_col = _input_columns[chunks[c].src_col_index];
+      CUDF_EXPECTS(input_col.schema_idx == chunks[c].src_col_schema,
+                   "Column/page schema index mismatch");
+      if (is_dict_chunk(chunks[c])) {
+        chunks[c].str_dict_index = _pass_itm_data->str_dict_index.data() + str_ofs;
+        str_ofs += pages[page_count].num_input_values;
+      }
+
+      // column_data_base will always point to leaf data, even for nested types.
+      page_count += chunks[c].max_num_pages;
+    }
+
+    if (total_str_dict_indexes > 0) {
+      chunks.host_to_device_async(_stream);
+      gpu::BuildStringDictionaryIndex(chunks.device_ptr(), chunks.size(), _stream);
+    }
+  }
+
+  // intermediate data we will need for further chunked reads
+  if (has_lists || chunk_read_limit > 0) {
+    // computes:
+    // PageNestingInfo::num_rows for each page. the true number of rows (taking repetition into
+    // account), not just the number of values. PageNestingInfo::size for each level of nesting, for
+    // each page.
+    //
+    // we will be applying a later "trim" pass if skip_rows/num_rows is being used, which can happen
+    // if:
+    // - user has passed custom row bounds
+    // - we will be doing a chunked read
+    gpu::ComputePageSizes(pages,
+                          chunks,
+                          0,  // 0-max size_t. process all possible rows
+                          std::numeric_limits<size_t>::max(),
+                          true,                  // compute num_rows
+                          chunk_read_limit > 0,  // compute string sizes
+                          _pass_itm_data->level_type_size,
+                          _stream);
+
+    // computes:
+    // PageInfo::chunk_row (the absolute start row index) for all pages
+    // Note: this is doing some redundant work for pages in flat hierarchies.  chunk_row has already
+    // been computed during header decoding. the overall amount of work here is very small though.
+    auto key_input  = thrust::make_transform_iterator(pages.device_ptr(), get_page_chunk_idx{});
+    auto page_input = thrust::make_transform_iterator(pages.device_ptr(), get_page_num_rows{});
+    thrust::exclusive_scan_by_key(rmm::exec_policy(_stream),
+                                  key_input,
+                                  key_input + pages.size(),
+                                  page_input,
+                                  chunk_row_output_iter{pages.device_ptr()});
+
+    // retrieve pages back
+    pages.device_to_host_sync(_stream);
+
+    // print_pages(pages, _stream);
+  }
+
+  // preserve page ordering data for string decoder
+  _pass_itm_data->page_keys  = std::move(page_keys);
+  _pass_itm_data->page_index = std::move(page_index);
+
+  // compute splits if necessary. otherwise return a single split representing
+  // the whole file.
+  _pass_itm_data->output_chunk_read_info =
+    _output_chunk_read_limit > 0
+      ? compute_splits(pages, *_pass_itm_data, num_rows, chunk_read_limit, _stream)
+      : std::vector<gpu::chunk_read_info>{{skip_rows, num_rows}};
+}
+
+void reader::impl::allocate_columns(size_t skip_rows, size_t num_rows, bool uses_custom_row_bounds)
+{
+  auto const& chunks = _pass_itm_data->chunks;
+  auto& pages        = _pass_itm_data->pages_info;
+
+  // Should not reach here if there is no page data.
+  CUDF_EXPECTS(pages.size() > 0, "There is no page to parse");
+
+  // computes:
+  // PageNestingInfo::batch_size for each level of nesting, for each page, taking row bounds into
+  // account. PageInfo::skipped_values, which tells us where to start decoding in the input to
+  // respect the user bounds. It is only necessary to do this second pass if uses_custom_row_bounds
+  // is set (if the user has specified artificial bounds).
+  if (uses_custom_row_bounds) {
+    gpu::ComputePageSizes(pages,
+                          chunks,
+                          skip_rows,
+                          num_rows,
+                          false,  // num_rows is already computed
+                          false,  // no need to compute string sizes
+                          _pass_itm_data->level_type_size,
+                          _stream);
+
+    // print_pages(pages, _stream);
+  }
+
+  // iterate over all input columns and allocate any associated output
+  // buffers if they are not part of a list hierarchy. mark down
+  // if we have any list columns that need further processing.
+  bool has_lists = false;
+  for (size_t idx = 0; idx < _input_columns.size(); idx++) {
+    auto const& input_col  = _input_columns[idx];
+    size_t const max_depth = input_col.nesting_depth();
+
+    auto* cols = &_output_buffers;
+    for (size_t l_idx = 0; l_idx < max_depth; l_idx++) {
+      auto& out_buf = (*cols)[input_col.nesting[l_idx]];
+      cols          = &out_buf.children;
+
+      // if this has a list parent, we have to get column sizes from the
+      // data computed during gpu::ComputePageSizes
+      if (out_buf.user_data & PARQUET_COLUMN_BUFFER_FLAG_HAS_LIST_PARENT) {
+        has_lists = true;
+      }
+      // if we haven't already processed this column because it is part of a struct hierarchy
+      else if (out_buf.size == 0) {
+        // add 1 for the offset if this is a list column
+        out_buf.create(
+          out_buf.type.id() == type_id::LIST && l_idx < max_depth ? num_rows + 1 : num_rows,
+          _stream,
+          _mr);
+      }
+    }
+  }
+
+  // compute output column sizes by examining the pages of the -input- columns
+  if (has_lists) {
+    auto& page_index = _pass_itm_data->page_index;
+
+    std::vector<input_col_info> h_cols_info;
+    h_cols_info.reserve(_input_columns.size());
+    std::transform(_input_columns.cbegin(),
+                   _input_columns.cend(),
+                   std::back_inserter(h_cols_info),
+                   [](auto& col) -> input_col_info {
+                     return {col.schema_idx, static_cast<size_type>(col.nesting_depth())};
+                   });
+
+    auto const max_depth =
+      (*std::max_element(h_cols_info.cbegin(),
+                         h_cols_info.cend(),
+                         [](auto& l, auto& r) { return l.nesting_depth < r.nesting_depth; }))
+        .nesting_depth;
+
+    auto const d_cols_info = cudf::detail::make_device_uvector_async(
+      h_cols_info, _stream, rmm::mr::get_current_device_resource());
+
+    auto const num_keys = _input_columns.size() * max_depth * pages.size();
+    // size iterator. indexes pages by sorted order
+    rmm::device_uvector<size_type> size_input{num_keys, _stream};
+    thrust::transform(
+      rmm::exec_policy(_stream),
+      thrust::make_counting_iterator<size_type>(0),
+      thrust::make_counting_iterator<size_type>(num_keys),
+      size_input.begin(),
+      get_page_nesting_size{
+        d_cols_info.data(), max_depth, pages.size(), pages.device_ptr(), page_index.begin()});
+    auto const reduction_keys =
+      cudf::detail::make_counting_transform_iterator(0, get_reduction_key{pages.size()});
+    cudf::detail::hostdevice_vector<size_t> sizes{_input_columns.size() * max_depth, _stream};
+
+    // find the size of each column
+    thrust::reduce_by_key(rmm::exec_policy(_stream),
+                          reduction_keys,
+                          reduction_keys + num_keys,
+                          size_input.cbegin(),
+                          thrust::make_discard_iterator(),
+                          sizes.d_begin());
+
+    // for nested hierarchies, compute per-page start offset
+    thrust::exclusive_scan_by_key(
+      rmm::exec_policy(_stream),
+      reduction_keys,
+      reduction_keys + num_keys,
+      size_input.cbegin(),
+      start_offset_output_iterator{
+        pages.device_ptr(), page_index.begin(), 0, d_cols_info.data(), max_depth, pages.size()});
+
+    sizes.device_to_host_sync(_stream);
+    for (size_type idx = 0; idx < static_cast<size_type>(_input_columns.size()); idx++) {
+      auto const& input_col = _input_columns[idx];
+      auto* cols            = &_output_buffers;
+      for (size_type l_idx = 0; l_idx < static_cast<size_type>(input_col.nesting_depth());
+           l_idx++) {
+        auto& out_buf = (*cols)[input_col.nesting[l_idx]];
+        cols          = &out_buf.children;
+        // if this buffer is part of a list hierarchy, we need to determine it's
+        // final size and allocate it here.
+        //
+        // for struct columns, higher levels of the output columns are shared between input
+        // columns. so don't compute any given level more than once.
+        if ((out_buf.user_data & PARQUET_COLUMN_BUFFER_FLAG_HAS_LIST_PARENT) && out_buf.size == 0) {
+          auto size = sizes[(idx * max_depth) + l_idx];
+
+          // if this is a list column add 1 for non-leaf levels for the terminating offset
+          if (out_buf.type.id() == type_id::LIST && l_idx < max_depth) { size++; }
+
+          // allocate
+          out_buf.create(size, _stream, _mr);
+        }
+      }
+    }
+  }
+}
+
+std::vector<size_t> reader::impl::calculate_page_string_offsets()
+{
+  auto& chunks           = _pass_itm_data->chunks;
+  auto& pages            = _pass_itm_data->pages_info;
+  auto const& page_keys  = _pass_itm_data->page_keys;
+  auto const& page_index = _pass_itm_data->page_index;
+
+  std::vector<size_t> col_sizes(_input_columns.size(), 0L);
+  rmm::device_uvector<size_t> d_col_sizes(col_sizes.size(), _stream);
+
+  // use page_index to fetch page string sizes in the proper order
+  auto val_iter = thrust::make_transform_iterator(
+    page_index.begin(), page_to_string_size{pages.device_ptr(), chunks.device_ptr()});
+
+  // do scan by key to calculate string offsets for each page
+  thrust::exclusive_scan_by_key(rmm::exec_policy(_stream),
+                                page_keys.begin(),
+                                page_keys.end(),
+                                val_iter,
+                                page_offset_output_iter{pages.device_ptr(), page_index.data()});
+
+  // now sum up page sizes
+  rmm::device_uvector<int> reduce_keys(col_sizes.size(), _stream);
+  thrust::reduce_by_key(rmm::exec_policy(_stream),
+                        page_keys.begin(),
+                        page_keys.end(),
+                        val_iter,
+                        reduce_keys.begin(),
+                        d_col_sizes.begin());
+
+  cudaMemcpyAsync(col_sizes.data(),
+                  d_col_sizes.data(),
+                  sizeof(size_t) * col_sizes.size(),
+                  cudaMemcpyDeviceToHost,
+                  _stream);
+  _stream.synchronize();
+
+  return col_sizes;
+}
+
+}  // namespace cudf::io::detail::parquet
diff --git a/cpp/src/io/parquet/rle_stream.cuh b/cpp/src/io/parquet/rle_stream.cuh
new file mode 100644
index 0000000..2545a07
--- /dev/null
+++ b/cpp/src/io/parquet/rle_stream.cuh
@@ -0,0 +1,365 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "parquet_gpu.hpp"
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+
+namespace cudf::io::parquet::gpu {
+
+template <int num_threads>
+constexpr int rle_stream_required_run_buffer_size()
+{
+  constexpr int num_rle_stream_decode_warps = (num_threads / cudf::detail::warp_size) - 1;
+  return (num_rle_stream_decode_warps * 2);
+}
+
+/**
+ * @brief Read a 32-bit varint integer
+ *
+ * @param[in,out] cur The current data position, updated after the read
+ * @param[in] end The end data position
+ *
+ * @return The 32-bit value read
+ */
+inline __device__ uint32_t get_vlq32(uint8_t const*& cur, uint8_t const* end)
+{
+  uint32_t v = *cur++;
+  if (v >= 0x80 && cur < end) {
+    v = (v & 0x7f) | ((*cur++) << 7);
+    if (v >= (0x80 << 7) && cur < end) {
+      v = (v & ((0x7f << 7) | 0x7f)) | ((*cur++) << 14);
+      if (v >= (0x80 << 14) && cur < end) {
+        v = (v & ((0x7f << 14) | (0x7f << 7) | 0x7f)) | ((*cur++) << 21);
+        if (v >= (0x80 << 21) && cur < end) {
+          v = (v & ((0x7f << 21) | (0x7f << 14) | (0x7f << 7) | 0x7f)) | ((*cur++) << 28);
+        }
+      }
+    }
+  }
+  return v;
+}
+
+// an individual batch. processed by a warp.
+// batches should be in shared memory.
+template <typename level_t>
+struct rle_batch {
+  uint8_t const* run_start;  // start of the run we are part of
+  int run_offset;            // value offset of this batch from the start of the run
+  level_t* output;
+  int level_run;
+  int size;
+
+  __device__ inline void decode(uint8_t const* const end, int level_bits, int lane, int warp_id)
+  {
+    int output_pos = 0;
+    int remain     = size;
+
+    // for bitpacked/literal runs, total size is always a multiple of 8. so we need to take care if
+    // we are not starting/ending exactly on a run boundary
+    uint8_t const* cur;
+    if (level_run & 1) {
+      int const effective_offset = cudf::util::round_down_safe(run_offset, 8);
+      int const lead_values      = (run_offset - effective_offset);
+      output_pos -= lead_values;
+      remain += lead_values;
+      cur = run_start + ((effective_offset >> 3) * level_bits);
+    }
+
+    // if this is a repeated run, compute the repeated value
+    int level_val;
+    if (!(level_run & 1)) {
+      level_val = run_start[0];
+      if (level_bits > 8) { level_val |= run_start[1] << 8; }
+    }
+
+    // process
+    while (remain > 0) {
+      int const batch_len = min(32, remain);
+
+      // if this is a literal run. each thread computes its own level_val
+      if (level_run & 1) {
+        int const batch_len8 = (batch_len + 7) >> 3;
+        if (lane < batch_len) {
+          int bitpos                = lane * level_bits;
+          uint8_t const* cur_thread = cur + (bitpos >> 3);
+          bitpos &= 7;
+          level_val = 0;
+          if (cur_thread < end) { level_val = cur_thread[0]; }
+          cur_thread++;
+          if (level_bits > 8 - bitpos && cur_thread < end) {
+            level_val |= cur_thread[0] << 8;
+            cur_thread++;
+            if (level_bits > 16 - bitpos && cur_thread < end) { level_val |= cur_thread[0] << 16; }
+          }
+          level_val = (level_val >> bitpos) & ((1 << level_bits) - 1);
+        }
+
+        cur += batch_len8 * level_bits;
+      }
+
+      // store level_val
+      if (lane < batch_len && (lane + output_pos) >= 0) { output[lane + output_pos] = level_val; }
+      remain -= batch_len;
+      output_pos += batch_len;
+    }
+  }
+};
+
+// a single rle run. may be broken up into multiple rle_batches
+template <typename level_t>
+struct rle_run {
+  int size;  // total size of the run
+  int output_pos;
+  uint8_t const* start;
+  int level_run;  // level_run header value
+  int remaining;
+
+  __device__ __inline__ rle_batch<level_t> next_batch(level_t* const output, int max_size)
+  {
+    int const batch_len  = min(max_size, remaining);
+    int const run_offset = size - remaining;
+    remaining -= batch_len;
+    return rle_batch<level_t>{start, run_offset, output, level_run, batch_len};
+  }
+};
+
+// a stream of rle_runs
+template <typename level_t, int decode_threads>
+struct rle_stream {
+  static constexpr int num_rle_stream_decode_threads = decode_threads;
+  // the -1 here is for the look-ahead warp that fills in the list of runs to be decoded
+  // in an overlapped manner. so if we had 16 total warps:
+  // - warp 0 would be filling in batches of runs to be processed
+  // - warps 1-15 would be decoding the previous batch of runs generated
+  static constexpr int num_rle_stream_decode_warps =
+    (num_rle_stream_decode_threads / cudf::detail::warp_size) - 1;
+
+  static constexpr int run_buffer_size = rle_stream_required_run_buffer_size<decode_threads>();
+
+  int level_bits;
+  uint8_t const* start;
+  uint8_t const* cur;
+  uint8_t const* end;
+
+  int max_output_values;
+  int total_values;
+  int cur_values;
+
+  level_t* output;
+
+  rle_run<level_t>* runs;
+  int run_index;
+  int run_count;
+  int output_pos;
+  bool spill;
+
+  int next_batch_run_start;
+  int next_batch_run_count;
+
+  __device__ rle_stream(rle_run<level_t>* _runs) : runs(_runs) {}
+
+  __device__ void init(int _level_bits,
+                       uint8_t const* _start,
+                       uint8_t const* _end,
+                       int _max_output_values,
+                       level_t* _output,
+                       int _total_values)
+  {
+    level_bits = _level_bits;
+    start      = _start;
+    cur        = _start;
+    end        = _end;
+
+    max_output_values = _max_output_values;
+    output            = _output;
+
+    run_index            = 0;
+    run_count            = 0;
+    output_pos           = 0;
+    spill                = false;
+    next_batch_run_start = 0;
+    next_batch_run_count = 0;
+
+    total_values = _total_values;
+    cur_values   = 0;
+  }
+
+  __device__ inline thrust::pair<int, int> get_run_batch()
+  {
+    return {next_batch_run_start, next_batch_run_count};
+  }
+
+  // fill in up to num_rle_stream_decode_warps runs or until we reach the max_count limit.
+  // this function is the critical hotspot.  please be very careful altering it.
+  __device__ inline void fill_run_batch(int max_count)
+  {
+    // if we spilled over, we've already got a run at the beginning
+    next_batch_run_start = spill ? run_index - 1 : run_index;
+    spill                = false;
+
+    // generate runs until we either run out of warps to decode them with, or
+    // we cross the output limit.
+    while (run_count < num_rle_stream_decode_warps && output_pos < max_count && cur < end) {
+      auto& run = runs[rolling_index<run_buffer_size>(run_index)];
+
+      // Encoding::RLE
+
+      // bytes for the varint header
+      uint8_t const* _cur = cur;
+      int const level_run = get_vlq32(_cur, end);
+      int run_bytes       = _cur - cur;
+
+      // literal run
+      if (level_run & 1) {
+        int const run_size  = (level_run >> 1) * 8;
+        run.size            = run_size;
+        int const run_size8 = (run_size + 7) >> 3;
+        run_bytes += run_size8 * level_bits;
+      }
+      // repeated value run
+      else {
+        run.size = (level_run >> 1);
+        run_bytes++;
+        // can this ever be > 16?  it effectively encodes nesting depth so that would require
+        // a nesting depth > 64k.
+        if (level_bits > 8) { run_bytes++; }
+      }
+      run.output_pos = output_pos;
+      run.start      = _cur;
+      run.level_run  = level_run;
+      run.remaining  = run.size;
+      cur += run_bytes;
+
+      output_pos += run.size;
+      run_count++;
+      run_index++;
+    }
+
+    // the above loop computes a batch of runs to be processed. mark down
+    // the number of runs because the code after this point resets run_count
+    // for the next batch. each batch is returned via get_next_batch().
+    next_batch_run_count = run_count;
+
+    // -------------------------------------
+    // prepare for the next run:
+
+    // if we've reached the value output limit on the last run
+    if (output_pos >= max_count) {
+      // first, see if we've spilled over
+      auto const& src       = runs[rolling_index<run_buffer_size>(run_index - 1)];
+      int const spill_count = output_pos - max_count;
+
+      // a spill has occurred in the current run. spill the extra values over into the beginning of
+      // the next run.
+      if (spill_count > 0) {
+        auto& spill_run      = runs[rolling_index<run_buffer_size>(run_index)];
+        spill_run            = src;
+        spill_run.output_pos = 0;
+        spill_run.remaining  = spill_count;
+
+        run_count = 1;
+        run_index++;
+        output_pos = spill_run.remaining;
+        spill      = true;
+      }
+      // no actual spill needed. just reset the output pos
+      else {
+        output_pos = 0;
+        run_count  = 0;
+      }
+    }
+    // didn't cross the limit, so reset the run count
+    else {
+      run_count = 0;
+    }
+  }
+
+  __device__ inline int decode_next(int t)
+  {
+    int const output_count = min(max_output_values, (total_values - cur_values));
+
+    // special case. if level_bits == 0, just return all zeros. this should tremendously speed up
+    // a very common case: columns with no nulls, especially if they are non-nested
+    if (level_bits == 0) {
+      int written = 0;
+      while (written < output_count) {
+        int const batch_size = min(num_rle_stream_decode_threads, output_count - written);
+        if (t < batch_size) { output[written + t] = 0; }
+        written += batch_size;
+      }
+      cur_values += output_count;
+      return output_count;
+    }
+
+    // otherwise, full decode.
+    int const warp_id        = t / cudf::detail::warp_size;
+    int const warp_decode_id = warp_id - 1;
+    int const warp_lane      = t % cudf::detail::warp_size;
+
+    __shared__ int run_start;
+    __shared__ int num_runs;
+    __shared__ int values_processed;
+    if (!t) {
+      // carryover from the last call.
+      thrust::tie(run_start, num_runs) = get_run_batch();
+      values_processed                 = 0;
+    }
+    __syncthreads();
+
+    do {
+      // warp 0 reads ahead and generates batches of runs to be decoded by remaining warps.
+      if (!warp_id) {
+        // fill the next set of runs. fill_runs will generally be the bottleneck for any
+        // kernel that uses an rle_stream.
+        if (warp_lane == 0) { fill_run_batch(output_count); }
+      }
+      // remaining warps decode the runs
+      else if (warp_decode_id < num_runs) {
+        // each warp handles 1 run, regardless of size.
+        // TODO: having each warp handle exactly 32 values would be ideal. as an example, the
+        // repetition levels for one of the list benchmarks decodes in ~3ms total, while the
+        // definition levels take ~11ms - the difference is entirely due to long runs in the
+        // definition levels.
+        auto& run  = runs[rolling_index<run_buffer_size>(run_start + warp_decode_id)];
+        auto batch = run.next_batch(output + run.output_pos,
+                                    min(run.remaining, (output_count - run.output_pos)));
+        batch.decode(end, level_bits, warp_lane, warp_decode_id);
+        // last warp updates total values processed
+        if (warp_lane == 0 && warp_decode_id == num_runs - 1) {
+          values_processed = run.output_pos + batch.size;
+        }
+      }
+      __syncthreads();
+
+      // if we haven't run out of space, retrieve the next batch. otherwise leave it for the next
+      // call.
+      if (!t && values_processed < output_count) {
+        thrust::tie(run_start, num_runs) = get_run_batch();
+      }
+      __syncthreads();
+    } while (num_runs > 0 && values_processed < output_count);
+
+    cur_values += values_processed;
+
+    // valid for every thread
+    return values_processed;
+  }
+};
+
+}  // namespace cudf::io::parquet::gpu
diff --git a/cpp/src/io/parquet/writer_impl.cu b/cpp/src/io/parquet/writer_impl.cu
new file mode 100644
index 0000000..a124f35
--- /dev/null
+++ b/cpp/src/io/parquet/writer_impl.cu
@@ -0,0 +1,2398 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file writer_impl.cu
+ * @brief cuDF-IO parquet writer class implementation
+ */
+
+#include "compact_protocol_reader.hpp"
+#include "compact_protocol_writer.hpp"
+#include "parquet_common.hpp"
+#include "parquet_gpu.cuh"
+#include "writer_impl.hpp"
+
+#include <io/comp/nvcomp_adapter.hpp>
+#include <io/statistics/column_statistics.cuh>
+#include <io/utilities/column_utils.cuh>
+#include <io/utilities/config_utils.hpp>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/linked_column.hpp>
+#include <cudf/detail/utilities/pinned_host_vector.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/detail/dremel.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+
+#include <algorithm>
+#include <cstring>
+#include <numeric>
+#include <utility>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace parquet {
+using namespace cudf::io::parquet;
+using namespace cudf::io;
+
+struct aggregate_writer_metadata {
+  aggregate_writer_metadata(host_span<partition_info const> partitions,
+                            host_span<std::map<std::string, std::string> const> kv_md,
+                            host_span<SchemaElement const> tbl_schema,
+                            size_type num_columns,
+                            statistics_freq stats_granularity)
+    : version(1),
+      schema(std::vector<SchemaElement>(tbl_schema.begin(), tbl_schema.end())),
+      files(partitions.size())
+  {
+    for (size_t i = 0; i < partitions.size(); ++i) {
+      this->files[i].num_rows = partitions[i].num_rows;
+    }
+
+    if (stats_granularity != statistics_freq::STATISTICS_NONE) {
+      ColumnOrder default_order = {ColumnOrder::TYPE_ORDER};
+      this->column_orders       = std::vector<ColumnOrder>(num_columns, default_order);
+    }
+
+    for (size_t p = 0; p < kv_md.size(); ++p) {
+      std::transform(kv_md[p].begin(),
+                     kv_md[p].end(),
+                     std::back_inserter(this->files[p].key_value_metadata),
+                     [](auto const& kv) {
+                       return KeyValue{kv.first, kv.second};
+                     });
+    }
+  }
+
+  aggregate_writer_metadata(aggregate_writer_metadata const&) = default;
+
+  void update_files(host_span<partition_info const> partitions)
+  {
+    CUDF_EXPECTS(partitions.size() == this->files.size(),
+                 "New partitions must be same size as previously passed number of partitions");
+    for (size_t i = 0; i < partitions.size(); ++i) {
+      this->files[i].num_rows += partitions[i].num_rows;
+    }
+  }
+
+  FileMetaData get_metadata(size_t part)
+  {
+    CUDF_EXPECTS(part < files.size(), "Invalid part index queried");
+    FileMetaData meta{};
+    meta.version            = this->version;
+    meta.schema             = this->schema;
+    meta.num_rows           = this->files[part].num_rows;
+    meta.row_groups         = this->files[part].row_groups;
+    meta.key_value_metadata = this->files[part].key_value_metadata;
+    meta.created_by         = this->created_by;
+    meta.column_orders      = this->column_orders;
+    return meta;
+  }
+
+  void set_file_paths(host_span<std::string const> column_chunks_file_path)
+  {
+    for (size_t p = 0; p < this->files.size(); ++p) {
+      auto& file            = this->files[p];
+      auto const& file_path = column_chunks_file_path[p];
+      for (auto& rowgroup : file.row_groups) {
+        for (auto& col : rowgroup.columns) {
+          col.file_path = file_path;
+        }
+      }
+    }
+  }
+
+  FileMetaData get_merged_metadata()
+  {
+    FileMetaData merged_md;
+    for (size_t p = 0; p < this->files.size(); ++p) {
+      auto& file = this->files[p];
+      if (p == 0) {
+        merged_md = this->get_metadata(0);
+      } else {
+        merged_md.row_groups.insert(merged_md.row_groups.end(),
+                                    std::make_move_iterator(file.row_groups.begin()),
+                                    std::make_move_iterator(file.row_groups.end()));
+        merged_md.num_rows += file.num_rows;
+      }
+    }
+    return merged_md;
+  }
+
+  std::vector<size_t> num_row_groups_per_file()
+  {
+    std::vector<size_t> global_rowgroup_base;
+    std::transform(this->files.begin(),
+                   this->files.end(),
+                   std::back_inserter(global_rowgroup_base),
+                   [](auto const& part) { return part.row_groups.size(); });
+    return global_rowgroup_base;
+  }
+
+  [[nodiscard]] bool schema_matches(std::vector<SchemaElement> const& schema) const
+  {
+    return this->schema == schema;
+  }
+  auto& file(size_t p) { return files[p]; }
+  [[nodiscard]] size_t num_files() const { return files.size(); }
+
+ private:
+  int32_t version = 0;
+  std::vector<SchemaElement> schema;
+  struct per_file_metadata {
+    int64_t num_rows = 0;
+    std::vector<RowGroup> row_groups;
+    std::vector<KeyValue> key_value_metadata;
+    std::vector<OffsetIndex> offset_indexes;
+    std::vector<std::vector<uint8_t>> column_indexes;
+  };
+  std::vector<per_file_metadata> files;
+  std::string created_by                                   = "";
+  thrust::optional<std::vector<ColumnOrder>> column_orders = thrust::nullopt;
+};
+
+namespace {
+
+/**
+ * @brief Function that translates GDF compression to parquet compression.
+ *
+ * @param compression The compression type
+ * @return The supported Parquet compression
+ */
+parquet::Compression to_parquet_compression(compression_type compression)
+{
+  switch (compression) {
+    case compression_type::AUTO:
+    case compression_type::SNAPPY: return parquet::Compression::SNAPPY;
+    case compression_type::ZSTD: return parquet::Compression::ZSTD;
+    case compression_type::NONE: return parquet::Compression::UNCOMPRESSED;
+    default: CUDF_FAIL("Unsupported compression type");
+  }
+}
+
+/**
+ * @brief Convert a mask of encodings to a vector.
+ *
+ * @param encodings Vector of `Encoding`s to populate
+ * @param enc_mask Mask of encodings used
+ */
+void update_chunk_encodings(std::vector<Encoding>& encodings, uint32_t enc_mask)
+{
+  for (uint8_t enc = 0; enc < static_cast<uint8_t>(Encoding::NUM_ENCODINGS); enc++) {
+    auto const enc_enum = static_cast<Encoding>(enc);
+    if ((enc_mask & gpu::encoding_to_mask(enc_enum)) != 0) { encodings.push_back(enc_enum); }
+  }
+}
+
+/**
+ * @brief Compute size (in bytes) of the data stored in the given column.
+ *
+ * @param column The input column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return The data size of the input
+ */
+size_t column_size(column_view const& column, rmm::cuda_stream_view stream)
+{
+  if (column.is_empty()) { return 0; }
+
+  if (is_fixed_width(column.type())) {
+    return size_of(column.type()) * column.size();
+  } else if (column.type().id() == type_id::STRING) {
+    auto const scol = strings_column_view(column);
+    return cudf::detail::get_value<size_type>(scol.offsets(), column.size(), stream) -
+           cudf::detail::get_value<size_type>(scol.offsets(), 0, stream);
+  } else if (column.type().id() == type_id::STRUCT) {
+    auto const scol = structs_column_view(column);
+    size_t ret      = 0;
+    for (int i = 0; i < scol.num_children(); i++) {
+      ret += column_size(scol.get_sliced_child(i), stream);
+    }
+    return ret;
+  } else if (column.type().id() == type_id::LIST) {
+    auto const lcol = lists_column_view(column);
+    return column_size(lcol.get_sliced_child(stream), stream);
+  }
+
+  CUDF_FAIL("Unexpected compound type");
+}
+
+// checks to see if the given column has a fixed size.  This doesn't
+// check every row, so assumes string and list columns are not fixed, even
+// if each row is the same width.
+// TODO: update this if FIXED_LEN_BYTE_ARRAY is ever supported for writes.
+bool is_col_fixed_width(column_view const& column)
+{
+  if (column.type().id() == type_id::STRUCT) {
+    return std::all_of(column.child_begin(), column.child_end(), is_col_fixed_width);
+  }
+
+  return is_fixed_width(column.type());
+}
+
+/**
+ * @brief Extends SchemaElement to add members required in constructing parquet_column_view
+ *
+ * Added members are:
+ * 1. leaf_column: Pointer to leaf linked_column_view which points to the corresponding data stream
+ *    of a leaf schema node. For non-leaf struct node, this is nullptr.
+ * 2. stats_dtype: datatype for statistics calculation required for the data stream of a leaf node.
+ * 3. ts_scale: scale to multiply or divide timestamp by in order to convert timestamp to parquet
+ *    supported types
+ */
+struct schema_tree_node : public SchemaElement {
+  cudf::detail::LinkedColPtr leaf_column;
+  statistics_dtype stats_dtype;
+  int32_t ts_scale;
+
+  // TODO(fut): Think about making schema a class that holds a vector of schema_tree_nodes. The
+  // function construct_schema_tree could be its constructor. It can have method to get the per
+  // column nullability given a schema node index corresponding to a leaf schema. Much easier than
+  // that is a method to get path in schema, given a leaf node
+};
+
+struct leaf_schema_fn {
+  schema_tree_node& col_schema;
+  cudf::detail::LinkedColPtr const& col;
+  column_in_metadata const& col_meta;
+  bool timestamp_is_int96;
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, bool>, void> operator()()
+  {
+    col_schema.type        = Type::BOOLEAN;
+    col_schema.stats_dtype = statistics_dtype::dtype_bool;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, int8_t>, void> operator()()
+  {
+    col_schema.type           = Type::INT32;
+    col_schema.converted_type = ConvertedType::INT_8;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int8;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, int16_t>, void> operator()()
+  {
+    col_schema.type           = Type::INT32;
+    col_schema.converted_type = ConvertedType::INT_16;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int16;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, int32_t>, void> operator()()
+  {
+    col_schema.type        = Type::INT32;
+    col_schema.stats_dtype = statistics_dtype::dtype_int32;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, int64_t>, void> operator()()
+  {
+    col_schema.type        = Type::INT64;
+    col_schema.stats_dtype = statistics_dtype::dtype_int64;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, uint8_t>, void> operator()()
+  {
+    col_schema.type           = Type::INT32;
+    col_schema.converted_type = ConvertedType::UINT_8;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int8;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, uint16_t>, void> operator()()
+  {
+    col_schema.type           = Type::INT32;
+    col_schema.converted_type = ConvertedType::UINT_16;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int16;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, uint32_t>, void> operator()()
+  {
+    col_schema.type           = Type::INT32;
+    col_schema.converted_type = ConvertedType::UINT_32;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int32;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, uint64_t>, void> operator()()
+  {
+    col_schema.type           = Type::INT64;
+    col_schema.converted_type = ConvertedType::UINT_64;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int64;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, float>, void> operator()()
+  {
+    col_schema.type        = Type::FLOAT;
+    col_schema.stats_dtype = statistics_dtype::dtype_float32;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, double>, void> operator()()
+  {
+    col_schema.type        = Type::DOUBLE;
+    col_schema.stats_dtype = statistics_dtype::dtype_float64;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::string_view>, void> operator()()
+  {
+    col_schema.type = Type::BYTE_ARRAY;
+    if (col_meta.is_enabled_output_as_binary()) {
+      col_schema.converted_type = ConvertedType::UNKNOWN;
+      col_schema.stats_dtype    = statistics_dtype::dtype_byte_array;
+    } else {
+      col_schema.converted_type = ConvertedType::UTF8;
+      col_schema.stats_dtype    = statistics_dtype::dtype_string;
+    }
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::timestamp_D>, void> operator()()
+  {
+    col_schema.type           = Type::INT32;
+    col_schema.converted_type = ConvertedType::DATE;
+    col_schema.stats_dtype    = statistics_dtype::dtype_int32;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::timestamp_s>, void> operator()()
+  {
+    col_schema.type = (timestamp_is_int96) ? Type::INT96 : Type::INT64;
+    col_schema.converted_type =
+      (timestamp_is_int96) ? ConvertedType::UNKNOWN : ConvertedType::TIMESTAMP_MILLIS;
+    col_schema.stats_dtype = statistics_dtype::dtype_timestamp64;
+    col_schema.ts_scale    = 1000;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::timestamp_ms>, void> operator()()
+  {
+    col_schema.type = (timestamp_is_int96) ? Type::INT96 : Type::INT64;
+    col_schema.converted_type =
+      (timestamp_is_int96) ? ConvertedType::UNKNOWN : ConvertedType::TIMESTAMP_MILLIS;
+    col_schema.stats_dtype = statistics_dtype::dtype_timestamp64;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::timestamp_us>, void> operator()()
+  {
+    col_schema.type = (timestamp_is_int96) ? Type::INT96 : Type::INT64;
+    col_schema.converted_type =
+      (timestamp_is_int96) ? ConvertedType::UNKNOWN : ConvertedType::TIMESTAMP_MICROS;
+    col_schema.stats_dtype = statistics_dtype::dtype_timestamp64;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::timestamp_ns>, void> operator()()
+  {
+    col_schema.type           = (timestamp_is_int96) ? Type::INT96 : Type::INT64;
+    col_schema.converted_type = ConvertedType::UNKNOWN;
+    col_schema.stats_dtype    = statistics_dtype::dtype_timestamp64;
+    if (timestamp_is_int96) {
+      col_schema.ts_scale = -1000;  // negative value indicates division by absolute value
+    }
+    // set logical type if it's not int96
+    else {
+      col_schema.logical_type.isset.TIMESTAMP            = true;
+      col_schema.logical_type.TIMESTAMP.unit.isset.NANOS = true;
+    }
+  }
+
+  //  unsupported outside cudf for parquet 1.0.
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::duration_D>, void> operator()()
+  {
+    col_schema.type                                = Type::INT32;
+    col_schema.converted_type                      = ConvertedType::TIME_MILLIS;
+    col_schema.stats_dtype                         = statistics_dtype::dtype_int32;
+    col_schema.ts_scale                            = 24 * 60 * 60 * 1000;
+    col_schema.logical_type.isset.TIME             = true;
+    col_schema.logical_type.TIME.unit.isset.MILLIS = true;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::duration_s>, void> operator()()
+  {
+    col_schema.type                                = Type::INT32;
+    col_schema.converted_type                      = ConvertedType::TIME_MILLIS;
+    col_schema.stats_dtype                         = statistics_dtype::dtype_int32;
+    col_schema.ts_scale                            = 1000;
+    col_schema.logical_type.isset.TIME             = true;
+    col_schema.logical_type.TIME.unit.isset.MILLIS = true;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::duration_ms>, void> operator()()
+  {
+    col_schema.type                                = Type::INT32;
+    col_schema.converted_type                      = ConvertedType::TIME_MILLIS;
+    col_schema.stats_dtype                         = statistics_dtype::dtype_int32;
+    col_schema.logical_type.isset.TIME             = true;
+    col_schema.logical_type.TIME.unit.isset.MILLIS = true;
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::duration_us>, void> operator()()
+  {
+    col_schema.type                                = Type::INT64;
+    col_schema.converted_type                      = ConvertedType::TIME_MICROS;
+    col_schema.stats_dtype                         = statistics_dtype::dtype_int64;
+    col_schema.logical_type.isset.TIME             = true;
+    col_schema.logical_type.TIME.unit.isset.MICROS = true;
+  }
+
+  //  unsupported outside cudf for parquet 1.0.
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, cudf::duration_ns>, void> operator()()
+  {
+    col_schema.type                               = Type::INT64;
+    col_schema.stats_dtype                        = statistics_dtype::dtype_int64;
+    col_schema.logical_type.isset.TIME            = true;
+    col_schema.logical_type.TIME.unit.isset.NANOS = true;
+  }
+
+  template <typename T>
+  std::enable_if_t<cudf::is_fixed_point<T>(), void> operator()()
+  {
+    if (std::is_same_v<T, numeric::decimal32>) {
+      col_schema.type              = Type::INT32;
+      col_schema.stats_dtype       = statistics_dtype::dtype_int32;
+      col_schema.decimal_precision = MAX_DECIMAL32_PRECISION;
+    } else if (std::is_same_v<T, numeric::decimal64>) {
+      col_schema.type              = Type::INT64;
+      col_schema.stats_dtype       = statistics_dtype::dtype_decimal64;
+      col_schema.decimal_precision = MAX_DECIMAL64_PRECISION;
+    } else if (std::is_same_v<T, numeric::decimal128>) {
+      col_schema.type              = Type::FIXED_LEN_BYTE_ARRAY;
+      col_schema.type_length       = sizeof(__int128_t);
+      col_schema.stats_dtype       = statistics_dtype::dtype_decimal128;
+      col_schema.decimal_precision = MAX_DECIMAL128_PRECISION;
+    } else {
+      CUDF_FAIL("Unsupported fixed point type for parquet writer");
+    }
+    col_schema.converted_type = ConvertedType::DECIMAL;
+    col_schema.decimal_scale = -col->type().scale();  // parquet and cudf disagree about scale signs
+    if (col_meta.is_decimal_precision_set()) {
+      CUDF_EXPECTS(col_meta.get_decimal_precision() >= col_schema.decimal_scale,
+                   "Precision must be equal to or greater than scale!");
+      if (col_schema.type == Type::INT64 and col_meta.get_decimal_precision() < 10) {
+        CUDF_LOG_WARN("Parquet writer: writing a decimal column with precision < 10 as int64");
+      }
+      col_schema.decimal_precision = col_meta.get_decimal_precision();
+    }
+  }
+
+  template <typename T>
+  std::enable_if_t<cudf::is_nested<T>(), void> operator()()
+  {
+    CUDF_FAIL("This functor is only meant for physical data types");
+  }
+
+  template <typename T>
+  std::enable_if_t<cudf::is_dictionary<T>(), void> operator()()
+  {
+    CUDF_FAIL("Dictionary columns are not supported for writing");
+  }
+};
+
+inline bool is_col_nullable(cudf::detail::LinkedColPtr const& col,
+                            column_in_metadata const& col_meta,
+                            single_write_mode write_mode)
+{
+  if (col_meta.is_nullability_defined()) {
+    CUDF_EXPECTS(col_meta.nullable() or col->null_count() == 0,
+                 "Mismatch in metadata prescribed nullability and input column. "
+                 "Metadata for input column with nulls cannot prescribe nullability = false");
+    return col_meta.nullable();
+  }
+  // For chunked write, when not provided nullability, we assume the worst case scenario
+  // that all columns are nullable.
+  return write_mode == single_write_mode::NO or col->nullable();
+}
+
+/**
+ * @brief Construct schema from input columns and per-column input options
+ *
+ * Recursively traverses through linked_columns and corresponding metadata to construct schema tree.
+ * The resulting schema tree is stored in a vector in pre-order traversal order.
+ */
+std::vector<schema_tree_node> construct_schema_tree(
+  cudf::detail::LinkedColVector const& linked_columns,
+  table_input_metadata& metadata,
+  single_write_mode write_mode,
+  bool int96_timestamps)
+{
+  std::vector<schema_tree_node> schema;
+  schema_tree_node root{};
+  root.type            = UNDEFINED_TYPE;
+  root.repetition_type = NO_REPETITION_TYPE;
+  root.name            = "schema";
+  root.num_children    = linked_columns.size();
+  root.parent_idx      = -1;  // root schema has no parent
+  schema.push_back(std::move(root));
+
+  std::function<void(cudf::detail::LinkedColPtr const&, column_in_metadata&, size_t)> add_schema =
+    [&](cudf::detail::LinkedColPtr const& col, column_in_metadata& col_meta, size_t parent_idx) {
+      bool col_nullable = is_col_nullable(col, col_meta, write_mode);
+
+      auto set_field_id = [&schema, parent_idx](schema_tree_node& s,
+                                                column_in_metadata const& col_meta) {
+        if (schema[parent_idx].name != "list" and col_meta.is_parquet_field_id_set()) {
+          s.field_id = col_meta.get_parquet_field_id();
+        }
+      };
+
+      auto is_last_list_child = [](cudf::detail::LinkedColPtr col) {
+        if (col->type().id() != type_id::LIST) { return false; }
+        auto const child_col_type =
+          col->children[lists_column_view::child_column_index]->type().id();
+        return child_col_type == type_id::UINT8;
+      };
+
+      // There is a special case for a list<int8> column with one byte column child. This column can
+      // have a special flag that indicates we write this out as binary instead of a list. This is a
+      // more efficient storage mechanism for a single-depth list of bytes, but is a departure from
+      // original cuIO behavior so it is locked behind the option. If the option is selected on a
+      // column that isn't a single-depth list<int8> the code will throw.
+      if (col_meta.is_enabled_output_as_binary() && is_last_list_child(col)) {
+        CUDF_EXPECTS(col_meta.num_children() == 2 or col_meta.num_children() == 0,
+                     "Binary column's corresponding metadata should have zero or two children!");
+        if (col_meta.num_children() > 0) {
+          CUDF_EXPECTS(col->children[lists_column_view::child_column_index]->children.empty(),
+                       "Binary column must not be nested!");
+        }
+
+        schema_tree_node col_schema{};
+        col_schema.type            = Type::BYTE_ARRAY;
+        col_schema.converted_type  = ConvertedType::UNKNOWN;
+        col_schema.stats_dtype     = statistics_dtype::dtype_byte_array;
+        col_schema.repetition_type = col_nullable ? OPTIONAL : REQUIRED;
+        col_schema.name = (schema[parent_idx].name == "list") ? "element" : col_meta.get_name();
+        col_schema.parent_idx  = parent_idx;
+        col_schema.leaf_column = col;
+        set_field_id(col_schema, col_meta);
+        col_schema.output_as_byte_array = col_meta.is_enabled_output_as_binary();
+        schema.push_back(col_schema);
+      } else if (col->type().id() == type_id::STRUCT) {
+        // if struct, add current and recursively call for all children
+        schema_tree_node struct_schema{};
+        struct_schema.repetition_type =
+          col_nullable ? FieldRepetitionType::OPTIONAL : FieldRepetitionType::REQUIRED;
+
+        struct_schema.name = (schema[parent_idx].name == "list") ? "element" : col_meta.get_name();
+        struct_schema.num_children = col->children.size();
+        struct_schema.parent_idx   = parent_idx;
+        set_field_id(struct_schema, col_meta);
+        schema.push_back(std::move(struct_schema));
+
+        auto struct_node_index = schema.size() - 1;
+        // for (auto child_it = col->children.begin(); child_it < col->children.end(); child_it++) {
+        //   add_schema(*child_it, struct_node_index);
+        // }
+        CUDF_EXPECTS(col->children.size() == static_cast<size_t>(col_meta.num_children()),
+                     "Mismatch in number of child columns between input table and metadata");
+        for (size_t i = 0; i < col->children.size(); ++i) {
+          add_schema(col->children[i], col_meta.child(i), struct_node_index);
+        }
+      } else if (col->type().id() == type_id::LIST && !col_meta.is_map()) {
+        // List schema is denoted by two levels for each nesting level and one final level for leaf.
+        // The top level is the same name as the column name.
+        // So e.g. List<List<int>> is denoted in the schema by
+        // "col_name" : { "list" : { "element" : { "list" : { "element" } } } }
+
+        schema_tree_node list_schema_1{};
+        list_schema_1.converted_type = ConvertedType::LIST;
+        list_schema_1.repetition_type =
+          col_nullable ? FieldRepetitionType::OPTIONAL : FieldRepetitionType::REQUIRED;
+        list_schema_1.name = (schema[parent_idx].name == "list") ? "element" : col_meta.get_name();
+        list_schema_1.num_children = 1;
+        list_schema_1.parent_idx   = parent_idx;
+        set_field_id(list_schema_1, col_meta);
+        schema.push_back(std::move(list_schema_1));
+
+        schema_tree_node list_schema_2{};
+        list_schema_2.repetition_type = FieldRepetitionType::REPEATED;
+        list_schema_2.name            = "list";
+        list_schema_2.num_children    = 1;
+        list_schema_2.parent_idx      = schema.size() - 1;  // Parent is list_schema_1, last added.
+        schema.push_back(std::move(list_schema_2));
+
+        CUDF_EXPECTS(col_meta.num_children() == 2,
+                     "List column's metadata should have exactly two children");
+
+        add_schema(col->children[lists_column_view::child_column_index],
+                   col_meta.child(lists_column_view::child_column_index),
+                   schema.size() - 1);
+      } else if (col->type().id() == type_id::LIST && col_meta.is_map()) {
+        // Map schema is denoted by a list of struct
+        // e.g. List<Struct<String,String>> will be
+        // "col_name" : { "key_value" : { "key", "value" } }
+
+        // verify the List child structure is a struct<left_child, right_child>
+        column_view struct_col = *col->children[lists_column_view::child_column_index];
+        CUDF_EXPECTS(struct_col.type().id() == type_id::STRUCT, "Map should be a List of struct");
+        CUDF_EXPECTS(struct_col.num_children() == 2,
+                     "Map should be a List of struct with two children only but found " +
+                       std::to_string(struct_col.num_children()));
+
+        schema_tree_node map_schema{};
+        map_schema.converted_type = ConvertedType::MAP;
+        map_schema.repetition_type =
+          col_nullable ? FieldRepetitionType::OPTIONAL : FieldRepetitionType::REQUIRED;
+        map_schema.name = col_meta.get_name();
+        if (col_meta.is_parquet_field_id_set()) {
+          map_schema.field_id = col_meta.get_parquet_field_id();
+        }
+        map_schema.num_children = 1;
+        map_schema.parent_idx   = parent_idx;
+        schema.push_back(std::move(map_schema));
+
+        schema_tree_node repeat_group{};
+        repeat_group.repetition_type = FieldRepetitionType::REPEATED;
+        repeat_group.name            = "key_value";
+        repeat_group.num_children    = 2;
+        repeat_group.parent_idx      = schema.size() - 1;  // Parent is map_schema, last added.
+        schema.push_back(std::move(repeat_group));
+
+        CUDF_EXPECTS(col_meta.num_children() == 2,
+                     "List column's metadata should have exactly two children");
+        CUDF_EXPECTS(col_meta.child(lists_column_view::child_column_index).num_children() == 2,
+                     "Map struct column should have exactly two children");
+        // verify the col meta of children of the struct have name key and value
+        auto& left_child_meta = col_meta.child(lists_column_view::child_column_index).child(0);
+        left_child_meta.set_name("key");
+        left_child_meta.set_nullability(false);
+
+        auto& right_child_meta = col_meta.child(lists_column_view::child_column_index).child(1);
+        right_child_meta.set_name("value");
+        // check the repetition type of key is required i.e. the col should be non-nullable
+        auto key_col = col->children[lists_column_view::child_column_index]->children[0];
+        CUDF_EXPECTS(!is_col_nullable(key_col, left_child_meta, write_mode),
+                     "key column cannot be nullable. For chunked writing, explicitly set the "
+                     "nullability to false in metadata");
+        // process key
+        size_type struct_col_index = schema.size() - 1;
+        add_schema(key_col, left_child_meta, struct_col_index);
+        // process value
+        add_schema(col->children[lists_column_view::child_column_index]->children[1],
+                   right_child_meta,
+                   struct_col_index);
+
+      } else {
+        // if leaf, add current
+        if (col->type().id() == type_id::STRING) {
+          CUDF_EXPECTS(col_meta.num_children() == 2 or col_meta.num_children() == 0,
+                       "String column's corresponding metadata should have zero or two children");
+        } else {
+          CUDF_EXPECTS(col_meta.num_children() == 0,
+                       "Leaf column's corresponding metadata cannot have children");
+        }
+
+        schema_tree_node col_schema{};
+
+        bool timestamp_is_int96 = int96_timestamps or col_meta.is_enabled_int96_timestamps();
+
+        cudf::type_dispatcher(col->type(),
+                              leaf_schema_fn{col_schema, col, col_meta, timestamp_is_int96});
+
+        col_schema.repetition_type = col_nullable ? OPTIONAL : REQUIRED;
+        col_schema.name = (schema[parent_idx].name == "list") ? "element" : col_meta.get_name();
+        col_schema.parent_idx  = parent_idx;
+        col_schema.leaf_column = col;
+        set_field_id(col_schema, col_meta);
+        schema.push_back(col_schema);
+      }
+    };
+
+  CUDF_EXPECTS(metadata.column_metadata.size() == linked_columns.size(),
+               "Mismatch in the number of columns and the corresponding metadata elements");
+  // Add all linked_columns to schema using parent_idx = 0 (root)
+  for (size_t i = 0; i < linked_columns.size(); ++i) {
+    add_schema(linked_columns[i], metadata.column_metadata[i], 0);
+  }
+
+  return schema;
+}
+
+/**
+ * @brief Class to store parquet specific information for one data stream.
+ *
+ * Contains information about a single data stream. In case of struct columns, a data stream is one
+ * of the child leaf columns that contains data.
+ * e.g. A column Struct<int, List<float>> contains 2 data streams:
+ * - Struct<int>
+ * - Struct<List<float>>
+ *
+ */
+struct parquet_column_view {
+  parquet_column_view(schema_tree_node const& schema_node,
+                      std::vector<schema_tree_node> const& schema_tree,
+                      rmm::cuda_stream_view stream);
+
+  [[nodiscard]] gpu::parquet_column_device_view get_device_view(rmm::cuda_stream_view stream) const;
+
+  [[nodiscard]] column_view cudf_column_view() const { return cudf_col; }
+  [[nodiscard]] parquet::Type physical_type() const { return schema_node.type; }
+  [[nodiscard]] parquet::ConvertedType converted_type() const { return schema_node.converted_type; }
+
+  std::vector<std::string> const& get_path_in_schema() { return path_in_schema; }
+
+  // LIST related member functions
+  [[nodiscard]] uint8_t max_def_level() const noexcept { return _max_def_level; }
+  [[nodiscard]] uint8_t max_rep_level() const noexcept { return _max_rep_level; }
+  [[nodiscard]] bool is_list() const noexcept { return _is_list; }
+
+ private:
+  // Schema related members
+  schema_tree_node schema_node;
+  std::vector<std::string> path_in_schema;
+  uint8_t _max_def_level = 0;
+  uint8_t _max_rep_level = 0;
+  rmm::device_uvector<uint8_t> _d_nullability;
+
+  column_view cudf_col;
+
+  // List-related members
+  bool _is_list;
+  rmm::device_uvector<size_type>
+    _dremel_offsets;  ///< For each row, the absolute offset into the repetition and definition
+                      ///< level vectors. O(num rows)
+  rmm::device_uvector<uint8_t> _rep_level;
+  rmm::device_uvector<uint8_t> _def_level;
+  std::vector<uint8_t> _nullability;
+  size_type _data_count = 0;
+};
+
+parquet_column_view::parquet_column_view(schema_tree_node const& schema_node,
+                                         std::vector<schema_tree_node> const& schema_tree,
+                                         rmm::cuda_stream_view stream)
+  : schema_node(schema_node),
+    _d_nullability(0, stream),
+    _dremel_offsets(0, stream),
+    _rep_level(0, stream),
+    _def_level(0, stream)
+{
+  // Construct single inheritance column_view from linked_column_view
+  auto curr_col                           = schema_node.leaf_column.get();
+  column_view single_inheritance_cudf_col = *curr_col;
+  while (curr_col->parent) {
+    auto const& parent = *curr_col->parent;
+
+    // For list columns, we still need to retain the offset child column.
+    auto children =
+      (parent.type().id() == type_id::LIST)
+        ? std::vector<column_view>{*parent.children[lists_column_view::offsets_column_index],
+                                   single_inheritance_cudf_col}
+        : std::vector<column_view>{single_inheritance_cudf_col};
+
+    single_inheritance_cudf_col = column_view(parent.type(),
+                                              parent.size(),
+                                              parent.head(),
+                                              parent.null_mask(),
+                                              parent.null_count(),
+                                              parent.offset(),
+                                              children);
+
+    curr_col = curr_col->parent;
+  }
+  cudf_col = single_inheritance_cudf_col;
+
+  // Construct path_in_schema by travelling up in the schema_tree
+  std::vector<std::string> path;
+  auto curr_schema_node = schema_node;
+  do {
+    path.push_back(curr_schema_node.name);
+    if (curr_schema_node.parent_idx != -1) {
+      curr_schema_node = schema_tree[curr_schema_node.parent_idx];
+    }
+  } while (curr_schema_node.parent_idx != -1);
+  path_in_schema = std::vector<std::string>(path.crbegin(), path.crend());
+
+  // Calculate max definition level by counting the number of levels that are optional (nullable)
+  // and max repetition level by counting the number of REPEATED levels in this column's hierarchy
+  uint16_t max_def_level = 0;
+  uint16_t max_rep_level = 0;
+  curr_schema_node       = schema_node;
+  while (curr_schema_node.parent_idx != -1) {
+    if (curr_schema_node.repetition_type == parquet::REPEATED or
+        curr_schema_node.repetition_type == parquet::OPTIONAL) {
+      ++max_def_level;
+    }
+    if (curr_schema_node.repetition_type == parquet::REPEATED) { ++max_rep_level; }
+    curr_schema_node = schema_tree[curr_schema_node.parent_idx];
+  }
+  CUDF_EXPECTS(max_def_level < 256, "Definition levels above 255 are not supported");
+  CUDF_EXPECTS(max_rep_level < 256, "Definition levels above 255 are not supported");
+
+  _max_def_level = max_def_level;
+  _max_rep_level = max_rep_level;
+
+  // Construct nullability vector using repetition_type from schema.
+  std::vector<uint8_t> r_nullability;
+  curr_schema_node = schema_node;
+  while (curr_schema_node.parent_idx != -1) {
+    if (not curr_schema_node.is_stub()) {
+      r_nullability.push_back(curr_schema_node.repetition_type == FieldRepetitionType::OPTIONAL);
+    }
+    curr_schema_node = schema_tree[curr_schema_node.parent_idx];
+  }
+  _nullability = std::vector<uint8_t>(r_nullability.crbegin(), r_nullability.crend());
+  // TODO(cp): Explore doing this for all columns in a single go outside this ctor. Maybe using
+  // hostdevice_vector. Currently this involves a cudaMemcpyAsync for each column.
+  _d_nullability = cudf::detail::make_device_uvector_async(
+    _nullability, stream, rmm::mr::get_current_device_resource());
+
+  _is_list = (_max_rep_level > 0);
+
+  if (cudf_col.is_empty()) { return; }
+
+  if (_is_list) {
+    // Top level column's offsets are not applied to all children. Get the effective offset and
+    // size of the leaf column
+    // Calculate row offset into dremel data (repetition/definition values) and the respective
+    // definition and repetition levels
+    cudf::detail::dremel_data dremel =
+      get_dremel_data(cudf_col, _nullability, schema_node.output_as_byte_array, stream);
+    _dremel_offsets = std::move(dremel.dremel_offsets);
+    _rep_level      = std::move(dremel.rep_level);
+    _def_level      = std::move(dremel.def_level);
+    _data_count     = dremel.leaf_data_size;  // Needed for knowing what size dictionary to allocate
+
+    stream.synchronize();
+  } else {
+    // For non-list struct, the size of the root column is the same as the size of the leaf column
+    _data_count = cudf_col.size();
+  }
+}
+
+gpu::parquet_column_device_view parquet_column_view::get_device_view(rmm::cuda_stream_view) const
+{
+  auto desc        = gpu::parquet_column_device_view{};  // Zero out all fields
+  desc.stats_dtype = schema_node.stats_dtype;
+  desc.ts_scale    = schema_node.ts_scale;
+
+  if (is_list()) {
+    desc.level_offsets = _dremel_offsets.data();
+    desc.rep_values    = _rep_level.data();
+    desc.def_values    = _def_level.data();
+  }
+  desc.num_rows             = cudf_col.size();
+  desc.physical_type        = physical_type();
+  desc.converted_type       = converted_type();
+  desc.output_as_byte_array = schema_node.output_as_byte_array;
+
+  desc.level_bits = CompactProtocolReader::NumRequiredBits(max_rep_level()) << 4 |
+                    CompactProtocolReader::NumRequiredBits(max_def_level());
+  desc.nullability = _d_nullability.data();
+  return desc;
+}
+
+/**
+ * @brief Gather row group fragments
+ *
+ * This calculates fragments to be used in determining row group boundaries.
+ *
+ * @param frag Destination row group fragments
+ * @param col_desc column description array
+ * @param partitions Information about partitioning of table
+ * @param part_frag_offset A Partition's offset into fragment array
+ * @param fragment_size Number of rows per fragment
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void init_row_group_fragments(cudf::detail::hostdevice_2dvector<gpu::PageFragment>& frag,
+                              device_span<gpu::parquet_column_device_view const> col_desc,
+                              host_span<partition_info const> partitions,
+                              device_span<int const> part_frag_offset,
+                              uint32_t fragment_size,
+                              rmm::cuda_stream_view stream)
+{
+  auto d_partitions = cudf::detail::make_device_uvector_async(
+    partitions, stream, rmm::mr::get_current_device_resource());
+  gpu::InitRowGroupFragments(frag, col_desc, d_partitions, part_frag_offset, fragment_size, stream);
+  frag.device_to_host_sync(stream);
+}
+
+/**
+ * @brief Recalculate page fragments
+ *
+ * This calculates fragments to be used to determine page boundaries within
+ * column chunks.
+ *
+ * @param frag Destination page fragments
+ * @param frag_sizes Array of fragment sizes for each column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void calculate_page_fragments(device_span<gpu::PageFragment> frag,
+                              host_span<size_type const> frag_sizes,
+                              rmm::cuda_stream_view stream)
+{
+  auto d_frag_sz = cudf::detail::make_device_uvector_async(
+    frag_sizes, stream, rmm::mr::get_current_device_resource());
+  gpu::CalculatePageFragments(frag, d_frag_sz, stream);
+}
+
+/**
+ * @brief Gather per-fragment statistics
+ *
+ * @param frag_stats output statistics
+ * @param frags Input page fragments
+ * @param int96_timestamps Flag to indicate if timestamps will be written as INT96
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void gather_fragment_statistics(device_span<statistics_chunk> frag_stats,
+                                device_span<gpu::PageFragment const> frags,
+                                bool int96_timestamps,
+                                rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<statistics_group> frag_stats_group(frag_stats.size(), stream);
+
+  gpu::InitFragmentStatistics(frag_stats_group, frags, stream);
+  detail::calculate_group_statistics<detail::io_file_format::PARQUET>(
+    frag_stats.data(), frag_stats_group.data(), frag_stats.size(), stream, int96_timestamps);
+  stream.synchronize();
+}
+
+auto to_nvcomp_compression_type(Compression codec)
+{
+  if (codec == Compression::SNAPPY) return nvcomp::compression_type::SNAPPY;
+  if (codec == Compression::ZSTD) return nvcomp::compression_type::ZSTD;
+  CUDF_FAIL("Unsupported compression type");
+}
+
+auto page_alignment(Compression codec)
+{
+  if (codec == Compression::UNCOMPRESSED or
+      nvcomp::is_compression_disabled(to_nvcomp_compression_type(codec))) {
+    return 1u;
+  }
+
+  return 1u << nvcomp::compress_input_alignment_bits(to_nvcomp_compression_type(codec));
+}
+
+size_t max_compression_output_size(Compression codec, uint32_t compression_blocksize)
+{
+  if (codec == Compression::UNCOMPRESSED) return 0;
+
+  return compress_max_output_chunk_size(to_nvcomp_compression_type(codec), compression_blocksize);
+}
+
+auto init_page_sizes(hostdevice_2dvector<gpu::EncColumnChunk>& chunks,
+                     device_span<gpu::parquet_column_device_view const> col_desc,
+                     uint32_t num_columns,
+                     size_t max_page_size_bytes,
+                     size_type max_page_size_rows,
+                     bool write_v2_headers,
+                     Compression compression_codec,
+                     rmm::cuda_stream_view stream)
+{
+  if (chunks.is_empty()) { return cudf::detail::hostdevice_vector<size_type>{}; }
+
+  chunks.host_to_device_async(stream);
+  // Calculate number of pages and store in respective chunks
+  gpu::InitEncoderPages(chunks,
+                        {},
+                        {},
+                        {},
+                        col_desc,
+                        num_columns,
+                        max_page_size_bytes,
+                        max_page_size_rows,
+                        page_alignment(compression_codec),
+                        write_v2_headers,
+                        nullptr,
+                        nullptr,
+                        stream);
+  chunks.device_to_host_sync(stream);
+
+  int num_pages = 0;
+  for (auto& chunk : chunks.host_view().flat_view()) {
+    chunk.first_page = num_pages;
+    num_pages += chunk.num_pages;
+  }
+  chunks.host_to_device_async(stream);
+
+  // Now that we know the number of pages, allocate an array to hold per page size and get it
+  // populated
+  cudf::detail::hostdevice_vector<size_type> page_sizes(num_pages, stream);
+  gpu::InitEncoderPages(chunks,
+                        {},
+                        page_sizes,
+                        {},
+                        col_desc,
+                        num_columns,
+                        max_page_size_bytes,
+                        max_page_size_rows,
+                        page_alignment(compression_codec),
+                        write_v2_headers,
+                        nullptr,
+                        nullptr,
+                        stream);
+  page_sizes.device_to_host_sync(stream);
+
+  // Get per-page max compressed size
+  cudf::detail::hostdevice_vector<size_type> comp_page_sizes(num_pages, stream);
+  std::transform(page_sizes.begin(),
+                 page_sizes.end(),
+                 comp_page_sizes.begin(),
+                 [compression_codec](auto page_size) {
+                   return max_compression_output_size(compression_codec, page_size);
+                 });
+  comp_page_sizes.host_to_device_async(stream);
+
+  // Use per-page max compressed size to calculate chunk.compressed_size
+  gpu::InitEncoderPages(chunks,
+                        {},
+                        {},
+                        comp_page_sizes,
+                        col_desc,
+                        num_columns,
+                        max_page_size_bytes,
+                        max_page_size_rows,
+                        page_alignment(compression_codec),
+                        write_v2_headers,
+                        nullptr,
+                        nullptr,
+                        stream);
+  chunks.device_to_host_sync(stream);
+  return comp_page_sizes;
+}
+
+size_t max_page_bytes(Compression compression, size_t max_page_size_bytes)
+{
+  if (compression == parquet::Compression::UNCOMPRESSED) { return max_page_size_bytes; }
+
+  auto const ncomp_type   = to_nvcomp_compression_type(compression);
+  auto const nvcomp_limit = nvcomp::is_compression_disabled(ncomp_type)
+                              ? std::nullopt
+                              : nvcomp::compress_max_allowed_chunk_size(ncomp_type);
+
+  auto max_size = std::min(nvcomp_limit.value_or(max_page_size_bytes), max_page_size_bytes);
+  // page size must fit in a 32-bit signed integer
+  return std::min<size_t>(max_size, std::numeric_limits<int32_t>::max());
+}
+
+std::pair<std::vector<rmm::device_uvector<size_type>>, std::vector<rmm::device_uvector<size_type>>>
+build_chunk_dictionaries(hostdevice_2dvector<gpu::EncColumnChunk>& chunks,
+                         host_span<gpu::parquet_column_device_view const> col_desc,
+                         device_2dspan<gpu::PageFragment const> frags,
+                         Compression compression,
+                         dictionary_policy dict_policy,
+                         size_t max_dict_size,
+                         rmm::cuda_stream_view stream)
+{
+  // At this point, we know all chunks and their sizes. We want to allocate dictionaries for each
+  // chunk that can have dictionary
+
+  auto h_chunks = chunks.host_view().flat_view();
+
+  std::vector<rmm::device_uvector<size_type>> dict_data;
+  std::vector<rmm::device_uvector<size_type>> dict_index;
+
+  if (h_chunks.empty()) { return std::pair(std::move(dict_data), std::move(dict_index)); }
+
+  if (dict_policy == dictionary_policy::NEVER) {
+    thrust::for_each(
+      h_chunks.begin(), h_chunks.end(), [](auto& chunk) { chunk.use_dictionary = false; });
+    chunks.host_to_device_async(stream);
+    return std::pair(std::move(dict_data), std::move(dict_index));
+  }
+
+  // Allocate slots for each chunk
+  std::vector<rmm::device_uvector<gpu::slot_type>> hash_maps_storage;
+  hash_maps_storage.reserve(h_chunks.size());
+  for (auto& chunk : h_chunks) {
+    if (col_desc[chunk.col_desc_id].physical_type == Type::BOOLEAN ||
+        (col_desc[chunk.col_desc_id].output_as_byte_array &&
+         col_desc[chunk.col_desc_id].physical_type == Type::BYTE_ARRAY)) {
+      chunk.use_dictionary = false;
+    } else {
+      chunk.use_dictionary = true;
+      // cuCollections suggests using a hash map of size N * (1/0.7) = num_values * 1.43
+      // https://github.com/NVIDIA/cuCollections/blob/3a49fc71/include/cuco/static_map.cuh#L190-L193
+      auto& inserted_map   = hash_maps_storage.emplace_back(chunk.num_values * 1.43, stream);
+      chunk.dict_map_slots = inserted_map.data();
+      chunk.dict_map_size  = inserted_map.size();
+    }
+  }
+
+  chunks.host_to_device_async(stream);
+
+  gpu::initialize_chunk_hash_maps(chunks.device_view().flat_view(), stream);
+  gpu::populate_chunk_hash_maps(frags, stream);
+
+  chunks.device_to_host_sync(stream);
+
+  // Make decision about which chunks have dictionary
+  for (auto& ck : h_chunks) {
+    if (not ck.use_dictionary) { continue; }
+    std::tie(ck.use_dictionary, ck.dict_rle_bits) = [&]() -> std::pair<bool, uint8_t> {
+      // calculate size of chunk if dictionary is used
+
+      // If we have N unique values then the idx for the last value is N - 1 and nbits is the number
+      // of bits required to encode indices into the dictionary
+      auto max_dict_index = (ck.num_dict_entries > 0) ? ck.num_dict_entries - 1 : 0;
+      auto nbits          = std::max(CompactProtocolReader::NumRequiredBits(max_dict_index), 1);
+
+      // We don't use dictionary if the indices are > MAX_DICT_BITS bits because that's the maximum
+      // bitpacking bitsize we efficiently support
+      if (nbits > MAX_DICT_BITS) { return {false, 0}; }
+
+      auto rle_byte_size = util::div_rounding_up_safe(ck.num_values * nbits, 8);
+      auto dict_enc_size = ck.uniq_data_size + rle_byte_size;
+      if (ck.plain_data_size <= dict_enc_size) { return {false, 0}; }
+
+      // don't use dictionary if it gets too large for the given compression codec
+      if (dict_policy == dictionary_policy::ADAPTIVE) {
+        auto const unique_size = static_cast<size_t>(ck.uniq_data_size);
+        if (unique_size > max_page_bytes(compression, max_dict_size)) { return {false, 0}; }
+      }
+
+      return {true, nbits};
+    }();
+  }
+
+  // TODO: (enh) Deallocate hash map storage for chunks that don't use dict and clear pointers.
+
+  dict_data.reserve(h_chunks.size());
+  dict_index.reserve(h_chunks.size());
+  for (auto& chunk : h_chunks) {
+    if (not chunk.use_dictionary) { continue; }
+
+    size_t dict_data_size     = std::min(MAX_DICT_SIZE, chunk.dict_map_size);
+    auto& inserted_dict_data  = dict_data.emplace_back(dict_data_size, stream);
+    auto& inserted_dict_index = dict_index.emplace_back(chunk.num_values, stream);
+    chunk.dict_data           = inserted_dict_data.data();
+    chunk.dict_index          = inserted_dict_index.data();
+  }
+  chunks.host_to_device_async(stream);
+  gpu::collect_map_entries(chunks.device_view().flat_view(), stream);
+  gpu::get_dictionary_indices(frags, stream);
+
+  return std::pair(std::move(dict_data), std::move(dict_index));
+}
+
+/**
+ * @brief Initialize encoder pages.
+ *
+ * @param chunks Column chunk array
+ * @param col_desc Column description array
+ * @param pages Encoder pages array
+ * @param comp_page_sizes Per-page max compressed size
+ * @param page_stats Page statistics array
+ * @param frag_stats Fragment statistics array
+ * @param num_columns Total number of columns
+ * @param num_pages Total number of pages
+ * @param num_stats_bfr Number of statistics buffers
+ * @param compression Compression format
+ * @param max_page_size_bytes Maximum uncompressed page size, in bytes
+ * @param max_page_size_rows Maximum page size, in rows
+ * @param write_v2_headers True if version 2 page headers are to be written
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void init_encoder_pages(hostdevice_2dvector<gpu::EncColumnChunk>& chunks,
+                        device_span<gpu::parquet_column_device_view const> col_desc,
+                        device_span<gpu::EncPage> pages,
+                        cudf::detail::hostdevice_vector<size_type>& comp_page_sizes,
+                        statistics_chunk* page_stats,
+                        statistics_chunk* frag_stats,
+                        uint32_t num_columns,
+                        uint32_t num_pages,
+                        uint32_t num_stats_bfr,
+                        Compression compression,
+                        size_t max_page_size_bytes,
+                        size_type max_page_size_rows,
+                        bool write_v2_headers,
+                        rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<statistics_merge_group> page_stats_mrg(num_stats_bfr, stream);
+  chunks.host_to_device_async(stream);
+  InitEncoderPages(chunks,
+                   pages,
+                   {},
+                   comp_page_sizes,
+                   col_desc,
+                   num_columns,
+                   max_page_size_bytes,
+                   max_page_size_rows,
+                   page_alignment(compression),
+                   write_v2_headers,
+                   (num_stats_bfr) ? page_stats_mrg.data() : nullptr,
+                   (num_stats_bfr > num_pages) ? page_stats_mrg.data() + num_pages : nullptr,
+                   stream);
+  if (num_stats_bfr > 0) {
+    detail::merge_group_statistics<detail::io_file_format::PARQUET>(
+      page_stats, frag_stats, page_stats_mrg.data(), num_pages, stream);
+    if (num_stats_bfr > num_pages) {
+      detail::merge_group_statistics<detail::io_file_format::PARQUET>(
+        page_stats + num_pages,
+        page_stats,
+        page_stats_mrg.data() + num_pages,
+        num_stats_bfr - num_pages,
+        stream);
+    }
+  }
+  stream.synchronize();
+}
+
+/**
+ * @brief Encode a batch of pages.
+ *
+ * @throws rmm::bad_alloc if there is insufficient space for temporary buffers
+ *
+ * @param chunks column chunk array
+ * @param pages encoder pages array
+ * @param pages_in_batch number of pages in this batch
+ * @param first_page_in_batch first page in batch
+ * @param rowgroups_in_batch number of rowgroups in this batch
+ * @param first_rowgroup first rowgroup in batch
+ * @param page_stats optional page-level statistics (nullptr if none)
+ * @param chunk_stats optional chunk-level statistics (nullptr if none)
+ * @param column_stats optional page-level statistics for column index (nullptr if none)
+ * @param comp_stats optional compression statistics (nullopt if none)
+ * @param compression compression format
+ * @param column_index_truncate_length maximum length of min or max values in column index, in bytes
+ * @param write_v2_headers True if V2 page headers should be written
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+void encode_pages(hostdevice_2dvector<gpu::EncColumnChunk>& chunks,
+                  device_span<gpu::EncPage> pages,
+                  uint32_t pages_in_batch,
+                  uint32_t first_page_in_batch,
+                  uint32_t rowgroups_in_batch,
+                  uint32_t first_rowgroup,
+                  statistics_chunk const* page_stats,
+                  statistics_chunk const* chunk_stats,
+                  statistics_chunk const* column_stats,
+                  std::optional<writer_compression_statistics>& comp_stats,
+                  Compression compression,
+                  int32_t column_index_truncate_length,
+                  bool write_v2_headers,
+                  rmm::cuda_stream_view stream)
+{
+  auto batch_pages = pages.subspan(first_page_in_batch, pages_in_batch);
+
+  auto batch_pages_stats =
+    (page_stats != nullptr)
+      ? device_span<statistics_chunk const>(page_stats + first_page_in_batch, pages_in_batch)
+      : device_span<statistics_chunk const>();
+
+  uint32_t max_comp_pages =
+    (compression != parquet::Compression::UNCOMPRESSED) ? pages_in_batch : 0;
+
+  rmm::device_uvector<device_span<uint8_t const>> comp_in(max_comp_pages, stream);
+  rmm::device_uvector<device_span<uint8_t>> comp_out(max_comp_pages, stream);
+  rmm::device_uvector<compression_result> comp_res(max_comp_pages, stream);
+  thrust::fill(rmm::exec_policy(stream),
+               comp_res.begin(),
+               comp_res.end(),
+               compression_result{0, compression_status::FAILURE});
+
+  gpu::EncodePages(batch_pages, write_v2_headers, comp_in, comp_out, comp_res, stream);
+  switch (compression) {
+    case parquet::Compression::SNAPPY:
+      if (nvcomp::is_compression_disabled(nvcomp::compression_type::SNAPPY)) {
+        gpu_snap(comp_in, comp_out, comp_res, stream);
+      } else {
+        nvcomp::batched_compress(
+          nvcomp::compression_type::SNAPPY, comp_in, comp_out, comp_res, stream);
+      }
+      break;
+    case parquet::Compression::ZSTD: {
+      if (auto const reason = nvcomp::is_compression_disabled(nvcomp::compression_type::ZSTD);
+          reason) {
+        CUDF_FAIL("Compression error: " + reason.value());
+      }
+      nvcomp::batched_compress(nvcomp::compression_type::ZSTD, comp_in, comp_out, comp_res, stream);
+
+      break;
+    }
+    case parquet::Compression::UNCOMPRESSED: break;
+    default: CUDF_FAIL("invalid compression type");
+  }
+
+  // TBD: Not clear if the official spec actually allows dynamically turning off compression at the
+  // chunk-level
+
+  auto d_chunks_in_batch = chunks.device_view().subspan(first_rowgroup, rowgroups_in_batch);
+  DecideCompression(d_chunks_in_batch.flat_view(), stream);
+  EncodePageHeaders(batch_pages, comp_res, batch_pages_stats, chunk_stats, stream);
+  GatherPages(d_chunks_in_batch.flat_view(), pages, stream);
+
+  if (column_stats != nullptr) {
+    EncodeColumnIndexes(d_chunks_in_batch.flat_view(),
+                        {column_stats, pages.size()},
+                        column_index_truncate_length,
+                        stream);
+  }
+
+  auto h_chunks_in_batch = chunks.host_view().subspan(first_rowgroup, rowgroups_in_batch);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(h_chunks_in_batch.data(),
+                                d_chunks_in_batch.data(),
+                                d_chunks_in_batch.flat_view().size_bytes(),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  if (comp_stats.has_value()) {
+    comp_stats.value() += collect_compression_statistics(comp_in, comp_res, stream);
+  }
+  stream.synchronize();
+}
+
+/**
+ * @brief Function to calculate the memory needed to encode the column index of the given
+ * column chunk.
+ *
+ * @param ck pointer to column chunk
+ * @param column_index_truncate_length maximum length of min or max values in column index, in bytes
+ * @return Computed buffer size needed to encode the column index
+ */
+size_t column_index_buffer_size(gpu::EncColumnChunk* ck, int32_t column_index_truncate_length)
+{
+  // encoding the column index for a given chunk requires:
+  //   each list (4 of them) requires 6 bytes of overhead
+  //     (1 byte field header, 1 byte type, 4 bytes length)
+  //   1 byte overhead for boundary_order
+  //   1 byte overhead for termination
+  //   sizeof(char) for boundary_order
+  //   sizeof(bool) * num_pages for null_pages
+  //   (ck_max_stats_len + 4) * num_pages * 2 for min/max values
+  //     (each binary requires 4 bytes length + ck_max_stats_len)
+  //   sizeof(int64_t) * num_pages for null_counts
+  //
+  // so 26 bytes overhead + sizeof(char) +
+  //    (sizeof(bool) + sizeof(int64_t) + 2 * (4 + ck_max_stats_len)) * num_pages
+  //
+  // we already have ck->ck_stat_size = 48 + 2 * ck_max_stats_len
+  // all of the overhead and non-stats data can fit in under 48 bytes
+  //
+  // so we can simply use ck_stat_size * num_pages
+  //
+  // add on some extra padding at the end (plus extra 7 bytes of alignment padding)
+  // for scratch space to do stats truncation.
+  //
+  // calculating this per-chunk because the sizes can be wildly different.
+  constexpr size_t padding = 7;
+  return ck->ck_stat_size * ck->num_pages + column_index_truncate_length + padding;
+}
+
+/**
+ * @brief Fill the table metadata with default column names.
+ *
+ * @param table_meta The table metadata to fill
+ */
+void fill_table_meta(std::unique_ptr<table_input_metadata> const& table_meta)
+{
+  // Fill unnamed columns' names in table_meta
+  std::function<void(column_in_metadata&, std::string)> add_default_name =
+    [&](column_in_metadata& col_meta, std::string default_name) {
+      if (col_meta.get_name().empty()) col_meta.set_name(default_name);
+      for (size_type i = 0; i < col_meta.num_children(); ++i) {
+        add_default_name(col_meta.child(i), col_meta.get_name() + "_" + std::to_string(i));
+      }
+    };
+  for (size_t i = 0; i < table_meta->column_metadata.size(); ++i) {
+    add_default_name(table_meta->column_metadata[i], "_col" + std::to_string(i));
+  }
+}
+
+/**
+ * @brief Perform the processing steps needed to convert the input table into the output Parquet
+ * data for writing, such as compression and encoding.
+ *
+ * @param[in,out] table_meta The table metadata
+ * @param input The input table
+ * @param partitions Optional partitions to divide the table into, if specified then must be same
+ *        size as number of sinks
+ * @param kv_meta Optional user metadata
+ * @param curr_agg_meta The current aggregate writer metadata
+ * @param max_page_fragment_size_opt Optional maximum number of rows in a page fragment
+ * @param max_row_group_size Maximum row group size, in bytes
+ * @param max_page_size_bytes Maximum uncompressed page size, in bytes
+ * @param max_row_group_rows Maximum row group size, in rows
+ * @param max_page_size_rows Maximum page size, in rows
+ * @param column_index_truncate_length maximum length of min or max values in column index, in bytes
+ * @param stats_granularity Level of statistics requested in output file
+ * @param compression Compression format
+ * @param collect_statistics Flag to indicate if statistics should be collected
+ * @param dict_policy Policy for dictionary use
+ * @param max_dictionary_size Maximum dictionary size, in bytes
+ * @param single_write_mode Flag to indicate that we are guaranteeing a single table write
+ * @param int96_timestamps Flag to indicate if timestamps will be written as INT96
+ * @param write_v2_headers True if V2 page headers are to be written
+ * @param out_sink Sink for checking if device write is supported, should not be used to write any
+ *        data in this function
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return A tuple of the intermediate results containing the processed data
+ */
+auto convert_table_to_parquet_data(table_input_metadata& table_meta,
+                                   table_view const& input,
+                                   host_span<partition_info const> partitions,
+                                   host_span<std::map<std::string, std::string> const> kv_meta,
+                                   std::unique_ptr<aggregate_writer_metadata> const& curr_agg_meta,
+                                   std::optional<size_type> max_page_fragment_size_opt,
+                                   size_t max_row_group_size,
+                                   size_t max_page_size_bytes,
+                                   size_type max_row_group_rows,
+                                   size_type max_page_size_rows,
+                                   int32_t column_index_truncate_length,
+                                   statistics_freq stats_granularity,
+                                   Compression compression,
+                                   bool collect_compression_statistics,
+                                   dictionary_policy dict_policy,
+                                   size_t max_dictionary_size,
+                                   single_write_mode write_mode,
+                                   bool int96_timestamps,
+                                   bool write_v2_headers,
+                                   host_span<std::unique_ptr<data_sink> const> out_sink,
+                                   rmm::cuda_stream_view stream)
+{
+  auto vec         = table_to_linked_columns(input);
+  auto schema_tree = construct_schema_tree(vec, table_meta, write_mode, int96_timestamps);
+  // Construct parquet_column_views from the schema tree leaf nodes.
+  std::vector<parquet_column_view> parquet_columns;
+
+  for (schema_tree_node const& schema_node : schema_tree) {
+    if (schema_node.leaf_column) { parquet_columns.emplace_back(schema_node, schema_tree, stream); }
+  }
+
+  // Mass allocation of column_device_views for each parquet_column_view
+  std::vector<column_view> cudf_cols;
+  cudf_cols.reserve(parquet_columns.size());
+  for (auto const& parq_col : parquet_columns) {
+    cudf_cols.push_back(parq_col.cudf_column_view());
+  }
+  table_view single_streams_table(cudf_cols);
+  size_type num_columns = single_streams_table.num_columns();
+
+  std::vector<SchemaElement> this_table_schema(schema_tree.begin(), schema_tree.end());
+
+  // Initialize column description
+  cudf::detail::hostdevice_vector<gpu::parquet_column_device_view> col_desc(parquet_columns.size(),
+                                                                            stream);
+  std::transform(
+    parquet_columns.begin(), parquet_columns.end(), col_desc.host_ptr(), [&](auto const& pcol) {
+      return pcol.get_device_view(stream);
+    });
+
+  // Init page fragments
+  // 5000 is good enough for up to ~200-character strings. Longer strings and deeply nested columns
+  // will start producing fragments larger than the desired page size, so calculate fragment sizes
+  // for each leaf column.  Skip if the fragment size is not the default.
+  size_type max_page_fragment_size =
+    max_page_fragment_size_opt.value_or(default_max_page_fragment_size);
+
+  std::vector<size_type> column_frag_size(num_columns, max_page_fragment_size);
+
+  if (input.num_rows() > 0 && not max_page_fragment_size_opt.has_value()) {
+    std::vector<size_t> column_sizes;
+    std::transform(single_streams_table.begin(),
+                   single_streams_table.end(),
+                   std::back_inserter(column_sizes),
+                   [&](auto const& column) { return column_size(column, stream); });
+
+    // adjust global fragment size if a single fragment will overrun a rowgroup
+    auto const table_size  = std::reduce(column_sizes.begin(), column_sizes.end());
+    auto const avg_row_len = util::div_rounding_up_safe<size_t>(table_size, input.num_rows());
+    if (avg_row_len > 0) {
+      auto const rg_frag_size = util::div_rounding_up_safe(max_row_group_size, avg_row_len);
+      max_page_fragment_size  = std::min<size_type>(rg_frag_size, max_page_fragment_size);
+    }
+
+    // dividing page size by average row length will tend to overshoot the desired
+    // page size when there's high variability in the row lengths. instead, shoot
+    // for multiple fragments per page to smooth things out. using 2 was too
+    // unbalanced in final page sizes, so using 4 which seems to be a good
+    // compromise at smoothing things out without getting fragment sizes too small.
+    auto frag_size_fn = [&](auto const& col, size_type col_size) {
+      int const target_frags_per_page = is_col_fixed_width(col) ? 1 : 4;
+      auto const avg_len =
+        target_frags_per_page * util::div_rounding_up_safe<size_type>(col_size, input.num_rows());
+      if (avg_len > 0) {
+        auto const frag_size = util::div_rounding_up_safe<size_type>(max_page_size_bytes, avg_len);
+        return std::min<size_type>(max_page_fragment_size, frag_size);
+      } else {
+        return max_page_fragment_size;
+      }
+    };
+
+    std::transform(single_streams_table.begin(),
+                   single_streams_table.end(),
+                   column_sizes.begin(),
+                   column_frag_size.begin(),
+                   frag_size_fn);
+  }
+
+  // Fragments are calculated in two passes. In the first pass, a uniform number of fragments
+  // per column is used. This is done to satisfy the requirement that each column chunk within
+  // a row group has the same number of rows. After the row group (and thus column chunk)
+  // boundaries are known, a second pass is done to calculate fragments to be used in determining
+  // page boundaries within each column chunk.
+  std::vector<int> num_frag_in_part;
+  std::transform(partitions.begin(),
+                 partitions.end(),
+                 std::back_inserter(num_frag_in_part),
+                 [max_page_fragment_size](auto const& part) {
+                   return util::div_rounding_up_unsafe(part.num_rows, max_page_fragment_size);
+                 });
+
+  size_type num_fragments = std::reduce(num_frag_in_part.begin(), num_frag_in_part.end());
+
+  std::vector<int> part_frag_offset;  // Store the idx of the first fragment in each partition
+  std::exclusive_scan(
+    num_frag_in_part.begin(), num_frag_in_part.end(), std::back_inserter(part_frag_offset), 0);
+  part_frag_offset.push_back(part_frag_offset.back() + num_frag_in_part.back());
+
+  auto d_part_frag_offset = cudf::detail::make_device_uvector_async(
+    part_frag_offset, stream, rmm::mr::get_current_device_resource());
+  cudf::detail::hostdevice_2dvector<gpu::PageFragment> row_group_fragments(
+    num_columns, num_fragments, stream);
+
+  // Create table_device_view so that corresponding column_device_view data
+  // can be written into col_desc members
+  // These are unused but needs to be kept alive.
+  auto parent_column_table_device_view = table_device_view::create(single_streams_table, stream);
+  rmm::device_uvector<column_device_view> leaf_column_views(0, stream);
+
+  if (num_fragments != 0) {
+    // Move column info to device
+    col_desc.host_to_device_async(stream);
+    leaf_column_views = create_leaf_column_device_views<gpu::parquet_column_device_view>(
+      col_desc, *parent_column_table_device_view, stream);
+
+    init_row_group_fragments(row_group_fragments,
+                             col_desc,
+                             partitions,
+                             d_part_frag_offset,
+                             max_page_fragment_size,
+                             stream);
+  }
+
+  std::unique_ptr<aggregate_writer_metadata> agg_meta;
+  if (!curr_agg_meta) {
+    agg_meta = std::make_unique<aggregate_writer_metadata>(
+      partitions, kv_meta, this_table_schema, num_columns, stats_granularity);
+  } else {
+    agg_meta = std::make_unique<aggregate_writer_metadata>(*curr_agg_meta);
+
+    // verify the user isn't passing mismatched tables
+    CUDF_EXPECTS(agg_meta->schema_matches(this_table_schema),
+                 "Mismatch in schema between multiple calls to write_chunk");
+
+    agg_meta->update_files(partitions);
+  }
+
+  auto global_rowgroup_base = agg_meta->num_row_groups_per_file();
+
+  // Decide row group boundaries based on uncompressed data size
+  size_type num_rowgroups = 0;
+
+  std::vector<int> num_rg_in_part(partitions.size());
+  for (size_t p = 0; p < partitions.size(); ++p) {
+    size_type curr_rg_num_rows = 0;
+    size_t curr_rg_data_size   = 0;
+    int first_frag_in_rg       = part_frag_offset[p];
+    int last_frag_in_part      = part_frag_offset[p + 1] - 1;
+    for (auto f = first_frag_in_rg; f <= last_frag_in_part; ++f) {
+      size_t fragment_data_size = 0;
+      for (auto c = 0; c < num_columns; c++) {
+        fragment_data_size += row_group_fragments[c][f].fragment_data_size;
+      }
+      size_type fragment_num_rows = row_group_fragments[0][f].num_rows;
+
+      // If the fragment size gets larger than rg limit then break off a rg
+      if (f > first_frag_in_rg &&  // There has to be at least one fragment in row group
+          (curr_rg_data_size + fragment_data_size > max_row_group_size ||
+           curr_rg_num_rows + fragment_num_rows > max_row_group_rows)) {
+        auto& rg    = agg_meta->file(p).row_groups.emplace_back();
+        rg.num_rows = curr_rg_num_rows;
+        num_rowgroups++;
+        num_rg_in_part[p]++;
+        curr_rg_num_rows  = 0;
+        curr_rg_data_size = 0;
+        first_frag_in_rg  = f;
+      }
+      curr_rg_num_rows += fragment_num_rows;
+      curr_rg_data_size += fragment_data_size;
+
+      // TODO: (wishful) refactor to consolidate with above if block
+      if (f == last_frag_in_part) {
+        auto& rg    = agg_meta->file(p).row_groups.emplace_back();
+        rg.num_rows = curr_rg_num_rows;
+        num_rowgroups++;
+        num_rg_in_part[p]++;
+      }
+    }
+  }
+
+  std::vector<int> first_rg_in_part;
+  std::exclusive_scan(
+    num_rg_in_part.begin(), num_rg_in_part.end(), std::back_inserter(first_rg_in_part), 0);
+
+  // Initialize row groups and column chunks
+  auto const num_chunks = num_rowgroups * num_columns;
+  hostdevice_2dvector<gpu::EncColumnChunk> chunks(num_rowgroups, num_columns, stream);
+
+  // total fragments per column (in case they are non-uniform)
+  std::vector<size_type> frags_per_column(num_columns, 0);
+
+  for (size_t p = 0; p < partitions.size(); ++p) {
+    int f               = part_frag_offset[p];
+    size_type start_row = partitions[p].start_row;
+    for (int r = 0; r < num_rg_in_part[p]; r++) {
+      size_t global_r = global_rowgroup_base[p] + r;  // Number of rowgroups already in file/part
+      auto& row_group = agg_meta->file(p).row_groups[global_r];
+      uint32_t fragments_in_chunk =
+        util::div_rounding_up_unsafe(row_group.num_rows, max_page_fragment_size);
+      row_group.total_byte_size = 0;
+      row_group.columns.resize(num_columns);
+      for (int c = 0; c < num_columns; c++) {
+        gpu::EncColumnChunk& ck = chunks[r + first_rg_in_part[p]][c];
+
+        ck                   = {};
+        ck.col_desc          = col_desc.device_ptr() + c;
+        ck.col_desc_id       = c;
+        ck.fragments         = &row_group_fragments.device_view()[c][f];
+        ck.stats             = nullptr;
+        ck.start_row         = start_row;
+        ck.num_rows          = (uint32_t)row_group.num_rows;
+        ck.first_fragment    = c * num_fragments + f;
+        ck.encodings         = 0;
+        auto chunk_fragments = row_group_fragments[c].subspan(f, fragments_in_chunk);
+        // In fragment struct, add a pointer to the chunk it belongs to
+        // In each fragment in chunk_fragments, update the chunk pointer here.
+        for (auto& frag : chunk_fragments) {
+          frag.chunk = &chunks.device_view()[r + first_rg_in_part[p]][c];
+        }
+        ck.num_values = std::accumulate(
+          chunk_fragments.begin(), chunk_fragments.end(), 0, [](uint32_t l, auto r) {
+            return l + r.num_values;
+          });
+        ck.plain_data_size = std::accumulate(
+          chunk_fragments.begin(), chunk_fragments.end(), 0, [](int sum, gpu::PageFragment frag) {
+            return sum + frag.fragment_data_size;
+          });
+        auto& column_chunk_meta          = row_group.columns[c].meta_data;
+        column_chunk_meta.type           = parquet_columns[c].physical_type();
+        column_chunk_meta.path_in_schema = parquet_columns[c].get_path_in_schema();
+        column_chunk_meta.codec          = UNCOMPRESSED;
+        column_chunk_meta.num_values     = ck.num_values;
+
+        frags_per_column[c] += util::div_rounding_up_unsafe(
+          row_group.num_rows, std::min(column_frag_size[c], max_page_fragment_size));
+      }
+      f += fragments_in_chunk;
+      start_row += (uint32_t)row_group.num_rows;
+    }
+  }
+
+  row_group_fragments.host_to_device_async(stream);
+  [[maybe_unused]] auto dict_info_owner = build_chunk_dictionaries(
+    chunks, col_desc, row_group_fragments, compression, dict_policy, max_dictionary_size, stream);
+
+  // The code preceding this used a uniform fragment size for all columns. Now recompute
+  // fragments with a (potentially) varying number of fragments per column.
+
+  // first figure out the total number of fragments and calculate the start offset for each column
+  std::vector<size_type> frag_offsets(num_columns, 0);
+  std::exclusive_scan(frags_per_column.begin(), frags_per_column.end(), frag_offsets.begin(), 0);
+  size_type const total_frags =
+    frags_per_column.empty() ? 0 : frag_offsets.back() + frags_per_column.back();
+
+  rmm::device_uvector<statistics_chunk> frag_stats(0, stream);
+  cudf::detail::hostdevice_vector<gpu::PageFragment> page_fragments(total_frags, stream);
+
+  // update fragments and/or prepare for fragment statistics calculation if necessary
+  if (total_frags != 0) {
+    if (stats_granularity != statistics_freq::STATISTICS_NONE) {
+      frag_stats.resize(total_frags, stream);
+    }
+
+    for (int c = 0; c < num_columns; c++) {
+      auto frag_offset     = frag_offsets[c];
+      auto const frag_size = column_frag_size[c];
+
+      for (size_t p = 0; p < partitions.size(); ++p) {
+        for (int r = 0; r < num_rg_in_part[p]; r++) {
+          auto const global_r   = global_rowgroup_base[p] + r;
+          auto const& row_group = agg_meta->file(p).row_groups[global_r];
+          uint32_t const fragments_in_chunk =
+            util::div_rounding_up_unsafe(row_group.num_rows, frag_size);
+          gpu::EncColumnChunk& ck = chunks[r + first_rg_in_part[p]][c];
+          ck.fragments            = page_fragments.device_ptr(frag_offset);
+          ck.first_fragment       = frag_offset;
+
+          // update the chunk pointer here for each fragment in chunk.fragments
+          for (uint32_t i = 0; i < fragments_in_chunk; i++) {
+            page_fragments[frag_offset + i].chunk =
+              &chunks.device_view()[r + first_rg_in_part[p]][c];
+          }
+
+          if (not frag_stats.is_empty()) { ck.stats = frag_stats.data() + frag_offset; }
+          frag_offset += fragments_in_chunk;
+        }
+      }
+    }
+
+    chunks.host_to_device_async(stream);
+
+    // re-initialize page fragments
+    page_fragments.host_to_device_async(stream);
+    calculate_page_fragments(page_fragments, column_frag_size, stream);
+
+    // and gather fragment statistics
+    if (not frag_stats.is_empty()) {
+      gather_fragment_statistics(frag_stats,
+                                 {page_fragments.device_ptr(), static_cast<size_t>(total_frags)},
+                                 int96_timestamps,
+                                 stream);
+    }
+  }
+
+  // Build chunk dictionaries and count pages. Sends chunks to device.
+  cudf::detail::hostdevice_vector<size_type> comp_page_sizes = init_page_sizes(chunks,
+                                                                               col_desc,
+                                                                               num_columns,
+                                                                               max_page_size_bytes,
+                                                                               max_page_size_rows,
+                                                                               write_v2_headers,
+                                                                               compression,
+                                                                               stream);
+
+  // Find which partition a rg belongs to
+  std::vector<int> rg_to_part;
+  for (size_t p = 0; p < num_rg_in_part.size(); ++p) {
+    std::fill_n(std::back_inserter(rg_to_part), num_rg_in_part[p], p);
+  }
+
+  // Batch processing is no longer supported.
+  // This line disables batch processing (so batch size will no longer be limited at 1GB as before).
+  // TODO: All the relevant code will be removed in the follow-up work:
+  // https://github.com/rapidsai/cudf/issues/13440
+  auto const max_bytes_in_batch = std::numeric_limits<size_t>::max();
+
+  // Initialize batches of rowgroups to encode (mainly to limit peak memory usage)
+  std::vector<size_type> batch_list;
+  size_type num_pages          = 0;
+  size_t max_uncomp_bfr_size   = 0;
+  size_t max_comp_bfr_size     = 0;
+  size_t max_chunk_bfr_size    = 0;
+  size_type max_pages_in_batch = 0;
+  size_t bytes_in_batch        = 0;
+  size_t comp_bytes_in_batch   = 0;
+  size_t column_index_bfr_size = 0;
+  for (size_type r = 0, groups_in_batch = 0, pages_in_batch = 0; r <= num_rowgroups; r++) {
+    size_t rowgroup_size      = 0;
+    size_t comp_rowgroup_size = 0;
+    if (r < num_rowgroups) {
+      for (int i = 0; i < num_columns; i++) {
+        gpu::EncColumnChunk* ck = &chunks[r][i];
+        ck->first_page          = num_pages;
+        num_pages += ck->num_pages;
+        pages_in_batch += ck->num_pages;
+        rowgroup_size += ck->bfr_size;
+        comp_rowgroup_size += ck->compressed_size;
+        max_chunk_bfr_size =
+          std::max(max_chunk_bfr_size, (size_t)std::max(ck->bfr_size, ck->compressed_size));
+        if (stats_granularity == statistics_freq::STATISTICS_COLUMN) {
+          column_index_bfr_size += column_index_buffer_size(ck, column_index_truncate_length);
+        }
+      }
+    }
+    // TBD: We may want to also shorten the batch if we have enough pages (not just based on size)
+    if ((r == num_rowgroups) ||
+        (groups_in_batch != 0 && bytes_in_batch + rowgroup_size > max_bytes_in_batch)) {
+      max_uncomp_bfr_size = std::max(max_uncomp_bfr_size, bytes_in_batch);
+      max_comp_bfr_size   = std::max(max_comp_bfr_size, comp_bytes_in_batch);
+      max_pages_in_batch  = std::max(max_pages_in_batch, pages_in_batch);
+      if (groups_in_batch != 0) {
+        batch_list.push_back(groups_in_batch);
+        groups_in_batch = 0;
+      }
+      bytes_in_batch      = 0;
+      comp_bytes_in_batch = 0;
+      pages_in_batch      = 0;
+    }
+    bytes_in_batch += rowgroup_size;
+    comp_bytes_in_batch += comp_rowgroup_size;
+    groups_in_batch++;
+  }
+
+  // Clear compressed buffer size if compression has been turned off
+  if (compression == parquet::Compression::UNCOMPRESSED) { max_comp_bfr_size = 0; }
+
+  // Initialize data pointers in batch
+  uint32_t const num_stats_bfr =
+    (stats_granularity != statistics_freq::STATISTICS_NONE) ? num_pages + num_chunks : 0;
+
+  // Buffers need to be padded.
+  // Required by `gpuGatherPages`.
+  rmm::device_buffer uncomp_bfr(
+    cudf::util::round_up_safe(max_uncomp_bfr_size, BUFFER_PADDING_MULTIPLE), stream);
+  rmm::device_buffer comp_bfr(cudf::util::round_up_safe(max_comp_bfr_size, BUFFER_PADDING_MULTIPLE),
+                              stream);
+
+  rmm::device_buffer col_idx_bfr(column_index_bfr_size, stream);
+  rmm::device_uvector<gpu::EncPage> pages(num_pages, stream);
+
+  // This contains stats for both the pages and the rowgroups. TODO: make them separate.
+  rmm::device_uvector<statistics_chunk> page_stats(num_stats_bfr, stream);
+  auto bfr_i = static_cast<uint8_t*>(col_idx_bfr.data());
+  for (auto b = 0, r = 0; b < static_cast<size_type>(batch_list.size()); b++) {
+    auto bfr   = static_cast<uint8_t*>(uncomp_bfr.data());
+    auto bfr_c = static_cast<uint8_t*>(comp_bfr.data());
+    for (auto j = 0; j < batch_list[b]; j++, r++) {
+      for (auto i = 0; i < num_columns; i++) {
+        gpu::EncColumnChunk& ck = chunks[r][i];
+        ck.uncompressed_bfr     = bfr;
+        ck.compressed_bfr       = bfr_c;
+        ck.column_index_blob    = bfr_i;
+        bfr += ck.bfr_size;
+        bfr_c += ck.compressed_size;
+        if (stats_granularity == statistics_freq::STATISTICS_COLUMN) {
+          ck.column_index_size = column_index_buffer_size(&ck, column_index_truncate_length);
+          bfr_i += ck.column_index_size;
+        }
+      }
+    }
+  }
+
+  if (num_pages != 0) {
+    init_encoder_pages(chunks,
+                       col_desc,
+                       {pages.data(), pages.size()},
+                       comp_page_sizes,
+                       (num_stats_bfr) ? page_stats.data() : nullptr,
+                       (num_stats_bfr) ? frag_stats.data() : nullptr,
+                       num_columns,
+                       num_pages,
+                       num_stats_bfr,
+                       compression,
+                       max_page_size_bytes,
+                       max_page_size_rows,
+                       write_v2_headers,
+                       stream);
+  }
+
+  // Check device write support for all chunks and initialize bounce_buffer.
+  bool all_device_write   = true;
+  uint32_t max_write_size = 0;
+  std::optional<writer_compression_statistics> comp_stats;
+  if (collect_compression_statistics) { comp_stats = writer_compression_statistics{}; }
+
+  // Encode row groups in batches
+  for (auto b = 0, r = 0; b < static_cast<size_type>(batch_list.size()); b++) {
+    // Count pages in this batch
+    auto const rnext               = r + batch_list[b];
+    auto const first_page_in_batch = chunks[r][0].first_page;
+    auto const first_page_in_next_batch =
+      (rnext < num_rowgroups) ? chunks[rnext][0].first_page : num_pages;
+    auto const pages_in_batch = first_page_in_next_batch - first_page_in_batch;
+
+    encode_pages(
+      chunks,
+      {pages.data(), pages.size()},
+      pages_in_batch,
+      first_page_in_batch,
+      batch_list[b],
+      r,
+      (stats_granularity == statistics_freq::STATISTICS_PAGE) ? page_stats.data() : nullptr,
+      (stats_granularity != statistics_freq::STATISTICS_NONE) ? page_stats.data() + num_pages
+                                                              : nullptr,
+      (stats_granularity == statistics_freq::STATISTICS_COLUMN) ? page_stats.data() : nullptr,
+      comp_stats,
+      compression,
+      column_index_truncate_length,
+      write_v2_headers,
+      stream);
+
+    bool need_sync{false};
+
+    for (; r < rnext; r++) {
+      int p           = rg_to_part[r];
+      int global_r    = global_rowgroup_base[p] + r - first_rg_in_part[p];
+      auto& row_group = agg_meta->file(p).row_groups[global_r];
+
+      for (auto i = 0; i < num_columns; i++) {
+        auto const& ck          = chunks[r][i];
+        auto const dev_bfr      = ck.is_compressed ? ck.compressed_bfr : ck.uncompressed_bfr;
+        auto& column_chunk_meta = row_group.columns[i].meta_data;
+
+        if (ck.is_compressed) { column_chunk_meta.codec = compression; }
+        if (!out_sink[p]->is_device_write_preferred(ck.compressed_size)) {
+          all_device_write = false;
+        }
+        max_write_size = std::max(max_write_size, ck.compressed_size);
+
+        update_chunk_encodings(column_chunk_meta.encodings, ck.encodings);
+
+        if (ck.ck_stat_size != 0) {
+          std::vector<uint8_t> const stats_blob = cudf::detail::make_std_vector_sync(
+            device_span<uint8_t const>(dev_bfr, ck.ck_stat_size), stream);
+          cudf::io::parquet::CompactProtocolReader cp(stats_blob.data(), stats_blob.size());
+          cp.read(&column_chunk_meta.statistics);
+          need_sync = true;
+        }
+
+        row_group.total_byte_size += ck.compressed_size;
+        column_chunk_meta.total_uncompressed_size = ck.bfr_size;
+        column_chunk_meta.total_compressed_size   = ck.compressed_size;
+      }
+    }
+
+    // Sync before calling the next `encode_pages` which may alter the stats data.
+    if (need_sync) { stream.synchronize(); }
+  }
+
+  auto bounce_buffer =
+    cudf::detail::pinned_host_vector<uint8_t>(all_device_write ? 0 : max_write_size);
+
+  return std::tuple{std::move(agg_meta),
+                    std::move(pages),
+                    std::move(chunks),
+                    std::move(global_rowgroup_base),
+                    std::move(first_rg_in_part),
+                    std::move(batch_list),
+                    std::move(rg_to_part),
+                    std::move(comp_stats),
+                    std::move(uncomp_bfr),
+                    std::move(comp_bfr),
+                    std::move(col_idx_bfr),
+                    std::move(bounce_buffer)};
+}
+
+}  // namespace
+
+writer::impl::impl(std::vector<std::unique_ptr<data_sink>> sinks,
+                   parquet_writer_options const& options,
+                   single_write_mode mode,
+                   rmm::cuda_stream_view stream)
+  : _stream(stream),
+    _compression(to_parquet_compression(options.get_compression())),
+    _max_row_group_size{options.get_row_group_size_bytes()},
+    _max_row_group_rows{options.get_row_group_size_rows()},
+    _max_page_size_bytes(max_page_bytes(_compression, options.get_max_page_size_bytes())),
+    _max_page_size_rows(options.get_max_page_size_rows()),
+    _stats_granularity(options.get_stats_level()),
+    _dict_policy(options.get_dictionary_policy()),
+    _max_dictionary_size(options.get_max_dictionary_size()),
+    _max_page_fragment_size(options.get_max_page_fragment_size()),
+    _int96_timestamps(options.is_enabled_int96_timestamps()),
+    _write_v2_headers(options.is_enabled_write_v2_headers()),
+    _column_index_truncate_length(options.get_column_index_truncate_length()),
+    _kv_meta(options.get_key_value_metadata()),
+    _single_write_mode(mode),
+    _out_sink(std::move(sinks)),
+    _compression_statistics{options.get_compression_statistics()}
+{
+  if (options.get_metadata()) {
+    _table_meta = std::make_unique<table_input_metadata>(*options.get_metadata());
+  }
+  init_state();
+}
+
+writer::impl::impl(std::vector<std::unique_ptr<data_sink>> sinks,
+                   chunked_parquet_writer_options const& options,
+                   single_write_mode mode,
+                   rmm::cuda_stream_view stream)
+  : _stream(stream),
+    _compression(to_parquet_compression(options.get_compression())),
+    _max_row_group_size{options.get_row_group_size_bytes()},
+    _max_row_group_rows{options.get_row_group_size_rows()},
+    _max_page_size_bytes(max_page_bytes(_compression, options.get_max_page_size_bytes())),
+    _max_page_size_rows(options.get_max_page_size_rows()),
+    _stats_granularity(options.get_stats_level()),
+    _dict_policy(options.get_dictionary_policy()),
+    _max_dictionary_size(options.get_max_dictionary_size()),
+    _max_page_fragment_size(options.get_max_page_fragment_size()),
+    _int96_timestamps(options.is_enabled_int96_timestamps()),
+    _write_v2_headers(options.is_enabled_write_v2_headers()),
+    _column_index_truncate_length(options.get_column_index_truncate_length()),
+    _kv_meta(options.get_key_value_metadata()),
+    _single_write_mode(mode),
+    _out_sink(std::move(sinks)),
+    _compression_statistics{options.get_compression_statistics()}
+{
+  if (options.get_metadata()) {
+    _table_meta = std::make_unique<table_input_metadata>(*options.get_metadata());
+  }
+  init_state();
+}
+
+writer::impl::~impl() { close(); }
+
+void writer::impl::init_state()
+{
+  _current_chunk_offset.resize(_out_sink.size());
+  // Write file header
+  file_header_s fhdr;
+  fhdr.magic = parquet_magic;
+  for (auto& sink : _out_sink) {
+    sink->host_write(&fhdr, sizeof(fhdr));
+  }
+  std::fill_n(_current_chunk_offset.begin(), _current_chunk_offset.size(), sizeof(file_header_s));
+}
+
+void writer::impl::update_compression_statistics(
+  std::optional<writer_compression_statistics> const& compression_stats)
+{
+  if (compression_stats.has_value() and _compression_statistics != nullptr) {
+    *_compression_statistics += compression_stats.value();
+  }
+}
+
+void writer::impl::write(table_view const& input, std::vector<partition_info> const& partitions)
+{
+  _last_write_successful = false;
+  CUDF_EXPECTS(not _closed, "Data has already been flushed to out and closed");
+
+  if (not _table_meta) { _table_meta = std::make_unique<table_input_metadata>(input); }
+  fill_table_meta(_table_meta);
+
+  // All kinds of memory allocation and data compressions/encoding are performed here.
+  // If any error occurs, such as out-of-memory exception, the internal state of the current
+  // writer is still intact.
+  [[maybe_unused]] auto [updated_agg_meta,
+                         pages,
+                         chunks,
+                         global_rowgroup_base,
+                         first_rg_in_part,
+                         batch_list,
+                         rg_to_part,
+                         comp_stats,
+                         uncomp_bfr,   // unused, but contains data for later write to sink
+                         comp_bfr,     // unused, but contains data for later write to sink
+                         col_idx_bfr,  // unused, but contains data for later write to sink
+                         bounce_buffer] = [&] {
+    try {
+      return convert_table_to_parquet_data(*_table_meta,
+                                           input,
+                                           partitions,
+                                           _kv_meta,
+                                           _agg_meta,
+                                           _max_page_fragment_size,
+                                           _max_row_group_size,
+                                           _max_page_size_bytes,
+                                           _max_row_group_rows,
+                                           _max_page_size_rows,
+                                           _column_index_truncate_length,
+                                           _stats_granularity,
+                                           _compression,
+                                           _compression_statistics != nullptr,
+                                           _dict_policy,
+                                           _max_dictionary_size,
+                                           _single_write_mode,
+                                           _int96_timestamps,
+                                           _write_v2_headers,
+                                           _out_sink,
+                                           _stream);
+    } catch (...) {  // catch any exception type
+      CUDF_LOG_ERROR(
+        "Parquet writer encountered exception during processing. "
+        "No data has been written to the sink.");
+      throw;  // this throws the same exception
+    }
+  }();
+
+  // Compression/encoding were all successful. Now write the intermediate results.
+  write_parquet_data_to_sink(updated_agg_meta,
+                             pages,
+                             chunks,
+                             global_rowgroup_base,
+                             first_rg_in_part,
+                             batch_list,
+                             rg_to_part,
+                             bounce_buffer);
+
+  update_compression_statistics(comp_stats);
+
+  _last_write_successful = true;
+}
+
+void writer::impl::write_parquet_data_to_sink(
+  std::unique_ptr<aggregate_writer_metadata>& updated_agg_meta,
+  device_span<gpu::EncPage const> pages,
+  host_2dspan<gpu::EncColumnChunk const> chunks,
+  host_span<size_t const> global_rowgroup_base,
+  host_span<int const> first_rg_in_part,
+  host_span<size_type const> batch_list,
+  host_span<int const> rg_to_part,
+  host_span<uint8_t> bounce_buffer)
+{
+  _agg_meta              = std::move(updated_agg_meta);
+  auto const num_columns = chunks.size().second;
+
+  for (auto b = 0, r = 0; b < static_cast<size_type>(batch_list.size()); b++) {
+    auto const rnext = r + batch_list[b];
+    std::vector<std::future<void>> write_tasks;
+
+    for (; r < rnext; r++) {
+      int const p        = rg_to_part[r];
+      int const global_r = global_rowgroup_base[p] + r - first_rg_in_part[p];
+      auto& row_group    = _agg_meta->file(p).row_groups[global_r];
+
+      for (std::size_t i = 0; i < num_columns; i++) {
+        auto const& ck     = chunks[r][i];
+        auto const dev_bfr = ck.is_compressed ? ck.compressed_bfr : ck.uncompressed_bfr;
+
+        // Skip the range [0, ck.ck_stat_size) since it has already been copied to host
+        // and stored in _agg_meta before.
+        if (_out_sink[p]->is_device_write_preferred(ck.compressed_size)) {
+          write_tasks.push_back(_out_sink[p]->device_write_async(
+            dev_bfr + ck.ck_stat_size, ck.compressed_size, _stream));
+        } else {
+          CUDF_EXPECTS(bounce_buffer.size() >= ck.compressed_size,
+                       "Bounce buffer was not properly initialized.");
+          CUDF_CUDA_TRY(cudaMemcpyAsync(bounce_buffer.data(),
+                                        dev_bfr + ck.ck_stat_size,
+                                        ck.compressed_size,
+                                        cudaMemcpyDefault,
+                                        _stream.value()));
+          _stream.synchronize();
+          _out_sink[p]->host_write(bounce_buffer.data(), ck.compressed_size);
+        }
+
+        auto& column_chunk_meta = row_group.columns[i].meta_data;
+        column_chunk_meta.data_page_offset =
+          _current_chunk_offset[p] + ((ck.use_dictionary) ? ck.dictionary_size : 0);
+        column_chunk_meta.dictionary_page_offset =
+          (ck.use_dictionary) ? _current_chunk_offset[p] : 0;
+        _current_chunk_offset[p] += ck.compressed_size;
+      }
+    }
+    for (auto const& task : write_tasks) {
+      task.wait();
+    }
+  }
+
+  if (_stats_granularity == statistics_freq::STATISTICS_COLUMN) {
+    // need pages on host to create offset_indexes
+    auto const h_pages = cudf::detail::make_host_vector_sync(pages, _stream);
+
+    // add column and offset indexes to metadata
+    for (auto b = 0, r = 0; b < static_cast<size_type>(batch_list.size()); b++) {
+      auto const rnext   = r + batch_list[b];
+      auto curr_page_idx = chunks[r][0].first_page;
+      for (; r < rnext; r++) {
+        int const p           = rg_to_part[r];
+        int const global_r    = global_rowgroup_base[p] + r - first_rg_in_part[p];
+        auto const& row_group = _agg_meta->file(p).row_groups[global_r];
+        for (std::size_t i = 0; i < num_columns; i++) {
+          gpu::EncColumnChunk const& ck = chunks[r][i];
+          auto const& column_chunk_meta = row_group.columns[i].meta_data;
+
+          // start transfer of the column index
+          std::vector<uint8_t> column_idx;
+          column_idx.resize(ck.column_index_size);
+          CUDF_CUDA_TRY(cudaMemcpyAsync(column_idx.data(),
+                                        ck.column_index_blob,
+                                        ck.column_index_size,
+                                        cudaMemcpyDefault,
+                                        _stream.value()));
+
+          // calculate offsets while the column index is transferring
+          int64_t curr_pg_offset = column_chunk_meta.data_page_offset;
+
+          OffsetIndex offset_idx;
+          for (uint32_t pg = 0; pg < ck.num_pages; pg++) {
+            auto const& enc_page = h_pages[curr_page_idx++];
+
+            // skip dict pages
+            if (enc_page.page_type == PageType::DICTIONARY_PAGE) { continue; }
+
+            int32_t this_page_size = enc_page.hdr_size + enc_page.max_data_size;
+            // first_row_idx is relative to start of row group
+            PageLocation loc{curr_pg_offset, this_page_size, enc_page.start_row - ck.start_row};
+            offset_idx.page_locations.push_back(loc);
+            curr_pg_offset += this_page_size;
+          }
+
+          _stream.synchronize();
+          _agg_meta->file(p).offset_indexes.emplace_back(std::move(offset_idx));
+          _agg_meta->file(p).column_indexes.emplace_back(std::move(column_idx));
+        }
+      }
+    }
+  }
+}
+
+std::unique_ptr<std::vector<uint8_t>> writer::impl::close(
+  std::vector<std::string> const& column_chunks_file_path)
+{
+  if (_closed) { return nullptr; }
+  _closed = true;
+  if (not _last_write_successful) { return nullptr; }
+  for (size_t p = 0; p < _out_sink.size(); p++) {
+    std::vector<uint8_t> buffer;
+    CompactProtocolWriter cpw(&buffer);
+    file_ender_s fendr;
+
+    if (_stats_granularity == statistics_freq::STATISTICS_COLUMN) {
+      auto& fmd = _agg_meta->file(p);
+
+      // write column indices, updating column metadata along the way
+      int chunkidx = 0;
+      for (auto& r : fmd.row_groups) {
+        for (auto& c : r.columns) {
+          auto const& index     = fmd.column_indexes[chunkidx++];
+          c.column_index_offset = _out_sink[p]->bytes_written();
+          c.column_index_length = index.size();
+          _out_sink[p]->host_write(index.data(), index.size());
+        }
+      }
+
+      // write offset indices, updating column metadata along the way
+      chunkidx = 0;
+      for (auto& r : fmd.row_groups) {
+        for (auto& c : r.columns) {
+          auto const& offsets = fmd.offset_indexes[chunkidx++];
+          buffer.resize(0);
+          int32_t len           = cpw.write(offsets);
+          c.offset_index_offset = _out_sink[p]->bytes_written();
+          c.offset_index_length = len;
+          _out_sink[p]->host_write(buffer.data(), buffer.size());
+        }
+      }
+    }
+
+    buffer.resize(0);
+    fendr.footer_len = static_cast<uint32_t>(cpw.write(_agg_meta->get_metadata(p)));
+    fendr.magic      = parquet_magic;
+    _out_sink[p]->host_write(buffer.data(), buffer.size());
+    _out_sink[p]->host_write(&fendr, sizeof(fendr));
+    _out_sink[p]->flush();
+  }
+
+  // Optionally output raw file metadata with the specified column chunk file path
+  if (column_chunks_file_path.size() > 0) {
+    CUDF_EXPECTS(column_chunks_file_path.size() == _agg_meta->num_files(),
+                 "Expected one column chunk path per output file");
+    _agg_meta->set_file_paths(column_chunks_file_path);
+    file_header_s fhdr = {parquet_magic};
+    std::vector<uint8_t> buffer;
+    CompactProtocolWriter cpw(&buffer);
+    buffer.insert(buffer.end(),
+                  reinterpret_cast<uint8_t const*>(&fhdr),
+                  reinterpret_cast<uint8_t const*>(&fhdr) + sizeof(fhdr));
+    file_ender_s fendr;
+    fendr.magic      = parquet_magic;
+    fendr.footer_len = static_cast<uint32_t>(cpw.write(_agg_meta->get_merged_metadata()));
+    buffer.insert(buffer.end(),
+                  reinterpret_cast<uint8_t const*>(&fendr),
+                  reinterpret_cast<uint8_t const*>(&fendr) + sizeof(fendr));
+    return std::make_unique<std::vector<uint8_t>>(std::move(buffer));
+  } else {
+    return {nullptr};
+  }
+  return nullptr;
+}
+
+// Forward to implementation
+writer::writer(std::vector<std::unique_ptr<data_sink>> sinks,
+               parquet_writer_options const& options,
+               single_write_mode mode,
+               rmm::cuda_stream_view stream)
+  : _impl(std::make_unique<impl>(std::move(sinks), options, mode, stream))
+{
+}
+
+writer::writer(std::vector<std::unique_ptr<data_sink>> sinks,
+               chunked_parquet_writer_options const& options,
+               single_write_mode mode,
+               rmm::cuda_stream_view stream)
+  : _impl(std::make_unique<impl>(std::move(sinks), options, mode, stream))
+{
+}
+
+// Destructor within this translation unit
+writer::~writer() = default;
+
+// Forward to implementation
+void writer::write(table_view const& table, std::vector<partition_info> const& partitions)
+{
+  _impl->write(
+    table, partitions.empty() ? std::vector<partition_info>{{0, table.num_rows()}} : partitions);
+}
+
+// Forward to implementation
+std::unique_ptr<std::vector<uint8_t>> writer::close(
+  std::vector<std::string> const& column_chunks_file_path)
+{
+  return _impl->close(column_chunks_file_path);
+}
+
+std::unique_ptr<std::vector<uint8_t>> writer::merge_row_group_metadata(
+  std::vector<std::unique_ptr<std::vector<uint8_t>>> const& metadata_list)
+{
+  std::vector<uint8_t> output;
+  CompactProtocolWriter cpw(&output);
+  FileMetaData md;
+
+  md.row_groups.reserve(metadata_list.size());
+  for (auto const& blob : metadata_list) {
+    CompactProtocolReader cpreader(
+      blob.get()->data(),
+      std::max<size_t>(blob.get()->size(), sizeof(file_ender_s)) - sizeof(file_ender_s));
+    cpreader.skip_bytes(sizeof(file_header_s));  // Skip over file header
+    if (md.num_rows == 0) {
+      cpreader.read(&md);
+    } else {
+      FileMetaData tmp;
+      cpreader.read(&tmp);
+      md.row_groups.insert(md.row_groups.end(),
+                           std::make_move_iterator(tmp.row_groups.begin()),
+                           std::make_move_iterator(tmp.row_groups.end()));
+      md.num_rows += tmp.num_rows;
+    }
+  }
+
+  // Thrift-encode the resulting output
+  file_header_s fhdr;
+  file_ender_s fendr;
+  fhdr.magic = parquet_magic;
+  output.insert(output.end(),
+                reinterpret_cast<uint8_t const*>(&fhdr),
+                reinterpret_cast<uint8_t const*>(&fhdr) + sizeof(fhdr));
+  fendr.footer_len = static_cast<uint32_t>(cpw.write(md));
+  fendr.magic      = parquet_magic;
+  output.insert(output.end(),
+                reinterpret_cast<uint8_t const*>(&fendr),
+                reinterpret_cast<uint8_t const*>(&fendr) + sizeof(fendr));
+  return std::make_unique<std::vector<uint8_t>>(std::move(output));
+}
+
+}  // namespace parquet
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/parquet/writer_impl.hpp b/cpp/src/io/parquet/writer_impl.hpp
new file mode 100644
index 0000000..89ef85b
--- /dev/null
+++ b/cpp/src/io/parquet/writer_impl.hpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file writer_impl.hpp
+ * @brief cuDF-IO Parquet writer class implementation header
+ */
+
+#pragma once
+
+#include "parquet.hpp"
+#include "parquet_gpu.hpp"
+
+#include <cudf/io/data_sink.hpp>
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/io/detail/parquet.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace io {
+namespace detail {
+namespace parquet {
+// Forward internal classes
+struct aggregate_writer_metadata;
+
+using namespace cudf::io::parquet;
+using namespace cudf::io;
+using cudf::detail::device_2dspan;
+using cudf::detail::host_2dspan;
+using cudf::detail::hostdevice_2dvector;
+
+/**
+ * @brief Implementation for parquet writer
+ */
+class writer::impl {
+ public:
+  /**
+   * @brief Constructor with writer options.
+   *
+   * @param sink data_sink's for storing dataset
+   * @param options Settings for controlling behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit impl(std::vector<std::unique_ptr<data_sink>> sinks,
+                parquet_writer_options const& options,
+                single_write_mode mode,
+                rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Constructor with chunked writer options.
+   *
+   * @param sink data_sink's for storing dataset
+   * @param options Settings for controlling behavior
+   * @param mode Option to write at once or in chunks
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  explicit impl(std::vector<std::unique_ptr<data_sink>> sinks,
+                chunked_parquet_writer_options const& options,
+                single_write_mode mode,
+                rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Destructor to complete any incomplete write and release resources.
+   */
+  ~impl();
+
+  /**
+   * @brief Initializes the states before writing.
+   */
+  void init_state();
+
+  /**
+   * @brief Updates writer-level statistics with data from the current table.
+   *
+   * @param compression_stats Optional compression statistics from the current table
+   */
+  void update_compression_statistics(
+    std::optional<writer_compression_statistics> const& compression_stats);
+
+  /**
+   * @brief Writes a single subtable as part of a larger parquet file/table write,
+   * normally used for chunked writing.
+   *
+   * @throws rmm::bad_alloc if there is insufficient space for temporary buffers
+   *
+   * @param[in] table The table information to be written
+   * @param[in] partitions Optional partitions to divide the table into. If specified, must be same
+   * size as number of sinks.
+   */
+  void write(table_view const& table, std::vector<partition_info> const& partitions);
+
+  /**
+   * @brief Finishes the chunked/streamed write process.
+   *
+   * @param[in] column_chunks_file_path Column chunks file path to be set in the raw output metadata
+   * @return A parquet-compatible blob that contains the data for all rowgroups in the list only if
+   * `column_chunks_file_path` is provided, else null.
+   */
+  std::unique_ptr<std::vector<uint8_t>> close(
+    std::vector<std::string> const& column_chunks_file_path = {});
+
+ private:
+  /**
+   * @brief Write the intermediate Parquet data into the data sink.
+   *
+   * The intermediate data is generated from processing (compressing/encoding) a cuDF input table
+   * by `convert_table_to_parquet_data` called in the `write()` function.
+   *
+   * @param updated_agg_meta The updated aggregate data after processing the input
+   * @param pages Encoded pages
+   * @param chunks Column chunks
+   * @param global_rowgroup_base Numbers of rowgroups in each file/partition
+   * @param first_rg_in_part The first rowgroup in each partition
+   * @param batch_list The batches of rowgroups to encode
+   * @param rg_to_part A map from rowgroup to partition
+   * @param[out] bounce_buffer Temporary host output buffer
+   */
+  void write_parquet_data_to_sink(std::unique_ptr<aggregate_writer_metadata>& updated_agg_meta,
+                                  device_span<gpu::EncPage const> pages,
+                                  host_2dspan<gpu::EncColumnChunk const> chunks,
+                                  host_span<size_t const> global_rowgroup_base,
+                                  host_span<int const> first_rg_in_part,
+                                  host_span<size_type const> batch_list,
+                                  host_span<int const> rg_to_part,
+                                  host_span<uint8_t> bounce_buffer);
+
+  // Cuda stream to be used
+  rmm::cuda_stream_view _stream;
+
+  // Writer options.
+  Compression const _compression;
+  size_t const _max_row_group_size;
+  size_type const _max_row_group_rows;
+  size_t const _max_page_size_bytes;
+  size_type const _max_page_size_rows;
+  statistics_freq const _stats_granularity;
+  dictionary_policy const _dict_policy;
+  size_t const _max_dictionary_size;
+  std::optional<size_type> const _max_page_fragment_size;
+  bool const _int96_timestamps;
+  bool const _write_v2_headers;
+  int32_t const _column_index_truncate_length;
+  std::vector<std::map<std::string, std::string>> const _kv_meta;  // Optional user metadata.
+  single_write_mode const _single_write_mode;  // Special parameter only used by `write()` to
+                                               // indicate that we are guaranteeing a single table
+                                               // write. This enables some internal optimizations.
+  std::vector<std::unique_ptr<data_sink>> const _out_sink;
+
+  // Internal states, filled during `write()` and written to sink during `write` and `close()`.
+  std::unique_ptr<table_input_metadata> _table_meta;
+  std::unique_ptr<aggregate_writer_metadata> _agg_meta;
+  std::vector<std::size_t> _current_chunk_offset;  // To track if the last write(table) call
+                                                   // completed successfully current write
+                                                   // position for rowgroups/chunks.
+  std::shared_ptr<writer_compression_statistics> _compression_statistics;  // Optional output
+  bool _last_write_successful = false;
+  bool _closed                = false;  // To track if the output has been written to sink.
+};
+
+}  // namespace parquet
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/byte_array_view.cuh b/cpp/src/io/statistics/byte_array_view.cuh
new file mode 100644
index 0000000..0fe6c17
--- /dev/null
+++ b/cpp/src/io/statistics/byte_array_view.cuh
@@ -0,0 +1,180 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/span.hpp>
+
+namespace cudf::io::statistics {
+
+/**
+ * @brief Wrapper for a row of a list<int8> or list<uint8> column. This is analogous to
+ * `string_view` in type. It was created due to the need for comparison operators for cub reduce on
+ * statistics. Otherwise, it is a device_span in all but name.
+ *
+ */
+class byte_array_view {
+ public:
+  using element_type = std::byte const;  ///< The type of the elements in the byte array
+
+  constexpr byte_array_view() noexcept {}
+  /**
+   * @brief Constructs a byte_array_view from a pointer and a size.
+   *
+   * @param data Pointer to the first element in the byte array.
+   * @param size The number of elements in the byte array.
+   */
+  constexpr byte_array_view(element_type* data, std::size_t size) : _data(data, size) {}
+  constexpr byte_array_view(byte_array_view const&) noexcept = default;  ///< Copy constructor
+  /**
+   * @brief Copy assignment operator.
+   *
+   * @return Reference to this byte_array_view.
+   */
+  constexpr byte_array_view& operator=(byte_array_view const&) noexcept = default;
+
+  /**
+   * @brief Returns a reference to the idx-th element of the byte_array_view.
+   *
+   * The behavior is undefined if idx is out of range (i.e., if it is greater than or equal to
+   * size()).
+   *
+   * @param idx The index of the element to access.
+   * @return A reference to the idx-th element of the byte_array_view, i.e., `_data.data()[idx]`.
+   */
+  [[nodiscard]] constexpr element_type& operator[](std::size_t idx) const { return _data[idx]; }
+
+  /**
+   * @brief Returns a pointer to the beginning of the byte_array_view.
+   *
+   * @return A pointer to the first element of the byte_array_view.
+   */
+  [[nodiscard]] constexpr element_type* data() const noexcept { return _data.data(); }
+
+  /**
+   * @brief Returns the number of elements in the byte_array_view.
+   *
+   * @return The number of elements in the byte_array_view.
+   */
+  [[nodiscard]] constexpr std::size_t size() const noexcept { return _data.size(); }
+
+  /**
+   * @brief Returns the size of the byte_array_view in bytes.
+   *
+   * @return The size of the byte_array_view in bytes
+   */
+  [[nodiscard]] constexpr std::size_t size_bytes() const noexcept { return _data.size_bytes(); }
+
+  /**
+   * @brief Comparing target byte_array_view with this byte_array_view. Each byte in the array is
+   * compared.
+   *
+   * @param byte_array_view Target byte_array_view to compare with this byte_array_view.
+   * @return 0  If they compare equal.
+   *         <0 Either the value of the first byte of this byte_array_view that does not match is
+   * lower in the arg byte_array_view, or all compared bytes match but the arg byte_array_view is
+   * shorter. >0 Either the value of the first byte of this byte_array_view that does not match is
+   * greater in the arg byte_array_view, or all compared bytes match but the arg byte_array_view is
+   * longer.
+   */
+  [[nodiscard]] __device__ inline int32_t compare(byte_array_view const& rhs) const
+  {
+    auto const len1  = size_bytes();
+    auto const len2  = rhs.size_bytes();
+    auto const* ptr1 = this->data();
+    auto const* ptr2 = rhs.data();
+    if ((ptr1 == ptr2) && (len1 == len2)) { return 0; }
+    // if I am max, I am greater than the argument
+    if (ptr1 == nullptr && len1 == std::numeric_limits<std::size_t>::max()) { return 1; }
+    // if the argument is max, it is greater than me
+    if (ptr2 == nullptr && len2 == std::numeric_limits<std::size_t>::max()) { return -1; }
+    std::size_t idx = 0;
+    for (; (idx < len1) && (idx < len2); ++idx) {
+      if (ptr1[idx] != ptr2[idx]) {
+        return static_cast<int32_t>(ptr1[idx]) - static_cast<int32_t>(ptr2[idx]);
+      }
+    }
+    // if the argument ran out of data, it is less than me
+    if (idx < len1) return 1;
+    // if I ran out of data first, I am less than the argument
+    if (idx < len2) return -1;
+    return 0;
+  }
+
+  /**
+   * @brief Returns true if this byte_array_view is ordered before rhs.
+   *
+   * @param rhs Target byte_array_view to compare with this byte_array_view.
+   * @return true if this byte_array_view is ordered before rhs
+   */
+  [[nodiscard]] __device__ inline bool operator<(byte_array_view const& rhs) const
+  {
+    return compare(rhs) < 0;
+  }
+  /**
+   * @brief Returns true if rhs is ordered before this byte_array_view.
+   *
+   * @param rhs Target byte_array_view to compare with this byte_array_view.
+   * @return true if rhs is ordered before this byte_array_view
+   */
+  [[nodiscard]] __device__ inline bool operator>(byte_array_view const& rhs) const
+  {
+    return compare(rhs) > 0;
+  }
+
+  /**
+   * @brief Returns true if this byte_array_view is ordered before rhs.
+   *
+   * @param rhs Target byte_array_view to compare with this byte_array_view.
+   * @return true if this byte_array_view is ordered before rhs
+   */
+  [[nodiscard]] __device__ inline bool operator<=(byte_array_view const& rhs) const
+  {
+    return compare(rhs) <= 0;
+  }
+  /**
+   * @brief Returns true if rhs is ordered before this byte_array_view.
+   *
+   * @param rhs Target byte_array_view to compare with this byte_array_view.
+   * @return true if rhs is ordered before this byte_array_view
+   */
+  [[nodiscard]] __device__ inline bool operator>=(byte_array_view const& rhs) const
+  {
+    return compare(rhs) >= 0;
+  }
+
+  /**
+   * @brief Return minimum value associated with the byte_array_view type
+   *
+   * @return An empty byte_array_view
+   */
+  [[nodiscard]] __device__ inline static byte_array_view min() { return byte_array_view(); }
+
+  /**
+   * @brief Return a byte_array_view to interpret as maximum value
+   *
+   * @return A byte_array_view value which represents the largest possible byte_array_view
+   */
+  [[nodiscard]] __device__ inline static byte_array_view max()
+  {
+    return byte_array_view(nullptr, std::numeric_limits<std::size_t>::max());
+  }
+
+ private:
+  device_span<element_type> _data{};
+};
+
+}  // namespace cudf::io::statistics
diff --git a/cpp/src/io/statistics/column_statistics.cuh b/cpp/src/io/statistics/column_statistics.cuh
new file mode 100644
index 0000000..f71fb95
--- /dev/null
+++ b/cpp/src/io/statistics/column_statistics.cuh
@@ -0,0 +1,417 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file column_statistics.cuh
+ * @brief Functors for statistics calculation to be used in ORC and PARQUET
+ */
+
+#pragma once
+
+#include "temp_storage_wrapper.cuh"
+
+#include "typed_statistics_chunk.cuh"
+
+#include "statistics.cuh"
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief shared state for statistics calculation kernel
+ */
+struct stats_state_s {
+  stats_column_desc col{};   ///< Column information
+  statistics_group group{};  ///< Group description
+  statistics_chunk ck{};     ///< Output statistics chunk
+};
+
+/**
+ * @brief shared state for statistics merge kernel
+ */
+struct merge_state_s {
+  stats_column_desc col{};         ///< Column information
+  statistics_merge_group group{};  ///< Group description
+  statistics_chunk ck{};           ///< Resulting statistics chunk
+};
+
+template <int dimension>
+using block_reduce_storage = detail::block_reduce_storage<dimension>;
+
+/**
+ * @brief Functor to calculate the statistics of rows in a column belonging to a
+ * statistics group
+ *
+ * @tparam block_size Dimension of the block
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <int block_size,
+          detail::io_file_format IO,
+          detail::is_int96_timestamp INT96 = detail::is_int96_timestamp::YES>
+struct calculate_group_statistics_functor {
+  block_reduce_storage<block_size>& temp_storage;
+
+  /**
+   * @brief Construct a statistics calculator
+   *
+   * @param d_temp_storage Temporary storage to be used by cub calls
+   */
+  __device__ calculate_group_statistics_functor(block_reduce_storage<block_size>& d_temp_storage)
+    : temp_storage(d_temp_storage)
+  {
+  }
+
+  template <typename T,
+            std::enable_if_t<detail::statistics_type_category<T, IO>::ignore>* = nullptr>
+  __device__ void operator()(stats_state_s&, uint32_t)
+  {
+    // No-op for unsupported aggregation types
+  }
+
+  template <typename T>
+  __device__ T get_element(stats_state_s const& s, uint32_t row)
+  {
+    return cudf::io::get_element<T>(*s.col.leaf_column, row);
+  }
+
+  /**
+   * @brief Iterates through the rows specified by statistics group and stores the combined
+   * statistics into the statistics chunk.
+   *
+   * @param s Statistics state which specifies the column, the group being worked and the chunk
+   * the results will be stored into
+   * @param t thread id
+   */
+  template <typename T,
+            std::enable_if_t<detail::statistics_type_category<T, IO>::include_extrema and
+                             (IO != detail::io_file_format::PARQUET or
+                              !std::is_same_v<T, list_view>)>* = nullptr>
+  __device__ void operator()(stats_state_s& s, uint32_t t)
+  {
+    // Temporarily disable stats writing for int96 timestamps
+    // TODO: https://github.com/rapidsai/cudf/issues/10438
+    if constexpr (cudf::is_timestamp<T>() and IO == detail::io_file_format::PARQUET and
+                  INT96 == detail::is_int96_timestamp::YES) {
+      return;
+    }
+
+    detail::storage_wrapper<block_size> storage(temp_storage);
+
+    using type_convert = detail::type_conversion<detail::conversion_map<IO, INT96>>;
+    using CT           = typename type_convert::template type<T>;
+    typed_statistics_chunk<CT, detail::statistics_type_category<T, IO>::include_aggregate> chunk;
+
+    for (uint32_t i = 0; i < s.group.num_rows; i += block_size) {
+      uint32_t r   = i + t;
+      uint32_t row = r + s.group.start_row;
+      if (r < s.group.num_rows) {
+        if (s.col.leaf_column->is_valid(row)) {
+          auto converted_value = type_convert::convert(get_element<T>(s, row));
+          chunk.reduce(converted_value);
+        } else {
+          chunk.null_count++;
+        }
+      }
+    }
+
+    chunk = block_reduce(chunk, storage);
+
+    if (t == 0) {
+      // parquet wants total null count in stats, not just count of null leaf values
+      if constexpr (IO == detail::io_file_format::PARQUET) {
+        chunk.null_count += s.group.non_leaf_nulls;
+      }
+      s.ck = get_untyped_chunk(chunk);
+    }
+  }
+
+  template <typename T,
+            std::enable_if_t<detail::statistics_type_category<T, IO>::include_extrema and
+                             IO == detail::io_file_format::PARQUET and
+                             std::is_same_v<T, list_view>>* = nullptr>
+  __device__ void operator()(stats_state_s& s, uint32_t t)
+  {
+    operator()<statistics::byte_array_view>(s, t);
+  }
+
+  template <
+    typename T,
+    std::enable_if_t<detail::statistics_type_category<T, IO>::include_count and
+                     not detail::statistics_type_category<T, IO>::include_extrema>* = nullptr>
+  __device__ void operator()(stats_state_s& s, uint32_t t)
+  {
+    detail::storage_wrapper<block_size> storage(temp_storage);
+    typed_statistics_chunk<uint32_t, false> chunk;
+
+    for (uint32_t i = 0; i < s.group.num_rows; i += block_size) {
+      uint32_t r   = i + t;
+      uint32_t row = r + s.group.start_row;
+      if (r < s.group.num_rows) {
+        if (s.col.leaf_column->is_valid(row)) {
+          chunk.non_nulls++;
+        } else {
+          chunk.null_count++;
+        }
+      }
+    }
+    cub::BlockReduce<uint32_t, block_size>(storage.template get<uint32_t>()).Sum(chunk.non_nulls);
+
+    if (t == 0) { s.ck = get_untyped_chunk(chunk); }
+  }
+};
+
+/**
+ * @brief Functor to merge the statistics chunks of a column belonging to a
+ * merge group
+ *
+ * @tparam block_size Dimension of the block
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <int block_size, detail::io_file_format IO>
+struct merge_group_statistics_functor {
+  block_reduce_storage<block_size>& temp_storage;
+
+  __device__ merge_group_statistics_functor(block_reduce_storage<block_size>& d_temp_storage)
+    : temp_storage(d_temp_storage)
+  {
+  }
+
+  template <typename T,
+            std::enable_if_t<detail::statistics_type_category<T, IO>::ignore>* = nullptr>
+  __device__ void operator()(merge_state_s& s,
+                             statistics_chunk const* chunks,
+                             uint32_t const num_chunks,
+                             uint32_t t)
+  {
+    // No-op for unsupported aggregation types
+  }
+
+  template <typename T,
+            std::enable_if_t<detail::statistics_type_category<T, IO>::include_extrema and
+                             (IO == detail::io_file_format::ORC or
+                              !std::is_same_v<T, list_view>)>* = nullptr>
+  __device__ void operator()(merge_state_s& s,
+                             statistics_chunk const* chunks,
+                             uint32_t const num_chunks,
+                             uint32_t t)
+  {
+    detail::storage_wrapper<block_size> storage(temp_storage);
+
+    typed_statistics_chunk<T, detail::statistics_type_category<T, IO>::include_aggregate> chunk;
+
+    for (uint32_t i = t; i < num_chunks; i += block_size) {
+      chunk.reduce(chunks[i]);
+    }
+    chunk.has_minmax = (chunk.minimum_value <= chunk.maximum_value);
+
+    chunk = block_reduce(chunk, storage);
+
+    if (t == 0) { s.ck = get_untyped_chunk(chunk); }
+  }
+
+  template <typename T,
+            std::enable_if_t<detail::statistics_type_category<T, IO>::include_extrema and
+                             IO == detail::io_file_format::PARQUET and
+                             std::is_same_v<T, list_view>>* = nullptr>
+  __device__ void operator()(merge_state_s& s,
+                             statistics_chunk const* chunks,
+                             uint32_t const num_chunks,
+                             uint32_t t)
+  {
+    operator()<statistics::byte_array_view>(s, chunks, num_chunks, t);
+  }
+
+  template <
+    typename T,
+    std::enable_if_t<detail::statistics_type_category<T, IO>::include_count and
+                     not detail::statistics_type_category<T, IO>::include_extrema>* = nullptr>
+  __device__ void operator()(merge_state_s& s,
+                             statistics_chunk const* chunks,
+                             uint32_t const num_chunks,
+                             uint32_t t)
+  {
+    detail::storage_wrapper<block_size> storage(temp_storage);
+    typed_statistics_chunk<uint32_t, false> chunk;
+
+    for (uint32_t i = t; i < num_chunks; i += block_size) {
+      chunk.reduce(chunks[i]);
+    }
+
+    chunk = block_reduce(chunk, storage);
+
+    if (t == 0) { s.ck = get_untyped_chunk(chunk); }
+  }
+};
+
+/**
+ * @brief Function to cooperatively load an object from a pointer
+ *
+ * If the pointer is nullptr then the members of the object are set to 0
+ *
+ * @param[out] destination Object being loaded
+ * @param[in] source Source object
+ * @tparam T Type of object
+ */
+template <typename T>
+__device__ void cooperative_load(T& destination, T const* source = nullptr)
+{
+  using load_type = std::conditional_t<((sizeof(T) % sizeof(uint32_t)) == 0), uint32_t, uint8_t>;
+  if (source == nullptr) {
+    for (auto i = threadIdx.x; i < (sizeof(T) / sizeof(load_type)); i += blockDim.x) {
+      reinterpret_cast<load_type*>(&destination)[i] = load_type{0};
+    }
+  } else {
+    for (auto i = threadIdx.x; i < sizeof(T) / sizeof(load_type); i += blockDim.x) {
+      reinterpret_cast<load_type*>(&destination)[i] = reinterpret_cast<load_type const*>(source)[i];
+    }
+  }
+}
+
+/**
+ * @brief Kernel to calculate group statistics
+ *
+ * @param[out] chunks Statistics results [num_chunks]
+ * @param[in] groups Statistics row groups [num_chunks]
+ * @tparam block_size Dimension of the block
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <int block_size, detail::io_file_format IO>
+__global__ void __launch_bounds__(block_size, 1)
+  gpu_calculate_group_statistics(statistics_chunk* chunks,
+                                 statistics_group const* groups,
+                                 bool const int96_timestamps)
+{
+  __shared__ __align__(8) stats_state_s state;
+  __shared__ block_reduce_storage<block_size> storage;
+
+  // Load state members
+  cooperative_load(state.group, &groups[blockIdx.x]);
+  cooperative_load(state.ck);
+  __syncthreads();
+  cooperative_load(state.col, state.group.col);
+  __syncthreads();
+
+  // Calculate statistics
+  if constexpr (IO == detail::io_file_format::PARQUET) {
+    // Do not convert ns to us for int64 timestamps
+    if (not int96_timestamps) {
+      type_dispatcher(
+        state.col.leaf_column->type(),
+        calculate_group_statistics_functor<block_size, IO, detail::is_int96_timestamp::NO>(storage),
+        state,
+        threadIdx.x);
+    }
+    // Temporarily disable stats writing for int96 timestamps
+    // TODO: https://github.com/rapidsai/cudf/issues/10438
+    else {
+      type_dispatcher(
+        state.col.leaf_column->type(),
+        calculate_group_statistics_functor<block_size, IO, detail::is_int96_timestamp::YES>(
+          storage),
+        state,
+        threadIdx.x);
+    }
+  } else {
+    type_dispatcher(state.col.leaf_column->type(),
+                    calculate_group_statistics_functor<block_size, IO>(storage),
+                    state,
+                    threadIdx.x);
+  }
+  __syncthreads();
+
+  cooperative_load(chunks[blockIdx.x], &state.ck);
+}
+
+namespace detail {
+
+/**
+ * @brief Launches kernel to calculate group statistics
+ *
+ * @param[out] chunks Statistics results [num_chunks]
+ * @param[in] groups Statistics row groups [num_chunks]
+ * @param[in] num_chunks Number of chunks & rowgroups
+ * @param[in] stream CUDA stream to use
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <detail::io_file_format IO>
+void calculate_group_statistics(statistics_chunk* chunks,
+                                statistics_group const* groups,
+                                uint32_t num_chunks,
+                                rmm::cuda_stream_view stream,
+                                bool const int96_timestamps = false)
+{
+  constexpr int block_size = 256;
+  gpu_calculate_group_statistics<block_size, IO>
+    <<<num_chunks, block_size, 0, stream.value()>>>(chunks, groups, int96_timestamps);
+}
+
+/**
+ * @brief Kernel to merge column statistics
+ *
+ * @param[out] chunks_out Statistics results [num_chunks]
+ * @param[in] chunks_in Input statistics
+ * @param[in] groups Statistics groups [num_chunks]
+ * @tparam block_size Dimension of the block
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <int block_size, detail::io_file_format IO>
+__global__ void __launch_bounds__(block_size, 1)
+  gpu_merge_group_statistics(statistics_chunk* chunks_out,
+                             statistics_chunk const* chunks_in,
+                             statistics_merge_group const* groups)
+{
+  __shared__ __align__(8) merge_state_s state;
+  __shared__ block_reduce_storage<block_size> storage;
+
+  cooperative_load(state.group, &groups[blockIdx.x]);
+  __syncthreads();
+
+  type_dispatcher(state.group.col_dtype,
+                  merge_group_statistics_functor<block_size, IO>(storage),
+                  state,
+                  chunks_in + state.group.start_chunk,
+                  state.group.num_chunks,
+                  threadIdx.x);
+  __syncthreads();
+
+  cooperative_load(chunks_out[blockIdx.x], &state.ck);
+}
+
+/**
+ * @brief Launches kernel to merge column statistics
+ *
+ * @param[out] chunks_out Statistics results [num_chunks]
+ * @param[in] chunks_in Input statistics
+ * @param[in] groups Statistics groups [num_chunks]
+ * @param[in] num_chunks Number of chunks & groups
+ * @param[in] stream CUDA stream to use
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <detail::io_file_format IO>
+void merge_group_statistics(statistics_chunk* chunks_out,
+                            statistics_chunk const* chunks_in,
+                            statistics_merge_group const* groups,
+                            uint32_t num_chunks,
+                            rmm::cuda_stream_view stream)
+{
+  constexpr int block_size = 256;
+  gpu_merge_group_statistics<block_size, IO>
+    <<<num_chunks, block_size, 0, stream.value()>>>(chunks_out, chunks_in, groups);
+}
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/conversion_type_select.cuh b/cpp/src/io/statistics/conversion_type_select.cuh
new file mode 100644
index 0000000..b76a5fc
--- /dev/null
+++ b/cpp/src/io/statistics/conversion_type_select.cuh
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file conversion_type_select.cuh
+ * @brief Utility classes for timestamp and duration conversion for PARQUET and ORC
+ */
+
+#pragma once
+
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+template <int, int, typename>
+class DetectInnerIteration;
+
+template <int N0, typename... T>
+class DetectInnerIteration<N0, 0, std::tuple<T...>> {
+ public:
+  static constexpr bool is_duplicate =
+    std::is_same_v<typename std::tuple_element<N0, std::tuple<T...>>::type,
+                   typename std::tuple_element<0, std::tuple<T...>>::type>;
+};
+
+template <int N0, int N1, typename... T>
+class DetectInnerIteration<N0, N1, std::tuple<T...>> {
+ public:
+  static constexpr bool is_duplicate =
+    std::is_same_v<typename std::tuple_element<N0, std::tuple<T...>>::type,
+                   typename std::tuple_element<N1, std::tuple<T...>>::type> ||
+    DetectInnerIteration<N0, N1 - 1, std::tuple<T...>>::is_duplicate;
+};
+
+template <int, typename>
+class DetectIteration;
+
+template <typename... T>
+class DetectIteration<0, std::tuple<T...>> {
+ public:
+  static constexpr bool is_duplicate = false;
+};
+
+template <int N, typename... T>
+class DetectIteration<N, std::tuple<T...>> {
+ public:
+  static constexpr bool is_duplicate =
+    DetectInnerIteration<N, N - 1, std::tuple<T...>>::is_duplicate ||
+    DetectIteration<N - 1, std::tuple<T...>>::is_duplicate;
+};
+
+template <typename>
+class Detect;
+
+/**
+ * @brief Utility class to detect multiple occurrences of a type in the first element of pairs in a
+ * tuple For eg. with the following tuple :
+ *
+ * using conversion_types =
+ * std::tuple<
+ *  std::pair<int, A>,
+ *  std::pair<char, B>,
+ *  std::pair<int, C>,
+ *  std::pair<int, D>,
+ *  std::pair<unsigned, E>,
+ *  std::pair<unsigned, F>>;
+ *
+ * Detect<conversion_types>::is_duplicate will evaluate to true at compile time.
+ * Here std::pair<int, A>, std::pair<int, C> and std::pair<int, D> are treated as duplicates
+ * and std::pair<unsigned, E> and std::pair<unsigned, F>> are treated as duplicates.
+ *
+ * @tparam T... Parameter pack of pairs of types
+ */
+template <typename... T>
+class Detect<std::tuple<T...>> {
+ public:
+  static constexpr bool is_duplicate =
+    DetectIteration<(sizeof...(T) - 1), std::tuple<T...>>::is_duplicate;
+};
+
+template <typename>
+class ConversionTypeSelect;
+
+template <typename I0>
+class ConversionTypeSelect<std::tuple<I0>> {
+ public:
+  template <typename T>
+  using type = std::conditional_t<std::is_same_v<T, typename std::tuple_element<0, I0>::type>,
+                                  typename std::tuple_element<1, I0>::type,
+                                  T>;
+};
+
+/**
+ * @brief Utility to select between types based on an input type
+ *
+ * using Conversion = std::tuple<
+ *  std::pair<cudf::timestamp_s, cudf::timestamp_ms>,
+ *  std::pair<cudf::timestamp_ns, cudf::timestamp_us>,
+ *  std::pair<cudf::duration_s, cudf::duration_ms>,
+ *  std::pair<cudf::duration_ns, cudf::duration_us>>
+ *
+ * using type = ConversionTypeSelect<Conversion>::type<cudf::duration_ns>
+ * Here type will resolve to cudf::duration_us
+ * If the type passed does not match any entries the type is returned as it is
+ * This utility takes advantage of Detect class to reject any tuple with duplicate first
+ * entries at compile time
+ *
+ * @tparam T... Parameter pack of pairs of types
+ */
+template <typename I0, typename... In>
+class ConversionTypeSelect<std::tuple<I0, In...>> {
+ public:
+  template <typename T>
+  using type =
+    std::conditional_t<std::is_same_v<T, typename std::tuple_element<0, I0>::type>,
+                       typename std::tuple_element<1, I0>::type,
+                       typename ConversionTypeSelect<std::tuple<In...>>::template type<T>>;
+
+  static_assert(not Detect<std::tuple<I0, In...>>::is_duplicate,
+                "Type tuple has duplicate first entries");
+};
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/orc_column_statistics.cu b/cpp/src/io/statistics/orc_column_statistics.cu
new file mode 100644
index 0000000..f3356b3
--- /dev/null
+++ b/cpp/src/io/statistics/orc_column_statistics.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file orc_column_statistics.cu
+ * @brief Template specialization for ORC statistics calls
+ */
+
+#include "column_statistics.cuh"
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+template <>
+void merge_group_statistics<detail::io_file_format::ORC>(statistics_chunk* chunks_out,
+                                                         statistics_chunk const* chunks_in,
+                                                         statistics_merge_group const* groups,
+                                                         uint32_t num_chunks,
+                                                         rmm::cuda_stream_view stream);
+template <>
+void calculate_group_statistics<detail::io_file_format::ORC>(statistics_chunk* chunks,
+                                                             statistics_group const* groups,
+                                                             uint32_t num_chunks,
+                                                             rmm::cuda_stream_view stream,
+                                                             bool int96_timestamp);
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/parquet_column_statistics.cu b/cpp/src/io/statistics/parquet_column_statistics.cu
new file mode 100644
index 0000000..091f08d
--- /dev/null
+++ b/cpp/src/io/statistics/parquet_column_statistics.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file parquet_column_statistics.cu
+ * @brief Template specialization for PARQUET statistics calls
+ */
+
+#include "column_statistics.cuh"
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+template <>
+void merge_group_statistics<detail::io_file_format::PARQUET>(statistics_chunk* chunks_out,
+                                                             statistics_chunk const* chunks_in,
+                                                             statistics_merge_group const* groups,
+                                                             uint32_t num_chunks,
+                                                             rmm::cuda_stream_view stream);
+template <>
+void calculate_group_statistics<detail::io_file_format::PARQUET>(statistics_chunk* chunks,
+                                                                 statistics_group const* groups,
+                                                                 uint32_t num_chunks,
+                                                                 rmm::cuda_stream_view stream,
+                                                                 bool int96_timestamp);
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/statistics.cuh b/cpp/src/io/statistics/statistics.cuh
new file mode 100644
index 0000000..b6e698f
--- /dev/null
+++ b/cpp/src/io/statistics/statistics.cuh
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file statistics.cuh
+ * @brief Common structures and utility functions for statistics
+ */
+
+#pragma once
+
+#include "byte_array_view.cuh"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+
+enum statistics_dtype {
+  dtype_none,
+  dtype_bool,
+  dtype_int8,
+  dtype_int16,
+  dtype_int32,
+  dtype_date32,
+  dtype_int64,
+  dtype_timestamp64,
+  dtype_decimal64,
+  dtype_decimal128,
+  dtype_float32,
+  dtype_float64,
+  dtype_string,
+  dtype_byte_array,
+};
+
+struct stats_column_desc {
+  statistics_dtype stats_dtype;  //!< physical data type of column
+  uint32_t num_rows;             //!< number of rows in column
+  uint32_t num_values;  //!< Number of data values in column. Different from num_rows in case of
+                        //!< nested columns
+  int32_t ts_scale;     //!< timestamp scale (>0: multiply by scale, <0: divide by -scale)
+
+  column_device_view const* leaf_column;    //!< Pointer to leaf column
+  column_device_view const* parent_column;  //!< Pointer to parent column; nullptr if not list type
+};
+
+template <typename ReturnType, typename InternalType>
+struct t_array_stats {
+  InternalType const* ptr;  //!< ptr to data
+  size_type length;         //!< length of data
+  __host__ __device__ __forceinline__ volatile t_array_stats& operator=(
+    ReturnType const& val) volatile
+  {
+    ptr    = val.data();
+    length = val.size_bytes();
+    return *this;
+  }
+  __host__ __device__ __forceinline__ operator ReturnType() volatile
+  {
+    return ReturnType(ptr, length);
+  }
+  __host__ __device__ __forceinline__ operator ReturnType() const
+  {
+    return ReturnType(ptr, length);
+  }
+  __host__ __device__ __forceinline__ operator ReturnType() { return ReturnType(ptr, length); }
+};
+using string_stats     = t_array_stats<string_view, char>;
+using byte_array_view  = statistics::byte_array_view;
+using byte_array_stats = t_array_stats<byte_array_view, byte_array_view::element_type>;
+
+union statistics_val {
+  string_stats str_val;       //!< string columns
+  byte_array_stats byte_val;  //!< byte array columns
+  double fp_val;              //!< float columns
+  int64_t i_val;              //!< integer columns
+  uint64_t u_val;             //!< unsigned integer columns
+  __int128_t d128_val;        //!< decimal128 columns
+};
+
+struct statistics_chunk {
+  uint32_t non_nulls{};        //!< number of non-null values in chunk
+  uint32_t null_count{};       //!< number of null values in chunk
+  statistics_val min_value{};  //!< minimum value in chunk
+  statistics_val max_value{};  //!< maximum value in chunk
+  statistics_val sum{};        //!< sum of chunk
+  uint8_t has_minmax{};        //!< Nonzero if min_value and max_values are valid
+  uint8_t has_sum{};           //!< Nonzero if sum is valid
+};
+
+struct statistics_group {
+  stats_column_desc const* col{};  //!< Column information
+  uint32_t start_row{};            //!< Start row of this group
+  uint32_t num_rows{};             //!< Number of rows in group
+  uint32_t non_leaf_nulls{};       //!< Number of null non-leaf values in the group
+};
+
+struct statistics_merge_group {
+  data_type col_dtype;                       //!< Column data type
+  statistics_dtype stats_dtype{dtype_none};  //!< Statistics data type for this column
+  uint32_t start_chunk{};                    //!< Start chunk of this group
+  uint32_t num_chunks{};                     //!< Number of chunks in group
+};
+
+template <typename T, std::enable_if_t<!std::is_same_v<T, statistics::byte_array_view>>* = nullptr>
+__device__ T get_element(column_device_view const& col, uint32_t row)
+{
+  return col.element<T>(row);
+}
+
+template <typename T, std::enable_if_t<std::is_same_v<T, statistics::byte_array_view>>* = nullptr>
+__device__ T get_element(column_device_view const& col, uint32_t row)
+{
+  using et              = typename T::element_type;
+  size_type const index = row + col.offset();  // account for this view's _offset
+  auto const* d_offsets = col.child(lists_column_view::offsets_column_index).data<size_type>();
+  auto const* d_data    = col.child(lists_column_view::child_column_index).data<et>();
+  auto const offset     = d_offsets[index];
+  return T(d_data + offset, d_offsets[index + 1] - offset);
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/statistics_type_identification.cuh b/cpp/src/io/statistics/statistics_type_identification.cuh
new file mode 100644
index 0000000..ea8c71f
--- /dev/null
+++ b/cpp/src/io/statistics/statistics_type_identification.cuh
@@ -0,0 +1,288 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file statistics_type_identification.cuh
+ * @brief Utility classes to identify extrema, aggregate and conversion types for ORC and PARQUET
+ */
+
+#pragma once
+
+#include "byte_array_view.cuh"
+
+#include <cudf/fixed_point/fixed_point.hpp>
+
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cudf/strings/string_view.cuh>
+
+#include <cudf/wrappers/durations.hpp>
+
+#include <cudf/utilities/traits.hpp>
+
+#include "conversion_type_select.cuh"
+
+#include <tuple>
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+using cudf::io::statistics::byte_array_view;
+
+enum class io_file_format { ORC, PARQUET };
+enum class is_int96_timestamp { YES, NO };
+
+template <io_file_format IO, is_int96_timestamp INT96>
+struct conversion_map;
+
+// Every timestamp or duration type is converted to nanoseconds in ORC statistics
+template <is_int96_timestamp INT96>
+struct conversion_map<io_file_format::ORC, INT96> {
+  using types = std::tuple<std::pair<cudf::timestamp_s, cudf::timestamp_ns>,
+                           std::pair<cudf::timestamp_us, cudf::timestamp_ns>,
+                           std::pair<cudf::timestamp_ns, cudf::timestamp_ns>,
+                           std::pair<cudf::duration_s, cudf::duration_ns>,
+                           std::pair<cudf::duration_us, cudf::duration_ns>,
+                           std::pair<cudf::duration_ns, cudf::duration_ns>>;
+};
+
+// In Parquet timestamps and durations with second resolution are converted to
+// milliseconds. Timestamps and durations with nanosecond resolution are
+// converted to microseconds.
+template <>
+struct conversion_map<io_file_format::PARQUET, is_int96_timestamp::YES> {
+  using types = std::tuple<std::pair<cudf::timestamp_s, cudf::timestamp_ms>,
+                           std::pair<cudf::timestamp_ns, cudf::timestamp_us>,
+                           std::pair<cudf::duration_s, cudf::duration_ms>,
+                           std::pair<cudf::duration_ns, cudf::duration_us>>;
+};
+// int64 nanosecond timestamp won't be converted
+template <>
+struct conversion_map<io_file_format::PARQUET, is_int96_timestamp::NO> {
+  using types = std::tuple<std::pair<cudf::timestamp_s, cudf::timestamp_ms>,
+                           std::pair<cudf::duration_s, cudf::duration_ms>>;
+};
+
+/**
+ * @brief Utility class to help conversion of timestamps and durations to their
+ * representation type
+ *
+ * @tparam conversion A conversion_map structure
+ */
+template <typename conversion>
+class type_conversion {
+  using type_selector = ConversionTypeSelect<typename conversion::types>;
+
+ public:
+  template <typename T>
+  using type = typename type_selector::template type<T>;
+
+  template <typename T>
+  static constexpr __device__ typename type_selector::template type<T> convert(T const& elem)
+  {
+    using Type = typename type_selector::template type<T>;
+    if constexpr (cudf::is_duration<T>()) {
+      return cuda::std::chrono::duration_cast<Type>(elem);
+    } else if constexpr (cudf::is_timestamp<T>()) {
+      using Duration = typename Type::duration;
+      return cuda::std::chrono::time_point_cast<Duration>(elem);
+    } else {
+      return elem;
+    }
+    return Type{};
+  }
+};
+
+template <class T>
+struct dependent_false : std::false_type {};
+
+/**
+ * @brief Utility class to convert a leaf column element into its extrema type
+ *
+ * @tparam T Column type
+ */
+template <typename T>
+class extrema_type {
+ private:
+  using integral_extrema_type = typename std::conditional_t<std::is_signed_v<T>, int64_t, uint64_t>;
+
+  using arithmetic_extrema_type =
+    typename std::conditional_t<std::is_integral_v<T>, integral_extrema_type, double>;
+
+  using non_arithmetic_extrema_type = typename std::conditional_t<
+    cudf::is_fixed_point<T>() or cudf::is_duration<T>() or cudf::is_timestamp<T>(),
+    typename std::conditional_t<cudf::is_fixed_point<T>(), __int128_t, int64_t>,
+    typename std::conditional_t<
+      std::is_same_v<T, string_view>,
+      string_view,
+      std::conditional_t<std::is_same_v<T, byte_array_view>, byte_array_view, void>>>;
+
+  // unsigned int/bool -> uint64_t
+  // signed int        -> int64_t
+  // float/double      -> double
+  // decimal32/64/128  -> __int128_t
+  // duration_[T]      -> int64_t
+  // string_view       -> string_view
+  // byte_array_view   -> byte_array_view
+  // timestamp_[T]     -> int64_t
+
+ public:
+  // Does type T have an extrema?
+  static constexpr bool is_supported =
+    std::is_arithmetic_v<T> or std::is_same_v<T, string_view> or cudf::is_duration<T>() or
+    cudf::is_timestamp<T>() or cudf::is_fixed_point<T>() or std::is_same_v<T, byte_array_view>;
+
+  using type = typename std::
+    conditional_t<std::is_arithmetic_v<T>, arithmetic_extrema_type, non_arithmetic_extrema_type>;
+
+  /**
+   * @brief Function that converts an element of a leaf column into its extrema type
+   */
+  __device__ static type convert(T const& val)
+  {
+    if constexpr (std::is_arithmetic_v<T> or std::is_same_v<T, string_view> or
+                  std::is_same_v<T, byte_array_view>) {
+      return val;
+    } else if constexpr (cudf::is_fixed_point<T>()) {
+      return val.value();
+    } else if constexpr (cudf::is_duration<T>()) {
+      return val.count();
+    } else if constexpr (cudf::is_timestamp<T>()) {
+      return val.time_since_epoch().count();
+    } else {
+      static_assert(dependent_false<T>::value, "aggregation_type does not exist");
+    }
+    return type{};
+  }
+};
+
+/**
+ * @brief Utility class to convert a leaf column element into its aggregate type
+ *
+ * @tparam T Column type
+ */
+template <typename T>
+class aggregation_type {
+ private:
+  using integral_aggregation_type =
+    typename std::conditional_t<std::is_signed_v<T>, int64_t, uint64_t>;
+
+  using arithmetic_aggregation_type =
+    typename std::conditional_t<std::is_integral_v<T>, integral_aggregation_type, double>;
+
+  using non_arithmetic_aggregation_type = typename std::conditional_t<
+    cudf::is_fixed_point<T>() or cudf::is_duration<T>() or
+      cudf::is_timestamp<T>()  // To be disabled with static_assert
+      or std::is_same_v<T, string_view> or std::is_same_v<T, byte_array_view>,
+    typename std::conditional_t<std::is_same_v<T, numeric::decimal128>, __int128_t, int64_t>,
+    void>;
+
+  // unsigned int/bool -> uint64_t
+  // signed int        -> int64_t
+  // float/double      -> double
+  // decimal32/64      -> int64_t
+  // decimal128        -> __int128_t
+  // duration_[T]      -> int64_t
+  // string_view       -> int64_t
+  // byte_array        -> int64_t
+  // NOTE : timestamps do not have an aggregation type
+
+ public:
+  // Does type T aggregate?
+  static constexpr bool is_supported = std::is_arithmetic_v<T> or std::is_same_v<T, string_view> or
+                                       cudf::is_duration<T>() or cudf::is_fixed_point<T>() or
+                                       std::is_same_v<T, byte_array_view>;
+
+  using type = typename std::conditional_t<std::is_arithmetic_v<T>,
+                                           arithmetic_aggregation_type,
+                                           non_arithmetic_aggregation_type>;
+
+  /**
+   * @brief Function that converts an element of a leaf column into its aggregate type
+   */
+  __device__ static type convert(T const& val)
+  {
+    if constexpr (std::is_same_v<T, string_view> or std::is_same_v<T, byte_array_view>) {
+      return val.size_bytes();
+    } else if constexpr (std::is_integral_v<T>) {
+      return val;
+    } else if constexpr (std::is_floating_point_v<T>) {
+      return isnan(val) ? 0 : val;
+    } else if constexpr (cudf::is_fixed_point<T>()) {
+      return val.value();
+    } else if constexpr (cudf::is_duration<T>()) {
+      return val.count();
+    } else if constexpr (cudf::is_timestamp<T>()) {
+      static_assert(dependent_false<T>::value, "aggregation_type for timestamps do not exist");
+    } else {
+      static_assert(dependent_false<T>::value, "aggregation_type for supplied type do not exist");
+    }
+    return type{};
+  }
+};
+
+template <typename T>
+__inline__ __device__ constexpr T minimum_identity()
+{
+  if constexpr (std::is_same_v<T, string_view>) {
+    return string_view::max();
+  } else if constexpr (std::is_same_v<T, byte_array_view>) {
+    return byte_array_view::max();
+  }
+  return cuda::std::numeric_limits<T>::max();
+}
+
+template <typename T>
+__inline__ __device__ constexpr T maximum_identity()
+{
+  if constexpr (std::is_same_v<T, string_view>) {
+    return string_view::min();
+  } else if constexpr (std::is_same_v<T, byte_array_view>) {
+    return byte_array_view::min();
+  }
+  return cuda::std::numeric_limits<T>::lowest();
+}
+
+/**
+ * @brief Utility class to identify whether a type T is aggregated or ignored
+ * for ORC or PARQUET
+ *
+ * @tparam T Leaf column type
+ * @tparam IO File format for which statistics calculation is being done
+ */
+template <typename T, io_file_format IO>
+class statistics_type_category {
+ public:
+  // Types that calculate the sum of elements encountered
+  static constexpr bool include_aggregate =
+    (IO == io_file_format::PARQUET) ? false : aggregation_type<T>::is_supported;
+
+  // Types for which sum does not make sense, but extrema do
+  static constexpr bool include_extrema =
+    aggregation_type<T>::is_supported or cudf::is_timestamp<T>() or
+    (std::is_same_v<T, cudf::list_view> and IO == io_file_format::PARQUET);
+
+  // Types for which only value count makes sense (e.g. nested)
+  static constexpr bool include_count = (IO == io_file_format::ORC) ? true : include_extrema;
+
+  // Do not calculate statistics for any other type
+  static constexpr bool ignore = not(include_count);
+};
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/temp_storage_wrapper.cuh b/cpp/src/io/statistics/temp_storage_wrapper.cuh
new file mode 100644
index 0000000..b2e31ce
--- /dev/null
+++ b/cpp/src/io/statistics/temp_storage_wrapper.cuh
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file temp_storage_wrapper.cuh
+ * @brief Temporary storage for cub calls and helper wrapper class
+ */
+
+#pragma once
+
+#include "byte_array_view.cuh"
+#include "statistics.cuh"
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cub/cub.cuh>
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+template <typename T, int block_size>
+using cub_temp_storage = typename cub::BlockReduce<T, block_size>::TempStorage;
+using statistics::byte_array_view;
+
+#define MEMBER_NAME(TYPE) TYPE##_stats
+
+#define DECLARE_MEMBER(TYPE) cub_temp_storage<TYPE, block_size> MEMBER_NAME(TYPE);
+
+/**
+ * @brief Templated union to hold temporary storage to be used by cub reduce
+ * calls
+ *
+ * @tparam block_size Dimension of the block
+ */
+template <int block_size>
+union block_reduce_storage {
+  DECLARE_MEMBER(bool)
+  DECLARE_MEMBER(int8_t)
+  DECLARE_MEMBER(int16_t)
+  DECLARE_MEMBER(int32_t)
+  DECLARE_MEMBER(int64_t)
+  DECLARE_MEMBER(__int128_t)
+  DECLARE_MEMBER(uint8_t)
+  DECLARE_MEMBER(uint16_t)
+  DECLARE_MEMBER(uint32_t)
+  DECLARE_MEMBER(uint64_t)
+  DECLARE_MEMBER(float)
+  DECLARE_MEMBER(double)
+  DECLARE_MEMBER(string_view)
+  DECLARE_MEMBER(byte_array_view)
+};
+
+#define STORAGE_WRAPPER_GET(TYPE)                                                                 \
+  template <typename T>                                                                           \
+  __device__ std::enable_if_t<std::is_same_v<T, TYPE>, cub_temp_storage<TYPE, block_size>&> get() \
+  {                                                                                               \
+    return storage.MEMBER_NAME(TYPE);                                                             \
+  }
+
+/**
+ * @brief Templated wrapper for block_reduce_storage to return member reference based on requested
+ * type
+ *
+ * @tparam block_size Dimension of the block
+ */
+template <int block_size>
+struct storage_wrapper {
+  block_reduce_storage<block_size>& storage;
+  __device__ storage_wrapper(block_reduce_storage<block_size>& _temp_storage)
+    : storage(_temp_storage)
+  {
+  }
+
+  STORAGE_WRAPPER_GET(bool);
+  STORAGE_WRAPPER_GET(int8_t);
+  STORAGE_WRAPPER_GET(int16_t);
+  STORAGE_WRAPPER_GET(int32_t);
+  STORAGE_WRAPPER_GET(int64_t);
+  STORAGE_WRAPPER_GET(__int128_t);
+  STORAGE_WRAPPER_GET(uint8_t);
+  STORAGE_WRAPPER_GET(uint16_t);
+  STORAGE_WRAPPER_GET(uint32_t);
+  STORAGE_WRAPPER_GET(uint64_t);
+  STORAGE_WRAPPER_GET(float);
+  STORAGE_WRAPPER_GET(double);
+  STORAGE_WRAPPER_GET(string_view);
+  STORAGE_WRAPPER_GET(byte_array_view);
+};
+
+#undef DECLARE_MEMBER
+#undef MEMBER_NAME
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/statistics/typed_statistics_chunk.cuh b/cpp/src/io/statistics/typed_statistics_chunk.cuh
new file mode 100644
index 0000000..e6ec147
--- /dev/null
+++ b/cpp/src/io/statistics/typed_statistics_chunk.cuh
@@ -0,0 +1,276 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file typed_statistics_chunk.cuh
+ * @brief Templated wrapper to generalize statistics chunk reduction and aggregation
+ * across different leaf column types
+ */
+
+#pragma once
+
+#include "byte_array_view.cuh"
+#include "statistics.cuh"
+#include "statistics_type_identification.cuh"
+#include "temp_storage_wrapper.cuh"
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <math_constants.h>
+
+#include <thrust/extrema.h>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Class used to get reference to members of unions related to statistics calculations
+ */
+class union_member {
+  template <typename U, typename V>
+  using reference_type = std::conditional_t<std::is_const_v<U>, V const&, V&>;
+
+ public:
+  template <typename T, typename U>
+  using type = std::conditional_t<
+    std::is_same_v<std::remove_cv_t<T>, string_view>,
+    reference_type<U, string_stats>,
+    std::conditional_t<std::is_same_v<std::remove_cv_t<T>, statistics::byte_array_view>,
+                       reference_type<U, byte_array_stats>,
+                       reference_type<U, T>>>;
+
+  template <typename T, typename U>
+  __device__ static std::enable_if_t<std::is_integral_v<T> and std::is_unsigned_v<T>, type<T, U>>
+  get(U& val)
+  {
+    return val.u_val;
+  }
+
+  template <typename T, typename U>
+  __device__ static std::enable_if_t<std::is_integral_v<T> and std::is_signed_v<T>, type<T, U>> get(
+    U& val)
+  {
+    return val.i_val;
+  }
+
+  template <typename T, typename U>
+  __device__ static std::enable_if_t<std::is_same_v<T, __int128_t>, type<T, U>> get(U& val)
+  {
+    return val.d128_val;
+  }
+
+  template <typename T, typename U>
+  __device__ static std::enable_if_t<std::is_floating_point_v<T>, type<T, U>> get(U& val)
+  {
+    return val.fp_val;
+  }
+
+  template <typename T, typename U>
+  __device__ static std::enable_if_t<std::is_same_v<T, string_view>, type<T, U>> get(U& val)
+  {
+    return val.str_val;
+  }
+
+  template <typename T, typename U>
+  __device__ static std::enable_if_t<std::is_same_v<T, statistics::byte_array_view>, type<T, U>>
+  get(U& val)
+  {
+    return val.byte_val;
+  }
+};
+
+/**
+ * @brief Templated structure used for merging and gathering of statistics chunks
+ *
+ * This uses the reduce function to compute the minimum, maximum and aggregate
+ * values simultaneously.
+ *
+ * @tparam T The input type associated with the chunk
+ * @tparam is_aggregation_supported Set to true if input type is meant to be aggregated
+ */
+template <typename T, bool is_aggregation_supported>
+struct typed_statistics_chunk {};
+
+template <typename T>
+struct typed_statistics_chunk<T, true> {
+  using E = typename detail::extrema_type<T>::type;
+  using A = typename detail::aggregation_type<T>::type;
+
+  uint32_t non_nulls{0};   //!< number of non-null values in chunk
+  uint32_t null_count{0};  //!< number of null values in chunk
+
+  E minimum_value;
+  E maximum_value;
+  A aggregate;
+
+  uint8_t has_minmax{false};  //!< Nonzero if min_value and max_values are valid
+  uint8_t has_sum{false};     //!< Nonzero if sum is valid
+
+  __device__ typed_statistics_chunk()
+    : minimum_value(detail::minimum_identity<E>()),
+      maximum_value(detail::maximum_identity<E>()),
+      aggregate(0)
+  {
+  }
+
+  __device__ void reduce(T const& elem)
+  {
+    non_nulls++;
+    minimum_value = thrust::min<E>(minimum_value, detail::extrema_type<T>::convert(elem));
+    maximum_value = thrust::max<E>(maximum_value, detail::extrema_type<T>::convert(elem));
+    aggregate += detail::aggregation_type<T>::convert(elem);
+    has_minmax = true;
+  }
+
+  __device__ void reduce(statistics_chunk const& chunk)
+  {
+    if (chunk.has_minmax) {
+      minimum_value = thrust::min<E>(minimum_value, union_member::get<E>(chunk.min_value));
+      maximum_value = thrust::max<E>(maximum_value, union_member::get<E>(chunk.max_value));
+    }
+    if (chunk.has_sum) { aggregate += union_member::get<A>(chunk.sum); }
+    non_nulls += chunk.non_nulls;
+    null_count += chunk.null_count;
+  }
+};
+
+template <typename T>
+struct typed_statistics_chunk<T, false> {
+  using E = typename detail::extrema_type<T>::type;
+
+  uint32_t non_nulls{0};   //!< number of non-null values in chunk
+  uint32_t null_count{0};  //!< number of null values in chunk
+
+  E minimum_value;
+  E maximum_value;
+
+  uint8_t has_minmax{false};  //!< Nonzero if min_value and max_values are valid
+  uint8_t has_sum{false};     //!< Nonzero if sum is valid
+
+  __device__ typed_statistics_chunk()
+    : minimum_value(detail::minimum_identity<E>()), maximum_value(detail::maximum_identity<E>())
+  {
+  }
+
+  __device__ void reduce(T const& elem)
+  {
+    non_nulls++;
+    minimum_value = thrust::min<E>(minimum_value, detail::extrema_type<T>::convert(elem));
+    maximum_value = thrust::max<E>(maximum_value, detail::extrema_type<T>::convert(elem));
+    has_minmax    = true;
+  }
+
+  __device__ void reduce(statistics_chunk const& chunk)
+  {
+    if (chunk.has_minmax) {
+      minimum_value = thrust::min<E>(minimum_value, union_member::get<E>(chunk.min_value));
+      maximum_value = thrust::max<E>(maximum_value, union_member::get<E>(chunk.max_value));
+    }
+    non_nulls += chunk.non_nulls;
+    null_count += chunk.null_count;
+  }
+};
+
+/**
+ * @brief Function to reduce members of a typed_statistics_chunk across a thread block
+ *
+ * @tparam T Type associated with typed_statistics_chunk
+ * @tparam block_size Dimension of the thread block
+ * @param chunk The input typed_statistics_chunk
+ * @param storage Temporary storage to be used by cub calls
+ */
+template <typename T, bool include_aggregate, int block_size>
+__inline__ __device__ typed_statistics_chunk<T, include_aggregate> block_reduce(
+  typed_statistics_chunk<T, include_aggregate>& chunk, detail::storage_wrapper<block_size>& storage)
+{
+  typed_statistics_chunk<T, include_aggregate> output_chunk = chunk;
+
+  using E              = typename detail::extrema_type<T>::type;
+  using extrema_reduce = cub::BlockReduce<E, block_size>;
+  using count_reduce   = cub::BlockReduce<uint32_t, block_size>;
+  output_chunk.minimum_value =
+    extrema_reduce(storage.template get<E>()).Reduce(output_chunk.minimum_value, cub::Min());
+  __syncthreads();
+  output_chunk.maximum_value =
+    extrema_reduce(storage.template get<E>()).Reduce(output_chunk.maximum_value, cub::Max());
+  __syncthreads();
+  output_chunk.non_nulls =
+    count_reduce(storage.template get<uint32_t>()).Sum(output_chunk.non_nulls);
+  __syncthreads();
+  output_chunk.null_count =
+    count_reduce(storage.template get<uint32_t>()).Sum(output_chunk.null_count);
+  __syncthreads();
+  output_chunk.has_minmax = __syncthreads_or(output_chunk.has_minmax);
+
+  // FIXME : Is another syncthreads needed here?
+  if constexpr (include_aggregate) {
+    if (output_chunk.has_minmax) {
+      using A                = typename detail::aggregation_type<T>::type;
+      using aggregate_reduce = cub::BlockReduce<A, block_size>;
+      output_chunk.aggregate =
+        aggregate_reduce(storage.template get<A>()).Sum(output_chunk.aggregate);
+    }
+  }
+  return output_chunk;
+}
+
+/**
+ * @brief Function to convert typed_statistics_chunk into statistics_chunk
+ *
+ * @tparam T Type associated with typed_statistics_chunk
+ * @param chunk The input typed_statistics_chunk
+ */
+template <typename T, bool include_aggregate>
+__inline__ __device__ statistics_chunk
+get_untyped_chunk(typed_statistics_chunk<T, include_aggregate> const& chunk)
+{
+  using E = typename detail::extrema_type<T>::type;
+  statistics_chunk stat{};
+  stat.non_nulls  = chunk.non_nulls;
+  stat.null_count = chunk.null_count;
+  stat.has_minmax = chunk.has_minmax;
+  stat.has_sum    = [&]() {
+    // invalidate the sum if overflow or underflow is possible
+    if constexpr (std::is_floating_point_v<E> or std::is_integral_v<E>) {
+      if (!chunk.has_minmax) { return true; }
+      return std::numeric_limits<E>::max() / chunk.non_nulls >=
+               static_cast<E>(chunk.maximum_value) and
+             std::numeric_limits<E>::lowest() / chunk.non_nulls <=
+               static_cast<E>(chunk.minimum_value);
+    }
+    return true;
+  }();
+  if (chunk.has_minmax) {
+    if constexpr (std::is_floating_point_v<E>) {
+      union_member::get<E>(stat.min_value) =
+        (chunk.minimum_value != 0.0) ? chunk.minimum_value : CUDART_NEG_ZERO;
+      union_member::get<E>(stat.max_value) =
+        (chunk.maximum_value != 0.0) ? chunk.maximum_value : CUDART_ZERO;
+    } else {
+      union_member::get<E>(stat.min_value) = chunk.minimum_value;
+      union_member::get<E>(stat.max_value) = chunk.maximum_value;
+    }
+    if constexpr (include_aggregate) {
+      using A                        = typename detail::aggregation_type<T>::type;
+      union_member::get<A>(stat.sum) = chunk.aggregate;
+    }
+  }
+  return stat;
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/text/bgzip_data_chunk_source.cu b/cpp/src/io/text/bgzip_data_chunk_source.cu
new file mode 100644
index 0000000..77647c1
--- /dev/null
+++ b/cpp/src/io/text/bgzip_data_chunk_source.cu
@@ -0,0 +1,382 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "io/text/device_data_chunks.hpp"
+
+#include <io/comp/nvcomp_adapter.hpp>
+#include <io/utilities/config_utils.hpp>
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/pinned_host_vector.hpp>
+#include <cudf/io/text/data_chunk_source_factories.hpp>
+#include <cudf/io/text/detail/bgzip_utils.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+
+#include <fstream>
+#include <limits>
+
+namespace cudf::io::text {
+namespace {
+
+/**
+ * @brief Transforms offset tuples of the form [compressed_begin, compressed_end,
+ * decompressed_begin, decompressed_end] into span tuples of the form [compressed_device_span,
+ * decompressed_device_span] based on the provided pointers.
+ */
+struct bgzip_nvcomp_transform_functor {
+  uint8_t const* compressed_ptr;
+  uint8_t* decompressed_ptr;
+
+  __device__ thrust::tuple<device_span<uint8_t const>, device_span<uint8_t>> operator()(
+    thrust::tuple<std::size_t, std::size_t, std::size_t, std::size_t> t)
+  {
+    auto const compressed_begin   = thrust::get<0>(t);
+    auto const compressed_end     = thrust::get<1>(t);
+    auto const decompressed_begin = thrust::get<2>(t);
+    auto const decompressed_end   = thrust::get<3>(t);
+    return thrust::make_tuple(device_span<uint8_t const>{compressed_ptr + compressed_begin,
+                                                         compressed_end - compressed_begin},
+                              device_span<uint8_t>{decompressed_ptr + decompressed_begin,
+                                                   decompressed_end - decompressed_begin});
+  }
+};
+
+class bgzip_data_chunk_reader : public data_chunk_reader {
+ private:
+  template <typename T>
+  static void copy_to_device(cudf::detail::pinned_host_vector<T> const& host,
+                             rmm::device_uvector<T>& device,
+                             rmm::cuda_stream_view stream)
+  {
+    // Buffer needs to be padded.
+    // Required by `inflate_kernel`.
+    device.resize(cudf::util::round_up_safe(host.size(), BUFFER_PADDING_MULTIPLE), stream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      device.data(), host.data(), host.size() * sizeof(T), cudaMemcpyDefault, stream.value()));
+  }
+
+  struct decompression_blocks {
+    static constexpr std::size_t default_buffer_alloc =
+      1 << 24;  // 16MB buffer allocation, resized on demand
+    static constexpr std::size_t default_offset_alloc =
+      1 << 16;  // 64k offset allocation, resized on demand
+
+    cudaEvent_t event;
+    cudf::detail::pinned_host_vector<char> h_compressed_blocks;
+    cudf::detail::pinned_host_vector<std::size_t> h_compressed_offsets;
+    cudf::detail::pinned_host_vector<std::size_t> h_decompressed_offsets;
+    rmm::device_uvector<char> d_compressed_blocks;
+    rmm::device_uvector<char> d_decompressed_blocks;
+    rmm::device_uvector<std::size_t> d_compressed_offsets;
+    rmm::device_uvector<std::size_t> d_decompressed_offsets;
+    rmm::device_uvector<device_span<uint8_t const>> d_compressed_spans;
+    rmm::device_uvector<device_span<uint8_t>> d_decompressed_spans;
+    rmm::device_uvector<compression_result> d_decompression_results;
+    std::size_t compressed_size_with_headers{};
+    std::size_t max_decompressed_size{};
+    // this is usually equal to decompressed_size()
+    // unless we are in the last chunk, where it's limited by _local_end
+    std::size_t available_decompressed_size{};
+    std::size_t read_pos{};
+    bool is_decompressed{};
+
+    decompression_blocks(rmm::cuda_stream_view init_stream)
+      : d_compressed_blocks(0, init_stream),
+        d_decompressed_blocks(0, init_stream),
+        d_compressed_offsets(0, init_stream),
+        d_decompressed_offsets(0, init_stream),
+        d_compressed_spans(0, init_stream),
+        d_decompressed_spans(0, init_stream),
+        d_decompression_results(0, init_stream)
+    {
+      CUDF_CUDA_TRY(cudaEventCreate(&event));
+      h_compressed_blocks.reserve(default_buffer_alloc);
+      h_compressed_offsets.reserve(default_offset_alloc);
+      h_compressed_offsets.push_back(0);
+      h_decompressed_offsets.reserve(default_offset_alloc);
+      h_decompressed_offsets.push_back(0);
+    }
+
+    void decompress(rmm::cuda_stream_view stream)
+    {
+      if (is_decompressed) { return; }
+      copy_to_device(h_compressed_blocks, d_compressed_blocks, stream);
+      copy_to_device(h_compressed_offsets, d_compressed_offsets, stream);
+      copy_to_device(h_decompressed_offsets, d_decompressed_offsets, stream);
+      d_decompressed_blocks.resize(decompressed_size(), stream);
+      d_compressed_spans.resize(num_blocks(), stream);
+      d_decompressed_spans.resize(num_blocks(), stream);
+      d_decompression_results.resize(num_blocks(), stream);
+
+      auto offset_it = thrust::make_zip_iterator(d_compressed_offsets.begin(),
+                                                 d_compressed_offsets.begin() + 1,
+                                                 d_decompressed_offsets.begin(),
+                                                 d_decompressed_offsets.begin() + 1);
+      auto span_it =
+        thrust::make_zip_iterator(d_compressed_spans.begin(), d_decompressed_spans.begin());
+      thrust::transform(
+        rmm::exec_policy_nosync(stream),
+        offset_it,
+        offset_it + num_blocks(),
+        span_it,
+        bgzip_nvcomp_transform_functor{reinterpret_cast<uint8_t const*>(d_compressed_blocks.data()),
+                                       reinterpret_cast<uint8_t*>(d_decompressed_blocks.data())});
+      if (decompressed_size() > 0) {
+        if (nvcomp::is_decompression_disabled(nvcomp::compression_type::DEFLATE)) {
+          gpuinflate(d_compressed_spans,
+                     d_decompressed_spans,
+                     d_decompression_results,
+                     gzip_header_included::NO,
+                     stream);
+        } else {
+          cudf::io::nvcomp::batched_decompress(cudf::io::nvcomp::compression_type::DEFLATE,
+                                               d_compressed_spans,
+                                               d_decompressed_spans,
+                                               d_decompression_results,
+                                               max_decompressed_size,
+                                               decompressed_size(),
+                                               stream);
+        }
+      }
+      is_decompressed = true;
+    }
+
+    void reset()
+    {
+      h_compressed_blocks.resize(0);
+      h_compressed_offsets.resize(1);
+      h_decompressed_offsets.resize(1);
+      // shrinking doesn't allocate/free, so we don't need to worry about streams
+      auto stream = cudf::get_default_stream();
+      d_compressed_blocks.resize(0, stream);
+      d_decompressed_blocks.resize(0, stream);
+      d_compressed_offsets.resize(0, stream);
+      d_decompressed_offsets.resize(0, stream);
+      d_compressed_spans.resize(0, stream);
+      d_decompressed_spans.resize(0, stream);
+      d_decompression_results.resize(0, stream);
+      compressed_size_with_headers = 0;
+      max_decompressed_size        = 0;
+      available_decompressed_size  = 0;
+      read_pos                     = 0;
+      is_decompressed              = false;
+    }
+
+    [[nodiscard]] std::size_t num_blocks() const { return h_compressed_offsets.size() - 1; }
+
+    [[nodiscard]] std::size_t compressed_size() const { return h_compressed_offsets.back(); }
+
+    [[nodiscard]] std::size_t decompressed_size() const { return h_decompressed_offsets.back(); }
+
+    [[nodiscard]] std::size_t remaining_size() const
+    {
+      return available_decompressed_size - read_pos;
+    }
+
+    void read_block(detail::bgzip::header header, std::istream& stream)
+    {
+      h_compressed_blocks.resize(h_compressed_blocks.size() + header.data_size());
+      stream.read(h_compressed_blocks.data() + compressed_size(), header.data_size());
+    }
+
+    void add_block_offsets(detail::bgzip::header header, detail::bgzip::footer footer)
+    {
+      max_decompressed_size =
+        std::max<std::size_t>(footer.decompressed_size, max_decompressed_size);
+      h_compressed_offsets.push_back(compressed_size() + header.data_size());
+      h_decompressed_offsets.push_back(decompressed_size() + footer.decompressed_size);
+    }
+
+    void consume_bytes(std::size_t size)
+    {
+      CUDF_EXPECTS(size <= remaining_size(), "out of bounds");
+      read_pos += size;
+    }
+  };
+
+  void read_next_compressed_chunk(std::size_t requested_size)
+  {
+    std::swap(_curr_blocks, _prev_blocks);
+    if (_curr_blocks.is_decompressed) {
+      // synchronize on the last decompression + copy, so we don't clobber any buffers
+      CUDF_CUDA_TRY(cudaEventSynchronize(_curr_blocks.event));
+    }
+    _curr_blocks.reset();
+    // read chunks until we have enough decompressed data
+    while (_curr_blocks.decompressed_size() < requested_size) {
+      // calling peek on an already EOF stream causes it to fail, we need to avoid that
+      if (_data_stream->eof()) { break; }
+      // peek is necessary if we are already at the end, but didn't try to read another byte
+      _data_stream->peek();
+      if (_data_stream->eof() || _compressed_pos > _compressed_end) { break; }
+      auto header = detail::bgzip::read_header(*_data_stream);
+      _curr_blocks.read_block(header, *_data_stream);
+      auto footer = detail::bgzip::read_footer(*_data_stream);
+      _curr_blocks.add_block_offsets(header, footer);
+      // for the last GZIP block, we restrict ourselves to the bytes up to _local_end
+      // but only for the reader, not for decompression!
+      if (_compressed_pos == _compressed_end) {
+        _curr_blocks.available_decompressed_size += _local_end;
+        _compressed_pos += header.block_size;
+        break;
+      } else {
+        _curr_blocks.available_decompressed_size += footer.decompressed_size;
+        _compressed_pos += header.block_size;
+      }
+    }
+  }
+
+  constexpr static std::size_t chunk_load_size = 1 << 24;  // load 16 MB of data by default
+
+ public:
+  bgzip_data_chunk_reader(std::unique_ptr<std::istream> input_stream,
+                          uint64_t virtual_begin,
+                          uint64_t virtual_end)
+    : _data_stream(std::move(input_stream)),
+      _prev_blocks{cudf::get_default_stream()},  // here we can use the default stream because
+      _curr_blocks{cudf::get_default_stream()},  // we only initialize empty device_uvectors
+      _local_end{virtual_end & 0xFFFFu},
+      _compressed_pos{virtual_begin >> 16},
+      _compressed_end{virtual_end >> 16}
+  {
+    // set failbit to throw on IO failures
+    _data_stream->exceptions(std::istream::failbit);
+    // seek to the beginning of the provided compressed offset
+    _data_stream->seekg(_compressed_pos, std::ios_base::cur);
+    // read the first blocks
+    read_next_compressed_chunk(chunk_load_size);
+    // seek to the beginning of the provided local offset
+    auto const local_pos = virtual_begin & 0xFFFFu;
+    if (local_pos > 0) {
+      CUDF_EXPECTS(_curr_blocks.h_decompressed_offsets.size() > 1 &&
+                     local_pos < _curr_blocks.h_decompressed_offsets[1],
+                   "local part of virtual offset is out of bounds");
+      _curr_blocks.consume_bytes(local_pos);
+    }
+  }
+
+  void skip_bytes(std::size_t read_size) override
+  {
+    while (read_size > _curr_blocks.remaining_size()) {
+      read_size -= _curr_blocks.remaining_size();
+      _curr_blocks.consume_bytes(_curr_blocks.remaining_size());
+      read_next_compressed_chunk(chunk_load_size);
+      // calling peek on an already EOF stream causes it to fail, we need to avoid that
+      if (_data_stream->eof()) { break; }
+      // peek is necessary if we are already at the end, but didn't try to read another byte
+      _data_stream->peek();
+      if (_data_stream->eof() || _compressed_pos > _compressed_end) { break; }
+    }
+    read_size = std::min(read_size, _curr_blocks.remaining_size());
+    _curr_blocks.consume_bytes(read_size);
+  }
+
+  std::unique_ptr<device_data_chunk> get_next_chunk(std::size_t read_size,
+                                                    rmm::cuda_stream_view stream) override
+  {
+    CUDF_FUNC_RANGE();
+    if (read_size <= _curr_blocks.remaining_size()) {
+      _curr_blocks.decompress(stream);
+      rmm::device_uvector<char> data(read_size, stream);
+      CUDF_CUDA_TRY(
+        cudaMemcpyAsync(data.data(),
+                        _curr_blocks.d_decompressed_blocks.data() + _curr_blocks.read_pos,
+                        read_size,
+                        cudaMemcpyDefault,
+                        stream.value()));
+      // record the host-to-device copy, decompression and device copy
+      CUDF_CUDA_TRY(cudaEventRecord(_curr_blocks.event, stream.value()));
+      _curr_blocks.consume_bytes(read_size);
+      return std::make_unique<device_uvector_data_chunk>(std::move(data));
+    }
+    read_next_compressed_chunk(read_size /* - _curr_blocks.remaining_size()*/);
+    _prev_blocks.decompress(stream);
+    _curr_blocks.decompress(stream);
+    read_size = std::min(read_size, _prev_blocks.remaining_size() + _curr_blocks.remaining_size());
+    rmm::device_uvector<char> data(read_size, stream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(data.data(),
+                                  _prev_blocks.d_decompressed_blocks.data() + _prev_blocks.read_pos,
+                                  _prev_blocks.remaining_size(),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    CUDF_CUDA_TRY(cudaMemcpyAsync(data.data() + _prev_blocks.remaining_size(),
+                                  _curr_blocks.d_decompressed_blocks.data() + _curr_blocks.read_pos,
+                                  read_size - _prev_blocks.remaining_size(),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+    // record the host-to-device copy, decompression and device copy
+    CUDF_CUDA_TRY(cudaEventRecord(_curr_blocks.event, stream.value()));
+    CUDF_CUDA_TRY(cudaEventRecord(_prev_blocks.event, stream.value()));
+    read_size -= _prev_blocks.remaining_size();
+    _prev_blocks.consume_bytes(_prev_blocks.remaining_size());
+    _curr_blocks.consume_bytes(read_size);
+    return std::make_unique<device_uvector_data_chunk>(std::move(data));
+  }
+
+ private:
+  std::unique_ptr<std::istream> _data_stream;
+  decompression_blocks _prev_blocks;
+  decompression_blocks _curr_blocks;
+  std::size_t _local_end;
+  std::size_t _compressed_pos;
+  std::size_t _compressed_end;
+};
+
+class bgzip_data_chunk_source : public data_chunk_source {
+ public:
+  bgzip_data_chunk_source(std::string_view filename, uint64_t virtual_begin, uint64_t virtual_end)
+    : _filename{filename}, _virtual_begin{virtual_begin}, _virtual_end{virtual_end}
+  {
+  }
+
+  [[nodiscard]] std::unique_ptr<data_chunk_reader> create_reader() const override
+  {
+    return std::make_unique<bgzip_data_chunk_reader>(
+      std::make_unique<std::ifstream>(_filename, std::ifstream::in), _virtual_begin, _virtual_end);
+  }
+
+ private:
+  std::string _filename;
+  uint64_t _virtual_begin;
+  uint64_t _virtual_end;
+};
+
+}  // namespace
+
+std::unique_ptr<data_chunk_source> make_source_from_bgzip_file(std::string_view filename,
+                                                               uint64_t virtual_begin,
+                                                               uint64_t virtual_end)
+{
+  return std::make_unique<bgzip_data_chunk_source>(filename, virtual_begin, virtual_end);
+}
+
+std::unique_ptr<data_chunk_source> make_source_from_bgzip_file(std::string_view filename)
+{
+  return std::make_unique<bgzip_data_chunk_source>(
+    filename, 0, std::numeric_limits<uint64_t>::max());
+}
+
+}  // namespace cudf::io::text
diff --git a/cpp/src/io/text/bgzip_utils.cpp b/cpp/src/io/text/bgzip_utils.cpp
new file mode 100644
index 0000000..43e2c26
--- /dev/null
+++ b/cpp/src/io/text/bgzip_utils.cpp
@@ -0,0 +1,179 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <zlib.h>
+
+#include <cudf/io/text/detail/bgzip_utils.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <algorithm>
+#include <array>
+#include <fstream>
+#include <limits>
+
+namespace cudf::io::text::detail::bgzip {
+namespace {
+
+template <typename IntType>
+IntType read_int(char* data)
+{
+  IntType result{};
+  // we assume little-endian
+  std::memcpy(&result, &data[0], sizeof(result));
+  return result;
+}
+
+template <typename T>
+void write_int(std::ostream& output_stream, T val)
+{
+  std::array<char, sizeof(T)> bytes;
+  // we assume little-endian
+  std::memcpy(&bytes[0], &val, sizeof(T));
+  output_stream.write(bytes.data(), bytes.size());
+}
+
+}  // namespace
+
+std::array<char, 4> constexpr extra_blocklen_field_header{{66, 67, 2, 0}};
+
+header read_header(std::istream& input_stream)
+{
+  std::array<char, 12> buffer{};
+  input_stream.read(buffer.data(), sizeof(buffer));
+  std::array<uint8_t, 4> constexpr expected_header{{31, 139, 8, 4}};
+  CUDF_EXPECTS(
+    std::equal(
+      expected_header.begin(), expected_header.end(), reinterpret_cast<uint8_t*>(buffer.data())),
+    "malformed BGZIP header");
+  // we ignore the remaining bytes of the fixed header, since they don't matter to us
+  auto const extra_length = read_int<uint16_t>(&buffer[10]);
+  uint16_t extra_offset{};
+  // read all the extra subfields
+  while (extra_offset < extra_length) {
+    auto const remaining_size = extra_length - extra_offset;
+    CUDF_EXPECTS(remaining_size >= 4, "invalid extra field length");
+    // a subfield consists of 2 identifier bytes and a uint16 length
+    // 66/67 identifies a BGZIP block size field, we skip all other fields
+    input_stream.read(buffer.data(), 4);
+    extra_offset += 4;
+    auto const subfield_size = read_int<uint16_t>(&buffer[2]);
+    if (buffer[0] == extra_blocklen_field_header[0] &&
+        buffer[1] == extra_blocklen_field_header[1]) {
+      // the block size subfield contains a single uint16 value, which is block_size - 1
+      CUDF_EXPECTS(
+        buffer[2] == extra_blocklen_field_header[2] && buffer[3] == extra_blocklen_field_header[3],
+        "malformed BGZIP extra subfield");
+      input_stream.read(buffer.data(), sizeof(uint16_t));
+      input_stream.seekg(remaining_size - 6, std::ios_base::cur);
+      auto const block_size_minus_one = read_int<uint16_t>(&buffer[0]);
+      return {block_size_minus_one + 1, extra_length};
+    } else {
+      input_stream.seekg(subfield_size, std::ios_base::cur);
+      extra_offset += subfield_size;
+    }
+  }
+  CUDF_FAIL("missing BGZIP size extra subfield");
+}
+
+footer read_footer(std::istream& input_stream)
+{
+  std::array<char, 8> buffer{};
+  input_stream.read(buffer.data(), sizeof(buffer));
+  return {read_int<uint32_t>(&buffer[0]), read_int<uint32_t>(&buffer[4])};
+}
+
+void write_footer(std::ostream& output_stream, host_span<char const> data)
+{
+  // compute crc32 with zlib, this allows checking the generated files with external tools
+  write_int<uint32_t>(output_stream, crc32(0, (unsigned char*)data.data(), data.size()));
+  write_int<uint32_t>(output_stream, data.size());
+}
+
+void write_header(std::ostream& output_stream,
+                  uint16_t compressed_size,
+                  host_span<char const> pre_size_subfield,
+                  host_span<char const> post_size_subfield)
+{
+  std::array<uint8_t, 10> constexpr header_data{{
+    31,   // magic number
+    139,  // magic number
+    8,    // compression type: deflate
+    4,    // flags: extra header
+    0,    // mtime
+    0,    // mtime
+    0,    // mtime
+    0,    // mtime: irrelevant
+    4,    // xfl: irrelevant
+    3     // OS: irrelevant
+  }};
+  output_stream.write(reinterpret_cast<char const*>(header_data.data()), header_data.size());
+  auto const extra_size = pre_size_subfield.size() + extra_blocklen_field_header.size() +
+                          sizeof(uint16_t) + post_size_subfield.size();
+  auto const block_size =
+    header_data.size() + sizeof(uint16_t) + extra_size + compressed_size + 2 * sizeof(uint32_t);
+  write_int<uint16_t>(output_stream, extra_size);
+  output_stream.write(pre_size_subfield.data(), pre_size_subfield.size());
+  output_stream.write(extra_blocklen_field_header.data(), extra_blocklen_field_header.size());
+  CUDF_EXPECTS(block_size - 1 <= std::numeric_limits<uint16_t>::max(), "block size overflow");
+  write_int<uint16_t>(output_stream, block_size - 1);
+  output_stream.write(post_size_subfield.data(), post_size_subfield.size());
+}
+
+void write_uncompressed_block(std::ostream& output_stream,
+                              host_span<char const> data,
+                              host_span<char const> pre_size_subfields,
+                              host_span<char const> post_size_subfields)
+{
+  CUDF_EXPECTS(data.size() <= std::numeric_limits<uint16_t>::max(), "data size overflow");
+  write_header(output_stream, data.size() + 5, pre_size_subfields, post_size_subfields);
+  write_int<uint8_t>(output_stream, 1);
+  write_int<uint16_t>(output_stream, data.size());
+  write_int<uint16_t>(output_stream, ~static_cast<uint16_t>(data.size()));
+  output_stream.write(data.data(), data.size());
+  write_footer(output_stream, data);
+}
+
+void write_compressed_block(std::ostream& output_stream,
+                            host_span<char const> data,
+                            host_span<char const> pre_size_subfields,
+                            host_span<char const> post_size_subfields)
+{
+  CUDF_EXPECTS(data.size() <= std::numeric_limits<uint16_t>::max(), "data size overflow");
+  z_stream deflate_stream{};
+  // let's make sure we have enough space to store the data
+  std::vector<char> compressed_out(data.size() * 2 + 256);
+  deflate_stream.next_in   = reinterpret_cast<unsigned char*>(const_cast<char*>(data.data()));
+  deflate_stream.avail_in  = data.size();
+  deflate_stream.next_out  = reinterpret_cast<unsigned char*>(compressed_out.data());
+  deflate_stream.avail_out = compressed_out.size();
+  CUDF_EXPECTS(
+    deflateInit2(&deflate_stream,        // stream
+                 Z_DEFAULT_COMPRESSION,  // compression level
+                 Z_DEFLATED,             // method
+                 -15,  // log2 of window size (negative value means no ZLIB header/footer)
+                 9,    // mem level: best performance/most memory usage for compression
+                 Z_DEFAULT_STRATEGY  // strategy
+                 ) == Z_OK,
+    "deflateInit failed");
+  CUDF_EXPECTS(deflate(&deflate_stream, Z_FINISH) == Z_STREAM_END, "deflate failed");
+  CUDF_EXPECTS(deflateEnd(&deflate_stream) == Z_OK, "deflateEnd failed");
+  write_header(output_stream, deflate_stream.total_out, pre_size_subfields, post_size_subfields);
+  output_stream.write(compressed_out.data(), deflate_stream.total_out);
+  write_footer(output_stream, data);
+}
+
+}  // namespace cudf::io::text::detail::bgzip
diff --git a/cpp/src/io/text/byte_range_info.cpp b/cpp/src/io/text/byte_range_info.cpp
new file mode 100644
index 0000000..290e045
--- /dev/null
+++ b/cpp/src/io/text/byte_range_info.cpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/io/text/byte_range_info.hpp>
+
+#include <limits>
+
+namespace cudf {
+namespace io {
+namespace text {
+
+byte_range_info create_byte_range_info_max() { return {0, std::numeric_limits<int64_t>::max()}; }
+
+std::vector<byte_range_info> create_byte_range_infos_consecutive(int64_t total_bytes,
+                                                                 int64_t range_count)
+{
+  auto range_size = util::div_rounding_up_safe(total_bytes, range_count);
+  auto ranges     = std::vector<byte_range_info>();
+
+  ranges.reserve(range_size);
+
+  for (int64_t i = 0; i < range_count; i++) {
+    auto offset = i * range_size;
+    auto size   = std::min(range_size, total_bytes - offset);
+    ranges.emplace_back(offset, size);
+  }
+
+  return ranges;
+}
+
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/text/data_chunk_source_factories.cpp b/cpp/src/io/text/data_chunk_source_factories.cpp
new file mode 100644
index 0000000..9d1d049
--- /dev/null
+++ b/cpp/src/io/text/data_chunk_source_factories.cpp
@@ -0,0 +1,339 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "io/text/device_data_chunks.hpp"
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/pinned_host_vector.hpp>
+#include <cudf/io/text/data_chunk_source_factories.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/host_vector.h>
+
+#include <fstream>
+
+namespace cudf::io::text {
+
+namespace {
+
+struct host_ticket {
+  cudaEvent_t event;
+  cudf::detail::pinned_host_vector<char> buffer;
+};
+
+/**
+ * @brief A reader which produces owning chunks of device memory which contain a copy of the data
+ * from an istream.
+ */
+class datasource_chunk_reader : public data_chunk_reader {
+  constexpr static int num_tickets = 2;
+
+ public:
+  datasource_chunk_reader(datasource* source) : _source(source)
+  {
+    // create an event to track the completion of the last device-to-host copy.
+    for (auto& ticket : _tickets) {
+      CUDF_CUDA_TRY(cudaEventCreate(&(ticket.event)));
+    }
+  }
+
+  ~datasource_chunk_reader() override
+  {
+    for (auto& ticket : _tickets) {
+      CUDF_CUDA_TRY(cudaEventDestroy(ticket.event));
+    }
+  }
+
+  void skip_bytes(std::size_t size) override
+  {
+    _offset += std::min(_source->size() - _offset, size);
+  };
+
+  std::unique_ptr<device_data_chunk> get_next_chunk(std::size_t read_size,
+                                                    rmm::cuda_stream_view stream) override
+  {
+    CUDF_FUNC_RANGE();
+
+    read_size = std::min(_source->size() - _offset, read_size);
+
+    // get a device buffer containing read data on the device.
+    auto chunk = rmm::device_uvector<char>(read_size, stream);
+
+    if (_source->supports_device_read() && _source->is_device_read_preferred(read_size)) {
+      _source->device_read(_offset, read_size, reinterpret_cast<uint8_t*>(chunk.data()), stream);
+    } else {
+      auto& h_ticket = _tickets[_next_ticket_idx];
+
+      _next_ticket_idx = (_next_ticket_idx + 1) % num_tickets;
+
+      // synchronize on the last host-to-device copy, so we don't clobber the host buffer.
+      CUDF_CUDA_TRY(cudaEventSynchronize(h_ticket.event));
+
+      // resize the host buffer as necessary to contain the requested number of bytes
+      if (h_ticket.buffer.size() < read_size) { h_ticket.buffer.resize(read_size); }
+
+      _source->host_read(_offset, read_size, reinterpret_cast<uint8_t*>(h_ticket.buffer.data()));
+
+      // copy the host-pinned data on to device
+      CUDF_CUDA_TRY(cudaMemcpyAsync(
+        chunk.data(), h_ticket.buffer.data(), read_size, cudaMemcpyDefault, stream.value()));
+
+      // record the host-to-device copy.
+      CUDF_CUDA_TRY(cudaEventRecord(h_ticket.event, stream.value()));
+    }
+
+    _offset += read_size;
+
+    // return the device buffer so it can be processed.
+    return std::make_unique<device_uvector_data_chunk>(std::move(chunk));
+  }
+
+ private:
+  std::size_t _offset          = 0;
+  std::size_t _next_ticket_idx = 0;
+  std::array<host_ticket, num_tickets> _tickets{};
+  datasource* _source;
+};
+
+/**
+ * @brief A reader which produces owning chunks of device memory which contain a copy of the data
+ * from an istream.
+ */
+class istream_data_chunk_reader : public data_chunk_reader {
+  constexpr static int num_tickets = 2;
+
+ public:
+  istream_data_chunk_reader(std::unique_ptr<std::istream> datastream)
+    : _datastream(std::move(datastream))
+  {
+    // create an event to track the completion of the last device-to-host copy.
+    for (auto& ticket : _tickets) {
+      CUDF_CUDA_TRY(cudaEventCreate(&(ticket.event)));
+    }
+  }
+
+  ~istream_data_chunk_reader() override
+  {
+    for (auto& ticket : _tickets) {
+      CUDF_CUDA_TRY(cudaEventDestroy(ticket.event));
+    }
+  }
+
+  void skip_bytes(std::size_t size) override { _datastream->ignore(size); };
+
+  std::unique_ptr<device_data_chunk> get_next_chunk(std::size_t read_size,
+                                                    rmm::cuda_stream_view stream) override
+  {
+    CUDF_FUNC_RANGE();
+
+    auto& h_ticket = _tickets[_next_ticket_idx];
+
+    _next_ticket_idx = (_next_ticket_idx + 1) % num_tickets;
+
+    // synchronize on the last host-to-device copy, so we don't clobber the host buffer.
+    CUDF_CUDA_TRY(cudaEventSynchronize(h_ticket.event));
+
+    // resize the host buffer as necessary to contain the requested number of bytes
+    if (h_ticket.buffer.size() < read_size) { h_ticket.buffer.resize(read_size); }
+
+    // read data from the host istream in to the pinned host memory buffer
+    _datastream->read(h_ticket.buffer.data(), read_size);
+
+    // adjust the read size to reflect how many bytes were actually read from the data stream
+    read_size = _datastream->gcount();
+
+    // get a device buffer containing read data on the device.
+    auto chunk = rmm::device_uvector<char>(read_size, stream);
+
+    // copy the host-pinned data on to device
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      chunk.data(), h_ticket.buffer.data(), read_size, cudaMemcpyDefault, stream.value()));
+
+    // record the host-to-device copy.
+    CUDF_CUDA_TRY(cudaEventRecord(h_ticket.event, stream.value()));
+
+    // return the device buffer so it can be processed.
+    return std::make_unique<device_uvector_data_chunk>(std::move(chunk));
+  }
+
+ private:
+  std::size_t _next_ticket_idx = 0;
+  std::array<host_ticket, num_tickets> _tickets{};
+  std::unique_ptr<std::istream> _datastream;
+};
+
+/**
+ * @brief A reader which produces owning chunks of device memory which contain a copy of the data
+ * from a host span.
+ */
+class host_span_data_chunk_reader : public data_chunk_reader {
+ public:
+  host_span_data_chunk_reader(cudf::host_span<char const> data) : _data(data) {}
+
+  void skip_bytes(std::size_t read_size) override
+  {
+    _position += std::min(read_size, _data.size() - _position);
+  }
+
+  std::unique_ptr<device_data_chunk> get_next_chunk(std::size_t read_size,
+                                                    rmm::cuda_stream_view stream) override
+  {
+    CUDF_FUNC_RANGE();
+
+    read_size = std::min(read_size, _data.size() - _position);
+
+    // get a device buffer containing read data on the device.
+    auto chunk = rmm::device_uvector<char>(read_size, stream);
+
+    // copy the host data to device
+    CUDF_CUDA_TRY(cudaMemcpyAsync(  //
+      chunk.data(),
+      _data.data() + _position,
+      read_size,
+      cudaMemcpyDefault,
+      stream.value()));
+
+    _position += read_size;
+
+    // return the device buffer so it can be processed.
+    return std::make_unique<device_uvector_data_chunk>(std::move(chunk));
+  }
+
+ private:
+  std::size_t _position = 0;
+  cudf::host_span<char const> _data;
+};
+
+/**
+ * @brief A reader which produces view of device memory which represent a subset of the input device
+ * span.
+ */
+class device_span_data_chunk_reader : public data_chunk_reader {
+ public:
+  device_span_data_chunk_reader(device_span<char const> data) : _data(data) {}
+
+  void skip_bytes(std::size_t read_size) override
+  {
+    _position += std::min(read_size, _data.size() - _position);
+  }
+
+  std::unique_ptr<device_data_chunk> get_next_chunk(std::size_t read_size,
+                                                    rmm::cuda_stream_view stream) override
+  {
+    // limit the read size to the number of bytes remaining in the device_span.
+    read_size = std::min(read_size, _data.size() - _position);
+
+    // create a view over the device span
+    auto chunk_span = _data.subspan(_position, read_size);
+
+    // increment position
+    _position += read_size;
+
+    // return the view over device memory so it can be processed.
+    return std::make_unique<device_span_data_chunk>(chunk_span);
+  }
+
+ private:
+  device_span<char const> _data;
+  uint64_t _position = 0;
+};
+
+/**
+ * @brief A datasource-based data chunk source which creates a datasource_chunk_reader.
+ */
+class datasource_chunk_source : public data_chunk_source {
+ public:
+  datasource_chunk_source(datasource& source) : _source(&source) {}
+  [[nodiscard]] std::unique_ptr<data_chunk_reader> create_reader() const override
+  {
+    return std::make_unique<datasource_chunk_reader>(_source);
+  }
+
+ private:
+  datasource* _source;
+};
+
+/**
+ * @brief A file data source which creates an istream_data_chunk_reader.
+ */
+class file_data_chunk_source : public data_chunk_source {
+ public:
+  file_data_chunk_source(std::string_view filename) : _filename(filename) {}
+  [[nodiscard]] std::unique_ptr<data_chunk_reader> create_reader() const override
+  {
+    return std::make_unique<istream_data_chunk_reader>(
+      std::make_unique<std::ifstream>(_filename, std::ifstream::in));
+  }
+
+ private:
+  std::string _filename;
+};
+
+/**
+ * @brief A host string data source which creates an host_span_data_chunk_reader.
+ */
+class host_span_data_chunk_source : public data_chunk_source {
+ public:
+  host_span_data_chunk_source(host_span<char const> data) : _data(data) {}
+  [[nodiscard]] std::unique_ptr<data_chunk_reader> create_reader() const override
+  {
+    return std::make_unique<host_span_data_chunk_reader>(_data);
+  }
+
+ private:
+  host_span<char const> _data;
+};
+
+/**
+ * @brief A device span data source which creates an istream_data_chunk_reader.
+ */
+class device_span_data_chunk_source : public data_chunk_source {
+ public:
+  device_span_data_chunk_source(device_span<char const> data) : _data(data) {}
+  [[nodiscard]] std::unique_ptr<data_chunk_reader> create_reader() const override
+  {
+    return std::make_unique<device_span_data_chunk_reader>(_data);
+  }
+
+ private:
+  device_span<char const> _data;
+};
+
+}  // namespace
+
+std::unique_ptr<data_chunk_source> make_source(datasource& data)
+{
+  return std::make_unique<datasource_chunk_source>(data);
+}
+
+std::unique_ptr<data_chunk_source> make_source(host_span<char const> data)
+{
+  return std::make_unique<host_span_data_chunk_source>(data);
+}
+
+std::unique_ptr<data_chunk_source> make_source_from_file(std::string_view filename)
+{
+  return std::make_unique<file_data_chunk_source>(filename);
+}
+
+std::unique_ptr<data_chunk_source> make_source(cudf::string_scalar& data)
+{
+  auto data_span = device_span<char const>(data.data(), data.size());
+  return std::make_unique<device_span_data_chunk_source>(data_span);
+}
+
+}  // namespace cudf::io::text
diff --git a/cpp/src/io/text/device_data_chunks.hpp b/cpp/src/io/text/device_data_chunks.hpp
new file mode 100644
index 0000000..3f971ae
--- /dev/null
+++ b/cpp/src/io/text/device_data_chunks.hpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/io/text/data_chunk_source.hpp>
+
+namespace cudf::io::text {
+
+class device_span_data_chunk : public device_data_chunk {
+ public:
+  device_span_data_chunk(device_span<char const> data) : _data(data) {}
+
+  [[nodiscard]] char const* data() const override { return _data.data(); }
+  [[nodiscard]] std::size_t size() const override { return _data.size(); }
+  operator device_span<char const>() const override { return _data; }
+
+ private:
+  device_span<char const> _data;
+};
+
+class device_uvector_data_chunk : public device_data_chunk {
+ public:
+  device_uvector_data_chunk(rmm::device_uvector<char>&& data) : _data(std::move(data)) {}
+
+  [[nodiscard]] char const* data() const override { return _data.data(); }
+  [[nodiscard]] std::size_t size() const override { return _data.size(); }
+  operator device_span<char const>() const override { return _data; }
+
+ private:
+  rmm::device_uvector<char> _data;
+};
+
+}  // namespace cudf::io::text
diff --git a/cpp/src/io/text/multibyte_split.cu b/cpp/src/io/text/multibyte_split.cu
new file mode 100644
index 0000000..772bcad
--- /dev/null
+++ b/cpp/src/io/text/multibyte_split.cu
@@ -0,0 +1,589 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/utilities/output_builder.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/stream_pool.hpp>
+#include <cudf/io/text/byte_range_info.hpp>
+#include <cudf/io/text/data_chunk_source.hpp>
+#include <cudf/io/text/detail/multistate.hpp>
+#include <cudf/io/text/detail/tile_state.hpp>
+#include <cudf/io/text/multibyte_split.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/find.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <cub/block/block_load.cuh>
+#include <cub/block/block_scan.cuh>
+
+#include <cstdint>
+#include <limits>
+#include <memory>
+#include <numeric>
+#include <optional>
+
+namespace {
+
+using cudf::io::text::detail::multistate;
+
+int32_t constexpr ITEMS_PER_THREAD = 64;
+int32_t constexpr THREADS_PER_TILE = 128;
+int32_t constexpr ITEMS_PER_TILE   = ITEMS_PER_THREAD * THREADS_PER_TILE;
+int32_t constexpr TILES_PER_CHUNK  = 4096;
+int32_t constexpr ITEMS_PER_CHUNK  = ITEMS_PER_TILE * TILES_PER_CHUNK;
+
+constexpr multistate transition_init(char c, cudf::device_span<char const> delim)
+{
+  auto result = multistate();
+
+  result.enqueue(0, 0);
+
+  for (std::size_t i = 0; i < delim.size(); i++) {
+    if (delim[i] == c) { result.enqueue(i, i + 1); }
+  }
+
+  return result;
+}
+
+constexpr multistate transition(char c, multistate state, cudf::device_span<char const> delim)
+{
+  auto result = multistate();
+
+  result.enqueue(0, 0);
+
+  for (uint8_t i = 0; i < state.size(); i++) {
+    auto const tail = state.get_tail(i);
+    if (tail < delim.size() && delim[tail] == c) { result.enqueue(state.get_head(i), tail + 1); }
+  }
+
+  return result;
+}
+
+struct PatternScan {
+  using BlockScan         = cub::BlockScan<multistate, THREADS_PER_TILE>;
+  using BlockScanCallback = cudf::io::text::detail::scan_tile_state_callback<multistate>;
+
+  struct _TempStorage {
+    typename BlockScan::TempStorage scan;
+  };
+
+  _TempStorage& _temp_storage;
+
+  using TempStorage = cub::Uninitialized<_TempStorage>;
+
+  __device__ inline PatternScan(TempStorage& temp_storage) : _temp_storage(temp_storage.Alias()) {}
+
+  __device__ inline void Scan(cudf::size_type tile_idx,
+                              cudf::io::text::detail::scan_tile_state_view<multistate> tile_state,
+                              cudf::device_span<char const> delim,
+                              char (&thread_data)[ITEMS_PER_THREAD],
+                              multistate& thread_multistate)
+  {
+    thread_multistate = transition_init(thread_data[0], delim);
+
+    for (uint32_t i = 1; i < ITEMS_PER_THREAD; i++) {
+      thread_multistate = transition(thread_data[i], thread_multistate, delim);
+    }
+
+    auto prefix_callback = BlockScanCallback(tile_state, tile_idx);
+
+    BlockScan(_temp_storage.scan)
+      .ExclusiveSum(thread_multistate, thread_multistate, prefix_callback);
+  }
+};
+
+// type aliases to distinguish between row offsets and character offsets
+using output_offset = int64_t;
+using byte_offset   = int64_t;
+
+// multibyte_split works by splitting up inputs in to 32 inputs (bytes) per thread, and transforming
+// them in to data structures called "multistates". these multistates are created by searching a
+// trie, but instead of a tradition trie where the search begins at a single node at the beginning,
+// we allow our search to begin anywhere within the trie tree. The position within the trie tree is
+// stored as a "partial match path", which indicates "we can get from here to there by a set of
+// specific transitions". By scanning together multistates, we effectively know "we can get here
+// from the beginning by following the inputs". By doing this, each thread knows exactly what state
+// it begins in. From there, each thread can then take deterministic action. In this case, the
+// deterministic action is counting and outputting delimiter offsets when a delimiter is found.
+
+__global__ void multibyte_split_init_kernel(
+  cudf::size_type base_tile_idx,
+  cudf::size_type num_tiles,
+  cudf::io::text::detail::scan_tile_state_view<multistate> tile_multistates,
+  cudf::io::text::detail::scan_tile_state_view<output_offset> tile_output_offsets,
+  cudf::io::text::detail::scan_tile_status status =
+    cudf::io::text::detail::scan_tile_status::invalid)
+{
+  auto const thread_idx = cudf::detail::grid_1d::global_thread_id();
+  if (thread_idx < num_tiles) {
+    auto const tile_idx = base_tile_idx + thread_idx;
+    tile_multistates.set_status(tile_idx, status);
+    tile_output_offsets.set_status(tile_idx, status);
+  }
+}
+
+__global__ __launch_bounds__(THREADS_PER_TILE) void multibyte_split_kernel(
+  cudf::size_type base_tile_idx,
+  byte_offset base_input_offset,
+  output_offset base_output_offset,
+  cudf::io::text::detail::scan_tile_state_view<multistate> tile_multistates,
+  cudf::io::text::detail::scan_tile_state_view<output_offset> tile_output_offsets,
+  cudf::device_span<char const> delim,
+  cudf::device_span<char const> chunk_input_chars,
+  cudf::split_device_span<byte_offset> row_offsets)
+{
+  using InputLoad =
+    cub::BlockLoad<char, THREADS_PER_TILE, ITEMS_PER_THREAD, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
+  using OffsetScan         = cub::BlockScan<output_offset, THREADS_PER_TILE>;
+  using OffsetScanCallback = cudf::io::text::detail::scan_tile_state_callback<output_offset>;
+
+  __shared__ union {
+    typename InputLoad::TempStorage input_load;
+    typename PatternScan::TempStorage pattern_scan;
+    typename OffsetScan::TempStorage offset_scan;
+  } temp_storage;
+
+  auto const tile_idx          = base_tile_idx + blockIdx.x;
+  auto const tile_input_offset = blockIdx.x * ITEMS_PER_TILE;
+  auto const thread_input_offset =
+    tile_input_offset + cudf::thread_index_type{threadIdx.x} * ITEMS_PER_THREAD;
+  auto const thread_input_size =
+    std::max<cudf::size_type>(chunk_input_chars.size() - thread_input_offset, 0);
+
+  // STEP 1: Load inputs
+
+  char thread_chars[ITEMS_PER_THREAD];
+
+  InputLoad(temp_storage.input_load)
+    .Load(chunk_input_chars.data() + tile_input_offset,
+          thread_chars,
+          chunk_input_chars.size() - tile_input_offset);
+
+  // STEP 2: Scan inputs to determine absolute thread states
+
+  multistate thread_multistate;
+
+  __syncthreads();  // required before temp_memory re-use
+  PatternScan(temp_storage.pattern_scan)
+    .Scan(tile_idx, tile_multistates, delim, thread_chars, thread_multistate);
+
+  // STEP 3: Flag matches
+
+  output_offset thread_offset{};
+  uint32_t thread_match_mask[(ITEMS_PER_THREAD + 31) / 32]{};
+
+  for (int32_t i = 0; i < ITEMS_PER_THREAD; i++) {
+    thread_multistate       = transition(thread_chars[i], thread_multistate, delim);
+    auto const thread_state = thread_multistate.max_tail();
+    auto const is_match     = i < thread_input_size and thread_state == delim.size();
+    thread_match_mask[i / 32] |= uint32_t{is_match} << (i % 32);
+    thread_offset += output_offset{is_match};
+  }
+
+  // STEP 4: Scan flags to determine absolute thread output offset
+
+  auto prefix_callback = OffsetScanCallback(tile_output_offsets, tile_idx);
+
+  __syncthreads();  // required before temp_memory re-use
+  OffsetScan(temp_storage.offset_scan).ExclusiveSum(thread_offset, thread_offset, prefix_callback);
+
+  // Step 5: Assign outputs from each thread using match offsets.
+
+  for (int32_t i = 0; i < ITEMS_PER_THREAD; i++) {
+    auto const is_match = (thread_match_mask[i / 32] >> (i % 32)) & 1u;
+    if (is_match) {
+      auto const match_end = base_input_offset + thread_input_offset + i + 1;
+      row_offsets[thread_offset - base_output_offset] = match_end;
+      thread_offset++;
+    }
+  }
+}
+
+__global__ __launch_bounds__(THREADS_PER_TILE) void byte_split_kernel(
+  cudf::size_type base_tile_idx,
+  byte_offset base_input_offset,
+  output_offset base_output_offset,
+  cudf::io::text::detail::scan_tile_state_view<output_offset> tile_output_offsets,
+  char delim,
+  cudf::device_span<char const> chunk_input_chars,
+  cudf::split_device_span<byte_offset> row_offsets)
+{
+  using InputLoad =
+    cub::BlockLoad<char, THREADS_PER_TILE, ITEMS_PER_THREAD, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
+  using OffsetScan         = cub::BlockScan<output_offset, THREADS_PER_TILE>;
+  using OffsetScanCallback = cudf::io::text::detail::scan_tile_state_callback<output_offset>;
+
+  __shared__ union {
+    typename InputLoad::TempStorage input_load;
+    typename OffsetScan::TempStorage offset_scan;
+  } temp_storage;
+
+  auto const tile_idx          = base_tile_idx + blockIdx.x;
+  auto const tile_input_offset = blockIdx.x * ITEMS_PER_TILE;
+  auto const thread_input_offset =
+    tile_input_offset + cudf::thread_index_type{threadIdx.x} * ITEMS_PER_THREAD;
+  auto const thread_input_size =
+    std::max<cudf::size_type>(chunk_input_chars.size() - thread_input_offset, 0);
+
+  // STEP 1: Load inputs
+
+  char thread_chars[ITEMS_PER_THREAD];
+
+  InputLoad(temp_storage.input_load)
+    .Load(chunk_input_chars.data() + tile_input_offset,
+          thread_chars,
+          chunk_input_chars.size() - tile_input_offset);
+
+  // STEP 2: Flag matches
+
+  output_offset thread_offset{};
+  uint32_t thread_match_mask[(ITEMS_PER_THREAD + 31) / 32]{};
+
+  for (int32_t i = 0; i < ITEMS_PER_THREAD; i++) {
+    auto const is_match = i < thread_input_size and thread_chars[i] == delim;
+    thread_match_mask[i / 32] |= uint32_t{is_match} << (i % 32);
+    thread_offset += output_offset{is_match};
+  }
+
+  // STEP 3: Scan flags to determine absolute thread output offset
+
+  auto prefix_callback = OffsetScanCallback(tile_output_offsets, tile_idx);
+
+  __syncthreads();  // required before temp_memory re-use
+  OffsetScan(temp_storage.offset_scan).ExclusiveSum(thread_offset, thread_offset, prefix_callback);
+
+  // Step 4: Assign outputs from each thread using match offsets.
+
+  for (int32_t i = 0; i < ITEMS_PER_THREAD; i++) {
+    auto const is_match = (thread_match_mask[i / 32] >> (i % 32)) & 1u;
+    if (is_match) {
+      auto const match_end = base_input_offset + thread_input_offset + i + 1;
+      row_offsets[thread_offset - base_output_offset] = match_end;
+      thread_offset++;
+    }
+  }
+}
+
+}  // namespace
+
+namespace cudf {
+namespace io {
+namespace text {
+namespace detail {
+
+std::unique_ptr<cudf::column> multibyte_split(cudf::io::text::data_chunk_source const& source,
+                                              std::string const& delimiter,
+                                              byte_range_info byte_range,
+                                              bool strip_delimiters,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (byte_range.empty()) { return make_empty_column(type_id::STRING); }
+
+  auto device_delim = cudf::string_scalar(delimiter, true, stream, mr);
+
+  auto sorted_delim = delimiter;
+  std::sort(sorted_delim.begin(), sorted_delim.end());
+  auto [_last_char, _last_char_count, max_duplicate_tokens] = std::accumulate(
+    sorted_delim.begin(), sorted_delim.end(), std::make_tuple('\0', 0, 0), [](auto acc, char c) {
+      if (std::get<0>(acc) != c) {
+        std::get<0>(acc) = c;
+        std::get<1>(acc) = 0;
+      }
+      std::get<1>(acc)++;
+      std::get<2>(acc) = std::max(std::get<1>(acc), std::get<2>(acc));
+      return acc;
+    });
+
+  CUDF_EXPECTS(max_duplicate_tokens < multistate::max_segment_count,
+               "delimiter contains too many duplicate tokens to produce a deterministic result.");
+
+  CUDF_EXPECTS(delimiter.size() < multistate::max_segment_value,
+               "delimiter contains too many total tokens to produce a deterministic result.");
+
+  auto const concurrency = 2;
+
+  // must be at least 32 when using warp-reduce on partials
+  // must be at least 1 more than max possible concurrent tiles
+  // best when at least 32 more than max possible concurrent tiles, due to rolling `invalid`s
+  auto num_tile_states = std::max(32, TILES_PER_CHUNK * concurrency + 32);
+  auto tile_multistates =
+    scan_tile_state<multistate>(num_tile_states, stream, rmm::mr::get_current_device_resource());
+  auto tile_offsets =
+    scan_tile_state<output_offset>(num_tile_states, stream, rmm::mr::get_current_device_resource());
+
+  multibyte_split_init_kernel<<<TILES_PER_CHUNK,
+                                THREADS_PER_TILE,
+                                0,
+                                stream.value()>>>(  //
+    -TILES_PER_CHUNK,
+    TILES_PER_CHUNK,
+    tile_multistates,
+    tile_offsets,
+    cudf::io::text::detail::scan_tile_status::oob);
+
+  auto multistate_seed = multistate();
+  multistate_seed.enqueue(0, 0);  // this represents the first state in the pattern.
+
+  // Seeding the tile state with an identity value allows the 0th tile to follow the same logic as
+  // the Nth tile, assuming it can look up an inclusive prefix. Without this seed, the 0th block
+  // would have to follow separate logic.
+  cudf::detail::device_single_thread(
+    [tm = scan_tile_state_view<multistate>(tile_multistates),
+     to = scan_tile_state_view<output_offset>(tile_offsets),
+     multistate_seed] __device__() mutable {
+      tm.set_inclusive_prefix(-1, multistate_seed);
+      to.set_inclusive_prefix(-1, 0);
+    },
+    stream);
+
+  auto reader               = source.create_reader();
+  auto chunk_offset         = std::max<byte_offset>(0, byte_range.offset() - delimiter.size());
+  auto const byte_range_end = byte_range.offset() + byte_range.size();
+  reader->skip_bytes(chunk_offset);
+  // amortize output chunk allocations over 8 worst-case outputs. This limits the overallocation
+  constexpr auto max_growth = 8;
+  output_builder<byte_offset> row_offset_storage(ITEMS_PER_CHUNK, max_growth, stream);
+  output_builder<char> char_storage(ITEMS_PER_CHUNK, max_growth, stream);
+
+  auto streams = cudf::detail::fork_streams(stream, concurrency);
+
+  cudaEvent_t last_launch_event;
+  CUDF_CUDA_TRY(cudaEventCreate(&last_launch_event));
+
+  auto& read_stream     = streams[0];
+  auto& scan_stream     = streams[1];
+  auto chunk            = reader->get_next_chunk(ITEMS_PER_CHUNK, read_stream);
+  int64_t base_tile_idx = 0;
+  std::optional<byte_offset> first_row_offset;
+  std::optional<byte_offset> last_row_offset;
+  bool found_last_offset = false;
+  if (byte_range.offset() == 0) { first_row_offset = 0; }
+  std::swap(read_stream, scan_stream);
+
+  while (chunk->size() > 0) {
+    // if we found the last delimiter, or didn't find delimiters inside the byte range at all: abort
+    if (last_row_offset.has_value() or
+        (not first_row_offset.has_value() and chunk_offset >= byte_range_end)) {
+      break;
+    }
+
+    auto tiles_in_launch =
+      cudf::util::div_rounding_up_safe(chunk->size(), static_cast<std::size_t>(ITEMS_PER_TILE));
+
+    auto row_offsets = row_offset_storage.next_output(scan_stream);
+
+    // reset the next chunk of tile state
+    multibyte_split_init_kernel<<<tiles_in_launch,
+                                  THREADS_PER_TILE,
+                                  0,
+                                  scan_stream.value()>>>(  //
+      base_tile_idx,
+      tiles_in_launch,
+      tile_multistates,
+      tile_offsets);
+
+    CUDF_CUDA_TRY(cudaStreamWaitEvent(scan_stream.value(), last_launch_event));
+
+    if (delimiter.size() == 1) {
+      // the single-byte case allows for a much more efficient kernel, so we special-case it
+      byte_split_kernel<<<tiles_in_launch,
+                          THREADS_PER_TILE,
+                          0,
+                          scan_stream.value()>>>(  //
+        base_tile_idx,
+        chunk_offset,
+        row_offset_storage.size(),
+        tile_offsets,
+        delimiter[0],
+        *chunk,
+        row_offsets);
+    } else {
+      multibyte_split_kernel<<<tiles_in_launch,
+                               THREADS_PER_TILE,
+                               0,
+                               scan_stream.value()>>>(  //
+        base_tile_idx,
+        chunk_offset,
+        row_offset_storage.size(),
+        tile_multistates,
+        tile_offsets,
+        {device_delim.data(), static_cast<std::size_t>(device_delim.size())},
+        *chunk,
+        row_offsets);
+    }
+
+    // load the next chunk
+    auto next_chunk = reader->get_next_chunk(ITEMS_PER_CHUNK, read_stream);
+    // while that is running, determine how many offsets we output (synchronizes)
+    auto const new_offsets = [&] {
+      auto const new_offsets_unclamped =
+        tile_offsets.get_inclusive_prefix(base_tile_idx + tiles_in_launch - 1, scan_stream) -
+        static_cast<output_offset>(row_offset_storage.size());
+      // if we are not in the last chunk, we can use all offsets
+      if (chunk_offset + static_cast<output_offset>(chunk->size()) < byte_range_end) {
+        return new_offsets_unclamped;
+      }
+      // if we are in the last chunk, we need to find the first out-of-bounds offset
+      auto const it = thrust::make_counting_iterator(output_offset{});
+      auto const end_loc =
+        *thrust::find_if(rmm::exec_policy_nosync(scan_stream),
+                         it,
+                         it + new_offsets_unclamped,
+                         [row_offsets, byte_range_end] __device__(output_offset i) {
+                           return row_offsets[i] >= byte_range_end;
+                         });
+      // if we had no out-of-bounds offset, we copy all offsets
+      if (end_loc == new_offsets_unclamped) { return end_loc; }
+      // otherwise we copy only up to (including) the first out-of-bounds delimiter
+      found_last_offset = true;
+      return end_loc + 1;
+    }();
+    row_offset_storage.advance_output(new_offsets, scan_stream);
+    // determine if we found the first or last field offset for the byte range
+    if (new_offsets > 0 and not first_row_offset) {
+      first_row_offset = row_offset_storage.front_element(scan_stream);
+    }
+    if (found_last_offset) { last_row_offset = row_offset_storage.back_element(scan_stream); }
+    // copy over the characters we need, if we already encountered the first field delimiter
+    if (first_row_offset.has_value()) {
+      auto const begin = chunk->data() + std::max<byte_offset>(0, *first_row_offset - chunk_offset);
+      auto const sentinel = last_row_offset.value_or(std::numeric_limits<byte_offset>::max());
+      auto const end =
+        chunk->data() + std::min<byte_offset>(sentinel - chunk_offset, chunk->size());
+      auto const output_size = end - begin;
+      auto char_output       = char_storage.next_output(scan_stream);
+      thrust::copy(rmm::exec_policy_nosync(scan_stream), begin, end, char_output.begin());
+      char_storage.advance_output(output_size, scan_stream);
+    }
+
+    CUDF_CUDA_TRY(cudaEventRecord(last_launch_event, scan_stream.value()));
+
+    std::swap(read_stream, scan_stream);
+    base_tile_idx += tiles_in_launch;
+    chunk_offset += chunk->size();
+    chunk = std::move(next_chunk);
+  }
+
+  CUDF_CUDA_TRY(cudaEventDestroy(last_launch_event));
+
+  cudf::detail::join_streams(streams, stream);
+
+  // if the input was empty, we didn't find a delimiter at all,
+  // or the first delimiter was also the last: empty output
+  if (chunk_offset == 0 or not first_row_offset.has_value() or
+      first_row_offset == last_row_offset) {
+    return make_empty_column(type_id::STRING);
+  }
+
+  auto chars          = char_storage.gather(stream, mr);
+  auto global_offsets = row_offset_storage.gather(stream, mr);
+
+  // insert an offset at the beginning if we started at the beginning of the input
+  bool const insert_begin = first_row_offset.value_or(0) == 0;
+  // insert an offset at the end if we have not terminated the last row
+  bool const insert_end =
+    not(last_row_offset.has_value() or
+        (global_offsets.size() > 0 and global_offsets.back_element(stream) == chunk_offset));
+  rmm::device_uvector<int32_t> offsets{
+    global_offsets.size() + insert_begin + insert_end, stream, mr};
+  if (insert_begin) { offsets.set_element_to_zero_async(0, stream); }
+  if (insert_end) {
+    offsets.set_element(offsets.size() - 1, chunk_offset - *first_row_offset, stream);
+  }
+  thrust::transform(rmm::exec_policy(stream),
+                    global_offsets.begin(),
+                    global_offsets.end(),
+                    offsets.begin() + insert_begin,
+                    [baseline = *first_row_offset] __device__(byte_offset global_offset) {
+                      return static_cast<int32_t>(global_offset - baseline);
+                    });
+  auto string_count = offsets.size() - 1;
+  if (strip_delimiters) {
+    auto it = cudf::detail::make_counting_transform_iterator(
+      0,
+      [ofs        = offsets.data(),
+       chars      = chars.data(),
+       delim_size = static_cast<size_type>(delimiter.size()),
+       last_row   = static_cast<size_type>(string_count) - 1,
+       insert_end] __device__(size_type row) {
+        auto const begin = ofs[row];
+        auto const len   = ofs[row + 1] - begin;
+        if (row == last_row && insert_end) {
+          return thrust::make_pair(chars + begin, len);
+        } else {
+          return thrust::make_pair(chars + begin, std::max<size_type>(0, len - delim_size));
+        };
+      });
+    return cudf::strings::detail::make_strings_column(it, it + string_count, stream, mr);
+  } else {
+    return cudf::make_strings_column(string_count, std::move(offsets), std::move(chars), {}, 0);
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> multibyte_split(cudf::io::text::data_chunk_source const& source,
+                                              std::string const& delimiter,
+                                              std::optional<byte_range_info> byte_range,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  return multibyte_split(
+    source, delimiter, parse_options{byte_range.value_or(create_byte_range_info_max())}, mr);
+}
+
+std::unique_ptr<cudf::column> multibyte_split(cudf::io::text::data_chunk_source const& source,
+                                              std::string const& delimiter,
+                                              parse_options options,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  auto stream = cudf::get_default_stream();
+
+  auto result = detail::multibyte_split(
+    source, delimiter, options.byte_range, options.strip_delimiters, stream, mr);
+
+  return result;
+}
+
+std::unique_ptr<cudf::column> multibyte_split(cudf::io::text::data_chunk_source const& source,
+                                              std::string const& delimiter,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  return multibyte_split(source, delimiter, parse_options{}, mr);
+}
+
+}  // namespace text
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/arrow_io_source.cpp b/cpp/src/io/utilities/arrow_io_source.cpp
new file mode 100644
index 0000000..d647f3c
--- /dev/null
+++ b/cpp/src/io/utilities/arrow_io_source.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/io/arrow_io_source.hpp>
+
+#include <arrow/buffer.h>
+#include <arrow/filesystem/filesystem.h>
+#include <arrow/result.h>
+
+#include <memory>
+#include <string>
+
+namespace cudf::io {
+
+/**
+ * @brief Implementation for an owning buffer where `arrow::Buffer` holds the data.
+ */
+class arrow_io_buffer : public datasource::buffer {
+  std::shared_ptr<arrow::Buffer> arrow_buffer;
+
+ public:
+  explicit arrow_io_buffer(std::shared_ptr<arrow::Buffer> arrow_buffer) : arrow_buffer(arrow_buffer)
+  {
+  }
+  [[nodiscard]] size_t size() const override { return arrow_buffer->size(); }
+  [[nodiscard]] uint8_t const* data() const override { return arrow_buffer->data(); }
+};
+
+arrow_io_source::arrow_io_source(std::string const& arrow_uri)
+{
+  std::string const uri_start_delimiter = "//";
+  std::string const uri_end_delimiter   = "?";
+
+  auto const result = arrow::fs::FileSystemFromUri(arrow_uri);
+  CUDF_EXPECTS(result.ok(), "Failed to generate Arrow Filesystem instance from URI.");
+  filesystem = result.ValueOrDie();
+
+  // Parse the path from the URI
+  auto const start = [&]() {
+    auto const delim_start = arrow_uri.find(uri_start_delimiter);
+    return delim_start == std::string::npos ? 0 : delim_start + uri_start_delimiter.size();
+  }();
+  auto const end  = arrow_uri.find(uri_end_delimiter) - start;
+  auto const path = arrow_uri.substr(start, end);
+
+  auto const in_stream = filesystem->OpenInputFile(path);
+  CUDF_EXPECTS(in_stream.ok(), "Failed to open Arrow RandomAccessFile");
+  arrow_file = in_stream.ValueOrDie();
+}
+
+std::unique_ptr<datasource::buffer> arrow_io_source::host_read(size_t offset, size_t size)
+{
+  auto const result = arrow_file->ReadAt(offset, size);
+  CUDF_EXPECTS(result.ok(), "Cannot read file data");
+  return std::make_unique<arrow_io_buffer>(result.ValueOrDie());
+}
+
+size_t arrow_io_source::host_read(size_t offset, size_t size, uint8_t* dst)
+{
+  auto const result = arrow_file->ReadAt(offset, size, dst);
+  CUDF_EXPECTS(result.ok(), "Cannot read file data");
+  return result.ValueOrDie();
+}
+
+[[nodiscard]] size_t arrow_io_source::size() const
+{
+  auto const result = arrow_file->GetSize();
+  CUDF_EXPECTS(result.ok(), "Cannot get file size");
+  return result.ValueOrDie();
+}
+
+}  // namespace cudf::io
diff --git a/cpp/src/io/utilities/block_utils.cuh b/cpp/src/io/utilities/block_utils.cuh
new file mode 100644
index 0000000..f028b0b
--- /dev/null
+++ b/cpp/src/io/utilities/block_utils.cuh
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include <cstdint>
+
+namespace cudf {
+namespace io {
+
+template <typename T>
+inline __device__ T shuffle(T var, int lane = 0)
+{
+  return __shfl_sync(~0, var, lane);
+}
+
+template <typename T>
+inline __device__ T shuffle_xor(T var, uint32_t delta)
+{
+  return __shfl_xor_sync(~0, var, delta);
+}
+
+inline __device__ void syncwarp() { __syncwarp(); }
+
+inline __device__ uint32_t ballot(int pred) { return __ballot_sync(~0, pred); }
+
+// Warp reduction helpers
+template <typename T>
+inline __device__ T WarpReduceOr2(T acc)
+{
+  return acc | shuffle_xor(acc, 1);
+}
+template <typename T>
+inline __device__ T WarpReduceOr4(T acc)
+{
+  acc = WarpReduceOr2(acc);
+  return acc | shuffle_xor(acc, 2);
+}
+template <typename T>
+inline __device__ T WarpReduceOr8(T acc)
+{
+  acc = WarpReduceOr4(acc);
+  return acc | shuffle_xor(acc, 4);
+}
+template <typename T>
+inline __device__ T WarpReduceOr16(T acc)
+{
+  acc = WarpReduceOr8(acc);
+  return acc | shuffle_xor(acc, 8);
+}
+template <typename T>
+inline __device__ T WarpReduceOr32(T acc)
+{
+  acc = WarpReduceOr16(acc);
+  return acc | shuffle_xor(acc, 16);
+}
+
+template <typename T>
+inline __device__ T WarpReducePos2(T pos, uint32_t t)
+{
+  T tmp = shuffle(pos, t & 0x1e);
+  pos += (t & 1) ? tmp : 0;
+  return pos;
+}
+template <typename T>
+inline __device__ T WarpReducePos4(T pos, uint32_t t)
+{
+  T tmp;
+  pos = WarpReducePos2(pos, t);
+  tmp = shuffle(pos, (t & 0x1c) | 1);
+  pos += (t & 2) ? tmp : 0;
+  return pos;
+}
+template <typename T>
+inline __device__ T WarpReducePos8(T pos, uint32_t t)
+{
+  T tmp;
+  pos = WarpReducePos4(pos, t);
+  tmp = shuffle(pos, (t & 0x18) | 3);
+  pos += (t & 4) ? tmp : 0;
+  return pos;
+}
+template <typename T>
+inline __device__ T WarpReducePos16(T pos, uint32_t t)
+{
+  T tmp;
+  pos = WarpReducePos8(pos, t);
+  tmp = shuffle(pos, (t & 0x10) | 7);
+  pos += (t & 8) ? tmp : 0;
+  return pos;
+}
+template <typename T>
+inline __device__ T WarpReducePos32(T pos, uint32_t t)
+{
+  T tmp;
+  pos = WarpReducePos16(pos, t);
+  tmp = shuffle(pos, 0xf);
+  pos += (t & 16) ? tmp : 0;
+  return pos;
+}
+
+inline __device__ double Int128ToDouble_rn(uint64_t lo, int64_t hi)
+{
+  double sign;
+  if (hi < 0) {
+    sign = -1.0;
+    lo   = (~lo) + 1;
+    hi   = (~hi) + (lo == 0);
+  } else {
+    sign = 1.0;
+  }
+  return sign * __fma_rn(__ll2double_rn(hi), 4294967296.0 * 4294967296.0, __ull2double_rn(lo));
+}
+
+inline __device__ uint32_t unaligned_load32(uint8_t const* p)
+{
+  uint32_t ofs    = 3 & reinterpret_cast<uintptr_t>(p);
+  auto const* p32 = reinterpret_cast<uint32_t const*>(p - ofs);
+  uint32_t v      = p32[0];
+  return (ofs) ? __funnelshift_r(v, p32[1], ofs * 8) : v;
+}
+
+inline __device__ uint64_t unaligned_load64(uint8_t const* p)
+{
+  uint32_t ofs    = 3 & reinterpret_cast<uintptr_t>(p);
+  auto const* p32 = reinterpret_cast<uint32_t const*>(p - ofs);
+  uint32_t v0     = p32[0];
+  uint32_t v1     = p32[1];
+  if (ofs) {
+    v0 = __funnelshift_r(v0, v1, ofs * 8);
+    v1 = __funnelshift_r(v1, p32[2], ofs * 8);
+  }
+  return (((uint64_t)v1) << 32) | v0;
+}
+
+template <unsigned int nthreads, bool sync_before_store>
+inline __device__ void memcpy_block(void* dstv, void const* srcv, uint32_t len, uint32_t t)
+{
+  auto* dst       = static_cast<uint8_t*>(dstv);
+  auto const* src = static_cast<uint8_t const*>(srcv);
+  uint32_t dst_align_bytes, src_align_bytes, src_align_bits;
+  // Align output to 32-bit
+  dst_align_bytes = 3 & -reinterpret_cast<intptr_t>(dst);
+  if (dst_align_bytes != 0) {
+    uint32_t align_len = min(dst_align_bytes, len);
+    uint8_t b;
+    if (t < align_len) { b = src[t]; }
+    if (sync_before_store) { __syncthreads(); }
+    if (t < align_len) { dst[t] = b; }
+    src += align_len;
+    dst += align_len;
+    len -= align_len;
+  }
+  src_align_bytes = (uint32_t)(3 & reinterpret_cast<uintptr_t>(src));
+  src_align_bits  = src_align_bytes * 8;
+  while (len >= 4) {
+    auto const* src32 = reinterpret_cast<uint32_t const*>(src - src_align_bytes);
+    uint32_t copy_cnt = min(len >> 2, nthreads);
+    uint32_t v;
+    if (t < copy_cnt) {
+      v = src32[t];
+      if (src_align_bits != 0) { v = __funnelshift_r(v, src32[t + 1], src_align_bits); }
+    }
+    if (sync_before_store) { __syncthreads(); }
+    if (t < copy_cnt) { reinterpret_cast<uint32_t*>(dst)[t] = v; }
+    src += copy_cnt * 4;
+    dst += copy_cnt * 4;
+    len -= copy_cnt * 4;
+  }
+  if (len != 0) {
+    uint8_t b;
+    if (t < len) { b = src[t]; }
+    if (sync_before_store) { __syncthreads(); }
+    if (t < len) { dst[t] = b; }
+  }
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/column_buffer.cpp b/cpp/src/io/utilities/column_buffer.cpp
new file mode 100644
index 0000000..f3a43cb
--- /dev/null
+++ b/cpp/src/io/utilities/column_buffer.cpp
@@ -0,0 +1,356 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file column_buffer.cpp
+ * @brief cuDF-IO column_buffer class implementation
+ */
+
+#include "column_buffer.hpp"
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+namespace cudf::io::detail {
+
+void gather_column_buffer::allocate_strings_data(rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(type.id() == type_id::STRING, "allocate_strings_data called for non-string column");
+  // The contents of _strings will never be directly returned to the user.
+  // Due to the fact that make_strings_column copies the input data to
+  // produce its outputs, _strings is actually a temporary. As a result, we
+  // do not pass the provided mr to the call to
+  // make_zeroed_device_uvector_async here and instead let it use the
+  // default rmm memory resource.
+  _strings = std::make_unique<rmm::device_uvector<string_index_pair>>(
+    cudf::detail::make_zeroed_device_uvector_async<string_index_pair>(
+      size, stream, rmm::mr::get_current_device_resource()));
+}
+
+std::unique_ptr<column> gather_column_buffer::make_string_column_impl(rmm::cuda_stream_view stream)
+{
+  // make_strings_column allocates new memory, it does not simply move
+  // from the inputs, so we need to pass it the memory resource given to
+  // the buffer on construction so that the memory is allocated using the
+  // resource that the calling code expected.
+  return make_strings_column(*_strings, stream, _mr);
+}
+
+void inline_column_buffer::allocate_strings_data(rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(type.id() == type_id::STRING, "allocate_strings_data called for non-string column");
+  // size + 1 for final offset. _string_data will be initialized later.
+  _data = create_data(data_type{type_id::INT32}, size + 1, stream, _mr);
+}
+
+void inline_column_buffer::create_string_data(size_t num_bytes, rmm::cuda_stream_view stream)
+{
+  _string_data = rmm::device_buffer(num_bytes, stream, _mr);
+}
+
+std::unique_ptr<column> inline_column_buffer::make_string_column_impl(rmm::cuda_stream_view stream)
+{
+  // no need for copies, just transfer ownership of the data_buffers to the columns
+  auto const state = mask_state::UNALLOCATED;
+  auto str_col =
+    _string_data.is_empty()
+      ? make_empty_column(data_type{type_id::INT8})
+      : std::make_unique<column>(data_type{type_id::INT8},
+                                 string_size(),
+                                 std::move(_string_data),
+                                 cudf::detail::create_null_mask(size, state, stream, _mr),
+                                 state_null_count(state, size),
+                                 std::vector<std::unique_ptr<column>>{});
+  auto offsets_col =
+    std::make_unique<column>(data_type{type_to_id<size_type>()},
+                             size + 1,
+                             std::move(_data),
+                             cudf::detail::create_null_mask(size + 1, state, stream, _mr),
+                             state_null_count(state, size + 1),
+                             std::vector<std::unique_ptr<column>>{});
+
+  return make_strings_column(
+    size, std::move(offsets_col), std::move(str_col), null_count(), std::move(_null_mask));
+}
+
+namespace {
+
+/**
+ * @brief Recursively copy `name` and `user_data` fields of one buffer to another.
+ *
+ * @param buff The old output buffer
+ * @param new_buff The new output buffer
+ */
+template <class string_policy>
+void copy_buffer_data(string_policy const& buff, string_policy& new_buff)
+{
+  new_buff.name      = buff.name;
+  new_buff.user_data = buff.user_data;
+  for (auto const& child : buff.children) {
+    auto& new_child = new_buff.children.emplace_back(string_policy(child.type, child.is_nullable));
+    copy_buffer_data(child, new_child);
+  }
+}
+
+}  // namespace
+
+template <class string_policy>
+void column_buffer_base<string_policy>::create(size_type _size,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  size = _size;
+  _mr  = mr;
+
+  switch (type.id()) {
+    case type_id::STRING: static_cast<string_policy*>(this)->allocate_strings_data(stream); break;
+
+    // list columns store a buffer of int32's as offsets to represent
+    // their individual rows
+    case type_id::LIST: _data = create_data(data_type{type_id::INT32}, size, stream, _mr); break;
+
+    // struct columns store no data themselves.  just validity and children.
+    case type_id::STRUCT: break;
+
+    default: _data = create_data(type, size, stream, _mr); break;
+  }
+  if (is_nullable) {
+    _null_mask = cudf::detail::create_null_mask(
+      size, mask_state::ALL_NULL, rmm::cuda_stream_view(stream), _mr);
+  }
+}
+
+template <class string_policy>
+string_policy column_buffer_base<string_policy>::empty_like(string_policy const& input)
+{
+  auto new_buff = string_policy(input.type, input.is_nullable);
+  copy_buffer_data(input, new_buff);
+  return new_buff;
+}
+
+template <class string_policy>
+std::unique_ptr<column> make_column(column_buffer_base<string_policy>& buffer,
+                                    column_name_info* schema_info,
+                                    std::optional<reader_column_schema> const& schema,
+                                    rmm::cuda_stream_view stream)
+{
+  if (schema_info != nullptr) {
+    schema_info->name        = buffer.name;
+    schema_info->is_nullable = buffer.is_nullable;
+  }
+
+  switch (buffer.type.id()) {
+    case type_id::STRING:
+      if (schema.value_or(reader_column_schema{}).is_enabled_convert_binary_to_strings()) {
+        if (schema_info != nullptr) {
+          schema_info->children.push_back(column_name_info{"offsets"});
+          schema_info->children.push_back(column_name_info{"chars"});
+        }
+
+        // make_strings_column allocates new memory, it does not simply move
+        // from the inputs, so we need to pass it the memory resource given to
+        // the buffer on construction so that the memory is allocated using the
+        // resource that the calling code expected.
+        return buffer.make_string_column(stream);
+      } else {
+        // convert to binary
+        auto const string_col = buffer.make_string_column(stream);
+        auto const num_rows   = string_col->size();
+        auto const null_count = string_col->null_count();
+        auto col_content      = string_col->release();
+
+        // convert to uint8 column, strings are currently stored as int8
+        auto contents =
+          col_content.children[strings_column_view::chars_column_index].release()->release();
+        auto data = contents.data.release();
+
+        auto uint8_col = std::make_unique<column>(
+          data_type{type_id::UINT8}, data->size(), std::move(*data), rmm::device_buffer{}, 0);
+
+        if (schema_info != nullptr) {
+          schema_info->children.push_back(column_name_info{"offsets"});
+          schema_info->children.push_back(column_name_info{"binary"});
+        }
+
+        return make_lists_column(
+          num_rows,
+          std::move(col_content.children[strings_column_view::offsets_column_index]),
+          std::move(uint8_col),
+          null_count,
+          std::move(*col_content.null_mask));
+      }
+
+    case type_id::LIST: {
+      // make offsets column
+      auto offsets = std::make_unique<column>(
+        data_type{type_id::INT32}, buffer.size, std::move(buffer._data), rmm::device_buffer{}, 0);
+
+      column_name_info* child_info = nullptr;
+      if (schema_info != nullptr) {
+        schema_info->children.push_back(column_name_info{"offsets"});
+        schema_info->children.push_back(column_name_info{""});
+        child_info = &schema_info->children.back();
+      }
+
+      CUDF_EXPECTS(not schema.has_value() or schema->get_num_children() > 0,
+                   "Invalid schema provided for read, expected child data for list!");
+      auto const child_schema = schema.has_value()
+                                  ? std::make_optional<reader_column_schema>(schema->child(0))
+                                  : std::nullopt;
+
+      // make child column
+      CUDF_EXPECTS(buffer.children.size() > 0, "Encountered malformed column_buffer");
+      auto child = make_column<string_policy>(buffer.children[0], child_info, child_schema, stream);
+
+      // make the final list column (note : size is the # of offsets, so our actual # of rows is 1
+      // less)
+      return make_lists_column(buffer.size - 1,
+                               std::move(offsets),
+                               std::move(child),
+                               buffer._null_count,
+                               std::move(buffer._null_mask),
+                               stream,
+                               buffer._mr);
+    } break;
+
+    case type_id::STRUCT: {
+      std::vector<std::unique_ptr<cudf::column>> output_children;
+      output_children.reserve(buffer.children.size());
+      for (size_t i = 0; i < buffer.children.size(); ++i) {
+        column_name_info* child_info = nullptr;
+        if (schema_info != nullptr) {
+          schema_info->children.push_back(column_name_info{""});
+          child_info = &schema_info->children.back();
+        }
+
+        CUDF_EXPECTS(not schema.has_value() or schema->get_num_children() > i,
+                     "Invalid schema provided for read, expected more child data for struct!");
+        auto const child_schema = schema.has_value()
+                                    ? std::make_optional<reader_column_schema>(schema->child(i))
+                                    : std::nullopt;
+
+        output_children.emplace_back(
+          make_column<string_policy>(buffer.children[i], child_info, child_schema, stream));
+      }
+
+      return make_structs_column(buffer.size,
+                                 std::move(output_children),
+                                 buffer._null_count,
+                                 std::move(buffer._null_mask),
+                                 stream,
+                                 buffer._mr);
+    } break;
+
+    default: {
+      return std::make_unique<column>(buffer.type,
+                                      buffer.size,
+                                      std::move(buffer._data),
+                                      std::move(buffer._null_mask),
+                                      buffer._null_count);
+    }
+  }
+}
+
+/**
+ * @copydoc cudf::io::detail::empty_like
+ */
+template <class string_policy>
+std::unique_ptr<column> empty_like(column_buffer_base<string_policy>& buffer,
+                                   column_name_info* schema_info,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (schema_info != nullptr) { schema_info->name = buffer.name; }
+
+  switch (buffer.type.id()) {
+    case type_id::LIST: {
+      // make offsets column
+      auto offsets = cudf::make_empty_column(type_id::INT32);
+
+      column_name_info* child_info = nullptr;
+      if (schema_info != nullptr) {
+        schema_info->children.push_back(column_name_info{"offsets"});
+        schema_info->children.push_back(column_name_info{""});
+        child_info = &schema_info->children.back();
+      }
+
+      // make child column
+      CUDF_EXPECTS(buffer.children.size() > 0, "Encountered malformed column_buffer");
+      auto child =
+        cudf::io::detail::empty_like<string_policy>(buffer.children[0], child_info, stream, mr);
+
+      // make the final list column
+      return make_lists_column(
+        0, std::move(offsets), std::move(child), 0, rmm::device_buffer{0, stream, mr}, stream, mr);
+    } break;
+
+    case type_id::STRUCT: {
+      std::vector<std::unique_ptr<cudf::column>> output_children;
+      output_children.reserve(buffer.children.size());
+      std::transform(buffer.children.begin(),
+                     buffer.children.end(),
+                     std::back_inserter(output_children),
+                     [&](auto& col) {
+                       column_name_info* child_info = nullptr;
+                       if (schema_info != nullptr) {
+                         schema_info->children.push_back(column_name_info{""});
+                         child_info = &schema_info->children.back();
+                       }
+                       return cudf::io::detail::empty_like<string_policy>(
+                         col, child_info, stream, mr);
+                     });
+
+      return make_structs_column(
+        0, std::move(output_children), 0, rmm::device_buffer{0, stream, mr}, stream, mr);
+    } break;
+
+    default: return cudf::make_empty_column(buffer.type);
+  }
+}
+
+using pointer_type = gather_column_buffer;
+using string_type  = inline_column_buffer;
+
+using pointer_column_buffer = column_buffer_base<pointer_type>;
+using string_column_buffer  = column_buffer_base<string_type>;
+
+template std::unique_ptr<column> make_column<string_type>(
+  string_column_buffer& buffer,
+  column_name_info* schema_info,
+  std::optional<reader_column_schema> const& schema,
+  rmm::cuda_stream_view stream);
+
+template std::unique_ptr<column> make_column<pointer_type>(
+  pointer_column_buffer& buffer,
+  column_name_info* schema_info,
+  std::optional<reader_column_schema> const& schema,
+  rmm::cuda_stream_view stream);
+
+template std::unique_ptr<column> empty_like<string_type>(string_column_buffer& buffer,
+                                                         column_name_info* schema_info,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr);
+
+template std::unique_ptr<column> empty_like<pointer_type>(pointer_column_buffer& buffer,
+                                                          column_name_info* schema_info,
+                                                          rmm::cuda_stream_view stream,
+                                                          rmm::mr::device_memory_resource* mr);
+
+template class column_buffer_base<pointer_type>;
+template class column_buffer_base<string_type>;
+}  // namespace cudf::io::detail
diff --git a/cpp/src/io/utilities/column_buffer.hpp b/cpp/src/io/utilities/column_buffer.hpp
new file mode 100644
index 0000000..2ee7c17
--- /dev/null
+++ b/cpp/src/io/utilities/column_buffer.hpp
@@ -0,0 +1,258 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file column_buffer.hpp
+ * @brief cuDF-IO Column-backing buffer utilities
+ */
+
+#pragma once
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/pair.h>
+
+namespace cudf {
+namespace io {
+namespace detail {
+/**
+ * @brief Creates a `device_buffer` for holding `column` data.
+ *
+ * @param type The intended data type to populate
+ * @param size The number of elements to be represented by the mask
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned device_buffer
+ *
+ * @return `rmm::device_buffer` Device buffer allocation
+ */
+inline rmm::device_buffer create_data(data_type type,
+                                      size_type size,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  std::size_t data_size = size_of(type) * size;
+
+  rmm::device_buffer data(data_size, stream, mr);
+  CUDF_CUDA_TRY(cudaMemsetAsync(data.data(), 0, data_size, stream.value()));
+
+  return data;
+}
+
+using string_index_pair = thrust::pair<char const*, size_type>;
+
+// forward declare friend functions
+template <typename string_policy>
+class column_buffer_base;
+
+/**
+ * @brief Creates a column from an existing set of device memory buffers.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ *
+ * @param buffer Column buffer descriptors
+ * @param schema_info Schema information for the column to write optionally.
+ * @param schema Optional schema used to control string to binary conversions.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return `std::unique_ptr<cudf::column>` Column from the existing device data
+ */
+template <class string_policy>
+std::unique_ptr<column> make_column(column_buffer_base<string_policy>& buffer,
+                                    column_name_info* schema_info,
+                                    std::optional<reader_column_schema> const& schema,
+                                    rmm::cuda_stream_view stream);
+
+template <typename string_policy>
+class column_buffer_base {
+ public:
+  column_buffer_base() = default;
+
+  // construct without a known size. call create() later to actually allocate memory
+  column_buffer_base(data_type _type, bool _is_nullable) : type(_type), is_nullable(_is_nullable) {}
+
+  column_buffer_base(data_type _type,
+                     size_type _size,
+                     bool _is_nullable,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr)
+    : column_buffer_base(_type, _is_nullable)
+  {
+  }
+
+  // move constructor
+  column_buffer_base(column_buffer_base&& col)            = default;
+  column_buffer_base& operator=(column_buffer_base&& col) = default;
+
+  // copy constructor
+  column_buffer_base(column_buffer_base const& col)            = delete;
+  column_buffer_base& operator=(column_buffer_base const& col) = delete;
+
+  // instantiate a column of known type with a specified size.  Allows deferred creation for
+  // preprocessing steps such as in the Parquet reader
+  void create(size_type _size, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr);
+
+  // Create a new column_buffer that has empty data but with the same basic information as the
+  // input column, including same type, nullability, name, and user_data.
+  static string_policy empty_like(string_policy const& input);
+
+  void set_null_mask(rmm::device_buffer&& mask) { _null_mask = std::move(mask); }
+
+  template <typename T = uint32_t>
+  auto null_mask()
+  {
+    return static_cast<T*>(_null_mask.data());
+  }
+  auto null_mask_size() { return _null_mask.size(); }
+  auto& null_count() { return _null_count; }
+
+  auto data() { return static_cast<string_policy*>(this)->data_impl(); }
+  auto data() const { return static_cast<string_policy const*>(this)->data_impl(); }
+  auto data_size() const { return static_cast<string_policy const*>(this)->data_size_impl(); }
+
+  std::unique_ptr<column> make_string_column(rmm::cuda_stream_view stream)
+  {
+    return static_cast<string_policy*>(this)->make_string_column_impl(stream);
+  }
+
+ protected:
+  rmm::device_buffer _data{};
+  rmm::device_buffer _null_mask{};
+  size_type _null_count{0};
+  rmm::mr::device_memory_resource* _mr;
+
+ public:
+  data_type type{type_id::EMPTY};
+  bool is_nullable{false};
+  size_type size{0};
+  uint32_t user_data{0};  // arbitrary user data
+  std::string name;
+
+  std::vector<string_policy> children;
+
+  friend std::unique_ptr<column> make_column<string_policy>(
+    column_buffer_base& buffer,
+    column_name_info* schema_info,
+    std::optional<reader_column_schema> const& schema,
+    rmm::cuda_stream_view stream);
+};
+
+// column buffer that uses a string_index_pair for strings data, requiring a gather step when
+// creating a string column
+class gather_column_buffer : public column_buffer_base<gather_column_buffer> {
+ public:
+  gather_column_buffer() = default;
+
+  // construct without a known size. call create() later to actually allocate memory
+  gather_column_buffer(data_type _type, bool _is_nullable)
+    : column_buffer_base<gather_column_buffer>(_type, _is_nullable)
+  {
+  }
+
+  gather_column_buffer(data_type _type,
+                       size_type _size,
+                       bool _is_nullable,
+                       rmm::cuda_stream_view stream,
+                       rmm::mr::device_memory_resource* mr)
+    : column_buffer_base<gather_column_buffer>(_type, _size, _is_nullable, stream, mr)
+  {
+    create(_size, stream, mr);
+  }
+
+  void allocate_strings_data(rmm::cuda_stream_view stream);
+
+  void* data_impl() { return _strings ? _strings->data() : _data.data(); }
+  void const* data_impl() const { return _strings ? _strings->data() : _data.data(); }
+  size_t data_size_impl() const { return _strings ? _strings->size() : _data.size(); }
+
+  std::unique_ptr<column> make_string_column_impl(rmm::cuda_stream_view stream);
+
+ public:
+  std::unique_ptr<rmm::device_uvector<string_index_pair>> _strings;
+};
+
+// column buffer that stores string data internally which can be passed directly when
+// creating a string column
+class inline_column_buffer : public column_buffer_base<inline_column_buffer> {
+ public:
+  inline_column_buffer() = default;
+
+  // construct without a known size. call create() later to actually allocate memory
+  inline_column_buffer(data_type _type, bool _is_nullable)
+    : column_buffer_base<inline_column_buffer>(_type, _is_nullable)
+  {
+  }
+
+  inline_column_buffer(data_type _type,
+                       size_type _size,
+                       bool _is_nullable,
+                       rmm::cuda_stream_view stream,
+                       rmm::mr::device_memory_resource* mr)
+    : column_buffer_base<inline_column_buffer>(_type, _size, _is_nullable, stream, mr)
+  {
+    create(_size, stream, mr);
+  }
+
+  void allocate_strings_data(rmm::cuda_stream_view stream);
+
+  void* data_impl() { return _data.data(); }
+  void const* data_impl() const { return _data.data(); }
+  size_t data_size_impl() const { return _data.size(); }
+  std::unique_ptr<column> make_string_column_impl(rmm::cuda_stream_view stream);
+
+  void create_string_data(size_t num_bytes, rmm::cuda_stream_view stream);
+  void* string_data() { return _string_data.data(); }
+  void const* string_data() const { return _string_data.data(); }
+  size_t string_size() const { return _string_data.size(); }
+
+ private:
+  rmm::device_buffer _string_data{};
+};
+
+using column_buffer = gather_column_buffer;
+
+/**
+ * @brief Creates an equivalent empty column from an existing set of device memory buffers.
+ *
+ * This function preserves nested column type information by producing complete/identical
+ * column hierarchies.
+ *
+ * @throws std::bad_alloc if device memory allocation fails
+ *
+ * @param buffer Column buffer descriptors
+ * @param schema_info Schema information for the column to write optionally.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @return `std::unique_ptr<cudf::column>` Column from the existing device data
+ */
+template <class string_policy>
+std::unique_ptr<column> empty_like(column_buffer_base<string_policy>& buffer,
+                                   column_name_info* schema_info,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/column_type_histogram.hpp b/cpp/src/io/utilities/column_type_histogram.hpp
new file mode 100644
index 0000000..88f4e58
--- /dev/null
+++ b/cpp/src/io/utilities/column_type_histogram.hpp
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Per-column histogram struct containing detected occurrences of each dtype
+ */
+struct column_type_histogram {
+  cudf::size_type null_count{};
+  cudf::size_type float_count{};
+  cudf::size_type datetime_count{};
+  cudf::size_type string_count{};
+  cudf::size_type negative_small_int_count{};
+  cudf::size_type positive_small_int_count{};
+  cudf::size_type big_int_count{};
+  cudf::size_type bool_count{};
+  auto total_count() const
+  {
+    return null_count + float_count + datetime_count + string_count + negative_small_int_count +
+           positive_small_int_count + big_int_count + bool_count;
+  }
+};
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/column_utils.cuh b/cpp/src/io/utilities/column_utils.cuh
new file mode 100644
index 0000000..5f4bf64
--- /dev/null
+++ b/cpp/src/io/utilities/column_utils.cuh
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <io/statistics/statistics.cuh>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Create column_device_view pointers from leaf columns
+ *
+ * A device_uvector is created to store the leaves of parent columns. The
+ * column descriptor array is updated to point to these leaf columns.
+ *
+ * @tparam ColumnDescriptor Struct describing properties of columns with
+ * pointers to leaf and parent columns
+ *
+ * @param col_desc Column description array
+ * @param parent_table_device_view Table device view containing parent columns
+ * @param stream CUDA stream to use
+ *
+ * @return Device array containing leaf column device views
+ */
+template <typename ColumnDescriptor>
+rmm::device_uvector<column_device_view> create_leaf_column_device_views(
+  typename cudf::device_span<ColumnDescriptor> col_desc,
+  table_device_view const& parent_table_device_view,
+  rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<column_device_view> leaf_column_views(parent_table_device_view.num_columns(),
+                                                            stream);
+  auto leaf_columns = cudf::device_span<column_device_view>{leaf_column_views};
+
+  auto iter = thrust::make_counting_iterator<size_type>(0);
+  thrust::for_each(
+    rmm::exec_policy(stream),
+    iter,
+    iter + parent_table_device_view.num_columns(),
+    [col_desc, parent_col_view = parent_table_device_view, leaf_columns] __device__(
+      size_type index) {
+      col_desc[index].parent_column = parent_col_view.begin() + index;
+      column_device_view col        = parent_col_view.column(index);
+      // traverse till leaf column
+      while (col.type().id() == type_id::LIST || col.type().id() == type_id::STRUCT) {
+        auto const child = (col.type().id() == type_id::LIST)
+                             ? col.child(lists_column_view::child_column_index)
+                             : col.child(0);
+        // stop early if writing a byte array
+        if (col_desc[index].stats_dtype == dtype_byte_array &&
+            child.type().id() == type_id::UINT8) {
+          break;
+        }
+        col = child;
+      }
+      // Store leaf_column to device storage
+      column_device_view* leaf_col_ptr = leaf_columns.begin() + index;
+      *leaf_col_ptr                    = col;
+      col_desc[index].leaf_column      = leaf_col_ptr;
+    });
+
+  return leaf_column_views;
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/config_utils.cpp b/cpp/src/io/utilities/config_utils.cpp
new file mode 100644
index 0000000..0f89613
--- /dev/null
+++ b/cpp/src/io/utilities/config_utils.cpp
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "config_utils.hpp"
+
+#include <cudf/utilities/error.hpp>
+
+#include <cstdlib>
+#include <string>
+
+namespace cudf::io::detail {
+
+namespace cufile_integration {
+
+namespace {
+/**
+ * @brief Defines which cuFile usage to enable.
+ */
+enum class usage_policy : uint8_t { OFF, GDS, ALWAYS, KVIKIO };
+
+/**
+ * @brief Get the current usage policy.
+ */
+usage_policy get_env_policy()
+{
+  static auto const env_val = getenv_or<std::string>("LIBCUDF_CUFILE_POLICY", "KVIKIO");
+  if (env_val == "OFF") return usage_policy::OFF;
+  if (env_val == "GDS") return usage_policy::GDS;
+  if (env_val == "ALWAYS") return usage_policy::ALWAYS;
+  if (env_val == "KVIKIO") return usage_policy::KVIKIO;
+  CUDF_FAIL("Invalid LIBCUDF_CUFILE_POLICY value: " + env_val);
+}
+}  // namespace
+
+bool is_always_enabled() { return get_env_policy() == usage_policy::ALWAYS; }
+
+bool is_gds_enabled() { return is_always_enabled() or get_env_policy() == usage_policy::GDS; }
+
+bool is_kvikio_enabled() { return get_env_policy() == usage_policy::KVIKIO; }
+
+}  // namespace cufile_integration
+
+namespace nvcomp_integration {
+
+namespace {
+/**
+ * @brief Defines which nvCOMP usage to enable.
+ */
+enum class usage_policy : uint8_t { OFF, STABLE, ALWAYS };
+
+/**
+ * @brief Get the current usage policy.
+ */
+usage_policy get_env_policy()
+{
+  static auto const env_val = getenv_or<std::string>("LIBCUDF_NVCOMP_POLICY", "STABLE");
+  if (env_val == "OFF") return usage_policy::OFF;
+  if (env_val == "STABLE") return usage_policy::STABLE;
+  if (env_val == "ALWAYS") return usage_policy::ALWAYS;
+  CUDF_FAIL("Invalid LIBCUDF_NVCOMP_POLICY value: " + env_val);
+}
+}  // namespace
+
+bool is_all_enabled() { return get_env_policy() == usage_policy::ALWAYS; }
+
+bool is_stable_enabled() { return is_all_enabled() or get_env_policy() == usage_policy::STABLE; }
+
+}  // namespace nvcomp_integration
+
+}  // namespace cudf::io::detail
diff --git a/cpp/src/io/utilities/config_utils.hpp b/cpp/src/io/utilities/config_utils.hpp
new file mode 100644
index 0000000..74df137
--- /dev/null
+++ b/cpp/src/io/utilities/config_utils.hpp
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/detail/utilities/logger.hpp>
+
+#include <sstream>
+#include <string>
+
+namespace cudf::io::detail {
+
+/**
+ * @brief Returns the value of the environment variable, or a default value if the variable is not
+ * present.
+ */
+template <typename T>
+T getenv_or(std::string_view env_var_name, T default_val)
+{
+  auto const env_val = std::getenv(env_var_name.data());
+  if (env_val != nullptr) {
+    CUDF_LOG_INFO("Environment variable {} read as {}", env_var_name, env_val);
+  } else {
+    CUDF_LOG_INFO(
+      "Environment variable {} is not set, using default value {}", env_var_name, default_val);
+  }
+
+  if (env_val == nullptr) { return default_val; }
+
+  std::stringstream sstream(env_val);
+  T converted_val;
+  sstream >> converted_val;
+  return converted_val;
+}
+
+namespace cufile_integration {
+
+/**
+ * @brief Returns true if cuFile and its compatibility mode are enabled.
+ */
+bool is_always_enabled();
+
+/**
+ * @brief Returns true if only direct IO through cuFile is enabled (compatibility mode is disabled).
+ */
+bool is_gds_enabled();
+
+/**
+ * @brief Returns true if KvikIO is enabled.
+ */
+bool is_kvikio_enabled();
+
+}  // namespace cufile_integration
+
+namespace nvcomp_integration {
+
+/**
+ * @brief Returns true if all nvCOMP uses are enabled.
+ */
+bool is_all_enabled();
+
+/**
+ * @brief Returns true if stable nvCOMP use is enabled.
+ */
+bool is_stable_enabled();
+
+}  // namespace nvcomp_integration
+
+}  // namespace cudf::io::detail
diff --git a/cpp/src/io/utilities/data_casting.cu b/cpp/src/io/utilities/data_casting.cu
new file mode 100644
index 0000000..9e5c5c7
--- /dev/null
+++ b/cpp/src/io/utilities/data_casting.cu
@@ -0,0 +1,984 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/utilities/parsing_utils.cuh>
+#include <io/utilities/string_parsing.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/functional.h>
+#include <thrust/transform_reduce.h>
+
+#include <cub/cub.cuh>
+
+#include <memory>
+#include <type_traits>
+
+namespace cudf::io::json::detail {
+
+constexpr auto SINGLE_THREAD_THRESHOLD = 128;
+constexpr auto WARP_THRESHOLD          = 128 * 128;  // 16K
+
+// Unicode code point escape sequence
+static constexpr char UNICODE_SEQ = 0x7F;
+
+// Invalid escape sequence
+static constexpr char NON_ESCAPE_CHAR = 0x7E;
+
+// Unicode code point escape sequence prefix comprises '\' and 'u' characters
+static constexpr size_type UNICODE_ESC_PREFIX = 2;
+
+// Unicode code point escape sequence comprises four hex characters
+static constexpr size_type UNICODE_HEX_DIGIT_COUNT = 4;
+
+// A unicode code point escape sequence is \uXXXX
+static auto constexpr NUM_UNICODE_ESC_SEQ_CHARS = UNICODE_ESC_PREFIX + UNICODE_HEX_DIGIT_COUNT;
+
+static constexpr auto UTF16_HIGH_SURROGATE_BEGIN = 0xD800;
+static constexpr auto UTF16_HIGH_SURROGATE_END   = 0xDC00;
+static constexpr auto UTF16_LOW_SURROGATE_BEGIN  = 0xDC00;
+static constexpr auto UTF16_LOW_SURROGATE_END    = 0xE000;
+
+/**
+ * @brief Describing whether data casting of a certain item succeed, the item was parsed to null, or
+ * whether type casting failed.
+ */
+enum class data_casting_result { PARSING_SUCCESS, PARSED_TO_NULL, PARSING_FAILURE };
+
+/**
+ * @brief Providing additional information about the type casting result.
+ */
+struct data_casting_result_info {
+  // Number of bytes written to output
+  size_type bytes;
+  // Whether parsing succeeded, item was parsed to null, or failed
+  data_casting_result result;
+};
+
+/**
+ * @brief Returns the character to output for a given escaped character that's following a
+ * backslash.
+ *
+ * @param escaped_char The character following the backslash.
+ * @return The character to output for a given character that's following a backslash
+ */
+__device__ __forceinline__ char get_escape_char(char escaped_char)
+{
+  switch (escaped_char) {
+    case '"': return '"';
+    case '\\': return '\\';
+    case '/': return '/';
+    case 'b': return '\b';
+    case 'f': return '\f';
+    case 'n': return '\n';
+    case 'r': return '\r';
+    case 't': return '\t';
+    case 'u': return UNICODE_SEQ;
+    default: return NON_ESCAPE_CHAR;
+  }
+}
+
+/**
+ * @brief Parses the hex value from the four hex digits of a unicode code point escape sequence
+ * \uXXXX.
+ *
+ * @param str Pointer to the first (most-significant) hex digit
+ * @return The parsed hex value if successful, -1 otherwise.
+ */
+__device__ __forceinline__ int32_t parse_unicode_hex(char const* str)
+{
+  // Prepare result
+  int32_t result = 0, base = 1;
+  constexpr int32_t hex_radix = 16;
+
+  // Iterate over hex digits right-to-left
+  size_type index = UNICODE_HEX_DIGIT_COUNT;
+  while (index-- > 0) {
+    char const ch = str[index];
+    if (ch >= '0' && ch <= '9') {
+      result += static_cast<int32_t>((ch - '0') + 0) * base;
+      base *= hex_radix;
+    } else if (ch >= 'A' && ch <= 'F') {
+      result += static_cast<int32_t>((ch - 'A') + 10) * base;
+      base *= hex_radix;
+    } else if (ch >= 'a' && ch <= 'f') {
+      result += static_cast<int32_t>((ch - 'a') + 10) * base;
+      base *= hex_radix;
+    } else {
+      return -1;
+    }
+  }
+  return result;
+}
+
+/**
+ * @brief Writes the UTF-8 byte sequence to \p out_it and returns the number of bytes written to
+ * \p out_it
+ */
+constexpr size_type write_utf8_char(char_utf8 character, char*& out_it)
+{
+  auto const bytes = (out_it == nullptr) ? strings::detail::bytes_in_char_utf8(character)
+                                         : strings::detail::from_char_utf8(character, out_it);
+  if (out_it) out_it += bytes;
+  return bytes;
+}
+
+/**
+ * @brief Processes a string, replaces escape sequences and optionally strips off the quote
+ * characters.
+ *
+ * @tparam in_iterator_t A bidirectional input iterator type whose value_type is convertible to
+ * char
+ * @param in_begin Iterator to the first item to process
+ * @param in_end Iterator to one past the last item to process
+ * @param d_buffer Output character buffer to the first item to write
+ * @param options Settings for controlling string processing behavior
+ * @return A struct of (num_bytes_written, parsing_success_result), where num_bytes_written is
+ * the number of bytes written to d_buffer, parsing_success_result is enum value indicating whether
+ * parsing succeeded, item was parsed to null, or failed.
+ */
+template <typename in_iterator_t>
+__device__ __forceinline__ data_casting_result_info
+process_string(in_iterator_t in_begin,
+               in_iterator_t in_end,
+               char* d_buffer,
+               cudf::io::parse_options_view const& options)
+{
+  int32_t bytes           = 0;
+  auto const num_in_chars = thrust::distance(in_begin, in_end);
+  // String values are indicated by keeping the quote character
+  bool const is_string_value =
+    num_in_chars >= 2LL &&
+    (options.quotechar == '\0' ||
+     (*in_begin == options.quotechar) && (*thrust::prev(in_end) == options.quotechar));
+
+  // Copy literal/numeric value
+  if (not is_string_value) {
+    bytes += (in_end - in_begin);
+    if (d_buffer) d_buffer = thrust::copy(thrust::seq, in_begin, in_end, d_buffer);
+    return {bytes, data_casting_result::PARSING_SUCCESS};
+  }
+  char constexpr backslash_char = '\\';
+
+  // Escape-flag, set after encountering a backslash character
+  bool is_prev_char_escape = false;
+
+  // Exclude beginning and ending quote chars from string range
+  if (!options.keepquotes) {
+    ++in_begin;
+    --in_end;
+  }
+
+  // Iterate over the input
+  while (in_begin != in_end) {
+    // Copy single character to output
+    if (!is_prev_char_escape) {
+      is_prev_char_escape = (*in_begin == backslash_char);
+      if (!is_prev_char_escape) {
+        if (d_buffer) *d_buffer++ = *in_begin;
+        ++bytes;
+      }
+      ++in_begin;
+      continue;
+    }
+
+    // Previous char indicated beginning of escape sequence
+    // Reset escape flag for next loop iteration
+    is_prev_char_escape = false;
+
+    // Check the character that is supposed to be escaped
+    auto escaped_char = get_escape_char(*in_begin);
+
+    // We escaped an invalid escape character -> "fail"/null for this item
+    if (escaped_char == NON_ESCAPE_CHAR) { return {bytes, data_casting_result::PARSING_FAILURE}; }
+
+    // Regular, single-character escape
+    if (escaped_char != UNICODE_SEQ) {
+      if (d_buffer) *d_buffer++ = escaped_char;
+      ++bytes;
+      ++in_begin;
+      continue;
+    }
+
+    // This is an escape sequence of a unicode code point: \uXXXX,
+    // where each X in XXXX represents a hex digit
+    // Skip over the 'u' char from \uXXXX to the first hex digit
+    ++in_begin;
+
+    // Make sure that there's at least 4 characters left from the
+    // input, which are expected to be hex digits
+    if (thrust::distance(in_begin, in_end) < UNICODE_HEX_DIGIT_COUNT) {
+      return {bytes, data_casting_result::PARSING_FAILURE};
+    }
+
+    auto hex_val = parse_unicode_hex(in_begin);
+
+    // Couldn't parse hex values from the four-character sequence -> "fail"/null for this item
+    if (hex_val < 0) { return {bytes, data_casting_result::PARSING_FAILURE}; }
+
+    // Skip over the four hex digits
+    thrust::advance(in_begin, UNICODE_HEX_DIGIT_COUNT);
+
+    // If this may be a UTF-16 encoded surrogate pair:
+    // we expect another \uXXXX sequence
+    int32_t hex_low_val = 0;
+    if (hex_val >= UTF16_HIGH_SURROGATE_BEGIN && hex_val < UTF16_HIGH_SURROGATE_END &&
+        thrust::distance(in_begin, in_end) >= NUM_UNICODE_ESC_SEQ_CHARS &&
+        *in_begin == backslash_char && *thrust::next(in_begin) == 'u') {
+      // Try to parse hex value following the '\' and 'u' characters from what may be a UTF16 low
+      // surrogate
+      hex_low_val = parse_unicode_hex(thrust::next(in_begin, 2));
+    }
+
+    // This is indeed a UTF16 surrogate pair
+    if (hex_val >= UTF16_HIGH_SURROGATE_BEGIN && hex_val < UTF16_HIGH_SURROGATE_END &&
+        hex_low_val >= UTF16_LOW_SURROGATE_BEGIN && hex_low_val < UTF16_LOW_SURROGATE_END) {
+      // Skip over the second \uXXXX sequence
+      thrust::advance(in_begin, NUM_UNICODE_ESC_SEQ_CHARS);
+
+      // Compute UTF16-encoded code point
+      uint32_t unicode_code_point = 0x10000 + ((hex_val - UTF16_HIGH_SURROGATE_BEGIN) << 10) +
+                                    (hex_low_val - UTF16_LOW_SURROGATE_BEGIN);
+      auto utf8_chars = strings::detail::codepoint_to_utf8(unicode_code_point);
+      bytes += write_utf8_char(utf8_chars, d_buffer);
+    } else {
+      // Just a single \uXXXX sequence
+      auto utf8_chars = strings::detail::codepoint_to_utf8(hex_val);
+      bytes += write_utf8_char(utf8_chars, d_buffer);
+    }
+  }
+
+  // The last character of the input is a backslash -> "fail"/null for this item
+  if (is_prev_char_escape) { return {bytes, data_casting_result::PARSING_FAILURE}; }
+  return {bytes, data_casting_result::PARSING_SUCCESS};
+}
+
+/**
+ * @brief Data structure to hold 1 bit per thread with previous `UNICODE_LOOK_BACK` bits stored in a
+ * warp.
+ *
+ * @tparam num_warps number of warps in the block
+ */
+template <unsigned num_warps>
+struct bitfield_warp {
+  static constexpr auto UNICODE_LOOK_BACK{5};
+  // 5 because for skipping unicode hex chars, look back up to 5 chars are needed.
+  // 5+32 for each warp.
+  bool is_slash[num_warps][UNICODE_LOOK_BACK + cudf::detail::warp_size];
+
+  /// Sets all bits to 0
+  __device__ void reset(unsigned warp_id)
+  {
+    if (threadIdx.x % cudf::detail::warp_size < UNICODE_LOOK_BACK) {
+      is_slash[warp_id][threadIdx.x % cudf::detail::warp_size] = 0;
+    }
+    is_slash[warp_id][threadIdx.x % cudf::detail::warp_size + UNICODE_LOOK_BACK] = 0;
+  }
+
+  /// Shifts UNICODE_LOOK_BACK bits to the left to hold the previous UNICODE_LOOK_BACK bits
+  __device__ void shift(unsigned warp_id)
+  {
+    if (threadIdx.x % cudf::detail::warp_size < UNICODE_LOOK_BACK)
+      is_slash[warp_id][threadIdx.x % cudf::detail::warp_size] =
+        is_slash[warp_id][cudf::detail::warp_size + threadIdx.x % cudf::detail::warp_size];
+    __syncwarp();
+  }
+
+  /// Each thread in a warp sets its own bit.
+  __device__ void set_bits(unsigned warp_id, bool is_escaping_backslash)
+  {
+    is_slash[warp_id][UNICODE_LOOK_BACK + threadIdx.x % cudf::detail::warp_size] =
+      is_escaping_backslash;
+    __syncwarp();
+  }
+
+  /// Each thread in a warp gets the requested bit.
+  __device__ bool get_bit(unsigned warp_id, int bit_index)
+  {
+    return is_slash[warp_id][UNICODE_LOOK_BACK + bit_index];
+  }
+};
+
+/**
+ * @brief Data structure to hold 1 bit per thread with previous `UNICODE_LOOK_BACK` bits stored in a
+ * block.
+ *
+ * @tparam num_warps number of warps in the block
+ */
+template <unsigned num_warps>
+struct bitfield_block {
+  static constexpr auto UNICODE_LOOK_BACK{5};
+  // 5 because for skipping unicode hex chars, look back up to 5 chars are needed.
+  // 5 + num_warps*32 for entire block
+  bool is_slash[UNICODE_LOOK_BACK + num_warps * cudf::detail::warp_size];
+
+  /// Sets all bits to 0
+  __device__ void reset(unsigned warp_id)
+  {
+    if (threadIdx.x < UNICODE_LOOK_BACK) { is_slash[threadIdx.x] = 0; }
+    is_slash[threadIdx.x + UNICODE_LOOK_BACK] = 0;
+  }
+
+  /// Shifts UNICODE_LOOK_BACK bits to the left to hold the previous UNICODE_LOOK_BACK bits
+  __device__ void shift(unsigned warp_id)
+  {
+    if (threadIdx.x < UNICODE_LOOK_BACK)
+      is_slash[threadIdx.x] = is_slash[num_warps * cudf::detail::warp_size + threadIdx.x];
+    __syncthreads();
+  }
+
+  /// Each thread in a block sets its own bit.
+  __device__ void set_bits(unsigned warp_id, bool is_escaping_backslash)
+  {
+    is_slash[UNICODE_LOOK_BACK + threadIdx.x] = is_escaping_backslash;
+    __syncthreads();
+  }
+
+  /// Each thread in a block gets the requested bit.
+  __device__ bool get_bit(unsigned warp_id, int bit_index)
+  {
+    return is_slash[UNICODE_LOOK_BACK + bit_index];
+  }
+};
+
+// Algorithm: warp/block parallel version of string_parse and process_string()
+// Decoding character classes (u8, u16, \*, *):
+// character      count: input->output
+// \uXXXX         6->2/3/4
+// \uXXXX\uXXXX  12->2/3/4
+// \"             2->1
+// *              1->1
+//
+// ERROR conditions. (all collaborating threads quit)
+// c=='\' & curr_idx == end_idx-1;
+// [c-1]=='\' &  get_escape[c]==NEC
+// [c-1]=='\' &  [c]=='u' & end_idx-curr_idx < UNICODE_HEX_DIGIT_COUNT
+// [c-1]=='\' &  [c]=='u' & end_idx-curr_idx >= UNICODE_HEX_DIGIT_COUNT && non-hex
+//
+// skip conditions. (current thread skips this char, no output)
+// c=='\' skip. (Escaping char only)
+// [c-2]=='\' && [c-1]=='u' for [2,1], [3,2] [4,5], [5, 6], skip.
+//
+// write conditions. (write to d_buffer)
+// [c-1]!='\' &  [c]!='\' write [c]
+// [c-1]!='\' &  [c]=='\' skip (already covered in skip conditions)
+// [c-1]=='\' &  [c]!=NEC && [c]!=UNICODE_SEQ, write [c]
+// [c-1]=='\' &  [c]=='u' & end_idx-curr_idx >= UNICODE_HEX_DIGIT_COUNT && hex, DECODE
+// [c+1:4]=curr_hex_val
+//        // if [c+5]=='\' & [c+6]=='u' & end_idx-curr_idx >= UNICODE_HEX_DIGIT_COUNT &&
+//        hex,DECODE [c+7:4]=next_hex_val
+//        // if [c-7]=='\' & [c-6]=='u' & end_idx-curr_idx >= UNICODE_HEX_DIGIT_COUNT &&
+//        hex,DECODE [c-5:4]=prev_hex_val prev_hex_val, curr_hex_val, next_hex_val
+//        // if prev_hex_val in high, curr_hex_val in low, skip.
+//        // if curr_hex_val in high, next_hex_val in low, write [u16]
+// if curr_hex_val not in high, write [u8]
+// before writing, find num of output characters per threads,
+// then do intra-warp/intra-block scan for out_idx
+// propagate offset from next iteration to carry forward.
+// Uses 1 warp per string or 1 block per string
+
+/**
+ * @brief Warp/Block parallel version of string_parse functor
+ *
+ * @tparam is_warp True if 1 warp per string, False if 1 block per string
+ * @tparam num_warps Number of warps per block
+ * @tparam str_tuple_it Iterator type for tuple with string pointer and its length
+ * @param str_tuples iterator of tuple with string pointer and its length
+ * @param total_out_strings Number of string rows to be processed
+ * @param str_counter Counter to keep track of processed number of strings
+ * @param null_mask Null mask
+ * @param null_count_data pointer to store null count
+ * @param options Settings for controlling string processing behavior
+ * @param d_offsets Offsets to identify where to store the results for each string
+ * @param d_chars Character array to store the characters of strings
+ */
+template <bool is_warp, size_type num_warps, typename str_tuple_it>
+__global__ void parse_fn_string_parallel(str_tuple_it str_tuples,
+                                         size_type total_out_strings,
+                                         size_type* str_counter,
+                                         bitmask_type* null_mask,
+                                         size_type* null_count_data,
+                                         cudf::io::parse_options_view const options,
+                                         size_type* d_offsets,
+                                         char* d_chars)
+{
+  constexpr auto BLOCK_SIZE =
+    is_warp ? cudf::detail::warp_size : cudf::detail::warp_size * num_warps;
+  size_type lane = is_warp ? (threadIdx.x % BLOCK_SIZE) : threadIdx.x;
+
+  // get 1-string index per warp/block
+  auto get_next_string = [&]() {
+    if constexpr (is_warp) {
+      size_type istring;
+      if (lane == 0) { istring = atomicAdd(str_counter, 1); }
+      return __shfl_sync(0xffffffff, istring, 0);
+    } else {
+      // Ensure lane 0 doesn't update istring before all threads have read the previous iteration's
+      // istring value
+      __syncthreads();
+      __shared__ size_type istring;
+      if (lane == 0) { istring = atomicAdd(str_counter, 1); }
+      __syncthreads();
+      return istring;
+    }
+  };
+  // grid-stride loop.
+  for (size_type istring = get_next_string(); istring < total_out_strings;
+       istring           = get_next_string()) {
+    // skip nulls
+    if (null_mask != nullptr && not bit_is_set(null_mask, istring)) {
+      if (!d_chars && lane == 0) d_offsets[istring] = 0;
+      continue;  // gride-stride return;
+    }
+
+    auto in_begin           = str_tuples[istring].first;
+    auto in_end             = in_begin + str_tuples[istring].second;
+    auto const num_in_chars = str_tuples[istring].second;
+    if constexpr (is_warp) {
+      if (num_in_chars <= SINGLE_THREAD_THRESHOLD or num_in_chars > WARP_THRESHOLD) continue;
+    } else {
+      if (num_in_chars <= WARP_THRESHOLD) continue;
+    }
+
+    // Check if the value corresponds to the null literal
+    if (!d_chars) {
+      auto const is_null_literal = serialized_trie_contains(
+        options.trie_na, {in_begin, static_cast<std::size_t>(num_in_chars)});
+      if (is_null_literal && null_mask != nullptr) {
+        if (lane == 0) {
+          clear_bit(null_mask, istring);
+          atomicAdd(null_count_data, 1);
+          if (!d_chars) d_offsets[istring] = 0;
+        }
+        continue;  // gride-stride return;
+      }
+    }
+    // String values are indicated by keeping the quote character
+    bool const is_string_value =
+      num_in_chars >= 2LL &&
+      (options.quotechar == '\0' ||
+       (*in_begin == options.quotechar) && (*thrust::prev(in_end) == options.quotechar));
+    char* d_buffer = d_chars ? d_chars + d_offsets[istring] : nullptr;
+
+    // Copy literal/numeric value
+    if (not is_string_value) {
+      if (!d_chars) {
+        if (lane == 0) { d_offsets[istring] = in_end - in_begin; }
+      } else {
+        for (thread_index_type char_index = lane; char_index < (in_end - in_begin);
+             char_index += BLOCK_SIZE) {
+          d_buffer[char_index] = in_begin[char_index];
+        }
+      }
+      continue;  // gride-stride return;
+    }
+
+    // Exclude beginning and ending quote chars from string range
+    if (!options.keepquotes) {
+      ++in_begin;
+      --in_end;
+    }
+    // warp-parallelized or block-parallelized process_string()
+
+    auto is_hex = [](auto ch) {
+      return (ch >= '0' && ch <= '9') || (ch >= 'A' && ch <= 'F') || (ch >= 'a' && ch <= 'f');
+    };
+
+    // for backslash scan calculation: is_previous_escaping_backslash
+    [[maybe_unused]] auto warp_id = threadIdx.x / cudf::detail::warp_size;
+    bool init_state_reg;
+    __shared__ bool init_state_shared;
+    size_type last_offset_reg;
+    __shared__ size_type last_offset_shared;
+    bool& init_state(is_warp ? init_state_reg : init_state_shared);
+    size_type& last_offset(is_warp ? last_offset_reg : last_offset_shared);
+    if (is_warp || lane == 0) {
+      init_state  = false;
+      last_offset = 0;
+    }
+    using bitfield =
+      std::conditional_t<is_warp, bitfield_warp<num_warps>, bitfield_block<num_warps>>;
+    __shared__ bitfield is_slash;
+    is_slash.reset(warp_id);
+    __syncthreads();
+    // 0-31, 32-63, ... i*32-n.
+    // entire warp executes but with mask.
+    for (thread_index_type char_index = lane;
+         char_index < cudf::util::round_up_safe(in_end - in_begin, static_cast<long>(BLOCK_SIZE));
+         char_index += BLOCK_SIZE) {
+      bool const is_within_bounds = char_index < (in_end - in_begin);
+      auto const c                = is_within_bounds ? in_begin[char_index] : '\0';
+      auto const prev_c = (char_index > 0 and is_within_bounds) ? in_begin[char_index - 1] : '\0';
+      auto const escaped_char = get_escape_char(c);
+
+      bool is_escaping_backslash{false};
+      [[maybe_unused]] bool is_prev_escaping_backslash{false};
+      // To check current is backslash by checking if previous is backslash.
+      // curr = !prev & c=='\\'
+      // So, scan is required from beginning of string.
+      // State table approach (intra-warp FST) (intra-block FST)
+      // 2 states: Not-Slash(NS), Slash(S).
+      // prev  /   *
+      // NS    S  NS
+      //  S   NS  NS
+      // After inclusive scan, all current S states translate to escaping backslash.
+      // All escaping backslash should be skipped.
+
+      struct state_table {
+        // using bit fields instead of state[2]
+        bool state0 : 1;
+        bool state1 : 1;
+        bool inline __device__ get(bool init_state) const { return init_state ? state1 : state0; }
+      };
+      state_table curr{is_within_bounds && c == '\\', false};  // state transition vector.
+      auto composite_op = [](state_table op1, state_table op2) {
+        // equivalent of state_table{op2.state[op1.state[0]], op2.state[op1.state[1]]};
+        return state_table{op1.state0 ? op2.state1 : op2.state0,
+                           op1.state1 ? op2.state1 : op2.state0};
+      };
+      state_table scanned;
+      // inclusive scan of escaping backslashes
+      if constexpr (is_warp) {
+        using SlashScan = cub::WarpScan<state_table>;
+        __shared__ typename SlashScan::TempStorage temp_slash[num_warps];
+        SlashScan(temp_slash[warp_id]).InclusiveScan(curr, scanned, composite_op);
+        is_escaping_backslash = scanned.get(init_state);
+        init_state            = __shfl_sync(~0u, is_escaping_backslash, BLOCK_SIZE - 1);
+        __syncwarp();
+        is_slash.shift(warp_id);
+        is_slash.set_bits(warp_id, is_escaping_backslash);
+        is_prev_escaping_backslash = is_slash.get_bit(warp_id, lane - 1);
+      } else {
+        using SlashScan = cub::BlockScan<state_table, BLOCK_SIZE>;
+        __shared__ typename SlashScan::TempStorage temp_slash;
+        SlashScan(temp_slash).InclusiveScan(curr, scanned, composite_op);
+        is_escaping_backslash = scanned.get(init_state);
+        __syncthreads();
+        if (threadIdx.x == BLOCK_SIZE - 1) init_state = is_escaping_backslash;
+        __syncthreads();
+        is_slash.shift(warp_id);
+        is_slash.set_bits(warp_id, is_escaping_backslash);
+        is_prev_escaping_backslash = is_slash.get_bit(warp_id, lane - 1);
+        // There is another __syncthreads() at the end of for-loop.
+      }
+
+      // String with parsing errors are made as null
+      bool error = false;
+      if (is_within_bounds) {
+        // curr=='\' and end, or prev=='\' and curr=='u' and end-curr < UNICODE_HEX_DIGIT_COUNT
+        // or prev=='\' and curr=='u' and end-curr >= UNICODE_HEX_DIGIT_COUNT and any non-hex
+        error |= (is_escaping_backslash /*c == '\\'*/ && char_index == (in_end - in_begin) - 1);
+        error |= (is_prev_escaping_backslash && escaped_char == NON_ESCAPE_CHAR);
+        error |= (is_prev_escaping_backslash && c == 'u' &&
+                  ((in_begin + char_index + UNICODE_HEX_DIGIT_COUNT >= in_end) |
+                   !is_hex(in_begin[char_index + 1]) | !is_hex(in_begin[char_index + 2]) |
+                   !is_hex(in_begin[char_index + 3]) | !is_hex(in_begin[char_index + 4])));
+      }
+      // Make sure all threads have no errors before continuing
+      if constexpr (is_warp) {
+        error = __any_sync(~0u, error);
+      } else {
+        using ErrorReduce = cub::BlockReduce<bool, BLOCK_SIZE>;
+        __shared__ typename ErrorReduce::TempStorage temp_storage_error;
+        __shared__ bool error_reduced;
+        error_reduced = ErrorReduce(temp_storage_error).Sum(error);  // TODO use cub::LogicalOR.
+        // only valid in thread0, so shared memory is used for broadcast.
+        __syncthreads();
+        error = error_reduced;
+      }
+      // If any thread has an error, skip the rest of the string and make this string as null
+      if (error) {
+        if (!d_chars && lane == 0) {
+          if (null_mask != nullptr) {
+            clear_bit(null_mask, istring);
+            atomicAdd(null_count_data, 1);
+          }
+          last_offset        = 0;
+          d_offsets[istring] = 0;
+        }
+        if constexpr (!is_warp) { __syncthreads(); }
+        break;  // gride-stride return;
+      }
+
+      // Skipping non-copied escaped characters
+      bool skip = !is_within_bounds;  // false;
+      // skip \ for \" \\ \/ \b \f \n \r \t \uXXXX
+      skip |= is_escaping_backslash;
+      if (is_within_bounds) {
+        // skip X for each X in \uXXXX
+        skip |=
+          char_index >= 2 && is_slash.get_bit(warp_id, lane - 2) && in_begin[char_index - 1] == 'u';
+        skip |=
+          char_index >= 3 && is_slash.get_bit(warp_id, lane - 3) && in_begin[char_index - 2] == 'u';
+        skip |=
+          char_index >= 4 && is_slash.get_bit(warp_id, lane - 4) && in_begin[char_index - 3] == 'u';
+        skip |=
+          char_index >= 5 && is_slash.get_bit(warp_id, lane - 5) && in_begin[char_index - 4] == 'u';
+      }
+      int this_num_out = 0;
+      cudf::char_utf8 write_char{};
+
+      if (!skip) {
+        // 1. Unescaped character
+        if (!is_prev_escaping_backslash) {
+          this_num_out = 1;
+          // writes char directly for non-unicode
+        } else {
+          // 2. Escaped character
+          if (escaped_char != UNICODE_SEQ) {
+            this_num_out = 1;
+            // writes char directly for non-unicode
+          } else {
+            // 3. Unicode
+            // UTF8 \uXXXX
+            auto hex_val     = parse_unicode_hex(in_begin + char_index + 1);
+            auto hex_low_val = 0;
+            // UTF16 \uXXXX\uXXXX
+            // Note: no need for scanned_backslash below because we already know that
+            // only '\u' check is enough.
+            if (hex_val >= UTF16_HIGH_SURROGATE_BEGIN && hex_val < UTF16_HIGH_SURROGATE_END &&
+                (in_begin + char_index + UNICODE_HEX_DIGIT_COUNT + NUM_UNICODE_ESC_SEQ_CHARS) <
+                  in_end &&
+                in_begin[char_index + NUM_UNICODE_ESC_SEQ_CHARS - 1] == '\\' &&
+                in_begin[char_index + NUM_UNICODE_ESC_SEQ_CHARS] == 'u') {
+              hex_low_val = parse_unicode_hex(in_begin + char_index + 1 + 6);
+            }
+            if (hex_val >= UTF16_HIGH_SURROGATE_BEGIN && hex_val < UTF16_HIGH_SURROGATE_END &&
+                hex_low_val >= UTF16_LOW_SURROGATE_BEGIN && hex_low_val < UTF16_LOW_SURROGATE_END) {
+              // Compute UTF16-encoded code point
+              uint32_t unicode_code_point = 0x10000 +
+                                            ((hex_val - UTF16_HIGH_SURROGATE_BEGIN) << 10) +
+                                            (hex_low_val - UTF16_LOW_SURROGATE_BEGIN);
+              write_char   = strings::detail::codepoint_to_utf8(unicode_code_point);
+              this_num_out = strings::detail::bytes_in_char_utf8(write_char);
+            } else {
+              // if hex_val is high surrogate, ideally it should be parsing failure.
+              // but skipping it as other parsers do this too.
+              if (hex_val >= UTF16_LOW_SURROGATE_BEGIN && hex_val < UTF16_LOW_SURROGATE_END) {
+                // Ideally this should be skipped if previous char is high surrogate.
+                skip         = true;
+                this_num_out = 0;
+                write_char   = 0;
+              } else {
+                // if UTF8
+                write_char   = strings::detail::codepoint_to_utf8(hex_val);
+                this_num_out = strings::detail::bytes_in_char_utf8(write_char);
+              }
+            }
+          }
+        }
+      }  // !skip end.
+      {
+        // compute offset to write output for each thread
+        size_type offset;
+        if constexpr (is_warp) {
+          using OffsetScan = cub::WarpScan<size_type>;
+          __shared__ typename OffsetScan::TempStorage temp_storage[num_warps];
+          OffsetScan(temp_storage[warp_id]).ExclusiveSum(this_num_out, offset);
+        } else {
+          using OffsetScan = cub::BlockScan<size_type, BLOCK_SIZE>;
+          __shared__ typename OffsetScan::TempStorage temp_storage;
+          OffsetScan(temp_storage).ExclusiveSum(this_num_out, offset);
+          __syncthreads();
+        }
+        offset += last_offset;
+        // Write output
+        if (d_chars && !skip) {
+          auto const is_not_unicode = (!is_prev_escaping_backslash) || escaped_char != UNICODE_SEQ;
+          if (is_not_unicode) {
+            *(d_buffer + offset) = (!is_prev_escaping_backslash) ? c : escaped_char;
+          } else {
+            strings::detail::from_char_utf8(write_char, d_buffer + offset);
+          }
+        }
+        offset += this_num_out;
+        if constexpr (is_warp) {
+          last_offset = __shfl_sync(0xffffffff, offset, BLOCK_SIZE - 1);
+        } else {
+          __syncthreads();
+          if (threadIdx.x == BLOCK_SIZE - 1) last_offset = offset;
+          __syncthreads();
+        }
+      }
+    }  // char for-loop
+    if (!d_chars && lane == 0) { d_offsets[istring] = last_offset; }
+  }  // grid-stride for-loop
+}
+
+template <typename str_tuple_it>
+struct string_parse {
+  str_tuple_it str_tuples;
+  bitmask_type* null_mask;
+  size_type* null_count_data;
+  cudf::io::parse_options_view const options;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (null_mask != nullptr && not bit_is_set(null_mask, idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const in_begin     = str_tuples[idx].first;
+    auto const in_end       = in_begin + str_tuples[idx].second;
+    auto const num_in_chars = str_tuples[idx].second;
+
+    if (num_in_chars > SINGLE_THREAD_THRESHOLD) return;
+
+    // Check if the value corresponds to the null literal
+    if (!d_chars) {
+      auto const is_null_literal = serialized_trie_contains(
+        options.trie_na, {in_begin, static_cast<std::size_t>(num_in_chars)});
+      if (is_null_literal && null_mask != nullptr) {
+        clear_bit(null_mask, idx);
+        atomicAdd(null_count_data, 1);
+        if (!d_chars) d_offsets[idx] = 0;
+        return;
+      }
+    }
+
+    char* d_buffer        = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    auto str_process_info = process_string(in_begin, in_end, d_buffer, options);
+    if (str_process_info.result != data_casting_result::PARSING_SUCCESS) {
+      if (null_mask != nullptr) {
+        clear_bit(null_mask, idx);
+        atomicAdd(null_count_data, 1);
+      }
+      if (!d_chars) d_offsets[idx] = 0;
+    } else {
+      if (!d_chars) d_offsets[idx] = str_process_info.bytes;
+    }
+  }
+};
+
+template <typename SymbolT>
+struct to_string_view_pair {
+  SymbolT const* data;
+  to_string_view_pair(SymbolT const* _data) : data(_data) {}
+  __device__ auto operator()(thrust::tuple<size_type, size_type> ip)
+  {
+    return thrust::pair<char const*, std::size_t>{data + thrust::get<0>(ip),
+                                                  static_cast<std::size_t>(thrust::get<1>(ip))};
+  }
+};
+
+template <typename string_view_pair_it>
+static std::unique_ptr<column> parse_string(string_view_pair_it str_tuples,
+                                            size_type col_size,
+                                            rmm::device_buffer&& null_mask,
+                                            rmm::device_scalar<size_type>& d_null_count,
+                                            cudf::io::parse_options_view const& options,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  //  CUDF_FUNC_RANGE();
+
+  auto const max_length = thrust::transform_reduce(
+    rmm::exec_policy(stream),
+    str_tuples,
+    str_tuples + col_size,
+    [] __device__(auto t) { return t.second; },
+    size_type{0},
+    thrust::maximum<size_type>{});
+
+  auto offsets = cudf::make_numeric_column(
+    data_type{type_to_id<size_type>()}, col_size + 1, cudf::mask_state::UNALLOCATED, stream, mr);
+  auto d_offsets       = offsets->mutable_view().data<size_type>();
+  auto null_count_data = d_null_count.data();
+
+  auto single_thread_fn = string_parse<decltype(str_tuples)>{
+    str_tuples, static_cast<bitmask_type*>(null_mask.data()), null_count_data, options, d_offsets};
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     col_size,
+                     single_thread_fn);
+
+  constexpr auto warps_per_block  = 8;
+  constexpr int threads_per_block = cudf::detail::warp_size * warps_per_block;
+  auto num_blocks                 = cudf::util::div_rounding_up_safe(col_size, warps_per_block);
+  auto str_counter                = cudf::numeric_scalar(size_type{0}, true, stream);
+
+  // TODO run these independent kernels in parallel streams.
+  if (max_length > SINGLE_THREAD_THRESHOLD) {
+    parse_fn_string_parallel<true, warps_per_block>
+      <<<num_blocks, threads_per_block, 0, stream.value()>>>(
+        str_tuples,
+        col_size,
+        str_counter.data(),
+        static_cast<bitmask_type*>(null_mask.data()),
+        null_count_data,
+        options,
+        d_offsets,
+        nullptr);
+  }
+
+  if (max_length > WARP_THRESHOLD) {
+    // for strings longer than WARP_THRESHOLD, 1 block per string
+    str_counter.set_value(0, stream);
+    parse_fn_string_parallel<false, warps_per_block>
+      <<<num_blocks, threads_per_block, 0, stream.value()>>>(
+        str_tuples,
+        col_size,
+        str_counter.data(),
+        static_cast<bitmask_type*>(null_mask.data()),
+        null_count_data,
+        options,
+        d_offsets,
+        nullptr);
+  }
+  auto const bytes =
+    cudf::detail::sizes_to_offsets(d_offsets, d_offsets + col_size + 1, d_offsets, stream);
+  CUDF_EXPECTS(bytes <= std::numeric_limits<size_type>::max(),
+               "Size of output exceeds the column size limit",
+               std::overflow_error);
+
+  // CHARS column
+  std::unique_ptr<column> chars =
+    strings::detail::create_chars_child_column(static_cast<size_type>(bytes), stream, mr);
+  auto d_chars = chars->mutable_view().data<char>();
+
+  single_thread_fn.d_chars = d_chars;
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     col_size,
+                     single_thread_fn);
+
+  if (max_length > SINGLE_THREAD_THRESHOLD) {
+    str_counter.set_value(0, stream);
+    parse_fn_string_parallel<true, warps_per_block>
+      <<<num_blocks, threads_per_block, 0, stream.value()>>>(
+        str_tuples,
+        col_size,
+        str_counter.data(),
+        static_cast<bitmask_type*>(null_mask.data()),
+        null_count_data,
+        options,
+        d_offsets,
+        d_chars);
+  }
+
+  if (max_length > WARP_THRESHOLD) {
+    str_counter.set_value(0, stream);
+    // for strings longer than WARP_THRESHOLD, 1 block per string
+    parse_fn_string_parallel<false, warps_per_block>
+      <<<num_blocks, threads_per_block, 0, stream.value()>>>(
+        str_tuples,
+        col_size,
+        str_counter.data(),
+        static_cast<bitmask_type*>(null_mask.data()),
+        null_count_data,
+        options,
+        d_offsets,
+        d_chars);
+  }
+
+  return make_strings_column(col_size,
+                             std::move(offsets),
+                             std::move(chars),
+                             d_null_count.value(stream),
+                             std::move(null_mask));
+}
+
+std::unique_ptr<column> parse_data(
+  const char* data,
+  thrust::zip_iterator<thrust::tuple<const size_type*, const size_type*>> offset_length_begin,
+  size_type col_size,
+  data_type col_type,
+  rmm::device_buffer&& null_mask,
+  size_type null_count,
+  cudf::io::parse_options_view const& options,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (col_size == 0) { return make_empty_column(col_type); }
+  auto d_null_count    = rmm::device_scalar<size_type>(null_count, stream);
+  auto null_count_data = d_null_count.data();
+  if (null_mask.is_empty()) {
+    null_mask = cudf::detail::create_null_mask(col_size, mask_state::ALL_VALID, stream, mr);
+  }
+
+  // Prepare iterator that returns (string_ptr, string_length)-pairs needed by type conversion
+  auto str_tuples = thrust::make_transform_iterator(offset_length_begin, to_string_view_pair{data});
+
+  if (col_type == cudf::data_type{cudf::type_id::STRING}) {
+    return parse_string(
+      str_tuples, col_size, std::move(null_mask), d_null_count, options, stream, mr);
+  }
+
+  auto out_col =
+    make_fixed_width_column(col_type, col_size, std::move(null_mask), null_count, stream, mr);
+  auto output_dv_ptr = mutable_column_device_view::create(*out_col, stream);
+
+  // use `ConvertFunctor` to convert non-string values
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    col_size,
+    [str_tuples, col = *output_dv_ptr, options, col_type, null_count_data] __device__(
+      size_type row) {
+      if (col.is_null(row)) { return; }
+      auto const in = str_tuples[row];
+
+      auto const is_null_literal =
+        serialized_trie_contains(options.trie_na, {in.first, static_cast<size_t>(in.second)});
+
+      if (is_null_literal) {
+        col.set_null(row);
+        atomicAdd(null_count_data, 1);
+        return;
+      }
+
+      // If this is a string value, remove quotes
+      auto [in_begin, in_end] = trim_quotes(in.first, in.first + in.second, options.quotechar);
+
+      auto const is_parsed = cudf::type_dispatcher(col_type,
+                                                   ConvertFunctor{},
+                                                   in_begin,
+                                                   in_end,
+                                                   col.data<char>(),
+                                                   row,
+                                                   col_type,
+                                                   options,
+                                                   false);
+      if (not is_parsed) {
+        col.set_null(row);
+        atomicAdd(null_count_data, 1);
+      }
+    });
+
+  out_col->set_null_count(d_null_count.value(stream));
+
+  return out_col;
+}
+
+}  // namespace cudf::io::json::detail
diff --git a/cpp/src/io/utilities/data_sink.cpp b/cpp/src/io/utilities/data_sink.cpp
new file mode 100644
index 0000000..aaad686
--- /dev/null
+++ b/cpp/src/io/utilities/data_sink.cpp
@@ -0,0 +1,217 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <fstream>
+
+#include "file_io_utilities.hpp"
+#include <cudf/io/data_sink.hpp>
+#include <cudf/utilities/error.hpp>
+#include <io/utilities/config_utils.hpp>
+
+#include <kvikio/file_handle.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace io {
+/**
+ * @brief Implementation class for storing data into a local file.
+ */
+class file_sink : public data_sink {
+ public:
+  explicit file_sink(std::string const& filepath)
+  {
+    _output_stream.open(filepath, std::ios::out | std::ios::binary | std::ios::trunc);
+    CUDF_EXPECTS(_output_stream.is_open(), "Cannot open output file");
+
+    if (detail::cufile_integration::is_kvikio_enabled()) {
+      _kvikio_file = kvikio::FileHandle(filepath, "w");
+      CUDF_LOG_INFO("Writing a file using kvikIO, with compatibility mode {}.",
+                    _kvikio_file.is_compat_mode_on() ? "on" : "off");
+    } else {
+      _cufile_out = detail::make_cufile_output(filepath);
+    }
+  }
+
+  virtual ~file_sink() { flush(); }
+
+  void host_write(void const* data, size_t size) override
+  {
+    _output_stream.seekp(_bytes_written);
+    _output_stream.write(static_cast<char const*>(data), size);
+    _bytes_written += size;
+  }
+
+  void flush() override { _output_stream.flush(); }
+
+  size_t bytes_written() override { return _bytes_written; }
+
+  [[nodiscard]] bool supports_device_write() const override
+  {
+    return !_kvikio_file.closed() || _cufile_out != nullptr;
+  }
+
+  [[nodiscard]] bool is_device_write_preferred(size_t size) const override
+  {
+    if (size < _gds_write_preferred_threshold) { return false; }
+    return supports_device_write();
+  }
+
+  std::future<void> device_write_async(void const* gpu_data,
+                                       size_t size,
+                                       rmm::cuda_stream_view stream) override
+  {
+    if (!supports_device_write()) CUDF_FAIL("Device writes are not supported for this file.");
+
+    size_t offset = _bytes_written;
+    _bytes_written += size;
+
+    if (!_kvikio_file.closed()) {
+      // KvikIO's `pwrite()` returns a `std::future<size_t>` so we convert it
+      // to `std::future<void>`
+      return std::async(std::launch::deferred, [this, gpu_data, size, offset] {
+        _kvikio_file.pwrite(gpu_data, size, offset).get();
+      });
+    }
+    return _cufile_out->write_async(gpu_data, offset, size);
+  }
+
+  void device_write(void const* gpu_data, size_t size, rmm::cuda_stream_view stream) override
+  {
+    return device_write_async(gpu_data, size, stream).get();
+  }
+
+ private:
+  std::ofstream _output_stream;
+  size_t _bytes_written = 0;
+  std::unique_ptr<detail::cufile_output_impl> _cufile_out;
+  kvikio::FileHandle _kvikio_file;
+  // The write size above which GDS is faster then d2h-copy + posix-write
+  static constexpr size_t _gds_write_preferred_threshold = 128 << 10;  // 128KB
+};
+
+/**
+ * @brief Implementation class for storing data into a std::vector.
+ */
+class host_buffer_sink : public data_sink {
+ public:
+  explicit host_buffer_sink(std::vector<char>* buffer) : buffer_(buffer) {}
+
+  virtual ~host_buffer_sink() { flush(); }
+
+  void host_write(void const* data, size_t size) override
+  {
+    auto char_array = static_cast<char const*>(data);
+    buffer_->insert(buffer_->end(), char_array, char_array + size);
+  }
+
+  void flush() override {}
+
+  size_t bytes_written() override { return buffer_->size(); }
+
+ private:
+  std::vector<char>* buffer_;
+};
+
+/**
+ * @brief Implementation class for voiding data (no io performed)
+ */
+class void_sink : public data_sink {
+ public:
+  explicit void_sink() {}
+
+  virtual ~void_sink() {}
+
+  void host_write(void const* data, size_t size) override { _bytes_written += size; }
+
+  [[nodiscard]] bool supports_device_write() const override { return true; }
+
+  void device_write(void const* gpu_data, size_t size, rmm::cuda_stream_view stream) override
+  {
+    _bytes_written += size;
+  }
+
+  std::future<void> device_write_async(void const* gpu_data,
+                                       size_t size,
+                                       rmm::cuda_stream_view stream) override
+  {
+    _bytes_written += size;
+    return std::async(std::launch::deferred, [] {});
+  }
+
+  void flush() override {}
+
+  size_t bytes_written() override { return _bytes_written; }
+
+ private:
+  size_t _bytes_written;
+};
+
+class user_sink_wrapper : public data_sink {
+ public:
+  explicit user_sink_wrapper(cudf::io::data_sink* const user_sink_) : user_sink(user_sink_) {}
+
+  virtual ~user_sink_wrapper() {}
+
+  void host_write(void const* data, size_t size) override { user_sink->host_write(data, size); }
+
+  [[nodiscard]] bool supports_device_write() const override
+  {
+    return user_sink->supports_device_write();
+  }
+
+  void device_write(void const* gpu_data, size_t size, rmm::cuda_stream_view stream) override
+  {
+    CUDF_EXPECTS(user_sink->supports_device_write(),
+                 "device_write() was called on a data_sink that doesn't support it");
+    user_sink->device_write(gpu_data, size, stream);
+  }
+
+  std::future<void> device_write_async(void const* gpu_data,
+                                       size_t size,
+                                       rmm::cuda_stream_view stream) override
+  {
+    CUDF_EXPECTS(user_sink->supports_device_write(),
+                 "device_write_async() was called on a data_sink that doesn't support it");
+    return user_sink->device_write_async(gpu_data, size, stream);
+  }
+
+  void flush() override { user_sink->flush(); }
+
+  size_t bytes_written() override { return user_sink->bytes_written(); }
+
+ private:
+  cudf::io::data_sink* const user_sink;
+};
+
+std::unique_ptr<data_sink> data_sink::create(std::string const& filepath)
+{
+  return std::make_unique<file_sink>(filepath);
+}
+
+std::unique_ptr<data_sink> data_sink::create(std::vector<char>* buffer)
+{
+  return std::make_unique<host_buffer_sink>(buffer);
+}
+
+std::unique_ptr<data_sink> data_sink::create() { return std::make_unique<void_sink>(); }
+
+std::unique_ptr<data_sink> data_sink::create(cudf::io::data_sink* const user_sink)
+{
+  return std::make_unique<user_sink_wrapper>(user_sink);
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/datasource.cpp b/cpp/src/io/utilities/datasource.cpp
new file mode 100644
index 0000000..7a7121a
--- /dev/null
+++ b/cpp/src/io/utilities/datasource.cpp
@@ -0,0 +1,425 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "file_io_utilities.hpp"
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/arrow_io_source.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <io/utilities/config_utils.hpp>
+
+#include <kvikio/file_handle.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <arrow/io/memory.h>
+
+#include <fcntl.h>
+#include <sys/mman.h>
+#include <unistd.h>
+
+#include <unordered_map>
+
+namespace cudf {
+namespace io {
+namespace {
+
+/**
+ * @brief Base class for file input. Only implements direct device reads.
+ */
+class file_source : public datasource {
+ public:
+  explicit file_source(char const* filepath) : _file(filepath, O_RDONLY)
+  {
+    if (detail::cufile_integration::is_kvikio_enabled()) {
+      _kvikio_file = kvikio::FileHandle(filepath);
+      CUDF_LOG_INFO("Reading a file using kvikIO, with compatibility mode {}.",
+                    _kvikio_file.is_compat_mode_on() ? "on" : "off");
+    } else {
+      _cufile_in = detail::make_cufile_input(filepath);
+    }
+  }
+
+  virtual ~file_source() = default;
+
+  [[nodiscard]] bool supports_device_read() const override
+  {
+    return !_kvikio_file.closed() || _cufile_in != nullptr;
+  }
+
+  [[nodiscard]] bool is_device_read_preferred(size_t size) const override
+  {
+    if (size < _gds_read_preferred_threshold) { return false; }
+    return supports_device_read();
+  }
+
+  std::future<size_t> device_read_async(size_t offset,
+                                        size_t size,
+                                        uint8_t* dst,
+                                        rmm::cuda_stream_view stream) override
+  {
+    CUDF_EXPECTS(supports_device_read(), "Device reads are not supported for this file.");
+
+    auto const read_size = std::min(size, _file.size() - offset);
+    if (!_kvikio_file.closed()) { return _kvikio_file.pread(dst, read_size, offset); }
+    return _cufile_in->read_async(offset, read_size, dst, stream);
+  }
+
+  size_t device_read(size_t offset,
+                     size_t size,
+                     uint8_t* dst,
+                     rmm::cuda_stream_view stream) override
+  {
+    return device_read_async(offset, size, dst, stream).get();
+  }
+
+  std::unique_ptr<datasource::buffer> device_read(size_t offset,
+                                                  size_t size,
+                                                  rmm::cuda_stream_view stream) override
+  {
+    rmm::device_buffer out_data(size, stream);
+    size_t read = device_read(offset, size, reinterpret_cast<uint8_t*>(out_data.data()), stream);
+    out_data.resize(read, stream);
+    return datasource::buffer::create(std::move(out_data));
+  }
+
+  [[nodiscard]] size_t size() const override { return _file.size(); }
+
+ protected:
+  detail::file_wrapper _file;
+
+ private:
+  std::unique_ptr<detail::cufile_input_impl> _cufile_in;
+  kvikio::FileHandle _kvikio_file;
+  // The read size above which GDS is faster then posix-read + h2d-copy
+  static constexpr size_t _gds_read_preferred_threshold = 128 << 10;  // 128KB
+};
+
+/**
+ * @brief Memoized pageableMemoryAccessUsesHostPageTables device property.
+ */
+[[nodiscard]] bool pageableMemoryAccessUsesHostPageTables()
+{
+  static std::unordered_map<int, bool> result_cache{};
+
+  int deviceId{};
+  CUDF_CUDA_TRY(cudaGetDevice(&deviceId));
+
+  if (result_cache.find(deviceId) == result_cache.end()) {
+    cudaDeviceProp props{};
+    CUDF_CUDA_TRY(cudaGetDeviceProperties(&props, deviceId));
+    result_cache[deviceId] = (props.pageableMemoryAccessUsesHostPageTables == 1);
+    CUDF_LOG_INFO(
+      "Device {} pageableMemoryAccessUsesHostPageTables: {}", deviceId, result_cache[deviceId]);
+  }
+
+  return result_cache[deviceId];
+}
+
+/**
+ * @brief Implementation class for reading from a file using memory mapped access.
+ *
+ * Unlike Arrow's memory mapped IO class, this implementation allows memory mapping a subset of the
+ * file where the starting offset may not be zero.
+ */
+class memory_mapped_source : public file_source {
+ public:
+  explicit memory_mapped_source(char const* filepath, size_t offset, size_t size)
+    : file_source(filepath)
+  {
+    if (_file.size() != 0) {
+      map(_file.desc(), offset, size);
+      register_mmap_buffer();
+    }
+  }
+
+  ~memory_mapped_source() override
+  {
+    if (_map_addr != nullptr) {
+      munmap(_map_addr, _map_size);
+      unregister_mmap_buffer();
+    }
+  }
+
+  std::unique_ptr<buffer> host_read(size_t offset, size_t size) override
+  {
+    CUDF_EXPECTS(offset >= _map_offset, "Requested offset is outside mapping");
+
+    // Clamp length to available data in the mapped region
+    auto const read_size = std::min(size, _map_size - (offset - _map_offset));
+
+    return std::make_unique<non_owning_buffer>(
+      static_cast<uint8_t*>(_map_addr) + (offset - _map_offset), read_size);
+  }
+
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override
+  {
+    CUDF_EXPECTS(offset >= _map_offset, "Requested offset is outside mapping");
+
+    // Clamp length to available data in the mapped region
+    auto const read_size = std::min(size, _map_size - (offset - _map_offset));
+
+    auto const src = static_cast<uint8_t*>(_map_addr) + (offset - _map_offset);
+    std::memcpy(dst, src, read_size);
+    return read_size;
+  }
+
+ private:
+  /**
+   * @brief Page-locks (registers) the memory range of the mapped file.
+   *
+   * Fixes nvbugs/4215160
+   */
+  void register_mmap_buffer()
+  {
+    if (_map_addr == nullptr or _map_size == 0 or not pageableMemoryAccessUsesHostPageTables()) {
+      return;
+    }
+
+    auto const result = cudaHostRegister(_map_addr, _map_size, cudaHostRegisterDefault);
+    if (result == cudaSuccess) {
+      _is_map_registered = true;
+    } else {
+      CUDF_LOG_WARN("cudaHostRegister failed with {} ({})", result, cudaGetErrorString(result));
+    }
+  }
+
+  /**
+   * @brief Unregisters the memory range of the mapped file.
+   */
+  void unregister_mmap_buffer()
+  {
+    if (not _is_map_registered) { return; }
+
+    auto const result = cudaHostUnregister(_map_addr);
+    if (result != cudaSuccess) {
+      CUDF_LOG_WARN("cudaHostUnregister failed with {} ({})", result, cudaGetErrorString(result));
+    }
+  }
+
+  void map(int fd, size_t offset, size_t size)
+  {
+    CUDF_EXPECTS(offset < _file.size(), "Offset is past end of file");
+
+    // Offset for `mmap()` must be page aligned
+    _map_offset = offset & ~(sysconf(_SC_PAGESIZE) - 1);
+
+    if (size == 0 || (offset + size) > _file.size()) { size = _file.size() - offset; }
+
+    // Size for `mmap()` needs to include the page padding
+    _map_size = size + (offset - _map_offset);
+
+    // Check if accessing a region within already mapped area
+    _map_addr = mmap(nullptr, _map_size, PROT_READ, MAP_PRIVATE, fd, _map_offset);
+    CUDF_EXPECTS(_map_addr != MAP_FAILED, "Cannot create memory mapping");
+  }
+
+ private:
+  size_t _map_size        = 0;
+  size_t _map_offset      = 0;
+  void* _map_addr         = nullptr;
+  bool _is_map_registered = false;
+};
+
+/**
+ * @brief Implementation class for reading from a file using `read` calls
+ *
+ * Potentially faster than `memory_mapped_source` when only a small portion of the file is read
+ * through the host.
+ */
+class direct_read_source : public file_source {
+ public:
+  explicit direct_read_source(char const* filepath) : file_source(filepath) {}
+
+  std::unique_ptr<buffer> host_read(size_t offset, size_t size) override
+  {
+    lseek(_file.desc(), offset, SEEK_SET);
+
+    // Clamp length to available data
+    ssize_t const read_size = std::min(size, _file.size() - offset);
+
+    std::vector<uint8_t> v(read_size);
+    CUDF_EXPECTS(read(_file.desc(), v.data(), read_size) == read_size, "read failed");
+    return buffer::create(std::move(v));
+  }
+
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override
+  {
+    lseek(_file.desc(), offset, SEEK_SET);
+
+    // Clamp length to available data
+    auto const read_size = std::min(size, _file.size() - offset);
+
+    CUDF_EXPECTS(read(_file.desc(), dst, read_size) == static_cast<ssize_t>(read_size),
+                 "read failed");
+    return read_size;
+  }
+};
+
+/**
+ * @brief Implementation class for reading from a device buffer source
+ */
+class device_buffer_source final : public datasource {
+ public:
+  explicit device_buffer_source(cudf::device_span<std::byte const> d_buffer) : _d_buffer{d_buffer}
+  {
+  }
+
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override
+  {
+    auto const count  = std::min(size, this->size() - offset);
+    auto const stream = cudf::get_default_stream();
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(dst, _d_buffer.data() + offset, count, cudaMemcpyDefault, stream.value()));
+    stream.synchronize();
+    return count;
+  }
+
+  std::unique_ptr<buffer> host_read(size_t offset, size_t size) override
+  {
+    auto const count  = std::min(size, this->size() - offset);
+    auto const stream = cudf::get_default_stream();
+    auto h_data       = cudf::detail::make_std_vector_async(
+      cudf::device_span<std::byte const>{_d_buffer.data() + offset, count}, stream);
+    stream.synchronize();
+    return std::make_unique<owning_buffer<std::vector<std::byte>>>(std::move(h_data));
+  }
+
+  [[nodiscard]] bool supports_device_read() const override { return true; }
+
+  std::future<size_t> device_read_async(size_t offset,
+                                        size_t size,
+                                        uint8_t* dst,
+                                        rmm::cuda_stream_view stream) override
+  {
+    auto const count = std::min(size, this->size() - offset);
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(dst, _d_buffer.data() + offset, count, cudaMemcpyDefault, stream.value()));
+    return std::async(std::launch::deferred, [count] { return count; });
+  }
+
+  size_t device_read(size_t offset,
+                     size_t size,
+                     uint8_t* dst,
+                     rmm::cuda_stream_view stream) override
+  {
+    return device_read_async(offset, size, dst, stream).get();
+  }
+
+  std::unique_ptr<buffer> device_read(size_t offset,
+                                      size_t size,
+                                      rmm::cuda_stream_view stream) override
+  {
+    return std::make_unique<non_owning_buffer>(
+      reinterpret_cast<uint8_t const*>(_d_buffer.data() + offset), size);
+  }
+
+  [[nodiscard]] size_t size() const override { return _d_buffer.size(); }
+
+ private:
+  cudf::device_span<std::byte const> _d_buffer;  ///< A non-owning view of the existing device data
+};
+
+/**
+ * @brief Wrapper class for user implemented data sources
+ *
+ * Holds the user-implemented object with a non-owning pointer; The user object is not deleted
+ * when the wrapper object is destroyed.
+ * All API calls are forwarded to the user datasource object.
+ */
+class user_datasource_wrapper : public datasource {
+ public:
+  explicit user_datasource_wrapper(datasource* const source) : source(source) {}
+
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override
+  {
+    return source->host_read(offset, size, dst);
+  }
+
+  std::unique_ptr<buffer> host_read(size_t offset, size_t size) override
+  {
+    return source->host_read(offset, size);
+  }
+
+  [[nodiscard]] bool supports_device_read() const override
+  {
+    return source->supports_device_read();
+  }
+
+  size_t device_read(size_t offset,
+                     size_t size,
+                     uint8_t* dst,
+                     rmm::cuda_stream_view stream) override
+  {
+    return source->device_read(offset, size, dst, stream);
+  }
+
+  std::unique_ptr<buffer> device_read(size_t offset,
+                                      size_t size,
+                                      rmm::cuda_stream_view stream) override
+  {
+    return source->device_read(offset, size, stream);
+  }
+
+  [[nodiscard]] size_t size() const override { return source->size(); }
+
+ private:
+  datasource* const source;  ///< A non-owning pointer to the user-implemented datasource
+};
+
+}  // namespace
+
+std::unique_ptr<datasource> datasource::create(std::string const& filepath,
+                                               size_t offset,
+                                               size_t size)
+{
+#ifdef CUFILE_FOUND
+  if (detail::cufile_integration::is_always_enabled()) {
+    // avoid mmap as GDS is expected to be used for most reads
+    return std::make_unique<direct_read_source>(filepath.c_str());
+  }
+#endif
+  // Use our own memory mapping implementation for direct file reads
+  return std::make_unique<memory_mapped_source>(filepath.c_str(), offset, size);
+}
+
+std::unique_ptr<datasource> datasource::create(host_buffer const& buffer)
+{
+  return create(
+    cudf::host_span<std::byte const>{reinterpret_cast<std::byte const*>(buffer.data), buffer.size});
+}
+
+std::unique_ptr<datasource> datasource::create(cudf::host_span<std::byte const> buffer)
+{
+  // Use Arrow IO buffer class for zero-copy reads of host memory
+  return std::make_unique<arrow_io_source>(std::make_shared<arrow::io::BufferReader>(
+    reinterpret_cast<uint8_t const*>(buffer.data()), buffer.size()));
+}
+
+std::unique_ptr<datasource> datasource::create(cudf::device_span<std::byte const> buffer)
+{
+  return std::make_unique<device_buffer_source>(buffer);
+}
+
+std::unique_ptr<datasource> datasource::create(datasource* source)
+{
+  // instantiate a wrapper that forwards the calls to the user implementation
+  return std::make_unique<user_datasource_wrapper>(source);
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/file_io_utilities.cpp b/cpp/src/io/utilities/file_io_utilities.cpp
new file mode 100644
index 0000000..28eae8b
--- /dev/null
+++ b/cpp/src/io/utilities/file_io_utilities.cpp
@@ -0,0 +1,348 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "file_io_utilities.hpp"
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <io/utilities/config_utils.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <dlfcn.h>
+
+#include <fstream>
+#include <numeric>
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+size_t get_file_size(int file_descriptor)
+{
+  struct stat st;
+  CUDF_EXPECTS(fstat(file_descriptor, &st) != -1, "Cannot query file size");
+  return static_cast<size_t>(st.st_size);
+}
+
+file_wrapper::file_wrapper(std::string const& filepath, int flags)
+  : fd(open(filepath.c_str(), flags)), _size{get_file_size(fd)}
+{
+  CUDF_EXPECTS(fd != -1, "Cannot open file " + filepath);
+}
+
+file_wrapper::file_wrapper(std::string const& filepath, int flags, mode_t mode)
+  : fd(open(filepath.c_str(), flags, mode)), _size{get_file_size(fd)}
+{
+  CUDF_EXPECTS(fd != -1, "Cannot open file " + filepath);
+}
+
+file_wrapper::~file_wrapper() { close(fd); }
+
+#ifdef CUFILE_FOUND
+
+/**
+ * @brief Class that dynamically loads the cuFile library and manages the cuFile driver.
+ */
+class cufile_shim {
+ private:
+  cufile_shim();
+  void modify_cufile_json() const;
+  void load_cufile_lib();
+
+  void* cf_lib                              = nullptr;
+  decltype(cuFileDriverOpen)* driver_open   = nullptr;
+  decltype(cuFileDriverClose)* driver_close = nullptr;
+
+  std::unique_ptr<cudf::logic_error> init_error;
+  auto is_valid() const noexcept { return init_error == nullptr; }
+
+ public:
+  cufile_shim(cufile_shim const&)            = delete;
+  cufile_shim& operator=(cufile_shim const&) = delete;
+
+  static cufile_shim const* instance();
+
+  ~cufile_shim()
+  {
+    if (driver_close != nullptr) driver_close();
+    if (cf_lib != nullptr) dlclose(cf_lib);
+  }
+
+  decltype(cuFileHandleRegister)* handle_register     = nullptr;
+  decltype(cuFileHandleDeregister)* handle_deregister = nullptr;
+  decltype(cuFileRead)* read                          = nullptr;
+  decltype(cuFileWrite)* write                        = nullptr;
+};
+
+void cufile_shim::modify_cufile_json() const
+{
+  std::string const json_path_env_var = "CUFILE_ENV_PATH_JSON";
+  static temp_directory tmp_config_dir{"cudf_cufile_config"};
+
+  // Modify the config file based on the policy
+  auto const config_file_path = getenv_or<std::string>(json_path_env_var, "/etc/cufile.json");
+  std::ifstream user_config_file(config_file_path);
+  // Modified config file is stored in a temporary directory
+  auto const cudf_config_path = tmp_config_dir.path() + "cufile.json";
+  std::ofstream cudf_config_file(cudf_config_path);
+
+  std::string line;
+  while (std::getline(user_config_file, line)) {
+    std::string const tag = "\"allow_compat_mode\"";
+    if (line.find(tag) != std::string::npos) {
+      // TODO: only replace the true/false value instead of replacing the whole line
+      // Enable compatibility mode when cuDF does not fall back to host path
+      cudf_config_file << tag << ": "
+                       << (cufile_integration::is_always_enabled() ? "true" : "false") << ",\n";
+    } else {
+      cudf_config_file << line << '\n';
+    }
+
+    // Point libcufile to the modified config file
+    CUDF_EXPECTS(setenv(json_path_env_var.c_str(), cudf_config_path.c_str(), 0) == 0,
+                 "Failed to set the cuFile config file environment variable.");
+  }
+}
+
+void cufile_shim::load_cufile_lib()
+{
+  for (auto&& name : {"libcufile.so.0",
+                      // Prior to CUDA 11.7.1, although ABI
+                      // compatibility was maintained, some (at least
+                      // Debian) packages do not have the .0 symlink,
+                      // instead request the exact version.
+                      "libcufile.so.1.3.0" /* 11.7.0 */,
+                      "libcufile.so.1.2.1" /* 11.6.2, 11.6.1 */,
+                      "libcufile.so.1.2.0" /* 11.6.0 */,
+                      "libcufile.so.1.1.1" /* 11.5.1 */,
+                      "libcufile.so.1.1.0" /* 11.5.0 */,
+                      "libcufile.so.1.0.2" /* 11.4.4, 11.4.3, 11.4.2 */,
+                      "libcufile.so.1.0.1" /* 11.4.1 */,
+                      "libcufile.so.1.0.0" /* 11.4.0 */}) {
+    cf_lib = dlopen(name, RTLD_LAZY | RTLD_LOCAL | RTLD_NODELETE);
+    if (cf_lib != nullptr) break;
+  }
+  CUDF_EXPECTS(cf_lib != nullptr, "Failed to load cuFile library");
+  driver_open = reinterpret_cast<decltype(driver_open)>(dlsym(cf_lib, "cuFileDriverOpen"));
+  CUDF_EXPECTS(driver_open != nullptr, "could not find cuFile cuFileDriverOpen symbol");
+  driver_close = reinterpret_cast<decltype(driver_close)>(dlsym(cf_lib, "cuFileDriverClose"));
+  CUDF_EXPECTS(driver_close != nullptr, "could not find cuFile cuFileDriverClose symbol");
+  handle_register =
+    reinterpret_cast<decltype(handle_register)>(dlsym(cf_lib, "cuFileHandleRegister"));
+  CUDF_EXPECTS(handle_register != nullptr, "could not find cuFile cuFileHandleRegister symbol");
+  handle_deregister =
+    reinterpret_cast<decltype(handle_deregister)>(dlsym(cf_lib, "cuFileHandleDeregister"));
+  CUDF_EXPECTS(handle_deregister != nullptr, "could not find cuFile cuFileHandleDeregister symbol");
+  read = reinterpret_cast<decltype(read)>(dlsym(cf_lib, "cuFileRead"));
+  CUDF_EXPECTS(read != nullptr, "could not find cuFile cuFileRead symbol");
+  write = reinterpret_cast<decltype(write)>(dlsym(cf_lib, "cuFileWrite"));
+  CUDF_EXPECTS(write != nullptr, "could not find cuFile cuFileWrite symbol");
+}
+
+cufile_shim::cufile_shim()
+{
+  try {
+    modify_cufile_json();
+    load_cufile_lib();
+
+    CUDF_EXPECTS(driver_open().err == CU_FILE_SUCCESS, "Failed to initialize cuFile driver");
+  } catch (cudf::logic_error const& err) {
+    init_error = std::make_unique<cudf::logic_error>(err);
+  }
+}
+
+cufile_shim const* cufile_shim::instance()
+{
+  static cufile_shim _instance;
+  // Defer throwing to avoid repeated attempts to load the library
+  if (!_instance.is_valid()) CUDF_FAIL("" + std::string(_instance.init_error->what()));
+
+  return &_instance;
+}
+
+void cufile_registered_file::register_handle()
+{
+  CUfileDescr_t cufile_desc{};
+  cufile_desc.handle.fd = _file.desc();
+  cufile_desc.type      = CU_FILE_HANDLE_TYPE_OPAQUE_FD;
+  CUDF_EXPECTS(shim->handle_register(&cf_handle, &cufile_desc).err == CU_FILE_SUCCESS,
+               "Cannot register file handle with cuFile");
+}
+
+cufile_registered_file::~cufile_registered_file() { shim->handle_deregister(cf_handle); }
+
+cufile_input_impl::cufile_input_impl(std::string const& filepath)
+  : shim{cufile_shim::instance()},
+    cf_file(shim, filepath, O_RDONLY | O_DIRECT),
+    // The benefit from multithreaded read plateaus around 16 threads
+    pool(getenv_or("LIBCUDF_CUFILE_THREAD_COUNT", 16))
+{
+  pool.sleep_duration = 10;
+}
+
+namespace {
+
+template <typename DataT,
+          typename F,
+          typename ResultT = std::invoke_result_t<F, DataT*, size_t, size_t>>
+std::vector<std::future<ResultT>> make_sliced_tasks(
+  F function, DataT* ptr, size_t offset, size_t size, cudf::detail::thread_pool& pool)
+{
+  constexpr size_t default_max_slice_size = 4 * 1024 * 1024;
+  static auto const max_slice_size = getenv_or("LIBCUDF_CUFILE_SLICE_SIZE", default_max_slice_size);
+  auto const slices                = make_file_io_slices(size, max_slice_size);
+  std::vector<std::future<ResultT>> slice_tasks;
+  std::transform(slices.cbegin(), slices.cend(), std::back_inserter(slice_tasks), [&](auto& slice) {
+    return pool.submit(function, ptr + slice.offset, slice.size, offset + slice.offset);
+  });
+  return slice_tasks;
+}
+
+}  // namespace
+
+std::future<size_t> cufile_input_impl::read_async(size_t offset,
+                                                  size_t size,
+                                                  uint8_t* dst,
+                                                  rmm::cuda_stream_view stream)
+{
+  int device;
+  CUDF_CUDA_TRY(cudaGetDevice(&device));
+
+  auto read_slice = [device, gds_read = shim->read, file_handle = cf_file.handle()](
+                      void* dst, size_t size, size_t offset) -> ssize_t {
+    CUDF_CUDA_TRY(cudaSetDevice(device));
+    auto read_size = gds_read(file_handle, dst, size, offset, 0);
+    CUDF_EXPECTS(read_size != -1, "cuFile error reading from a file");
+    return read_size;
+  };
+
+  auto slice_tasks = make_sliced_tasks(read_slice, dst, offset, size, pool);
+
+  auto waiter = [](auto slice_tasks) -> size_t {
+    return std::accumulate(slice_tasks.begin(), slice_tasks.end(), 0, [](auto sum, auto& task) {
+      return sum + task.get();
+    });
+  };
+  // The future returned from this function is deferred, not async because we want to avoid creating
+  // threads for each read_async call. This overhead is significant in case of multiple small reads.
+  return std::async(std::launch::deferred, waiter, std::move(slice_tasks));
+}
+
+cufile_output_impl::cufile_output_impl(std::string const& filepath)
+  : shim{cufile_shim::instance()},
+    cf_file(shim, filepath, O_CREAT | O_RDWR | O_DIRECT, 0664),
+    pool(getenv_or("LIBCUDF_CUFILE_THREAD_COUNT", 16))
+{
+}
+
+std::future<void> cufile_output_impl::write_async(void const* data, size_t offset, size_t size)
+{
+  int device;
+  CUDF_CUDA_TRY(cudaGetDevice(&device));
+
+  auto write_slice = [device, gds_write = shim->write, file_handle = cf_file.handle()](
+                       void const* src, size_t size, size_t offset) -> void {
+    CUDF_CUDA_TRY(cudaSetDevice(device));
+    auto write_size = gds_write(file_handle, src, size, offset, 0);
+    CUDF_EXPECTS(write_size != -1 and write_size == static_cast<decltype(write_size)>(size),
+                 "cuFile error writing to a file");
+  };
+
+  auto source      = static_cast<uint8_t const*>(data);
+  auto slice_tasks = make_sliced_tasks(write_slice, source, offset, size, pool);
+
+  auto waiter = [](auto slice_tasks) -> void {
+    for (auto const& task : slice_tasks) {
+      task.wait();
+    }
+  };
+  // The future returned from this function is deferred, not async because we want to avoid creating
+  // threads for each write_async call. This overhead is significant in case of multiple small
+  // writes.
+  return std::async(std::launch::deferred, waiter, std::move(slice_tasks));
+}
+#else
+cufile_input_impl::cufile_input_impl(std::string const& filepath)
+{
+  CUDF_FAIL("Cannot create cuFile source, current build was compiled without cuFile headers");
+}
+
+cufile_output_impl::cufile_output_impl(std::string const& filepath)
+{
+  CUDF_FAIL("Cannot create cuFile sink, current build was compiled without cuFile headers");
+}
+#endif
+
+std::unique_ptr<cufile_input_impl> make_cufile_input(std::string const& filepath)
+{
+  if (cufile_integration::is_gds_enabled()) {
+    try {
+      auto cufile_in = std::make_unique<cufile_input_impl>(filepath);
+      CUDF_LOG_INFO("File successfully opened for reading with GDS.");
+      return cufile_in;
+    } catch (...) {
+      if (cufile_integration::is_always_enabled()) {
+        CUDF_LOG_ERROR(
+          "Failed to open file for reading with GDS. Enable bounce buffer fallback to read this "
+          "file.");
+        throw;
+      }
+      CUDF_LOG_INFO(
+        "Failed to open file for reading with GDS. Data will be read from the file using a bounce "
+        "buffer (possible performance impact).");
+    }
+  }
+  return {};
+}
+
+std::unique_ptr<cufile_output_impl> make_cufile_output(std::string const& filepath)
+{
+  if (cufile_integration::is_gds_enabled()) {
+    try {
+      auto cufile_out = std::make_unique<cufile_output_impl>(filepath);
+      CUDF_LOG_INFO("File successfully opened for writing with GDS.");
+      return cufile_out;
+    } catch (...) {
+      if (cufile_integration::is_always_enabled()) {
+        CUDF_LOG_ERROR(
+          "Failed to open file for writing with GDS. Enable bounce buffer fallback to write to "
+          "this file.");
+        throw;
+      }
+      CUDF_LOG_INFO(
+        "Failed to open file for writing with GDS. Data will be written to the file using a bounce "
+        "buffer (possible performance impact).");
+    }
+  }
+  return {};
+}
+
+std::vector<file_io_slice> make_file_io_slices(size_t size, size_t max_slice_size)
+{
+  max_slice_size      = std::max(1024ul, max_slice_size);
+  auto const n_slices = util::div_rounding_up_safe(size, max_slice_size);
+  std::vector<file_io_slice> slices;
+  slices.reserve(n_slices);
+  std::generate_n(std::back_inserter(slices), n_slices, [&, idx = 0]() mutable {
+    auto const slice_offset = idx++ * max_slice_size;
+    auto const slice_size   = std::min(size - slice_offset, max_slice_size);
+    return file_io_slice{slice_offset, slice_size};
+  });
+
+  return slices;
+}
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/file_io_utilities.hpp b/cpp/src/io/utilities/file_io_utilities.hpp
new file mode 100644
index 0000000..b55dd3b
--- /dev/null
+++ b/cpp/src/io/utilities/file_io_utilities.hpp
@@ -0,0 +1,224 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#ifdef CUFILE_FOUND
+#include "thread_pool.hpp"
+
+#include <cudf_test/file_utilities.hpp>
+#include <cufile.h>
+#endif
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <string>
+
+namespace cudf {
+namespace io {
+namespace detail {
+
+/**
+ * @brief Class that provides RAII for file handling.
+ */
+class file_wrapper {
+  int fd = -1;
+  size_t _size;
+
+ public:
+  explicit file_wrapper(std::string const& filepath, int flags);
+  explicit file_wrapper(std::string const& filepath, int flags, mode_t mode);
+  ~file_wrapper();
+  [[nodiscard]] auto size() const { return _size; }
+  [[nodiscard]] auto desc() const { return fd; }
+};
+
+/**
+ * @brief Interface class for cufile input.
+ */
+class cufile_input {
+ public:
+  /**
+   * @brief Asynchronously reads into existing device memory.
+   *
+   *  @throws cudf::logic_error on cuFile error
+   *
+   * @param offset Number of bytes from the start
+   * @param size Number of bytes to read
+   * @param dst Address of the existing device memory
+   * @param stream CUDA stream to use
+   *
+   * @return The number of bytes read as an std::future
+   */
+  virtual std::future<size_t> read_async(size_t offset,
+                                         size_t size,
+                                         uint8_t* dst,
+                                         rmm::cuda_stream_view stream) = 0;
+};
+
+/**
+ * @brief Interface class for cufile output.
+ */
+class cufile_output {
+ public:
+  /**
+   * @brief Asynchronously writes the data from a device buffer into a file.
+   *
+   * It is the caller's responsibility to not invalidate `data` until the result from this function
+   * is synchronized.
+   *
+   * @throws cudf::logic_error on cuFile error
+   *
+   * @param data Pointer to the buffer to be written into the output file
+   * @param offset Number of bytes from the start
+   * @param size Number of bytes to write
+   */
+  virtual std::future<void> write_async(void const* data, size_t offset, size_t size) = 0;
+};
+
+#ifdef CUFILE_FOUND
+
+class cufile_shim;
+
+/**
+ * @brief Class that provides RAII for cuFile file registration.
+ */
+struct cufile_registered_file {
+  void register_handle();
+
+ public:
+  cufile_registered_file(cufile_shim const* shim, std::string const& filepath, int flags)
+    : _file(filepath, flags), shim{shim}
+  {
+    register_handle();
+  }
+
+  cufile_registered_file(cufile_shim const* shim,
+                         std::string const& filepath,
+                         int flags,
+                         mode_t mode)
+    : _file(filepath, flags, mode), shim{shim}
+  {
+    register_handle();
+  }
+
+  [[nodiscard]] auto const& handle() const noexcept { return cf_handle; }
+
+  ~cufile_registered_file();
+
+ private:
+  file_wrapper const _file;
+  CUfileHandle_t cf_handle = nullptr;
+  cufile_shim const* shim  = nullptr;
+};
+
+/**
+ * @brief Adapter for the `cuFileRead` API.
+ *
+ * Exposes APIs to read directly from a file into device memory.
+ */
+class cufile_input_impl final : public cufile_input {
+ public:
+  cufile_input_impl(std::string const& filepath);
+
+  std::future<size_t> read_async(size_t offset,
+                                 size_t size,
+                                 uint8_t* dst,
+                                 rmm::cuda_stream_view stream) override;
+
+ private:
+  cufile_shim const* shim = nullptr;
+  cufile_registered_file const cf_file;
+  cudf::detail::thread_pool pool;
+};
+
+/**
+ * @brief Adapter for the `cuFileWrite` API.
+ *
+ * Exposes an API to write directly into a file from device memory.
+ */
+class cufile_output_impl final : public cufile_output {
+ public:
+  cufile_output_impl(std::string const& filepath);
+
+  std::future<void> write_async(void const* data, size_t offset, size_t size) override;
+
+ private:
+  cufile_shim const* shim = nullptr;
+  cufile_registered_file const cf_file;
+  cudf::detail::thread_pool pool;
+};
+#else
+
+class cufile_input_impl final : public cufile_input {
+ public:
+  cufile_input_impl(std::string const& filepath);
+  std::future<size_t> read_async(size_t offset,
+                                 size_t size,
+                                 uint8_t* dst,
+                                 rmm::cuda_stream_view stream) override
+  {
+    CUDF_FAIL("Only used to compile without cufile library, should not be called");
+  }
+};
+
+class cufile_output_impl final : public cufile_output {
+ public:
+  cufile_output_impl(std::string const& filepath);
+  std::future<void> write_async(void const* data, size_t offset, size_t size) override
+  {
+    CUDF_FAIL("Only used to compile without cufile library, should not be called");
+  }
+};
+#endif
+
+/**
+ * @brief Creates a `cufile_input_impl` object
+ *
+ * Returns a null pointer if an exception occurs in the `cufile_input_impl` constructor, or if the
+ * cuFile library is not installed.
+ */
+std::unique_ptr<cufile_input_impl> make_cufile_input(std::string const& filepath);
+
+/**
+ * @brief Creates a `cufile_output_impl` object
+ *
+ * Returns a null pointer if an exception occurs in the `cufile_output_impl` constructor, or if the
+ * cuFile library is not installed.
+ */
+std::unique_ptr<cufile_output_impl> make_cufile_output(std::string const& filepath);
+
+/**
+ * @brief Byte range to be read/written in a single operation.
+ */
+struct file_io_slice {
+  size_t offset;
+  size_t size;
+};
+
+/**
+ * @brief Split the total number of bytes to read/write into slices to enable parallel IO.
+ *
+ * If `max_slice_size` is below 1024, 1024 will be used instead to prevent potential misuse.
+ */
+std::vector<file_io_slice> make_file_io_slices(size_t size, size_t max_slice_size);
+
+}  // namespace detail
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/hostdevice_span.hpp b/cpp/src/io/utilities/hostdevice_span.hpp
new file mode 100644
index 0000000..539e8e8
--- /dev/null
+++ b/cpp/src/io/utilities/hostdevice_span.hpp
@@ -0,0 +1,179 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::detail {
+
+template <typename T>
+class hostdevice_span {
+ public:
+  using value_type = T;
+
+  hostdevice_span()                       = default;
+  ~hostdevice_span()                      = default;
+  hostdevice_span(hostdevice_span const&) = default;  ///< Copy constructor
+  hostdevice_span(hostdevice_span&&)      = default;  ///< Move constructor
+
+  hostdevice_span(T* cpu_data, T* gpu_data, size_t size)
+    : _size(size), _host_data(cpu_data), _device_data(gpu_data)
+  {
+  }
+
+  /**
+   * @brief Copy assignment operator.
+   *
+   * @return Reference to this hostdevice_span.
+   */
+  constexpr hostdevice_span& operator=(hostdevice_span const&) noexcept = default;
+
+  /**
+   * @brief Converts a hostdevice view into a device span.
+   *
+   * @tparam T The device span type.
+   * @return A typed device span of the hostdevice view's data.
+   */
+  [[nodiscard]] operator cudf::device_span<T>() const
+  {
+    return cudf::device_span(_device_data, size());
+  }
+
+  /**
+   * @brief Returns the underlying device data.
+   *
+   * @tparam T The type to cast to
+   * @return T const* Typed pointer to underlying data
+   */
+  [[nodiscard]] T* device_ptr(size_t offset = 0) const noexcept { return _device_data + offset; }
+
+  /**
+   * @brief Return first element in device data.
+   *
+   * @tparam T The desired type
+   * @return T const* Pointer to the first element
+   */
+  [[nodiscard]] T* device_begin() const noexcept { return device_ptr(); }
+
+  /**
+   * @brief Return one past the last element in device_data.
+   *
+   * @tparam T The desired type
+   * @return T const* Pointer to one past the last element
+   */
+  [[nodiscard]] T* device_end() const noexcept { return device_begin() + size(); }
+
+  /**
+   * @brief Converts a hostdevice_span into a host span.
+   *
+   * @tparam T The host span type.
+   * @return A typed host span of the hostdevice_span's data.
+   */
+  [[nodiscard]] operator cudf::host_span<T>() const noexcept
+  {
+    return cudf::host_span<T>(_host_data, size());
+  }
+
+  /**
+   * @brief Returns the underlying host data.
+   *
+   * @tparam T The type to cast to
+   * @return T* Typed pointer to underlying data
+   */
+  [[nodiscard]] T* host_ptr(size_t offset = 0) const noexcept { return _host_data + offset; }
+
+  /**
+   * @brief Return first element in host data.
+   *
+   * @tparam T The desired type
+   * @return T const* Pointer to the first element
+   */
+  [[nodiscard]] T* host_begin() const noexcept { return host_ptr(); }
+
+  /**
+   * @brief Return one past the last elementin host data.
+   *
+   * @tparam T The desired type
+   * @return T const* Pointer to one past the last element
+   */
+  [[nodiscard]] T* host_end() const noexcept { return host_begin() + size(); }
+
+  /**
+   * @brief Returns the number of elements in the view
+   *
+   * @return The number of elements in the view
+   */
+  [[nodiscard]] std::size_t size() const noexcept { return _size; }
+
+  /**
+   * @brief Returns true if `size()` returns zero, or false otherwise
+   *
+   * @return True if `size()` returns zero, or false otherwise
+   */
+  [[nodiscard]] bool is_empty() const noexcept { return size() == 0; }
+
+  [[nodiscard]] size_t size_bytes() const noexcept { return sizeof(T) * size(); }
+
+  [[nodiscard]] T& operator[](size_t i) { return _host_data[i]; }
+  [[nodiscard]] T const& operator[](size_t i) const { return _host_data[i]; }
+
+  /**
+   * @brief Obtains a hostdevice_span that is a view over the `count` elements of this
+   * hostdevice_span starting at offset
+   *
+   * @param offset The offset of the first element in the subspan
+   * @param count The number of elements in the subspan
+   * @return A subspan of the sequence, of requested count and offset
+   */
+  constexpr hostdevice_span<T> subspan(size_t offset, size_t count) const noexcept
+  {
+    return hostdevice_span<T>(_host_data + offset, _device_data + offset, count);
+  }
+
+  void host_to_device_async(rmm::cuda_stream_view stream)
+  {
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(device_ptr(), host_ptr(), size_bytes(), cudaMemcpyDefault, stream.value()));
+  }
+
+  void host_to_device_sync(rmm::cuda_stream_view stream)
+  {
+    host_to_device_async(stream);
+    stream.synchronize();
+  }
+
+  void device_to_host_async(rmm::cuda_stream_view stream)
+  {
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(host_ptr(), device_ptr(), size_bytes(), cudaMemcpyDefault, stream.value()));
+  }
+
+  void device_to_host_sync(rmm::cuda_stream_view stream)
+  {
+    device_to_host_async(stream);
+    stream.synchronize();
+  }
+
+ private:
+  size_t _size{};     ///< Number of elements
+  T* _device_data{};  ///< Pointer to device memory containing elements
+  T* _host_data{};    ///< Pointer to host memory containing elements
+};
+
+}  // namespace cudf::detail
diff --git a/cpp/src/io/utilities/hostdevice_vector.hpp b/cpp/src/io/utilities/hostdevice_vector.hpp
new file mode 100644
index 0000000..a6a93c4
--- /dev/null
+++ b/cpp/src/io/utilities/hostdevice_vector.hpp
@@ -0,0 +1,258 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "config_utils.hpp"
+#include "hostdevice_span.hpp"
+
+#include <cudf/detail/utilities/pinned_host_vector.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/host_vector.h>
+
+#include <variant>
+
+namespace cudf::detail {
+
+inline bool hostdevice_vector_uses_pageable_buffer()
+{
+  static bool const use_pageable =
+    cudf::io::detail::getenv_or("LIBCUDF_IO_PREFER_PAGEABLE_TMP_MEMORY", 0);
+  return use_pageable;
+}
+
+/**
+ * @brief A helper class that wraps fixed-length device memory for the GPU, and
+ * a mirror host pinned memory for the CPU.
+ *
+ * This abstraction allocates a specified fixed chunk of device memory that can
+ * initialized upfront, or gradually initialized as required.
+ * The host-side memory can be used to manipulate data on the CPU before and
+ * after operating on the same data on the GPU.
+ */
+template <typename T>
+class hostdevice_vector {
+ public:
+  using value_type = T;
+
+  hostdevice_vector() : hostdevice_vector(0, cudf::get_default_stream()) {}
+
+  explicit hostdevice_vector(size_t size, rmm::cuda_stream_view stream)
+    : hostdevice_vector(size, size, stream)
+  {
+  }
+
+  explicit hostdevice_vector(size_t initial_size, size_t max_size, rmm::cuda_stream_view stream)
+    : d_data(0, stream)
+  {
+    CUDF_EXPECTS(initial_size <= max_size, "initial_size cannot be larger than max_size");
+
+    if (hostdevice_vector_uses_pageable_buffer()) {
+      h_data_owner = thrust::host_vector<T>();
+    } else {
+      h_data_owner = cudf::detail::pinned_host_vector<T>();
+    }
+
+    std::visit(
+      [&](auto&& v) {
+        v.reserve(max_size);
+        v.resize(initial_size);
+        host_data = v.data();
+      },
+      h_data_owner);
+
+    current_size = initial_size;
+    d_data.resize(max_size, stream);
+  }
+
+  void push_back(T const& data)
+  {
+    CUDF_EXPECTS(size() < capacity(),
+                 "Cannot insert data into hostdevice_vector because capacity has been exceeded.");
+    host_data[current_size++] = data;
+  }
+
+  [[nodiscard]] size_t capacity() const noexcept { return d_data.size(); }
+  [[nodiscard]] size_t size() const noexcept { return current_size; }
+  [[nodiscard]] size_t size_bytes() const noexcept { return sizeof(T) * size(); }
+  [[nodiscard]] bool empty() const noexcept { return size() == 0; }
+
+  [[nodiscard]] T& operator[](size_t i) { return host_data[i]; }
+  [[nodiscard]] T const& operator[](size_t i) const { return host_data[i]; }
+
+  [[nodiscard]] T* host_ptr(size_t offset = 0) { return host_data + offset; }
+  [[nodiscard]] T const* host_ptr(size_t offset = 0) const { return host_data + offset; }
+
+  [[nodiscard]] T* begin() { return host_ptr(); }
+  [[nodiscard]] T const* begin() const { return host_ptr(); }
+
+  [[nodiscard]] T* end() { return host_ptr(size()); }
+  [[nodiscard]] T const* end() const { return host_ptr(size()); }
+
+  [[nodiscard]] T* device_ptr(size_t offset = 0) { return d_data.data() + offset; }
+  [[nodiscard]] T const* device_ptr(size_t offset = 0) const { return d_data.data() + offset; }
+
+  [[nodiscard]] T* d_begin() { return device_ptr(); }
+  [[nodiscard]] T const* d_begin() const { return device_ptr(); }
+
+  [[nodiscard]] T* d_end() { return device_ptr(size()); }
+  [[nodiscard]] T const* d_end() const { return device_ptr(size()); }
+
+  /**
+   * @brief Returns the specified element from device memory
+   *
+   * @note This function incurs a device to host memcpy and should be used sparingly.
+   * @note This function synchronizes `stream`.
+   *
+   * @throws rmm::out_of_range exception if `element_index >= size()`
+   *
+   * @param element_index Index of the desired element
+   * @param stream The stream on which to perform the copy
+   * @return The value of the specified element
+   */
+  [[nodiscard]] T element(std::size_t element_index, rmm::cuda_stream_view stream) const
+  {
+    return d_data.element(element_index, stream);
+  }
+
+  operator cudf::host_span<T>() { return {host_ptr(), size()}; }
+  operator cudf::host_span<T const>() const { return {host_ptr(), size()}; }
+
+  operator cudf::device_span<T>() { return {device_ptr(), size()}; }
+  operator cudf::device_span<T const>() const { return {device_ptr(), size()}; }
+
+  void host_to_device_async(rmm::cuda_stream_view stream)
+  {
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(device_ptr(), host_ptr(), size_bytes(), cudaMemcpyDefault, stream.value()));
+  }
+
+  void host_to_device_sync(rmm::cuda_stream_view stream)
+  {
+    host_to_device_async(stream);
+    stream.synchronize();
+  }
+
+  void device_to_host_async(rmm::cuda_stream_view stream)
+  {
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(host_ptr(), device_ptr(), size_bytes(), cudaMemcpyDefault, stream.value()));
+  }
+
+  void device_to_host_sync(rmm::cuda_stream_view stream)
+  {
+    device_to_host_async(stream);
+    stream.synchronize();
+  }
+
+  /**
+   * @brief Converts a hostdevice_vector into a hostdevice_span.
+   *
+   * @return A typed hostdevice_span of the hostdevice_vector's data
+   */
+  [[nodiscard]] operator hostdevice_span<T>()
+  {
+    return hostdevice_span<T>{host_data, d_data.data(), size()};
+  }
+
+  /**
+   * @brief Converts a part of a hostdevice_vector into a hostdevice_span.
+   *
+   * @param offset The offset of the first element in the subspan
+   * @param count The number of elements in the subspan
+   * @return A typed hostdevice_span of the hostdevice_vector's data
+   */
+  [[nodiscard]] hostdevice_span<T> subspan(size_t offset, size_t count)
+  {
+    CUDF_EXPECTS(offset < d_data.size(), "Offset is out of bounds.");
+    CUDF_EXPECTS(count <= d_data.size() - offset,
+                 "The span with given offset and count is out of bounds.");
+    return hostdevice_span<T>{host_data + offset, d_data.data() + offset, count};
+  }
+
+ private:
+  std::variant<thrust::host_vector<T>, cudf::detail::pinned_host_vector<T>> h_data_owner;
+  T* host_data        = nullptr;
+  size_t current_size = 0;
+  rmm::device_uvector<T> d_data;
+};
+
+/**
+ * @brief Wrapper around hostdevice_vector to enable two-dimensional indexing.
+ *
+ * Does not incur additional allocations.
+ */
+template <typename T>
+class hostdevice_2dvector {
+ public:
+  hostdevice_2dvector(size_t rows, size_t columns, rmm::cuda_stream_view stream)
+    : _size{rows, columns}, _data{rows * columns, stream}
+  {
+  }
+
+  operator device_2dspan<T>() { return {_data.device_ptr(), _size}; }
+  operator device_2dspan<T const>() const { return {_data.device_ptr(), _size}; }
+
+  device_2dspan<T> device_view() { return static_cast<device_2dspan<T>>(*this); }
+  device_2dspan<T> device_view() const { return static_cast<device_2dspan<T const>>(*this); }
+
+  operator host_2dspan<T>() { return {_data.host_ptr(), _size}; }
+  operator host_2dspan<T const>() const { return {_data.host_ptr(), _size}; }
+
+  host_2dspan<T> host_view() { return static_cast<host_2dspan<T>>(*this); }
+  host_2dspan<T> host_view() const { return static_cast<host_2dspan<T const>>(*this); }
+
+  host_span<T> operator[](size_t row)
+  {
+    return {_data.host_ptr() + host_2dspan<T>::flatten_index(row, 0, _size), _size.second};
+  }
+
+  host_span<T const> operator[](size_t row) const
+  {
+    return {_data.host_ptr() + host_2dspan<T>::flatten_index(row, 0, _size), _size.second};
+  }
+
+  auto size() const noexcept { return _size; }
+  auto count() const noexcept { return _size.first * _size.second; }
+  auto is_empty() const noexcept { return count() == 0; }
+
+  T* base_host_ptr(size_t offset = 0) { return _data.host_ptr(offset); }
+  T* base_device_ptr(size_t offset = 0) { return _data.device_ptr(offset); }
+
+  T const* base_host_ptr(size_t offset = 0) const { return _data.host_ptr(offset); }
+
+  T const* base_device_ptr(size_t offset = 0) const { return _data.device_ptr(offset); }
+
+  size_t size_bytes() const noexcept { return _data.size_bytes(); }
+
+  void host_to_device_async(rmm::cuda_stream_view stream) { _data.host_to_device_async(stream); }
+  void host_to_device_sync(rmm::cuda_stream_view stream) { _data.host_to_device_sync(stream); }
+
+  void device_to_host_async(rmm::cuda_stream_view stream) { _data.device_to_host_async(stream); }
+  void device_to_host_sync(rmm::cuda_stream_view stream) { _data.device_to_host_sync(stream); }
+
+ private:
+  hostdevice_vector<T> _data;
+  typename host_2dspan<T>::size_type _size;
+};
+
+}  // namespace cudf::detail
diff --git a/cpp/src/io/utilities/output_builder.cuh b/cpp/src/io/utilities/output_builder.cuh
new file mode 100644
index 0000000..1858912
--- /dev/null
+++ b/cpp/src/io/utilities/output_builder.cuh
@@ -0,0 +1,357 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <thrust/copy.h>
+
+#include <iterator>
+
+namespace cudf {
+
+template <typename T>
+class split_device_span_iterator;
+
+/**
+ * @brief A device span consisting of two separate device_spans acting as if they were part of a
+ * single span. The first head.size() entries are served from the first span, the remaining
+ * tail.size() entries are served from the second span.
+ *
+ * @tparam T The type of elements in the span.
+ */
+template <typename T>
+class split_device_span {
+ public:
+  using element_type    = T;
+  using value_type      = std::remove_cv<T>;
+  using size_type       = std::size_t;
+  using difference_type = std::ptrdiff_t;
+  using pointer         = T*;
+  using iterator        = split_device_span_iterator<T>;
+  using const_pointer   = T const*;
+  using reference       = T&;
+  using const_reference = T const&;
+
+  split_device_span() = default;
+
+  explicit constexpr split_device_span(device_span<T> head, device_span<T> tail = {})
+    : _head{head}, _tail{tail}
+  {
+  }
+
+  [[nodiscard]] constexpr reference operator[](size_type i) const
+  {
+    return i < _head.size() ? _head[i] : _tail[i - _head.size()];
+  }
+
+  [[nodiscard]] constexpr size_type size() const { return _head.size() + _tail.size(); }
+
+  [[nodiscard]] constexpr device_span<T> head() const { return _head; }
+
+  [[nodiscard]] constexpr device_span<T> tail() const { return _tail; }
+
+  [[nodiscard]] constexpr iterator begin() const;
+
+  [[nodiscard]] constexpr iterator end() const;
+
+ private:
+  device_span<T> _head;
+  device_span<T> _tail;
+};
+
+/**
+ * @brief A random access iterator indexing into a split_device_span.
+ *
+ * @tparam T The type of elements in the underlying span.
+ */
+template <typename T>
+class split_device_span_iterator {
+  using it = split_device_span_iterator;
+
+ public:
+  using size_type         = std::size_t;
+  using difference_type   = std::ptrdiff_t;
+  using value_type        = T;
+  using pointer           = value_type*;
+  using reference         = value_type&;
+  using iterator_category = std::random_access_iterator_tag;
+
+  split_device_span_iterator() = default;
+
+  constexpr split_device_span_iterator(split_device_span<T> span, size_type offset)
+    : _span{span}, _offset{offset}
+  {
+  }
+
+  [[nodiscard]] constexpr reference operator*() const { return _span[_offset]; }
+
+  [[nodiscard]] constexpr reference operator[](size_type i) const { return _span[_offset + i]; }
+
+  [[nodiscard]] constexpr friend bool operator==(it const& lhs, it const& rhs)
+  {
+    return lhs._offset == rhs._offset;
+  }
+
+  [[nodiscard]] constexpr friend bool operator!=(it const& lhs, it const& rhs)
+  {
+    return !(lhs == rhs);
+  }
+  [[nodiscard]] constexpr friend bool operator<(it const& lhs, it const& rhs)
+  {
+    return lhs._offset < rhs._offset;
+  }
+
+  [[nodiscard]] constexpr friend bool operator>=(it const& lhs, it const& rhs)
+  {
+    return !(lhs < rhs);
+  }
+
+  [[nodiscard]] constexpr friend bool operator>(it const& lhs, it const& rhs) { return rhs < lhs; }
+
+  [[nodiscard]] constexpr friend bool operator<=(it const& lhs, it const& rhs)
+  {
+    return !(lhs > rhs);
+  }
+
+  [[nodiscard]] constexpr friend difference_type operator-(it const& lhs, it const& rhs)
+  {
+    return lhs._offset - rhs._offset;
+  }
+
+  [[nodiscard]] constexpr friend it operator+(it lhs, difference_type i) { return lhs += i; }
+
+  constexpr it& operator+=(difference_type i)
+  {
+    _offset += i;
+    return *this;
+  }
+
+  constexpr it& operator-=(difference_type i) { return *this += -i; }
+
+  constexpr it& operator++() { return *this += 1; }
+
+  constexpr it& operator--() { return *this -= 1; }
+
+  constexpr it operator++(int)
+  {
+    auto result = *this;
+    ++*this;
+    return result;
+  }
+
+  constexpr it operator--(int)
+  {
+    auto result = *this;
+    --*this;
+    return result;
+  }
+
+ private:
+  split_device_span<T> _span;
+  size_type _offset;
+};
+
+template <typename T>
+[[nodiscard]] constexpr split_device_span_iterator<T> split_device_span<T>::begin() const
+{
+  return {*this, 0};
+}
+
+template <typename T>
+[[nodiscard]] constexpr split_device_span_iterator<T> split_device_span<T>::end() const
+{
+  return {*this, size()};
+}
+
+/**
+ * @brief A chunked storage class that provides preallocated memory for algorithms with known
+ * worst-case output size. It provides functionality to retrieve the next chunk to write to, for
+ * reporting how much memory was actually written and for gathering all previously written outputs
+ * into a single contiguous vector.
+ *
+ * @tparam T The output element type.
+ */
+template <typename T>
+class output_builder {
+ public:
+  using size_type = typename rmm::device_uvector<T>::size_type;
+
+  /**
+   * @brief Initializes an output builder with given worst-case output size and stream.
+   *
+   * @param max_write_size the maximum number of elements that will be written into a
+   *                       split_device_span returned from `next_output`.
+   * @param stream the stream used to allocate the first chunk of memory.
+   * @param mr optional, the memory resource to use for allocation.
+   */
+  output_builder(size_type max_write_size,
+                 size_type max_growth,
+                 rmm::cuda_stream_view stream,
+                 rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+    : _size{0}, _max_write_size{max_write_size}, _max_growth{max_growth}
+  {
+    CUDF_EXPECTS(max_write_size > 0, "Internal error");
+    _chunks.emplace_back(0, stream, mr);
+    _chunks.back().reserve(max_write_size * 2, stream);
+  }
+
+  output_builder(output_builder&&)                 = delete;
+  output_builder(output_builder const&)            = delete;
+  output_builder& operator=(output_builder&&)      = delete;
+  output_builder& operator=(output_builder const&) = delete;
+
+  /**
+   * @brief Returns the next free chunk of `max_write_size` elements from the underlying storage.
+   * Must be followed by a call to `advance_output` after the memory has been written to.
+   *
+   * @param stream The stream to allocate a new chunk of memory with, if necessary.
+   *               This should be the stream that will write to the `split_device_span`.
+   * @return A `split_device_span` starting directly after the last output and providing at least
+   *         `max_write_size` entries of storage.
+   */
+  [[nodiscard]] split_device_span<T> next_output(rmm::cuda_stream_view stream)
+  {
+    auto head_it   = _chunks.end() - (_chunks.size() > 1 and _chunks.back().is_empty() ? 2 : 1);
+    auto head_span = get_free_span(*head_it);
+    if (head_span.size() >= _max_write_size) { return split_device_span<T>{head_span}; }
+    if (head_it == _chunks.end() - 1) {
+      // insert a new device_uvector of double size
+      auto const next_chunk_size =
+        std::min(_max_growth * _max_write_size, 2 * _chunks.back().capacity());
+      _chunks.emplace_back(0, stream, _chunks.back().memory_resource());
+      _chunks.back().reserve(next_chunk_size, stream);
+    }
+    auto tail_span = get_free_span(_chunks.back());
+    CUDF_EXPECTS(head_span.size() + tail_span.size() >= _max_write_size, "Internal error");
+    return split_device_span<T>{head_span, tail_span};
+  }
+
+  /**
+   * @brief Advances the output sizes after a `split_device_span` returned from `next_output` was
+   *        written to.
+   *
+   * @param actual_size The number of elements that were written to the result of the previous
+   *                    `next_output` call.
+   * @param stream The stream on which to resize the vectors. Since this function will not
+   *               reallocate, this only changes the stream of the internally stored vectors,
+   *               impacting their subsequent copy and destruction behavior.
+   */
+  void advance_output(size_type actual_size, rmm::cuda_stream_view stream)
+  {
+    CUDF_EXPECTS(actual_size <= _max_write_size, "Internal error");
+    if (_chunks.size() < 2) {
+      auto const new_size = _chunks.back().size() + actual_size;
+      inplace_resize(_chunks.back(), new_size, stream);
+    } else {
+      auto& tail              = _chunks.back();
+      auto& prev              = _chunks.rbegin()[1];
+      auto const prev_advance = std::min(actual_size, prev.capacity() - prev.size());
+      auto const tail_advance = actual_size - prev_advance;
+      inplace_resize(prev, prev.size() + prev_advance, stream);
+      inplace_resize(tail, tail.size() + tail_advance, stream);
+    }
+    _size += actual_size;
+  }
+
+  /**
+   * @brief Returns the first element that was written to the output.
+   *        Requires a previous call to `next_output` and `advance_output` and `size() > 0`.
+   * @param stream The stream used to access the element.
+   * @return The first element that was written to the output.
+   */
+  [[nodiscard]] T front_element(rmm::cuda_stream_view stream) const
+  {
+    return _chunks.front().front_element(stream);
+  }
+
+  /**
+   * @brief Returns the last element that was written to the output.
+   *        Requires a previous call to `next_output` and `advance_output` and `size() > 0`.
+   * @param stream The stream used to access the element.
+   * @return The last element that was written to the output.
+   */
+  [[nodiscard]] T back_element(rmm::cuda_stream_view stream) const
+  {
+    auto const& last_nonempty_chunk =
+      _chunks.size() > 1 and _chunks.back().is_empty() ? _chunks.rbegin()[1] : _chunks.back();
+    return last_nonempty_chunk.back_element(stream);
+  }
+
+  [[nodiscard]] size_type size() const { return _size; }
+
+  /**
+   * @brief Gathers all previously written outputs into a single contiguous vector.
+   *
+   * @param stream The stream used to allocate and gather the output vector. All previous write
+   *               operations to the output buffer must have finished or happened on this stream.
+   * @param mr The memory resource used to allocate the output vector.
+   * @return The output vector.
+   */
+  rmm::device_uvector<T> gather(rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr) const
+  {
+    rmm::device_uvector<T> output{size(), stream, mr};
+    auto output_it = output.begin();
+    for (auto const& chunk : _chunks) {
+      output_it = thrust::copy(
+        rmm::exec_policy_nosync(stream), chunk.begin(), chunk.begin() + chunk.size(), output_it);
+    }
+    return output;
+  }
+
+ private:
+  /**
+   * @brief Resizes a vector without reallocating
+   *
+   * @param vector The vector
+   * @param new_size The new size. Must be smaller than the vector's capacity
+   * @param stream The stream on which to resize the vector. Since this function will not
+   *               reallocate, this only changes the stream of `vector`, impacting its subsequent
+   *               copy and destruction behavior.
+   */
+  static void inplace_resize(rmm::device_uvector<T>& vector,
+                             size_type new_size,
+                             rmm::cuda_stream_view stream)
+  {
+    CUDF_EXPECTS(new_size <= vector.capacity(), "Internal error");
+    vector.resize(new_size, stream);
+  }
+
+  /**
+   * @brief Returns the span consisting of all currently unused elements in the vector
+   *        (`i >= size() and i < capacity()`).
+   *
+   * @param vector The vector.
+   * @return The span of unused elements.
+   */
+  static device_span<T> get_free_span(rmm::device_uvector<T>& vector)
+  {
+    return device_span<T>{vector.data() + vector.size(), vector.capacity() - vector.size()};
+  }
+
+  size_type _size;
+  size_type _max_write_size;
+  size_type _max_growth;
+  std::vector<rmm::device_uvector<T>> _chunks;
+};
+
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/parsing_utils.cu b/cpp/src/io/utilities/parsing_utils.cu
new file mode 100644
index 0000000..06b86f3
--- /dev/null
+++ b/cpp/src/io/utilities/parsing_utils.cu
@@ -0,0 +1,221 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/pair.h>
+
+#include <rmm/device_buffer.hpp>
+
+#include <algorithm>
+
+namespace cudf {
+namespace io {
+namespace {
+// When processing the input in chunks, this is the maximum size of each chunk.
+// Only one chunk is loaded on the GPU at a time, so this value is chosen to
+// be small enough to fit on the GPU in most cases.
+constexpr size_t max_chunk_bytes = 256 * 1024 * 1024;  // 256MB
+
+constexpr int bytes_per_find_thread = 64;
+
+using pos_key_pair = thrust::pair<uint64_t, char>;
+
+template <typename T>
+constexpr T divCeil(T dividend, T divisor) noexcept
+{
+  return (dividend + divisor - 1) / divisor;
+}
+
+/**
+ * @brief Sets the specified element of the array to the passed value
+ */
+template <class T, class V>
+__device__ __forceinline__ void setElement(T* array, cudf::size_type idx, T const& t, V const&)
+{
+  array[idx] = t;
+}
+
+/**
+ * @brief Sets the specified element of the array of pairs using the two passed
+ * parameters.
+ */
+template <class T, class V>
+__device__ __forceinline__ void setElement(thrust::pair<T, V>* array,
+                                           cudf::size_type idx,
+                                           T const& t,
+                                           V const& v)
+{
+  array[idx] = {t, v};
+}
+
+/**
+ * @brief Overloads the setElement() functions for void* arrays.
+ * Does not do anything, indexing is not allowed with void* arrays.
+ */
+template <class T, class V>
+__device__ __forceinline__ void setElement(void*, cudf::size_type, T const&, V const&)
+{
+}
+
+/**
+ * @brief CUDA kernel that finds all occurrences of a character in the given
+ * character array. If the 'positions' parameter is not void*,
+ * positions of all occurrences are stored in the output array.
+ *
+ * @param[in] data Pointer to the input character array
+ * @param[in] size Number of bytes in the input array
+ * @param[in] offset Offset to add to the output positions
+ * @param[in] key Character to find in the array
+ * @param[in,out] count Pointer to the number of found occurrences
+ * @param[out] positions Array containing the output positions
+ */
+template <class T>
+__global__ void count_and_set_positions(char const* data,
+                                        uint64_t size,
+                                        uint64_t offset,
+                                        char const key,
+                                        cudf::size_type* count,
+                                        T* positions)
+{
+  // thread IDs range per block, so also need the block id
+  auto const tid = cudf::detail::grid_1d::global_thread_id();
+  auto const did = tid * bytes_per_find_thread;
+
+  char const* raw = (data + did);
+
+  long const byteToProcess =
+    ((did + bytes_per_find_thread) < size) ? bytes_per_find_thread : (size - did);
+
+  // Process the data
+  for (long i = 0; i < byteToProcess; i++) {
+    if (raw[i] == key) {
+      auto const idx = atomicAdd(count, (cudf::size_type)1);
+      setElement(positions, idx, did + offset + i, key);
+    }
+  }
+}
+
+}  // namespace
+
+template <class T>
+cudf::size_type find_all_from_set(device_span<char const> data,
+                                  std::vector<char> const& keys,
+                                  uint64_t result_offset,
+                                  T* positions,
+                                  rmm::cuda_stream_view stream)
+{
+  int block_size    = 0;  // suggested thread count to use
+  int min_grid_size = 0;  // minimum block count required
+  CUDF_CUDA_TRY(
+    cudaOccupancyMaxPotentialBlockSize(&min_grid_size, &block_size, count_and_set_positions<T>));
+  int const grid_size = divCeil(data.size(), (size_t)block_size);
+
+  auto d_count = cudf::detail::make_zeroed_device_uvector_async<cudf::size_type>(
+    1, stream, rmm::mr::get_current_device_resource());
+  for (char key : keys) {
+    count_and_set_positions<T><<<grid_size, block_size, 0, stream.value()>>>(
+      data.data(), data.size(), result_offset, key, d_count.data(), positions);
+  }
+
+  return cudf::detail::make_std_vector_sync(d_count, stream)[0];
+}
+
+template <class T>
+cudf::size_type find_all_from_set(host_span<char const> data,
+                                  std::vector<char> const& keys,
+                                  uint64_t result_offset,
+                                  T* positions,
+                                  rmm::cuda_stream_view stream)
+{
+  rmm::device_buffer d_chunk(std::min(max_chunk_bytes, data.size()), stream);
+  auto d_count = cudf::detail::make_zeroed_device_uvector_async<cudf::size_type>(
+    1, stream, rmm::mr::get_current_device_resource());
+
+  int block_size    = 0;  // suggested thread count to use
+  int min_grid_size = 0;  // minimum block count required
+  CUDF_CUDA_TRY(
+    cudaOccupancyMaxPotentialBlockSize(&min_grid_size, &block_size, count_and_set_positions<T>));
+
+  size_t const chunk_count = divCeil(data.size(), max_chunk_bytes);
+  for (size_t ci = 0; ci < chunk_count; ++ci) {
+    auto const chunk_offset = ci * max_chunk_bytes;
+    auto const h_chunk      = data.data() + chunk_offset;
+    int const chunk_bytes = std::min((size_t)(data.size() - ci * max_chunk_bytes), max_chunk_bytes);
+    auto const chunk_bits = divCeil(chunk_bytes, bytes_per_find_thread);
+    int const grid_size   = divCeil(chunk_bits, block_size);
+
+    // Copy chunk to device
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(d_chunk.data(), h_chunk, chunk_bytes, cudaMemcpyDefault, stream.value()));
+
+    for (char key : keys) {
+      count_and_set_positions<T>
+        <<<grid_size, block_size, 0, stream.value()>>>(static_cast<char*>(d_chunk.data()),
+                                                       chunk_bytes,
+                                                       chunk_offset + result_offset,
+                                                       key,
+                                                       d_count.data(),
+                                                       positions);
+    }
+  }
+
+  return cudf::detail::make_std_vector_sync(d_count, stream)[0];
+}
+
+template cudf::size_type find_all_from_set<uint64_t>(device_span<char const> data,
+                                                     std::vector<char> const& keys,
+                                                     uint64_t result_offset,
+                                                     uint64_t* positions,
+                                                     rmm::cuda_stream_view stream);
+
+template cudf::size_type find_all_from_set<pos_key_pair>(device_span<char const> data,
+                                                         std::vector<char> const& keys,
+                                                         uint64_t result_offset,
+                                                         pos_key_pair* positions,
+                                                         rmm::cuda_stream_view stream);
+
+template cudf::size_type find_all_from_set<uint64_t>(host_span<char const> data,
+                                                     std::vector<char> const& keys,
+                                                     uint64_t result_offset,
+                                                     uint64_t* positions,
+                                                     rmm::cuda_stream_view stream);
+
+template cudf::size_type find_all_from_set<pos_key_pair>(host_span<char const> data,
+                                                         std::vector<char> const& keys,
+                                                         uint64_t result_offset,
+                                                         pos_key_pair* positions,
+                                                         rmm::cuda_stream_view stream);
+
+cudf::size_type count_all_from_set(device_span<char const> data,
+                                   std::vector<char> const& keys,
+                                   rmm::cuda_stream_view stream)
+{
+  return find_all_from_set<void>(data, keys, 0, nullptr, stream);
+}
+
+cudf::size_type count_all_from_set(host_span<char const> data,
+                                   std::vector<char> const& keys,
+                                   rmm::cuda_stream_view stream)
+{
+  return find_all_from_set<void>(data, keys, 0, nullptr, stream);
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/parsing_utils.cuh b/cpp/src/io/utilities/parsing_utils.cuh
new file mode 100644
index 0000000..43d62fc
--- /dev/null
+++ b/cpp/src/io/utilities/parsing_utils.cuh
@@ -0,0 +1,716 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <io/csv/datetime.cuh>
+#include <io/utilities/trie.cuh>
+
+#include <cudf/io/types.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/strings/detail/convert/fixed_point.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include "column_type_histogram.hpp"
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/mismatch.h>
+
+#include <optional>
+
+using cudf::device_span;
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Non-owning view for json type inference options
+ */
+struct json_inference_options_view {
+  char quote_char;
+  cudf::detail::trie_view trie_true;
+  cudf::detail::trie_view trie_false;
+  cudf::detail::trie_view trie_na;
+};
+
+/**
+ * @brief Structure for holding various options used when parsing and
+ * converting CSV/json data to cuDF data type values.
+ */
+struct parse_options_view {
+  char delimiter;
+  char terminator;
+  char quotechar;
+  char decimal;
+  char thousands;
+  char comment;
+  bool keepquotes;
+  bool doublequote;
+  bool dayfirst;
+  bool skipblanklines;
+  cudf::detail::trie_view trie_true;
+  cudf::detail::trie_view trie_false;
+  cudf::detail::trie_view trie_na;
+  bool multi_delimiter;
+};
+
+struct parse_options {
+  char delimiter;
+  char terminator;
+  char quotechar;
+  char decimal;
+  char thousands;
+  char comment;
+  bool keepquotes;
+  bool doublequote;
+  bool dayfirst;
+  bool skipblanklines;
+  cudf::detail::optional_trie trie_true;
+  cudf::detail::optional_trie trie_false;
+  cudf::detail::optional_trie trie_na;
+  bool multi_delimiter;
+
+  [[nodiscard]] json_inference_options_view json_view() const
+  {
+    return {quotechar,
+            cudf::detail::make_trie_view(trie_true),
+            cudf::detail::make_trie_view(trie_false),
+            cudf::detail::make_trie_view(trie_na)};
+  }
+
+  [[nodiscard]] parse_options_view view() const
+  {
+    return {delimiter,
+            terminator,
+            quotechar,
+            decimal,
+            thousands,
+            comment,
+            keepquotes,
+            doublequote,
+            dayfirst,
+            skipblanklines,
+            cudf::detail::make_trie_view(trie_true),
+            cudf::detail::make_trie_view(trie_false),
+            cudf::detail::make_trie_view(trie_na),
+            multi_delimiter};
+  }
+};
+
+/**
+ * @brief Returns the escaped characters for a given character.
+ *
+ * @param escaped_char The character to escape.
+ * @return The escaped characters for a given character.
+ */
+__device__ __forceinline__ thrust::pair<char, char> get_escaped_char(char escaped_char)
+{
+  switch (escaped_char) {
+    case '"': return {'\\', '"'};
+    case '\\': return {'\\', '\\'};
+    case '/': return {'\\', '/'};
+    case '\b': return {'\\', 'b'};
+    case '\f': return {'\\', 'f'};
+    case '\n': return {'\\', 'n'};
+    case '\r': return {'\\', 'r'};
+    case '\t': return {'\\', 't'};
+    // case 'u': return UNICODE_SEQ;
+    default: return {'\0', escaped_char};
+  }
+}
+
+/**
+ * @brief Returns the numeric value of an ASCII/UTF-8 character.
+ * Handles hexadecimal digits, both uppercase and lowercase
+ * for integral types and only decimal digits for floating point types.
+ * If the character is not a valid numeric digit then `0` is returned and
+ * valid_flag is set to false.
+ *
+ * @param c ASCII or UTF-8 character
+ * @param valid_flag Set to false if input is not valid. Unchanged otherwise.
+ *
+ * @return uint8_t Numeric value of the character, or `0`
+ */
+template <typename T, bool as_hex = false>
+constexpr uint8_t decode_digit(char c, bool* valid_flag)
+{
+  if (c >= '0' && c <= '9') return c - '0';
+  if constexpr (as_hex and std::is_integral_v<T>) {
+    if (c >= 'a' && c <= 'f') return c - 'a' + 10;
+    if (c >= 'A' && c <= 'F') return c - 'A' + 10;
+  }
+
+  *valid_flag = false;
+  return 0;
+}
+
+// Converts character to lowercase.
+constexpr char to_lower(char const c) { return c >= 'A' && c <= 'Z' ? c + ('a' - 'A') : c; }
+
+/**
+ * @brief Checks if string is infinity, case insensitive with/without sign
+ * Valid infinity strings are inf, +inf, -inf, infinity, +infinity, -infinity
+ * String comparison is case insensitive.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @return true if string is valid infinity, else false.
+ */
+constexpr bool is_infinity(char const* begin, char const* end)
+{
+  if (*begin == '-' || *begin == '+') begin++;
+  char const* cinf = "infinity";
+  auto index       = begin;
+  while (index < end) {
+    if (*cinf != to_lower(*index)) break;
+    index++;
+    cinf++;
+  }
+  return ((index == begin + 3 || index == begin + 8) && index >= end);
+}
+
+/**
+ * @brief Parses a character string and returns its numeric value.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @param opts The global parsing behavior options
+ * @param error_result Value to return on parse error
+ * @tparam base Base (radix) to use for conversion
+ *
+ * @return The parsed and converted value
+ */
+template <typename T, int base = 10>
+__host__ __device__ std::optional<T> parse_numeric(char const* begin,
+                                                   char const* end,
+                                                   parse_options_view const& opts)
+{
+  T value{};
+  bool all_digits_valid = true;
+  constexpr bool as_hex = (base == 16);
+
+  // Handle negative values if necessary
+  int32_t sign = (*begin == '-') ? -1 : 1;
+
+  // Handle infinity
+  if (std::is_floating_point_v<T> && is_infinity(begin, end)) {
+    return sign * std::numeric_limits<T>::infinity();
+  }
+  if (*begin == '-' || *begin == '+') begin++;
+
+  // Skip over the "0x" prefix for hex notation
+  if (base == 16 && begin + 2 < end && *begin == '0' && *(begin + 1) == 'x') { begin += 2; }
+
+  // Handle the whole part of the number
+  // auto index = begin;
+  while (begin < end) {
+    if (*begin == opts.decimal) {
+      ++begin;
+      break;
+    } else if (base == 10 && (*begin == 'e' || *begin == 'E')) {
+      break;
+    } else if (*begin != opts.thousands && *begin != '+') {
+      value = (value * base) + decode_digit<T, as_hex>(*begin, &all_digits_valid);
+    }
+    ++begin;
+  }
+
+  if (std::is_floating_point_v<T>) {
+    // Handle fractional part of the number if necessary
+    double divisor = 1;
+    while (begin < end) {
+      if (*begin == 'e' || *begin == 'E') {
+        ++begin;
+        break;
+      } else if (*begin != opts.thousands && *begin != '+') {
+        divisor /= base;
+        value += decode_digit<T, as_hex>(*begin, &all_digits_valid) * divisor;
+      }
+      ++begin;
+    }
+
+    // Handle exponential part of the number if necessary
+    if (begin < end) {
+      int32_t const exponent_sign = *begin == '-' ? -1 : 1;
+      if (*begin == '-' || *begin == '+') { ++begin; }
+      int32_t exponent = 0;
+      while (begin < end) {
+        exponent = (exponent * 10) + decode_digit<T, as_hex>(*(begin++), &all_digits_valid);
+      }
+      if (exponent != 0) { value *= exp10(double(exponent * exponent_sign)); }
+    }
+  }
+  if (!all_digits_valid) { return std::optional<T>{}; }
+
+  return value * sign;
+}
+
+namespace gpu {
+/**
+ * @brief CUDA kernel iterates over the data until the end of the current field
+ *
+ * Also iterates over (one or more) delimiter characters after the field.
+ * Function applies to formats with field delimiters and line terminators.
+ *
+ * @param begin Pointer to the first element of the string
+ * @param end Pointer to the first element after the string
+ * @param opts A set of parsing options
+ * @param escape_char A boolean value to signify whether to consider `\` as escape character or
+ * just a character.
+ *
+ * @return Pointer to the last character in the field, including the
+ *  delimiter(s) following the field data
+ */
+__device__ __inline__ char const* seek_field_end(char const* begin,
+                                                 char const* end,
+                                                 parse_options_view const& opts,
+                                                 bool escape_char = false)
+{
+  bool quotation   = false;
+  auto current     = begin;
+  bool escape_next = false;
+  while (current < end) {
+    // Use simple logic to ignore control chars between any quote seq
+    // Handles nominal cases including doublequotes within quotes, but
+    // may not output exact failures as PANDAS for malformed fields.
+    // Check for instances such as "a2\"bc" and "\\" if `escape_char` is true.
+
+    if (*current == opts.quotechar and not escape_next) {
+      quotation = !quotation;
+    } else if (!quotation) {
+      if (*current == opts.delimiter) {
+        while (opts.multi_delimiter && (current + 1 < end) && *(current + 1) == opts.delimiter) {
+          ++current;
+        }
+        break;
+      } else if (*current == opts.terminator) {
+        break;
+      } else if (*current == '\r' && (current + 1 < end && *(current + 1) == '\n')) {
+        --end;
+        break;
+      }
+    }
+
+    if (escape_char) {
+      // If a escape character is encountered, escape next character in next loop.
+      if (not escape_next and *current == '\\') {
+        escape_next = true;
+      } else {
+        escape_next = false;
+      }
+    }
+
+    if (current < end) { current++; }
+  }
+  return current;
+}
+
+/**
+ * @brief Lexicographically compare digits in input against string
+ * representing an integer
+ *
+ * @param raw_data The pointer to beginning of character string
+ * @param golden The pointer to beginning of character string representing
+ * the value to be compared against
+ * @return bool True if integer represented by character string is less
+ * than or equal to golden data
+ */
+template <int N>
+__device__ __inline__ bool less_equal_than(char const* data, char const (&golden)[N])
+{
+  auto mismatch_pair = thrust::mismatch(thrust::seq, data, data + N - 1, golden);
+  if (mismatch_pair.first != data + N - 1) {
+    return *mismatch_pair.first <= *mismatch_pair.second;
+  } else {
+    // Exact match
+    return true;
+  }
+}
+
+/**
+ * @brief Determine which counter to increment when a sequence of digits
+ * and a parity sign is encountered.
+ *
+ * @param raw_data The pointer to beginning of character string
+ * @param digit_count Total number of digits
+ * @param stats Reference to structure with counters
+ * @return Pointer to appropriate counter that belong to
+ * the interpreted data type
+ */
+__device__ __inline__ cudf::size_type* infer_integral_field_counter(char const* data_begin,
+                                                                    char const* data_end,
+                                                                    bool is_negative,
+                                                                    column_type_histogram& stats)
+{
+  static constexpr char uint64_max_abs[] = "18446744073709551615";
+  static constexpr char int64_min_abs[]  = "9223372036854775808";
+  static constexpr char int64_max_abs[]  = "9223372036854775807";
+
+  auto digit_count = data_end - data_begin;
+
+  // Remove preceding zeros
+  if (digit_count >= (sizeof(int64_max_abs) - 1)) {
+    // Trim zeros at the beginning of raw_data
+    while (*data_begin == '0' && (data_begin < data_end)) {
+      data_begin++;
+    }
+  }
+  digit_count = data_end - data_begin;
+
+  // After trimming the number of digits could be less than maximum
+  // int64 digit count
+  if (digit_count < (sizeof(int64_max_abs) - 1)) {  // CASE 0 : Accept validity
+    // If the length of the string representing the integer is smaller
+    // than string length of Int64Max then count this as an integer
+    // representable by int64
+    // If digit_count is 0 then ignore - sign, i.e. -000..00 should
+    // be treated as a positive small integer
+    return is_negative && (digit_count != 0) ? &stats.negative_small_int_count
+                                             : &stats.positive_small_int_count;
+  } else if (digit_count > (sizeof(uint64_max_abs) - 1)) {  // CASE 1 : Reject validity
+    // If the length of the string representing the integer is greater
+    // than string length of UInt64Max then count this as a string
+    // since it cannot be represented as an int64 or uint64
+    return &stats.string_count;
+  } else if (digit_count == (sizeof(uint64_max_abs) - 1) && is_negative) {
+    // A negative integer of length UInt64Max digit count cannot be represented
+    // as a 64 bit integer
+    return &stats.string_count;
+  }
+
+  if (digit_count == (sizeof(int64_max_abs) - 1) && is_negative) {
+    return less_equal_than(data_begin, int64_min_abs) ? &stats.negative_small_int_count
+                                                      : &stats.string_count;
+  } else if (digit_count == (sizeof(int64_max_abs) - 1) && !is_negative) {
+    return less_equal_than(data_begin, int64_max_abs) ? &stats.positive_small_int_count
+                                                      : &stats.big_int_count;
+  } else if (digit_count == (sizeof(uint64_max_abs) - 1)) {
+    return less_equal_than(data_begin, uint64_max_abs) ? &stats.big_int_count : &stats.string_count;
+  }
+
+  return &stats.string_count;
+}
+
+}  // namespace gpu
+
+/**
+ * @brief Searches the input character array for each of characters in a set.
+ * Sums up the number of occurrences. If the 'positions' parameter is not void*,
+ * positions of all occurrences are stored in the output device array.
+ *
+ * @param[in] d_data Input character array in device memory
+ * @param[in] keys Vector containing the keys to count in the buffer
+ * @param[in] result_offset Offset to add to the output positions
+ * @param[out] positions Array containing the output positions
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return cudf::size_type total number of occurrences
+ */
+template <class T>
+cudf::size_type find_all_from_set(device_span<char const> data,
+                                  std::vector<char> const& keys,
+                                  uint64_t result_offset,
+                                  T* positions,
+                                  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Searches the input character array for each of characters in a set.
+ * Sums up the number of occurrences. If the 'positions' parameter is not void*,
+ * positions of all occurrences are stored in the output device array.
+ *
+ * Does not load the entire file into the GPU memory at any time, so it can
+ * be used to parse large files. Output array needs to be preallocated.
+ *
+ * @param[in] h_data Pointer to the input character array
+ * @param[in] h_size Number of bytes in the input array
+ * @param[in] keys Vector containing the keys to count in the buffer
+ * @param[in] result_offset Offset to add to the output positions
+ * @param[out] positions Array containing the output positions
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return cudf::size_type total number of occurrences
+ */
+template <class T>
+cudf::size_type find_all_from_set(host_span<char const> data,
+                                  std::vector<char> const& keys,
+                                  uint64_t result_offset,
+                                  T* positions,
+                                  rmm::cuda_stream_view stream);
+
+/**
+ * @brief Searches the input character array for each of characters in a set
+ * and sums up the number of occurrences.
+ *
+ * @param d_data Input data buffer in device memory
+ * @param keys Vector containing the keys to count in the buffer
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return cudf::size_type total number of occurrences
+ */
+cudf::size_type count_all_from_set(device_span<char const> data,
+                                   std::vector<char> const& keys,
+                                   rmm::cuda_stream_view stream);
+
+/**
+ * @brief Searches the input character array for each of characters in a set
+ * and sums up the number of occurrences.
+ *
+ * Does not load the entire buffer into the GPU memory at any time, so it can
+ * be used with buffers of any size.
+ *
+ * @param h_data Pointer to the data in host memory
+ * @param h_size Size of the input data, in bytes
+ * @param keys Vector containing the keys to count in the buffer
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return cudf::size_type total number of occurrences
+ */
+cudf::size_type count_all_from_set(host_span<char const> data,
+                                   std::vector<char> const& keys,
+                                   rmm::cuda_stream_view stream);
+
+/**
+ * @brief Checks whether the given character is a whitespace character.
+ *
+ * @param ch The character to check
+ *
+ * @return True if the input is whitespace, False otherwise
+ */
+__inline__ __device__ bool is_whitespace(char ch) { return ch == '\t' || ch == ' '; }
+
+/**
+ * @brief Skips past the current character if it matches the given value.
+ */
+template <typename It>
+__inline__ __device__ It skip_character(It const& it, char ch)
+{
+  return it + (*it == ch);
+}
+
+/**
+ * @brief Adjusts the range to ignore starting/trailing whitespace and quotation characters.
+ *
+ * @param begin Pointer to the first character in the parsing range
+ * @param end Pointer to the first character after the parsing range
+ * @param quotechar The character used to denote quotes; '\0' if none
+ *
+ * @return Trimmed range
+ */
+__inline__ __device__ std::pair<char const*, char const*> trim_whitespaces_quotes(
+  char const* begin, char const* end, char quotechar = '\0')
+{
+  auto not_whitespace = [] __device__(auto c) { return !is_whitespace(c); };
+
+  auto const trim_begin = thrust::find_if(thrust::seq, begin, end, not_whitespace);
+  auto const trim_end   = thrust::find_if(thrust::seq,
+                                        thrust::make_reverse_iterator(end),
+                                        thrust::make_reverse_iterator(trim_begin),
+                                        not_whitespace);
+
+  return {skip_character(trim_begin, quotechar), skip_character(trim_end, quotechar).base()};
+}
+
+/**
+ * @brief Adjusts the range to ignore starting/trailing whitespace characters.
+ *
+ * @param begin Pointer to the first character in the parsing range
+ * @param end Pointer to the first character after the parsing range
+ *
+ * @return Trimmed range
+ */
+__inline__ __device__ std::pair<char const*, char const*> trim_whitespaces(char const* begin,
+                                                                           char const* end)
+{
+  auto not_whitespace = [] __device__(auto c) { return !is_whitespace(c); };
+
+  auto const trim_begin = thrust::find_if(thrust::seq, begin, end, not_whitespace);
+  auto const trim_end   = thrust::find_if(thrust::seq,
+                                        thrust::make_reverse_iterator(end),
+                                        thrust::make_reverse_iterator(trim_begin),
+                                        not_whitespace);
+
+  return {trim_begin, trim_end.base()};
+}
+
+/**
+ * @brief Adjusts the range to ignore starting/trailing quotation characters.
+ *
+ * @param begin Pointer to the first character in the parsing range
+ * @param end Pointer to the first character after the parsing range
+ * @param quotechar The character used to denote quotes. Provide '\0' if no quotes should be
+ * trimmed.
+ *
+ * @return Trimmed range
+ */
+__inline__ __device__ std::pair<char const*, char const*> trim_quotes(char const* begin,
+                                                                      char const* end,
+                                                                      char quotechar)
+{
+  if ((thrust::distance(begin, end) >= 2 && *begin == quotechar &&
+       *thrust::prev(end) == quotechar)) {
+    thrust::advance(begin, 1);
+    thrust::advance(end, -1);
+  }
+  return {begin, end};
+}
+
+struct ConvertFunctor {
+  /**
+   * @brief Dispatch for numeric types whose values can be convertible to
+   * 0 or 1 to represent boolean false/true, based upon checking against a
+   * true/false values list.
+   *
+   * @return bool Whether the parsed value is valid.
+   */
+  template <typename T,
+            CUDF_ENABLE_IF(std::is_integral_v<T> and !std::is_same_v<T, bool> and
+                           !cudf::is_fixed_point<T>())>
+  __host__ __device__ __forceinline__ bool operator()(char const* begin,
+                                                      char const* end,
+                                                      void* out_buffer,
+                                                      size_t row,
+                                                      data_type const output_type,
+                                                      parse_options_view const& opts,
+                                                      bool as_hex = false)
+  {
+    auto const value = [as_hex, &opts, begin, end]() -> std::optional<T> {
+      // Check for user-specified true/false values
+      auto const field_len = static_cast<size_t>(end - begin);
+      if (serialized_trie_contains(opts.trie_true, {begin, field_len})) { return 1; }
+      if (serialized_trie_contains(opts.trie_false, {begin, field_len})) { return 0; }
+      return as_hex ? cudf::io::parse_numeric<T, 16>(begin, end, opts)
+                    : cudf::io::parse_numeric<T>(begin, end, opts);
+    }();
+    if (value.has_value()) { static_cast<T*>(out_buffer)[row] = *value; }
+
+    return value.has_value();
+  }
+
+  /**
+   * @brief Dispatch for fixed point types.
+   *
+   * @return bool Whether the parsed value is valid.
+   */
+  template <typename T, CUDF_ENABLE_IF(cudf::is_fixed_point<T>())>
+  __host__ __device__ __forceinline__ bool operator()(char const* begin,
+                                                      char const* end,
+                                                      void* out_buffer,
+                                                      size_t row,
+                                                      data_type const output_type,
+                                                      parse_options_view const& opts,
+                                                      bool as_hex)
+  {
+    // TODO decide what's invalid input and update parsing functions
+    static_cast<device_storage_type_t<T>*>(out_buffer)[row] =
+      [&opts, output_type, begin, end]() -> device_storage_type_t<T> {
+      return strings::detail::parse_decimal<device_storage_type_t<T>>(
+        begin, end, output_type.scale());
+    }();
+
+    return true;
+  }
+
+  /**
+   * @brief Dispatch for boolean type types.
+   */
+  template <typename T, CUDF_ENABLE_IF(std::is_same_v<T, bool>)>
+  __host__ __device__ __forceinline__ bool operator()(char const* begin,
+                                                      char const* end,
+                                                      void* out_buffer,
+                                                      size_t row,
+                                                      data_type const output_type,
+                                                      parse_options_view const& opts,
+                                                      bool as_hex)
+  {
+    auto const value = [&opts, begin, end]() -> std::optional<T> {
+      // Check for user-specified true/false values
+      auto const field_len = static_cast<size_t>(end - begin);
+      if (serialized_trie_contains(opts.trie_true, {begin, field_len})) {
+        return static_cast<T>(true);
+      }
+      if (serialized_trie_contains(opts.trie_false, {begin, field_len})) {
+        return static_cast<T>(false);
+      }
+      return cudf::io::parse_numeric<T>(begin, end, opts);
+    }();
+    if (value.has_value()) { static_cast<T*>(out_buffer)[row] = *value; }
+
+    return value.has_value();
+  }
+
+  /**
+   * @brief Dispatch for floating points, which are set to NaN if the input
+   * is not valid. In such case, the validity mask is set to zero too.
+   */
+  template <typename T, CUDF_ENABLE_IF(std::is_floating_point_v<T>)>
+  __host__ __device__ __forceinline__ bool operator()(char const* begin,
+                                                      char const* end,
+                                                      void* out_buffer,
+                                                      size_t row,
+                                                      data_type const output_type,
+                                                      parse_options_view const& opts,
+                                                      bool as_hex)
+  {
+    auto const value = [&opts, begin, end]() -> std::optional<T> {
+      // Check for user-specified true/false values
+      auto const field_len = static_cast<size_t>(end - begin);
+      if (serialized_trie_contains(opts.trie_true, {begin, field_len})) {
+        return static_cast<T>(true);
+      }
+      if (serialized_trie_contains(opts.trie_false, {begin, field_len})) {
+        return static_cast<T>(false);
+      }
+      return cudf::io::parse_numeric<T>(begin, end, opts);
+    }();
+    if (value.has_value()) { static_cast<T*>(out_buffer)[row] = *value; }
+
+    return value.has_value() and !std::isnan(*value);
+  }
+
+  /**
+   * @brief Dispatch for remaining supported types, i.e., timestamp and duration types.
+   */
+  template <typename T,
+            CUDF_ENABLE_IF(!std::is_integral_v<T> and !std::is_floating_point_v<T> and
+                           !cudf::is_fixed_point<T>())>
+  __host__ __device__ __forceinline__ bool operator()(char const* begin,
+                                                      char const* end,
+                                                      void* out_buffer,
+                                                      size_t row,
+                                                      data_type const output_type,
+                                                      parse_options_view const& opts,
+                                                      bool as_hex)
+  {
+    // TODO decide what's invalid input and update parsing functions
+    if constexpr (cudf::is_timestamp<T>()) {
+      static_cast<T*>(out_buffer)[row] = to_timestamp<T>(begin, end, opts.dayfirst);
+    } else if constexpr (cudf::is_duration<T>()) {
+      static_cast<T*>(out_buffer)[row] = to_duration<T>(begin, end);
+    } else {
+      return false;
+    }
+    return true;
+  }
+};
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/row_selection.cpp b/cpp/src/io/utilities/row_selection.cpp
new file mode 100644
index 0000000..ae5c7b5
--- /dev/null
+++ b/cpp/src/io/utilities/row_selection.cpp
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/utilities/row_selection.hpp>
+
+#include <cudf/utilities/error.hpp>
+
+#include <algorithm>
+#include <limits>
+
+namespace cudf::io::detail {
+
+std::pair<uint64_t, size_type> skip_rows_num_rows_from_options(
+  uint64_t skip_rows, std::optional<size_type> const& num_rows, uint64_t num_source_rows)
+{
+  auto const rows_to_skip = std::min(skip_rows, num_source_rows);
+  if (not num_rows.has_value()) {
+    CUDF_EXPECTS(num_source_rows - rows_to_skip <= std::numeric_limits<size_type>::max(),
+                 "The requested number of rows exceeds the column size limit",
+                 std::overflow_error);
+    return {rows_to_skip, num_source_rows - rows_to_skip};
+  }
+  // Limit the number of rows to the end of the input
+  return {
+    rows_to_skip,
+    static_cast<size_type>(std::min<uint64_t>(num_rows.value(), num_source_rows - rows_to_skip))};
+}
+
+}  // namespace cudf::io::detail
diff --git a/cpp/src/io/utilities/row_selection.hpp b/cpp/src/io/utilities/row_selection.hpp
new file mode 100644
index 0000000..2117268
--- /dev/null
+++ b/cpp/src/io/utilities/row_selection.hpp
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/types.hpp>
+
+#include <cstdint>
+#include <optional>
+#include <utility>
+
+namespace cudf::io::detail {
+
+/**
+ * @brief Adjusts the input skip_rows and num_rows options to the actual number of rows to
+ * skip/read, based on the number of rows in the ORC file(s).
+ *
+ * @param skip_rows skip_rows as passed by the user
+ * @param num_rows Optional num_rows as passed by the user
+ * @param num_source_rows number of rows in the ORC file(s)
+ * @return A std::pair containing the number of rows to skip and the number of rows to read
+ *
+ * @throw std::overflow_exception The requested number of rows exceeds the column size limit
+ */
+std::pair<uint64_t, size_type> skip_rows_num_rows_from_options(
+  uint64_t skip_rows, std::optional<size_type> const& num_rows, uint64_t num_source_rows);
+
+}  // namespace cudf::io::detail
diff --git a/cpp/src/io/utilities/string_parsing.hpp b/cpp/src/io/utilities/string_parsing.hpp
new file mode 100644
index 0000000..12fc0a5
--- /dev/null
+++ b/cpp/src/io/utilities/string_parsing.hpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <io/utilities/parsing_utils.cuh>
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/tuple.h>
+
+namespace cudf::io {
+namespace detail {
+
+/**
+ * @brief Infers data type for a given JSON string input `data`.
+ *
+ * @throw cudf::logic_error if input size is 0
+ * @throw cudf::logic_error if date time is not inferred as string
+ * @throw cudf::logic_error if data type inference failed
+ *
+ * @param options View of inference options
+ * @param data JSON string input
+ * @param offset_length_begin The beginning of an offset-length tuple sequence
+ * @param size Size of the string input
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return The inferred data type
+ */
+cudf::data_type infer_data_type(
+  cudf::io::json_inference_options_view const& options,
+  device_span<char const> data,
+  thrust::zip_iterator<thrust::tuple<const size_type*, const size_type*>> offset_length_begin,
+  std::size_t const size,
+  rmm::cuda_stream_view stream);
+}  // namespace detail
+
+namespace json::detail {
+
+/**
+ * @brief Parses the data from an iterator of string views, casting it to the given target data type
+ *
+ * @param data string input base pointer
+ * @param offset_length_begin The beginning of an offset-length tuple sequence
+ * @param col_size The total number of items of this column
+ * @param col_type The column's target data type
+ * @param null_mask A null mask that renders certain items from the input invalid
+ * @param options Settings for controlling the processing behavior
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr The resource to be used for device memory allocation
+ * @return The column that contains the parsed data
+ */
+std::unique_ptr<column> parse_data(
+  const char* data,
+  thrust::zip_iterator<thrust::tuple<const size_type*, const size_type*>> offset_length_begin,
+  size_type col_size,
+  data_type col_type,
+  rmm::device_buffer&& null_mask,
+  size_type null_count,
+  cudf::io::parse_options_view const& options,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+}  // namespace json::detail
+}  // namespace cudf::io
diff --git a/cpp/src/io/utilities/thread_pool.hpp b/cpp/src/io/utilities/thread_pool.hpp
new file mode 100644
index 0000000..74a2531
--- /dev/null
+++ b/cpp/src/io/utilities/thread_pool.hpp
@@ -0,0 +1,381 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+/**
+ * Modified from https://github.com/bshoshany/thread-pool
+ * @copyright Copyright (c) 2021 Barak Shoshany. Licensed under the MIT license.
+ *            See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
+ */
+
+#include <atomic>       // std::atomic
+#include <chrono>       // std::chrono
+#include <cstdint>      // std::int_fast64_t, std::uint_fast32_t
+#include <functional>   // std::function
+#include <future>       // std::future, std::promise
+#include <memory>       // std::shared_ptr, std::unique_ptr
+#include <mutex>        // std::mutex, std::scoped_lock
+#include <queue>        // std::queue
+#include <thread>       // std::this_thread, std::thread
+#include <type_traits>  // std::decay_t, std::enable_if_t, std::is_void_v, std::invoke_result_t
+#include <utility>      // std::move, std::swap
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief A C++17 thread pool class. The user submits tasks to be executed into a queue. Whenever a
+ * thread becomes available, it pops a task from the queue and executes it. Each task is
+ * automatically assigned a future, which can be used to wait for the task to finish executing
+ * and/or obtain its eventual return value.
+ */
+class thread_pool {
+  using ui32 = int;
+
+ public:
+  /**
+   * @brief Construct a new thread pool.
+   *
+   * @param _thread_count The number of threads to use. The default value is the total number of
+   * hardware threads available, as reported by the implementation. With a hyperthreaded CPU, this
+   * will be twice the number of CPU cores. If the argument is zero, the default value will be used
+   * instead.
+   */
+  thread_pool(ui32 const& _thread_count = std::thread::hardware_concurrency())
+    : thread_count(_thread_count ? _thread_count : std::thread::hardware_concurrency()),
+      threads(new std::thread[_thread_count ? _thread_count : std::thread::hardware_concurrency()])
+  {
+    create_threads();
+  }
+
+  /**
+   * @brief Destruct the thread pool. Waits for all tasks to complete, then destroys all threads.
+   * Note that if the variable paused is set to true, then any tasks still in the queue will never
+   * be executed.
+   */
+  ~thread_pool()
+  {
+    wait_for_tasks();
+    running = false;
+    destroy_threads();
+  }
+
+  /**
+   * @brief Get the number of tasks currently waiting in the queue to be executed by the threads.
+   *
+   * @return The number of queued tasks.
+   */
+  [[nodiscard]] size_t get_tasks_queued() const
+  {
+    std::scoped_lock const lock(queue_mutex);
+    return tasks.size();
+  }
+
+  /**
+   * @brief Get the number of tasks currently being executed by the threads.
+   *
+   * @return The number of running tasks.
+   */
+  [[nodiscard]] ui32 get_tasks_running() const { return tasks_total - (ui32)get_tasks_queued(); }
+
+  /**
+   * @brief Get the total number of unfinished tasks - either still in the queue, or running in a
+   * thread.
+   *
+   * @return The total number of tasks.
+   */
+  [[nodiscard]] ui32 get_tasks_total() const { return tasks_total; }
+
+  /**
+   * @brief Get the number of threads in the pool.
+   *
+   * @return The number of threads.
+   */
+  [[nodiscard]] ui32 get_thread_count() const { return thread_count; }
+
+  /**
+   * @brief Parallelize a loop by splitting it into blocks, submitting each block separately to the
+   * thread pool, and waiting for all blocks to finish executing. The loop will be equivalent to:
+   * for (T i = first_index; i <= last_index; i++) loop(i);
+   *
+   * @tparam T The type of the loop index. Should be a signed or unsigned integer.
+   * @tparam F The type of the function to loop through.
+   * @param first_index The first index in the loop (inclusive).
+   * @param last_index The last index in the loop (inclusive).
+   * @param loop The function to loop through. Should take exactly one argument, the loop index.
+   * @param num_tasks The maximum number of tasks to split the loop into. The default is to use the
+   * number of threads in the pool.
+   */
+  template <typename T, typename F>
+  void parallelize_loop(T first_index, T last_index, F const& loop, ui32 num_tasks = 0)
+  {
+    if (num_tasks == 0) num_tasks = thread_count;
+    if (last_index < first_index) std::swap(last_index, first_index);
+    size_t total_size = last_index - first_index + 1;
+    size_t block_size = total_size / num_tasks;
+    if (block_size == 0) {
+      block_size = 1;
+      num_tasks  = (ui32)total_size > 1 ? (ui32)total_size : 1;
+    }
+    std::atomic<ui32> blocks_running = 0;
+    for (ui32 t = 0; t < num_tasks; t++) {
+      T start = (T)(t * block_size + first_index);
+      T end   = (t == num_tasks - 1) ? last_index : (T)((t + 1) * block_size + first_index - 1);
+      blocks_running++;
+      push_task([start, end, &loop, &blocks_running] {
+        for (T i = start; i <= end; i++)
+          loop(i);
+        blocks_running--;
+      });
+    }
+    while (blocks_running != 0) {
+      sleep_or_yield();
+    }
+  }
+
+  /**
+   * @brief Push a function with no arguments or return value into the task queue.
+   *
+   * @tparam F The type of the function.
+   * @param task The function to push.
+   */
+  template <typename F>
+  void push_task(F const& task)
+  {
+    tasks_total++;
+    {
+      std::scoped_lock const lock(queue_mutex);
+      tasks.push(std::function<void()>(task));
+    }
+  }
+
+  /**
+   * @brief Push a function with arguments, but no return value, into the task queue.
+   * @details The function is wrapped inside a lambda in order to hide the arguments, as the tasks
+   * in the queue must be of type std::function<void()>, so they cannot have any arguments or return
+   * value. If no arguments are provided, the other overload will be used, in order to avoid the
+   * (slight) overhead of using a lambda.
+   *
+   * @tparam F The type of the function.
+   * @tparam A The types of the arguments.
+   * @param task The function to push.
+   * @param args The arguments to pass to the function.
+   */
+  template <typename F, typename... A>
+  void push_task(F const& task, A const&... args)
+  {
+    push_task([task, args...] { task(args...); });
+  }
+
+  /**
+   * @brief Reset the number of threads in the pool. Waits for all currently running tasks to be
+   * completed, then destroys all threads in the pool and creates a new thread pool with the new
+   * number of threads. Any tasks that were waiting in the queue before the pool was reset will then
+   * be executed by the new threads. If the pool was paused before resetting it, the new pool will
+   * be paused as well.
+   *
+   * @param _thread_count The number of threads to use. The default value is the total number of
+   * hardware threads available, as reported by the implementation. With a hyperthreaded CPU, this
+   * will be twice the number of CPU cores. If the argument is zero, the default value will be used
+   * instead.
+   */
+  void reset(ui32 const& _thread_count = std::thread::hardware_concurrency())
+  {
+    bool was_paused = paused;
+    paused          = true;
+    wait_for_tasks();
+    running = false;
+    destroy_threads();
+    thread_count = _thread_count ? _thread_count : std::thread::hardware_concurrency();
+    threads.reset(new std::thread[thread_count]);
+    paused = was_paused;
+    create_threads();
+    running = true;
+  }
+
+  /**
+   * @brief Submit a function with zero or more arguments and a return value into the task queue,
+   * and get a future for its eventual returned value.
+   *
+   * @tparam F The type of the function.
+   * @tparam A The types of the zero or more arguments to pass to the function.
+   * @tparam R The return type of the function.
+   * @param task The function to submit.
+   * @param args The zero or more arguments to pass to the function.
+   * @return A future to be used later to obtain the function's returned value, waiting for it to
+   * finish its execution if needed.
+   */
+  template <typename F,
+            typename... A,
+            typename R = std::invoke_result_t<std::decay_t<F>, std::decay_t<A>...>>
+  std::future<R> submit(F const& task, A const&... args)
+  {
+    std::shared_ptr<std::promise<R>> promise(new std::promise<R>);
+    std::future<R> future = promise->get_future();
+    push_task([task, args..., promise] {
+      try {
+        if constexpr (std::is_void_v<R>) {
+          task(args...);
+          promise->set_value();
+        } else {
+          promise->set_value(task(args...));
+        }
+      } catch (...) {
+        promise->set_exception(std::current_exception());
+      };
+    });
+    return future;
+  }
+
+  /**
+   * @brief Wait for tasks to be completed. Normally, this function waits for all tasks, both those
+   * that are currently running in the threads and those that are still waiting in the queue.
+   * However, if the variable paused is set to true, this function only waits for the currently
+   * running tasks (otherwise it would wait forever). To wait for a specific task, use submit()
+   * instead, and call the wait() member function of the generated future.
+   */
+  void wait_for_tasks()
+  {
+    while (true) {
+      if (!paused) {
+        if (tasks_total == 0) break;
+      } else {
+        if (get_tasks_running() == 0) break;
+      }
+      sleep_or_yield();
+    }
+  }
+
+  /**
+   * @brief An atomic variable indicating to the workers to pause. When set to true, the workers
+   * temporarily stop popping new tasks out of the queue, although any tasks already executed will
+   * keep running until they are done. Set to false again to resume popping tasks.
+   */
+  std::atomic<bool> paused = false;
+
+  /**
+   * @brief The duration, in microseconds, that the worker function should sleep for when it cannot
+   * find any tasks in the queue. If set to 0, then instead of sleeping, the worker function will
+   * execute std::this_thread::yield() if there are no tasks in the queue. The default value is
+   * 1000.
+   */
+  ui32 sleep_duration = 1000;
+
+ private:
+  /**
+   * @brief Create the threads in the pool and assign a worker to each thread.
+   */
+  void create_threads()
+  {
+    for (ui32 i = 0; i < thread_count; i++) {
+      threads[i] = std::thread(&thread_pool::worker, this);
+    }
+  }
+
+  /**
+   * @brief Destroy the threads in the pool by joining them.
+   */
+  void destroy_threads()
+  {
+    for (ui32 i = 0; i < thread_count; i++) {
+      threads[i].join();
+    }
+  }
+
+  /**
+   * @brief Try to pop a new task out of the queue.
+   *
+   * @param task A reference to the task. Will be populated with a function if the queue is not
+   * empty.
+   * @return true if a task was found, false if the queue is empty.
+   */
+  bool pop_task(std::function<void()>& task)
+  {
+    std::scoped_lock const lock(queue_mutex);
+    if (tasks.empty())
+      return false;
+    else {
+      task = std::move(tasks.front());
+      tasks.pop();
+      return true;
+    }
+  }
+
+  /**
+   * @brief Sleep for sleep_duration microseconds. If that variable is set to zero, yield instead.
+   *
+   */
+  void sleep_or_yield()
+  {
+    if (sleep_duration)
+      std::this_thread::sleep_for(std::chrono::microseconds(sleep_duration));
+    else
+      std::this_thread::yield();
+  }
+
+  /**
+   * @brief A worker function to be assigned to each thread in the pool. Continuously pops tasks out
+   * of the queue and executes them, as long as the atomic variable running is set to true.
+   */
+  void worker()
+  {
+    while (running) {
+      std::function<void()> task;
+      if (!paused && pop_task(task)) {
+        task();
+        tasks_total--;
+      } else {
+        sleep_or_yield();
+      }
+    }
+  }
+
+  /**
+   * @brief A mutex to synchronize access to the task queue by different threads.
+   */
+  mutable std::mutex queue_mutex;
+
+  /**
+   * @brief An atomic variable indicating to the workers to keep running. When set to false, the
+   * workers permanently stop working.
+   */
+  std::atomic<bool> running = true;
+
+  /**
+   * @brief A queue of tasks to be executed by the threads.
+   */
+  std::queue<std::function<void()>> tasks;
+
+  /**
+   * @brief The number of threads in the pool.
+   */
+  ui32 thread_count;
+
+  /**
+   * @brief A smart pointer to manage the memory allocated for the threads.
+   */
+  std::unique_ptr<std::thread[]> threads;
+
+  /**
+   * @brief An atomic variable to keep track of the total number of unfinished tasks - either still
+   * in the queue, or running in a thread.
+   */
+  std::atomic<ui32> tasks_total = 0;
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/time_utils.cuh b/cpp/src/io/utilities/time_utils.cuh
new file mode 100644
index 0000000..687766c
--- /dev/null
+++ b/cpp/src/io/utilities/time_utils.cuh
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+namespace cudf {
+namespace io {
+
+/**
+ * @brief Lookup table to compute power of ten
+ */
+static const __device__ __constant__ int32_t powers_of_ten[10] = {
+  1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000};
+
+struct get_period {
+  template <typename T>
+  constexpr int32_t operator()()
+  {
+    if constexpr (is_chrono<T>()) { return T::period::den; }
+    CUDF_FAIL("Invalid, non chrono type");
+  }
+};
+
+/**
+ * @brief Function that translates cuDF time unit to clock frequency
+ */
+constexpr int32_t to_clockrate(type_id timestamp_type_id)
+{
+  return timestamp_type_id == type_id::EMPTY
+           ? 0
+           : type_dispatcher(data_type{timestamp_type_id}, get_period{});
+}
+
+}  // namespace io
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/trie.cu b/cpp/src/io/utilities/trie.cu
new file mode 100644
index 0000000..3be1a83
--- /dev/null
+++ b/cpp/src/io/utilities/trie.cu
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @brief Serialized trie implementation for C++/CUDA
+ * @file trie.cu
+ */
+
+#include "trie.cuh"
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <cuda_runtime.h>
+
+#include <deque>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+
+rmm::device_uvector<serial_trie_node> create_serialized_trie(std::vector<std::string> const& keys,
+                                                             rmm::cuda_stream_view stream)
+{
+  if (keys.empty()) { return rmm::device_uvector<serial_trie_node>{0, stream}; }
+
+  static constexpr int alphabet_size = std::numeric_limits<char>::max() + 1;
+  struct TreeTrieNode {
+    using TrieNodePtr = std::unique_ptr<TreeTrieNode>;
+    std::array<TrieNodePtr, alphabet_size> children;
+    bool is_end_of_word = false;
+  };
+
+  // Construct a tree-structured trie
+  // The trie takes a lot of memory, but the lookup is fast:
+  // allows direct addressing of children nodes
+  TreeTrieNode tree_trie;
+  for (auto const& key : keys) {
+    auto* current_node = &tree_trie;
+
+    for (char const character : key) {
+      if (current_node->children[character] == nullptr)
+        current_node->children[character] = std::make_unique<TreeTrieNode>();
+
+      current_node = current_node->children[character].get();
+    }
+
+    current_node->is_end_of_word = true;
+  }
+
+  struct IndexedTrieNode {
+    TreeTrieNode const* const pnode;
+    int16_t const idx;
+    IndexedTrieNode(TreeTrieNode const* const node, int16_t index) : pnode(node), idx(index) {}
+  };
+
+  // Serialize the tree trie
+  std::deque<IndexedTrieNode> to_visit;
+  std::vector<serial_trie_node> nodes;
+
+  // If the Tree trie matches empty strings, the root node is marked as 'end of word'.
+  // The first node in the serialized trie is also used to match empty strings, so we're
+  // initializing it using the `is_end_of_word` value from the root node.
+  nodes.push_back(serial_trie_node(trie_terminating_character, tree_trie.is_end_of_word));
+
+  // Add root node to queue. this node is not included to the serialized trie
+  to_visit.emplace_back(&tree_trie, -1);
+  while (!to_visit.empty()) {
+    auto const node_and_idx = to_visit.front();
+    auto const node         = node_and_idx.pnode;
+    auto const idx          = node_and_idx.idx;
+    to_visit.pop_front();
+
+    bool has_children = false;
+    for (size_t i = 0; i < node->children.size(); ++i) {
+      if (node->children[i] != nullptr) {
+        // Update the children offset of the parent node, unless at the root
+        if (idx >= 0 && nodes[idx].children_offset < 0) {
+          nodes[idx].children_offset = static_cast<uint16_t>(nodes.size() - idx);
+        }
+        // Add node to the trie
+        nodes.push_back(serial_trie_node(static_cast<char>(i), node->children[i]->is_end_of_word));
+        // Add to the queue, with the index within the new trie
+        to_visit.emplace_back(node->children[i].get(), static_cast<uint16_t>(nodes.size()) - 1);
+
+        has_children = true;
+      }
+    }
+    // Only add the terminating character if any nodes were added
+    if (has_children) { nodes.push_back(serial_trie_node(trie_terminating_character)); }
+  }
+  return cudf::detail::make_device_uvector_sync(
+    nodes, stream, rmm::mr::get_current_device_resource());
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/trie.cuh b/cpp/src/io/utilities/trie.cuh
new file mode 100644
index 0000000..677743d
--- /dev/null
+++ b/cpp/src/io/utilities/trie.cuh
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @brief Serialized trie implementation for C++/CUDA
+ * @file trie.cuh
+ */
+
+#pragma once
+
+#include <cudf/utilities/span.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace detail {
+static constexpr char trie_terminating_character = '\n';
+
+/**
+ * @brief Node in the serialized trie.
+ *
+ * A serialized trie is an array of nodes. Each node represents a matching character, except for the
+ * last child node, which denotes the end of the children list. Children of a node are stored
+ * contiguously. The `children_offset` member is the offset between the node and its first child.
+ * Matching is successful if all characters are matched and the final node is the last character of
+ * a word (i.e. `is_leaf` is true).
+ *
+ */
+struct serial_trie_node {
+  int16_t children_offset{-1};
+  char character{trie_terminating_character};
+  bool is_leaf{false};
+  explicit serial_trie_node(char c, bool leaf = false) noexcept : character(c), is_leaf(leaf) {}
+};
+
+using trie          = rmm::device_uvector<serial_trie_node>;
+using optional_trie = std::optional<trie>;
+using trie_view     = device_span<serial_trie_node const>;
+
+inline trie_view make_trie_view(optional_trie const& t)
+{
+  if (!t) return {};
+  return trie_view{t->data(), t->size()};
+}
+
+/**
+ * @brief Creates a serialized trie for cache-friendly string search.
+ *
+ * The resulting trie is a compact array - children array size is equal to the
+ * actual number of children nodes, not the size of the alphabet.
+ *
+ * @param keys Array of strings to insert into the trie
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return A host vector of nodes representing the serialized trie
+ */
+trie create_serialized_trie(std::vector<std::string> const& keys, rmm::cuda_stream_view stream);
+
+/*
+ * @brief Searches for a string in a serialized trie.
+ *
+ * Can be executed on host or device, as long as the data is available
+ *
+ * @param trie Pointer to the array of nodes that make up the trie
+ * @param key Pointer to the start of the string to find
+ * @param key_len Length of the string to find
+ *
+ * @return Boolean value; true if string is found, false otherwise
+ */
+__host__ __device__ inline bool serialized_trie_contains(device_span<serial_trie_node const> trie,
+                                                         device_span<char const> key)
+{
+  if (trie.empty()) { return false; }
+  if (key.empty()) { return trie.front().is_leaf; }
+  auto curr_node = trie.begin() + 1;
+  for (auto curr_key = key.begin(); curr_key < key.end(); ++curr_key) {
+    // Don't jump away from root node
+    if (curr_key != key.begin()) { curr_node += curr_node->children_offset; }
+    // Search for the next character in the array of children nodes
+    // Nodes are sorted - terminate search if the node is larger or equal
+    while (curr_node->character != trie_terminating_character && curr_node->character < *curr_key) {
+      ++curr_node;
+    }
+    // Could not find the next character, done with the search
+    if (curr_node->character != *curr_key) { return false; }
+  }
+  // Even if the node is present, return true only if that node is at the end of a word
+  return curr_node->is_leaf;
+}
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/io/utilities/type_inference.cu b/cpp/src/io/utilities/type_inference.cu
new file mode 100644
index 0000000..79a5c8f
--- /dev/null
+++ b/cpp/src/io/utilities/type_inference.cu
@@ -0,0 +1,292 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/utilities/column_type_histogram.hpp>
+#include <io/utilities/string_parsing.hpp>
+#include <io/utilities/trie.cuh>
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/device_scalar.hpp>
+
+#include <cub/block/block_reduce.cuh>
+
+#include <cstddef>
+
+namespace cudf::io::detail {
+/**
+ * @brief Custom column_type_histogram sum reduction callable
+ */
+struct custom_sum {
+  __device__ inline cudf::io::column_type_histogram operator()(
+    cudf::io::column_type_histogram const& lhs, cudf::io::column_type_histogram const& rhs)
+  {
+    return {lhs.null_count + rhs.null_count,
+            lhs.float_count + rhs.float_count,
+            lhs.datetime_count + rhs.datetime_count,
+            lhs.string_count + rhs.string_count,
+            lhs.negative_small_int_count + rhs.negative_small_int_count,
+            lhs.positive_small_int_count + rhs.positive_small_int_count,
+            lhs.big_int_count + rhs.big_int_count,
+            lhs.bool_count + rhs.bool_count};
+  }
+};
+
+/**
+ * @brief Returns true if the input character is a valid digit.
+ * Supports both decimal and hexadecimal digits (uppercase and lowercase).
+ *
+ * @param c Character to check
+ * @param is_hex Whether to check as a hexadecimal
+ *
+ * @return `true` if it is digit-like, `false` otherwise
+ */
+__device__ __inline__ bool is_digit(char const c, bool const is_hex = false)
+{
+  if (c >= '0' && c <= '9') return true;
+
+  if (is_hex) {
+    if (c >= 'A' && c <= 'F') return true;
+    if (c >= 'a' && c <= 'f') return true;
+  }
+
+  return false;
+}
+
+/**
+ * @brief Returns true if the counters indicate a potentially valid float.
+ * False positives are possible because positions are not taken into account.
+ * For example, field "e.123-" would match the pattern.
+ */
+__device__ __inline__ bool is_like_float(std::size_t len,
+                                         uint32_t digit_cnt,
+                                         uint32_t decimal_cnt,
+                                         uint32_t dash_cnt,
+                                         uint32_t exponent_cnt)
+{
+  // Can't have more than one exponent and one decimal point
+  if (decimal_cnt > 1) return false;
+  if (exponent_cnt > 1) return false;
+  // Without the exponent or a decimal point, this is an integer, not a float
+  if (decimal_cnt == 0 && exponent_cnt == 0) return false;
+
+  // Can only have one '-' per component
+  if (dash_cnt > 1 + exponent_cnt) return false;
+
+  // If anything other than these characters is present, it's not a float
+  if (digit_cnt + decimal_cnt + dash_cnt + exponent_cnt != len) return false;
+
+  // Needs at least 1 digit, 2 if exponent is present
+  if (digit_cnt < 1 + exponent_cnt) return false;
+
+  return true;
+}
+
+/**
+ * @brief Constructs column type histogram for a given column string input `data`.
+ *
+ * @tparam BlockSize Number of threads in each block
+ * @tparam OptionsView Type of inference options view
+ * @tparam ColumnStringIter Iterator type whose `value_type` is a
+ * `thrust::tuple<offset_t, length_t>`, where `offset_t` and `length_t` are of integral type and
+ * `offset_t` needs to be convertible to `std::size_t`.
+ *
+ * @param[in] options View of inference options
+ * @param[in] data JSON string input
+ * @param[in] offset_length_begin The beginning of an offset-length tuple sequence
+ * @param[in] size Size of the string input
+ * @param[out] column_info Histogram of column type counters
+ */
+template <int BlockSize, typename OptionsView, typename ColumnStringIter>
+__global__ void infer_column_type_kernel(OptionsView options,
+                                         device_span<char const> data,
+                                         ColumnStringIter offset_length_begin,
+                                         std::size_t size,
+                                         cudf::io::column_type_histogram* column_info)
+{
+  auto thread_type_histogram = cudf::io::column_type_histogram{};
+
+  for (auto idx = threadIdx.x + blockDim.x * blockIdx.x; idx < size;
+       idx += gridDim.x * blockDim.x) {
+    auto const field_offset = thrust::get<0>(*(offset_length_begin + idx));
+    auto const field_len    = thrust::get<1>(*(offset_length_begin + idx));
+    auto const field_begin  = data.begin() + field_offset;
+
+    if (cudf::detail::serialized_trie_contains(
+          options.trie_na, {field_begin, static_cast<std::size_t>(field_len)})) {
+      ++thread_type_histogram.null_count;
+      continue;
+    }
+
+    // Handling strings
+    if (field_len >= 2 and *field_begin == options.quote_char and
+        field_begin[field_len - 1] == options.quote_char) {
+      ++thread_type_histogram.string_count;
+      continue;
+    }
+
+    uint32_t digit_count    = 0;
+    uint32_t decimal_count  = 0;
+    uint32_t slash_count    = 0;
+    uint32_t dash_count     = 0;
+    uint32_t plus_count     = 0;
+    uint32_t colon_count    = 0;
+    uint32_t exponent_count = 0;
+    uint32_t other_count    = 0;
+
+    auto const maybe_hex =
+      (field_len > 2 && field_begin[0] == '0' && field_begin[1] == 'x') ||
+      (field_len > 3 && field_begin[0] == '-' && field_begin[1] == '0' && field_begin[2] == 'x');
+    auto const field_end = field_begin + field_len;
+
+    for (auto pos = field_begin; pos < field_end; ++pos) {
+      if (is_digit(*pos, maybe_hex)) {
+        digit_count++;
+        continue;
+      }
+      // Looking for unique characters that will help identify column types
+      switch (*pos) {
+        case '.': decimal_count++; break;
+        case '-': dash_count++; break;
+        case '+': plus_count++; break;
+        case '/': slash_count++; break;
+        case ':': colon_count++; break;
+        case 'e':
+        case 'E':
+          if (!maybe_hex && pos > field_begin && pos < field_end - 1) exponent_count++;
+          break;
+        default: other_count++; break;
+      }
+    }
+
+    // All characters must be digits in an integer, except for the starting sign and 'x' in the
+    // hexadecimal prefix
+    auto const int_req_number_cnt =
+      static_cast<uint32_t>(field_len) -
+      ((*field_begin == '-' || *field_begin == '+') && field_len > 1) - maybe_hex;
+    if (cudf::detail::serialized_trie_contains(
+          options.trie_true, {field_begin, static_cast<std::size_t>(field_len)}) ||
+        cudf::detail::serialized_trie_contains(
+          options.trie_false, {field_begin, static_cast<std::size_t>(field_len)})) {
+      ++thread_type_histogram.bool_count;
+    } else if (digit_count == int_req_number_cnt) {
+      auto const is_negative = (*field_begin == '-');
+      char const* data_begin = field_begin + (is_negative || (*field_begin == '+'));
+      cudf::size_type* ptr   = cudf::io::gpu::infer_integral_field_counter(
+        data_begin, data_begin + digit_count, is_negative, thread_type_histogram);
+      ++*ptr;
+    } else if (is_like_float(
+                 field_len, digit_count, decimal_count, dash_count + plus_count, exponent_count)) {
+      ++thread_type_histogram.float_count;
+    }
+    // All invalid JSON values are treated as string
+    else {
+      ++thread_type_histogram.string_count;
+    }
+  }  // grid-stride for loop
+
+  using BlockReduce = cub::BlockReduce<cudf::io::column_type_histogram, BlockSize>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  auto const block_type_histogram =
+    BlockReduce(temp_storage).Reduce(thread_type_histogram, custom_sum{});
+  if (threadIdx.x == 0) {
+    atomicAdd(&column_info->null_count, block_type_histogram.null_count);
+    atomicAdd(&column_info->float_count, block_type_histogram.float_count);
+    atomicAdd(&column_info->datetime_count, block_type_histogram.datetime_count);
+    atomicAdd(&column_info->string_count, block_type_histogram.string_count);
+    atomicAdd(&column_info->negative_small_int_count,
+              block_type_histogram.negative_small_int_count);
+    atomicAdd(&column_info->positive_small_int_count,
+              block_type_histogram.positive_small_int_count);
+    atomicAdd(&column_info->big_int_count, block_type_histogram.big_int_count);
+    atomicAdd(&column_info->bool_count, block_type_histogram.bool_count);
+  }
+}
+
+/**
+ * @brief Constructs column type histogram for a given column string input `data`.
+ *
+ * @tparam OptionsView Type of inference options view
+ * @tparam ColumnStringIter Iterator type whose `value_type` is a
+ * `thrust::tuple<offset_t, length_t>`, where `offset_t` and `length_t` are of integral type and
+ * `offset_t` needs to be convertible to `std::size_t`.
+ *
+ * @param options View of inference options
+ * @param data JSON string input
+ * @param offset_length_begin The beginning of an offset-length tuple sequence
+ * @param size Size of the string input
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return A histogram containing column-specific type counters
+ */
+template <typename OptionsView, typename ColumnStringIter>
+cudf::io::column_type_histogram infer_column_type(OptionsView const& options,
+                                                  cudf::device_span<char const> data,
+                                                  ColumnStringIter offset_length_begin,
+                                                  std::size_t const size,
+                                                  rmm::cuda_stream_view stream)
+{
+  constexpr int block_size = 128;
+
+  auto const grid_size = (size + block_size - 1) / block_size;
+  auto d_column_info   = rmm::device_scalar<cudf::io::column_type_histogram>(stream);
+  CUDF_CUDA_TRY(cudaMemsetAsync(
+    d_column_info.data(), 0, sizeof(cudf::io::column_type_histogram), stream.value()));
+
+  infer_column_type_kernel<block_size><<<grid_size, block_size, 0, stream.value()>>>(
+    options, data, offset_length_begin, size, d_column_info.data());
+
+  return d_column_info.value(stream);
+}
+
+cudf::data_type infer_data_type(
+  cudf::io::json_inference_options_view const& options,
+  device_span<char const> data,
+  thrust::zip_iterator<thrust::tuple<const size_type*, const size_type*>> offset_length_begin,
+  std::size_t const size,
+  rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(size != 0, "No data available for data type inference.\n");
+
+  auto const h_column_info = infer_column_type(options, data, offset_length_begin, size, stream);
+
+  auto get_type_id = [&](auto const& cinfo) {
+    auto int_count_total =
+      cinfo.big_int_count + cinfo.negative_small_int_count + cinfo.positive_small_int_count;
+    if (cinfo.null_count == static_cast<cudf::size_type>(size)) {
+      // Entire column is NULL; allocate the smallest amount of memory
+      return type_id::INT8;
+    } else if (cinfo.string_count > 0) {
+      return type_id::STRING;
+    } else if (cinfo.datetime_count > 0) {
+      CUDF_FAIL("Date time is inferred as string.\n");
+    } else if (cinfo.float_count > 0) {
+      return type_id::FLOAT64;
+    } else if (cinfo.big_int_count == 0 && int_count_total != 0) {
+      return type_id::INT64;
+    } else if (cinfo.big_int_count != 0 && cinfo.negative_small_int_count != 0) {
+      return type_id::STRING;
+    } else if (cinfo.big_int_count != 0) {
+      return type_id::UINT64;
+    } else if (cinfo.bool_count > 0) {
+      return type_id::BOOL8;
+    }
+    CUDF_FAIL("Data type inference failed.\n");
+  };
+  return cudf::data_type{get_type_id(h_column_info)};
+}
+}  // namespace cudf::io::detail
diff --git a/cpp/src/jit/cache.cpp b/cpp/src/jit/cache.cpp
new file mode 100644
index 0000000..46b347d
--- /dev/null
+++ b/cpp/src/jit/cache.cpp
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/error.hpp>
+
+#include <cuda.h>
+#include <jitify2.hpp>
+
+#include <cstddef>
+#include <filesystem>
+
+namespace cudf {
+namespace jit {
+
+// Get the directory in home to use for storing the cache
+std::filesystem::path get_user_home_cache_dir()
+{
+  auto home_dir = std::getenv("HOME");
+  if (home_dir != nullptr) {
+    return std::filesystem::path(home_dir) / ".cudf";
+  } else {
+    return std::filesystem::path();
+  }
+}
+
+// Default `LIBCUDF_KERNEL_CACHE_PATH` to `$HOME/.cudf/$CUDF_VERSION`.
+// This definition can be overridden at compile time by specifying a
+// `-DLIBCUDF_KERNEL_CACHE_PATH=/kernel/cache/path` CMake argument.
+// Use `std::filesystem` for cross-platform path resolution and dir
+// creation. This path is used in the `getCacheDir()` function below.
+#if !defined(LIBCUDF_KERNEL_CACHE_PATH)
+#define LIBCUDF_KERNEL_CACHE_PATH get_user_home_cache_dir()
+#endif
+
+/**
+ * @brief Get the string path to the JITIFY kernel cache directory.
+ *
+ * This path can be overridden at runtime by defining an environment variable
+ * named `LIBCUDF_KERNEL_CACHE_PATH`. The value of this variable must be a path
+ * under which the process' user has read/write privileges.
+ *
+ * This function returns a path to the cache directory, creating it if it
+ * doesn't exist.
+ *
+ * The default cache directory is `$HOME/.cudf/$CUDF_VERSION`. If no overrides
+ * are used and if $HOME is not defined, returns an empty path and file
+ * caching is not used.
+ */
+std::filesystem::path get_cache_dir()
+{
+  // The environment variable always overrides the
+  // default/compile-time value of `LIBCUDF_KERNEL_CACHE_PATH`
+  auto kernel_cache_path_env = std::getenv("LIBCUDF_KERNEL_CACHE_PATH");
+  auto kernel_cache_path     = std::filesystem::path(
+    kernel_cache_path_env != nullptr ? kernel_cache_path_env : LIBCUDF_KERNEL_CACHE_PATH);
+
+  // Cache path could be empty when env HOME is unset or LIBCUDF_KERNEL_CACHE_PATH is defined to be
+  // empty, to disallow use of file cache at runtime.
+  if (not kernel_cache_path.empty()) {
+    kernel_cache_path /= std::string{CUDF_STRINGIFY(CUDF_VERSION)};
+
+    // Make per device cache based on compute capability. This is to avoid multiple devices of
+    // different compute capability to access the same kernel cache.
+    int device;
+    int cc_major;
+    int cc_minor;
+    CUDF_CUDA_TRY(cudaGetDevice(&device));
+    CUDF_CUDA_TRY(cudaDeviceGetAttribute(&cc_major, cudaDevAttrComputeCapabilityMajor, device));
+    CUDF_CUDA_TRY(cudaDeviceGetAttribute(&cc_minor, cudaDevAttrComputeCapabilityMinor, device));
+    int cc = cc_major * 10 + cc_minor;
+
+    kernel_cache_path /= std::to_string(cc);
+
+    try {
+      // `mkdir -p` the kernel cache path if it doesn't exist
+      std::filesystem::create_directories(kernel_cache_path);
+    } catch (std::exception const& e) {
+      // if directory creation fails for any reason, return empty path
+      return std::filesystem::path();
+    }
+  }
+  return kernel_cache_path;
+}
+
+std::string get_program_cache_dir()
+{
+#if defined(JITIFY_USE_CACHE)
+  return get_cache_dir().string();
+#else
+  return {};
+#endif
+}
+
+std::size_t try_parse_numeric_env_var(char const* const env_name, std::size_t default_val)
+{
+  auto const value = std::getenv(env_name);
+  return value != nullptr ? std::stoull(value) : default_val;
+}
+
+jitify2::ProgramCache<>& get_program_cache(jitify2::PreprocessedProgramData preprog)
+{
+  static std::mutex caches_mutex{};
+  static std::unordered_map<std::string, std::unique_ptr<jitify2::ProgramCache<>>> caches{};
+
+  std::lock_guard<std::mutex> caches_lock(caches_mutex);
+
+  auto existing_cache = caches.find(preprog.name());
+
+  if (existing_cache == caches.end()) {
+    auto const kernel_limit_proc =
+      try_parse_numeric_env_var("LIBCUDF_KERNEL_CACHE_LIMIT_PER_PROCESS", 10'000);
+    auto const kernel_limit_disk =
+      try_parse_numeric_env_var("LIBCUDF_KERNEL_CACHE_LIMIT_DISK", 100'000);
+
+    // if kernel_limit_disk is zero, jitify will assign it the value of kernel_limit_proc.
+    // to avoid this, we treat zero as "disable disk caching" by not providing the cache dir.
+    auto const cache_dir = kernel_limit_disk == 0 ? std::string{} : get_program_cache_dir();
+
+    auto const res =
+      caches.insert({preprog.name(),
+                     std::make_unique<jitify2::ProgramCache<>>(
+                       kernel_limit_proc, preprog, nullptr, cache_dir, kernel_limit_disk)});
+    existing_cache = res.first;
+  }
+
+  return *(existing_cache->second);
+}
+
+}  // namespace jit
+}  // namespace cudf
diff --git a/cpp/src/jit/cache.hpp b/cpp/src/jit/cache.hpp
new file mode 100644
index 0000000..df8d427
--- /dev/null
+++ b/cpp/src/jit/cache.hpp
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <jitify2.hpp>
+#include <memory>
+
+namespace cudf {
+namespace jit {
+
+jitify2::ProgramCache<>& get_program_cache(jitify2::PreprocessedProgramData preprog);
+
+}  // namespace jit
+}  // namespace cudf
diff --git a/cpp/src/jit/parser.cpp b/cpp/src/jit/parser.cpp
new file mode 100644
index 0000000..1bc126d
--- /dev/null
+++ b/cpp/src/jit/parser.cpp
@@ -0,0 +1,416 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "parser.hpp"
+
+#include <cudf/utilities/error.hpp>
+
+#include <algorithm>
+#include <cctype>
+#include <map>
+#include <set>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace jit {
+constexpr char percent_escape[] = "_";
+
+inline bool is_white(char const c) { return c == ' ' || c == '\n' || c == '\r' || c == '\t'; }
+
+std::string ptx_parser::escape_percent(std::string const& src)
+{
+  // b/c we're transforming into inline ptx we aren't allowed to have register names starting with %
+  auto f = std::find_if_not(src.begin(), src.end(), [](auto c) { return is_white(c) || c == '['; });
+  if (f != src.end() && *f == '%') {
+    std::string output = src;
+    output.replace(std::distance(src.begin(), f), 1, percent_escape);
+    return output;
+  }
+  return src;
+}
+
+std::string ptx_parser::remove_nonalphanumeric(std::string const& src)
+{
+  std::string out = src;
+  auto f = std::find_if_not(out.begin(), out.end(), [](auto c) { return is_white(c) || c == '['; });
+  auto l = std::find_if(f, out.end(), [](auto c) { return is_white(c) || c == ']'; });
+  std::replace_if(
+    f, l, [](auto c) { return !isalnum(c) && c != '_'; }, '_');
+  return std::string(f, l);
+}
+
+std::string ptx_parser::register_type_to_contraint(std::string const& src)
+{
+  if (src == ".b8" || src == ".u8" || src == ".s8")
+    return "h";
+  else if (src == ".u16" || src == ".s16" || src == ".b16" || src == ".f16")
+    return "h";
+  else if (src == ".b32" || src == ".u32" || src == ".s32" || src == ".f16x2")
+    return "r";
+  else if (src == ".u64" || src == ".b64" || src == ".s64")
+    return "l";
+  else if (src == ".f32")
+    return "f";
+  else if (src == ".f64")
+    return "d";
+  else
+    return "x_reg";
+}
+
+std::string ptx_parser::register_type_to_cpp_type(std::string const& register_type)
+{
+  if (register_type == ".b8" || register_type == ".s8" || register_type == ".u8")
+    return "char";
+  else if (register_type == ".u16")
+    return "short int";
+  else if (register_type == ".s16")
+    return "short int";
+  else if (register_type == ".f16")
+    return "half";
+  else if (register_type == ".u32")
+    return "int";
+  else if (register_type == ".s32")
+    return "int";
+  else if (register_type == ".f16x2")
+    return "half2";
+  else if (register_type == ".u64")
+    return "long int";
+  else if (register_type == ".s64")
+    return "long int";
+  else if (register_type == ".f32")
+    return "float";
+  else if (register_type == ".f64")
+    return "double";
+  else
+    return "x_cpptype";
+}
+
+std::string ptx_parser::parse_instruction(std::string const& src)
+{
+  // I am assuming for an instruction statement the starting phrase is an
+  // instruction.
+  size_t const length = src.size();
+  std::string output;
+  std::string suffix;
+
+  std::string original_code = "\n   /**   " + src + "  */\n";
+
+  int piece_count = 0;
+
+  size_t start                      = 0;
+  size_t stop                       = 0;
+  bool is_instruction               = true;
+  bool is_param_loading_instruction = false;
+  std::string constraint;
+  std::string register_type;
+  bool blank = true;
+  std::string cpp_typename;
+  while (stop < length) {
+    while (start < length && (is_white(src[start]) || src[start] == ',' || src[start] == '{' ||
+                              src[start] == '}')) {  // running to the first non-white character.
+      if (src[start] == ',') output += ',';
+      if (src[start] == '{') output += '{';
+      if (src[start] == '}') output += '}';
+      start++;
+    }
+    stop = start;
+    if (stop < length) {
+      blank = false;
+      output += " ";
+    } else {
+      break;
+    }
+    if (src[start] == '[') {
+      while (stop < length && src[stop] != ']') {
+        stop++;
+      }
+      stop++;
+    } else {
+      while (stop < length && !is_white(src[stop]) && src[stop] != ',' && src[stop] != ':') {
+        stop++;
+      }
+      if (src[stop] == ':') {
+        // This is a branch
+        stop++;
+        output += std::string(src, start, stop - start);
+        start = stop;
+        continue;
+      }
+    }
+    std::string piece = std::string(src, start, stop - start);
+    if (is_instruction) {
+      if (piece.find("ld.param") != std::string::npos) {
+        is_param_loading_instruction = true;
+        register_type                = std::string(piece, 8, stop - 8);
+        // This is the ld.param sentence
+        cpp_typename = register_type_to_cpp_type(register_type);
+        if (cpp_typename == "int" || cpp_typename == "short int" || cpp_typename == "char") {
+          // The trick to support `ld` statement whose destination reg. wider than
+          // the instruction width, e.g.
+          //
+          //  "ld.param.s32 %rd0, [...];",
+          //
+          // where %rd0 is a 64-bit register. Directly converting to "mov" instruction
+          // does not work since "register widening" is ONLY allowed for
+          // "ld", "st", and "cvt". So we use cvt instead and something like
+          // "cvt.s32.s32". This keep the same operation behavior and when compiling to
+          // SASS code "usually" (in cases I have seen) this is optimized away, thus
+          // gives no performance penalty.
+          output += " cvt" + register_type + register_type;
+        } else {
+          output += " mov" + register_type;
+        }
+        constraint = register_type_to_contraint(register_type);
+      } else if (piece.find("st.param") != std::string::npos) {
+        return "asm volatile (\"" + output +
+               "/** *** The way we parse the CUDA PTX assumes the function returns the return "
+               "value through the first function parameter. Thus the `st.param.***` instructions "
+               "are not processed. *** */" +
+               "\");" + original_code;  // Our port does not support return value;
+      } else if (piece[0] == '@') {
+        output += " @" + remove_nonalphanumeric(piece.substr(1, piece.size() - 1));
+      } else {
+        output += " " + piece;
+      }
+      is_instruction = false;
+    } else {
+      // Here it should be the registers.
+      if (piece_count == 2 && is_param_loading_instruction) {
+        // This is the source of the parameter loading instruction
+        output += " %0";
+        if (cpp_typename == "char") {
+          suffix = ": : \"" + constraint + "\"( static_cast<short>(" +
+                   remove_nonalphanumeric(piece) + "))";
+        } else {
+          suffix = ": : \"" + constraint + "\"(" + remove_nonalphanumeric(piece) + ")";
+        }
+        // Here we get to see the actual type of the input arguments.
+        input_arg_list[remove_nonalphanumeric(piece)] = register_type_to_cpp_type(register_type);
+      } else {
+        output += escape_percent(std::string(src, start, stop - start));
+      }
+    }
+    start = stop;
+    piece_count++;
+  }
+  if (!blank) output += ";";
+  return "asm volatile (\"" + output + "\"" + suffix + ");" + original_code;
+}
+
+std::string ptx_parser::parse_statement(std::string const& src)
+{
+  auto f = std::find_if_not(src.cbegin(), src.cend(), [](auto c) { return is_white(c); });
+  return f == src.cend() ? " \n" : parse_instruction(std::string(f, src.cend()));
+}
+
+std::vector<std::string> ptx_parser::parse_function_body(std::string const& src)
+{
+  auto f = src.cbegin();
+  std::vector<std::string> statements;
+
+  while (f < src.cend()) {
+    auto l = std::find(f, src.cend(), ';');
+    statements.push_back(parse_statement(std::string(f, l)));
+    f = ++l;
+  }
+  return statements;
+}
+
+std::string ptx_parser::parse_param(std::string const& src)
+{
+  auto i = 0;
+  auto f = src.cbegin();
+
+  while (f < src.cend() && i <= 3) {
+    f      = std::find_if_not(f, src.cend(), [](auto c) { return is_white(c); });
+    auto l = std::find_if(f, src.cend(), [](auto c) { return is_white(c); });
+    if (++i == 3) return remove_nonalphanumeric(std::string(f, l));
+    f = l;
+  }
+  return "";
+}
+
+std::string ptx_parser::parse_param_list(std::string const& src)
+{
+  auto f = src.begin();
+
+  auto item_count = 0;
+  std::string output{};
+
+  while (f < src.end()) {
+    auto l = std::find(f, src.end(), ',');
+
+    output += [&, name = parse_param(std::string(f, l))] {
+      if (pointer_arg_list.find(item_count) != pointer_arg_list.end()) {
+        if (item_count == 0) {
+          return output_arg_type + "* " + name;
+        } else {
+          // On a 64-bit machine inside the PTX function body a pointer is
+          // literally just a uint_64 so here is doesn't make sense to
+          // have the type of the pointer. Thus we will just use void* here.
+          return ",\n  const void* " + name;
+        }
+      } else {
+        if (input_arg_list.count(name)) {
+          return ", \n  " + input_arg_list[name] + " " + name;
+        } else {
+          // This parameter isn't used in the function body so we just pretend
+          // it's an int. After being inlined they are gone anyway.
+          return ", \n  int " + name;
+        }
+      }
+    }();
+
+    f = ++l;
+    item_count++;
+  }
+
+  return "\n  " + output + "\n";
+}
+
+std::string ptx_parser::parse_function_header(std::string const& src)
+{
+  // Essentially we only need the information inside the two pairs of parentheses.
+  auto f = [&] {
+    auto i = std::find_if_not(src.cbegin(), src.cend(), [](auto c) { return is_white(c); });
+    if (i != src.cend() && *i == '(')  // This function has a return type
+      // First Pass: output param list
+      i = std::find_if_not(std::next(i), src.cend(), [](auto c) { return c == ')'; });
+    // The function name
+    i = std::find_if_not(std::next(i), src.cend(), [](auto c) { return is_white(c) || c == '('; });
+    // Second Pass: input param list
+    return std::next(std::find(i, src.cend(), '('));
+  }();
+
+  auto l = std::find(f, src.cend(), ')');
+
+  auto const input_arg = parse_param_list(std::string(f, l));
+  return "\n__device__ __inline__ void " + function_name + "(" + input_arg + "){" + "\n";
+}
+
+std::string remove_comments(std::string const& src)
+{
+  std::string output;
+  auto f = src.cbegin();
+  while (f < src.cend()) {
+    auto l = std::find(f, src.cend(), '/');
+    output.append(f, l);  // push chunk instead of 1 char at a time
+    f = std::next(l);     // skip over '/'
+    if (l < src.cend()) {
+      char n = f < src.cend() ? *f : '?';
+      if (n == '/') {                        // found "//"
+        f = std::find(f, src.cend(), '\n');  // skip to end of line
+      } else if (n == '*') {                 // found "/*"
+        auto term = std::string("*/");       // skip to end of next "*/"
+        f         = std::search(std::next(f), src.cend(), term.cbegin(), term.cend()) + term.size();
+      } else {
+        output.push_back('/');  // lone '/' should be pushed into output
+      }
+    }
+  }
+  return output;
+}
+
+// The interface
+std::string ptx_parser::parse()
+{
+  std::string no_comments = remove_comments(ptx);
+
+  input_arg_list.clear();
+  auto const _func = std::string(".func");  // Go directly to the .func mark
+  auto f = std::search(no_comments.cbegin(), no_comments.cend(), _func.cbegin(), _func.cend()) +
+           _func.size();
+
+  CUDF_EXPECTS(f < no_comments.cend(), "No function (.func) found in the input ptx code.\n");
+
+  auto l = std::find(f, no_comments.cend(), '{');
+
+  auto f2 = std::next(l);
+  auto l2 = std::find_if(f2, no_comments.cend(), [brace_count = 0](auto c) mutable {
+    if (c == '{') ++brace_count;
+    if (c == '}') {
+      if (brace_count == 0) return true;  // find matching } to first found {
+      --brace_count;
+    }
+    return false;
+  });
+
+  // DO NOT CHANGE ORDER - parse_function_body must be called before parse_function_header
+  // because the function parameter types are inferred from their corresponding load
+  // instructions in the function body
+  auto const fn_body_output   = parse_function_body(std::string(f2, l2));
+  auto const fn_header_output = parse_function_header(std::string(f, l));
+
+  // Don't use std::accumulate until C++20 when rvalue references are supported
+  auto final_output = fn_header_output + "\n asm volatile (\"{\");";
+  for (auto const& line : fn_body_output)
+    final_output += line.find("ret;") != std::string::npos ? "  asm volatile (\"bra RETTGT;\");\n"
+                                                           : "  " + line + "\n";
+  return final_output + " asm volatile (\"RETTGT:}\");}";
+}
+
+ptx_parser::ptx_parser(std::string const& ptx_,
+                       std::string const& function_name_,
+                       std::string const& output_arg_type_,
+                       std::set<int> const& pointer_arg_list_)
+  : ptx(ptx_),
+    function_name(function_name_),
+    output_arg_type(output_arg_type_),
+    pointer_arg_list(pointer_arg_list_)
+{
+}
+
+// The interface
+std::string parse_single_function_cuda(std::string const& src, std::string const& function_name)
+{
+  std::string no_comments = remove_comments(src);
+
+  // For CUDA device function we just need to find the function
+  // name and replace it with the specified one.
+  size_t const length = no_comments.size();
+  size_t start        = 0;
+  size_t stop         = start;
+
+  while (stop < length && no_comments[stop] != '(') {
+    stop++;
+  }
+  CUDF_EXPECTS(stop != length && stop != 0,
+               "No CUDA device function found in the input CUDA code.\n");
+
+  stop--;
+
+  while (stop > 0 && is_white(no_comments[stop])) {
+    stop--;
+  }
+  CUDF_EXPECTS(stop != 0 || !is_white(no_comments[0]),
+               "No CUDA device function name found in the input CUDA code.\n");
+
+  start = stop;
+  while (start > 0 && !is_white(no_comments[start])) {
+    start--;
+  }
+  start++;
+  stop++;
+  CUDF_EXPECTS(start < stop, "No CUDA device function name found in the input CUDA code.\n");
+
+  no_comments.replace(start, stop - start, function_name);
+
+  return no_comments;
+}
+
+}  // namespace jit
+}  // namespace cudf
diff --git a/cpp/src/jit/parser.hpp b/cpp/src/jit/parser.hpp
new file mode 100644
index 0000000..86f869c
--- /dev/null
+++ b/cpp/src/jit/parser.hpp
@@ -0,0 +1,242 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <map>
+#include <set>
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace jit {
+/**
+ * @brief Parse and transform a piece of PTX code that contains the implementation
+ * of a `__device__` function into a CUDA `__device__` `__inline__` function.
+ *
+ * @param `src` The input PTX code.
+ * @param `function_name` The User defined function that the output CUDA function
+ * will have.
+ * @param `output_arg_type` The output type of the PTX function, e.g. "int", "int64_t"
+ * @return The output CUDA `__device__` `__inline__` function
+ */
+class ptx_parser {
+ private:
+  std::string ptx;
+
+  std::string function_name;
+
+  std::string output_arg_type;
+
+  std::set<int> pointer_arg_list;
+
+  std::map<std::string, std::string> input_arg_list;
+
+ private:
+  /**
+   * @brief parse and transform header part of the PTX code into a CUDA header
+   *
+   * The result always has `__device__ __inline__ void`. The types of the input
+   * parameters are determined from, in descending order of priority:
+   *  1. The first parameter is always of type "`output_arg_type`*"
+   *  2. All other parameters marked in pointer_arg_list are of type "const void*"
+   *  3. For parameters that are used in the function body their types are
+   *      inferred from their corresponding parameter loading instructions
+   *  4. Unused parameters are always of type "int"
+   *
+   * @param src The header part of the PTX code
+   * @return The parsed CUDA header
+
+   As an example:
+
+    .visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241Eff(
+      .param .b64 _ZN8__main__7add$241Eff_param_0,
+      .param .b32 _ZN8__main__7add$241Eff_param_1,
+      .param .b32 _ZN8__main__7add$241Eff_param_2
+    )
+
+   will be transformed to
+
+    __device__ __inline__ void GENERIC_BINARY_OP(
+        float* _ZN8__main__7add_241Eff_param_0,
+        float _ZN8__main__7add_241Eff_param_1,
+        float _ZN8__main__7add_241Eff_param_2
+    )
+
+   */
+  std::string parse_function_header(std::string const& src);
+
+  /**
+   * @brief parse and transform input parameter list of the PTX code into the
+   * corresponding CUDA form
+   *
+   * @param src The input parameter list part of the PTX code
+   * @return The parsed CUDA input parameter list
+   */
+  std::string parse_param_list(std::string const& src);
+
+  /**
+   * @brief parse and transform an input parameter line of the PTX code into the
+   * corresponding CUDA form
+   *
+   * @param src The input parameter line of the PTX code
+   * @return The parsed CUDA input parameter
+   */
+  static std::string parse_param(std::string const& src);
+
+  /**
+   * @brief parse function body of the PTX code into statements by `;`s.
+   *
+   * @param src The function body of the PTX code
+   * @return The parsed statements
+   */
+  std::vector<std::string> parse_function_body(std::string const& src);
+
+  /**
+   * @brief Remove leading white characters and call `parse_instruction`.
+   *
+   * @param src The statement to be parsed.
+   * @return The resulting CUDA statement.
+   */
+  std::string parse_statement(std::string const& src);
+
+  /**
+   * @brief Convert the input PTX instruction into an inline PTX
+   * statement without changing (exceptions exist).
+   *
+   * Non-alphanumeric characters in register identifiers, except underscores, are replaced with
+   underscore. Example:
+   *
+   *  fma.rn.f32 	%f4, %f3, %f1, %f2
+   *
+   *    ---> asm volatile ("  fma.rn.f32 _f4, _f3, _f1, _f2;");
+   *
+   * If a register from the input parameters list is used in an instruction
+   * its type is inferred from the instruction and saved in the `input_arg_list`
+   * to be used in when parsing the function header.
+   *
+   * See the document at https://github.com/hummingtree/cudf/wiki/PTX-parser
+   * for the detailed description about the exceptions.
+   *
+   * @param src The statement to be parsed.
+   * @return The resulting CUDA inline PTX statement.
+   */
+  std::string parse_instruction(std::string const& src);
+
+  /**
+   * @brief Convert register type (e.g. ".f32") to the corresponding
+   * C++ type (e.g. "float")
+   *
+   * See the implementation for details
+   *
+   * @param src The input code
+   * @return The resulting code
+   */
+  static std::string register_type_to_cpp_type(std::string const& register_type);
+
+  /**
+   * @brief Convert register type (e.g. ".f32") to the corresponding
+   * constraint in inline PTX syntax (e.g. "f")
+   *
+   * See the implementation for details
+   *
+   * @param src The input code
+   * @return The resulting code
+   */
+  static std::string register_type_to_contraint(std::string const& src);
+
+  /**
+   * @brief Replace any non-alphanumeric characters that are not underscore with
+   * underscore. The leading `[` and trailing `]` are exempted, e.g.
+   *
+   *  "[t$5]" --> "[t_5]"
+   *
+   * @param src The input code
+   * @return The resulting code
+   */
+  static std::string remove_nonalphanumeric(std::string const& src);
+
+  /**
+   * @brief Replace leading `%` in register identifiers with `_`.
+   *
+   * According to PTX document `%` can only appear at the start of a register
+   * identifier. At the same time `%` is not allowed in inline PTX. This function
+   * first looks for the register identifier and if it starts with `%` replaces it
+   * with `_`.
+   *
+   * @param src The input code
+   * @return The resulting code
+   */
+  static std::string escape_percent(std::string const& src);
+
+ public:
+  ptx_parser() = delete;
+
+  /**
+   * @brief Constructor of the `ptx_parser` class
+   *
+   * @param ptx_ The input PTX code that contains the function whose
+   * CUDA is to be generated.
+   * @param function_name_ The function name of the output CUDA function
+   * @param output_arg_type_ The C++ type of the output parameter of the
+   * function.
+   * @param pointer_arg_list_ A list of the parameters that are pointers.
+   */
+  ptx_parser(std::string const& ptx_,
+             std::string const& function_name_,
+             std::string const& output_arg_type_,
+             std::set<int> const& pointer_arg_list_);
+
+  // parse the source!!!
+  std::string parse();
+};
+
+/**
+ * @brief Parse and Transform a piece of PTX code that contains the implementation
+ * of a device function into a CUDA device function.
+ *
+ * @param src The input PTX code.
+ * @param function_name The User defined function that the output CUDA function
+ * will have.
+ * @param output_arg_type output_arg_type The C++ type of the output parameter of the
+ * function
+ * @param pointer_arg_list A list of the parameters that are pointers.
+ * @return The output CUDA device function
+ */
+inline std::string parse_single_function_ptx(std::string const& src,
+                                             std::string const& function_name,
+                                             std::string const& output_arg_type,
+                                             std::set<int> const& pointer_arg_list = {0})
+{
+  ptx_parser instance(src, function_name, output_arg_type, pointer_arg_list);
+
+  return instance.parse();
+}
+
+/**
+ * @brief In a piece of CUDA code that contains the implementation
+ * of a device function, locate the function and replace its function name
+ * with the specified one.
+ *
+ * @param src The input CUDA code.
+ * @param function_name The User defined function that the output CUDA function
+ * will have.
+ * @return The output CUDA device function
+ */
+std::string parse_single_function_cuda(std::string const& src, std::string const& function_name);
+
+}  // namespace jit
+}  // namespace cudf
diff --git a/cpp/src/jit/util.cpp b/cpp/src/jit/util.cpp
new file mode 100644
index 0000000..0585e02
--- /dev/null
+++ b/cpp/src/jit/util.cpp
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <string>
+
+namespace cudf {
+namespace jit {
+struct get_data_ptr_functor {
+  /**
+   * @brief Gets the data pointer from a column_view
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  void const* operator()(column_view const& view)
+  {
+    return static_cast<void const*>(view.template data<T>());
+  }
+
+  template <typename T, CUDF_ENABLE_IF(not is_rep_layout_compatible<T>())>
+  void const* operator()(column_view const& view)
+  {
+    CUDF_FAIL("Invalid data type for JIT operation");
+  }
+
+  /**
+   * @brief Gets the data pointer from a scalar
+   */
+  template <typename T, CUDF_ENABLE_IF(is_rep_layout_compatible<T>())>
+  void const* operator()(scalar const& s)
+  {
+    using ScalarType = scalar_type_t<T>;
+    auto s1          = static_cast<ScalarType const*>(&s);
+    return static_cast<void const*>(s1->data());
+  }
+
+  template <typename T, CUDF_ENABLE_IF(not is_rep_layout_compatible<T>())>
+  void const* operator()(scalar const& s)
+  {
+    CUDF_FAIL("Invalid data type for JIT operation");
+  }
+};
+
+void const* get_data_ptr(column_view const& view)
+{
+  return type_dispatcher<dispatch_storage_type>(view.type(), get_data_ptr_functor{}, view);
+}
+
+void const* get_data_ptr(scalar const& s)
+{
+  return type_dispatcher<dispatch_storage_type>(s.type(), get_data_ptr_functor{}, s);
+}
+
+}  // namespace jit
+}  // namespace cudf
diff --git a/cpp/src/jit/util.hpp b/cpp/src/jit/util.hpp
new file mode 100644
index 0000000..a7e9ba6
--- /dev/null
+++ b/cpp/src/jit/util.hpp
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <string>
+
+namespace cudf {
+namespace jit {
+/**
+ * @brief Get the raw pointer to data in a (mutable_)column_view
+ */
+void const* get_data_ptr(column_view const& view);
+
+/**
+ * @brief Get the raw pointer to data in a scalar
+ */
+void const* get_data_ptr(scalar const& s);
+
+}  // namespace jit
+}  // namespace cudf
diff --git a/cpp/src/join/conditional_join.cu b/cpp/src/join/conditional_join.cu
new file mode 100644
index 0000000..c307352
--- /dev/null
+++ b/cpp/src/join/conditional_join.cu
@@ -0,0 +1,426 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/join.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <join/conditional_join.hpp>
+#include <join/conditional_join_kernels.cuh>
+#include <join/join_common_utils.cuh>
+#include <join/join_common_utils.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace detail {
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_join(table_view const& left,
+                 table_view const& right,
+                 ast::expression const& binary_predicate,
+                 join_kind join_type,
+                 std::optional<std::size_t> output_size,
+                 rmm::cuda_stream_view stream,
+                 rmm::mr::device_memory_resource* mr)
+{
+  // We can immediately filter out cases where the right table is empty. In
+  // some cases, we return all the rows of the left table with a corresponding
+  // null index for the right table; in others, we return an empty output.
+  auto right_num_rows{right.num_rows()};
+  auto left_num_rows{left.num_rows()};
+  if (right_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, and full all return all the row indices from left
+      // with a corresponding NULL from the right.
+      case join_kind::LEFT_JOIN:
+      case join_kind::LEFT_ANTI_JOIN:
+      case join_kind::FULL_JOIN:
+        return get_trivial_left_join_indices(left, stream, rmm::mr::get_current_device_resource());
+      // Inner and left semi joins return empty output because no matches can exist.
+      case join_kind::INNER_JOIN:
+      case join_kind::LEFT_SEMI_JOIN:
+        return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                         std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  } else if (left_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, left semi, and inner joins all return empty sets.
+      case join_kind::LEFT_JOIN:
+      case join_kind::LEFT_ANTI_JOIN:
+      case join_kind::INNER_JOIN:
+      case join_kind::LEFT_SEMI_JOIN:
+        return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                         std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+      // Full joins need to return the trivial complement.
+      case join_kind::FULL_JOIN: {
+        auto ret_flipped =
+          get_trivial_left_join_indices(right, stream, rmm::mr::get_current_device_resource());
+        return std::pair(std::move(ret_flipped.second), std::move(ret_flipped.first));
+      }
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  }
+
+  // If evaluating the expression may produce null outputs we create a nullable
+  // output column and follow the null-supporting expression evaluation code
+  // path.
+  auto const has_nulls = binary_predicate.may_evaluate_null(left, right, stream);
+
+  auto const parser =
+    ast::detail::expression_parser{binary_predicate, left, right, has_nulls, stream, mr};
+  CUDF_EXPECTS(parser.output_type().id() == type_id::BOOL8,
+               "The expression must produce a boolean output.");
+
+  auto left_table  = table_device_view::create(left, stream);
+  auto right_table = table_device_view::create(right, stream);
+
+  // For inner joins we support optimizing the join by launching one thread for
+  // whichever table is larger rather than always using the left table.
+  auto swap_tables = (join_type == join_kind::INNER_JOIN) && (right_num_rows > left_num_rows);
+  detail::grid_1d const config(swap_tables ? right_num_rows : left_num_rows,
+                               DEFAULT_JOIN_BLOCK_SIZE);
+  auto const shmem_size_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+  join_kind const kernel_join_type =
+    join_type == join_kind::FULL_JOIN ? join_kind::LEFT_JOIN : join_type;
+
+  // If the join size was not provided as an input, compute it here.
+  std::size_t join_size;
+  if (output_size.has_value()) {
+    join_size = *output_size;
+  } else {
+    // Allocate storage for the counter used to get the size of the join output
+    rmm::device_scalar<std::size_t> size(0, stream, mr);
+    if (has_nulls) {
+      compute_conditional_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, true>
+        <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+          *left_table,
+          *right_table,
+          kernel_join_type,
+          parser.device_expression_data,
+          swap_tables,
+          size.data());
+    } else {
+      compute_conditional_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, false>
+        <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+          *left_table,
+          *right_table,
+          kernel_join_type,
+          parser.device_expression_data,
+          swap_tables,
+          size.data());
+    }
+    join_size = size.value(stream);
+  }
+
+  // The initial early exit clauses guarantee that we will not reach this point
+  // unless both the left and right tables are non-empty. Under that
+  // constraint, neither left nor full joins can return an empty result since
+  // at minimum we are guaranteed null matches for all non-matching rows. In
+  // all other cases (inner, left semi, and left anti joins) if we reach this
+  // point we can safely return an empty result.
+  if (join_size == 0) {
+    return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                     std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+  }
+
+  rmm::device_scalar<size_type> write_index(0, stream);
+
+  auto left_indices  = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+  auto right_indices = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+
+  auto const& join_output_l = left_indices->data();
+  auto const& join_output_r = right_indices->data();
+  if (has_nulls) {
+    conditional_join<DEFAULT_JOIN_BLOCK_SIZE, DEFAULT_JOIN_CACHE_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_table,
+        *right_table,
+        kernel_join_type,
+        join_output_l,
+        join_output_r,
+        write_index.data(),
+        parser.device_expression_data,
+        join_size,
+        swap_tables);
+  } else {
+    conditional_join<DEFAULT_JOIN_BLOCK_SIZE, DEFAULT_JOIN_CACHE_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_table,
+        *right_table,
+        kernel_join_type,
+        join_output_l,
+        join_output_r,
+        write_index.data(),
+        parser.device_expression_data,
+        join_size,
+        swap_tables);
+  }
+
+  auto join_indices = std::pair(std::move(left_indices), std::move(right_indices));
+
+  // For full joins, get the indices in the right table that were not joined to
+  // by any row in the left table.
+  if (join_type == join_kind::FULL_JOIN) {
+    auto complement_indices = detail::get_left_join_indices_complement(
+      join_indices.second, left_num_rows, right_num_rows, stream, mr);
+    join_indices = detail::concatenate_vector_pairs(join_indices, complement_indices, stream);
+  }
+  return join_indices;
+}
+
+std::size_t compute_conditional_join_output_size(table_view const& left,
+                                                 table_view const& right,
+                                                 ast::expression const& binary_predicate,
+                                                 join_kind join_type,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  // Until we add logic to handle the number of non-matches in the right table,
+  // full joins are not supported in this function. Note that this does not
+  // prevent actually performing full joins since we do that by calculating the
+  // left join and then concatenating the complementary right indices.
+  CUDF_EXPECTS(join_type != join_kind::FULL_JOIN,
+               "Size estimation is not available for full joins.");
+
+  // We can immediately filter out cases where one table is empty. In
+  // some cases, we return all the rows of the other table with a corresponding
+  // null index for the empty table; in others, we return an empty output.
+  auto right_num_rows{right.num_rows()};
+  auto left_num_rows{left.num_rows()};
+  if (right_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, and full all return all the row indices from left
+      // with a corresponding NULL from the right.
+      case join_kind::LEFT_JOIN:
+      case join_kind::LEFT_ANTI_JOIN:
+      case join_kind::FULL_JOIN: return left_num_rows;
+      // Inner and left semi joins return empty output because no matches can exist.
+      case join_kind::INNER_JOIN:
+      case join_kind::LEFT_SEMI_JOIN: return 0;
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  } else if (left_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, left semi, and inner joins all return empty sets.
+      case join_kind::LEFT_JOIN:
+      case join_kind::LEFT_ANTI_JOIN:
+      case join_kind::INNER_JOIN:
+      case join_kind::LEFT_SEMI_JOIN: return 0;
+      // Full joins need to return the trivial complement.
+      case join_kind::FULL_JOIN: return right_num_rows;
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  }
+
+  // Prepare output column. Whether or not the output column is nullable is
+  // determined by whether any of the columns in the input table are nullable.
+  // If none of the input columns actually contain nulls, we can still use the
+  // non-nullable version of the expression evaluation code path for
+  // performance, so we capture that information as well.
+  auto const has_nulls = binary_predicate.may_evaluate_null(left, right, stream);
+
+  auto const parser =
+    ast::detail::expression_parser{binary_predicate, left, right, has_nulls, stream, mr};
+  CUDF_EXPECTS(parser.output_type().id() == type_id::BOOL8,
+               "The expression must produce a boolean output.");
+
+  auto left_table  = table_device_view::create(left, stream);
+  auto right_table = table_device_view::create(right, stream);
+
+  // For inner joins we support optimizing the join by launching one thread for
+  // whichever table is larger rather than always using the left table.
+  auto swap_tables = (join_type == join_kind::INNER_JOIN) && (right_num_rows > left_num_rows);
+  detail::grid_1d const config(swap_tables ? right_num_rows : left_num_rows,
+                               DEFAULT_JOIN_BLOCK_SIZE);
+  auto const shmem_size_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+
+  // Allocate storage for the counter used to get the size of the join output
+  rmm::device_scalar<std::size_t> size(0, stream, mr);
+
+  // Determine number of output rows without actually building the output to simply
+  // find what the size of the output will be.
+  if (has_nulls) {
+    compute_conditional_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_table,
+        *right_table,
+        join_type,
+        parser.device_expression_data,
+        swap_tables,
+        size.data());
+  } else {
+    compute_conditional_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_table,
+        *right_table,
+        join_type,
+        parser.device_expression_data,
+        swap_tables,
+        size.data());
+  }
+  return size.value(stream);
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_inner_join(table_view const& left,
+                       table_view const& right,
+                       ast::expression const& binary_predicate,
+                       std::optional<std::size_t> output_size,
+                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::conditional_join(left,
+                                  right,
+                                  binary_predicate,
+                                  detail::join_kind::INNER_JOIN,
+                                  output_size,
+                                  cudf::get_default_stream(),
+                                  mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_left_join(table_view const& left,
+                      table_view const& right,
+                      ast::expression const& binary_predicate,
+                      std::optional<std::size_t> output_size,
+                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::conditional_join(left,
+                                  right,
+                                  binary_predicate,
+                                  detail::join_kind::LEFT_JOIN,
+                                  output_size,
+                                  cudf::get_default_stream(),
+                                  mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_full_join(table_view const& left,
+                      table_view const& right,
+                      ast::expression const& binary_predicate,
+                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::conditional_join(left,
+                                  right,
+                                  binary_predicate,
+                                  detail::join_kind::FULL_JOIN,
+                                  {},
+                                  cudf::get_default_stream(),
+                                  mr);
+}
+
+std::unique_ptr<rmm::device_uvector<size_type>> conditional_left_semi_join(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  std::optional<std::size_t> output_size,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return std::move(detail::conditional_join(left,
+                                            right,
+                                            binary_predicate,
+                                            detail::join_kind::LEFT_SEMI_JOIN,
+                                            output_size,
+                                            cudf::get_default_stream(),
+                                            mr)
+                     .first);
+}
+
+std::unique_ptr<rmm::device_uvector<size_type>> conditional_left_anti_join(
+  table_view const& left,
+  table_view const& right,
+  ast::expression const& binary_predicate,
+  std::optional<std::size_t> output_size,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return std::move(detail::conditional_join(left,
+                                            right,
+                                            binary_predicate,
+                                            detail::join_kind::LEFT_ANTI_JOIN,
+                                            output_size,
+                                            cudf::get_default_stream(),
+                                            mr)
+                     .first);
+}
+
+std::size_t conditional_inner_join_size(table_view const& left,
+                                        table_view const& right,
+                                        ast::expression const& binary_predicate,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_conditional_join_output_size(
+    left, right, binary_predicate, detail::join_kind::INNER_JOIN, cudf::get_default_stream(), mr);
+}
+
+std::size_t conditional_left_join_size(table_view const& left,
+                                       table_view const& right,
+                                       ast::expression const& binary_predicate,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_conditional_join_output_size(
+    left, right, binary_predicate, detail::join_kind::LEFT_JOIN, cudf::get_default_stream(), mr);
+}
+
+std::size_t conditional_left_semi_join_size(table_view const& left,
+                                            table_view const& right,
+                                            ast::expression const& binary_predicate,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return std::move(detail::compute_conditional_join_output_size(left,
+                                                                right,
+                                                                binary_predicate,
+                                                                detail::join_kind::LEFT_SEMI_JOIN,
+                                                                cudf::get_default_stream(),
+                                                                mr));
+}
+
+std::size_t conditional_left_anti_join_size(table_view const& left,
+                                            table_view const& right,
+                                            ast::expression const& binary_predicate,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return std::move(detail::compute_conditional_join_output_size(left,
+                                                                right,
+                                                                binary_predicate,
+                                                                detail::join_kind::LEFT_ANTI_JOIN,
+                                                                cudf::get_default_stream(),
+                                                                mr));
+}
+
+}  // namespace cudf
diff --git a/cpp/src/join/conditional_join.hpp b/cpp/src/join/conditional_join.hpp
new file mode 100644
index 0000000..9bc6024
--- /dev/null
+++ b/cpp/src/join/conditional_join.hpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "join_common_utils.hpp"
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Computes the join operation between two tables and returns the
+ * output indices of left and right table as a combined table
+ *
+ * @param left  Table of left columns to join
+ * @param right Table of right  columns to join
+ * tables have been flipped, meaning the output indices should also be flipped
+ * @param JoinKind The type of join to be performed
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return Join output indices vector pair
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+conditional_join(table_view const& left,
+                 table_view const& right,
+                 ast::expression const& binary_predicate,
+                 join_kind JoinKind,
+                 std::optional<std::size_t> output_size,
+                 rmm::cuda_stream_view stream,
+                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Computes the size of a join operation between two tables without
+ * materializing the result and returns the total size value.
+ *
+ * @param left  Table of left columns to join
+ * @param right Table of right  columns to join
+ * tables have been flipped, meaning the output indices should also be flipped
+ * @param JoinKind The type of join to be performed
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return Join output indices vector pair
+ */
+std::size_t compute_conditional_join_output_size(table_view const& left,
+                                                 table_view const& right,
+                                                 ast::expression const& binary_predicate,
+                                                 join_kind JoinKind,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/join/conditional_join_kernels.cuh b/cpp/src/join/conditional_join_kernels.cuh
new file mode 100644
index 0000000..f665aba
--- /dev/null
+++ b/cpp/src/join/conditional_join_kernels.cuh
@@ -0,0 +1,276 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <join/join_common_utils.cuh>
+#include <join/join_common_utils.hpp>
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/table_device_view.cuh>
+
+#include <cub/cub.cuh>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Computes the output size of joining the left table to the right table.
+ *
+ * This method uses a nested loop to iterate over the left and right tables and count the number of
+ * matches according to a boolean expression.
+ *
+ * @tparam block_size The number of threads per block for this kernel
+ * @tparam has_nulls Whether or not the inputs may contain nulls.
+ *
+ * @param[in] left_table The left table
+ * @param[in] right_table The right table
+ * @param[in] join_type The type of join to be performed
+ * @param[in] device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param[in] swap_tables If true, the kernel was launched with one thread per right row and
+ * the kernel needs to internally loop over left rows. Otherwise, loop over right rows.
+ * @param[out] output_size The resulting output size
+ */
+template <int block_size, bool has_nulls>
+__global__ void compute_conditional_join_output_size(
+  table_device_view left_table,
+  table_device_view right_table,
+  join_kind join_type,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size)
+{
+  // The (required) extern storage of the shared memory array leads to
+  // conflicting declarations between different templates. The easiest
+  // workaround is to declare an arbitrary (here char) array type then cast it
+  // after the fact to the appropriate type.
+  extern __shared__ char raw_intermediate_storage[];
+  cudf::ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<cudf::ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+  auto thread_intermediate_storage =
+    &intermediate_storage[threadIdx.x * device_expression_data.num_intermediates];
+
+  std::size_t thread_counter{0};
+  auto const start_idx = cudf::detail::grid_1d::global_thread_id();
+  auto const stride    = cudf::detail::grid_1d::grid_stride();
+
+  cudf::thread_index_type const left_num_rows  = left_table.num_rows();
+  cudf::thread_index_type const right_num_rows = right_table.num_rows();
+  auto const outer_num_rows                    = (swap_tables ? right_num_rows : left_num_rows);
+  auto const inner_num_rows                    = (swap_tables ? left_num_rows : right_num_rows);
+
+  auto evaluator = cudf::ast::detail::expression_evaluator<has_nulls>(
+    left_table, right_table, device_expression_data);
+
+  for (cudf::thread_index_type outer_row_index = start_idx; outer_row_index < outer_num_rows;
+       outer_row_index += stride) {
+    bool found_match = false;
+    for (cudf::thread_index_type inner_row_index = 0; inner_row_index < inner_num_rows;
+         ++inner_row_index) {
+      auto output_dest = cudf::ast::detail::value_expression_result<bool, has_nulls>();
+      cudf::size_type const left_row_index  = swap_tables ? inner_row_index : outer_row_index;
+      cudf::size_type const right_row_index = swap_tables ? outer_row_index : inner_row_index;
+      evaluator.evaluate(
+        output_dest, left_row_index, right_row_index, 0, thread_intermediate_storage);
+      if (output_dest.is_valid() && output_dest.value()) {
+        if ((join_type != join_kind::LEFT_ANTI_JOIN) &&
+            !(join_type == join_kind::LEFT_SEMI_JOIN && found_match)) {
+          ++thread_counter;
+        }
+        found_match = true;
+      }
+    }
+    if ((join_type == join_kind::LEFT_JOIN || join_type == join_kind::LEFT_ANTI_JOIN ||
+         join_type == join_kind::FULL_JOIN) &&
+        (!found_match)) {
+      ++thread_counter;
+    }
+  }
+
+  using BlockReduce = cub::BlockReduce<cudf::size_type, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t block_counter = BlockReduce(temp_storage).Sum(thread_counter);
+
+  // Add block counter to global counter
+  if (threadIdx.x == 0) {
+    cuda::atomic_ref<std::size_t, cuda::thread_scope_device> ref{*output_size};
+    ref.fetch_add(block_counter, cuda::std::memory_order_relaxed);
+  }
+}
+
+/**
+ * @brief Performs a join conditioned on a predicate to find all matching rows
+ * between the left and right tables and generate the output for the desired
+ * Join operation.
+ *
+ * @tparam block_size The number of threads per block for this kernel
+ * @tparam output_cache_size The side of the shared memory buffer to cache join
+ * output results
+ * @tparam has_nulls Whether or not the inputs may contain nulls.
+ *
+ * @param[in] left_table The left table
+ * @param[in] right_table The right table
+ * @param[in] join_type The type of join to be performed
+ * @param[out] join_output_l The left result of the join operation
+ * @param[out] join_output_r The right result of the join operation
+ * @param[in,out] current_idx A global counter used by threads to coordinate
+ * writes to the global output
+ * @param device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param[in] max_size The maximum size of the output
+ * @param[in] swap_tables If true, the kernel was launched with one thread per right row and
+ * the kernel needs to internally loop over left rows. Otherwise, loop over right rows.
+ */
+template <cudf::size_type block_size, cudf::size_type output_cache_size, bool has_nulls>
+__global__ void conditional_join(table_device_view left_table,
+                                 table_device_view right_table,
+                                 join_kind join_type,
+                                 cudf::size_type* join_output_l,
+                                 cudf::size_type* join_output_r,
+                                 cudf::size_type* current_idx,
+                                 cudf::ast::detail::expression_device_view device_expression_data,
+                                 cudf::size_type const max_size,
+                                 bool const swap_tables)
+{
+  constexpr int num_warps = block_size / detail::warp_size;
+  __shared__ cudf::size_type current_idx_shared[num_warps];
+  __shared__ cudf::size_type join_shared_l[num_warps][output_cache_size];
+  __shared__ cudf::size_type join_shared_r[num_warps][output_cache_size];
+
+  // Normally the casting of a shared memory array is used to create multiple
+  // arrays of different types from the shared memory buffer, but here it is
+  // used to circumvent conflicts between arrays of different types between
+  // different template instantiations due to the extern specifier.
+  extern __shared__ char raw_intermediate_storage[];
+  cudf::ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<cudf::ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+  auto thread_intermediate_storage =
+    &intermediate_storage[threadIdx.x * device_expression_data.num_intermediates];
+
+  int const warp_id                            = threadIdx.x / detail::warp_size;
+  int const lane_id                            = threadIdx.x % detail::warp_size;
+  cudf::thread_index_type const left_num_rows  = left_table.num_rows();
+  cudf::thread_index_type const right_num_rows = right_table.num_rows();
+  cudf::thread_index_type const outer_num_rows = (swap_tables ? right_num_rows : left_num_rows);
+  cudf::thread_index_type const inner_num_rows = (swap_tables ? left_num_rows : right_num_rows);
+
+  if (0 == lane_id) { current_idx_shared[warp_id] = 0; }
+
+  __syncwarp();
+
+  auto outer_row_index = cudf::detail::grid_1d::global_thread_id();
+
+  unsigned int const activemask = __ballot_sync(0xffff'ffffu, outer_row_index < outer_num_rows);
+
+  auto evaluator = cudf::ast::detail::expression_evaluator<has_nulls>(
+    left_table, right_table, device_expression_data);
+
+  if (outer_row_index < outer_num_rows) {
+    bool found_match = false;
+    for (thread_index_type inner_row_index(0); inner_row_index < inner_num_rows;
+         ++inner_row_index) {
+      auto output_dest           = cudf::ast::detail::value_expression_result<bool, has_nulls>();
+      auto const left_row_index  = swap_tables ? inner_row_index : outer_row_index;
+      auto const right_row_index = swap_tables ? outer_row_index : inner_row_index;
+      evaluator.evaluate(
+        output_dest, left_row_index, right_row_index, 0, thread_intermediate_storage);
+
+      if (output_dest.is_valid() && output_dest.value()) {
+        // If the rows are equal, then we have found a true match
+        // In the case of left anti joins we only add indices from left after
+        // the loop if we have found _no_ matches from the right.
+        // In the case of left semi joins we only add the first match (note
+        // that the current logic relies on the fact that we process all right
+        // table rows for a single left table row on a single thread so that no
+        // synchronization of found_match is required).
+        if ((join_type != join_kind::LEFT_ANTI_JOIN) &&
+            !(join_type == join_kind::LEFT_SEMI_JOIN && found_match)) {
+          add_pair_to_cache(left_row_index,
+                            right_row_index,
+                            current_idx_shared,
+                            warp_id,
+                            join_shared_l[warp_id],
+                            join_shared_r[warp_id]);
+        }
+        found_match = true;
+      }
+
+      __syncwarp(activemask);
+
+      // flush output cache if next iteration does not fit
+      auto const do_flush   = current_idx_shared[warp_id] + detail::warp_size >= output_cache_size;
+      auto const flush_mask = __ballot_sync(activemask, do_flush);
+      if (do_flush) {
+        flush_output_cache<num_warps, output_cache_size>(flush_mask,
+                                                         max_size,
+                                                         warp_id,
+                                                         lane_id,
+                                                         current_idx,
+                                                         current_idx_shared,
+                                                         join_shared_l,
+                                                         join_shared_r,
+                                                         join_output_l,
+                                                         join_output_r);
+        __syncwarp(flush_mask);
+        if (0 == lane_id) { current_idx_shared[warp_id] = 0; }
+      }
+      __syncwarp(activemask);
+    }
+
+    // Left, left anti, and full joins all require saving left columns that
+    // aren't present in the right.
+    if ((join_type == join_kind::LEFT_JOIN || join_type == join_kind::LEFT_ANTI_JOIN ||
+         join_type == join_kind::FULL_JOIN) &&
+        (!found_match)) {
+      // TODO: This code assumes that swap_tables is false for all join
+      // kinds aside from inner joins. Once the code is generalized to handle
+      // other joins we'll want to switch the variable in the line below back
+      // to the left_row_index, but for now we can assume that they are
+      // equivalent inside this conditional.
+      add_pair_to_cache(outer_row_index,
+                        static_cast<cudf::size_type>(JoinNoneValue),
+                        current_idx_shared,
+                        warp_id,
+                        join_shared_l[warp_id],
+                        join_shared_r[warp_id]);
+    }
+
+    __syncwarp(activemask);
+
+    // final flush of output cache
+    auto const do_flush   = current_idx_shared[warp_id] > 0;
+    auto const flush_mask = __ballot_sync(activemask, do_flush);
+    if (do_flush) {
+      flush_output_cache<num_warps, output_cache_size>(flush_mask,
+                                                       max_size,
+                                                       warp_id,
+                                                       lane_id,
+                                                       current_idx,
+                                                       current_idx_shared,
+                                                       join_shared_l,
+                                                       join_shared_r,
+                                                       join_output_l,
+                                                       join_output_r);
+    }
+  }
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/cross_join.cu b/cpp/src/join/cross_join.cu
new file mode 100644
index 0000000..07057ac
--- /dev/null
+++ b/cpp/src/join/cross_join.cu
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/repeat.hpp>
+#include <cudf/detail/reshape.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/join.hpp>
+#include <cudf/reshape.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+/**
+ * @copydoc cudf::cross_join
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::table> cross_join(cudf::table_view const& left,
+                                        cudf::table_view const& right,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(0 != left.num_columns(), "Left table is empty");
+  CUDF_EXPECTS(0 != right.num_columns(), "Right table is empty");
+
+  // If left or right table has no rows, return an empty table with all columns
+  if ((0 == left.num_rows()) || (0 == right.num_rows())) {
+    auto empty_left_columns  = empty_like(left)->release();
+    auto empty_right_columns = empty_like(right)->release();
+    std::move(empty_right_columns.begin(),
+              empty_right_columns.end(),
+              std::back_inserter(empty_left_columns));
+    return std::make_unique<table>(std::move(empty_left_columns));
+  }
+
+  // Repeat left table
+  auto left_repeated = detail::repeat(left, right.num_rows(), stream, mr);
+
+  // Tile right table
+  auto right_tiled = detail::tile(right, left.num_rows(), stream, mr);
+
+  // Concatenate all repeated/tiled columns into one table
+  auto left_repeated_columns = left_repeated->release();
+  auto right_tiled_columns   = right_tiled->release();
+  std::move(right_tiled_columns.begin(),
+            right_tiled_columns.end(),
+            std::back_inserter(left_repeated_columns));
+
+  return std::make_unique<table>(std::move(left_repeated_columns));
+}
+}  // namespace detail
+
+std::unique_ptr<cudf::table> cross_join(cudf::table_view const& left,
+                                        cudf::table_view const& right,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::cross_join(left, right, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/join/hash_join.cu b/cpp/src/join/hash_join.cu
new file mode 100644
index 0000000..453257a
--- /dev/null
+++ b/cpp/src/join/hash_join.cu
@@ -0,0 +1,649 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "join_common_utils.cuh"
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/join.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/join.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/tuple.h>
+#include <thrust/uninitialized_fill.h>
+
+#include <cstddef>
+#include <iostream>
+#include <numeric>
+
+namespace cudf {
+namespace detail {
+namespace {
+/**
+ * @brief Calculates the exact size of the join output produced when
+ * joining two tables together.
+ *
+ * @throw cudf::logic_error if join is not INNER_JOIN or LEFT_JOIN
+ *
+ * @param build_table The right hand table
+ * @param probe_table The left hand table
+ * @param preprocessed_build shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           build_table
+ * @param preprocessed_probe shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           probe_table
+ * @param hash_table A hash table built on the build table that maps the index
+ *                   of every row to the hash value of that row
+ * @param join The type of join to be performed
+ * @param has_nulls Flag to denote if build or probe tables have nested nulls
+ * @param nulls_equal Flag to denote nulls are equal or not
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return The exact size of the output of the join operation
+ */
+std::size_t compute_join_output_size(
+  table_view const& build_table,
+  table_view const& probe_table,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const& preprocessed_build,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const& preprocessed_probe,
+  cudf::detail::multimap_type const& hash_table,
+  join_kind join,
+  bool has_nulls,
+  cudf::null_equality nulls_equal,
+  rmm::cuda_stream_view stream)
+{
+  size_type const build_table_num_rows{build_table.num_rows()};
+  size_type const probe_table_num_rows{probe_table.num_rows()};
+
+  // If the build table is empty, we know exactly how large the output
+  // will be for the different types of joins and can return immediately
+  if (0 == build_table_num_rows) {
+    switch (join) {
+      // Inner join with an empty table will have no output
+      case join_kind::INNER_JOIN: return 0;
+
+      // Left join with an empty table will have an output of NULL rows
+      // equal to the number of rows in the probe table
+      case join_kind::LEFT_JOIN: return probe_table_num_rows;
+
+      default: CUDF_FAIL("Unsupported join type");
+    }
+  }
+
+  auto const probe_nulls = cudf::nullate::DYNAMIC{has_nulls};
+
+  auto const row_hash           = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe         = row_hash.device_hasher(probe_nulls);
+  auto const empty_key_sentinel = hash_table.get_empty_key_sentinel();
+  auto const iter               = cudf::detail::make_counting_transform_iterator(
+    0, make_pair_function{hash_probe, empty_key_sentinel});
+
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const comparator_helper = [&](auto device_comparator) {
+    pair_equality equality{device_comparator};
+
+    if (join == join_kind::LEFT_JOIN) {
+      return hash_table.pair_count_outer(
+        iter, iter + probe_table_num_rows, equality, stream.value());
+    } else {
+      return hash_table.pair_count(iter, iter + probe_table_num_rows, equality, stream.value());
+    }
+  };
+
+  if (cudf::detail::has_nested_columns(probe_table)) {
+    auto const device_comparator = row_comparator.equal_to<true>(has_nulls, nulls_equal);
+    return comparator_helper(device_comparator);
+  } else {
+    auto const device_comparator = row_comparator.equal_to<false>(has_nulls, nulls_equal);
+    return comparator_helper(device_comparator);
+  }
+}
+
+/**
+ * @brief Probes the `hash_table` built from `build_table` for tuples in `probe_table`,
+ * and returns the output indices of `build_table` and `probe_table` as a combined table.
+ * Behavior is undefined if the provided `output_size` is smaller than the actual output size.
+ *
+ * @param build_table Table of build side columns to join
+ * @param probe_table Table of probe side columns to join
+ * @param preprocessed_build shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           build_table
+ * @param preprocessed_probe shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           probe_table
+ * @param hash_table Hash table built from `build_table`
+ * @param join The type of join to be performed
+ * @param has_nulls Flag to denote if build or probe tables have nested nulls
+ * @param compare_nulls Controls whether null join-key values should match or not
+ * @param output_size Optional value which allows users to specify the exact output size
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned vectors
+ *
+ * @return Join output indices vector pair.
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+probe_join_hash_table(
+  cudf::table_view const& build_table,
+  cudf::table_view const& probe_table,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const& preprocessed_build,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const& preprocessed_probe,
+  cudf::detail::multimap_type const& hash_table,
+  join_kind join,
+  bool has_nulls,
+  null_equality compare_nulls,
+  std::optional<std::size_t> output_size,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // Use the output size directly if provided. Otherwise, compute the exact output size
+  auto const probe_join_type =
+    (join == cudf::detail::join_kind::FULL_JOIN) ? cudf::detail::join_kind::LEFT_JOIN : join;
+
+  std::size_t const join_size = output_size ? *output_size
+                                            : compute_join_output_size(build_table,
+                                                                       probe_table,
+                                                                       preprocessed_build,
+                                                                       preprocessed_probe,
+                                                                       hash_table,
+                                                                       probe_join_type,
+                                                                       has_nulls,
+                                                                       compare_nulls,
+                                                                       stream);
+
+  // If output size is zero, return immediately
+  if (join_size == 0) {
+    return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                     std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+  }
+
+  auto left_indices  = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+  auto right_indices = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+
+  auto const probe_nulls = cudf::nullate::DYNAMIC{has_nulls};
+
+  auto const row_hash           = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe         = row_hash.device_hasher(probe_nulls);
+  auto const empty_key_sentinel = hash_table.get_empty_key_sentinel();
+  auto const iter               = cudf::detail::make_counting_transform_iterator(
+    0, make_pair_function{hash_probe, empty_key_sentinel});
+
+  cudf::size_type const probe_table_num_rows = probe_table.num_rows();
+
+  auto const out1_zip_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_discard_iterator(), left_indices->begin()));
+  auto const out2_zip_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_discard_iterator(), right_indices->begin()));
+
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const comparator_helper = [&](auto device_comparator) {
+    pair_equality equality{device_comparator};
+
+    if (join == cudf::detail::join_kind::FULL_JOIN or join == cudf::detail::join_kind::LEFT_JOIN) {
+      [[maybe_unused]] auto [out1_zip_end, out2_zip_end] =
+        hash_table.pair_retrieve_outer(iter,
+                                       iter + probe_table_num_rows,
+                                       out1_zip_begin,
+                                       out2_zip_begin,
+                                       equality,
+                                       stream.value());
+
+      if (join == cudf::detail::join_kind::FULL_JOIN) {
+        auto const actual_size = thrust::distance(out1_zip_begin, out1_zip_end);
+        left_indices->resize(actual_size, stream);
+        right_indices->resize(actual_size, stream);
+      }
+    } else {
+      hash_table.pair_retrieve(iter,
+                               iter + probe_table_num_rows,
+                               out1_zip_begin,
+                               out2_zip_begin,
+                               equality,
+                               stream.value());
+    }
+  };
+
+  if (cudf::detail::has_nested_columns(probe_table)) {
+    auto const device_comparator = row_comparator.equal_to<true>(probe_nulls, compare_nulls);
+    comparator_helper(device_comparator);
+  } else {
+    auto const device_comparator = row_comparator.equal_to<false>(probe_nulls, compare_nulls);
+    comparator_helper(device_comparator);
+  }
+
+  return std::pair(std::move(left_indices), std::move(right_indices));
+}
+
+/**
+ * @brief Probes the `hash_table` built from `build_table` for tuples in `probe_table` twice,
+ * and returns the output size of a full join operation between `build_table` and `probe_table`.
+ * TODO: this is a temporary solution as part of `full_join_size`. To be refactored during
+ * cuco integration.
+ *
+ * @param build_table Table of build side columns to join
+ * @param probe_table Table of probe side columns to join
+ * @param preprocessed_build shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           build_table
+ * @param preprocessed_probe shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           probe_table
+ * @param hash_table Hash table built from `build_table`
+ * @param has_nulls Flag to denote if build or probe tables have nested nulls
+ * @param compare_nulls Controls whether null join-key values should match or not
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the intermediate vectors
+ *
+ * @return Output size of full join.
+ */
+std::size_t get_full_join_size(
+  cudf::table_view const& build_table,
+  cudf::table_view const& probe_table,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const& preprocessed_build,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const& preprocessed_probe,
+  cudf::detail::multimap_type const& hash_table,
+  bool has_nulls,
+  null_equality compare_nulls,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  std::size_t join_size = compute_join_output_size(build_table,
+                                                   probe_table,
+                                                   preprocessed_build,
+                                                   preprocessed_probe,
+                                                   hash_table,
+                                                   cudf::detail::join_kind::LEFT_JOIN,
+                                                   has_nulls,
+                                                   compare_nulls,
+                                                   stream);
+
+  // If output size is zero, return immediately
+  if (join_size == 0) { return join_size; }
+
+  auto left_indices  = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+  auto right_indices = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+
+  auto const probe_nulls = cudf::nullate::DYNAMIC{has_nulls};
+
+  auto const row_hash           = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe         = row_hash.device_hasher(probe_nulls);
+  auto const empty_key_sentinel = hash_table.get_empty_key_sentinel();
+  auto const iter               = cudf::detail::make_counting_transform_iterator(
+    0, make_pair_function{hash_probe, empty_key_sentinel});
+
+  cudf::size_type const probe_table_num_rows = probe_table.num_rows();
+
+  auto const out1_zip_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_discard_iterator(), left_indices->begin()));
+  auto const out2_zip_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_discard_iterator(), right_indices->begin()));
+
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const comparator_helper = [&](auto device_comparator) {
+    pair_equality equality{device_comparator};
+    hash_table.pair_retrieve_outer(
+      iter, iter + probe_table_num_rows, out1_zip_begin, out2_zip_begin, equality, stream.value());
+  };
+  if (cudf::detail::has_nested_columns(probe_table)) {
+    auto const device_comparator = row_comparator.equal_to<true>(probe_nulls, compare_nulls);
+    comparator_helper(device_comparator);
+  } else {
+    auto const device_comparator = row_comparator.equal_to<false>(probe_nulls, compare_nulls);
+    comparator_helper(device_comparator);
+  }
+
+  // Release intermediate memory allocation
+  left_indices->resize(0, stream);
+
+  auto const left_table_row_count  = probe_table.num_rows();
+  auto const right_table_row_count = build_table.num_rows();
+
+  std::size_t left_join_complement_size;
+
+  // If left table is empty then all rows of the right table should be represented in the joined
+  // indices.
+  if (left_table_row_count == 0) {
+    left_join_complement_size = right_table_row_count;
+  } else {
+    // Assume all the indices in invalid_index_map are invalid
+    auto invalid_index_map =
+      std::make_unique<rmm::device_uvector<size_type>>(right_table_row_count, stream);
+    thrust::uninitialized_fill(
+      rmm::exec_policy(stream), invalid_index_map->begin(), invalid_index_map->end(), int32_t{1});
+
+    // Functor to check for index validity since left joins can create invalid indices
+    valid_range<size_type> valid(0, right_table_row_count);
+
+    // invalid_index_map[index_ptr[i]] = 0 for i = 0 to right_table_row_count
+    // Thus specifying that those locations are valid
+    thrust::scatter_if(rmm::exec_policy(stream),
+                       thrust::make_constant_iterator(0),
+                       thrust::make_constant_iterator(0) + right_indices->size(),
+                       right_indices->begin(),      // Index locations
+                       right_indices->begin(),      // Stencil - Check if index location is valid
+                       invalid_index_map->begin(),  // Output indices
+                       valid);                      // Stencil Predicate
+
+    // Create list of indices that have been marked as invalid
+    left_join_complement_size = thrust::count_if(rmm::exec_policy(stream),
+                                                 invalid_index_map->begin(),
+                                                 invalid_index_map->end(),
+                                                 thrust::identity());
+  }
+  return join_size + left_join_complement_size;
+}
+}  // namespace
+
+template <typename Hasher>
+hash_join<Hasher>::hash_join(cudf::table_view const& build,
+                             bool has_nulls,
+                             cudf::null_equality compare_nulls,
+                             rmm::cuda_stream_view stream)
+  : _has_nulls(has_nulls),
+    _is_empty{build.num_rows() == 0},
+    _nulls_equal{compare_nulls},
+    _hash_table{compute_hash_table_size(build.num_rows()),
+                cuco::empty_key{std::numeric_limits<hash_value_type>::max()},
+                cuco::empty_value{cudf::detail::JoinNoneValue},
+                stream.value(),
+                detail::hash_table_allocator_type{default_allocator<char>{}, stream}},
+    _build{build},
+    _preprocessed_build{
+      cudf::experimental::row::equality::preprocessed_table::create(_build, stream)}
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(0 != build.num_columns(), "Hash join build table is empty");
+
+  if (_is_empty) { return; }
+
+  auto const row_bitmask =
+    cudf::detail::bitmask_and(build, stream, rmm::mr::get_current_device_resource()).first;
+  cudf::detail::build_join_hash_table(_build,
+                                      _preprocessed_build,
+                                      _hash_table,
+                                      _has_nulls,
+                                      _nulls_equal,
+                                      reinterpret_cast<bitmask_type const*>(row_bitmask.data()),
+                                      stream);
+}
+
+template <typename Hasher>
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join<Hasher>::inner_join(cudf::table_view const& probe,
+                              std::optional<std::size_t> output_size,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr) const
+{
+  CUDF_FUNC_RANGE();
+  return compute_hash_join(probe, cudf::detail::join_kind::INNER_JOIN, output_size, stream, mr);
+}
+
+template <typename Hasher>
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join<Hasher>::left_join(cudf::table_view const& probe,
+                             std::optional<std::size_t> output_size,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr) const
+{
+  CUDF_FUNC_RANGE();
+  return compute_hash_join(probe, cudf::detail::join_kind::LEFT_JOIN, output_size, stream, mr);
+}
+
+template <typename Hasher>
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join<Hasher>::full_join(cudf::table_view const& probe,
+                             std::optional<std::size_t> output_size,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr) const
+{
+  CUDF_FUNC_RANGE();
+  return compute_hash_join(probe, cudf::detail::join_kind::FULL_JOIN, output_size, stream, mr);
+}
+
+template <typename Hasher>
+std::size_t hash_join<Hasher>::inner_join_size(cudf::table_view const& probe,
+                                               rmm::cuda_stream_view stream) const
+{
+  CUDF_FUNC_RANGE();
+
+  // Return directly if build table is empty
+  if (_is_empty) { return 0; }
+
+  CUDF_EXPECTS(_has_nulls || !cudf::has_nested_nulls(probe),
+               "Probe table has nulls while build table was not hashed with null check.");
+
+  auto const preprocessed_probe =
+    cudf::experimental::row::equality::preprocessed_table::create(probe, stream);
+
+  return cudf::detail::compute_join_output_size(_build,
+                                                probe,
+                                                _preprocessed_build,
+                                                preprocessed_probe,
+                                                _hash_table,
+                                                cudf::detail::join_kind::INNER_JOIN,
+                                                _has_nulls,
+                                                _nulls_equal,
+                                                stream);
+}
+
+template <typename Hasher>
+std::size_t hash_join<Hasher>::left_join_size(cudf::table_view const& probe,
+                                              rmm::cuda_stream_view stream) const
+{
+  CUDF_FUNC_RANGE();
+
+  // Trivial left join case - exit early
+  if (_is_empty) { return probe.num_rows(); }
+
+  CUDF_EXPECTS(_has_nulls || !cudf::has_nested_nulls(probe),
+               "Probe table has nulls while build table was not hashed with null check.");
+
+  auto const preprocessed_probe =
+    cudf::experimental::row::equality::preprocessed_table::create(probe, stream);
+
+  return cudf::detail::compute_join_output_size(_build,
+                                                probe,
+                                                _preprocessed_build,
+                                                preprocessed_probe,
+                                                _hash_table,
+                                                cudf::detail::join_kind::LEFT_JOIN,
+                                                _has_nulls,
+                                                _nulls_equal,
+                                                stream);
+}
+
+template <typename Hasher>
+std::size_t hash_join<Hasher>::full_join_size(cudf::table_view const& probe,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr) const
+{
+  CUDF_FUNC_RANGE();
+
+  // Trivial left join case - exit early
+  if (_is_empty) { return probe.num_rows(); }
+
+  CUDF_EXPECTS(_has_nulls || !cudf::has_nested_nulls(probe),
+               "Probe table has nulls while build table was not hashed with null check.");
+
+  auto const preprocessed_probe =
+    cudf::experimental::row::equality::preprocessed_table::create(probe, stream);
+
+  return cudf::detail::get_full_join_size(_build,
+                                          probe,
+                                          _preprocessed_build,
+                                          preprocessed_probe,
+                                          _hash_table,
+                                          _has_nulls,
+                                          _nulls_equal,
+                                          stream,
+                                          mr);
+}
+
+template <typename Hasher>
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join<Hasher>::probe_join_indices(cudf::table_view const& probe_table,
+                                      cudf::detail::join_kind join,
+                                      std::optional<std::size_t> output_size,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr) const
+{
+  // Trivial left join case - exit early
+  if (_is_empty and join != cudf::detail::join_kind::INNER_JOIN) {
+    return get_trivial_left_join_indices(probe_table, stream, mr);
+  }
+
+  CUDF_EXPECTS(!_is_empty, "Hash table of hash join is null.");
+
+  CUDF_EXPECTS(_has_nulls || !cudf::has_nested_nulls(probe_table),
+               "Probe table has nulls while build table was not hashed with null check.");
+
+  auto const preprocessed_probe =
+    cudf::experimental::row::equality::preprocessed_table::create(probe_table, stream);
+  auto join_indices = cudf::detail::probe_join_hash_table(_build,
+                                                          probe_table,
+                                                          _preprocessed_build,
+                                                          preprocessed_probe,
+                                                          _hash_table,
+                                                          join,
+                                                          _has_nulls,
+                                                          _nulls_equal,
+                                                          output_size,
+                                                          stream,
+                                                          mr);
+
+  if (join == cudf::detail::join_kind::FULL_JOIN) {
+    auto complement_indices = detail::get_left_join_indices_complement(
+      join_indices.second, probe_table.num_rows(), _build.num_rows(), stream, mr);
+    join_indices = detail::concatenate_vector_pairs(join_indices, complement_indices, stream);
+  }
+  return join_indices;
+}
+
+template <typename Hasher>
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join<Hasher>::compute_hash_join(cudf::table_view const& probe,
+                                     cudf::detail::join_kind join,
+                                     std::optional<std::size_t> output_size,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+{
+  CUDF_EXPECTS(0 != probe.num_columns(), "Hash join probe table is empty");
+
+  CUDF_EXPECTS(_build.num_columns() == probe.num_columns(),
+               "Mismatch in number of columns to be joined on");
+
+  CUDF_EXPECTS(_has_nulls || !cudf::has_nested_nulls(probe),
+               "Probe table has nulls while build table was not hashed with null check.");
+
+  if (is_trivial_join(probe, _build, join)) {
+    return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                     std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+  }
+
+  CUDF_EXPECTS(std::equal(std::cbegin(_build),
+                          std::cend(_build),
+                          std::cbegin(probe),
+                          std::cend(probe),
+                          [](auto const& b, auto const& p) { return b.type() == p.type(); }),
+               "Mismatch in joining column data types");
+
+  return probe_join_indices(probe, join, output_size, stream, mr);
+}
+}  // namespace detail
+
+hash_join::~hash_join() = default;
+
+hash_join::hash_join(cudf::table_view const& build,
+                     null_equality compare_nulls,
+                     rmm::cuda_stream_view stream)
+  // If we cannot know beforehand about null existence then let's assume that there are nulls.
+  : hash_join(build, nullable_join::YES, compare_nulls, stream)
+{
+}
+
+hash_join::hash_join(cudf::table_view const& build,
+                     nullable_join has_nulls,
+                     null_equality compare_nulls,
+                     rmm::cuda_stream_view stream)
+  : _impl{std::make_unique<impl_type const>(
+      build, has_nulls == nullable_join::YES, compare_nulls, stream)}
+{
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join::inner_join(cudf::table_view const& probe,
+                      std::optional<std::size_t> output_size,
+                      rmm::cuda_stream_view stream,
+                      rmm::mr::device_memory_resource* mr) const
+{
+  return _impl->inner_join(probe, output_size, stream, mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join::left_join(cudf::table_view const& probe,
+                     std::optional<std::size_t> output_size,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr) const
+{
+  return _impl->left_join(probe, output_size, stream, mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+hash_join::full_join(cudf::table_view const& probe,
+                     std::optional<std::size_t> output_size,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr) const
+{
+  return _impl->full_join(probe, output_size, stream, mr);
+}
+
+std::size_t hash_join::inner_join_size(cudf::table_view const& probe,
+                                       rmm::cuda_stream_view stream) const
+{
+  return _impl->inner_join_size(probe, stream);
+}
+
+std::size_t hash_join::left_join_size(cudf::table_view const& probe,
+                                      rmm::cuda_stream_view stream) const
+{
+  return _impl->left_join_size(probe, stream);
+}
+
+std::size_t hash_join::full_join_size(cudf::table_view const& probe,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr) const
+{
+  return _impl->full_join_size(probe, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/join/join.cu b/cpp/src/join/join.cu
new file mode 100644
index 0000000..ae025b1
--- /dev/null
+++ b/cpp/src/join/join.cu
@@ -0,0 +1,150 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "join_common_utils.hpp"
+
+#include <cudf/detail/gather.cuh>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/join.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+inner_join(table_view const& left_input,
+           table_view const& right_input,
+           null_equality compare_nulls,
+           rmm::cuda_stream_view stream,
+           rmm::mr::device_memory_resource* mr)
+{
+  // Make sure any dictionary columns have matched key sets.
+  // This will return any new dictionary columns created as well as updated table_views.
+  auto matched = cudf::dictionary::detail::match_dictionaries(
+    {left_input, right_input},
+    stream,
+    rmm::mr::get_current_device_resource());  // temporary objects returned
+
+  // now rebuild the table views with the updated ones
+  auto const left      = matched.second.front();
+  auto const right     = matched.second.back();
+  auto const has_nulls = cudf::has_nested_nulls(left) || cudf::has_nested_nulls(right)
+                           ? cudf::nullable_join::YES
+                           : cudf::nullable_join::NO;
+
+  // For `inner_join`, we can freely choose either the `left` or `right` table to use for
+  // building/probing the hash map. Because building is typically more expensive than probing, we
+  // build the hash map from the smaller table.
+  if (right.num_rows() > left.num_rows()) {
+    cudf::hash_join hj_obj(left, has_nulls, compare_nulls, stream);
+    auto [right_result, left_result] = hj_obj.inner_join(right, std::nullopt, stream, mr);
+    return std::pair(std::move(left_result), std::move(right_result));
+  } else {
+    cudf::hash_join hj_obj(right, has_nulls, compare_nulls, stream);
+    return hj_obj.inner_join(left, std::nullopt, stream, mr);
+  }
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+left_join(table_view const& left_input,
+          table_view const& right_input,
+          null_equality compare_nulls,
+          rmm::cuda_stream_view stream,
+          rmm::mr::device_memory_resource* mr)
+{
+  // Make sure any dictionary columns have matched key sets.
+  // This will return any new dictionary columns created as well as updated table_views.
+  auto matched = cudf::dictionary::detail::match_dictionaries(
+    {left_input, right_input},  // these should match
+    stream,
+    rmm::mr::get_current_device_resource());  // temporary objects returned
+  // now rebuild the table views with the updated ones
+  table_view const left  = matched.second.front();
+  table_view const right = matched.second.back();
+  auto const has_nulls   = cudf::has_nested_nulls(left) || cudf::has_nested_nulls(right)
+                             ? cudf::nullable_join::YES
+                             : cudf::nullable_join::NO;
+
+  cudf::hash_join hj_obj(right, has_nulls, compare_nulls, stream);
+  return hj_obj.left_join(left, std::nullopt, stream, mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+full_join(table_view const& left_input,
+          table_view const& right_input,
+          null_equality compare_nulls,
+          rmm::cuda_stream_view stream,
+          rmm::mr::device_memory_resource* mr)
+{
+  // Make sure any dictionary columns have matched key sets.
+  // This will return any new dictionary columns created as well as updated table_views.
+  auto matched = cudf::dictionary::detail::match_dictionaries(
+    {left_input, right_input},  // these should match
+    stream,
+    rmm::mr::get_current_device_resource());  // temporary objects returned
+  // now rebuild the table views with the updated ones
+  table_view const left  = matched.second.front();
+  table_view const right = matched.second.back();
+  auto const has_nulls   = cudf::has_nested_nulls(left) || cudf::has_nested_nulls(right)
+                             ? cudf::nullable_join::YES
+                             : cudf::nullable_join::NO;
+
+  cudf::hash_join hj_obj(right, has_nulls, compare_nulls, stream);
+  return hj_obj.full_join(left, std::nullopt, stream, mr);
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+inner_join(table_view const& left,
+           table_view const& right,
+           null_equality compare_nulls,
+           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::inner_join(left, right, compare_nulls, cudf::get_default_stream(), mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+left_join(table_view const& left,
+          table_view const& right,
+          null_equality compare_nulls,
+          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::left_join(left, right, compare_nulls, cudf::get_default_stream(), mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+full_join(table_view const& left,
+          table_view const& right,
+          null_equality compare_nulls,
+          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::full_join(left, right, compare_nulls, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/join/join_common_utils.cuh b/cpp/src/join/join_common_utils.cuh
new file mode 100644
index 0000000..f3ce6de
--- /dev/null
+++ b/cpp/src/join/join_common_utils.cuh
@@ -0,0 +1,327 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "join_common_utils.hpp"
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+/**
+ * @brief Remaps a hash value to a new value if it is equal to the specified sentinel value.
+ *
+ * @param hash The hash value to potentially remap
+ * @param sentinel The reserved value
+ */
+template <typename H, typename S>
+constexpr auto remap_sentinel_hash(H hash, S sentinel)
+{
+  // Arbitrarily choose hash - 1
+  return (hash == sentinel) ? (hash - 1) : hash;
+}
+
+/**
+ * @brief Device functor to create a pair of {hash_value, row_index} for a given row.
+ *
+ * @tparam T Type of row index, must be convertible to `size_type`.
+ * @tparam Hasher The type of internal hasher to compute row hash.
+ */
+template <typename Hasher, typename T = size_type>
+class make_pair_function {
+ public:
+  CUDF_HOST_DEVICE make_pair_function(Hasher const& hash, hash_value_type const empty_key_sentinel)
+    : _hash{hash}, _empty_key_sentinel{empty_key_sentinel}
+  {
+  }
+
+  __device__ __forceinline__ auto operator()(size_type i) const noexcept
+  {
+    // Compute the hash value of row `i`
+    auto row_hash_value = remap_sentinel_hash(_hash(i), _empty_key_sentinel);
+    return cuco::make_pair(row_hash_value, T{i});
+  }
+
+ private:
+  Hasher _hash;
+  hash_value_type const _empty_key_sentinel;
+};
+
+/**
+ * @brief Device functor to determine if a row is valid.
+ */
+class row_is_valid {
+ public:
+  row_is_valid(bitmask_type const* row_bitmask) : _row_bitmask{row_bitmask} {}
+
+  __device__ __inline__ bool operator()(size_type const& i) const noexcept
+  {
+    return cudf::bit_is_set(_row_bitmask, i);
+  }
+
+ private:
+  bitmask_type const* _row_bitmask;
+};
+
+/**
+ * @brief Device functor to determine if two pairs are identical.
+ *
+ * This equality comparator is designed for use with cuco::static_multimap's
+ * pair* APIs, which will compare equality based on comparing (key, value)
+ * pairs. In the context of joins, these pairs are of the form
+ * (row_hash, row_id). A hash probe hit indicates that hash of a probe row's hash is
+ * equal to the hash of the hash of some row in the multimap, at which point we need an
+ * equality comparator that will check whether the contents of the rows are
+ * identical. This comparator does so by verifying key equality (i.e. that
+ * probe_row_hash == build_row_hash) and then using a row_equality_comparator
+ * to compare the contents of the row indices that are stored as the payload in
+ * the hash map.
+ *
+ * @tparam Comparator The row comparator type to perform row equality comparison from row indices.
+ */
+template <typename DeviceComparator>
+class pair_equality {
+ public:
+  pair_equality(DeviceComparator check_row_equality)
+    : _check_row_equality{std::move(check_row_equality)}
+  {
+  }
+
+  // The parameters are build/probe rather than left/right because the operator
+  // is called by cuco's kernels with parameters in this order (note that this
+  // is an implementation detail that we should eventually stop relying on by
+  // defining operators with suitable heterogeneous typing). Rather than
+  // converting to left/right semantics, we can operate directly on build/probe
+  template <typename LhsPair, typename RhsPair>
+  __device__ __forceinline__ bool operator()(LhsPair const& lhs, RhsPair const& rhs) const noexcept
+  {
+    using experimental::row::lhs_index_type;
+    using experimental::row::rhs_index_type;
+
+    return lhs.first == rhs.first and
+           _check_row_equality(lhs_index_type{rhs.second}, rhs_index_type{lhs.second});
+  }
+
+ private:
+  DeviceComparator _check_row_equality;
+};
+
+/**
+ * @brief Computes the trivial left join operation for the case when the
+ * right table is empty.
+ *
+ * In this case all the valid indices of the left table
+ * are returned with their corresponding right indices being set to
+ * JoinNoneValue, i.e. -1.
+ *
+ * @param left Table of left columns to join
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the result
+ *
+ * @return Join output indices vector pair
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+get_trivial_left_join_indices(table_view const& left,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Builds the hash table based on the given `build_table`.
+ *
+ * @tparam MultimapType The type of the hash table
+ *
+ * @param build Table of columns used to build join hash.
+ * @param preprocessed_build shared_ptr to cudf::experimental::row::equality::preprocessed_table for
+ *                           build
+ * @param hash_table Build hash table.
+ * @param has_nulls Flag to denote if build or probe tables have nested nulls
+ * @param nulls_equal Flag to denote nulls are equal or not.
+ * @param bitmask Bitmask to denote whether a row is valid.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ *
+ */
+template <typename MultimapType>
+void build_join_hash_table(
+  cudf::table_view const& build,
+  std::shared_ptr<experimental::row::equality::preprocessed_table> const& preprocessed_build,
+  MultimapType& hash_table,
+  bool has_nulls,
+  null_equality nulls_equal,
+  [[maybe_unused]] bitmask_type const* bitmask,
+  rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(0 != build.num_columns(), "Selected build dataset is empty");
+  CUDF_EXPECTS(0 != build.num_rows(), "Build side table has no rows");
+
+  auto const row_hash   = experimental::row::hash::row_hasher{preprocessed_build};
+  auto const hash_build = row_hash.device_hasher(nullate::DYNAMIC{has_nulls});
+
+  auto const empty_key_sentinel = hash_table.get_empty_key_sentinel();
+  make_pair_function pair_func{hash_build, empty_key_sentinel};
+
+  auto const iter = cudf::detail::make_counting_transform_iterator(0, pair_func);
+
+  size_type const build_table_num_rows{build.num_rows()};
+  if (nulls_equal == cudf::null_equality::EQUAL or (not nullable(build))) {
+    hash_table.insert(iter, iter + build_table_num_rows, stream.value());
+  } else {
+    thrust::counting_iterator<size_type> stencil(0);
+    row_is_valid pred{bitmask};
+
+    // insert valid rows
+    hash_table.insert_if(iter, iter + build_table_num_rows, stencil, pred, stream.value());
+  }
+}
+
+// Convenient alias for a pair of unique pointers to device uvectors.
+using VectorPair = std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+                             std::unique_ptr<rmm::device_uvector<size_type>>>;
+
+/**
+ * @brief Takes two pairs of vectors and returns a single pair where the first
+ * element is a vector made from concatenating the first elements of both input
+ * pairs and the second element is a vector made from concatenating the second
+ * elements of both input pairs.
+ *
+ * This function's primary use is for computing the indices of a full join by
+ * first performing a left join, then separately getting the complementary
+ * right join indices, then finally calling this function to concatenate the
+ * results. In this case, each input VectorPair contains the left and right
+ * indices from a join.
+ *
+ * Note that this is a destructive operation, in that at least one of a or b
+ * will be invalidated (by a move) by this operation. Calling code should
+ * assume that neither input VectorPair is valid after this function executes.
+ *
+ * @param a The first pair of vectors.
+ * @param b The second pair of vectors.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ *
+ * @return A pair of vectors containing the concatenated output.
+ */
+VectorPair concatenate_vector_pairs(VectorPair& a, VectorPair& b, rmm::cuda_stream_view stream);
+
+/**
+ * @brief  Creates a table containing the complement of left join indices.
+ *
+ * This table has two columns. The first one is filled with JoinNoneValue(-1)
+ * and the second one contains values from 0 to right_table_row_count - 1
+ * excluding those found in the right_indices column.
+ *
+ * @param right_indices Vector of indices
+ * @param left_table_row_count Number of rows of left table
+ * @param right_table_row_count Number of rows of right table
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned vectors.
+ *
+ * @return Pair of vectors containing the left join indices complement
+ */
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+get_left_join_indices_complement(std::unique_ptr<rmm::device_uvector<size_type>>& right_indices,
+                                 size_type left_table_row_count,
+                                 size_type right_table_row_count,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Device functor to determine if an index is contained in a range.
+ */
+template <typename T>
+struct valid_range {
+  T start, stop;
+  __host__ __device__ valid_range(T const begin, T const end) : start(begin), stop(end) {}
+
+  __host__ __device__ __forceinline__ bool operator()(T const index)
+  {
+    return ((index >= start) && (index < stop));
+  }
+};
+
+/**
+ * @brief Adds a pair of indices to the shared memory cache
+ *
+ * @param[in] first The first index in the pair
+ * @param[in] second The second index in the pair
+ * @param[in,out] current_idx_shared Pointer to shared index that determines
+ * where in the shared memory cache the pair will be written
+ * @param[in] warp_id The ID of the warp of the calling the thread
+ * @param[out] joined_shared_l Pointer to the shared memory cache for left indices
+ * @param[out] joined_shared_r Pointer to the shared memory cache for right indices
+ */
+__inline__ __device__ void add_pair_to_cache(size_type const first,
+                                             size_type const second,
+                                             size_type* current_idx_shared,
+                                             int const warp_id,
+                                             size_type* joined_shared_l,
+                                             size_type* joined_shared_r)
+{
+  size_type my_current_idx{atomicAdd(current_idx_shared + warp_id, size_type(1))};
+
+  // its guaranteed to fit into the shared cache
+  joined_shared_l[my_current_idx] = first;
+  joined_shared_r[my_current_idx] = second;
+}
+
+template <int num_warps, cudf::size_type output_cache_size>
+__device__ void flush_output_cache(unsigned int const activemask,
+                                   cudf::size_type const max_size,
+                                   int const warp_id,
+                                   int const lane_id,
+                                   cudf::size_type* current_idx,
+                                   cudf::size_type current_idx_shared[num_warps],
+                                   size_type join_shared_l[num_warps][output_cache_size],
+                                   size_type join_shared_r[num_warps][output_cache_size],
+                                   size_type* join_output_l,
+                                   size_type* join_output_r)
+{
+  // count how many active threads participating here which could be less than warp_size
+  int num_threads               = __popc(activemask);
+  cudf::size_type output_offset = 0;
+
+  if (0 == lane_id) { output_offset = atomicAdd(current_idx, current_idx_shared[warp_id]); }
+
+  // No warp sync is necessary here because we are assuming that ShuffleIndex
+  // is internally using post-CUDA 9.0 synchronization-safe primitives
+  // (__shfl_sync instead of __shfl). __shfl is technically not guaranteed to
+  // be safe by the compiler because it is not required by the standard to
+  // converge divergent branches before executing.
+  output_offset = cub::ShuffleIndex<detail::warp_size>(output_offset, 0, activemask);
+
+  for (int shared_out_idx = lane_id; shared_out_idx < current_idx_shared[warp_id];
+       shared_out_idx += num_threads) {
+    cudf::size_type thread_offset = output_offset + shared_out_idx;
+    if (thread_offset < max_size) {
+      join_output_l[thread_offset] = join_shared_l[warp_id][shared_out_idx];
+      join_output_r[thread_offset] = join_shared_r[warp_id][shared_out_idx];
+    }
+  }
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/join_common_utils.hpp b/cpp/src/join/join_common_utils.hpp
new file mode 100644
index 0000000..e96505e
--- /dev/null
+++ b/cpp/src/join/join_common_utils.hpp
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/detail/join.hpp>
+#include <cudf/hashing/detail/default_hash.cuh>
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/join.hpp>
+#include <cudf/table/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <cuco/static_map.cuh>
+#include <cuco/static_multimap.cuh>
+
+#include <cuda/atomic>
+
+#include <limits>
+
+namespace cudf {
+namespace detail {
+
+constexpr int DEFAULT_JOIN_BLOCK_SIZE = 128;
+constexpr int DEFAULT_JOIN_CACHE_SIZE = 128;
+constexpr size_type JoinNoneValue     = std::numeric_limits<size_type>::min();
+
+using pair_type = cuco::pair<hash_value_type, size_type>;
+
+using hash_type = cuco::murmurhash3_32<hash_value_type>;
+
+using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+
+using multimap_type = cudf::hash_join::impl_type::map_type;
+
+// Multimap type used for mixed joins. TODO: This is a temporary alias used
+// until the mixed joins are converted to using CGs properly. Right now it's
+// using a cooperative group of size 1.
+using mixed_multimap_type = cuco::static_multimap<hash_value_type,
+                                                  size_type,
+                                                  cuda::thread_scope_device,
+                                                  hash_table_allocator_type,
+                                                  cuco::double_hashing<1, hash_type, hash_type>>;
+
+using semi_map_type = cuco::
+  static_map<hash_value_type, size_type, cuda::thread_scope_device, hash_table_allocator_type>;
+
+using row_hash_legacy =
+  cudf::row_hasher<cudf::hashing::detail::default_hash, cudf::nullate::DYNAMIC>;
+
+using row_equality_legacy = cudf::row_equality_comparator<cudf::nullate::DYNAMIC>;
+
+bool is_trivial_join(table_view const& left, table_view const& right, join_kind join_type);
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/join/join_utils.cu b/cpp/src/join/join_utils.cu
new file mode 100644
index 0000000..7fa6642
--- /dev/null
+++ b/cpp/src/join/join_utils.cu
@@ -0,0 +1,158 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "join_common_utils.cuh"
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+#include <thrust/uninitialized_fill.h>
+
+namespace cudf {
+namespace detail {
+
+bool is_trivial_join(table_view const& left, table_view const& right, join_kind join_type)
+{
+  // If there is nothing to join, then send empty table with all columns
+  if (left.is_empty() || right.is_empty()) { return true; }
+
+  // If left join and the left table is empty, return immediately
+  if ((join_kind::LEFT_JOIN == join_type) && (0 == left.num_rows())) { return true; }
+
+  // If Inner Join and either table is empty, return immediately
+  if ((join_kind::INNER_JOIN == join_type) && ((0 == left.num_rows()) || (0 == right.num_rows()))) {
+    return true;
+  }
+
+  // If left semi join (contains) and right table is empty,
+  // return immediately
+  if ((join_kind::LEFT_SEMI_JOIN == join_type) && (0 == right.num_rows())) { return true; }
+
+  return false;
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+get_trivial_left_join_indices(table_view const& left,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  auto left_indices = std::make_unique<rmm::device_uvector<size_type>>(left.num_rows(), stream, mr);
+  thrust::sequence(rmm::exec_policy(stream), left_indices->begin(), left_indices->end(), 0);
+  auto right_indices =
+    std::make_unique<rmm::device_uvector<size_type>>(left.num_rows(), stream, mr);
+  thrust::uninitialized_fill(
+    rmm::exec_policy(stream), right_indices->begin(), right_indices->end(), JoinNoneValue);
+  return std::pair(std::move(left_indices), std::move(right_indices));
+}
+
+VectorPair concatenate_vector_pairs(VectorPair& a, VectorPair& b, rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS((a.first->size() == a.second->size()),
+               "Mismatch between sizes of vectors in vector pair");
+  CUDF_EXPECTS((b.first->size() == b.second->size()),
+               "Mismatch between sizes of vectors in vector pair");
+  if (a.first->is_empty()) {
+    return std::move(b);
+  } else if (b.first->is_empty()) {
+    return std::move(a);
+  }
+  auto original_size = a.first->size();
+  a.first->resize(a.first->size() + b.first->size(), stream);
+  a.second->resize(a.second->size() + b.second->size(), stream);
+  thrust::copy(
+    rmm::exec_policy(stream), b.first->begin(), b.first->end(), a.first->begin() + original_size);
+  thrust::copy(rmm::exec_policy(stream),
+               b.second->begin(),
+               b.second->end(),
+               a.second->begin() + original_size);
+  return std::move(a);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+get_left_join_indices_complement(std::unique_ptr<rmm::device_uvector<size_type>>& right_indices,
+                                 size_type left_table_row_count,
+                                 size_type right_table_row_count,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  // Get array of indices that do not appear in right_indices
+
+  // Vector allocated for unmatched result
+  auto right_indices_complement =
+    std::make_unique<rmm::device_uvector<size_type>>(right_table_row_count, stream);
+
+  // If left table is empty in a full join call then all rows of the right table
+  // should be represented in the joined indices. This is an optimization since
+  // if left table is empty and full join is called all the elements in
+  // right_indices will be JoinNoneValue, i.e. -1. This if path should
+  // produce exactly the same result as the else path but will be faster.
+  if (left_table_row_count == 0) {
+    thrust::sequence(rmm::exec_policy(stream),
+                     right_indices_complement->begin(),
+                     right_indices_complement->end(),
+                     0);
+  } else {
+    // Assume all the indices in invalid_index_map are invalid
+    auto invalid_index_map =
+      std::make_unique<rmm::device_uvector<size_type>>(right_table_row_count, stream);
+    thrust::uninitialized_fill(
+      rmm::exec_policy(stream), invalid_index_map->begin(), invalid_index_map->end(), int32_t{1});
+
+    // Functor to check for index validity since left joins can create invalid indices
+    valid_range<size_type> valid(0, right_table_row_count);
+
+    // invalid_index_map[index_ptr[i]] = 0 for i = 0 to right_table_row_count
+    // Thus specifying that those locations are valid
+    thrust::scatter_if(rmm::exec_policy(stream),
+                       thrust::make_constant_iterator(0),
+                       thrust::make_constant_iterator(0) + right_indices->size(),
+                       right_indices->begin(),      // Index locations
+                       right_indices->begin(),      // Stencil - Check if index location is valid
+                       invalid_index_map->begin(),  // Output indices
+                       valid);                      // Stencil Predicate
+    size_type begin_counter = static_cast<size_type>(0);
+    size_type end_counter   = static_cast<size_type>(right_table_row_count);
+
+    // Create list of indices that have been marked as invalid
+    size_type indices_count = thrust::copy_if(rmm::exec_policy(stream),
+                                              thrust::make_counting_iterator(begin_counter),
+                                              thrust::make_counting_iterator(end_counter),
+                                              invalid_index_map->begin(),
+                                              right_indices_complement->begin(),
+                                              thrust::identity{}) -
+                              right_indices_complement->begin();
+    right_indices_complement->resize(indices_count, stream);
+  }
+
+  auto left_invalid_indices =
+    std::make_unique<rmm::device_uvector<size_type>>(right_indices_complement->size(), stream);
+  thrust::uninitialized_fill(rmm::exec_policy(stream),
+                             left_invalid_indices->begin(),
+                             left_invalid_indices->end(),
+                             JoinNoneValue);
+
+  return std::pair(std::move(left_invalid_indices), std::move(right_indices_complement));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join.cu b/cpp/src/join/mixed_join.cu
new file mode 100644
index 0000000..3d902bf
--- /dev/null
+++ b/cpp/src/join/mixed_join.cu
@@ -0,0 +1,594 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "join_common_utils.cuh"
+#include "join_common_utils.hpp"
+#include "mixed_join_kernels.cuh"
+
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/join.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/scan.h>
+
+#include <optional>
+#include <utility>
+
+namespace cudf {
+namespace detail {
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  join_kind join_type,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> const& output_size_data,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(left_conditional.num_rows() == left_equality.num_rows(),
+               "The left conditional and equality tables must have the same number of rows.");
+  CUDF_EXPECTS(right_conditional.num_rows() == right_equality.num_rows(),
+               "The right conditional and equality tables must have the same number of rows.");
+
+  CUDF_EXPECTS((join_type != join_kind::LEFT_SEMI_JOIN) && (join_type != join_kind::LEFT_ANTI_JOIN),
+               "Left semi and anti joins should use mixed_join_semi.");
+
+  auto const right_num_rows{right_conditional.num_rows()};
+  auto const left_num_rows{left_conditional.num_rows()};
+  auto const swap_tables = (join_type == join_kind::INNER_JOIN) && (right_num_rows > left_num_rows);
+
+  // The "outer" table is the larger of the two tables. The kernels are
+  // launched with one thread per row of the outer table, which also means that
+  // it is the probe table for the hash
+  auto const outer_num_rows{swap_tables ? right_num_rows : left_num_rows};
+
+  // We can immediately filter out cases where the right table is empty. In
+  // some cases, we return all the rows of the left table with a corresponding
+  // null index for the right table; in others, we return an empty output.
+  if (right_num_rows == 0) {
+    switch (join_type) {
+      // Left and full joins all return all the row indices from
+      // left with a corresponding NULL from the right.
+      case join_kind::LEFT_JOIN:
+      case join_kind::FULL_JOIN:
+        return get_trivial_left_join_indices(
+          left_conditional, stream, rmm::mr::get_current_device_resource());
+      // Inner joins return empty output because no matches can exist.
+      case join_kind::INNER_JOIN:
+        return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                         std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  } else if (left_num_rows == 0) {
+    switch (join_type) {
+      // Left and inner joins all return empty sets.
+      case join_kind::LEFT_JOIN:
+      case join_kind::INNER_JOIN:
+        return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                         std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+      // Full joins need to return the trivial complement.
+      case join_kind::FULL_JOIN: {
+        auto ret_flipped = get_trivial_left_join_indices(
+          right_conditional, stream, rmm::mr::get_current_device_resource());
+        return std::pair(std::move(ret_flipped.second), std::move(ret_flipped.first));
+      }
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  }
+
+  // If evaluating the expression may produce null outputs we create a nullable
+  // output column and follow the null-supporting expression evaluation code
+  // path.
+  auto const has_nulls = cudf::nullate::DYNAMIC{
+    cudf::has_nulls(left_equality) || cudf::has_nulls(right_equality) ||
+    binary_predicate.may_evaluate_null(left_conditional, right_conditional, stream)};
+
+  auto const parser = ast::detail::expression_parser{
+    binary_predicate, left_conditional, right_conditional, has_nulls, stream, mr};
+  CUDF_EXPECTS(parser.output_type().id() == type_id::BOOL8,
+               "The expression must produce a boolean output.");
+
+  // TODO: The non-conditional join impls start with a dictionary matching,
+  // figure out what that is and what it's needed for (and if conditional joins
+  // need to do the same).
+  auto& probe     = swap_tables ? right_equality : left_equality;
+  auto& build     = swap_tables ? left_equality : right_equality;
+  auto probe_view = table_device_view::create(probe, stream);
+  auto build_view = table_device_view::create(build, stream);
+
+  // Don't use multimap_type because we want a CG size of 1.
+  mixed_multimap_type hash_table{
+    compute_hash_table_size(build.num_rows()),
+    cuco::empty_key{std::numeric_limits<hash_value_type>::max()},
+    cuco::empty_value{cudf::detail::JoinNoneValue},
+    stream.value(),
+    detail::hash_table_allocator_type{default_allocator<char>{}, stream}};
+
+  // TODO: To add support for nested columns we will need to flatten in many
+  // places. However, this probably isn't worth adding any time soon since we
+  // won't be able to support AST conditions for those types anyway.
+  auto const row_bitmask =
+    cudf::detail::bitmask_and(build, stream, rmm::mr::get_current_device_resource()).first;
+  auto const preprocessed_build =
+    experimental::row::equality::preprocessed_table::create(build, stream);
+  build_join_hash_table(build,
+                        preprocessed_build,
+                        hash_table,
+                        has_nulls,
+                        compare_nulls,
+                        static_cast<bitmask_type const*>(row_bitmask.data()),
+                        stream);
+  auto hash_table_view = hash_table.get_device_view();
+
+  auto left_conditional_view  = table_device_view::create(left_conditional, stream);
+  auto right_conditional_view = table_device_view::create(right_conditional, stream);
+
+  // For inner joins we support optimizing the join by launching one thread for
+  // whichever table is larger rather than always using the left table.
+  detail::grid_1d const config(outer_num_rows, DEFAULT_JOIN_BLOCK_SIZE);
+  auto const shmem_size_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+  join_kind const kernel_join_type =
+    join_type == join_kind::FULL_JOIN ? join_kind::LEFT_JOIN : join_type;
+
+  // If the join size data was not provided as an input, compute it here.
+  std::size_t join_size;
+  // Using an optional because we only need to allocate a new vector if one was
+  // not passed as input, and rmm::device_uvector is not default constructible
+  std::optional<rmm::device_uvector<size_type>> matches_per_row{};
+  device_span<size_type const> matches_per_row_span{};
+
+  auto const preprocessed_probe =
+    experimental::row::equality::preprocessed_table::create(probe, stream);
+  auto const row_hash   = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe = row_hash.device_hasher(has_nulls);
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const equality_probe = row_comparator.equal_to<false>(has_nulls, compare_nulls);
+
+  if (output_size_data.has_value()) {
+    join_size            = output_size_data->first;
+    matches_per_row_span = output_size_data->second;
+  } else {
+    // Allocate storage for the counter used to get the size of the join output
+    rmm::device_scalar<std::size_t> size(0, stream, mr);
+
+    matches_per_row =
+      rmm::device_uvector<size_type>{static_cast<std::size_t>(outer_num_rows), stream, mr};
+    // Note that the view goes out of scope after this else statement, but the
+    // data owned by matches_per_row stays alive so the data pointer is valid.
+    auto mutable_matches_per_row_span = cudf::device_span<size_type>{
+      matches_per_row->begin(), static_cast<std::size_t>(outer_num_rows)};
+    matches_per_row_span = cudf::device_span<size_type const>{
+      matches_per_row->begin(), static_cast<std::size_t>(outer_num_rows)};
+    if (has_nulls) {
+      compute_mixed_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, true>
+        <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+          *left_conditional_view,
+          *right_conditional_view,
+          *probe_view,
+          *build_view,
+          hash_probe,
+          equality_probe,
+          kernel_join_type,
+          hash_table_view,
+          parser.device_expression_data,
+          swap_tables,
+          size.data(),
+          mutable_matches_per_row_span);
+    } else {
+      compute_mixed_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, false>
+        <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+          *left_conditional_view,
+          *right_conditional_view,
+          *probe_view,
+          *build_view,
+          hash_probe,
+          equality_probe,
+          kernel_join_type,
+          hash_table_view,
+          parser.device_expression_data,
+          swap_tables,
+          size.data(),
+          mutable_matches_per_row_span);
+    }
+    join_size = size.value(stream);
+  }
+
+  // The initial early exit clauses guarantee that we will not reach this point
+  // unless both the left and right tables are non-empty. Under that
+  // constraint, neither left nor full joins can return an empty result since
+  // at minimum we are guaranteed null matches for all non-matching rows. In
+  // all other cases (inner, left semi, and left anti joins) if we reach this
+  // point we can safely return an empty result.
+  if (join_size == 0) {
+    return std::pair(std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr),
+                     std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr));
+  }
+
+  // Given the number of matches per row, we need to compute the offsets for insertion.
+  auto join_result_offsets =
+    rmm::device_uvector<size_type>{static_cast<std::size_t>(outer_num_rows), stream, mr};
+  thrust::exclusive_scan(rmm::exec_policy{stream},
+                         matches_per_row_span.begin(),
+                         matches_per_row_span.end(),
+                         join_result_offsets.begin());
+
+  auto left_indices  = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+  auto right_indices = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+
+  auto const& join_output_l = left_indices->data();
+  auto const& join_output_r = right_indices->data();
+
+  if (has_nulls) {
+    mixed_join<DEFAULT_JOIN_BLOCK_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        kernel_join_type,
+        hash_table_view,
+        join_output_l,
+        join_output_r,
+        parser.device_expression_data,
+        join_result_offsets.data(),
+        swap_tables);
+  } else {
+    mixed_join<DEFAULT_JOIN_BLOCK_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        kernel_join_type,
+        hash_table_view,
+        join_output_l,
+        join_output_r,
+        parser.device_expression_data,
+        join_result_offsets.data(),
+        swap_tables);
+  }
+
+  auto join_indices = std::pair(std::move(left_indices), std::move(right_indices));
+
+  // For full joins, get the indices in the right table that were not joined to
+  // by any row in the left table.
+  if (join_type == join_kind::FULL_JOIN) {
+    auto complement_indices = detail::get_left_join_indices_complement(
+      join_indices.second, left_num_rows, right_num_rows, stream, mr);
+    join_indices = detail::concatenate_vector_pairs(join_indices, complement_indices, stream);
+  }
+  return join_indices;
+}
+
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>>
+compute_mixed_join_output_size(table_view const& left_equality,
+                               table_view const& right_equality,
+                               table_view const& left_conditional,
+                               table_view const& right_conditional,
+                               ast::expression const& binary_predicate,
+                               null_equality compare_nulls,
+                               join_kind join_type,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  // Until we add logic to handle the number of non-matches in the right table,
+  // full joins are not supported in this function. Note that this does not
+  // prevent actually performing full joins since we do that by calculating the
+  // left join and then concatenating the complementary right indices.
+  CUDF_EXPECTS(join_type != join_kind::FULL_JOIN,
+               "Size estimation is not available for full joins.");
+
+  CUDF_EXPECTS(
+    (join_type != join_kind::LEFT_SEMI_JOIN) && (join_type != join_kind::LEFT_ANTI_JOIN),
+    "Left semi and anti join size estimation should use compute_mixed_join_output_size_semi.");
+
+  CUDF_EXPECTS(left_conditional.num_rows() == left_equality.num_rows(),
+               "The left conditional and equality tables must have the same number of rows.");
+  CUDF_EXPECTS(right_conditional.num_rows() == right_equality.num_rows(),
+               "The right conditional and equality tables must have the same number of rows.");
+
+  auto const right_num_rows{right_conditional.num_rows()};
+  auto const left_num_rows{left_conditional.num_rows()};
+  auto const swap_tables = (join_type == join_kind::INNER_JOIN) && (right_num_rows > left_num_rows);
+
+  // The "outer" table is the larger of the two tables. The kernels are
+  // launched with one thread per row of the outer table, which also means that
+  // it is the probe table for the hash
+  auto const outer_num_rows{swap_tables ? right_num_rows : left_num_rows};
+
+  auto matches_per_row = std::make_unique<rmm::device_uvector<size_type>>(
+    static_cast<std::size_t>(outer_num_rows), stream, mr);
+  auto matches_per_row_span = cudf::device_span<size_type>{
+    matches_per_row->begin(), static_cast<std::size_t>(outer_num_rows)};
+
+  // We can immediately filter out cases where one table is empty. In
+  // some cases, we return all the rows of the other table with a corresponding
+  // null index for the empty table; in others, we return an empty output.
+  if (right_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, and full all return all the row indices from left
+      // with a corresponding NULL from the right.
+      case join_kind::LEFT_JOIN:
+      case join_kind::FULL_JOIN: {
+        thrust::fill(matches_per_row->begin(), matches_per_row->end(), 1);
+        return {left_num_rows, std::move(matches_per_row)};
+      }
+      // Inner and left semi joins return empty output because no matches can exist.
+      case join_kind::INNER_JOIN: {
+        thrust::fill(matches_per_row->begin(), matches_per_row->end(), 0);
+        return {0, std::move(matches_per_row)};
+      }
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  } else if (left_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, left semi, and inner joins all return empty sets.
+      case join_kind::LEFT_JOIN:
+      case join_kind::INNER_JOIN: {
+        thrust::fill(matches_per_row->begin(), matches_per_row->end(), 0);
+        return {0, std::move(matches_per_row)};
+      }
+      // Full joins need to return the trivial complement.
+      case join_kind::FULL_JOIN: {
+        thrust::fill(matches_per_row->begin(), matches_per_row->end(), 1);
+        return {right_num_rows, std::move(matches_per_row)};
+      }
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  }
+
+  // If evaluating the expression may produce null outputs we create a nullable
+  // output column and follow the null-supporting expression evaluation code
+  // path.
+  auto const has_nulls = cudf::nullate::DYNAMIC{
+    cudf::has_nulls(left_equality) || cudf::has_nulls(right_equality) ||
+    binary_predicate.may_evaluate_null(left_conditional, right_conditional, stream)};
+
+  auto const parser = ast::detail::expression_parser{
+    binary_predicate, left_conditional, right_conditional, has_nulls, stream, mr};
+  CUDF_EXPECTS(parser.output_type().id() == type_id::BOOL8,
+               "The expression must produce a boolean output.");
+
+  // TODO: The non-conditional join impls start with a dictionary matching,
+  // figure out what that is and what it's needed for (and if conditional joins
+  // need to do the same).
+  auto& probe     = swap_tables ? right_equality : left_equality;
+  auto& build     = swap_tables ? left_equality : right_equality;
+  auto probe_view = table_device_view::create(probe, stream);
+  auto build_view = table_device_view::create(build, stream);
+
+  // Don't use multimap_type because we want a CG size of 1.
+  mixed_multimap_type hash_table{
+    compute_hash_table_size(build.num_rows()),
+    cuco::empty_key{std::numeric_limits<hash_value_type>::max()},
+    cuco::empty_value{cudf::detail::JoinNoneValue},
+    stream.value(),
+    detail::hash_table_allocator_type{default_allocator<char>{}, stream}};
+
+  // TODO: To add support for nested columns we will need to flatten in many
+  // places. However, this probably isn't worth adding any time soon since we
+  // won't be able to support AST conditions for those types anyway.
+  auto const row_bitmask =
+    cudf::detail::bitmask_and(build, stream, rmm::mr::get_current_device_resource()).first;
+  auto const preprocessed_build =
+    experimental::row::equality::preprocessed_table::create(build, stream);
+  build_join_hash_table(build,
+                        preprocessed_build,
+                        hash_table,
+                        has_nulls,
+                        compare_nulls,
+                        static_cast<bitmask_type const*>(row_bitmask.data()),
+                        stream);
+  auto hash_table_view = hash_table.get_device_view();
+
+  auto left_conditional_view  = table_device_view::create(left_conditional, stream);
+  auto right_conditional_view = table_device_view::create(right_conditional, stream);
+
+  // For inner joins we support optimizing the join by launching one thread for
+  // whichever table is larger rather than always using the left table.
+  detail::grid_1d const config(outer_num_rows, DEFAULT_JOIN_BLOCK_SIZE);
+  auto const shmem_size_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+
+  // Allocate storage for the counter used to get the size of the join output
+  rmm::device_scalar<std::size_t> size(0, stream, mr);
+
+  auto const preprocessed_probe =
+    experimental::row::equality::preprocessed_table::create(probe, stream);
+  auto const row_hash   = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe = row_hash.device_hasher(has_nulls);
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const equality_probe = row_comparator.equal_to<false>(has_nulls, compare_nulls);
+
+  // Determine number of output rows without actually building the output to simply
+  // find what the size of the output will be.
+  if (has_nulls) {
+    compute_mixed_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        join_type,
+        hash_table_view,
+        parser.device_expression_data,
+        swap_tables,
+        size.data(),
+        matches_per_row_span);
+  } else {
+    compute_mixed_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        join_type,
+        hash_table_view,
+        parser.device_expression_data,
+        swap_tables,
+        size.data(),
+        matches_per_row_span);
+  }
+
+  return {size.value(stream), std::move(matches_per_row)};
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_inner_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> const output_size_data,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::mixed_join(left_equality,
+                            right_equality,
+                            left_conditional,
+                            right_conditional,
+                            binary_predicate,
+                            compare_nulls,
+                            detail::join_kind::INNER_JOIN,
+                            output_size_data,
+                            cudf::get_default_stream(),
+                            mr);
+}
+
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_inner_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_mixed_join_output_size(left_equality,
+                                                right_equality,
+                                                left_conditional,
+                                                right_conditional,
+                                                binary_predicate,
+                                                compare_nulls,
+                                                detail::join_kind::INNER_JOIN,
+                                                cudf::get_default_stream(),
+                                                mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_left_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> const output_size_data,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::mixed_join(left_equality,
+                            right_equality,
+                            left_conditional,
+                            right_conditional,
+                            binary_predicate,
+                            compare_nulls,
+                            detail::join_kind::LEFT_JOIN,
+                            output_size_data,
+                            cudf::get_default_stream(),
+                            mr);
+}
+
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_left_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_mixed_join_output_size(left_equality,
+                                                right_equality,
+                                                left_conditional,
+                                                right_conditional,
+                                                binary_predicate,
+                                                compare_nulls,
+                                                detail::join_kind::LEFT_JOIN,
+                                                cudf::get_default_stream(),
+                                                mr);
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>,
+          std::unique_ptr<rmm::device_uvector<size_type>>>
+mixed_full_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> const output_size_data,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::mixed_join(left_equality,
+                            right_equality,
+                            left_conditional,
+                            right_conditional,
+                            binary_predicate,
+                            compare_nulls,
+                            detail::join_kind::FULL_JOIN,
+                            output_size_data,
+                            cudf::get_default_stream(),
+                            mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_common_utils.cuh b/cpp/src/join/mixed_join_common_utils.cuh
new file mode 100644
index 0000000..38e5b75
--- /dev/null
+++ b/cpp/src/join/mixed_join_common_utils.cuh
@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <join/join_common_utils.hpp>
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+
+namespace cudf {
+namespace detail {
+
+using row_hash =
+  cudf::experimental::row::hash::device_row_hasher<cudf::hashing::detail::default_hash,
+                                                   cudf::nullate::DYNAMIC>;
+
+// // This alias is used by mixed_joins, which support only non-nested types
+using row_equality = cudf::experimental::row::equality::strong_index_comparator_adapter<
+  cudf::experimental::row::equality::device_row_comparator<false, cudf::nullate::DYNAMIC>>;
+
+/**
+ * @brief Equality comparator for use with cuco map methods that require expression evaluation.
+ *
+ * This class just defines the construction of the class and the necessary
+ * attributes, specifically the equality operator for the non-conditional parts
+ * of the operator and the evaluator used for the conditional.
+ */
+template <bool has_nulls>
+struct expression_equality {
+  __device__ expression_equality(
+    cudf::ast::detail::expression_evaluator<has_nulls> const& evaluator,
+    cudf::ast::detail::IntermediateDataType<has_nulls>* thread_intermediate_storage,
+    bool const swap_tables,
+    row_equality const& equality_probe)
+    : evaluator{evaluator},
+      thread_intermediate_storage{thread_intermediate_storage},
+      swap_tables{swap_tables},
+      equality_probe{equality_probe}
+  {
+  }
+
+  cudf::ast::detail::IntermediateDataType<has_nulls>* thread_intermediate_storage;
+  cudf::ast::detail::expression_evaluator<has_nulls> const& evaluator;
+  bool const swap_tables;
+  row_equality const& equality_probe;
+};
+
+/**
+ * @brief Equality comparator for cuco::static_map queries.
+ *
+ * This equality comparator is designed for use with cuco::static_map's APIs. A
+ * probe hit indicates that the hashes of the keys are equal, at which point
+ * this comparator checks whether the keys themselves are equal (using the
+ * provided equality_probe) and then evaluates the conditional expression
+ */
+template <bool has_nulls>
+struct single_expression_equality : expression_equality<has_nulls> {
+  using expression_equality<has_nulls>::expression_equality;
+
+  // The parameters are build/probe rather than left/right because the operator
+  // is called by cuco's kernels with parameters in this order (note that this
+  // is an implementation detail that we should eventually stop relying on by
+  // defining operators with suitable heterogeneous typing). Rather than
+  // converting to left/right semantics, we can operate directly on build/probe
+  // until we get to the expression evaluator, which needs to convert back to
+  // left/right semantics because the conditional expression need not be
+  // commutative.
+  // TODO: The input types should really be size_type.
+  __device__ __forceinline__ bool operator()(hash_value_type const build_row_index,
+                                             hash_value_type const probe_row_index) const noexcept
+  {
+    using cudf::experimental::row::lhs_index_type;
+    using cudf::experimental::row::rhs_index_type;
+
+    auto output_dest = cudf::ast::detail::value_expression_result<bool, has_nulls>();
+    // Two levels of checks:
+    // 1. The contents of the columns involved in the equality condition are equal.
+    // 2. The predicate evaluated on the relevant columns (already encoded in the evaluator)
+    // evaluates to true.
+    if (this->equality_probe(lhs_index_type{probe_row_index}, rhs_index_type{build_row_index})) {
+      auto const lrow_idx = this->swap_tables ? build_row_index : probe_row_index;
+      auto const rrow_idx = this->swap_tables ? probe_row_index : build_row_index;
+      this->evaluator.evaluate(output_dest,
+                               static_cast<size_type>(lrow_idx),
+                               static_cast<size_type>(rrow_idx),
+                               0,
+                               this->thread_intermediate_storage);
+      return (output_dest.is_valid() && output_dest.value());
+    }
+    return false;
+  }
+};
+
+/**
+ * @brief Equality comparator for cuco::static_multimap queries.
+ *
+ * This equality comparator is designed for use with cuco::static_multimap's
+ * pair* APIs, which will compare equality based on comparing (key, value)
+ * pairs. In the context of joins, these pairs are of the form
+ * (row_hash, row_id). A hash probe hit indicates that hash of a probe row's hash is
+ * equal to the hash of the hash of some row in the multimap, at which point we need an
+ * equality comparator that will check whether the contents of the rows are
+ * identical. This comparator does so by verifying key equality (i.e. that
+ * probe_row_hash == build_row_hash) and then using a row_equality_comparator
+ * to compare the contents of the row indices that are stored as the payload in
+ * the hash map.
+ */
+template <bool has_nulls>
+struct pair_expression_equality : public expression_equality<has_nulls> {
+  using expression_equality<has_nulls>::expression_equality;
+
+  // The parameters are build/probe rather than left/right because the operator
+  // is called by cuco's kernels with parameters in this order (note that this
+  // is an implementation detail that we should eventually stop relying on by
+  // defining operators with suitable heterogeneous typing). Rather than
+  // converting to left/right semantics, we can operate directly on build/probe
+  // until we get to the expression evaluator, which needs to convert back to
+  // left/right semantics because the conditional expression need not be
+  // commutative.
+  __device__ __forceinline__ bool operator()(pair_type const& build_row,
+                                             pair_type const& probe_row) const noexcept
+  {
+    using cudf::experimental::row::lhs_index_type;
+    using cudf::experimental::row::rhs_index_type;
+
+    auto output_dest = cudf::ast::detail::value_expression_result<bool, has_nulls>();
+    // Three levels of checks:
+    // 1. Row hashes of the columns involved in the equality condition are equal.
+    // 2. The contents of the columns involved in the equality condition are equal.
+    // 3. The predicate evaluated on the relevant columns (already encoded in the evaluator)
+    // evaluates to true.
+    if ((probe_row.first == build_row.first) &&
+        this->equality_probe(lhs_index_type{probe_row.second}, rhs_index_type{build_row.second})) {
+      auto const lrow_idx = this->swap_tables ? build_row.second : probe_row.second;
+      auto const rrow_idx = this->swap_tables ? probe_row.second : build_row.second;
+      this->evaluator.evaluate(
+        output_dest, lrow_idx, rrow_idx, 0, this->thread_intermediate_storage);
+      return (output_dest.is_valid() && output_dest.value());
+    }
+    return false;
+  }
+};
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_kernel.cu b/cpp/src/join/mixed_join_kernel.cu
new file mode 100644
index 0000000..61cfa16
--- /dev/null
+++ b/cpp/src/join/mixed_join_kernel.cu
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "mixed_join_kernel.cuh"
+
+namespace cudf {
+namespace detail {
+
+template __global__ void mixed_join<DEFAULT_JOIN_BLOCK_SIZE, false>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+  size_type* join_output_l,
+  size_type* join_output_r,
+  cudf::ast::detail::expression_device_view device_expression_data,
+  cudf::size_type const* join_result_offsets,
+  bool const swap_tables);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_kernel.cuh b/cpp/src/join/mixed_join_kernel.cuh
new file mode 100644
index 0000000..efe575e
--- /dev/null
+++ b/cpp/src/join/mixed_join_kernel.cuh
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "join_common_utils.cuh"
+#include "join_common_utils.hpp"
+#include "mixed_join_common_utils.cuh"
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <cooperative_groups.h>
+
+#include <cub/cub.cuh>
+#include <thrust/iterator/discard_iterator.h>
+
+namespace cudf {
+namespace detail {
+
+namespace cg = cooperative_groups;
+
+template <cudf::size_type block_size, bool has_nulls>
+__launch_bounds__(block_size) __global__
+  void mixed_join(table_device_view left_table,
+                  table_device_view right_table,
+                  table_device_view probe,
+                  table_device_view build,
+                  row_hash const hash_probe,
+                  row_equality const equality_probe,
+                  join_kind const join_type,
+                  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+                  size_type* join_output_l,
+                  size_type* join_output_r,
+                  cudf::ast::detail::expression_device_view device_expression_data,
+                  cudf::size_type const* join_result_offsets,
+                  bool const swap_tables)
+{
+  // Normally the casting of a shared memory array is used to create multiple
+  // arrays of different types from the shared memory buffer, but here it is
+  // used to circumvent conflicts between arrays of different types between
+  // different template instantiations due to the extern specifier.
+  extern __shared__ char raw_intermediate_storage[];
+  cudf::ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<cudf::ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+  auto thread_intermediate_storage =
+    &intermediate_storage[threadIdx.x * device_expression_data.num_intermediates];
+
+  cudf::size_type const left_num_rows  = left_table.num_rows();
+  cudf::size_type const right_num_rows = right_table.num_rows();
+  auto const outer_num_rows            = (swap_tables ? right_num_rows : left_num_rows);
+
+  cudf::size_type outer_row_index = threadIdx.x + blockIdx.x * block_size;
+
+  auto evaluator = cudf::ast::detail::expression_evaluator<has_nulls>(
+    left_table, right_table, device_expression_data);
+
+  auto const empty_key_sentinel = hash_table_view.get_empty_key_sentinel();
+  make_pair_function pair_func{hash_probe, empty_key_sentinel};
+
+  if (outer_row_index < outer_num_rows) {
+    // Figure out the number of elements for this key.
+    cg::thread_block_tile<1> this_thread = cg::this_thread();
+    // Figure out the number of elements for this key.
+    auto query_pair = pair_func(outer_row_index);
+    auto equality   = pair_expression_equality<has_nulls>{
+      evaluator, thread_intermediate_storage, swap_tables, equality_probe};
+
+    auto probe_key_begin       = thrust::make_discard_iterator();
+    auto probe_value_begin     = swap_tables ? join_output_r + join_result_offsets[outer_row_index]
+                                             : join_output_l + join_result_offsets[outer_row_index];
+    auto contained_key_begin   = thrust::make_discard_iterator();
+    auto contained_value_begin = swap_tables ? join_output_l + join_result_offsets[outer_row_index]
+                                             : join_output_r + join_result_offsets[outer_row_index];
+
+    if (join_type == join_kind::LEFT_JOIN || join_type == join_kind::FULL_JOIN) {
+      hash_table_view.pair_retrieve_outer(this_thread,
+                                          query_pair,
+                                          probe_key_begin,
+                                          probe_value_begin,
+                                          contained_key_begin,
+                                          contained_value_begin,
+                                          equality);
+    } else {
+      hash_table_view.pair_retrieve(this_thread,
+                                    query_pair,
+                                    probe_key_begin,
+                                    probe_value_begin,
+                                    contained_key_begin,
+                                    contained_value_begin,
+                                    equality);
+    }
+  }
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_kernel_nulls.cu b/cpp/src/join/mixed_join_kernel_nulls.cu
new file mode 100644
index 0000000..518f8ed
--- /dev/null
+++ b/cpp/src/join/mixed_join_kernel_nulls.cu
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "mixed_join_kernel.cuh"
+
+namespace cudf {
+namespace detail {
+
+template __global__ void mixed_join<DEFAULT_JOIN_BLOCK_SIZE, true>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+  size_type* join_output_l,
+  size_type* join_output_r,
+  cudf::ast::detail::expression_device_view device_expression_data,
+  cudf::size_type const* join_result_offsets,
+  bool const swap_tables);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_kernels.cuh b/cpp/src/join/mixed_join_kernels.cuh
new file mode 100644
index 0000000..2cd4d0c
--- /dev/null
+++ b/cpp/src/join/mixed_join_kernels.cuh
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <join/join_common_utils.hpp>
+#include <join/mixed_join_common_utils.cuh>
+
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Computes the output size of joining the left table to the right table.
+ *
+ * This method probes the hash table with each row in the probe table using a
+ * custom equality comparator that also checks that the conditional expression
+ * evaluates to true between the left/right tables when a match is found
+ * between probe and build rows.
+ *
+ * @tparam block_size The number of threads per block for this kernel
+ * @tparam has_nulls Whether or not the inputs may contain nulls.
+ *
+ * @param[in] left_table The left table
+ * @param[in] right_table The right table
+ * @param[in] probe The table with which to probe the hash table for matches.
+ * @param[in] build The table with which the hash table was built.
+ * @param[in] hash_probe The hasher used for the probe table.
+ * @param[in] equality_probe The equality comparator used when probing the hash table.
+ * @param[in] join_type The type of join to be performed
+ * @param[in] hash_table_view The hash table built from `build`.
+ * @param[in] device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param[in] swap_tables If true, the kernel was launched with one thread per right row and
+ * the kernel needs to internally loop over left rows. Otherwise, loop over right rows.
+ * @param[out] output_size The resulting output size
+ * @param[out] matches_per_row The number of matches in one pair of
+ * equality/conditional tables for each row in the other pair of tables. If
+ * swap_tables is true, matches_per_row corresponds to the right_table,
+ * otherwise it corresponds to the left_table. Note that corresponding swap of
+ * left/right tables to determine which is the build table and which is the
+ * probe table has already happened on the host.
+ */
+template <int block_size, bool has_nulls>
+__global__ void compute_mixed_join_output_size(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row);
+
+/**
+ * @brief Performs a join using the combination of a hash lookup to identify
+ * equal rows between one pair of tables and the evaluation of an expression
+ * containing an arbitrary expression.
+ *
+ * This method probes the hash table with each row in the probe table using a
+ * custom equality comparator that also checks that the conditional expression
+ * evaluates to true between the left/right tables when a match is found
+ * between probe and build rows.
+ *
+ * @tparam block_size The number of threads per block for this kernel
+ * @tparam has_nulls Whether or not the inputs may contain nulls.
+ *
+ * @param[in] left_table The left table
+ * @param[in] right_table The right table
+ * @param[in] probe The table with which to probe the hash table for matches.
+ * @param[in] build The table with which the hash table was built.
+ * @param[in] hash_probe The hasher used for the probe table.
+ * @param[in] equality_probe The equality comparator used when probing the hash table.
+ * @param[in] join_type The type of join to be performed
+ * @param[in] hash_table_view The hash table built from `build`.
+ * @param[out] join_output_l The left result of the join operation
+ * @param[out] join_output_r The right result of the join operation
+ * @param[in] device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param[in] join_result_offsets The starting indices in join_output[l|r]
+ * where the matches for each row begin. Equivalent to a prefix sum of
+ * matches_per_row.
+ * @param[in] swap_tables If true, the kernel was launched with one thread per right row and
+ * the kernel needs to internally loop over left rows. Otherwise, loop over right rows.
+ */
+template <cudf::size_type block_size, bool has_nulls>
+__global__ void mixed_join(table_device_view left_table,
+                           table_device_view right_table,
+                           table_device_view probe,
+                           table_device_view build,
+                           row_hash const hash_probe,
+                           row_equality const equality_probe,
+                           join_kind const join_type,
+                           cudf::detail::mixed_multimap_type::device_view hash_table_view,
+                           size_type* join_output_l,
+                           size_type* join_output_r,
+                           cudf::ast::detail::expression_device_view device_expression_data,
+                           cudf::size_type const* join_result_offsets,
+                           bool const swap_tables);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_kernels_semi.cu b/cpp/src/join/mixed_join_kernels_semi.cu
new file mode 100644
index 0000000..e31e35f
--- /dev/null
+++ b/cpp/src/join/mixed_join_kernels_semi.cu
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <join/join_common_utils.cuh>
+#include <join/join_common_utils.hpp>
+#include <join/mixed_join_common_utils.cuh>
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <cub/cub.cuh>
+
+namespace cudf {
+namespace detail {
+
+namespace cg = cooperative_groups;
+
+template <cudf::size_type block_size, bool has_nulls>
+__launch_bounds__(block_size) __global__
+  void mixed_join_semi(table_device_view left_table,
+                       table_device_view right_table,
+                       table_device_view probe,
+                       table_device_view build,
+                       row_hash const hash_probe,
+                       row_equality const equality_probe,
+                       join_kind const join_type,
+                       cudf::detail::semi_map_type::device_view hash_table_view,
+                       size_type* join_output_l,
+                       cudf::ast::detail::expression_device_view device_expression_data,
+                       cudf::size_type const* join_result_offsets,
+                       bool const swap_tables)
+{
+  // Normally the casting of a shared memory array is used to create multiple
+  // arrays of different types from the shared memory buffer, but here it is
+  // used to circumvent conflicts between arrays of different types between
+  // different template instantiations due to the extern specifier.
+  extern __shared__ char raw_intermediate_storage[];
+  cudf::ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<cudf::ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+  auto thread_intermediate_storage =
+    &intermediate_storage[threadIdx.x * device_expression_data.num_intermediates];
+
+  cudf::size_type const left_num_rows  = left_table.num_rows();
+  cudf::size_type const right_num_rows = right_table.num_rows();
+  auto const outer_num_rows            = (swap_tables ? right_num_rows : left_num_rows);
+
+  cudf::size_type outer_row_index = threadIdx.x + blockIdx.x * block_size;
+
+  auto evaluator = cudf::ast::detail::expression_evaluator<has_nulls>(
+    left_table, right_table, device_expression_data);
+
+  if (outer_row_index < outer_num_rows) {
+    // Figure out the number of elements for this key.
+    auto equality = single_expression_equality<has_nulls>{
+      evaluator, thread_intermediate_storage, swap_tables, equality_probe};
+
+    if ((join_type == join_kind::LEFT_ANTI_JOIN) !=
+        (hash_table_view.contains(outer_row_index, hash_probe, equality))) {
+      *(join_output_l + join_result_offsets[outer_row_index]) = outer_row_index;
+    }
+  }
+}
+
+template __global__ void mixed_join_semi<DEFAULT_JOIN_BLOCK_SIZE, true>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::semi_map_type::device_view hash_table_view,
+  size_type* join_output_l,
+  cudf::ast::detail::expression_device_view device_expression_data,
+  cudf::size_type const* join_result_offsets,
+  bool const swap_tables);
+
+template __global__ void mixed_join_semi<DEFAULT_JOIN_BLOCK_SIZE, false>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::semi_map_type::device_view hash_table_view,
+  size_type* join_output_l,
+  cudf::ast::detail::expression_device_view device_expression_data,
+  cudf::size_type const* join_result_offsets,
+  bool const swap_tables);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_kernels_semi.cuh b/cpp/src/join/mixed_join_kernels_semi.cuh
new file mode 100644
index 0000000..8e4966e
--- /dev/null
+++ b/cpp/src/join/mixed_join_kernels_semi.cuh
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <join/join_common_utils.hpp>
+#include <join/mixed_join_common_utils.cuh>
+
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Computes the output size of joining the left table to the right table for semi/anti joins.
+ *
+ * This method probes the hash table with each row in the probe table using a
+ * custom equality comparator that also checks that the conditional expression
+ * evaluates to true between the left/right tables when a match is found
+ * between probe and build rows.
+ *
+ * @tparam block_size The number of threads per block for this kernel
+ * @tparam has_nulls Whether or not the inputs may contain nulls.
+ *
+ * @param[in] left_table The left table
+ * @param[in] right_table The right table
+ * @param[in] probe The table with which to probe the hash table for matches.
+ * @param[in] build The table with which the hash table was built.
+ * @param[in] hash_probe The hasher used for the probe table.
+ * @param[in] equality_probe The equality comparator used when probing the hash table.
+ * @param[in] join_type The type of join to be performed
+ * @param[in] hash_table_view The hash table built from `build`.
+ * @param[in] device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param[in] swap_tables If true, the kernel was launched with one thread per right row and
+ * the kernel needs to internally loop over left rows. Otherwise, loop over right rows.
+ * @param[out] output_size The resulting output size
+ * @param[out] matches_per_row The number of matches in one pair of
+ * equality/conditional tables for each row in the other pair of tables. If
+ * swap_tables is true, matches_per_row corresponds to the right_table,
+ * otherwise it corresponds to the left_table. Note that corresponding swap of
+ * left/right tables to determine which is the build table and which is the
+ * probe table has already happened on the host.
+ */
+template <int block_size, bool has_nulls>
+__global__ void compute_mixed_join_output_size_semi(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::semi_map_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row);
+
+/**
+ * @brief Performs a semi/anti join using the combination of a hash lookup to
+ * identify equal rows between one pair of tables and the evaluation of an
+ * expression containing an arbitrary expression.
+ *
+ * This method probes the hash table with each row in the probe table using a
+ * custom equality comparator that also checks that the conditional expression
+ * evaluates to true between the left/right tables when a match is found
+ * between probe and build rows.
+ *
+ * @tparam block_size The number of threads per block for this kernel
+ * @tparam has_nulls Whether or not the inputs may contain nulls.
+ *
+ * @param[in] left_table The left table
+ * @param[in] right_table The right table
+ * @param[in] probe The table with which to probe the hash table for matches.
+ * @param[in] build The table with which the hash table was built.
+ * @param[in] hash_probe The hasher used for the probe table.
+ * @param[in] equality_probe The equality comparator used when probing the hash table.
+ * @param[in] join_type The type of join to be performed
+ * @param[in] hash_table_view The hash table built from `build`.
+ * @param[out] join_output_l The left result of the join operation
+ * @param[in] device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param[in] join_result_offsets The starting indices in join_output[l|r]
+ * where the matches for each row begin. Equivalent to a prefix sum of
+ * matches_per_row.
+ * @param[in] swap_tables If true, the kernel was launched with one thread per right row and
+ * the kernel needs to internally loop over left rows. Otherwise, loop over right rows.
+ */
+template <cudf::size_type block_size, bool has_nulls>
+__global__ void mixed_join_semi(table_device_view left_table,
+                                table_device_view right_table,
+                                table_device_view probe,
+                                table_device_view build,
+                                row_hash const hash_probe,
+                                row_equality const equality_probe,
+                                join_kind const join_type,
+                                cudf::detail::semi_map_type::device_view hash_table_view,
+                                size_type* join_output_l,
+                                cudf::ast::detail::expression_device_view device_expression_data,
+                                cudf::size_type const* join_result_offsets,
+                                bool const swap_tables);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_semi.cu b/cpp/src/join/mixed_join_semi.cu
new file mode 100644
index 0000000..f619ed0
--- /dev/null
+++ b/cpp/src/join/mixed_join_semi.cu
@@ -0,0 +1,626 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "join_common_utils.cuh"
+#include "join_common_utils.hpp"
+#include "mixed_join_kernels_semi.cuh"
+
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/join.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+
+#include <optional>
+#include <utility>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+/**
+ * @brief Device functor to create a pair of hash value and index for a given row.
+ */
+struct make_pair_function_semi {
+  __device__ __forceinline__ cudf::detail::pair_type operator()(size_type i) const noexcept
+  {
+    // The value is irrelevant since we only ever use the hash map to check for
+    // membership of a particular row index.
+    return cuco::make_pair(static_cast<hash_value_type>(i), 0);
+  }
+};
+
+/**
+ * @brief Equality comparator that composes two row_equality comparators.
+ */
+class double_row_equality {
+ public:
+  double_row_equality(row_equality equality_comparator, row_equality conditional_comparator)
+    : _equality_comparator{equality_comparator}, _conditional_comparator{conditional_comparator}
+  {
+  }
+
+  __device__ bool operator()(size_type lhs_row_index, size_type rhs_row_index) const noexcept
+  {
+    using experimental::row::lhs_index_type;
+    using experimental::row::rhs_index_type;
+
+    return _equality_comparator(lhs_index_type{lhs_row_index}, rhs_index_type{rhs_row_index}) &&
+           _conditional_comparator(lhs_index_type{lhs_row_index}, rhs_index_type{rhs_row_index});
+  }
+
+ private:
+  row_equality _equality_comparator;
+  row_equality _conditional_comparator;
+};
+
+}  // namespace
+
+std::unique_ptr<rmm::device_uvector<size_type>> mixed_join_semi(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  join_kind join_type,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS((join_type != join_kind::INNER_JOIN) && (join_type != join_kind::LEFT_JOIN) &&
+                 (join_type != join_kind::FULL_JOIN),
+               "Inner, left, and full joins should use mixed_join.");
+
+  CUDF_EXPECTS(left_conditional.num_rows() == left_equality.num_rows(),
+               "The left conditional and equality tables must have the same number of rows.");
+  CUDF_EXPECTS(right_conditional.num_rows() == right_equality.num_rows(),
+               "The right conditional and equality tables must have the same number of rows.");
+
+  auto const right_num_rows{right_conditional.num_rows()};
+  auto const left_num_rows{left_conditional.num_rows()};
+  auto const swap_tables = (join_type == join_kind::INNER_JOIN) && (right_num_rows > left_num_rows);
+
+  // The "outer" table is the larger of the two tables. The kernels are
+  // launched with one thread per row of the outer table, which also means that
+  // it is the probe table for the hash
+  auto const outer_num_rows{swap_tables ? right_num_rows : left_num_rows};
+
+  // We can immediately filter out cases where the right table is empty. In
+  // some cases, we return all the rows of the left table with a corresponding
+  // null index for the right table; in others, we return an empty output.
+  if (right_num_rows == 0) {
+    switch (join_type) {
+      // Anti and semi return all the row indices from left
+      // with a corresponding NULL from the right.
+      case join_kind::LEFT_ANTI_JOIN:
+        return get_trivial_left_join_indices(
+                 left_conditional, stream, rmm::mr::get_current_device_resource())
+          .first;
+      // Inner and left semi joins return empty output because no matches can exist.
+      case join_kind::LEFT_SEMI_JOIN:
+        return std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr);
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  } else if (left_num_rows == 0) {
+    switch (join_type) {
+      // Anti and semi joins both return empty sets.
+      case join_kind::LEFT_ANTI_JOIN:
+      case join_kind::LEFT_SEMI_JOIN:
+        return std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr);
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  }
+
+  // If evaluating the expression may produce null outputs we create a nullable
+  // output column and follow the null-supporting expression evaluation code
+  // path.
+  auto const has_nulls = cudf::nullate::DYNAMIC{
+    cudf::has_nulls(left_equality) || cudf::has_nulls(right_equality) ||
+    binary_predicate.may_evaluate_null(left_conditional, right_conditional, stream)};
+
+  auto const parser = ast::detail::expression_parser{
+    binary_predicate, left_conditional, right_conditional, has_nulls, stream, mr};
+  CUDF_EXPECTS(parser.output_type().id() == type_id::BOOL8,
+               "The expression must produce a boolean output.");
+
+  // TODO: The non-conditional join impls start with a dictionary matching,
+  // figure out what that is and what it's needed for (and if conditional joins
+  // need to do the same).
+  auto& probe                 = swap_tables ? right_equality : left_equality;
+  auto& build                 = swap_tables ? left_equality : right_equality;
+  auto probe_view             = table_device_view::create(probe, stream);
+  auto build_view             = table_device_view::create(build, stream);
+  auto left_conditional_view  = table_device_view::create(left_conditional, stream);
+  auto right_conditional_view = table_device_view::create(right_conditional, stream);
+
+  auto const preprocessed_build =
+    experimental::row::equality::preprocessed_table::create(build, stream);
+  auto const preprocessed_probe =
+    experimental::row::equality::preprocessed_table::create(probe, stream);
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const equality_probe = row_comparator.equal_to<false>(has_nulls, compare_nulls);
+
+  semi_map_type hash_table{compute_hash_table_size(build.num_rows()),
+                           cuco::empty_key{std::numeric_limits<hash_value_type>::max()},
+                           cuco::empty_value{cudf::detail::JoinNoneValue},
+                           detail::hash_table_allocator_type{default_allocator<char>{}, stream},
+                           stream.value()};
+
+  // Create hash table containing all keys found in right table
+  // TODO: To add support for nested columns we will need to flatten in many
+  // places. However, this probably isn't worth adding any time soon since we
+  // won't be able to support AST conditions for those types anyway.
+  auto const build_nulls    = cudf::nullate::DYNAMIC{cudf::has_nulls(build)};
+  auto const row_hash_build = cudf::experimental::row::hash::row_hasher{preprocessed_build};
+  auto const hash_build     = row_hash_build.device_hasher(build_nulls);
+  // Since we may see multiple rows that are identical in the equality tables
+  // but differ in the conditional tables, the equality comparator used for
+  // insertion must account for both sets of tables. An alternative solution
+  // would be to use a multimap, but that solution would store duplicates where
+  // equality and conditional rows are equal, so this approach is preferable.
+  // One way to make this solution even more efficient would be to only include
+  // the columns of the conditional table that are used by the expression, but
+  // that requires additional plumbing through the AST machinery and is out of
+  // scope for now.
+  auto const row_comparator_build =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_build, preprocessed_build};
+  auto const equality_build_equality =
+    row_comparator_build.equal_to<false>(build_nulls, compare_nulls);
+  auto const preprocessed_build_condtional =
+    experimental::row::equality::preprocessed_table::create(
+      swap_tables ? left_conditional : right_conditional, stream);
+  auto const row_comparator_conditional_build =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_build_condtional,
+                                                            preprocessed_build_condtional};
+  auto const equality_build_conditional =
+    row_comparator_conditional_build.equal_to<false>(build_nulls, compare_nulls);
+  double_row_equality equality_build{equality_build_equality, equality_build_conditional};
+  make_pair_function_semi pair_func_build{};
+
+  auto iter = cudf::detail::make_counting_transform_iterator(0, pair_func_build);
+
+  // skip rows that are null here.
+  if ((compare_nulls == null_equality::EQUAL) or (not nullable(build))) {
+    hash_table.insert(iter, iter + right_num_rows, hash_build, equality_build, stream.value());
+  } else {
+    thrust::counting_iterator<cudf::size_type> stencil(0);
+    auto const [row_bitmask, _] =
+      cudf::detail::bitmask_and(build, stream, rmm::mr::get_current_device_resource());
+    row_is_valid pred{static_cast<bitmask_type const*>(row_bitmask.data())};
+
+    // insert valid rows
+    hash_table.insert_if(
+      iter, iter + right_num_rows, stencil, pred, hash_build, equality_build, stream.value());
+  }
+
+  auto hash_table_view = hash_table.get_device_view();
+
+  // For inner joins we support optimizing the join by launching one thread for
+  // whichever table is larger rather than always using the left table.
+  detail::grid_1d const config(outer_num_rows, DEFAULT_JOIN_BLOCK_SIZE);
+  auto const shmem_size_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+  join_kind const kernel_join_type =
+    join_type == join_kind::FULL_JOIN ? join_kind::LEFT_JOIN : join_type;
+
+  // If the join size data was not provided as an input, compute it here.
+  std::size_t join_size;
+  // Using an optional because we only need to allocate a new vector if one was
+  // not passed as input, and rmm::device_uvector is not default constructible
+  std::optional<rmm::device_uvector<size_type>> matches_per_row{};
+  device_span<size_type const> matches_per_row_span{};
+
+  auto const row_hash   = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe = row_hash.device_hasher(has_nulls);
+
+  if (output_size_data.has_value()) {
+    join_size            = output_size_data->first;
+    matches_per_row_span = output_size_data->second;
+  } else {
+    // Allocate storage for the counter used to get the size of the join output
+    rmm::device_scalar<std::size_t> size(0, stream, mr);
+
+    matches_per_row =
+      rmm::device_uvector<size_type>{static_cast<std::size_t>(outer_num_rows), stream, mr};
+    // Note that the view goes out of scope after this else statement, but the
+    // data owned by matches_per_row stays alive so the data pointer is valid.
+    auto mutable_matches_per_row_span = cudf::device_span<size_type>{
+      matches_per_row->begin(), static_cast<std::size_t>(outer_num_rows)};
+    matches_per_row_span = cudf::device_span<size_type const>{
+      matches_per_row->begin(), static_cast<std::size_t>(outer_num_rows)};
+    if (has_nulls) {
+      compute_mixed_join_output_size_semi<DEFAULT_JOIN_BLOCK_SIZE, true>
+        <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+          *left_conditional_view,
+          *right_conditional_view,
+          *probe_view,
+          *build_view,
+          hash_probe,
+          equality_probe,
+          kernel_join_type,
+          hash_table_view,
+          parser.device_expression_data,
+          swap_tables,
+          size.data(),
+          mutable_matches_per_row_span);
+    } else {
+      compute_mixed_join_output_size_semi<DEFAULT_JOIN_BLOCK_SIZE, false>
+        <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+          *left_conditional_view,
+          *right_conditional_view,
+          *probe_view,
+          *build_view,
+          hash_probe,
+          equality_probe,
+          kernel_join_type,
+          hash_table_view,
+          parser.device_expression_data,
+          swap_tables,
+          size.data(),
+          mutable_matches_per_row_span);
+    }
+    join_size = size.value(stream);
+  }
+
+  if (join_size == 0) { return std::make_unique<rmm::device_uvector<size_type>>(0, stream, mr); }
+
+  // Given the number of matches per row, we need to compute the offsets for insertion.
+  auto join_result_offsets =
+    rmm::device_uvector<size_type>{static_cast<std::size_t>(outer_num_rows), stream, mr};
+  thrust::exclusive_scan(rmm::exec_policy{stream},
+                         matches_per_row_span.begin(),
+                         matches_per_row_span.end(),
+                         join_result_offsets.begin());
+
+  auto left_indices = std::make_unique<rmm::device_uvector<size_type>>(join_size, stream, mr);
+  auto const& join_output_l = left_indices->data();
+
+  if (has_nulls) {
+    mixed_join_semi<DEFAULT_JOIN_BLOCK_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        kernel_join_type,
+        hash_table_view,
+        join_output_l,
+        parser.device_expression_data,
+        join_result_offsets.data(),
+        swap_tables);
+  } else {
+    mixed_join_semi<DEFAULT_JOIN_BLOCK_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        kernel_join_type,
+        hash_table_view,
+        join_output_l,
+        parser.device_expression_data,
+        join_result_offsets.data(),
+        swap_tables);
+  }
+
+  return left_indices;
+}
+
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>>
+compute_mixed_join_output_size_semi(table_view const& left_equality,
+                                    table_view const& right_equality,
+                                    table_view const& left_conditional,
+                                    table_view const& right_conditional,
+                                    ast::expression const& binary_predicate,
+                                    null_equality compare_nulls,
+                                    join_kind join_type,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(
+    (join_type != join_kind::INNER_JOIN) && (join_type != join_kind::LEFT_JOIN) &&
+      (join_type != join_kind::FULL_JOIN),
+    "Inner, left, and full join size estimation should use compute_mixed_join_output_size.");
+
+  CUDF_EXPECTS(left_conditional.num_rows() == left_equality.num_rows(),
+               "The left conditional and equality tables must have the same number of rows.");
+  CUDF_EXPECTS(right_conditional.num_rows() == right_equality.num_rows(),
+               "The right conditional and equality tables must have the same number of rows.");
+
+  auto const right_num_rows{right_conditional.num_rows()};
+  auto const left_num_rows{left_conditional.num_rows()};
+  auto const swap_tables = (join_type == join_kind::INNER_JOIN) && (right_num_rows > left_num_rows);
+
+  // The "outer" table is the larger of the two tables. The kernels are
+  // launched with one thread per row of the outer table, which also means that
+  // it is the probe table for the hash
+  auto const outer_num_rows{swap_tables ? right_num_rows : left_num_rows};
+
+  auto matches_per_row = std::make_unique<rmm::device_uvector<size_type>>(
+    static_cast<std::size_t>(outer_num_rows), stream, mr);
+  auto matches_per_row_span = cudf::device_span<size_type>{
+    matches_per_row->begin(), static_cast<std::size_t>(outer_num_rows)};
+
+  // We can immediately filter out cases where one table is empty. In
+  // some cases, we return all the rows of the other table with a corresponding
+  // null index for the empty table; in others, we return an empty output.
+  if (right_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, and full all return all the row indices from left
+      // with a corresponding NULL from the right.
+      case join_kind::LEFT_ANTI_JOIN: {
+        thrust::fill(matches_per_row->begin(), matches_per_row->end(), 1);
+        return {left_num_rows, std::move(matches_per_row)};
+      }
+      // Inner and left semi joins return empty output because no matches can exist.
+      case join_kind::LEFT_SEMI_JOIN: return {0, std::move(matches_per_row)};
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  } else if (left_num_rows == 0) {
+    switch (join_type) {
+      // Left, left anti, left semi, and inner joins all return empty sets.
+      case join_kind::LEFT_ANTI_JOIN:
+      case join_kind::LEFT_SEMI_JOIN: {
+        thrust::fill(matches_per_row->begin(), matches_per_row->end(), 0);
+        return {0, std::move(matches_per_row)};
+      }
+      default: CUDF_FAIL("Invalid join kind."); break;
+    }
+  }
+
+  // If evaluating the expression may produce null outputs we create a nullable
+  // output column and follow the null-supporting expression evaluation code
+  // path.
+  auto const has_nulls = cudf::nullate::DYNAMIC{
+    cudf::has_nulls(left_equality) || cudf::has_nulls(right_equality) ||
+    binary_predicate.may_evaluate_null(left_conditional, right_conditional, stream)};
+
+  auto const parser = ast::detail::expression_parser{
+    binary_predicate, left_conditional, right_conditional, has_nulls, stream, mr};
+  CUDF_EXPECTS(parser.output_type().id() == type_id::BOOL8,
+               "The expression must produce a boolean output.");
+
+  // TODO: The non-conditional join impls start with a dictionary matching,
+  // figure out what that is and what it's needed for (and if conditional joins
+  // need to do the same).
+  auto& probe                 = swap_tables ? right_equality : left_equality;
+  auto& build                 = swap_tables ? left_equality : right_equality;
+  auto probe_view             = table_device_view::create(probe, stream);
+  auto build_view             = table_device_view::create(build, stream);
+  auto left_conditional_view  = table_device_view::create(left_conditional, stream);
+  auto right_conditional_view = table_device_view::create(right_conditional, stream);
+
+  auto const preprocessed_build =
+    experimental::row::equality::preprocessed_table::create(build, stream);
+  auto const preprocessed_probe =
+    experimental::row::equality::preprocessed_table::create(probe, stream);
+  auto const row_comparator =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_probe, preprocessed_build};
+  auto const equality_probe = row_comparator.equal_to<false>(has_nulls, compare_nulls);
+
+  semi_map_type hash_table{compute_hash_table_size(build.num_rows()),
+                           cuco::empty_key{std::numeric_limits<hash_value_type>::max()},
+                           cuco::empty_value{cudf::detail::JoinNoneValue},
+                           detail::hash_table_allocator_type{default_allocator<char>{}, stream},
+                           stream.value()};
+
+  // Create hash table containing all keys found in right table
+  // TODO: To add support for nested columns we will need to flatten in many
+  // places. However, this probably isn't worth adding any time soon since we
+  // won't be able to support AST conditions for those types anyway.
+  auto const build_nulls    = cudf::nullate::DYNAMIC{cudf::has_nulls(build)};
+  auto const row_hash_build = cudf::experimental::row::hash::row_hasher{preprocessed_build};
+  auto const hash_build     = row_hash_build.device_hasher(build_nulls);
+  // Since we may see multiple rows that are identical in the equality tables
+  // but differ in the conditional tables, the equality comparator used for
+  // insertion must account for both sets of tables. An alternative solution
+  // would be to use a multimap, but that solution would store duplicates where
+  // equality and conditional rows are equal, so this approach is preferable.
+  // One way to make this solution even more efficient would be to only include
+  // the columns of the conditional table that are used by the expression, but
+  // that requires additional plumbing through the AST machinery and is out of
+  // scope for now.
+  auto const row_comparator_build =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_build, preprocessed_build};
+  auto const equality_build_equality =
+    row_comparator_build.equal_to<false>(build_nulls, compare_nulls);
+  auto const preprocessed_build_condtional =
+    experimental::row::equality::preprocessed_table::create(
+      swap_tables ? left_conditional : right_conditional, stream);
+  auto const row_comparator_conditional_build =
+    cudf::experimental::row::equality::two_table_comparator{preprocessed_build_condtional,
+                                                            preprocessed_build_condtional};
+  auto const equality_build_conditional =
+    row_comparator_conditional_build.equal_to<false>(build_nulls, compare_nulls);
+  double_row_equality equality_build{equality_build_equality, equality_build_conditional};
+  make_pair_function_semi pair_func_build{};
+
+  auto iter = cudf::detail::make_counting_transform_iterator(0, pair_func_build);
+
+  // skip rows that are null here.
+  if ((compare_nulls == null_equality::EQUAL) or (not nullable(build))) {
+    hash_table.insert(iter, iter + right_num_rows, hash_build, equality_build, stream.value());
+  } else {
+    thrust::counting_iterator<cudf::size_type> stencil(0);
+    auto const [row_bitmask, _] =
+      cudf::detail::bitmask_and(build, stream, rmm::mr::get_current_device_resource());
+    row_is_valid pred{static_cast<bitmask_type const*>(row_bitmask.data())};
+
+    // insert valid rows
+    hash_table.insert_if(
+      iter, iter + right_num_rows, stencil, pred, hash_build, equality_build, stream.value());
+  }
+
+  auto hash_table_view = hash_table.get_device_view();
+
+  // For inner joins we support optimizing the join by launching one thread for
+  // whichever table is larger rather than always using the left table.
+  detail::grid_1d const config(outer_num_rows, DEFAULT_JOIN_BLOCK_SIZE);
+  auto const shmem_size_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+
+  // Allocate storage for the counter used to get the size of the join output
+  rmm::device_scalar<std::size_t> size(0, stream, mr);
+
+  auto const row_hash   = cudf::experimental::row::hash::row_hasher{preprocessed_probe};
+  auto const hash_probe = row_hash.device_hasher(has_nulls);
+
+  // Determine number of output rows without actually building the output to simply
+  // find what the size of the output will be.
+  if (has_nulls) {
+    compute_mixed_join_output_size_semi<DEFAULT_JOIN_BLOCK_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        join_type,
+        hash_table_view,
+        parser.device_expression_data,
+        swap_tables,
+        size.data(),
+        matches_per_row_span);
+  } else {
+    compute_mixed_join_output_size_semi<DEFAULT_JOIN_BLOCK_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_size_per_block, stream.value()>>>(
+        *left_conditional_view,
+        *right_conditional_view,
+        *probe_view,
+        *build_view,
+        hash_probe,
+        equality_probe,
+        join_type,
+        hash_table_view,
+        parser.device_expression_data,
+        swap_tables,
+        size.data(),
+        matches_per_row_span);
+  }
+
+  return {size.value(stream), std::move(matches_per_row)};
+}
+
+}  // namespace detail
+
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_left_semi_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_mixed_join_output_size_semi(left_equality,
+                                                     right_equality,
+                                                     left_conditional,
+                                                     right_conditional,
+                                                     binary_predicate,
+                                                     compare_nulls,
+                                                     detail::join_kind::LEFT_SEMI_JOIN,
+                                                     cudf::get_default_stream(),
+                                                     mr);
+}
+
+std::unique_ptr<rmm::device_uvector<size_type>> mixed_left_semi_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::mixed_join_semi(left_equality,
+                                 right_equality,
+                                 left_conditional,
+                                 right_conditional,
+                                 binary_predicate,
+                                 compare_nulls,
+                                 detail::join_kind::LEFT_SEMI_JOIN,
+                                 output_size_data,
+                                 cudf::get_default_stream(),
+                                 mr);
+}
+
+std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<size_type>>> mixed_left_anti_join_size(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_mixed_join_output_size_semi(left_equality,
+                                                     right_equality,
+                                                     left_conditional,
+                                                     right_conditional,
+                                                     binary_predicate,
+                                                     compare_nulls,
+                                                     detail::join_kind::LEFT_ANTI_JOIN,
+                                                     cudf::get_default_stream(),
+                                                     mr);
+}
+
+std::unique_ptr<rmm::device_uvector<size_type>> mixed_left_anti_join(
+  table_view const& left_equality,
+  table_view const& right_equality,
+  table_view const& left_conditional,
+  table_view const& right_conditional,
+  ast::expression const& binary_predicate,
+  null_equality compare_nulls,
+  std::optional<std::pair<std::size_t, device_span<size_type const>>> output_size_data,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::mixed_join_semi(left_equality,
+                                 right_equality,
+                                 left_conditional,
+                                 right_conditional,
+                                 binary_predicate,
+                                 compare_nulls,
+                                 detail::join_kind::LEFT_ANTI_JOIN,
+                                 output_size_data,
+                                 cudf::get_default_stream(),
+                                 mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_size_kernel.cu b/cpp/src/join/mixed_join_size_kernel.cu
new file mode 100644
index 0000000..4011acb
--- /dev/null
+++ b/cpp/src/join/mixed_join_size_kernel.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "mixed_join_size_kernel.cuh"
+
+namespace cudf {
+namespace detail {
+
+template __global__ void compute_mixed_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, false>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_size_kernel.cuh b/cpp/src/join/mixed_join_size_kernel.cuh
new file mode 100644
index 0000000..ef377da
--- /dev/null
+++ b/cpp/src/join/mixed_join_size_kernel.cuh
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "join_common_utils.cuh"
+#include "join_common_utils.hpp"
+#include "mixed_join_common_utils.cuh"
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <cooperative_groups.h>
+
+#include <cub/cub.cuh>
+#include <thrust/iterator/discard_iterator.h>
+
+namespace cudf {
+namespace detail {
+namespace cg = cooperative_groups;
+
+template <int block_size, bool has_nulls>
+__launch_bounds__(block_size) __global__ void compute_mixed_join_output_size(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row)
+{
+  // The (required) extern storage of the shared memory array leads to
+  // conflicting declarations between different templates. The easiest
+  // workaround is to declare an arbitrary (here char) array type then cast it
+  // after the fact to the appropriate type.
+  extern __shared__ char raw_intermediate_storage[];
+  cudf::ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<cudf::ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+  auto thread_intermediate_storage =
+    intermediate_storage + (threadIdx.x * device_expression_data.num_intermediates);
+
+  std::size_t thread_counter{0};
+  cudf::size_type const start_idx      = threadIdx.x + blockIdx.x * block_size;
+  cudf::size_type const stride         = block_size * gridDim.x;
+  cudf::size_type const left_num_rows  = left_table.num_rows();
+  cudf::size_type const right_num_rows = right_table.num_rows();
+  auto const outer_num_rows            = (swap_tables ? right_num_rows : left_num_rows);
+
+  auto evaluator = cudf::ast::detail::expression_evaluator<has_nulls>(
+    left_table, right_table, device_expression_data);
+
+  auto const empty_key_sentinel = hash_table_view.get_empty_key_sentinel();
+  make_pair_function pair_func{hash_probe, empty_key_sentinel};
+
+  // Figure out the number of elements for this key.
+  cg::thread_block_tile<1> this_thread = cg::this_thread();
+  // TODO: Address asymmetry in operator.
+  auto count_equality = pair_expression_equality<has_nulls>{
+    evaluator, thread_intermediate_storage, swap_tables, equality_probe};
+
+  for (cudf::size_type outer_row_index = start_idx; outer_row_index < outer_num_rows;
+       outer_row_index += stride) {
+    auto query_pair = pair_func(outer_row_index);
+    if (join_type == join_kind::LEFT_JOIN || join_type == join_kind::FULL_JOIN) {
+      matches_per_row[outer_row_index] =
+        hash_table_view.pair_count_outer(this_thread, query_pair, count_equality);
+    } else {
+      matches_per_row[outer_row_index] =
+        hash_table_view.pair_count(this_thread, query_pair, count_equality);
+    }
+    thread_counter += matches_per_row[outer_row_index];
+  }
+
+  using BlockReduce = cub::BlockReduce<cudf::size_type, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t block_counter = BlockReduce(temp_storage).Sum(thread_counter);
+
+  // Add block counter to global counter
+  if (threadIdx.x == 0) {
+    cuda::atomic_ref<std::size_t, cuda::thread_scope_device> ref{*output_size};
+    ref.fetch_add(block_counter, cuda::std::memory_order_relaxed);
+  }
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_size_kernel_nulls.cu b/cpp/src/join/mixed_join_size_kernel_nulls.cu
new file mode 100644
index 0000000..2868113
--- /dev/null
+++ b/cpp/src/join/mixed_join_size_kernel_nulls.cu
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "mixed_join_size_kernel.cuh"
+
+namespace cudf {
+namespace detail {
+
+template __global__ void compute_mixed_join_output_size<DEFAULT_JOIN_BLOCK_SIZE, true>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::mixed_multimap_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row);
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/join/mixed_join_size_kernels_semi.cu b/cpp/src/join/mixed_join_size_kernels_semi.cu
new file mode 100644
index 0000000..fd7bf02
--- /dev/null
+++ b/cpp/src/join/mixed_join_size_kernels_semi.cu
@@ -0,0 +1,122 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <join/join_common_utils.cuh>
+#include <join/join_common_utils.hpp>
+#include <join/mixed_join_common_utils.cuh>
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <cub/cub.cuh>
+
+namespace cudf {
+namespace detail {
+
+namespace cg = cooperative_groups;
+
+template <int block_size, bool has_nulls>
+__launch_bounds__(block_size) __global__ void compute_mixed_join_output_size_semi(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::semi_map_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row)
+{
+  // The (required) extern storage of the shared memory array leads to
+  // conflicting declarations between different templates. The easiest
+  // workaround is to declare an arbitrary (here char) array type then cast it
+  // after the fact to the appropriate type.
+  extern __shared__ char raw_intermediate_storage[];
+  cudf::ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<cudf::ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+  auto thread_intermediate_storage =
+    intermediate_storage + (threadIdx.x * device_expression_data.num_intermediates);
+
+  std::size_t thread_counter{0};
+  cudf::size_type const start_idx      = threadIdx.x + blockIdx.x * block_size;
+  cudf::size_type const stride         = block_size * gridDim.x;
+  cudf::size_type const left_num_rows  = left_table.num_rows();
+  cudf::size_type const right_num_rows = right_table.num_rows();
+  auto const outer_num_rows            = (swap_tables ? right_num_rows : left_num_rows);
+
+  auto evaluator = cudf::ast::detail::expression_evaluator<has_nulls>(
+    left_table, right_table, device_expression_data);
+
+  // TODO: Address asymmetry in operator.
+  auto equality = single_expression_equality<has_nulls>{
+    evaluator, thread_intermediate_storage, swap_tables, equality_probe};
+
+  for (cudf::size_type outer_row_index = start_idx; outer_row_index < outer_num_rows;
+       outer_row_index += stride) {
+    matches_per_row[outer_row_index] =
+      ((join_type == join_kind::LEFT_ANTI_JOIN) !=
+       (hash_table_view.contains(outer_row_index, hash_probe, equality)));
+    thread_counter += matches_per_row[outer_row_index];
+  }
+
+  using BlockReduce = cub::BlockReduce<cudf::size_type, block_size>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  std::size_t block_counter = BlockReduce(temp_storage).Sum(thread_counter);
+
+  // Add block counter to global counter
+  if (threadIdx.x == 0) {
+    cuda::atomic_ref<std::size_t, cuda::thread_scope_device> ref{*output_size};
+    ref.fetch_add(block_counter, cuda::std::memory_order_relaxed);
+  }
+}
+
+template __global__ void compute_mixed_join_output_size_semi<DEFAULT_JOIN_BLOCK_SIZE, true>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::semi_map_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row);
+
+template __global__ void compute_mixed_join_output_size_semi<DEFAULT_JOIN_BLOCK_SIZE, false>(
+  table_device_view left_table,
+  table_device_view right_table,
+  table_device_view probe,
+  table_device_view build,
+  row_hash const hash_probe,
+  row_equality const equality_probe,
+  join_kind const join_type,
+  cudf::detail::semi_map_type::device_view hash_table_view,
+  ast::detail::expression_device_view device_expression_data,
+  bool const swap_tables,
+  std::size_t* output_size,
+  cudf::device_span<cudf::size_type> matches_per_row);
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/join/semi_join.cu b/cpp/src/join/semi_join.cu
new file mode 100644
index 0000000..dcb6835
--- /dev/null
+++ b/cpp/src/join/semi_join.cu
@@ -0,0 +1,116 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <join/join_common_utils.hpp>
+
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/join.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+
+std::unique_ptr<rmm::device_uvector<cudf::size_type>> left_semi_anti_join(
+  join_kind const kind,
+  cudf::table_view const& left_keys,
+  cudf::table_view const& right_keys,
+  null_equality compare_nulls,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(0 != left_keys.num_columns(), "Left table is empty");
+  CUDF_EXPECTS(0 != right_keys.num_columns(), "Right table is empty");
+
+  if (is_trivial_join(left_keys, right_keys, kind)) {
+    return std::make_unique<rmm::device_uvector<cudf::size_type>>(0, stream, mr);
+  }
+  if ((join_kind::LEFT_ANTI_JOIN == kind) && (0 == right_keys.num_rows())) {
+    auto result =
+      std::make_unique<rmm::device_uvector<cudf::size_type>>(left_keys.num_rows(), stream, mr);
+    thrust::sequence(rmm::exec_policy(stream), result->begin(), result->end());
+    return result;
+  }
+
+  // Materialize a `flagged` boolean array to generate a gather map.
+  // Previously, the gather map was generated directly without this array but by calling to
+  // `map.contains` inside the `thrust::copy_if` kernel. However, that led to increasing register
+  // usage and reducing performance, as reported here: https://github.com/rapidsai/cudf/pull/10511.
+  auto const flagged = cudf::detail::contains(right_keys,
+                                              left_keys,
+                                              compare_nulls,
+                                              nan_equality::ALL_EQUAL,
+                                              stream,
+                                              rmm::mr::get_current_device_resource());
+
+  auto const left_num_rows = left_keys.num_rows();
+  auto gather_map =
+    std::make_unique<rmm::device_uvector<cudf::size_type>>(left_num_rows, stream, mr);
+
+  // gather_map_end will be the end of valid data in gather_map
+  auto gather_map_end =
+    thrust::copy_if(rmm::exec_policy(stream),
+                    thrust::counting_iterator<size_type>(0),
+                    thrust::counting_iterator<size_type>(left_num_rows),
+                    gather_map->begin(),
+                    [kind, d_flagged = flagged.begin()] __device__(size_type const idx) {
+                      return *(d_flagged + idx) == (kind == join_kind::LEFT_SEMI_JOIN);
+                    });
+
+  gather_map->resize(thrust::distance(gather_map->begin(), gather_map_end), stream);
+  return gather_map;
+}
+
+}  // namespace detail
+
+std::unique_ptr<rmm::device_uvector<cudf::size_type>> left_semi_join(
+  cudf::table_view const& left,
+  cudf::table_view const& right,
+  null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::left_semi_anti_join(
+    detail::join_kind::LEFT_SEMI_JOIN, left, right, compare_nulls, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<rmm::device_uvector<cudf::size_type>> left_anti_join(
+  cudf::table_view const& left,
+  cudf::table_view const& right,
+  null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::left_semi_anti_join(
+    detail::join_kind::LEFT_ANTI_JOIN, left, right, compare_nulls, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/labeling/label_bins.cu b/cpp/src/labeling/label_bins.cu
new file mode 100644
index 0000000..1a60378
--- /dev/null
+++ b/cpp/src/labeling/label_bins.cu
@@ -0,0 +1,250 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/label_bins.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/labeling/label_bins.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+#include <limits>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+// Sentinel used to indicate that an input value should be placed in the null
+// bin.
+// NOTE: In theory if a user decided to specify 2^31 bins this would fail. We
+// could make this an error in Python, but that is such a crazy edge case...
+constexpr size_type NULL_VALUE{std::numeric_limits<size_type>::max()};
+
+/*
+ * Functor for finding bins using thrust::transform.
+ *
+ * This functor is stateful, in the sense that it stores (for read-only use)
+ * pointers to the edge ranges on construction to enable natural use with
+ * thrust::transform semantics.  To handle null values, this functor assumes
+ * that the input iterators have already been shifted to exclude the range
+ * containing nulls. The `edge_index_shift` parameter is used to return the
+ * index of a value's bin accounting for this shift.
+ */
+template <typename T,
+          typename RandomAccessIterator,
+          typename LeftComparator,
+          typename RightComparator>
+struct bin_finder {
+  bin_finder(RandomAccessIterator left_begin,
+             RandomAccessIterator left_end,
+             RandomAccessIterator right_begin)
+    : m_left_begin(left_begin), m_left_end(left_end), m_right_begin(right_begin)
+  {
+  }
+
+  __device__ size_type operator()(thrust::pair<T, bool> input_value) const
+  {
+    // Immediately return sentinel for null inputs.
+    if (!input_value.second) return NULL_VALUE;
+
+    T value    = input_value.first;
+    auto bound = thrust::lower_bound(thrust::seq, m_left_begin, m_left_end, value, m_left_comp);
+
+    // Exit early and return sentinel for values that lie below the interval.
+    if (bound == m_left_begin) { return NULL_VALUE; }
+
+    auto index = thrust::distance(m_left_begin, thrust::prev(bound));
+    return (m_right_comp(value, m_right_begin[index])) ? index : NULL_VALUE;
+  }
+
+  RandomAccessIterator const
+    m_left_begin{};  // The beginning of the range containing the left bin edges.
+  RandomAccessIterator const m_left_end{};  // The end of the range containing the left bin edges.
+  RandomAccessIterator const
+    m_right_begin{};                   // The beginning of the range containing the right bin edges.
+  LeftComparator const m_left_comp{};  // Comparator used for left edges.
+  RightComparator const m_right_comp{};  // Comparator used for right edges.
+};
+
+// Functor to identify rows that should be filtered out based on the sentinel set by
+// bin_finder::operator().
+struct filter_null_sentinel {
+  __device__ bool operator()(size_type i) { return i != NULL_VALUE; }
+};
+
+// Bin the input by the edges in left_edges and right_edges.
+template <typename T, typename LeftComparator, typename RightComparator>
+std::unique_ptr<column> label_bins(column_view const& input,
+                                   column_view const& left_edges,
+                                   column_view const& right_edges,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  auto output = make_numeric_column(
+    data_type(type_to_id<size_type>()), input.size(), mask_state::UNALLOCATED, stream, mr);
+  auto output_mutable_view = output->mutable_view();
+  auto output_begin        = output_mutable_view.begin<size_type>();
+  auto output_end          = output_mutable_view.end<size_type>();
+
+  // These device column views are necessary for creating iterators that work
+  // for columns of compound types. The column_view iterators fail for compound
+  // types because they return raw pointers to the start of the data. The output
+  // does not require these iterators because it's always a primitive type.
+  auto input_device_view       = column_device_view::create(input, stream);
+  auto left_edges_device_view  = column_device_view::create(left_edges, stream);
+  auto right_edges_device_view = column_device_view::create(right_edges, stream);
+
+  auto left_begin  = left_edges_device_view->begin<T>();
+  auto left_end    = left_edges_device_view->end<T>();
+  auto right_begin = right_edges_device_view->begin<T>();
+
+  using RandomAccessIterator = decltype(left_edges_device_view->begin<T>());
+
+  if (input.has_nulls()) {
+    thrust::transform(rmm::exec_policy(stream),
+                      input_device_view->pair_begin<T, true>(),
+                      input_device_view->pair_end<T, true>(),
+                      output_begin,
+                      bin_finder<T, RandomAccessIterator, LeftComparator, RightComparator>(
+                        left_begin, left_end, right_begin));
+  } else {
+    thrust::transform(rmm::exec_policy(stream),
+                      input_device_view->pair_begin<T, false>(),
+                      input_device_view->pair_end<T, false>(),
+                      output_begin,
+                      bin_finder<T, RandomAccessIterator, LeftComparator, RightComparator>(
+                        left_begin, left_end, right_begin));
+  }
+
+  auto mask_and_count = valid_if(output_begin, output_end, filter_null_sentinel(), stream, mr);
+
+  output->set_null_mask(std::move(mask_and_count.first), mask_and_count.second);
+  return output;
+}
+
+template <typename T>
+constexpr auto is_supported_bin_type()
+{
+  return cudf::is_relationally_comparable<T, T>() && cudf::is_equality_comparable<T, T>();
+}
+
+struct bin_type_dispatcher {
+  template <typename T, typename... Args>
+  std::enable_if_t<not detail::is_supported_bin_type<T>(), std::unique_ptr<column>> operator()(
+    Args&&...)
+  {
+    CUDF_FAIL("Type not support for cudf::bin");
+  }
+
+  template <typename T>
+  std::enable_if_t<detail::is_supported_bin_type<T>(), std::unique_ptr<column>> operator()(
+    column_view const& input,
+    column_view const& left_edges,
+    inclusive left_inclusive,
+    column_view const& right_edges,
+    inclusive right_inclusive,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    if ((left_inclusive == inclusive::YES) && (right_inclusive == inclusive::YES))
+      return label_bins<T, thrust::less_equal<T>, thrust::less_equal<T>>(
+        input, left_edges, right_edges, stream, mr);
+    if ((left_inclusive == inclusive::YES) && (right_inclusive == inclusive::NO))
+      return label_bins<T, thrust::less_equal<T>, thrust::less<T>>(
+        input, left_edges, right_edges, stream, mr);
+    if ((left_inclusive == inclusive::NO) && (right_inclusive == inclusive::YES))
+      return label_bins<T, thrust::less<T>, thrust::less_equal<T>>(
+        input, left_edges, right_edges, stream, mr);
+    if ((left_inclusive == inclusive::NO) && (right_inclusive == inclusive::NO))
+      return label_bins<T, thrust::less<T>, thrust::less<T>>(
+        input, left_edges, right_edges, stream, mr);
+
+    CUDF_FAIL("Undefined inclusive setting.");
+  }
+};
+
+}  // anonymous namespace
+
+/// Bin the input by the edges in left_edges and right_edges.
+std::unique_ptr<column> label_bins(column_view const& input,
+                                   column_view const& left_edges,
+                                   inclusive left_inclusive,
+                                   column_view const& right_edges,
+                                   inclusive right_inclusive,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE()
+  CUDF_EXPECTS((input.type() == left_edges.type()) && (input.type() == right_edges.type()),
+               "The input and edge columns must have the same types.");
+  CUDF_EXPECTS(left_edges.size() == right_edges.size(),
+               "The left and right edge columns must be of the same length.");
+  CUDF_EXPECTS(!left_edges.has_nulls() && !right_edges.has_nulls(),
+               "The left and right edge columns cannot contain nulls.");
+
+  // Handle empty inputs.
+  if (input.is_empty()) { return make_empty_column(type_to_id<size_type>()); }
+
+  return type_dispatcher<dispatch_storage_type>(input.type(),
+                                                detail::bin_type_dispatcher{},
+                                                input,
+                                                left_edges,
+                                                left_inclusive,
+                                                right_edges,
+                                                right_inclusive,
+                                                stream,
+                                                mr);
+}
+
+}  // namespace detail
+
+/// Bin the input by the edges in left_edges and right_edges.
+std::unique_ptr<column> label_bins(column_view const& input,
+                                   column_view const& left_edges,
+                                   inclusive left_inclusive,
+                                   column_view const& right_edges,
+                                   inclusive right_inclusive,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::label_bins(input,
+                            left_edges,
+                            left_inclusive,
+                            right_edges,
+                            right_inclusive,
+                            cudf::get_default_stream(),
+                            mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/lists/combine/concatenate_list_elements.cu b/cpp/src/lists/combine/concatenate_list_elements.cu
new file mode 100644
index 0000000..fbe2977
--- /dev/null
+++ b/cpp/src/lists/combine/concatenate_list_elements.cu
@@ -0,0 +1,281 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/combine.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+namespace {
+/**
+ * @brief Concatenate lists within the same row into one list, ignoring any null list during
+ * concatenation.
+ */
+std::unique_ptr<column> concatenate_lists_ignore_null(column_view const& input,
+                                                      bool build_null_mask,
+                                                      rmm::cuda_stream_view stream,
+                                                      rmm::mr::device_memory_resource* mr)
+{
+  auto const num_rows = input.size();
+
+  auto out_offsets = make_numeric_column(
+    data_type{type_to_id<size_type>()}, num_rows + 1, mask_state::UNALLOCATED, stream, mr);
+
+  auto const d_out_offsets  = out_offsets->mutable_view().template begin<size_type>();
+  auto const d_row_offsets  = lists_column_view(input).offsets_begin();
+  auto const d_list_offsets = lists_column_view(lists_column_view(input).child()).offsets_begin();
+
+  // Concatenating the lists at the same row by converting the entry offsets from the child column
+  // into row offsets of the root column. Those entry offsets are subtracted by the first entry
+  // offset to output zero-based offsets.
+  auto const iter = thrust::make_counting_iterator<size_type>(0);
+  thrust::transform(rmm::exec_policy(stream),
+                    iter,
+                    iter + num_rows + 1,
+                    d_out_offsets,
+                    [d_row_offsets, d_list_offsets] __device__(auto const idx) {
+                      auto const start_offset = d_list_offsets[d_row_offsets[0]];
+                      return d_list_offsets[d_row_offsets[idx]] - start_offset;
+                    });
+
+  // The child column of the output lists column is just copied from the input column.
+  auto out_entries = std::make_unique<column>(
+    lists_column_view(lists_column_view(input).get_sliced_child(stream)).get_sliced_child(stream),
+    stream,
+    mr);
+
+  auto [null_mask, null_count] = [&] {
+    if (!build_null_mask)
+      return std::pair(cudf::detail::copy_bitmask(input, stream, mr), input.null_count());
+
+    // The output row will be null only if all lists on the input row are null.
+    auto const lists_dv_ptr = column_device_view::create(lists_column_view(input).child(), stream);
+    return cudf::detail::valid_if(
+      iter,
+      iter + num_rows,
+      [d_row_offsets, lists_dv = *lists_dv_ptr, iter] __device__(auto const idx) {
+        return thrust::any_of(
+          thrust::seq,
+          iter + d_row_offsets[idx],
+          iter + d_row_offsets[idx + 1],
+          [&] __device__(auto const list_idx) { return lists_dv.is_valid(list_idx); });
+      },
+      stream,
+      mr);
+  }();
+
+  return make_lists_column(num_rows,
+                           std::move(out_offsets),
+                           std::move(out_entries),
+                           null_count,
+                           null_count > 0 ? std::move(null_mask) : rmm::device_buffer{},
+                           stream,
+                           mr);
+}
+
+/**
+ * @brief Generate list offsets and list validities for the output lists column.
+ *
+ * This function is called only when (has_null_list == true and null_policy == NULLIFY_OUTPUT_ROW).
+ */
+std::pair<std::unique_ptr<column>, rmm::device_uvector<int8_t>>
+generate_list_offsets_and_validities(column_view const& input,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  auto const num_rows = input.size();
+
+  auto const lists_of_lists_dv_ptr = column_device_view::create(input, stream);
+  auto const lists_dv_ptr   = column_device_view::create(lists_column_view(input).child(), stream);
+  auto const d_row_offsets  = lists_column_view(input).offsets_begin();
+  auto const d_list_offsets = lists_column_view(lists_column_view(input).child()).offsets_begin();
+
+  // The array of int8_t stores validities for the output list elements.
+  auto validities = rmm::device_uvector<int8_t>(num_rows, stream);
+
+  // Compute output list sizes and validities.
+  auto sizes_itr = cudf::detail::make_counting_transform_iterator(
+    0,
+    [lists_of_lists_dv = *lists_of_lists_dv_ptr,
+     lists_dv          = *lists_dv_ptr,
+     d_row_offsets,
+     d_list_offsets,
+     d_validities = validities.begin()] __device__(auto const idx) {
+      if (d_row_offsets[idx] == d_row_offsets[idx + 1]) {  // This is a null/empty row.
+        d_validities[idx] = static_cast<int8_t>(lists_of_lists_dv.is_valid(idx));
+        return size_type{0};
+      }
+      // The output row will not be null only if all lists on the input row are not null.
+      auto const iter = thrust::make_counting_iterator<size_type>(0);
+      auto const is_valid =
+        thrust::all_of(thrust::seq,
+                       iter + d_row_offsets[idx],
+                       iter + d_row_offsets[idx + 1],
+                       [&] __device__(auto const list_idx) { return lists_dv.is_valid(list_idx); });
+      d_validities[idx] = static_cast<int8_t>(is_valid);
+      if (!is_valid) { return size_type{0}; }
+
+      // Compute size of the output list as sum of sizes of all lists in the current input row.
+      return d_list_offsets[d_row_offsets[idx + 1]] - d_list_offsets[d_row_offsets[idx]];
+    });
+  // Compute offsets from sizes.
+  auto out_offsets = std::get<0>(
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + num_rows, stream, mr));
+
+  return {std::move(out_offsets), std::move(validities)};
+}
+
+/**
+ * @brief Gather entries from the input lists column, ignoring rows that have null list elements.
+ *
+ * This function is called only when (has_null_list == true and null_policy == NULLIFY_OUTPUT_ROW).
+ */
+std::unique_ptr<column> gather_list_entries(column_view const& input,
+                                            column_view const& output_list_offsets,
+                                            size_type num_rows,
+                                            size_type num_output_entries,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  auto const child_col      = lists_column_view(input).child();
+  auto const entry_col      = lists_column_view(child_col).child();
+  auto const d_row_offsets  = lists_column_view(input).offsets_begin();
+  auto const d_list_offsets = lists_column_view(child_col).offsets_begin();
+  auto gather_map           = rmm::device_uvector<size_type>(num_output_entries, stream);
+
+  // Fill the gather map with indices of the lists from the child column of the input column.
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    num_rows,
+    [d_row_offsets,
+     d_list_offsets,
+     d_indices = gather_map.begin(),
+     d_out_list_offsets =
+       output_list_offsets.template begin<size_type>()] __device__(size_type const idx) {
+      // The output row has been identified as a null/empty list during list size computation.
+      if (d_out_list_offsets[idx + 1] == d_out_list_offsets[idx]) { return; }
+
+      // The indices of the list elements on the row `idx` of the input column.
+      thrust::sequence(thrust::seq,
+                       d_indices + d_out_list_offsets[idx],
+                       d_indices + d_out_list_offsets[idx + 1],
+                       d_list_offsets[d_row_offsets[idx]]);
+    });
+
+  auto result = cudf::detail::gather(table_view{{entry_col}},
+                                     gather_map,
+                                     out_of_bounds_policy::DONT_CHECK,
+                                     cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                     stream,
+                                     mr);
+  return std::move(result->release()[0]);
+}
+
+std::unique_ptr<column> concatenate_lists_nullifying_rows(column_view const& input,
+                                                          rmm::cuda_stream_view stream,
+                                                          rmm::mr::device_memory_resource* mr)
+{
+  // Generate offsets and validities of the output lists column.
+  auto [list_offsets, list_validities] = generate_list_offsets_and_validities(input, stream, mr);
+  auto const offsets_view              = list_offsets->view();
+
+  auto const num_rows = input.size();
+  auto const num_output_entries =
+    cudf::detail::get_value<size_type>(offsets_view, num_rows, stream);
+
+  auto list_entries =
+    gather_list_entries(input, offsets_view, num_rows, num_output_entries, stream, mr);
+  auto [null_mask, null_count] = cudf::detail::valid_if(
+    list_validities.begin(), list_validities.end(), thrust::identity{}, stream, mr);
+
+  return make_lists_column(num_rows,
+                           std::move(list_offsets),
+                           std::move(list_entries),
+                           null_count,
+                           null_count ? std::move(null_mask) : rmm::device_buffer{},
+                           stream,
+                           mr);
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::lists::concatenate_list_elements
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> concatenate_list_elements(column_view const& input,
+                                                  concatenate_null_policy null_policy,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type().id() == type_id::LIST,
+               "Input column must be a lists column.",
+               std::invalid_argument);
+
+  auto const child = lists_column_view(input).child();
+  CUDF_EXPECTS(child.type().id() == type_id::LIST,
+               "Child of the input lists column must also be a lists column.",
+               std::invalid_argument);
+
+  if (input.size() == 0) { return cudf::empty_like(input); }
+
+  bool const has_null_list = child.has_nulls();
+  return (null_policy == concatenate_null_policy::IGNORE || !has_null_list)
+           ? concatenate_lists_ignore_null(input, has_null_list, stream, mr)
+           : concatenate_lists_nullifying_rows(input, stream, mr);
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::lists::concatenate_list_elements
+ */
+std::unique_ptr<column> concatenate_list_elements(column_view const& input,
+                                                  concatenate_null_policy null_policy,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate_list_elements(input, null_policy, cudf::get_default_stream(), mr);
+}
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/combine/concatenate_rows.cu b/cpp/src/lists/combine/concatenate_rows.cu
new file mode 100644
index 0000000..658538b
--- /dev/null
+++ b/cpp/src/lists/combine/concatenate_rows.cu
@@ -0,0 +1,315 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/combine.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_checks.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Generates the new set of offsets that regroups the concatenated-by-column inputs
+ * into concatenated-by-rows inputs, and the associated null mask.
+ *
+ * If we have the following input columns:
+ *
+ * s1 = [{0, 1}, {2, 3, 4}, {5}, {},           {6, 7}]
+ * s2 = [{8},    {9},       {},  {10, 11, 12}, {13, 14, 15, 16}]
+ *
+ * We can rearrange the child data using a normal concatenate and a gather such that
+ * the resulting values are in the correct order. For the above example, the
+ * child column would look like:
+ *
+ * {0, 1, 8, 2, 3, 4, 9, 5, 10, 11, 12, 6, 7, 13, 14, 15}
+ *
+ * Because we did a regular concatenate (and a subsequent gather to reorder the rows),
+ * the top level rows of the list column would look like:
+ *
+ * (2N rows)
+ * [{0, 1}, {8}, {2, 3, 4}, {9}, {5}, {10, 11, 12}, {6, 7}, {13, 14, 15, 16}]
+ *
+ * What we really want is:
+ *
+ * (N rows)
+ * [{0, 1, 8}, {2, 3, 4, 9}, {5}, {10, 11, 12}, {6, 7, 13, 14, 15, 16}]
+ *
+ * We can do this by recomputing a new offsets column that does this regrouping.
+ *
+ */
+std::tuple<std::unique_ptr<column>, rmm::device_buffer, size_type>
+generate_regrouped_offsets_and_null_mask(table_device_view const& input,
+                                         bool build_null_mask,
+                                         concatenate_null_policy null_policy,
+                                         device_span<size_type const> row_null_counts,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  // outgoing offsets.
+  auto offsets = cudf::make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, input.num_rows() + 1, mask_state::UNALLOCATED, stream, mr);
+
+  auto keys = thrust::make_transform_iterator(thrust::make_counting_iterator(size_t{0}),
+                                              [num_columns = input.num_columns()] __device__(
+                                                size_t i) -> size_type { return i / num_columns; });
+
+  // generate sizes for the regrouped rows
+  auto values = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(size_t{0}),
+    [input, row_null_counts = row_null_counts.data(), null_policy] __device__(
+      size_t i) -> size_type {
+      auto const col_index = i % input.num_columns();
+      auto const row_index = i / input.num_columns();
+
+      // nullify the whole output row
+      if (row_null_counts) {
+        if ((null_policy == concatenate_null_policy::NULLIFY_OUTPUT_ROW &&
+             row_null_counts[row_index] > 0) ||
+            (null_policy == concatenate_null_policy::IGNORE &&
+             row_null_counts[row_index] == input.num_columns())) {
+          return 0;
+        }
+      }
+      auto offsets =
+        input.column(col_index).child(lists_column_view::offsets_column_index).data<size_type>() +
+        input.column(col_index).offset();
+      return offsets[row_index + 1] - offsets[row_index];
+    });
+
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        keys,
+                        keys + (input.num_rows() * input.num_columns()),
+                        values,
+                        thrust::make_discard_iterator(),
+                        offsets->mutable_view().begin<size_type>());
+
+  // convert to offsets
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         offsets->view().begin<size_type>(),
+                         offsets->view().begin<size_type>() + input.num_rows() + 1,
+                         offsets->mutable_view().begin<size_type>(),
+                         0);
+
+  // generate appropriate null mask
+  auto [null_mask, null_count] = [&]() {
+    // if the input doesn't contain nulls, no work to do
+    if (!build_null_mask) {
+      return std::pair<rmm::device_buffer, size_type>{rmm::device_buffer{}, 0};
+    }
+
+    // row is null if -all- input rows are null
+    if (null_policy == concatenate_null_policy::IGNORE) {
+      return cudf::detail::valid_if(
+        row_null_counts.begin(),
+        row_null_counts.begin() + input.num_rows(),
+        [num_columns = input.num_columns()] __device__(size_type null_count) {
+          return null_count != num_columns;
+        },
+        stream,
+        mr);
+    }
+
+    // row is null if -any- input rows are null
+    return cudf::detail::valid_if(
+      row_null_counts.begin(),
+      row_null_counts.begin() + input.num_rows(),
+      [] __device__(size_type null_count) { return null_count == 0; },
+      stream,
+      mr);
+  }();
+
+  return {std::move(offsets), std::move(null_mask), null_count};
+}
+
+rmm::device_uvector<size_type> generate_null_counts(table_device_view const& input,
+                                                    rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<size_type> null_counts(input.num_rows(), stream);
+
+  auto keys = thrust::make_transform_iterator(thrust::make_counting_iterator(size_t{0}),
+                                              [num_columns = input.num_columns()] __device__(
+                                                size_t i) -> size_type { return i / num_columns; });
+
+  auto null_values = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(size_t{0}), [input] __device__(size_t i) -> size_type {
+      auto const col_index = i % input.num_columns();
+      auto const row_index = i / input.num_columns();
+      auto const& col      = input.column(col_index);
+      return col.null_mask() ? (bit_is_set(col.null_mask(), row_index + col.offset()) ? 0 : 1) : 0;
+    });
+
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        keys,
+                        keys + (input.num_rows() * input.num_columns()),
+                        null_values,
+                        thrust::make_discard_iterator(),
+                        null_counts.data());
+
+  return null_counts;
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::lists::concatenate_rows
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> concatenate_rows(table_view const& input,
+                                         concatenate_null_policy null_policy,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.num_columns() > 0, "The input table must have at least one column.");
+
+  auto const entry_type = lists_column_view(*input.begin()).child().type();
+  CUDF_EXPECTS(
+    std::all_of(input.begin(),
+                input.end(),
+                [](column_view const& col) { return col.type().id() == cudf::type_id::LIST; }),
+    "All columns of the input table must be of lists column type.");
+  CUDF_EXPECTS(
+    std::all_of(std::next(input.begin()),
+                input.end(),
+                [a = *input.begin()](column_view const& b) { return column_types_equal(a, b); }),
+    "The types of entries in the input columns must be the same.");
+
+  auto const num_rows = input.num_rows();
+  auto const num_cols = input.num_columns();
+  if (num_rows == 0) { return cudf::empty_like(input.column(0)); }
+  if (num_cols == 1) { return std::make_unique<column>(*(input.begin()), stream, mr); }
+
+  // concatenate the input table into one column.
+  std::vector<column_view> cols(input.num_columns());
+  std::copy(input.begin(), input.end(), cols.begin());
+  auto concat = cudf::detail::concatenate(cols, stream, rmm::mr::get_current_device_resource());
+
+  // whether or not we should be generating a null mask at all
+  auto const build_null_mask = concat->has_nulls();
+
+  auto input_dv = table_device_view::create(input, stream);
+
+  // if the output needs a null mask, generate a vector of null counts per row of input, where the
+  // count is the number of columns that contain a null for a given row.
+  auto row_null_counts = build_null_mask ? generate_null_counts(*input_dv, stream)
+                                         : rmm::device_uvector<size_type>{0, stream};
+
+  // if we have nulls, overlay an appropriate null mask onto the
+  // concatenated column so that gather() sanitizes out the child data of rows that will ultimately
+  // be nullified.
+  if (build_null_mask) {
+    auto [null_mask, null_count] = [&]() {
+      auto iter = thrust::make_counting_iterator(size_t{0});
+
+      // IGNORE.  Output row is nullified if all input rows are null.
+      if (null_policy == concatenate_null_policy::IGNORE) {
+        return cudf::detail::valid_if(
+          iter,
+          iter + (input.num_rows() * input.num_columns()),
+          [num_rows        = input.num_rows(),
+           num_columns     = input.num_columns(),
+           row_null_counts = row_null_counts.data()] __device__(size_t i) -> size_type {
+            auto const row_index = i % num_rows;
+            return row_null_counts[row_index] != num_columns;
+          },
+          stream,
+          rmm::mr::get_current_device_resource());
+      }
+      // NULLIFY_OUTPUT_ROW.  Output row is nullfied if any input row is null
+      return cudf::detail::valid_if(
+        iter,
+        iter + (input.num_rows() * input.num_columns()),
+        [num_rows        = input.num_rows(),
+         row_null_counts = row_null_counts.data()] __device__(size_t i) -> size_type {
+          auto const row_index = i % num_rows;
+          return row_null_counts[row_index] == 0;
+        },
+        stream,
+        rmm::mr::get_current_device_resource());
+    }();
+    concat->set_null_mask(std::move(null_mask), null_count);
+  }
+
+  // perform the gather to rearrange the rows in desired child order. this will produce -almost-
+  // what we want. the data of the children will be exactly what we want, but will be grouped as if
+  // we had concatenated all the rows together instead of concatenating within the rows.  To fix
+  // this we can simply swap in a new set of offsets that re-groups them.  bmo
+  auto iter = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(size_t{0}),
+    [num_columns = input.num_columns(),
+     num_rows    = input.num_rows()] __device__(size_t i) -> size_type {
+      auto const src_col_index    = i % num_columns;
+      auto const src_row_index    = i / num_columns;
+      auto const concat_row_index = (src_col_index * num_rows) + src_row_index;
+      return concat_row_index;
+    });
+  auto gathered = cudf::detail::gather(table_view({*concat}),
+                                       iter,
+                                       iter + (input.num_columns() * input.num_rows()),
+                                       out_of_bounds_policy::DONT_CHECK,
+                                       stream,
+                                       mr);
+
+  // generate regrouped offsets and null mask
+  auto [offsets, null_mask, null_count] = generate_regrouped_offsets_and_null_mask(
+    *input_dv, build_null_mask, null_policy, row_null_counts, stream, mr);
+
+  // reassemble the underlying child data with the regrouped offsets and null mask
+  column& col   = gathered->get_column(0);
+  auto contents = col.release();
+  return cudf::make_lists_column(
+    input.num_rows(),
+    std::move(offsets),
+    std::move(contents.children[lists_column_view::child_column_index]),
+    null_count,
+    std::move(null_mask),
+    stream,
+    mr);
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::lists::concatenate_rows
+ */
+std::unique_ptr<column> concatenate_rows(table_view const& input,
+                                         concatenate_null_policy null_policy,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate_rows(input, null_policy, cudf::get_default_stream(), mr);
+}
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/contains.cu b/cpp/src/lists/contains.cu
new file mode 100644
index 0000000..df1d043
--- /dev/null
+++ b/cpp/src/lists/contains.cu
@@ -0,0 +1,406 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/detail/contains.hpp>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+#include <cudf/lists/list_device_view.cuh>
+#include <cudf/lists/lists_column_device_view.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/row_operators.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+#include <type_traits>
+
+namespace cudf::lists {
+namespace {
+
+/**
+ * @brief A sentinel value used for marking that a given key has not been found in the search list.
+ *
+ * The value should be `-1` as indicated in the public API documentation.
+ */
+auto constexpr __device__ NOT_FOUND_SENTINEL = size_type{-1};
+
+/**
+ * @brief A sentinel value used for marking that a given output row should be null.
+ *
+ * This value should be different from `NOT_FOUND_SENTINEL`.
+ */
+auto constexpr __device__ NULL_SENTINEL = std::numeric_limits<size_type>::min();
+
+/**
+ * @brief Check if the given type is a supported non-nested type in `cudf::lists::contains`.
+ */
+template <typename Element>
+static auto constexpr is_supported_non_nested_type()
+{
+  return cudf::is_fixed_width<Element>() || std::is_same_v<Element, cudf::string_view>;
+}
+
+/**
+ * @brief Check if the given type is supported in `cudf::lists::contains`.
+ */
+struct is_supported_type_fn {
+  template <typename Element>
+  auto constexpr operator()()
+  {
+    return is_supported_non_nested_type<Element>() || cudf::is_nested<Element>();
+  }
+};
+
+/**
+ * @brief Return a pair of index iterators {begin, end} to loop through elements within a
+ * list.
+ *
+ * Depending on the value of `forward`, a pair of forward or reverse iterators will be
+ * returned, allowing to loop through elements in the list in first-to-last or last-to-first
+ * order.
+ *
+ * Note that the element indices always restart to `0` at the first position in each list.
+ *
+ * @tparam forward A boolean value indicating whether we want to iterate elements in the list
+ *         by forward or reverse order.
+ * @param size The number of elements in the list.
+ * @return A pair of {begin, end} iterators to iterate through the range `[0, size)`.
+ */
+template <bool forward>
+__device__ auto element_index_pair_iter(size_type const size)
+{
+  auto const begin = thrust::make_counting_iterator(0);
+  auto const end   = thrust::make_counting_iterator(size);
+
+  if constexpr (forward) {
+    return thrust::pair{begin, end};
+  } else {
+    return thrust::pair{thrust::make_reverse_iterator(end), thrust::make_reverse_iterator(begin)};
+  }
+}
+
+/**
+ * @brief Functor to perform searching for index of a key element in a given list, specialized
+ * for nested types.
+ */
+template <typename KeyValidityIter, typename EqComparator>
+struct search_list_fn {
+  duplicate_find_option const find_option;
+  KeyValidityIter const key_validity_iter;
+  EqComparator const d_comp;
+
+  search_list_fn(duplicate_find_option const find_option,
+                 KeyValidityIter const key_validity_iter,
+                 EqComparator const& d_comp)
+    : find_option(find_option), key_validity_iter(key_validity_iter), d_comp(d_comp)
+  {
+  }
+
+  __device__ size_type operator()(list_device_view const list) const
+  {
+    // A null list or null key will result in a null output row.
+    if (list.is_null() || !key_validity_iter[list.row_index()]) { return NULL_SENTINEL; }
+
+    return find_option == duplicate_find_option::FIND_FIRST ? search_list_op<true>(list)
+                                                            : search_list_op<false>(list);
+  }
+
+ private:
+  template <bool forward>
+  __device__ inline size_type search_list_op(list_device_view const list) const
+  {
+    using cudf::experimental::row::lhs_index_type;
+    using cudf::experimental::row::rhs_index_type;
+
+    auto const [begin, end] = element_index_pair_iter<forward>(list.size());
+    auto const found_iter =
+      thrust::find_if(thrust::seq, begin, end, [=] __device__(auto const idx) {
+        return !list.is_null(idx) && d_comp(static_cast<lhs_index_type>(list.element_offset(idx)),
+                                            static_cast<rhs_index_type>(list.row_index()));
+      });
+    // If the key is found, return its found position in the list from `found_iter`.
+    return found_iter == end ? NOT_FOUND_SENTINEL : *found_iter;
+  }
+};
+
+/**
+ * @brief Function to search for index of key element(s) in the corresponding rows of a lists
+ * column, specialized for nested types.
+ */
+template <typename InputIterator, typename OutputIterator, typename DeviceComp>
+void index_of(InputIterator input_it,
+              size_type num_rows,
+              OutputIterator output_it,
+              column_view const& child,
+              column_view const& search_keys,
+              duplicate_find_option find_option,
+              DeviceComp d_comp,
+              rmm::cuda_stream_view stream)
+{
+  auto const keys_dv_ptr       = column_device_view::create(search_keys, stream);
+  auto const key_validity_iter = cudf::detail::make_validity_iterator<true>(*keys_dv_ptr);
+  thrust::transform(rmm::exec_policy(stream),
+                    input_it,
+                    input_it + num_rows,
+                    output_it,
+                    search_list_fn{find_option, key_validity_iter, d_comp});
+}
+
+/**
+ * @brief Dispatch function to search for index of key element(s) in the corresponding rows of a
+ * lists column.
+ */
+std::unique_ptr<column> dispatch_index_of(lists_column_view const& lists,
+                                          column_view const& search_keys,
+                                          duplicate_find_option find_option,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(cudf::type_dispatcher(search_keys.type(), is_supported_type_fn{}),
+               "Unsupported type in `dispatch_index_of` function.");
+  // Access the child column through `child()` method, not `get_sliced_child()`.
+  // This is because slicing offset has already been taken into account during row
+  // comparisons.
+  auto const child = lists.child();
+
+  CUDF_EXPECTS(child.type() == search_keys.type(),
+               "Type/Scale of search key does not match list column element type.",
+               cudf::data_type_error);
+  CUDF_EXPECTS(search_keys.type().id() != type_id::EMPTY, "Type cannot be empty.");
+
+  auto const search_keys_have_nulls = search_keys.has_nulls();
+
+  auto const num_rows = lists.size();
+
+  auto const lists_cdv_ptr = column_device_view::create(lists.parent(), stream);
+  auto const input_it      = cudf::detail::make_counting_transform_iterator(
+    size_type{0},
+    [lists = cudf::detail::lists_column_device_view{*lists_cdv_ptr}] __device__(auto const idx) {
+      return list_device_view{lists, idx};
+    });
+
+  auto out_positions = make_numeric_column(
+    data_type{type_to_id<size_type>()}, num_rows, cudf::mask_state::UNALLOCATED, stream, mr);
+  auto const output_it = out_positions->mutable_view().template begin<size_type>();
+
+  auto const keys_tview  = cudf::table_view{{search_keys}};
+  auto const child_tview = cudf::table_view{{child}};
+  auto const has_nulls   = has_nested_nulls(child_tview) || has_nested_nulls(keys_tview);
+  auto const comparator =
+    cudf::experimental::row::equality::two_table_comparator(child_tview, keys_tview, stream);
+  if (cudf::is_nested(search_keys.type())) {
+    auto const d_comp = comparator.equal_to<true>(nullate::DYNAMIC{has_nulls});
+    index_of(input_it, num_rows, output_it, child, search_keys, find_option, d_comp, stream);
+  } else {
+    auto const d_comp = comparator.equal_to<false>(nullate::DYNAMIC{has_nulls});
+    index_of(input_it, num_rows, output_it, child, search_keys, find_option, d_comp, stream);
+  }
+
+  if (search_keys_have_nulls || lists.has_nulls()) {
+    auto [null_mask, null_count] = cudf::detail::valid_if(
+      output_it,
+      output_it + num_rows,
+      [] __device__(auto const idx) { return idx != NULL_SENTINEL; },
+      stream,
+      mr);
+    out_positions->set_null_mask(std::move(null_mask), null_count);
+  }
+  return out_positions;
+}
+
+/**
+ * @brief Converts key-positions vector (from `index_of()`) to a BOOL8 vector, indicating if
+ * the search key(s) were found.
+ */
+std::unique_ptr<column> to_contains(std::unique_ptr<column>&& key_positions,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(key_positions->type().id() == type_to_id<size_type>(),
+               "Expected input column of type cudf::size_type.");
+  auto const positions_begin = key_positions->view().template begin<size_type>();
+  auto result                = make_numeric_column(
+    data_type{type_id::BOOL8}, key_positions->size(), mask_state::UNALLOCATED, stream, mr);
+  thrust::transform(rmm::exec_policy(stream),
+                    positions_begin,
+                    positions_begin + key_positions->size(),
+                    result->mutable_view().template begin<bool>(),
+                    [] __device__(auto const i) {
+                      // position == NOT_FOUND_SENTINEL: the list does not contain the search key.
+                      return i != NOT_FOUND_SENTINEL;
+                    });
+
+  auto const null_count                             = key_positions->null_count();
+  [[maybe_unused]] auto [data, null_mask, children] = key_positions->release();
+  result->set_null_mask(std::move(*null_mask.release()), null_count);
+
+  return result;
+}
+}  // namespace
+
+namespace detail {
+std::unique_ptr<column> index_of(lists_column_view const& lists,
+                                 cudf::scalar const& search_key,
+                                 duplicate_find_option find_option,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  if (!search_key.is_valid(stream)) {
+    return make_numeric_column(data_type{cudf::type_to_id<size_type>()},
+                               lists.size(),
+                               cudf::create_null_mask(lists.size(), mask_state::ALL_NULL, mr),
+                               lists.size(),
+                               stream,
+                               mr);
+  }
+  if (lists.size() == 0) {
+    return make_numeric_column(
+      data_type{type_to_id<size_type>()}, 0, cudf::mask_state::UNALLOCATED, stream, mr);
+  }
+
+  auto search_key_col = cudf::make_column_from_scalar(search_key, lists.size(), stream, mr);
+  return index_of(lists, search_key_col->view(), find_option, stream, mr);
+}
+
+std::unique_ptr<column> index_of(lists_column_view const& lists,
+                                 column_view const& search_keys,
+                                 duplicate_find_option find_option,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(search_keys.size() == lists.size(),
+               "Number of search keys must match list column size.");
+  return dispatch_index_of(lists, search_keys, find_option, stream, mr);
+}
+
+std::unique_ptr<column> contains(lists_column_view const& lists,
+                                 cudf::scalar const& search_key,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto key_indices = index_of(lists,
+                              search_key,
+                              duplicate_find_option::FIND_FIRST,
+                              stream,
+                              rmm::mr::get_current_device_resource());
+  return to_contains(std::move(key_indices), stream, mr);
+}
+
+std::unique_ptr<column> contains(lists_column_view const& lists,
+                                 column_view const& search_keys,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(search_keys.size() == lists.size(),
+               "Number of search keys must match list column size.");
+
+  auto key_indices = index_of(lists,
+                              search_keys,
+                              duplicate_find_option::FIND_FIRST,
+                              stream,
+                              rmm::mr::get_current_device_resource());
+  return to_contains(std::move(key_indices), stream, mr);
+}
+
+std::unique_ptr<column> contains_nulls(lists_column_view const& lists,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto const lists_cv      = lists.parent();
+  auto output              = make_numeric_column(data_type{type_to_id<bool>()},
+                                    lists.size(),
+                                    copy_bitmask(lists_cv, stream, mr),
+                                    lists_cv.null_count(),
+                                    stream,
+                                    mr);
+  auto const out_begin     = output->mutable_view().template begin<bool>();
+  auto const lists_cdv_ptr = column_device_view::create(lists_cv, stream);
+
+  thrust::tabulate(rmm::exec_policy(stream),
+                   out_begin,
+                   out_begin + lists.size(),
+                   [lists = cudf::detail::lists_column_device_view{*lists_cdv_ptr}] __device__(
+                     auto const list_idx) {
+                     auto const list = list_device_view{lists, list_idx};
+                     return list.is_null() ||
+                            thrust::any_of(thrust::seq,
+                                           thrust::make_counting_iterator(0),
+                                           thrust::make_counting_iterator(list.size()),
+                                           [&list](auto const idx) { return list.is_null(idx); });
+                   });
+
+  return output;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> contains(lists_column_view const& lists,
+                                 cudf::scalar const& search_key,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains(lists, search_key, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> contains(lists_column_view const& lists,
+                                 column_view const& search_keys,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains(lists, search_keys, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> contains_nulls(lists_column_view const& lists,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains_nulls(lists, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> index_of(lists_column_view const& lists,
+                                 cudf::scalar const& search_key,
+                                 duplicate_find_option find_option,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::index_of(lists, search_key, find_option, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> index_of(lists_column_view const& lists,
+                                 column_view const& search_keys,
+                                 duplicate_find_option find_option,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::index_of(lists, search_keys, find_option, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf::lists
diff --git a/cpp/src/lists/copying/concatenate.cu b/cpp/src/lists/copying/concatenate.cu
new file mode 100644
index 0000000..ddd0dfb
--- /dev/null
+++ b/cpp/src/lists/copying/concatenate.cu
@@ -0,0 +1,144 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/concatenate_masks.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <memory>
+#include <numeric>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Merges the offsets child columns of multiple list columns into one.
+ *
+ * Since offsets are all relative to the start of their respective column,
+ * all offsets are shifted to account for the new starting position
+ *
+ * @param[in] columns               Vector of lists columns to concatenate
+ * @param[in] total_list_count      Total number of lists contained in the columns
+ * @param[in] stream                CUDA stream used for device memory operations
+ * and kernel launches.
+ * @param[in] mr                    Device memory resource used to allocate the
+ * returned column's device memory.
+ */
+std::unique_ptr<column> merge_offsets(host_span<lists_column_view const> columns,
+                                      size_type total_list_count,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  // outgoing offsets
+  auto merged_offsets = cudf::make_fixed_width_column(
+    data_type{type_id::INT32}, total_list_count + 1, mask_state::UNALLOCATED, stream, mr);
+  mutable_column_device_view d_merged_offsets(*merged_offsets, 0, 0);
+
+  // merge offsets
+  // TODO : this could probably be done as a single gpu operation if done as a kernel.
+  size_type shift = 0;
+  size_type count = 0;
+  std::for_each(columns.begin(), columns.end(), [&](lists_column_view const& c) {
+    if (c.size() > 0) {
+      // handle sliced columns
+      int const local_shift =
+        shift -
+        (c.offset() > 0 ? cudf::detail::get_value<size_type>(c.offsets(), c.offset(), stream) : 0);
+      column_device_view offsets(c.offsets(), nullptr, nullptr);
+      thrust::transform(
+        rmm::exec_policy(stream),
+        offsets.begin<size_type>() + c.offset(),
+        offsets.begin<size_type>() + c.offset() + c.size() + 1,
+        d_merged_offsets.begin<size_type>() + count,
+        [local_shift] __device__(size_type offset) { return offset + local_shift; });
+
+      shift += c.get_sliced_child(stream).size();
+      count += c.size();
+    }
+  });
+
+  return merged_offsets;
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::lists::detail::concatenate
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  std::vector<lists_column_view> lists_columns;
+  lists_columns.reserve(columns.size());
+  std::transform(
+    columns.begin(), columns.end(), std::back_inserter(lists_columns), [](column_view const& c) {
+      return lists_column_view(c);
+    });
+
+  // concatenate children. also prep data needed for offset merging
+  std::vector<column_view> children;
+  children.reserve(columns.size());
+  size_type total_list_count = 0;
+  std::for_each(lists_columns.begin(),
+                lists_columns.end(),
+                [&total_list_count, &children, stream](lists_column_view const& l) {
+                  // count total # of lists
+                  total_list_count += l.size();
+                  children.push_back(l.get_sliced_child(stream));
+                });
+  auto data = cudf::detail::concatenate(children, stream, mr);
+
+  // merge offsets
+  auto offsets = merge_offsets(lists_columns, total_list_count, stream, mr);
+
+  // if any of the input columns have nulls, construct the output mask
+  bool const has_nulls =
+    std::any_of(columns.begin(), columns.end(), [](auto const& col) { return col.has_nulls(); });
+  rmm::device_buffer null_mask = create_null_mask(
+    total_list_count, has_nulls ? mask_state::UNINITIALIZED : mask_state::UNALLOCATED);
+  auto null_mask_data = static_cast<bitmask_type*>(null_mask.data());
+  auto const null_count =
+    has_nulls ? cudf::detail::concatenate_masks(columns, null_mask_data, stream) : size_type{0};
+
+  // assemble into outgoing list column
+  return make_lists_column(total_list_count,
+                           std::move(offsets),
+                           std::move(data),
+                           null_count,
+                           std::move(null_mask),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/copying/copying.cu b/cpp/src/lists/copying/copying.cu
new file mode 100644
index 0000000..2d3826c
--- /dev/null
+++ b/cpp/src/lists/copying/copying.cu
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/copy_range.cuh>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <iostream>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+// New lists column from a subset of a lists_column_view
+std::unique_ptr<cudf::column> copy_slice(lists_column_view const& lists,
+                                         size_type start,
+                                         size_type end,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if (lists.is_empty() or start == end) { return cudf::empty_like(lists.parent()); }
+  if (end < 0 || end > lists.size()) end = lists.size();
+  CUDF_EXPECTS(((start >= 0) && (start < end)), "Invalid slice range.");
+  auto lists_count   = end - start;
+  auto offsets_count = lists_count + 1;  // num_offsets always 1 more than num_lists
+
+  // Account for the offset of the view:
+  start += lists.offset();
+  end += lists.offset();
+
+  // Offsets at the beginning and end of the slice:
+  auto offsets_data = lists.offsets().data<cudf::size_type>();
+  auto start_offset = cudf::detail::get_value<size_type>(lists.offsets(), start, stream);
+  auto end_offset   = cudf::detail::get_value<size_type>(lists.offsets(), end, stream);
+
+  rmm::device_uvector<cudf::size_type> out_offsets(offsets_count, stream);
+
+  // Compute the offsets column of the result:
+  thrust::transform(
+    rmm::exec_policy(stream),
+    offsets_data + start,
+    offsets_data + end + 1,  // size of offsets column is 1 greater than slice length
+    out_offsets.data(),
+    [start_offset] __device__(cudf::size_type i) { return i - start_offset; });
+  auto offsets = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                                offsets_count,
+                                                out_offsets.release(),
+                                                rmm::device_buffer{},
+                                                0);
+
+  // Compute the child column of the result.
+  // If the child of this lists column is itself a lists column, we call copy_slice() on it.
+  // Otherwise, it is a column of the leaf type, so we call slice() on it and copy the resulting
+  // view into a cudf::column:
+  auto child =
+    (lists.child().type() == cudf::data_type{type_id::LIST})
+      ? copy_slice(lists_column_view(lists.child()), start_offset, end_offset, stream, mr)
+      : std::make_unique<cudf::column>(
+          cudf::detail::slice(lists.child(), {start_offset, end_offset}, stream).front(),
+          stream,
+          mr);
+
+  // Compute the null mask of the result:
+  auto null_mask = cudf::detail::copy_bitmask(lists.null_mask(), start, end, stream, mr);
+
+  auto null_count = cudf::detail::null_count(
+    static_cast<bitmask_type const*>(null_mask.data()), 0, end - start, stream);
+
+  return make_lists_column(lists_count,
+                           std::move(offsets),
+                           std::move(child),
+                           null_count,
+                           std::move(null_mask),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/copying/gather.cu b/cpp/src/lists/copying/gather.cu
new file mode 100644
index 0000000..bd270b6
--- /dev/null
+++ b/cpp/src/lists/copying/gather.cu
@@ -0,0 +1,184 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/gather.cuh>
+#include <cudf/lists/detail/gather.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief List gatherer function object.
+ *
+ * The iterator needed for gathering at level N+1 needs to reference the offsets
+ * from level N and the "base" offsets used from level N-1.  An example of
+ * the gather map needed for level N+1 (see documentation for make_gather_data for
+ * the full example)
+ *
+ * @code{.pseudo}
+ * level N-1 offsets               : [0, 2, 5, 10], gather map[0, 2]
+ *
+ * level N offsets                 : [0, 2, 7]
+ * "base" offsets from level N-1   : [0, 5]
+ *
+ * desired output sequence for the level N+1 gather map
+ * [0, 1, 5, 6, 7, 8, 9]
+ *
+ * The generation of this sequence in this functor works as follows
+ *
+ * step 1, generate row index sequence
+ * [0, 0, 1, 1, 1, 1, 1]
+ * step 2, generate row subindex sequence
+ * [0, 1, 0, 1, 2, 3, 4]
+ * step 3, add base offsets to get the final sequence
+ * [0, 1, 5, 6, 7, 8, 9]
+ * @endcode
+ */
+struct list_gatherer {
+  using argument_type = size_type;
+  using result_type   = size_type;
+
+  size_t offset_count;
+  size_type const* base_offsets;
+  size_type const* offsets;
+
+  list_gatherer(gather_data const& gd)
+    : offset_count{gd.base_offsets.size()},
+      base_offsets{gd.base_offsets.data()},
+      offsets{gd.offsets->mutable_view().data<size_type>()}
+  {
+  }
+
+  __device__ result_type operator()(argument_type index)
+  {
+    // the "upper bound" of the span for a given offset is always offsets+1;
+    size_type const* upper_bound_start = offsets + 1;
+    // "step 1" from above
+    auto const bound =
+      thrust::upper_bound(thrust::seq, upper_bound_start, upper_bound_start + offset_count, index);
+    size_type offset_index = thrust::distance(upper_bound_start, bound);
+    // "step 2" from above
+    size_type offset_subindex = offset_index == 0 ? index : index - offsets[offset_index];
+    // "step 3" from above
+    return offset_subindex + base_offsets[offset_index];
+  }
+};
+
+/**
+ * @copydoc cudf::lists::detail::gather_list_leaf
+ */
+std::unique_ptr<column> gather_list_leaf(column_view const& column,
+                                         gather_data const& gd,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  // gather map iterator for this level (N)
+  auto gather_map_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<size_type>(0), list_gatherer{gd});
+  size_type gather_map_size = gd.gather_map_size;
+
+  // call the normal gather
+  // note : we don't need to bother checking for out-of-bounds here since
+  // our inputs at this stage aren't coming from the user.
+  auto gather_table = cudf::detail::gather(cudf::table_view({column}),
+                                           gather_map_begin,
+                                           gather_map_begin + gather_map_size,
+                                           out_of_bounds_policy::DONT_CHECK,
+                                           stream,
+                                           mr);
+  auto leaf_column  = std::move(gather_table->release().front());
+
+  if (column.null_count() == 0) { leaf_column->set_null_mask(rmm::device_buffer{}, 0); }
+
+  return leaf_column;
+}
+
+/**
+ * @copydoc cudf::lists::detail::gather_list_nested
+ */
+std::unique_ptr<column> gather_list_nested(cudf::lists_column_view const& list,
+                                           gather_data& gd,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  // gather map iterator for this level (N)
+  auto gather_map_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<size_type>(0), list_gatherer{gd});
+  size_type gather_map_size = gd.gather_map_size;
+
+  // if the gather map is empty, return an empty column
+  if (gather_map_size == 0) { return empty_like(list.parent()); }
+
+  // gather the bitmask, if relevant
+  rmm::device_buffer null_mask{0, stream, mr};
+  size_type null_count = list.null_count();
+  if (null_count > 0) {
+    auto list_cdv = column_device_view::create(list.parent(), stream);
+    auto validity = cudf::detail::valid_if(
+      gather_map_begin,
+      gather_map_begin + gather_map_size,
+      [cdv = *list_cdv] __device__(int index) { return cdv.is_valid(index); },
+      stream,
+      mr);
+    null_mask  = std::move(validity.first);
+    null_count = validity.second;
+  }
+
+  // generate gather_data for next level (N+1), potentially recycling the temporary
+  // base_offsets buffer.
+  gather_data child_gd = make_gather_data<false>(
+    list, gather_map_begin, gather_map_size, std::move(gd.base_offsets), stream, mr);
+
+  // the nesting case.
+  if (list.child().type() == cudf::data_type{type_id::LIST}) {
+    // gather children.
+    auto child = gather_list_nested(list.get_sliced_child(stream), child_gd, stream, mr);
+
+    // return the nested column
+    return make_lists_column(gather_map_size,
+                             std::move(child_gd.offsets),
+                             std::move(child),
+                             null_count,
+                             std::move(null_mask),
+                             stream,
+                             mr);
+  }
+
+  // it's a leaf.  do a regular gather
+  auto child = gather_list_leaf(list.get_sliced_child(stream), child_gd, stream, mr);
+
+  // assemble final column
+  return make_lists_column(gather_map_size,
+                           std::move(child_gd.offsets),
+                           std::move(child),
+                           null_count,
+                           std::move(null_mask),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/copying/scatter_helper.cu b/cpp/src/lists/copying/scatter_helper.cu
new file mode 100644
index 0000000..ca53587
--- /dev/null
+++ b/cpp/src/lists/copying/scatter_helper.cu
@@ -0,0 +1,483 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/detail/copying.hpp>
+#include <cudf/lists/detail/scatter_helper.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/utilities/span.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+/**
+ * @brief Constructs null mask for a scattered list's child column
+ *
+ * @param parent_list_vector Vector of unbound_list_view, for parent lists column
+ * @param parent_list_offsets List column offsets for parent lists column
+ * @param source_lists Source lists column for scatter operation
+ * @param target_lists Target lists column for scatter operation
+ * @param num_child_rows Number of rows in child column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate child column's null mask
+ * @return std::pair<rmm::device_buffer, size_type> Child column's null mask and null row count
+ */
+std::pair<rmm::device_buffer, size_type> construct_child_nullmask(
+  rmm::device_uvector<unbound_list_view> const& parent_list_vector,
+  column_view const& parent_list_offsets,
+  cudf::detail::lists_column_device_view const& source_lists,
+  cudf::detail::lists_column_device_view const& target_lists,
+  size_type num_child_rows,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto is_valid_predicate = [d_list_vector  = parent_list_vector.begin(),
+                             d_offsets      = parent_list_offsets.template data<size_type>(),
+                             d_offsets_size = parent_list_offsets.size(),
+                             source_lists,
+                             target_lists] __device__(auto const& i) {
+    auto list_start =
+      thrust::upper_bound(thrust::seq, d_offsets, d_offsets + d_offsets_size, i) - 1;
+    auto list_index    = list_start - d_offsets;
+    auto element_index = i - *list_start;
+
+    auto list_row = d_list_vector[list_index];
+    return !list_row.bind_to_column(source_lists, target_lists).is_null(element_index);
+  };
+
+  return cudf::detail::valid_if(thrust::make_counting_iterator<size_type>(0),
+                                thrust::make_counting_iterator<size_type>(num_child_rows),
+                                is_valid_predicate,
+                                stream,
+                                mr);
+}
+
+/**
+ * @brief (type_dispatch endpoint) Functor that constructs the child column result
+ *        of `scatter()`ing a list column.
+ *
+ * The protocol is as follows:
+ *
+ * Inputs:
+ *  1. list_vector:  A device_uvector of unbound_list_view, with each element
+ *                   indicating the position, size, and which column the list
+ *                   row came from.
+ *  2. list_offsets: The offsets column for the (outer) lists column, each offset
+ *                   marking the beginning of a list row.
+ *  3. source_list:  The lists-column that is the source of the scatter().
+ *  4. target_list:  The lists-column that is the target of the scatter().
+ *
+ * Output: A (possibly non-list) child column, which may be used in combination
+ *         with list_offsets to fully construct the outer list.
+ *
+ * Example:
+ *
+ * Consider the following scatter operation of two `list<int>` columns:
+ *
+ * 1. Source:      [{9,9,9,9}, {8,8,8}], i.e.
+ *    a. Child:    [9,9,9,9,8,8,8]
+ *    b. Offsets:  [0,      4,    7]
+ *
+ * 2. Target:      [{1,1}, {2,2}, {3,3}], i.e.
+ *    a. Child:    [1,1,2,2,3,3]
+ *    b. Offsets:  [0,  2,  4,  6]
+ *
+ * 3. Scatter-map: [2, 0]
+ *
+ * 4. Expected output: [{8,8,8}, {2,2}, {9,9,9,9}], i.e.
+ *    a. Child:        [8,8,8,2,2,9,9,9,9]  <--- THIS
+ *    b. Offsets:      [0,    3,  5,     9]
+ *
+ * `list_child_constructor` constructs the Expected Child column indicated above.
+ *
+ * `list_child_constructor` expects to be called with the `Source`/`Target`
+ * lists columns, along with the following:
+ *
+ * 1. list_vector: [ S[1](3), T[1](2), S[0](4) ]
+ *    Each unbound_list_view (e.g. S[1](3)) indicates:
+ *      a. Which column the row is bound to: S == Source, T == Target
+ *      b. The list index. E.g. S[1] indicates the 2nd list row of the Source column.
+ *      c. The row size.   E.g. S[1](3) indicates that the row has 3 elements.
+ *
+ * 2. list_offsets: [0, 3, 5, 9]
+ *    The caller may construct this with an `inclusive_scan()` on `list_vector`
+ *    element sizes.
+ */
+struct list_child_constructor {
+ private:
+  /**
+   * @brief Determine whether the child column type is supported with scattering lists.
+   *
+   * @tparam T The data type of the child column of the list being scattered.
+   */
+  template <typename T>
+  struct is_supported_child_type {
+    static bool const value = cudf::is_fixed_width<T>() || std::is_same_v<T, string_view> ||
+                              std::is_same_v<T, list_view> || std::is_same_v<T, struct_view>;
+  };
+
+ public:
+  // SFINAE catch-all, for unsupported child column types.
+  template <typename T, typename... Args>
+  std::enable_if_t<!is_supported_child_type<T>::value, std::unique_ptr<column>> operator()(
+    Args&&... args)
+  {
+    CUDF_FAIL("list_child_constructor unsupported!");
+  }
+
+  /**
+   * @brief Implementation for fixed_width child column types.
+   */
+  template <typename T>
+  std::enable_if_t<cudf::is_fixed_width<T>(), std::unique_ptr<column>> operator()(
+    rmm::device_uvector<unbound_list_view> const& list_vector,
+    cudf::column_view const& list_offsets,
+    cudf::lists_column_view const& source_lists_column_view,
+    cudf::lists_column_view const& target_lists_column_view,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto source_column_device_view =
+      column_device_view::create(source_lists_column_view.parent(), stream);
+    auto target_column_device_view =
+      column_device_view::create(target_lists_column_view.parent(), stream);
+    auto source_lists = cudf::detail::lists_column_device_view(*source_column_device_view);
+    auto target_lists = cudf::detail::lists_column_device_view(*target_column_device_view);
+
+    auto const num_child_rows{
+      cudf::detail::get_value<size_type>(list_offsets, list_offsets.size() - 1, stream)};
+
+    auto child_null_mask =
+      source_lists_column_view.child().nullable() || target_lists_column_view.child().nullable()
+        ? construct_child_nullmask(
+            list_vector, list_offsets, source_lists, target_lists, num_child_rows, stream, mr)
+        : std::pair(rmm::device_buffer{}, 0);
+
+    auto child_column = cudf::make_fixed_width_column(source_lists_column_view.child().type(),
+                                                      num_child_rows,
+                                                      std::move(child_null_mask.first),
+                                                      child_null_mask.second,
+                                                      stream,
+                                                      mr);
+
+    thrust::transform(
+      rmm::exec_policy_nosync(stream),
+      thrust::make_counting_iterator(0),
+      thrust::make_counting_iterator(child_column->size()),
+      child_column->mutable_view().begin<T>(),
+      [offset_begin  = list_offsets.begin<size_type>(),
+       offset_size   = list_offsets.size(),
+       d_list_vector = list_vector.begin(),
+       source_lists,
+       target_lists] __device__(auto index) {
+        auto const list_index_iter =
+          thrust::upper_bound(thrust::seq, offset_begin, offset_begin + offset_size, index);
+        auto const list_index =
+          static_cast<size_type>(thrust::distance(offset_begin, list_index_iter) - 1);
+        auto const intra_index = static_cast<size_type>(index - offset_begin[list_index]);
+        auto actual_list_row = d_list_vector[list_index].bind_to_column(source_lists, target_lists);
+        return actual_list_row.template element<T>(intra_index);
+      });
+
+    child_column->set_null_count(child_null_mask.second);
+
+    return child_column;
+  }
+
+  /**
+   * @brief Implementation for list child columns that contain strings.
+   */
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, string_view>, std::unique_ptr<column>> operator()(
+    rmm::device_uvector<unbound_list_view> const& list_vector,
+    cudf::column_view const& list_offsets,
+    cudf::lists_column_view const& source_lists_column_view,
+    cudf::lists_column_view const& target_lists_column_view,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto source_column_device_view =
+      column_device_view::create(source_lists_column_view.parent(), stream);
+    auto target_column_device_view =
+      column_device_view::create(target_lists_column_view.parent(), stream);
+    auto source_lists = cudf::detail::lists_column_device_view(*source_column_device_view);
+    auto target_lists = cudf::detail::lists_column_device_view(*target_column_device_view);
+
+    auto const num_child_rows{
+      cudf::detail::get_value<size_type>(list_offsets, list_offsets.size() - 1, stream)};
+
+    if (num_child_rows == 0) { return make_empty_column(type_id::STRING); }
+
+    auto string_views = rmm::device_uvector<string_view>(num_child_rows, stream);
+
+    auto const null_string_view = string_view{nullptr, 0};  // placeholder for factory function
+
+    thrust::transform(
+      rmm::exec_policy_nosync(stream),
+      thrust::make_counting_iterator<size_type>(0),
+      thrust::make_counting_iterator<size_type>(string_views.size()),
+      string_views.begin(),
+      [offset_begin  = list_offsets.begin<size_type>(),
+       offset_size   = list_offsets.size(),
+       d_list_vector = list_vector.begin(),
+       source_lists,
+       target_lists,
+       null_string_view] __device__(auto index) {
+        auto const list_index_iter =
+          thrust::upper_bound(thrust::seq, offset_begin, offset_begin + offset_size, index);
+        auto const list_index =
+          static_cast<size_type>(thrust::distance(offset_begin, list_index_iter) - 1);
+        auto const intra_index = static_cast<size_type>(index - offset_begin[list_index]);
+        auto row_index         = d_list_vector[list_index].row_index();
+        auto actual_list_row = d_list_vector[list_index].bind_to_column(source_lists, target_lists);
+        auto lists_column    = actual_list_row.get_column();
+        auto lists_offsets_ptr    = lists_column.offsets().template data<size_type>();
+        auto child_strings_column = lists_column.child();
+        auto strings_offset       = lists_offsets_ptr[row_index] + intra_index;
+
+        if (child_strings_column.is_null(strings_offset)) { return null_string_view; }
+        auto const d_str = child_strings_column.template element<string_view>(strings_offset);
+        // ensure a string from an all-empty column is not mapped to the null placeholder
+        auto const empty_string_view = string_view{};
+        return d_str.empty() ? empty_string_view : d_str;
+      });
+
+    // string_views should now have been populated with source and target references.
+    auto sv_span = cudf::device_span<string_view const>(string_views);
+    return cudf::make_strings_column(sv_span, null_string_view, stream, mr);
+  }
+
+  /**
+   * @brief (Recursively) Constructs a child column that is itself a list column.
+   */
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, list_view>, std::unique_ptr<column>> operator()(
+    rmm::device_uvector<unbound_list_view> const& list_vector,
+    cudf::column_view const& list_offsets,
+    cudf::lists_column_view const& source_lists_column_view,
+    cudf::lists_column_view const& target_lists_column_view,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto source_column_device_view =
+      column_device_view::create(source_lists_column_view.parent(), stream);
+    auto target_column_device_view =
+      column_device_view::create(target_lists_column_view.parent(), stream);
+    auto source_lists = cudf::detail::lists_column_device_view(*source_column_device_view);
+    auto target_lists = cudf::detail::lists_column_device_view(*target_column_device_view);
+
+    auto const num_child_rows{
+      cudf::detail::get_value<size_type>(list_offsets, list_offsets.size() - 1, stream)};
+
+    if (num_child_rows == 0) {
+      // make an empty lists column using the input child type
+      return empty_like(source_lists_column_view.child());
+    }
+
+    auto child_list_views = rmm::device_uvector<unbound_list_view>(num_child_rows, stream, mr);
+
+    // Convert from parent list_device_view instances to child list_device_views.
+    // For instance, if a parent list_device_view has 3 elements, it should have 3 corresponding
+    // child list_device_view instances.
+    thrust::transform(
+      rmm::exec_policy_nosync(stream),
+      thrust::make_counting_iterator<size_type>(0),
+      thrust::make_counting_iterator<size_type>(child_list_views.size()),
+      child_list_views.begin(),
+      [offset_begin  = list_offsets.begin<size_type>(),
+       offset_size   = list_offsets.size(),
+       d_list_vector = list_vector.begin(),
+       source_lists,
+       target_lists] __device__(auto index) {
+        auto const list_index_iter =
+          thrust::upper_bound(thrust::seq, offset_begin, offset_begin + offset_size, index);
+        auto const list_index =
+          static_cast<size_type>(thrust::distance(offset_begin, list_index_iter) - 1);
+        auto const intra_index = static_cast<size_type>(index - offset_begin[list_index]);
+        auto label             = d_list_vector[list_index].label();
+        auto row_index         = d_list_vector[list_index].row_index();
+        auto actual_list_row = d_list_vector[list_index].bind_to_column(source_lists, target_lists);
+        auto lists_column    = actual_list_row.get_column();
+        auto child_lists_column = lists_column.child();
+        auto lists_offsets_ptr  = lists_column.offsets().template data<size_type>();
+        auto child_lists_offsets_ptr =
+          child_lists_column.child(lists_column_view::offsets_column_index)
+            .template data<size_type>();
+        auto child_row_index = lists_offsets_ptr[row_index] + intra_index;
+        auto size =
+          child_lists_offsets_ptr[child_row_index + 1] - child_lists_offsets_ptr[child_row_index];
+        return unbound_list_view{label, child_row_index, size};
+      });
+
+    // child_list_views should now have been populated, with source and target references.
+
+    auto begin = thrust::make_transform_iterator(
+      child_list_views.begin(), [] __device__(auto const& row) { return row.size(); });
+
+    auto child_offsets = std::get<0>(
+      cudf::detail::make_offsets_child_column(begin, begin + child_list_views.size(), stream, mr));
+
+    auto child_column = cudf::type_dispatcher<dispatch_storage_type>(
+      source_lists_column_view.child().child(1).type(),
+      list_child_constructor{},
+      child_list_views,
+      child_offsets->view(),
+      cudf::lists_column_view(source_lists_column_view.child()),
+      cudf::lists_column_view(target_lists_column_view.child()),
+      stream,
+      mr);
+
+    auto child_null_mask =
+      source_lists_column_view.child().nullable() || target_lists_column_view.child().nullable()
+        ? construct_child_nullmask(
+            list_vector, list_offsets, source_lists, target_lists, num_child_rows, stream, mr)
+        : std::pair(rmm::device_buffer{}, 0);
+
+    return cudf::make_lists_column(num_child_rows,
+                                   std::move(child_offsets),
+                                   std::move(child_column),
+                                   child_null_mask.second,            // Null count
+                                   std::move(child_null_mask.first),  // Null mask
+                                   stream,
+                                   mr);
+  }
+
+  /**
+   * @brief (Recursively) constructs child columns that are structs.
+   */
+  template <typename T>
+  std::enable_if_t<std::is_same_v<T, struct_view>, std::unique_ptr<column>> operator()(
+    rmm::device_uvector<unbound_list_view> const& list_vector,
+    cudf::column_view const& list_offsets,
+    cudf::lists_column_view const& source_lists_column_view,
+    cudf::lists_column_view const& target_lists_column_view,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto const source_column_device_view =
+      column_device_view::create(source_lists_column_view.parent(), stream);
+    auto const target_column_device_view =
+      column_device_view::create(target_lists_column_view.parent(), stream);
+    auto const source_lists = cudf::detail::lists_column_device_view(*source_column_device_view);
+    auto const target_lists = cudf::detail::lists_column_device_view(*target_column_device_view);
+
+    auto const source_structs = source_lists_column_view.child();
+    auto const target_structs = target_lists_column_view.child();
+
+    auto const num_child_rows{
+      cudf::detail::get_value<size_type>(list_offsets, list_offsets.size() - 1, stream)};
+
+    auto const num_struct_members =
+      std::distance(source_structs.child_begin(), source_structs.child_end());
+    std::vector<std::unique_ptr<column>> child_columns;
+    child_columns.reserve(num_struct_members);
+
+    auto project_member_as_list_view = [](column_view const& structs_member,
+                                          cudf::size_type const& structs_list_num_rows,
+                                          column_view const& structs_list_offsets,
+                                          bitmask_type const* structs_list_nullmask,
+                                          cudf::size_type const& structs_list_null_count) {
+      return lists_column_view(column_view(data_type{type_id::LIST},
+                                           structs_list_num_rows,
+                                           nullptr,
+                                           structs_list_nullmask,
+                                           structs_list_null_count,
+                                           0,
+                                           {structs_list_offsets, structs_member}));
+    };
+
+    auto const iter_source_member_as_list = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<cudf::size_type>(0), [&](auto child_idx) {
+        return project_member_as_list_view(source_structs.child(child_idx),
+                                           source_lists_column_view.size(),
+                                           source_lists_column_view.offsets(),
+                                           source_lists_column_view.null_mask(),
+                                           source_lists_column_view.null_count());
+      });
+
+    auto const iter_target_member_as_list = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<cudf::size_type>(0), [&](auto child_idx) {
+        return project_member_as_list_view(target_structs.child(child_idx),
+                                           target_lists_column_view.size(),
+                                           target_lists_column_view.offsets(),
+                                           target_lists_column_view.null_mask(),
+                                           target_lists_column_view.null_count());
+      });
+
+    std::transform(iter_source_member_as_list,
+                   iter_source_member_as_list + num_struct_members,
+                   iter_target_member_as_list,
+                   std::back_inserter(child_columns),
+                   [&](auto source_struct_member_list_view, auto target_struct_member_list_view) {
+                     return cudf::type_dispatcher<dispatch_storage_type>(
+                       source_struct_member_list_view.child().type(),
+                       list_child_constructor{},
+                       list_vector,
+                       list_offsets,
+                       source_struct_member_list_view,
+                       target_struct_member_list_view,
+                       stream,
+                       mr);
+                   });
+
+    auto child_null_mask =
+      source_lists_column_view.child().nullable() || target_lists_column_view.child().nullable()
+        ? construct_child_nullmask(
+            list_vector, list_offsets, source_lists, target_lists, num_child_rows, stream, mr)
+        : std::pair(rmm::device_buffer{}, 0);
+
+    return cudf::make_structs_column(num_child_rows,
+                                     std::move(child_columns),
+                                     child_null_mask.second,
+                                     std::move(child_null_mask.first),
+                                     stream,
+                                     mr);
+  }
+};
+
+std::unique_ptr<column> build_lists_child_column_recursive(
+  data_type child_column_type,
+  rmm::device_uvector<unbound_list_view> const& list_vector,
+  cudf::column_view const& list_offsets,
+  cudf::lists_column_view const& source_lists_column_view,
+  cudf::lists_column_view const& target_lists_column_view,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  return cudf::type_dispatcher<dispatch_storage_type>(child_column_type,
+                                                      list_child_constructor{},
+                                                      list_vector,
+                                                      list_offsets,
+                                                      source_lists_column_view,
+                                                      target_lists_column_view,
+                                                      stream,
+                                                      mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/copying/segmented_gather.cu b/cpp/src/lists/copying/segmented_gather.cu
new file mode 100644
index 0000000..79d33e7
--- /dev/null
+++ b/cpp/src/lists/copying/segmented_gather.cu
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/detail/copy_range.cuh>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/lists/detail/gather.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+std::unique_ptr<column> segmented_gather(lists_column_view const& value_column,
+                                         lists_column_view const& gather_map,
+                                         out_of_bounds_policy bounds_policy,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_index_type(gather_map.child().type()),
+               "Gather map should be list column of index type");
+  CUDF_EXPECTS(!gather_map.has_nulls(), "Gather map contains nulls", std::invalid_argument);
+  CUDF_EXPECTS(value_column.size() == gather_map.size(),
+               "Gather map and list column should be same size");
+
+  auto const gather_map_sliced_child = gather_map.get_sliced_child(stream);
+  auto const gather_map_size         = gather_map_sliced_child.size();
+  auto const gather_index_begin      = gather_map.offsets_begin() + 1;
+  auto const gather_index_end        = gather_map.offsets_end();
+  auto const value_offsets           = value_column.offsets_begin();
+  auto const value_device_view       = column_device_view::create(value_column.parent(), stream);
+  auto const map_begin =
+    cudf::detail::indexalator_factory::make_input_iterator(gather_map_sliced_child);
+  auto const out_of_bounds = [] __device__(auto const index, auto const list_size) {
+    return index >= list_size || (index < 0 && -index > list_size);
+  };
+
+  // Calculate Flattened gather indices  (value_offset[row]+sub_index
+  auto transformer = [values_lists_view = *value_device_view,
+                      value_offsets,
+                      map_begin,
+                      gather_index_begin,
+                      gather_index_end,
+                      bounds_policy,
+                      out_of_bounds] __device__(size_type index) -> size_type {
+    // Get each row's offset. (Each row is a list).
+    auto offset_idx =
+      thrust::upper_bound(
+        thrust::seq, gather_index_begin, gather_index_end, gather_index_begin[-1] + index) -
+      gather_index_begin;
+    // Get each sub_index in list in each row of gather_map.
+    auto sub_index    = map_begin[index];
+    auto list_is_null = values_lists_view.is_null(offset_idx);
+    auto list_size = list_is_null ? 0 : (value_offsets[offset_idx + 1] - value_offsets[offset_idx]);
+    auto wrapped_sub_index  = sub_index < 0 ? sub_index + list_size : sub_index;
+    auto constexpr null_idx = cuda::std::numeric_limits<cudf::size_type>::max();
+    // Add sub_index to value_column offsets, to get gather indices of child of value_column
+    return (bounds_policy == out_of_bounds_policy::NULLIFY && out_of_bounds(sub_index, list_size))
+             ? null_idx
+             : value_offsets[offset_idx] + wrapped_sub_index - value_offsets[0];
+  };
+  auto child_gather_index_begin = cudf::detail::make_counting_transform_iterator(0, transformer);
+
+  // Call gather on child of value_column
+  auto child_table = cudf::detail::gather(table_view({value_column.get_sliced_child(stream)}),
+                                          child_gather_index_begin,
+                                          child_gather_index_begin + gather_map_size,
+                                          bounds_policy,
+                                          stream,
+                                          mr);
+  auto child       = std::move(child_table->release().front());
+
+  // Create list offsets from gather_map.
+  auto output_offset = cudf::detail::allocate_like(
+    gather_map.offsets(), gather_map.size() + 1, mask_allocation_policy::RETAIN, stream, mr);
+  auto output_offset_view = output_offset->mutable_view();
+  cudf::detail::copy_range_in_place(gather_map.offsets(),
+                                    output_offset_view,
+                                    gather_map.offset(),
+                                    gather_map.offset() + output_offset_view.size(),
+                                    0,
+                                    stream);
+  // Assemble list column & return
+  auto null_mask       = cudf::detail::copy_bitmask(value_column.parent(), stream, mr);
+  size_type null_count = value_column.null_count();
+  return make_lists_column(gather_map.size(),
+                           std::move(output_offset),
+                           std::move(child),
+                           null_count,
+                           std::move(null_mask),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> segmented_gather(lists_column_view const& source_column,
+                                         lists_column_view const& gather_map_list,
+                                         out_of_bounds_policy bounds_policy,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::segmented_gather(
+    source_column, gather_map_list, bounds_policy, cudf::get_default_stream(), mr);
+}
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/count_elements.cu b/cpp/src/lists/count_elements.cu
new file mode 100644
index 0000000..40a14d8
--- /dev/null
+++ b/cpp/src/lists/count_elements.cu
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/count_elements.hpp>
+#include <cudf/lists/list_device_view.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+/**
+ * @brief Returns a numeric column containing lengths of each element
+ *
+ * @param input Input lists column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New size_type column with lengths
+ */
+std::unique_ptr<column> count_elements(lists_column_view const& input,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto device_column = cudf::column_device_view::create(input.parent(), stream);
+  auto d_column      = *device_column;
+  // create output column
+  auto output = make_fixed_width_column(data_type{type_to_id<size_type>()},
+                                        input.size(),
+                                        cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                        input.null_count(),
+                                        stream,
+                                        mr);
+
+  // fill in the sizes
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(input.size()),
+                    output->mutable_view().begin<size_type>(),
+                    list_size_functor{d_column});
+
+  output->set_null_count(input.null_count());  // reset null count
+  return output;
+}
+
+}  // namespace detail
+
+// external APIS
+
+std::unique_ptr<column> count_elements(lists_column_view const& input,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::count_elements(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/dremel.cu b/cpp/src/lists/dremel.cu
new file mode 100644
index 0000000..2b1978b
--- /dev/null
+++ b/cpp/src/lists/dremel.cu
@@ -0,0 +1,486 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/detail/dremel.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/gather.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+
+namespace cudf::detail {
+namespace {
+/**
+ * @brief Functor to get definition level value for a nested struct column until the leaf level or
+ * the first list level.
+ *
+ */
+struct def_level_fn {
+  column_device_view const* parent_col;
+  uint8_t const* d_nullability;
+  uint8_t sub_level_start;
+  uint8_t curr_def_level;
+  bool always_nullable;
+
+  __device__ uint32_t operator()(size_type i)
+  {
+    uint32_t def       = curr_def_level;
+    uint8_t l          = sub_level_start;
+    bool is_col_struct = false;
+    auto col           = *parent_col;
+    do {
+      // If col not nullable then it does not contribute to def levels
+      if (always_nullable or d_nullability[l]) {
+        if (not col.nullable() or bit_is_set(col.null_mask(), i)) {
+          ++def;
+        } else {  // We have found the shallowest level at which this row is null
+          break;
+        }
+      }
+      is_col_struct = (col.type().id() == type_id::STRUCT);
+      if (is_col_struct) {
+        col = col.child(0);
+        ++l;
+      }
+    } while (is_col_struct);
+    return def;
+  }
+};
+
+dremel_data get_encoding(column_view h_col,
+                         std::vector<uint8_t> nullability,
+                         bool output_as_byte_array,
+                         bool always_nullable,
+                         rmm::cuda_stream_view stream)
+{
+  auto get_list_level = [](column_view col) {
+    while (col.type().id() == type_id::STRUCT) {
+      col = col.child(0);
+    }
+    return col;
+  };
+
+  auto get_empties = [&](column_view col, size_type start, size_type end) {
+    auto lcv = lists_column_view(get_list_level(col));
+    rmm::device_uvector<size_type> empties_idx(lcv.size(), stream);
+    rmm::device_uvector<size_type> empties(lcv.size(), stream);
+    auto d_off = lcv.offsets().data<size_type>();
+
+    auto empties_idx_end =
+      thrust::copy_if(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(start),
+                      thrust::make_counting_iterator(end),
+                      empties_idx.begin(),
+                      [d_off] __device__(auto i) { return d_off[i] == d_off[i + 1]; });
+    auto empties_end = thrust::gather(rmm::exec_policy(stream),
+                                      empties_idx.begin(),
+                                      empties_idx_end,
+                                      lcv.offsets().begin<size_type>(),
+                                      empties.begin());
+
+    auto empties_size = empties_end - empties.begin();
+    return std::make_tuple(std::move(empties), std::move(empties_idx), empties_size);
+  };
+
+  // Check if there are empty lists with empty offsets in this column
+  bool has_empty_list_offsets = false;
+  {
+    auto curr_col = h_col;
+    while (is_nested(curr_col.type())) {
+      if (curr_col.type().id() == type_id::LIST) {
+        auto lcv = lists_column_view(curr_col);
+        if (lcv.offsets().size() == 0) {
+          has_empty_list_offsets = true;
+          break;
+        }
+        curr_col = lcv.child();
+      } else if (curr_col.type().id() == type_id::STRUCT) {
+        curr_col = curr_col.child(0);
+      }
+    }
+  }
+  std::unique_ptr<column> empty_list_offset_col;
+  if (has_empty_list_offsets) {
+    empty_list_offset_col = make_fixed_width_column(data_type(type_id::INT32), 1);
+    CUDF_CUDA_TRY(cudaMemsetAsync(
+      empty_list_offset_col->mutable_view().head(), 0, sizeof(size_type), stream.value()));
+    std::function<column_view(column_view const&)> normalize_col = [&](column_view const& col) {
+      auto children = [&]() -> std::vector<column_view> {
+        if (col.type().id() == type_id::LIST) {
+          auto lcol = lists_column_view(col);
+          auto offset_col =
+            lcol.offsets().head() == nullptr ? empty_list_offset_col->view() : lcol.offsets();
+          return {offset_col, normalize_col(lcol.child())};
+        } else if (col.type().id() == type_id::STRUCT) {
+          return {normalize_col(col.child(0))};
+        } else {
+          return {col.child_begin(), col.child_end()};
+        }
+      }();
+      return column_view(col.type(),
+                         col.size(),
+                         col.head(),
+                         col.null_mask(),
+                         col.null_count(),
+                         col.offset(),
+                         std::move(children));
+    };
+    h_col = normalize_col(h_col);
+  }
+
+  auto curr_col = h_col;
+  std::vector<column_view> nesting_levels;
+  std::vector<uint8_t> def_at_level;
+  std::vector<uint8_t> start_at_sub_level;
+  uint8_t curr_nesting_level_idx = 0;
+
+  if (nullability.empty()) {
+    while (is_nested(curr_col.type())) {
+      nullability.push_back(curr_col.nullable());
+      curr_col = curr_col.type().id() == type_id::LIST ? curr_col.child(1) : curr_col.child(0);
+    }
+    nullability.push_back(curr_col.nullable());
+  }
+  curr_col = h_col;
+
+  auto add_def_at_level = [&](column_view col) {
+    // Add up all def level contributions in this column all the way till the first list column
+    // appears in the hierarchy or until we get to leaf
+    uint32_t def = 0;
+    start_at_sub_level.push_back(curr_nesting_level_idx);
+    while (col.type().id() == type_id::STRUCT) {
+      def += (always_nullable or nullability[curr_nesting_level_idx]) ? 1 : 0;
+      col = col.child(0);
+      ++curr_nesting_level_idx;
+    }
+    // At the end of all those structs is either a list column or the leaf. List column contributes
+    // at least one def level. Leaf contributes 1 level only if it is nullable.
+    def += (col.type().id() == type_id::LIST ? 1 : 0) +
+           (always_nullable or nullability[curr_nesting_level_idx] ? 1 : 0);
+    def_at_level.push_back(def);
+    ++curr_nesting_level_idx;
+  };
+  while (cudf::is_nested(curr_col.type())) {
+    nesting_levels.push_back(curr_col);
+    add_def_at_level(curr_col);
+    while (curr_col.type().id() == type_id::STRUCT) {
+      // Go down the hierarchy until we get to the LIST or the leaf level
+      curr_col = curr_col.child(0);
+    }
+    if (curr_col.type().id() == type_id::LIST) {
+      auto child = curr_col.child(lists_column_view::child_column_index);
+      if (output_as_byte_array && child.type().id() == type_id::UINT8) {
+        // consider this the bottom
+        break;
+      }
+      curr_col = child;
+      if (not is_nested(curr_col.type())) {
+        // Special case: when the leaf data column is the immediate child of the list col then we
+        // want it to be included right away. Otherwise the struct containing it will be included in
+        // the next iteration of this loop.
+        nesting_levels.push_back(curr_col);
+        add_def_at_level(curr_col);
+        break;
+      }
+    }
+  }
+
+  [[maybe_unused]] auto [device_view_owners, d_nesting_levels] =
+    contiguous_copy_column_device_views<column_device_view>(nesting_levels, stream);
+
+  auto max_def_level = def_at_level.back();
+  thrust::exclusive_scan(
+    thrust::host, def_at_level.begin(), def_at_level.end(), def_at_level.begin());
+  max_def_level += def_at_level.back();
+
+  // Sliced list column views only have offsets applied to top level. Get offsets for each level.
+  rmm::device_uvector<size_type> d_column_offsets(nesting_levels.size(), stream);
+  rmm::device_uvector<size_type> d_column_ends(nesting_levels.size(), stream);
+
+  auto d_col = column_device_view::create(h_col, stream);
+  cudf::detail::device_single_thread(
+    [offset_at_level  = d_column_offsets.data(),
+     end_idx_at_level = d_column_ends.data(),
+     level_max        = d_column_offsets.size(),
+     col              = *d_col] __device__() {
+      auto curr_col           = col;
+      size_type off           = curr_col.offset();
+      size_type end           = off + curr_col.size();
+      size_type level         = 0;
+      offset_at_level[level]  = off;
+      end_idx_at_level[level] = end;
+      ++level;
+      // Apply offset recursively until we get to leaf data
+      // Skip doing the following for any structs we encounter in between.
+      while (curr_col.type().id() == type_id::LIST or curr_col.type().id() == type_id::STRUCT) {
+        if (curr_col.type().id() == type_id::LIST) {
+          off = curr_col.child(lists_column_view::offsets_column_index).element<size_type>(off);
+          end = curr_col.child(lists_column_view::offsets_column_index).element<size_type>(end);
+          if (level < level_max) {
+            offset_at_level[level]  = off;
+            end_idx_at_level[level] = end;
+            ++level;
+          }
+          curr_col = curr_col.child(lists_column_view::child_column_index);
+        } else {
+          curr_col = curr_col.child(0);
+        }
+      }
+    },
+    stream);
+
+  thrust::host_vector<size_type> column_offsets =
+    cudf::detail::make_host_vector_async(d_column_offsets, stream);
+  thrust::host_vector<size_type> column_ends =
+    cudf::detail::make_host_vector_async(d_column_ends, stream);
+  stream.synchronize();
+
+  size_t max_vals_size = 0;
+  for (size_t l = 0; l < column_offsets.size(); ++l) {
+    max_vals_size += column_ends[l] - column_offsets[l];
+  }
+
+  auto d_nullability = cudf::detail::make_device_uvector_async(
+    nullability, stream, rmm::mr::get_current_device_resource());
+
+  rmm::device_uvector<uint8_t> rep_level(max_vals_size, stream);
+  rmm::device_uvector<uint8_t> def_level(max_vals_size, stream);
+
+  rmm::device_uvector<uint8_t> temp_rep_vals(max_vals_size, stream);
+  rmm::device_uvector<uint8_t> temp_def_vals(max_vals_size, stream);
+  rmm::device_uvector<size_type> new_offsets(0, stream);
+  size_type curr_rep_values_size = 0;
+  {
+    // At this point, curr_col contains the leaf column. Max nesting level is
+    // nesting_levels.size().
+
+    // We are going to start by merging the last column in nesting_levels (the leaf, which is at the
+    // index `nesting_levels.size() - 1`) with the second-to-last (which is at
+    // `nesting_levels.size() - 2`).
+    size_t level              = nesting_levels.size() - 2;
+    curr_col                  = nesting_levels[level];
+    auto lcv                  = lists_column_view(get_list_level(curr_col));
+    auto offset_size_at_level = column_ends[level] - column_offsets[level] + 1;
+
+    // Get empties at this level
+    auto [empties, empties_idx, empties_size] =
+      get_empties(nesting_levels[level], column_offsets[level], column_ends[level]);
+
+    // Merge empty at deepest parent level with the rep, def level vals at leaf level
+
+    auto input_parent_rep_it = thrust::make_constant_iterator(level);
+    auto input_parent_def_it =
+      thrust::make_transform_iterator(empties_idx.begin(),
+                                      def_level_fn{d_nesting_levels + level,
+                                                   d_nullability.data(),
+                                                   start_at_sub_level[level],
+                                                   def_at_level[level],
+                                                   always_nullable});
+
+    // `nesting_levels.size()` == no of list levels + leaf. Max repetition level = no of list levels
+    auto input_child_rep_it = thrust::make_constant_iterator(nesting_levels.size() - 1);
+    auto input_child_def_it =
+      thrust::make_transform_iterator(thrust::make_counting_iterator(column_offsets[level + 1]),
+                                      def_level_fn{d_nesting_levels + level + 1,
+                                                   d_nullability.data(),
+                                                   start_at_sub_level[level + 1],
+                                                   def_at_level[level + 1],
+                                                   always_nullable});
+
+    // Zip the input and output value iterators so that merge operation is done only once
+    auto input_parent_zip_it =
+      thrust::make_zip_iterator(thrust::make_tuple(input_parent_rep_it, input_parent_def_it));
+
+    auto input_child_zip_it =
+      thrust::make_zip_iterator(thrust::make_tuple(input_child_rep_it, input_child_def_it));
+
+    auto output_zip_it =
+      thrust::make_zip_iterator(thrust::make_tuple(rep_level.begin(), def_level.begin()));
+
+    auto ends = thrust::merge_by_key(rmm::exec_policy(stream),
+                                     empties.begin(),
+                                     empties.begin() + empties_size,
+                                     thrust::make_counting_iterator(column_offsets[level + 1]),
+                                     thrust::make_counting_iterator(column_ends[level + 1]),
+                                     input_parent_zip_it,
+                                     input_child_zip_it,
+                                     thrust::make_discard_iterator(),
+                                     output_zip_it);
+
+    curr_rep_values_size = ends.second - output_zip_it;
+
+    // Scan to get distance by which each offset value is shifted due to the insertion of empties
+    auto scan_it = cudf::detail::make_counting_transform_iterator(
+      column_offsets[level],
+      [off = lcv.offsets().data<size_type>(), size = lcv.offsets().size()] __device__(
+        auto i) -> int { return (i + 1 < size) && (off[i] == off[i + 1]); });
+    rmm::device_uvector<size_type> scan_out(offset_size_at_level, stream);
+    thrust::exclusive_scan(
+      rmm::exec_policy(stream), scan_it, scan_it + offset_size_at_level, scan_out.begin());
+
+    // Add scan output to existing offsets to get new offsets into merged rep level values
+    new_offsets = rmm::device_uvector<size_type>(offset_size_at_level, stream);
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator(0),
+                       offset_size_at_level,
+                       [off      = lcv.offsets().data<size_type>() + column_offsets[level],
+                        scan_out = scan_out.data(),
+                        new_off  = new_offsets.data()] __device__(auto i) {
+                         new_off[i] = off[i] - off[0] + scan_out[i];
+                       });
+
+    // Set rep level values at level starts to appropriate rep level
+    auto scatter_it = thrust::make_constant_iterator(level);
+    thrust::scatter(rmm::exec_policy(stream),
+                    scatter_it,
+                    scatter_it + new_offsets.size() - 1,
+                    new_offsets.begin(),
+                    rep_level.begin());
+  }
+
+  // Having already merged the last two levels, we are now going to merge the result with the
+  // third-last level which is at index `nesting_levels.size() - 3`.
+  for (int level = nesting_levels.size() - 3; level >= 0; level--) {
+    curr_col                  = nesting_levels[level];
+    auto lcv                  = lists_column_view(get_list_level(curr_col));
+    auto offset_size_at_level = column_ends[level] - column_offsets[level] + 1;
+
+    // Get empties at this level
+    auto [empties, empties_idx, empties_size] =
+      get_empties(nesting_levels[level], column_offsets[level], column_ends[level]);
+
+    auto offset_transformer = [new_child_offsets = new_offsets.data(),
+                               child_start       = column_offsets[level + 1]] __device__(auto x) {
+      return new_child_offsets[x - child_start];  // (x - child's offset)
+    };
+
+    // We will be reading from old rep_levels and writing again to rep_levels. Swap the current
+    // rep values into temp_rep_vals so it can become the input and rep_levels can again be output.
+    std::swap(temp_rep_vals, rep_level);
+    std::swap(temp_def_vals, def_level);
+
+    // Merge empty at parent level with the rep, def level vals at current level
+    auto transformed_empties = thrust::make_transform_iterator(empties.begin(), offset_transformer);
+
+    auto input_parent_rep_it = thrust::make_constant_iterator(level);
+    auto input_parent_def_it =
+      thrust::make_transform_iterator(empties_idx.begin(),
+                                      def_level_fn{d_nesting_levels + level,
+                                                   d_nullability.data(),
+                                                   start_at_sub_level[level],
+                                                   def_at_level[level],
+                                                   always_nullable});
+
+    // Zip the input and output value iterators so that merge operation is done only once
+    auto input_parent_zip_it =
+      thrust::make_zip_iterator(thrust::make_tuple(input_parent_rep_it, input_parent_def_it));
+
+    auto input_child_zip_it =
+      thrust::make_zip_iterator(thrust::make_tuple(temp_rep_vals.begin(), temp_def_vals.begin()));
+
+    auto output_zip_it =
+      thrust::make_zip_iterator(thrust::make_tuple(rep_level.begin(), def_level.begin()));
+
+    auto ends = thrust::merge_by_key(rmm::exec_policy(stream),
+                                     transformed_empties,
+                                     transformed_empties + empties_size,
+                                     thrust::make_counting_iterator(0),
+                                     thrust::make_counting_iterator(curr_rep_values_size),
+                                     input_parent_zip_it,
+                                     input_child_zip_it,
+                                     thrust::make_discard_iterator(),
+                                     output_zip_it);
+
+    curr_rep_values_size = ends.second - output_zip_it;
+
+    // Scan to get distance by which each offset value is shifted due to the insertion of dremel
+    // level value fof an empty list
+    auto scan_it = cudf::detail::make_counting_transform_iterator(
+      column_offsets[level],
+      [off = lcv.offsets().data<size_type>(), size = lcv.offsets().size()] __device__(
+        auto i) -> int { return (i + 1 < size) && (off[i] == off[i + 1]); });
+    rmm::device_uvector<size_type> scan_out(offset_size_at_level, stream);
+    thrust::exclusive_scan(
+      rmm::exec_policy(stream), scan_it, scan_it + offset_size_at_level, scan_out.begin());
+
+    // Add scan output to existing offsets to get new offsets into merged rep level values
+    rmm::device_uvector<size_type> temp_new_offsets(offset_size_at_level, stream);
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator(0),
+                       offset_size_at_level,
+                       [off      = lcv.offsets().data<size_type>() + column_offsets[level],
+                        scan_out = scan_out.data(),
+                        new_off  = temp_new_offsets.data(),
+                        offset_transformer] __device__(auto i) {
+                         new_off[i] = offset_transformer(off[i]) + scan_out[i];
+                       });
+    new_offsets = std::move(temp_new_offsets);
+
+    // Set rep level values at level starts to appropriate rep level
+    auto scatter_it = thrust::make_constant_iterator(level);
+    thrust::scatter(rmm::exec_policy(stream),
+                    scatter_it,
+                    scatter_it + new_offsets.size() - 1,
+                    new_offsets.begin(),
+                    rep_level.begin());
+  }
+
+  size_t level_vals_size = new_offsets.back_element(stream);
+  rep_level.resize(level_vals_size, stream);
+  def_level.resize(level_vals_size, stream);
+
+  stream.synchronize();
+
+  size_type leaf_data_size = column_ends.back() - column_offsets.back();
+
+  return dremel_data{std::move(new_offsets),
+                     std::move(rep_level),
+                     std::move(def_level),
+                     leaf_data_size,
+                     max_def_level};
+}
+}  // namespace
+
+dremel_data get_dremel_data(column_view h_col,
+                            std::vector<uint8_t> nullability,
+                            bool output_as_byte_array,
+                            rmm::cuda_stream_view stream)
+{
+  return get_encoding(h_col, nullability, output_as_byte_array, false, stream);
+}
+
+dremel_data get_comparator_data(column_view h_col,
+                                std::vector<uint8_t> nullability,
+                                bool output_as_byte_array,
+                                rmm::cuda_stream_view stream)
+{
+  return get_encoding(h_col, nullability, output_as_byte_array, true, stream);
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/lists/explode.cu b/cpp/src/lists/explode.cu
new file mode 100644
index 0000000..4db3254
--- /dev/null
+++ b/cpp/src/lists/explode.cu
@@ -0,0 +1,347 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/explode.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+#include <memory>
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+// explode column gather map uses cudf::out_of_bounds_policy::NULLIFY to
+// fill nulls where there are invalid indices
+constexpr size_type InvalidIndex = -1;
+
+namespace {
+
+std::unique_ptr<table> build_table(
+  table_view const& input_table,
+  size_type const explode_column_idx,
+  column_view const& sliced_child,
+  cudf::device_span<size_type const> gather_map,
+  thrust::optional<cudf::device_span<size_type const>> explode_col_gather_map,
+  thrust::optional<rmm::device_uvector<size_type>> position_array,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto select_iter = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0),
+    [explode_column_idx](size_type i) { return i >= explode_column_idx ? i + 1 : i; });
+
+  auto gathered_table =
+    detail::gather(input_table.select(select_iter, select_iter + input_table.num_columns() - 1),
+                   gather_map.begin(),
+                   gather_map.end(),
+                   cudf::out_of_bounds_policy::DONT_CHECK,
+                   stream,
+                   mr);
+
+  std::vector<std::unique_ptr<column>> columns = gathered_table->release();
+
+  columns.insert(columns.begin() + explode_column_idx,
+                 explode_col_gather_map
+                   ? std::move(detail::gather(table_view({sliced_child}),
+                                              explode_col_gather_map->begin(),
+                                              explode_col_gather_map->end(),
+                                              cudf::out_of_bounds_policy::NULLIFY,
+                                              stream,
+                                              mr)
+                                 ->release()[0])
+                   : std::make_unique<column>(sliced_child, stream, mr));
+
+  if (position_array) {
+    size_type position_size = position_array->size();
+    // build the null mask for position based on invalid entries in gather map
+    auto nullmask = explode_col_gather_map ? valid_if(
+                                               explode_col_gather_map->begin(),
+                                               explode_col_gather_map->end(),
+                                               [] __device__(auto i) { return i != InvalidIndex; },
+                                               stream,
+                                               mr)
+                                           : std::pair<rmm::device_buffer, size_type>{
+                                               rmm::device_buffer(0, stream), size_type{0}};
+
+    columns.insert(columns.begin() + explode_column_idx,
+                   std::make_unique<column>(data_type(type_to_id<size_type>()),
+                                            position_size,
+                                            position_array->release(),
+                                            std::move(nullmask.first),
+                                            nullmask.second));
+  }
+
+  return std::make_unique<table>(std::move(columns));
+}
+}  // namespace
+
+std::unique_ptr<table> explode(table_view const& input_table,
+                               size_type const explode_column_idx,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  lists_column_view explode_col{input_table.column(explode_column_idx)};
+  auto sliced_child = explode_col.get_sliced_child(stream);
+  rmm::device_uvector<size_type> gather_map(sliced_child.size(), stream);
+
+  // Sliced columns may require rebasing of the offsets.
+  auto offsets = explode_col.offsets_begin();
+  // offsets + 1 here to skip the 0th offset, which removes a - 1 operation later.
+  auto offsets_minus_one = thrust::make_transform_iterator(
+    thrust::next(offsets), [offsets] __device__(auto i) { return (i - offsets[0]) - 1; });
+  auto counting_iter = thrust::make_counting_iterator(0);
+
+  // This looks like an off-by-one bug, but what is going on here is that we need to reduce each
+  // result from `lower_bound` by 1 to build the correct gather map. This can be accomplished by
+  // skipping the first entry and using the result of `lower_bound` directly.
+  thrust::lower_bound(rmm::exec_policy(stream),
+                      offsets_minus_one,
+                      offsets_minus_one + explode_col.size(),
+                      counting_iter,
+                      counting_iter + gather_map.size(),
+                      gather_map.begin());
+
+  return build_table(input_table,
+                     explode_column_idx,
+                     sliced_child,
+                     gather_map,
+                     thrust::nullopt,
+                     thrust::nullopt,
+                     stream,
+                     mr);
+}
+
+std::unique_ptr<table> explode_position(table_view const& input_table,
+                                        size_type const explode_column_idx,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  lists_column_view explode_col{input_table.column(explode_column_idx)};
+  auto sliced_child = explode_col.get_sliced_child(stream);
+  rmm::device_uvector<size_type> gather_map(sliced_child.size(), stream);
+
+  // Sliced columns may require rebasing of the offsets.
+  auto offsets = explode_col.offsets_begin();
+  // offsets + 1 here to skip the 0th offset, which removes a - 1 operation later.
+  auto offsets_minus_one = thrust::make_transform_iterator(
+    offsets + 1, [offsets] __device__(auto i) { return (i - offsets[0]) - 1; });
+  auto counting_iter = thrust::make_counting_iterator(0);
+
+  rmm::device_uvector<size_type> pos(sliced_child.size(), stream, mr);
+
+  // This looks like an off-by-one bug, but what is going on here is that we need to reduce each
+  // result from `lower_bound` by 1 to build the correct gather map. This can be accomplished by
+  // skipping the first entry and using the result of `lower_bound` directly.
+  thrust::transform(
+    rmm::exec_policy(stream),
+    counting_iter,
+    counting_iter + gather_map.size(),
+    gather_map.begin(),
+    [position_array = pos.data(),
+     offsets_minus_one,
+     offsets,
+     offset_size = explode_col.size()] __device__(auto idx) -> size_type {
+      auto lb_idx = thrust::distance(
+        offsets_minus_one,
+        thrust::lower_bound(thrust::seq, offsets_minus_one, offsets_minus_one + offset_size, idx));
+      position_array[idx] = idx - (offsets[lb_idx] - offsets[0]);
+      return lb_idx;
+    });
+
+  return build_table(input_table,
+                     explode_column_idx,
+                     sliced_child,
+                     gather_map,
+                     thrust::nullopt,
+                     std::move(pos),
+                     stream,
+                     mr);
+}
+
+std::unique_ptr<table> explode_outer(table_view const& input_table,
+                                     size_type const explode_column_idx,
+                                     bool include_position,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  lists_column_view explode_col{input_table.column(explode_column_idx)};
+  auto sliced_child  = explode_col.get_sliced_child(stream);
+  auto counting_iter = thrust::make_counting_iterator(0);
+  auto offsets       = explode_col.offsets_begin();
+
+  // number of nulls or empty lists found so far in the explode column
+  rmm::device_uvector<size_type> null_or_empty_offset(explode_col.size(), stream);
+
+  auto null_or_empty = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0),
+    [offsets, offsets_size = explode_col.size() - 1] __device__(int idx) {
+      return (idx > offsets_size || (offsets[idx + 1] != offsets[idx])) ? 0 : 1;
+    });
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         null_or_empty,
+                         null_or_empty + explode_col.size(),
+                         null_or_empty_offset.begin());
+
+  auto null_or_empty_count =
+    null_or_empty_offset.size() > 0 ? null_or_empty_offset.back_element(stream) : 0;
+  if (null_or_empty_count == 0) {
+    // performance penalty to run the below loop if there are no nulls or empty lists.
+    // run simple explode instead
+    return include_position ? explode_position(input_table, explode_column_idx, stream, mr)
+                            : explode(input_table, explode_column_idx, stream, mr);
+  }
+
+  auto gather_map_size = sliced_child.size() + null_or_empty_count;
+
+  rmm::device_uvector<size_type> gather_map(gather_map_size, stream);
+  rmm::device_uvector<size_type> explode_col_gather_map(gather_map_size, stream);
+  rmm::device_uvector<size_type> pos(include_position ? gather_map_size : 0, stream, mr);
+
+  // offsets + 1 here to skip the 0th offset, which removes a - 1 operation later.
+  auto offsets_minus_one = thrust::make_transform_iterator(
+    thrust::next(offsets), [offsets] __device__(auto i) { return (i - offsets[0]) - 1; });
+
+  auto fill_gather_maps = [offsets_minus_one,
+                           gather_map_p             = gather_map.begin(),
+                           explode_col_gather_map_p = explode_col_gather_map.begin(),
+                           position_array           = pos.begin(),
+                           sliced_child_size        = sliced_child.size(),
+                           null_or_empty_offset_p   = null_or_empty_offset.begin(),
+                           include_position,
+                           offsets,
+                           null_or_empty,
+                           offset_size = explode_col.offsets().size() - 1] __device__(auto idx) {
+    if (idx < sliced_child_size) {
+      auto lb_idx =
+        thrust::distance(offsets_minus_one,
+                         thrust::lower_bound(
+                           thrust::seq, offsets_minus_one, offsets_minus_one + (offset_size), idx));
+      auto index_to_write                      = null_or_empty_offset_p[lb_idx] + idx;
+      gather_map_p[index_to_write]             = lb_idx;
+      explode_col_gather_map_p[index_to_write] = idx;
+      if (include_position) {
+        position_array[index_to_write] = idx - (offsets[lb_idx] - offsets[0]);
+      }
+    }
+    if (null_or_empty[idx]) {
+      auto invalid_index          = null_or_empty_offset_p[idx] == 0
+                                      ? offsets[idx]
+                                      : offsets[idx] + null_or_empty_offset_p[idx] - 1;
+      gather_map_p[invalid_index] = idx;
+
+      explode_col_gather_map_p[invalid_index] = InvalidIndex;
+      if (include_position) { position_array[invalid_index] = 0; }
+    }
+  };
+
+  // we need to do this loop at least explode_col times or we may not properly fill in null and
+  // empty entries.
+  auto loop_count = std::max(sliced_child.size(), explode_col.size());
+
+  // Fill in gather map with all the child column's entries
+  thrust::for_each(
+    rmm::exec_policy(stream), counting_iter, counting_iter + loop_count, fill_gather_maps);
+
+  return build_table(
+    input_table,
+    explode_column_idx,
+    sliced_child,
+    gather_map,
+    explode_col_gather_map,
+    include_position ? std::move(pos) : thrust::optional<rmm::device_uvector<size_type>>{},
+    stream,
+    mr);
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::explode(table_view const&, size_type, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<table> explode(table_view const& input_table,
+                               size_type explode_column_idx,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(input_table.column(explode_column_idx).type().id() == type_id::LIST,
+               "Unsupported non-list column");
+  return detail::explode(input_table, explode_column_idx, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc cudf::explode_position(table_view const&, size_type, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<table> explode_position(table_view const& input_table,
+                                        size_type explode_column_idx,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(input_table.column(explode_column_idx).type().id() == type_id::LIST,
+               "Unsupported non-list column");
+  return detail::explode_position(input_table, explode_column_idx, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc cudf::explode_outer(table_view const&, size_type, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<table> explode_outer(table_view const& input_table,
+                                     size_type explode_column_idx,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(input_table.column(explode_column_idx).type().id() == type_id::LIST,
+               "Unsupported non-list column");
+  return detail::explode_outer(
+    input_table, explode_column_idx, false, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc cudf::explode_outer_position(table_view const&, size_type,
+ * rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<table> explode_outer_position(table_view const& input_table,
+                                              size_type explode_column_idx,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(input_table.column(explode_column_idx).type().id() == type_id::LIST,
+               "Unsupported non-list column");
+  return detail::explode_outer(
+    input_table, explode_column_idx, true, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/lists/extract.cu b/cpp/src/lists/extract.cu
new file mode 100644
index 0000000..5d4a20d
--- /dev/null
+++ b/cpp/src/lists/extract.cu
@@ -0,0 +1,221 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/lists/detail/extract.hpp>
+#include <cudf/lists/detail/gather.cuh>
+#include <cudf/lists/extract.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/fill.h>
+#include <thrust/iterator/constant_iterator.h>
+
+#include <limits>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+namespace {
+
+/**
+ * @brief Helper to construct a column of indices, for use with `segmented_gather()`.
+ *
+ * When indices are specified as a column, e.g. `{5, -4, 3, -2, 1, null}`,
+ * the column returned is:                      `{5, -4, 3, -2, 1, MAX_SIZE_TYPE}`.
+ * All null indices are replaced with `MAX_SIZE_TYPE = numeric_limits<size_type>::max()`.
+ *
+ * The returned column can then be used to construct a lists column, for use
+ * with `segmented_gather()`.
+ */
+std::unique_ptr<cudf::column> make_index_child(column_view const& indices,
+                                               size_type,
+                                               rmm::cuda_stream_view stream)
+{
+  // New column, near identical to `indices`, except with null values replaced.
+  // `segmented_gather()` on a null index should produce a null row.
+  if (not indices.nullable()) { return std::make_unique<column>(indices, stream); }
+
+  auto const d_indices = column_device_view::create(indices, stream);
+  // Replace null indices with MAX_SIZE_TYPE, so that gather() returns null for them.
+  auto const null_replaced_iter_begin =
+    cudf::detail::make_null_replacement_iterator(*d_indices, std::numeric_limits<size_type>::max());
+  auto index_child =
+    make_numeric_column(data_type{type_id::INT32}, indices.size(), mask_state::UNALLOCATED, stream);
+  thrust::copy_n(rmm::exec_policy(stream),
+                 null_replaced_iter_begin,
+                 indices.size(),
+                 index_child->mutable_view().begin<size_type>());
+  return index_child;
+}
+
+/**
+ * @brief Helper to construct a column of indices, for use with `segmented_gather()`.
+ *
+ * When indices are specified as a size_type, e.g. `7`,
+ * the column returned is: `{ 7, 7, 7, 7, 7 }`.
+ *
+ * The returned column can then be used to construct a lists column, for use
+ * with `segmented_gather()`.
+ */
+std::unique_ptr<cudf::column> make_index_child(size_type index,
+                                               size_type num_rows,
+                                               rmm::cuda_stream_view stream)
+{
+  auto index_child =  // [index, index, index, ..., index]
+    make_numeric_column(data_type{type_id::INT32}, num_rows, mask_state::UNALLOCATED, stream);
+  thrust::fill_n(
+    rmm::exec_policy(stream), index_child->mutable_view().begin<size_type>(), num_rows, index);
+  return index_child;
+}
+
+/**
+ * @brief Helper to construct offsets column for an index vector.
+ *
+ * Constructs the sequence: `{ 0, 1, 2, 3, ... num_lists + 1}`.
+ * This may be used to construct an "index-list" column, where each list row
+ * has a single element.
+ */
+std::unique_ptr<cudf::column> make_index_offsets(size_type num_lists, rmm::cuda_stream_view stream)
+{
+  return cudf::detail::sequence(num_lists + 1,
+                                cudf::scalar_type_t<size_type>(0, true, stream),
+                                stream,
+                                rmm::mr::get_current_device_resource());
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::lists::extract_list_element
+ * @tparam index_t The type used to specify the index values (either column_view or size_type)
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename index_t>
+std::unique_ptr<column> extract_list_element_impl(lists_column_view lists_column,
+                                                  index_t const& index,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  auto const num_lists = lists_column.size();
+  if (num_lists == 0) { return empty_like(lists_column.child()); }
+
+  // Given an index (or indices vector), an index lists column may be constructed,
+  // with each list row having a single element.
+  // E.g.
+  // 1. If index = 7, index_lists_column = { {7}, {7}, {7}, {7}, ... }.
+  // 2. If indices = {4, 3, 2, 1, null},
+  //    index_lists_column = { {4}, {3}, {2}, {1}, {MAX_SIZE_TYPE} }.
+
+  auto const index_lists_column = make_lists_column(num_lists,
+                                                    make_index_offsets(num_lists, stream),
+                                                    make_index_child(index, num_lists, stream),
+                                                    0,
+                                                    {},
+                                                    stream);
+
+  // We want the output of `segmented_gather` to be a lists column in which each list has exactly
+  // one element, even for the null lists.
+  // Thus, the input into `segmented_gather` should not be nullable.
+  auto const lists_column_removed_null_mask = lists_column_view{
+    column_view{data_type{type_id::LIST},
+                lists_column.size(),
+                nullptr,  // data
+                nullptr,  // null_mask
+                0,        // null_count
+                lists_column.offset(),
+                std::vector<column_view>{lists_column.child_begin(), lists_column.child_end()}}};
+
+  auto extracted_lists = segmented_gather(lists_column_removed_null_mask,
+                                          index_lists_column->view(),
+                                          out_of_bounds_policy::NULLIFY,
+                                          stream,
+                                          mr);
+
+  auto output =
+    std::move(extracted_lists->release().children[lists_column_view::child_column_index]);
+  if (!lists_column.has_nulls()) { return output; }
+
+  // The input lists column may have non-empty nulls if it is nullable, although this is rare.
+  // In such cases, the extracted elements corresponding to these non-empty nulls may not be null.
+  // Thus, we need to superimpose nulls from the input column into the output to make sure each
+  // input null list always results in a null output row.
+  return cudf::structs::detail::superimpose_nulls(
+    lists_column.null_mask(), lists_column.null_count(), std::move(output), stream, mr);
+}
+
+/**
+ * @copydoc cudf::lists::extract_list_element
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> extract_list_element(lists_column_view lists_column,
+                                             size_type const index,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  return detail::extract_list_element_impl(lists_column, index, stream, mr);
+}
+
+std::unique_ptr<column> extract_list_element(lists_column_view lists_column,
+                                             column_view const& indices,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  return detail::extract_list_element_impl(lists_column, indices, stream, mr);
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::lists::extract_list_element(lists_column_view const&,
+ *                                            size_type,
+ *                                            rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<column> extract_list_element(lists_column_view const& lists_column,
+                                             size_type index,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_list_element(lists_column, index, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc cudf::lists::extract_list_element(lists_column_view const&,
+ *                                            column_view const&,
+ *                                            rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<column> extract_list_element(lists_column_view const& lists_column,
+                                             column_view const& indices,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  CUDF_EXPECTS(indices.size() == lists_column.size(),
+               "Index column must have as many elements as lists column.");
+  return detail::extract_list_element(lists_column, indices, cudf::get_default_stream(), mr);
+}
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/interleave_columns.cu b/cpp/src/lists/interleave_columns.cu
new file mode 100644
index 0000000..e80d639
--- /dev/null
+++ b/cpp/src/lists/interleave_columns.cu
@@ -0,0 +1,423 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+namespace {
+/**
+ * @brief Generate list offsets and list validities for the output lists column from the table_view
+ * of the input lists columns.
+ */
+std::pair<std::unique_ptr<column>, rmm::device_uvector<int8_t>>
+generate_list_offsets_and_validities(table_view const& input,
+                                     bool has_null_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  auto const num_cols         = input.num_columns();
+  auto const num_rows         = input.num_rows();
+  auto const num_output_lists = num_rows * num_cols;
+  auto const table_dv_ptr     = table_device_view::create(input, stream);
+
+  // The output offsets column.
+  auto list_offsets = make_numeric_column(
+    data_type{type_to_id<size_type>()}, num_output_lists + 1, mask_state::UNALLOCATED, stream, mr);
+  auto const d_offsets = list_offsets->mutable_view().template begin<size_type>();
+
+  // The array of int8_t to store validities for list elements.
+  auto validities = rmm::device_uvector<int8_t>(has_null_mask ? num_output_lists : 0, stream);
+
+  // Compute list sizes and validities.
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(num_output_lists),
+    d_offsets,
+    [num_cols,
+     table_dv     = *table_dv_ptr,
+     d_validities = validities.begin(),
+     has_null_mask] __device__(size_type const idx) {
+      auto const col_id     = idx % num_cols;
+      auto const list_id    = idx / num_cols;
+      auto const& lists_col = table_dv.column(col_id);
+      if (has_null_mask) { d_validities[idx] = static_cast<int8_t>(lists_col.is_valid(list_id)); }
+      auto const list_offsets =
+        lists_col.child(lists_column_view::offsets_column_index).template data<size_type>() +
+        lists_col.offset();
+      return list_offsets[list_id + 1] - list_offsets[list_id];
+    });
+
+  // Compute offsets from sizes.
+  thrust::exclusive_scan(
+    rmm::exec_policy(stream), d_offsets, d_offsets + num_output_lists + 1, d_offsets);
+
+  return {std::move(list_offsets), std::move(validities)};
+}
+
+/**
+ * @brief Concatenate all input columns into one column and gather its rows to generate an output
+ * column that is the result of interleaving the input columns.
+ */
+std::unique_ptr<column> concatenate_and_gather_lists(host_span<column_view const> columns_to_concat,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  // Concatenate all columns into a single (temporary) column.
+  auto const concatenated_col =
+    cudf::detail::concatenate(columns_to_concat, stream, rmm::mr::get_current_device_resource());
+
+  // The number of input columns is known to be non-zero thus it's safe to call `front()` here.
+  auto const num_cols       = columns_to_concat.size();
+  auto const num_input_rows = columns_to_concat.front().size();
+
+  // Generate the gather map that interleaves the input columns.
+  auto const iter_gather = cudf::detail::make_counting_transform_iterator(
+    0, [num_cols, num_input_rows] __device__(auto const idx) {
+      auto const source_col_idx = idx % num_cols;
+      auto const source_row_idx = idx / num_cols;
+      return source_col_idx * num_input_rows + source_row_idx;
+    });
+
+  // The gather API should be able to handle any data type for the input columns.
+  auto result = cudf::detail::gather(table_view{{concatenated_col->view()}},
+                                     iter_gather,
+                                     iter_gather + concatenated_col->size(),
+                                     out_of_bounds_policy::DONT_CHECK,
+                                     stream,
+                                     mr);
+  return std::move(result->release()[0]);
+}
+
+/**
+ * @brief Compute string sizes, string validities, and interleave string lists functor.
+ *
+ * This functor is executed twice. In the first pass, the sizes and validities of the output strings
+ * will be computed. In the second pass, this will interleave the lists of strings of the given
+ * table containing those lists.
+ */
+struct compute_string_sizes_and_interleave_lists_fn {
+  table_device_view const table_dv;
+
+  // Store list offsets of the output lists column.
+  size_type const* const dst_list_offsets;
+
+  // Flag to specify whether to compute string validities.
+  bool const has_null_mask;
+
+  // Store offsets of the strings.
+  size_type* d_offsets{nullptr};
+
+  // If d_chars == nullptr: only compute sizes and validities of the output strings.
+  // If d_chars != nullptr: only interleave lists of strings.
+  char* d_chars{nullptr};
+
+  // We need to set `1` or `0` for the validities of the strings in the child column.
+  int8_t* d_validities{nullptr};
+
+  __device__ void operator()(size_type const idx)
+  {
+    auto const num_cols = table_dv.num_columns();
+    auto const col_id   = idx % num_cols;
+    auto const list_id  = idx / num_cols;
+
+    auto const& lists_col = table_dv.column(col_id);
+    if (has_null_mask and lists_col.is_null(list_id)) { return; }
+
+    auto const list_offsets =
+      lists_col.child(lists_column_view::offsets_column_index).template data<size_type>() +
+      lists_col.offset();
+    auto const& str_col = lists_col.child(lists_column_view::child_column_index);
+    auto const str_offsets =
+      str_col.child(strings_column_view::offsets_column_index).template data<size_type>();
+
+    // The range of indices of the strings within the source list.
+    auto const start_str_idx = list_offsets[list_id];
+    auto const end_str_idx   = list_offsets[list_id + 1];
+
+    // In case of empty list (i.e. it doesn't contain any string element), we just ignore it because
+    // there will not be anything to store for that list in the child column.
+    if (start_str_idx == end_str_idx) { return; }
+
+    // read_idx and write_idx are indices of string elements.
+    size_type write_idx = dst_list_offsets[idx];
+
+    if (not d_chars) {  // just compute sizes and validities of strings within a list
+      for (auto read_idx = start_str_idx; read_idx < end_str_idx; ++read_idx, ++write_idx) {
+        if (has_null_mask) {
+          d_validities[write_idx] = static_cast<int8_t>(str_col.is_valid(read_idx));
+        }
+        d_offsets[write_idx] = str_offsets[read_idx + 1] - str_offsets[read_idx];
+      }
+    } else {  // just copy the entire memory region containing all strings in the list
+      // start_byte and end_byte are indices of character of the string elements.
+      auto const start_byte = str_offsets[start_str_idx];
+      auto const end_byte   = str_offsets[end_str_idx];
+      if (start_byte < end_byte) {
+        auto const input_ptr =
+          str_col.child(strings_column_view::chars_column_index).template data<char>() + start_byte;
+        auto const output_ptr = d_chars + d_offsets[write_idx];
+        thrust::copy(thrust::seq, input_ptr, input_ptr + end_byte - start_byte, output_ptr);
+      }
+    }
+  }
+};
+
+// Error case when no other overload or specialization is available
+template <typename T, typename Enable = void>
+struct interleave_list_entries_impl {
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Called `interleave_list_entries_fn()` on non-supported types.");
+  }
+};
+
+template <typename T>
+struct interleave_list_entries_impl<T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>> {
+  std::unique_ptr<column> operator()(table_view const& input,
+                                     column_view const& output_list_offsets,
+                                     size_type num_output_lists,
+                                     size_type num_output_entries,
+                                     bool data_has_null_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const noexcept
+  {
+    auto const table_dv_ptr = table_device_view::create(input, stream);
+    auto comp_fn            = compute_string_sizes_and_interleave_lists_fn{
+      *table_dv_ptr, output_list_offsets.template begin<size_type>(), data_has_null_mask};
+
+    auto validities =
+      rmm::device_uvector<int8_t>(data_has_null_mask ? num_output_entries : 0, stream);
+    comp_fn.d_validities = validities.data();
+
+    auto [offsets_column, chars_column] = cudf::strings::detail::make_strings_children(
+      comp_fn, num_output_lists, num_output_entries, stream, mr);
+
+    auto [null_mask, null_count] =
+      cudf::detail::valid_if(validities.begin(), validities.end(), thrust::identity{}, stream, mr);
+
+    return make_strings_column(num_output_entries,
+                               std::move(offsets_column),
+                               std::move(chars_column),
+                               null_count,
+                               std::move(null_mask));
+  }
+};
+
+template <typename T>
+struct interleave_list_entries_impl<T, std::enable_if_t<cudf::is_fixed_width<T>()>> {
+  std::unique_ptr<column> operator()(table_view const& input,
+                                     column_view const& output_list_offsets,
+                                     size_type num_output_lists,
+                                     size_type num_output_entries,
+                                     bool data_has_null_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const noexcept
+  {
+    auto const num_cols     = input.num_columns();
+    auto const table_dv_ptr = table_device_view::create(input, stream);
+
+    // The output child column.
+    auto output        = cudf::detail::allocate_like(lists_column_view(*input.begin()).child(),
+                                              num_output_entries,
+                                              mask_allocation_policy::NEVER,
+                                              stream,
+                                              mr);
+    auto output_dv_ptr = mutable_column_device_view::create(*output, stream);
+
+    // The array of int8_t to store entry validities.
+    auto validities =
+      rmm::device_uvector<int8_t>(data_has_null_mask ? num_output_entries : 0, stream);
+
+    thrust::for_each_n(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator<size_type>(0),
+      num_output_lists,
+      [num_cols,
+       table_dv     = *table_dv_ptr,
+       d_validities = validities.begin(),
+       d_offsets    = output_list_offsets.template begin<size_type>(),
+       d_output     = output_dv_ptr->template begin<T>(),
+       data_has_null_mask] __device__(size_type const idx) {
+        auto const col_id     = idx % num_cols;
+        auto const list_id    = idx / num_cols;
+        auto const& lists_col = table_dv.column(col_id);
+        auto const list_offsets =
+          lists_col.child(lists_column_view::offsets_column_index).template data<size_type>() +
+          lists_col.offset();
+        auto const& data_col = lists_col.child(lists_column_view::child_column_index);
+
+        // The range of indices of the entries within the source list.
+        auto const start_idx = list_offsets[list_id];
+        auto const end_idx   = list_offsets[list_id + 1];
+
+        auto const write_start = d_offsets[idx];
+
+        // Fill the validities array if necessary.
+        if (data_has_null_mask) {
+          for (auto read_idx = start_idx, write_idx = write_start; read_idx < end_idx;
+               ++read_idx, ++write_idx) {
+            d_validities[write_idx] = static_cast<int8_t>(data_col.is_valid(read_idx));
+          }
+        }
+
+        // Do a copy for the entire list entries.
+        auto const input_ptr =
+          reinterpret_cast<char const*>(data_col.template data<T>() + start_idx);
+        auto const output_ptr = reinterpret_cast<char*>(&d_output[write_start]);
+        thrust::copy(
+          thrust::seq, input_ptr, input_ptr + sizeof(T) * (end_idx - start_idx), output_ptr);
+      });
+
+    if (data_has_null_mask) {
+      auto [null_mask, null_count] = cudf::detail::valid_if(
+        validities.begin(), validities.end(), thrust::identity{}, stream, mr);
+      if (null_count > 0) { output->set_null_mask(null_mask, null_count); }
+    }
+
+    return output;
+  }
+};
+
+/**
+ * @brief Struct used in type_dispatcher to interleave list entries of the input lists columns and
+ * output the results into a destination column.
+ */
+struct interleave_list_entries_fn {
+  template <class T>
+  std::unique_ptr<column> operator()(table_view const& input,
+                                     column_view const& output_list_offsets,
+                                     size_type num_output_lists,
+                                     size_type num_output_entries,
+                                     bool data_has_null_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    return interleave_list_entries_impl<T>{}(input,
+                                             output_list_offsets,
+                                             num_output_lists,
+                                             num_output_entries,
+                                             data_has_null_mask,
+                                             stream,
+                                             mr);
+  }
+};
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::lists::detail::interleave_columns
+ *
+ */
+std::unique_ptr<column> interleave_columns(table_view const& input,
+                                           bool has_null_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto const entry_type = lists_column_view(*input.begin()).child().type();
+  for (auto const& col : input) {
+    CUDF_EXPECTS(col.type().id() == type_id::LIST,
+                 "All columns of the input table must be of lists column type.");
+
+    auto const child_col = lists_column_view(col).child();
+    CUDF_EXPECTS(entry_type == child_col.type(),
+                 "The types of entries in the input columns must be the same.");
+  }
+
+  if (input.num_rows() == 0) { return cudf::empty_like(input.column(0)); }
+  if (input.num_columns() == 1) { return std::make_unique<column>(*(input.begin()), stream, mr); }
+
+  // For nested types, we rely on the `concatenate_and_gather` method, which costs more memory due
+  // to concatenation of the input columns into a temporary column. For non-nested types, we can
+  // directly interleave the input columns into the output column for better efficiency.
+  if (cudf::is_nested(entry_type)) {
+    auto const input_columns = std::vector<column_view>(input.begin(), input.end());
+    return concatenate_and_gather_lists(host_span<column_view const>{input_columns}, stream, mr);
+  }
+
+  // Generate offsets of the output lists column.
+  auto [list_offsets, list_validities] =
+    generate_list_offsets_and_validities(input, has_null_mask, stream, mr);
+  auto const offsets_view = list_offsets->view();
+
+  // Copy entries from the input lists columns to the output lists column - this needed to be
+  // specialized for different types.
+  auto const num_output_lists = input.num_rows() * input.num_columns();
+  auto const num_output_entries =
+    cudf::detail::get_value<size_type>(offsets_view, num_output_lists, stream);
+  auto const data_has_null_mask =
+    std::any_of(std::cbegin(input), std::cend(input), [](auto const& col) {
+      return col.child(lists_column_view::child_column_index).nullable();
+    });
+  auto list_entries = type_dispatcher<dispatch_storage_type>(entry_type,
+                                                             interleave_list_entries_fn{},
+                                                             input,
+                                                             offsets_view,
+                                                             num_output_lists,
+                                                             num_output_entries,
+                                                             data_has_null_mask,
+                                                             stream,
+                                                             mr);
+
+  if (not has_null_mask) {
+    return make_lists_column(num_output_lists,
+                             std::move(list_offsets),
+                             std::move(list_entries),
+                             0,
+                             rmm::device_buffer{},
+                             stream,
+                             mr);
+  }
+
+  auto [null_mask, null_count] = cudf::detail::valid_if(
+    list_validities.begin(), list_validities.end(), thrust::identity{}, stream, mr);
+  return make_lists_column(num_output_lists,
+                           std::move(list_offsets),
+                           std::move(list_entries),
+                           null_count,
+                           null_count ? std::move(null_mask) : rmm::device_buffer{},
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/lists_column_factories.cu b/cpp/src/lists/lists_column_factories.cu
new file mode 100644
index 0000000..278e5af
--- /dev/null
+++ b/cpp/src/lists/lists_column_factories.cu
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/sequence.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+std::unique_ptr<cudf::column> make_lists_column_from_scalar(list_scalar const& value,
+                                                            size_type size,
+                                                            rmm::cuda_stream_view stream,
+                                                            rmm::mr::device_memory_resource* mr)
+{
+  if (size == 0) {
+    return make_lists_column(0,
+                             make_empty_column(type_to_id<size_type>()),
+                             empty_like(value.view()),
+                             0,
+                             cudf::detail::create_null_mask(0, mask_state::UNALLOCATED, stream, mr),
+                             stream,
+                             mr);
+  }
+  auto mr_final = size == 1 ? mr : rmm::mr::get_current_device_resource();
+
+  // Handcraft a 1-row column
+  auto sizes_itr = thrust::constant_iterator<size_type>(value.view().size());
+  auto offsets   = std::get<0>(
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + 1, stream, mr_final));
+  size_type null_count = value.is_valid(stream) ? 0 : 1;
+  auto null_mask_state = null_count ? mask_state::ALL_NULL : mask_state::UNALLOCATED;
+  auto null_mask       = cudf::detail::create_null_mask(1, null_mask_state, stream, mr_final);
+
+  if (size == 1) {
+    auto child = std::make_unique<column>(value.view(), stream, mr_final);
+    return make_lists_column(
+      1, std::move(offsets), std::move(child), null_count, std::move(null_mask), stream, mr_final);
+  }
+
+  auto children_views   = std::vector<column_view>{offsets->view(), value.view()};
+  auto one_row_col_view = column_view(data_type{type_id::LIST},
+                                      1,
+                                      nullptr,
+                                      static_cast<bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      children_views);
+
+  auto begin = thrust::make_constant_iterator(0);
+  auto res   = cudf::detail::gather(table_view({one_row_col_view}),
+                                  begin,
+                                  begin + size,
+                                  out_of_bounds_policy::DONT_CHECK,
+                                  stream,
+                                  mr_final);
+  return std::move(res->release()[0]);
+}
+
+std::unique_ptr<column> make_empty_lists_column(data_type child_type,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  auto offsets = make_empty_column(data_type(type_to_id<size_type>()));
+  auto child   = make_empty_column(child_type);
+  return make_lists_column(
+    0, std::move(offsets), std::move(child), 0, rmm::device_buffer{}, stream, mr);
+}
+
+std::unique_ptr<column> make_all_nulls_lists_column(size_type size,
+                                                    data_type child_type,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  auto offsets = [&] {
+    auto offsets_buff =
+      cudf::detail::make_zeroed_device_uvector_async<size_type>(size + 1, stream, mr);
+    return std::make_unique<column>(std::move(offsets_buff), rmm::device_buffer{}, 0);
+  }();
+  auto child     = make_empty_column(child_type);
+  auto null_mask = cudf::detail::create_null_mask(size, mask_state::ALL_NULL, stream, mr);
+  return make_lists_column(
+    size, std::move(offsets), std::move(child), size, std::move(null_mask), stream, mr);
+}
+
+}  // namespace detail
+}  // namespace lists
+
+/**
+ * @copydoc cudf::make_lists_column
+ */
+std::unique_ptr<column> make_lists_column(size_type num_rows,
+                                          std::unique_ptr<column> offsets_column,
+                                          std::unique_ptr<column> child_column,
+                                          size_type null_count,
+                                          rmm::device_buffer&& null_mask,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  if (null_count > 0) { CUDF_EXPECTS(null_mask.size() > 0, "Column with nulls must be nullable."); }
+  CUDF_EXPECTS(
+    (num_rows == 0 && offsets_column->size() == 0) || num_rows == offsets_column->size() - 1,
+    "Invalid offsets column size for lists column.");
+  CUDF_EXPECTS(offsets_column->null_count() == 0, "Offsets column should not contain nulls");
+  CUDF_EXPECTS(child_column != nullptr, "Must pass a valid child column");
+
+  // Save type_id of the child column for later use.
+  auto const child_type_id = child_column->type().id();
+
+  std::vector<std::unique_ptr<column>> children;
+  children.emplace_back(std::move(offsets_column));
+  children.emplace_back(std::move(child_column));
+
+  auto output = std::make_unique<column>(cudf::data_type{type_id::LIST},
+                                         num_rows,
+                                         rmm::device_buffer{},
+                                         std::move(null_mask),
+                                         null_count,
+                                         std::move(children));
+
+  // We need to enforce all null lists to be empty.
+  // `has_nonempty_nulls` is less expensive than `purge_nonempty_nulls` and can save some
+  // run time if we don't have any non-empty nulls.
+  if (auto const output_cv = output->view(); detail::has_nonempty_nulls(output_cv, stream)) {
+    return detail::purge_nonempty_nulls(output_cv, stream, mr);
+  }
+
+  return output;
+}
+
+}  // namespace cudf
diff --git a/cpp/src/lists/lists_column_view.cu b/cpp/src/lists/lists_column_view.cu
new file mode 100644
index 0000000..6e2ed02
--- /dev/null
+++ b/cpp/src/lists/lists_column_view.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+
+lists_column_view::lists_column_view(column_view const& lists_column) : column_view(lists_column)
+{
+  CUDF_EXPECTS(type().id() == type_id::LIST, "lists_column_view only supports lists");
+}
+
+column_view lists_column_view::parent() const { return static_cast<column_view>(*this); }
+
+column_view lists_column_view::offsets() const
+{
+  CUDF_EXPECTS(num_children() == 2, "lists column has an incorrect number of children");
+  return column_view::child(offsets_column_index);
+}
+
+column_view lists_column_view::child() const
+{
+  CUDF_EXPECTS(num_children() == 2, "lists column has an incorrect number of children");
+  return column_view::child(child_column_index);
+}
+
+column_view lists_column_view::get_sliced_child(rmm::cuda_stream_view stream) const
+{
+  // if I have a positive offset, I need to slice my child
+  if (offset() > 0) {
+    // theoretically this function could always do this step and be correct, but get_value<>
+    // actually hits the gpu so it's best to avoid it if possible.
+    size_type child_offset_start = cudf::detail::get_value<size_type>(offsets(), offset(), stream);
+    size_type child_offset_end =
+      cudf::detail::get_value<size_type>(offsets(), offset() + size(), stream);
+    return cudf::detail::slice(child(), {child_offset_start, child_offset_end}, stream).front();
+  }
+
+  // if I don't have a positive offset, but I am shorter than my offsets() would otherwise indicate,
+  // I need to do a split and return the front.
+  if (size() < offsets().size() - 1) {
+    size_type child_offset = cudf::detail::get_value<size_type>(offsets(), size(), stream);
+    return cudf::detail::slice(child(), {0, child_offset}, stream).front();
+  }
+
+  // otherwise just return the child directly
+  return child();
+}
+
+}  // namespace cudf
diff --git a/cpp/src/lists/reverse.cu b/cpp/src/lists/reverse.cu
new file mode 100644
index 0000000..a2af85b
--- /dev/null
+++ b/cpp/src/lists/reverse.cu
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "utilities.hpp"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/reverse.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf::lists {
+namespace detail {
+
+std::unique_ptr<column> reverse(lists_column_view const& input,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return cudf::empty_like(input.parent()); }
+
+  auto const child = input.get_sliced_child(stream);
+
+  // The labels are also a map from each list element to its corresponding zero-based list index.
+  auto const labels =
+    generate_labels(input, child.size(), stream, rmm::mr::get_current_device_resource());
+
+  // The offsets of the output lists column.
+  auto out_offsets = get_normalized_offsets(input, stream, mr);
+
+  // Build a gather map to copy the output list elements from the input list elements.
+  auto gather_map = rmm::device_uvector<size_type>(child.size(), stream);
+
+  // Build a segmented reversed order for the child column.
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::counting_iterator<size_type>(0),
+                     child.size(),
+                     [list_offsets = out_offsets->view().begin<size_type>(),
+                      list_indices = labels->view().begin<size_type>(),
+                      gather_map   = gather_map.begin()] __device__(auto const idx) {
+                       auto const list_idx     = list_indices[idx];
+                       auto const begin_offset = list_offsets[list_idx];
+                       auto const end_offset   = list_offsets[list_idx + 1];
+
+                       // Reverse the order of elements within each list.
+                       gather_map[idx] = begin_offset + (end_offset - idx - 1);
+                     });
+
+  auto child_segmented_reversed =
+    cudf::detail::gather(table_view{{child}},
+                         device_span<size_type const>{gather_map.data(), gather_map.size()},
+                         out_of_bounds_policy::DONT_CHECK,
+                         cudf::detail::negative_index_policy::NOT_ALLOWED,
+                         stream,
+                         mr);
+
+  return cudf::make_lists_column(input.size(),
+                                 std::move(out_offsets),
+                                 std::move(child_segmented_reversed->release().front()),
+                                 input.null_count(),
+                                 cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                 stream,
+                                 mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> reverse(lists_column_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::reverse(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf::lists
diff --git a/cpp/src/lists/segmented_sort.cu b/cpp/src/lists/segmented_sort.cu
new file mode 100644
index 0000000..49054eb
--- /dev/null
+++ b/cpp/src/lists/segmented_sort.cu
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Create output offsets for segmented sort
+ *
+ * This creates a normalized set of offsets from the offsets child column of the input.
+ */
+std::unique_ptr<column> build_output_offsets(lists_column_view const& input,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  auto output_offset = make_numeric_column(
+    input.offsets().type(), input.size() + 1, mask_state::UNALLOCATED, stream, mr);
+  thrust::transform(rmm::exec_policy(stream),
+                    input.offsets_begin(),
+                    input.offsets_end(),
+                    output_offset->mutable_view().begin<size_type>(),
+                    [first = input.offsets_begin()] __device__(auto offset_index) {
+                      return offset_index - *first;
+                    });
+  return output_offset;
+}
+
+}  // namespace
+
+std::unique_ptr<column> sort_lists(lists_column_view const& input,
+                                   order column_order,
+                                   null_order null_precedence,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return empty_like(input.parent());
+
+  auto output_offset = build_output_offsets(input, stream, mr);
+  auto const child   = input.get_sliced_child(stream);
+
+  auto const sorted_child_table = cudf::detail::segmented_sort_by_key(table_view{{child}},
+                                                                      table_view{{child}},
+                                                                      output_offset->view(),
+                                                                      {column_order},
+                                                                      {null_precedence},
+                                                                      stream,
+                                                                      mr);
+
+  return make_lists_column(input.size(),
+                           std::move(output_offset),
+                           std::move(sorted_child_table->release().front()),
+                           input.null_count(),
+                           cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+
+std::unique_ptr<column> stable_sort_lists(lists_column_view const& input,
+                                          order column_order,
+                                          null_order null_precedence,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return empty_like(input.parent()); }
+
+  auto output_offset = build_output_offsets(input, stream, mr);
+  auto const child   = input.get_sliced_child(stream);
+
+  auto const sorted_child_table = cudf::detail::stable_segmented_sort_by_key(table_view{{child}},
+                                                                             table_view{{child}},
+                                                                             output_offset->view(),
+                                                                             {column_order},
+                                                                             {null_precedence},
+                                                                             stream,
+                                                                             mr);
+
+  return make_lists_column(input.size(),
+                           std::move(output_offset),
+                           std::move(sorted_child_table->release().front()),
+                           input.null_count(),
+                           cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+}  // namespace detail
+
+std::unique_ptr<column> sort_lists(lists_column_view const& input,
+                                   order column_order,
+                                   null_order null_precedence,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sort_lists(input, column_order, null_precedence, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> stable_sort_lists(lists_column_view const& input,
+                                          order column_order,
+                                          null_order null_precedence,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::stable_sort_lists(
+    input, column_order, null_precedence, cudf::get_default_stream(), mr);
+}
+
+}  // namespace lists
+}  // namespace cudf
diff --git a/cpp/src/lists/sequences.cu b/cpp/src/lists/sequences.cu
new file mode 100644
index 0000000..aaee560
--- /dev/null
+++ b/cpp/src/lists/sequences.cu
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+#include <cudf/lists/filling.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+
+#include <limits>
+#include <optional>
+#include <stdexcept>
+
+namespace cudf::lists {
+namespace detail {
+namespace {
+template <typename T>
+struct tabulator {
+  size_type const n_lists;
+  size_type const n_elements;
+
+  T const* const starts;
+  T const* const steps;
+  size_type const* const offsets;
+
+  template <typename U>
+  static std::enable_if_t<!cudf::is_duration<U>(), T> __device__ multiply(U x, size_type times)
+  {
+    return x * static_cast<T>(times);
+  }
+
+  template <typename U>
+  static std::enable_if_t<cudf::is_duration<U>(), T> __device__ multiply(U x, size_type times)
+  {
+    return T{x.count() * times};
+  }
+
+  auto __device__ operator()(size_type idx) const
+  {
+    auto const list_idx_end = thrust::upper_bound(thrust::seq, offsets, offsets + n_lists, idx);
+    auto const list_idx     = thrust::distance(offsets, list_idx_end) - 1;
+    auto const list_offset  = offsets[list_idx];
+    auto const list_step    = steps ? steps[list_idx] : T{1};
+    return starts[list_idx] + multiply(list_step, idx - list_offset);
+  }
+};
+
+template <typename T, typename Enable = void>
+struct sequences_functor {
+  template <typename... Args>
+  static std::unique_ptr<column> invoke(Args&&...)
+  {
+    CUDF_FAIL("Unsupported per-list sequence type-agg combination.");
+  }
+};
+
+struct sequences_dispatcher {
+  template <typename T>
+  std::unique_ptr<column> operator()(size_type n_lists,
+                                     size_type n_elements,
+                                     column_view const& starts,
+                                     std::optional<column_view> const& steps,
+                                     size_type const* offsets,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return sequences_functor<T>::invoke(n_lists, n_elements, starts, steps, offsets, stream, mr);
+  }
+};
+
+template <typename T>
+static constexpr bool is_supported()
+{
+  return (cudf::is_numeric<T>() && !cudf::is_boolean<T>()) || cudf::is_duration<T>();
+}
+
+template <typename T>
+struct sequences_functor<T, std::enable_if_t<is_supported<T>()>> {
+  static std::unique_ptr<column> invoke(size_type n_lists,
+                                        size_type n_elements,
+                                        column_view const& starts,
+                                        std::optional<column_view> const& steps,
+                                        size_type const* offsets,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    auto result =
+      make_fixed_width_column(starts.type(), n_elements, mask_state::UNALLOCATED, stream, mr);
+    if (starts.is_empty()) { return result; }
+
+    auto const result_begin = result->mutable_view().template begin<T>();
+
+    // Use pointers instead of column_device_view to access start and step values should be enough.
+    // This is because we don't need to check for nulls and only support numeric and duration types.
+    auto const starts_begin = starts.template begin<T>();
+    auto const steps_begin  = steps ? steps.value().template begin<T>() : nullptr;
+
+    auto const op = tabulator<T>{n_lists, n_elements, starts_begin, steps_begin, offsets};
+    thrust::tabulate(rmm::exec_policy(stream), result_begin, result_begin + n_elements, op);
+
+    return result;
+  }
+};
+
+std::unique_ptr<column> sequences(column_view const& starts,
+                                  std::optional<column_view> const& steps,
+                                  column_view const& sizes,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!starts.has_nulls() && !sizes.has_nulls(),
+               "starts and sizes input columns must not have nulls.");
+  CUDF_EXPECTS(starts.size() == sizes.size(),
+               "starts and sizes input columns must have the same number of rows.");
+  CUDF_EXPECTS(cudf::is_index_type(sizes.type()), "Input sizes column must be of integer types.");
+
+  if (steps) {
+    auto const& steps_cv = steps.value();
+    CUDF_EXPECTS(!steps_cv.has_nulls(), "steps input column must not have nulls.");
+    CUDF_EXPECTS(starts.size() == steps_cv.size(),
+                 "starts and steps input columns must have the same number of rows.");
+    CUDF_EXPECTS(starts.type() == steps_cv.type(),
+                 "starts and steps input columns must have the same type.");
+  }
+
+  auto const n_lists = starts.size();
+  if (n_lists == 0) { return make_empty_lists_column(starts.type(), stream, mr); }
+
+  // Generate list offsets for the output.
+  auto list_offsets = make_numeric_column(
+    data_type(type_to_id<size_type>()), n_lists + 1, mask_state::UNALLOCATED, stream, mr);
+  auto const offsets_begin  = list_offsets->mutable_view().template begin<size_type>();
+  auto const sizes_input_it = cudf::detail::indexalator_factory::make_input_iterator(sizes);
+  // First copy the sizes since the exclusive_scan tries to read (n_lists+1) values
+  thrust::copy_n(rmm::exec_policy(stream), sizes_input_it, sizes.size(), offsets_begin);
+
+  auto const n_elements = cudf::detail::sizes_to_offsets(
+    offsets_begin, offsets_begin + list_offsets->size(), offsets_begin, stream);
+  CUDF_EXPECTS(n_elements <= std::numeric_limits<size_type>::max(),
+               "Size of output exceeds the column size limit",
+               std::overflow_error);
+
+  auto child = type_dispatcher(starts.type(),
+                               sequences_dispatcher{},
+                               n_lists,
+                               static_cast<size_type>(n_elements),
+                               starts,
+                               steps,
+                               offsets_begin,
+                               stream,
+                               mr);
+
+  return make_lists_column(n_lists,
+                           std::move(list_offsets),
+                           std::move(child),
+                           0,
+                           rmm::device_buffer(0, stream, mr),
+                           stream,
+                           mr);
+}
+
+}  // anonymous namespace
+
+std::unique_ptr<column> sequences(column_view const& starts,
+                                  column_view const& sizes,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  return sequences(starts, std::nullopt, sizes, stream, mr);
+}
+
+std::unique_ptr<column> sequences(column_view const& starts,
+                                  column_view const& steps,
+                                  column_view const& sizes,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  return sequences(starts, std::optional<column_view>{steps}, sizes, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> sequences(column_view const& starts,
+                                  column_view const& sizes,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sequences(starts, sizes, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> sequences(column_view const& starts,
+                                  column_view const& steps,
+                                  column_view const& sizes,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sequences(starts, steps, sizes, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf::lists
diff --git a/cpp/src/lists/set_operations.cu b/cpp/src/lists/set_operations.cu
new file mode 100644
index 0000000..5687a49
--- /dev/null
+++ b/cpp/src/lists/set_operations.cu
@@ -0,0 +1,319 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "utilities.hpp"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/lists/detail/combine.hpp>
+#include <cudf/lists/detail/set_operations.hpp>
+#include <cudf/lists/detail/stream_compaction.hpp>
+#include <cudf/utilities/type_checks.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/functional.h>
+#include <thrust/reduce.h>
+#include <thrust/scatter.h>
+#include <thrust/uninitialized_fill.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf::lists {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Check if two input lists columns are valid input into the list operations.
+ * @param lhs The left lists column
+ * @param rhs The right lists column
+ */
+void check_compatibility(lists_column_view const& lhs, lists_column_view const& rhs)
+{
+  CUDF_EXPECTS(lhs.size() == rhs.size(), "The input lists column must have the same size.");
+  CUDF_EXPECTS(column_types_equal(lhs.child(), rhs.child()),
+               "The input lists columns must have children having the same type structure");
+}
+
+}  // namespace
+
+std::unique_ptr<column> have_overlap(lists_column_view const& lhs,
+                                     lists_column_view const& rhs,
+                                     null_equality nulls_equal,
+                                     nan_equality nans_equal,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  check_compatibility(lhs, rhs);
+
+  // Algorithm:
+  // - Generate labels for lhs and rhs child elements.
+  // - Check existence for rows of the table {rhs_labels, rhs_child} in the table
+  //   {lhs_labels, lhs_child}.
+  // - `reduce_by_key` with keys are rhs_labels and `logical_or` reduction on the existence reults
+  //   computed in the previous step.
+
+  auto const lhs_child = lhs.get_sliced_child(stream);
+  auto const rhs_child = rhs.get_sliced_child(stream);
+  auto const lhs_labels =
+    generate_labels(lhs, lhs_child.size(), stream, rmm::mr::get_current_device_resource());
+  auto const rhs_labels =
+    generate_labels(rhs, rhs_child.size(), stream, rmm::mr::get_current_device_resource());
+  auto const lhs_table = table_view{{lhs_labels->view(), lhs_child}};
+  auto const rhs_table = table_view{{rhs_labels->view(), rhs_child}};
+
+  // Check existence for each row of the rhs_table in lhs_table.
+  auto const contained = cudf::detail::contains(
+    lhs_table, rhs_table, nulls_equal, nans_equal, stream, rmm::mr::get_current_device_resource());
+
+  auto const num_rows = lhs.size();
+
+  // This stores the unique label values, used as scatter map.
+  auto list_indices = rmm::device_uvector<size_type>(num_rows, stream);
+
+  // Stores the result of checking overlap for non-empty lists.
+  auto overlap_results = rmm::device_uvector<bool>(num_rows, stream);
+
+  auto const labels_begin           = rhs_labels->view().begin<size_type>();
+  auto const end                    = thrust::reduce_by_key(rmm::exec_policy(stream),
+                                         labels_begin,  // keys
+                                         labels_begin + rhs_labels->size(),  // keys
+                                         contained.begin(),  // values to reduce
+                                         list_indices.begin(),     // out keys
+                                         overlap_results.begin(),  // out values
+                                         thrust::equal_to{},  // comp for keys
+                                         thrust::logical_or{});  // reduction op for values
+  auto const num_non_empty_segments = thrust::distance(overlap_results.begin(), end.second);
+
+  auto [null_mask, null_count] =
+    cudf::detail::bitmask_and(table_view{{lhs.parent(), rhs.parent()}}, stream, mr);
+  auto result = make_numeric_column(
+    data_type{type_to_id<bool>()}, num_rows, std::move(null_mask), null_count, stream, mr);
+  auto const result_begin = result->mutable_view().begin<bool>();
+
+  // `overlap_results` only stores the results of non-empty lists.
+  // We need to initialize `false` for the entire output array then scatter these results over.
+  thrust::uninitialized_fill(
+    rmm::exec_policy(stream), result_begin, result_begin + num_rows, false);
+  thrust::scatter(rmm::exec_policy(stream),
+                  overlap_results.begin(),
+                  overlap_results.begin() + num_non_empty_segments,
+                  list_indices.begin(),
+                  result_begin);
+
+  // Reset null count, which was invalidated when calling to `mutable_view()`.
+  result->set_null_count(null_count);
+
+  return result;
+}
+
+std::unique_ptr<column> intersect_distinct(lists_column_view const& lhs,
+                                           lists_column_view const& rhs,
+                                           null_equality nulls_equal,
+                                           nan_equality nans_equal,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  check_compatibility(lhs, rhs);
+
+  // Algorithm:
+  // - Generate labels for lhs and rhs child elements.
+  // - Check existence for rows of the table {rhs_labels, rhs_child} in the table
+  //   {lhs_labels, lhs_child}.
+  // - Extract rows of the rhs table using the existence results computed in the previous step.
+  // - Remove duplicate rows, and build the output lists.
+
+  auto const lhs_child = lhs.get_sliced_child(stream);
+  auto const rhs_child = rhs.get_sliced_child(stream);
+  auto const lhs_labels =
+    generate_labels(lhs, lhs_child.size(), stream, rmm::mr::get_current_device_resource());
+  auto const rhs_labels =
+    generate_labels(rhs, rhs_child.size(), stream, rmm::mr::get_current_device_resource());
+  auto const lhs_table = table_view{{lhs_labels->view(), lhs_child}};
+  auto const rhs_table = table_view{{rhs_labels->view(), rhs_child}};
+
+  auto const contained = cudf::detail::contains(
+    lhs_table, rhs_table, nulls_equal, nans_equal, stream, rmm::mr::get_current_device_resource());
+
+  auto const intersect_table = cudf::detail::copy_if(
+    rhs_table,
+    [contained = contained.begin()] __device__(auto const idx) { return contained[idx]; },
+    stream,
+    rmm::mr::get_current_device_resource());
+
+  // A stable algorithm is required to ensure that list labels remain contiguous.
+  auto out_table = cudf::detail::stable_distinct(intersect_table->view(),
+                                                 {0, 1},  // indices of key columns
+                                                 duplicate_keep_option::KEEP_ANY,
+                                                 nulls_equal,
+                                                 nans_equal,
+                                                 stream,
+                                                 mr);
+
+  auto const num_rows = lhs.size();
+  auto out_offsets    = reconstruct_offsets(out_table->get_column(0).view(), num_rows, stream, mr);
+  auto [null_mask, null_count] =
+    cudf::detail::bitmask_and(table_view{{lhs.parent(), rhs.parent()}}, stream, mr);
+  auto output = make_lists_column(num_rows,
+                                  std::move(out_offsets),
+                                  std::move(out_table->release().back()),
+                                  null_count,
+                                  std::move(null_mask),
+                                  stream,
+                                  mr);
+
+  if (auto const output_cv = output->view(); cudf::detail::has_nonempty_nulls(output_cv, stream)) {
+    return cudf::detail::purge_nonempty_nulls(output_cv, stream, mr);
+  }
+  return output;
+}
+
+std::unique_ptr<column> union_distinct(lists_column_view const& lhs,
+                                       lists_column_view const& rhs,
+                                       null_equality nulls_equal,
+                                       nan_equality nans_equal,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  check_compatibility(lhs, rhs);
+
+  // Algorithm: `return distinct(concatenate_rows(lhs, rhs))`.
+
+  auto const union_col =
+    lists::detail::concatenate_rows(table_view{{lhs.parent(), rhs.parent()}},
+                                    concatenate_null_policy::NULLIFY_OUTPUT_ROW,
+                                    stream,
+                                    rmm::mr::get_current_device_resource());
+
+  return cudf::lists::detail::distinct(
+    lists_column_view{union_col->view()}, nulls_equal, nans_equal, stream, mr);
+}
+
+std::unique_ptr<column> difference_distinct(lists_column_view const& lhs,
+                                            lists_column_view const& rhs,
+                                            null_equality nulls_equal,
+                                            nan_equality nans_equal,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  check_compatibility(lhs, rhs);
+
+  // Algorithm:
+  // - Generate labels for lhs and rhs child elements.
+  // - Check existence for rows of the table {lhs_labels, lhs_child} in the table
+  //   {rhs_labels, rhs_child}.
+  // - Invert the existence results computed in the previous step, resulting in difference results.
+  // - Extract rows of the lhs table using that difference results.
+  // - Remove duplicate rows, and build the output lists.
+
+  auto const lhs_child = lhs.get_sliced_child(stream);
+  auto const rhs_child = rhs.get_sliced_child(stream);
+  auto const lhs_labels =
+    generate_labels(lhs, lhs_child.size(), stream, rmm::mr::get_current_device_resource());
+  auto const rhs_labels =
+    generate_labels(rhs, rhs_child.size(), stream, rmm::mr::get_current_device_resource());
+  auto const lhs_table = table_view{{lhs_labels->view(), lhs_child}};
+  auto const rhs_table = table_view{{rhs_labels->view(), rhs_child}};
+
+  auto const contained = cudf::detail::contains(
+    rhs_table, lhs_table, nulls_equal, nans_equal, stream, rmm::mr::get_current_device_resource());
+
+  auto const difference_table = cudf::detail::copy_if(
+    lhs_table,
+    [contained = contained.begin()] __device__(auto const idx) { return !contained[idx]; },
+    stream,
+    rmm::mr::get_current_device_resource());
+
+  // A stable algorithm is required to ensure that list labels remain contiguous.
+  auto out_table = cudf::detail::stable_distinct(difference_table->view(),
+                                                 {0, 1},  // indices of key columns
+                                                 duplicate_keep_option::KEEP_ANY,
+                                                 nulls_equal,
+                                                 nans_equal,
+                                                 stream,
+                                                 mr);
+
+  auto const num_rows = lhs.size();
+  auto out_offsets    = reconstruct_offsets(out_table->get_column(0).view(), num_rows, stream, mr);
+  auto [null_mask, null_count] =
+    cudf::detail::bitmask_and(table_view{{lhs.parent(), rhs.parent()}}, stream, mr);
+
+  auto output = make_lists_column(num_rows,
+                                  std::move(out_offsets),
+                                  std::move(out_table->release().back()),
+                                  null_count,
+                                  std::move(null_mask),
+                                  stream,
+                                  mr);
+
+  if (auto const output_cv = output->view(); cudf::detail::has_nonempty_nulls(output_cv, stream)) {
+    return cudf::detail::purge_nonempty_nulls(output_cv, stream, mr);
+  }
+  return output;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> have_overlap(lists_column_view const& lhs,
+                                     lists_column_view const& rhs,
+                                     null_equality nulls_equal,
+                                     nan_equality nans_equal,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::have_overlap(lhs, rhs, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> intersect_distinct(lists_column_view const& lhs,
+                                           lists_column_view const& rhs,
+                                           null_equality nulls_equal,
+                                           nan_equality nans_equal,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::intersect_distinct(
+    lhs, rhs, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> union_distinct(lists_column_view const& lhs,
+                                       lists_column_view const& rhs,
+                                       null_equality nulls_equal,
+                                       nan_equality nans_equal,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::union_distinct(lhs, rhs, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> difference_distinct(lists_column_view const& lhs,
+                                            lists_column_view const& rhs,
+                                            null_equality nulls_equal,
+                                            nan_equality nans_equal,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::difference_distinct(
+    lhs, rhs, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf::lists
diff --git a/cpp/src/lists/stream_compaction/apply_boolean_mask.cu b/cpp/src/lists/stream_compaction/apply_boolean_mask.cu
new file mode 100644
index 0000000..ad43fbd
--- /dev/null
+++ b/cpp/src/lists/stream_compaction/apply_boolean_mask.cu
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/fill.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/replace.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/lists/detail/stream_compaction.hpp>
+#include <cudf/lists/stream_compaction.hpp>
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+
+namespace cudf::lists {
+namespace detail {
+
+std::unique_ptr<column> apply_boolean_mask(lists_column_view const& input,
+                                           lists_column_view const& boolean_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(boolean_mask.child().type().id() == type_id::BOOL8, "Mask must be of type BOOL8.");
+  CUDF_EXPECTS(input.size() == boolean_mask.size(),
+               "Boolean masks column must have same number of rows as input.");
+  auto const num_rows = input.size();
+
+  if (num_rows == 0) { return cudf::empty_like(input.parent()); }
+
+  auto constexpr offset_data_type = data_type{type_id::INT32};
+
+  auto const boolean_mask_sliced_child = boolean_mask.get_sliced_child(stream);
+
+  auto const make_filtered_child = [&] {
+    auto filtered =
+      cudf::detail::apply_boolean_mask(
+        cudf::table_view{{input.get_sliced_child(stream)}}, boolean_mask_sliced_child, stream, mr)
+        ->release();
+    return std::move(filtered.front());
+  };
+
+  auto const make_output_offsets = [&] {
+    auto boolean_mask_sliced_offsets =
+      cudf::detail::slice(
+        boolean_mask.offsets(), {boolean_mask.offset(), boolean_mask.size() + 1}, stream)
+        .front();
+    auto const sizes =
+      cudf::reduction::detail::segmented_sum(boolean_mask_sliced_child,
+                                             boolean_mask_sliced_offsets,
+                                             offset_data_type,
+                                             null_policy::EXCLUDE,
+                                             std::nullopt,
+                                             stream,
+                                             rmm::mr::get_current_device_resource());
+    auto const d_sizes     = column_device_view::create(*sizes, stream);
+    auto const sizes_begin = cudf::detail::make_null_replacement_iterator(*d_sizes, size_type{0});
+    auto const sizes_end   = sizes_begin + sizes->size();
+    auto output_offsets    = cudf::make_numeric_column(
+      offset_data_type, num_rows + 1, mask_state::UNALLOCATED, stream, mr);
+    auto output_offsets_view = output_offsets->mutable_view();
+
+    // Could have attempted an exclusive_scan(), but it would not compute the last entry.
+    // Instead, inclusive_scan(), followed by writing `0` to the head of the offsets column.
+    thrust::inclusive_scan(
+      rmm::exec_policy(stream), sizes_begin, sizes_end, output_offsets_view.begin<size_type>() + 1);
+    CUDF_CUDA_TRY(cudaMemsetAsync(
+      output_offsets_view.begin<size_type>(), 0, sizeof(size_type), stream.value()));
+    return output_offsets;
+  };
+
+  return cudf::make_lists_column(input.size(),
+                                 make_output_offsets(),
+                                 make_filtered_child(),
+                                 input.null_count(),
+                                 cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                 stream,
+                                 mr);
+}
+}  // namespace detail
+
+std::unique_ptr<column> apply_boolean_mask(lists_column_view const& input,
+                                           lists_column_view const& boolean_mask,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::apply_boolean_mask(input, boolean_mask, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf::lists
diff --git a/cpp/src/lists/stream_compaction/distinct.cu b/cpp/src/lists/stream_compaction/distinct.cu
new file mode 100644
index 0000000..48d8bab
--- /dev/null
+++ b/cpp/src/lists/stream_compaction/distinct.cu
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <lists/utilities.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <memory>
+#include <utility>
+
+namespace cudf::lists {
+namespace detail {
+
+std::unique_ptr<column> distinct(lists_column_view const& input,
+                                 null_equality nulls_equal,
+                                 nan_equality nans_equal,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  // Algorithm:
+  // - Generate labels for the child elements.
+  // - Get distinct rows of the table {labels, child} using `stable_distinct`.
+  // - Build the output lists column from the output distinct rows above.
+
+  if (input.is_empty()) { return empty_like(input.parent()); }
+
+  auto const child = input.get_sliced_child(stream);
+  auto const labels =
+    generate_labels(input, child.size(), stream, rmm::mr::get_current_device_resource());
+
+  auto const distinct_table =
+    cudf::detail::stable_distinct(table_view{{labels->view(), child}},  // input table
+                                  std::vector<size_type>{0, 1},         // keys
+                                  duplicate_keep_option::KEEP_ANY,
+                                  nulls_equal,
+                                  nans_equal,
+                                  stream,
+                                  mr);
+
+  auto out_offsets =
+    reconstruct_offsets(distinct_table->get_column(0).view(), input.size(), stream, mr);
+
+  return make_lists_column(input.size(),
+                           std::move(out_offsets),
+                           std::move(distinct_table->release().back()),
+                           input.null_count(),
+                           cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> distinct(lists_column_view const& input,
+                                 null_equality nulls_equal,
+                                 nan_equality nans_equal,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::distinct(input, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf::lists
diff --git a/cpp/src/lists/utilities.cu b/cpp/src/lists/utilities.cu
new file mode 100644
index 0000000..2c4966c
--- /dev/null
+++ b/cpp/src/lists/utilities.cu
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "utilities.hpp"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/labeling/label_segments.cuh>
+
+namespace cudf::lists::detail {
+
+std::unique_ptr<column> generate_labels(lists_column_view const& input,
+                                        size_type n_elements,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  auto labels = make_numeric_column(
+    data_type(type_to_id<size_type>()), n_elements, cudf::mask_state::UNALLOCATED, stream, mr);
+  auto const labels_begin = labels->mutable_view().template begin<size_type>();
+  cudf::detail::label_segments(
+    input.offsets_begin(), input.offsets_end(), labels_begin, labels_begin + n_elements, stream);
+  return labels;
+}
+
+std::unique_ptr<column> reconstruct_offsets(column_view const& labels,
+                                            size_type n_lists,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+
+{
+  auto out_offsets = make_numeric_column(
+    data_type{type_to_id<size_type>()}, n_lists + 1, mask_state::UNALLOCATED, stream, mr);
+
+  auto const labels_begin  = labels.template begin<size_type>();
+  auto const offsets_begin = out_offsets->mutable_view().template begin<size_type>();
+  cudf::detail::labels_to_offsets(labels_begin,
+                                  labels_begin + labels.size(),
+                                  offsets_begin,
+                                  offsets_begin + out_offsets->size(),
+                                  stream);
+  return out_offsets;
+}
+
+std::unique_ptr<column> get_normalized_offsets(lists_column_view const& input,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return empty_like(input.offsets()); }
+
+  auto out_offsets = make_numeric_column(data_type(type_to_id<size_type>()),
+                                         input.size() + 1,
+                                         cudf::mask_state::UNALLOCATED,
+                                         stream,
+                                         mr);
+  thrust::transform(rmm::exec_policy(stream),
+                    input.offsets_begin(),
+                    input.offsets_end(),
+                    out_offsets->mutable_view().begin<size_type>(),
+                    [d_offsets = input.offsets_begin()] __device__(auto const offset_val) {
+                      // The first offset value, used for zero-normalizing offsets.
+                      return offset_val - *d_offsets;
+                    });
+  return out_offsets;
+}
+
+}  // namespace cudf::lists::detail
diff --git a/cpp/src/lists/utilities.hpp b/cpp/src/lists/utilities.hpp
new file mode 100644
index 0000000..c881e82
--- /dev/null
+++ b/cpp/src/lists/utilities.hpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+namespace cudf::lists::detail {
+
+/**
+ * @brief Generate list labels for elements in the child column of the input lists column.
+ *
+ * @param input The input lists column
+ * @param n_elements The number of elements in the child column of the input lists column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned object
+ * @return A column containing list labels corresponding to each element in the child column
+ */
+std::unique_ptr<column> generate_labels(lists_column_view const& input,
+                                        size_type n_elements,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Reconstruct an offsets column from the input list labels column.
+ *
+ * @param labels The list labels corresponding to each list element
+ * @param n_lists The number of lists to build the offsets column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned object
+ * @return The output offsets column
+ */
+std::unique_ptr<column> reconstruct_offsets(column_view const& labels,
+                                            size_type n_lists,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Generate 0-based list offsets from the offsets of the input lists column.
+ *
+ * @param input The input lists column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned object
+ * @return The output offsets column with values start from 0
+ */
+std::unique_ptr<column> get_normalized_offsets(lists_column_view const& input,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::lists::detail
diff --git a/cpp/src/merge/merge.cu b/cpp/src/merge/merge.cu
new file mode 100644
index 0000000..c0765b4
--- /dev/null
+++ b/cpp/src/merge/merge.cu
@@ -0,0 +1,555 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/merge.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/detail/merge.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/strings/detail/merge.cuh>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/merge.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+#include <queue>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+using detail::side;
+using index_type = detail::index_type;
+
+/**
+ * @brief Merges the bits of two validity bitmasks.
+ *
+ * Merges the bits from two column_device_views into the destination validity buffer
+ * according to `merged_indices` map such that bit `i` in `out_validity`
+ * will be equal to bit `thrust::get<1>(merged_indices[i])` from `left_dcol`
+ * if `thrust::get<0>(merged_indices[i])` equals `side::LEFT`; otherwise,
+ * from `right_dcol`.
+ *
+ * `left_dcol` and `right_dcol` must not overlap.
+ *
+ * @tparam left_have_valids Indicates whether left_dcol mask is unallocated (hence, ALL_VALID)
+ * @tparam right_have_valids Indicates whether right_dcol mask is unallocated (hence ALL_VALID)
+ * @param[in] left_dcol The left column_device_view whose bits will be merged
+ * @param[in] right_dcol The right column_device_view whose bits will be merged
+ * @param[out] out_validity The output validity buffer after merging the left and right buffers
+ * @param[in] num_destination_rows The number of rows in the out_validity buffer
+ * @param[in] merged_indices The map that indicates the source of the input and index
+ * to be copied to the output. Length must be equal to `num_destination_rows`
+ */
+template <bool left_have_valids, bool right_have_valids>
+__global__ void materialize_merged_bitmask_kernel(
+  column_device_view left_dcol,
+  column_device_view right_dcol,
+  bitmask_type* out_validity,
+  size_type const num_destination_rows,
+  index_type const* const __restrict__ merged_indices)
+{
+  auto const stride = detail::grid_1d::grid_stride();
+
+  auto tid = detail::grid_1d::global_thread_id();
+
+  auto active_threads = __ballot_sync(0xffff'ffffu, tid < num_destination_rows);
+
+  while (tid < num_destination_rows) {
+    auto const destination_row     = static_cast<size_type>(tid);
+    auto const [src_side, src_row] = merged_indices[destination_row];
+    bool const from_left{src_side == side::LEFT};
+    bool source_bit_is_valid{true};
+    if (left_have_valids && from_left) {
+      source_bit_is_valid = left_dcol.is_valid_nocheck(src_row);
+    } else if (right_have_valids && !from_left) {
+      source_bit_is_valid = right_dcol.is_valid_nocheck(src_row);
+    }
+
+    // Use ballot to find all valid bits in this warp and create the output
+    // bitmask element
+    bitmask_type const result_mask{__ballot_sync(active_threads, source_bit_is_valid)};
+
+    // Only one thread writes output
+    if (0 == threadIdx.x % warpSize) { out_validity[word_index(destination_row)] = result_mask; }
+
+    tid += stride;
+    active_threads = __ballot_sync(active_threads, tid < num_destination_rows);
+  }
+}
+
+void materialize_bitmask(column_view const& left_col,
+                         column_view const& right_col,
+                         bitmask_type* out_validity,
+                         size_type num_elements,
+                         index_type const* merged_indices,
+                         rmm::cuda_stream_view stream)
+{
+  constexpr size_type BLOCK_SIZE{256};
+  detail::grid_1d grid_config{num_elements, BLOCK_SIZE};
+
+  auto p_left_dcol  = column_device_view::create(left_col, stream);
+  auto p_right_dcol = column_device_view::create(right_col, stream);
+
+  auto left_valid  = *p_left_dcol;
+  auto right_valid = *p_right_dcol;
+
+  if (left_col.has_nulls()) {
+    if (right_col.has_nulls()) {
+      materialize_merged_bitmask_kernel<true, true>
+        <<<grid_config.num_blocks, grid_config.num_threads_per_block, 0, stream.value()>>>(
+          left_valid, right_valid, out_validity, num_elements, merged_indices);
+    } else {
+      materialize_merged_bitmask_kernel<true, false>
+        <<<grid_config.num_blocks, grid_config.num_threads_per_block, 0, stream.value()>>>(
+          left_valid, right_valid, out_validity, num_elements, merged_indices);
+    }
+  } else {
+    if (right_col.has_nulls()) {
+      materialize_merged_bitmask_kernel<false, true>
+        <<<grid_config.num_blocks, grid_config.num_threads_per_block, 0, stream.value()>>>(
+          left_valid, right_valid, out_validity, num_elements, merged_indices);
+    } else {
+      CUDF_FAIL("materialize_merged_bitmask_kernel<false, false>() should never be called.");
+    }
+  }
+
+  CUDF_CHECK_CUDA(stream.value());
+}
+
+struct side_index_generator {
+  side _side;
+
+  __device__ index_type operator()(size_type i) const noexcept { return index_type{_side, i}; }
+};
+
+/**
+ * @brief Generates the row indices and source side (left or right) in accordance with the index
+ * columns.
+ *
+ *
+ * @tparam index_type Indicates the type to be used to collect index and side information;
+ * @param[in] left_table The left table_view to be merged
+ * @param[in] right_table The right table_view to be merged
+ * @param[in] column_order Sort order types of index columns
+ * @param[in] null_precedence Array indicating the order of nulls with respect to non-nulls for the
+ * index columns
+ * @param[in] nullable Flag indicating if at least one of the table_view arguments has nulls
+ * (defaults to true)
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ *
+ * @return A device_uvector of merged indices
+ */
+index_vector generate_merged_indices(table_view const& left_table,
+                                     table_view const& right_table,
+                                     std::vector<order> const& column_order,
+                                     std::vector<null_order> const& null_precedence,
+                                     bool nullable,
+                                     rmm::cuda_stream_view stream)
+{
+  size_type const left_size  = left_table.num_rows();
+  size_type const right_size = right_table.num_rows();
+  size_type const total_size = left_size + right_size;
+
+  auto left_gen    = side_index_generator{side::LEFT};
+  auto right_gen   = side_index_generator{side::RIGHT};
+  auto left_begin  = cudf::detail::make_counting_transform_iterator(0, left_gen);
+  auto right_begin = cudf::detail::make_counting_transform_iterator(0, right_gen);
+
+  index_vector merged_indices(total_size, stream);
+
+  auto lhs_device_view = table_device_view::create(left_table, stream);
+  auto rhs_device_view = table_device_view::create(right_table, stream);
+
+  auto d_column_order = cudf::detail::make_device_uvector_async(
+    column_order, stream, rmm::mr::get_current_device_resource());
+
+  if (nullable) {
+    auto d_null_precedence = cudf::detail::make_device_uvector_async(
+      null_precedence, stream, rmm::mr::get_current_device_resource());
+
+    auto ineq_op = detail::row_lexicographic_tagged_comparator<true>(
+      *lhs_device_view, *rhs_device_view, d_column_order.data(), d_null_precedence.data());
+    thrust::merge(rmm::exec_policy(stream),
+                  left_begin,
+                  left_begin + left_size,
+                  right_begin,
+                  right_begin + right_size,
+                  merged_indices.begin(),
+                  ineq_op);
+  } else {
+    auto ineq_op = detail::row_lexicographic_tagged_comparator<false>(
+      *lhs_device_view, *rhs_device_view, d_column_order.data());
+    thrust::merge(rmm::exec_policy(stream),
+                  left_begin,
+                  left_begin + left_size,
+                  right_begin,
+                  right_begin + right_size,
+                  merged_indices.begin(),
+                  ineq_op);
+  }
+
+  CUDF_CHECK_CUDA(stream.value());
+
+  return merged_indices;
+}
+
+/**
+ * @brief Generate merged column given row-order of merged tables
+ *  (ordered according to indices of key_cols) and the 2 columns to merge.
+ */
+struct column_merger {
+  explicit column_merger(index_vector const& row_order) : row_order_(row_order) {}
+
+  template <typename Element, CUDF_ENABLE_IF(not is_rep_layout_compatible<Element>())>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("Unsupported type for merge.");
+  }
+
+  // column merger operator;
+  //
+  template <typename Element>
+  std::enable_if_t<is_rep_layout_compatible<Element>(), std::unique_ptr<column>> operator()(
+    column_view const& lcol,
+    column_view const& rcol,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr) const
+  {
+    auto lsz         = lcol.size();
+    auto merged_size = lsz + rcol.size();
+    auto merged_col  = cudf::detail::allocate_like(lcol.has_nulls() ? lcol : rcol,
+                                                  merged_size,
+                                                  cudf::mask_allocation_policy::RETAIN,
+                                                  stream,
+                                                  mr);
+
+    //"gather" data from lcol, rcol according to row_order_ "map"
+    //(directly calling gather() won't work because
+    // lcol, rcol indices overlap!)
+    //
+    cudf::mutable_column_view merged_view = merged_col->mutable_view();
+
+    // initialize null_mask to all valid:
+    //
+    // Note: this initialization in conjunction with
+    // _conditionally_ calling materialize_bitmask() below covers
+    // the case materialize_merged_bitmask_kernel<false, false>()
+    // which won't be called anymore (because of the _condition_
+    // below)
+    //
+    cudf::detail::set_null_mask(merged_view.null_mask(), 0, merged_view.size(), true, stream);
+
+    // set the null count:
+    //
+    merged_col->set_null_count(lcol.null_count() + rcol.null_count());
+
+    // to resolve view.data()'s types use: Element
+    //
+    auto const d_lcol = lcol.data<Element>();
+    auto const d_rcol = rcol.data<Element>();
+
+    // capture lcol, rcol
+    // and "gather" into merged_view.data()[indx_merged]
+    // from lcol or rcol, depending on side;
+    //
+    thrust::transform(rmm::exec_policy(stream),
+                      row_order_.begin(),
+                      row_order_.end(),
+                      merged_view.begin<Element>(),
+                      [d_lcol, d_rcol] __device__(index_type const& index_pair) {
+                        auto const [side, index] = index_pair;
+                        return side == side::LEFT ? d_lcol[index] : d_rcol[index];
+                      });
+
+    // CAVEAT: conditional call below is erroneous without
+    // set_null_mask() call (see TODO above):
+    //
+    if (lcol.has_nulls() || rcol.has_nulls()) {
+      // resolve null mask:
+      //
+      materialize_bitmask(
+        lcol, rcol, merged_view.null_mask(), merged_view.size(), row_order_.data(), stream);
+    }
+
+    return merged_col;
+  }
+
+ private:
+  index_vector const& row_order_;
+};
+
+// specialization for strings
+template <>
+std::unique_ptr<column> column_merger::operator()<cudf::string_view>(
+  column_view const& lcol,
+  column_view const& rcol,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  auto column = strings::detail::merge<index_type>(strings_column_view(lcol),
+                                                   strings_column_view(rcol),
+                                                   row_order_.begin(),
+                                                   row_order_.end(),
+                                                   stream,
+                                                   mr);
+  if (lcol.has_nulls() || rcol.has_nulls()) {
+    auto merged_view = column->mutable_view();
+    materialize_bitmask(
+      lcol, rcol, merged_view.null_mask(), merged_view.size(), row_order_.data(), stream);
+  }
+  return column;
+}
+
+// specialization for dictionary
+template <>
+std::unique_ptr<column> column_merger::operator()<cudf::dictionary32>(
+  column_view const& lcol,
+  column_view const& rcol,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  auto result = cudf::dictionary::detail::merge(
+    cudf::dictionary_column_view(lcol), cudf::dictionary_column_view(rcol), row_order_, stream, mr);
+
+  // set the validity mask
+  if (lcol.has_nulls() || rcol.has_nulls()) {
+    auto merged_view = result->mutable_view();
+    materialize_bitmask(
+      lcol, rcol, merged_view.null_mask(), merged_view.size(), row_order_.data(), stream);
+  }
+  return result;
+}
+
+// specialization for structs
+template <>
+std::unique_ptr<column> column_merger::operator()<cudf::struct_view>(
+  column_view const& lcol,
+  column_view const& rcol,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  // merge each child.
+  auto const lhs = structs_column_view{lcol};
+  auto const rhs = structs_column_view{rcol};
+
+  auto it = cudf::detail::make_counting_transform_iterator(
+    0, [&, merger = column_merger{row_order_}](size_type i) {
+      return cudf::type_dispatcher<dispatch_storage_type>(lhs.child(i).type(),
+                                                          merger,
+                                                          lhs.get_sliced_child(i, stream),
+                                                          rhs.get_sliced_child(i, stream),
+                                                          stream,
+                                                          mr);
+    });
+
+  auto merged_children   = std::vector<std::unique_ptr<column>>(it, it + lhs.num_children());
+  auto const merged_size = lcol.size() + rcol.size();
+
+  // materialize the output buffer
+  rmm::device_buffer validity =
+    lcol.has_nulls() || rcol.has_nulls()
+      ? create_null_mask(merged_size, mask_state::UNINITIALIZED, stream, mr)
+      : rmm::device_buffer{};
+  if (lcol.has_nulls() || rcol.has_nulls()) {
+    materialize_bitmask(lcol,
+                        rcol,
+                        static_cast<bitmask_type*>(validity.data()),
+                        merged_size,
+                        row_order_.data(),
+                        stream);
+  }
+
+  return make_structs_column(merged_size,
+                             std::move(merged_children),
+                             lcol.null_count() + rcol.null_count(),
+                             std::move(validity),
+                             stream,
+                             mr);
+}
+
+using table_ptr_type = std::unique_ptr<cudf::table>;
+
+table_ptr_type merge(cudf::table_view const& left_table,
+                     cudf::table_view const& right_table,
+                     std::vector<cudf::size_type> const& key_cols,
+                     std::vector<cudf::order> const& column_order,
+                     std::vector<cudf::null_order> const& null_precedence,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr)
+{
+  // collect index columns for lhs, rhs, resp.
+  //
+  cudf::table_view index_left_view{left_table.select(key_cols)};
+  cudf::table_view index_right_view{right_table.select(key_cols)};
+  bool const nullable = cudf::has_nulls(index_left_view) || cudf::has_nulls(index_right_view);
+
+  // extract merged row order according to indices:
+  //
+  auto const merged_indices = generate_merged_indices(
+    index_left_view, index_right_view, column_order, null_precedence, nullable, stream);
+
+  // create merged table:
+  //
+  auto const n_cols = left_table.num_columns();
+  std::vector<std::unique_ptr<column>> merged_cols;
+  merged_cols.reserve(n_cols);
+
+  column_merger merger{merged_indices};
+  transform(left_table.begin(),
+            left_table.end(),
+            right_table.begin(),
+            std::back_inserter(merged_cols),
+            [&](auto const& left_col, auto const& right_col) {
+              return cudf::type_dispatcher<dispatch_storage_type>(
+                left_col.type(), merger, left_col, right_col, stream, mr);
+            });
+
+  return std::make_unique<cudf::table>(std::move(merged_cols));
+}
+
+struct merge_queue_item {
+  table_view view;
+  table_ptr_type table;
+  // Priority is a separate member to ensure that moving from an object
+  // does not change its priority (which would ruin the queue invariant)
+  cudf::size_type priority = 0;
+
+  merge_queue_item(table_view const& view, table_ptr_type&& table)
+    : view{view}, table{std::move(table)}, priority{-view.num_rows()}
+  {
+  }
+
+  bool operator<(merge_queue_item const& other) const { return priority < other.priority; }
+};
+
+// Helper function to ensure that moving out of the priority_queue is "atomic"
+template <typename T>
+T top_and_pop(std::priority_queue<T>& q)
+{
+  auto moved = std::move(const_cast<T&>(q.top()));
+  q.pop();
+  return moved;
+}
+
+}  // anonymous namespace
+
+table_ptr_type merge(std::vector<table_view> const& tables_to_merge,
+                     std::vector<cudf::size_type> const& key_cols,
+                     std::vector<cudf::order> const& column_order,
+                     std::vector<cudf::null_order> const& null_precedence,
+                     rmm::cuda_stream_view stream,
+                     rmm::mr::device_memory_resource* mr)
+{
+  if (tables_to_merge.empty()) { return std::make_unique<cudf::table>(); }
+
+  auto const& first_table = tables_to_merge.front();
+  auto const n_cols       = first_table.num_columns();
+
+  CUDF_EXPECTS(std::all_of(tables_to_merge.cbegin(),
+                           tables_to_merge.cend(),
+                           [n_cols](auto const& tbl) { return n_cols == tbl.num_columns(); }),
+               "Mismatched number of columns");
+  CUDF_EXPECTS(
+    std::all_of(tables_to_merge.cbegin(),
+                tables_to_merge.cend(),
+                [&](auto const& tbl) { return cudf::have_same_types(first_table, tbl); }),
+    "Mismatched column types");
+
+  CUDF_EXPECTS(!key_cols.empty(), "Empty key_cols");
+  CUDF_EXPECTS(key_cols.size() <= static_cast<size_t>(n_cols), "Too many values in key_cols");
+
+  CUDF_EXPECTS(key_cols.size() == column_order.size(),
+               "Mismatched size between key_cols and column_order");
+
+  // This utility will ensure all corresponding dictionary columns have matching keys.
+  // It will return any new dictionary columns created as well as updated table_views.
+  auto matched = cudf::dictionary::detail::match_dictionaries(
+    tables_to_merge, stream, rmm::mr::get_current_device_resource());
+  auto merge_tables = matched.second;
+
+  // A queue of (table view, table) pairs
+  std::priority_queue<merge_queue_item> merge_queue;
+  // The table pointer is null if we do not own the table (input tables)
+  std::for_each(merge_tables.begin(), merge_tables.end(), [&](auto const& table) {
+    if (table.num_rows() > 0) merge_queue.emplace(table, table_ptr_type());
+  });
+
+  // If there is only one non-empty table_view, return its copy
+  if (merge_queue.size() == 1) {
+    return std::make_unique<cudf::table>(merge_queue.top().view, stream, mr);
+  }
+  // No inputs have rows, return a table with same columns as the first one
+  if (merge_queue.empty()) { return empty_like(first_table); }
+
+  // Pick the two smallest tables and merge them
+  // Until there is only one table left in the queue
+  while (merge_queue.size() > 1) {
+    // To delete the intermediate table at the end of the block
+    auto const left_table = top_and_pop(merge_queue);
+    // Deallocated at the end of the block
+    auto const right_table = top_and_pop(merge_queue);
+
+    // Only use mr for the output table
+    auto const& new_tbl_mr = merge_queue.empty() ? mr : rmm::mr::get_current_device_resource();
+    auto merged_table      = merge(left_table.view,
+                              right_table.view,
+                              key_cols,
+                              column_order,
+                              null_precedence,
+                              stream,
+                              new_tbl_mr);
+
+    auto const merged_table_view = merged_table->view();
+    merge_queue.emplace(merged_table_view, std::move(merged_table));
+  }
+
+  return std::move(top_and_pop(merge_queue).table);
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::table> merge(std::vector<table_view> const& tables_to_merge,
+                                   std::vector<cudf::size_type> const& key_cols,
+                                   std::vector<cudf::order> const& column_order,
+                                   std::vector<cudf::null_order> const& null_precedence,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::merge(
+    tables_to_merge, key_cols, column_order, null_precedence, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/partitioning/partitioning.cu b/cpp/src/partitioning/partitioning.cu
new file mode 100644
index 0000000..7b66763
--- /dev/null
+++ b/cpp/src/partitioning/partitioning.cu
@@ -0,0 +1,843 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/scatter.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/partitioning.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+#include <cub/block/block_scan.cuh>
+#include <cub/device/device_histogram.cuh>
+
+namespace cudf {
+namespace {
+// Launch configuration for optimized hash partition
+constexpr size_type OPTIMIZED_BLOCK_SIZE                     = 512;
+constexpr size_type OPTIMIZED_ROWS_PER_THREAD                = 8;
+constexpr size_type ELEMENTS_PER_THREAD                      = 2;
+constexpr size_type THRESHOLD_FOR_OPTIMIZED_PARTITION_KERNEL = 1024;
+
+// Launch configuration for fallback hash partition
+constexpr size_type FALLBACK_BLOCK_SIZE      = 256;
+constexpr size_type FALLBACK_ROWS_PER_THREAD = 1;
+
+/**
+ * @brief  Functor to map a hash value to a particular 'bin' or partition number
+ * that uses the modulo operation.
+ */
+template <typename hash_value_t>
+class modulo_partitioner {
+ public:
+  modulo_partitioner(size_type num_partitions) : divisor{num_partitions} {}
+
+  __device__ size_type operator()(hash_value_t hash_value) const { return hash_value % divisor; }
+
+ private:
+  const size_type divisor;
+};
+
+template <typename T>
+bool is_power_two(T number)
+{
+  return (0 == (number & (number - 1)));
+}
+
+/**
+ * @brief  Functor to map a hash value to a particular 'bin' or partition number
+ * that uses a bitwise mask. Only works when num_partitions is a power of 2.
+ *
+ * For n % d, if d is a power of two, then it can be computed more efficiently
+ * via a single bitwise AND as: n & (d - 1)
+ */
+template <typename hash_value_t>
+class bitwise_partitioner {
+ public:
+  bitwise_partitioner(size_type num_partitions) : mask{(num_partitions - 1)}
+  {
+    assert(is_power_two(num_partitions));
+  }
+
+  __device__ size_type operator()(hash_value_t hash_value) const
+  {
+    return hash_value & mask;  // hash_value & (num_partitions - 1)
+  }
+
+ private:
+  const size_type mask;
+};
+
+/**
+ * @brief Computes which partition each row of a device_table will belong to
+ based on hashing each row, and applying a partition function to the hash value.
+   Records the size of each partition for each thread block as well as the
+ global size of each partition across all thread blocks.
+ *
+ * @param[in] the_table The table whose rows will be partitioned
+ * @param[in] num_rows The number of rows in the table
+ * @param[in] num_partitions The number of partitions to divide the rows into
+ * @param[in] the_partitioner The functor that maps a rows hash value to a
+ partition number
+ * @param[out] row_partition_numbers Array that holds which partition each row
+ belongs to
+ * @param[out] row_partition_offset Array that holds the offset of each row in
+ its partition of
+ * the thread block
+ * @param[out] block_partition_sizes Array that holds the size of each partition
+ for each block,
+ * i.e., { {block0 partition0 size, block1 partition0 size, ...},
+         {block0 partition1 size, block1 partition1 size, ...},
+         ...
+         {block0 partition(num_partitions-1) size, block1
+ partition(num_partitions -1) size, ...} }
+ * @param[out] global_partition_sizes The number of rows in each partition.
+ */
+template <class row_hasher_t, typename partitioner_type>
+__global__ void compute_row_partition_numbers(row_hasher_t the_hasher,
+                                              size_type const num_rows,
+                                              size_type const num_partitions,
+                                              partitioner_type const the_partitioner,
+                                              size_type* __restrict__ row_partition_numbers,
+                                              size_type* __restrict__ row_partition_offset,
+                                              size_type* __restrict__ block_partition_sizes,
+                                              size_type* __restrict__ global_partition_sizes)
+{
+  // Accumulate histogram of the size of each partition in shared memory
+  extern __shared__ size_type shared_partition_sizes[];
+
+  auto tid          = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+
+  // Initialize local histogram
+  size_type partition_number = threadIdx.x;
+  while (partition_number < num_partitions) {
+    shared_partition_sizes[partition_number] = 0;
+    partition_number += blockDim.x;
+  }
+
+  __syncthreads();
+
+  // Compute the hash value for each row, store it to the array of hash values
+  // and compute the partition to which the hash value belongs and increment
+  // the shared memory counter for that partition
+  while (tid < num_rows) {
+    auto const row_number                = static_cast<size_type>(tid);
+    hash_value_type const row_hash_value = the_hasher(row_number);
+
+    size_type const partition_number = the_partitioner(row_hash_value);
+
+    row_partition_numbers[row_number] = partition_number;
+
+    row_partition_offset[row_number] =
+      atomicAdd(&(shared_partition_sizes[partition_number]), size_type(1));
+
+    tid += stride;
+  }
+
+  __syncthreads();
+
+  // Flush shared memory histogram to global memory
+  partition_number = threadIdx.x;
+  while (partition_number < num_partitions) {
+    size_type const block_partition_size = shared_partition_sizes[partition_number];
+
+    // Update global size of each partition
+    atomicAdd(&global_partition_sizes[partition_number], block_partition_size);
+
+    // Record the size of this partition in this block
+    size_type const write_location        = partition_number * gridDim.x + blockIdx.x;
+    block_partition_sizes[write_location] = block_partition_size;
+    partition_number += blockDim.x;
+  }
+}
+
+/**
+ * @brief  Given an array of partition numbers, computes the final output
+ location for each element in the output such that all rows with the same
+ partition are contiguous in memory.
+ *
+ * @param row_partition_numbers The array that records the partition number for
+ each row
+ * @param num_rows The number of rows
+ * @param num_partitions THe number of partitions
+ * @param[out] block_partition_offsets Array that holds the offset of each
+ partition for each thread block,
+ * i.e., { {block0 partition0 offset, block1 partition0 offset, ...},
+         {block0 partition1 offset, block1 partition1 offset, ...},
+         ...
+         {block0 partition(num_partitions-1) offset, block1
+ partition(num_partitions -1) offset, ...} }
+ */
+__global__ void compute_row_output_locations(size_type* __restrict__ row_partition_numbers,
+                                             size_type const num_rows,
+                                             size_type const num_partitions,
+                                             size_type* __restrict__ block_partition_offsets)
+{
+  // Shared array that holds the offset of this blocks partitions in
+  // global memory
+  extern __shared__ size_type shared_partition_offsets[];
+
+  // Initialize array of this blocks offsets from global array
+  size_type partition_number = threadIdx.x;
+  while (partition_number < num_partitions) {
+    shared_partition_offsets[partition_number] =
+      block_partition_offsets[partition_number * gridDim.x + blockIdx.x];
+    partition_number += blockDim.x;
+  }
+  __syncthreads();
+
+  auto tid          = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+
+  // Get each row's partition number, and get it's output location by
+  // incrementing block's offset counter for that partition number
+  // and store the row's output location in-place
+  while (tid < num_rows) {
+    auto const row_number = static_cast<size_type>(tid);
+    // Get partition number of this row
+    size_type const partition_number = row_partition_numbers[row_number];
+
+    // Get output location based on partition number by incrementing the
+    // corresponding partition offset for this block
+    size_type const row_output_location =
+      atomicAdd(&(shared_partition_offsets[partition_number]), size_type(1));
+
+    // Store the row's output location in-place
+    row_partition_numbers[row_number] = row_output_location;
+
+    tid += stride;
+  }
+}
+
+/**
+ * @brief Move one column from the input table to the hashed table.
+ *
+ * @param[in] input_buf Data buffer of the column in the input table
+ * @param[out] output_buf Preallocated data buffer of the column in the output
+ * table
+ * @param[in] num_rows The number of rows in each column
+ * @param[in] num_partitions The number of partitions to divide the rows into
+ * @param[in] row_partition_numbers Array that holds which partition each row
+ * belongs to
+ * @param[in] row_partition_offset Array that holds the offset of each row in
+ * its partition of the thread block.
+ * @param[in] block_partition_sizes Array that holds the size of each partition
+ * for each block
+ * @param[in] scanned_block_partition_sizes The scan of block_partition_sizes
+ */
+template <typename InputIter, typename DataType>
+__global__ void copy_block_partitions(InputIter input_iter,
+                                      DataType* __restrict__ output_buf,
+                                      size_type const num_rows,
+                                      size_type const num_partitions,
+                                      size_type const* __restrict__ row_partition_numbers,
+                                      size_type const* __restrict__ row_partition_offset,
+                                      size_type const* __restrict__ block_partition_sizes,
+                                      size_type const* __restrict__ scanned_block_partition_sizes)
+{
+  extern __shared__ char shared_memory[];
+  auto block_output = reinterpret_cast<DataType*>(shared_memory);
+  auto partition_offset_shared =
+    reinterpret_cast<size_type*>(block_output + OPTIMIZED_BLOCK_SIZE * OPTIMIZED_ROWS_PER_THREAD);
+  auto partition_offset_global = partition_offset_shared + num_partitions + 1;
+
+  using BlockScan = cub::BlockScan<size_type, OPTIMIZED_BLOCK_SIZE>;
+  __shared__ typename BlockScan::TempStorage temp_storage;
+
+  // use ELEMENTS_PER_THREAD=2 to support up to 1024 partitions
+  size_type temp_histo[ELEMENTS_PER_THREAD];
+
+  for (int i = 0; i < ELEMENTS_PER_THREAD; ++i) {
+    if (ELEMENTS_PER_THREAD * threadIdx.x + i < num_partitions) {
+      temp_histo[i] =
+        block_partition_sizes[blockIdx.x + (ELEMENTS_PER_THREAD * threadIdx.x + i) * gridDim.x];
+    } else {
+      temp_histo[i] = 0;
+    }
+  }
+
+  __syncthreads();
+
+  BlockScan(temp_storage).InclusiveSum(temp_histo, temp_histo);
+
+  __syncthreads();
+
+  if (threadIdx.x == 0) { partition_offset_shared[0] = 0; }
+
+  // Calculate the offset in shared memory of each partition in this thread
+  // block
+  for (int i = 0; i < ELEMENTS_PER_THREAD; ++i) {
+    if (ELEMENTS_PER_THREAD * threadIdx.x + i < num_partitions) {
+      partition_offset_shared[ELEMENTS_PER_THREAD * threadIdx.x + i + 1] = temp_histo[i];
+    }
+  }
+
+  // Fetch the offset in the output buffer of each partition in this thread
+  // block
+  for (size_type ipartition = threadIdx.x; ipartition < num_partitions; ipartition += blockDim.x) {
+    partition_offset_global[ipartition] =
+      scanned_block_partition_sizes[ipartition * gridDim.x + blockIdx.x];
+  }
+
+  __syncthreads();
+
+  // Fetch the input data to shared memory
+  for (auto tid = cudf::detail::grid_1d::global_thread_id(); tid < num_rows;
+       tid += cudf::detail::grid_1d::grid_stride()) {
+    auto const row_number      = static_cast<size_type>(tid);
+    size_type const ipartition = row_partition_numbers[row_number];
+
+    block_output[partition_offset_shared[ipartition] + row_partition_offset[row_number]] =
+      input_iter[row_number];
+  }
+
+  __syncthreads();
+
+  // Copy data from shared memory to output using 32 threads for each partition
+  constexpr int nthreads_partition = 32;
+  static_assert(OPTIMIZED_BLOCK_SIZE % nthreads_partition == 0,
+                "BLOCK_SIZE must be divisible by number of threads");
+
+  for (size_type ipartition = threadIdx.x / nthreads_partition; ipartition < num_partitions;
+       ipartition += OPTIMIZED_BLOCK_SIZE / nthreads_partition) {
+    size_type const nelements_partition =
+      partition_offset_shared[ipartition + 1] - partition_offset_shared[ipartition];
+
+    for (size_type row_offset = threadIdx.x % nthreads_partition; row_offset < nelements_partition;
+         row_offset += nthreads_partition) {
+      output_buf[partition_offset_global[ipartition] + row_offset] =
+        block_output[partition_offset_shared[ipartition] + row_offset];
+    }
+  }
+}
+
+template <typename InputIter, typename OutputIter>
+void copy_block_partitions_impl(InputIter const input,
+                                OutputIter output,
+                                size_type num_rows,
+                                size_type num_partitions,
+                                size_type const* row_partition_numbers,
+                                size_type const* row_partition_offset,
+                                size_type const* block_partition_sizes,
+                                size_type const* scanned_block_partition_sizes,
+                                size_type grid_size,
+                                rmm::cuda_stream_view stream)
+{
+  // We need 3 chunks of shared memory:
+  // 1. BLOCK_SIZE * ROWS_PER_THREAD elements of size_type for copying to output
+  // 2. num_partitions + 1 elements of size_type for per-block partition offsets
+  // 3. num_partitions + 1 elements of size_type for global partition offsets
+  int const smem = OPTIMIZED_BLOCK_SIZE * OPTIMIZED_ROWS_PER_THREAD * sizeof(*output) +
+                   (num_partitions + 1) * sizeof(size_type) * 2;
+
+  copy_block_partitions<<<grid_size, OPTIMIZED_BLOCK_SIZE, smem, stream.value()>>>(
+    input,
+    output,
+    num_rows,
+    num_partitions,
+    row_partition_numbers,
+    row_partition_offset,
+    block_partition_sizes,
+    scanned_block_partition_sizes);
+}
+
+rmm::device_uvector<size_type> compute_gather_map(size_type num_rows,
+                                                  size_type num_partitions,
+                                                  size_type const* row_partition_numbers,
+                                                  size_type const* row_partition_offset,
+                                                  size_type const* block_partition_sizes,
+                                                  size_type const* scanned_block_partition_sizes,
+                                                  size_type grid_size,
+                                                  rmm::cuda_stream_view stream)
+{
+  auto sequence = thrust::make_counting_iterator(0);
+  rmm::device_uvector<size_type> gather_map(num_rows, stream);
+
+  copy_block_partitions_impl(sequence,
+                             gather_map.begin(),
+                             num_rows,
+                             num_partitions,
+                             row_partition_numbers,
+                             row_partition_offset,
+                             block_partition_sizes,
+                             scanned_block_partition_sizes,
+                             grid_size,
+                             stream);
+
+  return gather_map;
+}
+
+struct copy_block_partitions_dispatcher {
+  template <typename DataType>
+  constexpr static bool is_copy_block_supported()
+  {
+    // The shared-memory used for fixed-width types in the copy_block_partitions_impl function
+    // will be too large for any DataType greater than int64_t.
+    return is_fixed_width<DataType>() && (sizeof(DataType) <= sizeof(int64_t));
+  }
+
+  template <typename DataType, CUDF_ENABLE_IF(is_copy_block_supported<DataType>())>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     size_type const num_partitions,
+                                     size_type const* row_partition_numbers,
+                                     size_type const* row_partition_offset,
+                                     size_type const* block_partition_sizes,
+                                     size_type const* scanned_block_partition_sizes,
+                                     size_type grid_size,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    rmm::device_buffer output(input.size() * sizeof(DataType), stream, mr);
+
+    copy_block_partitions_impl(input.data<DataType>(),
+                               static_cast<DataType*>(output.data()),
+                               input.size(),
+                               num_partitions,
+                               row_partition_numbers,
+                               row_partition_offset,
+                               block_partition_sizes,
+                               scanned_block_partition_sizes,
+                               grid_size,
+                               stream);
+
+    return std::make_unique<column>(
+      input.type(), input.size(), std::move(output), rmm::device_buffer{}, 0);
+  }
+
+  template <typename DataType, CUDF_ENABLE_IF(not is_copy_block_supported<DataType>())>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     size_type const num_partitions,
+                                     size_type const* row_partition_numbers,
+                                     size_type const* row_partition_offset,
+                                     size_type const* block_partition_sizes,
+                                     size_type const* scanned_block_partition_sizes,
+                                     size_type grid_size,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    // Use move_to_output_buffer to create an equivalent gather map
+    auto gather_map = compute_gather_map(input.size(),
+                                         num_partitions,
+                                         row_partition_numbers,
+                                         row_partition_offset,
+                                         block_partition_sizes,
+                                         scanned_block_partition_sizes,
+                                         grid_size,
+                                         stream);
+
+    auto gather_table = cudf::detail::gather(cudf::table_view({input}),
+                                             gather_map,
+                                             out_of_bounds_policy::DONT_CHECK,
+                                             cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                             stream,
+                                             mr);
+    return std::move(gather_table->release().front());
+  }
+};
+
+// NOTE hash_has_nulls must be true if table_to_hash has nulls
+template <template <typename> class hash_function, bool hash_has_nulls>
+std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition_table(
+  table_view const& input,
+  table_view const& table_to_hash,
+  size_type num_partitions,
+  uint32_t seed,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto const num_rows = table_to_hash.num_rows();
+
+  bool const use_optimization{num_partitions <= THRESHOLD_FOR_OPTIMIZED_PARTITION_KERNEL};
+  auto const block_size = use_optimization ? OPTIMIZED_BLOCK_SIZE : FALLBACK_BLOCK_SIZE;
+  auto const rows_per_thread =
+    use_optimization ? OPTIMIZED_ROWS_PER_THREAD : FALLBACK_ROWS_PER_THREAD;
+  auto const rows_per_block = block_size * rows_per_thread;
+
+  // NOTE grid_size is non-const to workaround lambda capture bug in gcc 5.4
+  auto grid_size = util::div_rounding_up_safe(num_rows, rows_per_block);
+
+  // Allocate array to hold which partition each row belongs to
+  auto row_partition_numbers = rmm::device_uvector<size_type>(num_rows, stream);
+
+  // Array to hold the size of each partition computed by each block
+  //  i.e., { {block0 partition0 size, block1 partition0 size, ...},
+  //          {block0 partition1 size, block1 partition1 size, ...},
+  //          ...
+  //          {block0 partition(num_partitions-1) size, block1
+  //          partition(num_partitions -1) size, ...} }
+  auto block_partition_sizes = rmm::device_uvector<size_type>(grid_size * num_partitions, stream);
+
+  auto scanned_block_partition_sizes =
+    rmm::device_uvector<size_type>(grid_size * num_partitions, stream);
+
+  // Holds the total number of rows in each partition
+  auto global_partition_sizes = cudf::detail::make_zeroed_device_uvector_async<size_type>(
+    num_partitions, stream, rmm::mr::get_current_device_resource());
+
+  auto row_partition_offset = cudf::detail::make_zeroed_device_uvector_async<size_type>(
+    num_rows, stream, rmm::mr::get_current_device_resource());
+
+  auto const row_hasher = experimental::row::hash::row_hasher(table_to_hash, stream);
+  auto const hasher =
+    row_hasher.device_hasher<hash_function>(nullate::DYNAMIC{hash_has_nulls}, seed);
+
+  // If the number of partitions is a power of two, we can compute the partition
+  // number of each row more efficiently with bitwise operations
+  if (is_power_two(num_partitions)) {
+    // Determines how the mapping between hash value and partition number is
+    // computed
+    using partitioner_type = bitwise_partitioner<hash_value_type>;
+
+    // Computes which partition each row belongs to by hashing the row and
+    // performing a partitioning operator on the hash value. Also computes the
+    // number of rows in each partition both for each thread block as well as
+    // across all blocks
+    compute_row_partition_numbers<<<grid_size,
+                                    block_size,
+                                    num_partitions * sizeof(size_type),
+                                    stream.value()>>>(hasher,
+                                                      num_rows,
+                                                      num_partitions,
+                                                      partitioner_type(num_partitions),
+                                                      row_partition_numbers.data(),
+                                                      row_partition_offset.data(),
+                                                      block_partition_sizes.data(),
+                                                      global_partition_sizes.data());
+  } else {
+    // Determines how the mapping between hash value and partition number is
+    // computed
+    using partitioner_type = modulo_partitioner<hash_value_type>;
+
+    // Computes which partition each row belongs to by hashing the row and
+    // performing a partitioning operator on the hash value. Also computes the
+    // number of rows in each partition both for each thread block as well as
+    // across all blocks
+    compute_row_partition_numbers<<<grid_size,
+                                    block_size,
+                                    num_partitions * sizeof(size_type),
+                                    stream.value()>>>(hasher,
+                                                      num_rows,
+                                                      num_partitions,
+                                                      partitioner_type(num_partitions),
+                                                      row_partition_numbers.data(),
+                                                      row_partition_offset.data(),
+                                                      block_partition_sizes.data(),
+                                                      global_partition_sizes.data());
+  }
+
+  // Compute exclusive scan of all blocks' partition sizes in-place to determine
+  // the starting point for each blocks portion of each partition in the output
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         block_partition_sizes.begin(),
+                         block_partition_sizes.end(),
+                         scanned_block_partition_sizes.data());
+
+  // Compute exclusive scan of size of each partition to determine offset
+  // location of each partition in final output.
+  // TODO This can be done independently on a separate stream
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         global_partition_sizes.begin(),
+                         global_partition_sizes.end(),
+                         global_partition_sizes.begin());
+
+  // Copy the result of the exclusive scan to the output offsets array
+  // to indicate the starting point for each partition in the output
+  auto const partition_offsets =
+    cudf::detail::make_std_vector_async(global_partition_sizes, stream);
+
+  // When the number of partitions is less than a threshold, we can apply an
+  // optimization using shared memory to copy values to the output buffer.
+  // Otherwise, fallback to using scatter.
+  if (use_optimization) {
+    std::vector<std::unique_ptr<column>> output_cols(input.num_columns());
+
+    // Copy input to output by partition per column
+    std::transform(input.begin(), input.end(), output_cols.begin(), [&](auto const& col) {
+      return cudf::type_dispatcher<dispatch_storage_type>(col.type(),
+                                                          copy_block_partitions_dispatcher{},
+                                                          col,
+                                                          num_partitions,
+                                                          row_partition_numbers.data(),
+                                                          row_partition_offset.data(),
+                                                          block_partition_sizes.data(),
+                                                          scanned_block_partition_sizes.data(),
+                                                          grid_size,
+                                                          stream,
+                                                          mr);
+    });
+
+    if (has_nested_nulls(input)) {
+      // Use copy_block_partitions to compute a gather map
+      auto gather_map = compute_gather_map(num_rows,
+                                           num_partitions,
+                                           row_partition_numbers.data(),
+                                           row_partition_offset.data(),
+                                           block_partition_sizes.data(),
+                                           scanned_block_partition_sizes.data(),
+                                           grid_size,
+                                           stream);
+
+      // Handle bitmask using gather to take advantage of ballot_sync
+      detail::gather_bitmask(
+        input, gather_map.begin(), output_cols, detail::gather_bitmask_op::DONT_CHECK, stream, mr);
+    }
+
+    stream.synchronize();  // Async D2H copy must finish before returning host vec
+    return std::pair(std::make_unique<table>(std::move(output_cols)), std::move(partition_offsets));
+  } else {
+    // Compute a scatter map from input to output such that the output rows are
+    // sorted by partition number
+    auto row_output_locations{row_partition_numbers.data()};
+    auto scanned_block_partition_sizes_ptr{scanned_block_partition_sizes.data()};
+    compute_row_output_locations<<<grid_size,
+                                   block_size,
+                                   num_partitions * sizeof(size_type),
+                                   stream.value()>>>(
+      row_output_locations, num_rows, num_partitions, scanned_block_partition_sizes_ptr);
+
+    // Use the resulting scatter map to materialize the output
+    auto output = detail::scatter(input, row_partition_numbers, input, stream, mr);
+
+    stream.synchronize();  // Async D2H copy must finish before returning host vec
+    return std::pair(std::move(output), std::move(partition_offsets));
+  }
+}
+
+struct dispatch_map_type {
+  /**
+   * @brief Partitions the table `t` according to the `partition_map`.
+   *
+   * Algorithm:
+   * - Compute the histogram of the size each partition
+   * - Compute the exclusive scan of the histogram to get the offset for each
+   * partition in the final partitioned output
+   * - Use a transform iterator to materialize the scatter map of the rows from
+   * `t` into the final output.
+   *
+   * @note JH: It would likely be more efficient to avoid the atomic increments
+   * in the transform iterator. It would probably be faster to compute a
+   * per-thread block histogram and compute an exclusive scan of all of the
+   * per-block histograms (like in hash partition). But I'm purposefully trying
+   * to reduce memory pressure by avoiding intermediate materializations. Plus,
+   * atomics resolve in L2 and should be pretty fast since all the offsets will
+   * fit in L2.
+   *
+   */
+  template <typename MapType>
+  std::enable_if_t<is_index_type<MapType>(),
+                   std::pair<std::unique_ptr<table>, std::vector<size_type>>>
+  operator()(table_view const& t,
+             column_view const& partition_map,
+             size_type num_partitions,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr) const
+  {
+    // Build a histogram of the number of rows in each partition
+    rmm::device_uvector<size_type> histogram(num_partitions + 1, stream);
+    std::size_t temp_storage_bytes{};
+    std::size_t const num_levels = num_partitions + 1;
+    size_type const lower_level  = 0;
+    size_type const upper_level  = num_partitions;
+    cub::DeviceHistogram::HistogramEven(nullptr,
+                                        temp_storage_bytes,
+                                        partition_map.begin<MapType>(),
+                                        histogram.data(),
+                                        num_levels,
+                                        lower_level,
+                                        upper_level,
+                                        partition_map.size(),
+                                        stream.value());
+
+    rmm::device_buffer temp_storage(temp_storage_bytes, stream);
+
+    cub::DeviceHistogram::HistogramEven(temp_storage.data(),
+                                        temp_storage_bytes,
+                                        partition_map.begin<MapType>(),
+                                        histogram.data(),
+                                        num_levels,
+                                        lower_level,
+                                        upper_level,
+                                        partition_map.size(),
+                                        stream.value());
+
+    // `histogram` was created with an extra entry at the end such that an
+    // exclusive scan will put the total number of rows at the end
+    thrust::exclusive_scan(
+      rmm::exec_policy(stream), histogram.begin(), histogram.end(), histogram.begin());
+
+    // Copy offsets to host before the transform below modifies the histogram
+    auto const partition_offsets = cudf::detail::make_std_vector_sync(histogram, stream);
+
+    // Unfortunately need to materialize the scatter map because
+    // `detail::scatter` requires multiple passes through the iterator
+    rmm::device_uvector<size_type> scatter_map(partition_map.size(), stream);
+
+    // For each `partition_map[i]`, atomically increment the corresponding
+    // partition offset to determine `i`s location in the output
+    thrust::transform(rmm::exec_policy(stream),
+                      partition_map.begin<MapType>(),
+                      partition_map.end<MapType>(),
+                      scatter_map.begin(),
+                      [offsets = histogram.data()] __device__(auto partition_number) {
+                        return atomicAdd(&offsets[partition_number], 1);
+                      });
+
+    // Scatter the rows into their partitions
+    auto scattered = detail::scatter(t, scatter_map, t, stream, mr);
+
+    return std::pair(std::move(scattered), std::move(partition_offsets));
+  }
+
+  template <typename MapType, typename... Args>
+  std::enable_if_t<not is_index_type<MapType>(),
+                   std::pair<std::unique_ptr<table>, std::vector<size_type>>>
+  operator()(Args&&...) const
+  {
+    CUDF_FAIL("Unexpected, non-integral partition map.");
+  }
+};
+}  // namespace
+
+namespace detail {
+namespace {
+
+/**
+ * @brief This hash function simply returns the input value cast to the
+ * result_type of the functor.
+ */
+template <typename Key>
+struct IdentityHash {
+  using result_type        = uint32_t;
+  constexpr IdentityHash() = default;
+  constexpr IdentityHash(uint32_t) {}
+
+  template <typename return_type = result_type>
+  constexpr std::enable_if_t<!std::is_arithmetic_v<Key>, return_type> operator()(
+    Key const& key) const
+  {
+    CUDF_UNREACHABLE("IdentityHash does not support this data type");
+  }
+
+  template <typename return_type = result_type>
+  constexpr std::enable_if_t<std::is_arithmetic_v<Key>, return_type> operator()(
+    Key const& key) const
+  {
+    return static_cast<result_type>(key);
+  }
+};
+
+template <template <typename> class hash_function>
+std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
+  table_view const& input,
+  std::vector<size_type> const& columns_to_hash,
+  int num_partitions,
+  uint32_t seed,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto table_to_hash = input.select(columns_to_hash);
+
+  // Return empty result if there are no partitions or nothing to hash
+  if (num_partitions <= 0 || input.num_rows() == 0 || table_to_hash.num_columns() == 0) {
+    return std::pair(empty_like(input), std::vector<size_type>(num_partitions, 0));
+  }
+
+  if (has_nested_nulls(table_to_hash)) {
+    return hash_partition_table<hash_function, true>(
+      input, table_to_hash, num_partitions, seed, stream, mr);
+  } else {
+    return hash_partition_table<hash_function, false>(
+      input, table_to_hash, num_partitions, seed, stream, mr);
+  }
+}
+}  // namespace
+
+std::pair<std::unique_ptr<table>, std::vector<size_type>> partition(
+  table_view const& t,
+  column_view const& partition_map,
+  size_type num_partitions,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(t.num_rows() == partition_map.size(),
+               "Size mismatch between table and partition map.");
+  CUDF_EXPECTS(not partition_map.has_nulls(), "Unexpected null values in partition_map.");
+
+  if (num_partitions == 0 or t.num_rows() == 0) {
+    // The output offsets vector must have size `num_partitions + 1` as per documentation.
+    return std::pair(empty_like(t), std::vector<size_type>(num_partitions + 1, 0));
+  }
+
+  return cudf::type_dispatcher(
+    partition_map.type(), dispatch_map_type{}, t, partition_map, num_partitions, stream, mr);
+}
+}  // namespace detail
+
+// Partition based on hash values
+std::pair<std::unique_ptr<table>, std::vector<size_type>> hash_partition(
+  table_view const& input,
+  std::vector<size_type> const& columns_to_hash,
+  int num_partitions,
+  hash_id hash_function,
+  uint32_t seed,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  switch (hash_function) {
+    case (hash_id::HASH_IDENTITY):
+      for (size_type const& column_id : columns_to_hash) {
+        if (!is_numeric(input.column(column_id).type()))
+          CUDF_FAIL("IdentityHash does not support this data type");
+      }
+      return detail::hash_partition<cudf::detail::IdentityHash>(
+        input, columns_to_hash, num_partitions, seed, stream, mr);
+    case (hash_id::HASH_MURMUR3):
+      return detail::hash_partition<cudf::hashing::detail::MurmurHash3_x86_32>(
+        input, columns_to_hash, num_partitions, seed, stream, mr);
+    default: CUDF_FAIL("Unsupported hash function in hash_partition");
+  }
+}
+
+// Partition based on an explicit partition map
+std::pair<std::unique_ptr<table>, std::vector<size_type>> partition(
+  table_view const& t,
+  column_view const& partition_map,
+  size_type num_partitions,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::partition(t, partition_map, num_partitions, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/partitioning/round_robin.cu b/cpp/src/partitioning/round_robin.cu
new file mode 100644
index 0000000..32c72f6
--- /dev/null
+++ b/cpp/src/partitioning/round_robin.cu
@@ -0,0 +1,277 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/tuple.h>
+
+#include <algorithm>
+#include <cmath>  // for std::ceil()
+#include <memory>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace {
+
+/**
+ * @brief Handles the "degenerate" case num_partitions >= num_rows.
+ *
+ * Specifically,
+ * If num_partitions == nrows:
+ * Then, offsets = [0..nrows-1]
+ * gather_row_indices = rotate [0..nrows-1] right by start_partition positions;
+ *
+ * If num_partitions > nrows:
+ * Then, let:
+ * dbg = generate a directed bipartite graph with num_partitions nodes and nrows edges,
+ * so that node j has an edge to node (j+start_partition) % num_partitions, for j = 0,...,nrows-1;
+ *
+ * transpose_dbg = transpose graph of dbg; (i.e., (i -> j) edge in dbg means (j -> i) edge in
+ * transpose);
+ *
+ * (offsets, indices) = (row_offsets, col_indices) of transpose_dbg;
+ * where (row_offsets, col_indices) are the CSR format of the graph;
+ *
+ * @param[in] input The input table to be round-robin partitioned
+ * @param[in] num_partitions Number of partitions for the table
+ * @param[in] start_partition Index of the 1st partition
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ * @param[in] mr Device memory resource used to allocate the returned table's device memory
+ *
+ * @returns A std::pair consisting of a unique_ptr to the partitioned table and the partition
+ * offsets for each partition within the table
+ */
+std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> degenerate_partitions(
+  cudf::table_view const& input,
+  cudf::size_type num_partitions,
+  cudf::size_type start_partition,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto nrows = input.num_rows();
+
+  // iterator for partition index rotated right by start_partition positions:
+  auto rotated_iter_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    [num_partitions, start_partition] __device__(auto index) {
+      return (index + num_partitions - start_partition) % num_partitions;
+    });
+
+  if (num_partitions == nrows) {
+    rmm::device_uvector<cudf::size_type> partition_offsets(num_partitions, stream);
+    thrust::sequence(rmm::exec_policy(stream), partition_offsets.begin(), partition_offsets.end());
+
+    auto uniq_tbl = cudf::detail::gather(input,
+                                         rotated_iter_begin,
+                                         rotated_iter_begin + nrows,  // map
+                                         cudf::out_of_bounds_policy::DONT_CHECK,
+                                         stream,
+                                         mr);
+
+    return std::pair(std::move(uniq_tbl),
+                     cudf::detail::make_std_vector_sync(partition_offsets, stream));
+  } else {  //( num_partitions > nrows )
+    rmm::device_uvector<cudf::size_type> d_row_indices(nrows, stream);
+
+    // copy rotated right partition indexes that
+    // fall in the interval [0, nrows):
+    //(this relies on a _stable_ copy_if())
+    thrust::copy_if(rmm::exec_policy(stream),
+                    rotated_iter_begin,
+                    rotated_iter_begin + num_partitions,
+                    d_row_indices.begin(),
+                    [nrows] __device__(auto index) { return (index < nrows); });
+
+    //...and then use the result, d_row_indices, as gather map:
+    auto uniq_tbl = cudf::detail::gather(input,
+                                         d_row_indices.begin(),
+                                         d_row_indices.end(),  // map
+                                         cudf::out_of_bounds_policy::DONT_CHECK,
+                                         stream,
+                                         mr);
+
+    // offsets (part 1: compute partition sizes);
+    // iterator for number of edges of the transposed bipartite graph;
+    // this composes rotated_iter transform (above) iterator with
+    // calculating number of edges of transposed bi-graph:
+    auto nedges_iter_begin = thrust::make_transform_iterator(
+      rotated_iter_begin, [nrows] __device__(auto index) { return (index < nrows ? 1 : 0); });
+
+    // offsets (part 2: compute partition offsets):
+    rmm::device_uvector<cudf::size_type> partition_offsets(num_partitions, stream);
+    thrust::exclusive_scan(rmm::exec_policy(stream),
+                           nedges_iter_begin,
+                           nedges_iter_begin + num_partitions,
+                           partition_offsets.begin());
+
+    return std::pair(std::move(uniq_tbl),
+                     cudf::detail::make_std_vector_sync(partition_offsets, stream));
+  }
+}
+}  // namespace
+
+namespace cudf {
+namespace detail {
+std::pair<std::unique_ptr<table>, std::vector<cudf::size_type>> round_robin_partition(
+  table_view const& input,
+  cudf::size_type num_partitions,
+  cudf::size_type start_partition,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto nrows = input.num_rows();
+
+  CUDF_EXPECTS(num_partitions > 0, "Incorrect number of partitions. Must be greater than 0.");
+  CUDF_EXPECTS(start_partition < num_partitions,
+               "Incorrect start_partition index. Must be less than number of partitions.");
+  CUDF_EXPECTS(
+    start_partition >= 0,
+    "Incorrect start_partition index. Must be positive.");  // since cudf::size_type is an alias for
+                                                            // int32_t, it _can_ be negative
+
+  if (nrows == 0) {
+    return std::pair(empty_like(input), std::vector<size_type>(num_partitions, 0));
+  }
+
+  // handle degenerate case:
+  //
+  if (num_partitions >= nrows) {
+    return degenerate_partitions(input, num_partitions, start_partition, stream, mr);
+  }
+
+  auto np_max_size = nrows % num_partitions;  // # partitions of max size
+
+  // handle case when nr `mod` np == 0;
+  // fix for bug: https://github.com/rapidsai/cudf/issues/4043
+  auto num_partitions_max_size = (np_max_size > 0 ? np_max_size : num_partitions);
+
+  cudf::size_type max_partition_size = std::ceil(
+    static_cast<double>(nrows) / static_cast<double>(num_partitions));  // max size of partitions
+
+  auto total_max_partitions_size = num_partitions_max_size * max_partition_size;
+  auto num_partitions_min_size   = num_partitions - num_partitions_max_size;
+
+  // delta is the number of positions to rotate right
+  // the original range [0,1,...,n-1]
+  // and is calculated by accumulating the first
+  //`start_partition` partition sizes from the end;
+  // i.e.,
+  // the partition sizes array (of size p) being:
+  //[m,m,...,m,(m-1),...,(m-1)]
+  //(with num_partitions_max_size sizes `m` at the beginning;
+  // and (p-num_partitions_max_size) sizes `(m-1)` at the end)
+  // we accumulate the 1st `start_partition` entries from the end:
+  //
+  auto delta = (start_partition > num_partitions_min_size
+                  ? num_partitions_min_size * (max_partition_size - 1) +
+                      (start_partition - num_partitions_min_size) * max_partition_size
+                  : start_partition * (max_partition_size - 1));
+
+  auto iter_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    [nrows,
+     num_partitions,
+     max_partition_size,
+     num_partitions_max_size,
+     total_max_partitions_size,
+     delta] __device__(auto index0) {
+      // rotate original index right by delta positions;
+      // this is the effect of applying start_partition:
+      //
+      auto rotated_index = (index0 + nrows - delta) % nrows;
+
+      // using rotated_index = given index0, rotated;
+      // the algorithm below calculates the src round-robin row,
+      // by calculating the partition_index and the index_within_partition:
+      //
+      auto index_within_partition =
+        (rotated_index <= total_max_partitions_size
+           ? rotated_index % max_partition_size
+           : (rotated_index - total_max_partitions_size) % (max_partition_size - 1));
+      auto partition_index =
+        (rotated_index <= total_max_partitions_size
+           ? rotated_index / max_partition_size
+           : num_partitions_max_size +
+               (rotated_index - total_max_partitions_size) / (max_partition_size - 1));
+      return num_partitions * index_within_partition + partition_index;
+    });
+
+  auto uniq_tbl = cudf::detail::gather(
+    input, iter_begin, iter_begin + nrows, cudf::out_of_bounds_policy::DONT_CHECK, stream, mr);
+  auto ret_pair = std::pair(std::move(uniq_tbl), std::vector<cudf::size_type>(num_partitions));
+
+  // this has the effect of rotating the set of partition sizes
+  // right by start_partition positions:
+  //
+  auto rotated_iter_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    [num_partitions, start_partition, max_partition_size, num_partitions_max_size](auto index) {
+      return ((index + num_partitions - start_partition) % num_partitions < num_partitions_max_size
+                ? max_partition_size
+                : max_partition_size - 1);
+    });
+
+  // then exclusive_scan on the resulting
+  // rotated partition sizes to get the partition offsets
+  // corresponding to start_partition:
+  // Since:
+  //"num_partitions is usually going to be relatively small
+  //(<1,000), as such, it's probably more expensive to do this on the device.
+  // Instead, do it on the host directly into the std::vector and avoid the memcpy." - JH
+  //
+  thrust::exclusive_scan(
+    thrust::host, rotated_iter_begin, rotated_iter_begin + num_partitions, ret_pair.second.begin());
+
+  return ret_pair;
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> round_robin_partition(
+  table_view const& input,
+  cudf::size_type num_partitions,
+  cudf::size_type start_partition     = 0,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  CUDF_FUNC_RANGE();
+  return detail::round_robin_partition(
+    input, num_partitions, start_partition, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/quantiles/quantile.cu b/cpp/src/quantiles/quantile.cu
new file mode 100644
index 0000000..4a9c2e3
--- /dev/null
+++ b/cpp/src/quantiles/quantile.cu
@@ -0,0 +1,196 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <quantiles/quantiles_util.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/transform.h>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+template <bool exact, typename SortMapIterator>
+struct quantile_functor {
+  SortMapIterator ordered_indices;
+  size_type size;
+  std::vector<double> const& q;
+  interpolation interp;
+  bool retain_types;
+  rmm::cuda_stream_view stream;
+  rmm::mr::device_memory_resource* mr;
+
+  template <typename T>
+  std::enable_if_t<not std::is_arithmetic_v<T> and not cudf::is_fixed_point<T>(),
+                   std::unique_ptr<column>>
+  operator()(column_view const& input)
+  {
+    CUDF_FAIL("quantile does not support non-numeric types");
+  }
+
+  template <typename T>
+  std::enable_if_t<std::is_arithmetic_v<T> or cudf::is_fixed_point<T>(), std::unique_ptr<column>>
+  operator()(column_view const& input)
+  {
+    using StorageType   = cudf::device_storage_type_t<T>;
+    using ExactResult   = std::conditional_t<exact and not cudf::is_fixed_point<T>(), double, T>;
+    using StorageResult = cudf::device_storage_type_t<ExactResult>;
+
+    auto const type =
+      is_fixed_point(input.type()) ? input.type() : data_type{type_to_id<StorageResult>()};
+    auto output = make_fixed_width_column(type, q.size(), mask_state::UNALLOCATED, stream, mr);
+
+    if (output->size() == 0) { return output; }
+
+    if (input.is_empty()) {
+      auto mask = cudf::detail::create_null_mask(output->size(), mask_state::ALL_NULL, stream, mr);
+      output->set_null_mask(std::move(mask), output->size());
+      return output;
+    }
+
+    auto d_input  = column_device_view::create(input, stream);
+    auto d_output = mutable_column_device_view::create(output->mutable_view(), stream);
+
+    auto q_device =
+      cudf::detail::make_device_uvector_sync(q, stream, rmm::mr::get_current_device_resource());
+
+    if (!cudf::is_dictionary(input.type())) {
+      auto sorted_data =
+        thrust::make_permutation_iterator(input.data<StorageType>(), ordered_indices);
+      thrust::transform(rmm::exec_policy(stream),
+                        q_device.begin(),
+                        q_device.end(),
+                        d_output->template begin<StorageResult>(),
+                        [sorted_data, interp = interp, size = size] __device__(double q) {
+                          return select_quantile_data<StorageResult>(sorted_data, size, q, interp);
+                        });
+    } else {
+      auto sorted_data = thrust::make_permutation_iterator(
+        dictionary::detail::make_dictionary_iterator<T>(*d_input), ordered_indices);
+      thrust::transform(rmm::exec_policy(stream),
+                        q_device.begin(),
+                        q_device.end(),
+                        d_output->template begin<StorageResult>(),
+                        [sorted_data, interp = interp, size = size] __device__(double q) {
+                          return select_quantile_data<StorageResult>(sorted_data, size, q, interp);
+                        });
+    }
+
+    if (input.nullable()) {
+      auto sorted_validity = thrust::make_transform_iterator(
+        ordered_indices,
+        [input = *d_input] __device__(size_type idx) { return input.is_valid_nocheck(idx); });
+
+      auto [mask, null_count] = valid_if(
+        q_device.begin(),
+        q_device.end(),
+        [sorted_validity, interp = interp, size = size] __device__(double q) {
+          return select_quantile_validity(sorted_validity, size, q, interp);
+        },
+        stream,
+        mr);
+
+      output->set_null_mask(std::move(mask), null_count);
+    }
+
+    return output;
+  }
+};
+
+template <bool exact, typename SortMapIterator>
+std::unique_ptr<column> quantile(column_view const& input,
+                                 SortMapIterator ordered_indices,
+                                 size_type size,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 bool retain_types,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto functor = quantile_functor<exact, SortMapIterator>{
+    ordered_indices, size, q, interp, retain_types, stream, mr};
+
+  auto input_type = cudf::is_dictionary(input.type()) && !input.is_empty()
+                      ? dictionary_column_view(input).keys().type()
+                      : input.type();
+
+  return type_dispatcher(input_type, functor, input);
+}
+
+std::unique_ptr<column> quantile(column_view const& input,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 column_view const& indices,
+                                 bool exact,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  if (indices.is_empty()) {
+    auto begin = thrust::make_counting_iterator<size_type>(0);
+    if (exact) {
+      return quantile<true>(input, begin, input.size(), q, interp, exact, stream, mr);
+    } else {
+      return quantile<false>(input, begin, input.size(), q, interp, exact, stream, mr);
+    }
+
+  } else {
+    CUDF_EXPECTS(indices.type() == data_type{type_to_id<size_type>()},
+                 "`indices` type must be `INT32`.");
+    if (exact) {
+      return quantile<true>(
+        input, indices.begin<size_type>(), indices.size(), q, interp, exact, stream, mr);
+    } else {
+      return quantile<false>(
+        input, indices.begin<size_type>(), indices.size(), q, interp, exact, stream, mr);
+    }
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> quantile(column_view const& input,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 column_view const& ordered_indices,
+                                 bool exact,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::quantile(input, q, interp, ordered_indices, exact, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/quantiles/quantiles.cu b/cpp/src/quantiles/quantiles.cu
new file mode 100644
index 0000000..c6760e7
--- /dev/null
+++ b/cpp/src/quantiles/quantiles.cu
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <quantiles/quantiles_util.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <memory>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+template <typename SortMapIterator>
+std::unique_ptr<table> quantiles(table_view const& input,
+                                 SortMapIterator sortmap,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto quantile_idx_lookup = [sortmap, interp, size = input.num_rows()] __device__(double q) {
+    auto selector = [sortmap] __device__(auto idx) { return sortmap[idx]; };
+    return detail::select_quantile<size_type>(selector, size, q, interp);
+  };
+
+  auto const q_device =
+    cudf::detail::make_device_uvector_async(q, stream, rmm::mr::get_current_device_resource());
+
+  auto quantile_idx_iter = thrust::make_transform_iterator(q_device.begin(), quantile_idx_lookup);
+
+  return detail::gather(input,
+                        quantile_idx_iter,
+                        quantile_idx_iter + q.size(),
+                        out_of_bounds_policy::DONT_CHECK,
+                        stream,
+                        mr);
+}
+
+std::unique_ptr<table> quantiles(table_view const& input,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 cudf::sorted is_input_sorted,
+                                 std::vector<order> const& column_order,
+                                 std::vector<null_order> const& null_precedence,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  if (q.empty()) { return empty_like(input); }
+
+  CUDF_EXPECTS(interp == interpolation::HIGHER || interp == interpolation::LOWER ||
+                 interp == interpolation::NEAREST,
+               "multi-column quantiles require a non-arithmetic interpolation strategy.");
+
+  CUDF_EXPECTS(input.num_rows() > 0, "multi-column quantiles require at least one input row.");
+
+  if (is_input_sorted == sorted::YES) {
+    return detail::quantiles(
+      input, thrust::make_counting_iterator<size_type>(0), q, interp, stream, mr);
+  } else {
+    auto sorted_idx = detail::sorted_order(
+      input, column_order, null_precedence, stream, rmm::mr::get_current_device_resource());
+    return detail::quantiles(input, sorted_idx->view().data<size_type>(), q, interp, stream, mr);
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> quantiles(table_view const& input,
+                                 std::vector<double> const& q,
+                                 interpolation interp,
+                                 cudf::sorted is_input_sorted,
+                                 std::vector<order> const& column_order,
+                                 std::vector<null_order> const& null_precedence,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::quantiles(input,
+                           q,
+                           interp,
+                           is_input_sorted,
+                           column_order,
+                           null_precedence,
+                           cudf::get_default_stream(),
+                           mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/quantiles/quantiles_util.hpp b/cpp/src/quantiles/quantiles_util.hpp
new file mode 100644
index 0000000..5edb323
--- /dev/null
+++ b/cpp/src/quantiles/quantiles_util.hpp
@@ -0,0 +1,219 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cmath>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+namespace cudf {
+namespace detail {
+template <typename Result, typename T>
+CUDF_HOST_DEVICE inline Result get_array_value(T const* devarr, size_type location)
+{
+  T result;
+#if defined(__CUDA_ARCH__)
+  result = devarr[location];
+#else
+  CUDF_CUDA_TRY(cudaMemcpy(&result, devarr + location, sizeof(T), cudaMemcpyDefault));
+#endif
+  return static_cast<Result>(result);
+}
+
+namespace interpolate {
+template <typename Result, typename T>
+CUDF_HOST_DEVICE inline Result linear(T lhs, T rhs, double frac)
+{
+  // TODO: safe operation to avoid overflow/underflow
+  // double can fully represent int8-32 value range.
+  // Since the fraction part of double is 52 bits,
+  // double cannot fully represent int64.
+  // Underflow may occur when converting int64 to double
+  // detail: https://github.com/rapidsai/cudf/issues/1417
+
+  auto dlhs             = static_cast<double>(lhs);
+  auto drhs             = static_cast<double>(rhs);
+  double one_minus_frac = 1.0 - frac;
+  return static_cast<Result>(one_minus_frac * dlhs + frac * drhs);
+}
+
+template <typename Result, typename T>
+CUDF_HOST_DEVICE inline Result midpoint(T lhs, T rhs)
+{
+  // TODO: try std::midpoint (C++20) if available
+  auto dlhs = static_cast<double>(lhs);
+  auto drhs = static_cast<double>(rhs);
+  return static_cast<Result>(dlhs / 2 + drhs / 2);
+}
+
+template <typename Result>
+CUDF_HOST_DEVICE inline Result midpoint(int64_t lhs, int64_t rhs)
+{
+  // caring to avoid integer overflow and underflow between int64_t and Result( double )
+  int64_t half = lhs / 2 + rhs / 2;
+  int64_t rest = lhs % 2 + rhs % 2;
+  return static_cast<Result>(static_cast<Result>(half) + static_cast<Result>(rest) * 0.5);
+}
+
+template <>
+CUDF_HOST_DEVICE inline int64_t midpoint(int64_t lhs, int64_t rhs)
+{
+  // caring to avoid integer overflow
+  int64_t half   = lhs / 2 + rhs / 2;
+  int64_t rest   = lhs % 2 + rhs % 2;
+  int64_t result = half;
+
+  // rounding toward zero
+  result += (half >= 0 && rest != 0) ? rest / 2 : 0;
+  result += (half < 0 && rest != 0) ? 1 : 0;
+
+  return result;
+}
+
+}  // namespace interpolate
+
+struct quantile_index {
+  size_type lower;
+  size_type higher;
+  size_type nearest;
+  double fraction;
+
+  CUDF_HOST_DEVICE inline quantile_index(size_type count, double quantile)
+  {
+    quantile = std::min(std::max(quantile, 0.0), 1.0);
+
+    double val = quantile * (count - 1);
+    lower      = std::floor(val);
+    higher     = static_cast<size_type>(std::ceil(val));
+    nearest    = static_cast<size_type>(std::nearbyint(val));
+    fraction   = val - lower;
+  }
+};
+
+#ifdef __CUDACC__
+#pragma nv_exec_check_disable
+#endif
+/* @brief computes a quantile value.
+ *
+ * Computes a value for a quantile by interpolating between two values on either
+ * side of the desired quantile.
+ *
+ * `get_value` must have signature: `T <T>(size_type)` where T can be
+ * `static_cast` to `Result`.
+ *
+ * @param[in] get_value Gets the value at a given index in range [0, size].
+ * @param[in] size      Number of values indexed by `get_value`.
+ * @param[in] q         Desired quantile in range [0, 1].
+ * @param[in] interp    Strategy used to interpolate between the two values
+ *                      on either side of the desired quantile.
+ *
+ * @returns Value of the desired quantile.
+ */
+template <typename Result, typename ValueAccessor>
+CUDF_HOST_DEVICE inline Result select_quantile(ValueAccessor get_value,
+                                               size_type size,
+                                               double q,
+                                               interpolation interp)
+{
+  if (size < 2) { return get_value(0); }
+
+  quantile_index idx(size, q);
+
+  switch (interp) {
+    case interpolation::LINEAR:
+      return interpolate::linear<Result>(get_value(idx.lower), get_value(idx.higher), idx.fraction);
+
+    case interpolation::MIDPOINT:
+      return interpolate::midpoint<Result>(get_value(idx.lower), get_value(idx.higher));
+
+    case interpolation::LOWER: return static_cast<Result>(get_value(idx.lower));
+
+    case interpolation::HIGHER: return static_cast<Result>(get_value(idx.higher));
+
+    case interpolation::NEAREST: return static_cast<Result>(get_value(idx.nearest));
+
+    default: {
+#ifndef __CUDA_ARCH__
+      CUDF_FAIL("Invalid interpolation operation for quantiles.");
+#else
+      CUDF_UNREACHABLE("Invalid interpolation operation for quantiles");
+#endif
+    }
+  }
+}
+
+template <typename Result, typename Iterator>
+CUDF_HOST_DEVICE inline Result select_quantile_data(Iterator begin,
+                                                    size_type size,
+                                                    double q,
+                                                    interpolation interp)
+{
+  if (size == 0) return static_cast<Result>(*begin);
+
+  quantile_index idx(size, q);
+
+  switch (interp) {
+    case interpolation::LOWER: return static_cast<Result>(*(begin + idx.lower));
+
+    case interpolation::HIGHER: return static_cast<Result>(*(begin + idx.higher));
+
+    case interpolation::NEAREST: return static_cast<Result>(*(begin + idx.nearest));
+
+    case interpolation::LINEAR:
+      return interpolate::linear<Result>(*(begin + idx.lower), *(begin + idx.higher), idx.fraction);
+
+    case interpolation::MIDPOINT:
+      return interpolate::midpoint<Result>(*(begin + idx.lower), *(begin + idx.higher));
+    default: {
+#ifndef __CUDA_ARCH__
+      CUDF_FAIL("Invalid interpolation operation for quantiles.");
+#else
+      CUDF_UNREACHABLE("Invalid interpolation operation for quantiles");
+#endif
+    }
+  }
+}
+
+template <typename Iterator>
+CUDF_HOST_DEVICE inline bool select_quantile_validity(Iterator begin,
+                                                      size_type size,
+                                                      double q,
+                                                      interpolation interp)
+{
+  quantile_index idx(size, q);
+
+  switch (interp) {
+    case interpolation::HIGHER: return *(begin + idx.higher);
+
+    case interpolation::LOWER: return *(begin + idx.lower);
+
+    case interpolation::NEAREST: return *(begin + idx.nearest);
+
+    case interpolation::LINEAR:
+    case interpolation::MIDPOINT: return *(begin + idx.lower) and *(begin + idx.higher);
+    default: {
+#ifndef __CUDA_ARCH__
+      CUDF_FAIL("Invalid interpolation operation for quantiles.");
+#else
+      CUDF_UNREACHABLE("Invalid interpolation operation for quantiles");
+#endif
+    }
+  }
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/quantiles/tdigest/tdigest.cu b/cpp/src/quantiles/tdigest/tdigest.cu
new file mode 100644
index 0000000..79a25f7
--- /dev/null
+++ b/cpp/src/quantiles/tdigest/tdigest.cu
@@ -0,0 +1,413 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <quantiles/tdigest/tdigest_util.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+
+using namespace cudf::tdigest;
+
+namespace cudf {
+namespace tdigest {
+namespace detail {
+
+// https://developer.nvidia.com/blog/lerp-faster-cuda/
+template <typename T>
+__device__ inline T lerp(T v0, T v1, T t)
+{
+  return fma(t, v1, fma(-t, v0, v0));
+}
+
+struct centroid {
+  double mean;
+  double weight;
+};
+
+struct make_centroid {
+  double const* means;
+  double const* weights;
+  __device__ centroid operator()(size_type i) { return {means[i], weights[i]}; }
+};
+
+// kernel for computing percentiles on input tdigest (mean, weight) centroid data.
+template <typename CentroidIter>
+__global__ void compute_percentiles_kernel(device_span<size_type const> tdigest_offsets,
+                                           column_device_view percentiles,
+                                           CentroidIter centroids_,
+                                           double const* min_,
+                                           double const* max_,
+                                           double const* cumulative_weight_,
+                                           double* output)
+{
+  auto const tid = cudf::detail::grid_1d::global_thread_id();
+
+  auto const num_tdigests  = tdigest_offsets.size() - 1;
+  auto const tdigest_index = tid / percentiles.size();
+  if (tdigest_index >= num_tdigests) { return; }
+  auto const pindex = tid % percentiles.size();
+
+  // size of the digest we're querying
+  auto const tdigest_size = tdigest_offsets[tdigest_index + 1] - tdigest_offsets[tdigest_index];
+  // no work to do. values will be set to null
+  if (tdigest_size == 0 || !percentiles.is_valid(pindex)) { return; }
+
+  output[tid] = [&]() {
+    double const percentage         = percentiles.element<double>(pindex);
+    double const* cumulative_weight = cumulative_weight_ + tdigest_offsets[tdigest_index];
+
+    // centroids for this particular tdigest
+    CentroidIter centroids = centroids_ + tdigest_offsets[tdigest_index];
+
+    // min and max for the digest
+    double const* min_val = min_ + tdigest_index;
+    double const* max_val = max_ + tdigest_index;
+
+    double const total_weight = cumulative_weight[tdigest_size - 1];
+
+    // The following Arrow code serves as a basis for this computation
+    // https://github.com/apache/arrow/blob/master/cpp/src/arrow/util/tdigest.cc#L280
+    double const weighted_q = percentage * total_weight;
+    if (weighted_q <= 1) {
+      return *min_val;
+    } else if (weighted_q >= total_weight - 1) {
+      return *max_val;
+    }
+
+    // determine what centroid this weighted quantile falls within.
+    size_type const centroid_index = static_cast<size_type>(thrust::distance(
+      cumulative_weight,
+      thrust::lower_bound(
+        thrust::seq, cumulative_weight, cumulative_weight + tdigest_size, weighted_q)));
+    centroid c                     = centroids[centroid_index];
+
+    // diff == how far from the "center" of the centroid we are,
+    // in unit weights.
+    // visually:
+    //
+    // centroid of weight 7
+    //        C       <-- center of the centroid
+    //    |-------|
+    //      | |  |
+    //      X Y  Z
+    // X has a diff of -2 (2 units to the left of the center of the centroid)
+    // Y has a diff of 0 (directly in the middle of the centroid)
+    // Z has a diff of 3 (3 units to the right of the center of the centroid)
+    double const diff = weighted_q + c.weight / 2 - cumulative_weight[centroid_index];
+
+    // if we're completely within a centroid of weight 1, just return that.
+    if (c.weight == 1 && std::abs(diff) < 0.5) { return c.mean; }
+
+    // otherwise, interpolate between two centroids.
+
+    // get the two centroids we want to interpolate between
+    auto const look_left  = diff < 0;
+    auto const [lhs, rhs] = [&]() {
+      if (look_left) {
+        // if we're at the first centroid, "left" of us is the min value
+        auto const first_centroid = centroid_index == 0;
+        auto const lhs = first_centroid ? centroid{*min_val, 0} : centroids[centroid_index - 1];
+        auto const rhs = c;
+        return std::pair<centroid, centroid>{lhs, rhs};
+      } else {
+        // if we're at the last centroid, "right" of us is the max value
+        auto const last_centroid = (centroid_index == tdigest_size - 1);
+        auto const lhs           = c;
+        auto const rhs = last_centroid ? centroid{*max_val, 0} : centroids[centroid_index + 1];
+        return std::pair<centroid, centroid>{lhs, rhs};
+      }
+    }();
+
+    // compute interpolation value t
+
+    // total interpolation range. the total range of "space" between the lhs and rhs centroids.
+    auto const tip = lhs.weight / 2 + rhs.weight / 2;
+    // if we're looking left, diff is negative, so shift it so that we are interpolating
+    // from lhs -> rhs.
+    auto const t = (look_left) ? (diff + tip) / tip : diff / tip;
+
+    // interpolate
+    return lerp(lhs.mean, rhs.mean, t);
+  }();
+}
+
+/**
+ * @brief Calculate approximate percentiles on a provided tdigest column.
+ *
+ * Produces a LIST column where each row `i` represents output from querying the
+ * corresponding tdigest of from row `i` in `input`. The length of each output list
+ * is the number of percentiles specified in `percentiles`
+ *
+ * @param input           tdigest input data. One tdigest per row.
+ * @param percentiles     Desired percentiles in range [0, 1].
+ * @param stream          CUDA stream used for device memory operations and kernel launches
+ * @param mr              Device memory resource used to allocate the returned column's device
+ * memory
+ *
+ * @returns Column of doubles containing requested percentile values.
+ */
+std::unique_ptr<column> compute_approx_percentiles(tdigest_column_view const& input,
+                                                   column_view const& percentiles,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  tdigest_column_view tdv(input);
+
+  // offsets, representing the size of each tdigest
+  auto offsets = tdv.centroids().offsets();
+
+  // compute summed weights
+  auto weight             = tdv.weights();
+  auto cumulative_weights = cudf::make_fixed_width_column(data_type{type_id::FLOAT64},
+                                                          weight.size(),
+                                                          mask_state::UNALLOCATED,
+                                                          stream,
+                                                          rmm::mr::get_current_device_resource());
+  auto keys               = cudf::detail::make_counting_transform_iterator(
+    0,
+    [offsets_begin = offsets.begin<size_type>(),
+     offsets_end   = offsets.end<size_type>()] __device__(size_type i) {
+      return thrust::distance(
+        offsets_begin,
+        thrust::prev(thrust::upper_bound(thrust::seq, offsets_begin, offsets_end, i)));
+    });
+  thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                keys,
+                                keys + weight.size(),
+                                weight.begin<double>(),
+                                cumulative_weights->mutable_view().begin<double>());
+
+  auto percentiles_cdv = column_device_view::create(percentiles, stream);
+
+  // leaf is a column of size input.size() * percentiles.size()
+  auto const num_output_values = input.size() * percentiles.size();
+
+  // null percentiles become null results.
+  auto [null_mask, null_count] = [&]() {
+    return percentiles.null_count() != 0
+             ? cudf::detail::valid_if(
+                 thrust::make_counting_iterator<size_type>(0),
+                 thrust::make_counting_iterator<size_type>(0) + num_output_values,
+                 [percentiles = *percentiles_cdv] __device__(size_type i) {
+                   return percentiles.is_valid(i % percentiles.size());
+                 },
+                 stream,
+                 mr)
+             : std::pair<rmm::device_buffer, size_type>{rmm::device_buffer{}, 0};
+  }();
+
+  auto result = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, num_output_values, std::move(null_mask), null_count, stream, mr);
+
+  auto centroids = cudf::detail::make_counting_transform_iterator(
+    0, make_centroid{tdv.means().begin<double>(), tdv.weights().begin<double>()});
+
+  constexpr size_type block_size = 256;
+  cudf::detail::grid_1d const grid(percentiles.size() * input.size(), block_size);
+  compute_percentiles_kernel<<<grid.num_blocks, block_size, 0, stream.value()>>>(
+    {offsets.begin<size_type>(), static_cast<size_t>(offsets.size())},
+    *percentiles_cdv,
+    centroids,
+    tdv.min_begin(),
+    tdv.max_begin(),
+    cumulative_weights->view().begin<double>(),
+    result->mutable_view().begin<double>());
+
+  return result;
+}
+
+std::unique_ptr<column> make_tdigest_column(size_type num_rows,
+                                            std::unique_ptr<column>&& centroid_means,
+                                            std::unique_ptr<column>&& centroid_weights,
+                                            std::unique_ptr<column>&& tdigest_offsets,
+                                            std::unique_ptr<column>&& min_values,
+                                            std::unique_ptr<column>&& max_values,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(tdigest_offsets->size() == num_rows + 1,
+               "Encountered unexpected offset count in make_tdigest_column");
+  CUDF_EXPECTS(centroid_means->size() == centroid_weights->size(),
+               "Encountered unexpected centroid size mismatch in make_tdigest_column");
+  CUDF_EXPECTS(min_values->size() == num_rows,
+               "Encountered unexpected min value count in make_tdigest_column");
+  CUDF_EXPECTS(max_values->size() == num_rows,
+               "Encountered unexpected max value count in make_tdigest_column");
+
+  // inner struct column
+  auto const centroids_size = centroid_means->size();
+  std::vector<std::unique_ptr<column>> inner_children;
+  inner_children.push_back(std::move(centroid_means));
+  inner_children.push_back(std::move(centroid_weights));
+  auto tdigest_data =
+    cudf::make_structs_column(centroids_size, std::move(inner_children), 0, {}, stream, mr);
+
+  // grouped into lists
+  auto tdigest = cudf::make_lists_column(
+    num_rows, std::move(tdigest_offsets), std::move(tdigest_data), 0, {}, stream, mr);
+
+  // create the final column
+  std::vector<std::unique_ptr<column>> children;
+  children.push_back(std::move(tdigest));
+  children.push_back(std::move(min_values));
+  children.push_back(std::move(max_values));
+  return make_structs_column(num_rows, std::move(children), 0, {}, stream, mr);
+}
+
+std::unique_ptr<column> make_empty_tdigest_column(rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  auto offsets = cudf::make_fixed_width_column(
+    data_type(type_id::INT32), 2, mask_state::UNALLOCATED, stream, mr);
+  thrust::fill(rmm::exec_policy(stream),
+               offsets->mutable_view().begin<size_type>(),
+               offsets->mutable_view().end<size_type>(),
+               0);
+
+  auto min_col =
+    cudf::make_numeric_column(data_type(type_id::FLOAT64), 1, mask_state::UNALLOCATED, stream, mr);
+  thrust::fill(rmm::exec_policy(stream),
+               min_col->mutable_view().begin<double>(),
+               min_col->mutable_view().end<double>(),
+               0);
+  auto max_col =
+    cudf::make_numeric_column(data_type(type_id::FLOAT64), 1, mask_state::UNALLOCATED, stream, mr);
+  thrust::fill(rmm::exec_policy(stream),
+               max_col->mutable_view().begin<double>(),
+               max_col->mutable_view().end<double>(),
+               0);
+
+  return make_tdigest_column(1,
+                             make_empty_column(type_id::FLOAT64),
+                             make_empty_column(type_id::FLOAT64),
+                             std::move(offsets),
+                             std::move(min_col),
+                             std::move(max_col),
+                             stream,
+                             mr);
+}
+
+/**
+ * @brief Create an empty tdigest scalar.
+ *
+ * An empty tdigest scalar is a struct_scalar that contains a single row of length 0
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ *
+ * @returns An empty tdigest scalar.
+ */
+std::unique_ptr<scalar> make_empty_tdigest_scalar(rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  auto contents = make_empty_tdigest_column(stream, mr)->release();
+  return std::make_unique<struct_scalar>(
+    std::move(*std::make_unique<table>(std::move(contents.children))), true, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> percentile_approx(tdigest_column_view const& input,
+                                          column_view const& percentiles,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  tdigest_column_view tdv(input);
+  CUDF_EXPECTS(percentiles.type().id() == type_id::FLOAT64,
+               "percentile_approx expects float64 percentile inputs");
+
+  // output is a list column with each row containing percentiles.size() percentile values
+  auto offsets = cudf::make_fixed_width_column(
+    data_type{type_id::INT32}, input.size() + 1, mask_state::UNALLOCATED, stream, mr);
+  auto const all_empty_rows =
+    thrust::count_if(rmm::exec_policy(stream),
+                     detail::size_begin(input),
+                     detail::size_begin(input) + input.size(),
+                     [] __device__(auto const x) { return x == 0; }) == input.size();
+  auto row_size_iter = thrust::make_constant_iterator(all_empty_rows ? 0 : percentiles.size());
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         row_size_iter,
+                         row_size_iter + input.size() + 1,
+                         offsets->mutable_view().begin<size_type>());
+
+  if (percentiles.size() == 0 || all_empty_rows) {
+    return cudf::make_lists_column(
+      input.size(),
+      std::move(offsets),
+      cudf::make_empty_column(type_id::FLOAT64),
+      input.size(),
+      cudf::detail::create_null_mask(
+        input.size(), mask_state::ALL_NULL, rmm::cuda_stream_view(stream), mr),
+      stream,
+      mr);
+  }
+
+  // if any of the input digests are empty, nullify the corresponding output rows (values will be
+  // uninitialized)
+  auto [bitmask, null_count] = [stream, mr, &tdv]() {
+    auto tdigest_is_empty = thrust::make_transform_iterator(
+      detail::size_begin(tdv),
+      [] __device__(size_type tdigest_size) -> size_type { return tdigest_size == 0; });
+    auto const null_count =
+      thrust::reduce(rmm::exec_policy(stream), tdigest_is_empty, tdigest_is_empty + tdv.size(), 0);
+    if (null_count == 0) {
+      return std::pair<rmm::device_buffer, size_type>{rmm::device_buffer{}, null_count};
+    }
+    return cudf::detail::valid_if(
+      tdigest_is_empty, tdigest_is_empty + tdv.size(), thrust::logical_not{}, stream, mr);
+  }();
+
+  return cudf::make_lists_column(input.size(),
+                                 std::move(offsets),
+                                 detail::compute_approx_percentiles(input, percentiles, stream, mr),
+                                 null_count,
+                                 std::move(bitmask),
+                                 stream,
+                                 mr);
+}
+
+}  // namespace tdigest
+
+std::unique_ptr<column> percentile_approx(tdigest_column_view const& input,
+                                          column_view const& percentiles,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return tdigest::percentile_approx(input, percentiles, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/quantiles/tdigest/tdigest_aggregation.cu b/cpp/src/quantiles/tdigest/tdigest_aggregation.cu
new file mode 100644
index 0000000..9e8b75a
--- /dev/null
+++ b/cpp/src/quantiles/tdigest/tdigest_aggregation.cu
@@ -0,0 +1,1294 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <quantiles/tdigest/tdigest_util.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/merge.cuh>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/merge.h>
+#include <thrust/pair.h>
+#include <thrust/reduce.h>
+#include <thrust/remove.h>
+#include <thrust/replace.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace tdigest {
+namespace detail {
+
+namespace {
+
+// the most representative point within a cluster of similar
+// values. {mean, weight}
+// NOTE: Using a tuple here instead of a struct to take advantage of
+// thrust zip iterators for output.
+using centroid = thrust::tuple<double, double, bool>;
+
+// make a centroid from a scalar with a weight of 1.
+template <typename T>
+struct make_centroid {
+  column_device_view const col;
+
+  centroid operator() __device__(size_type index) const
+  {
+    auto const is_valid = col.is_valid(index);
+    auto const mean     = is_valid ? static_cast<double>(col.element<T>(index)) : 0.0;
+    auto const weight   = is_valid ? 1.0 : 0.0;
+    return {mean, weight, is_valid};
+  }
+};
+
+// make a centroid from a scalar with a weight of 1. this functor
+// assumes any value index it is passed is not null
+template <typename T>
+struct make_centroid_no_nulls {
+  column_device_view const col;
+
+  centroid operator() __device__(size_type index) const
+  {
+    return {static_cast<double>(col.element<T>(index)), 1.0, true};
+  }
+};
+
+// make a centroid from an input stream of mean/weight values.
+struct make_weighted_centroid {
+  double const* mean;
+  double const* weight;
+
+  centroid operator() __device__(size_type index) { return {mean[index], weight[index], true}; }
+};
+
+// merge two centroids
+struct merge_centroids {
+  centroid operator() __device__(centroid const& lhs, centroid const& rhs) const
+  {
+    bool const lhs_valid = thrust::get<2>(lhs);
+    bool const rhs_valid = thrust::get<2>(rhs);
+    if (!lhs_valid && !rhs_valid) { return {0, 0, false}; }
+    if (!lhs_valid) { return rhs; }
+    if (!rhs_valid) { return lhs; }
+
+    double const lhs_mean   = thrust::get<0>(lhs);
+    double const rhs_mean   = thrust::get<0>(rhs);
+    double const lhs_weight = thrust::get<1>(lhs);
+    double const rhs_weight = thrust::get<1>(rhs);
+    double const new_weight = lhs_weight + rhs_weight;
+    return {(lhs_mean * lhs_weight + rhs_mean * rhs_weight) / new_weight, new_weight, true};
+  }
+};
+
+/**
+ * @brief A functor which returns the nearest cumulative weight in the grouped input stream prior to
+ * the specified next weight limit.
+ *
+ * This functor assumes the weight for all scalars is simply 1. Under this assumption,
+ * the nearest weight that will be <= the next limit is simply the nearest integer < the limit,
+ * which we can get by just taking floor(next_limit).  For example if our next limit is 3.56, the
+ * nearest whole number <= it is floor(3.56) == 3.
+ */
+struct nearest_value_scalar_weights_grouped {
+  size_type const* group_offsets;
+
+  thrust::pair<double, int> operator() __device__(double next_limit, size_type group_index) const
+  {
+    double const f                   = floor(next_limit);
+    auto const relative_weight_index = max(0, static_cast<int>(next_limit) - 1);
+    auto const group_size            = group_offsets[group_index + 1] - group_offsets[group_index];
+    return {f, relative_weight_index < group_size ? relative_weight_index : group_size - 1};
+  }
+};
+
+/**
+ * @brief A functor which returns the nearest cumulative weight in the input stream prior to the
+ * specified next weight limit.
+ *
+ * This functor assumes the weight for all scalars is simply 1. Under this assumption,
+ * the nearest weight that will be <= the next limit is simply the nearest integer < the limit,
+ * which we can get by just taking floor(next_limit).  For example if our next limit is 3.56, the
+ * nearest whole number <= it is floor(3.56) == 3.
+ */
+struct nearest_value_scalar_weights {
+  size_type const input_size;
+
+  thrust::pair<double, int> operator() __device__(double next_limit, size_type) const
+  {
+    double const f                   = floor(next_limit);
+    auto const relative_weight_index = max(0, static_cast<int>(next_limit) - 1);
+    return {f, relative_weight_index < input_size ? relative_weight_index : input_size - 1};
+  }
+};
+
+/**
+ * @brief A functor which returns the nearest cumulative weight in the input stream prior to the
+ * specified next weight limit.
+ *
+ * This functor assumes we are dealing with grouped, sorted, weighted centroids.
+ */
+template <typename GroupOffsetsIter>
+struct nearest_value_centroid_weights {
+  double const* cumulative_weights;
+  GroupOffsetsIter outer_offsets;  // groups
+  size_type const* inner_offsets;  // tdigests within a group
+
+  thrust::pair<double, int> operator() __device__(double next_limit, size_type group_index) const
+  {
+    auto const tdigest_begin = outer_offsets[group_index];
+    auto const tdigest_end   = outer_offsets[group_index + 1];
+    auto const num_weights   = inner_offsets[tdigest_end] - inner_offsets[tdigest_begin];
+    // NOTE: as it is today, this functor will never be called for any digests that are empty, but
+    // I'll leave this check here for safety.
+    if (num_weights == 0) { return thrust::pair<double, int>{0, 0}; }
+    double const* group_cumulative_weights = cumulative_weights + inner_offsets[tdigest_begin];
+
+    auto const index = ((thrust::lower_bound(thrust::seq,
+                                             group_cumulative_weights,
+                                             group_cumulative_weights + num_weights,
+                                             next_limit)) -
+                        group_cumulative_weights);
+
+    return index == 0 ? thrust::pair<double, int>{0, 0}
+                      : thrust::pair<double, int>{group_cumulative_weights[index - 1],
+                                                  static_cast<int>(index) - 1};
+  }
+};
+
+/**
+ * @brief A functor which returns the cumulative input weight for a given index in a
+ * set of grouped input values.
+ *
+ * This functor assumes the weight for all scalars is simply 1. Under this assumption,
+ * the cumulative weight for a given value index I is simply I+1.
+ */
+struct cumulative_scalar_weight_grouped {
+  cudf::device_span<size_type const> group_offsets;
+  cudf::device_span<size_type const> group_labels;
+  std::tuple<size_type, size_type, double> operator() __device__(size_type value_index) const
+  {
+    auto const group_index          = group_labels[value_index];
+    auto const relative_value_index = value_index - group_offsets[group_index];
+    return {group_index, relative_value_index, relative_value_index + 1};
+  }
+};
+
+/**
+ * @brief A functor which returns the cumulative input weight for a given index in a
+ * set of input values.
+ *
+ * This functor assumes the weight for all scalars is simply 1. Under this assumption,
+ * the cumulative weight for a given value index I is simply I+1.
+ */
+struct cumulative_scalar_weight {
+  std::tuple<size_type, size_type, double> operator() __device__(size_type value_index) const
+  {
+    return {0, value_index, value_index + 1};
+  }
+};
+
+/**
+ * @brief A functor which returns the cumulative input weight for a given index in a
+ * set of grouped input centroids.
+ *
+ * This functor assumes we are dealing with grouped, weighted centroids.
+ */
+template <typename GroupLabelsIter, typename GroupOffsetsIter>
+struct cumulative_centroid_weight {
+  double const* cumulative_weights;
+  GroupLabelsIter group_labels;
+  GroupOffsetsIter outer_offsets;                    // groups
+  cudf::device_span<size_type const> inner_offsets;  // tdigests with a group
+
+  std::tuple<size_type, size_type, double> operator() __device__(size_type value_index) const
+  {
+    auto const tdigest_index =
+      static_cast<size_type>(
+        thrust::upper_bound(thrust::seq, inner_offsets.begin(), inner_offsets.end(), value_index) -
+        inner_offsets.begin()) -
+      1;
+    auto const group_index                 = group_labels[tdigest_index];
+    auto const first_tdigest_index         = outer_offsets[group_index];
+    auto const first_weight_index          = inner_offsets[first_tdigest_index];
+    auto const relative_value_index        = value_index - first_weight_index;
+    double const* group_cumulative_weights = cumulative_weights + first_weight_index;
+
+    return {group_index, relative_value_index, group_cumulative_weights[relative_value_index]};
+  }
+};
+
+// retrieve group info (total weight, size, start offset) of scalar inputs by group index.
+struct scalar_group_info_grouped {
+  size_type const* group_valid_counts;
+  size_type const* group_offsets;
+
+  __device__ thrust::tuple<double, size_type, size_type> operator()(size_type group_index) const
+  {
+    return {static_cast<double>(group_valid_counts[group_index]),
+            group_offsets[group_index + 1] - group_offsets[group_index],
+            group_offsets[group_index]};
+  }
+};
+
+// retrieve group info (total weight, size, start offset) of scalar inputs
+struct scalar_group_info {
+  double const total_weight;
+  size_type const size;
+
+  __device__ thrust::tuple<double, size_type, size_type> operator()(size_type) const
+  {
+    return {total_weight, size, 0};
+  }
+};
+
+// retrieve group info of centroid inputs by group index
+template <typename GroupOffsetsIter>
+struct centroid_group_info {
+  double const* cumulative_weights;
+  GroupOffsetsIter outer_offsets;
+  size_type const* inner_offsets;
+
+  __device__ thrust::tuple<double, size_type, size_type> operator()(size_type group_index) const
+  {
+    // if there's no weights in this group of digests at all, return 0.
+    auto const group_start       = inner_offsets[outer_offsets[group_index]];
+    auto const group_end         = inner_offsets[outer_offsets[group_index + 1]];
+    auto const num_weights       = group_end - group_start;
+    auto const last_weight_index = group_end - 1;
+    return num_weights == 0
+             ? thrust::tuple<double, size_type, size_type>{0, num_weights, group_start}
+             : thrust::tuple<double, size_type, size_type>{
+                 cumulative_weights[last_weight_index], num_weights, group_start};
+  }
+};
+
+struct tdigest_min {
+  __device__ double operator()(thrust::tuple<double, size_type> const& t) const
+  {
+    auto const min  = thrust::get<0>(t);
+    auto const size = thrust::get<1>(t);
+    return size > 0 ? min : std::numeric_limits<double>::max();
+  }
+};
+
+struct tdigest_max {
+  __device__ double operator()(thrust::tuple<double, size_type> const& t) const
+  {
+    auto const max  = thrust::get<0>(t);
+    auto const size = thrust::get<1>(t);
+    return size > 0 ? max : std::numeric_limits<double>::lowest();
+  }
+};
+
+// a monotonically increasing scale function which produces a distribution
+// of centroids that is more densely packed in the middle of the input
+// than at the ends.
+__device__ double scale_func_k1(double quantile, double delta_norm)
+{
+  double k = delta_norm * asin(2.0 * quantile - 1.0);
+  k += 1.0;
+  double const q = (sin(k / delta_norm) + 1.0) / 2.0;
+  return q;
+}
+
+// convert a single-row tdigest column to a scalar.
+std::unique_ptr<scalar> to_tdigest_scalar(std::unique_ptr<column>&& tdigest,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(tdigest->size() == 1,
+               "Encountered invalid tdigest column when converting to scalar");
+  auto contents = tdigest->release();
+  return std::make_unique<struct_scalar>(table(std::move(contents.children)), true, stream, mr);
+}
+
+/**
+ * @brief Compute a set of cluster limits (brackets, essentially) for a
+ * given tdigest based on the specified delta and the total weight of values
+ * to be added.
+ *
+ * The number of clusters generated will always be <= delta_, where delta_ is
+ * a reasonably small number likely << 10000.
+ *
+ * Each input group gets an independent set of clusters generated. 1 thread
+ * per group.
+ *
+ * This kernel is called in a two-pass style.  Once to compute the per-group
+ * cluster sizes and total # of clusters, and once to compute the actual
+ * weight limits per cluster.
+ *
+ * @param delta               tdigest compression level
+ * @param num_groups          The number of input groups
+ * @param nearest_weight      A functor which returns the nearest weight in the input
+ * stream that falls before our current cluster limit
+ * @param group_info          A functor which returns the info for the specified group (total
+ * weight, size and start offset)
+ * @param group_cluster_wl    Output.  The set of cluster weight limits for each group.
+ * @param group_num_clusters  Output.  The number of output clusters for each input group.
+ * @param group_cluster_offsets  Offsets per-group to the start of it's clusters
+ * @param has_nulls Whether or not the input contains nulls
+ *
+ */
+
+template <typename GroupInfo, typename NearestWeightFunc, typename CumulativeWeight>
+__global__ void generate_cluster_limits_kernel(int delta,
+                                               size_type num_groups,
+                                               NearestWeightFunc nearest_weight,
+                                               GroupInfo group_info,
+                                               CumulativeWeight cumulative_weight,
+                                               double* group_cluster_wl,
+                                               size_type* group_num_clusters,
+                                               size_type const* group_cluster_offsets,
+                                               bool has_nulls)
+{
+  int const tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+  auto const group_index = tid;
+  if (group_index >= num_groups) { return; }
+
+  // we will generate at most delta clusters.
+  double const delta_norm = static_cast<double>(delta) / (2.0 * M_PI);
+  double total_weight;
+  size_type group_size, group_start;
+  thrust::tie(total_weight, group_size, group_start) = group_info(group_index);
+
+  // start at the correct place based on our cluster offset.
+  double* cluster_wl =
+    group_cluster_wl ? group_cluster_wl + group_cluster_offsets[group_index] : nullptr;
+
+  // a group with nothing in it.
+  group_num_clusters[group_index] = 0;
+  if (total_weight <= 0) {
+    // if the input contains nulls we can potentially have a group that generates no
+    // clusters because -all- of the input values are null.  in that case, the reduce_by_key call
+    // in the tdigest generation step will need a location to store the unused reduction value for
+    // that group of nulls. these "stubs" will be postprocessed out afterwards.
+    if (has_nulls) { group_num_clusters[group_index] = 1; }
+    return;
+  }
+
+  double cur_limit        = 0.0;
+  double cur_weight       = 0.0;
+  double next_limit       = -1.0;
+  int last_inserted_index = -1;  // group-relative index into the input stream
+
+  // compute the first cluster limit
+  double nearest_w;
+  int nearest_w_index;  // group-relative index into the input stream
+  while (true) {
+    cur_weight = next_limit < 0 ? 0 : max(cur_weight + 1, nearest_w);
+    if (cur_weight >= total_weight) { break; }
+
+    // based on where we are closing the cluster off (not including the incoming weight),
+    // compute the next cluster limit
+    double const quantile = cur_weight / total_weight;
+    next_limit            = total_weight * scale_func_k1(quantile, delta_norm);
+
+    // if the next limit is < the cur limit, we're past the end of the distribution, so we're done.
+    if (next_limit <= cur_limit) {
+      if (cluster_wl) { cluster_wl[group_num_clusters[group_index]] = total_weight; }
+      group_num_clusters[group_index]++;
+      break;
+    }
+
+    // compute the weight we will be at in the input values just before closing off the current
+    // cluster (because adding the next value will cross the current limit).
+    // NOTE: can't use structured bindings here.
+    thrust::tie(nearest_w, nearest_w_index) = nearest_weight(next_limit, group_index);
+
+    // because of the way the scale functions work, it is possible to generate clusters
+    // in such a way that we end up with "gaps" where there are no input values that
+    // fall into a given cluster.  An example would be this:
+    //
+    // cluster weight limits = 0.00003, 1.008, 3.008
+    //
+    // input values(weight) = A(1), B(2), C(3)
+    //
+    // naively inserting these values into the clusters simply by taking a lower_bound,
+    // we would get the following distribution of input values into those 3 clusters.
+    //  (), (A), (B,C)
+    //
+    // whereas what we really want is:
+    //
+    //  (A), (B), (C)
+    //
+    // to fix this, we will artificially adjust the output cluster limits to guarantee
+    // at least 1 input value will be put in each cluster during the reduction step.
+    // this does not affect final centroid results as we still use the "real" weight limits
+    // to compute subsequent clusters - the purpose is only to allow cluster selection
+    // during the reduction step to be trivial.
+    //
+    double adjusted_next_limit = next_limit;
+    int adjusted_w_index       = nearest_w_index;
+    if ((last_inserted_index < 0) ||  // if we haven't inserted anything yet
+        (nearest_w_index ==
+         last_inserted_index)) {  // if we land in the same bucket as the previous cap
+
+      // force the value into this bucket
+      adjusted_w_index = (last_inserted_index == group_size - 1)
+                           ? last_inserted_index
+                           : max(adjusted_w_index, last_inserted_index + 1);
+
+      // the "adjusted" cluster limit must be high enough so that this value will fall in the
+      // bucket. NOTE: cumulative_weight expects an absolute index into the input value stream, not
+      // a group-relative index
+      [[maybe_unused]] auto [r, i, adjusted_w] = cumulative_weight(adjusted_w_index + group_start);
+      adjusted_next_limit                      = max(next_limit, adjusted_w);
+
+      // update the weight with our adjusted value.
+      nearest_w = adjusted_w;
+    }
+    if (cluster_wl) { cluster_wl[group_num_clusters[group_index]] = adjusted_next_limit; }
+    last_inserted_index = adjusted_w_index;
+
+    group_num_clusters[group_index]++;
+    cur_limit = next_limit;
+  }
+}
+
+/**
+ * @brief Compute a set of cluster limits (brackets, essentially) for a
+ * given tdigest based on the specified delta and the total weight of values
+ * to be added.
+ *
+ * The number of clusters generated will always be <= delta_, where delta_ is
+ * a reasonably small number likely << 10000.
+ *
+ * Each input group gets an independent set of clusters generated.
+ *
+ * @param delta_             tdigest compression level
+ * @param num_groups         The number of input groups
+ * @param nearest_weight     A functor which returns the nearest weight in the input
+ * stream that falls before our current cluster limit
+ * @param group_info         A functor which returns the info for the specified group (total weight,
+ * size and start offset)
+ * @param has_nulls          Whether or not the input data contains nulls
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A tuple containing the set of cluster weight limits for each group, a set of
+ * list-style offsets indicating group sizes, and the total number of clusters
+ */
+template <typename GroupInfo, typename NearestWeight, typename CumulativeWeight>
+std::tuple<rmm::device_uvector<double>, std::unique_ptr<column>, size_type>
+generate_group_cluster_info(int delta,
+                            size_type num_groups,
+                            NearestWeight nearest_weight,
+                            GroupInfo group_info,
+                            CumulativeWeight cumulative_weight,
+                            bool has_nulls,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  constexpr size_type block_size = 256;
+  cudf::detail::grid_1d const grid(num_groups, block_size);
+
+  // compute number of clusters per group
+  // each thread computes 1 set of clusters (# of cluster sets == # of groups)
+  rmm::device_uvector<size_type> group_num_clusters(num_groups, stream);
+  generate_cluster_limits_kernel<<<grid.num_blocks, block_size, 0, stream.value()>>>(
+    delta,
+    num_groups,
+    nearest_weight,
+    group_info,
+    cumulative_weight,
+    nullptr,
+    group_num_clusters.begin(),
+    nullptr,
+    has_nulls);
+
+  // generate group cluster offsets (where the clusters for a given group start and end)
+  auto group_cluster_offsets = cudf::make_numeric_column(
+    data_type{type_id::INT32}, num_groups + 1, mask_state::UNALLOCATED, stream, mr);
+  auto cluster_size = cudf::detail::make_counting_transform_iterator(
+    0, [group_num_clusters = group_num_clusters.begin(), num_groups] __device__(size_type index) {
+      return index == num_groups ? 0 : group_num_clusters[index];
+    });
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         cluster_size,
+                         cluster_size + num_groups + 1,
+                         group_cluster_offsets->mutable_view().begin<size_type>(),
+                         0);
+
+  // total # of clusters
+  size_type total_clusters =
+    cudf::detail::get_value<size_type>(group_cluster_offsets->view(), num_groups, stream);
+
+  // fill in the actual cluster weight limits
+  rmm::device_uvector<double> group_cluster_wl(total_clusters, stream);
+  generate_cluster_limits_kernel<<<grid.num_blocks, block_size, 0, stream.value()>>>(
+    delta,
+    num_groups,
+    nearest_weight,
+    group_info,
+    cumulative_weight,
+    group_cluster_wl.begin(),
+    group_num_clusters.begin(),
+    group_cluster_offsets->view().begin<size_type>(),
+    has_nulls);
+
+  return {std::move(group_cluster_wl),
+          std::move(group_cluster_offsets),
+          static_cast<size_type>(total_clusters)};
+}
+
+std::unique_ptr<column> build_output_column(size_type num_rows,
+                                            std::unique_ptr<column>&& means,
+                                            std::unique_ptr<column>&& weights,
+                                            std::unique_ptr<column>&& offsets,
+                                            std::unique_ptr<column>&& min_col,
+                                            std::unique_ptr<column>&& max_col,
+                                            bool has_nulls,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  // whether or not this weight is a stub
+  auto is_stub_weight = [weights = weights->view().begin<double>()] __device__(size_type i) {
+    return weights[i] == 0;
+  };
+  // whether or not this particular tdigest is a stub
+  auto is_stub_digest = [offsets = offsets->view().begin<size_type>(), is_stub_weight] __device__(
+                          size_type i) { return is_stub_weight(offsets[i]) ? 1 : 0; };
+
+  size_type const num_stubs = [&]() {
+    if (!has_nulls) { return 0; }
+    auto iter = cudf::detail::make_counting_transform_iterator(0, is_stub_digest);
+    return thrust::reduce(rmm::exec_policy(stream), iter, iter + num_rows);
+  }();
+
+  // if there are no stub tdigests, we can return immediately.
+  if (num_stubs == 0) {
+    return cudf::tdigest::detail::make_tdigest_column(num_rows,
+                                                      std::move(means),
+                                                      std::move(weights),
+                                                      std::move(offsets),
+                                                      std::move(min_col),
+                                                      std::move(max_col),
+                                                      stream,
+                                                      mr);
+  }
+
+  // otherwise we need to strip out the stubs.
+  auto remove_stubs = [&](column_view const& col, size_type num_stubs) {
+    auto result = cudf::make_numeric_column(
+      data_type{type_id::FLOAT64}, col.size() - num_stubs, mask_state::UNALLOCATED, stream, mr);
+    thrust::remove_copy_if(rmm::exec_policy(stream),
+                           col.begin<double>(),
+                           col.end<double>(),
+                           thrust::make_counting_iterator(0),
+                           result->mutable_view().begin<double>(),
+                           is_stub_weight);
+    return result;
+  };
+  // remove from the means and weights column
+  auto _means   = remove_stubs(*means, num_stubs);
+  auto _weights = remove_stubs(*weights, num_stubs);
+
+  // adjust offsets.
+  rmm::device_uvector<size_type> sizes(num_rows, stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(0) + num_rows,
+                    sizes.begin(),
+                    [offsets = offsets->view().begin<size_type>()] __device__(size_type i) {
+                      return offsets[i + 1] - offsets[i];
+                    });
+  auto iter = cudf::detail::make_counting_transform_iterator(
+    0, [sizes = sizes.begin(), is_stub_digest, num_rows] __device__(size_type i) {
+      return i == num_rows || is_stub_digest(i) ? 0 : sizes[i];
+    });
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         iter,
+                         iter + num_rows + 1,
+                         offsets->mutable_view().begin<size_type>(),
+                         0);
+
+  // assemble final column
+  return cudf::tdigest::detail::make_tdigest_column(num_rows,
+                                                    std::move(_means),
+                                                    std::move(_weights),
+                                                    std::move(offsets),
+                                                    std::move(min_col),
+                                                    std::move(max_col),
+                                                    stream,
+                                                    mr);
+}
+
+/**
+ * @brief Compute a column of tdigests.
+ *
+ * Assembles the output tdigest column based on the specified delta, a stream of
+ * input values (either scalar or centroids), and an assortment of per-group
+ * clustering information.
+ *
+ * This function is effectively just a reduce_by_key that performs a reduction
+ * from input values -> centroid clusters as defined by the cluster weight
+ * boundaries.
+ *
+ * @param delta              tdigest compression level
+ * @param values_begin       Beginning of the range of input values.
+ * @param values_end         End of the range of input values.
+ * @param cumulative_weight  Functor which returns cumulative weight and group information for
+ * an absolute input value index.
+ * @param min_col            Column containing the minimum value per group.
+ * @param max_col            Column containing the maximum value per group.
+ * @param group_cluster_wl   Cluster weight limits for each group.
+ * @param group_cluster_offsets R-value reference of offsets into the cluster weight limits.
+ * @param total_clusters     Total number of clusters in all groups.
+ * @param has_nulls          Whether or not the input contains nulls
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ *
+ * @returns A tdigest column with 1 row per output tdigest.
+ */
+template <typename CentroidIter, typename CumulativeWeight>
+std::unique_ptr<column> compute_tdigests(int delta,
+                                         CentroidIter centroids_begin,
+                                         CentroidIter centroids_end,
+                                         CumulativeWeight group_cumulative_weight,
+                                         std::unique_ptr<column>&& min_col,
+                                         std::unique_ptr<column>&& max_col,
+                                         rmm::device_uvector<double> const& group_cluster_wl,
+                                         std::unique_ptr<column>&& group_cluster_offsets,
+                                         size_type total_clusters,
+                                         bool has_nulls,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  // the output for each group is a column of data that represents the tdigest. since we want 1 row
+  // per group, each row will be a list the length of the tdigest for that group. so our output
+  // column is of the form:
+  // struct {
+  //   centroids for the digest
+  //   list {
+  //     struct {
+  //       double    // mean
+  //       double    // weight
+  //     }
+  //   }
+  //   double       // min
+  //   double       // max
+  // }
+  //
+  if (total_clusters == 0) { return cudf::tdigest::detail::make_empty_tdigest_column(stream, mr); }
+
+  // each input group represents an individual tdigest.  within each tdigest, we want the keys
+  // to represent cluster indices (for example, if a tdigest had 100 clusters, the keys should fall
+  // into the range 0-99).  But since we have multiple tdigests, we need to keep the keys unique
+  // between the groups, so we add our group start offset.
+  auto keys = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0),
+    [delta,
+     group_cluster_wl      = group_cluster_wl.data(),
+     group_cluster_offsets = group_cluster_offsets->view().begin<size_type>(),
+     group_cumulative_weight] __device__(size_type value_index) -> size_type {
+      // get group index, relative value index within the group and cumulative weight.
+      [[maybe_unused]] auto [group_index, relative_value_index, cumulative_weight] =
+        group_cumulative_weight(value_index);
+
+      auto const num_clusters =
+        group_cluster_offsets[group_index + 1] - group_cluster_offsets[group_index];
+      if (num_clusters == 0) { return group_cluster_offsets[group_index]; }
+
+      // compute start of cluster weight limits for this group
+      double const* weight_limits = group_cluster_wl + group_cluster_offsets[group_index];
+
+      // local cluster index
+      size_type const group_cluster_index =
+        min(num_clusters - 1,
+            static_cast<size_type>(
+              thrust::lower_bound(
+                thrust::seq, weight_limits, weight_limits + num_clusters, cumulative_weight) -
+              weight_limits));
+
+      // add the cluster offset to generate a globally unique key
+      return group_cluster_index + group_cluster_offsets[group_index];
+    });
+
+  // mean and weight data
+  auto centroid_means = cudf::make_numeric_column(
+    data_type{type_id::FLOAT64}, total_clusters, mask_state::UNALLOCATED, stream, mr);
+  auto centroid_weights = cudf::make_numeric_column(
+    data_type{type_id::FLOAT64}, total_clusters, mask_state::UNALLOCATED, stream, mr);
+  // reduce the centroids down by key.
+  cudf::mutable_column_view mean_col(*centroid_means);
+  cudf::mutable_column_view weight_col(*centroid_weights);
+
+  // reduce the centroids into the clusters
+  auto output = thrust::make_zip_iterator(thrust::make_tuple(
+    mean_col.begin<double>(), weight_col.begin<double>(), thrust::make_discard_iterator()));
+
+  auto const num_values = std::distance(centroids_begin, centroids_end);
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        keys,
+                        keys + num_values,                // keys
+                        centroids_begin,                  // values
+                        thrust::make_discard_iterator(),  // key output
+                        output,                           // output
+                        thrust::equal_to{},               // key equality check
+                        merge_centroids{});
+
+  // create final tdigest column
+  return build_output_column(group_cluster_offsets->size() - 1,
+                             std::move(centroid_means),
+                             std::move(centroid_weights),
+                             std::move(group_cluster_offsets),
+                             std::move(min_col),
+                             std::move(max_col),
+                             has_nulls,
+                             stream,
+                             mr);
+}
+
+// return the min/max value of scalar inputs by group index
+template <typename T>
+struct get_scalar_minmax_grouped {
+  column_device_view const col;
+  device_span<size_type const> group_offsets;
+  size_type const* group_valid_counts;
+
+  __device__ thrust::tuple<double, double> operator()(size_type group_index)
+  {
+    auto const valid_count = group_valid_counts[group_index];
+    return valid_count > 0
+             ? thrust::make_tuple(
+                 static_cast<double>(col.element<T>(group_offsets[group_index])),
+                 static_cast<double>(col.element<T>(group_offsets[group_index] + valid_count - 1)))
+             : thrust::make_tuple(0.0, 0.0);
+  }
+};
+
+// return the min/max value of scalar inputs
+template <typename T>
+struct get_scalar_minmax {
+  column_device_view const col;
+  size_type const valid_count;
+
+  __device__ thrust::tuple<double, double> operator()(size_type)
+  {
+    return valid_count > 0
+             ? thrust::make_tuple(static_cast<double>(col.element<T>(0)),
+                                  static_cast<double>(col.element<T>(valid_count - 1)))
+             : thrust::make_tuple(0.0, 0.0);
+  }
+};
+
+struct typed_group_tdigest {
+  template <typename T,
+            std::enable_if_t<cudf::is_numeric<T>() || cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     cudf::device_span<size_type const> group_offsets,
+                                     cudf::device_span<size_type const> group_labels,
+                                     cudf::device_span<size_type const> group_valid_counts,
+                                     size_type num_groups,
+                                     int delta,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    // first, generate cluster weight information for each input group
+    auto [group_cluster_wl, group_cluster_offsets, total_clusters] = generate_group_cluster_info(
+      delta,
+      num_groups,
+      nearest_value_scalar_weights_grouped{group_offsets.begin()},
+      scalar_group_info_grouped{group_valid_counts.begin(), group_offsets.begin()},
+      cumulative_scalar_weight_grouped{group_offsets, group_labels},
+      col.null_count() > 0,
+      stream,
+      mr);
+
+    // device column view. handy because the .element() function
+    // automatically handles fixed-point conversions for us
+    auto d_col = cudf::column_device_view::create(col, stream);
+
+    // compute min and max columns
+    auto min_col = cudf::make_numeric_column(
+      data_type{type_id::FLOAT64}, num_groups, mask_state::UNALLOCATED, stream, mr);
+    auto max_col = cudf::make_numeric_column(
+      data_type{type_id::FLOAT64}, num_groups, mask_state::UNALLOCATED, stream, mr);
+    thrust::transform(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator(0),
+      thrust::make_counting_iterator(0) + num_groups,
+      thrust::make_zip_iterator(thrust::make_tuple(min_col->mutable_view().begin<double>(),
+                                                   max_col->mutable_view().begin<double>())),
+      get_scalar_minmax_grouped<T>{*d_col, group_offsets, group_valid_counts.begin()});
+
+    // for simple input values, the "centroids" all have a weight of 1.
+    auto scalar_to_centroid =
+      cudf::detail::make_counting_transform_iterator(0, make_centroid<T>{*d_col});
+
+    // generate the final tdigest
+    return compute_tdigests(delta,
+                            scalar_to_centroid,
+                            scalar_to_centroid + col.size(),
+                            cumulative_scalar_weight_grouped{group_offsets, group_labels},
+                            std::move(min_col),
+                            std::move(max_col),
+                            group_cluster_wl,
+                            std::move(group_cluster_offsets),
+                            total_clusters,
+                            col.null_count() > 0,
+                            stream,
+                            mr);
+  }
+
+  template <typename T,
+            typename... Args,
+            std::enable_if_t<!cudf::is_numeric<T>() && !cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Non-numeric type in group_tdigest");
+  }
+};
+
+struct typed_reduce_tdigest {
+  // this function assumes col is sorted in ascending order with nulls at the end
+  template <
+    typename T,
+    typename std::enable_if_t<cudf::is_numeric<T>() || cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     int delta,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    // treat this the same as the groupby path with a single group.  Note:  even though
+    // there is only 1 group there are still multiple keys within the group that represent
+    // the clustering of (N input values) -> (1 output centroid), so the final computation
+    // remains a reduce_by_key() and not a reduce().
+    //
+    // additionally we get a few optimizations.
+    // - since we only ever have 1 "group" that is sorted with nulls at the end,
+    //   we can simply process just the non-null values and act as if the column
+    //   is non-nullable, allowing us to process fewer values than if we were doing a groupby.
+    //
+    // - several of the functors used during the reduction are cheaper than during a groupby.
+
+    auto const valid_count = col.size() - col.null_count();
+
+    // first, generate cluster weight information for each input group
+    auto [cluster_wl, cluster_offsets, total_clusters] =
+      generate_group_cluster_info(delta,
+                                  1,
+                                  nearest_value_scalar_weights{valid_count},
+                                  scalar_group_info{static_cast<double>(valid_count), valid_count},
+                                  cumulative_scalar_weight{},
+                                  false,
+                                  stream,
+                                  mr);
+
+    // device column view. handy because the .element() function
+    // automatically handles fixed-point conversions for us
+    auto d_col = cudf::column_device_view::create(col, stream);
+
+    // compute min and max columns
+    auto min_col = cudf::make_numeric_column(
+      data_type{type_id::FLOAT64}, 1, mask_state::UNALLOCATED, stream, mr);
+    auto max_col = cudf::make_numeric_column(
+      data_type{type_id::FLOAT64}, 1, mask_state::UNALLOCATED, stream, mr);
+    thrust::transform(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator(0),
+      thrust::make_counting_iterator(0) + 1,
+      thrust::make_zip_iterator(thrust::make_tuple(min_col->mutable_view().begin<double>(),
+                                                   max_col->mutable_view().begin<double>())),
+      get_scalar_minmax<T>{*d_col, valid_count});
+
+    // for simple input values, the "centroids" all have a weight of 1.
+    auto scalar_to_centroid =
+      cudf::detail::make_counting_transform_iterator(0, make_centroid_no_nulls<T>{*d_col});
+
+    // generate the final tdigest and wrap it in a struct_scalar
+    return to_tdigest_scalar(compute_tdigests(delta,
+                                              scalar_to_centroid,
+                                              scalar_to_centroid + valid_count,
+                                              cumulative_scalar_weight{},
+                                              std::move(min_col),
+                                              std::move(max_col),
+                                              cluster_wl,
+                                              std::move(cluster_offsets),
+                                              total_clusters,
+                                              false,
+                                              stream,
+                                              mr),
+                             stream,
+                             mr);
+  }
+
+  template <
+    typename T,
+    typename... Args,
+    typename std::enable_if_t<!cudf::is_numeric<T>() && !cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(Args&&...)
+  {
+    CUDF_FAIL("Non-numeric type in group_tdigest");
+  }
+};
+
+// utility for merge_tdigests.
+template <typename GroupOffsetsIter>
+struct group_num_weights_func {
+  GroupOffsetsIter outer_offsets;
+  size_type const* inner_offsets;
+
+  __device__ size_type operator()(size_type group_index)
+  {
+    auto const tdigest_begin = outer_offsets[group_index];
+    auto const tdigest_end   = outer_offsets[group_index + 1];
+    return inner_offsets[tdigest_end] - inner_offsets[tdigest_begin];
+  }
+};
+
+// utility for merge_tdigests.
+struct group_is_empty {
+  __device__ bool operator()(size_type group_size) { return group_size == 0; }
+};
+
+// utility for merge_tdigests.
+template <typename GroupLabelsIter>
+struct group_key_func {
+  GroupLabelsIter group_labels;
+  size_type const* inner_offsets;
+  size_type num_inner_offsets;
+
+  __device__ size_type operator()(size_type index)
+  {
+    // what -original- tdigest index this absolute index corresponds to
+    auto const iter = thrust::prev(
+      thrust::upper_bound(thrust::seq, inner_offsets, inner_offsets + num_inner_offsets, index));
+    auto const tdigest_index = thrust::distance(inner_offsets, iter);
+
+    // what group index the original tdigest belongs to
+    return group_labels[tdigest_index];
+  }
+};
+
+template <typename HGroupOffsetIter, typename GroupOffsetIter, typename GroupLabelIter>
+std::unique_ptr<column> merge_tdigests(tdigest_column_view const& tdv,
+                                       HGroupOffsetIter h_outer_offsets,
+                                       GroupOffsetIter group_offsets,
+                                       GroupLabelIter group_labels,
+                                       size_t num_group_labels,
+                                       size_type num_groups,
+                                       int max_centroids,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  // thrust::merge and thrust::merge_by_key don't provide what we need.  What we would need is an
+  // algorithm like a super-merge that takes two layers of keys: one which identifies the outer
+  // grouping of tdigests, and one which identifies the inner groupings of the tdigests within the
+  // outer groups.
+  // TODO: investigate replacing the iterative merge with a single stable_sort_by_key.
+
+  // bring tdigest offsets back to the host
+  auto tdigest_offsets = tdv.centroids().offsets();
+  std::vector<size_type> h_inner_offsets(tdigest_offsets.size());
+  cudaMemcpyAsync(h_inner_offsets.data(),
+                  tdigest_offsets.begin<size_type>(),
+                  sizeof(size_type) * tdigest_offsets.size(),
+                  cudaMemcpyDefault,
+                  stream);
+
+  stream.synchronize();
+
+  // extract all means and weights into a table
+  cudf::table_view tdigests_unsliced({tdv.means(), tdv.weights()});
+
+  // generate the merged (but not yet compressed) tdigests for each group.
+  std::vector<std::unique_ptr<table>> tdigests;
+  tdigests.reserve(num_groups);
+  std::transform(h_outer_offsets,
+                 h_outer_offsets + num_groups,
+                 std::next(h_outer_offsets),
+                 std::back_inserter(tdigests),
+                 [&](auto tdigest_start, auto tdigest_end) {
+                   // the range of tdigests in this group
+                   auto const num_tdigests = tdigest_end - tdigest_start;
+
+                   // slice each tdigest from the input
+                   std::vector<table_view> unmerged_tdigests;
+                   unmerged_tdigests.reserve(num_tdigests);
+                   auto offset_iter = std::next(h_inner_offsets.begin(), tdigest_start);
+                   std::transform(
+                     offset_iter,
+                     offset_iter + num_tdigests,
+                     std::next(offset_iter),
+                     std::back_inserter(unmerged_tdigests),
+                     [&](size_type start, size_type end) {
+                       return cudf::detail::slice(tdigests_unsliced, {start, end}, stream);
+                     });
+
+                   // merge
+                   return cudf::detail::merge(unmerged_tdigests,
+                                              {0},
+                                              {order::ASCENDING},
+                                              {},
+                                              stream,
+                                              rmm::mr::get_current_device_resource());
+                 });
+
+  // generate min and max values
+  auto merged_min_col = cudf::make_numeric_column(
+    data_type{type_id::FLOAT64}, num_groups, mask_state::UNALLOCATED, stream, mr);
+  auto min_iter =
+    thrust::make_transform_iterator(thrust::make_zip_iterator(thrust::make_tuple(
+                                      tdv.min_begin(), cudf::tdigest::detail::size_begin(tdv))),
+                                    tdigest_min{});
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        group_labels,
+                        group_labels + num_group_labels,
+                        min_iter,
+                        thrust::make_discard_iterator(),
+                        merged_min_col->mutable_view().begin<double>(),
+                        thrust::equal_to{},  // key equality check
+                        thrust::minimum{});
+
+  auto merged_max_col = cudf::make_numeric_column(
+    data_type{type_id::FLOAT64}, num_groups, mask_state::UNALLOCATED, stream, mr);
+  auto max_iter =
+    thrust::make_transform_iterator(thrust::make_zip_iterator(thrust::make_tuple(
+                                      tdv.max_begin(), cudf::tdigest::detail::size_begin(tdv))),
+                                    tdigest_max{});
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        group_labels,
+                        group_labels + num_group_labels,
+                        max_iter,
+                        thrust::make_discard_iterator(),
+                        merged_max_col->mutable_view().begin<double>(),
+                        thrust::equal_to{},  // key equality check
+                        thrust::maximum{});
+
+  // for any empty groups, set the min and max to be 0. not technically necessary but it makes
+  // testing simpler.
+  auto group_num_weights = cudf::detail::make_counting_transform_iterator(
+    0,
+    group_num_weights_func<decltype(group_offsets)>{group_offsets,
+                                                    tdigest_offsets.begin<size_type>()});
+  thrust::replace_if(rmm::exec_policy(stream),
+                     merged_min_col->mutable_view().begin<double>(),
+                     merged_min_col->mutable_view().end<double>(),
+                     group_num_weights,
+                     group_is_empty{},
+                     0);
+  thrust::replace_if(rmm::exec_policy(stream),
+                     merged_max_col->mutable_view().begin<double>(),
+                     merged_max_col->mutable_view().end<double>(),
+                     group_num_weights,
+                     group_is_empty{},
+                     0);
+
+  // concatenate all the merged tdigests back into one table.
+  std::vector<table_view> tdigest_views;
+  tdigest_views.reserve(num_groups);
+  std::transform(tdigests.begin(),
+                 tdigests.end(),
+                 std::back_inserter(tdigest_views),
+                 [](std::unique_ptr<table> const& t) { return t->view(); });
+  auto merged =
+    cudf::detail::concatenate(tdigest_views, stream, rmm::mr::get_current_device_resource());
+
+  // generate cumulative weights
+  auto merged_weights     = merged->get_column(1).view();
+  auto cumulative_weights = cudf::make_numeric_column(
+    data_type{type_id::FLOAT64}, merged_weights.size(), mask_state::UNALLOCATED, stream);
+  auto keys = cudf::detail::make_counting_transform_iterator(
+    0,
+    group_key_func<decltype(group_labels)>{
+      group_labels, tdigest_offsets.begin<size_type>(), tdigest_offsets.size()});
+  thrust::inclusive_scan_by_key(rmm::exec_policy(stream),
+                                keys,
+                                keys + cumulative_weights->size(),
+                                merged_weights.begin<double>(),
+                                cumulative_weights->mutable_view().begin<double>());
+
+  auto const delta = max_centroids;
+
+  // generate cluster info
+  auto [group_cluster_wl, group_cluster_offsets, total_clusters] = generate_group_cluster_info(
+    delta,
+    num_groups,
+    nearest_value_centroid_weights<decltype(group_offsets)>{
+      cumulative_weights->view().begin<double>(),
+      group_offsets,
+      tdigest_offsets.begin<size_type>()},
+    centroid_group_info<decltype(group_offsets)>{cumulative_weights->view().begin<double>(),
+                                                 group_offsets,
+                                                 tdigest_offsets.begin<size_type>()},
+    cumulative_centroid_weight<decltype(group_labels), decltype(group_offsets)>{
+      cumulative_weights->view().begin<double>(),
+      group_labels,
+      group_offsets,
+      {tdigest_offsets.begin<size_type>(), static_cast<size_t>(tdigest_offsets.size())}},
+    false,
+    stream,
+    mr);
+
+  // input centroid values
+  auto centroids = cudf::detail::make_counting_transform_iterator(
+    0,
+    make_weighted_centroid{merged->get_column(0).view().begin<double>(),
+                           merged_weights.begin<double>()});
+
+  // compute the tdigest
+  return compute_tdigests(
+    delta,
+    centroids,
+    centroids + merged->num_rows(),
+    cumulative_centroid_weight<decltype(group_labels), decltype(group_offsets)>{
+      cumulative_weights->view().begin<double>(),
+      group_labels,
+      group_offsets,
+      {tdigest_offsets.begin<size_type>(), static_cast<size_t>(tdigest_offsets.size())}},
+    std::move(merged_min_col),
+    std::move(merged_max_col),
+    group_cluster_wl,
+    std::move(group_cluster_offsets),
+    total_clusters,
+    false,
+    stream,
+    mr);
+}
+
+}  // anonymous namespace
+
+std::unique_ptr<scalar> reduce_tdigest(column_view const& col,
+                                       int max_centroids,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  if (col.size() == 0) { return cudf::tdigest::detail::make_empty_tdigest_scalar(stream, mr); }
+
+  // since this isn't coming out of a groupby, we need to sort the inputs in ascending
+  // order with nulls at the end.
+  table_view t({col});
+  auto sorted = cudf::detail::sort(
+    t, {order::ASCENDING}, {null_order::AFTER}, stream, rmm::mr::get_current_device_resource());
+
+  auto const delta = max_centroids;
+  return cudf::type_dispatcher(
+    col.type(), typed_reduce_tdigest{}, sorted->get_column(0), delta, stream, mr);
+}
+
+std::unique_ptr<scalar> reduce_merge_tdigest(column_view const& input,
+                                             int max_centroids,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  tdigest_column_view tdv(input);
+
+  if (input.size() == 0) { return cudf::tdigest::detail::make_empty_tdigest_scalar(stream, mr); }
+
+  auto h_group_offsets = cudf::detail::make_counting_transform_iterator(
+    0, [size = input.size()](size_type i) { return i == 0 ? 0 : size; });
+  auto group_offsets = cudf::detail::make_counting_transform_iterator(
+    0, [size = input.size()] __device__(size_type i) { return i == 0 ? 0 : size; });
+  auto group_labels = thrust::make_constant_iterator(0);
+  return to_tdigest_scalar(merge_tdigests(tdv,
+                                          h_group_offsets,
+                                          group_offsets,
+                                          group_labels,
+                                          input.size(),
+                                          1,
+                                          max_centroids,
+                                          stream,
+                                          mr),
+                           stream,
+                           mr);
+}
+
+std::unique_ptr<column> group_tdigest(column_view const& col,
+                                      cudf::device_span<size_type const> group_offsets,
+                                      cudf::device_span<size_type const> group_labels,
+                                      cudf::device_span<size_type const> group_valid_counts,
+                                      size_type num_groups,
+                                      int max_centroids,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  if (col.size() == 0) { return cudf::tdigest::detail::make_empty_tdigest_column(stream, mr); }
+
+  auto const delta = max_centroids;
+  return cudf::type_dispatcher(col.type(),
+                               typed_group_tdigest{},
+                               col,
+                               group_offsets,
+                               group_labels,
+                               group_valid_counts,
+                               num_groups,
+                               delta,
+                               stream,
+                               mr);
+}
+
+std::unique_ptr<column> group_merge_tdigest(column_view const& input,
+                                            cudf::device_span<size_type const> group_offsets,
+                                            cudf::device_span<size_type const> group_labels,
+                                            size_type num_groups,
+                                            int max_centroids,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  tdigest_column_view tdv(input);
+
+  if (num_groups == 0 || input.size() == 0) {
+    return cudf::tdigest::detail::make_empty_tdigest_column(stream, mr);
+  }
+
+  // bring group offsets back to the host
+  std::vector<size_type> h_group_offsets(group_offsets.size());
+  cudaMemcpyAsync(h_group_offsets.data(),
+                  group_offsets.begin(),
+                  sizeof(size_type) * group_offsets.size(),
+                  cudaMemcpyDefault,
+                  stream);
+
+  return merge_tdigests(tdv,
+                        h_group_offsets.begin(),
+                        group_offsets.data(),
+                        group_labels.data(),
+                        group_labels.size(),
+                        num_groups,
+                        max_centroids,
+                        stream,
+                        mr);
+}
+
+}  // namespace detail
+}  // namespace tdigest
+}  // namespace cudf
diff --git a/cpp/src/quantiles/tdigest/tdigest_column_view.cpp b/cpp/src/quantiles/tdigest/tdigest_column_view.cpp
new file mode 100644
index 0000000..a9f86ac
--- /dev/null
+++ b/cpp/src/quantiles/tdigest/tdigest_column_view.cpp
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+
+namespace cudf {
+namespace tdigest {
+
+tdigest_column_view::tdigest_column_view(column_view const& col) : column_view(col)
+{
+  // sanity check that this is actually tdigest data
+  CUDF_EXPECTS(col.type().id() == type_id::STRUCT, "Encountered invalid tdigest column");
+  CUDF_EXPECTS(col.size() > 0, "tdigest columns must have > 0 rows");
+  CUDF_EXPECTS(col.offset() == 0, "Encountered a sliced tdigest column");
+  CUDF_EXPECTS(not col.nullable(), "Encountered nullable tdigest column");
+
+  structs_column_view scv(col);
+  CUDF_EXPECTS(scv.num_children() == 3, "Encountered invalid tdigest column");
+  CUDF_EXPECTS(scv.child(min_column_index).type().id() == type_id::FLOAT64,
+               "Encountered invalid tdigest column");
+  CUDF_EXPECTS(scv.child(max_column_index).type().id() == type_id::FLOAT64,
+               "Encountered invalid tdigest column");
+
+  lists_column_view lcv(scv.child(centroid_column_index));
+  auto data = lcv.child();
+  CUDF_EXPECTS(data.type().id() == type_id::STRUCT, "Encountered invalid tdigest column");
+  CUDF_EXPECTS(data.num_children() == 2,
+               "Encountered tdigest column with an invalid number of children");
+  auto mean = data.child(mean_column_index);
+  CUDF_EXPECTS(mean.type().id() == type_id::FLOAT64, "Encountered invalid tdigest mean column");
+  auto weight = data.child(weight_column_index);
+  CUDF_EXPECTS(weight.type().id() == type_id::FLOAT64, "Encountered invalid tdigest weight column");
+}
+
+lists_column_view tdigest_column_view::centroids() const { return child(centroid_column_index); }
+
+column_view tdigest_column_view::means() const
+{
+  auto c = centroids();
+  structs_column_view inner(c.parent().child(lists_column_view::child_column_index));
+  return inner.child(mean_column_index);
+}
+
+column_view tdigest_column_view::weights() const
+{
+  auto c = centroids();
+  structs_column_view inner(c.parent().child(lists_column_view::child_column_index));
+  return inner.child(weight_column_index);
+}
+
+double const* tdigest_column_view::min_begin() const
+{
+  return child(min_column_index).begin<double>();
+}
+
+double const* tdigest_column_view::max_begin() const
+{
+  return child(max_column_index).begin<double>();
+}
+
+}  // namespace tdigest
+}  // namespace cudf
diff --git a/cpp/src/quantiles/tdigest/tdigest_util.cuh b/cpp/src/quantiles/tdigest/tdigest_util.cuh
new file mode 100644
index 0000000..d0e6484
--- /dev/null
+++ b/cpp/src/quantiles/tdigest/tdigest_util.cuh
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+
+namespace cudf {
+namespace tdigest {
+namespace detail {
+
+/**
+ * @brief Functor to compute the size of each tdigest of a column
+ */
+struct tdigest_size_fn {
+  size_type const* offsets;  ///< Offsets of the t-digest column
+  /**
+   * @brief Returns size of the each tdigest in the column
+   *
+   * @param tdigest_index Index of the tdigest in the column
+   * @return Size of the tdigest
+   */
+  __device__ size_type operator()(size_type tdigest_index)
+  {
+    return offsets[tdigest_index + 1] - offsets[tdigest_index];
+  }
+};
+
+/**
+ * @brief Returns an iterator that returns the size of each tdigest
+ * in the column (each row is 1 digest)
+ *
+ * @return An iterator that returns the size of each tdigest in the column
+ */
+inline auto size_begin(tdigest_column_view const& tdv)
+{
+  return cudf::detail::make_counting_transform_iterator(
+    0, tdigest_size_fn{tdv.centroids().offsets_begin()});
+}
+
+}  // namespace detail
+}  // namespace tdigest
+}  // namespace cudf
diff --git a/cpp/src/reductions/all.cu b/cpp/src/reductions/all.cu
new file mode 100644
index 0000000..4717c06
--- /dev/null
+++ b/cpp/src/reductions/all.cu
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+namespace {
+
+/**
+ * @brief Compute reduction all() for dictionary columns.
+ *
+ * This compiles 10x faster than using thrust::reduce or the
+ * cudf::simple::reduction::detail::reduce utility.
+ * Both of these use the CUB DeviceReduce which aggressively inlines
+ * the input iterator logic.
+ */
+struct all_fn {
+  template <typename Iterator>
+  struct all_true_fn {
+    __device__ void operator()(size_type idx)
+    {
+      if (*d_result && (iter[idx] != *d_result)) {
+        cuda::atomic_ref<int32_t, cuda::thread_scope_device> ref{*d_result};
+        ref.fetch_and(0, cuda::std::memory_order_relaxed);
+      }
+    }
+    Iterator iter;
+    int32_t* d_result;
+  };
+
+  template <typename T, std::enable_if_t<std::is_arithmetic_v<T>>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const d_dict = cudf::column_device_view::create(input, stream);
+    auto const iter   = [&] {
+      auto null_iter = op::min{}.template get_null_replacing_element_transformer<bool>();
+      auto pair_iter =
+        cudf::dictionary::detail::make_dictionary_pair_iterator<T>(*d_dict, input.has_nulls());
+      return thrust::make_transform_iterator(pair_iter, null_iter);
+    }();
+    auto d_result = rmm::device_scalar<int32_t>(1, stream, rmm::mr::get_current_device_resource());
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       input.size(),
+                       all_true_fn<decltype(iter)>{iter, d_result.data()});
+    return std::make_unique<numeric_scalar<bool>>(d_result.value(stream), true, stream, mr);
+  }
+  template <typename T, std::enable_if_t<!std::is_arithmetic_v<T>>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Unexpected key type for dictionary in reduction all()");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::scalar> all(column_view const& col,
+                                  cudf::data_type const output_dtype,
+                                  std::optional<std::reference_wrapper<scalar const>> init,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(output_dtype == cudf::data_type(cudf::type_id::BOOL8),
+               "all() operation can be applied with output type `BOOL8` only");
+
+  if (cudf::is_dictionary(col.type())) {
+    return cudf::type_dispatcher(
+      dictionary_column_view(col).keys().type(), detail::all_fn{}, col, stream, mr);
+  }
+  using reducer = simple::detail::bool_result_element_dispatcher<op::min>;
+  // dispatch for non-dictionary types
+  return cudf::type_dispatcher(col.type(), reducer{}, col, init, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/any.cu b/cpp/src/reductions/any.cu
new file mode 100644
index 0000000..f3093df
--- /dev/null
+++ b/cpp/src/reductions/any.cu
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+namespace {
+
+/**
+ * @brief Compute reduction any() for dictionary columns.
+ *
+ * This compiles 10x faster than using thrust::reduce or the
+ * cudf::simple::reduction::detail::reduce utility.
+ * Both of these use the CUB DeviceReduce which aggressively inlines
+ * the input iterator logic.
+ */
+struct any_fn {
+  template <typename Iterator>
+  struct any_true_fn {
+    __device__ void operator()(size_type idx)
+    {
+      if (!*d_result && (iter[idx] != *d_result)) {
+        cuda::atomic_ref<int32_t, cuda::thread_scope_device> ref{*d_result};
+        ref.fetch_or(1, cuda::std::memory_order_relaxed);
+      }
+    }
+    Iterator iter;
+    int32_t* d_result;
+  };
+
+  template <typename T, std::enable_if_t<std::is_arithmetic_v<T>>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const d_dict = cudf::column_device_view::create(input, stream);
+    auto const iter   = [&] {
+      auto null_iter = op::max{}.template get_null_replacing_element_transformer<bool>();
+      auto pair_iter =
+        cudf::dictionary::detail::make_dictionary_pair_iterator<T>(*d_dict, input.has_nulls());
+      return thrust::make_transform_iterator(pair_iter, null_iter);
+    }();
+    auto d_result = rmm::device_scalar<int32_t>(0, stream, rmm::mr::get_current_device_resource());
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       input.size(),
+                       any_true_fn<decltype(iter)>{iter, d_result.data()});
+    return std::make_unique<numeric_scalar<bool>>(d_result.value(stream), true, stream, mr);
+  }
+  template <typename T, std::enable_if_t<!std::is_arithmetic_v<T>>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Unexpected key type for dictionary in reduction any()");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::scalar> any(column_view const& col,
+                                  cudf::data_type const output_dtype,
+                                  std::optional<std::reference_wrapper<scalar const>> init,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(output_dtype == cudf::data_type(cudf::type_id::BOOL8),
+               "any() operation can be applied with output type `bool8` only");
+
+  if (cudf::is_dictionary(col.type())) {
+    return cudf::type_dispatcher(
+      dictionary_column_view(col).keys().type(), detail::any_fn{}, col, stream, mr);
+  }
+  using reducer = simple::detail::bool_result_element_dispatcher<op::max>;
+  // dispatch for non-dictionary types
+  return cudf::type_dispatcher(col.type(), reducer{}, col, init, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/collect_ops.cu b/cpp/src/reductions/collect_ops.cu
new file mode 100644
index 0000000..743eddb
--- /dev/null
+++ b/cpp/src/reductions/collect_ops.cu
@@ -0,0 +1,118 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+namespace {
+
+/**
+ * @brief Check if we need to handle nulls in the input column.
+ *
+ * @param input The input column
+ * @param null_handling The null handling policy
+ * @return A boolean value indicating if we need to handle nulls
+ */
+bool need_handle_nulls(column_view const& input, null_policy null_handling)
+{
+  return null_handling == null_policy::EXCLUDE && input.has_nulls();
+}
+
+}  // namespace
+
+std::unique_ptr<scalar> collect_list(column_view const& col,
+                                     null_policy null_handling,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  if (need_handle_nulls(col, null_handling)) {
+    auto d_view             = column_device_view::create(col, stream);
+    auto filter             = cudf::detail::validity_accessor(*d_view);
+    auto null_purged_table  = cudf::detail::copy_if(table_view{{col}}, filter, stream, mr);
+    column* null_purged_col = null_purged_table->release().front().release();
+    null_purged_col->set_null_mask(rmm::device_buffer{0, stream, mr}, 0);
+    return std::make_unique<list_scalar>(std::move(*null_purged_col), true, stream, mr);
+  } else {
+    return make_list_scalar(col, stream, mr);
+  }
+}
+
+std::unique_ptr<scalar> merge_lists(lists_column_view const& col,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto flatten_col = col.get_sliced_child(stream);
+  return make_list_scalar(flatten_col, stream, mr);
+}
+
+std::unique_ptr<scalar> collect_set(column_view const& col,
+                                    null_policy null_handling,
+                                    null_equality nulls_equal,
+                                    nan_equality nans_equal,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  // `input_as_collect_list` is the result of the input column that has been processed to obey
+  // the given null handling behavior.
+  [[maybe_unused]] auto const [input_as_collect_list, unused_scalar] = [&] {
+    if (need_handle_nulls(col, null_handling)) {
+      // Only call `collect_list` when we need to handle nulls.
+      auto scalar = collect_list(col, null_handling, stream, mr);
+      return std::pair(static_cast<list_scalar*>(scalar.get())->view(), std::move(scalar));
+    }
+
+    return std::pair(col, std::unique_ptr<scalar>(nullptr));
+  }();
+
+  auto distinct_table = cudf::detail::distinct(table_view{{input_as_collect_list}},
+                                               std::vector<size_type>{0},
+                                               duplicate_keep_option::KEEP_ANY,
+                                               nulls_equal,
+                                               nans_equal,
+                                               stream,
+                                               mr);
+
+  return std::make_unique<list_scalar>(std::move(distinct_table->get_column(0)), true, stream, mr);
+}
+
+std::unique_ptr<scalar> merge_sets(lists_column_view const& col,
+                                   null_equality nulls_equal,
+                                   nan_equality nans_equal,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  auto flatten_col    = col.get_sliced_child(stream);
+  auto distinct_table = cudf::detail::distinct(table_view{{flatten_col}},
+                                               std::vector<size_type>{0},
+                                               duplicate_keep_option::KEEP_ANY,
+                                               nulls_equal,
+                                               nans_equal,
+                                               stream,
+                                               mr);
+  return std::make_unique<list_scalar>(std::move(distinct_table->get_column(0)), true, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/compound.cuh b/cpp/src/reductions/compound.cuh
new file mode 100644
index 0000000..3428130
--- /dev/null
+++ b/cpp/src/reductions/compound.cuh
@@ -0,0 +1,159 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/reduction/detail/reduction.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace reduction {
+namespace compound {
+namespace detail {
+/**
+ * @brief Multi-step reduction for operations such as mean, variance, and standard deviation.
+ *
+ * @tparam ElementType  the input column data-type
+ * @tparam ResultType   the output data-type
+ * @tparam Op           the compound operator derived from `cudf::reduction::op::compound_op`
+ *
+ * @param col input column view
+ * @param output_dtype data type of return type and typecast elements of input column
+ * @param ddof Delta degrees of freedom used for standard deviation and variance. The divisor used
+ * is N - ddof, where N represents the number of elements.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Output scalar in device memory
+ */
+template <typename ElementType, typename ResultType, typename Op>
+std::unique_ptr<scalar> compound_reduction(column_view const& col,
+                                           data_type const output_dtype,
+                                           size_type ddof,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto const valid_count = col.size() - col.null_count();
+
+  // reduction by iterator
+  auto dcol = cudf::column_device_view::create(col, stream);
+  std::unique_ptr<scalar> result;
+  Op compound_op{};
+
+  if (!cudf::is_dictionary(col.type())) {
+    if (col.has_nulls()) {
+      auto it = thrust::make_transform_iterator(
+        dcol->pair_begin<ElementType, true>(),
+        compound_op.template get_null_replacing_element_transformer<ResultType>());
+      result = cudf::reduction::detail::reduce<Op, decltype(it), ResultType>(
+        it, col.size(), compound_op, valid_count, ddof, stream, mr);
+    } else {
+      auto it = thrust::make_transform_iterator(
+        dcol->begin<ElementType>(), compound_op.template get_element_transformer<ResultType>());
+      result = cudf::reduction::detail::reduce<Op, decltype(it), ResultType>(
+        it, col.size(), compound_op, valid_count, ddof, stream, mr);
+    }
+  } else {
+    auto it = thrust::make_transform_iterator(
+      cudf::dictionary::detail::make_dictionary_pair_iterator<ElementType>(*dcol, col.has_nulls()),
+      compound_op.template get_null_replacing_element_transformer<ResultType>());
+    result = cudf::reduction::detail::reduce<Op, decltype(it), ResultType>(
+      it, col.size(), compound_op, valid_count, ddof, stream, mr);
+  }
+
+  // set scalar is valid
+  result->set_valid_async(col.null_count() < col.size(), stream);
+  return result;
+};
+
+// @brief result type dispatcher for compound reduction (a.k.a. mean, var, std)
+template <typename ElementType, typename Op>
+struct result_type_dispatcher {
+ private:
+  template <typename ResultType>
+  static constexpr bool is_supported_v()
+  {
+    // the operator `mean`, `var`, `std` only accepts
+    // floating points as output dtype
+    return std::is_floating_point_v<ResultType>;
+  }
+
+ public:
+  template <typename ResultType, std::enable_if_t<is_supported_v<ResultType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     cudf::data_type const output_dtype,
+                                     size_type ddof,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return compound_reduction<ElementType, ResultType, Op>(col, output_dtype, ddof, stream, mr);
+  }
+
+  template <typename ResultType, std::enable_if_t<not is_supported_v<ResultType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     cudf::data_type const output_dtype,
+                                     size_type ddof,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_FAIL("Unsupported output data type");
+  }
+};
+
+// @brief input column element dispatcher for compound reduction (a.k.a. mean, var, std)
+template <typename Op>
+struct element_type_dispatcher {
+ private:
+  // return true if ElementType is arithmetic type
+  template <typename ElementType>
+  static constexpr bool is_supported_v()
+  {
+    return std::is_arithmetic_v<ElementType>;
+  }
+
+ public:
+  template <typename ElementType, std::enable_if_t<is_supported_v<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     cudf::data_type const output_dtype,
+                                     size_type ddof,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return cudf::type_dispatcher(
+      output_dtype, result_type_dispatcher<ElementType, Op>(), col, output_dtype, ddof, stream, mr);
+  }
+
+  template <typename ElementType, std::enable_if_t<not is_supported_v<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     cudf::data_type const output_dtype,
+                                     size_type ddof,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_FAIL(
+      "Reduction operators other than `min` and `max`"
+      " are not supported for non-arithmetic types");
+  }
+};
+
+}  // namespace detail
+}  // namespace compound
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/histogram.cu b/cpp/src/reductions/histogram.cu
new file mode 100644
index 0000000..fa84bbe
--- /dev/null
+++ b/cpp/src/reductions/histogram.cu
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/hash_reduce_by_row.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/tuple.h>
+
+#include <cuda/atomic>
+
+#include <optional>
+
+namespace cudf::reduction::detail {
+
+namespace {
+
+// Always use 64-bit signed integer for storing count.
+using histogram_count_type = int64_t;
+
+/**
+ * @brief The functor to accumulate the frequency of each distinct rows in the input table.
+ */
+template <typename MapView, typename KeyHasher, typename KeyEqual, typename CountType>
+struct reduce_fn : cudf::detail::reduce_by_row_fn_base<MapView, KeyHasher, KeyEqual, CountType> {
+  CountType const* d_partial_output;
+
+  reduce_fn(MapView const& d_map,
+            KeyHasher const& d_hasher,
+            KeyEqual const& d_equal,
+            CountType* const d_output,
+            CountType const* const d_partial_output)
+    : cudf::detail::reduce_by_row_fn_base<MapView, KeyHasher, KeyEqual, CountType>{d_map,
+                                                                                   d_hasher,
+                                                                                   d_equal,
+                                                                                   d_output},
+      d_partial_output{d_partial_output}
+  {
+  }
+
+  // Count the number of rows in each group of rows that are compared equal.
+  __device__ void operator()(size_type const idx) const
+  {
+    auto const increment = d_partial_output ? d_partial_output[idx] : CountType{1};
+    auto const count =
+      cuda::atomic_ref<CountType, cuda::thread_scope_device>(*this->get_output_ptr(idx));
+    count.fetch_add(increment, cuda::std::memory_order_relaxed);
+  }
+};
+
+/**
+ * @brief The builder to construct an instance of `reduce_fn` functor.
+ */
+template <typename CountType>
+struct reduce_func_builder {
+  CountType const* const d_partial_output;
+
+  reduce_func_builder(CountType const* const d_partial_output) : d_partial_output{d_partial_output}
+  {
+  }
+
+  template <typename MapView, typename KeyHasher, typename KeyEqual>
+  auto build(MapView const& d_map,
+             KeyHasher const& d_hasher,
+             KeyEqual const& d_equal,
+             CountType* const d_output)
+  {
+    return reduce_fn<MapView, KeyHasher, KeyEqual, CountType>{
+      d_map, d_hasher, d_equal, d_output, d_partial_output};
+  }
+};
+
+/**
+ * @brief Specialized functor to check for not-zero of the second component of the input.
+ */
+struct is_not_zero {
+  template <typename Pair>
+  __device__ bool operator()(Pair const input) const
+  {
+    return thrust::get<1>(input) != 0;
+  }
+};
+
+/**
+ * @brief Building a histogram by gathering distinct rows from the input table and their
+ * corresponding distinct counts.
+ *
+ * @param input The input table
+ * @param distinct_indices Indices of the distinct rows
+ * @param distinct_counts Distinct counts corresponding to the distinct rows
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned object's device memory
+ * @return A list_scalar storing the output histogram
+ */
+auto gather_histogram(table_view const& input,
+                      device_span<size_type const> distinct_indices,
+                      std::unique_ptr<column>&& distinct_counts,
+                      rmm::cuda_stream_view stream,
+                      rmm::mr::device_memory_resource* mr)
+{
+  auto distinct_rows = cudf::detail::gather(input,
+                                            distinct_indices,
+                                            out_of_bounds_policy::DONT_CHECK,
+                                            cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                            stream,
+                                            mr);
+
+  std::vector<std::unique_ptr<column>> struct_children;
+  struct_children.emplace_back(std::move(distinct_rows->release().front()));
+  struct_children.emplace_back(std::move(distinct_counts));
+  auto output_structs = make_structs_column(
+    static_cast<size_type>(distinct_indices.size()), std::move(struct_children), 0, {}, stream, mr);
+
+  return std::make_unique<cudf::list_scalar>(
+    std::move(*output_structs.release()), true, stream, mr);
+}
+
+}  // namespace
+
+std::unique_ptr<column> make_empty_histogram_like(column_view const& values)
+{
+  std::vector<std::unique_ptr<column>> struct_children;
+  struct_children.emplace_back(empty_like(values));
+  struct_children.emplace_back(make_numeric_column(data_type{type_id::INT64}, 0));
+  return std::make_unique<column>(data_type{type_id::STRUCT},
+                                  0,
+                                  rmm::device_buffer{},
+                                  rmm::device_buffer{},
+                                  0,
+                                  std::move(struct_children));
+}
+
+std::pair<std::unique_ptr<rmm::device_uvector<size_type>>, std::unique_ptr<column>>
+compute_row_frequencies(table_view const& input,
+                        std::optional<column_view> const& partial_counts,
+                        rmm::cuda_stream_view stream,
+                        rmm::mr::device_memory_resource* mr)
+{
+  auto const has_nested_columns = cudf::detail::has_nested_columns(input);
+
+  // Nested types are not tested, thus we just throw exception if we see such input for now.
+  // We should remove this check after having enough tests.
+  CUDF_EXPECTS(!has_nested_columns,
+               "Nested types are not yet supported in histogram aggregation.",
+               std::invalid_argument);
+
+  auto map = cudf::detail::hash_map_type{
+    compute_hash_table_size(input.num_rows()),
+    cuco::empty_key{-1},
+    cuco::empty_value{std::numeric_limits<size_type>::min()},
+    cudf::detail::hash_table_allocator_type{default_allocator<char>{}, stream},
+    stream.value()};
+
+  auto const preprocessed_input =
+    cudf::experimental::row::hash::preprocessed_table::create(input, stream);
+  auto const has_nulls = nullate::DYNAMIC{cudf::has_nested_nulls(input)};
+
+  auto const row_hasher = cudf::experimental::row::hash::row_hasher(preprocessed_input);
+  auto const key_hasher = row_hasher.device_hasher(has_nulls);
+  auto const row_comp   = cudf::experimental::row::equality::self_comparator(preprocessed_input);
+
+  auto const pair_iter = cudf::detail::make_counting_transform_iterator(
+    size_type{0}, [] __device__(size_type const i) { return cuco::make_pair(i, i); });
+
+  // Always compare NaNs as equal.
+  using nan_equal_comparator =
+    cudf::experimental::row::equality::nan_equal_physical_equality_comparator;
+  auto const value_comp = nan_equal_comparator{};
+
+  if (has_nested_columns) {
+    auto const key_equal = row_comp.equal_to<true>(has_nulls, null_equality::EQUAL, value_comp);
+    map.insert(pair_iter, pair_iter + input.num_rows(), key_hasher, key_equal, stream.value());
+  } else {
+    auto const key_equal = row_comp.equal_to<false>(has_nulls, null_equality::EQUAL, value_comp);
+    map.insert(pair_iter, pair_iter + input.num_rows(), key_hasher, key_equal, stream.value());
+  }
+
+  // Gather the indices of distinct rows.
+  auto distinct_indices = std::make_unique<rmm::device_uvector<size_type>>(
+    static_cast<size_type>(map.get_size()), stream, mr);
+
+  // Store the number of occurrences of each distinct row.
+  auto distinct_counts = make_numeric_column(data_type{type_to_id<histogram_count_type>()},
+                                             static_cast<size_type>(map.get_size()),
+                                             mask_state::UNALLOCATED,
+                                             stream,
+                                             mr);
+
+  // Compute frequencies (aka distinct counts) for the input rows.
+  // Note that we consider null and NaNs as always equal.
+  auto const reduction_results = cudf::detail::hash_reduce_by_row(
+    map,
+    preprocessed_input,
+    input.num_rows(),
+    has_nulls,
+    has_nested_columns,
+    null_equality::EQUAL,
+    nan_equality::ALL_EQUAL,
+    reduce_func_builder<histogram_count_type>{
+      partial_counts ? partial_counts.value().begin<histogram_count_type>() : nullptr},
+    histogram_count_type{0},
+    stream,
+    rmm::mr::get_current_device_resource());
+
+  auto const input_it = thrust::make_zip_iterator(
+    thrust::make_tuple(thrust::make_counting_iterator(0), reduction_results.begin()));
+  auto const output_it = thrust::make_zip_iterator(thrust::make_tuple(
+    distinct_indices->begin(), distinct_counts->mutable_view().begin<histogram_count_type>()));
+
+  // Reduction results above are either group sizes of equal rows, or `0`.
+  // The final output is non-zero group sizes only.
+  thrust::copy_if(
+    rmm::exec_policy(stream), input_it, input_it + input.num_rows(), output_it, is_not_zero{});
+
+  return {std::move(distinct_indices), std::move(distinct_counts)};
+}
+
+std::unique_ptr<cudf::scalar> histogram(column_view const& input,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  // Empty group should be handled before reaching here.
+  CUDF_EXPECTS(input.size() > 0, "Input should not be empty.", std::invalid_argument);
+
+  auto const input_tv = table_view{{input}};
+  auto [distinct_indices, distinct_counts] =
+    compute_row_frequencies(input_tv, std::nullopt, stream, mr);
+  return gather_histogram(input_tv, *distinct_indices, std::move(distinct_counts), stream, mr);
+}
+
+std::unique_ptr<cudf::scalar> merge_histogram(column_view const& input,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  // Empty group should be handled before reaching here.
+  CUDF_EXPECTS(input.size() > 0, "Input should not be empty.", std::invalid_argument);
+  CUDF_EXPECTS(!input.has_nulls(), "The input column must not have nulls.", std::invalid_argument);
+  CUDF_EXPECTS(input.type().id() == type_id::STRUCT && input.num_children() == 2,
+               "The input must be a structs column having two children.",
+               std::invalid_argument);
+  CUDF_EXPECTS(cudf::is_integral(input.child(1).type()) && !input.child(1).has_nulls(),
+               "The second child of the input column must be of integral type and without nulls.",
+               std::invalid_argument);
+
+  auto const structs_cv   = structs_column_view{input};
+  auto const input_values = structs_cv.get_sliced_child(0, stream);
+  auto const input_counts = structs_cv.get_sliced_child(1, stream);
+
+  auto const values_tv = table_view{{input_values}};
+  auto [distinct_indices, distinct_counts] =
+    compute_row_frequencies(values_tv, input_counts, stream, mr);
+  return gather_histogram(values_tv, *distinct_indices, std::move(distinct_counts), stream, mr);
+}
+
+}  // namespace cudf::reduction::detail
diff --git a/cpp/src/reductions/max.cu b/cpp/src/reductions/max.cu
new file mode 100644
index 0000000..1cf2b6f
--- /dev/null
+++ b/cpp/src/reductions/max.cu
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> max(column_view const& col,
+                                  cudf::data_type const output_dtype,
+                                  std::optional<std::reference_wrapper<scalar const>> init,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto const input_type =
+    cudf::is_dictionary(col.type()) ? cudf::dictionary_column_view(col).keys().type() : col.type();
+  CUDF_EXPECTS(input_type == output_dtype, "max() operation requires matching output type");
+  auto const dispatch_type = cudf::is_dictionary(col.type())
+                               ? cudf::dictionary_column_view(col).indices().type()
+                               : col.type();
+
+  using reducer = simple::detail::same_element_type_dispatcher<op::max>;
+  return cudf::type_dispatcher(dispatch_type, reducer{}, col, init, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/mean.cu b/cpp/src/reductions/mean.cu
new file mode 100644
index 0000000..e646609
--- /dev/null
+++ b/cpp/src/reductions/mean.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compound.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> mean(column_view const& col,
+                                   cudf::data_type const output_dtype,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  auto col_type =
+    cudf::is_dictionary(col.type()) ? dictionary_column_view(col).keys().type() : col.type();
+
+  using reducer = compound::detail::element_type_dispatcher<op::mean>;
+  return cudf::type_dispatcher(
+    col_type, reducer(), col, output_dtype, /* ddof is not used for mean*/ 1, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/min.cu b/cpp/src/reductions/min.cu
new file mode 100644
index 0000000..792965e
--- /dev/null
+++ b/cpp/src/reductions/min.cu
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+std::unique_ptr<cudf::scalar> min(column_view const& col,
+                                  data_type const output_dtype,
+                                  std::optional<std::reference_wrapper<scalar const>> init,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto const input_type =
+    cudf::is_dictionary(col.type()) ? cudf::dictionary_column_view(col).keys().type() : col.type();
+  CUDF_EXPECTS(input_type == output_dtype, "min() operation requires matching output type");
+  auto const dispatch_type = cudf::is_dictionary(col.type())
+                               ? cudf::dictionary_column_view(col).indices().type()
+                               : col.type();
+
+  using reducer = simple::detail::same_element_type_dispatcher<op::min>;
+  return cudf::type_dispatcher(dispatch_type, reducer{}, col, init, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/minmax.cu b/cpp/src/reductions/minmax.cu
new file mode 100644
index 0000000..c4eb091
--- /dev/null
+++ b/cpp/src/reductions/minmax.cu
@@ -0,0 +1,283 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/extrema.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/iterator_traits.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/transform_reduce.h>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Basic element for the minmax reduce operation.
+ *
+ * Stores the minimum and maximum values that have been encountered so far
+ */
+template <typename T>
+struct minmax_pair {
+  T min_val;
+  T max_val;
+
+  __host__ __device__ minmax_pair()
+    : min_val(cudf::DeviceMin::identity<T>()), max_val(cudf::DeviceMax::identity<T>()){};
+  __host__ __device__ minmax_pair(T val) : min_val(val), max_val(val){};
+  __host__ __device__ minmax_pair(T min_val_, T max_val_) : min_val(min_val_), max_val(max_val_){};
+};
+
+/**
+ * @brief Reduce for the minmax operation and return a device scalar.
+ *
+ * @tparam Op Binary operator functor
+ * @tparam InputIterator Input iterator Type
+ * @tparam OutputType Output scalar type
+ * @param d_in input iterator
+ * @param num_items number of items to reduce
+ * @param binary_op binary operator used to reduce
+ * @param stream CUDA stream to run kernels on.
+ * @return rmm::device_scalar<OutputType>
+ */
+template <typename Op,
+          typename InputIterator,
+          typename OutputType = typename thrust::iterator_value<InputIterator>::type>
+rmm::device_scalar<OutputType> reduce_device(InputIterator d_in,
+                                             size_type num_items,
+                                             Op binary_op,
+                                             rmm::cuda_stream_view stream)
+{
+  OutputType identity{};
+  rmm::device_scalar<OutputType> result{identity, stream};
+
+  // Allocate temporary storage
+  size_t storage_bytes = 0;
+  cub::DeviceReduce::Reduce(
+    nullptr, storage_bytes, d_in, result.data(), num_items, binary_op, identity, stream.value());
+  auto temp_storage = rmm::device_buffer{storage_bytes, stream};
+
+  // Run reduction
+  cub::DeviceReduce::Reduce(temp_storage.data(),
+                            storage_bytes,
+                            d_in,
+                            result.data(),
+                            num_items,
+                            binary_op,
+                            identity,
+                            stream.value());
+
+  return result;
+}
+
+/**
+ * @brief Functor that accepts two minmax_pairs and returns a
+ * minmax_pair whose minimum and maximum values are the min() and max()
+ * respectively of the minimums and maximums of the input pairs.
+ */
+template <typename T>
+struct minmax_binary_op
+  : public thrust::binary_function<minmax_pair<T>, minmax_pair<T>, minmax_pair<T>> {
+  __device__ minmax_pair<T> operator()(minmax_pair<T> const& lhs, minmax_pair<T> const& rhs) const
+  {
+    return minmax_pair<T>{thrust::min(lhs.min_val, rhs.min_val),
+                          thrust::max(lhs.max_val, rhs.max_val)};
+  }
+};
+
+/**
+ * @brief Creates a minmax_pair<T> from a T
+ */
+template <typename T>
+struct create_minmax {
+  __device__ minmax_pair<T> operator()(T e) { return minmax_pair<T>{e}; }
+};
+
+/**
+ * @brief Functor that takes a thrust::pair<T, bool> and produces a minmax_pair
+ * that is <T, T> for minimum and maximum or <cudf::DeviceMin::identity<T>(),
+ * cudf::DeviceMax::identity<T>()>
+ */
+template <typename T>
+struct create_minmax_with_nulls {
+  __device__ minmax_pair<T> operator()(thrust::pair<T, bool> i)
+  {
+    return i.second ? minmax_pair<T>{i.first} : minmax_pair<T>{};
+  }
+};
+
+/**
+ * @brief Dispatch functor for minmax operation.
+ *
+ * This uses the reduce function to compute the min and max values
+ * simultaneously for a column of data.
+ *
+ * @tparam T The input column's type
+ */
+struct minmax_functor {
+  template <typename T>
+  static constexpr bool is_supported()
+  {
+    return !(std::is_same_v<T, cudf::list_view> || std::is_same_v<T, cudf::struct_view>);
+  }
+
+  template <typename T>
+  auto reduce(column_view const& col, rmm::cuda_stream_view stream)
+  {
+    auto device_col = column_device_view::create(col, stream);
+    // compute minimum and maximum values
+    if (col.has_nulls()) {
+      auto pair_to_minmax = thrust::make_transform_iterator(
+        make_pair_iterator<T, true>(*device_col), create_minmax_with_nulls<T>{});
+      return reduce_device(pair_to_minmax, col.size(), minmax_binary_op<T>{}, stream);
+    } else {
+      auto col_to_minmax =
+        thrust::make_transform_iterator(device_col->begin<T>(), create_minmax<T>{});
+      return reduce_device(col_to_minmax, col.size(), minmax_binary_op<T>{}, stream);
+    }
+  }
+
+  /**
+   * @brief Functor to copy a minmax_pair result to individual scalar instances.
+   *
+   * @tparam T type of the data
+   * @tparam ResultType result type to assign min, max to minmax_pair<T>
+   */
+  template <typename T, typename ResultType = minmax_pair<T>>
+  struct assign_min_max {
+    __device__ void operator()()
+    {
+      *min_data = result->min_val;
+      *max_data = result->max_val;
+    }
+
+    ResultType* result;
+    T* min_data;
+    T* max_data;
+  };
+
+  template <typename T,
+            std::enable_if_t<is_supported<T>() and !std::is_same_v<T, cudf::string_view> and
+                             !cudf::is_dictionary<T>()>* = nullptr>
+  std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> operator()(
+    cudf::column_view const& col, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+  {
+    using storage_type = device_storage_type_t<T>;
+    // compute minimum and maximum values
+    auto dev_result = reduce<storage_type>(col, stream);
+    // create output scalars
+    using ScalarType = cudf::scalar_type_t<T>;
+    auto minimum     = new ScalarType(T{}, true, stream, mr);
+    auto maximum     = new ScalarType(T{}, true, stream, mr);
+    // copy dev_result to the output scalars
+    device_single_thread(
+      assign_min_max<storage_type>{dev_result.data(), minimum->data(), maximum->data()}, stream);
+    return {std::unique_ptr<scalar>(minimum), std::unique_ptr<scalar>(maximum)};
+  }
+
+  /**
+   * @brief Specialization for strings column.
+   */
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>* = nullptr>
+  std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> operator()(
+    cudf::column_view const& col, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+  {
+    // compute minimum and maximum values
+    auto dev_result = reduce<cudf::string_view>(col, stream);
+    // copy the minmax_pair to the host; does not copy the strings
+    using OutputType = minmax_pair<cudf::string_view>;
+    OutputType host_result;
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      &host_result, dev_result.data(), sizeof(OutputType), cudaMemcpyDefault, stream.value()));
+    // strings are copied to create the scalars here
+    return {std::make_unique<string_scalar>(host_result.min_val, true, stream, mr),
+            std::make_unique<string_scalar>(host_result.max_val, true, stream, mr)};
+  }
+
+  /**
+   * @brief Specialization for dictionary column.
+   */
+  template <typename T, std::enable_if_t<cudf::is_dictionary<T>()>* = nullptr>
+  std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> operator()(
+    cudf::column_view const& col, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+  {
+    // compute minimum and maximum values
+    auto dev_result = reduce<T>(col, stream);
+    // copy the minmax_pair to the host to call get_element
+    using OutputType = minmax_pair<T>;
+    OutputType host_result;
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      &host_result, dev_result.data(), sizeof(OutputType), cudaMemcpyDefault, stream.value()));
+    // get the keys for those indexes
+    auto const keys = dictionary_column_view(col).keys();
+    return {detail::get_element(keys, static_cast<size_type>(host_result.min_val), stream, mr),
+            detail::get_element(keys, static_cast<size_type>(host_result.max_val), stream, mr)};
+  }
+
+  template <typename T, std::enable_if_t<!is_supported<T>()>* = nullptr>
+  std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> operator()(
+    cudf::column_view const&, rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("type not supported for minmax() operation");
+  }
+};
+
+}  // namespace
+
+/**
+ * @copydoc cudf::minmax
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> minmax(
+  cudf::column_view const& col, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  if (col.null_count() == col.size()) {
+    // this handles empty and all-null columns
+    // return scalars with valid==false
+    return {make_default_constructed_scalar(col.type(), stream, mr),
+            make_default_constructed_scalar(col.type(), stream, mr)};
+  }
+
+  return type_dispatcher(col.type(), minmax_functor{}, col, stream, mr);
+}
+}  // namespace detail
+
+std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> minmax(
+  column_view const& col, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::minmax(col, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/reductions/nested_type_minmax_util.cuh b/cpp/src/reductions/nested_type_minmax_util.cuh
new file mode 100644
index 0000000..3cf390d
--- /dev/null
+++ b/cpp/src/reductions/nested_type_minmax_util.cuh
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/reduction/detail/reduction_operators.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+/**
+ * @brief Binary operator ArgMin/ArgMax with index values into the input table.
+ */
+template <typename DeviceComparator>
+struct row_arg_minmax_fn {
+  size_type const num_rows;
+  DeviceComparator const comp;
+  bool const is_arg_min;
+
+  row_arg_minmax_fn(size_type num_rows_, DeviceComparator comp_, bool const is_arg_min_)
+    : num_rows{num_rows_}, comp{std::move(comp_)}, is_arg_min{is_arg_min_}
+  {
+  }
+
+  // This function is explicitly prevented from inlining, because it calls to
+  // `DeviceComparator::operator()` which is inlined and very heavy-weight. Inlining
+  // this would result in huge code and significantly compile time when instantiated and
+  // used with `thrust::reduce_by_key` or `thrust::scan_by_key`.
+  __attribute__((noinline)) __device__ auto operator()(size_type lhs_idx, size_type rhs_idx) const
+  {
+    // The extra bounds checking is due to issue github.com/rapidsai/cudf/issues/9156 and
+    // github.com/NVIDIA/thrust/issues/1525
+    // where invalid random values may be passed here by thrust::reduce_by_key
+    if (lhs_idx < 0 || lhs_idx >= num_rows) { return rhs_idx; }
+    if (rhs_idx < 0 || rhs_idx >= num_rows) { return lhs_idx; }
+
+    // Return `lhs_idx` iff:
+    //   row(lhs_idx) <  row(rhs_idx) and finding ArgMin, or
+    //   row(lhs_idx) >= row(rhs_idx) and finding ArgMax.
+    return comp(lhs_idx, rhs_idx) == is_arg_min ? lhs_idx : rhs_idx;
+  }
+};
+
+/**
+ * @brief The null order when comparing a null with non-null elements. Currently support only the
+ * default null order: nulls are compared as LESS than any other non-null elements.
+ */
+auto static constexpr DEFAULT_NULL_ORDER = cudf::null_order::BEFORE;
+
+/**
+ * @brief The utility class to provide a binary operator object for lexicographic comparison of
+ * nested-type elements.
+ *
+ * The binary operator provided by this class has an explicit non-inline `operator()` method to
+ * prevent excessive compile time when working with `thrust::reduce_by_key`.
+ *
+ * When it is a structs or a lists column, top-level NULLs are compared as larger than all other
+ * non-null elements - if finding for ARGMIN, or smaller than all other non-null elements - if
+ * finding for ARGMAX. This helps achieve the results of finding the min or max element when nulls
+ * are excluded from the operations, returning null only when all the input elements are nulls.
+ */
+class comparison_binop_generator {
+ private:
+  cudf::table_view const input_tview;
+  bool const has_nulls;
+  bool const is_min_op;
+  rmm::cuda_stream_view stream;
+
+  // Contains data used in `row_comparator` below, thus needs to be kept alive as a member variable.
+  std::unique_ptr<cudf::structs::detail::flattened_table> const flattened_input;
+
+  // Contains data used in the returned binop, thus needs to be kept alive as a member variable.
+  cudf::experimental::row::lexicographic::self_comparator row_comparator;
+
+  comparison_binop_generator(column_view const& input_,
+                             bool is_min_op_,
+                             rmm::cuda_stream_view stream_)
+    : input_tview{cudf::table_view{{input_}}},
+      has_nulls{cudf::has_nested_nulls(input_tview)},
+      is_min_op{is_min_op_},
+      stream{stream_},
+      flattened_input{cudf::structs::detail::flatten_nested_columns(
+        input_tview,
+        {},
+        std::vector<null_order>{DEFAULT_NULL_ORDER},
+        cudf::structs::detail::column_nullability::MATCH_INCOMING,
+        stream,
+        rmm::mr::get_current_device_resource())},
+      row_comparator{[&input_,
+                      &input_tview     = input_tview,
+                      &flattened_input = flattened_input,
+                      is_min_op_,
+                      stream_]() {
+        if (is_min_op_ && input_.has_nulls()) {
+          // If the input column is nested type (struct/list) and has nulls (at the top level), null
+          // structs/lists are excluded from the operations. That is equivalent to considering
+          // top-level nulls as larger than all other non-null elements (if finding for ARGMIN), or
+          // smaller than all other non-null elements (if finding for ARGMAX).
+
+          if (input_.type().id() == cudf::type_id::STRUCT) {
+            // For struct type, it is simple: Just set a separate null order (`null_order::AFTER`)
+            // for the top level column, which is stored at the first position in the null_orders
+            // array resulted from struct flattening.
+            auto null_orders    = flattened_input->null_orders();
+            null_orders.front() = cudf::null_order::AFTER;
+            return cudf::experimental::row::lexicographic::self_comparator{
+              flattened_input->flattened_columns(), {}, null_orders, stream_};
+          } else {
+            // For list type, we cannot set a separate null order for the top level column.
+            // Thus, we have to workaround this by creating a dummy (empty) struct column view
+            // having the same null mask as the input lists column.
+            // This dummy column will have a different null order (`null_order::AFTER`).
+            auto const null_orders =
+              std::vector<null_order>{cudf::null_order::AFTER, DEFAULT_NULL_ORDER};
+            auto const dummy_struct = column_view{data_type{type_id::STRUCT},
+                                                  input_.size(),
+                                                  nullptr,
+                                                  input_.null_mask(),
+                                                  input_.null_count(),
+                                                  0,
+                                                  {}};
+            return cudf::experimental::row::lexicographic::self_comparator{
+              cudf::table_view{{dummy_struct, input_}}, {}, null_orders, stream_};
+          }
+        } else {
+          return cudf::experimental::row::lexicographic::self_comparator{
+            input_tview, {}, std::vector<null_order>{DEFAULT_NULL_ORDER}, stream_};
+        }
+      }()}
+  {
+  }
+
+ public:
+  auto binop() const
+  {
+    auto const device_comp = row_comparator.less<true>(cudf::nullate::DYNAMIC{has_nulls});
+    return row_arg_minmax_fn(input_tview.num_rows(), device_comp, is_min_op);
+  }
+
+  template <typename BinOp>
+  static auto create(column_view const& input, rmm::cuda_stream_view stream)
+  {
+    CUDF_EXPECTS(cudf::is_nested(input.type()),
+                 "This utility class is designed exclusively for nested input types.");
+    return comparison_binop_generator(input,
+                                      std::is_same_v<BinOp, cudf::reduction::detail::op::min> ||
+                                        std::is_same_v<BinOp, cudf::DeviceMin>,
+                                      stream);
+  }
+
+  template <cudf::aggregation::Kind K>
+  static auto create(column_view const& input, rmm::cuda_stream_view stream)
+  {
+    CUDF_EXPECTS(cudf::is_nested(input.type()),
+                 "This utility class is designed exclusively for nested input types.");
+    return comparison_binop_generator(
+      input, K == cudf::aggregation::MIN || K == cudf::aggregation::ARGMIN, stream);
+  }
+};
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/nth_element.cu b/cpp/src/reductions/nth_element.cu
new file mode 100644
index 0000000..ef58ec3
--- /dev/null
+++ b/cpp/src/reductions/nth_element.cu
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scan.h>
+
+namespace cudf::reduction::detail {
+
+std::unique_ptr<cudf::scalar> nth_element(column_view const& col,
+                                          size_type n,
+                                          null_policy null_handling,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(n >= -col.size() and n < col.size(), "Index out of bounds");
+  auto wrap_n = [n](size_type size) { return (n < 0 ? size + n : n); };
+  if (null_handling == null_policy::EXCLUDE and col.has_nulls()) {
+    auto valid_count = col.size() - col.null_count();
+    n                = wrap_n(valid_count);
+    CUDF_EXPECTS(n >= 0 and n < valid_count, "Index out of bounds");
+    auto dcol = column_device_view::create(col, stream);
+    auto bitmask_iterator =
+      thrust::make_transform_iterator(cudf::detail::make_validity_iterator(*dcol),
+                                      [] __device__(auto b) { return static_cast<size_type>(b); });
+    rmm::device_uvector<size_type> null_skipped_index(col.size(), stream);
+    // null skipped index for valids only.
+    thrust::inclusive_scan(rmm::exec_policy(stream),
+                           bitmask_iterator,
+                           bitmask_iterator + col.size(),
+                           null_skipped_index.begin());
+
+    auto n_pos = thrust::upper_bound(
+      rmm::exec_policy(stream), null_skipped_index.begin(), null_skipped_index.end(), n);
+    auto null_skipped_n = n_pos - null_skipped_index.begin();
+    return cudf::detail::get_element(col, null_skipped_n, stream, mr);
+  } else {
+    n = wrap_n(col.size());
+    return cudf::detail::get_element(col, n, stream, mr);
+  }
+}
+
+}  // namespace cudf::reduction::detail
diff --git a/cpp/src/reductions/product.cu b/cpp/src/reductions/product.cu
new file mode 100644
index 0000000..2e48381
--- /dev/null
+++ b/cpp/src/reductions/product.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> product(column_view const& col,
+                                      cudf::data_type const output_dtype,
+                                      std::optional<std::reference_wrapper<scalar const>> init,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  return cudf::type_dispatcher(
+    cudf::is_dictionary(col.type()) ? dictionary_column_view(col).keys().type() : col.type(),
+    simple::detail::element_type_dispatcher<op::product>{},
+    col,
+    output_dtype,
+    init,
+    stream,
+    mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/reductions.cpp b/cpp/src/reductions/reductions.cpp
new file mode 100644
index 0000000..23171ba
--- /dev/null
+++ b/cpp/src/reductions/reductions.cpp
@@ -0,0 +1,224 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/quantiles.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/reduction/detail/histogram.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+struct reduce_dispatch_functor {
+  column_view const col;
+  data_type output_dtype;
+  std::optional<std::reference_wrapper<scalar const>> init;
+  rmm::mr::device_memory_resource* mr;
+  rmm::cuda_stream_view stream;
+
+  reduce_dispatch_functor(column_view const& col,
+                          data_type output_dtype,
+                          std::optional<std::reference_wrapper<scalar const>> init,
+                          rmm::cuda_stream_view stream,
+                          rmm::mr::device_memory_resource* mr)
+    : col(col), output_dtype(output_dtype), init(init), mr(mr), stream(stream)
+  {
+  }
+
+  template <aggregation::Kind k>
+  std::unique_ptr<scalar> operator()(reduce_aggregation const& agg)
+  {
+    switch (k) {
+      case aggregation::SUM: return sum(col, output_dtype, init, stream, mr);
+      case aggregation::PRODUCT: return product(col, output_dtype, init, stream, mr);
+      case aggregation::MIN: return min(col, output_dtype, init, stream, mr);
+      case aggregation::MAX: return max(col, output_dtype, init, stream, mr);
+      case aggregation::ANY: return any(col, output_dtype, init, stream, mr);
+      case aggregation::ALL: return all(col, output_dtype, init, stream, mr);
+      case aggregation::HISTOGRAM: return histogram(col, stream, mr);
+      case aggregation::MERGE_HISTOGRAM: return merge_histogram(col, stream, mr);
+      case aggregation::SUM_OF_SQUARES: return sum_of_squares(col, output_dtype, stream, mr);
+      case aggregation::MEAN: return mean(col, output_dtype, stream, mr);
+      case aggregation::VARIANCE: {
+        auto var_agg = static_cast<cudf::detail::var_aggregation const&>(agg);
+        return variance(col, output_dtype, var_agg._ddof, stream, mr);
+      }
+      case aggregation::STD: {
+        auto var_agg = static_cast<cudf::detail::std_aggregation const&>(agg);
+        return standard_deviation(col, output_dtype, var_agg._ddof, stream, mr);
+      }
+      case aggregation::MEDIAN: {
+        auto current_mr     = rmm::mr::get_current_device_resource();
+        auto sorted_indices = cudf::detail::sorted_order(
+          table_view{{col}}, {}, {null_order::AFTER}, stream, current_mr);
+        auto valid_sorted_indices =
+          cudf::detail::split(*sorted_indices, {col.size() - col.null_count()}, stream)[0];
+        auto col_ptr = cudf::detail::quantile(
+          col, {0.5}, interpolation::LINEAR, valid_sorted_indices, true, stream, current_mr);
+        return cudf::detail::get_element(*col_ptr, 0, stream, mr);
+      }
+      case aggregation::QUANTILE: {
+        auto quantile_agg = static_cast<cudf::detail::quantile_aggregation const&>(agg);
+        CUDF_EXPECTS(quantile_agg._quantiles.size() == 1,
+                     "Reduction quantile accepts only one quantile value");
+        auto current_mr     = rmm::mr::get_current_device_resource();
+        auto sorted_indices = cudf::detail::sorted_order(
+          table_view{{col}}, {}, {null_order::AFTER}, stream, current_mr);
+        auto valid_sorted_indices =
+          cudf::detail::split(*sorted_indices, {col.size() - col.null_count()}, stream)[0];
+
+        auto col_ptr = cudf::detail::quantile(col,
+                                              quantile_agg._quantiles,
+                                              quantile_agg._interpolation,
+                                              valid_sorted_indices,
+                                              true,
+                                              stream,
+                                              current_mr);
+        return cudf::detail::get_element(*col_ptr, 0, stream, mr);
+      }
+      case aggregation::NUNIQUE: {
+        auto nunique_agg = static_cast<cudf::detail::nunique_aggregation const&>(agg);
+        return cudf::make_fixed_width_scalar(
+          cudf::detail::distinct_count(
+            col, nunique_agg._null_handling, nan_policy::NAN_IS_VALID, stream),
+          stream,
+          mr);
+      }
+      case aggregation::NTH_ELEMENT: {
+        auto nth_agg = static_cast<cudf::detail::nth_element_aggregation const&>(agg);
+        return nth_element(col, nth_agg._n, nth_agg._null_handling, stream, mr);
+      }
+      case aggregation::COLLECT_LIST: {
+        auto col_agg = static_cast<cudf::detail::collect_list_aggregation const&>(agg);
+        return collect_list(col, col_agg._null_handling, stream, mr);
+      }
+      case aggregation::COLLECT_SET: {
+        auto col_agg = static_cast<cudf::detail::collect_set_aggregation const&>(agg);
+        return collect_set(
+          col, col_agg._null_handling, col_agg._nulls_equal, col_agg._nans_equal, stream, mr);
+      }
+      case aggregation::MERGE_LISTS: {
+        return merge_lists(col, stream, mr);
+      }
+      case aggregation::MERGE_SETS: {
+        auto col_agg = static_cast<cudf::detail::merge_sets_aggregation const&>(agg);
+        return merge_sets(col, col_agg._nulls_equal, col_agg._nans_equal, stream, mr);
+      }
+      case aggregation::TDIGEST: {
+        CUDF_EXPECTS(output_dtype.id() == type_id::STRUCT,
+                     "Tdigest aggregations expect output type to be STRUCT");
+        auto td_agg = static_cast<cudf::detail::tdigest_aggregation const&>(agg);
+        return tdigest::detail::reduce_tdigest(col, td_agg.max_centroids, stream, mr);
+      }
+      case aggregation::MERGE_TDIGEST: {
+        CUDF_EXPECTS(output_dtype.id() == type_id::STRUCT,
+                     "Tdigest aggregations expect output type to be STRUCT");
+        auto td_agg = static_cast<cudf::detail::merge_tdigest_aggregation const&>(agg);
+        return tdigest::detail::reduce_merge_tdigest(col, td_agg.max_centroids, stream, mr);
+      }
+      default: CUDF_FAIL("Unsupported reduction operator");
+    }
+  }
+};
+
+std::unique_ptr<scalar> reduce(column_view const& col,
+                               reduce_aggregation const& agg,
+                               data_type output_dtype,
+                               std::optional<std::reference_wrapper<scalar const>> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!init.has_value() || col.type() == init.value().get().type(),
+               "column and initial value must be the same type");
+  if (init.has_value() && !(agg.kind == aggregation::SUM || agg.kind == aggregation::PRODUCT ||
+                            agg.kind == aggregation::MIN || agg.kind == aggregation::MAX ||
+                            agg.kind == aggregation::ANY || agg.kind == aggregation::ALL)) {
+    CUDF_FAIL(
+      "Initial value is only supported for SUM, PRODUCT, MIN, MAX, ANY, and ALL aggregation types");
+  }
+
+  // Returns default scalar if input column is empty or all null
+  if (col.size() <= col.null_count()) {
+    if (agg.kind == aggregation::TDIGEST || agg.kind == aggregation::MERGE_TDIGEST) {
+      return tdigest::detail::make_empty_tdigest_scalar(stream, mr);
+    }
+
+    if (agg.kind == aggregation::HISTOGRAM) {
+      return std::make_unique<list_scalar>(
+        std::move(*reduction::detail::make_empty_histogram_like(col)), true, stream, mr);
+    }
+    if (agg.kind == aggregation::MERGE_HISTOGRAM) {
+      return std::make_unique<list_scalar>(
+        std::move(*reduction::detail::make_empty_histogram_like(col.child(0))), true, stream, mr);
+    }
+
+    if (output_dtype.id() == type_id::LIST) {
+      if (col.type() == output_dtype) { return make_empty_scalar_like(col, stream, mr); }
+      // Under some circumstance, the output type will become the List of input type,
+      // such as: collect_list or collect_set. So, we have to handcraft the default scalar.
+      auto scalar = make_list_scalar(empty_like(col)->view(), stream, mr);
+      scalar->set_valid_async(false, stream);
+      return scalar;
+    }
+    if (output_dtype.id() == type_id::STRUCT) { return make_empty_scalar_like(col, stream, mr); }
+
+    auto result = make_default_constructed_scalar(output_dtype, stream, mr);
+    if (agg.kind == aggregation::ANY || agg.kind == aggregation::ALL) {
+      // empty input should return false for ANY and return true for ALL
+      dynamic_cast<numeric_scalar<bool>*>(result.get())
+        ->set_value(agg.kind == aggregation::ALL, stream);
+    }
+    return result;
+  }
+
+  return cudf::detail::aggregation_dispatcher(
+    agg.kind, reduce_dispatch_functor{col, output_dtype, init, stream, mr}, agg);
+}
+}  // namespace detail
+}  // namespace reduction
+
+std::unique_ptr<scalar> reduce(column_view const& col,
+                               reduce_aggregation const& agg,
+                               data_type output_dtype,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return reduction::detail::reduce(
+    col, agg, output_dtype, std::nullopt, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<scalar> reduce(column_view const& col,
+                               reduce_aggregation const& agg,
+                               data_type output_dtype,
+                               std::optional<std::reference_wrapper<scalar const>> init,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return reduction::detail::reduce(col, agg, output_dtype, init, cudf::get_default_stream(), mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/reductions/scan/rank_scan.cu b/cpp/src/reductions/scan/rank_scan.cu
new file mode 100644
index 0000000..5387630
--- /dev/null
+++ b/cpp/src/reductions/scan/rank_scan.cu
@@ -0,0 +1,155 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+template <typename device_comparator_type, typename value_resolver>
+struct rank_equality_functor {
+  rank_equality_functor(device_comparator_type comparator, value_resolver resolver)
+    : _comparator(comparator), _resolver(resolver)
+  {
+  }
+
+  auto __device__ operator()(size_type row_index) const noexcept
+  {
+    return _resolver(row_index == 0 || !_comparator(row_index, row_index - 1), row_index);
+  }
+
+ private:
+  device_comparator_type _comparator;
+  value_resolver _resolver;
+};
+
+/**
+ * @brief generate row ranks or dense ranks using a row comparison then scan the results
+ *
+ * @tparam value_resolver flag value resolver with boolean first and row number arguments
+ * @tparam scan_operator scan function ran on the flag values
+ * @param order_by input column to generate ranks for
+ * @param resolver flag value resolver
+ * @param scan_op scan operation ran on the flag results
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return std::unique_ptr<column> rank values
+ */
+template <typename value_resolver, typename scan_operator>
+std::unique_ptr<column> rank_generator(column_view const& order_by,
+                                       value_resolver resolver,
+                                       scan_operator scan_op,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto const order_by_tview = table_view{{order_by}};
+  auto comp = cudf::experimental::row::equality::self_comparator(order_by_tview, stream);
+
+  auto ranks = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, order_by.size(), mask_state::UNALLOCATED, stream, mr);
+  auto mutable_ranks = ranks->mutable_view();
+
+  auto const comparator_helper = [&](auto const device_comparator) {
+    thrust::tabulate(rmm::exec_policy(stream),
+                     mutable_ranks.begin<size_type>(),
+                     mutable_ranks.end<size_type>(),
+                     rank_equality_functor<decltype(device_comparator), value_resolver>(
+                       device_comparator, resolver));
+  };
+
+  if (cudf::detail::has_nested_columns(order_by_tview)) {
+    auto const device_comparator =
+      comp.equal_to<true>(nullate::DYNAMIC{has_nested_nulls(table_view({order_by}))});
+    comparator_helper(device_comparator);
+  } else {
+    auto const device_comparator =
+      comp.equal_to<false>(nullate::DYNAMIC{has_nested_nulls(table_view({order_by}))});
+    comparator_helper(device_comparator);
+  }
+
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         mutable_ranks.begin<size_type>(),
+                         mutable_ranks.end<size_type>(),
+                         mutable_ranks.begin<size_type>(),
+                         scan_op);
+  return ranks;
+}
+
+}  // namespace
+
+std::unique_ptr<column> inclusive_dense_rank_scan(column_view const& order_by,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  return rank_generator(
+    order_by,
+    [] __device__(bool const unequal, size_type const) { return unequal ? 1 : 0; },
+    DeviceSum{},
+    stream,
+    mr);
+}
+
+std::unique_ptr<column> inclusive_rank_scan(column_view const& order_by,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!cudf::structs::detail::is_or_has_nested_lists(order_by),
+               "Unsupported list type in rank scan.");
+  return rank_generator(
+    order_by,
+    [] __device__(bool unequal, auto row_index) { return unequal ? row_index + 1 : 0; },
+    DeviceMax{},
+    stream,
+    mr);
+}
+
+std::unique_ptr<column> inclusive_one_normalized_percent_rank_scan(
+  column_view const& order_by, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  auto const rank_column =
+    inclusive_rank_scan(order_by, stream, rmm::mr::get_current_device_resource());
+  auto const rank_view = rank_column->view();
+
+  // Result type for min 0-index percent rank is independent of input type.
+  using result_type        = double;
+  auto percent_rank_result = cudf::make_fixed_width_column(
+    data_type{type_to_id<result_type>()}, rank_view.size(), mask_state::UNALLOCATED, stream, mr);
+
+  thrust::transform(rmm::exec_policy(stream),
+                    rank_view.begin<size_type>(),
+                    rank_view.end<size_type>(),
+                    percent_rank_result->mutable_view().begin<result_type>(),
+                    [n_rows = rank_view.size()] __device__(auto const rank) {
+                      return n_rows == 1 ? 0.0 : ((rank - 1.0) / (n_rows - 1));
+                    });
+  return percent_rank_result;
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/reductions/scan/scan.cpp b/cpp/src/reductions/scan/scan.cpp
new file mode 100644
index 0000000..2871ee2
--- /dev/null
+++ b/cpp/src/reductions/scan/scan.cpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/scan.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+namespace cudf {
+
+namespace detail {
+std::unique_ptr<column> scan(column_view const& input,
+                             scan_aggregation const& agg,
+                             scan_type inclusive,
+                             null_policy null_handling,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  if (agg.kind == aggregation::RANK) {
+    CUDF_EXPECTS(inclusive == scan_type::INCLUSIVE,
+                 "Rank aggregation operator requires an inclusive scan");
+    auto const& rank_agg = static_cast<cudf::detail::rank_aggregation const&>(agg);
+    if (rank_agg._method == rank_method::MIN) {
+      if (rank_agg._percentage == rank_percentage::NONE) {
+        return inclusive_rank_scan(input, stream, mr);
+      } else if (rank_agg._percentage == rank_percentage::ONE_NORMALIZED) {
+        return inclusive_one_normalized_percent_rank_scan(input, stream, mr);
+      }
+    } else if (rank_agg._method == rank_method::DENSE) {
+      return inclusive_dense_rank_scan(input, stream, mr);
+    }
+    CUDF_FAIL("Unsupported rank aggregation method for inclusive scan");
+  }
+
+  return inclusive == scan_type::EXCLUSIVE
+           ? detail::scan_exclusive(input, agg, null_handling, stream, mr)
+           : detail::scan_inclusive(input, agg, null_handling, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> scan(column_view const& input,
+                             scan_aggregation const& agg,
+                             scan_type inclusive,
+                             null_policy null_handling,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::scan(input, agg, inclusive, null_handling, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/reductions/scan/scan.cuh b/cpp/src/reductions/scan/scan.cuh
new file mode 100644
index 0000000..e575bde
--- /dev/null
+++ b/cpp/src/reductions/scan/scan.cuh
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/reduction.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <utility>
+
+namespace cudf {
+namespace detail {
+
+// logical-and scan of the null mask of the input view
+std::pair<rmm::device_buffer, size_type> mask_scan(column_view const& input_view,
+                                                   scan_type inclusive,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr);
+
+template <template <typename> typename DispatchFn>
+std::unique_ptr<column> scan_agg_dispatch(column_view const& input,
+                                          scan_aggregation const& agg,
+                                          bitmask_type const* output_mask,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  switch (agg.kind) {
+    case aggregation::SUM:
+      return type_dispatcher<dispatch_storage_type>(
+        input.type(), DispatchFn<DeviceSum>(), input, output_mask, stream, mr);
+    case aggregation::MIN:
+      return type_dispatcher<dispatch_storage_type>(
+        input.type(), DispatchFn<DeviceMin>(), input, output_mask, stream, mr);
+    case aggregation::MAX:
+      return type_dispatcher<dispatch_storage_type>(
+        input.type(), DispatchFn<DeviceMax>(), input, output_mask, stream, mr);
+    case aggregation::PRODUCT:
+      // a product scan on a decimal type with non-zero scale would result in each element having
+      // a different scale, and because scale is stored once per column, this is not possible
+      if (is_fixed_point(input.type())) CUDF_FAIL("decimal32/64/128 cannot support product scan");
+      return type_dispatcher<dispatch_storage_type>(
+        input.type(), DispatchFn<DeviceProduct>(), input, output_mask, stream, mr);
+    default: CUDF_FAIL("Unsupported aggregation operator for scan");
+  }
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/reductions/scan/scan_exclusive.cu b/cpp/src/reductions/scan/scan_exclusive.cu
new file mode 100644
index 0000000..3fb1fc6
--- /dev/null
+++ b/cpp/src/reductions/scan/scan_exclusive.cu
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "scan.cuh"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/null_mask.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/scan.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+/**
+ * @brief Dispatcher for running a scan operation on an input column
+ *
+ * @tparam Op device binary operator (e.g. min, max, sum)
+ */
+template <typename Op>
+struct scan_dispatcher {
+ public:
+  /**
+   * @brief Creates a new column from input column by applying exclusive scan operation
+   *
+   * @tparam T type of input column
+   *
+   * @param input  Input column view
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   * @return Output column with scan results
+   */
+  template <typename T, std::enable_if_t<cuda::std::is_arithmetic_v<T>>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     bitmask_type const*,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto output_column =
+      detail::allocate_like(input, input.size(), mask_allocation_policy::NEVER, stream, mr);
+    mutable_column_view output = output_column->mutable_view();
+
+    auto d_input  = column_device_view::create(input, stream);
+    auto identity = Op::template identity<T>();
+
+    auto begin = make_null_replacement_iterator(*d_input, identity, input.has_nulls());
+    thrust::exclusive_scan(
+      rmm::exec_policy(stream), begin, begin + input.size(), output.data<T>(), identity, Op{});
+
+    CUDF_CHECK_CUDA(stream.value());
+    return output_column;
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<not cuda::std::is_arithmetic_v<T>, std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Non-arithmetic types not supported for exclusive scan");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> scan_exclusive(column_view const& input,
+                                       scan_aggregation const& agg,
+                                       null_policy null_handling,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto [mask, null_count] = [&] {
+    if (null_handling == null_policy::EXCLUDE) {
+      return std::make_pair(std::move(detail::copy_bitmask(input, stream, mr)), input.null_count());
+    } else if (input.nullable()) {
+      return mask_scan(input, scan_type::EXCLUSIVE, stream, mr);
+    }
+    return std::make_pair(rmm::device_buffer{}, size_type{0});
+  }();
+
+  auto output = scan_agg_dispatch<scan_dispatcher>(
+    input, agg, static_cast<bitmask_type*>(mask.data()), stream, mr);
+  output->set_null_mask(mask, null_count);
+
+  return output;
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/reductions/scan/scan_inclusive.cu b/cpp/src/reductions/scan/scan_inclusive.cu
new file mode 100644
index 0000000..e74fce6
--- /dev/null
+++ b/cpp/src/reductions/scan/scan_inclusive.cu
@@ -0,0 +1,310 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <reductions/nested_type_minmax_util.cuh>
+#include <reductions/scan/scan.cuh>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/reduction.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/find.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+
+// logical-and scan of the null mask of the input view
+std::pair<rmm::device_buffer, size_type> mask_scan(column_view const& input_view,
+                                                   scan_type inclusive,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_buffer mask =
+    detail::create_null_mask(input_view.size(), mask_state::UNINITIALIZED, stream, mr);
+  auto d_input   = column_device_view::create(input_view, stream);
+  auto valid_itr = detail::make_validity_iterator(*d_input);
+
+  auto first_null_position = [&] {
+    size_type const first_null =
+      thrust::find_if_not(
+        rmm::exec_policy(stream), valid_itr, valid_itr + input_view.size(), thrust::identity{}) -
+      valid_itr;
+    size_type const exclusive_offset = (inclusive == scan_type::EXCLUSIVE) ? 1 : 0;
+    return std::min(input_view.size(), first_null + exclusive_offset);
+  }();
+
+  set_null_mask(static_cast<bitmask_type*>(mask.data()), 0, first_null_position, true, stream);
+  set_null_mask(
+    static_cast<bitmask_type*>(mask.data()), first_null_position, input_view.size(), false, stream);
+  return {std::move(mask), input_view.size() - first_null_position};
+}
+
+namespace {
+
+/**
+ * @brief Min/Max inclusive scan operator
+ *
+ * This operator will accept index values, check them and then
+ * run the `Op` operation on the individual element objects.
+ * The returned result is the appropriate index value.
+ *
+ * This was specifically created to workaround a thrust issue
+ * https://github.com/NVIDIA/thrust/issues/1479
+ * where invalid values are passed to the operator.
+ */
+template <typename Element, typename Op>
+struct min_max_scan_operator {
+  column_device_view const col;      ///< strings column device view
+  Element const null_replacement{};  ///< value used when element is null
+  bool const has_nulls;              ///< true if col has null elements
+
+  min_max_scan_operator(column_device_view const& col, bool has_nulls = true)
+    : col{col}, null_replacement{Op::template identity<Element>()}, has_nulls{has_nulls}
+  {
+    // verify validity bitmask is non-null, otherwise, is_null_nocheck() will crash
+    if (has_nulls) CUDF_EXPECTS(col.nullable(), "column with nulls must have a validity bitmask");
+  }
+
+  __device__ inline size_type operator()(size_type lhs, size_type rhs) const
+  {
+    // thrust::inclusive_scan may pass us garbage values so we need to protect ourselves;
+    // in these cases the return value does not matter since the result is not used
+    if (lhs < 0 || rhs < 0 || lhs >= col.size() || rhs >= col.size()) return 0;
+    Element d_lhs =
+      has_nulls && col.is_null_nocheck(lhs) ? null_replacement : col.element<Element>(lhs);
+    Element d_rhs =
+      has_nulls && col.is_null_nocheck(rhs) ? null_replacement : col.element<Element>(rhs);
+    return Op{}(d_lhs, d_rhs) == d_lhs ? lhs : rhs;
+  }
+};
+
+template <typename Op, typename T>
+struct scan_functor {
+  static std::unique_ptr<column> invoke(column_view const& input_view,
+                                        bitmask_type const*,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    auto output_column = detail::allocate_like(
+      input_view, input_view.size(), mask_allocation_policy::NEVER, stream, mr);
+    mutable_column_view result = output_column->mutable_view();
+
+    auto d_input = column_device_view::create(input_view, stream);
+    auto const begin =
+      make_null_replacement_iterator(*d_input, Op::template identity<T>(), input_view.has_nulls());
+    thrust::inclusive_scan(
+      rmm::exec_policy(stream), begin, begin + input_view.size(), result.data<T>(), Op{});
+
+    CUDF_CHECK_CUDA(stream.value());
+    return output_column;
+  }
+};
+
+struct null_iterator {
+  bitmask_type const* mask;
+  __device__ bool operator()(size_type idx) const { return !bit_is_set(mask, idx); }
+};
+
+template <typename Op>
+struct scan_functor<Op, cudf::string_view> {
+  static std::unique_ptr<column> invoke(column_view const& input_view,
+                                        bitmask_type const* mask,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    auto d_input = column_device_view::create(input_view, stream);
+
+    // build indices of the scan operation results
+    rmm::device_uvector<size_type> result_map(input_view.size(), stream);
+    thrust::inclusive_scan(
+      rmm::exec_policy(stream),
+      thrust::counting_iterator<size_type>(0),
+      thrust::counting_iterator<size_type>(input_view.size()),
+      result_map.begin(),
+      min_max_scan_operator<cudf::string_view, Op>{*d_input, input_view.has_nulls()});
+
+    if (input_view.has_nulls()) {
+      // fill the null rows with out-of-bounds values so gather records them as null;
+      // this prevents un-sanitized null entries in the output
+      auto null_itr = detail::make_counting_transform_iterator(0, null_iterator{mask});
+      auto oob_val  = thrust::constant_iterator<size_type>(input_view.size());
+      thrust::scatter_if(rmm::exec_policy(stream),
+                         oob_val,
+                         oob_val + input_view.size(),
+                         thrust::counting_iterator<size_type>(0),
+                         null_itr,
+                         result_map.data());
+    }
+
+    // call gather using the indices to build the output column
+    auto result_table = cudf::detail::gather(cudf::table_view({input_view}),
+                                             result_map,
+                                             out_of_bounds_policy::NULLIFY,
+                                             negative_index_policy::NOT_ALLOWED,
+                                             stream,
+                                             mr);
+    return std::move(result_table->release().front());
+  }
+};
+
+template <typename Op>
+struct scan_functor<Op, cudf::struct_view> {
+  static std::unique_ptr<column> invoke(column_view const& input,
+                                        bitmask_type const*,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    // Create a gather map containing indices of the prefix min/max elements.
+    auto gather_map = rmm::device_uvector<size_type>(input.size(), stream);
+    auto const binop_generator =
+      cudf::reduction::detail::comparison_binop_generator::create<Op>(input, stream);
+    thrust::inclusive_scan(rmm::exec_policy(stream),
+                           thrust::counting_iterator<size_type>(0),
+                           thrust::counting_iterator<size_type>(input.size()),
+                           gather_map.begin(),
+                           binop_generator.binop());
+
+    // Gather the children columns of the input column. Must use `get_sliced_child` to properly
+    // handle input in case it is a sliced view.
+    auto const input_children = [&] {
+      auto const it = cudf::detail::make_counting_transform_iterator(
+        0, [structs_view = structs_column_view{input}, &stream](auto const child_idx) {
+          return structs_view.get_sliced_child(child_idx, stream);
+        });
+      return std::vector<column_view>(it, it + input.num_children());
+    }();
+
+    // Gather the children elements of the prefix min/max struct elements for the output.
+    auto scanned_children = cudf::detail::gather(table_view{input_children},
+                                                 gather_map,
+                                                 out_of_bounds_policy::DONT_CHECK,
+                                                 negative_index_policy::NOT_ALLOWED,
+                                                 stream,
+                                                 mr)
+                              ->release();
+
+    // Don't need to set a null mask because that will be handled at the caller.
+    return make_structs_column(
+      input.size(), std::move(scanned_children), 0, rmm::device_buffer{0, stream, mr}, stream, mr);
+  }
+};
+
+/**
+ * @brief Dispatcher for running a Scan operation on an input column
+ *
+ * @tparam Op device binary operator
+ */
+template <typename Op>
+struct scan_dispatcher {
+ private:
+  template <typename T>
+  static constexpr bool is_supported()
+  {
+    if constexpr (std::is_same_v<T, cudf::struct_view>) {
+      return std::is_same_v<Op, DeviceMin> || std::is_same_v<Op, DeviceMax>;
+    } else {
+      return std::is_invocable_v<Op, T, T> && !cudf::is_dictionary<T>();
+    }
+  }
+
+ public:
+  /**
+   * @brief Creates a new column from the input column by applying the scan operation
+   *
+   * @param input Input column view
+   * @param null_handling How null row entries are to be processed
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory
+   * @return
+   *
+   * @tparam T type of input column
+   */
+  template <typename T, std::enable_if_t<is_supported<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     bitmask_type const* output_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return scan_functor<Op, T>::invoke(input, output_mask, stream, mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!is_supported<T>(), std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type for inclusive scan operation");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> scan_inclusive(column_view const& input,
+                                       scan_aggregation const& agg,
+                                       null_policy null_handling,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto [mask, null_count] = [&] {
+    if (null_handling == null_policy::EXCLUDE) {
+      return std::make_pair(std::move(detail::copy_bitmask(input, stream, mr)), input.null_count());
+    } else if (input.nullable()) {
+      return mask_scan(input, scan_type::INCLUSIVE, stream, mr);
+    }
+    return std::make_pair(rmm::device_buffer{}, size_type{0});
+  }();
+
+  auto output = scan_agg_dispatch<scan_dispatcher>(
+    input, agg, static_cast<bitmask_type*>(mask.data()), stream, mr);
+  output->set_null_mask(mask, null_count);
+
+  // If the input is a structs column, we also need to push down nulls from the parent output column
+  // into the children columns.
+  if (input.type().id() == type_id::STRUCT && output->has_nulls()) {
+    auto const num_rows   = output->size();
+    auto const null_count = output->null_count();
+    auto content          = output->release();
+
+    // Build new children columns.
+    auto const null_mask = reinterpret_cast<bitmask_type const*>(content.null_mask->data());
+    std::for_each(content.children.begin(),
+                  content.children.end(),
+                  [null_mask, null_count, stream, mr](auto& child) {
+                    child = structs::detail::superimpose_nulls(
+                      null_mask, null_count, std::move(child), stream, mr);
+                  });
+
+    // Replace the children columns.
+    output = cudf::make_structs_column(
+      num_rows, std::move(content.children), null_count, std::move(*content.null_mask), stream, mr);
+  }
+
+  return output;
+}
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/all.cu b/cpp/src/reductions/segmented/all.cu
new file mode 100644
index 0000000..b81a088
--- /dev/null
+++ b/cpp/src/reductions/segmented/all.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_all(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  cudf::data_type const output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(output_dtype == cudf::data_type(cudf::type_id::BOOL8),
+               "segmented_all() operation requires output type `BOOL8`");
+
+  using reducer = simple::detail::bool_result_column_dispatcher<op::min>;
+  // A minimum over bool types is used to implement all()
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, null_handling, init, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/any.cu b/cpp/src/reductions/segmented/any.cu
new file mode 100644
index 0000000..9210fbd
--- /dev/null
+++ b/cpp/src/reductions/segmented/any.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_any(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  cudf::data_type const output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(output_dtype == cudf::data_type(cudf::type_id::BOOL8),
+               "segmented_any() operation requires output type `BOOL8`");
+
+  using reducer = simple::detail::bool_result_column_dispatcher<op::max>;
+  // A maximum over bool types is used to implement any()
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, null_handling, init, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/compound.cuh b/cpp/src/reductions/segmented/compound.cuh
new file mode 100644
index 0000000..395ad4c
--- /dev/null
+++ b/cpp/src/reductions/segmented/compound.cuh
@@ -0,0 +1,175 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "counts.hpp"
+#include "update_validity.hpp"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.cuh>
+#include <cudf/reduction/detail/segmented_reduction.cuh>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/adjacent_difference.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace reduction {
+namespace compound {
+namespace detail {
+/**
+ * @brief Multi-step reduction for operations such as mean, variance, and standard deviation.
+ *
+ * @tparam InputType  the input column data-type
+ * @tparam ResultType the output data-type
+ * @tparam Op         the compound operator derived from `cudf::reduction::op::compound_op`
+ *
+ * @param col Input column view
+ * @param offsets Indices identifying segments
+ * @param null_handling Indicates if null elements should be included in the reduction
+ * @param ddof Delta degrees of freedom used for standard deviation and variance.
+ *             The divisor used is N - ddof, where N represents the number of elements.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Segmented reduce result
+ */
+template <typename InputType, typename ResultType, typename Op>
+std::unique_ptr<column> compound_segmented_reduction(column_view const& col,
+                                                     device_span<size_type const> offsets,
+                                                     null_policy null_handling,
+                                                     size_type ddof,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  auto d_col              = cudf::column_device_view::create(col, stream);
+  auto compound_op        = Op{};
+  auto const num_segments = offsets.size() - 1;
+
+  auto result = make_fixed_width_column(
+    data_type{type_to_id<ResultType>()}, num_segments, mask_state::UNALLOCATED, stream, mr);
+  auto out_itr = result->mutable_view().template begin<ResultType>();
+
+  // Compute counts
+  rmm::device_uvector<size_type> counts =
+    cudf::reduction::detail::segmented_counts(col.null_mask(),
+                                              col.has_nulls(),
+                                              offsets,
+                                              null_handling,
+                                              stream,
+                                              rmm::mr::get_current_device_resource());
+
+  // Run segmented reduction
+  if (col.has_nulls()) {
+    auto nrt = compound_op.template get_null_replacing_element_transformer<ResultType>();
+    auto itr = thrust::make_transform_iterator(d_col->pair_begin<InputType, true>(), nrt);
+    cudf::reduction::detail::segmented_reduce(
+      itr, offsets.begin(), offsets.end(), out_itr, compound_op, ddof, counts.data(), stream);
+  } else {
+    auto et  = compound_op.template get_element_transformer<ResultType>();
+    auto itr = thrust::make_transform_iterator(d_col->begin<InputType>(), et);
+    cudf::reduction::detail::segmented_reduce(
+      itr, offsets.begin(), offsets.end(), out_itr, compound_op, ddof, counts.data(), stream);
+  }
+
+  // Compute the output null mask
+  cudf::reduction::detail::segmented_update_validity(
+    *result, col, offsets, null_handling, std::nullopt, stream, mr);
+
+  return result;
+};
+
+template <typename ElementType, typename Op>
+struct compound_float_output_dispatcher {
+ private:
+  template <typename ResultType>
+  static constexpr bool is_supported_v()
+  {
+    return std::is_floating_point_v<ResultType>;
+  }
+
+ public:
+  template <typename ResultType, std::enable_if_t<is_supported_v<ResultType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     null_policy null_handling,
+                                     size_type ddof,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return compound_segmented_reduction<ElementType, ResultType, Op>(
+      col, offsets, null_handling, ddof, stream, mr);
+  }
+
+  template <typename ResultType, std::enable_if_t<not is_supported_v<ResultType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     device_span<size_type const>,
+                                     null_policy,
+                                     size_type,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Unsupported output data type");
+  }
+};
+
+template <typename Op>
+struct compound_segmented_dispatcher {
+ private:
+  template <typename ElementType>
+  static constexpr bool is_supported_v()
+  {
+    return std::is_arithmetic_v<ElementType>;
+  }
+
+ public:
+  template <typename ElementType, std::enable_if_t<is_supported_v<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     cudf::data_type const output_dtype,
+                                     null_policy null_handling,
+                                     size_type ddof,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return cudf::type_dispatcher(output_dtype,
+                                 compound_float_output_dispatcher<ElementType, Op>(),
+                                 col,
+                                 offsets,
+                                 null_handling,
+                                 ddof,
+                                 stream,
+                                 mr);
+  }
+
+  template <typename ElementType, std::enable_if_t<not is_supported_v<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     device_span<size_type const>,
+                                     cudf::data_type const,
+                                     null_policy,
+                                     size_type,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Compound operators are not supported for non-arithmetic types");
+  }
+};
+
+}  // namespace detail
+}  // namespace compound
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/counts.cu b/cpp/src/reductions/segmented/counts.cu
new file mode 100644
index 0000000..b9064ad
--- /dev/null
+++ b/cpp/src/reductions/segmented/counts.cu
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "counts.hpp"
+
+#include <cudf/detail/null_mask.cuh>
+
+#include <thrust/adjacent_difference.h>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+rmm::device_uvector<size_type> segmented_counts(bitmask_type const* null_mask,
+                                                bool has_nulls,
+                                                device_span<size_type const> offsets,
+                                                null_policy null_handling,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  auto const num_segments = offsets.size() - 1;
+
+  if (has_nulls && (null_handling == null_policy::EXCLUDE)) {
+    return cudf::detail::segmented_count_bits(null_mask,
+                                              offsets.begin(),
+                                              offsets.end() - 1,
+                                              offsets.begin() + 1,
+                                              cudf::detail::count_bits_policy::SET_BITS,
+                                              stream,
+                                              mr);
+  }
+
+  rmm::device_uvector<size_type> valid_counts(num_segments, stream, mr);
+  thrust::adjacent_difference(
+    rmm::exec_policy(stream), offsets.begin() + 1, offsets.end(), valid_counts.begin());
+  return valid_counts;
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/counts.hpp b/cpp/src/reductions/segmented/counts.hpp
new file mode 100644
index 0000000..c5ee1fa
--- /dev/null
+++ b/cpp/src/reductions/segmented/counts.hpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf {
+class column_device_view;
+
+namespace reduction {
+namespace detail {
+
+/**
+ * @brief Compute the number of elements per segment
+ *
+ * If `null_handling == null_policy::EXCLUDE`, the count for each
+ * segment omits any null entries. Otherwise, this returns the number
+ * of elements in each segment.
+ *
+ * @param null_mask Null values over which the segment offsets apply
+ * @param has_nulls True if d_col contains any nulls
+ * @param offsets Indices to segment boundaries
+ * @param null_handling How null entries are processed within each segment
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return The number of elements in each segment
+ */
+rmm::device_uvector<size_type> segmented_counts(bitmask_type const* null_mask,
+                                                bool has_nulls,
+                                                device_span<size_type const> offsets,
+                                                null_policy null_handling,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/max.cu b/cpp/src/reductions/segmented/max.cu
new file mode 100644
index 0000000..c07c8fb
--- /dev/null
+++ b/cpp/src/reductions/segmented/max.cu
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_max(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  cudf::data_type const output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(col.type() == output_dtype,
+               "segmented_max() operation requires matching output type");
+  using reducer = simple::detail::same_column_type_dispatcher<op::max>;
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, null_handling, init, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/mean.cu b/cpp/src/reductions/segmented/mean.cu
new file mode 100644
index 0000000..99f1533
--- /dev/null
+++ b/cpp/src/reductions/segmented/mean.cu
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compound.cuh"
+
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_mean(column_view const& col,
+                                             device_span<size_type const> offsets,
+                                             cudf::data_type const output_dtype,
+                                             null_policy null_handling,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  using reducer            = compound::detail::compound_segmented_dispatcher<op::mean>;
+  constexpr size_type ddof = 1;  // ddof for mean calculation
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, output_dtype, null_handling, ddof, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/min.cu b/cpp/src/reductions/segmented/min.cu
new file mode 100644
index 0000000..f1597f9
--- /dev/null
+++ b/cpp/src/reductions/segmented/min.cu
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_min(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  data_type const output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(col.type() == output_dtype,
+               "segmented_min() operation requires matching output type");
+  using reducer = simple::detail::same_column_type_dispatcher<op::min>;
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, null_handling, init, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/nunique.cu b/cpp/src/reductions/segmented/nunique.cu
new file mode 100644
index 0000000..bd1efb4
--- /dev/null
+++ b/cpp/src/reductions/segmented/nunique.cu
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "update_validity.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/labeling/label_segments.cuh>
+#include <cudf/reduction/detail/segmented_reduction.cuh>
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+namespace {
+template <typename ComparatorType>
+struct is_unique_fn {
+  column_device_view const d_col;
+  ComparatorType row_equal;
+  null_policy null_handling;
+  size_type const* offsets;
+  size_type const* labels;
+
+  __device__ size_type operator()(size_type idx) const
+  {
+    if (null_handling == null_policy::EXCLUDE && d_col.is_null(idx)) { return 0; }
+    return static_cast<size_type>(offsets[labels[idx]] == idx || (!row_equal(idx, idx - 1)));
+  }
+};
+}  // namespace
+
+std::unique_ptr<cudf::column> segmented_nunique(column_view const& col,
+                                                device_span<size_type const> offsets,
+                                                null_policy null_handling,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  // only support non-nested types
+  CUDF_EXPECTS(!cudf::is_nested(col.type()),
+               "segmented reduce nunique only supports non-nested column types");
+
+  // compute the unique identifiers within each segment
+  auto const identifiers = [&] {
+    auto const d_col = column_device_view::create(col, stream);
+    auto const comparator =
+      cudf::experimental::row::equality::self_comparator{table_view({col}), stream};
+    auto const row_equal =
+      comparator.equal_to<false>(cudf::nullate::DYNAMIC{col.has_nulls()}, null_equality::EQUAL);
+
+    auto labels = rmm::device_uvector<size_type>(col.size(), stream);
+    cudf::detail::label_segments(
+      offsets.begin(), offsets.end(), labels.begin(), labels.end(), stream);
+    auto fn = is_unique_fn<decltype(row_equal)>{
+      *d_col, row_equal, null_handling, offsets.data(), labels.data()};
+
+    auto identifiers = rmm::device_uvector<size_type>(col.size(), stream);
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(col.size()),
+                      identifiers.begin(),
+                      fn);
+    return identifiers;
+  }();
+
+  auto result = cudf::make_numeric_column(data_type(type_to_id<size_type>()),
+                                          static_cast<size_type>(offsets.size() - 1),
+                                          cudf::mask_state::UNALLOCATED,
+                                          stream,
+                                          mr);
+
+  // Sum the unique identifiers within each segment
+  auto add_op = op::sum{};
+  cudf::reduction::detail::segmented_reduce(identifiers.begin(),
+                                            offsets.begin(),
+                                            offsets.end(),
+                                            result->mutable_view().data<size_type>(),
+                                            add_op.get_binary_op(),
+                                            0,
+                                            stream);
+
+  // Compute the output null mask
+  // - only empty segments are tagged as null
+  // - nulls are counted appropriately above per null_handling policy
+  auto const bitmask_col = null_handling == null_policy::EXCLUDE ? col : result->view();
+  cudf::reduction::detail::segmented_update_validity(
+    *result, bitmask_col, offsets, null_policy::EXCLUDE, std::nullopt, stream, mr);
+
+  return result;
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/product.cu b/cpp/src/reductions/segmented/product.cu
new file mode 100644
index 0000000..ea9c6f4
--- /dev/null
+++ b/cpp/src/reductions/segmented/product.cu
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+std::unique_ptr<cudf::column> segmented_product(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  cudf::data_type const output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  using reducer = simple::detail::column_type_dispatcher<op::product>;
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, output_dtype, null_handling, init, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/reductions.cpp b/cpp/src/reductions/segmented/reductions.cpp
new file mode 100644
index 0000000..cee8256
--- /dev/null
+++ b/cpp/src/reductions/segmented/reductions.cpp
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+struct segmented_reduce_dispatch_functor {
+  column_view const& col;
+  device_span<size_type const> offsets;
+  data_type output_dtype;
+  null_policy null_handling;
+  std::optional<std::reference_wrapper<scalar const>> init;
+  rmm::cuda_stream_view stream;
+  rmm::mr::device_memory_resource* mr;
+
+  segmented_reduce_dispatch_functor(column_view const& segmented_values,
+                                    device_span<size_type const> offsets,
+                                    data_type output_dtype,
+                                    null_policy null_handling,
+                                    std::optional<std::reference_wrapper<scalar const>> init,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+    : col(segmented_values),
+      offsets(offsets),
+      output_dtype(output_dtype),
+      null_handling(null_handling),
+      init(init),
+      stream(stream),
+      mr(mr)
+  {
+  }
+
+  segmented_reduce_dispatch_functor(column_view const& segmented_values,
+                                    device_span<size_type const> offsets,
+                                    data_type output_dtype,
+                                    null_policy null_handling,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+    : segmented_reduce_dispatch_functor(
+        segmented_values, offsets, output_dtype, null_handling, std::nullopt, stream, mr)
+  {
+  }
+
+  template <segmented_reduce_aggregation::Kind k>
+  std::unique_ptr<column> operator()(segmented_reduce_aggregation const& agg)
+  {
+    switch (k) {
+      case segmented_reduce_aggregation::SUM:
+        return segmented_sum(col, offsets, output_dtype, null_handling, init, stream, mr);
+      case segmented_reduce_aggregation::PRODUCT:
+        return segmented_product(col, offsets, output_dtype, null_handling, init, stream, mr);
+      case segmented_reduce_aggregation::MIN:
+        return segmented_min(col, offsets, output_dtype, null_handling, init, stream, mr);
+      case segmented_reduce_aggregation::MAX:
+        return segmented_max(col, offsets, output_dtype, null_handling, init, stream, mr);
+      case segmented_reduce_aggregation::ANY:
+        return segmented_any(col, offsets, output_dtype, null_handling, init, stream, mr);
+      case segmented_reduce_aggregation::ALL:
+        return segmented_all(col, offsets, output_dtype, null_handling, init, stream, mr);
+      case segmented_reduce_aggregation::SUM_OF_SQUARES:
+        return segmented_sum_of_squares(col, offsets, output_dtype, null_handling, stream, mr);
+      case segmented_reduce_aggregation::MEAN:
+        return segmented_mean(col, offsets, output_dtype, null_handling, stream, mr);
+      case segmented_reduce_aggregation::VARIANCE: {
+        auto var_agg = static_cast<cudf::detail::var_aggregation const&>(agg);
+        return segmented_variance(
+          col, offsets, output_dtype, null_handling, var_agg._ddof, stream, mr);
+      }
+      case segmented_reduce_aggregation::STD: {
+        auto var_agg = static_cast<cudf::detail::std_aggregation const&>(agg);
+        return segmented_standard_deviation(
+          col, offsets, output_dtype, null_handling, var_agg._ddof, stream, mr);
+      }
+      case segmented_reduce_aggregation::NUNIQUE:
+        return segmented_nunique(col, offsets, null_handling, stream, mr);
+      default: CUDF_FAIL("Unsupported aggregation type.");
+    }
+  }
+};
+
+std::unique_ptr<column> segmented_reduce(column_view const& segmented_values,
+                                         device_span<size_type const> offsets,
+                                         segmented_reduce_aggregation const& agg,
+                                         data_type output_dtype,
+                                         null_policy null_handling,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!init.has_value() || segmented_values.type() == init.value().get().type(),
+               "column and initial value must be the same type");
+  if (init.has_value() && !(agg.kind == aggregation::SUM || agg.kind == aggregation::PRODUCT ||
+                            agg.kind == aggregation::MIN || agg.kind == aggregation::MAX ||
+                            agg.kind == aggregation::ANY || agg.kind == aggregation::ALL)) {
+    CUDF_FAIL(
+      "Initial value is only supported for SUM, PRODUCT, MIN, MAX, ANY, and ALL aggregation types");
+  }
+  CUDF_EXPECTS(offsets.size() > 0, "`offsets` should have at least 1 element.");
+
+  return cudf::detail::aggregation_dispatcher(
+    agg.kind,
+    segmented_reduce_dispatch_functor{
+      segmented_values, offsets, output_dtype, null_handling, init, stream, mr},
+    agg);
+}
+}  // namespace detail
+}  // namespace reduction
+
+std::unique_ptr<column> segmented_reduce(column_view const& segmented_values,
+                                         device_span<size_type const> offsets,
+                                         segmented_reduce_aggregation const& agg,
+                                         data_type output_dtype,
+                                         null_policy null_handling,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return reduction::detail::segmented_reduce(segmented_values,
+                                             offsets,
+                                             agg,
+                                             output_dtype,
+                                             null_handling,
+                                             std::nullopt,
+                                             cudf::get_default_stream(),
+                                             mr);
+}
+
+std::unique_ptr<column> segmented_reduce(column_view const& segmented_values,
+                                         device_span<size_type const> offsets,
+                                         segmented_reduce_aggregation const& agg,
+                                         data_type output_dtype,
+                                         null_policy null_handling,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return reduction::detail::segmented_reduce(segmented_values,
+                                             offsets,
+                                             agg,
+                                             output_dtype,
+                                             null_handling,
+                                             init,
+                                             cudf::get_default_stream(),
+                                             mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/simple.cuh b/cpp/src/reductions/segmented/simple.cuh
new file mode 100644
index 0000000..05a871e
--- /dev/null
+++ b/cpp/src/reductions/segmented/simple.cuh
@@ -0,0 +1,496 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "counts.hpp"
+#include "update_validity.hpp"
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/element_argminmax.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/reduction/detail/segmented_reduction.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/reduce.h>
+
+#include <optional>
+#include <type_traits>
+
+namespace cudf {
+namespace reduction {
+namespace simple {
+namespace detail {
+
+/**
+ * @brief Segment reduction for 'sum', 'product', 'min', 'max', 'sum of squares', etc
+ * which directly compute the reduction by a single step reduction call.
+ *
+ * @tparam InputType    the input column data-type
+ * @tparam ResultType   the output data-type
+ * @tparam Op           the operator of cudf::reduction::op::
+
+ * @param col Input column of data to reduce
+ * @param offsets Indices to segment boundaries
+ * @param null_handling How null entries are processed within each segment
+ * @param init Optional initial value of the reduction
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Output column in device memory
+ */
+template <typename InputType, typename ResultType, typename Op>
+std::unique_ptr<column> simple_segmented_reduction(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto dcol               = cudf::column_device_view::create(col, stream);
+  auto simple_op          = Op{};
+  auto const num_segments = offsets.size() - 1;
+
+  auto const binary_op = simple_op.get_binary_op();
+
+  // Cast initial value
+  ResultType initial_value = [&] {
+    if (init.has_value() && init.value().get().is_valid()) {
+      using ScalarType = cudf::scalar_type_t<InputType>;
+      auto input_value = static_cast<ScalarType const*>(&init.value().get())->value(stream);
+      return static_cast<ResultType>(input_value);
+    } else {
+      return simple_op.template get_identity<ResultType>();
+    }
+  }();
+
+  auto const result_type =
+    cudf::is_fixed_point(col.type()) ? col.type() : data_type{type_to_id<ResultType>()};
+  auto result =
+    make_fixed_width_column(result_type, num_segments, mask_state::UNALLOCATED, stream, mr);
+  auto outit = result->mutable_view().template begin<ResultType>();
+
+  if (col.has_nulls()) {
+    auto f  = simple_op.template get_null_replacing_element_transformer<ResultType>();
+    auto it = thrust::make_transform_iterator(dcol->pair_begin<InputType, true>(), f);
+    cudf::reduction::detail::segmented_reduce(
+      it, offsets.begin(), offsets.end(), outit, binary_op, initial_value, stream);
+  } else {
+    auto f  = simple_op.template get_element_transformer<ResultType>();
+    auto it = thrust::make_transform_iterator(dcol->begin<InputType>(), f);
+    cudf::reduction::detail::segmented_reduce(
+      it, offsets.begin(), offsets.end(), outit, binary_op, initial_value, stream);
+  }
+
+  // Compute the output null mask
+  cudf::reduction::detail::segmented_update_validity(
+    *result, col, offsets, null_handling, init, stream, mr);
+
+  return result;
+}
+
+/**
+ * @brief String segmented reduction for 'min', 'max'.
+ *
+ * This algorithm uses argmin/argmax as a custom comparator to build a gather
+ * map, then builds the output.
+ *
+ * @tparam InputType    the input column data-type
+ * @tparam Op           the operator of cudf::reduction::op::
+
+ * @param col Input column of data to reduce
+ * @param offsets Indices to segment boundaries
+ * @param null_handling How null entries are processed within each segment
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Output column in device memory
+ */
+
+template <typename InputType,
+          typename Op,
+          CUDF_ENABLE_IF(std::is_same_v<Op, cudf::reduction::detail::op::min> ||
+                         std::is_same_v<Op, cudf::reduction::detail::op::max>)>
+std::unique_ptr<column> string_segmented_reduction(column_view const& col,
+                                                   device_span<size_type const> offsets,
+                                                   null_policy null_handling,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  // Pass to simple_segmented_reduction, get indices to gather, perform gather here.
+  auto device_col = cudf::column_device_view::create(col, stream);
+
+  auto it                 = thrust::make_counting_iterator(0);
+  auto const num_segments = static_cast<size_type>(offsets.size()) - 1;
+
+  bool constexpr is_argmin = std::is_same_v<Op, cudf::reduction::detail::op::min>;
+  auto string_comparator =
+    cudf::detail::element_argminmax_fn<InputType>{*device_col, col.has_nulls(), is_argmin};
+  auto constexpr identity =
+    is_argmin ? cudf::detail::ARGMIN_SENTINEL : cudf::detail::ARGMAX_SENTINEL;
+
+  auto gather_map = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, num_segments, mask_state::UNALLOCATED, stream, mr);
+
+  auto gather_map_it = gather_map->mutable_view().begin<size_type>();
+
+  cudf::reduction::detail::segmented_reduce(
+    it, offsets.begin(), offsets.end(), gather_map_it, string_comparator, identity, stream);
+
+  auto result = std::move(cudf::detail::gather(table_view{{col}},
+                                               *gather_map,
+                                               cudf::out_of_bounds_policy::NULLIFY,
+                                               cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                               stream,
+                                               mr)
+                            ->release()[0]);
+
+  // Compute the output null mask
+  cudf::reduction::detail::segmented_update_validity(
+    *result, col, offsets, null_handling, std::nullopt, stream, mr);
+
+  return result;
+}
+
+template <typename InputType,
+          typename Op,
+          CUDF_ENABLE_IF(!std::is_same_v<Op, cudf::reduction::detail::op::min>() &&
+                         !std::is_same_v<Op, cudf::reduction::detail::op::max>())>
+std::unique_ptr<column> string_segmented_reduction(column_view const& col,
+                                                   device_span<size_type const> offsets,
+                                                   null_policy null_handling,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL("Segmented reduction on string column only supports min and max reduction.");
+}
+
+/**
+ * @brief Specialization for fixed-point segmented reduction
+ *
+ * @tparam InputType    the input column data-type
+ * @tparam Op           the operator of cudf::reduction::op::
+
+ * @param col Input column of data to reduce
+ * @param offsets Indices to segment boundaries
+ * @param null_handling How null entries are processed within each segment
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Output column in device memory
+ */
+template <typename InputType, typename Op>
+std::unique_ptr<column> fixed_point_segmented_reduction(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  using RepType = device_storage_type_t<InputType>;
+  auto result =
+    simple_segmented_reduction<RepType, RepType, Op>(col, offsets, null_handling, init, stream, mr);
+  auto const scale = [&] {
+    if constexpr (std::is_same_v<Op, cudf::reduction::detail::op::product>) {
+      // The product aggregation requires updating the scale of the fixed-point output column.
+      // The output scale needs to be the maximum count of all segments multiplied by
+      // the input scale value.
+      rmm::device_uvector<size_type> const counts =
+        cudf::reduction::detail::segmented_counts(col.null_mask(),
+                                                  col.has_nulls(),
+                                                  offsets,
+                                                  null_policy::EXCLUDE,  // do not count nulls
+                                                  stream,
+                                                  rmm::mr::get_current_device_resource());
+
+      auto const max_count = thrust::reduce(rmm::exec_policy(stream),
+                                            counts.begin(),
+                                            counts.end(),
+                                            size_type{0},
+                                            thrust::maximum<size_type>{});
+
+      auto const new_scale = numeric::scale_type{col.type().scale() * max_count};
+
+      // adjust values in each segment to match the new scale
+      auto const d_col = column_device_view::create(col, stream);
+      thrust::transform(rmm::exec_policy(stream),
+                        d_col->begin<InputType>(),
+                        d_col->end<InputType>(),
+                        d_col->begin<InputType>(),
+                        [new_scale] __device__(auto fp) { return fp.rescaled(new_scale); });
+      return new_scale;
+    }
+
+    if constexpr (std::is_same_v<Op, cudf::reduction::detail::op::sum_of_squares>) {
+      return numeric::scale_type{col.type().scale() * 2};
+    }
+
+    return numeric::scale_type{col.type().scale()};
+  }();
+
+  auto const size       = result->size();        // get these before
+  auto const null_count = result->null_count();  // release() is called
+  auto contents         = result->release();
+
+  return std::make_unique<column>(data_type{type_to_id<InputType>(), scale},
+                                  size,
+                                  std::move(*(contents.data.release())),
+                                  std::move(*(contents.null_mask.release())),
+                                  null_count);
+}
+
+/**
+ * @brief Call reduce and return a column of type bool.
+ *
+ * This is used by operations `any()` and `all()`.
+ *
+ * @tparam Op The reduce operation to execute on the column.
+ */
+template <typename Op>
+struct bool_result_column_dispatcher {
+  template <typename ElementType, std::enable_if_t<cudf::is_numeric<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     null_policy null_handling,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return simple_segmented_reduction<ElementType, bool, Op>(
+      col, offsets, null_handling, init, stream, mr);
+  }
+
+  template <typename ElementType, std::enable_if_t<not cudf::is_numeric<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     device_span<size_type const>,
+                                     null_policy,
+                                     std::optional<std::reference_wrapper<scalar const>>,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Reduction operator not supported for this type");
+  }
+};
+
+/**
+ * @brief Call reduce and return a column of type matching the input column.
+ *
+ * This is used by operations `min()` and `max()`.
+ *
+ * @tparam Op The reduce operation to execute on the column.
+ */
+template <typename Op>
+struct same_column_type_dispatcher {
+ private:
+  template <typename ElementType>
+  static constexpr bool is_supported()
+  {
+    return !(cudf::is_dictionary<ElementType>() || std::is_same_v<ElementType, cudf::list_view> ||
+             std::is_same_v<ElementType, cudf::struct_view>);
+  }
+
+ public:
+  template <typename ElementType,
+            CUDF_ENABLE_IF(is_supported<ElementType>() &&
+                           !std::is_same_v<ElementType, string_view> &&
+                           !cudf::is_fixed_point<ElementType>())>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     null_policy null_handling,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return simple_segmented_reduction<ElementType, ElementType, Op>(
+      col, offsets, null_handling, init, stream, mr);
+  }
+
+  template <typename ElementType,
+            CUDF_ENABLE_IF(is_supported<ElementType>() && std::is_same_v<ElementType, string_view>)>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     null_policy null_handling,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    if (init.has_value()) { CUDF_FAIL("Initial value not supported for strings"); }
+
+    return string_segmented_reduction<ElementType, Op>(col, offsets, null_handling, stream, mr);
+  }
+
+  template <typename ElementType,
+            CUDF_ENABLE_IF(is_supported<ElementType>() && cudf::is_fixed_point<ElementType>())>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     null_policy null_handling,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return fixed_point_segmented_reduction<ElementType, Op>(
+      col, offsets, null_handling, init, stream, mr);
+  }
+
+  template <typename ElementType, CUDF_ENABLE_IF(!is_supported<ElementType>())>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     device_span<size_type const>,
+                                     null_policy,
+                                     std::optional<std::reference_wrapper<scalar const>>,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Reduction operator not supported for this type");
+  }
+};
+
+/**
+ * @brief Call reduce and return a column of the type specified.
+ *
+ * This is used by operations such as sum(), product(), sum_of_squares(), etc
+ * It only supports numeric types. If the output type is not the
+ * same as the input type, an extra cast operation may occur.
+ *
+ * @tparam Op The reduce operation to execute on the column.
+ */
+template <typename Op>
+struct column_type_dispatcher {
+  /**
+   * @brief Specialization for reducing floating-point column types to any output type.
+   *
+   * This is called when the output_type does not match the ElementType.
+   * The input values are promoted to double (via transform-iterator) for the
+   * reduce calculation. The result is then cast to the specified output_type.
+   */
+  template <typename ElementType,
+            typename std::enable_if_t<std::is_floating_point<ElementType>::value>* = nullptr>
+  std::unique_ptr<column> reduce_numeric(column_view const& col,
+                                         device_span<size_type const> offsets,
+                                         data_type const output_type,
+                                         null_policy null_handling,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+  {
+    // Floats are computed in double precision and then cast to the output type
+    auto result = simple_segmented_reduction<ElementType, double, Op>(
+      col, offsets, null_handling, init, stream, mr);
+    if (output_type == result->type()) { return result; }
+    return cudf::detail::cast(*result, output_type, stream, mr);
+  }
+
+  /**
+   * @brief Specialization for reducing integer column types to any output type.
+   *
+   * This is called when the output_type does not match the ElementType.
+   * The input values are promoted to int64_t (via transform-iterator) for the
+   * reduce calculation. The result is then cast to the specified output_type.
+   *
+   * For uint64_t case, the only reasonable output_type is also UINT64 and
+   * this is not called when the input/output types match.
+   */
+  template <typename ElementType,
+            typename std::enable_if_t<std::is_integral<ElementType>::value>* = nullptr>
+  std::unique_ptr<column> reduce_numeric(column_view const& col,
+                                         device_span<size_type const> offsets,
+                                         data_type const output_type,
+                                         null_policy null_handling,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+  {
+    // Integers are computed in int64 precision and then cast to the output type.
+    auto result = simple_segmented_reduction<ElementType, int64_t, Op>(
+      col, offsets, null_handling, init, stream, mr);
+    if (output_type == result->type()) { return result; }
+    return cudf::detail::cast(*result, output_type, stream, mr);
+  }
+
+  /**
+   * @brief Called by the type-dispatcher to reduce the input column `col` using
+   * the `Op` operation.
+   *
+   * @tparam ElementType The input column type or key type
+   * @param col Input column (must be numeric)
+   * @param offsets Indices to segment boundaries
+   * @param output_type Requested type of the output column
+   * @param null_handling How null entries are processed within each segment
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned scalar's device memory
+   */
+  template <typename ElementType,
+            typename std::enable_if_t<cudf::is_numeric<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     data_type const output_type,
+                                     null_policy null_handling,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    // If the output type matches the input type, then reduce using that type
+    if (output_type.id() == cudf::type_to_id<ElementType>()) {
+      return simple_segmented_reduction<ElementType, ElementType, Op>(
+        col, offsets, null_handling, init, stream, mr);
+    }
+    // otherwise, reduce and map to output type
+    return reduce_numeric<ElementType>(col, offsets, output_type, null_handling, init, stream, mr);
+  }
+
+  template <typename ElementType, std::enable_if_t<cudf::is_fixed_point<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& col,
+                                     device_span<size_type const> offsets,
+                                     data_type const output_type,
+                                     null_policy null_handling,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(output_type == col.type(), "Output type must be same as input column type.");
+    return fixed_point_segmented_reduction<ElementType, Op>(
+      col, offsets, null_handling, init, stream, mr);
+  }
+
+  template <typename ElementType,
+            std::enable_if_t<not cudf::is_numeric<ElementType>() and
+                             not cudf::is_fixed_point<ElementType>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     device_span<size_type const>,
+                                     data_type const,
+                                     null_policy,
+                                     std::optional<std::reference_wrapper<scalar const>>,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Reduction operator not supported for this type");
+  }
+};
+
+}  // namespace detail
+}  // namespace simple
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/std.cu b/cpp/src/reductions/segmented/std.cu
new file mode 100644
index 0000000..5f5ced6
--- /dev/null
+++ b/cpp/src/reductions/segmented/std.cu
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compound.cuh"
+
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_standard_deviation(column_view const& col,
+                                                           device_span<size_type const> offsets,
+                                                           cudf::data_type const output_dtype,
+                                                           null_policy null_handling,
+                                                           size_type ddof,
+                                                           rmm::cuda_stream_view stream,
+                                                           rmm::mr::device_memory_resource* mr)
+{
+  using reducer = compound::detail::compound_segmented_dispatcher<op::standard_deviation>;
+  return cudf::type_dispatcher(
+    col.type(), reducer(), col, offsets, output_dtype, null_handling, ddof, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/sum.cu b/cpp/src/reductions/segmented/sum.cu
new file mode 100644
index 0000000..7e84961
--- /dev/null
+++ b/cpp/src/reductions/segmented/sum.cu
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_sum(
+  column_view const& col,
+  device_span<size_type const> offsets,
+  cudf::data_type const output_dtype,
+  null_policy null_handling,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  using reducer = simple::detail::column_type_dispatcher<op::sum>;
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, output_dtype, null_handling, init, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/sum_of_squares.cu b/cpp/src/reductions/segmented/sum_of_squares.cu
new file mode 100644
index 0000000..6c3f286
--- /dev/null
+++ b/cpp/src/reductions/segmented/sum_of_squares.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_sum_of_squares(column_view const& col,
+                                                       device_span<size_type const> offsets,
+                                                       cudf::data_type const output_dtype,
+                                                       null_policy null_handling,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  using reducer = simple::detail::column_type_dispatcher<op::sum_of_squares>;
+  return cudf::type_dispatcher(
+    col.type(), reducer{}, col, offsets, output_dtype, null_handling, std::nullopt, stream, mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/update_validity.cu b/cpp/src/reductions/segmented/update_validity.cu
new file mode 100644
index 0000000..8d70f96
--- /dev/null
+++ b/cpp/src/reductions/segmented/update_validity.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "update_validity.hpp"
+
+#include <cudf/detail/null_mask.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/span.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+void segmented_update_validity(column& result,
+                               column_view const& col,
+                               device_span<size_type const> offsets,
+                               null_policy null_handling,
+                               std::optional<std::reference_wrapper<scalar const>> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto [output_null_mask, output_null_count] = cudf::detail::segmented_null_mask_reduction(
+    col.null_mask(),
+    offsets.begin(),
+    offsets.end() - 1,
+    offsets.begin() + 1,
+    null_handling,
+    init.has_value() ? std::optional(init.value().get().is_valid()) : std::nullopt,
+    stream,
+    mr);
+  result.set_null_mask(std::move(output_null_mask), output_null_count);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/update_validity.hpp b/cpp/src/reductions/segmented/update_validity.hpp
new file mode 100644
index 0000000..0003b98
--- /dev/null
+++ b/cpp/src/reductions/segmented/update_validity.hpp
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <optional>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+/**
+ * @brief Compute the validity mask and set it on the result column
+ *
+ * If `null_handling == null_policy::INCLUDE`, all elements in a segment must be valid for the
+ * reduced value to be valid.
+ * If `null_handling == null_policy::EXCLUDE`, the reduced value is valid if any element
+ * in the segment is valid.
+ *
+ * @param result Result of segmented reduce to update the null mask
+ * @param col Input column before reduce
+ * @param offsets Indices to segment boundaries
+ * @param null_handling How null entries are processed within each segment
+ * @param init Optional initial value
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+void segmented_update_validity(column& result,
+                               column_view const& col,
+                               device_span<size_type const> offsets,
+                               null_policy null_handling,
+                               std::optional<std::reference_wrapper<scalar const>> init,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/segmented/var.cu b/cpp/src/reductions/segmented/var.cu
new file mode 100644
index 0000000..4ac815b
--- /dev/null
+++ b/cpp/src/reductions/segmented/var.cu
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compound.cuh"
+
+#include <cudf/reduction/detail/segmented_reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::column> segmented_variance(column_view const& col,
+                                                 device_span<size_type const> offsets,
+                                                 cudf::data_type const output_dtype,
+                                                 null_policy null_handling,
+                                                 size_type ddof,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  using reducer = compound::detail::compound_segmented_dispatcher<op::variance>;
+  return cudf::type_dispatcher(
+    col.type(), reducer(), col, offsets, output_dtype, null_handling, ddof, stream, mr);
+}
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/simple.cuh b/cpp/src/reductions/simple.cuh
new file mode 100644
index 0000000..9bb01c7
--- /dev/null
+++ b/cpp/src/reductions/simple.cuh
@@ -0,0 +1,478 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "nested_type_minmax_util.cuh"
+
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction.cuh>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/reduce.h>
+
+namespace cudf {
+namespace reduction {
+namespace simple {
+namespace detail {
+/**
+ * @brief Reduction for 'sum', 'product', 'min', 'max', 'sum of squares'
+ * which directly compute the reduction by a single step reduction call
+ *
+ * @tparam ElementType  the input column data-type
+ * @tparam ResultType   the output data-type
+ * @tparam Op           the operator of cudf::reduction::op::
+
+ * @param col Input column of data to reduce
+ * @param init Optional initial value of the reduction
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Output scalar in device memory
+ */
+template <typename ElementType, typename ResultType, typename Op>
+std::unique_ptr<scalar> simple_reduction(column_view const& col,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  // reduction by iterator
+  auto dcol      = cudf::column_device_view::create(col, stream);
+  auto simple_op = Op{};
+
+  // Cast initial value
+  std::optional<ResultType> const initial_value = [&] {
+    if (init.has_value() && init.value().get().is_valid()) {
+      using ScalarType = cudf::scalar_type_t<ElementType>;
+      auto input_value = static_cast<ScalarType const*>(&init.value().get())->value(stream);
+      return std::optional<ResultType>(static_cast<ResultType>(input_value));
+    } else {
+      return std::optional<ResultType>(std::nullopt);
+    }
+  }();
+
+  auto result = [&] {
+    if (col.has_nulls()) {
+      auto f  = simple_op.template get_null_replacing_element_transformer<ResultType>();
+      auto it = thrust::make_transform_iterator(dcol->pair_begin<ElementType, true>(), f);
+      return cudf::reduction::detail::reduce(it, col.size(), simple_op, initial_value, stream, mr);
+    } else {
+      auto f  = simple_op.template get_element_transformer<ResultType>();
+      auto it = thrust::make_transform_iterator(dcol->begin<ElementType>(), f);
+      return cudf::reduction::detail::reduce(it, col.size(), simple_op, initial_value, stream, mr);
+    }
+  }();
+
+  // set scalar is valid
+  result->set_valid_async(
+    col.null_count() < col.size() && (!init.has_value() || init.value().get().is_valid()), stream);
+  return result;
+}
+
+/**
+ * @brief Reduction for `sum`, `product`, `min` and `max` for decimal types
+ *
+ * @tparam DecimalXX  The `decimal32`, `decimal64` or `decimal128` type
+ * @tparam Op         The operator of cudf::reduction::op::
+ *
+ * @param col         Input column of data to reduce
+ * @param init        Optional initial value of the reduction
+ * @param stream      Used for device memory operations and kernel launches
+ * @param mr          Device memory resource used to allocate the returned scalar's device memory
+ * @return            Output scalar in device memory
+ */
+template <typename DecimalXX, typename Op>
+std::unique_ptr<scalar> fixed_point_reduction(
+  column_view const& col,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  using Type = device_storage_type_t<DecimalXX>;
+
+  auto result = simple_reduction<Type, Type, Op>(col, init, stream, mr);
+
+  auto const scale = [&] {
+    if (std::is_same_v<Op, cudf::reduction::detail::op::product>) {
+      auto const valid_count = static_cast<int32_t>(col.size() - col.null_count());
+      return numeric::scale_type{col.type().scale() * (valid_count + (init.has_value() ? 1 : 0))};
+    } else if (std::is_same_v<Op, cudf::reduction::detail::op::sum_of_squares>) {
+      return numeric::scale_type{col.type().scale() * 2};
+    }
+    return numeric::scale_type{col.type().scale()};
+  }();
+
+  auto const val = static_cast<cudf::scalar_type_t<Type>*>(result.get());
+  auto result_scalar =
+    cudf::make_fixed_point_scalar<DecimalXX>(val->value(stream), scale, stream, mr);
+  result_scalar->set_valid_async(
+    col.null_count() < col.size() && (!init.has_value() || init.value().get().is_valid()), stream);
+  return result_scalar;
+}
+
+/**
+ * @brief Reduction for 'sum', 'product', 'sum of squares' for dictionary columns.
+ *
+ * @tparam ElementType  The key type of the input dictionary column.
+ * @tparam ResultType   The output data-type for the resulting scalar
+ * @tparam Op           The operator of cudf::reduction::op::
+ *
+ * @param col Input dictionary column of data to reduce
+ * @param init Optional initial value of the reduction
+ * @param stream Used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned scalar's device memory
+ * @return Output scalar in device memory
+ */
+template <typename ElementType, typename ResultType, typename Op>
+std::unique_ptr<scalar> dictionary_reduction(
+  column_view const& col,
+  std::optional<std::reference_wrapper<scalar const>> init,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (init.has_value()) { CUDF_FAIL("Initial value not supported for dictionary reductions"); }
+
+  auto dcol      = cudf::column_device_view::create(col, stream);
+  auto simple_op = Op{};
+
+  auto result = [&] {
+    auto f = simple_op.template get_null_replacing_element_transformer<ResultType>();
+    auto p =
+      cudf::dictionary::detail::make_dictionary_pair_iterator<ElementType>(*dcol, col.has_nulls());
+    auto it = thrust::make_transform_iterator(p, f);
+    return cudf::reduction::detail::reduce(it, col.size(), simple_op, {}, stream, mr);
+  }();
+
+  // set scalar is valid
+  result->set_valid_async(
+    col.null_count() < col.size() && (!init.has_value() || init.value().get().is_valid()), stream);
+  return result;
+}
+
+/**
+ * @brief Convert a numeric scalar to another numeric scalar.
+ *
+ * The input value and validity are cast to the output scalar.
+ *
+ * @tparam InputType The type of the input scalar to copy from
+ * @tparam OutputType The output scalar type to copy to
+ */
+template <typename InputType, typename OutputType>
+struct assign_scalar_fn {
+  __device__ void operator()()
+  {
+    d_output.set_value(static_cast<OutputType>(d_input.value()));
+    d_output.set_valid(d_input.is_valid());
+  }
+
+  cudf::numeric_scalar_device_view<InputType> d_input;
+  cudf::numeric_scalar_device_view<OutputType> d_output;
+};
+
+/**
+ * @brief A type-dispatcher functor for converting a numeric scalar.
+ *
+ * The InputType is known and the dispatch is on the ResultType
+ * which is the output numeric scalar type.
+ *
+ * @tparam InputType The scalar type to convert from
+ */
+template <typename InputType>
+struct cast_numeric_scalar_fn {
+ private:
+  template <typename ResultType>
+  static constexpr bool is_supported()
+  {
+    return cudf::is_convertible<InputType, ResultType>::value && cudf::is_numeric<ResultType>();
+  }
+
+ public:
+  template <typename ResultType, std::enable_if_t<is_supported<ResultType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(numeric_scalar<InputType>* input,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto d_input  = cudf::get_scalar_device_view(*input);
+    auto result   = std::make_unique<numeric_scalar<ResultType>>(ResultType{}, true, stream, mr);
+    auto d_output = cudf::get_scalar_device_view(*result);
+    cudf::detail::device_single_thread(assign_scalar_fn<InputType, ResultType>{d_input, d_output},
+                                       stream);
+    return result;
+  }
+
+  template <typename ResultType, std::enable_if_t<not is_supported<ResultType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(numeric_scalar<InputType>*,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("input data type is not convertible to output data type");
+  }
+};
+
+/**
+ * @brief Call reduce and return a scalar of type bool.
+ *
+ * This is used by operations `any()` and `all()`.
+ *
+ * @tparam Op The reduce operation to execute on the column.
+ */
+template <typename Op>
+struct bool_result_element_dispatcher {
+  template <typename ElementType, std::enable_if_t<std::is_arithmetic_v<ElementType>>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return simple_reduction<ElementType, bool, Op>(col, init, stream, mr);
+  }
+
+  template <typename ElementType,
+            std::enable_if_t<not std::is_arithmetic_v<ElementType>>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const&,
+                                     std::optional<std::reference_wrapper<scalar const>>,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Reduction operator not supported for this type");
+  }
+};
+
+/**
+ * @brief Call reduce and return a scalar of type matching the input column.
+ *
+ * This is used by operations `min()` and `max()`.
+ *
+ * @tparam Op The reduce operation to execute on the column.
+ */
+template <typename Op>
+struct same_element_type_dispatcher {
+ private:
+  template <typename ElementType>
+  static constexpr bool is_supported()
+  {
+    return !cudf::is_dictionary<ElementType>();
+  }
+
+  template <typename IndexType, std::enable_if_t<cudf::is_index_type<IndexType>()>* = nullptr>
+  std::unique_ptr<scalar> resolve_key(column_view const& keys,
+                                      scalar const& keys_index,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+  {
+    auto& index = static_cast<numeric_scalar<IndexType> const&>(keys_index);
+    return cudf::detail::get_element(keys, index.value(stream), stream, mr);
+  }
+
+  template <typename IndexType, std::enable_if_t<!cudf::is_index_type<IndexType>()>* = nullptr>
+  std::unique_ptr<scalar> resolve_key(column_view const&,
+                                      scalar const&,
+                                      rmm::cuda_stream_view,
+                                      rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("index type expected for dictionary column");
+  }
+
+ public:
+  template <typename ElementType,
+            std::enable_if_t<cudf::is_nested<ElementType>() &&
+                             (std::is_same_v<Op, cudf::reduction::detail::op::min> ||
+                              std::is_same_v<Op, cudf::reduction::detail::op::max>)>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& input,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    if (init.has_value()) { CUDF_FAIL("Initial value not supported for nested type reductions"); }
+
+    if (input.is_empty()) { return cudf::make_empty_scalar_like(input, stream, mr); }
+
+    // We will do reduction to find the ARGMIN/ARGMAX index, then return the element at that index.
+    auto const binop_generator =
+      cudf::reduction::detail::comparison_binop_generator::create<Op>(input, stream);
+    auto const minmax_idx = thrust::reduce(rmm::exec_policy(stream),
+                                           thrust::make_counting_iterator(0),
+                                           thrust::make_counting_iterator(input.size()),
+                                           size_type{0},
+                                           binop_generator.binop());
+
+    return cudf::detail::get_element(input, minmax_idx, stream, mr);
+  }
+
+  template <typename ElementType,
+            std::enable_if_t<is_supported<ElementType>() && !cudf::is_nested<ElementType>() &&
+                             !cudf::is_fixed_point<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    if (!cudf::is_dictionary(col.type())) {
+      return simple_reduction<ElementType, ElementType, Op>(col, init, stream, mr);
+    }
+    auto index = simple_reduction<ElementType, ElementType, Op>(
+      dictionary_column_view(col).get_indices_annotated(),
+      init,
+      stream,
+      rmm::mr::get_current_device_resource());
+    return resolve_key<ElementType>(dictionary_column_view(col).keys(), *index, stream, mr);
+  }
+
+  template <typename ElementType, std::enable_if_t<cudf::is_fixed_point<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return fixed_point_reduction<ElementType, Op>(col, init, stream, mr);
+  }
+
+  template <typename ElementType, std::enable_if_t<not is_supported<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const&,
+                                     std::optional<std::reference_wrapper<scalar const>>,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Reduction operator not supported for this type");
+  }
+};
+
+/**
+ * @brief Call reduce and return a scalar of the type specified.
+ *
+ * This is used by operations sum(), product(), and sum_of_squares().
+ * It only supports numeric types. If the output type is not the
+ * same as the input type, an extra cast operation may incur.
+ *
+ * @tparam Op The reduce operation to execute on the column.
+ */
+template <typename Op>
+struct element_type_dispatcher {
+  /**
+   * @brief Specialization for reducing floating-point column types to any output type.
+   */
+  template <typename ElementType,
+            std::enable_if_t<std::is_floating_point_v<ElementType>>* = nullptr>
+  std::unique_ptr<scalar> reduce_numeric(column_view const& col,
+                                         data_type const output_type,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+  {
+    auto result = !cudf::is_dictionary(col.type())
+                    ? simple_reduction<ElementType, double, Op>(col, init, stream, mr)
+                    : dictionary_reduction<ElementType, double, Op>(col, init, stream, mr);
+    if (output_type == result->type()) return result;
+
+    // this will cast the result to the output_type
+    return cudf::type_dispatcher(output_type,
+                                 cast_numeric_scalar_fn<double>{},
+                                 static_cast<numeric_scalar<double>*>(result.get()),
+                                 stream,
+                                 mr);
+  }
+
+  /**
+   * @brief Specialization for reducing integer column types to any output type.
+   */
+  template <typename ElementType, std::enable_if_t<std::is_integral_v<ElementType>>* = nullptr>
+  std::unique_ptr<scalar> reduce_numeric(column_view const& col,
+                                         data_type const output_type,
+                                         std::optional<std::reference_wrapper<scalar const>> init,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+  {
+    auto result = !cudf::is_dictionary(col.type())
+                    ? simple_reduction<ElementType, int64_t, Op>(col, init, stream, mr)
+                    : dictionary_reduction<ElementType, int64_t, Op>(col, init, stream, mr);
+    if (output_type == result->type()) return result;
+
+    // this will cast the result to the output_type
+    return cudf::type_dispatcher(output_type,
+                                 cast_numeric_scalar_fn<int64_t>{},
+                                 static_cast<numeric_scalar<int64_t>*>(result.get()),
+                                 stream,
+                                 mr);
+  }
+
+  /**
+   * @brief Called by the type-dispatcher to reduce the input column `col` using
+   * the `Op` operation.
+   *
+   * @tparam ElementType The input column type or key type
+   * @param col Input column (must be numeric)
+   * @param output_type Requested type of the scalar result
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @param mr Device memory resource used to allocate the returned scalar's device memory
+   */
+  template <typename ElementType, std::enable_if_t<cudf::is_numeric<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     data_type const output_type,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    if (output_type.id() == cudf::type_to_id<ElementType>())
+      return !cudf::is_dictionary(col.type())
+               ? simple_reduction<ElementType, ElementType, Op>(col, init, stream, mr)
+               : dictionary_reduction<ElementType, ElementType, Op>(col, init, stream, mr);
+    // reduce and map to output type
+    return reduce_numeric<ElementType>(col, output_type, init, stream, mr);
+  }
+
+  /**
+   * @brief Specialization for reducing fixed_point column types to fixed_point number
+   */
+  template <typename ElementType, std::enable_if_t<cudf::is_fixed_point<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const& col,
+                                     data_type const output_type,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(output_type == col.type(), "Output type must be same as input column type.");
+
+    return fixed_point_reduction<ElementType, Op>(col, init, stream, mr);
+  }
+
+  template <typename ElementType,
+            std::enable_if_t<not cudf::is_numeric<ElementType>() and
+                             not cudf::is_fixed_point<ElementType>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(column_view const&,
+                                     data_type const,
+                                     std::optional<std::reference_wrapper<scalar const>> init,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Reduction operator not supported for this type");
+  }
+};
+
+}  // namespace detail
+}  // namespace simple
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/std.cu b/cpp/src/reductions/std.cu
new file mode 100644
index 0000000..9df8363
--- /dev/null
+++ b/cpp/src/reductions/std.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compound.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> standard_deviation(column_view const& col,
+                                                 cudf::data_type const output_dtype,
+                                                 size_type ddof,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  // TODO: add cuda version check when the fix is available
+#if !defined(__CUDACC_DEBUG__)
+  using reducer = compound::detail::element_type_dispatcher<op::standard_deviation>;
+  auto col_type =
+    cudf::is_dictionary(col.type()) ? dictionary_column_view(col).keys().type() : col.type();
+  return cudf::type_dispatcher(col_type, reducer(), col, output_dtype, ddof, stream, mr);
+#else
+  // workaround for bug 200529165 which causes compilation error only at device debug build
+  // hopefully the bug will be fixed in future cuda version (still failing in 11.2)
+  CUDF_FAIL("var/std reductions are not supported at debug build.");
+#endif
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/sum.cu b/cpp/src/reductions/sum.cu
new file mode 100644
index 0000000..85c6b32
--- /dev/null
+++ b/cpp/src/reductions/sum.cu
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> sum(column_view const& col,
+                                  cudf::data_type const output_dtype,
+                                  std::optional<std::reference_wrapper<scalar const>> init,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  return cudf::type_dispatcher(
+    cudf::is_dictionary(col.type()) ? dictionary_column_view(col).keys().type() : col.type(),
+    simple::detail::element_type_dispatcher<op::sum>{},
+    col,
+    output_dtype,
+    init,
+    stream,
+    mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/sum_of_squares.cu b/cpp/src/reductions/sum_of_squares.cu
new file mode 100644
index 0000000..7b85c4e
--- /dev/null
+++ b/cpp/src/reductions/sum_of_squares.cu
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "simple.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> sum_of_squares(column_view const& col,
+                                             cudf::data_type const output_dtype,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  return cudf::type_dispatcher(
+    cudf::is_dictionary(col.type()) ? dictionary_column_view(col).keys().type() : col.type(),
+    simple::detail::element_type_dispatcher<op::sum_of_squares>{},
+    col,
+    output_dtype,
+    std::nullopt,
+    stream,
+    mr);
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/reductions/var.cu b/cpp/src/reductions/var.cu
new file mode 100644
index 0000000..d559531
--- /dev/null
+++ b/cpp/src/reductions/var.cu
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "compound.cuh"
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/reduction/detail/reduction_functions.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace reduction {
+namespace detail {
+
+std::unique_ptr<cudf::scalar> variance(column_view const& col,
+                                       cudf::data_type const output_dtype,
+                                       size_type ddof,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  // TODO: add cuda version check when the fix is available
+#if !defined(__CUDACC_DEBUG__)
+  using reducer = compound::detail::element_type_dispatcher<op::variance>;
+  auto col_type =
+    cudf::is_dictionary(col.type()) ? dictionary_column_view(col).keys().type() : col.type();
+  return cudf::type_dispatcher(col_type, reducer(), col, output_dtype, ddof, stream, mr);
+#else
+  // workaround for bug 200529165 which causes compilation error only at device debug build
+  // hopefully the bug will be fixed in future cuda version (still failing in 11.2)
+  CUDF_FAIL("var/std reductions are not supported at debug build.");
+#endif
+}
+
+}  // namespace detail
+}  // namespace reduction
+}  // namespace cudf
diff --git a/cpp/src/replace/clamp.cu b/cpp/src/replace/clamp.cu
new file mode 100644
index 0000000..950cb48
--- /dev/null
+++ b/cpp/src/replace/clamp.cu
@@ -0,0 +1,408 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+template <typename Transformer>
+std::pair<std::unique_ptr<column>, std::unique_ptr<column>> form_offsets_and_char_column(
+  cudf::column_device_view input,
+  size_type,
+  Transformer offsets_transformer,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  std::unique_ptr<column> offsets_column{};
+  auto strings_count = input.size();
+  size_type bytes    = 0;
+
+  if (input.nullable()) {
+    auto input_begin =
+      cudf::detail::make_null_replacement_iterator<string_view>(input, string_view{});
+    auto offsets_transformer_itr =
+      thrust::make_transform_iterator(input_begin, offsets_transformer);
+    std::tie(offsets_column, bytes) = cudf::detail::make_offsets_child_column(
+      offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  } else {
+    auto offsets_transformer_itr =
+      thrust::make_transform_iterator(input.begin<string_view>(), offsets_transformer);
+    std::tie(offsets_column, bytes) = cudf::detail::make_offsets_child_column(
+      offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  }
+
+  // build chars column
+  auto chars_column = cudf::strings::detail::create_chars_child_column(bytes, stream, mr);
+
+  return std::pair(std::move(offsets_column), std::move(chars_column));
+}
+
+template <typename OptionalScalarIterator, typename ReplaceScalarIterator>
+std::unique_ptr<cudf::column> clamp_string_column(strings_column_view const& input,
+                                                  OptionalScalarIterator lo_itr,
+                                                  ReplaceScalarIterator lo_replace_itr,
+                                                  OptionalScalarIterator hi_itr,
+                                                  ReplaceScalarIterator hi_replace_itr,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  auto input_device_column = column_device_view::create(input.parent(), stream);
+  auto d_input             = *input_device_column;
+  size_type null_count     = input.null_count();
+
+  // build offset column
+  auto offsets_transformer = [lo_itr, hi_itr, lo_replace_itr, hi_replace_itr] __device__(
+                               string_view element, bool is_valid = true) {
+    const auto d_lo         = (*lo_itr).value_or(element);
+    const auto d_hi         = (*hi_itr).value_or(element);
+    const auto d_lo_replace = *(*lo_replace_itr);
+    const auto d_hi_replace = *(*hi_replace_itr);
+    size_type bytes         = 0;
+
+    if (is_valid) {
+      if (element < d_lo) {
+        bytes = d_lo_replace.size_bytes();
+      } else if (d_hi < element) {
+        bytes = d_hi_replace.size_bytes();
+      } else {
+        bytes = element.size_bytes();
+      }
+    }
+    return bytes;
+  };
+
+  auto [offsets_column, chars_column] =
+    form_offsets_and_char_column(d_input, null_count, offsets_transformer, stream, mr);
+
+  auto d_offsets = offsets_column->view().template data<size_type>();
+  auto d_chars   = chars_column->mutable_view().template data<char>();
+  // fill in chars
+  auto copy_transformer =
+    [d_input, lo_itr, hi_itr, lo_replace_itr, hi_replace_itr, d_offsets, d_chars] __device__(
+      size_type idx) {
+      if (d_input.is_null(idx)) { return; }
+      auto input_element      = d_input.element<string_view>(idx);
+      const auto d_lo         = (*lo_itr).value_or(input_element);
+      const auto d_hi         = (*hi_itr).value_or(input_element);
+      const auto d_lo_replace = *(*lo_replace_itr);
+      const auto d_hi_replace = *(*hi_replace_itr);
+
+      if (input_element < d_lo) {
+        memcpy(d_chars + d_offsets[idx], d_lo_replace.data(), d_lo_replace.size_bytes());
+      } else if (d_hi < input_element) {
+        memcpy(d_chars + d_offsets[idx], d_hi_replace.data(), d_hi_replace.size_bytes());
+      } else {
+        memcpy(d_chars + d_offsets[idx], input_element.data(), input_element.size_bytes());
+      }
+    };
+
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     input.size(),
+                     copy_transformer);
+
+  return make_strings_column(input.size(),
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             input.null_count(),
+                             std::move(cudf::detail::copy_bitmask(input.parent(), stream, mr)));
+}
+
+template <typename T, typename OptionalScalarIterator, typename ReplaceScalarIterator>
+std::enable_if_t<cudf::is_fixed_width<T>(), std::unique_ptr<cudf::column>> clamper(
+  column_view const& input,
+  OptionalScalarIterator lo_itr,
+  ReplaceScalarIterator lo_replace_itr,
+  OptionalScalarIterator hi_itr,
+  ReplaceScalarIterator hi_replace_itr,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto output =
+    detail::allocate_like(input, input.size(), mask_allocation_policy::NEVER, stream, mr);
+  // mask will not change
+  if (input.nullable()) {
+    output->set_null_mask(cudf::detail::copy_bitmask(input, stream, mr), input.null_count());
+  }
+
+  auto output_device_view =
+    cudf::mutable_column_device_view::create(output->mutable_view(), stream);
+  auto input_device_view = cudf::column_device_view::create(input, stream);
+  auto scalar_zip_itr =
+    thrust::make_zip_iterator(thrust::make_tuple(lo_itr, lo_replace_itr, hi_itr, hi_replace_itr));
+
+  auto trans = [] __device__(auto element_optional, auto scalar_tuple) {
+    if (element_optional.has_value()) {
+      auto lo_optional = thrust::get<0>(scalar_tuple);
+      auto hi_optional = thrust::get<2>(scalar_tuple);
+      if (lo_optional.has_value() and (*element_optional < *lo_optional)) {
+        return *(thrust::get<1>(scalar_tuple));
+      } else if (hi_optional.has_value() and (*element_optional > *hi_optional)) {
+        return *(thrust::get<3>(scalar_tuple));
+      }
+    }
+
+    return *element_optional;
+  };
+
+  auto input_pair_iterator =
+    make_optional_iterator<T>(*input_device_view, nullate::DYNAMIC{input.has_nulls()});
+  thrust::transform(rmm::exec_policy(stream),
+                    input_pair_iterator,
+                    input_pair_iterator + input.size(),
+                    scalar_zip_itr,
+                    output_device_view->begin<T>(),
+                    trans);
+
+  return output;
+}
+
+template <typename T, typename OptionalScalarIterator, typename ReplaceScalarIterator>
+std::enable_if_t<std::is_same_v<T, string_view>, std::unique_ptr<cudf::column>> clamper(
+  column_view const& input,
+  OptionalScalarIterator lo_itr,
+  ReplaceScalarIterator lo_replace_itr,
+  OptionalScalarIterator hi_itr,
+  ReplaceScalarIterator hi_replace_itr,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  return clamp_string_column(input, lo_itr, lo_replace_itr, hi_itr, hi_replace_itr, stream, mr);
+}
+
+}  // namespace
+
+template <typename T, typename OptionalScalarIterator, typename ReplaceScalarIterator>
+std::unique_ptr<column> clamp(column_view const& input,
+                              OptionalScalarIterator lo_itr,
+                              ReplaceScalarIterator lo_replace_itr,
+                              OptionalScalarIterator hi_itr,
+                              ReplaceScalarIterator hi_replace_itr,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  return clamper<T>(input, lo_itr, lo_replace_itr, hi_itr, hi_replace_itr, stream, mr);
+}
+
+struct dispatch_clamp {
+  template <typename T>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     scalar const& lo,
+                                     scalar const& lo_replace,
+                                     scalar const& hi,
+                                     scalar const& hi_replace,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(lo.type() == input.type(), "mismatching types of scalar and input");
+
+    auto lo_itr         = make_optional_iterator<T>(lo, nullate::YES{});
+    auto hi_itr         = make_optional_iterator<T>(hi, nullate::YES{});
+    auto lo_replace_itr = make_optional_iterator<T>(lo_replace, nullate::NO{});
+    auto hi_replace_itr = make_optional_iterator<T>(hi_replace, nullate::NO{});
+
+    return clamp<T>(input, lo_itr, lo_replace_itr, hi_itr, hi_replace_itr, stream, mr);
+  }
+};
+
+template <>
+std::unique_ptr<column> dispatch_clamp::operator()<cudf::list_view>(
+  column_view const& input,
+  scalar const& lo,
+  scalar const& lo_replace,
+  scalar const& hi,
+  scalar const& hi_replace,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL("clamp for list_view not supported");
+}
+
+template <>
+std::unique_ptr<column> dispatch_clamp::operator()<struct_view>(column_view const& input,
+                                                                scalar const& lo,
+                                                                scalar const& lo_replace,
+                                                                scalar const& hi,
+                                                                scalar const& hi_replace,
+                                                                rmm::cuda_stream_view stream,
+                                                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL("clamp for struct_view not supported");
+}
+
+template <>
+std::unique_ptr<column> dispatch_clamp::operator()<cudf::dictionary32>(
+  column_view const& input,
+  scalar const& lo,
+  scalar const& lo_replace,
+  scalar const& hi,
+  scalar const& hi_replace,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // add lo_replace and hi_replace to keys
+  auto matched_column = [&] {
+    auto matched_view              = dictionary_column_view(input);
+    std::unique_ptr<column> result = nullptr;
+    auto add_scalar_key            = [&](scalar const& key, scalar const& key_replace) {
+      if (key.is_valid(stream)) {
+        result = dictionary::detail::add_keys(
+          matched_view, make_column_from_scalar(key_replace, 1, stream)->view(), stream, mr);
+        matched_view = dictionary_column_view(result->view());
+      }
+    };
+    add_scalar_key(lo, lo_replace);
+    add_scalar_key(hi, hi_replace);
+    return result;
+  }();
+  auto matched_view = dictionary_column_view(matched_column->view());
+  auto default_mr   = rmm::mr::get_current_device_resource();
+
+  // get the indexes for lo_replace and for hi_replace
+  auto lo_replace_index =
+    dictionary::detail::get_index(matched_view, lo_replace, stream, default_mr);
+  auto hi_replace_index =
+    dictionary::detail::get_index(matched_view, hi_replace, stream, default_mr);
+
+  // get the closest indexes for lo and for hi
+  auto lo_index = dictionary::detail::get_insert_index(matched_view, lo, stream, default_mr);
+  auto hi_index = dictionary::detail::get_insert_index(matched_view, hi, stream, default_mr);
+
+  // call clamp with the scalar indexes and the matched indices
+  auto matched_indices = matched_view.get_indices_annotated();
+  auto new_indices     = cudf::type_dispatcher<dispatch_storage_type>(matched_indices.type(),
+                                                                  dispatch_clamp{},
+                                                                  matched_indices,
+                                                                  *lo_index,
+                                                                  *lo_replace_index,
+                                                                  *hi_index,
+                                                                  *hi_replace_index,
+                                                                  stream,
+                                                                  mr);
+
+  auto const indices_type = new_indices->type();
+  auto const output_size  = new_indices->size();
+  auto const null_count   = new_indices->null_count();
+  auto contents           = new_indices->release();
+  auto indices_column     = std::make_unique<column>(indices_type,
+                                                 static_cast<size_type>(output_size),
+                                                 std::move(*(contents.data.release())),
+                                                 rmm::device_buffer{},
+                                                 0);
+
+  // take the keys from the matched column allocated using mr
+  std::unique_ptr<column> keys_column(std::move(matched_column->release().children.back()));
+
+  // create column with keys_column and indices_column
+  return make_dictionary_column(std::move(keys_column),
+                                std::move(indices_column),
+                                std::move(*(contents.null_mask.release())),
+                                null_count);
+}
+
+/**
+ * @copydoc cudf::clamp(column_view const& input,
+                                      scalar const& lo,
+                                      scalar const& lo_replace,
+                                      scalar const& hi,
+                                      scalar const& hi_replace,
+                                      rmm::mr::device_memory_resource* mr);
+ *
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> clamp(column_view const& input,
+                              scalar const& lo,
+                              scalar const& lo_replace,
+                              scalar const& hi,
+                              scalar const& hi_replace,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(lo.type() == hi.type(), "mismatching types of limit scalars");
+  CUDF_EXPECTS(lo_replace.type() == hi_replace.type(), "mismatching types of replace scalars");
+  CUDF_EXPECTS(lo.type() == lo_replace.type(), "mismatching types of limit and replace scalars");
+
+  if ((not lo.is_valid(stream) and not hi.is_valid(stream)) or (input.is_empty())) {
+    // There will be no change
+    return std::make_unique<column>(input, stream, mr);
+  }
+
+  if (lo.is_valid(stream)) {
+    CUDF_EXPECTS(lo_replace.is_valid(stream), "lo_replace can't be null if lo is not null");
+  }
+  if (hi.is_valid(stream)) {
+    CUDF_EXPECTS(hi_replace.is_valid(stream), "hi_replace can't be null if hi is not null");
+  }
+
+  return cudf::type_dispatcher<dispatch_storage_type>(
+    input.type(), dispatch_clamp{}, input, lo, lo_replace, hi, hi_replace, stream, mr);
+}
+
+}  // namespace detail
+
+// clamp input at lo and hi with lo_replace and hi_replace
+std::unique_ptr<column> clamp(column_view const& input,
+                              scalar const& lo,
+                              scalar const& lo_replace,
+                              scalar const& hi,
+                              scalar const& hi_replace,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::clamp(input, lo, lo_replace, hi, hi_replace, stream, mr);
+}
+
+// clamp input at lo and hi
+std::unique_ptr<column> clamp(column_view const& input,
+                              scalar const& lo,
+                              scalar const& hi,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::clamp(input, lo, lo, hi, hi, stream, mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/replace/nans.cu b/cpp/src/replace/nans.cu
new file mode 100644
index 0000000..2fcb934
--- /dev/null
+++ b/cpp/src/replace/nans.cu
@@ -0,0 +1,249 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy_if_else.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/replace.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+struct replace_nans_functor {
+  template <typename T, typename Replacement>
+  std::enable_if_t<std::is_floating_point_v<T>, std::unique_ptr<column>> operator()(
+    column_view const& input,
+    Replacement const& replacement,
+    bool replacement_nullable,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(input.type() == replacement.type(),
+                 "Input and replacement must be of the same type");
+
+    if (input.is_empty()) { return cudf::make_empty_column(input.type()); }
+
+    auto input_device_view = column_device_view::create(input, stream);
+    size_type size         = input.size();
+
+    auto predicate = [dinput = *input_device_view] __device__(auto i) {
+      return dinput.is_null(i) or !std::isnan(dinput.element<T>(i));
+    };
+
+    auto input_iterator =
+      make_optional_iterator<T>(*input_device_view, nullate::DYNAMIC{input.has_nulls()});
+    auto replacement_iterator =
+      make_optional_iterator<T>(replacement, nullate::DYNAMIC{replacement_nullable});
+    return copy_if_else(input.has_nulls() or replacement_nullable,
+                        input_iterator,
+                        input_iterator + size,
+                        replacement_iterator,
+                        predicate,
+                        input.type(),
+                        stream,
+                        mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!std::is_floating_point_v<T>, std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("NAN is not supported in a Non-floating point type column");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> replace_nans(column_view const& input,
+                                     column_view const& replacement,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.size() == replacement.size(),
+               "Input and replacement must be of the same size");
+
+  return type_dispatcher(input.type(),
+                         replace_nans_functor{},
+                         input,
+                         *column_device_view::create(replacement, stream),
+                         replacement.nullable(),
+                         stream,
+                         mr);
+}
+
+std::unique_ptr<column> replace_nans(column_view const& input,
+                                     scalar const& replacement,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(
+    input.type(), replace_nans_functor{}, input, replacement, true, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> replace_nans(column_view const& input,
+                                     column_view const& replacement,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_nans(input, replacement, stream, mr);
+}
+
+std::unique_ptr<column> replace_nans(column_view const& input,
+                                     scalar const& replacement,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_nans(input, replacement, stream, mr);
+}
+
+}  // namespace cudf
+
+namespace {  // anonymous
+
+template <typename T>
+struct normalize_nans_and_zeros_lambda {
+  cudf::column_device_view in;
+  T __device__ operator()(cudf::size_type i)
+  {
+    auto e = in.element<T>(i);
+    if (isnan(e)) { return std::numeric_limits<T>::quiet_NaN(); }
+    if (T{0.0} == e) { return T{0.0}; }
+    return e;
+  }
+};
+
+/**
+ * @brief Functor called by the `type_dispatcher` in order to invoke and instantiate
+ *        `normalize_nans_and_zeros` with the appropriate data types.
+ */
+struct normalize_nans_and_zeros_kernel_forwarder {
+  // floats and doubles. what we really care about.
+  template <typename T, std::enable_if_t<std::is_floating_point_v<T>>* = nullptr>
+  void operator()(cudf::column_device_view in,
+                  cudf::mutable_column_device_view out,
+                  rmm::cuda_stream_view stream)
+  {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(in.size()),
+                      out.head<T>(),
+                      normalize_nans_and_zeros_lambda<T>{in});
+  }
+
+  // if we get in here for anything but a float or double, that's a problem.
+  template <typename T, typename... Args>
+  std::enable_if_t<not std::is_floating_point_v<T>, void> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unexpected non floating-point type.");
+  }
+};
+
+}  // end anonymous namespace
+
+namespace cudf {
+namespace detail {
+void normalize_nans_and_zeros(mutable_column_view in_out, rmm::cuda_stream_view stream)
+{
+  if (in_out.is_empty()) { return; }
+  CUDF_EXPECTS(
+    in_out.type() == data_type(type_id::FLOAT32) || in_out.type() == data_type(type_id::FLOAT64),
+    "Expects float or double input");
+
+  // wrapping the in_out data in a column_view so we can call the same lower level code.
+  // that we use for the non in-place version.
+  column_view input = in_out;
+
+  // to device. unique_ptr which gets automatically cleaned up when we leave
+  auto device_in = column_device_view::create(input, stream);
+
+  // from device. unique_ptr which gets automatically cleaned up when we leave.
+  auto device_out = mutable_column_device_view::create(in_out, stream);
+
+  // invoke the actual kernel.
+  cudf::type_dispatcher(
+    input.type(), normalize_nans_and_zeros_kernel_forwarder{}, *device_in, *device_out, stream);
+}
+
+std::unique_ptr<column> normalize_nans_and_zeros(column_view const& input,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  // output. copies the input
+  auto out = std::make_unique<column>(input, stream, mr);
+
+  // from device. unique_ptr which gets automatically cleaned up when we leave.
+  auto out_view = out->mutable_view();
+  detail::normalize_nans_and_zeros(out_view, stream);
+  out->set_null_count(input.null_count());
+
+  return out;
+}
+
+}  // namespace detail
+
+/**
+ * @brief Makes all Nans and zeroes positive.
+ *
+ * Converts floating point values from @p in_out using the following rules:
+ *        Convert  -NaN  -> NaN
+ *        Convert  -0.0  -> 0.0
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ */
+std::unique_ptr<column> normalize_nans_and_zeros(column_view const& input,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::normalize_nans_and_zeros(input, stream, mr);
+}
+
+/**
+ * @brief Makes all Nans and zeroes positive.
+ *
+ * Converts floating point values from @p in_out using the following rules:
+ *        Convert  -NaN  -> NaN
+ *        Convert  -0.0  -> 0.0
+ *
+ * @throws cudf::logic_error if column does not have floating point data type.
+ * @param[in, out] in_out mutable_column_view representing input data. data is processed in-place
+ */
+void normalize_nans_and_zeros(mutable_column_view& in_out, rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  detail::normalize_nans_and_zeros(in_out, cudf::get_default_stream());
+}
+
+}  // namespace cudf
diff --git a/cpp/src/replace/nulls.cu b/cpp/src/replace/nulls.cu
new file mode 100644
index 0000000..2eb624d
--- /dev/null
+++ b/cpp/src/replace/nulls.cu
@@ -0,0 +1,474 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/replace.hpp>
+#include <cudf/detail/replace/nulls.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/dictionary/detail/replace.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/reverse_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace {  // anonymous
+
+static constexpr int BLOCK_SIZE = 256;
+
+template <int phase, bool replacement_has_nulls>
+__global__ void replace_nulls_strings(cudf::column_device_view input,
+                                      cudf::column_device_view replacement,
+                                      cudf::bitmask_type* output_valid,
+                                      cudf::size_type* offsets,
+                                      char* chars,
+                                      cudf::size_type* valid_counter)
+{
+  cudf::size_type nrows = input.size();
+  auto i                = cudf::detail::grid_1d::global_thread_id();
+  auto const stride     = cudf::detail::grid_1d::grid_stride();
+
+  uint32_t active_mask = 0xffff'ffff;
+  active_mask          = __ballot_sync(active_mask, i < nrows);
+  auto const lane_id{threadIdx.x % cudf::detail::warp_size};
+  uint32_t valid_sum{0};
+
+  while (i < nrows) {
+    bool input_is_valid  = input.is_valid_nocheck(i);
+    bool output_is_valid = true;
+
+    if (replacement_has_nulls && !input_is_valid) {
+      output_is_valid = replacement.is_valid_nocheck(i);
+    }
+
+    cudf::string_view out;
+    if (input_is_valid) {
+      out = input.element<cudf::string_view>(i);
+    } else if (output_is_valid) {
+      out = replacement.element<cudf::string_view>(i);
+    }
+
+    bool nonzero_output = (input_is_valid || output_is_valid);
+
+    if (phase == 0) {
+      offsets[i]       = nonzero_output ? out.size_bytes() : 0;
+      uint32_t bitmask = __ballot_sync(active_mask, output_is_valid);
+      if (0 == lane_id) {
+        output_valid[cudf::word_index(i)] = bitmask;
+        valid_sum += __popc(bitmask);
+      }
+    } else if (phase == 1) {
+      if (nonzero_output) std::memcpy(chars + offsets[i], out.data(), out.size_bytes());
+    }
+
+    i += stride;
+    active_mask = __ballot_sync(active_mask, i < nrows);
+  }
+
+  // Compute total valid count for this block and add it to global count
+  uint32_t block_valid_count = cudf::detail::single_lane_block_sum_reduce<BLOCK_SIZE, 0>(valid_sum);
+  // one thread computes and adds to output_valid_count
+  if (threadIdx.x == 0) { atomicAdd(valid_counter, block_valid_count); }
+}
+
+template <typename Type, bool replacement_has_nulls>
+__global__ void replace_nulls(cudf::column_device_view input,
+                              cudf::column_device_view replacement,
+                              cudf::mutable_column_device_view output,
+                              cudf::size_type* output_valid_count)
+{
+  cudf::size_type nrows = input.size();
+  auto i                = cudf::detail::grid_1d::global_thread_id();
+  auto const stride     = cudf::detail::grid_1d::grid_stride();
+
+  uint32_t active_mask = 0xffff'ffff;
+  active_mask          = __ballot_sync(active_mask, i < nrows);
+  auto const lane_id{threadIdx.x % cudf::detail::warp_size};
+  uint32_t valid_sum{0};
+
+  while (i < nrows) {
+    bool input_is_valid  = input.is_valid_nocheck(i);
+    bool output_is_valid = true;
+    if (input_is_valid) {
+      output.data<Type>()[i] = input.element<Type>(i);
+    } else {
+      if (replacement_has_nulls) { output_is_valid = replacement.is_valid_nocheck(i); }
+      output.data<Type>()[i] = replacement.element<Type>(i);
+    }
+
+    /* output valid counts calculations*/
+    if (replacement_has_nulls) {
+      uint32_t bitmask = __ballot_sync(active_mask, output_is_valid);
+      if (0 == lane_id) {
+        output.set_mask_word(cudf::word_index(i), bitmask);
+        valid_sum += __popc(bitmask);
+      }
+    }
+
+    i += stride;
+    active_mask = __ballot_sync(active_mask, i < nrows);
+  }
+  if (replacement_has_nulls) {
+    // Compute total valid count for this block and add it to global count
+    uint32_t block_valid_count =
+      cudf::detail::single_lane_block_sum_reduce<BLOCK_SIZE, 0>(valid_sum);
+    // one thread computes and adds to output_valid_count
+    if (threadIdx.x == 0) { atomicAdd(output_valid_count, block_valid_count); }
+  }
+}
+
+/**
+ * @brief Functor called by the `type_dispatcher` in order to invoke and instantiate
+ *        `replace_nulls` with the appropriate data types.
+ */
+struct replace_nulls_column_kernel_forwarder {
+  template <typename col_type, CUDF_ENABLE_IF(cudf::is_rep_layout_compatible<col_type>())>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           cudf::column_view const& replacement,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    cudf::size_type nrows = input.size();
+    cudf::detail::grid_1d grid{nrows, BLOCK_SIZE};
+
+    auto output =
+      cudf::detail::allocate_like(input,
+                                  input.size(),
+                                  replacement.has_nulls() ? cudf::mask_allocation_policy::ALWAYS
+                                                          : cudf::mask_allocation_policy::NEVER,
+                                  stream,
+                                  mr);
+
+    auto output_view = output->mutable_view();
+
+    auto replace = replace_nulls<col_type, false>;
+    if (output_view.nullable()) replace = replace_nulls<col_type, true>;
+
+    auto device_in          = cudf::column_device_view::create(input, stream);
+    auto device_out         = cudf::mutable_column_device_view::create(output_view, stream);
+    auto device_replacement = cudf::column_device_view::create(replacement, stream);
+
+    rmm::device_scalar<cudf::size_type> valid_counter(0, stream);
+    cudf::size_type* valid_count = valid_counter.data();
+
+    replace<<<grid.num_blocks, BLOCK_SIZE, 0, stream.value()>>>(
+      *device_in, *device_replacement, *device_out, valid_count);
+
+    if (output_view.nullable()) {
+      output->set_null_count(output->size() - valid_counter.value(stream));
+    }
+
+    return output;
+  }
+
+  template <typename col_type, CUDF_ENABLE_IF(not cudf::is_rep_layout_compatible<col_type>())>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const&,
+                                           cudf::column_view const&,
+                                           rmm::cuda_stream_view,
+                                           rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("No specialization exists for the given type.");
+  }
+};
+
+template <>
+std::unique_ptr<cudf::column> replace_nulls_column_kernel_forwarder::operator()<cudf::string_view>(
+  cudf::column_view const& input,
+  cudf::column_view const& replacement,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_scalar<cudf::size_type> valid_counter(0, stream);
+  cudf::size_type* valid_count = valid_counter.data();
+
+  auto replace_first  = replace_nulls_strings<0, false>;
+  auto replace_second = replace_nulls_strings<1, false>;
+  if (replacement.has_nulls()) {
+    replace_first  = replace_nulls_strings<0, true>;
+    replace_second = replace_nulls_strings<1, true>;
+  }
+
+  // Create new offsets column to use in kernel
+  std::unique_ptr<cudf::column> sizes = cudf::make_numeric_column(
+    cudf::data_type(cudf::type_id::INT32), input.size(), cudf::mask_state::UNALLOCATED, stream);
+
+  auto sizes_view         = sizes->mutable_view();
+  auto device_in          = cudf::column_device_view::create(input, stream);
+  auto device_replacement = cudf::column_device_view::create(replacement, stream);
+
+  rmm::device_buffer valid_bits =
+    cudf::detail::create_null_mask(input.size(), cudf::mask_state::UNINITIALIZED, stream, mr);
+
+  // Call first pass kernel to get sizes in offsets
+  cudf::detail::grid_1d grid{input.size(), BLOCK_SIZE, 1};
+  replace_first<<<grid.num_blocks, BLOCK_SIZE, 0, stream.value()>>>(
+    *device_in,
+    *device_replacement,
+    reinterpret_cast<cudf::bitmask_type*>(valid_bits.data()),
+    sizes_view.begin<cudf::size_type>(),
+    nullptr,
+    valid_count);
+
+  auto [offsets, bytes] = cudf::detail::make_offsets_child_column(
+    sizes_view.begin<int32_t>(), sizes_view.end<int32_t>(), stream, mr);
+
+  auto offsets_view = offsets->mutable_view();
+
+  // Allocate chars array and output null mask
+  std::unique_ptr<cudf::column> output_chars =
+    cudf::strings::detail::create_chars_child_column(bytes, stream, mr);
+
+  auto output_chars_view = output_chars->mutable_view();
+
+  replace_second<<<grid.num_blocks, BLOCK_SIZE, 0, stream.value()>>>(
+    *device_in,
+    *device_replacement,
+    reinterpret_cast<cudf::bitmask_type*>(valid_bits.data()),
+    offsets_view.begin<cudf::size_type>(),
+    output_chars_view.data<char>(),
+    valid_count);
+
+  return cudf::make_strings_column(input.size(),
+                                   std::move(offsets),
+                                   std::move(output_chars),
+                                   input.size() - valid_counter.value(stream),
+                                   std::move(valid_bits));
+}
+
+template <>
+std::unique_ptr<cudf::column> replace_nulls_column_kernel_forwarder::operator()<cudf::dictionary32>(
+  cudf::column_view const& input,
+  cudf::column_view const& replacement,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  cudf::dictionary_column_view dict_input(input);
+  cudf::dictionary_column_view dict_repl(replacement);
+  return cudf::dictionary::detail::replace_nulls(dict_input, dict_repl, stream, mr);
+}
+
+template <typename T>
+struct replace_nulls_functor {
+  T const* value_it;
+  replace_nulls_functor(T const* _value_it) : value_it(_value_it) {}
+  __device__ T operator()(T input, bool is_valid) { return is_valid ? input : *value_it; }
+};
+
+/**
+ * @brief Functor called by the `type_dispatcher` in order to invoke and instantiate
+ *        `replace_nulls` with the appropriate data types.
+ */
+struct replace_nulls_scalar_kernel_forwarder {
+  template <typename col_type, std::enable_if_t<cudf::is_fixed_width<col_type>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           cudf::scalar const& replacement,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_EXPECTS(input.type() == replacement.type(), "Data type mismatch");
+    std::unique_ptr<cudf::column> output = cudf::detail::allocate_like(
+      input, input.size(), cudf::mask_allocation_policy::NEVER, stream, mr);
+    auto output_view = output->mutable_view();
+
+    using ScalarType = cudf::scalar_type_t<col_type>;
+    auto& s1         = static_cast<ScalarType const&>(replacement);
+    auto device_in   = cudf::column_device_view::create(input, stream);
+
+    auto func = replace_nulls_functor<col_type>{s1.data()};
+    thrust::transform(rmm::exec_policy(stream),
+                      input.data<col_type>(),
+                      input.data<col_type>() + input.size(),
+                      cudf::detail::make_validity_iterator(*device_in),
+                      output_view.data<col_type>(),
+                      func);
+    return output;
+  }
+
+  template <typename col_type, std::enable_if_t<not cudf::is_fixed_width<col_type>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const&,
+                                           cudf::scalar const&,
+                                           rmm::cuda_stream_view,
+                                           rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("No specialization exists for the given type.");
+  }
+};
+
+template <>
+std::unique_ptr<cudf::column> replace_nulls_scalar_kernel_forwarder::operator()<cudf::string_view>(
+  cudf::column_view const& input,
+  cudf::scalar const& replacement,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type() == replacement.type(), "Data type mismatch");
+  cudf::strings_column_view input_s(input);
+  cudf::string_scalar const& repl = static_cast<cudf::string_scalar const&>(replacement);
+  return cudf::strings::detail::replace_nulls(input_s, repl, stream, mr);
+}
+
+template <>
+std::unique_ptr<cudf::column> replace_nulls_scalar_kernel_forwarder::operator()<cudf::dictionary32>(
+  cudf::column_view const& input,
+  cudf::scalar const& replacement,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  cudf::dictionary_column_view dict_input(input);
+  return cudf::dictionary::detail::replace_nulls(dict_input, replacement, stream, mr);
+}
+
+/**
+ * @brief Function used by replace_nulls policy
+ */
+
+std::unique_ptr<cudf::column> replace_nulls_policy_impl(cudf::column_view const& input,
+                                                        cudf::replace_policy const& replace_policy,
+                                                        rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  auto device_in = cudf::column_device_view::create(input, stream);
+  auto index     = thrust::make_counting_iterator<cudf::size_type>(0);
+  auto valid_it  = cudf::detail::make_validity_iterator(*device_in);
+  auto in_begin  = thrust::make_zip_iterator(thrust::make_tuple(index, valid_it));
+
+  rmm::device_uvector<cudf::size_type> gather_map(input.size(), stream);
+  auto gm_begin = thrust::make_zip_iterator(
+    thrust::make_tuple(gather_map.begin(), thrust::make_discard_iterator()));
+
+  auto func = cudf::detail::replace_policy_functor();
+  if (replace_policy == cudf::replace_policy::PRECEDING) {
+    thrust::inclusive_scan(
+      rmm::exec_policy(stream), in_begin, in_begin + input.size(), gm_begin, func);
+  } else {
+    auto in_rbegin = thrust::make_reverse_iterator(in_begin + input.size());
+    auto gm_rbegin = thrust::make_reverse_iterator(gm_begin + gather_map.size());
+    thrust::inclusive_scan(
+      rmm::exec_policy(stream), in_rbegin, in_rbegin + input.size(), gm_rbegin, func);
+  }
+
+  auto output = cudf::detail::gather(cudf::table_view({input}),
+                                     gather_map,
+                                     cudf::out_of_bounds_policy::DONT_CHECK,
+                                     cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                     stream,
+                                     mr);
+
+  return std::move(output->release()[0]);
+}
+
+}  // end anonymous namespace
+
+namespace cudf {
+namespace detail {
+
+std::unique_ptr<cudf::column> replace_nulls(cudf::column_view const& input,
+                                            cudf::column_view const& replacement,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type() == replacement.type(), "Data type mismatch");
+  CUDF_EXPECTS(replacement.size() == input.size(), "Column size mismatch");
+
+  if (input.is_empty()) { return cudf::empty_like(input); }
+  if (!input.has_nulls()) { return std::make_unique<cudf::column>(input, stream, mr); }
+
+  return cudf::type_dispatcher<dispatch_storage_type>(
+    input.type(), replace_nulls_column_kernel_forwarder{}, input, replacement, stream, mr);
+}
+
+std::unique_ptr<cudf::column> replace_nulls(cudf::column_view const& input,
+                                            cudf::scalar const& replacement,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return cudf::empty_like(input); }
+  if (!input.has_nulls() || !replacement.is_valid(stream)) {
+    return std::make_unique<cudf::column>(input, stream, mr);
+  }
+
+  return cudf::type_dispatcher<dispatch_storage_type>(
+    input.type(), replace_nulls_scalar_kernel_forwarder{}, input, replacement, stream, mr);
+}
+
+std::unique_ptr<cudf::column> replace_nulls(cudf::column_view const& input,
+                                            cudf::replace_policy const& replace_policy,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return cudf::empty_like(input); }
+  if (!input.has_nulls()) { return std::make_unique<cudf::column>(input, stream, mr); }
+
+  return replace_nulls_policy_impl(input, replace_policy, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> replace_nulls(cudf::column_view const& input,
+                                            cudf::column_view const& replacement,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_nulls(input, replacement, stream, mr);
+}
+
+std::unique_ptr<cudf::column> replace_nulls(cudf::column_view const& input,
+                                            cudf::scalar const& replacement,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_nulls(input, replacement, stream, mr);
+}
+
+std::unique_ptr<cudf::column> replace_nulls(column_view const& input,
+                                            replace_policy const& replace_policy,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_nulls(input, replace_policy, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/replace/replace.cu b/cpp/src/replace/replace.cu
new file mode 100644
index 0000000..9341929
--- /dev/null
+++ b/cpp/src/replace/replace.cu
@@ -0,0 +1,550 @@
+/*
+ * Copyright 2018 BlazingDB, Inc.
+
+ *     Copyright 2018 Cristhian Alberto Gonzales Castillo <cristhian@blazingdb.com>
+ *     Copyright 2018 Alexander Ocsa <alexander@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/replace.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/pair.h>
+#include <thrust/tuple.h>
+
+namespace {  // anonymous
+
+static constexpr int BLOCK_SIZE = 256;
+
+// return the new_value for output column at index `idx`
+template <class T, bool replacement_has_nulls>
+__device__ auto get_new_value(cudf::size_type idx,
+                              T const* __restrict__ input_data,
+                              T const* __restrict__ values_to_replace_begin,
+                              T const* __restrict__ values_to_replace_end,
+                              T const* __restrict__ d_replacement_values,
+                              cudf::bitmask_type const* __restrict__ replacement_valid)
+{
+  auto found_ptr =
+    thrust::find(thrust::seq, values_to_replace_begin, values_to_replace_end, input_data[idx]);
+  T new_value{};
+  bool output_is_valid{true};
+
+  if (found_ptr != values_to_replace_end) {
+    auto d    = thrust::distance(values_to_replace_begin, found_ptr);
+    new_value = d_replacement_values[d];
+    if (replacement_has_nulls) { output_is_valid = cudf::bit_is_set(replacement_valid, d); }
+  } else {
+    new_value = input_data[idx];
+  }
+  return thrust::make_pair(new_value, output_is_valid);
+}
+
+__device__ int get_new_string_value(cudf::size_type idx,
+                                    cudf::column_device_view& input,
+                                    cudf::column_device_view& values_to_replace,
+                                    cudf::column_device_view&)
+{
+  cudf::string_view input_string = input.element<cudf::string_view>(idx);
+  int match                      = -1;
+  for (int i = 0; i < values_to_replace.size(); i++) {
+    cudf::string_view value_string = values_to_replace.element<cudf::string_view>(i);
+    if (input_string == value_string) {
+      match = i;
+      break;
+    }
+  }
+  return match;
+}
+
+/**
+ * @brief Kernel which does the first pass of strings replace.
+ *
+ * It computes the output null_mask, null_count, and the offsets.
+ *
+ * @param input The input column to replace strings in.
+ * @param values_to_replace The string values to replace.
+ * @param replacement The replacement values.
+ * @param offsets The column which will contain the offsets of the new string column
+ * @param indices Temporary column used to store the replacement indices
+ * @param output_valid The output null_mask
+ * @param output_valid_count The output valid count
+ */
+template <bool input_has_nulls, bool replacement_has_nulls>
+__global__ void replace_strings_first_pass(cudf::column_device_view input,
+                                           cudf::column_device_view values_to_replace,
+                                           cudf::column_device_view replacement,
+                                           cudf::mutable_column_device_view offsets,
+                                           cudf::mutable_column_device_view indices,
+                                           cudf::bitmask_type* output_valid,
+                                           cudf::size_type* __restrict__ output_valid_count)
+{
+  cudf::size_type nrows = input.size();
+  auto tid              = cudf::detail::grid_1d::global_thread_id();
+  auto const stride     = cudf::detail::grid_1d::grid_stride();
+  uint32_t active_mask  = 0xffff'ffffu;
+  active_mask           = __ballot_sync(active_mask, tid < nrows);
+  auto const lane_id{threadIdx.x % cudf::detail::warp_size};
+  uint32_t valid_sum{0};
+
+  while (tid < nrows) {
+    auto const idx      = static_cast<cudf::size_type>(tid);
+    bool input_is_valid = true;
+
+    if (input_has_nulls) input_is_valid = input.is_valid_nocheck(idx);
+    bool output_is_valid = input_is_valid;
+
+    if (input_is_valid) {
+      int result               = get_new_string_value(idx, input, values_to_replace, replacement);
+      cudf::string_view output = (result == -1) ? input.element<cudf::string_view>(idx)
+                                                : replacement.element<cudf::string_view>(result);
+      offsets.data<cudf::size_type>()[idx] = output.size_bytes();
+      indices.data<cudf::size_type>()[idx] = result;
+      if (replacement_has_nulls && result != -1) {
+        output_is_valid = replacement.is_valid_nocheck(result);
+      }
+    } else {
+      offsets.data<cudf::size_type>()[idx] = 0;
+      indices.data<cudf::size_type>()[idx] = -1;
+    }
+
+    uint32_t bitmask = __ballot_sync(active_mask, output_is_valid);
+    if (0 == lane_id) {
+      output_valid[cudf::word_index(idx)] = bitmask;
+      valid_sum += __popc(bitmask);
+    }
+
+    tid += stride;
+    active_mask = __ballot_sync(active_mask, tid < nrows);
+  }
+
+  // Compute total valid count for this block and add it to global count
+  uint32_t block_valid_count = cudf::detail::single_lane_block_sum_reduce<BLOCK_SIZE, 0>(valid_sum);
+  // one thread computes and adds to output_valid_count
+  if (threadIdx.x == 0) { atomicAdd(output_valid_count, block_valid_count); }
+}
+
+/**
+ * @brief Kernel which does the second pass of strings replace.
+ *
+ * It copies the string data needed from input and replacement into the new strings column chars
+ * column.
+ *
+ * @param input The input column
+ * @param replacement The replacement values
+ * @param offsets The offsets column of the new strings column
+ * @param strings The chars column of the new strings column
+ * @param indices Temporary column used to store the replacement indices.
+ */
+template <bool input_has_nulls, bool replacement_has_nulls>
+__global__ void replace_strings_second_pass(cudf::column_device_view input,
+                                            cudf::column_device_view replacement,
+                                            cudf::mutable_column_device_view offsets,
+                                            cudf::mutable_column_device_view strings,
+                                            cudf::mutable_column_device_view indices)
+{
+  cudf::size_type nrows = input.size();
+  auto tid              = cudf::detail::grid_1d::global_thread_id();
+  auto const stride     = cudf::detail::grid_1d::grid_stride();
+
+  while (tid < nrows) {
+    auto const idx         = static_cast<cudf::size_type>(tid);
+    auto const replace_idx = indices.element<cudf::size_type>(idx);
+    bool output_is_valid   = true;
+    bool input_is_valid    = true;
+
+    if (input_has_nulls) {
+      input_is_valid  = input.is_valid_nocheck(idx);
+      output_is_valid = input_is_valid;
+    }
+    if (replacement_has_nulls && replace_idx != -1) {
+      output_is_valid = replacement.is_valid_nocheck(replace_idx);
+    }
+    if (output_is_valid) {
+      cudf::string_view output = (replace_idx == -1)
+                                   ? input.element<cudf::string_view>(idx)
+                                   : replacement.element<cudf::string_view>(replace_idx);
+      std::memcpy(strings.data<char>() + offsets.data<cudf::size_type>()[idx],
+                  output.data(),
+                  output.size_bytes());
+    }
+
+    tid += stride;
+  }
+}
+
+/**
+ * @brief Kernel that replaces elements from `output_data` given the following
+ *        rule: replace all `values_to_replace[i]` in [values_to_replace_begin`,
+ *        `values_to_replace_end`) present in `output_data` with `d_replacement_values[i]`.
+ *
+ * @tparam input_has_nulls `true` if output column has valid mask, `false` otherwise
+ * @tparam replacement_has_nulls `true` if replacement_values column has valid mask, `false`
+ * otherwise The input_has_nulls and replacement_has_nulls template parameters allows us to
+ * specialize this kernel for the different scenario for performance without writing different
+ * kernel.
+ *
+ * @param[in] input_data Device array with the data to be modified
+ * @param[in] input_valid Valid mask associated with input_data
+ * @param[out] output_data Device array to store the data from input_data
+ * @param[out] output_valid Valid mask associated with output_data
+ * @param[out] output_valid_count #valid in output column
+ * @param[in] nrows # rows in `output_data`
+ * @param[in] values_to_replace_begin Device pointer to the beginning of the sequence
+ * of old values to be replaced
+ * @param[in] values_to_replace_end  Device pointer to the end of the sequence
+ * of old values to be replaced
+ * @param[in] d_replacement_values Device array with the new values
+ * @param[in] replacement_valid Valid mask associated with d_replacement_values
+ */
+template <class T, bool input_has_nulls, bool replacement_has_nulls>
+__global__ void replace_kernel(cudf::column_device_view input,
+                               cudf::mutable_column_device_view output,
+                               cudf::size_type* __restrict__ output_valid_count,
+                               cudf::size_type nrows,
+                               cudf::column_device_view values_to_replace,
+                               cudf::column_device_view replacement)
+{
+  T* __restrict__ output_data = output.data<T>();
+
+  auto tid          = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+
+  uint32_t active_mask = 0xffff'ffffu;
+  active_mask          = __ballot_sync(active_mask, tid < nrows);
+  auto const lane_id{threadIdx.x % cudf::detail::warp_size};
+  uint32_t valid_sum{0};
+
+  while (tid < nrows) {
+    auto const idx = static_cast<cudf::size_type>(tid);
+    bool output_is_valid{true};
+    bool input_is_valid{true};
+    if (input_has_nulls) {
+      input_is_valid  = input.is_valid_nocheck(idx);
+      output_is_valid = input_is_valid;
+    }
+    if (input_is_valid)
+      thrust::tie(output_data[idx], output_is_valid) = get_new_value<T, replacement_has_nulls>(
+        idx,
+        input.data<T>(),
+        values_to_replace.data<T>(),
+        values_to_replace.data<T>() + values_to_replace.size(),
+        replacement.data<T>(),
+        replacement.null_mask());
+
+    /* output valid counts calculations*/
+    if (input_has_nulls or replacement_has_nulls) {
+      uint32_t bitmask = __ballot_sync(active_mask, output_is_valid);
+      if (0 == lane_id) {
+        output.set_mask_word(cudf::word_index(idx), bitmask);
+        valid_sum += __popc(bitmask);
+      }
+    }
+
+    tid += stride;
+    active_mask = __ballot_sync(active_mask, tid < nrows);
+  }
+  if (input_has_nulls or replacement_has_nulls) {
+    // Compute total valid count for this block and add it to global count
+    uint32_t block_valid_count =
+      cudf::detail::single_lane_block_sum_reduce<BLOCK_SIZE, 0>(valid_sum);
+    // one thread computes and adds to output_valid_count
+    if (threadIdx.x == 0) { atomicAdd(output_valid_count, block_valid_count); }
+  }
+}
+
+/**
+ * @brief Functor called by the `type_dispatcher` in order to invoke and instantiate
+ *        `replace_kernel` with the appropriate data types.
+ */
+struct replace_kernel_forwarder {
+  template <typename col_type, std::enable_if_t<cudf::is_fixed_width<col_type>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input_col,
+                                           cudf::column_view const& values_to_replace,
+                                           cudf::column_view const& replacement_values,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    rmm::device_scalar<cudf::size_type> valid_counter(0, stream);
+    cudf::size_type* valid_count = valid_counter.data();
+
+    auto replace = [&] {
+      if (input_col.has_nulls())
+        return replacement_values.has_nulls() ? replace_kernel<col_type, true, true>
+                                              : replace_kernel<col_type, true, false>;
+      else
+        return replacement_values.has_nulls() ? replace_kernel<col_type, false, true>
+                                              : replace_kernel<col_type, false, false>;
+    }();
+
+    auto output = [&] {
+      auto const mask_allocation_policy = input_col.has_nulls() || replacement_values.has_nulls()
+                                            ? cudf::mask_allocation_policy::ALWAYS
+                                            : cudf::mask_allocation_policy::NEVER;
+      return cudf::detail::allocate_like(
+        input_col, input_col.size(), mask_allocation_policy, stream, mr);
+    }();
+
+    auto output_view = output->mutable_view();
+    auto grid        = cudf::detail::grid_1d{output_view.size(), BLOCK_SIZE, 1};
+
+    auto device_in                 = cudf::column_device_view::create(input_col, stream);
+    auto device_out                = cudf::mutable_column_device_view::create(output_view, stream);
+    auto device_values_to_replace  = cudf::column_device_view::create(values_to_replace, stream);
+    auto device_replacement_values = cudf::column_device_view::create(replacement_values, stream);
+
+    replace<<<grid.num_blocks, BLOCK_SIZE, 0, stream.value()>>>(*device_in,
+                                                                *device_out,
+                                                                valid_count,
+                                                                output_view.size(),
+                                                                *device_values_to_replace,
+                                                                *device_replacement_values);
+
+    if (output_view.nullable()) {
+      output->set_null_count(output->size() - valid_counter.value(stream));
+    }
+    return output;
+  }
+
+  template <typename col_type, std::enable_if_t<not cudf::is_fixed_width<col_type>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const&,
+                                           cudf::column_view const&,
+                                           cudf::column_view const&,
+                                           rmm::cuda_stream_view,
+                                           rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("No specialization exists for this type");
+  }
+};
+
+template <>
+std::unique_ptr<cudf::column> replace_kernel_forwarder::operator()<cudf::string_view>(
+  cudf::column_view const& input_col,
+  cudf::column_view const& values_to_replace,
+  cudf::column_view const& replacement_values,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_scalar<cudf::size_type> valid_counter(0, stream);
+  cudf::size_type* valid_count = valid_counter.data();
+
+  auto replace_first  = replace_strings_first_pass<true, false>;
+  auto replace_second = replace_strings_second_pass<true, false>;
+  if (input_col.has_nulls()) {
+    if (replacement_values.has_nulls()) {
+      replace_first  = replace_strings_first_pass<true, true>;
+      replace_second = replace_strings_second_pass<true, true>;
+    }
+  } else {
+    if (replacement_values.has_nulls()) {
+      replace_first  = replace_strings_first_pass<false, true>;
+      replace_second = replace_strings_second_pass<false, true>;
+    } else {
+      replace_first  = replace_strings_first_pass<false, false>;
+      replace_second = replace_strings_second_pass<false, false>;
+    }
+  }
+
+  // Create new offsets column to use in kernel
+  std::unique_ptr<cudf::column> sizes =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                              input_col.size(),
+                              cudf::mask_state::UNALLOCATED,
+                              stream);
+  std::unique_ptr<cudf::column> indices =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                              input_col.size(),
+                              cudf::mask_state::UNALLOCATED,
+                              stream);
+
+  auto sizes_view   = sizes->mutable_view();
+  auto indices_view = indices->mutable_view();
+
+  auto device_in                = cudf::column_device_view::create(input_col, stream);
+  auto device_values_to_replace = cudf::column_device_view::create(values_to_replace, stream);
+  auto device_replacement       = cudf::column_device_view::create(replacement_values, stream);
+  auto device_sizes             = cudf::mutable_column_device_view::create(sizes_view, stream);
+  auto device_indices           = cudf::mutable_column_device_view::create(indices_view, stream);
+
+  rmm::device_buffer valid_bits =
+    cudf::detail::create_null_mask(input_col.size(), cudf::mask_state::UNINITIALIZED, stream, mr);
+
+  // Call first pass kernel to get sizes in offsets
+  cudf::detail::grid_1d grid{input_col.size(), BLOCK_SIZE, 1};
+  replace_first<<<grid.num_blocks, BLOCK_SIZE, 0, stream.value()>>>(
+    *device_in,
+    *device_values_to_replace,
+    *device_replacement,
+    *device_sizes,
+    *device_indices,
+    reinterpret_cast<cudf::bitmask_type*>(valid_bits.data()),
+    valid_count);
+
+  auto [offsets, bytes] = cudf::detail::make_offsets_child_column(
+    sizes_view.begin<cudf::size_type>(), sizes_view.end<cudf::size_type>(), stream, mr);
+  auto offsets_view   = offsets->mutable_view();
+  auto device_offsets = cudf::mutable_column_device_view::create(offsets_view, stream);
+
+  // Allocate chars array and output null mask
+  cudf::size_type null_count = input_col.size() - valid_counter.value(stream);
+  std::unique_ptr<cudf::column> output_chars =
+    cudf::strings::detail::create_chars_child_column(bytes, stream, mr);
+
+  auto output_chars_view = output_chars->mutable_view();
+  auto device_chars      = cudf::mutable_column_device_view::create(output_chars_view, stream);
+
+  replace_second<<<grid.num_blocks, BLOCK_SIZE, 0, stream.value()>>>(
+    *device_in, *device_replacement, *device_offsets, *device_chars, *device_indices);
+
+  return cudf::make_strings_column(input_col.size(),
+                                   std::move(offsets),
+                                   std::move(output_chars),
+                                   null_count,
+                                   std::move(valid_bits));
+}
+
+template <>
+std::unique_ptr<cudf::column> replace_kernel_forwarder::operator()<cudf::dictionary32>(
+  cudf::column_view const& input_col,
+  cudf::column_view const& values_to_replace,
+  cudf::column_view const& replacement_values,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto input        = cudf::dictionary_column_view(input_col);
+  auto values       = cudf::dictionary_column_view(values_to_replace);
+  auto replacements = cudf::dictionary_column_view(replacement_values);
+
+  auto matched_input = [&] {
+    auto new_keys = cudf::detail::concatenate(
+      std::vector<cudf::column_view>({values.keys(), replacements.keys()}),
+      stream,
+      rmm::mr::get_current_device_resource());
+    return cudf::dictionary::detail::add_keys(input, new_keys->view(), stream, mr);
+  }();
+  auto matched_view   = cudf::dictionary_column_view(matched_input->view());
+  auto matched_values = cudf::dictionary::detail::set_keys(
+    values, matched_view.keys(), stream, rmm::mr::get_current_device_resource());
+  auto matched_replacements = cudf::dictionary::detail::set_keys(
+    replacements, matched_view.keys(), stream, rmm::mr::get_current_device_resource());
+
+  auto indices_type = matched_view.indices().type();
+  auto new_indices  = cudf::type_dispatcher<cudf::dispatch_storage_type>(
+    indices_type,
+    replace_kernel_forwarder{},
+    matched_view.get_indices_annotated(),
+    cudf::dictionary_column_view(matched_values->view()).indices(),
+    cudf::dictionary_column_view(matched_replacements->view()).get_indices_annotated(),
+    stream,
+    mr);
+  auto null_count     = new_indices->null_count();
+  auto contents       = new_indices->release();
+  auto indices_column = std::make_unique<cudf::column>(
+    indices_type, input.size(), std::move(*(contents.data.release())), rmm::device_buffer{}, 0);
+  std::unique_ptr<cudf::column> keys_column(std::move(matched_input->release().children.back()));
+  return cudf::make_dictionary_column(std::move(keys_column),
+                                      std::move(indices_column),
+                                      std::move(*(contents.null_mask.release())),
+                                      null_count);
+}
+
+}  // end anonymous namespace
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<cudf::column> find_and_replace_all(cudf::column_view const& input_col,
+                                                   cudf::column_view const& values_to_replace,
+                                                   cudf::column_view const& replacement_values,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(values_to_replace.size() == replacement_values.size(),
+               "values_to_replace and replacement_values size mismatch.");
+
+  CUDF_EXPECTS(
+    input_col.type() == values_to_replace.type() && input_col.type() == replacement_values.type(),
+    "Columns type mismatch");
+  CUDF_EXPECTS(not values_to_replace.has_nulls(), "values_to_replace must not have nulls");
+
+  if (input_col.is_empty() or values_to_replace.is_empty() or replacement_values.is_empty()) {
+    return std::make_unique<cudf::column>(input_col, stream, mr);
+  }
+
+  return cudf::type_dispatcher<dispatch_storage_type>(input_col.type(),
+                                                      replace_kernel_forwarder{},
+                                                      input_col,
+                                                      values_to_replace,
+                                                      replacement_values,
+                                                      stream,
+                                                      mr);
+}
+
+}  // namespace detail
+
+/**
+ * @brief Replace elements from `input_col` according to the mapping `values_to_replace` to
+ *        `replacement_values`, that is, replace all `values_to_replace[i]` present in `input_col`
+ *        with `replacement_values[i]`.
+ *
+ * @param[in] input_col column_view of the data to be modified
+ * @param[in] values_to_replace column_view of the old values to be replaced
+ * @param[in] replacement_values column_view of the new values
+ *
+ * @returns output cudf::column with the modified data
+ */
+std::unique_ptr<cudf::column> find_and_replace_all(cudf::column_view const& input_col,
+                                                   cudf::column_view const& values_to_replace,
+                                                   cudf::column_view const& replacement_values,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  return detail::find_and_replace_all(input_col, values_to_replace, replacement_values, stream, mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/reshape/byte_cast.cu b/cpp/src/reshape/byte_cast.cu
new file mode 100644
index 0000000..5f89b6d
--- /dev/null
+++ b/cpp/src/reshape/byte_cast.cu
@@ -0,0 +1,192 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+#include <cudf/reshape.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+// Data type of the output data column after conversion.
+constexpr data_type output_type{type_id::UINT8};
+
+template <typename T, typename Enable = void>
+struct byte_list_conversion_fn {
+  template <typename... Args>
+  static std::unique_ptr<column> invoke(Args&&...)
+  {
+    CUDF_FAIL("Unsupported non-numeric and non-string column");
+  }
+};
+
+struct byte_list_conversion_dispatcher {
+  template <typename T>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     flip_endianness configuration,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    return byte_list_conversion_fn<T>::invoke(input, configuration, stream, mr);
+  }
+};
+
+template <typename T>
+struct byte_list_conversion_fn<T, std::enable_if_t<cudf::is_numeric<T>()>> {
+  static std::unique_ptr<column> invoke(column_view const& input,
+                                        flip_endianness configuration,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    if (input.size() == 0) {
+      return cudf::lists::detail::make_empty_lists_column(output_type, stream, mr);
+    }
+    if (input.size() == input.null_count()) {
+      return cudf::lists::detail::make_all_nulls_lists_column(
+        input.size(), output_type, stream, mr);
+    }
+
+    auto const num_bytes = static_cast<size_type>(input.size() * sizeof(T));
+    auto byte_column =
+      make_numeric_column(output_type, num_bytes, mask_state::UNALLOCATED, stream, mr);
+
+    auto const d_inp = reinterpret_cast<char const*>(input.data<T>());
+    auto const d_out = byte_column->mutable_view().data<char>();
+
+    if (configuration == flip_endianness::YES) {
+      thrust::for_each(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator(0),
+                       thrust::make_counting_iterator(num_bytes),
+                       [d_inp, d_out] __device__(auto index) {
+                         constexpr auto mask = static_cast<size_type>(sizeof(T) - 1);
+                         d_out[index]        = d_inp[index + mask - ((index & mask) << 1)];
+                       });
+    } else {
+      thrust::copy_n(rmm::exec_policy(stream), d_inp, num_bytes, d_out);
+    }
+
+    auto const it = thrust::make_constant_iterator(cudf::size_of(input.type()));
+    auto offsets_column =
+      std::get<0>(cudf::detail::make_offsets_child_column(it, it + input.size(), stream, mr));
+
+    auto result = make_lists_column(input.size(),
+                                    std::move(offsets_column),
+                                    std::move(byte_column),
+                                    input.null_count(),
+                                    detail::copy_bitmask(input, stream, mr),
+                                    stream,
+                                    mr);
+
+    // If any nulls are present, the corresponding lists must be purged so that
+    // the result is sanitized.
+    if (auto const result_cv = result->view();
+        cudf::detail::has_nonempty_nulls(result_cv, stream)) {
+      return cudf::detail::purge_nonempty_nulls(result_cv, stream, mr);
+    }
+
+    return result;
+  }
+};
+
+template <typename T>
+struct byte_list_conversion_fn<T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>> {
+  static std::unique_ptr<column> invoke(column_view const& input,
+                                        flip_endianness,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+  {
+    if (input.size() == 0) {
+      return cudf::lists::detail::make_empty_lists_column(output_type, stream, mr);
+    }
+    if (input.size() == input.null_count()) {
+      return cudf::lists::detail::make_all_nulls_lists_column(
+        input.size(), output_type, stream, mr);
+    }
+
+    auto col_content     = std::make_unique<column>(input, stream, mr)->release();
+    auto chars_contents  = col_content.children[strings_column_view::chars_column_index]->release();
+    auto const num_chars = chars_contents.data->size();
+    auto uint8_col       = std::make_unique<column>(
+      output_type, num_chars, std::move(*(chars_contents.data)), rmm::device_buffer{}, 0);
+
+    auto result = make_lists_column(
+      input.size(),
+      std::move(col_content.children[cudf::strings_column_view::offsets_column_index]),
+      std::move(uint8_col),
+      input.null_count(),
+      detail::copy_bitmask(input, stream, mr),
+      stream,
+      mr);
+
+    // If any nulls are present, the corresponding lists must be purged so that
+    // the result is sanitized.
+    if (auto const result_cv = result->view();
+        cudf::detail::has_nonempty_nulls(result_cv, stream)) {
+      return cudf::detail::purge_nonempty_nulls(result_cv, stream, mr);
+    }
+
+    return result;
+  }
+};
+
+}  // namespace
+
+/**
+ * @copydoc cudf::byte_cast(column_view const&, flip_endianness, rmm::mr::device_memory_resource*)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> byte_cast(column_view const& input,
+                                  flip_endianness endian_configuration,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(
+    input.type(), byte_list_conversion_dispatcher{}, input, endian_configuration, stream, mr);
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::byte_cast(column_view const&, flip_endianness, rmm::mr::device_memory_resource*)
+ */
+std::unique_ptr<column> byte_cast(column_view const& input,
+                                  flip_endianness endian_configuration,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::byte_cast(input, endian_configuration, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/reshape/interleave_columns.cu b/cpp/src/reshape/interleave_columns.cu
new file mode 100644
index 0000000..d803d78
--- /dev/null
+++ b/cpp/src/reshape/interleave_columns.cu
@@ -0,0 +1,299 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/reshape.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/detail/interleave_columns.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+// Error case when no other overload or specialization is available
+template <typename T, typename Enable = void>
+struct interleave_columns_impl {
+  template <typename... Args>
+  std::unique_ptr<column> operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported type in `interleave_columns`.");
+  }
+};
+
+struct interleave_columns_functor {
+  template <typename T>
+  std::unique_ptr<cudf::column> operator()(table_view const& input,
+                                           bool create_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    return interleave_columns_impl<T>{}(input, create_mask, stream, mr);
+  }
+};
+
+template <typename T>
+struct interleave_columns_impl<T, std::enable_if_t<std::is_same_v<T, cudf::list_view>>> {
+  std::unique_ptr<column> operator()(table_view const& lists_columns,
+                                     bool create_mask,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return lists::detail::interleave_columns(lists_columns, create_mask, stream, mr);
+  }
+};
+
+template <typename T>
+struct interleave_columns_impl<T, std::enable_if_t<std::is_same_v<T, cudf::struct_view>>> {
+  std::unique_ptr<cudf::column> operator()(table_view const& structs_columns,
+                                           bool create_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    // We can safely call `column(0)` as the number of columns is known to be non zero.
+    auto const num_children = structs_columns.column(0).num_children();
+    CUDF_EXPECTS(
+      std::all_of(structs_columns.begin(),
+                  structs_columns.end(),
+                  [num_children](auto const& col) { return col.num_children() == num_children; }),
+      "Number of children of the input structs columns must be the same");
+
+    auto const num_columns = structs_columns.num_columns();
+    auto const num_rows    = structs_columns.num_rows();
+    auto const output_size = num_columns * num_rows;
+
+    // Interleave the children of the structs columns.
+    std::vector<std::unique_ptr<cudf::column>> output_struct_members;
+    for (size_type child_idx = 0; child_idx < num_children; ++child_idx) {
+      // Collect children columns from the input structs columns at index `child_idx`.
+      auto const child_iter = thrust::make_transform_iterator(
+        structs_columns.begin(), [&stream = stream, child_idx](auto const& col) {
+          return structs_column_view(col).get_sliced_child(child_idx, stream);
+        });
+      auto children = std::vector<column_view>(child_iter, child_iter + num_columns);
+
+      auto const child_type = children.front().type();
+      CUDF_EXPECTS(
+        std::all_of(children.cbegin(),
+                    children.cend(),
+                    [child_type](auto const& col) { return child_type == col.type(); }),
+        "Children of the input structs columns at the same child index must have the same type");
+
+      auto const children_nullable = std::any_of(
+        children.cbegin(), children.cend(), [](auto const& col) { return col.nullable(); });
+      output_struct_members.emplace_back(
+        type_dispatcher<dispatch_storage_type>(child_type,
+                                               interleave_columns_functor{},
+                                               table_view{std::move(children)},
+                                               children_nullable,
+                                               stream,
+                                               mr));
+    }
+
+    auto const create_mask_fn = [&] {
+      auto const input_dv_ptr = table_device_view::create(structs_columns, stream);
+      auto const validity_fn  = [input_dv = *input_dv_ptr, num_columns] __device__(auto const idx) {
+        return input_dv.column(idx % num_columns).is_valid(idx / num_columns);
+      };
+      return cudf::detail::valid_if(thrust::make_counting_iterator<size_type>(0),
+                                    thrust::make_counting_iterator<size_type>(output_size),
+                                    validity_fn,
+                                    stream,
+                                    mr);
+    };
+
+    // Only create null mask if at least one input structs column is nullable.
+    auto [null_mask, null_count] =
+      create_mask ? create_mask_fn() : std::pair{rmm::device_buffer{0, stream, mr}, size_type{0}};
+    return make_structs_column(
+      output_size, std::move(output_struct_members), null_count, std::move(null_mask), stream, mr);
+  }
+};
+
+template <typename T>
+struct interleave_columns_impl<T, std::enable_if_t<std::is_same_v<T, cudf::string_view>>> {
+  std::unique_ptr<cudf::column> operator()(table_view const& strings_columns,
+                                           bool create_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    auto num_columns = strings_columns.num_columns();
+    if (num_columns == 1)  // Single strings column returns a copy
+      return std::make_unique<column>(*(strings_columns.begin()), stream, mr);
+
+    auto strings_count = strings_columns.num_rows();
+    if (strings_count == 0)  // All columns have 0 rows
+      return make_empty_column(type_id::STRING);
+
+    // Create device views from the strings columns.
+    auto table       = table_device_view::create(strings_columns, stream);
+    auto d_table     = *table;
+    auto num_strings = num_columns * strings_count;
+
+    std::pair<rmm::device_buffer, size_type> valid_mask{};
+    if (create_mask) {
+      // Create resulting null mask
+      valid_mask = cudf::detail::valid_if(
+        thrust::make_counting_iterator<size_type>(0),
+        thrust::make_counting_iterator<size_type>(num_strings),
+        [num_columns, d_table] __device__(size_type idx) {
+          auto source_row_idx = idx % num_columns;
+          auto source_col_idx = idx / num_columns;
+          return !d_table.column(source_row_idx).is_null(source_col_idx);
+        },
+        stream,
+        mr);
+    }
+
+    auto const null_count = valid_mask.second;
+
+    // Build offsets column by computing sizes of each string in the output
+    auto offsets_transformer = [num_columns, d_table] __device__(size_type idx) {
+      // First compute the column and the row this item belongs to
+      auto source_row_idx = idx % num_columns;
+      auto source_col_idx = idx / num_columns;
+      return d_table.column(source_row_idx).is_valid(source_col_idx)
+               ? d_table.column(source_row_idx).element<string_view>(source_col_idx).size_bytes()
+               : 0;
+    };
+    auto offsets_transformer_itr = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<size_type>(0), offsets_transformer);
+    auto [offsets_column, bytes] = cudf::detail::make_offsets_child_column(
+      offsets_transformer_itr, offsets_transformer_itr + num_strings, stream, mr);
+    auto d_results_offsets = offsets_column->view().template data<int32_t>();
+
+    // Create the chars column
+    auto chars_column = strings::detail::create_chars_child_column(bytes, stream, mr);
+    // Fill the chars column
+    auto d_results_chars = chars_column->mutable_view().template data<char>();
+    thrust::for_each_n(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator<size_type>(0),
+      num_strings,
+      [num_columns, d_table, d_results_offsets, d_results_chars] __device__(size_type idx) {
+        auto source_row_idx = idx % num_columns;
+        auto source_col_idx = idx / num_columns;
+
+        // Do not write to buffer if the column value for this row is null
+        if (d_table.column(source_row_idx).is_null(source_col_idx)) return;
+
+        size_type offset = d_results_offsets[idx];
+        char* d_buffer   = d_results_chars + offset;
+        strings::detail::copy_string(
+          d_buffer, d_table.column(source_row_idx).element<string_view>(source_col_idx));
+      });
+
+    return make_strings_column(num_strings,
+                               std::move(offsets_column),
+                               std::move(chars_column),
+                               null_count,
+                               std::move(valid_mask.first));
+  }
+};
+
+template <typename T>
+struct interleave_columns_impl<T, std::enable_if_t<cudf::is_fixed_width<T>()>> {
+  std::unique_ptr<cudf::column> operator()(table_view const& input,
+                                           bool create_mask,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    auto arch_column = input.column(0);
+    auto output_size = input.num_columns() * input.num_rows();
+    auto output =
+      detail::allocate_like(arch_column, output_size, mask_allocation_policy::NEVER, stream, mr);
+    auto device_input  = table_device_view::create(input, stream);
+    auto device_output = mutable_column_device_view::create(*output, stream);
+    auto index_begin   = thrust::make_counting_iterator<size_type>(0);
+    auto index_end     = thrust::make_counting_iterator<size_type>(output_size);
+
+    auto func_value = [input   = *device_input,
+                       divisor = input.num_columns()] __device__(size_type idx) {
+      return input.column(idx % divisor).element<T>(idx / divisor);
+    };
+
+    if (not create_mask) {
+      thrust::transform(
+        rmm::exec_policy(stream), index_begin, index_end, device_output->begin<T>(), func_value);
+
+      return output;
+    }
+
+    auto func_validity = [input   = *device_input,
+                          divisor = input.num_columns()] __device__(size_type idx) {
+      return input.column(idx % divisor).is_valid(idx / divisor);
+    };
+
+    thrust::transform_if(rmm::exec_policy(stream),
+                         index_begin,
+                         index_end,
+                         device_output->begin<T>(),
+                         func_value,
+                         func_validity);
+
+    auto [mask, null_count] = valid_if(index_begin, index_end, func_validity, stream, mr);
+
+    output->set_null_mask(std::move(mask), null_count);
+
+    return output;
+  }
+};
+
+}  // anonymous namespace
+
+std::unique_ptr<column> interleave_columns(table_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.num_columns() > 0, "input must have at least one column to determine dtype.");
+
+  auto const dtype = input.column(0).type();
+  CUDF_EXPECTS(std::all_of(std::cbegin(input),
+                           std::cend(input),
+                           [dtype](auto const& col) { return dtype == col.type(); }),
+               "Input columns must have the same type");
+
+  auto const output_needs_mask = std::any_of(
+    std::cbegin(input), std::cend(input), [](auto const& col) { return col.nullable(); });
+
+  return type_dispatcher<dispatch_storage_type>(
+    dtype, detail::interleave_columns_functor{}, input, output_needs_mask, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> interleave_columns(table_view const& input,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::interleave_columns(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/reshape/tile.cu b/cpp/src/reshape/tile.cu
new file mode 100644
index 0000000..9d76c50
--- /dev/null
+++ b/cpp/src/reshape/tile.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/reshape.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <memory>
+
+namespace cudf {
+namespace {
+struct tile_functor {
+  size_type count;
+  size_type __device__ operator()(size_type i) { return i % count; }
+};
+
+}  // anonymous namespace
+
+namespace detail {
+std::unique_ptr<table> tile(table_view const& in,
+                            size_type count,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(count >= 0, "Count cannot be negative");
+
+  auto const in_num_rows = in.num_rows();
+
+  if (count == 0 or in_num_rows == 0) { return empty_like(in); }
+
+  auto out_num_rows = in_num_rows * count;
+  auto tiled_it     = cudf::detail::make_counting_transform_iterator(0, tile_functor{in_num_rows});
+
+  return detail::gather(
+    in, tiled_it, tiled_it + out_num_rows, out_of_bounds_policy::DONT_CHECK, stream, mr);
+}
+}  // namespace detail
+
+std::unique_ptr<table> tile(table_view const& in,
+                            size_type count,
+                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::tile(in, count, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/lead_lag_nested.cuh b/cpp/src/rolling/detail/lead_lag_nested.cuh
new file mode 100644
index 0000000..d2fe9fa
--- /dev/null
+++ b/cpp/src/rolling/detail/lead_lag_nested.cuh
@@ -0,0 +1,208 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/scatter.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <vector>
+
+namespace cudf::detail {
+namespace {
+
+/**
+ * @brief Predicate to find indices at which LEAD/LAG evaluated to null.
+ */
+template <typename GatherMapIter>
+class is_null_index_predicate_impl {
+ public:
+  is_null_index_predicate_impl(size_type input_size, GatherMapIter gather_)
+    : _null_index{input_size}, _gather{gather_}
+  {
+  }
+
+  bool __device__ operator()(size_type i) const { return _gather[i] == _null_index; }
+
+ private:
+  size_type const _null_index;  // Index value to use to output NULL for LEAD/LAG calculation.
+  GatherMapIter _gather;        // Iterator for gather-map entries.
+};
+
+/**
+ * @brief Helper to construct is_null_index_predicate_impl
+ */
+template <typename GatherMapIter>
+is_null_index_predicate_impl<GatherMapIter> is_null_index_predicate(size_type input_size,
+                                                                    GatherMapIter gather)
+{
+  return is_null_index_predicate_impl<GatherMapIter>{input_size, gather};
+}
+
+}  // namespace
+
+/**
+ * @brief Helper function to calculate LEAD/LAG for nested-type input columns.
+ *
+ * @tparam PrecedingIterator Iterator-type that returns the preceding bounds
+ * @tparam FollowingIterator Iterator-type that returns the following bounds
+ * @param[in] op Aggregation kind.
+ * @param[in] input Nested-type input column for LEAD/LAG calculation
+ * @param[in] default_outputs Default values to use as outputs, if LEAD/LAG
+ *                            offset crosses column/group boundaries
+ * @param[in] preceding Iterator to retrieve preceding window bounds
+ * @param[in] following Iterator to retrieve following window bounds
+ * @param[in] row_offset Lead/Lag offset, indicating which row after/before
+ *                       the current row is to be returned
+ * @param[in] stream CUDA stream for device memory operations/allocations
+ * @param[in] mr device_memory_resource for device memory allocations
+ */
+template <typename PrecedingIter, typename FollowingIter>
+std::unique_ptr<column> compute_lead_lag_for_nested(aggregation::Kind op,
+                                                    column_view const& input,
+                                                    column_view const& default_outputs,
+                                                    PrecedingIter preceding,
+                                                    FollowingIter following,
+                                                    size_type row_offset,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(op == aggregation::LEAD || op == aggregation::LAG,
+               "Unexpected aggregation type in compute_lead_lag_for_nested");
+  CUDF_EXPECTS(default_outputs.type().id() == input.type().id(),
+               "Defaults column type must match input column.");  // Because LEAD/LAG.
+
+  CUDF_EXPECTS(default_outputs.is_empty() || (input.size() == default_outputs.size()),
+               "Number of defaults must match input column.");
+
+  // For LEAD(0)/LAG(0), no computation need be performed.
+  // Return copy of input.
+  if (row_offset == 0) { return std::make_unique<column>(input, stream, mr); }
+
+  // Algorithm:
+  //
+  // 1. Construct gather_map with the LEAD/LAG offset applied to the indices.
+  //    E.g. A gather_map of:
+  //        {0, 1, 2, 3, ..., N-3, N-2, N-1}
+  //    would select the input column, unchanged.
+  //
+  //    For LEAD(2), the following gather_map is used:
+  //        {3, 4, 5, 6, ..., N-1, NULL_INDEX, NULL_INDEX}
+  //    where `NULL_INDEX` selects `NULL` for the gather.
+  //
+  //    Similarly, LAG(2) is implemented using the following gather_map:
+  //        {NULL_INDEX, NULL_INDEX, 0, 1, 2...}
+  //
+  // 2. Gather input column based on the gather_map.
+  // 3. If default outputs are available, scatter contents of `default_outputs`
+  //    to all positions where nulls where gathered in step 2.
+  //
+  // Note: Step 3 can be switched to use `copy_if_else()`, once it supports
+  //       nested types.
+
+  auto static constexpr size_data_type = data_type{type_to_id<size_type>()};
+
+  auto gather_map_column =
+    make_numeric_column(size_data_type, input.size(), mask_state::UNALLOCATED, stream);
+  auto gather_map = gather_map_column->mutable_view();
+
+  auto const input_size = input.size();
+  auto const null_index = input.size();
+  if (op == aggregation::LEAD) {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(size_type{0}),
+                      thrust::make_counting_iterator(size_type{input.size()}),
+                      gather_map.begin<size_type>(),
+                      [following, input_size, null_index, row_offset] __device__(size_type i) {
+                        // Note: grouped_*rolling_window() trims preceding/following to
+                        // the beginning/end of the group. `rolling_window()` does not.
+                        // Must trim _following[i] so as not to go past the column end.
+                        auto _following = min(following[i], input_size - i - 1);
+                        return (row_offset > _following) ? null_index : (i + row_offset);
+                      });
+  } else {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(size_type{0}),
+                      thrust::make_counting_iterator(size_type{input.size()}),
+                      gather_map.begin<size_type>(),
+                      [preceding, input_size, null_index, row_offset] __device__(size_type i) {
+                        // Note: grouped_*rolling_window() trims preceding/following to
+                        // the beginning/end of the group. `rolling_window()` does not.
+                        // Must trim _preceding[i] so as not to go past the column start.
+                        auto _preceding = min(preceding[i], i + 1);
+                        return (row_offset > (_preceding - 1)) ? null_index : (i - row_offset);
+                      });
+  }
+
+  auto output_with_nulls = cudf::detail::gather(table_view{std::vector<column_view>{input}},
+                                                gather_map_column->view(),
+                                                out_of_bounds_policy::NULLIFY,
+                                                cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                                stream,
+                                                mr);
+
+  if (default_outputs.is_empty()) { return std::move(output_with_nulls->release()[0]); }
+
+  // Must scatter defaults.
+  auto scatter_map = rmm::device_uvector<size_type>(input.size(), stream);
+
+  // Find all indices at which LEAD/LAG computed nulls previously.
+  auto scatter_map_end =
+    thrust::copy_if(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator(size_type{0}),
+                    thrust::make_counting_iterator(size_type{input.size()}),
+                    scatter_map.begin(),
+                    is_null_index_predicate(input.size(), gather_map.begin<size_type>()));
+
+  scatter_map.resize(thrust::distance(scatter_map.begin(), scatter_map_end), stream);
+  // Bail early, if all LEAD/LAG computations succeeded. No defaults need be substituted.
+  if (scatter_map.is_empty()) { return std::move(output_with_nulls->release()[0]); }
+
+  // Gather only those default values that are to be substituted.
+  auto gathered_defaults =
+    cudf::detail::gather(table_view{std::vector<column_view>{default_outputs}},
+                         scatter_map,
+                         out_of_bounds_policy::DONT_CHECK,
+                         cudf::detail::negative_index_policy::NOT_ALLOWED,
+                         stream,
+                         rmm::mr::get_current_device_resource());
+
+  // Scatter defaults into locations where LEAD/LAG computed nulls.
+  auto scattered_results = cudf::detail::scatter(
+    table_view{std::vector<column_view>{gathered_defaults->release()[0]->view()}},
+    scatter_map,
+    table_view{std::vector<column_view>{output_with_nulls->release()[0]->view()}},
+    stream,
+    mr);
+  return std::move(scattered_results->release()[0]);
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/rolling/detail/nth_element.cuh b/cpp/src/rolling/detail/nth_element.cuh
new file mode 100644
index 0000000..bd3cbb3
--- /dev/null
+++ b/cpp/src/rolling/detail/nth_element.cuh
@@ -0,0 +1,174 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/utilities/bit.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/reverse_iterator.h>
+
+#include <limits>
+
+namespace cudf::detail::rolling {
+
+/**
+ * @brief Functor to construct gather-map indices for NTH_ELEMENT rolling aggregation.
+ *
+ * By definition, the `N`th element is deemed null (i.e. the gather index is set to "nullify")
+ * for the following cases:
+ *   1. The window has fewer elements than `min_periods`.
+ *   2. N falls outside the window, i.e. N ∉ [-window_size, window_size).
+ *   3. `null_handling == EXCLUDE`, and the window has fewer than `N` non-null elements.
+ *
+ * If none of the above holds true, the result is non-null. How the value is determined
+ * depends on `null_handling`:
+ *   1. `null_handling == INCLUDE`: The required value is the `N`th value from the window's start.
+ *       i.e. the gather index is window_start + N (adjusted for negative N).
+ *   2. `null_handling == EXCLUDE`: The required value is the `N`th non-null value from the
+ *       window's start. i.e. Return index of the `N`th non-null value.
+ */
+template <null_policy null_handling, typename PrecedingIter, typename FollowingIter>
+struct gather_index_calculator {
+  size_type n;
+  bitmask_type const* input_nullmask;
+  bool exclude_nulls;
+  PrecedingIter preceding;
+  FollowingIter following;
+  size_type min_periods;
+  rmm::cuda_stream_view stream;
+
+  static size_type constexpr NULL_INDEX =
+    std::numeric_limits<size_type>::min();  // For nullifying with gather.
+
+  gather_index_calculator(size_type n,
+                          column_view input,
+                          PrecedingIter preceding,
+                          FollowingIter following,
+                          size_type min_periods,
+                          rmm::cuda_stream_view stream)
+    : n{n},
+      input_nullmask{input.null_mask()},
+      exclude_nulls{null_handling == null_policy::EXCLUDE and input.has_nulls()},
+      preceding{preceding},
+      following{following},
+      min_periods{min_periods},
+      stream{stream}
+  {
+  }
+
+  /// For `null_policy::EXCLUDE`, find gather index for `N`th non-null value.
+  template <typename Iter>
+  size_type __device__ index_of_nth_non_null(Iter begin, size_type window_size) const
+  {
+    auto reqd_valid_count     = n >= 0 ? n : (-n - 1);
+    auto const pred_nth_valid = [&reqd_valid_count, input_nullmask = input_nullmask](size_type j) {
+      return cudf::bit_is_set(input_nullmask, j) && reqd_valid_count-- == 0;
+    };
+    auto const end   = begin + window_size;
+    auto const found = thrust::find_if(thrust::seq, begin, end, pred_nth_valid);
+    return found == end ? NULL_INDEX : *found;
+  }
+
+  size_type __device__ operator()(size_type i) const
+  {
+    // preceding[i] includes the current row.
+    auto const window_size = preceding[i] + following[i];
+    if (min_periods > window_size) { return NULL_INDEX; }
+
+    auto const wrapped_n = n >= 0 ? n : (window_size + n);
+    if (wrapped_n < 0 || wrapped_n > (window_size - 1)) {
+      return NULL_INDEX;  // n lies outside the window.
+    }
+
+    // Out of short-circuit exits.
+    // If nulls don't need to be excluded, a fixed window offset calculation is sufficient.
+    auto const window_start = i - preceding[i] + 1;
+    if (not exclude_nulls) { return window_start + wrapped_n; }
+
+    // Must exclude nulls. Must examine each row in the window.
+    auto const window_end = window_start + window_size;
+    return n >= 0 ? index_of_nth_non_null(thrust::make_counting_iterator(window_start), window_size)
+                  : index_of_nth_non_null(
+                      thrust::make_reverse_iterator(thrust::make_counting_iterator(window_end)),
+                      window_size);
+  }
+};
+
+/**
+ * @brief Helper function for NTH_ELEMENT window aggregation
+ *
+ * The `N`th element is deemed null for the following cases:
+ *    1. The window has fewer elements than `min_periods`.
+ *    2. N falls outside the window, i.e. N ∉ [-window_size, window_size).
+ *    3. `null_handling == EXCLUDE`, and the window has fewer than `N` non-null elements.
+ *
+ *  If none of the above holds true, the result is non-null. How the value is determined
+ *  depends on `null_handling`:
+ *    1. `null_handling == INCLUDE`: The required value is the `N`th value from the window's start.
+ *    2. `null_handling == EXCLUDE`: The required value is the `N`th *non-null* value from the
+ *        window's start. If the window has fewer than `N` non-null values, the result is null.
+ *
+ * @tparam null_handling Whether to include/exclude null rows in the window
+ * @tparam PrecedingIter Type of iterator for preceding window
+ * @tparam FollowingIter Type of iterator for following window
+ * @param n The index of the element to be returned
+ * @param input The input column
+ * @param preceding Iterator specifying the preceding window bound
+ * @param following Iterator specifying the following window bound
+ * @param min_periods The minimum number of rows required in the window
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return A column the `n`th element of the specified window for each row
+ */
+template <null_policy null_handling, typename PrecedingIter, typename FollowingIter>
+std::unique_ptr<column> nth_element(size_type n,
+                                    column_view const& input,
+                                    PrecedingIter preceding,
+                                    FollowingIter following,
+                                    size_type min_periods,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto const gather_iter = cudf::detail::make_counting_transform_iterator(
+    0,
+    gather_index_calculator<null_handling, PrecedingIter, FollowingIter>{
+      n, input, preceding, following, min_periods, stream});
+
+  auto gather_map = rmm::device_uvector<size_type>(input.size(), stream);
+  thrust::copy(
+    rmm::exec_policy(stream), gather_iter, gather_iter + input.size(), gather_map.begin());
+
+  auto gathered = cudf::detail::gather(table_view{{input}},
+                                       gather_map,
+                                       cudf::out_of_bounds_policy::NULLIFY,
+                                       negative_index_policy::NOT_ALLOWED,
+                                       stream,
+                                       mr)
+                    ->release();
+  return std::move(gathered.front());
+}
+
+}  // namespace cudf::detail::rolling
diff --git a/cpp/src/rolling/detail/optimized_unbounded_window.cpp b/cpp/src/rolling/detail/optimized_unbounded_window.cpp
new file mode 100644
index 0000000..f1a5c4c
--- /dev/null
+++ b/cpp/src/rolling/detail/optimized_unbounded_window.cpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby/sort_helper.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/groupby.hpp>
+#include <cudf/reduction/detail/reduction.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+namespace cudf::detail {
+
+bool can_optimize_unbounded_window(bool unbounded_preceding,
+                                   bool unbounded_following,
+                                   size_type min_periods,
+                                   rolling_aggregation const& agg)
+{
+  auto is_supported = [](auto const& agg) {
+    switch (agg.kind) {
+      case cudf::aggregation::Kind::COUNT_ALL: [[fallthrough]];
+      case cudf::aggregation::Kind::COUNT_VALID: [[fallthrough]];
+      case cudf::aggregation::Kind::SUM: [[fallthrough]];
+      case cudf::aggregation::Kind::MIN: [[fallthrough]];
+      case cudf::aggregation::Kind::MAX: return true;
+      default:
+        // COLLECT_LIST and COLLECT_SET can be added at a later date.
+        // Other aggregations do not fit into the [UNBOUNDED, UNBOUNDED]
+        // category. For instance:
+        // 1. Ranking functions (ROW_NUMBER, RANK, DENSE_RANK, PERCENT_RANK)
+        //    use [UNBOUNDED PRECEDING, CURRENT ROW].
+        // 2. LEAD/LAG are defined on finite row boundaries.
+        return false;
+    }
+  };
+
+  return unbounded_preceding && unbounded_following && (min_periods == 1) && is_supported(agg);
+}
+
+/// Converts rolling_aggregation to corresponding reduce/groupby_aggregation.
+template <typename Base>
+struct aggregation_converter {
+  template <aggregation::Kind k>
+  std::unique_ptr<Base> operator()() const
+  {
+    if constexpr (std::is_same_v<Base, cudf::groupby_aggregation> and
+                  k == aggregation::Kind::COUNT_ALL) {
+      // Note: COUNT_ALL cannot be used as a cudf::reduce_aggregation; cudf::reduce does not support
+      // it.
+      return cudf::make_count_aggregation<Base>(null_policy::INCLUDE);
+    } else if constexpr (std::is_same_v<Base, cudf::groupby_aggregation> and
+                         k == aggregation::Kind::COUNT_VALID) {
+      // Note: COUNT_ALL cannot be used as a cudf::reduce_aggregation; cudf::reduce does not support
+      // it.
+      return cudf::make_count_aggregation<Base>(null_policy::EXCLUDE);
+    } else if constexpr (k == aggregation::Kind::SUM) {
+      return cudf::make_sum_aggregation<Base>();
+    } else if constexpr (k == aggregation::Kind::MIN) {
+      return cudf::make_min_aggregation<Base>();
+    } else if constexpr (k == aggregation::Kind::MAX) {
+      return cudf::make_max_aggregation<Base>();
+    } else {
+      CUDF_FAIL("Unsupported aggregation kind for optimized unbounded windows.");
+    }
+  }
+};
+
+template <typename Base>
+std::unique_ptr<Base> convert_to(cudf::rolling_aggregation const& aggr)
+{
+  return cudf::detail::aggregation_dispatcher(aggr.kind, aggregation_converter<Base>{});
+}
+
+/// Compute unbounded rolling window via groupby-aggregation.
+/// Used for input that has groupby key columns.
+std::unique_ptr<column> aggregation_based_rolling_window(table_view const& group_keys,
+                                                         column_view const& input,
+                                                         rolling_aggregation const& aggr,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(group_keys.num_columns() > 0,
+               "Ungrouped rolling window not supported in aggregation path.");
+
+  auto agg_requests = std::vector<cudf::groupby::aggregation_request>{};
+  agg_requests.push_back(cudf::groupby::aggregation_request());
+  agg_requests.front().values = input;
+  agg_requests.front().aggregations.push_back(convert_to<cudf::groupby_aggregation>(aggr));
+
+  auto group_by = cudf::groupby::groupby{group_keys, cudf::null_policy::INCLUDE, cudf::sorted::YES};
+  auto aggregation_results           = group_by.aggregate(agg_requests, stream);
+  auto const& aggregation_result_col = aggregation_results.second.front().results.front();
+
+  using cudf::groupby::detail::sort::sort_groupby_helper;
+  auto helper = sort_groupby_helper{group_keys, cudf::null_policy::INCLUDE, cudf::sorted::YES, {}};
+  auto const& group_labels = helper.group_labels(stream);
+
+  auto result_columns = cudf::detail::gather(cudf::table_view{{*aggregation_result_col}},
+                                             group_labels,
+                                             cudf::out_of_bounds_policy::DONT_CHECK,
+                                             cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                             stream,
+                                             mr)
+                          ->release();
+  return std::move(result_columns.front());
+}
+
+/// Compute unbounded rolling window via cudf::reduce.
+/// Used for input that has no groupby keys. i.e. The window spans the column.
+std::unique_ptr<column> reduction_based_rolling_window(column_view const& input,
+                                                       rolling_aggregation const& aggr,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  auto const reduce_results = [&] {
+    auto const return_dtype = cudf::detail::target_type(input.type(), aggr.kind);
+    if (aggr.kind == aggregation::COUNT_ALL) {
+      return cudf::make_fixed_width_scalar(input.size(), stream);
+    } else if (aggr.kind == aggregation::COUNT_VALID) {
+      return cudf::make_fixed_width_scalar(input.size() - input.null_count(), stream);
+    } else {
+      return cudf::reduction::detail::reduce(input,
+                                             *convert_to<cudf::reduce_aggregation>(aggr),
+                                             return_dtype,
+                                             std::nullopt,
+                                             stream,
+                                             rmm::mr::get_current_device_resource());
+    }
+  }();
+  // Blow up results into separate column.
+  return cudf::make_column_from_scalar(*reduce_results, input.size(), stream, mr);
+}
+
+std::unique_ptr<column> optimized_unbounded_window(table_view const& group_keys,
+                                                   column_view const& input,
+                                                   rolling_aggregation const& aggr,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  return group_keys.num_columns() > 0
+           ? aggregation_based_rolling_window(group_keys, input, aggr, stream, mr)
+           : reduction_based_rolling_window(input, aggr, stream, mr);
+}
+}  // namespace cudf::detail
diff --git a/cpp/src/rolling/detail/optimized_unbounded_window.hpp b/cpp/src/rolling/detail/optimized_unbounded_window.hpp
new file mode 100644
index 0000000..5964390
--- /dev/null
+++ b/cpp/src/rolling/detail/optimized_unbounded_window.hpp
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace rmm::mr {
+class device_memory_resource;
+}
+
+namespace cudf {
+
+class rolling_aggregation;
+class table_view;
+
+namespace detail {
+/**
+ * @brief Checks if it is possible to optimize fully UNBOUNDED window function.
+ *
+ * @return true if the window aggregation can optimized, i.e. if it is unbounded-preceding,
+ * unbounded-following, if it has a supported aggregation type, and if min_periods is 1.
+ * @return false if the window aggregation cannot be optimized.
+ */
+bool can_optimize_unbounded_window(bool unbounded_preceding,
+                                   bool unbounded_following,
+                                   size_type min_periods,
+                                   rolling_aggregation const& agg);
+
+/**
+ * @brief Optimized bypass for fully UNBOUNDED window functions.
+ *
+ * @return the result column from running the unbounded window aggregation,
+ * via the optimized aggregation/reduction path.
+ */
+std::unique_ptr<column> optimized_unbounded_window(table_view const& group_keys,
+                                                   column_view const& input,
+                                                   rolling_aggregation const& aggr,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr);
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/range_comparator_utils.cuh b/cpp/src/rolling/detail/range_comparator_utils.cuh
new file mode 100644
index 0000000..009070d
--- /dev/null
+++ b/cpp/src/rolling/detail/range_comparator_utils.cuh
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/functional.h>
+
+#include <cmath>
+#include <limits>
+
+namespace cudf::detail {
+
+/// For order-by columns of signed types, bounds calculation might cause accidental
+/// overflow/underflows. This needs to be detected and handled appropriately
+/// for signed and unsigned types.
+
+/**
+ * @brief Add `delta` to value, and cap at numeric_limits::max(), for signed types.
+ */
+template <typename T, CUDF_ENABLE_IF(cuda::std::numeric_limits<T>::is_signed)>
+__host__ __device__ T add_safe(T const& value, T const& delta)
+{
+  if constexpr (std::is_floating_point_v<T>) {
+    if (std::isinf(value) or std::isnan(value)) { return value; }
+  }
+  // delta >= 0.
+  return (value < 0 || (cuda::std::numeric_limits<T>::max() - value) >= delta)
+           ? (value + delta)
+           : cuda::std::numeric_limits<T>::max();
+}
+
+/**
+ * @brief Add `delta` to value, and cap at numeric_limits::max(), for unsigned types.
+ */
+template <typename T, CUDF_ENABLE_IF(not cuda::std::numeric_limits<T>::is_signed)>
+__host__ __device__ T add_safe(T const& value, T const& delta)
+{
+  // delta >= 0.
+  return ((cuda::std::numeric_limits<T>::max() - value) >= delta)
+           ? (value + delta)
+           : cuda::std::numeric_limits<T>::max();
+}
+
+/**
+ * @brief Subtract `delta` from value, and cap at numeric_limits::lowest(), for signed types.
+ *
+ * Note: We use numeric_limits::lowest() instead of min() because for floats, lowest() returns
+ * the smallest finite value, as opposed to min() which returns the smallest _positive_ value.
+ */
+template <typename T, CUDF_ENABLE_IF(cuda::std::numeric_limits<T>::is_signed)>
+__host__ __device__ T subtract_safe(T const& value, T const& delta)
+{
+  if constexpr (std::is_floating_point_v<T>) {
+    if (std::isinf(value) or std::isnan(value)) { return value; }
+  }
+  // delta >= 0;
+  return (value >= 0 || (value - cuda::std::numeric_limits<T>::lowest()) >= delta)
+           ? (value - delta)
+           : cuda::std::numeric_limits<T>::lowest();
+}
+
+/**
+ * @brief Subtract `delta` from value, and cap at numeric_limits::lowest(), for unsigned types.
+ *
+ * Note: We use numeric_limits::lowest() instead of min() because for floats, lowest() returns
+ * the smallest finite value, as opposed to min() which returns the smallest _positive_ value.
+ *
+ * This distinction isn't truly relevant for this overload (because float is signed).
+ * lowest() is kept for uniformity.
+ */
+template <typename T, CUDF_ENABLE_IF(not cuda::std::numeric_limits<T>::is_signed)>
+__host__ __device__ T subtract_safe(T const& value, T const& delta)
+{
+  // delta >= 0;
+  return ((value - cuda::std::numeric_limits<T>::lowest()) >= delta)
+           ? (value - delta)
+           : cuda::std::numeric_limits<T>::lowest();
+}
+
+/**
+ * @brief Comparator for numeric order-by columns, handling floating point NaN values.
+ *
+ * This is required for binary search through sorted vectors that contain NaN values.
+ * With ascending sort, NaN values are stored at the end of the sequence, even
+ * greater than infinity.
+ * But thrust::less would have trouble locating it because:
+ * 1. thrust::less(NaN, 10) returns false
+ * 2. thrust::less(10, NaN) also returns false
+ *
+ * This comparator honors the position of NaN values vis-à-vis non-NaN values.
+ *
+ */
+struct nan_aware_less {
+  template <typename T, CUDF_ENABLE_IF(not cudf::is_floating_point<T>())>
+  __host__ __device__ bool operator()(T const& lhs, T const& rhs) const
+  {
+    return thrust::less<T>{}(lhs, rhs);
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_floating_point<T>())>
+  __host__ __device__ bool operator()(T const& lhs, T const& rhs) const
+  {
+    if (std::isnan(lhs)) { return false; }
+    return std::isnan(rhs) or thrust::less<T>{}(lhs, rhs);
+  }
+};
+
+/**
+ * @brief Comparator for numeric order-by columns, handling floating point NaN values.
+ *
+ * This is required for binary search through sorted vectors that contain NaN values.
+ * With descending sort, NaN values are stored at the beginning of the sequence, even
+ * greater than infinity.
+ * But thrust::greater would have trouble locating it because:
+ * 1. thrust::greater(NaN, 10) returns false
+ * 2. thrust::greater(10, NaN) also returns false
+ *
+ * This comparator honors the position of NaN values vis-à-vis non-NaN values.
+ *
+ */
+struct nan_aware_greater {
+  template <typename T>
+  __host__ __device__ bool operator()(T const& lhs, T const& rhs) const
+  {
+    return nan_aware_less{}(rhs, lhs);
+  }
+};
+}  // namespace cudf::detail
diff --git a/cpp/src/rolling/detail/range_window_bounds.hpp b/cpp/src/rolling/detail/range_window_bounds.hpp
new file mode 100644
index 0000000..8a53e93
--- /dev/null
+++ b/cpp/src/rolling/detail/range_window_bounds.hpp
@@ -0,0 +1,166 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/rolling/range_window_bounds.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/wrappers/durations.hpp>
+
+namespace cudf {
+namespace detail {
+
+/// Checks if the specified type is supported in a range_window_bounds.
+template <typename RangeType>
+constexpr bool is_supported_range_type()
+{
+  return cudf::is_duration<RangeType>() || cudf::is_fixed_point<RangeType>() ||
+         (cudf::is_numeric<RangeType>() && !cudf::is_boolean<RangeType>());
+}
+
+/// Checks if the specified type is a supported target type,
+/// as an order-by column, for comparisons with a range_window_bounds scalar.
+template <typename ColumnType>
+constexpr bool is_supported_order_by_column_type()
+{
+  return cudf::is_timestamp<ColumnType>() || cudf::is_fixed_point<ColumnType>() ||
+         (cudf::is_numeric<ColumnType>() && !cudf::is_boolean<ColumnType>()) ||
+         std::is_same_v<ColumnType, cudf::string_view>;
+}
+
+/// Range-comparable representation type for an orderby column type.
+/// This is the datatype used for range comparisons.
+///   1. For integral orderby column types `T`, comparisons are done as `T`.
+///      E.g. `range_type_for<int32_t>` == `int32_t`.
+///   2. For timestamp orderby columns:
+///      a. For `TIMESTAMP_DAYS`, the range-type is `DURATION_DAYS`.
+///         Comparisons are done in `int32_t`.
+///      b. For all other timestamp types, comparisons are done in `int64_t`.
+///   3. For decimal types, all comparisons are done with the rep type,
+///      after scaling the rep value to the same scale as the order by column:
+///      a. For decimal32, the range-type is `int32_t`.
+///      b. For decimal64, the range-type is `int64_t`.
+///      c. For decimal128, the range-type is `__int128_t`.
+template <typename ColumnType, typename = void>
+struct range_type_impl {
+  using type     = void;
+  using rep_type = void;
+};
+
+template <typename ColumnType>
+struct range_type_impl<
+  ColumnType,
+  std::enable_if_t<cudf::is_numeric<ColumnType>() && !cudf::is_boolean<ColumnType>(), void>> {
+  using type     = ColumnType;
+  using rep_type = ColumnType;
+};
+
+template <typename TimestampType>
+struct range_type_impl<TimestampType, std::enable_if_t<cudf::is_timestamp<TimestampType>(), void>> {
+  using type     = typename TimestampType::duration;
+  using rep_type = typename type::rep;
+};
+
+template <typename FixedPointType>
+struct range_type_impl<FixedPointType,
+                       std::enable_if_t<cudf::is_fixed_point<FixedPointType>(), void>> {
+  using type     = FixedPointType;
+  using rep_type = typename type::rep;
+};
+
+template <typename ColumnType>
+using range_type = typename range_type_impl<ColumnType>::type;
+
+template <typename ColumnType>
+using range_rep_type = typename range_type_impl<ColumnType>::rep_type;
+
+template <typename T>
+void assert_non_negative([[maybe_unused]] T const& value)
+{
+  if constexpr (std::numeric_limits<T>::is_signed) {
+    CUDF_EXPECTS(value >= T{0}, "Range scalar must be >= 0.");
+  }
+}
+
+template <typename RangeT,
+          typename RepT,
+          CUDF_ENABLE_IF(cudf::is_numeric<RangeT>() && !cudf::is_boolean<RangeT>())>
+RepT range_comparable_value_impl(scalar const& range_scalar,
+                                 bool,
+                                 data_type const&,
+                                 rmm::cuda_stream_view stream)
+{
+  auto val = static_cast<numeric_scalar<RangeT> const&>(range_scalar).value(stream);
+  assert_non_negative(val);
+  return val;
+}
+
+template <typename RangeT, typename RepT, CUDF_ENABLE_IF(cudf::is_duration<RangeT>())>
+RepT range_comparable_value_impl(scalar const& range_scalar,
+                                 bool,
+                                 data_type const&,
+                                 rmm::cuda_stream_view stream)
+{
+  auto val = static_cast<duration_scalar<RangeT> const&>(range_scalar).value(stream).count();
+  assert_non_negative(val);
+  return val;
+}
+
+template <typename RangeT, typename RepT, CUDF_ENABLE_IF(cudf::is_fixed_point<RangeT>())>
+RepT range_comparable_value_impl(scalar const& range_scalar,
+                                 bool is_unbounded,
+                                 data_type const& order_by_data_type,
+                                 rmm::cuda_stream_view stream)
+{
+  CUDF_EXPECTS(is_unbounded || range_scalar.type().scale() >= order_by_data_type.scale(),
+               "Range bounds scalar must match/exceed the scale of the orderby column.");
+  auto const fixed_point_value =
+    static_cast<fixed_point_scalar<RangeT> const&>(range_scalar).fixed_point_value(stream);
+  auto const value =
+    fixed_point_value.rescaled(numeric::scale_type{order_by_data_type.scale()}).value();
+  assert_non_negative(value);
+  return value;
+}
+
+/**
+ * @brief Fetch the value of the range_window_bounds scalar, for comparisons
+ *        with an orderby column's rows.
+ *
+ * @tparam OrderByType The type of the orderby column with which the range value will be compared
+ * @param range_bounds The range_window_bounds whose value is to be read
+ * @param order_by_data_type The data type for the order-by column
+ * @param stream The CUDA stream for device memory operations
+ * @return RepType Value of the range scalar
+ */
+template <typename OrderByType>
+range_rep_type<OrderByType> range_comparable_value(range_window_bounds const& range_bounds,
+                                                   data_type const& order_by_data_type,
+                                                   rmm::cuda_stream_view stream)
+{
+  auto const& range_scalar = range_bounds.range_scalar();
+  using range_type         = cudf::detail::range_type<OrderByType>;
+
+  CUDF_EXPECTS(range_scalar.type().id() == cudf::type_to_id<range_type>(),
+               "Range bounds scalar must match the type of the orderby column.");
+
+  using rep_type = cudf::detail::range_rep_type<OrderByType>;
+  return range_comparable_value_impl<range_type, rep_type>(
+    range_scalar, range_bounds.is_unbounded(), order_by_data_type, stream);
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/rolling.cuh b/cpp/src/rolling/detail/rolling.cuh
new file mode 100644
index 0000000..0648ef3
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling.cuh
@@ -0,0 +1,1390 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "lead_lag_nested.cuh"
+#include "nth_element.cuh"
+#include "rolling.hpp"
+#include "rolling_collect_list.cuh"
+#include "rolling_jit.hpp"
+
+#include <reductions/nested_type_minmax_util.cuh>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/aggregation/aggregation.cuh>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/groupby/sort_helper.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/lists/detail/stream_compaction.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <jit/cache.hpp>
+#include <jit/parser.hpp>
+#include <jit/util.hpp>
+
+#include <jit_preprocessed_files/rolling/jit/kernel.cu.jit.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/reduce.h>
+
+#include <cuda/std/climits>
+#include <cuda/std/limits>
+
+#include <memory>
+
+namespace cudf {
+
+namespace detail {
+
+/// Helper function to materialize preceding/following offsets.
+template <typename Calculator>
+std::unique_ptr<column> expand_to_column(Calculator const& calc,
+                                         size_type const& num_rows,
+                                         rmm::cuda_stream_view stream)
+{
+  auto window_column = cudf::make_numeric_column(
+    cudf::data_type{type_to_id<size_type>()}, num_rows, cudf::mask_state::UNALLOCATED, stream);
+
+  auto begin = cudf::detail::make_counting_transform_iterator(0, calc);
+
+  thrust::copy_n(
+    rmm::exec_policy(stream), begin, num_rows, window_column->mutable_view().data<size_type>());
+
+  return window_column;
+}
+
+/**
+ * @brief Operator for applying a generic (non-specialized) rolling aggregation on a single window.
+ */
+template <typename InputType, aggregation::Kind op>
+struct DeviceRolling {
+  size_type min_periods;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = op>
+  static constexpr bool is_supported()
+  {
+    return cudf::detail::is_valid_aggregation<T, O>() && has_corresponding_operator<O>() &&
+           // MIN/MAX only supports fixed width types
+           (((O == aggregation::MIN || O == aggregation::MAX) && cudf::is_fixed_width<T>()) ||
+            (O == aggregation::SUM) || (O == aggregation::MEAN));
+  }
+
+  // operations we do support
+  template <typename T = InputType, aggregation::Kind O = op>
+  explicit DeviceRolling(size_type _min_periods, std::enable_if_t<is_supported<T, O>()>* = nullptr)
+    : min_periods(_min_periods)
+  {
+  }
+
+  // operations we don't support
+  template <typename T = InputType, aggregation::Kind O = op>
+  explicit DeviceRolling(size_type _min_periods, std::enable_if_t<!is_supported<T, O>()>* = nullptr)
+    : min_periods(_min_periods)
+  {
+    CUDF_FAIL("Invalid aggregation/type pair");
+  }
+
+  // perform the windowing operation
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index) const
+  {
+    using AggOp = typename corresponding_operator<op>::type;
+    AggOp agg_op;
+
+    cudf::size_type count = 0;
+    OutputType val        = AggOp::template identity<OutputType>();
+
+    for (size_type j = start_index; j < end_index; j++) {
+      if (!has_nulls || input.is_valid(j)) {
+        OutputType element = input.element<device_storage_type_t<InputType>>(j);
+        val                = agg_op(element, val);
+        count++;
+      }
+    }
+
+    bool output_is_valid = (count >= min_periods);
+
+    // store the output value, one per thread
+    cudf::detail::rolling_store_output_functor<OutputType, op == aggregation::MEAN>{}(
+      output.element<OutputType>(current_index), val, count);
+
+    return output_is_valid;
+  }
+};
+
+/**
+ * @brief The base struct used for checking if the combination of input type and aggregation op is
+ * supported.
+ */
+template <typename InputType, aggregation::Kind op>
+struct DeviceRollingArgMinMaxBase {
+  size_type min_periods;
+  explicit DeviceRollingArgMinMaxBase(size_type _min_periods) : min_periods(_min_periods) {}
+
+  static constexpr bool is_supported()
+  {
+    // Right now only support ARGMIN/ARGMAX of strings and structs.
+    auto const type_supported =
+      std::is_same_v<InputType, cudf::string_view> || std::is_same_v<InputType, cudf::struct_view>;
+    auto const op_supported = op == aggregation::Kind::ARGMIN || op == aggregation::Kind::ARGMAX;
+
+    return type_supported && op_supported;
+  }
+};
+
+/**
+ * @brief Operator for applying an ARGMAX/ARGMIN rolling aggregation on a single window for string.
+ */
+template <aggregation::Kind op>
+struct DeviceRollingArgMinMaxString : DeviceRollingArgMinMaxBase<cudf::string_view, op> {
+  explicit DeviceRollingArgMinMaxString(size_type _min_periods)
+    : DeviceRollingArgMinMaxBase<cudf::string_view, op>(_min_periods)
+  {
+  }
+  using DeviceRollingArgMinMaxBase<cudf::string_view, op>::min_periods;
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index)
+  {
+    auto constexpr default_output = (op == aggregation::ARGMIN) ? ARGMIN_SENTINEL : ARGMAX_SENTINEL;
+
+    using InputType = cudf::string_view;
+    using AggOp     = typename corresponding_operator<op>::type;
+    AggOp agg_op;
+
+    cudf::size_type count = 0;
+    InputType val         = AggOp::template identity<InputType>();
+    OutputType val_index  = default_output;
+
+    for (size_type j = start_index; j < end_index; j++) {
+      if (!has_nulls || input.is_valid(j)) {
+        InputType element = input.element<InputType>(j);
+        val               = agg_op(element, val);
+        if (val == element) { val_index = j; }
+        count++;
+      }
+    }
+
+    bool output_is_valid = (count >= min_periods);
+    // Use the sentinel value (i.e., -1) for the output will help identify null elements while
+    // gathering for Min and Max.
+    output.element<OutputType>(current_index) = output_is_valid ? val_index : default_output;
+
+    // The gather mask shouldn't contain null values, so
+    // always return zero
+    return true;
+  }
+};
+
+/**
+ * @brief Operator for applying an ARGMAX/ARGMIN rolling aggregation on a single window for struct.
+ */
+template <aggregation::Kind op, typename Comparator>
+struct DeviceRollingArgMinMaxStruct : DeviceRollingArgMinMaxBase<cudf::struct_view, op> {
+  DeviceRollingArgMinMaxStruct(size_type _min_periods, Comparator const& _comp)
+    : DeviceRollingArgMinMaxBase<cudf::struct_view, op>(_min_periods), comp(_comp)
+  {
+  }
+  using DeviceRollingArgMinMaxBase<cudf::struct_view, op>::min_periods;
+  Comparator comp;
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index)
+  {
+    auto constexpr default_output = (op == aggregation::ARGMIN) ? ARGMIN_SENTINEL : ARGMAX_SENTINEL;
+
+    auto const valid_count =
+      has_nulls ? thrust::count_if(thrust::seq,
+                                   thrust::make_counting_iterator(start_index),
+                                   thrust::make_counting_iterator(end_index),
+                                   [&input](size_type idx) { return input.is_valid_nocheck(idx); })
+                : end_index - start_index;
+
+    // Use the sentinel value (i.e., -1) for the output will help identify null elements while
+    // gathering for Min and Max.
+    output.element<OutputType>(current_index) =
+      (valid_count >= min_periods) ? thrust::reduce(thrust::seq,
+                                                    thrust::make_counting_iterator(start_index),
+                                                    thrust::make_counting_iterator(end_index),
+                                                    size_type{start_index},
+                                                    comp)
+                                   : default_output;
+
+    // The gather mask shouldn't contain null values, so always return true.
+    return true;
+  }
+};
+
+/**
+ * @brief Operator for applying a COUNT_VALID rolling aggregation on a single window.
+ */
+template <typename InputType>
+struct DeviceRollingCountValid {
+  size_type min_periods;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = aggregation::COUNT_VALID>
+  static constexpr bool is_supported()
+  {
+    return true;
+  }
+
+  DeviceRollingCountValid(size_type _min_periods) : min_periods(_min_periods) {}
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index)
+  {
+    bool output_is_valid = ((end_index - start_index) >= min_periods);
+
+    if (output_is_valid) {
+      cudf::size_type count = 0;
+
+      if (!has_nulls) {
+        count = end_index - start_index;
+      } else {
+        count = thrust::count_if(thrust::seq,
+                                 thrust::make_counting_iterator(start_index),
+                                 thrust::make_counting_iterator(end_index),
+                                 [&input](auto i) { return input.is_valid_nocheck(i); });
+      }
+      output.element<OutputType>(current_index) = count;
+    }
+
+    return output_is_valid;
+  }
+};
+
+/**
+ * @brief Operator for applying a COUNT_ALL rolling aggregation on a single window.
+ */
+template <typename InputType>
+struct DeviceRollingCountAll {
+  size_type min_periods;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = aggregation::COUNT_ALL>
+  static constexpr bool is_supported()
+  {
+    return true;
+  }
+
+  DeviceRollingCountAll(size_type _min_periods) : min_periods(_min_periods) {}
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const&,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index)
+  {
+    cudf::size_type count = end_index - start_index;
+
+    bool output_is_valid                      = count >= min_periods;
+    output.element<OutputType>(current_index) = count;
+
+    return output_is_valid;
+  }
+};
+
+/**
+ * @brief Operator for applying a VAR rolling aggregation on a single window.
+ */
+template <typename InputType>
+struct DeviceRollingVariance {
+  size_type const min_periods;
+  size_type const ddof;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = aggregation::VARIANCE>
+  static constexpr bool is_supported()
+  {
+    return is_fixed_width<InputType>() and not is_chrono<InputType>();
+  }
+
+  DeviceRollingVariance(size_type _min_periods, size_type _ddof)
+    : min_periods(_min_periods), ddof{_ddof}
+  {
+  }
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index) const
+  {
+    using DeviceInputType = device_storage_type_t<InputType>;
+
+    // valid counts in the window
+    cudf::size_type const count =
+      has_nulls ? thrust::count_if(thrust::seq,
+                                   thrust::make_counting_iterator(start_index),
+                                   thrust::make_counting_iterator(end_index),
+                                   [&input](auto i) { return input.is_valid_nocheck(i); })
+                : end_index - start_index;
+
+    // Result will be null if any of the following conditions are met:
+    // - All inputs are null
+    // - Number of valid inputs is less than `min_periods`
+    bool output_is_valid = count > 0 and (count >= min_periods);
+
+    if (output_is_valid) {
+      if (count >= ddof) {
+        // Welford algorithm
+        // See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+        OutputType m{0}, m2{0};
+        size_type running_count{0};
+
+        for (size_type i = start_index; i < end_index; i++) {
+          if (has_nulls and input.is_null_nocheck(i)) { continue; }
+
+          OutputType const x = static_cast<OutputType>(input.element<DeviceInputType>(i));
+
+          running_count++;
+          OutputType const tmp1 = x - m;
+          m += tmp1 / running_count;
+          OutputType const tmp2 = x - m;
+          m2 += tmp1 * tmp2;
+        }
+        if constexpr (is_fixed_point<InputType>()) {
+          // For fixed_point types, the previous computed value used unscaled rep-value,
+          // the final result should be multiplied by the square of decimal `scale`.
+          OutputType scaleby = exp10(static_cast<double>(input.type().scale()));
+          scaleby *= scaleby;
+          output.element<OutputType>(current_index) = m2 / (count - ddof) * scaleby;
+        } else {
+          output.element<OutputType>(current_index) = m2 / (count - ddof);
+        }
+      } else {
+        output.element<OutputType>(current_index) =
+          cuda::std::numeric_limits<OutputType>::signaling_NaN();
+      }
+    }
+
+    return output_is_valid;
+  }
+};
+
+/**
+ * @brief Operator for applying a ROW_NUMBER rolling aggregation on a single window.
+ */
+template <typename InputType>
+struct DeviceRollingRowNumber {
+  size_type min_periods;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = aggregation::ROW_NUMBER>
+  static constexpr bool is_supported()
+  {
+    return true;
+  }
+
+  DeviceRollingRowNumber(size_type _min_periods) : min_periods(_min_periods) {}
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const&,
+                             column_device_view const&,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type end_index,
+                             size_type current_index)
+  {
+    bool output_is_valid                      = end_index - start_index >= min_periods;
+    output.element<OutputType>(current_index) = current_index - start_index + 1;
+
+    return output_is_valid;
+  }
+};
+
+struct agg_specific_empty_output {
+  template <typename InputType, aggregation::Kind op>
+  std::unique_ptr<column> operator()(column_view const& input, rolling_aggregation const&) const
+  {
+    using target_type = cudf::detail::target_type_t<InputType, op>;
+
+    if constexpr (std::is_same_v<cudf::detail::target_type_t<InputType, op>, void>) {
+      CUDF_FAIL("Unsupported combination of column-type and aggregation.");
+    }
+
+    if constexpr (cudf::is_fixed_width<target_type>()) {
+      return cudf::make_empty_column(type_to_id<target_type>());
+    }
+
+    if constexpr (op == aggregation::COLLECT_LIST) {
+      return cudf::make_lists_column(
+        0, make_empty_column(type_to_id<size_type>()), empty_like(input), 0, {});
+    }
+
+    return empty_like(input);
+  }
+};
+
+static std::unique_ptr<column> empty_output_for_rolling_aggregation(column_view const& input,
+                                                                    rolling_aggregation const& agg)
+{
+  // TODO:
+  //  Ideally, for UDF aggregations, the returned column would match
+  //  the agg's return type. It currently returns empty_like(input), because:
+  //    1. This preserves prior behavior for empty input columns.
+  //    2. There is insufficient information to construct nested return columns.
+  //       `cudf::make_udf_aggregation()` expresses the return type as a `data_type`
+  //        which cannot express recursively nested types (e.g. `STRUCT<LIST<INT32>>`.)
+  //    3. In any case, UDFs that return nested types are not currently supported.
+  //  Constructing a more accurate return type for UDFs will be taken up
+  //  at a later date.
+  return agg.kind == aggregation::CUDA || agg.kind == aggregation::PTX
+           ? empty_like(input)
+           : cudf::detail::dispatch_type_and_aggregation(
+               input.type(), agg.kind, agg_specific_empty_output{}, input, agg);
+}
+
+/**
+ * @brief Operator for applying a LEAD rolling aggregation on a single window.
+ */
+template <typename InputType>
+struct DeviceRollingLead {
+  size_type row_offset;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = aggregation::LEAD>
+  static constexpr bool is_supported()
+  {
+    return cudf::is_fixed_width<T>();
+  }
+
+  template <typename T = InputType, std::enable_if_t<is_supported<T>()>* = nullptr>
+  DeviceRollingLead(size_type _row_offset) : row_offset(_row_offset)
+  {
+  }
+
+  template <typename T = InputType, std::enable_if_t<!is_supported<T>()>* = nullptr>
+  DeviceRollingLead(size_type _row_offset) : row_offset(_row_offset)
+  {
+    CUDF_FAIL("Invalid aggregation/type pair");
+  }
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const& default_outputs,
+                             mutable_column_device_view& output,
+                             size_type,
+                             size_type end_index,
+                             size_type current_index)
+  {
+    // Offsets have already been normalized.
+
+    // Check if row is invalid.
+    if (row_offset > (end_index - current_index - 1)) {
+      // Invalid row marked. Use default value, if available.
+      if (default_outputs.size() == 0 || default_outputs.is_null(current_index)) { return false; }
+
+      output.element<OutputType>(current_index) =
+        default_outputs.element<OutputType>(current_index);
+      return true;
+    }
+
+    // Not an invalid row.
+    auto index   = current_index + row_offset;
+    auto is_null = input.is_null(index);
+    if (!is_null) {
+      output.element<OutputType>(current_index) =
+        input.element<device_storage_type_t<InputType>>(index);
+    }
+    return !is_null;
+  }
+};
+
+/**
+ * @brief Operator for applying a LAG rolling aggregation on a single window.
+ */
+template <typename InputType>
+struct DeviceRollingLag {
+  size_type row_offset;
+
+  // what operations do we support
+  template <typename T = InputType, aggregation::Kind O = aggregation::LAG>
+  static constexpr bool is_supported()
+  {
+    return cudf::is_fixed_width<T>();
+  }
+
+  template <typename T = InputType, std::enable_if_t<is_supported<T>()>* = nullptr>
+  DeviceRollingLag(size_type _row_offset) : row_offset(_row_offset)
+  {
+  }
+
+  template <typename T = InputType, std::enable_if_t<!is_supported<T>()>* = nullptr>
+  DeviceRollingLag(size_type _row_offset) : row_offset(_row_offset)
+  {
+    CUDF_FAIL("Invalid aggregation/type pair");
+  }
+
+  template <typename OutputType, bool has_nulls>
+  bool __device__ operator()(column_device_view const& input,
+                             column_device_view const& default_outputs,
+                             mutable_column_device_view& output,
+                             size_type start_index,
+                             size_type,
+                             size_type current_index)
+  {
+    // Offsets have already been normalized.
+
+    // Check if row is invalid.
+    if (row_offset > (current_index - start_index)) {
+      // Invalid row marked. Use default value, if available.
+      if (default_outputs.size() == 0 || default_outputs.is_null(current_index)) { return false; }
+
+      output.element<OutputType>(current_index) =
+        default_outputs.element<OutputType>(current_index);
+      return true;
+    }
+
+    // Not an invalid row.
+    auto index   = current_index - row_offset;
+    auto is_null = input.is_null(index);
+    if (!is_null) {
+      output.element<OutputType>(current_index) =
+        input.element<device_storage_type_t<InputType>>(index);
+    }
+    return !is_null;
+  }
+};
+
+/**
+ * @brief Maps an `InputType and `aggregation::Kind` value to its corresponding
+ * rolling window operator.
+ *
+ * @tparam InputType The input type to map to its corresponding operator
+ * @tparam k The `aggregation::Kind` value to map to its corresponding operator
+ */
+template <typename InputType, aggregation::Kind k>
+struct corresponding_rolling_operator {
+  using type = DeviceRolling<InputType, k>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::ARGMIN> {
+  using type = DeviceRollingArgMinMaxBase<InputType, aggregation::ARGMIN>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::ARGMAX> {
+  using type = DeviceRollingArgMinMaxBase<InputType, aggregation::ARGMAX>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::COUNT_VALID> {
+  using type = DeviceRollingCountValid<InputType>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::COUNT_ALL> {
+  using type = DeviceRollingCountAll<InputType>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::ROW_NUMBER> {
+  using type = DeviceRollingRowNumber<InputType>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::Kind::VARIANCE> {
+  using type = DeviceRollingVariance<InputType>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::Kind::LEAD> {
+  using type = DeviceRollingLead<InputType>;
+};
+
+template <typename InputType>
+struct corresponding_rolling_operator<InputType, aggregation::Kind::LAG> {
+  using type = DeviceRollingLag<InputType>;
+};
+
+/**
+ * @brief Functor for creating a device rolling operator based on input type and aggregation type.
+ */
+template <typename InputType, aggregation::Kind k, typename = void>
+struct create_rolling_operator {
+  auto operator()(size_type min_periods, rolling_aggregation const&)
+  {
+    return typename corresponding_rolling_operator<InputType, k>::type(min_periods);
+  }
+};
+
+template <typename InputType>
+struct create_rolling_operator<InputType, aggregation::Kind::VARIANCE> {
+  auto operator()(size_type min_periods, rolling_aggregation const& agg)
+  {
+    return DeviceRollingVariance<InputType>{
+      min_periods, dynamic_cast<cudf::detail::var_aggregation const&>(agg)._ddof};
+  }
+};
+
+template <typename InputType>
+struct create_rolling_operator<InputType, aggregation::Kind::LEAD> {
+  auto operator()(size_type, rolling_aggregation const& agg)
+  {
+    return DeviceRollingLead<InputType>{
+      dynamic_cast<cudf::detail::lead_lag_aggregation const&>(agg).row_offset};
+  }
+};
+
+template <typename InputType>
+struct create_rolling_operator<InputType, aggregation::Kind::LAG> {
+  auto operator()(size_type, rolling_aggregation const& agg)
+  {
+    return DeviceRollingLag<InputType>{
+      dynamic_cast<cudf::detail::lead_lag_aggregation const&>(agg).row_offset};
+  }
+};
+
+template <typename InputType, aggregation::Kind k>
+struct create_rolling_operator<
+  InputType,
+  k,
+  typename std::enable_if_t<std::is_same_v<InputType, cudf::string_view> &&
+                            (k == aggregation::Kind::ARGMIN || k == aggregation::Kind::ARGMAX)>> {
+  auto operator()(size_type min_periods, rolling_aggregation const&)
+  {
+    return DeviceRollingArgMinMaxString<k>{min_periods};
+  }
+};
+
+template <typename InputType, aggregation::Kind k>
+struct create_rolling_operator<
+  InputType,
+  k,
+  typename std::enable_if_t<std::is_same_v<InputType, cudf::struct_view> &&
+                            (k == aggregation::Kind::ARGMIN || k == aggregation::Kind::ARGMAX)>> {
+  template <typename Comparator>
+  auto operator()(size_type min_periods, Comparator const& comp)
+  {
+    return DeviceRollingArgMinMaxStruct<k, Comparator>{min_periods, comp};
+  }
+};
+
+/**
+ * @brief Rolling window specific implementation of simple_aggregations_collector.
+ *
+ * The purpose of this class is to preprocess incoming aggregation/type pairs and
+ * potentially transform them into other aggregation/type pairs. Typically when this
+ * happens, the equivalent aggregation/type implementation of finalize() will perform
+ * some postprocessing step.
+ *
+ * An example of this would be applying a MIN aggregation to strings. This cannot be done
+ * directly in the rolling operation, so instead the following happens:
+ *
+ * - the rolling_aggregation_preprocessor transforms the incoming MIN/string pair to
+ *   an ARGMIN/int pair.
+ * - The ARGMIN/int has the rolling operation applied to it, generating a list of indices
+ *   that can then be used as a gather map.
+ * - The rolling_aggregation_postprocessor then takes this gather map and performs a final
+ *   gather() on the input string data to generate the final output.
+ *
+ * Another example is COLLECT_LIST. COLLECT_LIST is odd in that it doesn't go through the
+ * normal gpu rolling kernel at all. It has a completely custom implementation. So the
+ * following happens:
+ *
+ * - the rolling_aggregation_preprocessor transforms the COLLECT_LIST aggregation into nothing,
+ *   since no actual rolling window operation will be performed.
+ * - the rolling_aggregation_postprocessor calls the specialized rolling_collect_list()
+ *   function to generate the final output.
+ *
+ */
+class rolling_aggregation_preprocessor final : public cudf::detail::simple_aggregations_collector {
+ public:
+  using cudf::detail::simple_aggregations_collector::visit;
+
+  // NOTE : all other aggregations are passed through unchanged via the default
+  // visit() function in the simple_aggregations_collector.
+
+  // MIN aggregations with strings are processed in 2 passes. The first pass performs
+  // the rolling operation on a ARGMIN aggregation to generate indices instead of values.
+  // Then a second pass uses those indices to gather the final strings.  This step
+  // translates the MIN -> ARGMIN aggregation
+  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                  cudf::detail::min_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(col_type.id() == type_id::STRING || col_type.id() == type_id::STRUCT
+                     ? make_argmin_aggregation()
+                     : make_min_aggregation());
+    return aggs;
+  }
+
+  // MAX aggregations with strings are processed in 2 passes. The first pass performs
+  // the rolling operation on a ARGMAX aggregation to generate indices instead of values.
+  // Then a second pass uses those indices to gather the final strings.  This step
+  // translates the MAX -> ARGMAX aggregation
+  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
+                                                  cudf::detail::max_aggregation const&) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(col_type.id() == type_id::STRING || col_type.id() == type_id::STRUCT
+                     ? make_argmax_aggregation()
+                     : make_max_aggregation());
+    return aggs;
+  }
+
+  // COLLECT_LIST aggregations do not perform a rolling operation at all. They get processed
+  // entirely in the finalize() step.
+  std::vector<std::unique_ptr<aggregation>> visit(
+    data_type, cudf::detail::collect_list_aggregation const&) override
+  {
+    return {};
+  }
+
+  // COLLECT_SET aggregations do not perform a rolling operation at all. They get processed
+  // entirely in the finalize() step.
+  std::vector<std::unique_ptr<aggregation>> visit(
+    data_type, cudf::detail::collect_set_aggregation const&) override
+  {
+    return {};
+  }
+
+  // STD aggregations depends on VARIANCE aggregation. Each element is applied
+  // with square-root in the finalize() step.
+  std::vector<std::unique_ptr<aggregation>> visit(data_type,
+                                                  cudf::detail::std_aggregation const& agg) override
+  {
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(make_variance_aggregation(agg._ddof));
+    return aggs;
+  }
+
+  // LEAD and LAG have custom behaviors for non fixed-width types.
+  std::vector<std::unique_ptr<aggregation>> visit(
+    data_type col_type, cudf::detail::lead_lag_aggregation const& agg) override
+  {
+    // no rolling operation for non-fixed width.  just a postprocess step at the end
+    if (!cudf::is_fixed_width(col_type)) { return {}; }
+    // otherwise, pass through
+    std::vector<std::unique_ptr<aggregation>> aggs;
+    aggs.push_back(agg.clone());
+    return aggs;
+  }
+
+  // NTH_ELEMENT aggregations are computed in finalize(). Skip preprocessing.
+  std::vector<std::unique_ptr<aggregation>> visit(
+    data_type, cudf::detail::nth_element_aggregation const&) override
+  {
+    return {};
+  }
+};
+
+/**
+ * @brief Rolling window specific implementation of aggregation_finalizer.
+ *
+ * The purpose of this class is to postprocess rolling window data depending on the
+ * aggregation/type pair. See the description of rolling_aggregation_preprocessor for
+ * a detailed description.
+ *
+ */
+template <typename PrecedingWindowIterator, typename FollowingWindowIterator>
+class rolling_aggregation_postprocessor final : public cudf::detail::aggregation_finalizer {
+ public:
+  using cudf::detail::aggregation_finalizer::visit;
+
+  rolling_aggregation_postprocessor(column_view const& _input,
+                                    column_view const& _default_outputs,
+                                    data_type _result_type,
+                                    PrecedingWindowIterator _preceding_window_begin,
+                                    FollowingWindowIterator _following_window_begin,
+                                    int _min_periods,
+                                    std::unique_ptr<column>&& _intermediate,
+                                    rmm::cuda_stream_view _stream,
+                                    rmm::mr::device_memory_resource* _mr)
+    :
+
+      input(_input),
+      default_outputs(_default_outputs),
+      result_type(_result_type),
+      preceding_window_begin(_preceding_window_begin),
+      following_window_begin(_following_window_begin),
+      min_periods(_min_periods),
+      intermediate(std::move(_intermediate)),
+      result(nullptr),
+      stream(_stream),
+      mr(_mr)
+  {
+  }
+
+  // all non-specialized aggregation types simply pass the intermediate result through.
+  void visit(aggregation const&) override { result = std::move(intermediate); }
+
+  // perform a final gather on the generated ARGMIN data
+  void visit(cudf::detail::min_aggregation const&) override
+  {
+    if (result_type.id() == type_id::STRING || result_type.id() == type_id::STRUCT) {
+      // The rows that represent null elements will have negative values in gather map,
+      // and that's why nullify_out_of_bounds/ignore_out_of_bounds is true.
+      auto output_table = detail::gather(table_view{{input}},
+                                         intermediate->view(),
+                                         cudf::out_of_bounds_policy::NULLIFY,
+                                         detail::negative_index_policy::NOT_ALLOWED,
+                                         stream,
+                                         mr);
+      result            = std::make_unique<cudf::column>(std::move(output_table->get_column(0)));
+    } else {
+      result = std::move(intermediate);
+    }
+  }
+
+  // perform a final gather on the generated ARGMAX data
+  void visit(cudf::detail::max_aggregation const&) override
+  {
+    if (result_type.id() == type_id::STRING || result_type.id() == type_id::STRUCT) {
+      // The rows that represent null elements will have negative values in gather map,
+      // and that's why nullify_out_of_bounds/ignore_out_of_bounds is true.
+      auto output_table = detail::gather(table_view{{input}},
+                                         intermediate->view(),
+                                         cudf::out_of_bounds_policy::NULLIFY,
+                                         detail::negative_index_policy::NOT_ALLOWED,
+                                         stream,
+                                         mr);
+      result            = std::make_unique<cudf::column>(std::move(output_table->get_column(0)));
+    } else {
+      result = std::move(intermediate);
+    }
+  }
+
+  // perform the actual COLLECT_LIST operation entirely.
+  void visit(cudf::detail::collect_list_aggregation const& agg) override
+  {
+    result = rolling_collect_list(input,
+                                  default_outputs,
+                                  preceding_window_begin,
+                                  following_window_begin,
+                                  min_periods,
+                                  agg._null_handling,
+                                  stream,
+                                  mr);
+  }
+
+  // perform the actual COLLECT_SET operation entirely.
+  void visit(cudf::detail::collect_set_aggregation const& agg) override
+  {
+    auto const collected_list = rolling_collect_list(input,
+                                                     default_outputs,
+                                                     preceding_window_begin,
+                                                     following_window_begin,
+                                                     min_periods,
+                                                     agg._null_handling,
+                                                     stream,
+                                                     rmm::mr::get_current_device_resource());
+
+    result = lists::detail::distinct(
+      lists_column_view{collected_list->view()}, agg._nulls_equal, agg._nans_equal, stream, mr);
+  }
+
+  // perform the element-wise square root operation on result of VARIANCE
+  void visit(cudf::detail::std_aggregation const&) override
+  {
+    result = detail::unary_operation(intermediate->view(), unary_operator::SQRT, stream, mr);
+  }
+
+  std::unique_ptr<column> get_result()
+  {
+    CUDF_EXPECTS(result != nullptr,
+                 "Calling result on rolling aggregation postprocessor that has not been visited in "
+                 "rolling_window");
+    return std::move(result);
+  }
+
+  // LEAD and LAG have custom behaviors for non fixed-width types.
+  void visit(cudf::detail::lead_lag_aggregation const& agg) override
+  {
+    // if this is non-fixed width, run the custom lead-lag code
+    if (!cudf::is_fixed_width(result_type)) {
+      result =
+        cudf::detail::compute_lead_lag_for_nested<PrecedingWindowIterator, FollowingWindowIterator>(
+          agg.kind,
+          input,
+          default_outputs,
+          preceding_window_begin,
+          following_window_begin,
+          agg.row_offset,
+          stream,
+          mr);
+    }
+    // otherwise just pass through the intermediate
+    else {
+      result = std::move(intermediate);
+    }
+  }
+
+  // Nth_ELEMENT aggregation.
+  void visit(cudf::detail::nth_element_aggregation const& agg) override
+  {
+    result =
+      agg._null_handling == null_policy::EXCLUDE
+        ? rolling::nth_element<null_policy::EXCLUDE>(
+            agg._n, input, preceding_window_begin, following_window_begin, min_periods, stream, mr)
+        : rolling::nth_element<null_policy::INCLUDE>(
+            agg._n, input, preceding_window_begin, following_window_begin, min_periods, stream, mr);
+  }
+
+ private:
+  column_view input;
+  column_view default_outputs;
+  data_type result_type;
+  PrecedingWindowIterator preceding_window_begin;
+  FollowingWindowIterator following_window_begin;
+  int min_periods;
+  std::unique_ptr<column> intermediate;
+  std::unique_ptr<column> result;
+  rmm::cuda_stream_view stream;
+  rmm::mr::device_memory_resource* mr;
+};
+
+/**
+ * @brief Computes the rolling window function
+ *
+ * @tparam OutputType Datatype of `output`
+ * @tparam block_size CUDA block size for the kernel
+ * @tparam has_nulls true if the input column has nulls
+ * @tparam DeviceRollingOperator An operator that performs a single windowing operation
+ * @tparam PrecedingWindowIterator iterator type (inferred)
+ * @tparam FollowingWindowIterator iterator type (inferred)
+ * @param[in] input Input column device view
+ * @param[in] default_outputs A column of per-row default values to be returned instead
+ *            of nulls for certain aggregation types.
+ * @param[out] output Output column device view
+ * @param[out] output_valid_count Output count of valid values
+ * @param[in] device_operator The operator used to perform a single window operation
+ * @param[in] preceding_window_begin Rolling window size iterator, accumulates from
+ *            in_col[i-preceding_window] to in_col[i] inclusive
+ * @param[in] following_window_begin Rolling window size iterator in the forward
+ *            direction, accumulates from in_col[i] to in_col[i+following_window] inclusive
+ */
+template <typename OutputType,
+          int block_size,
+          bool has_nulls,
+          typename DeviceRollingOperator,
+          typename PrecedingWindowIterator,
+          typename FollowingWindowIterator>
+__launch_bounds__(block_size) __global__
+  void gpu_rolling(column_device_view input,
+                   column_device_view default_outputs,
+                   mutable_column_device_view output,
+                   size_type* __restrict__ output_valid_count,
+                   DeviceRollingOperator device_operator,
+                   PrecedingWindowIterator preceding_window_begin,
+                   FollowingWindowIterator following_window_begin)
+{
+  thread_index_type i            = blockIdx.x * block_size + threadIdx.x;
+  thread_index_type const stride = block_size * gridDim.x;
+
+  size_type warp_valid_count{0};
+
+  auto active_threads = __ballot_sync(0xffff'ffffu, i < input.size());
+  while (i < input.size()) {
+    // to prevent overflow issues when computing bounds use int64_t
+    int64_t const preceding_window = preceding_window_begin[i];
+    int64_t const following_window = following_window_begin[i];
+
+    // compute bounds
+    auto const start = static_cast<size_type>(
+      min(static_cast<int64_t>(input.size()), max(int64_t{0}, i - preceding_window + 1)));
+    auto const end = static_cast<size_type>(
+      min(static_cast<int64_t>(input.size()), max(int64_t{0}, i + following_window + 1)));
+    auto const start_index = min(start, end);
+    auto const end_index   = max(start, end);
+
+    // aggregate
+    // TODO: We should explore using shared memory to avoid redundant loads.
+    //       This might require separating the kernel into a special version
+    //       for dynamic and static sizes.
+
+    volatile bool output_is_valid = false;
+    output_is_valid               = device_operator.template operator()<OutputType, has_nulls>(
+      input, default_outputs, output, start_index, end_index, i);
+
+    // set the mask
+    cudf::bitmask_type const result_mask{__ballot_sync(active_threads, output_is_valid)};
+
+    // only one thread writes the mask
+    if (0 == threadIdx.x % cudf::detail::warp_size) {
+      output.set_mask_word(cudf::word_index(i), result_mask);
+      warp_valid_count += __popc(result_mask);
+    }
+
+    // process next element
+    i += stride;
+    active_threads = __ballot_sync(active_threads, i < input.size());
+  }
+
+  // sum the valid counts across the whole block
+  size_type block_valid_count =
+    cudf::detail::single_lane_block_sum_reduce<block_size, 0>(warp_valid_count);
+
+  if (threadIdx.x == 0) { atomicAdd(output_valid_count, block_valid_count); }
+}
+
+/**
+ * @brief Type/aggregation dispatched functor for launching the gpu rolling window
+ *        kernel.
+ */
+template <typename InputType>
+struct rolling_window_launcher {
+  template <aggregation::Kind op,
+            typename PrecedingWindowIterator,
+            typename FollowingWindowIterator>
+  std::enable_if_t<corresponding_rolling_operator<InputType, op>::type::is_supported(),
+                   std::unique_ptr<column>>
+  operator()(column_view const& input,
+             column_view const& default_outputs,
+             PrecedingWindowIterator preceding_window_begin,
+             FollowingWindowIterator following_window_begin,
+             int min_periods,
+             [[maybe_unused]] rolling_aggregation const& agg,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr)
+  {
+    auto const do_rolling = [&](auto const& device_op) {
+      auto output = make_fixed_width_column(
+        target_type(input.type(), op), input.size(), mask_state::UNINITIALIZED, stream, mr);
+
+      auto const d_inp_ptr         = column_device_view::create(input, stream);
+      auto const d_default_out_ptr = column_device_view::create(default_outputs, stream);
+      auto const d_out_ptr = mutable_column_device_view::create(output->mutable_view(), stream);
+      auto d_valid_count   = rmm::device_scalar<size_type>{0, stream};
+
+      auto constexpr block_size = 256;
+      auto const grid           = cudf::detail::grid_1d(input.size(), block_size);
+      using OutType             = device_storage_type_t<target_type_t<InputType, op>>;
+
+      if (input.has_nulls()) {
+        gpu_rolling<OutType, block_size, true>
+          <<<grid.num_blocks, block_size, 0, stream.value()>>>(*d_inp_ptr,
+                                                               *d_default_out_ptr,
+                                                               *d_out_ptr,
+                                                               d_valid_count.data(),
+                                                               device_op,
+                                                               preceding_window_begin,
+                                                               following_window_begin);
+      } else {
+        gpu_rolling<OutType, block_size, false>
+          <<<grid.num_blocks, block_size, 0, stream.value()>>>(*d_inp_ptr,
+                                                               *d_default_out_ptr,
+                                                               *d_out_ptr,
+                                                               d_valid_count.data(),
+                                                               device_op,
+                                                               preceding_window_begin,
+                                                               following_window_begin);
+      }
+
+      auto const valid_count = d_valid_count.value(stream);
+      output->set_null_count(output->size() - valid_count);
+
+      return output;
+    };  // end do_rolling
+
+    auto constexpr is_arg_minmax =
+      op == aggregation::Kind::ARGMIN || op == aggregation::Kind::ARGMAX;
+
+    if constexpr (is_arg_minmax && std::is_same_v<InputType, cudf::struct_view>) {
+      // Using comp_generator to create a LESS operator for finding ARGMIN/ARGMAX of structs.
+      auto const comp_generator =
+        cudf::reduction::detail::comparison_binop_generator::create<op>(input, stream);
+      auto const device_op =
+        create_rolling_operator<InputType, op>{}(min_periods, comp_generator.binop());
+      return do_rolling(device_op);
+    } else {  // all the remaining rolling operations
+      auto const device_op = create_rolling_operator<InputType, op>{}(min_periods, agg);
+      return do_rolling(device_op);
+    }
+  }
+
+  template <aggregation::Kind op,
+            typename PrecedingWindowIterator,
+            typename FollowingWindowIterator>
+  std::enable_if_t<!corresponding_rolling_operator<InputType, op>::type::is_supported(),
+                   std::unique_ptr<column>>
+  operator()(column_view const&,
+             column_view const&,
+             PrecedingWindowIterator,
+             FollowingWindowIterator,
+             int,
+             rolling_aggregation const&,
+             rmm::cuda_stream_view,
+             rmm::mr::device_memory_resource*)
+  {
+    CUDF_FAIL("Invalid aggregation type/pair");
+  }
+};
+
+/**
+ * @brief Functor for performing the high level rolling logic.
+ *
+ * This does 3 basic things:
+ *
+ * - It calls the preprocess step on incoming aggregation/type pairs
+ * - It calls the aggregation-dispatched gpu-rolling operation
+ * - It calls the final postprocess step
+ */
+struct dispatch_rolling {
+  template <typename InputType, typename PrecedingWindowIterator, typename FollowingWindowIterator>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     column_view const& default_outputs,
+                                     PrecedingWindowIterator preceding_window_begin,
+                                     FollowingWindowIterator following_window_begin,
+                                     size_type min_periods,
+                                     rolling_aggregation const& agg,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    // do any preprocessing of aggregations (eg, MIN -> ARGMIN, COLLECT_LIST -> nothing)
+    rolling_aggregation_preprocessor preprocessor;
+    auto preprocessed_aggs = agg.get_simple_aggregations(input.type(), preprocessor);
+    CUDF_EXPECTS(preprocessed_aggs.size() <= 1,
+                 "Encountered a non-trivial rolling aggregation result");
+
+    // perform the rolling window if we produced an aggregation to use
+    auto intermediate = preprocessed_aggs.size() > 0
+                          ? aggregation_dispatcher(
+                              dynamic_cast<rolling_aggregation const&>(*preprocessed_aggs[0]).kind,
+                              rolling_window_launcher<InputType>{},
+                              input,
+                              default_outputs,
+                              preceding_window_begin,
+                              following_window_begin,
+                              min_periods,
+                              dynamic_cast<rolling_aggregation const&>(*preprocessed_aggs[0]),
+                              stream,
+                              mr)
+                          : nullptr;
+
+    // finalize.
+    auto const result_type = target_type(input.type(), agg.kind);
+    rolling_aggregation_postprocessor postprocessor(input,
+                                                    default_outputs,
+                                                    result_type,
+                                                    preceding_window_begin,
+                                                    following_window_begin,
+                                                    min_periods,
+                                                    std::move(intermediate),
+                                                    stream,
+                                                    mr);
+    agg.finalize(postprocessor);
+    return postprocessor.get_result();
+  }
+};
+
+// Applies a user-defined rolling window function to the values in a column.
+template <typename PrecedingWindowIterator, typename FollowingWindowIterator>
+std::unique_ptr<column> rolling_window_udf(column_view const& input,
+                                           PrecedingWindowIterator preceding_window,
+                                           std::string const& preceding_window_str,
+                                           FollowingWindowIterator following_window,
+                                           std::string const& following_window_str,
+                                           size_type min_periods,
+                                           rolling_aggregation const& agg,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  static_assert(warp_size == cudf::detail::size_in_bits<cudf::bitmask_type>(),
+                "bitmask_type size does not match CUDA warp size");
+
+  if (input.has_nulls()) {
+    CUDF_FAIL("Currently the UDF version of rolling window does NOT support inputs with nulls.");
+  }
+
+  min_periods = std::max(min_periods, 0);
+
+  auto& udf_agg = dynamic_cast<udf_aggregation const&>(agg);
+
+  std::string hash = "prog_rolling." + std::to_string(std::hash<std::string>{}(udf_agg._source));
+
+  std::string cuda_source;
+  switch (udf_agg.kind) {
+    case aggregation::Kind::PTX:
+      cuda_source += cudf::jit::parse_single_function_ptx(udf_agg._source,
+                                                          udf_agg._function_name,
+                                                          cudf::type_to_name(udf_agg._output_type),
+                                                          {0, 5});  // args 0 and 5 are pointers.
+      break;
+    case aggregation::Kind::CUDA:
+      cuda_source += cudf::jit::parse_single_function_cuda(udf_agg._source, udf_agg._function_name);
+      break;
+    default: CUDF_FAIL("Unsupported UDF type.");
+  }
+
+  std::unique_ptr<column> output = make_numeric_column(
+    udf_agg._output_type, input.size(), cudf::mask_state::UNINITIALIZED, stream, mr);
+
+  auto output_view = output->mutable_view();
+  rmm::device_scalar<size_type> device_valid_count{0, stream};
+
+  std::string kernel_name =
+    jitify2::reflection::Template("cudf::rolling::jit::gpu_rolling_new")  //
+      .instantiate(cudf::type_to_name(input.type()),  // list of template arguments
+                   cudf::type_to_name(output->type()),
+                   udf_agg._operator_name,
+                   preceding_window_str.c_str(),
+                   following_window_str.c_str());
+
+  cudf::jit::get_program_cache(*rolling_jit_kernel_cu_jit)
+    .get_kernel(
+      kernel_name, {}, {{"rolling/jit/operation-udf.hpp", cuda_source}}, {"-arch=sm_."})  //
+    ->configure_1d_max_occupancy(0, 0, 0, stream.value())                                 //
+    ->launch(input.size(),
+             cudf::jit::get_data_ptr(input),
+             input.null_mask(),
+             cudf::jit::get_data_ptr(output_view),
+             output_view.null_mask(),
+             device_valid_count.data(),
+             preceding_window,
+             following_window,
+             min_periods);
+
+  output->set_null_count(output->size() - device_valid_count.value(stream));
+
+  // check the stream for debugging
+  CUDF_CHECK_CUDA(stream.value());
+
+  return output;
+}
+
+/**
+ * @copydoc cudf::rolling_window(column_view const& input,
+ *                               PrecedingWindowIterator preceding_window_begin,
+ *                               FollowingWindowIterator following_window_begin,
+ *                               size_type min_periods,
+ *                               rolling_aggregation const& agg,
+ *                               rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename PrecedingWindowIterator, typename FollowingWindowIterator>
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& default_outputs,
+                                       PrecedingWindowIterator preceding_window_begin,
+                                       FollowingWindowIterator following_window_begin,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  static_assert(warp_size == cudf::detail::size_in_bits<cudf::bitmask_type>(),
+                "bitmask_type size does not match CUDA warp size");
+
+  if (input.is_empty()) { return cudf::detail::empty_output_for_rolling_aggregation(input, agg); }
+
+  if (cudf::is_dictionary(input.type())) {
+    CUDF_EXPECTS(agg.kind == aggregation::COUNT_ALL || agg.kind == aggregation::COUNT_VALID ||
+                   agg.kind == aggregation::ROW_NUMBER || agg.kind == aggregation::MIN ||
+                   agg.kind == aggregation::MAX || agg.kind == aggregation::LEAD ||
+                   agg.kind == aggregation::LAG,
+                 "Invalid aggregation for dictionary column");
+  }
+
+  if (agg.kind != aggregation::LEAD && agg.kind != aggregation::LAG) {
+    CUDF_EXPECTS(default_outputs.is_empty(),
+                 "Only LEAD/LAG window functions support default values.");
+  }
+
+  min_periods = std::max(min_periods, 0);
+
+  auto input_col = cudf::is_dictionary(input.type())
+                     ? dictionary_column_view(input).get_indices_annotated()
+                     : input;
+
+  auto output = cudf::type_dispatcher(input_col.type(),
+                                      dispatch_rolling{},
+                                      input_col,
+                                      default_outputs,
+                                      preceding_window_begin,
+                                      following_window_begin,
+                                      min_periods,
+                                      agg,
+                                      stream,
+                                      mr);
+
+  if (!cudf::is_dictionary(input.type())) return output;
+
+  // dictionary column post processing
+  if (agg.kind == aggregation::COUNT_ALL || agg.kind == aggregation::COUNT_VALID ||
+      agg.kind == aggregation::ROW_NUMBER) {
+    return output;
+  }
+
+  // output is new dictionary indices (including nulls)
+  auto keys = std::make_unique<column>(dictionary_column_view(input).keys(), stream, mr);
+  auto const indices_type = output->type();        // capture these
+  auto const output_size  = output->size();        // before calling
+  auto const null_count   = output->null_count();  // release()
+  auto contents           = output->release();
+  // create indices column from output column data
+  auto indices = std::make_unique<column>(indices_type,
+                                          output_size,
+                                          std::move(*(contents.data.release())),
+                                          rmm::device_buffer{0, stream, mr},
+                                          0);
+  // create dictionary from keys and indices
+  return make_dictionary_column(
+    std::move(keys), std::move(indices), std::move(*(contents.null_mask.release())), null_count);
+}
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/rolling.hpp b/cpp/src/rolling/detail/rolling.hpp
new file mode 100644
index 0000000..d2dfa2f
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling.hpp
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/utilities/traits.hpp>
+
+namespace cudf {
+// helper functions - used in the rolling window implementation and tests
+
+namespace detail {
+
+// store functor
+template <typename T, bool is_mean = false>
+struct rolling_store_output_functor {
+  CUDF_HOST_DEVICE inline void operator()(T& out, T& val, size_type count) { out = val; }
+};
+
+// Specialization for MEAN
+template <typename _T>
+struct rolling_store_output_functor<_T, true> {
+  // SFINAE for non-bool types
+  template <typename T                                                             = _T,
+            std::enable_if_t<!(cudf::is_boolean<T>() || cudf::is_timestamp<T>())>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(T& out, T& val, size_type count)
+  {
+    out = val / count;
+  }
+
+  // SFINAE for bool type
+  template <typename T = _T, std::enable_if_t<cudf::is_boolean<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(T& out, T& val, size_type count)
+  {
+    out = static_cast<int32_t>(val) / count;
+  }
+
+  // SFINAE for timestamp types
+  template <typename T = _T, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+  CUDF_HOST_DEVICE inline void operator()(T& out, T& val, size_type count)
+  {
+    out = static_cast<T>(val.time_since_epoch() / count);
+  }
+};
+
+/**
+ * @copydoc cudf::rolling_window(column_view const& input,
+ *                               column_view const& default_outputs,
+ *                               size_type preceding_window,
+ *                               size_type following_window,
+ *                               size_type min_periods,
+ *                               rolling_aggregation const& agg,
+ *                               rmm::mr::device_memory_resource* mr)
+ *
+ * @param stream CUDA stream to use for device memory operations
+ */
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& default_outputs,
+                                       size_type preceding_window,
+                                       size_type following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+
+/**
+ * @copydoc cudf::rolling_window(column_view const& input,
+ *                               column_view const& preceding_window,
+ *                               column_view const& following_window,
+ *                               size_type min_periods,
+ *                               rolling_aggregation const& agg,
+ *                               rmm::mr::device_memory_resource* mr);
+ *
+ * @param stream CUDA stream to use for device memory operations
+ */
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& preceding_window,
+                                       column_view const& following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr);
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/rolling_collect_list.cu b/cpp/src/rolling/detail/rolling_collect_list.cu
new file mode 100644
index 0000000..85dced0
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling_collect_list.cu
@@ -0,0 +1,163 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "rolling_collect_list.cuh"
+
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @see cudf::detail::get_list_child_to_list_row_mapping
+ */
+std::unique_ptr<column> get_list_child_to_list_row_mapping(cudf::column_view const& offsets,
+                                                           rmm::cuda_stream_view stream)
+{
+  // First, scatter the count for each repeated offset (except the first and last),
+  // into a column of N `0`s, where N == number of child rows.
+  // For example:
+  //   offsets        == [0, 2, 5, 8, 11, 13]
+  //   scatter result == [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]
+  //
+  // An example with empty list row at index 2:
+  //   offsets        == [0, 2, 5, 5, 8, 11, 13]
+  //   scatter result == [0, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 1, 0]
+  //
+  auto const num_child_rows{
+    cudf::detail::get_value<size_type>(offsets, offsets.size() - 1, stream)};
+  auto per_row_mapping = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, num_child_rows, mask_state::UNALLOCATED, stream);
+  auto per_row_mapping_begin = per_row_mapping->mutable_view().template begin<size_type>();
+  thrust::fill_n(rmm::exec_policy(stream), per_row_mapping_begin, num_child_rows, 0);
+
+  auto const begin = thrust::make_counting_iterator<size_type>(0);
+  thrust::scatter_if(rmm::exec_policy(stream),
+                     begin,
+                     begin + offsets.size() - 1,
+                     offsets.begin<size_type>(),
+                     begin,  // stencil iterator
+                     per_row_mapping_begin,
+                     [offset = offsets.begin<size_type>()] __device__(auto i) {
+                       return offset[i] != offset[i + 1];
+                     });  // [0,0,1,0,0,3,...]
+
+  // Next, generate mapping with inclusive_scan(max) on the scatter result.
+  // For the example above:
+  //   scatter result == [0, 0, 1, 0, 0, 2, 0, 0, 3, 0, 0, 4, 0]
+  //   inclusive_scan == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4]
+  //
+  // For the case with an empty list at index 2:
+  //   scatter result == [0, 0, 1, 0, 0, 3, 0, 0, 4, 0, 0, 5, 0]
+  //   inclusive_scan == [0, 0, 1, 1, 1, 3, 3, 3, 4, 4, 4, 5, 5]
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         per_row_mapping_begin,
+                         per_row_mapping_begin + num_child_rows,
+                         per_row_mapping_begin,
+                         thrust::maximum{});
+  return per_row_mapping;
+}
+
+/**
+ * @see cudf::detail::count_child_nulls
+ */
+size_type count_child_nulls(column_view const& input,
+                            std::unique_ptr<column> const& gather_map,
+                            rmm::cuda_stream_view stream)
+{
+  auto input_device_view = column_device_view::create(input, stream);
+
+  auto input_row_is_null = [d_input = *input_device_view] __device__(auto i) {
+    return d_input.is_null_nocheck(i);
+  };
+
+  return thrust::count_if(rmm::exec_policy(stream),
+                          gather_map->view().begin<size_type>(),
+                          gather_map->view().end<size_type>(),
+                          input_row_is_null);
+}
+
+/**
+ * @see cudf::detail::rolling_collect_list
+ */
+std::pair<std::unique_ptr<column>, std::unique_ptr<column>> purge_null_entries(
+  column_view const& input,
+  column_view const& gather_map,
+  column_view const& offsets,
+  size_type num_child_nulls,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto input_device_view = column_device_view::create(input, stream);
+
+  auto input_row_not_null = [d_input = *input_device_view] __device__(auto i) {
+    return d_input.is_valid_nocheck(i);
+  };
+
+  // Purge entries in gather_map that correspond to null input.
+  auto new_gather_map = make_fixed_width_column(data_type{type_to_id<size_type>()},
+                                                gather_map.size() - num_child_nulls,
+                                                mask_state::UNALLOCATED,
+                                                stream);
+  thrust::copy_if(rmm::exec_policy(stream),
+                  gather_map.template begin<size_type>(),
+                  gather_map.template end<size_type>(),
+                  new_gather_map->mutable_view().template begin<size_type>(),
+                  input_row_not_null);
+
+  // Recalculate offsets after null entries are purged.
+  auto new_sizes = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, input.size(), mask_state::UNALLOCATED, stream);
+
+  thrust::tabulate(rmm::exec_policy(stream),
+                   new_sizes->mutable_view().template begin<size_type>(),
+                   new_sizes->mutable_view().template end<size_type>(),
+                   [d_gather_map  = gather_map.template begin<size_type>(),
+                    d_old_offsets = offsets.template begin<size_type>(),
+                    input_row_not_null] __device__(auto i) {
+                     return thrust::count_if(thrust::seq,
+                                             d_gather_map + d_old_offsets[i],
+                                             d_gather_map + d_old_offsets[i + 1],
+                                             input_row_not_null);
+                   });
+
+  auto new_offsets = std::get<0>(
+    cudf::detail::make_offsets_child_column(new_sizes->view().template begin<size_type>(),
+                                            new_sizes->view().template end<size_type>(),
+                                            stream,
+                                            mr));
+
+  return std::make_pair<std::unique_ptr<column>, std::unique_ptr<column>>(std::move(new_gather_map),
+                                                                          std::move(new_offsets));
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/rolling_collect_list.cuh b/cpp/src/rolling/detail/rolling_collect_list.cuh
new file mode 100644
index 0000000..39d15ed
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling_collect_list.cuh
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/extrema.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Creates the offsets child of the result of the `COLLECT_LIST` window aggregation
+ *
+ * Given the input column, the preceding/following window bounds, and `min_periods`,
+ * the sizes of each list row may be computed. These values can then be used to
+ * calculate the offsets for the result of `COLLECT_LIST`.
+ *
+ * Note: If `min_periods` exceeds the number of observations for a window, the size
+ * is set to `0` (since the result is `null`).
+ */
+template <typename PrecedingIter, typename FollowingIter>
+std::unique_ptr<column> create_collect_offsets(size_type input_size,
+                                               PrecedingIter preceding_begin,
+                                               FollowingIter following_begin,
+                                               size_type min_periods,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  // Materialize offsets column.
+  auto static constexpr size_data_type = data_type{type_to_id<size_type>()};
+  auto sizes = make_fixed_width_column(size_data_type, input_size, mask_state::UNALLOCATED, stream);
+  auto mutable_sizes = sizes->mutable_view();
+
+  // Consider the following preceding/following values:
+  //    preceding = [1,2,2,2,2]
+  //    following = [1,1,1,1,0]
+  // The sum of the vectors should yield the window sizes:
+  //  prec + foll = [2,3,3,3,2]
+  //
+  // If min_periods=2, all rows have at least `min_periods` observations.
+  // But if min_periods=3, rows at indices 0 and 4 have too few observations, and must return
+  // null. The sizes at these positions must be 0, i.e.
+  //  prec + foll = [0,3,3,3,0]
+  thrust::transform(rmm::exec_policy(stream),
+                    preceding_begin,
+                    preceding_begin + input_size,
+                    following_begin,
+                    mutable_sizes.begin<size_type>(),
+                    [min_periods] __device__(auto const preceding, auto const following) {
+                      return (preceding + following) < min_periods ? 0 : (preceding + following);
+                    });
+
+  // Convert `sizes` to an offsets column, via inclusive_scan():
+  auto offsets_column = std::get<0>(cudf::detail::make_offsets_child_column(
+    sizes->view().begin<size_type>(), sizes->view().end<size_type>(), stream, mr));
+  return offsets_column;
+}
+
+/**
+ * @brief Generate mapping of each row in the COLLECT_LIST result's child column
+ * to the index of the row it belongs to.
+ *
+ *  If
+ *         input col == [A,B,C,D,E]
+ *    and  preceding == [1,2,2,2,2],
+ *    and  following == [1,1,1,1,0],
+ *  then,
+ *        collect result       == [ [A,B], [A,B,C], [B,C,D], [C,D,E], [D,E] ]
+ *   i.e. result offset column == [0,2,5,8,11,13],
+ *    and result child  column == [A,B,A,B,C,B,C,D,C,D,E,D,E].
+ *  Mapping back to `input`    == [0,1,0,1,2,1,2,3,2,3,4,3,4]
+ */
+std::unique_ptr<column> get_list_child_to_list_row_mapping(cudf::column_view const& offsets,
+                                                           rmm::cuda_stream_view stream);
+
+/**
+ * @brief Create gather map to generate the child column of the result of
+ * the `COLLECT_LIST` window aggregation.
+ */
+template <typename PrecedingIter>
+std::unique_ptr<column> create_collect_gather_map(column_view const& child_offsets,
+                                                  column_view const& per_row_mapping,
+                                                  PrecedingIter preceding_iter,
+                                                  rmm::cuda_stream_view stream)
+{
+  auto gather_map = make_fixed_width_column(
+    data_type{type_to_id<size_type>()}, per_row_mapping.size(), mask_state::UNALLOCATED, stream);
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(per_row_mapping.size()),
+    gather_map->mutable_view().template begin<size_type>(),
+    [d_offsets =
+       child_offsets.template begin<size_type>(),  // E.g. [0,   2,     5,     8,     11, 13]
+     d_groups =
+       per_row_mapping.template begin<size_type>(),  // E.g. [0,0, 1,1,1, 2,2,2, 3,3,3, 4,4]
+     d_prev = preceding_iter] __device__(auto i) {
+      auto group              = d_groups[i];
+      auto group_start_offset = d_offsets[group];
+      auto relative_index     = i - group_start_offset;
+
+      return (group - d_prev[group] + 1) + relative_index;
+    });
+  return gather_map;
+}
+
+/**
+ * @brief Count null entries in result of COLLECT_LIST.
+ */
+size_type count_child_nulls(column_view const& input,
+                            std::unique_ptr<column> const& gather_map,
+                            rmm::cuda_stream_view stream);
+
+/**
+ * @brief Purge entries for null inputs from gather_map, and adjust offsets.
+ */
+std::pair<std::unique_ptr<column>, std::unique_ptr<column>> purge_null_entries(
+  column_view const& input,
+  column_view const& gather_map,
+  column_view const& offsets,
+  size_type num_child_nulls,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+template <typename PrecedingIter, typename FollowingIter>
+std::unique_ptr<column> rolling_collect_list(column_view const& input,
+                                             column_view const& default_outputs,
+                                             PrecedingIter preceding_begin_raw,
+                                             FollowingIter following_begin_raw,
+                                             size_type min_periods,
+                                             null_policy null_handling,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(default_outputs.is_empty(),
+               "COLLECT_LIST window function does not support default values.");
+
+  if (input.is_empty()) return empty_like(input);
+
+  // Fix up preceding/following iterators to respect column boundaries,
+  // similar to gpu_rolling().
+  // `rolling_window()` does not fix up preceding/following so as not to read past
+  // column boundaries.
+  // `grouped_rolling_window()` and `time_range_based_grouped_rolling_window() do.
+  auto preceding_begin = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<size_type>(0), [preceding_begin_raw] __device__(auto i) {
+      return thrust::min(preceding_begin_raw[i], i + 1);
+    });
+  auto following_begin =
+    thrust::make_transform_iterator(thrust::make_counting_iterator<size_type>(0),
+                                    [following_begin_raw, size = input.size()] __device__(auto i) {
+                                      return thrust::min(following_begin_raw[i], size - i - 1);
+                                    });
+
+  // Materialize collect list's offsets.
+  auto offsets =
+    create_collect_offsets(input.size(), preceding_begin, following_begin, min_periods, stream, mr);
+
+  // Map each element of the collect() result's child column
+  // to the index where it appears in the input.
+  auto per_row_mapping = get_list_child_to_list_row_mapping(offsets->view(), stream);
+
+  // Generate gather map to produce the collect() result's child column.
+  auto gather_map =
+    create_collect_gather_map(offsets->view(), per_row_mapping->view(), preceding_begin, stream);
+
+  // If gather_map collects null elements, and null_policy == EXCLUDE,
+  // those elements must be filtered out, and offsets recomputed.
+  if (null_handling == null_policy::EXCLUDE && input.has_nulls()) {
+    auto num_child_nulls = count_child_nulls(input, gather_map, stream);
+    if (num_child_nulls != 0) {
+      std::tie(gather_map, offsets) =
+        purge_null_entries(input, *gather_map, *offsets, num_child_nulls, stream, mr);
+    }
+  }
+
+  // gather(), to construct child column.
+  auto gather_output = cudf::detail::gather(table_view{std::vector<column_view>{input}},
+                                            gather_map->view(),
+                                            cudf::out_of_bounds_policy::DONT_CHECK,
+                                            cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                            stream,
+                                            mr);
+
+  auto [null_mask, null_count] = valid_if(
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(input.size()),
+    [preceding_begin, following_begin, min_periods] __device__(auto i) {
+      return (preceding_begin[i] + following_begin[i]) >= min_periods;
+    },
+    stream,
+    mr);
+
+  return make_lists_column(input.size(),
+                           std::move(offsets),
+                           std::move(gather_output->release()[0]),
+                           null_count,
+                           std::move(null_mask),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/rolling_fixed_window.cu b/cpp/src/rolling/detail/rolling_fixed_window.cu
new file mode 100644
index 0000000..e951db9
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling_fixed_window.cu
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "rolling.cuh"
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+
+#include <thrust/extrema.h>
+
+namespace cudf::detail {
+
+// Applies a fixed-size rolling window function to the values in a column.
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& default_outputs,
+                                       size_type preceding_window,
+                                       size_type following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (input.is_empty()) { return cudf::detail::empty_output_for_rolling_aggregation(input, agg); }
+
+  CUDF_EXPECTS((min_periods >= 0), "min_periods must be non-negative");
+
+  CUDF_EXPECTS((default_outputs.is_empty() || default_outputs.size() == input.size()),
+               "Defaults column must be either empty or have as many rows as the input column.");
+
+  CUDF_EXPECTS(-(preceding_window - 1) <= following_window,
+               "Preceding window bounds must precede the following window bounds.");
+
+  if (agg.kind == aggregation::CUDA || agg.kind == aggregation::PTX) {
+    // TODO: In future, might need to clamp preceding/following to column boundaries.
+    return cudf::detail::rolling_window_udf(input,
+                                            preceding_window,
+                                            "cudf::size_type",
+                                            following_window,
+                                            "cudf::size_type",
+                                            min_periods,
+                                            agg,
+                                            stream,
+                                            mr);
+  } else {
+    // Clamp preceding/following to column boundaries.
+    // E.g. If preceding_window == 2, then for a column of 5 elements, preceding_window will be:
+    //      [1, 2, 2, 2, 1]
+
+    auto const preceding_calc = [preceding_window] __device__(size_type i) {
+      return thrust::min(i + 1, preceding_window);
+    };
+
+    auto const following_calc = [col_size = input.size(),
+                                 following_window] __device__(size_type i) {
+      return thrust::min(col_size - i - 1, following_window);
+    };
+
+    auto const preceding_column = expand_to_column(preceding_calc, input.size(), stream);
+    auto const following_column = expand_to_column(following_calc, input.size(), stream);
+    return cudf::detail::rolling_window(input,
+                                        default_outputs,
+                                        preceding_column->view().begin<cudf::size_type>(),
+                                        following_column->view().begin<cudf::size_type>(),
+                                        min_periods,
+                                        agg,
+                                        stream,
+                                        mr);
+  }
+}
+}  // namespace cudf::detail
diff --git a/cpp/src/rolling/detail/rolling_jit.hpp b/cpp/src/rolling/detail/rolling_jit.hpp
new file mode 100644
index 0000000..615cba8
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling_jit.hpp
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/types.hpp>
+
+namespace cudf {
+
+namespace detail {
+
+template <class T>
+T minimum(T a, T b)
+{
+  return b < a ? b : a;
+}
+
+struct preceding_window_wrapper {
+  cudf::size_type const* d_group_offsets;
+  cudf::size_type const* d_group_labels;
+  cudf::size_type preceding_window;
+
+  cudf::size_type operator[](cudf::size_type idx)
+  {
+    auto group_label = d_group_labels[idx];
+    auto group_start = d_group_offsets[group_label];
+    return minimum(preceding_window, idx - group_start + 1);  // Preceding includes current row.
+  }
+};
+
+struct following_window_wrapper {
+  cudf::size_type const* d_group_offsets;
+  cudf::size_type const* d_group_labels;
+  cudf::size_type following_window;
+
+  cudf::size_type operator[](cudf::size_type idx)
+  {
+    auto group_label = d_group_labels[idx];
+    auto group_end =
+      d_group_offsets[group_label +
+                      1];  // Cannot fall off the end, since offsets is capped with `input.size()`.
+    return minimum(following_window, (group_end - 1) - idx);
+  }
+};
+
+}  // namespace detail
+
+}  // namespace cudf
diff --git a/cpp/src/rolling/detail/rolling_variable_window.cu b/cpp/src/rolling/detail/rolling_variable_window.cu
new file mode 100644
index 0000000..fcddabe
--- /dev/null
+++ b/cpp/src/rolling/detail/rolling_variable_window.cu
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "rolling.cuh"
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/extrema.h>
+#include <thrust/iterator/constant_iterator.h>
+
+namespace cudf::detail {
+
+// Applies a variable-size rolling window function to the values in a column.
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& preceding_window,
+                                       column_view const& following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (preceding_window.is_empty() || following_window.is_empty() || input.is_empty()) {
+    return cudf::detail::empty_output_for_rolling_aggregation(input, agg);
+  }
+
+  CUDF_EXPECTS(preceding_window.type().id() == type_id::INT32 &&
+                 following_window.type().id() == type_id::INT32,
+               "preceding_window/following_window must have type_id::INT32 type");
+
+  CUDF_EXPECTS(preceding_window.size() == input.size() && following_window.size() == input.size(),
+               "preceding_window/following_window size must match input size");
+
+  if (agg.kind == aggregation::CUDA || agg.kind == aggregation::PTX) {
+    // TODO: In future, might need to clamp preceding/following to column boundaries.
+    return cudf::detail::rolling_window_udf(input,
+                                            preceding_window.begin<size_type>(),
+                                            "cudf::size_type*",
+                                            following_window.begin<size_type>(),
+                                            "cudf::size_type*",
+                                            min_periods,
+                                            agg,
+                                            stream,
+                                            mr);
+  } else {
+    auto defaults_col =
+      cudf::is_dictionary(input.type()) ? dictionary_column_view(input).indices() : input;
+    // Clamp preceding/following to column boundaries.
+    // E.g. If preceding_window == [2, 2, 2, 2, 2] for a column of 5 elements, the new
+    // preceding_window will be: [1, 2, 2, 2, 1]
+    auto const preceding_window_begin = cudf::detail::make_counting_transform_iterator(
+      0, [preceding = preceding_window.begin<size_type>()] __device__(size_type i) {
+        return thrust::min(i + 1, preceding[i]);
+      });
+    auto const following_window_begin = cudf::detail::make_counting_transform_iterator(
+      0,
+      [col_size = input.size(), following = following_window.begin<size_type>()] __device__(
+        size_type i) { return thrust::min(col_size - i - 1, following[i]); });
+    return cudf::detail::rolling_window(input,
+                                        empty_like(defaults_col)->view(),
+                                        preceding_window_begin,
+                                        following_window_begin,
+                                        min_periods,
+                                        agg,
+                                        stream,
+                                        mr);
+  }
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/rolling/grouped_rolling.cu b/cpp/src/rolling/grouped_rolling.cu
new file mode 100644
index 0000000..7ac784b
--- /dev/null
+++ b/cpp/src/rolling/grouped_rolling.cu
@@ -0,0 +1,1292 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/optimized_unbounded_window.hpp"
+#include "detail/range_comparator_utils.cuh"
+#include "detail/range_window_bounds.hpp"
+#include "detail/rolling.cuh"
+#include "detail/rolling_jit.hpp"
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/rolling.hpp>
+#include <cudf/detail/utilities/assert.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/rolling/range_window_bounds.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/partition.h>
+
+namespace cudf {
+std::unique_ptr<column> grouped_rolling_window(table_view const& group_keys,
+                                               column_view const& input,
+                                               size_type preceding_window,
+                                               size_type following_window,
+                                               size_type min_periods,
+                                               rolling_aggregation const& aggr,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  return grouped_rolling_window(group_keys,
+                                input,
+                                window_bounds::get(preceding_window),
+                                window_bounds::get(following_window),
+                                min_periods,
+                                aggr,
+                                mr);
+}
+
+std::unique_ptr<column> grouped_rolling_window(table_view const& group_keys,
+                                               column_view const& input,
+                                               window_bounds preceding_window,
+                                               window_bounds following_window,
+                                               size_type min_periods,
+                                               rolling_aggregation const& aggr,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  return grouped_rolling_window(group_keys,
+                                input,
+                                empty_like(input)->view(),
+                                preceding_window,
+                                following_window,
+                                min_periods,
+                                aggr,
+                                mr);
+}
+
+std::unique_ptr<column> grouped_rolling_window(table_view const& group_keys,
+                                               column_view const& input,
+                                               column_view const& default_outputs,
+                                               size_type preceding_window,
+                                               size_type following_window,
+                                               size_type min_periods,
+                                               rolling_aggregation const& aggr,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  return grouped_rolling_window(group_keys,
+                                input,
+                                default_outputs,
+                                window_bounds::get(preceding_window),
+                                window_bounds::get(following_window),
+                                min_periods,
+                                aggr,
+                                mr);
+}
+
+namespace detail {
+
+/// Preceding window calculation functor.
+template <bool preceding_less_than_1>
+struct row_based_preceding_calc {
+  cudf::size_type const* _group_offsets_begin;
+  cudf::size_type const* _group_labels_begin;
+  cudf::size_type const _preceding_window;
+
+  row_based_preceding_calc(rmm::device_uvector<cudf::size_type> const& group_offsets,
+                           rmm::device_uvector<cudf::size_type> const& group_labels,
+                           cudf::size_type const& preceding_window)
+    : _group_offsets_begin(group_offsets.data()),
+      _group_labels_begin(group_labels.data()),
+      _preceding_window(preceding_window)
+  {
+  }
+
+  __device__ cudf::size_type operator()(cudf::size_type const& idx) const
+  {
+    auto group_label = _group_labels_begin[idx];
+    if constexpr (preceding_less_than_1) {  // where 1 indicates only the current row.
+      auto group_end = _group_offsets_begin[group_label + 1];
+      return thrust::maximum{}(_preceding_window, -(group_end - 1 - idx));
+    } else {
+      auto group_start = _group_offsets_begin[group_label];
+      return thrust::minimum{}(_preceding_window,
+                               idx - group_start + 1);  // Preceding includes current row.
+    }
+  }
+};
+
+/// Helper to materialize preceding-window column, corrected to respect group boundaries.
+/// E.g. If preceding window == 5, then,
+///   1. For the first row in the group, the preceding is set to 1,
+///   2. For the next row in the group, preceding is set to 2, etc.
+std::unique_ptr<cudf::column> make_preceding_column(
+  rmm::device_uvector<cudf::size_type> const& group_offsets,
+  rmm::device_uvector<cudf::size_type> const& group_labels,
+  cudf::size_type const& preceding_window,
+  cudf::size_type const& num_rows,
+  rmm::cuda_stream_view stream)
+{
+  if (preceding_window < 1) {
+    auto const calc = row_based_preceding_calc<true>(group_offsets, group_labels, preceding_window);
+    return cudf::detail::expand_to_column(calc, num_rows, stream);
+  } else {
+    auto const calc =
+      row_based_preceding_calc<false>(group_offsets, group_labels, preceding_window);
+    return cudf::detail::expand_to_column(calc, num_rows, stream);
+  }
+}
+
+/// Following window calculation functor.
+template <bool following_less_than_0>
+struct row_based_following_calc {
+  cudf::size_type const* _group_offsets_begin;
+  cudf::size_type const* _group_labels_begin;
+  cudf::size_type const _following_window;
+
+  row_based_following_calc(rmm::device_uvector<cudf::size_type> const& group_offsets,
+                           rmm::device_uvector<cudf::size_type> const& group_labels,
+                           cudf::size_type const& following_window)
+    : _group_offsets_begin(group_offsets.data()),
+      _group_labels_begin(group_labels.data()),
+      _following_window(following_window)
+  {
+  }
+
+  __device__ cudf::size_type operator()(cudf::size_type const& idx) const
+  {
+    auto group_label = _group_labels_begin[idx];
+    if constexpr (following_less_than_0) {
+      auto group_start = _group_offsets_begin[group_label];
+      return thrust::maximum{}(_following_window, -(idx - group_start) - 1);
+    } else {
+      auto group_end =
+        _group_offsets_begin[group_label + 1];  // Cannot fall off the end, since offsets
+                                                // is capped with `input.size()`.
+      return thrust::minimum{}(_following_window, (group_end - 1) - idx);
+    }
+  }
+};
+
+/// Helper to materialize following-window column, corrected to respect group boundaries.
+/// i.e. If following window == 5, then:
+///   1. For the last row in the group, the following is set to 0.
+///   2. For the second last row in the group, following is set to 1, etc.
+std::unique_ptr<cudf::column> make_following_column(
+  rmm::device_uvector<cudf::size_type> const& group_offsets,
+  rmm::device_uvector<cudf::size_type> const& group_labels,
+  cudf::size_type const& following_window,
+  cudf::size_type const& num_rows,
+  rmm::cuda_stream_view stream)
+{
+  if (following_window < 0) {
+    auto const calc = row_based_following_calc<true>(group_offsets, group_labels, following_window);
+    return cudf::detail::expand_to_column(calc, num_rows, stream);
+  } else {
+    auto const calc =
+      row_based_following_calc<false>(group_offsets, group_labels, following_window);
+    return cudf::detail::expand_to_column(calc, num_rows, stream);
+  }
+}
+
+std::unique_ptr<column> grouped_rolling_window(table_view const& group_keys,
+                                               column_view const& input,
+                                               column_view const& default_outputs,
+                                               window_bounds preceding_window_bounds,
+                                               window_bounds following_window_bounds,
+                                               size_type min_periods,
+                                               rolling_aggregation const& aggr,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (input.is_empty()) { return cudf::detail::empty_output_for_rolling_aggregation(input, aggr); }
+
+  CUDF_EXPECTS((group_keys.num_columns() == 0 || group_keys.num_rows() == input.size()),
+               "Size mismatch between group_keys and input vector.");
+
+  CUDF_EXPECTS((min_periods >= 0), "min_periods must be non-negative");
+
+  CUDF_EXPECTS((default_outputs.is_empty() || default_outputs.size() == input.size()),
+               "Defaults column must be either empty or have as many rows as the input column.");
+
+  // Detect and bypass fully UNBOUNDED windows.
+  if (can_optimize_unbounded_window(preceding_window_bounds.is_unbounded(),
+                                    following_window_bounds.is_unbounded(),
+                                    min_periods,
+                                    aggr)) {
+    return optimized_unbounded_window(group_keys, input, aggr, stream, mr);
+  }
+
+  auto const preceding_window = preceding_window_bounds.value();
+  auto const following_window = following_window_bounds.value();
+
+  CUDF_EXPECTS(-(preceding_window - 1) <= following_window,
+               "Preceding window bounds must precede the following window bounds.");
+
+  if (group_keys.num_columns() == 0) {
+    // No Groupby columns specified. Treat as one big group.
+    return rolling_window(
+      input, default_outputs, preceding_window, following_window, min_periods, aggr, mr);
+  }
+
+  using sort_groupby_helper = cudf::groupby::detail::sort::sort_groupby_helper;
+
+  sort_groupby_helper helper{group_keys, cudf::null_policy::INCLUDE, cudf::sorted::YES, {}};
+  auto const& group_offsets{helper.group_offsets(stream)};
+  auto const& group_labels{helper.group_labels(stream)};
+
+  // `group_offsets` are interpreted in adjacent pairs, each pair representing the offsets
+  // of the first, and one past the last elements in a group.
+  //
+  // If `group_offsets` is not empty, it must contain at least two offsets:
+  //   a. 0, indicating the first element in `input`
+  //   b. input.size(), indicating one past the last element in `input`.
+  //
+  // Thus, for an input of 1000 rows,
+  //   0. [] indicates a single group, spanning the entire column.
+  //   1  [10] is invalid.
+  //   2. [0, 1000] indicates a single group, spanning the entire column (thus, equivalent to no
+  //   groups.)
+  //   3. [0, 500, 1000] indicates two equal-sized groups: [0,500), and [500,1000).
+
+  assert(group_offsets.size() >= 2 && group_offsets.element(0, stream) == 0 &&
+         group_offsets.element(group_offsets.size() - 1, stream) == input.size() &&
+         "Must have at least one group.");
+
+  if (aggr.kind == aggregation::CUDA || aggr.kind == aggregation::PTX) {
+    cudf::detail::preceding_window_wrapper grouped_preceding_window{
+      group_offsets.data(), group_labels.data(), preceding_window};
+
+    cudf::detail::following_window_wrapper grouped_following_window{
+      group_offsets.data(), group_labels.data(), following_window};
+
+    return cudf::detail::rolling_window_udf(input,
+                                            grouped_preceding_window,
+                                            "cudf::detail::preceding_window_wrapper",
+                                            grouped_following_window,
+                                            "cudf::detail::following_window_wrapper",
+                                            min_periods,
+                                            aggr,
+                                            stream,
+                                            mr);
+  } else {
+    auto const preceding_column =
+      make_preceding_column(group_offsets, group_labels, preceding_window, input.size(), stream);
+    auto const following_column =
+      make_following_column(group_offsets, group_labels, following_window, input.size(), stream);
+    return cudf::detail::rolling_window(input,
+                                        default_outputs,
+                                        preceding_column->view().begin<cudf::size_type>(),
+                                        following_column->view().begin<cudf::size_type>(),
+                                        min_periods,
+                                        aggr,
+                                        stream,
+                                        mr);
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> grouped_rolling_window(table_view const& group_keys,
+                                               column_view const& input,
+                                               column_view const& default_outputs,
+                                               window_bounds preceding_window_bounds,
+                                               window_bounds following_window_bounds,
+                                               size_type min_periods,
+                                               rolling_aggregation const& aggr,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  return detail::grouped_rolling_window(group_keys,
+                                        input,
+                                        default_outputs,
+                                        preceding_window_bounds,
+                                        following_window_bounds,
+                                        min_periods,
+                                        aggr,
+                                        cudf::get_default_stream(),
+                                        mr);
+}
+
+namespace {
+
+/**
+ * @brief For a specified idx, find the lowest value of the (sorted) orderby column that
+ * participates in a range-window query.
+ */
+template <typename ElementT, typename ElementIter>
+__device__ ElementT compute_lowest_in_window(ElementIter orderby_iter,
+                                             size_type idx,
+                                             [[maybe_unused]] ElementT delta)
+{
+  if constexpr (std::is_same_v<ElementT, cudf::string_view>) {
+    return orderby_iter[idx];
+  } else {
+    return cudf::detail::subtract_safe(orderby_iter[idx], delta);
+  }
+}
+
+/**
+ * @brief For a specified idx, find the highest value of the (sorted) orderby column that
+ * participates in a range-window query.
+ */
+template <typename ElementT, typename ElementIter>
+__device__ ElementT compute_highest_in_window(ElementIter orderby_iter,
+                                              size_type idx,
+                                              [[maybe_unused]] ElementT delta)
+{
+  if constexpr (std::is_same_v<ElementT, cudf::string_view>) {
+    return orderby_iter[idx];
+  } else {
+    return cudf::detail::add_safe(orderby_iter[idx], delta);
+  }
+}
+
+/**
+ * Accessor for values in an order-by column, on the device.
+ */
+template <typename T>
+struct device_value_accessor {
+  column_device_view const col;  ///< column view of column in device
+
+  /// Checks that the type used to access device values matches the rep-type
+  /// of the order-by column.
+  struct is_correct_range_rep {
+    template <typename U>  /// Order-by type.
+    constexpr bool operator()() const
+    {
+      return std::is_same_v<T, cudf::detail::range_rep_type<U>>;
+    }
+  };
+
+  /**
+   * @brief constructor
+   *
+   * @param[in] col_ column device view of cudf column
+   */
+  explicit __device__ device_value_accessor(column_device_view const& col_) : col{col_}
+  {
+    // For non-timestamp types, T must match the order-by column's type.
+    // For timestamp types, T must match the range rep type for the order-by column.
+    cudf_assert((type_id_matches_device_storage_type<T>(col.type().id()) or
+                 cudf::type_dispatcher(col.type(), is_correct_range_rep{})) &&
+                "data type mismatch when accessing the order-by column");
+  }
+
+  /**
+   * @brief Returns the value of element at index `i`
+   * @param[in] i index of element
+   * @return value of element at index `i`
+   */
+  __device__ T operator()(cudf::size_type i) const { return col.element<T>(i); }
+};
+
+template <typename T>
+using const_device_iterator =
+  thrust::transform_iterator<device_value_accessor<T>, thrust::counting_iterator<size_type>>;
+
+/// This is a stand-in for the `cudf::column_device_view::begin<T>()`, which is `__host__` only.
+/// For range window functions, one might need to iterate over the order-by column, per row.
+template <typename T, CUDF_ENABLE_IF(cudf::column_device_view::has_element_accessor<T>())>
+[[nodiscard]] __device__ const_device_iterator<T> begin(cudf::column_device_view const& col)
+{
+  return const_device_iterator<T>{thrust::make_counting_iterator<cudf::size_type>(0),
+                                  device_value_accessor<T>{col}};
+}
+
+/// Given a single, ungrouped order-by column, return the indices corresponding
+/// to the first null element, and (one past) the last null timestamp.
+/// The input column is sorted, with all null values clustered either
+/// at the beginning of the column or at the end.
+/// If no null values are founds, null_begin and null_end are 0.
+std::tuple<size_type, size_type> get_null_bounds_for_orderby_column(
+  column_view const& orderby_column)
+{
+  auto const num_rows  = orderby_column.size();
+  auto const num_nulls = orderby_column.null_count();
+
+  if (num_nulls == num_rows || num_nulls == 0) {
+    // Short-circuit: All nulls, or no nulls.
+    return std::make_tuple(0, num_nulls);
+  }
+
+  auto const first_row_is_null = orderby_column.null_count(0, 1) == 1;
+
+  return first_row_is_null ? std::make_tuple(0, num_nulls)
+                           : std::make_tuple(num_rows - num_nulls, num_rows);
+}
+
+/// Range window computation, with
+///   1. no grouping keys specified
+///   2. rows in ASCENDING order.
+/// Treat as one single group.
+template <typename T>
+std::unique_ptr<column> range_window_ASC(column_view const& input,
+                                         column_view const& orderby_column,
+                                         T preceding_window,
+                                         bool preceding_window_is_unbounded,
+                                         T following_window,
+                                         bool following_window_is_unbounded,
+                                         size_type min_periods,
+                                         rolling_aggregation const& aggr,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  auto [h_nulls_begin_idx, h_nulls_end_idx] = get_null_bounds_for_orderby_column(orderby_column);
+  auto const p_orderby_device_view = cudf::column_device_view::create(orderby_column, stream);
+
+  auto const preceding_calculator =
+    [nulls_begin_idx     = h_nulls_begin_idx,
+     nulls_end_idx       = h_nulls_end_idx,
+     orderby_device_view = *p_orderby_device_view,
+     preceding_window,
+     preceding_window_is_unbounded] __device__(size_type idx) -> size_type {
+    if (preceding_window_is_unbounded) {
+      return idx + 1;  // Technically `idx - 0 + 1`,
+                       // where 0 == Group start,
+                       // and   1 accounts for the current row
+    }
+    if (idx >= nulls_begin_idx && idx < nulls_end_idx) {
+      // Current row is in the null group.
+      // Must consider beginning of null-group as window start.
+      return idx - nulls_begin_idx + 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+    // orderby[idx] not null. Binary search the group, excluding null group.
+    // If nulls_begin_idx == 0, either
+    //  1. NULLS FIRST ordering: Binary search starts where nulls_end_idx.
+    //  2. NO NULLS: Binary search starts at 0 (also nulls_end_idx).
+    // Otherwise, NULLS LAST ordering. Start at 0.
+    auto const group_start      = nulls_begin_idx == 0 ? nulls_end_idx : 0;
+    auto const lowest_in_window = compute_lowest_in_window(d_orderby, idx, preceding_window);
+
+    return ((d_orderby + idx) - thrust::lower_bound(thrust::seq,
+                                                    d_orderby + group_start,
+                                                    d_orderby + idx,
+                                                    lowest_in_window,
+                                                    cudf::detail::nan_aware_less{})) +
+           1;  // Add 1, for `preceding` to account for current row.
+  };
+
+  auto const preceding_column =
+    cudf::detail::expand_to_column(preceding_calculator, input.size(), stream);
+
+  auto const following_calculator =
+    [nulls_begin_idx     = h_nulls_begin_idx,
+     nulls_end_idx       = h_nulls_end_idx,
+     num_rows            = input.size(),
+     orderby_device_view = *p_orderby_device_view,
+     following_window,
+     following_window_is_unbounded] __device__(size_type idx) -> size_type {
+    if (following_window_is_unbounded) { return num_rows - idx - 1; }
+    if (idx >= nulls_begin_idx && idx < nulls_end_idx) {
+      // Current row is in the null group.
+      // Window ends at the end of the null group.
+      return nulls_end_idx - idx - 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+    // orderby[idx] not null. Binary search the group, excluding null group.
+    // If nulls_begin_idx == 0, either
+    //  1. NULLS FIRST ordering: Binary search ends at num_rows.
+    //  2. NO NULLS: Binary search also ends at num_rows.
+    // Otherwise, NULLS LAST ordering. End at nulls_begin_idx.
+
+    auto const group_end         = nulls_begin_idx == 0 ? num_rows : nulls_begin_idx;
+    auto const highest_in_window = compute_highest_in_window(d_orderby, idx, following_window);
+
+    return (thrust::upper_bound(thrust::seq,
+                                d_orderby + idx,
+                                d_orderby + group_end,
+                                highest_in_window,
+                                cudf::detail::nan_aware_less{}) -
+            (d_orderby + idx)) -
+           1;
+  };
+
+  auto const following_column =
+    cudf::detail::expand_to_column(following_calculator, input.size(), stream);
+
+  return cudf::detail::rolling_window(
+    input, preceding_column->view(), following_column->view(), min_periods, aggr, stream, mr);
+}
+
+// Given an orderby column grouped as specified in group_offsets,
+// return the following two vectors:
+//  1. Vector with one entry per group, indicating the offset in the group
+//     where the null values begin.
+//  2. Vector with one entry per group, indicating the offset in the group
+//     where the null values end. (i.e. 1 past the last null.)
+// Each group in the input orderby column must be sorted,
+// with null values clustered at either the start or the end of each group.
+// If there are no nulls for any given group, (nulls_begin, nulls_end) == (0,0).
+std::tuple<rmm::device_uvector<size_type>, rmm::device_uvector<size_type>>
+get_null_bounds_for_orderby_column(column_view const& orderby_column,
+                                   cudf::device_span<size_type const> group_offsets,
+                                   rmm::cuda_stream_view stream)
+{
+  // For each group, the null values are clustered at the beginning or the end of the group.
+  // These nulls cannot participate, except in their own window.
+
+  auto const num_groups = group_offsets.size() - 1;
+
+  if (orderby_column.has_nulls()) {
+    auto null_start = rmm::device_uvector<size_type>(num_groups, stream);
+    auto null_end   = rmm::device_uvector<size_type>(num_groups, stream);
+
+    auto p_orderby_device_view = column_device_view::create(orderby_column, stream);
+
+    // Null timestamps exist. Find null bounds, per group.
+    thrust::for_each(
+      rmm::exec_policy(stream),
+      thrust::make_counting_iterator(static_cast<size_type>(0)),
+      thrust::make_counting_iterator(static_cast<size_type>(num_groups)),
+      [d_orderby       = *p_orderby_device_view,
+       d_group_offsets = group_offsets.data(),
+       d_null_start    = null_start.data(),
+       d_null_end      = null_end.data()] __device__(auto group_label) {
+        auto group_start           = d_group_offsets[group_label];
+        auto group_end             = d_group_offsets[group_label + 1];
+        auto first_element_is_null = d_orderby.is_null_nocheck(group_start);
+        auto last_element_is_null  = d_orderby.is_null_nocheck(group_end - 1);
+        if (!first_element_is_null && !last_element_is_null) {
+          // Short circuit: No nulls.
+          d_null_start[group_label] = group_start;
+          d_null_end[group_label]   = group_start;
+        } else if (first_element_is_null && last_element_is_null) {
+          // Short circuit: All nulls.
+          d_null_start[group_label] = group_start;
+          d_null_end[group_label]   = group_end;
+        } else if (first_element_is_null) {
+          // NULLS FIRST.
+          d_null_start[group_label] = group_start;
+          d_null_end[group_label]   = *thrust::partition_point(
+            thrust::seq,
+            thrust::make_counting_iterator(group_start),
+            thrust::make_counting_iterator(group_end),
+            [&d_orderby] __device__(auto i) { return d_orderby.is_null_nocheck(i); });
+        } else {
+          // NULLS LAST.
+          d_null_end[group_label]   = group_end;
+          d_null_start[group_label] = *thrust::partition_point(
+            thrust::seq,
+            thrust::make_counting_iterator(group_start),
+            thrust::make_counting_iterator(group_end),
+            [&d_orderby] __device__(auto i) { return d_orderby.is_valid_nocheck(i); });
+        }
+      });
+
+    return std::make_tuple(std::move(null_start), std::move(null_end));
+  } else {
+    // The returned vectors have num_groups items, but the input offsets have num_groups+1
+    // Drop the last element using a span
+    auto const group_offsets_span =
+      cudf::device_span<cudf::size_type const>(group_offsets.data(), num_groups);
+
+    // When there are no nulls, just copy the input group offsets to the output.
+    return std::make_tuple(cudf::detail::make_device_uvector_async(
+                             group_offsets_span, stream, rmm::mr::get_current_device_resource()),
+                           cudf::detail::make_device_uvector_async(
+                             group_offsets_span, stream, rmm::mr::get_current_device_resource()));
+  }
+}
+
+// Range window computation, for orderby column in ASCENDING order.
+template <typename T>
+std::unique_ptr<column> range_window_ASC(column_view const& input,
+                                         column_view const& orderby_column,
+                                         rmm::device_uvector<cudf::size_type> const& group_offsets,
+                                         rmm::device_uvector<cudf::size_type> const& group_labels,
+                                         T preceding_window,
+                                         bool preceding_window_is_unbounded,
+                                         T following_window,
+                                         bool following_window_is_unbounded,
+                                         size_type min_periods,
+                                         rolling_aggregation const& aggr,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  auto [null_start, null_end] =
+    get_null_bounds_for_orderby_column(orderby_column, group_offsets, stream);
+  auto const p_orderby_device_view = cudf::column_device_view::create(orderby_column, stream);
+
+  auto const preceding_calculator =
+    [d_group_offsets     = group_offsets.data(),
+     d_group_labels      = group_labels.data(),
+     orderby_device_view = *p_orderby_device_view,
+     d_nulls_begin       = null_start.data(),
+     d_nulls_end         = null_end.data(),
+     preceding_window,
+     preceding_window_is_unbounded] __device__(size_type idx) -> size_type {
+    auto const group_label = d_group_labels[idx];
+    auto const group_start = d_group_offsets[group_label];
+    auto const nulls_begin = d_nulls_begin[group_label];
+    auto const nulls_end   = d_nulls_end[group_label];
+
+    if (preceding_window_is_unbounded) { return idx - group_start + 1; }
+
+    // If idx lies in the null-range, the window is the null range.
+    if (idx >= nulls_begin && idx < nulls_end) {
+      // Current row is in the null group.
+      // The window starts at the start of the null group.
+      return idx - nulls_begin + 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+
+    // orderby[idx] not null. Search must exclude the null group.
+    // If nulls_begin == group_start, either of the following is true:
+    //  1. NULLS FIRST ordering: Search must begin at nulls_end.
+    //  2. NO NULLS: Search must begin at group_start (which also equals nulls_end.)
+    // Otherwise, NULLS LAST ordering. Search must start at nulls group_start.
+    auto const search_start     = nulls_begin == group_start ? nulls_end : group_start;
+    auto const lowest_in_window = compute_lowest_in_window(d_orderby, idx, preceding_window);
+
+    return ((d_orderby + idx) - thrust::lower_bound(thrust::seq,
+                                                    d_orderby + search_start,
+                                                    d_orderby + idx,
+                                                    lowest_in_window,
+                                                    cudf::detail::nan_aware_less{})) +
+           1;  // Add 1, for `preceding` to account for current row.
+  };
+
+  auto const preceding_column =
+    cudf::detail::expand_to_column(preceding_calculator, input.size(), stream);
+
+  auto const following_calculator =
+    [d_group_offsets     = group_offsets.data(),
+     d_group_labels      = group_labels.data(),
+     orderby_device_view = *p_orderby_device_view,
+     d_nulls_begin       = null_start.data(),
+     d_nulls_end         = null_end.data(),
+     following_window,
+     following_window_is_unbounded] __device__(size_type idx) -> size_type {
+    auto const group_label = d_group_labels[idx];
+    auto const group_start = d_group_offsets[group_label];
+    auto const group_end =
+      d_group_offsets[group_label + 1];  // Cannot fall off the end, since offsets
+                                         // is capped with `input.size()`.
+    auto const nulls_begin = d_nulls_begin[group_label];
+    auto const nulls_end   = d_nulls_end[group_label];
+
+    if (following_window_is_unbounded) { return (group_end - idx) - 1; }
+
+    // If idx lies in the null-range, the window is the null range.
+    if (idx >= nulls_begin && idx < nulls_end) {
+      // Current row is in the null group.
+      // The window ends at the end of the null group.
+      return nulls_end - idx - 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+
+    // orderby[idx] not null. Search must exclude the null group.
+    // If nulls_begin == group_start, either of the following is true:
+    //  1. NULLS FIRST ordering: Search ends at group_end.
+    //  2. NO NULLS: Search ends at group_end.
+    // Otherwise, NULLS LAST ordering. Search ends at nulls_begin.
+    auto const search_end        = nulls_begin == group_start ? group_end : nulls_begin;
+    auto const highest_in_window = compute_highest_in_window(d_orderby, idx, following_window);
+
+    return (thrust::upper_bound(thrust::seq,
+                                d_orderby + idx,
+                                d_orderby + search_end,
+                                highest_in_window,
+                                cudf::detail::nan_aware_less{}) -
+            (d_orderby + idx)) -
+           1;
+  };
+
+  auto const following_column =
+    cudf::detail::expand_to_column(following_calculator, input.size(), stream);
+
+  return cudf::detail::rolling_window(
+    input, preceding_column->view(), following_column->view(), min_periods, aggr, stream, mr);
+}
+
+/// Range window computation, with
+///   1. no grouping keys specified
+///   2. rows in DESCENDING order.
+/// Treat as one single group.
+template <typename T>
+std::unique_ptr<column> range_window_DESC(column_view const& input,
+                                          column_view const& orderby_column,
+                                          T preceding_window,
+                                          bool preceding_window_is_unbounded,
+                                          T following_window,
+                                          bool following_window_is_unbounded,
+                                          size_type min_periods,
+                                          rolling_aggregation const& aggr,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  auto [h_nulls_begin_idx, h_nulls_end_idx] = get_null_bounds_for_orderby_column(orderby_column);
+  auto const p_orderby_device_view = cudf::column_device_view::create(orderby_column, stream);
+
+  auto const preceding_calculator =
+    [nulls_begin_idx     = h_nulls_begin_idx,
+     nulls_end_idx       = h_nulls_end_idx,
+     orderby_device_view = *p_orderby_device_view,
+     preceding_window,
+     preceding_window_is_unbounded] __device__(size_type idx) -> size_type {
+    if (preceding_window_is_unbounded) {
+      return idx + 1;  // Technically `idx - 0 + 1`,
+                       // where 0 == Group start,
+                       // and   1 accounts for the current row
+    }
+    if (idx >= nulls_begin_idx && idx < nulls_end_idx) {
+      // Current row is in the null group.
+      // Must consider beginning of null-group as window start.
+      return idx - nulls_begin_idx + 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+    // orderby[idx] not null. Binary search the group, excluding null group.
+    // If nulls_begin_idx == 0, either
+    //  1. NULLS FIRST ordering: Binary search starts where nulls_end_idx.
+    //  2. NO NULLS: Binary search starts at 0 (also nulls_end_idx).
+    // Otherwise, NULLS LAST ordering. Start at 0.
+    auto const group_start       = nulls_begin_idx == 0 ? nulls_end_idx : 0;
+    auto const highest_in_window = compute_highest_in_window(d_orderby, idx, preceding_window);
+
+    return ((d_orderby + idx) - thrust::lower_bound(thrust::seq,
+                                                    d_orderby + group_start,
+                                                    d_orderby + idx,
+                                                    highest_in_window,
+                                                    cudf::detail::nan_aware_greater{})) +
+           1;  // Add 1, for `preceding` to account for current row.
+  };
+
+  auto const preceding_column =
+    cudf::detail::expand_to_column(preceding_calculator, input.size(), stream);
+
+  auto const following_calculator =
+    [nulls_begin_idx     = h_nulls_begin_idx,
+     nulls_end_idx       = h_nulls_end_idx,
+     num_rows            = input.size(),
+     orderby_device_view = *p_orderby_device_view,
+     following_window,
+     following_window_is_unbounded] __device__(size_type idx) -> size_type {
+    if (following_window_is_unbounded) { return (num_rows - idx) - 1; }
+    if (idx >= nulls_begin_idx && idx < nulls_end_idx) {
+      // Current row is in the null group.
+      // Window ends at the end of the null group.
+      return nulls_end_idx - idx - 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+    // orderby[idx] not null. Search must exclude null group.
+    // If nulls_begin_idx = 0, either
+    //  1. NULLS FIRST ordering: Search ends at num_rows.
+    //  2. NO NULLS: Search also ends at num_rows.
+    // Otherwise, NULLS LAST ordering: End at nulls_begin_idx.
+
+    auto const group_end        = nulls_begin_idx == 0 ? num_rows : nulls_begin_idx;
+    auto const lowest_in_window = compute_lowest_in_window(d_orderby, idx, following_window);
+
+    return (thrust::upper_bound(thrust::seq,
+                                d_orderby + idx,
+                                d_orderby + group_end,
+                                lowest_in_window,
+                                cudf::detail::nan_aware_greater{}) -
+            (d_orderby + idx)) -
+           1;
+  };
+
+  auto const following_column =
+    cudf::detail::expand_to_column(following_calculator, input.size(), stream);
+
+  return cudf::detail::rolling_window(
+    input, preceding_column->view(), following_column->view(), min_periods, aggr, stream, mr);
+}
+
+// Range window computation, for rows in DESCENDING order.
+template <typename T>
+std::unique_ptr<column> range_window_DESC(column_view const& input,
+                                          column_view const& orderby_column,
+                                          rmm::device_uvector<cudf::size_type> const& group_offsets,
+                                          rmm::device_uvector<cudf::size_type> const& group_labels,
+                                          T preceding_window,
+                                          bool preceding_window_is_unbounded,
+                                          T following_window,
+                                          bool following_window_is_unbounded,
+                                          size_type min_periods,
+                                          rolling_aggregation const& aggr,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  auto [null_start, null_end] =
+    get_null_bounds_for_orderby_column(orderby_column, group_offsets, stream);
+  auto const p_orderby_device_view = cudf::column_device_view::create(orderby_column, stream);
+
+  auto const preceding_calculator =
+    [d_group_offsets     = group_offsets.data(),
+     d_group_labels      = group_labels.data(),
+     orderby_device_view = *p_orderby_device_view,
+     d_nulls_begin       = null_start.data(),
+     d_nulls_end         = null_end.data(),
+     preceding_window,
+     preceding_window_is_unbounded] __device__(size_type idx) -> size_type {
+    auto const group_label = d_group_labels[idx];
+    auto const group_start = d_group_offsets[group_label];
+    auto const nulls_begin = d_nulls_begin[group_label];
+    auto const nulls_end   = d_nulls_end[group_label];
+
+    if (preceding_window_is_unbounded) { return (idx - group_start) + 1; }
+
+    // If idx lies in the null-range, the window is the null range.
+    if (idx >= nulls_begin && idx < nulls_end) {
+      // Current row is in the null group.
+      // The window starts at the start of the null group.
+      return idx - nulls_begin + 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+    // orderby[idx] not null. Search must exclude the null group.
+    // If nulls_begin == group_start, either of the following is true:
+    //  1. NULLS FIRST ordering: Search must begin at nulls_end.
+    //  2. NO NULLS: Search must begin at group_start (which also equals nulls_end.)
+    // Otherwise, NULLS LAST ordering. Search must start at nulls group_start.
+    auto const search_start      = nulls_begin == group_start ? nulls_end : group_start;
+    auto const highest_in_window = compute_highest_in_window(d_orderby, idx, preceding_window);
+
+    return ((d_orderby + idx) - thrust::lower_bound(thrust::seq,
+                                                    d_orderby + search_start,
+                                                    d_orderby + idx,
+                                                    highest_in_window,
+                                                    cudf::detail::nan_aware_greater{})) +
+           1;  // Add 1, for `preceding` to account for current row.
+  };
+
+  auto const preceding_column =
+    cudf::detail::expand_to_column(preceding_calculator, input.size(), stream);
+
+  auto const following_calculator =
+    [d_group_offsets     = group_offsets.data(),
+     d_group_labels      = group_labels.data(),
+     orderby_device_view = *p_orderby_device_view,
+     d_nulls_begin       = null_start.data(),
+     d_nulls_end         = null_end.data(),
+     following_window,
+     following_window_is_unbounded] __device__(size_type idx) -> size_type {
+    auto const group_label = d_group_labels[idx];
+    auto const group_start = d_group_offsets[group_label];
+    auto const group_end   = d_group_offsets[group_label + 1];
+    auto const nulls_begin = d_nulls_begin[group_label];
+    auto const nulls_end   = d_nulls_end[group_label];
+
+    if (following_window_is_unbounded) { return (group_end - idx) - 1; }
+
+    // If idx lies in the null-range, the window is the null range.
+    if (idx >= nulls_begin && idx < nulls_end) {
+      // Current row is in the null group.
+      // The window ends at the end of the null group.
+      return nulls_end - idx - 1;
+    }
+
+    auto const d_orderby = begin<T>(orderby_device_view);
+    // orderby[idx] not null. Search must exclude the null group.
+    // If nulls_begin == group_start, either of the following is true:
+    //  1. NULLS FIRST ordering: Search ends at group_end.
+    //  2. NO NULLS: Search ends at group_end.
+    // Otherwise, NULLS LAST ordering. Search ends at nulls_begin.
+    auto const search_end       = nulls_begin == group_start ? group_end : nulls_begin;
+    auto const lowest_in_window = compute_lowest_in_window(d_orderby, idx, following_window);
+
+    return (thrust::upper_bound(thrust::seq,
+                                d_orderby + idx,
+                                d_orderby + search_end,
+                                lowest_in_window,
+                                cudf::detail::nan_aware_greater{}) -
+            (d_orderby + idx)) -
+           1;
+  };
+
+  auto const following_column =
+    cudf::detail::expand_to_column(following_calculator, input.size(), stream);
+
+  if (aggr.kind == aggregation::CUDA || aggr.kind == aggregation::PTX) {
+    CUDF_FAIL("Ranged rolling window does NOT (yet) support UDF.");
+  } else {
+    return cudf::detail::rolling_window(
+      input, preceding_column->view(), following_column->view(), min_periods, aggr, stream, mr);
+  }
+}
+
+template <typename OrderByT>
+std::unique_ptr<column> grouped_range_rolling_window_impl(
+  column_view const& input,
+  column_view const& orderby_column,
+  cudf::order const& order_of_orderby_column,
+  rmm::device_uvector<cudf::size_type> const& group_offsets,
+  rmm::device_uvector<cudf::size_type> const& group_labels,
+  range_window_bounds const& preceding_window,
+  range_window_bounds const& following_window,
+  size_type min_periods,
+  rolling_aggregation const& aggr,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto [preceding_value, following_value] = [&] {
+    if constexpr (std::is_same_v<OrderByT, cudf::string_view>) {
+      CUDF_EXPECTS(
+        preceding_window.is_unbounded() || preceding_window.is_current_row(),
+        "For STRING order-by column, preceding range has to be either UNBOUNDED or CURRENT ROW.");
+      CUDF_EXPECTS(
+        following_window.is_unbounded() || following_window.is_current_row(),
+        "For STRING order-by column, following range has to be either UNBOUNDED or CURRENT ROW.");
+      return std::pair{cudf::string_view{}, cudf::string_view{}};
+    } else {
+      return std::pair{
+        detail::range_comparable_value<OrderByT>(preceding_window, orderby_column.type(), stream),
+        detail::range_comparable_value<OrderByT>(following_window, orderby_column.type(), stream)};
+    }
+  }();
+
+  if (order_of_orderby_column == cudf::order::ASCENDING) {
+    return group_offsets.is_empty() ? range_window_ASC(input,
+                                                       orderby_column,
+                                                       preceding_value,
+                                                       preceding_window.is_unbounded(),
+                                                       following_value,
+                                                       following_window.is_unbounded(),
+                                                       min_periods,
+                                                       aggr,
+                                                       stream,
+                                                       mr)
+                                    : range_window_ASC(input,
+                                                       orderby_column,
+                                                       group_offsets,
+                                                       group_labels,
+                                                       preceding_value,
+                                                       preceding_window.is_unbounded(),
+                                                       following_value,
+                                                       following_window.is_unbounded(),
+                                                       min_periods,
+                                                       aggr,
+                                                       stream,
+                                                       mr);
+  } else {
+    return group_offsets.is_empty() ? range_window_DESC(input,
+                                                        orderby_column,
+                                                        preceding_value,
+                                                        preceding_window.is_unbounded(),
+                                                        following_value,
+                                                        following_window.is_unbounded(),
+                                                        min_periods,
+                                                        aggr,
+                                                        stream,
+                                                        mr)
+                                    : range_window_DESC(input,
+                                                        orderby_column,
+                                                        group_offsets,
+                                                        group_labels,
+                                                        preceding_value,
+                                                        preceding_window.is_unbounded(),
+                                                        following_value,
+                                                        following_window.is_unbounded(),
+                                                        min_periods,
+                                                        aggr,
+                                                        stream,
+                                                        mr);
+  }
+}
+
+struct dispatch_grouped_range_rolling_window {
+  template <typename OrderByColumnType, typename... Args>
+  std::enable_if_t<!detail::is_supported_order_by_column_type<OrderByColumnType>(),
+                   std::unique_ptr<column>>
+  operator()(Args&&...) const
+  {
+    CUDF_FAIL("Unsupported OrderBy column type.");
+  }
+
+  template <typename OrderByColumnType>
+  std::enable_if_t<detail::is_supported_order_by_column_type<OrderByColumnType>(),
+                   std::unique_ptr<column>>
+  operator()(column_view const& input,
+             column_view const& orderby_column,
+             cudf::order const& order_of_orderby_column,
+             rmm::device_uvector<cudf::size_type> const& group_offsets,
+             rmm::device_uvector<cudf::size_type> const& group_labels,
+             range_window_bounds const& preceding_window,
+             range_window_bounds const& following_window,
+             size_type min_periods,
+             rolling_aggregation const& aggr,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr) const
+  {
+    return grouped_range_rolling_window_impl<OrderByColumnType>(input,
+                                                                orderby_column,
+                                                                order_of_orderby_column,
+                                                                group_offsets,
+                                                                group_labels,
+                                                                preceding_window,
+                                                                following_window,
+                                                                min_periods,
+                                                                aggr,
+                                                                stream,
+                                                                mr);
+  }
+};
+
+/**
+ * @brief Functor to convert from size_type (number of days) to appropriate duration type.
+ */
+struct to_duration_bounds {
+  template <typename OrderBy, std::enable_if_t<cudf::is_timestamp<OrderBy>(), void>* = nullptr>
+  range_window_bounds operator()(size_type num_days) const
+  {
+    using DurationT = typename OrderBy::duration;
+    return range_window_bounds::get(duration_scalar<DurationT>{duration_D{num_days}, true});
+  }
+
+  template <typename OrderBy, std::enable_if_t<!cudf::is_timestamp<OrderBy>(), void>* = nullptr>
+  range_window_bounds operator()(size_type) const
+  {
+    CUDF_FAIL("Expected timestamp orderby column.");
+  }
+};
+
+/**
+ * @brief Get duration type corresponding to specified timestamp type.
+ */
+data_type get_duration_type_for(cudf::data_type timestamp_type)
+{
+  switch (timestamp_type.id()) {
+    case type_id::TIMESTAMP_DAYS: return data_type{type_id::DURATION_DAYS};
+    case type_id::TIMESTAMP_SECONDS: return data_type{type_id::DURATION_SECONDS};
+    case type_id::TIMESTAMP_MILLISECONDS: return data_type{type_id::DURATION_MILLISECONDS};
+    case type_id::TIMESTAMP_MICROSECONDS: return data_type{type_id::DURATION_MICROSECONDS};
+    case type_id::TIMESTAMP_NANOSECONDS: return data_type{type_id::DURATION_NANOSECONDS};
+    default: CUDF_FAIL("Expected timestamp orderby column.");
+  }
+}
+
+/**
+ * @brief Bridge function to convert from size_type (number of days) to appropriate duration type.
+ *
+ * This helps adapt the old `grouped_time_range_rolling_window()` functions that took a "number of
+ * days" to the new `range_window_bounds` interface.
+ *
+ * @param num_days Window bounds specified in number of days in `size_type`
+ * @param timestamp_type Data-type of the orderby column to which the `num_days` is to be adapted.
+ * @return range_window_bounds A `range_window_bounds` to be used with the new API.
+ */
+range_window_bounds to_range_bounds(cudf::size_type num_days, cudf::data_type timestamp_type)
+{
+  return cudf::type_dispatcher(timestamp_type, to_duration_bounds{}, num_days);
+}
+
+/**
+ * @brief Bridge function to convert from `window_bounds` (in days) to appropriate duration type.
+ *
+ * This helps adapt the old `grouped_time_range_rolling_window()` functions that took a
+ * `window_bounds` to the new `range_window_bounds` interface.
+ *
+ * @param days_bounds The static window-width `window_bounds` object
+ * @param timestamp_type Data-type of the orderby column to which the `num_days` is to be adapted.
+ * @return range_window_bounds A `range_window_bounds` to be used with the new API.
+ */
+range_window_bounds to_range_bounds(cudf::window_bounds const& days_bounds,
+                                    cudf::data_type timestamp_type)
+{
+  return days_bounds.is_unbounded()
+           ? range_window_bounds::unbounded(get_duration_type_for(timestamp_type))
+           : cudf::type_dispatcher(timestamp_type, to_duration_bounds{}, days_bounds.value());
+}
+
+}  // namespace
+
+namespace detail {
+
+/**
+ * @copydoc  std::unique_ptr<column> grouped_range_rolling_window(
+ *               table_view const& group_keys,
+ *               column_view const& orderby_column,
+ *               cudf::order const& order,
+ *               column_view const& input,
+ *               range_window_bounds const& preceding,
+ *               range_window_bounds const& following,
+ *               size_type min_periods,
+ *               rolling_aggregation const& aggr,
+ *               rmm::mr::device_memory_resource* mr );
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> grouped_range_rolling_window(table_view const& group_keys,
+                                                     column_view const& order_by_column,
+                                                     cudf::order const& order,
+                                                     column_view const& input,
+                                                     range_window_bounds const& preceding,
+                                                     range_window_bounds const& following,
+                                                     size_type min_periods,
+                                                     rolling_aggregation const& aggr,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  if (input.is_empty()) { return cudf::detail::empty_output_for_rolling_aggregation(input, aggr); }
+
+  CUDF_EXPECTS((group_keys.num_columns() == 0 || group_keys.num_rows() == input.size()),
+               "Size mismatch between group_keys and input vector.");
+
+  CUDF_EXPECTS((min_periods > 0), "min_periods must be positive");
+
+  // Detect and bypass fully UNBOUNDED windows.
+  if (can_optimize_unbounded_window(
+        preceding.is_unbounded(), following.is_unbounded(), min_periods, aggr)) {
+    return optimized_unbounded_window(group_keys, input, aggr, stream, mr);
+  }
+
+  using sort_groupby_helper = cudf::groupby::detail::sort::sort_groupby_helper;
+  using index_vector        = sort_groupby_helper::index_vector;
+
+  index_vector group_offsets(0, stream), group_labels(0, stream);
+  if (group_keys.num_columns() > 0) {
+    sort_groupby_helper helper{group_keys, cudf::null_policy::INCLUDE, cudf::sorted::YES, {}};
+    group_offsets = index_vector(helper.group_offsets(stream), stream);
+    group_labels  = index_vector(helper.group_labels(stream), stream);
+  }
+
+  return cudf::type_dispatcher(order_by_column.type(),
+                               dispatch_grouped_range_rolling_window{},
+                               input,
+                               order_by_column,
+                               order,
+                               group_offsets,
+                               group_labels,
+                               preceding,
+                               following,
+                               min_periods,
+                               aggr,
+                               stream,
+                               mr);
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc std::unique_ptr<column> grouped_time_range_rolling_window(
+ *              table_view const& group_keys,
+ *              column_view const& timestamp_column,
+ *              cudf::order const& timestamp_order,
+ *              column_view const& input,
+ *              size_type preceding_window_in_days,
+ *              size_type following_window_in_days,
+ *              size_type min_periods,
+ *              rolling_aggregation const& aggr,
+ *              rmm::mr::device_memory_resource* mr);
+ */
+std::unique_ptr<column> grouped_time_range_rolling_window(table_view const& group_keys,
+                                                          column_view const& timestamp_column,
+                                                          cudf::order const& timestamp_order,
+                                                          column_view const& input,
+                                                          size_type preceding_window_in_days,
+                                                          size_type following_window_in_days,
+                                                          size_type min_periods,
+                                                          rolling_aggregation const& aggr,
+                                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto preceding = to_range_bounds(preceding_window_in_days, timestamp_column.type());
+  auto following = to_range_bounds(following_window_in_days, timestamp_column.type());
+
+  return detail::grouped_range_rolling_window(group_keys,
+                                              timestamp_column,
+                                              timestamp_order,
+                                              input,
+                                              preceding,
+                                              following,
+                                              min_periods,
+                                              aggr,
+                                              cudf::get_default_stream(),
+                                              mr);
+}
+
+/**
+ * @copydoc grouped_time_range_rolling_window(
+ *            table_view const& group_keys,
+ *            column_view const& timestamp_column,
+ *            cudf::order const& timestamp_order,
+ *            column_view const& input,
+ *            window_bounds preceding_window_in_days,
+ *            window_bounds following_window_in_days,
+ *            size_type min_periods,
+ *            rolling_aggregation const& aggr,
+ *            rmm::mr::device_memory_resource* mr);
+ */
+std::unique_ptr<column> grouped_time_range_rolling_window(table_view const& group_keys,
+                                                          column_view const& timestamp_column,
+                                                          cudf::order const& timestamp_order,
+                                                          column_view const& input,
+                                                          window_bounds preceding_window_in_days,
+                                                          window_bounds following_window_in_days,
+                                                          size_type min_periods,
+                                                          rolling_aggregation const& aggr,
+                                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  range_window_bounds preceding =
+    to_range_bounds(preceding_window_in_days, timestamp_column.type());
+  range_window_bounds following =
+    to_range_bounds(following_window_in_days, timestamp_column.type());
+
+  return detail::grouped_range_rolling_window(group_keys,
+                                              timestamp_column,
+                                              timestamp_order,
+                                              input,
+                                              preceding,
+                                              following,
+                                              min_periods,
+                                              aggr,
+                                              cudf::get_default_stream(),
+                                              mr);
+}
+
+/**
+ * @copydoc grouped_range_rolling_window(
+ *               table_view const& group_keys,
+ *               column_view const& orderby_column,
+ *               cudf::order const& order,
+ *               column_view const& input,
+ *               range_window_bounds const& preceding,
+ *               range_window_bounds const& following,
+ *               size_type min_periods,
+ *               rolling_aggregation const& aggr,
+ *               rmm::mr::device_memory_resource* mr );
+ */
+std::unique_ptr<column> grouped_range_rolling_window(table_view const& group_keys,
+                                                     column_view const& timestamp_column,
+                                                     cudf::order const& timestamp_order,
+                                                     column_view const& input,
+                                                     range_window_bounds const& preceding,
+                                                     range_window_bounds const& following,
+                                                     size_type min_periods,
+                                                     rolling_aggregation const& aggr,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::grouped_range_rolling_window(group_keys,
+                                              timestamp_column,
+                                              timestamp_order,
+                                              input,
+                                              preceding,
+                                              following,
+                                              min_periods,
+                                              aggr,
+                                              cudf::get_default_stream(),
+                                              mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/rolling/jit/kernel.cu b/cpp/src/rolling/jit/kernel.cu
new file mode 100644
index 0000000..06b224c
--- /dev/null
+++ b/cpp/src/rolling/jit/kernel.cu
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <rolling/detail/rolling_jit.hpp>
+#include <rolling/jit/operation.hpp>
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+
+namespace cudf {
+namespace rolling {
+namespace jit {
+
+template <typename WindowType>
+cudf::size_type __device__ get_window(WindowType window, cudf::thread_index_type index)
+{
+  return window[index];
+}
+
+template <>
+cudf::size_type __device__ get_window(cudf::size_type window, cudf::thread_index_type index)
+{
+  return window;
+}
+
+template <typename InType,
+          typename OutType,
+          class agg_op,
+          typename PrecedingWindowType,
+          typename FollowingWindowType>
+__global__ void gpu_rolling_new(cudf::size_type nrows,
+                                InType const* const __restrict__ in_col,
+                                cudf::bitmask_type const* const __restrict__ in_col_valid,
+                                OutType* __restrict__ out_col,
+                                cudf::bitmask_type* __restrict__ out_col_valid,
+                                cudf::size_type* __restrict__ output_valid_count,
+                                PrecedingWindowType preceding_window_begin,
+                                FollowingWindowType following_window_begin,
+                                cudf::size_type min_periods)
+{
+  cudf::thread_index_type i            = blockIdx.x * blockDim.x + threadIdx.x;
+  cudf::thread_index_type const stride = blockDim.x * gridDim.x;
+
+  cudf::size_type warp_valid_count{0};
+
+  auto active_threads = __ballot_sync(0xffff'ffffu, i < nrows);
+  while (i < nrows) {
+    int64_t const preceding_window = get_window(preceding_window_begin, i);
+    int64_t const following_window = get_window(following_window_begin, i);
+
+    // compute bounds
+    auto const start = static_cast<cudf::size_type>(
+      min(static_cast<int64_t>(nrows), max(int64_t{0}, i - preceding_window + 1)));
+    auto const end = static_cast<cudf::size_type>(
+      min(static_cast<int64_t>(nrows), max(int64_t{0}, i + following_window + 1)));
+    auto const start_index = min(start, end);
+    auto const end_index   = max(start, end);
+
+    // aggregate
+    // TODO: We should explore using shared memory to avoid redundant loads.
+    //       This might require separating the kernel into a special version
+    //       for dynamic and static sizes.
+    cudf::size_type count = end_index - start_index;
+    OutType val           = agg_op::template operate<OutType, InType>(in_col, start_index, count);
+
+    // check if we have enough input samples
+    bool const output_is_valid = (count >= min_periods);
+
+    // set the mask
+    unsigned int const result_mask = __ballot_sync(active_threads, output_is_valid);
+
+    // store the output value, one per thread
+    if (output_is_valid) { out_col[i] = val; }
+
+    // only one thread writes the mask
+    if (0 == cudf::intra_word_index(i)) {
+      out_col_valid[cudf::word_index(i)] = result_mask;
+      warp_valid_count += __popc(result_mask);
+    }
+
+    // process next element
+    i += stride;
+    active_threads = __ballot_sync(active_threads, i < nrows);
+  }
+
+  // TODO: likely faster to do a single_lane_block_reduce and a single
+  // atomic per block but that requires jitifying single_lane_block_reduce...
+  if (0 == cudf::intra_word_index(threadIdx.x)) { atomicAdd(output_valid_count, warp_valid_count); }
+}
+
+}  // namespace jit
+}  // namespace rolling
+}  // namespace cudf
diff --git a/cpp/src/rolling/jit/operation-udf.hpp b/cpp/src/rolling/jit/operation-udf.hpp
new file mode 100644
index 0000000..eaab211
--- /dev/null
+++ b/cpp/src/rolling/jit/operation-udf.hpp
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// This file serves as a placeholder for user defined functions, so jitify can choose to override it
+// at runtime.
diff --git a/cpp/src/rolling/jit/operation.hpp b/cpp/src/rolling/jit/operation.hpp
new file mode 100644
index 0000000..22943f0
--- /dev/null
+++ b/cpp/src/rolling/jit/operation.hpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/types.hpp>
+
+#include <rolling/jit/operation-udf.hpp>
+
+#pragma once
+
+struct rolling_udf_ptx {
+  template <typename OutType, typename InType>
+  static OutType operate(InType const* in_col, cudf::size_type start, cudf::size_type count)
+  {
+    OutType ret;
+    rolling_udf(&ret, 0, 0, 0, 0, &in_col[start], count, sizeof(InType));
+    return ret;
+  }
+};
+
+struct rolling_udf_cuda {
+  template <typename OutType, typename InType>
+  static OutType operate(InType const* in_col, cudf::size_type start, cudf::size_type count)
+  {
+    OutType ret;
+    rolling_udf(&ret, in_col, start, count);
+    return ret;
+  }
+};
diff --git a/cpp/src/rolling/range_window_bounds.cpp b/cpp/src/rolling/range_window_bounds.cpp
new file mode 100644
index 0000000..a136f15
--- /dev/null
+++ b/cpp/src/rolling/range_window_bounds.cpp
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/range_window_bounds.hpp"
+#include <cudf/rolling/range_window_bounds.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/wrappers/durations.hpp>
+
+namespace cudf {
+namespace {
+
+/**
+ * @brief Factory to (copy) construct scalars.
+ *
+ * Derived types of scalars are cloned, to be adopted by `range_window_bounds`.
+ * This makes it possible to copy construct and copy assign `range_window_bounds` objects.
+ */
+struct range_scalar_constructor {
+  template <typename T, CUDF_ENABLE_IF(not detail::is_supported_range_type<T>())>
+  std::unique_ptr<scalar> operator()(scalar const& range_scalar_) const
+  {
+    CUDF_FAIL(
+      "Unsupported range type. "
+      "Only durations, fixed-point, and non-boolean numeric range types are allowed.");
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_duration<T>())>
+  std::unique_ptr<scalar> operator()(scalar const& range_scalar_) const
+  {
+    return std::make_unique<duration_scalar<T>>(
+      static_cast<duration_scalar<T> const&>(range_scalar_));
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_numeric<T>() && not cudf::is_boolean<T>())>
+  std::unique_ptr<scalar> operator()(scalar const& range_scalar_) const
+  {
+    return std::make_unique<numeric_scalar<T>>(
+      static_cast<numeric_scalar<T> const&>(range_scalar_));
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_fixed_point<T>())>
+  std::unique_ptr<scalar> operator()(scalar const& range_scalar_) const
+  {
+    return std::make_unique<fixed_point_scalar<T>>(
+      static_cast<fixed_point_scalar<T> const&>(range_scalar_));
+  }
+};
+}  // namespace
+
+range_window_bounds::range_window_bounds(extent_type extent_, std::unique_ptr<scalar> range_scalar_)
+  : _extent{extent_}, _range_scalar{std::move(range_scalar_)}
+{
+  CUDF_EXPECTS(_range_scalar.get(), "Range window scalar cannot be null.");
+  CUDF_EXPECTS(_extent == extent_type::UNBOUNDED || _extent == extent_type::CURRENT_ROW ||
+                 _range_scalar->is_valid(),
+               "Bounded Range window scalar must be valid.");
+}
+
+range_window_bounds range_window_bounds::unbounded(data_type type)
+{
+  return {extent_type::UNBOUNDED, make_default_constructed_scalar(type)};
+}
+
+range_window_bounds range_window_bounds::current_row(data_type type)
+{
+  return {extent_type::CURRENT_ROW, make_default_constructed_scalar(type)};
+}
+
+range_window_bounds range_window_bounds::get(scalar const& boundary)
+{
+  return {extent_type::BOUNDED,
+          cudf::type_dispatcher(boundary.type(), range_scalar_constructor{}, boundary)};
+}
+
+}  // namespace cudf
diff --git a/cpp/src/rolling/rolling.cu b/cpp/src/rolling/rolling.cu
new file mode 100644
index 0000000..5c78cc4
--- /dev/null
+++ b/cpp/src/rolling/rolling.cu
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "detail/rolling.cuh"
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+namespace cudf {
+
+// Applies a fixed-size rolling window function to the values in a column, with default output
+// specified
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& default_outputs,
+                                       size_type preceding_window,
+                                       size_type following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rolling_window(input,
+                                default_outputs,
+                                preceding_window,
+                                following_window,
+                                min_periods,
+                                agg,
+                                cudf::get_default_stream(),
+                                mr);
+}
+
+// Applies a fixed-size rolling window function to the values in a column, without default specified
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       size_type preceding_window,
+                                       size_type following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  auto defaults =
+    cudf::is_dictionary(input.type()) ? dictionary_column_view(input).indices() : input;
+  return detail::rolling_window(input,
+                                empty_like(defaults)->view(),
+                                preceding_window,
+                                following_window,
+                                min_periods,
+                                agg,
+                                cudf::get_default_stream(),
+                                mr);
+}
+
+// Applies a variable-size rolling window function to the values in a column.
+std::unique_ptr<column> rolling_window(column_view const& input,
+                                       column_view const& preceding_window,
+                                       column_view const& following_window,
+                                       size_type min_periods,
+                                       rolling_aggregation const& agg,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rolling_window(
+    input, preceding_window, following_window, min_periods, agg, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/round/round.cu b/cpp/src/round/round.cu
new file mode 100644
index 0000000..41cce57
--- /dev/null
+++ b/cpp/src/round/round.cu
@@ -0,0 +1,357 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy_range.cuh>
+#include <cudf/detail/fill.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/round.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/fixed_point/temporary.hpp>
+#include <cudf/round.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+#include <thrust/uninitialized_fill.h>
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+namespace {  // anonymous
+
+inline float __device__ generic_round(float f) { return roundf(f); }
+inline double __device__ generic_round(double d) { return ::round(d); }
+
+inline float __device__ generic_round_half_even(float f) { return rintf(f); }
+inline double __device__ generic_round_half_even(double d) { return rint(d); }
+
+inline float __device__ generic_modf(float a, float* b) { return modff(a, b); }
+inline double __device__ generic_modf(double a, double* b) { return modf(a, b); }
+
+template <typename T, std::enable_if_t<cuda::std::is_signed_v<T>>* = nullptr>
+T __device__ generic_abs(T value)
+{
+  return numeric::detail::abs(value);
+}
+
+template <typename T, std::enable_if_t<not cuda::std::is_signed_v<T>>* = nullptr>
+T __device__ generic_abs(T value)
+{
+  return value;
+}
+
+template <typename T, std::enable_if_t<cuda::std::is_signed_v<T>>* = nullptr>
+int16_t __device__ generic_sign(T value)
+{
+  return value < 0 ? -1 : 1;
+}
+
+// this is needed to suppress warning: pointless comparison of unsigned integer with zero
+template <typename T, std::enable_if_t<not cuda::std::is_signed_v<T>>* = nullptr>
+int16_t __device__ generic_sign(T)
+{
+  return 1;
+}
+
+template <typename T>
+constexpr inline auto is_supported_round_type()
+{
+  return (cudf::is_numeric<T>() && not std::is_same_v<T, bool>) || cudf::is_fixed_point<T>();
+}
+
+template <typename T>
+struct half_up_zero {
+  T n;  // unused in the decimal_places = 0 case
+  template <typename U = T, std::enable_if_t<cudf::is_floating_point<U>()>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    return generic_round(e);
+  }
+
+  template <typename U = T, std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  __device__ U operator()(U)
+  {
+    assert(false);  // Should never get here. Just for compilation
+    return U{};
+  }
+};
+
+template <typename T>
+struct half_up_positive {
+  T n;
+  template <typename U = T, std::enable_if_t<cudf::is_floating_point<U>()>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    T integer_part;
+    T const fractional_part = generic_modf(e, &integer_part);
+    return integer_part + generic_round(fractional_part * n) / n;
+  }
+
+  template <typename U = T, std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  __device__ U operator()(U)
+  {
+    assert(false);  // Should never get here. Just for compilation
+    return U{};
+  }
+};
+
+template <typename T>
+struct half_up_negative {
+  T n;
+  template <typename U = T, std::enable_if_t<cudf::is_floating_point<U>()>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    return generic_round(e / n) * n;
+  }
+
+  template <typename U = T, std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    auto const down = (e / n) * n;  // result from rounding down
+    return down + generic_sign(e) * (generic_abs(e - down) >= n / 2 ? n : 0);
+  }
+};
+
+template <typename T>
+struct half_even_zero {
+  T n;  // unused in the decimal_places = 0 case
+  template <typename U = T, std::enable_if_t<cudf::is_floating_point<U>()>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    return generic_round_half_even(e);
+  }
+
+  template <typename U = T, std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  __device__ U operator()(U)
+  {
+    assert(false);  // Should never get here. Just for compilation
+    return U{};
+  }
+};
+
+template <typename T>
+struct half_even_positive {
+  T n;
+  template <typename U = T, std::enable_if_t<cudf::is_floating_point<U>()>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    T integer_part;
+    T const fractional_part = generic_modf(e, &integer_part);
+    return integer_part + generic_round_half_even(fractional_part * n) / n;
+  }
+
+  template <typename U = T, std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  __device__ U operator()(U)
+  {
+    assert(false);  // Should never get here. Just for compilation
+    return U{};
+  }
+};
+
+template <typename T>
+struct half_even_negative {
+  T n;
+  template <typename U = T, std::enable_if_t<cudf::is_floating_point<U>()>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    return generic_round_half_even(e / n) * n;
+  }
+
+  template <typename U = T, std::enable_if_t<cuda::std::is_integral_v<U>>* = nullptr>
+  __device__ U operator()(U e)
+  {
+    auto const down_over_n = e / n;            // use this to determine HALF_EVEN case
+    auto const down        = down_over_n * n;  // result from rounding down
+    auto const diff        = generic_abs(e - down);
+    auto const adjustment =
+      (diff > n / 2) or (diff == n / 2 && generic_abs(down_over_n) % 2 == 1) ? n : 0;
+    return down + generic_sign(e) * adjustment;
+  }
+};
+
+template <typename T>
+struct half_up_fixed_point {
+  T n;
+  __device__ T operator()(T e) { return half_up_negative<T>{n}(e) / n; }
+};
+
+template <typename T>
+struct half_even_fixed_point {
+  T n;
+  __device__ T operator()(T e) { return half_even_negative<T>{n}(e) / n; }
+};
+
+template <typename T,
+          template <typename>
+          typename RoundFunctor,
+          std::enable_if_t<not cudf::is_fixed_point<T>()>* = nullptr>
+std::unique_ptr<column> round_with(column_view const& input,
+                                   int32_t decimal_places,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  using Functor = RoundFunctor<T>;
+
+  if (decimal_places >= 0 && std::is_integral_v<T>)
+    return std::make_unique<cudf::column>(input, stream, mr);
+
+  auto result = cudf::make_fixed_width_column(
+    input.type(), input.size(), copy_bitmask(input, stream, mr), input.null_count(), stream, mr);
+
+  auto out_view = result->mutable_view();
+  T const n     = std::pow(10, std::abs(decimal_places));
+
+  thrust::transform(
+    rmm::exec_policy(stream), input.begin<T>(), input.end<T>(), out_view.begin<T>(), Functor{n});
+
+  result->set_null_count(input.null_count());
+
+  return result;
+}
+
+template <typename T,
+          template <typename>
+          typename RoundFunctor,
+          std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+std::unique_ptr<column> round_with(column_view const& input,
+                                   int32_t decimal_places,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  using namespace numeric;
+  using Type                   = device_storage_type_t<T>;
+  using FixedPointRoundFunctor = RoundFunctor<Type>;
+
+  if (input.type().scale() == -decimal_places)
+    return std::make_unique<cudf::column>(input, stream, mr);
+
+  auto const result_type = data_type{input.type().id(), scale_type{-decimal_places}};
+
+  // if rounding to more precision than fixed_point is capable of, just need to rescale
+  // note: decimal_places has the opposite sign of numeric::scale_type (therefore have to negate)
+  if (input.type().scale() > -decimal_places)
+    return cudf::detail::cast(input, result_type, stream, mr);
+
+  auto result = cudf::make_fixed_width_column(
+    result_type, input.size(), copy_bitmask(input, stream, mr), input.null_count(), stream, mr);
+
+  auto out_view = result->mutable_view();
+
+  auto const scale_movement = -decimal_places - input.type().scale();
+  // If scale_movement is larger than max precision of current type, the pow operation will
+  // overflow. Under this circumstance, we can simply output a zero column because no digits can
+  // survive such a large scale movement.
+  if (scale_movement > cuda::std::numeric_limits<Type>::digits10) {
+    thrust::uninitialized_fill(rmm::exec_policy(stream),
+                               out_view.template begin<Type>(),
+                               out_view.template end<Type>(),
+                               static_cast<Type>(0));
+  } else {
+    Type n = 10;
+    for (int i = 1; i < scale_movement; ++i) {
+      n *= 10;
+    }
+    thrust::transform(rmm::exec_policy(stream),
+                      input.begin<Type>(),
+                      input.end<Type>(),
+                      out_view.begin<Type>(),
+                      FixedPointRoundFunctor{n});
+  }
+
+  result->set_null_count(input.null_count());
+
+  return result;
+}
+
+struct round_type_dispatcher {
+  template <typename T, typename... Args>
+  std::enable_if_t<not is_supported_round_type<T>(), std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Type not support for cudf::round");
+  }
+
+  template <typename T>
+  std::enable_if_t<is_supported_round_type<T>(), std::unique_ptr<column>> operator()(
+    column_view const& input,
+    int32_t decimal_places,
+    cudf::rounding_method method,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    // clang-format off
+    switch (method) {
+      case cudf::rounding_method::HALF_UP:
+        if      (is_fixed_point<T>()) return round_with<T, half_up_fixed_point>(input, decimal_places, stream, mr);
+        else if (decimal_places == 0) return round_with<T, half_up_zero       >(input, decimal_places, stream, mr);
+        else if (decimal_places >  0) return round_with<T, half_up_positive   >(input, decimal_places, stream, mr);
+        else                          return round_with<T, half_up_negative   >(input, decimal_places, stream, mr);
+      case cudf::rounding_method::HALF_EVEN:
+        if      (is_fixed_point<T>()) return round_with<T, half_even_fixed_point>(input, decimal_places, stream, mr);
+        else if (decimal_places == 0) return round_with<T, half_even_zero       >(input, decimal_places, stream, mr);
+        else if (decimal_places >  0) return round_with<T, half_even_positive   >(input, decimal_places, stream, mr);
+        else                          return round_with<T, half_even_negative   >(input, decimal_places, stream, mr);
+      default: CUDF_FAIL("Undefined rounding method");
+    }
+    // clang-format on
+  }
+};
+
+}  // anonymous namespace
+
+std::unique_ptr<column> round(column_view const& input,
+                              int32_t decimal_places,
+                              cudf::rounding_method method,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(cudf::is_numeric(input.type()) || cudf::is_fixed_point(input.type()),
+               "Only integral/floating point/fixed point currently supported.");
+
+  if (input.is_empty()) {
+    if (is_fixed_point(input.type())) {
+      auto const type = data_type{input.type().id(), numeric::scale_type{-decimal_places}};
+      return make_empty_column(type);
+    }
+    return empty_like(input);
+  }
+
+  return type_dispatcher(
+    input.type(), round_type_dispatcher{}, input, decimal_places, method, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> round(column_view const& input,
+                              int32_t decimal_places,
+                              rounding_method method,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::round(input, decimal_places, method, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/scalar/scalar.cpp b/cpp/src/scalar/scalar.cpp
new file mode 100644
index 0000000..6fb05e6
--- /dev/null
+++ b/cpp/src/scalar/scalar.cpp
@@ -0,0 +1,600 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <string>
+
+namespace cudf {
+
+scalar::scalar(data_type type,
+               bool is_valid,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr)
+  : _type(type), _is_valid(is_valid, stream, mr)
+{
+}
+
+scalar::scalar(scalar const& other,
+               rmm::cuda_stream_view stream,
+               rmm::mr::device_memory_resource* mr)
+  : _type(other.type()), _is_valid(other._is_valid, stream, mr)
+{
+}
+
+data_type scalar::type() const noexcept { return _type; }
+
+void scalar::set_valid_async(bool is_valid, rmm::cuda_stream_view stream)
+{
+  _is_valid.set_value_async(is_valid, stream);
+}
+
+bool scalar::is_valid(rmm::cuda_stream_view stream) const { return _is_valid.value(stream); }
+
+bool* scalar::validity_data() { return _is_valid.data(); }
+
+bool const* scalar::validity_data() const { return _is_valid.data(); }
+
+string_scalar::string_scalar(std::string const& string,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::STRING), is_valid, stream, mr),
+    _data(string.data(), string.size(), stream, mr)
+{
+  CUDF_EXPECTS(
+    string.size() <= static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max()),
+    "Data exceeds the string size limit",
+    std::overflow_error);
+}
+
+string_scalar::string_scalar(string_scalar const& other,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(other, stream, mr), _data(other._data, stream, mr)
+{
+}
+
+string_scalar::string_scalar(rmm::device_scalar<value_type>& data,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : string_scalar(data.value(stream), is_valid, stream, mr)
+{
+}
+
+string_scalar::string_scalar(value_type const& source,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::STRING), is_valid, stream, mr),
+    _data(source.data(), source.size_bytes(), stream, mr)
+{
+}
+
+string_scalar::string_scalar(rmm::device_buffer&& data,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::STRING), is_valid, stream, mr), _data(std::move(data))
+{
+}
+
+string_scalar::value_type string_scalar::value(rmm::cuda_stream_view stream) const
+{
+  return value_type{data(), size()};
+}
+
+size_type string_scalar::size() const { return _data.size(); }
+
+char const* string_scalar::data() const { return static_cast<char const*>(_data.data()); }
+
+string_scalar::operator std::string() const { return this->to_string(cudf::get_default_stream()); }
+
+std::string string_scalar::to_string(rmm::cuda_stream_view stream) const
+{
+  std::string result;
+  result.resize(_data.size());
+  CUDF_CUDA_TRY(
+    cudaMemcpyAsync(&result[0], _data.data(), _data.size(), cudaMemcpyDefault, stream.value()));
+  stream.synchronize();
+  return result;
+}
+
+template <typename T>
+fixed_point_scalar<T>::fixed_point_scalar(rep_type value,
+                                          numeric::scale_type scale,
+                                          bool is_valid,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar{data_type{type_to_id<T>(), static_cast<int32_t>(scale)}, is_valid, stream, mr},
+    _data{value, stream, mr}
+{
+}
+
+template <typename T>
+fixed_point_scalar<T>::fixed_point_scalar(rep_type value,
+                                          bool is_valid,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar{data_type{type_to_id<T>(), 0}, is_valid, stream, mr}, _data{value, stream, mr}
+{
+}
+
+template <typename T>
+fixed_point_scalar<T>::fixed_point_scalar(T value,
+                                          bool is_valid,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar{data_type{type_to_id<T>(), value.scale()}, is_valid, stream, mr},
+    _data{value.value(), stream, mr}
+{
+}
+
+template <typename T>
+fixed_point_scalar<T>::fixed_point_scalar(rmm::device_scalar<rep_type>&& data,
+                                          numeric::scale_type scale,
+                                          bool is_valid,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar{data_type{type_to_id<T>(), scale}, is_valid, stream, mr}, _data{std::move(data)}
+{
+}
+
+template <typename T>
+fixed_point_scalar<T>::fixed_point_scalar(fixed_point_scalar<T> const& other,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar{other, stream, mr}, _data(other._data, stream, mr)
+{
+}
+
+template <typename T>
+typename fixed_point_scalar<T>::rep_type fixed_point_scalar<T>::value(
+  rmm::cuda_stream_view stream) const
+{
+  return _data.value(stream);
+}
+
+template <typename T>
+T fixed_point_scalar<T>::fixed_point_value(rmm::cuda_stream_view stream) const
+{
+  return value_type{
+    numeric::scaled_integer<rep_type>{_data.value(stream), numeric::scale_type{type().scale()}}};
+}
+
+template <typename T>
+fixed_point_scalar<T>::operator value_type() const
+{
+  return this->fixed_point_value(cudf::get_default_stream());
+}
+
+template <typename T>
+typename fixed_point_scalar<T>::rep_type* fixed_point_scalar<T>::data()
+{
+  return _data.data();
+}
+
+template <typename T>
+typename fixed_point_scalar<T>::rep_type const* fixed_point_scalar<T>::data() const
+{
+  return _data.data();
+}
+
+/**
+ * @brief These define the valid fixed-point scalar types.
+ *
+ * See `is_fixed_point` in @see cudf/utilities/traits.hpp
+ *
+ * Adding a new supported type only requires adding the appropriate line here
+ * and does not require updating the scalar.hpp file.
+ */
+template class fixed_point_scalar<numeric::decimal32>;
+template class fixed_point_scalar<numeric::decimal64>;
+template class fixed_point_scalar<numeric::decimal128>;
+
+namespace detail {
+
+template <typename T>
+fixed_width_scalar<T>::fixed_width_scalar(T value,
+                                          bool is_valid,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_to_id<T>()), is_valid, stream, mr), _data(value, stream, mr)
+{
+}
+
+template <typename T>
+fixed_width_scalar<T>::fixed_width_scalar(rmm::device_scalar<T>&& data,
+                                          bool is_valid,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_to_id<T>()), is_valid, stream, mr), _data{std::move(data)}
+{
+}
+
+template <typename T>
+fixed_width_scalar<T>::fixed_width_scalar(fixed_width_scalar<T> const& other,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+  : scalar{other, stream, mr}, _data(other._data, stream, mr)
+{
+}
+
+template <typename T>
+void fixed_width_scalar<T>::set_value(T value, rmm::cuda_stream_view stream)
+{
+  _data.set_value_async(value, stream);
+  this->set_valid_async(true, stream);
+}
+
+template <typename T>
+T fixed_width_scalar<T>::value(rmm::cuda_stream_view stream) const
+{
+  return _data.value(stream);
+}
+
+template <typename T>
+T* fixed_width_scalar<T>::data()
+{
+  return _data.data();
+}
+
+template <typename T>
+T const* fixed_width_scalar<T>::data() const
+{
+  return _data.data();
+}
+
+template <typename T>
+fixed_width_scalar<T>::operator value_type() const
+{
+  return this->value(cudf::get_default_stream());
+}
+
+/**
+ * @brief These define the valid fixed-width scalar types.
+ *
+ * See `is_fixed_width` in @see cudf/utilities/traits.hpp
+ *
+ * Adding a new supported type only requires adding the appropriate line here
+ * and does not require updating the scalar.hpp file.
+ */
+template class fixed_width_scalar<bool>;
+template class fixed_width_scalar<int8_t>;
+template class fixed_width_scalar<int16_t>;
+template class fixed_width_scalar<int32_t>;
+template class fixed_width_scalar<int64_t>;
+template class fixed_width_scalar<__int128_t>;
+template class fixed_width_scalar<uint8_t>;
+template class fixed_width_scalar<uint16_t>;
+template class fixed_width_scalar<uint32_t>;
+template class fixed_width_scalar<uint64_t>;
+template class fixed_width_scalar<float>;
+template class fixed_width_scalar<double>;
+template class fixed_width_scalar<timestamp_D>;
+template class fixed_width_scalar<timestamp_s>;
+template class fixed_width_scalar<timestamp_ms>;
+template class fixed_width_scalar<timestamp_us>;
+template class fixed_width_scalar<timestamp_ns>;
+template class fixed_width_scalar<duration_D>;
+template class fixed_width_scalar<duration_s>;
+template class fixed_width_scalar<duration_ms>;
+template class fixed_width_scalar<duration_us>;
+template class fixed_width_scalar<duration_ns>;
+
+}  // namespace detail
+
+template <typename T>
+numeric_scalar<T>::numeric_scalar(T value,
+                                  bool is_valid,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+  : detail::fixed_width_scalar<T>(value, is_valid, stream, mr)
+{
+}
+
+template <typename T>
+numeric_scalar<T>::numeric_scalar(rmm::device_scalar<T>&& data,
+                                  bool is_valid,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+  : detail::fixed_width_scalar<T>(std::forward<rmm::device_scalar<T>>(data), is_valid, stream, mr)
+{
+}
+
+template <typename T>
+numeric_scalar<T>::numeric_scalar(numeric_scalar<T> const& other,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+  : detail::fixed_width_scalar<T>{other, stream, mr}
+{
+}
+
+/**
+ * @brief These define the valid numeric scalar types.
+ *
+ * See `is_numeric` in @see cudf/utilities/traits.hpp
+ *
+ * Adding a new supported type only requires adding the appropriate line here
+ * and does not require updating the scalar.hpp file.
+ */
+template class numeric_scalar<bool>;
+template class numeric_scalar<int8_t>;
+template class numeric_scalar<int16_t>;
+template class numeric_scalar<int32_t>;
+template class numeric_scalar<int64_t>;
+template class numeric_scalar<__int128_t>;
+template class numeric_scalar<uint8_t>;
+template class numeric_scalar<uint16_t>;
+template class numeric_scalar<uint32_t>;
+template class numeric_scalar<uint64_t>;
+template class numeric_scalar<float>;
+template class numeric_scalar<double>;
+
+template <typename T>
+chrono_scalar<T>::chrono_scalar(T value,
+                                bool is_valid,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+  : detail::fixed_width_scalar<T>(value, is_valid, stream, mr)
+{
+}
+
+template <typename T>
+chrono_scalar<T>::chrono_scalar(rmm::device_scalar<T>&& data,
+                                bool is_valid,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+  : detail::fixed_width_scalar<T>(std::forward<rmm::device_scalar<T>>(data), is_valid, stream, mr)
+{
+}
+
+template <typename T>
+chrono_scalar<T>::chrono_scalar(chrono_scalar<T> const& other,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+  : detail::fixed_width_scalar<T>{other, stream, mr}
+{
+}
+
+/**
+ * @brief These define the valid chrono scalar types.
+ *
+ * See `is_chrono` in @see cudf/utilities/traits.hpp
+ *
+ * Adding a new supported type only requires adding the appropriate line here
+ * and does not require updating the scalar.hpp file.
+ */
+template class chrono_scalar<timestamp_D>;
+template class chrono_scalar<timestamp_s>;
+template class chrono_scalar<timestamp_ms>;
+template class chrono_scalar<timestamp_us>;
+template class chrono_scalar<timestamp_ns>;
+template class chrono_scalar<duration_D>;
+template class chrono_scalar<duration_s>;
+template class chrono_scalar<duration_ms>;
+template class chrono_scalar<duration_us>;
+template class chrono_scalar<duration_ns>;
+
+template <typename T>
+duration_scalar<T>::duration_scalar(rep_type value,
+                                    bool is_valid,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+  : chrono_scalar<T>(T{value}, is_valid, stream, mr)
+{
+}
+
+template <typename T>
+duration_scalar<T>::duration_scalar(duration_scalar<T> const& other,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+  : chrono_scalar<T>{other, stream, mr}
+{
+}
+
+template <typename T>
+typename duration_scalar<T>::rep_type duration_scalar<T>::count()
+{
+  return this->value().count();
+}
+
+/**
+ * @brief These define the valid duration scalar types.
+ *
+ * See `is_duration` in @see cudf/utilities/traits.hpp
+ *
+ * Adding a new supported type only requires adding the appropriate line here
+ * and does not require updating the scalar.hpp file.
+ */
+template class duration_scalar<duration_D>;
+template class duration_scalar<duration_s>;
+template class duration_scalar<duration_ms>;
+template class duration_scalar<duration_us>;
+template class duration_scalar<duration_ns>;
+
+template <typename T>
+typename timestamp_scalar<T>::rep_type timestamp_scalar<T>::ticks_since_epoch()
+{
+  return this->value().time_since_epoch().count();
+}
+
+/**
+ * @brief These define the valid timestamp scalar types.
+ *
+ * See `is_timestamp` in @see cudf/utilities/traits.hpp
+ *
+ * Adding a new supported type only requires adding the appropriate line here
+ * and does not require updating the scalar.hpp file.
+ */
+template class timestamp_scalar<timestamp_D>;
+template class timestamp_scalar<timestamp_s>;
+template class timestamp_scalar<timestamp_ms>;
+template class timestamp_scalar<timestamp_us>;
+template class timestamp_scalar<timestamp_ns>;
+
+template <typename T>
+template <typename D>
+timestamp_scalar<T>::timestamp_scalar(D const& value,
+                                      bool is_valid,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+  : chrono_scalar<T>(T{typename T::duration{value}}, is_valid, stream, mr)
+{
+}
+
+template <typename T>
+timestamp_scalar<T>::timestamp_scalar(timestamp_scalar<T> const& other,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+  : chrono_scalar<T>{other, stream, mr}
+{
+}
+
+#define TS_CTOR(TimestampType, DurationType)                  \
+  template timestamp_scalar<TimestampType>::timestamp_scalar( \
+    DurationType const&, bool, rmm::cuda_stream_view, rmm::mr::device_memory_resource*);
+
+/**
+ * @brief These are the valid combinations of duration types to timestamp types.
+ */
+TS_CTOR(timestamp_D, duration_D)
+TS_CTOR(timestamp_D, int32_t)
+TS_CTOR(timestamp_s, duration_D)
+TS_CTOR(timestamp_s, duration_s)
+TS_CTOR(timestamp_s, int64_t)
+TS_CTOR(timestamp_ms, duration_D)
+TS_CTOR(timestamp_ms, duration_s)
+TS_CTOR(timestamp_ms, duration_ms)
+TS_CTOR(timestamp_ms, int64_t)
+TS_CTOR(timestamp_us, duration_D)
+TS_CTOR(timestamp_us, duration_s)
+TS_CTOR(timestamp_us, duration_ms)
+TS_CTOR(timestamp_us, duration_us)
+TS_CTOR(timestamp_us, int64_t)
+TS_CTOR(timestamp_ns, duration_D)
+TS_CTOR(timestamp_ns, duration_s)
+TS_CTOR(timestamp_ns, duration_ms)
+TS_CTOR(timestamp_ns, duration_us)
+TS_CTOR(timestamp_ns, duration_ns)
+TS_CTOR(timestamp_ns, int64_t)
+
+list_scalar::list_scalar(cudf::column_view const& data,
+                         bool is_valid,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::LIST), is_valid, stream, mr), _data(data, stream, mr)
+{
+}
+
+list_scalar::list_scalar(cudf::column&& data,
+                         bool is_valid,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::LIST), is_valid, stream, mr), _data(std::move(data))
+{
+}
+
+list_scalar::list_scalar(list_scalar const& other,
+                         rmm::cuda_stream_view stream,
+                         rmm::mr::device_memory_resource* mr)
+  : scalar{other, stream, mr}, _data(other._data, stream, mr)
+{
+}
+
+column_view list_scalar::view() const { return _data.view(); }
+
+struct_scalar::struct_scalar(struct_scalar const& other,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar{other, stream, mr}, _data(other._data, stream, mr)
+{
+}
+
+struct_scalar::struct_scalar(table_view const& data,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::STRUCT), is_valid, stream, mr),
+    _data{init_data(table{data}, is_valid, stream, mr)}
+{
+  assert_valid_size();
+}
+
+struct_scalar::struct_scalar(host_span<column_view const> data,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::STRUCT), is_valid, stream, mr),
+    _data{init_data(
+      table{table_view{std::vector<column_view>{data.begin(), data.end()}}}, is_valid, stream, mr)}
+{
+  assert_valid_size();
+}
+
+struct_scalar::struct_scalar(table&& data,
+                             bool is_valid,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+  : scalar(data_type(type_id::STRUCT), is_valid, stream, mr),
+    _data{init_data(std::move(data), is_valid, stream, mr)}
+{
+  assert_valid_size();
+}
+
+table_view struct_scalar::view() const { return _data.view(); }
+
+void struct_scalar::assert_valid_size()
+{
+  auto const tv = _data.view();
+  CUDF_EXPECTS(
+    std::all_of(tv.begin(), tv.end(), [](column_view const& col) { return col.size() == 1; }),
+    "Struct scalar inputs must have exactly 1 row");
+}
+
+table struct_scalar::init_data(table&& data,
+                               bool is_valid,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  if (is_valid) { return std::move(data); }
+
+  auto data_cols = data.release();
+
+  // push validity mask down
+  auto const validity = cudf::detail::create_null_mask(
+    1, mask_state::ALL_NULL, stream, rmm::mr::get_current_device_resource());
+  for (auto& col : data_cols) {
+    col = cudf::structs::detail::superimpose_nulls(
+      static_cast<bitmask_type const*>(validity.data()), 1, std::move(col), stream, mr);
+  }
+
+  return table{std::move(data_cols)};
+}
+
+}  // namespace cudf
diff --git a/cpp/src/scalar/scalar_factories.cpp b/cpp/src/scalar/scalar_factories.cpp
new file mode 100644
index 0000000..3a2920f
--- /dev/null
+++ b/cpp/src/scalar/scalar_factories.cpp
@@ -0,0 +1,202 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf/detail/copy.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace {
+struct scalar_construction_helper {
+  template <typename T,
+            typename ScalarType                                                = scalar_type_t<T>,
+            std::enable_if_t<is_fixed_width<T>() and not is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    using Type = device_storage_type_t<T>;
+    auto s     = new ScalarType(Type{}, false, stream, mr);
+    return std::unique_ptr<scalar>(s);
+  }
+
+  template <typename T,
+            typename ScalarType                    = scalar_type_t<T>,
+            std::enable_if_t<is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    using Type = device_storage_type_t<T>;
+    auto s     = new ScalarType(Type{}, numeric::scale_type{0}, false, stream, mr);
+    return std::unique_ptr<scalar>(s);
+  }
+
+  template <typename T, typename... Args, std::enable_if_t<not is_fixed_width<T>()>* = nullptr>
+  std::unique_ptr<scalar> operator()(Args... args) const
+  {
+    CUDF_FAIL("Invalid type.");
+  }
+};
+}  // namespace
+
+// Allocate storage for a single numeric element
+std::unique_ptr<scalar> make_numeric_scalar(data_type type,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_numeric(type), "Invalid, non-numeric type.");
+
+  return type_dispatcher(type, scalar_construction_helper{}, stream, mr);
+}
+
+// Allocate storage for a single timestamp element
+std::unique_ptr<scalar> make_timestamp_scalar(data_type type,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_timestamp(type), "Invalid, non-timestamp type.");
+
+  return type_dispatcher(type, scalar_construction_helper{}, stream, mr);
+}
+
+// Allocate storage for a single duration element
+std::unique_ptr<scalar> make_duration_scalar(data_type type,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_duration(type), "Invalid, non-duration type.");
+
+  return type_dispatcher(type, scalar_construction_helper{}, stream, mr);
+}
+
+// Allocate storage for a single fixed width element
+std::unique_ptr<scalar> make_fixed_width_scalar(data_type type,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_fixed_width(type), "Invalid, non-fixed-width type.");
+
+  return type_dispatcher(type, scalar_construction_helper{}, stream, mr);
+}
+
+std::unique_ptr<scalar> make_list_scalar(column_view elements,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  return std::make_unique<list_scalar>(elements, true, stream, mr);
+}
+
+std::unique_ptr<scalar> make_struct_scalar(table_view const& data,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  return std::make_unique<struct_scalar>(data, true, stream, mr);
+}
+
+std::unique_ptr<scalar> make_struct_scalar(host_span<column_view const> data,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  return std::make_unique<struct_scalar>(data, true, stream, mr);
+}
+
+namespace {
+struct default_scalar_functor {
+  data_type type;
+
+  template <typename T, std::enable_if_t<not is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::scalar> operator()(rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    return make_fixed_width_scalar(data_type(type_to_id<T>()), stream, mr);
+  }
+
+  template <typename T, std::enable_if_t<is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::scalar> operator()(rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    auto const scale_ = numeric::scale_type{type.scale()};
+    auto s            = make_fixed_point_scalar<T>(0, scale_, stream, mr);
+    s->set_valid_async(false, stream);
+    return s;
+  }
+};
+
+template <>
+std::unique_ptr<cudf::scalar> default_scalar_functor::operator()<string_view>(
+  rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  return std::unique_ptr<scalar>(new string_scalar("", false, stream, mr));
+}
+
+template <>
+std::unique_ptr<cudf::scalar> default_scalar_functor::operator()<dictionary32>(
+  rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL("dictionary type not supported");
+}
+
+template <>
+std::unique_ptr<cudf::scalar> default_scalar_functor::operator()<list_view>(
+  rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL("list_view type not supported");
+}
+
+template <>
+std::unique_ptr<cudf::scalar> default_scalar_functor::operator()<struct_view>(
+  rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FAIL("struct_view type not supported");
+}
+
+}  // namespace
+
+std::unique_ptr<scalar> make_default_constructed_scalar(data_type type,
+                                                        rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  return type_dispatcher(type, default_scalar_functor{type}, stream, mr);
+}
+
+std::unique_ptr<scalar> make_empty_scalar_like(column_view const& column,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  std::unique_ptr<scalar> result;
+  switch (column.type().id()) {
+    case type_id::LIST: {
+      auto const empty_child = empty_like(lists_column_view(column).child());
+      result                 = make_list_scalar(empty_child->view(), stream, mr);
+      result->set_valid_async(false, stream);
+      break;
+    }
+    case type_id::STRUCT:
+      // The input column must have at least 1 row to extract a scalar (row) from it.
+      result = detail::get_element(column, 0, stream, mr);
+      result->set_valid_async(false, stream);
+      break;
+    default: result = make_default_constructed_scalar(column.type(), stream, mr);
+  }
+  return result;
+}
+
+}  // namespace cudf
diff --git a/cpp/src/search/contains_column.cu b/cpp/src/search/contains_column.cu
new file mode 100644
index 0000000..4363bd2
--- /dev/null
+++ b/cpp/src/search/contains_column.cu
@@ -0,0 +1,164 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <hash/unordered_multiset.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/uninitialized_fill.h>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+struct contains_column_dispatch {
+  template <typename Element, typename Haystack>
+  struct contains_fn {
+    bool __device__ operator()(size_type const idx) const
+    {
+      if (needles_have_nulls && needles.is_null_nocheck(idx)) {
+        // Exit early. The value doesn't matter, and will be masked as a null element.
+        return true;
+      }
+
+      return haystack.contains(needles.template element<Element>(idx));
+    }
+
+    Haystack const haystack;
+    column_device_view const needles;
+    bool const needles_have_nulls;
+  };
+
+  template <typename Element, CUDF_ENABLE_IF(!is_nested<Element>())>
+  std::unique_ptr<column> operator()(column_view const& haystack,
+                                     column_view const& needles,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto result = make_numeric_column(data_type{type_to_id<bool>()},
+                                      needles.size(),
+                                      copy_bitmask(needles, stream, mr),
+                                      needles.null_count(),
+                                      stream,
+                                      mr);
+    if (needles.is_empty()) { return result; }
+
+    auto const out_begin = result->mutable_view().template begin<bool>();
+    if (haystack.is_empty()) {
+      thrust::uninitialized_fill(
+        rmm::exec_policy(stream), out_begin, out_begin + needles.size(), false);
+      return result;
+    }
+
+    auto const haystack_set = cudf::detail::unordered_multiset<Element>::create(haystack, stream);
+    auto const haystack_set_dv = haystack_set.to_device();
+    auto const needles_cdv_ptr = column_device_view::create(needles, stream);
+
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(needles.size()),
+                      out_begin,
+                      contains_fn<Element, decltype(haystack_set_dv)>{
+                        haystack_set_dv, *needles_cdv_ptr, needles.has_nulls()});
+
+    result->set_null_count(needles.null_count());
+
+    return result;
+  }
+
+  template <typename Element, CUDF_ENABLE_IF(is_nested<Element>())>
+  std::unique_ptr<column> operator()(column_view const& haystack,
+                                     column_view const& needles,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto result_v = detail::contains(table_view{{haystack}},
+                                     table_view{{needles}},
+                                     null_equality::EQUAL,
+                                     nan_equality::ALL_EQUAL,
+                                     stream,
+                                     mr);
+    return std::make_unique<column>(
+      std::move(result_v), copy_bitmask(needles, stream, mr), needles.null_count());
+  }
+};
+
+template <>
+std::unique_ptr<column> contains_column_dispatch::operator()<dictionary32>(
+  column_view const& haystack_in,
+  column_view const& needles_in,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr) const
+{
+  dictionary_column_view const haystack(haystack_in);
+  dictionary_column_view const needles(needles_in);
+  // first combine keys so both dictionaries have the same set
+  auto needles_matched = dictionary::detail::add_keys(
+    needles, haystack.keys(), stream, rmm::mr::get_current_device_resource());
+  auto const needles_view = dictionary_column_view(needles_matched->view());
+  auto haystack_matched   = dictionary::detail::set_keys(
+    haystack, needles_view.keys(), stream, rmm::mr::get_current_device_resource());
+  auto const haystack_view = dictionary_column_view(haystack_matched->view());
+
+  // now just use the indices for the contains
+  column_view const haystack_indices = haystack_view.get_indices_annotated();
+  column_view const needles_indices  = needles_view.get_indices_annotated();
+  return cudf::type_dispatcher(haystack_indices.type(),
+                               contains_column_dispatch{},
+                               haystack_indices,
+                               needles_indices,
+                               stream,
+                               mr);
+}
+
+}  // namespace
+
+std::unique_ptr<column> contains(column_view const& haystack,
+                                 column_view const& needles,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(haystack.type() == needles.type(), "DTYPE mismatch");
+
+  return cudf::type_dispatcher(
+    haystack.type(), contains_column_dispatch{}, haystack, needles, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> contains(column_view const& haystack,
+                                 column_view const& needles,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains(haystack, needles, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/search/contains_scalar.cu b/cpp/src/search/contains_scalar.cu
new file mode 100644
index 0000000..0b344ec
--- /dev/null
+++ b/cpp/src/search/contains_scalar.cu
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Get the underlying value of a scalar through a scalar device view.
+ *
+ * @tparam Element The scalar's value type
+ * @tparam ScalarDView Type of the input scalar device view
+ * @param d_scalar The input scalar device view
+ */
+template <typename Element, typename ScalarDView>
+__device__ auto inline get_scalar_value(ScalarDView d_scalar)
+{
+  if constexpr (cudf::is_fixed_point<Element>()) {
+    return d_scalar.rep();
+  } else {
+    return d_scalar.value();
+  }
+}
+
+struct contains_scalar_dispatch {
+  // SFINAE with conditional return type because we need to support device lambda in this function.
+  // This is required due to a limitation of nvcc.
+  template <typename Element>
+  std::enable_if_t<!is_nested<Element>(), bool> operator()(column_view const& haystack,
+                                                           scalar const& needle,
+                                                           rmm::cuda_stream_view stream) const
+  {
+    CUDF_EXPECTS(haystack.type() == needle.type(), "Scalar and column types must match");
+    // Don't need to check for needle validity. If it is invalid, it should be handled by the caller
+    // before dispatching to this function.
+
+    using DType           = device_storage_type_t<Element>;
+    auto const d_haystack = column_device_view::create(haystack, stream);
+    auto const d_needle   = get_scalar_device_view(
+      static_cast<cudf::scalar_type_t<Element>&>(const_cast<scalar&>(needle)));
+
+    auto const begin =
+      d_haystack->optional_begin<DType>(cudf::nullate::DYNAMIC{haystack.has_nulls()});
+    auto const end = d_haystack->optional_end<DType>(cudf::nullate::DYNAMIC{haystack.has_nulls()});
+
+    return thrust::count_if(
+             rmm::exec_policy(stream), begin, end, [d_needle] __device__(auto const val_pair) {
+               auto needle = get_scalar_value<Element>(d_needle);
+               return val_pair.has_value() && (needle == *val_pair);
+             }) > 0;
+  }
+
+  template <typename Element>
+  std::enable_if_t<is_nested<Element>(), bool> operator()(column_view const& haystack,
+                                                          scalar const& needle,
+                                                          rmm::cuda_stream_view stream) const
+  {
+    CUDF_EXPECTS(haystack.type() == needle.type(), "Scalar and column types must match");
+    // Don't need to check for needle validity. If it is invalid, it should be handled by the caller
+    // before dispatching to this function.
+    // In addition, haystack and needle structure compatibility will be checked later on by
+    // constructor of the table comparator.
+
+    auto const haystack_tv   = table_view{{haystack}};
+    auto const needle_as_col = make_column_from_scalar(needle, 1, stream);
+    auto const needle_tv     = table_view{{needle_as_col->view()}};
+    auto const has_nulls     = has_nested_nulls(haystack_tv) || has_nested_nulls(needle_tv);
+
+    auto const comparator =
+      cudf::experimental::row::equality::two_table_comparator(haystack_tv, needle_tv, stream);
+
+    auto const begin = cudf::experimental::row::lhs_iterator(0);
+    auto const end   = begin + haystack.size();
+    using cudf::experimental::row::rhs_index_type;
+
+    auto const check_nulls      = haystack.has_nulls();
+    auto const haystack_cdv_ptr = column_device_view::create(haystack, stream);
+
+    auto const d_comp = comparator.equal_to<true>(nullate::DYNAMIC{has_nulls});
+
+    // Using a temporary buffer for intermediate transform results from the lambda containing
+    // the comparator speeds up compile-time significantly without much degradation in
+    // runtime performance over using the comparator in a transform iterator with thrust::count_if.
+    auto d_results = rmm::device_uvector<bool>(haystack.size(), stream);
+    thrust::transform(
+      rmm::exec_policy(stream),
+      begin,
+      end,
+      d_results.begin(),
+      [d_comp, check_nulls, d_haystack = *haystack_cdv_ptr] __device__(auto const idx) {
+        if (check_nulls && d_haystack.is_null_nocheck(static_cast<size_type>(idx))) {
+          return false;
+        }
+        return d_comp(idx, rhs_index_type{0});  // compare haystack[idx] == needle[0].
+      });
+
+    return thrust::count(rmm::exec_policy(stream), d_results.begin(), d_results.end(), true) > 0;
+  }
+};
+
+template <>
+bool contains_scalar_dispatch::operator()<cudf::dictionary32>(column_view const& haystack,
+                                                              scalar const& needle,
+                                                              rmm::cuda_stream_view stream) const
+{
+  auto const dict_col = cudf::dictionary_column_view(haystack);
+  // first, find the needle in the dictionary's key set
+  auto const index = cudf::dictionary::detail::get_index(
+    dict_col, needle, stream, rmm::mr::get_current_device_resource());
+  // if found, check the index is actually in the indices column
+  return index->is_valid(stream) && cudf::type_dispatcher(dict_col.indices().type(),
+                                                          contains_scalar_dispatch{},
+                                                          dict_col.indices(),
+                                                          *index,
+                                                          stream);
+}
+
+}  // namespace
+
+bool contains(column_view const& haystack, scalar const& needle, rmm::cuda_stream_view stream)
+{
+  if (haystack.is_empty()) { return false; }
+  if (not needle.is_valid(stream)) { return haystack.has_nulls(); }
+
+  return cudf::type_dispatcher(
+    haystack.type(), contains_scalar_dispatch{}, haystack, needle, stream);
+}
+
+}  // namespace detail
+
+bool contains(column_view const& haystack, scalar const& needle, rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains(haystack, needle, stream);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/search/contains_table.cu b/cpp/src/search/contains_table.cu
new file mode 100644
index 0000000..43624ba
--- /dev/null
+++ b/cpp/src/search/contains_table.cu
@@ -0,0 +1,286 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <join/join_common_utils.cuh>
+
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <cuco/static_set.cuh>
+
+#include <type_traits>
+
+namespace cudf::detail {
+
+namespace {
+
+using cudf::experimental::row::lhs_index_type;
+using cudf::experimental::row::rhs_index_type;
+
+/**
+ * @brief An hasher adapter wrapping both haystack hasher and needles hasher
+ */
+template <typename HaystackHasher, typename NeedleHasher>
+struct hasher_adapter {
+  hasher_adapter(HaystackHasher const& haystack_hasher, NeedleHasher const& needle_hasher)
+    : _haystack_hasher{haystack_hasher}, _needle_hasher{needle_hasher}
+  {
+  }
+
+  __device__ constexpr auto operator()(lhs_index_type idx) const noexcept
+  {
+    return _haystack_hasher(static_cast<size_type>(idx));
+  }
+
+  __device__ constexpr auto operator()(rhs_index_type idx) const noexcept
+  {
+    return _needle_hasher(static_cast<size_type>(idx));
+  }
+
+ private:
+  HaystackHasher const _haystack_hasher;
+  NeedleHasher const _needle_hasher;
+};
+
+/**
+ * @brief An comparator adapter wrapping both self comparator and two table comparator
+ */
+template <typename SelfEqual, typename TwoTableEqual>
+struct comparator_adapter {
+  comparator_adapter(SelfEqual const& self_equal, TwoTableEqual const& two_table_equal)
+    : _self_equal{self_equal}, _two_table_equal{two_table_equal}
+  {
+  }
+
+  __device__ constexpr auto operator()(lhs_index_type lhs_index,
+                                       lhs_index_type rhs_index) const noexcept
+  {
+    auto const lhs = static_cast<size_type>(lhs_index);
+    auto const rhs = static_cast<size_type>(rhs_index);
+
+    return _self_equal(lhs, rhs);
+  }
+
+  __device__ constexpr auto operator()(lhs_index_type lhs_index,
+                                       rhs_index_type rhs_index) const noexcept
+  {
+    return _two_table_equal(lhs_index, rhs_index);
+  }
+
+ private:
+  SelfEqual const _self_equal;
+  TwoTableEqual const _two_table_equal;
+};
+
+/**
+ * @brief Build a row bitmask for the input table.
+ *
+ * The output bitmask will have invalid bits corresponding to the input rows having nulls (at
+ * any nested level) and vice versa.
+ *
+ * @param input The input table
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return A pair of pointer to the output bitmask and the buffer containing the bitmask
+ */
+std::pair<rmm::device_buffer, bitmask_type const*> build_row_bitmask(table_view const& input,
+                                                                     rmm::cuda_stream_view stream)
+{
+  auto const nullable_columns = get_nullable_columns(input);
+  CUDF_EXPECTS(nullable_columns.size() > 0,
+               "The input table has nulls thus it should have nullable columns.");
+
+  // If there are more than one nullable column, we compute `bitmask_and` of their null masks.
+  // Otherwise, we have only one nullable column and can use its null mask directly.
+  if (nullable_columns.size() > 1) {
+    auto row_bitmask =
+      cudf::detail::bitmask_and(
+        table_view{nullable_columns}, stream, rmm::mr::get_current_device_resource())
+        .first;
+    auto const row_bitmask_ptr = static_cast<bitmask_type const*>(row_bitmask.data());
+    return std::pair(std::move(row_bitmask), row_bitmask_ptr);
+  }
+
+  return std::pair(rmm::device_buffer{0, stream}, nullable_columns.front().null_mask());
+}
+
+/**
+ * @brief Invokes the given `func` with desired comparators based on the specified `compare_nans`
+ * parameter
+ *
+ * @tparam HasNested Flag indicating whether there are nested columns in haystack or needles
+ * @tparam Hasher Type of device hash function
+ * @tparam Func Type of the helper function doing `contains` check
+ *
+ * @param compare_nulls Control whether nulls should be compared as equal or not
+ * @param compare_nans Control whether floating-point NaNs values should be compared as equal or not
+ * @param haystack_has_nulls Flag indicating whether haystack has nulls or not
+ * @param has_any_nulls Flag indicating whether there are nested nulls is either haystack or needles
+ * @param self_equal Self table comparator
+ * @param two_table_equal Two table comparator
+ * @param d_hasher Device hash functor
+ * @param func The input functor to invoke
+ */
+template <bool HasNested, typename Hasher, typename Func>
+void dispatch_nan_comparator(
+  null_equality compare_nulls,
+  nan_equality compare_nans,
+  bool haystack_has_nulls,
+  bool has_any_nulls,
+  cudf::experimental::row::equality::self_comparator self_equal,
+  cudf::experimental::row::equality::two_table_comparator two_table_equal,
+  Hasher const& d_hasher,
+  Func&& func)
+{
+  // Distinguish probing scheme CG sizes between nested and flat types for better performance
+  auto const probing_scheme = [&]() {
+    if constexpr (HasNested) {
+      return cuco::experimental::linear_probing<4, Hasher>{d_hasher};
+    } else {
+      return cuco::experimental::linear_probing<1, Hasher>{d_hasher};
+    }
+  }();
+
+  if (compare_nans == nan_equality::ALL_EQUAL) {
+    using nan_equal_comparator =
+      cudf::experimental::row::equality::nan_equal_physical_equality_comparator;
+    auto const d_self_equal = self_equal.equal_to<HasNested>(
+      nullate::DYNAMIC{haystack_has_nulls}, compare_nulls, nan_equal_comparator{});
+    auto const d_two_table_equal = two_table_equal.equal_to<HasNested>(
+      nullate::DYNAMIC{has_any_nulls}, compare_nulls, nan_equal_comparator{});
+    func(d_self_equal, d_two_table_equal, probing_scheme);
+  } else {
+    using nan_unequal_comparator = cudf::experimental::row::equality::physical_equality_comparator;
+    auto const d_self_equal      = self_equal.equal_to<HasNested>(
+      nullate::DYNAMIC{haystack_has_nulls}, compare_nulls, nan_unequal_comparator{});
+    auto const d_two_table_equal = two_table_equal.equal_to<HasNested>(
+      nullate::DYNAMIC{has_any_nulls}, compare_nulls, nan_unequal_comparator{});
+    func(d_self_equal, d_two_table_equal, probing_scheme);
+  }
+}
+
+}  // namespace
+
+rmm::device_uvector<bool> contains(table_view const& haystack,
+                                   table_view const& needles,
+                                   null_equality compare_nulls,
+                                   nan_equality compare_nans,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(cudf::have_same_types(haystack, needles), "Column types mismatch");
+
+  auto const haystack_has_nulls = has_nested_nulls(haystack);
+  auto const needles_has_nulls  = has_nested_nulls(needles);
+  auto const has_any_nulls      = haystack_has_nulls || needles_has_nulls;
+
+  auto const preprocessed_needles =
+    cudf::experimental::row::equality::preprocessed_table::create(needles, stream);
+  auto const preprocessed_haystack =
+    cudf::experimental::row::equality::preprocessed_table::create(haystack, stream);
+
+  auto const haystack_hasher   = cudf::experimental::row::hash::row_hasher(preprocessed_haystack);
+  auto const d_haystack_hasher = haystack_hasher.device_hasher(nullate::DYNAMIC{has_any_nulls});
+  auto const needle_hasher     = cudf::experimental::row::hash::row_hasher(preprocessed_needles);
+  auto const d_needle_hasher   = needle_hasher.device_hasher(nullate::DYNAMIC{has_any_nulls});
+  auto const d_hasher          = hasher_adapter{d_haystack_hasher, d_needle_hasher};
+
+  auto const self_equal = cudf::experimental::row::equality::self_comparator(preprocessed_haystack);
+  auto const two_table_equal = cudf::experimental::row::equality::two_table_comparator(
+    preprocessed_haystack, preprocessed_needles);
+
+  // The output vector.
+  auto contained = rmm::device_uvector<bool>(needles.num_rows(), stream, mr);
+
+  auto const haystack_iter = cudf::detail::make_counting_transform_iterator(
+    size_type{0}, [] __device__(auto idx) { return lhs_index_type{idx}; });
+  auto const needles_iter = cudf::detail::make_counting_transform_iterator(
+    size_type{0}, [] __device__(auto idx) { return rhs_index_type{idx}; });
+
+  auto const helper_func =
+    [&](auto const& d_self_equal, auto const& d_two_table_equal, auto const& probing_scheme) {
+      auto const d_equal = comparator_adapter{d_self_equal, d_two_table_equal};
+
+      auto set = cuco::experimental::static_set{
+        cuco::experimental::extent{compute_hash_table_size(haystack.num_rows())},
+        cuco::empty_key{lhs_index_type{-1}},
+        d_equal,
+        probing_scheme,
+        detail::hash_table_allocator_type{default_allocator<lhs_index_type>{}, stream},
+        stream.value()};
+
+      if (haystack_has_nulls && compare_nulls == null_equality::UNEQUAL) {
+        auto const bitmask_buffer_and_ptr = build_row_bitmask(haystack, stream);
+        auto const row_bitmask_ptr        = bitmask_buffer_and_ptr.second;
+
+        // If the haystack table has nulls but they are compared unequal, don't insert them.
+        // Otherwise, it was known to cause performance issue:
+        // - https://github.com/rapidsai/cudf/pull/6943
+        // - https://github.com/rapidsai/cudf/pull/8277
+        set.insert_if_async(haystack_iter,
+                            haystack_iter + haystack.num_rows(),
+                            thrust::counting_iterator<size_type>(0),  // stencil
+                            row_is_valid{row_bitmask_ptr},
+                            stream.value());
+      } else {
+        set.insert_async(haystack_iter, haystack_iter + haystack.num_rows(), stream.value());
+      }
+
+      if (needles_has_nulls && compare_nulls == null_equality::UNEQUAL) {
+        auto const bitmask_buffer_and_ptr = build_row_bitmask(needles, stream);
+        auto const row_bitmask_ptr        = bitmask_buffer_and_ptr.second;
+        set.contains_if_async(needles_iter,
+                              needles_iter + needles.num_rows(),
+                              thrust::counting_iterator<size_type>(0),  // stencil
+                              row_is_valid{row_bitmask_ptr},
+                              contained.begin(),
+                              stream.value());
+      } else {
+        set.contains_async(
+          needles_iter, needles_iter + needles.num_rows(), contained.begin(), stream.value());
+      }
+    };
+
+  if (cudf::detail::has_nested_columns(haystack)) {
+    dispatch_nan_comparator<true>(compare_nulls,
+                                  compare_nans,
+                                  haystack_has_nulls,
+                                  has_any_nulls,
+                                  self_equal,
+                                  two_table_equal,
+                                  d_hasher,
+                                  helper_func);
+  } else {
+    dispatch_nan_comparator<false>(compare_nulls,
+                                   compare_nans,
+                                   haystack_has_nulls,
+                                   has_any_nulls,
+                                   self_equal,
+                                   two_table_equal,
+                                   d_hasher,
+                                   helper_func);
+  }
+
+  return contained;
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/search/search_ordered.cu b/cpp/src/search/search_ordered.cu
new file mode 100644
index 0000000..3b5dbef
--- /dev/null
+++ b/cpp/src/search/search_ordered.cu
@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/detail/update_keys.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+std::unique_ptr<column> search_ordered(table_view const& haystack,
+                                       table_view const& needles,
+                                       bool find_first,
+                                       std::vector<order> const& column_order,
+                                       std::vector<null_order> const& null_precedence,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(
+    column_order.empty() or static_cast<std::size_t>(haystack.num_columns()) == column_order.size(),
+    "Mismatch between number of columns and column order.");
+  CUDF_EXPECTS(null_precedence.empty() or
+                 static_cast<std::size_t>(haystack.num_columns()) == null_precedence.size(),
+               "Mismatch between number of columns and null precedence.");
+
+  // Allocate result column
+  auto result = make_numeric_column(
+    data_type{type_to_id<size_type>()}, needles.num_rows(), mask_state::UNALLOCATED, stream, mr);
+  auto const out_it = result->mutable_view().data<size_type>();
+
+  // Handle empty inputs
+  if (haystack.num_rows() == 0) {
+    CUDF_CUDA_TRY(
+      cudaMemsetAsync(out_it, 0, needles.num_rows() * sizeof(size_type), stream.value()));
+    return result;
+  }
+
+  // This utility will ensure all corresponding dictionary columns have matching keys.
+  // It will return any new dictionary columns created as well as updated table_views.
+  auto const matched = dictionary::detail::match_dictionaries(
+    {haystack, needles}, stream, rmm::mr::get_current_device_resource());
+  auto const& matched_haystack = matched.second.front();
+  auto const& matched_needles  = matched.second.back();
+
+  auto const comparator = cudf::experimental::row::lexicographic::two_table_comparator(
+    matched_haystack, matched_needles, column_order, null_precedence, stream);
+  auto const has_nulls = has_nested_nulls(matched_haystack) or has_nested_nulls(matched_needles);
+
+  auto const haystack_it = cudf::experimental::row::lhs_iterator(0);
+  auto const needles_it  = cudf::experimental::row::rhs_iterator(0);
+
+  if (cudf::detail::has_nested_columns(haystack) || cudf::detail::has_nested_columns(needles)) {
+    auto const d_comparator = comparator.less<true>(nullate::DYNAMIC{has_nulls});
+    if (find_first) {
+      thrust::lower_bound(rmm::exec_policy(stream),
+                          haystack_it,
+                          haystack_it + haystack.num_rows(),
+                          needles_it,
+                          needles_it + needles.num_rows(),
+                          out_it,
+                          d_comparator);
+    } else {
+      thrust::upper_bound(rmm::exec_policy(stream),
+                          haystack_it,
+                          haystack_it + haystack.num_rows(),
+                          needles_it,
+                          needles_it + needles.num_rows(),
+                          out_it,
+                          d_comparator);
+    }
+  } else {
+    auto const d_comparator = comparator.less<false>(nullate::DYNAMIC{has_nulls});
+    if (find_first) {
+      thrust::lower_bound(rmm::exec_policy(stream),
+                          haystack_it,
+                          haystack_it + haystack.num_rows(),
+                          needles_it,
+                          needles_it + needles.num_rows(),
+                          out_it,
+                          d_comparator);
+    } else {
+      thrust::upper_bound(rmm::exec_policy(stream),
+                          haystack_it,
+                          haystack_it + haystack.num_rows(),
+                          needles_it,
+                          needles_it + needles.num_rows(),
+                          out_it,
+                          d_comparator);
+    }
+  }
+  return result;
+}
+}  // namespace
+
+std::unique_ptr<column> lower_bound(table_view const& haystack,
+                                    table_view const& needles,
+                                    std::vector<order> const& column_order,
+                                    std::vector<null_order> const& null_precedence,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  return search_ordered(haystack, needles, true, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<column> upper_bound(table_view const& haystack,
+                                    table_view const& needles,
+                                    std::vector<order> const& column_order,
+                                    std::vector<null_order> const& null_precedence,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  return search_ordered(haystack, needles, false, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> lower_bound(table_view const& haystack,
+                                    table_view const& needles,
+                                    std::vector<order> const& column_order,
+                                    std::vector<null_order> const& null_precedence,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::lower_bound(haystack, needles, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<column> upper_bound(table_view const& haystack,
+                                    table_view const& needles,
+                                    std::vector<order> const& column_order,
+                                    std::vector<null_order> const& null_precedence,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::upper_bound(haystack, needles, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/sort/is_sorted.cu b/cpp/src/sort/is_sorted.cu
new file mode 100644
index 0000000..39476a2
--- /dev/null
+++ b/cpp/src/sort/is_sorted.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+
+bool is_sorted(cudf::table_view const& in,
+               std::vector<order> const& column_order,
+               std::vector<null_order> const& null_precedence,
+               rmm::cuda_stream_view stream)
+{
+  auto const comparator =
+    experimental::row::lexicographic::self_comparator{in, column_order, null_precedence, stream};
+
+  if (cudf::detail::has_nested_columns(in)) {
+    auto const device_comparator = comparator.less<true>(has_nested_nulls(in));
+
+    // Using a temporary buffer for intermediate transform results from the lambda containing
+    // the comparator speeds up compile-time significantly over using the comparator directly
+    // in thrust::is_sorted.
+    auto d_results = rmm::device_uvector<bool>(in.num_rows(), stream);
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::counting_iterator<size_type>(0),
+                      thrust::counting_iterator<size_type>(in.num_rows()),
+                      d_results.begin(),
+                      [device_comparator] __device__(auto idx) -> bool {
+                        return (idx == 0) || device_comparator(idx - 1, idx);
+                      });
+
+    return thrust::count(rmm::exec_policy(stream), d_results.begin(), d_results.end(), false) == 0;
+  } else {
+    auto const device_comparator = comparator.less<false>(has_nested_nulls(in));
+
+    return thrust::is_sorted(rmm::exec_policy(stream),
+                             thrust::counting_iterator<size_type>(0),
+                             thrust::counting_iterator<size_type>(in.num_rows()),
+                             device_comparator);
+  }
+}
+
+}  // namespace detail
+
+bool is_sorted(cudf::table_view const& in,
+               std::vector<order> const& column_order,
+               std::vector<null_order> const& null_precedence,
+               rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  if (in.num_columns() == 0 || in.num_rows() == 0) { return true; }
+
+  if (not column_order.empty()) {
+    CUDF_EXPECTS(static_cast<unsigned int>(in.num_columns()) == column_order.size(),
+                 "Number of columns in the table doesn't match the vector column_order's size .\n");
+  }
+
+  if (not null_precedence.empty()) {
+    CUDF_EXPECTS(
+      static_cast<unsigned int>(in.num_columns()) == null_precedence.size(),
+      "Number of columns in the table doesn't match the vector null_precedence's size .\n");
+  }
+
+  return detail::is_sorted(in, column_order, null_precedence, stream);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/sort/rank.cu b/cpp/src/sort/rank.cu
new file mode 100644
index 0000000..3ead8cf
--- /dev/null
+++ b/cpp/src/sort/rank.cu
@@ -0,0 +1,376 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+template <typename PermutationIteratorType, typename DeviceComparatorType>
+struct unique_functor {
+  unique_functor(PermutationIteratorType permute, DeviceComparatorType device_comparator)
+    : _permute(permute), _device_comparator(device_comparator)
+  {
+  }
+
+  auto __device__ operator()(size_type index) const noexcept
+  {
+    return static_cast<size_type>(index == 0 ||
+                                  not _device_comparator(_permute[index], _permute[index - 1]));
+  }
+
+ private:
+  PermutationIteratorType _permute;
+  DeviceComparatorType _device_comparator;
+};
+
+// Assign rank from 1 to n unique values. Equal values get same rank value.
+rmm::device_uvector<size_type> sorted_dense_rank(column_view input_col,
+                                                 column_view sorted_order_view,
+                                                 rmm::cuda_stream_view stream)
+{
+  auto const t_input    = table_view{{input_col}};
+  auto const comparator = cudf::experimental::row::equality::self_comparator{t_input, stream};
+
+  auto const sorted_index_order = thrust::make_permutation_iterator(
+    sorted_order_view.begin<size_type>(), thrust::make_counting_iterator<size_type>(0));
+
+  auto const input_size = input_col.size();
+  rmm::device_uvector<size_type> dense_rank_sorted(input_size, stream);
+
+  auto const comparator_helper = [&](auto const device_comparator) {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(input_size),
+                      dense_rank_sorted.data(),
+                      unique_functor<decltype(sorted_index_order), decltype(device_comparator)>{
+                        sorted_index_order, device_comparator});
+  };
+
+  if (cudf::detail::has_nested_columns(t_input)) {
+    auto const device_comparator =
+      comparator.equal_to<true>(nullate::DYNAMIC{has_nested_nulls(t_input)});
+    comparator_helper(device_comparator);
+  } else {
+    auto const device_comparator =
+      comparator.equal_to<false>(nullate::DYNAMIC{has_nested_nulls(t_input)});
+    comparator_helper(device_comparator);
+  }
+
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         dense_rank_sorted.begin(),
+                         dense_rank_sorted.end(),
+                         dense_rank_sorted.data());
+
+  return dense_rank_sorted;
+}
+
+/**
+ * @brief Breaks the ties among equal value groups using binary operator and
+ * transform this tied value to final rank.
+ *
+ * @param dense_rank dense rank of sorted input column (acts as key for value
+ * groups).
+ * @param tie_iter  iterator of rank to break ties among equal value groups.
+ * @param sorted_order_view sorted order indices of input column
+ * @param rank_iter output rank iterator
+ * @param tie_breaker tie breaking operator. For example, maximum & minimum.
+ * @param transformer transform after tie breaking (useful for average).
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+template <typename TieType,
+          typename outputIterator,
+          typename TieBreaker,
+          typename Transformer,
+          typename TieIterator>
+void tie_break_ranks_transform(cudf::device_span<size_type const> dense_rank_sorted,
+                               TieIterator tie_iter,
+                               column_view const& sorted_order_view,
+                               outputIterator rank_iter,
+                               TieBreaker tie_breaker,
+                               Transformer transformer,
+                               rmm::cuda_stream_view stream)
+{
+  auto const input_size = sorted_order_view.size();
+  // algorithm: reduce_by_key(dense_rank, 1, n, reduction_tie_breaker)
+  // reduction_tie_breaker = min, max, min_count
+  rmm::device_uvector<TieType> tie_sorted(sorted_order_view.size(), stream);
+  thrust::reduce_by_key(rmm::exec_policy(stream),
+                        dense_rank_sorted.begin(),
+                        dense_rank_sorted.end(),
+                        tie_iter,
+                        thrust::make_discard_iterator(),
+                        tie_sorted.begin(),
+                        thrust::equal_to{},
+                        tie_breaker);
+  auto sorted_tied_rank = thrust::make_transform_iterator(
+    dense_rank_sorted.begin(),
+    [tied_rank = tie_sorted.begin(), transformer] __device__(auto dense_pos) {
+      return transformer(tied_rank[dense_pos - 1]);
+    });
+  thrust::scatter(rmm::exec_policy(stream),
+                  sorted_tied_rank,
+                  sorted_tied_rank + input_size,
+                  sorted_order_view.begin<size_type>(),
+                  rank_iter);
+}
+
+template <typename outputType>
+void rank_first(column_view sorted_order_view,
+                mutable_column_view rank_mutable_view,
+                rmm::cuda_stream_view stream)
+{
+  // stable sort order ranking (no ties)
+  thrust::scatter(rmm::exec_policy(stream),
+                  thrust::make_counting_iterator<size_type>(1),
+                  thrust::make_counting_iterator<size_type>(rank_mutable_view.size() + 1),
+                  sorted_order_view.begin<size_type>(),
+                  rank_mutable_view.begin<outputType>());
+}
+
+template <typename outputType>
+void rank_dense(cudf::device_span<size_type const> dense_rank_sorted,
+                column_view sorted_order_view,
+                mutable_column_view rank_mutable_view,
+                rmm::cuda_stream_view stream)
+{
+  // All equal values have same rank and rank always increases by 1 between groups
+  thrust::scatter(rmm::exec_policy(stream),
+                  dense_rank_sorted.begin(),
+                  dense_rank_sorted.end(),
+                  sorted_order_view.begin<size_type>(),
+                  rank_mutable_view.begin<outputType>());
+}
+
+template <typename outputType>
+void rank_min(cudf::device_span<size_type const> group_keys,
+              column_view sorted_order_view,
+              mutable_column_view rank_mutable_view,
+              rmm::cuda_stream_view stream)
+{
+  // min of first in the group
+  // All equal values have min of ranks among them.
+  // algorithm: reduce_by_key(dense_rank, 1, n, min), scatter
+  tie_break_ranks_transform<size_type>(group_keys,
+                                       thrust::make_counting_iterator<size_type>(1),
+                                       sorted_order_view,
+                                       rank_mutable_view.begin<outputType>(),
+                                       thrust::minimum{},
+                                       thrust::identity{},
+                                       stream);
+}
+
+template <typename outputType>
+void rank_max(cudf::device_span<size_type const> group_keys,
+              column_view sorted_order_view,
+              mutable_column_view rank_mutable_view,
+              rmm::cuda_stream_view stream)
+{
+  // max of first in the group
+  // All equal values have max of ranks among them.
+  // algorithm: reduce_by_key(dense_rank, 1, n, max), scatter
+  tie_break_ranks_transform<size_type>(group_keys,
+                                       thrust::make_counting_iterator<size_type>(1),
+                                       sorted_order_view,
+                                       rank_mutable_view.begin<outputType>(),
+                                       thrust::maximum{},
+                                       thrust::identity{},
+                                       stream);
+}
+
+// Returns index, count
+template <typename T>
+struct index_counter {
+  __device__ T operator()(size_type i) { return T{i, 1}; }
+};
+
+void rank_average(cudf::device_span<size_type const> group_keys,
+                  column_view sorted_order_view,
+                  mutable_column_view rank_mutable_view,
+                  rmm::cuda_stream_view stream)
+{
+  // k, k+1, .. k+n-1
+  // average = (n*k+ n*(n-1)/2)/n
+  // average = k + (n-1)/2 = min + (count-1)/2
+  // Calculate Min of ranks and Count of equal values
+  // algorithm: reduce_by_key(dense_rank, 1, n, min_count)
+  //            transform(min+(count-1)/2), scatter
+  using MinCount = thrust::pair<size_type, size_type>;
+  tie_break_ranks_transform<MinCount>(
+    group_keys,
+    // Use device functor with return type. Cannot use device lambda due to limitation.
+    // https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#extended-lambda-restrictions
+    cudf::detail::make_counting_transform_iterator(1, index_counter<MinCount>{}),
+    sorted_order_view,
+    rank_mutable_view.begin<double>(),
+    [] __device__(auto rank_count1, auto rank_count2) {
+      return MinCount{std::min(rank_count1.first, rank_count2.first),
+                      rank_count1.second + rank_count2.second};
+    },
+    [] __device__(MinCount minrank_count) {  // min+(count-1)/2
+      return static_cast<double>(thrust::get<0>(minrank_count)) +
+             (static_cast<double>(thrust::get<1>(minrank_count)) - 1) / 2.0;
+    },
+    stream);
+}
+
+}  // anonymous namespace
+
+std::unique_ptr<column> rank(column_view const& input,
+                             rank_method method,
+                             order column_order,
+                             null_policy null_handling,
+                             null_order null_precedence,
+                             bool percentage,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  data_type const output_type         = (percentage or method == rank_method::AVERAGE)
+                                          ? data_type(type_id::FLOAT64)
+                                          : data_type(type_to_id<size_type>());
+  std::unique_ptr<column> rank_column = [&null_handling, &output_type, &input, &stream, &mr] {
+    // na_option=keep assign NA to NA values
+    if (null_handling == null_policy::EXCLUDE)
+      return make_numeric_column(output_type,
+                                 input.size(),
+                                 detail::copy_bitmask(input, stream, mr),
+                                 input.null_count(),
+                                 stream,
+                                 mr);
+    else
+      return make_numeric_column(output_type, input.size(), mask_state::UNALLOCATED, stream, mr);
+  }();
+  auto rank_mutable_view = rank_column->mutable_view();
+
+  std::unique_ptr<column> sorted_order =
+    (method == rank_method::FIRST)
+      ? detail::stable_sorted_order(
+          table_view{{input}}, {column_order}, {null_precedence}, stream, mr)
+      : detail::sorted_order(table_view{{input}}, {column_order}, {null_precedence}, stream, mr);
+  column_view sorted_order_view = sorted_order->view();
+
+  // dense: All equal values have same rank and rank always increases by 1 between groups
+  // acts as key for min, max, average to denote equal value groups
+  rmm::device_uvector<size_type> const dense_rank_sorted =
+    [&method, &input, &sorted_order_view, &stream] {
+      if (method != rank_method::FIRST)
+        return sorted_dense_rank(input, sorted_order_view, stream);
+      else
+        return rmm::device_uvector<size_type>(0, stream);
+    }();
+
+  if (output_type.id() == type_id::FLOAT64) {
+    switch (method) {
+      case rank_method::FIRST:
+        rank_first<double>(sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::DENSE:
+        rank_dense<double>(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::MIN:
+        rank_min<double>(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::MAX:
+        rank_max<double>(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::AVERAGE:
+        rank_average(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      default: CUDF_FAIL("Unexpected rank_method for rank()");
+    }
+  } else {
+    switch (method) {
+      case rank_method::FIRST:
+        rank_first<size_type>(sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::DENSE:
+        rank_dense<size_type>(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::MIN:
+        rank_min<size_type>(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::MAX:
+        rank_max<size_type>(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      case rank_method::AVERAGE:
+        rank_average(dense_rank_sorted, sorted_order_view, rank_mutable_view, stream);
+        break;
+      default: CUDF_FAIL("Unexpected rank_method for rank()");
+    }
+  }
+
+  // pct inplace transform
+  if (percentage) {
+    auto rank_iter = rank_mutable_view.begin<double>();
+    size_type const count =
+      (null_handling == null_policy::EXCLUDE) ? input.size() - input.null_count() : input.size();
+    auto drs            = dense_rank_sorted.data();
+    bool const is_dense = (method == rank_method::DENSE);
+    thrust::transform(rmm::exec_policy(stream),
+                      rank_iter,
+                      rank_iter + input.size(),
+                      rank_iter,
+                      [is_dense, drs, count] __device__(double r) -> double {
+                        return is_dense ? r / drs[count - 1] : r / count;
+                      });
+  }
+  return rank_column;
+}
+}  // namespace detail
+
+std::unique_ptr<column> rank(column_view const& input,
+                             rank_method method,
+                             order column_order,
+                             null_policy null_handling,
+                             null_order null_precedence,
+                             bool percentage,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rank(
+    input, method, column_order, null_handling, null_precedence, percentage, stream, mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/sort/segmented_sort.cu b/cpp/src/sort/segmented_sort.cu
new file mode 100644
index 0000000..d945734
--- /dev/null
+++ b/cpp/src/sort/segmented_sort.cu
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "segmented_sort_impl.cuh"
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+
+rmm::device_uvector<size_type> get_segment_indices(size_type num_rows,
+                                                   column_view const& offsets,
+                                                   rmm::cuda_stream_view stream)
+{
+  rmm::device_uvector<size_type> segment_ids(num_rows, stream);
+
+  auto offset_begin  = offsets.begin<size_type>();
+  auto offset_end    = offsets.end<size_type>();
+  auto counting_iter = thrust::make_counting_iterator<size_type>(0);
+  thrust::transform(rmm::exec_policy(stream),
+                    counting_iter,
+                    counting_iter + segment_ids.size(),
+                    segment_ids.begin(),
+                    [offset_begin, offset_end] __device__(auto idx) {
+                      if (offset_begin == offset_end || idx < *offset_begin) { return idx; }
+                      if (idx >= *(offset_end - 1)) { return idx + 1; }
+                      return static_cast<size_type>(
+                        *thrust::upper_bound(thrust::seq, offset_begin, offset_end, idx));
+                    });
+  return segment_ids;
+}
+
+std::unique_ptr<column> segmented_sorted_order(table_view const& keys,
+                                               column_view const& segment_offsets,
+                                               std::vector<order> const& column_order,
+                                               std::vector<null_order> const& null_precedence,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  return segmented_sorted_order_common<sort_method::UNSTABLE>(
+    keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> segmented_sort_by_key(table_view const& values,
+                                             table_view const& keys,
+                                             column_view const& segment_offsets,
+                                             std::vector<order> const& column_order,
+                                             std::vector<null_order> const& null_precedence,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  return segmented_sort_by_key_common<sort_method::UNSTABLE>(
+    values, keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> segmented_sorted_order(table_view const& keys,
+                                               column_view const& segment_offsets,
+                                               std::vector<order> const& column_order,
+                                               std::vector<null_order> const& null_precedence,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::segmented_sorted_order(
+    keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> segmented_sort_by_key(table_view const& values,
+                                             table_view const& keys,
+                                             column_view const& segment_offsets,
+                                             std::vector<order> const& column_order,
+                                             std::vector<null_order> const& null_precedence,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::segmented_sort_by_key(
+    values, keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/sort/segmented_sort_impl.cuh b/cpp/src/sort/segmented_sort_impl.cuh
new file mode 100644
index 0000000..5d11bf0
--- /dev/null
+++ b/cpp/src/sort/segmented_sort_impl.cuh
@@ -0,0 +1,328 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/sorting.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <cub/device/device_segmented_sort.cuh>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief The enum specifying which sorting method to use (stable or unstable).
+ */
+enum class sort_method { STABLE, UNSTABLE };
+
+/**
+ * @brief Functor performs faster segmented sort on eligible columns
+ */
+template <sort_method method>
+struct column_fast_sort_fn {
+  /**
+   * @brief Run-time check for faster segmented sort on an eligible column
+   *
+   * Fast segmented sort can handle integral types including
+   * decimal types if dispatch_storage_type is used but it does not support int128.
+   */
+  static bool is_fast_sort_supported(column_view const& col)
+  {
+    return !col.has_nulls() and
+           (cudf::is_integral(col.type()) ||
+            (cudf::is_fixed_point(col.type()) and (col.type().id() != type_id::DECIMAL128)));
+  }
+
+  /**
+   * @brief Compile-time check for supporting fast segmented sort for a specific type
+   *
+   * The dispatch_storage_type means we can check for integral types to
+   * include fixed-point types but the CUB limitation means we need to exclude int128.
+   */
+  template <typename T>
+  static constexpr bool is_fast_sort_supported()
+  {
+    return cudf::is_integral<T>() and !std::is_same_v<__int128, T>;
+  }
+
+  template <typename T>
+  void fast_sort(column_view const& input,
+                 column_view const& segment_offsets,
+                 mutable_column_view& indices,
+                 bool ascending,
+                 rmm::cuda_stream_view stream)
+  {
+    // CUB's segmented sort functions cannot accept iterators.
+    // We create a temporary column here for it to use.
+    auto temp_col                   = cudf::detail::allocate_like(input,
+                                                input.size(),
+                                                mask_allocation_policy::NEVER,
+                                                stream,
+                                                rmm::mr::get_current_device_resource());
+    mutable_column_view output_view = temp_col->mutable_view();
+
+    // DeviceSegmentedSort is faster than DeviceSegmentedRadixSort at this time
+    auto fast_sort_impl = [stream](bool ascending, [[maybe_unused]] auto&&... args) {
+      rmm::device_buffer d_temp_storage;
+      size_t temp_storage_bytes = 0;
+      if (ascending) {
+        if constexpr (method == sort_method::STABLE) {
+          cub::DeviceSegmentedSort::StableSortPairs(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+          d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+          cub::DeviceSegmentedSort::StableSortPairs(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+        } else {
+          cub::DeviceSegmentedSort::SortPairs(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+          d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+          cub::DeviceSegmentedSort::SortPairs(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+        }
+      } else {
+        if constexpr (method == sort_method::STABLE) {
+          cub::DeviceSegmentedSort::StableSortPairsDescending(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+          d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+          cub::DeviceSegmentedSort::StableSortPairsDescending(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+        } else {
+          cub::DeviceSegmentedSort::SortPairsDescending(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+          d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+          cub::DeviceSegmentedSort::SortPairsDescending(
+            d_temp_storage.data(), temp_storage_bytes, std::forward<decltype(args)>(args)...);
+        }
+      }
+    };
+
+    fast_sort_impl(ascending,
+                   input.begin<T>(),
+                   output_view.begin<T>(),
+                   indices.begin<size_type>(),
+                   indices.begin<size_type>(),
+                   input.size(),
+                   segment_offsets.size() - 1,
+                   segment_offsets.begin<size_type>(),
+                   segment_offsets.begin<size_type>() + 1,
+                   stream.value());
+  }
+
+  template <typename T, CUDF_ENABLE_IF(is_fast_sort_supported<T>())>
+  void operator()(column_view const& input,
+                  column_view const& segment_offsets,
+                  mutable_column_view& indices,
+                  bool ascending,
+                  rmm::cuda_stream_view stream)
+  {
+    fast_sort<T>(input, segment_offsets, indices, ascending, stream);
+  }
+
+  template <typename T, CUDF_ENABLE_IF(!is_fast_sort_supported<T>())>
+  void operator()(
+    column_view const&, column_view const&, mutable_column_view&, bool, rmm::cuda_stream_view)
+  {
+    CUDF_FAIL("Column type cannot be used with fast-sort function");
+  }
+};
+
+/**
+ * @brief Performs faster sort on eligible columns
+ *
+ * Check the `is_fast_sort_supported()==true` on the input column before using this function.
+ *
+ * @param input Column to sort
+ * @param segment_offsets Identifies segments to sort within
+ * @param column_order Sort ascending or descending
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+template <sort_method method>
+std::unique_ptr<column> fast_segmented_sorted_order(column_view const& input,
+                                                    column_view const& segment_offsets,
+                                                    order const& column_order,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  // Unfortunately, CUB's segmented sort functions cannot accept iterators.
+  // We have to build a pre-filled sequence of indices as input.
+  auto sorted_indices =
+    cudf::detail::sequence(input.size(), numeric_scalar<size_type>{0, true, stream}, stream, mr);
+  auto indices_view = sorted_indices->mutable_view();
+
+  cudf::type_dispatcher<dispatch_storage_type>(input.type(),
+                                               column_fast_sort_fn<method>{},
+                                               input,
+                                               segment_offsets,
+                                               indices_view,
+                                               column_order == order::ASCENDING,
+                                               stream);
+  return sorted_indices;
+}
+
+/**
+ * @brief Builds indices to identify segments to sort
+ *
+ * The segments are added to the input table-view keys so they
+ * are lexicographically sorted within the segmented groups.
+ *
+ * ```
+ * Example 1:
+ * num_rows = 10
+ * offsets = {0, 3, 7, 10}
+ * segment-indices -> { 3,3,3, 7,7,7,7, 10,10,10 }
+ * ```
+ *
+ * ```
+ * Example 2: (offsets do not cover all indices)
+ * num_rows = 10
+ * offsets = {3, 7}
+ * segment-indices -> { 0,1,2, 7,7,7,7, 8,9,10 }
+ * ```
+ *
+ * @param num_rows Total number of rows in the input keys to sort
+ * @param offsets The offsets identifying the segments
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+rmm::device_uvector<size_type> get_segment_indices(size_type num_rows,
+                                                   column_view const& offsets,
+                                                   rmm::cuda_stream_view stream);
+
+/**
+ * @brief Segmented sorted-order utility
+ *
+ * Returns the indices that map the column to a segmented sorted table.
+ * Automatically handles calling accelerated code paths as appropriate.
+ *
+ * @tparam method Specifies sort is stable or not
+ * @param keys Table to sort
+ * @param segment_offsets Identifies the segments within the keys
+ * @param column_order Sort order for each column in the keys
+ * @param null_precedence Where to place the null entries for each column
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource to allocate any returned objects
+ */
+template <sort_method method>
+std::unique_ptr<column> segmented_sorted_order_common(
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (keys.num_rows() == 0 || keys.num_columns() == 0) {
+    return cudf::make_empty_column(type_to_id<size_type>());
+  }
+
+  CUDF_EXPECTS(segment_offsets.type() == data_type(type_to_id<size_type>()),
+               "segment offsets should be size_type");
+
+  if (not column_order.empty()) {
+    CUDF_EXPECTS(static_cast<std::size_t>(keys.num_columns()) == column_order.size(),
+                 "Mismatch between number of columns and column order.");
+  }
+
+  if (not null_precedence.empty()) {
+    CUDF_EXPECTS(static_cast<std::size_t>(keys.num_columns()) == null_precedence.size(),
+                 "Mismatch between number of columns and null_precedence size.");
+  }
+
+  // the average row size for which to prefer fast sort
+  constexpr cudf::size_type MAX_AVG_LIST_SIZE_FOR_FAST_SORT{100};
+  // the maximum row count for which to prefer fast sort
+  constexpr cudf::size_type MAX_LIST_SIZE_FOR_FAST_SORT{1 << 18};
+
+  // fast-path for single column sort:
+  // - single-column table
+  // - not stable-sort
+  // - no nulls and allowable fixed-width type
+  // - size and width are limited -- based on benchmark results
+  if (keys.num_columns() == 1 and
+      column_fast_sort_fn<method>::is_fast_sort_supported(keys.column(0)) and
+      (segment_offsets.size() > 0) and
+      (((keys.num_rows() / segment_offsets.size()) < MAX_AVG_LIST_SIZE_FOR_FAST_SORT) or
+       (keys.num_rows() < MAX_LIST_SIZE_FOR_FAST_SORT))) {
+    auto const col_order = column_order.empty() ? order::ASCENDING : column_order.front();
+    return fast_segmented_sorted_order<method>(
+      keys.column(0), segment_offsets, col_order, stream, mr);
+  }
+
+  // Get segment id of each element in all segments.
+  auto segment_ids = get_segment_indices(keys.num_rows(), segment_offsets, stream);
+
+  // insert segment id before all columns.
+  std::vector<column_view> keys_with_segid;
+  keys_with_segid.reserve(keys.num_columns() + 1);
+  keys_with_segid.push_back(column_view(
+    data_type(type_to_id<size_type>()), segment_ids.size(), segment_ids.data(), nullptr, 0));
+  keys_with_segid.insert(keys_with_segid.end(), keys.begin(), keys.end());
+  auto segid_keys = table_view(keys_with_segid);
+
+  auto prepend_default = [](auto const& vector, auto default_value) {
+    if (vector.empty()) return vector;
+    std::remove_cv_t<std::remove_reference_t<decltype(vector)>> pre_vector;
+    pre_vector.reserve(pre_vector.size() + 1);
+    pre_vector.push_back(default_value);
+    pre_vector.insert(pre_vector.end(), vector.begin(), vector.end());
+    return pre_vector;
+  };
+  auto child_column_order    = prepend_default(column_order, order::ASCENDING);
+  auto child_null_precedence = prepend_default(null_precedence, null_order::AFTER);
+
+  // return sorted order of child columns
+  if constexpr (method == sort_method::STABLE) {
+    return detail::stable_sorted_order(
+      segid_keys, child_column_order, child_null_precedence, stream, mr);
+  } else {
+    return detail::sorted_order(segid_keys, child_column_order, child_null_precedence, stream, mr);
+  }
+}
+
+template <sort_method method>
+std::unique_ptr<table> segmented_sort_by_key_common(table_view const& values,
+                                                    table_view const& keys,
+                                                    column_view const& segment_offsets,
+                                                    std::vector<order> const& column_order,
+                                                    std::vector<null_order> const& null_precedence,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(values.num_rows() == keys.num_rows(),
+               "Mismatch in number of rows for values and keys");
+  auto sorted_order = segmented_sorted_order_common<method>(keys,
+                                                            segment_offsets,
+                                                            column_order,
+                                                            null_precedence,
+                                                            stream,
+                                                            rmm::mr::get_current_device_resource());
+  // Gather segmented sort of child value columns
+  return detail::gather(values,
+                        sorted_order->view(),
+                        out_of_bounds_policy::DONT_CHECK,
+                        detail::negative_index_policy::NOT_ALLOWED,
+                        stream,
+                        mr);
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/sort/sort.cu b/cpp/src/sort/sort.cu
new file mode 100644
index 0000000..46edae7
--- /dev/null
+++ b/cpp/src/sort/sort.cu
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "sort_impl.cuh"
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/sort.h>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<column> sorted_order(table_view const& input,
+                                     std::vector<order> const& column_order,
+                                     std::vector<null_order> const& null_precedence,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  return sorted_order<false>(input, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> sort_by_key(table_view const& values,
+                                   table_view const& keys,
+                                   std::vector<order> const& column_order,
+                                   std::vector<null_order> const& null_precedence,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(values.num_rows() == keys.num_rows(),
+               "Mismatch in number of rows for values and keys");
+
+  auto sorted_order = detail::sorted_order(
+    keys, column_order, null_precedence, stream, rmm::mr::get_current_device_resource());
+
+  return detail::gather(values,
+                        sorted_order->view(),
+                        out_of_bounds_policy::DONT_CHECK,
+                        detail::negative_index_policy::NOT_ALLOWED,
+                        stream,
+                        mr);
+}
+
+struct inplace_column_sort_fn {
+  template <typename T, std::enable_if_t<cudf::is_fixed_width<T>()>* = nullptr>
+  void operator()(mutable_column_view& col, bool ascending, rmm::cuda_stream_view stream) const
+  {
+    CUDF_EXPECTS(!col.has_nulls(), "Nulls not supported for in-place sort");
+    if (ascending) {
+      thrust::sort(rmm::exec_policy(stream), col.begin<T>(), col.end<T>(), thrust::less<T>());
+    } else {
+      thrust::sort(rmm::exec_policy(stream), col.begin<T>(), col.end<T>(), thrust::greater<T>());
+    }
+  }
+
+  template <typename T, std::enable_if_t<!cudf::is_fixed_width<T>()>* = nullptr>
+  void operator()(mutable_column_view&, bool, rmm::cuda_stream_view) const
+  {
+    CUDF_FAIL("Column type must be relationally comparable and fixed-width");
+  }
+};
+
+std::unique_ptr<table> sort(table_view const& input,
+                            std::vector<order> const& column_order,
+                            std::vector<null_order> const& null_precedence,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  // fast-path sort conditions: single, non-floating-point, fixed-width column with no nulls
+  if (input.num_columns() == 1 && !input.column(0).has_nulls() &&
+      cudf::is_fixed_width(input.column(0).type()) &&
+      !cudf::is_floating_point(input.column(0).type())) {
+    auto output    = std::make_unique<column>(input.column(0), stream, mr);
+    auto view      = output->mutable_view();
+    bool ascending = (column_order.empty() ? true : column_order.front() == order::ASCENDING);
+    cudf::type_dispatcher<dispatch_storage_type>(
+      output->type(), inplace_column_sort_fn{}, view, ascending, stream);
+    std::vector<std::unique_ptr<column>> columns;
+    columns.emplace_back(std::move(output));
+    return std::make_unique<table>(std::move(columns));
+  }
+  return detail::sort_by_key(
+    input, input, column_order, null_precedence, cudf::get_default_stream(), mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> sorted_order(table_view const& input,
+                                     std::vector<order> const& column_order,
+                                     std::vector<null_order> const& null_precedence,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sorted_order(input, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> sort(table_view const& input,
+                            std::vector<order> const& column_order,
+                            std::vector<null_order> const& null_precedence,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sort(input, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> sort_by_key(table_view const& values,
+                                   table_view const& keys,
+                                   std::vector<order> const& column_order,
+                                   std::vector<null_order> const& null_precedence,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::sort_by_key(values, keys, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/sort/sort_column.cu b/cpp/src/sort/sort_column.cu
new file mode 100644
index 0000000..9df0425
--- /dev/null
+++ b/cpp/src/sort/sort_column.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "sort_column_impl.cuh"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/sequence.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc
+ * sorted_order(column_view&,order,null_order,rmm::cuda_stream_view,rmm::mr::device_memory_resource*)
+ */
+template <>
+std::unique_ptr<column> sorted_order<false>(column_view const& input,
+                                            order column_order,
+                                            null_order null_precedence,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  auto sorted_indices = cudf::make_numeric_column(
+    data_type(type_to_id<size_type>()), input.size(), mask_state::UNALLOCATED, stream, mr);
+  mutable_column_view indices_view = sorted_indices->mutable_view();
+  thrust::sequence(
+    rmm::exec_policy(stream), indices_view.begin<size_type>(), indices_view.end<size_type>(), 0);
+  cudf::type_dispatcher<dispatch_storage_type>(input.type(),
+                                               column_sorted_order_fn<false>{},
+                                               input,
+                                               indices_view,
+                                               column_order == order::ASCENDING,
+                                               null_precedence,
+                                               stream);
+  return sorted_indices;
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/sort/sort_column_impl.cuh b/cpp/src/sort/sort_column_impl.cuh
new file mode 100644
index 0000000..5abc6bd
--- /dev/null
+++ b/cpp/src/sort/sort_column_impl.cuh
@@ -0,0 +1,203 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Sort indices of a single column.
+ *
+ * This API offers fast sorting for primitive types. It cannot handle nested types and will not
+ * consider `NaN` as equivalent to other `NaN`.
+ *
+ * @tparam stable Whether to use stable sort
+ * @param input Column to sort. The column data is not modified.
+ * @param column_order Ascending or descending sort order
+ * @param null_precedence How null rows are to be ordered
+ * @param stable True if sort should be stable
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return Sorted indices for the input column.
+ */
+template <bool stable>
+std::unique_ptr<column> sorted_order(column_view const& input,
+                                     order column_order,
+                                     null_order null_precedence,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr);
+
+/**
+ * @brief Comparator functor needed for single column sort.
+ *
+ * @tparam Column element type.
+ */
+template <typename T>
+struct simple_comparator {
+  __device__ bool operator()(size_type lhs, size_type rhs)
+  {
+    if (has_nulls) {
+      bool lhs_null{d_column.is_null(lhs)};
+      bool rhs_null{d_column.is_null(rhs)};
+      if (lhs_null || rhs_null) {
+        if (!ascending) thrust::swap(lhs_null, rhs_null);
+        return (null_precedence == cudf::null_order::BEFORE ? !rhs_null : !lhs_null);
+      }
+    }
+    return relational_compare(d_column.element<T>(lhs), d_column.element<T>(rhs)) ==
+           (ascending ? weak_ordering::LESS : weak_ordering::GREATER);
+  }
+  column_device_view const d_column;
+  bool has_nulls;
+  bool ascending;
+  null_order null_precedence{};
+};
+
+template <bool stable>
+struct column_sorted_order_fn {
+  /**
+   * @brief Compile time check for allowing faster sort.
+   *
+   * Faster sort is defined for fixed-width types where only
+   * the primitive comparators thrust::greater or thrust::less
+   * are needed.
+   *
+   * Floating point is removed here for special handling of NaNs
+   * which require the row-comparator.
+   */
+  template <typename T>
+  static constexpr bool is_faster_sort_supported()
+  {
+    return cudf::is_fixed_width<T>() && !cudf::is_floating_point<T>();
+  }
+
+  /**
+   * @brief Sorts fixed-width columns using faster thrust sort.
+   *
+   * Should not be called if `input.has_nulls()==true`
+   *
+   * @param input Column to sort
+   * @param indices Output sorted indices
+   * @param ascending True if sort order is ascending
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  template <typename T>
+  void faster_sort(column_view const& input,
+                   mutable_column_view& indices,
+                   bool ascending,
+                   rmm::cuda_stream_view stream)
+  {
+    // A thrust sort on a column of primitive types will use a radix sort.
+    // For other fixed-width types, thrust will use merge-sort.
+    // But this also requires making a copy of the input data.
+    auto temp_col = column(input, stream);
+    auto d_col    = temp_col.mutable_view();
+
+    auto const do_sort = [&](auto const comp) {
+      // Compiling `thrust::*sort*` APIs is expensive.
+      // Thus, we should optimize that by using constexpr condition to only compile what we need.
+      if constexpr (stable) {
+        thrust::stable_sort_by_key(rmm::exec_policy(stream),
+                                   d_col.begin<T>(),
+                                   d_col.end<T>(),
+                                   indices.begin<size_type>(),
+                                   comp);
+      } else {
+        thrust::sort_by_key(rmm::exec_policy(stream),
+                            d_col.begin<T>(),
+                            d_col.end<T>(),
+                            indices.begin<size_type>(),
+                            comp);
+      }
+    };
+
+    if (ascending) {
+      do_sort(thrust::less<T>{});
+    } else {
+      do_sort(thrust::greater<T>{});
+    }
+  }
+
+  /**
+   * @brief Sorts a single column with a relationally comparable type.
+   *
+   * This is used when a comparator is required.
+   *
+   * @param input Column to sort
+   * @param indices Output sorted indices
+   * @param ascending True if sort order is ascending
+   * @param null_precedence How null rows are to be ordered
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   */
+  template <typename T>
+  void sorted_order(column_view const& input,
+                    mutable_column_view& indices,
+                    bool ascending,
+                    null_order null_precedence,
+                    rmm::cuda_stream_view stream)
+  {
+    auto keys = column_device_view::create(input, stream);
+    auto comp = simple_comparator<T>{*keys, input.has_nulls(), ascending, null_precedence};
+    // Compiling `thrust::*sort*` APIs is expensive.
+    // Thus, we should optimize that by using constexpr condition to only compile what we need.
+    if constexpr (stable) {
+      thrust::stable_sort(
+        rmm::exec_policy(stream), indices.begin<size_type>(), indices.end<size_type>(), comp);
+    } else {
+      thrust::sort(
+        rmm::exec_policy(stream), indices.begin<size_type>(), indices.end<size_type>(), comp);
+    }
+  }
+
+  template <typename T, CUDF_ENABLE_IF(cudf::is_relationally_comparable<T, T>())>
+  void operator()(column_view const& input,
+                  mutable_column_view& indices,
+                  bool ascending,
+                  null_order null_precedence,
+                  rmm::cuda_stream_view stream)
+  {
+    if constexpr (is_faster_sort_supported<T>()) {
+      if (input.has_nulls()) {
+        sorted_order<T>(input, indices, ascending, null_precedence, stream);
+      } else {
+        faster_sort<T>(input, indices, ascending, stream);
+      }
+    } else {
+      sorted_order<T>(input, indices, ascending, null_precedence, stream);
+    }
+  }
+
+  template <typename T, CUDF_ENABLE_IF(not cudf::is_relationally_comparable<T, T>())>
+  void operator()(column_view const&, mutable_column_view&, bool, null_order, rmm::cuda_stream_view)
+  {
+    CUDF_FAIL("Column type must be relationally comparable");
+  }
+};
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/sort/sort_impl.cuh b/cpp/src/sort/sort_impl.cuh
new file mode 100644
index 0000000..ef56da0
--- /dev/null
+++ b/cpp/src/sort/sort_impl.cuh
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "sort_column_impl.cuh"
+
+#include <cudf/column/column_factories.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc
+ * sorted_order(table_view&,std::vector<order>,std::vector<null_order>,rmm::mr::device_memory_resource*)
+ *
+ * @tparam stable Whether to use stable sort
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+template <bool stable>
+std::unique_ptr<column> sorted_order(table_view input,
+                                     std::vector<order> const& column_order,
+                                     std::vector<null_order> const& null_precedence,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  if (input.num_rows() == 0 or input.num_columns() == 0) {
+    return cudf::make_numeric_column(data_type(type_to_id<size_type>()), 0);
+  }
+
+  if (not column_order.empty()) {
+    CUDF_EXPECTS(static_cast<std::size_t>(input.num_columns()) == column_order.size(),
+                 "Mismatch between number of columns and column order.");
+  }
+
+  if (not null_precedence.empty()) {
+    CUDF_EXPECTS(static_cast<std::size_t>(input.num_columns()) == null_precedence.size(),
+                 "Mismatch between number of columns and null_precedence size.");
+  }
+
+  // fast-path for single column sort
+  if (input.num_columns() == 1 and not cudf::is_nested(input.column(0).type())) {
+    auto const single_col = input.column(0);
+    auto const col_order  = column_order.empty() ? order::ASCENDING : column_order.front();
+    auto const null_prec  = null_precedence.empty() ? null_order::BEFORE : null_precedence.front();
+    return sorted_order<stable>(single_col, col_order, null_prec, stream, mr);
+  }
+
+  std::unique_ptr<column> sorted_indices = cudf::make_numeric_column(
+    data_type(type_to_id<size_type>()), input.num_rows(), mask_state::UNALLOCATED, stream, mr);
+  mutable_column_view mutable_indices_view = sorted_indices->mutable_view();
+  thrust::sequence(rmm::exec_policy(stream),
+                   mutable_indices_view.begin<size_type>(),
+                   mutable_indices_view.end<size_type>(),
+                   0);
+
+  auto const do_sort = [&](auto const comparator) {
+    // Compiling `thrust::*sort*` APIs is expensive.
+    // Thus, we should optimize that by using constexpr condition to only compile what we need.
+    if constexpr (stable) {
+      thrust::stable_sort(rmm::exec_policy(stream),
+                          mutable_indices_view.begin<size_type>(),
+                          mutable_indices_view.end<size_type>(),
+                          comparator);
+    } else {
+      thrust::sort(rmm::exec_policy(stream),
+                   mutable_indices_view.begin<size_type>(),
+                   mutable_indices_view.end<size_type>(),
+                   comparator);
+    }
+  };
+
+  auto const comp = cudf::experimental::row::lexicographic::self_comparator(
+    input, column_order, null_precedence, stream);
+  if (cudf::detail::has_nested_columns(input)) {
+    auto const comparator = comp.less<true>(nullate::DYNAMIC{has_nested_nulls(input)});
+    do_sort(comparator);
+  } else {
+    auto const comparator = comp.less<false>(nullate::DYNAMIC{has_nested_nulls(input)});
+    do_sort(comparator);
+  }
+
+  return sorted_indices;
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/sort/stable_segmented_sort.cu b/cpp/src/sort/stable_segmented_sort.cu
new file mode 100644
index 0000000..4725d65
--- /dev/null
+++ b/cpp/src/sort/stable_segmented_sort.cu
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "segmented_sort_impl.cuh"
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+namespace cudf {
+namespace detail {
+
+std::unique_ptr<column> stable_segmented_sorted_order(
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  return segmented_sorted_order_common<sort_method::STABLE>(
+    keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> stable_segmented_sort_by_key(table_view const& values,
+                                                    table_view const& keys,
+                                                    column_view const& segment_offsets,
+                                                    std::vector<order> const& column_order,
+                                                    std::vector<null_order> const& null_precedence,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  return segmented_sort_by_key_common<sort_method::STABLE>(
+    values, keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> stable_segmented_sorted_order(
+  table_view const& keys,
+  column_view const& segment_offsets,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::stable_segmented_sorted_order(
+    keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> stable_segmented_sort_by_key(table_view const& values,
+                                                    table_view const& keys,
+                                                    column_view const& segment_offsets,
+                                                    std::vector<order> const& column_order,
+                                                    std::vector<null_order> const& null_precedence,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::stable_segmented_sort_by_key(
+    values, keys, segment_offsets, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/sort/stable_sort.cu b/cpp/src/sort/stable_sort.cu
new file mode 100644
index 0000000..cf602dc
--- /dev/null
+++ b/cpp/src/sort/stable_sort.cu
@@ -0,0 +1,83 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "sort_impl.cuh"
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<column> stable_sorted_order(table_view const& input,
+                                            std::vector<order> const& column_order,
+                                            std::vector<null_order> const& null_precedence,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  return sorted_order<true>(input, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> stable_sort_by_key(table_view const& values,
+                                          table_view const& keys,
+                                          std::vector<order> const& column_order,
+                                          std::vector<null_order> const& null_precedence,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(values.num_rows() == keys.num_rows(),
+               "Mismatch in number of rows for values and keys");
+
+  auto sorted_order = detail::stable_sorted_order(
+    keys, column_order, null_precedence, stream, rmm::mr::get_current_device_resource());
+
+  return detail::gather(values,
+                        sorted_order->view(),
+                        out_of_bounds_policy::DONT_CHECK,
+                        detail::negative_index_policy::NOT_ALLOWED,
+                        stream,
+                        mr);
+}
+}  // namespace detail
+
+std::unique_ptr<column> stable_sorted_order(table_view const& input,
+                                            std::vector<order> const& column_order,
+                                            std::vector<null_order> const& null_precedence,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::stable_sorted_order(input, column_order, null_precedence, stream, mr);
+}
+
+std::unique_ptr<table> stable_sort_by_key(table_view const& values,
+                                          table_view const& keys,
+                                          std::vector<order> const& column_order,
+                                          std::vector<null_order> const& null_precedence,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::stable_sort_by_key(values, keys, column_order, null_precedence, stream, mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/sort/stable_sort_column.cu b/cpp/src/sort/stable_sort_column.cu
new file mode 100644
index 0000000..be519ea
--- /dev/null
+++ b/cpp/src/sort/stable_sort_column.cu
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "sort_column_impl.cuh"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/sequence.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @copydoc
+ * sorted_order(column_view&,order,null_order,rmm::cuda_stream_view,rmm::mr::device_memory_resource*)
+ */
+template <>
+std::unique_ptr<column> sorted_order<true>(column_view const& input,
+                                           order column_order,
+                                           null_order null_precedence,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto sorted_indices = cudf::make_numeric_column(
+    data_type(type_to_id<size_type>()), input.size(), mask_state::UNALLOCATED, stream, mr);
+  mutable_column_view indices_view = sorted_indices->mutable_view();
+  thrust::sequence(
+    rmm::exec_policy(stream), indices_view.begin<size_type>(), indices_view.end<size_type>(), 0);
+  cudf::type_dispatcher<dispatch_storage_type>(input.type(),
+                                               column_sorted_order_fn<true>{},
+                                               input,
+                                               indices_view,
+                                               column_order == order::ASCENDING,
+                                               null_precedence,
+                                               stream);
+  return sorted_indices;
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/apply_boolean_mask.cu b/cpp/src/stream_compaction/apply_boolean_mask.cu
new file mode 100644
index 0000000..8f707f6
--- /dev/null
+++ b/cpp/src/stream_compaction/apply_boolean_mask.cu
@@ -0,0 +1,98 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <algorithm>
+
+namespace {
+// Returns true if the mask is true and valid (non-null) for index i
+// This is the filter functor for apply_boolean_mask
+template <bool has_nulls = true>
+struct boolean_mask_filter {
+  boolean_mask_filter(cudf::column_device_view const& boolean_mask) : boolean_mask{boolean_mask} {}
+
+  __device__ inline bool operator()(cudf::size_type i)
+  {
+    if (true == has_nulls) {
+      bool valid   = boolean_mask.is_valid(i);
+      bool is_true = boolean_mask.data<bool>()[i];
+
+      return is_true && valid;
+    } else {
+      return boolean_mask.data<bool>()[i];
+    }
+  }
+
+ protected:
+  cudf::column_device_view boolean_mask;
+};
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+/*
+ * Filters a table_view using a column_view of boolean values as a mask.
+ *
+ * calls copy_if() with the `boolean_mask_filter` functor.
+ */
+std::unique_ptr<table> apply_boolean_mask(table_view const& input,
+                                          column_view const& boolean_mask,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  if (boolean_mask.is_empty()) { return empty_like(input); }
+
+  CUDF_EXPECTS(boolean_mask.type().id() == type_id::BOOL8, "Mask must be Boolean type");
+  // zero-size inputs are OK, but otherwise input size must match mask size
+  CUDF_EXPECTS(input.num_rows() == 0 || input.num_rows() == boolean_mask.size(),
+               "Column size mismatch");
+
+  auto device_boolean_mask = cudf::column_device_view::create(boolean_mask, stream);
+
+  if (boolean_mask.has_nulls()) {
+    return detail::copy_if(input, boolean_mask_filter<true>{*device_boolean_mask}, stream, mr);
+  } else {
+    return detail::copy_if(input, boolean_mask_filter<false>{*device_boolean_mask}, stream, mr);
+  }
+}
+
+}  // namespace detail
+
+/*
+ * Filters a table_view using a column_view of boolean values as a mask.
+ */
+std::unique_ptr<table> apply_boolean_mask(table_view const& input,
+                                          column_view const& boolean_mask,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::apply_boolean_mask(input, boolean_mask, cudf::get_default_stream(), mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/distinct.cu b/cpp/src/stream_compaction/distinct.cu
new file mode 100644
index 0000000..cc1e342
--- /dev/null
+++ b/cpp/src/stream_compaction/distinct.cu
@@ -0,0 +1,179 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "distinct_helpers.hpp"
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+
+#include <utility>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+
+rmm::device_uvector<size_type> get_distinct_indices(table_view const& input,
+                                                    duplicate_keep_option keep,
+                                                    null_equality nulls_equal,
+                                                    nan_equality nans_equal,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  if (input.num_rows() == 0 or input.num_columns() == 0) {
+    return rmm::device_uvector<size_type>(0, stream, mr);
+  }
+
+  auto map = hash_map_type{compute_hash_table_size(input.num_rows()),
+                           cuco::empty_key{-1},
+                           cuco::empty_value{std::numeric_limits<size_type>::min()},
+                           detail::hash_table_allocator_type{default_allocator<char>{}, stream},
+                           stream.value()};
+
+  auto const preprocessed_input =
+    cudf::experimental::row::hash::preprocessed_table::create(input, stream);
+  auto const has_nulls          = nullate::DYNAMIC{cudf::has_nested_nulls(input)};
+  auto const has_nested_columns = cudf::detail::has_nested_columns(input);
+
+  auto const row_hasher = cudf::experimental::row::hash::row_hasher(preprocessed_input);
+  auto const key_hasher = row_hasher.device_hasher(has_nulls);
+
+  auto const row_comp = cudf::experimental::row::equality::self_comparator(preprocessed_input);
+
+  auto const pair_iter = cudf::detail::make_counting_transform_iterator(
+    size_type{0}, [] __device__(size_type const i) { return cuco::make_pair(i, i); });
+
+  auto const insert_keys = [&](auto const value_comp) {
+    if (has_nested_columns) {
+      auto const key_equal = row_comp.equal_to<true>(has_nulls, nulls_equal, value_comp);
+      map.insert(pair_iter, pair_iter + input.num_rows(), key_hasher, key_equal, stream.value());
+    } else {
+      auto const key_equal = row_comp.equal_to<false>(has_nulls, nulls_equal, value_comp);
+      map.insert(pair_iter, pair_iter + input.num_rows(), key_hasher, key_equal, stream.value());
+    }
+  };
+
+  if (nans_equal == nan_equality::ALL_EQUAL) {
+    using nan_equal_comparator =
+      cudf::experimental::row::equality::nan_equal_physical_equality_comparator;
+    insert_keys(nan_equal_comparator{});
+  } else {
+    using nan_unequal_comparator = cudf::experimental::row::equality::physical_equality_comparator;
+    insert_keys(nan_unequal_comparator{});
+  }
+
+  auto output_indices = rmm::device_uvector<size_type>(map.get_size(), stream, mr);
+
+  // If we don't care about order, just gather indices of distinct keys taken from map.
+  if (keep == duplicate_keep_option::KEEP_ANY) {
+    map.retrieve_all(output_indices.begin(), thrust::make_discard_iterator(), stream.value());
+    return output_indices;
+  }
+
+  // For other keep options, reduce by row on rows that compare equal.
+  auto const reduction_results = reduce_by_row(map,
+                                               std::move(preprocessed_input),
+                                               input.num_rows(),
+                                               has_nulls,
+                                               has_nested_columns,
+                                               keep,
+                                               nulls_equal,
+                                               nans_equal,
+                                               stream,
+                                               rmm::mr::get_current_device_resource());
+
+  // Extract the desired output indices from reduction results.
+  auto const map_end = [&] {
+    if (keep == duplicate_keep_option::KEEP_NONE) {
+      // Reduction results with `KEEP_NONE` are either group sizes of equal rows, or `0`.
+      // Thus, we only output index of the rows in the groups having group size of `1`.
+      return thrust::copy_if(rmm::exec_policy(stream),
+                             thrust::make_counting_iterator(0),
+                             thrust::make_counting_iterator(input.num_rows()),
+                             output_indices.begin(),
+                             [reduction_results = reduction_results.begin()] __device__(
+                               auto const idx) { return reduction_results[idx] == size_type{1}; });
+    }
+
+    // Reduction results with `KEEP_FIRST` and `KEEP_LAST` are row indices of the first/last row in
+    // each group of equal rows (which are the desired output indices), or the value given by
+    // `reduction_init_value()`.
+    return thrust::copy_if(rmm::exec_policy(stream),
+                           reduction_results.begin(),
+                           reduction_results.end(),
+                           output_indices.begin(),
+                           [init_value = reduction_init_value(keep)] __device__(auto const idx) {
+                             return idx != init_value;
+                           });
+  }();
+
+  output_indices.resize(thrust::distance(output_indices.begin(), map_end), stream);
+  return output_indices;
+}
+
+std::unique_ptr<table> distinct(table_view const& input,
+                                std::vector<size_type> const& keys,
+                                duplicate_keep_option keep,
+                                null_equality nulls_equal,
+                                nan_equality nans_equal,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  if (input.num_rows() == 0 or input.num_columns() == 0 or keys.empty()) {
+    return empty_like(input);
+  }
+
+  auto const gather_map = get_distinct_indices(input.select(keys),
+                                               keep,
+                                               nulls_equal,
+                                               nans_equal,
+                                               stream,
+                                               rmm::mr::get_current_device_resource());
+  return detail::gather(input,
+                        gather_map,
+                        out_of_bounds_policy::DONT_CHECK,
+                        negative_index_policy::NOT_ALLOWED,
+                        stream,
+                        mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> distinct(table_view const& input,
+                                std::vector<size_type> const& keys,
+                                duplicate_keep_option keep,
+                                null_equality nulls_equal,
+                                nan_equality nans_equal,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::distinct(
+    input, keys, keep, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/distinct_count.cu b/cpp/src/stream_compaction/distinct_count.cu
new file mode 100644
index 0000000..ac4811a
--- /dev/null
+++ b/cpp/src/stream_compaction/distinct_count.cu
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "stream_compaction_common.cuh"
+#include "stream_compaction_common.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <cuco/static_set.cuh>
+
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/logical.h>
+
+#include <cmath>
+#include <cstddef>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+namespace {
+/**
+ * @brief Functor to check for `NaN` at an index in a `column_device_view`.
+ *
+ * @tparam T The type of `column_device_view`
+ */
+template <typename T>
+struct check_for_nan {
+  /*
+   * @brief Construct from a column_device_view.
+   *
+   * @param[in] input The `column_device_view`
+   */
+  check_for_nan(cudf::column_device_view input) : _input{input} {}
+
+  /**
+   * @brief Operator to be called to check for `NaN` at `index` in `_input`
+   *
+   * @param[in] index The index at which the `NaN` needs to be checked in `input`
+   *
+   * @returns bool true if value at `index` is `NaN` and not null, else false
+   */
+  __device__ bool operator()(size_type index) const noexcept
+  {
+    return std::isnan(_input.data<T>()[index]) and _input.is_valid(index);
+  }
+
+  cudf::column_device_view _input;
+};
+
+/**
+ * @brief A structure to be used along with type_dispatcher to check if a
+ * `column_view` has `NaN`.
+ */
+struct has_nans {
+  /**
+   * @brief Checks if `input` has `NaN`
+   *
+   * @note This will be applicable only for floating point type columns.
+   *
+   * @param[in] input The `column_view` which will be checked for `NaN`
+   * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+   *
+   * @returns bool true if `input` has `NaN` else false
+   */
+  template <typename T, std::enable_if_t<std::is_floating_point_v<T>>* = nullptr>
+  bool operator()(column_view const& input, rmm::cuda_stream_view stream)
+  {
+    auto input_device_view = cudf::column_device_view::create(input, stream);
+    auto device_view       = *input_device_view;
+    return thrust::any_of(rmm::exec_policy(stream),
+                          thrust::counting_iterator<cudf::size_type>(0),
+                          thrust::counting_iterator<cudf::size_type>(input.size()),
+                          check_for_nan<T>(device_view));
+  }
+
+  /**
+   * @brief Checks if `input` has `NaN`
+   *
+   * @note This will be applicable only for non-floating point type columns. And
+   * non-floating point columns can never have `NaN`, so it will always return
+   * false
+   *
+   * @param[in] input The `column_view` which will be checked for `NaN`
+   * @param[in] stream CUDA stream used for device memory operations and kernel launches.
+   *
+   * @returns bool Always false as non-floating point columns can't have `NaN`
+   */
+  template <typename T, std::enable_if_t<not std::is_floating_point_v<T>>* = nullptr>
+  bool operator()(column_view const&, rmm::cuda_stream_view)
+  {
+    return false;
+  }
+};
+}  // namespace
+
+cudf::size_type distinct_count(table_view const& keys,
+                               null_equality nulls_equal,
+                               rmm::cuda_stream_view stream)
+{
+  auto const num_rows = keys.num_rows();
+  if (num_rows == 0) { return 0; }  // early exit for empty input
+  auto const has_nulls = nullate::DYNAMIC{cudf::has_nested_nulls(keys)};
+
+  auto const preprocessed_input =
+    cudf::experimental::row::hash::preprocessed_table::create(keys, stream);
+  auto const row_hasher = cudf::experimental::row::hash::row_hasher(preprocessed_input);
+  auto const hash_key   = row_hasher.device_hasher(has_nulls);
+  auto const row_comp   = cudf::experimental::row::equality::self_comparator(preprocessed_input);
+
+  auto const comparator_helper = [&](auto const row_equal) {
+    using hasher_type = decltype(hash_key);
+    auto key_set      = cuco::experimental::static_set{
+      cuco::experimental::extent{compute_hash_table_size(num_rows)},
+      cuco::empty_key<cudf::size_type>{-1},
+      row_equal,
+      cuco::experimental::linear_probing<1, hasher_type>{hash_key},
+      detail::hash_table_allocator_type{default_allocator<char>{}, stream},
+      stream.value()};
+
+    auto const iter = thrust::counting_iterator<cudf::size_type>(0);
+    // when nulls are equal, we skip hashing any row that has a null
+    // in every column to improve efficiency.
+    if (nulls_equal == null_equality::EQUAL and has_nulls) {
+      thrust::counting_iterator<size_type> stencil(0);
+      // We must consider a row if any of its column entries is valid,
+      // hence OR together the validities of the columns.
+      auto const [row_bitmask, null_count] =
+        cudf::detail::bitmask_or(keys, stream, rmm::mr::get_current_device_resource());
+
+      // Unless all columns have a null mask, row_bitmask will be
+      // null, and null_count will be zero. Equally, unless there is
+      // some row which is null in all columns, null_count will be
+      // zero. So, it is only when null_count is not zero that we need
+      // to do a filtered insertion.
+      if (null_count > 0) {
+        row_validity pred{static_cast<bitmask_type const*>(row_bitmask.data())};
+        return key_set.insert_if(iter, iter + num_rows, stencil, pred, stream.value()) + 1;
+      }
+    }
+    // otherwise, insert all
+    return key_set.insert(iter, iter + num_rows, stream.value());
+  };
+
+  if (cudf::detail::has_nested_columns(keys)) {
+    auto const row_equal = row_comp.equal_to<true>(has_nulls, nulls_equal);
+    return comparator_helper(row_equal);
+  } else {
+    auto const row_equal = row_comp.equal_to<false>(has_nulls, nulls_equal);
+    return comparator_helper(row_equal);
+  }
+}
+
+cudf::size_type distinct_count(column_view const& input,
+                               null_policy null_handling,
+                               nan_policy nan_handling,
+                               rmm::cuda_stream_view stream)
+{
+  if (0 == input.size() or input.null_count() == input.size()) { return 0; }
+
+  auto count = detail::distinct_count(table_view{{input}}, null_equality::EQUAL, stream);
+
+  // Check for nulls. If the null policy is EXCLUDE and null values were found,
+  // we decrement the count.
+  auto const has_null = input.has_nulls();
+  if (null_handling == null_policy::EXCLUDE and has_null) { --count; }
+
+  // Check for NaNs. There are two cases that can lead to decrementing the
+  // count. The first case is when the input has no nulls, but has NaN values
+  // handled as a null via NAN_IS_NULL and has a policy to EXCLUDE null values
+  // from the count. The second case is when the input has null values and NaN
+  // values handled as nulls via NAN_IS_NULL. Regardless of whether the null
+  // policy is set to EXCLUDE, we decrement the count to avoid double-counting
+  // null and NaN as distinct entities.
+  auto const has_nan_as_null = (nan_handling == nan_policy::NAN_IS_NULL) and
+                               cudf::type_dispatcher(input.type(), has_nans{}, input, stream);
+  if (has_nan_as_null and (has_null or null_handling == null_policy::EXCLUDE)) { --count; }
+  return count;
+}
+}  // namespace detail
+
+cudf::size_type distinct_count(column_view const& input,
+                               null_policy null_handling,
+                               nan_policy nan_handling)
+{
+  CUDF_FUNC_RANGE();
+  return detail::distinct_count(input, null_handling, nan_handling, cudf::get_default_stream());
+}
+
+cudf::size_type distinct_count(table_view const& input, null_equality nulls_equal)
+{
+  CUDF_FUNC_RANGE();
+  return detail::distinct_count(input, nulls_equal, cudf::get_default_stream());
+}
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/distinct_helpers.cu b/cpp/src/stream_compaction/distinct_helpers.cu
new file mode 100644
index 0000000..8f36ec9
--- /dev/null
+++ b/cpp/src/stream_compaction/distinct_helpers.cu
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "distinct_helpers.hpp"
+
+#include <cudf/detail/hash_reduce_by_row.cuh>
+
+namespace cudf::detail {
+
+namespace {
+/**
+ * @brief The functor to find the first/last/all duplicate row for rows that compared equal.
+ */
+template <typename MapView, typename KeyHasher, typename KeyEqual>
+struct reduce_fn : reduce_by_row_fn_base<MapView, KeyHasher, KeyEqual, size_type> {
+  duplicate_keep_option const keep;
+
+  reduce_fn(MapView const& d_map,
+            KeyHasher const& d_hasher,
+            KeyEqual const& d_equal,
+            duplicate_keep_option const keep,
+            size_type* const d_output)
+    : reduce_by_row_fn_base<MapView, KeyHasher, KeyEqual, size_type>{d_map,
+                                                                     d_hasher,
+                                                                     d_equal,
+                                                                     d_output},
+      keep{keep}
+  {
+  }
+
+  __device__ void operator()(size_type const idx) const
+  {
+    auto const out_ptr = this->get_output_ptr(idx);
+
+    if (keep == duplicate_keep_option::KEEP_FIRST) {
+      // Store the smallest index of all rows that are equal.
+      atomicMin(out_ptr, idx);
+    } else if (keep == duplicate_keep_option::KEEP_LAST) {
+      // Store the greatest index of all rows that are equal.
+      atomicMax(out_ptr, idx);
+    } else {
+      // Count the number of rows in each group of rows that are compared equal.
+      atomicAdd(out_ptr, size_type{1});
+    }
+  }
+};
+
+/**
+ * @brief The builder to construct an instance of `reduce_fn` functor base on the given
+ * value of the `duplicate_keep_option` member variable.
+ */
+struct reduce_func_builder {
+  duplicate_keep_option const keep;
+
+  template <typename MapView, typename KeyHasher, typename KeyEqual>
+  auto build(MapView const& d_map,
+             KeyHasher const& d_hasher,
+             KeyEqual const& d_equal,
+             size_type* const d_output)
+  {
+    return reduce_fn<MapView, KeyHasher, KeyEqual>{d_map, d_hasher, d_equal, keep, d_output};
+  }
+};
+
+}  // namespace
+
+// This function is split from `distinct.cu` to improve compile time.
+rmm::device_uvector<size_type> reduce_by_row(
+  hash_map_type const& map,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const preprocessed_input,
+  size_type num_rows,
+  cudf::nullate::DYNAMIC has_nulls,
+  bool has_nested_columns,
+  duplicate_keep_option keep,
+  null_equality nulls_equal,
+  nan_equality nans_equal,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(keep != duplicate_keep_option::KEEP_ANY,
+               "This function should not be called with KEEP_ANY");
+
+  return hash_reduce_by_row(map,
+                            preprocessed_input,
+                            num_rows,
+                            has_nulls,
+                            has_nested_columns,
+                            nulls_equal,
+                            nans_equal,
+                            reduce_func_builder{keep},
+                            reduction_init_value(keep),
+                            stream,
+                            mr);
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/stream_compaction/distinct_helpers.hpp b/cpp/src/stream_compaction/distinct_helpers.hpp
new file mode 100644
index 0000000..b667d0b
--- /dev/null
+++ b/cpp/src/stream_compaction/distinct_helpers.hpp
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "stream_compaction_common.hpp"
+
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+namespace cudf::detail {
+
+/**
+ * @brief Return the reduction identity used to initialize results of `hash_reduce_by_row`.
+ *
+ * @param keep A value of `duplicate_keep_option` type, must not be `KEEP_ANY`.
+ * @return The initial reduction value.
+ */
+auto constexpr reduction_init_value(duplicate_keep_option keep)
+{
+  switch (keep) {
+    case duplicate_keep_option::KEEP_FIRST: return std::numeric_limits<size_type>::max();
+    case duplicate_keep_option::KEEP_LAST: return std::numeric_limits<size_type>::min();
+    case duplicate_keep_option::KEEP_NONE: return size_type{0};
+    default: CUDF_UNREACHABLE("This function should not be called with KEEP_ANY");
+  }
+}
+
+/**
+ * @brief Perform a reduction on groups of rows that are compared equal.
+ *
+ * This is essentially a reduce-by-key operation with keys are non-contiguous rows and are compared
+ * equal. A hash table is used to find groups of equal rows.
+ *
+ * Depending on the `keep` parameter, the reduction operation for each row group is:
+ * - If `keep == KEEP_FIRST`: min of row indices in the group.
+ * - If `keep == KEEP_LAST`: max of row indices in the group.
+ * - If `keep == KEEP_NONE`: count of equivalent rows (group size).
+ *
+ * Note that this function is not needed when `keep == KEEP_NONE`.
+ *
+ * At the beginning of the operation, the entire output array is filled with a value given by
+ * the `reduction_init_value()` function. Then, the reduction result for each row group is written
+ * into the output array at the index of an unspecified row in the group.
+ *
+ * @param map The auxiliary map to perform reduction
+ * @param preprocessed_input The preprocessed of the input rows for computing row hashing and row
+ *        comparisons
+ * @param num_rows The number of all input rows
+ * @param has_nulls Indicate whether the input rows has any nulls at any nested levels
+ * @param has_nested_columns Indicates whether the input table has any nested columns
+ * @param keep The parameter to determine what type of reduction to perform
+ * @param nulls_equal Flag to specify whether null elements should be considered as equal
+ * @param nans_equal Flag to specify whether NaN values in floating point column should be
+ *        considered equal.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned vector
+ * @return A device_uvector containing the reduction results
+ */
+rmm::device_uvector<size_type> reduce_by_row(
+  hash_map_type const& map,
+  std::shared_ptr<cudf::experimental::row::equality::preprocessed_table> const preprocessed_input,
+  size_type num_rows,
+  cudf::nullate::DYNAMIC has_nulls,
+  bool has_nested_columns,
+  duplicate_keep_option keep,
+  null_equality nulls_equal,
+  nan_equality nans_equal,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr);
+
+}  // namespace cudf::detail
diff --git a/cpp/src/stream_compaction/drop_nans.cu b/cpp/src/stream_compaction/drop_nans.cu
new file mode 100644
index 0000000..a645b46
--- /dev/null
+++ b/cpp/src/stream_compaction/drop_nans.cu
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+
+namespace {
+
+struct dispatch_is_not_nan {
+  template <typename T>
+  std::enable_if_t<std::is_floating_point_v<T>, bool> __device__
+  operator()(cudf::column_device_view col_device_view, cudf::size_type i)
+  {
+    return col_device_view.is_valid(i) ? not std::isnan(col_device_view.element<T>(i)) : true;
+  }
+
+  template <typename T>
+  std::enable_if_t<not std::is_floating_point_v<T>, bool> __device__
+  operator()(cudf::column_device_view, cudf::size_type)
+  {
+    return true;
+  }
+};
+
+// Returns true if the mask is true and it is not NaN for index i in at least keep_threshold
+// columns
+struct valid_table_filter {
+  __device__ inline bool operator()(cudf::size_type i)
+  {
+    auto valid = [i](auto col_device_view) {
+      return cudf::type_dispatcher(
+        col_device_view.type(), dispatch_is_not_nan{}, col_device_view, i);
+    };
+
+    auto count =
+      thrust::count_if(thrust::seq, keys_device_view.begin(), keys_device_view.end(), valid);
+
+    return (count >= keep_threshold);
+  }
+
+  valid_table_filter()  = delete;
+  ~valid_table_filter() = default;
+
+  valid_table_filter(cudf::table_device_view const& keys_device_view,
+                     cudf::size_type keep_threshold)
+    : keep_threshold(keep_threshold), keys_device_view(keys_device_view)
+  {
+  }
+
+ protected:
+  cudf::size_type keep_threshold;
+  cudf::size_type num_columns;
+  cudf::table_device_view keys_device_view;
+};
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+/*
+ * Filters a table to remove nans elements.
+ */
+std::unique_ptr<table> drop_nans(table_view const& input,
+                                 std::vector<size_type> const& keys,
+                                 cudf::size_type keep_threshold,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto keys_view = input.select(keys);
+  if (keys_view.num_columns() == 0 || keys_view.num_rows() == 0) {
+    return std::make_unique<table>(input, stream, mr);
+  }
+
+  if (std::any_of(keys_view.begin(), keys_view.end(), [](auto col) {
+        return not is_floating_point(col.type());
+      })) {
+    CUDF_FAIL("Key column is not of type floating-point");
+  }
+
+  auto keys_device_view = cudf::table_device_view::create(keys_view, stream);
+
+  return cudf::detail::copy_if(
+    input, valid_table_filter{*keys_device_view, keep_threshold}, stream, mr);
+}
+
+}  // namespace detail
+
+/*
+ * Filters a table to remove nan elements.
+ */
+std::unique_ptr<table> drop_nans(table_view const& input,
+                                 std::vector<size_type> const& keys,
+                                 cudf::size_type keep_threshold,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::drop_nans(input, keys, keep_threshold, cudf::get_default_stream(), mr);
+}
+/*
+ * Filters a table to remove nan elements.
+ */
+std::unique_ptr<table> drop_nans(table_view const& input,
+                                 std::vector<size_type> const& keys,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::drop_nans(input, keys, keys.size(), cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/drop_nulls.cu b/cpp/src/stream_compaction/drop_nulls.cu
new file mode 100644
index 0000000..6ea1fd4
--- /dev/null
+++ b/cpp/src/stream_compaction/drop_nulls.cu
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+
+namespace {
+// Returns true if the mask is true for index i in at least keep_threshold
+// columns
+struct valid_table_filter {
+  __device__ inline bool operator()(cudf::size_type i)
+  {
+    auto valid = [i](auto column_device_view) { return column_device_view.is_valid(i); };
+
+    auto count =
+      thrust::count_if(thrust::seq, keys_device_view.begin(), keys_device_view.end(), valid);
+
+    return (count >= keep_threshold);
+  }
+
+  valid_table_filter()  = delete;
+  ~valid_table_filter() = default;
+
+  valid_table_filter(cudf::table_device_view const& keys_device_view,
+                     cudf::size_type keep_threshold)
+    : keep_threshold(keep_threshold), keys_device_view(keys_device_view)
+  {
+  }
+
+ protected:
+  cudf::size_type keep_threshold;
+  cudf::size_type num_columns;
+  cudf::table_device_view keys_device_view;
+};
+
+}  // namespace
+
+namespace cudf {
+namespace detail {
+/*
+ * Filters a table to remove null elements.
+ */
+std::unique_ptr<table> drop_nulls(table_view const& input,
+                                  std::vector<size_type> const& keys,
+                                  cudf::size_type keep_threshold,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto keys_view = input.select(keys);
+  if (keys_view.num_columns() == 0 || keys_view.num_rows() == 0 || not cudf::has_nulls(keys_view)) {
+    return std::make_unique<table>(input, stream, mr);
+  }
+
+  auto keys_device_view = cudf::table_device_view::create(keys_view, stream);
+
+  return cudf::detail::copy_if(
+    input, valid_table_filter{*keys_device_view, keep_threshold}, stream, mr);
+}
+
+}  // namespace detail
+
+/*
+ * Filters a table to remove null elements.
+ */
+std::unique_ptr<table> drop_nulls(table_view const& input,
+                                  std::vector<size_type> const& keys,
+                                  cudf::size_type keep_threshold,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::drop_nulls(input, keys, keep_threshold, cudf::get_default_stream(), mr);
+}
+/*
+ * Filters a table to remove null elements.
+ */
+std::unique_ptr<table> drop_nulls(table_view const& input,
+                                  std::vector<size_type> const& keys,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::drop_nulls(input, keys, keys.size(), cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/stable_distinct.cu b/cpp/src/stream_compaction/stable_distinct.cu
new file mode 100644
index 0000000..45a2de9
--- /dev/null
+++ b/cpp/src/stream_compaction/stable_distinct.cu
@@ -0,0 +1,87 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/scatter.h>
+#include <thrust/uninitialized_fill.h>
+
+namespace cudf {
+namespace detail {
+
+std::unique_ptr<table> stable_distinct(table_view const& input,
+                                       std::vector<size_type> const& keys,
+                                       duplicate_keep_option keep,
+                                       null_equality nulls_equal,
+                                       nan_equality nans_equal,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  if (input.num_rows() == 0 or input.num_columns() == 0 or keys.empty()) {
+    return empty_like(input);
+  }
+
+  auto const distinct_indices = get_distinct_indices(input.select(keys),
+                                                     keep,
+                                                     nulls_equal,
+                                                     nans_equal,
+                                                     stream,
+                                                     rmm::mr::get_current_device_resource());
+
+  // The only difference between this implementation and the unstable version
+  // is that the stable implementation must retain the input order. The
+  // distinct indices are not sorted, so we cannot simply copy the rows in the
+  // order of the distinct indices and retain the input order. Instead, we use
+  // a boolean mask to indicate which rows to copy to the output. This avoids
+  // the need to sort the distinct indices, which is slower.
+
+  auto const output_markers = [&] {
+    auto markers = rmm::device_uvector<bool>(input.num_rows(), stream);
+    thrust::uninitialized_fill(rmm::exec_policy(stream), markers.begin(), markers.end(), false);
+    thrust::scatter(
+      rmm::exec_policy(stream),
+      thrust::constant_iterator<bool>(true, 0),
+      thrust::constant_iterator<bool>(true, static_cast<size_type>(distinct_indices.size())),
+      distinct_indices.begin(),
+      markers.begin());
+    return markers;
+  }();
+
+  return cudf::detail::apply_boolean_mask(
+    input, cudf::device_span<bool const>(output_markers), stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<table> stable_distinct(table_view const& input,
+                                       std::vector<size_type> const& keys,
+                                       duplicate_keep_option keep,
+                                       null_equality nulls_equal,
+                                       nan_equality nans_equal,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::stable_distinct(
+    input, keys, keep, nulls_equal, nans_equal, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/stream_compaction_common.cuh b/cpp/src/stream_compaction/stream_compaction_common.cuh
new file mode 100644
index 0000000..839672d
--- /dev/null
+++ b/cpp/src/stream_compaction/stream_compaction_common.cuh
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "stream_compaction_common.hpp"
+
+#include <cudf/stream_compaction.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Device functor to determine if a row is valid.
+ */
+class row_validity {
+ public:
+  row_validity(bitmask_type const* row_bitmask) : _row_bitmask{row_bitmask} {}
+
+  __device__ inline bool operator()(size_type const& i) const noexcept
+  {
+    return cudf::bit_is_set(_row_bitmask, i);
+  }
+
+ private:
+  bitmask_type const* _row_bitmask;
+};
+
+template <typename InputIterator, typename BinaryPredicate>
+struct unique_copy_fn {
+  /**
+   * @brief Functor for unique_copy()
+   *
+   * The logic here is equivalent to:
+   * @code
+   *   ((keep == duplicate_keep_option::KEEP_LAST) ||
+   *    (i == 0 || !comp(iter[i], iter[i - 1]))) &&
+   *   ((keep == duplicate_keep_option::KEEP_FIRST) ||
+   *    (i == last_index || !comp(iter[i], iter[i + 1])))
+   * @endcode
+   *
+   * It is written this way so that the `comp` comparator
+   * function appears only once minimizing the inlining
+   * required and reducing the compile time.
+   */
+  __device__ bool operator()(size_type i)
+  {
+    size_type boundary = 0;
+    size_type offset   = 1;
+    auto keep_option   = duplicate_keep_option::KEEP_LAST;
+    do {
+      if ((keep != keep_option) && (i != boundary) && comp(iter[i], iter[i - offset])) {
+        return false;
+      }
+      keep_option = duplicate_keep_option::KEEP_FIRST;
+      boundary    = last_index;
+      offset      = -offset;
+    } while (offset < 0);
+    return true;
+  }
+
+  InputIterator iter;
+  duplicate_keep_option const keep;
+  BinaryPredicate comp;
+  size_type const last_index;
+};
+
+/**
+ * @brief Copies unique elements from the range [first, last) to output iterator `output`.
+ *
+ * In a consecutive group of duplicate elements, depending on parameter `keep`,
+ * only the first element is copied, or the last element is copied or neither is copied.
+ *
+ * @return End of the range to which the elements are copied.
+ */
+template <typename InputIterator, typename OutputIterator, typename BinaryPredicate>
+OutputIterator unique_copy(InputIterator first,
+                           InputIterator last,
+                           OutputIterator output,
+                           BinaryPredicate comp,
+                           duplicate_keep_option const keep,
+                           rmm::cuda_stream_view stream)
+{
+  size_type const last_index = thrust::distance(first, last) - 1;
+  return thrust::copy_if(
+    rmm::exec_policy(stream),
+    first,
+    last,
+    thrust::counting_iterator<size_type>(0),
+    output,
+    unique_copy_fn<InputIterator, BinaryPredicate>{first, keep, comp, last_index});
+}
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/stream_compaction_common.hpp b/cpp/src/stream_compaction/stream_compaction_common.hpp
new file mode 100644
index 0000000..18c531e
--- /dev/null
+++ b/cpp/src/stream_compaction/stream_compaction_common.hpp
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/helper_functions.cuh>
+#include <cudf/table/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <cuco/static_map.cuh>
+
+#include <limits>
+
+namespace cudf {
+namespace detail {
+
+using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+
+using hash_map_type =
+  cuco::static_map<size_type, size_type, cuda::thread_scope_device, hash_table_allocator_type>;
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/unique.cu b/cpp/src/stream_compaction/unique.cu
new file mode 100644
index 0000000..db67daa
--- /dev/null
+++ b/cpp/src/stream_compaction/unique.cu
@@ -0,0 +1,128 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "stream_compaction_common.cuh"
+#include "stream_compaction_common.hpp"
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <utility>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<table> unique(table_view const& input,
+                              std::vector<size_type> const& keys,
+                              duplicate_keep_option keep,
+                              null_equality nulls_equal,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  // If keep is KEEP_ANY, just alias it to KEEP_FIRST.
+  if (keep == duplicate_keep_option::KEEP_ANY) { keep = duplicate_keep_option::KEEP_FIRST; }
+
+  auto const num_rows = input.num_rows();
+  if (num_rows == 0 or input.num_columns() == 0 or keys.empty()) { return empty_like(input); }
+
+  auto unique_indices = make_numeric_column(
+    data_type{type_to_id<size_type>()}, num_rows, mask_state::UNALLOCATED, stream, mr);
+  auto mutable_view = mutable_column_device_view::create(*unique_indices, stream);
+  auto keys_view    = input.select(keys);
+
+  auto comp = cudf::experimental::row::equality::self_comparator(keys_view, stream);
+
+  size_type const unique_size = [&] {
+    if (cudf::detail::has_nested_columns(keys_view)) {
+      // Using a temporary buffer for intermediate transform results from the functor containing
+      // the comparator speeds up compile-time significantly without much degradation in
+      // runtime performance over using the comparator directly in thrust::unique_copy.
+      auto row_equal =
+        comp.equal_to<true>(nullate::DYNAMIC{has_nested_nulls(keys_view)}, nulls_equal);
+      auto d_results = rmm::device_uvector<bool>(num_rows, stream);
+      auto itr       = thrust::make_counting_iterator<size_type>(0);
+      thrust::transform(
+        rmm::exec_policy(stream),
+        itr,
+        itr + num_rows,
+        d_results.begin(),
+        unique_copy_fn<decltype(itr), decltype(row_equal)>{itr, keep, row_equal, num_rows - 1});
+      auto result_end = thrust::copy_if(rmm::exec_policy(stream),
+                                        itr,
+                                        itr + num_rows,
+                                        d_results.begin(),
+                                        mutable_view->begin<size_type>(),
+                                        thrust::identity<bool>{});
+      return static_cast<size_type>(thrust::distance(mutable_view->begin<size_type>(), result_end));
+    } else {
+      // Using thrust::unique_copy with the comparator directly will compile more slowly but
+      // improves runtime by up to 2x over the transform/copy_if approach above.
+      auto row_equal =
+        comp.equal_to<false>(nullate::DYNAMIC{has_nested_nulls(keys_view)}, nulls_equal);
+      auto result_end = unique_copy(thrust::counting_iterator<size_type>(0),
+                                    thrust::counting_iterator<size_type>(num_rows),
+                                    mutable_view->begin<size_type>(),
+                                    row_equal,
+                                    keep,
+                                    stream);
+      return static_cast<size_type>(thrust::distance(mutable_view->begin<size_type>(), result_end));
+    }
+  }();
+  auto indices_view = cudf::detail::slice(column_view(*unique_indices), 0, unique_size, stream);
+
+  // gather unique rows and return
+  return detail::gather(input,
+                        indices_view,
+                        out_of_bounds_policy::DONT_CHECK,
+                        detail::negative_index_policy::NOT_ALLOWED,
+                        stream,
+                        mr);
+}
+}  // namespace detail
+
+std::unique_ptr<table> unique(table_view const& input,
+                              std::vector<size_type> const& keys,
+                              duplicate_keep_option const keep,
+                              null_equality nulls_equal,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::unique(input, keys, keep, nulls_equal, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/unique_count.cu b/cpp/src/stream_compaction/unique_count.cu
new file mode 100644
index 0000000..19607fe
--- /dev/null
+++ b/cpp/src/stream_compaction/unique_count.cu
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+
+cudf::size_type unique_count(table_view const& keys,
+                             null_equality nulls_equal,
+                             rmm::cuda_stream_view stream)
+{
+  auto const row_comp = cudf::experimental::row::equality::self_comparator(keys, stream);
+  if (cudf::detail::has_nested_columns(keys)) {
+    auto const comp =
+      row_comp.equal_to<true>(nullate::DYNAMIC{has_nested_nulls(keys)}, nulls_equal);
+    // Using a temporary buffer for intermediate transform results from the lambda containing
+    // the comparator speeds up compile-time significantly without much degradation in
+    // runtime performance over using the comparator directly in thrust::count_if.
+    auto d_results = rmm::device_uvector<bool>(keys.num_rows(), stream);
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(keys.num_rows()),
+                      d_results.begin(),
+                      [comp] __device__(auto i) { return (i == 0 or not comp(i, i - 1)); });
+
+    return static_cast<size_type>(
+      thrust::count(rmm::exec_policy(stream), d_results.begin(), d_results.end(), true));
+  } else {
+    auto const comp =
+      row_comp.equal_to<false>(nullate::DYNAMIC{has_nested_nulls(keys)}, nulls_equal);
+    // Using thrust::copy_if with the comparator directly will compile more slowly but
+    // improves runtime by up to 2x over the transform/count approach above.
+    return thrust::count_if(
+      rmm::exec_policy(stream),
+      thrust::counting_iterator<cudf::size_type>(0),
+      thrust::counting_iterator<cudf::size_type>(keys.num_rows()),
+      [comp] __device__(cudf::size_type i) { return (i == 0 or not comp(i, i - 1)); });
+  }
+}
+
+}  // namespace detail
+
+cudf::size_type unique_count(table_view const& input, null_equality nulls_equal)
+{
+  CUDF_FUNC_RANGE();
+  return detail::unique_count(input, nulls_equal, cudf::get_default_stream());
+}
+
+}  // namespace cudf
diff --git a/cpp/src/stream_compaction/unique_count_column.cu b/cpp/src/stream_compaction/unique_count_column.cu
new file mode 100644
index 0000000..16758b6
--- /dev/null
+++ b/cpp/src/stream_compaction/unique_count_column.cu
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/count.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <cmath>
+
+namespace cudf {
+namespace detail {
+namespace {
+/**
+ * @brief A functor to be used along with device type_dispatcher to check if
+ * the row `index` of `column_device_view` is `NaN`.
+ */
+struct check_nan {
+  // Check if a value is `NaN` for floating point type columns
+  template <typename T, std::enable_if_t<std::is_floating_point_v<T>>* = nullptr>
+  __device__ inline bool operator()(column_device_view const& input, size_type index)
+  {
+    return std::isnan(input.data<T>()[index]);
+  }
+  // Non-floating point type columns can never have `NaN`, so it will always return false.
+  template <typename T, std::enable_if_t<not std::is_floating_point_v<T>>* = nullptr>
+  __device__ inline bool operator()(column_device_view const&, size_type)
+  {
+    return false;
+  }
+};
+}  // namespace
+
+cudf::size_type unique_count(column_view const& input,
+                             null_policy null_handling,
+                             nan_policy nan_handling,
+                             rmm::cuda_stream_view stream)
+{
+  auto const num_rows = input.size();
+
+  if (num_rows == 0 or num_rows == input.null_count()) { return 0; }
+
+  auto const count_nulls      = null_handling == null_policy::INCLUDE;
+  auto const nan_is_null      = nan_handling == nan_policy::NAN_IS_NULL;
+  auto const should_check_nan = cudf::is_floating_point(input.type());
+  auto input_device_view      = cudf::column_device_view::create(input, stream);
+  auto device_view            = *input_device_view;
+  auto input_table_view       = table_view{{input}};
+  auto table_ptr              = cudf::table_device_view::create(input_table_view, stream);
+  row_equality_comparator comp(nullate::DYNAMIC{cudf::has_nulls(input_table_view)},
+                               *table_ptr,
+                               *table_ptr,
+                               null_equality::EQUAL);
+
+  return thrust::count_if(
+    rmm::exec_policy(stream),
+    thrust::counting_iterator<cudf::size_type>(0),
+    thrust::counting_iterator<cudf::size_type>(num_rows),
+    [count_nulls, nan_is_null, should_check_nan, device_view, comp] __device__(cudf::size_type i) {
+      auto const is_null = device_view.is_null(i);
+      auto const is_nan  = nan_is_null and should_check_nan and
+                          cudf::type_dispatcher(device_view.type(), check_nan{}, device_view, i);
+      if (not count_nulls and (is_null or (nan_is_null and is_nan))) { return false; }
+      if (i == 0) { return true; }
+      if (count_nulls and nan_is_null and (is_nan or is_null)) {
+        auto const prev_is_nan =
+          should_check_nan and
+          cudf::type_dispatcher(device_view.type(), check_nan{}, device_view, i - 1);
+        return not(prev_is_nan or device_view.is_null(i - 1));
+      }
+      return not comp(i, i - 1);
+    });
+}
+}  // namespace detail
+
+cudf::size_type unique_count(column_view const& input,
+                             null_policy null_handling,
+                             nan_policy nan_handling)
+{
+  CUDF_FUNC_RANGE();
+  return detail::unique_count(input, null_handling, nan_handling, cudf::get_default_stream());
+}
+
+}  // namespace cudf
diff --git a/cpp/src/strings/attributes.cu b/cpp/src/strings/attributes.cu
new file mode 100644
index 0000000..8dc1509
--- /dev/null
+++ b/cpp/src/strings/attributes.cu
@@ -0,0 +1,284 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/attributes.hpp>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+#include <cub/warp/warp_reduce.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Threshold to decide on using string or warp parallel functions.
+ *
+ * If the average byte length of a string in a column exceeds this value then
+ * the warp-parallel function is used.
+ * Otherwise, a regular string-parallel function is used.
+ *
+ * This value was found using the strings_lengths benchmark results.
+ */
+constexpr size_type AVG_CHAR_BYTES_THRESHOLD = 64;
+
+/**
+ * @brief Returns a numeric column containing lengths of each string in
+ * based on the provided unary function
+ *
+ * Any null string will result in a null entry for that row in the output column.
+ *
+ * @tparam UnaryFunction Device function that returns an integer given a string_view
+ * @param strings Strings instance for this operation
+ * @param ufn Function returns an integer for each string
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New column with lengths for each string
+ */
+template <typename UnaryFunction>
+std::unique_ptr<column> counts_fn(strings_column_view const& strings,
+                                  UnaryFunction& ufn,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  // create output column
+  auto results   = make_numeric_column(data_type{type_to_id<size_type>()},
+                                     strings.size(),
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto d_lengths = results->mutable_view().data<int32_t>();
+  // input column device view
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  // fill in the lengths
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(strings.size()),
+                    d_lengths,
+                    [d_strings, ufn] __device__(size_type idx) {
+                      return d_strings.is_null(idx)
+                               ? 0
+                               : static_cast<int32_t>(ufn(d_strings.element<string_view>(idx)));
+                    });
+  results->set_null_count(strings.null_count());  // reset null count
+  return results;
+}
+
+/**
+ * @brief Count characters using a warp per string
+ *
+ * @param d_strings Column with strings to count
+ * @param d_lengths Results of the counts per string
+ */
+__global__ void count_characters_parallel_fn(column_device_view const d_strings,
+                                             size_type* d_lengths)
+{
+  auto const idx    = cudf::detail::grid_1d::global_thread_id();
+  using warp_reduce = cub::WarpReduce<size_type>;
+  __shared__ typename warp_reduce::TempStorage temp_storage;
+
+  if (idx >= (d_strings.size() * cudf::detail::warp_size)) { return; }
+
+  auto const str_idx  = static_cast<size_type>(idx / cudf::detail::warp_size);
+  auto const lane_idx = static_cast<size_type>(idx % cudf::detail::warp_size);
+  if (d_strings.is_null(str_idx)) {
+    d_lengths[str_idx] = 0;
+    return;
+  }
+  auto const d_str   = d_strings.element<string_view>(str_idx);
+  auto const str_ptr = d_str.data();
+
+  size_type count = 0;
+  for (auto i = lane_idx; i < d_str.size_bytes(); i += cudf::detail::warp_size) {
+    count += static_cast<size_type>(is_begin_utf8_char(str_ptr[i]));
+  }
+  auto const char_count = warp_reduce(temp_storage).Sum(count);
+  if (lane_idx == 0) { d_lengths[str_idx] = char_count; }
+}
+
+std::unique_ptr<column> count_characters_parallel(strings_column_view const& input,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  // create output column
+  auto results = make_numeric_column(data_type{type_to_id<size_type>()},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+
+  auto const d_lengths = results->mutable_view().data<size_type>();
+  auto const d_strings = cudf::column_device_view::create(input.parent(), stream);
+
+  // fill in the lengths
+  constexpr int block_size = 256;
+  cudf::detail::grid_1d grid{input.size() * cudf::detail::warp_size, block_size};
+  count_characters_parallel_fn<<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+    *d_strings, d_lengths);
+
+  // reset null count after call to mutable_view()
+  results->set_null_count(input.null_count());
+
+  return results;
+}
+
+}  // namespace
+
+std::unique_ptr<column> count_characters(strings_column_view const& input,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if ((input.size() == input.null_count()) ||
+      ((input.chars_size() / (input.size() - input.null_count())) < AVG_CHAR_BYTES_THRESHOLD)) {
+    auto ufn = [] __device__(string_view const& d_str) { return d_str.length(); };
+    return counts_fn(input, ufn, stream, mr);
+  }
+
+  return count_characters_parallel(input, stream, mr);
+}
+
+std::unique_ptr<column> count_bytes(strings_column_view const& input,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto ufn = [] __device__(string_view const& d_str) { return d_str.size_bytes(); };
+  return counts_fn(input, ufn, stream, mr);
+}
+
+}  // namespace detail
+
+namespace {
+/**
+ * @brief Sets the code-point values for each character in the output
+ * integer memory for each string in the strings column.
+ *
+ * For each string, there is a sub-array in d_results with length equal
+ * to the number of characters in that string. The function here will
+ * write code-point values to that section as pointed to by the
+ * corresponding d_offsets value calculated for that string.
+ */
+struct code_points_fn {
+  column_device_view d_strings;
+  size_type* d_offsets;  // offset within d_results to fill with each string's code-point values
+  int32_t* d_results;    // base integer array output
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) return;
+    auto d_str  = d_strings.element<string_view>(idx);
+    auto result = d_results + d_offsets[idx];
+    thrust::copy(thrust::seq, d_str.begin(), d_str.end(), result);
+  }
+};
+
+}  // namespace
+
+namespace detail {
+//
+std::unique_ptr<column> code_points(strings_column_view const& input,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto strings_column = column_device_view::create(input.parent(), stream);
+  auto d_column       = *strings_column;
+
+  // create offsets vector to account for each string's character length
+  rmm::device_uvector<size_type> offsets(input.size() + 1, stream);
+  thrust::transform_inclusive_scan(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(input.size()),
+    offsets.begin() + 1,
+    [d_column] __device__(size_type idx) {
+      size_type length = 0;
+      if (!d_column.is_null(idx)) length = d_column.element<string_view>(idx).length();
+      return length;
+    },
+    thrust::plus<size_type>());
+
+  offsets.set_element_to_zero_async(0, stream);
+
+  // the total size is the number of characters in the entire column
+  size_type num_characters = offsets.back_element(stream);
+  // create output column with no nulls
+  auto results = make_numeric_column(
+    data_type{type_id::INT32}, num_characters, mask_state::UNALLOCATED, stream, mr);
+  auto results_view = results->mutable_view();
+  // fill column with character code-point values
+  auto d_results = results_view.data<int32_t>();
+  // now set the ranges from each strings' character values
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     input.size(),
+                     code_points_fn{d_column, offsets.data(), d_results});
+
+  results->set_null_count(0);
+  return results;
+}
+
+}  // namespace detail
+
+// external APIS
+
+std::unique_ptr<column> count_characters(strings_column_view const& input,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::count_characters(input, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> count_bytes(strings_column_view const& input,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::count_bytes(input, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> code_points(strings_column_view const& input,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::code_points(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/capitalize.cu b/cpp/src/strings/capitalize.cu
new file mode 100644
index 0000000..c555031
--- /dev/null
+++ b/cpp/src/strings/capitalize.cu
@@ -0,0 +1,315 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/capitalize.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+using char_info = thrust::pair<uint32_t, detail::character_flags_table_type>;
+
+/**
+ * @brief Returns the given character's info flags.
+ */
+__device__ char_info get_char_info(character_flags_table_type const* d_flags, char_utf8 chr)
+{
+  auto const code_point = detail::utf8_to_codepoint(chr);
+  auto const flag = code_point <= 0x00'FFFF ? d_flags[code_point] : character_flags_table_type{0};
+  return char_info{code_point, flag};
+}
+
+/**
+ * @brief Base class for capitalize and title functors.
+ *
+ * Utility functions here manage access to the character case and flags tables.
+ * Any derived class must supply a `capitalize_next` member function.
+ *
+ * @tparam Derived class uses the CRTP pattern to reuse code logic.
+ */
+template <typename Derived>
+struct base_fn {
+  character_flags_table_type const* d_flags;
+  character_cases_table_type const* d_case_table;
+  special_case_mapping const* d_special_case_mapping;
+  column_device_view const d_column;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  base_fn(column_device_view const& d_column)
+    : d_flags(get_character_flags_table()),
+      d_case_table(get_character_cases_table()),
+      d_special_case_mapping(get_special_case_mapping_table()),
+      d_column(d_column)
+  {
+  }
+
+  __device__ int32_t convert_char(char_info const& info, char* d_buffer) const
+  {
+    auto const code_point = info.first;
+    auto const flag       = info.second;
+
+    if (!IS_SPECIAL(flag)) {
+      auto const new_char = codepoint_to_utf8(d_case_table[code_point]);
+      return d_buffer ? detail::from_char_utf8(new_char, d_buffer)
+                      : detail::bytes_in_char_utf8(new_char);
+    }
+
+    special_case_mapping m = d_special_case_mapping[get_special_case_hash_index(code_point)];
+
+    auto const count  = IS_LOWER(flag) ? m.num_upper_chars : m.num_lower_chars;
+    auto const* chars = IS_LOWER(flag) ? m.upper : m.lower;
+    size_type bytes   = 0;
+    for (uint16_t idx = 0; idx < count; idx++) {
+      bytes += d_buffer
+                 ? detail::from_char_utf8(detail::codepoint_to_utf8(chars[idx]), d_buffer + bytes)
+                 : detail::bytes_in_char_utf8(detail::codepoint_to_utf8(chars[idx]));
+    }
+    return bytes;
+  }
+
+  /**
+   * @brief Operator called for each row in `d_column`.
+   *
+   * This logic is shared by capitalize() and title() functions.
+   * The derived class must supply a `capitalize_next` member function.
+   */
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto& derived    = static_cast<Derived&>(*this);
+    auto const d_str = d_column.element<string_view>(idx);
+    size_type bytes  = 0;
+    auto d_buffer    = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    bool capitalize  = true;
+    for (auto const chr : d_str) {
+      auto const info        = get_char_info(d_flags, chr);
+      auto const flag        = info.second;
+      auto const change_case = capitalize ? IS_LOWER(flag) : IS_UPPER(flag);
+
+      if (change_case) {
+        auto const char_bytes = convert_char(info, d_buffer);
+        bytes += char_bytes;
+        d_buffer += d_buffer ? char_bytes : 0;
+      } else {
+        if (d_buffer) {
+          d_buffer += detail::from_char_utf8(chr, d_buffer);
+        } else {
+          bytes += detail::bytes_in_char_utf8(chr);
+        }
+      }
+
+      // capitalize the next char if this one is a delimiter
+      capitalize = derived.capitalize_next(chr, flag);
+    }
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+/**
+ * @brief Capitalize functor.
+ *
+ * This capitalizes the first character of the string and lower-cases
+ * the remaining characters.
+ * If a delimiter is specified, capitalization continues within the string
+ * on the first eligible character after any delimiter.
+ */
+struct capitalize_fn : base_fn<capitalize_fn> {
+  string_view const d_delimiters;
+
+  capitalize_fn(column_device_view const& d_column, string_view const& d_delimiters)
+    : base_fn(d_column), d_delimiters(d_delimiters)
+  {
+  }
+
+  __device__ bool capitalize_next(char_utf8 const chr, character_flags_table_type const)
+  {
+    return !d_delimiters.empty() && (d_delimiters.find(chr) != string_view::npos);
+  }
+};
+
+/**
+ * @brief Title functor.
+ *
+ * This capitalizes the first letter of each word.
+ * The beginning of a word is identified as the first sequence_type
+ * character after a non-sequence_type character.
+ * Also, lower-case all other alphabetic characters.
+ */
+struct title_fn : base_fn<title_fn> {
+  string_character_types sequence_type;
+
+  title_fn(column_device_view const& d_column, string_character_types sequence_type)
+    : base_fn(d_column), sequence_type(sequence_type)
+  {
+  }
+
+  __device__ bool capitalize_next(char_utf8 const, character_flags_table_type const flag)
+  {
+    return (flag & sequence_type) == 0;
+  };
+};
+
+/**
+ * @brief Functor for determining title format for each string in a column.
+ *
+ * The first letter of each word should be upper-case (IS_UPPER).
+ * All other characters should be lower-case (IS_LOWER).
+ * Non-upper/lower-case (IS_UPPER_OR_LOWER) characters delimit words.
+ */
+struct is_title_fn {
+  character_flags_table_type const* d_flags;
+  column_device_view const d_column;
+
+  __device__ bool operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) { return false; }
+    auto const d_str = d_column.element<string_view>(idx);
+
+    bool at_least_one_valid    = false;  // requires one or more cased characters
+    bool should_be_capitalized = true;   // current character should be upper-case
+    for (auto const chr : d_str) {
+      auto const flag = get_char_info(d_flags, chr).second;
+      if (IS_UPPER_OR_LOWER(flag)) {
+        if (should_be_capitalized == !IS_UPPER(flag)) return false;
+        at_least_one_valid = true;
+      }
+      should_be_capitalized = !IS_UPPER_OR_LOWER(flag);
+    }
+    return at_least_one_valid;
+  }
+};
+
+/**
+ * @brief Common utility function for title() and capitalize().
+ *
+ * @tparam CapitalFn The specific functor.
+ * @param cfn The functor instance.
+ * @param input The input strings column.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used for allocating the new device_buffer
+ */
+template <typename CapitalFn>
+std::unique_ptr<column> capitalizer(CapitalFn cfn,
+                                    strings_column_view const& input,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto children = cudf::strings::detail::make_strings_children(cfn, input.size(), stream, mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace
+
+std::unique_ptr<column> capitalize(strings_column_view const& input,
+                                   string_scalar const& delimiters,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiters.is_valid(stream), "Delimiter must be a valid string");
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+  auto const d_column     = column_device_view::create(input.parent(), stream);
+  auto const d_delimiters = delimiters.value(stream);
+  return capitalizer(capitalize_fn{*d_column, d_delimiters}, input, stream, mr);
+}
+
+std::unique_ptr<column> title(strings_column_view const& input,
+                              string_character_types sequence_type,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+  auto d_column = column_device_view::create(input.parent(), stream);
+  return capitalizer(title_fn{*d_column, sequence_type}, input, stream, mr);
+}
+
+std::unique_ptr<column> is_title(strings_column_view const& input,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::BOOL8);
+  auto results  = make_numeric_column(data_type{type_id::BOOL8},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+  auto d_column = column_device_view::create(input.parent(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(input.size()),
+                    results->mutable_view().data<bool>(),
+                    is_title_fn{get_character_flags_table(), *d_column});
+  results->set_null_count(input.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> capitalize(strings_column_view const& input,
+                                   string_scalar const& delimiter,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::capitalize(input, delimiter, stream, mr);
+}
+
+std::unique_ptr<column> title(strings_column_view const& input,
+                              string_character_types sequence_type,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::title(input, sequence_type, stream, mr);
+}
+
+std::unique_ptr<column> is_title(strings_column_view const& input,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_title(input, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/case.cu b/cpp/src/strings/case.cu
new file mode 100644
index 0000000..8f4c2ee
--- /dev/null
+++ b/cpp/src/strings/case.cu
@@ -0,0 +1,337 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/case.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Threshold to decide on using string or warp parallel functions.
+ *
+ * If the average byte length of a string in a column exceeds this value then
+ * the warp-parallel function is used to compute the output sizes.
+ * Otherwise, a regular string-parallel function is used.
+ *
+ * This value was found using the strings_lengths benchmark results.
+ */
+constexpr size_type AVG_CHAR_BYTES_THRESHOLD = 64;
+
+/**
+ * @brief Utility functions for converting characters to upper or lower case
+ */
+struct convert_char_fn {
+  character_flags_table_type case_flag;
+  character_flags_table_type const* d_flags;
+  character_cases_table_type const* d_case_table;
+  special_case_mapping const* d_special_case_mapping;
+
+  // compute size or copy the bytes representing the special case mapping for this codepoint
+  __device__ size_type handle_special_case_bytes(uint32_t code_point,
+                                                 detail::character_flags_table_type flag,
+                                                 char* d_buffer = nullptr) const
+  {
+    special_case_mapping m = d_special_case_mapping[get_special_case_hash_index(code_point)];
+
+    size_type bytes   = 0;
+    auto const count  = IS_LOWER(flag) ? m.num_upper_chars : m.num_lower_chars;
+    auto const* chars = IS_LOWER(flag) ? m.upper : m.lower;
+    for (uint16_t idx = 0; idx < count; idx++) {
+      bytes += d_buffer
+                 ? detail::from_char_utf8(detail::codepoint_to_utf8(chars[idx]), d_buffer + bytes)
+                 : detail::bytes_in_char_utf8(detail::codepoint_to_utf8(chars[idx]));
+    }
+    return bytes;
+  }
+
+  // this is called for converting any UTF-8 characters
+  __device__ size_type process_character(char_utf8 chr, char* d_buffer = nullptr) const
+  {
+    auto const code_point = detail::utf8_to_codepoint(chr);
+
+    detail::character_flags_table_type flag = code_point <= 0x00'FFFF ? d_flags[code_point] : 0;
+
+    // we apply special mapping in two cases:
+    // - uncased characters with the special mapping flag: always
+    // - cased characters with the special mapping flag: when matching the input case_flag
+    if (IS_SPECIAL(flag) && ((flag & case_flag) || !IS_UPPER_OR_LOWER(flag))) {
+      return handle_special_case_bytes(code_point, case_flag, d_buffer);
+    }
+
+    char_utf8 const new_char =
+      (flag & case_flag) ? detail::codepoint_to_utf8(d_case_table[code_point]) : chr;
+    return (d_buffer) ? detail::from_char_utf8(new_char, d_buffer)
+                      : detail::bytes_in_char_utf8(new_char);
+  }
+
+  // special function for converting ASCII-only characters
+  __device__ char process_ascii(char chr)
+  {
+    return (case_flag & d_flags[chr]) ? static_cast<char>(d_case_table[chr]) : chr;
+  }
+};
+
+/**
+ * @brief Per string logic for case conversion functions
+ *
+ * This can be used in calls to make_strings_children.
+ */
+struct upper_lower_fn {
+  convert_char_fn converter;
+  column_device_view d_strings;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str = d_strings.element<string_view>(idx);
+    size_type bytes  = 0;
+    char* d_buffer   = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    for (auto itr = d_str.begin(); itr != d_str.end(); ++itr) {
+      auto const size = converter.process_character(*itr, d_buffer);
+      if (d_buffer) {
+        d_buffer += size;
+      } else {
+        bytes += size;
+      }
+    }
+    if (!d_buffer) { d_offsets[idx] = bytes; }
+  }
+};
+
+/**
+ * @brief Count output bytes in warp-parallel threads
+ *
+ * This executes as one warp per string and just computes the output sizes.
+ */
+struct count_bytes_fn {
+  convert_char_fn converter;
+  column_device_view d_strings;
+  size_type* d_offsets;
+
+  __device__ void operator()(size_type idx) const
+  {
+    auto const str_idx  = idx / cudf::detail::warp_size;
+    auto const lane_idx = idx % cudf::detail::warp_size;
+
+    // initialize the output for the atomicAdd
+    if (lane_idx == 0) { d_offsets[str_idx] = 0; }
+    __syncwarp();
+
+    if (d_strings.is_null(str_idx)) { return; }
+    auto const d_str   = d_strings.element<string_view>(str_idx);
+    auto const str_ptr = d_str.data();
+
+    size_type size = 0;
+    for (auto i = lane_idx; i < d_str.size_bytes(); i += cudf::detail::warp_size) {
+      auto const chr = str_ptr[i];
+      if (is_utf8_continuation_char(chr)) { continue; }
+      char_utf8 u8 = 0;
+      to_char_utf8(str_ptr + i, u8);
+      size += converter.process_character(u8);
+    }
+    // this is every so slightly faster than using the cub::warp_reduce
+    if (size > 0) {
+      cuda::atomic_ref<size_type, cuda::thread_scope_block> ref{*(d_offsets + str_idx)};
+      ref.fetch_add(size, cuda::std::memory_order_relaxed);
+    }
+  }
+};
+
+/**
+ * @brief Special functor for processing ASCII-only data
+ */
+struct ascii_converter_fn {
+  convert_char_fn converter;
+  __device__ char operator()(char chr) { return converter.process_ascii(chr); }
+};
+
+/**
+ * @brief Utility method for converting upper and lower case characters
+ * in a strings column
+ *
+ * @param input Strings to convert
+ * @param case_flag The character type to convert (upper, lower, or both)
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ * @return New strings column with characters converted
+ */
+std::unique_ptr<column> convert_case(strings_column_view const& input,
+                                     character_flags_table_type case_flag,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  if (input.size() == input.null_count()) {
+    return std::make_unique<column>(input.parent(), stream, mr);
+  }
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+  auto const d_flags   = get_character_flags_table();
+  auto const d_cases   = get_character_cases_table();
+  auto const d_special = get_special_case_mapping_table();
+
+  convert_char_fn ccfn{case_flag, d_flags, d_cases, d_special};
+  upper_lower_fn converter{ccfn, *d_strings};
+
+  // For smaller strings, use the regular string-parallel algorithm
+  if ((input.chars_size() / (input.size() - input.null_count())) < AVG_CHAR_BYTES_THRESHOLD) {
+    auto [offsets, chars] =
+      cudf::strings::detail::make_strings_children(converter, input.size(), stream, mr);
+    return make_strings_column(input.size(),
+                               std::move(offsets),
+                               std::move(chars),
+                               input.null_count(),
+                               cudf::detail::copy_bitmask(input.parent(), stream, mr));
+  }
+
+  // Check if the input contains any multi-byte characters.
+  // This check incurs ~20% performance hit for smaller strings and so we only use it
+  // after the threshold check above. The check makes very little impact for larger strings
+  // but results in a large performance gain when the input contains only single-byte characters.
+  // The count_if is faster than any_of or all_of: https://github.com/NVIDIA/thrust/issues/1016
+  bool const multi_byte_chars =
+    thrust::count_if(
+      rmm::exec_policy(stream), input.chars_begin(), input.chars_end(), [] __device__(auto chr) {
+        return is_utf8_continuation_char(chr);
+      }) > 0;
+  if (!multi_byte_chars) {
+    // optimization for ASCII-only case: copy the input column and inplace replace each character
+    auto result = std::make_unique<column>(input.parent(), stream, mr);
+    auto d_chars =
+      result->mutable_view().child(strings_column_view::chars_column_index).data<char>();
+    auto const chars_size = strings_column_view(result->view()).chars_size();
+    thrust::transform(
+      rmm::exec_policy(stream), d_chars, d_chars + chars_size, d_chars, ascii_converter_fn{ccfn});
+    result->set_null_count(input.null_count());
+    return result;
+  }
+
+  // This will use a warp-parallel algorithm to compute the output sizes for each string
+  // and then uses the normal string parallel functor to build the output.
+  auto offsets = make_numeric_column(
+    data_type{type_to_id<size_type>()}, input.size() + 1, mask_state::UNALLOCATED, stream, mr);
+  auto d_offsets = offsets->mutable_view().data<size_type>();
+
+  // first pass, compute output sizes
+  // note: tried to use segmented-reduce approach instead here and it was consistently slower
+  count_bytes_fn counter{ccfn, *d_strings, d_offsets};
+  auto const count_itr = thrust::make_counting_iterator<size_type>(0);
+  thrust::for_each_n(
+    rmm::exec_policy(stream), count_itr, input.size() * cudf::detail::warp_size, counter);
+
+  // convert sizes to offsets
+  auto const bytes =
+    cudf::detail::sizes_to_offsets(d_offsets, d_offsets + input.size() + 1, d_offsets, stream);
+  CUDF_EXPECTS(bytes <= std::numeric_limits<size_type>::max(),
+               "Size of output exceeds the column size limit",
+               std::overflow_error);
+
+  auto chars = create_chars_child_column(static_cast<size_type>(bytes), stream, mr);
+  // second pass, write output
+  converter.d_offsets = d_offsets;
+  converter.d_chars   = chars->mutable_view().data<char>();
+  thrust::for_each_n(rmm::exec_policy(stream), count_itr, input.size(), converter);
+
+  return make_strings_column(input.size(),
+                             std::move(offsets),
+                             std::move(chars),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace
+
+std::unique_ptr<column> to_lower(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  character_flags_table_type case_flag = IS_UPPER(0xFF);  // convert only upper case characters
+  return convert_case(strings, case_flag, stream, mr);
+}
+
+//
+std::unique_ptr<column> to_upper(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  character_flags_table_type case_flag = IS_LOWER(0xFF);  // convert only lower case characters
+  return convert_case(strings, case_flag, stream, mr);
+}
+
+//
+std::unique_ptr<column> swapcase(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  // convert only upper or lower case characters
+  character_flags_table_type case_flag = IS_LOWER(0xFF) | IS_UPPER(0xFF);
+  return convert_case(strings, case_flag, stream, mr);
+}
+
+}  // namespace detail
+
+// APIs
+
+std::unique_ptr<column> to_lower(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_lower(strings, stream, mr);
+}
+
+std::unique_ptr<column> to_upper(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_upper(strings, stream, mr);
+}
+
+std::unique_ptr<column> swapcase(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::swapcase(strings, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/char_types/char_cases.cu b/cpp/src/strings/char_types/char_cases.cu
new file mode 100644
index 0000000..1021d57
--- /dev/null
+++ b/cpp/src/strings/char_types/char_cases.cu
@@ -0,0 +1,201 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <algorithm>
+#include <array>
+#include <unordered_set>
+#include <vector>
+
+#include <cudf/utilities/error.hpp>
+
+//
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+struct special_case_mapping_in {
+  uint16_t num_upper_chars;
+  uint16_t upper[3];
+  uint16_t num_lower_chars;
+  uint16_t lower[3];
+};
+constexpr special_case_mapping_in codepoint_mapping_in[] = {
+  {2, {83, 83, 0}, 0, {0, 0, 0}},       {0, {0, 0, 0}, 2, {105, 775, 0}},
+  {2, {700, 78, 0}, 0, {0, 0, 0}},      {1, {452, 0, 0}, 1, {454, 0, 0}},
+  {1, {455, 0, 0}, 1, {457, 0, 0}},     {1, {458, 0, 0}, 1, {460, 0, 0}},
+  {2, {74, 780, 0}, 0, {0, 0, 0}},      {1, {497, 0, 0}, 1, {499, 0, 0}},
+  {3, {921, 776, 769}, 0, {0, 0, 0}},   {3, {933, 776, 769}, 0, {0, 0, 0}},
+  {2, {1333, 1362, 0}, 0, {0, 0, 0}},   {2, {72, 817, 0}, 0, {0, 0, 0}},
+  {2, {84, 776, 0}, 0, {0, 0, 0}},      {2, {87, 778, 0}, 0, {0, 0, 0}},
+  {2, {89, 778, 0}, 0, {0, 0, 0}},      {2, {65, 702, 0}, 0, {0, 0, 0}},
+  {2, {933, 787, 0}, 0, {0, 0, 0}},     {3, {933, 787, 768}, 0, {0, 0, 0}},
+  {3, {933, 787, 769}, 0, {0, 0, 0}},   {3, {933, 787, 834}, 0, {0, 0, 0}},
+  {2, {7944, 921, 0}, 0, {0, 0, 0}},    {2, {7945, 921, 0}, 0, {0, 0, 0}},
+  {2, {7946, 921, 0}, 0, {0, 0, 0}},    {2, {7947, 921, 0}, 0, {0, 0, 0}},
+  {2, {7948, 921, 0}, 0, {0, 0, 0}},    {2, {7949, 921, 0}, 0, {0, 0, 0}},
+  {2, {7950, 921, 0}, 0, {0, 0, 0}},    {2, {7951, 921, 0}, 0, {0, 0, 0}},
+  {2, {7944, 921, 0}, 1, {8064, 0, 0}}, {2, {7945, 921, 0}, 1, {8065, 0, 0}},
+  {2, {7946, 921, 0}, 1, {8066, 0, 0}}, {2, {7947, 921, 0}, 1, {8067, 0, 0}},
+  {2, {7948, 921, 0}, 1, {8068, 0, 0}}, {2, {7949, 921, 0}, 1, {8069, 0, 0}},
+  {2, {7950, 921, 0}, 1, {8070, 0, 0}}, {2, {7951, 921, 0}, 1, {8071, 0, 0}},
+  {2, {7976, 921, 0}, 0, {0, 0, 0}},    {2, {7977, 921, 0}, 0, {0, 0, 0}},
+  {2, {7978, 921, 0}, 0, {0, 0, 0}},    {2, {7979, 921, 0}, 0, {0, 0, 0}},
+  {2, {7980, 921, 0}, 0, {0, 0, 0}},    {2, {7981, 921, 0}, 0, {0, 0, 0}},
+  {2, {7982, 921, 0}, 0, {0, 0, 0}},    {2, {7983, 921, 0}, 0, {0, 0, 0}},
+  {2, {7976, 921, 0}, 1, {8080, 0, 0}}, {2, {7977, 921, 0}, 1, {8081, 0, 0}},
+  {2, {7978, 921, 0}, 1, {8082, 0, 0}}, {2, {7979, 921, 0}, 1, {8083, 0, 0}},
+  {2, {7980, 921, 0}, 1, {8084, 0, 0}}, {2, {7981, 921, 0}, 1, {8085, 0, 0}},
+  {2, {7982, 921, 0}, 1, {8086, 0, 0}}, {2, {7983, 921, 0}, 1, {8087, 0, 0}},
+  {2, {8040, 921, 0}, 0, {0, 0, 0}},    {2, {8041, 921, 0}, 0, {0, 0, 0}},
+  {2, {8042, 921, 0}, 0, {0, 0, 0}},    {2, {8043, 921, 0}, 0, {0, 0, 0}},
+  {2, {8044, 921, 0}, 0, {0, 0, 0}},    {2, {8045, 921, 0}, 0, {0, 0, 0}},
+  {2, {8046, 921, 0}, 0, {0, 0, 0}},    {2, {8047, 921, 0}, 0, {0, 0, 0}},
+  {2, {8040, 921, 0}, 1, {8096, 0, 0}}, {2, {8041, 921, 0}, 1, {8097, 0, 0}},
+  {2, {8042, 921, 0}, 1, {8098, 0, 0}}, {2, {8043, 921, 0}, 1, {8099, 0, 0}},
+  {2, {8044, 921, 0}, 1, {8100, 0, 0}}, {2, {8045, 921, 0}, 1, {8101, 0, 0}},
+  {2, {8046, 921, 0}, 1, {8102, 0, 0}}, {2, {8047, 921, 0}, 1, {8103, 0, 0}},
+  {2, {8122, 921, 0}, 0, {0, 0, 0}},    {2, {913, 921, 0}, 0, {0, 0, 0}},
+  {2, {902, 921, 0}, 0, {0, 0, 0}},     {2, {913, 834, 0}, 0, {0, 0, 0}},
+  {3, {913, 834, 921}, 0, {0, 0, 0}},   {2, {913, 921, 0}, 1, {8115, 0, 0}},
+  {2, {8138, 921, 0}, 0, {0, 0, 0}},    {2, {919, 921, 0}, 0, {0, 0, 0}},
+  {2, {905, 921, 0}, 0, {0, 0, 0}},     {2, {919, 834, 0}, 0, {0, 0, 0}},
+  {3, {919, 834, 921}, 0, {0, 0, 0}},   {2, {919, 921, 0}, 1, {8131, 0, 0}},
+  {3, {921, 776, 768}, 0, {0, 0, 0}},   {3, {921, 776, 769}, 0, {0, 0, 0}},
+  {2, {921, 834, 0}, 0, {0, 0, 0}},     {3, {921, 776, 834}, 0, {0, 0, 0}},
+  {3, {933, 776, 768}, 0, {0, 0, 0}},   {3, {933, 776, 769}, 0, {0, 0, 0}},
+  {2, {929, 787, 0}, 0, {0, 0, 0}},     {2, {933, 834, 0}, 0, {0, 0, 0}},
+  {3, {933, 776, 834}, 0, {0, 0, 0}},   {2, {8186, 921, 0}, 0, {0, 0, 0}},
+  {2, {937, 921, 0}, 0, {0, 0, 0}},     {2, {911, 921, 0}, 0, {0, 0, 0}},
+  {2, {937, 834, 0}, 0, {0, 0, 0}},     {3, {937, 834, 921}, 0, {0, 0, 0}},
+  {2, {937, 921, 0}, 1, {8179, 0, 0}},  {2, {70, 70, 0}, 0, {0, 0, 0}},
+  {2, {70, 73, 0}, 0, {0, 0, 0}},       {2, {70, 76, 0}, 0, {0, 0, 0}},
+  {3, {70, 70, 73}, 0, {0, 0, 0}},      {3, {70, 70, 76}, 0, {0, 0, 0}},
+  {2, {83, 84, 0}, 0, {0, 0, 0}},       {2, {83, 84, 0}, 0, {0, 0, 0}},
+  {2, {1348, 1350, 0}, 0, {0, 0, 0}},   {2, {1348, 1333, 0}, 0, {0, 0, 0}},
+  {2, {1348, 1339, 0}, 0, {0, 0, 0}},   {2, {1358, 1350, 0}, 0, {0, 0, 0}},
+  {2, {1348, 1341, 0}, 0, {0, 0, 0}},
+};
+constexpr std::array<uint16_t, 107> codepoints_in = {
+  223,   304,   329,   453,   456,   459,   496,   498,   912,   944,  1415, 7830,  7831,  7832,
+  7833,  7834,  8016,  8018,  8020,  8022,  8064,  8065,  8066,  8067, 8068, 8069,  8070,  8071,
+  8072,  8073,  8074,  8075,  8076,  8077,  8078,  8079,  8080,  8081, 8082, 8083,  8084,  8085,
+  8086,  8087,  8088,  8089,  8090,  8091,  8092,  8093,  8094,  8095, 8096, 8097,  8098,  8099,
+  8100,  8101,  8102,  8103,  8104,  8105,  8106,  8107,  8108,  8109, 8110, 8111,  8114,  8115,
+  8116,  8118,  8119,  8124,  8130,  8131,  8132,  8134,  8135,  8140, 8146, 8147,  8150,  8151,
+  8162,  8163,  8164,  8166,  8167,  8178,  8179,  8180,  8182,  8183, 8188, 64256, 64257, 64258,
+  64259, 64260, 64261, 64262, 64275, 64276, 64277, 64278, 64279,
+};
+constexpr std::array<uint16_t, 269> primes = {
+  227,  229,  233,  239,  241,  251,  257,  263,  269,  271,  277,  281,  283,  293,  307,  311,
+  313,  317,  331,  337,  347,  349,  353,  359,  367,  373,  379,  383,  389,  397,  401,  409,
+  419,  421,  431,  433,  439,  443,  449,  457,  461,  463,  467,  479,  487,  491,  499,  503,
+  509,  521,  523,  541,  547,  557,  563,  569,  571,  577,  587,  593,  599,  601,  607,  613,
+  617,  619,  631,  641,  643,  647,  653,  659,  661,  673,  677,  683,  691,  701,  709,  719,
+  727,  733,  739,  743,  751,  757,  761,  769,  773,  787,  797,  809,  811,  821,  823,  827,
+  829,  839,  853,  857,  859,  863,  877,  881,  883,  887,  907,  911,  919,  929,  937,  941,
+  947,  953,  967,  971,  977,  983,  991,  997,  1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049,
+  1051, 1061, 1063, 1069, 1087, 1091, 1093, 1097, 1103, 1109, 1117, 1123, 1129, 1151, 1153, 1163,
+  1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223, 1229, 1231, 1237, 1249, 1259, 1277, 1279, 1283,
+  1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373, 1381, 1399, 1409, 1423,
+  1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511,
+  1523, 1531, 1543, 1549, 1553, 1559, 1567, 1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619,
+  1621, 1627, 1637, 1657, 1663, 1667, 1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747,
+  1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811, 1823, 1831, 1847, 1861, 1867, 1871, 1873, 1877,
+  1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987, 1993, 1997, 1999, 2003,
+  2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083, 2087, 2089, 2099,
+};
+
+// find a prime number that generates no collisions for all possible input data
+uint16_t find_collision_proof_prime()
+{
+  for (auto const& prime : primes) {
+    std::unordered_set<uint16_t> keys;
+    std::for_each(std::cbegin(codepoints_in),
+                  std::cend(codepoints_in),
+                  [&](uint16_t const codepoint) { keys.insert(codepoint % prime); });
+    if (keys.size() == codepoints_in.size()) return prime;
+  }
+
+  // couldn't find a collision-proof prime
+  return 0;
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::strings::detail::generate_special_mapping_hash_table
+ */
+void generate_special_mapping_hash_table()
+{
+  uint16_t hash_prime = find_collision_proof_prime();
+  if (hash_prime == 0) { CUDF_FAIL("Could not find a usable prime number for hash table"); }
+
+  // generate hash index table
+  // size of the table is the prime #, since we're just doing (key % hash_prime)
+  std::vector<std::pair<bool, uint16_t>> hash_indices(hash_prime,
+                                                      std::pair<bool, uint16_t>(false, 0));
+  int index = 0;
+  std::for_each(std::begin(codepoints_in), std::end(codepoints_in), [&](uint16_t codepoint) {
+    hash_indices[codepoint % hash_prime].first  = true;
+    hash_indices[codepoint % hash_prime].second = index++;
+  });
+
+  // print out the code
+
+  // the mappings
+  printf("struct special_case_mapping {\n");
+  printf("   uint16_t num_upper_chars;\n");
+  printf("   uint16_t upper[3];\n");
+  printf("   uint16_t num_lower_chars;\n");
+  printf("   uint16_t lower[3];\n");
+  printf("};\n");
+  printf("constexpr special_case_mapping g_special_case_mappings[] = {\n");
+  bool prev_empty = false;
+  std::for_each(
+    hash_indices.begin(), hash_indices.end(), [&prev_empty](std::pair<bool, uint16_t> entry) {
+      if (entry.first) {
+        special_case_mapping_in m = codepoint_mapping_in[entry.second];
+        printf("%s   { %d, { %d, %d, %d }, %d, {%d, %d, %d} },\n",
+               prev_empty ? "\n" : "",
+               m.num_upper_chars,
+               m.upper[0],
+               m.upper[1],
+               m.upper[2],
+               m.num_lower_chars,
+               m.lower[0],
+               m.lower[1],
+               m.lower[2]);
+      } else {
+        printf("%s{},", prev_empty ? "" : "   ");
+      }
+      prev_empty = !entry.first;
+    });
+  printf("};\n");
+
+  printf(
+    "// the special case mapping table is a perfect hash table with no collisions, allowing us\n"
+    "// to 'hash' by simply modding by the incoming codepoint\n"
+    "constexpr uint16_t get_special_case_hash_index(uint32_t code_point){\n"
+    "   constexpr uint16_t special_case_prime = %d;\n"
+    "   return static_cast<uint16_t>(code_point %% special_case_prime);"
+    "\n}\n",
+    hash_prime);
+}
+
+}  // namespace detail
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/char_types/char_cases.h b/cpp/src/strings/char_types/char_cases.h
new file mode 100644
index 0000000..96d132b
--- /dev/null
+++ b/cpp/src/strings/char_types/char_cases.h
@@ -0,0 +1,5236 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/detail/char_tables.hpp>
+
+#include <cstdint>
+
+//
+// Each entry is the code-point's 'case opposite'
+// For example, if the code-point is an upper-case character, the entry is its lower-case
+// counterpart.
+//
+uint16_t const g_character_cases_table[] = {
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     97,    98,    99,    100,   101,
+  102,   103,   104,   105,   106,   107,   108,   109,   110,   111,   112,   113,   114,   115,
+  116,   117,   118,   119,   120,   121,   122,   0,     0,     0,     0,     0,     0,     65,
+  66,    67,    68,    69,    70,    71,    72,    73,    74,    75,    76,    77,    78,    79,
+  80,    81,    82,    83,    84,    85,    86,    87,    88,    89,    90,    0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     170,   0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     924,
+  0,     0,     0,     0,     186,   0,     0,     0,     0,     0,     224,   225,   226,   227,
+  228,   229,   230,   231,   232,   233,   234,   235,   236,   237,   238,   239,   240,   241,
+  242,   243,   244,   245,   246,   0,     248,   249,   250,   251,   252,   253,   254,   83,
+  192,   193,   194,   195,   196,   197,   198,   199,   200,   201,   202,   203,   204,   205,
+  206,   207,   208,   209,   210,   211,   212,   213,   214,   0,     216,   217,   218,   219,
+  220,   221,   222,   376,   257,   256,   259,   258,   261,   260,   263,   262,   265,   264,
+  267,   266,   269,   268,   271,   270,   273,   272,   275,   274,   277,   276,   279,   278,
+  281,   280,   283,   282,   285,   284,   287,   286,   289,   288,   291,   290,   293,   292,
+  295,   294,   297,   296,   299,   298,   301,   300,   303,   302,   105,   73,    307,   306,
+  309,   308,   311,   310,   312,   314,   313,   316,   315,   318,   317,   320,   319,   322,
+  321,   324,   323,   326,   325,   328,   327,   700,   331,   330,   333,   332,   335,   334,
+  337,   336,   339,   338,   341,   340,   343,   342,   345,   344,   347,   346,   349,   348,
+  351,   350,   353,   352,   355,   354,   357,   356,   359,   358,   361,   360,   363,   362,
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+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     65345, 65346, 65347, 65348, 65349, 65350, 65351, 65352, 65353, 65354, 65355,
+  65356, 65357, 65358, 65359, 65360, 65361, 65362, 65363, 65364, 65365, 65366, 65367, 65368, 65369,
+  65370, 0,     0,     0,     0,     0,     0,     65313, 65314, 65315, 65316, 65317, 65318, 65319,
+  65320, 65321, 65322, 65323, 65324, 65325, 65326, 65327, 65328, 65329, 65330, 65331, 65332, 65333,
+  65334, 65335, 65336, 65337, 65338, 0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,     0,
+  0,     0,
+};
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief The following structs and arrays are autogenerated using
+ * generate_special_mapping_hash_table().  They represent a 'perfect' hash table that
+ * allows special cased code points (those with the special case flag, 1<<7 set in the
+ * g_character_codepoint_flags array) to have their mapping information retrieved in
+ * constant time (codepoint % special_case_prime).
+ *
+ * 'special' cased characters are those defined as not having trivial single->single character
+ * mappings when having upper(), lower() or titlecase() operations applied.  Typically this is
+ * for cases where a single character maps to multiple, but there are also cases of
+ * non-reversible mappings, where:  codepoint != lower(upper(code_point)).  The mappings for
+ * these cases are explicitly specified by g_special_case_mappings.
+ *
+ * special_case_mapping, g_special_case_mappings, and special_case_prime can be regenerated with
+ * cudf::strings::detail::generate_special_mapping_hash_table
+ */
+
+constexpr special_case_mapping g_special_case_mappings[] = {
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {933, 787, 0}, 0, {0, 0, 0}},
+  {},
+  {3, {933, 787, 768}, 0, {0, 0, 0}},
+  {},
+  {3, {933, 787, 769}, 0, {0, 0, 0}},
+  {},
+  {3, {933, 787, 834}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {7944, 921, 0}, 0, {0, 0, 0}},
+  {2, {7945, 921, 0}, 0, {0, 0, 0}},
+  {2, {7946, 921, 0}, 0, {0, 0, 0}},
+  {2, {7947, 921, 0}, 0, {0, 0, 0}},
+  {2, {7948, 921, 0}, 0, {0, 0, 0}},
+  {2, {7949, 921, 0}, 0, {0, 0, 0}},
+  {2, {7950, 921, 0}, 0, {0, 0, 0}},
+  {2, {7951, 921, 0}, 0, {0, 0, 0}},
+  {2, {7944, 921, 0}, 1, {8064, 0, 0}},
+  {2, {7945, 921, 0}, 1, {8065, 0, 0}},
+  {2, {7946, 921, 0}, 1, {8066, 0, 0}},
+  {2, {7947, 921, 0}, 1, {8067, 0, 0}},
+  {2, {7948, 921, 0}, 1, {8068, 0, 0}},
+  {2, {7949, 921, 0}, 1, {8069, 0, 0}},
+  {2, {7950, 921, 0}, 1, {8070, 0, 0}},
+  {2, {7951, 921, 0}, 1, {8071, 0, 0}},
+  {2, {7976, 921, 0}, 0, {0, 0, 0}},
+  {2, {7977, 921, 0}, 0, {0, 0, 0}},
+  {2, {7978, 921, 0}, 0, {0, 0, 0}},
+  {2, {7979, 921, 0}, 0, {0, 0, 0}},
+  {2, {7980, 921, 0}, 0, {0, 0, 0}},
+  {2, {7981, 921, 0}, 0, {0, 0, 0}},
+  {2, {7982, 921, 0}, 0, {0, 0, 0}},
+  {2, {7983, 921, 0}, 0, {0, 0, 0}},
+  {2, {7976, 921, 0}, 1, {8080, 0, 0}},
+  {2, {7977, 921, 0}, 1, {8081, 0, 0}},
+  {2, {7978, 921, 0}, 1, {8082, 0, 0}},
+  {2, {7979, 921, 0}, 1, {8083, 0, 0}},
+  {2, {7980, 921, 0}, 1, {8084, 0, 0}},
+  {2, {7981, 921, 0}, 1, {8085, 0, 0}},
+  {2, {7982, 921, 0}, 1, {8086, 0, 0}},
+  {2, {7983, 921, 0}, 1, {8087, 0, 0}},
+  {2, {8040, 921, 0}, 0, {0, 0, 0}},
+  {2, {8041, 921, 0}, 0, {0, 0, 0}},
+  {2, {8042, 921, 0}, 0, {0, 0, 0}},
+  {2, {8043, 921, 0}, 0, {0, 0, 0}},
+  {2, {8044, 921, 0}, 0, {0, 0, 0}},
+  {2, {8045, 921, 0}, 0, {0, 0, 0}},
+  {2, {8046, 921, 0}, 0, {0, 0, 0}},
+  {2, {8047, 921, 0}, 0, {0, 0, 0}},
+  {2, {8040, 921, 0}, 1, {8096, 0, 0}},
+  {2, {8041, 921, 0}, 1, {8097, 0, 0}},
+  {2, {8042, 921, 0}, 1, {8098, 0, 0}},
+  {2, {8043, 921, 0}, 1, {8099, 0, 0}},
+  {2, {8044, 921, 0}, 1, {8100, 0, 0}},
+  {2, {8045, 921, 0}, 1, {8101, 0, 0}},
+  {2, {8046, 921, 0}, 1, {8102, 0, 0}},
+  {2, {8047, 921, 0}, 1, {8103, 0, 0}},
+  {},
+  {},
+  {2, {8122, 921, 0}, 0, {0, 0, 0}},
+  {2, {913, 921, 0}, 0, {0, 0, 0}},
+  {2, {902, 921, 0}, 0, {0, 0, 0}},
+  {},
+  {2, {913, 834, 0}, 0, {0, 0, 0}},
+  {3, {913, 834, 921}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {2, {913, 921, 0}, 1, {8115, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {8138, 921, 0}, 0, {0, 0, 0}},
+  {2, {919, 921, 0}, 0, {0, 0, 0}},
+  {2, {905, 921, 0}, 0, {0, 0, 0}},
+  {},
+  {2, {919, 834, 0}, 0, {0, 0, 0}},
+  {3, {919, 834, 921}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {2, {919, 921, 0}, 1, {8131, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {3, {921, 776, 768}, 0, {0, 0, 0}},
+  {3, {921, 776, 769}, 0, {0, 0, 0}},
+  {},
+  {},
+  {2, {921, 834, 0}, 0, {0, 0, 0}},
+  {3, {921, 776, 834}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {3, {933, 776, 768}, 0, {0, 0, 0}},
+  {3, {933, 776, 769}, 0, {0, 0, 0}},
+  {2, {929, 787, 0}, 0, {0, 0, 0}},
+  {},
+  {2, {933, 834, 0}, 0, {0, 0, 0}},
+  {3, {933, 776, 834}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {8186, 921, 0}, 0, {0, 0, 0}},
+  {2, {937, 921, 0}, 0, {0, 0, 0}},
+  {2, {911, 921, 0}, 0, {0, 0, 0}},
+  {},
+  {2, {937, 834, 0}, 0, {0, 0, 0}},
+  {3, {937, 834, 921}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {2, {937, 921, 0}, 1, {8179, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {83, 83, 0}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {0, {0, 0, 0}, 2, {105, 775, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {700, 78, 0}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {72, 817, 0}, 0, {0, 0, 0}},
+  {2, {84, 776, 0}, 0, {0, 0, 0}},
+  {2, {87, 778, 0}, 0, {0, 0, 0}},
+  {2, {89, 778, 0}, 0, {0, 0, 0}},
+  {2, {65, 702, 0}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {70, 70, 0}, 0, {0, 0, 0}},
+  {2, {70, 73, 0}, 0, {0, 0, 0}},
+  {2, {70, 76, 0}, 0, {0, 0, 0}},
+  {3, {70, 70, 73}, 0, {0, 0, 0}},
+  {3, {70, 70, 76}, 0, {0, 0, 0}},
+  {2, {83, 84, 0}, 0, {0, 0, 0}},
+  {2, {83, 84, 0}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {1348, 1350, 0}, 0, {0, 0, 0}},
+  {2, {1348, 1333, 0}, 0, {0, 0, 0}},
+  {2, {1348, 1339, 0}, 0, {0, 0, 0}},
+  {2, {1358, 1350, 0}, 0, {0, 0, 0}},
+  {2, {1348, 1341, 0}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {3, {921, 776, 769}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {2, {1333, 1362, 0}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {3, {933, 776, 769}, 0, {0, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {1, {452, 0, 0}, 1, {454, 0, 0}},
+  {},
+  {},
+  {1, {455, 0, 0}, 1, {457, 0, 0}},
+  {},
+  {},
+  {1, {458, 0, 0}, 1, {460, 0, 0}},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {},
+  {2, {74, 780, 0}, 0, {0, 0, 0}},
+  {},
+  {1, {497, 0, 0}, 1, {499, 0, 0}},
+};
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/char_types/char_flags.h b/cpp/src/strings/char_types/char_flags.h
new file mode 100644
index 0000000..5aa79fe
--- /dev/null
+++ b/cpp/src/strings/char_types/char_flags.h
@@ -0,0 +1,3488 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+
+//
+// 8-bit flag for each code-point.
+// Bit values assigned as follows
+//    7 - special casing. this character has unusual rules when it comes to
+//        upper casing, lower casing or title casing.  typically this is because of
+//        a single character mapping to multiple characters upon case change. codepoints
+//        with this flag set go through an auxiliary hash table (g_special_case_mappings)
+//        instead of the trivial lookup in g_character_cases_table.
+//    6 - lower
+//    5 - upper
+//    4 - space
+//    3 - alpha
+//    2 - digit
+//    1 - numeric
+//    0 - decimal
+//
+
+uint8_t const g_character_codepoint_flags[] = {
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   16,  16,  16,  16,  16,  0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   16,  16,  16,  16,  16,  0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   7,   7,   7,   7,   7,   7,   7,   7,   7,
+  7,   0,   0,   0,   0,   0,   0,   0,   40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,
+  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  0,   0,   0,   0,
+  0,   0,   72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,
+  72,  72,  72,  72,  72,  72,  72,  72,  72,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  16,  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   16,  0,   0,   0,   0,   0,   0,   0,   0,   0,   72,
+  0,   0,   0,   0,   0,   0,   0,   6,   6,   0,   72,  0,   0,   0,   6,   72,  0,   2,   2,
+  2,   0,   40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,
+  40,  40,  40,  40,  40,  40,  0,   40,  40,  40,  40,  40,  40,  40,  200, 72,  72,  72,  72,
+  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,
+  0,   72,  72,  72,  72,  72,  72,  72,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,
+  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  168, 72,  40,  72,  40,  72,  40,  72,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  40,  72,  40,  72,  40,  72,  200, 40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,
+  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  40,  72,  40,
+  72,  40,  72,  72,  72,  40,  40,  72,  40,  72,  40,  40,  72,  40,  40,  40,  72,  72,  40,
+  40,  40,  40,  72,  40,  40,  72,  40,  40,  40,  72,  72,  72,  40,  40,  72,  40,  40,  72,
+  40,  72,  40,  72,  40,  40,  72,  40,  72,  72,  40,  72,  40,  40,  72,  40,  40,  40,  72,
+  40,  72,  40,  40,  72,  72,  8,   40,  72,  72,  72,  8,   8,   8,   8,   40,  136, 72,  40,
+  136, 72,  40,  136, 72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  40,  72,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  40,  72,  200, 40,  136, 72,  40,  72,  40,  40,  40,  72,  40,  72,  40,  72,  40,  72,  40,
+  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,
+  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,
+  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  40,  72,  72,  72,  72,  72,  72,  72,
+  40,  40,  72,  40,  40,  72,  72,  40,  72,  40,  40,  40,  40,  72,  40,  72,  40,  72,  40,
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+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   200, 200,
+  200, 200, 200, 200, 200, 0,   0,   72,  72,  72,  72,  72,  0,   0,   0,   0,   0,   200, 200,
+  200, 200, 200, 0,   0,   0,   0,   0,   8,   0,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   0,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   0,   8,   8,   8,
+  8,   8,   0,   8,   0,   8,   8,   0,   8,   8,   0,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   0,   0,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   8,   8,   8,   8,   8,   0,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   7,   7,   7,   7,   7,   7,   7,
+  7,   7,   7,   0,   0,   0,   0,   0,   0,   0,   40,  40,  40,  40,  40,  40,  40,  40,  40,
+  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  40,  0,   0,
+  0,   0,   0,   0,   72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,
+  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  72,  0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,
+  8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   8,   0,   0,   0,
+  8,   8,   8,   8,   8,   8,   0,   0,   8,   8,   8,   8,   8,   8,   0,   0,   8,   8,   8,
+  8,   8,   8,   0,   0,   8,   8,   8,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
+  0,   0,   0,   0,   0,
+};
diff --git a/cpp/src/strings/char_types/char_types.cu b/cpp/src/strings/char_types/char_types.cu
new file mode 100644
index 0000000..0c0ad0a
--- /dev/null
+++ b/cpp/src/strings/char_types/char_types.cu
@@ -0,0 +1,239 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/char_types/char_types.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Returns true for each string where all characters match the given types.
+ *
+ * Only the characters that match to `verify_types` are checked.
+ * Returns false if no characters are checked or one character does not match `types`.
+ * Returns true if at least one character is checked and all checked characters match `types`.
+ */
+struct char_types_fn {
+  column_device_view const d_column;
+  character_flags_table_type const* d_flags;
+  string_character_types const types;
+  string_character_types const verify_types;
+
+  __device__ bool operator()(size_type idx) const
+  {
+    if (d_column.is_null(idx)) { return false; }
+    auto const d_str = d_column.element<string_view>(idx);
+    auto const end   = d_str.data() + d_str.size_bytes();
+
+    bool type_matched     = !d_str.empty();  // require at least one character;
+    size_type check_count = 0;               // count checked characters
+    for (auto itr = d_str.data(); type_matched && (itr < end); ++itr) {
+      uint8_t const chr = static_cast<uint8_t>(*itr);
+      if (is_utf8_continuation_char(chr)) { continue; }
+      auto u8 = static_cast<char_utf8>(chr);  // holds UTF8 value
+      // using max(int8) here since max(char)=255 on ARM systems
+      if (u8 > std::numeric_limits<int8_t>::max()) { to_char_utf8(itr, u8); }
+
+      // lookup flags in table by codepoint
+      auto const code_point = utf8_to_codepoint(u8);
+      auto const flag       = code_point <= 0x00'FFFF ? d_flags[code_point] : 0;
+
+      if ((verify_types & flag) ||                   // should flag be verified;
+          (flag == 0 && verify_types == ALL_TYPES))  // special edge case
+      {
+        type_matched = (types & flag) > 0;
+        ++check_count;
+      }
+    }
+
+    return type_matched && (check_count > 0);
+  }
+};
+}  // namespace
+
+std::unique_ptr<column> all_characters_of_type(strings_column_view const& input,
+                                               string_character_types types,
+                                               string_character_types verify_types,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  // create output column
+  auto results = make_numeric_column(data_type{type_id::BOOL8},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+  // get the static character types table
+  auto d_flags = detail::get_character_flags_table();
+
+  // set the output values by checking the character types for each string
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(input.size()),
+                    results->mutable_view().data<bool>(),
+                    char_types_fn{*d_strings, d_flags, types, verify_types});
+
+  results->set_null_count(input.null_count());
+  return results;
+}
+
+namespace {
+
+/**
+ * @brief Removes individual characters from a strings column based on character type.
+ *
+ * Types to remove are specified by `types_to_remove` OR
+ * types to not remove are specified by `types_to_keep`.
+ *
+ * This is called twice. The first pass calculates the size of each output string.
+ * The final pass copies the results to the output strings column memory.
+ */
+struct filter_chars_fn {
+  column_device_view const d_column;
+  character_flags_table_type const* d_flags;
+  string_character_types const types_to_remove;
+  string_character_types const types_to_keep;
+  string_view const d_replacement;  ///< optional replacement for removed characters
+  int32_t* d_offsets{};             ///< size of the output string stored here during first pass
+  char* d_chars{};                  ///< this is null only during the first pass
+
+  /**
+   * @brief Returns true if the given character should be replaced.
+   */
+  __device__ bool replace_char(char_utf8 ch)
+  {
+    auto const code_point = detail::utf8_to_codepoint(ch);
+    auto const flag       = code_point <= 0x00'FFFF ? d_flags[code_point] : 0;
+    if (flag == 0)  // all types pass unless specifically identified
+      return (types_to_remove == ALL_TYPES);
+    if (types_to_keep == ALL_TYPES)  // filter case
+      return (types_to_remove & flag) != 0;
+    return (types_to_keep & flag) == 0;  // keep case
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str  = d_column.element<string_view>(idx);
+    auto const in_ptr = d_str.data();
+    auto out_ptr      = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    auto nbytes       = d_str.size_bytes();
+
+    for (auto itr = d_str.begin(); itr != d_str.end(); ++itr) {
+      auto const char_size = bytes_in_char_utf8(*itr);
+      string_view const d_newchar =
+        replace_char(*itr) ? d_replacement : string_view(in_ptr + itr.byte_offset(), char_size);
+      nbytes += d_newchar.size_bytes() - char_size;
+      if (out_ptr) out_ptr = cudf::strings::detail::copy_string(out_ptr, d_newchar);
+    }
+    if (!out_ptr) d_offsets[idx] = nbytes;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> filter_characters_of_type(strings_column_view const& strings,
+                                                  string_character_types types_to_remove,
+                                                  string_scalar const& replacement,
+                                                  string_character_types types_to_keep,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(replacement.is_valid(stream), "Parameter replacement must be valid");
+  if (types_to_remove == ALL_TYPES)
+    CUDF_EXPECTS(types_to_keep != ALL_TYPES,
+                 "Parameters types_to_remove and types_to_keep must not be both ALL_TYPES");
+  else
+    CUDF_EXPECTS(types_to_keep == ALL_TYPES,
+                 "One of parameter types_to_remove and types_to_keep must be set to ALL_TYPES");
+
+  auto const strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  cudf::string_view d_replacement(replacement.data(), replacement.size());
+  filter_chars_fn filterer{*strings_column,
+                           detail::get_character_flags_table(),
+                           types_to_remove,
+                           types_to_keep,
+                           d_replacement};
+
+  // copy null mask from input column
+  rmm::device_buffer null_mask = cudf::detail::copy_bitmask(strings.parent(), stream, mr);
+
+  // this utility calls filterer to build the offsets and chars columns
+  auto children = cudf::strings::detail::make_strings_children(filterer, strings_count, stream, mr);
+
+  // return new strings column
+  return make_strings_column(strings_count,
+                             std::move(children.first),
+                             std::move(children.second),
+                             strings.null_count(),
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> all_characters_of_type(strings_column_view const& strings,
+                                               string_character_types types,
+                                               string_character_types verify_types,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::all_characters_of_type(
+    strings, types, verify_types, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> filter_characters_of_type(strings_column_view const& strings,
+                                                  string_character_types types_to_remove,
+                                                  string_scalar const& replacement,
+                                                  string_character_types types_to_keep,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::filter_characters_of_type(
+    strings, types_to_remove, replacement, types_to_keep, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/combine/concatenate.cu b/cpp/src/strings/combine/concatenate.cu
new file mode 100644
index 0000000..ba8acd2
--- /dev/null
+++ b/cpp/src/strings/combine/concatenate.cu
@@ -0,0 +1,295 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/detail/combine.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/logical.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+struct concat_strings_base {
+  table_device_view const d_table;
+  string_scalar_device_view const d_narep;
+  separator_on_nulls separate_nulls;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  /**
+   * @brief Concatenate each table row to a single output string.
+   *
+   * This will concatenate the strings from each row of the given table
+   * and apply the separator. The null-replacement string `d_narep` is
+   * used in place of any string in a row that contains a null entry.
+   *
+   * @param idx The current row to process
+   * @param d_separator String to place in between each column's row
+   */
+  __device__ void process_row(size_type idx, string_view const d_separator)
+  {
+    if (!d_narep.is_valid() &&
+        thrust::any_of(thrust::seq, d_table.begin(), d_table.end(), [idx](auto const& col) {
+          return col.is_null(idx);
+        })) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    char* d_buffer       = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    size_type bytes      = 0;
+    bool write_separator = false;
+
+    for (auto itr = d_table.begin(); itr < d_table.end(); ++itr) {
+      auto const d_column     = *itr;
+      bool const null_element = d_column.is_null(idx);
+
+      if (write_separator && (separate_nulls == separator_on_nulls::YES || !null_element)) {
+        if (d_buffer) d_buffer = detail::copy_string(d_buffer, d_separator);
+        bytes += d_separator.size_bytes();
+        write_separator = false;
+      }
+
+      // write out column's row data (or narep if the row is null)
+      auto const d_str = null_element ? d_narep.value() : d_column.element<string_view>(idx);
+      if (d_buffer) d_buffer = detail::copy_string(d_buffer, d_str);
+      bytes += d_str.size_bytes();
+
+      write_separator =
+        write_separator || (separate_nulls == separator_on_nulls::YES) || !null_element;
+    }
+
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+/**
+ * @brief Single separator concatenate functor
+ */
+struct concat_strings_fn : concat_strings_base {
+  string_view const d_separator;
+
+  concat_strings_fn(table_device_view const& d_table,
+                    string_view const& d_separator,
+                    string_scalar_device_view const& d_narep,
+                    separator_on_nulls separate_nulls)
+    : concat_strings_base{d_table, d_narep, separate_nulls}, d_separator(d_separator)
+  {
+  }
+
+  __device__ void operator()(size_type idx) { process_row(idx, d_separator); }
+};
+
+}  // namespace
+
+std::unique_ptr<column> concatenate(table_view const& strings_columns,
+                                    string_scalar const& separator,
+                                    string_scalar const& narep,
+                                    separator_on_nulls separate_nulls,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto const num_columns = strings_columns.num_columns();
+  CUDF_EXPECTS(num_columns > 1, "At least two columns must be specified");
+  // check all columns are of type string
+  CUDF_EXPECTS(std::all_of(strings_columns.begin(),
+                           strings_columns.end(),
+                           [](auto c) { return c.type().id() == type_id::STRING; }),
+               "All columns must be of type string");
+  auto const strings_count = strings_columns.num_rows();
+  if (strings_count == 0)  // empty begets empty
+    return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(separator.is_valid(stream), "Parameter separator must be a valid string_scalar");
+  string_view d_separator(separator.data(), separator.size());
+  auto d_narep = get_scalar_device_view(const_cast<string_scalar&>(narep));
+
+  // Create device views from the strings columns.
+  auto d_table = table_device_view::create(strings_columns, stream);
+  concat_strings_fn fn{*d_table, d_separator, d_narep, separate_nulls};
+  auto children = make_strings_children(fn, strings_count, stream, mr);
+
+  // create resulting null mask
+  auto [null_mask, null_count] = cudf::detail::valid_if(
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(strings_count),
+    [d_table = *d_table, d_narep] __device__(size_type idx) {
+      if (d_narep.is_valid()) return true;
+      return !thrust::any_of(
+        thrust::seq, d_table.begin(), d_table.end(), [idx](auto col) { return col.is_null(idx); });
+    },
+    stream,
+    mr);
+
+  return make_strings_column(strings_count,
+                             std::move(children.first),
+                             std::move(children.second),
+                             null_count,
+                             std::move(null_mask));
+}
+
+namespace {
+
+/**
+ * @brief Concatenate strings functor using multiple separators.
+ *
+ * A unique separator is provided for each row along with a string to use
+ * when a separator row is null `d_separator_narep`. The `d_narep` is
+ * used in place of a null entry in the strings columns.
+ */
+struct multi_separator_concat_fn : concat_strings_base {
+  column_device_view const d_separators;
+  string_scalar_device_view const d_separator_narep;
+
+  multi_separator_concat_fn(table_device_view const& d_table,
+                            column_device_view const& d_separators,
+                            string_scalar_device_view const& d_separator_narep,
+                            string_scalar_device_view const& d_narep,
+                            separator_on_nulls separate_nulls)
+    : concat_strings_base{d_table, d_narep, separate_nulls},
+      d_separators(d_separators),
+      d_separator_narep(d_separator_narep)
+  {
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_separators.is_null(idx) && !d_separator_narep.is_valid()) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const d_separator = d_separators.is_valid(idx) ? d_separators.element<string_view>(idx)
+                                                        : d_separator_narep.value();
+    // base class utility function handles the rest
+    process_row(idx, d_separator);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> concatenate(table_view const& strings_columns,
+                                    strings_column_view const& separators,
+                                    string_scalar const& separator_narep,
+                                    string_scalar const& col_narep,
+                                    separator_on_nulls separate_nulls,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto const num_columns = strings_columns.num_columns();
+  CUDF_EXPECTS(num_columns > 0, "At least one column must be specified");
+  // Check if all columns are of type string
+  CUDF_EXPECTS(std::all_of(strings_columns.begin(),
+                           strings_columns.end(),
+                           [](auto c) { return c.type().id() == type_id::STRING; }),
+               "All columns must be of type string");
+
+  auto const strings_count = strings_columns.num_rows();
+  CUDF_EXPECTS(strings_count == separators.size(),
+               "Separators column should be the same size as the strings columns");
+  if (strings_count == 0)  // Empty begets empty
+    return make_empty_column(type_id::STRING);
+
+  // Invalid output column strings - null rows
+  string_view const invalid_str{nullptr, 0};
+  auto const separator_rep = get_scalar_device_view(const_cast<string_scalar&>(separator_narep));
+  auto const col_rep       = get_scalar_device_view(const_cast<string_scalar&>(col_narep));
+  auto const separator_col_view_ptr = column_device_view::create(separators.parent(), stream);
+  auto const separator_col_view     = *separator_col_view_ptr;
+
+  // Create device views from the strings columns.
+  auto d_table = table_device_view::create(strings_columns, stream);
+
+  multi_separator_concat_fn mscf{
+    *d_table, separator_col_view, separator_rep, col_rep, separate_nulls};
+  auto children = make_strings_children(mscf, strings_count, stream, mr);
+
+  // Create resulting null mask
+  auto [null_mask, null_count] = cudf::detail::valid_if(
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(strings_count),
+    [d_table = *d_table, separator_col_view, separator_rep, col_rep] __device__(size_type idx) {
+      if (!separator_col_view.is_valid(idx) && !separator_rep.is_valid()) return false;
+      if (col_rep.is_valid()) return true;
+      return !thrust::any_of(
+        thrust::seq, d_table.begin(), d_table.end(), [idx](auto col) { return col.is_null(idx); });
+    },
+    stream,
+    mr);
+
+  return make_strings_column(strings_count,
+                             std::move(children.first),
+                             std::move(children.second),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+
+// APIs
+
+std::unique_ptr<column> concatenate(table_view const& strings_columns,
+                                    string_scalar const& separator,
+                                    string_scalar const& narep,
+                                    separator_on_nulls separate_nulls,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate(
+    strings_columns, separator, narep, separate_nulls, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> concatenate(table_view const& strings_columns,
+                                    strings_column_view const& separators,
+                                    string_scalar const& separator_narep,
+                                    string_scalar const& col_narep,
+                                    separator_on_nulls separate_nulls,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::concatenate(strings_columns,
+                             separators,
+                             separator_narep,
+                             col_narep,
+                             separate_nulls,
+                             cudf::get_default_stream(),
+                             mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/combine/join.cu b/cpp/src/strings/combine/join.cu
new file mode 100644
index 0000000..faf1be6
--- /dev/null
+++ b/cpp/src/strings/combine/join.cu
@@ -0,0 +1,190 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/detail/combine.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Threshold to decide on using string-per-thread vs the string-gather
+ * approaches.
+ *
+ * If the average byte length of a string in a column exceeds this value then
+ * the string-gather function is used.
+ * Otherwise, a regular string-parallel function is used.
+ *
+ * This value was found using the strings_join benchmark results.
+ */
+constexpr size_type AVG_CHAR_BYTES_THRESHOLD = 32;
+
+struct join_base_fn {
+  column_device_view const d_strings;
+  string_view d_separator;
+  string_scalar_device_view d_narep;
+
+  __device__ thrust::pair<string_view, string_view> process_string(size_type idx) const
+  {
+    string_view d_str{};
+    string_view d_sep = (idx + 1 < d_strings.size()) ? d_separator : d_str;
+    if (d_strings.is_null(idx)) {
+      if (d_narep.is_valid()) {
+        d_str = d_narep.value();
+      } else {
+        // if null and no narep, don't output a separator either
+        d_sep = d_str;
+      }
+    } else {
+      d_str = d_strings.element<string_view>(idx);
+    }
+    return {d_str, d_sep};
+  }
+};
+
+/**
+ * @brief Compute output sizes and write output bytes
+ *
+ * This functor is suitable for make_strings_children
+ */
+struct join_fn : public join_base_fn {
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  join_fn(column_device_view const d_strings,
+          string_view d_separator,
+          string_scalar_device_view d_narep)
+    : join_base_fn{d_strings, d_separator, d_narep}
+  {
+  }
+
+  __device__ void operator()(size_type idx) const
+  {
+    auto const [d_str, d_sep] = process_string(idx);
+
+    char* d_buffer  = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    size_type bytes = 0;
+    if (d_buffer) {
+      d_buffer = detail::copy_string(d_buffer, d_str);
+      d_buffer = detail::copy_string(d_buffer, d_sep);
+    } else {
+      bytes += d_str.size_bytes() + d_sep.size_bytes();
+    }
+    if (!d_chars) { d_offsets[idx] = bytes; }
+  }
+};
+
+struct join_gather_fn : public join_base_fn {
+  join_gather_fn(column_device_view const d_strings,
+                 string_view d_separator,
+                 string_scalar_device_view d_narep)
+    : join_base_fn{d_strings, d_separator, d_narep}
+  {
+  }
+
+  __device__ string_index_pair operator()(size_type idx) const
+  {
+    auto const [d_str, d_sep] = process_string(idx / 2);
+    // every other string is the separator
+    return idx % 2 ? string_index_pair{d_sep.data(), d_sep.size_bytes()}
+                   : string_index_pair{d_str.data(), d_str.size_bytes()};
+  }
+};
+}  // namespace
+
+std::unique_ptr<column> join_strings(strings_column_view const& input,
+                                     string_scalar const& separator,
+                                     string_scalar const& narep,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return make_empty_column(type_id::STRING); }
+
+  CUDF_EXPECTS(separator.is_valid(stream), "Parameter separator must be a valid string_scalar");
+
+  string_view d_separator(separator.data(), separator.size());
+  auto d_narep = get_scalar_device_view(const_cast<string_scalar&>(narep));
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  auto chars_column = [&] {
+    // build the strings column and commandeer the chars column
+    if ((input.size() == input.null_count()) ||
+        ((input.chars_size() / (input.size() - input.null_count())) <= AVG_CHAR_BYTES_THRESHOLD)) {
+      return std::get<1>(
+        make_strings_children(join_fn{*d_strings, d_separator, d_narep}, input.size(), stream, mr));
+    }
+    // dynamically feeds index pairs to build the output
+    auto indices = cudf::detail::make_counting_transform_iterator(
+      0, join_gather_fn{*d_strings, d_separator, d_narep});
+    auto joined_col = make_strings_column(indices, indices + (input.size() * 2), stream, mr);
+    return std::move(joined_col->release().children.back());
+  }();
+
+  // build the offsets: single string output has offsets [0,chars-size]
+  auto offsets = cudf::detail::make_device_uvector_async(
+    std::vector<size_type>({0, chars_column->size()}), stream, mr);
+  auto offsets_column = std::make_unique<column>(std::move(offsets), rmm::device_buffer{}, 0);
+
+  // build the null mask: only one output row so it is either all-valid or all-null
+  auto const null_count =
+    static_cast<size_type>(input.null_count() == input.size() && !narep.is_valid(stream));
+  auto null_mask = null_count
+                     ? cudf::detail::create_null_mask(1, cudf::mask_state::ALL_NULL, stream, mr)
+                     : rmm::device_buffer{0, stream, mr};
+
+  // perhaps this return a string_scalar instead of a single-row column
+  return make_strings_column(
+    1, std::move(offsets_column), std::move(chars_column), null_count, std::move(null_mask));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> join_strings(strings_column_view const& strings,
+                                     string_scalar const& separator,
+                                     string_scalar const& narep,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::join_strings(strings, separator, narep, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/combine/join_list_elements.cu b/cpp/src/strings/combine/join_list_elements.cu
new file mode 100644
index 0000000..eee59e3
--- /dev/null
+++ b/cpp/src/strings/combine/join_list_elements.cu
@@ -0,0 +1,336 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+/**
+ * @brief Compute string sizes, string validities, and concatenate strings functor.
+ *
+ * This functor is executed twice. In the first pass, the sizes and validities of the output strings
+ * will be computed. In the second pass, this will concatenate the strings within each list element
+ * of the given lists column and apply the separator. The null-replacement string scalar
+ * `string_narep_dv` (if valid) is used in place of any null string.
+ *
+ * @tparam Functor The functor which can check for validity of the input list at a given list index
+ * as well as access to the separator corresponding to the list index.
+ */
+template <class Functor>
+struct compute_size_and_concatenate_fn {
+  Functor const func;
+  column_device_view const lists_dv;
+  size_type const* const list_offsets;
+  column_device_view const strings_dv;
+  string_scalar_device_view const string_narep_dv;
+  separator_on_nulls const separate_nulls;
+  output_if_empty_list const empty_list_policy;
+
+  size_type* d_offsets{nullptr};
+
+  // If d_chars == nullptr: only compute sizes and validities of the output strings.
+  // If d_chars != nullptr: only concatenate strings.
+  char* d_chars{nullptr};
+
+  [[nodiscard]] __device__ bool output_is_null(size_type const idx,
+                                               size_type const start_idx,
+                                               size_type const end_idx) const noexcept
+  {
+    if (func.is_null_list(lists_dv, idx)) { return true; }
+    return empty_list_policy == output_if_empty_list::NULL_ELEMENT && start_idx == end_idx;
+  }
+
+  __device__ void operator()(size_type const idx) const noexcept
+  {
+    // If this is the second pass, and the row `idx` is known to be a null or empty string
+    if (d_chars && (d_offsets[idx] == d_offsets[idx + 1])) { return; }
+
+    // Indices of the strings within the list row
+    auto const start_idx = list_offsets[idx];
+    auto const end_idx   = list_offsets[idx + 1];
+
+    if (!d_chars && output_is_null(idx, start_idx, end_idx)) {
+      d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const separator   = func.separator(idx);
+    char* output_ptr       = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    bool write_separator   = false;
+    auto size_bytes        = size_type{0};
+    bool has_valid_element = false;
+
+    for (size_type str_idx = start_idx; str_idx < end_idx; ++str_idx) {
+      bool null_element = strings_dv.is_null(str_idx);
+      has_valid_element = has_valid_element || !null_element;
+
+      if (!d_chars && (null_element && !string_narep_dv.is_valid())) {
+        size_bytes = 0;
+        break;
+      }
+
+      if (write_separator && (separate_nulls == separator_on_nulls::YES || !null_element)) {
+        if (output_ptr) output_ptr = detail::copy_string(output_ptr, separator);
+        size_bytes += separator.size_bytes();
+        write_separator = false;
+      }
+
+      auto const d_str =
+        null_element ? string_narep_dv.value() : strings_dv.element<string_view>(str_idx);
+      if (output_ptr) output_ptr = detail::copy_string(output_ptr, d_str);
+      size_bytes += d_str.size_bytes();
+
+      write_separator =
+        write_separator || (separate_nulls == separator_on_nulls::YES) || !null_element;
+    }
+
+    // If there are all null elements, the output should be the same as having an empty list input:
+    // a null or an empty string
+    if (!d_chars) { d_offsets[idx] = has_valid_element ? size_bytes : 0; }
+  }
+};
+
+/**
+ * @brief Functor accompanying with `compute_size_and_concatenate_fn` for computing output string
+ * sizes, output string validities, and concatenating strings within list elements; used when the
+ * separator is a string scalar.
+ */
+struct scalar_separator_fn {
+  string_scalar_device_view const d_separator;
+
+  [[nodiscard]] __device__ bool is_null_list(column_device_view const& lists_dv,
+                                             size_type const idx) const noexcept
+  {
+    return lists_dv.is_null(idx);
+  }
+
+  [[nodiscard]] __device__ string_view separator(size_type const) const noexcept
+  {
+    return d_separator.value();
+  }
+};
+
+template <typename CompFn>
+struct validities_fn {
+  CompFn comp_fn;
+
+  validities_fn(CompFn comp_fn) : comp_fn(comp_fn) {}
+
+  __device__ bool operator()(size_type idx)
+  {
+    auto const start_idx = comp_fn.list_offsets[idx];
+    auto const end_idx   = comp_fn.list_offsets[idx + 1];
+    bool valid_output    = !comp_fn.output_is_null(idx, start_idx, end_idx);
+    if (valid_output) {
+      bool check_elements = false;
+      for (size_type str_idx = start_idx; str_idx < end_idx; ++str_idx) {
+        bool const valid_element = comp_fn.strings_dv.is_valid(str_idx);
+        check_elements           = check_elements || valid_element;
+        // if an element is null and narep is invalid, the output row is null
+        if (!valid_element && !comp_fn.string_narep_dv.is_valid()) { return false; }
+      }
+      // handle empty-list-as-null output policy setting
+      valid_output =
+        check_elements || comp_fn.empty_list_policy == output_if_empty_list::EMPTY_STRING;
+    }
+    return valid_output;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> join_list_elements(lists_column_view const& lists_strings_column,
+                                           string_scalar const& separator,
+                                           string_scalar const& narep,
+                                           separator_on_nulls separate_nulls,
+                                           output_if_empty_list empty_list_policy,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(lists_strings_column.child().type().id() == type_id::STRING,
+               "The input column must be a column of lists of strings");
+  CUDF_EXPECTS(separator.is_valid(stream), "Parameter separator must be a valid string_scalar");
+
+  auto const num_rows = lists_strings_column.size();
+  if (num_rows == 0) { return make_empty_column(type_id::STRING); }
+
+  // Accessing the child strings column of the lists column must be done by calling `child()` on the
+  // lists column, not `get_sliced_child()`. This is because calling to `offsets_begin()` on the
+  // lists column returns a pointer to the offsets of the original lists column, which may not start
+  // from `0`.
+  auto const strings_col     = strings_column_view(lists_strings_column.child());
+  auto const lists_dv_ptr    = column_device_view::create(lists_strings_column.parent(), stream);
+  auto const strings_dv_ptr  = column_device_view::create(strings_col.parent(), stream);
+  auto const sep_dv          = get_scalar_device_view(const_cast<string_scalar&>(separator));
+  auto const string_narep_dv = get_scalar_device_view(const_cast<string_scalar&>(narep));
+
+  auto const func = scalar_separator_fn{sep_dv};
+  auto const comp_fn =
+    compute_size_and_concatenate_fn<decltype(func)>{func,
+                                                    *lists_dv_ptr,
+                                                    lists_strings_column.offsets_begin(),
+                                                    *strings_dv_ptr,
+                                                    string_narep_dv,
+                                                    separate_nulls,
+                                                    empty_list_policy};
+
+  auto [offsets_column, chars_column] = make_strings_children(comp_fn, num_rows, stream, mr);
+  auto [null_mask, null_count] =
+    cudf::detail::valid_if(thrust::counting_iterator<size_type>(0),
+                           thrust::counting_iterator<size_type>(num_rows),
+                           validities_fn{comp_fn},
+                           stream,
+                           mr);
+
+  return make_strings_column(
+    num_rows, std::move(offsets_column), std::move(chars_column), null_count, std::move(null_mask));
+}
+
+namespace {
+/**
+ * @brief Functor accompanying with `compute_size_and_concatenate_fn` for computing output string
+ * sizes, output string validities, and concatenating strings within list elements; used when the
+ * separators are given as a strings column.
+ */
+struct column_separators_fn {
+  column_device_view const separators_dv;
+  string_scalar_device_view const sep_narep_dv;
+
+  [[nodiscard]] __device__ bool is_null_list(column_device_view const& lists_dv,
+                                             size_type const idx) const noexcept
+  {
+    return lists_dv.is_null(idx) || (separators_dv.is_null(idx) && !sep_narep_dv.is_valid());
+  }
+
+  [[nodiscard]] __device__ string_view separator(size_type const idx) const noexcept
+  {
+    return separators_dv.is_valid(idx) ? separators_dv.element<string_view>(idx)
+                                       : sep_narep_dv.value();
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> join_list_elements(lists_column_view const& lists_strings_column,
+                                           strings_column_view const& separators,
+                                           string_scalar const& separator_narep,
+                                           string_scalar const& string_narep,
+                                           separator_on_nulls separate_nulls,
+                                           output_if_empty_list empty_list_policy,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(lists_strings_column.child().type().id() == type_id::STRING,
+               "The input column must be a column of lists of strings");
+  CUDF_EXPECTS(lists_strings_column.size() == separators.size(),
+               "Separators column should be the same size as the lists columns");
+
+  auto const num_rows = lists_strings_column.size();
+  if (num_rows == 0) { return make_empty_column(type_id::STRING); }
+
+  // Accessing the child strings column of the lists column must be done by calling `child()` on the
+  // lists column, not `get_sliced_child()`. This is because calling to `offsets_begin()` on the
+  // lists column returns a pointer to the offsets of the original lists column, which may not start
+  // from `0`.
+  auto const strings_col     = strings_column_view(lists_strings_column.child());
+  auto const lists_dv_ptr    = column_device_view::create(lists_strings_column.parent(), stream);
+  auto const strings_dv_ptr  = column_device_view::create(strings_col.parent(), stream);
+  auto const string_narep_dv = get_scalar_device_view(const_cast<string_scalar&>(string_narep));
+  auto const sep_dv_ptr      = column_device_view::create(separators.parent(), stream);
+  auto const sep_narep_dv    = get_scalar_device_view(const_cast<string_scalar&>(separator_narep));
+
+  auto const func = column_separators_fn{*sep_dv_ptr, sep_narep_dv};
+  auto const comp_fn =
+    compute_size_and_concatenate_fn<decltype(func)>{func,
+                                                    *lists_dv_ptr,
+                                                    lists_strings_column.offsets_begin(),
+                                                    *strings_dv_ptr,
+                                                    string_narep_dv,
+                                                    separate_nulls,
+                                                    empty_list_policy};
+
+  auto [offsets_column, chars_column] = make_strings_children(comp_fn, num_rows, stream, mr);
+  auto [null_mask, null_count] =
+    cudf::detail::valid_if(thrust::counting_iterator<size_type>(0),
+                           thrust::counting_iterator<size_type>(num_rows),
+                           validities_fn{comp_fn},
+                           stream,
+                           mr);
+
+  return make_strings_column(
+    num_rows, std::move(offsets_column), std::move(chars_column), null_count, std::move(null_mask));
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> join_list_elements(lists_column_view const& lists_strings_column,
+                                           string_scalar const& separator,
+                                           string_scalar const& narep,
+                                           separator_on_nulls separate_nulls,
+                                           output_if_empty_list empty_list_policy,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::join_list_elements(lists_strings_column,
+                                    separator,
+                                    narep,
+                                    separate_nulls,
+                                    empty_list_policy,
+                                    cudf::get_default_stream(),
+                                    mr);
+}
+
+std::unique_ptr<column> join_list_elements(lists_column_view const& lists_strings_column,
+                                           strings_column_view const& separators,
+                                           string_scalar const& separator_narep,
+                                           string_scalar const& string_narep,
+                                           separator_on_nulls separate_nulls,
+                                           output_if_empty_list empty_list_policy,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::join_list_elements(lists_strings_column,
+                                    separators,
+                                    separator_narep,
+                                    string_narep,
+                                    separate_nulls,
+                                    empty_list_policy,
+                                    cudf::get_default_stream(),
+                                    mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/contains.cu b/cpp/src/strings/contains.cu
new file mode 100644
index 0000000..2253487
--- /dev/null
+++ b/cpp/src/strings/contains.cu
@@ -0,0 +1,151 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/count_matches.hpp>
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+/**
+ * @brief This functor handles both contains_re and match_re to regex-match a pattern
+ * to each string in a column.
+ */
+struct contains_fn {
+  column_device_view const d_strings;
+  bool const beginning_only;
+
+  __device__ bool operator()(size_type const idx,
+                             reprog_device const prog,
+                             int32_t const thread_idx)
+  {
+    if (d_strings.is_null(idx)) return false;
+    auto const d_str = d_strings.element<string_view>(idx);
+
+    size_type end = beginning_only ? 1    // match only the beginning of the string;
+                                   : -1;  // match anywhere in the string
+    return prog.find(thread_idx, d_str, d_str.begin(), end).has_value();
+  }
+};
+
+std::unique_ptr<column> contains_impl(strings_column_view const& input,
+                                      regex_program const& prog,
+                                      bool const beginning_only,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  auto results = make_numeric_column(data_type{type_id::BOOL8},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+  if (input.is_empty()) { return results; }
+
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  auto d_results       = results->mutable_view().data<bool>();
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+
+  launch_transform_kernel(
+    contains_fn{*d_strings, beginning_only}, *d_prog, d_results, input.size(), stream);
+
+  results->set_null_count(input.null_count());
+
+  return results;
+}
+
+}  // namespace
+
+std::unique_ptr<column> contains_re(strings_column_view const& input,
+                                    regex_program const& prog,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  return contains_impl(input, prog, false, stream, mr);
+}
+
+std::unique_ptr<column> matches_re(strings_column_view const& input,
+                                   regex_program const& prog,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  return contains_impl(input, prog, true, stream, mr);
+}
+
+std::unique_ptr<column> count_re(strings_column_view const& input,
+                                 regex_program const& prog,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+
+  auto result = count_matches(*d_strings, *d_prog, input.size(), stream, mr);
+  if (input.has_nulls()) {
+    result->set_null_mask(cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                          input.null_count());
+  }
+  return result;
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> contains_re(strings_column_view const& strings,
+                                    regex_program const& prog,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains_re(strings, prog, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> matches_re(strings_column_view const& strings,
+                                   regex_program const& prog,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::matches_re(strings, prog, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> count_re(strings_column_view const& strings,
+                                 regex_program const& prog,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::count_re(strings, prog, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_booleans.cu b/cpp/src/strings/convert/convert_booleans.cu
new file mode 100644
index 0000000..0d04fc7
--- /dev/null
+++ b/cpp/src/strings/convert/convert_booleans.cu
@@ -0,0 +1,166 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/convert/convert_booleans.hpp>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+// Convert strings column to boolean column
+std::unique_ptr<column> to_booleans(strings_column_view const& strings,
+                                    string_scalar const& true_string,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_numeric_column(data_type{type_id::BOOL8}, 0);
+
+  CUDF_EXPECTS(true_string.is_valid(stream) && true_string.size() > 0,
+               "Parameter true_string must not be empty.");
+  auto d_true = string_view(true_string.data(), true_string.size());
+
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  // create output column copying the strings' null-mask
+  auto results      = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto results_view = results->mutable_view();
+  auto d_results    = results_view.data<bool>();
+
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings_count),
+                    d_results,
+                    [d_strings, d_true] __device__(size_type idx) {
+                      bool result = false;
+                      if (!d_strings.is_null(idx))
+                        result = d_strings.element<string_view>(idx).compare(d_true) == 0;
+                      return result;
+                    });
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> to_booleans(strings_column_view const& strings,
+                                    string_scalar const& true_string,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_booleans(strings, true_string, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+
+namespace {
+struct from_booleans_fn {
+  column_device_view const d_column;
+  string_view d_true;
+  string_view d_false;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx) const
+  {
+    if (d_column.is_null(idx)) {
+      if (d_chars == nullptr) { d_offsets[idx] = 0; }
+      return;
+    }
+
+    if (d_chars != nullptr) {
+      auto const result = d_column.element<bool>(idx) ? d_true : d_false;
+      memcpy(d_chars + d_offsets[idx], result.data(), result.size_bytes());
+    } else {
+      d_offsets[idx] = d_column.element<bool>(idx) ? d_true.size_bytes() : d_false.size_bytes();
+    }
+  };
+};
+}  // namespace
+
+// Convert boolean column to strings column
+std::unique_ptr<column> from_booleans(column_view const& booleans,
+                                      string_scalar const& true_string,
+                                      string_scalar const& false_string,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = booleans.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(booleans.type().id() == type_id::BOOL8, "Input column must be boolean type");
+  CUDF_EXPECTS(true_string.is_valid(stream) && true_string.size() > 0,
+               "Parameter true_string must not be empty.");
+  auto d_true = string_view(true_string.data(), true_string.size());
+  CUDF_EXPECTS(false_string.is_valid(stream) && false_string.size() > 0,
+               "Parameter false_string must not be empty.");
+  auto d_false = string_view(false_string.data(), false_string.size());
+
+  auto column   = column_device_view::create(booleans, stream);
+  auto d_column = *column;
+
+  // copy null mask
+  rmm::device_buffer null_mask = cudf::detail::copy_bitmask(booleans, stream, mr);
+
+  auto [offsets, chars] =
+    make_strings_children(from_booleans_fn{d_column, d_true, d_false}, strings_count, stream, mr);
+
+  return make_strings_column(strings_count,
+                             std::move(offsets),
+                             std::move(chars),
+                             booleans.null_count(),
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> from_booleans(column_view const& booleans,
+                                      string_scalar const& true_string,
+                                      string_scalar const& false_string,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_booleans(booleans, true_string, false_string, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_datetime.cu b/cpp/src/strings/convert/convert_datetime.cu
new file mode 100644
index 0000000..8a953d7
--- /dev/null
+++ b/cpp/src/strings/convert/convert_datetime.cu
@@ -0,0 +1,1178 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/convert/convert_datetime.hpp>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+#include <map>
+#include <numeric>
+#include <vector>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Structure of date/time components
+ */
+struct timestamp_components {
+  int16_t year;
+  int8_t month;
+  int8_t day;
+  int16_t day_of_year;
+  int8_t hour;
+  int8_t minute;
+  int8_t second;
+  int8_t week;     ///< week of the year
+  int8_t weekday;  ///< day of the week
+  int32_t subsecond;
+  int32_t tz_minutes;
+};
+
+enum class format_char_type : int8_t {
+  literal,   ///< literal char type passed through
+  specifier  ///< timestamp format specifier
+};
+
+/**
+ * @brief Represents a format specifier or literal from a timestamp format string.
+ *
+ * Created by the format_compiler when parsing a format string.
+ */
+struct alignas(4) format_item {
+  format_char_type item_type;  // specifier or literal indicator
+  char value;                  // specifier or literal value
+  int8_t length;               // item length in bytes
+
+  static format_item new_specifier(char format_char, int8_t length)
+  {
+    return format_item{format_char_type::specifier, format_char, length};
+  }
+  static format_item new_literal(char literal)
+  {
+    return format_item{format_char_type::literal, literal, 1};
+  }
+};
+
+/**
+ * @brief The format-compiler parses a timestamp format string into a vector of
+ * `format_items`.
+ *
+ * The vector of `format_items` is used when parsing a string into timestamp
+ * components and when formatting a string from timestamp components.
+ */
+using specifier_map = std::map<char, int8_t>;
+
+struct format_compiler {
+  std::string_view const format;
+  rmm::device_uvector<format_item> d_items;
+
+  // clang-format off
+  // The specifiers are documented here (not all are supported):
+  // https://en.cppreference.com/w/cpp/chrono/system_clock/formatter
+  specifier_map specifiers = {
+    {'Y', 4}, {'y', 2}, {'m', 2}, {'d', 2}, {'H', 2}, {'I', 2}, {'M', 2},
+    {'S', 2}, {'f', 6}, {'z', 5}, {'Z', 3}, {'p', 2}, {'j', 3},
+    {'W', 2}, {'w', 1}, {'U', 2}, {'u', 1}};
+  // clang-format on
+
+  format_compiler(std::string_view fmt,
+                  rmm::cuda_stream_view stream,
+                  specifier_map extra_specifiers = {})
+    : format(fmt), d_items(0, stream)
+  {
+    specifiers.insert(extra_specifiers.begin(), extra_specifiers.end());
+    std::vector<format_item> items;
+    auto str    = format.data();
+    auto length = format.length();
+    while (length > 0) {
+      char ch = *str++;
+      length--;
+
+      // first check for a literal character
+      if (ch != '%') {
+        items.push_back(format_item::new_literal(ch));
+        continue;
+      }
+      CUDF_EXPECTS(length > 0, "Unfinished specifier in timestamp format");
+
+      ch = *str++;
+      length--;
+      if (ch == '%')  // escaped % char
+      {
+        items.push_back(format_item::new_literal(ch));
+        continue;
+      }
+      if (ch >= '0' && ch <= '9') {
+        CUDF_EXPECTS(*str == 'f', "precision not supported for specifier: " + std::string(1, *str));
+        specifiers[*str] = static_cast<int8_t>(ch - '0');
+        ch               = *str++;
+        length--;
+      }
+
+      // check if the specifier found is supported
+      CUDF_EXPECTS(specifiers.find(ch) != specifiers.end(),
+                   "invalid format specifier: " + std::string(1, ch));
+
+      // create the format item for this specifier
+      items.push_back(format_item::new_specifier(ch, specifiers[ch]));
+    }
+
+    // copy format_items to device memory
+    d_items = cudf::detail::make_device_uvector_async(
+      items, stream, rmm::mr::get_current_device_resource());
+  }
+
+  device_span<format_item const> format_items() { return device_span<format_item const>(d_items); }
+
+  [[nodiscard]] int8_t subsecond_precision() const { return specifiers.at('f'); }
+};
+
+/**
+ * @brief Specialized function to return the integer value reading up to the specified
+ * bytes or until an invalid character is encountered.
+ *
+ * @param str Beginning of characters to read.
+ * @param bytes Number of bytes in str to read.
+ * @return Integer value of valid characters read and how many bytes were not read.
+ */
+__device__ thrust::pair<int32_t, size_type> parse_int(char const* str, size_type bytes)
+{
+  int32_t value = 0;
+  while (bytes-- > 0) {
+    char chr = *str++;
+    if (chr < '0' || chr > '9') break;
+    value = (value * 10) + static_cast<int32_t>(chr - '0');
+  }
+  return thrust::make_pair(value, bytes + 1);
+}
+
+/**
+ * @brief This parses date/time characters into a timestamp integer
+ *
+ * @tparam T cudf::timestamp type
+ */
+template <typename T>
+struct parse_datetime {
+  column_device_view const d_strings;
+  device_span<format_item const> const d_format_items;
+  int8_t const subsecond_precision;
+
+  /**
+   * @brief Return power of ten value given an exponent.
+   *
+   * @return `1x10^exponent` for `0 <= exponent <= 9`
+   */
+  [[nodiscard]] __device__ constexpr int64_t power_of_ten(int32_t const exponent) const
+  {
+    constexpr int64_t powers_of_ten[] = {
+      1L, 10L, 100L, 1000L, 10000L, 100000L, 1000000L, 10000000L, 100000000L, 1000000000L};
+    return powers_of_ten[exponent];
+  }
+
+  __device__ bool format_contains(char specifier) const
+  {
+    return thrust::find_if(thrust::seq,
+                           d_format_items.begin(),
+                           d_format_items.end(),
+                           [specifier](auto const item) { return item.value == specifier; }) !=
+           d_format_items.end();
+  }
+
+  // Walk the format_items to parse the string into date/time components
+  [[nodiscard]] __device__ timestamp_components parse_into_parts(string_view const& d_string) const
+  {
+    timestamp_components timeparts = {1970, 1, 1, 0};  // init to epoch time
+
+    auto ptr    = d_string.data();
+    auto length = d_string.size_bytes();
+    for (auto item : d_format_items) {
+      if (item.value != 'f')
+        item.length = static_cast<int8_t>(std::min(static_cast<size_type>(item.length), length));
+
+      if (item.item_type == format_char_type::literal) {
+        // static character we'll just skip;
+        // consume item.length bytes from the input string
+        ptr += item.length;
+        length -= item.length;
+        continue;
+      }
+
+      size_type bytes_read = item.length;  // number of bytes processed
+      // special logic for each specifier
+      switch (item.value) {
+        case 'Y': {
+          auto const [year, left] = parse_int(ptr, item.length);
+          timeparts.year          = static_cast<int16_t>(year);
+          bytes_read -= left;
+          break;
+        }
+        case 'y': {
+          auto const [year, left] = parse_int(ptr, item.length);
+          timeparts.year          = static_cast<int16_t>(year + (year < 69 ? 2000 : 1900));
+          bytes_read -= left;
+          break;
+        }
+        case 'm': {
+          auto const [month, left] = parse_int(ptr, item.length);
+          timeparts.month          = static_cast<int8_t>(month);
+          bytes_read -= left;
+          break;
+        }
+        case 'd': {
+          auto const [day, left] = parse_int(ptr, item.length);
+          timeparts.day          = static_cast<int8_t>(day);
+          bytes_read -= left;
+          break;
+        }
+        case 'j': {
+          auto const [day, left] = parse_int(ptr, item.length);
+          timeparts.day_of_year  = static_cast<int16_t>(day);
+          bytes_read -= left;
+          break;
+        }
+        case 'H':
+        case 'I': {
+          auto const [hour, left] = parse_int(ptr, item.length);
+          timeparts.hour          = static_cast<int8_t>(hour);
+          bytes_read -= left;
+          break;
+        }
+        case 'M': {
+          auto const [minute, left] = parse_int(ptr, item.length);
+          timeparts.minute          = static_cast<int8_t>(minute);
+          bytes_read -= left;
+          break;
+        }
+        case 'S': {
+          auto const [second, left] = parse_int(ptr, item.length);
+          timeparts.second          = static_cast<int8_t>(second);
+          bytes_read -= left;
+          break;
+        }
+        case 'f': {
+          int32_t const read_size =
+            std::min(static_cast<int32_t>(item.length), static_cast<int32_t>(length));
+          auto const [fraction, left] = parse_int(ptr, read_size);
+          timeparts.subsecond =
+            static_cast<int32_t>(fraction * power_of_ten(item.length - read_size + left));
+          bytes_read = read_size - left;
+          break;
+        }
+        case 'p': {
+          string_view am_pm(ptr, 2);
+          auto hour = timeparts.hour;
+          if ((am_pm.compare("AM", 2) == 0) || (am_pm.compare("am", 2) == 0)) {
+            if (hour == 12) hour = 0;
+          } else if (hour < 12)
+            hour += 12;
+          timeparts.hour = hour;
+          break;
+        }
+        case 'U': [[fallthrough]];  // week of year: Sunday based
+        case 'W': {                 // week of year: Monday based
+          auto const [week, left] = parse_int(ptr, item.length);
+          timeparts.week          = static_cast<int8_t>(week + 1);
+          bytes_read -= left;
+          break;
+        }
+        case 'u': [[fallthrough]];  // day of week: Mon(1)-Sat(6),Sun(7)
+        case 'w': {                 // day of week; Sun(0),Mon(1)-Sat(6)
+          auto const [weekday, left] = parse_int(ptr, item.length);
+          timeparts.weekday          =  // 0 is mapped to 7 for chrono library
+            static_cast<int8_t>((item.value == 'w' && weekday == 0) ? 7 : weekday);
+          bytes_read -= left;
+          break;
+        }
+        case 'z': {
+          // 'z' format is +hh:mm -- single sign char and 2 chars each for hour and minute
+          if (item.length == 5) {
+            auto const sign     = *ptr == '-' ? 1 : -1;
+            auto const [hh, lh] = parse_int(ptr + 1, 2);
+            auto const [mm, lm] = parse_int(ptr + 3, 2);
+            // revert timezone back to UTC
+            timeparts.tz_minutes = sign * ((hh * 60) + mm);
+            bytes_read -= lh + lm;
+          }
+          break;
+        }
+        case 'Z': break;  // skip
+        default: break;
+      }
+      ptr += bytes_read;
+      length -= bytes_read;
+    }
+    return timeparts;
+  }
+
+  [[nodiscard]] __device__ int64_t timestamp_from_parts(timestamp_components const& timeparts) const
+  {
+    // Reference: https://howardhinnant.github.io/date/date.html#Reference
+    auto const days = [timeparts, this] {
+      // week and weekday prioritize over month/day
+      if ((timeparts.week > 0) && (timeparts.weekday > 0)) {
+        auto const y = cuda::std::chrono::year{timeparts.year};
+        // clang-format off
+        auto const start = format_contains('U')
+          ? cuda::std::chrono::sys_days{cuda::std::chrono::Sunday[1]/cuda::std::chrono::January/y}
+          : cuda::std::chrono::sys_days{cuda::std::chrono::Monday[1]/cuda::std::chrono::January/y};
+        // clang-format on
+        auto const days =  // compute days from year, weeks and weekday
+          start + cuda::std::chrono::weeks(timeparts.week - 1) - cuda::std::chrono::weeks{1} +
+          (cuda::std::chrono::weekday(timeparts.weekday) -
+           cuda::std::chrono::weekday{1});  // cuda::std::chrono::Monday causes compile error here
+        return days.time_since_epoch().count();
+      }
+      auto const ymd =  // chrono class handles the leap year calculations for us
+        cuda::std::chrono::year_month_day(
+          cuda::std::chrono::year{timeparts.year},
+          cuda::std::chrono::month{static_cast<uint32_t>(timeparts.month)},
+          cuda::std::chrono::day{static_cast<uint32_t>(timeparts.day)});
+      return cuda::std::chrono::sys_days(ymd).time_since_epoch().count();
+    }();
+
+    if constexpr (std::is_same_v<T, cudf::timestamp_D>) { return days; }
+
+    int64_t timestamp = (days * 24L * 3600L) + (timeparts.hour * 3600L) + (timeparts.minute * 60L) +
+                        timeparts.second + (timeparts.tz_minutes * 60L);
+
+    if constexpr (std::is_same_v<T, cudf::timestamp_s>) { return timestamp; }
+
+    int64_t const subsecond =
+      (timeparts.subsecond * power_of_ten(9 - subsecond_precision)) /  // normalize to nanoseconds
+      (1000000000L / T::period::type::den);                            // and rescale to T
+
+    timestamp *= T::period::type::den;
+    timestamp += subsecond;
+
+    return timestamp;
+  }
+
+  __device__ T operator()(size_type idx) const
+  {
+    T epoch_time{typename T::duration{0}};
+    if (d_strings.is_null(idx)) return epoch_time;
+    string_view d_str = d_strings.element<string_view>(idx);
+    if (d_str.empty()) return epoch_time;
+
+    auto const timeparts = parse_into_parts(d_str);
+
+    return T{T::duration(timestamp_from_parts(timeparts))};
+  }
+};
+
+/**
+ * @brief Type-dispatch operator to convert timestamp strings to native fixed-width-type
+ */
+struct dispatch_to_timestamps_fn {
+  template <typename T, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+  void operator()(column_device_view const& d_strings,
+                  std::string_view format,
+                  mutable_column_view& results_view,
+                  rmm::cuda_stream_view stream) const
+  {
+    format_compiler compiler(format, stream);
+    parse_datetime<T> pfn{d_strings, compiler.format_items(), compiler.subsecond_precision()};
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(results_view.size()),
+                      results_view.data<T>(),
+                      pfn);
+  }
+  template <typename T, std::enable_if_t<not cudf::is_timestamp<T>()>* = nullptr>
+  void operator()(column_device_view const&,
+                  std::string_view,
+                  mutable_column_view&,
+                  rmm::cuda_stream_view) const
+  {
+    CUDF_FAIL("Only timestamps type are expected");
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<cudf::column> to_timestamps(strings_column_view const& input,
+                                            data_type timestamp_type,
+                                            std::string_view format,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty())
+    return make_empty_column(timestamp_type);  // make_timestamp_column(timestamp_type, 0);
+
+  CUDF_EXPECTS(!format.empty(), "Format parameter must not be empty.");
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  auto results = make_timestamp_column(timestamp_type,
+                                       input.size(),
+                                       cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                       input.null_count(),
+                                       stream,
+                                       mr);
+
+  auto results_view = results->mutable_view();
+  cudf::type_dispatcher(
+    timestamp_type, dispatch_to_timestamps_fn(), *d_strings, format, results_view, stream);
+
+  results->set_null_count(input.null_count());
+  return results;
+}
+
+/**
+ * @brief Functor checks the strings against the given format items.
+ *
+ * This does no data conversion.
+ */
+struct check_datetime_format {
+  column_device_view const d_strings;
+  device_span<format_item const> const d_format_items;
+
+  /**
+   * @brief Check the specified characters are between ['0','9'].
+   *
+   * @param str Beginning of characters to check.
+   * @param bytes Number of bytes to check.
+   * @return true if all digits are 0-9
+   */
+  __device__ bool check_digits(char const* str, size_type bytes)
+  {
+    return thrust::all_of(thrust::seq, str, str + bytes, [] __device__(char chr) {
+      return (chr >= '0' && chr <= '9');
+    });
+  }
+
+  /**
+   * @brief Check the specified characters are between ['0','9']
+   * and the resulting integer is within [`min_value`, `max_value`].
+   *
+   * @param str Beginning of characters to check.
+   * @param bytes Number of bytes to check.
+   * @param min_value Inclusive minimum value
+   * @param max_value Inclusive maximum value
+   * @return If value is valid and number of bytes not successfully processed
+   */
+  __device__ thrust::pair<bool, size_type> check_value(char const* str,
+                                                       size_type const bytes,
+                                                       int const min_value,
+                                                       int const max_value)
+  {
+    if (*str < '0' || *str > '9') { return thrust::make_pair(false, bytes); }
+    int32_t value   = 0;
+    size_type count = bytes;
+    while (count-- > 0) {
+      char chr = *str++;
+      if (chr < '0' || chr > '9') break;
+      value = (value * 10) + static_cast<int32_t>(chr - '0');
+    }
+    return (value >= min_value && value <= max_value) ? thrust::make_pair(true, count + 1)
+                                                      : thrust::make_pair(false, bytes);
+  }
+
+  /**
+   * @brief Check the string matches the format.
+   *
+   * Walk the `format_items` as we read the string characters
+   * checking the characters are valid for each format specifier.
+   * The checking here is a little more strict than the actual
+   * parser used for conversion.
+   */
+  __device__ thrust::optional<timestamp_components> check_string(string_view const& d_string)
+  {
+    timestamp_components dateparts = {1970, 1, 1, 0};  // init to epoch time
+
+    auto ptr    = d_string.data();
+    auto length = d_string.size_bytes();
+    for (auto item : d_format_items) {
+      // eliminate static character values first
+      if (item.item_type == format_char_type::literal) {
+        // check static character matches
+        if (*ptr != item.value) return thrust::nullopt;
+        ptr += item.length;
+        length -= item.length;
+        continue;
+      }
+      // allow for specifiers to be truncated
+      if (item.value != 'f')
+        item.length = static_cast<int8_t>(std::min(static_cast<size_type>(item.length), length));
+
+      // special logic for each specifier
+      // reference: https://man7.org/linux/man-pages/man3/strptime.3.html
+      bool result          = false;
+      size_type bytes_read = item.length;
+      switch (item.value) {
+        case 'Y': {
+          auto const [year, left] = parse_int(ptr, item.length);
+          result                  = (left < item.length);
+          dateparts.year          = static_cast<int16_t>(year);
+          bytes_read -= left;
+          break;
+        }
+        case 'y': {
+          auto const [year, left] = parse_int(ptr, item.length);
+          result                  = (left < item.length);
+          dateparts.year          = static_cast<int16_t>(year + (year < 69 ? 2000 : 1900));
+          bytes_read -= left;
+          break;
+        }
+        case 'm': {
+          auto const [month, left] = parse_int(ptr, item.length);
+          result                   = (left < item.length);
+          dateparts.month          = static_cast<int8_t>(month);
+          bytes_read -= left;
+          break;
+        }
+        case 'd': {
+          auto const [day, left] = parse_int(ptr, item.length);
+          result                 = (left < item.length);
+          dateparts.day          = static_cast<int8_t>(day);  // value.value()
+          bytes_read -= left;
+          break;
+        }
+        case 'j': {
+          auto const cv = check_value(ptr, item.length, 1, 366);
+          result        = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'H': {
+          auto const cv = check_value(ptr, item.length, 0, 23);
+          result        = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'I': {
+          auto const cv = check_value(ptr, item.length, 1, 12);
+          result        = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'M': {
+          auto const cv = check_value(ptr, item.length, 0, 59);
+          result        = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'S': {
+          auto const cv = check_value(ptr, item.length, 0, 59);  // leap seconds not supported
+          result        = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'f': {
+          int32_t const read_size =
+            std::min(static_cast<int32_t>(item.length), static_cast<int32_t>(length));
+          result     = check_digits(ptr, read_size);
+          bytes_read = read_size;
+          break;
+        }
+        case 'p': {
+          if (item.length == 2) {
+            string_view am_pm(ptr, 2);
+            result = (am_pm.compare("AM", 2) == 0) || (am_pm.compare("am", 2) == 0) ||
+                     (am_pm.compare("PM", 2) == 0) || (am_pm.compare("pm", 2) == 0);
+          }
+          break;
+        }
+        case 'U': [[fallthrough]];
+        case 'W': {
+          auto const cv = check_value(ptr, item.length, 0, 53);
+          result        = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'u': [[fallthrough]];  // valid values: 1-7
+        case 'w': {                 // valid values: 0-6
+          auto const first = item.value == 'w' ? 0 : 1;
+          auto const cv    = check_value(ptr, item.length, first, first + 6);
+          result           = cv.first;
+          bytes_read -= cv.second;
+          break;
+        }
+        case 'z': {  // timezone offset
+          if (item.length == 5) {
+            auto const cvh = check_value(ptr + 1, 2, 0, 23);
+            auto const cvm = check_value(ptr + 3, 2, 0, 59);
+            result         = (*ptr == '-' || *ptr == '+') && cvh.first && cvm.first;
+            bytes_read -= cvh.second + cvm.second;
+          } else if (item.length == 1) {
+            result = *ptr == 'Z';
+          }
+          break;
+        }
+        case 'Z': result = true;  // skip
+        default: break;
+      }
+      if (!result) return thrust::nullopt;
+      ptr += bytes_read;
+      length -= bytes_read;
+    }
+    return dateparts;
+  }
+
+  __device__ bool operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) return false;
+
+    string_view d_str = d_strings.element<string_view>(idx);
+    if (d_str.empty()) return false;
+
+    auto const dateparts = check_string(d_str);
+    if (!dateparts.has_value()) return false;
+
+    auto const year  = dateparts.value().year;
+    auto const month = static_cast<uint32_t>(dateparts.value().month);
+    auto const day   = static_cast<uint32_t>(dateparts.value().day);
+    return cuda::std::chrono::year_month_day(cuda::std::chrono::year{year},
+                                             cuda::std::chrono::month{month},
+                                             cuda::std::chrono::day{day})
+      .ok();
+  }
+};
+
+std::unique_ptr<cudf::column> is_timestamp(strings_column_view const& input,
+                                           std::string_view const& format,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = input.size();
+  if (strings_count == 0) return make_empty_column(type_id::BOOL8);
+
+  CUDF_EXPECTS(!format.empty(), "Format parameter must not be empty.");
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  auto results   = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+  auto d_results = results->mutable_view().data<bool>();
+
+  format_compiler compiler(format, stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings_count),
+                    d_results,
+                    check_datetime_format{*d_strings, compiler.format_items()});
+
+  results->set_null_count(input.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<cudf::column> to_timestamps(strings_column_view const& input,
+                                            data_type timestamp_type,
+                                            std::string_view format,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_timestamps(input, timestamp_type, format, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> is_timestamp(strings_column_view const& input,
+                                           std::string_view format,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_timestamp(input, format, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+
+constexpr size_type format_names_size = 40;  // 2(am/pm) + 2x7(weekdays) + 2x12(months)
+constexpr size_type offset_weekdays   = 2;
+constexpr size_type offset_months     = 16;
+constexpr size_type days_in_week      = 7;
+constexpr size_type months_in_year    = 12;
+
+/**
+ * @brief Time components used by the date_time_formatter
+ */
+struct time_components {
+  int8_t hour;
+  int8_t minute;
+  int8_t second;
+  int32_t subsecond;
+};
+
+/**
+ * @brief Functor for from_timestamps to convert a timestamp to a string
+ *
+ * This is designed to be used with make_strings_children
+ */
+template <typename T>
+struct datetime_formatter_fn {
+  column_device_view const d_timestamps;
+  column_device_view const d_format_names;
+  device_span<format_item const> const d_format_items;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  /**
+   * @brief Specialized modulo expression that handles negative values.
+   *
+   * @code{.pseudo}
+   * Examples:
+   *     modulo(1,60)  ->  1
+   *     modulo(-1,60) -> 59
+   * @endcode
+   */
+  __device__ int32_t modulo_time(int64_t time, int64_t base) const
+  {
+    return static_cast<int32_t>(((time % base) + base) % base);
+  };
+
+  /**
+   * @brief This function handles converting units by dividing and adjusting for negative values.
+   *
+   * @code{.pseudo}
+   * Examples:
+   *     scale(-61,60) -> -2
+   *     scale(-60,60) -> -1
+   *     scale(-59,60) -> -1
+   *     scale( 59,60) ->  0
+   *     scale( 60,60) ->  1
+   *     scale( 61,60) ->  1
+   * @endcode
+   */
+  __device__ int64_t scale_time(int64_t time, int64_t base) const
+  {
+    return (time - ((time < 0) * (base - 1L))) / base;
+  };
+
+  __device__ time_components get_time_components(int64_t tstamp) const
+  {
+    time_components result = {0};
+    if constexpr (std::is_same_v<T, cudf::timestamp_D>) { return result; }
+
+    // Note: Tried using: cuda::std::chrono::hh_mm_ss(T::duration(timestamp));
+    // and retrieving the hour, minute, second, and subsecond values from it
+    // but it did not scale/modulo the components for negative timestamps
+    // correctly -- it simply did an abs(timestamp) as documented here:
+    // https://en.cppreference.com/w/cpp/chrono/hh_mm_ss/hh_mm_ss
+
+    if constexpr (not std::is_same_v<T, cudf::timestamp_s>) {
+      int64_t constexpr base = T::period::type::den;  // 1000=ms, 1000000=us, etc
+      auto const subsecond   = modulo_time(tstamp, base);
+      tstamp                 = tstamp / base - ((tstamp < 0) and (subsecond != 0));
+      result.subsecond       = subsecond;
+    }
+
+    result.hour   = modulo_time(scale_time(tstamp, 3600), 24);
+    result.minute = modulo_time(scale_time(tstamp, 60), 60);
+    result.second = modulo_time(tstamp, 60);
+
+    return result;
+  }
+
+  __device__ size_type compute_output_size(T const tstamp) const
+  {
+    // We only dissect the timestamp into components if needed
+    // by a specifier. And then we only do it once and reuse it.
+    // This can improve performance when not using uncommon specifiers.
+    thrust::optional<cuda::std::chrono::sys_days> days;
+
+    auto days_from_timestamp = [tstamp]() {
+      auto const count = tstamp.time_since_epoch().count();
+      return cuda::std::chrono::sys_days(static_cast<cudf::timestamp_D::duration>(
+        floor<cuda::std::chrono::days>(T::duration(count))));
+    };
+
+    size_type bytes = 0;  // output size
+    for (auto item : d_format_items) {
+      if (item.item_type == format_char_type::literal) {
+        bytes += item.length;
+        continue;
+      }
+
+      // only specifiers resulting in strings require special logic
+      switch (item.value) {
+        case 'a':    // weekday abbreviated
+        case 'A': {  // weekday full name
+          if (!days.has_value()) { days = days_from_timestamp(); }
+          auto const day_of_week =
+            cuda::std::chrono::year_month_weekday(days.value()).weekday().c_encoding();
+          auto const day_idx =
+            day_of_week + offset_weekdays + (item.value == 'a' ? days_in_week : 0);
+          if (day_idx < d_format_names.size())
+            bytes += d_format_names.element<cudf::string_view>(day_idx).size_bytes();
+          break;
+        }
+        case 'b':    // month abbreviated
+        case 'B': {  // month full name
+          if (!days.has_value()) { days = days_from_timestamp(); }
+          auto const month =
+            static_cast<uint32_t>(cuda::std::chrono::year_month_day(days.value()).month());
+          auto const month_idx =
+            month - 1 + offset_months + (item.value == 'b' ? months_in_year : 0);
+          if (month_idx < d_format_names.size())
+            bytes += d_format_names.element<cudf::string_view>(month_idx).size_bytes();
+          break;
+        }
+        case 'p':  // AM/PM
+        {
+          auto const times = get_time_components(tstamp.time_since_epoch().count());
+          bytes += d_format_names.size() > 1
+                     ? d_format_names.element<cudf::string_view>(static_cast<int>(times.hour >= 12))
+                         .size_bytes()
+                     : 2;
+          break;
+        }
+        default: {
+          bytes += item.length;
+          break;
+        }
+      }
+    }
+    return bytes;
+  }
+
+  // utility to create 0-padded integers (up to 9 chars)
+  __device__ char* int2str(char* str, int bytes, int val) const
+  {
+    char tmpl[9] = {'0', '0', '0', '0', '0', '0', '0', '0', '0'};
+    char* ptr    = tmpl;
+    while (val > 0) {
+      int digit = val % 10;
+      *ptr++    = '0' + digit;
+      val       = val / 10;
+    }
+    ptr = tmpl + bytes - 1;
+    while (bytes-- > 0)
+      *str++ = *ptr--;
+    return str;
+  }
+
+  // from https://howardhinnant.github.io/date/date.html
+  __device__ thrust::pair<int32_t, int32_t> get_iso_week_year(
+    cuda::std::chrono::year_month_day const& ymd) const
+  {
+    auto const days = cuda::std::chrono::sys_days(ymd);
+    auto year       = ymd.year();
+
+    auto iso_week_start = [](cuda::std::chrono::year const y) {
+      // clang-format off
+      return cuda::std::chrono::sys_days{cuda::std::chrono::Thursday[1]/cuda::std::chrono::January/y} -
+             (cuda::std::chrono::Thursday - cuda::std::chrono::Monday);
+      // clang-format on
+    };
+
+    auto start = iso_week_start(year);
+    if (days < start)
+      start = iso_week_start(--year);
+    else {
+      auto const next_start = iso_week_start(year + cuda::std::chrono::years{1});
+      if (days >= next_start) {
+        ++year;
+        start = next_start;
+      }
+    }
+    return thrust::make_pair(
+      (cuda::std::chrono::duration_cast<cuda::std::chrono::weeks>(days - start) +
+       cuda::std::chrono::weeks{1})  // always [1-53]
+        .count(),
+      static_cast<int32_t>(year));
+  }
+
+  __device__ int8_t get_week_of_year(cuda::std::chrono::sys_days const days,
+                                     cuda::std::chrono::sys_days const start) const
+  {
+    return days < start
+             ? 0
+             : (cuda::std::chrono::duration_cast<cuda::std::chrono::weeks>(days - start) +
+                cuda::std::chrono::weeks{1})
+                 .count();
+  }
+
+  __device__ int32_t get_day_of_year(cuda::std::chrono::year_month_day const& ymd) const
+  {
+    auto const month               = static_cast<uint32_t>(ymd.month());
+    auto const day                 = static_cast<uint32_t>(ymd.day());
+    int32_t const monthDayOffset[] = {0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334};
+    return static_cast<int32_t>(day + monthDayOffset[month - 1] +
+                                (month > 2 and ymd.year().is_leap()));
+  }
+
+  __device__ void timestamp_to_string(T const tstamp, char* ptr) const
+  {
+    auto const days = cuda::std::chrono::sys_days(
+      static_cast<cudf::timestamp_D::duration>(cuda::std::chrono::floor<cuda::std::chrono::days>(
+        T::duration(tstamp.time_since_epoch().count()))));
+    auto const ymd = cuda::std::chrono::year_month_day(days);
+
+    auto timeparts = get_time_components(tstamp.time_since_epoch().count());
+
+    // convert to characters using the format items
+    for (auto item : d_format_items) {
+      if (item.item_type == format_char_type::literal) {
+        *ptr++ = item.value;
+        continue;
+      }
+
+      // Value to use for int2str call at the end of the switch-statement.
+      // This simplifies the case statements and prevents a lot of extra inlining.
+      int32_t copy_value = -1;  // default set for non-int2str usage cases
+
+      // special logic for each specifier
+      switch (item.value) {
+        case 'Y':  // 4-digit year
+          copy_value = static_cast<int32_t>(ymd.year());
+          break;
+        case 'y':  // 2-digit year
+        {
+          auto year = static_cast<int32_t>(ymd.year());
+          // remove hundredths digits and above
+          copy_value = year - ((year / 100) * 100);
+          break;
+        }
+        case 'm':  // month
+          copy_value = static_cast<int32_t>(static_cast<uint32_t>(ymd.month()));
+          break;
+        case 'd':  // day of month
+          copy_value = static_cast<int32_t>(static_cast<uint32_t>(ymd.day()));
+          break;
+        case 'j':  // day of year
+          copy_value = get_day_of_year(ymd);
+          break;
+        case 'H':  // 24-hour
+          copy_value = timeparts.hour;
+          break;
+        case 'I':  // 12-hour
+        {
+          // 0 = 12am; 12 = 12pm; 6 = 06am; 18 = 06pm
+          copy_value = [h = timeparts.hour] {
+            if (h == 0) return 12;
+            return h > 12 ? h - 12 : h;
+          }();
+          break;
+        }
+        case 'M':  // minute
+          copy_value = timeparts.minute;
+          break;
+        case 'S':  // second
+          copy_value = timeparts.second;
+          break;
+        case 'f':  // sub-second
+        {
+          char subsecond_digits[] = "000000000";  // 9 max digits
+          int const digits        = [] {
+            if constexpr (std::is_same_v<T, cudf::timestamp_ms>) return 3;
+            if constexpr (std::is_same_v<T, cudf::timestamp_us>) return 6;
+            if constexpr (std::is_same_v<T, cudf::timestamp_ns>) return 9;
+            return 0;
+          }();
+          int2str(subsecond_digits, digits, timeparts.subsecond);
+          ptr = copy_and_increment(ptr, subsecond_digits, item.length);
+          break;
+        }
+        case 'p':  // am or pm
+        {
+          // 0 = 12am, 12 = 12pm
+          auto const am_pm = [&] {
+            if (d_format_names.size() > 1)
+              return d_format_names.element<cudf::string_view>(
+                static_cast<int>(timeparts.hour >= 12));
+            return string_view(timeparts.hour >= 12 ? "PM" : "AM", 2);
+          }();
+          ptr = copy_string(ptr, am_pm);
+          break;
+        }
+        case 'z':  // timezone -- always UTC
+          ptr = copy_and_increment(ptr, "+0000", 5);
+          break;
+        case 'Z':  // timezone string -- always UTC
+          ptr = copy_and_increment(ptr, "UTC", 3);
+          break;
+        case 'u':    // day of week ISO
+        case 'w': {  // day of week non-ISO
+          auto const day_of_week = static_cast<int32_t>(
+            cuda::std::chrono::year_month_weekday(days).weekday().c_encoding());
+          copy_value = day_of_week == 0 && item.value == 'u' ? 7 : day_of_week;
+          break;
+        }
+        // clang-format off
+        case 'U': {  // week of year: first week includes the first Sunday of the year
+          copy_value = get_week_of_year(days, cuda::std::chrono::sys_days{
+            cuda::std::chrono::Sunday[1]/cuda::std::chrono::January/ymd.year()});
+          break;
+        }
+        case 'W': {  // week of year: first week includes the first Monday of the year
+          copy_value = get_week_of_year(days, cuda::std::chrono::sys_days{
+            cuda::std::chrono::Monday[1]/cuda::std::chrono::January/ymd.year()});
+          break;
+        }
+        // clang-format on
+        case 'V':    // ISO week number
+        case 'G': {  // ISO year number
+          auto const [week, year] = get_iso_week_year(ymd);
+          copy_value              = item.value == 'G' ? year : week;
+          break;
+        }
+        case 'a':    // abbreviated day of the week
+        case 'A': {  // day of the week
+          auto const day_of_week =
+            cuda::std::chrono::year_month_weekday(days).weekday().c_encoding();
+          auto const day_idx =
+            day_of_week + offset_weekdays + (item.value == 'a' ? days_in_week : 0);
+          if (d_format_names.size())
+            ptr = copy_string(ptr, d_format_names.element<cudf::string_view>(day_idx));
+          break;
+        }
+        case 'b':    // abbreviated month of the year
+        case 'B': {  // month of the year
+          auto const month = static_cast<uint32_t>(ymd.month());
+          auto const month_idx =
+            month - 1 + offset_months + (item.value == 'b' ? months_in_year : 0);
+          if (d_format_names.size())
+            ptr = copy_string(ptr, d_format_names.element<cudf::string_view>(month_idx));
+          break;
+        }
+        default: break;
+      }
+      if (copy_value >= 0) ptr = int2str(ptr, item.length, copy_value);
+    }
+  }
+
+  __device__ void operator()(size_type idx) const
+  {
+    if (d_timestamps.is_null(idx)) {
+      if (!d_chars) { d_offsets[idx] = 0; }
+      return;
+    }
+    auto const tstamp = d_timestamps.element<T>(idx);
+    if (d_chars) {
+      timestamp_to_string(tstamp, d_chars + d_offsets[idx]);
+    } else {
+      d_offsets[idx] = compute_output_size(tstamp);
+    }
+  }
+};
+
+//
+using strings_children = std::pair<std::unique_ptr<cudf::column>, std::unique_ptr<cudf::column>>;
+struct dispatch_from_timestamps_fn {
+  template <typename T, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+  strings_children operator()(column_device_view const& d_timestamps,
+                              column_device_view const& d_format_names,
+                              device_span<format_item const> d_format_items,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr) const
+  {
+    return make_strings_children(
+      datetime_formatter_fn<T>{d_timestamps, d_format_names, d_format_items},
+      d_timestamps.size(),
+      stream,
+      mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_timestamp<T>(), strings_children> operator()(Args&&...) const
+  {
+    CUDF_FAIL("Only timestamps type are expected");
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<column> from_timestamps(column_view const& timestamps,
+                                        std::string_view format,
+                                        strings_column_view const& names,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  if (timestamps.is_empty()) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(!format.empty(), "Format parameter must not be empty.");
+  CUDF_EXPECTS(names.is_empty() || names.size() == format_names_size,
+               "Invalid size for format names.");
+
+  auto const d_names = column_device_view::create(names.parent(), stream);
+
+  // This API supports a few more specifiers than to_timestamps.
+  // clang-format off
+  format_compiler compiler(format, stream,
+    specifier_map{{'V', 2}, {'G', 4}, {'a', 3}, {'A', 3}, {'b', 3}, {'B', 3}});
+  // clang-format on
+  auto const d_format_items = compiler.format_items();
+  auto const d_timestamps   = column_device_view::create(timestamps, stream);
+
+  // dispatcher is called to handle the different timestamp types
+  auto [offsets_column, chars_column] = cudf::type_dispatcher(timestamps.type(),
+                                                              dispatch_from_timestamps_fn(),
+                                                              *d_timestamps,
+                                                              *d_names,
+                                                              d_format_items,
+                                                              stream,
+                                                              mr);
+
+  return make_strings_column(timestamps.size(),
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             timestamps.null_count(),
+                             cudf::detail::copy_bitmask(timestamps, stream, mr));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> from_timestamps(column_view const& timestamps,
+                                        std::string_view format,
+                                        strings_column_view const& names,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_timestamps(timestamps, format, names, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_durations.cu b/cpp/src/strings/convert/convert_durations.cu
new file mode 100644
index 0000000..6ab7082
--- /dev/null
+++ b/cpp/src/strings/convert/convert_durations.cu
@@ -0,0 +1,740 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/convert/int_to_string.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/transform_reduce.h>
+
+#include <map>
+#include <vector>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+// duration components timeparts structure
+struct alignas(4) duration_component {
+  int32_t day;        //-2,147,483,648 to 2,147,483,647
+  int32_t subsecond;  // 000000000 to 999999999
+  int8_t hour;        // 00 to 23
+  int8_t minute;      // 00 to 59
+  int8_t second;      // 00 to 59
+  bool is_negative;   // true/false
+};
+
+enum class format_char_type : int8_t {
+  literal,   // literal char type passed through
+  specifier  // duration format specifier
+};
+
+/**
+ * @brief Represents a format specifier or literal from a duration format string.
+ *
+ * Created by the format_compiler when parsing a format string.
+ */
+struct alignas(4) format_item {
+  format_char_type item_type;  // specifier or literal indicator
+  char value;                  // specifier or literal value
+  int8_t length;               // item length in bytes
+
+  static format_item new_specifier(char format_char, int8_t length)
+  {
+    return format_item{format_char_type::specifier, format_char, length};
+  }
+  static format_item new_delimiter(char literal)
+  {
+    return format_item{format_char_type::literal, literal, 1};
+  }
+};
+
+/**
+ * @brief The format_compiler parses a duration format string into a vector of
+ * format_items.
+ *
+ * The vector of format_items are used when parsing a string into duration
+ * components and when formatting a string from duration components.
+ */
+struct format_compiler {
+  std::string_view const format;
+  rmm::device_uvector<format_item> d_items;
+  format_compiler(std::string_view format, rmm::cuda_stream_view stream)
+    : format(format), d_items(0, stream)
+  {
+    static std::map<char, int8_t> const specifier_lengths = {
+      {'-', -1},  // '-' if negative
+      {'D', -1},  // 1 to 11 (not in std::format)
+      {'H', 2},   // HH
+      {'I', 2},   // HH
+      {'M', 2},   // MM
+      {'S', -1},  // 2 to 13 SS[.mmm][uuu][nnn] (uuu,nnn are not in std::format)
+      {'p', 2},   // AM/PM
+      {'R', 5},   // 5 HH:MM
+      {'T', 8},   // 8 HH:MM:SS"
+      {'r', 11}   // HH:MM:SS AM/PM
+    };
+    std::vector<format_item> items;
+    auto str    = format.data();
+    auto length = format.length();
+    bool negative_sign{true};
+    while (length > 0) {
+      char ch = *str++;
+      length--;
+      if (ch != '%') {
+        items.push_back(format_item::new_delimiter(ch));
+        continue;
+      }
+      CUDF_EXPECTS(length > 0, "Unfinished specifier in duration format");
+
+      ch = *str++;
+      length--;
+      if (ch == '%')  // escaped % char
+      {
+        items.push_back(format_item::new_delimiter(ch));
+        continue;
+      } else if (ch == 'n') {
+        items.push_back(format_item::new_delimiter('\n'));
+        continue;
+      } else if (ch == 't') {
+        items.push_back(format_item::new_delimiter('\t'));
+        continue;
+      }
+      if (ch == 'O') {
+        CUDF_EXPECTS(*str == 'H' || *str == 'I' || *str == 'M' || *str == 'S',
+                     "locale's alternative representation not supported for specifier: " +
+                       std::string(1, *str));
+        ch = *str++;
+        length--;
+        items.push_back(format_item::new_specifier(ch, 2));  // without sign
+        continue;
+      }
+      CUDF_EXPECTS(specifier_lengths.find(ch) != specifier_lengths.end(),
+                   "invalid format specifier: " + std::string(1, ch));
+
+      // negative sign should be present only once.
+      if (negative_sign) {
+        if (std::string("DHIMSRT").find_first_of(ch) != std::string::npos) {
+          items.push_back(format_item::new_specifier('-', specifier_lengths.at('-')));
+          negative_sign = false;
+        }
+      }
+
+      int8_t spec_length = specifier_lengths.at(ch);
+      items.push_back(format_item::new_specifier(ch, spec_length));
+    }
+
+    // create program in device memory
+    d_items.resize(items.size(), stream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(d_items.data(),
+                                  items.data(),
+                                  items.size() * sizeof(items[0]),
+                                  cudaMemcpyDefault,
+                                  stream.value()));
+  }
+
+  format_item const* compiled_format_items() { return d_items.data(); }
+
+  [[nodiscard]] size_type items_count() const { return static_cast<size_type>(d_items.size()); }
+};
+
+template <typename T>
+__device__ void dissect_duration(T duration, duration_component* timeparts)
+{
+  timeparts->is_negative = (duration < T{0});
+  timeparts->day         = cuda::std::chrono::duration_cast<duration_D>(duration).count();
+
+  if (cuda::std::is_same_v<T, duration_D>) return;
+
+  duration_s seconds = cuda::std::chrono::duration_cast<duration_s>(duration);
+  timeparts->hour =
+    (cuda::std::chrono::duration_cast<cuda::std::chrono::hours>(seconds) % duration_D(1)).count();
+  timeparts->minute = (cuda::std::chrono::duration_cast<cuda::std::chrono::minutes>(seconds) %
+                       cuda::std::chrono::hours(1))
+                        .count();
+  timeparts->second = (seconds % cuda::std::chrono::minutes(1)).count();
+  if (not cuda::std::is_same_v<T, duration_s>) {
+    timeparts->subsecond = (duration % duration_s(1)).count();
+  }
+}
+
+namespace {
+template <typename T>
+struct from_durations_fn {
+  column_device_view d_durations;
+  format_item const* d_format_items;
+  size_type items_count;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ int8_t format_length(char format_char, duration_component const* const timeparts) const
+  {
+    switch (format_char) {
+      case '-': return timeparts->is_negative; break;
+      case 'D': return count_digits(timeparts->day) - (timeparts->day < 0); break;
+      case 'S':
+        return 2 + (timeparts->subsecond == 0 ? 0 : [] {
+                 if (cuda::std::is_same_v<T, duration_ms>) return 3 + 1;  // +1 is for dot
+                 if (cuda::std::is_same_v<T, duration_us>) return 6 + 1;  // +1 is for dot
+                 if (cuda::std::is_same_v<T, duration_ns>) return 9 + 1;  // +1 is for dot
+                 return 0;
+               }());
+        break;
+      default: return 2;
+    }
+  }
+
+  // utility to create (optionally) 0-padded integers (up to 10 chars) without negative sign.
+  // min_digits==-1 indicates no 0-padding.
+  __device__ char* int2str(char* str, int min_digits, int32_t value)
+  {
+    constexpr int MAX_DIGITS = 10;  // largest 32-bit integer is 10 digits
+    assert(min_digits <= MAX_DIGITS);
+    if (value == 0) {
+      do {
+        *str++ = '0';
+      } while (--min_digits > 0);
+      return str;
+    }
+
+    char digits[MAX_DIGITS] = {'0', '0', '0', '0', '0', '0', '0', '0', '0', '0'};
+    int digits_idx          = 0;
+    while (value != 0) {
+      assert(digits_idx < MAX_DIGITS);
+      digits[digits_idx++] = '0' + std::abs(value % 10);
+      // next digit
+      value = value / 10;
+    }
+    digits_idx = std::max(digits_idx, min_digits);
+    // digits are backwards, reverse the string into the output
+    while (digits_idx-- > 0)
+      *str++ = digits[digits_idx];
+    return str;
+  }
+
+  __device__ char* int_to_2digitstr(char* str, int8_t value)
+  {
+    assert(value >= -99 && value <= 99);
+    value  = std::abs(value);
+    str[0] = '0' + value / 10;
+    str[1] = '0' + value % 10;
+    return str + 2;
+  }
+
+  inline __device__ char* day(char* ptr, duration_component const* timeparts)
+  {
+    return int2str(ptr, -1, timeparts->day);
+  }
+
+  inline __device__ char* hour_12(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(ptr, timeparts->hour % 12);
+  }
+  inline __device__ char* hour_24(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(ptr, timeparts->hour);
+  }
+  inline __device__ char* am_or_pm(char* ptr, duration_component const* timeparts)
+  {
+    *ptr++ = (timeparts->hour / 12 == 0 ? 'A' : 'P');
+    *ptr++ = 'M';
+    return ptr;
+  }
+  inline __device__ char* minute(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(ptr, timeparts->minute);
+  }
+  inline __device__ char* second(char* ptr, duration_component const* timeparts)
+  {
+    return int_to_2digitstr(ptr, timeparts->second);
+  }
+
+  inline __device__ char* subsecond(char* ptr, duration_component const* timeparts)
+  {
+    if (timeparts->subsecond == 0) return ptr;
+    int const digits = format_length('S', timeparts) - 3;
+    *ptr             = '.';
+    auto value       = timeparts->subsecond;
+    for (int idx = digits; idx > 0; idx--) {
+      *(ptr + idx) = '0' + std::abs(value % 10);
+      value /= 10;
+    }
+    return ptr + digits + 1;
+  }
+
+  __device__ char* format_from_parts(duration_component const* timeparts, char* ptr)
+  {
+    for (size_t idx = 0; idx < items_count; ++idx) {
+      auto item = d_format_items[idx];
+      if (item.item_type == format_char_type::literal) {
+        *ptr++ = item.value;
+        continue;
+      }
+      // special logic for each specifier
+      switch (item.value) {
+        case 'D':  // days
+          ptr = day(ptr, timeparts);
+          break;
+        case '-':  // - if value is negative
+          if (timeparts->is_negative) *ptr++ = '-';
+          break;
+        case 'H':  // 24-hour
+          ptr = hour_24(ptr, timeparts);
+          break;
+        case 'I':  // 12-hour
+          ptr = hour_12(ptr, timeparts);
+          break;
+        case 'M':  // minute
+          ptr = minute(ptr, timeparts);
+          break;
+        case 'S':  // second
+          ptr = second(ptr, timeparts);
+          if (item.length == 2) break;
+        case 'f':  // sub-second
+          ptr = subsecond(ptr, timeparts);
+          break;
+        case 'p': ptr = am_or_pm(ptr, timeparts); break;
+        case 'R':  // HH:MM 24-hour
+          ptr    = hour_24(ptr, timeparts);
+          *ptr++ = ':';
+          ptr    = minute(ptr, timeparts);
+          break;
+        case 'T':  // HH:MM:SS 24-hour
+          ptr    = hour_24(ptr, timeparts);
+          *ptr++ = ':';
+          ptr    = minute(ptr, timeparts);
+          *ptr++ = ':';
+          ptr    = second(ptr, timeparts);
+          break;
+        case 'r':  // HH:MM:SS AM/PM 12-hour
+          ptr    = hour_12(ptr, timeparts);
+          *ptr++ = ':';
+          ptr    = minute(ptr, timeparts);
+          *ptr++ = ':';
+          ptr    = second(ptr, timeparts);
+          *ptr++ = ' ';
+          ptr    = am_or_pm(ptr, timeparts);
+          break;
+        default:  // ignore everything else
+          break;
+      }
+    }
+    return ptr;
+  }
+
+  __device__ size_type string_size(T duration)
+  {
+    duration_component timeparts = {0};  // days, hours, minutes, seconds, subseconds(9)
+    dissect_duration(duration, &timeparts);
+    return thrust::transform_reduce(
+      thrust::seq,
+      d_format_items,
+      d_format_items + items_count,
+      [this, &timeparts] __device__(format_item item) -> size_type {
+        if (item.item_type == format_char_type::literal) { return 1; }
+        return (item.length != -1) ? item.length : format_length(item.value, &timeparts);
+      },
+      size_type{0},
+      thrust::plus<size_type>());
+  }
+
+  __device__ void set_chars(size_type idx)
+  {
+    auto duration                = d_durations.template element<T>(idx);
+    duration_component timeparts = {0};  // days, hours, minutes, seconds, subseconds(9)
+    dissect_duration(duration, &timeparts);
+    // convert to characters
+    format_from_parts(&timeparts, d_chars + d_offsets[idx]);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_durations.is_null(idx)) {
+      if (d_chars == nullptr) { d_offsets[idx] = 0; }
+      return;
+    }
+
+    if (d_chars != nullptr) {
+      set_chars(idx);
+    } else {
+      d_offsets[idx] = string_size(d_durations.template element<T>(idx));
+    }
+  }
+};
+}  // namespace
+
+/**
+ * @brief This dispatch method is for converting durations into strings.
+ *
+ * The template function declaration ensures only duration types are used.
+ */
+struct dispatch_from_durations_fn {
+  template <typename T, std::enable_if_t<cudf::is_duration<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& durations,
+                                     std::string_view format,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    CUDF_EXPECTS(!format.empty(), "Format parameter must not be empty.");
+
+    format_compiler compiler(format, stream);
+    auto d_format_items = compiler.compiled_format_items();
+
+    size_type strings_count = durations.size();
+    auto column             = column_device_view::create(durations, stream);
+    auto d_column           = *column;
+
+    // copy null mask
+    rmm::device_buffer null_mask = cudf::detail::copy_bitmask(durations, stream, mr);
+
+    auto [offsets, chars] =
+      make_strings_children(from_durations_fn<T>{d_column, d_format_items, compiler.items_count()},
+                            strings_count,
+                            stream,
+                            mr);
+
+    return make_strings_column(strings_count,
+                               std::move(offsets),
+                               std::move(chars),
+                               durations.null_count(),
+                               std::move(null_mask));
+  }
+
+  // non-duration types throw an exception
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_duration<T>(), std::unique_ptr<column>> operator()(Args&&...) const
+  {
+    CUDF_FAIL("Values for from_durations function must be a duration type.");
+  }
+};
+
+static const __device__ __constant__ int32_t powers_of_ten[10] = {
+  1L, 10L, 100L, 1000L, 10000L, 100000L, 1000000L, 10000000L, 100000000L, 1000000000L};
+
+// this parses duration string into a duration integer
+template <typename T>  // duration type
+struct parse_duration {
+  column_device_view const d_strings;
+  format_item const* d_format_items;
+  size_type items_count;
+
+  // function to parse string (maximum 10 digits) to integer.
+  __device__ int32_t str2int(char const* str, int8_t max_bytes, int8_t& actual_length)
+  {
+    char const* ptr = (*str == '-' || *str == '+') ? str + 1 : str;
+    int32_t value   = 0;
+    for (int8_t idx = 0; idx < max_bytes; ++idx) {
+      char chr = *ptr++;
+      if (chr < '0' || chr > '9') {
+        ptr--;  // roll back
+        break;
+      }
+      value = (value * 10) + static_cast<int32_t>(chr - '0');
+    }
+    actual_length += (ptr - str);
+    return (*str == '-') ? -value : value;
+  }
+
+  // function to parse fraction of decimal value with trailing zeros removed.
+  __device__ int32_t str2int_fixed(char const* str,
+                                   int8_t fixed_width,
+                                   size_type string_length,
+                                   int8_t& actual_length)
+  {
+    char const* ptr = (*str == '.') ? str + 1 : str;
+    int32_t value   = 0;
+    // parse till fixed_width or end of string.
+    for (int8_t idx = 0; idx < fixed_width && idx < string_length; ++idx) {
+      char chr = *ptr++;
+      if (chr < '0' || chr > '9') {
+        ptr--;  // roll back
+        break;
+      }
+      value = (value * 10) + static_cast<int32_t>(chr - '0');
+    }
+    auto parsed_length = ptr - str;
+    // compensate for missing trailing zeros
+    if (parsed_length < fixed_width) value *= powers_of_ten[fixed_width - parsed_length];
+    actual_length += parsed_length;
+    return value;
+  }
+
+  // parse 2 digit string to integer
+  __device__ int8_t parse_2digit_int(char const* str, int8_t& actual_length)
+  {
+    char const* ptr = (*str == '-' || *str == '+') ? str + 1 : str;
+    int8_t value    = 0;
+    if (*ptr >= '0' && *ptr <= '9') value = (value * 10) + static_cast<int32_t>(*ptr++ - '0');
+    if (*ptr >= '0' && *ptr <= '9') value = (value * 10) + static_cast<int32_t>(*ptr++ - '0');
+    actual_length += (ptr - str);
+    return (*str == '-') ? -value : value;
+  }
+  inline __device__ int8_t parse_hour(char const* str, int8_t& actual_length)
+  {
+    return parse_2digit_int(str, actual_length);
+  }
+  inline __device__ int8_t parse_minute(char const* str, int8_t& actual_length)
+  {
+    return parse_2digit_int(str, actual_length);
+  }
+  inline __device__ int8_t parse_second(char const* str, int8_t& actual_length)
+  {
+    return parse_2digit_int(str, actual_length);
+  }
+
+  // Walk the format_items to read the datetime string.
+  // Returns 0 if all ok.
+  __device__ int parse_into_parts(string_view const& d_string, duration_component* timeparts)
+  {
+    auto ptr    = d_string.data();
+    auto length = d_string.size_bytes();
+    int8_t hour_shift{0};
+    for (size_type idx = 0; idx < items_count; ++idx) {
+      auto item = d_format_items[idx];
+      if (length < item.length) return 1;
+      if (item.item_type == format_char_type::literal) {  // static character we'll just skip;
+        // consume item.length bytes from string
+        ptr += item.length;
+        length -= item.length;
+        continue;
+      }
+      timeparts->is_negative |= (*ptr == '-');
+
+      // special logic for each specifier
+      int8_t item_length{0};
+      switch (item.value) {
+        case 'D':  // day
+          timeparts->day = str2int(ptr, 11, item_length);
+          break;
+        case '-':  // skip
+          item_length = (*ptr == '-');
+          break;
+        case 'H':  // 24-hour
+          timeparts->hour = parse_hour(ptr, item_length);
+          hour_shift      = 0;
+          break;
+        case 'I':  // 12-hour
+          timeparts->hour = parse_hour(ptr, item_length);
+          break;
+        case 'M':  // minute
+          timeparts->minute = parse_minute(ptr, item_length);
+          break;
+        case 'S':  // [-]SS[.mmm][uuu][nnn]
+          timeparts->second = parse_second(ptr, item_length);
+          if ((item_length < length) && *(ptr + item_length) == '.') {
+            item_length++;
+            int64_t nanoseconds = str2int_fixed(
+              ptr + item_length, 9, length - item_length, item_length);  // normalize to nanoseconds
+            timeparts->subsecond = nanoseconds;
+          }
+          break;
+        case 'p':  // AM/PM
+          if (*ptr == 'P' && *(ptr + 1) == 'M')
+            hour_shift = 12;
+          else
+            hour_shift = 0;
+          item_length = 2;
+          break;
+        case 'R':  // [-]HH:SS
+          timeparts->hour = parse_hour(ptr, item_length);
+          hour_shift      = 0;
+          item_length++;  // :
+          timeparts->minute = parse_minute(ptr + item_length, item_length);
+          break;
+        case 'T':  // [-]HH:MM:SS
+          timeparts->hour = parse_hour(ptr, item_length);
+          hour_shift      = 0;
+          item_length++;  // :
+          timeparts->minute = parse_minute(ptr + item_length, item_length);
+          item_length++;  // :
+          timeparts->second = parse_second(ptr + item_length, item_length);
+          break;
+        case 'r':  // hh:MM:SS AM/PM
+          timeparts->hour = parse_hour(ptr, item_length);
+          item_length++;  // :
+          timeparts->minute = parse_minute(ptr + item_length, item_length);
+          item_length++;  // :
+          timeparts->second = parse_second(ptr + item_length, item_length);
+          item_length++;  // space
+          if (*(ptr + item_length) == 'P' && *(ptr + item_length + 1) == 'M')
+            hour_shift = 12;
+          else
+            hour_shift = 0;
+          item_length += 2;
+          break;
+        default: return 3;
+      }
+      ptr += item_length;
+      length -= item_length;
+    }
+    // negate all if duration has negative sign
+    if (timeparts->is_negative) {
+      auto negate          = [](auto i) { return (i < 0 ? i : -i); };
+      timeparts->day       = negate(timeparts->day);
+      timeparts->hour      = negate(timeparts->hour);
+      timeparts->minute    = negate(timeparts->minute);
+      timeparts->second    = negate(timeparts->second);
+      timeparts->subsecond = negate(timeparts->subsecond);
+      hour_shift           = -hour_shift;
+    }
+    timeparts->hour += hour_shift;
+    return 0;
+  }
+
+  inline __device__ int64_t duration_from_parts(duration_component const* timeparts)
+  {
+    int32_t days  = timeparts->day;
+    auto hour     = timeparts->hour;
+    auto minute   = timeparts->minute;
+    auto second   = timeparts->second;
+    auto duration = duration_D(days) + cuda::std::chrono::hours(hour) +
+                    cuda::std::chrono::minutes(minute) + duration_s(second);
+    if (cuda::std::is_same_v<T, duration_D>)
+      return cuda::std::chrono::duration_cast<duration_D>(duration).count();
+    else if (cuda::std::is_same_v<T, duration_s>)
+      return cuda::std::chrono::duration_cast<duration_s>(duration).count();
+
+    duration_ns subsecond(timeparts->subsecond);  // ns
+    if (cuda::std::is_same_v<T, duration_ms>) {
+      return cuda::std::chrono::duration_cast<duration_ms>(duration + subsecond).count();
+    } else if (cuda::std::is_same_v<T, duration_us>) {
+      return cuda::std::chrono::duration_cast<duration_us>(duration + subsecond).count();
+    } else if (cuda::std::is_same_v<T, duration_ns>)
+      return cuda::std::chrono::duration_cast<duration_ns>(duration + subsecond).count();
+    return cuda::std::chrono::duration_cast<duration_ns>(duration + subsecond).count();
+  }
+
+  __device__ T operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) return T{0};
+    string_view d_str = d_strings.element<string_view>(idx);
+    if (d_str.empty()) return T{0};
+    //
+    duration_component timeparts = {0};
+    if (parse_into_parts(d_str, &timeparts)) return T{0};  // unexpected parse case
+    //
+    return static_cast<T>(duration_from_parts(&timeparts));
+  }
+};
+
+/**
+ * @brief This dispatch method is for converting strings to durations.
+ *
+ * The template function declaration ensures only duration types are used.
+ */
+struct dispatch_to_durations_fn {
+  template <typename T, std::enable_if_t<cudf::is_duration<T>()>* = nullptr>
+  void operator()(column_device_view const& d_strings,
+                  std::string_view format,
+                  mutable_column_view& results_view,
+                  rmm::cuda_stream_view stream) const
+  {
+    format_compiler compiler(format, stream);
+    auto d_items   = compiler.compiled_format_items();
+    auto d_results = results_view.data<T>();
+    parse_duration<T> pfn{d_strings, d_items, compiler.items_count()};
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(results_view.size()),
+                      d_results,
+                      pfn);
+  }
+  template <typename T, std::enable_if_t<not cudf::is_duration<T>()>* = nullptr>
+  void operator()(column_device_view const&,
+                  std::string_view,
+                  mutable_column_view&,
+                  rmm::cuda_stream_view) const
+  {
+    CUDF_FAIL("Only durations type are expected for to_durations function");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> from_durations(column_view const& durations,
+                                       std::string_view format,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = durations.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  return type_dispatcher(
+    durations.type(), dispatch_from_durations_fn{}, durations, format, stream, mr);
+}
+
+std::unique_ptr<column> to_durations(strings_column_view const& strings,
+                                     data_type duration_type,
+                                     std::string_view format,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_duration_column(duration_type, 0);
+
+  CUDF_EXPECTS(!format.empty(), "Format parameter must not be empty.");
+
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_column       = *strings_column;
+
+  auto results      = make_duration_column(duration_type,
+                                      strings_count,
+                                      cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                      strings.null_count(),
+                                      stream,
+                                      mr);
+  auto results_view = results->mutable_view();
+  cudf::type_dispatcher(
+    duration_type, dispatch_to_durations_fn(), d_column, format, results_view, stream);
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> from_durations(column_view const& durations,
+                                       std::string_view format,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_durations(durations, format, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> to_durations(strings_column_view const& strings,
+                                     data_type duration_type,
+                                     std::string_view format,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_durations(strings, duration_type, format, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_fixed_point.cu b/cpp/src/strings/convert/convert_fixed_point.cu
new file mode 100644
index 0000000..51aab9f
--- /dev/null
+++ b/cpp/src/strings/convert/convert_fixed_point.cu
@@ -0,0 +1,351 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/convert/convert_fixed_point.hpp>
+#include <cudf/strings/detail/convert/fixed_point.cuh>
+#include <cudf/strings/detail/convert/fixed_point_to_string.cuh>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/generate.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/transform.h>
+
+#include <cuda/std/climits>
+#include <cuda/std/limits>
+#include <cuda/std/type_traits>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Converts strings into an integers and records decimal places.
+ *
+ * The conversion uses the provided scale to build the resulting
+ * integer. This can prevent overflow for strings with many digits.
+ */
+template <typename DecimalType>
+struct string_to_decimal_fn {
+  column_device_view const d_strings;
+  int32_t const scale;
+
+  string_to_decimal_fn(column_device_view const& d_strings, int32_t scale)
+    : d_strings(d_strings), scale(scale)
+  {
+  }
+
+  __device__ DecimalType operator()(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return 0; }
+    auto const d_str = d_strings.element<string_view>(idx);
+    if (d_str.empty()) { return 0; }
+
+    auto iter           = d_str.data();
+    auto const iter_end = d_str.data() + d_str.size_bytes();
+
+    return parse_decimal<DecimalType>(iter, iter_end, scale);
+  }
+};
+
+/**
+ * @brief This only checks the string format for valid decimal characters.
+ *
+ * This follows closely the logic above but just ensures there are valid
+ * characters for conversion and the integer component does not overflow.
+ */
+template <typename DecimalType>
+struct string_to_decimal_check_fn {
+  column_device_view const d_strings;
+  int32_t const scale;
+
+  string_to_decimal_check_fn(column_device_view const& d_strings, int32_t scale)
+    : d_strings{d_strings}, scale{scale}
+  {
+  }
+
+  __device__ bool operator()(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return false; }
+    auto const d_str = d_strings.element<string_view>(idx);
+    if (d_str.empty()) { return false; }
+
+    auto iter = d_str.data() + static_cast<int>((d_str.data()[0] == '-' || d_str.data()[0] == '+'));
+
+    auto const iter_end = d_str.data() + d_str.size_bytes();
+
+    using UnsignedDecimalType = cuda::std::make_unsigned_t<DecimalType>;
+    auto [value, exp_offset]  = parse_integer<UnsignedDecimalType>(iter, iter_end);
+
+    // only exponent notation is expected here
+    if ((iter < iter_end) && (*iter != 'e' && *iter != 'E')) { return false; }
+    ++iter;
+
+    int32_t exp_ten = 0;  // check exponent overflow
+    if (iter < iter_end) {
+      auto exp_result = parse_exponent<true>(iter, iter_end);
+      if (!exp_result) { return false; }
+      exp_ten = exp_result.value();
+    }
+    exp_ten += exp_offset;
+
+    // finally, check for overflow based on the exp_ten and scale values
+    return (exp_ten < scale) or
+           value <= static_cast<UnsignedDecimalType>(
+                      cuda::std::numeric_limits<DecimalType>::max() /
+                      static_cast<DecimalType>(exp10(static_cast<double>(exp_ten - scale))));
+  }
+};
+
+/**
+ * @brief The dispatch function for converting strings column to fixed-point column.
+ */
+struct dispatch_to_fixed_point_fn {
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(strings_column_view const& input,
+                                     data_type output_type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    using DecimalType = device_storage_type_t<T>;
+
+    auto const d_column = column_device_view::create(input.parent(), stream);
+
+    // create output column
+    auto results   = make_fixed_point_column(output_type,
+                                           input.size(),
+                                           cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                           input.null_count(),
+                                           stream,
+                                           mr);
+    auto d_results = results->mutable_view().data<DecimalType>();
+
+    // convert strings into decimal values
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(input.size()),
+                      d_results,
+                      string_to_decimal_fn<DecimalType>{*d_column, output_type.scale()});
+    results->set_null_count(input.null_count());
+    return results;
+  }
+
+  template <typename T, std::enable_if_t<not cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(strings_column_view const&,
+                                     data_type,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("Output for to_fixed_point must be a decimal type.");
+  }
+};
+
+}  // namespace
+
+// This will convert a strings column into any integer column type.
+std::unique_ptr<column> to_fixed_point(strings_column_view const& input,
+                                       data_type output_type,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(output_type);
+  return type_dispatcher(output_type, dispatch_to_fixed_point_fn{}, input, output_type, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> to_fixed_point(strings_column_view const& strings,
+                                       data_type output_type,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_fixed_point(strings, output_type, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+template <typename DecimalType>
+struct from_fixed_point_fn {
+  column_device_view d_decimals;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  /**
+   * @brief Converts a decimal element into a string.
+   *
+   * The value is converted into base-10 digits [0-9]
+   * plus the decimal point and a negative sign prefix.
+   */
+  __device__ void fixed_point_element_to_string(size_type idx)
+  {
+    auto const value = d_decimals.element<DecimalType>(idx);
+    auto const scale = d_decimals.type().scale();
+    char* d_buffer   = d_chars + d_offsets[idx];
+
+    fixed_point_to_string(value, scale, d_buffer);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_decimals.is_null(idx)) {
+      if (d_chars == nullptr) { d_offsets[idx] = 0; }
+      return;
+    }
+    if (d_chars != nullptr) {
+      fixed_point_element_to_string(idx);
+    } else {
+      d_offsets[idx] =
+        fixed_point_string_size(d_decimals.element<DecimalType>(idx), d_decimals.type().scale());
+    }
+  }
+};
+
+/**
+ * @brief The dispatcher functor for converting fixed-point values into strings.
+ */
+struct dispatch_from_fixed_point_fn {
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    using DecimalType = device_storage_type_t<T>;  // underlying value type
+
+    auto const d_column = column_device_view::create(input, stream);
+
+    auto [offsets, chars] =
+      make_strings_children(from_fixed_point_fn<DecimalType>{*d_column}, input.size(), stream, mr);
+
+    return make_strings_column(input.size(),
+                               std::move(offsets),
+                               std::move(chars),
+                               input.null_count(),
+                               cudf::detail::copy_bitmask(input, stream, mr));
+  }
+
+  template <typename T, std::enable_if_t<not cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("Values for from_fixed_point function must be a decimal type.");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> from_fixed_point(column_view const& input,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+  return type_dispatcher(input.type(), dispatch_from_fixed_point_fn{}, input, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> from_fixed_point(column_view const& input,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_fixed_point(input, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+
+struct dispatch_is_fixed_point_fn {
+  template <typename T, std::enable_if_t<cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(strings_column_view const& input,
+                                     data_type decimal_type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    using DecimalType = device_storage_type_t<T>;
+
+    auto const d_column = column_device_view::create(input.parent(), stream);
+
+    // create output column
+    auto results   = make_numeric_column(data_type{type_id::BOOL8},
+                                       input.size(),
+                                       cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                       input.null_count(),
+                                       stream,
+                                       mr);
+    auto d_results = results->mutable_view().data<bool>();
+
+    // check strings for valid fixed-point chars
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(input.size()),
+                      d_results,
+                      string_to_decimal_check_fn<DecimalType>{*d_column, decimal_type.scale()});
+    results->set_null_count(input.null_count());
+    return results;
+  }
+
+  template <typename T, std::enable_if_t<not cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(strings_column_view const&,
+                                     data_type,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("is_fixed_point is expecting a decimal type");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> is_fixed_point(strings_column_view const& input,
+                                       data_type decimal_type,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return cudf::make_empty_column(type_id::BOOL8);
+  return type_dispatcher(
+    decimal_type, dispatch_is_fixed_point_fn{}, input, decimal_type, stream, mr);
+}
+}  // namespace detail
+
+std::unique_ptr<column> is_fixed_point(strings_column_view const& input,
+                                       data_type decimal_type,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_fixed_point(input, decimal_type, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_floats.cu b/cpp/src/strings/convert/convert_floats.cu
new file mode 100644
index 0000000..3216758
--- /dev/null
+++ b/cpp/src/strings/convert/convert_floats.cu
@@ -0,0 +1,484 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/convert/convert_floats.hpp>
+#include <cudf/strings/detail/convert/string_to_float.cuh>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/transform.h>
+
+#include <cmath>
+#include <limits>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Converts strings column entries into floats.
+ *
+ * Used by the dispatch method to convert to different float types.
+ */
+template <typename FloatType>
+struct string_to_float_fn {
+  column_device_view const strings_column;  // strings to convert
+
+  __device__ FloatType operator()(size_type idx)
+  {
+    if (strings_column.is_null(idx)) return static_cast<FloatType>(0);
+    // The cast to FloatType will create predictable results for floats that are larger than the
+    // FloatType can hold
+    return static_cast<FloatType>(stod(strings_column.element<string_view>(idx)));
+  }
+};
+
+/**
+ * @brief The dispatch functions for converting strings to floats.
+ *
+ * The output_column is expected to be one of the float types only.
+ */
+struct dispatch_to_floats_fn {
+  template <typename FloatType, std::enable_if_t<std::is_floating_point_v<FloatType>>* = nullptr>
+  void operator()(column_device_view const& strings_column,
+                  mutable_column_view& output_column,
+                  rmm::cuda_stream_view stream) const
+  {
+    auto d_results = output_column.data<FloatType>();
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(strings_column.size()),
+                      d_results,
+                      string_to_float_fn<FloatType>{strings_column});
+  }
+  // non-integral types throw an exception
+  template <typename T, std::enable_if_t<not std::is_floating_point_v<T>>* = nullptr>
+  void operator()(column_device_view const&, mutable_column_view&, rmm::cuda_stream_view) const
+  {
+    CUDF_FAIL("Output for to_floats must be a float type.");
+  }
+};
+
+}  // namespace
+
+// This will convert a strings column into any float column type.
+std::unique_ptr<column> to_floats(strings_column_view const& strings,
+                                  data_type output_type,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_numeric_column(output_type, 0);
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  // create float output column copying the strings null-mask
+  auto results      = make_numeric_column(output_type,
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto results_view = results->mutable_view();
+  // fill output column with floats
+  type_dispatcher(output_type, dispatch_to_floats_fn{}, d_strings, results_view, stream);
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> to_floats(strings_column_view const& strings,
+                                  data_type output_type,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_floats(strings, output_type, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+/**
+ * @brief Code logic for converting float value into a string.
+ *
+ * The floating point components are dissected and used to fill an
+ * existing output char array.
+ */
+struct ftos_converter {
+  // significant digits is independent of scientific notation range
+  // digits more than this may require using long values instead of ints
+  static constexpr unsigned int significant_digits = 10;
+  // maximum power-of-10 that will fit in 32-bits
+  static constexpr unsigned int nine_digits = 1000000000;  // 1x10^9
+  // Range of numbers here is for normalizing the value.
+  // If the value is above or below the following limits, the output is converted to
+  // scientific notation in order to show (at most) the number of significant digits.
+  static constexpr double upper_limit = 1000000000;  // max is 1x10^9
+  static constexpr double lower_limit = 0.0001;      // printf uses scientific notation below this
+  // Tables for doing normalization: converting to exponent form
+  // IEEE double float has maximum exponent of 305 so these should cover everything
+  double const upper10[9]  = {10, 100, 10000, 1e8, 1e16, 1e32, 1e64, 1e128, 1e256};
+  double const lower10[9]  = {.1, .01, .0001, 1e-8, 1e-16, 1e-32, 1e-64, 1e-128, 1e-256};
+  double const blower10[9] = {1.0, .1, .001, 1e-7, 1e-15, 1e-31, 1e-63, 1e-127, 1e-255};
+
+  // utility for quickly converting known integer range to character array
+  __device__ char* int2str(int value, char* output)
+  {
+    if (value == 0) {
+      *output++ = '0';
+      return output;
+    }
+    char buffer[significant_digits];  // should be big-enough for significant digits
+    char* ptr = buffer;
+    while (value > 0) {
+      *ptr++ = (char)('0' + (value % 10));
+      value /= 10;
+    }
+    while (ptr != buffer)
+      *output++ = *--ptr;  // 54321 -> 12345
+    return output;
+  }
+
+  /**
+   * @brief Dissect a float value into integer, decimal, and exponent components.
+   *
+   * @return The number of decimal places.
+   */
+  __device__ int dissect_value(double value,
+                               unsigned int& integer,
+                               unsigned int& decimal,
+                               int& exp10)
+  {
+    int decimal_places = significant_digits - 1;
+    // normalize step puts value between lower-limit and upper-limit
+    // by adjusting the exponent up or down
+    exp10 = 0;
+    if (value > upper_limit) {
+      int fx = 256;
+      for (int idx = 8; idx >= 0; --idx) {
+        if (value >= upper10[idx]) {
+          value *= lower10[idx];
+          exp10 += fx;
+        }
+        fx = fx >> 1;
+      }
+    } else if ((value > 0.0) && (value < lower_limit)) {
+      int fx = 256;
+      for (int idx = 8; idx >= 0; --idx) {
+        if (value < blower10[idx]) {
+          value *= upper10[idx];
+          exp10 -= fx;
+        }
+        fx = fx >> 1;
+      }
+    }
+    //
+    unsigned int max_digits = nine_digits;
+    integer                 = (unsigned int)value;
+    for (unsigned int i = integer; i >= 10; i /= 10) {
+      --decimal_places;
+      max_digits /= 10;
+    }
+    double remainder = (value - (double)integer) * (double)max_digits;
+    decimal          = (unsigned int)remainder;
+    remainder -= (double)decimal;
+    decimal += (unsigned int)(2.0 * remainder);
+    if (decimal >= max_digits) {
+      decimal = 0;
+      ++integer;
+      if (exp10 && (integer >= 10)) {
+        ++exp10;
+        integer = 1;
+      }
+    }
+    //
+    while ((decimal % 10) == 0 && (decimal_places > 0)) {
+      decimal /= 10;
+      --decimal_places;
+    }
+    return decimal_places;
+  }
+
+  /**
+   * @brief Main kernel method for converting float value to char output array.
+   *
+   * Output need not be more than (significant_digits + 7) bytes:
+   * 7 = 1 sign, 1 decimal point, 1 exponent ('e'), 1 exponent-sign, 3 digits for exponent
+   *
+   * @param value Float value to convert.
+   * @param output Memory to write output characters.
+   * @return Number of bytes written.
+   */
+  __device__ int float_to_string(double value, char* output)
+  {
+    // check for valid value
+    if (std::isnan(value)) {
+      memcpy(output, "NaN", 3);
+      return 3;
+    }
+    bool bneg = false;
+    if (signbit(value)) {  // handles -0.0 too
+      value = -value;
+      bneg  = true;
+    }
+    if (std::isinf(value)) {
+      if (bneg)
+        memcpy(output, "-Inf", 4);
+      else
+        memcpy(output, "Inf", 3);
+      return bneg ? 4 : 3;
+    }
+
+    // dissect value into components
+    unsigned int integer = 0, decimal = 0;
+    int exp10          = 0;
+    int decimal_places = dissect_value(value, integer, decimal, exp10);
+    //
+    // now build the string from the
+    // components: sign, integer, decimal, exp10, decimal_places
+    //
+    // sign
+    char* ptr = output;
+    if (bneg) *ptr++ = '-';
+    // integer
+    ptr = int2str(integer, ptr);
+    // decimal
+    *ptr++ = '.';
+    if (decimal_places) {
+      char buffer[10];
+      char* pb = buffer;
+      while (decimal_places--) {
+        *pb++ = (char)('0' + (decimal % 10));
+        decimal /= 10;
+      }
+      while (pb != buffer)  // reverses the digits
+        *ptr++ = *--pb;     // e.g. 54321 -> 12345
+    } else
+      *ptr++ = '0';  // always include at least .0
+    // exponent
+    if (exp10) {
+      *ptr++ = 'e';
+      if (exp10 < 0) {
+        *ptr++ = '-';
+        exp10  = -exp10;
+      } else
+        *ptr++ = '+';
+      if (exp10 < 10) *ptr++ = '0';  // extra zero-pad
+      ptr = int2str(exp10, ptr);
+    }
+    // done
+    return (int)(ptr - output);  // number of bytes written
+  }
+
+  /**
+   * @brief Compute how man bytes are needed to hold the output string.
+   *
+   * @param value Float value to convert.
+   * @return Number of bytes required.
+   */
+  __device__ int compute_ftos_size(double value)
+  {
+    if (std::isnan(value)) return 3;  // NaN
+    bool bneg = false;
+    if (signbit(value)) {  // handles -0.0 too
+      value = -value;
+      bneg  = true;
+    }
+    if (std::isinf(value)) return 3 + (int)bneg;  // Inf
+
+    // dissect float into parts
+    unsigned int integer = 0, decimal = 0;
+    int exp10          = 0;
+    int decimal_places = dissect_value(value, integer, decimal, exp10);
+    // now count up the components
+    // sign
+    int count = (int)bneg;
+    // integer
+    count += (int)(integer == 0);
+    while (integer > 0) {
+      integer /= 10;
+      ++count;
+    }  // log10(integer)
+    // decimal
+    ++count;  // decimal point
+    if (decimal_places)
+      count += decimal_places;
+    else
+      ++count;  // always include .0
+    // exponent
+    if (exp10) {
+      count += 2;  // 'e±'
+      if (exp10 < 0) exp10 = -exp10;
+      count += (int)(exp10 < 10);  // padding
+      while (exp10 > 0) {
+        exp10 /= 10;
+        ++count;
+      }  // log10(exp10)
+    }
+    return count;
+  }
+};
+
+template <typename FloatType>
+struct from_floats_fn {
+  column_device_view d_floats;
+  size_type* d_offsets;
+  char* d_chars;
+
+  __device__ size_type compute_output_size(FloatType value)
+  {
+    ftos_converter fts;
+    return static_cast<size_type>(fts.compute_ftos_size(static_cast<double>(value)));
+  }
+
+  __device__ void float_to_string(size_type idx)
+  {
+    FloatType value = d_floats.element<FloatType>(idx);
+    ftos_converter fts;
+    fts.float_to_string(static_cast<double>(value), d_chars + d_offsets[idx]);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_floats.is_null(idx)) {
+      if (d_chars == nullptr) { d_offsets[idx] = 0; }
+      return;
+    }
+    if (d_chars != nullptr) {
+      float_to_string(idx);
+    } else {
+      d_offsets[idx] = compute_output_size(d_floats.element<FloatType>(idx));
+    }
+  }
+};
+
+/**
+ * @brief This dispatch method is for converting floats into strings.
+ *
+ * The template function declaration ensures only float types are allowed.
+ */
+struct dispatch_from_floats_fn {
+  template <typename FloatType, std::enable_if_t<std::is_floating_point_v<FloatType>>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& floats,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    size_type strings_count = floats.size();
+    auto column             = column_device_view::create(floats, stream);
+    auto d_column           = *column;
+
+    // copy the null mask
+    rmm::device_buffer null_mask = cudf::detail::copy_bitmask(floats, stream, mr);
+
+    auto [offsets, chars] =
+      make_strings_children(from_floats_fn<FloatType>{d_column}, strings_count, stream, mr);
+
+    return make_strings_column(strings_count,
+                               std::move(offsets),
+                               std::move(chars),
+                               floats.null_count(),
+                               std::move(null_mask));
+  }
+
+  // non-float types throw an exception
+  template <typename T, std::enable_if_t<not std::is_floating_point_v<T>>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("Values for from_floats function must be a float type.");
+  }
+};
+
+}  // namespace
+
+// This will convert all float column types into a strings column.
+std::unique_ptr<column> from_floats(column_view const& floats,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = floats.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  return type_dispatcher(floats.type(), dispatch_from_floats_fn{}, floats, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> from_floats(column_view const& floats, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_floats(floats, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+std::unique_ptr<column> is_float(strings_column_view const& strings,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_column       = *strings_column;
+  // create output column
+  auto results   = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings.size(),
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto d_results = results->mutable_view().data<bool>();
+  // check strings for valid float chars
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings.size()),
+                    d_results,
+                    [d_column] __device__(size_type idx) {
+                      if (d_column.is_null(idx)) return false;
+                      return is_float(d_column.element<string_view>(idx));
+                    });
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> is_float(strings_column_view const& strings,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_float(strings, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_hex.cu b/cpp/src/strings/convert/convert_hex.cu
new file mode 100644
index 0000000..bed682a
--- /dev/null
+++ b/cpp/src/strings/convert/convert_hex.cu
@@ -0,0 +1,304 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/convert/convert_integers.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+/**
+ * @brief Converts hex strings into an integers.
+ *
+ * Used by the dispatch method to convert to different integer types.
+ */
+template <typename IntegerType>
+struct hex_to_integer_fn {
+  column_device_view const strings_column;
+
+  /**
+   * @brief Converts a single hex string into an integer.
+   *
+   * Non-hexadecimal characters are ignored.
+   * This means it can handle "0x01A23" and "1a23".
+   *
+   * Overflow of the int64 type is not detected.
+   */
+  __device__ int64_t string_to_integer(string_view const& d_str)
+  {
+    int64_t result = 0, base = 1;
+    char const* str = d_str.data();
+    size_type index = d_str.size_bytes();
+    while (index-- > 0) {
+      char ch = str[index];
+      if (ch >= '0' && ch <= '9') {
+        result += static_cast<int64_t>(ch - 48) * base;
+        base *= 16;
+      } else if (ch >= 'A' && ch <= 'F') {
+        result += static_cast<int64_t>(ch - 55) * base;
+        base *= 16;
+      } else if (ch >= 'a' && ch <= 'f') {
+        result += static_cast<int64_t>(ch - 87) * base;
+        base *= 16;
+      }
+    }
+    return result;
+  }
+
+  __device__ IntegerType operator()(size_type idx)
+  {
+    if (strings_column.is_null(idx)) return static_cast<IntegerType>(0);
+    // the cast to IntegerType will create predictable results
+    // for integers that are larger than the IntegerType can hold
+    return static_cast<IntegerType>(string_to_integer(strings_column.element<string_view>(idx)));
+  }
+};
+
+/**
+ * @brief The dispatch functions for converting strings to integers.
+ *
+ * The output_column is expected to be one of the integer types only.
+ */
+struct dispatch_hex_to_integers_fn {
+  template <typename IntegerType, std::enable_if_t<std::is_integral_v<IntegerType>>* = nullptr>
+  void operator()(column_device_view const& strings_column,
+                  mutable_column_view& output_column,
+                  rmm::cuda_stream_view stream) const
+  {
+    auto d_results = output_column.data<IntegerType>();
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(strings_column.size()),
+                      d_results,
+                      hex_to_integer_fn<IntegerType>{strings_column});
+  }
+  // non-integral types throw an exception
+  template <typename T, typename... Args>
+  std::enable_if_t<not std::is_integral_v<T>, void> operator()(Args&&...) const
+  {
+    CUDF_FAIL("Output for hex_to_integers must be an integral type.");
+  }
+};
+
+template <>
+void dispatch_hex_to_integers_fn::operator()<bool>(column_device_view const&,
+                                                   mutable_column_view&,
+                                                   rmm::cuda_stream_view) const
+{
+  CUDF_FAIL("Output for hex_to_integers must not be a boolean type.");
+}
+
+/**
+ * @brief Functor to convert integers to hexadecimal strings
+ *
+ * @tparam IntegerType The specific integer type to convert from.
+ */
+template <typename IntegerType>
+struct integer_to_hex_fn {
+  column_device_view const d_column;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void byte_to_hex(uint8_t byte, char* hex)
+  {
+    hex[0] = [&] {
+      if (byte < 16) { return '0'; }
+      uint8_t const nibble = byte / 16;
+
+      byte = byte - (nibble * 16);
+      return static_cast<char>(nibble < 10 ? '0' + nibble : 'A' + (nibble - 10));
+    }();
+    hex[1] = byte < 10 ? '0' + byte : 'A' + (byte - 10);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) { d_offsets[idx] = 0; }
+      return;
+    }
+
+    // Reinterpret an integer value as a little-endian byte sequence.
+    // For example, 123456 becomes 0x40E2'0100
+    auto const value = d_column.element<IntegerType>(idx);
+    auto value_bytes = reinterpret_cast<uint8_t const*>(&value);
+
+    // compute the number of output bytes
+    int bytes      = sizeof(IntegerType);
+    int byte_index = sizeof(IntegerType);
+    while ((--byte_index > 0) && (value_bytes[byte_index] & 0xFF) == 0) {
+      --bytes;
+    }
+
+    // create output
+    byte_index = bytes - 1;
+    if (d_chars) {
+      auto d_buffer = d_chars + d_offsets[idx];
+      while (byte_index >= 0) {
+        byte_to_hex(value_bytes[byte_index], d_buffer);
+        d_buffer += 2;
+        --byte_index;
+      }
+    } else {
+      d_offsets[idx] = static_cast<size_type>(bytes) * 2;  // 2 hex characters per byte
+    }
+  }
+};
+
+struct dispatch_integers_to_hex_fn {
+  template <typename IntegerType, std::enable_if_t<std::is_integral_v<IntegerType>>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& input,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto const d_column = column_device_view::create(input, stream);
+
+    auto children = cudf::strings::detail::make_strings_children(
+      integer_to_hex_fn<IntegerType>{*d_column}, input.size(), stream, mr);
+
+    return make_strings_column(input.size(),
+                               std::move(children.first),
+                               std::move(children.second),
+                               input.null_count(),
+                               cudf::detail::copy_bitmask(input, stream, mr));
+  }
+  // non-integral types throw an exception
+  template <typename T, typename... Args>
+  std::enable_if_t<not std::is_integral_v<T>, std::unique_ptr<column>> operator()(Args...) const
+  {
+    CUDF_FAIL("integers_to_hex only supports integral type columns");
+  }
+};
+
+}  // namespace
+
+// This will convert a strings column into any integer column type.
+std::unique_ptr<column> hex_to_integers(strings_column_view const& strings,
+                                        data_type output_type,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(output_type);
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  // create integer output column copying the strings null-mask
+  auto results      = make_numeric_column(output_type,
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto results_view = results->mutable_view();
+  // fill output column with integers
+  type_dispatcher(output_type, dispatch_hex_to_integers_fn{}, d_strings, results_view, stream);
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+std::unique_ptr<column> is_hex(strings_column_view const& strings,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_column       = *strings_column;
+  // create output column
+  auto results   = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings.size(),
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto d_results = results->mutable_view().data<bool>();
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings.size()),
+                    d_results,
+                    [d_column] __device__(size_type idx) {
+                      if (d_column.is_null(idx)) return false;
+                      auto const d_str = d_column.element<string_view>(idx);
+                      if (d_str.empty()) return false;
+                      auto const starts_with_0x = [](auto const& sv) {
+                        return sv.length() > 1 && (sv.substr(0, 2) == string_view("0x", 2) ||
+                                                   sv.substr(0, 2) == string_view("0X", 2));
+                      };
+                      auto begin = d_str.begin() + (starts_with_0x(d_str) ? 2 : 0);
+                      auto end   = d_str.end();
+                      return (thrust::distance(begin, end) > 0) &&
+                             thrust::all_of(thrust::seq, begin, end, [] __device__(auto chr) {
+                               return (chr >= '0' && chr <= '9') || (chr >= 'A' && chr <= 'F') ||
+                                      (chr >= 'a' && chr <= 'f');
+                             });
+                    });
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+std::unique_ptr<column> integers_to_hex(column_view const& input,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return cudf::make_empty_column(type_id::STRING); }
+  return type_dispatcher(input.type(), dispatch_integers_to_hex_fn{}, input, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> hex_to_integers(strings_column_view const& strings,
+                                        data_type output_type,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::hex_to_integers(strings, output_type, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> is_hex(strings_column_view const& strings,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_hex(strings, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> integers_to_hex(column_view const& input,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::integers_to_hex(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_integers.cu b/cpp/src/strings/convert/convert_integers.cu
new file mode 100644
index 0000000..5597d28
--- /dev/null
+++ b/cpp/src/strings/convert/convert_integers.cu
@@ -0,0 +1,417 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/strings/convert/convert_integers.hpp>
+#include <cudf/strings/detail/convert/int_to_string.cuh>
+#include <cudf/strings/detail/convert/string_to_int.cuh>
+#include <cudf/strings/detail/converters.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+
+namespace detail {
+namespace {
+
+/**
+ * @brief This only checks if a string is a valid integer within the bounds of its storage type.
+ */
+template <typename IntegerType>
+struct string_to_integer_check_fn {
+  __device__ bool operator()(thrust::pair<string_view, bool> const& p) const
+  {
+    if (!p.second || p.first.empty()) { return false; }
+
+    auto const d_str = p.first.data();
+    if (d_str[0] == '-' && std::is_unsigned_v<IntegerType>) { return false; }
+
+    auto iter           = d_str + static_cast<int>((d_str[0] == '-' || d_str[0] == '+'));
+    auto const iter_end = d_str + p.first.size_bytes();
+    if (iter == iter_end) { return false; }
+
+    auto const sign = d_str[0] == '-' ? IntegerType{-1} : IntegerType{1};
+    auto const bound_val =
+      sign > 0 ? std::numeric_limits<IntegerType>::max() : std::numeric_limits<IntegerType>::min();
+
+    IntegerType value = 0;      // parse the string to integer and check for overflow along the way
+    while (iter != iter_end) {  // check all bytes for valid characters
+      auto const chr = *iter++;
+      // Check for valid character
+      if (chr < '0' || chr > '9') { return false; }
+
+      // Check for underflow and overflow:
+      auto const digit       = static_cast<IntegerType>(chr - '0');
+      auto const bound_check = (bound_val - sign * digit) / IntegerType{10} * sign;
+      if (value > bound_check) return false;
+      value = value * IntegerType{10} + digit;
+    }
+
+    return true;
+  }
+};
+
+/**
+ * @brief Returns `true` if all characters in the string
+ * are valid for conversion to an integer.
+ *
+ * Valid characters are in [-+0-9]. The sign character (+/-)
+ * is optional but if present must be the first character.
+ * An empty string returns `false`.
+ * No bounds checking is performed to verify if the integer will fit
+ * within a specific integer type.
+ *
+ * @param d_str String to check.
+ * @return true if string has valid integer characters
+ */
+inline __device__ bool is_integer(string_view const& d_str)
+{
+  if (d_str.empty()) return false;
+  auto const end = d_str.end();
+  auto begin     = d_str.begin();
+  if (*begin == '+' || *begin == '-') ++begin;
+  return (begin < end) && thrust::all_of(thrust::seq, begin, end, [] __device__(auto chr) {
+           return chr >= '0' && chr <= '9';
+         });
+}
+
+/**
+ * @brief The dispatch functions for checking if strings are valid integers.
+ */
+struct dispatch_is_integer_fn {
+  template <typename T, std::enable_if_t<std::is_integral_v<T>>* = nullptr>
+  std::unique_ptr<column> operator()(strings_column_view const& strings,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    auto const d_column = column_device_view::create(strings.parent(), stream);
+    auto results        = make_numeric_column(data_type{type_id::BOOL8},
+                                       strings.size(),
+                                       cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                       strings.null_count(),
+                                       stream,
+                                       mr);
+
+    auto d_results = results->mutable_view().data<bool>();
+    if (strings.has_nulls()) {
+      thrust::transform(rmm::exec_policy(stream),
+                        d_column->pair_begin<string_view, true>(),
+                        d_column->pair_end<string_view, true>(),
+                        d_results,
+                        string_to_integer_check_fn<T>{});
+    } else {
+      thrust::transform(rmm::exec_policy(stream),
+                        d_column->pair_begin<string_view, false>(),
+                        d_column->pair_end<string_view, false>(),
+                        d_results,
+                        string_to_integer_check_fn<T>{});
+    }
+
+    // Calling mutable_view() on a column invalidates it's null count so we need to set it back
+    results->set_null_count(strings.null_count());
+
+    return results;
+  }
+
+  template <typename T, std::enable_if_t<not std::is_integral_v<T>>* = nullptr>
+  std::unique_ptr<column> operator()(strings_column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("is_integer is expecting an integer type");
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> is_integer(strings_column_view const& strings,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  auto const d_column = column_device_view::create(strings.parent(), stream);
+  auto results        = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings.size(),
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+
+  auto d_results = results->mutable_view().data<bool>();
+  if (strings.has_nulls()) {
+    thrust::transform(
+      rmm::exec_policy(stream),
+      d_column->pair_begin<string_view, true>(),
+      d_column->pair_end<string_view, true>(),
+      d_results,
+      [] __device__(auto const& p) { return p.second ? is_integer(p.first) : false; });
+  } else {
+    thrust::transform(rmm::exec_policy(stream),
+                      d_column->pair_begin<string_view, false>(),
+                      d_column->pair_end<string_view, false>(),
+                      d_results,
+                      [] __device__(auto const& p) { return is_integer(p.first); });
+  }
+
+  // Calling mutable_view() on a column invalidates it's null count so we need to set it back
+  results->set_null_count(strings.null_count());
+
+  return results;
+}
+
+std::unique_ptr<column> is_integer(strings_column_view const& strings,
+                                   data_type int_type,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) { return cudf::make_empty_column(type_id::BOOL8); }
+  return type_dispatcher(int_type, dispatch_is_integer_fn{}, strings, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+std::unique_ptr<column> is_integer(strings_column_view const& strings,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_integer(strings, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> is_integer(strings_column_view const& strings,
+                                   data_type int_type,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_integer(strings, int_type, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+/**
+ * @brief Converts strings into an integers.
+ *
+ * Used by the dispatch method to convert to different integer types.
+ */
+template <typename IntegerType>
+struct string_to_integer_fn {
+  column_device_view const strings_column;  // strings to convert
+
+  __device__ IntegerType operator()(size_type idx)
+  {
+    if (strings_column.is_null(idx)) return static_cast<IntegerType>(0);
+    // the cast to IntegerType will create predictable results
+    // for integers that are larger than the IntegerType can hold
+    return static_cast<IntegerType>(string_to_integer(strings_column.element<string_view>(idx)));
+  }
+};
+
+/**
+ * @brief The dispatch functions for converting strings to integers.
+ *
+ * The output_column is expected to be one of the integer types only.
+ */
+struct dispatch_to_integers_fn {
+  template <typename IntegerType, std::enable_if_t<std::is_integral_v<IntegerType>>* = nullptr>
+  void operator()(column_device_view const& strings_column,
+                  mutable_column_view& output_column,
+                  rmm::cuda_stream_view stream) const
+  {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(strings_column.size()),
+                      output_column.data<IntegerType>(),
+                      string_to_integer_fn<IntegerType>{strings_column});
+  }
+  // non-integral types throw an exception
+  template <typename T, std::enable_if_t<not std::is_integral_v<T>>* = nullptr>
+  void operator()(column_device_view const&, mutable_column_view&, rmm::cuda_stream_view) const
+  {
+    CUDF_FAIL("Output for to_integers must be an integral type.");
+  }
+};
+
+template <>
+void dispatch_to_integers_fn::operator()<bool>(column_device_view const&,
+                                               mutable_column_view&,
+                                               rmm::cuda_stream_view) const
+{
+  CUDF_FAIL("Output for to_integers must not be a boolean type.");
+}
+
+}  // namespace
+
+// This will convert a strings column into any integer column type.
+std::unique_ptr<column> to_integers(strings_column_view const& strings,
+                                    data_type output_type,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_numeric_column(output_type, 0);
+
+  // Create integer output column copying the strings null-mask
+  auto results = make_numeric_column(output_type,
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  // Fill output column with integers
+  auto const strings_dev_view = column_device_view::create(strings.parent(), stream);
+  auto results_view           = results->mutable_view();
+  type_dispatcher(output_type, dispatch_to_integers_fn{}, *strings_dev_view, results_view, stream);
+
+  // Calling mutable_view() on a column invalidates it's null count so we need to set it back
+  results->set_null_count(strings.null_count());
+
+  return results;
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> to_integers(strings_column_view const& strings,
+                                    data_type output_type,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::to_integers(strings, output_type, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+template <typename IntegerType>
+struct from_integers_fn {
+  column_device_view d_integers;
+  size_type* d_offsets;
+  char* d_chars;
+
+  /**
+   * @brief Converts an integer element into a string.
+   *
+   * The integer is converted into base-10 using only characters [0-9].
+   * No formatting is done for the string other than prepending the '-'
+   * character for negative values.
+   */
+  __device__ void integer_element_to_string(size_type idx)
+  {
+    IntegerType value = d_integers.element<IntegerType>(idx);
+    char* d_buffer    = d_chars + d_offsets[idx];
+    integer_to_string(value, d_buffer);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_integers.is_null(idx)) {
+      if (d_chars == nullptr) { d_offsets[idx] = 0; }
+      return;
+    }
+    if (d_chars != nullptr) {
+      integer_element_to_string(idx);
+    } else {
+      d_offsets[idx] = count_digits(d_integers.element<IntegerType>(idx));
+    }
+  }
+};
+
+/**
+ * @brief This dispatch method is for converting integers into strings.
+ * The template function declaration ensures only integer types are used.
+ */
+struct dispatch_from_integers_fn {
+  template <typename IntegerType, std::enable_if_t<std::is_integral_v<IntegerType>>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const& integers,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr) const
+  {
+    size_type strings_count = integers.size();
+    auto column             = column_device_view::create(integers, stream);
+    auto d_column           = *column;
+
+    // copy the null mask
+    rmm::device_buffer null_mask = cudf::detail::copy_bitmask(integers, stream, mr);
+
+    auto [offsets, chars] =
+      make_strings_children(from_integers_fn<IntegerType>{d_column}, strings_count, stream, mr);
+
+    return make_strings_column(strings_count,
+                               std::move(offsets),
+                               std::move(chars),
+                               integers.null_count(),
+                               std::move(null_mask));
+  }
+
+  // non-integral types throw an exception
+  template <typename T, std::enable_if_t<not std::is_integral_v<T>>* = nullptr>
+  std::unique_ptr<column> operator()(column_view const&,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*) const
+  {
+    CUDF_FAIL("Values for from_integers function must be an integral type.");
+  }
+};
+
+template <>
+std::unique_ptr<column> dispatch_from_integers_fn::operator()<bool>(
+  column_view const&, rmm::cuda_stream_view, rmm::mr::device_memory_resource*) const
+{
+  CUDF_FAIL("Input for from_integers must not be a boolean type.");
+}
+
+}  // namespace
+
+// This will convert all integer column types into a strings column.
+std::unique_ptr<column> from_integers(column_view const& integers,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = integers.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  return type_dispatcher(integers.type(), dispatch_from_integers_fn{}, integers, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> from_integers(column_view const& integers,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::from_integers(integers, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_ipv4.cu b/cpp/src/strings/convert/convert_ipv4.cu
new file mode 100644
index 0000000..adb72cb
--- /dev/null
+++ b/cpp/src/strings/convert/convert_ipv4.cu
@@ -0,0 +1,240 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/convert/convert_ipv4.hpp>
+#include <cudf/strings/detail/convert/int_to_string.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+/**
+ * @brief Converts IPv4 strings into integers.
+ *
+ * Only single-byte characters are expected.
+ * No checking is done on the format of individual strings.
+ * Any character that is not [0-9] is considered a delimiter.
+ * This means "128-34-56-709" will parse successfully.
+ */
+struct ipv4_to_integers_fn {
+  column_device_view const d_strings;
+
+  __device__ int64_t operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) return 0;
+    string_view d_str  = d_strings.element<string_view>(idx);
+    uint32_t ipvals[4] = {0};  // IPV4 format: xxx.xxx.xxx.xxx
+    int32_t ipv_idx    = 0;
+    int32_t factor     = 1;
+    char const* in_ptr = d_str.data();
+    char const* end    = in_ptr + d_str.size_bytes();
+    while ((in_ptr < end) && (ipv_idx < 4)) {
+      char ch = *in_ptr++;
+      if (ch < '0' || ch > '9') {
+        ++ipv_idx;
+        factor = 1;
+      } else {
+        ipvals[ipv_idx] = (ipvals[ipv_idx] * factor) + static_cast<uint32_t>(ch - '0');
+        factor          = 10;
+      }
+    }
+    uint32_t result = (ipvals[0] << 24) + (ipvals[1] << 16) + (ipvals[2] << 8) + ipvals[3];
+    return static_cast<int64_t>(result);
+  }
+};
+
+}  // namespace
+
+// Convert strings column of IPv4 addresses to integers column
+std::unique_ptr<column> ipv4_to_integers(strings_column_view const& strings,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_numeric_column(data_type{type_id::INT64}, 0);
+
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  // create output column copying the strings' null-mask
+  auto results   = make_numeric_column(data_type{type_id::INT64},
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto d_results = results->mutable_view().data<int64_t>();
+  // fill output column with ipv4 integers
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings_count),
+                    d_results,
+                    ipv4_to_integers_fn{*strings_column});
+  // done
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> ipv4_to_integers(strings_column_view const& strings,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::ipv4_to_integers(strings, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+/**
+ * @brief Converts integers into IPv4 addresses.
+ *
+ * Each integer is divided into 8-bit sub-integers.
+ * The sub-integers are converted into 1-3 character digits.
+ * These are placed appropriately between '.' character.
+ */
+struct integers_to_ipv4_fn {
+  column_device_view const d_column;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const ip_number = d_column.element<int64_t>(idx);
+
+    char* out_ptr   = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    int shift_bits  = 24;
+    size_type bytes = 3;  // at least 3 dots: xxx.xxx.xxx.xxx
+#pragma unroll
+    for (int n = 0; n < 4; ++n) {
+      uint8_t value = static_cast<uint8_t>((ip_number >> shift_bits) & 0x00FF);
+      if (out_ptr) {
+        out_ptr += integer_to_string(value, out_ptr);
+        if ((n + 1) < 4) *out_ptr++ = '.';
+      } else {
+        bytes += count_digits(value);
+      }
+      shift_bits -= 8;
+    }
+
+    if (!d_chars) { d_offsets[idx] = bytes; }
+  }
+};
+
+}  // namespace
+
+// Convert integers into IPv4 addresses
+std::unique_ptr<column> integers_to_ipv4(column_view const& integers,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if (integers.is_empty()) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(integers.type().id() == type_id::INT64, "Input column must be type_id::INT64 type");
+
+  auto d_column = column_device_view::create(integers, stream);
+  auto children = cudf::strings::detail::make_strings_children(
+    integers_to_ipv4_fn{*d_column}, integers.size(), stream, mr);
+
+  return make_strings_column(integers.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             integers.null_count(),
+                             cudf::detail::copy_bitmask(integers, stream, mr));
+}
+
+std::unique_ptr<column> is_ipv4(strings_column_view const& strings,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_column       = *strings_column;
+  // create output column
+  auto results   = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings.size(),
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto d_results = results->mutable_view().data<bool>();
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings.size()),
+                    d_results,
+                    [d_column] __device__(size_type idx) {
+                      if (d_column.is_null(idx)) return false;
+                      auto const d_str = d_column.element<string_view>(idx);
+                      if (d_str.empty()) return false;
+                      constexpr int max_ip = 255;  // values must be in [0,255]
+                      int ip_vals[4]       = {-1, -1, -1, -1};
+                      int ipv_idx          = 0;  // index into ip_vals
+                      for (auto const ch : d_str) {
+                        if ((ch >= '0') && (ch <= '9')) {
+                          auto const ip_val    = ip_vals[ipv_idx];
+                          int const new_ip_val = static_cast<int>(ch - '0') +  // compute new value
+                                                 (ip_val < 0 ? 0 : (10 * ip_val));
+                          if (new_ip_val > max_ip) return false;
+                          ip_vals[ipv_idx] = new_ip_val;
+                        }
+                        // here ipv_idx is incremented only when ch=='.'
+                        else if (ch != '.' || (++ipv_idx > 3))
+                          return false;
+                      }
+                      // final check for any missing values
+                      return ip_vals[0] >= 0 && ip_vals[1] >= 0 && ip_vals[2] >= 0 &&
+                             ip_vals[3] >= 0;
+                    });
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> integers_to_ipv4(column_view const& integers,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::integers_to_ipv4(integers, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> is_ipv4(strings_column_view const& strings,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_ipv4(strings, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_lists.cu b/cpp/src/strings/convert/convert_lists.cu
new file mode 100644
index 0000000..3aef379
--- /dev/null
+++ b/cpp/src/strings/convert/convert_lists.cu
@@ -0,0 +1,243 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/convert/convert_lists.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+// position of the element separator string (e.g. comma ',') within the separators column
+constexpr size_type separator_index = 0;
+// position of the enclosure strings (e.g. []) within the separators column
+constexpr size_type left_brace_index  = 1;
+constexpr size_type right_brace_index = 2;
+
+/**
+ * @brief Pending separator type for `stack_item`
+ */
+enum class item_separator : int8_t { NONE, ELEMENT, LIST };
+
+/**
+ * @brief Stack item used to manage nested lists.
+ *
+ * Each item includes the current range and the pending separator.
+ */
+struct alignas(8) stack_item {
+  size_type left_idx;
+  size_type right_idx;
+  item_separator separator{item_separator::NONE};
+};
+
+/**
+ * @brief Formatting lists functor.
+ *
+ * This formats the input list column into individual strings using the
+ * specified separators and null-representation (na_rep) string.
+ *
+ * Recursion is simulated by using stack allocating per output string.
+ */
+struct format_lists_fn {
+  column_device_view const d_input;
+  column_device_view const d_separators;
+  string_view const d_na_rep;
+  stack_item* d_stack;
+  size_type const max_depth;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ column_device_view get_nested_child(size_type idx)
+  {
+    auto current = d_input;
+    while (idx > 0) {
+      current = current.child(cudf::lists_column_view::child_column_index);
+      --idx;
+    }
+    return current;
+  }
+
+  __device__ size_type write_separator(char*& d_output, size_type sep_idx = separator_index)
+  {
+    auto d_str = [&] {
+      if (d_separators.size() > sep_idx) return d_separators.element<string_view>(sep_idx);
+      if (sep_idx == left_brace_index) return string_view("[", 1);
+      if (sep_idx == right_brace_index) return string_view("]", 1);
+      return string_view(",", 1);
+    }();
+    if (d_output) d_output = copy_string(d_output, d_str);
+    return d_str.size_bytes();
+  }
+
+  __device__ size_type write_na_rep(char*& d_output)
+  {
+    if (d_output) d_output = copy_string(d_output, d_na_rep);
+    return d_na_rep.size_bytes();
+  }
+
+  __device__ size_type write_strings(column_device_view const& col,
+                                     size_type left_idx,
+                                     size_type right_idx,
+                                     char* d_output)
+  {
+    size_type bytes = 0;
+    for (size_type idx = left_idx; idx < right_idx; ++idx) {
+      if (col.is_null(idx)) {
+        bytes += write_na_rep(d_output);  // e.g. 'NULL'
+      } else {
+        auto d_str = col.element<string_view>(idx);
+        if (d_output) d_output = copy_string(d_output, d_str);
+        bytes += d_str.size_bytes();
+      }
+      if (idx + 1 < right_idx) {
+        bytes += write_separator(d_output);  // e.g. comma ','
+      }
+    }
+    return bytes;
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    size_type bytes = 0;
+    char* d_output  = d_chars ? d_chars + d_offsets[idx] : nullptr;
+
+    // push first item to the stack
+    auto item_stack         = d_stack + idx * max_depth;
+    auto stack_idx          = size_type{0};
+    item_stack[stack_idx++] = stack_item{idx, idx + 1};
+
+    // process until stack is empty
+    while (stack_idx > 0) {
+      --stack_idx;  // pop from stack
+      auto const item = item_stack[stack_idx];
+      auto const view = get_nested_child(stack_idx);
+
+      auto offsets   = view.child(cudf::lists_column_view::offsets_column_index);
+      auto d_offsets = offsets.data<size_type>() + view.offset();
+
+      // add pending separator
+      if (item.separator == item_separator::LIST) {
+        bytes += write_separator(d_output, right_brace_index);
+      } else if (item.separator == item_separator::ELEMENT) {
+        bytes += write_separator(d_output, separator_index);
+      }
+
+      // loop through the child elements for the current view
+      for (auto jdx = item.left_idx; jdx < item.right_idx; ++jdx) {
+        auto const lhs = d_offsets[jdx];
+        auto const rhs = d_offsets[jdx + 1];
+
+        if (view.is_null(jdx)) {
+          bytes += write_na_rep(d_output);  // e.g. 'NULL'
+        } else if (lhs == rhs) {            // e.g. '[]'
+          bytes += write_separator(d_output, left_brace_index);
+          bytes += write_separator(d_output, right_brace_index);
+        } else {
+          auto child = view.child(cudf::lists_column_view::child_column_index);
+          bytes += write_separator(d_output, left_brace_index);
+
+          // if child is a list type, then recurse into it
+          if (child.type().id() == type_id::LIST) {
+            // push current state to the stack
+            item_stack[stack_idx++] =
+              stack_item{jdx + 1,
+                         item.right_idx,
+                         jdx + 1 < item.right_idx ? item_separator::ELEMENT : item_separator::LIST};
+            // push child to the stack
+            item_stack[stack_idx++] = stack_item{lhs, rhs};
+            break;  // back to the stack (while-loop)
+          }
+
+          // otherwise, the child is a strings column;
+          // write out the string elements
+          auto const size = write_strings(child, lhs, rhs, d_output);
+          bytes += size;
+          if (d_output) d_output += size;
+
+          bytes += write_separator(d_output, right_brace_index);
+        }
+
+        // write element separator (e.g. comma ',') if not at the end
+        if (jdx + 1 < item.right_idx) { bytes += write_separator(d_output); }
+      }
+    }
+
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> format_list_column(lists_column_view const& input,
+                                           string_scalar const& na_rep,
+                                           strings_column_view const& separators,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(data_type{type_id::STRING});
+
+  size_type depth = 1;  // count the depth to the strings column
+  auto child_col  = input.child();
+  while (child_col.type().id() == type_id::LIST) {
+    child_col = cudf::lists_column_view(child_col).child();
+    ++depth;
+  }
+  CUDF_EXPECTS(child_col.type().id() == type_id::STRING, "lists child must be a STRING column");
+
+  CUDF_EXPECTS(separators.size() == 0 || separators.size() == 3,
+               "Invalid number of separator strings");
+  CUDF_EXPECTS(na_rep.is_valid(stream), "Null replacement string must be valid");
+
+  // create stack memory for processing nested lists
+  auto stack_buffer = rmm::device_uvector<stack_item>(input.size() * depth, stream);
+
+  auto const d_input      = column_device_view::create(input.parent(), stream);
+  auto const d_separators = column_device_view::create(separators.parent(), stream);
+  auto const d_na_rep     = na_rep.value(stream);
+
+  auto children = cudf::strings::detail::make_strings_children(
+    format_lists_fn{*d_input, *d_separators, d_na_rep, stack_buffer.data(), depth},
+    input.size(),
+    stream,
+    mr);
+
+  return make_strings_column(
+    input.size(), std::move(children.first), std::move(children.second), 0, rmm::device_buffer{});
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> format_list_column(lists_column_view const& input,
+                                           string_scalar const& na_rep,
+                                           strings_column_view const& separators,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::format_list_column(input, na_rep, separators, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/convert/convert_urls.cu b/cpp/src/strings/convert/convert_urls.cu
new file mode 100644
index 0000000..9efa148
--- /dev/null
+++ b/cpp/src/strings/convert/convert_urls.cu
@@ -0,0 +1,439 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/strings/convert/convert_urls.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cub/cub.cuh>
+
+#include <thrust/scan.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+//
+// This is the functor for the url_encode() method below.
+// Specific requirements are documented in custrings issue #321.
+// In summary it converts mostly non-ascii characters and control characters into UTF-8 hex
+// characters prefixed with '%'. For example, the space character must be converted to characters
+// '%20' where the '20' indicates the hex value for space in UTF-8. Likewise, multi-byte characters
+// are converted to multiple hex characters. For example, the é character is converted to characters
+// '%C3%A9' where 'C3A9' is the UTF-8 bytes xc3a9 for this character.
+//
+struct url_encoder_fn {
+  column_device_view const d_strings;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  // utility to create 2-byte hex characters from single binary byte
+  __device__ void byte_to_hex(uint8_t byte, char* hex)
+  {
+    hex[0] = '0';
+    if (byte >= 16) {
+      uint8_t hibyte = byte / 16;
+      hex[0]         = hibyte < 10 ? '0' + hibyte : 'A' + (hibyte - 10);
+      byte           = byte - (hibyte * 16);
+    }
+    hex[1] = byte < 10 ? '0' + byte : 'A' + (byte - 10);
+  }
+
+  __device__ bool should_not_url_encode(char ch)
+  {
+    return (
+      (ch >= '0' && ch <= '9') ||  // these are the characters
+      (ch >= 'A' && ch <= 'Z') ||  // that are not to be url encoded
+      (ch >= 'a' &&
+       ch <= 'z') ||  // reference: docs.python.org/3/library/urllib.parse.html#urllib.parse.quote
+      (ch == '.') ||
+      (ch == '_') || (ch == '~') || (ch == '-'));
+  }
+
+  // main part of the functor the performs the url-encoding
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    string_view d_str = d_strings.element<string_view>(idx);
+    //
+    char* out_ptr    = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    size_type nbytes = 0;
+    char hex[2];  // two-byte hex max
+    for (auto itr = d_str.begin(); itr != d_str.end(); ++itr) {
+      auto ch = *itr;
+      if (ch < 128) {
+        if (should_not_url_encode(static_cast<char>(ch))) {
+          nbytes++;
+          if (out_ptr) out_ptr = copy_and_increment(out_ptr, d_str.data() + itr.byte_offset(), 1);
+        } else  // url-encode everything else
+        {
+          nbytes += 3;
+          if (out_ptr) {
+            out_ptr = copy_and_increment(out_ptr, "%", 1);  // add the '%' prefix
+            byte_to_hex(static_cast<uint8_t>(ch), hex);     // convert to 2 hex chars
+            out_ptr = copy_and_increment(out_ptr, hex, 2);  // add them to the output
+          }
+        }
+      } else  // these are to be utf-8 url-encoded
+      {
+        uint8_t char_bytes[4];  // holds utf-8 bytes for one character
+        size_type char_width = from_char_utf8(ch, reinterpret_cast<char*>(char_bytes));
+        nbytes += char_width * 3;  // '%' plus 2 hex chars per byte (example: é is %C3%A9)
+        // process each byte in this current character
+        for (size_type chidx = 0; out_ptr && (chidx < char_width); ++chidx) {
+          out_ptr = copy_and_increment(out_ptr, "%", 1);  // add '%' prefix
+          byte_to_hex(char_bytes[chidx], hex);            // convert to 2 hex chars
+          out_ptr = copy_and_increment(out_ptr, hex, 2);  // add them to the output
+        }
+      }
+    }
+    if (!d_chars) d_offsets[idx] = nbytes;
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<column> url_encode(strings_column_view const& input,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+
+  auto d_column = column_device_view::create(input.parent(), stream);
+
+  auto children = cudf::strings::detail::make_strings_children(
+    url_encoder_fn{*d_column}, input.size(), stream, mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> url_encode(strings_column_view const& strings,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::url_encode(strings, cudf::get_default_stream(), mr);
+}
+
+namespace detail {
+namespace {
+
+// utility to convert a hex char into a single byte
+constexpr uint8_t hex_char_to_byte(char ch)
+{
+  if (ch >= '0' && ch <= '9') return (ch - '0');
+  if (ch >= 'A' && ch <= 'F') return (ch - 'A' + 10);  // in hex A=10,B=11,...,F=15
+  if (ch >= 'a' && ch <= 'f') return (ch - 'a' + 10);  // same for lower case
+  return 0;
+}
+
+constexpr bool is_hex_digit(char ch)
+{
+  return (ch >= '0' && ch <= '9') || (ch >= 'A' && ch <= 'F') || (ch >= 'a' && ch <= 'f');
+}
+
+__forceinline__ __device__ bool is_escape_char(char const* const ptr)
+{
+  return (ptr[0] == '%' && is_hex_digit(ptr[1]) && is_hex_digit(ptr[2]));
+}
+
+// helper function for converting an escaped sequence starting at `ptr` to a single byte
+__forceinline__ __device__ char escaped_sequence_to_byte(char const* const ptr)
+{
+  return (hex_char_to_byte(ptr[1]) << 4) | hex_char_to_byte(ptr[2]);
+}
+
+/**
+ * @brief Count the number of characters of each string after URL decoding.
+ *
+ * @tparam num_warps_per_threadblock Number of warps in a threadblock. This template argument must
+ * match the launch configuration, i.e. the kernel must be launched with
+ * `num_warps_per_threadblock * cudf::detail::warp_size` threads per threadblock.
+ * @tparam char_block_size Number of characters which will be loaded into the shared memory at a
+ * time.
+ *
+ * @param[in] in_strings Input string column.
+ * @param[out] out_counts Number of characters in each decode URL.
+ */
+template <size_type num_warps_per_threadblock, size_type char_block_size>
+__global__ void url_decode_char_counter(column_device_view const in_strings,
+                                        size_type* const out_counts)
+{
+  constexpr int halo_size = 2;
+  __shared__ char temporary_buffer[num_warps_per_threadblock][char_block_size + halo_size];
+  __shared__ typename cub::WarpReduce<int8_t>::TempStorage cub_storage[num_warps_per_threadblock];
+
+  auto const global_thread_id = cudf::detail::grid_1d::global_thread_id();
+  auto const global_warp_id   = static_cast<size_type>(global_thread_id / cudf::detail::warp_size);
+  auto const local_warp_id    = static_cast<size_type>(threadIdx.x / cudf::detail::warp_size);
+  auto const warp_lane        = static_cast<size_type>(threadIdx.x % cudf::detail::warp_size);
+  auto const nwarps     = static_cast<size_type>(gridDim.x * blockDim.x / cudf::detail::warp_size);
+  char* in_chars_shared = temporary_buffer[local_warp_id];
+
+  // Loop through strings, and assign each string to a warp.
+  for (size_type row_idx = global_warp_id; row_idx < in_strings.size(); row_idx += nwarps) {
+    if (in_strings.is_null(row_idx)) {
+      out_counts[row_idx] = 0;
+      continue;
+    }
+
+    auto const in_string     = in_strings.element<string_view>(row_idx);
+    auto const in_chars      = in_string.data();
+    auto const string_length = in_string.size_bytes();
+    auto const nblocks       = cudf::util::div_rounding_up_unsafe(string_length, char_block_size);
+    size_type escape_char_count = 0;
+
+    for (size_type block_idx = 0; block_idx < nblocks; block_idx++) {
+      auto const string_length_block =
+        std::min(char_block_size, string_length - char_block_size * block_idx);
+
+      // Each warp collectively loads input characters of the current block to the shared memory.
+      // When testing whether a location is the start of an escaped character, we need to access
+      // the current location as well as the next two locations. To avoid branches, two halo cells
+      // are added after the end of the block. If the cell is beyond the end of the string, 0s are
+      // filled in to make sure the last two characters of the string are not the start of an
+      // escaped sequence.
+      for (auto char_idx = warp_lane; char_idx < string_length_block + halo_size;
+           char_idx += cudf::detail::warp_size) {
+        auto const in_idx         = block_idx * char_block_size + char_idx;
+        in_chars_shared[char_idx] = in_idx < string_length ? in_chars[in_idx] : 0;
+      }
+
+      __syncwarp();
+
+      // `char_idx_start` represents the start character index of the current warp.
+      for (size_type char_idx_start = 0; char_idx_start < string_length_block;
+           char_idx_start += cudf::detail::warp_size) {
+        auto const char_idx = char_idx_start + warp_lane;
+        int8_t const is_ichar_escape_char =
+          (char_idx < string_length_block && is_escape_char(in_chars_shared + char_idx)) ? 1 : 0;
+
+        // Warp-wise reduction to calculate the number of escape characters.
+        // All threads in the warp participate in the reduction, even if `char_idx` is beyond
+        // `string_length_block`.
+        int8_t const total_escape_char =
+          cub::WarpReduce<int8_t>(cub_storage[local_warp_id]).Sum(is_ichar_escape_char);
+
+        if (warp_lane == 0) { escape_char_count += total_escape_char; }
+
+        __syncwarp();
+      }
+    }
+    // URL decoding replaces 3 bytes with 1 for each escape character.
+    if (warp_lane == 0) { out_counts[row_idx] = string_length - escape_char_count * 2; }
+  }
+}
+
+/**
+ * @brief Decode and copy from the input string column to the output char buffer.
+ *
+ * @tparam num_warps_per_threadblock Number of warps in a threadblock. This template argument must
+ * match the launch configuration, i.e. the kernel must be launched with
+ * `num_warps_per_threadblock * cudf::detail::warp_size` threads per threadblock.
+ * @tparam char_block_size Number of characters which will be loaded into the shared memory at a
+ * time.
+ *
+ * @param[in] in_strings Input string column.
+ * @param[out] out_chars Character buffer for the output string column.
+ * @param[in] out_offsets Offset value of each string associated with `out_chars`.
+ */
+template <size_type num_warps_per_threadblock, size_type char_block_size>
+__global__ void url_decode_char_replacer(column_device_view const in_strings,
+                                         char* const out_chars,
+                                         size_type const* const out_offsets)
+{
+  constexpr int halo_size = 2;
+  __shared__ char temporary_buffer[num_warps_per_threadblock][char_block_size + halo_size * 2];
+  __shared__ typename cub::WarpScan<int8_t>::TempStorage cub_storage[num_warps_per_threadblock];
+  __shared__ size_type out_idx[num_warps_per_threadblock];
+
+  auto const global_thread_id = cudf::detail::grid_1d::global_thread_id();
+  auto const global_warp_id   = static_cast<size_type>(global_thread_id / cudf::detail::warp_size);
+  auto const local_warp_id    = static_cast<size_type>(threadIdx.x / cudf::detail::warp_size);
+  auto const warp_lane        = static_cast<size_type>(threadIdx.x % cudf::detail::warp_size);
+  auto const nwarps     = static_cast<size_type>(gridDim.x * blockDim.x / cudf::detail::warp_size);
+  char* in_chars_shared = temporary_buffer[local_warp_id];
+
+  // Loop through strings, and assign each string to a warp
+  for (size_type row_idx = global_warp_id; row_idx < in_strings.size(); row_idx += nwarps) {
+    if (in_strings.is_null(row_idx)) continue;
+
+    auto const in_string     = in_strings.element<string_view>(row_idx);
+    auto const in_chars      = in_string.data();
+    auto const string_length = in_string.size_bytes();
+    auto out_chars_string    = out_chars + out_offsets[row_idx];
+    auto const nblocks       = cudf::util::div_rounding_up_unsafe(string_length, char_block_size);
+
+    // Use the last thread of the warp to initialize `out_idx` to 0.
+    if (warp_lane == cudf::detail::warp_size - 1) { out_idx[local_warp_id] = 0; }
+
+    for (size_type block_idx = 0; block_idx < nblocks; block_idx++) {
+      auto const string_length_block =
+        std::min(char_block_size, string_length - char_block_size * block_idx);
+
+      // Each warp collectively loads input characters of the current block to shared memory.
+      // Two halo cells before and after the block are added. The halo cells are used to test
+      // whether the current location as well as the previous two locations are escape characters,
+      // without branches.
+      for (auto char_idx = warp_lane; char_idx < string_length_block + halo_size * 2;
+           char_idx += cudf::detail::warp_size) {
+        auto const in_idx         = block_idx * char_block_size + char_idx - halo_size;
+        in_chars_shared[char_idx] = in_idx >= 0 && in_idx < string_length ? in_chars[in_idx] : 0;
+      }
+
+      __syncwarp();
+
+      // `char_idx_start` represents the start character index of the current warp.
+      for (size_type char_idx_start = 0; char_idx_start < string_length_block;
+           char_idx_start += cudf::detail::warp_size) {
+        auto const char_idx = char_idx_start + warp_lane;
+        // If the current character is part of an escape sequence starting at the previous two
+        // locations, the thread with the starting location should output the escaped character, and
+        // the current thread should not output a character.
+        int8_t const out_size =
+          (char_idx >= string_length_block || is_escape_char(in_chars_shared + char_idx) ||
+           is_escape_char(in_chars_shared + char_idx + 1))
+            ? 0
+            : 1;
+
+        // Warp-wise prefix sum to establish output location of the current thread.
+        // All threads in the warp participate in the prefix sum, even if `char_idx` is beyond
+        // `string_length_block`.
+        int8_t out_offset;
+        cub::WarpScan<int8_t>(cub_storage[local_warp_id]).ExclusiveSum(out_size, out_offset);
+
+        if (out_size == 1) {
+          char const* const ch_ptr = in_chars_shared + char_idx + halo_size;
+          char const ch =
+            is_escape_char(ch_ptr)
+              ?
+              // If the current location is the start of an escape sequence, load and decode.
+              escaped_sequence_to_byte(ch_ptr)
+              :
+              // If the current location is not the start of an escape sequence, load directly.
+              *ch_ptr;
+          out_chars_string[out_idx[local_warp_id] + out_offset] = ch;
+        }
+
+        if (warp_lane == cudf::detail::warp_size - 1) {
+          out_idx[local_warp_id] += (out_offset + out_size);
+        }
+
+        __syncwarp();
+      }
+    }
+  }
+}
+
+}  // namespace
+
+//
+std::unique_ptr<column> url_decode(strings_column_view const& strings,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  constexpr size_type num_warps_per_threadblock = 4;
+  constexpr size_type threadblock_size = num_warps_per_threadblock * cudf::detail::warp_size;
+  constexpr size_type char_block_size  = 256;
+  auto const num_threadblocks =
+    std::min(65536, cudf::util::div_rounding_up_unsafe(strings_count, num_warps_per_threadblock));
+
+  auto offset_count    = strings_count + 1;
+  auto const d_strings = column_device_view::create(strings.parent(), stream);
+
+  // build offsets column
+  auto offsets_column = make_numeric_column(
+    data_type{type_to_id<size_type>()}, offset_count, mask_state::UNALLOCATED, stream, mr);
+
+  // count number of bytes in each string after decoding and store it in offsets_column
+  auto offsets_view         = offsets_column->view();
+  auto offsets_mutable_view = offsets_column->mutable_view();
+  url_decode_char_counter<num_warps_per_threadblock, char_block_size>
+    <<<num_threadblocks, threadblock_size, 0, stream.value()>>>(
+      *d_strings, offsets_mutable_view.begin<size_type>());
+
+  // use scan to transform number of bytes into offsets
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         offsets_view.begin<size_type>(),
+                         offsets_view.end<size_type>(),
+                         offsets_mutable_view.begin<size_type>());
+
+  // copy the total number of characters of all strings combined (last element of the offset column)
+  // to the host memory
+  auto out_chars_bytes = cudf::detail::get_value<size_type>(offsets_view, offset_count - 1, stream);
+
+  // create the chars column
+  auto chars_column = create_chars_child_column(out_chars_bytes, stream, mr);
+  auto d_out_chars  = chars_column->mutable_view().data<char>();
+
+  // decode and copy the characters from the input column to the output column
+  url_decode_char_replacer<num_warps_per_threadblock, char_block_size>
+    <<<num_threadblocks, threadblock_size, 0, stream.value()>>>(
+      *d_strings, d_out_chars, offsets_column->view().begin<size_type>());
+
+  // copy null mask
+  rmm::device_buffer null_mask = cudf::detail::copy_bitmask(strings.parent(), stream, mr);
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             strings.null_count(),
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> url_decode(strings_column_view const& strings,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::url_decode(strings, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/copying/concatenate.cu b/cpp/src/strings/copying/concatenate.cu
new file mode 100644
index 0000000..26cd4ff
--- /dev/null
+++ b/cpp/src/strings/copying/concatenate.cu
@@ -0,0 +1,316 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/detail/concatenate.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_scalar.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/advance.h>
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+// Benchmark data, shared at https://github.com/rapidsai/cudf/pull/4703, shows
+// that the single kernel optimization generally performs better, but when the
+// number of chars/col is beyond a certain threshold memcpy performs better.
+// This heuristic estimates which strategy will give better performance by
+// comparing the mean chars/col with values from the above table.
+constexpr bool use_fused_kernel_heuristic(bool const has_nulls,
+                                          size_t const total_bytes,
+                                          size_t const num_columns)
+{
+  return has_nulls ? total_bytes < num_columns * 1572864  // midpoint of 1048576 and 2097152
+                   : total_bytes < num_columns * 393216;  // midpoint of 262144 and 524288
+}
+
+// Using a functor instead of a lambda as a workaround for:
+// error: The enclosing parent function ("create_strings_device_views") for an
+// extended __device__ lambda must not have deduced return type
+struct chars_size_transform {
+  __device__ size_t operator()(column_device_view const& col) const
+  {
+    if (col.size() > 0) {
+      constexpr auto offsets_index = strings_column_view::offsets_column_index;
+      auto d_offsets               = col.child(offsets_index).data<int32_t>();
+      return d_offsets[col.size() + col.offset()] - d_offsets[col.offset()];
+    } else {
+      return 0;
+    }
+  }
+};
+
+auto create_strings_device_views(host_span<column_view const> views, rmm::cuda_stream_view stream)
+{
+  CUDF_FUNC_RANGE();
+  // Assemble contiguous array of device views
+  auto [device_view_owners, device_views_ptr] =
+    contiguous_copy_column_device_views<column_device_view>(views, stream);
+
+  // Compute the partition offsets and size of offset column
+  // Note: Using 64-bit size_t so we can detect overflow of 32-bit size_type
+  auto input_offsets = std::vector<size_t>(views.size() + 1);
+  auto offset_it     = std::next(input_offsets.begin());
+  thrust::transform(
+    thrust::host, views.begin(), views.end(), offset_it, [](auto const& col) -> size_t {
+      return static_cast<size_t>(col.size());
+    });
+  thrust::inclusive_scan(thrust::host, offset_it, input_offsets.end(), offset_it);
+  auto d_input_offsets = cudf::detail::make_device_uvector_async(
+    input_offsets, stream, rmm::mr::get_current_device_resource());
+  auto const output_size = input_offsets.back();
+
+  // Compute the partition offsets and size of chars column
+  // Note: Using 64-bit size_t so we can detect overflow of 32-bit size_type
+  auto d_partition_offsets = rmm::device_uvector<size_t>(views.size() + 1, stream);
+  d_partition_offsets.set_element_to_zero_async(0, stream);  // zero first element
+
+  thrust::transform_inclusive_scan(rmm::exec_policy(stream),
+                                   device_views_ptr,
+                                   device_views_ptr + views.size(),
+                                   std::next(d_partition_offsets.begin()),
+                                   chars_size_transform{},
+                                   thrust::plus{});
+  auto const output_chars_size = d_partition_offsets.back_element(stream);
+  stream.synchronize();  // ensure copy of output_chars_size is complete before returning
+
+  return std::make_tuple(std::move(device_view_owners),
+                         device_views_ptr,
+                         std::move(d_input_offsets),
+                         std::move(d_partition_offsets),
+                         output_size,
+                         output_chars_size);
+}
+
+template <size_type block_size, bool Nullable>
+__global__ void fused_concatenate_string_offset_kernel(column_device_view const* input_views,
+                                                       size_t const* input_offsets,
+                                                       size_t const* partition_offsets,
+                                                       size_type const num_input_views,
+                                                       size_type const output_size,
+                                                       int32_t* output_data,
+                                                       bitmask_type* output_mask,
+                                                       size_type* out_valid_count)
+{
+  cudf::thread_index_type output_index = threadIdx.x + blockIdx.x * blockDim.x;
+  size_type warp_valid_count           = 0;
+
+  unsigned active_mask;
+  if (Nullable) { active_mask = __ballot_sync(0xFFFF'FFFFu, output_index < output_size); }
+  while (output_index < output_size) {
+    // Lookup input index by searching for output index in offsets
+    auto const offset_it            = thrust::prev(thrust::upper_bound(
+      thrust::seq, input_offsets, input_offsets + num_input_views, output_index));
+    size_type const partition_index = offset_it - input_offsets;
+
+    auto const offset_index      = output_index - *offset_it;
+    auto const& input_view       = input_views[partition_index];
+    constexpr auto offsets_child = strings_column_view::offsets_column_index;
+    auto const* input_data       = input_view.child(offsets_child).data<int32_t>();
+    output_data[output_index] =
+      input_data[offset_index + input_view.offset()]  // handle parent offset
+      - input_data[input_view.offset()]               // subtract first offset if non-zero
+      + partition_offsets[partition_index];           // add offset of source column
+
+    if (Nullable) {
+      bool const bit_is_set       = input_view.is_valid(offset_index);
+      bitmask_type const new_word = __ballot_sync(active_mask, bit_is_set);
+
+      // First thread writes bitmask word
+      if (threadIdx.x % cudf::detail::warp_size == 0) {
+        output_mask[word_index(output_index)] = new_word;
+      }
+
+      warp_valid_count += __popc(new_word);
+    }
+
+    output_index += blockDim.x * gridDim.x;
+    if (Nullable) { active_mask = __ballot_sync(active_mask, output_index < output_size); }
+  }
+
+  // Fill final offsets index with total size of char data
+  if (output_index == output_size) {
+    output_data[output_size] = partition_offsets[num_input_views];
+  }
+
+  if (Nullable) {
+    using cudf::detail::single_lane_block_sum_reduce;
+    auto block_valid_count = single_lane_block_sum_reduce<block_size, 0>(warp_valid_count);
+    if (threadIdx.x == 0) { atomicAdd(out_valid_count, block_valid_count); }
+  }
+}
+
+__global__ void fused_concatenate_string_chars_kernel(column_device_view const* input_views,
+                                                      size_t const* partition_offsets,
+                                                      size_type const num_input_views,
+                                                      size_type const output_size,
+                                                      char* output_data)
+{
+  cudf::thread_index_type output_index = threadIdx.x + blockIdx.x * blockDim.x;
+
+  while (output_index < output_size) {
+    // Lookup input index by searching for output index in offsets
+    auto const offset_it            = thrust::prev(thrust::upper_bound(
+      thrust::seq, partition_offsets, partition_offsets + num_input_views, output_index));
+    size_type const partition_index = offset_it - partition_offsets;
+
+    auto const offset_index = output_index - *offset_it;
+    auto const& input_view  = input_views[partition_index];
+
+    constexpr auto offsets_child   = strings_column_view::offsets_column_index;
+    auto const* input_offsets_data = input_view.child(offsets_child).data<int32_t>();
+
+    constexpr auto chars_child   = strings_column_view::chars_column_index;
+    auto const* input_chars_data = input_view.child(chars_child).data<char>();
+
+    auto const first_char     = input_offsets_data[input_view.offset()];
+    output_data[output_index] = input_chars_data[offset_index + first_char];
+
+    output_index += blockDim.x * gridDim.x;
+  }
+}
+
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  // Compute output sizes
+  auto const device_views         = create_strings_device_views(columns, stream);
+  auto const& d_views             = std::get<1>(device_views);
+  auto const& d_input_offsets     = std::get<2>(device_views);
+  auto const& d_partition_offsets = std::get<3>(device_views);
+  auto const strings_count        = std::get<4>(device_views);
+  auto const total_bytes          = std::get<5>(device_views);
+  auto const offsets_count        = strings_count + 1;
+
+  if (strings_count == 0) { return make_empty_column(type_id::STRING); }
+
+  CUDF_EXPECTS(offsets_count <= static_cast<std::size_t>(std::numeric_limits<size_type>::max()),
+               "total number of strings exceeds the column size limit",
+               std::overflow_error);
+  CUDF_EXPECTS(total_bytes <= static_cast<std::size_t>(std::numeric_limits<size_type>::max()),
+               "total size of strings exceeds the column size limit",
+               std::overflow_error);
+
+  bool const has_nulls =
+    std::any_of(columns.begin(), columns.end(), [](auto const& col) { return col.has_nulls(); });
+
+  // create chars column
+  auto chars_column = create_chars_child_column(total_bytes, stream, mr);
+  auto d_new_chars  = chars_column->mutable_view().data<char>();
+  chars_column->set_null_count(0);
+
+  // create offsets column
+  auto offsets_column = make_numeric_column(
+    data_type{type_id::INT32}, offsets_count, mask_state::UNALLOCATED, stream, mr);
+  auto d_new_offsets = offsets_column->mutable_view().data<int32_t>();
+  offsets_column->set_null_count(0);
+
+  rmm::device_buffer null_mask{0, stream, mr};
+  size_type null_count{};
+  if (has_nulls) {
+    null_mask =
+      cudf::detail::create_null_mask(strings_count, mask_state::UNINITIALIZED, stream, mr);
+  }
+
+  {  // Copy offsets columns with single kernel launch
+    rmm::device_scalar<size_type> d_valid_count(0, stream);
+
+    constexpr size_type block_size{256};
+    cudf::detail::grid_1d config(offsets_count, block_size);
+    auto const kernel = has_nulls ? fused_concatenate_string_offset_kernel<block_size, true>
+                                  : fused_concatenate_string_offset_kernel<block_size, false>;
+    kernel<<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+      d_views,
+      d_input_offsets.data(),
+      d_partition_offsets.data(),
+      static_cast<size_type>(columns.size()),
+      strings_count,
+      d_new_offsets,
+      reinterpret_cast<bitmask_type*>(null_mask.data()),
+      d_valid_count.data());
+
+    if (has_nulls) { null_count = strings_count - d_valid_count.value(stream); }
+  }
+
+  if (total_bytes > 0) {
+    // Use a heuristic to guess when the fused kernel will be faster than memcpy
+    if (use_fused_kernel_heuristic(has_nulls, total_bytes, columns.size())) {
+      // Use single kernel launch to copy chars columns
+      constexpr size_type block_size{256};
+      cudf::detail::grid_1d config(total_bytes, block_size);
+      auto const kernel = fused_concatenate_string_chars_kernel;
+      kernel<<<config.num_blocks, config.num_threads_per_block, 0, stream.value()>>>(
+        d_views,
+        d_partition_offsets.data(),
+        static_cast<size_type>(columns.size()),
+        total_bytes,
+        d_new_chars);
+    } else {
+      // Memcpy each input chars column (more efficient for very large strings)
+      for (auto column = columns.begin(); column != columns.end(); ++column) {
+        size_type column_size = column->size();
+        if (column_size == 0)  // nothing to do
+          continue;            // empty column may not have children
+        size_type column_offset   = column->offset();
+        column_view offsets_child = column->child(strings_column_view::offsets_column_index);
+        column_view chars_child   = column->child(strings_column_view::chars_column_index);
+
+        auto bytes_offset =
+          cudf::detail::get_value<size_type>(offsets_child, column_offset, stream);
+
+        // copy the chars column data
+        auto d_chars = chars_child.data<char>() + bytes_offset;
+        auto const bytes =
+          cudf::detail::get_value<size_type>(offsets_child, column_size + column_offset, stream) -
+          bytes_offset;
+
+        CUDF_CUDA_TRY(
+          cudaMemcpyAsync(d_new_chars, d_chars, bytes, cudaMemcpyDefault, stream.value()));
+
+        // get ready for the next column
+        d_new_chars += bytes;
+      }
+    }
+  }
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/copying/copying.cu b/cpp/src/strings/copying/copying.cu
new file mode 100644
index 0000000..e6796c2
--- /dev/null
+++ b/cpp/src/strings/copying/copying.cu
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/strings/detail/copying.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+std::unique_ptr<cudf::column> copy_slice(strings_column_view const& strings,
+                                         size_type start,
+                                         size_type end,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+  if (end < 0 || end > strings.size()) end = strings.size();
+  CUDF_EXPECTS(((start >= 0) && (start < end)), "Invalid start parameter value.");
+  auto const strings_count  = end - start;
+  auto const offsets_offset = start + strings.offset();
+
+  // slice the offsets child column
+  auto offsets_column = std::make_unique<cudf::column>(
+    cudf::detail::slice(
+      strings.offsets(), {offsets_offset, offsets_offset + strings_count + 1}, stream)
+      .front(),
+    stream,
+    mr);
+  auto const chars_offset =
+    offsets_offset == 0 ? 0 : cudf::detail::get_value<int32_t>(offsets_column->view(), 0, stream);
+  if (chars_offset > 0) {
+    // adjust the individual offset values only if needed
+    auto d_offsets = offsets_column->mutable_view();
+    thrust::transform(rmm::exec_policy(stream),
+                      d_offsets.begin<int32_t>(),
+                      d_offsets.end<int32_t>(),
+                      d_offsets.begin<int32_t>(),
+                      [chars_offset] __device__(auto offset) { return offset - chars_offset; });
+  }
+
+  // slice the chars child column
+  auto const data_size =
+    cudf::detail::get_value<int32_t>(offsets_column->view(), strings_count, stream);
+  auto chars_column = std::make_unique<cudf::column>(
+    cudf::detail::slice(strings.chars(), {chars_offset, chars_offset + data_size}, stream).front(),
+    stream,
+    mr);
+
+  // slice the null mask
+  auto null_mask = cudf::detail::copy_bitmask(
+    strings.null_mask(), offsets_offset, offsets_offset + strings_count, stream, mr);
+
+  auto null_count = cudf::detail::null_count(
+    static_cast<bitmask_type const*>(null_mask.data()), 0, strings_count, stream);
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/copying/shift.cu b/cpp/src/strings/copying/shift.cu
new file mode 100644
index 0000000..b54c433
--- /dev/null
+++ b/cpp/src/strings/copying/shift.cu
@@ -0,0 +1,134 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/strings/detail/copying.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf::strings::detail {
+
+namespace {
+
+struct output_sizes_fn {
+  column_device_view const d_column;  // input strings column
+  string_view const d_filler;
+  size_type const offset;
+
+  __device__ size_type get_string_size_at(size_type idx)
+  {
+    return d_column.is_null(idx) ? 0 : d_column.element<string_view>(idx).size_bytes();
+  }
+
+  __device__ size_type operator()(size_type idx)
+  {
+    auto const last_index = offset < 0 ? d_column.size() + offset : offset;
+    if (offset < 0) {
+      // shift left:  a,b,c,d,e,f -> b,c,d,e,f,x
+      return (idx < last_index) ? get_string_size_at(idx - offset) : d_filler.size_bytes();
+    } else {
+      // shift right:  a,b,c,d,e,f -> x,a,b,c,d,e
+      return (idx < last_index) ? d_filler.size_bytes() : get_string_size_at(idx - offset);
+    }
+  }
+};
+
+struct shift_chars_fn {
+  column_device_view const d_column;  // input strings column
+  string_view const d_filler;
+  size_type const offset;
+
+  __device__ char operator()(size_type idx)
+  {
+    if (offset < 0) {
+      auto const last_index = -offset;
+      if (idx < last_index) {
+        auto const first_index =
+          offset + d_column.child(strings_column_view::offsets_column_index)
+                     .element<size_type>(d_column.offset() + d_column.size());
+        return d_column.child(strings_column_view::chars_column_index)
+          .element<char>(idx + first_index);
+      } else {
+        auto const char_index = idx - last_index;
+        return d_filler.data()[char_index % d_filler.size_bytes()];
+      }
+    } else {
+      if (idx < offset) {
+        return d_filler.data()[idx % d_filler.size_bytes()];
+      } else {
+        return d_column.child(strings_column_view::chars_column_index)
+          .element<char>(idx - offset +
+                         d_column.child(strings_column_view::offsets_column_index)
+                           .element<size_type>(d_column.offset()));
+      }
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> shift(strings_column_view const& input,
+                              size_type offset,
+                              scalar const& fill_value,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  auto d_fill_str = static_cast<string_scalar const&>(fill_value).value(stream);
+
+  // adjust offset when greater than the size of the input
+  if (std::abs(offset) > input.size()) { offset = input.size(); }
+
+  // build the output offsets by computing the sizes of each output row
+  auto const d_input = column_device_view::create(input.parent(), stream);
+  auto sizes_itr     = cudf::detail::make_counting_transform_iterator(
+    0, output_sizes_fn{*d_input, d_fill_str, offset});
+  auto [offsets_column, total_bytes] =
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + input.size(), stream, mr);
+  auto offsets_view = offsets_column->view();
+
+  // compute the shift-offset for the output characters child column
+  auto const shift_offset = [&] {
+    auto const index = (offset < 0) ? input.size() + offset : offset;
+    return (offset < 0 ? -1 : 1) * cudf::detail::get_value<size_type>(offsets_view, index, stream);
+  }();
+
+  // create output chars child column
+  auto chars_column = create_chars_child_column(static_cast<size_type>(total_bytes), stream, mr);
+  auto d_chars      = mutable_column_device_view::create(chars_column->mutable_view(), stream);
+
+  // run kernel to shift all the characters
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::counting_iterator<size_type>(0),
+                    thrust::counting_iterator<size_type>(total_bytes),
+                    d_chars->data<char>(),
+                    shift_chars_fn{*d_input, d_fill_str, shift_offset});
+
+  // caller sets the null-mask
+  return make_strings_column(
+    input.size(), std::move(offsets_column), std::move(chars_column), 0, rmm::device_buffer{});
+}
+
+}  // namespace cudf::strings::detail
diff --git a/cpp/src/strings/count_matches.cu b/cpp/src/strings/count_matches.cu
new file mode 100644
index 0000000..6de5d43
--- /dev/null
+++ b/cpp/src/strings/count_matches.cu
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/count_matches.hpp>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+/**
+ * @brief Kernel counts the total matches for the given regex in each string.
+ */
+struct count_fn {
+  column_device_view const d_strings;
+
+  __device__ int32_t operator()(size_type const idx,
+                                reprog_device const prog,
+                                int32_t const thread_idx)
+  {
+    if (d_strings.is_null(idx)) return 0;
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const nchars = d_str.length();
+    int32_t count     = 0;
+
+    auto itr = d_str.begin();
+    while (itr.position() <= nchars) {
+      auto result = prog.find(thread_idx, d_str, itr);
+      if (!result) { break; }
+      ++count;
+      // increment the iterator is faster than creating a new one
+      // +1 if the match was on a virtual position (e.g. word boundary)
+      itr += (result->second - itr.position()) + (result->first == result->second);
+    }
+    return count;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> count_matches(column_device_view const& d_strings,
+                                      reprog_device& d_prog,
+                                      size_type output_size,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  assert(output_size >= d_strings.size() and "Unexpected output size");
+
+  auto results = make_numeric_column(
+    data_type{type_to_id<size_type>()}, output_size, mask_state::UNALLOCATED, stream, mr);
+
+  if (d_strings.size() == 0) return results;
+
+  auto d_results = results->mutable_view().data<int32_t>();
+
+  launch_transform_kernel(count_fn{d_strings}, d_prog, d_results, d_strings.size(), stream);
+
+  return results;
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/count_matches.hpp b/cpp/src/strings/count_matches.hpp
new file mode 100644
index 0000000..a4f76c1
--- /dev/null
+++ b/cpp/src/strings/count_matches.hpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+
+class column_device_view;
+
+namespace strings {
+namespace detail {
+
+class reprog_device;
+
+/**
+ * @brief Returns a column of regex match counts for each string in the given column.
+ *
+ * A null entry will result in a zero count for that output row.
+ *
+ * @param d_strings Device view of the input strings column.
+ * @param d_prog Regex instance to evaluate on each string.
+ * @param output_size Number of rows for the output column.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return Integer column of match counts
+ */
+std::unique_ptr<column> count_matches(column_device_view const& d_strings,
+                                      reprog_device& d_prog,
+                                      size_type output_size,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr);
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/extract/extract.cu b/cpp/src/strings/extract/extract.cu
new file mode 100644
index 0000000..532053e
--- /dev/null
+++ b/cpp/src/strings/extract/extract.cu
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/extract.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/pair.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+using string_index_pair = thrust::pair<char const*, size_type>;
+
+/**
+ * @brief This functor handles extracting strings by applying the compiled regex pattern
+ * and creating string_index_pairs for all the substrings.
+ */
+struct extract_fn {
+  column_device_view const d_strings;
+  cudf::detail::device_2dspan<string_index_pair> d_indices;
+
+  __device__ void operator()(size_type const idx,
+                             reprog_device const d_prog,
+                             int32_t const prog_idx)
+  {
+    auto const groups = d_prog.group_counts();
+    auto d_output     = d_indices[idx];
+
+    if (d_strings.is_valid(idx)) {
+      auto const d_str = d_strings.element<string_view>(idx);
+      auto const match = d_prog.find(prog_idx, d_str, d_str.begin());
+      if (match) {
+        auto const itr = d_str.begin() + match->first;
+        auto last_pos  = itr;
+        for (auto col_idx = 0; col_idx < groups; ++col_idx) {
+          auto const extracted = d_prog.extract(prog_idx, d_str, itr, match->second, col_idx);
+          if (extracted) {
+            auto const d_extracted = string_from_match(*extracted, d_str, last_pos);
+            d_output[col_idx] = string_index_pair{d_extracted.data(), d_extracted.size_bytes()};
+            last_pos += (extracted->second - last_pos.position());
+          } else {
+            d_output[col_idx] = string_index_pair{nullptr, 0};
+          }
+        }
+        return;
+      }
+    }
+
+    // if null row or no match found, fill the output with null entries
+    thrust::fill(thrust::seq, d_output.begin(), d_output.end(), string_index_pair{nullptr, 0});
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<table> extract(strings_column_view const& input,
+                               regex_program const& prog,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  auto const groups = d_prog->group_counts();
+  CUDF_EXPECTS(groups > 0, "Group indicators not found in regex pattern");
+
+  auto indices = rmm::device_uvector<string_index_pair>(input.size() * groups, stream);
+  auto d_indices =
+    cudf::detail::device_2dspan<string_index_pair>(indices.data(), input.size(), groups);
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+
+  launch_for_each_kernel(extract_fn{*d_strings, d_indices}, *d_prog, input.size(), stream);
+
+  // build a result column for each group
+  std::vector<std::unique_ptr<column>> results(groups);
+  auto make_strings_lambda = [&](size_type column_index) {
+    // this iterator transposes the extract results into column order
+    auto indices_itr =
+      thrust::make_permutation_iterator(indices.begin(),
+                                        cudf::detail::make_counting_transform_iterator(
+                                          0, [column_index, groups] __device__(size_type idx) {
+                                            return (idx * groups) + column_index;
+                                          }));
+    return make_strings_column(indices_itr, indices_itr + input.size(), stream, mr);
+  };
+
+  std::transform(thrust::make_counting_iterator<size_type>(0),
+                 thrust::make_counting_iterator<size_type>(groups),
+                 results.begin(),
+                 make_strings_lambda);
+
+  return std::make_unique<table>(std::move(results));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<table> extract(strings_column_view const& strings,
+                               regex_program const& prog,
+                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract(strings, prog, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/extract/extract_all.cu b/cpp/src/strings/extract/extract_all.cu
new file mode 100644
index 0000000..8a2f8f0
--- /dev/null
+++ b/cpp/src/strings/extract/extract_all.cu
@@ -0,0 +1,176 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/count_matches.hpp>
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/extract.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/transform_scan.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Functor extracts matched string pointers for each input string.
+ *
+ * For regex match within a string, the specified groups are extracted into
+ * the `d_indices` output vector.
+ * The `d_offsets` are pre-computed to identify the location of where each
+ * string's output groups are to be written.
+ */
+struct extract_fn {
+  column_device_view const d_strings;
+  size_type const* d_offsets;
+  string_index_pair* d_indices;
+
+  __device__ void operator()(size_type const idx,
+                             reprog_device const d_prog,
+                             int32_t const prog_idx)
+  {
+    if (d_strings.is_null(idx)) { return; }
+
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const nchars = d_str.length();
+
+    auto const groups    = d_prog.group_counts();
+    auto d_output        = d_indices + d_offsets[idx];
+    size_type output_idx = 0;
+
+    auto itr = d_str.begin();
+
+    while (itr.position() < nchars) {
+      // first, match the regex
+      auto const match = d_prog.find(prog_idx, d_str, itr);
+      if (!match) { break; }
+      itr += (match->first - itr.position());  // position to beginning of the match
+      auto last_pos = itr;
+      // extract each group into the output
+      for (auto group_idx = 0; group_idx < groups; ++group_idx) {
+        // result is an optional containing the bounds of the extracted string at group_idx
+        auto const extracted = d_prog.extract(prog_idx, d_str, itr, match->second, group_idx);
+        if (extracted) {
+          auto const d_result = string_from_match(*extracted, d_str, last_pos);
+          d_output[group_idx + output_idx] =
+            string_index_pair{d_result.data(), d_result.size_bytes()};
+        } else {
+          d_output[group_idx + output_idx] = string_index_pair{nullptr, 0};
+        }
+        last_pos += (extracted->second - last_pos.position());
+      }
+      // point to the end of this match to start the next match
+      itr += (match->second - itr.position());
+      output_idx += groups;
+    }
+  }
+};
+
+}  // namespace
+
+/**
+ * @copydoc cudf::strings::extract_all_record
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+std::unique_ptr<column> extract_all_record(strings_column_view const& input,
+                                           regex_program const& prog,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = input.size();
+  auto const d_strings     = column_device_view::create(input.parent(), stream);
+
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  // The extract pattern should always include groups.
+  auto const groups = d_prog->group_counts();
+  CUDF_EXPECTS(groups > 0, "extract_all requires group indicators in the regex pattern.");
+
+  // Get the match counts for each string.
+  // This column will become the output lists child offsets column.
+  auto offsets   = count_matches(*d_strings, *d_prog, strings_count + 1, stream, mr);
+  auto d_offsets = offsets->mutable_view().data<size_type>();
+
+  // Compute null output rows
+  auto [null_mask, null_count] = cudf::detail::valid_if(
+    d_offsets, d_offsets + strings_count, [] __device__(auto v) { return v > 0; }, stream, mr);
+
+  // Return an empty lists column if there are no valid rows
+  if (strings_count == null_count) {
+    return cudf::lists::detail::make_empty_lists_column(data_type{type_id::STRING}, stream, mr);
+  }
+
+  // Convert counts into offsets.
+  // Multiply each count by the number of groups.
+  thrust::transform_exclusive_scan(
+    rmm::exec_policy(stream),
+    d_offsets,
+    d_offsets + strings_count + 1,
+    d_offsets,
+    [groups] __device__(auto v) { return v * groups; },
+    size_type{0},
+    thrust::plus{});
+  auto const total_groups =
+    cudf::detail::get_value<size_type>(offsets->view(), strings_count, stream);
+
+  rmm::device_uvector<string_index_pair> indices(total_groups, stream);
+
+  launch_for_each_kernel(
+    extract_fn{*d_strings, d_offsets, indices.data()}, *d_prog, strings_count, stream);
+
+  auto strings_output = make_strings_column(indices.begin(), indices.end(), stream, mr);
+
+  // Build the lists column from the offsets and the strings.
+  return make_lists_column(strings_count,
+                           std::move(offsets),
+                           std::move(strings_output),
+                           null_count,
+                           std::move(null_mask),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> extract_all_record(strings_column_view const& strings,
+                                           regex_program const& prog,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::extract_all_record(strings, prog, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/filling/fill.cu b/cpp/src/strings/filling/fill.cu
new file mode 100644
index 0000000..0c4dd53
--- /dev/null
+++ b/cpp/src/strings/filling/fill.cu
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/detail/fill.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+std::unique_ptr<column> fill(strings_column_view const& strings,
+                             size_type begin,
+                             size_type end,
+                             string_scalar const& value,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  auto strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+  CUDF_EXPECTS((begin >= 0) && (end <= strings_count),
+               "Parameters [begin,end) are outside the range of the provided strings column");
+  CUDF_EXPECTS(begin <= end, "Parameters [begin,end) have invalid range values");
+  if (begin == end)  // return a copy
+    return std::make_unique<column>(strings.parent(), stream, mr);
+
+  // string_scalar.data() is null for valid, empty strings
+  auto d_value = get_scalar_device_view(const_cast<string_scalar&>(value));
+
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+
+  // create resulting null mask
+  auto valid_mask = [begin, end, d_value, value, d_strings, stream, mr] {
+    if (begin == 0 and end == d_strings.size() and value.is_valid(stream))
+      return std::pair(rmm::device_buffer{}, 0);
+    return cudf::detail::valid_if(
+      thrust::make_counting_iterator<size_type>(0),
+      thrust::make_counting_iterator<size_type>(d_strings.size()),
+      [d_strings, begin, end, d_value] __device__(size_type idx) {
+        return ((begin <= idx) && (idx < end)) ? d_value.is_valid() : !d_strings.is_null(idx);
+      },
+      stream,
+      mr);
+  }();
+  auto null_count               = valid_mask.second;
+  rmm::device_buffer& null_mask = valid_mask.first;
+
+  // build offsets column
+  auto offsets_transformer = [d_strings, begin, end, d_value] __device__(size_type idx) {
+    if (((begin <= idx) && (idx < end)) ? !d_value.is_valid() : d_strings.is_null(idx)) return 0;
+    return ((begin <= idx) && (idx < end)) ? d_value.size()
+                                           : d_strings.element<string_view>(idx).size_bytes();
+  };
+  auto offsets_transformer_itr = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<size_type>(0), offsets_transformer);
+  auto [offsets_column, bytes] = cudf::detail::make_offsets_child_column(
+    offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  auto d_offsets = offsets_column->view().data<int32_t>();
+
+  // create the chars column
+  auto chars_column = create_chars_child_column(bytes, stream, mr);
+  // fill the chars column
+  auto d_chars = chars_column->mutable_view().data<char>();
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    strings_count,
+    [d_strings, begin, end, d_value, d_offsets, d_chars] __device__(size_type idx) {
+      if (((begin <= idx) && (idx < end)) ? !d_value.is_valid() : d_strings.is_null(idx)) return;
+      string_view const d_str =
+        ((begin <= idx) && (idx < end)) ? d_value.value() : d_strings.element<string_view>(idx);
+      memcpy(d_chars + d_offsets[idx], d_str.data(), d_str.size_bytes());
+    });
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/filter_chars.cu b/cpp/src/strings/filter_chars.cu
new file mode 100644
index 0000000..3e38b5f
--- /dev/null
+++ b/cpp/src/strings/filter_chars.cu
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/translate.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/host_vector.h>
+#include <thrust/pair.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+using char_range = thrust::pair<char_utf8, char_utf8>;
+
+namespace {
+/**
+ * @brief This is the filter functor for replacing characters
+ * in each string given a vector of char_range values.
+ */
+struct filter_fn {
+  column_device_view const d_strings;
+  filter_type keep_characters;
+  rmm::device_uvector<char_range>::iterator table_begin;
+  rmm::device_uvector<char_range>::iterator table_end;
+  string_view const d_replacement;
+  int32_t* d_offsets{};
+  char* d_chars{};
+
+  /**
+   * @brief Return true if this character should be removed.
+   *
+   * @param ch Character to check
+   * @return True if character should be removed.
+   */
+  __device__ bool remove_char(char_utf8 ch)
+  {
+    auto const entry =
+      thrust::find_if(thrust::seq, table_begin, table_end, [ch] __device__(auto const& range) {
+        return (range.first <= ch) && (ch <= range.second);
+      });
+    // if keep==true and entry-not-found OR
+    // if keep==false and entry-found
+    return (keep_characters == filter_type::KEEP) == (entry == table_end);
+  }
+
+  /**
+   * @brief Execute the filter operation on each string.
+   *
+   * This is also used to calculate the size of the output.
+   *
+   * @param idx Index of the current string to process.
+   */
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str = d_strings.element<string_view>(idx);
+
+    auto nbytes  = d_str.size_bytes();
+    auto out_ptr = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    for (auto itr = d_str.begin(); itr != d_str.end(); ++itr) {
+      auto const char_size        = bytes_in_char_utf8(*itr);
+      string_view const d_newchar = remove_char(*itr)
+                                      ? d_replacement
+                                      : string_view(d_str.data() + itr.byte_offset(), char_size);
+      if (out_ptr)
+        out_ptr = cudf::strings::detail::copy_string(out_ptr, d_newchar);
+      else
+        nbytes += d_newchar.size_bytes() - char_size;
+    }
+    if (!out_ptr) d_offsets[idx] = nbytes;
+  }
+};
+
+}  // namespace
+
+/**
+ * @copydoc cudf::strings::filter_characters
+ */
+std::unique_ptr<column> filter_characters(
+  strings_column_view const& strings,
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> characters_to_filter,
+  filter_type keep_characters,
+  string_scalar const& replacement,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+  CUDF_EXPECTS(replacement.is_valid(stream), "Parameter replacement must be valid");
+  cudf::string_view d_replacement(replacement.data(), replacement.size());
+
+  // convert input table for copy to device memory
+  size_type table_size = static_cast<size_type>(characters_to_filter.size());
+  thrust::host_vector<char_range> htable(table_size);
+  std::transform(
+    characters_to_filter.begin(), characters_to_filter.end(), htable.begin(), [](auto entry) {
+      return char_range{entry.first, entry.second};
+    });
+  rmm::device_uvector<char_range> table =
+    cudf::detail::make_device_uvector_async(htable, stream, rmm::mr::get_current_device_resource());
+
+  auto d_strings = column_device_view::create(strings.parent(), stream);
+
+  // this utility calls the strip_fn to build the offsets and chars columns
+  filter_fn ffn{*d_strings, keep_characters, table.begin(), table.end(), d_replacement};
+  auto children = cudf::strings::detail::make_strings_children(ffn, strings.size(), stream, mr);
+
+  return make_strings_column(strings_count,
+                             std::move(children.first),
+                             std::move(children.second),
+                             strings.null_count(),
+                             cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::strings::filter_characters
+ */
+std::unique_ptr<column> filter_characters(
+  strings_column_view const& strings,
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> characters_to_filter,
+  filter_type keep_characters,
+  string_scalar const& replacement,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::filter_characters(
+    strings, characters_to_filter, keep_characters, replacement, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/json/json_path.cu b/cpp/src/strings/json/json_path.cu
new file mode 100644
index 0000000..c56752f
--- /dev/null
+++ b/cpp/src/strings/json/json_path.cu
@@ -0,0 +1,1057 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/json.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <io/utilities/parsing_utils.cuh>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+#include <thrust/scan.h>
+#include <thrust/tuple.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+// change to "\n" and 1 to make output more readable
+#define DEBUG_NEWLINE
+constexpr int DEBUG_NEWLINE_LEN = 0;
+
+/**
+ * @brief Result of calling a parse function.
+ *
+ * The primary use of this is to distinguish between "success" and
+ * "success but no data" return cases.  For example, if you are reading the
+ * values of an array you might call a parse function in a while loop. You
+ * would want to continue doing this until you either encounter an error (parse_result::ERROR)
+ * or you get nothing back (parse_result::EMPTY)
+ */
+enum class parse_result {
+  ERROR,          // failure
+  SUCCESS,        // success
+  MISSING_FIELD,  // success, but the field is missing
+  EMPTY,          // success, but no data
+};
+
+/**
+ * @brief Base parser class inherited by the (device-side) json_state class and
+ * (host-side) path_state class.
+ *
+ * Contains a number of useful utility functions common to parsing json and
+ * JSONPath strings.
+ */
+class parser {
+ protected:
+  CUDF_HOST_DEVICE inline parser() {}
+  CUDF_HOST_DEVICE inline parser(char const* _input, int64_t _input_len)
+    : input(_input), input_len(_input_len), pos(_input)
+  {
+    parse_whitespace();
+  }
+
+  CUDF_HOST_DEVICE inline parser(parser const& p)
+    : input(p.input), input_len(p.input_len), pos(p.pos)
+  {
+  }
+
+  CUDF_HOST_DEVICE inline bool eof(char const* p) { return p - input >= input_len; }
+  CUDF_HOST_DEVICE inline bool eof() { return eof(pos); }
+
+  CUDF_HOST_DEVICE inline bool parse_whitespace()
+  {
+    while (!eof()) {
+      if (is_whitespace(*pos)) {
+        pos++;
+      } else {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  CUDF_HOST_DEVICE inline bool is_hex_digit(char c)
+  {
+    return (c >= '0' && c <= '9') || (c >= 'a' && c <= 'f') || (c >= 'A' && c <= 'F');
+  }
+
+  CUDF_HOST_DEVICE inline int64_t chars_left() { return input_len - ((pos - input) + 1); }
+
+  /**
+   * @brief Parse an escape sequence.
+   *
+   * Must be a valid sequence as specified by the JSON format
+   * https://www.json.org/json-en.html
+   *
+   * @returns True on success or false on fail.
+   */
+  CUDF_HOST_DEVICE inline bool parse_escape_seq()
+  {
+    if (*pos != '\\') { return false; }
+    char c = *++pos;
+
+    // simple case
+    if (c == '\"' || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' ||
+        c == 't') {
+      pos++;
+      return true;
+    }
+
+    // hex digits: must be of the form uXXXX  where each X is a valid hex digit
+    if (c == 'u' && chars_left() >= 4 && is_hex_digit(pos[1]) && is_hex_digit(pos[2]) &&
+        is_hex_digit(pos[3]) && is_hex_digit(pos[4])) {
+      pos += 5;
+      return true;
+    }
+
+    // an illegal escape sequence.
+    return false;
+  }
+
+  /**
+   * @brief Parse a quote-enclosed JSON string.
+   *
+   * @param[out] str The resulting string.
+   * @param can_be_empty Parameter indicating whether it is valid for the string
+   * to not be present.
+   * @param quote Character expected as the surrounding quotes.  A value of 0
+   * indicates allowing either single or double quotes (but not a mixture of both).
+   * @returns A result code indicating success, failure or other result.
+   */
+  CUDF_HOST_DEVICE inline parse_result parse_string(string_view& str, bool can_be_empty, char quote)
+  {
+    str = string_view(nullptr, 0);
+
+    if (parse_whitespace()) {
+      // if the user specifies 0 for quote, allow either ' or ". otherwise
+      // use the char directly
+      if ((quote == 0 && (*pos == '\'' || *pos == '\"')) || (quote == *pos)) {
+        quote = *pos;
+
+        char const* start = ++pos;
+        while (!eof()) {
+          // handle escaped characters
+          if (*pos == '\\') {
+            if (!parse_escape_seq()) { return parse_result::ERROR; }
+          } else if (*pos == quote) {
+            str = string_view(start, pos - start);
+            pos++;
+            return parse_result::SUCCESS;
+          } else {
+            pos++;
+          }
+        }
+      }
+    }
+
+    return can_be_empty ? parse_result::EMPTY : parse_result::ERROR;
+  }
+
+ protected:
+  char const* input{nullptr};
+  int64_t input_len{0};
+  char const* pos{nullptr};
+
+  CUDF_HOST_DEVICE inline bool is_whitespace(char c) { return c <= ' '; }
+};
+
+/**
+ * @brief Output buffer object.  Used during the preprocess/size-computation step
+ * and the actual output step.
+ *
+ * There is an important distinction between two cases:
+ *
+ * - producing no output at all. that is, the query matched nothing in the input.
+ * - producing empty output. the query matched something in the input, but the
+ *   value of the result is an empty string.
+ *
+ * The `has_output` field is the flag which indicates whether or not the output
+ * from the query should be considered empty or null.
+ *
+ */
+struct json_output {
+  size_t output_max_len;
+  char* output;
+  thrust::optional<size_t> output_len;
+
+  __device__ void add_output(char const* str, size_t len)
+  {
+    if (output != nullptr) { memcpy(output + output_len.value_or(0), str, len); }
+    output_len = output_len.value_or(0) + len;
+  }
+
+  __device__ void add_output(string_view const& str) { add_output(str.data(), str.size_bytes()); }
+};
+
+enum json_element_type { NONE, OBJECT, ARRAY, VALUE };
+
+/**
+ * @brief Parsing class that holds the current state of the json to be parse and provides
+ * functions for navigating through it.
+ */
+class json_state : private parser {
+ public:
+  __device__ json_state() : parser() {}
+  __device__ json_state(char const* _input, int64_t _input_len, get_json_object_options _options)
+    : parser(_input, _input_len),
+
+      options(_options)
+  {
+  }
+
+  __device__ json_state(json_state const& j)
+    : parser(j),
+      cur_el_start(j.cur_el_start),
+      cur_el_type(j.cur_el_type),
+      parent_el_type(j.parent_el_type),
+      options(j.options)
+  {
+  }
+
+  // retrieve the entire current element into the output
+  __device__ parse_result extract_element(json_output* output, bool list_element)
+  {
+    char const* start = cur_el_start;
+    char const* end   = start;
+
+    // if we're a value type, do a simple value parse.
+    if (cur_el_type == VALUE) {
+      pos = cur_el_start;
+      if (parse_value() != parse_result::SUCCESS) { return parse_result::ERROR; }
+      end = pos;
+
+      // potentially strip quotes from individually returned string values.
+      if (options.get_strip_quotes_from_single_strings() && !list_element && is_quote(*start) &&
+          *(end - 1) == *start) {
+        start++;
+        end--;
+      }
+    }
+    // otherwise, march through everything inside
+    else {
+      int obj_count = 0;
+      int arr_count = 0;
+
+      while (!eof(end)) {
+        // parse strings explicitly so we handle all interesting corner cases (such as strings
+        // containing {, }, [ or ]
+        if (is_quote(*end)) {
+          string_view str;
+          pos = end;
+          if (parse_string(str, false, *end) == parse_result::ERROR) { return parse_result::ERROR; }
+          end = pos;
+        } else {
+          char const c = *end++;
+          switch (c) {
+            case '{': obj_count++; break;
+            case '}': obj_count--; break;
+            case '[': arr_count++; break;
+            case ']': arr_count--; break;
+            default: break;
+          }
+        }
+        if (obj_count == 0 && arr_count == 0) { break; }
+      }
+      if (obj_count > 0 || arr_count > 0) { return parse_result::ERROR; }
+      pos = end;
+    }
+
+    // parse trailing ,
+    if (parse_whitespace()) {
+      if (*pos == ',') { pos++; }
+    }
+
+    if (output != nullptr) { output->add_output({start, static_cast<size_type>(end - start)}); }
+    return parse_result::SUCCESS;
+  }
+
+  // skip the next element
+  __device__ parse_result skip_element() { return extract_element(nullptr, false); }
+
+  // advance to the next element
+  __device__ parse_result next_element() { return next_element_internal(false); }
+
+  // advance inside the current element
+  __device__ parse_result child_element(json_element_type expected_type)
+  {
+    if (expected_type != NONE && cur_el_type != expected_type) { return parse_result::ERROR; }
+
+    // if we succeed, record our parent element type.
+    auto const prev_el_type = cur_el_type;
+    auto const result       = next_element_internal(true);
+    if (result == parse_result::SUCCESS) { parent_el_type = prev_el_type; }
+    return result;
+  }
+
+  // return the next element that matches the specified name.
+  __device__ parse_result next_matching_element(string_view const& name, bool inclusive)
+  {
+    // if we're not including the current element, skip it
+    if (!inclusive) {
+      parse_result result = next_element_internal(false);
+      if (result != parse_result::SUCCESS) { return result; }
+    }
+    // loop until we find a match or there's nothing left
+    do {
+      if (name.size_bytes() == 1 && name.data()[0] == '*') {
+        return parse_result::SUCCESS;
+      } else if (cur_el_name == name) {
+        return parse_result::SUCCESS;
+      }
+      // next
+      parse_result result = next_element_internal(false);
+      if (result != parse_result::SUCCESS) {
+        return options.get_missing_fields_as_nulls() && result == parse_result::EMPTY
+                 ? parse_result::MISSING_FIELD
+                 : result;
+      }
+    } while (true);
+
+    return parse_result::ERROR;
+  }
+
+  /**
+   * @brief Parse a name field for a JSON element.
+   *
+   * When parsing JSON objects, it is not always a requirement that the name
+   * actually exists.  For example, the outer object bounded by {} here has
+   * no name, while the inner element "a" does.
+   *
+   * ```
+   * {
+   *    "a" : "b"
+   * }
+   * ```
+   *
+   * The user can specify whether or not the name string must be present via
+   * the `can_be_empty` flag.
+   *
+   * When a name is present, it must be followed by a colon `:`
+   *
+   * @param[out] name The resulting name.
+   * @param can_be_empty Parameter indicating whether it is valid for the name
+   * to not be present.
+   * @returns A result code indicating success, failure or other result.
+   */
+  CUDF_HOST_DEVICE inline parse_result parse_name(string_view& name, bool can_be_empty)
+  {
+    char const quote = options.get_allow_single_quotes() ? 0 : '\"';
+
+    if (parse_string(name, can_be_empty, quote) == parse_result::ERROR) {
+      return parse_result::ERROR;
+    }
+
+    // if we got a real string, the next char must be a :
+    if (name.size_bytes() > 0) {
+      if (!parse_whitespace()) { return parse_result::ERROR; }
+      if (*pos == ':') {
+        pos++;
+        return parse_result::SUCCESS;
+      }
+    }
+    return parse_result::EMPTY;
+  }
+
+ private:
+  /**
+   * @brief Parse a non-string JSON value.
+   *
+   * Non-string values include numbers, true, false, or null. This function does not
+   * do any validation of the value.
+   *
+   * @param val (Output) The string containing the parsed value
+   * @returns A result code indicating success, failure or other result.
+   */
+  CUDF_HOST_DEVICE inline parse_result parse_non_string_value(string_view& val)
+  {
+    if (!parse_whitespace()) { return parse_result::ERROR; }
+
+    // parse to the end of the value
+    char const* start = pos;
+    char const* end   = start;
+    while (!eof(end)) {
+      char const c = *end;
+      if (c == ',' || c == '}' || c == ']' || is_whitespace(c)) { break; }
+
+      // illegal chars
+      if (c == '[' || c == '{' || c == ':' || is_quote(c)) { return parse_result::ERROR; }
+      end++;
+    }
+    pos = end;
+
+    val = string_view(start, end - start);
+
+    return parse_result::SUCCESS;
+  }
+
+  // parse a value - either a string or a number/null/bool
+  __device__ parse_result parse_value()
+  {
+    if (!parse_whitespace()) { return parse_result::ERROR; }
+
+    // string or number?
+    string_view unused;
+    return is_quote(*pos) ? parse_string(unused, false, *pos) : parse_non_string_value(unused);
+  }
+
+  __device__ parse_result next_element_internal(bool child)
+  {
+    // if we're not getting a child element, skip the current element.
+    // this will leave pos as the first character -after- the close of
+    // the current element
+    if (!child && cur_el_start != nullptr) {
+      if (skip_element() == parse_result::ERROR) { return parse_result::ERROR; }
+      cur_el_start = nullptr;
+    }
+    // otherwise pos will be at the first character within the current element
+
+    // can only get the child of an object or array.
+    // this could theoretically be handled as an error, but the evaluators I've found
+    // seem to treat this as "it's nothing"
+    if (child && (cur_el_type == VALUE || cur_el_type == NONE)) { return parse_result::EMPTY; }
+
+    // what's next
+    if (!parse_whitespace()) { return parse_result::EMPTY; }
+    // if we're closing off a parent element, we're done
+    char const c = *pos;
+    if (c == ']' || c == '}') { return parse_result::EMPTY; }
+
+    // if we're not accessing elements of an array, check for name.
+    bool const array_access =
+      (cur_el_type == ARRAY && child) || (parent_el_type == ARRAY && !child);
+    if (!array_access && parse_name(cur_el_name, true) == parse_result::ERROR) {
+      return parse_result::ERROR;
+    }
+
+    // element type
+    if (!parse_whitespace()) { return parse_result::EMPTY; }
+    switch (*pos++) {
+      case '[': cur_el_type = ARRAY; break;
+      case '{': cur_el_type = OBJECT; break;
+
+      case ',':
+      case ':': return parse_result::ERROR;
+
+      case '\'':
+        if (!options.get_allow_single_quotes()) { return parse_result::ERROR; }
+        cur_el_type = VALUE;
+        break;
+
+      // value type
+      default: cur_el_type = VALUE; break;
+    }
+
+    // the start of the current element is always at the value, not the name
+    cur_el_start = pos - 1;
+    return parse_result::SUCCESS;
+  }
+
+  CUDF_HOST_DEVICE inline bool is_quote(char c)
+  {
+    return (c == '\"') || (options.get_allow_single_quotes() && (c == '\''));
+  }
+
+  char const* cur_el_start{nullptr};  // pointer to the first character of the -value- of the
+                                      // current element - not the name
+  string_view cur_el_name;            // name of the current element (if applicable)
+  json_element_type cur_el_type{json_element_type::NONE};     // type of the current element
+  json_element_type parent_el_type{json_element_type::NONE};  // parent element type
+  get_json_object_options options;                            // behavior options
+};
+
+enum class path_operator_type { ROOT, CHILD, CHILD_WILDCARD, CHILD_INDEX, ERROR, END };
+
+/**
+ * @brief A "command" operator used to query a json string.  A full query is
+ * an array of these operators applied to the incoming json string,
+ */
+struct path_operator {
+  CUDF_HOST_DEVICE inline path_operator() {}
+  CUDF_HOST_DEVICE inline path_operator(path_operator_type _type,
+                                        json_element_type _expected_type = NONE)
+    : type(_type), expected_type{_expected_type}
+  {
+  }
+
+  path_operator_type type{path_operator_type::ERROR};  // operator type
+  // the expected element type we're applying this operation to.
+  // for example:
+  //    - you cannot retrieve a subscripted field (eg [5]) from an object.
+  //    - you cannot retrieve a field by name (eg  .book) from an array.
+  //    - you -can- use .* for both arrays and objects
+  // a value of NONE implies any type accepted
+  json_element_type expected_type{NONE};  // the expected type of the element we're working with
+  string_view name;                       // name to match against (if applicable)
+  int index{-1};                          // index for subscript operator
+};
+
+/**
+ * @brief Parsing class that holds the current state of the JSONPath string to be parsed
+ * and provides functions for navigating through it. This is only called on the host
+ * during the preprocess step which builds a command buffer that the gpu uses.
+ */
+class path_state : private parser {
+ public:
+  path_state(char const* _path, size_t _path_len) : parser(_path, _path_len) {}
+
+  // get the next operator in the JSONPath string
+  path_operator get_next_operator()
+  {
+    if (eof()) { return {path_operator_type::END}; }
+
+    switch (*pos++) {
+      case '$': return {path_operator_type::ROOT};
+
+      case '.': {
+        path_operator op;
+        string_view term{".[", 2};
+        if (parse_path_name(op.name, term)) {
+          // this is another potential use case for __SPARK_BEHAVIORS / configurability
+          // Spark currently only handles the wildcard operator inside [*], it does
+          // not handle .*
+          if (op.name.size_bytes() == 1 && op.name.data()[0] == '*') {
+            op.type          = path_operator_type::CHILD_WILDCARD;
+            op.expected_type = NONE;
+          } else {
+            op.type          = path_operator_type::CHILD;
+            op.expected_type = OBJECT;
+          }
+          return op;
+        }
+      } break;
+
+      // 3 ways this can be used
+      // indices:   [0]
+      // name:      ['book']
+      // wildcard:  [*]
+      case '[': {
+        path_operator op;
+        string_view term{"]", 1};
+        bool const is_string = *pos == '\'';
+        if (parse_path_name(op.name, term)) {
+          pos++;
+          if (op.name.size_bytes() == 1 && op.name.data()[0] == '*') {
+            op.type          = path_operator_type::CHILD_WILDCARD;
+            op.expected_type = NONE;
+          } else {
+            if (is_string) {
+              op.type          = path_operator_type::CHILD;
+              op.expected_type = OBJECT;
+            } else {
+              op.type          = path_operator_type::CHILD_INDEX;
+              auto const value = cudf::io::parse_numeric<int>(
+                op.name.data(), op.name.data() + op.name.size_bytes(), json_opts);
+              op.index = value.value_or(-1);
+              CUDF_EXPECTS(op.index >= 0, "Invalid numeric index specified in JSONPath");
+              op.expected_type = ARRAY;
+            }
+          }
+          return op;
+        }
+      } break;
+
+      // wildcard operator
+      case '*': {
+        pos++;
+        return path_operator{path_operator_type::CHILD_WILDCARD};
+      } break;
+
+      default: CUDF_FAIL("Unrecognized JSONPath operator", std::invalid_argument); break;
+    }
+    return {path_operator_type::ERROR};
+  }
+
+ private:
+  cudf::io::parse_options_view json_opts{',', '\n', '\"', '.'};
+
+  bool parse_path_name(string_view& name, string_view const& terminators)
+  {
+    switch (*pos) {
+      case '*':
+        name = string_view(pos, 1);
+        pos++;
+        break;
+
+      case '\'':
+        if (parse_string(name, false, '\'') != parse_result::SUCCESS) { return false; }
+        break;
+
+      default: {
+        size_t const chars_left = input_len - (pos - input);
+        char const* end         = std::find_first_of(
+          pos, pos + chars_left, terminators.data(), terminators.data() + terminators.size_bytes());
+        if (end) {
+          name = string_view(pos, end - pos);
+          pos  = end;
+        } else {
+          name = string_view(pos, chars_left);
+          pos  = input + input_len;
+        }
+        break;
+      }
+    }
+
+    // an empty name is not valid
+    CUDF_EXPECTS(
+      name.size_bytes() > 0, "Invalid empty name in JSONPath query string", std::invalid_argument);
+
+    return true;
+  }
+};
+
+/**
+ * @brief Preprocess the incoming JSONPath string on the host to generate a
+ * command buffer for use by the GPU.
+ *
+ * @param json_path The incoming json path
+ * @param stream Cuda stream to perform any gpu actions on
+ * @returns A pair containing the command buffer, and maximum stack depth required.
+ */
+std::pair<thrust::optional<rmm::device_uvector<path_operator>>, int> build_command_buffer(
+  cudf::string_scalar const& json_path, rmm::cuda_stream_view stream)
+{
+  std::string h_json_path = json_path.to_string(stream);
+  path_state p_state(h_json_path.data(), static_cast<size_type>(h_json_path.size()));
+
+  std::vector<path_operator> h_operators;
+
+  path_operator op;
+  int max_stack_depth = 1;
+  do {
+    op = p_state.get_next_operator();
+    if (op.type == path_operator_type::ERROR) {
+      CUDF_FAIL("Encountered invalid JSONPath input string");
+    }
+    if (op.type == path_operator_type::CHILD_WILDCARD) { max_stack_depth++; }
+    // convert pointer to device pointer
+    if (op.name.size_bytes() > 0) {
+      op.name =
+        string_view(json_path.data() + (op.name.data() - h_json_path.data()), op.name.size_bytes());
+    }
+    if (op.type == path_operator_type::ROOT) {
+      CUDF_EXPECTS(h_operators.size() == 0, "Root operator ($) can only exist at the root");
+    }
+    // if we have not gotten a root operator to start, and we're not empty, quietly push a
+    // root operator now.
+    if (h_operators.size() == 0 && op.type != path_operator_type::ROOT &&
+        op.type != path_operator_type::END) {
+      h_operators.push_back(path_operator{path_operator_type::ROOT});
+    }
+    h_operators.push_back(op);
+  } while (op.type != path_operator_type::END);
+
+  auto const is_empty = h_operators.size() == 1 && h_operators[0].type == path_operator_type::END;
+  return is_empty ? std::pair(thrust::nullopt, 0)
+                  : std::pair(thrust::make_optional(cudf::detail::make_device_uvector_sync(
+                                h_operators, stream, rmm::mr::get_current_device_resource())),
+                              max_stack_depth);
+}
+
+#define PARSE_TRY(_x)                                                       \
+  do {                                                                      \
+    last_result = _x;                                                       \
+    if (last_result == parse_result::ERROR) { return parse_result::ERROR; } \
+  } while (0)
+
+/**
+ * @brief Parse a single json string using the provided command buffer
+ *
+ * @param j_state The incoming json string and associated parser
+ * @param commands The command buffer to be applied to the string. Always ends with a
+ * path_operator_type::END
+ * @param output Buffer user to store the results of the query
+ * @returns A result code indicating success/fail/empty.
+ */
+template <int max_command_stack_depth>
+__device__ parse_result parse_json_path(json_state& j_state,
+                                        path_operator const* commands,
+                                        json_output& output)
+{
+  // manually maintained context stack in lieu of calling parse_json_path recursively.
+  struct context {
+    json_state j_state;
+    path_operator const* commands;
+    bool list_element;
+    bool state_flag;
+  };
+  context stack[max_command_stack_depth];
+  int stack_pos     = 0;
+  auto push_context = [&stack, &stack_pos](json_state const& _j_state,
+                                           path_operator const* _commands,
+                                           bool _list_element = false,
+                                           bool _state_flag   = false) {
+    if (stack_pos == max_command_stack_depth - 1) { return false; }
+    stack[stack_pos++] = context{_j_state, _commands, _list_element, _state_flag};
+    return true;
+  };
+  auto pop_context = [&stack, &stack_pos](context& c) {
+    if (stack_pos > 0) {
+      c = stack[--stack_pos];
+      return true;
+    }
+    return false;
+  };
+  push_context(j_state, commands, false);
+
+  parse_result last_result = parse_result::SUCCESS;
+  context ctx;
+  int element_count = 0;
+  while (pop_context(ctx)) {
+    path_operator op = *ctx.commands;
+    switch (op.type) {
+      // whatever the first object is
+      case path_operator_type::ROOT:
+        PARSE_TRY(ctx.j_state.next_element());
+        push_context(ctx.j_state, ctx.commands + 1);
+        break;
+
+      // .name
+      // ['name']
+      // [1]
+      // will return a single thing
+      case path_operator_type::CHILD: {
+        PARSE_TRY(ctx.j_state.child_element(op.expected_type));
+        if (last_result == parse_result::SUCCESS) {
+          PARSE_TRY(ctx.j_state.next_matching_element(op.name, true));
+          if (last_result == parse_result::SUCCESS) {
+            push_context(ctx.j_state, ctx.commands + 1, ctx.list_element);
+          } else if (last_result == parse_result::MISSING_FIELD) {
+            if (ctx.list_element && element_count > 0) {
+              output.add_output({"," DEBUG_NEWLINE, 1 + DEBUG_NEWLINE_LEN});
+            }
+            output.add_output({"null", 4});
+            element_count++;
+          }
+        }
+      } break;
+
+      // .*
+      // [*]
+      // will return an array of things
+      case path_operator_type::CHILD_WILDCARD: {
+        // if we're on the first element of this wildcard
+        if (!ctx.state_flag) {
+          // we will only ever be returning 1 array
+          if (!ctx.list_element) { output.add_output({"[" DEBUG_NEWLINE, 1 + DEBUG_NEWLINE_LEN}); }
+
+          // step into the child element
+          PARSE_TRY(ctx.j_state.child_element(op.expected_type));
+          if (last_result == parse_result::EMPTY) {
+            if (!ctx.list_element) {
+              output.add_output({"]" DEBUG_NEWLINE, 1 + DEBUG_NEWLINE_LEN});
+            }
+            last_result = parse_result::SUCCESS;
+            break;
+          }
+
+          // first element
+          PARSE_TRY(ctx.j_state.next_matching_element({"*", 1}, true));
+          if (last_result == parse_result::EMPTY) {
+            if (!ctx.list_element) {
+              output.add_output({"]" DEBUG_NEWLINE, 1 + DEBUG_NEWLINE_LEN});
+            }
+            last_result = parse_result::SUCCESS;
+            break;
+          }
+
+          // re-push ourselves
+          push_context(ctx.j_state, ctx.commands, ctx.list_element, true);
+          // push the next command
+          push_context(ctx.j_state, ctx.commands + 1, true);
+        } else {
+          // next element
+          PARSE_TRY(ctx.j_state.next_matching_element({"*", 1}, false));
+          if (last_result == parse_result::EMPTY) {
+            if (!ctx.list_element) {
+              output.add_output({"]" DEBUG_NEWLINE, 1 + DEBUG_NEWLINE_LEN});
+            }
+            last_result = parse_result::SUCCESS;
+            break;
+          }
+
+          // re-push ourselves
+          push_context(ctx.j_state, ctx.commands, ctx.list_element, true);
+          // push the next command
+          push_context(ctx.j_state, ctx.commands + 1, true);
+        }
+      } break;
+
+      // [0]
+      // [1]
+      // etc
+      // returns a single thing
+      case path_operator_type::CHILD_INDEX: {
+        PARSE_TRY(ctx.j_state.child_element(op.expected_type));
+        if (last_result == parse_result::SUCCESS) {
+          string_view const any{"*", 1};
+          PARSE_TRY(ctx.j_state.next_matching_element(any, true));
+          if (last_result == parse_result::SUCCESS) {
+            int idx;
+            for (idx = 1; idx <= op.index; idx++) {
+              PARSE_TRY(ctx.j_state.next_matching_element(any, false));
+              if (last_result == parse_result::EMPTY) { break; }
+            }
+            // if we didn't end up at the index we requested, this is an invalid index
+            if (idx - 1 != op.index) { return parse_result::ERROR; }
+            push_context(ctx.j_state, ctx.commands + 1, ctx.list_element);
+          }
+        }
+      } break;
+
+      // some sort of error.
+      case path_operator_type::ERROR: return parse_result::ERROR; break;
+
+      // END case
+      default: {
+        if (ctx.list_element && element_count > 0) {
+          output.add_output({"," DEBUG_NEWLINE, 1 + DEBUG_NEWLINE_LEN});
+        }
+        PARSE_TRY(ctx.j_state.extract_element(&output, ctx.list_element));
+        if (ctx.list_element && last_result != parse_result::EMPTY) { element_count++; }
+      } break;
+    }
+  }
+
+  return parse_result::SUCCESS;
+}
+
+// hardcoding this for now. to reach a stack depth of 8 would require
+// a JSONPath containing 7 nested wildcards so this is probably reasonable.
+constexpr int max_command_stack_depth = 8;
+
+/**
+ * @brief Parse a single json string using the provided command buffer
+ *
+ * This function exists primarily as a shim for debugging purposes.
+ *
+ * @param input The incoming json string
+ * @param input_len Size of the incoming json string
+ * @param commands The command buffer to be applied to the string. Always ends with a
+ * path_operator_type::END
+ * @param out_buf Buffer user to store the results of the query (nullptr in the size computation
+ * step)
+ * @param out_buf_size Size of the output buffer
+ * @param options Options controlling behavior
+ * @returns A pair containing the result code the output buffer.
+ */
+__device__ thrust::pair<parse_result, json_output> get_json_object_single(
+  char const* input,
+  size_t input_len,
+  path_operator const* const commands,
+  char* out_buf,
+  size_t out_buf_size,
+  get_json_object_options options)
+{
+  json_state j_state(input, input_len, options);
+  json_output output{out_buf_size, out_buf};
+
+  auto const result = parse_json_path<max_command_stack_depth>(j_state, commands, output);
+
+  return {result, output};
+}
+
+/**
+ * @brief Kernel for running the JSONPath query.
+ *
+ * This kernel operates in a 2-pass way.  On the first pass, it computes
+ * output sizes.  On the second pass it fills in the provided output buffers
+ * (chars and validity)
+ *
+ * @param col Device view of the incoming string
+ * @param commands JSONPath command buffer
+ * @param output_offsets Buffer used to store the string offsets for the results of the query
+ * @param out_buf Buffer used to store the results of the query
+ * @param out_validity Output validity buffer
+ * @param out_valid_count Output count of # of valid bits
+ * @param options Options controlling behavior
+ */
+template <int block_size>
+__launch_bounds__(block_size) __global__
+  void get_json_object_kernel(column_device_view col,
+                              path_operator const* const commands,
+                              size_type* output_offsets,
+                              thrust::optional<char*> out_buf,
+                              thrust::optional<bitmask_type*> out_validity,
+                              thrust::optional<size_type*> out_valid_count,
+                              get_json_object_options options)
+{
+  auto tid          = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::thread_index_type{blockDim.x} * cudf::thread_index_type{gridDim.x};
+
+  size_type warp_valid_count{0};
+
+  auto active_threads = __ballot_sync(0xffff'ffffu, tid < col.size());
+  while (tid < col.size()) {
+    bool is_valid         = false;
+    string_view const str = col.element<string_view>(tid);
+    size_type output_size = 0;
+    if (str.size_bytes() > 0) {
+      char* dst = out_buf.has_value() ? out_buf.value() + output_offsets[tid] : nullptr;
+      size_t const dst_size =
+        out_buf.has_value() ? output_offsets[tid + 1] - output_offsets[tid] : 0;
+
+      parse_result result;
+      json_output out;
+      thrust::tie(result, out) =
+        get_json_object_single(str.data(), str.size_bytes(), commands, dst, dst_size, options);
+      output_size = out.output_len.value_or(0);
+      if (out.output_len.has_value() && result == parse_result::SUCCESS) { is_valid = true; }
+    }
+
+    // filled in only during the precompute step. during the compute step, the offsets
+    // are fed back in so we do -not- want to write them out
+    if (!out_buf.has_value()) { output_offsets[tid] = static_cast<size_type>(output_size); }
+
+    // validity filled in only during the output step
+    if (out_validity.has_value()) {
+      uint32_t mask = __ballot_sync(active_threads, is_valid);
+      // 0th lane of the warp writes the validity
+      if (!(tid % cudf::detail::warp_size)) {
+        out_validity.value()[cudf::word_index(tid)] = mask;
+        warp_valid_count += __popc(mask);
+      }
+    }
+
+    tid += stride;
+    active_threads = __ballot_sync(active_threads, tid < col.size());
+  }
+
+  // sum the valid counts across the whole block
+  if (out_valid_count) {
+    size_type block_valid_count =
+      cudf::detail::single_lane_block_sum_reduce<block_size, 0>(warp_valid_count);
+    if (threadIdx.x == 0) { atomicAdd(out_valid_count.value(), block_valid_count); }
+  }
+}
+
+/**
+ * @copydoc cudf::strings::detail::get_json_object
+ */
+std::unique_ptr<cudf::column> get_json_object(cudf::strings_column_view const& col,
+                                              cudf::string_scalar const& json_path,
+                                              get_json_object_options options,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  // preprocess the json_path into a command buffer
+  auto preprocess = build_command_buffer(json_path, stream);
+  CUDF_EXPECTS(std::get<1>(preprocess) <= max_command_stack_depth,
+               "Encountered JSONPath string that is too complex");
+
+  if (col.is_empty()) return make_empty_column(type_id::STRING);
+
+  // allocate output offsets buffer.
+  auto offsets = cudf::make_fixed_width_column(
+    data_type{type_id::INT32}, col.size() + 1, mask_state::UNALLOCATED, stream, mr);
+  cudf::mutable_column_view offsets_view(*offsets);
+
+  // if the query is empty, return a string column containing all nulls
+  if (!std::get<0>(preprocess).has_value()) {
+    return std::make_unique<column>(
+      data_type{type_id::STRING},
+      col.size(),
+      rmm::device_buffer{0, stream, mr},  // no data
+      cudf::detail::create_null_mask(col.size(), mask_state::ALL_NULL, stream, mr),
+      col.size());  // null count
+  }
+
+  constexpr int block_size = 512;
+  cudf::detail::grid_1d const grid{col.size(), block_size};
+  auto cdv = column_device_view::create(col.parent(), stream);
+  // preprocess sizes (returned in the offsets buffer)
+  get_json_object_kernel<block_size>
+    <<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+      *cdv,
+      std::get<0>(preprocess).value().data(),
+      offsets_view.head<size_type>(),
+      thrust::nullopt,
+      thrust::nullopt,
+      thrust::nullopt,
+      options);
+
+  // convert sizes to offsets
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         offsets_view.head<size_type>(),
+                         offsets_view.head<size_type>() + col.size() + 1,
+                         offsets_view.head<size_type>(),
+                         0);
+  size_type const output_size =
+    cudf::detail::get_value<size_type>(offsets_view, col.size(), stream);
+
+  // allocate output string column
+  auto chars = create_chars_child_column(output_size, stream, mr);
+
+  // potential optimization : if we know that all outputs are valid, we could skip creating
+  // the validity mask altogether
+  rmm::device_buffer validity =
+    cudf::detail::create_null_mask(col.size(), mask_state::UNINITIALIZED, stream, mr);
+
+  // compute results
+  cudf::mutable_column_view chars_view(*chars);
+  rmm::device_scalar<size_type> d_valid_count{0, stream};
+
+  get_json_object_kernel<block_size>
+    <<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+      *cdv,
+      std::get<0>(preprocess).value().data(),
+      offsets_view.head<size_type>(),
+      chars_view.head<char>(),
+      static_cast<bitmask_type*>(validity.data()),
+      d_valid_count.data(),
+      options);
+  return make_strings_column(col.size(),
+                             std::move(offsets),
+                             std::move(chars),
+                             col.size() - d_valid_count.value(stream),
+                             std::move(validity));
+}
+
+}  // namespace
+}  // namespace detail
+
+/**
+ * @copydoc cudf::strings::get_json_object
+ */
+std::unique_ptr<cudf::column> get_json_object(cudf::strings_column_view const& col,
+                                              cudf::string_scalar const& json_path,
+                                              get_json_object_options options,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::get_json_object(col, json_path, options, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/like.cu b/cpp/src/strings/like.cu
new file mode 100644
index 0000000..5b91f29
--- /dev/null
+++ b/cpp/src/strings/like.cu
@@ -0,0 +1,204 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+constexpr char multi_wildcard  = '%';
+constexpr char single_wildcard = '_';
+
+template <typename PatternIterator>
+struct like_fn {
+  column_device_view const d_strings;
+  PatternIterator const patterns_itr;
+  string_view const d_escape;
+
+  like_fn(column_device_view d_strings, PatternIterator patterns_itr, string_view d_escape)
+    : d_strings{d_strings}, patterns_itr{patterns_itr}, d_escape{d_escape}
+  {
+  }
+
+  __device__ bool operator()(size_type const idx)
+  {
+    if (d_strings.is_null(idx)) return false;
+    auto const d_str     = d_strings.element<string_view>(idx);
+    auto const d_pattern = patterns_itr[idx];
+
+    // incrementing by bytes instead of character improves performance 10-20%
+    auto target_itr  = d_str.data();
+    auto pattern_itr = d_pattern.begin();
+
+    auto const target_end  = target_itr + d_str.size_bytes();
+    auto const pattern_end = d_pattern.end();
+    auto const esc_char    = d_escape.empty() ? 0 : d_escape[0];
+
+    auto last_target_itr  = target_end;
+    auto last_pattern_itr = pattern_end;
+
+    bool result = true;
+    while (true) {
+      // walk through the pattern and check against the current character
+      while (pattern_itr < pattern_end) {
+        auto const escaped = *pattern_itr == esc_char;
+        auto const pattern_char =
+          escaped && (pattern_itr + 1 < pattern_end) ? *(++pattern_itr) : *pattern_itr;
+
+        if (escaped || (pattern_char != multi_wildcard)) {
+          size_type char_width = 0;
+          // check match with the current character
+          result = (target_itr != target_end);
+          if (result) {
+            if (escaped || pattern_char != single_wildcard) {
+              char_utf8 target_char = 0;
+              // retrieve the target character to compare with the current pattern_char
+              char_width = to_char_utf8(target_itr, target_char);
+              result     = (pattern_char == target_char);
+            }
+          }
+          if (!result) { break; }
+          ++pattern_itr;
+          target_itr += char_width ? char_width : bytes_in_utf8_byte(*target_itr);
+        } else {
+          // process wildcard '%'
+          result = true;
+          ++pattern_itr;
+          if (pattern_itr == pattern_end) {  // pattern ends with '%' so we are done
+            target_itr = target_end;
+            break;
+          }
+          // save positions
+          last_pattern_itr = pattern_itr;
+          last_target_itr  = target_itr;
+        }  // next pattern character
+      }
+
+      if (result && (target_itr == target_end)) { break; }  // success
+
+      result = false;
+      // check if exhausted either the pattern or the target string
+      if (last_pattern_itr == pattern_end || last_target_itr == target_end) { break; }
+
+      // restore saved positions
+      pattern_itr = last_pattern_itr;
+      last_target_itr += bytes_in_utf8_byte(*last_target_itr);
+      target_itr = last_target_itr;
+    }
+    return result;
+  }
+};
+
+template <typename PatternIterator>
+std::unique_ptr<column> like(strings_column_view const& input,
+                             PatternIterator const patterns_itr,
+                             string_view const& d_escape,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  auto results = make_numeric_column(data_type{type_id::BOOL8},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+  if (input.is_empty()) { return results; }
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(input.size()),
+                    results->mutable_view().data<bool>(),
+                    like_fn{*d_strings, patterns_itr, d_escape});
+
+  results->set_null_count(input.null_count());
+  return results;
+}
+
+}  // namespace
+
+std::unique_ptr<column> like(strings_column_view const& input,
+                             string_scalar const& pattern,
+                             string_scalar const& escape_character,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(pattern.is_valid(stream), "Parameter pattern must be valid");
+  CUDF_EXPECTS(escape_character.is_valid(stream), "Parameter escape_character must be valid");
+
+  auto const d_pattern    = pattern.value(stream);
+  auto const patterns_itr = thrust::make_constant_iterator(d_pattern);
+
+  return like(input, patterns_itr, escape_character.value(stream), stream, mr);
+}
+
+std::unique_ptr<column> like(strings_column_view const& input,
+                             strings_column_view const& patterns,
+                             string_scalar const& escape_character,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(patterns.size() == input.size(), "Number of patterns must match the input size");
+  CUDF_EXPECTS(patterns.has_nulls() == false, "Parameter patterns must not contain nulls");
+  CUDF_EXPECTS(escape_character.is_valid(stream), "Parameter escape_character must be valid");
+
+  auto const d_patterns   = column_device_view::create(patterns.parent(), stream);
+  auto const patterns_itr = d_patterns->begin<string_view>();
+
+  return like(input, patterns_itr, escape_character.value(stream), stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> like(strings_column_view const& input,
+                             string_scalar const& pattern,
+                             string_scalar const& escape_character,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::like(input, pattern, escape_character, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> like(strings_column_view const& input,
+                             strings_column_view const& patterns,
+                             string_scalar const& escape_character,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::like(input, patterns, escape_character, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/padding.cu b/cpp/src/strings/padding.cu
new file mode 100644
index 0000000..c501a8b
--- /dev/null
+++ b/cpp/src/strings/padding.cu
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/pad_impl.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/padding.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Base class for pad_fn and zfill_fn functors
+ *
+ * This handles the output size calculation while delegating the
+ * pad operation to Derived.
+ *
+ * @tparam Derived class uses the CRTP pattern to reuse code logic
+ *         and must include a `pad(string_view,char*)` member function.
+ */
+template <typename Derived>
+struct base_fn {
+  column_device_view const d_column;
+  size_type const width;
+  size_type const fill_char_size;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  base_fn(column_device_view const& d_column, size_type width, size_type fill_char_size)
+    : d_column(d_column), width(width), fill_char_size(fill_char_size)
+  {
+  }
+
+  __device__ void operator()(size_type idx) const
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const d_str    = d_column.element<string_view>(idx);
+    auto const& derived = static_cast<Derived const&>(*this);
+    if (d_chars) {
+      derived.pad(d_str, d_chars + d_offsets[idx]);
+    } else {
+      d_offsets[idx] = compute_padded_size(d_str, width, fill_char_size);
+    }
+  };
+};
+
+/**
+ * @brief Pads each string to specified width
+ *
+ * @tparam side Side of the string to pad
+ */
+template <side_type side>
+struct pad_fn : base_fn<pad_fn<side>> {
+  using Base = base_fn<pad_fn<side>>;
+
+  cudf::char_utf8 const d_fill_char;
+
+  pad_fn(column_device_view const& d_column,
+         size_type width,
+         size_type fill_char_size,
+         char_utf8 fill_char)
+    : Base(d_column, width, fill_char_size), d_fill_char(fill_char)
+  {
+  }
+
+  __device__ void pad(string_view d_str, char* output) const
+  {
+    pad_impl<side>(d_str, Base::width, d_fill_char, output);
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> pad(strings_column_view const& input,
+                            size_type width,
+                            side_type side,
+                            std::string_view fill_char,
+                            rmm::cuda_stream_view stream,
+                            rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+  CUDF_EXPECTS(!fill_char.empty(), "fill_char parameter must not be empty");
+  auto d_fill_char          = char_utf8{0};
+  auto const fill_char_size = to_char_utf8(fill_char.data(), d_fill_char);
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  auto children = [&] {
+    if (side == side_type::LEFT) {
+      auto fn = pad_fn<side_type::LEFT>{*d_strings, width, fill_char_size, d_fill_char};
+      return make_strings_children(fn, input.size(), stream, mr);
+    } else if (side == side_type::RIGHT) {
+      auto fn = pad_fn<side_type::RIGHT>{*d_strings, width, fill_char_size, d_fill_char};
+      return make_strings_children(fn, input.size(), stream, mr);
+    }
+    auto fn = pad_fn<side_type::BOTH>{*d_strings, width, fill_char_size, d_fill_char};
+    return make_strings_children(fn, input.size(), stream, mr);
+  }();
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+namespace {
+
+/**
+ * @brief Zero-fill each string to specified width
+ */
+struct zfill_fn : base_fn<zfill_fn> {
+  zfill_fn(column_device_view const& d_column, size_type width) : base_fn(d_column, width, 1) {}
+
+  __device__ void pad(string_view d_str, char* output) const { zfill_impl(d_str, width, output); }
+};
+}  // namespace
+
+std::unique_ptr<column> zfill(strings_column_view const& input,
+                              size_type width,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+  auto children  = make_strings_children(zfill_fn{*d_strings, width}, input.size(), stream, mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// Public APIs
+
+std::unique_ptr<column> pad(strings_column_view const& input,
+                            size_type width,
+                            side_type side,
+                            std::string_view fill_char,
+                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::pad(input, width, side, fill_char, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> zfill(strings_column_view const& input,
+                              size_type width,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::zfill(input, width, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/regcomp.cpp b/cpp/src/strings/regex/regcomp.cpp
new file mode 100644
index 0000000..b7a7f19
--- /dev/null
+++ b/cpp/src/strings/regex/regcomp.cpp
@@ -0,0 +1,1274 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/regex/regcomp.h>
+
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <array>
+#include <cctype>
+#include <numeric>
+#include <stack>
+#include <string>
+#include <tuple>
+#include <vector>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+// Bitmask of all operators
+#define OPERATOR_MASK 0200
+enum OperatorType : int32_t {
+  START        = 0200,  // Start, used for marker on stack
+  LBRA_NC      = 0203,  // non-capturing group
+  CAT          = 0205,  // Concatenation, implicit operator
+  STAR         = 0206,  // Closure, *
+  STAR_LAZY    = 0207,
+  PLUS         = 0210,  // a+ == aa*
+  PLUS_LAZY    = 0211,
+  QUEST        = 0212,  // a? == a|nothing, i.e. 0 or 1 a's
+  QUEST_LAZY   = 0213,
+  COUNTED      = 0214,  // counted repeat a{2} a{3,5}
+  COUNTED_LAZY = 0215,
+  NOP          = 0302,  // No operation, internal use only
+};
+#define ITEM_MASK 0300
+
+static reclass cclass_w(CCLASS_W);   // \w
+static reclass cclass_s(CCLASS_S);   // \s
+static reclass cclass_d(CCLASS_D);   // \d
+static reclass cclass_W(NCCLASS_W);  // \W
+static reclass cclass_S(NCCLASS_S);  // \S
+static reclass cclass_D(NCCLASS_D);  // \D
+
+// Tables for analyzing quantifiers
+std::array<int, 5> const valid_preceding_inst_types{{CHAR, CCLASS, NCCLASS, ANY, ANYNL}};
+std::array<char, 5> const quantifiers{{'*', '?', '+', '{', '|'}};
+// Valid regex characters that can be escaped and used as literals
+std::array<char, 33> const escapable_chars{
+  {'.', '-', '+',  '*', '\\', '?', '^', '$', '|', '{', '}', '(', ')', '[', ']', '<', '>',
+   '"', '~', '\'', '`', '_',  '@', '=', ';', ':', '!', '#', '%', '&', ',', '/', ' '}};
+
+/**
+ * @brief Converts UTF-8 string into fixed-width 32-bit character vector.
+ *
+ * No character conversion occurs.
+ * Each UTF-8 character is promoted into a 32-bit value.
+ * The last entry in the returned vector will be a 0 value.
+ * The fixed-width vector makes it easier to compile and faster to execute.
+ *
+ * @param pattern Regular expression encoded with UTF-8.
+ * @return Fixed-width 32-bit character vector.
+ */
+std::vector<char32_t> string_to_char32_vector(std::string_view pattern)
+{
+  size_type size  = static_cast<size_type>(pattern.size());
+  size_type count = std::count_if(pattern.cbegin(), pattern.cend(), [](char ch) {
+    return is_begin_utf8_char(static_cast<uint8_t>(ch));
+  });
+  std::vector<char32_t> result(count + 1);
+  char32_t* output_ptr  = result.data();
+  char const* input_ptr = pattern.data();
+  for (size_type idx = 0; idx < size; ++idx) {
+    char_utf8 output_character = 0;
+    size_type ch_width         = to_char_utf8(input_ptr, output_character);
+    input_ptr += ch_width;
+    idx += ch_width - 1;
+    *output_ptr++ = output_character;
+  }
+  result[count] = 0;  // last entry set to 0
+  return result;
+}
+
+}  // namespace
+
+int32_t reprog::add_inst(int32_t t)
+{
+  reinst inst;
+  inst.type        = t;
+  inst.u2.left_id  = 0;
+  inst.u1.right_id = 0;
+  return add_inst(inst);
+}
+
+int32_t reprog::add_inst(reinst const& inst)
+{
+  _insts.push_back(inst);
+  return static_cast<int32_t>(_insts.size() - 1);
+}
+
+int32_t reprog::add_class(reclass const& cls)
+{
+  _classes.push_back(cls);
+  return static_cast<int32_t>(_classes.size() - 1);
+}
+
+reinst& reprog::inst_at(int32_t id) { return _insts[id]; }
+
+reclass const& reprog::class_at(int32_t id) const { return _classes[id]; }
+
+void reprog::set_start_inst(int32_t id) { _startinst_id = id; }
+
+int32_t reprog::get_start_inst() const { return _startinst_id; }
+
+int32_t reprog::insts_count() const { return static_cast<int>(_insts.size()); }
+
+int32_t reprog::classes_count() const { return static_cast<int>(_classes.size()); }
+
+void reprog::set_groups_count(int32_t groups) { _num_capturing_groups = groups; }
+
+int32_t reprog::groups_count() const { return _num_capturing_groups; }
+
+reinst const* reprog::insts_data() const { return _insts.data(); }
+
+reclass const* reprog::classes_data() const { return _classes.data(); }
+
+int32_t const* reprog::starts_data() const { return _startinst_ids.data(); }
+
+int32_t reprog::starts_count() const { return static_cast<int>(_startinst_ids.size()); }
+
+static constexpr auto MAX_REGEX_CHAR = std::numeric_limits<char32_t>::max();
+
+/**
+ * @brief Converts pattern into regex classes
+ */
+class regex_parser {
+ public:
+  /**
+   * @brief Single parsed pattern element.
+   */
+  struct Item {
+    int32_t type;
+    union {
+      char32_t chr;
+      int32_t cclass_id;
+      struct {
+        int16_t n;
+        int16_t m;
+      } count;
+    } d;
+    Item(int32_t type, char32_t chr) : type{type}, d{chr} {}
+    Item(int32_t type, int32_t id) : type{type}, d{.cclass_id{id}} {}
+    Item(int32_t type, int16_t n, int16_t m) : type{type}, d{.count{n, m}} {}
+  };
+
+ private:
+  reprog& _prog;
+  char32_t const* const _pattern_begin;
+  char32_t const* _expr_ptr;
+  bool _lex_done{false};
+  regex_flags const _flags;
+  capture_groups const _capture;
+
+  int32_t _id_cclass_w{-1};  // alphanumeric [a-zA-Z0-9_]
+  int32_t _id_cclass_W{-1};  // not alphanumeric plus '\n'
+  int32_t _id_cclass_s{-1};  // whitespace including '\t', '\n', '\r'
+  int32_t _id_cclass_d{-1};  // digits [0-9]
+  int32_t _id_cclass_D{-1};  // not digits
+
+  char32_t _chr{};       // last lex'd char
+  int32_t _cclass_id{};  // last lex'd class
+  int16_t _min_count{};  // data for counted operators
+  int16_t _max_count{};
+
+  std::vector<Item> _items;
+  bool _has_counted{false};
+
+  /**
+   * @brief Parses octal characters at the current expression position
+   * to return the represented character
+   *
+   * Reads up to 3 octal digits. The first digit should be passed
+   * in `in_chr`.
+   *
+   * @param in_chr The first character of the octal pattern
+   * @return The resulting character
+   */
+  char32_t handle_octal(char32_t in_chr)
+  {
+    auto out_chr = in_chr - '0';
+    auto c       = *_expr_ptr;
+    auto digits  = 1;
+    while ((c >= '0') && (c <= '7') && (digits < 3)) {
+      out_chr = (out_chr * 8) | (c - '0');
+      c       = *(++_expr_ptr);
+      ++digits;
+    }
+    return out_chr;
+  }
+
+  /**
+   * @brief Parses 2 hex characters at the current expression position
+   * to return the represented character
+   *
+   * @return The resulting character
+   */
+  char32_t handle_hex()
+  {
+    std::string hex(1, static_cast<char>(*_expr_ptr++));
+    hex.append(1, static_cast<char>(*_expr_ptr++));
+    return static_cast<char32_t>(std::stol(hex, nullptr, 16));  // 16 = hex
+  }
+
+  /**
+   * @brief Returns the next character in the expression
+   *
+   * Handles quoted (escaped) special characters and detecting the end of the expression.
+   *
+   * @return is-backslash-escape and character
+   */
+  std::pair<bool, char32_t> next_char()
+  {
+    if (_lex_done) { return {true, 0}; }
+
+    auto c = *_expr_ptr++;
+    if (c == '\\') {
+      c = *_expr_ptr++;
+      return {true, c};
+    }
+
+    if (c == 0) { _lex_done = true; }
+
+    return {false, c};
+  }
+
+  // for \d and \D
+  void add_ascii_digit_class(std::vector<reclass_range>& ranges, bool negated = false)
+  {
+    if (!negated) {
+      ranges.push_back({'0', '9'});
+    } else {
+      ranges.push_back({0, '0' - 1});
+      ranges.push_back({'9' + 1, MAX_REGEX_CHAR});
+    }
+  }
+
+  // for \s and \S
+  void add_ascii_space_class(std::vector<reclass_range>& ranges, bool negated = false)
+  {
+    if (!negated) {
+      ranges.push_back({'\t', ' '});
+    } else {
+      ranges.push_back({0, '\t' - 1});
+      ranges.push_back({' ' + 1, MAX_REGEX_CHAR});
+    }
+  }
+
+  // for \w and \W
+  void add_ascii_word_class(std::vector<reclass_range>& ranges, bool negated = false)
+  {
+    add_ascii_digit_class(ranges, negated);
+    if (!negated) {
+      ranges.push_back({'a', 'z'});
+      ranges.push_back({'A', 'Z'});
+      ranges.push_back({'_', '_'});
+    } else {
+      ranges.back().last = 'A' - 1;
+      ranges.push_back({'Z' + 1, 'a' - 1});  // {'_'-1, '_' + 1}
+      ranges.push_back({'z' + 1, MAX_REGEX_CHAR});
+    }
+  }
+
+  int32_t build_cclass()
+  {
+    int32_t type = CCLASS;
+    std::vector<char32_t> literals;
+    int32_t builtins = 0;
+    std::vector<reclass_range> ranges;
+
+    auto [is_quoted, chr] = next_char();
+    // check for negation
+    if (!is_quoted && chr == '^') {
+      type                     = NCCLASS;
+      std::tie(is_quoted, chr) = next_char();
+    }
+
+    // parse class into a set of spans
+    auto count_char = 0;
+    while (true) {
+      count_char++;
+      if (chr == 0) { return 0; }  // malformed '[]'
+      if (is_quoted) {
+        switch (chr) {
+          case 'n': chr = '\n'; break;
+          case 'r': chr = '\r'; break;
+          case 't': chr = '\t'; break;
+          case 'a': chr = 0x07; break;
+          case 'b': chr = 0x08; break;
+          case 'f': chr = 0x0C; break;
+          case '0' ... '7': {
+            chr = handle_octal(chr);
+            break;
+          }
+          case 'x': {
+            chr = handle_hex();
+            break;
+          }
+          case 'w':
+          case 'W':
+            if (is_ascii(_flags)) {
+              add_ascii_word_class(ranges, chr == 'W');
+            } else {
+              builtins |= (chr == 'w' ? cclass_w.builtins : cclass_W.builtins);
+            }
+            std::tie(is_quoted, chr) = next_char();
+            continue;
+          case 's':
+          case 'S':
+            if (is_ascii(_flags)) {
+              add_ascii_space_class(ranges, chr == 'S');
+            } else {
+              builtins |= (chr == 's' ? cclass_s.builtins : cclass_S.builtins);
+            }
+            std::tie(is_quoted, chr) = next_char();
+            continue;
+          case 'd':
+          case 'D':
+            if (is_ascii(_flags)) {
+              add_ascii_digit_class(ranges, chr == 'D');
+            } else {
+              builtins |= (chr == 'd' ? cclass_d.builtins : cclass_D.builtins);
+            }
+            std::tie(is_quoted, chr) = next_char();
+            continue;
+        }
+      }
+      if (!is_quoted && chr == ']' && count_char > 1) { break; }  // done
+
+      // A hyphen '-' here signifies a range of characters in a '[]' class definition.
+      // The logic here also gracefully handles a dangling '-' appearing unquoted
+      // at the beginning '[-x]' or at the end '[x-]' or by itself '[-]'
+      // and treats the '-' as a literal value in this cclass in this case.
+      if (!is_quoted && chr == '-' && !literals.empty()) {
+        auto [q, n_chr] = next_char();
+        if (n_chr == 0) { return 0; }  // malformed: '[x-'
+
+        if (!q && n_chr == ']') {  // handles: '[x-]'
+          literals.push_back(chr);
+          literals.push_back(chr);  // add '-' as literal
+          break;
+        }
+        // normal case: '[a-z]'
+        // update end-range character
+        literals.back() = n_chr;
+      } else {
+        // add single literal
+        literals.push_back(chr);
+        literals.push_back(chr);
+      }
+      std::tie(is_quoted, chr) = next_char();
+    }
+
+    // transform pairs of literals to ranges
+    auto const counter = thrust::make_counting_iterator(0);
+    std::transform(
+      counter, counter + (literals.size() / 2), std::back_inserter(ranges), [&literals](auto idx) {
+        return reclass_range{literals[idx * 2], literals[idx * 2 + 1]};
+      });
+    // sort the ranges to help with detecting overlapping entries
+    std::sort(ranges.begin(), ranges.end(), [](auto l, auto r) {
+      return l.first == r.first ? l.last < r.last : l.first < r.first;
+    });
+    // combine overlapping entries: [a-f][c-g] => [a-g]
+    if (ranges.size() > 1) {
+      for (auto itr = ranges.begin() + 1; itr < ranges.end(); ++itr) {
+        auto const prev = *(itr - 1);
+        if (itr->first <= prev.last + 1) {
+          // if these 2 ranges intersect, expand the current one
+          *itr = reclass_range{prev.first, std::max(prev.last, itr->last)};
+        }
+      }
+    }
+    // remove any duplicates
+    auto const end = std::unique(
+      ranges.rbegin(), ranges.rend(), [](auto l, auto r) { return l.first == r.first; });
+    ranges.erase(ranges.begin(), ranges.begin() + std::distance(end, ranges.rend()));
+
+    _cclass_id = _prog.add_class(reclass{builtins, std::move(ranges)});
+    return type;
+  }
+
+  int32_t lex(int32_t dot_type)
+  {
+    _chr = 0;
+
+    auto [is_quoted, chr] = next_char();
+    if (is_quoted) {
+      switch (chr) {
+        case 't': chr = '\t'; break;
+        case 'n': chr = '\n'; break;
+        case 'r': chr = '\r'; break;
+        case 'a': chr = 0x07; break;
+        case 'f': chr = 0x0C; break;
+        case '0' ... '7': {
+          chr = handle_octal(chr);
+          break;
+        }
+        case 'x': {
+          chr = handle_hex();
+          break;
+        }
+        case 'w': {
+          if (is_ascii(_flags)) {
+            reclass cls;
+            add_ascii_word_class(cls.literals);
+            _cclass_id = _prog.add_class(cls);
+          } else {
+            if (_id_cclass_w < 0) { _id_cclass_w = _prog.add_class(cclass_w); }
+            _cclass_id = _id_cclass_w;
+          }
+          return CCLASS;
+        }
+        case 'W': {
+          if (is_ascii(_flags)) {
+            reclass cls;
+            add_ascii_word_class(cls.literals);
+            _cclass_id = _prog.add_class(cls);
+          } else {
+            if (_id_cclass_W < 0) {
+              reclass cls = cclass_w;
+              cls.literals.push_back({'\n', '\n'});
+              _id_cclass_W = _prog.add_class(cls);
+            }
+            _cclass_id = _id_cclass_W;
+          }
+          return NCCLASS;
+        }
+        case 's': {
+          if (is_ascii(_flags)) {
+            reclass cls;
+            add_ascii_space_class(cls.literals);
+            _cclass_id = _prog.add_class(cls);
+          } else {
+            if (_id_cclass_s < 0) { _id_cclass_s = _prog.add_class(cclass_s); }
+            _cclass_id = _id_cclass_s;
+          }
+          return CCLASS;
+        }
+        case 'S': {
+          if (is_ascii(_flags)) {
+            reclass cls;
+            add_ascii_space_class(cls.literals);
+            _cclass_id = _prog.add_class(cls);
+          } else {
+            if (_id_cclass_s < 0) { _id_cclass_s = _prog.add_class(cclass_s); }
+            _cclass_id = _id_cclass_s;
+            return NCCLASS;
+          }
+        }
+        case 'd': {
+          if (is_ascii(_flags)) {
+            reclass cls;
+            add_ascii_digit_class(cls.literals);
+            _cclass_id = _prog.add_class(cls);
+          } else {
+            if (_id_cclass_d < 0) { _id_cclass_d = _prog.add_class(cclass_d); }
+            _cclass_id = _id_cclass_d;
+          }
+          return CCLASS;
+        }
+        case 'D': {
+          if (is_ascii(_flags)) {
+            reclass cls;
+            add_ascii_digit_class(cls.literals);
+            _cclass_id = _prog.add_class(cls);
+          } else {
+            if (_id_cclass_D < 0) {
+              reclass cls = cclass_d;
+              cls.literals.push_back({'\n', '\n'});
+              _id_cclass_D = _prog.add_class(cls);
+            }
+            _cclass_id = _id_cclass_D;
+          }
+          return NCCLASS;
+        }
+        case 'b': return BOW;
+        case 'B': return NBOW;
+        case 'A': {
+          _chr = chr;
+          return BOL;
+        }
+        case 'Z': {
+          _chr = chr;
+          return EOL;
+        }
+        default: {
+          // let valid escapable chars fall through as literal CHAR
+          if (chr &&
+              (std::find(escapable_chars.begin(), escapable_chars.end(), static_cast<char>(chr)) !=
+               escapable_chars.end())) {
+            break;
+          }
+          // anything else is a bad escape so throw an error
+          CUDF_FAIL("invalid regex pattern: bad escape character at position " +
+                    std::to_string(_expr_ptr - _pattern_begin - 1));
+        }
+      }  // end-switch
+      _chr = chr;
+      return CHAR;
+    }
+
+    // handle regex characters
+    switch (chr) {
+      case 0: return END;
+      case '(':
+        if (*_expr_ptr == '?' && *(_expr_ptr + 1) == ':')  // non-capturing group
+        {
+          _expr_ptr += 2;
+          return LBRA_NC;
+        }
+        return (_capture == capture_groups::NON_CAPTURE) ? static_cast<int32_t>(LBRA_NC)
+                                                         : static_cast<int32_t>(LBRA);
+      case ')': return RBRA;
+      case '^': {
+        _chr = is_multiline(_flags) ? chr : '\n';
+        return BOL;
+      }
+      case '$': {
+        _chr = is_multiline(_flags) ? chr : '\n';
+        return EOL;
+      }
+      case '[': return build_cclass();
+      case '.': return dot_type;
+    }
+
+    if (std::find(quantifiers.begin(), quantifiers.end(), static_cast<char>(chr)) ==
+        quantifiers.end()) {
+      _chr = chr;
+      return CHAR;
+    }
+
+    // The quantifiers require at least one "real" previous item.
+    // We are throwing errors for invalid quantifiers.
+    // Another option is to just return CHAR silently here which effectively
+    // treats the chr character as a literal instead as a quantifier.
+    // This could lead to confusion where sometimes unescaped quantifier characters
+    // are treated as regex expressions and sometimes they are not.
+    if (_items.empty()) { CUDF_FAIL("invalid regex pattern: nothing to repeat at position 0"); }
+
+    // handle alternation instruction
+    if (chr == '|') return OR;
+
+    // Check that the previous item can be used with quantifiers.
+    // If the previous item is a capture group, we need to check items inside the
+    // capture group can be used with quantifiers too.
+    // (Note that capture groups can be nested).
+    auto previous_type = _items.back().type;
+    if (previous_type == RBRA) {  // previous item is a capture group
+      // look for matching LBRA
+      auto nested_count = 1;
+      auto lbra_itr =
+        std::find_if(_items.rbegin(), _items.rend(), [nested_count](auto const& item) mutable {
+          auto const is_closing = (item.type == RBRA);
+          auto const is_opening = (item.type == LBRA || item.type == LBRA_NC);
+          nested_count += is_closing - is_opening;
+          return is_opening && (nested_count == 0);
+        });
+      // search for the first valid item within the LBRA-RBRA range
+      auto first_valid = std::find_first_of(
+        _items.rbegin() + 1,
+        lbra_itr,
+        valid_preceding_inst_types.begin(),
+        valid_preceding_inst_types.end(),
+        [](auto const item, auto const valid_type) { return item.type == valid_type; });
+      // set previous_type to be checked in next if-statement
+      previous_type = (first_valid == lbra_itr) ? (--lbra_itr)->type : first_valid->type;
+    }
+
+    if (std::find(valid_preceding_inst_types.begin(),
+                  valid_preceding_inst_types.end(),
+                  previous_type) == valid_preceding_inst_types.end()) {
+      CUDF_FAIL("invalid regex pattern: nothing to repeat at position " +
+                std::to_string(_expr_ptr - _pattern_begin - 1));
+    }
+
+    // handle quantifiers
+    switch (chr) {
+      case '*':
+        if (*_expr_ptr == '?') {
+          _expr_ptr++;
+          return STAR_LAZY;
+        }
+        return STAR;
+      case '?':
+        if (*_expr_ptr == '?') {
+          _expr_ptr++;
+          return QUEST_LAZY;
+        }
+        return QUEST;
+      case '+':
+        if (*_expr_ptr == '?') {
+          _expr_ptr++;
+          return PLUS_LAZY;
+        }
+        return PLUS;
+      case '{':  // counted repetition: {n,m}
+      {
+        if (!std::isdigit(*_expr_ptr)) { break; }
+
+        // transform char32 to char until null, delimiter, non-digit or end is reached;
+        // returns the number of chars read/transformed
+        auto transform_until = [](char32_t const* input,
+                                  char32_t const* end,
+                                  char* output,
+                                  std::string_view const delimiters) -> int32_t {
+          int32_t count = 0;
+          while (*input != 0 && input < end) {
+            auto const ch = static_cast<char>(*input++);
+            // if ch not a digit or ch is a delimiter, we are done
+            if (!std::isdigit(ch) || delimiters.find(ch) != delimiters.npos) { break; }
+            output[count] = ch;
+            ++count;
+          }
+          output[count] = 0;  // null-terminate (for the atoi call)
+          return count;
+        };
+
+        constexpr auto max_read               = 4;    // 3 digits plus the delimiter
+        constexpr auto max_value              = 999;  // support only 3 digits
+        std::array<char, max_read + 1> buffer = {0};  //(max_read + 1);
+
+        // get left-side (n) value => min_count
+        auto bytes_read = transform_until(_expr_ptr, _expr_ptr + max_read, buffer.data(), "},");
+        if (_expr_ptr[bytes_read] != '}' && _expr_ptr[bytes_read] != ',') {
+          break;  // re-interpret as CHAR
+        }
+        auto count = std::atoi(buffer.data());
+        CUDF_EXPECTS(
+          count <= max_value,
+          "unsupported repeat value at " + std::to_string(_expr_ptr - _pattern_begin - 1));
+        _min_count = static_cast<int16_t>(count);
+
+        auto const expr_ptr_save = _expr_ptr;  // save in case ending '}' is not found
+        _expr_ptr += bytes_read;
+
+        // get optional right-side (m) value => max_count
+        _max_count = _min_count;
+        if (*_expr_ptr++ == ',') {
+          bytes_read = transform_until(_expr_ptr, _expr_ptr + max_read, buffer.data(), "}");
+          if (_expr_ptr[bytes_read] != '}') {
+            _expr_ptr = expr_ptr_save;  // abort, rollback and
+            break;                      // re-interpret as CHAR
+          }
+
+          count = std::atoi(buffer.data());
+          CUDF_EXPECTS(
+            count <= max_value,
+            "unsupported repeat value at " + std::to_string(_expr_ptr - _pattern_begin - 1));
+
+          // {n,m} and {n,} are both valid
+          _max_count = buffer[0] == 0 ? -1 : static_cast<int16_t>(count);
+          _expr_ptr += bytes_read + 1;
+        }
+
+        // {n,m}? pattern is lazy counted quantifier
+        if (*_expr_ptr == '?') {
+          _expr_ptr++;
+          return COUNTED_LAZY;
+        }
+        // otherwise, fixed counted quantifier
+        return COUNTED;
+      }
+    }
+    _chr = chr;
+    return CHAR;
+  }
+
+  std::vector<regex_parser::Item> expand_counted_items() const
+  {
+    std::vector<regex_parser::Item> const& in = _items;
+    std::vector<regex_parser::Item> out;
+    std::stack<int> lbra_stack;
+    auto repeat_start_index = -1;
+
+    for (std::size_t index = 0; index < in.size(); index++) {
+      auto const item = in[index];
+
+      if (item.type != COUNTED && item.type != COUNTED_LAZY) {
+        out.push_back(item);
+        if (item.type == LBRA || item.type == LBRA_NC) {
+          lbra_stack.push(index);
+          repeat_start_index = -1;
+        } else if (item.type == RBRA) {
+          repeat_start_index = lbra_stack.top();
+          lbra_stack.pop();
+        } else if ((item.type & ITEM_MASK) != OPERATOR_MASK) {
+          repeat_start_index = index;
+        }
+      } else {
+        // item is of type COUNTED or COUNTED_LAZY
+        // here we repeat the previous item(s) based on the count range in item
+
+        CUDF_EXPECTS(repeat_start_index >= 0, "regex: invalid counted quantifier location");
+
+        // range of affected item(s) to repeat
+        auto const begin = in.begin() + repeat_start_index;
+        auto const end   = in.begin() + index;
+        // count range values
+        auto const n = item.d.count.n;  // minimum count
+        auto const m = item.d.count.m;  // maximum count
+
+        assert(n >= 0 && "invalid repeat count value n");
+        // zero-repeat edge-case: need to erase the previous items
+        if (n == 0) { out.erase(out.end() - (index - repeat_start_index), out.end()); }
+
+        // minimum repeats (n)
+        for (int j = 1; j < n; j++) {
+          out.insert(out.end(), begin, end);
+        }
+
+        // optional maximum repeats (m)
+        if (m >= 0) {
+          for (int j = n; j < m; j++) {
+            out.push_back(regex_parser::Item{LBRA_NC, 0});
+            out.insert(out.end(), begin, end);
+          }
+          for (int j = n; j < m; j++) {
+            out.push_back(regex_parser::Item{RBRA, 0});
+            out.push_back(regex_parser::Item{item.type == COUNTED ? QUEST : QUEST_LAZY, 0});
+          }
+        } else {
+          // infinite repeats
+          if (n > 0) {  // append '+' after last repetition
+            out.push_back(regex_parser::Item{item.type == COUNTED ? PLUS : PLUS_LAZY, 0});
+          } else {  // copy it once then append '*'
+            out.insert(out.end(), begin, end);
+            out.push_back(regex_parser::Item{item.type == COUNTED ? STAR : STAR_LAZY, 0});
+          }
+        }
+      }
+    }
+    return out;
+  }
+
+ public:
+  regex_parser(char32_t const* pattern,
+               regex_flags const flags,
+               capture_groups const capture,
+               reprog& prog)
+    : _prog(prog), _pattern_begin(pattern), _expr_ptr(pattern), _flags(flags), _capture(capture)
+  {
+    auto const dot_type = is_dotall(_flags) ? ANYNL : ANY;
+
+    int32_t type = 0;
+    while ((type = lex(dot_type)) != END) {
+      auto const item = [type, chr = _chr, cid = _cclass_id, n = _min_count, m = _max_count] {
+        if (type == CCLASS || type == NCCLASS) return Item{type, cid};
+        if (type == COUNTED || type == COUNTED_LAZY) return Item{type, n, m};
+        return Item{type, chr};
+      }();
+      _items.push_back(item);
+      if (type == COUNTED || type == COUNTED_LAZY) _has_counted = true;
+    }
+  }
+
+  std::vector<regex_parser::Item> get_items() const
+  {
+    return _has_counted ? expand_counted_items() : _items;
+  }
+};
+
+/**
+ * @brief The compiler converts class list into instructions.
+ */
+class regex_compiler {
+  struct and_node {
+    int id_first;
+    int id_last;
+  };
+
+  struct re_operator {
+    int t;
+    int subid;
+  };
+
+  reprog& _prog;
+  std::stack<and_node> _and_stack;
+  std::stack<re_operator> _operator_stack;
+  bool _last_was_and;
+  int _bracket_count;
+  regex_flags _flags;
+
+  inline void push_and(int first, int last) { _and_stack.push({first, last}); }
+
+  inline and_node pop_and()
+  {
+    if (_and_stack.empty()) {
+      auto const inst_id = _prog.add_inst(NOP);
+      push_and(inst_id, inst_id);
+    }
+    auto const node = _and_stack.top();
+    _and_stack.pop();
+    return node;
+  }
+
+  inline void push_operator(int token, int subid = 0)
+  {
+    _operator_stack.push(re_operator{token, subid});
+  }
+
+  inline re_operator const pop_operator()
+  {
+    auto const op = _operator_stack.top();
+    _operator_stack.pop();
+    return op;
+  }
+
+  void eval_until(int min_token)
+  {
+    while (min_token == RBRA || _operator_stack.top().t >= min_token) {
+      auto const op = pop_operator();
+      switch (op.t) {
+        default:
+          // unknown operator
+          break;
+        case LBRA:  // expects matching RBRA
+        {
+          auto const operand                        = pop_and();
+          auto const id_inst2                       = _prog.add_inst(RBRA);
+          _prog.inst_at(id_inst2).u1.subid          = op.subid;
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst2;
+          auto const id_inst1                       = _prog.add_inst(LBRA);
+          _prog.inst_at(id_inst1).u1.subid          = op.subid;
+          _prog.inst_at(id_inst1).u2.next_id        = operand.id_first;
+          push_and(id_inst1, id_inst2);
+          return;
+        }
+        case OR: {
+          auto const operand2                        = pop_and();
+          auto const operand1                        = pop_and();
+          auto const id_inst2                        = _prog.add_inst(NOP);
+          _prog.inst_at(operand2.id_last).u2.next_id = id_inst2;
+          _prog.inst_at(operand1.id_last).u2.next_id = id_inst2;
+          auto const id_inst1                        = _prog.add_inst(OR);
+          _prog.inst_at(id_inst1).u1.right_id        = operand1.id_first;
+          _prog.inst_at(id_inst1).u2.left_id         = operand2.id_first;
+          push_and(id_inst1, id_inst2);
+          break;
+        }
+        case CAT: {
+          auto const operand2                        = pop_and();
+          auto const operand1                        = pop_and();
+          _prog.inst_at(operand1.id_last).u2.next_id = operand2.id_first;
+          push_and(operand1.id_first, operand2.id_last);
+          break;
+        }
+        case STAR: {
+          auto const operand                        = pop_and();
+          auto const id_inst1                       = _prog.add_inst(OR);
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst1;
+          _prog.inst_at(id_inst1).u1.right_id       = operand.id_first;
+          push_and(id_inst1, id_inst1);
+          break;
+        }
+        case STAR_LAZY: {
+          auto const operand                        = pop_and();
+          auto const id_inst1                       = _prog.add_inst(OR);
+          auto const id_inst2                       = _prog.add_inst(NOP);
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst1;
+          _prog.inst_at(id_inst1).u2.left_id        = operand.id_first;
+          _prog.inst_at(id_inst1).u1.right_id       = id_inst2;
+          push_and(id_inst1, id_inst2);
+          break;
+        }
+        case PLUS: {
+          auto const operand                        = pop_and();
+          auto const id_inst1                       = _prog.add_inst(OR);
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst1;
+          _prog.inst_at(id_inst1).u1.right_id       = operand.id_first;
+          push_and(operand.id_first, id_inst1);
+          break;
+        }
+        case PLUS_LAZY: {
+          auto const operand                        = pop_and();
+          auto const id_inst1                       = _prog.add_inst(OR);
+          auto const id_inst2                       = _prog.add_inst(NOP);
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst1;
+          _prog.inst_at(id_inst1).u2.left_id        = operand.id_first;
+          _prog.inst_at(id_inst1).u1.right_id       = id_inst2;
+          push_and(operand.id_first, id_inst2);
+          break;
+        }
+        case QUEST: {
+          auto const operand                        = pop_and();
+          auto const id_inst1                       = _prog.add_inst(OR);
+          auto const id_inst2                       = _prog.add_inst(NOP);
+          _prog.inst_at(id_inst1).u2.left_id        = id_inst2;
+          _prog.inst_at(id_inst1).u1.right_id       = operand.id_first;
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst2;
+          push_and(id_inst1, id_inst2);
+          break;
+        }
+        case QUEST_LAZY: {
+          auto const operand                        = pop_and();
+          auto const id_inst1                       = _prog.add_inst(OR);
+          auto const id_inst2                       = _prog.add_inst(NOP);
+          _prog.inst_at(id_inst1).u2.left_id        = operand.id_first;
+          _prog.inst_at(id_inst1).u1.right_id       = id_inst2;
+          _prog.inst_at(operand.id_last).u2.next_id = id_inst2;
+          push_and(id_inst1, id_inst2);
+          break;
+        }
+      }
+    }
+  }
+
+  void handle_operator(int token, int subid = 0)
+  {
+    if (token == RBRA && --_bracket_count < 0) {
+      // unmatched right paren
+      return;
+    }
+    if (token == LBRA) {
+      _bracket_count++;
+      if (_last_was_and) { handle_operator(CAT, subid); }
+    } else {
+      eval_until(token);
+    }
+    if (token != RBRA) { push_operator(token, subid); }
+
+    static std::vector<int> tokens{STAR, STAR_LAZY, QUEST, QUEST_LAZY, PLUS, PLUS_LAZY, RBRA};
+    _last_was_and =
+      std::any_of(tokens.cbegin(), tokens.cend(), [token](auto t) { return t == token; });
+  }
+
+  void handle_operand(int token, int subid = 0, char32_t yy = 0, int class_id = 0)
+  {
+    if (_last_was_and) { handle_operator(CAT, subid); }  // catenate is implicit
+
+    auto const inst_id = _prog.add_inst(token);
+    if (token == CCLASS || token == NCCLASS) {
+      _prog.inst_at(inst_id).u1.cls_id = class_id;
+    } else if (token == CHAR) {
+      _prog.inst_at(inst_id).u1.c = yy;
+    } else if (token == BOL || token == EOL) {
+      _prog.inst_at(inst_id).u1.c = yy;
+    }
+    push_and(inst_id, inst_id);
+    _last_was_and = true;
+  }
+
+ public:
+  regex_compiler(char32_t const* pattern,
+                 regex_flags const flags,
+                 capture_groups const capture,
+                 reprog& prog)
+    : _prog(prog), _last_was_and(false), _bracket_count(0), _flags(flags)
+  {
+    // Parse pattern into items
+    auto const items = regex_parser(pattern, _flags, capture, _prog).get_items();
+
+    int cur_subid{};
+    int push_subid{};
+
+    // Start with a low priority operator
+    push_operator(START - 1);
+
+    for (auto const item : items) {
+      auto token = item.type;
+
+      if (token == LBRA) {
+        ++cur_subid;
+        push_subid = cur_subid;
+      } else if (token == LBRA_NC) {
+        push_subid = 0;
+        token      = LBRA;
+      }
+
+      if ((token & ITEM_MASK) == OPERATOR_MASK) {
+        handle_operator(token, push_subid);
+      } else {
+        handle_operand(token, push_subid, item.d.chr, item.d.cclass_id);
+      }
+    }
+
+    // Close with a low priority operator
+    eval_until(START);
+    // Force END
+    handle_operand(END, push_subid);
+    eval_until(START);
+
+    CUDF_EXPECTS(_bracket_count == 0, "unmatched left parenthesis");
+
+    _prog.set_start_inst(_and_stack.top().id_first);
+    _prog.optimize();
+    _prog.check_for_errors();
+    _prog.finalize();
+    _prog.set_groups_count(cur_subid);
+  }
+};
+
+// Convert pattern into program
+reprog reprog::create_from(std::string_view pattern,
+                           regex_flags const flags,
+                           capture_groups const capture)
+{
+  reprog rtn;
+  auto pattern32 = string_to_char32_vector(pattern);
+  regex_compiler compiler(pattern32.data(), flags, capture, rtn);
+  // for debugging, it can be helpful to call rtn.print(flags) here to dump
+  // out the instructions that have been created from the given pattern
+  return rtn;
+}
+
+void reprog::optimize() { collapse_nops(); }
+
+void reprog::finalize() { build_start_ids(); }
+
+void reprog::collapse_nops()
+{
+  // treat non-capturing LBRAs/RBRAs as NOP
+  std::transform(_insts.begin(), _insts.end(), _insts.begin(), [](auto inst) {
+    if ((inst.type == LBRA || inst.type == RBRA) && (inst.u1.subid < 1)) { inst.type = NOP; }
+    return inst;
+  });
+
+  // functor for finding the next valid op
+  auto find_next_op = [insts = _insts](int id) {
+    while (insts[id].type == NOP) {
+      id = insts[id].u2.next_id;
+    }
+    return id;
+  };
+
+  // create new routes around NOP chains
+  std::transform(_insts.begin(), _insts.end(), _insts.begin(), [find_next_op](auto inst) {
+    if (inst.type != NOP) {
+      inst.u2.next_id = find_next_op(inst.u2.next_id);
+      if (inst.type == OR) { inst.u1.right_id = find_next_op(inst.u1.right_id); }
+    }
+    return inst;
+  });
+
+  // find starting op
+  _startinst_id = find_next_op(_startinst_id);
+
+  // build a map of op ids
+  // these are used to fix up the ids after the NOPs are removed
+  std::vector<int> id_map(insts_count());
+  std::transform_exclusive_scan(
+    _insts.begin(), _insts.end(), id_map.begin(), 0, std::plus<int>{}, [](auto inst) {
+      return static_cast<int>(inst.type != NOP);
+    });
+
+  // remove the NOP instructions
+  auto end = std::remove_if(_insts.begin(), _insts.end(), [](auto i) { return i.type == NOP; });
+  _insts.resize(std::distance(_insts.begin(), end));
+
+  // fix up the ids on the remaining instructions using the id_map
+  std::transform(_insts.begin(), _insts.end(), _insts.begin(), [id_map](auto inst) {
+    inst.u2.next_id = id_map[inst.u2.next_id];
+    if (inst.type == OR) { inst.u1.right_id = id_map[inst.u1.right_id]; }
+    return inst;
+  });
+
+  // fix up the start instruction id too
+  _startinst_id = id_map[_startinst_id];
+}
+
+// expand leading ORs to multiple startinst_ids
+void reprog::build_start_ids()
+{
+  _startinst_ids.clear();
+  std::stack<int> ids;
+  ids.push(_startinst_id);
+  while (!ids.empty()) {
+    int id = ids.top();
+    ids.pop();
+    reinst const& inst = _insts[id];
+    if (inst.type == OR) {
+      if (inst.u2.left_id != id)  // prevents infinite while-loop here
+        ids.push(inst.u2.left_id);
+      if (inst.u1.right_id != id)  // prevents infinite while-loop here
+        ids.push(inst.u1.right_id);
+    } else {
+      _startinst_ids.push_back(id);
+    }
+  }
+  _startinst_ids.push_back(-1);  // terminator mark
+}
+
+/**
+ * @brief Check a specific instruction for errors.
+ *
+ * Currently this is checking for an infinite-loop condition as documented in this issue:
+ * https://github.com/rapidsai/cudf/issues/10006
+ *
+ * Example instructions list created from pattern `(A?)+`
+ * ```
+ *   0:    CHAR c='A', next=2
+ *   1:      OR right=0, left=2, next=2
+ *   2:    RBRA id=1, next=4
+ *   3:    LBRA id=1, next=1
+ *   4:      OR right=3, left=5, next=5
+ *   5:     END
+ * ```
+ *
+ * Following the example above, the instruction at `id==1` (OR)
+ * is being checked. If the instruction path returns to `id==1`
+ * without including the `0==CHAR` or `5==END` as in this example,
+ * then this would cause the runtime to go into an infinite-loop.
+ *
+ * It appears this example pattern is not valid. But Python interprets
+ * its behavior similarly to pattern `(A*)`. Handling this in the same
+ * way does not look feasible with the current implementation.
+ *
+ * @throw cudf::logic_error if instruction logic error is found
+ *
+ * @param id Instruction to check if repeated.
+ * @param next_id Next instruction to process.
+ */
+void reprog::check_for_errors(int32_t id, int32_t next_id)
+{
+  auto inst = inst_at(next_id);
+  while (inst.type == LBRA || inst.type == RBRA) {
+    next_id = inst.u2.next_id;
+    inst    = inst_at(next_id);
+  }
+  if (inst.type == OR) {
+    CUDF_EXPECTS(next_id != id, "Unsupported regex pattern");
+    check_for_errors(id, inst.u2.left_id);
+    check_for_errors(id, inst.u1.right_id);
+  }
+}
+
+/**
+ * @brief Check regex instruction set for any errors.
+ *
+ * Currently, this checks for OR instructions that eventually point back to themselves with only
+ * intervening capture group instructions between causing an infinite-loop during runtime
+ * evaluation.
+ */
+void reprog::check_for_errors()
+{
+  for (auto id = 0; id < insts_count(); ++id) {
+    auto const inst = inst_at(id);
+    if (inst.type == OR) {
+      check_for_errors(id, inst.u2.left_id);
+      check_for_errors(id, inst.u1.right_id);
+    }
+  }
+}
+
+#ifndef NDEBUG
+void reprog::print(regex_flags const flags)
+{
+  printf("Flags = 0x%08x\n", static_cast<uint32_t>(flags));
+  printf("Instructions:\n");
+  for (std::size_t i = 0; i < _insts.size(); i++) {
+    reinst const& inst = _insts[i];
+    printf("%3zu: ", i);
+    switch (inst.type) {
+      default: printf("Unknown instruction: %d, next=%d", inst.type, inst.u2.next_id); break;
+      case CHAR:
+        if (inst.u1.c <= 32 || inst.u1.c >= 127) {
+          printf("   CHAR c='0x%02x', next=%d", static_cast<unsigned>(inst.u1.c), inst.u2.next_id);
+        } else {
+          printf("   CHAR c='%c', next=%d", inst.u1.c, inst.u2.next_id);
+        }
+        break;
+      case RBRA: printf("   RBRA id=%d, next=%d", inst.u1.subid, inst.u2.next_id); break;
+      case LBRA: printf("   LBRA id=%d, next=%d", inst.u1.subid, inst.u2.next_id); break;
+      case OR:
+        printf(
+          "     OR right=%d, left=%d, next=%d", inst.u1.right_id, inst.u2.left_id, inst.u2.next_id);
+        break;
+      case STAR: printf("   STAR next=%d", inst.u2.next_id); break;
+      case PLUS: printf("   PLUS next=%d", inst.u2.next_id); break;
+      case QUEST: printf("  QUEST next=%d", inst.u2.next_id); break;
+      case ANY: printf("    ANY next=%d", inst.u2.next_id); break;
+      case ANYNL: printf("  ANYNL next=%d", inst.u2.next_id); break;
+      case NOP: printf("    NOP next=%d", inst.u2.next_id); break;
+      case BOL: {
+        printf("    BOL c=");
+        if (inst.u1.c == '\n') {
+          printf("'\\n'");
+        } else {
+          printf("'%c'", inst.u1.c);
+        }
+        printf(", next=%d", inst.u2.next_id);
+        break;
+      }
+      case EOL: {
+        printf("    EOL c=");
+        if (inst.u1.c == '\n') {
+          printf("'\\n'");
+        } else {
+          printf("'%c'", inst.u1.c);
+        }
+        printf(", next=%d", inst.u2.next_id);
+        break;
+      }
+      case CCLASS: printf(" CCLASS cls=%d , next=%d", inst.u1.cls_id, inst.u2.next_id); break;
+      case NCCLASS: printf("NCCLASS cls=%d, next=%d", inst.u1.cls_id, inst.u2.next_id); break;
+      case BOW: printf("    BOW next=%d", inst.u2.next_id); break;
+      case NBOW: printf("   NBOW next=%d", inst.u2.next_id); break;
+      case END: printf("    END"); break;
+    }
+    printf("\n");
+  }
+
+  printf("startinst_id=%d\n", _startinst_id);
+  if (_startinst_ids.size() > 0) {
+    printf("startinst_ids: [");
+    for (size_t i = 0; i < _startinst_ids.size(); i++) {
+      printf(" %d", _startinst_ids[i]);
+    }
+    printf("]\n");
+  }
+
+  int count = static_cast<int>(_classes.size());
+  printf("\nClasses %d\n", count);
+  for (int i = 0; i < count; i++) {
+    reclass const& cls = _classes[i];
+    auto const size    = static_cast<int>(cls.literals.size());
+    printf("%2d: ", i);
+    for (int j = 0; j < size; ++j) {
+      auto const l = cls.literals[j];
+      char32_t c1  = l.first;
+      char32_t c2  = l.last;
+      if (c1 <= 32 || c1 >= 127 || c2 <= 32 || c2 >= 127) {
+        printf("0x%02x-0x%02x", static_cast<unsigned>(c1), static_cast<unsigned>(c2));
+      } else {
+        printf("%c-%c", static_cast<char>(c1), static_cast<char>(c2));
+      }
+      if ((j + 1) < size) { printf(", "); }
+    }
+    printf("\n");
+    if (cls.builtins) {
+      int mask = cls.builtins;
+      printf("   builtins(x%02X):", static_cast<unsigned>(mask));
+      if (mask & CCLASS_W) printf(" \\w");
+      if (mask & CCLASS_S) printf(" \\s");
+      if (mask & CCLASS_D) printf(" \\d");
+      if (mask & NCCLASS_W) printf(" \\W");
+      if (mask & NCCLASS_S) printf(" \\S");
+      if (mask & NCCLASS_D) printf(" \\D");
+    }
+    printf("\n");
+  }
+  if (_num_capturing_groups) { printf("Number of capturing groups: %d\n", _num_capturing_groups); }
+}
+#endif
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/regcomp.h b/cpp/src/strings/regex/regcomp.h
new file mode 100644
index 0000000..ab912ac
--- /dev/null
+++ b/cpp/src/strings/regex/regcomp.h
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/regex/flags.hpp>
+
+#include <string>
+#include <vector>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+/**
+ * @brief Actions and Tokens (regex instruction types)
+ *
+ * ```
+ *	02xx are operators, value == precedence
+ *	03xx are tokens, i.e. operands for operators
+ * ```
+ */
+enum InstType : int32_t {
+  CHAR    = 0177,  // Literal character
+  RBRA    = 0201,  // Right bracket, )
+  LBRA    = 0202,  // Left bracket, (
+  OR      = 0204,  // Alternation, |
+  ANY     = 0300,  // Any character except newline, .
+  ANYNL   = 0301,  // Any character including newline, .
+  BOL     = 0303,  // Beginning of line, ^
+  EOL     = 0304,  // End of line, $
+  CCLASS  = 0305,  // Character class, []
+  NCCLASS = 0306,  // Negated character class, [^ ]
+  BOW     = 0307,  // Boundary of word, \b
+  NBOW    = 0310,  // Not boundary of word, \B
+  END     = 0377   // Terminate: match found
+};
+
+/**
+ * @brief Range used for literals in reclass classes.
+ */
+struct reclass_range {
+  char32_t first{};  /// first character in span
+  char32_t last{};   /// last character in span (inclusive)
+};
+
+/**
+ * @brief Class type for regex compiler instruction.
+ */
+struct reclass {
+  int32_t builtins{0};  // bit mask identifying builtin classes
+  std::vector<reclass_range> literals;
+  reclass() {}
+  reclass(int m) : builtins(m) {}
+  reclass(int m, std::vector<reclass_range>&& l) : builtins(m), literals(std::move(l)) {}
+};
+
+constexpr int32_t CCLASS_W{1 << 0};   // [a-z], [A-Z], [0-9], and '_'
+constexpr int32_t CCLASS_S{1 << 1};   // all spaces or ctrl characters
+constexpr int32_t CCLASS_D{1 << 2};   // digits [0-9]
+constexpr int32_t NCCLASS_W{1 << 3};  // not CCLASS_W or '\n'
+constexpr int32_t NCCLASS_S{1 << 4};  // not CCLASS_S
+constexpr int32_t NCCLASS_D{1 << 5};  // not CCLASS_D or '\n'
+
+/**
+ * @brief Structure of an encoded regex instruction
+ */
+struct reinst {
+  int32_t type; /* operator type or instruction type */
+  union {
+    int32_t cls_id;   /* class pointer */
+    char32_t c;       /* character */
+    int32_t subid;    /* sub-expression id for RBRA and LBRA */
+    int32_t right_id; /* right child of OR */
+  } u1;
+  union {            /* regexec relies on these two being in the same union */
+    int32_t left_id; /* left child of OR */
+    int32_t next_id; /* next instruction for CAT & LBRA */
+  } u2;
+  int32_t reserved4;
+};
+
+/**
+ * @brief Regex program handles parsing a pattern into a vector
+ * of chained instructions.
+ */
+class reprog {
+ public:
+  reprog(reprog const&)            = default;
+  reprog(reprog&&)                 = default;
+  ~reprog()                        = default;
+  reprog& operator=(reprog const&) = default;
+  reprog& operator=(reprog&&)      = default;
+
+  /**
+   * @brief Parses the given regex pattern and produces an instance
+   * of this object
+   *
+   * @param pattern Regex pattern encoded as UTF-8
+   * @param flags For interpreting certain `pattern` characters
+   * @param capture For controlling how capture groups are processed
+   * @return Instance of reprog
+   */
+  static reprog create_from(std::string_view pattern,
+                            regex_flags const flags,
+                            capture_groups const capture = capture_groups::EXTRACT);
+
+  int32_t add_inst(int32_t type);
+  int32_t add_inst(reinst const& inst);
+  int32_t add_class(reclass const& cls);
+
+  void set_groups_count(int32_t groups);
+  [[nodiscard]] int32_t groups_count() const;
+
+  [[nodiscard]] int32_t insts_count() const;
+  [[nodiscard]] reinst& inst_at(int32_t id);
+  [[nodiscard]] reinst const* insts_data() const;
+
+  [[nodiscard]] int32_t classes_count() const;
+  [[nodiscard]] reclass const& class_at(int32_t id) const;
+  [[nodiscard]] reclass const* classes_data() const;
+
+  [[nodiscard]] int32_t const* starts_data() const;
+  [[nodiscard]] int32_t starts_count() const;
+
+  void set_start_inst(int32_t id);
+  [[nodiscard]] int32_t get_start_inst() const;
+
+  void optimize();
+  void finalize();
+  void check_for_errors();
+#ifndef NDEBUG
+  void print(regex_flags const flags);
+#endif
+
+ private:
+  std::vector<reinst> _insts;           // instructions
+  std::vector<reclass> _classes;        // data for CCLASS instructions
+  int32_t _startinst_id{};              // id of first instruction
+  std::vector<int32_t> _startinst_ids;  // short-cut to speed-up ORs
+  int32_t _num_capturing_groups{};
+
+  reprog() = default;
+  void collapse_nops();
+  void build_start_ids();
+  void check_for_errors(int32_t id, int32_t next_id);
+};
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/regex.cuh b/cpp/src/strings/regex/regex.cuh
new file mode 100644
index 0000000..c1abbd7
--- /dev/null
+++ b/cpp/src/strings/regex/regex.cuh
@@ -0,0 +1,311 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <strings/regex/regcomp.h>
+
+#include <cudf/strings/regex/flags.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+#include <cuda_runtime.h>
+
+#include <memory>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+struct relist;
+
+using match_pair   = thrust::pair<cudf::size_type, cudf::size_type>;
+using match_result = thrust::optional<match_pair>;
+
+constexpr int32_t MAX_SHARED_MEM      = 2048;  ///< Memory size for storing prog instruction data
+constexpr std::size_t MAX_WORKING_MEM = 0x01'FFFF'FFFF;  ///< Memory size for state data
+constexpr int32_t MINIMUM_THREADS     = 256;  // Minimum threads for computing working memory
+
+/**
+ * @brief Regex class stored on the device and executed by reprog_device.
+ *
+ * This class holds the unique data for any regex CCLASS instruction.
+ */
+struct alignas(16) reclass_device {
+  int32_t builtins{};
+  int32_t count{};
+  reclass_range const* literals{};
+
+  __device__ inline bool is_match(char32_t const ch, uint8_t const* flags) const;
+};
+
+class reprog;
+
+/**
+ * @brief Regex program of instructions/data for a specific regex pattern.
+ *
+ * Once created, the find/extract methods are used to evaluate the regex instructions
+ * against a single string.
+ *
+ * An instance of the class requires working memory for evaluating the regex
+ * instructions for the string. Determine the size of the required memory by
+ * calling either `working_memory_size()` or `compute_strided_working_memory()`.
+ * Once the buffer is allocated, pass the device pointer to the `set_working_memory()`
+ * member function.
+ */
+class reprog_device {
+ public:
+  reprog_device()                                = delete;
+  ~reprog_device()                               = default;
+  reprog_device(reprog_device const&)            = default;
+  reprog_device(reprog_device&&)                 = default;
+  reprog_device& operator=(reprog_device const&) = default;
+  reprog_device& operator=(reprog_device&&)      = default;
+
+  /**
+   * @brief Create device program instance from a regex program
+   *
+   * @param prog The regex program to create from
+   * @param stream CUDA stream used for device memory operations and kernel launches
+   * @return The program device object
+   */
+  static std::unique_ptr<reprog_device, std::function<void(reprog_device*)>> create(
+    reprog const& prog, rmm::cuda_stream_view stream);
+
+  /**
+   * @brief Called automatically by the unique_ptr returned from create().
+   */
+  void destroy();
+
+  /**
+   * @brief Returns the number of regex instructions.
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE int32_t insts_counts() const { return _insts_count; }
+
+  /**
+   * @brief Returns the number of regex groups found in the expression.
+   */
+  [[nodiscard]] CUDF_HOST_DEVICE inline int32_t group_counts() const
+  {
+    return _num_capturing_groups;
+  }
+
+  /**
+   * @brief Returns true if this is an empty program.
+   */
+  [[nodiscard]] __device__ inline bool is_empty() const;
+
+  /**
+   * @brief Returns the size needed for working memory for the given thread count.
+   *
+   * @param num_threads Number of threads to be executed in parallel
+   * @return Size of working memory in bytes
+   */
+  [[nodiscard]] std::size_t working_memory_size(int32_t num_threads) const;
+
+  /**
+   * @brief Compute working memory for the given thread count with a maximum size.
+   *
+   * The `min_rows` overrules the `requested_max_size`.
+   * That is, the `requested_max_size` may be
+   * exceeded to keep the number of rows greater than `min_rows`.
+   * Also, if `rows < min_rows` then `min_rows` is not enforced.
+   *
+   * @param rows Number of rows to execute in parallel
+   * @param min_rows The least number of rows to meet `max_size`
+   * @param requested_max_size Requested maximum bytes for the working memory
+   * @return The size of the working memory and the number of parallel rows it will support
+   */
+  [[nodiscard]] std::pair<std::size_t, int32_t> compute_strided_working_memory(
+    int32_t rows,
+    int32_t min_rows               = MINIMUM_THREADS,
+    std::size_t requested_max_size = MAX_WORKING_MEM) const;
+
+  /**
+   * @brief Set the device working memory buffer to use for the regex execution.
+   *
+   * @param buffer Device memory pointer.
+   * @param thread_count Number of threads the memory buffer will support.
+   * @param max_insts Set to the maximum instruction count if reusing the
+   *                  memory buffer for other regex calls.
+   */
+  void set_working_memory(void* buffer, int32_t thread_count, int32_t max_insts = 0);
+
+  /**
+   * @brief Returns the size of shared memory required to hold this instance.
+   *
+   * This can be called on the CPU for specifying the shared-memory size in the
+   * kernel launch parameters.
+   * This may return 0 if the MAX_SHARED_MEM value is exceeded.
+   */
+  [[nodiscard]] int32_t compute_shared_memory_size() const;
+
+  /**
+   * @brief Returns the thread count passed on `set_working_memory`.
+   */
+  [[nodiscard]] __device__ inline int32_t thread_count() const { return _thread_count; }
+
+  /**
+   * @brief Store this object into the given device pointer (e.g. shared memory).
+   *
+   * No data is stored if MAX_SHARED_MEM is exceeded for this object.
+   */
+  __device__ inline void store(void* buffer) const;
+
+  /**
+   * @brief Load an instance of this class from a device buffer (e.g. shared memory).
+   *
+   * Data is loaded from the given buffer if MAX_SHARED_MEM is not exceeded for the given object.
+   * Otherwise, a copy of the object is returned.
+   */
+  [[nodiscard]] __device__ static inline reprog_device load(reprog_device const prog, void* buffer);
+
+  /**
+   * @brief Does a find evaluation using the compiled expression on the given string.
+   *
+   * @param thread_idx The index used for mapping the state memory for this string in global memory.
+   * @param d_str The string to search.
+   * @param begin Position to begin the search within `d_str`.
+   * @param end Character position index to end the search within `d_str`.
+   *            Specify -1 to match any virtual positions past the end of the string.
+   * @return If match found, returns character positions of the matches.
+   */
+  __device__ inline match_result find(int32_t const thread_idx,
+                                      string_view const d_str,
+                                      string_view::const_iterator begin,
+                                      cudf::size_type end = -1) const;
+
+  /**
+   * @brief Does an extract evaluation using the compiled expression on the given string.
+   *
+   * This will find a specific capture group within the string.
+   * The find() function should be called first to locate the begin/end bounds of the
+   * the matched section.
+   *
+   * @param thread_idx The index used for mapping the state memory for this string in global memory.
+   * @param d_str The string to search.
+   * @param begin Position to begin the search within `d_str`.
+   * @param end Character position index to end the search within `d_str`.
+   * @param group_id The specific group to return its matching position values.
+   * @return If valid, returns the character position of the matched group in the given string,
+   */
+  __device__ inline match_result extract(int32_t const thread_idx,
+                                         string_view const d_str,
+                                         string_view::const_iterator begin,
+                                         cudf::size_type end,
+                                         cudf::size_type const group_id) const;
+
+ private:
+  struct reljunk {
+    relist* __restrict__ list1;
+    relist* __restrict__ list2;
+    int32_t starttype{};
+    char32_t startchar{};
+
+    __device__ inline reljunk(relist* list1, relist* list2, reinst const inst);
+    __device__ inline void swaplist();
+  };
+
+  /**
+   * @brief Returns the regex instruction object for a given id.
+   */
+  __device__ inline reinst get_inst(int32_t id) const;
+
+  /**
+   * @brief Returns the regex class object for a given id.
+   */
+  __device__ inline reclass_device get_class(int32_t id) const;
+
+  /**
+   * @brief Executes the regex pattern on the given string.
+   */
+  __device__ inline match_result regexec(string_view const d_str,
+                                         reljunk jnk,
+                                         string_view::const_iterator begin,
+                                         cudf::size_type end,
+                                         cudf::size_type const group_id = 0) const;
+
+  /**
+   * @brief Utility wrapper to setup state memory structures for calling regexec
+   */
+  __device__ inline match_result call_regexec(int32_t const thread_idx,
+                                              string_view const d_str,
+                                              string_view::const_iterator begin,
+                                              cudf::size_type end,
+                                              cudf::size_type const group_id = 0) const;
+
+  reprog_device(reprog const&);
+
+  int32_t _startinst_id;          // first instruction id
+  int32_t _num_capturing_groups;  // instruction groups
+  int32_t _insts_count;           // number of instructions
+  int32_t _starts_count;          // number of start-insts ids
+  int32_t _classes_count;         // number of classes
+  int32_t _max_insts;             // for partitioning working memory
+
+  uint8_t const* _codepoint_flags{};  // table of character types
+  reinst const* _insts{};             // array of regex instructions
+  int32_t const* _startinst_ids{};    // array of start instruction ids
+  reclass_device const* _classes{};   // array of regex classes
+
+  std::size_t _prog_size{};  // total size of this instance
+  void* _buffer{};           // working memory buffer
+  int32_t _thread_count{};   // threads available in working memory
+};
+
+/**
+ * @brief Return the size in bytes needed for working memory to
+ * execute insts_count instructions in parallel over num_threads threads.
+ *
+ * @param num_threads Number of parallel threads (usually one per string in a strings column)
+ * @param insts_count Number of instructions from a compiled regex pattern
+ * @return Number of bytes needed for working memory
+ */
+std::size_t compute_working_memory_size(int32_t num_threads, int32_t insts_count);
+
+/**
+ * @brief Converts a match_pair from character positions to byte positions
+ */
+__device__ __forceinline__ match_pair match_positions_to_bytes(match_pair const result,
+                                                               string_view d_str,
+                                                               string_view::const_iterator last)
+{
+  if (d_str.length() == d_str.size_bytes()) { return result; }
+  auto const begin = (last + (result.first - last.position())).byte_offset();
+  auto const end   = (last + (result.second - last.position())).byte_offset();
+  return {begin, end};
+}
+
+/**
+ * @brief Creates a string_view from a match result
+ */
+__device__ __forceinline__ string_view string_from_match(match_pair const result,
+                                                         string_view d_str,
+                                                         string_view::const_iterator last)
+{
+  auto const [begin, end] = match_positions_to_bytes(result, d_str, last);
+  return string_view(d_str.data() + begin, end - begin);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
+
+#include "./regex.inl"
diff --git a/cpp/src/strings/regex/regex.inl b/cpp/src/strings/regex/regex.inl
new file mode 100644
index 0000000..ce12dc1
--- /dev/null
+++ b/cpp/src/strings/regex/regex.inl
@@ -0,0 +1,431 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @brief This holds the state information when evaluating a string
+ * against a regex pattern.
+ *
+ * There are 2 instances of this per string managed in the reljunk class.
+ * As each regex instruction is evaluated for a string, the result is
+ * reflected here. The regexec function updates and manages this state data.
+ */
+struct alignas(8) relist {
+  /**
+   * @brief Compute the memory size for the state data.
+   */
+  constexpr inline static std::size_t data_size_for(int32_t insts)
+  {
+    return ((sizeof(ranges[0]) + sizeof(inst_ids[0])) * insts) +
+           cudf::util::div_rounding_up_unsafe(insts, 8);
+  }
+
+  /**
+   * @brief Compute the aligned memory allocation size.
+   */
+  constexpr inline static std::size_t alloc_size(int32_t insts, int32_t num_threads)
+  {
+    return cudf::util::round_up_unsafe<size_t>(data_size_for(insts) * num_threads, sizeof(restate));
+  }
+
+  struct alignas(16) restate {
+    int2 range;
+    int32_t inst_id;
+    int32_t reserved;
+  };
+
+  __device__ __forceinline__
+  relist(int16_t insts, int32_t num_threads, u_char* gp_ptr, int32_t index)
+    : masksize(cudf::util::div_rounding_up_unsafe(insts, 8)), stride(num_threads)
+  {
+    auto const rdata_size = sizeof(ranges[0]);
+    auto const idata_size = sizeof(inst_ids[0]);
+    ranges                = reinterpret_cast<decltype(ranges)>(gp_ptr + (index * rdata_size));
+    inst_ids =
+      reinterpret_cast<int16_t*>(gp_ptr + (rdata_size * stride * insts) + (index * idata_size));
+    mask = gp_ptr + ((rdata_size + idata_size) * stride * insts) + (index * masksize);
+  }
+
+  __device__ __forceinline__ void reset()
+  {
+    memset(mask, 0, masksize);
+    size = 0;
+  }
+
+  __device__ __forceinline__ bool activate(int32_t id, int32_t begin, int32_t end)
+  {
+    if (readMask(id)) { return false; }
+    writeMask(id);
+    inst_ids[size * stride] = static_cast<int16_t>(id);
+    ranges[size * stride]   = int2{begin, end};
+    ++size;
+    return true;
+  }
+
+  __device__ __forceinline__ restate get_state(int16_t idx) const
+  {
+    return restate{ranges[idx * stride], inst_ids[idx * stride]};
+  }
+
+  __device__ __forceinline__ int16_t get_size() const { return size; }
+
+ private:
+  int16_t size{};
+  int16_t const masksize;
+  int32_t const stride;
+  int2* __restrict__ ranges;       // pair per instruction
+  int16_t* __restrict__ inst_ids;  // one per instruction
+  u_char* __restrict__ mask;       // bit per instruction
+
+  __device__ __forceinline__ void writeMask(int32_t pos) const
+  {
+    u_char const uc = 1 << (pos & 7);
+    mask[pos >> 3] |= uc;
+  }
+
+  __device__ __forceinline__ bool readMask(int32_t pos) const
+  {
+    u_char const uc = mask[pos >> 3];
+    return static_cast<bool>((uc >> (pos & 7)) & 1);
+  }
+};
+
+__device__ __forceinline__ reprog_device::reljunk::reljunk(relist* list1,
+                                                           relist* list2,
+                                                           reinst const inst)
+  : list1(list1), list2(list2)
+{
+  if (inst.type == CHAR || inst.type == BOL) {
+    starttype = inst.type;
+    startchar = inst.u1.c;
+  }
+}
+
+__device__ __forceinline__ void reprog_device::reljunk::swaplist()
+{
+  auto tmp = list1;
+  list1    = list2;
+  list2    = tmp;
+}
+
+/**
+ * @brief Utility to check a specific character against this class instance.
+ *
+ * @param ch A 4-byte UTF-8 character.
+ * @param codepoint_flags Used for mapping a character to type for builtin classes.
+ * @return true if the character matches
+ */
+__device__ __forceinline__ bool reclass_device::is_match(char32_t const ch,
+                                                         uint8_t const* codepoint_flags) const
+{
+  for (int i = 0; i < count; ++i) {
+    auto const literal = literals[i];
+    if ((ch >= literal.first) && (ch <= literal.last)) { return true; }
+  }
+
+  if (!builtins) return false;
+  uint32_t codept = utf8_to_codepoint(ch);
+  if (codept > 0x00'FFFF) return false;
+  int8_t fl = codepoint_flags[codept];
+  if ((builtins & CCLASS_W) && ((ch == '_') || IS_ALPHANUM(fl)))  // \w
+    return true;
+  if ((builtins & CCLASS_S) && IS_SPACE(fl))  // \s
+    return true;
+  if ((builtins & CCLASS_D) && IS_DIGIT(fl))  // \d
+    return true;
+  if ((builtins & NCCLASS_W) && ((ch != '\n') && (ch != '_') && !IS_ALPHANUM(fl)))  // \W
+    return true;
+  if ((builtins & NCCLASS_S) && !IS_SPACE(fl))  // \S
+    return true;
+  if ((builtins & NCCLASS_D) && ((ch != '\n') && !IS_DIGIT(fl)))  // \D
+    return true;
+  //
+  return false;
+}
+
+__device__ __forceinline__ reinst reprog_device::get_inst(int32_t id) const { return _insts[id]; }
+
+__device__ __forceinline__ reclass_device reprog_device::get_class(int32_t id) const
+{
+  return _classes[id];
+}
+
+__device__ __forceinline__ bool reprog_device::is_empty() const
+{
+  return insts_counts() == 0 || get_inst(0).type == END;
+}
+
+__device__ __forceinline__ void reprog_device::store(void* buffer) const
+{
+  if (_prog_size > MAX_SHARED_MEM) { return; }
+
+  auto ptr = static_cast<u_char*>(buffer);
+
+  // create instance inside the given buffer
+  auto result = new (ptr) reprog_device(*this);
+
+  // add the insts array
+  ptr += sizeof(reprog_device);
+  auto insts     = reinterpret_cast<reinst*>(ptr);
+  result->_insts = insts;
+  for (int idx = 0; idx < _insts_count; ++idx)
+    *insts++ = _insts[idx];
+
+  // add the startinst_ids array
+  ptr += cudf::util::round_up_unsafe(_insts_count * sizeof(_insts[0]), sizeof(_startinst_ids[0]));
+  auto ids               = reinterpret_cast<int32_t*>(ptr);
+  result->_startinst_ids = ids;
+  for (int idx = 0; idx < _starts_count; ++idx)
+    *ids++ = _startinst_ids[idx];
+
+  // add the classes array
+  ptr += cudf::util::round_up_unsafe(_starts_count * sizeof(int32_t), sizeof(_classes[0]));
+  auto classes     = reinterpret_cast<reclass_device*>(ptr);
+  result->_classes = classes;
+  // fill in each class
+  auto d_ptr = reinterpret_cast<reclass_range*>(classes + _classes_count);
+  for (int idx = 0; idx < _classes_count; ++idx) {
+    classes[idx]          = _classes[idx];
+    classes[idx].literals = d_ptr;
+    for (int jdx = 0; jdx < _classes[idx].count; ++jdx)
+      *d_ptr++ = _classes[idx].literals[jdx];
+  }
+}
+
+__device__ __forceinline__ reprog_device reprog_device::load(reprog_device const prog, void* buffer)
+{
+  return (prog._prog_size > MAX_SHARED_MEM) ? reprog_device(prog)
+                                            : reinterpret_cast<reprog_device*>(buffer)[0];
+}
+
+/**
+ * @brief Evaluate a specific string against regex pattern compiled to this instance.
+ *
+ * This is the main function for executing the regex against an individual string.
+ *
+ * @param dstr String used for matching.
+ * @param jnk State data object for this string.
+ * @param[in,out] begin Character position to start evaluation. On return, it is the position of the
+ * match.
+ * @param[in,out] end Character position to stop evaluation. On return, it is the end of the matched
+ * substring.
+ * @param group_id Index of the group to match in a multi-group regex pattern.
+ * @return >0 if match found
+ */
+__device__ __forceinline__ match_result reprog_device::regexec(string_view const dstr,
+                                                               reljunk jnk,
+                                                               string_view::const_iterator itr,
+                                                               cudf::size_type end,
+                                                               cudf::size_type const group_id) const
+{
+  int32_t match       = 0;
+  auto begin          = itr.position();
+  auto pos            = begin;
+  auto eos            = end;
+  auto checkstart     = jnk.starttype != 0;
+  auto last_character = false;
+
+  jnk.list1->reset();
+  do {
+    // fast check for first CHAR or BOL
+    if (checkstart) {
+      auto startchar = static_cast<char_utf8>(jnk.startchar);
+      switch (jnk.starttype) {
+        case BOL:
+          if (pos == 0) break;
+          if (jnk.startchar != '^') { return thrust::nullopt; }
+          --pos;
+          startchar = static_cast<char_utf8>('\n');
+        case CHAR: {
+          auto const fidx = dstr.find(startchar, pos);
+          if (fidx == string_view::npos) { return thrust::nullopt; }
+          pos = fidx + (jnk.starttype == BOL);
+          break;
+        }
+      }
+      itr += (pos - itr.position());  // faster to increment position
+    }
+
+    if (((eos < 0) || (pos < eos)) && match == 0) {
+      auto ids = _startinst_ids;
+      while (*ids >= 0)
+        jnk.list1->activate(*ids++, (group_id == 0 ? pos : -1), -1);
+    }
+
+    last_character = itr.byte_offset() >= dstr.size_bytes();
+
+    char_utf8 const c = last_character ? 0 : *itr;
+
+    // expand the non-character types like: LBRA, RBRA, BOL, EOL, BOW, NBOW, and OR
+    bool expanded = false;
+    do {
+      jnk.list2->reset();
+      expanded = false;
+
+      for (int16_t i = 0; i < jnk.list1->get_size(); i++) {
+        auto state          = jnk.list1->get_state(i);
+        auto range          = state.range;
+        auto const inst     = get_inst(state.inst_id);
+        int32_t id_activate = -1;
+
+        switch (inst.type) {
+          case CHAR:
+          case ANY:
+          case ANYNL:
+          case CCLASS:
+          case NCCLASS:
+          case END: id_activate = state.inst_id; break;
+          case LBRA:
+            if (inst.u1.subid == group_id) range.x = pos;
+            id_activate = inst.u2.next_id;
+            expanded    = true;
+            break;
+          case RBRA:
+            if (inst.u1.subid == group_id) range.y = pos;
+            id_activate = inst.u2.next_id;
+            expanded    = true;
+            break;
+          case BOL:
+            if ((pos == 0) || ((inst.u1.c == '^') && (dstr[pos - 1] == '\n'))) {
+              id_activate = inst.u2.next_id;
+              expanded    = true;
+            }
+            break;
+          case EOL:
+            // after the last character OR:
+            // - for MULTILINE, if current character is new-line
+            // - for non-MULTILINE, the very last character of the string can also be a new-line
+            if (last_character ||
+                ((c == '\n') && (inst.u1.c != 'Z') &&
+                 ((inst.u1.c == '$') || (itr.byte_offset() + 1 == dstr.size_bytes())))) {
+              id_activate = inst.u2.next_id;
+              expanded    = true;
+            }
+            break;
+          case BOW:
+          case NBOW: {
+            auto const prev_c       = pos > 0 ? dstr[pos - 1] : 0;
+            auto const word_class   = reclass_device{CCLASS_W};
+            bool const curr_is_word = word_class.is_match(c, _codepoint_flags);
+            bool const prev_is_word = word_class.is_match(prev_c, _codepoint_flags);
+            if ((curr_is_word == prev_is_word) != (inst.type == BOW)) {
+              id_activate = inst.u2.next_id;
+              expanded    = true;
+            }
+            break;
+          }
+          case OR:
+            jnk.list2->activate(inst.u1.right_id, range.x, range.y);
+            id_activate = inst.u2.left_id;
+            expanded    = true;
+            break;
+        }
+        if (id_activate >= 0) jnk.list2->activate(id_activate, range.x, range.y);
+      }
+      jnk.swaplist();
+
+    } while (expanded);
+
+    // execute instructions
+    bool continue_execute = true;
+    jnk.list2->reset();
+    for (int16_t i = 0; continue_execute && i < jnk.list1->get_size(); i++) {
+      auto const state    = jnk.list1->get_state(i);
+      auto const range    = state.range;
+      auto const inst     = get_inst(state.inst_id);
+      int32_t id_activate = -1;
+
+      switch (inst.type) {
+        case CHAR:
+          if (inst.u1.c == c) id_activate = inst.u2.next_id;
+          break;
+        case ANY:
+          if (c != '\n') id_activate = inst.u2.next_id;
+          break;
+        case ANYNL: id_activate = inst.u2.next_id; break;
+        case NCCLASS:
+        case CCLASS: {
+          auto const cls = get_class(inst.u1.cls_id);
+          if (cls.is_match(static_cast<char32_t>(c), _codepoint_flags) == (inst.type == CCLASS)) {
+            id_activate = inst.u2.next_id;
+          }
+          break;
+        }
+        case END:
+          match = 1;
+          begin = range.x;
+          end   = group_id == 0 ? pos : range.y;
+          // done with execute
+          continue_execute = false;
+          break;
+      }
+      if (continue_execute && (id_activate >= 0))
+        jnk.list2->activate(id_activate, range.x, range.y);
+    }
+
+    ++pos;
+    ++itr;
+    jnk.swaplist();
+    checkstart = jnk.list1->get_size() == 0;
+  } while (!last_character && (!checkstart || !match));
+
+  return match ? match_result({begin, end}) : thrust::nullopt;
+}
+
+__device__ __forceinline__ match_result reprog_device::find(int32_t const thread_idx,
+                                                            string_view const dstr,
+                                                            string_view::const_iterator begin,
+                                                            cudf::size_type end) const
+{
+  return call_regexec(thread_idx, dstr, begin, end);
+}
+
+__device__ __forceinline__ match_result reprog_device::extract(int32_t const thread_idx,
+                                                               string_view const dstr,
+                                                               string_view::const_iterator begin,
+                                                               cudf::size_type end,
+                                                               cudf::size_type const group_id) const
+{
+  end = begin.position() + 1;
+  return call_regexec(thread_idx, dstr, begin, end, group_id + 1);
+}
+
+__device__ __forceinline__ match_result
+reprog_device::call_regexec(int32_t const thread_idx,
+                            string_view const dstr,
+                            string_view::const_iterator begin,
+                            cudf::size_type end,
+                            cudf::size_type const group_id) const
+{
+  auto gp_ptr = reinterpret_cast<u_char*>(_buffer);
+  relist list1(static_cast<int16_t>(_max_insts), _thread_count, gp_ptr, thread_idx);
+
+  gp_ptr += relist::alloc_size(_max_insts, _thread_count);
+  relist list2(static_cast<int16_t>(_max_insts), _thread_count, gp_ptr, thread_idx);
+
+  reljunk jnk(&list1, &list2, get_inst(_startinst_id));
+  return regexec(dstr, jnk, begin, end, group_id);
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/regex_program.cpp b/cpp/src/strings/regex/regex_program.cpp
new file mode 100644
index 0000000..28950d5
--- /dev/null
+++ b/cpp/src/strings/regex/regex_program.cpp
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "regex_program_impl.h"
+
+#include <cudf/strings/regex/regex_program.hpp>
+
+#include <memory>
+#include <string>
+
+namespace cudf {
+namespace strings {
+
+std::unique_ptr<regex_program> regex_program::create(std::string_view pattern,
+                                                     regex_flags flags,
+                                                     capture_groups capture)
+{
+  auto p = new regex_program(pattern, flags, capture);
+  return std::unique_ptr<regex_program>(p);
+}
+
+regex_program::~regex_program()                                = default;
+regex_program::regex_program(regex_program&& other)            = default;
+regex_program& regex_program::operator=(regex_program&& other) = default;
+
+regex_program::regex_program(std::string_view pattern, regex_flags flags, capture_groups capture)
+  : _pattern(pattern),
+    _flags(flags),
+    _impl(
+      std::make_unique<regex_program_impl>(detail::reprog::create_from(pattern, flags, capture)))
+{
+}
+
+std::string regex_program::pattern() const { return _pattern; }
+
+regex_flags regex_program::flags() const { return _flags; }
+
+capture_groups regex_program::capture() const { return _capture; }
+
+int32_t regex_program::instructions_count() const { return _impl->prog.insts_count(); }
+
+int32_t regex_program::groups_count() const { return _impl->prog.groups_count(); }
+
+std::size_t regex_program::compute_working_memory_size(int32_t num_strings) const
+{
+  return detail::compute_working_memory_size(num_strings, instructions_count());
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/regex_program_impl.h b/cpp/src/strings/regex/regex_program_impl.h
new file mode 100644
index 0000000..74cc190
--- /dev/null
+++ b/cpp/src/strings/regex/regex_program_impl.h
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "regcomp.h"
+#include "regex.cuh"
+
+#include <cudf/strings/regex/regex_program.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+
+/**
+ * @brief Implementation object for regex_program
+ *
+ * It encapsulates internal reprog object used for building its device equivalent
+ */
+struct regex_program::regex_program_impl {
+  detail::reprog prog;
+
+  regex_program_impl(detail::reprog const& p) : prog(p) {}
+  regex_program_impl(detail::reprog&& p) : prog(p) {}
+
+  // TODO: There will be other options added here in the future to handle issues
+  // 10852 and possibly others like 11979
+};
+
+struct regex_device_builder {
+  static auto create_prog_device(regex_program const& p, rmm::cuda_stream_view stream)
+  {
+    return detail::reprog_device::create(p._impl->prog, stream);
+  }
+};
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/regexec.cpp b/cpp/src/strings/regex/regexec.cpp
new file mode 100644
index 0000000..a82f035
--- /dev/null
+++ b/cpp/src/strings/regex/regexec.cpp
@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/regex/regcomp.h>
+#include <strings/regex/regex.cuh>
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+
+#include <algorithm>
+#include <functional>
+#include <numeric>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+// Copy reprog primitive values
+reprog_device::reprog_device(reprog const& prog)
+  : _startinst_id{prog.get_start_inst()},
+    _num_capturing_groups{prog.groups_count()},
+    _insts_count{prog.insts_count()},
+    _starts_count{prog.starts_count()},
+    _classes_count{prog.classes_count()},
+    _max_insts{prog.insts_count()},
+    _codepoint_flags{get_character_flags_table()}
+{
+}
+
+std::unique_ptr<reprog_device, std::function<void(reprog_device*)>> reprog_device::create(
+  reprog const& h_prog, rmm::cuda_stream_view stream)
+{
+  // compute size to hold all the member data
+  auto const insts_count   = h_prog.insts_count();
+  auto const classes_count = h_prog.classes_count();
+  auto const starts_count  = h_prog.starts_count();
+
+  // compute size of each section
+  auto insts_size    = insts_count * sizeof(_insts[0]);
+  auto startids_size = starts_count * sizeof(_startinst_ids[0]);
+  auto classes_size  = std::transform_reduce(
+    h_prog.classes_data(),
+    h_prog.classes_data() + h_prog.classes_count(),
+    classes_count * sizeof(_classes[0]),
+    std::plus<std::size_t>{},
+    [&h_prog](auto& cls) { return cls.literals.size() * sizeof(reclass_range); });
+  // make sure each section is aligned for the subsequent section's data type
+  auto const memsize = cudf::util::round_up_safe(insts_size, sizeof(_startinst_ids[0])) +
+                       cudf::util::round_up_safe(startids_size, sizeof(_classes[0])) +
+                       cudf::util::round_up_safe(classes_size, sizeof(char32_t));
+
+  // allocate memory to store all the prog data in a flat contiguous buffer
+  std::vector<u_char> h_buffer(memsize);                        // copy everything into here;
+  auto h_ptr    = h_buffer.data();                              // this is our running host ptr;
+  auto d_buffer = new rmm::device_buffer(memsize, stream);      // output device memory;
+  auto d_ptr    = reinterpret_cast<u_char*>(d_buffer->data());  // running device pointer
+
+  // create our device object; this is managed separately and returned to the caller
+  reprog_device* d_prog = new reprog_device(h_prog);
+
+  // copy the instructions array first (fixed-sized structs)
+  memcpy(h_ptr, h_prog.insts_data(), insts_size);
+  d_prog->_insts = reinterpret_cast<reinst*>(d_ptr);
+
+  // point to the end for the next section
+  insts_size = cudf::util::round_up_safe(insts_size, sizeof(_startinst_ids[0]));
+  h_ptr += insts_size;
+  d_ptr += insts_size;
+  // copy the startinst_ids next
+  memcpy(h_ptr, h_prog.starts_data(), startids_size);
+  d_prog->_startinst_ids = reinterpret_cast<int32_t*>(d_ptr);
+
+  // next section; align the size for next data type
+  startids_size = cudf::util::round_up_safe(startids_size, sizeof(_classes[0]));
+  h_ptr += startids_size;
+  d_ptr += startids_size;
+  // copy classes into flat memory: [class1,class2,...][char32 arrays]
+  auto classes     = reinterpret_cast<reclass_device*>(h_ptr);
+  d_prog->_classes = reinterpret_cast<reclass_device*>(d_ptr);
+  // get pointer to the end to handle variable length data
+  auto h_end = h_ptr + (classes_count * sizeof(reclass_device));
+  auto d_end = d_ptr + (classes_count * sizeof(reclass_device));
+  // place each class and append the variable length data
+  for (int32_t idx = 0; idx < classes_count; ++idx) {
+    auto const& h_class = h_prog.class_at(idx);
+    reclass_device d_class{h_class.builtins,
+                           static_cast<int32_t>(h_class.literals.size()),
+                           reinterpret_cast<reclass_range*>(d_end)};
+    *classes++ = d_class;
+    memcpy(h_end, h_class.literals.data(), h_class.literals.size() * sizeof(reclass_range));
+    h_end += h_class.literals.size() * sizeof(reclass_range);
+    d_end += h_class.literals.size() * sizeof(reclass_range);
+  }
+
+  // initialize the rest of the elements
+  d_prog->_max_insts = insts_count;
+  d_prog->_prog_size = memsize + sizeof(reprog_device);
+
+  // copy flat prog to device memory
+  CUDF_CUDA_TRY(
+    cudaMemcpyAsync(d_buffer->data(), h_buffer.data(), memsize, cudaMemcpyDefault, stream.value()));
+
+  // build deleter to cleanup device memory
+  auto deleter = [d_buffer](reprog_device* t) {
+    t->destroy();
+    delete d_buffer;
+  };
+
+  return std::unique_ptr<reprog_device, std::function<void(reprog_device*)>>(d_prog, deleter);
+}
+
+void reprog_device::destroy() { delete this; }
+
+std::size_t reprog_device::working_memory_size(int32_t num_threads) const
+{
+  return compute_working_memory_size(num_threads, insts_counts());
+}
+
+std::pair<std::size_t, int32_t> reprog_device::compute_strided_working_memory(
+  int32_t rows, int32_t min_rows, std::size_t requested_max_size) const
+{
+  auto thread_count = rows;
+  auto buffer_size  = working_memory_size(thread_count);
+  while ((buffer_size > requested_max_size) && (thread_count > min_rows)) {
+    thread_count = thread_count / 2;
+    buffer_size  = working_memory_size(thread_count);
+  }
+  // clamp to min_rows but only if rows is greater than min_rows
+  if (rows > min_rows && thread_count < min_rows) {
+    thread_count = min_rows;
+    buffer_size  = working_memory_size(thread_count);
+  }
+  return std::make_pair(buffer_size, thread_count);
+}
+
+void reprog_device::set_working_memory(void* buffer, int32_t thread_count, int32_t max_insts)
+{
+  _buffer       = buffer;
+  _thread_count = thread_count;
+  _max_insts    = _max_insts > 0 ? _max_insts : _insts_count;
+}
+
+int32_t reprog_device::compute_shared_memory_size() const
+{
+  return _prog_size < MAX_SHARED_MEM ? static_cast<int32_t>(_prog_size) : 0;
+}
+
+std::size_t compute_working_memory_size(int32_t num_threads, int32_t insts_count)
+{
+  return relist::alloc_size(insts_count, num_threads) * 2;
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/regex/utilities.cuh b/cpp/src/strings/regex/utilities.cuh
new file mode 100644
index 0000000..23b5306
--- /dev/null
+++ b/cpp/src/strings/regex/utilities.cuh
@@ -0,0 +1,156 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <strings/regex/regex.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/detail/utilities.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/scan.h>
+
+#include <stdexcept>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+constexpr auto regex_launch_kernel_block_size = 256;
+
+template <typename ForEachFunction>
+__global__ void for_each_kernel(ForEachFunction fn, reprog_device const d_prog, size_type size)
+{
+  extern __shared__ u_char shmem[];
+  if (threadIdx.x == 0) { d_prog.store(shmem); }
+  __syncthreads();
+  auto const s_prog = reprog_device::load(d_prog, shmem);
+
+  auto const thread_idx = threadIdx.x + blockIdx.x * blockDim.x;
+  auto const stride     = s_prog.thread_count();
+  if (thread_idx < stride) {
+    for (auto idx = thread_idx; idx < size; idx += stride) {
+      fn(idx, s_prog, thread_idx);
+    }
+  }
+}
+
+template <typename ForEachFunction>
+void launch_for_each_kernel(ForEachFunction fn,
+                            reprog_device& d_prog,
+                            size_type size,
+                            rmm::cuda_stream_view stream)
+{
+  auto [buffer_size, thread_count] = d_prog.compute_strided_working_memory(size);
+
+  auto d_buffer = rmm::device_buffer(buffer_size, stream);
+  d_prog.set_working_memory(d_buffer.data(), thread_count);
+
+  auto const shmem_size = d_prog.compute_shared_memory_size();
+  cudf::detail::grid_1d grid{thread_count, regex_launch_kernel_block_size};
+  for_each_kernel<<<grid.num_blocks, grid.num_threads_per_block, shmem_size, stream.value()>>>(
+    fn, d_prog, size);
+}
+
+template <typename TransformFunction, typename OutputType>
+__global__ void transform_kernel(TransformFunction fn,
+                                 reprog_device const d_prog,
+                                 OutputType* d_output,
+                                 size_type size)
+{
+  extern __shared__ u_char shmem[];
+  if (threadIdx.x == 0) { d_prog.store(shmem); }
+  __syncthreads();
+  auto const s_prog = reprog_device::load(d_prog, shmem);
+
+  auto const thread_idx = threadIdx.x + blockIdx.x * blockDim.x;
+  auto const stride     = s_prog.thread_count();
+  if (thread_idx < stride) {
+    for (auto idx = thread_idx; idx < size; idx += stride) {
+      d_output[idx] = fn(idx, s_prog, thread_idx);
+    }
+  }
+}
+
+template <typename TransformFunction, typename OutputType>
+void launch_transform_kernel(TransformFunction fn,
+                             reprog_device& d_prog,
+                             OutputType* d_output,
+                             size_type size,
+                             rmm::cuda_stream_view stream)
+{
+  auto [buffer_size, thread_count] = d_prog.compute_strided_working_memory(size);
+
+  auto d_buffer = rmm::device_buffer(buffer_size, stream);
+  d_prog.set_working_memory(d_buffer.data(), thread_count);
+
+  auto const shmem_size = d_prog.compute_shared_memory_size();
+  cudf::detail::grid_1d grid{thread_count, regex_launch_kernel_block_size};
+  transform_kernel<<<grid.num_blocks, grid.num_threads_per_block, shmem_size, stream.value()>>>(
+    fn, d_prog, d_output, size);
+}
+
+template <typename SizeAndExecuteFunction>
+auto make_strings_children(SizeAndExecuteFunction size_and_exec_fn,
+                           reprog_device& d_prog,
+                           size_type strings_count,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr)
+{
+  auto offsets = make_numeric_column(
+    data_type{type_id::INT32}, strings_count + 1, mask_state::UNALLOCATED, stream, mr);
+  auto d_offsets             = offsets->mutable_view().template data<int32_t>();
+  size_and_exec_fn.d_offsets = d_offsets;
+
+  auto [buffer_size, thread_count] = d_prog.compute_strided_working_memory(strings_count);
+
+  auto d_buffer = rmm::device_buffer(buffer_size, stream);
+  d_prog.set_working_memory(d_buffer.data(), thread_count);
+  auto const shmem_size = d_prog.compute_shared_memory_size();
+  cudf::detail::grid_1d grid{thread_count, 256};
+
+  // Compute the output size for each row
+  if (strings_count > 0) {
+    for_each_kernel<<<grid.num_blocks, grid.num_threads_per_block, shmem_size, stream.value()>>>(
+      size_and_exec_fn, d_prog, strings_count);
+  }
+
+  auto const char_bytes =
+    cudf::detail::sizes_to_offsets(d_offsets, d_offsets + strings_count + 1, d_offsets, stream);
+  CUDF_EXPECTS(char_bytes <= std::numeric_limits<size_type>::max(),
+               "Size of output exceeds the column size limit",
+               std::overflow_error);
+
+  // Now build the chars column
+  std::unique_ptr<column> chars =
+    create_chars_child_column(static_cast<size_type>(char_bytes), stream, mr);
+  if (char_bytes > 0) {
+    size_and_exec_fn.d_chars = chars->mutable_view().template data<char>();
+    for_each_kernel<<<grid.num_blocks, grid.num_threads_per_block, shmem_size, stream.value()>>>(
+      size_and_exec_fn, d_prog, strings_count);
+  }
+
+  return std::make_pair(std::move(offsets), std::move(chars));
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/repeat_strings.cu b/cpp/src/strings/repeat_strings.cu
new file mode 100644
index 0000000..396e1e6
--- /dev/null
+++ b/cpp/src/strings/repeat_strings.cu
@@ -0,0 +1,286 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+#include <thrust/transform_reduce.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+std::unique_ptr<string_scalar> repeat_string(string_scalar const& input,
+                                             size_type repeat_times,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  if (!input.is_valid(stream)) { return std::make_unique<string_scalar>("", false, stream, mr); }
+  if (input.size() == 0 || repeat_times <= 0) {
+    return std::make_unique<string_scalar>("", true, stream, mr);
+  }
+  if (repeat_times == 1) { return std::make_unique<string_scalar>(input, stream, mr); }
+
+  CUDF_EXPECTS(input.size() <= std::numeric_limits<size_type>::max() / repeat_times,
+               "The output size exceeds the column size limit",
+               std::overflow_error);
+
+  auto const str_size = input.size();
+  auto const iter     = thrust::make_counting_iterator(0);
+  auto buff           = rmm::device_buffer(repeat_times * input.size(), stream, mr);
+
+  // Pull data from the input string into each byte of the output string.
+  thrust::transform(rmm::exec_policy(stream),
+                    iter,
+                    iter + repeat_times * str_size,
+                    static_cast<char*>(buff.data()),
+                    [in_ptr = input.data(), str_size] __device__(auto const idx) {
+                      return in_ptr[idx % str_size];
+                    });
+
+  return std::make_unique<string_scalar>(std::move(buff));
+}
+
+namespace {
+/**
+ * @brief Generate a strings column in which each row is an empty string or a null.
+ *
+ * The output strings column has the same bitmask as the input column.
+ */
+auto generate_empty_output(strings_column_view const& input,
+                           size_type strings_count,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr)
+{
+  auto chars_column = create_chars_child_column(0, stream, mr);
+
+  auto offsets_column = make_numeric_column(
+    data_type{type_to_id<size_type>()}, strings_count + 1, mask_state::UNALLOCATED, stream, mr);
+  CUDF_CUDA_TRY(cudaMemsetAsync(offsets_column->mutable_view().template data<size_type>(),
+                                0,
+                                offsets_column->size() * sizeof(size_type),
+                                stream.value()));
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+/**
+ * @brief Functor to compute output string sizes and repeat the input strings.
+ *
+ * This functor is called only when `repeat_times > 0`. In addition, the total number of threads
+ * running this functor is `repeat_times * strings_count` (instead of `string_count`) for maximizing
+ * parallelism and better load-balancing.
+ */
+struct compute_size_and_repeat_fn {
+  column_device_view const strings_dv;
+  size_type const repeat_times;
+  bool const has_nulls;
+
+  size_type* d_offsets{nullptr};
+
+  // If d_chars == nullptr: only compute sizes of the output strings.
+  // If d_chars != nullptr: only repeat strings.
+  char* d_chars{nullptr};
+
+  // `idx` will be in the range of [0, repeat_times * strings_count).
+  __device__ void operator()(size_type const idx) const noexcept
+  {
+    auto const str_idx    = idx / repeat_times;  // value cycles in [0, string_count)
+    auto const repeat_idx = idx % repeat_times;  // value cycles in [0, repeat_times)
+    auto const is_valid   = !has_nulls || strings_dv.is_valid_nocheck(str_idx);
+
+    if (!d_chars && repeat_idx == 0) {
+      d_offsets[str_idx] =
+        is_valid ? repeat_times * strings_dv.element<string_view>(str_idx).size_bytes() : 0;
+    }
+
+    // Each input string will be copied by `repeat_times` threads into the output string.
+    if (d_chars && is_valid) {
+      auto const d_str    = strings_dv.element<string_view>(str_idx);
+      auto const str_size = d_str.size_bytes();
+      if (str_size > 0) {
+        auto const input_ptr  = d_str.data();
+        auto const output_ptr = d_chars + d_offsets[str_idx] + repeat_idx * str_size;
+        std::memcpy(output_ptr, input_ptr, str_size);
+      }
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> repeat_strings(strings_column_view const& input,
+                                       size_type repeat_times,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = input.size();
+  if (strings_count == 0) { return make_empty_column(type_id::STRING); }
+
+  if (repeat_times <= 0) {
+    // If the number of repetitions is not positive, each row of the output strings column will be
+    // either an empty string (if the input row is not null), or a null (if the input row is null).
+    return generate_empty_output(input, strings_count, stream, mr);
+  }
+
+  // If `repeat_times == 1`, just make a copy of the input.
+  if (repeat_times == 1) { return std::make_unique<column>(input.parent(), stream, mr); }
+
+  auto const strings_dv_ptr = column_device_view::create(input.parent(), stream);
+  auto const fn = compute_size_and_repeat_fn{*strings_dv_ptr, repeat_times, input.has_nulls()};
+
+  auto [offsets_column, chars_column] =
+    make_strings_children(fn, strings_count * repeat_times, strings_count, stream, mr);
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+namespace {
+/**
+ * @brief Functor to compute string sizes and repeat the input strings, each string is repeated by a
+ * separate number of times.
+ */
+template <class Iterator>
+struct compute_sizes_and_repeat_fn {
+  column_device_view const strings_dv;
+  column_device_view const repeat_times_dv;
+  Iterator const repeat_times_iter;
+  bool const strings_has_nulls;
+  bool const rtimes_has_nulls;
+
+  size_type* d_offsets{nullptr};
+
+  // If d_chars == nullptr: only compute sizes of the output strings.
+  // If d_chars != nullptr: only repeat strings.
+  char* d_chars{nullptr};
+
+  __device__ void operator()(size_type const idx) const noexcept
+  {
+    auto const string_is_valid = !strings_has_nulls || strings_dv.is_valid_nocheck(idx);
+    auto const rtimes_is_valid = !rtimes_has_nulls || repeat_times_dv.is_valid_nocheck(idx);
+
+    // Any null input (either string or repeat_times value) will result in a null output.
+    auto const is_valid = string_is_valid && rtimes_is_valid;
+    if (!is_valid) {
+      if (!d_chars) { d_offsets[idx] = 0; }
+      return;
+    }
+
+    auto repeat_times = repeat_times_iter[idx];
+    auto const d_str  = strings_dv.element<string_view>(idx);
+
+    if (!d_chars) {
+      // repeat_times could be negative
+      d_offsets[idx] = (repeat_times > 0) ? (repeat_times * d_str.size_bytes()) : 0;
+    } else {
+      auto output_ptr = d_chars + d_offsets[idx];
+      while (repeat_times-- > 0) {
+        output_ptr = copy_and_increment(output_ptr, d_str.data(), d_str.size_bytes());
+      }
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> repeat_strings(strings_column_view const& input,
+                                       column_view const& repeat_times,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.size() == repeat_times.size(), "The input columns must have the same size.");
+  CUDF_EXPECTS(cudf::is_index_type(repeat_times.type()),
+               "repeat_strings expects an integer type for the `repeat_times` input column.");
+
+  auto const strings_count = input.size();
+  if (strings_count == 0) { return make_empty_column(type_id::STRING); }
+
+  auto const strings_dv_ptr      = column_device_view::create(input.parent(), stream);
+  auto const repeat_times_dv_ptr = column_device_view::create(repeat_times, stream);
+  auto const repeat_times_iter =
+    cudf::detail::indexalator_factory::make_input_iterator(repeat_times);
+  auto const fn =
+    compute_sizes_and_repeat_fn<decltype(repeat_times_iter)>{*strings_dv_ptr,
+                                                             *repeat_times_dv_ptr,
+                                                             repeat_times_iter,
+                                                             input.has_nulls(),
+                                                             repeat_times.has_nulls()};
+
+  auto [offsets_column, chars_column] = make_strings_children(fn, strings_count, stream, mr);
+
+  // We generate new bitmask by AND of the two input columns' bitmasks.
+  // Note that if either of the input columns are nullable, the output column will also be nullable
+  // but may not have nulls.
+  auto [null_mask, null_count] =
+    cudf::detail::bitmask_and(table_view{{input.parent(), repeat_times}}, stream, mr);
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+}  // namespace detail
+
+std::unique_ptr<string_scalar> repeat_string(string_scalar const& input,
+                                             size_type repeat_times,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::repeat_string(input, repeat_times, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> repeat_strings(strings_column_view const& input,
+                                       size_type repeat_times,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::repeat_strings(input, repeat_times, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> repeat_strings(strings_column_view const& input,
+                                       column_view const& repeat_times,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::repeat_strings(input, repeat_times, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/replace/backref_re.cu b/cpp/src/strings/replace/backref_re.cu
new file mode 100644
index 0000000..31e06aa
--- /dev/null
+++ b/cpp/src/strings/replace/backref_re.cu
@@ -0,0 +1,158 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "backref_re.cuh"
+
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/replace_re.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <regex>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Return the capturing group index pattern to use with the given replacement string.
+ *
+ * Only two patterns are supported at this time `\d` and `${d}` where `d` is an integer in
+ * the range 0-99. The `\d` pattern is returned by default unless no `\d` pattern is found in
+ * the `repl` string,
+ *
+ * Reference: https://www.regular-expressions.info/refreplacebackref.html
+ */
+std::string get_backref_pattern(std::string_view repl)
+{
+  std::string const backslash_pattern = "\\\\(\\d+)";
+  std::string const bracket_pattern   = "\\$\\{(\\d+)\\}";
+  std::string const r{repl};
+  std::smatch m;
+  return std::regex_search(r, m, std::regex(backslash_pattern)) ? backslash_pattern
+                                                                : bracket_pattern;
+}
+/**
+ * @brief Parse the back-ref index and position values from a given replace format.
+ *
+ * The back-ref numbers are expected to be 1-based.
+ *
+ * Returns a modified string without back-ref indicators and a vector of back-ref
+ * byte position pairs. These are used by the device code to build the output
+ * string by placing the captured group elements into the replace format.
+ *
+ * For example, for input string 'hello \2 and \1' the returned `backref_type` vector
+ * contains `[(2,6),(1,11)]` and the returned string is 'hello  and '.
+ */
+std::pair<std::string, std::vector<backref_type>> parse_backrefs(std::string_view repl,
+                                                                 int const group_count)
+{
+  std::vector<backref_type> backrefs;
+  std::string str{repl};  // make a modifiable copy
+  std::smatch m;
+  std::regex ex(get_backref_pattern(repl));
+  std::string rtn;
+  size_type byte_offset = 0;
+  while (std::regex_search(str, m, ex) && !m.empty()) {
+    // parse the back-ref index number
+    size_type const index = static_cast<size_type>(std::atoi(std::string{m[1]}.c_str()));
+    CUDF_EXPECTS(index >= 0 && index <= group_count,
+                 "Group index numbers must be in the range 0 to group count");
+
+    // store the new byte offset and index value
+    size_type const position = static_cast<size_type>(m.position(0));
+    byte_offset += position;
+    backrefs.push_back({index, byte_offset});
+
+    // update the output string
+    rtn += str.substr(0, position);
+    // remove the back-ref pattern to continue parsing
+    str = str.substr(position + static_cast<size_type>(m.length(0)));
+  }
+  if (!str.empty())  // add the remainder
+    rtn += str;      // of the string
+  return {rtn, backrefs};
+}
+
+}  // namespace
+
+//
+std::unique_ptr<column> replace_with_backrefs(strings_column_view const& input,
+                                              regex_program const& prog,
+                                              std::string_view replacement,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(!prog.pattern().empty(), "Parameter pattern must not be empty");
+  CUDF_EXPECTS(!replacement.empty(), "Parameter replacement must not be empty");
+
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  // parse the repl string for back-ref indicators
+  auto group_count = std::min(99, d_prog->group_counts());  // group count should NOT exceed 99
+  auto const parse_result                    = parse_backrefs(replacement, group_count);
+  rmm::device_uvector<backref_type> backrefs = cudf::detail::make_device_uvector_async(
+    parse_result.second, stream, rmm::mr::get_current_device_resource());
+  string_scalar repl_scalar(parse_result.first, true, stream);
+  string_view const d_repl_template = repl_scalar.value();
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+
+  using BackRefIterator = decltype(backrefs.begin());
+  auto children         = make_strings_children(
+    backrefs_fn<BackRefIterator>{*d_strings, d_repl_template, backrefs.begin(), backrefs.end()},
+    *d_prog,
+    input.size(),
+    stream,
+    mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> replace_with_backrefs(strings_column_view const& strings,
+                                              regex_program const& prog,
+                                              std::string_view replacement,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_with_backrefs(strings, prog, replacement, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/replace/backref_re.cuh b/cpp/src/strings/replace/backref_re.cuh
new file mode 100644
index 0000000..aeaea40
--- /dev/null
+++ b/cpp/src/strings/replace/backref_re.cuh
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/regex/regex.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/pair.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+using backref_type = thrust::pair<size_type, size_type>;
+
+/**
+ * @brief This functor handles replacing strings by applying the compiled regex pattern
+ * and inserting the at the backref position indicated in the replacement template.
+ *
+ * The logic includes computing the size of each string and also writing the output.
+ */
+template <typename Iterator>
+struct backrefs_fn {
+  column_device_view const d_strings;
+  string_view const d_repl;  // string replacement template
+  Iterator backrefs_begin;
+  Iterator backrefs_end;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type const idx, reprog_device const prog, int32_t const prog_idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const in_ptr = d_str.data();
+    auto const nchars = d_str.length();      // number of characters in input string
+    auto nbytes       = d_str.size_bytes();  // number of bytes for the output string
+    auto out_ptr      = d_chars ? (d_chars + d_offsets[idx]) : nullptr;
+    auto itr          = d_str.begin();
+    auto last_pos     = itr;
+
+    // copy input to output replacing strings as we go
+    while (itr.position() <= nchars)  // inits the begin/end vars
+    {
+      auto const match = prog.find(prog_idx, d_str, itr);
+      if (!match) { break; }
+
+      auto const [start_pos, end_pos] = match_positions_to_bytes(*match, d_str, itr);
+      nbytes += d_repl.size_bytes() - (end_pos - start_pos);  // compute the output size
+
+      // copy the string data before the matched section
+      if (out_ptr) {
+        out_ptr = copy_and_increment(
+          out_ptr, in_ptr + last_pos.byte_offset(), start_pos - last_pos.byte_offset());
+      }
+      size_type lpos_template = 0;              // last end pos of replace template
+      auto const repl_ptr     = d_repl.data();  // replace template pattern
+
+      itr += (match->first - itr.position());
+      thrust::for_each(
+        thrust::seq, backrefs_begin, backrefs_end, [&] __device__(backref_type backref) {
+          if (out_ptr) {
+            auto const copy_length = backref.second - lpos_template;
+            out_ptr = copy_and_increment(out_ptr, repl_ptr + lpos_template, copy_length);
+            lpos_template += copy_length;
+          }
+          // extract the specific group's string for this backref's index
+          auto extracted = prog.extract(prog_idx, d_str, itr, match->second, backref.first - 1);
+          if (!extracted || (extracted->second < extracted->first)) {
+            return;  // no value for this backref number; that is ok
+          }
+          auto const d_str_ex = string_from_match(*extracted, d_str, itr);
+          nbytes += d_str_ex.size_bytes();
+          if (out_ptr) { out_ptr = copy_string(out_ptr, d_str_ex); }
+        });
+
+      // copy remainder of template
+      if (out_ptr && (lpos_template < d_repl.size_bytes())) {
+        out_ptr = copy_and_increment(
+          out_ptr, repl_ptr + lpos_template, d_repl.size_bytes() - lpos_template);
+      }
+
+      // setup to match the next section
+      last_pos += (match->second - last_pos.position());
+      itr = last_pos + (match->first == match->second);
+    }
+
+    // finally, copy remainder of input string
+    if (out_ptr) {
+      thrust::copy_n(
+        thrust::seq, in_ptr + itr.byte_offset(), d_str.size_bytes() - itr.byte_offset(), out_ptr);
+    } else {
+      d_offsets[idx] = nbytes;
+    }
+  }
+};
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/replace/multi.cu b/cpp/src/strings/replace/multi.cu
new file mode 100644
index 0000000..92ace4e
--- /dev/null
+++ b/cpp/src/strings/replace/multi.cu
@@ -0,0 +1,500 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/replace.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Threshold to decide on using string or character-parallel functions.
+ *
+ * If the average byte length of a string in a column exceeds this value then
+ * the character-parallel function is used.
+ * Otherwise, a regular string-parallel function is used.
+ *
+ * This value was found using the replace-multi benchmark results using an
+ * RTX A6000.
+ */
+constexpr size_type AVG_CHAR_BYTES_THRESHOLD = 256;
+
+/**
+ * @brief Type used for holding the target position (first) and the
+ * target index (second).
+ */
+using target_pair = thrust::pair<size_type, size_type>;
+
+/**
+ * @brief Helper functions for performing character-parallel replace
+ */
+struct replace_multi_parallel_fn {
+  __device__ char const* get_base_ptr() const
+  {
+    return d_strings.child(strings_column_view::chars_column_index).data<char>();
+  }
+
+  __device__ size_type const* get_offsets_ptr() const
+  {
+    return d_strings.child(strings_column_view::offsets_column_index).data<size_type>() +
+           d_strings.offset();
+  }
+
+  __device__ string_view const get_string(size_type idx) const
+  {
+    return d_strings.element<string_view>(idx);
+  }
+
+  __device__ string_view const get_replacement_string(size_type idx) const
+  {
+    return d_replacements.size() == 1 ? d_replacements[0] : d_replacements[idx];
+  }
+
+  __device__ bool is_valid(size_type idx) const { return d_strings.is_valid(idx); }
+
+  /**
+   * @brief Returns the index of the target string found at the given byte position
+   * in the input strings column
+   *
+   * @param idx Index of the byte position in the chars column
+   * @param chars_bytes Number of bytes in the chars column
+   */
+  __device__ thrust::optional<size_type> has_target(size_type idx, size_type chars_bytes) const
+  {
+    auto const d_offsets = get_offsets_ptr();
+    auto const d_chars   = get_base_ptr() + d_offsets[0] + idx;
+    size_type str_idx    = -1;
+    for (std::size_t t = 0; t < d_targets.size(); ++t) {
+      auto const d_tgt = d_targets[t];
+      if (!d_tgt.empty() && (idx + d_tgt.size_bytes() <= chars_bytes) &&
+          (d_tgt.compare(d_chars, d_tgt.size_bytes()) == 0)) {
+        if (str_idx < 0) {
+          auto const idx_itr =
+            thrust::upper_bound(thrust::seq, d_offsets, d_offsets + d_strings.size(), idx);
+          str_idx = thrust::distance(d_offsets, idx_itr) - 1;
+        }
+        auto const d_str = get_string(str_idx - d_offsets[0]);
+        if ((d_chars + d_tgt.size_bytes()) <= (d_str.data() + d_str.size_bytes())) { return t; }
+      }
+    }
+    return thrust::nullopt;
+  }
+
+  /**
+   * @brief Count the number of strings that will be produced by the replace
+   *
+   * This includes segments of the string that are not replaced as well as those
+   * that are replaced.
+   *
+   * @param idx Index of the row in d_strings to be processed
+   * @param d_positions Positions of the targets found in the chars column
+   * @param d_targets_offsets Offsets identify which target positions go with the current string
+   * @return Number of substrings resulting from the replace operations on this row
+   */
+  __device__ size_type count_strings(size_type idx,
+                                     target_pair const* d_positions,
+                                     size_type const* d_targets_offsets) const
+  {
+    if (!is_valid(idx)) { return 0; }
+
+    auto const d_str             = get_string(idx);
+    auto const d_str_end         = d_str.data() + d_str.size_bytes();
+    auto const base_ptr          = get_base_ptr();
+    auto const targets_positions = cudf::device_span<target_pair const>(
+      d_positions + d_targets_offsets[idx], d_targets_offsets[idx + 1] - d_targets_offsets[idx]);
+
+    size_type count = 1;  // always at least one string
+    auto str_ptr    = d_str.data();
+    for (auto d_pair : targets_positions) {
+      auto const d_pos   = d_pair.first;
+      auto const d_tgt   = d_targets[d_pair.second];
+      auto const tgt_ptr = base_ptr + d_pos;
+      if (str_ptr <= tgt_ptr && tgt_ptr < d_str_end) {
+        auto const keep_size = static_cast<size_type>(thrust::distance(str_ptr, tgt_ptr));
+        if (keep_size > 0) { count++; }  // don't bother counting empty strings
+
+        auto const d_repl = get_replacement_string(d_pair.second);
+        if (!d_repl.empty()) { count++; }
+
+        str_ptr += keep_size + d_tgt.size_bytes();
+      }
+    }
+
+    return count;
+  }
+
+  /**
+   * @brief Retrieve the strings for each row
+   *
+   * This will return string segments as string_index_pair objects for
+   * parts of the string that are not replaced interlaced with the
+   * appropriate replacement string where replacement targets are found.
+   *
+   * This function is called only once to produce both the string_index_pair objects
+   * and the output row size in bytes.
+   *
+   * @param idx Index of the row in d_strings
+   * @param d_offsets Offsets to identify where to store the results of the replace for this string
+   * @param d_positions The target positions found in the chars column
+   * @param d_targets_offsets The offsets to identify which target positions go with this string
+   * @param d_all_strings The output of all the produced string segments
+   * @return The size in bytes of the output string for this row
+   */
+  __device__ size_type get_strings(size_type idx,
+                                   size_type const* d_offsets,
+                                   target_pair const* d_positions,
+                                   size_type const* d_targets_offsets,
+                                   string_index_pair* d_all_strings) const
+  {
+    if (!is_valid(idx)) { return 0; }
+
+    auto const d_output  = d_all_strings + d_offsets[idx];
+    auto const d_str     = get_string(idx);
+    auto const d_str_end = d_str.data() + d_str.size_bytes();
+    auto const base_ptr  = get_base_ptr();
+
+    auto const targets_positions = cudf::device_span<target_pair const>(
+      d_positions + d_targets_offsets[idx], d_targets_offsets[idx + 1] - d_targets_offsets[idx]);
+
+    size_type output_idx  = 0;
+    size_type output_size = 0;
+    auto str_ptr          = d_str.data();
+    for (auto d_pair : targets_positions) {
+      auto const d_pos   = d_pair.first;
+      auto const d_tgt   = d_targets[d_pair.second];
+      auto const tgt_ptr = base_ptr + d_pos;
+      if (str_ptr <= tgt_ptr && tgt_ptr < d_str_end) {
+        auto const keep_size = static_cast<size_type>(thrust::distance(str_ptr, tgt_ptr));
+        if (keep_size > 0) { d_output[output_idx++] = string_index_pair{str_ptr, keep_size}; }
+        output_size += keep_size;
+
+        auto const d_repl = get_replacement_string(d_pair.second);
+        if (!d_repl.empty()) {
+          d_output[output_idx++] = string_index_pair{d_repl.data(), d_repl.size_bytes()};
+        }
+        output_size += d_repl.size_bytes();
+
+        str_ptr += keep_size + d_tgt.size_bytes();
+      }
+    }
+    // include any leftover parts of the string
+    if (str_ptr <= d_str_end) {
+      auto const left_size = static_cast<size_type>(thrust::distance(str_ptr, d_str_end));
+      d_output[output_idx] = string_index_pair{str_ptr, left_size};
+      output_size += left_size;
+    }
+    return output_size;
+  }
+
+  replace_multi_parallel_fn(column_device_view const& d_strings,
+                            device_span<string_view const> d_targets,
+                            device_span<string_view const> d_replacements)
+    : d_strings(d_strings), d_targets{d_targets}, d_replacements{d_replacements}
+  {
+  }
+
+ protected:
+  column_device_view d_strings;
+  device_span<string_view const> d_targets;
+  device_span<string_view const> d_replacements;
+};
+
+/**
+ * @brief Used by the copy-if function to produce target_pair objects
+ *
+ * Using an inplace lambda caused a runtime crash in thrust::copy_if
+ * (this happens sometimes when passing device lambdas to thrust algorithms)
+ */
+struct pair_generator {
+  __device__ target_pair operator()(int idx) const
+  {
+    auto pos = fn.has_target(idx, chars_bytes);
+    return target_pair{idx, pos.value_or(-1)};
+  }
+  replace_multi_parallel_fn fn;
+  size_type chars_bytes;
+};
+
+struct copy_if_fn {
+  __device__ bool operator()(target_pair pos) { return pos.second >= 0; }
+};
+
+std::unique_ptr<column> replace_character_parallel(strings_column_view const& input,
+                                                   strings_column_view const& targets,
+                                                   strings_column_view const& repls,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  auto const strings_count = input.size();
+  auto const chars_bytes =
+    cudf::detail::get_value<size_type>(input.offsets(), input.offset() + strings_count, stream) -
+    cudf::detail::get_value<size_type>(input.offsets(), input.offset(), stream);
+
+  auto d_targets =
+    create_string_vector_from_column(targets, stream, rmm::mr::get_current_device_resource());
+  auto d_replacements =
+    create_string_vector_from_column(repls, stream, rmm::mr::get_current_device_resource());
+
+  replace_multi_parallel_fn fn{*d_strings, d_targets, d_replacements};
+
+  // count the number of targets in the entire column
+  auto const target_count = thrust::count_if(rmm::exec_policy(stream),
+                                             thrust::make_counting_iterator<size_type>(0),
+                                             thrust::make_counting_iterator<size_type>(chars_bytes),
+                                             [fn, chars_bytes] __device__(size_type idx) {
+                                               return fn.has_target(idx, chars_bytes).has_value();
+                                             });
+  // Create a vector of every target position in the chars column.
+  // These may include overlapping targets which will be resolved later.
+  auto targets_positions = rmm::device_uvector<target_pair>(target_count, stream);
+  auto d_positions       = targets_positions.data();
+
+  auto const copy_itr =
+    cudf::detail::make_counting_transform_iterator(0, pair_generator{fn, chars_bytes});
+  auto const copy_end = thrust::copy_if(
+    rmm::exec_policy(stream), copy_itr, copy_itr + chars_bytes, d_positions, copy_if_fn{});
+
+  // create a vector of offsets to each string's set of target positions
+  auto const targets_offsets = [&] {
+    auto string_indices = rmm::device_uvector<size_type>(target_count, stream);
+
+    auto const pos_itr = cudf::detail::make_counting_transform_iterator(
+      0, [d_positions] __device__(auto idx) -> size_type { return d_positions[idx].first; });
+    auto pos_count = std::distance(d_positions, copy_end);
+
+    thrust::upper_bound(rmm::exec_policy(stream),
+                        input.offsets_begin(),
+                        input.offsets_end(),
+                        pos_itr,
+                        pos_itr + pos_count,
+                        string_indices.begin());
+
+    // compute offsets per string
+    auto targets_offsets   = rmm::device_uvector<size_type>(strings_count + 1, stream);
+    auto d_targets_offsets = targets_offsets.data();
+
+    // memset to zero-out the target counts for any null-entries or strings with no targets
+    thrust::uninitialized_fill(
+      rmm::exec_policy(stream), targets_offsets.begin(), targets_offsets.end(), 0);
+
+    // next, count the number of targets per string
+    auto d_string_indices = string_indices.data();
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       target_count,
+                       [d_string_indices, d_targets_offsets] __device__(size_type idx) {
+                         auto const str_idx = d_string_indices[idx] - 1;
+                         atomicAdd(d_targets_offsets + str_idx, 1);
+                       });
+    // finally, convert the counts into offsets
+    thrust::exclusive_scan(rmm::exec_policy(stream),
+                           targets_offsets.begin(),
+                           targets_offsets.end(),
+                           targets_offsets.begin());
+    return targets_offsets;
+  }();
+  auto const d_targets_offsets = targets_offsets.data();
+
+  // compute the number of string segments produced by replace in each string
+  auto counts = rmm::device_uvector<size_type>(strings_count, stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings_count),
+                    counts.begin(),
+                    [fn, d_positions, d_targets_offsets] __device__(size_type idx) -> size_type {
+                      return fn.count_strings(idx, d_positions, d_targets_offsets);
+                    });
+
+  // create offsets from the counts
+  auto offsets =
+    std::get<0>(cudf::detail::make_offsets_child_column(counts.begin(), counts.end(), stream, mr));
+  auto const total_strings =
+    cudf::detail::get_value<size_type>(offsets->view(), strings_count, stream);
+  auto const d_strings_offsets = offsets->view().data<size_type>();
+
+  // build a vector of all the positions for all the strings
+  auto indices   = rmm::device_uvector<string_index_pair>(total_strings, stream);
+  auto d_indices = indices.data();
+  auto d_sizes   = counts.data();  // reusing this vector to hold output sizes now
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    strings_count,
+    [fn, d_strings_offsets, d_positions, d_targets_offsets, d_indices, d_sizes] __device__(
+      size_type idx) {
+      d_sizes[idx] =
+        fn.get_strings(idx, d_strings_offsets, d_positions, d_targets_offsets, d_indices);
+    });
+
+  // use this utility to gather the string parts into a contiguous chars column
+  auto chars = make_strings_column(indices.begin(), indices.end(), stream, mr);
+
+  // create offsets from the sizes
+  offsets =
+    std::get<0>(cudf::detail::make_offsets_child_column(counts.begin(), counts.end(), stream, mr));
+
+  // build the strings columns from the chars and offsets
+  return make_strings_column(strings_count,
+                             std::move(offsets),
+                             std::move(chars->release().children.back()),
+                             input.null_count(),
+                             copy_bitmask(input.parent(), stream, mr));
+}
+
+/**
+ * @brief Function logic for the replace_string_parallel
+ *
+ * Performs the multi-replace operation with a thread per string.
+ * This performs best on smaller strings. @see AVG_CHAR_BYTES_THRESHOLD
+ */
+struct replace_multi_fn {
+  column_device_view const d_strings;
+  column_device_view const d_targets;
+  column_device_view const d_repls;
+  int32_t* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) { d_offsets[idx] = 0; }
+      return;
+    }
+    auto const d_str   = d_strings.element<string_view>(idx);
+    char const* in_ptr = d_str.data();
+
+    size_type bytes = d_str.size_bytes();
+    size_type spos  = 0;
+    size_type lpos  = 0;
+    char* out_ptr   = d_chars ? d_chars + d_offsets[idx] : nullptr;
+
+    // check each character against each target
+    while (spos < d_str.size_bytes()) {
+      for (int tgt_idx = 0; tgt_idx < d_targets.size(); ++tgt_idx) {
+        auto const d_tgt = d_targets.element<string_view>(tgt_idx);
+        if ((d_tgt.size_bytes() <= (d_str.size_bytes() - spos)) &&    // check fit
+            (d_tgt.compare(in_ptr + spos, d_tgt.size_bytes()) == 0))  // and match
+        {
+          auto const d_repl = (d_repls.size() == 1) ? d_repls.element<string_view>(0)
+                                                    : d_repls.element<string_view>(tgt_idx);
+          bytes += d_repl.size_bytes() - d_tgt.size_bytes();
+          if (out_ptr) {
+            out_ptr = copy_and_increment(out_ptr, in_ptr + lpos, spos - lpos);
+            out_ptr = copy_string(out_ptr, d_repl);
+            lpos    = spos + d_tgt.size_bytes();
+          }
+          spos += d_tgt.size_bytes() - 1;
+          break;
+        }
+      }
+      ++spos;
+    }
+    if (out_ptr)  // copy remainder
+      memcpy(out_ptr, in_ptr + lpos, d_str.size_bytes() - lpos);
+    else
+      d_offsets[idx] = bytes;
+  }
+};
+
+std::unique_ptr<column> replace_string_parallel(strings_column_view const& input,
+                                                strings_column_view const& targets,
+                                                strings_column_view const& repls,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  auto d_strings      = column_device_view::create(input.parent(), stream);
+  auto d_targets      = column_device_view::create(targets.parent(), stream);
+  auto d_replacements = column_device_view::create(repls.parent(), stream);
+
+  auto children = cudf::strings::detail::make_strings_children(
+    replace_multi_fn{*d_strings, *d_targets, *d_replacements}, input.size(), stream, mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace
+
+std::unique_ptr<column> replace(strings_column_view const& input,
+                                strings_column_view const& targets,
+                                strings_column_view const& repls,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return make_empty_column(type_id::STRING); }
+  CUDF_EXPECTS(((targets.size() > 0) && (targets.null_count() == 0)),
+               "Parameters targets must not be empty and must not have nulls");
+  CUDF_EXPECTS(((repls.size() > 0) && (repls.null_count() == 0)),
+               "Parameters repls must not be empty and must not have nulls");
+  if (repls.size() > 1)
+    CUDF_EXPECTS(repls.size() == targets.size(), "Sizes for targets and repls must match");
+
+  return (input.size() == input.null_count() ||
+          ((input.chars_size() / (input.size() - input.null_count())) < AVG_CHAR_BYTES_THRESHOLD))
+           ? replace_string_parallel(input, targets, repls, stream, mr)
+           : replace_character_parallel(input, targets, repls, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> replace(strings_column_view const& strings,
+                                strings_column_view const& targets,
+                                strings_column_view const& repls,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace(strings, targets, repls, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/replace/multi_re.cu b/cpp/src/strings/replace/multi_re.cu
new file mode 100644
index 0000000..867b443
--- /dev/null
+++ b/cpp/src/strings/replace/multi_re.cu
@@ -0,0 +1,216 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/regex/regex.cuh>
+#include <strings/regex/regex_program_impl.h>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/replace_re.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/find.h>
+#include <thrust/pair.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+// this is a [begin,end) pair of character positions when a substring is matched
+using found_range = thrust::pair<size_type, size_type>;
+
+/**
+ * @brief This functor handles replacing strings by applying the compiled regex patterns
+ * and inserting the corresponding new string within the matched range of characters.
+ */
+struct replace_multi_regex_fn {
+  column_device_view const d_strings;
+  device_span<reprog_device const> progs;  // array of regex progs
+  found_range* d_found_ranges;             // working array matched (begin,end) values
+  column_device_view const d_repls;        // replacement strings
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const number_of_patterns = static_cast<size_type>(progs.size());
+
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const nchars = d_str.length();      // number of characters in input string
+    auto nbytes       = d_str.size_bytes();  // number of bytes in input string
+    auto in_ptr       = d_str.data();        // input pointer
+    auto out_ptr      = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    auto itr          = d_str.begin();
+    auto last_pos     = itr;
+
+    found_range* d_ranges = d_found_ranges + (idx * number_of_patterns);
+
+    // initialize the working ranges memory to -1's
+    thrust::fill(thrust::seq, d_ranges, d_ranges + number_of_patterns, found_range{-1, 1});
+
+    // process string one character at a time
+    while (itr.position() < nchars) {
+      // this minimizes the regex-find calls by only calling it for stale patterns
+      // -- those that have not previously matched up to this point (ch_pos)
+      for (size_type ptn_idx = 0; ptn_idx < number_of_patterns; ++ptn_idx) {
+        if (d_ranges[ptn_idx].first >= itr.position()) {  // previously matched here
+          continue;                                       // or later in the string
+        }
+        reprog_device prog = progs[ptn_idx];
+
+        auto const result = !prog.is_empty() ? prog.find(idx, d_str, itr) : thrust::nullopt;
+        d_ranges[ptn_idx] =
+          result ? found_range{result->first, result->second} : found_range{nchars, nchars};
+      }
+      // all the ranges have been updated from each regex match;
+      // look for any that match at this character position (ch_pos)
+      auto const ptn_itr =
+        thrust::find_if(thrust::seq,
+                        d_ranges,
+                        d_ranges + number_of_patterns,
+                        [ch_pos = itr.position()](auto range) { return range.first == ch_pos; });
+      if (ptn_itr != d_ranges + number_of_patterns) {
+        // match found, compute and replace the string in the output
+        auto const ptn_idx = static_cast<size_type>(thrust::distance(d_ranges, ptn_itr));
+
+        auto d_repl = d_repls.size() > 1 ? d_repls.element<string_view>(ptn_idx)
+                                         : d_repls.element<string_view>(0);
+
+        auto const d_range = d_ranges[ptn_idx];
+        auto const [start_pos, end_pos] =
+          match_positions_to_bytes({d_range.first, d_range.second}, d_str, last_pos);
+        nbytes += d_repl.size_bytes() - (end_pos - start_pos);
+        if (out_ptr) {  // copy unmodified content plus new replacement string
+          out_ptr = copy_and_increment(
+            out_ptr, in_ptr + last_pos.byte_offset(), start_pos - last_pos.byte_offset());
+          out_ptr = copy_string(out_ptr, d_repl);
+        }
+        last_pos += (d_range.second - last_pos.position());
+        itr = last_pos - 1;
+      }
+      ++itr;
+    }
+    if (out_ptr) {  // copy the remainder
+      thrust::copy_n(thrust::seq,
+                     in_ptr + last_pos.byte_offset(),
+                     d_str.size_bytes() - last_pos.byte_offset(),
+                     out_ptr);
+    } else {
+      d_offsets[idx] = nbytes;
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> replace_re(strings_column_view const& input,
+                                   std::vector<std::string> const& patterns,
+                                   strings_column_view const& replacements,
+                                   regex_flags const flags,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return make_empty_column(type_id::STRING); }
+  if (patterns.empty()) {  // if no patterns; just return a copy
+    return std::make_unique<column>(input.parent(), stream, mr);
+  }
+
+  CUDF_EXPECTS(!replacements.has_nulls(), "Parameter replacements must not have any nulls");
+
+  // compile regexes into device objects
+  auto h_progs = std::vector<std::unique_ptr<reprog_device, std::function<void(reprog_device*)>>>(
+    patterns.size());
+  std::transform(
+    patterns.begin(), patterns.end(), h_progs.begin(), [flags, stream](auto const& ptn) {
+      auto h_prog = regex_program::create(ptn, flags, capture_groups::NON_CAPTURE);
+      return regex_device_builder::create_prog_device(*h_prog, stream);
+    });
+
+  // get the longest regex for the dispatcher
+  auto const max_prog =
+    std::max_element(h_progs.begin(), h_progs.end(), [](auto const& lhs, auto const& rhs) {
+      return lhs->insts_counts() < rhs->insts_counts();
+    });
+
+  auto d_max_prog        = **max_prog;
+  auto const buffer_size = d_max_prog.working_memory_size(input.size());
+  auto d_buffer          = rmm::device_buffer(buffer_size, stream);
+
+  // copy all the reprog_device instances to a device memory array
+  std::vector<reprog_device> progs;
+  std::transform(h_progs.begin(),
+                 h_progs.end(),
+                 std::back_inserter(progs),
+                 [d_buffer = d_buffer.data(), size = input.size()](auto& prog) {
+                   prog->set_working_memory(d_buffer, size);
+                   return *prog;
+                 });
+  auto d_progs =
+    cudf::detail::make_device_uvector_async(progs, stream, rmm::mr::get_current_device_resource());
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+  auto const d_repls   = column_device_view::create(replacements.parent(), stream);
+
+  auto found_ranges = rmm::device_uvector<found_range>(d_progs.size() * input.size(), stream);
+
+  auto children = make_strings_children(
+    replace_multi_regex_fn{*d_strings, d_progs, found_ranges.data(), *d_repls},
+    input.size(),
+    stream,
+    mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> replace_re(strings_column_view const& strings,
+                                   std::vector<std::string> const& patterns,
+                                   strings_column_view const& replacements,
+                                   regex_flags const flags,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_re(strings, patterns, replacements, flags, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/replace/replace.cu b/cpp/src/strings/replace/replace.cu
new file mode 100644
index 0000000..a622d1a
--- /dev/null
+++ b/cpp/src/strings/replace/replace.cu
@@ -0,0 +1,772 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/replace.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/remove.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Average string byte-length threshold for deciding character-level vs row-level parallel
+ * algorithm.
+ *
+ * This value was determined by running the replace string scalar benchmark against different
+ * power-of-2 string lengths and observing the point at which the performance only improved for
+ * all trials.
+ */
+constexpr size_type BYTES_PER_VALID_ROW_THRESHOLD = 64;
+
+/**
+ * @brief Function logic for the row-level parallelism replace API.
+ *
+ * This will perform a replace operation on each string.
+ */
+struct replace_row_parallel_fn {
+  column_device_view const d_strings;
+  string_view const d_target;
+  string_view const d_repl;
+  int32_t const max_repl;
+  int32_t* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str   = d_strings.element<string_view>(idx);
+    char const* in_ptr = d_str.data();
+
+    char* out_ptr = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    auto max_n    = (max_repl < 0) ? d_str.length() : max_repl;
+    auto bytes    = d_str.size_bytes();
+    auto position = d_str.find(d_target);
+
+    size_type last_pos = 0;
+    while ((position != string_view::npos) && (max_n > 0)) {
+      if (out_ptr) {
+        auto const curr_pos = d_str.byte_offset(position);
+        out_ptr = copy_and_increment(out_ptr, in_ptr + last_pos, curr_pos - last_pos);  // copy left
+        out_ptr = copy_string(out_ptr, d_repl);                                         // copy repl
+        last_pos = curr_pos + d_target.size_bytes();
+      } else {
+        bytes += d_repl.size_bytes() - d_target.size_bytes();
+      }
+      position = d_str.find(d_target, position + d_target.size_bytes());
+      --max_n;
+    }
+    if (out_ptr)  // copy whats left (or right depending on your point of view)
+      memcpy(out_ptr, in_ptr + last_pos, d_str.size_bytes() - last_pos);
+    else
+      d_offsets[idx] = bytes;
+  }
+};
+
+/**
+ * @brief Functor for detecting falsely-overlapped target positions.
+ *
+ * This functor examines target positions that have been flagged as potentially overlapped by
+ * a previous target position and identifies the overlaps that are false. A false overlap can occur
+ * when a target position is overlapped by another target position that is itself overlapped.
+ *
+ * For example, a target string of "+++" and string to search of "++++++" will generate 4 potential
+ * target positions at char offsets 0 through 3. The targets at offsets 1, 2, and 3 will be flagged
+ * as potential overlaps since a prior target position is within range of the target string length.
+ * The targets at offset 1 and 2 are true overlaps, since the footprint of the valid target at
+ * offset 0 overlaps with them. The target at offset 3 is not truly overlapped because it is only
+ * overlapped by invalid targets, targets that were themselves overlapped by a valid target.
+ */
+struct target_false_overlap_filter_fn {
+  size_type const* const d_overlap_pos_indices{};
+  size_type const* const d_target_positions{};
+  size_type const target_size{};
+
+  __device__ bool operator()(size_type overlap_idx) const
+  {
+    if (overlap_idx == 0) {
+      // The first overlap has no prior overlap to chain, so it should be kept as an overlap.
+      return false;
+    }
+
+    size_type const this_pos_idx = d_overlap_pos_indices[overlap_idx];
+
+    // Searching backwards for the first target position index of an overlap that is not adjacent
+    // to its overlap predecessor. The result will be the first overlap in this chain of overlaps.
+    size_type first_overlap_idx = overlap_idx;
+    size_type first_pos_idx     = this_pos_idx;
+    while (first_overlap_idx > 0) {
+      size_type prev_pos_idx = d_overlap_pos_indices[--first_overlap_idx];
+      if (prev_pos_idx + 1 != first_pos_idx) { break; }
+      first_pos_idx = prev_pos_idx;
+    }
+
+    // The prior target position to the first overlapped position in the chain is a valid target.
+    size_type valid_pos_idx = first_pos_idx - 1;
+    size_type valid_pos     = d_target_positions[valid_pos_idx];
+
+    // Walk forward from this valid target. Any targets within the range of this valid one are true
+    // overlaps. The first overlap beyond the range of this valid target is another valid target,
+    // as it was falsely overlapped by a target that was itself overlapped. Repeat until we get to
+    // the overlapped position being queried by this call.
+    while (valid_pos_idx < this_pos_idx) {
+      size_type next_pos_idx = valid_pos_idx + 1;
+      size_type next_pos     = d_target_positions[next_pos_idx];
+      // Every target position within the range of a valid target position is a true overlap.
+      while (next_pos < valid_pos + target_size) {
+        if (next_pos_idx == this_pos_idx) { return false; }
+        next_pos = d_target_positions[++next_pos_idx];
+      }
+      valid_pos_idx = next_pos_idx;
+      valid_pos     = next_pos;
+    }
+
+    // This was overlapped only by false overlaps and therefore is a valid target.
+    return true;
+  }
+};
+
+/**
+ * @brief Functor for replacing each target string with the replacement string.
+ *
+ * This will perform a replace operation at each target position.
+ */
+struct target_replacer_fn {
+  device_span<size_type const> const d_target_positions;
+  char const* const d_in_chars{};
+  char* const d_out_chars{};
+  size_type const target_size{};
+  string_view const d_repl;
+  int32_t const in_char_offset = 0;
+
+  __device__ void operator()(size_type input_idx) const
+  {
+    // Calculate the adjustment from input index to output index for each prior target position.
+    auto const repl_size         = d_repl.size_bytes();
+    auto const idx_delta_per_pos = repl_size - target_size;
+
+    // determine the number of target positions at or before this character position
+    size_type const* next_target_pos_ptr = thrust::upper_bound(
+      thrust::seq, d_target_positions.begin(), d_target_positions.end(), input_idx);
+    size_type const num_prev_targets = next_target_pos_ptr - d_target_positions.data();
+    size_type output_idx = input_idx - in_char_offset + idx_delta_per_pos * num_prev_targets;
+
+    if (num_prev_targets == 0) {
+      // not within a target string
+      d_out_chars[output_idx] = d_in_chars[input_idx];
+    } else {
+      // check if this input position is within a target string
+      size_type const prev_target_pos = *(next_target_pos_ptr - 1);
+      size_type target_idx            = input_idx - prev_target_pos;
+      if (target_idx < target_size) {
+        // within the target string, so the original calculation was off by one target string
+        output_idx -= idx_delta_per_pos;
+
+        // Copy the corresponding byte from the replacement string. If the replacement string is
+        // larger than the target string then the thread reading the last target byte is
+        // responsible for copying the remainder of the replacement string.
+        if (target_idx < repl_size) {
+          d_out_chars[output_idx++] = d_repl.data()[target_idx++];
+          if (target_idx == target_size) {
+            memcpy(d_out_chars + output_idx, d_repl.data() + target_idx, repl_size - target_idx);
+          }
+        }
+      } else {
+        // not within a target string
+        d_out_chars[output_idx] = d_in_chars[input_idx];
+      }
+    }
+  }
+};
+
+/**
+ * @brief Filter target positions that are overlapped by other, valid target positions.
+ *
+ * This performs an in-place modification of the target positions to remove any target positions
+ * that are overlapped by other, valid target positions. For example, if the target string is "++"
+ * and the string to search is "+++" then there will be two potential targets at character offsets
+ * 0 and 1. The target at offset 0 is valid and overlaps the target at offset 1, invalidating the
+ * target at offset 1.
+ *
+ * @param[in,out] d_target_positions Potential target positions to filter in-place.
+ * @param[in]     target_count       Number of potential target positions.
+ * @param[in]     target_size        Size of the target string in bytes.
+ * @param[in]     stream             CUDA stream to use for device operations.
+ * @return Number of target positions after filtering.
+ */
+size_type filter_overlap_target_positions(size_type* d_target_positions,
+                                          size_type target_count,
+                                          size_type target_size,
+                                          rmm::cuda_stream_view stream)
+{
+  auto overlap_detector = [d_target_positions, target_size] __device__(size_type pos_idx) -> bool {
+    return (pos_idx > 0)
+             ? d_target_positions[pos_idx] - d_target_positions[pos_idx - 1] < target_size
+             : false;
+  };
+
+  // count the potential number of overlapped target positions
+  size_type overlap_count =
+    thrust::count_if(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     thrust::make_counting_iterator<size_type>(target_count),
+                     overlap_detector);
+  if (overlap_count == 0) { return target_count; }
+
+  // create a vector indexing the potential overlapped target positions
+  rmm::device_uvector<size_type> potential_overlapped_pos_indices(overlap_count, stream);
+  auto d_potential_overlapped_pos_indices = potential_overlapped_pos_indices.data();
+  thrust::copy_if(rmm::exec_policy(stream),
+                  thrust::make_counting_iterator<size_type>(0),
+                  thrust::make_counting_iterator<size_type>(target_count),
+                  d_potential_overlapped_pos_indices,
+                  overlap_detector);
+
+  // filter out the false overlaps that are actually valid
+  rmm::device_uvector<size_type> overlapped_pos_indices(overlap_count, stream);
+  auto d_overlapped_pos_indices = overlapped_pos_indices.data();
+  auto overlap_end =
+    thrust::remove_copy_if(rmm::exec_policy(stream),
+                           d_potential_overlapped_pos_indices,
+                           d_potential_overlapped_pos_indices + overlap_count,
+                           thrust::make_counting_iterator<size_type>(0),
+                           d_overlapped_pos_indices,
+                           target_false_overlap_filter_fn{
+                             d_potential_overlapped_pos_indices, d_target_positions, target_size});
+  overlap_count = cudf::distance(d_overlapped_pos_indices, overlap_end);
+
+  // In-place remove any target positions that are overlapped by valid target positions
+  auto target_pos_end = thrust::remove_if(
+    rmm::exec_policy(stream),
+    d_target_positions,
+    d_target_positions + target_count,
+    thrust::make_counting_iterator<size_type>(0),
+    [d_overlapped_pos_indices, overlap_count] __device__(size_type target_position_idx) -> bool {
+      return thrust::binary_search(thrust::seq,
+                                   d_overlapped_pos_indices,
+                                   d_overlapped_pos_indices + overlap_count,
+                                   target_position_idx);
+    });
+  return cudf::distance(d_target_positions, target_pos_end);
+}
+
+/**
+ * @brief Filter target positions to remove any invalid target positions.
+ *
+ * This performs an in-place modification of the target positions to remove any target positions
+ * that are invalid, either by the target string overlapping a row boundary or being overlapped by
+ * another valid target string.
+ *
+ * @param[in,out] target_positions Potential target positions to filter in-place.
+ * @param[in]     d_offsets_span   Memory range encompassing the string column offsets.
+ * @param[in]     target_size      Size of the target string in bytes.
+ * @param[in]     stream           CUDA stream to use for device operations.
+ * @return Number of target positions after filtering.
+ */
+size_type filter_false_target_positions(rmm::device_uvector<size_type>& target_positions,
+                                        device_span<int32_t const> d_offsets_span,
+                                        size_type target_size,
+                                        rmm::cuda_stream_view stream)
+{
+  // In-place remove any positions for target strings that crossed string boundaries.
+  auto d_target_positions = target_positions.data();
+  auto target_pos_end =
+    thrust::remove_if(rmm::exec_policy(stream),
+                      d_target_positions,
+                      d_target_positions + target_positions.size(),
+                      [d_offsets_span, target_size] __device__(size_type target_pos) -> bool {
+                        // find the end of the string containing the start of this target
+                        size_type const* offset_ptr = thrust::upper_bound(
+                          thrust::seq, d_offsets_span.begin(), d_offsets_span.end(), target_pos);
+                        return target_pos + target_size > *offset_ptr;
+                      });
+  auto const target_count = cudf::distance(d_target_positions, target_pos_end);
+  if (target_count == 0) { return 0; }
+
+  // Filter out target positions that are the result of overlapping target matches.
+  return (target_count > 1)
+           ? filter_overlap_target_positions(d_target_positions, target_count, target_size, stream)
+           : target_count;
+}
+
+/**
+ * @brief Filter target positions beyond the maximum target replacements per row limit.
+ *
+ * This performs an in-place modification of the target positions to remove any target positions
+ * corresponding to targets that should not be replaced due to the maximum target replacement per
+ * row limit.
+ *
+ * @param[in,out] target_positions Target positions to filter in-place.
+ * @param[in]     target_count     Number of target positions.
+ * @param[in]     d_offsets_span   Memory range encompassing the string column offsets.
+ * @param[in]     max_repl_per_row Maximum target replacements per row limit.
+ * @param[in]     stream           CUDA stream to use for device operations.
+ * @return Number of target positions after filtering.
+ */
+size_type filter_maxrepl_target_positions(size_type* d_target_positions,
+                                          size_type target_count,
+                                          device_span<int32_t const> d_offsets_span,
+                                          size_type max_repl_per_row,
+                                          rmm::cuda_stream_view stream)
+{
+  auto pos_to_row_fn = [d_offsets_span] __device__(size_type target_pos) -> size_type {
+    auto upper_bound =
+      thrust::upper_bound(thrust::seq, d_offsets_span.begin(), d_offsets_span.end(), target_pos);
+    return thrust::distance(d_offsets_span.begin(), upper_bound);
+  };
+
+  // compute the match count per row for each target position
+  rmm::device_uvector<size_type> match_counts(target_count, stream);
+  auto d_match_counts = match_counts.data();
+  thrust::inclusive_scan_by_key(
+    rmm::exec_policy(stream),
+    thrust::make_transform_iterator(d_target_positions, pos_to_row_fn),
+    thrust::make_transform_iterator(d_target_positions + target_count, pos_to_row_fn),
+    thrust::make_constant_iterator<size_type>(1),
+    d_match_counts);
+
+  // In-place remove any positions that exceed the per-row match limit
+  auto target_pos_end =
+    thrust::remove_if(rmm::exec_policy(stream),
+                      d_target_positions,
+                      d_target_positions + target_count,
+                      d_match_counts,
+                      [max_repl_per_row] __device__(size_type match_count) -> bool {
+                        return match_count > max_repl_per_row;
+                      });
+
+  return cudf::distance(d_target_positions, target_pos_end);
+}
+
+/**
+ * @brief Scalar string replacement using a character-level parallel algorithm.
+ *
+ * Replaces occurrences of the target string with the replacement string using an algorithm with
+ * character-level parallelism. This algorithm will perform well when the strings in the string
+ * column are relatively long.
+ * @see BYTES_PER_VALID_ROW_THRESHOLD
+ *
+ * @param strings     String column to search for target strings.
+ * @param chars_start Offset of the first character in the string column.
+ * @param chars_end   Offset beyond the last character in the string column to search.
+ * @param d_target    String to search for within the string column.
+ * @param d_repl      Replacement string if target string is found.
+ * @param maxrepl     Maximum times to replace if target appears multiple times in a string.
+ * @param stream      CUDA stream to use for device operations
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace_char_parallel(strings_column_view const& strings,
+                                              size_type chars_start,
+                                              size_type chars_end,
+                                              string_view const& d_target,
+                                              string_view const& d_repl,
+                                              int32_t maxrepl,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = strings.size();
+  auto const offset_count  = strings_count + 1;
+  auto const d_offsets     = strings.offsets_begin();
+  auto const d_in_chars    = strings.chars_begin();
+  auto const chars_bytes   = chars_end - chars_start;
+  auto const target_size   = d_target.size_bytes();
+
+  // detect a target match at the specified byte position
+  device_span<char const> const d_chars_span(d_in_chars, chars_end);
+  auto target_detector = [d_chars_span, d_target] __device__(size_type char_idx) {
+    auto target_size = d_target.size_bytes();
+    auto target_ptr  = d_chars_span.begin() + char_idx;
+    return target_ptr + target_size <= d_chars_span.end() &&
+           d_target.compare(target_ptr, target_size) == 0;
+  };
+
+  // Count target string matches across all character positions, ignoring string boundaries and
+  // overlapping target strings. This may produce false-positives.
+  size_type target_count = thrust::count_if(rmm::exec_policy(stream),
+                                            thrust::make_counting_iterator<size_type>(chars_start),
+                                            thrust::make_counting_iterator<size_type>(chars_end),
+                                            target_detector);
+  if (target_count == 0) {
+    // nothing to replace, copy the input column
+    return std::make_unique<cudf::column>(strings.parent(), stream, mr);
+  }
+
+  // create a vector of the potential target match positions
+  rmm::device_uvector<size_type> target_positions(target_count, stream);
+  auto d_target_positions = target_positions.data();
+  thrust::copy_if(rmm::exec_policy(stream),
+                  thrust::make_counting_iterator<size_type>(chars_start),
+                  thrust::make_counting_iterator<size_type>(chars_end),
+                  d_target_positions,
+                  target_detector);
+
+  device_span<int32_t const> d_offsets_span(d_offsets, offset_count);
+  if (target_size > 1) {
+    target_count =
+      filter_false_target_positions(target_positions, d_offsets_span, target_size, stream);
+    if (target_count == 0) {
+      // nothing to replace, copy the input column
+      return std::make_unique<cudf::column>(strings.parent(), stream, mr);
+    }
+  }
+
+  // filter out any target positions that exceed the per-row match limit
+  if (maxrepl > 0 && target_count > maxrepl) {
+    target_count = filter_maxrepl_target_positions(
+      d_target_positions, target_count, d_offsets_span, maxrepl, stream);
+  }
+
+  // build the offsets column
+  auto offsets_column = make_numeric_column(
+    data_type{type_id::INT32}, offset_count, mask_state::UNALLOCATED, stream, mr);
+  auto offsets_view     = offsets_column->mutable_view();
+  auto delta_per_target = d_repl.size_bytes() - target_size;
+  device_span<size_type const> d_target_positions_span(d_target_positions, target_count);
+  auto offsets_update_fn =
+    [d_target_positions_span, delta_per_target, chars_start] __device__(int32_t offset) -> int32_t {
+    // determine the number of target positions occurring before this offset
+    size_type const* next_target_pos_ptr = thrust::lower_bound(
+      thrust::seq, d_target_positions_span.begin(), d_target_positions_span.end(), offset);
+    size_type num_prev_targets =
+      thrust::distance(d_target_positions_span.data(), next_target_pos_ptr);
+    return offset - chars_start + delta_per_target * num_prev_targets;
+  };
+  thrust::transform(rmm::exec_policy(stream),
+                    d_offsets_span.begin(),
+                    d_offsets_span.end(),
+                    offsets_view.begin<int32_t>(),
+                    offsets_update_fn);
+
+  // build the characters column
+  auto chars_column =
+    create_chars_child_column(chars_bytes + (delta_per_target * target_count), stream, mr);
+  auto d_out_chars = chars_column->mutable_view().data<char>();
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(chars_start),
+    chars_bytes,
+    target_replacer_fn{
+      d_target_positions_span, d_in_chars, d_out_chars, target_size, d_repl, chars_start});
+
+  // free the target positions buffer as it is no longer needed
+  (void)target_positions.release();
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             strings.null_count(),
+                             cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+/**
+ * @brief Scalar string replacement using a row-level parallel algorithm.
+ *
+ * Replaces occurrences of the target string with the replacement string using an algorithm with
+ * row-level parallelism. This algorithm will perform well when the strings in the string
+ * column are relatively short.
+ * @see BYTES_PER_VALID_ROW_THRESHOLD
+ *
+ * @param strings     String column to search for target strings.
+ * @param d_target    String to search for within the string column.
+ * @param d_repl      Replacement string if target string is found.
+ * @param maxrepl     Maximum times to replace if target appears multiple times in a string.
+ * @param stream      CUDA stream to use for device operations
+ * @param mr          Device memory resource used to allocate the returned column's device memory
+ * @return New strings column.
+ */
+std::unique_ptr<column> replace_row_parallel(strings_column_view const& strings,
+                                             string_view const& d_target,
+                                             string_view const& d_repl,
+                                             int32_t maxrepl,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  auto d_strings = column_device_view::create(strings.parent(), stream);
+
+  // this utility calls the given functor to build the offsets and chars columns
+  auto children = cudf::strings::detail::make_strings_children(
+    replace_row_parallel_fn{*d_strings, d_target, d_repl, maxrepl}, strings.size(), stream, mr);
+
+  return make_strings_column(strings.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             strings.null_count(),
+                             cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+}  // namespace
+
+/**
+ * @copydoc cudf::strings::detail::replace(strings_column_view const&, string_scalar const&,
+ * string_scalar const&, int32_t, rmm::cuda_stream_view, rmm::mr::device_memory_resource*)
+ */
+template <>
+std::unique_ptr<column> replace<replace_algorithm::AUTO>(strings_column_view const& strings,
+                                                         string_scalar const& target,
+                                                         string_scalar const& repl,
+                                                         int32_t maxrepl,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+  if (maxrepl == 0) return std::make_unique<cudf::column>(strings.parent(), stream, mr);
+  CUDF_EXPECTS(repl.is_valid(stream), "Parameter repl must be valid.");
+  CUDF_EXPECTS(target.is_valid(stream), "Parameter target must be valid.");
+  CUDF_EXPECTS(target.size() > 0, "Parameter target must not be empty string.");
+
+  string_view d_target(target.data(), target.size());
+  string_view d_repl(repl.data(), repl.size());
+
+  // determine range of characters in the base column
+  auto const strings_count = strings.size();
+  auto const offset_count  = strings_count + 1;
+  auto const d_offsets     = strings.offsets().data<int32_t>() + strings.offset();
+  size_type const chars_start =
+    (strings.offset() == 0)
+      ? 0
+      : cudf::detail::get_value<int32_t>(strings.offsets(), strings.offset(), stream);
+  size_type const chars_end   = (offset_count == strings.offsets().size())
+                                  ? strings.chars_size()
+                                  : cudf::detail::get_value<int32_t>(
+                                    strings.offsets(), strings.offset() + strings_count, stream);
+  size_type const chars_bytes = chars_end - chars_start;
+
+  auto const avg_bytes_per_row = chars_bytes / std::max(strings_count - strings.null_count(), 1);
+  return (avg_bytes_per_row < BYTES_PER_VALID_ROW_THRESHOLD)
+           ? replace_row_parallel(strings, d_target, d_repl, maxrepl, stream, mr)
+           : replace_char_parallel(
+               strings, chars_start, chars_end, d_target, d_repl, maxrepl, stream, mr);
+}
+
+template <>
+std::unique_ptr<column> replace<replace_algorithm::CHAR_PARALLEL>(
+  strings_column_view const& strings,
+  string_scalar const& target,
+  string_scalar const& repl,
+  int32_t maxrepl,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+  if (maxrepl == 0) return std::make_unique<cudf::column>(strings.parent(), stream, mr);
+  CUDF_EXPECTS(repl.is_valid(stream), "Parameter repl must be valid.");
+  CUDF_EXPECTS(target.is_valid(stream), "Parameter target must be valid.");
+  CUDF_EXPECTS(target.size() > 0, "Parameter target must not be empty string.");
+
+  string_view d_target(target.data(), target.size());
+  string_view d_repl(repl.data(), repl.size());
+
+  // determine range of characters in the base column
+  auto const strings_count = strings.size();
+  auto const offset_count  = strings_count + 1;
+  auto const d_offsets     = strings.offsets_begin();
+  size_type chars_start    = (strings.offset() == 0) ? 0
+                                                     : cudf::detail::get_value<int32_t>(
+                                                      strings.offsets(), strings.offset(), stream);
+  size_type chars_end      = (offset_count == strings.offsets().size())
+                               ? strings.chars_size()
+                               : cudf::detail::get_value<int32_t>(
+                              strings.offsets(), strings.offset() + strings_count, stream);
+  return replace_char_parallel(
+    strings, chars_start, chars_end, d_target, d_repl, maxrepl, stream, mr);
+}
+
+template <>
+std::unique_ptr<column> replace<replace_algorithm::ROW_PARALLEL>(
+  strings_column_view const& strings,
+  string_scalar const& target,
+  string_scalar const& repl,
+  int32_t maxrepl,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+  if (maxrepl == 0) return std::make_unique<cudf::column>(strings.parent(), stream, mr);
+  CUDF_EXPECTS(repl.is_valid(stream), "Parameter repl must be valid.");
+  CUDF_EXPECTS(target.is_valid(stream), "Parameter target must be valid.");
+  CUDF_EXPECTS(target.size() > 0, "Parameter target must not be empty string.");
+
+  string_view d_target(target.data(), target.size());
+  string_view d_repl(repl.data(), repl.size());
+  return replace_row_parallel(strings, d_target, d_repl, maxrepl, stream, mr);
+}
+
+namespace {
+/**
+ * @brief Function logic for the replace_slice API.
+ *
+ * This will perform a replace_slice operation on each string.
+ */
+struct replace_slice_fn {
+  column_device_view const d_strings;
+  string_view const d_repl;
+  size_type const start;
+  size_type const stop;
+  int32_t* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str   = d_strings.element<string_view>(idx);
+    auto const length  = d_str.length();
+    char const* in_ptr = d_str.data();
+    auto const begin   = d_str.byte_offset(((start < 0) || (start > length) ? length : start));
+    auto const end     = d_str.byte_offset(((stop < 0) || (stop > length) ? length : stop));
+
+    if (d_chars) {
+      char* out_ptr = d_chars + d_offsets[idx];
+
+      out_ptr = copy_and_increment(out_ptr, in_ptr, begin);  // copy beginning
+      out_ptr = copy_string(out_ptr, d_repl);                // insert replacement
+      out_ptr = copy_and_increment(out_ptr,                  // copy end
+                                   in_ptr + end,
+                                   d_str.size_bytes() - end);
+    } else {
+      d_offsets[idx] = d_str.size_bytes() + d_repl.size_bytes() - (end - begin);
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> replace_slice(strings_column_view const& strings,
+                                      string_scalar const& repl,
+                                      size_type start,
+                                      size_type stop,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+  CUDF_EXPECTS(repl.is_valid(stream), "Parameter repl must be valid.");
+  if (stop > 0) CUDF_EXPECTS(start <= stop, "Parameter start must be less than or equal to stop.");
+
+  string_view d_repl(repl.data(), repl.size());
+
+  auto d_strings = column_device_view::create(strings.parent(), stream);
+
+  // this utility calls the given functor to build the offsets and chars columns
+  auto children = cudf::strings::detail::make_strings_children(
+    replace_slice_fn{*d_strings, d_repl, start, stop}, strings.size(), stream, mr);
+
+  return make_strings_column(strings.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             strings.null_count(),
+                             cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+std::unique_ptr<column> replace_nulls(strings_column_view const& strings,
+                                      string_scalar const& repl,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+  CUDF_EXPECTS(repl.is_valid(stream), "Parameter repl must be valid.");
+
+  string_view d_repl(repl.data(), repl.size());
+
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+
+  // build offsets column
+  auto offsets_transformer_itr = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<int32_t>(0), [d_strings, d_repl] __device__(size_type idx) {
+      return d_strings.is_null(idx) ? d_repl.size_bytes()
+                                    : d_strings.element<string_view>(idx).size_bytes();
+    });
+  auto [offsets_column, bytes] = cudf::detail::make_offsets_child_column(
+    offsets_transformer_itr, offsets_transformer_itr + strings_count, stream, mr);
+  auto d_offsets = offsets_column->view().data<int32_t>();
+
+  // build chars column
+  auto chars_column = create_chars_child_column(bytes, stream, mr);
+  auto d_chars      = chars_column->mutable_view().data<char>();
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     strings_count,
+                     [d_strings, d_repl, d_offsets, d_chars] __device__(size_type idx) {
+                       string_view d_str = d_repl;
+                       if (!d_strings.is_null(idx)) d_str = d_strings.element<string_view>(idx);
+                       memcpy(d_chars + d_offsets[idx], d_str.data(), d_str.size_bytes());
+                     });
+
+  return make_strings_column(
+    strings_count, std::move(offsets_column), std::move(chars_column), 0, rmm::device_buffer{});
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> replace(strings_column_view const& strings,
+                                string_scalar const& target,
+                                string_scalar const& repl,
+                                int32_t maxrepl,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace(strings, target, repl, maxrepl, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> replace_slice(strings_column_view const& strings,
+                                      string_scalar const& repl,
+                                      size_type start,
+                                      size_type stop,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_slice(strings, repl, start, stop, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/replace/replace_re.cu b/cpp/src/strings/replace/replace_re.cu
new file mode 100644
index 0000000..81ddb93
--- /dev/null
+++ b/cpp/src/strings/replace/replace_re.cu
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/replace_re.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+/**
+ * @brief This functor handles replacing strings by applying the compiled regex pattern
+ * and inserting the new string within the matched range of characters.
+ */
+struct replace_regex_fn {
+  column_device_view const d_strings;
+  string_view const d_repl;
+  size_type const maxrepl;
+  size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type const idx, reprog_device const prog, int32_t const prog_idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const nchars = d_str.length();
+    auto nbytes       = d_str.size_bytes();              // number of bytes in input string
+    auto mxn      = maxrepl < 0 ? nchars + 1 : maxrepl;  // max possible replaces for this string
+    auto in_ptr   = d_str.data();                        // input pointer (i)
+    auto out_ptr  = d_chars ? d_chars + d_offsets[idx]   // output pointer (o)
+                            : nullptr;
+    auto itr      = d_str.begin();
+    auto last_pos = itr;
+
+    // copy input to output replacing strings as we go
+    while (mxn-- > 0 && itr.position() <= nchars && !prog.is_empty()) {
+      auto const match = prog.find(prog_idx, d_str, itr);
+      if (!match) { break; }  // no more matches
+
+      auto const [start_pos, end_pos] = match_positions_to_bytes(*match, d_str, last_pos);
+      nbytes += d_repl.size_bytes() - (end_pos - start_pos);  // add new size
+
+      if (out_ptr) {                                                       // replace:
+                                                                           // i:bbbbsssseeee
+        out_ptr = copy_and_increment(out_ptr,                              //   ^
+                                     in_ptr + last_pos.byte_offset(),      // o:bbbb
+                                     start_pos - last_pos.byte_offset());  //       ^
+        out_ptr = copy_string(out_ptr, d_repl);                            // o:bbbbrrrrrr
+      }                                                                    //  out_ptr ---^
+      last_pos += (match->second - last_pos.position());                   // i:bbbbsssseeee
+                                                                           //  in_ptr --^
+
+      itr = last_pos + (match->first == match->second);
+    }
+
+    if (out_ptr) {
+      thrust::copy_n(thrust::seq,                                  // copy the remainder
+                     in_ptr + last_pos.byte_offset(),              // o:bbbbrrrrrreeee
+                     d_str.size_bytes() - last_pos.byte_offset(),  //             ^   ^
+                     out_ptr);
+    } else {
+      d_offsets[idx] = nbytes;
+    }
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<column> replace_re(strings_column_view const& input,
+                                   regex_program const& prog,
+                                   string_scalar const& replacement,
+                                   std::optional<size_type> max_replace_count,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(replacement.is_valid(stream), "Parameter replacement must be valid");
+  string_view d_repl(replacement.data(), replacement.size());
+
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  auto const maxrepl = max_replace_count.value_or(-1);
+
+  auto const d_strings = column_device_view::create(input.parent(), stream);
+
+  auto children = make_strings_children(
+    replace_regex_fn{*d_strings, d_repl, maxrepl}, *d_prog, input.size(), stream, mr);
+
+  return make_strings_column(input.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             input.null_count(),
+                             cudf::detail::copy_bitmask(input.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> replace_re(strings_column_view const& strings,
+                                   regex_program const& prog,
+                                   string_scalar const& replacement,
+                                   std::optional<size_type> max_replace_count,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_re(
+    strings, prog, replacement, max_replace_count, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/reverse.cu b/cpp/src/strings/reverse.cu
new file mode 100644
index 0000000..090705a
--- /dev/null
+++ b/cpp/src/strings/reverse.cu
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/reverse.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Reverse individual characters in each string
+ */
+struct reverse_characters_fn {
+  column_device_view const d_strings;
+  size_type const* d_offsets;
+  char* d_chars;
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) { return; }
+    auto const d_str = d_strings.element<string_view>(idx);
+    // pointer to the end of the output string
+    auto d_output = d_chars + d_offsets[idx] + d_str.size_bytes();
+    for (auto const chr : d_str) {          // iterate through each character;
+      d_output -= bytes_in_char_utf8(chr);  // position output;
+      from_char_utf8(chr, d_output);        // place character into output
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> reverse(strings_column_view const& input,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return make_empty_column(type_id::STRING); }
+
+  // copy the column; replace data in the chars column
+  auto result = std::make_unique<column>(input.parent(), stream, mr);
+  auto const d_offsets =
+    result->view().child(strings_column_view::offsets_column_index).data<size_type>();
+  auto d_chars = result->mutable_view().child(strings_column_view::chars_column_index).data<char>();
+
+  auto const d_column = column_device_view::create(input.parent(), stream);
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::counting_iterator<size_type>(0),
+                     input.size(),
+                     reverse_characters_fn{*d_column, d_offsets, d_chars});
+
+  return result;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> reverse(strings_column_view const& input,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::reverse(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/search/find.cu b/cpp/src/strings/search/find.cu
new file mode 100644
index 0000000..1299e55
--- /dev/null
+++ b/cpp/src/strings/search/find.cu
@@ -0,0 +1,676 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/find.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <cuda/atomic>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Threshold to decide on using string or warp parallel functions.
+ *
+ * If the average byte length of a string in a column exceeds this value then
+ * a warp-parallel function is used.
+ *
+ * Note that this value is shared by find, rfind, and contains functions.
+ */
+constexpr size_type AVG_CHAR_BYTES_THRESHOLD = 64;
+
+/**
+ * @brief Find function handles a string per thread
+ */
+template <typename TargetIterator, bool forward = true>
+struct finder_fn {
+  column_device_view const d_strings;
+  TargetIterator const d_targets;
+  size_type const start;
+  size_type const stop;
+
+  __device__ size_type operator()(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return -1; }
+    auto const d_str = d_strings.element<string_view>(idx);
+    if (d_str.empty() && (start > 0)) { return -1; }
+    auto const d_target = d_targets[idx];
+
+    auto const length = d_str.length();
+    auto const begin  = (start > length) ? length : start;
+    auto const end    = (stop < 0) || (stop > length) ? length : stop;
+    return forward ? d_str.find(d_target, begin, end - begin)
+                   : d_str.rfind(d_target, begin, end - begin);
+  }
+};
+
+/**
+ * @brief Special logic handles an empty target for find/rfind
+ *
+ * where length = number of characters in the input string
+ * if forward = true:
+ *   return start iff (start <= length), otherwise return -1
+ * if forward = false:
+ *   return stop iff (0 <= stop <= length), otherwise return length
+ */
+template <bool forward = true>
+struct empty_target_fn {
+  column_device_view const d_strings;
+  size_type const start;
+  size_type const stop;
+
+  __device__ size_type operator()(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return -1; }
+    auto d_str = d_strings.element<string_view>(idx);
+
+    // common case shortcut
+    if (forward && start == 0) { return 0; }
+
+    auto const length = d_str.length();
+    if (start > length) { return -1; }
+    if constexpr (forward) { return start; }
+
+    return (stop < 0) || (stop > length) ? length : stop;
+  }
+};
+
+/**
+ * @brief String per warp function for find/rfind
+ */
+template <typename TargetIterator, bool forward = true>
+__global__ void finder_warp_parallel_fn(column_device_view const d_strings,
+                                        TargetIterator const d_targets,
+                                        size_type const start,
+                                        size_type const stop,
+                                        size_type* d_results)
+{
+  size_type const idx = static_cast<size_type>(threadIdx.x + blockIdx.x * blockDim.x);
+
+  if (idx >= (d_strings.size() * cudf::detail::warp_size)) { return; }
+
+  auto const str_idx  = idx / cudf::detail::warp_size;
+  auto const lane_idx = idx % cudf::detail::warp_size;
+
+  if (d_strings.is_null(str_idx)) { return; }
+
+  // initialize the output for the atomicMin/Max
+  if (lane_idx == 0) { d_results[str_idx] = forward ? std::numeric_limits<size_type>::max() : -1; }
+  __syncwarp();
+
+  auto const d_str    = d_strings.element<string_view>(str_idx);
+  auto const d_target = d_targets[str_idx];
+
+  auto const [begin, left_over] = bytes_to_character_position(d_str, start);
+  auto const start_char_pos     = start - left_over;  // keep track of character position
+
+  auto const end = [d_str, start, stop, begin = begin] {
+    if (stop < 0) { return d_str.size_bytes(); }
+    if (stop <= start) { return begin; }
+    // we count from `begin` instead of recounting from the beginning of the string
+    return begin + std::get<0>(bytes_to_character_position(
+                     string_view(d_str.data() + begin, d_str.size_bytes() - begin), stop - start));
+  }();
+
+  // each thread compares the target with the thread's individual starting byte
+  size_type position = forward ? std::numeric_limits<size_type>::max() : -1;
+  for (auto itr = begin + lane_idx; itr + d_target.size_bytes() <= end;
+       itr += cudf::detail::warp_size) {
+    if (d_target.compare(d_str.data() + itr, d_target.size_bytes()) == 0) {
+      position = itr;
+      if (forward) break;
+    }
+  }
+
+  // find stores the minimum position while rfind stores the maximum position
+  // note that this was slightly faster than using cub::WarpReduce
+  cuda::atomic_ref<size_type, cuda::thread_scope_block> ref{*(d_results + str_idx)};
+  forward ? ref.fetch_min(position, cuda::std::memory_order_relaxed)
+          : ref.fetch_max(position, cuda::std::memory_order_relaxed);
+  __syncwarp();
+
+  if (lane_idx == 0) {
+    // the final result needs to be fixed up convert max() to -1
+    // and a byte position to a character position
+    auto const result = d_results[str_idx];
+    d_results[str_idx] =
+      ((result < std::numeric_limits<size_type>::max()) && (result >= begin))
+        ? start_char_pos + characters_in_string(d_str.data() + begin, result - begin)
+        : -1;
+  }
+}
+
+template <typename TargetIterator, bool forward = true>
+void find_utility(strings_column_view const& input,
+                  TargetIterator const& target_itr,
+                  column& output,
+                  size_type start,
+                  size_type stop,
+                  rmm::cuda_stream_view stream)
+{
+  auto d_strings = column_device_view::create(input.parent(), stream);
+  auto d_results = output.mutable_view().data<size_type>();
+  if ((input.chars_size() / (input.size() - input.null_count())) > AVG_CHAR_BYTES_THRESHOLD) {
+    // warp-per-string runs faster for longer strings (but not shorter ones)
+    constexpr int block_size = 256;
+    cudf::detail::grid_1d grid{input.size() * cudf::detail::warp_size, block_size};
+    finder_warp_parallel_fn<TargetIterator, forward>
+      <<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+        *d_strings, target_itr, start, stop, d_results);
+  } else {
+    // string-per-thread function
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<size_type>(0),
+                      thrust::make_counting_iterator<size_type>(input.size()),
+                      d_results,
+                      finder_fn<TargetIterator, forward>{*d_strings, target_itr, start, stop});
+  }
+}
+
+template <bool forward = true>
+std::unique_ptr<column> find_fn(strings_column_view const& input,
+                                string_scalar const& target,
+                                size_type start,
+                                size_type stop,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(target.is_valid(stream), "Parameter target must be valid.");
+  CUDF_EXPECTS(start >= 0, "Parameter start must be positive integer or zero.");
+  if ((stop > 0) && (start > stop)) CUDF_FAIL("Parameter start must be less than stop.");
+
+  // create output column
+  auto results = make_numeric_column(data_type{type_to_id<size_type>()},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+  // if input is empty or all-null then we are done
+  if (input.size() == input.null_count()) { return results; }
+
+  auto d_target = string_view(target.data(), target.size());
+
+  // special logic for empty target results
+  if (d_target.empty()) {
+    auto d_strings = column_device_view::create(input.parent(), stream);
+    auto d_results = results->mutable_view().data<size_type>();
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::counting_iterator<size_type>(0),
+                      thrust::counting_iterator<size_type>(input.size()),
+                      d_results,
+                      empty_target_fn<forward>{*d_strings, start, stop});
+    return results;
+  }
+
+  // find-utility function fills in the results column
+  auto target_itr      = thrust::make_constant_iterator(d_target);
+  using TargetIterator = decltype(target_itr);
+  find_utility<TargetIterator, forward>(input, target_itr, *results, start, stop, stream);
+  results->set_null_count(input.null_count());
+  return results;
+}
+}  // namespace
+
+std::unique_ptr<column> find(strings_column_view const& input,
+                             string_scalar const& target,
+                             size_type start,
+                             size_type stop,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  return find_fn<true>(input, target, start, stop, stream, mr);
+}
+
+std::unique_ptr<column> rfind(strings_column_view const& input,
+                              string_scalar const& target,
+                              size_type start,
+                              size_type stop,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  return find_fn<false>(input, target, start, stop, stream, mr);
+}
+
+template <bool forward = true>
+std::unique_ptr<column> find(strings_column_view const& input,
+                             strings_column_view const& target,
+                             size_type start,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(start >= 0, "Parameter start must be positive integer or zero.");
+  CUDF_EXPECTS(input.size() == target.size(), "input and target columns must be the same size");
+
+  // create output column
+  auto results = make_numeric_column(
+    data_type{type_to_id<size_type>()}, input.size(), rmm::device_buffer{}, 0, stream, mr);
+  // if input is empty or all-null then we are done
+  if (input.size() == input.null_count()) { return results; }
+
+  // call find utility with target iterator
+  auto d_targets  = column_device_view::create(target.parent(), stream);
+  auto target_itr = cudf::detail::make_null_replacement_iterator<string_view>(
+    *d_targets, string_view{}, target.has_nulls());
+  find_utility<decltype(target_itr), forward>(input, target_itr, *results, start, -1, stream);
+
+  // AND the bitmasks from input and target
+  auto [null_mask, null_count] =
+    cudf::detail::bitmask_and(table_view({input.parent(), target.parent()}), stream, mr);
+  results->set_null_mask(std::move(null_mask), null_count);
+  return results;
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> find(strings_column_view const& strings,
+                             string_scalar const& target,
+                             size_type start,
+                             size_type stop,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::find(strings, target, start, stop, stream, mr);
+}
+
+std::unique_ptr<column> rfind(strings_column_view const& strings,
+                              string_scalar const& target,
+                              size_type start,
+                              size_type stop,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rfind(strings, target, start, stop, stream, mr);
+}
+
+std::unique_ptr<column> find(strings_column_view const& input,
+                             strings_column_view const& target,
+                             size_type start,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::find<true>(input, target, start, stream, mr);
+}
+
+namespace detail {
+namespace {
+
+/**
+ * @brief Check if `d_target` appears in a row in `d_strings`.
+ *
+ * This executes as a warp per string/row and performs well for longer strings.
+ * @see AVG_CHAR_BYTES_THRESHOLD
+ *
+ * @param d_strings Column of input strings
+ * @param d_target String to search for in each row of `d_strings`
+ * @param d_results Indicates which rows contain `d_target`
+ */
+__global__ void contains_warp_parallel_fn(column_device_view const d_strings,
+                                          string_view const d_target,
+                                          bool* d_results)
+{
+  size_type const idx = static_cast<size_type>(threadIdx.x + blockIdx.x * blockDim.x);
+  using warp_reduce   = cub::WarpReduce<bool>;
+  __shared__ typename warp_reduce::TempStorage temp_storage;
+
+  if (idx >= (d_strings.size() * cudf::detail::warp_size)) { return; }
+
+  auto const str_idx  = idx / cudf::detail::warp_size;
+  auto const lane_idx = idx % cudf::detail::warp_size;
+  if (d_strings.is_null(str_idx)) { return; }
+  // get the string for this warp
+  auto const d_str = d_strings.element<string_view>(str_idx);
+  // each thread of the warp will check just part of the string
+  auto found = false;
+  for (auto i = static_cast<size_type>(idx % cudf::detail::warp_size);
+       !found && (i + d_target.size_bytes()) < d_str.size_bytes();
+       i += cudf::detail::warp_size) {
+    // check the target matches this part of the d_str data
+    if (d_target.compare(d_str.data() + i, d_target.size_bytes()) == 0) { found = true; }
+  }
+  auto const result = warp_reduce(temp_storage).Reduce(found, cub::Max());
+  if (lane_idx == 0) { d_results[str_idx] = result; }
+}
+
+std::unique_ptr<column> contains_warp_parallel(strings_column_view const& input,
+                                               string_scalar const& target,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(target.is_valid(stream), "Parameter target must be valid.");
+  auto d_target = string_view(target.data(), target.size());
+
+  // create output column
+  auto results = make_numeric_column(data_type{type_id::BOOL8},
+                                     input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+
+  // fill the output with `false` unless the `d_target` is empty
+  auto results_view = results->mutable_view();
+  thrust::fill(rmm::exec_policy(stream),
+               results_view.begin<bool>(),
+               results_view.end<bool>(),
+               d_target.empty());
+
+  if (!d_target.empty()) {
+    // launch warp per string
+    auto const d_strings     = column_device_view::create(input.parent(), stream);
+    constexpr int block_size = 256;
+    cudf::detail::grid_1d grid{input.size() * cudf::detail::warp_size, block_size};
+    contains_warp_parallel_fn<<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+      *d_strings, d_target, results_view.data<bool>());
+  }
+  results->set_null_count(input.null_count());
+  return results;
+}
+
+/**
+ * @brief Utility to return a bool column indicating the presence of
+ * a given target string in a strings column.
+ *
+ * Null string entries return corresponding null output column entries.
+ *
+ * @tparam BoolFunction Return bool value given two strings.
+ *
+ * @param strings Column of strings to check for target.
+ * @param target UTF-8 encoded string to check in strings column.
+ * @param pfn Returns bool value if target is found in the given string.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New BOOL column.
+ */
+template <typename BoolFunction>
+std::unique_ptr<column> contains_fn(strings_column_view const& strings,
+                                    string_scalar const& target,
+                                    BoolFunction pfn,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::BOOL8);
+
+  CUDF_EXPECTS(target.is_valid(stream), "Parameter target must be valid.");
+  if (target.size() == 0)  // empty target string returns true
+  {
+    auto const true_scalar = make_fixed_width_scalar<bool>(true, stream);
+    auto results           = make_column_from_scalar(*true_scalar, strings.size(), stream, mr);
+    results->set_null_mask(cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                           strings.null_count());
+    return results;
+  }
+
+  auto d_target       = string_view(target.data(), target.size());
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  // create output column
+  auto results      = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings_count,
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto results_view = results->mutable_view();
+  auto d_results    = results_view.data<bool>();
+  // set the bool values by evaluating the passed function
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings_count),
+                    d_results,
+                    [d_strings, pfn, d_target] __device__(size_type idx) {
+                      if (!d_strings.is_null(idx))
+                        return bool{pfn(d_strings.element<string_view>(idx), d_target)};
+                      return false;
+                    });
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+/**
+ * @brief Utility to return a bool column indicating the presence of
+ * a string targets[i] in strings[i].
+ *
+ * Null string entries return corresponding null output column entries.
+ *
+ * @tparam BoolFunction Return bool value given two strings.
+ *
+ * @param strings Column of strings to check for `targets[i]`.
+ * @param targets Column of strings to be checked in `strings[i]``.
+ * @param pfn Returns bool value if target is found in the given string.
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ * @param mr Device memory resource used to allocate the returned column's device memory.
+ * @return New BOOL column.
+ */
+template <typename BoolFunction>
+std::unique_ptr<column> contains_fn(strings_column_view const& strings,
+                                    strings_column_view const& targets,
+                                    BoolFunction pfn,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::BOOL8);
+
+  CUDF_EXPECTS(targets.size() == strings.size(),
+               "strings and targets column must be the same size");
+
+  auto targets_column = column_device_view::create(targets.parent(), stream);
+  auto d_targets      = *targets_column;
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  // create output column
+  auto results      = make_numeric_column(data_type{type_id::BOOL8},
+                                     strings.size(),
+                                     cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                     strings.null_count(),
+                                     stream,
+                                     mr);
+  auto results_view = results->mutable_view();
+  auto d_results    = results_view.data<bool>();
+  // set the bool values by evaluating the passed function
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(strings.size()),
+    d_results,
+    [d_strings, pfn, d_targets] __device__(size_type idx) {
+      // empty target string returns true
+      if (d_targets.is_valid(idx) && d_targets.element<string_view>(idx).length() == 0) {
+        return true;
+      } else if (!d_strings.is_null(idx) && !d_targets.is_null(idx)) {
+        return bool{pfn(d_strings.element<string_view>(idx), d_targets.element<string_view>(idx))};
+      } else {
+        return false;
+      }
+    });
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+}  // namespace
+
+std::unique_ptr<column> contains(strings_column_view const& input,
+                                 string_scalar const& target,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  // use warp parallel when the average string width is greater than the threshold
+  if ((input.null_count() < input.size()) &&
+      ((input.chars_size() / input.size()) > AVG_CHAR_BYTES_THRESHOLD)) {
+    return contains_warp_parallel(input, target, stream, mr);
+  }
+
+  // benchmark measurements showed this to be faster for smaller strings
+  auto pfn = [] __device__(string_view d_string, string_view d_target) {
+    return d_string.find(d_target) != string_view::npos;
+  };
+  return contains_fn(input, target, pfn, stream, mr);
+}
+
+std::unique_ptr<column> contains(strings_column_view const& strings,
+                                 strings_column_view const& targets,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto pfn = [] __device__(string_view d_string, string_view d_target) {
+    return d_string.find(d_target) != string_view::npos;
+  };
+  return contains_fn(strings, targets, pfn, stream, mr);
+}
+
+std::unique_ptr<column> starts_with(strings_column_view const& strings,
+                                    string_scalar const& target,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto pfn = [] __device__(string_view d_string, string_view d_target) {
+    return (d_target.size_bytes() <= d_string.size_bytes()) &&
+           (d_target.compare(d_string.data(), d_target.size_bytes()) == 0);
+  };
+  return contains_fn(strings, target, pfn, stream, mr);
+}
+
+std::unique_ptr<column> starts_with(strings_column_view const& strings,
+                                    strings_column_view const& targets,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto pfn = [] __device__(string_view d_string, string_view d_target) {
+    return (d_target.size_bytes() <= d_string.size_bytes()) &&
+           (d_target.compare(d_string.data(), d_target.size_bytes()) == 0);
+  };
+  return contains_fn(strings, targets, pfn, stream, mr);
+}
+
+std::unique_ptr<column> ends_with(strings_column_view const& strings,
+                                  string_scalar const& target,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto pfn = [] __device__(string_view d_string, string_view d_target) {
+    auto const str_size = d_string.size_bytes();
+    auto const tgt_size = d_target.size_bytes();
+    return (tgt_size <= str_size) &&
+           (d_target.compare(d_string.data() + str_size - tgt_size, tgt_size) == 0);
+  };
+
+  return contains_fn(strings, target, pfn, stream, mr);
+}
+
+std::unique_ptr<column> ends_with(strings_column_view const& strings,
+                                  strings_column_view const& targets,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  auto pfn = [] __device__(string_view d_string, string_view d_target) {
+    auto const str_size = d_string.size_bytes();
+    auto const tgt_size = d_target.size_bytes();
+    return (tgt_size <= str_size) &&
+           (d_target.compare(d_string.data() + str_size - tgt_size, tgt_size) == 0);
+  };
+
+  return contains_fn(strings, targets, pfn, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> contains(strings_column_view const& strings,
+                                 string_scalar const& target,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains(strings, target, stream, mr);
+}
+
+std::unique_ptr<column> contains(strings_column_view const& strings,
+                                 strings_column_view const& targets,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::contains(strings, targets, stream, mr);
+}
+
+std::unique_ptr<column> starts_with(strings_column_view const& strings,
+                                    string_scalar const& target,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::starts_with(strings, target, stream, mr);
+}
+
+std::unique_ptr<column> starts_with(strings_column_view const& strings,
+                                    strings_column_view const& targets,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::starts_with(strings, targets, stream, mr);
+}
+
+std::unique_ptr<column> ends_with(strings_column_view const& strings,
+                                  string_scalar const& target,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::ends_with(strings, target, stream, mr);
+}
+
+std::unique_ptr<column> ends_with(strings_column_view const& strings,
+                                  strings_column_view const& targets,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::ends_with(strings, targets, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/search/find_multiple.cu b/cpp/src/strings/search/find_multiple.cu
new file mode 100644
index 0000000..fcaec83
--- /dev/null
+++ b/cpp/src/strings/search/find_multiple.cu
@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/find_multiple.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+std::unique_ptr<column> find_multiple(strings_column_view const& input,
+                                      strings_column_view const& targets,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = input.size();
+  auto const targets_count = targets.size();
+  CUDF_EXPECTS(targets_count > 0, "Must include at least one search target");
+  CUDF_EXPECTS(!targets.has_nulls(), "Search targets cannot contain null strings");
+
+  auto strings_column = column_device_view::create(input.parent(), stream);
+  auto d_strings      = *strings_column;
+  auto targets_column = column_device_view::create(targets.parent(), stream);
+  auto d_targets      = *targets_column;
+
+  auto const total_count = strings_count * targets_count;
+
+  // create output column
+  auto results = make_numeric_column(
+    data_type{type_id::INT32}, total_count, rmm::device_buffer{0, stream, mr}, 0, stream, mr);
+
+  // fill output column with position values
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(total_count),
+                    results->mutable_view().begin<int32_t>(),
+                    [d_strings, d_targets, targets_count] __device__(size_type idx) {
+                      size_type str_idx = idx / targets_count;
+                      if (d_strings.is_null(str_idx)) return -1;
+                      string_view d_str = d_strings.element<string_view>(str_idx);
+                      string_view d_tgt = d_targets.element<string_view>(idx % targets_count);
+                      return d_str.find(d_tgt);
+                    });
+  results->set_null_count(0);
+
+  auto offsets = cudf::detail::sequence(strings_count + 1,
+                                        numeric_scalar<size_type>(0, true, stream),
+                                        numeric_scalar<size_type>(targets_count, true, stream),
+                                        stream,
+                                        mr);
+  return make_lists_column(strings_count,
+                           std::move(offsets),
+                           std::move(results),
+                           0,
+                           rmm::device_buffer{0, stream, mr},
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+
+// external API
+std::unique_ptr<column> find_multiple(strings_column_view const& input,
+                                      strings_column_view const& targets,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::find_multiple(input, targets, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/search/findall.cu b/cpp/src/strings/search/findall.cu
new file mode 100644
index 0000000..acea4ff
--- /dev/null
+++ b/cpp/src/strings/search/findall.cu
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/count_matches.hpp>
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/findall.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/pair.h>
+#include <thrust/scan.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+using string_index_pair = thrust::pair<char const*, size_type>;
+
+namespace {
+
+/**
+ * @brief This functor handles extracting matched strings by applying the compiled regex pattern
+ * and creating string_index_pairs for all the substrings.
+ */
+struct findall_fn {
+  column_device_view const d_strings;
+  size_type const* d_offsets;
+  string_index_pair* d_indices;
+
+  __device__ void operator()(size_type const idx, reprog_device const prog, int32_t const prog_idx)
+  {
+    if (d_strings.is_null(idx)) { return; }
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const nchars = d_str.length();
+
+    auto d_output        = d_indices + d_offsets[idx];
+    size_type output_idx = 0;
+
+    auto itr = d_str.begin();
+    while (itr.position() < nchars) {
+      auto const match = prog.find(prog_idx, d_str, itr);
+      if (!match) { break; }
+
+      auto const d_result    = string_from_match(*match, d_str, itr);
+      d_output[output_idx++] = string_index_pair{d_result.data(), d_result.size_bytes()};
+
+      itr += (match->second - itr.position());
+    }
+  }
+};
+
+std::unique_ptr<column> findall_util(column_device_view const& d_strings,
+                                     reprog_device& d_prog,
+                                     size_type total_matches,
+                                     size_type const* d_offsets,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  rmm::device_uvector<string_index_pair> indices(total_matches, stream);
+
+  launch_for_each_kernel(
+    findall_fn{d_strings, d_offsets, indices.data()}, d_prog, d_strings.size(), stream);
+
+  return make_strings_column(indices.begin(), indices.end(), stream, mr);
+}
+
+}  // namespace
+
+//
+std::unique_ptr<column> findall(strings_column_view const& input,
+                                regex_program const& prog,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = input.size();
+  auto const d_strings     = column_device_view::create(input.parent(), stream);
+
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  // Create lists offsets column
+  auto offsets   = count_matches(*d_strings, *d_prog, strings_count + 1, stream, mr);
+  auto d_offsets = offsets->mutable_view().data<size_type>();
+
+  // Convert counts into offsets
+  thrust::exclusive_scan(
+    rmm::exec_policy(stream), d_offsets, d_offsets + strings_count + 1, d_offsets);
+
+  // Create indices vector with the total number of groups that will be extracted
+  auto const total_matches =
+    cudf::detail::get_value<size_type>(offsets->view(), strings_count, stream);
+
+  auto strings_output = findall_util(*d_strings, *d_prog, total_matches, d_offsets, stream, mr);
+
+  // Build the lists column from the offsets and the strings
+  return make_lists_column(strings_count,
+                           std::move(offsets),
+                           std::move(strings_output),
+                           input.null_count(),
+                           cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> findall(strings_column_view const& input,
+                                regex_program const& prog,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::findall(input, prog, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/slice.cu b/cpp/src/strings/slice.cu
new file mode 100644
index 0000000..cce6a19
--- /dev/null
+++ b/cpp/src/strings/slice.cu
@@ -0,0 +1,268 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/slice.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+/**
+ * @brief Function logic for compute_substrings_from_fn API
+ *
+ * This computes the output size and resolves the substring
+ */
+template <typename IndexIterator>
+struct substring_from_fn {
+  column_device_view const d_column;
+  IndexIterator const starts;
+  IndexIterator const stops;
+
+  __device__ string_view operator()(size_type idx) const
+  {
+    if (d_column.is_null(idx)) { return string_view{nullptr, 0}; }
+    auto const d_str  = d_column.template element<string_view>(idx);
+    auto const length = d_str.length();
+    auto const start  = std::max(starts[idx], 0);
+    if (start >= length) { return string_view{}; }
+
+    auto const stop = stops[idx];
+    auto const end  = (((stop < 0) || (stop > length)) ? length : stop);
+    return start < end ? d_str.substr(start, end - start) : string_view{};
+  }
+
+  substring_from_fn(column_device_view const& d_column, IndexIterator starts, IndexIterator stops)
+    : d_column(d_column), starts(starts), stops(stops)
+  {
+  }
+};
+
+/**
+ * @brief Function logic for the substring API.
+ *
+ * This will perform a substring operation on each string
+ * using the provided start, stop, and step parameters.
+ */
+struct substring_fn {
+  column_device_view const d_column;
+  numeric_scalar_device_view<size_type> const d_start;
+  numeric_scalar_device_view<size_type> const d_stop;
+  numeric_scalar_device_view<size_type> const d_step;
+  int32_t* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_column.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const d_str  = d_column.template element<string_view>(idx);
+    auto const length = d_str.length();
+    if (length == 0) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    size_type const step = d_step.is_valid() ? d_step.value() : 1;
+    auto const begin     = [&] {  // always inclusive
+      // when invalid, default depends on step
+      if (!d_start.is_valid()) return (step > 0) ? d_str.begin() : (d_str.end() - 1);
+      // normal positive position logic
+      auto start = d_start.value();
+      if (start >= 0) {
+        if (start < length) return d_str.begin() + start;
+        return d_str.end() + (step < 0 ? -1 : 0);
+      }
+      // handle negative position here
+      auto adjust = length + start;
+      if (adjust >= 0) return d_str.begin() + adjust;
+      return d_str.begin() + (step < 0 ? -1 : 0);
+    }();
+    auto const end = [&] {  // always exclusive
+      // when invalid, default depends on step
+      if (!d_stop.is_valid()) return step > 0 ? d_str.end() : (d_str.begin() - 1);
+      // normal positive position logic
+      auto stop = d_stop.value();
+      if (stop >= 0) return (stop < length) ? (d_str.begin() + stop) : d_str.end();
+      // handle negative position here
+      auto adjust = length + stop;
+      return d_str.begin() + (adjust >= 0 ? adjust : -1);
+    }();
+
+    size_type bytes = 0;
+    char* d_buffer  = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    auto itr        = begin;
+    while (step > 0 ? itr < end : end < itr) {
+      if (d_buffer) {
+        d_buffer += from_char_utf8(*itr, d_buffer);
+      } else {
+        bytes += bytes_in_char_utf8(*itr);
+      }
+      itr += step;
+    }
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+/**
+ * @brief Common utility function for the slice_strings APIs
+ *
+ * It wraps calling the functors appropriately to build the output strings column.
+ *
+ * The input iterators may have unique position values per string in `d_column`.
+ * This can also be called with constant value iterators to handle special
+ * slice functions if possible.
+ *
+ * @tparam IndexIterator Iterator type for character position values
+ *
+ * @param d_column Input strings column to substring
+ * @param starts Start positions index iterator
+ * @param stops Stop positions index iterator
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column's device memory
+ */
+template <typename IndexIterator>
+std::unique_ptr<column> compute_substrings_from_fn(column_device_view const& d_column,
+                                                   IndexIterator starts,
+                                                   IndexIterator stops,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  auto results = rmm::device_uvector<string_view>(d_column.size(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::counting_iterator<size_type>(0),
+                    thrust::counting_iterator<size_type>(d_column.size()),
+                    results.begin(),
+                    substring_from_fn{d_column, starts, stops});
+  return make_strings_column(results, string_view{nullptr, 0}, stream, mr);
+}
+
+}  // namespace
+
+//
+std::unique_ptr<column> slice_strings(strings_column_view const& strings,
+                                      numeric_scalar<size_type> const& start,
+                                      numeric_scalar<size_type> const& stop,
+                                      numeric_scalar<size_type> const& step,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+
+  auto const step_valid = step.is_valid(stream);
+  auto const step_value = step_valid ? step.value(stream) : 0;
+  if (step_valid) { CUDF_EXPECTS(step_value != 0, "Step parameter must not be 0"); }
+
+  auto const d_column = column_device_view::create(strings.parent(), stream);
+
+  // optimization for (step==1 and start < stop) -- expect this to be most common
+  if (step_value == 1 and start.is_valid(stream) and stop.is_valid(stream)) {
+    auto const start_value = start.value(stream);
+    auto const stop_value  = stop.value(stream);
+    // note that any negative values here must use the alternate function below
+    if ((start_value >= 0) && (start_value < stop_value)) {
+      // this is about 2x faster on long strings for this common case
+      return compute_substrings_from_fn(*d_column,
+                                        thrust::constant_iterator<size_type>(start_value),
+                                        thrust::constant_iterator<size_type>(stop_value),
+                                        stream,
+                                        mr);
+    }
+  }
+
+  auto const d_start = get_scalar_device_view(const_cast<numeric_scalar<size_type>&>(start));
+  auto const d_stop  = get_scalar_device_view(const_cast<numeric_scalar<size_type>&>(stop));
+  auto const d_step  = get_scalar_device_view(const_cast<numeric_scalar<size_type>&>(step));
+
+  auto [offsets, chars] = make_strings_children(
+    substring_fn{*d_column, d_start, d_stop, d_step}, strings.size(), stream, mr);
+
+  return make_strings_column(strings.size(),
+                             std::move(offsets),
+                             std::move(chars),
+                             strings.null_count(),
+                             cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+std::unique_ptr<column> slice_strings(strings_column_view const& strings,
+                                      column_view const& starts_column,
+                                      column_view const& stops_column,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  size_type strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+  CUDF_EXPECTS(starts_column.size() == strings_count,
+               "Parameter starts must have the same number of rows as strings.");
+  CUDF_EXPECTS(stops_column.size() == strings_count,
+               "Parameter stops must have the same number of rows as strings.");
+  CUDF_EXPECTS(starts_column.type() == stops_column.type(),
+               "Parameters starts and stops must be of the same type.");
+  CUDF_EXPECTS(starts_column.null_count() == 0, "Parameter starts must not contain nulls.");
+  CUDF_EXPECTS(stops_column.null_count() == 0, "Parameter stops must not contain nulls.");
+  CUDF_EXPECTS(starts_column.type().id() != data_type{type_id::BOOL8}.id(),
+               "Positions values must not be bool type.");
+  CUDF_EXPECTS(is_fixed_width(starts_column.type()), "Positions values must be fixed width type.");
+
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  auto starts_iter    = cudf::detail::indexalator_factory::make_input_iterator(starts_column);
+  auto stops_iter     = cudf::detail::indexalator_factory::make_input_iterator(stops_column);
+  return compute_substrings_from_fn(*strings_column, starts_iter, stops_iter, stream, mr);
+}
+
+}  // namespace detail
+
+// external API
+
+std::unique_ptr<column> slice_strings(strings_column_view const& strings,
+                                      numeric_scalar<size_type> const& start,
+                                      numeric_scalar<size_type> const& stop,
+                                      numeric_scalar<size_type> const& step,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::slice_strings(strings, start, stop, step, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> slice_strings(strings_column_view const& strings,
+                                      column_view const& starts_column,
+                                      column_view const& stops_column,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::slice_strings(
+    strings, starts_column, stops_column, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/split/partition.cu b/cpp/src/strings/split/partition.cu
new file mode 100644
index 0000000..0c7d119
--- /dev/null
+++ b/cpp/src/strings/split/partition.cu
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/split/partition.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/pair.h>
+
+#include <vector>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+using string_index_pair = thrust::pair<char const*, size_type>;
+
+namespace {
+//
+// Partition splits the string at the first occurrence of delimiter, and returns 3 elements
+// containing the part before the delimiter, the delimiter itself, and the part after the delimiter.
+// If the delimiter is not found, return 3 elements containing the string itself, followed by two
+// empty strings.
+//
+// strs = ["abcde", nullptr, "a_bc_def", "a__bc", "_ab_cd", "ab_cd_"]
+// results = partition(strs,"_")
+//     col0  col1    col2
+// 0  abcde  ""      ""
+// 1  null   null    null
+// 2  a      _       bc_déf
+// 3  a      _      _bc
+// 4  ""     _       ab_cd
+// 5  ab     _       cd_
+//
+struct partition_fn {
+  column_device_view const d_strings;    // strings to split
+  string_view const d_delimiter;         // delimiter for split
+  string_index_pair* d_indices_left{};   // the three
+  string_index_pair* d_indices_delim{};  // output columns
+  string_index_pair* d_indices_right{};  // amigos
+
+  partition_fn(column_device_view const& d_strings,
+               string_view const& d_delimiter,
+               rmm::device_uvector<string_index_pair>& indices_left,
+               rmm::device_uvector<string_index_pair>& indices_delim,
+               rmm::device_uvector<string_index_pair>& indices_right)
+    : d_strings(d_strings),
+      d_delimiter(d_delimiter),
+      d_indices_left(indices_left.data()),
+      d_indices_delim(indices_delim.data()),
+      d_indices_right(indices_right.data())
+  {
+  }
+
+  __device__ void set_null_entries(size_type idx)
+  {
+    if (d_indices_left) {
+      d_indices_left[idx]  = string_index_pair{nullptr, 0};
+      d_indices_delim[idx] = string_index_pair{nullptr, 0};
+      d_indices_right[idx] = string_index_pair{nullptr, 0};
+    }
+  }
+
+  __device__ size_type check_delimiter(size_type idx,
+                                       string_view const& d_str,
+                                       string_view::const_iterator& itr)
+  {
+    size_type offset = itr.byte_offset();
+    size_type pos    = -1;
+    if (d_delimiter.empty()) {
+      if (*itr <= ' ')  // whitespace delimited
+        pos = offset;
+    } else {
+      auto bytes = std::min(d_str.size_bytes() - offset, d_delimiter.size_bytes());
+      if (d_delimiter.compare(d_str.data() + offset, bytes) == 0) pos = offset;
+    }
+    if (pos >= 0)  // delimiter found, set results
+    {
+      d_indices_left[idx] = string_index_pair{d_str.data(), offset};
+      if (d_delimiter.empty()) {
+        d_indices_delim[idx] = string_index_pair{d_str.data() + offset, 1};
+        ++offset;
+      } else {
+        d_indices_delim[idx] = string_index_pair{d_delimiter.data(), d_delimiter.size_bytes()};
+        offset += d_delimiter.size_bytes();
+      }
+      d_indices_right[idx] = string_index_pair{d_str.data() + offset, d_str.size_bytes() - offset};
+    }
+    return pos;
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      set_null_entries(idx);
+      return;
+    }
+    string_view d_str = d_strings.element<string_view>(idx);
+    size_type pos     = -1;
+    for (auto itr = d_str.begin(); (pos < 0) && (itr < d_str.end()); ++itr)
+      pos = check_delimiter(idx, d_str, itr);
+    if (pos < 0)  // delimiter not found
+    {
+      d_indices_left[idx]  = string_index_pair{d_str.data(), d_str.size_bytes()};
+      d_indices_delim[idx] = string_index_pair{"", 0};  // two empty
+      d_indices_right[idx] = string_index_pair{"", 0};  // strings added
+    }
+  }
+};
+
+//
+// This follows most of the same logic as partition above except that the delimiter
+// search starts from the end of each string. Also, if no delimiter is found the
+// resulting array includes two empty strings followed by the original string.
+//
+// strs = ["abcde", nullptr, "a_bc_def", "a__bc", "_ab_cd", "ab_cd_"]
+// results = rpartition(strs,"_")
+//     col0  col1   col2
+// 0  ""     ""     abcde
+// 1  null   null   null
+// 2  a_bc   _      déf
+// 3  a_     _      bc
+// 4  ab     _      cd
+// 5  ab_cd  _      ""
+//
+struct rpartition_fn : public partition_fn {
+  rpartition_fn(column_device_view const& d_strings,
+                string_view const& d_delimiter,
+                rmm::device_uvector<string_index_pair>& indices_left,
+                rmm::device_uvector<string_index_pair>& indices_delim,
+                rmm::device_uvector<string_index_pair>& indices_right)
+    : partition_fn(d_strings, d_delimiter, indices_left, indices_delim, indices_right)
+  {
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      set_null_entries(idx);
+      return;
+    }
+    string_view d_str = d_strings.element<string_view>(idx);
+    size_type pos     = -1;
+    auto itr          = d_str.end();
+    while ((pos < 0) && (d_str.begin() < itr)) {
+      --itr;
+      pos = check_delimiter(idx, d_str, itr);
+    }
+    if (pos < 0)  // delimiter not found
+    {
+      d_indices_left[idx]  = string_index_pair{"", 0};  // two empty
+      d_indices_delim[idx] = string_index_pair{"", 0};  // strings
+      d_indices_right[idx] = string_index_pair{d_str.data(), d_str.size_bytes()};
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<table> partition(strings_column_view const& strings,
+                                 string_scalar const& delimiter,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+  auto strings_count = strings.size();
+  if (strings_count == 0) return std::make_unique<table>(std::vector<std::unique_ptr<column>>());
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  string_view d_delimiter(delimiter.data(), delimiter.size());
+  auto left_indices  = rmm::device_uvector<string_index_pair>(strings_count, stream);
+  auto delim_indices = rmm::device_uvector<string_index_pair>(strings_count, stream);
+  auto right_indices = rmm::device_uvector<string_index_pair>(strings_count, stream);
+  partition_fn partitioner(
+    *strings_column, d_delimiter, left_indices, delim_indices, right_indices);
+
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     strings_count,
+                     partitioner);
+  std::vector<std::unique_ptr<column>> results;
+  results.emplace_back(make_strings_column(left_indices, stream, mr));
+  results.emplace_back(make_strings_column(delim_indices, stream, mr));
+  results.emplace_back(make_strings_column(right_indices, stream, mr));
+  return std::make_unique<table>(std::move(results));
+}
+
+std::unique_ptr<table> rpartition(strings_column_view const& strings,
+                                  string_scalar const& delimiter,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+  auto strings_count = strings.size();
+  if (strings_count == 0) return std::make_unique<table>(std::vector<std::unique_ptr<column>>());
+  auto strings_column = column_device_view::create(strings.parent(), stream);
+  string_view d_delimiter(delimiter.data(), delimiter.size());
+  auto left_indices  = rmm::device_uvector<string_index_pair>(strings_count, stream);
+  auto delim_indices = rmm::device_uvector<string_index_pair>(strings_count, stream);
+  auto right_indices = rmm::device_uvector<string_index_pair>(strings_count, stream);
+  rpartition_fn partitioner(
+    *strings_column, d_delimiter, left_indices, delim_indices, right_indices);
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     strings_count,
+                     partitioner);
+
+  std::vector<std::unique_ptr<column>> results;
+  results.emplace_back(make_strings_column(left_indices, stream, mr));
+  results.emplace_back(make_strings_column(delim_indices, stream, mr));
+  results.emplace_back(make_strings_column(right_indices, stream, mr));
+  return std::make_unique<table>(std::move(results));
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<table> partition(strings_column_view const& strings,
+                                 string_scalar const& delimiter,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::partition(strings, delimiter, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<table> rpartition(strings_column_view const& strings,
+                                  string_scalar const& delimiter,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rpartition(strings, delimiter, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/split/split.cu b/cpp/src/strings/split/split.cu
new file mode 100644
index 0000000..bad7eef
--- /dev/null
+++ b/cpp/src/strings/split/split.cu
@@ -0,0 +1,452 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "split.cuh"
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/split_utils.cuh>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/split/split.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Generic split function called by split() and rsplit().
+ *
+ * This function will first count the number of delimiters in the entire strings
+ * column. Next it records the position of all the delimiters. These positions
+ * are used for the remainder of the code to build string_index_pair elements
+ * for each output column.
+ *
+ * The number of tokens for each string is computed by analyzing the delimiter
+ * position values and mapping them to each string.
+ * The number of output columns is determined by the string with the most tokens.
+ * Next the `string_index_pairs` for the entire column are created using the
+ * delimiter positions and their string indices vector.
+ *
+ * Finally, each column is built by creating a vector of tokens (`string_index_pairs`)
+ * according to their position in each string. The first token from each string goes
+ * into the first output column, the 2nd token from each string goes into the 2nd
+ * output column, etc.
+ *
+ * Output should be comparable to Pandas `split()` with `expand=True` but the
+ * rows/columns are transposed.
+ *
+ * ```
+ *   import pandas as pd
+ *   pd_series = pd.Series(['', None, 'a_b', '_a_b_', '__aa__bb__', '_a__bbb___c', '_aa_b__ccc__'])
+ *   print(pd_series.str.split(pat='_', expand=True))
+ *            0     1     2     3     4     5     6
+ *      0    ''  None  None  None  None  None  None
+ *      1  None  None  None  None  None  None  None
+ *      2     a     b  None  None  None  None  None
+ *      3    ''     a     b    ''  None  None  None
+ *      4    ''    ''    aa    ''    bb    ''    ''
+ *      5    ''     a    ''   bbb    ''    ''     c
+ *      6    ''    aa     b    ''   ccc    ''    ''
+ *
+ *   print(pd_series.str.split(pat='_', n=1, expand=True))
+ *            0            1
+ *      0    ''         None
+ *      1  None         None
+ *      2     a            b
+ *      3    ''         a_b_
+ *      4    ''    _aa__bb__
+ *      5    ''   a__bbb___c
+ *      6    ''  aa_b__ccc__
+ *
+ *   print(pd_series.str.split(pat='_', n=2, expand=True))
+ *            0     1         2
+ *      0    ''  None      None
+ *      1  None  None      None
+ *      2     a     b      None
+ *      3    ''     a        b_
+ *      4    ''        aa__bb__
+ *      5    ''     a  _bbb___c
+ *      6    ''    aa  b__ccc__
+ * ```
+ *
+ * @tparam Tokenizer provides unique functions for split/rsplit.
+ * @param strings_column The strings to split
+ * @param tokenizer Tokenizer for counting and producing tokens
+ * @return table of columns for the output of the split
+ */
+template <typename Tokenizer>
+std::unique_ptr<table> split_fn(strings_column_view const& input,
+                                Tokenizer tokenizer,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  std::vector<std::unique_ptr<column>> results;
+  if (input.size() == input.null_count()) {
+    results.push_back(std::make_unique<column>(input.parent(), stream, mr));
+    return std::make_unique<table>(std::move(results));
+  }
+
+  // builds the offsets and the vector of all tokens
+  auto [offsets, tokens] = split_helper(input, tokenizer, stream, mr);
+  auto const d_offsets   = offsets->view().template data<size_type>();
+  auto const d_tokens    = tokens.data();
+
+  // compute the maximum number of tokens for any string
+  auto const columns_count = thrust::transform_reduce(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(input.size()),
+    [d_offsets] __device__(auto idx) -> size_type { return d_offsets[idx + 1] - d_offsets[idx]; },
+    0,
+    thrust::maximum{});
+
+  // build strings columns for each token position
+  for (size_type col = 0; col < columns_count; ++col) {
+    auto itr = cudf::detail::make_counting_transform_iterator(
+      0, [d_tokens, d_offsets, col] __device__(size_type idx) {
+        auto const offset      = d_offsets[idx];
+        auto const token_count = d_offsets[idx + 1] - offset;
+        return (col < token_count) ? d_tokens[offset + col] : string_index_pair{nullptr, 0};
+      });
+    results.emplace_back(make_strings_column(itr, itr + input.size(), stream, mr));
+  }
+
+  return std::make_unique<table>(std::move(results));
+}
+
+/**
+ * @brief Base class for whitespace tokenizers.
+ *
+ * These are common methods used by both split and rsplit tokenizer functors.
+ */
+struct base_whitespace_split_tokenizer {
+  // count the tokens only between non-whitespace characters
+  __device__ size_type count_tokens(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) return 0;
+    string_view const d_str = d_strings.element<string_view>(idx);
+    return count_tokens_whitespace(d_str, max_tokens);
+  }
+
+  base_whitespace_split_tokenizer(column_device_view const& d_strings, size_type max_tokens)
+    : d_strings(d_strings), max_tokens(max_tokens)
+  {
+  }
+
+ protected:
+  column_device_view const d_strings;
+  size_type max_tokens;  // maximum number of tokens
+};
+
+/**
+ * @brief The tokenizer functions for split() with whitespace.
+ *
+ * The whitespace tokenizer has no delimiter and handles one or more
+ * consecutive whitespace characters as a single delimiter.
+ */
+struct whitespace_split_tokenizer_fn : base_whitespace_split_tokenizer {
+  /**
+   * @brief This will create tokens around each runs of whitespace characters.
+   *
+   * Each token is placed in `d_all_tokens` so they align consecutively
+   * with other tokens for the same output column.
+   * That is, `d_tokens[col * strings_count + string_index]` is the token at column `col`
+   * for string at `string_index`.
+   *
+   * @param idx Index of the string to process
+   * @param d_token_counts Token counts for each string
+   * @param d_all_tokens All output tokens for the strings column
+   */
+  __device__ void process_tokens(size_type idx,
+                                 size_type const* d_token_counts,
+                                 string_index_pair* d_all_tokens) const
+  {
+    string_index_pair* d_tokens = d_all_tokens + idx;
+    if (d_strings.is_null(idx)) return;
+    string_view const d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) return;
+    whitespace_string_tokenizer tokenizer(d_str);
+    size_type token_count = d_token_counts[idx];
+    size_type token_idx   = 0;
+    position_pair token{0, 0};
+    while (tokenizer.next_token() && (token_idx < token_count)) {
+      token = tokenizer.get_token();
+      d_tokens[d_strings.size() * (token_idx++)] =
+        string_index_pair{d_str.data() + token.first, (token.second - token.first)};
+    }
+    if (token_count == max_tokens)
+      d_tokens[d_strings.size() * (token_idx - 1)] =
+        string_index_pair{d_str.data() + token.first, (d_str.size_bytes() - token.first)};
+  }
+
+  whitespace_split_tokenizer_fn(column_device_view const& d_strings, size_type max_tokens)
+    : base_whitespace_split_tokenizer(d_strings, max_tokens)
+  {
+  }
+};
+
+/**
+ * @brief The tokenizer functions for rsplit() with whitespace.
+ *
+ * The whitespace tokenizer has no delimiter and handles one or more
+ * consecutive whitespace characters as a single delimiter.
+ *
+ * This one processes tokens from the end of each string.
+ */
+struct whitespace_rsplit_tokenizer_fn : base_whitespace_split_tokenizer {
+  /**
+   * @brief This will create tokens around each runs of whitespace characters.
+   *
+   * Each token is placed in `d_all_tokens` so they align consecutively
+   * with other tokens for the same output column.
+   * That is, `d_tokens[col * strings_count + string_index]` is the token at column `col`
+   * for string at `string_index`.
+   *
+   * @param idx Index of the string to process
+   * @param d_token_counts Token counts for each string
+   * @param d_all_tokens All output tokens for the strings column
+   */
+  __device__ void process_tokens(size_type idx,  // string position index
+                                 size_type const* d_token_counts,
+                                 string_index_pair* d_all_tokens) const
+  {
+    string_index_pair* d_tokens = d_all_tokens + idx;
+    if (d_strings.is_null(idx)) return;
+    string_view const d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) return;
+    whitespace_string_tokenizer tokenizer(d_str, true);
+    size_type token_count = d_token_counts[idx];
+    size_type token_idx   = 0;
+    position_pair token{0, 0};
+    while (tokenizer.prev_token() && (token_idx < token_count)) {
+      token = tokenizer.get_token();
+      d_tokens[d_strings.size() * (token_count - 1 - token_idx)] =
+        string_index_pair{d_str.data() + token.first, (token.second - token.first)};
+      ++token_idx;
+    }
+    if (token_count == max_tokens)
+      d_tokens[d_strings.size() * (token_count - token_idx)] =
+        string_index_pair{d_str.data(), token.second};
+  }
+
+  whitespace_rsplit_tokenizer_fn(column_device_view const& d_strings, size_type max_tokens)
+    : base_whitespace_split_tokenizer(d_strings, max_tokens)
+  {
+  }
+};
+
+/**
+ * @brief Generic split function called by split() and rsplit() using whitespace as a delimiter.
+ *
+ * The number of tokens for each string is computed by counting consecutive characters
+ * between runs of whitespace in each string. The number of output columns is determined
+ * by the string with the most tokens. Next the string_index_pairs for the entire column
+ * is created.
+ *
+ * Finally, each column is built by creating a vector of tokens (string_index_pairs)
+ * according to their position in each string. The first token from each string goes
+ * into the first output column, the 2nd token from each string goes into the 2nd
+ * output column, etc.
+ *
+ * This can be compared to Pandas `split()` with no delimiter and with `expand=True` but
+ * with the rows/columns transposed.
+ *
+ *  import pandas as pd
+ *  pd_series = pd.Series(['', None, 'a b', ' a b ', '  aa  bb  ', ' a  bbb   c', ' aa b  ccc  '])
+ *  print(pd_series.str.split(pat=None, expand=True))
+ *            0     1     2
+ *      0  None  None  None
+ *      1  None  None  None
+ *      2     a     b  None
+ *      3     a     b  None
+ *      4    aa    bb  None
+ *      5     a   bbb     c
+ *      6    aa     b   ccc
+ *
+ *  print(pd_series.str.split(pat=None, n=1, expand=True))
+ *            0         1
+ *      0  None      None
+ *      1  None      None
+ *      2     a         b
+ *      3     a        b
+ *      4    aa      bb
+ *      5     a   bbb   c
+ *      6    aa  b  ccc
+ *
+ *  print(pd_series.str.split(pat=None, n=2, expand=True))
+ *            0     1      2
+ *      0  None  None   None
+ *      1  None  None   None
+ *      2     a     b   None
+ *      3     a     b   None
+ *      4    aa    bb   None
+ *      5     a   bbb      c
+ *      6    aa     b  ccc
+ *
+ * @tparam Tokenizer provides unique functions for split/rsplit.
+ * @param strings_count The number of strings in the column
+ * @param tokenizer Tokenizer for counting and producing tokens
+ * @return table of columns for the output of the split
+ */
+template <typename Tokenizer>
+std::unique_ptr<table> whitespace_split_fn(size_type strings_count,
+                                           Tokenizer tokenizer,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  // compute the number of tokens per string
+  rmm::device_uvector<size_type> token_counts(strings_count, stream);
+  auto d_token_counts = token_counts.data();
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(strings_count),
+                    d_token_counts,
+                    [tokenizer] __device__(size_type idx) { return tokenizer.count_tokens(idx); });
+
+  // column count is the maximum number of tokens for any string
+  size_type const columns_count = thrust::reduce(
+    rmm::exec_policy(stream), token_counts.begin(), token_counts.end(), 0, thrust::maximum{});
+
+  std::vector<std::unique_ptr<column>> results;
+  // boundary case: if no columns, return one null column (issue #119)
+  if (columns_count == 0) {
+    results.push_back(std::make_unique<column>(
+      data_type{type_id::STRING},
+      strings_count,
+      rmm::device_buffer{0, stream, mr},  // no data
+      cudf::detail::create_null_mask(strings_count, mask_state::ALL_NULL, stream, mr),
+      strings_count));
+  }
+
+  // get the positions for every token
+  rmm::device_uvector<string_index_pair> tokens(columns_count * strings_count, stream);
+  string_index_pair* d_tokens = tokens.data();
+  thrust::fill(rmm::exec_policy(stream),
+               d_tokens,
+               d_tokens + (columns_count * strings_count),
+               string_index_pair{nullptr, 0});
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     strings_count,
+                     [tokenizer, d_token_counts, d_tokens] __device__(size_type idx) {
+                       tokenizer.process_tokens(idx, d_token_counts, d_tokens);
+                     });
+
+  // Create each column.
+  // - Each pair points to a string for that column for each row.
+  // - Create the strings column from the vector using the strings factory.
+  for (size_type col = 0; col < columns_count; ++col) {
+    auto column_tokens = d_tokens + (col * strings_count);
+    results.emplace_back(
+      make_strings_column(column_tokens, column_tokens + strings_count, stream, mr));
+  }
+  return std::make_unique<table>(std::move(results));
+}
+
+}  // namespace
+
+std::unique_ptr<table> split(strings_column_view const& strings_column,
+                             string_scalar const& delimiter,
+                             size_type maxsplit,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+
+  size_type max_tokens = maxsplit > 0 ? maxsplit + 1 : std::numeric_limits<size_type>::max();
+
+  auto strings_device_view = column_device_view::create(strings_column.parent(), stream);
+  if (delimiter.size() == 0) {
+    return whitespace_split_fn(strings_column.size(),
+                               whitespace_split_tokenizer_fn{*strings_device_view, max_tokens},
+                               stream,
+                               mr);
+  }
+
+  string_view d_delimiter(delimiter.data(), delimiter.size());
+  return split_fn(
+    strings_column, split_tokenizer_fn{*strings_device_view, d_delimiter, max_tokens}, stream, mr);
+}
+
+std::unique_ptr<table> rsplit(strings_column_view const& strings_column,
+                              string_scalar const& delimiter,
+                              size_type maxsplit,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+
+  size_type max_tokens = maxsplit > 0 ? maxsplit + 1 : std::numeric_limits<size_type>::max();
+
+  auto strings_device_view = column_device_view::create(strings_column.parent(), stream);
+  if (delimiter.size() == 0) {
+    return whitespace_split_fn(strings_column.size(),
+                               whitespace_rsplit_tokenizer_fn{*strings_device_view, max_tokens},
+                               stream,
+                               mr);
+  }
+
+  string_view d_delimiter(delimiter.data(), delimiter.size());
+  return split_fn(
+    strings_column, rsplit_tokenizer_fn{*strings_device_view, d_delimiter, max_tokens}, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<table> split(strings_column_view const& strings_column,
+                             string_scalar const& delimiter,
+                             size_type maxsplit,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split(strings_column, delimiter, maxsplit, stream, mr);
+}
+
+std::unique_ptr<table> rsplit(strings_column_view const& strings_column,
+                              string_scalar const& delimiter,
+                              size_type maxsplit,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rsplit(strings_column, delimiter, maxsplit, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/split/split.cuh b/cpp/src/strings/split/split.cuh
new file mode 100644
index 0000000..dc0b04a
--- /dev/null
+++ b/cpp/src/strings/split/split.cuh
@@ -0,0 +1,406 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/strings/detail/split_utils.cuh>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+#include <cuda/atomic>
+
+namespace cudf::strings::detail {
+
+/**
+ * @brief Base class for delimiter-based tokenizers
+ *
+ * These are common methods used by both split and rsplit tokenizer functors.
+ *
+ * The Derived class is required to implement the `process_tokens` function.
+ */
+template <typename Derived>
+struct base_split_tokenizer {
+  __device__ char const* get_base_ptr() const
+  {
+    return d_strings.child(strings_column_view::chars_column_index).data<char>();
+  }
+
+  __device__ string_view const get_string(size_type idx) const
+  {
+    return d_strings.element<string_view>(idx);
+  }
+
+  __device__ bool is_valid(size_type idx) const { return d_strings.is_valid(idx); }
+
+  /**
+   * @brief Returns `true` if the byte at `idx` is the start of the delimiter
+   *
+   * @param idx Index of a byte in the chars column
+   * @param d_offsets Offsets values to locate the chars ranges
+   * @param chars_bytes Total number of characters to process
+   * @return true if delimiter is found starting at position `idx`
+   */
+  __device__ bool is_delimiter(size_type idx,
+                               size_type const* d_offsets,
+                               size_type chars_bytes) const
+  {
+    auto const d_chars = get_base_ptr() + d_offsets[0];
+    if (idx + d_delimiter.size_bytes() > chars_bytes) { return false; }
+    return d_delimiter.compare(d_chars + idx, d_delimiter.size_bytes()) == 0;
+  }
+
+  /**
+   * @brief This counts the tokens for strings that contain delimiters
+   *
+   * Counting tokens is the same regardless if counting from the left
+   * or from the right. This logic counts from the left which is simpler.
+   * The count will be truncated appropriately to the max_tokens value.
+   *
+   * @param idx Index of input string
+   * @param d_positions Start positions of all the delimiters
+   * @param d_delimiter_offsets Offsets per string to delimiters in d_positions
+   */
+  __device__ size_type count_tokens(size_type idx,
+                                    size_type const* d_positions,
+                                    size_type const* d_delimiter_offsets) const
+  {
+    if (!is_valid(idx)) { return 0; }
+
+    auto const delim_size = d_delimiter.size_bytes();
+    auto const d_str      = get_string(idx);
+    auto const d_str_end  = d_str.data() + d_str.size_bytes();
+    auto const base_ptr   = get_base_ptr() + delim_size - 1;
+    auto const delimiters =
+      cudf::device_span<size_type const>(d_positions + d_delimiter_offsets[idx],
+                                         d_delimiter_offsets[idx + 1] - d_delimiter_offsets[idx]);
+
+    size_type token_count = 1;  // all strings will have at least one token
+    size_type last_pos    = delimiters[0] - delim_size;
+    for (auto d_pos : delimiters) {
+      // delimiter must fit in string && overlapping delimiters are ignored
+      if (((base_ptr + d_pos) < d_str_end) && ((d_pos - last_pos) >= delim_size)) {
+        ++token_count;
+        last_pos = d_pos;
+      }
+    }
+    // number of tokens is capped to max_tokens
+    return ((max_tokens > 0) && (token_count > max_tokens)) ? max_tokens : token_count;
+  }
+
+  /**
+   * @brief This will create tokens around each delimiter honoring the string boundaries
+   * in which the delimiter resides
+   *
+   * Each token is placed in `d_all_tokens` so they align consecutively
+   * with other tokens for the same output column.
+   *
+   * The actual token extraction is performed in the subclass process_tokens() function.
+   *
+   * @param idx Index of the string to tokenize
+   * @param d_tokens_offsets Token offsets for each string
+   * @param d_positions The beginning byte position of each delimiter
+   * @param d_delimiter_offsets Offsets to d_positions to each delimiter set per string
+   * @param d_all_tokens All output tokens for the strings column
+   */
+  __device__ void get_tokens(size_type idx,
+                             size_type const* d_tokens_offsets,
+                             size_type const* d_positions,
+                             size_type const* d_delimiter_offsets,
+                             string_index_pair* d_all_tokens) const
+  {
+    auto const d_tokens =  // this string's tokens output
+      cudf::device_span<string_index_pair>(d_all_tokens + d_tokens_offsets[idx],
+                                           d_tokens_offsets[idx + 1] - d_tokens_offsets[idx]);
+
+    if (!is_valid(idx)) { return; }
+
+    auto const d_str = get_string(idx);
+
+    // max_tokens already included in token counts
+    if (d_tokens.size() == 1) {
+      d_tokens[0] = string_index_pair{d_str.data(), d_str.size_bytes()};
+      return;
+    }
+
+    auto const delimiters =
+      cudf::device_span<size_type const>(d_positions + d_delimiter_offsets[idx],
+                                         d_delimiter_offsets[idx + 1] - d_delimiter_offsets[idx]);
+
+    auto& derived = static_cast<Derived const&>(*this);
+    derived.process_tokens(d_str, delimiters, d_tokens);
+  }
+
+  base_split_tokenizer(column_device_view const& d_strings,
+                       string_view const& d_delimiter,
+                       size_type max_tokens)
+    : d_strings(d_strings), d_delimiter(d_delimiter), max_tokens(max_tokens)
+  {
+  }
+
+ protected:
+  column_device_view const d_strings;  // strings to split
+  string_view const d_delimiter;       // delimiter for split
+  size_type max_tokens;                // maximum number of tokens to identify
+};
+
+/**
+ * @brief The tokenizer functions for forward splitting
+ */
+struct split_tokenizer_fn : base_split_tokenizer<split_tokenizer_fn> {
+  /**
+   * @brief This will create tokens around each delimiter honoring the string boundaries
+   *
+   * The tokens are processed from the beginning of each string ignoring overlapping
+   * delimiters and honoring the `max_tokens` value.
+   *
+   * @param d_str String to tokenize
+   * @param d_delimiters Positions of delimiters for this string
+   * @param d_tokens Output vector to store tokens for this string
+   */
+  __device__ void process_tokens(string_view const d_str,
+                                 device_span<size_type const> d_delimiters,
+                                 device_span<string_index_pair> d_tokens) const
+  {
+    auto const base_ptr    = get_base_ptr();  // d_positions values based on this
+    auto str_ptr           = d_str.data();
+    auto const str_end     = str_ptr + d_str.size_bytes();  // end of the string
+    auto const token_count = static_cast<size_type>(d_tokens.size());
+    auto const delim_size  = d_delimiter.size_bytes();
+
+    // build the index-pair of each token for this string
+    size_type token_idx = 0;
+    for (auto d_pos : d_delimiters) {
+      auto const next_delim = base_ptr + d_pos;
+      if (next_delim < str_ptr || ((next_delim + delim_size) > str_end)) { continue; }
+      auto const end_ptr = (token_idx + 1 < token_count) ? next_delim : str_end;
+
+      // store the token into the output vector
+      d_tokens[token_idx++] =
+        string_index_pair{str_ptr, static_cast<size_type>(thrust::distance(str_ptr, end_ptr))};
+
+      // setup for next token
+      str_ptr = end_ptr + delim_size;
+    }
+    // include anything leftover
+    if (token_idx < token_count) {
+      d_tokens[token_idx] =
+        string_index_pair{str_ptr, static_cast<size_type>(thrust::distance(str_ptr, str_end))};
+    }
+  }
+
+  split_tokenizer_fn(column_device_view const& d_strings,
+                     string_view const& d_delimiter,
+                     size_type max_tokens)
+    : base_split_tokenizer(d_strings, d_delimiter, max_tokens)
+  {
+  }
+};
+
+/**
+ * @brief The tokenizer functions for backwards splitting
+ *
+ * Same as split_tokenizer_fn except delimiters are searched from the end of each string.
+ */
+struct rsplit_tokenizer_fn : base_split_tokenizer<rsplit_tokenizer_fn> {
+  /**
+   * @brief This will create tokens around each delimiter honoring the string boundaries
+   *
+   * The tokens are processed from the end of each string ignoring overlapping
+   * delimiters and honoring the `max_tokens` value.
+   *
+   * @param d_str String to tokenize
+   * @param d_delimiters Positions of delimiters for this string
+   * @param d_tokens Output vector to store tokens for this string
+   */
+  __device__ void process_tokens(string_view const d_str,
+                                 device_span<size_type const> d_delimiters,
+                                 device_span<string_index_pair> d_tokens) const
+  {
+    auto const base_ptr    = get_base_ptr();  // d_positions values are based on this ptr
+    auto const str_begin   = d_str.data();    // beginning of the string
+    auto const token_count = static_cast<size_type>(d_tokens.size());
+    auto const delim_count = static_cast<size_type>(d_delimiters.size());
+    auto const delim_size  = d_delimiter.size_bytes();
+
+    // build the index-pair of each token for this string
+    auto str_ptr        = str_begin + d_str.size_bytes();
+    size_type token_idx = 0;
+    for (auto d = delim_count - 1; d >= 0; --d) {  // read right-to-left
+      auto const prev_delim = base_ptr + d_delimiters[d] + delim_size;
+      if (prev_delim > str_ptr || ((prev_delim - delim_size) < str_begin)) { continue; }
+      auto const start_ptr = (token_idx + 1 < token_count) ? prev_delim : str_begin;
+
+      // store the token into the output vector right-to-left
+      d_tokens[token_count - token_idx - 1] =
+        string_index_pair{start_ptr, static_cast<size_type>(thrust::distance(start_ptr, str_ptr))};
+
+      // setup for next token
+      str_ptr = start_ptr - delim_size;
+      ++token_idx;
+    }
+    // include anything leftover (rightover?)
+    if (token_idx < token_count) {
+      d_tokens[0] =
+        string_index_pair{str_begin, static_cast<size_type>(thrust::distance(str_begin, str_ptr))};
+    }
+  }
+
+  rsplit_tokenizer_fn(column_device_view const& d_strings,
+                      string_view const& d_delimiter,
+                      size_type max_tokens)
+    : base_split_tokenizer(d_strings, d_delimiter, max_tokens)
+  {
+  }
+};
+
+/**
+ * @brief Helper function used by split/rsplit and split_record/rsplit_record
+ *
+ * This function returns all the token/split positions within the input column as processed by
+ * the given tokenizer. It also returns the offsets for each set of tokens identified per string.
+ *
+ * @tparam Tokenizer Type of the tokenizer object
+ *
+ * @param input The input column of strings to split
+ * @param tokenizer Object used for counting and identifying delimiters and tokens
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned objects' device memory.
+ */
+template <typename Tokenizer>
+std::pair<std::unique_ptr<column>, rmm::device_uvector<string_index_pair>> split_helper(
+  strings_column_view const& input,
+  Tokenizer tokenizer,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = input.size();
+  auto const chars_bytes =
+    cudf::detail::get_value<size_type>(input.offsets(), input.offset() + strings_count, stream) -
+    cudf::detail::get_value<size_type>(input.offsets(), input.offset(), stream);
+
+  auto d_offsets = input.offsets_begin();
+
+  // count the number of delimiters in the entire column
+  auto const delimiter_count =
+    thrust::count_if(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     thrust::make_counting_iterator<size_type>(chars_bytes),
+                     [tokenizer, d_offsets, chars_bytes] __device__(size_type idx) {
+                       return tokenizer.is_delimiter(idx, d_offsets, chars_bytes);
+                     });
+  // Create a vector of every delimiter position in the chars column.
+  // These may include overlapping or otherwise out-of-bounds delimiters which
+  // will be resolved during token processing.
+  auto delimiter_positions = rmm::device_uvector<size_type>(delimiter_count, stream);
+  auto d_positions         = delimiter_positions.data();
+  auto const copy_end =
+    thrust::copy_if(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(chars_bytes),
+                    delimiter_positions.begin(),
+                    [tokenizer, d_offsets, chars_bytes] __device__(size_type idx) {
+                      return tokenizer.is_delimiter(idx, d_offsets, chars_bytes);
+                    });
+
+  // create a vector of offsets to each string's delimiter set within delimiter_positions
+  auto const delimiter_offsets = [&] {
+    // first, create a vector of string indices for each delimiter
+    auto string_indices = rmm::device_uvector<size_type>(delimiter_count, stream);
+    thrust::upper_bound(rmm::exec_policy(stream),
+                        d_offsets,
+                        d_offsets + strings_count,
+                        delimiter_positions.begin(),
+                        copy_end,
+                        string_indices.begin());
+
+    // compute delimiter offsets per string
+    auto delimiter_offsets   = rmm::device_uvector<size_type>(strings_count + 1, stream);
+    auto d_delimiter_offsets = delimiter_offsets.data();
+
+    // memset to zero-out the delimiter counts for any null-entries or strings with no delimiters
+    CUDF_CUDA_TRY(cudaMemsetAsync(
+      d_delimiter_offsets, 0, delimiter_offsets.size() * sizeof(size_type), stream.value()));
+
+    // next, count the number of delimiters per string
+    auto d_string_indices = string_indices.data();  // identifies strings with delimiters only
+    thrust::for_each_n(rmm::exec_policy(stream),
+                       thrust::make_counting_iterator<size_type>(0),
+                       delimiter_count,
+                       [d_string_indices, d_delimiter_offsets] __device__(size_type idx) {
+                         auto const str_idx = d_string_indices[idx] - 1;
+                         cuda::atomic_ref<size_type, cuda::thread_scope_device> ref{
+                           *(d_delimiter_offsets + str_idx)};
+                         ref.fetch_add(1, cuda::std::memory_order_relaxed);
+                       });
+    // finally, convert the delimiter counts into offsets
+    thrust::exclusive_scan(rmm::exec_policy(stream),
+                           delimiter_offsets.begin(),
+                           delimiter_offsets.end(),
+                           delimiter_offsets.begin());
+    return delimiter_offsets;
+  }();
+  auto const d_delimiter_offsets = delimiter_offsets.data();
+
+  // compute the number of tokens per string
+  auto token_counts = rmm::device_uvector<size_type>(strings_count, stream);
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(strings_count),
+    token_counts.begin(),
+    [tokenizer, d_positions, d_delimiter_offsets] __device__(size_type idx) -> size_type {
+      return tokenizer.count_tokens(idx, d_positions, d_delimiter_offsets);
+    });
+
+  // create offsets from the counts for return to the caller
+  auto offsets = std::get<0>(
+    cudf::detail::make_offsets_child_column(token_counts.begin(), token_counts.end(), stream, mr));
+  auto const total_tokens =
+    cudf::detail::get_value<size_type>(offsets->view(), strings_count, stream);
+  auto const d_tokens_offsets = offsets->view().data<size_type>();
+
+  // build a vector of all the token positions for all the strings
+  auto tokens   = rmm::device_uvector<string_index_pair>(total_tokens, stream);
+  auto d_tokens = tokens.data();
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    strings_count,
+    [tokenizer, d_tokens_offsets, d_positions, d_delimiter_offsets, d_tokens] __device__(
+      size_type idx) {
+      tokenizer.get_tokens(idx, d_tokens_offsets, d_positions, d_delimiter_offsets, d_tokens);
+    });
+
+  return std::make_pair(std::move(offsets), std::move(tokens));
+}
+
+}  // namespace cudf::strings::detail
diff --git a/cpp/src/strings/split/split_re.cu b/cpp/src/strings/split/split_re.cu
new file mode 100644
index 0000000..3be5937
--- /dev/null
+++ b/cpp/src/strings/split/split_re.cu
@@ -0,0 +1,377 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/count_matches.hpp>
+#include <strings/regex/regex_program_impl.h>
+#include <strings/regex/utilities.cuh>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/split/split_re.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/transform_reduce.h>
+#include <thrust/transform_scan.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+using string_index_pair = thrust::pair<char const*, size_type>;
+
+enum class split_direction {
+  FORWARD,  ///< for split logic
+  BACKWARD  ///< for rsplit logic
+};
+
+/**
+ * @brief Identify the tokens from the `idx'th` string element of `d_strings`.
+ *
+ * Each string's tokens are stored in the `d_tokens` vector.
+ * The `d_token_offsets` specifies the output position within `d_tokens`
+ * for each string.
+ */
+struct token_reader_fn {
+  column_device_view const d_strings;
+  split_direction const direction;
+  size_type const* d_token_offsets;
+  string_index_pair* d_tokens;
+
+  __device__ void operator()(size_type const idx, reprog_device const prog, int32_t const prog_idx)
+  {
+    if (d_strings.is_null(idx)) { return; }
+    auto const d_str  = d_strings.element<string_view>(idx);
+    auto const nchars = d_str.length();
+
+    auto const token_offset = d_token_offsets[idx];
+    auto const token_count  = d_token_offsets[idx + 1] - token_offset;
+    auto const d_result     = d_tokens + token_offset;  // store tokens here
+
+    size_type token_idx = 0;
+    auto itr            = d_str.begin();
+    auto last_pos       = itr;
+    while (itr.position() <= nchars) {
+      auto const match = prog.find(prog_idx, d_str, itr);
+      if (!match) { break; }
+
+      auto const start_pos = thrust::get<0>(match_positions_to_bytes(*match, d_str, last_pos));
+
+      // get the token (characters just before this match)
+      auto const token = string_index_pair{d_str.data() + last_pos.byte_offset(),
+                                           start_pos - last_pos.byte_offset()};
+      // store it if we have space
+      if (token_idx < token_count - 1) {
+        d_result[token_idx++] = token;
+      } else {
+        if (direction == split_direction::FORWARD) { break; }  // we are done
+        for (auto l = 0; l < token_idx - 1; ++l) {
+          d_result[l] = d_result[l + 1];  // shift left
+        }
+        d_result[token_idx - 1] = token;
+      }
+      // setup for next match
+      last_pos += (match->second - last_pos.position());
+      itr = last_pos + (match->first == match->second);
+    }
+
+    // set the last token to the remainder of the string
+    d_result[token_idx] = string_index_pair{d_str.data() + last_pos.byte_offset(),
+                                            d_str.size_bytes() - last_pos.byte_offset()};
+
+    if (direction == split_direction::BACKWARD) {
+      // update first entry -- this happens when max_tokens is hit before the end of the string
+      auto const first_offset =
+        d_result[0].first
+          ? static_cast<size_type>(thrust::distance(d_str.data(), d_result[0].first))
+          : 0;
+      if (first_offset) {
+        d_result[0] = string_index_pair{d_str.data(), first_offset + d_result[0].second};
+      }
+    }
+  }
+};
+
+/**
+ * @brief Call regex to split each input string into tokens.
+ *
+ * This will also convert the `offsets` values from counts to offsets.
+ *
+ * @param d_strings Strings to split
+ * @param d_prog Regex to evaluate against each string
+ * @param direction Whether tokens are generated forwards or backwards.
+ * @param max_tokens The maximum number of tokens for each split.
+ * @param offsets The number of matches on input.
+ *                The offsets for each token in each string on output.
+ * @param stream CUDA stream used for kernel launches.
+ */
+rmm::device_uvector<string_index_pair> generate_tokens(column_device_view const& d_strings,
+                                                       reprog_device& d_prog,
+                                                       split_direction direction,
+                                                       size_type maxsplit,
+                                                       mutable_column_view& offsets,
+                                                       rmm::cuda_stream_view stream)
+{
+  auto const strings_count = d_strings.size();
+
+  auto const max_tokens = maxsplit > 0 ? maxsplit : std::numeric_limits<size_type>::max();
+
+  auto const begin     = thrust::make_counting_iterator<size_type>(0);
+  auto const end       = thrust::make_counting_iterator<size_type>(strings_count);
+  auto const d_offsets = offsets.data<size_type>();
+
+  // convert match counts to token offsets
+  auto map_fn = [d_strings, d_offsets, max_tokens] __device__(auto idx) {
+    return d_strings.is_null(idx) ? 0 : std::min(d_offsets[idx], max_tokens) + 1;
+  };
+  thrust::transform_exclusive_scan(
+    rmm::exec_policy(stream), begin, end + 1, d_offsets, map_fn, 0, thrust::plus<size_type>{});
+
+  // the last offset entry is the total number of tokens to be generated
+  auto const total_tokens = cudf::detail::get_value<size_type>(offsets, strings_count, stream);
+
+  rmm::device_uvector<string_index_pair> tokens(total_tokens, stream);
+  if (total_tokens == 0) { return tokens; }
+
+  launch_for_each_kernel(token_reader_fn{d_strings, direction, d_offsets, tokens.data()},
+                         d_prog,
+                         d_strings.size(),
+                         stream);
+
+  return tokens;
+}
+
+/**
+ * @brief Returns string pair for the specified column for each string in `d_strings`
+ *
+ * This is used to build the table result of a split.
+ * Null is returned if the row is null or if the `column_index` is larger
+ * than the token count for that string.
+ */
+struct tokens_transform_fn {
+  column_device_view const d_strings;
+  string_index_pair const* d_tokens;
+  size_type const* d_token_offsets;
+  size_type const column_index;
+
+  __device__ string_index_pair operator()(size_type idx) const
+  {
+    auto const offset      = d_token_offsets[idx];
+    auto const token_count = d_token_offsets[idx + 1] - offset;
+    return (column_index >= token_count) || d_strings.is_null(idx)
+             ? string_index_pair{nullptr, 0}
+             : d_tokens[offset + column_index];
+  }
+};
+
+std::unique_ptr<table> split_re(strings_column_view const& input,
+                                regex_program const& prog,
+                                split_direction direction,
+                                size_type maxsplit,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!prog.pattern().empty(), "Parameter pattern must not be empty");
+
+  auto const strings_count = input.size();
+
+  std::vector<std::unique_ptr<column>> results;
+  if (strings_count == 0) {
+    results.push_back(make_empty_column(type_id::STRING));
+    return std::make_unique<table>(std::move(results));
+  }
+
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  // count the number of delimiters matched in each string
+  auto offsets = count_matches(
+    *d_strings, *d_prog, strings_count + 1, stream, rmm::mr::get_current_device_resource());
+  auto offsets_view = offsets->mutable_view();
+  auto d_offsets    = offsets_view.data<size_type>();
+
+  // get the split tokens from the input column; this also converts the counts into offsets
+  auto tokens = generate_tokens(*d_strings, *d_prog, direction, maxsplit, offsets_view, stream);
+
+  // the output column count is the maximum number of tokens generated for any input string
+  auto const columns_count = thrust::transform_reduce(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_type>(0),
+    thrust::make_counting_iterator<size_type>(strings_count),
+    [d_offsets] __device__(auto const idx) -> size_type {
+      return d_offsets[idx + 1] - d_offsets[idx];
+    },
+    0,
+    thrust::maximum<size_type>{});
+
+  // boundary case: if no columns, return one all-null column (custrings issue #119)
+  if (columns_count == 0) {
+    results.push_back(std::make_unique<column>(
+      data_type{type_id::STRING},
+      strings_count,
+      rmm::device_buffer{0, stream, mr},  // no data
+      cudf::detail::create_null_mask(strings_count, mask_state::ALL_NULL, stream, mr),
+      strings_count));
+    return std::make_unique<table>(std::move(results));
+  }
+
+  // convert the tokens into multiple strings columns
+  auto make_strings_lambda = [&](size_type column_index) {
+    // returns appropriate token for each row/column
+    auto indices_itr = cudf::detail::make_counting_transform_iterator(
+      0, tokens_transform_fn{*d_strings, tokens.data(), d_offsets, column_index});
+    return make_strings_column(indices_itr, indices_itr + strings_count, stream, mr);
+  };
+  // build a vector of columns
+  results.resize(columns_count);
+  std::transform(thrust::make_counting_iterator<size_type>(0),
+                 thrust::make_counting_iterator<size_type>(columns_count),
+                 results.begin(),
+                 make_strings_lambda);
+
+  return std::make_unique<table>(std::move(results));
+}
+
+std::unique_ptr<column> split_record_re(strings_column_view const& input,
+                                        regex_program const& prog,
+                                        split_direction direction,
+                                        size_type maxsplit,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!prog.pattern().empty(), "Parameter pattern must not be empty");
+
+  auto const strings_count = input.size();
+
+  // create device object from regex_program
+  auto d_prog = regex_device_builder::create_prog_device(prog, stream);
+
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  // count the number of delimiters matched in each string
+  auto offsets      = count_matches(*d_strings, *d_prog, strings_count + 1, stream, mr);
+  auto offsets_view = offsets->mutable_view();
+
+  // get the split tokens from the input column; this also converts the counts into offsets
+  auto tokens = generate_tokens(*d_strings, *d_prog, direction, maxsplit, offsets_view, stream);
+
+  // convert the tokens into one big strings column
+  auto strings_output = make_strings_column(tokens.begin(), tokens.end(), stream, mr);
+
+  // create a lists column using the offsets and the strings columns
+  return make_lists_column(strings_count,
+                           std::move(offsets),
+                           std::move(strings_output),
+                           input.null_count(),
+                           copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+
+}  // namespace
+
+std::unique_ptr<table> split_re(strings_column_view const& input,
+                                regex_program const& prog,
+                                size_type maxsplit,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  return split_re(input, prog, split_direction::FORWARD, maxsplit, stream, mr);
+}
+
+std::unique_ptr<column> split_record_re(strings_column_view const& input,
+                                        regex_program const& prog,
+                                        size_type maxsplit,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  return split_record_re(input, prog, split_direction::FORWARD, maxsplit, stream, mr);
+}
+
+std::unique_ptr<table> rsplit_re(strings_column_view const& input,
+                                 regex_program const& prog,
+                                 size_type maxsplit,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  return split_re(input, prog, split_direction::BACKWARD, maxsplit, stream, mr);
+}
+
+std::unique_ptr<column> rsplit_record_re(strings_column_view const& input,
+                                         regex_program const& prog,
+                                         size_type maxsplit,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  return split_record_re(input, prog, split_direction::BACKWARD, maxsplit, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<table> split_re(strings_column_view const& input,
+                                regex_program const& prog,
+                                size_type maxsplit,
+                                rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split_re(input, prog, maxsplit, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> split_record_re(strings_column_view const& input,
+                                        regex_program const& prog,
+                                        size_type maxsplit,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split_record_re(input, prog, maxsplit, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<table> rsplit_re(strings_column_view const& input,
+                                 regex_program const& prog,
+                                 size_type maxsplit,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rsplit_re(input, prog, maxsplit, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> rsplit_record_re(strings_column_view const& input,
+                                         regex_program const& prog,
+                                         size_type maxsplit,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::rsplit_record_re(input, prog, maxsplit, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/split/split_record.cu b/cpp/src/strings/split/split_record.cu
new file mode 100644
index 0000000..52f27c6
--- /dev/null
+++ b/cpp/src/strings/split/split_record.cu
@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "split.cuh"
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/detail/lists_column_factories.hpp>
+#include <cudf/strings/detail/split_utils.cuh>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/split/split.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+namespace {
+
+template <typename Tokenizer>
+std::unique_ptr<column> split_record_fn(strings_column_view const& input,
+                                        Tokenizer tokenizer,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) {
+    return cudf::lists::detail::make_empty_lists_column(data_type{type_id::STRING}, stream, mr);
+  }
+  if (input.size() == input.null_count()) {
+    auto offsets = std::make_unique<column>(input.offsets(), stream, mr);
+    auto results = make_empty_column(type_id::STRING);
+    return make_lists_column(input.size(),
+                             std::move(offsets),
+                             std::move(results),
+                             input.null_count(),
+                             copy_bitmask(input.parent(), stream, mr),
+                             stream,
+                             mr);
+  }
+
+  // builds the offsets and the vector of all tokens
+  auto [offsets, tokens] = split_helper(input, tokenizer, stream, mr);
+
+  // build a strings column from the tokens
+  auto strings_child = make_strings_column(tokens.begin(), tokens.end(), stream, mr);
+
+  return make_lists_column(input.size(),
+                           std::move(offsets),
+                           std::move(strings_child),
+                           input.null_count(),
+                           copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+
+enum class Direction { FORWARD, BACKWARD };
+
+/**
+ * @brief Identify the tokens from the `idx'th` string element of `d_strings`.
+ */
+template <Direction direction>
+struct whitespace_token_reader_fn {
+  column_device_view const d_strings;  // strings to split
+  size_type const max_tokens = std::numeric_limits<size_type>::max();
+  size_type const* d_token_offsets{};
+  string_index_pair* d_tokens{};
+
+  __device__ size_type count_tokens(size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return 0; }
+    auto const d_str = d_strings.element<string_view>(idx);
+    return count_tokens_whitespace(d_str, max_tokens);
+  }
+
+  __device__ void operator()(size_type idx)
+  {
+    auto const token_offset = d_token_offsets[idx];
+    auto const token_count  = d_token_offsets[idx + 1] - token_offset;
+    if (token_count == 0) { return; }
+    auto d_result = d_tokens + token_offset;
+
+    auto const d_str = d_strings.element<string_view>(idx);
+    whitespace_string_tokenizer tokenizer(d_str, direction != Direction::FORWARD);
+    size_type token_idx = 0;
+    position_pair token{0, 0};
+    if constexpr (direction == Direction::FORWARD) {
+      while (tokenizer.next_token() && (token_idx < token_count)) {
+        token = tokenizer.get_token();
+        d_result[token_idx++] =
+          string_index_pair{d_str.data() + token.first, token.second - token.first};
+      }
+      --token_idx;
+      token.second = d_str.size_bytes() - token.first;
+    } else {
+      while (tokenizer.prev_token() && (token_idx < token_count)) {
+        token = tokenizer.get_token();
+        d_result[token_count - 1 - token_idx] =
+          string_index_pair{d_str.data() + token.first, token.second - token.first};
+        ++token_idx;
+      }
+      token_idx   = token_count - token_idx;  // token_count - 1 - (token_idx-1)
+      token.first = 0;
+    }
+    // reset last token only if we hit the max
+    if (token_count == max_tokens)
+      d_result[token_idx] = string_index_pair{d_str.data() + token.first, token.second};
+  }
+};
+
+}  // namespace
+
+// The output is one list item per string
+template <typename TokenReader>
+std::unique_ptr<column> whitespace_split_record_fn(strings_column_view const& input,
+                                                   TokenReader reader,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  // create offsets column by counting the number of tokens per string
+  auto sizes_itr = cudf::detail::make_counting_transform_iterator(
+    0, [reader] __device__(auto idx) { return reader.count_tokens(idx); });
+  auto [offsets, total_tokens] =
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + input.size(), stream, mr);
+  auto d_offsets = offsets->view().template data<cudf::size_type>();
+
+  // split each string into an array of index-pair values
+  rmm::device_uvector<string_index_pair> tokens(total_tokens, stream);
+  reader.d_token_offsets = d_offsets;
+  reader.d_tokens        = tokens.data();
+  thrust::for_each_n(
+    rmm::exec_policy(stream), thrust::make_counting_iterator<size_type>(0), input.size(), reader);
+  // convert the index-pairs into one big strings column
+  auto strings_output = make_strings_column(tokens.begin(), tokens.end(), stream, mr);
+  // create a lists column using the offsets and the strings columns
+  return make_lists_column(input.size(),
+                           std::move(offsets),
+                           std::move(strings_output),
+                           input.null_count(),
+                           copy_bitmask(input.parent(), stream, mr),
+                           stream,
+                           mr);
+}
+
+template <Direction direction>
+std::unique_ptr<column> split_record(strings_column_view const& strings,
+                                     string_scalar const& delimiter,
+                                     size_type maxsplit,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+
+  // makes consistent with Pandas
+  size_type max_tokens = maxsplit > 0 ? maxsplit + 1 : std::numeric_limits<size_type>::max();
+
+  auto d_strings_column_ptr = column_device_view::create(strings.parent(), stream);
+  if (delimiter.size() == 0) {
+    return whitespace_split_record_fn(
+      strings,
+      whitespace_token_reader_fn<direction>{*d_strings_column_ptr, max_tokens},
+      stream,
+      mr);
+  } else {
+    string_view d_delimiter(delimiter.data(), delimiter.size());
+    if (direction == Direction::FORWARD) {
+      return split_record_fn(
+        strings, split_tokenizer_fn{*d_strings_column_ptr, d_delimiter, max_tokens}, stream, mr);
+    } else {
+      return split_record_fn(
+        strings, rsplit_tokenizer_fn{*d_strings_column_ptr, d_delimiter, max_tokens}, stream, mr);
+    }
+  }
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> split_record(strings_column_view const& strings,
+                                     string_scalar const& delimiter,
+                                     size_type maxsplit,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split_record<detail::Direction::FORWARD>(strings, delimiter, maxsplit, stream, mr);
+}
+
+std::unique_ptr<column> rsplit_record(strings_column_view const& strings,
+                                      string_scalar const& delimiter,
+                                      size_type maxsplit,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::split_record<detail::Direction::BACKWARD>(
+    strings, delimiter, maxsplit, stream, mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/strings_column_factories.cu b/cpp/src/strings/strings_column_factories.cu
new file mode 100644
index 0000000..6e3b939
--- /dev/null
+++ b/cpp/src/strings/strings_column_factories.cu
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+
+namespace cudf {
+
+namespace {
+struct string_view_to_pair {
+  string_view null_placeholder;
+  string_view_to_pair(string_view n) : null_placeholder(n) {}
+  __device__ thrust::pair<char const*, size_type> operator()(string_view const& i)
+  {
+    return (i.data() == null_placeholder.data())
+             ? thrust::pair<char const*, size_type>{nullptr, 0}
+             : thrust::pair<char const*, size_type>{i.data(), i.size_bytes()};
+  }
+};
+
+}  // namespace
+
+// Create a strings-type column from vector of pointer/size pairs
+std::unique_ptr<column> make_strings_column(
+  device_span<thrust::pair<char const*, size_type> const> strings,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  return cudf::strings::detail::make_strings_column(strings.begin(), strings.end(), stream, mr);
+}
+
+std::unique_ptr<column> make_strings_column(device_span<char> chars,
+                                            device_span<size_type> offsets,
+                                            size_type null_count,
+                                            rmm::device_buffer&& null_mask,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  return cudf::strings::detail::make_strings_column(chars.begin(),
+                                                    chars.end(),
+                                                    offsets.begin(),
+                                                    offsets.end(),
+                                                    null_count,
+                                                    std::move(null_mask),
+                                                    stream,
+                                                    mr);
+}
+
+std::unique_ptr<column> make_strings_column(device_span<string_view const> string_views,
+                                            string_view null_placeholder,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  auto it_pair =
+    thrust::make_transform_iterator(string_views.begin(), string_view_to_pair{null_placeholder});
+  return cudf::strings::detail::make_strings_column(
+    it_pair, it_pair + string_views.size(), stream, mr);
+}
+
+// Create a strings-type column from device vector of chars and vector of offsets.
+std::unique_ptr<column> make_strings_column(cudf::device_span<char const> strings,
+                                            cudf::device_span<size_type const> offsets,
+                                            cudf::device_span<bitmask_type const> valid_mask,
+                                            size_type null_count,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+
+  // build null bitmask
+  rmm::device_buffer null_mask{
+    valid_mask.data(), valid_mask.size() * sizeof(bitmask_type), stream, mr};
+
+  return cudf::strings::detail::make_strings_column(strings.begin(),
+                                                    strings.end(),
+                                                    offsets.begin(),
+                                                    offsets.end(),
+                                                    null_count,
+                                                    std::move(null_mask),
+                                                    stream,
+                                                    mr);
+}
+
+//
+std::unique_ptr<column> make_strings_column(size_type num_strings,
+                                            std::unique_ptr<column> offsets_column,
+                                            std::unique_ptr<column> chars_column,
+                                            size_type null_count,
+                                            rmm::device_buffer&& null_mask)
+{
+  CUDF_FUNC_RANGE();
+
+  if (null_count > 0) CUDF_EXPECTS(null_mask.size() > 0, "Column with nulls must be nullable.");
+  CUDF_EXPECTS(num_strings == offsets_column->size() - 1,
+               "Invalid offsets column size for strings column.");
+  CUDF_EXPECTS(offsets_column->null_count() == 0, "Offsets column should not contain nulls");
+  CUDF_EXPECTS(chars_column->null_count() == 0, "Chars column should not contain nulls");
+
+  std::vector<std::unique_ptr<column>> children;
+  children.emplace_back(std::move(offsets_column));
+  children.emplace_back(std::move(chars_column));
+  return std::make_unique<column>(data_type{type_id::STRING},
+                                  num_strings,
+                                  rmm::device_buffer{},
+                                  std::move(null_mask),
+                                  null_count,
+                                  std::move(children));
+}
+
+std::unique_ptr<column> make_strings_column(size_type num_strings,
+                                            rmm::device_uvector<size_type>&& offsets,
+                                            rmm::device_uvector<char>&& chars,
+                                            rmm::device_buffer&& null_mask,
+                                            size_type null_count)
+{
+  CUDF_FUNC_RANGE();
+
+  auto const offsets_size = static_cast<size_type>(offsets.size());
+  auto const chars_size   = static_cast<size_type>(chars.size());
+
+  if (null_count > 0) CUDF_EXPECTS(null_mask.size() > 0, "Column with nulls must be nullable.");
+
+  CUDF_EXPECTS(num_strings == offsets_size - 1, "Invalid offsets column size for strings column.");
+
+  auto offsets_column = std::make_unique<column>(  //
+    data_type{type_id::INT32},
+    offsets_size,
+    offsets.release(),
+    rmm::device_buffer(),
+    0);
+
+  auto chars_column = std::make_unique<column>(  //
+    data_type{type_id::INT8},
+    chars_size,
+    chars.release(),
+    rmm::device_buffer(),
+    0);
+
+  auto children = std::vector<std::unique_ptr<column>>();
+
+  children.emplace_back(std::move(offsets_column));
+  children.emplace_back(std::move(chars_column));
+
+  return std::make_unique<column>(data_type{type_id::STRING},
+                                  num_strings,
+                                  rmm::device_buffer{},
+                                  std::move(null_mask),
+                                  null_count,
+                                  std::move(children));
+}
+
+}  // namespace cudf
diff --git a/cpp/src/strings/strings_column_view.cpp b/cpp/src/strings/strings_column_view.cpp
new file mode 100644
index 0000000..4b20666
--- /dev/null
+++ b/cpp/src/strings/strings_column_view.cpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/error.hpp>
+
+namespace cudf {
+//
+strings_column_view::strings_column_view(column_view strings_column) : column_view(strings_column)
+{
+  CUDF_EXPECTS(type().id() == type_id::STRING, "strings_column_view only supports strings");
+}
+
+column_view strings_column_view::parent() const { return static_cast<column_view>(*this); }
+
+column_view strings_column_view::offsets() const
+{
+  CUDF_EXPECTS(num_children() > 0, "strings column has no children");
+  return child(offsets_column_index);
+}
+
+strings_column_view::offset_iterator strings_column_view::offsets_begin() const
+{
+  return offsets().begin<size_type>() + offset();
+}
+
+strings_column_view::offset_iterator strings_column_view::offsets_end() const
+{
+  return offsets_begin() + size() + 1;
+}
+
+column_view strings_column_view::chars() const
+{
+  CUDF_EXPECTS(num_children() > 0, "strings column has no children");
+  return child(chars_column_index);
+}
+
+size_type strings_column_view::chars_size() const noexcept
+{
+  if (size() == 0) return 0;
+  return chars().size();
+}
+
+strings_column_view::chars_iterator strings_column_view::chars_begin() const
+{
+  return chars().begin<char>();
+}
+
+strings_column_view::chars_iterator strings_column_view::chars_end() const
+{
+  return chars_begin() + chars_size();
+}
+
+}  // namespace cudf
diff --git a/cpp/src/strings/strings_scalar_factories.cpp b/cpp/src/strings/strings_scalar_factories.cpp
new file mode 100644
index 0000000..9c7f905
--- /dev/null
+++ b/cpp/src/strings/strings_scalar_factories.cpp
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/scalar/scalar.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+// Create a strings-type column from array of pointer/size pairs
+std::unique_ptr<scalar> make_string_scalar(std::string const& string,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto s = new string_scalar(string, true, stream, mr);
+  return std::unique_ptr<scalar>(s);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/strings/strip.cu b/cpp/src/strings/strip.cu
new file mode 100644
index 0000000..6fb7c67
--- /dev/null
+++ b/cpp/src/strings/strip.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/detail/strip.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/strip.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {
+
+/**
+ * @brief Strip characters from the beginning and/or end of a string
+ *
+ * This functor strips the beginning and/or end of each string
+ * of any characters found in d_to_strip or whitespace if
+ * d_to_strip is empty.
+ *
+ */
+struct strip_transform_fn {
+  column_device_view const d_strings;
+  side_type const side;  // right, left, or both
+  string_view const d_to_strip;
+
+  __device__ string_index_pair operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) { return string_index_pair{nullptr, 0}; }
+    auto const d_str      = d_strings.element<string_view>(idx);
+    auto const d_stripped = strip(d_str, d_to_strip, side);
+    return string_index_pair{d_stripped.data(), d_stripped.size_bytes()};
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<column> strip(strings_column_view const& input,
+                              side_type side,
+                              string_scalar const& to_strip,
+                              rmm::cuda_stream_view stream,
+                              rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) return make_empty_column(type_id::STRING);
+
+  CUDF_EXPECTS(to_strip.is_valid(stream), "Parameter to_strip must be valid");
+  string_view const d_to_strip(to_strip.data(), to_strip.size());
+
+  auto const d_column = column_device_view::create(input.parent(), stream);
+
+  auto result = rmm::device_uvector<string_index_pair>(input.size(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::counting_iterator<size_type>(0),
+                    thrust::counting_iterator<size_type>(input.size()),
+                    result.begin(),
+                    strip_transform_fn{*d_column, side, d_to_strip});
+
+  return make_strings_column(result.begin(), result.end(), stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> strip(strings_column_view const& input,
+                              side_type side,
+                              string_scalar const& to_strip,
+                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::strip(input, side, to_strip, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/translate.cu b/cpp/src/strings/translate.cu
new file mode 100644
index 0000000..e7b637c
--- /dev/null
+++ b/cpp/src/strings/translate.cu
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/translate.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+#include <thrust/host_vector.h>
+#include <thrust/pair.h>
+#include <thrust/sort.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+using translate_table = thrust::pair<char_utf8, char_utf8>;
+
+namespace {
+/**
+ * @brief This is the translate functor for replacing individual characters
+ * in each string.
+ */
+struct translate_fn {
+  column_device_view const d_strings;
+  rmm::device_uvector<translate_table>::iterator table_begin;
+  rmm::device_uvector<translate_table>::iterator table_end;
+  int32_t* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    string_view const d_str = d_strings.element<string_view>(idx);
+
+    size_type bytes = d_str.size_bytes();
+    char* out_ptr   = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    for (auto chr : d_str) {
+      auto const entry =
+        thrust::lower_bound(thrust::seq,
+                            table_begin,
+                            table_end,
+                            translate_table{chr, 0},
+                            [](auto const& lhs, auto const& rhs) { return lhs.first < rhs.first; });
+      if (entry != table_end && entry->first == chr) {
+        bytes -= bytes_in_char_utf8(chr);
+        chr = entry->second;
+        if (chr)  // if null, skip the character
+          bytes += bytes_in_char_utf8(chr);
+      }
+      if (chr && out_ptr) out_ptr += from_char_utf8(chr, out_ptr);
+    }
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+}  // namespace
+
+//
+std::unique_ptr<column> translate(strings_column_view const& strings,
+                                  std::vector<std::pair<char_utf8, char_utf8>> const& chars_table,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return make_empty_column(type_id::STRING);
+
+  size_type table_size = static_cast<size_type>(chars_table.size());
+  // convert input table
+  thrust::host_vector<translate_table> htable(table_size);
+  std::transform(chars_table.begin(), chars_table.end(), htable.begin(), [](auto entry) {
+    return translate_table{entry.first, entry.second};
+  });
+  // The size of this table is usually much less than 100 so it is was
+  // found to be more efficient to sort on the CPU than the GPU.
+  thrust::sort(htable.begin(), htable.end(), [](auto const& lhs, auto const& rhs) {
+    return lhs.first < rhs.first;
+  });
+  // copy translate table to device memory
+  rmm::device_uvector<translate_table> table =
+    cudf::detail::make_device_uvector_async(htable, stream, rmm::mr::get_current_device_resource());
+
+  auto d_strings = column_device_view::create(strings.parent(), stream);
+
+  auto children = make_strings_children(
+    translate_fn{*d_strings, table.begin(), table.end()}, strings.size(), stream, mr);
+
+  return make_strings_column(strings.size(),
+                             std::move(children.first),
+                             std::move(children.second),
+                             strings.null_count(),
+                             cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<column> translate(strings_column_view const& strings,
+                                  std::vector<std::pair<uint32_t, uint32_t>> const& chars_table,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::translate(strings, chars_table, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/utilities.cu b/cpp/src/strings/utilities.cu
new file mode 100644
index 0000000..c8c68d1
--- /dev/null
+++ b/cpp/src/strings/utilities.cu
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <strings/char_types/char_cases.h>
+#include <strings/char_types/char_flags.h>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+
+/**
+ * @copydoc create_string_vector_from_column
+ */
+rmm::device_uvector<string_view> create_string_vector_from_column(
+  cudf::strings_column_view const input,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto d_strings = column_device_view::create(input.parent(), stream);
+
+  auto strings_vector = rmm::device_uvector<string_view>(input.size(), stream, mr);
+
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<size_type>(0),
+                    thrust::make_counting_iterator<size_type>(input.size()),
+                    strings_vector.begin(),
+                    [d_strings = *d_strings] __device__(size_type idx) {
+                      // placeholder for factory function that takes a span of string_views
+                      auto const null_string_view = string_view{nullptr, 0};
+                      if (d_strings.is_null(idx)) { return null_string_view; }
+                      auto const d_str = d_strings.element<string_view>(idx);
+                      // special case when the entire column is filled with empty strings:
+                      // here the empty d_str may have a d_str.data() == nullptr
+                      auto const empty_string_view = string_view{};
+                      return d_str.empty() ? empty_string_view : d_str;
+                    });
+
+  return strings_vector;
+}
+
+std::unique_ptr<column> create_chars_child_column(cudf::size_type total_bytes,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  return make_numeric_column(
+    data_type{type_id::INT8}, total_bytes, mask_state::UNALLOCATED, stream, mr);
+}
+
+namespace {
+// The device variables are created here to avoid using a singleton that may cause issues
+// with RMM initialize/finalize. See PR #3159 for details on this approach.
+__device__ character_flags_table_type
+  character_codepoint_flags[sizeof(g_character_codepoint_flags)];
+__device__ character_cases_table_type character_cases_table[sizeof(g_character_cases_table)];
+__device__ special_case_mapping character_special_case_mappings[sizeof(g_special_case_mappings)];
+
+thread_safe_per_context_cache<character_flags_table_type> d_character_codepoint_flags;
+thread_safe_per_context_cache<character_cases_table_type> d_character_cases_table;
+thread_safe_per_context_cache<special_case_mapping> d_special_case_mappings;
+
+}  // namespace
+
+/**
+ * @copydoc cudf::strings::detail::get_character_flags_table
+ */
+character_flags_table_type const* get_character_flags_table()
+{
+  return d_character_codepoint_flags.find_or_initialize([&](void) {
+    character_flags_table_type* table = nullptr;
+    CUDF_CUDA_TRY(cudaMemcpyToSymbol(
+      character_codepoint_flags, g_character_codepoint_flags, sizeof(g_character_codepoint_flags)));
+    CUDF_CUDA_TRY(cudaGetSymbolAddress((void**)&table, character_codepoint_flags));
+    return table;
+  });
+}
+
+/**
+ * @copydoc cudf::strings::detail::get_character_cases_table
+ */
+character_cases_table_type const* get_character_cases_table()
+{
+  return d_character_cases_table.find_or_initialize([&](void) {
+    character_cases_table_type* table = nullptr;
+    CUDF_CUDA_TRY(cudaMemcpyToSymbol(
+      character_cases_table, g_character_cases_table, sizeof(g_character_cases_table)));
+    CUDF_CUDA_TRY(cudaGetSymbolAddress((void**)&table, character_cases_table));
+    return table;
+  });
+}
+
+/**
+ * @copydoc cudf::strings::detail::get_special_case_mapping_table
+ */
+special_case_mapping const* get_special_case_mapping_table()
+{
+  return d_special_case_mappings.find_or_initialize([&](void) {
+    special_case_mapping* table = nullptr;
+    CUDF_CUDA_TRY(cudaMemcpyToSymbol(
+      character_special_case_mappings, g_special_case_mappings, sizeof(g_special_case_mappings)));
+    CUDF_CUDA_TRY(cudaGetSymbolAddress((void**)&table, character_special_case_mappings));
+    return table;
+  });
+}
+
+}  // namespace detail
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/strings/wrap.cu b/cpp/src/strings/wrap.cu
new file mode 100644
index 0000000..335908d
--- /dev/null
+++ b/cpp/src/strings/wrap.cu
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/case.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace strings {
+namespace detail {
+namespace {  // anonym.
+
+// execute string wrap:
+//
+struct execute_wrap {
+  execute_wrap(column_device_view const d_column,
+               int32_t const* d_offsets,
+               char* d_chars,
+               size_type width)
+    : d_column_(d_column), d_offsets_(d_offsets), d_chars_(d_chars), width_(width)
+  {
+  }
+
+  __device__ int32_t operator()(size_type idx)
+  {
+    if (d_column_.is_null(idx)) return 0;  // null string
+
+    string_view d_str = d_column_.template element<string_view>(idx);
+    char* d_buffer    = d_chars_ + d_offsets_[idx];
+
+    int charOffsetToLastSpace = -1;
+    int byteOffsetToLastSpace = -1;
+    int spos                  = 0;
+    int bidx                  = 0;
+
+    for (auto itr = d_str.begin(); itr != d_str.end(); ++itr) {
+      auto const the_chr = *itr;
+      auto const pos     = itr.position();
+
+      // execute conditions:
+      if (the_chr <= ' ') {  // convert all whitespace to space
+        d_buffer[bidx]        = ' ';
+        byteOffsetToLastSpace = bidx;
+        charOffsetToLastSpace = pos;
+      }
+      if (pos - spos >= width_ && byteOffsetToLastSpace >= 0) {
+        d_buffer[byteOffsetToLastSpace] = '\n';
+        spos                            = charOffsetToLastSpace;
+        byteOffsetToLastSpace           = -1;
+        charOffsetToLastSpace           = -1;
+      }
+      bidx += detail::bytes_in_char_utf8(the_chr);
+    }
+    return 0;
+  }
+
+ private:
+  column_device_view const d_column_;
+  int32_t const* d_offsets_;
+  char* d_chars_;
+  size_type width_;
+};
+
+}  // namespace
+
+template <typename device_execute_functor>
+std::unique_ptr<column> wrap(strings_column_view const& strings,
+                             size_type width,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(width > 0, "Positive wrap width required");
+
+  auto strings_count = strings.size();
+  if (strings_count == 0) return make_empty_column(type_id::STRING);
+
+  auto strings_column  = column_device_view::create(strings.parent(), stream);
+  auto d_column        = *strings_column;
+  size_type null_count = strings.null_count();
+
+  // copy null mask
+  rmm::device_buffer null_mask = cudf::detail::copy_bitmask(strings.parent(), stream, mr);
+
+  // build offsets column
+  auto offsets_column = std::make_unique<column>(strings.offsets(), stream, mr);  // makes a copy
+  auto d_new_offsets  = offsets_column->view().template data<int32_t>();
+
+  auto chars_column = std::make_unique<column>(strings.chars(), stream, mr);  // makes a copy
+  auto d_chars      = chars_column->mutable_view().data<char>();
+
+  device_execute_functor d_execute_fctr{d_column, d_new_offsets, d_chars, width};
+
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<size_type>(0),
+                     strings_count,
+                     d_execute_fctr);
+
+  return make_strings_column(strings_count,
+                             std::move(offsets_column),
+                             std::move(chars_column),
+                             null_count,
+                             std::move(null_mask));
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> wrap(strings_column_view const& strings,
+                             size_type width,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::wrap<detail::execute_wrap>(strings, width, cudf::get_default_stream(), mr);
+}
+
+}  // namespace strings
+}  // namespace cudf
diff --git a/cpp/src/structs/copying/concatenate.cu b/cpp/src/structs/copying/concatenate.cu
new file mode 100644
index 0000000..27c8aa1
--- /dev/null
+++ b/cpp/src/structs/copying/concatenate.cu
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/concatenate_masks.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <algorithm>
+#include <memory>
+#include <numeric>
+
+namespace cudf {
+namespace structs {
+namespace detail {
+
+/**
+ * @copydoc cudf::structs::detail::concatenate
+ */
+std::unique_ptr<column> concatenate(host_span<column_view const> columns,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  // get ordered children
+  auto ordered_children = extract_ordered_struct_children(columns, stream);
+
+  // concatenate them
+  std::vector<std::unique_ptr<column>> children;
+  children.reserve(columns[0].num_children());
+  std::transform(ordered_children.begin(),
+                 ordered_children.end(),
+                 std::back_inserter(children),
+                 [mr, stream](host_span<column_view const> cols) {
+                   return cudf::detail::concatenate(cols, stream, mr);
+                 });
+
+  // get total length from concatenated children; if no child exists, we would compute it
+  auto const acc_size_fn = [](size_type s, column_view const& c) { return s + c.size(); };
+  auto const total_length =
+    !children.empty() ? children[0]->size()
+                      : std::accumulate(columns.begin(), columns.end(), size_type{0}, acc_size_fn);
+
+  // if any of the input columns have nulls, construct the output mask
+  bool const has_nulls =
+    std::any_of(columns.begin(), columns.end(), [](auto const& col) { return col.has_nulls(); });
+  rmm::device_buffer null_mask =
+    create_null_mask(total_length, has_nulls ? mask_state::UNINITIALIZED : mask_state::UNALLOCATED);
+  auto null_mask_data = static_cast<bitmask_type*>(null_mask.data());
+  auto const null_count =
+    has_nulls ? cudf::detail::concatenate_masks(columns, null_mask_data, stream) : size_type{0};
+
+  // assemble into outgoing list column
+  return make_structs_column(
+    total_length, std::move(children), null_count, std::move(null_mask), stream, mr);
+}
+
+}  // namespace detail
+}  // namespace structs
+}  // namespace cudf
diff --git a/cpp/src/structs/structs_column_factories.cu b/cpp/src/structs/structs_column_factories.cu
new file mode 100644
index 0000000..9e511c6
--- /dev/null
+++ b/cpp/src/structs/structs_column_factories.cu
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/types.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <memory>
+namespace cudf {
+
+/// Column factory that adopts child columns.
+std::unique_ptr<cudf::column> make_structs_column(
+  size_type num_rows,
+  std::vector<std::unique_ptr<column>>&& child_columns,
+  size_type null_count,
+  rmm::device_buffer&& null_mask,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(null_count <= 0 || !null_mask.is_empty(),
+               "Struct column with nulls must be nullable.");
+
+  CUDF_EXPECTS(std::all_of(child_columns.begin(),
+                           child_columns.end(),
+                           [&](auto const& child_col) { return num_rows == child_col->size(); }),
+               "Child columns must have the same number of rows as the Struct column.");
+
+  if (!null_mask.is_empty()) {
+    for (auto& child : child_columns) {
+      child = structs::detail::superimpose_nulls(static_cast<bitmask_type const*>(null_mask.data()),
+                                                 null_count,
+                                                 std::move(child),
+                                                 stream,
+                                                 mr);
+    }
+  }
+
+  return std::make_unique<column>(cudf::data_type{type_id::STRUCT},
+                                  num_rows,
+                                  rmm::device_buffer{},  // Empty data buffer. Structs hold no data.
+                                  std::move(null_mask),
+                                  null_count,
+                                  std::move(child_columns));
+}
+
+}  // namespace cudf
diff --git a/cpp/src/structs/structs_column_view.cpp b/cpp/src/structs/structs_column_view.cpp
new file mode 100644
index 0000000..b0284e9
--- /dev/null
+++ b/cpp/src/structs/structs_column_view.cpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+namespace cudf {
+
+structs_column_view::structs_column_view(column_view const& rhs) : column_view{rhs}
+{
+  CUDF_EXPECTS(type().id() == type_id::STRUCT, "structs_column_view only supports struct columns");
+}
+
+column_view structs_column_view::parent() const { return *this; }
+
+column_view structs_column_view::get_sliced_child(int index, rmm::cuda_stream_view stream) const
+{
+  std::vector<column_view> children;
+  children.reserve(child(index).num_children());
+  for (size_type i = 0; i < child(index).num_children(); i++) {
+    children.push_back(child(index).child(i));
+  }
+
+  return column_view{
+    child(index).type(),
+    size(),
+    child(index).head<uint8_t>(),
+    child(index).null_mask(),
+    child(index).null_count()
+      ? cudf::detail::null_count(child(index).null_mask(), offset(), offset() + size(), stream)
+      : 0,
+    offset(),
+    children};
+}
+
+}  // namespace cudf
diff --git a/cpp/src/structs/utilities.cpp b/cpp/src/structs/utilities.cpp
new file mode 100644
index 0000000..acb153f
--- /dev/null
+++ b/cpp/src/structs/utilities.cpp
@@ -0,0 +1,437 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf::structs::detail {
+
+/**
+ * @copydoc cudf::structs::detail::extract_ordered_struct_children
+ */
+std::vector<std::vector<column_view>> extract_ordered_struct_children(
+  host_span<column_view const> struct_cols, rmm::cuda_stream_view stream)
+{
+  auto const num_children = struct_cols[0].num_children();
+  auto const num_cols     = static_cast<size_type>(struct_cols.size());
+
+  std::vector<std::vector<column_view>> result;
+  result.reserve(num_children);
+
+  for (size_type child_index = 0; child_index < num_children; child_index++) {
+    std::vector<column_view> children;
+    children.reserve(num_cols);
+    for (size_type col_index = 0; col_index < num_cols; col_index++) {
+      structs_column_view scv(struct_cols[col_index]);
+
+      // all inputs must have the same # of children and they must all be of the
+      // same type.
+      CUDF_EXPECTS(struct_cols[0].num_children() == scv.num_children(),
+                   "Mismatch in number of children during struct concatenate");
+      CUDF_EXPECTS(struct_cols[0].child(child_index).type() == scv.child(child_index).type(),
+                   "Mismatch in child types during struct concatenate");
+      children.push_back(scv.get_sliced_child(child_index, stream));
+    }
+
+    result.push_back(std::move(children));
+  }
+
+  return result;
+}
+
+namespace {
+
+/**
+ * @brief Check whether the specified column is of type `STRUCT`.
+ */
+bool is_struct(cudf::column_view const& col) { return col.type().id() == type_id::STRUCT; }
+
+}  // namespace
+
+bool is_or_has_nested_lists(cudf::column_view const& col)
+{
+  auto is_list = [](cudf::column_view const& col) { return col.type().id() == type_id::LIST; };
+
+  return is_list(col) || std::any_of(col.child_begin(), col.child_end(), is_or_has_nested_lists);
+}
+
+/**
+ * @brief Flattens struct columns to constituent non-struct columns in the input table.
+ *
+ */
+struct table_flattener {
+  table_view input;
+  std::vector<order> const& column_order;
+  std::vector<null_order> const& null_precedence;
+  column_nullability nullability;
+  rmm::cuda_stream_view stream;
+  rmm::mr::device_memory_resource* mr;
+
+  temporary_nullable_data nullable_data;
+  std::vector<std::unique_ptr<column>> validity_as_column;
+  std::vector<column_view> flat_columns;
+  std::vector<order> flat_column_order;
+  std::vector<null_order> flat_null_precedence;
+
+  table_flattener(table_view const& input,
+                  std::vector<order> const& column_order,
+                  std::vector<null_order> const& null_precedence,
+                  column_nullability nullability,
+                  rmm::cuda_stream_view stream,
+                  rmm::mr::device_memory_resource* mr)
+    : column_order{column_order},
+      null_precedence{null_precedence},
+      nullability{nullability},
+      stream{stream},
+      mr{mr}
+  {
+    superimpose_nulls(input);
+  }
+
+  /**
+   * @brief Pushes down nulls from struct columns to children, saves the resulting
+   * column to `input`, and generated null masks to `superimposed_nullmasks`.
+   */
+  void superimpose_nulls(table_view const& input_table)
+  {
+    auto [table, tmp_nullable_data] = push_down_nulls(input_table, stream, mr);
+    this->input                     = std::move(table);
+    this->nullable_data             = std::move(tmp_nullable_data);
+  }
+
+  // Convert null_mask to BOOL8 columns and flatten the struct children in order.
+  void flatten_struct_column(structs_column_view const& col,
+                             order col_order,
+                             null_order col_null_order)
+  {
+    // Even if it is not required to extract the bitmask to a separate column,
+    // we should always do that if the structs column has any null element.
+    //
+    // In addition, we should check for null by calling to `has_nulls()`, not `nullable()`.
+    // This is because when comparing structs columns, if one column has bitmask while the other
+    // does not (and both columns do not have any null element) then flattening them using
+    // `nullable()` will result in tables with different number of columns.
+    //
+    // Notice that, for comparing structs columns when one column has null while the other
+    // doesn't, `nullability` must be passed in with value `column_nullability::FORCE` to make
+    // sure the flattening results are tables having the same number of columns.
+
+    if (nullability == column_nullability::FORCE || col.has_nulls()) {
+      validity_as_column.push_back(cudf::detail::is_valid(col, stream, mr));
+      if (col.has_nulls()) {
+        // copy bitmask is needed only if the column has null
+        validity_as_column.back()->set_null_mask(cudf::detail::copy_bitmask(col, stream, mr),
+                                                 col.null_count());
+      }
+      flat_columns.push_back(validity_as_column.back()->view());
+      if (not column_order.empty()) { flat_column_order.push_back(col_order); }  // doesn't matter.
+      if (not null_precedence.empty()) { flat_null_precedence.push_back(col_null_order); }
+    }
+    for (decltype(col.num_children()) i = 0; i < col.num_children(); ++i) {
+      auto const& child = col.get_sliced_child(i, stream);
+      if (child.type().id() == type_id::STRUCT) {
+        flatten_struct_column(structs_column_view{child}, col_order, col_null_order);
+      } else {
+        flat_columns.push_back(child);
+        if (not column_order.empty()) flat_column_order.push_back(col_order);
+        if (not null_precedence.empty()) flat_null_precedence.push_back(col_null_order);
+      }
+    }
+  }
+  // Note: possibly expand for flattening list columns too.
+
+  /**
+   * @copydoc flattened_table
+   *
+   * @return tuple with flattened table, flattened column order, flattened null precedence,
+   * vector of boolean columns (struct validity).
+   */
+  auto operator()()
+  {
+    for (auto i = 0; i < input.num_columns(); ++i) {
+      auto const& col = input.column(i);
+      if (col.type().id() == type_id::STRUCT) {
+        flatten_struct_column(structs_column_view{col},
+                              (column_order.empty() ? order() : column_order[i]),
+                              (null_precedence.empty() ? null_order() : null_precedence[i]));
+      } else {
+        flat_columns.push_back(col);
+        if (not column_order.empty()) flat_column_order.push_back(column_order[i]);
+        if (not null_precedence.empty()) flat_null_precedence.push_back(null_precedence[i]);
+      }
+    }
+
+    return std::make_unique<flattened_table>(table_view{flat_columns},
+                                             std::move(flat_column_order),
+                                             std::move(flat_null_precedence),
+                                             std::move(validity_as_column),
+                                             std::move(nullable_data));
+  }
+};
+
+std::unique_ptr<flattened_table> flatten_nested_columns(
+  table_view const& input,
+  std::vector<order> const& column_order,
+  std::vector<null_order> const& null_precedence,
+  column_nullability nullability,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  auto const has_struct = std::any_of(input.begin(), input.end(), is_struct);
+  if (not has_struct) {
+    return std::make_unique<flattened_table>(input,
+                                             column_order,
+                                             null_precedence,
+                                             std::vector<std::unique_ptr<column>>{},
+                                             temporary_nullable_data{});
+  }
+
+  return table_flattener{input, column_order, null_precedence, nullability, stream, mr}();
+}
+
+namespace {
+
+/**
+ * @brief Superimpose the given null mask into the input column without any sanitization for
+ * non-empty nulls.
+ *
+ * @copydoc cudf::structs::detail::superimpose_nulls
+ */
+std::unique_ptr<column> superimpose_nulls_no_sanitize(bitmask_type const* null_mask,
+                                                      size_type null_count,
+                                                      std::unique_ptr<column>&& input,
+                                                      rmm::cuda_stream_view stream,
+                                                      rmm::mr::device_memory_resource* mr)
+{
+  if (input->type().id() == cudf::type_id::EMPTY) {
+    // EMPTY columns should not have a null mask,
+    // so don't superimpose null mask on empty columns.
+    return std::move(input);
+  }
+
+  auto const num_rows = input->size();
+
+  if (!input->nullable()) {
+    input->set_null_mask(cudf::detail::copy_bitmask(null_mask, 0, num_rows, stream, mr),
+                         null_count);
+  } else {
+    auto current_mask = input->mutable_view().null_mask();
+    std::vector<bitmask_type const*> masks{reinterpret_cast<bitmask_type const*>(null_mask),
+                                           reinterpret_cast<bitmask_type const*>(current_mask)};
+    std::vector<size_type> begin_bits{0, 0};
+    auto const valid_count = cudf::detail::inplace_bitmask_and(
+      device_span<bitmask_type>(current_mask, num_bitmask_words(num_rows)),
+      masks,
+      begin_bits,
+      num_rows,
+      stream);
+    auto const new_null_count = num_rows - valid_count;
+    input->set_null_count(new_null_count);
+  }
+
+  // If the input is also a struct, repeat for all its children. Otherwise just return.
+  if (input->type().id() != cudf::type_id::STRUCT) { return std::move(input); }
+
+  auto const current_mask   = input->view().null_mask();
+  auto const new_null_count = input->null_count();  // this was just computed in the step above
+  auto content              = input->release();
+
+  // Build new children columns.
+  std::for_each(content.children.begin(),
+                content.children.end(),
+                [current_mask, new_null_count, stream, mr](auto& child) {
+                  child = superimpose_nulls_no_sanitize(
+                    current_mask, new_null_count, std::move(child), stream, mr);
+                });
+
+  // Replace the children columns.
+  return cudf::make_structs_column(num_rows,
+                                   std::move(content.children),
+                                   new_null_count,
+                                   std::move(*content.null_mask),
+                                   stream,
+                                   mr);
+}
+
+/**
+ * @brief Push down nulls from the given input column into its children columns without any
+ * sanitization for non-empty nulls.
+ *
+ * @copydoc cudf::structs::detail::push_down_nulls
+ */
+std::pair<column_view, temporary_nullable_data> push_down_nulls_no_sanitize(
+  column_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  auto ret_nullable_data = temporary_nullable_data{};
+  if (input.type().id() != type_id::STRUCT) {
+    // NOOP for non-STRUCT columns.
+    return {input, std::move(ret_nullable_data)};
+  }
+
+  auto const structs_view = structs_column_view{input};
+
+  // Function to rewrite child null mask.
+  auto const child_with_new_mask = [&](auto const& child_idx) {
+    auto child = structs_view.get_sliced_child(child_idx, stream);
+
+    // If struct is not nullable, child null mask is retained. NOOP.
+    if (not structs_view.nullable()) { return child; }
+
+    auto parent_child_null_masks =
+      std::vector<cudf::bitmask_type const*>{structs_view.null_mask(), child.null_mask()};
+
+    auto [new_child_mask, null_count] = [&] {
+      if (not child.nullable()) {
+        // Adopt parent STRUCT's null mask.
+        return std::pair{structs_view.null_mask(), 0};
+      }
+
+      // Both STRUCT and child are nullable. AND() for the child's new null mask.
+      //
+      // Note: ANDing only [offset(), offset()+size()) would not work. The null-mask produced thus
+      // would start at offset=0. The column-view attempts to apply its offset() to both the _data
+      // and the _null_mask(). It would be better to AND the bits from the beginning, and apply
+      // offset() uniformly.
+      // Alternatively, one could construct a big enough buffer, and use inplace_bitwise_and.
+      auto [new_mask, null_count] = cudf::detail::bitmask_and(parent_child_null_masks,
+                                                              std::vector<size_type>{0, 0},
+                                                              child.offset() + child.size(),
+                                                              stream,
+                                                              mr);
+      ret_nullable_data.new_null_masks.push_back(std::move(new_mask));
+      return std::pair{
+        reinterpret_cast<bitmask_type const*>(ret_nullable_data.new_null_masks.back().data()),
+        null_count};
+    }();
+
+    return column_view(child.type(),
+                       child.size(),
+                       child.head(),
+                       new_child_mask,
+                       null_count,
+                       child.offset(),
+                       std::vector<column_view>{child.child_begin(), child.child_end()});
+  };
+
+  auto const child_begin =
+    thrust::make_transform_iterator(thrust::make_counting_iterator(0), child_with_new_mask);
+  auto const child_end = child_begin + structs_view.num_children();
+  auto ret_children    = std::vector<column_view>{};
+
+  std::for_each(child_begin, child_end, [&](auto const& child) {
+    auto [processed_child, child_nullable_data] = push_down_nulls_no_sanitize(child, stream, mr);
+    ret_children.emplace_back(std::move(processed_child));
+    ret_nullable_data.emplace_back(std::move(child_nullable_data));
+  });
+
+  // Make column view out of newly constructed column_views, and all the validity buffers.
+
+  return std::pair{column_view(input.type(),
+                               input.size(),
+                               nullptr,
+                               input.null_mask(),
+                               input.null_count(),  // Alternatively, postpone.
+                               input.offset(),
+                               ret_children),
+                   std::move(ret_nullable_data)};
+}
+
+}  // namespace
+
+void temporary_nullable_data::emplace_back(temporary_nullable_data&& other)
+{
+  auto const move_append = [](auto& dst, auto& src) {
+    dst.insert(dst.end(), std::make_move_iterator(src.begin()), std::make_move_iterator(src.end()));
+  };
+  move_append(new_null_masks, other.new_null_masks);
+  move_append(new_columns, other.new_columns);
+}
+
+std::unique_ptr<column> superimpose_nulls(bitmask_type const* null_mask,
+                                          size_type null_count,
+                                          std::unique_ptr<column>&& input,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  input = superimpose_nulls_no_sanitize(null_mask, null_count, std::move(input), stream, mr);
+
+  if (auto const input_view = input->view(); cudf::detail::has_nonempty_nulls(input_view, stream)) {
+    // We can't call `purge_nonempty_nulls` for individual child column(s) that need to be
+    // sanitized. Instead, we have to call it from the top level column.
+    // This is to make sure all the columns (top level + all children) have consistent offsets.
+    // Otherwise, the sanitized children may have offsets that are different from the others and
+    // also different from the parent column, causing data corruption.
+    return cudf::detail::purge_nonempty_nulls(input_view, stream, mr);
+  }
+
+  return std::move(input);
+}
+
+std::pair<column_view, temporary_nullable_data> push_down_nulls(column_view const& input,
+                                                                rmm::cuda_stream_view stream,
+                                                                rmm::mr::device_memory_resource* mr)
+{
+  auto output = push_down_nulls_no_sanitize(input, stream, mr);
+
+  if (auto const output_view = output.first;
+      cudf::detail::has_nonempty_nulls(output_view, stream)) {
+    output.second.new_columns.emplace_back(
+      cudf::detail::purge_nonempty_nulls(output_view, stream, mr));
+    output.first = output.second.new_columns.back()->view();
+
+    // Don't need the temp null mask anymore, as we will create a new column.
+    // However, these null masks are still needed for `purge_nonempty_nulls` thus removing them
+    // must be done after calling it.
+    output.second.new_null_masks.clear();
+  }
+
+  return output;
+}
+
+std::pair<table_view, temporary_nullable_data> push_down_nulls(table_view const& table,
+                                                               rmm::cuda_stream_view stream,
+                                                               rmm::mr::device_memory_resource* mr)
+{
+  auto processed_columns = std::vector<column_view>{};
+  auto nullable_data     = temporary_nullable_data{};
+  for (auto const& col : table) {
+    auto [processed_col, col_nullable_data] = push_down_nulls(col, stream, mr);
+    processed_columns.emplace_back(std::move(processed_col));
+    nullable_data.emplace_back(std::move(col_nullable_data));
+  }
+  return {table_view{processed_columns}, std::move(nullable_data)};
+}
+
+bool contains_null_structs(column_view const& col)
+{
+  return (is_struct(col) && col.has_nulls()) ||
+         std::any_of(col.child_begin(), col.child_end(), contains_null_structs);
+}
+
+}  // namespace cudf::structs::detail
diff --git a/cpp/src/table/row_operators.cu b/cpp/src/table/row_operators.cu
new file mode 100644
index 0000000..770a7c7
--- /dev/null
+++ b/cpp/src/table/row_operators.cu
@@ -0,0 +1,876 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <lists/utilities.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/concatenate.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/detail/utilities/linked_column.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/type_checks.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace experimental {
+
+namespace {
+
+/**
+ * @brief Removes the offsets of struct column's children
+ *
+ * @param c The column whose children are to be un-sliced
+ * @return Children of `c` with offsets removed
+ */
+std::vector<column_view> unslice_children(column_view const& c)
+{
+  if (c.type().id() == type_id::STRUCT) {
+    auto child_it = thrust::make_transform_iterator(c.child_begin(), [](auto const& child) {
+      return column_view(
+        child.type(),
+        child.offset() + child.size(),  // This is hacky, we don't know the actual unsliced size but
+                                        // it is at least offset + size
+        child.head(),
+        child.null_mask(),
+        child.null_count(),
+        0,
+        unslice_children(child));
+    });
+    return {child_it, child_it + c.num_children()};
+  }
+  return {c.child_begin(), c.child_end()};
+};
+
+/**
+ * @brief Removes the child column offsets of struct columns in a table.
+ *
+ * Given a table, this replaces any struct columns with similar struct columns that have their
+ * offsets removed from their children. Structs that are children of list columns are not affected.
+ *
+ */
+table_view remove_struct_child_offsets(table_view table)
+{
+  std::vector<column_view> cols;
+  cols.reserve(table.num_columns());
+  std::transform(table.begin(), table.end(), std::back_inserter(cols), [&](column_view const& c) {
+    return column_view(c.type(),
+                       c.size(),
+                       c.head<uint8_t>(),
+                       c.null_mask(),
+                       c.null_count(),
+                       c.offset(),
+                       unslice_children(c));
+  });
+  return table_view(cols);
+}
+
+/**
+ * @brief The enum to specify whether the `decompose_structs` function will process lists columns
+ * (at any nested level) or will output them unchanged.
+ */
+enum class decompose_lists_column : bool { YES, NO };
+
+/**
+ * @brief Decompose all struct columns in a table
+ *
+ * If a structs column is a tree with N leaves, then this function decomposes the tree into
+ * N "linear trees" (branch factor == 1) and prunes common parents. Also returns a vector of
+ * per-column `depth`s.
+ *
+ * A `depth` value is the number of nested levels as parent of the column in the original,
+ * non-decomposed table, which are pruned during decomposition.
+ *
+ * Special handling is needed in the cases of structs column having lists as its first child. In
+ * such situations, the function decomposes the tree of N leaves into N+1 linear trees in which the
+ * second tree was generated by extracting out leaf of the first tree. This is to make sure there is
+ * no structs column having child lists column in the output. Note that structs with lists children
+ * in subsequent positions do not require any special treatment because the struct parent will be
+ * pruned for all subsequent children.
+ *
+ * For example, if the original table has a column `Struct<Struct<int, float>, decimal>`,
+ *
+ *      S1
+ *     / \
+ *    S2  d
+ *   / \
+ *  i   f
+ *
+ * then after decomposition, we get three columns:
+ * `Struct<Struct<int>>`, `float`, and `decimal`.
+ *
+ *  0   2   1  <- depths
+ *  S1
+ *  |
+ *  S2      d
+ *  |
+ *  i   f
+ *
+ * The depth of the first column is 0 because it contains all its parent levels, while the depth
+ * of the second column is 2 because two of its parent struct levels were pruned.
+ *
+ * Similarly, a struct column of type `Struct<int, Struct<float, decimal>>` is decomposed as follows
+ *
+ *     S1
+ *    / \
+ *   i   S2
+ *      / \
+ *     f   d
+ *
+ *  0   1   2  <- depths
+ *  S1  S2  d
+ *  |   |
+ *  i   f
+ *
+ * In the case of structs column with a lists column as its first child such as
+ * `Struct<List<int>, float>`, after decomposition we get three columns `Struct<>`,
+ * `List<int>`, and `float`.
+ *
+ * When list columns are present, depending on the input flag `decompose_lists`, the decomposition
+ * can be performed similarly to pure structs but list parent columns are NOT pruned. The list
+ * parents are still needed to define the range of elements in the leaf that belong to the same row.
+ *
+ * For example, if the original table has a column `List<Struct<int, float>>`,
+ *
+ *    L
+ *    |
+ *    S
+ *   / \
+ *  i   f
+ *
+ * after decomposition, we get two columns
+ *
+ *  L   L
+ *  |   |
+ *  S   f
+ *  |
+ *  i
+ *
+ * Note that the `decompose_lists` flag should be specified as follow:
+ *  - `decompose_lists_column::YES` when preprocessing a table for equality comparison.
+ *  - `decompose_lists_column::NO` when preprocessing a table for lexicographic comparison,
+ *    since we need to keep all lists columns intact to input into the next preprocessing step.
+ *
+ * @param table The table whose struct columns to decompose.
+ * @param decompose_lists Whether to decompose lists columns
+ * @param column_order The per-column order if using output with lexicographic comparison
+ * @param null_precedence The per-column null precedence
+ * @return A tuple containing a table with all struct columns decomposed, new corresponding column
+ *         orders and null precedences and depths of the linearized branches
+ */
+auto decompose_structs(table_view table,
+                       decompose_lists_column decompose_lists,
+                       host_span<order const> column_order         = {},
+                       host_span<null_order const> null_precedence = {})
+{
+  auto linked_columns = detail::table_to_linked_columns(table);
+
+  std::vector<column_view> verticalized_columns;
+  std::vector<order> new_column_order;
+  std::vector<null_order> new_null_precedence;
+  std::vector<int> verticalized_col_depths;
+  for (size_t col_idx = 0; col_idx < linked_columns.size(); ++col_idx) {
+    detail::linked_column_view const* col = linked_columns[col_idx].get();
+    if (is_nested(col->type())) {
+      // convert and insert
+      std::vector<std::vector<detail::linked_column_view const*>> flattened;
+      std::function<void(
+        detail::linked_column_view const*, std::vector<detail::linked_column_view const*>*, int)>
+        recursive_child = [&](detail::linked_column_view const* c,
+                              std::vector<detail::linked_column_view const*>* branch,
+                              int depth) {
+          branch->push_back(c);
+          if (decompose_lists == decompose_lists_column::YES && c->type().id() == type_id::LIST) {
+            recursive_child(
+              c->children[lists_column_view::child_column_index].get(), branch, depth + 1);
+          } else if (c->type().id() == type_id::STRUCT) {
+            for (size_t child_idx = 0; child_idx < c->children.size(); ++child_idx) {
+              // When child_idx == 0, we also cut off the current branch if its first child is a
+              // lists column.
+              // In such cases, the last column of the current branch will be `Struct<List,...>` and
+              // it will be modified to empty struct type `Struct<>` later on.
+              if (child_idx > 0 || c->children[0]->type().id() == type_id::LIST) {
+                verticalized_col_depths.push_back(depth + 1);
+                branch = &flattened.emplace_back();
+              }
+              recursive_child(c->children[child_idx].get(), branch, depth + 1);
+            }
+          }
+        };
+      auto& branch = flattened.emplace_back();
+      verticalized_col_depths.push_back(0);
+      recursive_child(col, &branch, 0);
+
+      for (auto const& branch : flattened) {
+        column_view temp_col = *branch.back();
+
+        // Change `Struct<List,...>` into empty struct type `Struct<>`.
+        if (temp_col.type().id() == type_id::STRUCT &&
+            (temp_col.num_children() > 0 && temp_col.child(0).type().id() == type_id::LIST)) {
+          temp_col = column_view(temp_col.type(),
+                                 temp_col.size(),
+                                 temp_col.head(),
+                                 temp_col.null_mask(),
+                                 temp_col.null_count(),
+                                 temp_col.offset(),
+                                 {});
+        }
+
+        for (auto it = branch.crbegin() + 1; it < branch.crend(); ++it) {
+          auto const& prev_col = *(*it);
+          auto children =
+            (prev_col.type().id() == type_id::LIST)
+              ? std::vector<column_view>{*prev_col
+                                            .children[lists_column_view::offsets_column_index],
+                                         temp_col}
+              : std::vector<column_view>{temp_col};
+          temp_col = column_view(prev_col.type(),
+                                 prev_col.size(),
+                                 nullptr,
+                                 prev_col.null_mask(),
+                                 prev_col.null_count(),
+                                 prev_col.offset(),
+                                 std::move(children));
+        }
+        // Traverse upward and include any list columns in the ancestors
+        for (detail::linked_column_view* parent = branch.front()->parent; parent;
+             parent                             = parent->parent) {
+          if (parent->type().id() == type_id::LIST) {
+            // Include this parent
+            temp_col = column_view(
+              parent->type(),
+              parent->size(),
+              nullptr,  // list has no data of its own
+              nullptr,  // If we're going through this then nullmask is already in another branch
+              0,
+              parent->offset(),
+              {*parent->children[lists_column_view::offsets_column_index], temp_col});
+          } else if (parent->type().id() == type_id::STRUCT) {
+            // Replace offset with parent's offset
+            temp_col = column_view(temp_col.type(),
+                                   parent->size(),
+                                   temp_col.head(),
+                                   temp_col.null_mask(),
+                                   temp_col.null_count(),
+                                   parent->offset(),
+                                   {temp_col.child_begin(), temp_col.child_end()});
+          }
+        }
+        verticalized_columns.push_back(temp_col);
+      }
+      if (not column_order.empty()) {
+        new_column_order.insert(new_column_order.end(), flattened.size(), column_order[col_idx]);
+      }
+      if (not null_precedence.empty()) {
+        new_null_precedence.insert(
+          new_null_precedence.end(), flattened.size(), null_precedence[col_idx]);
+      }
+    } else {
+      verticalized_columns.push_back(*col);
+      verticalized_col_depths.push_back(0);
+      if (not column_order.empty()) { new_column_order.push_back(column_order[col_idx]); }
+      if (not null_precedence.empty()) { new_null_precedence.push_back(null_precedence[col_idx]); }
+    }
+  }
+  return std::make_tuple(table_view(verticalized_columns),
+                         std::move(new_column_order),
+                         std::move(new_null_precedence),
+                         std::move(verticalized_col_depths));
+}
+
+/*
+ * This helper function generates dremel data for any list-type columns in a
+ * table. This data is necessary for lexicographic comparisons.
+ */
+auto list_lex_preprocess(table_view const& table, rmm::cuda_stream_view stream)
+{
+  std::vector<detail::dremel_data> dremel_data;
+  std::vector<detail::dremel_device_view> dremel_device_views;
+  for (auto const& col : table) {
+    if (col.type().id() == type_id::LIST) {
+      dremel_data.push_back(detail::get_comparator_data(col, {}, false, stream));
+      dremel_device_views.push_back(dremel_data.back());
+    }
+  }
+  auto d_dremel_device_views = detail::make_device_uvector_sync(
+    dremel_device_views, stream, rmm::mr::get_current_device_resource());
+  return std::make_tuple(std::move(dremel_data), std::move(d_dremel_device_views));
+}
+
+using column_checker_fn_t = std::function<void(column_view const&)>;
+
+/**
+ * @brief Check a table for compatibility with lexicographic comparison
+ *
+ * Checks whether a given table contains columns of non-relationally comparable types.
+ */
+void check_lex_compatibility(table_view const& input)
+{
+  // Basically check if there's any LIST of STRUCT or STRUCT of LIST hiding anywhere in the table
+  column_checker_fn_t check_column = [&](column_view const& c) {
+    if (c.type().id() == type_id::LIST) {
+      auto const& list_col = lists_column_view(c);
+      CUDF_EXPECTS(list_col.child().type().id() != type_id::STRUCT,
+                   "Cannot lexicographically compare a table with a LIST of STRUCT column");
+      check_column(list_col.child());
+    } else if (c.type().id() == type_id::STRUCT) {
+      for (auto child = c.child_begin(); child < c.child_end(); ++child) {
+        CUDF_EXPECTS(child->type().id() != type_id::LIST,
+                     "Cannot lexicographically compare a table with a STRUCT of LIST column");
+        check_column(*child);
+      }
+    }
+    if (not is_nested(c.type())) {
+      CUDF_EXPECTS(is_relationally_comparable(c.type()),
+                   "Cannot lexicographic compare a table with a column of type " +
+                     cudf::type_to_name(c.type()));
+    }
+  };
+  for (column_view const& c : input) {
+    check_column(c);
+  }
+}
+
+/**
+ * @brief Check a table for compatibility with equality comparison
+ *
+ * Checks whether a given table contains columns of non-equality comparable types.
+ */
+void check_eq_compatibility(table_view const& input)
+{
+  column_checker_fn_t check_column = [&](column_view const& c) {
+    if (not is_nested(c.type())) {
+      CUDF_EXPECTS(is_equality_comparable(c.type()),
+                   "Cannot compare equality for a table with a column of type " +
+                     cudf::type_to_name(c.type()));
+    }
+    for (auto child = c.child_begin(); child < c.child_end(); ++child) {
+      check_column(*child);
+    }
+  };
+  for (column_view const& c : input) {
+    check_column(c);
+  }
+}
+
+void check_shape_compatibility(table_view const& lhs, table_view const& rhs)
+{
+  CUDF_EXPECTS(lhs.num_columns() == rhs.num_columns(),
+               "Cannot compare tables with different number of columns");
+  for (size_type i = 0; i < lhs.num_columns(); ++i) {
+    CUDF_EXPECTS(column_types_equivalent(lhs.column(i), rhs.column(i)),
+                 "Cannot compare tables with different column types");
+  }
+}
+
+}  // namespace
+
+namespace row {
+
+namespace lexicographic {
+
+namespace {
+
+/**
+ * @brief Replace child of the input lists column by a new child column.
+ *
+ * If the input is not sliced, just replace the input child by the new_child.
+ * Otherwise, we have to generate new offsets and replace both the offsets and the child of the
+ * input by the new ones. This is because the new child was generated by ranking and always
+ * has zero offset, so it cannot replace the input child if it is sliced.
+ *
+ * The new generated offsets column needs to be returned and kept alive.
+ *
+ * @param[in] input The input column_view of type LIST
+ * @param[in] new_child A new child column to replace the existing child of the input
+ * @param[out] out_cols An array to store the new generated offsets (if applicable)
+ * @param[in] stream CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr Device memory resource used to allocate the returned column
+ * @return An output column_view with child replaced
+ */
+auto replace_child(column_view const& input,
+                   column_view const& new_child,
+                   std::vector<std::unique_ptr<column>>& out_cols,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+{
+  auto const make_output = [&input](auto const& offsets_cv, auto const& child_cv) {
+    return column_view{data_type{type_id::LIST},
+                       input.size(),
+                       nullptr,
+                       input.null_mask(),
+                       input.null_count(),
+                       0,
+                       {offsets_cv, child_cv}};
+  };
+
+  if (input.offset() == 0) {
+    return make_output(input.child(lists_column_view::offsets_column_index), new_child);
+  }
+
+  out_cols.emplace_back(
+    cudf::lists::detail::get_normalized_offsets(lists_column_view{input}, stream, mr));
+  return make_output(out_cols.back()->view(), new_child);
+}
+
+/**
+ * @brief Compute ranks of the input column.
+ *
+ * `Dense` rank type must be used for compute ranking of the input for later lexicographic
+ * comparison.
+ *
+ * To understand why, consider: `input = [ [{0, "a"}, {3, "c"}], [{0, "a"}, {2, "b"}] ]`.
+ * If first rank is used, `transformed_input = [ [0, 3], [1, 2] ]`. Comparing them will lead
+ * to the result row(0) < row(1) which is incorrect.
+ * With dense rank, `transformed_input = [ [0, 2], [0, 1] ]`, producing the correct output for
+ * lexicographic comparison.
+ *
+ * In addition, since the input column being ranked is always a nested child column instead of
+ * a top-level column, the column order for ranking should be fixed to the same value
+ * `order::ASCENDING` in all situations.
+ * For example, with the same input above, using column order as `order::ASCENDING` we will have
+ * `transformed_input = [ [0, 2], [0, 1] ]`. The output of sorting `transformed_input` will be
+ * exactly the same as sorting `input` regardless of the sorting order (ASC or DESC).
+ *
+ * @param input The input column to compute ranks
+ * @param column_null_order The flag indicating how nulls compare to non-null values
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column
+ * @return The output rank columns
+ */
+auto compute_ranks(column_view const& input,
+                   null_order column_null_order,
+                   rmm::cuda_stream_view stream,
+                   rmm::mr::device_memory_resource* mr)
+{
+  return cudf::detail::rank(input,
+                            rank_method::DENSE,
+                            order::ASCENDING,
+                            null_policy::EXCLUDE,
+                            column_null_order,
+                            false /*percentage*/,
+                            stream,
+                            mr);
+}
+
+/**
+ * @brief Transform any nested lists-of-structs column into lists-of-integers column.
+ *
+ * For a lists-of-structs column at any nested level, its child structs column will be replaced by a
+ * `size_type` column computed as its ranks.
+ *
+ * If the input column is not lists-of-structs, or does not contain lists-of-structs at any nested
+ * level, the input will be passed through without any changes.
+ *
+ * @param input The input column to transform
+ * @param column_null_order The flag indicating how nulls compare to non-null values
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column(s)
+ * @return A pair consisting of new column_view representing the transformed input, along with
+ *         an array containing its rank column(s) (of `size_type` type) and possibly new list
+ *         offsets generated during the transformation process
+ */
+std::pair<column_view, std::vector<std::unique_ptr<column>>> transform_lists_of_structs(
+  column_view const& input,
+  null_order column_null_order,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  std::vector<std::unique_ptr<column>> out_cols;
+
+  if (input.type().id() == type_id::LIST) {
+    auto const child = cudf::lists_column_view{input}.get_sliced_child(stream);
+
+    // Found a lists-of-structs column.
+    if (child.type().id() == type_id::STRUCT) {
+      out_cols.emplace_back(compute_ranks(child, column_null_order, stream, mr));
+      return {replace_child(input, out_cols.back()->view(), out_cols, stream, mr),
+              std::move(out_cols)};
+    }
+    // Found a lists-of-lists column.
+    else if (child.type().id() == type_id::LIST) {
+      // Recursively call transformation on the child column.
+      auto [new_child, out_cols_child] =
+        transform_lists_of_structs(child, column_null_order, stream, mr);
+
+      // Only transform the current column if its child has been transformed.
+      if (out_cols_child.size() > 0) {
+        out_cols.insert(out_cols.end(),
+                        std::make_move_iterator(out_cols_child.begin()),
+                        std::make_move_iterator(out_cols_child.end()));
+        return {replace_child(input, new_child, out_cols, stream, mr), std::move(out_cols)};
+      }
+      // else: child was not transformed so input is also not transformed.
+    }
+    // else: child is not STRUCT or LIST: no transformation.
+  }
+  // else: lhs.type().id() != type_id::LIST.
+  // In such situations, lhs.type().id() can still be type_id::STRUCT. However, any
+  // structs-of-lists should be decomposed into empty struct type `Struct<>` before being
+  // processed by this function so we do nothing here.
+
+  // Passthrough: nothing changed.
+  return {input, std::move(out_cols)};
+}
+
+/**
+ * @brief Transform any nested lists-of-structs column into lists-of-integers column.
+ *
+ * For a lists-of-structs column at any nested level, its child structs column will be replaced by a
+ * `size_type` column computed as its ranks. In addition, equivalent child columns of both input
+ * columns (i.e., child columns at the same order, same nested level) will be combined and
+ * ranked together.
+ *
+ * If the input columns are not lists-of-structs, or do not contain lists-of-structs at any nested
+ * level, there will not be any changes.
+ *
+ * @param lhs The input lhs column to transform
+ * @param rhs The input rhs column to transform
+ * @param column_null_order The flag indicating how nulls compare to non-null values
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr Device memory resource used to allocate the returned column(s)
+ * @return A tuple consisting of new column_view(s) representing the transformed input, along with
+ *         their rank column(s) (of `size_type` type) and possibly new list offsets generated
+ *         during the transformation process
+ */
+std::tuple<column_view,
+           column_view,
+           std::vector<std::unique_ptr<column>>,
+           std::vector<std::unique_ptr<column>>>
+transform_lists_of_structs(column_view const& lhs,
+                           column_view const& rhs,
+                           null_order column_null_order,
+                           rmm::cuda_stream_view stream,
+                           rmm::mr::device_memory_resource* mr)
+{
+  std::vector<std::unique_ptr<column>> out_cols_lhs;
+  std::vector<std::unique_ptr<column>> out_cols_rhs;
+
+  auto const make_output = [&](auto const& new_child_lhs, auto const& new_child_rhs) {
+    return std::tuple{replace_child(lhs, new_child_lhs, out_cols_lhs, stream, mr),
+                      replace_child(rhs, new_child_rhs, out_cols_rhs, stream, mr),
+                      std::move(out_cols_lhs),
+                      std::move(out_cols_rhs)};
+  };
+
+  if (lhs.type().id() == type_id::LIST) {
+    auto const child_lhs = cudf::lists_column_view{lhs}.get_sliced_child(stream);
+    auto const child_rhs = cudf::lists_column_view{rhs}.get_sliced_child(stream);
+
+    // Found a lists-of-structs column.
+    if (child_lhs.type().id() == type_id::STRUCT) {
+      auto const concatenated_children =
+        cudf::detail::concatenate(std::vector<column_view>{child_lhs, child_rhs},
+                                  stream,
+                                  rmm::mr::get_current_device_resource());
+
+      auto const ranks        = compute_ranks(concatenated_children->view(),
+                                       column_null_order,
+                                       stream,
+                                       rmm::mr::get_current_device_resource());
+      auto const ranks_slices = cudf::detail::slice(
+        ranks->view(),
+        {0, child_lhs.size(), child_lhs.size(), child_lhs.size() + child_rhs.size()},
+        stream);
+
+      out_cols_lhs.emplace_back(std::make_unique<column>(ranks_slices.front(), stream, mr));
+      out_cols_rhs.emplace_back(std::make_unique<column>(ranks_slices.back(), stream, mr));
+
+      return make_output(out_cols_lhs.back()->view(), out_cols_rhs.back()->view());
+
+    }
+    // Found a lists-of-lists column.
+    else if (child_lhs.type().id() == type_id::LIST) {
+      // Recursively call transformation on the child column.
+      auto [new_child_lhs, new_child_rhs, out_cols_child_lhs, out_cols_child_rhs] =
+        transform_lists_of_structs(child_lhs, child_rhs, column_null_order, stream, mr);
+
+      // Only transform the current pair of columns if their children have been transformed.
+      if (out_cols_child_lhs.size() > 0 || out_cols_child_rhs.size() > 0) {
+        out_cols_lhs.insert(out_cols_lhs.end(),
+                            std::make_move_iterator(out_cols_child_lhs.begin()),
+                            std::make_move_iterator(out_cols_child_lhs.end()));
+        out_cols_rhs.insert(out_cols_rhs.end(),
+                            std::make_move_iterator(out_cols_child_rhs.begin()),
+                            std::make_move_iterator(out_cols_child_rhs.end()));
+
+        return make_output(new_child_lhs, new_child_rhs);
+      }
+    }
+    // else: child is not STRUCT or LIST: just go to the end of this function, no transformation.
+  }
+  // else: lhs.type().id() != type_id::LIST.
+  // In such situations, lhs.type().id() can still be type_id::STRUCT. However, any
+  // structs-of-lists should be decomposed into empty struct type `Struct<>` before being
+  // processed by this function so we do nothing here.
+
+  // Passthrough: nothing changed.
+  return {lhs, rhs, std::move(out_cols_lhs), std::move(out_cols_rhs)};
+}
+
+}  // namespace
+
+std::shared_ptr<preprocessed_table> preprocessed_table::create(
+  table_view const& preprocessed_input,
+  std::vector<int>&& verticalized_col_depths,
+  std::vector<std::unique_ptr<column>>&& transformed_columns,
+  host_span<order const> column_order,
+  host_span<null_order const> null_precedence,
+  bool has_ranked_children,
+  rmm::cuda_stream_view stream)
+{
+  check_lex_compatibility(preprocessed_input);
+
+  auto d_table = table_device_view::create(preprocessed_input, stream);
+  auto d_column_order =
+    detail::make_device_uvector_async(column_order, stream, rmm::mr::get_current_device_resource());
+  auto d_null_precedence = detail::make_device_uvector_async(
+    null_precedence, stream, rmm::mr::get_current_device_resource());
+  auto d_depths = detail::make_device_uvector_async(
+    verticalized_col_depths, stream, rmm::mr::get_current_device_resource());
+
+  if (detail::has_nested_columns(preprocessed_input)) {
+    auto [dremel_data, d_dremel_device_view] = list_lex_preprocess(preprocessed_input, stream);
+    return std::shared_ptr<preprocessed_table>(
+      new preprocessed_table(std::move(d_table),
+                             std::move(d_column_order),
+                             std::move(d_null_precedence),
+                             std::move(d_depths),
+                             std::move(dremel_data),
+                             std::move(d_dremel_device_view),
+                             std::move(transformed_columns),
+                             has_ranked_children));
+  } else {
+    return std::shared_ptr<preprocessed_table>(
+      new preprocessed_table(std::move(d_table),
+                             std::move(d_column_order),
+                             std::move(d_null_precedence),
+                             std::move(d_depths),
+                             std::move(transformed_columns),
+                             has_ranked_children));
+  }
+}
+
+std::shared_ptr<preprocessed_table> preprocessed_table::create(
+  table_view const& input,
+  host_span<order const> column_order,
+  host_span<null_order const> null_precedence,
+  rmm::cuda_stream_view stream)
+{
+  auto [decomposed_input, new_column_order, new_null_precedence, verticalized_col_depths] =
+    decompose_structs(input, decompose_lists_column::NO, column_order, null_precedence);
+
+  // Transform any (nested) lists-of-structs column into lists-of-integers column.
+  std::vector<std::unique_ptr<column>> transformed_columns;
+  auto const transformed_input =
+    [&, &decomposed_input = decomposed_input, &new_null_precedence = new_null_precedence] {
+      std::vector<column_view> transformed_cvs;
+
+      for (size_type col_idx = 0; col_idx < decomposed_input.num_columns(); ++col_idx) {
+        auto const& lhs_col = decomposed_input.column(col_idx);
+
+        auto [transformed, curr_out_cols] = transform_lists_of_structs(
+          lhs_col,
+          null_precedence.empty() ? null_order::BEFORE : new_null_precedence[col_idx],
+          stream,
+          rmm::mr::get_current_device_resource());
+
+        transformed_cvs.emplace_back(std::move(transformed));
+        transformed_columns.insert(transformed_columns.end(),
+                                   std::make_move_iterator(curr_out_cols.begin()),
+                                   std::make_move_iterator(curr_out_cols.end()));
+      }
+
+      return table_view{transformed_cvs};
+    }();
+
+  auto const has_ranked_children = !transformed_columns.empty();
+  return create(transformed_input,
+                std::move(verticalized_col_depths),
+                std::move(transformed_columns),
+                new_column_order,
+                new_null_precedence,
+                has_ranked_children,
+                stream);
+}
+
+std::pair<std::shared_ptr<preprocessed_table>, std::shared_ptr<preprocessed_table>>
+preprocessed_table::create(table_view const& lhs,
+                           table_view const& rhs,
+                           host_span<order const> column_order,
+                           host_span<null_order const> null_precedence,
+                           rmm::cuda_stream_view stream)
+{
+  check_shape_compatibility(lhs, rhs);
+
+  auto [decomposed_lhs,
+        new_column_order_lhs,
+        new_null_precedence_lhs,
+        verticalized_col_depths_lhs] =
+    decompose_structs(lhs, decompose_lists_column::NO, column_order, null_precedence);
+
+  // Unused variables are new column order and null order for rhs, which are the same as for lhs
+  // so we don't need them.
+  [[maybe_unused]] auto [decomposed_rhs, unused0, unused1, verticalized_col_depths_rhs] =
+    decompose_structs(rhs, decompose_lists_column::NO, column_order, null_precedence);
+
+  // Transform any (nested) lists-of-structs column into lists-of-integers column.
+  std::vector<std::unique_ptr<column>> transformed_columns_lhs;
+  std::vector<std::unique_ptr<column>> transformed_columns_rhs;
+  auto const [transformed_lhs,
+              transformed_rhs] = [&,
+                                  &decomposed_lhs          = decomposed_lhs,
+                                  &decomposed_rhs          = decomposed_rhs,
+                                  &new_null_precedence_lhs = new_null_precedence_lhs] {
+    std::vector<column_view> transformed_lhs_cvs;
+    std::vector<column_view> transformed_rhs_cvs;
+
+    for (size_type col_idx = 0; col_idx < decomposed_lhs.num_columns(); ++col_idx) {
+      auto const& lhs_col = decomposed_lhs.column(col_idx);
+      auto const& rhs_col = decomposed_rhs.column(col_idx);
+
+      auto [transformed_lhs, transformed_rhs, curr_out_cols_lhs, curr_out_cols_rhs] =
+        transform_lists_of_structs(
+          lhs_col,
+          rhs_col,
+          null_precedence.empty() ? null_order::BEFORE : null_precedence[col_idx],
+          stream,
+          rmm::mr::get_current_device_resource());
+
+      transformed_lhs_cvs.emplace_back(std::move(transformed_lhs));
+      transformed_rhs_cvs.emplace_back(std::move(transformed_rhs));
+      transformed_columns_lhs.insert(transformed_columns_lhs.end(),
+                                     std::make_move_iterator(curr_out_cols_lhs.begin()),
+                                     std::make_move_iterator(curr_out_cols_lhs.end()));
+      transformed_columns_rhs.insert(transformed_columns_rhs.end(),
+                                     std::make_move_iterator(curr_out_cols_rhs.begin()),
+                                     std::make_move_iterator(curr_out_cols_rhs.end()));
+    }
+
+    return std::pair{table_view{transformed_lhs_cvs}, table_view{transformed_rhs_cvs}};
+  }();
+
+  // This should be the same for both lhs and rhs but not all the time, such as when one table
+  // has 0 rows while the other has >0 rows. So we check separately for each of them.
+  auto const has_ranked_children_lhs = !transformed_columns_lhs.empty();
+  auto const has_ranked_children_rhs = !transformed_columns_rhs.empty();
+
+  return {create(transformed_lhs,
+                 std::move(verticalized_col_depths_lhs),
+                 std::move(transformed_columns_lhs),
+                 new_column_order_lhs,
+                 new_null_precedence_lhs,
+                 has_ranked_children_lhs,
+                 stream),
+          create(transformed_rhs,
+                 std::move(verticalized_col_depths_rhs),
+                 std::move(transformed_columns_rhs),
+                 new_column_order_lhs,
+                 new_null_precedence_lhs,
+                 has_ranked_children_rhs,
+                 stream)};
+}
+
+preprocessed_table::preprocessed_table(
+  table_device_view_owner&& table,
+  rmm::device_uvector<order>&& column_order,
+  rmm::device_uvector<null_order>&& null_precedence,
+  rmm::device_uvector<size_type>&& depths,
+  std::vector<detail::dremel_data>&& dremel_data,
+  rmm::device_uvector<detail::dremel_device_view>&& dremel_device_views,
+  std::vector<std::unique_ptr<column>>&& transformed_columns,
+  bool has_ranked_children)
+  : _t(std::move(table)),
+    _column_order(std::move(column_order)),
+    _null_precedence(std::move(null_precedence)),
+    _depths(std::move(depths)),
+    _dremel_data(std::move(dremel_data)),
+    _dremel_device_views(std::move(dremel_device_views)),
+    _transformed_columns(std::move(transformed_columns)),
+    _has_ranked_children(has_ranked_children)
+{
+}
+
+preprocessed_table::preprocessed_table(table_device_view_owner&& table,
+                                       rmm::device_uvector<order>&& column_order,
+                                       rmm::device_uvector<null_order>&& null_precedence,
+                                       rmm::device_uvector<size_type>&& depths,
+                                       std::vector<std::unique_ptr<column>>&& transformed_columns,
+                                       bool has_ranked_children)
+  : _t(std::move(table)),
+    _column_order(std::move(column_order)),
+    _null_precedence(std::move(null_precedence)),
+    _depths(std::move(depths)),
+    _dremel_data{},
+    _dremel_device_views{},
+    _transformed_columns(std::move(transformed_columns)),
+    _has_ranked_children(has_ranked_children)
+{
+}
+
+two_table_comparator::two_table_comparator(table_view const& left,
+                                           table_view const& right,
+                                           host_span<order const> column_order,
+                                           host_span<null_order const> null_precedence,
+                                           rmm::cuda_stream_view stream)
+{
+  std::tie(d_left_table, d_right_table) =
+    preprocessed_table::create(left, right, column_order, null_precedence, stream);
+}
+
+}  // namespace lexicographic
+
+namespace equality {
+
+std::shared_ptr<preprocessed_table> preprocessed_table::create(table_view const& t,
+                                                               rmm::cuda_stream_view stream)
+{
+  check_eq_compatibility(t);
+
+  auto [null_pushed_table, nullable_data] =
+    structs::detail::push_down_nulls(t, stream, rmm::mr::get_current_device_resource());
+  auto struct_offset_removed_table = remove_struct_child_offsets(null_pushed_table);
+  auto verticalized_t =
+    std::get<0>(decompose_structs(struct_offset_removed_table, decompose_lists_column::YES));
+
+  auto d_t = table_device_view_owner(table_device_view::create(verticalized_t, stream));
+  return std::shared_ptr<preprocessed_table>(new preprocessed_table(
+    std::move(d_t), std::move(nullable_data.new_null_masks), std::move(nullable_data.new_columns)));
+}
+
+two_table_comparator::two_table_comparator(table_view const& left,
+                                           table_view const& right,
+                                           rmm::cuda_stream_view stream)
+  : d_left_table{preprocessed_table::create(left, stream)},
+    d_right_table{preprocessed_table::create(right, stream)}
+{
+  check_shape_compatibility(left, right);
+}
+
+}  // namespace equality
+
+}  // namespace row
+}  // namespace experimental
+}  // namespace cudf
diff --git a/cpp/src/table/table.cpp b/cpp/src/table/table.cpp
new file mode 100644
index 0000000..904ce54
--- /dev/null
+++ b/cpp/src/table/table.cpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+
+// Copy the columns from another table
+table::table(table const& other) : _num_rows{other.num_rows()}
+{
+  CUDF_FUNC_RANGE();
+  _columns.reserve(other._columns.size());
+  for (auto const& c : other._columns) {
+    _columns.emplace_back(std::make_unique<column>(*c));
+  }
+}
+
+// Move the contents of a vector `unique_ptr<column>`
+table::table(std::vector<std::unique_ptr<column>>&& columns) : _columns{std::move(columns)}
+{
+  if (num_columns() > 0) {
+    for (auto const& c : _columns) {
+      CUDF_EXPECTS(c, "Unexpected null column");
+      CUDF_EXPECTS(c->size() == _columns.front()->size(),
+                   "Column size mismatch: " + std::to_string(c->size()) +
+                     " != " + std::to_string(_columns.front()->size()));
+    }
+    _num_rows = _columns.front()->size();
+  } else {
+    _num_rows = 0;
+  }
+}
+
+// Copy the contents of a `table_view`
+table::table(table_view view, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+  : _num_rows{view.num_rows()}
+{
+  CUDF_FUNC_RANGE();
+  _columns.reserve(view.num_columns());
+  for (auto const& c : view) {
+    _columns.emplace_back(std::make_unique<column>(c, stream, mr));
+  }
+}
+
+// Create immutable view
+table_view table::view() const
+{
+  std::vector<column_view> views;
+  views.reserve(_columns.size());
+  for (auto const& c : _columns) {
+    views.push_back(c->view());
+  }
+  return table_view{views};
+}
+
+// Create mutable view
+mutable_table_view table::mutable_view()
+{
+  std::vector<mutable_column_view> views;
+  views.reserve(_columns.size());
+  for (auto const& c : _columns) {
+    views.push_back(c->mutable_view());
+  }
+  return mutable_table_view{views};
+}
+
+// Release ownership of columns
+std::vector<std::unique_ptr<column>> table::release()
+{
+  _num_rows = 0;
+  return std::move(_columns);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/table/table_device_view.cu b/cpp/src/table/table_device_view.cu
new file mode 100644
index 0000000..5f17574
--- /dev/null
+++ b/cpp/src/table/table_device_view.cu
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+template <typename ColumnDeviceView, typename HostTableView>
+void table_device_view_base<ColumnDeviceView, HostTableView>::destroy()
+{
+  delete _descendant_storage;
+  delete this;
+}
+
+template <typename ColumnDeviceView, typename HostTableView>
+table_device_view_base<ColumnDeviceView, HostTableView>::table_device_view_base(
+  HostTableView source_view, rmm::cuda_stream_view stream)
+  : _num_rows{source_view.num_rows()}, _num_columns{source_view.num_columns()}
+{
+  // The table's columns must be converted to ColumnDeviceView
+  // objects and copied into device memory for the table_device_view's
+  // _columns member.
+  if (source_view.num_columns() > 0) {
+    std::unique_ptr<rmm::device_buffer> descendant_storage_owner;
+    std::tie(descendant_storage_owner, _columns) =
+      contiguous_copy_column_device_views<ColumnDeviceView, HostTableView>(source_view, stream);
+    _descendant_storage = descendant_storage_owner.release();
+  }
+}
+
+// Explicit instantiation for a device table of immutable views
+template class table_device_view_base<column_device_view, table_view>;
+
+// Explicit instantiation for a device table of mutable views
+template class table_device_view_base<mutable_column_device_view, mutable_table_view>;
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/table/table_view.cpp b/cpp/src/table/table_view.cpp
new file mode 100644
index 0000000..0d1cabf
--- /dev/null
+++ b/cpp/src/table/table_view.cpp
@@ -0,0 +1,144 @@
+/*
+ * Copyright (c) 2018-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <cassert>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+template <typename ColumnView>
+table_view_base<ColumnView>::table_view_base(std::vector<ColumnView> const& cols) : _columns{cols}
+{
+  if (num_columns() > 0) {
+    std::for_each(_columns.begin(), _columns.end(), [this](ColumnView col) {
+      CUDF_EXPECTS(col.size() == _columns.front().size(), "Column size mismatch.");
+    });
+    _num_rows = _columns.front().size();
+  } else {
+    _num_rows = 0;
+  }
+}
+
+template <typename ViewType>
+auto concatenate_column_views(std::vector<ViewType> const& views)
+{
+  using ColumnView = typename ViewType::ColumnView;
+  std::vector<ColumnView> concat_cols;
+  for (auto& view : views) {
+    concat_cols.insert(concat_cols.end(), view.begin(), view.end());
+  }
+  return concat_cols;
+}
+
+template <typename ColumnView>
+ColumnView const& table_view_base<ColumnView>::column(size_type column_index) const
+{
+  return _columns.at(column_index);
+}
+
+// Explicit instantiation for a table of `column_view`s
+template class table_view_base<column_view>;
+
+// Explicit instantiation for a table of `mutable_column_view`s
+template class table_view_base<mutable_column_view>;
+}  // namespace detail
+
+// Returns a table_view with set of specified columns
+table_view table_view::select(std::vector<size_type> const& column_indices) const
+{
+  return select(column_indices.begin(), column_indices.end());
+}
+
+// Convert mutable view to immutable view
+mutable_table_view::operator table_view()
+{
+  std::vector<column_view> cols{begin(), end()};
+  return table_view{cols};
+}
+
+table_view::table_view(std::vector<table_view> const& views)
+  : table_view{concatenate_column_views(views)}
+{
+}
+
+mutable_table_view::mutable_table_view(std::vector<mutable_table_view> const& views)
+  : mutable_table_view{concatenate_column_views(views)}
+{
+}
+
+table_view scatter_columns(table_view const& source,
+                           std::vector<size_type> const& map,
+                           table_view const& target)
+{
+  std::vector<cudf::column_view> updated_columns(target.begin(), target.end());
+  // scatter(updated_table.begin(),updated_table.end(),indices.begin(),updated_columns.begin());
+  for (size_type idx = 0; idx < source.num_columns(); ++idx)
+    updated_columns[map[idx]] = source.column(idx);
+  return table_view{updated_columns};
+}
+
+std::vector<column_view> get_nullable_columns(table_view const& table)
+{
+  std::vector<column_view> result;
+  for (auto const& col : table) {
+    if (col.nullable()) { result.push_back(col); }
+    for (auto it = col.child_begin(); it != col.child_end(); ++it) {
+      auto const& child = *it;
+      if (child.size() == col.size()) {
+        auto const child_result = get_nullable_columns(table_view{{child}});
+        result.insert(result.end(), child_result.begin(), child_result.end());
+      }
+    }
+  }
+  return result;
+}
+
+namespace detail {
+
+template <typename TableView>
+bool is_relationally_comparable(TableView const& lhs, TableView const& rhs)
+{
+  return std::all_of(thrust::counting_iterator<size_type>(0),
+                     thrust::counting_iterator<size_type>(lhs.num_columns()),
+                     [lhs, rhs](auto const i) {
+                       return lhs.column(i).type() == rhs.column(i).type() and
+                              cudf::is_relationally_comparable(lhs.column(i).type());
+                     });
+}
+
+// Explicit template instantiation for a table of immutable views
+template bool is_relationally_comparable<table_view>(table_view const& lhs, table_view const& rhs);
+
+// Explicit template instantiation for a table of mutable views
+template bool is_relationally_comparable<mutable_table_view>(mutable_table_view const& lhs,
+                                                             mutable_table_view const& rhs);
+
+bool has_nested_columns(table_view const& table)
+{
+  return std::any_of(
+    table.begin(), table.end(), [](column_view const& col) { return is_nested(col.type()); });
+}
+
+}  // namespace detail
+}  // namespace cudf
diff --git a/cpp/src/text/detokenize.cu b/cpp/src/text/detokenize.cu
new file mode 100644
index 0000000..a17583c
--- /dev/null
+++ b/cpp/src/text/detokenize.cu
@@ -0,0 +1,181 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/indexalator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/iterator/counting_iterator.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+/**
+ * @brief Generate strings from tokens.
+ *
+ * Each string is created by appending all the tokens assigned to
+ * the same row. The `d_separator` is appended between each token.
+ */
+struct detokenizer_fn {
+  cudf::column_device_view const d_strings;  // these are the tokens
+  cudf::size_type const* d_row_map;          // indices sorted by output row
+  cudf::size_type const* d_token_offsets;    // to each input token array
+  cudf::string_view const d_separator;       // append after each token
+  cudf::size_type* d_offsets{};              // offsets to output buffer d_chars
+  char* d_chars{};                           // output buffer for characters
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    auto const offset      = d_token_offsets[idx];
+    auto d_tokens          = d_row_map + offset;
+    auto const token_count = d_token_offsets[idx + 1] - offset;
+    auto out_ptr           = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    cudf::size_type nbytes = 0;
+    for (cudf::size_type jdx = 0; jdx < token_count; ++jdx) {
+      auto const str_index = d_tokens[jdx];
+      if (d_strings.is_null(str_index)) continue;
+      auto const d_str = d_strings.element<cudf::string_view>(str_index);
+      if (out_ptr) {
+        out_ptr = cudf::strings::detail::copy_string(out_ptr, d_str);
+        if (jdx + 1 < token_count)
+          out_ptr = cudf::strings::detail::copy_string(out_ptr, d_separator);
+      } else {
+        nbytes += d_str.size_bytes();
+        nbytes += d_separator.size_bytes();
+      }
+    }
+    if (!d_chars) { d_offsets[idx] = (nbytes > 0) ? (nbytes - d_separator.size_bytes()) : 0; }
+  }
+};
+
+struct index_changed_fn {
+  cudf::detail::input_indexalator const d_rows;
+  cudf::size_type const* d_row_map;
+  __device__ bool operator()(cudf::size_type idx) const
+  {
+    return (idx == 0) || (d_rows[d_row_map[idx]] != d_rows[d_row_map[idx - 1]]);
+  }
+};
+
+/**
+ * @brief Convert the row indices into token offsets
+ *
+ * @param row_indices Indices where each token should land
+ * @param sorted_indices Map of row_indices sorted
+ * @param tokens_counts Token counts for each row
+ * @param stream CUDA stream used for kernel launches
+ */
+rmm::device_uvector<cudf::size_type> create_token_row_offsets(
+  cudf::column_view const& row_indices,
+  cudf::column_view const& sorted_indices,
+  cudf::size_type tokens_counts,
+  rmm::cuda_stream_view stream)
+{
+  index_changed_fn fn{cudf::detail::indexalator_factory::make_input_iterator(row_indices),
+                      sorted_indices.data<cudf::size_type>()};
+
+  auto const output_count =
+    thrust::count_if(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<cudf::size_type>(0),
+                     thrust::make_counting_iterator<cudf::size_type>(tokens_counts),
+                     fn);
+
+  auto tokens_offsets = rmm::device_uvector<cudf::size_type>(output_count + 1, stream);
+
+  thrust::copy_if(rmm::exec_policy(stream),
+                  thrust::make_counting_iterator<cudf::size_type>(0),
+                  thrust::make_counting_iterator<cudf::size_type>(tokens_counts),
+                  tokens_offsets.begin(),
+                  fn);
+
+  // set the last element to the total number of tokens
+  tokens_offsets.set_element(output_count, tokens_counts, stream);
+  return tokens_offsets;
+}
+
+}  // namespace
+
+/**
+ * @copydoc nvtext::detokenize
+ */
+std::unique_ptr<cudf::column> detokenize(cudf::strings_column_view const& strings,
+                                         cudf::column_view const& row_indices,
+                                         cudf::string_scalar const& separator,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(separator.is_valid(stream), "Parameter separator must be valid");
+  CUDF_EXPECTS(row_indices.size() == strings.size(),
+               "Parameter row_indices must be the same size as the input column");
+  CUDF_EXPECTS(not row_indices.has_nulls(), "Parameter row_indices must not have nulls");
+
+  auto tokens_counts = strings.size();
+  if (tokens_counts == 0)  // if no input strings, return an empty column
+    return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  // the indices may not be in order so we need to build a sorted map
+  auto sorted_rows = cudf::detail::stable_sorted_order(
+    cudf::table_view({row_indices}), {}, {}, stream, rmm::mr::get_current_device_resource());
+  auto const d_row_map = sorted_rows->view().data<cudf::size_type>();
+
+  // create offsets for the tokens for each output string
+  auto tokens_offsets =
+    create_token_row_offsets(row_indices, sorted_rows->view(), tokens_counts, stream);
+  auto const output_count = tokens_offsets.size() - 1;  // number of output strings
+
+  cudf::string_view const d_separator(separator.data(), separator.size());
+
+  auto children = cudf::strings::detail::make_strings_children(
+    detokenizer_fn{*strings_column, d_row_map, tokens_offsets.data(), d_separator},
+    output_count,
+    stream,
+    mr);
+
+  // make the output strings column from the offsets and chars column
+  return cudf::make_strings_column(
+    output_count, std::move(children.first), std::move(children.second), 0, rmm::device_buffer{});
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> detokenize(cudf::strings_column_view const& strings,
+                                         cudf::column_view const& row_indices,
+                                         cudf::string_scalar const& separator,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::detokenize(strings, row_indices, separator, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/edit_distance.cu b/cpp/src/text/edit_distance.cu
new file mode 100644
index 0000000..1460be4
--- /dev/null
+++ b/cpp/src/text/edit_distance.cu
@@ -0,0 +1,319 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/edit_distance.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Compute the Levenshtein distance for each string pair
+ *
+ * Documentation here: https://www.cuelogic.com/blog/the-levenshtein-algorithm
+ * And here: https://en.wikipedia.org/wiki/Levenshtein_distance
+ *
+ * @param d_str First string
+ * @param d_tgt Second string
+ * @param buffer Working buffer for intermediate calculations
+ * @return The edit distance value
+ */
+__device__ cudf::size_type compute_distance(cudf::string_view const& d_str,
+                                            cudf::string_view const& d_tgt,
+                                            cudf::size_type* buffer)
+{
+  auto const str_length = d_str.length();
+  auto const tgt_length = d_tgt.length();
+  if (str_length == 0) return tgt_length;
+  if (tgt_length == 0) return str_length;
+
+  auto begin = str_length < tgt_length ? d_str.begin() : d_tgt.begin();
+  auto itr   = str_length < tgt_length ? d_tgt.begin() : d_str.begin();
+  // .first is min and .second is max
+  auto const [n, m] = std::minmax(str_length, tgt_length);
+  // setup compute buffer pointers
+  auto v0 = buffer;
+  auto v1 = v0 + n + 1;
+  // initialize v0
+  thrust::sequence(thrust::seq, v0, v1);
+
+  for (int i = 0; i < m; ++i, ++itr) {
+    auto itr_tgt = begin;
+    v1[0]        = i + 1;
+    for (int j = 0; j < n; ++j, ++itr_tgt) {
+      auto sub_cost = v0[j] + (*itr != *itr_tgt);
+      auto del_cost = v0[j + 1] + 1;
+      auto ins_cost = v1[j] + 1;
+      v1[j + 1]     = std::min(std::min(sub_cost, del_cost), ins_cost);
+    }
+    thrust::swap(v0, v1);
+  }
+  return v0[n];
+}
+
+struct edit_distance_levenshtein_algorithm {
+  cudf::column_device_view d_strings;  // computing these
+  cudf::column_device_view d_targets;  // against these;
+  cudf::size_type* d_buffer;           // compute buffer for each string
+  std::ptrdiff_t const* d_offsets;     // locate sub-buffer for each string
+  cudf::size_type* d_results;          // edit distance values
+
+  __device__ void operator()(cudf::size_type idx) const
+  {
+    auto d_str =
+      d_strings.is_null(idx) ? cudf::string_view{} : d_strings.element<cudf::string_view>(idx);
+    auto d_tgt = [&] __device__ {  // d_targets is also allowed to have only one entry
+      if (d_targets.is_null(idx)) { return cudf::string_view{}; }
+      return d_targets.size() == 1 ? d_targets.element<cudf::string_view>(0)
+                                   : d_targets.element<cudf::string_view>(idx);
+    }();
+    d_results[idx] = compute_distance(d_str, d_tgt, d_buffer + d_offsets[idx]);
+  }
+};
+
+struct edit_distance_matrix_levenshtein_algorithm {
+  cudf::column_device_view d_strings;  // computing these against itself
+  cudf::size_type* d_buffer;           // compute buffer for each string
+  std::ptrdiff_t const* d_offsets;     // locate sub-buffer for each string
+  cudf::size_type* d_results;          // edit distance values
+
+  __device__ void operator()(cudf::size_type idx) const
+  {
+    auto const strings_count = d_strings.size();
+    auto const row           = idx / strings_count;
+    auto const col           = idx % strings_count;
+    if (row > col) return;  // bottom half is computed with the top half of matrix
+    cudf::string_view d_str1 =
+      d_strings.is_null(row) ? cudf::string_view{} : d_strings.element<cudf::string_view>(row);
+    cudf::string_view d_str2 =
+      d_strings.is_null(col) ? cudf::string_view{} : d_strings.element<cudf::string_view>(col);
+    auto work_buffer    = d_buffer + d_offsets[idx - ((row + 1) * (row + 2)) / 2];
+    auto const distance = (row == col) ? 0 : compute_distance(d_str1, d_str2, work_buffer);
+    d_results[idx]      = distance;                   // top half of matrix
+    d_results[col * strings_count + row] = distance;  // bottom half of matrix
+  }
+};
+
+}  // namespace
+
+/**
+ * @copydoc nvtext::edit_distance
+ */
+std::unique_ptr<cudf::column> edit_distance(cudf::strings_column_view const& strings,
+                                            cudf::strings_column_view const& targets,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  auto const strings_count = strings.size();
+  if (strings_count == 0) {
+    return cudf::make_empty_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()});
+  }
+  if (targets.size() > 1) {
+    CUDF_EXPECTS(strings_count == targets.size(), "targets.size() must equal strings.size()");
+  }
+
+  // create device columns from the input columns
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+  auto targets_column = cudf::column_device_view::create(targets.parent(), stream);
+  auto d_targets      = *targets_column;
+
+  // calculate the size of the compute-buffer;
+  rmm::device_uvector<std::ptrdiff_t> offsets(strings_count, stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(strings_count),
+                    offsets.begin(),
+                    [d_strings, d_targets] __device__(auto idx) {
+                      if (d_strings.is_null(idx) || d_targets.is_null(idx)) {
+                        return cudf::size_type{0};
+                      }
+                      auto d_str = d_strings.element<cudf::string_view>(idx);
+                      auto d_tgt = d_targets.size() == 1
+                                     ? d_targets.element<cudf::string_view>(0)
+                                     : d_targets.element<cudf::string_view>(idx);
+                      // just need 2 integers for each character of the shorter string
+                      return (std::min(d_str.length(), d_tgt.length()) + 1) * 2;
+                    });
+
+  // get the total size of the temporary compute buffer
+  int64_t compute_size =
+    thrust::reduce(rmm::exec_policy(stream), offsets.begin(), offsets.end(), int64_t{0});
+  // convert sizes to offsets in-place
+  thrust::exclusive_scan(rmm::exec_policy(stream), offsets.begin(), offsets.end(), offsets.begin());
+  // create the temporary compute buffer
+  rmm::device_uvector<cudf::size_type> compute_buffer(compute_size, stream);
+  auto d_buffer = compute_buffer.data();
+
+  auto results = cudf::make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                               strings_count,
+                                               rmm::device_buffer{0, stream, mr},
+                                               0,
+                                               stream,
+                                               mr);
+  auto d_results = results->mutable_view().data<cudf::size_type>();
+
+  // compute the edit distance into the output column
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    strings_count,
+    edit_distance_levenshtein_algorithm{d_strings, d_targets, d_buffer, offsets.data(), d_results});
+  return results;
+}
+
+/**
+ * @copydoc nvtext::edit_distance_matrix
+ */
+std::unique_ptr<cudf::column> edit_distance_matrix(cudf::strings_column_view const& strings,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  cudf::size_type strings_count = strings.size();
+  if (strings_count == 0) {
+    return cudf::make_empty_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()});
+  }
+  CUDF_EXPECTS(strings_count > 1, "the input strings must include at least 2 strings");
+  CUDF_EXPECTS(static_cast<size_t>(strings_count) * static_cast<size_t>(strings_count) <
+                 static_cast<std::size_t>(std::numeric_limits<cudf::size_type>().max()),
+               "too many strings to create the output column");
+
+  // create device column of the input strings column
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+
+  // Calculate the size of the compute-buffer.
+  // We only need memory for half the size of the output matrix since the edit distance calculation
+  // is commutative -- `distance(strings[i],strings[j]) == distance(strings[j],strings[i])`
+  cudf::size_type n_upper = (strings_count * (strings_count - 1)) / 2;
+  rmm::device_uvector<std::ptrdiff_t> offsets(n_upper, stream);
+  auto d_offsets = offsets.data();
+  CUDF_CUDA_TRY(cudaMemsetAsync(d_offsets, 0, n_upper * sizeof(cudf::size_type), stream.value()));
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    strings_count * strings_count,
+    [d_strings, d_offsets, strings_count] __device__(cudf::size_type idx) {
+      auto const row = idx / strings_count;
+      auto const col = idx % strings_count;
+      if (row >= col) return;  // compute only the top half
+      cudf::string_view const d_str1 =
+        d_strings.is_null(row) ? cudf::string_view{} : d_strings.element<cudf::string_view>(row);
+      cudf::string_view const d_str2 =
+        d_strings.is_null(col) ? cudf::string_view{} : d_strings.element<cudf::string_view>(col);
+      if (d_str1.empty() || d_str2.empty()) { return; }
+      // the temp size needed is 2 integers per character of the shorter string
+      d_offsets[idx - ((row + 1) * (row + 2)) / 2] =
+        (std::min(d_str1.length(), d_str2.length()) + 1) * 2;
+    });
+
+  // get the total size for the compute buffer
+  int64_t compute_size =
+    thrust::reduce(rmm::exec_policy(stream), offsets.begin(), offsets.end(), int64_t{0});
+  // convert sizes to offsets in-place
+  thrust::exclusive_scan(rmm::exec_policy(stream), offsets.begin(), offsets.end(), offsets.begin());
+  // create the compute buffer
+  rmm::device_uvector<cudf::size_type> compute_buffer(compute_size, stream);
+  auto d_buffer = compute_buffer.data();
+
+  // compute the edit distance into the output column
+  auto results = cudf::make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                               strings_count * strings_count,
+                                               rmm::device_buffer{0, stream, mr},
+                                               0,
+                                               stream,
+                                               mr);
+  auto d_results = results->mutable_view().data<cudf::size_type>();
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    strings_count * strings_count,
+    edit_distance_matrix_levenshtein_algorithm{d_strings, d_buffer, d_offsets, d_results});
+
+  // build a lists column of the results
+  auto offsets_column =
+    cudf::make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                  strings_count + 1,
+                                  rmm::device_buffer{0, stream, mr},
+                                  0,
+                                  stream,
+                                  mr);
+  thrust::transform_exclusive_scan(
+    rmm::exec_policy(stream),
+    thrust::counting_iterator<cudf::size_type>(0),
+    thrust::counting_iterator<cudf::size_type>(strings_count + 1),
+    offsets_column->mutable_view().data<cudf::size_type>(),
+    [strings_count] __device__(auto idx) { return strings_count; },
+    cudf::size_type{0},
+    thrust::plus<cudf::size_type>());
+  return cudf::make_lists_column(strings_count,
+                                 std::move(offsets_column),
+                                 std::move(results),
+                                 0,  // no nulls
+                                 rmm::device_buffer{0, stream, mr},
+                                 stream,
+                                 mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+/**
+ * @copydoc nvtext::edit_distance
+ */
+std::unique_ptr<cudf::column> edit_distance(cudf::strings_column_view const& strings,
+                                            cudf::strings_column_view const& targets,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::edit_distance(strings, targets, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc nvtext::edit_distance_matrix
+ */
+std::unique_ptr<cudf::column> edit_distance_matrix(cudf::strings_column_view const& strings,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::edit_distance_matrix(strings, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/generate_ngrams.cu b/cpp/src/text/generate_ngrams.cu
new file mode 100644
index 0000000..5f2f4d0
--- /dev/null
+++ b/cpp/src/text/generate_ngrams.cu
@@ -0,0 +1,337 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/detail/generate_ngrams.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy_if.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform_scan.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+/**
+ * @brief Generate ngrams from strings column.
+ *
+ * Adjacent strings are concatenated with the provided separator.
+ * The number of adjacent strings join depends on the specified ngrams value.
+ * For example: for bigrams (ngrams=2), pairs of strings are concatenated.
+ */
+struct ngram_generator_fn {
+  cudf::column_device_view const d_strings;
+  cudf::size_type ngrams;
+  cudf::string_view const d_separator;
+  cudf::size_type* d_offsets{};
+  char* d_chars{};
+
+  /**
+   * @brief Build ngram for each string.
+   *
+   * This is called for each thread and processed for each string.
+   * Each string will produce the number of ngrams specified.
+   *
+   * @param idx Index of the kernel thread.
+   * @return Number of bytes required for the string for this thread.
+   */
+  __device__ void operator()(cudf::size_type idx)
+  {
+    char* out_ptr         = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    cudf::size_type bytes = 0;
+    for (cudf::size_type n = 0; n < ngrams; ++n) {
+      auto const d_str = d_strings.element<cudf::string_view>(n + idx);
+      bytes += d_str.size_bytes();
+      if (out_ptr) out_ptr = cudf::strings::detail::copy_string(out_ptr, d_str);
+      if ((n + 1) >= ngrams) continue;
+      bytes += d_separator.size_bytes();
+      if (out_ptr) out_ptr = cudf::strings::detail::copy_string(out_ptr, d_separator);
+    }
+    if (!d_chars) d_offsets[idx] = bytes;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::column> generate_ngrams(cudf::strings_column_view const& strings,
+                                              cudf::size_type ngrams,
+                                              cudf::string_scalar const& separator,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(separator.is_valid(stream), "Parameter separator must be valid");
+  cudf::string_view const d_separator(separator.data(), separator.size());
+  CUDF_EXPECTS(ngrams > 1, "Parameter ngrams should be an integer value of 2 or greater");
+
+  auto strings_count = strings.size();
+  if (strings_count == 0)  // if no strings, return an empty column
+    return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+
+  // first create a new offsets vector removing nulls and empty strings from the input column
+  std::unique_ptr<cudf::column> non_empty_offsets_column = [&] {
+    cudf::column_view offsets_view(cudf::data_type{cudf::type_id::INT32},
+                                   strings_count + 1,
+                                   strings.offsets_begin(),
+                                   nullptr,
+                                   0);
+    auto table_offsets = cudf::detail::copy_if(
+                           cudf::table_view({offsets_view}),
+                           [d_strings, strings_count] __device__(cudf::size_type idx) {
+                             if (idx == strings_count) return true;
+                             if (d_strings.is_null(idx)) return false;
+                             return !d_strings.element<cudf::string_view>(idx).empty();
+                           },
+                           stream,
+                           rmm::mr::get_current_device_resource())
+                           ->release();
+    strings_count = table_offsets.front()->size() - 1;
+    auto result   = std::move(table_offsets.front());
+    return result;
+  }();  // this allows freeing the temporary table_offsets
+
+  CUDF_EXPECTS(strings_count >= ngrams, "Insufficient number of strings to generate ngrams");
+  // create a temporary column view from the non-empty offsets and chars column views
+  cudf::column_view strings_view(cudf::data_type{cudf::type_id::STRING},
+                                 strings_count,
+                                 nullptr,
+                                 nullptr,
+                                 0,
+                                 0,
+                                 {non_empty_offsets_column->view(), strings.chars()});
+  strings_column = cudf::column_device_view::create(strings_view, stream);
+  d_strings      = *strings_column;
+
+  // compute the number of strings of ngrams
+  auto const ngrams_count = strings_count - ngrams + 1;
+
+  auto children = cudf::strings::detail::make_strings_children(
+    ngram_generator_fn{d_strings, ngrams, d_separator}, ngrams_count, stream, mr);
+
+  // make the output strings column from the offsets and chars column
+  return cudf::make_strings_column(
+    ngrams_count, std::move(children.first), std::move(children.second), 0, rmm::device_buffer{});
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> generate_ngrams(cudf::strings_column_view const& strings,
+                                              cudf::size_type ngrams,
+                                              cudf::string_scalar const& separator,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::generate_ngrams(strings, ngrams, separator, stream, mr);
+}
+
+namespace detail {
+namespace {
+
+/**
+ * @brief Generate character ngrams for each string
+ *
+ * Each string produces many strings depending on the ngram width and the string size.
+ * This functor can be used with `make_strings_children` to build the offsets and
+ * the chars child columns.
+ */
+struct character_ngram_generator_fn {
+  cudf::column_device_view const d_strings;
+  cudf::size_type ngrams;
+  cudf::size_type const* d_ngram_offsets{};
+  cudf::size_type* d_offsets{};
+  char* d_chars{};
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) return;
+    auto const d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) return;
+    auto itr                = d_str.begin();
+    auto const ngram_offset = d_ngram_offsets[idx];
+    auto const ngram_count  = d_ngram_offsets[idx + 1] - ngram_offset;
+    auto d_sizes            = d_offsets + ngram_offset;
+    auto out_ptr            = d_chars ? d_chars + *d_sizes : nullptr;
+    for (cudf::size_type n = 0; n < ngram_count; ++n, ++itr) {
+      auto const begin = itr.byte_offset();
+      auto const end   = (itr + ngrams).byte_offset();
+      if (d_chars) {
+        out_ptr =
+          cudf::strings::detail::copy_and_increment(out_ptr, d_str.data() + begin, (end - begin));
+      } else {
+        *d_sizes++ = end - begin;
+      }
+    }
+  }
+};
+}  // namespace
+
+std::unique_ptr<cudf::column> generate_character_ngrams(cudf::strings_column_view const& strings,
+                                                        cudf::size_type ngrams,
+                                                        rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(ngrams > 1, "Parameter ngrams should be an integer value of 2 or greater");
+
+  auto const strings_count = strings.size();
+  if (strings_count == 0)  // if no strings, return an empty column
+    return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto const strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  auto const d_strings      = *strings_column;
+
+  // create a vector of ngram offsets for each string
+  rmm::device_uvector<cudf::size_type> ngram_offsets(strings_count + 1, stream);
+  thrust::transform_exclusive_scan(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    thrust::make_counting_iterator<cudf::size_type>(strings_count + 1),
+    ngram_offsets.begin(),
+    [d_strings, strings_count, ngrams] __device__(auto idx) {
+      if (d_strings.is_null(idx) || (idx == strings_count)) return 0;
+      auto const length = d_strings.element<cudf::string_view>(idx).length();
+      return std::max(0, static_cast<cudf::size_type>(length + 1 - ngrams));
+    },
+    cudf::size_type{0},
+    thrust::plus<cudf::size_type>());
+
+  // total ngrams count is the last entry
+  cudf::size_type const total_ngrams = ngram_offsets.back_element(stream);
+  CUDF_EXPECTS(total_ngrams > 0,
+               "Insufficient number of characters in each string to generate ngrams");
+
+  character_ngram_generator_fn generator{d_strings, ngrams, ngram_offsets.data()};
+  auto [offsets_column, chars_column] = cudf::strings::detail::make_strings_children(
+    generator, strings_count, total_ngrams, stream, mr);
+
+  return cudf::make_strings_column(
+    total_ngrams, std::move(offsets_column), std::move(chars_column), 0, rmm::device_buffer{});
+}
+
+namespace {
+/**
+ * @brief Computes the hash of each character ngram
+ *
+ * Each thread processes a single string. Substrings are resolved for every character
+ * of the string and hashed.
+ */
+struct character_ngram_hash_fn {
+  cudf::column_device_view const d_strings;
+  cudf::size_type ngrams;
+  cudf::size_type const* d_ngram_offsets;
+  cudf::hash_value_type* d_results;
+
+  __device__ void operator()(cudf::size_type idx) const
+  {
+    if (d_strings.is_null(idx)) return;
+    auto const d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) return;
+    auto itr                = d_str.begin();
+    auto const ngram_offset = d_ngram_offsets[idx];
+    auto const ngram_count  = d_ngram_offsets[idx + 1] - ngram_offset;
+    auto const hasher       = cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>{0};
+    auto d_hashes           = d_results + ngram_offset;
+    for (cudf::size_type n = 0; n < ngram_count; ++n, ++itr) {
+      auto const begin = itr.byte_offset();
+      auto const end   = (itr + ngrams).byte_offset();
+      auto const ngram = cudf::string_view(d_str.data() + begin, end - begin);
+      *d_hashes++      = hasher(ngram);
+    }
+  }
+};
+}  // namespace
+
+std::unique_ptr<cudf::column> hash_character_ngrams(cudf::strings_column_view const& input,
+                                                    cudf::size_type ngrams,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(ngrams >= 2, "Parameter ngrams should be an integer value of 2 or greater");
+
+  auto output_type = cudf::data_type{cudf::type_to_id<cudf::hash_value_type>()};
+  if (input.is_empty()) { return cudf::make_empty_column(output_type); }
+
+  auto const d_strings = cudf::column_device_view::create(input.parent(), stream);
+
+  // build offsets column by computing the number of ngrams per string
+  auto sizes_itr = cudf::detail::make_counting_transform_iterator(
+    0, [d_strings = *d_strings, ngrams] __device__(auto idx) {
+      if (d_strings.is_null(idx)) { return 0; }
+      auto const length = d_strings.element<cudf::string_view>(idx).length();
+      return std::max(0, static_cast<cudf::size_type>(length + 1 - ngrams));
+    });
+  auto [offsets, total_ngrams] =
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + input.size(), stream, mr);
+  auto d_offsets = offsets->view().data<cudf::size_type>();
+
+  CUDF_EXPECTS(total_ngrams > 0,
+               "Insufficient number of characters in each string to generate ngrams");
+
+  // compute ngrams and build hashes
+  auto hashes =
+    cudf::make_numeric_column(output_type, total_ngrams, cudf::mask_state::UNALLOCATED, stream, mr);
+  auto d_hashes = hashes->mutable_view().data<cudf::hash_value_type>();
+
+  character_ngram_hash_fn generator{*d_strings, ngrams, d_offsets, d_hashes};
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::counting_iterator<cudf::size_type>(0),
+                     input.size(),
+                     generator);
+
+  return make_lists_column(
+    input.size(), std::move(offsets), std::move(hashes), 0, rmm::device_buffer{}, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> generate_character_ngrams(cudf::strings_column_view const& strings,
+                                                        cudf::size_type ngrams,
+                                                        rmm::cuda_stream_view stream,
+                                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::generate_character_ngrams(strings, ngrams, stream, mr);
+}
+
+std::unique_ptr<cudf::column> hash_character_ngrams(cudf::strings_column_view const& strings,
+                                                    cudf::size_type ngrams,
+                                                    rmm::cuda_stream_view stream,
+                                                    rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::hash_character_ngrams(strings, ngrams, stream, mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/jaccard.cu b/cpp/src/text/jaccard.cu
new file mode 100644
index 0000000..9532484
--- /dev/null
+++ b/cpp/src/text/jaccard.cu
@@ -0,0 +1,307 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/detail/generate_ngrams.hpp>
+#include <nvtext/jaccard.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <cub/cub.cuh>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Retrieve the row data (span) for the given column/row-index
+ *
+ * @param d_input Input lists column
+ * @param idx Row index to retrieve
+ * @return A device-span of the row values
+ */
+__device__ auto get_row(cudf::column_device_view const& d_input, cudf::size_type idx)
+{
+  auto const offsets =
+    d_input.child(cudf::lists_column_view::offsets_column_index).data<cudf::size_type>();
+  auto const offset = offsets[idx];
+  auto const size   = offsets[idx + 1] - offset;
+  auto const begin =
+    d_input.child(cudf::lists_column_view::child_column_index).data<uint32_t>() + offset;
+  return cudf::device_span<uint32_t const>(begin, size);
+}
+
+/**
+ * @brief Count the unique values within each row of the input column
+ *
+ * This is called with a warp per row
+ */
+struct sorted_unique_fn {
+  cudf::column_device_view const d_input;
+  cudf::size_type* d_results;
+
+  // warp per row
+  __device__ void operator()(cudf::size_type idx) const
+  {
+    using warp_reduce = cub::WarpReduce<cudf::size_type>;
+    __shared__ typename warp_reduce::TempStorage temp_storage;
+
+    auto const row_idx  = idx / cudf::detail::warp_size;
+    auto const lane_idx = idx % cudf::detail::warp_size;
+    auto const row      = get_row(d_input, row_idx);
+    auto const begin    = row.begin();
+
+    cudf::size_type count = 0;
+    for (auto itr = begin + lane_idx; itr < row.end(); itr += cudf::detail::warp_size) {
+      count += (itr == begin || *itr != *(itr - 1));
+    }
+    auto const result = warp_reduce(temp_storage).Sum(count);
+    if (lane_idx == 0) { d_results[row_idx] = result; }
+  }
+};
+
+rmm::device_uvector<cudf::size_type> compute_unique_counts(cudf::column_view const& input,
+                                                           rmm::cuda_stream_view stream)
+{
+  auto const d_input = cudf::column_device_view::create(input, stream);
+  auto d_results     = rmm::device_uvector<cudf::size_type>(input.size(), stream);
+  sorted_unique_fn fn{*d_input, d_results.data()};
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::counting_iterator<cudf::size_type>(0),
+                     input.size() * cudf::detail::warp_size,
+                     fn);
+  return d_results;
+}
+
+/**
+ * @brief Count the number of common values within each row of the 2 input columns
+ *
+ * This is called with a warp per row
+ */
+struct sorted_intersect_fn {
+  cudf::column_device_view const d_input1;
+  cudf::column_device_view const d_input2;
+  cudf::size_type* d_results;
+
+  // warp per row
+  __device__ float operator()(cudf::size_type idx) const
+  {
+    using warp_reduce = cub::WarpReduce<cudf::size_type>;
+    __shared__ typename warp_reduce::TempStorage temp_storage;
+
+    auto const row_idx  = idx / cudf::detail::warp_size;
+    auto const lane_idx = idx % cudf::detail::warp_size;
+
+    auto const needles  = get_row(d_input1, row_idx);
+    auto const haystack = get_row(d_input2, row_idx);
+
+    auto begin     = haystack.begin();
+    auto const end = haystack.end();
+
+    // TODO: investigate cuCollections device-side static-map to match row values
+
+    cudf::size_type count = 0;
+    for (auto itr = needles.begin() + lane_idx; itr < needles.end() && begin < end;
+         itr += cudf::detail::warp_size) {
+      if (itr != needles.begin() && *itr == *(itr - 1)) { continue; }  // skip duplicates
+      // search haystack for this needle (*itr)
+      auto const found = thrust::lower_bound(thrust::seq, begin, end, *itr);
+      count += (found != end) && (*found == *itr);  // increment if found;
+      begin = found;                                // shorten the next lower-bound range
+    }
+    // sum up the counts across this warp
+    auto const result = warp_reduce(temp_storage).Sum(count);
+    if (lane_idx == 0) { d_results[row_idx] = result; }
+  }
+};
+
+rmm::device_uvector<cudf::size_type> compute_intersect_counts(cudf::column_view const& input1,
+                                                              cudf::column_view const& input2,
+                                                              rmm::cuda_stream_view stream)
+{
+  auto const d_input1 = cudf::column_device_view::create(input1, stream);
+  auto const d_input2 = cudf::column_device_view::create(input2, stream);
+  auto d_results      = rmm::device_uvector<cudf::size_type>(input1.size(), stream);
+  sorted_intersect_fn fn{*d_input1, *d_input2, d_results.data()};
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::counting_iterator<cudf::size_type>(0),
+                     input1.size() * cudf::detail::warp_size,
+                     fn);
+  return d_results;
+}
+
+/**
+ * @brief Compute the jaccard distance for each row
+ *
+ * Formula is J = |A ∩ B| / |A ∪ B|
+ *              = |A ∩ B| / (|A| + |B| - |A ∩ B|)
+ *
+ * where |A ∩ B| is number of common values between A and B
+ * and |x| is the number of unique values in x.
+ */
+struct jaccard_fn {
+  cudf::size_type const* d_uniques1;
+  cudf::size_type const* d_uniques2;
+  cudf::size_type const* d_intersects;
+
+  __device__ float operator()(cudf::size_type idx) const
+  {
+    auto const count1     = d_uniques1[idx];
+    auto const count2     = d_uniques2[idx];
+    auto const intersects = d_intersects[idx];
+    // the intersect values are in both sets so a union count
+    // would need to subtract the intersect count from one set
+    // (see formula in comment above)
+    auto const unions = count1 + count2 - intersects;
+    return unions ? (static_cast<float>(intersects) / static_cast<float>(unions)) : 0.f;
+  }
+};
+
+/**
+ * @brief Create hashes for each substring
+ *
+ * Uses the hash_character_ngrams to hash substrings of the input column.
+ * This returns a lists column where each row is the hashes for the substrings
+ * of the corresponding input string row.
+ *
+ * The hashes are then sorted using a segmented-sort as setup to
+ * perform the unique and intersect operations.
+ */
+std::unique_ptr<cudf::column> hash_substrings(cudf::strings_column_view const& col,
+                                              cudf::size_type width,
+                                              rmm::cuda_stream_view stream)
+{
+  auto hashes = hash_character_ngrams(col, width, stream, rmm::mr::get_current_device_resource());
+  auto const input   = cudf::lists_column_view(hashes->view());
+  auto const offsets = input.offsets_begin();
+  auto const data    = input.child().data<uint32_t>();
+
+  rmm::device_uvector<uint32_t> sorted(input.child().size(), stream);
+
+  // this is wicked fast and much faster than using cudf::lists::detail::sort_list
+  rmm::device_buffer d_temp_storage;
+  size_t temp_storage_bytes = 0;
+  cub::DeviceSegmentedSort::SortKeys(d_temp_storage.data(),
+                                     temp_storage_bytes,
+                                     data,
+                                     sorted.data(),
+                                     sorted.size(),
+                                     input.size(),
+                                     offsets,
+                                     offsets + 1,
+                                     stream.value());
+  d_temp_storage = rmm::device_buffer{temp_storage_bytes, stream};
+  cub::DeviceSegmentedSort::SortKeys(d_temp_storage.data(),
+                                     temp_storage_bytes,
+                                     data,
+                                     sorted.data(),
+                                     sorted.size(),
+                                     input.size(),
+                                     offsets,
+                                     offsets + 1,
+                                     stream.value());
+
+  auto contents = hashes->release();
+  // the offsets are taken from the hashes column since they are the same
+  // before and after the segmented-sort
+  return cudf::make_lists_column(
+    col.size(),
+    std::move(contents.children.front()),
+    std::make_unique<cudf::column>(std::move(sorted), rmm::device_buffer{}, 0),
+    0,
+    rmm::device_buffer{},
+    stream,
+    rmm::mr::get_current_device_resource());
+}
+}  // namespace
+
+std::unique_ptr<cudf::column> jaccard_index(cudf::strings_column_view const& input1,
+                                            cudf::strings_column_view const& input2,
+                                            cudf::size_type width,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(
+    input1.size() == input2.size(), "input columns must be the same size", std::invalid_argument);
+  CUDF_EXPECTS(width >= 2,
+               "Parameter width should be an integer value of 2 or greater",
+               std::invalid_argument);
+
+  constexpr auto output_type = cudf::data_type{cudf::type_id::FLOAT32};
+  if (input1.is_empty()) { return cudf::make_empty_column(output_type); }
+
+  auto const [d_uniques1, d_uniques2, d_intersects] = [&] {
+    // build hashes of the substrings
+    auto const hash1 = hash_substrings(input1, width, stream);
+    auto const hash2 = hash_substrings(input2, width, stream);
+
+    // compute the unique counts in each set and the intersection counts
+    auto d_uniques1   = compute_unique_counts(hash1->view(), stream);
+    auto d_uniques2   = compute_unique_counts(hash2->view(), stream);
+    auto d_intersects = compute_intersect_counts(hash1->view(), hash2->view(), stream);
+
+    return std::tuple{std::move(d_uniques1), std::move(d_uniques2), std::move(d_intersects)};
+  }();
+
+  auto results = cudf::make_numeric_column(
+    output_type, input1.size(), cudf::mask_state::UNALLOCATED, stream, mr);
+  auto d_results = results->mutable_view().data<float>();
+
+  // compute the jaccard using the unique counts and the intersect counts
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::counting_iterator<cudf::size_type>(0),
+                    thrust::counting_iterator<cudf::size_type>(results->size()),
+                    d_results,
+                    jaccard_fn{d_uniques1.data(), d_uniques2.data(), d_intersects.data()});
+
+  if (input1.null_count() || input2.null_count()) {
+    auto [null_mask, null_count] =
+      cudf::detail::bitmask_and(cudf::table_view({input1.parent(), input2.parent()}), stream, mr);
+    results->set_null_mask(null_mask, null_count);
+  }
+
+  return results;
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> jaccard_index(cudf::strings_column_view const& input1,
+                                            cudf::strings_column_view const& input2,
+                                            cudf::size_type width,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::jaccard_index(input1, input2, width, stream, mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/minhash.cu b/cpp/src/text/minhash.cu
new file mode 100644
index 0000000..f06eaa5
--- /dev/null
+++ b/cpp/src/text/minhash.cu
@@ -0,0 +1,279 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/minhash.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/hashing/detail/murmurhash3_x64_128.cuh>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+
+#include <limits>
+
+#include <cuda/atomic>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Compute the minhash of each string for each seed
+ *
+ * This is a warp-per-string algorithm where parallel threads within a warp
+ * work on substrings of a single string row.
+ *
+ * @tparam HashFunction hash function to use on each substring
+ *
+ * @param d_strings Strings column to process
+ * @param seeds Seeds for hashing each string
+ * @param width Substring window size in characters
+ * @param d_hashes Minhash output values for each string
+ */
+template <
+  typename HashFunction,
+  typename hash_value_type = std::
+    conditional_t<std::is_same_v<typename HashFunction::result_type, uint32_t>, uint32_t, uint64_t>>
+__global__ void minhash_kernel(cudf::column_device_view const d_strings,
+                               cudf::device_span<hash_value_type const> seeds,
+                               cudf::size_type width,
+                               hash_value_type* d_hashes)
+{
+  auto const idx = static_cast<std::size_t>(threadIdx.x + blockIdx.x * blockDim.x);
+  if (idx >= (static_cast<std::size_t>(d_strings.size()) *
+              static_cast<std::size_t>(cudf::detail::warp_size))) {
+    return;
+  }
+
+  auto const str_idx  = static_cast<cudf::size_type>(idx / cudf::detail::warp_size);
+  auto const lane_idx = static_cast<cudf::size_type>(idx % cudf::detail::warp_size);
+
+  if (d_strings.is_null(str_idx)) { return; }
+
+  auto const d_str    = d_strings.element<cudf::string_view>(str_idx);
+  auto const d_output = d_hashes + (str_idx * seeds.size());
+
+  // initialize hashes output for this string
+  if (lane_idx == 0) {
+    auto const init = d_str.empty() ? 0 : std::numeric_limits<hash_value_type>::max();
+    thrust::fill(thrust::seq, d_output, d_output + seeds.size(), init);
+  }
+  __syncwarp();
+
+  auto const begin = d_str.data() + lane_idx;
+  auto const end   = d_str.data() + d_str.size_bytes();
+
+  // each lane hashes 'width' substrings of d_str
+  for (auto itr = begin; itr < end; itr += cudf::detail::warp_size) {
+    if (cudf::strings::detail::is_utf8_continuation_char(*itr)) { continue; }
+    auto const check_str =  // used for counting 'width' characters
+      cudf::string_view(itr, static_cast<cudf::size_type>(thrust::distance(itr, end)));
+    auto const [bytes, left] = cudf::strings::detail::bytes_to_character_position(check_str, width);
+    if ((itr != d_str.data()) && (left > 0)) { continue; }  // true if past the end of the string
+
+    auto const hash_str = cudf::string_view(itr, bytes);
+    // hashing with each seed on the same section of the string is 10x faster than
+    // computing the substrings for each seed
+    for (std::size_t seed_idx = 0; seed_idx < seeds.size(); ++seed_idx) {
+      auto const hasher = HashFunction(seeds[seed_idx]);
+      // hash substring and store the min value
+      if constexpr (std::is_same_v<hash_value_type, uint32_t>) {
+        auto const hvalue = hasher(hash_str);
+        cuda::atomic_ref<hash_value_type, cuda::thread_scope_block> ref{*(d_output + seed_idx)};
+        ref.fetch_min(hvalue, cuda::std::memory_order_relaxed);
+      } else {
+        // This code path assumes the use of MurmurHash3_x64_128 which produces 2 uint64 values
+        // but only uses the first uint64 value as requested by the LLM team.
+        auto const hvalue = thrust::get<0>(hasher(hash_str));
+        cuda::atomic_ref<hash_value_type, cuda::thread_scope_block> ref{*(d_output + seed_idx)};
+        ref.fetch_min(hvalue, cuda::std::memory_order_relaxed);
+      }
+    }
+  }
+}
+
+template <
+  typename HashFunction,
+  typename hash_value_type = std::
+    conditional_t<std::is_same_v<typename HashFunction::result_type, uint32_t>, uint32_t, uint64_t>>
+std::unique_ptr<cudf::column> minhash_fn(cudf::strings_column_view const& input,
+                                         cudf::device_span<hash_value_type const> seeds,
+                                         cudf::size_type width,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!seeds.empty(), "Parameter seeds cannot be empty", std::invalid_argument);
+  CUDF_EXPECTS(width >= 2,
+               "Parameter width should be an integer value of 2 or greater",
+               std::invalid_argument);
+  CUDF_EXPECTS((static_cast<std::size_t>(input.size()) * seeds.size()) <
+                 static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max()),
+               "The number of seeds times the number of input rows exceeds the column size limit",
+               std::overflow_error);
+
+  auto const output_type = cudf::data_type{cudf::type_to_id<hash_value_type>()};
+  if (input.is_empty()) { return cudf::make_empty_column(output_type); }
+
+  auto const d_strings = cudf::column_device_view::create(input.parent(), stream);
+
+  auto hashes   = cudf::make_numeric_column(output_type,
+                                          input.size() * static_cast<cudf::size_type>(seeds.size()),
+                                          cudf::mask_state::UNALLOCATED,
+                                          stream,
+                                          mr);
+  auto d_hashes = hashes->mutable_view().data<hash_value_type>();
+
+  constexpr int block_size = 256;
+  cudf::detail::grid_1d grid{input.size() * cudf::detail::warp_size, block_size};
+  minhash_kernel<HashFunction><<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+    *d_strings, seeds, width, d_hashes);
+
+  return hashes;
+}
+
+std::unique_ptr<cudf::column> build_list_result(cudf::strings_column_view const& input,
+                                                std::unique_ptr<cudf::column>&& hashes,
+                                                cudf::size_type seeds_size,
+                                                rmm::cuda_stream_view stream,
+                                                rmm::mr::device_memory_resource* mr)
+{
+  // build the offsets for the output lists column
+  auto const zero = cudf::numeric_scalar<cudf::size_type>(0);
+  auto const size = cudf::numeric_scalar<cudf::size_type>(seeds_size);
+  auto offsets    = cudf::detail::sequence(input.size() + 1, zero, size, stream, mr);
+  hashes->set_null_mask(rmm::device_buffer{}, 0);  // children have no nulls
+
+  // build the lists column from the offsets and the hashes
+  auto result = make_lists_column(input.size(),
+                                  std::move(offsets),
+                                  std::move(hashes),
+                                  input.null_count(),
+                                  cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                  stream,
+                                  mr);
+  // expect this condition to be very rare
+  if (input.null_count() > 0) {
+    result = cudf::detail::purge_nonempty_nulls(result->view(), stream, mr);
+  }
+  return result;
+}
+}  // namespace
+
+std::unique_ptr<cudf::column> minhash(cudf::strings_column_view const& input,
+                                      cudf::numeric_scalar<uint32_t> seed,
+                                      cudf::size_type width,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  using HashFunction = cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>;
+  auto const seeds   = cudf::device_span<uint32_t const>{seed.data(), 1};
+  auto hashes        = detail::minhash_fn<HashFunction>(input, seeds, width, stream, mr);
+  hashes->set_null_mask(cudf::detail::copy_bitmask(input.parent(), stream, mr), input.null_count());
+  return hashes;
+}
+
+std::unique_ptr<cudf::column> minhash(cudf::strings_column_view const& input,
+                                      cudf::device_span<uint32_t const> seeds,
+                                      cudf::size_type width,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  using HashFunction = cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>;
+  auto hashes        = detail::minhash_fn<HashFunction>(input, seeds, width, stream, mr);
+  return build_list_result(input, std::move(hashes), seeds.size(), stream, mr);
+}
+
+std::unique_ptr<cudf::column> minhash64(cudf::strings_column_view const& input,
+                                        cudf::numeric_scalar<uint64_t> seed,
+                                        cudf::size_type width,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  using HashFunction = cudf::hashing::detail::MurmurHash3_x64_128<cudf::string_view>;
+  auto const seeds   = cudf::device_span<uint64_t const>{seed.data(), 1};
+  auto hashes        = detail::minhash_fn<HashFunction>(input, seeds, width, stream, mr);
+  hashes->set_null_mask(cudf::detail::copy_bitmask(input.parent(), stream, mr), input.null_count());
+  return hashes;
+}
+
+std::unique_ptr<cudf::column> minhash64(cudf::strings_column_view const& input,
+                                        cudf::device_span<uint64_t const> seeds,
+                                        cudf::size_type width,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  using HashFunction = cudf::hashing::detail::MurmurHash3_x64_128<cudf::string_view>;
+  auto hashes        = detail::minhash_fn<HashFunction>(input, seeds, width, stream, mr);
+  return build_list_result(input, std::move(hashes), seeds.size(), stream, mr);
+}
+}  // namespace detail
+
+std::unique_ptr<cudf::column> minhash(cudf::strings_column_view const& input,
+                                      cudf::numeric_scalar<uint32_t> seed,
+                                      cudf::size_type width,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::minhash(input, seed, width, stream, mr);
+}
+
+std::unique_ptr<cudf::column> minhash(cudf::strings_column_view const& input,
+                                      cudf::device_span<uint32_t const> seeds,
+                                      cudf::size_type width,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::minhash(input, seeds, width, stream, mr);
+}
+
+std::unique_ptr<cudf::column> minhash64(cudf::strings_column_view const& input,
+                                        cudf::numeric_scalar<uint64_t> seed,
+                                        cudf::size_type width,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::minhash64(input, seed, width, stream, mr);
+}
+
+std::unique_ptr<cudf::column> minhash64(cudf::strings_column_view const& input,
+                                        cudf::device_span<uint64_t const> seeds,
+                                        cudf::size_type width,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::minhash64(input, seeds, width, stream, mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/ngrams_tokenize.cu b/cpp/src/text/ngrams_tokenize.cu
new file mode 100644
index 0000000..73d8551
--- /dev/null
+++ b/cpp/src/text/ngrams_tokenize.cu
@@ -0,0 +1,275 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/utilities/tokenize_ops.cuh>
+
+#include <nvtext/detail/tokenize.hpp>
+#include <nvtext/ngrams_tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+#include <stdexcept>
+
+namespace nvtext {
+namespace detail {
+namespace {
+/**
+ * @brief This records the byte positions of each token within each string.
+ *
+ * The position values are recorded since we need to reference tokens
+ * within a string multiple times to generate the ngrams. For example,
+ * to generate tri-grams for string "aa b ccc dd" requires creating
+ * the following two strings ["aa_b_ccc","b_ccc_dd"]. Notice the
+ * tokens "b" and "ccc" needed to be copied twice for this string.
+ *
+ * Most of the work is done in the characters_tokenizer locating the tokens.
+ * This functor simply records the byte positions in the d_token_positions
+ * member.
+ */
+struct string_tokens_positions_fn {
+  cudf::column_device_view const d_strings;  // strings to tokenize
+  cudf::string_view const d_delimiter;       // delimiter to tokenize around
+  cudf::size_type const* d_token_offsets;    // offsets into the d_token_positions for each string
+  position_pair* d_token_positions;          // token positions in each string
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) return;
+    cudf::string_view d_str = d_strings.element<cudf::string_view>(idx);
+    // create tokenizer for this string
+    characters_tokenizer tokenizer(d_str, d_delimiter);
+    // record the token positions for this string
+    cudf::size_type token_index = 0;
+    auto token_positions        = d_token_positions + d_token_offsets[idx];
+    while (tokenizer.next_token())
+      token_positions[token_index++] = tokenizer.token_byte_positions();
+  }
+};
+
+/**
+ * @brief Generate the ngrams for each string.
+ *
+ * The ngrams for each string are placed contiguously within the section of memory
+ * assigned for the input string. At the same time, the size of each ngram is recorded
+ * in order to build the output offsets column.
+ *
+ * This functor can be called to compute the size of memory needed to write out
+ * each set of ngrams per string. Once the memory offsets (d_chars_offsets) are
+ * set and the output memory is allocated (d_chars), the ngrams for each string
+ * can be generated into the output buffer.
+ */
+struct ngram_builder_fn {
+  cudf::column_device_view const d_strings;  // strings to generate ngrams from
+  cudf::string_view const d_separator;       // separator to place between them 'grams
+  cudf::size_type const ngrams;              // ngram number to generate (2=bi-gram, 3=tri-gram)
+  cudf::size_type const* d_token_offsets;    // offsets for token position for each string
+  position_pair const* d_token_positions;    // token positions for each string
+  cudf::size_type const* d_chars_offsets{};  // offsets for each string's ngrams
+  char* d_chars{};                           // write ngram strings to here
+  cudf::size_type const* d_ngram_offsets{};  // offsets for sizes of each string's ngrams
+  cudf::size_type* d_ngram_sizes{};          // write ngram sizes to here
+
+  __device__ cudf::size_type operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) { return 0; }
+    auto const d_str            = d_strings.element<cudf::string_view>(idx);
+    auto const token_positions  = d_token_positions + d_token_offsets[idx];
+    auto const token_count      = d_token_offsets[idx + 1] - d_token_offsets[idx];
+    cudf::size_type nbytes      = 0;  // total number of output bytes needed for this string
+    cudf::size_type ngram_index = 0;
+    auto out_ptr                = d_chars ? d_chars + d_chars_offsets[idx] : nullptr;
+    auto d_sizes                = d_ngram_sizes ? d_ngram_sizes + d_ngram_offsets[idx] : nullptr;
+    // for ngrams=2, this will turn string "a b c d e" into "a_bb_cc_dd_e"
+    for (cudf::size_type token_index = (ngrams - 1); token_index < token_count; ++token_index) {
+      cudf::size_type length = 0;                          // calculate size of each ngram in bytes
+      for (cudf::size_type n = (ngrams - 1); n >= 0; --n)  // sliding window of tokens
+      {
+        auto const item = token_positions[token_index - n];
+        length += item.second - item.first;
+        if (out_ptr) {
+          out_ptr = cudf::strings::detail::copy_and_increment(
+            out_ptr, d_str.data() + item.first, item.second - item.first);
+        }
+        if (n > 0) {  // include the separator (except for the last one)
+          if (out_ptr) { out_ptr = cudf::strings::detail::copy_string(out_ptr, d_separator); }
+          length += d_separator.size_bytes();
+        }
+      }
+      if (d_sizes) { d_sizes[ngram_index++] = length; }
+      nbytes += length;
+    }
+    return nbytes;
+  }
+};
+
+}  // namespace
+
+// detail APIs
+
+std::unique_ptr<cudf::column> ngrams_tokenize(cudf::strings_column_view const& strings,
+                                              cudf::size_type ngrams,
+                                              cudf::string_scalar const& delimiter,
+                                              cudf::string_scalar const& separator,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+  cudf::string_view d_delimiter(delimiter.data(), delimiter.size());
+  CUDF_EXPECTS(separator.is_valid(stream), "Parameter separator must be valid");
+  cudf::string_view d_separator(separator.data(), separator.size());
+
+  CUDF_EXPECTS(ngrams >= 1, "Parameter ngrams should be an integer value of 1 or greater");
+  if (ngrams == 1)  // this is just a straight tokenize
+    return tokenize(strings, delimiter, stream, mr);
+  auto strings_count = strings.size();
+  if (strings.is_empty()) return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  auto d_strings      = *strings_column;
+
+  // Example for comments with ngrams=2
+  // ["a bb ccc","dd e"] => ["a_bb", "bb_ccc", "dd_e"]
+
+  // first, get the number of tokens per string to get the token-offsets
+  // Ex. token-counts = [3,2]; token-offsets = [0,3,5]
+  rmm::device_uvector<cudf::size_type> token_offsets(strings_count + 1, stream);
+  auto d_token_offsets = token_offsets.data();
+  thrust::transform_inclusive_scan(rmm::exec_policy(stream),
+                                   thrust::make_counting_iterator<cudf::size_type>(0),
+                                   thrust::make_counting_iterator<cudf::size_type>(strings_count),
+                                   d_token_offsets + 1,
+                                   strings_tokenizer{d_strings, d_delimiter},
+                                   thrust::plus<cudf::size_type>());
+  token_offsets.set_element_to_zero_async(0, stream);
+  auto const total_tokens = token_offsets.back_element(stream);  // Ex. 5 tokens
+
+  // get the token positions (in bytes) per string
+  // Ex. start/end pairs: [(0,1),(2,4),(5,8), (0,2),(3,4)]
+  rmm::device_uvector<position_pair> token_positions(total_tokens, stream);
+  auto d_token_positions = token_positions.data();
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    strings_count,
+    string_tokens_positions_fn{d_strings, d_delimiter, d_token_offsets, d_token_positions});
+
+  // compute the number of ngrams per string to get the total number of ngrams to generate
+  // Ex. ngram-counts = [2,1]; ngram-offsets = [0,2,3]; total = 3 bigrams
+  rmm::device_uvector<cudf::size_type> ngram_offsets(strings_count + 1, stream);
+  auto d_ngram_offsets = ngram_offsets.data();
+  thrust::transform_inclusive_scan(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    thrust::make_counting_iterator<cudf::size_type>(strings_count),
+    d_ngram_offsets + 1,
+    [d_token_offsets, ngrams] __device__(cudf::size_type idx) {
+      auto token_count = d_token_offsets[idx + 1] - d_token_offsets[idx];
+      return (token_count >= ngrams) ? token_count - ngrams + 1 : 0;
+    },
+    thrust::plus{});
+  ngram_offsets.set_element_to_zero_async(0, stream);
+  auto const total_ngrams = ngram_offsets.back_element(stream);
+
+  // Compute the total size of the ngrams for each string (not for each ngram)
+  // Ex. 2 bigrams in 1st string total to 10 bytes; 1 bigram in 2nd string is 4 bytes
+  //     => sizes = [10,4]; offsets = [0,10,14]
+  //
+  // This produces a set of offsets for the output memory where we can build adjacent
+  // ngrams for each string.
+  // Ex. bigram for first string produces 2 bigrams ("a_bb","bb_ccc") which
+  //     is built in memory like this: "a_bbbb_ccc"
+  rmm::device_uvector<cudf::size_type> chars_offsets(strings_count + 1, stream);
+  // First compute the output sizes for each string (this not the final output result)
+  thrust::transform(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    thrust::make_counting_iterator<cudf::size_type>(strings_count),
+    chars_offsets.begin(),
+    ngram_builder_fn{d_strings, d_separator, ngrams, d_token_offsets, d_token_positions});
+  // Convert the sizes to offsets
+  auto const output_chars_size = cudf::detail::sizes_to_offsets(
+    chars_offsets.begin(), chars_offsets.end(), chars_offsets.begin(), stream);
+  CUDF_EXPECTS(
+    output_chars_size <= static_cast<int64_t>(std::numeric_limits<cudf::size_type>::max()),
+    "Size of output exceeds the column size limit",
+    std::overflow_error);
+
+  // This will contain the size in bytes of each ngram to generate
+  rmm::device_uvector<cudf::size_type> ngram_sizes(total_ngrams, stream);
+
+  // build output chars column
+  auto chars_column = cudf::strings::detail::create_chars_child_column(
+    static_cast<cudf::size_type>(output_chars_size), stream, mr);
+  auto d_chars = chars_column->mutable_view().data<char>();
+  // Generate the ngrams into the chars column data buffer.
+  // The ngram_builder_fn functor also fills the ngram_sizes vector with the
+  // size of each ngram.
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<cudf::size_type>(0),
+                     strings_count,
+                     ngram_builder_fn{d_strings,
+                                      d_separator,
+                                      ngrams,
+                                      d_token_offsets,
+                                      d_token_positions,
+                                      chars_offsets.data(),
+                                      d_chars,
+                                      d_ngram_offsets,
+                                      ngram_sizes.data()});
+  // build the offsets column -- converting the ngram sizes into offsets
+  auto offsets_column = std::get<0>(
+    cudf::detail::make_offsets_child_column(ngram_sizes.begin(), ngram_sizes.end(), stream, mr));
+  chars_column->set_null_count(0);
+  offsets_column->set_null_count(0);
+  // create the output strings column
+  return make_strings_column(
+    total_ngrams, std::move(offsets_column), std::move(chars_column), 0, rmm::device_buffer{});
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<cudf::column> ngrams_tokenize(cudf::strings_column_view const& strings,
+                                              cudf::size_type ngrams,
+                                              cudf::string_scalar const& delimiter,
+                                              cudf::string_scalar const& separator,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::ngrams_tokenize(strings, ngrams, delimiter, separator, stream, mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/normalize.cu b/cpp/src/text/normalize.cu
new file mode 100644
index 0000000..1b07b07
--- /dev/null
+++ b/cpp/src/text/normalize.cu
@@ -0,0 +1,263 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/subword/detail/data_normalizer.hpp>
+#include <text/subword/detail/tokenizer_utils.cuh>
+#include <text/utilities/tokenize_ops.cuh>
+
+#include <nvtext/normalize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/transform_reduce.h>
+
+#include <limits>
+
+namespace nvtext {
+namespace detail {
+namespace {
+/**
+ * @brief Normalize spaces in a strings column.
+ *
+ * Repeated whitespace (code-point <= ' ') is replaced with a single space.
+ * Also, whitespace is trimmed from the beginning and end of each string.
+ *
+ * This functor can be called to compute the output size in bytes
+ * of each string and then called again to fill in the allocated buffer.
+ */
+struct normalize_spaces_fn {
+  cudf::column_device_view const d_strings;  // strings to normalize
+  cudf::size_type* d_offsets{};              // offsets into d_chars
+  char* d_chars{};                           // output buffer for characters
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    cudf::string_view const single_space(" ", 1);
+    auto const d_str = d_strings.element<cudf::string_view>(idx);
+    char* buffer     = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    char* optr       = buffer;  // running output pointer
+
+    cudf::size_type nbytes = 0;  // holds the number of bytes per output string
+
+    // create a tokenizer for this string with whitespace delimiter (default)
+    characters_tokenizer tokenizer(d_str);
+
+    // this will retrieve tokens automatically skipping runs of whitespace
+    while (tokenizer.next_token()) {
+      auto const token_pos = tokenizer.token_byte_positions();
+      auto const token =
+        cudf::string_view(d_str.data() + token_pos.first, token_pos.second - token_pos.first);
+      if (optr) {
+        // prepend space unless we are at the beginning
+        if (optr != buffer) { optr = cudf::strings::detail::copy_string(optr, single_space); }
+        // write token to output buffer
+        thrust::copy_n(thrust::seq, token.data(), token.size_bytes(), optr);
+        optr += token.size_bytes();
+      }
+      nbytes += token.size_bytes() + 1;  // token size plus a single space
+    }
+    // remove trailing space
+    if (!d_chars) d_offsets[idx] = (nbytes > 0) ? nbytes - 1 : 0;
+  }
+};
+
+// code-point to multi-byte range limits
+constexpr uint32_t UTF8_1BYTE = 0x0080;
+constexpr uint32_t UTF8_2BYTE = 0x0800;
+constexpr uint32_t UTF8_3BYTE = 0x01'0000;
+
+/**
+ * @brief Convert code-point arrays into UTF-8 bytes for each string.
+ */
+struct codepoint_to_utf8_fn {
+  cudf::column_device_view const d_strings;  // input strings
+  uint32_t const* cp_data;                   // full code-point array
+  cudf::size_type const* d_cp_offsets{};     // offsets to each string's code-point array
+  cudf::size_type* d_offsets{};              // offsets for the output strings
+  char* d_chars{};                           // buffer for the output strings column
+
+  /**
+   * @brief Return the number of bytes for the output string given its code-point array.
+   *
+   * @param str_cps code-points for the string
+   * @param count number of code-points in `str_cps`
+   * @return Number of bytes required for the output
+   */
+  __device__ cudf::size_type compute_output_size(uint32_t const* str_cps, uint32_t count)
+  {
+    return thrust::transform_reduce(
+      thrust::seq,
+      str_cps,
+      str_cps + count,
+      [](auto cp) { return 1 + (cp >= UTF8_1BYTE) + (cp >= UTF8_2BYTE) + (cp >= UTF8_3BYTE); },
+      0,
+      thrust::plus());
+  }
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+    auto const offset = d_cp_offsets[idx];
+    auto const count  = d_cp_offsets[idx + 1] - offset;  // number of code-points
+    auto str_cps      = cp_data + offset;                // code-points for this string
+    if (!d_chars) {
+      d_offsets[idx] = compute_output_size(str_cps, count);
+      return;
+    }
+    // convert each code-point to 1-4 UTF-8 encoded bytes
+    char* out_ptr = d_chars + d_offsets[idx];
+    for (uint32_t jdx = 0; jdx < count; ++jdx) {
+      uint32_t code_point = *str_cps++;
+      if (code_point < UTF8_1BYTE)  // ASCII range
+        *out_ptr++ = static_cast<char>(code_point);
+      else if (code_point < UTF8_2BYTE) {  // create two-byte UTF-8
+        // b00001xxx:byyyyyyyy => b110xxxyy:b10yyyyyy
+        *out_ptr++ = static_cast<char>((((code_point << 2) & 0x00'1F00) | 0x00'C000) >> 8);
+        *out_ptr++ = static_cast<char>((code_point & 0x3F) | 0x0080);
+      } else if (code_point < UTF8_3BYTE) {  // create three-byte UTF-8
+        // bxxxxxxxx:byyyyyyyy => b1110xxxx:b10xxxxyy:b10yyyyyy
+        *out_ptr++ = static_cast<char>((((code_point << 4) & 0x0F'0000) | 0x00E0'0000) >> 16);
+        *out_ptr++ = static_cast<char>((((code_point << 2) & 0x00'3F00) | 0x00'8000) >> 8);
+        *out_ptr++ = static_cast<char>((code_point & 0x3F) | 0x0080);
+      } else {  // create four-byte UTF-8
+        // maximum code-point value is 0x0011'0000
+        // b000xxxxx:byyyyyyyy:bzzzzzzzz => b11110xxx:b10xxyyyy:b10yyyyzz:b10zzzzzz
+        *out_ptr++ = static_cast<char>((((code_point << 6) & 0x0700'0000u) | 0xF000'0000u) >> 24);
+        *out_ptr++ = static_cast<char>((((code_point << 4) & 0x003F'0000u) | 0x0080'0000u) >> 16);
+        *out_ptr++ = static_cast<char>((((code_point << 2) & 0x00'3F00u) | 0x00'8000u) >> 8);
+        *out_ptr++ = static_cast<char>((code_point & 0x3F) | 0x0080);
+      }
+    }
+  }
+};
+
+}  // namespace
+
+// detail API
+std::unique_ptr<cudf::column> normalize_spaces(cudf::strings_column_view const& strings,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  // create device column
+  auto d_strings = cudf::column_device_view::create(strings.parent(), stream);
+
+  // build offsets and children using the normalize_space_fn
+  auto children = cudf::strings::detail::make_strings_children(
+    normalize_spaces_fn{*d_strings}, strings.size(), stream, mr);
+
+  return cudf::make_strings_column(strings.size(),
+                                   std::move(children.first),
+                                   std::move(children.second),
+                                   strings.null_count(),
+                                   cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+/**
+ * @copydoc nvtext::normalize_characters
+ */
+std::unique_ptr<cudf::column> normalize_characters(cudf::strings_column_view const& strings,
+                                                   bool do_lower_case,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  // create the normalizer and call it
+  auto result = [&] {
+    auto const cp_metadata = get_codepoint_metadata(stream);
+    auto const aux_table   = get_aux_codepoint_data(stream);
+    auto const normalizer  = data_normalizer(cp_metadata.data(), aux_table.data(), do_lower_case);
+    auto const offsets     = strings.offsets();
+    auto const d_offsets   = offsets.data<cudf::size_type>() + strings.offset();
+    auto const offset = cudf::detail::get_value<cudf::size_type>(offsets, strings.offset(), stream);
+    auto const d_chars = strings.chars().data<char>() + offset;
+    return normalizer.normalize(d_chars, d_offsets, strings.size(), stream);
+  }();
+
+  CUDF_EXPECTS(
+    result.first->size() < static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max()),
+    "output exceeds the column size limit",
+    std::overflow_error);
+
+  // convert the result into a strings column
+  // - the cp_chars are the new 4-byte code-point values for all the characters in the output
+  // - the cp_offsets identify which code-points go with which strings
+  uint32_t const* cp_chars          = result.first->data();
+  cudf::size_type const* cp_offsets = result.second->data();
+
+  auto d_strings = cudf::column_device_view::create(strings.parent(), stream);
+
+  // build offsets and children using the codepoint_to_utf8_fn
+  auto children = cudf::strings::detail::make_strings_children(
+    codepoint_to_utf8_fn{*d_strings, cp_chars, cp_offsets}, strings.size(), stream, mr);
+
+  return cudf::make_strings_column(strings.size(),
+                                   std::move(children.first),
+                                   std::move(children.second),
+                                   strings.null_count(),
+                                   cudf::detail::copy_bitmask(strings.parent(), stream, mr));
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<cudf::column> normalize_spaces(cudf::strings_column_view const& strings,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::normalize_spaces(strings, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc nvtext::normalize_characters
+ */
+std::unique_ptr<cudf::column> normalize_characters(cudf::strings_column_view const& strings,
+                                                   bool do_lower_case,
+                                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::normalize_characters(strings, do_lower_case, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/replace.cu b/cpp/src/text/replace.cu
new file mode 100644
index 0000000..34916e1
--- /dev/null
+++ b/cpp/src/text/replace.cu
@@ -0,0 +1,299 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/utilities/tokenize_ops.cuh>
+
+#include <nvtext/detail/tokenize.hpp>
+#include <nvtext/replace.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/pair.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+using replace_result = thrust::pair<bool, cudf::string_view>;
+
+struct base_token_replacer_fn {
+  cudf::column_device_view const d_strings;  ///< strings to tokenize
+  cudf::string_view const d_delimiter;       ///< delimiter characters for tokenizing
+  cudf::size_type* d_offsets{};              ///< for locating output string in d_chars
+  char* d_chars{};                           ///< output buffer
+
+  /**
+   * @brief Tokenizes each string and calls the provided `replacer` function
+   * for each token.
+   *
+   * @tparam ReplaceFn Should accept a `string_view` and return a `replace_result`
+   * @param idx Index of the current string to process
+   * @param replacer Function to call for each token to determined its replacement
+   */
+  template <typename ReplaceFn>
+  __device__ void process_string(cudf::size_type idx, ReplaceFn replacer)
+  {
+    if (d_strings.is_null(idx)) {
+      if (!d_chars) d_offsets[idx] = 0;
+      return;
+    }
+
+    auto const d_str  = d_strings.element<cudf::string_view>(idx);
+    auto const in_ptr = d_str.data();
+    auto out_ptr      = d_chars ? d_chars + d_offsets[idx] : nullptr;
+    auto nbytes       = d_str.size_bytes();  // count the output bytes
+    auto last_pos     = cudf::size_type{0};
+    auto tokenizer    = characters_tokenizer{d_str, d_delimiter};
+    // process each token
+    while (tokenizer.next_token()) {
+      auto const token_pos = tokenizer.token_byte_positions();
+      auto const token =
+        cudf::string_view{d_str.data() + token_pos.first, token_pos.second - token_pos.first};
+      // ask replacer if this token should be replaced
+      auto const result = replacer(token);
+      if (result.first) {  // first == replace indicator, second == new string
+        auto d_replacement = result.second;
+        nbytes += d_replacement.size_bytes() - token.size_bytes();
+        if (out_ptr) {
+          // copy over string up to the token location
+          out_ptr = cudf::strings::detail::copy_and_increment(
+            out_ptr, in_ptr + last_pos, token_pos.first - last_pos);
+          // copy over replacement string
+          out_ptr  = cudf::strings::detail::copy_string(out_ptr, d_replacement);
+          last_pos = token_pos.second;  // update last byte position for this string
+        }
+      }
+    }
+
+    // copy the remainder of the string's bytes to the output buffer
+    if (out_ptr)
+      memcpy(out_ptr, in_ptr + last_pos, d_str.size_bytes() - last_pos);
+    else
+      d_offsets[idx] = nbytes;
+  }
+};
+
+using strings_iterator = cudf::column_device_view::const_iterator<cudf::string_view>;
+
+/**
+ * @brief Functor to replace tokens in each string.
+ *
+ * This tokenizes a string using the given d_delimiter and replaces any tokens that match
+ * a string in d_targets_begin/end with those from the d_replacements column.
+ * Strings with no matching tokens are left unchanged.
+ *
+ * This should be called first to compute the size of each output string and then a second
+ * time to fill in the allocated output buffer for each string.
+ */
+struct replace_tokens_fn : base_token_replacer_fn {
+  strings_iterator d_targets_begin;  ///< strings to search for
+  strings_iterator d_targets_end;
+  cudf::column_device_view const d_replacements;  ///< replacement strings
+
+  replace_tokens_fn(cudf::column_device_view const& d_strings,
+                    cudf::string_view const& d_delimiter,
+                    strings_iterator d_targets_begin,
+                    strings_iterator d_targets_end,
+                    cudf::column_device_view const& d_replacements)
+    : base_token_replacer_fn{d_strings, d_delimiter},
+      d_targets_begin{d_targets_begin},
+      d_targets_end{d_targets_end},
+      d_replacements{d_replacements}
+  {
+  }
+
+  /**
+   * @brief Return replacement string for the given token.
+   *
+   * @param token Token candidate to be replaced.
+   * @return result pair specifies replacement condition and new string
+   */
+  __device__ replace_result token_replacement(cudf::string_view const& token)
+  {
+    // check if the token matches any of the targets
+    auto const found_itr = thrust::find(thrust::seq, d_targets_begin, d_targets_end, token);
+    if (found_itr != d_targets_end) {  // match found
+      // retrieve the corresponding replacement string or
+      // if only one repl string, use that one for all targets
+      auto const d_repl = [&] {
+        auto const repl_idx = thrust::distance(d_targets_begin, found_itr);
+        return d_replacements.size() == 1 ? d_replacements.element<cudf::string_view>(0)
+                                          : d_replacements.element<cudf::string_view>(repl_idx);
+      }();
+      return replace_result{true, d_repl};
+    }
+    // otherwise, do not replace this token
+    return replace_result{false, cudf::string_view()};
+  }
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    process_string(
+      idx, [this] __device__(cudf::string_view const& token) { return token_replacement(token); });
+  }
+};
+
+/**
+ * @brief Functor to filter tokens in each string.
+ *
+ * This tokenizes a string using the given d_delimiter and replaces any tokens
+ * that are shorter than min_token_length with a replacement string.
+ *
+ * This should be called first to compute the size of each output string and then
+ * a second time to fill in the allocated output buffer for each string.
+ */
+struct remove_small_tokens_fn : base_token_replacer_fn {
+  cudf::size_type min_token_length;       ///< minimum size for found tokens
+  cudf::string_view const d_replacement;  ///< replacement string
+
+  remove_small_tokens_fn(cudf::column_device_view const& d_strings,
+                         cudf::string_view const& d_delimiter,
+                         cudf::size_type min_token_length,
+                         cudf::string_view const& d_replacement)
+    : base_token_replacer_fn{d_strings, d_delimiter},
+      min_token_length{min_token_length},
+      d_replacement{d_replacement}
+  {
+  }
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    auto replacer = [this] __device__(cudf::string_view const& token) {
+      return replace_result{token.length() < min_token_length, d_replacement};
+    };
+    process_string(idx, replacer);
+  }
+};
+
+}  // namespace
+
+// detail APIs
+
+std::unique_ptr<cudf::column> replace_tokens(cudf::strings_column_view const& strings,
+                                             cudf::strings_column_view const& targets,
+                                             cudf::strings_column_view const& replacements,
+                                             cudf::string_scalar const& delimiter,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(!targets.has_nulls(), "Parameter targets must not have nulls");
+  CUDF_EXPECTS(!replacements.has_nulls(), "Parameter replacements must not have nulls");
+  if (replacements.size() != 1)
+    CUDF_EXPECTS(replacements.size() == targets.size(),
+                 "Parameter targets and replacements must be the same size");
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+
+  cudf::size_type const strings_count = strings.size();
+  if (strings_count == 0) return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto strings_column      = cudf::column_device_view::create(strings.parent(), stream);
+  auto targets_column      = cudf::column_device_view::create(targets.parent(), stream);
+  auto replacements_column = cudf::column_device_view::create(replacements.parent(), stream);
+  cudf::string_view d_delimiter(delimiter.data(), delimiter.size());
+  replace_tokens_fn replacer{*strings_column,
+                             d_delimiter,
+                             targets_column->begin<cudf::string_view>(),
+                             targets_column->end<cudf::string_view>(),
+                             *replacements_column};
+
+  // copy null mask from input column
+  rmm::device_buffer null_mask = cudf::detail::copy_bitmask(strings.parent(), stream, mr);
+
+  // this utility calls replacer to build the offsets and chars columns
+  auto children = cudf::strings::detail::make_strings_children(replacer, strings_count, stream, mr);
+
+  // return new strings column
+  return cudf::make_strings_column(strings_count,
+                                   std::move(children.first),
+                                   std::move(children.second),
+                                   strings.null_count(),
+                                   std::move(null_mask));
+}
+
+std::unique_ptr<cudf::column> filter_tokens(cudf::strings_column_view const& strings,
+                                            cudf::size_type min_token_length,
+                                            cudf::string_scalar const& replacement,
+                                            cudf::string_scalar const& delimiter,
+                                            rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(replacement.is_valid(stream), "Parameter replacement must be valid");
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+
+  cudf::size_type const strings_count = strings.size();
+  if (strings_count == 0) return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  cudf::string_view d_replacement(replacement.data(), replacement.size());
+  cudf::string_view d_delimiter(delimiter.data(), delimiter.size());
+  remove_small_tokens_fn filterer{*strings_column, d_delimiter, min_token_length, d_replacement};
+
+  // copy null mask from input column
+  rmm::device_buffer null_mask = cudf::detail::copy_bitmask(strings.parent(), stream, mr);
+
+  // this utility calls filterer to build the offsets and chars columns
+  auto children = cudf::strings::detail::make_strings_children(filterer, strings_count, stream, mr);
+
+  // return new strings column
+  return cudf::make_strings_column(strings_count,
+                                   std::move(children.first),
+                                   std::move(children.second),
+                                   strings.null_count(),
+                                   std::move(null_mask));
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<cudf::column> replace_tokens(cudf::strings_column_view const& strings,
+                                             cudf::strings_column_view const& targets,
+                                             cudf::strings_column_view const& replacements,
+                                             cudf::string_scalar const& delimiter,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::replace_tokens(
+    strings, targets, replacements, delimiter, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> filter_tokens(cudf::strings_column_view const& strings,
+                                            cudf::size_type min_token_length,
+                                            cudf::string_scalar const& replacement,
+                                            cudf::string_scalar const& delimiter,
+                                            rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::filter_tokens(
+    strings, min_token_length, replacement, delimiter, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/stemmer.cu b/cpp/src/text/stemmer.cu
new file mode 100644
index 0000000..2b2b842
--- /dev/null
+++ b/cpp/src/text/stemmer.cu
@@ -0,0 +1,285 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/stemmer.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Return true if character at current iterator position
+ * is a consonant.
+ *
+ * A consonant is a letter other than a, e, i, o or u, and other
+ * than y preceded by a consonant.
+ *
+ * For `toy` the consonants are `t` and `y`, and in `syzygy` they
+ * are `s`, `z` and `g`.
+ *
+ * A _vowel_ is defined as _not a consonant_.
+ *
+ * @param string_iterator Iterator positioned to the character to check.
+ * @return True if the character at the iterator is a consonant.
+ */
+__device__ bool is_consonant(cudf::string_view::const_iterator string_iterator)
+{
+  auto ch = *string_iterator;
+  cudf::string_view const d_vowels("aeiou", 5);
+  if (d_vowels.find(ch) != cudf::string_view::npos) return false;
+  if ((ch != 'y') || (string_iterator.position() == 0)) return true;
+  // for 'y' case, check previous character is a consonant
+  --string_iterator;
+  return d_vowels.find(*string_iterator) != cudf::string_view::npos;
+}
+
+/**
+ * @brief Functor for the detail::is_letter_fn called to return true/false
+ * indicating the specified character is a consonant or a vowel.
+ */
+template <typename PositionIterator>
+struct is_letter_fn {
+  cudf::column_device_view const d_strings;
+  letter_type ltype;
+  PositionIterator position_itr;
+
+  __device__ bool operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) return false;
+    auto const d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) return false;
+    auto const position = position_itr[idx];
+    auto const length   = d_str.length();
+    if ((position >= length) || (position < -length)) return false;
+    return is_consonant(d_str.begin() + ((position + length) % length))
+             ? ltype == letter_type::CONSONANT
+             : ltype == letter_type::VOWEL;
+  }
+};
+
+}  // namespace
+
+// details API
+
+template <typename PositionIterator>
+std::unique_ptr<cudf::column> is_letter(cudf::strings_column_view const& strings,
+                                        letter_type ltype,
+                                        PositionIterator position_itr,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) return cudf::make_empty_column(cudf::data_type{cudf::type_id::BOOL8});
+
+  // create empty output column
+  auto results =
+    cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::BOOL8},
+                                  strings.size(),
+                                  cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                  strings.null_count(),
+                                  stream,
+                                  mr);
+  // set values into output column
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(strings.size()),
+                    results->mutable_view().data<bool>(),
+                    is_letter_fn<PositionIterator>{*strings_column, ltype, position_itr});
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+namespace {
+
+/**
+ * @brief For dispatching index-type of indices parameter in the nvtext::is_letter API.
+ */
+struct dispatch_is_letter_fn {
+  template <typename T, std::enable_if_t<cudf::is_index_type<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::strings_column_view const& strings,
+                                           letter_type ltype,
+                                           cudf::column_view const& indices,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr) const
+  {
+    CUDF_EXPECTS(strings.size() == indices.size(),
+                 "strings column and indices column must be the same size");
+    CUDF_EXPECTS(!indices.has_nulls(), "indices column must not contain nulls");
+    // resolve and pass an iterator for the indices column to the detail function
+    return is_letter(strings, ltype, indices.begin<T>(), stream, mr);
+  }
+
+  template <typename T, typename... Args, std::enable_if_t<not cudf::is_index_type<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(Args&&...) const
+  {
+    CUDF_FAIL("The is_letter indices parameter must be an integer type.");
+  }
+};
+
+/**
+ * @brief Returns the measure for each string.
+ *
+ * Text description here is from https://tartarus.org/martin/PorterStemmer/def.txt
+ *
+ * A consonant will be denoted by `c`, a vowel by `v`. A list `ccc...` of length
+ * greater than 0 will be denoted by `C`, and a list `vvv...` of length greater
+ * than 0 will be denoted by `V`. Any word, or part of a word, therefore has one
+ * of the four forms:
+ *
+ * @code{.pseudo}
+ *     CVCV ... C
+ *     CVCV ... V
+ *     VCVC ... C
+ *     VCVC ... V
+ * @endcode
+ *
+ * These may all be represented by the single form `[C]VCVC ... [V]`
+ * where the square brackets denote arbitrary presence of their contents.
+ * Using `(VC){m}` to denote `VC` repeated `m` times, this may again be written as
+ * `[C](VC){m}[V]`.
+ *
+ * And `m` will be called the _measure_ of any word or word part when represented in
+ * this form. The case `m = 0` covers the null or empty string.
+ *
+ * Examples:
+ * @code{.pseudo}
+ * m=0:    TR,  EE,  TREE,  Y,  BY.
+ * m=1:    TROUBLE,  OATS,  TREES,  IVY.
+ * m=2:    TROUBLES,  PRIVATE,  OATEN,  ORRERY.
+ * @endcode
+ */
+struct porter_stemmer_measure_fn {
+  cudf::column_device_view const d_strings;  // strings to measure
+
+  __device__ cudf::size_type operator()(cudf::size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return 0; }
+    cudf::string_view d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) { return 0; }
+
+    cudf::size_type measure = 0;
+
+    auto itr       = d_str.begin();
+    bool vowel_run = !is_consonant(itr);
+    while (itr != d_str.end()) {
+      if (is_consonant(itr)) {
+        if (vowel_run) { measure++; }
+        vowel_run = false;
+      } else {
+        vowel_run = true;
+      }
+      ++itr;
+    }
+    return measure;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::column> porter_stemmer_measure(cudf::strings_column_view const& strings,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  if (strings.is_empty()) {
+    return cudf::make_empty_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()});
+  }
+
+  // create empty output column
+  auto results =
+    cudf::make_fixed_width_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                  strings.size(),
+                                  cudf::detail::copy_bitmask(strings.parent(), stream, mr),
+                                  strings.null_count(),
+                                  stream,
+                                  mr);
+  // compute measures into output column
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(strings.size()),
+                    results->mutable_view().data<cudf::size_type>(),
+                    porter_stemmer_measure_fn{*strings_column});
+  results->set_null_count(strings.null_count());
+  return results;
+}
+
+std::unique_ptr<cudf::column> is_letter(cudf::strings_column_view const& strings,
+                                        letter_type ltype,
+                                        cudf::column_view const& indices,
+                                        rmm::cuda_stream_view stream,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  return cudf::type_dispatcher(
+    indices.type(), dispatch_is_letter_fn{}, strings, ltype, indices, stream, mr);
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<cudf::column> is_letter(cudf::strings_column_view const& strings,
+                                        letter_type ltype,
+                                        cudf::size_type character_index,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_letter(strings,
+                           ltype,
+                           thrust::make_constant_iterator<cudf::size_type>(character_index),
+                           cudf::get_default_stream(),
+                           mr);
+}
+
+std::unique_ptr<cudf::column> is_letter(cudf::strings_column_view const& strings,
+                                        letter_type ltype,
+                                        cudf::column_view const& indices,
+                                        rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_letter(strings, ltype, indices, cudf::get_default_stream(), mr);
+}
+
+/**
+ * @copydoc nvtext::porter_stemmer_measure
+ */
+std::unique_ptr<cudf::column> porter_stemmer_measure(cudf::strings_column_view const& strings,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::porter_stemmer_measure(strings, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/bpe_tokenizer.cu b/cpp/src/text/subword/bpe_tokenizer.cu
new file mode 100644
index 0000000..13c744a
--- /dev/null
+++ b/cpp/src/text/subword/bpe_tokenizer.cu
@@ -0,0 +1,564 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/subword/bpe_tokenizer.cuh>
+
+#include <nvtext/bpe_tokenize.hpp>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/combine.hpp>
+#include <cudf/strings/detail/strings_children.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/merge.h>
+#include <thrust/pair.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace nvtext {
+namespace detail {
+
+namespace {
+
+template <typename CharType>
+constexpr bool is_whitespace(CharType ch)
+{
+  return ch <= ' ';
+}
+
+/**
+ * @brief Resolve a substring up to the first whitespace character.
+ *
+ * This will return a substring of the input starting with the first byte
+ * up to the first whitespace character found or the end of the string.
+ * Any whitespace is expected only at the end of the string.
+ *
+ * @param d_str Input string to resolve.
+ * @return Substring of the input excluding any trailing whitespace.
+ */
+__device__ cudf::string_view get_first_token(cudf::string_view const& d_str)
+{
+  auto const begin = d_str.data();
+  auto const end   = thrust::find_if(
+    thrust::seq, begin, begin + d_str.size_bytes(), [](auto ch) { return is_whitespace(ch); });
+  auto const size = static_cast<cudf::size_type>(thrust::distance(begin, end));
+  return cudf::string_view(begin, size);
+}
+
+/**
+ * @brief Main byte pair encoding algorithm function for each string.
+ *
+ * @see The byte_pair_encoding_fn::operator() function below for details.
+ */
+template <typename MapRefType>
+struct byte_pair_encoding_fn {
+  cudf::column_device_view const d_merges;
+  cudf::column_device_view const d_strings;
+  MapRefType const d_map;
+  cudf::size_type* d_sizes;  // output size of encoded string
+  string_hasher_type const hasher;
+  cudf::size_type* d_byte_indices;
+
+  /**
+   * @brief Parse the merge pair into components.
+   *
+   * The two substrings are separated by a single space.
+   *
+   * @param idx Index of merge pair to dissect.
+   * @return The left and right halves of the merge pair.
+   */
+  __device__ thrust::pair<cudf::string_view, cudf::string_view> dissect_merge_pair(
+    cudf::size_type idx)
+  {
+    auto const d_pair  = d_merges.element<cudf::string_view>(idx);
+    auto const lhs     = d_pair.data();
+    auto const end_str = d_pair.data() + d_pair.size_bytes();
+    auto const rhs     = thrust::find(thrust::seq, lhs, end_str, ' ');  // space always expected
+    // check for malformed pair entry to prevent segfault
+    if (rhs == end_str) { return thrust::make_pair(cudf::string_view{}, cudf::string_view{}); }
+    auto const lhs_size = static_cast<cudf::size_type>(thrust::distance(lhs, rhs));
+    auto const rhs_size = static_cast<cudf::size_type>(thrust::distance(rhs + 1, end_str));
+    return thrust::make_pair(cudf::string_view(lhs, lhs_size),
+                             cudf::string_view(rhs + 1, rhs_size));
+  }
+
+  /**
+   * @brief Get the next substring of the given string.
+   *
+   * This will find the next sequence of characters identified by the
+   * given byte indices iterator values. The beginning of the sequence
+   * starts at `begin` and the end of the sequence is the first non-zero
+   * index found between (begin,end) exclusive.
+   *
+   * @tparam Iterator The byte indices iterator type
+   * @param begin Start of indices to check
+   * @param end End of indices to check
+   * @param d_str String to substring
+   * @return The substring found.
+   */
+  template <typename Iterator>
+  __device__ cudf::string_view next_substr(Iterator begin,
+                                           Iterator end,
+                                           cudf::string_view const& d_str)
+  {
+    auto const next = thrust::find_if(thrust::seq, begin + 1, end, [](auto v) { return v != 0; });
+    auto const size = static_cast<cudf::size_type>(thrust::distance(begin, next));
+    return cudf::string_view(d_str.data() + *begin, size);
+  }
+
+  /**
+   * @brief Look up the pair of strings in the d_map/d_merges
+   *
+   * @param lhs Left half of the string
+   * @param rhs Right half of the string
+   * @return Position of merge pair within d_map
+   */
+  __device__ auto get_merge_pair(cudf::string_view const& lhs, cudf::string_view const& rhs)
+  {
+    __shared__ char shmem[48 * 1024];  // max for Pascal
+    auto const total_size         = lhs.size_bytes() + rhs.size_bytes() + 1;
+    auto const thread_memory_size = static_cast<cudf::size_type>(sizeof(shmem) / blockDim.x);
+
+    // Edge case check.
+    // Empirically found only two merge pair strings that were greater than 70 bytes
+    // and they both looked like ignorable errors.
+    if (thread_memory_size < total_size) { return d_map.end(); }
+
+    // build the target string in shared memory
+    char* ptr = &shmem[threadIdx.x * thread_memory_size];
+
+    // build a temp string like:  temp = lhs + ' ' + rhs
+    memcpy(ptr, lhs.data(), lhs.size_bytes());
+    memcpy(ptr + lhs.size_bytes(), " ", 1);
+    memcpy(ptr + lhs.size_bytes() + 1, rhs.data(), rhs.size_bytes());
+
+    auto const d_str = cudf::string_view(ptr, total_size);
+    return d_map.find(d_str);
+  }
+
+  /**
+   * @brief Byte encode each string.
+   *
+   * Each string is iteratively scanned for the minimum rank of adjacent substring pairs
+   * as found within the `d_map` table. Once the minimum pair is located, that pair
+   * is removed -- virtually by zero-ing the index value between any matching adjacent pairs.
+   *
+   * The iteration ends once there are no more adjacent pairs or there are no more
+   * matches found in `d_map`. At the end, the indices for each string reflect the
+   * encoding pattern and can be used to build the output.
+   *
+   * This function also computes the size of the encoded output of each string
+   * by simply counting the number of non-zero indices values remaining. This saves
+   * an extra kernel launch normally required to compute the offsets of the output column.
+   *
+   * @param idx The index of the string in `d_strings` to encode
+   */
+  __device__ void operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) {
+      d_sizes[idx] = 0;
+      return;
+    }
+    auto const d_str = get_first_token(d_strings.element<cudf::string_view>(idx));
+    if (d_str.empty()) {
+      d_sizes[idx] = 0;
+      return;
+    }
+
+    auto const offset = d_strings.child(cudf::strings_column_view::offsets_column_index)
+                          .element<cudf::size_type>(idx);
+    auto const d_indices = d_byte_indices + offset;
+
+    // initialize the byte indices for this string;
+    // set the index value to 0 for any intermediate UTF-8 bytes
+    thrust::transform(thrust::seq,
+                      thrust::make_counting_iterator<cudf::size_type>(0),
+                      thrust::make_counting_iterator<cudf::size_type>(d_str.size_bytes()),
+                      d_indices,
+                      [data = d_str.data()](auto idx) {
+                        auto const byte = static_cast<uint8_t>(data[idx]);
+                        return cudf::strings::detail::is_begin_utf8_char(byte) ? idx : 0;
+                      });
+
+    auto const begin = d_indices;
+    auto const end   = d_indices + d_str.size_bytes();
+
+    // keep processing the string until there are no more adjacent pairs found in d_map
+    cudf::size_type min_rank = 0;
+    while (min_rank < cuda::std::numeric_limits<cudf::size_type>::max()) {
+      // initialize working variables
+      min_rank = cuda::std::numeric_limits<cudf::size_type>::max();
+
+      auto lhs = next_substr(begin, end, d_str);
+      auto itr = begin + lhs.size_bytes();
+
+      auto min_itr  = itr;               // these are set along with
+      auto min_size = lhs.size_bytes();  // the min_rank variable
+
+      // check each adjacent pair against the d_map
+      while (itr < end) {
+        auto const rhs = next_substr(itr, end, d_str);
+        if (rhs.empty()) break;  // no more adjacent pairs
+
+        auto const map_itr = get_merge_pair(lhs, rhs);
+        if (map_itr != d_map.end()) {
+          // found a match; record the rank (and other min_ vars)
+          auto const rank = map_itr->second;
+          if (rank < min_rank) {
+            min_rank = rank;
+            min_itr  = itr;
+            min_size = rhs.size_bytes();
+          }
+        }
+        // next substring
+        lhs = rhs;
+        itr += rhs.size_bytes();
+      }
+
+      // if any pair matched, remove every occurrence from the string
+      if (min_rank < cuda::std::numeric_limits<cudf::size_type>::max()) {
+        // remove the first pair we found
+        itr  = min_itr;
+        *itr = 0;
+
+        // continue scanning for other occurrences in the remainder of the string
+        itr += min_size;
+        if (itr < end) {
+          auto const d_pair = dissect_merge_pair(min_rank);
+
+          lhs = next_substr(itr, end, d_str);
+          itr += lhs.size_bytes();
+          while (itr < end) {
+            auto rhs = next_substr(itr, end, d_str);
+            if (d_pair.first == lhs && d_pair.second == rhs) {
+              *itr = 0;  // removes the pair from this string
+              itr += rhs.size_bytes();
+              if (itr >= end) { break; }  // done checking for pairs
+              // skip to the next adjacent pair
+              rhs = next_substr(itr, end, d_str);
+            }
+            // next substring
+            lhs = rhs;
+            itr += rhs.size_bytes();
+          }
+        }
+      }
+    }
+
+    // compute and store the output size for this string's encoding
+    auto const encoded_size = d_str.size_bytes() +  // number of original bytes +
+                              thrust::count_if(     // number of non-zero byte indices
+                                thrust::seq,
+                                d_indices,
+                                d_indices + d_str.size_bytes(),
+                                [](auto v) { return v != 0; });
+    d_sizes[idx] = static_cast<cudf::size_type>(encoded_size);
+  }
+};
+
+/**
+ * @brief Build the output string encoding.
+ *
+ * This copies each string to the output inserting a space at each non-zero byte index.
+ *
+ * @code{.txt}
+ * d_strings =      ["helloworld", "testthis"]
+ * d_byte_indices = [ 0000050000    00004000]
+ * result is ["hello world", "test this"]
+ * @endcode
+ */
+struct build_encoding_fn {
+  cudf::column_device_view const d_strings;
+  cudf::size_type const* d_byte_indices;
+  cudf::size_type const* d_offsets;
+  char* d_chars{};
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) { return; }
+    auto const d_str = get_first_token(d_strings.element<cudf::string_view>(idx));
+    if (d_str.empty()) { return; }
+
+    auto const offset = d_strings.child(cudf::strings_column_view::offsets_column_index)
+                          .element<cudf::size_type>(idx);
+    auto const d_indices = d_byte_indices + offset;
+    auto d_output        = d_chars ? d_chars + d_offsets[idx] : nullptr;
+
+    // copy chars while indices[i]==0,
+    // insert space each time indices[i]!=0
+    auto const begin = d_indices;
+    auto const end   = d_indices + d_str.size_bytes();
+    auto d_input     = d_str.data();
+    *d_output++      = *d_input++;
+    auto itr         = begin + 1;
+    while (itr < end) {
+      if (*itr++) *d_output++ = ' ';
+      *d_output++ = *d_input++;
+    }
+    // https://github.com/rapidsai/cudf/pull/10270/files#r826319405
+  }
+};
+
+/**
+ * @brief Perform byte pair encoding on each string in the input column.
+ *
+ * The result is a strings column of the same size where each string has been encoded.
+ *
+ * The encoding is performed iteratively. Each pass determines the string's lowest
+ * ranked merge pair as determined by the strings in `merges_table`. This pair
+ * is removed (virtually) from each string before starting the next iteration.
+ *
+ * Once all pairs have exhausted for all strings, the output is constructed from
+ * the results by adding spaces between each remaining pair in each string.
+ *
+ * @param input Strings to encode.
+ * @param merge_pairs Merge pairs data and map used for encoding.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<cudf::column> byte_pair_encoding(
+  cudf::strings_column_view const& input,
+  bpe_merge_pairs::bpe_merge_pairs_impl const& merge_pairs,
+  rmm::cuda_stream_view stream)
+{
+  auto const d_merges = merge_pairs.get_merge_pairs();
+  CUDF_EXPECTS(d_merges.size() > 0, "Merge pairs table must not be empty");
+
+  // build working vector to hold index values per byte
+  rmm::device_uvector<cudf::size_type> d_byte_indices(input.chars().size(), stream);
+
+  auto const d_strings = cudf::column_device_view::create(input.parent(), stream);
+
+  auto offsets   = cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                           static_cast<cudf::size_type>(input.size() + 1),
+                                           cudf::mask_state::UNALLOCATED,
+                                           stream,
+                                           rmm::mr::get_current_device_resource());
+  auto d_offsets = offsets->mutable_view().data<cudf::size_type>();
+
+  auto map_ref = merge_pairs.get_merge_pairs_ref();
+  byte_pair_encoding_fn<decltype(map_ref)> fn{
+    d_merges, *d_strings, map_ref, d_offsets, string_hasher_type{}, d_byte_indices.data()};
+  thrust::for_each_n(
+    rmm::exec_policy(stream), thrust::make_counting_iterator<cudf::size_type>(0), input.size(), fn);
+
+  // build the output: add spaces between the remaining pairs in each string
+  thrust::exclusive_scan(
+    rmm::exec_policy(stream), d_offsets, d_offsets + input.size() + 1, d_offsets);
+
+  auto const bytes =
+    cudf::detail::get_value<cudf::size_type>(offsets->view(), input.size(), stream);
+  auto chars = cudf::strings::detail::create_chars_child_column(
+    bytes, stream, rmm::mr::get_current_device_resource());
+  auto d_chars = chars->mutable_view().data<char>();
+
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<cudf::size_type>(0),
+                     input.size(),
+                     build_encoding_fn{*d_strings, d_byte_indices.data(), d_offsets, d_chars});
+
+  return make_strings_column(
+    input.size(), std::move(offsets), std::move(chars), 0, rmm::device_buffer{});
+}
+
+/**
+ * @brief Detect space to not-space transitions inside each string.
+ *
+ * This handles sliced input and null strings as well.
+ * It is parallelized over bytes and returns true only for valid left edges
+ * -- non-space preceded by a space.
+ */
+struct edge_of_space_fn {
+  cudf::column_device_view const d_strings;
+  __device__ bool operator()(cudf::size_type offset)
+  {
+    auto const d_chars =
+      d_strings.child(cudf::strings_column_view::chars_column_index).data<char>();
+    if (is_whitespace(d_chars[offset]) || !is_whitespace(d_chars[offset - 1])) { return false; }
+
+    auto const offsets   = d_strings.child(cudf::strings_column_view::offsets_column_index);
+    auto const d_offsets = offsets.data<cudf::size_type>() + d_strings.offset();
+    // ignore offsets outside sliced range
+    if (offset < d_offsets[0] || offset >= d_offsets[d_strings.size()]) { return false; }
+
+    auto itr =
+      thrust::lower_bound(thrust::seq, d_offsets, d_offsets + d_strings.size() + 1, offset);
+    // ignore offsets at existing string boundaries
+    if (*itr == offset) { return false; }
+
+    // count only edges for valid strings
+    auto const index = static_cast<cudf::size_type>(thrust::distance(d_offsets, itr)) - 1;
+    return d_strings.is_valid(index);
+  }
+};
+
+/**
+ * @brief Create new offsets by identifying substrings by whitespace.
+ *
+ * This is similar to cudf::strings::split_record but does not fully split
+ * and only returns new offsets. The behavior is more like a view-only slice
+ * of the chars child with the result still including trailing delimiters.
+ *
+ * The encoding algorithm ignores the trailing whitespace of each string.
+ *
+ * @param input Strings to tokenize.
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @return New offsets including those at the edge of each space.
+ */
+std::unique_ptr<cudf::column> space_offsets(cudf::strings_column_view const& input,
+                                            cudf::column_device_view const& d_strings,
+                                            rmm::cuda_stream_view stream)
+{
+  // count space offsets
+  auto const begin = thrust::make_counting_iterator<cudf::size_type>(1);
+  auto const end   = thrust::make_counting_iterator<cudf::size_type>(input.chars().size());
+  edge_of_space_fn edge_of_space{d_strings};
+  auto const space_count = thrust::count_if(rmm::exec_policy(stream), begin, end, edge_of_space);
+
+  // copy space offsets
+  rmm::device_uvector<cudf::size_type> space_offsets(space_count, stream);
+  thrust::copy_if(rmm::exec_policy(stream), begin, end, space_offsets.data(), edge_of_space);
+
+  // create output offsets
+  auto result =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                              static_cast<cudf::size_type>(space_count + input.size() + 1),
+                              cudf::mask_state::UNALLOCATED,
+                              stream,
+                              rmm::mr::get_current_device_resource());
+
+  // combine current offsets with space offsets
+  thrust::merge(rmm::exec_policy(stream),
+                input.offsets_begin(),
+                input.offsets_end(),
+                space_offsets.begin(),
+                space_offsets.end(),
+                result->mutable_view().begin<cudf::size_type>());
+
+  return result;
+}
+
+/**
+ * @brief Build new offsets that can be used to build a list column for calling join.
+ *
+ * This essentially returns the number of tokens for each string.
+ */
+struct list_offsets_fn {
+  cudf::column_device_view const d_strings;
+  __device__ cudf::size_type operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) return 0;
+    auto const d_str = d_strings.element<cudf::string_view>(idx);
+    if (d_str.empty()) return 1;  // empty is a single valid result
+
+    auto const begin = thrust::make_counting_iterator<cudf::size_type>(1);
+    auto const end   = thrust::make_counting_iterator<cudf::size_type>(d_str.size_bytes());
+
+    // this counts the number of non-adjacent delimiters
+    auto const result =
+      thrust::count_if(thrust::seq, begin, end, [data = d_str.data()](auto chidx) {
+        return !is_whitespace(data[chidx]) && is_whitespace(data[chidx - 1]);
+      });
+    return static_cast<cudf::size_type>(result) + 1;
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::column> byte_pair_encoding(cudf::strings_column_view const& input,
+                                                 bpe_merge_pairs const& merge_pairs,
+                                                 cudf::string_scalar const& separator,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty() || input.chars_size() == 0)
+    return cudf::make_empty_column(cudf::type_id::STRING);
+
+  auto const d_strings = cudf::column_device_view::create(input.parent(), stream);
+  auto const offsets   = space_offsets(input, *d_strings, stream);
+
+  // build a view using the new offsets and the current input chars column
+  auto const input_view = cudf::column_view(cudf::data_type{cudf::type_id::STRING},
+                                            offsets->size() - 1,
+                                            nullptr,  // no parent data
+                                            nullptr,  // null-mask
+                                            0,        // null-count
+                                            0,        // offset
+                                            {offsets->view(), input.chars()});
+
+  // run BPE on this view
+  auto const bpe_column =
+    byte_pair_encoding(cudf::strings_column_view(input_view), *(merge_pairs.impl), stream);
+
+  // recombine the result:
+  // compute the offsets needed to build a list view
+  auto const list_offsets = [d_strings = *d_strings, stream] {
+    auto offsets_itr = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<cudf::size_type>(0), list_offsets_fn{d_strings});
+    auto offsets_column = std::get<0>(cudf::detail::make_offsets_child_column(
+      offsets_itr, offsets_itr + d_strings.size(), stream, rmm::mr::get_current_device_resource()));
+    return offsets_column;
+  }();
+
+  // build a list column_view using the BPE output and the list_offsets
+  auto const list_join = cudf::column_view(cudf::data_type{cudf::type_id::LIST},
+                                           input.size(),
+                                           nullptr,  // no parent data in list column
+                                           input.null_mask(),
+                                           input.null_count(),
+                                           0,
+                                           {list_offsets->view(), bpe_column->view()});
+
+  // build the output strings column
+  auto result =
+    cudf::strings::detail::join_list_elements(cudf::lists_column_view(list_join),
+                                              separator,
+                                              cudf::string_scalar(""),
+                                              cudf::strings::separator_on_nulls::NO,
+                                              cudf::strings::output_if_empty_list::EMPTY_STRING,
+                                              stream,
+                                              mr);
+  return result;
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> byte_pair_encoding(cudf::strings_column_view const& input,
+                                                 bpe_merge_pairs const& merges_table,
+                                                 cudf::string_scalar const& separator,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::byte_pair_encoding(input, merges_table, separator, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/bpe_tokenizer.cuh b/cpp/src/text/subword/bpe_tokenizer.cuh
new file mode 100644
index 0000000..2fa879e
--- /dev/null
+++ b/cpp/src/text/subword/bpe_tokenizer.cuh
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <nvtext/bpe_tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/strings/string_view.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <cuco/static_map.cuh>
+
+#include <cstdint>
+#include <type_traits>
+
+namespace nvtext {
+namespace detail {
+
+using hash_value_type    = uint32_t;
+using string_hasher_type = cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>;
+
+/**
+ * @brief Hasher function used for building and using the cuco static-map
+ *
+ * This takes advantage of heterogeneous lookup feature in cuco static-map which
+ * allows inserting with one type (index) and looking up with a different type (string).
+ */
+struct bpe_hasher {
+  cudf::column_device_view const d_strings;
+  string_hasher_type hasher{};
+  // used by insert
+  __device__ hash_value_type operator()(cudf::size_type index) const
+  {
+    return hasher(d_strings.element<cudf::string_view>(index));
+  }
+  // used by find
+  __device__ hash_value_type operator()(cudf::string_view const& s) const { return hasher(s); }
+};
+
+/**
+ * @brief Equal function used for building and using the cuco static-map
+ *
+ * This takes advantage of heterogeneous lookup feature in cuco static-map which
+ * allows inserting with one type (index) and looking up with a different type (string).
+ */
+struct bpe_equal {
+  cudf::column_device_view const d_strings;
+  // used by insert
+  __device__ bool operator()(cudf::size_type lhs, cudf::size_type rhs) const noexcept
+  {
+    return d_strings.element<cudf::string_view>(lhs) == d_strings.element<cudf::string_view>(rhs);
+  }
+  // used by find
+  __device__ bool operator()(cudf::size_type lhs, cudf::string_view const& rhs) const noexcept
+  {
+    return d_strings.element<cudf::string_view>(lhs) == rhs;
+  }
+};
+
+using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+
+using probe_scheme = cuco::experimental::linear_probing<1, bpe_hasher>;
+
+using merge_pairs_map_type = cuco::experimental::static_map<cudf::size_type,
+                                                            cudf::size_type,
+                                                            cuco::experimental::extent<std::size_t>,
+                                                            cuda::thread_scope_device,
+                                                            bpe_equal,
+                                                            probe_scheme,
+                                                            hash_table_allocator_type>;
+
+}  // namespace detail
+
+// since column_device_view::create returns is a little more than
+// std::unique_ptr<column_device_view> this helper simplifies the return type in a more maintainable
+// way
+using col_device_view = std::invoke_result_t<decltype(&cudf::column_device_view::create),
+                                             cudf::column_view,
+                                             rmm::cuda_stream_view>;
+
+struct bpe_merge_pairs::bpe_merge_pairs_impl {
+  std::unique_ptr<cudf::column> const merge_pairs;
+  col_device_view const d_merge_pairs;
+  std::unique_ptr<detail::merge_pairs_map_type> merge_pairs_map;
+
+  bpe_merge_pairs_impl(std::unique_ptr<cudf::column>&& merge_pairs,
+                       col_device_view&& d_merge_pairs,
+                       std::unique_ptr<detail::merge_pairs_map_type>&& merge_pairs_map);
+
+  auto const get_merge_pairs() const { return *d_merge_pairs; }
+  auto get_merge_pairs_ref() const { return merge_pairs_map->ref(cuco::experimental::op::find); }
+};
+
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/data_normalizer.cu b/cpp/src/text/subword/data_normalizer.cu
new file mode 100644
index 0000000..34eb95b
--- /dev/null
+++ b/cpp/src/text/subword/data_normalizer.cu
@@ -0,0 +1,349 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/subword/detail/data_normalizer.hpp>
+#include <text/subword/detail/tokenizer_utils.cuh>
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/remove.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Bit used to filter out invalid code points.
+ *
+ * When normalizing characters to code point values, if this bit is set,
+ * the code point should be filtered out before returning from the normalizer.
+ */
+constexpr uint32_t FILTER_BIT = 22;
+
+/**
+ * @brief Retrieve new code point from metadata value.
+ *
+ * @param metadata Value from the codepoint_metadata table.
+ * @return The replacement character if appropriate.
+ */
+__device__ uint32_t get_first_cp(uint32_t metadata) { return metadata & NEW_CP_MASK; }
+
+/**
+ * @brief Retrieve token category from the metadata value.
+ *
+ * Category values are 0-5:
+ * 0 - character should be padded
+ * 1 - pad character if lower-case
+ * 2 - character should be removed
+ * 3 - remove character if lower-case
+ * 4 - whitespace character -- always replace
+ * 5 - uncategorized
+ *
+ * @param metadata Value from the codepoint_metadata table.
+ * @return Category value.
+ */
+__device__ uint32_t extract_token_cat(uint32_t metadata)
+{
+  return (metadata >> TOKEN_CAT_SHIFT) & TOKEN_CAT_MASK;
+}
+
+/**
+ * @brief Return true if category of metadata value specifies the character should be replaced.
+ */
+__device__ bool should_remove_cp(uint32_t metadata, bool lower_case)
+{
+  auto const cat = extract_token_cat(metadata);
+  return (cat == TOKEN_CAT_REMOVE_CHAR) || (lower_case && (cat == TOKEN_CAT_REMOVE_CHAR_IF_LOWER));
+}
+
+/**
+ * @brief Return true if category of metadata value specifies the character should be padded.
+ */
+__device__ bool should_add_spaces(uint32_t metadata, bool lower_case)
+{
+  auto const cat = extract_token_cat(metadata);
+  return (cat == TOKEN_CAT_ADD_SPACE) || (lower_case && (cat == TOKEN_CAT_ADD_SPACE_IF_LOWER));
+}
+
+/**
+ * @brief Return true if category of metadata value specifies the character should be replaced.
+ */
+__device__ bool always_replace(uint32_t metadata)
+{
+  return extract_token_cat(metadata) == TOKEN_CAT_ALWAYS_REPLACE;
+}
+
+/**
+ * @brief Returns true if metadata value includes a multi-character transform bit equal to 1.
+ */
+__device__ bool is_multi_char_transform(uint32_t metadata)
+{
+  return (metadata >> MULTICHAR_SHIFT) & MULTICHAR_MASK;
+}
+
+/**
+ * @brief Returns true if the byte passed in could be a valid head byte for
+ * a utf8 character. That is, not binary `10xxxxxx`
+ */
+__device__ bool is_head_byte(unsigned char utf8_byte) { return (utf8_byte >> 6) != 2; }
+
+/**
+ * @brief Converts a UTF-8 character into a unicode code point value.
+ *
+ * If the byte at start_byte_for_thread is the first byte of a UTF-8 character (head byte),
+ * the UTF-8 character is converted to a unicode code point and returned.
+ *
+ * If the byte at start_byte_for_thread is not a head byte, 0 is returned.
+ *
+ * All threads start reading bytes from the pointer denoted by strings.
+ *
+ * @param strings A pointer to the start of the sequence of characters to be analyzed.
+ * @param start_byte_for_thread Which byte to start analyzing
+ * @return New code point value for this byte.
+ */
+__device__ uint32_t
+extract_code_points_from_utf8(unsigned char const* strings,
+                              size_t const total_bytes,
+                              cudf::thread_index_type const start_byte_for_thread)
+{
+  constexpr uint8_t max_utf8_blocks_for_char    = 4;
+  uint8_t utf8_blocks[max_utf8_blocks_for_char] = {0};
+
+  for (int i = 0; i < std::min(static_cast<size_t>(max_utf8_blocks_for_char),
+                               total_bytes - start_byte_for_thread);
+       ++i) {
+    utf8_blocks[i] = strings[start_byte_for_thread + i];
+  }
+
+  uint8_t const length_encoding_bits = utf8_blocks[0] >> 3;
+  // UTF-8 format is variable-width character encoding using up to 4 bytes.
+  // If the first byte is:
+  // - [x00-x7F] -- beginning of a 1-byte character (ASCII)
+  // - [xC0-xDF] -- beginning of a 2-byte character
+  // - [xE0-xEF] -- beginning of a 3-byte character
+  // - [xF0-xF7] -- beginning of a 3-byte character
+  // Anything else is an intermediate byte [x80-xBF].
+  // So shifted by 3 bits this becomes
+  // - [x00-x0F]  or leb < 16
+  // - [x18-x1B]  or 24 <= leb <= 27
+  // - [x1C-x1D]  or 28 <= leb <= 29
+  // - [x1E-x1F]  or leb >= 30
+  // The remaining bits are part of the value as specified by the mask
+  // specified by x's below.
+  // - b0xxxxxxx = x7F
+  // - b110xxxxx = x1F
+  // - b1110xxxx = x0F
+  // - b11110xxx = x07
+  using encoding_length_pair = thrust::pair<uint8_t, uint8_t>;
+  // Set the number of characters and the top masks based on the length encoding bits.
+  encoding_length_pair const char_encoding_length = [length_encoding_bits] {
+    if (length_encoding_bits < 16) return encoding_length_pair{1, 0x7F};
+    if (length_encoding_bits >= 24 && length_encoding_bits <= 27)
+      return encoding_length_pair{2, 0x1F};
+    if (length_encoding_bits == 28 || length_encoding_bits == 29)
+      return encoding_length_pair{3, 0x0F};
+    if (length_encoding_bits == 30) return encoding_length_pair{4, 0x07};
+    return encoding_length_pair{0, 0};
+  }();
+
+  // Now pack up the bits into a uint32_t.
+  // Move the first set of values into bits 19-24 in the 32-bit value.
+  uint32_t code_point = (utf8_blocks[0] & char_encoding_length.second) << 18;
+  // Move the remaining values which are 6 bits (mask b10xxxxxx = x3F)
+  // from the remaining bytes into successive positions in the 32-bit result.
+  code_point |= ((utf8_blocks[1] & 0x3F) << 12);
+  code_point |= ((utf8_blocks[2] & 0x3F) << 6);
+  code_point |= utf8_blocks[3] & 0x3F;
+
+  // Adjust the final result by shifting by the character length.
+  uint8_t const shift_amt = 24 - 6 * char_encoding_length.first;
+  code_point >>= shift_amt;
+  return code_point;
+}
+
+/**
+ * @brief Normalize the characters for the strings input.
+ *
+ * Characters are replaced, padded, or removed depending on the `do_lower_case` input
+ * as well as the metadata values for each code point found in `cp_metadata`.
+ *
+ * First, each character is converted from UTF-8 to a unicode code point value.
+ * This value is then looked up in the `cp_metadata` table to determine its fate.
+ * The end result is a set of code point values for each character.
+ * The normalized set of characters make it easier for the tokenizer to identify
+ * tokens and match up token ids.
+ *
+ * @param[in] strings The input strings with characters to normalize to code point values.
+ * @param[in] total_bytes Total number of bytes in the input `strings` vector.
+ * @param[in] cp_metadata The metadata lookup table for every unicode code point value.
+ * @param[in] aux_table Aux table for mapping some multi-byte code point values.
+ * @param[in] do_lower_case True if normalization should include lower-casing.
+ * @param[out] code_points The resulting code point values from normalization.
+ * @param[out] chars_per_thread Output number of code point values per string.
+ */
+__global__ void kernel_data_normalizer(unsigned char const* strings,
+                                       size_t const total_bytes,
+                                       uint32_t const* cp_metadata,
+                                       uint64_t const* aux_table,
+                                       bool const do_lower_case,
+                                       uint32_t* code_points,
+                                       uint32_t* chars_per_thread)
+{
+  constexpr uint32_t init_val                     = (1 << FILTER_BIT);
+  uint32_t replacement_code_points[MAX_NEW_CHARS] = {init_val, init_val, init_val};
+
+  cudf::thread_index_type const char_for_thread =
+    threadIdx.x + cudf::thread_index_type(blockIdx.x) * cudf::thread_index_type(blockDim.x);
+  uint32_t num_new_chars = 0;
+
+  if (char_for_thread < total_bytes) {
+    auto const code_point = extract_code_points_from_utf8(strings, total_bytes, char_for_thread);
+    auto const metadata   = cp_metadata[code_point];
+
+    if (is_head_byte(strings[char_for_thread]) && !should_remove_cp(metadata, do_lower_case)) {
+      num_new_chars = 1;
+      // Apply lower cases and accent stripping if necessary
+      auto const new_cp =
+        do_lower_case || always_replace(metadata) ? get_first_cp(metadata) : code_point;
+      replacement_code_points[0] = new_cp == 0 ? code_point : new_cp;
+
+      if (do_lower_case && is_multi_char_transform(metadata)) {
+        auto const next_cps          = aux_table[code_point];
+        replacement_code_points[1]   = static_cast<uint32_t>(next_cps >> 32);
+        auto const potential_next_cp = static_cast<uint32_t>(next_cps);
+        replacement_code_points[2] =
+          potential_next_cp != 0 ? potential_next_cp : replacement_code_points[2];
+        num_new_chars = 2 + (potential_next_cp != 0);
+      }
+
+      if (should_add_spaces(metadata, do_lower_case)) {
+        // Need to shift all existing code-points up one
+        // This is a rotate right. There is no thrust equivalent at this time.
+        for (int loc = num_new_chars; loc > 0; --loc) {
+          replacement_code_points[loc] = replacement_code_points[loc - 1];
+        }
+
+        // Write the required spaces at the end
+        replacement_code_points[0]                 = SPACE_CODE_POINT;
+        replacement_code_points[num_new_chars + 1] = SPACE_CODE_POINT;
+        num_new_chars += 2;
+      }
+    }
+  }
+
+  chars_per_thread[char_for_thread] = num_new_chars;
+
+  using BlockStore =
+    cub::BlockStore<uint32_t, THREADS_PER_BLOCK, MAX_NEW_CHARS, cub::BLOCK_STORE_WARP_TRANSPOSE>;
+  __shared__ typename BlockStore::TempStorage temp_storage;
+
+  // Now we perform coalesced writes back to global memory using cub.
+  uint32_t* block_base = code_points + blockIdx.x * blockDim.x * MAX_NEW_CHARS;
+  BlockStore(temp_storage).Store(block_base, replacement_code_points);
+}
+
+}  // namespace
+
+data_normalizer::data_normalizer(codepoint_metadata_type const* cp_metadata,
+                                 aux_codepoint_data_type const* aux_table,
+                                 bool do_lower_case)
+  : d_cp_metadata{cp_metadata}, d_aux_table{aux_table}, do_lower_case{do_lower_case}
+{
+}
+
+uvector_pair data_normalizer::normalize(char const* d_strings,
+                                        cudf::size_type const* d_offsets,
+                                        cudf::size_type num_strings,
+                                        rmm::cuda_stream_view stream) const
+{
+  if (num_strings == 0) {
+    return uvector_pair{std::make_unique<rmm::device_uvector<uint32_t>>(0, stream),
+                        std::make_unique<rmm::device_uvector<cudf::size_type>>(0, stream)};
+  }
+
+  // copy offsets to working memory
+  auto const num_offsets = num_strings + 1;
+  auto d_strings_offsets =
+    std::make_unique<rmm::device_uvector<cudf::size_type>>(num_offsets, stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::counting_iterator<cudf::size_type>(0),
+                    thrust::counting_iterator<cudf::size_type>(num_offsets),
+                    d_strings_offsets->begin(),
+                    [d_offsets] __device__(auto idx) {
+                      auto const offset = d_offsets[0];  // adjust for any offset to the offsets
+                      return d_offsets[idx] - offset;
+                    });
+  auto const bytes_count = d_strings_offsets->element(num_strings, stream);
+  if (bytes_count == 0) {  // if no bytes, nothing to do
+    return uvector_pair{std::make_unique<rmm::device_uvector<uint32_t>>(0, stream),
+                        std::make_unique<rmm::device_uvector<cudf::size_type>>(0, stream)};
+  }
+
+  cudf::detail::grid_1d const grid{bytes_count, THREADS_PER_BLOCK, 1};
+  size_t const threads_on_device  = grid.num_threads_per_block * grid.num_blocks;
+  size_t const max_new_char_total = MAX_NEW_CHARS * threads_on_device;
+
+  auto d_code_points = std::make_unique<rmm::device_uvector<uint32_t>>(max_new_char_total, stream);
+  rmm::device_uvector<uint32_t> d_chars_per_thread(threads_on_device, stream);
+
+  kernel_data_normalizer<<<grid.num_blocks, grid.num_threads_per_block, 0, stream.value()>>>(
+    reinterpret_cast<unsigned char const*>(d_strings),
+    bytes_count,
+    d_cp_metadata,
+    d_aux_table,
+    do_lower_case,
+    d_code_points->data(),
+    d_chars_per_thread.data());
+
+  // Remove the 'empty' code points from the vector
+  thrust::remove(rmm::exec_policy(stream),
+                 d_code_points->begin(),
+                 d_code_points->end(),
+                 uint32_t{1 << FILTER_BIT});
+
+  // We also need to prefix sum the number of characters up to an including
+  // the current character in order to get the new strings lengths.
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         d_chars_per_thread.begin(),
+                         d_chars_per_thread.end(),
+                         d_chars_per_thread.begin());
+
+  // This will reset the offsets to the new generated code point values
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<uint32_t>(1),
+    num_strings,
+    update_strings_lengths_fn{d_chars_per_thread.data(), d_strings_offsets->data()});
+
+  auto const num_chars = d_strings_offsets->element(num_strings, stream);
+  d_code_points->resize(num_chars, stream);  // should be smaller than original allocated size
+
+  // return the normalized code points and the new offsets
+  return uvector_pair(std::move(d_code_points), std::move(d_strings_offsets));
+}
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/detail/codepoint_metadata.ah b/cpp/src/text/subword/detail/codepoint_metadata.ah
new file mode 100644
index 0000000..e886203
--- /dev/null
+++ b/cpp/src/text/subword/detail/codepoint_metadata.ah
@@ -0,0 +1,13466 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+
+// this file is included only by data_normalizer.cu
+// this is the metadata for every unicode code point value
+// it is broken into pieces since only 10% of the values are unique
+
+// some magic numbers
+constexpr uint32_t codepoint_metadata_size = 1114112; // 0x11'0000
+constexpr uint32_t aux_codepoint_data_size = 119233;
+constexpr uint32_t codepoint_metadata_default_value = 83886080; // 0x0500'0000
+constexpr uint32_t aux_codepoint_default_value = 0;
+constexpr uint32_t cp_section1_end = 195104;
+constexpr uint32_t cp_section2_begin = 917505;
+constexpr uint32_t cp_section2_end   = 917999;
+constexpr uint32_t aux_section1_end   = 6978;
+constexpr uint32_t aux_section2_begin = 44032;
+constexpr uint32_t aux_section2_end   = 55203;
+constexpr uint32_t aux_section3_begin = 70475;
+constexpr uint32_t aux_section3_end   = 71099;
+constexpr uint32_t aux_section4_begin = 119134;
+constexpr uint32_t aux_section4_end   = 119232;
+
+using codepoint_metadata_type = uint32_t;
+using aux_codepoint_data_type = uint64_t;
+
+codepoint_metadata_type const codepoint_metadata[] = {
+33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,67108896,67108896,33554464,33554464,67108896,33554432,33554432,
+33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554464,33554464,33554464,33554464,
+67108896,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,0,0,0,0,0,0,
+0,83886177,83886178,83886179,83886180,83886181,83886182,83886183,83886184,83886185,83886186,83886187,83886188,83886189,83886190,83886191,
+83886192,83886193,83886194,83886195,83886196,83886197,83886198,83886199,83886200,83886201,83886202,0,0,0,0,0,
+0,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,
+83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,83886080,0,0,0,0,33554432,
+33554432,33554432,33554432,33554432,33554432,33554464,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,
+33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,33554432,
+67108896,0,83886080,83886080,83886080,83886080,83886080,0,83886080,83886080,83886080,0,83886080,33554432,83886080,83886080,
+83886080,83886080,83886080,83886080,83886080,83886080,0,0,83886080,83886080,83886080,0,83886080,83886080,83886080,0,
+83886177,83886177,83886177,83886177,83886177,83886177,85983462,83886179,83886181,83886181,83886181,83886181,83886185,83886185,83886185,83886185,
+85983472,83886190,83886191,83886191,83886191,83886191,83886191,83886080,85983480,83886197,83886197,83886197,83886197,83886201,85983486,83886080,
+83886177,83886177,83886177,83886177,83886177,83886177,83886080,83886179,83886181,83886181,83886181,83886181,83886185,83886185,83886185,83886185,
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+83886177,83886177,83886177,83886177,83886177,83886177,83886179,83886179,83886179,83886179,83886179,83886179,83886179,83886179,83886180,83886180,
+85983505,83886080,83886181,83886181,83886181,83886181,83886181,83886181,83886181,83886181,83886181,83886181,83886183,83886183,83886183,83886183,
+83886183,83886183,83886183,83886183,83886184,83886184,85983527,83886080,83886185,83886185,83886185,83886185,83886185,83886185,83886185,83886185,
+83886185,83886080,85983539,83886080,83886186,83886186,83886187,83886187,83886080,83886188,83886188,83886188,83886188,83886188,83886188,85983552,
+83886080,85983554,83886080,83886190,83886190,83886190,83886190,83886190,83886190,83886080,85983563,83886080,83886191,83886191,83886191,83886191,
+83886191,83886191,85983571,83886080,83886194,83886194,83886194,83886194,83886194,83886194,83886195,83886195,83886195,83886195,83886195,83886195,
+83886195,83886195,83886196,83886196,83886196,83886196,85983591,83886080,83886197,83886197,83886197,83886197,83886197,83886197,83886197,83886197,
+83886197,83886197,83886197,83886197,83886199,83886199,83886201,83886201,83886201,83886202,83886202,83886202,83886202,83886202,83886202,83886080,
+83886080,85983827,85983619,83886080,85983621,83886080,85983828,85983624,83886080,85983830,85983831,85983628,83886080,83886080,85983709,85983833,
+85983835,85983634,83886080,85983840,85983843,83886080,85983849,85983848,85983641,83886080,83886080,83886080,85983855,85983858,83886080,85983861,
+83886191,83886191,85983651,83886080,85983653,83886080,85983872,85983656,83886080,85983875,83886080,83886080,85983661,83886080,85983880,83886197,
+83886197,85983882,85983883,85983668,83886080,85983670,83886080,85983890,85983673,83886080,83886080,83886080,85983677,83886080,83886080,83886080,
+83886080,83886080,83886080,83886080,85983686,85983686,83886080,85983689,85983689,83886080,85983692,85983692,83886080,83886177,83886177,83886185,
+83886185,83886191,83886191,83886197,83886197,83886197,83886197,83886197,83886197,83886197,83886197,83886197,83886197,83886080,83886177,83886177,
+83886177,83886177,85983462,85983462,85983717,83886080,83886183,83886183,83886187,83886187,83886191,83886191,83886191,83886191,85983890,85983890,
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+};
+
+uint32_t cp_metadata_917505_917999[] = {
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+};
+
+aux_codepoint_data_type const aux_codepoint_data[] = {
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+
+uint64_t aux_cp_data_70475_71099[] = {
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+0,0,0,0,0,0,0,0,0,305316340170752,305316340170752};
+
+uint64_t aux_cp_data_119134_119232[] = {
+511706698612736,511706698612736,511706698731886,
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+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
+0,0,0,0,0,0,0,0,0,0,511706698612736,511706698612736,511706698731886,511706698731886,511706698731887,511706698731887
+};
diff --git a/cpp/src/text/subword/detail/cp_data.h b/cpp/src/text/subword/detail/cp_data.h
new file mode 100644
index 0000000..189ef14
--- /dev/null
+++ b/cpp/src/text/subword/detail/cp_data.h
@@ -0,0 +1,37 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cstdint>
+
+constexpr uint32_t NEW_CP_MASK = 0x1f'ffffu;
+
+constexpr uint32_t MULTICHAR_SHIFT = 23;
+constexpr uint32_t MULTICHAR_MASK  = 1;
+
+constexpr uint32_t TOKEN_CAT_SHIFT                = 24;
+constexpr uint32_t TOKEN_CAT_MASK                 = 7;
+constexpr uint32_t TOKEN_CAT_ADD_SPACE            = 0;
+constexpr uint32_t TOKEN_CAT_ADD_SPACE_IF_LOWER   = 1;
+constexpr uint32_t TOKEN_CAT_REMOVE_CHAR          = 2;
+constexpr uint32_t TOKEN_CAT_REMOVE_CHAR_IF_LOWER = 3;
+constexpr uint32_t TOKEN_CAT_ALWAYS_REPLACE       = 4;
+
+constexpr uint32_t SPACE_CODE_POINT = 32;
+constexpr uint32_t MAX_NEW_CHARS    = 3;
+
+using codepoint_metadata_type = uint32_t;
+using aux_codepoint_data_type = uint64_t;
diff --git a/cpp/src/text/subword/detail/data_normalizer.hpp b/cpp/src/text/subword/detail/data_normalizer.hpp
new file mode 100644
index 0000000..fb507b8
--- /dev/null
+++ b/cpp/src/text/subword/detail/data_normalizer.hpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <text/subword/detail/cp_data.h>
+
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+using uvector_pair = std::pair<std::unique_ptr<rmm::device_uvector<uint32_t>>,
+                               std::unique_ptr<rmm::device_uvector<cudf::size_type>>>;
+
+namespace nvtext {
+namespace detail {
+
+/**
+ * @brief Performs text cleaning for the tokenizers.
+ *
+ * Every instantiation of this class will transfer the meta data over to the GPU.
+ * It is advised to create one class and reuse that class as needed.
+ *
+ * Converts characters to lowercase, adds spaces around punctuation and multi-byte
+ * characters, strips accents from letters in the text and standardizes whitespace
+ * characters to all be the code point for the " " literal.
+ *
+ * The algorithm produces two vectors of integers `uvector_pair`.
+ * The first is the size of 3 uint32 values per input byte (of the strings buffer).
+ * The second is the same size as the input offsets vector -- number of strings + 1.
+ *
+ * A temporary buffer is created equal to 1 uint32 value per input byte.
+ * This means 16x the number bytes of the input strings buffer must be available
+ * to call the `normalize()` function in this class.
+ */
+class data_normalizer {
+ public:
+  /**
+   * @brief Create instance of the normalizer.
+   *
+   * @param cp_metadata The code point metadata table to use for normalization.
+   * @param aux_table The auxiliary code point table.
+   * @param do_lower_case If true, the normalizer will convert uppercase characters in the
+   *        input stream to lower case and strip accents from those characters.
+   *        If false, accented and uppercase characters are not transformed.
+   */
+  data_normalizer(codepoint_metadata_type const* cp_metadata,
+                  aux_codepoint_data_type const* aux_table,
+                  bool do_lower_case = true);
+
+  /**
+   * @brief Normalize a vector of strings.
+   *
+   * If `do_lower_case` is true, this function will convert each character to lowercase
+   * and strip accents from the characters. If false it will do all other conversions
+   * in the class description except lower-casing and punctuation stripping.
+   *
+   * The result of this function returns two pointers to GPU data.
+   * The first pointer is to a contiguous array of unicode code points corresponding to the
+   * characters in the text after running normalization. The second pointer is to the
+   * offsets of the strings in the code point array. That is, string `i` starts at
+   * `result.second->data()[i]`.
+   * This array will always be of length `num_strings + 1` since we need one entry
+   * for each input and a last entry which has the total number of bytes.
+   *
+   * @param d_strings A vector of strings which MUST be encoded in the UTF-8 format.
+   * @param d_offsets A vector of byte offsets to the beginning of individual strings in
+   *        the `d_strings` parameter.
+   * @param num_strings The number of strings identified in `d_strings`.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @return Two pointers to GPU data buffers. The first is a pointer
+   *         to the code points array and the second is a pointer to the offsets
+   *         used to locate the code points for each string.
+   */
+  uvector_pair normalize(char const* d_strings,
+                         cudf::size_type const* d_offsets,
+                         cudf::size_type num_strings,
+                         rmm::cuda_stream_view stream) const;
+
+ private:
+  bool const do_lower_case;
+  codepoint_metadata_type const* d_cp_metadata;
+  aux_codepoint_data_type const* d_aux_table;
+};
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/detail/hash_utils.cuh b/cpp/src/text/subword/detail/hash_utils.cuh
new file mode 100644
index 0000000..dc07371
--- /dev/null
+++ b/cpp/src/text/subword/detail/hash_utils.cuh
@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cstdint>
+
+namespace nvtext {
+namespace detail {
+
+// Used for hashing functions in this file
+constexpr uint64_t PRIME = 281474976710677;
+
+/**
+ * @brief This does a multiply mod 48 without overflow for the sdbm hash "pop" method.
+ *
+ * This method computes the bottom 48 bits of the result of multiplying two numbers
+ * respecting the restrictions specified by the parameters.
+ *
+ * It works by splitting `num` into 16 bit chunks and performing repeated multiplies.
+ * The result of all of those multiplies are added together.
+ *
+ * @param num_48bit A multiplicand that is at most 48 bits.
+ * @param num Any 64 bit number to multiply by num_48bit mod 2**48
+ * @return (num_48bit * num) mod 2**48
+ */
+__device__ uint64_t mul_mod_48(uint64_t num_48bit, uint64_t num)
+{
+  constexpr uint64_t mask          = (1ULL << 48) - 1;
+  constexpr uint8_t bit_chunk_size = 16;
+
+  uint64_t result = 0;
+#pragma unroll
+  for (uint8_t i = 0; i < sizeof(num) / 2; ++i) {
+    auto const shift_amt = bit_chunk_size * i;
+    auto const bottom_16 = static_cast<uint16_t>(num >> shift_amt);
+    // update result
+    result = result + ((num_48bit * bottom_16) << shift_amt);
+    result &= mask;
+  }
+  return result;
+}
+
+/**
+ * @brief Computes the sdbm hash for the sequence starting at sequence_start up to length sequences.
+ *
+ * A start value for the sdbm hash can optionally be given. This is useful when checking if elements
+ * starting with "##" exist in the table since we can pass in the hash of "##" as the start value.
+ *
+ * @param sequence_start Code points to hash
+ * @param length Number of code points to hash
+ * @param start_value Initializes the hash computation.
+ * @return The sdbm hash of all elements in range `[sequence_start, sequence_start + length)`
+ */
+__device__ uint64_t sdbm_hash(uint32_t const* sequence_start,
+                              uint32_t length,
+                              uint64_t start_value = 0)
+{
+  // This expression computes h_{i} = (65599*h{i-1} + new_val) mod 2^48 and was obtained from here:
+  // http://www.cse.yorku.ca/~oz/hash.html
+
+  constexpr uint64_t mask = (1ULL << 48) - 1;
+  uint64_t hash_value     = start_value;
+
+  for (int i = 0; i < length; ++i) {
+    hash_value = ((hash_value << 6) + (hash_value << 16) - hash_value) & mask;
+    hash_value = (hash_value + (sequence_start[i] & mask)) & mask;
+  }
+
+  return hash_value;
+}
+
+/**
+ * @brief Removes the last value added to the hash.
+ *
+ * If we have `current_hash = sdbm_hash("dog")` then, `prev_sdbm_hash(current_hash, cp(g))`
+ * returns the `sdbm_hash("do")` where it is assumed cp returns the unicode code point for a
+ * given letter.
+ *
+ * @param current_hash The current value used to compute the previous sdbm.
+ * @param last_val Last value used in the hash sequence.
+ * @return The hash value before that new value was added.
+ */
+__device__ uint64_t prev_sdbm_hash(uint64_t current_hash, uint32_t last_val)
+{
+  constexpr uint64_t mask = (1ULL << 48) - 1;
+  // Multiplicative inverse of 65599 under mod 2**48
+  constexpr uint64_t mod_inverse = 24320495251391;
+  uint64_t const prev_hash =
+    mul_mod_48(mod_inverse, current_hash) - mul_mod_48(mod_inverse, last_val);
+  return prev_hash & mask;
+}
+
+/**
+ * @brief The hash function used for accesses to the table.
+ *
+ * This is a universal hash function with parameters chosen to achieve perfect hashing.
+ *
+ * Algorithm is `((a*k + b) % PRIME) % table_size` where @ref PRIME is globally defined
+ * as 281474976710677
+ *
+ * @param key Value to hash
+ * @param a Outer table first constant
+ * @param b Outer table second constant
+ * @param table_size Number of bins in the hash table.
+ * @return The computed hash value.
+ */
+__device__ uint32_t hash(uint64_t key, uint64_t a, uint64_t b, uint32_t table_size)
+{
+  return ((a * key + b) % PRIME) % table_size;
+}
+
+/**
+ * @brief Retrieves the value associated with key in the hash table.
+ *
+ * If there is no value in the table with the input key, -1 is returned.
+ *
+ * This method will ALWAYS return the correct value if a key is in the table. However, some
+ * code point sequences may hash to the same key in which case an incorrect value is returned.
+ * This collision is rare and will not likely affect the model's performance.
+ *
+ * @param key The key to search for in the hash table
+ * @param hash_table A pointer to the flattened hash table
+ * @param bin_coefficients A pointer to the hashing parameters for each bin in the hash table.
+ * @param bin_offsets A pointer to the start of each bin in the hash table.
+ * @return -1 if key is not in the hash table. If the key is in the table returns an index in
+ *         [0, vocab_size) indicating the index for the token in the bert model.
+ */
+__device__ int retrieve(uint64_t const key,
+                        uint32_t const outer_table_a,
+                        uint32_t const outer_table_b,
+                        uint16_t const num_bins,
+                        uint64_t const* hash_table,
+                        uint64_t const* bin_coefficients,
+                        uint16_t const* bin_offsets)
+{
+  auto const hash_bin        = hash(key, outer_table_a, outer_table_b, num_bins);
+  auto const bin_params      = bin_coefficients[hash_bin];
+  auto const start_ht_offset = bin_offsets[hash_bin];
+
+  // The shift constants are due to how the hash coefficients are packed and are
+  // obtained from the python script perfect_hash.py which generates the expected tables.
+  auto const inner_bin_a = bin_params >> 16;
+  auto const inner_bin_b = (bin_params >> 9) & ((1 << 7) - 1);
+  auto const bin_size    = static_cast<uint8_t>(bin_params);
+
+  if (bin_size == 0) { return -1; }  // key hash has no bin parameters
+
+  auto const inner_offset = hash(key, inner_bin_a, inner_bin_b, bin_size);
+  auto const kv_pair      = hash_table[start_ht_offset + inner_offset];
+
+  auto const expected_key = kv_pair >> 16;
+  // extract value from encoded key-value
+  int value = kv_pair & ((1 << 16) - 1);
+  return key == expected_key ? value : -1;
+}
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/detail/tokenizer_utils.cuh b/cpp/src/text/subword/detail/tokenizer_utils.cuh
new file mode 100644
index 0000000..7cc0e7c
--- /dev/null
+++ b/cpp/src/text/subword/detail/tokenizer_utils.cuh
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <text/subword/detail/cp_data.h>
+
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cstdint>
+
+namespace nvtext {
+namespace detail {
+
+constexpr int THREADS_PER_BLOCK = 64;
+
+/**
+ * @brief In-place update of offsets values.
+ *
+ * In the `d_chars_up_to_idx`, the last character of each string is basically
+ * the offset (i.e. the number of characters) in that string.
+ *
+ * Example
+ * @code{.pseudo}
+ * // 3 strings with sizes 5,4,2
+ * d_offsets = [0,5,9,11]
+ * // code points generated per character (as offsets)
+ * // 2nd string has an extra code point at its first char
+ * d_chars_up_to_idx = [1,2,3,4,5,6,8,9,10,11,12]
+ * d_chars_up_to_idx[d_offsets[1-3]] is [5,10,12]
+ * => d_offsets becomes [0,5,10,12]
+ * @endcode
+ */
+struct update_strings_lengths_fn {
+  uint32_t const* d_chars_up_to_idx;
+  cudf::size_type* d_offsets;
+
+  __device__ void operator()(cudf::size_type idx)
+  {
+    auto const offset = d_offsets[idx];
+    d_offsets[idx]    = offset > 0 ? d_chars_up_to_idx[offset - 1] : 0;
+  }
+};
+
+/**
+ * @brief Retrieve the code point metadata table.
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+rmm::device_uvector<codepoint_metadata_type> get_codepoint_metadata(rmm::cuda_stream_view stream);
+
+/**
+ * @brief Retrieve the auxiliary code point metadata table.
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches.
+ */
+rmm::device_uvector<aux_codepoint_data_type> get_aux_codepoint_data(rmm::cuda_stream_view stream);
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/detail/wordpiece_tokenizer.hpp b/cpp/src/text/subword/detail/wordpiece_tokenizer.hpp
new file mode 100644
index 0000000..e191890
--- /dev/null
+++ b/cpp/src/text/subword/detail/wordpiece_tokenizer.hpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <text/subword/detail/data_normalizer.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace nvtext {
+
+struct hashed_vocabulary;
+
+namespace detail {
+
+/**
+ * @brief This splits words into tokens contained in the model vocabulary file.
+ *
+ * The tokenizer first normalizes the character bytes, identifies the words in
+ * each string, and then converts each word in to a integer token-id per the
+ * provided vocabulary hash table.
+ *
+ * The `tokenize()` function produces two device vectors `uvector_pair`.
+ * The first is the token-ids for each word identified in the input strings.
+ * The second is the offsets to identify which ids go with each string.
+ *
+ * Temporary buffers are created equal to 3 uint32 values plus 1 byte per input byte.
+ * Also the normalize step allocates an additional 16x bytes per input byte but 8x
+ * of this memory is reused by the `tokenize()` function.
+ * This means 13x + 8x = 21x the number bytes of the input strings buffer must be
+ * available to call the `tokenize()` function in this class.
+ */
+class wordpiece_tokenizer {
+ public:
+  /**
+   * @brief Creates a full tokenizer that cleans the text and splits it into tokens.
+   *
+   * @param vocab_table The preprocessed hashed vocabulary data.
+   * @param max_sequence_length Limit the number of token-ids per row in the output
+   * @param stride Each row in tensor-token-ids will replicate `max_sequence_length - stride`
+   *        token-ids from the previous row, unless it is the first string.
+   * @param do_truncate If true, the tokenizer will discard all the token-ids after
+   *        `max_sequence_length` for each input string. If false, it will use a
+   *        new row in the tensor-token-ids to continue generating the output.
+   * @param do_lower_case If true, the tokenizer will convert uppercase characters in the
+   *        input stream to lowercase and strip accents from those characters.
+   *        If false, accented and uppercase characters are not transformed.
+   * @param max_word_length The length of the longest word that will be tokenized. Words
+   *        longer than this will simply be replaced by the unknown token
+   *        specified in the `vocab_file`.
+   */
+  wordpiece_tokenizer(hashed_vocabulary const& vocab_table,
+                      uint32_t max_sequence_length,
+                      uint32_t stride,
+                      bool do_truncate,
+                      bool do_lower_case,
+                      uint32_t max_word_length = 200);
+
+  /**
+   * @brief Splits the input text into token ids.
+   *
+   * This class is simply a wrapper around the basic and word piece tokenizers.
+   *
+   * @param d_strings A vector of strings which MUST be encoded in the utf8 format.
+   * @param d_offsets A vector of byte offsets to the beginning of individual strings in
+   *        the `d_strings` parameter.
+   * @param num_strings The number of strings in `d_strings`.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @return Pointer to token-ids and token-id offsets
+   */
+  uvector_pair tokenize(char const* d_strings,
+                        cudf::size_type const* d_offsets,
+                        cudf::size_type num_strings,
+                        rmm::cuda_stream_view stream);
+
+ private:
+  /**
+   * @brief Splits the code points from the normalizer into tokens.
+   *
+   * @param[in,out] cps_and_offsets The output code points and offsets
+   *        from the normalizer.
+   *        The data is modified to contain the token ids and token counts
+   *        per string.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   */
+  void tokenize(uvector_pair& cps_and_offsets, rmm::cuda_stream_view stream);
+
+  hashed_vocabulary const& vocab_table;
+  data_normalizer normalizer;  // removes punctuation, accents, etc
+  uint32_t const max_sequence_length;
+  uint32_t const stride;
+  bool const do_truncate;
+  uint32_t const max_word_length;
+};
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/load_hash_file.cu b/cpp/src/text/subword/load_hash_file.cu
new file mode 100644
index 0000000..cb18d0e
--- /dev/null
+++ b/cpp/src/text/subword/load_hash_file.cu
@@ -0,0 +1,297 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/subword/detail/codepoint_metadata.ah>
+#include <text/subword/detail/tokenizer_utils.cuh>
+
+#include <nvtext/detail/load_hash_file.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/utilities.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+
+#include <algorithm>
+#include <cstdint>
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+namespace nvtext {
+namespace detail {
+
+/**
+ * @brief Retrieve the code point metadata table.
+ *
+ * Build the code point metadata table in device memory
+ * using the vector pieces from codepoint_metadata.ah
+ */
+rmm::device_uvector<codepoint_metadata_type> get_codepoint_metadata(rmm::cuda_stream_view stream)
+{
+  auto table_vector = rmm::device_uvector<codepoint_metadata_type>(codepoint_metadata_size, stream);
+  auto table        = table_vector.data();
+  thrust::fill(rmm::exec_policy(stream),
+               table + cp_section1_end,
+               table + codepoint_metadata_size,
+               codepoint_metadata_default_value);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(table,
+                                codepoint_metadata,
+                                cp_section1_end * sizeof(codepoint_metadata[0]),  // 1st section
+                                cudaMemcpyDefault,
+                                stream.value()));
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    table + cp_section2_begin,
+    cp_metadata_917505_917999,
+    (cp_section2_end - cp_section2_begin + 1) * sizeof(codepoint_metadata[0]),  // 2nd section
+    cudaMemcpyDefault,
+    stream.value()));
+  return table_vector;
+}
+
+/**
+ * @brief Retrieve the aux code point data table.
+ *
+ * Build the aux code point data table in device memory
+ * using the vector pieces from codepoint_metadata.ah
+ */
+rmm::device_uvector<aux_codepoint_data_type> get_aux_codepoint_data(rmm::cuda_stream_view stream)
+{
+  auto table_vector = rmm::device_uvector<aux_codepoint_data_type>(aux_codepoint_data_size, stream);
+  auto table        = table_vector.data();
+  thrust::fill(rmm::exec_policy(stream),
+               table + aux_section1_end,
+               table + aux_codepoint_data_size,
+               aux_codepoint_default_value);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(table,
+                                aux_codepoint_data,
+                                aux_section1_end * sizeof(aux_codepoint_data[0]),  // 1st section
+                                cudaMemcpyDefault,
+                                stream.value()));
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    table + aux_section2_begin,
+    aux_cp_data_44032_55203,
+    (aux_section2_end - aux_section2_begin + 1) * sizeof(aux_codepoint_data[0]),  // 2nd section
+    cudaMemcpyDefault,
+    stream.value()));
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    table + aux_section3_begin,
+    aux_cp_data_70475_71099,
+    (aux_section3_end - aux_section3_begin + 1) * sizeof(aux_codepoint_data[0]),  // 3rd section
+    cudaMemcpyDefault,
+    stream.value()));
+  CUDF_CUDA_TRY(cudaMemcpyAsync(
+    table + aux_section4_begin,
+    aux_cp_data_119134_119232,
+    (aux_section4_end - aux_section4_begin + 1) * sizeof(aux_codepoint_data[0]),  // 4th section
+    cudaMemcpyDefault,
+    stream.value()));
+  return table_vector;
+}
+
+namespace {
+/**
+ * @brief Convert string to uint32.
+ *
+ * This just wraps the std::stoi but provides a nice error message
+ * in case the hash file format is incorrect.
+ */
+uint32_t str_to_uint32(std::string const& str, uint64_t line_no)
+{
+  try {
+    return std::stoi(str);  // there is no std::stoui
+  } catch (std::exception const& exc) {
+    std::string message("Line ");
+    message += std::to_string(line_no) + ": ";
+    message += "cannot convert integer from '";
+    message += str;
+    message += "': ";
+    message += exc.what();
+    std::cerr << message << std::endl;
+    throw;
+  }
+}
+
+/**
+ * @brief Convert string to uint64.
+ *
+ * This just wraps the std::stoul but provides a nice error message
+ * in case the hash file format is incorrect.
+ */
+uint64_t str_to_uint64(std::string const& str, uint64_t line_no)
+{
+  try {
+    return std::stoul(str);
+  } catch (std::exception const& exc) {
+    std::string message("Line ");
+    message += std::to_string(line_no) + ": ";
+    message += "cannot convert integer from '";
+    message += str;
+    message += "': ";
+    message += exc.what();
+    std::cerr << message << std::endl;
+    throw;
+  }
+}
+}  // namespace
+
+/**
+ * @brief Loads a text file representing the hashed vocabulary into hashed_vocabulary struct.
+ *
+ * @code{.pseudo}
+ * Format of the file (ASCII text file with numbers):
+ * First 3 lines have the following values:
+ *  outer_hash_a
+ *  outer_hash_b
+ *  number-of-bins
+ * The next number-of-bins lines has two values in each line separated by a space
+ *  coefficient offset
+ *  ...
+ * Next line has the size (number of lines) of the table followed
+ * by the table values -- one value per line.
+ * The last three lines:
+ *  unknown_token_id
+ *  first_token_id
+ *  separator_token_id
+ * @endcode
+ *
+ * @param filename_hashed_vocabulary Path to text file containing hashed vocabulary
+ * @return object containing hash table elements for the wordpiece tokenizer
+ */
+std::unique_ptr<hashed_vocabulary> load_vocabulary_file(
+  std::string const& filename_hashed_vocabulary,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  hashed_vocabulary result;
+  std::ifstream hash_file(filename_hashed_vocabulary);
+  CUDF_EXPECTS(hash_file.good(), "Could not open " + filename_hashed_vocabulary);
+
+  uint64_t line_no = 1;
+  std::string line;
+  std::getline(hash_file, line);
+  result.outer_hash_a = str_to_uint32(line, line_no++);
+
+  std::getline(hash_file, line);
+  result.outer_hash_b = str_to_uint32(line, line_no++);
+
+  std::getline(hash_file, line);
+  result.num_bins = str_to_uint32(line, line_no++);
+
+  std::vector<uint64_t> bin_coefficients(result.num_bins);
+  std::vector<uint16_t> bin_offsets(result.num_bins);
+
+  for (int i = 0; i < result.num_bins; ++i) {
+    std::getline(hash_file, line);
+    size_t loc_of_space = line.find(" ");
+    CUDF_EXPECTS(loc_of_space != line.npos, "invalid hash file format");
+
+    std::string first_num  = line.substr(0, loc_of_space);
+    std::string second_num = line.substr(loc_of_space + 1, line.length());
+
+    bin_coefficients[i] = str_to_uint64(first_num, line_no);
+    bin_offsets[i]      = str_to_uint32(second_num, line_no);
+    ++line_no;
+  }
+
+  std::getline(hash_file, line);
+  uint64_t hash_table_length = str_to_uint64(line, line_no++);
+  std::vector<uint64_t> table(hash_table_length);
+
+  std::generate(table.begin(), table.end(), [&hash_file, &line_no]() {
+    std::string line;
+    std::getline(hash_file, line);
+    return str_to_uint64(line, line_no++);
+  });
+
+  std::getline(hash_file, line);
+  result.unknown_token_id = str_to_uint32(line, line_no++);
+
+  std::getline(hash_file, line);
+  result.first_token_id = str_to_uint32(line, line_no++);
+
+  std::getline(hash_file, line);
+  result.separator_token_id = str_to_uint32(line, line_no++);
+
+  // Transfer hash table to columns
+  result.table = cudf::make_numeric_column(cudf::data_type{cudf::type_id::UINT64},
+                                           table.size(),
+                                           cudf::mask_state::UNALLOCATED,
+                                           stream,
+                                           mr);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(result.table->mutable_view().data<uint64_t>(),
+                                table.data(),
+                                table.size() * sizeof(uint64_t),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  result.bin_coefficients = cudf::make_numeric_column(cudf::data_type{cudf::type_id::UINT64},
+                                                      bin_coefficients.size(),
+                                                      cudf::mask_state::UNALLOCATED,
+                                                      stream,
+                                                      mr);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(result.bin_coefficients->mutable_view().data<uint64_t>(),
+                                bin_coefficients.data(),
+                                bin_coefficients.size() * sizeof(uint64_t),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  result.bin_offsets = cudf::make_numeric_column(cudf::data_type{cudf::type_id::UINT16},
+                                                 bin_offsets.size(),
+                                                 cudf::mask_state::UNALLOCATED,
+                                                 stream,
+                                                 mr);
+  CUDF_CUDA_TRY(cudaMemcpyAsync(result.bin_offsets->mutable_view().data<uint16_t>(),
+                                bin_offsets.data(),
+                                bin_offsets.size() * sizeof(uint16_t),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  auto cp_metadata            = detail::get_codepoint_metadata(stream);
+  auto const cp_metadata_size = static_cast<cudf::size_type>(cp_metadata.size());
+  result.cp_metadata = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::UINT32},
+                                                      cp_metadata_size,
+                                                      cp_metadata.release(),
+                                                      rmm::device_buffer{},
+                                                      0);
+
+  auto aux_cp_table            = detail::get_aux_codepoint_data(stream);
+  auto const aux_cp_table_size = static_cast<cudf::size_type>(aux_cp_table.size());
+  result.aux_cp_table = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::UINT64},
+                                                       aux_cp_table_size,
+                                                       aux_cp_table.release(),
+                                                       rmm::device_buffer{},
+                                                       0);
+
+  return std::make_unique<hashed_vocabulary>(std::move(result));
+}
+
+}  // namespace detail
+
+std::unique_ptr<hashed_vocabulary> load_vocabulary_file(
+  std::string const& filename_hashed_vocabulary, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::load_vocabulary_file(filename_hashed_vocabulary, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/load_merges_file.cu b/cpp/src/text/subword/load_merges_file.cu
new file mode 100644
index 0000000..db6ad2e
--- /dev/null
+++ b/cpp/src/text/subword/load_merges_file.cu
@@ -0,0 +1,174 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/subword/bpe_tokenizer.cuh>
+
+#include <nvtext/bpe_tokenize.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/functional.h>
+
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Loads a text file of merge-pairs into a strings column.
+ *
+ * The line position in the file indicates the pair's rank.
+ *
+ * @code{.pseudo}
+ * Format of the file:
+ * #version ..
+ * a1 a2
+ * b1 b2
+ * c1 c2
+ * ...
+ * @endcode
+ *
+ * @param filename_merges Path to text file containing merge-pairs
+ * @return object containing table elements for the BPE function
+ */
+std::unique_ptr<cudf::column> load_file_to_column(std::string const& filename_merges,
+                                                  rmm::cuda_stream_view stream,
+                                                  rmm::mr::device_memory_resource* mr)
+{
+  std::ifstream merges_file(filename_merges);
+  CUDF_EXPECTS(merges_file.good(), "Could not open " + filename_merges);
+
+  std::vector<char> chars{};
+  std::vector<cudf::size_type> offsets(1, 0);
+
+  std::string line;
+  std::getline(merges_file, line);
+  std::string version = "#version";
+  if (line.substr(0, version.size()).compare(version) == 0) { std::getline(merges_file, line); }
+
+  // This is a text file delimited only by CR/LF.
+  // TODO: Look into using the CSV reader to load the strings column instead.
+  while (!line.empty()) {
+    chars.insert(chars.end(), std::cbegin(line), std::cend(line));
+    offsets.push_back(offsets.back() + line.length());
+    std::getline(merges_file, line);
+  }
+
+  CUDF_EXPECTS(!chars.empty(), "No data found in " + filename_merges);
+
+  auto d_chars   = cudf::detail::make_device_uvector_async(chars, stream, mr);
+  auto d_offsets = cudf::detail::make_device_uvector_async(offsets, stream, mr);
+  return cudf::make_strings_column(d_chars, d_offsets, {}, 0);
+}
+
+std::unique_ptr<detail::merge_pairs_map_type> initialize_merge_pairs_map(
+  cudf::column_device_view const& input, rmm::cuda_stream_view stream)
+{
+  // Ensure capacity is at least (size/0.7) as documented here:
+  // https://github.com/NVIDIA/cuCollections/blob/6ec8b6dcdeceea07ab4456d32461a05c18864411/include/cuco/static_map.cuh#L179-L182
+  auto merge_pairs_map = std::make_unique<merge_pairs_map_type>(
+    static_cast<size_t>(input.size() * 2),  // capacity is 2x;
+    cuco::empty_key{-1},
+    cuco::empty_value{-1},  // empty value is not used
+    bpe_equal{input},
+    probe_scheme{bpe_hasher{input}},
+    hash_table_allocator_type{default_allocator<char>{}, stream},
+    stream.value());
+
+  auto iter = cudf::detail::make_counting_transform_iterator(
+    0, [] __device__(cudf::size_type idx) { return cuco::make_pair(idx, idx); });
+
+  merge_pairs_map->insert_async(iter, iter + input.size(), stream.value());
+
+  return merge_pairs_map;
+}
+
+std::unique_ptr<bpe_merge_pairs::bpe_merge_pairs_impl> create_bpe_merge_pairs_impl(
+  std::unique_ptr<cudf::column>&& input, rmm::cuda_stream_view stream)
+{
+  auto d_input     = cudf::column_device_view::create(input->view(), stream);
+  auto merge_pairs = initialize_merge_pairs_map(*d_input, stream);
+  return std::make_unique<nvtext::bpe_merge_pairs::bpe_merge_pairs_impl>(
+    std::move(input), std::move(d_input), std::move(merge_pairs));
+}
+
+std::unique_ptr<bpe_merge_pairs::bpe_merge_pairs_impl> create_bpe_merge_pairs_impl(
+  cudf::strings_column_view const& input,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  return create_bpe_merge_pairs_impl(std::make_unique<cudf::column>(input.parent(), stream, mr),
+                                     stream);
+}
+
+}  // namespace
+
+std::unique_ptr<bpe_merge_pairs> load_merge_pairs_file(std::string const& filename_merges,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  auto input_column = load_file_to_column(filename_merges, stream, mr);
+  return std::make_unique<bpe_merge_pairs>(std::move(input_column), stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<bpe_merge_pairs> load_merge_pairs_file(std::string const& filename_merges,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::load_merge_pairs_file(filename_merges, cudf::get_default_stream(), mr);
+}
+
+bpe_merge_pairs::bpe_merge_pairs_impl::bpe_merge_pairs_impl(
+  std::unique_ptr<cudf::column>&& merge_pairs,
+  std::unique_ptr<cudf::column_device_view, std::function<void(cudf::column_device_view*)>>&&
+    d_merge_pairs,
+  std::unique_ptr<detail::merge_pairs_map_type>&& merge_pairs_map)
+  : merge_pairs(std::move(merge_pairs)),
+    d_merge_pairs(std::move(d_merge_pairs)),
+    merge_pairs_map(std::move(merge_pairs_map))
+{
+}
+
+bpe_merge_pairs::bpe_merge_pairs(std::unique_ptr<cudf::column>&& input,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource*)
+  : impl(detail::create_bpe_merge_pairs_impl(std::move(input), stream))
+{
+}
+
+bpe_merge_pairs::bpe_merge_pairs(cudf::strings_column_view const& input,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+  : impl(detail::create_bpe_merge_pairs_impl(input, stream, mr))
+{
+}
+
+bpe_merge_pairs::~bpe_merge_pairs() = default;
+
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/subword_tokenize.cu b/cpp/src/text/subword/subword_tokenize.cu
new file mode 100644
index 0000000..1a3084a
--- /dev/null
+++ b/cpp/src/text/subword/subword_tokenize.cu
@@ -0,0 +1,312 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <nvtext/detail/load_hash_file.hpp>
+#include <nvtext/subword_tokenize.hpp>
+#include <text/subword/detail/wordpiece_tokenizer.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform_scan.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+/**
+ * @brief Convert tokens and row2tensor map to final tensor data.
+ *
+ * @param[in] token_ids Tokens from tokenizer
+ * @param[in] offsets Offsets to each string's output row of tokens
+ * @param[in] row2tensor String to tensor token counts
+ * @param[in] row2row_within_tensor Token counts within sub-rows of the output
+ * @param[in] max_sequence_length Maximum number of tokens in a row
+ * @param[in] nrows_tensor_token_ids Total number of output tensor rows
+ * @param[in] stride Number of tokens in sub-rows
+ * @param[in] do_truncate True if tokens should not spill into sub-rows in the output
+ * @param[out] final_tensor Output vector of token-ids
+ * @param[out] attn_mask Identifies valid token id entries
+ * @param[out] metadata Additional data per row
+ */
+__global__ void kernel_compute_tensor_metadata(
+  // input
+  uint32_t const* token_ids,
+  cudf::size_type const* offsets,
+  uint32_t const* row2tensor,
+  uint32_t const* row2row_within_tensor,
+  uint32_t max_sequence_length,
+  uint32_t nrows_tensor_token_ids,
+  uint32_t stride,
+  bool do_truncate,
+  // output
+  uint32_t* final_tensor,
+  uint32_t* attn_mask,
+  uint32_t* metadata)
+{
+  cudf::thread_index_type const output_idx =
+    threadIdx.x + static_cast<cudf::thread_index_type>(blockIdx.x) *
+                    static_cast<cudf::thread_index_type>(blockDim.x);
+  if (output_idx >= (static_cast<cudf::thread_index_type>(nrows_tensor_token_ids) *
+                     static_cast<cudf::thread_index_type>(max_sequence_length))) {
+    return;
+  }
+
+  uint32_t const absolute_row_id         = output_idx / max_sequence_length;
+  uint32_t const tensor_id               = row2tensor[absolute_row_id];
+  uint32_t const row_within_tensor       = row2row_within_tensor[absolute_row_id];
+  uint32_t const offset_token_ids_tensor = offsets[tensor_id];
+  uint32_t const n_tokens_tensor         = offsets[tensor_id + 1] - offset_token_ids_tensor;
+  // check for last row within tensor
+  bool const last_row_of_tensor = (absolute_row_id == nrows_tensor_token_ids - 1) ||
+                                  (row2tensor[absolute_row_id + 1] != tensor_id);
+  // compute input offset to retrieve token ids
+  uint32_t const token_idx = output_idx % max_sequence_length;
+  uint32_t const row_offset_token_ids =
+    offset_token_ids_tensor + token_idx +
+    (row_within_tensor ? (max_sequence_length + (stride * (row_within_tensor - 1))) : 0);
+
+  if (row_within_tensor == 0) {
+    if (token_idx < n_tokens_tensor) {
+      // copy token ids
+      final_tensor[output_idx] = token_ids[row_offset_token_ids];
+      attn_mask[output_idx]    = 1;
+    } else {
+      // pad with 0
+      final_tensor[output_idx] = 0;
+      attn_mask[output_idx]    = 0;
+    }
+  } else {
+    uint32_t const n_replicates = max_sequence_length - stride;
+    if ((row_offset_token_ids - n_replicates) < (offset_token_ids_tensor + n_tokens_tensor)) {
+      // replicate elements from previous row or copy new tokens
+      final_tensor[output_idx] = token_ids[row_offset_token_ids - n_replicates];
+      attn_mask[output_idx]    = 1;
+    } else {
+      // pad with 0
+      final_tensor[output_idx] = 0;
+      attn_mask[output_idx]    = 0;
+    }
+  }
+
+  // write metadata
+  if (token_idx == 0) {
+    auto const metadata_idx    = absolute_row_id * 3;  // three metadata values per output row
+    metadata[metadata_idx]     = tensor_id;
+    metadata[metadata_idx + 1] = (row_within_tensor == 0) ? 0 : (max_sequence_length - stride) / 2;
+    metadata[metadata_idx + 2] = [&] {
+      if (!last_row_of_tensor) return max_sequence_length - (max_sequence_length - stride) / 2 - 1;
+      if (n_tokens_tensor <= max_sequence_length)  // we fit, all good
+        return (n_tokens_tensor > 0) ? (n_tokens_tensor - 1) : 0;
+      if (do_truncate) return (max_sequence_length - 1);
+
+      auto const final_row_value =
+        (max_sequence_length - stride) + (n_tokens_tensor - max_sequence_length) % stride;
+      return (final_row_value > 0) ? (final_row_value - 1) : 0;
+    }();
+  }
+}
+
+// this happens if there are no tokens in the input
+tokenizer_result build_empty_result(cudf::size_type size,
+                                    uint32_t max_sequence_length,
+                                    rmm::cuda_stream_view stream,
+                                    rmm::mr::device_memory_resource* mr)
+{
+  auto zero = cudf::numeric_scalar<uint32_t>(0, true, stream);
+  auto ids  = cudf::detail::sequence(size * max_sequence_length, zero, zero, stream, mr);
+  auto mask = cudf::detail::sequence(size * max_sequence_length, zero, zero, stream, mr);
+
+  auto metadata = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_id::UINT32}, size * 3, cudf::mask_state::UNALLOCATED, stream, mr);
+  thrust::tabulate(rmm::exec_policy(stream),
+                   metadata->mutable_view().begin<uint32_t>(),
+                   metadata->mutable_view().end<uint32_t>(),
+                   [] __device__(auto idx) { return ((idx % 3) == 0) ? idx : 0; });
+  metadata->set_null_count(0);
+
+  return tokenizer_result{
+    0, max_sequence_length, std::move(ids), std::move(mask), std::move(metadata)};
+}
+
+}  // namespace
+
+tokenizer_result subword_tokenize(cudf::strings_column_view const& strings,
+                                  hashed_vocabulary const& vocab_table,
+                                  uint32_t max_sequence_length,
+                                  uint32_t stride,
+                                  bool do_lower_case,
+                                  bool do_truncate,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(stride <= max_sequence_length,
+               "stride must be less than or equal to max_sequence_length");
+  auto const strings_count = strings.size();
+  if (strings_count == strings.null_count()) {  // empty or all-null returns empty
+    return tokenizer_result{0,
+                            max_sequence_length,
+                            cudf::make_empty_column(cudf::data_type{cudf::type_id::UINT32}),
+                            cudf::make_empty_column(cudf::data_type{cudf::type_id::UINT32}),
+                            cudf::make_empty_column(cudf::data_type{cudf::type_id::UINT32})};
+  }
+  CUDF_EXPECTS(
+    max_sequence_length <=
+      (static_cast<std::size_t>(std::numeric_limits<cudf::size_type>::max()) / strings_count),
+    "max_sequence_length times number of input rows exceeds the column size limit",
+    std::overflow_error);
+
+  auto const offsets   = strings.offsets();
+  auto const d_offsets = offsets.data<cudf::size_type>() + strings.offset();
+  auto const offset  = cudf::detail::get_value<cudf::size_type>(offsets, strings.offset(), stream);
+  auto const d_chars = strings.chars().data<char>() + offset;
+
+  // Create tokenizer
+  wordpiece_tokenizer tokenizer(
+    vocab_table, max_sequence_length, stride, do_truncate, do_lower_case);
+  // Run tokenizer
+  auto const tokens = tokenizer.tokenize(d_chars, d_offsets, strings_count, stream);
+  // assign output components
+  auto device_token_ids = tokens.first->data();
+  auto device_offsets   = tokens.second->data();
+
+  // Format output from tokenizer
+  // Each string can create 1 or more tensor entries.
+  // Compute the string-per-tensor offsets values by scanning
+  // over the number of tokens for each string.
+  rmm::device_uvector<uint32_t> offsets_per_tensor(strings_count + 1, stream);
+  auto d_offsets_per_tensor = offsets_per_tensor.data();
+
+  thrust::transform_exclusive_scan(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<cudf::size_type>(0),
+    thrust::make_counting_iterator<cudf::size_type>(strings_count + 1),
+    offsets_per_tensor.begin(),
+    [device_offsets, do_truncate, max_sequence_length, stride, strings_count] __device__(
+      cudf::size_type idx) {
+      uint32_t const num_tokens =
+        idx < strings_count ? device_offsets[idx + 1] - device_offsets[idx] : 0;
+      if (do_truncate || num_tokens <= max_sequence_length) return uint32_t{1};
+      return 1 + ((num_tokens - max_sequence_length + stride - 1) / stride);
+    },
+    uint32_t{0},
+    thrust::plus<uint32_t>());
+  // last element is the total number of output rows
+  uint32_t const nrows_tensor_token_ids = offsets_per_tensor.element(strings_count, stream);
+  // if there are no tokens at all, build a specific empty result
+  if (nrows_tensor_token_ids == 0) {
+    return build_empty_result(strings_count, max_sequence_length, stream, mr);
+  }
+
+  // compute global_row to tensor, and global_row to within_tensor_row correspondence
+  rmm::device_uvector<uint32_t> row2tensor(nrows_tensor_token_ids, stream);
+  auto d_row2tensor = row2tensor.data();
+  rmm::device_uvector<uint32_t> row2row_within_tensor(nrows_tensor_token_ids, stream);
+  auto d_row2row_within_tensor = row2row_within_tensor.data();
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<uint32_t>(0),
+    strings_count,
+    [d_offsets_per_tensor, d_row2tensor, d_row2row_within_tensor] __device__(auto idx) {
+      uint32_t offset = d_offsets_per_tensor[idx];
+      uint32_t nrows  = d_offsets_per_tensor[idx + 1] - offset;
+      for (uint32_t jdx = 0; jdx < nrows; ++jdx) {
+        d_row2tensor[jdx + offset]            = idx;
+        d_row2row_within_tensor[jdx + offset] = jdx;
+      }
+    });
+
+  // create output data columns
+  auto tensor_token_ids = cudf::make_numeric_column(cudf::data_type{cudf::type_id::UINT32},
+                                                    nrows_tensor_token_ids * max_sequence_length,
+                                                    cudf::mask_state::UNALLOCATED,
+                                                    stream,
+                                                    mr);
+  auto tensor_attention_mask =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_id::UINT32},
+                              nrows_tensor_token_ids * max_sequence_length,
+                              cudf::mask_state::UNALLOCATED,
+                              stream,
+                              mr);
+  auto tensor_metadata = cudf::make_numeric_column(cudf::data_type{cudf::type_id::UINT32},
+                                                   nrows_tensor_token_ids * 3,
+                                                   cudf::mask_state::UNALLOCATED,
+                                                   stream,
+                                                   mr);
+
+  // compute final-tensor, mask, and metadata
+  constexpr int block_size = 256;
+  cudf::detail::grid_1d const grid{
+    static_cast<cudf::size_type>(nrows_tensor_token_ids * max_sequence_length), block_size};
+  kernel_compute_tensor_metadata<<<grid.num_blocks,
+                                   grid.num_threads_per_block,
+                                   0,
+                                   stream.value()>>>(
+    device_token_ids,
+    device_offsets,
+    d_row2tensor,
+    d_row2row_within_tensor,
+    max_sequence_length,
+    nrows_tensor_token_ids,
+    stride,
+    do_truncate,
+    tensor_token_ids->mutable_view().data<uint32_t>(),
+    tensor_attention_mask->mutable_view().data<uint32_t>(),
+    tensor_metadata->mutable_view().data<uint32_t>());
+
+  return tokenizer_result{nrows_tensor_token_ids,
+                          max_sequence_length,
+                          std::move(tensor_token_ids),
+                          std::move(tensor_attention_mask),
+                          std::move(tensor_metadata)};
+}
+
+}  // namespace detail
+
+tokenizer_result subword_tokenize(cudf::strings_column_view const& strings,
+                                  hashed_vocabulary const& vocabulary_table,
+                                  uint32_t max_sequence_length,
+                                  uint32_t stride,
+                                  bool do_lower_case,
+                                  bool do_truncate,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::subword_tokenize(strings,
+                                  vocabulary_table,
+                                  max_sequence_length,
+                                  stride,
+                                  do_lower_case,
+                                  do_truncate,
+                                  cudf::get_default_stream(),
+                                  mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/subword/wordpiece_tokenizer.cu b/cpp/src/text/subword/wordpiece_tokenizer.cu
new file mode 100644
index 0000000..3b91201
--- /dev/null
+++ b/cpp/src/text/subword/wordpiece_tokenizer.cu
@@ -0,0 +1,562 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/subword/detail/hash_utils.cuh>
+#include <text/subword/detail/tokenizer_utils.cuh>
+#include <text/subword/detail/wordpiece_tokenizer.hpp>
+
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/error.hpp>
+#include <nvtext/subword_tokenize.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/execution_policy.h>
+#include <thrust/fill.h>
+#include <thrust/find.h>
+#include <thrust/for_each.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/remove.h>
+#include <thrust/transform.h>
+#include <thrust/transform_scan.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+/**
+ * @brief Initializes the token-ids, word-indices, and token counts vectors.
+ *
+ * Each thread process a single code point from `code_points`.
+ * This also locates the start and end of each word within the `code_points` buffer.
+ * A word start is identified as a non-space character that appears right after a space.
+ * A word end is identified as a space character that appears right after a non-space one.
+ * If the code point at this thread does not represent a word start or word end,
+ * a max uint32_t value is written to the appropriate vector instead.
+ * A post processing step is required to filter the relevant values in these
+ * vectors.
+ *
+ * It is guaranteed that the same number of valid values will be written to both the
+ * start and end indices and that after the select step, the two arrays will be aligned.
+ * That is, `start_word_indices[word]` and `end_word_indices[word]` are the start and
+ * end for the same word.
+ *
+ * Memory required is 13 bytes per code point values:
+ * - 4 bytes each for `start_word_indices` and `end_word_indices`
+ * - 4 bytes for each `token_ids`
+ * - 1 byte for each `tokens_per_word`
+ * Also, there is a code point value for each byte in the input strings.
+ *
+ * @param[in] code_points A pointer to the code points in the strings after normalization.
+ * @param[out] start_word_indices An array of size `num_code_points` which will contain the
+ *        starting index for each word.
+ * @param[out] end_word_indices An array of size `num_code_points` which will contain the
+ *        ending index for each word.
+ * @param num_code_points The total number of code_points.
+ * @param[out] token_ids An array of size `num_code_points` which will hold the token ids.
+ *        This kernel just sets all the values to max uint32_t.
+ * @param[out] tokens_per_word An array of size `num_code_points` which hold the number of
+ *        tokens. This kernel just sets all the values to 0.
+ */
+__global__ void init_data_and_mark_word_start_and_ends(uint32_t const* code_points,
+                                                       uint32_t* start_word_indices,
+                                                       uint32_t* end_word_indices,
+                                                       size_t num_code_points,
+                                                       uint32_t* token_ids,
+                                                       uint8_t* tokens_per_word)
+{
+  cudf::thread_index_type char_for_thread = static_cast<cudf::thread_index_type>(blockDim.x) *
+                                              static_cast<cudf::thread_index_type>(blockIdx.x) +
+                                            threadIdx.x;
+
+  // Deal with the start_word_indices array
+  if (char_for_thread < num_code_points) {
+    uint32_t val_to_write = std::numeric_limits<uint32_t>::max();
+    if ((code_points[char_for_thread] != SPACE_CODE_POINT) && (char_for_thread > 0) &&
+        (code_points[char_for_thread - 1] == SPACE_CODE_POINT)) {
+      val_to_write = char_for_thread;
+    }
+    start_word_indices[char_for_thread] = val_to_write;
+
+    // Deal with the end_word_indices_array
+    val_to_write = std::numeric_limits<uint32_t>::max();
+    if ((code_points[char_for_thread] != SPACE_CODE_POINT) &&
+        (char_for_thread + 1 < num_code_points) &&
+        (code_points[char_for_thread + 1] == SPACE_CODE_POINT)) {
+      val_to_write = char_for_thread + 1;
+    }
+    end_word_indices[char_for_thread] = val_to_write;
+
+    token_ids[char_for_thread]       = std::numeric_limits<uint32_t>::max();
+    tokens_per_word[char_for_thread] = 0;
+  }
+}
+
+/**
+ * @brief Resolves the string boundaries for the start and end words.
+ *
+ * This kernel should be called after `init_data_and_mark_word_start_and_ends` with at
+ * least `num_strings` total threads.
+ *
+ * The start and end indices are updated to honor the string boundaries
+ * within the strings array. This corrects any word ranges that span across
+ * individual strings.
+ *
+ * @param code_points A pointer to the code points in the strings.
+ * @param strings_offsets An array containing the index of the starting character of each string
+ *        with an extra space at the end containing the total number of characters. As a result,
+ *        this array is of length num_strings + 1.
+ * @param start_word_indices An array which will contain the starting index for each word scattered
+ *        throughout. If an index does not represent a word start, the max-uint32_t value is written
+ *        to indicate this.
+ * @param end_word_indices An array which will contain the one past the end index for each word
+ *        scattered throughout. If an index does not represent a word end, the max uint32_t value is
+ *        written to indicate this.
+ * @param num_strings The total number of strings to be processed.
+ */
+__global__ void mark_string_start_and_ends(uint32_t const* code_points,
+                                           cudf::size_type const* strings_offsets,
+                                           uint32_t* start_word_indices,
+                                           uint32_t* end_word_indices,
+                                           uint32_t num_strings)
+{
+  cudf::thread_index_type idx = static_cast<cudf::thread_index_type>(blockDim.x) *
+                                  static_cast<cudf::thread_index_type>(blockIdx.x) +
+                                threadIdx.x;
+  // Ensure the starting character of each strings is written to the word start array.
+  if (idx <= num_strings) {
+    auto const offset = strings_offsets[idx];
+
+    if ((idx < num_strings) && (code_points[offset] != SPACE_CODE_POINT)) {
+      start_word_indices[offset] = offset;
+    }
+
+    if ((offset > 0) && (code_points[offset - 1] != SPACE_CODE_POINT)) {
+      end_word_indices[offset - 1] = offset;
+    }
+  }
+}
+
+/**
+ * @brief Currently supported special tokens.
+ *
+ * Code logic expects these to be 3 upper-case characters along
+ * with a single trailing space.
+ */
+__constant__ char special_tokens[35]{"BOS EOS UNK SEP PAD CLS MASK "};
+constexpr cudf::size_type MIN_ST_WIDTH = 4;  // Min token size in special_tokens
+constexpr cudf::size_type MAX_ST_WIDTH = 5;  // Max token size in special_tokens
+
+struct mark_special_tokens {
+  /**
+   * @brief Check given code-point array to the list of known
+   * special tokens.
+   */
+  __device__ bool is_special_token(uint32_t const* token, cudf::size_type size) const
+  {
+    if (size < MIN_ST_WIDTH || size > MAX_ST_WIDTH) return false;
+    char str_token[MAX_ST_WIDTH];
+    // convert code-points to chars
+    thrust::transform(thrust::seq, token, token + size, str_token, [](uint32_t cp) {
+      // also upper-case them to match again special_tokens array
+      return static_cast<char>(cp >= 'a' ? cp - 'a' + 'A' : cp);
+    });
+    // search the special tokens array for the str_token
+    cudf::string_view tokens(special_tokens, sizeof(special_tokens));
+    return tokens.find(str_token, size) != cudf::string_view::npos;
+  }
+
+  /**
+   * @brief Check code-points for special tokens and adjust indices.
+   *
+   * Tokens will appear in the `code_points` array as:
+   * `_[_ttt_]_` where `_` are single space characters and
+   *                   ttt is the variable-length token name
+   *
+   * The logic below uses the following variables to represent position
+   * values in the `code_points` array after locating a special token:
+   * ```
+   * _ [ _ t t t _  ] _
+   *   ^   ^     ^  ^
+   *   si  sp    ep ei
+   * ```
+   * where `si` is `start_index`
+   *       `sp` is `start_pos`
+   *       `ep` is `end_pos`
+   *       `ei` is `end_index`
+   *
+   * When a special token is found, the `code_points` are adjusted
+   * to remove the spaces and capitalize the name.
+   * ```
+   * _ [ _ t t t _ ] _  is updated to
+   * _ [ T T T ] _ ] _
+   * ```
+   * This is required for the downstream word-piece tokenizer to
+   * match it to the vocabulary hash table.
+   *
+   * The `start_word_indices` and `end_word_indices` are updated to
+   * identify the token and to ignore the extra trailing `]` character.
+   */
+  __device__ void operator()(size_t idx) const
+  {
+    uint32_t const start_index = start_word_indices[idx];
+    if ((start_index == std::numeric_limits<uint32_t>::max()) ||
+        ((start_index + MIN_ST_WIDTH + 2) > num_code_points))
+      return;
+    if (code_points[start_index] != '[') return;
+
+    // check for matching end bracket
+    uint32_t const start_pos = start_index + 2;  // after the space delimiter
+    // search for next start-word and then check it is a ']'
+    uint32_t const end_index = [&] {
+      auto const begin = start_word_indices + start_pos;
+      auto const width =
+        std::min(static_cast<size_t>(MAX_ST_WIDTH + 1), (num_code_points - start_pos));
+      auto const end = begin + width;
+      // checking the next start-word is more reliable than arbitrarily searching for ']'
+      // in case the text is split across string rows
+      auto const iter = thrust::find_if(thrust::seq, begin + 1, end, [](auto swi) {
+        return swi != std::numeric_limits<uint32_t>::max();
+      });
+      return iter == end ? start_index : static_cast<uint32_t>(iter - start_word_indices);
+    }();
+    if (code_points[end_index] != ']') return;
+
+    // check for special token
+    auto const size = static_cast<cudf::size_type>(end_index - start_pos);
+    if (!is_special_token(code_points + start_pos, size)) return;
+
+    // special token found
+    // adjust code-points
+    auto const end_pos = end_index - 2;
+    // change _[_ttt_]_ to _[TTT]_
+    for (auto left_idx = start_pos - 1; left_idx <= end_pos; ++left_idx) {
+      auto const cp         = code_points[left_idx + 1];
+      code_points[left_idx] = cp >= 'a' ? cp - 'a' + 'A' : cp;
+    }
+    code_points[end_pos] = ']';
+
+    // erase the intermediate indices
+    thrust::fill(thrust::seq,
+                 start_word_indices + start_index + 1,  // keep the first one
+                 start_word_indices + end_index + 1,
+                 std::numeric_limits<uint32_t>::max());
+    thrust::fill(thrust::seq,
+                 end_word_indices + start_index,
+                 end_word_indices + end_index + 1,
+                 std::numeric_limits<uint32_t>::max());
+
+    // reset the new end-word index
+    end_word_indices[end_pos] = end_pos + 1;
+  }
+
+  uint32_t* const code_points;
+  uint32_t* const start_word_indices;
+  uint32_t* const end_word_indices;
+  size_t const num_code_points;
+};
+
+/**
+ * @brief Converts words into token ids.
+ *
+ * Each thread is assigned a word to convert based on the `hash_table`. Each thread converts
+ * its word and writes the number of tokens it found in the `tokens_per_word` array.
+ *
+ * The `tokens_per_word` array is kept to the length `num_code_points + 1`. This means each thread
+ * can write its number of tokens to the `tokens_per_word` corresponding to the starting
+ * character of each word. Since strings must start at some word, we can prefix sum this array
+ * and use the strings_lengths code point offsets to directly index the number of tokens in each
+ * string.
+ *
+ * The `token_ids` array should be initialized to the max uint32_t before calling this kernel.
+ *
+ * @param code_points An array containing all of the code points to be processed
+ * @param hash_table An array containing the flattened hash table with key, value pairs
+ *        packed in 64-bits
+ * @param bin_coefficients A pointer to the GPU pointer containing the hashing parameters for
+ *        each hash bin on the GPU.
+ * @param bin_offsets: A pointer to the GPU pointer containing the start index of each bin in
+ *        the flattened hash table.
+ * @param token_ids The index for each token found during tokenization. This is of length
+ *        num_code_points. In most cases, multiple characters will collapse to one token. In these
+ *        cases, the max uint32_t will be in place. Cub will be used later to filter out these
+ *        invalid ids later.
+ * @param word_starts An array of length `num_code_points`. The first total word elements contains
+ *        the index of the first character for each word.
+ * @param word_ends An array of length num_code_points. The first total_words elements contains the
+ *        past the end index for each word. This array is kept aligned with the initial
+ *        token_ids array containing the word start code points.
+ *        `word_ends[word] - filtered_start_indices[word] = word_length`
+ * @param tokens_per_word An array of size num_code_points that will contain the number of tokens in
+ *        each word in a string. This array can be exclusive summed and the result used in
+ *        conjunction with the strings lengths array to find the tokens in each string. This is
+ *        possible since the number of tokens in each word will be placed at the index corresponding
+ *        to the start character of a word. If we assume prefix_summed is the prefix sum of the
+ *        tokens_per_word array, then `prefix_summed[strings_lengths[string_idx] - 1]` is the number
+ *        of tokens found before the start of string.
+ * @param unk_token_id The token id to be place for unknown tokens
+ * @param max_word_length The maximum length of a word. Any word longer than this length is
+ *        replaced by the unknown token.
+ * @param total_words The total number of white space separated words
+ * @param outer_hash_a_param The a parameter for the outer hash
+ * @param outer_hash_b_param: The b parameter for the outer hash
+ * @param num_outer_bins: The number of bins for the outer hash
+ */
+__global__ void kernel_wordpiece_tokenizer(uint32_t const* code_points,
+                                           uint64_t const* hash_table,
+                                           uint64_t const* bin_coefficients,
+                                           uint16_t const* bin_offsets,
+                                           uint16_t unk_token_id,
+                                           uint32_t outer_hash_a_param,
+                                           uint32_t outer_hash_b_param,
+                                           uint16_t num_outer_bins,
+                                           uint32_t const* word_starts,
+                                           uint32_t const* word_ends,
+                                           uint32_t max_word_length,
+                                           uint32_t total_words,
+                                           uint32_t* token_ids,
+                                           uint8_t* tokens_per_word)
+{
+  cudf::thread_index_type word_to_tokenize = static_cast<cudf::thread_index_type>(blockDim.x) *
+                                               static_cast<cudf::thread_index_type>(blockIdx.x) +
+                                             threadIdx.x;
+
+  if (word_to_tokenize >= total_words) return;
+  // Each thread gets the start code_point offset for each word and resets the token_id memory to
+  // the default value. In a post processing step, all of these values will be removed.
+  auto const token_start = word_starts[word_to_tokenize];
+  auto const token_end   = word_ends[word_to_tokenize];
+  auto const word_length = token_end - token_start;
+
+  // The sdbm hash of "##"
+  constexpr uint32_t hashtag_hash = 2296000;
+  uint16_t num_values_tokenized   = 0;
+  // initialize start, end
+  uint32_t start = token_start;
+  uint32_t end   = token_end;
+
+  if (word_length > max_word_length) {
+    start                        = token_end;
+    num_values_tokenized         = 1;
+    token_ids[token_start]       = unk_token_id;
+    tokens_per_word[token_start] = num_values_tokenized;
+  }
+
+  while (start < token_end) {
+    end = token_end;
+    // init token_id to no token
+    int token_id = -1;
+    // compute current length
+    uint32_t const length = token_end - start;
+    uint64_t substr_hash =
+      sdbm_hash(code_points + start, length, start == token_start ? 0 : hashtag_hash);
+    while (start < end) {
+      token_id = retrieve(substr_hash,
+                          outer_hash_a_param,
+                          outer_hash_b_param,
+                          num_outer_bins,
+                          hash_table,
+                          bin_coefficients,
+                          bin_offsets);
+      if (token_id != -1) { break; }
+      --end;
+      // Pop off the last value from the substr hash
+      substr_hash = prev_sdbm_hash(substr_hash, code_points[end]);
+    }
+
+    if (token_id == -1) {
+      end      = token_end;
+      token_id = unk_token_id;
+      // We need to clean up the global array. This case is very uncommon.
+      //  Only 0.016% of words cannot be resolved to a token from the squad dev set.
+      for (uint32_t i = 1; i < num_values_tokenized; ++i) {
+        token_ids[token_start + i] = std::numeric_limits<uint32_t>::max();
+      }
+      num_values_tokenized = 0;
+    }
+
+    token_ids[token_start + num_values_tokenized] = token_id;
+    ++num_values_tokenized;
+    start = end;
+  }
+
+  tokens_per_word[token_start] = num_values_tokenized;
+}
+
+}  // namespace
+
+wordpiece_tokenizer::wordpiece_tokenizer(hashed_vocabulary const& vocab_table,
+                                         uint32_t max_sequence_length,
+                                         uint32_t stride,
+                                         bool do_truncate,
+                                         bool do_lower_case,
+                                         uint32_t max_word_length)
+  : vocab_table(vocab_table),
+    normalizer(vocab_table.cp_metadata->view().data<codepoint_metadata_type>(),
+               vocab_table.aux_cp_table->view().data<aux_codepoint_data_type>(),
+               do_lower_case),
+    max_sequence_length{max_sequence_length},
+    stride(stride),
+    do_truncate(do_truncate),
+    max_word_length{max_word_length}
+{
+}
+
+uvector_pair wordpiece_tokenizer::tokenize(char const* d_strings,
+                                           cudf::size_type const* d_offsets,
+                                           cudf::size_type num_strings,
+                                           rmm::cuda_stream_view stream)
+{
+  auto cps_and_offsets = normalizer.normalize(d_strings, d_offsets, num_strings, stream);
+  tokenize(cps_and_offsets, stream);
+  return uvector_pair(std::move(cps_and_offsets.first), std::move(cps_and_offsets.second));
+}
+
+struct copy_if_fn {  // inline lambda not allowed in private or protected member function
+  __device__ bool operator()(uint32_t cp) { return cp != std::numeric_limits<uint32_t>::max(); }
+};
+
+struct tranform_fn {  // just converting uint8 value to uint32
+  __device__ uint32_t operator()(uint8_t count) { return count; }
+};
+
+void wordpiece_tokenizer::tokenize(uvector_pair& cps_and_offsets, rmm::cuda_stream_view stream)
+{
+  auto device_code_points     = cps_and_offsets.first->data();
+  auto const num_code_points  = cps_and_offsets.first->size();
+  auto device_strings_offsets = cps_and_offsets.second->data();
+  auto const num_strings      = cps_and_offsets.second->size() - 1;
+
+  size_t const four_byte_cp_chunks = 1 + (num_code_points - 1) / sizeof(uint32_t);
+  size_t const rounded_num_cps     = sizeof(uint32_t) * four_byte_cp_chunks;
+  rmm::device_uvector<uint8_t> device_tokens_per_word(rounded_num_cps, stream);
+  rmm::device_uvector<uint32_t> device_token_ids(num_code_points, stream);
+  rmm::device_uvector<uint32_t> device_word_indices(2 * num_code_points, stream);
+
+  // make device_start_word_indices and device_end_word_indices contiguous
+  uint32_t* device_start_word_indices = device_word_indices.data();
+  uint32_t* device_end_word_indices   = device_start_word_indices + num_code_points;
+
+  cudf::detail::grid_1d const grid_init{static_cast<cudf::size_type>(num_code_points),
+                                        THREADS_PER_BLOCK};
+  detail::init_data_and_mark_word_start_and_ends<<<grid_init.num_blocks,
+                                                   grid_init.num_threads_per_block,
+                                                   0,
+                                                   stream.value()>>>(device_code_points,
+                                                                     device_start_word_indices,
+                                                                     device_end_word_indices,
+                                                                     num_code_points,
+                                                                     device_token_ids.data(),
+                                                                     device_tokens_per_word.data());
+  CUDF_CHECK_CUDA(stream.value());
+
+  cudf::detail::grid_1d const grid_mark{static_cast<cudf::size_type>(num_strings + 1),
+                                        THREADS_PER_BLOCK};
+  detail::mark_string_start_and_ends<<<grid_mark.num_blocks,
+                                       grid_mark.num_threads_per_block,
+                                       0,
+                                       stream.value()>>>(device_code_points,
+                                                         device_strings_offsets,
+                                                         device_start_word_indices,
+                                                         device_end_word_indices,
+                                                         num_strings);
+  CUDF_CHECK_CUDA(stream.value());
+
+  // check for special tokens and adjust indices
+  thrust::for_each_n(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator<size_t>(0),
+    num_code_points,
+    mark_special_tokens{
+      device_code_points, device_start_word_indices, device_end_word_indices, num_code_points});
+
+  // Now start_word_indices has the word starts scattered throughout the array. We need to select
+  // all values not equal to the max uint32_t and place them at the start of the array. We leverage
+  // the fact that the start_word_indices and the end_word indices are contiguous to only launch one
+  // device select kernel.
+  auto itr_end = thrust::remove(rmm::exec_policy(stream),
+                                device_word_indices.begin(),
+                                device_word_indices.end(),
+                                std::numeric_limits<uint32_t>::max());
+
+  // The number of tokens selected will be double the number of words since we
+  // select from both the start and end index arrays.
+  uint32_t const num_words = thrust::distance(device_word_indices.begin(), itr_end) / 2;
+
+  // We need to change the end_word_indices pointer after the selection is complete
+  device_end_word_indices = device_start_word_indices + num_words;
+
+  if (num_words > 0) {
+    cudf::detail::grid_1d const grid{static_cast<cudf::size_type>(num_words), THREADS_PER_BLOCK};
+    detail::kernel_wordpiece_tokenizer<<<grid.num_blocks,
+                                         grid.num_threads_per_block,
+                                         0,
+                                         stream.value()>>>(
+      device_code_points,
+      vocab_table.table->view().data<uint64_t>(),
+      vocab_table.bin_coefficients->view().data<uint64_t>(),
+      vocab_table.bin_offsets->view().data<uint16_t>(),
+      vocab_table.unknown_token_id,
+      vocab_table.outer_hash_a,
+      vocab_table.outer_hash_b,
+      vocab_table.num_bins,
+      device_start_word_indices,
+      device_end_word_indices,
+      max_word_length,
+      num_words,
+      device_token_ids.data(),
+      device_tokens_per_word.data());
+    CUDF_CHECK_CUDA(stream.value());
+  }
+
+  // Repurpose the input array for the token ids. In the worst case, each code point ends up being a
+  // token so this will always have enough memory to store the contiguous tokens.
+  uint32_t* contiguous_token_ids = device_code_points;
+  auto const copy_size           =  // thrust::copy_if limited to copying int-max values
+    std::min(device_token_ids.size(), static_cast<std::size_t>(std::numeric_limits<int>::max()));
+  auto ids_itr       = device_token_ids.begin();
+  auto const ids_end = device_token_ids.end();
+  while (ids_itr != ids_end) {
+    auto const copy_end  = (static_cast<std::size_t>(std::distance(ids_itr, ids_end)) <= copy_size)
+                             ? ids_end
+                             : ids_itr + copy_size;
+    contiguous_token_ids = thrust::copy_if(
+      rmm::exec_policy(stream), ids_itr, copy_end, contiguous_token_ids, copy_if_fn{});
+    ids_itr = copy_end;
+  }
+
+  // Repurpose start word indices since it is the same size and type as the required output.
+  uint32_t* token_id_counts = device_start_word_indices;
+  thrust::transform_inclusive_scan(rmm::exec_policy(stream),
+                                   device_tokens_per_word.data(),
+                                   device_tokens_per_word.data() + num_code_points,
+                                   token_id_counts,
+                                   tranform_fn{},
+                                   thrust::plus<uint32_t>());
+
+  // Update the device_strings_offsets using the token_id_counts
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<uint32_t>(1),
+                     num_strings,
+                     update_strings_lengths_fn{token_id_counts, device_strings_offsets});
+}
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/tokenize.cu b/cpp/src/text/tokenize.cu
new file mode 100644
index 0000000..16b9f25
--- /dev/null
+++ b/cpp/src/text/tokenize.cu
@@ -0,0 +1,274 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/utilities/tokenize_ops.cuh>
+
+#include <nvtext/detail/tokenize.hpp>
+#include <nvtext/tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/strings/detail/strings_column_factories.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/count.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+namespace nvtext {
+namespace detail {
+namespace {
+// common pattern for token_count functions
+template <typename TokenCounter>
+std::unique_ptr<cudf::column> token_count_fn(cudf::size_type strings_count,
+                                             TokenCounter tokenizer,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  // create output column
+  auto token_counts =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                              strings_count,
+                              cudf::mask_state::UNALLOCATED,
+                              stream,
+                              mr);
+  auto d_token_counts = token_counts->mutable_view().data<cudf::size_type>();
+  // add the counts to the column
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(strings_count),
+                    d_token_counts,
+                    tokenizer);
+  return token_counts;
+}
+
+// common pattern for tokenize functions
+template <typename Tokenizer>
+std::unique_ptr<cudf::column> tokenize_fn(cudf::size_type strings_count,
+                                          Tokenizer tokenizer,
+                                          rmm::cuda_stream_view stream,
+                                          rmm::mr::device_memory_resource* mr)
+{
+  // get the number of tokens in each string
+  auto const token_counts =
+    token_count_fn(strings_count, tokenizer, stream, rmm::mr::get_current_device_resource());
+  auto d_token_counts = token_counts->view();
+  // create token-index offsets from the counts
+  rmm::device_uvector<cudf::size_type> token_offsets(strings_count + 1, stream);
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         d_token_counts.template begin<cudf::size_type>(),
+                         d_token_counts.template end<cudf::size_type>(),
+                         token_offsets.begin() + 1);
+  token_offsets.set_element_to_zero_async(0, stream);
+  auto const total_tokens = token_offsets.back_element(stream);
+  // build a list of pointers to each token
+  rmm::device_uvector<string_index_pair> tokens(total_tokens, stream);
+  // now go get the tokens
+  tokenizer.d_offsets = token_offsets.data();
+  tokenizer.d_tokens  = tokens.data();
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<cudf::size_type>(0),
+                     strings_count,
+                     tokenizer);
+  // create the strings column using the tokens pointers
+  return cudf::strings::detail::make_strings_column(tokens.begin(), tokens.end(), stream, mr);
+}
+
+}  // namespace
+
+// detail APIs
+
+// zero or more character tokenizer
+std::unique_ptr<cudf::column> tokenize(cudf::strings_column_view const& strings,
+                                       cudf::string_scalar const& delimiter,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+  cudf::string_view d_delimiter(delimiter.data(), delimiter.size());
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  return tokenize_fn(strings.size(), strings_tokenizer{*strings_column, d_delimiter}, stream, mr);
+}
+
+// zero or more character token counter
+std::unique_ptr<cudf::column> count_tokens(cudf::strings_column_view const& strings,
+                                           cudf::string_scalar const& delimiter,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+  cudf::string_view d_delimiter(delimiter.data(), delimiter.size());
+  auto strings_column = cudf::column_device_view::create(strings.parent(), stream);
+  return token_count_fn(
+    strings.size(), strings_tokenizer{*strings_column, d_delimiter}, stream, mr);
+}
+
+// one or more string delimiter tokenizer
+std::unique_ptr<cudf::column> tokenize(cudf::strings_column_view const& strings,
+                                       cudf::strings_column_view const& delimiters,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiters.size() > 0, "Parameter delimiters must not be empty");
+  CUDF_EXPECTS(!delimiters.has_nulls(), "Parameter delimiters must not have nulls");
+  auto strings_column    = cudf::column_device_view::create(strings.parent(), stream);
+  auto delimiters_column = cudf::column_device_view::create(delimiters.parent(), stream);
+  return tokenize_fn(
+    strings.size(),
+    multi_delimiter_strings_tokenizer{*strings_column,
+                                      delimiters_column->begin<cudf::string_view>(),
+                                      delimiters_column->end<cudf::string_view>()},
+    stream,
+    mr);
+}
+
+// one or more string delimiter token counter
+std::unique_ptr<cudf::column> count_tokens(cudf::strings_column_view const& strings,
+                                           cudf::strings_column_view const& delimiters,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiters.size() > 0, "Parameter delimiters must not be empty");
+  CUDF_EXPECTS(!delimiters.has_nulls(), "Parameter delimiters must not have nulls");
+  auto strings_column    = cudf::column_device_view::create(strings.parent(), stream);
+  auto delimiters_column = cudf::column_device_view::create(delimiters.parent(), stream);
+  return token_count_fn(
+    strings.size(),
+    multi_delimiter_strings_tokenizer{*strings_column,
+                                      delimiters_column->begin<cudf::string_view>(),
+                                      delimiters_column->end<cudf::string_view>()},
+    stream,
+    mr);
+}
+
+// tokenize on every character
+std::unique_ptr<cudf::column> character_tokenize(cudf::strings_column_view const& strings_column,
+                                                 rmm::cuda_stream_view stream,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  auto strings_count = strings_column.size();
+  if (strings_count == 0) {
+    return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  }
+
+  auto offsets = strings_column.offsets();
+  auto offset  = cudf::detail::get_value<cudf::size_type>(offsets, strings_column.offset(), stream);
+  auto chars_bytes = cudf::detail::get_value<cudf::size_type>(
+                       offsets, strings_column.offset() + strings_count, stream) -
+                     offset;
+  auto d_chars = strings_column.chars().data<uint8_t>();  // unsigned is necessary for checking bits
+  d_chars += offset;
+
+  // To minimize memory, count the number of characters so we can
+  // build the output offsets without an intermediate buffer.
+  // In the worst case each byte is a character so the output is 4x the input.
+  cudf::size_type num_characters = thrust::count_if(
+    rmm::exec_policy(stream), d_chars, d_chars + chars_bytes, [] __device__(uint8_t byte) {
+      return cudf::strings::detail::is_begin_utf8_char(byte);
+    });
+
+  // no characters check -- this could happen in all-empty or all-null strings column
+  if (num_characters == 0) {
+    return cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  }
+
+  // create output offsets column
+  // -- conditionally copy a counting iterator where
+  //    the first byte of each character is located
+  auto offsets_column =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                              num_characters + 1,
+                              cudf::mask_state::UNALLOCATED,
+                              stream,
+                              mr);
+  auto d_new_offsets = offsets_column->mutable_view().begin<cudf::size_type>();
+  thrust::copy_if(
+    rmm::exec_policy(stream),
+    thrust::counting_iterator<cudf::size_type>(0),
+    thrust::counting_iterator<cudf::size_type>(chars_bytes + 1),
+    d_new_offsets,
+    [d_chars, chars_bytes] __device__(auto idx) {
+      // this will also set the final value to the size chars_bytes
+      return idx < chars_bytes ? cudf::strings::detail::is_begin_utf8_char(d_chars[idx]) : true;
+    });
+
+  // create the output chars column -- just a copy of the input's chars column
+  cudf::column_view chars_view(
+    cudf::data_type{cudf::type_id::INT8}, chars_bytes, d_chars, nullptr, 0);
+  auto chars_column = std::make_unique<cudf::column>(chars_view, stream, mr);
+
+  // return new strings column
+  return cudf::make_strings_column(
+    num_characters, std::move(offsets_column), std::move(chars_column), 0, rmm::device_buffer{});
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<cudf::column> tokenize(cudf::strings_column_view const& strings,
+                                       cudf::string_scalar const& delimiter,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::tokenize(strings, delimiter, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> tokenize(cudf::strings_column_view const& strings,
+                                       cudf::strings_column_view const& delimiters,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::tokenize(strings, delimiters, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> count_tokens(cudf::strings_column_view const& strings,
+                                           cudf::string_scalar const& delimiter,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::count_tokens(strings, delimiter, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> count_tokens(cudf::strings_column_view const& strings,
+                                           cudf::strings_column_view const& delimiters,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::count_tokens(strings, delimiters, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<cudf::column> character_tokenize(cudf::strings_column_view const& strings,
+                                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::character_tokenize(strings, cudf::get_default_stream(), mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/text/utilities/tokenize_ops.cuh b/cpp/src/text/utilities/tokenize_ops.cuh
new file mode 100644
index 0000000..a84e94a
--- /dev/null
+++ b/cpp/src/text/utilities/tokenize_ops.cuh
@@ -0,0 +1,255 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/logical.h>
+#include <thrust/pair.h>
+
+namespace nvtext {
+namespace detail {
+using string_index_pair = thrust::pair<char const*, cudf::size_type>;
+using position_pair     = thrust::pair<cudf::size_type, cudf::size_type>;
+
+/**
+ * @brief Tokenizer class that use multi-character delimiters.
+ *
+ * This is common code for tokenize, token-counters, normalize functions.
+ * If an empty delimiter string is specified, then whitespace
+ * (code-point <= ' ') is used to identify tokens.
+ *
+ * After instantiating this object, use the `next_token()` method
+ * to parse tokens and the `token_byte_positions()` to retrieve the
+ * current token's byte offsets within the string.
+ */
+struct characters_tokenizer {
+  /**
+   * @brief Constructor for characters_tokenizer.
+   *
+   * @param d_str The string to tokenize.
+   * @param d_delimiter The (optional) delimiter to locate tokens.
+   */
+  __device__ characters_tokenizer(cudf::string_view const& d_str,
+                                  cudf::string_view const& d_delimiter = cudf::string_view{})
+    : d_str{d_str},
+      d_delimiter{d_delimiter},
+      spaces{true},
+      current_position{0},
+      start_position(0),
+      end_position(d_str.size_bytes())
+  {
+  }
+
+  /**
+   * @brief Return true if the given character is a delimiter.
+   *
+   * For empty delimiter, whitespace code-point is checked.
+   *
+   * @param chr The character to test.
+   * @return true if the character is a delimiter
+   */
+  __device__ bool is_delimiter(cudf::char_utf8 chr) const
+  {
+    return d_delimiter.empty() ? (chr <= ' ') :  // whitespace check
+             thrust::any_of(thrust::seq,
+                            d_delimiter.begin(),
+                            d_delimiter.end(),
+                            [chr] __device__(cudf::char_utf8 c) { return c == chr; });
+  }
+
+  /**
+   * @brief Identifies the bounds of the next token in the given
+   * string at the specified iterator position.
+   *
+   * For empty delimiter, whitespace code-point is checked.
+   * Starting at the current_position, a token
+   * start position is identified when a delimiter is
+   * not found. Once found, the end position is identified
+   * when a delimiter or the end of the string is found.
+   *
+   * @return true if a token has been found
+   */
+  __device__ bool next_token()
+  {
+    auto const src_ptr = d_str.data();
+    if (current_position >= d_str.size_bytes()) { return false; }
+    if (current_position != 0) {  // skip these 2 lines the first time through
+      current_position += cudf::strings::detail::bytes_in_char_utf8(src_ptr[current_position]);
+      start_position = current_position;
+    }
+    if (start_position >= d_str.size_bytes()) { return false; }
+    // continue search for the next token
+    end_position = d_str.size_bytes();
+    while (current_position < d_str.size_bytes()) {
+      cudf::char_utf8 ch   = 0;
+      auto const chr_width = cudf::strings::detail::to_char_utf8(src_ptr + current_position, ch);
+      if (spaces == is_delimiter(ch)) {
+        current_position += chr_width;
+        if (spaces) {
+          start_position = current_position;
+        } else {
+          end_position = current_position;
+        }
+        continue;
+      }
+      spaces = !spaces;
+      if (spaces) {
+        end_position = current_position;
+        break;
+      }
+      current_position += chr_width;
+    }
+    return start_position < end_position;
+  }
+
+  /**
+   * @brief Returns the byte offsets for the current token
+   * within this string.
+   *
+   * @return Byte positions of the current token.
+   */
+  __device__ position_pair token_byte_positions() const
+  {
+    return position_pair{start_position, end_position};
+  }
+
+ private:
+  cudf::string_view const d_str;        ///< string to tokenize
+  cudf::string_view const d_delimiter;  ///< delimiter characters
+  bool spaces;                          ///< true if current position is delimiter
+  cudf::size_type current_position;     ///< current position in d_str
+  cudf::size_type start_position;       ///< starting byte position of token found
+  cudf::size_type end_position;         ///< ending byte position (exclusive) of token found
+};
+
+/**
+ * @brief Tokenizing function for multi-character delimiter.
+ *
+ * The first pass simply counts the tokens so the size of the output
+ * vector can be calculated. The second pass places the token
+ * positions into the d_tokens vector.
+ */
+struct strings_tokenizer {
+  cudf::column_device_view const d_strings;  ///< strings to tokenize
+  cudf::string_view const d_delimiter;       ///< delimiter characters to tokenize around
+  cudf::size_type* d_offsets{};              ///< offsets into the d_tokens vector for each string
+  string_index_pair* d_tokens{};             ///< token positions in device memory
+
+  /**
+   * @brief Identifies the token positions within each string.
+   *
+   * This counts the tokens in each string and also places the token positions
+   * into the d_tokens member.
+   *
+   * @param idx Index of the string to tokenize in the d_strings column.
+   * @return The number of tokens for this string.
+   */
+  __device__ cudf::size_type operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) return 0;
+    auto d_str = d_strings.element<cudf::string_view>(idx);
+    // create tokenizer for this string
+    characters_tokenizer tokenizer(d_str, d_delimiter);
+    string_index_pair* d_str_tokens = d_tokens ? d_tokens + d_offsets[idx] : nullptr;
+    cudf::size_type token_idx       = 0;
+    while (tokenizer.next_token()) {
+      if (d_str_tokens) {
+        auto token_pos = tokenizer.token_byte_positions();
+        d_str_tokens[token_idx] =
+          string_index_pair{d_str.data() + token_pos.first, (token_pos.second - token_pos.first)};
+      }
+      ++token_idx;
+    }
+    return token_idx;  // number of tokens found
+  }
+};
+
+// delimiters' iterator = delimiterator
+using delimiterator = cudf::column_device_view::const_iterator<cudf::string_view>;
+
+/**
+ * @brief Tokenizes strings using multiple string delimiters.
+ *
+ * One or more strings are used as delimiters to identify tokens inside
+ * each string of a given strings column.
+ */
+struct multi_delimiter_strings_tokenizer {
+  cudf::column_device_view const d_strings;  ///< strings column to tokenize
+  delimiterator delimiters_begin;            ///< first delimiter
+  delimiterator delimiters_end;              ///< last delimiter
+  cudf::size_type* d_offsets{};              ///< offsets into the d_tokens output vector
+  string_index_pair* d_tokens{};             ///< token positions found for each string
+
+  /**
+   * @brief Identifies the token positions within each string.
+   *
+   * This counts the tokens in each string and also places the token positions
+   * into the d_tokens member.
+   *
+   * @param idx Index of the string to tokenize in the d_strings column.
+   * @return The number of tokens for this string.
+   */
+  __device__ cudf::size_type operator()(cudf::size_type idx)
+  {
+    if (d_strings.is_null(idx)) return 0;
+    cudf::string_view d_str   = d_strings.element<cudf::string_view>(idx);
+    auto d_str_tokens         = d_tokens ? d_tokens + d_offsets[idx] : nullptr;
+    auto data_ptr             = d_str.data();
+    cudf::size_type last_pos  = 0;
+    cudf::size_type token_idx = 0;
+    // check for delimiters at each character position
+    for (auto itr = d_str.begin(); itr != d_str.end(); ++itr) {
+      auto curr_ptr = data_ptr + itr.byte_offset();
+      cudf::string_view sub_str(
+        curr_ptr, static_cast<cudf::size_type>(data_ptr + d_str.size_bytes() - curr_ptr));
+      // look for delimiter at current position
+      auto itr_find = thrust::find_if(
+        thrust::seq,
+        delimiters_begin,
+        delimiters_end,
+        [sub_str] __device__(cudf::string_view const& d_delim) {
+          return !d_delim.empty() && (d_delim.size_bytes() <= sub_str.size_bytes()) &&
+                 d_delim.compare(sub_str.data(), d_delim.size_bytes()) == 0;
+        });
+      if (itr_find != delimiters_end) {  // found delimiter
+        auto token_size = static_cast<cudf::size_type>((curr_ptr - data_ptr) - last_pos);
+        if (token_size > 0)  // we only care about non-zero sized tokens
+        {
+          if (d_str_tokens)
+            d_str_tokens[token_idx] = string_index_pair{data_ptr + last_pos, token_size};
+          ++token_idx;
+        }
+        last_pos = (curr_ptr - data_ptr) + (*itr_find).size_bytes();  // point past delimiter
+        itr += (*itr_find).length() - 1;
+      }
+    }
+    if (last_pos < d_str.size_bytes())  // left-over tokens
+    {
+      if (d_str_tokens)
+        d_str_tokens[token_idx] =
+          string_index_pair{data_ptr + last_pos, d_str.size_bytes() - last_pos};
+      ++token_idx;
+    }
+    return token_idx;  // this is the number of tokens found for this string
+  }
+};
+
+}  // namespace detail
+}  // namespace nvtext
diff --git a/cpp/src/text/vocabulary_tokenize.cu b/cpp/src/text/vocabulary_tokenize.cu
new file mode 100644
index 0000000..f998c9e
--- /dev/null
+++ b/cpp/src/text/vocabulary_tokenize.cu
@@ -0,0 +1,257 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <text/utilities/tokenize_ops.cuh>
+
+#include <nvtext/tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/hashing/detail/hash_allocator.cuh>
+#include <cudf/hashing/detail/murmurhash3_x86_32.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/polymorphic_allocator.hpp>
+
+#include <cuco/static_map.cuh>
+
+namespace nvtext {
+namespace detail {
+namespace {
+
+using string_hasher_type = cudf::hashing::detail::MurmurHash3_x86_32<cudf::string_view>;
+using hash_value_type    = string_hasher_type::result_type;
+
+/**
+ * @brief Hasher function used for building and using the cuco static-map
+ *
+ * This takes advantage of heterogeneous lookup feature in cuco static-map which
+ * allows inserting with one type (index) and looking up with a different type (string).
+ */
+struct vocab_hasher {
+  cudf::column_device_view const d_strings;
+  string_hasher_type hasher{};
+  // used by insert
+  __device__ hash_value_type operator()(cudf::size_type index) const
+  {
+    return hasher(d_strings.element<cudf::string_view>(index));
+  }
+  // used by find
+  __device__ hash_value_type operator()(cudf::string_view const& s) const { return hasher(s); }
+};
+
+/**
+ * @brief Equal function used for building and using the cuco static-map
+ *
+ * This takes advantage of heterogeneous lookup feature in cuco static-map which
+ * allows inserting with one type (index) and looking up with a different type (string).
+ */
+struct vocab_equal {
+  cudf::column_device_view const d_strings;
+  // used by insert
+  __device__ bool operator()(cudf::size_type lhs, cudf::size_type rhs) const noexcept
+  {
+    return lhs == rhs;  // all rows are expected to be unique
+  }
+  // used by find
+  __device__ bool operator()(cudf::size_type lhs, cudf::string_view const& rhs) const noexcept
+  {
+    return d_strings.element<cudf::string_view>(lhs) == rhs;
+  }
+};
+
+using hash_table_allocator_type = rmm::mr::stream_allocator_adaptor<default_allocator<char>>;
+using probe_scheme              = cuco::experimental::linear_probing<1, vocab_hasher>;
+using vocabulary_map_type       = cuco::experimental::static_map<cudf::size_type,
+                                                           cudf::size_type,
+                                                           cuco::experimental::extent<std::size_t>,
+                                                           cuda::thread_scope_device,
+                                                           vocab_equal,
+                                                           probe_scheme,
+                                                           hash_table_allocator_type>;
+}  // namespace
+}  // namespace detail
+
+// since column_device_view::create returns is a little more than
+// std::unique_ptr<column_device_view> this helper simplifies the return type in a maintainable way
+using col_device_view = std::invoke_result_t<decltype(&cudf::column_device_view::create),
+                                             cudf::column_view,
+                                             rmm::cuda_stream_view>;
+
+struct tokenize_vocabulary::tokenize_vocabulary_impl {
+  std::unique_ptr<cudf::column> const vocabulary;
+  col_device_view const d_vocabulary;
+  std::unique_ptr<detail::vocabulary_map_type> vocabulary_map;
+
+  auto get_map_ref() const { return vocabulary_map->ref(cuco::experimental::op::find); }
+
+  tokenize_vocabulary_impl(std::unique_ptr<cudf::column>&& vocab,
+                           col_device_view&& d_vocab,
+                           std::unique_ptr<detail::vocabulary_map_type>&& map)
+    : vocabulary(std::move(vocab)), d_vocabulary(std::move(d_vocab)), vocabulary_map(std::move(map))
+  {
+  }
+};
+
+struct key_pair {
+  __device__ auto operator()(cudf::size_type idx) const noexcept
+  {
+    return cuco::make_pair(idx, idx);
+  }
+};
+
+tokenize_vocabulary::tokenize_vocabulary(cudf::strings_column_view const& input,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(not input.is_empty(), "vocabulary must not be empty");
+  CUDF_EXPECTS(not input.has_nulls(), "vocabulary must not have nulls");
+
+  // need to hold a copy of the input
+  auto vocabulary   = std::make_unique<cudf::column>(input.parent(), stream, mr);
+  auto d_vocabulary = cudf::column_device_view::create(vocabulary->view(), stream);
+
+  auto vocab_map = std::make_unique<detail::vocabulary_map_type>(
+    static_cast<size_t>(vocabulary->size() * 2),
+    cuco::empty_key{-1},
+    cuco::empty_value{-1},
+    detail::vocab_equal{*d_vocabulary},
+    detail::probe_scheme{detail::vocab_hasher{*d_vocabulary}},
+    detail::hash_table_allocator_type{default_allocator<char>{}, stream},
+    stream.value());
+
+  // the row index is the token id (value for each key in the map)
+  auto iter = cudf::detail::make_counting_transform_iterator(0, key_pair{});
+  vocab_map->insert_async(iter, iter + vocabulary->size(), stream.value());
+
+  _impl = new tokenize_vocabulary_impl(
+    std::move(vocabulary), std::move(d_vocabulary), std::move(vocab_map));
+}
+tokenize_vocabulary::~tokenize_vocabulary() { delete _impl; }
+
+std::unique_ptr<tokenize_vocabulary> load_vocabulary(cudf::strings_column_view const& input,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return std::make_unique<tokenize_vocabulary>(input, stream, mr);
+}
+
+namespace detail {
+namespace {
+
+/**
+ * @brief Tokenizes each string and uses the map to assign token id values
+ *
+ * @tparam MapRefType Type of the static_map reference for calling find()
+ */
+template <typename MapRefType>
+struct vocabulary_tokenizer_fn {
+  cudf::column_device_view const d_strings;
+  cudf::string_view const d_delimiter;
+  MapRefType d_map;
+  cudf::size_type const default_id;
+  cudf::size_type const* d_offsets;
+  cudf::size_type* d_results;
+
+  __device__ void operator()(cudf::size_type idx) const
+  {
+    if (d_strings.is_null(idx)) { return; }
+
+    auto const d_str = d_strings.element<cudf::string_view>(idx);
+    characters_tokenizer tokenizer(d_str, d_delimiter);
+    auto d_tokens = d_results + d_offsets[idx];
+
+    cudf::size_type token_idx = 0;
+    while (tokenizer.next_token()) {
+      auto const pos   = tokenizer.token_byte_positions();
+      auto const token = cudf::string_view{d_str.data() + pos.first, (pos.second - pos.first)};
+      // lookup token in map
+      auto const itr = d_map.find(token);
+      auto const id  = (itr != d_map.end()) ? itr->second : default_id;
+      // set value into the output
+      d_tokens[token_idx++] = id;
+    }
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::column> tokenize_with_vocabulary(cudf::strings_column_view const& input,
+                                                       tokenize_vocabulary const& vocabulary,
+                                                       cudf::string_scalar const& delimiter,
+                                                       cudf::size_type default_id,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(delimiter.is_valid(stream), "Parameter delimiter must be valid");
+
+  auto const output_type = cudf::data_type{cudf::type_to_id<cudf::size_type>()};
+  if (input.is_empty()) { return cudf::make_empty_column(output_type); }
+
+  // count the tokens per string and build the offsets from the counts
+  auto const d_strings   = cudf::column_device_view::create(input.parent(), stream);
+  auto const d_delimiter = delimiter.value(stream);
+  auto const sizes_itr =
+    cudf::detail::make_counting_transform_iterator(0, strings_tokenizer{*d_strings, d_delimiter});
+  auto [token_offsets, total_count] =
+    cudf::detail::make_offsets_child_column(sizes_itr, sizes_itr + input.size(), stream, mr);
+
+  // build the output column to hold all the token ids
+  auto tokens =
+    cudf::make_numeric_column(output_type, total_count, cudf::mask_state::UNALLOCATED, stream, mr);
+  auto map_ref   = vocabulary._impl->get_map_ref();
+  auto d_offsets = token_offsets->view().data<cudf::size_type>();
+  auto d_tokens  = tokens->mutable_view().data<cudf::size_type>();
+  vocabulary_tokenizer_fn<decltype(map_ref)> tokenizer{
+    *d_strings, d_delimiter, map_ref, default_id, d_offsets, d_tokens};
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator<cudf::size_type>(0),
+                     input.size(),
+                     tokenizer);
+
+  return cudf::make_lists_column(input.size(),
+                                 std::move(token_offsets),
+                                 std::move(tokens),
+                                 input.null_count(),
+                                 cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                 stream,
+                                 mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> tokenize_with_vocabulary(cudf::strings_column_view const& input,
+                                                       tokenize_vocabulary const& vocabulary,
+                                                       cudf::string_scalar const& delimiter,
+                                                       cudf::size_type default_id,
+                                                       rmm::cuda_stream_view stream,
+                                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::tokenize_with_vocabulary(input, vocabulary, delimiter, default_id, stream, mr);
+}
+
+}  // namespace nvtext
diff --git a/cpp/src/transform/bools_to_mask.cu b/cpp/src/transform/bools_to_mask.cu
new file mode 100644
index 0000000..e558b51
--- /dev/null
+++ b/cpp/src/transform/bools_to_mask.cu
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type> bools_to_mask(
+  column_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type().id() == type_id::BOOL8, "Input is not of type bool");
+
+  if (input.is_empty()) { return std::pair(std::make_unique<rmm::device_buffer>(), 0); }
+
+  auto input_device_view_ptr = column_device_view::create(input, stream);
+  auto input_device_view     = *input_device_view_ptr;
+  auto pred                  = [] __device__(bool element) { return element; };
+  if (input.nullable()) {
+    // Nulls are considered false
+    auto input_begin = make_null_replacement_iterator<bool>(input_device_view, false);
+
+    auto mask = detail::valid_if(input_begin, input_begin + input.size(), pred, stream, mr);
+
+    return std::pair(std::make_unique<rmm::device_buffer>(std::move(mask.first)), mask.second);
+  } else {
+    auto mask = detail::valid_if(
+      input_device_view.begin<bool>(), input_device_view.end<bool>(), pred, stream, mr);
+
+    return std::pair(std::make_unique<rmm::device_buffer>(std::move(mask.first)), mask.second);
+  }
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type> bools_to_mask(
+  column_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::bools_to_mask(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/transform/compute_column.cu b/cpp/src/transform/compute_column.cu
new file mode 100644
index 0000000..224dd93
--- /dev/null
+++ b/cpp/src/transform/compute_column.cu
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/ast/detail/expression_evaluator.cuh>
+#include <cudf/ast/detail/expression_parser.hpp>
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+
+namespace cudf {
+namespace detail {
+
+/**
+ * @brief Kernel for evaluating an expression on a table to produce a new column.
+ *
+ * This evaluates an expression over a table to produce a new column. Also called an n-ary
+ * transform.
+ *
+ * @tparam max_block_size The size of the thread block, used to set launch
+ * bounds and minimize register usage.
+ * @tparam has_nulls whether or not the output column may contain nulls.
+ *
+ * @param table The table device view used for evaluation.
+ * @param device_expression_data Container of device data required to evaluate the desired
+ * expression.
+ * @param output_column The destination for the results of evaluating the expression.
+ */
+template <cudf::size_type max_block_size, bool has_nulls>
+__launch_bounds__(max_block_size) __global__
+  void compute_column_kernel(table_device_view const table,
+                             ast::detail::expression_device_view device_expression_data,
+                             mutable_column_device_view output_column)
+{
+  // The (required) extern storage of the shared memory array leads to
+  // conflicting declarations between different templates. The easiest
+  // workaround is to declare an arbitrary (here char) array type then cast it
+  // after the fact to the appropriate type.
+  extern __shared__ char raw_intermediate_storage[];
+  ast::detail::IntermediateDataType<has_nulls>* intermediate_storage =
+    reinterpret_cast<ast::detail::IntermediateDataType<has_nulls>*>(raw_intermediate_storage);
+
+  auto thread_intermediate_storage =
+    &intermediate_storage[threadIdx.x * device_expression_data.num_intermediates];
+  auto start_idx    = cudf::detail::grid_1d::global_thread_id();
+  auto const stride = cudf::detail::grid_1d::grid_stride();
+  auto evaluator =
+    cudf::ast::detail::expression_evaluator<has_nulls>(table, device_expression_data);
+
+  for (thread_index_type row_index = start_idx; row_index < table.num_rows(); row_index += stride) {
+    auto output_dest = ast::detail::mutable_column_expression_result<has_nulls>(output_column);
+    evaluator.evaluate(output_dest, row_index, thread_intermediate_storage);
+  }
+}
+
+std::unique_ptr<column> compute_column(table_view const& table,
+                                       ast::expression const& expr,
+                                       rmm::cuda_stream_view stream,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  // If evaluating the expression may produce null outputs we create a nullable
+  // output column and follow the null-supporting expression evaluation code
+  // path.
+  auto const has_nulls = expr.may_evaluate_null(table, stream);
+
+  auto const parser = ast::detail::expression_parser{expr, table, has_nulls, stream, mr};
+
+  auto const output_column_mask_state =
+    has_nulls ? mask_state::UNINITIALIZED : mask_state::UNALLOCATED;
+
+  auto output_column = cudf::make_fixed_width_column(
+    parser.output_type(), table.num_rows(), output_column_mask_state, stream, mr);
+  if (table.num_rows() == 0) { return output_column; }
+  auto mutable_output_device =
+    cudf::mutable_column_device_view::create(output_column->mutable_view(), stream);
+
+  // Configure kernel parameters
+  auto const& device_expression_data = parser.device_expression_data;
+  int device_id;
+  CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+  int shmem_limit_per_block;
+  CUDF_CUDA_TRY(
+    cudaDeviceGetAttribute(&shmem_limit_per_block, cudaDevAttrMaxSharedMemoryPerBlock, device_id));
+  auto constexpr MAX_BLOCK_SIZE = 128;
+  auto const block_size =
+    parser.shmem_per_thread != 0
+      ? std::min(MAX_BLOCK_SIZE, shmem_limit_per_block / parser.shmem_per_thread)
+      : MAX_BLOCK_SIZE;
+  auto const config          = cudf::detail::grid_1d{table.num_rows(), block_size};
+  auto const shmem_per_block = parser.shmem_per_thread * config.num_threads_per_block;
+
+  // Execute the kernel
+  auto table_device = table_device_view::create(table, stream);
+  if (has_nulls) {
+    cudf::detail::compute_column_kernel<MAX_BLOCK_SIZE, true>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_per_block, stream.value()>>>(
+        *table_device, device_expression_data, *mutable_output_device);
+  } else {
+    cudf::detail::compute_column_kernel<MAX_BLOCK_SIZE, false>
+      <<<config.num_blocks, config.num_threads_per_block, shmem_per_block, stream.value()>>>(
+        *table_device, device_expression_data, *mutable_output_device);
+  }
+  CUDF_CHECK_CUDA(stream.value());
+  output_column->set_null_count(
+    cudf::detail::null_count(mutable_output_device->null_mask(), 0, output_column->size(), stream));
+  return output_column;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> compute_column(table_view const& table,
+                                       ast::expression const& expr,
+                                       rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::compute_column(table, expr, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/transform/encode.cu b/cpp/src/transform/encode.cu
new file mode 100644
index 0000000..c0e0c83
--- /dev/null
+++ b/cpp/src/transform/encode.cu
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/search.hpp>
+#include <cudf/detail/sorting.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <memory>
+#include <numeric>
+#include <utility>
+#include <vector>
+
+namespace cudf {
+namespace detail {
+
+std::pair<std::unique_ptr<table>, std::unique_ptr<column>> encode(
+  table_view const& input_table, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  auto const num_cols = input_table.num_columns();
+
+  std::vector<size_type> drop_keys(num_cols);
+  std::iota(drop_keys.begin(), drop_keys.end(), 0);
+
+  auto distinct_keys = cudf::detail::distinct(input_table,
+                                              drop_keys,
+                                              duplicate_keep_option::KEEP_ANY,
+                                              null_equality::EQUAL,
+                                              nan_equality::ALL_EQUAL,
+                                              stream,
+                                              mr);
+
+  std::vector<order> column_order(num_cols, order::ASCENDING);
+  std::vector<null_order> null_precedence(num_cols, null_order::AFTER);
+  auto sorted_unique_keys =
+    cudf::detail::sort(distinct_keys->view(), column_order, null_precedence, stream, mr);
+
+  auto indices_column = cudf::detail::lower_bound(
+    sorted_unique_keys->view(), input_table, column_order, null_precedence, stream, mr);
+
+  return std::pair(std::move(sorted_unique_keys), std::move(indices_column));
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<cudf::table>, std::unique_ptr<cudf::column>> encode(
+  cudf::table_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::encode(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/transform/jit/kernel.cu b/cpp/src/transform/jit/kernel.cu
new file mode 100644
index 0000000..0170cc5
--- /dev/null
+++ b/cpp/src/transform/jit/kernel.cu
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Include Jitify's cstddef header first
+#include <cstddef>
+
+#include <cuda/std/climits>
+#include <cuda/std/cstddef>
+#include <cuda/std/limits>
+#include <cuda/std/type_traits>
+
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <transform/jit/operation-udf.hpp>
+
+#include <cudf/types.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+namespace cudf {
+namespace transformation {
+namespace jit {
+
+template <typename TypeOut, typename TypeIn>
+__global__ void kernel(cudf::size_type size, TypeOut* out_data, TypeIn* in_data)
+{
+  // cannot use global_thread_id utility due to a JIT build issue by including
+  // the `cudf/detail/utilities/cuda.cuh` header
+  thread_index_type const start  = threadIdx.x + blockIdx.x * blockDim.x;
+  thread_index_type const stride = blockDim.x * gridDim.x;
+
+  for (auto i = start; i < static_cast<thread_index_type>(size); i += stride) {
+    GENERIC_UNARY_OP(&out_data[i], in_data[i]);
+  }
+}
+
+}  // namespace jit
+}  // namespace transformation
+}  // namespace cudf
diff --git a/cpp/src/transform/jit/operation-udf.hpp b/cpp/src/transform/jit/operation-udf.hpp
new file mode 100644
index 0000000..eaab211
--- /dev/null
+++ b/cpp/src/transform/jit/operation-udf.hpp
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+// This file serves as a placeholder for user defined functions, so jitify can choose to override it
+// at runtime.
diff --git a/cpp/src/transform/mask_to_bools.cu b/cpp/src/transform/mask_to_bools.cu
new file mode 100644
index 0000000..1b9a58c
--- /dev/null
+++ b/cpp/src/transform/mask_to_bools.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<column> mask_to_bools(bitmask_type const* bitmask,
+                                      size_type begin_bit,
+                                      size_type end_bit,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  auto const length = end_bit - begin_bit;
+  CUDF_EXPECTS(length >= 0, "begin_bit should be less than or equal to end_bit");
+  CUDF_EXPECTS((bitmask != nullptr) or (length == 0), "nullmask is null");
+
+  auto out_col =
+    make_fixed_width_column(data_type(type_id::BOOL8), length, mask_state::UNALLOCATED, stream, mr);
+
+  if (length > 0) {
+    auto mutable_view = out_col->mutable_view();
+
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator<cudf::size_type>(begin_bit),
+                      thrust::make_counting_iterator<cudf::size_type>(end_bit),
+                      mutable_view.begin<bool>(),
+                      [bitmask] __device__(auto index) { return bit_is_set(bitmask, index); });
+  }
+
+  return out_col;
+}
+}  // namespace detail
+
+std::unique_ptr<column> mask_to_bools(bitmask_type const* bitmask,
+                                      size_type begin_bit,
+                                      size_type end_bit,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::mask_to_bools(bitmask, begin_bit, end_bit, cudf::get_default_stream(), mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/transform/nans_to_nulls.cu b/cpp/src/transform/nans_to_nulls.cu
new file mode 100644
index 0000000..3c02409
--- /dev/null
+++ b/cpp/src/transform/nans_to_nulls.cu
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+struct dispatch_nan_to_null {
+  template <typename T>
+  std::enable_if_t<std::is_floating_point_v<T>,
+                   std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type>>
+  operator()(column_view const& input,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr)
+  {
+    auto input_device_view_ptr = column_device_view::create(input, stream);
+    auto input_device_view     = *input_device_view_ptr;
+
+    if (input.nullable()) {
+      auto pred = [input_device_view] __device__(cudf::size_type idx) {
+        return not(std::isnan(input_device_view.element<T>(idx)) ||
+                   input_device_view.is_null_nocheck(idx));
+      };
+
+      auto mask = detail::valid_if(thrust::make_counting_iterator<cudf::size_type>(0),
+                                   thrust::make_counting_iterator<cudf::size_type>(input.size()),
+                                   pred,
+                                   stream,
+                                   mr);
+
+      return std::pair(std::make_unique<rmm::device_buffer>(std::move(mask.first)), mask.second);
+    } else {
+      auto pred = [input_device_view] __device__(cudf::size_type idx) {
+        return not(std::isnan(input_device_view.element<T>(idx)));
+      };
+
+      auto mask = detail::valid_if(thrust::make_counting_iterator<cudf::size_type>(0),
+                                   thrust::make_counting_iterator<cudf::size_type>(input.size()),
+                                   pred,
+                                   stream,
+                                   mr);
+
+      return std::pair(std::make_unique<rmm::device_buffer>(std::move(mask.first)), mask.second);
+    }
+  }
+
+  template <typename T>
+  std::enable_if_t<!std::is_floating_point_v<T>,
+                   std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type>>
+  operator()(column_view const& input,
+             rmm::cuda_stream_view stream,
+             rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_FAIL("Input column can't be a non-floating type");
+  }
+};
+
+std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type> nans_to_nulls(
+  column_view const& input, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
+{
+  if (input.is_empty()) { return std::pair(std::make_unique<rmm::device_buffer>(), 0); }
+
+  return cudf::type_dispatcher(input.type(), dispatch_nan_to_null{}, input, stream, mr);
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<rmm::device_buffer>, cudf::size_type> nans_to_nulls(
+  column_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::nans_to_nulls(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/transform/one_hot_encode.cu b/cpp/src/transform/one_hot_encode.cu
new file mode 100644
index 0000000..c7bb40e
--- /dev/null
+++ b/cpp/src/transform/one_hot_encode.cu
@@ -0,0 +1,118 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <algorithm>
+
+namespace cudf {
+namespace detail {
+
+template <typename DeviceComparatorType>
+struct ohe_equality_functor {
+  ohe_equality_functor(size_type input_size, DeviceComparatorType d_equal)
+    : _input_size(input_size), _d_equal(d_equal)
+  {
+  }
+
+  auto __device__ operator()(size_type i) const noexcept
+  {
+    auto const element_index  = cudf::experimental::row::lhs_index_type{i % _input_size};
+    auto const category_index = cudf::experimental::row::rhs_index_type{i / _input_size};
+    return _d_equal(element_index, category_index);
+  }
+
+ private:
+  size_type _input_size;
+  DeviceComparatorType _d_equal;
+};
+
+std::pair<std::unique_ptr<column>, table_view> one_hot_encode(column_view const& input,
+                                                              column_view const& categories,
+                                                              rmm::cuda_stream_view stream,
+                                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(input.type() == categories.type(), "Mismatch type between input and categories.");
+
+  if (categories.is_empty()) { return {make_empty_column(type_id::BOOL8), table_view{}}; }
+
+  if (input.is_empty()) {
+    auto empty_data = make_empty_column(type_id::BOOL8);
+    std::vector<column_view> views(categories.size(), empty_data->view());
+    return {std::move(empty_data), table_view{views}};
+  }
+
+  auto const total_size = input.size() * categories.size();
+  auto all_encodings =
+    make_numeric_column(data_type{type_id::BOOL8}, total_size, mask_state::UNALLOCATED, stream, mr);
+
+  auto const t_lhs = table_view{{input}};
+  auto const t_rhs = table_view{{categories}};
+  auto const comparator =
+    cudf::experimental::row::equality::two_table_comparator{t_lhs, t_rhs, stream};
+
+  auto const comparator_helper = [&](auto const d_equal) {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(total_size),
+                      all_encodings->mutable_view().begin<bool>(),
+                      ohe_equality_functor<decltype(d_equal)>(input.size(), d_equal));
+  };
+
+  if (cudf::detail::has_nested_columns(t_lhs) or cudf::detail::has_nested_columns(t_rhs)) {
+    auto const d_equal = comparator.equal_to<true>(
+      nullate::DYNAMIC{has_nested_nulls(t_lhs) || has_nested_nulls(t_rhs)});
+    comparator_helper(d_equal);
+  } else {
+    auto const d_equal = comparator.equal_to<false>(
+      nullate::DYNAMIC{has_nested_nulls(t_lhs) || has_nested_nulls(t_rhs)});
+    comparator_helper(d_equal);
+  }
+
+  auto const split_iter =
+    make_counting_transform_iterator(1, [width = input.size()](auto i) { return i * width; });
+  std::vector<size_type> split_indices(split_iter, split_iter + categories.size() - 1);
+
+  auto encodings_view = table_view{detail::split(all_encodings->view(), split_indices, stream)};
+
+  return {std::move(all_encodings), encodings_view};
+}
+
+}  // namespace detail
+
+std::pair<std::unique_ptr<column>, table_view> one_hot_encode(column_view const& input,
+                                                              column_view const& categories,
+                                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::one_hot_encode(input, categories, cudf::get_default_stream(), mr);
+}
+}  // namespace cudf
diff --git a/cpp/src/transform/row_bit_count.cu b/cpp/src/transform/row_bit_count.cu
new file mode 100644
index 0000000..b151b44
--- /dev/null
+++ b/cpp/src/transform/row_bit_count.cu
@@ -0,0 +1,546 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/optional.h>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace detail {
+
+namespace {
+
+/**
+ * @brief Struct which contains per-column information necessary to
+ * traverse a column hierarchy on the gpu.
+ *
+ * When `row_bit_count` is called, the input column hierarchy is flattened into a
+ * vector of column_device_views.  For each one of them, we store a column_info
+ * struct.   The `depth` field represents the depth of the column in the original
+ * hierarchy.
+ *
+ * As we traverse the hierarchy for each input row, we maintain a span representing
+ * the start and end rows for the current nesting depth.  At depth 0, this span is
+ * always just 1 row.  As we cross list boundaries int the hierarchy, this span
+ * grows. So for each column we visit we always know how many rows of it are relevant
+ * and can compute it's contribution to the overall size.
+ *
+ *  An example using a list<list<int>> column, computing the size of row 1.
+ *
+ *  { {{1, 2}, {3, 4}, {5, 6}}, {{7}, {8, 9, 10}, {11, 12, 13, 14}} }
+ *
+ *  L0 = List<List<int32_t>>:
+ *  Length : 2
+ *  Offsets : 0, 3, 6
+ *     L1 = List<int32_t>:
+ *     Length : 6
+ *     Offsets : 0, 2, 4, 6, 7, 10, 14
+ *        I = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
+ *
+ *
+ *  span0 = [1, 2]                                        row 1 is represented by the span [1, 2]
+ *  span1 = [L0.offsets[span0[0]], L0.offsets[span0[1]]]  expand by the offsets of L0
+ *  span1 = [3, 6]                                        span applied to children of L0
+ *  span2 = [L1.offsets[span1[0]], L1.offsets[span1[1]]]  expand by the offsets of L1
+ *  span2 = [6, 14]                                       span applied to children of L1
+ *
+ *  The total size of our row is computed as:
+ *  (span0[1] - span0[0]) * sizeof(int)        the cost of the offsets for L0
+ *                 +
+ *  (span1[1] - span1[0]) * sizeof(int)        the cost of the offsets for L1
+ *                 +
+ *  (span2[1] - span2[0]) * sizeof(int)        the cost of the integers in I
+ *
+ * `depth` represents our depth in the source column hierarchy.
+ *
+ * "branches" within the spans can occur when we have lists inside of structs.
+ * consider a case where we are entering a struct<list, float> with a span of [4, 8].
+ * The internal list column will change that span to something else, say [5, 9].
+ * But when we finish processing the list column, the final float column wants to
+ * go back and use the original span [4, 8].
+ *
+ * [4, 8]  [5, 9]   [4, 8]
+ * struct< list<>   float>
+ *
+ * To accomplish this we maintain a stack of spans. Pushing the current span
+ * whenever we enter a branch, and popping a span whenever we leave a branch.
+ *
+ * `branch_depth_start` represents the branch depth as we reach a new column.
+ * if `branch_depth_start` is < the last branch depth we saw, we are returning
+ * from a branch and should pop off the stack.
+ *
+ * `branch_depth_end` represents the new branch depth caused by this column.
+ * if branch_depth_end > branch_depth_start, we are branching and need to
+ * push the current span on the stack.
+ *
+ */
+struct column_info {
+  size_type depth;
+  size_type branch_depth_start;
+  size_type branch_depth_end;
+};
+
+/**
+ * @brief Struct which contains hierarchy information precomputed on the host.
+ *
+ * If the input data contains only fixed-width types, this preprocess step
+ * produces the value `simple_per_row_size` which is a constant for every
+ * row in the output.  We can use this value and skip the more complicated
+ * processing for lists, structs and strings entirely if `complex_type_count`
+ * is 0.
+ *
+ */
+struct hierarchy_info {
+  hierarchy_info() {}
+
+  // These two fields act as an optimization. If we find that the entire table
+  // is just fixed-width types, we do not need to do the more expensive kernel call that
+  // traverses the individual columns. So if complex_type_count is 0, we can just
+  // return a column where every row contains the value simple_per_row_size
+  size_type simple_per_row_size{0};  // in bits
+  size_type complex_type_count{0};
+
+  // max depth of span branches present in the hierarchy.
+  size_type max_branch_depth{0};
+};
+
+/**
+ * @brief Function which flattens the incoming column hierarchy into a vector
+ * of column_views and produces accompanying column_info and hierarchy_info
+ * metadata.
+ *
+ * @param begin: Beginning of a range of column views
+ * @param end: End of a range of column views
+ * @param out: (output) Flattened vector of output column_views
+ * @param info: (output) Additional per-output column_view metadata needed by the gpu
+ * @param h_info: (output) Information about the hierarchy
+ * @param cur_depth: Current absolute depth in the hierarchy
+ * @param cur_branch_depth: Current branch depth
+ * @param parent_index: Index into `out` representing our owning parent column
+ */
+template <typename ColIter>
+void flatten_hierarchy(ColIter begin,
+                       ColIter end,
+                       std::vector<cudf::column_view>& out,
+                       std::vector<column_info>& info,
+                       hierarchy_info& h_info,
+                       rmm::cuda_stream_view stream,
+                       size_type cur_depth                = 0,
+                       size_type cur_branch_depth         = 0,
+                       thrust::optional<int> parent_index = {});
+
+/**
+ * @brief Type-dispatched functor called by flatten_hierarchy.
+ *
+ */
+struct flatten_functor {
+  // fixed width
+  template <typename T, std::enable_if_t<cudf::is_fixed_width<T>()>* = nullptr>
+  void operator()(column_view const& col,
+                  std::vector<cudf::column_view>& out,
+                  std::vector<column_info>& info,
+                  hierarchy_info& h_info,
+                  rmm::cuda_stream_view,
+                  size_type cur_depth,
+                  size_type cur_branch_depth,
+                  thrust::optional<int>)
+  {
+    out.push_back(col);
+    info.push_back({cur_depth, cur_branch_depth, cur_branch_depth});
+    h_info.simple_per_row_size +=
+      (sizeof(device_storage_type_t<T>) * CHAR_BIT) + (col.nullable() ? 1 : 0);
+  }
+
+  // strings
+  template <typename T, std::enable_if_t<std::is_same_v<T, string_view>>* = nullptr>
+  void operator()(column_view const& col,
+                  std::vector<cudf::column_view>& out,
+                  std::vector<column_info>& info,
+                  hierarchy_info& h_info,
+                  rmm::cuda_stream_view,
+                  size_type cur_depth,
+                  size_type cur_branch_depth,
+                  thrust::optional<int>)
+  {
+    out.push_back(col);
+    info.push_back({cur_depth, cur_branch_depth, cur_branch_depth});
+    h_info.complex_type_count++;
+  }
+
+  // lists
+  template <typename T, std::enable_if_t<std::is_same_v<T, list_view>>* = nullptr>
+  void operator()(column_view const& col,
+                  std::vector<cudf::column_view>& out,
+                  std::vector<column_info>& info,
+                  hierarchy_info& h_info,
+                  rmm::cuda_stream_view stream,
+                  size_type cur_depth,
+                  size_type cur_branch_depth,
+                  thrust::optional<int> parent_index)
+  {
+    // track branch depth as we reach this list and after we pass it
+    auto const branch_depth_start = cur_branch_depth;
+    auto const is_list_inside_struct =
+      parent_index && out[parent_index.value()].type().id() == type_id::STRUCT;
+    if (is_list_inside_struct) {
+      cur_branch_depth++;
+      h_info.max_branch_depth = max(h_info.max_branch_depth, cur_branch_depth);
+    }
+    size_type const branch_depth_end = cur_branch_depth;
+
+    out.push_back(col);
+    info.push_back({cur_depth, branch_depth_start, branch_depth_end});
+
+    lists_column_view lcv(col);
+    auto iter = cudf::detail::make_counting_transform_iterator(
+      0, [col = lcv.get_sliced_child(stream)](auto) { return col; });
+    h_info.complex_type_count++;
+
+    flatten_hierarchy(
+      iter, iter + 1, out, info, h_info, stream, cur_depth + 1, cur_branch_depth, out.size() - 1);
+  }
+
+  // structs
+  template <typename T, std::enable_if_t<std::is_same_v<T, struct_view>>* = nullptr>
+  void operator()(column_view const& col,
+                  std::vector<cudf::column_view>& out,
+                  std::vector<column_info>& info,
+                  hierarchy_info& h_info,
+                  rmm::cuda_stream_view stream,
+                  size_type cur_depth,
+                  size_type cur_branch_depth,
+                  thrust::optional<int>)
+  {
+    out.push_back(col);
+    info.push_back({cur_depth, cur_branch_depth, cur_branch_depth});
+
+    h_info.simple_per_row_size += col.nullable() ? 1 : 0;
+
+    structs_column_view scv(col);
+    auto iter = cudf::detail::make_counting_transform_iterator(
+      0, [&scv, &stream](auto i) { return scv.get_sliced_child(i, stream); });
+    flatten_hierarchy(iter,
+                      iter + scv.num_children(),
+                      out,
+                      info,
+                      h_info,
+                      stream,
+                      cur_depth + 1,
+                      cur_branch_depth,
+                      out.size() - 1);
+  }
+
+  // everything else
+  template <typename T, typename... Args>
+  std::enable_if_t<!cudf::is_fixed_width<T>() && !std::is_same_v<T, string_view> &&
+                     !std::is_same_v<T, list_view> && !std::is_same_v<T, struct_view>,
+                   void>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported column type in row_bit_count");
+  }
+};
+
+template <typename ColIter>
+void flatten_hierarchy(ColIter begin,
+                       ColIter end,
+                       std::vector<cudf::column_view>& out,
+                       std::vector<column_info>& info,
+                       hierarchy_info& h_info,
+                       rmm::cuda_stream_view stream,
+                       size_type cur_depth,
+                       size_type cur_branch_depth,
+                       thrust::optional<int> parent_index)
+{
+  std::for_each(begin, end, [&](column_view const& col) {
+    cudf::type_dispatcher(col.type(),
+                          flatten_functor{},
+                          col,
+                          out,
+                          info,
+                          h_info,
+                          stream,
+                          cur_depth,
+                          cur_branch_depth,
+                          parent_index);
+  });
+}
+
+/**
+ * @brief Struct representing a span of rows.
+ *
+ */
+struct row_span {
+  size_type row_start, row_end;
+};
+
+/**
+ * @brief Functor for computing the size, in bits, of a `row_span` of rows for a given
+ * `column_device_view`
+ *
+ */
+struct row_size_functor {
+  /**
+   * @brief Computes size in bits of a span of rows in a fixed-width column.
+   *
+   * Computed as :   ((# of rows) * sizeof(data type) * 8)
+   *                 +
+   *                 1 bit per row for validity if applicable.
+   */
+  template <typename T>
+  __device__ size_type operator()(column_device_view const& col, row_span const& span)
+  {
+    auto const num_rows{span.row_end - span.row_start};
+    auto const element_size  = sizeof(device_storage_type_t<T>) * CHAR_BIT;
+    auto const validity_size = col.nullable() ? 1 : 0;
+    return (element_size + validity_size) * num_rows;
+  }
+};
+
+/**
+ * @brief Computes size in bits of a span of rows in a strings column.
+ *
+ * Computed as :   ((# of rows) * sizeof(offset) * 8) + (total # of characters * 8))
+ *                 +
+ *                 1 bit per row for validity if applicable.
+ */
+template <>
+__device__ size_type row_size_functor::operator()<string_view>(column_device_view const& col,
+                                                               row_span const& span)
+{
+  auto const num_rows{span.row_end - span.row_start};
+  if (num_rows == 0) {
+    // For empty columns, the `span` cannot have a row size.
+    return 0;
+  }
+
+  auto const& offsets = col.child(strings_column_view::offsets_column_index);
+  auto const row_start{span.row_start + col.offset()};
+  auto const row_end{span.row_end + col.offset()};
+  if (row_start == row_end) {
+    // Empty row contributes 0 bits to row_bit_count().
+    // Note: Validity bit doesn't count either. There are no rows in the child column
+    //       corresponding to this span.
+    return 0;
+  }
+
+  auto const offsets_size  = sizeof(size_type) * CHAR_BIT;
+  auto const validity_size = col.nullable() ? 1 : 0;
+  auto const chars_size =
+    (offsets.data<size_type>()[row_end] - offsets.data<size_type>()[row_start]) * CHAR_BIT;
+  return ((offsets_size + validity_size) * num_rows) + chars_size;
+}
+
+/**
+ * @brief Computes size in bits of a span of rows in a list column.
+ *
+ * Computed as :   ((# of rows) * sizeof(offset) * 8)
+ *                 +
+ *                 1 bit per row for validity if applicable.
+ */
+template <>
+__device__ size_type row_size_functor::operator()<list_view>(column_device_view const& col,
+                                                             row_span const& span)
+{
+  auto const num_rows{span.row_end - span.row_start};
+
+  auto const offsets_size  = sizeof(size_type) * CHAR_BIT;
+  auto const validity_size = col.nullable() ? 1 : 0;
+  return (offsets_size + validity_size) * num_rows;
+}
+
+/**
+ * @brief Computes size in bits of a span of rows in a struct column.
+ *
+ * Computed as :   1 bit per row for validity if applicable.
+ */
+template <>
+__device__ size_type row_size_functor::operator()<struct_view>(column_device_view const& col,
+                                                               row_span const& span)
+{
+  auto const num_rows{span.row_end - span.row_start};
+  return (col.nullable() ? 1 : 0) * num_rows;  // cost of validity
+}
+
+/**
+ * @brief Kernel for computing per-row sizes in bits.
+ *
+ * @param cols An span of column_device_views representing a column hierarchy
+ * @param info An span of column_info structs corresponding the elements in `cols`
+ * @param output Output span of size (# rows) where per-row bit sizes are stored
+ * @param max_branch_depth Maximum depth of the span stack needed per-thread
+ */
+__global__ void compute_row_sizes(device_span<column_device_view const> cols,
+                                  device_span<column_info const> info,
+                                  device_span<size_type> output,
+                                  size_type max_branch_depth)
+{
+  extern __shared__ row_span thread_branch_stacks[];
+  int const tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+  auto const num_rows = output.size();
+  if (tid >= num_rows) { return; }
+
+  // my_branch_stack points to the last span prior to branching. a branch occurs only
+  // when we are inside of a list contained within a struct column.
+  row_span* my_branch_stack = thread_branch_stacks + (threadIdx.x * max_branch_depth);
+  size_type branch_depth{0};
+
+  // current row span - always starts at 1 row.
+  row_span cur_span{tid, tid + 1};
+
+  // output size
+  size_type& size = output[tid];
+  size            = 0;
+
+  size_type last_branch_depth{0};
+  for (size_type idx = 0; idx < cols.size(); idx++) {
+    column_device_view const& col = cols[idx];
+
+    // if we've returned from a branch, pop to the proper span
+    if (info[idx].branch_depth_start < last_branch_depth) {
+      branch_depth = info[idx].branch_depth_start;
+      cur_span     = my_branch_stack[branch_depth];
+    }
+    // if we're entering a new branch, push the current span
+    // NOTE: this case can happen (a pop and a push by the same column)
+    // when we have a struct<list, list>
+    if (info[idx].branch_depth_end > info[idx].branch_depth_start) {
+      my_branch_stack[branch_depth++] = cur_span;
+    }
+
+    // if we're back at depth 0, this is a new top-level column, so reset
+    // span info
+    if (info[idx].depth == 0) {
+      branch_depth      = 0;
+      last_branch_depth = 0;
+      cur_span          = row_span{tid, tid + 1};
+    }
+
+    // add the contributing size of this row
+    size += cudf::type_dispatcher(col.type(), row_size_functor{}, col, cur_span);
+
+    // if this is a list column, update the working span from our offsets
+    if (col.type().id() == type_id::LIST && col.size() > 0) {
+      column_device_view const& offsets = col.child(lists_column_view::offsets_column_index);
+      auto const base_offset            = offsets.data<size_type>()[col.offset()];
+      cur_span.row_start =
+        offsets.data<size_type>()[cur_span.row_start + col.offset()] - base_offset;
+      cur_span.row_end = offsets.data<size_type>()[cur_span.row_end + col.offset()] - base_offset;
+    }
+
+    last_branch_depth = info[idx].branch_depth_end;
+  }
+}
+
+}  // anonymous namespace
+
+/**
+ * @copydoc cudf::detail::row_bit_count
+ *
+ */
+std::unique_ptr<column> row_bit_count(table_view const& t,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  // no rows
+  if (t.num_rows() <= 0) { return cudf::make_empty_column(type_id::INT32); }
+
+  // flatten the hierarchy and determine some information about it.
+  std::vector<cudf::column_view> cols;
+  std::vector<column_info> info;
+  hierarchy_info h_info;
+  flatten_hierarchy(t.begin(), t.end(), cols, info, h_info, stream);
+  CUDF_EXPECTS(info.size() == cols.size(), "Size/info mismatch");
+
+  // create output buffer and view
+  auto output = cudf::make_fixed_width_column(
+    data_type{type_id::INT32}, t.num_rows(), mask_state::UNALLOCATED, stream, mr);
+  mutable_column_view mcv = output->mutable_view();
+
+  // simple case.  if we have no complex types (lists, strings, etc), the per-row size is already
+  // trivially computed
+  if (h_info.complex_type_count <= 0) {
+    thrust::fill(rmm::exec_policy(stream),
+                 mcv.begin<size_type>(),
+                 mcv.end<size_type>(),
+                 h_info.simple_per_row_size);
+    return output;
+  }
+
+  // create a contiguous block of column_device_views
+  auto d_cols = contiguous_copy_column_device_views<column_device_view>(cols, stream);
+
+  // move stack info to the gpu
+  rmm::device_uvector<column_info> d_info =
+    cudf::detail::make_device_uvector_async(info, stream, rmm::mr::get_current_device_resource());
+
+  // each thread needs to maintain a stack of row spans of size max_branch_depth. we will use
+  // shared memory to do this rather than allocating a potentially gigantic temporary buffer
+  // of memory of size (# input rows * sizeof(row_span) * max_branch_depth).
+  auto const shmem_per_thread = sizeof(row_span) * h_info.max_branch_depth;
+  int device_id;
+  CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+  int shmem_limit_per_block;
+  CUDF_CUDA_TRY(
+    cudaDeviceGetAttribute(&shmem_limit_per_block, cudaDevAttrMaxSharedMemoryPerBlock, device_id));
+  constexpr int max_block_size = 256;
+  auto const block_size =
+    shmem_per_thread != 0
+      ? std::min(max_block_size, shmem_limit_per_block / static_cast<int>(shmem_per_thread))
+      : max_block_size;
+  auto const shared_mem_size = shmem_per_thread * block_size;
+  // should we be aborting if we reach some extremely small block size, or just if we hit 0?
+  CUDF_EXPECTS(block_size > 0, "Encountered a column hierarchy too complex for row_bit_count");
+
+  cudf::detail::grid_1d grid{t.num_rows(), block_size, 1};
+  compute_row_sizes<<<grid.num_blocks, block_size, shared_mem_size, stream.value()>>>(
+    {std::get<1>(d_cols), cols.size()},
+    {d_info.data(), info.size()},
+    {mcv.data<size_type>(), static_cast<std::size_t>(t.num_rows())},
+    h_info.max_branch_depth);
+
+  return output;
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::row_bit_count
+ *
+ */
+std::unique_ptr<column> row_bit_count(table_view const& t, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::row_bit_count(t, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/transform/transform.cpp b/cpp/src/transform/transform.cpp
new file mode 100644
index 0000000..57a18b6
--- /dev/null
+++ b/cpp/src/transform/transform.cpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/transform.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <jit_preprocessed_files/transform/jit/kernel.cu.jit.hpp>
+
+#include <jit/cache.hpp>
+#include <jit/parser.hpp>
+#include <jit/util.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace transformation {
+namespace jit {
+
+void unary_operation(mutable_column_view output,
+                     column_view input,
+                     std::string const& udf,
+                     data_type output_type,
+                     bool is_ptx,
+                     rmm::cuda_stream_view stream)
+{
+  std::string kernel_name =
+    jitify2::reflection::Template("cudf::transformation::jit::kernel")  //
+      .instantiate(cudf::type_to_name(output.type()),  // list of template arguments
+                   cudf::type_to_name(input.type()));
+
+  std::string cuda_source =
+    is_ptx ? cudf::jit::parse_single_function_ptx(udf,  //
+                                                  "GENERIC_UNARY_OP",
+                                                  cudf::type_to_name(output_type),
+                                                  {0})
+           : cudf::jit::parse_single_function_cuda(udf,  //
+                                                   "GENERIC_UNARY_OP");
+
+  cudf::jit::get_program_cache(*transform_jit_kernel_cu_jit)
+    .get_kernel(
+      kernel_name, {}, {{"transform/jit/operation-udf.hpp", cuda_source}}, {"-arch=sm_."})  //
+    ->configure_1d_max_occupancy(0, 0, 0, stream.value())                                   //
+    ->launch(output.size(),                                                                 //
+             cudf::jit::get_data_ptr(output),
+             cudf::jit::get_data_ptr(input));
+}
+
+}  // namespace jit
+}  // namespace transformation
+
+namespace detail {
+std::unique_ptr<column> transform(column_view const& input,
+                                  std::string const& unary_udf,
+                                  data_type output_type,
+                                  bool is_ptx,
+                                  rmm::cuda_stream_view stream,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_fixed_width(input.type()), "Unexpected non-fixed-width type.");
+
+  std::unique_ptr<column> output = make_fixed_width_column(
+    output_type, input.size(), copy_bitmask(input), input.null_count(), stream, mr);
+
+  if (input.is_empty()) { return output; }
+
+  mutable_column_view output_view = *output;
+
+  // transform
+  transformation::jit::unary_operation(output_view, input, unary_udf, output_type, is_ptx, stream);
+
+  return output;
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> transform(column_view const& input,
+                                  std::string const& unary_udf,
+                                  data_type output_type,
+                                  bool is_ptx,
+                                  rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::transform(input, unary_udf, output_type, is_ptx, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/transpose/transpose.cu b/cpp/src/transpose/transpose.cu
new file mode 100644
index 0000000..45c2e65
--- /dev/null
+++ b/cpp/src/transpose/transpose.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/reshape.hpp>
+#include <cudf/detail/transpose.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/transpose.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf {
+namespace detail {
+std::pair<std::unique_ptr<column>, table_view> transpose(table_view const& input,
+                                                         rmm::cuda_stream_view stream,
+                                                         rmm::mr::device_memory_resource* mr)
+{
+  // If there are no rows in the input, return successfully
+  if (input.num_columns() == 0 || input.num_rows() == 0) {
+    return {std::make_unique<column>(), table_view{}};
+  }
+
+  // Check datatype homogeneity
+  auto const dtype = input.column(0).type();
+  CUDF_EXPECTS(
+    std::all_of(
+      input.begin(), input.end(), [dtype](auto const& col) { return dtype == col.type(); }),
+    "Column type mismatch");
+
+  auto output_column = cudf::detail::interleave_columns(input, stream, mr);
+  auto one_iter      = thrust::make_counting_iterator<size_type>(1);
+  auto splits_iter   = thrust::make_transform_iterator(
+    one_iter, [width = input.num_columns()](size_type idx) { return idx * width; });
+  auto splits = std::vector<size_type>(splits_iter, splits_iter + input.num_rows() - 1);
+  auto output_column_views = detail::split(output_column->view(), splits, stream);
+
+  return {std::move(output_column), table_view(output_column_views)};
+}
+}  // namespace detail
+
+std::pair<std::unique_ptr<column>, table_view> transpose(table_view const& input,
+                                                         rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::transpose(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/unary/cast_ops.cu b/cpp/src/unary/cast_ops.cu
new file mode 100644
index 0000000..1c81f26
--- /dev/null
+++ b/cpp/src/unary/cast_ops.cu
@@ -0,0 +1,424 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/detail/binaryop.hpp>
+#include <cudf/detail/fill.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace detail {
+namespace {  // anonymous namespace
+template <typename _TargetT>
+struct unary_cast {
+  template <
+    typename SourceT,
+    typename TargetT                                                                = _TargetT,
+    std::enable_if_t<(cudf::is_numeric<SourceT>() && cudf::is_numeric<TargetT>())>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    return static_cast<TargetT>(element);
+  }
+
+  template <
+    typename SourceT,
+    typename TargetT                                                                    = _TargetT,
+    std::enable_if_t<(cudf::is_timestamp<SourceT>() && cudf::is_timestamp<TargetT>())>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    // Convert source tick counts into target tick counts without blindly truncating them
+    // by dividing the respective duration time periods (which may not work for time before
+    // UNIX epoch)
+    return TargetT{cuda::std::chrono::floor<TargetT::duration>(element.time_since_epoch())};
+  }
+
+  template <
+    typename SourceT,
+    typename TargetT                                                                  = _TargetT,
+    std::enable_if_t<(cudf::is_duration<SourceT>() && cudf::is_duration<TargetT>())>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    return TargetT{cuda::std::chrono::floor<TargetT>(element)};
+  }
+
+  template <
+    typename SourceT,
+    typename TargetT                                                               = _TargetT,
+    std::enable_if_t<cudf::is_numeric<SourceT>() && cudf::is_duration<TargetT>()>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    return TargetT{static_cast<typename TargetT::rep>(element)};
+  }
+
+  template <
+    typename SourceT,
+    typename TargetT                                                                   = _TargetT,
+    std::enable_if_t<(cudf::is_timestamp<SourceT>() && cudf::is_duration<TargetT>())>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    return TargetT{cuda::std::chrono::floor<TargetT>(element.time_since_epoch())};
+  }
+
+  template <
+    typename SourceT,
+    typename TargetT                                                               = _TargetT,
+    std::enable_if_t<cudf::is_duration<SourceT>() && cudf::is_numeric<TargetT>()>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    return static_cast<TargetT>(element.count());
+  }
+
+  template <
+    typename SourceT,
+    typename TargetT                                                                   = _TargetT,
+    std::enable_if_t<(cudf::is_duration<SourceT>() && cudf::is_timestamp<TargetT>())>* = nullptr>
+  __device__ inline TargetT operator()(SourceT const element)
+  {
+    return TargetT{cuda::std::chrono::floor<TargetT::duration>(element)};
+  }
+};
+
+template <typename _SourceT, typename _TargetT>
+struct fixed_point_unary_cast {
+  numeric::scale_type scale;
+  using FixedPointT = std::conditional_t<cudf::is_fixed_point<_SourceT>(), _SourceT, _TargetT>;
+  using DeviceT     = device_storage_type_t<FixedPointT>;
+
+  template <
+    typename SourceT                                                                     = _SourceT,
+    typename TargetT                                                                     = _TargetT,
+    std::enable_if_t<(cudf::is_fixed_point<_SourceT>() && cudf::is_numeric<TargetT>())>* = nullptr>
+  __device__ inline TargetT operator()(DeviceT const element)
+  {
+    auto const fp = SourceT{numeric::scaled_integer<DeviceT>{element, scale}};
+    return static_cast<TargetT>(fp);
+  }
+
+  template <
+    typename SourceT                                                                     = _SourceT,
+    typename TargetT                                                                     = _TargetT,
+    std::enable_if_t<(cudf::is_numeric<_SourceT>() && cudf::is_fixed_point<TargetT>())>* = nullptr>
+  __device__ inline DeviceT operator()(SourceT const element)
+  {
+    return TargetT{element, scale}.value();
+  }
+};
+
+template <typename From, typename To>
+constexpr inline auto is_supported_non_fixed_point_cast()
+{
+  return cudf::is_fixed_width<To>() &&
+         // Disallow fixed_point here (requires different specialization)
+         !(cudf::is_fixed_point<From>() || cudf::is_fixed_point<To>()) &&
+         // Disallow conversions between timestamps and numeric
+         !(cudf::is_timestamp<From>() && is_numeric<To>()) &&
+         !(cudf::is_timestamp<To>() && is_numeric<From>());
+}
+
+template <typename From, typename To>
+constexpr inline auto is_supported_fixed_point_cast()
+{
+  return (cudf::is_fixed_point<From>() && cudf::is_numeric<To>()) ||
+         (cudf::is_numeric<From>() && cudf::is_fixed_point<To>()) ||
+         (cudf::is_fixed_point<From>() && cudf::is_fixed_point<To>());
+}
+
+template <typename From, typename To>
+constexpr inline auto is_supported_cast()
+{
+  return is_supported_non_fixed_point_cast<From, To>() || is_supported_fixed_point_cast<From, To>();
+}
+
+template <typename From, typename To>
+struct device_cast {
+  __device__ To operator()(From element) { return static_cast<To>(element); }
+};
+
+/**
+ * @brief Takes a `fixed_point` column_view as @p input and returns a `fixed_point` column with new
+ * @p scale
+ *
+ * @tparam T     Type of the `fixed_point` column_view (`decimal32`, `decimal64` or `decimal128`)
+ * @param input  Input `column_view`
+ * @param scale  `scale` of the returned `column`
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ * @param mr     Device memory resource used to allocate the returned column's device memory
+ *
+ * @return std::unique_ptr<column> Returned column with new @p scale
+ */
+template <typename T, std::enable_if_t<is_fixed_point<T>()>* = nullptr>
+std::unique_ptr<column> rescale(column_view input,
+                                numeric::scale_type scale,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  using namespace numeric;
+  using RepType = device_storage_type_t<T>;
+
+  auto const type = cudf::data_type{cudf::type_to_id<T>(), scale};
+  if (input.type().scale() >= scale) {
+    auto const scalar = make_fixed_point_scalar<T>(0, scale_type{scale}, stream);
+    return detail::binary_operation(input, *scalar, binary_operator::ADD, type, stream, mr);
+  } else {
+    auto const diff = input.type().scale() - scale;
+    // The value of fixed point scalar will overflow if the scale difference is larger than the
+    // max digits of underlying integral type. Under this condition, the output values can be
+    // nothing other than zero value. Therefore, we simply return a zero column.
+    if (-diff > cuda::std::numeric_limits<RepType>::digits10) {
+      auto const scalar  = make_fixed_point_scalar<T>(0, scale_type{scale}, stream);
+      auto output_column = make_column_from_scalar(*scalar, input.size(), stream, mr);
+      if (input.nullable()) {
+        auto const null_mask = copy_bitmask(input, stream, mr);
+        output_column->set_null_mask(std::move(null_mask), input.null_count());
+      }
+      return output_column;
+    }
+
+    RepType scalar_value = 10;
+    for (int i = 1; i < -diff; ++i) {
+      scalar_value *= 10;
+    }
+
+    auto const scalar = make_fixed_point_scalar<T>(scalar_value, scale_type{diff}, stream);
+    return detail::binary_operation(input, *scalar, binary_operator::DIV, type, stream, mr);
+  }
+};
+
+template <typename _SourceT>
+struct dispatch_unary_cast_to {
+  column_view input;
+
+  dispatch_unary_cast_to(column_view inp) : input(inp) {}
+
+  template <typename TargetT,
+            typename SourceT                                                         = _SourceT,
+            std::enable_if_t<is_supported_non_fixed_point_cast<SourceT, TargetT>()>* = nullptr>
+  std::unique_ptr<column> operator()(data_type type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const size = input.size();
+    auto output =
+      std::make_unique<column>(type,
+                               size,
+                               rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                               detail::copy_bitmask(input, stream, mr),
+                               input.null_count());
+
+    mutable_column_view output_mutable = *output;
+
+    thrust::transform(rmm::exec_policy(stream),
+                      input.begin<SourceT>(),
+                      input.end<SourceT>(),
+                      output_mutable.begin<TargetT>(),
+                      unary_cast<TargetT>{});
+
+    return output;
+  }
+
+  template <
+    typename TargetT,
+    typename SourceT                                                                  = _SourceT,
+    std::enable_if_t<cudf::is_fixed_point<SourceT>() && cudf::is_numeric<TargetT>()>* = nullptr>
+  std::unique_ptr<column> operator()(data_type type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const size = input.size();
+    auto output =
+      std::make_unique<column>(type,
+                               size,
+                               rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                               copy_bitmask(input, stream, mr),
+                               input.null_count());
+
+    mutable_column_view output_mutable = *output;
+
+    using DeviceT    = device_storage_type_t<SourceT>;
+    auto const scale = numeric::scale_type{input.type().scale()};
+
+    thrust::transform(rmm::exec_policy(stream),
+                      input.begin<DeviceT>(),
+                      input.end<DeviceT>(),
+                      output_mutable.begin<TargetT>(),
+                      fixed_point_unary_cast<SourceT, TargetT>{scale});
+
+    return output;
+  }
+
+  template <
+    typename TargetT,
+    typename SourceT                                                                  = _SourceT,
+    std::enable_if_t<cudf::is_numeric<SourceT>() && cudf::is_fixed_point<TargetT>()>* = nullptr>
+  std::unique_ptr<column> operator()(data_type type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    auto const size = input.size();
+    auto output =
+      std::make_unique<column>(type,
+                               size,
+                               rmm::device_buffer{size * cudf::size_of(type), stream, mr},
+                               copy_bitmask(input, stream, mr),
+                               input.null_count());
+
+    mutable_column_view output_mutable = *output;
+
+    using DeviceT    = device_storage_type_t<TargetT>;
+    auto const scale = numeric::scale_type{type.scale()};
+
+    thrust::transform(rmm::exec_policy(stream),
+                      input.begin<SourceT>(),
+                      input.end<SourceT>(),
+                      output_mutable.begin<DeviceT>(),
+                      fixed_point_unary_cast<SourceT, TargetT>{scale});
+
+    return output;
+  }
+
+  template <typename TargetT,
+            typename SourceT                                    = _SourceT,
+            std::enable_if_t<cudf::is_fixed_point<SourceT>() && cudf::is_fixed_point<TargetT>() &&
+                             std::is_same_v<SourceT, TargetT>>* = nullptr>
+  std::unique_ptr<column> operator()(data_type type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    if (input.type() == type) {
+      return std::make_unique<column>(input, stream, mr);  // TODO add test for this
+    }
+
+    return detail::rescale<TargetT>(input, numeric::scale_type{type.scale()}, stream, mr);
+  }
+
+  template <typename TargetT,
+            typename SourceT                                        = _SourceT,
+            std::enable_if_t<cudf::is_fixed_point<SourceT>() && cudf::is_fixed_point<TargetT>() &&
+                             not std::is_same_v<SourceT, TargetT>>* = nullptr>
+  std::unique_ptr<column> operator()(data_type type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    using namespace numeric;
+    using SourceDeviceT = device_storage_type_t<SourceT>;
+    using TargetDeviceT = device_storage_type_t<TargetT>;
+
+    auto casted = [&]() {
+      auto const size = input.size();
+      auto output     = std::make_unique<column>(cudf::data_type{type.id(), input.type().scale()},
+                                             size,
+                                             rmm::device_buffer{size * cudf::size_of(type), stream},
+                                             copy_bitmask(input, stream, mr),
+                                             input.null_count());
+
+      mutable_column_view output_mutable = *output;
+
+      thrust::transform(rmm::exec_policy(stream),
+                        input.begin<SourceDeviceT>(),
+                        input.end<SourceDeviceT>(),
+                        output_mutable.begin<TargetDeviceT>(),
+                        device_cast<SourceDeviceT, TargetDeviceT>{});
+
+      return output;
+    };
+
+    if (input.type().scale() == type.scale()) return casted();
+
+    if constexpr (sizeof(SourceDeviceT) < sizeof(TargetDeviceT)) {
+      // device_cast BEFORE rescale when SourceDeviceT is < TargetDeviceT
+      auto temporary = casted();
+      return detail::rescale<TargetT>(*temporary, scale_type{type.scale()}, stream, mr);
+    } else {
+      // device_cast AFTER rescale when SourceDeviceT is > TargetDeviceT to avoid overflow
+      auto temporary = detail::rescale<SourceT>(input, scale_type{type.scale()}, stream, mr);
+      return detail::cast(*temporary, type, stream, mr);
+    }
+  }
+
+  template <typename TargetT,
+            typename SourceT                                             = _SourceT,
+            std::enable_if_t<not is_supported_cast<SourceT, TargetT>()>* = nullptr>
+  std::unique_ptr<column> operator()(data_type,
+                                     rmm::cuda_stream_view,
+                                     rmm::mr::device_memory_resource*)
+
+  {
+    if (!cudf::is_fixed_width<TargetT>())
+      CUDF_FAIL("Column type must be numeric or chrono or decimal32/64/128");
+    else if (cudf::is_fixed_point<SourceT>())
+      CUDF_FAIL("Currently only decimal32/64/128 to floating point/integral is supported");
+    else if (cudf::is_timestamp<SourceT>() && is_numeric<TargetT>())
+      CUDF_FAIL("Timestamps can be created only from duration");
+    else
+      CUDF_FAIL("Timestamps cannot be converted to numeric without converting it to a duration");
+  }
+};
+
+struct dispatch_unary_cast_from {
+  column_view input;
+
+  dispatch_unary_cast_from(column_view inp) : input(inp) {}
+
+  template <typename T, std::enable_if_t<cudf::is_fixed_width<T>()>* = nullptr>
+  std::unique_ptr<column> operator()(data_type type,
+                                     rmm::cuda_stream_view stream,
+                                     rmm::mr::device_memory_resource* mr)
+  {
+    return type_dispatcher(type, dispatch_unary_cast_to<T>{input}, type, stream, mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!cudf::is_fixed_width<T>(), std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("Column type must be numeric or chrono or decimal32/64/128");
+  }
+};
+}  // anonymous namespace
+
+std::unique_ptr<column> cast(column_view const& input,
+                             data_type type,
+                             rmm::cuda_stream_view stream,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_EXPECTS(is_fixed_width(type), "Unary cast type must be fixed-width.");
+
+  return type_dispatcher(input.type(), detail::dispatch_unary_cast_from{input}, type, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> cast(column_view const& input,
+                             data_type type,
+                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::cast(input, type, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/unary/math_ops.cu b/cpp/src/unary/math_ops.cu
new file mode 100644
index 0000000..d0cae81
--- /dev/null
+++ b/cpp/src/unary/math_ops.cu
@@ -0,0 +1,651 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/copying.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/dictionary/detail/encode.hpp>
+#include <cudf/dictionary/detail/iterator.cuh>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/transform.h>
+
+#include <cmath>
+#include <type_traits>
+
+namespace cudf {
+namespace detail {
+namespace {
+
+// trig functions
+
+struct DeviceSin {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::sin(data);
+  }
+};
+
+struct DeviceCos {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::cos(data);
+  }
+};
+
+struct DeviceTan {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::tan(data);
+  }
+};
+
+struct DeviceArcSin {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::asin(data);
+  }
+};
+
+struct DeviceArcCos {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::acos(data);
+  }
+};
+
+struct DeviceArcTan {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::atan(data);
+  }
+};
+
+struct DeviceSinH {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::sinh(data);
+  }
+};
+
+struct DeviceCosH {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::cosh(data);
+  }
+};
+
+struct DeviceTanH {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::tanh(data);
+  }
+};
+
+struct DeviceArcSinH {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::asinh(data);
+  }
+};
+
+struct DeviceArcCosH {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::acosh(data);
+  }
+};
+
+struct DeviceArcTanH {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::atanh(data);
+  }
+};
+
+// exponential functions
+
+struct DeviceExp {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::exp(data);
+  }
+};
+
+struct DeviceLog {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::log(data);
+  }
+};
+
+struct DeviceSqrt {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::sqrt(data);
+  }
+};
+
+struct DeviceCbrt {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::cbrt(data);
+  }
+};
+
+// rounding functions
+
+struct DeviceCeil {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::ceil(data);
+  }
+};
+
+struct DeviceFloor {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return std::floor(data);
+  }
+};
+
+struct DeviceAbs {
+  template <typename T>
+  std::enable_if_t<std::is_signed_v<T>, T> __device__ operator()(T data)
+  {
+    return std::abs(data);
+  }
+  template <typename T>
+  std::enable_if_t<!std::is_signed_v<T>, T> __device__ operator()(T data)
+  {
+    return data;
+  }
+};
+
+struct DeviceRInt {
+  template <typename T>
+  std::enable_if_t<std::is_floating_point_v<T>, T> __device__ operator()(T data)
+  {
+    return std::rint(data);
+  }
+
+  // Dummy to handle other types, will never be executed
+  template <typename T>
+  std::enable_if_t<!std::is_floating_point_v<T>, T> __device__ operator()(T data)
+  {
+    return data;
+  }
+};
+
+// bitwise op
+
+struct DeviceInvert {
+  template <typename T>
+  __device__ T operator()(T data)
+  {
+    return ~data;
+  }
+};
+
+// logical op
+
+struct DeviceNot {
+  template <typename T>
+  __device__ bool operator()(T data)
+  {
+    return !data;
+  }
+};
+
+// fixed_point ops
+
+/*
+ * Ceiling is calculated using integer division. When we divide by `n`, we get the integer part of
+ * the `fixed_point` number. For a negative number, this is all that is needed since the ceiling
+ * operation is defined as the least integer greater than the value. For a positive number, we may
+ * need to round up if the `fixed_point` number has a fractional part. This is handled by comparing
+ * the truncated value to the original value and if they are not equal, the result needs to be
+ * incremented by `n`.
+ */
+template <typename T>
+struct fixed_point_ceil {
+  T n;  // 10^-scale (value required to determine integer part of fixed_point number)
+  __device__ T operator()(T data)
+  {
+    T const a = (data / n) * n;                  // result of integer division
+    return a + (data > 0 && a != data ? n : 0);  // add 1 if positive and not round number
+  }
+};
+
+/*
+ * Floor is calculated using integer division. When we divide by `n`, we get the integer part of
+ * the `fixed_point` number. For a positive number, this is all that is needed since the floor
+ * operation is defined as the greatest integer less than the value. For a negative number, we may
+ * need to round down if the `fixed_point` number has a fractional part. This is handled by
+ * comparing the truncated value to the original value and if they are not equal, the result needs
+ * to be decremented by `n`.
+ */
+template <typename T>
+struct fixed_point_floor {
+  T n;  // 10^-scale (value required to determine integer part of fixed_point number)
+  __device__ T operator()(T data)
+  {
+    T const a = (data / n) * n;                  // result of integer division
+    return a - (data < 0 && a != data ? n : 0);  // subtract 1 if negative and not round number
+  }
+};
+
+template <typename T>
+struct fixed_point_abs {
+  T n;
+  __device__ T operator()(T data) { return numeric::detail::abs(data); }
+};
+
+template <typename T, template <typename> typename FixedPointFunctor>
+std::unique_ptr<column> unary_op_with(column_view const& input,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource* mr)
+{
+  using Type                     = device_storage_type_t<T>;
+  using FixedPointUnaryOpFunctor = FixedPointFunctor<Type>;
+
+  // When scale is >= 0 and unary_operator is CEIL or FLOOR, the unary_operation is a no-op
+  if (input.type().scale() >= 0 &&
+      (std::is_same_v<FixedPointUnaryOpFunctor, fixed_point_ceil<Type>> ||
+       std::is_same_v<FixedPointUnaryOpFunctor, fixed_point_floor<Type>>))
+    return std::make_unique<cudf::column>(input, stream, mr);
+
+  auto result = cudf::make_fixed_width_column(
+    input.type(), input.size(), copy_bitmask(input, stream, mr), input.null_count(), stream, mr);
+
+  auto out_view = result->mutable_view();
+
+  Type n = 10;
+  for (int i = 1; i < -input.type().scale(); ++i) {
+    n *= 10;
+  }
+
+  thrust::transform(rmm::exec_policy(stream),
+                    input.begin<Type>(),
+                    input.end<Type>(),
+                    out_view.begin<Type>(),
+                    FixedPointUnaryOpFunctor{n});
+
+  result->set_null_count(input.null_count());
+
+  return result;
+}
+
+template <typename OutputType, typename UFN, typename InputIterator>
+std::unique_ptr<cudf::column> transform_fn(InputIterator begin,
+                                           InputIterator end,
+                                           rmm::device_buffer&& null_mask,
+                                           size_type null_count,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto const size = cudf::distance(begin, end);
+
+  std::unique_ptr<cudf::column> output =
+    make_fixed_width_column(data_type{type_to_id<OutputType>()},
+                            size,
+                            std::forward<rmm::device_buffer>(null_mask),
+                            null_count,
+                            stream,
+                            mr);
+  if (size == 0) return output;
+
+  auto output_view = output->mutable_view();
+  thrust::transform(rmm::exec_policy(stream), begin, end, output_view.begin<OutputType>(), UFN{});
+  output->set_null_count(null_count);
+  return output;
+}
+
+template <typename T, typename UFN>
+std::unique_ptr<cudf::column> transform_fn(cudf::dictionary_column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+{
+  auto dictionary_view = cudf::column_device_view::create(input.parent(), stream);
+  auto dictionary_itr  = dictionary::detail::make_dictionary_iterator<T>(*dictionary_view);
+  auto default_mr      = rmm::mr::get_current_device_resource();
+  // call unary-op using temporary output buffer
+  auto output = transform_fn<T, UFN>(dictionary_itr,
+                                     dictionary_itr + input.size(),
+                                     detail::copy_bitmask(input.parent(), stream, default_mr),
+                                     input.null_count(),
+                                     stream,
+                                     default_mr);
+  return cudf::dictionary::detail::encode(
+    output->view(), dictionary::detail::get_indices_type_for_size(output->size()), stream, mr);
+}
+
+template <typename UFN>
+struct MathOpDispatcher {
+  template <typename T, std::enable_if_t<std::is_arithmetic_v<T>>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    return transform_fn<T, UFN>(input.begin<T>(),
+                                input.end<T>(),
+                                cudf::detail::copy_bitmask(input, stream, mr),
+                                input.null_count(),
+                                stream,
+                                mr);
+  }
+
+  struct dictionary_dispatch {
+    template <typename T, std::enable_if_t<std::is_arithmetic_v<T>>* = nullptr>
+    std::unique_ptr<cudf::column> operator()(cudf::dictionary_column_view const& input,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+    {
+      return transform_fn<T, UFN>(input, stream, mr);
+    }
+
+    template <typename T, typename... Args>
+    std::enable_if_t<!std::is_arithmetic_v<T>, std::unique_ptr<cudf::column>> operator()(Args&&...)
+    {
+      CUDF_FAIL("dictionary keys must be numeric for this operation");
+    }
+  };
+
+  template <
+    typename T,
+    std::enable_if_t<!std::is_arithmetic_v<T> and std::is_same_v<T, dictionary32>>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    if (input.is_empty()) return empty_like(input);
+    auto dictionary_col = dictionary_column_view(input);
+    return type_dispatcher(
+      dictionary_col.keys().type(), dictionary_dispatch{}, dictionary_col, stream, mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!std::is_arithmetic_v<T> and !std::is_same_v<T, dictionary32>,
+                   std::unique_ptr<cudf::column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported data type for operation");
+  }
+};
+
+template <typename UFN>
+struct BitwiseOpDispatcher {
+  template <typename T, std::enable_if_t<std::is_integral_v<T>>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    return transform_fn<T, UFN>(input.begin<T>(),
+                                input.end<T>(),
+                                cudf::detail::copy_bitmask(input, stream, mr),
+                                input.null_count(),
+                                stream,
+                                mr);
+  }
+
+  struct dictionary_dispatch {
+    template <typename T, std::enable_if_t<std::is_integral_v<T>>* = nullptr>
+    std::unique_ptr<cudf::column> operator()(cudf::dictionary_column_view const& input,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+    {
+      return transform_fn<T, UFN>(input, stream, mr);
+    }
+
+    template <typename T, typename... Args>
+    std::enable_if_t<!std::is_integral_v<T>, std::unique_ptr<cudf::column>> operator()(Args&&...)
+    {
+      CUDF_FAIL("dictionary keys type not supported for this operation");
+    }
+  };
+
+  template <typename T,
+            std::enable_if_t<!std::is_integral_v<T> and std::is_same_v<T, dictionary32>>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    if (input.is_empty()) return empty_like(input);
+    auto dictionary_col = dictionary_column_view(input);
+    return type_dispatcher(
+      dictionary_col.keys().type(), dictionary_dispatch{}, dictionary_col, stream, mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!std::is_integral_v<T> and !std::is_same_v<T, dictionary32>,
+                   std::unique_ptr<cudf::column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported datatype for operation");
+  }
+};
+
+template <typename UFN>
+struct LogicalOpDispatcher {
+ private:
+  template <typename T>
+  static constexpr bool is_supported()
+  {
+    return std::is_arithmetic_v<T> || std::is_same_v<T, bool>;
+  }
+
+ public:
+  template <typename T, std::enable_if_t<is_supported<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    return transform_fn<bool, UFN>(input.begin<T>(),
+                                   input.end<T>(),
+                                   cudf::detail::copy_bitmask(input, stream, mr),
+                                   input.null_count(),
+
+                                   stream,
+                                   mr);
+  }
+
+  struct dictionary_dispatch {
+    template <typename T, std::enable_if_t<is_supported<T>()>* = nullptr>
+    std::unique_ptr<cudf::column> operator()(cudf::dictionary_column_view const& input,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+    {
+      auto dictionary_view = cudf::column_device_view::create(input.parent(), stream);
+      auto dictionary_itr  = dictionary::detail::make_dictionary_iterator<T>(*dictionary_view);
+      return transform_fn<bool, UFN>(dictionary_itr,
+                                     dictionary_itr + input.size(),
+                                     cudf::detail::copy_bitmask(input.parent(), stream, mr),
+                                     input.null_count(),
+                                     stream,
+                                     mr);
+    }
+
+    template <typename T, typename... Args>
+    std::enable_if_t<!is_supported<T>(), std::unique_ptr<cudf::column>> operator()(Args&&...)
+    {
+      CUDF_FAIL("dictionary keys type not supported for this operation");
+    }
+  };
+
+  template <typename T,
+            std::enable_if_t<!is_supported<T>() and std::is_same_v<T, dictionary32>>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& input,
+                                           rmm::cuda_stream_view stream,
+                                           rmm::mr::device_memory_resource* mr)
+  {
+    if (input.is_empty()) return make_empty_column(cudf::data_type{cudf::type_id::BOOL8});
+    auto dictionary_col = dictionary_column_view(input);
+    return type_dispatcher(
+      dictionary_col.keys().type(), dictionary_dispatch{}, dictionary_col, stream, mr);
+  }
+
+  template <typename T, typename... Args>
+  std::enable_if_t<!is_supported<T>() and !std::is_same_v<T, dictionary32>,
+                   std::unique_ptr<cudf::column>>
+  operator()(Args&&...)
+  {
+    CUDF_FAIL("Unsupported datatype for operation");
+  }
+};
+
+struct FixedPointOpDispatcher {
+  template <typename T, typename... Args>
+  std::enable_if_t<not cudf::is_fixed_point<T>(), std::unique_ptr<column>> operator()(Args&&...)
+  {
+    CUDF_FAIL("FixedPointOpDispatcher only for fixed_point");
+  }
+
+  template <typename T>
+  std::enable_if_t<cudf::is_fixed_point<T>(), std::unique_ptr<column>> operator()(
+    column_view const& input,
+    cudf::unary_operator op,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    // clang-format off
+    switch (op) {
+      case cudf::unary_operator::CEIL:  return unary_op_with<T, fixed_point_ceil>(input, stream, mr);
+      case cudf::unary_operator::FLOOR: return unary_op_with<T, fixed_point_floor>(input, stream, mr);
+      case cudf::unary_operator::ABS:   return unary_op_with<T, fixed_point_abs>(input, stream, mr);
+      default: CUDF_FAIL("Unsupported fixed_point unary operation");
+    }
+    // clang-format on
+  }
+};
+
+}  // namespace
+
+std::unique_ptr<cudf::column> unary_operation(cudf::column_view const& input,
+                                              cudf::unary_operator op,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  if (cudf::is_fixed_point(input.type()))
+    return type_dispatcher(input.type(), detail::FixedPointOpDispatcher{}, input, op, stream, mr);
+
+  switch (op) {
+    case cudf::unary_operator::SIN:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceSin>{}, input, stream, mr);
+    case cudf::unary_operator::COS:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceCos>{}, input, stream, mr);
+    case cudf::unary_operator::TAN:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceTan>{}, input, stream, mr);
+    case cudf::unary_operator::ARCSIN:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceArcSin>{}, input, stream, mr);
+    case cudf::unary_operator::ARCCOS:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceArcCos>{}, input, stream, mr);
+    case cudf::unary_operator::ARCTAN:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceArcTan>{}, input, stream, mr);
+    case cudf::unary_operator::SINH:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceSinH>{}, input, stream, mr);
+    case cudf::unary_operator::COSH:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceCosH>{}, input, stream, mr);
+    case cudf::unary_operator::TANH:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceTanH>{}, input, stream, mr);
+    case cudf::unary_operator::ARCSINH:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceArcSinH>{}, input, stream, mr);
+    case cudf::unary_operator::ARCCOSH:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceArcCosH>{}, input, stream, mr);
+    case cudf::unary_operator::ARCTANH:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceArcTanH>{}, input, stream, mr);
+    case cudf::unary_operator::EXP:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceExp>{}, input, stream, mr);
+    case cudf::unary_operator::LOG:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceLog>{}, input, stream, mr);
+    case cudf::unary_operator::SQRT:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceSqrt>{}, input, stream, mr);
+    case cudf::unary_operator::CBRT:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceCbrt>{}, input, stream, mr);
+    case cudf::unary_operator::CEIL:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceCeil>{}, input, stream, mr);
+    case cudf::unary_operator::FLOOR:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceFloor>{}, input, stream, mr);
+    case cudf::unary_operator::ABS:
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceAbs>{}, input, stream, mr);
+    case cudf::unary_operator::RINT:
+      CUDF_EXPECTS(
+        (input.type().id() == type_id::FLOAT32) or (input.type().id() == type_id::FLOAT64),
+        "rint expects floating point values");
+      return cudf::type_dispatcher(
+        input.type(), detail::MathOpDispatcher<detail::DeviceRInt>{}, input, stream, mr);
+    case cudf::unary_operator::BIT_INVERT:
+      return cudf::type_dispatcher(
+        input.type(), detail::BitwiseOpDispatcher<detail::DeviceInvert>{}, input, stream, mr);
+    case cudf::unary_operator::NOT:
+      return cudf::type_dispatcher(
+        input.type(), detail::LogicalOpDispatcher<detail::DeviceNot>{}, input, stream, mr);
+    default: CUDF_FAIL("Undefined unary operation");
+  }
+}
+
+}  // namespace detail
+
+std::unique_ptr<cudf::column> unary_operation(cudf::column_view const& input,
+                                              cudf::unary_operator op,
+                                              rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::unary_operation(input, op, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/unary/nan_ops.cu b/cpp/src/unary/nan_ops.cu
new file mode 100644
index 0000000..2cf8346
--- /dev/null
+++ b/cpp/src/unary/nan_ops.cu
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace detail {
+struct nan_dispatcher {
+  template <typename T, typename Predicate>
+  std::enable_if_t<std::is_floating_point_v<T>, std::unique_ptr<column>> operator()(
+    cudf::column_view const& input,
+    Predicate predicate,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    auto input_device_view = column_device_view::create(input, stream);
+
+    if (input.has_nulls()) {
+      auto input_pair_iterator = make_pair_iterator<T, true>(*input_device_view);
+      return true_if(input_pair_iterator,
+                     input_pair_iterator + input.size(),
+                     input.size(),
+                     predicate,
+                     stream,
+                     mr);
+    } else {
+      auto input_pair_iterator = make_pair_iterator<T, false>(*input_device_view);
+      return true_if(input_pair_iterator,
+                     input_pair_iterator + input.size(),
+                     input.size(),
+                     predicate,
+                     stream,
+                     mr);
+    }
+  }
+
+  template <typename T, typename Predicate>
+  std::enable_if_t<!std::is_floating_point_v<T>, std::unique_ptr<column>> operator()(
+    cudf::column_view const& input,
+    Predicate predicate,
+    rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource* mr)
+  {
+    CUDF_FAIL("NAN is not supported in a Non-floating point type column");
+  }
+};
+
+std::unique_ptr<column> is_nan(cudf::column_view const& input,
+                               rmm::cuda_stream_view stream,
+                               rmm::mr::device_memory_resource* mr)
+{
+  auto predicate = [] __device__(auto element_validity_pair) {
+    return element_validity_pair.second and std::isnan(element_validity_pair.first);
+  };
+
+  return cudf::type_dispatcher(input.type(), nan_dispatcher{}, input, predicate, stream, mr);
+}
+
+std::unique_ptr<column> is_not_nan(cudf::column_view const& input,
+                                   rmm::cuda_stream_view stream,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  auto predicate = [] __device__(auto element_validity_pair) {
+    return !element_validity_pair.second or !std::isnan(element_validity_pair.first);
+  };
+
+  return cudf::type_dispatcher(input.type(), nan_dispatcher{}, input, predicate, stream, mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> is_nan(cudf::column_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_nan(input, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> is_not_nan(cudf::column_view const& input,
+                                   rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_not_nan(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/unary/null_ops.cu b/cpp/src/unary/null_ops.cu
new file mode 100644
index 0000000..e64c68f
--- /dev/null
+++ b/cpp/src/unary/null_ops.cu
@@ -0,0 +1,71 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/detail/unary.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+namespace cudf {
+namespace detail {
+std::unique_ptr<column> is_null(cudf::column_view const& input,
+                                rmm::cuda_stream_view stream,
+                                rmm::mr::device_memory_resource* mr)
+{
+  auto input_device_view = column_device_view::create(input, stream);
+  auto device_view       = *input_device_view;
+  auto predicate = [device_view] __device__(auto index) { return (device_view.is_null(index)); };
+  return detail::true_if(thrust::make_counting_iterator(0),
+                         thrust::make_counting_iterator(input.size()),
+                         input.size(),
+                         predicate,
+                         stream,
+                         mr);
+}
+
+std::unique_ptr<column> is_valid(cudf::column_view const& input,
+                                 rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  auto input_device_view = column_device_view::create(input, stream);
+  auto device_view       = *input_device_view;
+  auto predicate = [device_view] __device__(auto index) { return device_view.is_valid(index); };
+  return detail::true_if(thrust::make_counting_iterator(0),
+                         thrust::make_counting_iterator(input.size()),
+                         input.size(),
+                         predicate,
+                         stream,
+                         mr);
+}
+
+}  // namespace detail
+
+std::unique_ptr<column> is_null(cudf::column_view const& input, rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_null(input, cudf::get_default_stream(), mr);
+}
+
+std::unique_ptr<column> is_valid(cudf::column_view const& input,
+                                 rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::is_valid(input, cudf::get_default_stream(), mr);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/unary/unary_ops.cuh b/cpp/src/unary/unary_ops.cuh
new file mode 100644
index 0000000..d0003bb
--- /dev/null
+++ b/cpp/src/unary/unary_ops.cuh
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace unary {
+template <typename T, typename Tout, typename F>
+struct launcher {
+  static std::unique_ptr<cudf::column> launch(cudf::column_view const& input,
+                                              cudf::unary_operator op,
+                                              rmm::cuda_stream_view stream,
+                                              rmm::mr::device_memory_resource* mr)
+  {
+    std::unique_ptr<cudf::column> output = [&] {
+      if (op == cudf::unary_operator::NOT) {
+        auto type = cudf::data_type{cudf::type_id::BOOL8};
+        auto size = input.size();
+
+        return std::make_unique<column>(type,
+                                        size,
+                                        rmm::device_buffer{size * cudf::size_of(type), 0, mr},
+                                        cudf::detail::copy_bitmask(input, stream, mr),
+                                        input.null_count());
+
+      } else {
+        return cudf::detail::allocate_like(
+          input, input.size(), mask_allocation_policy::NEVER, stream, mr);
+      }
+    }();
+
+    if (input.is_empty()) return output;
+
+    auto output_view = output->mutable_view();
+
+    CUDF_EXPECTS(input.size() > 0, "Launcher requires input size to be non-zero.");
+    CUDF_EXPECTS(input.size() == output_view.size(),
+                 "Launcher requires input and output size to be equal.");
+
+    if (input.nullable())
+      output->set_null_mask(
+        rmm::device_buffer{input.null_mask(), bitmask_allocation_size_bytes(input.size())},
+        input.null_count());
+
+    thrust::transform(
+      rmm::exec_policy(stream), input.begin<T>(), input.end<T>(), output_view.begin<Tout>(), F{});
+
+    CUDF_CHECK_CUDA(stream.value());
+
+    return output;
+  }
+};
+
+}  // namespace unary
+}  // namespace cudf
diff --git a/cpp/src/utilities/default_stream.cpp b/cpp/src/utilities/default_stream.cpp
new file mode 100644
index 0000000..c21436a
--- /dev/null
+++ b/cpp/src/utilities/default_stream.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/default_stream.hpp>
+
+namespace cudf {
+
+namespace detail {
+
+#if defined(CUDF_USE_PER_THREAD_DEFAULT_STREAM)
+rmm::cuda_stream_view const default_stream_value{rmm::cuda_stream_per_thread};
+#else
+rmm::cuda_stream_view const default_stream_value{};
+#endif
+
+}  // namespace detail
+
+/**
+ * @brief Check if per-thread default stream is enabled.
+ *
+ * @return true if PTDS is enabled, false otherwise.
+ */
+bool is_ptds_enabled()
+{
+#ifdef CUDA_API_PER_THREAD_DEFAULT_STREAM
+  return true;
+#else
+  return false;
+#endif
+}
+
+rmm::cuda_stream_view const get_default_stream() { return detail::default_stream_value; }
+}  // namespace cudf
diff --git a/cpp/src/utilities/linked_column.cpp b/cpp/src/utilities/linked_column.cpp
new file mode 100644
index 0000000..805cc1d
--- /dev/null
+++ b/cpp/src/utilities/linked_column.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/linked_column.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+namespace cudf::detail {
+
+linked_column_view::linked_column_view(column_view const& col) : linked_column_view(nullptr, col) {}
+
+linked_column_view::linked_column_view(linked_column_view* parent, column_view const& col)
+  : column_view_base(col), parent(parent)
+{
+  children.reserve(col.num_children());
+  std::transform(
+    col.child_begin(), col.child_end(), std::back_inserter(children), [&](column_view const& c) {
+      return std::make_shared<linked_column_view>(this, c);
+    });
+}
+
+linked_column_view::operator column_view() const
+{
+  auto child_it = thrust::make_transform_iterator(
+    children.begin(), [](auto const& c) { return static_cast<column_view>(*c); });
+  return column_view(this->type(),
+                     this->size(),
+                     this->head(),
+                     this->null_mask(),
+                     this->null_count(),
+                     this->offset(),
+                     std::vector<column_view>(child_it, child_it + children.size()));
+}
+
+LinkedColVector table_to_linked_columns(table_view const& table)
+{
+  auto linked_it = thrust::make_transform_iterator(
+    table.begin(), [](auto const& c) { return std::make_shared<linked_column_view>(c); });
+  return LinkedColVector(linked_it, linked_it + table.num_columns());
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/utilities/logger.cpp b/cpp/src/utilities/logger.cpp
new file mode 100644
index 0000000..9195085
--- /dev/null
+++ b/cpp/src/utilities/logger.cpp
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/logger.hpp>
+
+#include "spdlog/sinks/stdout_sinks.h"
+#include <spdlog/sinks/basic_file_sink.h>
+
+#include <string>
+
+namespace {
+
+/**
+ * @brief Creates a sink for libcudf logging.
+ *
+ * Returns a file sink if the file name has been specified, otherwise returns a stderr sink.
+ */
+[[nodiscard]] spdlog::sink_ptr make_libcudf_sink()
+{
+  if (auto filename = std::getenv("LIBCUDF_DEBUG_LOG_FILE"); filename != nullptr) {
+    return std::make_shared<spdlog::sinks::basic_file_sink_mt>(filename, true);
+  } else {
+    return std::make_shared<spdlog::sinks::stderr_sink_mt>();
+  }
+}
+
+/**
+ * @brief Converts the level name into the `spdlog` level enum.
+ */
+[[nodiscard]] spdlog::level::level_enum libcudf_log_level()
+{
+  auto const env_level = std::getenv("LIBCUDF_LOGGING_LEVEL");
+  if (env_level == nullptr) { return spdlog::level::warn; }
+
+  auto const env_lvl_str = std::string(env_level);
+  if (env_lvl_str == "TRACE") return spdlog::level::trace;
+  if (env_lvl_str == "DEBUG") return spdlog::level::debug;
+  if (env_lvl_str == "INFO") return spdlog::level::info;
+  if (env_lvl_str == "WARN") return spdlog::level::warn;
+  if (env_lvl_str == "ERROR") return spdlog::level::err;
+  if (env_lvl_str == "CRITICAL") return spdlog::level::critical;
+  if (env_lvl_str == "OFF") return spdlog::level::off;
+
+  CUDF_FAIL("Invalid value for LIBCUDF_LOGGING_LEVEL environment variable");
+}
+
+/**
+ * @brief Simple wrapper around a spdlog::logger that performs cuDF-specific initialization.
+ */
+struct logger_wrapper {
+  spdlog::logger logger_;
+
+  logger_wrapper() : logger_{"CUDF", make_libcudf_sink()}
+  {
+    logger_.set_pattern("[%6t][%H:%M:%S:%f][%-6l] %v");
+    logger_.set_level(libcudf_log_level());
+    logger_.flush_on(spdlog::level::warn);
+  }
+};
+
+}  // namespace
+
+spdlog::logger& cudf::logger()
+{
+  static logger_wrapper wrapped{};
+  return wrapped.logger_;
+}
diff --git a/cpp/src/utilities/stacktrace.cpp b/cpp/src/utilities/stacktrace.cpp
new file mode 100644
index 0000000..48cf632
--- /dev/null
+++ b/cpp/src/utilities/stacktrace.cpp
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/stacktrace.hpp>
+
+#if defined(__GNUC__) && defined(CUDF_BUILD_STACKTRACE_DEBUG)
+#include <cxxabi.h>
+#include <execinfo.h>
+
+#include <cstdlib>
+#include <cstring>
+#include <sstream>
+#endif  // defined(__GNUC__) && defined(CUDF_BUILD_STACKTRACE_DEBUG)
+
+namespace cudf::detail {
+
+std::string get_stacktrace(capture_last_stackframe capture_last_frame)
+{
+#if defined(__GNUC__) && defined(CUDF_BUILD_STACKTRACE_DEBUG)
+  constexpr int max_stack_depth = 64;
+  void* stack[max_stack_depth];
+
+  auto const depth   = backtrace(stack, max_stack_depth);
+  auto const modules = backtrace_symbols(stack, depth);
+
+  if (modules == nullptr) { return "No stacktrace could be captured!"; }
+
+  std::stringstream ss;
+
+  // Skip one more depth to avoid including the stackframe of this function.
+  auto const skip_depth = 1 + (capture_last_frame == capture_last_stackframe::YES ? 0 : 1);
+  for (auto i = skip_depth; i < depth; ++i) {
+    // Each modules[i] string contains a mangled name in the format like following:
+    // `module_name(function_name+0x012) [0x01234567890a]`
+    // We need to extract function name and function offset.
+    char* begin_func_name   = std::strstr(modules[i], "(");
+    char* begin_func_offset = std::strstr(modules[i], "+");
+    char* end_func_offset   = std::strstr(modules[i], ")");
+
+    auto const frame_idx = i - skip_depth;
+    if (begin_func_name && begin_func_offset && end_func_offset &&
+        begin_func_name < begin_func_offset) {
+      // Split `modules[i]` into separate null-terminated strings.
+      // After this, mangled function name will then be [begin_func_name, begin_func_offset), and
+      // function offset is in [begin_func_offset, end_func_offset).
+      *(begin_func_name++)   = '\0';
+      *(begin_func_offset++) = '\0';
+      *end_func_offset       = '\0';
+
+      // We need to demangle function name.
+      int status{0};
+      char* func_name = abi::__cxa_demangle(begin_func_name, nullptr, nullptr, &status);
+
+      ss << "#" << frame_idx << ": " << modules[i] << " : "
+         << (status == 0 /*demangle success*/ ? func_name : begin_func_name) << "+"
+         << begin_func_offset << "\n";
+      free(func_name);
+    } else {
+      ss << "#" << frame_idx << ": " << modules[i] << "\n";
+    }
+  }
+
+  free(modules);
+
+  return ss.str();
+#else
+#ifdef CUDF_BUILD_STACKTRACE_DEBUG
+  return "Stacktrace is only supported when built with a GNU compiler.";
+#else
+  return "libcudf was not built with stacktrace support.";
+#endif  // CUDF_BUILD_STACKTRACE_DEBUG
+#endif  // __GNUC__
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/utilities/stream_pool.cpp b/cpp/src/utilities/stream_pool.cpp
new file mode 100644
index 0000000..b3b2088
--- /dev/null
+++ b/cpp/src/utilities/stream_pool.cpp
@@ -0,0 +1,256 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/logger.hpp>
+#include <cudf/detail/utilities/stream_pool.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream_pool.hpp>
+
+#include <algorithm>
+#include <cstddef>
+#include <memory>
+#include <mutex>
+#include <vector>
+
+namespace cudf::detail {
+
+namespace {
+
+// TODO: what is a good number here. what's the penalty for making it larger?
+// Dave Baranec rule of thumb was max_streams_needed * num_concurrent_threads,
+// where num_concurrent_threads was estimated to be 4. so using 32 will allow
+// for 8 streams per thread, which should be plenty (decoding will be up to 4
+// kernels when delta_byte_array decoding is added). rmm::cuda_stream_pool
+// defaults to 16.
+std::size_t constexpr STREAM_POOL_SIZE = 32;
+
+// FIXME: "borrowed" from rmm...remove when this stream pool is moved there
+#ifdef NDEBUG
+#define CUDF_ASSERT_CUDA_SUCCESS(_call) \
+  do {                                  \
+    (_call);                            \
+  } while (0);
+#else
+#define CUDF_ASSERT_CUDA_SUCCESS(_call)                                         \
+  do {                                                                          \
+    cudaError_t const status__ = (_call);                                       \
+    if (status__ != cudaSuccess) {                                              \
+      std::cerr << "CUDA Error detected. " << cudaGetErrorName(status__) << " " \
+                << cudaGetErrorString(status__) << std::endl;                   \
+    }                                                                           \
+    /* NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-array-to-pointer-decay) */   \
+    assert(status__ == cudaSuccess);                                            \
+  } while (0)
+#endif
+
+class cuda_stream_pool {
+ public:
+  // matching type used in rmm::cuda_stream_pool::get_stream(stream_id)
+  using stream_id_type = std::size_t;
+
+  virtual ~cuda_stream_pool() = default;
+
+  /**
+   * @brief Get a `cuda_stream_view` of a stream in the pool.
+   *
+   * This function is thread safe with respect to other calls to the same function.
+   *
+   * @return Stream view.
+   */
+  virtual rmm::cuda_stream_view get_stream() = 0;
+
+  /**
+   * @brief Get a `cuda_stream_view` of the stream associated with `stream_id`.
+   *
+   * Equivalent values of `stream_id` return a `cuda_stream_view` to the same underlying stream.
+   * This function is thread safe with respect to other calls to the same function.
+   *
+   * @param stream_id Unique identifier for the desired stream
+   * @return Requested stream view.
+   */
+  virtual rmm::cuda_stream_view get_stream(stream_id_type stream_id) = 0;
+
+  /**
+   * @brief Get a set of `cuda_stream_view` objects from the pool.
+   *
+   * An attempt is made to ensure that the returned vector does not contain duplicate
+   * streams, but this cannot be guaranteed if `count` is greater than the value returned by
+   * `get_stream_pool_size()`.
+   *
+   * This function is thread safe with respect to other calls to the same function.
+   *
+   * @param count The number of stream views to return.
+   * @return Vector containing `count` stream views.
+   */
+  virtual std::vector<rmm::cuda_stream_view> get_streams(std::size_t count) = 0;
+
+  /**
+   * @brief Get the number of stream objects in the pool.
+   *
+   * This function is thread safe with respect to other calls to the same function.
+   *
+   * @return the number of stream objects in the pool
+   */
+  virtual std::size_t get_stream_pool_size() const = 0;
+};
+
+/**
+ * @brief Implementation of `cuda_stream_pool` that wraps an `rmm::cuda_stram_pool`.
+ */
+class rmm_cuda_stream_pool : public cuda_stream_pool {
+  rmm::cuda_stream_pool _pool;
+
+ public:
+  rmm_cuda_stream_pool() : _pool{STREAM_POOL_SIZE} {}
+  rmm::cuda_stream_view get_stream() override { return _pool.get_stream(); }
+  rmm::cuda_stream_view get_stream(stream_id_type stream_id) override
+  {
+    return _pool.get_stream(stream_id);
+  }
+
+  std::vector<rmm::cuda_stream_view> get_streams(std::size_t count) override
+  {
+    if (count > STREAM_POOL_SIZE) {
+      CUDF_LOG_WARN("get_streams called with count ({}) > pool size ({})", count, STREAM_POOL_SIZE);
+    }
+    auto streams = std::vector<rmm::cuda_stream_view>();
+    for (uint32_t i = 0; i < count; i++) {
+      streams.emplace_back(_pool.get_stream());
+    }
+    return streams;
+  }
+
+  std::size_t get_stream_pool_size() const override { return STREAM_POOL_SIZE; }
+};
+
+/**
+ * @brief Implementation of `cuda_stream_pool` that always returns `cudf::get_default_stream()`
+ */
+class debug_cuda_stream_pool : public cuda_stream_pool {
+ public:
+  rmm::cuda_stream_view get_stream() override { return cudf::get_default_stream(); }
+  rmm::cuda_stream_view get_stream(stream_id_type stream_id) override
+  {
+    return cudf::get_default_stream();
+  }
+
+  std::vector<rmm::cuda_stream_view> get_streams(std::size_t count) override
+  {
+    return std::vector<rmm::cuda_stream_view>(count, cudf::get_default_stream());
+  }
+
+  std::size_t get_stream_pool_size() const override { return 1UL; }
+};
+
+/**
+ * @brief Initialize global stream pool.
+ */
+cuda_stream_pool* create_global_cuda_stream_pool()
+{
+  if (getenv("LIBCUDF_USE_DEBUG_STREAM_POOL")) return new debug_cuda_stream_pool();
+
+  return new rmm_cuda_stream_pool();
+}
+
+// FIXME: these will be available in rmm soon
+inline int get_num_cuda_devices()
+{
+  rmm::cuda_device_id::value_type num_dev{};
+  CUDF_CUDA_TRY(cudaGetDeviceCount(&num_dev));
+  return num_dev;
+}
+
+rmm::cuda_device_id get_current_cuda_device()
+{
+  int device_id;
+  CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+  return rmm::cuda_device_id{device_id};
+}
+
+/**
+ * @brief RAII struct to wrap a cuda event and ensure its proper destruction.
+ */
+struct cuda_event {
+  cuda_event() { CUDF_CUDA_TRY(cudaEventCreateWithFlags(&e_, cudaEventDisableTiming)); }
+  virtual ~cuda_event() { CUDF_ASSERT_CUDA_SUCCESS(cudaEventDestroy(e_)); }
+
+  operator cudaEvent_t() { return e_; }
+
+ private:
+  cudaEvent_t e_;
+};
+
+/**
+ * @brief Returns a cudaEvent_t for the current thread.
+ *
+ * The returned event is valid for the current device.
+ *
+ * @return A cudaEvent_t unique to the current thread and valid on the current device.
+ */
+cudaEvent_t event_for_thread()
+{
+  thread_local std::vector<std::unique_ptr<cuda_event>> thread_events(get_num_cuda_devices());
+  auto const device_id = get_current_cuda_device();
+  if (not thread_events[device_id.value()]) {
+    thread_events[device_id.value()] = std::make_unique<cuda_event>();
+  }
+  return *thread_events[device_id.value()];
+}
+
+/**
+ * @brief Returns a reference to the global stream pool for the current device.
+ * @return `cuda_stream_pool` valid on the current device.
+ */
+cuda_stream_pool& global_cuda_stream_pool()
+{
+  // using bare pointers here to deliberately allow them to leak. otherwise we wind up with
+  // seg faults trying to destroy stream objects after the context has shut down.
+  static std::vector<cuda_stream_pool*> pools(get_num_cuda_devices());
+  static std::mutex mutex;
+  auto const device_id = get_current_cuda_device();
+
+  std::lock_guard<std::mutex> lock(mutex);
+  if (pools[device_id.value()] == nullptr) {
+    pools[device_id.value()] = create_global_cuda_stream_pool();
+  }
+  return *pools[device_id.value()];
+}
+
+}  // anonymous namespace
+
+std::vector<rmm::cuda_stream_view> fork_streams(rmm::cuda_stream_view stream, std::size_t count)
+{
+  auto const streams = global_cuda_stream_pool().get_streams(count);
+  auto const event   = event_for_thread();
+  CUDF_CUDA_TRY(cudaEventRecord(event, stream));
+  std::for_each(streams.begin(), streams.end(), [&](auto& strm) {
+    CUDF_CUDA_TRY(cudaStreamWaitEvent(strm, event, 0));
+  });
+  return streams;
+}
+
+void join_streams(host_span<rmm::cuda_stream_view const> streams, rmm::cuda_stream_view stream)
+{
+  auto const event = event_for_thread();
+  std::for_each(streams.begin(), streams.end(), [&](auto& strm) {
+    CUDF_CUDA_TRY(cudaEventRecord(event, strm));
+    CUDF_CUDA_TRY(cudaStreamWaitEvent(stream, event, 0));
+  });
+}
+
+}  // namespace cudf::detail
diff --git a/cpp/src/utilities/traits.cpp b/cpp/src/utilities/traits.cpp
new file mode 100644
index 0000000..bc10dd7
--- /dev/null
+++ b/cpp/src/utilities/traits.cpp
@@ -0,0 +1,409 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda_runtime.h>
+
+#include <cudf/strings/string_view.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf/wrappers/dictionary.hpp>
+
+namespace cudf {
+
+namespace {
+/**
+ * @brief Helper functor to check if a specified type `T` supports relational comparisons.
+ *
+ */
+struct unary_relationally_comparable_functor {
+  /**
+   * @brief Returns true if `T` supports relational comparisons.
+   *
+   * @tparam T Type to check
+   * @return true if `T` supports relational comparisons
+   */
+  template <typename T>
+  inline bool operator()() const
+  {
+    return cudf::is_relationally_comparable<T, T>();
+  }
+};
+}  // namespace
+
+/**
+ * @brief Checks whether `data_type` `type` supports relational comparisons.
+ *
+ * @param type Data_type for comparison.
+ * @return true If `type` supports relational comparisons.
+ * @return false If `type` does not support relational comparisons.
+ */
+bool is_relationally_comparable(data_type type)
+{
+  return type_dispatcher(type, unary_relationally_comparable_functor{});
+}
+
+namespace {
+/**
+ * @brief Helper functor to check if a specified type `T` supports equality comparisons.
+ *
+ */
+struct unary_equality_comparable_functor {
+  /**
+   * @brief Checks whether `T` supports equality comparisons.
+   *
+   * @tparam T Type to check
+   * @return true if `T` supports equality comparisons
+   */
+  template <typename T>
+  bool operator()() const
+  {
+    return cudf::is_equality_comparable<T, T>();
+  }
+};
+}  // namespace
+
+/**
+ * @brief Checks whether `data_type` `type` supports equality comparisons.
+ *
+ * @param type Data_type for comparison.
+ * @return true If `type` supports equality comparisons.
+ * @return false If `type` does not support equality comparisons.
+ */
+bool is_equality_comparable(data_type type)
+{
+  return cudf::type_dispatcher(type, unary_equality_comparable_functor{});
+}
+
+struct is_numeric_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_numeric<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a numeric `data_type`.
+ *
+ * "Numeric" types are fundamental integral/floating point types such as `INT*`
+ * or `FLOAT*`. Types that wrap a numeric type are not considered numeric, e.g.,
+ *`TIMESTAMP`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is numeric
+ * @return false `type` is not numeric
+ */
+bool is_numeric(data_type type) { return cudf::type_dispatcher(type, is_numeric_impl{}); }
+
+struct is_index_type_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_index_type<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether the type `type` is a index type.
+ *
+ * A type `T` is considered an index type if it is valid to use
+ * elements of type `T` to index into a column. I.e.,
+ * index types are integral types such as 'INT*' apart from 'bool'.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is index type
+ * @return false `type` is not index type
+ */
+bool is_index_type(data_type type) { return cudf::type_dispatcher(type, is_index_type_impl{}); }
+
+struct is_unsigned_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_unsigned<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a unsigned numeric `data_type`.
+ *
+ * "Unsigned Numeric" types are fundamental integral types such as `UINT*`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is unsigned numeric
+ * @return false `type` is signed numeric
+ */
+bool is_unsigned(data_type type) { return cudf::type_dispatcher(type, is_unsigned_impl{}); }
+
+struct is_integral_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_integral<T>();
+  }
+};
+
+bool is_integral(data_type type) { return cudf::type_dispatcher(type, is_integral_impl{}); }
+
+struct is_floating_point_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_floating_point<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a floating point `data_type`.
+ *
+ * "Floating point" types are fundamental floating point types such as `FLOAT*`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is floating point
+ * @return false `type` is not floating point
+ */
+bool is_floating_point(data_type type)
+{
+  return cudf::type_dispatcher(type, is_floating_point_impl{});
+}
+
+struct is_boolean_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_boolean<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a Boolean `data_type`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a Boolean
+ * @return false `type` is not a Boolean
+ */
+bool is_boolean(data_type type) { return cudf::type_dispatcher(type, is_boolean_impl{}); }
+
+struct is_fixed_point_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_fixed_point<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a fixed point `data_type`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a fixed point type
+ * @return false `type` is not a fixed point type
+ */
+bool is_fixed_point(data_type type) { return cudf::type_dispatcher(type, is_fixed_point_impl{}); }
+
+struct is_timestamp_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_timestamp<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a timestamp `data_type`.
+ *
+ * "Timestamp" types are int32_t or int64_t durations since the unix epoch.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a timestamp
+ * @return false `type` is not a timestamp
+ */
+bool is_timestamp(data_type type) { return cudf::type_dispatcher(type, is_timestamp_impl{}); }
+
+struct is_duration_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_duration<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a duration `data_type`.
+ *
+ * "Duration" types are int32_t or int64_t tick counts representing a time interval.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a duration
+ * @return false `type` is not a duration
+ */
+bool is_duration(data_type type) { return cudf::type_dispatcher(type, is_duration_impl{}); }
+
+struct is_chrono_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_chrono<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a chrono `data_type`.
+ *
+ * Chrono types include cudf timestamp types, which represent a point in time, and cudf
+ * duration types that represent a time interval.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a chrono type
+ * @return false `type` is not a chrono type
+ */
+bool is_chrono(data_type type) { return cudf::type_dispatcher(type, is_chrono_impl{}); }
+
+struct is_dictionary_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_dictionary<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a dictionary `data_type`.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a dictionary type
+ * @return false `type` is not a dictionary type
+ */
+bool is_dictionary(data_type type) { return cudf::type_dispatcher(type, is_dictionary_impl{}); }
+
+struct is_fixed_width_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_fixed_width<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether elements of `type` are fixed-width.
+ *
+ * Elements of a fixed-width type all have the same size in bytes.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is fixed-width
+ * @return false  `type` is variable-width
+ */
+bool is_fixed_width(data_type type) { return cudf::type_dispatcher(type, is_fixed_width_impl{}); }
+
+struct is_compound_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_compound<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether elements of `type` are compound.
+ *
+ * `column`s with "compound" elements are logically a single column of elements,
+ * but may be concretely implemented with two or more `column`s. For example, a
+ * `STRING` column could contain a `column` of offsets and a child `column` of
+ * characters.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a compound type
+ * @return false `type` is a simple type
+ */
+bool is_compound(data_type type) { return cudf::type_dispatcher(type, is_compound_impl{}); }
+
+struct is_nested_impl {
+  template <typename T>
+  constexpr bool operator()()
+  {
+    return is_nested<T>();
+  }
+};
+
+/**
+ * @brief Indicates whether `type` is a nested type
+ *
+ * "Nested" types are distinct from compound types in that they
+ * can have an arbitrarily deep list of descendants of the same
+ * type. Strings are not a nested type, but lists are.
+ *
+ * @param type The `data_type` to verify
+ * @return true `type` is a nested type
+ * @return false `type` is not a nested type
+ */
+bool is_nested(data_type type) { return cudf::type_dispatcher(type, is_nested_impl{}); }
+
+namespace {
+template <typename FromType>
+struct is_bit_castable_to_impl {
+  template <typename ToType, std::enable_if_t<is_compound<ToType>()>* = nullptr>
+  constexpr bool operator()()
+  {
+    return false;
+  }
+
+  template <typename ToType, std::enable_if_t<not is_compound<ToType>()>* = nullptr>
+  constexpr bool operator()()
+  {
+    if (not cuda::std::is_trivially_copyable_v<FromType> ||
+        not cuda::std::is_trivially_copyable_v<ToType>) {
+      return false;
+    }
+    constexpr auto from_size = sizeof(cudf::device_storage_type_t<FromType>);
+    constexpr auto to_size   = sizeof(cudf::device_storage_type_t<ToType>);
+    return from_size == to_size;
+  }
+};
+
+struct is_bit_castable_from_impl {
+  template <typename FromType, std::enable_if_t<is_compound<FromType>()>* = nullptr>
+  constexpr bool operator()(data_type)
+  {
+    return false;
+  }
+
+  template <typename FromType, std::enable_if_t<not is_compound<FromType>()>* = nullptr>
+  constexpr bool operator()(data_type to)
+  {
+    return cudf::type_dispatcher(to, is_bit_castable_to_impl<FromType>{});
+  }
+};
+}  // namespace
+
+/**
+ * @brief Indicates whether `from` is bit-castable to `to`.
+ *
+ * This casting is based on std::bit_cast. Data types that have the same size and are trivially
+ * copyable are eligible for this casting.
+ *
+ * See `cudf::bit_cast()` which returns a zero-copy `column_view` when casting between
+ * bit-castable types.
+ *
+ * @param from The `data_type` to convert from
+ * @param to The `data_type` to convert to
+ * @return `true` if the types are castable
+ */
+bool is_bit_castable(data_type from, data_type to)
+{
+  return type_dispatcher(from, is_bit_castable_from_impl{}, to);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/utilities/type_checks.cpp b/cpp/src/utilities/type_checks.cpp
new file mode 100644
index 0000000..d6f5c65
--- /dev/null
+++ b/cpp/src/utilities/type_checks.cpp
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/type_checks.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+
+namespace cudf {
+namespace {
+
+struct columns_equal_fn {
+  template <typename T>
+  bool operator()(column_view const&, column_view const&)
+  {
+    return true;
+  }
+};
+
+template <>
+bool columns_equal_fn::operator()<dictionary32>(column_view const& lhs, column_view const& rhs)
+{
+  auto const kidx = dictionary_column_view::keys_column_index;
+  return lhs.num_children() > 0 and rhs.num_children() > 0
+           ? lhs.child(kidx).type() == rhs.child(kidx).type()
+           : lhs.is_empty() and rhs.is_empty();
+}
+
+template <>
+bool columns_equal_fn::operator()<list_view>(column_view const& lhs, column_view const& rhs)
+{
+  auto const& ci = lists_column_view::child_column_index;
+  return column_types_equal(lhs.child(ci), rhs.child(ci));
+}
+
+template <>
+bool columns_equal_fn::operator()<struct_view>(column_view const& lhs, column_view const& rhs)
+{
+  return lhs.num_children() == rhs.num_children() and
+         std::all_of(thrust::make_counting_iterator(0),
+                     thrust::make_counting_iterator(lhs.num_children()),
+                     [&](auto i) { return column_types_equal(lhs.child(i), rhs.child(i)); });
+}
+
+};  // namespace
+
+// Implementation note: avoid using double dispatch for this function
+// as it increases code paths to NxN for N types.
+bool column_types_equal(column_view const& lhs, column_view const& rhs)
+{
+  if (lhs.type() != rhs.type()) { return false; }
+  return type_dispatcher(lhs.type(), columns_equal_fn{}, lhs, rhs);
+}
+
+bool column_types_equivalent(column_view const& lhs, column_view const& rhs)
+{
+  if (lhs.type().id() != rhs.type().id()) { return false; }
+  return type_dispatcher(lhs.type(), columns_equal_fn{}, lhs, rhs);
+}
+
+}  // namespace cudf
diff --git a/cpp/src/utilities/type_dispatcher.cpp b/cpp/src/utilities/type_dispatcher.cpp
new file mode 100644
index 0000000..cb87daa
--- /dev/null
+++ b/cpp/src/utilities/type_dispatcher.cpp
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/type_dispatcher.hpp>
+
+namespace cudf {
+
+std::string type_to_name(data_type type) { return type_dispatcher(type, type_to_name_impl{}); }
+
+}  // namespace cudf
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
new file mode 100644
index 0000000..04939f3
--- /dev/null
+++ b/cpp/tests/CMakeLists.txt
@@ -0,0 +1,644 @@
+# =============================================================================
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+# ##################################################################################################
+# enable testing ################################################################################
+# ##################################################################################################
+enable_testing()
+
+include(rapids-test)
+rapids_test_init()
+
+# This function takes in a test name and test source and handles setting all of the associated
+# properties and linking to build the test
+function(ConfigureTest CMAKE_TEST_NAME)
+  set(options)
+  set(one_value GPUS PERCENT STREAM_MODE)
+  set(multi_value)
+  cmake_parse_arguments(_CUDF_TEST "${options}" "${one_value}" "${multi_value}" ${ARGN})
+  if(NOT DEFINED _CUDF_TEST_GPUS AND NOT DEFINED _CUDF_TEST_PERCENT)
+    set(_CUDF_TEST_GPUS 1)
+    set(_CUDF_TEST_PERCENT 15)
+  endif()
+  if(NOT DEFINED _CUDF_TEST_GPUS)
+    set(_CUDF_TEST_GPUS 1)
+  endif()
+  if(NOT DEFINED _CUDF_TEST_PERCENT)
+    set(_CUDF_TEST_PERCENT 100)
+  endif()
+  if(NOT DEFINED _CUDF_TEST_STREAM_MODE)
+    set(_CUDF_TEST_STREAM_MODE cudf)
+  endif()
+
+  add_executable(${CMAKE_TEST_NAME} ${_CUDF_TEST_UNPARSED_ARGUMENTS})
+  set_target_properties(
+    ${CMAKE_TEST_NAME}
+    PROPERTIES RUNTIME_OUTPUT_DIRECTORY "$<BUILD_INTERFACE:${CUDF_BINARY_DIR}/gtests>"
+               INSTALL_RPATH "\$ORIGIN/../../../lib"
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               # For std:: support of __int128_t. Can be removed once using cuda::std
+               CXX_EXTENSIONS ON
+               CUDA_STANDARD 17
+               CUDA_STANDARD_REQUIRED ON
+  )
+
+  target_link_libraries(
+    ${CMAKE_TEST_NAME} PRIVATE cudftestutil GTest::gmock_main GTest::gtest_main
+                               $<TARGET_NAME_IF_EXISTS:conda_env>
+  )
+  rapids_test_add(
+    NAME ${CMAKE_TEST_NAME}
+    COMMAND ${CMAKE_TEST_NAME}
+    GPUS ${_CUDF_TEST_GPUS}
+    PERCENT ${_CUDF_TEST_PERCENT}
+    INSTALL_COMPONENT_SET testing
+  )
+
+  set_tests_properties(
+    ${CMAKE_TEST_NAME}
+    PROPERTIES
+      ENVIRONMENT
+      "GTEST_CUDF_STREAM_MODE=new_${_CUDF_TEST_STREAM_MODE}_default;LD_PRELOAD=$<TARGET_FILE:cudf_identify_stream_usage_mode_${_CUDF_TEST_STREAM_MODE}>"
+  )
+endfunction()
+
+# ##################################################################################################
+# test sources ##################################################################################
+# ##################################################################################################
+
+# ##################################################################################################
+# * column tests ----------------------------------------------------------------------------------
+ConfigureTest(
+  COLUMN_TEST
+  column/bit_cast_test.cpp
+  column/column_device_view_test.cu
+  column/column_test.cpp
+  column/column_view_device_span_test.cpp
+  column/column_view_shallow_test.cpp
+  column/compound_test.cu
+)
+
+# ##################################################################################################
+# * scalar tests ----------------------------------------------------------------------------------
+ConfigureTest(SCALAR_TEST scalar/scalar_test.cpp scalar/scalar_device_view_test.cu)
+
+# ##################################################################################################
+# * timestamps tests ------------------------------------------------------------------------------
+ConfigureTest(TIMESTAMPS_TEST wrappers/timestamps_test.cu)
+
+# ##################################################################################################
+# * cudf tests ------------------------------------------------------------------------------------
+ConfigureTest(ERROR_TEST error/error_handling_test.cu)
+
+# ##################################################################################################
+# * groupby tests ---------------------------------------------------------------------------------
+ConfigureTest(
+  GROUPBY_TEST
+  groupby/argmin_tests.cpp
+  groupby/argmax_tests.cpp
+  groupby/collect_list_tests.cpp
+  groupby/collect_set_tests.cpp
+  groupby/correlation_tests.cpp
+  groupby/count_scan_tests.cpp
+  groupby/count_tests.cpp
+  groupby/covariance_tests.cpp
+  groupby/groupby_test_util.cpp
+  groupby/groups_tests.cpp
+  groupby/histogram_tests.cpp
+  groupby/keys_tests.cpp
+  groupby/lists_tests.cpp
+  groupby/m2_tests.cpp
+  groupby/min_tests.cpp
+  groupby/max_scan_tests.cpp
+  groupby/max_tests.cpp
+  groupby/mean_tests.cpp
+  groupby/median_tests.cpp
+  groupby/merge_m2_tests.cpp
+  groupby/merge_lists_tests.cpp
+  groupby/merge_sets_tests.cpp
+  groupby/min_scan_tests.cpp
+  groupby/nth_element_tests.cpp
+  groupby/nunique_tests.cpp
+  groupby/product_tests.cpp
+  groupby/quantile_tests.cpp
+  groupby/rank_scan_tests.cpp
+  groupby/replace_nulls_tests.cpp
+  groupby/shift_tests.cpp
+  groupby/std_tests.cpp
+  groupby/structs_tests.cpp
+  groupby/sum_of_squares_tests.cpp
+  groupby/sum_scan_tests.cpp
+  groupby/sum_tests.cpp
+  groupby/tdigest_tests.cu
+  groupby/var_tests.cpp
+  GPUS 1
+  PERCENT 100
+)
+
+# ##################################################################################################
+# * join tests ------------------------------------------------------------------------------------
+ConfigureTest(
+  JOIN_TEST join/join_tests.cpp join/conditional_join_tests.cu join/cross_join_tests.cpp
+  join/semi_anti_join_tests.cpp join/mixed_join_tests.cu
+)
+
+# ##################################################################################################
+# * is_sorted tests -------------------------------------------------------------------------------
+ConfigureTest(IS_SORTED_TEST sort/is_sorted_tests.cpp)
+
+# ##################################################################################################
+# * datetime tests --------------------------------------------------------------------------------
+ConfigureTest(DATETIME_OPS_TEST datetime/datetime_ops_test.cpp)
+
+# ##################################################################################################
+# * hashing tests ---------------------------------------------------------------------------------
+ConfigureTest(
+  HASHING_TEST
+  hashing/md5_test.cpp
+  hashing/murmurhash3_x86_32_test.cpp
+  hashing/murmurhash3_x64_128_test.cpp
+  hashing/spark_murmurhash3_x86_32_test.cpp
+  hashing/xxhash_64_test.cpp
+)
+
+# ##################################################################################################
+# * partitioning tests ----------------------------------------------------------------------------
+ConfigureTest(
+  PARTITIONING_TEST partitioning/hash_partition_test.cpp partitioning/round_robin_test.cpp
+  partitioning/partition_test.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * hash_map tests --------------------------------------------------------------------------------
+ConfigureTest(HASH_MAP_TEST hash_map/map_test.cu)
+
+# ##################################################################################################
+# * quantiles tests -------------------------------------------------------------------------------
+ConfigureTest(
+  QUANTILES_TEST quantiles/percentile_approx_test.cpp quantiles/quantile_test.cpp
+  quantiles/quantiles_test.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * reduction tests -------------------------------------------------------------------------------
+ConfigureTest(
+  REDUCTIONS_TEST
+  reductions/collect_ops_tests.cpp
+  reductions/rank_tests.cpp
+  reductions/reduction_tests.cpp
+  reductions/scan_tests.cpp
+  reductions/segmented_reduction_tests.cpp
+  reductions/list_rank_test.cpp
+  reductions/tdigest_tests.cu
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * replace tests ---------------------------------------------------------------------------------
+ConfigureTest(REPLACE_TEST replace/replace_tests.cpp)
+
+ConfigureTest(REPLACE_NULLS_TEST replace/replace_nulls_tests.cpp)
+
+ConfigureTest(REPLACE_NANS_TEST replace/replace_nans_tests.cpp)
+
+ConfigureTest(NORMALIZE_REPLACE_TEST replace/normalize_replace_tests.cpp)
+
+ConfigureTest(CLAMP_TEST replace/clamp_test.cpp)
+
+# ##################################################################################################
+# * fixed_point tests -----------------------------------------------------------------------------
+ConfigureTest(FIXED_POINT_TEST fixed_point/fixed_point_tests.cpp fixed_point/fixed_point_tests.cu)
+
+# ##################################################################################################
+# * unary tests -----------------------------------------------------------------------------------
+ConfigureTest(UNARY_TEST unary/math_ops_test.cpp unary/unary_ops_test.cpp unary/cast_tests.cpp)
+
+# ##################################################################################################
+# * round tests -----------------------------------------------------------------------------------
+ConfigureTest(ROUND_TEST round/round_tests.cpp)
+
+# ##################################################################################################
+# * binary tests ----------------------------------------------------------------------------------
+ConfigureTest(
+  BINARYOP_TEST
+  binaryop/binop-verify-input-test.cpp
+  binaryop/binop-null-test.cpp
+  binaryop/binop-compiled-test.cpp
+  binaryop/binop-compiled-fixed_point-test.cpp
+  binaryop/binop-generic-ptx-test.cpp
+)
+
+# ##################################################################################################
+# * unary transform tests -------------------------------------------------------------------------
+ConfigureTest(
+  TRANSFORM_TEST
+  transform/integration/unary_transform_test.cpp
+  transform/nans_to_null_test.cpp
+  transform/mask_to_bools_test.cpp
+  transform/bools_to_mask_test.cpp
+  transform/row_bit_count_test.cu
+  transform/one_hot_encode_tests.cpp
+)
+
+# ##################################################################################################
+# * interop tests -------------------------------------------------------------------------
+ConfigureTest(
+  INTEROP_TEST interop/to_arrow_test.cpp interop/from_arrow_test.cpp interop/dlpack_test.cpp
+)
+
+# ##################################################################################################
+# * io tests --------------------------------------------------------------------------------------
+ConfigureTest(DECOMPRESSION_TEST io/comp/decomp_test.cpp)
+ConfigureTest(ROW_SELECTION_TEST io/row_selection_test.cpp)
+
+ConfigureTest(
+  CSV_TEST io/csv_test.cpp
+  GPUS 1
+  PERCENT 30
+)
+ConfigureTest(
+  FILE_IO_TEST io/file_io_test.cpp
+  GPUS 1
+  PERCENT 30
+)
+ConfigureTest(
+  ORC_TEST io/orc_test.cpp
+  GPUS 1
+  PERCENT 30
+)
+ConfigureTest(
+  PARQUET_TEST io/parquet_test.cpp io/parquet_chunked_reader_test.cpp
+  GPUS 1
+  PERCENT 30
+)
+ConfigureTest(
+  JSON_TEST io/json_test.cpp io/json_chunked_reader.cpp
+  GPUS 1
+  PERCENT 30
+)
+ConfigureTest(JSON_WRITER_TEST io/json_writer.cpp)
+ConfigureTest(JSON_TYPE_CAST_TEST io/json_type_cast_test.cu)
+ConfigureTest(NESTED_JSON_TEST io/nested_json_test.cpp io/json_tree.cpp)
+ConfigureTest(ARROW_IO_SOURCE_TEST io/arrow_io_source_test.cpp)
+ConfigureTest(MULTIBYTE_SPLIT_TEST io/text/multibyte_split_test.cpp)
+ConfigureTest(
+  DATA_CHUNK_SOURCE_TEST io/text/data_chunk_source_test.cpp
+  GPUS 1
+  PERCENT 30
+)
+target_link_libraries(DATA_CHUNK_SOURCE_TEST PRIVATE ZLIB::ZLIB)
+ConfigureTest(LOGICAL_STACK_TEST io/fst/logical_stack_test.cu)
+ConfigureTest(FST_TEST io/fst/fst_test.cu)
+ConfigureTest(TYPE_INFERENCE_TEST io/type_inference_test.cu)
+if(CUDF_ENABLE_ARROW_S3)
+  target_compile_definitions(ARROW_IO_SOURCE_TEST PRIVATE "S3_ENABLED")
+endif()
+
+# ##################################################################################################
+# * sort tests ------------------------------------------------------------------------------------
+ConfigureTest(
+  SORT_TEST sort/segmented_sort_tests.cpp sort/sort_nested_types_tests.cpp sort/sort_test.cpp
+  sort/stable_sort_tests.cpp sort/rank_test.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * copying tests ---------------------------------------------------------------------------------
+ConfigureTest(
+  COPYING_TEST
+  copying/concatenate_tests.cpp
+  copying/copy_if_else_nested_tests.cpp
+  copying/copy_range_tests.cpp
+  copying/copy_tests.cpp
+  copying/detail_gather_tests.cu
+  copying/gather_list_tests.cpp
+  copying/gather_str_tests.cpp
+  copying/gather_struct_tests.cpp
+  copying/gather_tests.cpp
+  copying/get_value_tests.cpp
+  copying/pack_tests.cpp
+  copying/purge_nonempty_nulls_tests.cpp
+  copying/sample_tests.cpp
+  copying/scatter_tests.cpp
+  copying/scatter_list_tests.cpp
+  copying/scatter_list_scalar_tests.cpp
+  copying/scatter_struct_tests.cpp
+  copying/scatter_struct_scalar_tests.cpp
+  copying/segmented_gather_list_tests.cpp
+  copying/shift_tests.cpp
+  copying/slice_tests.cpp
+  copying/split_tests.cpp
+  copying/utility_tests.cpp
+  copying/reverse_tests.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * utilities tests -------------------------------------------------------------------------------
+ConfigureTest(
+  UTILITIES_TEST
+  utilities_tests/type_list_tests.cpp
+  utilities_tests/column_utilities_tests.cpp
+  utilities_tests/column_wrapper_tests.cpp
+  utilities_tests/lists_column_wrapper_tests.cpp
+  utilities_tests/logger_tests.cpp
+  utilities_tests/default_stream_tests.cpp
+  utilities_tests/type_check_tests.cpp
+)
+
+# ##################################################################################################
+# * span tests -------------------------------------------------------------------------------
+
+# This test must be split into two executables so that one can use the preload library and one does
+# not. The one that doesn't includes a thrust::device_vector copy, which is always synchronous on
+# the default stream and is out of libcudf's control (but must be tested).
+set(_allowlist_filter SpanTest.CanConstructFromDeviceContainers)
+
+ConfigureTest(SPAN_TEST utilities_tests/span_tests.cu)
+ConfigureTest(SPAN_TEST_DEVICE_VECTOR utilities_tests/span_tests.cu)
+
+# Overwrite the environments set by ConfigureTest
+set_tests_properties(
+  SPAN_TEST
+  PROPERTIES
+    ENVIRONMENT
+    "GTEST_FILTER=-${_allowlist_filter};GTEST_CUDF_STREAM_MODE=new_cudf_default;LD_PRELOAD=$<TARGET_FILE:cudf_identify_stream_usage_mode_cudf>"
+)
+set_tests_properties(
+  SPAN_TEST_DEVICE_VECTOR PROPERTIES ENVIRONMENT "GTEST_FILTER=${_allowlist_filter}"
+)
+
+# ##################################################################################################
+# * iterator tests --------------------------------------------------------------------------------
+ConfigureTest(
+  ITERATOR_TEST
+  iterator/indexalator_test.cu
+  iterator/optional_iterator_test_chrono.cu
+  iterator/optional_iterator_test_numeric.cu
+  iterator/pair_iterator_test_chrono.cu
+  iterator/pair_iterator_test_numeric.cu
+  iterator/scalar_iterator_test.cu
+  iterator/sizes_to_offsets_iterator_test.cu
+  iterator/value_iterator.cpp
+  iterator/value_iterator_test_chrono.cu
+  iterator/value_iterator_test_numeric.cu
+  iterator/value_iterator_test_strings.cu
+  iterator/value_iterator_test_transform.cu
+)
+
+# ##################################################################################################
+# * device atomics tests --------------------------------------------------------------------------
+ConfigureTest(DEVICE_ATOMICS_TEST device_atomics/device_atomics_test.cu)
+
+# ##################################################################################################
+# * transpose tests -------------------------------------------------------------------------------
+ConfigureTest(
+  TRANSPOSE_TEST transpose/transpose_test.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * table tests -----------------------------------------------------------------------------------
+ConfigureTest(
+  TABLE_TEST table/table_tests.cpp table/table_view_tests.cu table/row_operators_tests.cpp
+  table/experimental_row_operator_tests.cu table/row_operator_tests_utilities.cu
+)
+
+# ##################################################################################################
+# * sorted-merge tests ----------------------------------------------------------------------------
+ConfigureTest(
+  MERGE_TEST merge/merge_test.cpp merge/merge_dictionary_test.cpp merge/merge_string_test.cpp
+)
+
+# ##################################################################################################
+# * stream compaction tests -----------------------------------------------------------------------
+ConfigureTest(
+  STREAM_COMPACTION_TEST
+  stream_compaction/apply_boolean_mask_tests.cpp
+  stream_compaction/distinct_count_tests.cpp
+  stream_compaction/distinct_tests.cpp
+  stream_compaction/drop_nans_tests.cpp
+  stream_compaction/drop_nulls_tests.cpp
+  stream_compaction/stable_distinct_tests.cpp
+  stream_compaction/unique_count_tests.cpp
+  stream_compaction/unique_tests.cpp
+)
+
+# ##################################################################################################
+# * rolling tests ---------------------------------------------------------------------------------
+ConfigureTest(
+  ROLLING_TEST
+  rolling/collect_ops_test.cpp
+  rolling/empty_input_test.cpp
+  rolling/grouped_rolling_range_test.cpp
+  rolling/grouped_rolling_test.cpp
+  rolling/lead_lag_test.cpp
+  rolling/nth_element_test.cpp
+  rolling/offset_row_window_test.cpp
+  rolling/range_comparator_test.cu
+  rolling/range_rolling_window_test.cpp
+  rolling/range_window_bounds_test.cpp
+  rolling/rolling_test.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * filling test ----------------------------------------------------------------------------------
+ConfigureTest(
+  FILLING_TEST filling/fill_tests.cpp filling/repeat_tests.cpp filling/sequence_tests.cpp
+)
+
+# ##################################################################################################
+# * search test -----------------------------------------------------------------------------------
+ConfigureTest(
+  SEARCH_TEST search/search_dictionary_test.cpp search/search_list_test.cpp
+  search/search_struct_test.cpp search/search_test.cpp
+)
+
+# ##################################################################################################
+# * reshape test ----------------------------------------------------------------------------------
+ConfigureTest(
+  RESHAPE_TEST reshape/byte_cast_tests.cpp reshape/interleave_columns_tests.cpp
+  reshape/tile_tests.cpp
+)
+
+# ##################################################################################################
+# * traits test -----------------------------------------------------------------------------------
+ConfigureTest(TRAITS_TEST types/traits_test.cpp)
+
+# ##################################################################################################
+# * factories test --------------------------------------------------------------------------------
+ConfigureTest(FACTORIES_TEST scalar/factories_test.cpp column/factories_test.cpp)
+
+# ##################################################################################################
+# * dispatcher test -------------------------------------------------------------------------------
+ConfigureTest(DISPATCHER_TEST types/type_dispatcher_test.cu)
+
+# ##################################################################################################
+# * strings test ----------------------------------------------------------------------------------
+ConfigureTest(
+  STRINGS_TEST
+  strings/array_tests.cpp
+  strings/attrs_tests.cpp
+  strings/booleans_tests.cpp
+  strings/case_tests.cpp
+  strings/chars_types_tests.cpp
+  strings/combine/concatenate_tests.cpp
+  strings/combine/join_list_elements_tests.cpp
+  strings/combine/join_strings_tests.cpp
+  strings/concatenate_tests.cpp
+  strings/contains_tests.cpp
+  strings/datetime_tests.cpp
+  strings/durations_tests.cpp
+  strings/extract_tests.cpp
+  strings/factories_test.cu
+  strings/fill_tests.cpp
+  strings/findall_tests.cpp
+  strings/find_tests.cpp
+  strings/find_multiple_tests.cpp
+  strings/fixed_point_tests.cpp
+  strings/floats_tests.cpp
+  strings/format_lists_tests.cpp
+  strings/integers_tests.cpp
+  strings/ipv4_tests.cpp
+  strings/json_tests.cpp
+  strings/like_tests.cpp
+  strings/pad_tests.cpp
+  strings/repeat_strings_tests.cpp
+  strings/replace_regex_tests.cpp
+  strings/replace_tests.cpp
+  strings/reverse_tests.cpp
+  strings/slice_tests.cpp
+  strings/split_tests.cpp
+  strings/strip_tests.cpp
+  strings/translate_tests.cpp
+  strings/urls_tests.cpp
+)
+
+# ##################################################################################################
+# * structs test ----------------------------------------------------------------------------------
+ConfigureTest(STRUCTS_TEST structs/structs_column_tests.cpp structs/utilities_tests.cpp)
+
+# ##################################################################################################
+# * nvtext test -----------------------------------------------------------------------------------
+ConfigureTest(
+  TEXT_TEST
+  text/bpe_tests.cpp
+  text/edit_distance_tests.cpp
+  text/jaccard_tests.cpp
+  text/minhash_tests.cpp
+  text/ngrams_tests.cpp
+  text/ngrams_tokenize_tests.cpp
+  text/normalize_tests.cpp
+  text/replace_tests.cpp
+  text/stemmer_tests.cpp
+  text/subword_tests.cpp
+  text/tokenize_tests.cpp
+)
+
+# ##################################################################################################
+# * bitmask tests ---------------------------------------------------------------------------------
+ConfigureTest(
+  BITMASK_TEST bitmask/valid_if_tests.cu bitmask/set_nullmask_tests.cu bitmask/bitmask_tests.cpp
+  bitmask/is_element_valid_tests.cpp
+)
+
+# ##################################################################################################
+# * dictionary tests ------------------------------------------------------------------------------
+ConfigureTest(
+  DICTIONARY_TEST
+  dictionary/add_keys_test.cpp
+  dictionary/decode_test.cpp
+  dictionary/encode_test.cpp
+  dictionary/factories_test.cpp
+  dictionary/fill_test.cpp
+  dictionary/gather_test.cpp
+  dictionary/remove_keys_test.cpp
+  dictionary/scatter_test.cpp
+  dictionary/search_test.cpp
+  dictionary/set_keys_test.cpp
+  dictionary/slice_test.cpp
+)
+
+# ##################################################################################################
+# * encode tests -----------------------------------------------------------------------------------
+ConfigureTest(ENCODE_TEST encode/encode_tests.cpp)
+
+# ##################################################################################################
+# * ast tests -------------------------------------------------------------------------------------
+ConfigureTest(AST_TEST ast/transform_tests.cpp)
+
+# ##################################################################################################
+# * lists tests ----------------------------------------------------------------------------------
+ConfigureTest(
+  LISTS_TEST
+  lists/combine/concatenate_list_elements_tests.cpp
+  lists/combine/concatenate_rows_tests.cpp
+  lists/contains_tests.cpp
+  lists/count_elements_tests.cpp
+  lists/explode_tests.cpp
+  lists/extract_tests.cpp
+  lists/reverse_tests.cpp
+  lists/sequences_tests.cpp
+  lists/set_operations/difference_distinct_tests.cpp
+  lists/set_operations/have_overlap_tests.cpp
+  lists/set_operations/intersect_distinct_tests.cpp
+  lists/set_operations/union_distinct_tests.cpp
+  lists/sort_lists_tests.cpp
+  lists/stream_compaction/apply_boolean_mask_tests.cpp
+  lists/stream_compaction/distinct_tests.cpp
+  GPUS 1
+  PERCENT 70
+)
+
+# ##################################################################################################
+# * bin tests ----------------------------------------------------------------------------------
+ConfigureTest(LABEL_BINS_TEST labeling/label_bins_tests.cpp)
+
+# ##################################################################################################
+# * stream testing ---------------------------------------------------------------------------------
+ConfigureTest(
+  STREAM_IDENTIFICATION_TEST identify_stream_usage/test_default_stream_identification.cu
+)
+
+ConfigureTest(STREAM_CONCATENATE_TEST streams/concatenate_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_COPYING_TEST streams/copying_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_FILLING_TEST streams/filling_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_GROUPBY_TEST streams/groupby_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_HASHING_TEST streams/hash_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_INTEROP_TEST streams/interop_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_REPLACE_TEST streams/replace_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_SEARCH_TEST streams/search_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_DICTIONARY_TEST streams/dictionary_test.cpp STREAM_MODE testing)
+ConfigureTest(
+  STREAM_STRINGS_TEST streams/strings/case_test.cpp streams/strings/find_test.cpp STREAM_MODE
+  testing
+)
+ConfigureTest(STREAM_SORTING_TEST streams/sorting_test.cpp STREAM_MODE testing)
+ConfigureTest(STREAM_TEXT_TEST streams/text/ngrams_test.cpp STREAM_MODE testing)
+
+# ##################################################################################################
+# Install tests ####################################################################################
+# ##################################################################################################
+rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcudf)
diff --git a/cpp/tests/ast/transform_tests.cpp b/cpp/tests/ast/transform_tests.cpp
new file mode 100644
index 0000000..c0109a4
--- /dev/null
+++ b/cpp/tests/ast/transform_tests.cpp
@@ -0,0 +1,728 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <limits>
+#include <random>
+#include <type_traits>
+#include <vector>
+
+template <typename T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T>;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+struct TransformTest : public cudf::test::BaseFixture {};
+
+TEST_F(TransformTest, ColumnReference)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{10, 7, 20, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+
+  auto const& expected = c_0;
+  auto result          = cudf::compute_column(table, col_ref_0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, Literal)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{10, 7, 20, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto literal_value = cudf::numeric_scalar<int32_t>(42);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expected = column_wrapper<int32_t>{42, 42, 42, 42};
+  auto result   = cudf::compute_column(table, literal);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, NullLiteral)
+{
+  auto c_0   = column_wrapper<int32_t>{0, 0, 0, 0};
+  auto table = cudf::table_view{{c_0}};
+
+  auto literal_value = cudf::numeric_scalar<int32_t>(-123);
+  literal_value.set_valid_async(false);
+  auto literal = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::IDENTITY, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<int32_t>({-123, -123, -123, -123}, {0, 0, 0, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, IsNull)
+{
+  auto c_0   = column_wrapper<int32_t>{{0, 1, 2, 0}, {0, 1, 1, 0}};
+  auto table = cudf::table_view{{c_0}};
+
+  // result of IS_NULL on literal, will be a column of table size, with all values set to
+  // !literal.is_valid(). The table values are irrelevant.
+  auto literal_value = cudf::numeric_scalar<int32_t>(-123);
+  auto literal       = cudf::ast::literal(literal_value);
+  auto expression    = cudf::ast::operation(cudf::ast::ast_operator::IS_NULL, literal);
+
+  auto result    = cudf::compute_column(table, expression);
+  auto expected1 = column_wrapper<bool>({0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, result->view(), verbosity);
+
+  literal_value.set_valid_async(false);
+  result         = cudf::compute_column(table, expression);
+  auto expected2 = column_wrapper<bool>({1, 1, 1, 1}, cudf::test::iterators::no_nulls());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result->view(), verbosity);
+
+  auto col_ref_0   = cudf::ast::column_reference(0);
+  auto expression2 = cudf::ast::operation(cudf::ast::ast_operator::IS_NULL, col_ref_0);
+  result           = cudf::compute_column(table, expression2);
+  auto expected3   = column_wrapper<bool>({1, 0, 0, 1}, cudf::test::iterators::no_nulls());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicAddition)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{10, 7, 20, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<int32_t>{13, 27, 21, 50};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicAdditionEmptyTable)
+{
+  auto c_0   = column_wrapper<int32_t>{};
+  auto c_1   = column_wrapper<int32_t>{};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<int32_t>{};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicAdditionCast)
+{
+  auto c_0   = column_wrapper<int64_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int8_t>{10, 7, 20, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto cast       = cudf::ast::operation(cudf::ast::ast_operator::CAST_TO_INT64, col_ref_1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0, cast);
+
+  auto expected = column_wrapper<int64_t>{13, 27, 21, 50};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicEquality)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{3, 7, 1, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{true, false, true, false};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicAdditionLarge)
+{
+  auto a     = thrust::make_counting_iterator(0);
+  auto col   = column_wrapper<int32_t>(a, a + 2000);
+  auto table = cudf::table_view{{col, col}};
+
+  auto col_ref    = cudf::ast::column_reference(0);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref, col_ref);
+
+  auto b        = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  auto expected = column_wrapper<int32_t>(b, b + 2000);
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, LessComparator)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{10, 7, 20, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{true, false, true, false};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, LessComparatorLarge)
+{
+  auto a     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  auto b     = thrust::make_counting_iterator(500);
+  auto c_0   = column_wrapper<int32_t>(a, a + 2000);
+  auto c_1   = column_wrapper<int32_t>(b, b + 2000);
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, col_ref_1);
+
+  auto c        = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i < 500; });
+  auto expected = column_wrapper<bool>(c, c + 2000);
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, MultiLevelTreeArithmetic)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{10, 7, 20, 0};
+  auto c_2   = column_wrapper<int32_t>{-3, 66, 2, -99};
+  auto table = cudf::table_view{{c_0, c_1, c_2}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+  auto col_ref_2 = cudf::ast::column_reference(2);
+
+  auto expression_left_subtree =
+    cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0, col_ref_1);
+
+  auto expression_right_subtree =
+    cudf::ast::operation(cudf::ast::ast_operator::SUB, col_ref_2, col_ref_0);
+
+  auto expression_tree = cudf::ast::operation(
+    cudf::ast::ast_operator::ADD, expression_left_subtree, expression_right_subtree);
+
+  auto result   = cudf::compute_column(table, expression_tree);
+  auto expected = column_wrapper<int32_t>{7, 73, 22, -99};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, MultiLevelTreeArithmeticLarge)
+{
+  auto a     = thrust::make_counting_iterator(0);
+  auto b     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i + 1; });
+  auto c     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  auto c_0   = column_wrapper<int32_t>(a, a + 2000);
+  auto c_1   = column_wrapper<int32_t>(b, b + 2000);
+  auto c_2   = column_wrapper<int32_t>(c, c + 2000);
+  auto table = cudf::table_view{{c_0, c_1, c_2}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+  auto col_ref_2 = cudf::ast::column_reference(2);
+
+  auto expr_left_subtree = cudf::ast::operation(cudf::ast::ast_operator::MUL, col_ref_0, col_ref_1);
+  auto expr_right_subtree =
+    cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_2, col_ref_0);
+  auto expr_tree =
+    cudf::ast::operation(cudf::ast::ast_operator::SUB, expr_left_subtree, expr_right_subtree);
+
+  auto result = cudf::compute_column(table, expr_tree);
+  auto calc   = [](auto i) { return (i * (i + 1)) - (i + (i * 2)); };
+  auto d      = cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return calc(i); });
+  auto expected = column_wrapper<int32_t>(d, d + 2000);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, ImbalancedTreeArithmetic)
+{
+  auto c_0   = column_wrapper<double>{0.15, 0.37, 4.2, 21.3};
+  auto c_1   = column_wrapper<double>{0.0, -42.0, 1.0, 98.6};
+  auto c_2   = column_wrapper<double>{0.6, std::numeric_limits<double>::infinity(), 0.999, 1.0};
+  auto table = cudf::table_view{{c_0, c_1, c_2}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+  auto col_ref_2 = cudf::ast::column_reference(2);
+
+  auto expression_right_subtree =
+    cudf::ast::operation(cudf::ast::ast_operator::MUL, col_ref_0, col_ref_1);
+
+  auto expression_tree =
+    cudf::ast::operation(cudf::ast::ast_operator::SUB, col_ref_2, expression_right_subtree);
+
+  auto result = cudf::compute_column(table, expression_tree);
+  auto expected =
+    column_wrapper<double>{0.6, std::numeric_limits<double>::infinity(), -3.201, -2099.18};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, MultiLevelTreeComparator)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{10, 7, 20, 0};
+  auto c_2   = column_wrapper<int32_t>{-3, 66, 2, -99};
+  auto table = cudf::table_view{{c_0, c_1, c_2}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+  auto col_ref_2 = cudf::ast::column_reference(2);
+
+  auto expression_left_subtree =
+    cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, col_ref_0, col_ref_1);
+
+  auto expression_right_subtree =
+    cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_2, col_ref_0);
+
+  auto expression_tree = cudf::ast::operation(
+    cudf::ast::ast_operator::LOGICAL_AND, expression_left_subtree, expression_right_subtree);
+
+  auto result   = cudf::compute_column(table, expression_tree);
+  auto expected = column_wrapper<bool>{false, true, false, false};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, MultiTypeOperationFailure)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<double>{0.15, 0.77, 4.2, 21.3};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+
+  auto expression_0_plus_1 =
+    cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0, col_ref_1);
+  auto expression_1_plus_0 =
+    cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_1, col_ref_0);
+
+  // Operations on different types are not allowed
+  EXPECT_THROW(cudf::compute_column(table, expression_0_plus_1), cudf::logic_error);
+  EXPECT_THROW(cudf::compute_column(table, expression_1_plus_0), cudf::logic_error);
+}
+
+TEST_F(TransformTest, LiteralComparison)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0     = cudf::ast::column_reference(0);
+  auto literal_value = cudf::numeric_scalar<int32_t>(41);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_0, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<bool>{false, false, false, true};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, UnaryNot)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 0, 1, 50};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::NOT, col_ref_0);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<bool>{false, true, false, false};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, UnaryTrigonometry)
+{
+  auto c_0   = column_wrapper<double>{0.0, M_PI / 4, M_PI / 3};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+
+  auto expected_sin   = column_wrapper<double>{0.0, std::sqrt(2) / 2, std::sqrt(3.0) / 2.0};
+  auto expression_sin = cudf::ast::operation(cudf::ast::ast_operator::SIN, col_ref_0);
+  auto result_sin     = cudf::compute_column(table, expression_sin);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_sin, result_sin->view(), verbosity);
+
+  auto expected_cos   = column_wrapper<double>{1.0, std::sqrt(2) / 2, 0.5};
+  auto expression_cos = cudf::ast::operation(cudf::ast::ast_operator::COS, col_ref_0);
+  auto result_cos     = cudf::compute_column(table, expression_cos);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_cos, result_cos->view(), verbosity);
+
+  auto expected_tan   = column_wrapper<double>{0.0, 1.0, std::sqrt(3.0)};
+  auto expression_tan = cudf::ast::operation(cudf::ast::ast_operator::TAN, col_ref_0);
+  auto result_tan     = cudf::compute_column(table, expression_tan);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_tan, result_tan->view(), verbosity);
+}
+
+TEST_F(TransformTest, ArityCheckFailure)
+{
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  EXPECT_THROW(cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0), cudf::logic_error);
+  EXPECT_THROW(cudf::ast::operation(cudf::ast::ast_operator::ABS, col_ref_0, col_ref_0),
+               cudf::logic_error);
+}
+
+TEST_F(TransformTest, StringComparison)
+{
+  auto c_0   = cudf::test::strings_column_wrapper({"a", "bb", "ccc", "dddd"});
+  auto c_1   = cudf::test::strings_column_wrapper({"aa", "b", "cccc", "ddd"});
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{true, false, true, false};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, StringScalarComparison)
+{
+  auto c_0 =
+    cudf::test::strings_column_wrapper({"1", "12", "123", "23"}, {true, true, false, true});
+  auto table = cudf::table_view{{c_0}};
+
+  auto literal_value = cudf::string_scalar("2");
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, literal);
+
+  auto expected = column_wrapper<bool>{{true, true, true, false}, {true, true, false, true}};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+
+  // compare with null literal
+  literal_value.set_valid_async(false);
+  auto expected2 = column_wrapper<bool>{{false, false, false, false}, {false, false, false, false}};
+  auto result2   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, NumericScalarComparison)
+{
+  auto c_0   = column_wrapper<int32_t>{1, 12, 123, 23};
+  auto table = cudf::table_view{{c_0}};
+
+  auto literal_value = cudf::numeric_scalar<int32_t>(2);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, literal);
+
+  auto expected = column_wrapper<bool>{true, false, false, false};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, CopyColumn)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 0, 1, 50};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::IDENTITY, col_ref_0);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<int32_t>{3, 0, 1, 50};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, CopyLiteral)
+{
+  auto c_0   = column_wrapper<int32_t>{0, 0, 0, 0};
+  auto table = cudf::table_view{{c_0}};
+
+  auto literal_value = cudf::numeric_scalar<int32_t>(-123);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::IDENTITY, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<int32_t>{-123, -123, -123, -123};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, TrueDiv)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 0, 1, 50};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0     = cudf::ast::column_reference(0);
+  auto literal_value = cudf::numeric_scalar<int32_t>(2);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::TRUE_DIV, col_ref_0, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<double>{1.5, 0.0, 0.5, 25.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, FloorDiv)
+{
+  auto c_0   = column_wrapper<double>{3.0, 0.0, 1.0, 50.0};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0     = cudf::ast::column_reference(0);
+  auto literal_value = cudf::numeric_scalar<double>(2.0);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::FLOOR_DIV, col_ref_0, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<double>{1.0, 0.0, 0.0, 25.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, Mod)
+{
+  auto c_0   = column_wrapper<double>{3.0, 0.0, -1.0, -50.0};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0     = cudf::ast::column_reference(0);
+  auto literal_value = cudf::numeric_scalar<double>(2.0);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::MOD, col_ref_0, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<double>{1.0, 0.0, -1.0, 0.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, PyMod)
+{
+  auto c_0   = column_wrapper<double>{3.0, 0.0, -1.0, -50.0};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0     = cudf::ast::column_reference(0);
+  auto literal_value = cudf::numeric_scalar<double>(2.0);
+  auto literal       = cudf::ast::literal(literal_value);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::PYMOD, col_ref_0, literal);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<double>{1.0, 0.0, 1.0, 0.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicEqualityNullEqualNoNulls)
+{
+  auto c_0   = column_wrapper<int32_t>{3, 20, 1, 50};
+  auto c_1   = column_wrapper<int32_t>{3, 7, 1, 0};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::NULL_EQUAL, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{true, false, true, false};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicEqualityNormalEqualWithNulls)
+{
+  auto c_0   = column_wrapper<int32_t>{{3, 20, 1, 50}, {1, 1, 0, 0}};
+  auto c_1   = column_wrapper<int32_t>{{3, 7, 1, 0}, {1, 1, 0, 0}};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{{true, false, true, true}, {1, 1, 0, 0}};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicEqualityNulls)
+{
+  auto c_0   = column_wrapper<int32_t>{{3, 20, 1, 2, 50}, {1, 1, 0, 1, 0}};
+  auto c_1   = column_wrapper<int32_t>{{3, 7, 1, 2, 0}, {1, 1, 1, 0, 0}};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::NULL_EQUAL, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{{true, false, false, false, true}, {1, 1, 1, 1, 1}};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, UnaryNotNulls)
+{
+  auto c_0   = column_wrapper<int32_t>{{3, 0, 0, 50}, {0, 0, 1, 1}};
+  auto table = cudf::table_view{{c_0}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::NOT, col_ref_0);
+
+  auto result   = cudf::compute_column(table, expression);
+  auto expected = column_wrapper<bool>{{false, true, true, false}, {0, 0, 1, 1}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicAdditionNulls)
+{
+  auto c_0   = column_wrapper<int32_t>{{3, 20, 1, 50}, {0, 0, 1, 1}};
+  auto c_1   = column_wrapper<int32_t>{{10, 7, 20, 0}, {0, 1, 0, 1}};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<int32_t>{{0, 0, 0, 50}, {0, 0, 0, 1}};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, BasicAdditionLargeNulls)
+{
+  auto N = 2000;
+  auto a = thrust::make_counting_iterator(0);
+
+  auto validities = std::vector<int32_t>(N);
+  std::fill(validities.begin(), validities.begin() + N / 2, 0);
+  std::fill(validities.begin() + (N / 2), validities.end(), 0);
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::shuffle(validities.begin(), validities.end(), gen);
+
+  auto col   = column_wrapper<int32_t>(a, a + N, validities.begin());
+  auto table = cudf::table_view{{col}};
+
+  auto col_ref    = cudf::ast::column_reference(0);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::ADD, col_ref, col_ref);
+
+  auto b        = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  auto expected = column_wrapper<int32_t>(b, b + N, validities.begin());
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, NullLogicalAnd)
+{
+  auto c_0   = column_wrapper<bool>{{false, false, true, true, false, false, true, true},
+                                    {1, 1, 1, 1, 1, 0, 0, 0}};
+  auto c_1   = column_wrapper<bool>{{false, true, false, true, true, true, false, true},
+                                    {1, 1, 1, 1, 0, 1, 1, 0}};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+  auto expression =
+    cudf::ast::operation(cudf::ast::ast_operator::NULL_LOGICAL_AND, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{{false, false, false, true, false, false, false, true},
+                                       {1, 1, 1, 1, 1, 0, 1, 0}};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+TEST_F(TransformTest, NullLogicalOr)
+{
+  auto c_0   = column_wrapper<bool>{{false, false, true, true, false, false, true, true},
+                                    {1, 1, 1, 1, 1, 0, 1, 0}};
+  auto c_1   = column_wrapper<bool>{{false, true, false, true, true, true, false, true},
+                                    {1, 1, 1, 1, 0, 1, 0, 0}};
+  auto table = cudf::table_view{{c_0, c_1}};
+
+  auto col_ref_0 = cudf::ast::column_reference(0);
+  auto col_ref_1 = cudf::ast::column_reference(1);
+  auto expression =
+    cudf::ast::operation(cudf::ast::ast_operator::NULL_LOGICAL_OR, col_ref_0, col_ref_1);
+
+  auto expected = column_wrapper<bool>{{false, true, true, true, false, true, true, true},
+                                       {1, 1, 1, 1, 0, 1, 1, 0}};
+  auto result   = cudf::compute_column(table, expression);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view(), verbosity);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/binaryop/assert-binops.h b/cpp/tests/binaryop/assert-binops.h
new file mode 100644
index 0000000..6933d15
--- /dev/null
+++ b/cpp/tests/binaryop/assert-binops.h
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <limits>
+
+// This is used to convert the expected binop result computed by the test utilities and the
+// result returned by the binop operation into string, which is then used for display purposes
+// when the values do not match.
+struct stringify_out_values {
+  template <typename TypeOut>
+  std::string operator()(cudf::size_type i, TypeOut lhs, TypeOut rhs) const
+  {
+    std::stringstream out_str;
+    out_str << "[" << i << "]:\n";
+    if constexpr (cudf::is_fixed_point<TypeOut>()) {
+      out_str << "lhs: " << std::string(lhs) << "\nrhs: " << std::string(rhs);
+    } else if constexpr (cudf::is_timestamp<TypeOut>()) {
+      out_str << "lhs: " << lhs.time_since_epoch().count()
+              << "\nrhs: " << rhs.time_since_epoch().count();
+    } else if constexpr (cudf::is_duration<TypeOut>()) {
+      out_str << "lhs: " << lhs.count() << "\nrhs: " << rhs.count();
+    } else {
+      out_str << "lhs: " << lhs << "\nrhs: " << rhs;
+    }
+    return out_str.str();
+  }
+};
+
+// This comparator can be used to compare two values that are within a max ULP error.
+// This is typically used to compare floating point values computed on CPU and GPU which is
+// expected to be *near* equal, or when computing large numbers can yield ULP errors
+//
+// TODO: This should not be used in favor of the built-in one in column_utilities
+template <typename TypeOut>
+struct NearEqualComparator {
+  double ulp_;
+
+  NearEqualComparator(double ulp) : ulp_(ulp) {}
+
+  bool operator()(TypeOut const& lhs, TypeOut const& rhs) const
+  {
+    return (std::fabs(lhs - rhs) <=
+              std::numeric_limits<TypeOut>::epsilon() * std::fabs(lhs + rhs) * ulp_ ||
+            std::fabs(lhs - rhs) < std::numeric_limits<TypeOut>::min());
+  }
+};
+
+template <typename TypeOut,
+          typename TypeLhs,
+          typename TypeRhs,
+          typename TypeOp,
+          typename ValueComparator = std::equal_to<TypeOut>,
+          typename ScalarType      = cudf::scalar_type_t<TypeLhs>>
+void ASSERT_BINOP(cudf::column_view const& out,
+                  cudf::scalar const& lhs,
+                  cudf::column_view const& rhs,
+                  TypeOp&& op,
+                  ValueComparator const& value_comparator = ValueComparator())
+{
+  auto lhs_h    = static_cast<ScalarType const&>(lhs).operator TypeLhs();
+  auto rhs_h    = cudf::test::to_host<TypeRhs>(rhs);
+  auto rhs_data = rhs_h.first;
+  auto out_h    = cudf::test::to_host<TypeOut>(out);
+  auto out_data = out_h.first;
+
+  ASSERT_EQ(out_data.size(), rhs_data.size());
+  for (size_t i = 0; i < out_data.size(); ++i) {
+    auto lhs = out_data[i];
+    auto rhs = (TypeOut)(op(lhs_h, rhs_data[i]));
+    // TODO: This is incorrectly comparing row values that may be null
+    EXPECT_TRUE(value_comparator(lhs, rhs)) << stringify_out_values{}(i, lhs, rhs);
+  }
+
+  if (rhs.nullable()) {
+    EXPECT_TRUE(out.nullable());
+    auto rhs_valid = rhs_h.second;
+    auto out_valid = out_h.second;
+
+    uint32_t lhs_valid = (lhs.is_valid() ? std::numeric_limits<cudf::bitmask_type>::max() : 0);
+    ASSERT_EQ(out_valid.size(), rhs_valid.size());
+    for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+      EXPECT_EQ(out_valid[i], (lhs_valid & rhs_valid[i]));
+    }
+  } else {
+    if (lhs.is_valid()) {
+      EXPECT_FALSE(out.nullable());
+    } else {
+      auto out_valid = out_h.second;
+      for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+        EXPECT_EQ(out_valid[i], cudf::bitmask_type{0});
+      }
+    }
+  }
+}
+
+template <typename TypeOut,
+          typename TypeLhs,
+          typename TypeRhs,
+          typename TypeOp,
+          typename ValueComparator = std::equal_to<TypeOut>,
+          typename ScalarType      = cudf::scalar_type_t<TypeRhs>>
+void ASSERT_BINOP(cudf::column_view const& out,
+                  cudf::column_view const& lhs,
+                  cudf::scalar const& rhs,
+                  TypeOp&& op,
+                  ValueComparator const& value_comparator = ValueComparator())
+{
+  auto rhs_h    = static_cast<ScalarType const&>(rhs).operator TypeRhs();
+  auto lhs_h    = cudf::test::to_host<TypeLhs>(lhs);
+  auto lhs_data = lhs_h.first;
+  auto out_h    = cudf::test::to_host<TypeOut>(out);
+  auto out_data = out_h.first;
+
+  ASSERT_EQ(out_data.size(), lhs_data.size());
+  for (size_t i = 0; i < out_data.size(); ++i) {
+    auto lhs = out_data[i];
+    auto rhs = (TypeOut)(op(lhs_data[i], rhs_h));
+    // TODO: This is incorrectly comparing row values that may be null
+    EXPECT_TRUE(value_comparator(lhs, rhs)) << stringify_out_values{}(i, lhs, rhs);
+  }
+
+  if (lhs.nullable()) {
+    EXPECT_TRUE(out.nullable());
+    auto lhs_valid = lhs_h.second;
+    auto out_valid = out_h.second;
+
+    uint32_t rhs_valid = (rhs.is_valid() ? std::numeric_limits<cudf::bitmask_type>::max() : 0);
+    ASSERT_EQ(out_valid.size(), lhs_valid.size());
+    for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+      EXPECT_EQ(out_valid[i], (rhs_valid & lhs_valid[i]));
+    }
+  } else {
+    if (rhs.is_valid()) {
+      EXPECT_FALSE(out.nullable());
+    } else {
+      auto out_valid = out_h.second;
+      for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+        EXPECT_EQ(out_valid[i], cudf::bitmask_type{0});
+      }
+    }
+  }
+}
+
+template <typename TypeOut,
+          typename TypeLhs,
+          typename TypeRhs,
+          typename TypeOp,
+          typename ValueComparator = std::equal_to<TypeOut>>
+void ASSERT_BINOP(cudf::column_view const& out,
+                  cudf::column_view const& lhs,
+                  cudf::column_view const& rhs,
+                  TypeOp&& op,
+                  ValueComparator const& value_comparator = ValueComparator())
+{
+  auto lhs_h    = cudf::test::to_host<TypeLhs>(lhs);
+  auto lhs_data = lhs_h.first;
+  auto rhs_h    = cudf::test::to_host<TypeRhs>(rhs);
+  auto rhs_data = rhs_h.first;
+  auto out_h    = cudf::test::to_host<TypeOut>(out);
+  auto out_data = out_h.first;
+
+  ASSERT_EQ(out_data.size(), lhs_data.size());
+  ASSERT_EQ(out_data.size(), rhs_data.size());
+  for (size_t i = 0; i < out_data.size(); ++i) {
+    auto lhs = out_data[i];
+    auto rhs = (TypeOut)(op(lhs_data[i], rhs_data[i]));
+    // TODO: This is incorrectly comparing row values that may be null
+    EXPECT_TRUE(value_comparator(lhs, rhs)) << stringify_out_values{}(i, lhs, rhs);
+  }
+
+  if (lhs.nullable() and rhs.nullable()) {
+    EXPECT_TRUE(out.nullable());
+    auto lhs_valid = lhs_h.second;
+    auto rhs_valid = rhs_h.second;
+    auto out_valid = out_h.second;
+
+    ASSERT_EQ(out_valid.size(), lhs_valid.size());
+    ASSERT_EQ(out_valid.size(), rhs_valid.size());
+    for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+      EXPECT_EQ(out_valid[i], (lhs_valid[i] & rhs_valid[i]));
+    }
+  } else if (not lhs.nullable() and rhs.nullable()) {
+    EXPECT_TRUE(out.nullable());
+    auto rhs_valid = rhs_h.second;
+    auto out_valid = out_h.second;
+
+    ASSERT_EQ(out_valid.size(), rhs_valid.size());
+    for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+      EXPECT_EQ(out_valid[i], rhs_valid[i]);
+    }
+  } else if (lhs.nullable() and not rhs.nullable()) {
+    EXPECT_TRUE(out.nullable());
+    auto lhs_valid = lhs_h.second;
+    auto out_valid = out_h.second;
+
+    ASSERT_EQ(out_valid.size(), lhs_valid.size());
+    for (cudf::size_type i = 0; i < cudf::num_bitmask_words(out_data.size()); ++i) {
+      EXPECT_EQ(out_valid[i], lhs_valid[i]);
+    }
+  } else {
+    EXPECT_FALSE(out.nullable());
+  }
+}
diff --git a/cpp/tests/binaryop/binop-compiled-fixed_point-test.cpp b/cpp/tests/binaryop/binop-compiled-fixed_point-test.cpp
new file mode 100644
index 0000000..8b1e987
--- /dev/null
+++ b/cpp/tests/binaryop/binop-compiled-fixed_point-test.cpp
@@ -0,0 +1,845 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/binaryop.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+template <typename T>
+struct FixedPointCompiledTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+using wrapper = cudf::test::fixed_width_column_wrapper<T>;
+TYPED_TEST_SUITE(FixedPointCompiledTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpAdd)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const sz = std::size_t{1000};
+
+  auto begin      = cudf::detail::make_counting_transform_iterator(1, [](auto i) {
+    return decimalXX{i, scale_type{0}};
+  });
+  auto const vec1 = std::vector<decimalXX>(begin, begin + sz);
+  auto const vec2 = std::vector<decimalXX>(sz, decimalXX{2, scale_type{0}});
+  auto expected   = std::vector<decimalXX>(sz);
+
+  std::transform(std::cbegin(vec1),
+                 std::cend(vec1),
+                 std::cbegin(vec2),
+                 std::begin(expected),
+                 std::plus<decimalXX>());
+
+  auto const lhs          = wrapper<decimalXX>(vec1.begin(), vec1.end());
+  auto const rhs          = wrapper<decimalXX>(vec2.begin(), vec2.end());
+  auto const expected_col = wrapper<decimalXX>(expected.begin(), expected.end());
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::ADD,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_col, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpMultiply)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const sz = std::size_t{1000};
+
+  auto begin      = cudf::detail::make_counting_transform_iterator(1, [](auto i) {
+    return decimalXX{i, scale_type{0}};
+  });
+  auto const vec1 = std::vector<decimalXX>(begin, begin + sz);
+  auto const vec2 = std::vector<decimalXX>(sz, decimalXX{2, scale_type{0}});
+  auto expected   = std::vector<decimalXX>(sz);
+
+  std::transform(std::cbegin(vec1),
+                 std::cend(vec1),
+                 std::cbegin(vec2),
+                 std::begin(expected),
+                 std::multiplies<decimalXX>());
+
+  auto const lhs          = wrapper<decimalXX>(vec1.begin(), vec1.end());
+  auto const rhs          = wrapper<decimalXX>(vec2.begin(), vec2.end());
+  auto const expected_col = wrapper<decimalXX>(expected.begin(), expected.end());
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::MUL,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::MUL, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_col, result->view());
+}
+
+template <typename T>
+using fp_wrapper = cudf::test::fixed_point_column_wrapper<T>;
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpMultiply2)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = fp_wrapper<RepType>{{10, 10, 10, 10, 10}, scale_type{0}};
+  auto const expected = fp_wrapper<RepType>{{110, 220, 330, 440, 550}, scale_type{-1}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::MUL,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::MUL, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpDiv)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{10, 30, 50, 70}, scale_type{-1}};
+  auto const rhs      = fp_wrapper<RepType>{{4, 4, 4, 4}, scale_type{0}};
+  auto const expected = fp_wrapper<RepType>{{2, 7, 12, 17}, scale_type{-1}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::DIV,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpDiv2)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{10, 30, 50, 70}, scale_type{-1}};
+  auto const rhs      = fp_wrapper<RepType>{{4, 4, 4, 4}, scale_type{-2}};
+  auto const expected = fp_wrapper<RepType>{{2, 7, 12, 17}, scale_type{1}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::DIV,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpDiv3)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{10, 30, 50, 70}, scale_type{-1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(12, scale_type{-1});
+  auto const expected = fp_wrapper<RepType>{{0, 2, 4, 5}, scale_type{0}};
+
+  auto const type = cudf::binary_operation_fixed_point_output_type(
+    cudf::binary_operator::DIV, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpDiv4)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto begin = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 11; });
+  auto result_begin =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i * 11) / 12; });
+  auto const lhs      = fp_wrapper<RepType>(begin, begin + 1000, scale_type{-1});
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(12, scale_type{-1});
+  auto const expected = fp_wrapper<RepType>(result_begin, result_begin + 1000, scale_type{0});
+
+  auto const type = cudf::binary_operation_fixed_point_output_type(
+    cudf::binary_operator::DIV, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpAdd2)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = fp_wrapper<RepType>{{100, 200, 300, 400, 500}, scale_type{-2}};
+  auto const expected = fp_wrapper<RepType>{{210, 420, 630, 840, 1050}, scale_type{-2}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::ADD,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpAdd3)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{1100, 2200, 3300, 4400, 5500}, scale_type{-3}};
+  auto const rhs      = fp_wrapper<RepType>{{100, 200, 300, 400, 500}, scale_type{-2}};
+  auto const expected = fp_wrapper<RepType>{{2100, 4200, 6300, 8400, 10500}, scale_type{-3}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::ADD,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpAdd4)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(100, scale_type{-2});
+  auto const expected = fp_wrapper<RepType>{{210, 320, 430, 540, 650}, scale_type{-2}};
+
+  auto const type = cudf::binary_operation_fixed_point_output_type(
+    cudf::binary_operator::ADD, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpAdd5)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = cudf::make_fixed_point_scalar<decimalXX>(100, scale_type{-2});
+  auto const rhs      = fp_wrapper<RepType>{{11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const expected = fp_wrapper<RepType>{{210, 320, 430, 540, 650}, scale_type{-2}};
+
+  auto const type = cudf::binary_operation_fixed_point_output_type(
+    cudf::binary_operator::ADD, lhs->type(), static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(*lhs, rhs, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpAdd6)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col = fp_wrapper<RepType>{{30, 4, 5, 6, 7, 8}, scale_type{0}};
+
+  auto const expected1 = fp_wrapper<RepType>{{60, 8, 10, 12, 14, 16}, scale_type{0}};
+  auto const expected2 = fp_wrapper<RepType>{{6, 0, 1, 1, 1, 1}, scale_type{1}};
+  auto const type1     = cudf::data_type{cudf::type_to_id<decimalXX>(), 0};
+  auto const type2     = cudf::data_type{cudf::type_to_id<decimalXX>(), 1};
+  auto const result1   = cudf::binary_operation(col, col, cudf::binary_operator::ADD, type1);
+  auto const result2   = cudf::binary_operation(col, col, cudf::binary_operator::ADD, type2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result2->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, result1->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointCast)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col      = fp_wrapper<RepType>{{6, 8, 10, 12, 14, 16}, scale_type{0}};
+  auto const expected = fp_wrapper<RepType>{{0, 0, 1, 1, 1, 1}, scale_type{1}};
+  auto const type     = cudf::data_type{cudf::type_to_id<decimalXX>(), 1};
+  auto const result   = cudf::cast(col, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpMultiplyScalar)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(100, scale_type{-1});
+  auto const expected = fp_wrapper<RepType>{{1100, 2200, 3300, 4400, 5500}, scale_type{-2}};
+
+  auto const type = cudf::binary_operation_fixed_point_output_type(
+    cudf::binary_operator::MUL, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::MUL, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpSimplePlus)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{150, 200}, scale_type{-2}};
+  auto const rhs      = fp_wrapper<RepType>{{2250, 1005}, scale_type{-3}};
+  auto const expected = fp_wrapper<RepType>{{3750, 3005}, scale_type{-3}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::ADD,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpEqualSimple)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const trues    = std::vector<bool>(4, true);
+  auto const col1     = fp_wrapper<RepType>{{1, 2, 3, 4}, scale_type{0}};
+  auto const col2     = fp_wrapper<RepType>{{100, 200, 300, 400}, scale_type{-2}};
+  auto const expected = wrapper<bool>(trues.begin(), trues.end());
+
+  auto const result = cudf::binary_operation(
+    col1, col2, cudf::binary_operator::EQUAL, cudf::data_type{cudf::type_id::BOOL8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpEqualSimpleScale0)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const trues    = std::vector<bool>(4, true);
+  auto const col      = fp_wrapper<RepType>{{1, 2, 3, 4}, scale_type{0}};
+  auto const expected = wrapper<bool>(trues.begin(), trues.end());
+
+  auto const result = cudf::binary_operation(
+    col, col, cudf::binary_operator::EQUAL, cudf::data_type{cudf::type_id::BOOL8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpEqualSimpleScale0Null)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col1     = fp_wrapper<RepType>{{1, 2, 3, 4}, {1, 1, 1, 1}, scale_type{0}};
+  auto const col2     = fp_wrapper<RepType>{{1, 2, 3, 4}, {0, 0, 0, 0}, scale_type{0}};
+  auto const expected = wrapper<bool>{{0, 1, 0, 1}, {0, 0, 0, 0}};
+
+  auto const result = cudf::binary_operation(
+    col1, col2, cudf::binary_operator::EQUAL, cudf::data_type{cudf::type_id::BOOL8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpEqualSimpleScale2Null)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col1     = fp_wrapper<RepType>{{1, 2, 3, 4}, {1, 1, 1, 1}, scale_type{-2}};
+  auto const col2     = fp_wrapper<RepType>{{1, 2, 3, 4}, {0, 0, 0, 0}, scale_type{0}};
+  auto const expected = wrapper<bool>{{0, 1, 0, 1}, {0, 0, 0, 0}};
+
+  auto const result = cudf::binary_operation(
+    col1, col2, cudf::binary_operator::EQUAL, cudf::data_type{cudf::type_id::BOOL8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpEqualLessGreater)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const sz = std::size_t{1000};
+
+  // TESTING binary op ADD
+
+  auto begin = cudf::detail::make_counting_transform_iterator(1, [](auto e) { return e * 1000; });
+  auto const vec1 = std::vector<RepType>(begin, begin + sz);
+  auto const vec2 = std::vector<RepType>(sz, 0);
+
+  auto const iota_3  = fp_wrapper<RepType>(vec1.begin(), vec1.end(), scale_type{-3});
+  auto const zeros_3 = fp_wrapper<RepType>(vec2.begin(), vec2.end(), scale_type{-1});
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::ADD,
+                                                   static_cast<cudf::column_view>(iota_3).type(),
+                                                   static_cast<cudf::column_view>(zeros_3).type());
+  auto const iota_3_after_add =
+    cudf::binary_operation(zeros_3, iota_3, cudf::binary_operator::ADD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(iota_3, iota_3_after_add->view());
+
+  // TESTING binary op EQUAL, LESS, GREATER
+
+  auto const trues    = std::vector<bool>(sz, true);
+  auto const true_col = wrapper<bool>(trues.begin(), trues.end());
+
+  auto const btype = cudf::data_type{cudf::type_id::BOOL8};
+  auto const equal_result =
+    cudf::binary_operation(iota_3, iota_3_after_add->view(), cudf::binary_operator::EQUAL, btype);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(true_col, equal_result->view());
+
+  auto const less_result =
+    cudf::binary_operation(zeros_3, iota_3_after_add->view(), cudf::binary_operator::LESS, btype);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(true_col, less_result->view());
+
+  auto const greater_result = cudf::binary_operation(
+    iota_3_after_add->view(), zeros_3, cudf::binary_operator::GREATER, btype);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(true_col, greater_result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpNullMaxSimple)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col1     = fp_wrapper<RepType>{{40, 30, 20, 10, 0}, {1, 0, 1, 1, 0}, scale_type{-2}};
+  auto const col2     = fp_wrapper<RepType>{{10, 20, 30, 40, 0}, {1, 1, 1, 0, 0}, scale_type{-2}};
+  auto const expected = fp_wrapper<RepType>{{40, 20, 30, 10, 0}, {1, 1, 1, 1, 0}, scale_type{-2}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::NULL_MAX,
+                                                   static_cast<cudf::column_view>(col1).type(),
+                                                   static_cast<cudf::column_view>(col2).type());
+  auto const result = cudf::binary_operation(col1, col2, cudf::binary_operator::NULL_MAX, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpNullMinSimple)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col1     = fp_wrapper<RepType>{{40, 30, 20, 10, 0}, {1, 1, 1, 0, 0}, scale_type{-1}};
+  auto const col2     = fp_wrapper<RepType>{{10, 20, 30, 40, 0}, {1, 0, 1, 1, 0}, scale_type{-1}};
+  auto const expected = fp_wrapper<RepType>{{10, 30, 20, 40, 0}, {1, 1, 1, 1, 0}, scale_type{-1}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::NULL_MIN,
+                                                   static_cast<cudf::column_view>(col1).type(),
+                                                   static_cast<cudf::column_view>(col2).type());
+  auto const result = cudf::binary_operation(col1, col2, cudf::binary_operator::NULL_MIN, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpNullEqualsSimple)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col1     = fp_wrapper<RepType>{{400, 300, 300, 100}, {1, 1, 1, 0}, scale_type{-2}};
+  auto const col2     = fp_wrapper<RepType>{{40, 200, 20, 400}, {1, 0, 1, 0}, scale_type{-1}};
+  auto const expected = wrapper<bool>{{1, 0, 0, 1}, {1, 1, 1, 1}};
+
+  auto const result = cudf::binary_operation(
+    col1, col2, cudf::binary_operator::NULL_EQUALS, cudf::data_type{cudf::type_id::BOOL8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{100, 300, 500, 700}, scale_type{-2}};
+  auto const rhs      = fp_wrapper<RepType>{{4, 4, 4, 4}, scale_type{0}};
+  auto const expected = fp_wrapper<RepType>{{25, 75, 125, 175}, scale_type{-2}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), -2};
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div2)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{100000, 300000, 500000, 700000}, scale_type{-3}};
+  auto const rhs      = fp_wrapper<RepType>{{20, 20, 20, 20}, scale_type{-1}};
+  auto const expected = fp_wrapper<RepType>{{5000, 15000, 25000, 35000}, scale_type{-2}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), -2};
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div3)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{10000, 30000, 50000, 70000}, scale_type{-2}};
+  auto const rhs      = fp_wrapper<RepType>{{3, 9, 3, 3}, scale_type{0}};
+  auto const expected = fp_wrapper<RepType>{{3333, 3333, 16666, 23333}, scale_type{-2}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), -2};
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div4)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{10, 30, 50, 70}, scale_type{1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(3, scale_type{0});
+  auto const expected = fp_wrapper<RepType>{{3, 10, 16, 23}, scale_type{1}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), 1};
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div6)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs = cudf::make_fixed_point_scalar<decimalXX>(3000, scale_type{-3});
+  auto const rhs = fp_wrapper<RepType>{{10, 30, 50, 70}, scale_type{-1}};
+
+  auto const expected = fp_wrapper<RepType>{{300, 100, 60, 42}, scale_type{-2}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), -2};
+  auto const result = cudf::binary_operation(*lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div7)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs = cudf::make_fixed_point_scalar<decimalXX>(1200, scale_type{0});
+  auto const rhs = fp_wrapper<RepType>{{100, 200, 300, 500, 600, 800, 1200, 1300}, scale_type{-2}};
+
+  auto const expected = fp_wrapper<RepType>{{12, 6, 4, 2, 2, 1, 1, 0}, scale_type{2}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), 2};
+  auto const result = cudf::binary_operation(*lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div8)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{4000, 6000, 80000}, scale_type{-1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(5000, scale_type{-3});
+  auto const expected = fp_wrapper<RepType>{{0, 1, 16}, scale_type{2}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), 2};
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div9)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{10, 20, 30}, scale_type{2}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(7, scale_type{1});
+  auto const expected = fp_wrapper<RepType>{{1, 2, 4}, scale_type{1}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), 1};
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div10)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{100, 200, 300}, scale_type{1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(7, scale_type{0});
+  auto const expected = fp_wrapper<RepType>{{14, 28, 42}, scale_type{1}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), 1};
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOp_Div11)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{1000, 2000, 3000}, scale_type{1}};
+  auto const rhs      = fp_wrapper<RepType>{{7, 7, 7}, scale_type{0}};
+  auto const expected = fp_wrapper<RepType>{{142, 285, 428}, scale_type{1}};
+
+  auto const type   = cudf::data_type{cudf::type_to_id<decimalXX>(), 1};
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpThrows)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const col           = fp_wrapper<RepType>{{100, 300, 500, 700}, scale_type{-2}};
+  auto const non_bool_type = cudf::data_type{cudf::type_to_id<decimalXX>(), -2};
+  EXPECT_THROW(cudf::binary_operation(col, col, cudf::binary_operator::LESS, non_bool_type),
+               cudf::data_type_error);
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpModSimple)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{-33, -22, -11, 11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = fp_wrapper<RepType>{{10, 10, 10, 10, 10, 10, 10, 10}, scale_type{-1}};
+  auto const expected = fp_wrapper<RepType>{{-3, -2, -1, 1, 2, 3, 4, 5}, scale_type{-1}};
+
+  auto const type =
+    cudf::binary_operation_fixed_point_output_type(cudf::binary_operator::MOD,
+                                                   static_cast<cudf::column_view>(lhs).type(),
+                                                   static_cast<cudf::column_view>(rhs).type());
+  auto const result = cudf::binary_operation(lhs, rhs, cudf::binary_operator::MOD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpPModSimple)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{-33, -22, -11, 11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = fp_wrapper<RepType>{{10, 10, 10, 10, 10, 10, 10, 10}, scale_type{-1}};
+  auto const expected = fp_wrapper<RepType>{{7, 8, 9, 1, 2, 3, 4, 5}, scale_type{-1}};
+
+  for (auto const op : {cudf::binary_operator::PMOD, cudf::binary_operator::PYMOD}) {
+    auto const type = cudf::binary_operation_fixed_point_output_type(
+      op, static_cast<cudf::column_view>(lhs).type(), static_cast<cudf::column_view>(rhs).type());
+    auto const result = cudf::binary_operation(lhs, rhs, op, type);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpModSimple2)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{-33, -22, -11, 11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(10, scale_type{-1});
+  auto const expected = fp_wrapper<RepType>{{-3, -2, -1, 1, 2, 3, 4, 5}, scale_type{-1}};
+
+  auto const type = cudf::binary_operation_fixed_point_output_type(
+    cudf::binary_operator::MOD, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+  auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::MOD, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpPModAndPyModSimple2)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const lhs      = fp_wrapper<RepType>{{-33, -22, -11, 11, 22, 33, 44, 55}, scale_type{-1}};
+  auto const rhs      = cudf::make_fixed_point_scalar<decimalXX>(10, scale_type{-1});
+  auto const expected = fp_wrapper<RepType>{{7, 8, 9, 1, 2, 3, 4, 5}, scale_type{-1}};
+
+  for (auto const op : {cudf::binary_operator::PMOD, cudf::binary_operator::PYMOD}) {
+    auto const type = cudf::binary_operation_fixed_point_output_type(
+      op, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+    auto const result = cudf::binary_operation(lhs, *rhs, op, type);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpMod)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  auto constexpr N = 1000;
+
+  for (auto scale : {-1, -2, -3}) {
+    auto const iota = thrust::make_counting_iterator(-500);
+    auto const lhs  = fp_wrapper<RepType>{iota, iota + N, scale_type{-1}};
+    auto const rhs  = cudf::make_fixed_point_scalar<decimalXX>(7, scale_type{scale});
+
+    auto const factor   = static_cast<int>(std::pow(10, -1 - scale));
+    auto const f        = [factor](auto i) { return (i * factor) % 7; };
+    auto const exp_iter = cudf::detail::make_counting_transform_iterator(-500, f);
+    auto const expected = fp_wrapper<RepType>{exp_iter, exp_iter + N, scale_type{scale}};
+
+    auto const type = cudf::binary_operation_fixed_point_output_type(
+      cudf::binary_operator::MOD, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+    auto const result = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::MOD, type);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TYPED_TEST(FixedPointCompiledTest, FixedPointBinaryOpPModAndPyMod)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  auto constexpr N = 1000;
+
+  for (auto const scale : {-1, -2, -3}) {
+    auto const iota = thrust::make_counting_iterator(-500);
+    auto const lhs  = fp_wrapper<RepType>{iota, iota + N, scale_type{-1}};
+    auto const rhs  = cudf::make_fixed_point_scalar<decimalXX>(7, scale_type{scale});
+
+    auto const factor   = static_cast<int>(std::pow(10, -1 - scale));
+    auto const f        = [factor](auto i) { return (((i * factor) % 7) + 7) % 7; };
+    auto const exp_iter = cudf::detail::make_counting_transform_iterator(-500, f);
+    auto const expected = fp_wrapper<RepType>{exp_iter, exp_iter + N, scale_type{scale}};
+
+    for (auto const op : {cudf::binary_operator::PMOD, cudf::binary_operator::PYMOD}) {
+      auto const type = cudf::binary_operation_fixed_point_output_type(
+        op, static_cast<cudf::column_view>(lhs).type(), rhs->type());
+      auto const result = cudf::binary_operation(lhs, *rhs, op, type);
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+    }
+  }
+}
+
+template <typename T>
+struct FixedPointTest_64_128_Reps : public cudf::test::BaseFixture {};
+
+using Decimal64And128Types = cudf::test::Types<numeric::decimal64, numeric::decimal128>;
+TYPED_TEST_SUITE(FixedPointTest_64_128_Reps, Decimal64And128Types);
+
+TYPED_TEST(FixedPointTest_64_128_Reps, FixedPoint_64_128_ComparisonTests)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  for (auto const rhs_value : {10000000000000000, 100000000000000000}) {
+    auto const lhs       = fp_wrapper<RepType>{{33041, 97290, 36438, 25379, 48473}, scale_type{2}};
+    auto const rhs       = cudf::make_fixed_point_scalar<decimalXX>(rhs_value, scale_type{0});
+    auto const trues     = wrapper<bool>{{1, 1, 1, 1, 1}};
+    auto const falses    = wrapper<bool>{{0, 0, 0, 0, 0}};
+    auto const bool_type = cudf::data_type{cudf::type_id::BOOL8};
+
+    auto const a = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::LESS, bool_type);
+    auto const b = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::LESS_EQUAL, bool_type);
+    auto const c = cudf::binary_operation(lhs, *rhs, cudf::binary_operator::GREATER, bool_type);
+    auto const d =
+      cudf::binary_operation(lhs, *rhs, cudf::binary_operator::GREATER_EQUAL, bool_type);
+    auto const e = cudf::binary_operation(*rhs, lhs, cudf::binary_operator::GREATER, bool_type);
+    auto const f =
+      cudf::binary_operation(*rhs, lhs, cudf::binary_operator::GREATER_EQUAL, bool_type);
+    auto const g = cudf::binary_operation(*rhs, lhs, cudf::binary_operator::LESS, bool_type);
+    auto const h = cudf::binary_operation(*rhs, lhs, cudf::binary_operator::LESS_EQUAL, bool_type);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(a->view(), trues);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(b->view(), trues);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(c->view(), falses);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(d->view(), falses);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e->view(), trues);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(f->view(), trues);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(g->view(), falses);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(h->view(), falses);
+  }
+}
diff --git a/cpp/tests/binaryop/binop-compiled-test.cpp b/cpp/tests/binaryop/binop-compiled-test.cpp
new file mode 100644
index 0000000..3c5adaf
--- /dev/null
+++ b/cpp/tests/binaryop/binop-compiled-test.cpp
@@ -0,0 +1,786 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/binaryop/assert-binops.h>
+#include <tests/binaryop/binop-fixture.hpp>
+#include <tests/binaryop/util/operation.h>
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/binaryop.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <limits>
+#include <type_traits>
+
+template <typename T>
+auto lhs_random_column(cudf::size_type size)
+{
+  return BinaryOperationTest::make_random_wrapped_column<T>(size);
+}
+
+template <>
+auto lhs_random_column<std::string>(cudf::size_type size)
+{
+  return cudf::test::strings_column_wrapper({"eee", "bb", "<null>", "", "aa", "bbb", "ééé"},
+                                            {1, 1, 0, 1, 1, 1, 1});
+}
+template <typename T>
+auto rhs_random_column(cudf::size_type size)
+{
+  return BinaryOperationTest::make_random_wrapped_column<T>(size);
+}
+template <>
+auto rhs_random_column<std::string>(cudf::size_type size)
+{
+  return cudf::test::strings_column_wrapper({"ééé", "bbb", "aa", "", "<null>", "bb", "eee"},
+                                            {1, 1, 1, 1, 0, 1, 1});
+}
+
+// combinations to test
+//     n  t   d
+// n n.n n.t n.d
+// t t.n t.t t.d
+// d d.n d.t d.d
+
+constexpr cudf::size_type col_size = 10000;
+template <typename T>
+struct BinaryOperationCompiledTest : public BinaryOperationTest {
+  using TypeOut = cudf::test::GetType<T, 0>;
+  using TypeLhs = cudf::test::GetType<T, 1>;
+  using TypeRhs = cudf::test::GetType<T, 2>;
+
+  template <template <typename... Ty> class FunctorOP>
+  void test(cudf::binary_operator op)
+  {
+    using OPERATOR = FunctorOP<TypeOut, TypeLhs, TypeRhs>;
+
+    auto lhs = lhs_random_column<TypeLhs>(col_size);
+    auto rhs = rhs_random_column<TypeRhs>(col_size);
+
+    auto out = cudf::binary_operation(lhs, rhs, op, cudf::data_type(cudf::type_to_id<TypeOut>()));
+    ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, OPERATOR());
+
+    auto s_lhs = this->template make_random_wrapped_scalar<TypeLhs>();
+    auto s_rhs = this->template make_random_wrapped_scalar<TypeRhs>();
+    s_lhs.set_valid_async(true);
+    s_rhs.set_valid_async(true);
+
+    out = cudf::binary_operation(lhs, s_rhs, op, cudf::data_type(cudf::type_to_id<TypeOut>()));
+    ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, s_rhs, OPERATOR());
+    out = cudf::binary_operation(s_lhs, rhs, op, cudf::data_type(cudf::type_to_id<TypeOut>()));
+    ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, s_lhs, rhs, OPERATOR());
+
+    s_lhs.set_valid_async(false);
+    s_rhs.set_valid_async(false);
+    out = cudf::binary_operation(lhs, s_rhs, op, cudf::data_type(cudf::type_to_id<TypeOut>()));
+    ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, s_rhs, OPERATOR());
+    out = cudf::binary_operation(s_lhs, rhs, op, cudf::data_type(cudf::type_to_id<TypeOut>()));
+    ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, s_lhs, rhs, OPERATOR());
+  }
+};
+
+// ADD
+//     n      t     d
+// n n + n
+// t      	     	t + d
+// d      	d + t	d + d
+
+using namespace numeric;
+
+using Add_types =
+  cudf::test::Types<cudf::test::Types<bool, bool, float>,
+                    cudf::test::Types<int16_t, double, uint8_t>,
+                    cudf::test::Types<cudf::timestamp_s, cudf::timestamp_s, cudf::duration_s>,
+                    cudf::test::Types<cudf::timestamp_ns, cudf::duration_ms, cudf::timestamp_us>,
+                    cudf::test::Types<cudf::duration_us, cudf::duration_us, cudf::duration_D>,
+                    // cudf::test::Types<duration_s, int16_t, int64_t>, //valid
+                    cudf::test::Types<decimal32, decimal32, decimal32>,
+                    cudf::test::Types<decimal64, decimal64, decimal64>,
+                    cudf::test::Types<decimal128, decimal128, decimal128>,
+                    cudf::test::Types<int, decimal32, decimal32>,
+                    cudf::test::Types<int, decimal64, decimal64>,
+                    cudf::test::Types<int, decimal128, decimal128>,
+                    // Extras
+                    cudf::test::Types<cudf::duration_D, cudf::duration_D, cudf::duration_D>,
+                    cudf::test::Types<cudf::timestamp_D, cudf::timestamp_D, cudf::duration_D>,
+                    cudf::test::Types<cudf::timestamp_s, cudf::timestamp_D, cudf::duration_s>,
+                    cudf::test::Types<cudf::timestamp_ms, cudf::timestamp_ms, cudf::duration_s>,
+                    cudf::test::Types<cudf::timestamp_ns, cudf::timestamp_ms, cudf::duration_ns>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_Add : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Add, Add_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Add, Vector_Vector)
+{
+  this->template test<cudf::library::operation::Add>(cudf::binary_operator::ADD);
+}
+
+// SUB
+//     n      t     d
+// n n - n
+// t      	t - t	t - d
+// d      	     	d - d
+
+using Sub_types = cudf::test::Types<
+  cudf::test::Types<int32_t, bool, float>,                                    // n - n
+  cudf::test::Types<cudf::duration_D, cudf::timestamp_D, cudf::timestamp_D>,  // t - t
+  cudf::test::Types<cudf::timestamp_s, cudf::timestamp_D, cudf::duration_s>,  // t - d
+  cudf::test::Types<cudf::duration_ns, cudf::duration_us, cudf::duration_s>,  // d - d
+  cudf::test::Types<cudf::duration_us, cudf::duration_us, cudf::duration_s>,  // d - d
+  cudf::test::Types<decimal32, decimal32, decimal32>,
+  cudf::test::Types<decimal64, decimal64, decimal64>,
+  cudf::test::Types<decimal128, decimal128, decimal128>,
+  cudf::test::Types<int, decimal32, decimal32>,
+  cudf::test::Types<int, decimal64, decimal64>,
+  cudf::test::Types<int, decimal128, decimal128>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_Sub : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Sub, Sub_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Sub, Vector_Vector)
+{
+  this->template test<cudf::library::operation::Sub>(cudf::binary_operator::SUB);
+}
+
+// MUL
+//     n      t     d
+// n n * n	     	n * d
+// t
+// d d * n
+using Mul_types =
+  cudf::test::Types<cudf::test::Types<int32_t, u_int64_t, float>,
+                    cudf::test::Types<cudf::duration_s, u_int64_t, cudf::duration_s>,
+                    cudf::test::Types<cudf::duration_ms, cudf::duration_D, int16_t>,
+                    cudf::test::Types<cudf::duration_ns, cudf::duration_us, uint8_t>,
+                    cudf::test::Types<decimal32, decimal32, decimal32>,
+                    cudf::test::Types<decimal64, decimal64, decimal64>,
+                    cudf::test::Types<decimal128, decimal128, decimal128>,
+                    cudf::test::Types<int, decimal32, decimal32>,
+                    cudf::test::Types<int, decimal64, decimal64>,
+                    cudf::test::Types<int, decimal128, decimal128>,
+                    cudf::test::Types<decimal32, int, int>,
+                    cudf::test::Types<decimal64, int, int>,
+                    cudf::test::Types<decimal128, int, int>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_Mul : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Mul, Mul_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Mul, Vector_Vector)
+{
+  this->template test<cudf::library::operation::Mul>(cudf::binary_operator::MUL);
+}
+
+// DIV
+//     n      t     d
+// n n / n
+// t
+// d d / n	     	d / d
+using Div_types =
+  cudf::test::Types<cudf::test::Types<int16_t, u_int64_t, u_int64_t>,
+                    cudf::test::Types<double, int8_t, int64_t>,
+                    cudf::test::Types<cudf::duration_ms, cudf::duration_s, u_int32_t>,
+                    cudf::test::Types<cudf::duration_ns, cudf::duration_D, int16_t>,
+                    cudf::test::Types<double, cudf::duration_D, cudf::duration_ns>,
+                    cudf::test::Types<float, cudf::duration_ms, cudf::duration_ns>,
+                    cudf::test::Types<u_int64_t, cudf::duration_us, cudf::duration_ns>,
+                    cudf::test::Types<decimal32, decimal32, decimal32>,
+                    cudf::test::Types<decimal64, decimal64, decimal64>,
+                    cudf::test::Types<decimal128, decimal128, decimal128>,
+                    cudf::test::Types<int, decimal32, decimal32>,
+                    cudf::test::Types<int, decimal64, decimal64>,
+                    cudf::test::Types<int, decimal128, decimal128>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_Div : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Div, Div_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Div, Vector_Vector)
+{
+  this->template test<cudf::library::operation::Div>(cudf::binary_operator::DIV);
+}
+
+// TRUE-DIV
+//     n      t     d
+// n n / n
+// t
+// d
+using TrueDiv_types = cudf::test::Types<cudf::test::Types<int16_t, u_int64_t, u_int64_t>,
+                                        cudf::test::Types<double, int8_t, int64_t>,
+                                        cudf::test::Types<int8_t, bool, u_int32_t>,
+                                        cudf::test::Types<u_int64_t, float, int16_t>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_TrueDiv : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_TrueDiv, TrueDiv_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_TrueDiv, Vector_Vector)
+{
+  this->template test<cudf::library::operation::TrueDiv>(cudf::binary_operator::TRUE_DIV);
+}
+// FLOOR_DIV
+//     n      t     d
+// n n / n
+// t
+// d
+TYPED_TEST(BinaryOperationCompiledTest_TrueDiv, FloorDiv_Vector_Vector)
+{
+  this->template test<cudf::library::operation::FloorDiv>(cudf::binary_operator::FLOOR_DIV);
+}
+
+// MOD
+//     n      t     d
+// n n % n
+// t
+// d d % n	     	d % d
+using Mod_types =
+  cudf::test::Types<cudf::test::Types<int16_t, u_int64_t, u_int64_t>,
+                    cudf::test::Types<double, int8_t, int64_t>,
+                    cudf::test::Types<cudf::duration_ms, cudf::duration_s, u_int32_t>,
+                    cudf::test::Types<cudf::duration_D, cudf::duration_D, int16_t>,
+                    cudf::test::Types<cudf::duration_ns, cudf::duration_D, int16_t>,
+                    cudf::test::Types<cudf::duration_ns, cudf::duration_us, cudf::duration_ns>>;
+template <typename T>
+struct BinaryOperationCompiledTest_Mod : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Mod, Mod_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Mod, Vector_Vector)
+{
+  this->template test<cudf::library::operation::Mod>(cudf::binary_operator::MOD);
+}
+
+// PYMOD
+//     n      t     d
+// n n % n
+// t
+// d      	     	d % d
+using PyMod_types =
+  cudf::test::Types<cudf::test::Types<int16_t, u_int64_t, u_int64_t>,
+                    cudf::test::Types<double, int8_t, int64_t>,
+                    cudf::test::Types<double, double, double>,
+                    cudf::test::Types<cudf::duration_ns, cudf::duration_us, cudf::duration_ns>>;
+template <typename T>
+struct BinaryOperationCompiledTest_PyMod : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_PyMod, PyMod_types);
+TYPED_TEST(BinaryOperationCompiledTest_PyMod, Vector_Vector)
+{
+  this->template test<cudf::library::operation::PyMod>(cudf::binary_operator::PYMOD);
+}
+
+// POW
+//     n      t     d
+// n n ^ n
+// t
+// d
+
+using Pow_types = cudf::test::Types<cudf::test::Types<double, int64_t, int64_t>,
+                                    cudf::test::Types<float, float, float>,
+                                    cudf::test::Types<int, int32_t, float>,
+                                    cudf::test::Types<float, int, int>,
+                                    cudf::test::Types<double, int64_t, int32_t>,
+                                    cudf::test::Types<double, double, double>,
+                                    cudf::test::Types<double, float, double>,
+                                    cudf::test::Types<double, int32_t, int64_t>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_FloatOps : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_FloatOps, Pow_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_FloatOps, Pow_Vector_Vector)
+{
+  using TypeOut = typename TestFixture::TypeOut;
+  using TypeLhs = typename TestFixture::TypeLhs;
+  using TypeRhs = typename TestFixture::TypeRhs;
+
+  using POW = cudf::library::operation::Pow<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs = []() {
+    // resulting value can not be represented by the target type => behavior is undefined
+    // -2147483648 in host, 2147483647 in device
+    if constexpr (std::is_same_v<TypeOut, int>) {
+      auto elements =
+        cudf::detail::make_counting_transform_iterator(1, [](auto i) { return i % 5; });
+      return cudf::test::fixed_width_column_wrapper<TypeLhs>(elements, elements + 100);
+    }
+    return lhs_random_column<TypeLhs>(100);
+  }();
+  auto rhs = rhs_random_column<TypeRhs>(100);
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::POW, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, POW(), NearEqualComparator<TypeOut>{2});
+}
+
+// LOG_BASE
+//     n      t     d
+// n log(n, n)
+// t
+// d
+TYPED_TEST(BinaryOperationCompiledTest_FloatOps, LogBase_Vector_Vector)
+{
+  using TypeOut = typename TestFixture::TypeOut;
+  using TypeLhs = typename TestFixture::TypeLhs;
+  using TypeRhs = typename TestFixture::TypeRhs;
+
+  using LOG_BASE = cudf::library::operation::LogBase<TypeOut, TypeLhs, TypeRhs>;
+
+  // Make sure there are no zeros
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    1, [](auto i) { return sizeof(TypeLhs) > 4 ? std::pow(2, i) : i + 30; });
+  cudf::test::fixed_width_column_wrapper<TypeLhs> lhs(elements, elements + 50);
+
+  // Find log to the base 7
+  auto rhs_elements = cudf::detail::make_counting_transform_iterator(0, [](auto) { return 7; });
+  cudf::test::fixed_width_column_wrapper<TypeRhs> rhs(rhs_elements, rhs_elements + 50);
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::LOG_BASE, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, LOG_BASE());
+}
+
+// ATAN2
+//     n      t     d
+// n ATan2(n, n)
+// t
+// d
+TYPED_TEST(BinaryOperationCompiledTest_FloatOps, ATan2_Vector_Vector)
+{
+  using TypeOut = typename TestFixture::TypeOut;
+  using TypeLhs = typename TestFixture::TypeLhs;
+  using TypeRhs = typename TestFixture::TypeRhs;
+
+  using ATAN2 = cudf::library::operation::ATan2<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs = lhs_random_column<TypeLhs>(col_size);
+  auto rhs = rhs_random_column<TypeRhs>(col_size);
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ATAN2, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, ATAN2(), NearEqualComparator<TypeOut>{2});
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_FloatOps, PMod_Vector_Vector)
+{
+  this->template test<cudf::library::operation::PMod>(cudf::binary_operator::PMOD);
+}
+
+using IntPow_types = cudf::test::Types<cudf::test::Types<int32_t, int32_t, int32_t>,
+                                       cudf::test::Types<int64_t, int64_t, int64_t>>;
+template <typename T>
+struct BinaryOperationCompiledTest_IntPow : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_IntPow, IntPow_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_IntPow, IntPow_SpecialCases)
+{
+  // This tests special values for which integer powers are required. Casting
+  // to double and casting the result back to int results in floating point
+  // losses, like 3**1 == 2.
+  using TypeOut = typename TestFixture::TypeOut;
+  using TypeLhs = typename TestFixture::TypeLhs;
+  using TypeRhs = typename TestFixture::TypeRhs;
+
+  auto lhs      = cudf::test::fixed_width_column_wrapper<TypeLhs>({3, -3, 8, -8});
+  auto rhs      = cudf::test::fixed_width_column_wrapper<TypeRhs>({1, 1, 7, 7});
+  auto expected = cudf::test::fixed_width_column_wrapper<TypeOut>({3, -3, 2097152, -2097152});
+
+  auto result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::INT_POW, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST(BinaryOperationCompiledTestFloorDivInt64, FloorDivInt64Positive)
+{
+  // This tests special values for which integer floor division is
+  // incorrect if round-tripped through casting to double precision.
+  // Double precision floating point does not have enough resolution
+  // to represent these integers distinctly, so if we were to cast to
+  // double, we would get three identical results (all wrong!).
+  auto lhs =
+    cudf::test::fixed_width_column_wrapper<int64_t>({std::numeric_limits<int64_t>::max(),
+                                                     std::numeric_limits<int64_t>::max() - 10,
+                                                     std::numeric_limits<int64_t>::max() - 100});
+  auto rhs      = cudf::test::fixed_width_column_wrapper<int64_t>({10, 10, 10});
+  auto expected = cudf::test::fixed_width_column_wrapper<int64_t>(
+    {std::numeric_limits<int64_t>::max() / 10,
+     (std::numeric_limits<int64_t>::max() - 10) / 10,
+     (std::numeric_limits<int64_t>::max() - 100) / 10});
+
+  auto result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::FLOOR_DIV, cudf::data_type(cudf::type_to_id<int64_t>()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST(BinaryOperationCompiledTestFloorDivInt64, FloorDivInt64RoundNegativeInf)
+{
+  // Floor division should round towards negative infinity. Which is
+  // distinct from default integral division in C++ which rounds
+  // towards zero (truncation)
+  auto lhs =
+    cudf::test::fixed_width_column_wrapper<int64_t>({std::numeric_limits<int64_t>::min(),
+                                                     std::numeric_limits<int64_t>::min() + 10,
+                                                     std::numeric_limits<int64_t>::min() + 100});
+  auto rhs = cudf::test::fixed_width_column_wrapper<int64_t>({10, 10, 10});
+  // int64_t::min() is not divisible by 10, so there is a non-zero
+  // remainder which should be rounded down.
+  auto expected = cudf::test::fixed_width_column_wrapper<int64_t>(
+    {std::numeric_limits<int64_t>::min() / 10 - 1,
+     (std::numeric_limits<int64_t>::min() + 10) / 10 - 1,
+     (std::numeric_limits<int64_t>::min() + 100) / 10 - 1});
+
+  auto result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::FLOOR_DIV, cudf::data_type(cudf::type_to_id<int64_t>()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+// Bit Operations
+//     n      t     d
+// n n . n
+// t
+// d
+
+// i.i, i.u, u.i, u.u -> i
+// i.i, i.u, u.i, u.u -> u
+using Bit_types = cudf::test::Types<cudf::test::Types<int16_t, int8_t, int16_t>,
+                                    cudf::test::Types<int64_t, int32_t, uint16_t>,
+                                    cudf::test::Types<int64_t, uint64_t, int64_t>,
+                                    cudf::test::Types<int16_t, uint32_t, uint8_t>,
+                                    // cudf::test::Types<bool, int8_t, uint8_t>, // valid
+                                    cudf::test::Types<uint16_t, int8_t, int16_t>,
+                                    cudf::test::Types<uint64_t, int32_t, uint16_t>,
+                                    cudf::test::Types<uint64_t, uint64_t, int64_t>,
+                                    cudf::test::Types<uint16_t, uint8_t, uint32_t>>;
+template <typename T>
+struct BinaryOperationCompiledTest_Bit : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Bit, Bit_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Bit, BitwiseAnd_Vector_Vector)
+{
+  this->template test<cudf::library::operation::BitwiseAnd>(cudf::binary_operator::BITWISE_AND);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Bit, BitwiseOr_Vector_Vector)
+{
+  this->template test<cudf::library::operation::BitwiseOr>(cudf::binary_operator::BITWISE_OR);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Bit, BitwiseXor_Vector_Vector)
+{
+  this->template test<cudf::library::operation::BitwiseXor>(cudf::binary_operator::BITWISE_XOR);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Bit, ShiftLeft_Vector_Vector)
+{
+  this->template test<cudf::library::operation::ShiftLeft>(cudf::binary_operator::SHIFT_LEFT);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Bit, ShiftRight_Vector_Vector)
+{
+  this->template test<cudf::library::operation::ShiftRight>(cudf::binary_operator::SHIFT_RIGHT);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Bit, ShiftRightUnsigned_Vector_Vector)
+{
+  this->template test<cudf::library::operation::ShiftRightUnsigned>(
+    cudf::binary_operator::SHIFT_RIGHT_UNSIGNED);
+}
+
+// Logical Operations
+//     n      t     d
+// n n . n
+// t
+// d
+using Logical_types = cudf::test::Types<cudf::test::Types<bool, int8_t, int16_t>,
+                                        cudf::test::Types<bool, int32_t, uint16_t>,
+                                        cudf::test::Types<bool, uint64_t, double>,
+                                        cudf::test::Types<bool, int8_t, int16_t>,
+                                        cudf::test::Types<bool, float, uint16_t>,
+                                        cudf::test::Types<bool, uint64_t, int64_t>,
+                                        cudf::test::Types<bool, uint8_t, uint32_t>,
+                                        cudf::test::Types<bool, uint64_t, int64_t>>;
+template <typename T>
+struct BinaryOperationCompiledTest_Logical : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Logical, Logical_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Logical, LogicalAnd_Vector_Vector)
+{
+  this->template test<cudf::library::operation::LogicalAnd>(cudf::binary_operator::LOGICAL_AND);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Logical, LogicalOr_Vector_Vector)
+{
+  this->template test<cudf::library::operation::LogicalOr>(cudf::binary_operator::LOGICAL_OR);
+}
+
+template <typename T>
+using column_wrapper = std::conditional_t<std::is_same_v<T, std::string>,
+                                          cudf::test::strings_column_wrapper,
+                                          cudf::test::fixed_width_column_wrapper<T>>;
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs, class OP>
+auto NullOp_Result(cudf::column_view lhs, cudf::column_view rhs)
+{
+  auto [lhs_data, lhs_mask] = cudf::test::to_host<TypeLhs>(lhs);
+  auto [rhs_data, rhs_mask] = cudf::test::to_host<TypeRhs>(rhs);
+  std::vector<TypeOut> result(lhs.size());
+  std::vector<bool> result_mask;
+  std::transform(thrust::make_counting_iterator(0),
+                 thrust::make_counting_iterator(lhs.size()),
+                 result.begin(),
+                 [&lhs_data, &lhs_mask, &rhs_data, &rhs_mask, &result_mask](auto i) -> TypeOut {
+                   auto lhs_valid    = lhs_mask.data() and cudf::bit_is_set(lhs_mask.data(), i);
+                   auto rhs_valid    = rhs_mask.data() and cudf::bit_is_set(rhs_mask.data(), i);
+                   bool output_valid = lhs_valid or rhs_valid;
+                   auto result = OP{}(lhs_data[i], rhs_data[i], lhs_valid, rhs_valid, output_valid);
+                   result_mask.push_back(output_valid);
+                   return result;
+                 });
+  return column_wrapper<TypeOut>(result.cbegin(), result.cend(), result_mask.cbegin());
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Logical, NullLogicalAnd_Vector_Vector)
+{
+  using TypeOut  = bool;
+  using TypeLhs  = typename TestFixture::TypeLhs;
+  using TypeRhs  = typename TestFixture::TypeRhs;
+  using NULL_AND = cudf::library::operation::NullLogicalAnd<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_AND>(lhs, rhs);
+
+  auto const result = cudf::binary_operation(lhs,
+                                             rhs,
+                                             cudf::binary_operator::NULL_LOGICAL_AND,
+                                             cudf::data_type(cudf::type_to_id<TypeOut>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Logical, NullLogicalOr_Vector_Vector)
+{
+  using TypeOut = bool;
+  using TypeLhs = typename TestFixture::TypeLhs;
+  using TypeRhs = typename TestFixture::TypeRhs;
+  using NULL_OR = cudf::library::operation::NullLogicalOr<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_OR>(lhs, rhs);
+
+  auto const result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::NULL_LOGICAL_OR, cudf::data_type(cudf::type_to_id<TypeOut>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+// Comparison Operations ==, !=, <, >, <=, >=
+// n<!=>n, t<!=>t, d<!=>d, s<!=>s, dc<!=>dc
+using Comparison_types =
+  cudf::test::Types<cudf::test::Types<bool, int8_t, int16_t>,
+                    cudf::test::Types<bool, uint32_t, uint16_t>,
+                    cudf::test::Types<bool, uint64_t, double>,
+                    cudf::test::Types<bool, cudf::timestamp_D, cudf::timestamp_s>,
+                    cudf::test::Types<bool, cudf::timestamp_ns, cudf::timestamp_us>,
+                    cudf::test::Types<bool, cudf::duration_ns, cudf::duration_ns>,
+                    cudf::test::Types<bool, cudf::duration_us, cudf::duration_s>,
+                    cudf::test::Types<bool, std::string, std::string>,
+                    cudf::test::Types<bool, decimal32, decimal32>,
+                    cudf::test::Types<bool, decimal64, decimal64>,
+                    cudf::test::Types<bool, decimal128, decimal128>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_Comparison : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_Comparison, Comparison_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_Comparison, Equal_Vector_Vector)
+{
+  this->template test<cudf::library::operation::Equal>(cudf::binary_operator::EQUAL);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Comparison, NotEqual_Vector_Vector)
+{
+  this->template test<cudf::library::operation::NotEqual>(cudf::binary_operator::NOT_EQUAL);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Comparison, Less_Vector_Vector)
+{
+  this->template test<cudf::library::operation::Less>(cudf::binary_operator::LESS);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Comparison, Greater_Vector_Vector)
+{
+  this->template test<cudf::library::operation::Greater>(cudf::binary_operator::GREATER);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Comparison, LessEqual_Vector_Vector)
+{
+  this->template test<cudf::library::operation::LessEqual>(cudf::binary_operator::LESS_EQUAL);
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_Comparison, GreaterEqual_Vector_Vector)
+{
+  this->template test<cudf::library::operation::GreaterEqual>(cudf::binary_operator::GREATER_EQUAL);
+}
+
+// Null Operations NullMax, NullMin
+// Min(n,n) , Min(t,t), Min(d, d), Min(s, s), Min(dc, dc), Min(n,dc), Min(dc, n)
+//    n   t   d  s  dc
+// n  .             .
+// t      .
+// d          .
+// s             .
+// dc .             .
+using Null_types =
+  cudf::test::Types<cudf::test::Types<int16_t, int8_t, int16_t>,
+                    cudf::test::Types<uint16_t, uint32_t, uint16_t>,
+                    cudf::test::Types<double, uint64_t, double>,
+                    cudf::test::Types<cudf::timestamp_s, cudf::timestamp_D, cudf::timestamp_s>,
+                    cudf::test::Types<cudf::duration_ns, cudf::duration_us, cudf::duration_s>,
+                    // cudf::test::Types<std::string, std::string, std::string>, // only fixed-width
+                    cudf::test::Types<decimal32, decimal32, decimal32>,
+                    cudf::test::Types<decimal64, decimal64, decimal64>,
+                    cudf::test::Types<decimal128, decimal128, decimal128>,
+                    cudf::test::Types<decimal32, uint32_t, decimal32>,
+                    cudf::test::Types<decimal64, uint32_t, decimal64>,
+                    cudf::test::Types<decimal128, uint32_t, decimal128>,
+                    cudf::test::Types<int64_t, decimal32, decimal32>,
+                    cudf::test::Types<int64_t, decimal64, decimal64>,
+                    cudf::test::Types<int64_t, decimal128, decimal128>>;
+
+template <typename T>
+struct BinaryOperationCompiledTest_NullOps : public BinaryOperationCompiledTest<T> {};
+TYPED_TEST_SUITE(BinaryOperationCompiledTest_NullOps, Null_types);
+
+TYPED_TEST(BinaryOperationCompiledTest_NullOps, NullEquals_Vector_Vector)
+{
+  using TypeOut     = bool;
+  using TypeLhs     = typename TestFixture::TypeLhs;
+  using TypeRhs     = typename TestFixture::TypeRhs;
+  using NULL_EQUALS = cudf::library::operation::NullEquals<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_EQUALS>(lhs, rhs);
+
+  auto const result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::NULL_EQUALS, cudf::data_type(cudf::type_to_id<TypeOut>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+using BinaryOperationCompiledTest_NullOpsString =
+  BinaryOperationCompiledTest_NullOps<cudf::test::Types<std::string, std::string, std::string>>;
+TEST_F(BinaryOperationCompiledTest_NullOpsString, NullEquals_Vector_Vector)
+{
+  using TypeOut     = bool;
+  using TypeLhs     = std::string;
+  using TypeRhs     = std::string;
+  using NULL_EQUALS = cudf::library::operation::NullEquals<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_EQUALS>(lhs, rhs);
+
+  auto const result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::NULL_EQUALS, cudf::data_type(cudf::type_to_id<TypeOut>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_NullOps, NullMax_Vector_Vector)
+{
+  using TypeOut  = typename TestFixture::TypeOut;
+  using TypeLhs  = typename TestFixture::TypeLhs;
+  using TypeRhs  = typename TestFixture::TypeRhs;
+  using NULL_MAX = cudf::library::operation::NullMax<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_MAX>(lhs, rhs);
+
+  auto const result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::NULL_MAX, cudf::data_type(cudf::type_to_id<TypeOut>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(BinaryOperationCompiledTest_NullOps, NullMin_Vector_Vector)
+{
+  using TypeOut  = typename TestFixture::TypeOut;
+  using TypeLhs  = typename TestFixture::TypeLhs;
+  using TypeRhs  = typename TestFixture::TypeRhs;
+  using NULL_MIN = cudf::library::operation::NullMin<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_MIN>(lhs, rhs);
+
+  auto const result = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::NULL_MIN, cudf::data_type(cudf::type_to_id<TypeOut>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(BinaryOperationCompiledTest_NullOpsString, NullMax_Vector_Vector)
+{
+  using TypeOut  = std::string;
+  using TypeLhs  = std::string;
+  using TypeRhs  = std::string;
+  using NULL_MAX = cudf::library::operation::NullMax<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_MAX>(lhs, rhs);
+
+  auto const result =
+    cudf::binary_operation(lhs,
+                           rhs,
+                           cudf::binary_operator::NULL_MAX,
+                           cudf::data_type(cudf::type_to_id<cudf::string_view>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->view());
+}
+
+TEST_F(BinaryOperationCompiledTest_NullOpsString, NullMin_Vector_Vector)
+{
+  using TypeOut  = std::string;
+  using TypeLhs  = std::string;
+  using TypeRhs  = std::string;
+  using NULL_MIN = cudf::library::operation::NullMin<TypeOut, TypeLhs, TypeRhs>;
+
+  auto lhs            = lhs_random_column<TypeLhs>(col_size);
+  auto rhs            = rhs_random_column<TypeRhs>(col_size);
+  auto const expected = NullOp_Result<TypeOut, TypeLhs, TypeRhs, NULL_MIN>(lhs, rhs);
+
+  auto const result =
+    cudf::binary_operation(lhs,
+                           rhs,
+                           cudf::binary_operator::NULL_MIN,
+                           cudf::data_type(cudf::type_to_id<cudf::string_view>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/binaryop/binop-fixture.hpp b/cpp/tests/binaryop/binop-fixture.hpp
new file mode 100644
index 0000000..bc3820d
--- /dev/null
+++ b/cpp/tests/binaryop/binop-fixture.hpp
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/detail/iterator.cuh>
+
+#include <string>
+#include <type_traits>
+
+struct BinaryOperationTest : public cudf::test::BaseFixture {
+  BinaryOperationTest() {}
+
+  static constexpr int r_min = 1;
+  static constexpr int r_max = 10;
+
+  template <typename T>
+  static auto make_data_iter(cudf::test::UniformRandomGenerator<T>& rand_gen)
+  {
+    return cudf::detail::make_counting_transform_iterator(
+      0, [&](auto row) { return rand_gen.generate(); });
+  }
+
+  static auto make_validity_iter()
+  {
+    cudf::test::UniformRandomGenerator<uint8_t> rand_gen(r_min, r_max);
+    uint8_t mod_base = rand_gen.generate();
+    return cudf::detail::make_counting_transform_iterator(
+      0, [mod_base](auto row) { return (row % mod_base) > 0; });
+  }
+
+  template <typename T>
+  static auto make_random_wrapped_column(cudf::size_type size)
+  {
+    cudf::test::UniformRandomGenerator<T> rand_gen(r_min, r_max);
+    auto data_iter     = make_data_iter(rand_gen);
+    auto validity_iter = make_validity_iter();
+
+    return cudf::test::fixed_width_column_wrapper<T>(data_iter, data_iter + size, validity_iter);
+  }
+
+  template <typename T, std::enable_if_t<!std::is_same_v<T, std::string>>* = nullptr>
+  auto make_random_wrapped_scalar()
+  {
+    cudf::test::UniformRandomGenerator<T> rand_gen(r_min, r_max);
+    return cudf::scalar_type_t<T>(rand_gen.generate());
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, std::string>>* = nullptr>
+  auto make_random_wrapped_scalar()
+  {
+    cudf::test::UniformRandomGenerator<uint8_t> rand_gen(r_min, r_max);
+    uint8_t size = rand_gen.generate();
+    std::string str{"ஔⒶbc⁂∰ൠ \tنж水✉♪✿™"};
+    return cudf::scalar_type_t<T>(str.substr(0, size));
+  }
+};
diff --git a/cpp/tests/binaryop/binop-generic-ptx-test.cpp b/cpp/tests/binaryop/binop-generic-ptx-test.cpp
new file mode 100644
index 0000000..03cc87a
--- /dev/null
+++ b/cpp/tests/binaryop/binop-generic-ptx-test.cpp
@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/binaryop/assert-binops.h>
+#include <tests/binaryop/binop-fixture.hpp>
+#include <tests/binaryop/util/operation.h>
+#include <tests/binaryop/util/runtime_support.h>
+
+#include <cudf/binaryop.hpp>
+
+struct BinaryOperationGenericPTXTest : public BinaryOperationTest {
+ protected:
+  void SetUp() override
+  {
+    if (!can_do_runtime_jit()) { GTEST_SKIP() << "Skipping tests that require 11.5 runtime"; }
+  }
+};
+
+TEST_F(BinaryOperationGenericPTXTest, CAdd_Vector_Vector_FP32_FP32_FP32)
+{
+  // c = a*a*a + b
+  char const* ptx =
+    R"***(
+//
+// Generated by NVIDIA NVVM Compiler
+//
+// Compiler Build ID: CL-26218862
+// Cuda compilation tools, release 10.1, V10.1.168
+// Based on LLVM 3.4svn
+//
+
+.version 6.4
+.target sm_70
+.address_size 64
+
+	// .globl	_ZN8__main__7add$241Eff
+.common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241Eff;
+.common .global .align 8 .u64 _ZN08NumbaEnv5numba7targets7numbers13int_power$242Efx;
+
+.visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241Eff(
+	.param .b64 _ZN8__main__7add$241Eff_param_0,
+	.param .b32 _ZN8__main__7add$241Eff_param_1,
+	.param .b32 _ZN8__main__7add$241Eff_param_2
+)
+{
+	.reg .f32 	%f<5>;
+	.reg .b32 	%r<2>;
+	.reg .b64 	%rd<2>;
+
+
+	ld.param.u64 	%rd1, [_ZN8__main__7add$241Eff_param_0];
+	ld.param.f32 	%f1, [_ZN8__main__7add$241Eff_param_1];
+	ld.param.f32 	%f2, [_ZN8__main__7add$241Eff_param_2];
+	mul.f32 	%f3, %f1, %f1;
+	fma.rn.f32 	%f4, %f3, %f1, %f2;
+	st.f32 	[%rd1], %f4;
+	mov.u32 	%r1, 0;
+	st.param.b32	[func_retval0+0], %r1;
+	ret;
+}
+)***";
+
+  using TypeOut = float;
+  using TypeLhs = float;
+  using TypeRhs = float;
+
+  auto CADD = [](TypeLhs a, TypeRhs b) { return a * a * a + b; };
+
+  auto lhs = make_random_wrapped_column<TypeLhs>(500);
+  auto rhs = make_random_wrapped_column<TypeRhs>(500);
+
+  auto out = cudf::binary_operation(lhs, rhs, ptx, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  // pow has a max ULP error of 2 per CUDA programming guide
+  ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, CADD, NearEqualComparator<TypeOut>{2});
+}
+
+TEST_F(BinaryOperationGenericPTXTest, CAdd_Vector_Vector_INT64_INT32_INT32)
+{
+  // c = a*a*a + b
+  char const* ptx =
+    R"***(
+//
+// Generated by NVIDIA NVVM Compiler
+//
+// Compiler Build ID: CL-26218862
+// Cuda compilation tools, release 10.1, V10.1.168
+// Based on LLVM 3.4svn
+//
+
+.version 6.4
+.target sm_70
+.address_size 64
+
+	// .globl	_ZN8__main__7add$241Eii
+.common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241Eii;
+.common .global .align 8 .u64 _ZN08NumbaEnv5numba7targets7numbers14int_power_impl12$3clocals$3e13int_power$242Exx;
+
+.visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241Eii(
+	.param .b64 _ZN8__main__7add$241Eii_param_0,
+	.param .b32 _ZN8__main__7add$241Eii_param_1,
+	.param .b32 _ZN8__main__7add$241Eii_param_2
+)
+{
+	.reg .b32 	%r<3>;
+	.reg .b64 	%rd<7>;
+
+
+	ld.param.u64 	%rd1, [_ZN8__main__7add$241Eii_param_0];
+	ld.param.u32 	%r1, [_ZN8__main__7add$241Eii_param_1];
+	cvt.s64.s32	%rd2, %r1;
+	mul.wide.s32 	%rd3, %r1, %r1;
+	mul.lo.s64 	%rd4, %rd3, %rd2;
+	ld.param.s32 	%rd5, [_ZN8__main__7add$241Eii_param_2];
+	add.s64 	%rd6, %rd4, %rd5;
+	st.u64 	[%rd1], %rd6;
+	mov.u32 	%r2, 0;
+	st.param.b32	[func_retval0+0], %r2;
+	ret;
+}
+)***";
+
+  using TypeOut = int64_t;
+  using TypeLhs = int32_t;
+  using TypeRhs = int32_t;
+
+  auto CADD = [](TypeLhs a, TypeRhs b) { return a * a * a + b; };
+
+  auto lhs = make_random_wrapped_column<TypeLhs>(500);
+  auto rhs = make_random_wrapped_column<TypeRhs>(500);
+
+  auto out = cudf::binary_operation(lhs, rhs, ptx, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, CADD);
+}
+
+TEST_F(BinaryOperationGenericPTXTest, CAdd_Vector_Vector_INT64_INT32_INT64)
+{
+  // c = a*a*a + b*b
+  char const* ptx =
+    R"***(
+//
+// Generated by NVIDIA NVVM Compiler
+//
+// Compiler Build ID: CL-24817639
+// Cuda compilation tools, release 10.0, V10.0.130
+// Based on LLVM 3.4svn
+//
+
+.version 6.3
+.target sm_70
+.address_size 64
+
+	// .globl	_ZN8__main__7add$241Eix
+.common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241Eix;
+.common .global .align 8 .u64 _ZN08NumbaEnv5numba7targets7numbers14int_power_impl12$3clocals$3e13int_power$242Exx;
+
+.visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241Eix(
+	.param .b64 _ZN8__main__7add$241Eix_param_0,
+	.param .b32 _ZN8__main__7add$241Eix_param_1,
+	.param .b64 _ZN8__main__7add$241Eix_param_2
+)
+{
+	.reg .b32 	%r<3>;
+	.reg .b64 	%rd<8>;
+
+
+	ld.param.u64 	%rd1, [_ZN8__main__7add$241Eix_param_0];
+	ld.param.u32 	%r1, [_ZN8__main__7add$241Eix_param_1];
+	ld.param.u64 	%rd2, [_ZN8__main__7add$241Eix_param_2];
+	cvt.s64.s32	%rd3, %r1;
+	mul.wide.s32 	%rd4, %r1, %r1;
+	mul.lo.s64 	%rd5, %rd4, %rd3;
+	mul.lo.s64 	%rd6, %rd2, %rd2;
+	add.s64 	%rd7, %rd6, %rd5;
+	st.u64 	[%rd1], %rd7;
+	mov.u32 	%r2, 0;
+	st.param.b32	[func_retval0+0], %r2;
+	ret;
+}
+
+)***";
+
+  using TypeOut = int64_t;
+  using TypeLhs = int32_t;
+  using TypeRhs = int64_t;
+
+  auto CADD = [](TypeLhs a, TypeRhs b) { return a * a * a + b * b; };
+
+  auto lhs = make_random_wrapped_column<TypeLhs>(500);
+  auto rhs = make_random_wrapped_column<TypeRhs>(500);
+
+  auto out = cudf::binary_operation(lhs, rhs, ptx, cudf::data_type(cudf::type_to_id<TypeOut>()));
+
+  ASSERT_BINOP<TypeOut, TypeLhs, TypeRhs>(*out, lhs, rhs, CADD);
+}
diff --git a/cpp/tests/binaryop/binop-null-test.cpp b/cpp/tests/binaryop/binop-null-test.cpp
new file mode 100644
index 0000000..641648f
--- /dev/null
+++ b/cpp/tests/binaryop/binop-null-test.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/binaryop/util/runtime_support.h>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/binaryop.hpp>
+
+struct BinaryOperationNullTest : public cudf::test::BaseFixture {
+ protected:
+  void SetUp() override
+  {
+    if (!can_do_runtime_jit()) { GTEST_SKIP() << "Skipping tests that require 11.5 runtime"; }
+  }
+};
+
+TEST_F(BinaryOperationNullTest, Scalar_Null_Vector_Valid)
+{
+  auto lhs = cudf::scalar_type_t<int32_t>(0);
+  lhs.set_valid_async(false);
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                             cudf::test::iterators::no_nulls());
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  auto expected = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, cudf::test::iterators::all_nulls());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected);
+}
+
+TEST_F(BinaryOperationNullTest, Scalar_Valid_Vector_NonNullable)
+{
+  auto lhs = cudf::scalar_type_t<int32_t>(1);
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  auto expected = cudf::test::fixed_width_column_wrapper<int32_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected);
+}
+
+TEST_F(BinaryOperationNullTest, Scalar_Null_Vector_NonNullable)
+{
+  auto lhs = cudf::scalar_type_t<int32_t>(0);
+  lhs.set_valid_async(false);
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  auto expected = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, cudf::test::iterators::all_nulls());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected);
+}
+
+TEST_F(BinaryOperationNullTest, Vector_Null_Scalar_Valid)
+{
+  auto lhs = cudf::scalar_type_t<int32_t>(1);
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+                                                             cudf::test::iterators::all_nulls());
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), rhs);
+}
+
+TEST_F(BinaryOperationNullTest, Vector_Null_Vector_Valid)
+{
+  auto lhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+                                                             cudf::test::iterators::all_nulls());
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                             cudf::test::iterators::no_nulls());
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), lhs);
+}
+
+TEST_F(BinaryOperationNullTest, Vector_Null_Vector_NonNullable)
+{
+  auto lhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+                                                             cudf::test::iterators::all_nulls());
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), lhs);
+}
+
+TEST_F(BinaryOperationNullTest, Vector_Valid_Vector_NonNullable)
+{
+  auto lhs = cudf::test::fixed_width_column_wrapper<int32_t>({9, 8, 7, 6, 5, 4, 3, 2, 1, 0},
+                                                             cudf::test::iterators::no_nulls());
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  auto expected = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {9, 9, 9, 9, 9, 9, 9, 9, 9, 9}, cudf::test::iterators::no_nulls());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected);
+}
+
+TEST_F(BinaryOperationNullTest, Vector_NonNullable_Vector_NonNullable)
+{
+  auto lhs = cudf::test::fixed_width_column_wrapper<int32_t>({9, 8, 7, 6, 5, 4, 3, 2, 1, 0});
+  auto rhs = cudf::test::fixed_width_column_wrapper<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+
+  auto out = cudf::binary_operation(
+    lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT32));
+
+  auto expected = cudf::test::fixed_width_column_wrapper<int32_t>({9, 9, 9, 9, 9, 9, 9, 9, 9, 9});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected);
+}
diff --git a/cpp/tests/binaryop/binop-verify-input-test.cpp b/cpp/tests/binaryop/binop-verify-input-test.cpp
new file mode 100644
index 0000000..1346dcd
--- /dev/null
+++ b/cpp/tests/binaryop/binop-verify-input-test.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/binaryop.hpp>
+
+struct BinopVerifyInputTest : public cudf::test::BaseFixture {};
+
+TEST_F(BinopVerifyInputTest, Vector_Scalar_ErrorOutputVectorType)
+{
+  auto lhs = cudf::scalar_type_t<int64_t>(1);
+  auto rhs = cudf::test::fixed_width_column_wrapper<int64_t>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  EXPECT_THROW(
+    cudf::binary_operation(
+      lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::NUM_TYPE_IDS)),
+    cudf::logic_error);
+}
+
+TEST_F(BinopVerifyInputTest, Vector_Vector_ErrorSecondOperandVectorZeroSize)
+{
+  auto lhs = cudf::test::fixed_width_column_wrapper<int64_t>{1};
+  auto rhs = cudf::test::fixed_width_column_wrapper<int64_t>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  EXPECT_THROW(cudf::binary_operation(
+                 lhs, rhs, cudf::binary_operator::ADD, cudf::data_type(cudf::type_id::INT64)),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/binaryop/util/operation.h b/cpp/tests/binaryop/util/operation.h
new file mode 100644
index 0000000..f14fe85
--- /dev/null
+++ b/cpp/tests/binaryop/util/operation.h
@@ -0,0 +1,447 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cmath>
+#include <cstdint>
+#include <cudf/utilities/traits.hpp>
+#include <type_traits>
+
+namespace cudf {
+namespace library {
+namespace operation {
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Add {
+  // Allow sum between chronos only when both input and output types
+  // are chronos. Unsupported combinations will fail to compile
+  template <typename OutT           = TypeOut,
+            std::enable_if_t<cudf::is_chrono<OutT>() && cudf::is_chrono<TypeLhs>() &&
+                               cudf::is_chrono<TypeRhs>(),
+                             void>* = nullptr>
+  OutT operator()(TypeLhs lhs, TypeRhs rhs) const
+  {
+    return lhs + rhs;
+  }
+
+  template <typename OutT           = TypeOut,
+            std::enable_if_t<!cudf::is_chrono<OutT>() || !cudf::is_chrono<TypeLhs>() ||
+                               !cudf::is_chrono<TypeRhs>(),
+                             void>* = nullptr>
+  OutT operator()(TypeLhs lhs, TypeRhs rhs) const
+  {
+    using TypeCommon = typename std::common_type<OutT, TypeLhs, TypeRhs>::type;
+    return static_cast<OutT>(static_cast<TypeCommon>(lhs) + static_cast<TypeCommon>(rhs));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Sub {
+  // Allow difference between chronos only when both input and output types
+  // are chronos. Unsupported combinations will fail to compile
+  template <typename OutT           = TypeOut,
+            std::enable_if_t<cudf::is_chrono<OutT>() && cudf::is_chrono<TypeLhs>() &&
+                               cudf::is_chrono<TypeRhs>(),
+                             void>* = nullptr>
+  OutT operator()(TypeLhs lhs, TypeRhs rhs) const
+  {
+    return lhs - rhs;
+  }
+
+  template <typename OutT           = TypeOut,
+            std::enable_if_t<!cudf::is_chrono<OutT>() || !cudf::is_chrono<TypeLhs>() ||
+                               !cudf::is_chrono<TypeRhs>(),
+                             void>* = nullptr>
+  OutT operator()(TypeLhs lhs, TypeRhs rhs) const
+  {
+    using TypeCommon = typename std::common_type<OutT, TypeLhs, TypeRhs>::type;
+    return static_cast<OutT>(static_cast<TypeCommon>(lhs) - static_cast<TypeCommon>(rhs));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Mul {
+  template <typename OutT                                              = TypeOut,
+            std::enable_if_t<!cudf::is_duration_t<OutT>::value, void>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) const
+  {
+    using TypeCommon = typename std::common_type<TypeOut, TypeLhs, TypeRhs>::type;
+    return static_cast<TypeOut>(static_cast<TypeCommon>(lhs) * static_cast<TypeCommon>(rhs));
+  }
+
+  template <typename OutT                                             = TypeOut,
+            std::enable_if_t<cudf::is_duration_t<OutT>::value, void>* = nullptr>
+  TypeOut operator()(TypeLhs x, TypeRhs y) const
+  {
+    return DurationProduct<TypeOut>(x, y);
+  }
+
+  template <typename OutT,
+            typename LhsT,
+            typename RhsT,
+            std::enable_if_t<(cudf::is_duration_t<LhsT>::value && std::is_integral_v<RhsT>) ||
+                               (cudf::is_duration_t<RhsT>::value && std::is_integral_v<LhsT>),
+                             void>* = nullptr>
+  OutT DurationProduct(LhsT x, RhsT y) const
+  {
+    return x * y;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Div {
+  template <typename LhsT                                              = TypeLhs,
+            std::enable_if_t<!cudf::is_duration_t<LhsT>::value, void>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    using TypeCommon = typename std::common_type<TypeOut, TypeLhs, TypeRhs>::type;
+    return static_cast<TypeOut>(static_cast<TypeCommon>(lhs) / static_cast<TypeCommon>(rhs));
+  }
+
+  template <typename LhsT                                             = TypeLhs,
+            std::enable_if_t<cudf::is_duration_t<LhsT>::value, void>* = nullptr>
+  TypeOut operator()(TypeLhs x, TypeRhs y) const
+  {
+    return DurationDivide<TypeOut>(x, y);
+  }
+
+  template <
+    typename OutT,
+    typename LhsT,
+    typename RhsT,
+    std::enable_if_t<(std::is_integral_v<RhsT> || cudf::is_duration<RhsT>()), void>* = nullptr>
+  OutT DurationDivide(LhsT x, RhsT y) const
+  {
+    return x / y;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct TrueDiv {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(static_cast<double>(lhs) / static_cast<double>(rhs));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct FloorDiv {
+  template <typename OutT                                                         = TypeOut,
+            typename LhsT                                                         = TypeLhs,
+            typename RhsT                                                         = TypeRhs,
+            std::enable_if_t<(std::is_integral_v<std::common_type_t<LhsT, RhsT>> and
+                              std::is_signed_v<std::common_type_t<LhsT, RhsT>>)>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    if ((lhs ^ rhs) >= 0) {
+      return lhs / rhs;
+    } else {
+      auto const quotient  = lhs / rhs;
+      auto const remainder = lhs % rhs;
+      return quotient - !!remainder;
+    }
+  }
+
+  template <typename OutT                                                          = TypeOut,
+            typename LhsT                                                          = TypeLhs,
+            typename RhsT                                                          = TypeRhs,
+            std::enable_if_t<(std::is_integral_v<std::common_type_t<LhsT, RhsT>> and
+                              !std::is_signed_v<std::common_type_t<LhsT, RhsT>>)>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return lhs / rhs;
+  }
+
+  template <typename OutT                                                              = TypeOut,
+            typename LhsT                                                              = TypeLhs,
+            typename RhsT                                                              = TypeRhs,
+            std::enable_if_t<(std::is_same_v<std::common_type_t<LhsT, RhsT>, float>)>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(std::floor(lhs / rhs));
+  }
+
+  template <typename OutT                                                               = TypeOut,
+            typename LhsT                                                               = TypeLhs,
+            typename RhsT                                                               = TypeRhs,
+            std::enable_if_t<(std::is_same_v<std::common_type_t<LhsT, RhsT>, double>)>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(std::floor(lhs / rhs));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Mod {
+  template <typename OutT = TypeOut,
+            typename LhsT = TypeLhs,
+            typename RhsT = TypeRhs,
+            std::enable_if_t<
+              (std::is_integral_v<typename std::common_type<OutT, LhsT, RhsT>::type>)>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    using TypeCommon = typename std::common_type<TypeOut, TypeLhs, TypeRhs>::type;
+    return static_cast<TypeOut>(static_cast<TypeCommon>(lhs) % static_cast<TypeCommon>(rhs));
+  }
+
+  template <typename OutT                                                                 = TypeOut,
+            typename LhsT                                                                 = TypeLhs,
+            typename RhsT                                                                 = TypeRhs,
+            std::enable_if_t<(
+              std::is_same_v<typename std::common_type<OutT, LhsT, RhsT>::type, float>)>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(fmod(static_cast<float>(lhs), static_cast<float>(rhs)));
+  }
+
+  template <
+    typename OutT = TypeOut,
+    typename LhsT = TypeLhs,
+    typename RhsT = TypeRhs,
+    std::enable_if_t<(std::is_same_v<typename std::common_type<OutT, LhsT, RhsT>::type, double>)>* =
+      nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(fmod(static_cast<double>(lhs), static_cast<double>(rhs)));
+  }
+
+  // Mod with duration types - duration % (integral or a duration) = duration
+  template <typename LhsT                                       = TypeLhs,
+            typename OutT                                       = TypeOut,
+            std::enable_if_t<cudf::is_duration_t<LhsT>::value &&
+                             cudf::is_duration_t<OutT>::value>* = nullptr>
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return lhs % rhs;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Pow {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(pow(static_cast<double>(lhs), static_cast<double>(rhs)));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Equal {
+  TypeOut operator()(TypeLhs x, TypeRhs y) { return (x == y); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct NotEqual {
+  TypeOut operator()(TypeLhs x, TypeRhs y) { return (x != y); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Less {
+  TypeOut operator()(TypeLhs x, TypeRhs y) { return (x < y); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct Greater {
+  TypeOut operator()(TypeLhs x, TypeRhs y) { return (x > y); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct LessEqual {
+  TypeOut operator()(TypeLhs x, TypeRhs y) { return (x <= y); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct GreaterEqual {
+  TypeOut operator()(TypeLhs x, TypeRhs y) { return (x >= y); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct BitwiseAnd {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return (lhs & rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct BitwiseOr {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return (lhs | rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct BitwiseXor {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return (lhs ^ rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct LogicalAnd {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return static_cast<TypeOut>(lhs && rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct LogicalOr {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return static_cast<TypeOut>(lhs || rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct ShiftLeft {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return static_cast<TypeOut>(lhs << rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct ShiftRight {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs) { return static_cast<TypeOut>(lhs >> rhs); }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct ShiftRightUnsigned {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(static_cast<std::make_unsigned_t<TypeLhs>>(lhs) >> rhs);
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct LogBase {
+  TypeOut operator()(TypeLhs lhs, TypeRhs rhs)
+  {
+    return static_cast<TypeOut>(std::log(static_cast<double>(lhs)) /
+                                std::log(static_cast<double>(rhs)));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct PMod {
+  using CommonArgsT = typename std::common_type<TypeLhs, TypeRhs>::type;
+
+  TypeOut operator()(TypeLhs x, TypeRhs y) const
+  {
+    CommonArgsT xconv{static_cast<CommonArgsT>(x)};
+    CommonArgsT yconv{static_cast<CommonArgsT>(y)};
+    auto rem = std::fmod(xconv, yconv);
+    if (rem < 0) rem = std::fmod(rem + yconv, yconv);
+    return static_cast<TypeOut>(rem);
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct ATan2 {
+  TypeOut operator()(TypeLhs x, TypeRhs y) const
+  {
+    return static_cast<TypeOut>(std::atan2(static_cast<double>(x), static_cast<double>(y)));
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct PyMod {
+  TypeOut operator()(TypeLhs x, TypeRhs y) const
+  {
+    if constexpr (std::is_floating_point_v<TypeLhs> or std::is_floating_point_v<TypeRhs>) {
+      double x1 = static_cast<double>(x);
+      double y1 = static_cast<double>(y);
+      return fmod(fmod(x1, y1) + y1, y1);
+    } else {
+      return ((x % y) + y) % y;
+    }
+    return {};
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct NullLogicalAnd {
+  TypeOut operator()(TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) const
+  {
+    if (lhs_valid && !x) {
+      output_valid = true;
+      return false;
+    }
+    if (rhs_valid && !y) {
+      output_valid = true;
+      return false;
+    }
+    if (lhs_valid && rhs_valid) {
+      output_valid = true;
+      return true;
+    }
+    output_valid = false;
+    return false;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct NullLogicalOr {
+  TypeOut operator()(TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) const
+  {
+    if (lhs_valid && x) {
+      output_valid = true;
+      return true;
+    }
+    if (rhs_valid && y) {
+      output_valid = true;
+      return true;
+    }
+    if (lhs_valid && rhs_valid) {
+      output_valid = true;
+      return false;
+    }
+    output_valid = false;
+    return false;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct NullEquals {
+  TypeOut operator()(TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) const
+  {
+    output_valid = true;
+    if (!lhs_valid && !rhs_valid) return true;
+    using common_t = std::common_type_t<TypeLhs, TypeRhs>;
+    if (lhs_valid && rhs_valid) return static_cast<common_t>(x) == static_cast<common_t>(y);
+    return false;
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct NullMax {
+  TypeOut operator()(TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) const
+  {
+    output_valid = lhs_valid or rhs_valid;
+    if (lhs_valid or rhs_valid) {
+      return (lhs_valid and (!rhs_valid or static_cast<TypeOut>(x) > static_cast<TypeOut>(y)))
+               ? static_cast<TypeOut>(x)
+               : static_cast<TypeOut>(y);
+    } else
+      return TypeOut{};
+  }
+};
+
+template <typename TypeOut, typename TypeLhs, typename TypeRhs>
+struct NullMin {
+  TypeOut operator()(TypeLhs x, TypeRhs y, bool lhs_valid, bool rhs_valid, bool& output_valid) const
+  {
+    output_valid = lhs_valid or rhs_valid;
+    if (lhs_valid or rhs_valid) {
+      return (lhs_valid and (!rhs_valid or static_cast<TypeOut>(x) < static_cast<TypeOut>(y)))
+               ? static_cast<TypeOut>(x)
+               : static_cast<TypeOut>(y);
+    } else
+      return TypeOut{};
+  }
+};
+
+}  // namespace operation
+}  // namespace library
+}  // namespace cudf
diff --git a/cpp/tests/binaryop/util/runtime_support.h b/cpp/tests/binaryop/util/runtime_support.h
new file mode 100644
index 0000000..250d34a
--- /dev/null
+++ b/cpp/tests/binaryop/util/runtime_support.h
@@ -0,0 +1,29 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cuda_runtime.h>
+
+inline bool can_do_runtime_jit()
+{
+  // We require a CUDA NVRTC of 11.5+ to do runtime jit
+  // as we need support for __int128
+
+  int runtime      = 0;
+  auto error_value = cudaRuntimeGetVersion(&runtime);
+  return (error_value == cudaSuccess) && (runtime >= 11050);
+}
diff --git a/cpp/tests/bitmask/bitmask_tests.cpp b/cpp/tests/bitmask/bitmask_tests.cpp
new file mode 100644
index 0000000..ab008b5
--- /dev/null
+++ b/cpp/tests/bitmask/bitmask_tests.cpp
@@ -0,0 +1,752 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+struct BitmaskUtilitiesTest : public cudf::test::BaseFixture {};
+
+TEST_F(BitmaskUtilitiesTest, StateNullCount)
+{
+  EXPECT_EQ(0, cudf::state_null_count(cudf::mask_state::UNALLOCATED, 42));
+  EXPECT_EQ(42, cudf::state_null_count(cudf::mask_state::ALL_NULL, 42));
+  EXPECT_EQ(0, cudf::state_null_count(cudf::mask_state::ALL_VALID, 42));
+  EXPECT_THROW(cudf::state_null_count(cudf::mask_state::UNINITIALIZED, 42), std::invalid_argument);
+}
+
+TEST_F(BitmaskUtilitiesTest, BitmaskAllocationSize)
+{
+  EXPECT_EQ(0u, cudf::bitmask_allocation_size_bytes(0));
+  EXPECT_EQ(64u, cudf::bitmask_allocation_size_bytes(1));
+  EXPECT_EQ(64u, cudf::bitmask_allocation_size_bytes(512));
+  EXPECT_EQ(128u, cudf::bitmask_allocation_size_bytes(513));
+  EXPECT_EQ(128u, cudf::bitmask_allocation_size_bytes(1023));
+  EXPECT_EQ(128u, cudf::bitmask_allocation_size_bytes(1024));
+  EXPECT_EQ(192u, cudf::bitmask_allocation_size_bytes(1025));
+}
+
+TEST_F(BitmaskUtilitiesTest, NumBitmaskWords)
+{
+  EXPECT_EQ(0, cudf::num_bitmask_words(0));
+  EXPECT_EQ(1, cudf::num_bitmask_words(1));
+  EXPECT_EQ(1, cudf::num_bitmask_words(31));
+  EXPECT_EQ(1, cudf::num_bitmask_words(32));
+  EXPECT_EQ(2, cudf::num_bitmask_words(33));
+  EXPECT_EQ(2, cudf::num_bitmask_words(63));
+  EXPECT_EQ(2, cudf::num_bitmask_words(64));
+  EXPECT_EQ(3, cudf::num_bitmask_words(65));
+}
+
+struct CountBitmaskTest : public cudf::test::BaseFixture {};
+
+TEST_F(CountBitmaskTest, NullMask)
+{
+  EXPECT_THROW(cudf::detail::count_set_bits(nullptr, 0, 32, cudf::get_default_stream()),
+               cudf::logic_error);
+  EXPECT_EQ(32, cudf::detail::valid_count(nullptr, 0, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 32, 7, 25};
+  EXPECT_THROW(cudf::detail::segmented_count_set_bits(nullptr, indices, cudf::get_default_stream()),
+               cudf::logic_error);
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(nullptr, indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{32, 18}));
+}
+
+// Utility to construct a mask vector. If fill_valid is false (default), it is initialized to all
+// null. Otherwise it is initialized to all valid.
+rmm::device_uvector<cudf::bitmask_type> make_mask(cudf::size_type size, bool fill_valid = false)
+{
+  if (!fill_valid) {
+    return cudf::detail::make_zeroed_device_uvector_sync<cudf::bitmask_type>(
+      size, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  } else {
+    auto ret = rmm::device_uvector<cudf::bitmask_type>(size, cudf::get_default_stream());
+    CUDF_CUDA_TRY(cudaMemsetAsync(ret.data(),
+                                  ~cudf::bitmask_type{0},
+                                  size * sizeof(cudf::bitmask_type),
+                                  cudf::get_default_stream().value()));
+    return ret;
+  }
+}
+
+TEST_F(CountBitmaskTest, NegativeStart)
+{
+  auto mask = make_mask(1);
+  EXPECT_THROW(cudf::detail::count_set_bits(mask.data(), -1, 32, cudf::get_default_stream()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::detail::valid_count(mask.data(), -1, 32, cudf::get_default_stream()),
+               cudf::logic_error);
+
+  std::vector<cudf::size_type> indices = {0, 16, -1, 32};
+  EXPECT_THROW(
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream()),
+    cudf::logic_error);
+}
+
+TEST_F(CountBitmaskTest, StartLargerThanStop)
+{
+  auto mask = make_mask(1);
+  EXPECT_THROW(cudf::detail::count_set_bits(mask.data(), 32, 31, cudf::get_default_stream()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::detail::valid_count(mask.data(), 32, 31, cudf::get_default_stream()),
+               cudf::logic_error);
+
+  std::vector<cudf::size_type> indices = {0, 16, 31, 30};
+  EXPECT_THROW(
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream()),
+    cudf::logic_error);
+}
+
+TEST_F(CountBitmaskTest, EmptyRange)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(0, cudf::detail::count_set_bits(mask.data(), 17, 17, cudf::get_default_stream()));
+  EXPECT_EQ(0, cudf::detail::valid_count(mask.data(), 17, 17, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 0, 17, 17};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+}
+
+TEST_F(CountBitmaskTest, SingleWordAllZero)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(0, cudf::detail::count_set_bits(mask.data(), 0, 32, cudf::get_default_stream()));
+  EXPECT_EQ(0, cudf::detail::valid_count(mask.data(), 0, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 32, 0, 32};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+  auto valid_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+}
+
+TEST_F(CountBitmaskTest, SingleBitAllZero)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(0, cudf::detail::count_set_bits(mask.data(), 17, 18, cudf::get_default_stream()));
+  EXPECT_EQ(0, cudf::detail::valid_count(mask.data(), 17, 18, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {17, 18, 7, 8};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+}
+
+TEST_F(CountBitmaskTest, SingleBitAllSet)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(1, cudf::detail::count_set_bits(mask.data(), 13, 14, cudf::get_default_stream()));
+  EXPECT_EQ(1, cudf::detail::valid_count(mask.data(), 13, 14, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {13, 14, 0, 1};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{1, 1}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{1, 1}));
+}
+
+TEST_F(CountBitmaskTest, SingleWordAllBitsSet)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(32, cudf::detail::count_set_bits(mask.data(), 0, 32, cudf::get_default_stream()));
+  EXPECT_EQ(32, cudf::detail::valid_count(mask.data(), 0, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 32, 0, 32};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{32, 32}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{32, 32}));
+}
+
+TEST_F(CountBitmaskTest, SingleWordPreSlack)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(25, cudf::detail::count_set_bits(mask.data(), 7, 32, cudf::get_default_stream()));
+  EXPECT_EQ(25, cudf::detail::valid_count(mask.data(), 7, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {7, 32, 8, 32};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{25, 24}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{25, 24}));
+}
+
+TEST_F(CountBitmaskTest, SingleWordPostSlack)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(17, cudf::detail::count_set_bits(mask.data(), 0, 17, cudf::get_default_stream()));
+  EXPECT_EQ(17, cudf::detail::valid_count(mask.data(), 0, 17, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 17, 0, 18};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{17, 18}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{17, 18}));
+}
+
+TEST_F(CountBitmaskTest, SingleWordSubset)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(30, cudf::detail::count_set_bits(mask.data(), 1, 31, cudf::get_default_stream()));
+  EXPECT_EQ(30, cudf::detail::valid_count(mask.data(), 1, 31, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {1, 31, 7, 17};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{30, 10}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{30, 10}));
+}
+
+TEST_F(CountBitmaskTest, SingleWordSubset2)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(28, cudf::detail::count_set_bits(mask.data(), 2, 30, cudf::get_default_stream()));
+  EXPECT_EQ(28, cudf::detail::valid_count(mask.data(), 2, 30, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {4, 16, 2, 30};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{12, 28}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{12, 28}));
+}
+
+TEST_F(CountBitmaskTest, MultipleWordsAllBits)
+{
+  auto mask = make_mask(10, true);
+  EXPECT_EQ(320, cudf::detail::count_set_bits(mask.data(), 0, 320, cudf::get_default_stream()));
+  EXPECT_EQ(320, cudf::detail::valid_count(mask.data(), 0, 320, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 320, 0, 320};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{320, 320}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{320, 320}));
+}
+
+TEST_F(CountBitmaskTest, MultipleWordsSubsetWordBoundary)
+{
+  auto mask = make_mask(10, true);
+  EXPECT_EQ(256, cudf::detail::count_set_bits(mask.data(), 32, 288, cudf::get_default_stream()));
+  EXPECT_EQ(256, cudf::detail::valid_count(mask.data(), 32, 288, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {32, 192, 32, 288};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{160, 256}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{160, 256}));
+}
+
+TEST_F(CountBitmaskTest, MultipleWordsSplitWordBoundary)
+{
+  auto mask = make_mask(10, true);
+  EXPECT_EQ(2, cudf::detail::count_set_bits(mask.data(), 31, 33, cudf::get_default_stream()));
+  EXPECT_EQ(2, cudf::detail::valid_count(mask.data(), 31, 33, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {31, 33, 60, 67};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{2, 7}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{2, 7}));
+}
+
+TEST_F(CountBitmaskTest, MultipleWordsSubset)
+{
+  auto mask = make_mask(10, true);
+  EXPECT_EQ(226, cudf::detail::count_set_bits(mask.data(), 67, 293, cudf::get_default_stream()));
+  EXPECT_EQ(226, cudf::detail::valid_count(mask.data(), 67, 293, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {67, 293, 37, 319};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{226, 282}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{226, 282}));
+}
+
+TEST_F(CountBitmaskTest, MultipleWordsSingleBit)
+{
+  auto mask = make_mask(10, true);
+  EXPECT_EQ(1, cudf::detail::count_set_bits(mask.data(), 67, 68, cudf::get_default_stream()));
+  EXPECT_EQ(1, cudf::detail::valid_count(mask.data(), 67, 68, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {67, 68, 31, 32, 192, 193};
+  auto set_counts =
+    cudf::detail::segmented_count_set_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(set_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{1, 1, 1}));
+  auto valid_counts =
+    cudf::detail::segmented_valid_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(valid_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{1, 1, 1}));
+}
+
+using CountUnsetBitsTest = CountBitmaskTest;
+
+TEST_F(CountUnsetBitsTest, SingleBitAllSet)
+{
+  auto mask = make_mask(1, true);
+  EXPECT_EQ(0, cudf::detail::count_unset_bits(mask.data(), 13, 14, cudf::get_default_stream()));
+  EXPECT_EQ(0, cudf::detail::null_count(mask.data(), 13, 14, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {13, 14, 31, 32};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+  auto null_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+}
+
+TEST_F(CountUnsetBitsTest, NullMask)
+{
+  EXPECT_THROW(cudf::detail::count_unset_bits(nullptr, 0, 32, cudf::get_default_stream()),
+               cudf::logic_error);
+  EXPECT_EQ(0, cudf::detail::null_count(nullptr, 0, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 32, 7, 25};
+  EXPECT_THROW(
+    cudf::detail::segmented_count_unset_bits(nullptr, indices, cudf::get_default_stream()),
+    cudf::logic_error);
+  auto null_counts =
+    cudf::detail::segmented_null_count(nullptr, indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{0, 0}));
+}
+
+TEST_F(CountUnsetBitsTest, SingleWordAllBits)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(32, cudf::detail::count_unset_bits(mask.data(), 0, 32, cudf::get_default_stream()));
+  EXPECT_EQ(32, cudf::detail::null_count(mask.data(), 0, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 32, 0, 32};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{32, 32}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{32, 32}));
+}
+
+TEST_F(CountUnsetBitsTest, SingleWordPreSlack)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(25, cudf::detail::count_unset_bits(mask.data(), 7, 32, cudf::get_default_stream()));
+  EXPECT_EQ(25, cudf::detail::null_count(mask.data(), 7, 32, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {7, 32, 8, 32};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{25, 24}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{25, 24}));
+}
+
+TEST_F(CountUnsetBitsTest, SingleWordPostSlack)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(17, cudf::detail::count_unset_bits(mask.data(), 0, 17, cudf::get_default_stream()));
+  EXPECT_EQ(17, cudf::detail::null_count(mask.data(), 0, 17, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 17, 0, 18};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{17, 18}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{17, 18}));
+}
+
+TEST_F(CountUnsetBitsTest, SingleWordSubset)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(30, cudf::detail::count_unset_bits(mask.data(), 1, 31, cudf::get_default_stream()));
+  EXPECT_EQ(30, cudf::detail::null_count(mask.data(), 1, 31, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {1, 31, 7, 17};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{30, 10}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{30, 10}));
+}
+
+TEST_F(CountUnsetBitsTest, SingleWordSubset2)
+{
+  auto mask = make_mask(1);
+  EXPECT_EQ(28, cudf::detail::count_unset_bits(mask.data(), 2, 30, cudf::get_default_stream()));
+  EXPECT_EQ(28, cudf::detail::null_count(mask.data(), 2, 30, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {4, 16, 2, 30};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{12, 28}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{12, 28}));
+}
+
+TEST_F(CountUnsetBitsTest, MultipleWordsAllBits)
+{
+  auto mask = make_mask(10);
+  EXPECT_EQ(320, cudf::detail::count_unset_bits(mask.data(), 0, 320, cudf::get_default_stream()));
+  EXPECT_EQ(320, cudf::detail::null_count(mask.data(), 0, 320, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {0, 320, 0, 320};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{320, 320}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{320, 320}));
+}
+
+TEST_F(CountUnsetBitsTest, MultipleWordsSubsetWordBoundary)
+{
+  auto mask = make_mask(10);
+  EXPECT_EQ(256, cudf::detail::count_unset_bits(mask.data(), 32, 288, cudf::get_default_stream()));
+  EXPECT_EQ(256, cudf::detail::null_count(mask.data(), 32, 288, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {32, 192, 32, 288};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{160, 256}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{160, 256}));
+}
+
+TEST_F(CountUnsetBitsTest, MultipleWordsSplitWordBoundary)
+{
+  auto mask = make_mask(10);
+  EXPECT_EQ(2, cudf::detail::count_unset_bits(mask.data(), 31, 33, cudf::get_default_stream()));
+  EXPECT_EQ(2, cudf::detail::null_count(mask.data(), 31, 33, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {31, 33, 60, 67};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{2, 7}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{2, 7}));
+}
+
+TEST_F(CountUnsetBitsTest, MultipleWordsSubset)
+{
+  auto mask = make_mask(10);
+  EXPECT_EQ(226, cudf::detail::count_unset_bits(mask.data(), 67, 293, cudf::get_default_stream()));
+  EXPECT_EQ(226, cudf::detail::null_count(mask.data(), 67, 293, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {67, 293, 37, 319};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{226, 282}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{226, 282}));
+}
+
+TEST_F(CountUnsetBitsTest, MultipleWordsSingleBit)
+{
+  auto mask = make_mask(10);
+  EXPECT_EQ(1, cudf::detail::count_unset_bits(mask.data(), 67, 68, cudf::get_default_stream()));
+  EXPECT_EQ(1, cudf::detail::null_count(mask.data(), 67, 68, cudf::get_default_stream()));
+
+  std::vector<cudf::size_type> indices = {67, 68, 31, 32, 192, 193};
+  auto unset_counts =
+    cudf::detail::segmented_count_unset_bits(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(unset_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{1, 1, 1}));
+  auto null_counts =
+    cudf::detail::segmented_null_count(mask.data(), indices, cudf::get_default_stream());
+  EXPECT_THAT(null_counts, ::testing::ElementsAreArray(std::vector<cudf::size_type>{1, 1, 1}));
+}
+
+struct CopyBitmaskTest : public cudf::test::BaseFixture, cudf::test::UniformRandomGenerator<int> {
+  CopyBitmaskTest() : cudf::test::UniformRandomGenerator<int>{0, 1} {}
+};
+
+void cleanEndWord(rmm::device_buffer& mask, int begin_bit, int end_bit)
+{
+  auto ptr = static_cast<cudf::bitmask_type*>(mask.data());
+
+  auto number_of_mask_words = cudf::num_bitmask_words(static_cast<size_t>(end_bit - begin_bit));
+  auto number_of_bits       = end_bit - begin_bit;
+  if (number_of_bits % 32 != 0) {
+    cudf::bitmask_type end_mask = 0;
+    CUDF_CUDA_TRY(
+      cudaMemcpy(&end_mask, ptr + number_of_mask_words - 1, sizeof(end_mask), cudaMemcpyDefault));
+    end_mask = end_mask & ((1 << (number_of_bits % 32)) - 1);
+    CUDF_CUDA_TRY(
+      cudaMemcpy(ptr + number_of_mask_words - 1, &end_mask, sizeof(end_mask), cudaMemcpyDefault));
+  }
+}
+
+TEST_F(CopyBitmaskTest, NegativeStart)
+{
+  auto mask = make_mask(1);
+  EXPECT_THROW(cudf::copy_bitmask(mask.data(), -1, 32), cudf::logic_error);
+}
+
+TEST_F(CopyBitmaskTest, StartLargerThanStop)
+{
+  auto mask = make_mask(1);
+  EXPECT_THROW(cudf::copy_bitmask(mask.data(), 32, 31), cudf::logic_error);
+}
+
+TEST_F(CopyBitmaskTest, EmptyRange)
+{
+  auto mask = make_mask(1);
+  auto buff = cudf::copy_bitmask(mask.data(), 17, 17);
+  EXPECT_EQ(0, static_cast<int>(buff.size()));
+}
+
+TEST_F(CopyBitmaskTest, NullPtr)
+{
+  auto buff = cudf::copy_bitmask(nullptr, 17, 17);
+  EXPECT_EQ(0, static_cast<int>(buff.size()));
+}
+
+TEST_F(CopyBitmaskTest, TestZeroOffset)
+{
+  std::vector<int> validity_bit(1000);
+  for (auto& m : validity_bit) {
+    m = this->generate();
+  }
+  auto input_mask =
+    std::get<0>(cudf::test::detail::make_null_mask(validity_bit.begin(), validity_bit.end()));
+
+  int begin_bit         = 0;
+  int end_bit           = 800;
+  auto gold_splice_mask = std::get<0>(cudf::test::detail::make_null_mask(
+    validity_bit.begin() + begin_bit, validity_bit.begin() + end_bit));
+
+  auto splice_mask = cudf::copy_bitmask(
+    static_cast<cudf::bitmask_type const*>(input_mask.data()), begin_bit, end_bit);
+
+  cleanEndWord(splice_mask, begin_bit, end_bit);
+  auto number_of_bits = end_bit - begin_bit;
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    gold_splice_mask.data(), splice_mask.data(), cudf::num_bitmask_words(number_of_bits));
+}
+
+TEST_F(CopyBitmaskTest, TestNonZeroOffset)
+{
+  std::vector<int> validity_bit(1000);
+  for (auto& m : validity_bit) {
+    m = this->generate();
+  }
+  auto input_mask =
+    std::get<0>(cudf::test::detail::make_null_mask(validity_bit.begin(), validity_bit.end()));
+
+  int begin_bit         = 321;
+  int end_bit           = 998;
+  auto gold_splice_mask = std::get<0>(cudf::test::detail::make_null_mask(
+    validity_bit.begin() + begin_bit, validity_bit.begin() + end_bit));
+
+  auto splice_mask = cudf::copy_bitmask(
+    static_cast<cudf::bitmask_type const*>(input_mask.data()), begin_bit, end_bit);
+
+  cleanEndWord(splice_mask, begin_bit, end_bit);
+  auto number_of_bits = end_bit - begin_bit;
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    gold_splice_mask.data(), splice_mask.data(), cudf::num_bitmask_words(number_of_bits));
+}
+
+TEST_F(CopyBitmaskTest, TestCopyColumnViewVectorContiguous)
+{
+  cudf::data_type t{cudf::type_id::INT32};
+  cudf::size_type num_elements = 1001;
+  std::vector<int> validity_bit(num_elements);
+  for (auto& m : validity_bit) {
+    m = this->generate();
+  }
+  auto [gold_mask, null_count] =
+    cudf::test::detail::make_null_mask(validity_bit.begin(), validity_bit.end());
+
+  rmm::device_buffer copy_mask{gold_mask, cudf::get_default_stream()};
+  cudf::column original{t,
+                        num_elements,
+                        rmm::device_buffer{num_elements * sizeof(int), cudf::get_default_stream()},
+                        std::move(copy_mask),
+                        null_count};
+  std::vector<cudf::size_type> indices{0,
+                                       104,
+                                       104,
+                                       128,
+                                       128,
+                                       152,
+                                       152,
+                                       311,
+                                       311,
+                                       491,
+                                       491,
+                                       583,
+                                       583,
+                                       734,
+                                       734,
+                                       760,
+                                       760,
+                                       num_elements};
+  std::vector<cudf::column_view> views    = cudf::slice(original, indices);
+  rmm::device_buffer concatenated_bitmask = cudf::concatenate_masks(views);
+  cleanEndWord(concatenated_bitmask, 0, num_elements);
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    concatenated_bitmask.data(), gold_mask.data(), cudf::num_bitmask_words(num_elements));
+}
+
+TEST_F(CopyBitmaskTest, TestCopyColumnViewVectorDiscontiguous)
+{
+  cudf::data_type t{cudf::type_id::INT32};
+  cudf::size_type num_elements = 1001;
+  std::vector<int> validity_bit(num_elements);
+  for (auto& m : validity_bit) {
+    m = this->generate();
+  }
+  auto gold_mask =
+    std::get<0>(cudf::test::detail::make_null_mask(validity_bit.begin(), validity_bit.end()));
+  std::vector<cudf::size_type> split{0, 104, 128, 152, 311, 491, 583, 734, 760, num_elements};
+
+  std::vector<cudf::column> cols;
+  std::vector<cudf::column_view> views;
+  for (unsigned i = 0; i < split.size() - 1; i++) {
+    auto [null_mask, null_count] = cudf::test::detail::make_null_mask(
+      validity_bit.begin() + split[i], validity_bit.begin() + split[i + 1]);
+    cols.emplace_back(
+      t,
+      split[i + 1] - split[i],
+      rmm::device_buffer{sizeof(int) * (split[i + 1] - split[i]), cudf::get_default_stream()},
+      std::move(null_mask),
+      null_count);
+    views.push_back(cols.back());
+  }
+  rmm::device_buffer concatenated_bitmask = cudf::concatenate_masks(views);
+  cleanEndWord(concatenated_bitmask, 0, num_elements);
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    concatenated_bitmask.data(), gold_mask.data(), cudf::num_bitmask_words(num_elements));
+}
+
+struct MergeBitmaskTest : public cudf::test::BaseFixture {};
+
+TEST_F(MergeBitmaskTest, TestBitmaskAnd)
+{
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 0, 1, 1}, {0, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 2, 1, 0, 255}, {1, 1, 0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col3({0, 2, 1, 0, 255});
+
+  auto const input1 = cudf::table_view({bools_col3});
+  auto const input2 = cudf::table_view({bools_col1, bools_col2});
+  auto const input3 = cudf::table_view({bools_col1, bools_col2, bools_col3});
+
+  auto [result1_mask, result1_null_count] = cudf::bitmask_and(input1);
+  auto [result2_mask, result2_null_count] = cudf::bitmask_and(input2);
+  auto [result3_mask, result3_null_count] = cudf::bitmask_and(input3);
+
+  constexpr cudf::size_type gold_null_count = 3;
+
+  EXPECT_EQ(result1_null_count, 0);
+  EXPECT_EQ(result2_null_count, gold_null_count);
+  EXPECT_EQ(result3_null_count, gold_null_count);
+
+  auto odd_indices =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto odd =
+    std::get<0>(cudf::test::detail::make_null_mask(odd_indices, odd_indices + input2.num_rows()));
+
+  EXPECT_EQ(nullptr, result1_mask.data());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    result2_mask.data(), odd.data(), cudf::num_bitmask_words(input2.num_rows()));
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    result3_mask.data(), odd.data(), cudf::num_bitmask_words(input2.num_rows()));
+}
+
+TEST_F(MergeBitmaskTest, TestBitmaskOr)
+{
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 0, 1, 1}, {1, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 2, 1, 0, 255}, {0, 0, 1, 0, 1});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col3({0, 2, 1, 0, 255});
+
+  auto const input1 = cudf::table_view({bools_col3});
+  auto const input2 = cudf::table_view({bools_col1, bools_col2});
+  auto const input3 = cudf::table_view({bools_col1, bools_col2, bools_col3});
+
+  auto [result1_mask, result1_null_count] = cudf::bitmask_or(input1);
+  auto [result2_mask, result2_null_count] = cudf::bitmask_or(input2);
+  auto [result3_mask, result3_null_count] = cudf::bitmask_or(input3);
+
+  EXPECT_EQ(result1_null_count, 0);
+  EXPECT_EQ(result2_null_count, 1);
+  EXPECT_EQ(result3_null_count, 0);
+
+  auto all_but_index3 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+  auto null3 = std::get<0>(
+    cudf::test::detail::make_null_mask(all_but_index3, all_but_index3 + input2.num_rows()));
+
+  EXPECT_EQ(nullptr, result1_mask.data());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+    result2_mask.data(), null3.data(), cudf::num_bitmask_words(input2.num_rows()));
+  EXPECT_EQ(nullptr, result3_mask.data());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/bitmask/is_element_valid_tests.cpp b/cpp/tests/bitmask/is_element_valid_tests.cpp
new file mode 100644
index 0000000..224b989
--- /dev/null
+++ b/cpp/tests/bitmask/is_element_valid_tests.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/is_element_valid.hpp>
+#include <cudf/detail/iterator.cuh>
+
+#include <thrust/iterator/counting_iterator.h>
+
+struct IsElementValidTest : public cudf::test::BaseFixture {};
+
+TEST_F(IsElementValidTest, IsElementValidBasic)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 1, 1, 1, 1}, {1, 0, 0, 0, 1});
+  EXPECT_TRUE(cudf::detail::is_element_valid_sync(col, 0, cudf::get_default_stream()));
+  EXPECT_FALSE(cudf::detail::is_element_valid_sync(col, 1, cudf::get_default_stream()));
+  EXPECT_FALSE(cudf::detail::is_element_valid_sync(col, 2, cudf::get_default_stream()));
+  EXPECT_FALSE(cudf::detail::is_element_valid_sync(col, 3, cudf::get_default_stream()));
+  EXPECT_TRUE(cudf::detail::is_element_valid_sync(col, 4, cudf::get_default_stream()));
+}
+
+TEST_F(IsElementValidTest, IsElementValidLarge)
+{
+  auto filter              = [](auto i) { return static_cast<bool>(i % 3); };
+  auto val                 = thrust::make_counting_iterator(0);
+  auto valid               = cudf::detail::make_counting_transform_iterator(0, filter);
+  cudf::size_type num_rows = 1000;
+
+  cudf::test::fixed_width_column_wrapper<int32_t> col(val, val + num_rows, valid);
+
+  for (int i = 0; i < num_rows; i++) {
+    EXPECT_EQ(cudf::detail::is_element_valid_sync(col, i, cudf::get_default_stream()), filter(i));
+  }
+}
+
+TEST_F(IsElementValidTest, IsElementValidOffset)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 1, 1, 1, 1}, {1, 0, 0, 0, 1});
+  {
+    auto offset_col = cudf::slice(col, {1, 5}).front();
+    EXPECT_FALSE(cudf::detail::is_element_valid_sync(offset_col, 0, cudf::get_default_stream()));
+    EXPECT_FALSE(cudf::detail::is_element_valid_sync(offset_col, 1, cudf::get_default_stream()));
+    EXPECT_FALSE(cudf::detail::is_element_valid_sync(offset_col, 2, cudf::get_default_stream()));
+    EXPECT_TRUE(cudf::detail::is_element_valid_sync(offset_col, 3, cudf::get_default_stream()));
+  }
+  {
+    auto offset_col = cudf::slice(col, {2, 5}).front();
+    EXPECT_FALSE(cudf::detail::is_element_valid_sync(offset_col, 0, cudf::get_default_stream()));
+    EXPECT_FALSE(cudf::detail::is_element_valid_sync(offset_col, 1, cudf::get_default_stream()));
+    EXPECT_TRUE(cudf::detail::is_element_valid_sync(offset_col, 2, cudf::get_default_stream()));
+  }
+}
+
+TEST_F(IsElementValidTest, IsElementValidOffsetLarge)
+{
+  auto filter              = [](auto i) { return static_cast<bool>(i % 3); };
+  cudf::size_type offset   = 37;
+  auto val                 = thrust::make_counting_iterator(0);
+  auto valid               = cudf::detail::make_counting_transform_iterator(0, filter);
+  cudf::size_type num_rows = 1000;
+
+  cudf::test::fixed_width_column_wrapper<int32_t> col(val, val + num_rows, valid);
+  auto offset_col = cudf::slice(col, {offset, num_rows}).front();
+
+  for (int i = 0; i < offset_col.size(); i++) {
+    EXPECT_EQ(cudf::detail::is_element_valid_sync(offset_col, i, cudf::get_default_stream()),
+              filter(i + offset));
+  }
+}
diff --git a/cpp/tests/bitmask/set_nullmask_tests.cu b/cpp/tests/bitmask/set_nullmask_tests.cu
new file mode 100644
index 0000000..19e3202
--- /dev/null
+++ b/cpp/tests/bitmask/set_nullmask_tests.cu
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <algorithm>
+#include <iostream>
+
+#include <cudf_test/base_fixture.hpp>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+struct valid_bit_functor {
+  cudf::bitmask_type const* _null_mask;
+  __device__ bool operator()(cudf::size_type element_index) const noexcept
+  {
+    return cudf::bit_is_set(_null_mask, element_index);
+  }
+};
+
+std::ostream& operator<<(std::ostream& stream, thrust::host_vector<bool> const& bits)
+{
+  for (auto _bit : bits)
+    stream << int(_bit);
+  return stream;
+}
+
+struct SetBitmaskTest : public cudf::test::BaseFixture {
+  void expect_bitmask_equal(cudf::bitmask_type const* bitmask,  // Device Ptr
+                            cudf::size_type start_bit,
+                            thrust::host_vector<bool> const& expect,
+                            rmm::cuda_stream_view stream = cudf::get_default_stream())
+  {
+    rmm::device_uvector<bool> result(expect.size(), stream);
+    auto counting_iter = thrust::counting_iterator<cudf::size_type>{0};
+    thrust::transform(rmm::exec_policy(stream),
+                      counting_iter + start_bit,
+                      counting_iter + start_bit + expect.size(),
+                      result.begin(),
+                      valid_bit_functor{bitmask});
+
+    auto host_result = cudf::detail::make_host_vector_sync(result, stream);
+    EXPECT_THAT(host_result, testing::ElementsAreArray(expect));
+  }
+
+  void test_set_null_range(cudf::size_type size,
+                           cudf::size_type begin,
+                           cudf::size_type end,
+                           bool valid)
+  {
+    thrust::host_vector<bool> expected(end - begin, valid);
+    // TEST
+    rmm::device_buffer mask = create_null_mask(size, cudf::mask_state::UNINITIALIZED);
+    // valid ? cudf::mask_state::ALL_NULL : cudf::mask_state::ALL_VALID);
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask.data()), begin, end, valid);
+    expect_bitmask_equal(static_cast<cudf::bitmask_type*>(mask.data()), begin, expected);
+  }
+
+  void test_null_partition(cudf::size_type size, cudf::size_type middle, bool valid)
+  {
+    thrust::host_vector<bool> expected(size);
+    std::generate(expected.begin(), expected.end(), [n = 0, middle, valid]() mutable {
+      auto i = n++;
+      return (!valid) ^ (i < middle);
+    });
+    // TEST
+    rmm::device_buffer mask = create_null_mask(size, cudf::mask_state::UNINITIALIZED);
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask.data()), 0, middle, valid);
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask.data()), middle, size, !valid);
+    expect_bitmask_equal(static_cast<cudf::bitmask_type*>(mask.data()), 0, expected);
+  }
+};
+
+// tests for set_null_mask
+TEST_F(SetBitmaskTest, fill_range)
+{
+  cudf::size_type size = 121;
+  for (auto begin = 0; begin < size; begin += 5)
+    for (auto end = begin + 1; end <= size; end += 7) {
+      this->test_set_null_range(size, begin, end, true);
+      this->test_set_null_range(size, begin, end, false);
+    }
+}
+
+TEST_F(SetBitmaskTest, null_mask_partition)
+{
+  cudf::size_type size = 64;
+  for (auto middle = 1; middle < size; middle++) {
+    this->test_null_partition(size, middle, true);
+    this->test_null_partition(size, middle, false);
+  }
+}
+
+TEST_F(SetBitmaskTest, error_range)
+{
+  cudf::size_type size = 121;
+  using size_pair      = std::pair<cudf::size_type, cudf::size_type>;
+  std::vector<size_pair> begin_end_fail{
+    {-1, size},  // begin>=0
+    {-2, -1},    // begin>=0
+    {9, 8},      // begin<=end
+  };
+  for (auto begin_end : begin_end_fail) {
+    auto begin = begin_end.first, end = begin_end.second;
+    EXPECT_ANY_THROW(this->test_set_null_range(size, begin, end, true));
+    EXPECT_ANY_THROW(this->test_set_null_range(size, begin, end, false));
+  }
+  std::vector<size_pair> begin_end_pass{
+    {0, size},         // begin>=0
+    {0, 1},            // begin>=0
+    {8, 8},            // begin==end
+    {8, 9},            // begin<=end
+    {size - 1, size},  // begin<=end
+  };
+  for (auto begin_end : begin_end_pass) {
+    auto begin = begin_end.first, end = begin_end.second;
+    EXPECT_NO_THROW(this->test_set_null_range(size, begin, end, true));
+    EXPECT_NO_THROW(this->test_set_null_range(size, begin, end, false));
+  }
+}
diff --git a/cpp/tests/bitmask/valid_if_tests.cu b/cpp/tests/bitmask/valid_if_tests.cu
new file mode 100644
index 0000000..164f8d7
--- /dev/null
+++ b/cpp/tests/bitmask/valid_if_tests.cu
@@ -0,0 +1,103 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+struct ValidIfTest : public cudf::test::BaseFixture {};
+
+struct odds_valid {
+  __host__ __device__ bool operator()(cudf::size_type i) { return i % 2; }
+};
+struct all_valid {
+  __host__ __device__ bool operator()(cudf::size_type i) { return true; }
+};
+struct all_null {
+  __host__ __device__ bool operator()(cudf::size_type i) { return false; }
+};
+
+TEST_F(ValidIfTest, EmptyRange)
+{
+  auto actual        = cudf::detail::valid_if(thrust::make_counting_iterator(0),
+                                       thrust::make_counting_iterator(0),
+                                       odds_valid{},
+                                       cudf::get_default_stream(),
+                                       rmm::mr::get_current_device_resource());
+  auto const& buffer = actual.first;
+  EXPECT_EQ(0u, buffer.size());
+  EXPECT_EQ(nullptr, buffer.data());
+  EXPECT_EQ(0, actual.second);
+}
+
+TEST_F(ValidIfTest, InvalidRange)
+{
+  EXPECT_THROW(cudf::detail::valid_if(thrust::make_counting_iterator(1),
+                                      thrust::make_counting_iterator(0),
+                                      odds_valid{},
+                                      cudf::get_default_stream(),
+                                      rmm::mr::get_current_device_resource()),
+               cudf::logic_error);
+}
+
+TEST_F(ValidIfTest, OddsValid)
+{
+  auto iter     = cudf::detail::make_counting_transform_iterator(0, odds_valid{});
+  auto expected = cudf::test::detail::make_null_mask(iter, iter + 10000);
+  auto actual   = cudf::detail::valid_if(thrust::make_counting_iterator(0),
+                                       thrust::make_counting_iterator(10000),
+                                       odds_valid{},
+                                       cudf::get_default_stream(),
+                                       rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(expected.first.data(), actual.first.data(), expected.first.size());
+  EXPECT_EQ(5000, actual.second);
+  EXPECT_EQ(expected.second, actual.second);
+}
+
+TEST_F(ValidIfTest, AllValid)
+{
+  auto iter     = cudf::detail::make_counting_transform_iterator(0, all_valid{});
+  auto expected = cudf::test::detail::make_null_mask(iter, iter + 10000);
+  auto actual   = cudf::detail::valid_if(thrust::make_counting_iterator(0),
+                                       thrust::make_counting_iterator(10000),
+                                       all_valid{},
+                                       cudf::get_default_stream(),
+                                       rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(expected.first.data(), actual.first.data(), expected.first.size());
+  EXPECT_EQ(0, actual.second);
+  EXPECT_EQ(expected.second, actual.second);
+}
+
+TEST_F(ValidIfTest, AllNull)
+{
+  auto iter     = cudf::detail::make_counting_transform_iterator(0, all_null{});
+  auto expected = cudf::test::detail::make_null_mask(iter, iter + 10000);
+  auto actual   = cudf::detail::valid_if(thrust::make_counting_iterator(0),
+                                       thrust::make_counting_iterator(10000),
+                                       all_null{},
+                                       cudf::get_default_stream(),
+                                       rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(expected.first.data(), actual.first.data(), expected.first.size());
+  EXPECT_EQ(10000, actual.second);
+  EXPECT_EQ(expected.second, actual.second);
+}
diff --git a/cpp/tests/column/bit_cast_test.cpp b/cpp/tests/column/bit_cast_test.cpp
new file mode 100644
index 0000000..a6654bb
--- /dev/null
+++ b/cpp/tests/column/bit_cast_test.cpp
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <random>
+
+template <typename T, typename T2 = void>
+struct rep_type_impl {
+  using type = void;
+};
+
+template <typename T>
+struct rep_type_impl<T, std::enable_if_t<cudf::is_timestamp<T>()>> {
+  using type = typename T::duration::rep;
+};
+
+template <typename T>
+struct rep_type_impl<T, std::enable_if_t<cudf::is_duration<T>()>> {
+  using type = typename T::rep;
+};
+
+template <typename T>
+struct rep_type_impl<T, std::enable_if_t<cudf::is_fixed_point<T>()>> {
+  using type = typename T::rep;
+};
+
+template <typename T>
+using rep_type_t = typename rep_type_impl<T>::type;
+
+template <typename T>
+struct ColumnViewAllTypesTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ColumnViewAllTypesTests, cudf::test::FixedWidthTypes);
+
+template <typename FromType, typename ToType, typename Iterator>
+void do_bit_cast(cudf::column_view const& column_view, Iterator begin, Iterator end)
+{
+  auto mutable_column_view = reinterpret_cast<cudf::mutable_column_view const&>(column_view);
+  cudf::data_type to_type{cudf::type_to_id<ToType>()};
+
+  if (std::is_same_v<FromType, ToType>) {
+    // Cast to same to_type
+    auto output  = cudf::bit_cast(column_view, column_view.type());
+    auto output1 = cudf::bit_cast(mutable_column_view, mutable_column_view.type());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, column_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1, mutable_column_view);
+  } else if (std::is_same_v<rep_type_t<FromType>, ToType> ||
+             std::is_same_v<FromType, rep_type_t<ToType>>) {
+    // Cast integer to timestamp or vice versa
+    auto output  = cudf::bit_cast(column_view, to_type);
+    auto output1 = cudf::bit_cast(mutable_column_view, to_type);
+    cudf::test::fixed_width_column_wrapper<ToType, cudf::size_type> expected(begin, end);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1, expected);
+  } else {
+    if (cuda::std::is_trivially_copyable_v<FromType> &&
+        cuda::std::is_trivially_copyable_v<ToType>) {
+      constexpr auto from_size = sizeof(cudf::device_storage_type_t<FromType>);
+      constexpr auto to_size   = sizeof(cudf::device_storage_type_t<ToType>);
+      if (from_size == to_size) {
+        // Cast from FromType to ToType
+        auto output1         = cudf::bit_cast(column_view, to_type);
+        auto output1_mutable = cudf::bit_cast(mutable_column_view, to_type);
+
+        // Cast back from ToType to FromType
+        cudf::data_type from_type{cudf::type_to_id<FromType>()};
+        auto output2         = cudf::bit_cast(output1, from_type);
+        auto output2_mutable = cudf::bit_cast(output1_mutable, from_type);
+
+        CUDF_TEST_EXPECT_COLUMNS_EQUAL(output2, column_view);
+        CUDF_TEST_EXPECT_COLUMNS_EQUAL(output2_mutable, mutable_column_view);
+      } else {
+        // Not allow to cast if sizes are mismatched
+        EXPECT_THROW(cudf::bit_cast(column_view, to_type), cudf::logic_error);
+        EXPECT_THROW(cudf::bit_cast(mutable_column_view, to_type), cudf::logic_error);
+      }
+    } else {
+      // Not allow to cast if any of from/to types is not trivially copyable
+      EXPECT_THROW(cudf::bit_cast(column_view, to_type), cudf::logic_error);
+      EXPECT_THROW(cudf::bit_cast(mutable_column_view, to_type), cudf::logic_error);
+    }
+  }
+}
+
+TYPED_TEST(ColumnViewAllTypesTests, BitCast)
+{
+  auto begin = thrust::make_counting_iterator(1);
+  auto end   = thrust::make_counting_iterator(16);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, cudf::size_type> input(begin, end);
+
+  do_bit_cast<TypeParam, int8_t>(input, begin, end);
+  do_bit_cast<TypeParam, int16_t>(input, begin, end);
+  do_bit_cast<TypeParam, int32_t>(input, begin, end);
+  do_bit_cast<TypeParam, int64_t>(input, begin, end);
+  do_bit_cast<TypeParam, float>(input, begin, end);
+  do_bit_cast<TypeParam, double>(input, begin, end);
+  do_bit_cast<TypeParam, bool>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::duration_D>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::duration_s>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::duration_ms>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::duration_us>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::duration_ns>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::timestamp_D>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::timestamp_s>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::timestamp_ms>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::timestamp_us>(input, begin, end);
+  do_bit_cast<TypeParam, cudf::timestamp_ns>(input, begin, end);
+}
diff --git a/cpp/tests/column/column_device_view_test.cu b/cpp/tests/column/column_device_view_test.cu
new file mode 100644
index 0000000..21bf587
--- /dev/null
+++ b/cpp/tests/column/column_device_view_test.cu
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+
+struct ColumnDeviceViewTest : public cudf::test::BaseFixture {};
+
+TEST_F(ColumnDeviceViewTest, Sample)
+{
+  using T = int32_t;
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+  cudf::test::fixed_width_column_wrapper<T> input({1, 2, 3, 4, 5, 6});
+  auto output            = cudf::allocate_like(input);
+  auto input_device_view = cudf::column_device_view::create(input, stream);
+  auto output_device_view =
+    cudf::mutable_column_device_view::create(output->mutable_view(), stream);
+
+  EXPECT_NO_THROW(thrust::copy(rmm::exec_policy(stream),
+                               input_device_view->begin<T>(),
+                               input_device_view->end<T>(),
+                               output_device_view->begin<T>()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, output->view());
+}
+
+TEST_F(ColumnDeviceViewTest, MismatchingType)
+{
+  using T = int32_t;
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+  cudf::test::fixed_width_column_wrapper<T> input({1, 2, 3, 4, 5, 6});
+  auto output            = cudf::allocate_like(input);
+  auto input_device_view = cudf::column_device_view::create(input, stream);
+  auto output_device_view =
+    cudf::mutable_column_device_view::create(output->mutable_view(), stream);
+
+  EXPECT_THROW(thrust::copy(rmm::exec_policy(stream),
+                            input_device_view->begin<T>(),
+                            input_device_view->end<T>(),
+                            output_device_view->begin<int64_t>()),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/column/column_test.cpp b/cpp/tests/column/column_test.cpp
new file mode 100644
index 0000000..b278e49
--- /dev/null
+++ b/cpp/tests/column/column_test.cpp
@@ -0,0 +1,648 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <numeric>
+#include <random>
+
+template <typename T>
+struct TypedColumnTest : public cudf::test::BaseFixture {
+  cudf::data_type type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+
+  TypedColumnTest(rmm::cuda_stream_view stream = cudf::get_default_stream())
+    : data{_num_elements * cudf::size_of(type()), stream},
+      mask{cudf::bitmask_allocation_size_bytes(_num_elements), stream}
+  {
+    std::vector<char> h_data(std::max(data.size(), mask.size()));
+    std::iota(h_data.begin(), h_data.end(), 0);
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(data.data(), h_data.data(), data.size(), cudaMemcpyDefault, stream.value()));
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(mask.data(), h_data.data(), mask.size(), cudaMemcpyDefault, stream.value()));
+  }
+
+  cudf::size_type num_elements() { return _num_elements; }
+
+  std::random_device r;
+  std::default_random_engine generator{r()};
+  std::uniform_int_distribution<cudf::size_type> distribution{200, 1000};
+  cudf::size_type _num_elements{distribution(generator)};
+  rmm::device_buffer data{};
+  rmm::device_buffer mask{};
+  rmm::device_buffer all_valid_mask{create_null_mask(num_elements(), cudf::mask_state::ALL_VALID)};
+  rmm::device_buffer all_null_mask{create_null_mask(num_elements(), cudf::mask_state::ALL_NULL)};
+};
+
+TYPED_TEST_SUITE(TypedColumnTest, cudf::test::Types<int32_t>);
+
+/**
+ * @brief Verifies equality of the properties and data of a `column`'s views.
+ *
+ * @param col The `column` to verify
+ */
+void verify_column_views(cudf::column col)
+{
+  cudf::column_view view                 = col;
+  cudf::mutable_column_view mutable_view = col;
+  EXPECT_EQ(col.type(), view.type());
+  EXPECT_EQ(col.type(), mutable_view.type());
+  EXPECT_EQ(col.size(), view.size());
+  EXPECT_EQ(col.size(), mutable_view.size());
+  EXPECT_EQ(col.null_count(), view.null_count());
+  EXPECT_EQ(col.null_count(), mutable_view.null_count());
+  EXPECT_EQ(col.nullable(), view.nullable());
+  EXPECT_EQ(col.nullable(), mutable_view.nullable());
+  EXPECT_EQ(col.num_children(), view.num_children());
+  EXPECT_EQ(col.num_children(), mutable_view.num_children());
+  EXPECT_EQ(view.head(), mutable_view.head());
+  EXPECT_EQ(view.data<char>(), mutable_view.data<char>());
+  EXPECT_EQ(view.offset(), mutable_view.offset());
+}
+
+TYPED_TEST(TypedColumnTest, DefaultNullCountNoMask)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  EXPECT_FALSE(col.nullable());
+  EXPECT_FALSE(col.has_nulls());
+  EXPECT_EQ(0, col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, DefaultNullCountEmptyMask)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  EXPECT_FALSE(col.nullable());
+  EXPECT_FALSE(col.has_nulls());
+  EXPECT_EQ(0, col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, DefaultNullCountAllValid)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  EXPECT_TRUE(col.nullable());
+  EXPECT_FALSE(col.has_nulls());
+  EXPECT_EQ(0, col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, ExplicitNullCountAllValid)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  EXPECT_TRUE(col.nullable());
+  EXPECT_FALSE(col.has_nulls());
+  EXPECT_EQ(0, col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, DefaultNullCountAllNull)
+{
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   std::move(this->data),
+                   std::move(this->all_null_mask),
+                   this->num_elements()};
+  EXPECT_TRUE(col.nullable());
+  EXPECT_TRUE(col.has_nulls());
+  EXPECT_EQ(this->num_elements(), col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, ExplicitNullCountAllNull)
+{
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   std::move(this->data),
+                   std::move(this->all_null_mask),
+                   this->num_elements()};
+  EXPECT_TRUE(col.nullable());
+  EXPECT_TRUE(col.has_nulls());
+  EXPECT_EQ(this->num_elements(), col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, SetNullCountNoMask)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  EXPECT_THROW(col.set_null_count(1), cudf::logic_error);
+}
+
+TYPED_TEST(TypedColumnTest, SetEmptyNullMaskNonZeroNullCount)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  rmm::device_buffer empty_null_mask{};
+  EXPECT_THROW(col.set_null_mask(std::move(empty_null_mask), this->num_elements()),
+               cudf::logic_error);
+}
+
+TYPED_TEST(TypedColumnTest, SetInvalidSizeNullMaskNonZeroNullCount)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  auto invalid_size_null_mask =
+    create_null_mask(std::min(this->num_elements() - 50, 0), cudf::mask_state::ALL_VALID);
+  EXPECT_THROW(
+    col.set_null_mask(invalid_size_null_mask, this->num_elements(), cudf::get_default_stream()),
+    cudf::logic_error);
+}
+
+TYPED_TEST(TypedColumnTest, SetNullCountEmptyMask)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  EXPECT_THROW(col.set_null_count(1), cudf::logic_error);
+}
+
+TYPED_TEST(TypedColumnTest, SetNullCountAllValid)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  EXPECT_NO_THROW(col.set_null_count(0));
+  EXPECT_EQ(0, col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, SetNullCountAllNull)
+{
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   std::move(this->data),
+                   std::move(this->all_null_mask),
+                   this->num_elements()};
+  EXPECT_NO_THROW(col.set_null_count(this->num_elements()));
+  EXPECT_EQ(this->num_elements(), col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, ResetNullCountAllNull)
+{
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   std::move(this->data),
+                   std::move(this->all_null_mask),
+                   this->num_elements()};
+
+  EXPECT_EQ(this->num_elements(), col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, ResetNullCountAllValid)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  EXPECT_EQ(0, col.null_count());
+}
+
+TYPED_TEST(TypedColumnTest, CopyDataNoMask)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  EXPECT_EQ(this->type(), col.type());
+  EXPECT_FALSE(col.nullable());
+  EXPECT_EQ(0, col.null_count());
+  EXPECT_EQ(this->num_elements(), col.size());
+  EXPECT_EQ(0, col.num_children());
+
+  verify_column_views(col);
+
+  // Verify deep copy
+  cudf::column_view v = col;
+  EXPECT_NE(v.head(), this->data.data());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(v.head(), this->data.data(), this->data.size());
+}
+
+TYPED_TEST(TypedColumnTest, MoveDataNoMask)
+{
+  void* original_data = this->data.data();
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  EXPECT_EQ(this->type(), col.type());
+  EXPECT_FALSE(col.nullable());
+  EXPECT_EQ(0, col.null_count());
+  EXPECT_EQ(this->num_elements(), col.size());
+  EXPECT_EQ(0, col.num_children());
+
+  verify_column_views(col);
+
+  // Verify shallow copy
+  cudf::column_view v = col;
+  EXPECT_EQ(v.head(), original_data);
+}
+
+TYPED_TEST(TypedColumnTest, CopyDataAndMask)
+{
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   rmm::device_buffer{this->data, cudf::get_default_stream()},
+                   rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+                   0};
+  EXPECT_EQ(this->type(), col.type());
+  EXPECT_TRUE(col.nullable());
+  EXPECT_EQ(0, col.null_count());
+  EXPECT_EQ(this->num_elements(), col.size());
+  EXPECT_EQ(0, col.num_children());
+
+  verify_column_views(col);
+
+  // Verify deep copy
+  cudf::column_view v = col;
+  EXPECT_NE(v.head(), this->data.data());
+  EXPECT_NE(v.null_mask(), this->all_valid_mask.data());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(v.head(), this->data.data(), this->data.size());
+  CUDF_TEST_EXPECT_EQUAL_BUFFERS(v.null_mask(), this->all_valid_mask.data(), this->mask.size());
+}
+
+TYPED_TEST(TypedColumnTest, MoveDataAndMask)
+{
+  void* original_data = this->data.data();
+  void* original_mask = this->all_valid_mask.data();
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  EXPECT_EQ(this->type(), col.type());
+  EXPECT_TRUE(col.nullable());
+  EXPECT_EQ(0, col.null_count());
+  EXPECT_EQ(this->num_elements(), col.size());
+  EXPECT_EQ(0, col.num_children());
+
+  verify_column_views(col);
+
+  // Verify shallow copy
+  cudf::column_view v = col;
+  EXPECT_EQ(v.head(), original_data);
+  EXPECT_EQ(v.null_mask(), original_mask);
+}
+
+TYPED_TEST(TypedColumnTest, CopyConstructorNoMask)
+{
+  cudf::column original{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+  cudf::column copy{original};
+  verify_column_views(copy);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(original, copy);
+
+  // Verify deep copy
+  cudf::column_view original_view = original;
+  cudf::column_view copy_view     = copy;
+  EXPECT_NE(original_view.head(), copy_view.head());
+}
+
+TYPED_TEST(TypedColumnTest, CopyConstructorWithMask)
+{
+  cudf::column original{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  cudf::column copy{original};
+  verify_column_views(copy);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(original, copy);
+
+  // Verify deep copy
+  cudf::column_view original_view = original;
+  cudf::column_view copy_view     = copy;
+  EXPECT_NE(original_view.head(), copy_view.head());
+  EXPECT_NE(original_view.null_mask(), copy_view.null_mask());
+}
+
+TYPED_TEST(TypedColumnTest, MoveConstructorNoMask)
+{
+  cudf::column original{
+    this->type(), this->num_elements(), std::move(this->data), rmm::device_buffer{}, 0};
+
+  auto original_data = original.view().head();
+
+  cudf::column moved_to{std::move(original)};
+
+  EXPECT_EQ(0, original.size());
+  EXPECT_EQ(cudf::data_type{cudf::type_id::EMPTY}, original.type());
+
+  verify_column_views(moved_to);
+
+  // Verify move
+  cudf::column_view moved_to_view = moved_to;
+  EXPECT_EQ(original_data, moved_to_view.head());
+}
+
+TYPED_TEST(TypedColumnTest, MoveConstructorWithMask)
+{
+  cudf::column original{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  auto original_data = original.view().head();
+  auto original_mask = original.view().null_mask();
+  cudf::column moved_to{std::move(original)};
+  verify_column_views(moved_to);
+
+  EXPECT_EQ(0, original.size());
+  EXPECT_EQ(cudf::data_type{cudf::type_id::EMPTY}, original.type());
+
+  // Verify move
+  cudf::column_view moved_to_view = moved_to;
+  EXPECT_EQ(original_data, moved_to_view.head());
+  EXPECT_EQ(original_mask, moved_to_view.null_mask());
+}
+
+TYPED_TEST(TypedColumnTest, DeviceUvectorConstructorNoMask)
+{
+  auto data = cudf::device_span<TypeParam const>(static_cast<TypeParam*>(this->data.data()),
+                                                 this->num_elements());
+
+  auto original = cudf::detail::make_device_uvector_async(
+    data, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto original_data = original.data();
+  cudf::column moved_to{std::move(original), rmm::device_buffer{}, 0};
+  verify_column_views(moved_to);
+
+  // Verify move
+  cudf::column_view moved_to_view = moved_to;
+  EXPECT_EQ(original_data, moved_to_view.head());
+}
+
+TYPED_TEST(TypedColumnTest, DeviceUvectorConstructorWithMask)
+{
+  auto data = cudf::device_span<TypeParam const>(static_cast<TypeParam*>(this->data.data()),
+                                                 this->num_elements());
+
+  auto original = cudf::detail::make_device_uvector_async(
+    data, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto original_data = original.data();
+  auto original_mask = this->all_valid_mask.data();
+  cudf::column moved_to{std::move(original), std::move(this->all_valid_mask), 0};
+  verify_column_views(moved_to);
+
+  // Verify move
+  cudf::column_view moved_to_view = moved_to;
+  EXPECT_EQ(original_data, moved_to_view.head());
+  EXPECT_EQ(original_mask, moved_to_view.null_mask());
+}
+
+TYPED_TEST(TypedColumnTest, ConstructWithChildren)
+{
+  std::vector<std::unique_ptr<cudf::column>> children;
+
+  children.emplace_back(std::make_unique<cudf::column>(
+    cudf::data_type{cudf::type_id::INT8},
+    42,
+    rmm::device_buffer{this->data, cudf::get_default_stream()},
+    rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+    0));
+  children.emplace_back(std::make_unique<cudf::column>(
+    cudf::data_type{cudf::type_id::FLOAT64},
+    314,
+    rmm::device_buffer{this->data, cudf::get_default_stream()},
+    rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+    0));
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   rmm::device_buffer{this->data, cudf::get_default_stream()},
+                   rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+                   0,
+                   std::move(children)};
+
+  verify_column_views(col);
+  EXPECT_EQ(2, col.num_children());
+  EXPECT_EQ(cudf::data_type{cudf::type_id::INT8}, col.child(0).type());
+  EXPECT_EQ(42, col.child(0).size());
+  EXPECT_EQ(cudf::data_type{cudf::type_id::FLOAT64}, col.child(1).type());
+  EXPECT_EQ(314, col.child(1).size());
+}
+
+TYPED_TEST(TypedColumnTest, ReleaseNoChildren)
+{
+  cudf::column col{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  auto original_data = col.view().head();
+  auto original_mask = col.view().null_mask();
+
+  cudf::column::contents contents = col.release();
+  EXPECT_EQ(original_data, contents.data->data());
+  EXPECT_EQ(original_mask, contents.null_mask->data());
+  EXPECT_EQ(0u, contents.children.size());
+  EXPECT_EQ(0, col.size());
+  EXPECT_EQ(0, col.null_count());
+  EXPECT_EQ(cudf::data_type{cudf::type_id::EMPTY}, col.type());
+  EXPECT_EQ(0, col.num_children());
+}
+
+TYPED_TEST(TypedColumnTest, ReleaseWithChildren)
+{
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.emplace_back(std::make_unique<cudf::column>(
+    this->type(),
+    this->num_elements(),
+    rmm::device_buffer{this->data, cudf::get_default_stream()},
+    rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+    0));
+  children.emplace_back(std::make_unique<cudf::column>(
+    this->type(),
+    this->num_elements(),
+    rmm::device_buffer{this->data, cudf::get_default_stream()},
+    rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+    0));
+  cudf::column col{this->type(),
+                   this->num_elements(),
+                   rmm::device_buffer{this->data, cudf::get_default_stream()},
+                   rmm::device_buffer{this->all_valid_mask, cudf::get_default_stream()},
+                   0,
+                   std::move(children)};
+
+  auto original_data = col.view().head();
+  auto original_mask = col.view().null_mask();
+
+  cudf::column::contents contents = col.release();
+  EXPECT_EQ(original_data, contents.data->data());
+  EXPECT_EQ(original_mask, contents.null_mask->data());
+  EXPECT_EQ(2u, contents.children.size());
+  EXPECT_EQ(0, col.size());
+  EXPECT_EQ(0, col.null_count());
+  EXPECT_EQ(cudf::data_type{cudf::type_id::EMPTY}, col.type());
+  EXPECT_EQ(0, col.num_children());
+}
+
+TYPED_TEST(TypedColumnTest, ColumnViewConstructorWithMask)
+{
+  cudf::column original{
+    this->type(), this->num_elements(), std::move(this->data), std::move(this->all_valid_mask), 0};
+  cudf::column_view original_view = original;
+  cudf::column copy{original_view};
+  verify_column_views(copy);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(original, copy);
+
+  // Verify deep copy
+  cudf::column_view copy_view = copy;
+  EXPECT_NE(original_view.head(), copy_view.head());
+  EXPECT_NE(original_view.null_mask(), copy_view.null_mask());
+}
+
+template <typename T>
+struct ListsColumnTest : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(ListsColumnTest, NumericTypesNotBool);
+
+TYPED_TEST(ListsColumnTest, ListsColumnViewConstructor)
+{
+  cudf::test::lists_column_wrapper<TypeParam> list{{1, 2}, {3, 4}, {5, 6, 7}, {8, 9}};
+
+  auto result = std::make_unique<cudf::column>(list);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(list, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedColumnViewConstructor)
+{
+  cudf::test::lists_column_wrapper<TypeParam> list{{1, 2}, {3, 4}, {5, 6, 7}, {8, 9}};
+  cudf::test::lists_column_wrapper<TypeParam> expect{{3, 4}, {5, 6, 7}};
+
+  auto sliced = cudf::slice(list, {1, 3}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedIncludesEmpty)
+{
+  cudf::test::lists_column_wrapper<TypeParam> list{{1, 2}, {}, {3, 4}, {8, 9}};
+  cudf::test::lists_column_wrapper<TypeParam> expect{{}, {3, 4}};
+
+  auto sliced = cudf::slice(list, {1, 3}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedNonNestedEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Column of List<int>
+  LCW list{{1, 2}, {}, {3, 4}, {8, 9}};
+  // Column of 1 row, an empty List<int>
+  LCW expect{LCW{}};
+
+  auto sliced = cudf::slice(list, {1, 2}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedNestedEmpty)
+{
+  using LCW     = cudf::test::lists_column_wrapper<TypeParam>;
+  using FWCW_SZ = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  // Column of List<List<int>>, with incomplete hierarchy
+  LCW list{{LCW{1}, LCW{2}},
+           {},  // < ----------- empty List<List<int>>, slice this
+           {LCW{3}, LCW{4, 5}}};
+
+  // Make 1-row column of type List<List<int>>, the row data contains 0 element.
+  // Well-formed memory layout:
+  // type: List<List<int>>
+  // Length: 1
+  // Mask: 1
+  // Offsets: 0, 0
+  //    List<int>
+  //    Length: 0
+  //    Offset:
+  //        INT
+  //        Length: 0
+  auto leaf      = std::make_unique<cudf::column>(cudf::column(LCW{}));
+  auto offset    = std::make_unique<cudf::column>(cudf::column(FWCW_SZ{0, 0}));
+  auto null_mask = cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED);
+  auto expect =
+    cudf::make_lists_column(1, std::move(offset), std::move(leaf), 0, std::move(null_mask));
+
+  auto sliced = cudf::slice(list, {1, 2}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expect, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedZeroSliceLengthNested)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Column of List<List<int>>, with incomplete hierarchy
+  LCW list{{LCW{1}, LCW{2}}, {}, {LCW{3}, LCW{4, 5}}};
+
+  auto expect = cudf::empty_like(list);
+
+  auto sliced = cudf::slice(list, {0, 0}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expect, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedZeroSliceLengthNonNested)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+
+  LCW list{{1, 2}, {}, {3, 4}, {8, 9}};
+
+  auto expect = cudf::empty_like(list);
+
+  auto sliced = cudf::slice(list, {0, 0}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expect, result->view());
+}
+
+TYPED_TEST(ListsColumnTest, ListsSlicedColumnViewConstructorWithNulls)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  auto expect_valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 != 0; });
+
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+
+  cudf::test::lists_column_wrapper<TypeParam> list{
+    {{{{1, 2}, {3, 4}}, valids}, LCW{}, {{{5, 6, 7}, LCW{}, {8, 9}}, valids}, LCW{}, LCW{}},
+    valids};
+
+  cudf::test::lists_column_wrapper<TypeParam> expect{
+    {LCW{}, {{{5, 6, 7}, LCW{}, {8, 9}}, valids}, LCW{}, LCW{}}, expect_valids};
+
+  auto sliced = cudf::slice(list, {1, 5}).front();
+  auto result = std::make_unique<cudf::column>(sliced);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, result->view());
+
+  // TODO: null mask equality is being checked separately because
+  // expect_columns_equal doesn't do the check for lists columns.
+  // This is fixed in https://github.com/rapidsai/cudf/pull/5904,
+  // so we should remove this check after that's merged:
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    cudf::mask_to_bools(result->view().null_mask(), 0, 4)->view(),
+    cudf::mask_to_bools(static_cast<cudf::column_view>(expect).null_mask(), 0, 4)->view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/column/column_view_device_span_test.cpp b/cpp/tests/column/column_view_device_span_test.cpp
new file mode 100644
index 0000000..7daf687
--- /dev/null
+++ b/cpp/tests/column/column_view_device_span_test.cpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <memory>
+
+template <typename T, CUDF_ENABLE_IF(cudf::is_numeric<T>() or cudf::is_chrono<T>())>
+std::unique_ptr<cudf::column> example_column()
+{
+  auto begin = thrust::make_counting_iterator(1);
+  auto end   = thrust::make_counting_iterator(16);
+  return cudf::test::fixed_width_column_wrapper<T>(begin, end).release();
+}
+
+template <typename T>
+struct ColumnViewDeviceSpanTests : public cudf::test::BaseFixture {};
+
+using DeviceSpanTypes = cudf::test::FixedWidthTypesWithoutFixedPoint;
+TYPED_TEST_SUITE(ColumnViewDeviceSpanTests, DeviceSpanTypes);
+
+TYPED_TEST(ColumnViewDeviceSpanTests, conversion_round_trip)
+{
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+
+  // Test implicit conversion, round trip
+  cudf::device_span<TypeParam const> device_span_from_col_view = col_view;
+  cudf::column_view col_view_from_device_span                  = device_span_from_col_view;
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_view, col_view_from_device_span);
+}
+
+struct ColumnViewDeviceSpanErrorTests : public cudf::test::BaseFixture {};
+
+TEST_F(ColumnViewDeviceSpanErrorTests, type_mismatch)
+{
+  auto col      = example_column<int32_t>();
+  auto col_view = cudf::column_view{*col};
+  EXPECT_THROW((void)cudf::device_span<float const>{col_view}, cudf::logic_error);
+}
+
+TEST_F(ColumnViewDeviceSpanErrorTests, nullable_column)
+{
+  auto col = example_column<int32_t>();
+  col->set_null_mask(cudf::create_null_mask(col->size(), cudf::mask_state::ALL_NULL), col->size());
+  auto col_view = cudf::column_view{*col};
+  EXPECT_THROW((void)cudf::device_span<int32_t const>{col_view}, cudf::logic_error);
+}
diff --git a/cpp/tests/column/column_view_shallow_test.cpp b/cpp/tests/column/column_view_shallow_test.cpp
new file mode 100644
index 0000000..3e56506
--- /dev/null
+++ b/cpp/tests/column/column_view_shallow_test.cpp
@@ -0,0 +1,436 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <memory>
+#include <type_traits>
+
+// fixed_width, dict, string, list, struct
+template <typename T, std::enable_if_t<cudf::is_fixed_width<T>()>* = nullptr>
+std::unique_ptr<cudf::column> example_column()
+{
+  auto begin = thrust::make_counting_iterator(1);
+  auto end   = thrust::make_counting_iterator(16);
+  return cudf::test::fixed_width_column_wrapper<T>(begin, end).release();
+}
+
+template <typename T, std::enable_if_t<cudf::is_dictionary<T>()>* = nullptr>
+std::unique_ptr<cudf::column> example_column()
+{
+  return cudf::test::dictionary_column_wrapper<std::string>(
+           {"fff", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "", ""}, {1, 1, 1, 1, 1, 1, 1, 1, 0})
+    .release();
+}
+
+template <typename T,
+          std::enable_if_t<std::is_same_v<T, std::string> or
+                           std::is_same_v<T, cudf::string_view>>* = nullptr>
+std::unique_ptr<cudf::column> example_column()
+
+{
+  return cudf::test::strings_column_wrapper(
+           {"fff", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "", ""})
+    .release();
+}
+
+template <typename T, std::enable_if_t<std::is_same_v<T, cudf::list_view>>* = nullptr>
+std::unique_ptr<cudf::column> example_column()
+{
+  return cudf::test::lists_column_wrapper<int>({{1, 2, 3}, {4, 5}, {}, {6, 7, 8}}).release();
+}
+
+template <typename T, std::enable_if_t<std::is_same_v<T, cudf::struct_view>>* = nullptr>
+std::unique_ptr<cudf::column> example_column()
+{
+  auto begin    = thrust::make_counting_iterator(1);
+  auto end      = thrust::make_counting_iterator(16);
+  auto member_0 = cudf::test::fixed_width_column_wrapper<int32_t>(begin, end);
+  auto member_1 = cudf::test::fixed_width_column_wrapper<int32_t>(begin + 10, end + 10);
+  return cudf::test::structs_column_wrapper({member_0, member_1}).release();
+}
+
+template <typename T>
+struct ColumnViewShallowTests : public cudf::test::BaseFixture {};
+
+using AllTypes = cudf::test::Concat<cudf::test::AllTypes, cudf::test::CompoundTypes>;
+TYPED_TEST_SUITE(ColumnViewShallowTests, AllTypes);
+
+// Test for fixed_width, dict, string, list, struct
+// column_view, column_view = same hash.
+// column_view, make a copy = same hash.
+// new column_view from column = same hash
+// column_view, copy column = diff hash
+// column_view, diff column = diff hash.
+//
+// column_view old, update data + new column_view     = same hash.
+// column_view old, add null_mask + new column_view   = diff hash.
+// column_view old, update nulls + new column_view    = same hash.
+// column_view old, set_null_count + new column_view  = same hash.
+//
+// column_view, sliced[0, size) = same hash (for split too)
+// column_view, sliced[n:)      = diff hash (for split too)
+// column_view, bit_cast        = diff hash
+//
+// mutable_column_view, column_view = same hash
+// mutable_column_view, modified mutable_column_view = same hash
+//
+// update the children column data  = same hash
+// update the children column_views = diff hash
+
+TYPED_TEST(ColumnViewShallowTests, shallow_hash_basic)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // same = same hash
+  {
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_view));
+  }
+  // copy column_view = same hash
+  {
+    auto col_view_copy = col_view;
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_view_copy));
+  }
+
+  // new column_view from column = same hash
+  {
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_view_new));
+  }
+
+  // copy column = diff hash
+  {
+    auto col_new       = std::make_unique<cudf::column>(*col);
+    auto col_view_copy = col_new->view();
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_view_copy));
+  }
+
+  // column_view, diff column = diff hash.
+  {
+    auto col_diff      = example_column<TypeParam>();
+    auto col_view_diff = cudf::column_view{*col_diff};
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_view_diff));
+  }
+}
+TYPED_TEST(ColumnViewShallowTests, shallow_hash_update_data)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // update data + new column_view = same hash.
+  {
+    // update data by modifying some bits: fixed_width, string, dict, list, struct
+    if constexpr (cudf::is_fixed_width<TypeParam>()) {
+      // Update data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->mutable_view().head());
+      cudf::set_null_mask(data, 2, 64, true);
+    } else {
+      // Update child(0).data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->child(0).mutable_view().head());
+      cudf::set_null_mask(data, 2, 64, true);
+    }
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_view_new));
+  }
+  // add null_mask + new column_view = diff hash.
+  {
+    col->set_null_mask(cudf::create_null_mask(col->size(), cudf::mask_state::ALL_VALID), 0);
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_view_new));
+    [[maybe_unused]] auto const nulls = col_view_new.null_count();
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_view_new));
+    auto col_view_new2 = cudf::column_view{*col};
+    EXPECT_EQ(shallow_hash(col_view_new), shallow_hash(col_view_new2));
+  }
+  col_view = cudf::column_view{*col};  // updating after adding null_mask
+  // update nulls + new column_view = same hash.
+  {
+    cudf::set_null_mask(col->mutable_view().null_mask(), 2, 4, false);
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_view_new));
+  }
+  // set_null_count + new column_view = same hash.
+  {
+    col->set_null_count(col->size());
+    auto col_view_new2 = cudf::column_view{*col};
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_view_new2));
+  }
+}
+
+TYPED_TEST(ColumnViewShallowTests, shallow_hash_slice)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // column_view, sliced[0, size)  = same hash (for split too)
+  {
+    auto col_sliced = cudf::slice(col_view, {0, col_view.size()});
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_sliced[0]));
+    auto col_split = cudf::split(col_view, {0});
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_split[0]));
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_split[1]));
+  }
+  // column_view, sliced[n:]       = diff hash (for split too)
+  {
+    auto col_sliced = cudf::slice(col_view, {1, col_view.size()});
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_sliced[0]));
+    auto col_split = cudf::split(col_view, {1});
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_split[0]));
+    EXPECT_NE(shallow_hash(col_view), shallow_hash(col_split[1]));
+  }
+  // column_view, col copy sliced[0, 0)  = same hash (empty column)
+  {
+    auto col_new        = std::make_unique<cudf::column>(*col);
+    auto col_new_view   = col_new->view();
+    auto col_sliced     = cudf::slice(col_view, {0, 0, 1, 1, col_view.size(), col_view.size()});
+    auto col_new_sliced = cudf::slice(col_new_view, {0, 0, 1, 1, col_view.size(), col_view.size()});
+
+    EXPECT_EQ(shallow_hash(col_sliced[0]), shallow_hash(col_sliced[1]));
+    EXPECT_EQ(shallow_hash(col_sliced[1]), shallow_hash(col_sliced[2]));
+    EXPECT_EQ(shallow_hash(col_sliced[0]), shallow_hash(col_new_sliced[0]));
+    EXPECT_EQ(shallow_hash(col_sliced[1]), shallow_hash(col_new_sliced[1]));
+    EXPECT_EQ(shallow_hash(col_sliced[2]), shallow_hash(col_new_sliced[2]));
+  }
+
+  // column_view, bit_cast         = diff hash
+  {
+    if constexpr (std::is_integral_v<TypeParam> and not std::is_same_v<TypeParam, bool>) {
+      using newType    = std::conditional_t<std::is_signed_v<TypeParam>,
+                                         std::make_unsigned_t<TypeParam>,
+                                         std::make_signed_t<TypeParam>>;
+      auto new_type    = cudf::data_type(cudf::type_to_id<newType>());
+      auto col_bitcast = cudf::bit_cast(col_view, new_type);
+      EXPECT_NE(shallow_hash(col_view), shallow_hash(col_bitcast));
+    }
+  }
+}
+
+TYPED_TEST(ColumnViewShallowTests, shallow_hash_mutable)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // mutable_column_view, column_view = same hash
+  {
+    auto col_mutable = cudf::mutable_column_view{*col};
+    EXPECT_EQ(shallow_hash(col_mutable), shallow_hash(col_view));
+  }
+  // mutable_column_view, modified mutable_column_view = same hash
+  // update the children column data = same hash
+  {
+    auto col_mutable = cudf::mutable_column_view{*col};
+    if constexpr (cudf::is_fixed_width<TypeParam>()) {
+      // Update data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->mutable_view().head());
+      cudf::set_null_mask(data, 1, 32, false);
+    } else {
+      // Update child(0).data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->child(0).mutable_view().head());
+      cudf::set_null_mask(data, 1, 32, false);
+    }
+    EXPECT_EQ(shallow_hash(col_view), shallow_hash(col_mutable));
+    auto col_mutable_new = cudf::mutable_column_view{*col};
+    EXPECT_EQ(shallow_hash(col_mutable), shallow_hash(col_mutable_new));
+  }
+  // update the children column_views = diff hash
+  {
+    if constexpr (cudf::is_nested<TypeParam>()) {
+      col->child(0).set_null_mask(
+        cudf::create_null_mask(col->child(0).size(), cudf::mask_state::ALL_NULL),
+        col->child(0).size());
+      auto col_child_updated = cudf::mutable_column_view{*col};
+      EXPECT_NE(shallow_hash(col_view), shallow_hash(col_child_updated));
+    }
+  }
+}
+
+TYPED_TEST(ColumnViewShallowTests, is_shallow_equivalent_basic)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // same = same hash
+  {
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_view));
+  }
+  // copy column_view = same hash
+  {
+    auto col_view_copy = col_view;
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_view_copy));
+  }
+
+  // new column_view from column = same hash
+  {
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_view_new));
+  }
+
+  // copy column = diff hash
+  {
+    auto col_new       = std::make_unique<cudf::column>(*col);
+    auto col_view_copy = col_new->view();
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_view_copy));
+  }
+
+  // column_view, diff column = diff hash.
+  {
+    auto col_diff      = example_column<TypeParam>();
+    auto col_view_diff = cudf::column_view{*col_diff};
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_view_diff));
+  }
+}
+TYPED_TEST(ColumnViewShallowTests, is_shallow_equivalent_update_data)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // update data + new column_view = same hash.
+  {
+    // update data by modifying some bits: fixed_width, string, dict, list, struct
+    if constexpr (cudf::is_fixed_width<TypeParam>()) {
+      // Update data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->mutable_view().head());
+      cudf::set_null_mask(data, 2, 64, true);
+    } else {
+      // Update child(0).data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->child(0).mutable_view().head());
+      cudf::set_null_mask(data, 2, 64, true);
+    }
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_view_new));
+  }
+  // add null_mask + new column_view = diff hash.
+  {
+    col->set_null_mask(cudf::create_null_mask(col->size(), cudf::mask_state::ALL_VALID), 0);
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_view_new));
+    [[maybe_unused]] auto const nulls = col_view_new.null_count();
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_view_new));
+    auto col_view_new2 = cudf::column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_view_new, col_view_new2));
+  }
+  col_view = cudf::column_view{*col};  // updating after adding null_mask
+  // update nulls + new column_view = same hash.
+  {
+    cudf::set_null_mask(col->mutable_view().null_mask(), 2, 4, false);
+    auto col_view_new = cudf::column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_view_new));
+  }
+  // set_null_count + new column_view = same hash.
+  {
+    col->set_null_count(col->size());
+    auto col_view_new2 = cudf::column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_view_new2));
+  }
+}
+
+TYPED_TEST(ColumnViewShallowTests, is_shallow_equivalent_slice)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // column_view, sliced[0, size)  = same hash (for split too)
+  {
+    auto col_sliced = cudf::slice(col_view, {0, col_view.size()});
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_sliced[0]));
+    auto col_split = cudf::split(col_view, {0});
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_split[0]));
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_split[1]));
+  }
+  // column_view, sliced[n:]       = diff hash (for split too)
+  {
+    auto col_sliced = cudf::slice(col_view, {1, col_view.size()});
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_sliced[0]));
+    auto col_split = cudf::split(col_view, {1});
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_split[0]));
+    EXPECT_FALSE(is_shallow_equivalent(col_view, col_split[1]));
+  }
+  // column_view, col copy sliced[0, 0)  = same hash (empty column)
+  {
+    auto col_new        = std::make_unique<cudf::column>(*col);
+    auto col_new_view   = col_new->view();
+    auto col_sliced     = cudf::slice(col_view, {0, 0, 1, 1, col_view.size(), col_view.size()});
+    auto col_new_sliced = cudf::slice(col_new_view, {0, 0, 1, 1, col_view.size(), col_view.size()});
+
+    EXPECT_TRUE(is_shallow_equivalent(col_sliced[0], col_sliced[1]));
+    EXPECT_TRUE(is_shallow_equivalent(col_sliced[1], col_sliced[2]));
+    EXPECT_TRUE(is_shallow_equivalent(col_sliced[0], col_new_sliced[0]));
+    EXPECT_TRUE(is_shallow_equivalent(col_sliced[1], col_new_sliced[1]));
+    EXPECT_TRUE(is_shallow_equivalent(col_sliced[2], col_new_sliced[2]));
+  }
+
+  // column_view, bit_cast         = diff hash
+  {
+    if constexpr (std::is_integral_v<TypeParam> and not std::is_same_v<TypeParam, bool>) {
+      using newType    = std::conditional_t<std::is_signed_v<TypeParam>,
+                                         std::make_unsigned_t<TypeParam>,
+                                         std::make_signed_t<TypeParam>>;
+      auto new_type    = cudf::data_type(cudf::type_to_id<newType>());
+      auto col_bitcast = cudf::bit_cast(col_view, new_type);
+      EXPECT_FALSE(is_shallow_equivalent(col_view, col_bitcast));
+    }
+  }
+}
+
+TYPED_TEST(ColumnViewShallowTests, is_shallow_equivalent_mutable)
+{
+  using namespace cudf::detail;
+  auto col      = example_column<TypeParam>();
+  auto col_view = cudf::column_view{*col};
+  // mutable_column_view, column_view = same hash
+  {
+    auto col_mutable = cudf::mutable_column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_mutable, col_view));
+  }
+  // mutable_column_view, modified mutable_column_view = same hash
+  // update the children column data = same hash
+  {
+    auto col_mutable = cudf::mutable_column_view{*col};
+    if constexpr (cudf::is_fixed_width<TypeParam>()) {
+      // Update data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->mutable_view().head());
+      cudf::set_null_mask(data, 1, 32, false);
+    } else {
+      // Update child(0).data
+      auto data = reinterpret_cast<cudf::bitmask_type*>(col->child(0).mutable_view().head());
+      cudf::set_null_mask(data, 1, 32, false);
+    }
+    EXPECT_TRUE(is_shallow_equivalent(col_view, col_mutable));
+    auto col_mutable_new = cudf::mutable_column_view{*col};
+    EXPECT_TRUE(is_shallow_equivalent(col_mutable, col_mutable_new));
+  }
+  // update the children column_views = diff hash
+  {
+    if constexpr (cudf::is_nested<TypeParam>()) {
+      col->child(0).set_null_mask(
+        cudf::create_null_mask(col->child(0).size(), cudf::mask_state::ALL_NULL), col->size());
+      auto col_child_updated = cudf::mutable_column_view{*col};
+      EXPECT_FALSE(is_shallow_equivalent(col_view, col_child_updated));
+    }
+  }
+}
diff --git a/cpp/tests/column/compound_test.cu b/cpp/tests/column/compound_test.cu
new file mode 100644
index 0000000..fc34828
--- /dev/null
+++ b/cpp/tests/column/compound_test.cu
@@ -0,0 +1,216 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/sequence.h>
+
+#include <vector>
+
+struct CompoundColumnTest : public cudf::test::BaseFixture {};
+
+template <typename ColumnDeviceView>
+struct checker_for_level1 {
+  ColumnDeviceView d_column;
+  bool __device__ operator()(int32_t idx)
+  {
+    int32_t val1 = d_column.child(0).template element<int32_t>(idx);
+    int32_t val2 = d_column.child(1).template element<int32_t>(idx);
+    int32_t val3 = d_column.child(2).template element<int32_t>(idx);
+    return ((val1 + 100) == val2) && ((val2 + 100) == val3);
+  }
+};
+
+template <typename ColumnDeviceView>
+struct checker_for_level2 {
+  ColumnDeviceView d_column;
+  bool __device__ operator()(int32_t idx)
+  {
+    bool bcheck = true;
+    for (int i = 0; i < 2 && bcheck; ++i) {
+      auto child   = d_column.child(i);
+      int32_t val1 = child.child(0).template element<int32_t>(idx);
+      int32_t val2 = child.child(1).template element<int32_t>(idx);
+      int32_t val3 = child.child(2).template element<int32_t>(idx);
+      bcheck       = ((val1 + 100) == val2) && ((val2 + 100) == val3);
+    }
+    return bcheck;
+  }
+};
+
+TEST_F(CompoundColumnTest, ChildrenLevel1)
+{
+  rmm::device_uvector<int32_t> data(1000, cudf::get_default_stream());
+  thrust::sequence(rmm::exec_policy(cudf::get_default_stream()), data.begin(), data.end(), 1);
+
+  auto null_mask = cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED);
+  rmm::device_buffer data1{data.data() + 100, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data2{data.data() + 200, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data3{data.data() + 300, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  auto child1 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   100,
+                                   std::move(data1),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto child2 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   200,
+                                   std::move(data2),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto child3 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   300,
+                                   std::move(data3),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.emplace_back(std::move(child1));
+  children.emplace_back(std::move(child2));
+  children.emplace_back(std::move(child3));
+
+  auto parent = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::STRING},
+                                               100,
+                                               rmm::device_buffer{},
+                                               rmm::device_buffer{},
+                                               0,
+                                               std::move(children));
+
+  {
+    auto column = cudf::column_device_view::create(parent->view());
+    EXPECT_TRUE(thrust::any_of(rmm::exec_policy(cudf::get_default_stream()),
+                               thrust::make_counting_iterator<int32_t>(0),
+                               thrust::make_counting_iterator<int32_t>(100),
+                               checker_for_level1<cudf::column_device_view>{*column}));
+  }
+  {
+    auto column = cudf::mutable_column_device_view::create(parent->mutable_view());
+    EXPECT_TRUE(thrust::any_of(rmm::exec_policy(cudf::get_default_stream()),
+                               thrust::make_counting_iterator<int32_t>(0),
+                               thrust::make_counting_iterator<int32_t>(100),
+                               checker_for_level1<cudf::mutable_column_device_view>{*column}));
+  }
+}
+
+TEST_F(CompoundColumnTest, ChildrenLevel2)
+{
+  rmm::device_uvector<int32_t> data(1000, cudf::get_default_stream());
+  thrust::sequence(rmm::exec_policy(cudf::get_default_stream()), data.begin(), data.end(), 1);
+
+  auto null_mask = cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED);
+  rmm::device_buffer data11{data.data() + 100, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data12{data.data() + 200, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data13{data.data() + 300, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data21{data.data() + 400, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data22{data.data() + 500, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  rmm::device_buffer data23{data.data() + 600, 100 * sizeof(int32_t), cudf::get_default_stream()};
+  auto gchild11 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   100,
+                                   std::move(data11),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto gchild12 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   200,
+                                   std::move(data12),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto gchild13 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   300,
+                                   std::move(data13),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto gchild21 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   400,
+                                   std::move(data21),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto gchild22 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   500,
+                                   std::move(data22),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+  auto gchild23 =
+    std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::INT32},
+                                   600,
+                                   std::move(data23),
+                                   cudf::create_null_mask(100, cudf::mask_state::UNALLOCATED),
+                                   0);
+
+  std::vector<std::unique_ptr<cudf::column>> gchildren1;
+  gchildren1.emplace_back(std::move(gchild11));
+  gchildren1.emplace_back(std::move(gchild12));
+  gchildren1.emplace_back(std::move(gchild13));
+  std::vector<std::unique_ptr<cudf::column>> gchildren2;
+  gchildren2.emplace_back(std::move(gchild21));
+  gchildren2.emplace_back(std::move(gchild22));
+  gchildren2.emplace_back(std::move(gchild23));
+
+  auto children1 = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::STRING},
+                                                  100,
+                                                  rmm::device_buffer{},
+                                                  rmm::device_buffer{},
+                                                  0,
+                                                  std::move(gchildren1));
+  auto children2 = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::STRING},
+                                                  100,
+                                                  rmm::device_buffer{},
+                                                  rmm::device_buffer{},
+                                                  0,
+                                                  std::move(gchildren2));
+
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.emplace_back(std::move(children1));
+  children.emplace_back(std::move(children2));
+  auto parent = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::STRING},
+                                               100,
+                                               rmm::device_buffer{},
+                                               rmm::device_buffer{},
+                                               0,
+                                               std::move(children));
+
+  {
+    auto column = cudf::column_device_view::create(parent->view());
+    EXPECT_TRUE(thrust::any_of(rmm::exec_policy(cudf::get_default_stream()),
+                               thrust::make_counting_iterator<int32_t>(0),
+                               thrust::make_counting_iterator<int32_t>(100),
+                               checker_for_level2<cudf::column_device_view>{*column}));
+  }
+  {
+    auto column = cudf::mutable_column_device_view::create(parent->mutable_view());
+    EXPECT_TRUE(thrust::any_of(rmm::exec_policy(cudf::get_default_stream()),
+                               thrust::make_counting_iterator<int32_t>(0),
+                               thrust::make_counting_iterator<int32_t>(100),
+                               checker_for_level2<cudf::mutable_column_device_view>{*column}));
+  }
+}
diff --git a/cpp/tests/column/factories_test.cpp b/cpp/tests/column/factories_test.cpp
new file mode 100644
index 0000000..b06d097
--- /dev/null
+++ b/cpp/tests/column/factories_test.cpp
@@ -0,0 +1,770 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+class ColumnFactoryTest : public cudf::test::BaseFixture {
+  cudf::size_type _size{1000};
+
+ public:
+  cudf::size_type size() { return _size; }
+};
+
+template <typename T>
+class NumericFactoryTest : public ColumnFactoryTest {};
+
+TYPED_TEST_SUITE(NumericFactoryTest, cudf::test::NumericTypes);
+
+TYPED_TEST(NumericFactoryTest, EmptyNoMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 0, cudf::mask_state::UNALLOCATED);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), 0);
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, EmptyAllValidMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 0, cudf::mask_state::ALL_VALID);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), 0);
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, EmptyAllNullMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 0, cudf::mask_state::ALL_NULL);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), 0);
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, NoMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::UNALLOCATED);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, UnitializedMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::UNINITIALIZED);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, AllValidMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::ALL_VALID);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, AllNullMask)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::ALL_NULL);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(this->size(), column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, NullMaskAsParm)
+{
+  rmm::device_buffer null_mask{create_null_mask(this->size(), cudf::mask_state::ALL_NULL)};
+  auto column = cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                          this->size(),
+                                          std::move(null_mask),
+                                          this->size());
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(this->size(), column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, NullMaskAsEmptyParm)
+{
+  auto column = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), rmm::device_buffer{}, 0);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+class NonNumericFactoryTest : public ColumnFactoryTest,
+                              public testing::WithParamInterface<cudf::type_id> {};
+
+// All non-numeric types should throw
+TEST_P(NonNumericFactoryTest, NonNumericThrow)
+{
+  auto construct = [this]() {
+    auto column = cudf::make_numeric_column(
+      cudf::data_type{GetParam()}, this->size(), cudf::mask_state::UNALLOCATED);
+  };
+  EXPECT_THROW(construct(), cudf::logic_error);
+}
+
+INSTANTIATE_TEST_CASE_P(NonNumeric,
+                        NonNumericFactoryTest,
+                        testing::ValuesIn(cudf::test::non_numeric_type_ids));
+
+template <typename T>
+class FixedWidthFactoryTest : public ColumnFactoryTest {};
+
+TYPED_TEST_SUITE(FixedWidthFactoryTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(FixedWidthFactoryTest, EmptyNoMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 0, cudf::mask_state::UNALLOCATED);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+}
+
+template <typename T>
+class EmptyFactoryTest : public ColumnFactoryTest {};
+
+TYPED_TEST_SUITE(EmptyFactoryTest, cudf::test::AllTypes);
+
+TYPED_TEST(EmptyFactoryTest, Empty)
+{
+  auto type   = cudf::data_type{cudf::type_to_id<TypeParam>()};
+  auto column = cudf::make_empty_column(type);
+  EXPECT_EQ(type, column->type());
+  EXPECT_EQ(column->size(), 0);
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, EmptyAllValidMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 0, cudf::mask_state::ALL_VALID);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), 0);
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, EmptyAllNullMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 0, cudf::mask_state::ALL_NULL);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), 0);
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, NoMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::UNALLOCATED);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, UnitializedMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::UNINITIALIZED);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, AllValidMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::ALL_VALID);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, AllNullMask)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), cudf::mask_state::ALL_NULL);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(this->size(), column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, NullMaskAsParm)
+{
+  rmm::device_buffer null_mask{create_null_mask(this->size(), cudf::mask_state::ALL_NULL)};
+  auto column = cudf::make_fixed_width_column(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                              this->size(),
+                                              std::move(null_mask),
+                                              this->size());
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(this->size(), column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(FixedWidthFactoryTest, NullMaskAsEmptyParm)
+{
+  auto column = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), rmm::device_buffer{}, 0);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+class NonFixedWidthFactoryTest : public ColumnFactoryTest,
+                                 public testing::WithParamInterface<cudf::type_id> {};
+
+// All non-fixed types should throw
+TEST_P(NonFixedWidthFactoryTest, NonFixedWidthThrow)
+{
+  auto construct = [this]() {
+    auto column = cudf::make_fixed_width_column(
+      cudf::data_type{GetParam()}, this->size(), cudf::mask_state::UNALLOCATED);
+  };
+  EXPECT_THROW(construct(), cudf::logic_error);
+}
+
+INSTANTIATE_TEST_CASE_P(NonFixedWidth,
+                        NonFixedWidthFactoryTest,
+                        testing::ValuesIn(cudf::test::non_fixed_width_type_ids));
+
+TYPED_TEST(NumericFactoryTest, FromScalar)
+{
+  cudf::numeric_scalar<TypeParam> value(12);
+  auto column = cudf::make_column_from_scalar(value, 10);
+  EXPECT_EQ(column->type(), value.type());
+  EXPECT_EQ(10, column->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, FromNullScalar)
+{
+  cudf::numeric_scalar<TypeParam> value(0, false);
+  auto column = cudf::make_column_from_scalar(value, 10);
+  EXPECT_EQ(column->type(), value.type());
+  EXPECT_EQ(10, column->size());
+  EXPECT_EQ(10, column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(NumericFactoryTest, FromScalarWithZeroSize)
+{
+  cudf::numeric_scalar<TypeParam> value(7);
+  auto column = cudf::make_column_from_scalar(value, 0);
+  EXPECT_EQ(column->type(), value.type());
+  EXPECT_EQ(0, column->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TEST_F(ColumnFactoryTest, FromStringScalar)
+{
+  cudf::string_scalar value("hello");
+  auto column = cudf::make_column_from_scalar(value, 1);
+  EXPECT_EQ(1, column->size());
+  EXPECT_EQ(column->type(), value.type());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_TRUE(column->num_children() > 0);
+}
+
+TEST_F(ColumnFactoryTest, FromNullStringScalar)
+{
+  cudf::string_scalar value("", false);
+  auto column = cudf::make_column_from_scalar(value, 2);
+  EXPECT_EQ(2, column->size());
+  EXPECT_EQ(column->type(), value.type());
+  EXPECT_EQ(2, column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_TRUE(column->num_children() > 0);
+}
+
+TEST_F(ColumnFactoryTest, FromStringScalarWithZeroSize)
+{
+  cudf::string_scalar value("hello");
+  auto column = cudf::make_column_from_scalar(value, 0);
+  EXPECT_EQ(0, column->size());
+  EXPECT_EQ(column->type(), value.type());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+}
+
+TEST_F(ColumnFactoryTest, DictionaryFromStringScalar)
+{
+  cudf::string_scalar value("hello");
+  auto column = cudf::make_dictionary_from_scalar(value, 1);
+  EXPECT_EQ(1, column->size());
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_id::DICTIONARY32});
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_EQ(2, column->num_children());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+}
+
+TEST_F(ColumnFactoryTest, DictionaryFromStringScalarError)
+{
+  cudf::string_scalar value("hello", false);
+  EXPECT_THROW(cudf::make_dictionary_from_scalar(value, 1), cudf::logic_error);
+}
+
+template <typename T>
+class ListsFixedWidthLeafTest : public ColumnFactoryTest {};
+
+TYPED_TEST_SUITE(ListsFixedWidthLeafTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ListsFixedWidthLeafTest, FromNonNested)
+{
+  using FCW     = cudf::test::fixed_width_column_wrapper<TypeParam>;
+  using LCW     = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using valid_t = std::vector<cudf::valid_type>;
+
+  auto s   = cudf::make_list_scalar(FCW({1, -1, 3}, {1, 0, 1}));
+  auto col = cudf::make_column_from_scalar(*s, 3);
+
+  auto expected = LCW{LCW({1, 2, 3}, valid_t{1, 0, 1}.begin()),
+                      LCW({1, 2, 3}, valid_t{1, 0, 1}.begin()),
+                      LCW({1, 2, 3}, valid_t{1, 0, 1}.begin())};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, expected);
+}
+
+TYPED_TEST(ListsFixedWidthLeafTest, FromNested)
+{
+  using LCW     = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using valid_t = std::vector<cudf::valid_type>;
+
+#define row_data \
+  LCW({LCW({-1, -1, 3}, valid_t{0, 0, 1}.begin()), LCW{}, LCW{}}, valid_t{1, 0, 1}.begin())
+
+  auto s   = cudf::make_list_scalar(row_data);
+  auto col = cudf::make_column_from_scalar(*s, 5);
+
+  auto expected = LCW{row_data, row_data, row_data, row_data, row_data};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, expected);
+
+#undef row_data
+}
+
+template <typename T>
+class ListsDictionaryLeafTest : public ColumnFactoryTest {};
+
+TYPED_TEST_SUITE(ListsDictionaryLeafTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ListsDictionaryLeafTest, FromNonNested)
+{
+  using DCW      = cudf::test::dictionary_column_wrapper<TypeParam>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto s   = cudf::make_list_scalar(DCW({1, 3, -1, 1, 3}, {1, 1, 0, 1, 1}));
+  auto col = cudf::make_column_from_scalar(*s, 2);
+
+  DCW leaf({1, 3, -1, 1, 3, 1, 3, -1, 1, 3}, {1, 1, 0, 1, 1, 1, 1, 0, 1, 1});
+  offset_t offsets{0, 5, 10};
+  auto mask = cudf::create_null_mask(2, cudf::mask_state::UNALLOCATED);
+
+  auto expected = cudf::make_lists_column(2, offsets.release(), leaf.release(), 0, std::move(mask));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, *expected);
+}
+
+TYPED_TEST(ListsDictionaryLeafTest, FromNested)
+{
+  using DCW      = cudf::test::dictionary_column_wrapper<TypeParam>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  DCW leaf({1, 3, -1, 1, 3, 1, 3, -1, 1, 3}, {1, 1, 0, 1, 1, 1, 1, 0, 1, 1});
+  offset_t offsets{0, 3, 3, 6, 6, 10};
+  auto mask = cudf::create_null_mask(5, cudf::mask_state::ALL_VALID);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask.data()), 1, 2, false);
+  auto data = cudf::make_lists_column(5, offsets.release(), leaf.release(), 0, std::move(mask));
+
+  auto s   = cudf::make_list_scalar(*data);
+  auto col = cudf::make_column_from_scalar(*s, 3);
+
+  DCW leaf2(
+    {1, 3, -1, 1, 3, 1, 3, -1, 1, 3, 1, 3, -1, 1, 3,
+     1, 3, -1, 1, 3, 1, 3, -1, 1, 3, 1, 3, -1, 1, 3},
+    {1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1});
+  offset_t offsets2{0, 3, 3, 6, 6, 10, 13, 13, 16, 16, 20, 23, 23, 26, 26, 30};
+  auto mask2 = cudf::create_null_mask(15, cudf::mask_state::ALL_VALID);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask2.data()), 1, 2, false);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask2.data()), 6, 7, false);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask2.data()), 11, 12, false);
+  auto nested =
+    cudf::make_lists_column(15, offsets2.release(), leaf2.release(), 3, std::move(mask2));
+
+  offset_t offsets3{0, 5, 10, 15};
+  auto mask3 = cudf::create_null_mask(3, cudf::mask_state::UNALLOCATED);
+  auto expected =
+    cudf::make_lists_column(3, offsets3.release(), std::move(nested), 0, std::move(mask3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, *expected);
+}
+
+class ListsStringLeafTest : public ColumnFactoryTest {};
+
+TEST_F(ListsStringLeafTest, FromNonNested)
+{
+  using SCW     = cudf::test::strings_column_wrapper;
+  using LCW     = cudf::test::lists_column_wrapper<cudf::string_view>;
+  using valid_t = std::vector<cudf::valid_type>;
+
+  auto s   = cudf::make_list_scalar(SCW({"xx", "", "z"}, {true, false, true}));
+  auto col = cudf::make_column_from_scalar(*s, 4);
+
+  auto expected = LCW{LCW({"xx", "", "z"}, valid_t{1, 0, 1}.begin()),
+                      LCW({"xx", "", "z"}, valid_t{1, 0, 1}.begin()),
+                      LCW({"xx", "", "z"}, valid_t{1, 0, 1}.begin()),
+                      LCW({"xx", "", "z"}, valid_t{1, 0, 1}.begin())};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, expected);
+}
+
+TEST_F(ListsStringLeafTest, FromNested)
+{
+  using LCW     = cudf::test::lists_column_wrapper<cudf::string_view>;
+  using valid_t = std::vector<cudf::valid_type>;
+
+#define row_data                                                              \
+  LCW({LCW{},                                                                 \
+       LCW({"@@", "rapids", "", "四", "ら"}, valid_t{1, 1, 0, 1, 1}.begin()), \
+       LCW{},                                                                 \
+       LCW({"hello", ""}, valid_t{1, 0}.begin())},                            \
+      valid_t{0, 1, 1, 1}.begin())
+
+  auto s = cudf::make_list_scalar(row_data);
+
+  auto col = cudf::make_column_from_scalar(*s, 3);
+
+  auto expected = LCW{row_data, row_data, row_data};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, expected);
+#undef row_data
+}
+
+template <typename T>
+class ListsStructsLeafTest : public ColumnFactoryTest {
+ protected:
+  using SCW = cudf::test::structs_column_wrapper;
+  /**
+   * @brief Create a structs column that contains 3 fields: int, string, List<int>
+   */
+  template <typename MaskIterator>
+  SCW make_test_structs_column(cudf::test::fixed_width_column_wrapper<T> field1,
+                               cudf::test::strings_column_wrapper field2,
+                               cudf::test::lists_column_wrapper<T, int32_t> field3,
+                               MaskIterator mask)
+  {
+    return SCW{{field1, field2, field3}, mask};
+  }
+};
+
+TYPED_TEST_SUITE(ListsStructsLeafTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ListsStructsLeafTest, FromNonNested)
+{
+  using LCWinner_t = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using StringCW   = cudf::test::strings_column_wrapper;
+  using offset_t   = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using valid_t    = std::vector<cudf::valid_type>;
+
+  auto data = this->make_test_structs_column(
+    {{1, 3, 5, 2, 4}, {1, 0, 1, 0, 1}},
+    StringCW({"fleur", "flower", "", "花", "はな"}, {true, true, false, true, true}),
+    LCWinner_t({{1, 2}, {}, {4, 5}, {-1}, {}}, valid_t{1, 1, 1, 1, 0}.begin()),
+    valid_t{1, 1, 1, 0, 1}.begin());
+  auto s   = cudf::make_list_scalar(data);
+  auto col = cudf::make_column_from_scalar(*s, 2);
+
+  auto leaf = this->make_test_structs_column(
+    {{1, 3, 5, 2, 4, 1, 3, 5, 2, 4}, {1, 0, 1, 0, 1, 1, 0, 1, 0, 1}},
+    StringCW({"fleur", "flower", "", "花", "はな", "fleur", "flower", "", "花", "はな"},
+             {true, true, false, true, true, true, true, false, true, true}),
+    LCWinner_t({{1, 2}, {}, {4, 5}, {-1}, {}, {1, 2}, {}, {4, 5}, {-1}, {}},
+               valid_t{1, 1, 1, 1, 0, 1, 1, 1, 1, 0}.begin()),
+    valid_t{1, 1, 1, 0, 1, 1, 1, 1, 0, 1}.begin());
+  auto expected = cudf::make_lists_column(2,
+                                          offset_t{0, 5, 10}.release(),
+                                          leaf.release(),
+                                          0,
+                                          cudf::create_null_mask(2, cudf::mask_state::UNALLOCATED));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *expected);
+}
+
+TYPED_TEST(ListsStructsLeafTest, FromNested)
+{
+  using LCWinner_t = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using StringCW   = cudf::test::strings_column_wrapper;
+  using offset_t   = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using valid_t    = std::vector<cudf::valid_type>;
+  auto leaf        = this->make_test_structs_column(
+    {{1, 2}, {0, 1}},
+    StringCW({"étoile", "星"}, {true, true}),
+    LCWinner_t({LCWinner_t{}, LCWinner_t{42}}, valid_t{1, 1}.begin()),
+    valid_t{0, 1}.begin());
+  auto mask = cudf::create_null_mask(3, cudf::mask_state::ALL_VALID);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask.data()), 0, 1, false);
+  auto data =
+    cudf::make_lists_column(3, offset_t{0, 0, 1, 2}.release(), leaf.release(), 1, std::move(mask));
+  auto s = cudf::make_list_scalar(*data);
+
+  auto col = cudf::make_column_from_scalar(*s, 3);
+
+  auto leaf2 = this->make_test_structs_column(
+    {{1, 2, 1, 2, 1, 2}, {0, 1, 0, 1, 0, 1}},
+    StringCW({"étoile", "星", "étoile", "星", "étoile", "星"},
+             {true, true, true, true, true, true}),
+    LCWinner_t(
+      {LCWinner_t{}, LCWinner_t{42}, LCWinner_t{}, LCWinner_t{42}, LCWinner_t{}, LCWinner_t{42}},
+      valid_t{1, 1, 1, 1, 1, 1}.begin()),
+    valid_t{0, 1, 0, 1, 0, 1}.begin());
+  auto mask2 = cudf::create_null_mask(9, cudf::mask_state::ALL_VALID);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask2.data()), 0, 1, false);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask2.data()), 3, 4, false);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(mask2.data()), 6, 7, false);
+  auto data2 = cudf::make_lists_column(
+    9, offset_t{0, 0, 1, 2, 2, 3, 4, 4, 5, 6}.release(), leaf2.release(), 3, std::move(mask2));
+  auto expected = cudf::make_lists_column(3,
+                                          offset_t{0, 3, 6, 9}.release(),
+                                          std::move(data2),
+                                          0,
+                                          cudf::create_null_mask(3, cudf::mask_state::UNALLOCATED));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*col, *expected);
+}
+
+class ListsZeroLengthColumnTest : public ColumnFactoryTest {
+ protected:
+  using StructsCW = cudf::test::structs_column_wrapper;
+  StructsCW make_test_structs_column(cudf::test::fixed_width_column_wrapper<int32_t> field1,
+                                     cudf::test::strings_column_wrapper field2,
+                                     cudf::test::lists_column_wrapper<int32_t> field3)
+  {
+    return StructsCW{field1, field2, field3};
+  }
+};
+
+TEST_F(ListsZeroLengthColumnTest, MixedTypes)
+{
+  using FCW      = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using StringCW = cudf::test::strings_column_wrapper;
+  using LCW      = cudf::test::lists_column_wrapper<int32_t>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  {
+    auto s   = cudf::make_list_scalar(FCW{1, 2, 3});
+    auto got = cudf::make_column_from_scalar(*s, 0);
+    auto expected =
+      cudf::make_lists_column(0,
+                              offset_t{}.release(),
+                              FCW{}.release(),
+                              0,
+                              cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*got, *expected);
+  }
+
+  {
+    auto s      = cudf::make_list_scalar(LCW{LCW{1, 2, 3}, LCW{}, LCW{5, 6}});
+    auto got    = cudf::make_column_from_scalar(*s, 0);
+    auto nested = cudf::make_lists_column(0,
+                                          offset_t{}.release(),
+                                          FCW{}.release(),
+                                          0,
+                                          cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED));
+    auto expected =
+      cudf::make_lists_column(0,
+                              offset_t{}.release(),
+                              std::move(nested),
+                              0,
+                              cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*got, *expected);
+  }
+
+  {
+    auto s = cudf::make_list_scalar(
+      this->make_test_structs_column({1, 2, 3}, StringCW({"x", "", "y"}), LCW{{5, 6}, {}, {7}}));
+    auto got = cudf::make_column_from_scalar(*s, 0);
+
+    std::vector<std::unique_ptr<cudf::column>> children;
+    children.emplace_back(FCW{}.release());
+    children.emplace_back(StringCW{}.release());
+    children.emplace_back(LCW{}.release());
+    auto nested = cudf::make_structs_column(
+      0, std::move(children), 0, cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED));
+
+    auto expected =
+      cudf::make_lists_column(0,
+                              offset_t{}.release(),
+                              std::move(nested),
+                              0,
+                              cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*got, *expected);
+  }
+}
+
+TEST_F(ListsZeroLengthColumnTest, SuperimposeNulls)
+{
+  using FCW      = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using StringCW = cudf::test::strings_column_wrapper;
+  using LCW      = cudf::test::lists_column_wrapper<int32_t>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto const lists = [&] {
+    auto child = this
+                   ->make_test_structs_column(FCW{1, 2, 3, 4, 5},
+                                              StringCW({"a", "b", "c", "d", "e"}),
+                                              LCW{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {10, 11}, {12}})
+                   .release();
+    auto offsets = offset_t{0, 3, 3, 5}.release();
+
+    auto const valid_iter        = cudf::test::iterators::null_at(2);
+    auto [null_mask, null_count] = cudf::test::detail::make_null_mask(valid_iter, valid_iter + 3);
+
+    return cudf::make_lists_column(
+      3, std::move(offsets), std::move(child), null_count, std::move(null_mask));
+  }();
+
+  auto const expected_child =
+    this
+      ->make_test_structs_column(
+        FCW{1, 2, 3}, StringCW({"a", "b", "c"}), LCW{{1, 2, 3}, {4, 5, 6}, {7, 8, 9}})
+      .release();
+  auto const expected_offsets = offset_t{0, 3, 3, 3}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_child,
+                                 lists->child(cudf::lists_column_view::child_column_index));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_offsets,
+                                 lists->child(cudf::lists_column_view::offsets_column_index));
+}
+
+void struct_from_scalar(bool is_valid)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  cudf::test::fixed_width_column_wrapper<int> col0{1};
+  cudf::test::strings_column_wrapper col1{"abc"};
+  cudf::test::lists_column_wrapper<int> col2{{1, 2, 3}};
+  cudf::test::lists_column_wrapper<int> col3{LCW{}};
+
+  std::vector<cudf::column_view> src_children({col0, col1, col2, col3});
+  auto value = cudf::struct_scalar(src_children, is_valid);
+  cudf::test::structs_column_wrapper struct_col({col0, col1, col2, col3}, {is_valid});
+
+  auto const num_rows = 32;
+  auto result         = cudf::make_column_from_scalar(value, num_rows);
+
+  // generate a column of size num_rows
+  std::vector<cudf::column_view> cols;
+  auto iter = thrust::make_counting_iterator(0);
+  std::transform(iter, iter + num_rows, std::back_inserter(cols), [&](int i) {
+    return static_cast<cudf::column_view>(struct_col);
+  });
+  auto expected = cudf::concatenate(cols);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TEST_F(ColumnFactoryTest, FromStructScalar) { struct_from_scalar(true); }
+
+TEST_F(ColumnFactoryTest, FromStructScalarNull) { struct_from_scalar(false); }
+
+TEST_F(ColumnFactoryTest, FromScalarErrors)
+{
+  cudf::string_scalar ss("hello world");
+  EXPECT_THROW(cudf::make_column_from_scalar(ss, 214748365), std::overflow_error);
+
+  using FCW = cudf::test::fixed_width_column_wrapper<int8_t>;
+  auto s    = cudf::make_list_scalar(FCW({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}));
+  EXPECT_THROW(cudf::make_column_from_scalar(*s, 214748365), std::overflow_error);
+}
diff --git a/cpp/tests/copying/concatenate_tests.cpp b/cpp/tests/copying/concatenate_tests.cpp
new file mode 100644
index 0000000..c81f177
--- /dev/null
+++ b/cpp/tests/copying/concatenate_tests.cpp
@@ -0,0 +1,1690 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <numeric>
+#include <stdexcept>
+#include <string>
+
+template <typename T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T>;
+
+using s_col_wrapper = cudf::test::strings_column_wrapper;
+
+using CVector     = std::vector<std::unique_ptr<cudf::column>>;
+using column      = cudf::column;
+using column_view = cudf::column_view;
+using TView       = cudf::table_view;
+using Table       = cudf::table;
+
+template <typename T>
+struct TypedColumnTest : public cudf::test::BaseFixture {
+  cudf::data_type type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+
+  TypedColumnTest(rmm::cuda_stream_view stream = cudf::get_default_stream())
+    : data{_num_elements * cudf::size_of(type()), stream},
+      mask{cudf::bitmask_allocation_size_bytes(_num_elements), stream}
+  {
+    auto typed_data = static_cast<char*>(data.data());
+    auto typed_mask = static_cast<char*>(mask.data());
+    std::vector<char> h_data(data.size());
+    std::iota(h_data.begin(), h_data.end(), char{0});
+    std::vector<char> h_mask(mask.size());
+    std::iota(h_mask.begin(), h_mask.end(), char{0});
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(typed_data, h_data.data(), data.size(), cudaMemcpyDefault, stream.value()));
+    CUDF_CUDA_TRY(
+      cudaMemcpyAsync(typed_mask, h_mask.data(), mask.size(), cudaMemcpyDefault, stream.value()));
+    _null_count = cudf::detail::null_count(
+      static_cast<cudf::bitmask_type*>(mask.data()), 0, _num_elements, stream);
+    stream.synchronize();
+  }
+
+  cudf::size_type num_elements() const { return _num_elements; }
+  cudf::size_type null_count() const { return _null_count; }
+
+  std::random_device r;
+  std::default_random_engine generator{r()};
+  std::uniform_int_distribution<cudf::size_type> distribution{200, 1000};
+  cudf::size_type _num_elements{distribution(generator)};
+  rmm::device_buffer data{};
+  rmm::device_buffer mask{};
+  cudf::size_type _null_count{};
+  rmm::device_buffer all_valid_mask{create_null_mask(num_elements(), cudf::mask_state::ALL_VALID)};
+  rmm::device_buffer all_null_mask{create_null_mask(num_elements(), cudf::mask_state::ALL_NULL)};
+};
+
+TYPED_TEST_SUITE(TypedColumnTest, cudf::test::Types<int32_t>);
+
+TYPED_TEST(TypedColumnTest, ConcatenateEmptyColumns)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> empty_first{};
+  cudf::test::fixed_width_column_wrapper<TypeParam> empty_second{};
+  cudf::test::fixed_width_column_wrapper<TypeParam> empty_third{};
+  std::vector<column_view> columns_to_concat({empty_first, empty_second, empty_third});
+
+  auto concat = cudf::concatenate(columns_to_concat);
+
+  auto expected_type = cudf::column_view(empty_first).type();
+  EXPECT_EQ(concat->size(), 0);
+  EXPECT_EQ(concat->type(), expected_type);
+}
+
+TYPED_TEST(TypedColumnTest, ConcatenateNoColumns)
+{
+  std::vector<column_view> columns_to_concat{};
+  EXPECT_THROW(cudf::concatenate(columns_to_concat), cudf::logic_error);
+}
+
+TYPED_TEST(TypedColumnTest, ConcatenateColumnView)
+{
+  column original{this->type(),
+                  this->num_elements(),
+                  std::move(this->data),
+                  std::move(this->mask),
+                  this->null_count()};
+  std::vector<cudf::size_type> indices{0,
+                                       this->num_elements() / 3,
+                                       this->num_elements() / 3,
+                                       this->num_elements() / 2,
+                                       this->num_elements() / 2,
+                                       this->num_elements()};
+  std::vector<cudf::column_view> views = cudf::slice(original, indices);
+
+  auto concatenated_col = cudf::concatenate(views);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(original, *concatenated_col);
+}
+
+struct StringColumnTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringColumnTest, ConcatenateColumnView)
+{
+  std::vector<char const*> h_strings{"aaa",
+                                     "bb",
+                                     "",
+                                     "cccc",
+                                     "d",
+                                     "ééé",
+                                     "ff",
+                                     "gggg",
+                                     "",
+                                     "h",
+                                     "iiii",
+                                     "jjj",
+                                     "k",
+                                     "lllllll",
+                                     "mmmmm",
+                                     "n",
+                                     "oo",
+                                     "ppp"};
+  cudf::test::strings_column_wrapper strings1(h_strings.data(), h_strings.data() + 6);
+  cudf::test::strings_column_wrapper strings2(h_strings.data() + 6, h_strings.data() + 10);
+  cudf::test::strings_column_wrapper strings3(h_strings.data() + 10,
+                                              h_strings.data() + h_strings.size());
+
+  std::vector<cudf::column_view> strings_columns;
+  strings_columns.push_back(strings1);
+  strings_columns.push_back(strings2);
+  strings_columns.push_back(strings3);
+
+  auto results = cudf::concatenate(strings_columns);
+
+  cudf::test::strings_column_wrapper expected(h_strings.begin(), h_strings.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringColumnTest, ConcatenateColumnViewLarge)
+{
+  // Test large concatenate, causes out of bound device memory errors if kernel
+  // indexing is not int64_t.
+  // 1.5GB bytes, 5k columns
+  constexpr size_t num_strings        = 10000;
+  constexpr size_t string_length      = 150000;
+  constexpr size_t strings_per_column = 2;
+  constexpr size_t num_columns        = num_strings / strings_per_column;
+
+  std::vector<std::string> strings;
+  std::vector<char const*> h_strings;
+  std::vector<cudf::test::strings_column_wrapper> strings_column_wrappers;
+  std::vector<cudf::column_view> strings_columns;
+
+  std::string s(string_length, 'a');
+  for (size_t i = 0; i < num_strings; ++i)
+    h_strings.push_back(s.data());
+
+  for (size_t i = 0; i < num_columns; ++i)
+    strings_column_wrappers.push_back(cudf::test::strings_column_wrapper(
+      h_strings.data() + i * strings_per_column, h_strings.data() + (i + 1) * strings_per_column));
+  for (auto& wrapper : strings_column_wrappers)
+    strings_columns.push_back(wrapper);
+
+  auto results = cudf::concatenate(strings_columns);
+
+  cudf::test::strings_column_wrapper expected(h_strings.begin(), h_strings.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringColumnTest, ConcatenateTooManyColumns)
+{
+  std::vector<char const*> h_strings{"aaa",
+                                     "bb",
+                                     "",
+                                     "cccc",
+                                     "d",
+                                     "ééé",
+                                     "ff",
+                                     "gggg",
+                                     "",
+                                     "h",
+                                     "iiii",
+                                     "jjj",
+                                     "k",
+                                     "lllllll",
+                                     "mmmmm",
+                                     "n",
+                                     "oo",
+                                     "ppp"};
+
+  std::vector<char const*> expected_strings;
+  std::vector<cudf::test::strings_column_wrapper> wrappers;
+  std::vector<cudf::column_view> strings_columns;
+  for (int i = 0; i < 200; ++i) {
+    wrappers.emplace_back(h_strings.data(), h_strings.data() + h_strings.size());
+    strings_columns.push_back(wrappers[i]);
+    expected_strings.insert(expected_strings.end(), h_strings.begin(), h_strings.end());
+  }
+  cudf::test::strings_column_wrapper expected(expected_strings.data(),
+                                              expected_strings.data() + expected_strings.size());
+  auto results = cudf::concatenate(strings_columns);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+struct TableTest : public cudf::test::BaseFixture {};
+
+TEST_F(TableTest, ConcatenateTables)
+{
+  std::vector<char const*> h_strings{
+    "Lorem", "ipsum", "dolor", "sit", "amet", "consectetur", "adipiscing", "elit"};
+
+  CVector cols_gold;
+  column_wrapper<int8_t> col1_gold{{1, 2, 3, 4, 5, 6, 7, 8}};
+  column_wrapper<int16_t> col2_gold{{1, 2, 3, 4, 5, 6, 7, 8}};
+  s_col_wrapper col3_gold(h_strings.data(), h_strings.data() + h_strings.size());
+  cols_gold.push_back(col1_gold.release());
+  cols_gold.push_back(col2_gold.release());
+  cols_gold.push_back(col3_gold.release());
+  Table gold_table(std::move(cols_gold));
+
+  CVector cols_table1;
+  column_wrapper<int8_t> col1_table1{{1, 2, 3, 4}};
+  column_wrapper<int16_t> col2_table1{{1, 2, 3, 4}};
+  s_col_wrapper col3_table1(h_strings.data(), h_strings.data() + 4);
+  cols_table1.push_back(col1_table1.release());
+  cols_table1.push_back(col2_table1.release());
+  cols_table1.push_back(col3_table1.release());
+  Table t1(std::move(cols_table1));
+
+  CVector cols_table2;
+  column_wrapper<int8_t> col1_table2{{5, 6, 7, 8}};
+  column_wrapper<int16_t> col2_table2{{5, 6, 7, 8}};
+  s_col_wrapper col3_table2(h_strings.data() + 4, h_strings.data() + h_strings.size());
+  cols_table2.push_back(col1_table2.release());
+  cols_table2.push_back(col2_table2.release());
+  cols_table2.push_back(col3_table2.release());
+  Table t2(std::move(cols_table2));
+
+  auto concat_table = cudf::concatenate(std::vector<TView>({t1, t2}));
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*concat_table, gold_table);
+}
+
+TEST_F(TableTest, ConcatenateTablesWithOffsets)
+{
+  column_wrapper<int32_t> col1_1{{5, 4, 3, 5, 8, 5, 6}};
+  cudf::test::strings_column_wrapper col2_1(
+    {"dada", "egg", "avocado", "dada", "kite", "dog", "ln"});
+  cudf::table_view table_view_in1{{col1_1, col2_1}};
+
+  column_wrapper<int32_t> col1_2{{5, 8, 5, 6, 15, 14, 13}};
+  cudf::test::strings_column_wrapper col2_2(
+    {"dada", "kite", "dog", "ln", "dado", "greg", "spinach"});
+  cudf::table_view table_view_in2{{col1_2, col2_2}};
+
+  std::vector<cudf::size_type> split_indexes1{3};
+  std::vector<cudf::table_view> partitioned1 = cudf::split(table_view_in1, split_indexes1);
+
+  std::vector<cudf::size_type> split_indexes2{3};
+  std::vector<cudf::table_view> partitioned2 = cudf::split(table_view_in2, split_indexes2);
+
+  {
+    std::vector<cudf::table_view> table_views_to_concat;
+    table_views_to_concat.push_back(partitioned1[1]);
+    table_views_to_concat.push_back(partitioned2[1]);
+    std::unique_ptr<cudf::table> concatenated_tables = cudf::concatenate(table_views_to_concat);
+
+    column_wrapper<int32_t> exp1_1{{5, 8, 5, 6, 6, 15, 14, 13}};
+    cudf::test::strings_column_wrapper exp2_1(
+      {"dada", "kite", "dog", "ln", "ln", "dado", "greg", "spinach"});
+    cudf::table_view table_view_exp1{{exp1_1, exp2_1}};
+    CUDF_TEST_EXPECT_TABLES_EQUAL(concatenated_tables->view(), table_view_exp1);
+  }
+  {
+    std::vector<cudf::table_view> table_views_to_concat;
+    table_views_to_concat.push_back(partitioned1[0]);
+    table_views_to_concat.push_back(partitioned2[1]);
+    std::unique_ptr<cudf::table> concatenated_tables = cudf::concatenate(table_views_to_concat);
+
+    column_wrapper<int32_t> exp1_1{{5, 4, 3, 6, 15, 14, 13}};
+    cudf::test::strings_column_wrapper exp2_1(
+      {"dada", "egg", "avocado", "ln", "dado", "greg", "spinach"});
+    cudf::table_view table_view_exp1{{exp1_1, exp2_1}};
+    CUDF_TEST_EXPECT_TABLES_EQUAL(concatenated_tables->view(), table_view_exp1);
+  }
+  {
+    std::vector<cudf::table_view> table_views_to_concat;
+    table_views_to_concat.push_back(partitioned1[1]);
+    table_views_to_concat.push_back(partitioned2[0]);
+    std::unique_ptr<cudf::table> concatenated_tables = cudf::concatenate(table_views_to_concat);
+
+    column_wrapper<int32_t> exp1_1{{5, 8, 5, 6, 5, 8, 5}};
+    cudf::test::strings_column_wrapper exp2_1({"dada", "kite", "dog", "ln", "dada", "kite", "dog"});
+    cudf::table_view table_view_exp{{exp1_1, exp2_1}};
+    CUDF_TEST_EXPECT_TABLES_EQUAL(concatenated_tables->view(), table_view_exp);
+  }
+}
+
+TEST_F(TableTest, ConcatenateTablesWithOffsetsAndNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1_1{{5, 4, 3, 5, 8, 5, 6},
+                                                         {0, 1, 1, 1, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col2_1({"dada", "egg", "avocado", "dada", "kite", "dog", "ln"},
+                                            {1, 1, 1, 0, 1, 1, 1});
+  cudf::table_view table_view_in1{{col1_1, col2_1}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> col1_2{{5, 8, 5, 6, 15, 14, 13},
+                                                         {1, 1, 1, 1, 1, 1, 0}};
+  cudf::test::strings_column_wrapper col2_2(
+    {"dada", "kite", "dog", "ln", "dado", "greg", "spinach"}, {1, 0, 1, 1, 1, 1, 1});
+  cudf::table_view table_view_in2{{col1_2, col2_2}};
+
+  std::vector<cudf::size_type> split_indexes1{3};
+  std::vector<cudf::table_view> partitioned1 = cudf::split(table_view_in1, split_indexes1);
+
+  std::vector<cudf::size_type> split_indexes2{3};
+  std::vector<cudf::table_view> partitioned2 = cudf::split(table_view_in2, split_indexes2);
+
+  {
+    std::vector<cudf::table_view> table_views_to_concat;
+    table_views_to_concat.push_back(partitioned1[1]);
+    table_views_to_concat.push_back(partitioned2[1]);
+    std::unique_ptr<cudf::table> concatenated_tables = cudf::concatenate(table_views_to_concat);
+
+    cudf::test::fixed_width_column_wrapper<int32_t> exp1_1{{5, 8, 5, 6, 6, 15, 14, 13},
+                                                           {1, 1, 1, 1, 1, 1, 1, 0}};
+    cudf::test::strings_column_wrapper exp2_1(
+      {"dada", "kite", "dog", "ln", "ln", "dado", "greg", "spinach"}, {0, 1, 1, 1, 1, 1, 1, 1});
+    cudf::table_view table_view_exp1{{exp1_1, exp2_1}};
+    CUDF_TEST_EXPECT_TABLES_EQUAL(concatenated_tables->view(), table_view_exp1);
+  }
+  {
+    std::vector<cudf::table_view> table_views_to_concat;
+    table_views_to_concat.push_back(partitioned1[1]);
+    table_views_to_concat.push_back(partitioned2[0]);
+    std::unique_ptr<cudf::table> concatenated_tables = cudf::concatenate(table_views_to_concat);
+
+    cudf::test::fixed_width_column_wrapper<int32_t> exp1_1{5, 8, 5, 6, 5, 8, 5};
+    cudf::test::strings_column_wrapper exp2_1({"dada", "kite", "dog", "ln", "dada", "kite", "dog"},
+                                              {0, 1, 1, 1, 1, 0, 1});
+    cudf::table_view table_view_exp1{{exp1_1, exp2_1}};
+    CUDF_TEST_EXPECT_TABLES_EQUAL(concatenated_tables->view(), table_view_exp1);
+  }
+}
+
+struct OverflowTest : public cudf::test::BaseFixture {};
+
+TEST_F(OverflowTest, OverflowTest)
+{
+  // should concatenate up to size_type::max rows.
+  {
+    // 5 x size + size_last adds to size_type::max
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(250) * 1024 * 1024);
+    constexpr auto size_last = static_cast<cudf::size_type>(836763647);
+
+    auto many_chars = cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size);
+    auto many_chars_last =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size_last);
+
+    cudf::table_view tbl({*many_chars});
+    cudf::table_view tbl_last({*many_chars_last});
+    std::vector<cudf::table_view> table_views_to_concat({tbl, tbl, tbl, tbl, tbl, tbl_last});
+    std::unique_ptr<cudf::table> concatenated_tables = cudf::concatenate(table_views_to_concat);
+    EXPECT_NO_THROW(cudf::get_default_stream().synchronize());
+    ASSERT_EQ(concatenated_tables->num_rows(), std::numeric_limits<cudf::size_type>::max());
+  }
+
+  // primitive column
+  {
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    // try and concatenate 6 char columns of size 1 billion each
+    auto many_chars = cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size);
+
+    cudf::table_view tbl({*many_chars});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({tbl, tbl, tbl, tbl, tbl, tbl})),
+                 std::overflow_error);
+  }
+
+  // string column, overflow on chars
+  {
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    // try and concatenate 6 string columns of with 1 billion chars in each
+    auto offsets    = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, size};
+    auto many_chars = cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size);
+    auto col        = cudf::make_strings_column(
+      1, offsets.release(), std::move(many_chars), 0, rmm::device_buffer{});
+
+    cudf::table_view tbl({*col});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({tbl, tbl, tbl, tbl, tbl, tbl})),
+                 std::overflow_error);
+  }
+
+  // string column, overflow on offsets (rows)
+  {
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    // try and concatenate 6 string columns 1 billion rows each
+    auto many_offsets =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT32}, size + 1);
+    auto chars = cudf::test::fixed_width_column_wrapper<int8_t>{0, 1, 2};
+    auto col   = cudf::make_strings_column(
+      size, std::move(many_offsets), chars.release(), 0, rmm::device_buffer{});
+
+    cudf::table_view tbl({*col});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({tbl, tbl, tbl, tbl, tbl, tbl})),
+                 std::overflow_error);
+  }
+
+  // list<struct>, structs too long
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(512) * 1024 * 1024);
+
+    // struct
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size));
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size));
+    auto struct_col =
+      cudf::make_structs_column(inner_size, std::move(children), 0, rmm::device_buffer{});
+
+    // list
+    auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, inner_size};
+    auto col =
+      cudf::make_lists_column(1, offsets.release(), std::move(struct_col), 0, rmm::device_buffer{});
+
+    cudf::table_view tbl({*col});
+    auto tables =
+      std::vector<cudf::table_view>({tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl});
+    EXPECT_THROW(cudf::concatenate(tables), std::overflow_error);
+  }
+
+  // struct<int, list>, list child too long
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(512) * 1024 * 1024);
+    constexpr cudf::size_type size = 3;
+
+    // list
+    auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 0, inner_size};
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size);
+    auto list_col =
+      cudf::make_lists_column(3, offsets.release(), std::move(many_chars), 0, rmm::device_buffer{});
+
+    // struct
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT32}, size));
+    children.push_back(std::move(list_col));
+    auto col = cudf::make_structs_column(size, std::move(children), 0, rmm::device_buffer{});
+
+    cudf::table_view tbl({*col});
+    auto tables =
+      std::vector<cudf::table_view>({tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl, tbl});
+    EXPECT_THROW(cudf::concatenate(tables), std::overflow_error);
+  }
+}
+
+TEST_F(OverflowTest, Presliced)
+{
+  // primitive column
+  {
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    // try and concatenate 4 char columns of size ~1/2 billion each
+    auto many_chars = cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size);
+    auto sliced     = cudf::split(*many_chars, {511 * 1024 * 1024});
+
+    // 511 * 1024 * 1024, should succeed
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a}));
+
+    // 513 * 1024 * 1024, should fail
+    cudf::table_view b({sliced[1]});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({b, b, b, b})),
+                 std::overflow_error);
+  }
+
+  // struct<int8> column
+  {
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    // try and concatenate 4 char columns of size ~1/2 billion each
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size));
+    auto struct_col = cudf::make_structs_column(size, std::move(children), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::split(*struct_col, {511 * 1024 * 1024});
+
+    // 511 * 1024 * 1024, should succeed
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a}));
+
+    // 513 * 1024 * 1024, should fail
+    cudf::table_view b({sliced[1]});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({b, b, b, b})),
+                 std::overflow_error);
+  }
+
+  // strings, overflow on chars
+  {
+    constexpr cudf::size_type total_chars_size = 1024 * 1024 * 1024;
+    constexpr cudf::size_type string_size      = 64;
+    constexpr cudf::size_type num_rows         = total_chars_size / string_size;
+
+    // try and concatenate 4 string columns of with ~1/2 billion chars in each
+    auto offset_gen = cudf::detail::make_counting_transform_iterator(
+      0, [string_size](cudf::size_type index) { return index * string_size; });
+    cudf::test::fixed_width_column_wrapper<int> offsets(offset_gen, offset_gen + num_rows + 1);
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, total_chars_size);
+    auto col = cudf::make_strings_column(
+      num_rows, offsets.release(), std::move(many_chars), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::split(*col, {(num_rows / 2) - 1});
+
+    // (num_rows / 2) - 1 should succeed
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a}));
+
+    // (num_rows / 2) + 1 should fail
+    cudf::table_view b({sliced[1]});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({b, b, b, b})),
+                 std::overflow_error);
+  }
+
+  // strings, overflow on offsets
+  {
+    constexpr cudf::size_type total_chars_size = 1024 * 1024 * 1024;
+    constexpr cudf::size_type string_size      = 1;
+    constexpr cudf::size_type num_rows         = total_chars_size / string_size;
+
+    // try and concatenate 4 string columns of with ~1/2 billion chars in each
+    auto offsets = cudf::sequence(num_rows + 1,
+                                  cudf::numeric_scalar<cudf::size_type>(0),
+                                  cudf::numeric_scalar<cudf::size_type>(string_size));
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, total_chars_size);
+    auto col = cudf::make_strings_column(
+      num_rows, std::move(offsets), std::move(many_chars), 0, rmm::device_buffer{});
+
+    // should pass (with 2 rows to spare)
+    // leaving this disabled as it typically runs out of memory on a T4
+    /*
+    {
+      auto sliced = cudf::split(*col, {(num_rows / 2) + 1});
+      cudf::table_view b({sliced[1]});
+      cudf::concatenate(std::vector<cudf::table_view>({b, b, b, b}));
+    }
+    */
+
+    // should fail by 1 row (2,147,483,647 rows which is fine, but that requires 2,147,483,648
+    // offsets, which is not)
+    {
+      auto a = cudf::split(*col, {(num_rows / 2)});
+      cudf::table_view ta({a[1]});
+
+      auto b = cudf::split(*col, {(num_rows / 2) + 1});
+      cudf::table_view tb({b[1]});
+
+      EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({ta, ta, ta, tb})),
+                   std::overflow_error);
+    }
+  }
+
+  // list<struct>, structs too long
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    // struct
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size));
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size));
+    auto struct_col =
+      cudf::make_structs_column(inner_size, std::move(children), 0, rmm::device_buffer{});
+
+    // list
+    constexpr cudf::size_type list_size = inner_size / 4;
+    auto offsets                        = cudf::test::fixed_width_column_wrapper<int>{
+      0, list_size, list_size * 2, list_size * 3, inner_size};
+    auto col =
+      cudf::make_lists_column(4, offsets.release(), std::move(struct_col), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::split(*col, {2});
+    cudf::table_view tbl({sliced[1]});
+    auto tables = std::vector<cudf::table_view>({tbl, tbl, tbl, tbl});
+    EXPECT_THROW(cudf::concatenate(tables), std::overflow_error);
+  }
+
+  // list<struct>, overflow on offsets
+  {
+    constexpr cudf::size_type inner_size = 1024 * 1024 * 1024;
+    constexpr cudf::size_type list_size  = 1;
+    constexpr cudf::size_type num_rows   = inner_size / list_size;
+
+    // struct
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size));
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size));
+    auto struct_col =
+      cudf::make_structs_column(inner_size, std::move(children), 0, rmm::device_buffer{});
+
+    // try and concatenate 4 struct columns of with ~1/2 billion elements in each
+    auto offsets = cudf::sequence(num_rows + 1,
+                                  cudf::numeric_scalar<cudf::size_type>(0),
+                                  cudf::numeric_scalar<cudf::size_type>(list_size));
+
+    auto col = cudf::make_strings_column(
+      num_rows, std::move(offsets), std::move(struct_col), 0, rmm::device_buffer{});
+
+    // should pass (with 2 rows to spare)
+    // leaving this disabled as it typically runs out of memory on a T4
+    /*
+    {
+      auto sliced = cudf::split(*col, {(num_rows / 2) + 1});
+      cudf::table_view b({sliced[1]});
+      cudf::concatenate(std::vector<cudf::table_view>({b, b, b, b}));
+    }
+    */
+
+    // should fail by 1 row (2,147,483,647 rows which is fine, but that requires 2,147,483,648
+    // offsets, which is not)
+    {
+      auto a = cudf::split(*col, {(num_rows / 2)});
+      cudf::table_view ta({a[1]});
+
+      auto b = cudf::split(*col, {(num_rows / 2) + 1});
+      cudf::table_view tb({b[1]});
+
+      EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({ta, ta, ta, tb})),
+                   std::overflow_error);
+    }
+  }
+
+  // struct<int8, list>, list child elements too long
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+    constexpr cudf::size_type num_rows  = 4;
+    constexpr cudf::size_type list_size = inner_size / num_rows;
+
+    // list
+    auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+      0, list_size, (list_size * 2) - 1, list_size * 3, inner_size};
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size);
+    auto list_col = cudf::make_lists_column(
+      num_rows, offsets.release(), std::move(many_chars), 0, rmm::device_buffer{});
+
+    // struct
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, num_rows));
+    children.push_back(std::move(list_col));
+    auto struct_col =
+      cudf::make_structs_column(num_rows, std::move(children), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::split(*struct_col, {2});
+
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a}));
+
+    cudf::table_view b({sliced[1]});
+    EXPECT_THROW(cudf::concatenate(std::vector<cudf::table_view>({b, b, b, b})),
+                 std::overflow_error);
+  }
+}
+
+TEST_F(OverflowTest, BigColumnsSmallSlices)
+{
+  // test : many small slices of large columns. the idea is to make sure
+  // that we are respecting the offset/sizes of the slices and not attempting to
+  // concatenate the entire large initial columns
+
+  // primitive column
+  {
+    constexpr auto size = static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+
+    auto many_chars = cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, size);
+    auto sliced     = cudf::slice(*many_chars, {16, 32});
+
+    // 192 total rows
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a, a, a, a, a, a, a, a, a}));
+  }
+
+  // strings column
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+    constexpr cudf::size_type num_rows    = 1024;
+    constexpr cudf::size_type string_size = inner_size / num_rows;
+
+    auto offsets = cudf::sequence(num_rows + 1,
+                                  cudf::numeric_scalar<cudf::size_type>(0),
+                                  cudf::numeric_scalar<cudf::size_type>(string_size));
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size);
+    auto col = cudf::make_strings_column(
+      num_rows, std::move(offsets), std::move(many_chars), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::slice(*col, {16, 32});
+
+    // 192 outer rows
+    // 201,326,592 inner rows
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a, a, a, a, a, a, a, a, a}));
+  }
+
+  // list<int8> column
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+    constexpr cudf::size_type num_rows  = 1024;
+    constexpr cudf::size_type list_size = inner_size / num_rows;
+
+    auto offsets = cudf::sequence(num_rows + 1,
+                                  cudf::numeric_scalar<cudf::size_type>(0),
+                                  cudf::numeric_scalar<cudf::size_type>(list_size));
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size);
+    auto col = cudf::make_lists_column(
+      num_rows, std::move(offsets), std::move(many_chars), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::slice(*col, {16, 32});
+
+    // 192 outer rows
+    // 201,326,592 inner rows
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a, a, a, a, a, a, a, a, a}));
+  }
+
+  // struct<int8, list>
+  {
+    constexpr auto inner_size =
+      static_cast<cudf::size_type>(static_cast<uint32_t>(1024) * 1024 * 1024);
+    constexpr cudf::size_type num_rows  = 1024;
+    constexpr cudf::size_type list_size = inner_size / num_rows;
+
+    auto offsets = cudf::sequence(num_rows + 1,
+                                  cudf::numeric_scalar<cudf::size_type>(0),
+                                  cudf::numeric_scalar<cudf::size_type>(list_size));
+    auto many_chars =
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, inner_size);
+    auto list_col = cudf::make_lists_column(
+      num_rows, std::move(offsets), std::move(many_chars), 0, rmm::device_buffer{});
+
+    // struct
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(
+      cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT8}, num_rows));
+    children.push_back(std::move(list_col));
+    auto struct_col =
+      cudf::make_structs_column(num_rows, std::move(children), 0, rmm::device_buffer{});
+
+    auto sliced = cudf::slice(*struct_col, {16, 32});
+
+    // 192 outer rows
+    // 201,326,592 inner rows
+    cudf::table_view a({sliced[0]});
+    cudf::concatenate(std::vector<cudf::table_view>({a, a, a, a, a, a, a, a, a, a, a, a}));
+  }
+}
+
+struct StructsColumnTest : public cudf::test::BaseFixture {};
+
+TEST_F(StructsColumnTest, ConcatenateStructs)
+{
+  auto count_iter = thrust::make_counting_iterator(0);
+
+  // 1. String "names" column.
+  std::vector<std::vector<std::string>> names(
+    {{"Vimes", "Carrot"}, {"Angua", "Cheery"}, {}, {"Detritus", "Slant"}});
+  std::vector<std::vector<bool>> names_validity({{1, 1}, {1, 1}, {}, {1, 1}});
+  std::vector<cudf::test::strings_column_wrapper> name_cols;
+  std::transform(count_iter, count_iter + names.size(), std::back_inserter(name_cols), [&](int i) {
+    return cudf::test::strings_column_wrapper(
+      names[i].begin(), names[i].end(), names_validity[i].begin());
+  });
+
+  // 2. Numeric "ages" column.
+  std::vector<std::vector<int>> ages({{5, 10}, {15, 20}, {}, {25, 30}});
+  std::vector<std::vector<bool>> ages_validity({{1, 1}, {1, 1}, {}, {0, 1}});
+  std::vector<cudf::test::fixed_width_column_wrapper<int>> age_cols;
+  std::transform(count_iter, count_iter + ages.size(), std::back_inserter(age_cols), [&](int i) {
+    return cudf::test::fixed_width_column_wrapper<int>(
+      ages[i].begin(), ages[i].end(), ages_validity[i].begin());
+  });
+
+  // 3. Boolean "is_human" column.
+  std::vector<std::vector<bool>> is_human({{true, true}, {false, false}, {}, {false, false}});
+  std::vector<std::vector<bool>> is_human_validity({{1, 1}, {1, 0}, {}, {1, 1}});
+  std::vector<cudf::test::fixed_width_column_wrapper<bool>> is_human_cols;
+  std::transform(
+    count_iter, count_iter + is_human.size(), std::back_inserter(is_human_cols), [&](int i) {
+      return cudf::test::fixed_width_column_wrapper<bool>(
+        is_human[i].begin(), is_human[i].end(), is_human_validity[i].begin());
+    });
+
+  // build expected output
+  std::vector<std::unique_ptr<column>> expected_children;
+  auto name_col_vec =
+    std::vector<column_view>({name_cols[0], name_cols[1], name_cols[2], name_cols[3]});
+  auto age_col_vec = std::vector<column_view>({age_cols[0], age_cols[1], age_cols[2], age_cols[3]});
+  auto is_human_col_vec = std::vector<column_view>(
+    {is_human_cols[0], is_human_cols[1], is_human_cols[2], is_human_cols[3]});
+  expected_children.push_back(cudf::concatenate(name_col_vec));
+  expected_children.push_back(cudf::concatenate(age_col_vec));
+  expected_children.push_back(cudf::concatenate(is_human_col_vec));
+  std::vector<bool> struct_validity({1, 0, 1, 1, 1, 0});
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(struct_validity.begin(), struct_validity.end());
+  auto expected =
+    make_structs_column(6, std::move(expected_children), null_count, std::move(null_mask));
+
+  // concatenate as structs
+  std::vector<cudf::test::structs_column_wrapper> src;
+  src.push_back(
+    cudf::test::structs_column_wrapper({name_cols[0], age_cols[0], is_human_cols[0]}, {1, 0}));
+  src.push_back(
+    cudf::test::structs_column_wrapper({name_cols[1], age_cols[1], is_human_cols[1]}, {1, 1}));
+  src.push_back(
+    cudf::test::structs_column_wrapper({name_cols[2], age_cols[2], is_human_cols[2]}, {}));
+  src.push_back(
+    cudf::test::structs_column_wrapper({name_cols[3], age_cols[3], is_human_cols[3]}, {1, 0}));
+
+  // concatenate
+  auto result = cudf::concatenate(std::vector<column_view>({src[0], src[1], src[2], src[3]}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TEST_F(StructsColumnTest, ConcatenateEmptyStructs)
+{
+  auto expected = cudf::make_structs_column(10, {}, 0, rmm::device_buffer());
+  auto first    = cudf::make_structs_column(5, {}, 0, rmm::device_buffer());
+  auto second   = cudf::make_structs_column(2, {}, 0, rmm::device_buffer());
+  auto third    = cudf::make_structs_column(0, {}, 0, rmm::device_buffer());
+  auto fourth   = cudf::make_structs_column(3, {}, 0, rmm::device_buffer());
+
+  // concatenate
+  auto result = cudf::concatenate(std::vector<column_view>({*first, *second, *third, *fourth}));
+  ASSERT_EQ(result->size(), expected->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TEST_F(StructsColumnTest, ConcatenateSplitStructs)
+{
+  auto count_iter = thrust::make_counting_iterator(0);
+
+  std::vector<int> splits({2});
+
+  // 1. String "names" column.
+  std::vector<std::vector<std::string>> names(
+    {{"Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant"},
+     {"Bill", "Bob", "Sam", "Fred", "Tom"}});
+  std::vector<std::vector<bool>> names_validity({{1, 1, 1, 1, 1, 1}, {0, 1, 0, 1, 0}});
+  std::vector<cudf::test::strings_column_wrapper> name_cols;
+  std::transform(count_iter, count_iter + names.size(), std::back_inserter(name_cols), [&](int i) {
+    return cudf::test::strings_column_wrapper(
+      names[i].begin(), names[i].end(), names_validity[i].begin());
+  });
+
+  // 2. Numeric "ages" column.
+  std::vector<std::vector<int>> ages({{5, 10, 15, 20, 25, 30}, {11, 16, 17, 41, 42}});
+  std::vector<std::vector<bool>> ages_validity({{1, 1, 1, 1, 0, 1}, {1, 1, 1, 0, 0}});
+  std::vector<cudf::test::fixed_width_column_wrapper<int>> age_cols;
+  std::transform(count_iter, count_iter + ages.size(), std::back_inserter(age_cols), [&](int i) {
+    return cudf::test::fixed_width_column_wrapper<int>(
+      ages[i].begin(), ages[i].end(), ages_validity[i].begin());
+  });
+
+  // 3. Boolean "is_human" column.
+  std::vector<std::vector<bool>> is_human(
+    {{true, true, false, false, false, false}, {true, true, true, false, true}});
+  std::vector<std::vector<bool>> is_human_validity({{1, 1, 1, 0, 1, 1}, {0, 0, 0, 1, 1}});
+  std::vector<cudf::test::fixed_width_column_wrapper<bool>> is_human_cols;
+  std::transform(
+    count_iter, count_iter + is_human.size(), std::back_inserter(is_human_cols), [&](int i) {
+      return cudf::test::fixed_width_column_wrapper<bool>(
+        is_human[i].begin(), is_human[i].end(), is_human_validity[i].begin());
+    });
+
+  // split the columns, keep the one on the end
+  std::vector<column_view> split_names_cols(
+    {cudf::split(name_cols[0], splits)[1], cudf::split(name_cols[1], splits)[1]});
+  std::vector<column_view> split_ages_cols(
+    {cudf::split(age_cols[0], splits)[1], cudf::split(age_cols[1], splits)[1]});
+  std::vector<column_view> split_is_human_cols(
+    {cudf::split(is_human_cols[0], splits)[1], cudf::split(is_human_cols[1], splits)[1]});
+
+  // build expected output
+  std::vector<std::unique_ptr<column>> expected_children;
+  auto expected_names = std::vector<column_view>({split_names_cols[0], split_names_cols[1]});
+  auto expected_ages  = std::vector<column_view>({split_ages_cols[0], split_ages_cols[1]});
+  auto expected_is_human =
+    std::vector<column_view>({split_is_human_cols[0], split_is_human_cols[1]});
+  expected_children.push_back(cudf::concatenate(expected_names));
+  expected_children.push_back(cudf::concatenate(expected_ages));
+  expected_children.push_back(cudf::concatenate(expected_is_human));
+  auto expected = make_structs_column(7, std::move(expected_children), 0, rmm::device_buffer{});
+
+  // concatenate as structs
+  std::vector<cudf::test::structs_column_wrapper> src;
+  for (size_t idx = 0; idx < split_names_cols.size(); idx++) {
+    std::vector<std::unique_ptr<column>> inputs;
+    inputs.push_back(std::make_unique<column>(split_names_cols[idx]));
+    inputs.push_back(std::make_unique<column>(split_ages_cols[idx]));
+    inputs.push_back(std::make_unique<column>(split_is_human_cols[idx]));
+    src.push_back(cudf::test::structs_column_wrapper(std::move(inputs)));
+  }
+
+  // concatenate
+
+  auto result = cudf::concatenate(std::vector<column_view>({src[0], src[1]}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TEST_F(StructsColumnTest, ConcatenateStructsNested)
+{
+  // includes Struct<Struct> and Struct<List>
+
+  auto count_iter = thrust::make_counting_iterator(0);
+
+  // inner structs
+  std::vector<cudf::test::structs_column_wrapper> inner_structs;
+  {
+    // 1. String "names" column.
+    std::vector<std::vector<std::string>> names(
+      {{"Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant"},
+       {"Bill", "Bob", "Sam", "Fred", "Tom"}});
+    std::vector<std::vector<bool>> names_validity({{1, 1, 1, 1, 1, 1}, {0, 1, 0, 1, 0}});
+    std::vector<cudf::test::strings_column_wrapper> name_cols;
+    std::transform(
+      count_iter, count_iter + names.size(), std::back_inserter(name_cols), [&](int i) {
+        return cudf::test::strings_column_wrapper(
+          names[i].begin(), names[i].end(), names_validity[i].begin());
+      });
+
+    // 2. Numeric "ages" column.
+    std::vector<std::vector<int>> ages({{5, 10, 15, 20, 25, 30}, {11, 16, 17, 41, 42}});
+    std::vector<std::vector<bool>> ages_validity({{1, 1, 1, 1, 0, 1}, {1, 1, 1, 0, 0}});
+    std::vector<cudf::test::fixed_width_column_wrapper<int>> age_cols;
+    std::transform(count_iter, count_iter + ages.size(), std::back_inserter(age_cols), [&](int i) {
+      return cudf::test::fixed_width_column_wrapper<int>(
+        ages[i].begin(), ages[i].end(), ages_validity[i].begin());
+    });
+
+    for (size_t idx = 0; idx < names.size(); idx++) {
+      std::vector<std::unique_ptr<column>> children;
+      children.push_back(name_cols[idx].release());
+      children.push_back(age_cols[idx].release());
+      inner_structs.push_back(cudf::test::structs_column_wrapper(std::move(children)));
+    }
+  }
+
+  // inner lists
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  std::vector<cudf::test::lists_column_wrapper<cudf::string_view>> inner_lists;
+  {
+    inner_lists.push_back(cudf::test::lists_column_wrapper<cudf::string_view>{
+      {"abc", "d"}, {"ef", "ghi", "j"}, {"klm", "no"}, LCW{}, LCW{"whee"}, {"xyz", "ab", "g"}});
+
+    inner_lists.push_back(cudf::test::lists_column_wrapper<cudf::string_view>{
+      {"er", "hyj"}, {"", "", "uvw"}, LCW{}, LCW{"oipq", "te"}, LCW{"yay", "bonk"}});
+  }
+
+  // build expected output
+  std::vector<std::unique_ptr<column>> expected_children;
+
+  expected_children.push_back(
+    cudf::concatenate(std::vector<column_view>({inner_structs[0], inner_structs[1]})));
+  expected_children.push_back(
+    cudf::concatenate(std::vector<column_view>({inner_lists[0], inner_lists[1]})));
+  auto expected = make_structs_column(11, std::move(expected_children), 0, rmm::device_buffer{});
+
+  // concatenate as structs
+  std::vector<cudf::test::structs_column_wrapper> src;
+  for (size_t idx = 0; idx < inner_structs.size(); idx++) {
+    std::vector<std::unique_ptr<column>> inputs;
+    inputs.push_back(std::make_unique<column>(inner_structs[idx]));
+    inputs.push_back(std::make_unique<column>(inner_lists[idx]));
+    src.push_back(cudf::test::structs_column_wrapper(std::move(inputs)));
+  }
+
+  // concatenate
+  auto result = cudf::concatenate(std::vector<column_view>({src[0], src[1]}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+struct ListsColumnTest : public cudf::test::BaseFixture {};
+
+TEST_F(ListsColumnTest, ConcatenateLists)
+{
+  {
+    cudf::test::lists_column_wrapper<int> a{0, 1, 2, 3};
+    cudf::test::lists_column_wrapper<int> b{4, 5, 6, 7, 8, 9, 10};
+    cudf::test::lists_column_wrapper<int> expected{{0, 1, 2, 3}, {4, 5, 6, 7, 8, 9, 10}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{{0, 1, 1}, {2, 3}, {4, 5}};
+    cudf::test::lists_column_wrapper<int> b{{6}, {8, 9, 9, 9}, {10, 11}};
+    cudf::test::lists_column_wrapper<int> expected{
+      {0, 1, 1}, {2, 3}, {4, 5}, {6}, {8, 9, 9, 9}, {10, 11}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{{0, 1}, {2, 3, 4, 5}, {6, 7, 8}};
+    cudf::test::lists_column_wrapper<int> b{{9}, {10, 11}, {12, 13, 14, 15}};
+    cudf::test::lists_column_wrapper<int> expected{
+      {0, 1}, {2, 3, 4, 5}, {6, 7, 8}, {9}, {10, 11}, {12, 13, 14, 15}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TEST_F(ListsColumnTest, ConcatenateEmptyLists)
+{
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<int>;
+  {
+    cudf::test::lists_column_wrapper<int> a;
+    cudf::test::lists_column_wrapper<int> b{4, 5, 6, 7};
+    cudf::test::lists_column_wrapper<int> expected{4, 5, 6, 7};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a, b, c;
+    cudf::test::lists_column_wrapper<int> d{4, 5, 6, 7};
+    cudf::test::lists_column_wrapper<int> expected{4, 5, 6, 7};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b, c, d}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{LCW{}};
+    cudf::test::lists_column_wrapper<int> b{4, 5, 6, 7};
+    cudf::test::lists_column_wrapper<int> expected{LCW{}, {4, 5, 6, 7}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{LCW{}}, b{LCW{}}, c{LCW{}};
+    cudf::test::lists_column_wrapper<int> d{4, 5, 6, 7};
+    cudf::test::lists_column_wrapper<int> expected{LCW{}, LCW{}, LCW{}, {4, 5, 6, 7}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b, c, d}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{1, 2};
+    cudf::test::lists_column_wrapper<int> b{LCW{}}, c{LCW{}};
+    cudf::test::lists_column_wrapper<int> d{4, 5, 6, 7};
+    cudf::test::lists_column_wrapper<int> expected{{1, 2}, LCW{}, LCW{}, {4, 5, 6, 7}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b, c, d}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TEST_F(ListsColumnTest, ConcatenateListsWithNulls)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // nulls in the leaves
+  {
+    cudf::test::lists_column_wrapper<int> a{{{0, 1, 2, 3}, valids}};
+    cudf::test::lists_column_wrapper<int> b{{{4, 6, 7}, valids}};
+    cudf::test::lists_column_wrapper<int> expected{{{0, 1, 2, 3}, valids}, {{4, 6, 7}, valids}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TEST_F(ListsColumnTest, ConcatenateNestedLists)
+{
+  {
+    cudf::test::lists_column_wrapper<int> a{{{0, 1}, {2}}, {{4, 5, 6, 7, 8, 9, 10}}};
+    cudf::test::lists_column_wrapper<int> b{{{6, 7}}, {{8, 9, 10}, {11, 12}}};
+    cudf::test::lists_column_wrapper<int> expected{
+      {{0, 1}, {2}}, {{4, 5, 6, 7, 8, 9, 10}}, {{6, 7}}, {{8, 9, 10}, {11, 12}}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{
+      {{{0, 1, 2}, {3, 4}}, {{5}, {6, 7}}, {{8, 9}}},
+      {{{10}, {11, 12}}, {{13, 14, 15, 16}, {15, 16}}, {{17, 18}, {19, 20}}},
+      {{{50}, {51, 52}}, {{54}, {55, 16}}, {{57, 18}, {59, 60}}}};
+
+    cudf::test::lists_column_wrapper<int> b{
+      {{{21, 22}, {23, 24}}, {{25}, {26, 27}}, {{28, 29, 30}}},
+      {{{31, 32}, {33, 34}}, {{35, 36}, {37, 38}}, {{39, 40}}},
+      {{{71, 72}, {74}}, {{75, 76, 77, 78}, {77, 78}}, {{79, 80, 81}}}};
+
+    cudf::test::lists_column_wrapper<int> expected{
+      {{{0, 1, 2}, {3, 4}}, {{5}, {6, 7}}, {{8, 9}}},
+      {{{10}, {11, 12}}, {{13, 14, 15, 16}, {15, 16}}, {{17, 18}, {19, 20}}},
+      {{{50}, {51, 52}}, {{54}, {55, 16}}, {{57, 18}, {59, 60}}},
+      {{{21, 22}, {23, 24}}, {{25}, {26, 27}}, {{28, 29, 30}}},
+      {{{31, 32}, {33, 34}}, {{35, 36}, {37, 38}}, {{39, 40}}},
+      {{{71, 72}, {74}}, {{75, 76, 77, 78}, {77, 78}}, {{79, 80, 81}}}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TEST_F(ListsColumnTest, ConcatenateNestedEmptyLists)
+{
+  using T = int;
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<T>;
+  {
+    cudf::test::lists_column_wrapper<T> a{{LCW{}}, {{0, 1}, {2, 3}}};
+    cudf::test::lists_column_wrapper<int> b{{{6, 7}}, {LCW{}, {11, 12}}};
+    cudf::test::lists_column_wrapper<int> expected{
+      {LCW{}}, {{0, 1}, {2, 3}}, {{6, 7}}, {LCW{}, {11, 12}}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{
+      {{{0, 1, 2}, LCW{}}, {{5}, {6, 7}}, {{8, 9}}},
+      {{LCW{}}, {{17, 18}, {19, 20}}},
+      {{LCW{}}},
+      {{{50}, {51, 52}}, {{53, 54}, {55, 16, 17}}, {{59, 60}}}};
+
+    cudf::test::lists_column_wrapper<int> b{
+      {{{21, 22}, {23, 24}}, {LCW{}, {26, 27}}, {{28, 29, 30}}},
+      {{{31, 32}, {33, 34}}, {{35, 36}, {37, 38}, {1, 2}}, {{39, 40}}},
+      {{LCW{}}}};
+
+    cudf::test::lists_column_wrapper<int> expected{
+      {{{0, 1, 2}, LCW{}}, {{5}, {6, 7}}, {{8, 9}}},
+      {{LCW{}}, {{17, 18}, {19, 20}}},
+      {{LCW{}}},
+      {{{50}, {51, 52}}, {{53, 54}, {55, 16, 17}}, {{59, 60}}},
+      {{{21, 22}, {23, 24}}, {LCW{}, {26, 27}}, {{28, 29, 30}}},
+      {{{31, 32}, {33, 34}}, {{35, 36}, {37, 38}, {1, 2}}, {{39, 40}}},
+      {{LCW{}}}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TEST_F(ListsColumnTest, ConcatenateNestedListsWithNulls)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // nulls in the lists
+  {
+    cudf::test::lists_column_wrapper<int> a{{{{0, 1}, {2, 3}}, valids}};
+    cudf::test::lists_column_wrapper<int> b{{{{4}, {6, 7}}, valids}};
+
+    cudf::test::lists_column_wrapper<int> expected{{{{0, 1}, {2, 3}}, valids},
+                                                   {{{4}, {6, 7}}, valids}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  // nulls in the lists -and- the values
+  {
+    cudf::test::lists_column_wrapper<int> a{{{{{0}, valids}, {2, 3}}, valids}, {{4, 5}}};
+    cudf::test::lists_column_wrapper<int> b{{{6, 7}}, {{{{8, 9, 10}, valids}, {11, 12}}, valids}};
+    cudf::test::lists_column_wrapper<int> expected{{{{{0}, valids}, {2, 3}}, valids},
+                                                   {{4, 5}},
+                                                   {{6, 7}},
+                                                   {{{{8, 9, 10}, valids}, {11, 12}}, valids}};
+
+    auto result = cudf::concatenate(std::vector<column_view>({a, b}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TEST_F(ListsColumnTest, ConcatenateMismatchedHierarchies)
+{
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<int>;
+  {
+    cudf::test::lists_column_wrapper<int> a{{{{LCW{}}}}};
+    cudf::test::lists_column_wrapper<int> b{{{LCW{}}}};
+    cudf::test::lists_column_wrapper<int> c{{LCW{}}};
+
+    EXPECT_THROW(cudf::concatenate(std::vector<column_view>({a, b, c})), cudf::logic_error);
+  }
+
+  {
+    std::vector<bool> valids{false};
+    cudf::test::lists_column_wrapper<int> a{{{{{LCW{}}}}, valids.begin()}};
+    cudf::test::lists_column_wrapper<int> b{{{LCW{}}}};
+    cudf::test::lists_column_wrapper<int> c{{LCW{}}};
+
+    EXPECT_THROW(cudf::concatenate(std::vector<column_view>({a, b, c})), cudf::logic_error);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{{{{LCW{}}}}};
+    cudf::test::lists_column_wrapper<int> b{1, 2, 3};
+    cudf::test::lists_column_wrapper<int> c{{3, 4, 5}};
+
+    EXPECT_THROW(cudf::concatenate(std::vector<column_view>({a, b, c})), cudf::logic_error);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{{{1, 2, 3}}};
+    cudf::test::lists_column_wrapper<int> b{{4, 5}};
+
+    EXPECT_THROW(cudf::concatenate(std::vector<column_view>({a, b})), cudf::logic_error);
+  }
+}
+
+TEST_F(ListsColumnTest, SlicedColumns)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  {
+    cudf::test::lists_column_wrapper<int> a{{{1, 1, 1}, {2, 2}, {3, 3}},
+                                            {{4, 4, 4}, {5, 5}, {6, 6}},
+                                            {{7, 7, 7}, {8, 8}, {9, 9}},
+                                            {{10, 10, 10}, {11, 11}, {12, 12}}};
+    auto split_a = cudf::split(a, {2});
+
+    cudf::test::lists_column_wrapper<int> b{{{-1, -1, -1, -1}, {-2}},
+                                            {{-3, -3, -3, -3}, {-4}},
+                                            {{-5, -5, -5, -5}, {-6}},
+                                            {{-7, -7, -7, -7}, {-8}}};
+    auto split_b = cudf::split(b, {2});
+
+    cudf::test::lists_column_wrapper<int> expected0{{{1, 1, 1}, {2, 2}, {3, 3}},
+                                                    {{4, 4, 4}, {5, 5}, {6, 6}},
+                                                    {{-1, -1, -1, -1}, {-2}},
+                                                    {{-3, -3, -3, -3}, {-4}}};
+
+    auto result0 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result0, expected0);
+
+    cudf::test::lists_column_wrapper<int> expected1{{{1, 1, 1}, {2, 2}, {3, 3}},
+                                                    {{4, 4, 4}, {5, 5}, {6, 6}},
+                                                    {{-5, -5, -5, -5}, {-6}},
+                                                    {{-7, -7, -7, -7}, {-8}}};
+
+    auto result1 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result1, expected1);
+
+    cudf::test::lists_column_wrapper<int> expected2{
+      {{7, 7, 7}, {8, 8}, {9, 9}},
+      {{10, 10, 10}, {11, 11}, {12, 12}},
+      {{-1, -1, -1, -1}, {-2}},
+      {{-3, -3, -3, -3}, {-4}},
+    };
+
+    auto result2 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result2, expected2);
+
+    cudf::test::lists_column_wrapper<int> expected3{{{7, 7, 7}, {8, 8}, {9, 9}},
+                                                    {{10, 10, 10}, {11, 11}, {12, 12}},
+                                                    {{-5, -5, -5, -5}, {-6}},
+                                                    {{-7, -7, -7, -7}, {-8}}};
+
+    auto result3 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result3, expected3);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{
+      {{{1, 1, 1}, {2, 2}}, {{3, 3}}, {{10, 9, 16}, {8, 7, 1}, {6, 8, 2}}},
+      {LCW{}, {LCW{}}, {{6, 6}, {2}}},
+      {LCW{}, LCW{}},
+      {LCW{}, LCW{}, {{10, 10, 10}, {11, 11}, {12, 12}}, LCW{}}};
+    auto split_a = cudf::split(a, {2});
+
+    cudf::test::lists_column_wrapper<int> b{
+      {{LCW{}}},
+      {LCW{}, {LCW{}}},
+      {{{1, 2, 9}, LCW{}}, {{5, 6, 7, 8, 9}, {0}, {15, 17}}},
+      {{LCW{}}},
+    };
+    auto split_b = cudf::split(b, {2});
+
+    cudf::test::lists_column_wrapper<int> expected0{
+      {{{1, 1, 1}, {2, 2}}, {{3, 3}}, {{10, 9, 16}, {8, 7, 1}, {6, 8, 2}}},
+      {LCW{}, {LCW{}}, {{6, 6}, {2}}},
+      {{LCW{}}},
+      {LCW{}, {LCW{}}}};
+
+    auto result0 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[0]}));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result0, expected0);
+
+    cudf::test::lists_column_wrapper<int> expected1{
+      {{{1, 1, 1}, {2, 2}}, {{3, 3}}, {{10, 9, 16}, {8, 7, 1}, {6, 8, 2}}},
+      {LCW{}, {LCW{}}, {{6, 6}, {2}}},
+      {{{1, 2, 9}, LCW{}}, {{5, 6, 7, 8, 9}, {0}, {15, 17}}},
+      {{LCW{}}},
+    };
+
+    auto result1 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result1, expected1);
+
+    cudf::test::lists_column_wrapper<int> expected2{
+      {LCW{}, LCW{}},
+      {LCW{}, LCW{}, {{10, 10, 10}, {11, 11}, {12, 12}}, LCW{}},
+      {{LCW{}}},
+      {LCW{}, {LCW{}}}};
+
+    auto result2 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result2, expected2);
+
+    cudf::test::lists_column_wrapper<int> expected3{
+      {LCW{}, LCW{}},
+      {LCW{}, LCW{}, {{10, 10, 10}, {11, 11}, {12, 12}}, LCW{}},
+      {{{1, 2, 9}, LCW{}}, {{5, 6, 7, 8, 9}, {0}, {15, 17}}},
+      {{LCW{}}},
+    };
+
+    auto result3 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result3, expected3);
+  }
+}
+
+TEST_F(ListsColumnTest, SlicedColumnsWithNulls)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  {
+    cudf::test::lists_column_wrapper<int> a{{{{1, 1, 1}, valids}, {2, 2}, {{3, 3}, valids}},
+                                            {{{4, 4, 4}, {{5, 5}, valids}, {6, 6}}, valids},
+                                            {{7, 7, 7}, {8, 8}, {9, 9}},
+                                            {{{10, 10, 10}, {11, 11}, {{12, 12}, valids}}, valids}};
+    auto split_a = cudf::split(a, {3});
+
+    cudf::test::lists_column_wrapper<int> b{{{{{-1, -1, -1, -1}, valids}, {-2}}, valids},
+                                            {{{{-3, -3, -3, -3}, valids}, {-4}}, valids},
+                                            {{{{-5, -5, -5, -5}, valids}, {-6}}, valids},
+                                            {{{{-7, -7, -7, -7}, valids}, {-8}}, valids}};
+    auto split_b = cudf::split(b, {3});
+
+    cudf::test::lists_column_wrapper<int> expected0{{{{1, 1, 1}, valids}, {2, 2}, {{3, 3}, valids}},
+                                                    {{{4, 4, 4}, {{5, 5}, valids}, {6, 6}}, valids},
+                                                    {{7, 7, 7}, {8, 8}, {9, 9}},
+                                                    {{{{-1, -1, -1, -1}, valids}, {-2}}, valids},
+                                                    {{{{-3, -3, -3, -3}, valids}, {-4}}, valids},
+                                                    {{{{-5, -5, -5, -5}, valids}, {-6}}, valids}};
+
+    auto result0 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result0, expected0);
+
+    cudf::test::lists_column_wrapper<int> expected1{{{{1, 1, 1}, valids}, {2, 2}, {{3, 3}, valids}},
+                                                    {{{4, 4, 4}, {{5, 5}, valids}, {6, 6}}, valids},
+                                                    {{7, 7, 7}, {8, 8}, {9, 9}},
+                                                    {{{{-7, -7, -7, -7}, valids}, {-8}}, valids}};
+
+    auto result1 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result1, expected1);
+
+    cudf::test::lists_column_wrapper<int> expected2{
+      {{{10, 10, 10}, {11, 11}, {{12, 12}, valids}}, valids},
+      {{{{-1, -1, -1, -1}, valids}, {-2}}, valids},
+      {{{{-3, -3, -3, -3}, valids}, {-4}}, valids},
+      {{{{-5, -5, -5, -5}, valids}, {-6}}, valids}};
+
+    auto result2 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result2, expected2);
+
+    cudf::test::lists_column_wrapper<int> expected3{
+      {{{10, 10, 10}, {11, 11}, {{12, 12}, valids}}, valids},
+      {{{{-7, -7, -7, -7}, valids}, {-8}}, valids}};
+
+    auto result3 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result3, expected3);
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> a{
+      {{{{1, 1, 1}, valids}, {2, 2}},
+       {{{3, 3}}, valids},
+       {{{10, 9, 16}, valids}, {8, 7, 1}, {{6, 8, 2}, valids}}},
+      {{LCW{}, {{LCW{}}, valids}, {{6, 6}, {2}}}, valids},
+      {{{LCW{}, LCW{}}, valids}},
+      {LCW{}, LCW{}, {{{10, 10, 10}, {{11, 11}, valids}, {12, 12}}, valids}, LCW{}}};
+    auto split_a = cudf::split(a, {3});
+
+    cudf::test::lists_column_wrapper<int> b{
+      {{{LCW{}}, valids}},
+      {{LCW{}, {{LCW{}}, valids}}, valids},
+      {{{{1, 2, 9}, LCW{}}, {{5, 6, 7, 8, 9}, {0}, {15, 17}}}, valids},
+      {{LCW{}}},
+    };
+    auto split_b = cudf::split(b, {3});
+
+    cudf::test::lists_column_wrapper<int> expected0{
+      {{{{1, 1, 1}, valids}, {2, 2}},
+       {{{3, 3}}, valids},
+       {{{10, 9, 16}, valids}, {8, 7, 1}, {{6, 8, 2}, valids}}},
+      {{LCW{}, {{LCW{}}, valids}, {{6, 6}, {2}}}, valids},
+      {{{LCW{}, LCW{}}, valids}},
+      {{{LCW{}}, valids}},
+      {{LCW{}, {{LCW{}}, valids}}, valids},
+      {{{{1, 2, 9}, LCW{}}, {{5, 6, 7, 8, 9}, {0}, {15, 17}}}, valids},
+    };
+
+    auto result0 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result0, expected0);
+
+    cudf::test::lists_column_wrapper<int> expected1{
+      {{{{1, 1, 1}, valids}, {2, 2}},
+       {{{3, 3}}, valids},
+       {{{10, 9, 16}, valids}, {8, 7, 1}, {{6, 8, 2}, valids}}},
+      {{LCW{}, {{LCW{}}, valids}, {{6, 6}, {2}}}, valids},
+      {{{LCW{}, LCW{}}, valids}},
+      {{LCW{}}},
+    };
+
+    auto result1 = cudf::concatenate(std::vector<column_view>({split_a[0], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result1, expected1);
+
+    cudf::test::lists_column_wrapper<int> expected2{
+      {LCW{}, LCW{}, {{{10, 10, 10}, {{11, 11}, valids}, {12, 12}}, valids}, LCW{}},
+      {{{LCW{}}, valids}},
+      {{LCW{}, {{LCW{}}, valids}}, valids},
+      {{{{1, 2, 9}, LCW{}}, {{5, 6, 7, 8, 9}, {0}, {15, 17}}}, valids},
+    };
+
+    auto result2 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[0]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result2, expected2);
+
+    cudf::test::lists_column_wrapper<int> expected3{
+      {LCW{}, LCW{}, {{{10, 10, 10}, {{11, 11}, valids}, {12, 12}}, valids}, LCW{}},
+      {{LCW{}}},
+    };
+
+    auto result3 = cudf::concatenate(std::vector<column_view>({split_a[1], split_b[1]}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result3, expected3);
+  }
+}
+
+TEST_F(ListsColumnTest, ListOfStructs)
+{
+  auto count_iter = thrust::make_counting_iterator(0);
+
+  // inner structs
+  std::vector<cudf::test::structs_column_wrapper> inner_structs;
+  {
+    // 1. String "names" column.
+    std::vector<std::vector<std::string>> names(
+      {{"Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant"},
+       {},
+       {},
+       {"Bill", "Bob", "Sam", "Fred", "Tom"}});
+    std::vector<std::vector<bool>> names_validity({{1, 1, 1, 1, 1, 1}, {}, {}, {0, 1, 0, 1, 0}});
+    std::vector<cudf::test::strings_column_wrapper> name_cols;
+    std::transform(
+      count_iter, count_iter + names.size(), std::back_inserter(name_cols), [&](int i) {
+        return cudf::test::strings_column_wrapper(
+          names[i].begin(), names[i].end(), names_validity[i].begin());
+      });
+
+    // 2. Numeric "ages" column.
+    std::vector<std::vector<int>> ages({{5, 10, 15, 20, 25, 30}, {}, {}, {11, 16, 17, 41, 42}});
+    std::vector<std::vector<bool>> ages_validity({{1, 1, 1, 1, 0, 1}, {}, {}, {1, 1, 1, 0, 0}});
+    std::vector<cudf::test::fixed_width_column_wrapper<int>> age_cols;
+    std::transform(count_iter, count_iter + ages.size(), std::back_inserter(age_cols), [&](int i) {
+      return cudf::test::fixed_width_column_wrapper<int>(
+        ages[i].begin(), ages[i].end(), ages_validity[i].begin());
+    });
+
+    for (size_t idx = 0; idx < names.size(); idx++) {
+      std::vector<std::unique_ptr<column>> children;
+      children.push_back(name_cols[idx].release());
+      children.push_back(age_cols[idx].release());
+      inner_structs.push_back(cudf::test::structs_column_wrapper(std::move(children)));
+    }
+  }
+
+  // build expected output
+  auto struct_views = std::vector<column_view>(
+    {inner_structs[0], inner_structs[1], inner_structs[2], inner_structs[3]});
+  auto expected_child = cudf::concatenate(struct_views);
+  cudf::test::fixed_width_column_wrapper<int> offsets_w{0, 1, 1, 1, 1, 4, 6, 6, 6, 10, 11};
+  auto expected =
+    make_lists_column(10, offsets_w.release(), std::move(expected_child), 0, rmm::device_buffer{});
+
+  // lists
+  std::vector<cudf::test::fixed_width_column_wrapper<int>> offsets;
+  offsets.push_back({0, 1, 1, 1, 1, 4, 6, 6});
+  offsets.push_back({0});
+  offsets.push_back({0});
+  offsets.push_back({0, 0, 4, 5});
+
+  // concatenate as lists
+  std::vector<std::unique_ptr<column>> src;
+  for (size_t idx = 0; idx < inner_structs.size(); idx++) {
+    int size = static_cast<column_view>(offsets[idx]).size() - 1;
+    src.push_back(make_lists_column(
+      size, offsets[idx].release(), inner_structs[idx].release(), 0, rmm::device_buffer{}));
+  }
+
+  // concatenate
+  auto result = cudf::concatenate(std::vector<column_view>({*src[0], *src[1], *src[2], *src[3]}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+struct FixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointConcatentate)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<decimalXX>;
+
+  auto begin =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return decimalXX{i}; });
+  auto const vec = std::vector<decimalXX>(begin, begin + 1000);
+
+  auto const a = fw_wrapper(vec.begin(), /***/ vec.begin() + 300);
+  auto const b = fw_wrapper(vec.begin() + 300, vec.begin() + 700);
+  auto const c = fw_wrapper(vec.begin() + 700, vec.end());
+
+  auto const results  = cudf::concatenate(std::vector<cudf::column_view>{a, b, c});
+  auto const expected = fw_wrapper(vec.begin(), vec.end());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(FixedPointTest, FixedPointConcatentate)
+{
+  using namespace numeric;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<int32_t>;
+
+  auto begin     = thrust::make_counting_iterator(0);
+  auto const vec = std::vector<int32_t>(begin, begin + 1000);
+
+  auto const a = fp_wrapper(vec.begin(), /***/ vec.begin() + 300, scale_type{-2});
+  auto const b = fp_wrapper(vec.begin() + 300, vec.begin() + 700, scale_type{-2});
+  auto const c = fp_wrapper(vec.begin() + 700, vec.end(), /*****/ scale_type{-2});
+
+  auto const results  = cudf::concatenate(std::vector<cudf::column_view>{a, b, c});
+  auto const expected = fp_wrapper(vec.begin(), vec.end(), scale_type{-2});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(FixedPointTest, FixedPointScaleMismatch)
+{
+  using namespace numeric;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<int32_t>;
+
+  auto begin     = thrust::make_counting_iterator(0);
+  auto const vec = std::vector<int32_t>(begin, begin + 1000);
+
+  auto const a = fp_wrapper(vec.begin(), /***/ vec.begin() + 300, scale_type{-1});
+  auto const b = fp_wrapper(vec.begin() + 300, vec.begin() + 700, scale_type{-2});
+  auto const c = fp_wrapper(vec.begin() + 700, vec.end(), /*****/ scale_type{-3});
+
+  EXPECT_THROW(cudf::concatenate(std::vector<cudf::column_view>{a, b, c}), cudf::logic_error);
+}
+
+struct DictionaryConcatTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryConcatTest, StringsKeys)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"eee", "aaa", "ddd", "bbb", "", "", "ccc", "ccc", "ccc", "eee", "aaa"},
+    {1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1});
+  auto dictionary = cudf::dictionary::encode(strings);
+
+  std::vector<cudf::size_type> splits{0, 2, 2, 5, 5, 7, 7, 7, 7, 11};
+  std::vector<cudf::column_view> views = cudf::slice(dictionary->view(), splits);
+  // concatenate should recreate the original column
+  auto result  = cudf::concatenate(views);
+  auto decoded = cudf::dictionary::decode(result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, strings);
+}
+
+template <typename T>
+struct DictionaryConcatTestFW : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(DictionaryConcatTestFW, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(DictionaryConcatTestFW, FixedWidthKeys)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> original(
+    {20, 10, 0, 5, 15, 15, 10, 5, 20}, {1, 1, 0, 1, 1, 1, 1, 1, 1});
+  auto dictionary = cudf::dictionary::encode(original);
+  std::vector<cudf::size_type> splits{0, 3, 3, 5, 5, 9};
+  std::vector<cudf::column_view> views = cudf::slice(dictionary->view(), splits);
+  // concatenated result should equal the original column
+  auto result  = cudf::concatenate(views);
+  auto decoded = cudf::dictionary::decode(result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, original);
+}
+
+TEST_F(DictionaryConcatTest, ErrorsTest)
+{
+  cudf::test::strings_column_wrapper strings({"aaa", "ddd", "bbb"});
+  auto dictionary1 = cudf::dictionary::encode(strings);
+  cudf::test::fixed_width_column_wrapper<int32_t> integers({10, 30, 20});
+  auto dictionary2 = cudf::dictionary::encode(integers);
+  std::vector<cudf::column_view> views({dictionary1->view(), dictionary2->view()});
+  EXPECT_THROW(cudf::concatenate(views), cudf::logic_error);
+  std::vector<cudf::column_view> empty;
+  EXPECT_THROW(cudf::concatenate(empty), cudf::logic_error);
+}
diff --git a/cpp/tests/copying/copy_if_else_nested_tests.cpp b/cpp/tests/copying/copy_if_else_nested_tests.cpp
new file mode 100644
index 0000000..579e1bd
--- /dev/null
+++ b/cpp/tests/copying/copy_if_else_nested_tests.cpp
@@ -0,0 +1,513 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+using namespace cudf::test::iterators;
+
+struct CopyIfElseNestedTest : cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedCopyIfElseNestedTest : CopyIfElseNestedTest {};
+
+TYPED_TEST_SUITE(TypedCopyIfElseNestedTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(TypedCopyIfElseNestedTest, Structs)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto lhs_ints_child     = ints{0, 1, 2, 3, 4, 5, 6};
+  auto lhs_strings_child  = strings{"0", "1", "2", "3", "4", "5", "6"};
+  auto lhs_structs_column = structs{{lhs_ints_child, lhs_strings_child}}.release();
+
+  auto rhs_ints_child     = ints{0, 11, 22, 33, 44, 55, 66};
+  auto rhs_strings_child  = strings{"00", "11", "22", "33", "44", "55", "66"};
+  auto rhs_structs_column = structs{{rhs_ints_child, rhs_strings_child}}.release();
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto result_column = cudf::copy_if_else(
+    lhs_structs_column->view(), rhs_structs_column->view(), selector_column->view());
+
+  auto expected_ints    = ints{0, 1, 22, 3, 4, 55, 6};
+  auto expected_strings = strings{"0", "1", "22", "3", "4", "55", "6"};
+  auto expected_result  = structs{{expected_ints, expected_strings}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_column->view(), expected_result->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, StructsWithNulls)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto null_at_0 = null_at(0);
+  auto null_at_3 = null_at(3);
+  auto null_at_5 = null_at(5);
+
+  auto lhs_ints_child     = ints{{0, 1, 2, 3, 4, 5, 6}, null_at_0};
+  auto lhs_strings_child  = strings{"0", "1", "2", "3", "4", "5", "6"};
+  auto lhs_structs_column = structs{{lhs_ints_child, lhs_strings_child}, null_at_3}.release();
+
+  auto rhs_ints_child     = ints{0, 11, 22, 33, 44, 55, 66};
+  auto rhs_strings_child  = strings{{"00", "11", "22", "33", "44", "55", "66"}, null_at_5};
+  auto rhs_structs_column = structs{{rhs_ints_child, rhs_strings_child}}.release();
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto result_column = cudf::copy_if_else(
+    lhs_structs_column->view(), rhs_structs_column->view(), selector_column->view());
+
+  auto null_at_0_3 = nulls_at(std::vector<cudf::size_type>{0, 3});
+  auto null_at_3_5 = nulls_at(std::vector<cudf::size_type>{3, 5});
+
+  auto expected_ints    = ints{{-1, 1, 22, 3, 4, 55, 6}, null_at_0_3};
+  auto expected_strings = strings{{"0", "1", "22", "", "4", "", "6"}, null_at_3_5};
+  auto expected_result  = structs{{expected_ints, expected_strings}, null_at_3}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_column->view(), expected_result->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, LongerStructsWithNulls)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto selector_column = bools{1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,
+                               0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,
+                               0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0}
+                           .release();
+  auto lhs_child_1 =
+    ints{{27, -80, -24, 76,  -56, 42,  5,   13,  -69, -77, 61,   -77,  72,  0,   31,  118, -30,
+          86, 125, 0,   0,   0,   75,  -49, 125, 60,  116, 118,  64,   20,  -70, -18, 0,   -25,
+          22, -46, -89, -9,  27,  -56, -77, 123, 0,   -90, 87,   -113, -37, 22,  -22, -53, 73,
+          99, 113, -2,  -24, 113, 75,  6,   82,  -58, 122, -123, -127, 19,  -62, -24},
+         nulls_at(std::vector<cudf::size_type>{13, 19, 20, 21, 32, 42})};
+
+  auto lhs_structs_column = structs{{lhs_child_1}}.release();
+  auto result_column      = cudf::copy_if_else(
+    lhs_structs_column->view(), lhs_structs_column->view(), selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_column->view(), lhs_structs_column->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarStructBothInvalid)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto lhs_child_ints    = ints{11};
+  auto lhs_child_strings = strings{"11"};
+  auto lhs_children      = std::vector<cudf::column_view>{{lhs_child_ints, lhs_child_strings}};
+  auto lhs_scalar        = cudf::struct_scalar{lhs_children, false};
+
+  auto rhs_child_ints    = ints{{22}, null_at(0)};
+  auto rhs_child_strings = strings{"22"};
+  auto rhs_children      = std::vector<cudf::column_view>{{rhs_child_ints, rhs_child_strings}};
+  auto rhs_scalar        = cudf::struct_scalar{rhs_children, false};
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto expected_ints    = ints{-11, -11, -22, -11, -11, -22, -11};
+  auto expected_strings = strings{"-11", "-11", "-22", "-11", "-22", "-11", "-11"};
+  auto expected_result  = structs{{expected_ints, expected_strings}, all_nulls()}.release();
+
+  auto result_column = cudf::copy_if_else(lhs_scalar, rhs_scalar, selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarStructBothValid)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto lhs_child_ints    = ints{11};
+  auto lhs_child_strings = strings{{"11"}, null_at(0)};
+  auto lhs_children      = std::vector<cudf::column_view>{{lhs_child_ints, lhs_child_strings}};
+  auto lhs_scalar        = cudf::make_struct_scalar(lhs_children);
+
+  auto rhs_child_ints    = ints{{22}, null_at(0)};
+  auto rhs_child_strings = strings{"22"};
+  auto rhs_children      = std::vector<cudf::column_view>{{rhs_child_ints, rhs_child_strings}};
+  auto rhs_scalar        = cudf::make_struct_scalar(rhs_children);
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto expected_ints =
+    ints{{11, 11, -22, 11, 11, -22, 11}, nulls_at(std::vector<cudf::size_type>{2, 5})};
+  auto expected_strings = strings{{"NA", "NA", "22", "NA", "NA", "22", "NA"},
+                                  nulls_at(std::vector<cudf::size_type>{0, 1, 3, 4, 6})};
+  auto expected_result  = structs{{expected_ints, expected_strings}}.release();
+
+  auto result_column = cudf::copy_if_else(*lhs_scalar, *rhs_scalar, selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarStructLeft)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto lhs_child_ints    = ints{11};
+  auto lhs_child_strings = strings{{"11"}, null_at(0)};
+  auto lhs_children      = std::vector<cudf::column_view>{{lhs_child_ints, lhs_child_strings}};
+  auto lhs_scalar        = cudf::make_struct_scalar(lhs_children);
+
+  auto rhs_child_ints    = ints{{22, 22, 22, 22, 22, 22, 22}, null_at(2)};
+  auto rhs_child_strings = strings{"22", "22", "22", "22", "22", "22", "22"};
+  auto rhs_column        = structs{{rhs_child_ints, rhs_child_strings}, null_at(5)}.release();
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto lhs_column = cudf::make_column_from_scalar(*lhs_scalar, selector_column->size());
+
+  auto expected_ints    = ints{{11, 11, -22, 11, 11, 22, 11}, null_at(2)};
+  auto expected_strings = strings{{"NA", "NA", "22", "NA", "NA", "22", "NA"},
+                                  nulls_at(std::vector<cudf::size_type>{0, 1, 3, 4, 6})};
+  auto expected_result  = structs{{expected_ints, expected_strings}, null_at(5)}.release();
+
+  auto result_column = cudf::copy_if_else(*lhs_scalar, rhs_column->view(), selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarStructRight)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+  auto lhs_child_ints =
+    ints{{11, 11, 11, 11, 11, 11, 11}, nulls_at(std::vector<cudf::size_type>{1, 4})};
+  auto lhs_child_strings = strings{"11", "11", "11", "11", "11", "11", "11"};
+  auto lhs_column        = structs{{lhs_child_ints, lhs_child_strings}, null_at(6)}.release();
+
+  auto rhs_child_ints    = ints{{22}, null_at(0)};
+  auto rhs_child_strings = strings{"22"};
+  auto rhs_children      = std::vector<cudf::column_view>{{rhs_child_ints, rhs_child_strings}};
+  auto rhs_scalar        = cudf::make_struct_scalar(rhs_children);
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto expected_ints =
+    ints{{11, 11, -22, 11, 11, -22, 11}, nulls_at(std::vector<cudf::size_type>{1, 2, 4, 5})};
+  auto expected_strings = strings{"11", "11", "22", "11", "11", "22", "11"};
+  auto expected_result  = structs{{expected_ints, expected_strings}, null_at(6)}.release();
+
+  auto result_column = cudf::copy_if_else(lhs_column->view(), *rhs_scalar, selector_column->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, Lists)
+{
+  using T = TypeParam;
+
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto lhs =
+    lcw{{0, 0}, {1, 1}, {2, 2}, {3, 3, 3}, {4, 4, 4, 4}, {5, 5, 5, 5, 5}, {6, 6, 6, 6, 6, 6}}
+      .release();
+
+  auto rhs = lcw{{0, 0},
+                 {11, 11},
+                 {22, 22},
+                 {33, 33, 33},
+                 {44, 44, 44, 44},
+                 {55, 55, 55, 55, 55},
+                 {66, 66, 66, 66, 66, 66}}
+               .release();
+
+  auto selector_column =
+    cudf::test::fixed_width_column_wrapper<bool, int32_t>{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto result_column = cudf::copy_if_else(lhs->view(), rhs->view(), selector_column->view());
+
+  auto expected_output =
+    lcw{{0, 0}, {1, 1}, {22, 22}, {3, 3, 3}, {4, 4, 4, 4}, {55, 55, 55, 55, 55}, {6, 6, 6, 6, 6, 6}}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_column->view(), expected_output->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ListsWithNulls)
+{
+  using T = TypeParam;
+
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto null_at_0 = null_at(0);
+  auto null_at_4 = null_at(4);
+  auto null_at_5 = null_at(5);
+
+  auto lhs = lcw{{{0, 0},
+                  {1, 1},
+                  lcw{{2, 2}, null_at_0},
+                  lcw{{3, 3, 3}, null_at_0},
+                  {4, 4, 4, 4},
+                  {5, 5, 5, 5, 5},
+                  {6, 6, 6, 6, 6, 6}},
+                 null_at_4}
+               .release();
+
+  auto rhs = lcw{{{0, 0},
+                  {11, 11},
+                  {22, 22},
+                  {33, 33, 33},
+                  {44, 44, 44, 44},
+                  {55, 55, 55, 55, 55},
+                  {66, 66, 66, 66, 66, 66}},
+                 null_at_5}
+               .release();
+
+  auto selector_column =
+    cudf::test::fixed_width_column_wrapper<bool, int32_t>{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto result_column = cudf::copy_if_else(lhs->view(), rhs->view(), selector_column->view());
+
+  auto null_at_4_5 = nulls_at(std::vector{4, 5});
+
+  auto expected_output =
+    lcw{{{0, 0}, {1, 1}, {22, 22}, lcw{{3, 3, 3}, null_at_0}, {}, {}, {6, 6, 6, 6, 6, 6}},
+        null_at_4_5}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_column->view(), expected_output->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ListsWithStructs)
+{
+  using T = TypeParam;
+
+  using ints    = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using strings = cudf::test::strings_column_wrapper;
+  using structs = cudf::test::structs_column_wrapper;
+  using bools   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+  using offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type, int32_t>;
+
+  auto const null_at_0 = null_at(0);
+  auto const null_at_3 = null_at(3);
+  auto const null_at_4 = null_at(4);
+  auto const null_at_6 = null_at(6);
+  auto const null_at_7 = null_at(7);
+  auto const null_at_8 = null_at(8);
+
+  auto lhs_ints    = ints{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, null_at_3};
+  auto lhs_strings = strings{{"0", "1", "2", "3", "4", "5", "6", "7", "8", "9"}, null_at_4};
+  auto lhs_structs = structs{{lhs_ints, lhs_strings}}.release();
+  auto lhs_offsets = offsets{0, 2, 4, 6, 10, 10}.release();
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_at_4, null_at_4 + 5);
+  auto const lhs               = cudf::make_lists_column(
+    5, std::move(lhs_offsets), std::move(lhs_structs), null_count, std::move(null_mask));
+
+  auto rhs_ints = ints{{0, 11, 22, 33, 44, 55, 66, 77, 88, 99}, null_at_6};
+  auto rhs_strings =
+    strings{{"00", "11", "22", "33", "44", "55", "66", "77", "88", "99"}, null_at_7};
+  auto rhs_structs = structs{{rhs_ints, rhs_strings}, null_at_8};
+  auto rhs_offsets = offsets{0, 0, 4, 6, 8, 10};
+
+  std::tie(null_mask, null_count) = cudf::test::detail::make_null_mask(null_at_0, null_at_0 + 5);
+  auto const rhs                  = cudf::make_lists_column(
+    5, rhs_offsets.release(), rhs_structs.release(), null_count, std::move(null_mask));
+
+  auto selector_column = bools{1, 0, 1, 0, 1}.release();
+
+  auto result_column = cudf::copy_if_else(lhs->view(), rhs->view(), selector_column->view());
+
+  auto const null_at_6_9 = nulls_at(std::vector{6, 9});
+  auto expected_ints     = ints{{0, 1, 0, 11, 22, 33, 4, 5, -1, 77}, null_at_8};
+  auto expected_strings =
+    strings{{"0", "1", "00", "11", "22", "33", "", "5", "66", ""}, null_at_6_9};
+  auto expected_structs = structs{{expected_ints, expected_strings}};
+  auto expected_offsets = offsets{0, 2, 6, 8, 10, 10};
+
+  std::tie(null_mask, null_count) = cudf::test::detail::make_null_mask(null_at_4, null_at_4 + 5);
+  auto const expected             = cudf::make_lists_column(
+    5, expected_offsets.release(), expected_structs.release(), null_count, std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_column->view(), expected->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarListBothInvalid)
+{
+  using T = TypeParam;
+
+  using ints  = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using bools = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+  using lcw   = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto lhs_scalar = cudf::list_scalar{ints{33, 33, 33}, false};
+  auto rhs_scalar = cudf::list_scalar{ints{22, 22}, false};
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto expected = lcw{{
+                        {-33, -33, -33},
+                        {-33, -33, -33},
+                        {-22, -22},
+                        {-33, -33, -33},
+                        {-33, -33, -33},
+                        {-22, -22},
+                        {-33, -33, -33},
+                      },
+                      all_nulls()}
+                    .release();
+
+  auto result = cudf::copy_if_else(lhs_scalar, rhs_scalar, selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), result->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarListBothValid)
+{
+  using T = TypeParam;
+
+  using ints  = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using bools = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+  using lcw   = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto lhs_scalar = cudf::make_list_scalar(ints{{33, 33, 33}, null_at(1)});
+  auto rhs_scalar = cudf::make_list_scalar(ints{{22, 22}, null_at(0)});
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto expected =
+    lcw{
+      lcw{{33, 33, 33}, null_at(1)},
+      lcw{{33, 33, 33}, null_at(1)},
+      lcw{{22, 22}, null_at(0)},
+      lcw{{33, 33, 33}, null_at(1)},
+      lcw{{33, 33, 33}, null_at(1)},
+      lcw{{22, 22}, null_at(0)},
+      lcw{{33, 33, 33}, null_at(1)},
+    }
+      .release();
+
+  auto result = cudf::copy_if_else(*lhs_scalar, *rhs_scalar, selector_column->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), result->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarListLeft)
+{
+  using T = TypeParam;
+
+  using ints  = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using bools = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+  using lcw   = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto lhs_scalar = cudf::make_list_scalar(ints{{33, 33, 33}, null_at(1)});
+  auto rhs_column = lcw{{{-2, -1},
+                         {-2, -1, 0},
+                         {21, 22},
+                         {-20, -10, 0},
+                         {-200, -100, 0, 100},
+                         lcw{{23, 24, 25, 26, 27, 28}, null_at(1)},
+                         {-400}},
+                        null_at(2)}
+                      .release();
+
+  auto selector_column = bools{1, 1, 0, 1, 1, 0, 1}.release();
+
+  auto expected = lcw{{lcw{{33, 33, 33}, null_at(1)},
+                       lcw{{33, -33, 33}, null_at(1)},
+                       {-21, -22},
+                       lcw{{33, -33, 33}, null_at(1)},
+                       lcw{{33, -33, 33}, null_at(1)},
+                       lcw{{23, -24, 25, 26, 27, 28}, null_at(1)},
+                       lcw{{33, -33, 33}, null_at(1)}},
+                      null_at(2)}
+                    .release();
+
+  auto result = cudf::copy_if_else(*lhs_scalar, rhs_column->view(), selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), result->view());
+}
+
+TYPED_TEST(TypedCopyIfElseNestedTest, ScalarListRight)
+{
+  using T = TypeParam;
+
+  using ints  = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  using bools = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+  using lcw   = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto lhs_column = lcw{{{-2, -1},
+                         {-2, -1, 0},
+                         {21, 22},
+                         {-20, -10, 0},
+                         {-200, -100, 0, 100},
+                         lcw{{23, 24, 25, 26, 27, 28}, null_at(1)},
+                         {-400}},
+                        null_at(2)}
+                      .release();
+
+  auto rhs_scalar = cudf::make_list_scalar(ints{{33, 33, 33}, null_at(1)});
+
+  auto selector_column = bools{0, 0, 1, 0, 0, 1, 0}.release();
+
+  auto expected = lcw{{
+                        lcw{{33, -33, 33}, null_at(1)},
+                        lcw{{33, -33, 33}, null_at(1)},
+                        {-21, -22},
+                        lcw{{33, -33, 33}, null_at(1)},
+                        lcw{{33, -33, 33}, null_at(1)},
+                        lcw{{23, -24, 25, 26, 27, 28}, null_at(1)},
+                        lcw{{33, -33, 33}, null_at(1)},
+                      },
+                      null_at(2)}
+                    .release();
+
+  auto result = cudf::copy_if_else(lhs_column->view(), *rhs_scalar, selector_column->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), result->view());
+}
diff --git a/cpp/tests/copying/copy_range_tests.cpp b/cpp/tests/copying/copy_range_tests.cpp
new file mode 100644
index 0000000..96fbdcb
--- /dev/null
+++ b/cpp/tests/copying/copy_range_tests.cpp
@@ -0,0 +1,520 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/encode.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+auto all_valid  = [](cudf::size_type row) { return true; };
+auto even_valid = [](cudf::size_type row) { return (row % 2 == 0); };
+
+template <typename T>
+class CopyRangeTypedTestFixture : public cudf::test::BaseFixture {
+ public:
+  static constexpr cudf::size_type column_size{1000};
+
+  void test(cudf::column_view const& source,
+            cudf::column_view const& expected,
+            cudf::mutable_column_view& target,
+            cudf::size_type source_begin,
+            cudf::size_type source_end,
+            cudf::size_type target_begin)
+  {
+    static_assert(cudf::is_fixed_width<T>(), "this code assumes fixed-width types.");
+
+    // test the out-of-place version first
+
+    const cudf::column_view immutable_view{target};
+    auto p_ret = cudf::copy_range(source, immutable_view, source_begin, source_end, target_begin);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*p_ret, expected);
+
+    // test the in-place version second
+
+    EXPECT_NO_THROW(
+      cudf::copy_range_in_place(source, target, source_begin, source_end, target_begin));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(target, expected);
+  }
+};
+
+TYPED_TEST_SUITE(CopyRangeTypedTestFixture, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(CopyRangeTypedTestFixture, CopyWithNulls)
+{
+  using T = TypeParam;
+
+  cudf::size_type size{CopyRangeTypedTestFixture<T>::column_size};
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{size - 50};
+  cudf::size_type target_begin{30};
+  auto target_end = target_begin + (source_end - source_begin);
+  auto row_diff   = source_begin - target_begin;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target(
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(0) + size,
+    cudf::detail::make_counting_transform_iterator(0, all_valid));
+
+  auto source_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  cudf::test::fixed_width_column_wrapper<T, typename decltype(source_elements)::value_type> source(
+    source_elements,
+    source_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, even_valid));
+
+  auto expected_elements =
+    cudf::detail::make_counting_transform_iterator(0, [target_begin, target_end, row_diff](auto i) {
+      return ((i >= target_begin) && (i < target_end)) ? (i + row_diff) * 2 : i;
+    });
+  cudf::test::fixed_width_column_wrapper<T, typename decltype(expected_elements)::value_type>
+    expected(expected_elements,
+             expected_elements + size,
+             cudf::detail::make_counting_transform_iterator(
+               0, [target_begin, target_end, row_diff](auto i) {
+                 return ((i >= target_begin) && (i < target_end)) ? even_valid(i + row_diff)
+                                                                  : all_valid(i);
+               }));
+
+  cudf::mutable_column_view target_view{target};
+  this->test(source, expected, target_view, source_begin, source_end, target_begin);
+}
+
+TYPED_TEST(CopyRangeTypedTestFixture, CopyNoNulls)
+{
+  using T = TypeParam;
+
+  cudf::size_type size{CopyRangeTypedTestFixture<T>::column_size};
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{size - 50};
+  cudf::size_type target_begin{30};
+  auto target_end = target_begin + (source_end - source_begin);
+  auto row_diff   = source_begin - target_begin;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto source_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  cudf::test::fixed_width_column_wrapper<T, typename decltype(source_elements)::value_type> source(
+    source_elements, source_elements + size);
+
+  auto expected_elements =
+    cudf::detail::make_counting_transform_iterator(0, [target_begin, target_end, row_diff](auto i) {
+      return ((i >= target_begin) && (i < target_end)) ? (i + row_diff) * 2 : i;
+    });
+  cudf::test::fixed_width_column_wrapper<T, typename decltype(expected_elements)::value_type>
+    expected(expected_elements, expected_elements + size);
+
+  cudf::mutable_column_view target_view{target};
+  this->test(source, expected, target_view, source_begin, source_end, target_begin);
+}
+
+TYPED_TEST(CopyRangeTypedTestFixture, CopyWithNullsNonzeroOffset)
+{
+  using T = TypeParam;
+
+  cudf::size_type size{CopyRangeTypedTestFixture<T>::column_size};
+  cudf::size_type source_offset{27};
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{50};
+  cudf::size_type target_offset{58};
+  cudf::size_type target_begin{30};
+  auto target_end = target_begin + (source_end - source_begin);
+  auto row_diff   = (source_offset + source_begin) - (target_offset + target_begin);
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target(
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(0) + size,
+    cudf::detail::make_counting_transform_iterator(0, all_valid));
+
+  cudf::mutable_column_view tmp = target;
+  cudf::mutable_column_view target_slice(tmp.type(),
+                                         tmp.size() - target_offset,
+                                         tmp.head<T>(),
+                                         tmp.null_mask(),
+                                         tmp.null_count(),
+                                         target_offset);
+
+  auto source_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  cudf::test::fixed_width_column_wrapper<T, typename decltype(source_elements)::value_type> source(
+    source_elements,
+    source_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, even_valid));
+
+  auto source_slice = cudf::slice(source, std::vector<cudf::size_type>{source_offset, size})[0];
+
+  auto expected_elements = cudf::detail::make_counting_transform_iterator(
+    0, [target_offset, target_begin, target_end, row_diff](auto i) {
+      return ((i >= target_offset + target_begin) && (i < target_offset + target_end))
+               ? (i + row_diff) * 2
+               : i;
+    });
+  cudf::test::fixed_width_column_wrapper<T, typename decltype(expected_elements)::value_type>
+    expected(expected_elements,
+             expected_elements + size,
+             cudf::detail::make_counting_transform_iterator(
+               0, [target_offset, target_begin, target_end, row_diff](auto i) {
+                 return ((i >= target_offset + target_begin) && (i < target_offset + target_end))
+                          ? even_valid(i + row_diff)
+                          : all_valid(i);
+               }));
+
+  auto expected_slice = cudf::slice(expected, std::vector<cudf::size_type>{target_offset, size})[0];
+
+  this->test(source_slice, expected_slice, target_slice, source_begin, source_end, target_begin);
+}
+
+class CopyRangeTestFixture : public cudf::test::BaseFixture {};
+
+TEST_F(CopyRangeTestFixture, CopyWithNullsString)
+{
+  cudf::size_type size{100};
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{50};
+  cudf::size_type target_begin{30};
+  auto target_end = target_begin + (source_end - source_begin);
+  auto row_diff   = source_begin - target_begin;
+
+  auto target_elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "#" + std::to_string(i); });
+  auto target = cudf::test::strings_column_wrapper(
+    target_elements,
+    target_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, all_valid));
+
+  auto source_elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "#" + std::to_string(i * 2); });
+  auto source = cudf::test::strings_column_wrapper(
+    source_elements,
+    source_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, even_valid));
+
+  auto expected_elements =
+    cudf::detail::make_counting_transform_iterator(0, [target_begin, target_end, row_diff](auto i) {
+      auto num = std::to_string(((i >= target_begin) && (i < target_end)) ? (i + row_diff) * 2 : i);
+      return "#" + num;
+    });
+  auto expected = cudf::test::strings_column_wrapper(
+    expected_elements,
+    expected_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, [target_begin, target_end, row_diff](auto i) {
+      return ((i >= target_begin) && (i < target_end)) ? even_valid(i + row_diff) : all_valid(i);
+    }));
+
+  auto p_ret = cudf::copy_range(source, target, source_begin, source_end, target_begin);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*p_ret, expected);
+}
+
+TEST_F(CopyRangeTestFixture, CopyNoNullsString)
+{
+  cudf::size_type size{100};
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{50};
+  cudf::size_type target_begin{30};
+  auto target_end = target_begin + (source_end - source_begin);
+  auto row_diff   = source_begin - target_begin;
+
+  auto target_elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "#" + std::to_string(i); });
+  auto target = cudf::test::strings_column_wrapper(target_elements, target_elements + size);
+
+  auto source_elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "#" + std::to_string(i * 2); });
+  auto source = cudf::test::strings_column_wrapper(source_elements, source_elements + size);
+
+  auto expected_elements =
+    cudf::detail::make_counting_transform_iterator(0, [target_begin, target_end, row_diff](auto i) {
+      auto num = std::to_string(((i >= target_begin) && (i < target_end)) ? (i + row_diff) * 2 : i);
+      return "#" + num;
+    });
+  auto expected = cudf::test::strings_column_wrapper(expected_elements, expected_elements + size);
+
+  auto p_ret = cudf::copy_range(source, target, source_begin, source_end, target_begin);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*p_ret, expected);
+}
+
+TEST_F(CopyRangeTestFixture, CopyWithNullsNonzeroOffsetString)
+{
+  cudf::size_type size{200};
+  cudf::size_type source_offset{27};
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{50};
+  cudf::size_type target_offset{58};
+  cudf::size_type target_begin{30};
+  auto target_end = target_begin + (source_end - source_begin);
+  auto row_diff   = (source_offset + source_begin) - (target_offset + target_begin);
+
+  auto target_elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "#" + std::to_string(i); });
+  auto target = cudf::test::strings_column_wrapper(
+    target_elements,
+    target_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, all_valid));
+
+  auto target_slice = cudf::slice(target, std::vector<cudf::size_type>{target_offset, size})[0];
+
+  auto source_elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "#" + std::to_string(i * 2); });
+  auto source = cudf::test::strings_column_wrapper(
+    source_elements,
+    source_elements + size,
+    cudf::detail::make_counting_transform_iterator(0, even_valid));
+
+  auto source_slice = cudf::slice(source, std::vector<cudf::size_type>{source_offset, size})[0];
+
+  auto expected_elements = cudf::detail::make_counting_transform_iterator(
+    0, [target_offset, target_begin, target_end, row_diff](auto i) {
+      auto num =
+        std::to_string(((i >= target_offset + target_begin) && (i < target_offset + target_end))
+                         ? (i + row_diff) * 2
+                         : i);
+      return "#" + num;
+    });
+  auto expected = cudf::test::strings_column_wrapper(
+    expected_elements,
+    expected_elements + size,
+    cudf::detail::make_counting_transform_iterator(
+      0, [target_offset, target_begin, target_end, row_diff](auto i) {
+        return ((i >= target_offset + target_begin) && (i < target_offset + target_end))
+                 ? even_valid(i + row_diff)
+                 : all_valid(i);
+      }));
+
+  auto expected_slice = cudf::slice(expected, std::vector<cudf::size_type>{target_offset, size})[0];
+
+  auto p_ret = cudf::copy_range(source_slice, target_slice, source_begin, source_end, target_begin);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*p_ret, expected_slice);
+}
+
+TEST_F(CopyRangeTestFixture, CopyDictionary)
+{
+  cudf::size_type source_begin{1};
+  cudf::size_type source_end{5};
+  cudf::size_type target_begin{3};
+
+  std::vector<std::string> source_elements({"e", "b", "a", "c", "c", "e", "b", "c", "e", "b"});
+  std::vector<std::string> target_elements({"a", "e", "d", "c", "c", "b", "b", "a", "f", "f"});
+  std::vector<std::string> expected_elements(target_elements);
+  std::copy(source_elements.begin() + source_begin,
+            source_elements.begin() + source_end,
+            expected_elements.begin() + target_begin);
+
+  {
+    auto source = cudf::dictionary::encode(
+      cudf::test::strings_column_wrapper(source_elements.begin(), source_elements.end()));
+    auto target = cudf::dictionary::encode(
+      cudf::test::strings_column_wrapper(target_elements.begin(), target_elements.end()));
+
+    auto result =
+      cudf::copy_range(source->view(), target->view(), source_begin, source_end, target_begin);
+    auto decoded = cudf::dictionary::decode(cudf::dictionary_column_view(result->view()));
+
+    auto expected =
+      cudf::test::strings_column_wrapper(expected_elements.begin(), expected_elements.end());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+  }
+
+  auto source_validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 3; });
+  auto target_validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 3 && i != 9; });
+  auto expected_validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 5 && i != 9; });
+  {
+    auto source = cudf::dictionary::encode(cudf::test::strings_column_wrapper(
+      source_elements.begin(), source_elements.end(), source_validity));
+    auto target = cudf::dictionary::encode(cudf::test::strings_column_wrapper(
+      target_elements.begin(), target_elements.end(), target_validity));
+
+    auto result =
+      cudf::copy_range(source->view(), target->view(), source_begin, source_end, target_begin);
+    auto decoded = cudf::dictionary::decode(cudf::dictionary_column_view(result->view()));
+
+    auto expected = cudf::test::strings_column_wrapper(
+      expected_elements.begin(), expected_elements.end(), expected_validity);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+  }
+}
+
+class CopyRangeErrorTestFixture : public cudf::test::BaseFixture {};
+
+TEST_F(CopyRangeErrorTestFixture, InvalidInplaceCall)
+{
+  cudf::size_type size{100};
+
+  auto target = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto source = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(0) + size,
+    cudf::detail::make_counting_transform_iterator(0, even_valid));
+
+  cudf::mutable_column_view target_view{target};
+  // source has null values but target is not nullable.
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 0, size, 0), cudf::logic_error);
+
+  std::vector<std::string> strings{"", "this", "is", "a", "column", "of", "strings"};
+  auto target_string = cudf::test::strings_column_wrapper(strings.begin(), strings.end());
+  auto source_string = cudf::test::strings_column_wrapper(strings.begin(), strings.end());
+
+  cudf::mutable_column_view target_view_string{target_string};
+  EXPECT_THROW(cudf::copy_range_in_place(source_string, target_view_string, 0, size, 0),
+               cudf::logic_error);
+}
+
+TEST_F(CopyRangeErrorTestFixture, InvalidRange)
+{
+  cudf::size_type size{100};
+
+  auto target = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto source = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  cudf::mutable_column_view target_view{target};
+  cudf::column_view source_view{source};
+
+  // empty_range == no-op, this is valid
+  EXPECT_NO_THROW(cudf::copy_range_in_place(source, target_view, 0, 0, 0));
+  EXPECT_NO_THROW(auto p_ret = cudf::copy_range(source, target, 0, 0, 0));
+
+  // source_begin is negative
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, -1, size, 0), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, -1, size, 0), cudf::logic_error);
+
+  // source_begin > source_end
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 10, 5, 0), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, 10, 5, 0), cudf::logic_error);
+
+  // source_begin >= source.size()
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 101, 100, 0), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, 101, 100, 0), cudf::logic_error);
+
+  // source_end > source.size()
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 99, 101, 0), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, 99, 101, 0), cudf::logic_error);
+
+  // target_begin < 0
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 50, 100, -5), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, 50, 100, -5), cudf::logic_error);
+
+  // target_begin >= target.size()
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 50, 100, 100), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, 50, 100, 100), cudf::logic_error);
+
+  // target_begin + (source_end - source_begin) > target.size()
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 50, 100, 80), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::copy_range(source, target, 50, 100, 80), cudf::logic_error);
+
+  // Empty column
+  target      = cudf::test::fixed_width_column_wrapper<int32_t>{};
+  source      = cudf::test::fixed_width_column_wrapper<int32_t>{};
+  target_view = target;
+  source_view = source;
+
+  // empty column == no-op, this is valid
+  EXPECT_NO_THROW(cudf::copy_range_in_place(source_view, target_view, 0, source_view.size(), 0));
+  EXPECT_NO_THROW(auto p_ret = cudf::copy_range(source_view, target, 0, source_view.size(), 0));
+}
+
+TEST_F(CopyRangeErrorTestFixture, DTypeMismatch)
+{
+  cudf::size_type size{100};
+
+  auto target = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto source = cudf::test::fixed_width_column_wrapper<float>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  cudf::mutable_column_view target_view{target};
+
+  EXPECT_THROW(cudf::copy_range_in_place(source, target_view, 0, 100, 0), cudf::logic_error);
+  EXPECT_THROW(cudf::copy_range(source, target, 0, 100, 0), cudf::logic_error);
+
+  auto dict_target = cudf::dictionary::encode(target);
+  auto dict_source = cudf::dictionary::encode(source);
+  EXPECT_THROW(cudf::copy_range(dict_source->view(), dict_target->view(), 0, 100, 0),
+               cudf::logic_error);
+}
+
+template <typename T>
+struct FixedPointTypesCopyRange : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTypesCopyRange, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTypesCopyRange, FixedPointSimple)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const source   = fp_wrapper{{110, 220, 330, 440, 550, 660}, scale_type{-2}};
+  auto const target   = fp_wrapper{{0, 0, 0, 0, 0, 0}, scale_type{-2}};
+  auto const expected = fp_wrapper{{0, 220, 330, 440, 0, 0}, scale_type{-2}};
+  auto const result   = cudf::copy_range(source, target, 1, 4, 1);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTypesCopyRange, FixedPointLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto s = thrust::make_counting_iterator(-1000);
+  auto t = thrust::make_constant_iterator(0);
+  auto e =
+    cudf::detail::make_counting_transform_iterator(500, [](int i) { return i < 1000 ? i : 0; });
+
+  auto const source   = fp_wrapper{s, s + 2000, scale_type{-1}};
+  auto const target   = fp_wrapper{t, t + 2000, scale_type{-1}};
+  auto const expected = fp_wrapper{e, e + 2000, scale_type{-1}};
+  auto const result   = cudf::copy_range(source, target, 1500, 2000, 0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTypesCopyRange, FixedPointScaleMismatch)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const source = fp_wrapper{{110, 220, 330, 440, 550, 660}, scale_type{-2}};
+  auto const target = fp_wrapper{{0, 0, 0, 0, 0, 0}, scale_type{-3}};
+
+  EXPECT_THROW(cudf::copy_range(source, target, 1, 4, 1), cudf::logic_error);
+}
diff --git a/cpp/tests/copying/copy_tests.cpp b/cpp/tests/copying/copy_tests.cpp
new file mode 100644
index 0000000..7379373
--- /dev/null
+++ b/cpp/tests/copying/copy_tests.cpp
@@ -0,0 +1,719 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+template <typename T>
+struct CopyTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(CopyTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+#define wrapper cudf::test::fixed_width_column_wrapper
+
+TYPED_TEST(CopyTest, CopyIfElseTestShort)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 0, 0};
+
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5}, {1, 1, 1, 1});
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6}, {1, 1, 1, 1});
+  wrapper<T, int32_t> expected_w({5, 6, 6, 6});
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseTestManyNulls)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{{1, 0, 0, 0, 0, 0, 1}, {1, 1, 1, 1, 1, 1, 0}};
+
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5, 5, 5, 5}, {1, 1, 1, 1, 1, 1, 1});
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6, 6, 6, 6}, {1, 0, 0, 0, 0, 0, 1});
+  wrapper<T, int32_t> expected_w({5, 6, 6, 6, 6, 6, 6}, {1, 0, 0, 0, 0, 0, 1});
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseTestLong)
+{
+  using T = TypeParam;
+
+  // make sure we span at least 2 warps
+  int num_els = 64;
+
+  bool mask[] = {true, false, true, false, true, true, true,  true,  true,  true,  true, true, true,
+                 true, true,  true, true,  true, true, false, false, false, false, true, true, true,
+                 true, true,  true, true,  true, true, false, false, false, false, true, true, true,
+                 true, true,  true, true,  true, true, true,  true,  true,  true,  true, true, true,
+                 true, true,  true, true,  true, true, true,  true,  true,  true,  true, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els);
+
+  bool lhs_v[] = {true, true, true, true, false, false, true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true};
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+                             5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+                             5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5},
+                            lhs_v);
+
+  bool rhs_v[] = {true, true, true, true, true, true, false, false, true, true, true, true, true,
+                  true, true, true, true, true, true, true,  true,  true, true, true, true, true,
+                  true, true, true, true, true, true, true,  true,  true, true, true, true, true,
+                  true, true, true, true, true, true, true,  true,  true, true, true, true, true,
+                  true, true, true, true, true, true, true,  true,  true, true, true, true};
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
+                             6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
+                             6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6},
+                            rhs_v);
+
+  bool exp_v[] = {true, true, true, true, false, false, true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true, true,
+                  true, true, true, true, true,  true,  true, true, true, true, true, true};
+  wrapper<T, int32_t> expected_w({5, 6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6,
+                                  6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5,
+                                  5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5},
+                                 exp_v);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseTestMultipleBlocks)
+{
+  using T = TypeParam;
+
+  int num = 1000;  // larger than a single block
+  std::vector<int32_t> h_lhs(num, 5);
+  std::vector<int32_t> h_rhs(num, 6);
+  std::vector<bool> h_mask(num, false);
+  std::vector<bool> h_validity(num, true);
+  h_validity[0] = 0;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> lhs_w(
+    h_lhs.begin(), h_lhs.end(), h_validity.begin());
+  cudf::test::fixed_width_column_wrapper<T, int32_t> rhs_w(
+    h_rhs.begin(), h_rhs.end(), h_validity.begin());
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(h_mask.begin(), h_mask.end());
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), rhs_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseTestEmptyInputs)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{};
+
+  wrapper<T> lhs_w{};
+  wrapper<T> rhs_w{};
+  wrapper<T> expected_w{};
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseMixedInputValidity)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 1, 1};
+
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5}, {1, 1, 1, 0});
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6}, {1, 0, 1, 1});
+  wrapper<T, int32_t> expected_w({5, 6, 5, 5}, {1, 0, 1, 0});
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseMixedInputValidity2)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 1, 1};
+
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5}, {1, 1, 1, 0});
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6});
+  wrapper<T, int32_t> expected_w({5, 6, 5, 5}, {1, 1, 1, 0});
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseMixedInputValidity3)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 1, 1};
+
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5});
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6}, {1, 0, 1, 1});
+  wrapper<T, int32_t> expected_w({5, 6, 5, 5}, {1, 0, 1, 1});
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseMixedInputValidity4)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 1, 1};
+
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5});
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6});
+  wrapper<T, int32_t> expected_w({5, 6, 5, 5});
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTest, CopyIfElseBadInputLength)
+{
+  using T = TypeParam;
+
+  // mask length mismatch
+  {
+    cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 1, 1};
+
+    wrapper<T, int32_t> lhs_w({5, 5, 5, 5});
+    wrapper<T, int32_t> rhs_w({6, 6, 6, 6});
+
+    EXPECT_THROW(cudf::copy_if_else(lhs_w, rhs_w, mask_w), cudf::logic_error);
+  }
+
+  // column length mismatch
+  {
+    cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 1, 1, 1};
+
+    wrapper<T, int32_t> lhs_w({5, 5, 5});
+    wrapper<T, int32_t> rhs_w({6, 6, 6, 6});
+
+    EXPECT_THROW(cudf::copy_if_else(lhs_w, rhs_w, mask_w), cudf::logic_error);
+  }
+}
+
+struct CopyEmptyNested : public cudf::test::BaseFixture {};
+
+TEST_F(CopyEmptyNested, CopyIfElseTestEmptyNestedColumns)
+{
+  // lists
+  {
+    cudf::test::lists_column_wrapper<cudf::string_view> col{{{"abc", "def"}, {"xyz"}}};
+    auto lhs = cudf::empty_like(col);
+    auto rhs = cudf::empty_like(col);
+    cudf::test::fixed_width_column_wrapper<bool> mask{};
+
+    auto expected = empty_like(col);
+
+    auto out = cudf::copy_if_else(*lhs, *rhs, mask);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), *expected);
+  }
+
+  // structs
+  {
+    cudf::test::lists_column_wrapper<cudf::string_view> _col0{{{"abc", "def"}, {"xyz"}}};
+    auto col0 = cudf::empty_like(_col0);
+    cudf::test::fixed_width_column_wrapper<int> col1;
+
+    std::vector<std::unique_ptr<cudf::column>> cols;
+    cols.push_back(std::move(col0));
+    cols.push_back(col1.release());
+    cudf::test::structs_column_wrapper struct_col(std::move(cols));
+    auto lhs = cudf::empty_like(struct_col);
+    auto rhs = cudf::empty_like(struct_col);
+
+    cudf::test::fixed_width_column_wrapper<bool> mask{};
+
+    auto expected = cudf::empty_like(struct_col);
+
+    auto out = cudf::copy_if_else(*lhs, *rhs, mask);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), *expected);
+  }
+}
+
+TEST_F(CopyEmptyNested, CopyIfElseTestEmptyNestedScalars)
+{
+  // lists
+  {
+    cudf::test::lists_column_wrapper<cudf::string_view> _col{{{"abc", "def"}, {"xyz"}}};
+    std::unique_ptr<cudf::scalar> lhs = cudf::get_element(_col, 0);
+    std::unique_ptr<cudf::scalar> rhs = cudf::get_element(_col, 0);
+
+    cudf::test::fixed_width_column_wrapper<bool> mask{};
+
+    auto expected = empty_like(_col);
+
+    auto out = cudf::copy_if_else(*lhs, *rhs, mask);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), *expected);
+  }
+
+  // structs
+  {
+    cudf::test::lists_column_wrapper<cudf::string_view> col0{{{"abc", "def"}, {"xyz"}}};
+    cudf::test::fixed_width_column_wrapper<int> col1{1};
+
+    cudf::table_view tbl({col0, col1});
+    cudf::struct_scalar lhs(tbl);
+    cudf::struct_scalar rhs(tbl);
+
+    std::vector<std::unique_ptr<cudf::column>> cols;
+    cols.push_back(col0.release());
+    cols.push_back(col1.release());
+    cudf::test::structs_column_wrapper struct_col(std::move(cols));
+
+    cudf::test::fixed_width_column_wrapper<bool> mask{};
+
+    auto expected = cudf::empty_like(struct_col);
+
+    auto out = cudf::copy_if_else(lhs, rhs, mask);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), *expected);
+  }
+}
+
+template <typename T>
+struct CopyTestNumeric : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(CopyTestNumeric, cudf::test::NumericTypes);
+
+TYPED_TEST(CopyTestNumeric, CopyIfElseTestScalarColumn)
+{
+  using T = TypeParam;
+
+  int num_els = 4;
+
+  bool mask[] = {true, false, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els);
+
+  cudf::numeric_scalar<T> lhs_w(5);
+
+  auto const rhs = cudf::test::make_type_param_vector<T>({6, 6, 6, 6});
+  bool rhs_v[]   = {true, false, true, true};
+  wrapper<T> rhs_w(rhs.begin(), rhs.end(), rhs_v);
+
+  auto const expected = cudf::test::make_type_param_vector<T>({5, 6, 6, 5});
+  wrapper<T> expected_w(expected.begin(), expected.end(), rhs_v);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTestNumeric, CopyIfElseTestColumnScalar)
+{
+  using T = TypeParam;
+
+  int num_els = 4;
+
+  bool mask[]   = {true, false, false, true};
+  bool mask_v[] = {true, true, true, false};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els, mask_v);
+
+  auto const lhs = cudf::test::make_type_param_vector<T>({5, 5, 5, 5});
+  bool lhs_v[]   = {false, true, true, true};
+  wrapper<T> lhs_w(lhs.begin(), lhs.end(), lhs_v);
+
+  cudf::numeric_scalar<T> rhs_w(6);
+
+  auto const expected = cudf::test::make_type_param_vector<T>({5, 6, 6, 6});
+  wrapper<T> expected_w(expected.begin(), expected.end(), lhs_v);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTestNumeric, CopyIfElseTestScalarScalar)
+{
+  using T = TypeParam;
+
+  int num_els = 4;
+
+  bool mask[] = {true, false, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els);
+
+  cudf::numeric_scalar<T> lhs_w(5);
+  cudf::numeric_scalar<T> rhs_w(6, false);
+
+  auto const expected = cudf::test::make_type_param_vector<T>({5, 6, 6, 5});
+  wrapper<T> expected_w(expected.begin(), expected.end(), mask);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+template <typename T>
+struct create_chrono_scalar {
+  template <typename ChronoT = T, typename... Args>
+  std::enable_if_t<std::is_same_v<typename cudf::is_timestamp_t<ChronoT>::type, std::true_type>,
+                   cudf::timestamp_scalar<ChronoT>>
+  operator()(Args&&... args) const
+  {
+    return cudf::timestamp_scalar<T>(std::forward<Args>(args)...);
+  }
+
+  template <typename ChronoT = T, typename... Args>
+  std::enable_if_t<std::is_same_v<typename cudf::is_duration_t<ChronoT>::type, std::true_type>,
+                   cudf::duration_scalar<ChronoT>>
+  operator()(Args&&... args) const
+  {
+    return cudf::duration_scalar<T>(std::forward<Args>(args)...);
+  }
+};
+
+template <typename T>
+struct CopyTestChrono : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(CopyTestChrono, cudf::test::ChronoTypes);
+
+TYPED_TEST(CopyTestChrono, CopyIfElseTestScalarColumn)
+{
+  using T = TypeParam;
+
+  int num_els = 4;
+
+  bool mask[] = {true, false, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els);
+
+  auto lhs_w = create_chrono_scalar<T>{}(cudf::test::make_type_param_scalar<T>(5), true);
+
+  bool rhs_v[] = {true, false, true, true};
+  wrapper<T, int32_t> rhs_w({6, 6, 6, 6}, rhs_v);
+
+  wrapper<T, int32_t> expected_w({5, 6, 6, 5}, rhs_v);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTestChrono, CopyIfElseTestColumnScalar)
+{
+  using T = TypeParam;
+
+  int num_els = 4;
+
+  bool mask[] = {true, false, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els);
+
+  bool lhs_v[] = {false, true, true, true};
+  wrapper<T, int32_t> lhs_w({5, 5, 5, 5}, lhs_v);
+
+  auto rhs_w = create_chrono_scalar<T>{}(cudf::test::make_type_param_scalar<T>(6), true);
+
+  wrapper<T, int32_t> expected_w({5, 6, 6, 5}, lhs_v);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+TYPED_TEST(CopyTestChrono, CopyIfElseTestScalarScalar)
+{
+  using T = TypeParam;
+
+  int num_els = 4;
+
+  bool mask[] = {true, false, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + num_els);
+
+  auto lhs_w = create_chrono_scalar<T>{}(cudf::test::make_type_param_scalar<T>(5), true);
+  auto rhs_w = create_chrono_scalar<T>{}(cudf::test::make_type_param_scalar<T>(6), false);
+
+  wrapper<T, int32_t> expected_w({5, 6, 6, 5}, mask);
+
+  auto out = cudf::copy_if_else(lhs_w, rhs_w, mask_w);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(out->view(), expected_w);
+}
+
+struct CopyTestUntyped : public cudf::test::BaseFixture {};
+
+TEST_F(CopyTestUntyped, CopyIfElseTypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 1, 1, 1};
+
+  wrapper<float> lhs_w{5, 5, 5, 5};
+  wrapper<int32_t> rhs_w{6, 6, 6, 6};
+
+  EXPECT_THROW(cudf::copy_if_else(lhs_w, rhs_w, mask_w), cudf::logic_error);
+}
+
+struct StringsCopyIfElseTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsCopyIfElseTest, CopyIfElse)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<char const*> h_strings1{"eee", "bb", "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings1(h_strings1.begin(), h_strings1.end(), valids);
+  std::vector<char const*> h_strings2{"zz", "", "yyy", "w", "ééé", "ooo"};
+  cudf::test::strings_column_wrapper strings2(h_strings2.begin(), h_strings2.end(), valids);
+
+  bool mask[]   = {true, true, false, true, false, true};
+  bool mask_v[] = {true, true, true, true, true, false};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + 6, mask_v);
+
+  auto results = cudf::copy_if_else(strings1, strings2, mask_w);
+
+  std::vector<char const*> h_expected;
+  for (cudf::size_type idx = 0; idx < static_cast<cudf::size_type>(h_strings1.size()); ++idx) {
+    if (mask[idx] and mask_v[idx])
+      h_expected.push_back(h_strings1[idx]);
+    else
+      h_expected.push_back(h_strings2[idx]);
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end(), valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCopyIfElseTest, CopyIfElseScalarColumn)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<char const*> h_string1{"eee"};
+  cudf::string_scalar strings1{h_string1[0]};
+  std::vector<char const*> h_strings2{"zz", "", "yyy", "w", "ééé", "ooo"};
+  cudf::test::strings_column_wrapper strings2(h_strings2.begin(), h_strings2.end(), valids);
+
+  bool mask[]   = {true, false, true, false, true, false};
+  bool mask_v[] = {true, true, true, true, true, false};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + 6, mask_v);
+
+  auto results = cudf::copy_if_else(strings1, strings2, mask_w);
+
+  std::vector<char const*> h_expected;
+  for (cudf::size_type idx = 0; idx < static_cast<cudf::size_type>(h_strings2.size()); ++idx) {
+    if (mask[idx] and mask_v[idx]) {
+      h_expected.push_back(h_string1[0]);
+    } else {
+      h_expected.push_back(h_strings2[idx]);
+    }
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end(), valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCopyIfElseTest, CopyIfElseColumnScalar)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<char const*> h_string1{"eee"};
+  cudf::string_scalar strings1{h_string1[0]};
+  std::vector<char const*> h_strings2{"zz", "", "yyy", "w", "ééé", "ooo"};
+  cudf::test::strings_column_wrapper strings2(h_strings2.begin(), h_strings2.end(), valids);
+
+  bool mask[] = {false, true, true, true, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + 6);
+
+  auto results = cudf::copy_if_else(strings2, strings1, mask_w);
+
+  std::vector<char const*> h_expected;
+  for (cudf::size_type idx = 0; idx < static_cast<cudf::size_type>(h_strings2.size()); ++idx) {
+    if (mask[idx]) {
+      h_expected.push_back(h_strings2[idx]);
+    } else {
+      h_expected.push_back(h_string1[0]);
+    }
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end(), valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCopyIfElseTest, CopyIfElseScalarScalar)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<char const*> h_string1{"eee"};
+  cudf::string_scalar string1{h_string1[0]};
+  std::vector<char const*> h_string2{"aaa"};
+  cudf::string_scalar string2{h_string2[0], false};
+
+  constexpr cudf::size_type mask_size = 6;
+  bool mask[]                         = {true, false, true, false, true, false};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + mask_size);
+
+  auto results = cudf::copy_if_else(string1, string2, mask_w);
+
+  std::vector<char const*> h_expected;
+  for (bool idx : mask) {
+    if (idx) {
+      h_expected.push_back(h_string1[0]);
+    } else {
+      h_expected.push_back(h_string2[0]);
+    }
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end(), valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+template <typename T>
+struct FixedPointTypes : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTypes, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTypes, FixedPointSimple)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const mask     = cudf::test::fixed_width_column_wrapper<bool>{0, 1, 1, 1, 0, 0};
+  auto const a        = fp_wrapper{{110, 220, 330, 440, 550, 660}, scale_type{-2}};
+  auto const b        = fp_wrapper{{0, 0, 0, 0, 0, 0}, scale_type{-2}};
+  auto const expected = fp_wrapper{{0, 220, 330, 440, 0, 0}, scale_type{-2}};
+  auto const result   = cudf::copy_if_else(a, b, mask);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTypes, FixedPointLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto a = thrust::make_counting_iterator(-1000);
+  auto b = thrust::make_constant_iterator(0);
+  auto m = cudf::detail::make_counting_transform_iterator(-1000, [](int i) { return i > 0; });
+  auto e =
+    cudf::detail::make_counting_transform_iterator(-1000, [](int i) { return std::max(0, i); });
+
+  auto const mask     = cudf::test::fixed_width_column_wrapper<bool>(m, m + 2000);
+  auto const A        = fp_wrapper{a, a + 2000, scale_type{-3}};
+  auto const B        = fp_wrapper{b, b + 2000, scale_type{-3}};
+  auto const expected = fp_wrapper{e, e + 2000, scale_type{-3}};
+  auto const result   = cudf::copy_if_else(A, B, mask);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTypes, FixedPointScaleMismatch)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const mask = cudf::test::fixed_width_column_wrapper<bool>{0, 1, 1, 1, 0, 0};
+  auto const a    = fp_wrapper{{110, 220, 330, 440, 550, 660}, scale_type{-2}};
+  auto const b    = fp_wrapper{{0, 0, 0, 0, 0, 0}, scale_type{-1}};
+
+  EXPECT_THROW(cudf::copy_if_else(a, b, mask), cudf::logic_error);
+}
+
+struct DictionaryCopyIfElseTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryCopyIfElseTest, ColumnColumn)
+{
+  auto valids = cudf::test::iterators::null_at(2);
+  std::vector<char const*> h_strings1{"eee", "bb", "", "aa", "bb", "ééé"};
+  cudf::test::dictionary_column_wrapper<std::string> input1(
+    h_strings1.begin(), h_strings1.end(), valids);
+  std::vector<char const*> h_strings2{"zz", "bb", "", "aa", "ééé", "ooo"};
+  cudf::test::dictionary_column_wrapper<std::string> input2(
+    h_strings2.begin(), h_strings2.end(), valids);
+
+  bool mask[]   = {true, true, false, true, false, true};
+  bool mask_v[] = {true, true, true, true, true, false};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + 6, mask_v);
+
+  auto results = cudf::copy_if_else(input1, input2, mask_w);
+  auto decoded = cudf::dictionary::decode(cudf::dictionary_column_view(results->view()));
+
+  std::vector<char const*> h_expected;
+  for (cudf::size_type idx = 0; idx < static_cast<cudf::size_type>(h_strings1.size()); ++idx) {
+    if (mask[idx] and mask_v[idx])
+      h_expected.push_back(h_strings1[idx]);
+    else
+      h_expected.push_back(h_strings2[idx]);
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end(), valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(decoded->view(), expected);
+}
+
+TEST_F(DictionaryCopyIfElseTest, ColumnScalar)
+{
+  std::string h_string{"eee"};
+  cudf::string_scalar input1{h_string};
+  std::vector<char const*> h_strings{"zz", "", "yyy", "w", "ééé", "ooo"};
+  auto valids = cudf::test::iterators::null_at(1);
+  cudf::test::dictionary_column_wrapper<std::string> input2(
+    h_strings.begin(), h_strings.end(), valids);
+
+  bool mask[] = {false, true, true, true, false, true};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w(mask, mask + 6);
+
+  auto results = cudf::copy_if_else(input2, input1, mask_w);
+  auto decoded = cudf::dictionary::decode(cudf::dictionary_column_view(results->view()));
+
+  std::vector<char const*> h_expected1;
+  std::vector<char const*> h_expected2;
+  for (cudf::size_type idx = 0; idx < static_cast<cudf::size_type>(h_strings.size()); ++idx) {
+    if (mask[idx]) {
+      h_expected1.push_back(h_strings[idx]);
+      h_expected2.push_back(h_string.c_str());
+    } else {
+      h_expected1.push_back(h_string.c_str());
+      h_expected2.push_back(h_strings[idx]);
+    }
+  }
+
+  cudf::test::strings_column_wrapper expected1(h_expected1.begin(), h_expected1.end(), valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(decoded->view(), expected1);
+
+  results = cudf::copy_if_else(input1, input2, mask_w);
+  decoded = cudf::dictionary::decode(cudf::dictionary_column_view(results->view()));
+
+  cudf::test::strings_column_wrapper expected2(h_expected2.begin(), h_expected2.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(decoded->view(), expected2);
+}
+
+TEST_F(DictionaryCopyIfElseTest, TypeMismatch)
+{
+  cudf::test::dictionary_column_wrapper<int32_t> input1({1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<double> input2({1.0, 1.0, 1.0, 1.0});
+  cudf::test::fixed_width_column_wrapper<bool> mask({1, 0, 0, 1});
+
+  EXPECT_THROW(cudf::copy_if_else(input1, input2, mask), cudf::logic_error);
+
+  cudf::string_scalar input3{"1"};
+  EXPECT_THROW(cudf::copy_if_else(input1, input3, mask), cudf::logic_error);
+  EXPECT_THROW(cudf::copy_if_else(input3, input2, mask), cudf::logic_error);
+  EXPECT_THROW(cudf::copy_if_else(input2, input3, mask), cudf::logic_error);
+}
diff --git a/cpp/tests/copying/detail_gather_tests.cu b/cpp/tests/copying/detail_gather_tests.cu
new file mode 100644
index 0000000..17ced5c
--- /dev/null
+++ b/cpp/tests/copying/detail_gather_tests.cu
@@ -0,0 +1,122 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.cuh>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/sequence.h>
+
+template <typename T>
+class GatherTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GatherTest, cudf::test::NumericTypes);
+
+// This test exercises using different iterator types as gather map inputs
+// to cudf::detail::gather -- device_uvector and raw pointers.
+TYPED_TEST(GatherTest, GatherDetailDeviceVectorTest)
+{
+  constexpr cudf::size_type source_size{1000};
+  rmm::device_uvector<cudf::size_type> gather_map(source_size, cudf::get_default_stream());
+  thrust::sequence(
+    rmm::exec_policy_nosync(cudf::get_default_stream()), gather_map.begin(), gather_map.end());
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column(data, data + source_size);
+
+  cudf::table_view source_table({source_column});
+
+  // test with device vector iterators
+  {
+    std::unique_ptr<cudf::table> result =
+      cudf::detail::gather(source_table,
+                           gather_map.begin(),
+                           gather_map.end(),
+                           cudf::out_of_bounds_policy::DONT_CHECK,
+                           cudf::get_default_stream(),
+                           rmm::mr::get_current_device_resource());
+
+    for (auto i = 0; i < source_table.num_columns(); ++i) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(source_table.column(i), result->view().column(i));
+    }
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(source_table, result->view());
+  }
+
+  // test with raw pointers
+  {
+    std::unique_ptr<cudf::table> result =
+      cudf::detail::gather(source_table,
+                           gather_map.begin(),
+                           gather_map.data() + gather_map.size(),
+                           cudf::out_of_bounds_policy::DONT_CHECK,
+                           cudf::get_default_stream(),
+                           rmm::mr::get_current_device_resource());
+
+    for (auto i = 0; i < source_table.num_columns(); ++i) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(source_table.column(i), result->view().column(i));
+    }
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(source_table, result->view());
+  }
+}
+
+TYPED_TEST(GatherTest, GatherDetailInvalidIndexTest)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column(data, data + source_size);
+  auto gather_map_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2) ? -1 : i; });
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(gather_map_data,
+                                                             gather_map_data + (source_size * 2));
+
+  cudf::table_view source_table({source_column});
+  std::unique_ptr<cudf::table> result =
+    cudf::detail::gather(source_table,
+                         gather_map,
+                         cudf::out_of_bounds_policy::NULLIFY,
+                         cudf::detail::negative_index_policy::NOT_ALLOWED,
+                         cudf::get_default_stream(),
+                         rmm::mr::get_current_device_resource());
+
+  auto expect_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2) ? 0 : i; });
+  auto expect_valid = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return (i % 2) || (i >= source_size) ? 0 : 1; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_column(
+    expect_data, expect_data + (source_size * 2), expect_valid);
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_column, result->view().column(i));
+  }
+}
diff --git a/cpp/tests/copying/gather_list_tests.cpp b/cpp/tests/copying/gather_list_tests.cpp
new file mode 100644
index 0000000..247090a
--- /dev/null
+++ b/cpp/tests/copying/gather_list_tests.cpp
@@ -0,0 +1,437 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+template <typename T>
+class GatherTestListTyped : public cudf::test::BaseFixture {};
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FixedPointTypes,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::DurationTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(GatherTestListTyped, FixedWidthTypesNotBool);
+
+class GatherTestList : public cudf::test::BaseFixture {};
+
+// to disambiguate between {} == 0 and {} == List{0}
+// Also, see note about compiler issues when declaring nested
+// empty lists in lists_column_wrapper documentation
+template <typename T>
+using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+TYPED_TEST(GatherTestListTyped, Gather)
+{
+  using T = TypeParam;
+
+  // List<T>
+  LCW<T> list{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}};
+  cudf::test::fixed_width_column_wrapper<int> gather_map{0, 2};
+
+  cudf::table_view source_table({list});
+  auto results = cudf::gather(source_table, gather_map);
+
+  LCW<T> expected{{1, 2, 3, 4}, {6, 7}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TYPED_TEST(GatherTestListTyped, GatherNothing)
+{
+  using T = TypeParam;
+
+  // List<T>
+  {
+    LCW<T> list{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}};
+    cudf::test::fixed_width_column_wrapper<int> gather_map{};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map);
+
+    LCW<T> expected;
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+
+  // List<T>
+  {
+    cudf::test::lists_column_wrapper<int> list{{{{1, 2, 3, 4}, {5}}}, {{{6, 7}, {8, 9, 10}}}};
+    cudf::test::fixed_width_column_wrapper<int> gather_map{};
+
+    cudf::table_view source_table({list});
+    auto result = cudf::gather(source_table, gather_map);
+
+    // the result should preserve the full List<List<List<int>>> hierarchy
+    // even though it is empty past the first level
+    cudf::lists_column_view lcv(result->view().column(0));
+    EXPECT_EQ(lcv.size(), 0);
+    EXPECT_EQ(lcv.child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(lcv.child().size(), 0);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().size(), 0);
+    EXPECT_EQ(
+      cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().type().id(),
+      cudf::type_id::INT32);
+    EXPECT_EQ(cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().size(),
+              0);
+  }
+}
+
+TYPED_TEST(GatherTestListTyped, GatherNulls)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<T>
+  LCW<T> list{{{1, 2, 3, 4}, valids}, {5}, {{6, 7}, valids}, {{8, 9, 10}, valids}};
+  cudf::test::fixed_width_column_wrapper<int> gather_map{0, 2};
+
+  cudf::table_view source_table({list});
+  auto results = cudf::gather(source_table, gather_map);
+
+  LCW<T> expected{{{1, 2, 3, 4}, valids}, {{6, 7}, valids}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TYPED_TEST(GatherTestListTyped, GatherNested)
+{
+  using T = TypeParam;
+
+  // List<List<T>>
+  {
+    LCW<T> list{{{2, 3}, {4, 5}},
+                {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    cudf::test::fixed_width_column_wrapper<int> gather_map{0, 2};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map);
+
+    LCW<T> expected{{{2, 3}, {4, 5}}, {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+
+  // List<List<List<T>>>
+  {
+    LCW<T> list{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+                {{{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}},
+                {{LCW<T>{0}}},
+                {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+                 {{0, 1, 3}, {5}},
+                 {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+                {{{10, 20}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}};
+    cudf::test::fixed_width_column_wrapper<int> gather_map{1, 2, 4};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map);
+
+    LCW<T> expected{{{{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}},
+                    {{LCW<T>{0}}},
+                    {{{10, 20}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+}
+
+TYPED_TEST(GatherTestListTyped, GatherOutOfOrder)
+{
+  using T = TypeParam;
+
+  // List<List<T>>
+  {
+    LCW<T> list{{{2, 3}, {4, 5}},
+                {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    cudf::test::fixed_width_column_wrapper<int> gather_map{1, 2, 0};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map);
+
+    LCW<T> expected{{{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                    {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}},
+                    {{2, 3}, {4, 5}}};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+}
+
+TYPED_TEST(GatherTestListTyped, GatherNestedNulls)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<List<T>>
+  {
+    LCW<T> list{{{{2, 3}, valids}, {4, 5}},
+                {{{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}, valids},
+                {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}},
+                {{{{25, 26}, valids}, {27, 28}, {{29, 30}, valids}, {31, 32}, {33, 34}}, valids}};
+
+    cudf::test::fixed_width_column_wrapper<int> gather_map{0, 1, 3};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map);
+
+    LCW<T> expected{
+      {{{2, 3}, valids}, {4, 5}},
+      {{{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}, valids},
+      {{{{25, 26}, valids}, {27, 28}, {{29, 30}, valids}, {31, 32}, {33, 34}}, valids}};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+
+  // List<List<List<T>>>
+  {
+    LCW<T> list{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+                {{{15, 16}, {{27, 28}, valids}, {{37, 38}, valids}, {47, 48}, {57, 58}}},
+                {{LCW<T>{0}}},
+                {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+                 {{0, 1, 3}, {5}},
+                 {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+                {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids}};
+
+    cudf::test::fixed_width_column_wrapper<int> gather_map{1, 2, 4};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map);
+
+    LCW<T> expected{{{{15, 16}, {{27, 28}, valids}, {{37, 38}, valids}, {47, 48}, {57, 58}}},
+                    {{LCW<T>{0}}},
+                    {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids}};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+}
+
+TYPED_TEST(GatherTestListTyped, GatherNestedWithEmpties)
+{
+  using T = TypeParam;
+
+  LCW<T> list{{{2, 3}, LCW<T>{}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}, {LCW<T>{}}};
+  cudf::test::fixed_width_column_wrapper<int> gather_map{0, 2};
+
+  cudf::table_view source_table({list});
+  auto results = cudf::gather(source_table, gather_map);
+
+  LCW<T> expected{{{2, 3}, LCW<T>{}}, {LCW<T>{}}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TYPED_TEST(GatherTestListTyped, GatherDetailInvalidIndex)
+{
+  using T = TypeParam;
+
+  // List<List<T>>
+  {
+    LCW<T> list{{{2, 3}, {4, 5}},
+                {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    cudf::test::fixed_width_column_wrapper<int> gather_map{0, 15, 16, 2};
+
+    cudf::table_view source_table({list});
+    auto results = cudf::gather(source_table, gather_map, cudf::out_of_bounds_policy::NULLIFY);
+
+    std::vector<int32_t> expected_validity{1, 0, 0, 1};
+    LCW<T> expected{{{{2, 3}, {4, 5}},
+                     {LCW<T>{}},
+                     {LCW<T>{}},
+                     {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}},
+                    expected_validity.begin()};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+  }
+}
+
+TEST_F(GatherTestList, GatherIncompleteHierarchies)
+{
+  using LCW = cudf::test::lists_column_wrapper<int32_t>;
+
+  {
+    // List<List<List<int>, but rows 1 and 2 are empty at the very top.
+    // We expect to get back a "full" hierarchy of type List<List<List<int>> anyway.
+    cudf::test::lists_column_wrapper<int32_t> list{{{{1, 2}}}, LCW{}, LCW{}};
+
+    cudf::table_view source_table({list});
+
+    cudf::test::fixed_width_column_wrapper<int32_t> row1_map{1};
+    auto result = cudf::gather(source_table, row1_map);
+
+    // the result should preserve the full List<List<List<int>>> hierarchy
+    // even though it is empty past the first level
+    cudf::lists_column_view lcv(result->view().column(0));
+    EXPECT_EQ(lcv.size(), 1);
+    EXPECT_EQ(lcv.child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(lcv.child().size(), 0);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().size(), 0);
+    EXPECT_EQ(
+      cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().type().id(),
+      cudf::type_id::INT32);
+    EXPECT_EQ(cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().size(),
+              0);
+  }
+
+  {
+    // List<List<List<int>, gathering nothing.
+    // We expect to get back a "full" hierarchy of type List<List<List<int>> anyway.
+    cudf::test::lists_column_wrapper<int32_t> list{{{{1, 2}}}, LCW{}};
+
+    cudf::table_view source_table({list});
+
+    cudf::test::fixed_width_column_wrapper<int32_t> empty_map{};
+    auto result = cudf::gather(source_table, empty_map);
+
+    // the result should preserve the full List<List<List<int>>> hierarchy
+    // even though it is empty past the first level
+    cudf::lists_column_view lcv(result->view().column(0));
+    EXPECT_EQ(lcv.size(), 0);
+    EXPECT_EQ(lcv.child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(lcv.child().size(), 0);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().size(), 0);
+    EXPECT_EQ(
+      cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().type().id(),
+      cudf::type_id::INT32);
+    EXPECT_EQ(cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().size(),
+              0);
+  }
+}
+
+TYPED_TEST(GatherTestListTyped, GatherSliced)
+{
+  using T = TypeParam;
+  {
+    LCW<T> a{
+      {{1, 1, 1}, {2, 2}, {3, 3}},
+      {{4, 4, 4}, {5, 5}, {6, 6}},
+      {{7, 7, 7}, {8, 8}, {9, 9}},
+      {{10, 10, 10}, {11, 11}, {12, 12}},
+      {{20, 20, 20, 20}, {25}},
+      {{30, 30, 30, 30}, {40}},
+      {{50, 50, 50, 50}, {6, 13}},
+      {{70, 70, 70, 70}, {80}},
+    };
+    auto split_a = cudf::split(a, {3});
+    cudf::table_view tbl0({split_a[0]});
+    cudf::table_view tbl1({split_a[1]});
+
+    auto result0 = cudf::gather(tbl0, cudf::test::fixed_width_column_wrapper<int>{1, 2});
+    LCW<T> expected0{
+      {{4, 4, 4}, {5, 5}, {6, 6}},
+      {{7, 7, 7}, {8, 8}, {9, 9}},
+    };
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected0, result0->get_column(0).view());
+
+    auto result1 = cudf::gather(tbl1, cudf::test::fixed_width_column_wrapper<int>{0, 3});
+    LCW<T> expected1{
+      {{10, 10, 10}, {11, 11}, {12, 12}},
+      {{50, 50, 50, 50}, {6, 13}},
+    };
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, result1->get_column(0).view());
+  }
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<List<List<T>>>
+  {
+    LCW<T> list{
+      // slice 0
+      {{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+
+      {{{15, 16}, {{27, 28}, valids}, {{37, 38}, valids}, {47, 48}, {57, 58}},
+       {{11, 12}, {{42, 43, 44}, valids}, {{77, 78}, valids}}},
+
+      // slice 1
+      {{LCW<T>{0}}},
+      {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+       {{0, 1, 3}, {5}},
+       {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+      {{{{1, 6}, {60, 70, 80, 100}}, {{10, 11, 13}, {15}}, {{11, 12, 13, 14, 15}}}, valids},
+
+      // slice 2
+      {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids},
+      {{{{10, 20, 30}}, {LCW<T>{30}}, {{{20, 30}, valids}, {62, 72, 82}}}, valids}};
+
+    auto sliced = cudf::slice(list, {0, 1, 2, 5, 5, 7});
+
+    // gather from slice 0
+    {
+      cudf::table_view tbl({sliced[0]});
+
+      cudf::test::fixed_width_column_wrapper<int> map{0};
+      auto result = cudf::gather(tbl, map);
+      LCW<T> expected{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}}};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->get_column(0).view());
+    }
+
+    // gather from slice 1
+    {
+      cudf::table_view tbl({sliced[1]});
+
+      cudf::test::fixed_width_column_wrapper<int> map{1, 2, 0, 1};
+      auto result = cudf::gather(tbl, map);
+      LCW<T> expected{
+        {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+         {{0, 1, 3}, {5}},
+         {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+
+        {{{{1, 6}, {60, 70, 80, 100}}, {{10, 11, 13}, {15}}, {{11, 12, 13, 14, 15}}}, valids},
+
+        {{LCW<T>{0}}},
+
+        {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+         {{0, 1, 3}, {5}},
+         {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+      };
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->get_column(0).view());
+    }
+
+    // gather from slice 2
+    {
+      cudf::table_view tbl({sliced[2]});
+
+      cudf::test::fixed_width_column_wrapper<int> map{1, 0, 0, 1, 1, 0};
+      auto result = cudf::gather(tbl, map);
+      LCW<T> expected{{{{{10, 20, 30}}, {LCW<T>{30}}, {{{20, 30}, valids}, {62, 72, 82}}}, valids},
+                      {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids},
+                      {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids},
+                      {{{{10, 20, 30}}, {LCW<T>{30}}, {{{20, 30}, valids}, {62, 72, 82}}}, valids},
+                      {{{{10, 20, 30}}, {LCW<T>{30}}, {{{20, 30}, valids}, {62, 72, 82}}}, valids},
+                      {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids}};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->get_column(0).view());
+    }
+  }
+}
diff --git a/cpp/tests/copying/gather_str_tests.cpp b/cpp/tests/copying/gather_str_tests.cpp
new file mode 100644
index 0000000..22af600
--- /dev/null
+++ b/cpp/tests/copying/gather_str_tests.cpp
@@ -0,0 +1,159 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+class GatherTestStr : public cudf::test::BaseFixture {};
+
+TEST_F(GatherTestStr, StringColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{1, 2, 3, 4, 5, 6}, {1, 1, 0, 1, 0, 1}};
+  cudf::test::strings_column_wrapper col2{{"This", "is", "not", "a", "string", "type"},
+                                          {1, 1, 1, 1, 1, 0}};
+  cudf::table_view source_table{{col1, col2}};
+
+  cudf::test::fixed_width_column_wrapper<int16_t> gather_map{{0, 1, 3, 4}};
+
+  cudf::test::fixed_width_column_wrapper<int16_t> exp_col1{{1, 2, 4, 5}, {1, 1, 1, 0}};
+  cudf::test::strings_column_wrapper exp_col2{{"This", "is", "a", "string"}, {1, 1, 1, 1}};
+  cudf::table_view expected{{exp_col1, exp_col2}};
+
+  auto got = cudf::gather(source_table, gather_map);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(GatherTestStr, GatherSlicedStringsColumn)
+{
+  cudf::test::strings_column_wrapper strings{{"This", "is", "not", "a", "string", "type"},
+                                             {1, 1, 1, 1, 1, 0}};
+  std::vector<cudf::size_type> slice_indices{0, 2, 2, 3, 3, 6};
+  auto sliced_strings = cudf::slice(strings, slice_indices);
+  {
+    cudf::test::fixed_width_column_wrapper<int16_t> gather_map{{1, 0, 1}};
+    cudf::test::strings_column_wrapper expected_strings{{"is", "This", "is"}, {1, 1, 1}};
+    cudf::table_view expected{{expected_strings}};
+    auto result = cudf::gather(cudf::table_view{{sliced_strings[0]}}, gather_map);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result->view());
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<int16_t> gather_map{{0, 0, 0}};
+    cudf::test::strings_column_wrapper expected_strings{{"not", "not", "not"}, {1, 1, 1}};
+    cudf::table_view expected{{expected_strings}};
+    auto result = cudf::gather(cudf::table_view{{sliced_strings[1]}}, gather_map);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result->view());
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<int16_t> gather_map{{2, 1, 0}};
+    cudf::test::strings_column_wrapper expected_strings{{"", "string", "a"}, {0, 1, 1}};
+    cudf::table_view expected{{expected_strings}};
+    auto result = cudf::gather(cudf::table_view{{sliced_strings[2]}}, gather_map);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(GatherTestStr, Gather)
+{
+  std::vector<char const*> h_strings{"eee", "bb", "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  cudf::table_view source_table({strings});
+
+  std::vector<int32_t> h_map{4, 1, 5, 2, 7};
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(h_map.begin(), h_map.end());
+  auto results = cudf::detail::gather(source_table,
+                                      gather_map,
+                                      cudf::out_of_bounds_policy::NULLIFY,
+                                      cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                      cudf::get_default_stream(),
+                                      rmm::mr::get_current_device_resource());
+
+  std::vector<char const*> h_expected;
+  std::vector<int32_t> expected_validity;
+  for (auto itr = h_map.begin(); itr != h_map.end(); ++itr) {
+    auto index = *itr;
+    if ((0 <= index) && (index < static_cast<decltype(index)>(h_strings.size()))) {
+      h_expected.push_back(h_strings[index]);
+      expected_validity.push_back(1);
+    } else {
+      h_expected.push_back("");
+      expected_validity.push_back(0);
+    }
+  }
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), expected_validity.begin());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TEST_F(GatherTestStr, GatherDontCheckOutOfBounds)
+{
+  std::vector<char const*> h_strings{"eee", "bb", "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  cudf::table_view source_table({strings});
+
+  std::vector<int32_t> h_map{3, 4, 0, 0};
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(h_map.begin(), h_map.end());
+  auto results = cudf::detail::gather(source_table,
+                                      gather_map,
+                                      cudf::out_of_bounds_policy::DONT_CHECK,
+                                      cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                      cudf::get_default_stream(),
+                                      rmm::mr::get_current_device_resource());
+
+  std::vector<char const*> h_expected;
+  for (auto itr = h_map.begin(); itr != h_map.end(); ++itr) {
+    h_expected.push_back(h_strings[*itr]);
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TEST_F(GatherTestStr, GatherEmptyMapStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> gather_map;
+  auto results = cudf::detail::gather(cudf::table_view({zero_size_strings_column->view()}),
+                                      gather_map,
+                                      cudf::out_of_bounds_policy::NULLIFY,
+                                      cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                      cudf::get_default_stream(),
+                                      rmm::mr::get_current_device_resource());
+  cudf::test::expect_column_empty(results->get_column(0).view());
+}
+
+TEST_F(GatherTestStr, GatherZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING);
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map({0});
+  cudf::test::strings_column_wrapper expected{std::pair<std::string, bool>{"", false}};
+  auto results = cudf::detail::gather(cudf::table_view({zero_size_strings_column->view()}),
+                                      gather_map,
+                                      cudf::out_of_bounds_policy::NULLIFY,
+                                      cudf::detail::negative_index_policy::NOT_ALLOWED,
+                                      cudf::get_default_stream(),
+                                      rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, results->get_column(0).view());
+}
diff --git a/cpp/tests/copying/gather_struct_tests.cpp b/cpp/tests/copying/gather_struct_tests.cpp
new file mode 100644
index 0000000..2bc18c7
--- /dev/null
+++ b/cpp/tests/copying/gather_struct_tests.cpp
@@ -0,0 +1,472 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <memory>
+
+using vector_of_columns = std::vector<std::unique_ptr<cudf::column>>;
+using gather_map_t      = std::vector<cudf::size_type>;
+using offsets           = cudf::test::fixed_width_column_wrapper<int32_t>;
+using structs           = cudf::test::structs_column_wrapper;
+using strings           = cudf::test::strings_column_wrapper;
+using bools             = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+
+// Test validity iterator utilities.
+using cudf::test::iterators::no_nulls;
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+template <typename T>
+using numerics = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+template <typename T>
+using lists = cudf::test::lists_column_wrapper<T, int32_t>;
+
+auto constexpr null_index = std::numeric_limits<cudf::size_type>::max();
+
+struct StructGatherTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedStructGatherTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedStructGatherTest, cudf::test::FixedWidthTypes);
+
+namespace {
+template <typename ElementTo, typename SourceElementT = ElementTo>
+struct column_wrapper_constructor {
+  template <typename ValueIter, typename ValidityIter>
+  auto operator()(ValueIter begin, ValueIter end, ValidityIter validity_begin) const
+  {
+    return cudf::test::fixed_width_column_wrapper<ElementTo, SourceElementT>{
+      begin, end, validity_begin};
+  }
+};
+
+template <>
+struct column_wrapper_constructor<std::string, std::string> {
+  template <typename ValueIter, typename ValidityIter>
+  cudf::test::strings_column_wrapper operator()(ValueIter begin,
+                                                ValueIter end,
+                                                ValidityIter validity_begin) const
+  {
+    return cudf::test::strings_column_wrapper{begin, end, validity_begin};
+  }
+};
+
+template <typename ElementTo,
+          typename SourceElementT     = ElementTo,
+          typename InputValidityIter  = decltype(null_at(0)),
+          typename StructValidityIter = InputValidityIter>
+auto get_expected_column(std::vector<SourceElementT> const& input_values,
+                         InputValidityIter input_validity,
+                         StructValidityIter struct_validity,
+                         std::vector<int32_t> const& gather_map)
+{
+  auto is_valid =  // Validity predicate.
+    [&input_values, &input_validity, &struct_validity, &gather_map](auto gather_index) {
+      assert(
+        (gather_index >= 0 && gather_index < static_cast<cudf::size_type>(gather_map.size())) &&
+        "Gather-index out of range.");
+
+      auto i = gather_map[gather_index];  // Index into input_values.
+
+      return (i >= 0 && i < static_cast<int>(input_values.size())) && struct_validity[i] &&
+             input_validity[i];
+    };
+
+  auto expected_row_count = gather_map.size();
+  auto gather_iter        = cudf::detail::make_counting_transform_iterator(
+    0, [is_valid, &input_values, &gather_map](auto i) {
+      return is_valid(i) ? input_values[gather_map[i]] : SourceElementT{};
+    });
+
+  return column_wrapper_constructor<ElementTo, SourceElementT>()(
+    gather_iter,
+    gather_iter + expected_row_count,
+    cudf::detail::make_counting_transform_iterator(0, is_valid));
+}
+
+auto do_gather(cudf::column_view const& input, gather_map_t const& gather_map)
+{
+  auto result = cudf::gather(cudf::table_view{{input}},
+                             offsets(gather_map.begin(), gather_map.end()),
+                             cudf::out_of_bounds_policy::NULLIFY);
+  return std::move(result->release()[0]);
+}
+}  // namespace
+
+TYPED_TEST(TypedStructGatherTest, TestSimpleStructGather)
+{
+  // Testing gather() on struct<string, numeric, bool>.
+
+  // 1. String "names" column.
+  auto const names =
+    std::vector<std::string>{"Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant"};
+  auto const names_validity = no_nulls();
+
+  // 2. Numeric "ages" column.
+  auto const ages          = std::vector<int32_t>{5, 10, 15, 20, 25, 30};
+  auto const ages_validity = null_at(4);
+
+  // 3. Boolean "is_human" column.
+  auto const is_human          = {true, true, false, false, false, false};
+  auto const is_human_validity = null_at(3);
+
+  // Assemble struct column.
+  auto const struct_validity = null_at(5);
+  auto const struct_column   = [&] {
+    auto names_member    = ::strings(names.begin(), names.end(), names_validity);
+    auto ages_member     = ::numerics<TypeParam>(ages.begin(), ages.end(), ages_validity);
+    auto is_human_member = ::bools(is_human.begin(), is_human.end(), is_human_validity);
+    return structs{{names_member, ages_member, is_human_member}, struct_validity};
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map = gather_map_t{null_index, 4, 3, 2, 1};
+
+  auto const output = do_gather(struct_column, gather_map);
+
+  auto const expected_output = [&] {
+    auto names_member =
+      get_expected_column<std::string>(names, names_validity, struct_validity, gather_map);
+    auto ages_member =
+      get_expected_column<TypeParam, int32_t>(ages, ages_validity, struct_validity, gather_map);
+    auto is_human_member =
+      get_expected_column<bool>(std::vector<bool>(is_human.begin(), is_human.end()),
+                                is_human_validity,
+                                struct_validity,
+                                gather_map);
+    return structs{{names_member, ages_member, is_human_member}, null_at(0)};
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output->view(), expected_output);
+}
+
+TYPED_TEST(TypedStructGatherTest, TestSlicedStructsColumnGatherNoNulls)
+{
+  auto const structs_original = [] {
+    auto child1 =
+      cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    auto child2 = cudf::test::strings_column_wrapper{
+      "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Ten"};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expected = [] {
+    auto child1 = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{6, 10, 8};
+    auto child2 = cudf::test::strings_column_wrapper{"Six", "Ten", "Eight"};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const structs    = cudf::slice(structs_original, {4, 10})[0];
+  auto const gather_map = cudf::test::fixed_width_column_wrapper<int32_t>{1, 5, 3};
+  auto const result     = cudf::gather(cudf::table_view{{structs}}, gather_map)->get_column(0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.view(), expected);
+}
+
+TYPED_TEST(TypedStructGatherTest, TestSlicedStructsColumnGatherWithNulls)
+{
+  auto constexpr null = int32_t{0};  // null at child
+  auto constexpr XXX  = int32_t{0};  // null at parent
+
+  auto const structs_original = [] {
+    auto child1 = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+      {null, XXX, 3, null, null, 6, XXX, null, null, 10}, nulls_at({0, 3, 4, 7, 8})};
+    auto child2 = cudf::test::strings_column_wrapper{{"One",
+                                                      "" /*NULL at both parent and child*/,
+                                                      "Three",
+                                                      "" /*NULL*/,
+                                                      "Five",
+                                                      "" /*NULL*/,
+                                                      "" /*NULL at parent*/,
+                                                      "" /*NULL*/,
+                                                      "Nine",
+                                                      "" /*NULL*/},
+                                                     nulls_at({1, 3, 5, 7, 9})};
+    return cudf::test::structs_column_wrapper{{child1, child2}, nulls_at({1, 6})};
+  }();
+
+  auto const expected = [] {
+    auto child1 =
+      cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{{6, 10, null, XXX}, null_at(2)};
+    auto child2 =
+      cudf::test::strings_column_wrapper{{
+                                           "" /*NULL*/, "" /*NULL*/, "Nine", "" /*NULL at parent*/
+                                         },
+                                         nulls_at({0, 1})};
+    return cudf::test::structs_column_wrapper{{child1, child2}, null_at(3)};
+  }();
+
+  auto const structs    = cudf::slice(structs_original, {4, 10})[0];
+  auto const gather_map = cudf::test::fixed_width_column_wrapper<int32_t>{1, 5, 4, 2};
+  auto const result     = cudf::gather(cudf::table_view{{structs}}, gather_map)->get_column(0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.view(), expected);
+}
+
+TYPED_TEST(TypedStructGatherTest, TestNullifyOnNonNullInput)
+{
+  // Test that the null masks of the struct output (and its children) are set correctly,
+  // for an input struct column whose members are not nullable.
+
+  // 1. String "names" column.
+  auto const names =
+    std::vector<std::string>{"Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant"};
+
+  // 2. Numeric "ages" column.
+  auto const ages = std::vector<int32_t>{5, 10, 15, 20, 25, 30};
+
+  // 3. Boolean "is_human" column.
+  auto const is_human = {true, true, false, false, false, false};
+
+  // Assemble struct column.
+  auto const struct_column = [&] {
+    auto names_member    = ::strings(names.begin(), names.end());
+    auto ages_member     = ::numerics<TypeParam>(ages.begin(), ages.end());
+    auto is_human_member = ::bools(is_human.begin(), is_human.end());
+    return structs({names_member, ages_member, is_human_member});
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map = gather_map_t{null_index, 4, 3, 2, 1};
+
+  auto const output = do_gather(struct_column, gather_map);
+
+  auto const expected_output = [&] {
+    auto names_member = get_expected_column<std::string>(names, no_nulls(), no_nulls(), gather_map);
+    auto ages_member =
+      get_expected_column<TypeParam, int32_t>(ages, no_nulls(), no_nulls(), gather_map);
+    auto is_human_member = get_expected_column<bool>(
+      std::vector<bool>(is_human.begin(), is_human.end()), no_nulls(), no_nulls(), gather_map);
+    return cudf::test::structs_column_wrapper{{names_member, ages_member, is_human_member},
+                                              null_at(0)};
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output->view(), expected_output);
+}
+
+TYPED_TEST(TypedStructGatherTest, TestGatherStructOfLists)
+{
+  // Testing gather() on struct<list<numeric>>
+
+  auto lists_column_exemplar = [] {
+    return lists<TypeParam>{
+      {{5}, {10, 15}, {20, 25, 30}, {35, 40, 45, 50}, {55, 60, 65}, {70, 75}, {80}, {}, {}},
+      nulls_at({0, 3, 6, 9})};
+  };
+
+  // Assemble struct column.
+  auto const structs_column = [&] {
+    auto lists_column = lists_column_exemplar();
+    return cudf::test::structs_column_wrapper{{lists_column}};
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map = gather_map_t{null_index, 4, 3, 2, 1, 7, 3};
+
+  auto const gathered_structs = do_gather(structs_column, gather_map);
+
+  // Verify that the gathered struct column's list member presents as if
+  // it had itself been gathered individually.
+
+  auto const expected_gathered_list_column = [&] {
+    auto const list_column_before_gathering = lists_column_exemplar();
+    return do_gather(list_column_before_gathering, gather_map);
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_gathered_list_column->view(),
+                                      gathered_structs->view().child(0));
+}
+
+TYPED_TEST(TypedStructGatherTest, TestGatherStructOfListsOfLists)
+{
+  // Testing gather() on struct<list<list<numeric>>>
+
+  auto const lists_column_exemplar = []() {
+    return lists<TypeParam>{{{{5, 5}},
+                             {{10, 15}},
+                             {{20, 25}, {30}},
+                             {{35, 40}, {45, 50}},
+                             {{55}, {60, 65}},
+                             {{70, 75}},
+                             {{80, 80}},
+                             {},
+                             {}},
+                            nulls_at({0, 3, 6, 9})};
+  };
+
+  auto const structs_column = [&] {
+    auto lists_column = lists_column_exemplar();
+    return cudf::test::structs_column_wrapper{{lists_column}};
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map = gather_map_t{null_index, 4, 3, 2, 1, 7, 3};
+
+  auto const gathered_structs = do_gather(structs_column, gather_map);
+
+  // Verify that the gathered struct column's list member presents as if
+  // it had itself been gathered individually.
+
+  auto const expected_gathered_list_column = [&] {
+    auto const list_column_before_gathering = lists_column_exemplar();
+    return do_gather(list_column_before_gathering, gather_map);
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_gathered_list_column->view(),
+                                      gathered_structs->view().child(0));
+}
+
+TYPED_TEST(TypedStructGatherTest, TestGatherStructOfStructs)
+{
+  // Testing gather() on struct<struct<numeric>>
+
+  auto const numeric_column_exemplar = []() {
+    return numerics<TypeParam>{{5, 10, 15, 20, 25, 30, 35, 45, 50, 55, 60, 65, 70, 75},
+                               nulls_at({0, 3, 6, 9, 12, 15})};
+  };
+
+  auto const struct_of_structs_column = [&] {
+    auto numeric_column = numeric_column_exemplar();
+    auto structs_column = cudf::test::structs_column_wrapper{{numeric_column}};
+    return cudf::test::structs_column_wrapper{{structs_column}};
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map       = gather_map_t{null_index, 4, 3, 2, 1, 7, 3};
+  auto const gathered_structs = do_gather(struct_of_structs_column, gather_map);
+
+  // Verify that the underlying numeric column presents as if
+  // it had itself been gathered individually.
+
+  auto const expected_gathered_column = [&] {
+    auto const numeric_column_before_gathering = numeric_column_exemplar();
+    return do_gather(numeric_column_before_gathering, gather_map);
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_gathered_column->view(),
+                                      gathered_structs->view().child(0).child(0));
+}
+
+TYPED_TEST(TypedStructGatherTest, TestGatherStructOfListOfStructs)
+{
+  // Testing gather() on struct<list<struct<numeric>>>
+
+  auto const struct_of_list_of_structs = [&] {
+    auto numeric_column =
+      numerics<TypeParam>{{5, 10, 15, 20, 25, 30, 35, 45, 50, 55, 60, 65, 70, 75}};
+    auto structs_column         = structs{{numeric_column}}.release();
+    auto list_of_structs_column = cudf::make_lists_column(
+      7, offsets{0, 2, 4, 6, 8, 10, 12, 14}.release(), std::move(structs_column), 0, {});
+
+    std::vector<std::unique_ptr<cudf::column>> vector_of_columns;
+    vector_of_columns.push_back(std::move(list_of_structs_column));
+    return structs{std::move(vector_of_columns)};
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map       = gather_map_t{null_index, 4, 3, 2, 1};
+  auto const gathered_structs = do_gather(struct_of_list_of_structs, gather_map);
+
+  // Construct expected gather result.
+
+  auto expected_gather_result = [&] {
+    auto expected_numeric_col = numerics<TypeParam>{{70, 75, 50, 55, 35, 45, 25, 30, 15, 20}};
+    auto expected_struct_col  = structs{{expected_numeric_col}}.release();
+    auto expected_list_of_structs_column = cudf::make_lists_column(
+      5, offsets{0, 2, 4, 6, 8, 10}.release(), std::move(expected_struct_col), 0, {});
+    std::vector<std::unique_ptr<cudf::column>> expected_vector_of_columns;
+    expected_vector_of_columns.push_back(std::move(expected_list_of_structs_column));
+    return structs{std::move(expected_vector_of_columns), {0, 1, 1, 1, 1}};
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_gather_result, gathered_structs->view());
+}
+
+TYPED_TEST(TypedStructGatherTest, TestGatherStructOfStructsWithValidity)
+{
+  // Testing gather() on struct<struct<numeric>>
+
+  using validity_iter_t = decltype(nulls_at({0}));
+
+  // Factory to construct numeric column with configurable null-mask.
+  auto const numeric_column_exemplar = [](validity_iter_t validity) {
+    return numerics<TypeParam>{{5, 10, 15, 20, 25, 30, 35, 45, 50, 55, 60, 65, 70, 75}, validity};
+  };
+
+  // Construct struct-of-struct-of-numerics.
+  auto struct_of_structs_column = [&] {
+    // Every 3rd element is null.
+    auto numeric_column = numeric_column_exemplar(nulls_at({0, 3, 6, 9, 12, 15}));
+    // 12th element is null.
+    auto structs_column = cudf::test::structs_column_wrapper{{numeric_column}, nulls_at({11})};
+    return cudf::test::structs_column_wrapper{{structs_column}};
+  }();
+
+  // Gather to new struct column.
+  auto const gather_map       = gather_map_t{null_index, 4, 3, 2, 1, 7, 3};
+  auto const gathered_structs = do_gather(struct_of_structs_column, gather_map);
+
+  // Verify that the underlying numeric column presents as if
+  // it had itself been gathered individually.
+
+  auto const expected_gathered_column = [&] {
+    // Every 3rd element *and* the 12th element are null.
+    auto const final_validity                  = nulls_at({0, 3, 6, 9, 11, 12, 15});
+    auto const numeric_column_before_gathering = numeric_column_exemplar(final_validity);
+    return do_gather(numeric_column_before_gathering, gather_map);
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_gathered_column->view(),
+                                      gathered_structs->view().child(0).child(0));
+}
+
+TYPED_TEST(TypedStructGatherTest, TestEmptyGather)
+{
+  auto const struct_column = [&] {
+    auto ages = numerics<TypeParam>{{5, 10, 15, 20, 25, 30}, null_at(4)};
+    return cudf::test::structs_column_wrapper{{ages}, null_at(5)};
+  }();
+
+  auto const empty_gather_map = gather_map_t{};
+  auto const gathered_structs = do_gather(struct_column, empty_gather_map);
+
+  // Expect empty struct column gathered.
+  auto const expected_empty_column = [&] {
+    auto expected_empty_numerics = numerics<TypeParam>{};
+    return cudf::test::structs_column_wrapper{{expected_empty_numerics}};
+  }();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_empty_column, gathered_structs->view());
+}
diff --git a/cpp/tests/copying/gather_tests.cpp b/cpp/tests/copying/gather_tests.cpp
new file mode 100644
index 0000000..3120b73
--- /dev/null
+++ b/cpp/tests/copying/gather_tests.cpp
@@ -0,0 +1,244 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+template <typename T>
+class GatherTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GatherTest, cudf::test::NumericTypes);
+
+TYPED_TEST(GatherTest, IdentityTest)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column(data, data + source_size);
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(data, data + source_size);
+
+  cudf::table_view source_table({source_column});
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(source_table.column(i), result->view().column(i));
+  }
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(source_table, result->view());
+}
+
+TYPED_TEST(GatherTest, ReverseIdentityTest)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto reversed_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return source_size - 1 - i; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column(data, data + source_size);
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(reversed_data,
+                                                             reversed_data + source_size);
+
+  cudf::table_view source_table({source_column});
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_column(reversed_data,
+                                                                  reversed_data + source_size);
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_column, result->view().column(i));
+  }
+}
+
+TYPED_TEST(GatherTest, EveryOtherNullOdds)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  // Every other element is valid
+  auto data     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column(
+    data, data + source_size, validity);
+
+  // Gather odd-valued indices
+  auto map_data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(map_data,
+                                                             map_data + (source_size / 2));
+
+  cudf::table_view source_table({source_column});
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+
+  auto expect_data  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 0; });
+  auto expect_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 0; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_column(
+    expect_data, expect_data + source_size / 2, expect_valid);
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_column, result->view().column(i));
+  }
+}
+
+TYPED_TEST(GatherTest, EveryOtherNullEvens)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  // Every other element is valid
+  auto data     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column(
+    data, data + source_size, validity);
+
+  // Gather even-valued indices
+  auto map_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2 + 1; });
+
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(map_data,
+                                                             map_data + (source_size / 2));
+
+  cudf::table_view source_table({source_column});
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+
+  auto expect_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2 + 1; });
+  auto expect_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 1; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_column(
+    expect_data, expect_data + source_size / 2, expect_valid);
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_column, result->view().column(i));
+  }
+}
+
+TYPED_TEST(GatherTest, AllNull)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  // Every element is invalid
+  auto data     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 0; });
+
+  // Create a gather map that gathers to random locations
+  std::vector<cudf::size_type> host_map_data(source_size);
+  std::iota(host_map_data.begin(), host_map_data.end(), 0);
+  std::mt19937 g(0);
+  std::shuffle(host_map_data.begin(), host_map_data.end(), g);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> source_column{
+    data, data + source_size, validity};
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(host_map_data.begin(),
+                                                             host_map_data.end());
+
+  cudf::table_view source_table({source_column});
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+
+  // Check that the result is also all invalid
+  CUDF_TEST_EXPECT_TABLES_EQUAL(source_table, result->view());
+}
+
+TYPED_TEST(GatherTest, MultiColReverseIdentityTest)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  constexpr cudf::size_type n_cols = 3;
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto reversed_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return source_size - 1 - i; });
+
+  std::vector<cudf::test::fixed_width_column_wrapper<TypeParam>> source_column_wrappers;
+  std::vector<cudf::column_view> source_columns;
+
+  for (int i = 0; i < n_cols; ++i) {
+    source_column_wrappers.push_back(
+      cudf::test::fixed_width_column_wrapper<TypeParam>(data, data + source_size));
+    source_columns.push_back(source_column_wrappers[i]);
+  }
+
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(reversed_data,
+                                                             reversed_data + source_size);
+
+  cudf::table_view source_table{source_columns};
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_column(reversed_data,
+                                                                  reversed_data + source_size);
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_column, result->view().column(i));
+  }
+}
+
+TYPED_TEST(GatherTest, MultiColNulls)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  static_assert(0 == source_size % 2, "Size of source data must be a multiple of 2.");
+
+  constexpr cudf::size_type n_cols = 3;
+
+  auto data     = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  std::vector<cudf::test::fixed_width_column_wrapper<TypeParam>> source_column_wrappers;
+  std::vector<cudf::column_view> source_columns;
+
+  for (int i = 0; i < n_cols; ++i) {
+    source_column_wrappers.push_back(
+      cudf::test::fixed_width_column_wrapper<TypeParam>(data, data + source_size, validity));
+    source_columns.push_back(source_column_wrappers[i]);
+  }
+
+  auto reversed_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return source_size - 1 - i; });
+
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(reversed_data,
+                                                             reversed_data + source_size);
+
+  cudf::table_view source_table{source_columns};
+
+  std::unique_ptr<cudf::table> result = std::move(cudf::gather(source_table, gather_map));
+
+  // Expected data
+  auto expect_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return source_size - i - 1; });
+  auto expect_valid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i + 1) % 2; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_column(
+    expect_data, expect_data + source_size, expect_valid);
+
+  for (auto i = 0; i < source_table.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_column, result->view().column(i));
+  }
+}
diff --git a/cpp/tests/copying/get_value_tests.cpp b/cpp/tests/copying/get_value_tests.cpp
new file mode 100644
index 0000000..d322fbe
--- /dev/null
+++ b/cpp/tests/copying/get_value_tests.cpp
@@ -0,0 +1,923 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+using namespace cudf::test::iterators;
+
+template <typename T>
+struct FixedWidthGetValueTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedWidthGetValueTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(FixedWidthGetValueTest, BasicGet)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({9, 8, 7, 6});
+  auto s = cudf::get_element(col, 0);
+
+  using ScalarType = cudf::scalar_type_t<TypeParam>;
+  auto typed_s     = static_cast<ScalarType const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ(cudf::test::make_type_param_scalar<TypeParam>(9), typed_s->value());
+}
+
+TYPED_TEST(FixedWidthGetValueTest, GetFromNullable)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({9, 8, 7, 6}, {0, 1, 0, 1});
+  auto s = cudf::get_element(col, 1);
+
+  using ScalarType = cudf::scalar_type_t<TypeParam>;
+  auto typed_s     = static_cast<ScalarType const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ(cudf::test::make_type_param_scalar<TypeParam>(8), typed_s->value());
+}
+
+TYPED_TEST(FixedWidthGetValueTest, GetNull)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({9, 8, 7, 6}, {0, 1, 0, 1});
+  auto s = cudf::get_element(col, 2);
+
+  EXPECT_FALSE(s->is_valid());
+}
+
+TYPED_TEST(FixedWidthGetValueTest, IndexOutOfBounds)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({9, 8, 7, 6}, {0, 1, 0, 1});
+
+  // Test for out of bounds indexes in both directions.
+  EXPECT_THROW(cudf::get_element(col, -1), cudf::logic_error);
+  EXPECT_THROW(cudf::get_element(col, 4), cudf::logic_error);
+}
+
+struct StringGetValueTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringGetValueTest, BasicGet)
+{
+  cudf::test::strings_column_wrapper col{"this", "is", "a", "test"};
+  auto s = cudf::get_element(col, 3);
+
+  auto typed_s = static_cast<cudf::string_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ("test", typed_s->to_string());
+}
+
+TEST_F(StringGetValueTest, GetEmpty)
+{
+  cudf::test::strings_column_wrapper col{"this", "is", "", "test"};
+  auto s = cudf::get_element(col, 2);
+
+  auto typed_s = static_cast<cudf::string_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ("", typed_s->to_string());
+}
+
+TEST_F(StringGetValueTest, GetFromNullable)
+{
+  cudf::test::strings_column_wrapper col({"this", "is", "a", "test"}, {0, 1, 0, 1});
+  auto s = cudf::get_element(col, 1);
+
+  auto typed_s = static_cast<cudf::string_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ("is", typed_s->to_string());
+}
+
+TEST_F(StringGetValueTest, GetNull)
+{
+  cudf::test::strings_column_wrapper col({"this", "is", "a", "test"}, {0, 1, 0, 1});
+  auto s = cudf::get_element(col, 2);
+
+  EXPECT_FALSE(s->is_valid());
+}
+
+template <typename T>
+struct DictionaryGetValueTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(DictionaryGetValueTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(DictionaryGetValueTest, BasicGet)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> keys({6, 7, 8, 9});
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices{0, 0, 1, 2, 1, 3, 3, 2};
+  auto col = cudf::make_dictionary_column(keys, indices);
+
+  auto s = cudf::get_element(*col, 2);
+
+  using ScalarType = cudf::scalar_type_t<TypeParam>;
+  auto typed_s     = static_cast<ScalarType const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ(cudf::test::make_type_param_scalar<TypeParam>(7), typed_s->value());
+}
+
+TYPED_TEST(DictionaryGetValueTest, GetFromNullable)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> keys({6, 7, 8, 9});
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices({0, 0, 1, 2, 1, 3, 3, 2},
+                                                           {0, 1, 0, 1, 1, 1, 0, 0});
+  auto col = cudf::make_dictionary_column(keys, indices);
+
+  auto s = cudf::get_element(*col, 3);
+
+  using ScalarType = cudf::scalar_type_t<TypeParam>;
+  auto typed_s     = static_cast<ScalarType const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  EXPECT_EQ(cudf::test::make_type_param_scalar<TypeParam>(8), typed_s->value());
+}
+
+TYPED_TEST(DictionaryGetValueTest, GetNull)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> keys({6, 7, 8, 9});
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices({0, 0, 1, 2, 1, 3, 3, 2},
+                                                           {0, 1, 0, 1, 1, 1, 0, 0});
+  auto col = cudf::make_dictionary_column(keys, indices);
+
+  auto s = cudf::get_element(*col, 2);
+
+  EXPECT_FALSE(s->is_valid());
+}
+
+/*
+ * Lists test grid:
+ * Dim1 nestedness:          {Nested, Non-nested}
+ * Dim2 validity, emptiness: {Null element, Non-null non-empty list, Non-null empty list}
+ * Dim3 leaf data type:      {Fixed-width, string, struct}
+ */
+
+template <typename T>
+struct ListGetFixedWidthValueTest : public cudf::test::BaseFixture {
+  auto odds_valid()
+  {
+    return cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  }
+  auto nth_valid(cudf::size_type x)
+  {
+    return cudf::detail::make_counting_transform_iterator(0, [=](auto i) { return x == i; });
+  }
+};
+
+TYPED_TEST_SUITE(ListGetFixedWidthValueTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ListGetFixedWidthValueTest, NonNestedGetNonNullNonEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  LCW col{LCW({1, 2, 34}, this->odds_valid()), LCW{}, LCW{1}, LCW{}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_data({1, 2, 34}, this->odds_valid());
+  cudf::size_type index = 0;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetFixedWidthValueTest, NonNestedGetNonNullEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  LCW col{LCW{1, 2, 34}, LCW{}, LCW{1}, LCW{}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_data{};
+  cudf::size_type index = 1;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetFixedWidthValueTest, NonNestedGetNull)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FCW = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  LCW col({LCW{1, 2, 34}, LCW{}, LCW{1}, LCW{}}, this->odds_valid());
+  cudf::size_type index = 2;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_FALSE(s->is_valid());
+  // Test preserve column hierarchy
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(typed_s->view(), FCW{});
+}
+
+TYPED_TEST(ListGetFixedWidthValueTest, NestedGetNonNullNonEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // clang-format off
+  LCW col{
+    LCW{LCW{1, 2}, LCW{34}},
+    LCW{},
+    LCW{LCW{1}},
+    LCW{LCW{42}, LCW{10}}
+  };
+  // clang-format on
+  LCW expected_data{LCW{42}, LCW{10}};
+
+  cudf::size_type index = 3;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetFixedWidthValueTest, NestedGetNonNullNonEmptyPreserveNull)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  std::vector<cudf::valid_type> valid{0, 1, 1};
+  // clang-format off
+  LCW col{
+    LCW{LCW{1, 2}, LCW{34}},
+    LCW{},
+    LCW{LCW{1}},
+    LCW({LCW{42}, LCW{10}, LCW({1, 3, 2}, this->nth_valid(1))}, valid.begin())
+  };
+  // clang-format on
+  LCW expected_data({LCW{42}, LCW{10}, LCW({1, 3, 2}, this->nth_valid(1))}, valid.begin());
+  cudf::size_type index = 3;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetFixedWidthValueTest, NestedGetNonNullEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // clang-format off
+  LCW col{
+    LCW{LCW{1, 2}, LCW{34}},
+    LCW{},
+    LCW{LCW{1}},
+    LCW{LCW{42}, LCW{10}}
+  };
+  // clang-format on
+  LCW expected_data{};
+  cudf::size_type index = 1;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetFixedWidthValueTest, NestedGetNull)
+{
+  using LCW      = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FCW      = cudf::test::fixed_width_column_wrapper<TypeParam>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  std::vector<cudf::valid_type> valid{1, 0, 1, 0};
+  // clang-format off
+  LCW col(
+    {
+      LCW{LCW{1, 2}, LCW{34}},
+      LCW{},
+      LCW{LCW{1}},
+      LCW{LCW{42}, LCW{10}}
+    }, valid.begin());
+  // clang-format on
+  cudf::size_type index = 1;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  auto expected_data =
+    cudf::make_lists_column(0, offset_t{}.release(), FCW{}.release(), 0, rmm::device_buffer{});
+
+  EXPECT_FALSE(s->is_valid());
+  // Test preserve column hierarchy
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(typed_s->view(), *expected_data);
+}
+
+struct ListGetStringValueTest : public cudf::test::BaseFixture {
+  auto odds_valid()
+  {
+    return cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  }
+  auto nth_valid(cudf::size_type x)
+  {
+    return cudf::detail::make_counting_transform_iterator(0, [=](auto i) { return x == i; });
+  }
+};
+
+TEST_F(ListGetStringValueTest, NonNestedGetNonNullNonEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  LCW col{LCW({"aaa", "Héllo"}, this->odds_valid()), LCW{}, LCW{""}, LCW{"42"}};
+  cudf::test::strings_column_wrapper expected_data({"aaa", "Héllo"}, this->odds_valid());
+  cudf::size_type index = 0;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TEST_F(ListGetStringValueTest, NonNestedGetNonNullEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  LCW col{LCW{"aaa", "Héllo"}, LCW{}, LCW{""}, LCW{"42"}};
+  cudf::test::strings_column_wrapper expected_data{};
+  cudf::size_type index = 1;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TEST_F(ListGetStringValueTest, NonNestedGetNull)
+{
+  using LCW      = cudf::test::lists_column_wrapper<cudf::string_view>;
+  using StringCW = cudf::test::strings_column_wrapper;
+
+  std::vector<cudf::valid_type> valid{1, 0, 0, 1};
+  LCW col({LCW{"aaa", "Héllo"}, LCW{}, LCW{""}, LCW{"42"}}, valid.begin());
+  cudf::size_type index = 2;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_FALSE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(typed_s->view(), StringCW{});
+}
+
+TEST_F(ListGetStringValueTest, NestedGetNonNullNonEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  // clang-format off
+  LCW col{
+    LCW{LCW{"aaa", "Héllo"}},
+    LCW{},
+    LCW{LCW{""}, LCW({"string", "str2", "xyz"}, this->nth_valid(0))},
+    LCW{LCW{"42"}, LCW{"21"}}
+  };
+  // clang-format on
+  LCW expected_data{LCW{""}, LCW({"string", "str2", "xyz"}, this->nth_valid(0))};
+  cudf::size_type index = 2;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TEST_F(ListGetStringValueTest, NestedGetNonNullNonEmptyPreserveNull)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  std::vector<cudf::valid_type> valid{0, 1, 1};
+  // clang-format off
+  LCW col{
+    LCW{LCW{"aaa", "Héllo"}},
+    LCW{},
+    LCW({LCW{""}, LCW{"cc"}, LCW({"string", "str2", "xyz"}, this->nth_valid(0))}, valid.begin()),
+    LCW{LCW{"42"}, LCW{"21"}}
+  };
+  // clang-format on
+  LCW expected_data({LCW{""}, LCW{"cc"}, LCW({"string", "str2", "xyz"}, this->nth_valid(0))},
+                    valid.begin());
+  cudf::size_type index = 2;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_data, typed_s->view());
+}
+
+TEST_F(ListGetStringValueTest, NestedGetNonNullEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  // clang-format off
+  LCW col{
+    LCW{LCW{"aaa", "Héllo"}},
+    LCW{LCW{""}},
+    LCW{LCW{"42"}, LCW{"21"}},
+    LCW{}
+  };
+  // clang-format on
+  LCW expected_data{};
+  cudf::size_type index = 3;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  // Relax to equivalent. `expected_data` leaf string column does not
+  // allocate offset and byte array, but `typed_s` does.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_data, typed_s->view());
+}
+
+TEST_F(ListGetStringValueTest, NestedGetNull)
+{
+  using LCW      = cudf::test::lists_column_wrapper<cudf::string_view>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using StringCW = cudf::test::strings_column_wrapper;
+
+  std::vector<cudf::valid_type> valid{0, 0, 1, 1};
+  // clang-format off
+  LCW col(
+    {
+      LCW{LCW{"aaa", "Héllo"}},
+      LCW{LCW{""}},
+      LCW{LCW{"42"}, LCW{"21"}},
+      LCW{}
+    }, valid.begin());
+  // clang-format on
+  cudf::size_type index = 0;
+
+  auto s       = cudf::get_element(col, index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  auto expected_data =
+    cudf::make_lists_column(0, offset_t{}.release(), StringCW{}.release(), 0, rmm::device_buffer{});
+
+  EXPECT_FALSE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_data, typed_s->view());
+}
+
+/**
+ * @brief Some shared helper functions used by lists of structs test.
+ */
+template <typename T>
+struct ListGetStructValueTest : public cudf::test::BaseFixture {
+  using SCW        = cudf::test::structs_column_wrapper;
+  using LCWinner_t = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  /**
+   * @brief Create a lists column
+   *
+   * @note Different from `cudf::make_lists_column`, this allows setting the `null_mask`
+   * in `initializer_list`. However this is an expensive function because it repeatedly
+   * calls `cudf::set_null_mask` for each row.
+   */
+  std::unique_ptr<cudf::column> make_test_lists_column(
+    cudf::size_type num_lists,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets,
+    std::unique_ptr<cudf::column> child,
+    std::initializer_list<cudf::valid_type> null_mask)
+  {
+    cudf::size_type null_count = num_lists - std::accumulate(null_mask.begin(), null_mask.end(), 0);
+    auto d_null_mask           = cudf::create_null_mask(
+      num_lists, null_count == 0 ? cudf::mask_state::UNALLOCATED : cudf::mask_state::ALL_NULL);
+    if (null_count > 0) {
+      std::for_each(
+        thrust::make_counting_iterator(0), thrust::make_counting_iterator(num_lists), [&](auto i) {
+          if (*(null_mask.begin() + i)) {
+            cudf::set_null_mask(
+              static_cast<cudf::bitmask_type*>(d_null_mask.data()), i, i + 1, true);
+          }
+        });
+    }
+    return cudf::make_lists_column(
+      num_lists, offsets.release(), std::move(child), null_count, std::move(d_null_mask));
+  }
+
+  /**
+   * @brief Create a structs column that contains 3 fields: int, string, List<int>
+   */
+  template <typename MaskIterator>
+  SCW make_test_structs_column(cudf::test::fixed_width_column_wrapper<T> field1,
+                               cudf::test::strings_column_wrapper field2,
+                               cudf::test::lists_column_wrapper<T, int32_t> field3,
+                               MaskIterator mask)
+  {
+    return SCW{{field1, field2, field3}, mask};
+  }
+
+  /**
+   * @brief Create a 0-length structs column
+   */
+  SCW zero_length_struct() { return SCW{}; }
+
+  /**
+   * @brief Concatenate structs columns, allow specifying inputs in `initializer_list`
+   */
+  std::unique_ptr<cudf::column> concat(std::initializer_list<SCW> rows)
+  {
+    std::vector<cudf::column_view> views;
+    std::transform(rows.begin(), rows.end(), std::back_inserter(views), [](auto& r) {
+      return cudf::column_view(r);
+    });
+    return cudf::concatenate(views);
+  }
+
+  /**
+   * @brief Test data setup: row 0 of structs column
+   */
+  SCW row0()
+  {
+    // {int: 1, string: NULL, list: NULL}
+    return this->make_test_structs_column({{1}, {1}},
+                                          cudf::test::strings_column_wrapper({"aa"}, {false}),
+                                          LCWinner_t({{}}, all_nulls()),
+                                          no_nulls());
+  }
+
+  /**
+   * @brief Test data setup: row 1 of structs column
+   */
+  SCW row1()
+  {
+    // NULL
+    return this->make_test_structs_column({-1}, {""}, LCWinner_t{-1}, all_nulls());
+  }
+
+  /**
+   * @brief Test data setup: row 2 of structs column
+   */
+  SCW row2()
+  {
+    // {int: 3, string: "xyz", list: [3, 8, 4]}
+    return this->make_test_structs_column({{3}, {1}},
+                                          cudf::test::strings_column_wrapper({"xyz"}, {true}),
+                                          LCWinner_t({{3, 8, 4}}, no_nulls()),
+                                          no_nulls());
+  }
+
+  /**
+   * @brief Test data setup: a 3-row structs column
+   */
+  std::unique_ptr<cudf::column> leaf_data()
+  {
+    // 3 rows:
+    // {int: 1, string: NULL, list: NULL}
+    // NULL
+    // {int: 3, string: "xyz", list: [3, 8, 4]}
+    return this->concat({row0(), row1(), row2()});
+  }
+};
+
+TYPED_TEST_SUITE(ListGetStructValueTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ListGetStructValueTest, NonNestedGetNonNullNonEmpty)
+{
+  // 2-rows
+  // [{1, NULL, NULL}, NULL]
+  // [{3, "xyz", [3, 8, 4]}] <- cudf::get_element(1)
+
+  auto list_column      = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  cudf::size_type index = 1;
+  auto expected_data    = this->row2();
+
+  auto s       = cudf::get_element(list_column->view(), index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  // Relax to equivalent. The nested list column in struct allocates `null_mask`.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NonNestedGetNonNullNonEmpty2)
+{
+  // 2-rows
+  // [{1, NULL, NULL}, NULL] <- cudf::get_element(0)
+  // [{3, "xyz", [3, 8, 4]}]
+
+  auto list_column      = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  cudf::size_type index = 0;
+  auto expected_data    = this->concat({this->row0(), this->row1()});
+
+  auto s       = cudf::get_element(list_column->view(), index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NonNestedGetNonNullEmpty)
+{
+  // 3-rows
+  // [{1, NULL, NULL}, NULL]
+  // [{3, "xyz", [3, 8, 4]}]
+  // []                      <- cudf::get_element(2)
+
+  auto list_column = this->make_test_lists_column(3, {0, 2, 3, 3}, this->leaf_data(), {1, 1, 1});
+  cudf::size_type index = 2;
+  // For well-formed list column, an empty list still holds the complete structure of
+  // a 0-length structs column
+  auto expected_data = this->zero_length_struct();
+
+  auto s       = cudf::get_element(list_column->view(), index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  // Relax to equivalent. The nested list column in struct allocates `null_mask`.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NonNestedGetNull)
+{
+  // 2-rows
+  // NULL                    <- cudf::get_element(0)
+  // [{3, "xyz", [3, 8, 4]}]
+
+  using valid_t = std::vector<cudf::valid_type>;
+
+  auto list_column      = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {0, 1});
+  cudf::size_type index = 0;
+
+  auto s       = cudf::get_element(list_column->view(), index);
+  auto typed_s = static_cast<cudf::list_scalar const*>(s.get());
+
+  auto expected_data = this->make_test_structs_column({}, {}, {}, valid_t{}.begin());
+
+  EXPECT_FALSE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(typed_s->view(), expected_data);
+}
+
+TYPED_TEST(ListGetStructValueTest, NestedGetNonNullNonEmpty)
+{
+  // 2-rows
+  // [[{1, NULL, NULL}, NULL], [{3, "xyz", [3, 8, 4]}]] <- cudf::get_element(0)
+  // []
+
+  auto list_column   = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  auto expected_data = std::make_unique<cudf::column>(*list_column);
+
+  auto list_column_nested =
+    this->make_test_lists_column(2, {0, 2, 2}, std::move(list_column), {1, 1});
+
+  cudf::size_type index = 0;
+  auto s                = cudf::get_element(list_column_nested->view(), index);
+  auto typed_s          = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NestedGetNonNullNonEmpty2)
+{
+  // 2-rows
+  // [[{1, NULL, NULL}, NULL]] <- cudf::get_element(0)
+  // [[{3, "xyz", [3, 8, 4]}]]
+
+  auto list_column = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  auto list_column_nested =
+    this->make_test_lists_column(2, {0, 1, 2}, std::move(list_column), {1, 1});
+
+  auto expected_data =
+    this->make_test_lists_column(1, {0, 2}, this->concat({this->row0(), this->row1()}), {1});
+
+  cudf::size_type index = 0;
+  auto s                = cudf::get_element(list_column_nested->view(), index);
+  auto typed_s          = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NestedGetNonNullNonEmpty3)
+{
+  // 2-rows
+  // [[{1, NULL, NULL}, NULL]]
+  // [[{3, "xyz", [3, 8, 4]}]] <- cudf::get_element(1)
+
+  auto list_column = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  auto list_column_nested =
+    this->make_test_lists_column(2, {0, 1, 2}, std::move(list_column), {1, 1});
+
+  auto expected_data = this->make_test_lists_column(1, {0, 1}, this->row2().release(), {1});
+
+  cudf::size_type index = 1;
+  auto s                = cudf::get_element(list_column_nested->view(), index);
+  auto typed_s          = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  // Relax to equivalent. For `get_element`, the nested list column in struct
+  // allocates `null_mask`.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NestedGetNonNullEmpty)
+{
+  // 3-rows
+  // [[{1, NULL, NULL}, NULL]]
+  // []                        <- cudf::get_element(1)
+  // [[{3, "xyz", [3, 8, 4]}]]
+
+  auto list_column = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  auto list_column_nested =
+    this->make_test_lists_column(3, {0, 1, 1, 2}, std::move(list_column), {1, 1, 1});
+
+  auto expected_data =
+    this->make_test_lists_column(0, {0}, this->zero_length_struct().release(), {1});
+
+  cudf::size_type index = 1;
+  auto s                = cudf::get_element(list_column_nested->view(), index);
+  auto typed_s          = static_cast<cudf::list_scalar const*>(s.get());
+
+  EXPECT_TRUE(s->is_valid());
+  // Relax to equivalent. The sliced version still has the array for fields
+  // allocated.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_data, typed_s->view());
+}
+
+TYPED_TEST(ListGetStructValueTest, NestedGetNull)
+{
+  // 3-rows
+  // [[{1, NULL, NULL}, NULL]]
+  // []
+  // NULL                      <- cudf::get_element(2)
+
+  using valid_t  = std::vector<cudf::valid_type>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto list_column = this->make_test_lists_column(2, {0, 2, 3}, this->leaf_data(), {1, 1});
+  auto list_column_nested =
+    this->make_test_lists_column(3, {0, 1, 1, 2}, std::move(list_column), {1, 1, 0});
+
+  cudf::size_type index = 2;
+  auto s                = cudf::get_element(list_column_nested->view(), index);
+  auto typed_s          = static_cast<cudf::list_scalar const*>(s.get());
+
+  auto nested = this->make_test_structs_column({}, {}, {}, valid_t{}.begin());
+  auto expected_data =
+    cudf::make_lists_column(0, offset_t{}.release(), nested.release(), 0, rmm::device_buffer{});
+
+  EXPECT_FALSE(s->is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_data, typed_s->view());
+}
+
+struct StructGetValueTest : public cudf::test::BaseFixture {};
+template <typename T>
+struct StructGetValueTestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(StructGetValueTestTyped, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(StructGetValueTestTyped, mixed_types_valid)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // col fields
+  cudf::test::fixed_width_column_wrapper<TypeParam> f1{1, 2, 3};
+  cudf::test::strings_column_wrapper f2{"aa", "bbb", "c"};
+  cudf::test::dictionary_column_wrapper<TypeParam, int32_t> f3{42, 42, 24};
+  LCW f4{LCW{8, 8, 8}, LCW{9, 9}, LCW{10}};
+
+  cudf::test::structs_column_wrapper col{f1, f2, f3, f4};
+
+  cudf::size_type index = 2;
+  auto s                = cudf::get_element(col, index);
+  auto typed_s          = static_cast<cudf::struct_scalar const*>(s.get());
+
+  // expect fields
+  cudf::test::fixed_width_column_wrapper<TypeParam> ef1{3};
+  cudf::test::strings_column_wrapper ef2{"c"};
+  cudf::test::dictionary_column_wrapper<TypeParam, int32_t> ef3{24};
+  LCW ef4{LCW{10}};
+
+  cudf::table_view expect_data{{ef1, ef2, ef3, ef4}};
+
+  EXPECT_TRUE(typed_s->is_valid());
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expect_data, typed_s->view());
+}
+
+TYPED_TEST(StructGetValueTestTyped, mixed_types_valid_with_nulls)
+{
+  using LCW             = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using validity_mask_t = std::vector<cudf::valid_type>;
+
+  // col fields
+  cudf::test::fixed_width_column_wrapper<TypeParam> f1({1, 2, 3}, {true, false, true});
+  cudf::test::strings_column_wrapper f2({"aa", "bbb", "c"}, {false, false, true});
+  cudf::test::dictionary_column_wrapper<TypeParam, uint32_t> f3(
+    {42, 42, 24}, validity_mask_t{true, true, true}.begin());
+  LCW f4({LCW{8, 8, 8}, LCW{9, 9}, LCW{10}}, validity_mask_t{false, false, false}.begin());
+
+  cudf::test::structs_column_wrapper col{f1, f2, f3, f4};
+
+  cudf::size_type index = 1;
+  auto s                = cudf::get_element(col, index);
+  auto typed_s          = static_cast<cudf::struct_scalar const*>(s.get());
+
+  // expect fields
+  cudf::test::fixed_width_column_wrapper<TypeParam> ef1({-1}, {false});
+  cudf::test::strings_column_wrapper ef2({""}, {false});
+
+  cudf::test::dictionary_column_wrapper<TypeParam, uint32_t> x({42}, {true});
+  cudf::dictionary_column_view dict_col(x);
+  cudf::test::fixed_width_column_wrapper<TypeParam> new_key{24};
+  auto ef3 = cudf::dictionary::add_keys(dict_col, new_key);
+
+  LCW ef4({LCW{10}}, validity_mask_t{false}.begin());
+
+  cudf::table_view expect_data{{ef1, ef2, *ef3, ef4}};
+
+  EXPECT_TRUE(typed_s->is_valid());
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expect_data, typed_s->view());
+}
+
+TYPED_TEST(StructGetValueTestTyped, mixed_types_invalid)
+{
+  using LCW             = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using validity_mask_t = std::vector<cudf::valid_type>;
+
+  // col fields
+  cudf::test::fixed_width_column_wrapper<TypeParam> f1{1, 2, 3};
+  cudf::test::strings_column_wrapper f2{"aa", "bbb", "c"};
+  cudf::test::dictionary_column_wrapper<TypeParam, uint32_t> f3{42, 42, 24};
+  LCW f4{LCW{8, 8, 8}, LCW{9, 9}, LCW{10}};
+
+  cudf::test::structs_column_wrapper col({f1, f2, f3, f4},
+                                         validity_mask_t{false, true, true}.begin());
+
+  cudf::size_type index = 0;
+  auto s                = cudf::get_element(col, index);
+  auto typed_s          = static_cast<cudf::struct_scalar const*>(s.get());
+
+  EXPECT_FALSE(typed_s->is_valid());
+
+  // expect to preserve types along column hierarchy.
+  EXPECT_EQ(typed_s->view().column(0).type().id(), cudf::type_to_id<TypeParam>());
+  EXPECT_EQ(typed_s->view().column(1).type().id(), cudf::type_id::STRING);
+  EXPECT_EQ(typed_s->view().column(2).type().id(), cudf::type_id::DICTIONARY32);
+  EXPECT_EQ(typed_s->view().column(2).child(1).type().id(), cudf::type_to_id<TypeParam>());
+  EXPECT_EQ(typed_s->view().column(3).type().id(), cudf::type_id::LIST);
+  EXPECT_EQ(typed_s->view().column(3).child(1).type().id(), cudf::type_to_id<TypeParam>());
+}
+
+TEST_F(StructGetValueTest, multi_level_nested)
+{
+  using LCW             = cudf::test::lists_column_wrapper<int32_t, int32_t>;
+  using validity_mask_t = std::vector<cudf::valid_type>;
+
+  // col fields
+  LCW l3({LCW{1, 1, 1}, LCW{2, 2}, LCW{3}}, validity_mask_t{false, true, true}.begin());
+  cudf::test::structs_column_wrapper l2{l3};
+  auto l1 =
+    cudf::make_lists_column(1,
+                            cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 3}.release(),
+                            l2.release(),
+                            0,
+                            cudf::create_null_mask(1, cudf::mask_state::UNALLOCATED));
+  std::vector<std::unique_ptr<cudf::column>> l0_fields;
+  l0_fields.emplace_back(std::move(l1));
+  cudf::test::structs_column_wrapper l0(std::move(l0_fields));
+
+  cudf::size_type index = 0;
+  auto s                = cudf::get_element(l0, index);
+  auto typed_s          = static_cast<cudf::struct_scalar const*>(s.get());
+
+  // Expect fields
+  cudf::column_view cv = cudf::column_view(l0);
+  cudf::table_view fields(std::vector<cudf::column_view>(cv.child_begin(), cv.child_end()));
+
+  EXPECT_TRUE(typed_s->is_valid());
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(fields, typed_s->view());
+}
diff --git a/cpp/tests/copying/pack_tests.cpp b/cpp/tests/copying/pack_tests.cpp
new file mode 100644
index 0000000..8a33e01
--- /dev/null
+++ b/cpp/tests/copying/pack_tests.cpp
@@ -0,0 +1,542 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/contiguous_split.hpp>
+
+struct PackUnpackTest : public cudf::test::BaseFixture {
+  void run_test(cudf::table_view const& t)
+  {
+    // verify pack/unpack works
+    auto packed   = cudf::pack(t);
+    auto unpacked = cudf::unpack(packed);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(t, unpacked);
+
+    // verify pack_metadata itself works
+    auto metadata = cudf::pack_metadata(
+      unpacked, reinterpret_cast<uint8_t const*>(packed.gpu_data->data()), packed.gpu_data->size());
+    EXPECT_EQ(metadata.size(), packed.metadata->size());
+    EXPECT_EQ(
+      std::equal(metadata.data(), metadata.data() + metadata.size(), packed.metadata->data()),
+      true);
+  }
+  void run_test(std::vector<cudf::column_view> const& t) { run_test(cudf::table_view{t}); }
+};
+
+TEST_F(PackUnpackTest, SingleColumnFixedWidth)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> col1({1, 2, 3, 4, 5, 6, 7},
+                                                       {1, 1, 1, 0, 1, 0, 1});
+
+  this->run_test({col1});
+}
+
+TEST_F(PackUnpackTest, SingleColumnFixedWidthNonNullable)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> col1({1, 2, 3, 4, 5, 6, 7});
+
+  this->run_test({col1});
+}
+
+TEST_F(PackUnpackTest, MultiColumnFixedWidth)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1({1, 2, 3, 4, 5, 6, 7},
+                                                       {1, 1, 1, 0, 1, 0, 1});
+  cudf::test::fixed_width_column_wrapper<float> col2({7, 8, 6, 5, 4, 3, 2}, {1, 0, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<double> col3({8, 4, 2, 0, 7, 1, 3}, {0, 1, 1, 1, 1, 1, 1});
+
+  this->run_test({col1, col2, col3});
+}
+
+TEST_F(PackUnpackTest, MultiColumnWithStrings)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1({1, 2, 3, 4, 5, 6, 7},
+                                                       {1, 1, 1, 0, 1, 0, 1});
+  cudf::test::strings_column_wrapper col2({"Lorem", "ipsum", "dolor", "sit", "amet", "ort", "ral"},
+                                          {1, 0, 1, 1, 1, 0, 1});
+  cudf::test::strings_column_wrapper col3({"", "this", "is", "a", "column", "of", "strings"});
+
+  this->run_test({col1, col2, col3});
+}
+// clang-format on
+
+TEST_F(PackUnpackTest, EmptyColumns)
+{
+  {
+    auto empty_string = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+    cudf::table_view src_table({static_cast<cudf::column_view>(*empty_string)});
+    this->run_test(src_table);
+  }
+
+  {
+    cudf::test::strings_column_wrapper str{"abc"};
+    auto empty_string = cudf::empty_like(str);
+    cudf::table_view src_table({static_cast<cudf::column_view>(*empty_string)});
+    this->run_test(src_table);
+  }
+
+  {
+    cudf::test::fixed_width_column_wrapper<int> col0;
+    cudf::test::dictionary_column_wrapper<int> col1;
+    cudf::test::strings_column_wrapper col2;
+    cudf::test::lists_column_wrapper<int> col3;
+    cudf::test::structs_column_wrapper col4({});
+
+    cudf::table_view src_table({col0, col1, col2, col3, col4});
+    this->run_test(src_table);
+  }
+}
+
+std::vector<std::unique_ptr<cudf::column>> generate_lists(bool include_validity)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  if (include_validity) {
+    auto valids =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+    cudf::test::lists_column_wrapper<int> list0{{1, 2, 3},
+                                                {4, 5},
+                                                {6},
+                                                {{7, 8}, valids},
+                                                {9, 10, 11},
+                                                LCW{},
+                                                LCW{},
+                                                {{-1, -2, -3, -4, -5}, valids},
+                                                {{100, -200}, valids}};
+
+    cudf::test::lists_column_wrapper<int> list1{{{{1, 2, 3}, valids}, {4, 5}},
+                                                {{LCW{}, LCW{}, {7, 8}, LCW{}}, valids},
+                                                {LCW{6}},
+                                                {{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids},
+                                                {{LCW{}, {-1, -2, -3, -4, -5}}, valids},
+                                                {LCW{}},
+                                                {LCW{-10}, {-100, -200}},
+                                                {{-10, -200}, LCW{}, {8, 9}},
+                                                {LCW{8}, LCW{}, LCW{9}, {5, 6}}};
+
+    std::vector<std::unique_ptr<cudf::column>> out;
+    out.push_back(list0.release());
+    out.push_back(list1.release());
+    return out;
+  }
+
+  cudf::test::lists_column_wrapper<int> list0{
+    {1, 2, 3}, {4, 5}, {6}, {7, 8}, {9, 10, 11}, LCW{}, LCW{}, {-1, -2, -3, -4, -5}, {-100, -200}};
+
+  cudf::test::lists_column_wrapper<int> list1{{{1, 2, 3}, {4, 5}},
+                                              {LCW{}, LCW{}, {7, 8}, LCW{}},
+                                              {LCW{6}},
+                                              {{7, 8}, {9, 10, 11}, LCW{}},
+                                              {LCW{}, {-1, -2, -3, -4, -5}},
+                                              {LCW{}},
+                                              {{-10}, {-100, -200}},
+                                              {{-10, -200}, LCW{}, {8, 9}},
+                                              {LCW{8}, LCW{}, LCW{9}, {5, 6}}};
+
+  std::vector<std::unique_ptr<cudf::column>> out;
+  out.push_back(list0.release());
+  out.push_back(list1.release());
+  return out;
+}
+
+std::vector<std::unique_ptr<cudf::column>> generate_structs(bool include_validity)
+{
+  // 1. String "names" column.
+  std::vector<std::string> names{
+    "Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant", "Fred", "Todd", "Kevin"};
+  cudf::test::strings_column_wrapper names_column(names.begin(), names.end());
+
+  // 2. Numeric "ages" column.
+  std::vector<int> ages{5, 10, 15, 20, 25, 30, 100, 101, 102};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 0, 1};
+  auto ages_column =
+    include_validity
+      ? cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end(), ages_validity.begin())
+      : cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end());
+
+  // 3. Boolean "is_human" column.
+  std::vector<bool> is_human{true, true, false, false, false, false, true, true, true};
+  std::vector<bool> is_human_validity{1, 1, 1, 0, 1, 1, 1, 1, 0};
+  auto is_human_col =
+    include_validity
+      ? cudf::test::fixed_width_column_wrapper<bool>(
+          is_human.begin(), is_human.end(), is_human_validity.begin())
+      : cudf::test::fixed_width_column_wrapper<bool>(is_human.begin(), is_human.end());
+
+  // Assemble struct column.
+  auto const struct_validity = std::vector<bool>{1, 1, 1, 1, 1, 0, 0, 1, 0};
+  auto struct_column =
+    include_validity
+      ? cudf::test::structs_column_wrapper({names_column, ages_column, is_human_col},
+                                           struct_validity.begin())
+      : cudf::test::structs_column_wrapper({names_column, ages_column, is_human_col});
+
+  std::vector<std::unique_ptr<cudf::column>> out;
+  out.push_back(struct_column.release());
+  return out;
+}
+
+std::vector<std::unique_ptr<cudf::column>> generate_struct_of_list()
+{
+  // 1. String "names" column.
+  std::vector<std::string> names{
+    "Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant", "Fred", "Todd", "Kevin"};
+  cudf::test::strings_column_wrapper names_column(names.begin(), names.end());
+
+  // 2. Numeric "ages" column.
+  std::vector<int> ages{5, 10, 15, 20, 25, 30, 100, 101, 102};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 0, 1};
+  auto ages_column =
+    cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end(), ages_validity.begin());
+
+  // 3. List column
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  std::vector<bool> list_validity{1, 1, 1, 1, 1, 0, 1, 0, 1};
+  cudf::test::lists_column_wrapper<cudf::string_view> list(
+    {{{"abc", "d", "edf"}, {"jjj"}},
+     {{"dgaer", "-7"}, LCW{}},
+     {LCW{}},
+     {{"qwerty"}, {"ral", "ort", "tal"}, {"five", "six"}},
+     {LCW{}, LCW{}, {"eight", "nine"}},
+     {LCW{}},
+     {{"fun"}, {"a", "bc", "def", "ghij", "klmno", "pqrstu"}},
+     {{"seven", "zz"}, LCW{}, {"xyzzy"}},
+     {LCW{"negative 3", "  ", "cleveland"}}},
+    list_validity.begin());
+
+  // Assemble struct column.
+  auto const struct_validity = std::vector<bool>{1, 1, 1, 1, 1, 0, 0, 1, 0};
+  auto struct_column =
+    cudf::test::structs_column_wrapper({names_column, ages_column, list}, struct_validity.begin());
+
+  std::vector<std::unique_ptr<cudf::column>> out;
+  out.push_back(struct_column.release());
+  return out;
+}
+
+std::vector<std::unique_ptr<cudf::column>> generate_list_of_struct()
+{
+  // 1. String "names" column.
+  std::vector<std::string> names{"Vimes",
+                                 "Carrot",
+                                 "Angua",
+                                 "Cheery",
+                                 "Detritus",
+                                 "Slant",
+                                 "Fred",
+                                 "Todd",
+                                 "Kevin",
+                                 "Abc",
+                                 "Def",
+                                 "Xyz",
+                                 "Five",
+                                 "Seventeen",
+                                 "Dol",
+                                 "Est"};
+  cudf::test::strings_column_wrapper names_column(names.begin(), names.end());
+
+  // 2. Numeric "ages" column.
+  std::vector<int> ages{5, 10, 15, 20, 25, 30, 100, 101, 102, -1, -2, -3, -4, -5, -6, -7};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1};
+  auto ages_column =
+    cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end(), ages_validity.begin());
+
+  // Assemble struct column.
+  auto const struct_validity = std::vector<bool>{1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1};
+  auto struct_column =
+    cudf::test::structs_column_wrapper({names_column, ages_column}, struct_validity.begin());
+
+  // 3. List column
+  std::vector<bool> list_validity{1, 1, 1, 1, 1, 0, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<int> offsets{0, 1, 4, 5, 7, 7, 10, 13, 14, 16};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_validity.begin(), list_validity.begin() + 9);
+  auto list = cudf::make_lists_column(
+    9, offsets.release(), struct_column.release(), null_count, std::move(null_mask));
+
+  std::vector<std::unique_ptr<cudf::column>> out;
+  out.push_back(std::move(list));
+  return out;
+}
+
+TEST_F(PackUnpackTest, Lists)
+{
+  // lists
+  {
+    auto cols = generate_lists(false);
+    std::vector<cudf::column_view> col_views;
+    std::transform(cols.begin(),
+                   cols.end(),
+                   std::back_inserter(col_views),
+                   [](std::unique_ptr<cudf::column> const& col) {
+                     return static_cast<cudf::column_view>(*col);
+                   });
+    cudf::table_view src_table(col_views);
+    this->run_test(src_table);
+  }
+
+  // lists with validity
+  {
+    auto cols = generate_lists(true);
+    std::vector<cudf::column_view> col_views;
+    std::transform(cols.begin(),
+                   cols.end(),
+                   std::back_inserter(col_views),
+                   [](std::unique_ptr<cudf::column> const& col) {
+                     return static_cast<cudf::column_view>(*col);
+                   });
+    cudf::table_view src_table(col_views);
+    this->run_test(src_table);
+  }
+}
+
+TEST_F(PackUnpackTest, Structs)
+{
+  // structs
+  {
+    auto cols = generate_structs(false);
+    std::vector<cudf::column_view> col_views;
+    std::transform(cols.begin(),
+                   cols.end(),
+                   std::back_inserter(col_views),
+                   [](std::unique_ptr<cudf::column> const& col) {
+                     return static_cast<cudf::column_view>(*col);
+                   });
+    cudf::table_view src_table(col_views);
+    this->run_test(src_table);
+  }
+
+  // structs with validity
+  {
+    auto cols = generate_structs(true);
+    std::vector<cudf::column_view> col_views;
+    std::transform(cols.begin(),
+                   cols.end(),
+                   std::back_inserter(col_views),
+                   [](std::unique_ptr<cudf::column> const& col) {
+                     return static_cast<cudf::column_view>(*col);
+                   });
+    cudf::table_view src_table(col_views);
+    this->run_test(src_table);
+  }
+}
+
+TEST_F(PackUnpackTest, NestedTypes)
+{
+  // build one big table containing, lists, structs, structs<list>, list<struct>
+  std::vector<cudf::column_view> col_views;
+
+  auto lists = generate_lists(true);
+  std::transform(
+    lists.begin(),
+    lists.end(),
+    std::back_inserter(col_views),
+    [](std::unique_ptr<cudf::column> const& col) { return static_cast<cudf::column_view>(*col); });
+
+  auto structs = generate_structs(true);
+  std::transform(
+    structs.begin(),
+    structs.end(),
+    std::back_inserter(col_views),
+    [](std::unique_ptr<cudf::column> const& col) { return static_cast<cudf::column_view>(*col); });
+
+  auto struct_of_list = generate_struct_of_list();
+  std::transform(
+    struct_of_list.begin(),
+    struct_of_list.end(),
+    std::back_inserter(col_views),
+    [](std::unique_ptr<cudf::column> const& col) { return static_cast<cudf::column_view>(*col); });
+
+  auto list_of_struct = generate_list_of_struct();
+  std::transform(
+    list_of_struct.begin(),
+    list_of_struct.end(),
+    std::back_inserter(col_views),
+    [](std::unique_ptr<cudf::column> const& col) { return static_cast<cudf::column_view>(*col); });
+
+  cudf::table_view src_table(col_views);
+  this->run_test(src_table);
+}
+
+TEST_F(PackUnpackTest, NestedEmpty)
+{
+  // this produces an empty strings column with no children,
+  // nested inside a list
+  {
+    auto empty_string = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+    auto offsets      = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list         = cudf::make_lists_column(
+      1, offsets.release(), std::move(empty_string), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+    this->run_test(src_table);
+  }
+
+  // this produces an empty strings column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::strings_column_wrapper str{"abc"};
+    auto empty_string = cudf::empty_like(str);
+    auto offsets      = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list         = cudf::make_lists_column(
+      1, offsets.release(), std::move(empty_string), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+    this->run_test(src_table);
+  }
+
+  // this produces an empty lists column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::lists_column_wrapper<float> listw{{1.0f, 2.0f}, {3.0f, 4.0f}};
+    auto empty_list = cudf::empty_like(listw);
+    auto offsets    = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list =
+      cudf::make_lists_column(1, offsets.release(), std::move(empty_list), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+    this->run_test(src_table);
+  }
+
+  // this produces an empty lists column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::lists_column_wrapper<float> listw{{1.0f, 2.0f}, {3.0f, 4.0f}};
+    auto empty_list = cudf::empty_like(listw);
+    auto offsets    = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list =
+      cudf::make_lists_column(1, offsets.release(), std::move(empty_list), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+    this->run_test(src_table);
+  }
+
+  // this produces an empty struct column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::fixed_width_column_wrapper<int> ints{0, 1, 2, 3, 4};
+    cudf::test::fixed_width_column_wrapper<float> floats{4, 3, 2, 1, 0};
+    auto struct_column = cudf::test::structs_column_wrapper({ints, floats});
+    auto empty_struct  = cudf::empty_like(struct_column);
+    auto offsets       = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list          = cudf::make_lists_column(
+      1, offsets.release(), std::move(empty_struct), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+    this->run_test(src_table);
+  }
+}
+
+TEST_F(PackUnpackTest, NestedSliced)
+{
+  // list
+  {
+    auto valids =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+    using LCW = cudf::test::lists_column_wrapper<int>;
+
+    cudf::test::lists_column_wrapper<int> col0{{{{1, 2, 3}, valids}, {4, 5}},
+                                               {{LCW{}, LCW{}, {7, 8}, LCW{}}, valids},
+                                               {{6, 12}},
+                                               {{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids},
+                                               {{LCW{}, {-1, -2, -3, -4, -5}}, valids},
+                                               {LCW{}},
+                                               {{-10}, {-100, -200}}};
+
+    cudf::test::strings_column_wrapper col1{
+      "Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant", "Fred"};
+    cudf::test::fixed_width_column_wrapper<float> col2{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};
+
+    std::vector<std::unique_ptr<cudf::column>> children;
+    children.push_back(std::make_unique<cudf::column>(col2));
+    children.push_back(std::make_unique<cudf::column>(col0));
+    children.push_back(std::make_unique<cudf::column>(col1));
+    auto col3 = cudf::make_structs_column(
+      static_cast<cudf::column_view>(col0).size(), std::move(children), 0, rmm::device_buffer{});
+
+    cudf::table_view t({col0, col1, col2, *col3});
+    this->run_test(t);
+  }
+
+  // struct
+  {
+    cudf::test::fixed_width_column_wrapper<int> a{0, 1, 2, 3, 4, 5, 6, 7};
+    cudf::test::fixed_width_column_wrapper<float> b{{0, -1, -2, -3, -4, -5, -6, -7},
+                                                    {1, 1, 1, 0, 0, 0, 0, 1}};
+    cudf::test::strings_column_wrapper c{{"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx"},
+                                         {0, 0, 1, 1, 1, 1, 1, 1}};
+    std::vector<bool> list_validity{1, 0, 1, 0, 1, 0, 1, 1};
+    cudf::test::lists_column_wrapper<int16_t> d{
+      {{0, 1}, {2, 3, 4}, {5, 6}, {7}, {8, 9, 10}, {11, 12}, {}, {15, 16, 17}},
+      list_validity.begin()};
+    cudf::test::fixed_width_column_wrapper<int> _a{10, 20, 30, 40, 50, 60, 70, 80};
+    cudf::test::fixed_width_column_wrapper<float> _b{-10, -20, -30, -40, -50, -60, -70, -80};
+    cudf::test::strings_column_wrapper _c{"aa", "", "ccc", "dddd", "eeeee", "f", "gg", "hhh"};
+    cudf::test::structs_column_wrapper e({_a, _b, _c}, {1, 1, 1, 0, 1, 1, 1, 0});
+    cudf::test::structs_column_wrapper s({a, b, c, d, e}, {1, 1, 0, 1, 1, 1, 1, 1});
+
+    auto split = cudf::split(s, {2, 5});
+
+    this->run_test(cudf::table_view({split[0]}));
+    this->run_test(cudf::table_view({split[1]}));
+    this->run_test(cudf::table_view({split[2]}));
+  }
+}
+
+TEST_F(PackUnpackTest, EmptyTable)
+{
+  // no columns
+  {
+    cudf::table_view t;
+    this->run_test(t);
+  }
+
+  // no rows
+  {
+    cudf::test::fixed_width_column_wrapper<int> a;
+    cudf::test::strings_column_wrapper b;
+    cudf::test::lists_column_wrapper<float> c;
+    cudf::table_view t({a, b, c});
+    this->run_test(t);
+  }
+}
+
+TEST_F(PackUnpackTest, SlicedEmpty)
+{
+  // empty sliced column. this is specifically testing the corner case:
+  // - a sliced column of size 0
+  // - having children that are of size > 0
+  //
+  cudf::test::strings_column_wrapper a{"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx"};
+  cudf::test::lists_column_wrapper<int> b{
+    {0, 1}, {2}, {3, 4, 5}, {6, 7}, {8, 9}, {10}, {11, 12}, {13, 14}};
+  cudf::test::fixed_width_column_wrapper<float> c{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::strings_column_wrapper _a{"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx"};
+  cudf::test::lists_column_wrapper<float> _b{
+    {0, 1}, {2}, {3, 4, 5}, {6, 7}, {8, 9}, {10}, {11, 12}, {13, 14}};
+  cudf::test::fixed_width_column_wrapper<float> _c{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::structs_column_wrapper d({_a, _b, _c});
+
+  cudf::table_view t({a, b, c, d});
+
+  auto sliced = cudf::split(t, {0});
+  this->run_test(sliced[0]);
+}
diff --git a/cpp/tests/copying/purge_nonempty_nulls_tests.cpp b/cpp/tests/copying/purge_nonempty_nulls_tests.cpp
new file mode 100644
index 0000000..b55875f
--- /dev/null
+++ b/cpp/tests/copying/purge_nonempty_nulls_tests.cpp
@@ -0,0 +1,485 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+using cudf::test::iterators::no_nulls;
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+using T             = int32_t;  // The actual type of the leaf node isn't really important.
+using values_col_t  = cudf::test::fixed_width_column_wrapper<T>;
+using offsets_col_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+using gather_map_t  = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+template <typename T>
+using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+struct HasNonEmptyNullsTest : public cudf::test::BaseFixture {};
+
+TEST_F(HasNonEmptyNullsTest, TrivialTest)
+{
+  auto const input = LCW<T>{{{{1, 2, 3, 4}, null_at(2)},
+                             {5},
+                             {6, 7},  // <--- Will be set to NULL. Unsanitized row.
+                             {8, 9, 10}},
+                            no_nulls()}
+                       .release();
+  EXPECT_FALSE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*input));
+
+  // Set nullmask, post construction.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 2, 3, false, cudf::get_default_stream());
+  input->set_null_count(1);
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*input));
+}
+
+TEST_F(HasNonEmptyNullsTest, SlicedInputTest)
+{
+  auto const input = cudf::test::strings_column_wrapper{
+    {"" /*NULL*/, "111", "222", "333", "444", "" /*NULL*/, "", "777", "888", "" /*NULL*/, "101010"},
+    cudf::test::iterators::nulls_at({0, 5, 9})};
+
+  // Split into 2 columns from rows [0, 2) and [2, 10).
+  auto const result = cudf::split(input, {2});
+  for (auto const& col : result) {
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(col));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(col));
+  }
+}
+
+struct PurgeNonEmptyNullsTest : public cudf::test::BaseFixture {
+  /// Helper to run gather() on a single column, and extract the single column from the result.
+  std::unique_ptr<cudf::column> gather(cudf::column_view const& input,
+                                       gather_map_t const& gather_map)
+  {
+    auto gathered =
+      cudf::gather(cudf::table_view{{input}}, gather_map, cudf::out_of_bounds_policy::NULLIFY);
+    return std::move(gathered->release()[0]);
+  }
+
+  /// Verify that the result of `sanitize()` is equivalent to the unsanitized input,
+  /// except that the null rows are also empty.
+  void test_purge(cudf::column_view const& unpurged)
+  {
+    auto const purged = cudf::purge_nonempty_nulls(unpurged);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(unpurged, *purged);
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*purged));
+  }
+};
+
+// List<T>.
+TEST_F(PurgeNonEmptyNullsTest, SingleLevelList)
+{
+  auto const input = LCW<T>{{{{1, 2, 3, 4}, null_at(2)},
+                             {5},
+                             {6, 7},  // <--- Will be set to NULL. Unsanitized row.
+                             {8, 9, 10}},
+                            no_nulls()}
+                       .release();
+
+  // Set nullmask, post construction.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 2, 3, false, cudf::get_default_stream());
+  input->set_null_count(1);
+
+  test_purge(*input);
+
+  {
+    // Selecting all rows from input, in different order.
+    auto const results           = gather(input->view(), {1, 2, 0, 3});
+    auto const results_list_view = cudf::lists_column_view(*results);
+
+    auto const expected = LCW<T>{{{5},
+                                  {},  // NULL.
+                                  {{1, 2, 3, 4}, null_at(2)},
+                                  {8, 9, 10}},
+                                 null_at(1)};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_list_view.offsets(), offsets_col_t{0, 1, 1, 5, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_list_view.child(),
+                                   values_col_t{{5, 1, 2, 3, 4, 8, 9, 10}, null_at(3)});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+  {
+    // Test when gather selects rows preceded by unsanitized rows.
+    auto const results  = gather(input->view(), {3, 100, 0});
+    auto const expected = LCW<T>{{
+                                   {8, 9, 10},
+                                   {},  // NULL.
+                                   {{1, 2, 3, 4}, null_at(2)},
+                                 },
+                                 null_at(1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+  {
+    // Test when gather selects rows followed by unsanitized rows.
+    auto const results  = gather(input->view(), {1, 100, 0});
+    auto const expected = LCW<T>{{
+                                   {5},
+                                   {},  // NULL.
+                                   {{1, 2, 3, 4}, null_at(2)},
+                                 },
+                                 null_at(1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+  {
+    // Test when gather selects unsanitized row specifically.
+    auto const results            = gather(input->view(), {2});
+    auto const results_lists_view = cudf::lists_column_view(*results);
+    auto const expected           = LCW<T>{{
+                                   LCW<T>{}  // NULL.
+                                 },
+                                           null_at(0)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_lists_view.offsets(), offsets_col_t{0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_lists_view.child(), values_col_t{});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+}
+
+// List<List<T>>.
+TEST_F(PurgeNonEmptyNullsTest, TwoLevelList)
+{
+  auto const input =
+    LCW<T>{
+      {{{1, 2, 3}, {4, 5, 6, 7}, {8}, {9, 1}, {2}},
+       {{11, 12}, {13, 14, 15}, {16, 17, 18}, {19}},
+       {{21}, {22, 23}, {24, 25, 26}},
+       {{31, 32}, {33, 34, 35, 36}, {}, {37, 38}},  //<--- Will be set to NULL. Unsanitized row.
+       {{41}, {42, 43}}},
+      no_nulls()}
+      .release();
+  EXPECT_FALSE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*input));
+
+  // Set nullmask, post construction.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 3, 4, false, cudf::get_default_stream());
+  input->set_null_count(1);
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*input));
+
+  test_purge(*input);
+
+  {
+    // Verify that gather() output is sanitized.
+    auto const results            = gather(input->view(), {100, 3, 0, 1});
+    auto const results_lists_view = cudf::lists_column_view(*results);
+
+    auto const expected = LCW<T>{{
+                                   LCW<T>{},  // NULL, because of out of bounds.
+                                   LCW<T>{},  // NULL, because input row was null.
+                                   {{1, 2, 3}, {4, 5, 6, 7}, {8}, {9, 1}, {2}},  // i.e. input[0]
+                                   {{11, 12}, {13, 14, 15}, {16, 17, 18}, {19}}  // i.e. input[1]
+                                 },
+                                 nulls_at({0, 1})};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_lists_view.offsets(), offsets_col_t{0, 0, 0, 5, 9});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+      results_lists_view.child(),
+      LCW<T>{
+        {1, 2, 3}, {4, 5, 6, 7}, {8}, {9, 1}, {2}, {11, 12}, {13, 14, 15}, {16, 17, 18}, {19}});
+
+    auto const child_lists_view = cudf::lists_column_view(results_lists_view.child());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(child_lists_view.offsets(),
+                                   offsets_col_t{0, 3, 7, 8, 10, 11, 13, 16, 19, 20});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+      child_lists_view.child(),
+      values_col_t{1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 11, 12, 13, 14, 15, 16, 17, 18, 19});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+}
+
+// List<List<List<T>>>.
+TEST_F(PurgeNonEmptyNullsTest, ThreeLevelList)
+{
+  auto const input = LCW<T>{{{{{1, 2}, {3}}, {{4, 5}, {6, 7}}, {{8, 8}, {}}, {{9, 1}}, {{2, 3}}},
+                             {{{11, 12}}, {{13}, {14, 15}}, {{16, 17, 18}}, {{19, 19}, {}}},
+                             {{{21, 21}}, {{22, 23}, {}}, {{24, 25}, {26}}},
+                             {{{31, 32}, {}},
+                              {{33, 34, 35}, {36}},
+                              {},
+                              {{37, 38}}},  //<--- Will be set to NULL. Unsanitized row.
+                             {{{41, 41, 41}}, {{42, 43}}}},
+                            no_nulls()}
+                       .release();
+  EXPECT_FALSE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*input));
+
+  // Set nullmask, post construction.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 3, 4, false, cudf::get_default_stream());
+  input->set_null_count(1);
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*input));
+
+  test_purge(*input);
+
+  {
+    auto const results            = gather(input->view(), {100, 3, 0, 1});
+    auto const results_lists_view = cudf::lists_column_view(*results);
+
+    auto const expected = LCW<T>{
+      {
+        LCW<T>{},  // NULL, because of out of bounds.
+        LCW<T>{},  // NULL, because input row was null.
+        {{{1, 2}, {3}}, {{4, 5}, {6, 7}}, {{8, 8}, {}}, {{9, 1}}, {{2, 3}}},  // i.e. input[0]
+        {{{11, 12}}, {{13}, {14, 15}}, {{16, 17, 18}}, {{19, 19}, {}}}        // i.e. input[1]
+      },
+      nulls_at({0, 1})};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_lists_view.offsets(), offsets_col_t{0, 0, 0, 5, 9});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_lists_view.child(),
+                                   LCW<T>{{{1, 2}, {3}},
+                                          {{4, 5}, {6, 7}},
+                                          {{8, 8}, {}},
+                                          {{9, 1}},
+                                          {{2, 3}},
+                                          {{11, 12}},
+                                          {{13}, {14, 15}},
+                                          {{16, 17, 18}},
+                                          {{19, 19}, {}}});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+}
+
+// List<string>.
+TEST_F(PurgeNonEmptyNullsTest, ListOfStrings)
+{
+  using T = cudf::string_view;
+
+  auto const input = LCW<T>{{{{"1", "22", "", "4444"}, null_at(2)},
+                             {"55555"},
+                             {"666666", "7777777"},  // <--- Will be set to NULL. Unsanitized row.
+                             {"88888888", "999999999", "1010101010"},
+                             {"11", "22", "33", "44"},
+                             {"55", "66", "77", "88"}},
+                            no_nulls()}
+                       .release();
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*input));
+
+  // Set nullmask, post construction.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 2, 3, false, cudf::get_default_stream());
+  input->set_null_count(1);
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*input));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*input));
+
+  test_purge(*input);
+
+  {
+    // Selecting all rows from input, in different order.
+    auto const results           = gather(input->view(), {1, 2, 0, 3});
+    auto const results_list_view = cudf::lists_column_view(*results);
+
+    auto const expected = LCW<T>{{{"55555"},
+                                  {},  // NULL.
+                                  {{"1", "22", "", "4444"}, null_at(2)},
+                                  {"88888888", "999999999", "1010101010"}},
+                                 null_at(1)};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_list_view.offsets(), offsets_col_t{0, 1, 1, 5, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+      results_list_view.child(),
+      cudf::test::strings_column_wrapper{
+        {"55555", "1", "22", "", "4444", "88888888", "999999999", "1010101010"}, null_at(3)});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+  {
+    // Gathering from a sliced column.
+    auto const sliced = cudf::slice({input->view()}, {1, 5})[0];  // Lop off 1 row at each end.
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(sliced));
+    EXPECT_TRUE(cudf::has_nonempty_nulls(sliced));
+
+    auto const results           = gather(sliced, {1, 2, 0, 3});
+    auto const results_list_view = cudf::lists_column_view(*results);
+    auto const expected          = LCW<T>{{
+                                   {},
+                                   {"88888888", "999999999", "1010101010"},
+                                   {"55555"},
+                                   {"11", "22", "33", "44"},
+                                 },
+                                          null_at(0)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_list_view.offsets(), offsets_col_t{0, 0, 3, 4, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+      results_list_view.child(),
+      cudf::test::strings_column_wrapper{
+        "88888888", "999999999", "1010101010", "55555", "11", "22", "33", "44"});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*results));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*results));
+  }
+}
+
+// List<string>.
+TEST_F(PurgeNonEmptyNullsTest, UnsanitizedListOfUnsanitizedStrings)
+{
+  auto strings =
+    cudf::test::strings_column_wrapper{
+      {"1", "22", "3", "44", "5", "66", "7", "8888", "9", "1010"},  //<--- "8888" will be
+                                                                    // unsanitized.
+      no_nulls()}
+      .release();
+  EXPECT_FALSE(cudf::may_have_nonempty_nulls(*strings));
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*strings));
+
+  // Set strings nullmask, post construction.
+  cudf::set_null_mask(strings->mutable_view().null_mask(), 7, 8, false);
+  strings->set_null_count(1);
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*strings));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*strings));
+
+  test_purge(*strings);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(cudf::strings_column_view(*strings).offsets(),
+                                 offsets_col_t{0, 1, 3, 4, 6, 7, 9, 10, 14, 15, 19}
+                                 // 10-14 indicates that "8888" is unsanitized.
+  );
+
+  // Construct a list column from the strings column.
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(no_nulls(), no_nulls() + 4);
+  auto const lists             = cudf::make_lists_column(4,
+                                             offsets_col_t{0, 4, 5, 7, 10}.release(),
+                                             std::move(strings),
+                                             null_count,
+                                             std::move(null_mask));
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*lists));
+
+  // The child column has non-empty nulls but it has already been sanitized during lists column
+  // construction.
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*lists));
+
+  // Set lists nullmask, post construction.
+  cudf::detail::set_null_mask(
+    lists->mutable_view().null_mask(), 2, 3, false, cudf::get_default_stream());
+  lists->set_null_count(1);
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*lists));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*lists));
+
+  test_purge(*lists);
+
+  // At this point,
+  // 1. {"66", "7"} will be unsanitized.
+  // 2. {"8888", "9", "1010"} will be actually be {NULL, "9", "1010"}.
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    cudf::lists_column_view(*lists).offsets(),
+    offsets_col_t{0, 4, 5, 7, 10});  // 5-7 indicates that list row#2 is unsanitized.
+
+  auto const result   = gather(lists->view(), {1, 2, 0, 3});
+  auto const expected = LCW<cudf::string_view>{{{"5"},
+                                                {},  // NULL.
+                                                {"1", "22", "3", "44"},
+                                                {{"", "9", "1010"}, null_at(0)}},
+                                               null_at(1)};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+
+  // Ensure row#2 has been sanitized.
+  auto const results_lists_view = cudf::lists_column_view(*result);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_lists_view.offsets(), offsets_col_t{0, 1, 1, 5, 8}
+                                 // 1-1 indicates that row#2 is sanitized.
+  );
+
+  // Ensure that "8888" has been sanitized, and stored as "".
+  auto const child_strings_view = cudf::strings_column_view(results_lists_view.child());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(child_strings_view.offsets(),
+                                 offsets_col_t{0, 1, 2, 4, 5, 7, 7, 8, 12});
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*result));
+  EXPECT_FALSE(cudf::has_nonempty_nulls(*result));
+}
+
+// Struct<List<T>>.
+TEST_F(PurgeNonEmptyNullsTest, StructOfList)
+{
+  auto const structs_input = [] {
+    auto child = LCW<T>{{{{1, 2, 3, 4}, null_at(2)},
+                         {5},
+                         {6, 7},  //<--- Unsanitized row.
+                         {8, 9, 10}},
+                        no_nulls()};
+    EXPECT_FALSE(cudf::has_nonempty_nulls(child));
+    return cudf::test::structs_column_wrapper{{child}}.release();
+  }();
+  auto [null_mask, null_count] = [&] {
+    auto const valid_iter = null_at(2);
+    return cudf::test::detail::make_null_mask(valid_iter, valid_iter + structs_input->size());
+  }();
+
+  // Manually set the null mask for the columns, leaving the null at list index 2 unsanitized.
+  structs_input->child(0).set_null_mask(null_mask, null_count, cudf::get_default_stream());
+  structs_input->set_null_mask(std::move(null_mask), null_count);
+
+  EXPECT_TRUE(cudf::may_have_nonempty_nulls(*structs_input));
+  EXPECT_TRUE(cudf::has_nonempty_nulls(*structs_input));
+
+  test_purge(*structs_input);
+
+  // At this point, even though the structs column has a null at index 2,
+  // the child column has a non-empty list row at index 2: {6, 7}.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(cudf::lists_column_view(structs_input->child(0)).child(),
+                                 values_col_t{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, null_at(2)});
+
+  {
+    // Test rearrange.
+    auto const gather_map      = gather_map_t{1, 2, 0, 3};
+    auto const result          = gather(structs_input->view(), gather_map);
+    auto const expected_result = [] {
+      auto child = LCW<T>{{{5},
+                           LCW<T>{},  //<--- Now, sanitized.
+                           {{1, 2, 3, 4}, null_at(2)},
+                           {8, 9, 10}},
+                          null_at(1)};
+      return cudf::test::structs_column_wrapper{{child}, null_at(1)};
+    }();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected_result);
+    auto const results_child = cudf::lists_column_view(result->child(0));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_child.offsets(), offsets_col_t{0, 1, 1, 5, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results_child.child(),
+                                   values_col_t{{5, 1, 2, 3, 4, 8, 9, 10}, null_at(3)});
+    EXPECT_TRUE(cudf::may_have_nonempty_nulls(*result));
+    EXPECT_FALSE(cudf::has_nonempty_nulls(*result));
+  }
+}
diff --git a/cpp/tests/copying/reverse_tests.cpp b/cpp/tests/copying/reverse_tests.cpp
new file mode 100644
index 0000000..e4b2d31
--- /dev/null
+++ b/cpp/tests/copying/reverse_tests.cpp
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/tabulate.h>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+template <typename T>
+class ReverseTypedTestFixture : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ReverseTypedTestFixture, cudf::test::AllTypes);
+TYPED_TEST(ReverseTypedTestFixture, ReverseTable)
+{
+  using T = TypeParam;
+  constexpr cudf::size_type num_values{10};
+
+  auto input = cudf::test::fixed_width_column_wrapper<T, int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + num_values);
+
+  auto expected_elements = cudf::detail::make_counting_transform_iterator(
+    0, [num_values] __device__(auto i) { return num_values - i - 1; });
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T, typename decltype(expected_elements)::value_type>(
+      expected_elements, expected_elements + num_values);
+
+  auto input_table = cudf::table_view{{input}};
+  auto const p_ret = cudf::reverse(input_table);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected, verbosity);
+}
+
+TYPED_TEST(ReverseTypedTestFixture, ReverseColumn)
+{
+  using T = TypeParam;
+  constexpr cudf::size_type num_values{10};
+
+  auto input = cudf::test::fixed_width_column_wrapper<T, int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + num_values);
+
+  auto expected_elements = cudf::detail::make_counting_transform_iterator(
+    0, [num_values] __device__(auto i) { return num_values - i - 1; });
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T, typename decltype(expected_elements)::value_type>(
+      expected_elements, expected_elements + num_values);
+
+  auto const column_ret = cudf::reverse(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(column_ret->view(), expected, verbosity);
+}
+
+TYPED_TEST(ReverseTypedTestFixture, ReverseNullable)
+{
+  using T = TypeParam;
+  constexpr cudf::size_type num_values{20};
+
+  std::vector<int64_t> input_values(num_values);
+  std::iota(input_values.begin(), input_values.end(), 1);
+
+  thrust::host_vector<bool> input_valids(num_values);
+  thrust::tabulate(
+    thrust::seq, input_valids.begin(), input_valids.end(), [](auto i) { return not(i % 2); });
+
+  std::vector<T> expected_values(input_values.size());
+  thrust::host_vector<bool> expected_valids(input_valids.size());
+
+  std::transform(std::make_reverse_iterator(input_values.end()),
+                 std::make_reverse_iterator(input_values.begin()),
+                 expected_values.begin(),
+                 [](auto i) { return cudf::test::make_type_param_scalar<T>(i); });
+  std::reverse_copy(input_valids.begin(), input_valids.end(), expected_valids.begin());
+
+  cudf::test::fixed_width_column_wrapper<T, int64_t> input(
+    input_values.begin(), input_values.end(), input_valids.begin());
+
+  cudf::test::fixed_width_column_wrapper<T> expected(
+    expected_values.begin(), expected_values.end(), expected_valids.begin());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::reverse(input_table);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+TYPED_TEST(ReverseTypedTestFixture, ZeroSizeInput)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T, int32_t> input(thrust::make_counting_iterator(0),
+                                                           thrust::make_counting_iterator(0));
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected(thrust::make_counting_iterator(0),
+                                                              thrust::make_counting_iterator(0));
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::reverse(input_table);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+class ReverseStringTestFixture : public cudf::test::BaseFixture {};
+
+TEST_F(ReverseStringTestFixture, ReverseNullable)
+{
+  constexpr cudf::size_type num_values{20};
+
+  std::vector<std::string> input_values(num_values);
+  thrust::host_vector<bool> input_valids(num_values);
+
+  thrust::tabulate(thrust::seq, input_values.begin(), input_values.end(), [](auto i) {
+    return "#" + std::to_string(i);
+  });
+  thrust::tabulate(
+    thrust::seq, input_valids.begin(), input_valids.end(), [](auto i) { return not(i % 2); });
+
+  std::vector<std::string> expected_values(input_values.size());
+  thrust::host_vector<bool> expected_valids(input_valids.size());
+
+  std::reverse_copy(input_values.begin(), input_values.end(), expected_values.begin());
+  std::reverse_copy(input_valids.begin(), input_valids.end(), expected_valids.begin());
+
+  auto input = cudf::test::strings_column_wrapper(
+    input_values.begin(), input_values.end(), input_valids.begin());
+
+  auto expected = cudf::test::strings_column_wrapper(
+    expected_values.begin(), expected_values.end(), expected_valids.begin());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::reverse(input_table);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+TEST_F(ReverseStringTestFixture, ZeroSizeInput)
+{
+  std::vector<std::string> input_values{};
+  auto input = cudf::test::strings_column_wrapper(input_values.begin(), input_values.end());
+
+  auto count = cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0));
+
+  auto expected = cudf::test::strings_column_wrapper(input_values.begin(), input_values.end());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::reverse(input_table);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
diff --git a/cpp/tests/copying/sample_tests.cpp b/cpp/tests/copying/sample_tests.cpp
new file mode 100644
index 0000000..2f76e3f
--- /dev/null
+++ b/cpp/tests/copying/sample_tests.cpp
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+struct SampleTest : public cudf::test::BaseFixture {};
+
+TEST_F(SampleTest, FailCaseRowMultipleSampling)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{1, 2, 3, 4, 5};
+  // size of input table is smaller than number of samples
+  // and sampling same row multiple times is disallowed,
+  // this combination can't work.
+  cudf::size_type const n_samples = 10;
+  cudf::table_view input({col1});
+
+  EXPECT_THROW(cudf::sample(input, n_samples, cudf::sample_with_replacement::FALSE, 0),
+               cudf::logic_error);
+}
+
+TEST_F(SampleTest, RowMultipleSamplingDisallowed)
+{
+  cudf::size_type const n_samples = 1024;
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<int16_t> col1(data, data + n_samples);
+
+  cudf::table_view input({col1});
+
+  for (int i = 0; i < 10; i++) {
+    auto out_table  = cudf::sample(input, n_samples, cudf::sample_with_replacement::FALSE, i);
+    auto sorted_out = cudf::sort(out_table->view());
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(input, sorted_out->view());
+  }
+}
+
+TEST_F(SampleTest, TestReproducibilityWithSeed)
+{
+  cudf::size_type const n_samples = 1024;
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<int16_t> col1(data, data + n_samples);
+
+  cudf::table_view input({col1});
+
+  auto expected_1 = cudf::sample(input, n_samples, cudf::sample_with_replacement::FALSE, 1);
+  for (int i = 0; i < 2; i++) {
+    auto out = cudf::sample(input, n_samples, cudf::sample_with_replacement::FALSE, 1);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_1->view(), out->view());
+  }
+
+  auto expected_2 = cudf::sample(input, n_samples, cudf::sample_with_replacement::TRUE, 1);
+  for (int i = 0; i < 2; i++) {
+    auto out = cudf::sample(input, n_samples, cudf::sample_with_replacement::TRUE, 1);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_2->view(), out->view());
+  }
+}
+
+struct SampleBasicTest : public SampleTest,
+                         public ::testing::WithParamInterface<
+                           std::tuple<cudf::size_type, cudf::sample_with_replacement>> {};
+
+TEST_P(SampleBasicTest, CombinationOfParameters)
+{
+  cudf::size_type const table_size   = 1024;
+  auto const [n_samples, multi_smpl] = GetParam();
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<int16_t> col1(data, data + table_size);
+  cudf::test::fixed_width_column_wrapper<float> col2(data, data + table_size);
+
+  cudf::table_view input({col1, col2});
+  auto out_table = cudf::sample(input, n_samples, multi_smpl, 0);
+
+  EXPECT_EQ(out_table->num_rows(), n_samples);
+}
+
+INSTANTIATE_TEST_CASE_P(
+  SampleTest,
+  SampleBasicTest,
+  ::testing::Values(std::make_tuple(0, cudf::sample_with_replacement::TRUE),
+                    std::make_tuple(0, cudf::sample_with_replacement::FALSE),
+                    std::make_tuple(512, cudf::sample_with_replacement::TRUE),
+                    std::make_tuple(512, cudf::sample_with_replacement::FALSE),
+                    std::make_tuple(1024, cudf::sample_with_replacement::TRUE),
+                    std::make_tuple(1024, cudf::sample_with_replacement::FALSE),
+                    std::make_tuple(2048, cudf::sample_with_replacement::TRUE)));
diff --git a/cpp/tests/copying/scatter_list_scalar_tests.cpp b/cpp/tests/copying/scatter_list_scalar_tests.cpp
new file mode 100644
index 0000000..42d2e00
--- /dev/null
+++ b/cpp/tests/copying/scatter_list_scalar_tests.cpp
@@ -0,0 +1,453 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+using mask_vector = std::vector<cudf::valid_type>;
+using size_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+class ScatterListScalarTests : public cudf::test::BaseFixture {};
+
+std::unique_ptr<cudf::column> single_scalar_scatter(cudf::column_view const& target,
+                                                    cudf::scalar const& slr,
+                                                    cudf::column_view const& scatter_map)
+{
+  std::vector<std::reference_wrapper<const cudf::scalar>> slrs{slr};
+  cudf::table_view targets{{target}};
+  auto result = cudf::scatter(slrs, scatter_map, targets);
+  return std::move(result->release()[0]);
+}
+
+template <typename T>
+class ScatterListOfFixedWidthScalarTest : public ScatterListScalarTests {};
+
+TYPED_TEST_SUITE(ScatterListOfFixedWidthScalarTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+// Test grid
+// Dim1 : {Fixed width, strings, lists, structs}
+// Dim2 : {Null scalar, Non-null empty scalar, Non-null non-empty scalar}
+// Dim3 : {Nullable target, non-nullable target row}
+
+TYPED_TEST(ScatterListOfFixedWidthScalarTest, Basic)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FCW = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(FCW({2, 2, 2}, {1, 0, 1}), true);
+  LCW col{LCW{1, 1, 1}, LCW{8, 8}, LCW{10, 10, 10, 10}, LCW{5}};
+  size_column scatter_map{3, 1, 0};
+
+  LCW expected{LCW({2, 2, 2}, mask_vector{1, 0, 1}.begin()),
+               LCW({2, 2, 2}, mask_vector{1, 0, 1}.begin()),
+               LCW{10, 10, 10, 10},
+               LCW({2, 2, 2}, mask_vector{1, 0, 1}.begin())};
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TYPED_TEST(ScatterListOfFixedWidthScalarTest, EmptyValidScalar)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FCW = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(FCW{}, true);
+  LCW col{LCW{1, 1, 1},
+          LCW{8, 8},
+          LCW({10, 10, 10, 10}, mask_vector{1, 0, 1, 0}.begin()),
+          LCW{5},
+          LCW{42, 42}};
+  size_column scatter_map{1, 0};
+
+  LCW expected{
+    LCW{}, LCW{}, LCW({10, 10, 10, 10}, mask_vector{1, 0, 1, 0}.begin()), LCW{5}, LCW{42, 42}};
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TYPED_TEST(ScatterListOfFixedWidthScalarTest, NullScalar)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FCW = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(FCW{}, false);
+  LCW col{LCW({1, 1, 1}, mask_vector{0, 0, 1}.begin()), LCW{8, 8}, LCW{10, 10, 10, 10}, LCW{5}};
+  size_column scatter_map{3, 1};
+
+  LCW expected({LCW({1, 1, 1}, mask_vector{0, 0, 1}.begin()), LCW{}, LCW{10, 10, 10, 10}, LCW{}},
+               mask_vector{1, 0, 1, 0}.begin());
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TYPED_TEST(ScatterListOfFixedWidthScalarTest, NullableTargetRow)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FCW = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(FCW{9, 9}, true);
+  LCW col({LCW{4, 4}, LCW{}, LCW{8, 8, 8}, LCW{}, LCW{9, 9, 9}},
+          mask_vector{1, 0, 1, 0, 1}.begin());
+  size_column scatter_map{0, 1};
+
+  LCW expected({LCW{9, 9}, LCW{9, 9}, LCW{8, 8, 8}, LCW{}, LCW{9, 9, 9}},
+               mask_vector{1, 1, 1, 0, 1}.begin());
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+class ScatterListOfStringScalarTest : public ScatterListScalarTests {};
+
+TEST_F(ScatterListOfStringScalarTest, Basic)
+{
+  using LCW      = cudf::test::lists_column_wrapper<cudf::string_view, int32_t>;
+  using StringCW = cudf::test::strings_column_wrapper;
+
+  auto slr = std::make_unique<cudf::list_scalar>(
+    StringCW({"Hello!", "", "你好！", "صباح الخير!", "", "こんにちは！"},
+             {true, false, true, true, false, true}),
+    true);
+  LCW col{LCW({"xx", "yy"}, mask_vector{0, 1}.begin()), LCW{""}, LCW{"a", "bab", "bacab"}};
+
+  size_column scatter_map{2, 1};
+
+  LCW expected{LCW({"xx", "yy"}, mask_vector{0, 1}.begin()),
+               LCW({"Hello!", "", "你好！", "صباح الخير!", "", "こんにちは！"},
+                   mask_vector{1, 0, 1, 1, 0, 1}.begin()),
+               LCW({"Hello!", "", "你好！", "صباح الخير!", "", "こんにちは！"},
+                   mask_vector{1, 0, 1, 1, 0, 1}.begin())};
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(ScatterListOfStringScalarTest, EmptyValidScalar)
+{
+  using LCW      = cudf::test::lists_column_wrapper<cudf::string_view, int32_t>;
+  using StringCW = cudf::test::strings_column_wrapper;
+
+  auto slr = std::make_unique<cudf::list_scalar>(StringCW{}, true);
+
+  LCW col{LCW({"xx", "yy"}, mask_vector{0, 1}.begin()),
+          LCW{""},
+          LCW{"a", "bab", "bacab"},
+          LCW{"888", "777"}};
+
+  size_column scatter_map{0, 3};
+
+  LCW expected{LCW{}, LCW{""}, LCW{"a", "bab", "bacab"}, LCW{}};
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(ScatterListOfStringScalarTest, NullScalar)
+{
+  using LCW      = cudf::test::lists_column_wrapper<cudf::string_view, int32_t>;
+  using StringCW = cudf::test::strings_column_wrapper;
+
+  auto slr = std::make_unique<cudf::list_scalar>(StringCW{}, false);
+  LCW col{LCW{"xx", "yy"},
+          LCW({""}, mask_vector{0}.begin()),
+          LCW{"a", "bab", "bacab"},
+          LCW{"888", "777"}};
+
+  size_column scatter_map{1, 2};
+
+  LCW expected({LCW{"xx", "yy"}, LCW{}, LCW{}, LCW{"888", "777"}}, mask_vector{1, 0, 0, 1}.begin());
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(ScatterListOfStringScalarTest, NullableTargetRow)
+{
+  using LCW      = cudf::test::lists_column_wrapper<cudf::string_view, int32_t>;
+  using StringCW = cudf::test::strings_column_wrapper;
+
+  auto slr = std::make_unique<cudf::list_scalar>(
+    StringCW({"Hello!", "", "こんにちは！"}, {true, false, true}), true);
+  LCW col({LCW{"xx", "yy"}, LCW({""}, mask_vector{0}.begin()), LCW{}, LCW{"888", "777"}},
+          mask_vector{1, 1, 0, 1}.begin());
+
+  size_column scatter_map{3, 2};
+
+  LCW expected({LCW{"xx", "yy"},
+                LCW({""}, mask_vector{0}.begin()),
+                LCW({"Hello!", "", "こんにちは！"}, mask_vector{1, 0, 1}.begin()),
+                LCW({"Hello!", "", "こんにちは！"}, mask_vector{1, 0, 1}.begin())},
+               mask_vector{1, 1, 1, 1}.begin());
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+template <typename T>
+class ScatterListOfListScalarTest : public ScatterListScalarTests {};
+
+TYPED_TEST_SUITE(ScatterListOfListScalarTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(ScatterListOfListScalarTest, Basic)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(
+    LCW({LCW{1, 2, 3}, LCW{4}, LCW{}, LCW{5, 6}}, mask_vector{1, 1, 0, 1}.begin()), true);
+  LCW col({LCW({LCW{88, 88}, LCW{}, LCW{9, 9, 9}}, mask_vector{1, 0, 1}.begin()),
+           LCW{LCW{66}, LCW{}, LCW({77, 77, 77, 77}, mask_vector{1, 0, 0, 1}.begin())},
+           LCW{LCW{55, 55}, LCW{}, LCW{10, 10, 10}},
+           LCW{LCW{44, 44}}});
+
+  size_column scatter_map{1, 2, 3};
+
+  LCW expected({LCW({LCW{88, 88}, LCW{}, LCW{9, 9, 9}}, mask_vector{1, 0, 1}.begin()),
+                LCW({LCW{1, 2, 3}, LCW{4}, LCW{}, LCW{5, 6}}, mask_vector{1, 1, 0, 1}.begin()),
+                LCW({LCW{1, 2, 3}, LCW{4}, LCW{}, LCW{5, 6}}, mask_vector{1, 1, 0, 1}.begin()),
+                LCW({LCW{1, 2, 3}, LCW{4}, LCW{}, LCW{5, 6}}, mask_vector{1, 1, 0, 1}.begin())});
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TYPED_TEST(ScatterListOfListScalarTest, EmptyValidScalar)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(LCW{}, true);
+  LCW col({LCW({LCW{88, 88}, LCW{}, LCW{9, 9, 9}}, mask_vector{1, 0, 1}.begin()),
+           LCW{LCW{66}, LCW{}, LCW({77, 77, 77, 77}, mask_vector{1, 0, 0, 1}.begin())},
+           LCW{LCW{55, 55}, LCW{}, LCW{10, 10, 10}},
+           LCW{LCW{44, 44}}});
+
+  size_column scatter_map{3, 0};
+
+  LCW expected({LCW{},
+                LCW{LCW{66}, LCW{}, LCW({77, 77, 77, 77}, mask_vector{1, 0, 0, 1}.begin())},
+                LCW{LCW{55, 55}, LCW{}, LCW{10, 10, 10}},
+                LCW{}});
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TYPED_TEST(ScatterListOfListScalarTest, NullScalar)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(LCW{}, false);
+  LCW col({LCW({LCW{88, 88}, LCW{}, LCW{9, 9, 9}}, mask_vector{1, 0, 1}.begin()),
+           LCW{LCW{66}, LCW{}, LCW({77, 77, 77, 77}, mask_vector{1, 0, 0, 1}.begin())},
+           LCW{LCW{44, 44}}});
+
+  size_column scatter_map{1, 0};
+
+  LCW expected({LCW{}, LCW{}, LCW{LCW{44, 44}}}, mask_vector{0, 0, 1}.begin());
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TYPED_TEST(ScatterListOfListScalarTest, NullableTargetRows)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto slr = std::make_unique<cudf::list_scalar>(
+    LCW({LCW{1, 1, 1}, LCW{3, 3}, LCW{}, LCW{4}}, mask_vector{1, 1, 0, 1}.begin()), true);
+
+  LCW col({LCW({LCW{88, 88}, LCW{}, LCW{9, 9, 9}}, mask_vector{1, 0, 1}.begin()),
+           LCW{LCW{66}, LCW{}, LCW({77, 77, 77, 77}, mask_vector{1, 0, 0, 1}.begin())},
+           LCW{LCW{44, 44}}},
+          mask_vector{1, 0, 1}.begin());
+
+  size_column scatter_map{1};
+
+  LCW expected({LCW({LCW{88, 88}, LCW{}, LCW{9, 9, 9}}, mask_vector{1, 0, 1}.begin()),
+                LCW({LCW{1, 1, 1}, LCW{3, 3}, LCW{}, LCW{4}}, mask_vector{1, 1, 0, 1}.begin()),
+                LCW{LCW{44, 44}}},
+               mask_vector{1, 1, 1}.begin());
+
+  auto result = single_scalar_scatter(col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+template <typename T>
+class ScatterListOfStructScalarTest : public ScatterListScalarTests {
+ protected:
+  cudf::test::structs_column_wrapper make_test_structs(
+    cudf::test::fixed_width_column_wrapper<T> field0,
+    cudf::test::strings_column_wrapper field1,
+    cudf::test::lists_column_wrapper<T, int32_t> field2,
+    std::vector<cudf::valid_type> mask)
+  {
+    return cudf::test::structs_column_wrapper({field0, field1, field2}, mask.begin());
+  }
+};
+
+TYPED_TEST_SUITE(ScatterListOfStructScalarTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(ScatterListOfStructScalarTest, Basic)
+{
+  using LCW      = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto data =
+    this->make_test_structs({{42, 42, 42}, {1, 0, 1}},
+                            {{"hello", "你好！", "bonjour!"}, {false, true, true}},
+                            LCW({LCW{88}, LCW{}, LCW{99, 99}}, mask_vector{1, 0, 1}.begin()),
+                            {1, 1, 0});
+  auto slr = std::make_unique<cudf::list_scalar>(data, true);
+
+  auto child = this->make_test_structs(
+    {{1, 1, 2, 3, 3, 3}, {0, 1, 1, 1, 0, 0}},
+    {{"x", "x", "yy", "", "zzz", "zzz"}, {true, true, true, false, true, true}},
+    LCW({LCW{10, 10}, LCW{}, LCW{10}, LCW{20, 20}, LCW{}, LCW{30, 30}},
+        mask_vector{1, 0, 1, 1, 0, 1}.begin()),
+    {1, 1, 0, 0, 1, 1});
+  offset_t offsets{0, 2, 2, 3, 6};
+  auto col =
+    cudf::make_lists_column(4, offsets.release(), child.release(), 0, rmm::device_buffer{});
+
+  size_column scatter_map{1, 3};
+
+  auto ex_child = this->make_test_structs(
+    {{1, 1, 42, 42, 42, 2, 42, 42, 42}, {0, 1, 1, 0, 1, 1, 1, 0, 1}},
+    {{"x", "x", "hello", "你好！", "bonjour!", "yy", "hello", "你好！", "bonjour!"},
+     {true, true, false, true, true, true, false, true, true}},
+    LCW({LCW{10, 10}, LCW{}, LCW{88}, LCW{}, LCW{99, 99}, LCW{10}, LCW{88}, LCW{}, LCW{99, 99}},
+        mask_vector{1, 0, 1, 0, 1, 1, 1, 0, 1}.begin()),
+    {1, 1, 1, 1, 0, 0, 1, 1, 0});
+  offset_t ex_offsets{0, 2, 5, 6, 9};
+  auto expected =
+    cudf::make_lists_column(4, ex_offsets.release(), ex_child.release(), 0, rmm::device_buffer{});
+
+  auto result = single_scalar_scatter(*col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TYPED_TEST(ScatterListOfStructScalarTest, EmptyValidScalar)
+{
+  using LCW      = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto data = this->make_test_structs({}, {}, LCW{}, {});
+  auto slr  = std::make_unique<cudf::list_scalar>(data, true);
+
+  auto child = this->make_test_structs(
+    {{1, 1, 2, 3, 3, 3}, {0, 1, 1, 1, 0, 0}},
+    {{"x", "x", "yy", "", "zzz", "zzz"}, {true, true, true, false, true, true}},
+    LCW({LCW{10, 10}, LCW{}, LCW{10}, LCW{20, 20}, LCW{}, LCW{30, 30}},
+        mask_vector{1, 0, 1, 1, 0, 1}.begin()),
+    {1, 1, 0, 0, 1, 1});
+  offset_t offsets{0, 2, 2, 3, 6};
+  auto col =
+    cudf::make_lists_column(4, offsets.release(), child.release(), 0, rmm::device_buffer{});
+
+  size_column scatter_map{0, 2};
+
+  auto ex_child =
+    this->make_test_structs({{3, 3, 3}, {1, 0, 0}},
+                            {{"", "zzz", "zzz"}, {false, true, true}},
+                            LCW({LCW{20, 20}, LCW{}, LCW{30, 30}}, mask_vector{1, 0, 1}.begin()),
+                            {0, 1, 1});
+  offset_t ex_offsets{0, 0, 0, 0, 3};
+  auto expected =
+    cudf::make_lists_column(4, ex_offsets.release(), ex_child.release(), 0, rmm::device_buffer{});
+
+  auto result = single_scalar_scatter(*col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TYPED_TEST(ScatterListOfStructScalarTest, NullScalar)
+{
+  using LCW      = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto data = this->make_test_structs({}, {}, {}, {});
+  auto slr  = std::make_unique<cudf::list_scalar>(data, false);
+
+  auto child = this->make_test_structs(
+    {{1, 1, 2, 3, 3, 3}, {0, 1, 1, 1, 0, 0}},
+    {{"x", "x", "yy", "", "zzz", "zzz"}, {true, true, true, false, true, true}},
+    LCW({LCW{10, 10}, LCW{}, LCW{10}, LCW{20, 20}, LCW{}, LCW{30, 30}},
+        mask_vector{1, 0, 1, 1, 0, 1}.begin()),
+    {1, 1, 1, 0, 1, 1});
+  offset_t offsets{0, 2, 2, 3, 6};
+  auto col =
+    cudf::make_lists_column(4, offsets.release(), child.release(), 0, rmm::device_buffer{});
+
+  size_column scatter_map{3, 1, 0};
+
+  auto ex_child = this->make_test_structs({2}, {"yy"}, LCW({10}, mask_vector{1}.begin()), {1});
+  offset_t ex_offsets{0, 0, 0, 1, 1};
+
+  auto null_mask = cudf::create_null_mask(4, cudf::mask_state::ALL_NULL);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 2, 3, true);
+  auto expected =
+    cudf::make_lists_column(4, ex_offsets.release(), ex_child.release(), 3, std::move(null_mask));
+
+  auto result = single_scalar_scatter(*col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TYPED_TEST(ScatterListOfStructScalarTest, NullableTargetRow)
+{
+  using LCW      = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using offset_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto data =
+    this->make_test_structs({{42, 42, 42}, {1, 0, 1}},
+                            {{"hello", "你好！", "bonjour!"}, {false, true, true}},
+                            LCW({LCW{88}, LCW{}, LCW{99, 99}}, mask_vector{1, 0, 1}.begin()),
+                            {1, 1, 0});
+  auto slr = std::make_unique<cudf::list_scalar>(data, true);
+
+  auto child = this->make_test_structs(
+    {{1, 1, 2, 3, 3, 3}, {0, 1, 1, 1, 0, 0}},
+    {{"x", "x", "yy", "", "zzz", "zzz"}, {true, true, true, false, true, true}},
+    LCW({LCW{10, 10}, LCW{}, LCW{10}, LCW{20, 20}, LCW{}, LCW{30, 30}},
+        mask_vector{1, 0, 1, 1, 0, 1}.begin()),
+    {1, 1, 1, 0, 1, 1});
+  offset_t offsets{0, 2, 2, 3, 6};
+  auto null_mask = cudf::create_null_mask(4, cudf::mask_state::ALL_VALID);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 1, 3, false);
+  auto col =
+    cudf::make_lists_column(4, offsets.release(), child.release(), 2, std::move(null_mask));
+
+  size_column scatter_map{3, 2};
+
+  auto ex_child = this->make_test_structs(
+    {{1, 1, 42, 42, 42, 42, 42, 42}, {0, 1, 1, 0, 1, 1, 0, 1}},
+    {{"x", "x", "hello", "你好！", "bonjour!", "hello", "你好！", "bonjour!"},
+     {true, true, false, true, true, false, true, true}},
+    LCW({LCW{10, 10}, LCW{}, LCW{88}, LCW{}, LCW{99, 99}, LCW{88}, LCW{}, LCW{99, 99}},
+        mask_vector{1, 0, 1, 0, 1, 1, 0, 1}.begin()),
+    {1, 1, 1, 1, 0, 1, 1, 0});
+  offset_t ex_offsets{0, 2, 2, 5, 8};
+
+  auto ex_null_mask = cudf::create_null_mask(4, cudf::mask_state::ALL_VALID);
+  cudf::set_null_mask(static_cast<cudf::bitmask_type*>(ex_null_mask.data()), 1, 2, false);
+  auto expected = cudf::make_lists_column(
+    4, ex_offsets.release(), ex_child.release(), 1, std::move(ex_null_mask));
+
+  auto result = single_scalar_scatter(*col, *slr, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
diff --git a/cpp/tests/copying/scatter_list_tests.cpp b/cpp/tests/copying/scatter_list_tests.cpp
new file mode 100644
index 0000000..9501bb2
--- /dev/null
+++ b/cpp/tests/copying/scatter_list_tests.cpp
@@ -0,0 +1,968 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+template <typename T>
+class TypedScatterListsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedScatterListsTest, cudf::test::FixedWidthTypes);
+
+class ScatterListsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST(TypedScatterListsTest, ListsOfFixedWidth)
+{
+  using T = TypeParam;
+
+  auto src_list_column = cudf::test::lists_column_wrapper<T, int32_t>{{9, 9, 9, 9}, {8, 8, 8}};
+
+  auto target_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}, {5, 5}, {6, 6}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0};
+
+  auto ret = cudf::scatter(
+    cudf::table_view({src_list_column}), scatter_map, cudf::table_view({target_list_column}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    ret->get_column(0),
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {8, 8, 8}, {1, 1}, {9, 9, 9, 9}, {3, 3}, {4, 4}, {5, 5}, {6, 6}});
+}
+
+TYPED_TEST(TypedScatterListsTest, SlicedInputLists)
+{
+  using T = TypeParam;
+
+  auto src_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {0, 0, 0, 0},
+    {9, 9, 9, 9},
+    {8, 8, 8},
+    {7, 7, 7}}.release();
+  auto src_sliced = cudf::slice(src_list_column->view(), {1, 3}).front();
+
+  auto target_list_column =
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}, {5, 5}, {6, 6}}
+      .release();
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0};
+
+  auto ret_1 = cudf::scatter(
+    cudf::table_view({src_sliced}), scatter_map, cudf::table_view({target_list_column->view()}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    ret_1->get_column(0),
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {8, 8, 8}, {1, 1}, {9, 9, 9, 9}, {3, 3}, {4, 4}, {5, 5}, {6, 6}});
+
+  auto target_sliced = cudf::slice(target_list_column->view(), {1, 6});
+
+  auto ret_2 =
+    cudf::scatter(cudf::table_view({src_sliced}), scatter_map, cudf::table_view({target_sliced}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    ret_2->get_column(0),
+    cudf::test::lists_column_wrapper<T, int32_t>{{8, 8, 8}, {2, 2}, {9, 9, 9, 9}, {4, 4}, {5, 5}});
+}
+
+TYPED_TEST(TypedScatterListsTest, EmptyListsOfFixedWidth)
+{
+  using T = TypeParam;
+
+  auto src_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {9, 9, 9, 9, 8, 8, 8},
+  };
+
+  // One null list row, and one row with nulls.
+  auto src_list_column = cudf::make_lists_column(
+    3,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 7, 7}.release(),
+    src_child.release(),
+    0,
+    {});
+
+  auto target_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}, {5, 5}, {6, 6}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0, 5};
+
+  auto ret = cudf::scatter(cudf::table_view({src_list_column->view()}),
+                           scatter_map,
+                           cudf::table_view({target_list_column}));
+
+  auto expected_child_ints = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {8, 8, 8, 1, 1, 9, 9, 9, 9, 3, 3, 4, 4, 6, 6}};
+  auto expected_lists_column = cudf::make_lists_column(
+    7,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 3, 5, 9, 11, 13, 13, 15}.release(),
+    expected_child_ints.release(),
+    0,
+    {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists_column->view(), ret->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, EmptyListsOfNullableFixedWidth)
+{
+  using T = TypeParam;
+
+  auto src_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{{9, 9, 9, 9, 8, 8, 8},
+                                                                      {1, 1, 1, 0, 1, 1, 1}};
+
+  // One null list row, and one row with nulls.
+  auto src_list_column = cudf::make_lists_column(
+    3,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 7, 7}.release(),
+    src_child.release(),
+    0,
+    {});
+
+  auto target_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}, {5, 5}, {6, 6}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0, 5};
+
+  auto ret = cudf::scatter(cudf::table_view({src_list_column->view()}),
+                           scatter_map,
+                           cudf::table_view({target_list_column}));
+
+  auto expected_child_ints = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {8, 8, 8, 1, 1, 9, 9, 9, 9, 3, 3, 4, 4, 6, 6}, {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1}};
+  auto expected_lists_column = cudf::make_lists_column(
+    7,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 3, 5, 9, 11, 13, 13, 15}.release(),
+    expected_child_ints.release(),
+    0,
+    {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists_column->view(), ret->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, NullableListsOfNullableFixedWidth)
+{
+  using T = TypeParam;
+
+  auto src_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{{9, 9, 9, 9, 8, 8, 8},
+                                                                      {1, 1, 1, 0, 1, 1, 1}};
+
+  auto src_list_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2; });
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(src_list_validity, src_list_validity + 3);
+  // One null list row, and one row with nulls.
+  auto src_list_column = cudf::make_lists_column(
+    3,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 7, 7}.release(),
+    src_child.release(),
+    null_count,
+    std::move(null_mask));
+
+  auto target_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}, {5, 5}, {6, 6}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0, 5};
+
+  auto ret = cudf::scatter(cudf::table_view({src_list_column->view()}),
+                           scatter_map,
+                           cudf::table_view({target_list_column}));
+
+  auto expected_child_ints = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {8, 8, 8, 1, 1, 9, 9, 9, 9, 3, 3, 4, 4, 6, 6}, {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto expected_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 5; });
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(expected_validity, expected_validity + 7);
+  auto expected_lists_column = cudf::make_lists_column(
+    7,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 3, 5, 9, 11, 13, 13, 15}.release(),
+    expected_child_ints.release(),
+    null_count,
+    std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists_column->view(), ret->get_column(0));
+}
+
+TEST_F(ScatterListsTest, ListsOfStrings)
+{
+  auto src_list_column = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {"all", "the", "leaves", "are", "brown"}, {"california", "dreaming"}};
+
+  auto target_list_column =
+    cudf::test::lists_column_wrapper<cudf::string_view>{{"zero"},
+                                                        {"one", "one"},
+                                                        {"two", "two"},
+                                                        {"three", "three", "three"},
+                                                        {"four", "four", "four", "four"}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<int32_t>{2, 0};
+
+  auto ret = cudf::scatter(
+    cudf::table_view({src_list_column}), scatter_map, cudf::table_view({target_list_column}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    cudf::test::lists_column_wrapper<cudf::string_view>{{"california", "dreaming"},
+                                                        {"one", "one"},
+                                                        {"all", "the", "leaves", "are", "brown"},
+                                                        {"three", "three", "three"},
+                                                        {"four", "four", "four", "four"}},
+    ret->get_column(0));
+}
+
+TEST_F(ScatterListsTest, ListsOfNullableStrings)
+{
+  auto src_strings_column = cudf::test::strings_column_wrapper{
+    {"all", "the", "leaves", "are", "brown", "california", "dreaming"}, {1, 1, 1, 0, 1, 0, 1}};
+
+  auto src_list_column = cudf::make_lists_column(
+    2,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 5, 7}.release(),
+    src_strings_column.release(),
+    0,
+    {});
+
+  auto target_list_column = cudf::test::lists_column_wrapper<cudf::string_view>{{"zero"},
+                                                                                {"one", "one"},
+                                                                                {"two", "two"},
+                                                                                {"three", "three"},
+                                                                                {"four", "four"},
+                                                                                {"five", "five"}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<int32_t>{2, 0};
+
+  auto ret = cudf::scatter(cudf::table_view({src_list_column->view()}),
+                           scatter_map,
+                           cudf::table_view({target_list_column}));
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {"california",
+     "dreaming",
+     "one",
+     "one",
+     "all",
+     "the",
+     "leaves",
+     "are",
+     "brown",
+     "three",
+     "three",
+     "four",
+     "four",
+     "five",
+     "five"},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0 && i != 7; })};
+
+  auto expected_lists = cudf::make_lists_column(
+    6,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 9, 11, 13, 15}.release(),
+    expected_strings.release(),
+    0,
+    {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), ret->get_column(0));
+}
+
+TEST_F(ScatterListsTest, EmptyListsOfNullableStrings)
+{
+  auto src_strings_column = cudf::test::strings_column_wrapper{
+    {"all", "the", "leaves", "are", "brown", "california", "dreaming"}, {1, 1, 1, 0, 1, 0, 1}};
+
+  auto src_list_column = cudf::make_lists_column(
+    3,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 5, 5, 7}.release(),
+    src_strings_column.release(),
+    0,
+    {});
+
+  auto target_list_column = cudf::test::lists_column_wrapper<cudf::string_view>{{"zero"},
+                                                                                {"one", "one"},
+                                                                                {"two", "two"},
+                                                                                {"three", "three"},
+                                                                                {"four", "four"},
+                                                                                {"five", "five"}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<int32_t>{2, 4, 0};
+
+  auto ret = cudf::scatter(cudf::table_view({src_list_column->view()}),
+                           scatter_map,
+                           cudf::table_view({target_list_column}));
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {"california",
+     "dreaming",
+     "one",
+     "one",
+     "all",
+     "the",
+     "leaves",
+     "are",
+     "brown",
+     "three",
+     "three",
+     "five",
+     "five"},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0 && i != 7; })};
+
+  auto expected_lists = cudf::make_lists_column(
+    6,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 9, 11, 11, 13}.release(),
+    expected_strings.release(),
+    0,
+    {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), ret->get_column(0));
+}
+
+TEST_F(ScatterListsTest, NullableListsOfNullableStrings)
+{
+  auto src_strings_column = cudf::test::strings_column_wrapper{
+    {"all", "the", "leaves", "are", "brown", "california", "dreaming"}, {1, 1, 1, 0, 1, 0, 1}};
+
+  auto src_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; });
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(src_validity, src_validity + 3);
+  auto src_list_column         = cudf::make_lists_column(
+    3,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 5, 5, 7}.release(),
+    src_strings_column.release(),
+    null_count,
+    std::move(null_mask));
+
+  auto target_list_column = cudf::test::lists_column_wrapper<cudf::string_view>{{"zero"},
+                                                                                {"one", "one"},
+                                                                                {"two", "two"},
+                                                                                {"three", "three"},
+                                                                                {"four", "four"},
+                                                                                {"five", "five"}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<int32_t>{2, 4, 0};
+
+  auto ret = cudf::scatter(cudf::table_view({src_list_column->view()}),
+                           scatter_map,
+                           cudf::table_view({target_list_column}));
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {"california",
+     "dreaming",
+     "one",
+     "one",
+     "all",
+     "the",
+     "leaves",
+     "are",
+     "brown",
+     "three",
+     "three",
+     "five",
+     "five"},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0 && i != 7; })};
+
+  auto expected_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 4; });
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(expected_validity, expected_validity + 6);
+  auto expected_lists = cudf::make_lists_column(
+    6,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 9, 11, 11, 13}.release(),
+    expected_strings.release(),
+    null_count,
+    std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), ret->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, ListsOfLists)
+{
+  using T = TypeParam;
+
+  auto src_list_column = cudf::test::lists_column_wrapper<T, int32_t>{{{1, 1, 1, 1}, {2, 2, 2, 2}},
+                                                                      {{3, 3, 3, 3}, {4, 4, 4, 4}}};
+
+  auto target_list_column =
+    cudf::test::lists_column_wrapper<T, int32_t>{{{9, 9, 9}, {8, 8, 8}, {7, 7, 7}},
+                                                 {{6, 6, 6}, {5, 5, 5}, {4, 4, 4}},
+                                                 {{3, 3, 3}, {2, 2, 2}, {1, 1, 1}},
+                                                 {{9, 9}, {8, 8}, {7, 7}},
+                                                 {{6, 6}, {5, 5}, {4, 4}},
+                                                 {{3, 3}, {2, 2}, {1, 1}}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0};
+
+  auto ret = cudf::scatter(
+    cudf::table_view({src_list_column}), scatter_map, cudf::table_view({target_list_column}));
+
+  auto expected = cudf::test::lists_column_wrapper<T, int32_t>{{{3, 3, 3, 3}, {4, 4, 4, 4}},
+                                                               {{6, 6, 6}, {5, 5, 5}, {4, 4, 4}},
+                                                               {{1, 1, 1, 1}, {2, 2, 2, 2}},
+                                                               {{9, 9}, {8, 8}, {7, 7}},
+                                                               {{6, 6}, {5, 5}, {4, 4}},
+                                                               {{3, 3}, {2, 2}, {1, 1}}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, ret->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, EmptyListsOfLists)
+{
+  using T = TypeParam;
+
+  auto src_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{1, 1, 1, 1}, {2, 2, 2, 2}}, {{3, 3, 3, 3}, {}}, {}};
+
+  auto target_list_column =
+    cudf::test::lists_column_wrapper<T, int32_t>{{{9, 9, 9}, {8, 8, 8}, {7, 7, 7}},
+                                                 {{6, 6, 6}, {5, 5, 5}, {4, 4, 4}},
+                                                 {{3, 3, 3}, {2, 2, 2}, {1, 1, 1}},
+                                                 {{9, 9}, {8, 8}, {7, 7}},
+                                                 {{6, 6}, {5, 5}, {4, 4}},
+                                                 {{3, 3}, {2, 2}, {1, 1}}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0, 4};
+
+  auto ret = cudf::scatter(
+    cudf::table_view({src_list_column}), scatter_map, cudf::table_view({target_list_column}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    cudf::test::lists_column_wrapper<T, int32_t>{{{3, 3, 3, 3}, {}},
+                                                 {{6, 6, 6}, {5, 5, 5}, {4, 4, 4}},
+                                                 {{1, 1, 1, 1}, {2, 2, 2, 2}},
+                                                 {{9, 9}, {8, 8}, {7, 7}},
+                                                 {},
+                                                 {{3, 3}, {2, 2}, {1, 1}}},
+    ret->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, NullListsOfLists)
+{
+  using T = TypeParam;
+
+  auto src_list_column = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{{1, 1, 1, 1}, {2, 2, 2, 2}}, {{3, 3, 3, 3}, {}}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2; })};
+
+  auto target_list_column =
+    cudf::test::lists_column_wrapper<T, int32_t>{{{9, 9, 9}, {8, 8, 8}, {7, 7, 7}},
+                                                 {{6, 6, 6}, {5, 5, 5}, {4, 4, 4}},
+                                                 {{3, 3, 3}, {2, 2, 2}, {1, 1, 1}},
+                                                 {{9, 9}, {8, 8}, {7, 7}},
+                                                 {{6, 6}, {5, 5}, {4, 4}},
+                                                 {{3, 3}, {2, 2}, {1, 1}}};
+
+  auto scatter_map = cudf::test::fixed_width_column_wrapper<cudf::size_type>{2, 0, 4};
+
+  auto ret = cudf::scatter(
+    cudf::table_view({src_list_column}), scatter_map, cudf::table_view({target_list_column}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {{{3, 3, 3, 3}, {}},
+       {{6, 6, 6}, {5, 5, 5}, {4, 4, 4}},
+       {{1, 1, 1, 1}, {2, 2, 2, 2}},
+       {{9, 9}, {8, 8}, {7, 7}},
+       {},
+       {{3, 3}, {2, 2}, {1, 1}}},
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 4; })},
+    ret->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, ListsOfStructs)
+{
+  using T               = TypeParam;
+  using offsets_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using numerics_column = cudf::test::fixed_width_column_wrapper<T>;
+
+  // clang-format off
+  auto source_numerics = numerics_column{
+    9, 9, 9, 9,
+    8, 8, 8
+  };
+
+  auto source_strings = cudf::test::strings_column_wrapper{
+    "nine", "nine", "nine", "nine",
+    "eight", "eight", "eight"
+  };
+  // clang-format on
+
+  auto source_structs = cudf::test::structs_column_wrapper{{source_numerics, source_strings}};
+
+  auto source_lists =
+    cudf::make_lists_column(2, offsets_column{0, 4, 7}.release(), source_structs.release(), 0, {});
+
+  // clang-format off
+  auto target_ints    = numerics_column{
+    0, 0,
+    1, 1,
+    2, 2,
+    3, 3,
+    4, 4,
+    5, 5
+  };
+
+  auto target_strings = cudf::test::strings_column_wrapper{
+    "zero",  "zero",
+    "one",   "one",
+    "two",   "two",
+    "three", "three",
+    "four",  "four",
+    "five",  "five"
+  };
+  // clang-format on
+
+  auto target_structs = cudf::test::structs_column_wrapper{{target_ints, target_strings}};
+
+  auto target_lists = cudf::make_lists_column(
+    6, offsets_column{0, 2, 4, 6, 8, 10, 12}.release(), target_structs.release(), 0, {});
+
+  auto scatter_map = offsets_column{2, 0};
+
+  auto scatter_result = cudf::scatter(cudf::table_view({source_lists->view()}),
+                                      scatter_map,
+                                      cudf::table_view({target_lists->view()}));
+
+  // clang-format off
+  auto expected_numerics = numerics_column{
+    8, 8, 8,
+    1, 1,
+    9, 9, 9, 9,
+    3, 3, 4, 4, 5, 5
+  };
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    "eight", "eight", "eight",
+    "one", "one",
+    "nine", "nine", "nine", "nine",
+    "three", "three",
+    "four", "four",
+    "five", "five"
+  };
+  // clang-format on
+
+  auto expected_structs = cudf::test::structs_column_wrapper{{expected_numerics, expected_strings}};
+
+  auto expected_lists = cudf::make_lists_column(
+    6, offsets_column{0, 3, 5, 9, 11, 13, 15}.release(), expected_structs.release(), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), scatter_result->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, ListsOfStructsWithNullMembers)
+{
+  using T               = TypeParam;
+  using offsets_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using numerics_column = cudf::test::fixed_width_column_wrapper<T>;
+
+  // clang-format off
+  auto source_numerics = numerics_column{
+    {
+      9, 9, 9, 9,
+      8, 8, 8
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; })
+  };
+
+  auto source_strings = cudf::test::strings_column_wrapper{
+    {
+      "nine",  "nine",  "nine", "nine",
+      "eight", "eight", "eight"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 5; })
+  };
+  // clang-format on
+
+  auto source_structs = cudf::test::structs_column_wrapper{{source_numerics, source_strings}};
+
+  auto source_lists =
+    cudf::make_lists_column(2, offsets_column{0, 4, 7}.release(), source_structs.release(), 0, {});
+
+  // clang-format off
+  auto target_ints    = numerics_column{
+    0, 0,
+    1, 1,
+    2, 2,
+    3, 3,
+    4, 4,
+    5, 5
+  };
+
+  auto target_strings = cudf::test::strings_column_wrapper{
+    "zero", "zero",
+    "one",  "one",
+    "two",  "two",
+    "three","three",
+    "four", "four",
+    "five", "five"
+  };
+  // clang-format on
+
+  auto target_structs = cudf::test::structs_column_wrapper{{target_ints, target_strings}};
+
+  auto target_lists = cudf::make_lists_column(
+    6, offsets_column{0, 2, 4, 6, 8, 10, 12}.release(), target_structs.release(), 0, {});
+  // clang-format on
+
+  auto scatter_map = offsets_column{2, 0};
+
+  auto scatter_result = cudf::scatter(cudf::table_view({source_lists->view()}),
+                                      scatter_map,
+                                      cudf::table_view({target_lists->view()}));
+
+  // clang-format off
+  auto expected_numerics = numerics_column{
+    {
+      8, 8, 8,
+      1, 1,
+      9, 9, 9, 9,
+      3, 3,
+      4, 4,
+      5, 5
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 8; })
+  };
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {
+      "eight", "eight", "eight",
+      "one",   "one",
+      "nine",  "nine",  "nine", "nine",
+      "three", "three",
+      "four",  "four",
+      "five",  "five"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; })
+  };
+  // clang-format on
+
+  auto expected_structs = cudf::test::structs_column_wrapper{{expected_numerics, expected_strings}};
+
+  auto expected_lists = cudf::make_lists_column(
+    6, offsets_column{0, 3, 5, 9, 11, 13, 15}.release(), expected_structs.release(), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), scatter_result->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, ListsOfNullStructs)
+{
+  using T               = TypeParam;
+  using offsets_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using numerics_column = cudf::test::fixed_width_column_wrapper<T>;
+
+  // clang-format off
+  auto source_numerics = numerics_column{
+    {
+      9, 9, 9, 9,
+      8, 8, 8
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; })
+  };
+
+  auto source_strings = cudf::test::strings_column_wrapper{
+    {
+      "nine",  "nine",  "nine", "nine",
+      "eight", "eight", "eight"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 5; })
+  };
+  // clang-format on
+
+  auto source_structs = cudf::test::structs_column_wrapper{
+    {source_numerics, source_strings},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; })};
+
+  auto source_lists =
+    cudf::make_lists_column(2, offsets_column{0, 4, 7}.release(), source_structs.release(), 0, {});
+
+  // clang-format off
+  auto target_ints    = numerics_column{
+    0, 0,
+    1, 1,
+    2, 2,
+    3, 3,
+    4, 4,
+    5, 5
+  };
+
+  auto target_strings = cudf::test::strings_column_wrapper{
+    "zero",  "zero",
+    "one",   "one",
+    "two",   "two",
+    "three", "three",
+    "four",  "four",
+    "five",  "five"
+  };
+  // clang-format on
+
+  auto target_structs = cudf::test::structs_column_wrapper{{target_ints, target_strings}};
+
+  auto target_lists = cudf::make_lists_column(
+    6, offsets_column{0, 2, 4, 6, 8, 10, 12}.release(), target_structs.release(), 0, {});
+
+  auto scatter_map = offsets_column{2, 0};
+
+  auto scatter_result = cudf::scatter(cudf::table_view({source_lists->view()}),
+                                      scatter_map,
+                                      cudf::table_view({target_lists->view()}));
+
+  // clang-format off
+  auto expected_numerics = numerics_column{
+    {
+      8, 8, 8,
+      1, 1,
+      9, 9, 9, 9,
+      3, 3,
+      4, 4,
+      5, 5
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i != 6) && (i != 8); })
+  };
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {
+      "eight", "eight", "eight",
+      "one",   "one",
+      "nine",  "nine",  "nine", "nine",
+      "three", "three",
+      "four",  "four",
+      "five",  "five"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i != 1) && (i != 6); })
+  };
+  // clang-format on
+
+  auto expected_structs = cudf::test::structs_column_wrapper{
+    {expected_numerics, expected_strings},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 6; })};
+
+  auto expected_lists = cudf::make_lists_column(
+    6, offsets_column{0, 3, 5, 9, 11, 13, 15}.release(), expected_structs.release(), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), scatter_result->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, EmptyListsOfStructs)
+{
+  using T               = TypeParam;
+  using offsets_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using numerics_column = cudf::test::fixed_width_column_wrapper<T>;
+
+  // clang-format off
+  auto source_numerics = numerics_column{
+    {
+      9, 9, 9, 9,
+      8, 8, 8
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; })
+  };
+
+  auto source_strings = cudf::test::strings_column_wrapper{
+    {
+      "nine",  "nine",  "nine", "nine",
+      "eight", "eight", "eight"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 5; })
+  };
+  // clang-format on
+
+  auto source_structs = cudf::test::structs_column_wrapper{
+    {source_numerics, source_strings},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; })};
+
+  auto source_lists = cudf::make_lists_column(
+    3, offsets_column{0, 4, 7, 7}.release(), source_structs.release(), 0, {});
+
+  // clang-format off
+  auto target_ints    = numerics_column{
+    0, 0,
+    1, 1,
+    2, 2,
+    3, 3,
+    4, 4,
+    5, 5
+  };
+
+  auto target_strings = cudf::test::strings_column_wrapper{
+    "zero",  "zero",
+    "one",   "one",
+    "two",   "two",
+    "three", "three",
+    "four",  "four",
+    "five",  "five"
+  };
+  // clang-format on
+
+  auto target_structs = cudf::test::structs_column_wrapper{{target_ints, target_strings}};
+
+  auto target_lists = cudf::make_lists_column(
+    6, offsets_column{0, 2, 4, 6, 8, 10, 12}.release(), target_structs.release(), 0, {});
+
+  auto scatter_map = offsets_column{2, 0, 4};
+
+  auto scatter_result = cudf::scatter(cudf::table_view({source_lists->view()}),
+                                      scatter_map,
+                                      cudf::table_view({target_lists->view()}));
+
+  // clang-format off
+  auto expected_numerics = numerics_column{
+    {
+      8, 8, 8,
+      1, 1,
+      9, 9, 9, 9,
+      3, 3,
+      5, 5
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i != 6) && (i != 8); })
+  };
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {
+      "eight", "eight", "eight",
+      "one",   "one",
+      "nine",  "nine",  "nine", "nine",
+      "three", "three",
+      "five",  "five"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i != 1) && (i != 6); })
+  };
+  // clang-format on
+
+  auto expected_structs = cudf::test::structs_column_wrapper{
+    {expected_numerics, expected_strings},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 6; })};
+
+  auto expected_lists = cudf::make_lists_column(
+    6, offsets_column{0, 3, 5, 9, 11, 11, 13}.release(), expected_structs.release(), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), scatter_result->get_column(0));
+}
+
+TYPED_TEST(TypedScatterListsTest, NullListsOfStructs)
+{
+  using T               = TypeParam;
+  using offsets_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+  using numerics_column = cudf::test::fixed_width_column_wrapper<T>;
+
+  // clang-format off
+  auto source_numerics = numerics_column{
+    {
+      9, 9, 9, 9,
+      8, 8, 8
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; })
+  };
+
+  auto source_strings = cudf::test::strings_column_wrapper{
+    {
+      "nine",  "nine",  "nine", "nine",
+      "eight", "eight", "eight"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 5; })
+  };
+  // clang-format on
+
+  auto source_structs = cudf::test::structs_column_wrapper{
+    {source_numerics, source_strings},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; })};
+
+  auto source_list_null_mask_begin =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2; });
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(
+    source_list_null_mask_begin, source_list_null_mask_begin + 3);
+  auto source_lists = cudf::make_lists_column(3,
+                                              offsets_column{0, 4, 7, 7}.release(),
+                                              source_structs.release(),
+                                              null_count,
+                                              std::move(null_mask));
+
+  // clang-format off
+  auto target_ints    = numerics_column{
+    0, 0,
+    1, 1,
+    2, 2,
+    3, 3,
+    4, 4,
+    5, 5
+  };
+  auto target_strings = cudf::test::strings_column_wrapper{
+    "zero",  "zero",
+    "one",   "one",
+    "two",   "two",
+    "three", "three",
+    "four",  "four",
+    "five",  "five"
+  };
+  // clang-format on
+
+  auto target_structs = cudf::test::structs_column_wrapper{{target_ints, target_strings}};
+
+  auto target_lists = cudf::make_lists_column(
+    6, offsets_column{0, 2, 4, 6, 8, 10, 12}.release(), target_structs.release(), 0, {});
+
+  auto scatter_map = offsets_column{2, 0, 4};
+
+  auto scatter_result = cudf::scatter(cudf::table_view({source_lists->view()}),
+                                      scatter_map,
+                                      cudf::table_view({target_lists->view()}));
+
+  // clang-format off
+  auto expected_numerics = numerics_column{
+    {
+      8, 8, 8,
+      1, 1,
+      9, 9, 9, 9,
+      3, 3,
+      5, 5
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i != 6) && (i != 8); })
+  };
+
+  auto expected_strings = cudf::test::strings_column_wrapper{
+    {
+      "eight", "eight", "eight",
+      "one",   "one",
+      "nine",  "nine",  "nine", "nine",
+      "three", "three",
+      "five",  "five"
+    },
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1 && i != 6; })
+  };
+  // clang-format on
+
+  auto expected_structs = cudf::test::structs_column_wrapper{
+    {expected_numerics, expected_strings},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 6; })};
+
+  auto expected_lists_null_mask_begin =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 4; });
+
+  std::tie(null_mask, null_count) = cudf::test::detail::make_null_mask(
+    expected_lists_null_mask_begin, expected_lists_null_mask_begin + 6);
+  auto expected_lists = cudf::make_lists_column(6,
+                                                offsets_column{0, 3, 5, 9, 11, 11, 13}.release(),
+                                                expected_structs.release(),
+                                                null_count,
+                                                std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_lists->view(), scatter_result->get_column(0));
+}
diff --git a/cpp/tests/copying/scatter_struct_scalar_tests.cpp b/cpp/tests/copying/scatter_struct_scalar_tests.cpp
new file mode 100644
index 0000000..78572b0
--- /dev/null
+++ b/cpp/tests/copying/scatter_struct_scalar_tests.cpp
@@ -0,0 +1,267 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+constexpr int32_t null{0};  // Mark for null child elements
+constexpr int32_t XXX{0};   // Mark for null struct elements
+
+using structs_col = cudf::test::structs_column_wrapper;
+
+template <typename T>
+struct TypedStructScalarScatterTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedStructScalarScatterTest, cudf::test::FixedWidthTypes);
+
+cudf::column scatter_single_scalar(cudf::scalar const& slr,
+                                   cudf::column_view scatter_map,
+                                   cudf::column_view target)
+{
+  auto result = cudf::scatter({slr}, scatter_map, cudf::table_view{{target}});
+  return result->get_column(0);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, Basic)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  cudf::test::strings_column_wrapper slr_f1{"hello"};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0, slr_f1}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{2};
+
+  // Target column
+  fixed_width_wrapper field0{11, 11, 22, 22, 33};
+  cudf::test::strings_column_wrapper field1{"aa", "aa", "bb", "bb", "cc"};
+  structs_col target{field0, field1};
+
+  // Expect column
+  fixed_width_wrapper ef0{11, 11, 777, 22, 33};
+  cudf::test::strings_column_wrapper ef1{"aa", "aa", "hello", "bb", "cc"};
+  structs_col expected{ef0, ef1};
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, FillNulls)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{3, 4};
+
+  // Target column
+  fixed_width_wrapper field0({11, 11, 22, null, XXX}, cudf::test::iterators::null_at(3));
+  structs_col target({field0}, cudf::test::iterators::null_at(4));
+
+  // Expect column
+  fixed_width_wrapper ef0{11, 11, 22, 777, 777};
+  structs_col expected{ef0};
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, ScatterNullElements)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  std::vector<cudf::column_view> source_fields{slr_f0};
+  auto slr = std::make_unique<cudf::struct_scalar>(source_fields, false);
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{0, 3, 4};
+
+  // Target column
+  fixed_width_wrapper field0({11, 11, 22, null, XXX}, cudf::test::iterators::null_at(3));
+  structs_col target({field0}, cudf::test::iterators::null_at(4));
+
+  // Expect column
+  fixed_width_wrapper ef0{XXX, 11, 22, XXX, XXX};
+  structs_col expected({ef0}, {false, true, true, false, false});
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, ScatterNullFields)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0({null}, {false});
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{0, 2};
+
+  // Target column
+  fixed_width_wrapper field0({11, 11, 22, null, XXX}, cudf::test::iterators::null_at(3));
+  structs_col target({field0}, cudf::test::iterators::null_at(4));
+
+  // Expect column
+  fixed_width_wrapper ef0({null, 11, null, null, XXX}, {false, true, false, false, true});
+  structs_col expected({ef0}, cudf::test::iterators::null_at(4));
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, NegativeIndices)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{-1, -5};
+
+  // Target column
+  fixed_width_wrapper field0({11, 11, 22, null, XXX}, cudf::test::iterators::null_at(3));
+  structs_col target({field0}, cudf::test::iterators::null_at(4));
+
+  // Expect column
+  fixed_width_wrapper ef0({777, 11, 22, null, 777}, cudf::test::iterators::null_at(3));
+  structs_col expected{ef0};
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, EmptyInputTest)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{};
+
+  // Target column
+  fixed_width_wrapper field0{};
+  structs_col target{field0};
+
+  // Expect column
+  fixed_width_wrapper ef0{};
+  structs_col expected{ef0};
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, EmptyScatterMapTest)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{};
+
+  // Target column
+  fixed_width_wrapper field0({11, 11, 22, null, XXX}, cudf::test::iterators::null_at(3));
+  structs_col target({field0}, cudf::test::iterators::null_at(4));
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(target, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, FixedWidthStringTypes)
+{
+  using fixed_width_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  // Source scalar
+  fixed_width_wrapper slr_f0{777};
+  cudf::test::strings_column_wrapper slr_f1{"hello"};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0, slr_f1}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{0, 2, 4};
+
+  // Target column
+  fixed_width_wrapper field0({11, 11, 22, null, XXX}, cudf::test::iterators::null_at(3));
+  cudf::test::strings_column_wrapper field1({"aa", "null", "ccc", "null", "XXX"},
+                                            {true, false, true, false, true});
+  structs_col target({field0, field1}, cudf::test::iterators::null_at(4));
+
+  // Expect column
+  fixed_width_wrapper ef0({777, 11, 777, null, 777}, cudf::test::iterators::null_at(3));
+  cudf::test::strings_column_wrapper ef1({"hello", "null", "hello", "null", "hello"},
+                                         {true, false, true, false, true});
+  structs_col expected{ef0, ef1};
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, got, verbosity);
+}
+
+TYPED_TEST(TypedStructScalarScatterTest, StructOfLists)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Source scalar
+  LCW slr_f0{777};
+  auto slr = cudf::make_struct_scalar(cudf::table_view{{slr_f0}});
+
+  // Scatter map
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> scatter_map{0, 1, 4};
+
+  // Target column
+  LCW field0({LCW{XXX}, LCW{22}, LCW{33, 44}, LCW{null}, LCW{55}},
+             cudf::test::iterators::null_at(3));
+  structs_col target({field0}, cudf::test::iterators::null_at(0));
+
+  // Expect column
+  LCW ef0({LCW{777}, LCW{777}, LCW{33, 44}, LCW{null}, LCW{777}},
+          cudf::test::iterators::null_at(3));
+  structs_col expected{ef0};
+
+  auto got = scatter_single_scalar(*slr, scatter_map, target);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, got, verbosity);
+}
diff --git a/cpp/tests/copying/scatter_struct_tests.cpp b/cpp/tests/copying/scatter_struct_tests.cpp
new file mode 100644
index 0000000..f678880
--- /dev/null
+++ b/cpp/tests/copying/scatter_struct_tests.cpp
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+
+using namespace cudf::test::iterators;
+
+using bools_col   = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+using strings_col = cudf::test::strings_column_wrapper;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};  // Mark for null child elements
+constexpr int32_t XXX{0};   // Mark for null struct elements
+
+template <typename T>
+struct TypedStructScatterTest : public cudf::test::BaseFixture {};
+
+using TestTypes = cudf::test::Concat<cudf::test::IntegralTypes,
+                                     cudf::test::FloatingPointTypes,
+                                     cudf::test::DurationTypes,
+                                     cudf::test::TimestampTypes>;
+
+TYPED_TEST_SUITE(TypedStructScatterTest, TestTypes);
+
+namespace {
+auto scatter_structs(std::unique_ptr<cudf::column> const& structs_src,
+                     std::unique_ptr<cudf::column> const& structs_tgt,
+                     std::unique_ptr<cudf::column> const& scatter_map)
+{
+  auto const source = cudf::table_view{std::vector<cudf::column_view>{structs_src->view()}};
+  auto const target = cudf::table_view{std::vector<cudf::column_view>{structs_tgt->view()}};
+  auto const result = cudf::scatter(source, scatter_map->view(), target);
+  return result->get_column(0);
+}
+}  // namespace
+
+// Test case when all input columns are empty
+TYPED_TEST(TypedStructScatterTest, EmptyInputTest)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_src     = col_wrapper{};
+  auto const structs_src = structs_col{{child_col_src}}.release();
+
+  auto child_col_tgt     = col_wrapper{};
+  auto const structs_tgt = structs_col{{child_col_tgt}}.release();
+
+  auto const scatter_map = int32s_col{}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_src, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+// Test case when only the scatter map is empty
+TYPED_TEST(TypedStructScatterTest, EmptyScatterMapTest)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_src     = col_wrapper{{0, 1, 2, 3, null, XXX}, null_at(4)};
+  auto const structs_src = structs_col{{child_col_src}, null_at(5)}.release();
+
+  auto child_col_tgt     = col_wrapper{{50, null, 70, XXX, 90, 100}, null_at(1)};
+  auto const structs_tgt = structs_col{{child_col_tgt}, null_at(3)}.release();
+
+  auto const scatter_map = int32s_col{}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_tgt, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, ScatterAsCopyTest)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_src     = col_wrapper{{0, 1, 2, 3, null, XXX}, null_at(4)};
+  auto const structs_src = structs_col{{child_col_src}, null_at(5)}.release();
+
+  auto child_col_tgt     = col_wrapper{{50, null, 70, XXX, 90, 100}, null_at(1)};
+  auto const structs_tgt = structs_col{{child_col_tgt}, null_at(3)}.release();
+
+  // Scatter as copy: the target should be the same as source
+  auto const scatter_map = int32s_col{0, 1, 2, 3, 4, 5}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_src, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, ScatterAsLeftShiftTest)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_src     = col_wrapper{{0, 1, 2, 3, null, XXX}, null_at(4)};
+  auto const structs_src = structs_col{{child_col_src}, null_at(5)}.release();
+
+  auto child_col_tgt     = col_wrapper{{50, null, 70, XXX, 90, 100}, null_at(1)};
+  auto const structs_tgt = structs_col{{child_col_tgt}, null_at(3)}.release();
+
+  auto child_col_expected = col_wrapper{{2, 3, null, XXX, 0, 1}, null_at(2)};
+  auto structs_expected   = structs_col{{child_col_expected}, null_at(3)}.release();
+
+  auto const scatter_map = int32s_col{-2, -1, 0, 1, 2, 3}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_expected, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, SimpleScatterTests)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  // Source data
+  auto child_col_src     = col_wrapper{{0, 1, 2, 3, null, XXX}, null_at(4)};
+  auto const structs_src = structs_col{{child_col_src}, null_at(5)}.release();
+
+  // Target data
+  auto child_col_tgt     = col_wrapper{{50, null, 70, XXX, 90, 100}, null_at(1)};
+  auto const structs_tgt = structs_col{{child_col_tgt}, null_at(3)}.release();
+
+  // Expected data
+  auto child_col_expected1     = col_wrapper{{1, null, 70, XXX, 0, 2}, null_at(1)};
+  auto const structs_expected1 = structs_col{{child_col_expected1}, null_at(3)}.release();
+  auto const scatter_map1      = int32s_col{-2, 0, 5}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_expected1, scatter_structs(structs_src, structs_tgt, scatter_map1), verbosity);
+
+  // Expected data
+  auto child_col_expected2     = col_wrapper{{1, null, 70, 3, 0, 2}, null_at(1)};
+  auto const structs_expected2 = structs_col{{child_col_expected2}, no_nulls()}.release();
+  auto const scatter_map2      = int32s_col{-2, 0, 5, 3}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_expected2, scatter_structs(structs_src, structs_tgt, scatter_map2), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, ComplexDataScatterTest)
+{
+  // Testing scatter() on struct<string, numeric, bool>.
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  // Source data
+  auto names_column_src =
+    strings_col{{"Newton", "Washington", "Cherry", "Kiwi", "Lemon", "Tomato" /*XXX*/}, no_nulls()};
+  auto ages_column_src = col_wrapper{{5, 10, 15, 20, null, XXX}, null_at(4)};
+  auto is_human_col_src =
+    bools_col{{true, true, false, false /*null*/, false, false /*XXX*/}, null_at(3)};
+  auto const structs_src =
+    structs_col{{names_column_src, ages_column_src, is_human_col_src}, null_at(5)}.release();
+
+  // Target data
+  auto names_column_tgt = strings_col{
+    {"String 0" /*null*/, "String 1", "String 2" /*XXX*/, "String 3", "String 4", "String 5"},
+    null_at(0)};
+  auto ages_column_tgt  = col_wrapper{{50, null, XXX, 80, 90, 100}, null_at(1)};
+  auto is_human_col_tgt = bools_col{{true, true, true /*XXX*/, true, true, true}, no_nulls()};
+  auto const structs_tgt =
+    structs_col{{names_column_tgt, ages_column_tgt, is_human_col_tgt}, null_at(2)}.release();
+
+  // Expected data
+  auto names_column_expected = strings_col{
+    {"String 0" /*null*/, "Lemon", "Kiwi", "Cherry", "Washington", "Newton"}, null_at(0)};
+  auto ages_column_expected = col_wrapper{{50, null, 20, 15, 10, 5}, null_at(1)};
+  auto is_human_col_expected =
+    bools_col{{true, false, false /*null*/, false, true, true}, null_at(2)};
+  auto const structs_expected =
+    structs_col{{names_column_expected, ages_column_expected, is_human_col_expected}, no_nulls()}
+      .release();
+
+  // The first element of the target is not overwritten
+  auto const scatter_map = int32s_col{-1, 4, 3, 2, 1}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_expected, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, ScatterStructOfListsTest)
+{
+  // Testing scatter() on struct<list<numeric>>
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Source data
+  auto lists_col_src =
+    lists_col{{{5}, {10, 15}, {20, 25, 30}, {35, 40, 45, 50}, {55, 60, 65}, {70, 75}, {80}, {}, {}},
+              // Valid for elements 0, 3, 6,...
+              cudf::detail::make_counting_transform_iterator(0, [](auto i) { return !(i % 3); })};
+  auto const structs_src = structs_col{{lists_col_src}}.release();
+
+  // Target data
+  auto lists_col_tgt =
+    lists_col{{{1}, {2, 3}, {4, 5, 6}, {7, 8}, {9}, {10, 11, 12, 13}, {}, {14}, {15, 16}},
+              // Valid for elements 1, 3, 5, 7,...
+              cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; })};
+  auto const structs_tgt = structs_col{{lists_col_tgt}}.release();
+
+  // Expected data
+  auto const validity_expected = std::vector<bool>{0, 1, 1, 0, 0, 1, 1, 0, 0};
+  auto lists_col_expected      = lists_col{
+         {{1}, {2, 3}, {80}, {70, 75}, {55, 60, 65}, {35, 40, 45, 50}, {5}, {10, 15}, {20, 25, 30}},
+    validity_expected.begin()};
+  auto const structs_expected = structs_col{{lists_col_expected}}.release();
+
+  // The first 2 elements of the target is not overwritten
+  auto const scatter_map = int32s_col{-3, -2, -1, 5, 4, 3, 2}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_expected, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, SourceSmallerThanTargetScatterTest)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_src     = col_wrapper{22, 55};
+  auto const structs_src = structs_col{{child_col_src}}.release();
+
+  auto child_col_tgt     = col_wrapper{0, 1, 2, 3, 4, 5, 6};
+  auto const structs_tgt = structs_col{{child_col_tgt}}.release();
+
+  auto child_col_expected     = col_wrapper{0, 1, 22, 3, 4, 55, 6};
+  auto const structs_expected = structs_col{{child_col_expected}}.release();
+
+  auto const scatter_map = int32s_col{2, 5}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *structs_expected, scatter_structs(structs_src, structs_tgt, scatter_map), verbosity);
+}
+
+TYPED_TEST(TypedStructScatterTest, IntStructNullMaskRegression)
+{
+  using col_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_tgt      = col_wrapper({0, null, 2}, null_at(1));
+  auto struct_col_tgt = structs_col({child_tgt}).release();
+
+  auto child_src      = col_wrapper{20};
+  auto struct_col_src = structs_col({child_src}).release();
+
+  auto scatter_map = int32s_col{2}.release();
+
+  auto expected_child  = col_wrapper({0, null, 20}, null_at(1));
+  auto expected_struct = structs_col({expected_child}).release();
+
+  auto const result = scatter_structs(struct_col_src, struct_col_tgt, scatter_map);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_struct, result, verbosity);
+}
diff --git a/cpp/tests/copying/scatter_tests.cpp b/cpp/tests/copying/scatter_tests.cpp
new file mode 100644
index 0000000..8194a74
--- /dev/null
+++ b/cpp/tests/copying/scatter_tests.cpp
@@ -0,0 +1,847 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/stream_compaction.hpp>
+
+class ScatterUntypedTests : public cudf::test::BaseFixture {};
+
+// Throw logic error if scatter map is longer than source
+TEST_F(ScatterUntypedTests, ScatterMapTooLong)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1, 0, 2, 4, 6});
+
+  auto const source_table = cudf::table_view({source, source});
+  auto const target_table = cudf::table_view({target, target});
+
+  EXPECT_THROW(cudf::scatter(source_table, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if scatter map has nulls
+TEST_F(ScatterUntypedTests, ScatterMapNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1}, {0, 1, 1, 1});
+
+  auto const source_table = cudf::table_view({source, source});
+  auto const target_table = cudf::table_view({target, target});
+
+  EXPECT_THROW(cudf::scatter(source_table, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if scatter map has nulls
+TEST_F(ScatterUntypedTests, ScatterScalarMapNulls)
+{
+  auto const source = cudf::scalar_type_t<int32_t>{100};
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1}, {0, 1, 1, 1});
+
+  auto const target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::scatter(source_vector, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if source and target have different number of columns
+TEST_F(ScatterUntypedTests, ScatterColumnNumberMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const source_table = cudf::table_view({source});
+  auto const target_table = cudf::table_view({target, target});
+
+  EXPECT_THROW(cudf::scatter(source_table, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if number of scalars doesn't match number of columns
+TEST_F(ScatterUntypedTests, ScatterScalarColumnNumberMismatch)
+{
+  auto const source = cudf::scalar_type_t<int32_t>(100);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const target_table = cudf::table_view({target, target});
+
+  EXPECT_THROW(cudf::scatter(source_vector, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if source and target have different data types
+TEST_F(ScatterUntypedTests, ScatterDataTypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<float> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const source_table = cudf::table_view({source});
+  auto const target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::scatter(source_table, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if source and target have different data types
+TEST_F(ScatterUntypedTests, ScatterScalarDataTypeMismatch)
+{
+  auto const source = cudf::scalar_type_t<int32_t>(100);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<float> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::scatter(source_vector, scatter_map, target_table), cudf::logic_error);
+}
+
+template <typename T>
+class ScatterIndexTypeTests : public cudf::test::BaseFixture {};
+
+using IndexTypes = cudf::test::Types<int8_t, int16_t, int32_t, int64_t>;
+TYPED_TEST_SUITE(ScatterIndexTypeTests, IndexTypes);
+
+// Validate that each of the index types work
+TYPED_TEST(ScatterIndexTypeTests, ScatterIndexType)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<TypeParam> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<TypeParam> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected({10, 3, 30, 2, 50, 1, 70, 4});
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+// Validate that each of the index types work
+TYPED_TEST(ScatterIndexTypeTests, ScatterScalarIndexType)
+{
+  auto const source = cudf::scalar_type_t<TypeParam>(100, true);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<TypeParam> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected({10, 100, 30, 100, 50, 100, 70, 100});
+
+  auto const target_table   = cudf::table_view({target});
+  auto const expected_table = cudf::table_view({expected});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+template <typename T>
+class ScatterInvalidIndexTypeTests : public cudf::test::BaseFixture {};
+
+// NOTE string types hit static assert in fixed_width_column_wrapper
+using InvalidIndexTypes = cudf::test::Concat<cudf::test::Types<float, double, bool>,
+                                             cudf::test::ChronoTypes,
+                                             cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(ScatterInvalidIndexTypeTests, InvalidIndexTypes);
+
+// Throw logic error if scatter map column has invalid data type
+TYPED_TEST(ScatterInvalidIndexTypeTests, ScatterInvalidIndexType)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const source_table = cudf::table_view({source, source});
+  auto const target_table = cudf::table_view({target, target});
+
+  EXPECT_THROW(cudf::scatter(source_table, scatter_map, target_table), cudf::logic_error);
+}
+
+// Throw logic error if scatter map column has invalid data type
+TYPED_TEST(ScatterInvalidIndexTypeTests, ScatterScalarInvalidIndexType)
+{
+  auto const source = cudf::scalar_type_t<int32_t>(100, true);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::scatter(source_vector, scatter_map, target_table), cudf::logic_error);
+}
+
+template <typename T>
+class ScatterDataTypeTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ScatterDataTypeTests, cudf::test::FixedWidthTypes);
+
+// Empty scatter map returns copy of input
+TYPED_TEST(ScatterDataTypeTests, EmptyScatterMap)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({});
+
+  auto const source_table = cudf::table_view({source, source});
+  auto const target_table = cudf::table_view({target, target});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  // Expect a copy of the input table
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), target_table);
+}
+
+// Empty scatter map returns copy of input
+TYPED_TEST(ScatterDataTypeTests, EmptyScalarScatterMap)
+{
+  auto const source =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(100), true);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({});
+
+  auto const target_table = cudf::table_view({target});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  // Expect a copy of the input table
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), target_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterNoNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected({10, 3, 30, 2, 50, 1, 70, 4});
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterBothNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> source({2, 4, 6, 8}, {1, 1, 0, 0});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80}, {0, 0, 0, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({1, 3, -3, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected({10, 2, 30, 4, 50, 6, 70, 8},
+                                                                      {0, 1, 0, 1, 1, 0, 1, 0});
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterSourceNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> source({2, 4, 6, 8}, {1, 1, 0, 0});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({1, 3, -3, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected({10, 2, 30, 4, 50, 6, 70, 8},
+                                                                      {1, 1, 1, 1, 1, 0, 1, 0});
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterTargetNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> source({2, 4, 6, 8});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80}, {0, 0, 0, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({1, 3, -3, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected({10, 2, 30, 4, 50, 6, 70, 8},
+                                                                      {0, 1, 0, 1, 1, 1, 1, 1});
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterScalarNoNulls)
+{
+  using Type = cudf::device_storage_type_t<TypeParam>;
+
+  auto const source =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<Type>(100), true);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected(
+    {10, 100, 30, 100, 50, 100, 70, 100});
+
+  auto const target_table   = cudf::table_view({target});
+  auto const expected_table = cudf::table_view({expected});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterScalarTargetNulls)
+{
+  using Type = cudf::device_storage_type_t<TypeParam>;
+
+  auto const source =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<Type>(100), true);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80}, {0, 0, 0, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected(
+    {10, 100, 30, 100, 50, 100, 70, 100}, {0, 1, 0, 1, 1, 1, 1, 1});
+
+  auto const target_table   = cudf::table_view({target});
+  auto const expected_table = cudf::table_view({expected});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterScalarSourceNulls)
+{
+  using Type = cudf::device_storage_type_t<TypeParam>;
+
+  auto const source =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<Type>(100), false);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected(
+    {10, 100, 30, 100, 50, 100, 70, 100}, {1, 0, 1, 0, 1, 0, 1, 0});
+
+  auto const target_table   = cudf::table_view({target});
+  auto const expected_table = cudf::table_view({expected});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterScalarBothNulls)
+{
+  using Type = cudf::device_storage_type_t<TypeParam>;
+
+  auto const source =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<Type>(100), false);
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> target(
+    {10, 20, 30, 40, 50, 60, 70, 80}, {0, 0, 0, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected(
+    {10, 100, 30, 100, 50, 100, 70, 100}, {0, 0, 0, 0, 1, 0, 1, 0});
+
+  auto const target_table   = cudf::table_view({target});
+  auto const expected_table = cudf::table_view({expected});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TYPED_TEST(ScatterDataTypeTests, ScatterSourceNullsLarge)
+{
+  constexpr cudf::size_type N{513};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> source({0, 0, 0, 0}, {0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({0, 1, 2, 3});
+  auto target_data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(target_data)::value_type>
+    target(target_data, target_data + N);
+
+  auto expect_data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto expect_valid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i > 3; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(expect_data)::value_type>
+    expected(expect_data, expect_data + N, expect_valid);
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+class ScatterStringsTests : public cudf::test::BaseFixture {};
+
+TEST_F(ScatterStringsTests, ScatterNoNulls)
+{
+  std::vector<char const*> h_source{"dog", "the", "jumps", "brown", "the"};
+  cudf::test::strings_column_wrapper source(h_source.begin(), h_source.end());
+
+  std::vector<char const*> h_target{
+    "a", "quick", "fire", "fox", "browses", "over", "a", "lazy", "web"};
+  cudf::test::strings_column_wrapper target(h_target.begin(), h_target.end());
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-1, -3, -5, 2, 0});
+
+  std::vector<char const*> h_expected{
+    "the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"};
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TEST_F(ScatterStringsTests, ScatterScalarNoNulls)
+{
+  auto const source = cudf::string_scalar("buffalo");
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  std::vector<char const*> h_target{
+    "Buffalo", "bison", "Buffalo", "bison", "bully", "bully", "Buffalo", "bison"};
+  cudf::test::strings_column_wrapper target(h_target.begin(), h_target.end());
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({1, 3, -4, -3, -1});
+
+  std::vector<char const*> h_expected{
+    "Buffalo", "buffalo", "Buffalo", "buffalo", "buffalo", "buffalo", "Buffalo", "buffalo"};
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+
+  auto const target_table   = cudf::table_view({target});
+  auto const expected_table = cudf::table_view({expected});
+
+  auto const result = cudf::scatter(source_vector, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), expected_table);
+}
+
+TEST_F(ScatterStringsTests, EmptyStrings)
+{
+  cudf::test::strings_column_wrapper input{"", "", ""};
+  cudf::table_view t({input});
+
+  // Test for issue 10717: all-empty-string column scatter
+  auto map    = cudf::test::fixed_width_column_wrapper<int32_t>({0});
+  auto result = cudf::scatter(t, map, t);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result->view(), t);
+}
+
+template <typename T>
+class BooleanMaskScatter : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(BooleanMaskScatter, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(BooleanMaskScatter, WithNoNullElementsInTarget)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T, int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+  auto source_table   = cudf::table_view({source});
+  auto target_table   = cudf::table_view({target});
+  auto expected_table = cudf::table_view({expected});
+
+  auto got = cudf::boolean_mask_scatter(source_table, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+TYPED_TEST(BooleanMaskScatter, WithNull)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T, int32_t> source_col1({1, 5, 6, 8, 9}, {1, 0, 1, 0, 1});
+  cudf::test::strings_column_wrapper source_col2({"This", "is", "cudf", "test", "column"},
+                                                 {1, 0, 0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target_col1({2, 2, 3, 4, 11, 12, 7, 7, 10, 10},
+                                                                 {1, 1, 0, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::strings_column_wrapper target_col2(
+    {"a", "bc", "cd", "ef", "gh", "ij", "jk", "lm", "no", "pq"}, {1, 1, 0, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected_col1({1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+                                                                   {1, 1, 0, 1, 0, 1, 1, 0, 1, 0});
+  cudf::test::strings_column_wrapper expected_col2(
+    {"This", "bc", "cd", "ef", "is", "cudf", "jk", "test", "column", "pq"},
+    {1, 1, 0, 1, 0, 0, 1, 1, 0, 0});
+  auto source_table   = cudf::table_view({source_col1, source_col2});
+  auto target_table   = cudf::table_view({target_col1, target_col2});
+  auto expected_table = cudf::table_view({expected_col1, expected_col2});
+
+  auto got = cudf::boolean_mask_scatter(source_table, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+class BooleanMaskScatterString : public cudf::test::BaseFixture {};
+
+TEST_F(BooleanMaskScatterString, NoNUll)
+{
+  cudf::test::strings_column_wrapper source({"This", "cudf"});
+  cudf::test::strings_column_wrapper target({"is", "is", "a", "udf", "api"});
+  cudf::test::fixed_width_column_wrapper<bool> mask({true, false, false, true, false});
+
+  cudf::test::strings_column_wrapper expected({"This", "is", "a", "cudf", "api"});
+  auto source_table   = cudf::table_view({source});
+  auto target_table   = cudf::table_view({target});
+  auto expected_table = cudf::table_view({expected});
+
+  auto got = cudf::boolean_mask_scatter(source_table, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+TEST_F(BooleanMaskScatterString, WithNUll)
+{
+  cudf::test::strings_column_wrapper source({"This", "cudf"}, {0, 1});
+  cudf::test::strings_column_wrapper target({"is", "is", "a", "udf", "api"}, {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<bool> mask({true, false, false, true, false});
+
+  cudf::test::strings_column_wrapper expected({"This", "is", "a", "cudf", "api"}, {0, 0, 0, 1, 1});
+  auto source_table   = cudf::table_view({source});
+  auto target_table   = cudf::table_view({target});
+  auto expected_table = cudf::table_view({expected});
+
+  auto got = cudf::boolean_mask_scatter(source_table, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+class BooleanMaskScatterFails : public cudf::test::BaseFixture {};
+
+TEST_F(BooleanMaskScatterFails, SourceAndTargetTypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<int64_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+  auto source_table = cudf::table_view({source});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(source_table, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterFails, BooleanMaskTypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<int8_t> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+  auto source_table = cudf::table_view({source});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(source_table, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterFails, BooleanMaskTargetSizeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true});
+  auto source_table = cudf::table_view({source});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(source_table, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterFails, NumberOfColumnMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true});
+  auto source_table = cudf::table_view({source, source});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(source_table, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterFails, MoreTruesInMaskThanSourceSize)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, true, false, true, true, false, true, true});
+  auto source_table = cudf::table_view({source, source});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(source_table, target_table, mask), cudf::logic_error);
+}
+
+template <typename T>
+struct BooleanMaskScalarScatter : public cudf::test::BaseFixture {
+  std::unique_ptr<cudf::scalar> form_scalar(T value, bool validity = true)
+  {
+    using ScalarType = cudf::scalar_type_t<T>;
+    std::unique_ptr<cudf::scalar> scalar{nullptr};
+
+    if (cudf::is_numeric<T>()) {
+      scalar = cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+    } else if (cudf::is_timestamp<T>()) {
+      scalar = cudf::make_timestamp_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+    } else if (cudf::is_duration<T>()) {
+      scalar = cudf::make_duration_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+    }
+
+    static_cast<ScalarType*>(scalar.get())->set_value(value);
+    static_cast<ScalarType*>(scalar.get())->set_valid_async(validity);
+
+    return scalar;
+  }
+};
+
+TYPED_TEST_SUITE(BooleanMaskScalarScatter, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(BooleanMaskScalarScatter, WithNoNullElementsInTarget)
+{
+  using T       = TypeParam;
+  T source      = cudf::test::make_type_param_scalar<T>(11);
+  bool validity = true;
+  auto scalar   = this->form_scalar(source, validity);
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({11, 2, 3, 4, 11, 11, 7, 11, 11, 10});
+  auto target_table   = cudf::table_view({target});
+  auto expected_table = cudf::table_view({expected});
+
+  auto got = cudf::boolean_mask_scatter(scalar_vect, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+TYPED_TEST(BooleanMaskScalarScatter, WithNull)
+{
+  using T       = TypeParam;
+  T source      = cudf::test::make_type_param_scalar<T>(11);
+  bool validity = false;
+  auto scalar_1 = this->form_scalar(source, validity);
+  auto scalar_2 = cudf::make_string_scalar("cudf");
+  scalar_2->set_valid_async(true);
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar_1);
+  scalar_vect.push_back(*scalar_2);
+  cudf::test::fixed_width_column_wrapper<T, int32_t> target_col1({2, 2, 3, 4, 11, 12, 7, 7, 10, 10},
+                                                                 {1, 1, 0, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::strings_column_wrapper target_col2(
+    {"a", "bc", "cd", "ef", "gh", "ij", "jk", "lm", "no", "pq"}, {1, 1, 0, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected_col1(
+    {11, 2, 3, 4, 11, 11, 7, 11, 11, 10}, {0, 1, 0, 1, 0, 0, 1, 0, 0, 0});
+  cudf::test::strings_column_wrapper expected_col2(
+    {"cudf", "bc", "cd", "ef", "cudf", "cudf", "jk", "cudf", "cudf", "pq"},
+    {1, 1, 0, 1, 1, 1, 1, 1, 1, 0});
+  auto target_table   = cudf::table_view({target_col1, target_col2});
+  auto expected_table = cudf::table_view({expected_col1, expected_col2});
+
+  auto got = cudf::boolean_mask_scatter(scalar_vect, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+class BooleanMaskScatterScalarString : public cudf::test::BaseFixture {};
+
+TEST_F(BooleanMaskScatterScalarString, NoNUll)
+{
+  auto scalar = cudf::make_string_scalar("cudf");
+  scalar->set_valid_async(true);
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+
+  cudf::test::strings_column_wrapper target({"is", "is", "a", "udf", "api"});
+  cudf::test::fixed_width_column_wrapper<bool> mask({true, false, false, true, false});
+
+  cudf::test::strings_column_wrapper expected({"cudf", "is", "a", "cudf", "api"});
+  auto target_table   = cudf::table_view({target});
+  auto expected_table = cudf::table_view({expected});
+
+  auto got = cudf::boolean_mask_scatter(scalar_vect, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+TEST_F(BooleanMaskScatterScalarString, WithNUll)
+{
+  auto scalar = cudf::make_string_scalar("cudf");
+  scalar->set_valid_async(true);
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+  cudf::test::strings_column_wrapper target({"is", "is", "a", "udf", "api"}, {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<bool> mask({true, false, true, true, false});
+
+  cudf::test::strings_column_wrapper expected({"cudf", "is", "cudf", "cudf", "api"},
+                                              {1, 0, 1, 1, 1});
+  auto target_table   = cudf::table_view({target});
+  auto expected_table = cudf::table_view({expected});
+  auto got            = cudf::boolean_mask_scatter(scalar_vect, target_table, mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, got->view());
+}
+
+class BooleanMaskScatterScalarFails : public cudf::test::BaseFixture {};
+
+TEST_F(BooleanMaskScatterScalarFails, SourceAndTargetTypeMismatch)
+{
+  auto scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<int32_t>()}));
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+  cudf::test::fixed_width_column_wrapper<int64_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(scalar_vect, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterScalarFails, BooleanMaskTypeMismatch)
+{
+  auto scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<int32_t>()}));
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<int8_t> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(scalar_vect, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterScalarFails, BooleanMaskTargetSizeMismatch)
+{
+  auto scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<int32_t>()}));
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(scalar_vect, target_table, mask), cudf::logic_error);
+}
+
+TEST_F(BooleanMaskScatterScalarFails, NumberOfColumnAndScalarMismatch)
+{
+  auto scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<int32_t>()}));
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalar_vect;
+  scalar_vect.push_back(*scalar);
+  scalar_vect.push_back(*scalar);
+  cudf::test::fixed_width_column_wrapper<int32_t> target({2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true});
+  auto target_table = cudf::table_view({target});
+
+  EXPECT_THROW(cudf::boolean_mask_scatter(scalar_vect, target_table, mask), cudf::logic_error);
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+template <typename T>
+using wrapper = cudf::test::fixed_width_column_wrapper<T>;
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointScatter)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const ONE   = decimalXX{1, scale_type{0}};
+  auto const TWO   = decimalXX{2, scale_type{0}};
+  auto const THREE = decimalXX{3, scale_type{0}};
+  auto const FOUR  = decimalXX{4, scale_type{0}};
+  auto const FIVE  = decimalXX{5, scale_type{0}};
+
+  auto const source      = wrapper<decimalXX>({ONE, TWO, THREE, FOUR, FIVE});
+  auto const target      = wrapper<decimalXX>({ONE, TWO, THREE, FOUR, FIVE, FOUR, THREE, TWO, ONE});
+  auto const scatter_map = wrapper<int32_t>({1, 2, -1, -3, -4});
+  auto const expected    = wrapper<decimalXX>({ONE, ONE, TWO, FOUR, FIVE, FIVE, FOUR, TWO, THREE});
+
+  auto const source_table   = cudf::table_view({source, source});
+  auto const target_table   = cudf::table_view({target, target});
+  auto const expected_table = cudf::table_view({expected, expected});
+
+  auto const result = cudf::scatter(source_table, scatter_map, target_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table, result->view());
+}
diff --git a/cpp/tests/copying/segmented_gather_list_tests.cpp b/cpp/tests/copying/segmented_gather_list_tests.cpp
new file mode 100644
index 0000000..dda54cb
--- /dev/null
+++ b/cpp/tests/copying/segmented_gather_list_tests.cpp
@@ -0,0 +1,621 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/lists/gather.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+
+#include <stdexcept>
+
+template <typename T>
+class SegmentedGatherTest : public cudf::test::BaseFixture {};
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::DurationTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(SegmentedGatherTest, FixedWidthTypesNotBool);
+
+// to disambiguate between {} == 0 and {} == List{0}
+// Also, see note about compiler issues when declaring nested
+// empty lists in lists_column_wrapper documentation
+template <typename T>
+using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+using namespace cudf::test::iterators;
+auto constexpr NULLIFY = cudf::out_of_bounds_policy::NULLIFY;
+
+TYPED_TEST(SegmentedGatherTest, Gather)
+{
+  using T = TypeParam;
+
+  // List<T>
+  LCW<T> list{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}};
+
+  {
+    // Straight-line case.
+    auto const gather_map = LCW<int>{{3, 2, 1, 0}, {0}, {0, 1}, {0, 2, 1}};
+    auto const expected   = LCW<T>{{4, 3, 2, 1}, {5}, {6, 7}, {8, 10, 9}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                       cudf::lists_column_view{gather_map});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+
+  {
+    // Nullify out-of-bounds values.
+    auto const gather_map = LCW<int>{{3, 2, 4, 0}, {0}, {0, -3}, {0, 2, 1}};
+    auto const expected = LCW<T>{{{4, 3, 2, 1}, null_at(2)}, {5}, {{6, 7}, null_at(1)}, {8, 10, 9}};
+    auto const results  = cudf::lists::segmented_gather(
+      cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherNothing)
+{
+  using T = TypeParam;
+
+  // List<T>
+  {
+    auto const list       = LCW<T>{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}};
+    auto const gather_map = LCW<int>{LCW<int>{}, LCW<int>{}, LCW<int>{}, LCW<int>{}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                       cudf::lists_column_view{gather_map});
+    auto const expected   = LCW<T>{LCW<T>{}, LCW<T>{}, LCW<T>{}, LCW<T>{}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  // List<List<T>>
+  {
+    auto const list       = LCW<T>{{{1, 2, 3, 4}, {5}}, {{6, 7}}, {{}, {8, 9, 10}}};
+    auto const gather_map = LCW<int>{LCW<int>{}, LCW<int>{}, LCW<int>{}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                       cudf::lists_column_view{gather_map});
+
+    // hack to get column of empty list of list
+    auto const expected_dummy = LCW<T>{{{1, 2, 3, 4}, {5}}, LCW<T>{}, LCW<T>{}, LCW<T>{}};
+    auto const expected       = cudf::split(expected_dummy, {1})[1];
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  // List<List<List<T>>>
+  {
+    auto const list       = LCW<T>{{{{1, 2, 3, 4}, {5}}}, {{{6, 7}, {8, 9, 10}}}};
+    auto const gather_map = LCW<int>{LCW<int>{}, LCW<int>{}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                       cudf::lists_column_view{gather_map});
+    // hack to get column of empty list of list of list
+    auto const expected_dummy = LCW<T>{{{{1, 2, 3, 4}}}, LCW<T>{}, LCW<T>{}};
+    auto const expected       = cudf::split(expected_dummy, {1})[1];
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+    // the result should preserve the full List<List<List<int>>> hierarchy
+    // even though it is empty past the first level
+    cudf::lists_column_view lcv(results->view());
+    EXPECT_EQ(lcv.size(), 2);
+    EXPECT_EQ(lcv.child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(lcv.child().size(), 0);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().type().id(), cudf::type_id::LIST);
+    EXPECT_EQ(cudf::lists_column_view(lcv.child()).child().size(), 0);
+    EXPECT_EQ(
+      cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().type().id(),
+      cudf::type_to_id<T>());
+    EXPECT_EQ(cudf::lists_column_view(cudf::lists_column_view(lcv.child()).child()).child().size(),
+              0);
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherNulls)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<T>
+  auto const list = LCW<T>{{{1, 2, 3, 4}, valids}, {5}, {{6, 7}, valids}, {{8, 9, 10}, valids}};
+
+  {
+    // Test gathering on lists that contain nulls.
+    auto const gather_map = LCW<int>{{0, 1}, LCW<int>{}, {1}, {2, 1, 0}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                       cudf::lists_column_view{gather_map});
+    auto const expected =
+      LCW<T>{{{1, 2}, valids}, LCW<T>{}, {{7}, valids + 1}, {{10, 9, 8}, valids}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+  {
+    // Test gathering on lists that contain nulls, with out-of-bounds indices.
+    auto const gather_map = LCW<int>{{10, -10}, LCW<int>{}, {1}, {2, -10, 0}};
+    auto const results    = cudf::lists::segmented_gather(
+      cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    auto const expected =
+      LCW<T>{{{0, 0}, nulls_at({0, 1})}, LCW<T>{}, {{7}, valids + 1}, {{10, 0, 8}, null_at(1)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherNested)
+{
+  using T = TypeParam;
+
+  // List<List<T>>
+  {
+    // clang-format off
+    auto const list       = LCW<T>{{{2, 3}, {4, 5}},
+                                   {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {-17, -18}}};
+    auto const gather_map = LCW<int>{{0, -2, -2}, {1}, {1, 0, -1, -5}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map});
+    auto const expected   = LCW<T>{{{2, 3}, {2, 3}, {2, 3}},
+                                   {{9, 10, 11}},
+                                   {{17, 18}, {15, 16}, {-17, -18}, {15, 16}}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+
+  // List<List<T>>, with out-of-bounds gather indices.
+  {
+    // clang-format off
+    auto const list       = LCW<T>{{{2, 3}, {4, 5}},
+                                   {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {-17, -18}}};
+    auto const gather_map = LCW<int>{{0, 2, -2}, {1}, {1, 0, -1, -6}};
+    auto const results =
+      cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    auto const expected = LCW<T>{{{{2, 3}, LCW<T>{}, {2, 3}}, null_at(1)},
+                                 {{9, 10, 11}},
+                                 {{{17, 18}, {15, 16}, {-17, -18}, LCW<T>{}}, null_at(3)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+
+  // List<List<List<T>>>
+  {
+    // clang-format off
+    auto const list = LCW<T>{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+                             {{{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}},
+                             {{LCW<T>{0}}},
+                             {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+                              {{0, 1, 3}, {5}},
+                              {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+                             {{{10, 20}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}};
+    auto const gather_map = LCW<int>{{1}, LCW<int>{}, {0}, {1}, {0, -1, 1}};
+    auto const results = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map});
+    auto const expected = LCW<T>{{{{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+                                 LCW<T>{},
+                                 {{LCW<T>{0}}},
+                                 {{{0, 1, 3}, {5}}},
+                                 {{{10, 20}}, {{40, 50}, {60, 70, 80}}, {LCW<T>{30}}}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+
+  // List<List<List<T>>>, with out-of-bounds gather indices.
+  {
+    auto const list       = LCW<T>{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+                                   {{{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}},
+                                   {{LCW<T>{0}}},
+                                   {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+                                    {{0, 1, 3}, {5}},
+                                    {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+                                   {{{10, 20}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}};
+    auto const gather_map = LCW<int>{{1}, LCW<int>{}, {0}, {1}, {0, -1, 3, -4}};
+    auto const results    = cudf::lists::segmented_gather(
+      cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    auto const expected =
+      LCW<T>{{{{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+             LCW<T>{},
+             {{LCW<T>{0}}},
+             {{{0, 1, 3}, {5}}},
+             {{{{10, 20}}, {{40, 50}, {60, 70, 80}}, LCW<T>{}, LCW<T>{}}, nulls_at({2, 3})}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherOutOfOrder)
+{
+  using T = TypeParam;
+
+  // List<List<T>>
+  {
+    // clang-format off
+    auto const list       = LCW<T>{{{2, 3}, {4, 5}},
+                                   {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    auto const gather_map = LCW<int>{{1, 0}, {1, 2, 0}, {4, 3, 2, 1, 0}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map});
+    auto const expected   = LCW<T>{{{4, 5}, {2, 3}},
+                                   {{9, 10, 11}, {12, 13, 14}, {6, 7, 8}},
+                                   {{17, 18}, {17, 18}, {17, 18}, {17, 18}, {15, 16}}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+
+  // List<List<T>>, with out-of-bounds gather indices.
+  {
+    // clang-format off
+    auto const list       = LCW<T>{{{2, 3}, {4, 5}},
+                                   {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    auto const gather_map = LCW<int>{{1, 0}, {3, -1, -4}, {5, 4, 3, 2, 1, 0}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    auto const expected   = LCW<T>{{{4, 5}, {2, 3}},
+                                   {{LCW<T>{}, {12, 13, 14}, LCW<T>{}}, nulls_at({0, 2})},
+                                   {{LCW<T>{}, {17, 18}, {17, 18}, {17, 18}, {17, 18}, {15, 16}}, null_at(0)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherNegatives)
+{
+  using T = TypeParam;
+
+  // List<List<T>>
+  {
+    // clang-format off
+    auto const list       = LCW<T>{{{2, 3}, {4, 5}},
+                                   {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    auto const gather_map = LCW<int>{{-1, 0}, {-2, -1, 0}, {-5, -4, -3, -2, -1, 0}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map});
+    auto const expected   = LCW<T>{{{4, 5}, {2, 3}},
+                                   {{9, 10, 11}, {12, 13, 14}, {6, 7, 8}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}, {15, 16}}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+  // List<List<T>>, with out-of-bounds gather indices.
+  {
+    // clang-format off
+    auto const list       = LCW<T>{{{2, 3}, {4, 5}},
+                                   {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}},
+                                   {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}}};
+    auto const gather_map = LCW<int>{{-1, 0}, {-2, -1, -4}, {-6, -4, -3, -2, -1, 0}};
+    auto const results    =
+      cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    auto const expected   = LCW<T>{{{4, 5}, {2, 3}},
+                                   {{{9, 10, 11}, {12, 13, 14}, LCW<T>{}}, null_at(2)},
+                                   {{LCW<T>{}, {17, 18}, {17, 18}, {17, 18}, {17, 18}, {15, 16}}, null_at(0)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherOnNonCompactedNullLists)
+{
+  using T          = TypeParam;
+  auto constexpr X = -1;  // Signifies null value.
+
+  // List<T>
+  auto list = LCW<T>{{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 0}, {}, {1, 2}, {3, 4, 5}}, no_nulls()};
+  auto const input = list.release();
+
+  // Set non-empty list row at index 5 to null.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 5, 6, false, cudf::get_default_stream());
+
+  auto const gather_map = LCW<int>{{-1, 2, 1, -4}, {0}, {-2, 1}, {0, 2, 1}, {}, {0}, {1, 2}};
+  auto const expected =
+    LCW<T>{{{4, 3, 2, 1}, {5}, {6, 7}, {8, 0, 9}, {}, {{X}, all_nulls()}, {4, 5}}, null_at(5)};
+  auto const results = cudf::lists::segmented_gather(cudf::lists_column_view{*input},
+                                                     cudf::lists_column_view{gather_map});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherNestedNulls)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<List<T>>
+  {
+    // clang-format off
+    auto const list = LCW<T>{{{{2, 3}, valids}, {4, 5}},
+                             {{{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}, valids},
+                             {{15, 16}, {17, 18}, {17, 18}, {17, 18}, {17, 18}},
+                             {{{{25, 26}, valids}, {27, 28}, {{29, 30}, valids}, {31, 32}, {33, 34}}, valids}};
+    auto const gather_map = LCW<int>{{0, 1}, {0, 2}, LCW<int>{}, {0, 1, 4}};
+    auto const results  = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map});
+    auto const expected = LCW<T>{{{{2, 3}, valids}, {4, 5}},
+                                 {{{6, 7, 8}, {12, 13, 14}}, no_nulls()},
+                                 LCW<T>{},
+                                 {{{{25, 26}, valids}, {27, 28}, {33, 34}}, valids}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+
+  // List<List<List<List<T>>>>
+  {
+    // clang-format off
+    auto const list = LCW<T>{{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+                              {{{15, 16}, {{27, 28}, valids}, {{37, 38}, valids}, {47, 48}, {57, 58}}},
+                              {{LCW<T>{0}}},
+                              {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+                                {{0, 1, 3}, {5}},
+                                {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+                              {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids}}};
+    auto const gather_map = LCW<int>{{1, 2, 4}};
+    auto const results    = cudf::lists::segmented_gather(cudf::lists_column_view{list}, cudf::lists_column_view{gather_map});
+    auto const expected   = LCW<T>{{{{{15, 16}, {{27, 28}, valids}, {{37, 38}, valids}, {47, 48}, {57, 58}}},
+                                    {{LCW<T>{0}}},
+                                    {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids}}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+    // clang-format on
+  }
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherNestedWithEmpties)
+{
+  using T = TypeParam;
+
+  auto const list = LCW<T>{{{2, 3}, LCW<T>{}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}, {LCW<T>{}}};
+  auto const gather_map = LCW<int>{LCW<int>{0}, LCW<int>{0}, LCW<int>{0}};
+  auto results          = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                               cudf::lists_column_view{gather_map});
+  auto const expected =
+    LCW<T>{{{2, 3}}, {{6, 7, 8}}, {LCW<T>{}}};  // skip one null, gather one null.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+}
+
+TYPED_TEST(SegmentedGatherTest, GatherSliced)
+{
+  using T = TypeParam;
+  {
+    auto const a = LCW<T>{
+      {{1, 1, 1}, {2, 2}, {3, 3}},
+      {{4, 4, 4}, {5, 5}, {6, 6}},
+      {{7, 7, 7}, {8, 8}, {9, 9}},
+      {{10, 10, 10}, {11, 11}, {12, 12}},
+      {{20, 20, 20, 20}, {25}},
+      {{30, 30, 30, 30}, {40}},
+      {{50, 50, 50, 50}, {6, 13}},
+      {{70, 70, 70, 70}, {80}},
+    };
+    auto const split_a = cudf::split(a, {3});
+
+    {
+      auto const list       = LCW<int>{{1, 2}, {0, 2}, {0, 1}};
+      auto const gather_map = cudf::lists_column_view{list};
+      auto const result =
+        cudf::lists::segmented_gather(cudf::lists_column_view{split_a[0]}, gather_map);
+      auto const expected = LCW<T>{
+        {{2, 2}, {3, 3}},
+        {{4, 4, 4}, {6, 6}},
+        {{7, 7, 7}, {8, 8}},
+      };
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+    }
+
+    {
+      auto const list       = LCW<int>{{0, 1}, LCW<int>{}, LCW<int>{}, {0, 1}, LCW<int>{}};
+      auto const gather_map = cudf::lists_column_view{list};
+      auto const result =
+        cudf::lists::segmented_gather(cudf::lists_column_view{split_a[1]}, gather_map);
+      auto const expected =
+        LCW<T>{{{10, 10, 10}, {11, 11}}, LCW<T>{}, LCW<T>{}, {{50, 50, 50, 50}, {6, 13}}, LCW<T>{}};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+    }
+  }
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<List<List<T>>>
+  {
+    LCW<T> list{
+      // slice 0
+      {{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}},
+
+      {{{15, 16}, {{27, 28}, valids}, {{37, 38}, valids}, {47, 48}, {57, 58}},
+       {{11, 12}, {{42, 43, 44}, valids}, {{77, 78}, valids}}},
+
+      // slice 1
+      {{LCW<T>{0}}},
+      {{{10}, {20, 30, 40, 50}, {60, 70, 80}},
+       {{0, 1, 3}, {5}},
+       {{11, 12, 13, 14, 15}, {16, 17}, {0}}},
+      {{{{1, 6}, {60, 70, 80, 100}}, {{10, 11, 13}, {15}}, {{11, 12, 13, 14, 15}}}, valids},
+
+      // slice 2
+      {{{{{10, 20}, valids}}, {LCW<T>{30}}, {{40, 50}, {60, 70, 80}}}, valids},
+      {{{{10, 20, 30}}, {LCW<T>{30}}, {{{20, 30}, valids}, {62, 72, 82}}}, valids}};
+
+    auto sliced = cudf::slice(list, {0, 1, 2, 5, 5, 7});
+
+    // gather from slice 0
+    {
+      LCW<int> map{{0, 1}};
+      auto result = cudf::lists::segmented_gather(cudf::lists_column_view{sliced[0]},
+                                                  cudf::lists_column_view{map});
+      LCW<T> expected{{{{2, 3}, {4, 5}}, {{6, 7, 8}, {9, 10, 11}, {12, 13, 14}}}};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->view());
+    }
+
+    // gather from slice 1
+    {
+      LCW<int16_t> map{{0}, {1, 2, 0, 1}, {0, 1, 2}};
+      auto result = cudf::lists::segmented_gather(cudf::lists_column_view{sliced[1]},
+                                                  cudf::lists_column_view{map});
+      LCW<T> expected{
+        {{LCW<T>{0}}},
+
+        {{{0, 1, 3}, {5}},
+         {{11, 12, 13, 14, 15}, {16, 17}, {0}},
+         {{10}, {20, 30, 40, 50}, {60, 70, 80}},
+         {{0, 1, 3}, {5}}},
+
+        {{{{1, 6}, {60, 70, 80, 100}}, {{10, 11, 13}, {15}}, {{11, 12, 13, 14, 15}}}, valids},
+      };
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->view());
+    }
+
+    // gather from slice 2
+    {
+      LCW<int> map{{1, 0, 0, 1, 1, 0}, {1, 0, 0, 1, 1, 2}};
+      auto result = cudf::lists::segmented_gather(cudf::lists_column_view{sliced[2]},
+                                                  cudf::lists_column_view{map});
+      std::vector<bool> expected_valids = {false, true, true, false, false, true};
+
+      LCW<T> expected{{{{LCW<T>{30}},
+                        {{{10, 20}, valids}},
+                        {{{10, 20}, valids}},
+                        {LCW<T>{30}},
+                        {LCW<T>{30}},
+                        {{{10, 20}, valids}}},
+                       expected_valids.begin()},
+                      {{{LCW<T>{30}},
+                        {{10, 20, 30}},
+                        {{10, 20, 30}},
+                        {LCW<T>{30}},
+                        {LCW<T>{30}},
+                        {{{20, 30}, valids}, {62, 72, 82}}},
+                       expected_valids.begin()}};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result->view());
+    }
+  }
+}
+
+using SegmentedGatherTestString = SegmentedGatherTest<cudf::string_view>;
+TEST_F(SegmentedGatherTestString, StringGather)
+{
+  using T = cudf::string_view;
+  // List<T>
+  {
+    auto const list       = LCW<T>{{"a", "b", "c", "d"}, {"1", "22", "333", "4"}, {"x", "y", "z"}};
+    auto const gather_map = LCW<int8_t>{{0, 1, 3, 2}, {1, 0, 3, 2}, LCW<int8_t>{}};
+    auto const expected   = LCW<T>{{"a", "b", "d", "c"}, {"22", "1", "4", "333"}, LCW<T>{}};
+    auto const result     = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                      cudf::lists_column_view{gather_map});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  // List<T>, with out-of-order gather indices.
+  {
+    auto const list       = LCW<T>{{"a", "b", "c", "d"}, {"1", "22", "333", "4"}, {"x", "y", "z"}};
+    auto const gather_map = LCW<int8_t>{{0, 1, 3, 4}, {1, -5, 3, 2}, LCW<int8_t>{}};
+    auto const expected   = LCW<T>{{{"a", "b", "d", "c"}, cudf::test::iterators::null_at(3)},
+                                   {{"22", "1", "4", "333"}, cudf::test::iterators::null_at(1)},
+                                   LCW<T>{}};
+    auto result           = cudf::lists::segmented_gather(
+      cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+using SegmentedGatherTestFloat = SegmentedGatherTest<float>;
+TEST_F(SegmentedGatherTestFloat, GatherMapSliced)
+{
+  using T = float;
+
+  // List<T>
+  {
+    auto const list = LCW<T>{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}, {11, 12}, {13, 14, 15, 16}};
+    auto const gather_map = LCW<int>{{3, 2, 1, 0}, {0}, {0, 1}, {0, 2, 1}, {0}, {1}};
+    // gather_map.offset: 0, 4, 5, 7, 10, 11, 12
+    auto const expected = LCW<T>{{4, 3, 2, 1}, {5}, {6, 7}, {8, 10, 9}, {11}, {14}};
+    auto const results  = cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                                       cudf::lists_column_view{gather_map});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+
+    auto const sliced  = cudf::split(list, {1, 4});
+    auto const split_m = cudf::split(gather_map, {1, 4});
+    auto const split_e = cudf::split(expected, {1, 4});
+
+    auto result0 = cudf::lists::segmented_gather(cudf::lists_column_view{sliced[0]},
+                                                 cudf::lists_column_view{split_m[0]});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(split_e[0], result0->view());
+    auto result1 = cudf::lists::segmented_gather(cudf::lists_column_view{sliced[1]},
+                                                 cudf::lists_column_view{split_m[1]});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(split_e[1], result1->view());
+    auto result2 = cudf::lists::segmented_gather(cudf::lists_column_view{sliced[2]},
+                                                 cudf::lists_column_view{split_m[2]});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(split_e[2], result2->view());
+  }
+
+  // List<T>, with out-of-bounds gather indices.
+  {
+    auto const list = LCW<T>{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}, {11, 12}, {13, 14, 15, 16}};
+    auto const gather_map = LCW<int>{{3, -5, 1, 0}, {0}, {0, 1}, {0, 2, 3}, {0}, {1}};
+    // gather_map.offset: 0, 4, 5, 7, 10, 11, 12
+    auto const expected =
+      LCW<T>{{{4, 0, 2, 1}, null_at(1)}, {5}, {6, 7}, {{8, 10, 9}, null_at(2)}, {11}, {14}};
+    auto results = cudf::lists::segmented_gather(
+      cudf::lists_column_view{list}, cudf::lists_column_view{gather_map}, NULLIFY);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+
+    auto const sliced  = cudf::split(list, {1, 4});
+    auto const split_m = cudf::split(gather_map, {1, 4});
+    auto const split_e = cudf::split(expected, {1, 4});
+
+    auto const result0 = cudf::lists::segmented_gather(
+      cudf::lists_column_view{sliced[0]}, cudf::lists_column_view{split_m[0]}, NULLIFY);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(split_e[0], result0->view());
+    auto const result1 = cudf::lists::segmented_gather(
+      cudf::lists_column_view{sliced[1]}, cudf::lists_column_view{split_m[1]}, NULLIFY);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(split_e[1], result1->view());
+    auto const result2 = cudf::lists::segmented_gather(
+      cudf::lists_column_view{sliced[2]}, cudf::lists_column_view{split_m[2]}, NULLIFY);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(split_e[2], result2->view());
+  }
+}
+
+TEST_F(SegmentedGatherTestFloat, Fails)
+{
+  using T = float;
+  // List<T>
+  LCW<T> list{{1, 2, 3, 4}, {5}, {6, 7}, {8, 9, 10}};
+  LCW<int8_t> size_mismatch_map{{3, 2, 1, 0}, {0}, {0, 1}};
+  cudf::test::fixed_width_column_wrapper<int> nonlist_map0{1, 2, 0, 1};
+  cudf::test::strings_column_wrapper nonlist_map1{"1", "2", "0", "1"};
+  LCW<cudf::string_view> nonlist_map2{{"1", "2", "0", "1"}};
+
+  // Input must be a list of integer indices. It should fail for integers,
+  // strings, or lists containing anything other than integers.
+  EXPECT_THROW(cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                             cudf::lists_column_view{nonlist_map0}),
+               cudf::logic_error);
+
+  EXPECT_THROW(cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                             cudf::lists_column_view{nonlist_map1}),
+               cudf::logic_error);
+
+  EXPECT_THROW(cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                             cudf::lists_column_view{nonlist_map2}),
+               cudf::logic_error);
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  LCW<int8_t> nulls_map{{{3, 2, 1, 0}, {0}, {0}, {0, 1}}, valids};
+
+  // Nulls are not supported in the gather map.
+  EXPECT_THROW(cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                             cudf::lists_column_view{nulls_map}),
+               std::invalid_argument);
+
+  // Gather map and list column sizes must be the same.
+  EXPECT_THROW(cudf::lists::segmented_gather(cudf::lists_column_view{list},
+                                             cudf::lists_column_view{size_mismatch_map}),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/copying/shift_tests.cpp b/cpp/tests/copying/shift_tests.cpp
new file mode 100644
index 0000000..17e56ea
--- /dev/null
+++ b/cpp/tests/copying/shift_tests.cpp
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <limits>
+#include <memory>
+
+using TestTypes = cudf::test::Types<int32_t>;
+
+template <typename T, typename ScalarType = cudf::scalar_type_t<T>>
+std::unique_ptr<cudf::scalar> make_scalar(
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  auto s = new ScalarType(cudf::test::make_type_param_scalar<T>(0), false, stream, mr);
+  return std::unique_ptr<cudf::scalar>(s);
+}
+
+template <typename T, typename ScalarType = cudf::scalar_type_t<T>>
+std::unique_ptr<cudf::scalar> make_scalar(
+  T value,
+  rmm::cuda_stream_view stream        = cudf::get_default_stream(),
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  auto s = new ScalarType(value, true, stream, mr);
+  return std::unique_ptr<cudf::scalar>(s);
+}
+
+template <typename T>
+constexpr auto highest()
+{
+  // chrono types do not have std::numeric_limits specializations and should use T::max()
+  // https://eel.is/c++draft/numeric.limits.general#6
+  if constexpr (cudf::is_chrono<T>()) return T::max();
+  return std::numeric_limits<T>::max();
+}
+
+template <typename T>
+constexpr auto lowest()
+{
+  // chrono types do not have std::numeric_limits specializations and should use T::min()
+  // https://eel.is/c++draft/numeric.limits.general#6
+  if constexpr (cudf::is_chrono<T>()) return T::min();
+  return std::numeric_limits<T>::lowest();
+}
+
+template <typename T>
+struct ShiftTestsTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ShiftTestsTyped, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ShiftTestsTyped, ColumnEmpty)
+{
+  using T = TypeParam;
+
+  std::vector<T> vals{};
+  std::vector<bool> mask{};
+
+  auto input    = cudf::test::fixed_width_column_wrapper<T>(vals.begin(), vals.end(), mask.begin());
+  auto expected = cudf::test::fixed_width_column_wrapper<T>(vals.begin(), vals.end(), mask.begin());
+
+  auto fill   = make_scalar<T>();
+  auto actual = cudf::shift(input, 5, *fill);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual);
+}
+
+TYPED_TEST(ShiftTestsTyped, NonNullColumn)
+{
+  using T = TypeParam;
+
+  auto input = cudf::test::fixed_width_column_wrapper<T>{lowest<T>(),
+                                                         cudf::test::make_type_param_scalar<T>(1),
+                                                         cudf::test::make_type_param_scalar<T>(2),
+                                                         cudf::test::make_type_param_scalar<T>(3),
+                                                         cudf::test::make_type_param_scalar<T>(4),
+                                                         cudf::test::make_type_param_scalar<T>(5),
+                                                         highest<T>()};
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T>{cudf::test::make_type_param_scalar<T>(7),
+                                              cudf::test::make_type_param_scalar<T>(7),
+                                              lowest<T>(),
+                                              cudf::test::make_type_param_scalar<T>(1),
+                                              cudf::test::make_type_param_scalar<T>(2),
+                                              cudf::test::make_type_param_scalar<T>(3),
+                                              cudf::test::make_type_param_scalar<T>(4)};
+
+  auto fill   = make_scalar<T>(cudf::test::make_type_param_scalar<T>(7));
+  auto actual = cudf::shift(input, 2, *fill);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual);
+}
+
+TYPED_TEST(ShiftTestsTyped, NegativeShift)
+{
+  using T = TypeParam;
+
+  auto input = cudf::test::fixed_width_column_wrapper<T>{lowest<T>(),
+                                                         cudf::test::make_type_param_scalar<T>(1),
+                                                         cudf::test::make_type_param_scalar<T>(2),
+                                                         cudf::test::make_type_param_scalar<T>(3),
+                                                         cudf::test::make_type_param_scalar<T>(4),
+                                                         cudf::test::make_type_param_scalar<T>(5),
+                                                         highest<T>()};
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T>{cudf::test::make_type_param_scalar<T>(4),
+                                              cudf::test::make_type_param_scalar<T>(5),
+                                              highest<T>(),
+                                              cudf::test::make_type_param_scalar<T>(7),
+                                              cudf::test::make_type_param_scalar<T>(7),
+                                              cudf::test::make_type_param_scalar<T>(7),
+                                              cudf::test::make_type_param_scalar<T>(7)};
+
+  auto fill   = make_scalar<T>(cudf::test::make_type_param_scalar<T>(7));
+  auto actual = cudf::shift(input, -4, *fill);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual);
+}
+
+TYPED_TEST(ShiftTestsTyped, NullScalar)
+{
+  using T = TypeParam;
+
+  auto input = cudf::test::fixed_width_column_wrapper<T>{lowest<T>(),
+                                                         cudf::test::make_type_param_scalar<T>(5),
+                                                         cudf::test::make_type_param_scalar<T>(0),
+                                                         cudf::test::make_type_param_scalar<T>(3),
+                                                         cudf::test::make_type_param_scalar<T>(0),
+                                                         cudf::test::make_type_param_scalar<T>(1),
+                                                         highest<T>()};
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T>({cudf::test::make_type_param_scalar<T>(0),
+                                               cudf::test::make_type_param_scalar<T>(0),
+                                               lowest<T>(),
+                                               cudf::test::make_type_param_scalar<T>(5),
+                                               cudf::test::make_type_param_scalar<T>(0),
+                                               cudf::test::make_type_param_scalar<T>(3),
+                                               cudf::test::make_type_param_scalar<T>(0)},
+                                              {0, 0, 1, 1, 1, 1, 1});
+
+  auto fill = make_scalar<T>();
+
+  auto actual = cudf::shift(input, 2, *fill);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual);
+}
+
+TYPED_TEST(ShiftTestsTyped, NullableColumn)
+{
+  using T = TypeParam;
+
+  auto input = cudf::test::fixed_width_column_wrapper<T, int32_t>({1, 2, 3, 4, 5}, {0, 1, 1, 1, 0});
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>({7, 7, 1, 2, 3}, {1, 1, 0, 1, 1});
+
+  auto fill   = make_scalar<T>(cudf::test::make_type_param_scalar<T>(7));
+  auto actual = cudf::shift(input, 2, *fill);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual);
+}
+
+TYPED_TEST(ShiftTestsTyped, MismatchFillValueDtypes)
+{
+  using T = TypeParam;
+
+  auto input = cudf::test::fixed_width_column_wrapper<T>{};
+
+  auto fill = cudf::string_scalar("");
+
+  EXPECT_THROW(cudf::shift(input, 5, fill), cudf::logic_error);
+}
+
+struct ShiftTests : public cudf::test::BaseFixture {};
+
+TEST_F(ShiftTests, StringsShiftTest)
+{
+  auto input =
+    cudf::test::strings_column_wrapper({"", "bb", "ccc", "ddddddé", ""}, {0, 1, 1, 1, 0});
+
+  auto fill    = cudf::string_scalar("xx");
+  auto results = cudf::shift(input, 2, fill);
+  auto expected_right =
+    cudf::test::strings_column_wrapper({"xx", "xx", "", "bb", "ccc"}, {1, 1, 0, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_right, *results);
+
+  results = cudf::shift(input, -2, fill);
+  auto expected_left =
+    cudf::test::strings_column_wrapper({"ccc", "ddddddé", "", "xx", "xx"}, {1, 1, 0, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_left, *results);
+
+  auto sliced = cudf::slice(input, {1, 4}).front();
+
+  results           = cudf::shift(sliced, 1, fill);
+  auto sliced_right = cudf::test::strings_column_wrapper({"xx", "bb", "ccc"}, {1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(sliced_right, *results);
+
+  results          = cudf::shift(sliced, -1, fill);
+  auto sliced_left = cudf::test::strings_column_wrapper({"ccc", "ddddddé", "xx"}, {1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(sliced_left, *results);
+}
+
+TEST_F(ShiftTests, StringsShiftNullFillTest)
+{
+  auto input = cudf::test::strings_column_wrapper(
+    {"a", "b", "c", "d", "e", "ff", "ggg", "hhhh", "iii", "jjjjj"});
+  auto phil = cudf::string_scalar("", false);
+
+  auto results  = cudf::shift(input, -1, phil);
+  auto expected = cudf::test::strings_column_wrapper(
+    {"b", "c", "d", "e", "ff", "ggg", "hhhh", "iii", "jjjjj", ""}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::shift(input, 1, phil);
+  expected = cudf::test::strings_column_wrapper(
+    {"", "a", "b", "c", "d", "e", "ff", "ggg", "hhhh", "iii"}, {0, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  auto sliced = cudf::slice(input, {5, 10}).front();
+  results     = cudf::shift(sliced, -2, phil);
+  expected = cudf::test::strings_column_wrapper({"hhhh", "iii", "jjjjj", "", ""}, {1, 1, 1, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::shift(sliced, 2, phil);
+  expected = cudf::test::strings_column_wrapper({"", "", "ff", "ggg", "hhhh"}, {0, 0, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(ShiftTests, OffsetGreaterThanSize)
+{
+  auto const input_str =
+    cudf::test::strings_column_wrapper({"", "bb", "ccc", "ddé", ""}, {0, 1, 1, 1, 0});
+  auto results      = cudf::shift(input_str, 6, cudf::string_scalar("xx"));
+  auto expected_str = cudf::test::strings_column_wrapper({"xx", "xx", "xx", "xx", "xx"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_str, *results);
+  results = cudf::shift(input_str, -6, cudf::string_scalar("xx"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_str, *results);
+
+  results      = cudf::shift(input_str, 6, cudf::string_scalar("", false));
+  expected_str = cudf::test::strings_column_wrapper({"", "", "", "", ""}, {0, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_str, *results);
+  results = cudf::shift(input_str, -6, cudf::string_scalar("", false));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_str, *results);
+
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>({0, 2, 3, 4, 0}, {0, 1, 1, 1, 0});
+  results       = cudf::shift(input, 6, cudf::numeric_scalar<int32_t>(9));
+  auto expected = cudf::test::fixed_width_column_wrapper<int32_t>({9, 9, 9, 9, 9});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results);
+  results = cudf::shift(input, -6, cudf::numeric_scalar<int32_t>(9));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results);
+
+  results  = cudf::shift(input, 6, cudf::numeric_scalar<int32_t>(0, false));
+  expected = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results);
+  results = cudf::shift(input, -6, cudf::numeric_scalar<int32_t>(0, false));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results);
+}
diff --git a/cpp/tests/copying/slice_tests.cpp b/cpp/tests/copying/slice_tests.cpp
new file mode 100644
index 0000000..b7f00b4
--- /dev/null
+++ b/cpp/tests/copying/slice_tests.cpp
@@ -0,0 +1,532 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <tests/copying/slice_tests.cuh>
+
+#include <string>
+#include <vector>
+
+template <typename T>
+struct SliceTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SliceTest, cudf::test::NumericTypes);
+
+TYPED_TEST(SliceTest, NumericColumnsWithNulls)
+{
+  using T = TypeParam;
+
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, valids);
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 2, 5, 9};
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> expected =
+    create_expected_columns<T>(indices, true);
+  std::vector<cudf::column_view> result = cudf::slice(col, indices);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], result[index]);
+  }
+}
+
+TYPED_TEST(SliceTest, NumericColumnsWithNullsAsColumn)
+{
+  using T = TypeParam;
+
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<T> input = create_fixed_columns<T>(start, size, valids);
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 2, 5, 9};
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> expected =
+    create_expected_columns<T>(indices, true);
+  std::vector<cudf::column_view> result = cudf::slice(input, indices);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    auto col = cudf::column(result[index]);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], col);
+  }
+}
+
+struct SliceStringTest : public SliceTest<std::string> {};
+
+TEST_F(SliceStringTest, StringWithNulls)
+{
+  std::vector<std::string> strings{
+    "", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"};
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  cudf::test::strings_column_wrapper s(strings.begin(), strings.end(), valids);
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 4, 1, 9};
+
+  std::vector<cudf::test::strings_column_wrapper> expected =
+    create_expected_string_columns(strings, indices, true);
+  std::vector<cudf::column_view> result = cudf::slice(s, indices);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], result[index]);
+  }
+}
+
+TEST_F(SliceStringTest, StringWithNullsAsColumn)
+{
+  std::vector<std::string> strings{
+    "", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"};
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  cudf::test::strings_column_wrapper s(strings.begin(), strings.end(), valids);
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 4, 1, 9};
+
+  std::vector<cudf::test::strings_column_wrapper> expected =
+    create_expected_string_columns(strings, indices, true);
+  std::vector<cudf::column_view> result = cudf::slice(s, indices);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    // this materializes a column to test slicing + materialization
+    auto result_col = cudf::column(result[index]);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], result_col);
+  }
+}
+
+struct SliceListTest : public SliceTest<int> {};
+
+TEST_F(SliceListTest, Lists)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  {
+    cudf::test::lists_column_wrapper<int> list{{1, 2, 3},
+                                               {4, 5},
+                                               {6},
+                                               {7, 8},
+                                               {9, 10, 11},
+                                               LCW{},
+                                               LCW{},
+                                               {-1, -2, -3, -4, -5},
+                                               {-10},
+                                               {-100, -200}};
+
+    std::vector<cudf::size_type> indices{1, 3, 2, 4, 1, 9};
+
+    std::vector<cudf::test::lists_column_wrapper<int>> expected;
+    expected.push_back(LCW{{4, 5}, {6}});
+    expected.push_back(LCW{{6}, {7, 8}});
+    expected.push_back(
+      LCW{{4, 5}, {6}, {7, 8}, {9, 10, 11}, LCW{}, LCW{}, {-1, -2, -3, -4, -5}, {-10}});
+
+    std::vector<cudf::column_view> result = cudf::slice(list, indices);
+    EXPECT_EQ(expected.size(), result.size());
+
+    for (unsigned long index = 0; index < result.size(); index++) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected[index], result[index]);
+    }
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> list{{{1, 2, 3}, {4, 5}},
+                                               {LCW{}, LCW{}, {7, 8}, LCW{}},
+                                               {{{6}}},
+                                               {{7, 8}, {9, 10, 11}, LCW{}},
+                                               {LCW{}, {-1, -2, -3, -4, -5}},
+                                               {LCW{}},
+                                               {{-10}, {-100, -200}}};
+
+    std::vector<cudf::size_type> indices{1, 3, 3, 6};
+
+    std::vector<cudf::test::lists_column_wrapper<int>> expected;
+    expected.push_back(LCW{{LCW{}, LCW{}, {7, 8}, LCW{}}, {{{6}}}});
+    expected.push_back(LCW{{{7, 8}, {9, 10, 11}, LCW{}}, {LCW{}, {-1, -2, -3, -4, -5}}, {LCW{}}});
+
+    std::vector<cudf::column_view> result = cudf::slice(list, indices);
+    EXPECT_EQ(expected.size(), result.size());
+
+    for (unsigned long index = 0; index < result.size(); index++) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected[index], result[index]);
+    }
+  }
+}
+
+TEST_F(SliceListTest, ListsWithNulls)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  {
+    cudf::test::lists_column_wrapper<int> list{{1, 2, 3},
+                                               {4, 5},
+                                               {6},
+                                               {{7, 8}, valids},
+                                               {9, 10, 11},
+                                               LCW{},
+                                               LCW{},
+                                               {{-1, -2, -3, -4, -5}, valids},
+                                               {-10},
+                                               {{-100, -200}, valids}};
+
+    std::vector<cudf::size_type> indices{1, 3, 2, 4, 1, 9};
+
+    std::vector<cudf::test::lists_column_wrapper<int>> expected;
+    expected.push_back(LCW{{4, 5}, {6}});
+    expected.push_back(LCW{{6}, {{7, 8}, valids}});
+    expected.push_back(LCW{{4, 5},
+                           {6},
+                           {{7, 8}, valids},
+                           {9, 10, 11},
+                           LCW{},
+                           LCW{},
+                           {{-1, -2, -3, -4, -5}, valids},
+                           {-10}});
+
+    std::vector<cudf::column_view> result = cudf::slice(list, indices);
+    EXPECT_EQ(expected.size(), result.size());
+
+    for (unsigned long index = 0; index < result.size(); index++) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected[index], result[index]);
+    }
+  }
+
+  {
+    cudf::test::lists_column_wrapper<int> list{{{{1, 2, 3}, valids}, {4, 5}},
+                                               {{LCW{}, LCW{}, {7, 8}, LCW{}}, valids},
+                                               {{{6}}},
+                                               {{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids},
+                                               {{LCW{}, {-1, -2, -3, -4, -5}}, valids},
+                                               {LCW{}},
+                                               {{-10}, {-100, -200}}};
+
+    std::vector<cudf::size_type> indices{1, 3, 3, 6};
+
+    std::vector<cudf::test::lists_column_wrapper<int>> expected;
+    expected.push_back(LCW{{{LCW{}, LCW{}, {7, 8}, LCW{}}, valids}, {{{6}}}});
+    expected.push_back(LCW{{{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids},
+                           {{LCW{}, {-1, -2, -3, -4, -5}}, valids},
+                           {LCW{}}});
+
+    std::vector<cudf::column_view> result = cudf::slice(list, indices);
+    EXPECT_EQ(expected.size(), result.size());
+
+    for (unsigned long index = 0; index < result.size(); index++) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected[index], result[index]);
+    }
+  }
+}
+
+struct SliceCornerCases : public SliceTest<int8_t> {};
+
+TEST_F(SliceCornerCases, EmptyColumn)
+{
+  cudf::column col{};
+  std::vector<cudf::size_type> indices{0, 0, 0, 0, 0, 0};
+
+  std::vector<cudf::column_view> result = cudf::slice(col.view(), indices);
+
+  unsigned long expected = 3;
+
+  EXPECT_EQ(expected, result.size());
+
+  auto type_match_count = std::count_if(result.cbegin(), result.cend(), [](auto const& col) {
+    return col.type().id() == cudf::type_id::EMPTY;
+  });
+  EXPECT_EQ(static_cast<std::size_t>(type_match_count), expected);
+}
+
+TEST_F(SliceCornerCases, EmptyIndices)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> indices{};
+
+  std::vector<cudf::column_view> result = cudf::slice(col, indices);
+
+  unsigned long expected = 0;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+TEST_F(SliceCornerCases, InvalidSetOfIndices)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> indices{11, 12};
+
+  EXPECT_THROW(cudf::slice(col, indices), cudf::logic_error);
+}
+
+TEST_F(SliceCornerCases, ImproperRange)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> indices{5, 4};
+
+  EXPECT_THROW(cudf::slice(col, indices), cudf::logic_error);
+}
+
+TEST_F(SliceCornerCases, NegativeOffset)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> indices{-1, 4};
+
+  EXPECT_THROW(cudf::slice(col, indices), cudf::logic_error);
+}
+
+template <typename T>
+struct SliceTableTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SliceTableTest, cudf::test::NumericTypes);
+
+TYPED_TEST(SliceTableTest, NumericColumnsWithNulls)
+{
+  using T = TypeParam;
+
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<T>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 2, 5, 9};
+  std::vector<cudf::table> expected = create_expected_tables<T>(num_cols, indices, true);
+
+  std::vector<cudf::table_view> result = cudf::slice(src_table, indices);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected[index], result[index]);
+  }
+}
+
+struct SliceStringTableTest : public SliceTableTest<std::string> {};
+
+TEST_F(SliceStringTableTest, StringWithNulls)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<std::string> strings[2] = {
+    {"", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"},
+    {"", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"}};
+  cudf::test::strings_column_wrapper sw[2] = {{strings[0].begin(), strings[0].end(), valids},
+                                              {strings[1].begin(), strings[1].end(), valids}};
+
+  std::vector<std::unique_ptr<cudf::column>> scols;
+  scols.push_back(sw[0].release());
+  scols.push_back(sw[1].release());
+  cudf::table src_table(std::move(scols));
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 4, 1, 9};
+
+  std::vector<cudf::table> expected = create_expected_string_tables(strings, indices, true);
+
+  std::vector<cudf::table_view> result = cudf::slice(src_table, indices);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(expected[index], result[index]);
+  }
+}
+
+struct SliceTableCornerCases : public SliceTableTest<int8_t> {};
+
+TEST_F(SliceTableCornerCases, EmptyTable)
+{
+  std::vector<cudf::size_type> indices{1, 3, 2, 4, 5, 9};
+
+  cudf::table src_table{};
+  std::vector<cudf::table_view> result = cudf::slice(src_table.view(), indices);
+
+  unsigned long expected = 3;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+TEST_F(SliceTableCornerCases, EmptyIndices)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+  std::vector<cudf::size_type> indices{};
+
+  std::vector<cudf::table_view> result = cudf::slice(src_table, indices);
+
+  unsigned long expected = 0;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+TEST_F(SliceTableCornerCases, InvalidSetOfIndices)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> indices{11, 12};
+
+  EXPECT_THROW(cudf::slice(src_table, indices), cudf::logic_error);
+}
+
+TEST_F(SliceTableCornerCases, ImproperRange)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> indices{5, 4};
+
+  EXPECT_THROW(cudf::slice(src_table, indices), cudf::logic_error);
+}
+
+TEST_F(SliceTableCornerCases, NegativeOffset)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> indices{-1, 4};
+
+  EXPECT_THROW(cudf::slice(src_table, indices), cudf::logic_error);
+}
+
+TEST_F(SliceTableCornerCases, MiscOffset)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{
+    {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
+     3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col3{
+    {3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0}};
+  std::vector<cudf::size_type> indices{19, 38};
+  std::vector<cudf::column_view> result = cudf::slice(col2, indices);
+  cudf::column result_column(result[0]);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col3, result_column);
+}
+
+TEST_F(SliceTableCornerCases, PreSlicedInputs)
+{
+  {
+    using LCW = cudf::test::lists_column_wrapper<float>;
+
+    cudf::test::fixed_width_column_wrapper<int> a{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                  {1, 1, 0, 1, 1, 1, 0, 0, 1, 0}};
+
+    cudf::test::fixed_width_column_wrapper<int> b{{0, -1, -2, -3, -4, -5, -6, -7, -8, -9},
+                                                  {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
+
+    cudf::test::strings_column_wrapper c{{"aa", "b", "", "ccc", "ddd", "e", "ff", "", "", "gggg"},
+                                         {0, 0, 1, 1, 0, 0, 1, 1, 1, 0}};
+
+    std::vector<bool> list_validity{1, 0, 1, 0, 1, 1, 0, 0, 1, 1};
+    cudf::test::lists_column_wrapper<float> d{
+      {{0, 1}, {2}, {3, 4, 5}, {6}, {7, 7}, {8, 9}, {10, 11}, {12, 13}, {}, {14, 15, 16}},
+      list_validity.begin()};
+
+    cudf::table_view t({a, b, c, d});
+
+    auto pre_sliced = cudf::slice(t, {0, 4, 4, 10});
+
+    auto result = cudf::slice(pre_sliced[1], {0, 1, 1, 6});
+
+    cudf::test::fixed_width_column_wrapper<int> e0_a({4}, {1});
+    cudf::test::fixed_width_column_wrapper<int> e0_b({-4}, {0});
+    cudf::test::strings_column_wrapper e0_c({""}, {0});
+    std::vector<bool> e0_list_validity{1};
+    cudf::test::lists_column_wrapper<float> e0_d({LCW{7, 7}}, e0_list_validity.begin());
+    cudf::table_view expected0({e0_a, e0_b, e0_c, e0_d});
+    CUDF_TEST_EXPECT_TABLES_EQUAL(result[0], expected0);
+
+    cudf::test::fixed_width_column_wrapper<int> e1_a{{5, 6, 7, 8, 9}, {1, 0, 0, 1, 0}};
+    cudf::test::fixed_width_column_wrapper<int> e1_b{{-5, -6, -7, -8, -9}, {0, 0, 0, 0, 0}};
+    cudf::test::strings_column_wrapper e1_c{{"e", "ff", "", "", "gggg"}, {0, 1, 1, 1, 0}};
+    std::vector<bool> e1_list_validity{1, 0, 0, 1, 1};
+    cudf::test::lists_column_wrapper<float> e1_d{{{8, 9}, {10, 11}, {12, 13}, {}, {14, 15, 16}},
+                                                 e1_list_validity.begin()};
+    cudf::table_view expected1({e1_a, e1_b, e1_c, e1_d});
+    CUDF_TEST_EXPECT_TABLES_EQUAL(result[1], expected1);
+  }
+}
diff --git a/cpp/tests/copying/slice_tests.cuh b/cpp/tests/copying/slice_tests.cuh
new file mode 100644
index 0000000..a180740
--- /dev/null
+++ b/cpp/tests/copying/slice_tests.cuh
@@ -0,0 +1,223 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+template <typename T, typename InputIterator>
+cudf::test::fixed_width_column_wrapper<T> create_fixed_columns(cudf::size_type start,
+                                                               cudf::size_type size,
+                                                               InputIterator valids)
+{
+  auto iter = cudf::detail::make_counting_transform_iterator(start, [](auto i) { return T(i); });
+
+  return cudf::test::fixed_width_column_wrapper<T>(iter, iter + size, valids);
+}
+
+template <typename T, typename InputIterator>
+cudf::table create_fixed_table(cudf::size_type num_cols,
+                               cudf::size_type start,
+                               cudf::size_type col_size,
+                               InputIterator valids)
+{
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  for (int idx = 0; idx < num_cols; idx++) {
+    cudf::test::fixed_width_column_wrapper<T> wrap =
+      create_fixed_columns<T>(start + (idx * num_cols), col_size, valids);
+    cols.push_back(wrap.release());
+  }
+  return cudf::table(std::move(cols));
+}
+
+template <typename T>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns(
+  std::vector<cudf::size_type> const& indices, bool nullable)
+{
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    auto iter =
+      cudf::detail::make_counting_transform_iterator(indices[index], [](auto i) { return T(i); });
+    if (not nullable) {
+      result.push_back(cudf::test::fixed_width_column_wrapper<T>(
+        iter, iter + (indices[index + 1] - indices[index])));
+    } else {
+      auto valids = cudf::detail::make_counting_transform_iterator(
+        indices[index], [](auto i) { return i % 2 == 0; });
+      result.push_back(cudf::test::fixed_width_column_wrapper<T>(
+        iter, iter + (indices[index + 1] - indices[index]), valids));
+    }
+  }
+
+  return result;
+}
+
+template <typename T>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns(
+  std::vector<cudf::size_type> const& indices, std::vector<bool> const& validity)
+{
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    auto iter =
+      cudf::detail::make_counting_transform_iterator(indices[index], [](auto i) { return T(i); });
+    result.push_back(cudf::test::fixed_width_column_wrapper<T>(
+      iter, iter + (indices[index + 1] - indices[index]), validity.begin() + indices[index]));
+  }
+
+  return result;
+}
+
+template <typename T, typename ElementIter>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns(
+  std::vector<cudf::size_type> const& indices, ElementIter begin, bool nullable)
+{
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    auto iter = begin + indices[index];
+
+    if (not nullable) {
+      result.push_back(cudf::test::fixed_width_column_wrapper<T>(
+        iter, iter + (indices[index + 1] - indices[index])));
+    } else {
+      auto valids = cudf::detail::make_counting_transform_iterator(
+        indices[index], [](auto i) { return i % 2 == 0; });
+      result.push_back(cudf::test::fixed_width_column_wrapper<T>(
+        iter, iter + (indices[index + 1] - indices[index]), valids));
+    }
+  }
+
+  return result;
+}
+
+template <typename T, typename ElementIter>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns(
+  std::vector<cudf::size_type> const& indices, ElementIter begin, std::vector<bool> const& validity)
+{
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    auto iter = begin + indices[index];
+    result.push_back(cudf::test::fixed_width_column_wrapper<T>(
+      iter, iter + (indices[index + 1] - indices[index]), validity.begin() + indices[index]));
+  }
+
+  return result;
+}
+
+template <typename T>
+std::vector<cudf::table> create_expected_tables(cudf::size_type num_cols,
+                                                std::vector<cudf::size_type> const& indices,
+                                                bool nullable)
+{
+  std::vector<cudf::table> result;
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    std::vector<std::unique_ptr<cudf::column>> cols = {};
+
+    for (int idx = 0; idx < num_cols; idx++) {
+      auto iter = cudf::detail::make_counting_transform_iterator(indices[index] + (idx * num_cols),
+                                                                 [](auto i) { return T(i); });
+
+      if (not nullable) {
+        cudf::test::fixed_width_column_wrapper<T> wrap(
+          iter, iter + (indices[index + 1] - indices[index]));
+        cols.push_back(wrap.release());
+      } else {
+        auto valids = cudf::detail::make_counting_transform_iterator(
+          indices[index], [](auto i) { return i % 2 == 0; });
+        cudf::test::fixed_width_column_wrapper<T> wrap(
+          iter, iter + (indices[index + 1] - indices[index]), valids);
+        cols.push_back(wrap.release());
+      }
+    }
+
+    result.push_back(cudf::table(std::move(cols)));
+  }
+
+  return result;
+}
+
+inline std::vector<cudf::test::strings_column_wrapper> create_expected_string_columns(
+  std::vector<std::string> const& strings,
+  std::vector<cudf::size_type> const& indices,
+  bool nullable)
+{
+  std::vector<cudf::test::strings_column_wrapper> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    if (not nullable) {
+      result.push_back(cudf::test::strings_column_wrapper(strings.begin() + indices[index],
+                                                          strings.begin() + indices[index + 1]));
+    } else {
+      auto valids = cudf::detail::make_counting_transform_iterator(
+        indices[index], [](auto i) { return i % 2 == 0; });
+      result.push_back(cudf::test::strings_column_wrapper(
+        strings.begin() + indices[index], strings.begin() + indices[index + 1], valids));
+    }
+  }
+
+  return result;
+}
+
+inline std::vector<cudf::test::strings_column_wrapper> create_expected_string_columns(
+  std::vector<std::string> const& strings,
+  std::vector<cudf::size_type> const& indices,
+  std::vector<bool> const& validity)
+{
+  std::vector<cudf::test::strings_column_wrapper> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    result.push_back(cudf::test::strings_column_wrapper(strings.begin() + indices[index],
+                                                        strings.begin() + indices[index + 1],
+                                                        validity.begin() + indices[index]));
+  }
+
+  return result;
+}
+
+inline std::vector<cudf::table> create_expected_string_tables(
+  std::vector<std::string> const strings[2],
+  std::vector<cudf::size_type> const& indices,
+  bool nullable)
+{
+  std::vector<cudf::table> result = {};
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    std::vector<std::unique_ptr<cudf::column>> cols = {};
+
+    for (int idx = 0; idx < 2; idx++) {
+      if (not nullable) {
+        cudf::test::strings_column_wrapper wrap(strings[idx].begin() + indices[index],
+                                                strings[idx].begin() + indices[index + 1]);
+        cols.push_back(wrap.release());
+      } else {
+        auto valids = cudf::detail::make_counting_transform_iterator(
+          indices[index], [](auto i) { return i % 2 == 0; });
+        cudf::test::strings_column_wrapper wrap(
+          strings[idx].begin() + indices[index], strings[idx].begin() + indices[index + 1], valids);
+        cols.push_back(wrap.release());
+      }
+    }
+
+    result.push_back(cudf::table(std::move(cols)));
+  }
+
+  return result;
+}
diff --git a/cpp/tests/copying/split_tests.cpp b/cpp/tests/copying/split_tests.cpp
new file mode 100644
index 0000000..842ba80
--- /dev/null
+++ b/cpp/tests/copying/split_tests.cpp
@@ -0,0 +1,2531 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/copying/slice_tests.cuh>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/contiguous_split.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/filling.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <string>
+#include <vector>
+
+std::vector<cudf::size_type> splits_to_indices(std::vector<cudf::size_type> splits,
+                                               cudf::size_type size)
+{
+  std::vector<cudf::size_type> indices{0};
+
+  std::for_each(splits.begin(), splits.end(), [&indices](auto split) {
+    indices.push_back(split);  // This for end
+    indices.push_back(split);  // This for the start
+  });
+
+  indices.push_back(size);  // This to include rest of the elements
+
+  return indices;
+}
+
+template <typename T>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns_for_splits(
+  std::vector<cudf::size_type> const& splits, cudf::size_type size, bool nullable)
+{
+  // convert splits to slice indices
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, size);
+  return create_expected_columns<T>(indices, nullable);
+}
+
+template <typename T>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns_for_splits(
+  std::vector<cudf::size_type> const& splits, std::vector<T> const& elements, bool nullable)
+{
+  // convert splits to slice indices
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, elements.size());
+  return create_expected_columns<T>(indices, elements.begin(), nullable);
+}
+
+template <typename T>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns_for_splits(
+  std::vector<cudf::size_type> const& splits,
+  cudf::size_type size,
+  std::vector<bool> const& validity)
+{
+  // convert splits to slice indices
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, size);
+  return create_expected_columns<T>(indices, validity);
+}
+
+template <typename T>
+std::vector<cudf::test::fixed_width_column_wrapper<T>> create_expected_columns_for_splits(
+  std::vector<cudf::size_type> const& splits,
+  std::vector<T> const& elements,
+  std::vector<bool> const& validity)
+{
+  // convert splits to slice indices
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, elements.size());
+  return create_expected_columns<T>(indices, elements.begin(), validity);
+}
+
+std::vector<cudf::test::strings_column_wrapper> create_expected_string_columns_for_splits(
+  std::vector<std::string> strings, std::vector<cudf::size_type> const& splits, bool nullable)
+{
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, strings.size());
+  return create_expected_string_columns(strings, indices, nullable);
+}
+
+std::vector<cudf::test::strings_column_wrapper> create_expected_string_columns_for_splits(
+  std::vector<std::string> strings,
+  std::vector<cudf::size_type> const& splits,
+  std::vector<bool> const& validity)
+{
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, strings.size());
+  return create_expected_string_columns(strings, indices, validity);
+}
+
+std::vector<std::vector<bool>> create_expected_validity(std::vector<cudf::size_type> const& splits,
+                                                        std::vector<bool> const& validity)
+{
+  std::vector<std::vector<bool>> result = {};
+  std::vector<cudf::size_type> indices  = splits_to_indices(splits, validity.size());
+
+  for (unsigned long index = 0; index < indices.size(); index += 2) {
+    result.push_back(
+      std::vector<bool>(validity.begin() + indices[index], validity.begin() + indices[index + 1]));
+  }
+
+  return result;
+}
+
+template <typename T>
+std::vector<cudf::table> create_expected_tables_for_splits(
+  cudf::size_type num_cols,
+  std::vector<cudf::size_type> const& splits,
+  cudf::size_type col_size,
+  bool nullable)
+{
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, col_size);
+  return create_expected_tables<T>(num_cols, indices, nullable);
+}
+
+std::vector<cudf::table> create_expected_string_tables_for_splits(
+  std::vector<std::string> const strings[2],
+  std::vector<cudf::size_type> const& splits,
+  bool nullable)
+{
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, strings[0].size());
+  return create_expected_string_tables(strings, indices, nullable);
+}
+
+std::vector<cudf::table> create_expected_string_tables_for_splits(
+  std::vector<std::string> const strings[2],
+  std::vector<bool> const validity[2],
+  std::vector<cudf::size_type> const& splits)
+{
+  std::vector<cudf::size_type> indices = splits_to_indices(splits, strings[0].size());
+  auto ret_cols_0 = create_expected_string_columns(strings[0], indices, validity[0]);
+  auto ret_cols_1 = create_expected_string_columns(strings[1], indices, validity[1]);
+
+  std::vector<cudf::table> ret_tables;
+  for (std::size_t i = 0; i < ret_cols_0.size(); ++i) {
+    std::vector<std::unique_ptr<cudf::column>> scols;
+    scols.push_back(ret_cols_0[i].release());
+    scols.push_back(ret_cols_1[i].release());
+    ret_tables.emplace_back(std::move(scols));
+  }
+  return ret_tables;
+}
+
+template <typename T>
+struct SplitTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SplitTest, cudf::test::NumericTypes);
+
+TYPED_TEST(SplitTest, SplitEndLessThanSize)
+{
+  using T = TypeParam;
+
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, valids);
+
+  std::vector<cudf::size_type> splits{2, 5, 7};
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> expected =
+    create_expected_columns_for_splits<T>(splits, size, true);
+  std::vector<cudf::column_view> result = cudf::split(col, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], result[index]);
+  }
+}
+
+TYPED_TEST(SplitTest, SplitEndToSize)
+{
+  using T = TypeParam;
+
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, valids);
+
+  std::vector<cudf::size_type> splits{2, 5, 10, 10, 10, 10};
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> expected =
+    create_expected_columns_for_splits<T>(splits, size, true);
+  std::vector<cudf::column_view> result = cudf::split(col, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], result[index]);
+  }
+}
+
+// common functions for testing split/contiguous_split
+template <typename T, typename SplitFunc, typename CompareFunc>
+void split_custom_column(SplitFunc Split,
+                         CompareFunc Compare,
+                         int size,
+                         std::vector<cudf::size_type> const& splits,
+                         bool include_validity)
+{
+  // the intent here is to stress the various boundary conditions in contiguous_split -
+  // especially the validity copying code.
+  cudf::size_type start = 0;
+
+  srand(824);
+
+  std::vector<std::string> base_strings(
+    {"banana", "pear", "apple", "pecans", "vanilla", "cat", "mouse", "green"});
+  auto string_randomizer = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0),
+    [&base_strings](cudf::size_type i) { return base_strings[rand() % base_strings.size()]; });
+
+  auto rvalids = cudf::detail::make_counting_transform_iterator(start, [include_validity](auto i) {
+    return include_validity
+             ? (static_cast<float>(rand()) / static_cast<float>(RAND_MAX) < 0.5f ? 0 : 1)
+             : 0;
+  });
+
+  std::vector<bool> valids{rvalids, rvalids + size};
+  cudf::test::fixed_width_column_wrapper<T> col =
+    create_fixed_columns<T>(start, size, valids.begin());
+
+  std::vector<bool> valids2{rvalids, rvalids + size};
+  std::vector<std::string> strings(string_randomizer, string_randomizer + size);
+  cudf::test::strings_column_wrapper col2(strings.begin(), strings.end(), valids2.begin());
+
+  std::vector<cudf::table_view> expected;
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> expected_fixed =
+    create_expected_columns_for_splits<T>(splits, size, valids);
+  std::vector<cudf::test::strings_column_wrapper> expected_strings =
+    create_expected_string_columns_for_splits(strings, splits, valids2);
+  std::transform(thrust::make_counting_iterator(static_cast<size_t>(0)),
+                 thrust::make_counting_iterator(expected_fixed.size()),
+                 std::back_inserter(expected),
+                 [&expected_fixed, &expected_strings](size_t i) {
+                   return cudf::table_view({expected_fixed[i], expected_strings[i]});
+                 });
+
+  cudf::table_view tbl({col, col2});
+  auto result = Split(tbl, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    Compare(expected[index], result[index]);
+  }
+}
+
+TYPED_TEST(SplitTest, LongColumn)
+{
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::table_view const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i), result.column(i));
+                    });
+    },
+    10002,
+    std::vector<cudf::size_type>{
+      2, 16, 31, 35, 64, 97, 158, 190, 638, 899, 900, 901, 996, 4200, 7131, 8111},
+    true);
+
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::table_view const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i), result.column(i));
+                    });
+    },
+    10002,
+    std::vector<cudf::size_type>{
+      2, 16, 31, 35, 64, 97, 158, 190, 638, 899, 900, 901, 996, 4200, 7131, 8111},
+    false);
+}
+
+struct SplitStringTest : public SplitTest<std::string> {};
+
+TEST_F(SplitStringTest, StringWithInvalids)
+{
+  std::vector<std::string> strings{
+    "", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"};
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  cudf::test::strings_column_wrapper s(strings.begin(), strings.end(), valids);
+
+  std::vector<cudf::size_type> splits{2, 5, 9};
+
+  std::vector<cudf::test::strings_column_wrapper> expected =
+    create_expected_string_columns_for_splits(strings, splits, true);
+  std::vector<cudf::column_view> result = cudf::split(s, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected[index], result[index]);
+  }
+}
+
+struct SplitCornerCases : public SplitTest<int8_t> {};
+
+TEST_F(SplitCornerCases, EmptyColumn)
+{
+  cudf::column col{};
+  std::vector<cudf::size_type> splits{2, 5, 9};
+
+  std::vector<cudf::column_view> result = cudf::split(col.view(), splits);
+
+  unsigned long expected = 1;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+TEST_F(SplitCornerCases, EmptyIndices)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> splits{};
+
+  std::vector<cudf::column_view> result = cudf::split(col, splits);
+
+  unsigned long expected = 1;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+TEST_F(SplitCornerCases, InvalidSetOfIndices)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> splits{11, 12};
+
+  EXPECT_THROW(cudf::split(col, splits), cudf::logic_error);
+}
+
+TEST_F(SplitCornerCases, ImproperRange)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> splits{5, 4};
+
+  EXPECT_THROW(cudf::split(col, splits), cudf::logic_error);
+}
+
+TEST_F(SplitCornerCases, NegativeValue)
+{
+  cudf::size_type start = 0;
+  cudf::size_type size  = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::test::fixed_width_column_wrapper<int8_t> col =
+    create_fixed_columns<int8_t>(start, size, valids);
+  std::vector<cudf::size_type> splits{-1, 4};
+
+  EXPECT_THROW(cudf::split(col, splits), cudf::logic_error);
+}
+
+// common functions for testing split/contiguous_split
+template <typename T, typename SplitFunc, typename CompareFunc>
+void split_end_less_than_size(SplitFunc Split, CompareFunc Compare)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<T>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> splits{2, 5, 7};
+  std::vector<cudf::table> expected =
+    create_expected_tables_for_splits<T>(num_cols, splits, col_size, true);
+
+  auto result = Split(src_table, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    Compare(expected[index], result[index]);
+  }
+}
+
+template <typename T, typename SplitFunc, typename CompareFunc>
+void split_end_to_size(SplitFunc Split, CompareFunc Compare)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<T>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> splits{2, 5, 10};
+  std::vector<cudf::table> expected =
+    create_expected_tables_for_splits<T>(num_cols, splits, col_size, true);
+
+  auto result = Split(src_table, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    Compare(expected[index], result[index]);
+  }
+}
+
+template <typename SplitFunc>
+void split_empty_table(SplitFunc Split, std::vector<cudf::size_type> const& splits = {2, 5, 6})
+{
+  cudf::table src_table{};
+  auto result = Split(src_table, splits);
+
+  unsigned long expected = 0;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+template <typename SplitFunc>
+void split_empty_indices(SplitFunc Split)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+  std::vector<cudf::size_type> splits{};
+
+  auto result = Split(src_table, splits);
+
+  unsigned long expected = 1;
+
+  EXPECT_EQ(expected, result.size());
+}
+
+template <typename SplitFunc>
+void split_invalid_indices(SplitFunc Split)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> splits{11, 12};
+
+  EXPECT_THROW(Split(src_table, splits), cudf::logic_error);
+}
+
+template <typename SplitFunc>
+void split_improper_range(SplitFunc Split)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> splits{5, 4};
+
+  EXPECT_THROW(Split(src_table, splits), cudf::logic_error);
+}
+
+template <typename SplitFunc>
+void split_negative_value(SplitFunc Split)
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  std::vector<cudf::size_type> splits{-1, 4};
+
+  EXPECT_THROW(Split(src_table, splits), cudf::logic_error);
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_empty_output_column_value(SplitFunc Split,
+                                     CompareFunc Compare,
+                                     std::vector<cudf::size_type> const& splits = {0, 2, 2})
+{
+  cudf::size_type start    = 0;
+  cudf::size_type col_size = 10;
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i % 2 == 0; });
+
+  cudf::size_type num_cols = 5;
+  cudf::table src_table    = create_fixed_table<int8_t>(num_cols, start, col_size, valids);
+
+  EXPECT_NO_THROW(Split(src_table, splits));
+
+  auto result = Split(src_table, splits);
+  EXPECT_NO_THROW(Compare(result[0], num_cols));
+}
+
+// regular splits
+template <typename T>
+struct SplitTableTest : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(SplitTableTest, cudf::test::NumericTypes);
+
+TYPED_TEST(SplitTableTest, SplitEndLessThanSize)
+{
+  split_end_less_than_size<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::table_view const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result);
+    });
+}
+
+TYPED_TEST(SplitTableTest, SplitEndToSize)
+{
+  split_end_to_size<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::table_view const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result);
+    });
+}
+
+struct SplitTableCornerCases : public SplitTest<int8_t> {};
+
+TEST_F(SplitTableCornerCases, EmptyTable)
+{
+  split_empty_table([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::split(t, splits);
+  });
+}
+
+TEST_F(SplitTableCornerCases, EmptyIndices)
+{
+  split_empty_indices([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::split(t, splits);
+  });
+}
+
+TEST_F(SplitTableCornerCases, InvalidSetOfIndices)
+{
+  split_invalid_indices([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::split(t, splits);
+  });
+}
+
+TEST_F(SplitTableCornerCases, ImproperRange)
+{
+  split_improper_range([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::split(t, splits);
+  });
+}
+
+TEST_F(SplitTableCornerCases, NegativeValue)
+{
+  split_negative_value([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::split(t, splits);
+  });
+}
+
+TEST_F(SplitTableCornerCases, EmptyOutputColumn)
+{
+  split_empty_output_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& t, int num_cols) { EXPECT_EQ(t.num_columns(), num_cols); });
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_string_with_invalids(SplitFunc Split,
+                                CompareFunc Compare,
+                                std::vector<cudf::size_type> splits = {2, 5, 9})
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<std::string> strings[2] = {
+    {"", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"},
+    {"", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"}};
+  cudf::test::strings_column_wrapper sw[2] = {{strings[0].begin(), strings[0].end(), valids},
+                                              {strings[1].begin(), strings[1].end(), valids}};
+
+  std::vector<std::unique_ptr<cudf::column>> scols;
+  scols.push_back(sw[0].release());
+  scols.push_back(sw[1].release());
+  cudf::table src_table(std::move(scols));
+
+  std::vector<cudf::table> expected =
+    create_expected_string_tables_for_splits(strings, splits, true);
+
+  auto result = Split(src_table, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    Compare(expected[index], result[index]);
+  }
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_empty_output_strings_column_value(SplitFunc Split,
+                                             CompareFunc Compare,
+                                             std::vector<cudf::size_type> const& splits = {0, 2, 2})
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<std::string> strings[2] = {
+    {"", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"},
+    {"", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"}};
+  cudf::test::strings_column_wrapper sw[2] = {{strings[0].begin(), strings[0].end(), valids},
+                                              {strings[1].begin(), strings[1].end(), valids}};
+
+  std::vector<std::unique_ptr<cudf::column>> scols;
+  scols.push_back(sw[0].release());
+  scols.push_back(sw[1].release());
+  cudf::table src_table(std::move(scols));
+
+  cudf::size_type num_cols = 2;
+
+  EXPECT_NO_THROW(Split(src_table, splits));
+
+  auto result = Split(src_table, splits);
+  EXPECT_NO_THROW(Compare(result[0], num_cols));
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_null_input_strings_column_value(SplitFunc Split, CompareFunc Compare)
+{
+  auto no_valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return false; });
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  std::vector<std::string> strings[2] = {
+    {"", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"},
+    {"", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"}};
+
+  std::vector<cudf::size_type> splits{2, 5, 9};
+
+  {
+    cudf::test::strings_column_wrapper empty_str_col{
+      strings[0].begin(), strings[0].end(), no_valids};
+    std::vector<std::unique_ptr<cudf::column>> scols;
+    scols.push_back(empty_str_col.release());
+    cudf::table empty_table(std::move(scols));
+    EXPECT_NO_THROW(Split(empty_table, splits));
+  }
+
+  cudf::test::strings_column_wrapper sw[2] = {{strings[0].begin(), strings[0].end(), no_valids},
+                                              {strings[1].begin(), strings[1].end(), valids}};
+  std::vector<std::unique_ptr<cudf::column>> scols;
+  scols.push_back(sw[0].release());
+  scols.push_back(sw[1].release());
+  cudf::table src_table(std::move(scols));
+  auto result = Split(src_table, splits);
+
+  std::vector<bool> validity_masks[2] = {std::vector<bool>(strings[0].size()),
+                                         std::vector<bool>(strings[0].size())};
+  std::generate(
+    validity_masks[1].begin(), validity_masks[1].end(), [i = 0]() mutable { return i++ % 2 == 0; });
+
+  auto expected = create_expected_string_tables_for_splits(strings, validity_masks, splits);
+
+  for (std::size_t i = 0; i < result.size(); ++i) {
+    Compare(expected[i], result[i]);
+  }
+}
+
+// split with strings
+struct SplitStringTableTest : public SplitTest<std::string> {};
+
+TEST_F(SplitStringTableTest, StringWithInvalids)
+{
+  split_string_with_invalids(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::table_view const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result);
+    });
+}
+
+TEST_F(SplitStringTableTest, EmptyOutputColumn)
+{
+  split_empty_output_strings_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table_view const& t, int num_cols) { EXPECT_EQ(t.num_columns(), num_cols); });
+}
+
+TEST_F(SplitStringTableTest, NullStringColumn)
+{
+  split_null_input_strings_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::table const& expected, cudf::table_view const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected.view(), result);
+    });
+}
+
+struct SplitNestedTypesTest : public cudf::test::BaseFixture {};
+
+// common functions for testing split/contiguous_split
+template <typename T, typename SplitFunc, typename CompareFunc>
+void split_lists(SplitFunc Split, CompareFunc Compare, bool split = true)
+{
+  using LCW = cudf::test::lists_column_wrapper<T>;
+
+  {
+    cudf::test::lists_column_wrapper<T> list{{1, 2, 3},
+                                             {4, 5},
+                                             {6},
+                                             {7, 8},
+                                             {9, 10, 11},
+                                             LCW{},
+                                             LCW{},
+                                             {-1, -2, -3, -4, -5},
+                                             {-10},
+                                             {-100, -200}};
+
+    if (split) {
+      std::vector<cudf::size_type> splits{0, 1, 4, 5, 6, 9};
+
+      std::vector<cudf::test::lists_column_wrapper<T>> expected;
+      expected.push_back(LCW{});
+      expected.push_back(LCW{{1, 2, 3}});
+      expected.push_back(LCW{{4, 5}, {6}, {7, 8}});
+      expected.push_back(LCW{{9, 10, 11}});
+      expected.push_back(LCW{LCW{}});
+      expected.push_back(LCW{LCW{}, {-1, -2, -3, -4, -5}, {-10}});
+      expected.push_back(LCW{{-100, -200}});
+
+      auto result = Split(list, splits);
+      EXPECT_EQ(expected.size(), result.size());
+
+      for (unsigned long index = 0; index < result.size(); index++) {
+        Compare(expected[index], result[index]);
+      }
+    } else {
+      auto result = Split(list, {});
+      EXPECT_EQ(1, result.size());
+      Compare(list, result[0]);
+    }
+  }
+
+  {
+    cudf::test::lists_column_wrapper<T> list{{{1, 2, 3}, {4, 5}},
+                                             {LCW{}, LCW{}, {7, 8}, LCW{}},
+                                             {LCW{6}},
+                                             {{7, 8}, {9, 10, 11}, LCW{}},
+                                             {LCW{}, {-1, -2, -3, -4, -5}},
+                                             {LCW{}},
+                                             {{-10}, {-100, -200}}};
+
+    if (split) {
+      std::vector<cudf::size_type> splits{1, 3, 4};
+
+      std::vector<cudf::test::lists_column_wrapper<T>> expected;
+      expected.push_back(LCW{{{1, 2, 3}, {4, 5}}});
+      expected.push_back(LCW{{LCW{}, LCW{}, {7, 8}, LCW{}}, {LCW{6}}});
+      expected.push_back(LCW{{{7, 8}, {9, 10, 11}, LCW{}}});
+      expected.push_back(LCW{{LCW{}, {-1, -2, -3, -4, -5}}, {LCW{}}, {{-10}, {-100, -200}}});
+
+      auto result = Split(list, splits);
+      EXPECT_EQ(expected.size(), result.size());
+
+      for (unsigned long index = 0; index < result.size(); index++) {
+        Compare(expected[index], result[index]);
+      }
+    } else {
+      auto result = Split(list, {});
+      EXPECT_EQ(1, result.size());
+      Compare(list, result[0]);
+    }
+  }
+}
+
+template <typename T, typename SplitFunc, typename CompareFunc>
+void split_lists_with_nulls(SplitFunc Split, CompareFunc Compare, bool split = true)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  {
+    cudf::test::lists_column_wrapper<T> list{{1, 2, 3},
+                                             {4, 5},
+                                             {6},
+                                             {{7, 8}, valids},
+                                             {9, 10, 11},
+                                             LCW{},
+                                             LCW{},
+                                             {{-1, -2, -3, -4, -5}, valids},
+                                             {-10},
+                                             {{-100, -200}, valids}};
+
+    if (split) {
+      std::vector<cudf::size_type> splits{0, 1, 4, 5, 6, 9};
+
+      std::vector<cudf::test::lists_column_wrapper<T>> expected;
+      expected.push_back(LCW{});
+      expected.push_back(LCW{{1, 2, 3}});
+      expected.push_back(LCW{{4, 5}, {6}, {{7, 8}, valids}});
+      expected.push_back(LCW{{9, 10, 11}});
+      expected.push_back(LCW{LCW{}});
+      expected.push_back(LCW{LCW{}, {{-1, -2, -3, -4, -5}, valids}, {-10}});
+      expected.push_back(LCW{{{-100, -200}, valids}});
+
+      auto result = Split(list, splits);
+      EXPECT_EQ(expected.size(), result.size());
+
+      for (unsigned long index = 0; index < result.size(); index++) {
+        Compare(expected[index], result[index]);
+      }
+    } else {
+      auto result = Split(list, {});
+      EXPECT_EQ(1, result.size());
+      Compare(list, result[0]);
+    }
+  }
+
+  {
+    cudf::test::lists_column_wrapper<T> list{{{{1, 2, 3}, valids}, {4, 5}},
+                                             {{LCW{}, LCW{}, {7, 8}, LCW{}}, valids},
+                                             {{{6}}},
+                                             {{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids},
+                                             {{LCW{}, {-1, -2, -3, -4, -5}}, valids},
+                                             {LCW{}},
+                                             {{-10}, {-100, -200}}};
+
+    if (split) {
+      std::vector<cudf::size_type> splits{1, 3, 4};
+
+      std::vector<cudf::test::lists_column_wrapper<T>> expected;
+      expected.push_back(LCW{{{{1, 2, 3}, valids}, {4, 5}}});
+      expected.push_back(LCW{{{LCW{}, LCW{}, {7, 8}, LCW{}}, valids}, {{{6}}}});
+      expected.push_back(LCW{{{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids}});
+      expected.push_back(
+        LCW{{{LCW{}, {-1, -2, -3, -4, -5}}, valids}, {LCW{}}, {{-10}, {-100, -200}}});
+
+      auto result = Split(list, splits);
+      EXPECT_EQ(expected.size(), result.size());
+
+      for (unsigned long index = 0; index < result.size(); index++) {
+        Compare(expected[index], result[index]);
+      }
+    } else {
+      auto result = Split(list, {});
+      EXPECT_EQ(1, result.size());
+      Compare(list, result[0]);
+    }
+  }
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_structs(bool include_validity, SplitFunc Split, CompareFunc Compare, bool split = true)
+{
+  // 1. String "names" column.
+  std::vector<std::string> names{
+    "Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant", "Fred", "Todd", "Kevin"};
+  std::vector<bool> names_validity{1, 1, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper names_column(names.begin(), names.end());
+
+  // 2. Numeric "ages" column.
+  std::vector<int> ages{5, 10, 15, 20, 25, 30, 100, 101, 102};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 0, 1};
+  auto ages_column =
+    include_validity
+      ? cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end(), ages_validity.begin())
+      : cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end());
+
+  // 3. Boolean "is_human" column.
+  std::vector<bool> is_human{true, true, false, false, false, false, true, true, true};
+  std::vector<bool> is_human_validity{1, 1, 1, 0, 1, 1, 1, 1, 0};
+  auto is_human_col =
+    include_validity
+      ? cudf::test::fixed_width_column_wrapper<bool>(
+          is_human.begin(), is_human.end(), is_human_validity.begin())
+      : cudf::test::fixed_width_column_wrapper<bool>(is_human.begin(), is_human.end());
+
+  // Assemble struct column.
+  auto const struct_validity = std::vector<bool>{1, 1, 1, 1, 1, 0, 0, 1, 0};
+  auto struct_column =
+    include_validity
+      ? cudf::test::structs_column_wrapper({names_column, ages_column, is_human_col},
+                                           struct_validity.begin())
+      : cudf::test::structs_column_wrapper({names_column, ages_column, is_human_col});
+
+  // split
+  std::vector<cudf::size_type> splits;
+  if (split) { splits = std::vector<cudf::size_type>({0, 1, 3, 8}); }
+  auto result = Split(struct_column, splits);
+
+  // expected outputs
+  auto expected_names = include_validity
+                          ? create_expected_string_columns_for_splits(names, splits, names_validity)
+                          : create_expected_string_columns_for_splits(names, splits, false);
+  auto expected_ages  = include_validity
+                          ? create_expected_columns_for_splits<int>(splits, ages, ages_validity)
+                          : create_expected_columns_for_splits<int>(splits, ages, false);
+  auto expected_is_human =
+    include_validity ? create_expected_columns_for_splits<bool>(splits, is_human, is_human_validity)
+                     : create_expected_columns_for_splits<bool>(splits, is_human, false);
+
+  auto expected_struct_validity = create_expected_validity(splits, struct_validity);
+
+  EXPECT_EQ(expected_names.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    auto expected = include_validity
+                      ? cudf::test::structs_column_wrapper(
+                          {expected_names[index], expected_ages[index], expected_is_human[index]},
+                          expected_struct_validity[index])
+                      : cudf::test::structs_column_wrapper(
+                          {expected_names[index], expected_ages[index], expected_is_human[index]});
+
+    Compare(expected, result[index]);
+  }
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_structs_no_children(SplitFunc Split, CompareFunc Compare, bool split = true)
+{
+  // no nulls
+  {
+    auto struct_column = cudf::make_structs_column(4, {}, 0, rmm::device_buffer{});
+    if (split) {
+      auto expected = cudf::make_structs_column(2, {}, 0, rmm::device_buffer{});
+
+      // split
+      std::vector<cudf::size_type> splits{2};
+      auto result = Split(*struct_column, splits);
+
+      EXPECT_EQ(result.size(), 2ul);
+      Compare(*expected, result[0]);
+      Compare(*expected, result[1]);
+    } else {
+      auto result = Split(*struct_column, {});
+      EXPECT_EQ(1, result.size());
+      Compare(*struct_column, result[0]);
+    }
+  }
+
+  // all nulls
+  {
+    std::vector<bool> struct_validity{false, false, false, false};
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(struct_validity.begin(), struct_validity.end());
+    auto struct_column = cudf::make_structs_column(4, {}, null_count, std::move(null_mask));
+
+    if (split) {
+      std::vector<bool> expected_validity{false, false};
+      std::tie(null_mask, null_count) =
+        cudf::test::detail::make_null_mask(expected_validity.begin(), expected_validity.end());
+      auto expected = cudf::make_structs_column(2, {}, null_count, std::move(null_mask));
+
+      // split
+      std::vector<cudf::size_type> splits{2};
+      auto result = Split(*struct_column, splits);
+
+      EXPECT_EQ(result.size(), 2ul);
+      Compare(*expected, result[0]);
+      Compare(*expected, result[1]);
+    } else {
+      auto result = Split(*struct_column, {});
+      EXPECT_EQ(1, result.size());
+      Compare(*struct_column, result[0]);
+    }
+  }
+
+  // no nulls, empty output column
+  {
+    auto struct_column = cudf::make_structs_column(4, {}, 0, rmm::device_buffer{});
+    if (split) {
+      auto expected0 = cudf::make_structs_column(4, {}, 0, rmm::device_buffer{});
+      auto expected1 = cudf::make_structs_column(0, {}, 0, rmm::device_buffer{});
+
+      // split
+      std::vector<cudf::size_type> splits{4};
+      auto result = Split(*struct_column, splits);
+
+      EXPECT_EQ(result.size(), 2ul);
+      Compare(*expected0, result[0]);
+      Compare(*expected1, result[1]);
+    } else {
+      auto result = Split(*struct_column, {});
+      EXPECT_EQ(1, result.size());
+      Compare(*struct_column, result[0]);
+    }
+  }
+
+  // all nulls, empty output column
+  {
+    std::vector<bool> struct_validity{false, false, false, false};
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(struct_validity.begin(), struct_validity.end());
+    auto struct_column = cudf::make_structs_column(4, {}, null_count, std::move(null_mask));
+
+    if (split) {
+      std::vector<bool> expected_validity0{false, false, false, false};
+      std::tie(null_mask, null_count) =
+        cudf::test::detail::make_null_mask(expected_validity0.begin(), expected_validity0.end());
+      auto expected0 = cudf::make_structs_column(4, {}, null_count, std::move(null_mask));
+
+      auto expected1 = cudf::make_structs_column(0, {}, 0, rmm::device_buffer{});
+
+      // split
+      std::vector<cudf::size_type> splits{4};
+      auto result = Split(*struct_column, splits);
+
+      EXPECT_EQ(result.size(), 2ul);
+      Compare(*expected0, result[0]);
+      Compare(*expected1, result[1]);
+    } else {
+      auto result = Split(*struct_column, {});
+      EXPECT_EQ(1, result.size());
+      Compare(*struct_column, result[0]);
+    }
+  }
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_nested_struct_of_list(SplitFunc Split, CompareFunc Compare, bool split = true)
+{
+  // Struct<List<List>>
+  using LCW = cudf::test::lists_column_wrapper<float>;
+
+  // 1. String "names" column.
+  std::vector<std::string> names{
+    "Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant", "Fred", "Todd", "Kevin"};
+  std::vector<bool> names_validity{1, 1, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper names_column(names.begin(), names.end());
+
+  // 2. Numeric "ages" column.
+  std::vector<int> ages{5, 10, 15, 20, 25, 30, 100, 101, 102};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 0, 1};
+  auto ages_column =
+    cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end(), ages_validity.begin());
+
+  // 3. List column
+  std::vector<bool> list_validity{1, 1, 1, 1, 1, 0, 1, 0, 1};
+  cudf::test::lists_column_wrapper<float> list({{{1, 2, 3}, {4}},
+                                                {{-1, -2}, LCW{}},
+                                                LCW{},
+                                                {{10}, {20, 30, 40}, {100, -100}},
+                                                {LCW{}, LCW{}, {8, 9}},
+                                                LCW{},
+                                                {{8}, {10, 9, 8, 7, 6, 5}},
+                                                {{5, 6}, LCW{}, {8}},
+                                                {LCW{-3, 4, -5}}},
+                                               list_validity.begin());
+
+  // Assemble struct column.
+  auto const struct_validity = std::vector<bool>{1, 1, 1, 1, 1, 0, 0, 1, 0};
+  auto struct_column =
+    cudf::test::structs_column_wrapper({names_column, ages_column, list}, struct_validity.begin());
+
+  if (split) {
+    std::vector<cudf::size_type> splits{1, 3, 8};
+    auto result = Split(struct_column, splits);
+    // expected results
+    auto expected_names = create_expected_string_columns_for_splits(names, splits, names_validity);
+    auto expected_ages  = create_expected_columns_for_splits<int>(splits, ages, ages_validity);
+    std::vector<cudf::test::lists_column_wrapper<float>> expected_lists;
+    expected_lists.push_back(LCW({{{1, 2, 3}, {4}}}));
+    expected_lists.push_back(LCW({{{-1, -2}, LCW{}}, LCW{}}));
+    std::vector<bool> ex_v{1, 1, 0, 1, 0};
+    expected_lists.push_back(LCW({{{10}, {20, 30, 40}, {100, -100}},
+                                  {LCW{}, LCW{}, {8, 9}},
+                                  LCW{},
+                                  {{8}, {10, 9, 8, 7, 6, 5}},
+                                  {{5, 6}, LCW{}, {8}}},
+                                 ex_v.begin()));
+    expected_lists.push_back(LCW({{LCW{-3, 4, -5}}}));
+
+    auto expected_struct_validity = create_expected_validity(splits, struct_validity);
+    EXPECT_EQ(expected_names.size(), result.size());
+
+    for (std::size_t index = 0; index < result.size(); index++) {
+      auto expected = cudf::test::structs_column_wrapper(
+        {expected_names[index], expected_ages[index], expected_lists[index]},
+        expected_struct_validity[index]);
+      Compare(expected, result[index]);
+    }
+  } else {
+    auto result = Split(struct_column, {});
+    Compare(struct_column, result[0]);
+  }
+}
+
+template <typename SplitFunc, typename CompareFunc>
+void split_nested_list_of_structs(SplitFunc Split, CompareFunc Compare, bool split = true)
+{
+  // List<Struct<List<>>
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  // 1. String "names" column.
+  std::vector<std::string> names{"Vimes",
+                                 "Carrot",
+                                 "Angua",
+                                 "Cheery",
+                                 "Detritus",
+                                 "Slant",
+                                 "Fred",
+                                 "Todd",
+                                 "Kevin",
+                                 "Jason",
+                                 "Clark",
+                                 "Bob",
+                                 "Mithun",
+                                 "Sameer",
+                                 "Tim",
+                                 "Mark",
+                                 "Herman",
+                                 "Will"};
+  std::vector<bool> names_validity{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper names_column(names.begin(), names.end());
+
+  // 2. Numeric "ages" column.
+  std::vector<int> ages{5, 10, 15, 20, 25, 30, 100, 101, 102, 26, 64, 12, 17, 16, 120, 44, 23, 50};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0};
+  auto ages_column =
+    cudf::test::fixed_width_column_wrapper<int>(ages.begin(), ages.end(), ages_validity.begin());
+
+  // 3. List column
+  std::vector<bool> list_validity{1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1};
+  cudf::test::lists_column_wrapper<cudf::string_view> list(
+    {{"ab", "cd", "ef"},
+     LCW{"gh"},
+     {"ijk", "lmn"},
+     LCW{},
+     LCW{"o"},
+     {"pqr", "stu", "vwx"},
+     {"yz", "aaaa"},
+     LCW{"bbbb"},
+     {"cccc", "ddd", "eee", "fff", "ggg", "hh"},
+     {"b", "cdr", "efh", "um"},
+     LCW{"gh", "iu"},
+     {"lmn"},
+     LCW{"org"},
+     LCW{},
+     {"stu", "vwx"},
+     {"yz", "aaaa", "kem"},
+     LCW{"bbbb"},
+     {"cccc", "eee", "faff", "jiea", "fff", "ggg", "hh"}},
+    list_validity.begin());
+
+  // Assembly struct column
+  auto const struct_validity =
+    std::vector<bool>{1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1};
+  auto struct_column =
+    cudf::test::structs_column_wrapper({names_column, ages_column, list}, struct_validity.begin());
+
+  // wrap in a list
+  std::vector<int> outer_offsets{0, 3, 4, 8, 13, 16, 17, 18};
+  cudf::test::fixed_width_column_wrapper<int> outer_offsets_col(outer_offsets.begin(),
+                                                                outer_offsets.end());
+  std::vector<bool> outer_validity{1, 1, 1, 0, 1, 1, 0};
+  auto [outer_null_mask, outer_null_count] =
+    cudf::test::detail::make_null_mask(outer_validity.begin(), outer_validity.end());
+  auto outer_list = make_lists_column(static_cast<cudf::size_type>(outer_validity.size()),
+                                      outer_offsets_col.release(),
+                                      struct_column.release(),
+                                      outer_null_count,
+                                      std::move(outer_null_mask));
+  if (split) {
+    std::vector<cudf::size_type> splits{1, 3, 7};
+    cudf::table_view tbl({static_cast<cudf::column_view>(*outer_list)});
+
+    // we are testing the results of contiguous_split against regular cudf::split, which may seem
+    // weird. however, cudf::split() is a simple operation that just sets offsets at the topmost
+    // output column, whereas contiguous_split is a deep copy of the data to contiguous output
+    // buffers. so as long as we believe the comparison code (expect_columns_equivalent) can compare
+    // these outputs correctly, this should be safe.
+    auto result   = Split(*outer_list, splits);
+    auto expected = cudf::split(static_cast<cudf::column_view>(*outer_list), splits);
+    ASSERT_EQ(result.size(), expected.size());
+
+    for (std::size_t index = 0; index < result.size(); index++) {
+      Compare(expected[index], result[index]);
+    }
+  } else {
+    auto result = Split(*outer_list, {});
+    EXPECT_EQ(1, result.size());
+    Compare(*outer_list, result[0]);
+  }
+}
+
+TEST_F(SplitNestedTypesTest, Lists)
+{
+  split_lists<int>(
+    [](cudf::column_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::column_view const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result);
+    });
+}
+
+TEST_F(SplitNestedTypesTest, ListsWithNulls)
+{
+  split_lists_with_nulls<int>(
+    [](cudf::column_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::column_view const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result);
+    });
+}
+
+TEST_F(SplitNestedTypesTest, Structs)
+{
+  split_structs(
+    false,
+    [](cudf::column_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::column_view const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result);
+    });
+}
+
+TEST_F(SplitNestedTypesTest, StructsWithNulls)
+{
+  split_structs(
+    true,
+    [](cudf::column_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::column_view const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result);
+    });
+}
+
+TEST_F(SplitNestedTypesTest, StructsNoChildren)
+{
+  split_structs_no_children(
+    [](cudf::column_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::column_view const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result);
+    });
+}
+
+TEST_F(SplitNestedTypesTest, StructsOfList)
+{
+  split_nested_struct_of_list(
+    [](cudf::column_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::column_view const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result);
+    });
+}
+
+template <typename T>
+struct ContiguousSplitTest : public cudf::test::BaseFixture {};
+
+std::vector<cudf::packed_table> do_chunked_pack(cudf::table_view const& input)
+{
+  auto mr = rmm::mr::get_current_device_resource();
+
+  rmm::device_buffer bounce_buff(1 * 1024 * 1024, cudf::get_default_stream(), mr);
+  auto bounce_buff_span =
+    cudf::device_span<uint8_t>(static_cast<uint8_t*>(bounce_buff.data()), bounce_buff.size());
+
+  auto chunked_pack = cudf::chunked_pack::create(input, bounce_buff_span.size(), mr);
+
+  // right size the final buffer
+  rmm::device_buffer final_buff(
+    chunked_pack->get_total_contiguous_size(), cudf::get_default_stream(), mr);
+
+  std::size_t final_buff_offset = 0;
+  while (chunked_pack->has_next()) {
+    auto bytes_copied = chunked_pack->next(bounce_buff_span);
+    cudaMemcpyAsync((uint8_t*)final_buff.data() + final_buff_offset,
+                    bounce_buff.data(),
+                    bytes_copied,
+                    cudaMemcpyDefault,
+                    cudf::get_default_stream());
+    final_buff_offset += bytes_copied;
+  }
+
+  auto packed_column_metas = chunked_pack->build_metadata();
+  // for chunked contig split, this is going to be a size 1 vector if we have
+  // results, or a size 0 if the original table was empty (no columns)
+  std::vector<cudf::packed_table> result;
+  if (packed_column_metas) {
+    result  = std::vector<cudf::packed_table>(1);
+    auto pc = cudf::packed_columns(std::move(packed_column_metas),
+                                   std::make_unique<rmm::device_buffer>(std::move(final_buff)));
+
+    auto unpacked = cudf::unpack(pc);
+    cudf::packed_table pt{std::move(unpacked), std::move(pc)};
+    result[0] = std::move(pt);
+  }
+  return result;
+}
+
+// the various utility functions in slice_tests.cuh don't like the chrono types
+using FixedWidthTypesWithoutChrono =
+  cudf::test::Concat<cudf::test::NumericTypes, cudf::test::FixedPointTypes>;
+
+TYPED_TEST_SUITE(ContiguousSplitTest, FixedWidthTypesWithoutChrono);
+
+TYPED_TEST(ContiguousSplitTest, LongColumn)
+{
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                          result.table.column(i));
+                    });
+    },
+    10002,
+    std::vector<cudf::size_type>{
+      2, 16, 31, 35, 64, 97, 158, 190, 638, 899, 900, 901, 996, 4200, 7131, 8111},
+    true);
+
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                          result.table.column(i));
+                    });
+    },
+    10002,
+    std::vector<cudf::size_type>{
+      2, 16, 31, 35, 64, 97, 158, 190, 638, 899, 900, 901, 996, 4200, 7131, 8111},
+    false);
+}
+
+TYPED_TEST(ContiguousSplitTest, LongColumnChunked)
+{
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+      return do_chunked_pack(t);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                          result.table.column(i));
+                    });
+    },
+    100002,
+    {},
+    true);
+
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+      return do_chunked_pack(t);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                          result.table.column(i));
+                    });
+    },
+    100002,
+    {},
+    false);
+}
+
+TYPED_TEST(ContiguousSplitTest, LongColumnBigSplits)
+{
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                          result.table.column(i));
+                    });
+    },
+    10007,
+    std::vector<cudf::size_type>{0, 3613, 7777, 10005, 10007},
+    true);
+
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i) {
+                      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                          result.table.column(i));
+                    });
+    },
+    10007,
+    std::vector<cudf::size_type>{0, 3613, 7777, 10005, 10007},
+    false);
+}
+
+// this is a useful test but a little too expensive to run all the time
+/*
+TYPED_TEST(ContiguousSplitTest, LongColumnTinySplits)
+{
+  std::vector<cudf::size_type> splits(thrust::make_counting_iterator(0),
+thrust::make_counting_iterator(10000));
+
+  split_custom_column<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      std::for_each(thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(expected.num_columns()),
+                    [&expected, &result](cudf::size_type i){
+
+        CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i), result.table.column(i));
+      });
+    },
+    10002,
+    splits
+    );
+}
+*/
+struct ContiguousSplitUntypedTest : public cudf::test::BaseFixture {};
+
+TEST_F(ContiguousSplitUntypedTest, ProgressiveSizes)
+{
+  constexpr int col_size = 256;
+
+  // stress test copying a wide amount of bytes.
+  for (int idx = 0; idx < col_size; idx++) {
+    split_custom_column<uint8_t>(
+      [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+        return cudf::contiguous_split(t, splits);
+      },
+      [](cudf::table_view const& expected, cudf::packed_table const& result) {
+        std::for_each(thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(expected.num_columns()),
+                      [&expected, &result](cudf::size_type i) {
+                        CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                            result.table.column(i));
+                      });
+      },
+      col_size,
+      std::vector<cudf::size_type>{idx},
+      true);
+
+    split_custom_column<uint8_t>(
+      [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+        return cudf::contiguous_split(t, splits);
+      },
+      [](cudf::table_view const& expected, cudf::packed_table const& result) {
+        std::for_each(thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(expected.num_columns()),
+                      [&expected, &result](cudf::size_type i) {
+                        CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                            result.table.column(i));
+                      });
+      },
+      col_size,
+      std::vector<cudf::size_type>{idx},
+      false);
+  }
+}
+
+TEST_F(ContiguousSplitUntypedTest, ProgressiveSizesChunked)
+{
+  constexpr int col_size = 4096;
+
+  // stress test copying a wide amount of bytes.
+  for (int idx = 2048; idx < col_size; idx += 128) {
+    split_custom_column<uint64_t>(
+      [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+        return do_chunked_pack(t);
+      },
+      [](cudf::table_view const& expected, cudf::packed_table const& result) {
+        std::for_each(thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(expected.num_columns()),
+                      [&expected, &result](cudf::size_type i) {
+                        CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                            result.table.column(i));
+                      });
+      },
+      col_size,
+      {},
+      true);
+
+    split_custom_column<uint64_t>(
+      [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+        return do_chunked_pack(t);
+      },
+      [](cudf::table_view const& expected, cudf::packed_table const& result) {
+        std::for_each(thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(expected.num_columns()),
+                      [&expected, &result](cudf::size_type i) {
+                        CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected.column(i),
+                                                            result.table.column(i));
+                      });
+      },
+      col_size,
+      {},
+      false);
+  }
+}
+
+TEST_F(ContiguousSplitUntypedTest, ValidityRepartition)
+{
+  // it is tricky to actually get the internal repartitioning/load-balancing code to add new splits
+  // inside a validity buffer.  Under almost all situations, the fraction of bytes that validity
+  // represents is so small compared to the bytes for all other data, that those buffers end up not
+  // getting subdivided. this test forces it happen by using a small, single column of int8's, which
+  // keeps the overall fraction that validity takes up large enough to cause a repartition.
+  srand(0);
+  auto rvalids                   = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    return static_cast<float>(rand()) / static_cast<float>(RAND_MAX) < 0.5f ? 0 : 1;
+  });
+  cudf::size_type const num_rows = 2000000;
+  auto col                       = cudf::sequence(num_rows, cudf::numeric_scalar<int8_t>{0});
+  auto [null_mask, null_count]   = cudf::test::detail::make_null_mask(rvalids, rvalids + num_rows);
+  col->set_null_mask(std::move(null_mask), null_count);
+
+  cudf::table_view t({*col});
+  auto result   = cudf::contiguous_split(t, {num_rows / 2});
+  auto expected = cudf::split(t, {num_rows / 2});
+  ASSERT_EQ(result.size(), expected.size());
+
+  for (size_t idx = 0; idx < result.size(); idx++) {
+    CUDF_TEST_EXPECT_TABLES_EQUAL(result[idx].table, expected[idx]);
+  }
+}
+
+TEST_F(ContiguousSplitUntypedTest, ValidityRepartitionChunked)
+{
+  srand(0);
+  auto rvalids                   = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    return static_cast<float>(rand()) / static_cast<float>(RAND_MAX) < 0.5f ? 0 : 1;
+  });
+  cudf::size_type const num_rows = 2000000;
+  auto col                       = cudf::sequence(num_rows, cudf::numeric_scalar<int8_t>{0});
+  auto [null_mask, null_count]   = cudf::test::detail::make_null_mask(rvalids, rvalids + num_rows);
+  col->set_null_mask(std::move(null_mask), null_count);
+
+  cudf::table_view t({*col});
+  auto result    = do_chunked_pack(t);
+  auto& expected = t;
+  EXPECT_EQ(1, result.size());
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(result[0].table, expected);
+}
+
+TEST_F(ContiguousSplitUntypedTest, ValidityEdgeCase)
+{
+  // tests an edge case where the splits cause the final validity data to be copied
+  // to be < 32 full bits, making sure we don't unintentionally read past the end of the input
+  auto col = cudf::make_numeric_column(
+    cudf::data_type{cudf::type_id::INT32}, 512, cudf::mask_state::ALL_VALID);
+  auto result   = cudf::contiguous_split(cudf::table_view{{*col}}, {510});
+  auto expected = cudf::split(cudf::table_view{{*col}}, {510});
+
+  EXPECT_EQ(expected.size(), result.size());
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected[index].column(0), result[index].table.column(0));
+  }
+}
+
+// This test requires about 25GB of device memory when used with the arena allocator
+TEST_F(ContiguousSplitUntypedTest, DISABLED_VeryLargeColumnTest)
+{
+  // tests an edge case where buf.elements * buf.element_size overflows an INT32.
+  auto col = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT64}, 400 * 1024 * 1024, cudf::mask_state::UNALLOCATED);
+  auto result = cudf::contiguous_split(cudf::table_view{{*col}}, {});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, result[0].table.column(0));
+}
+
+// This test requires about 25GB of device memory when used with the arena allocator
+TEST_F(ContiguousSplitUntypedTest, DISABLED_VeryLargeColumnTestChunked)
+{
+  // tests an edge case where buf.elements * buf.element_size overflows an INT32.
+  auto col = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT64}, 400 * 1024 * 1024, cudf::mask_state::UNALLOCATED);
+  auto result = do_chunked_pack(cudf::table_view{{*col}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, result[0].table.column(0));
+}
+
+// contiguous split with strings
+struct ContiguousSplitStringTableTest : public SplitTest<std::string> {};
+
+TEST_F(ContiguousSplitStringTableTest, StringWithInvalids)
+{
+  split_string_with_invalids(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.table);
+    });
+}
+
+TEST_F(ContiguousSplitStringTableTest, StringWithInvalidsChunked)
+{
+  split_string_with_invalids(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+      return do_chunked_pack(t);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.table);
+    },
+    {});
+}
+
+TEST_F(ContiguousSplitStringTableTest, EmptyInputColumn)
+{
+  // build a bunch of empty stuff
+  cudf::test::strings_column_wrapper sw;
+  cudf::test::lists_column_wrapper<int> lw;
+  cudf::test::fixed_width_column_wrapper<float> fw;
+  //
+  cudf::test::strings_column_wrapper ssw;
+  cudf::test::lists_column_wrapper<int> slw;
+  cudf::test::fixed_width_column_wrapper<float> sfw;
+  cudf::test::structs_column_wrapper st_w({sfw, ssw, slw});
+
+  cudf::table_view src_table({sw, lw, fw, st_w});
+
+  {
+    std::vector<cudf::size_type> splits;
+    auto result = cudf::contiguous_split(src_table, splits);
+    ASSERT_EQ(result.size(), 1);
+
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(src_table, result[0].table);
+  }
+
+  {
+    auto result = do_chunked_pack(src_table);
+    ASSERT_EQ(result.size(), 1);
+
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(src_table, result[0].table);
+  }
+
+  {
+    std::vector<cudf::size_type> splits{0, 0, 0, 0};
+    auto result = cudf::contiguous_split(src_table, splits);
+    ASSERT_EQ(result.size(), 5);
+
+    for (size_t idx = 0; idx < result.size(); idx++) {
+      CUDF_TEST_EXPECT_TABLES_EQUIVALENT(src_table, result[idx].table);
+    }
+  }
+}
+
+TEST_F(ContiguousSplitStringTableTest, EmptyOutputColumn)
+{
+  split_empty_output_strings_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::packed_table const& t, int num_cols) { EXPECT_EQ(t.table.num_columns(), num_cols); });
+}
+
+TEST_F(ContiguousSplitStringTableTest, EmptyOutputColumnChunked)
+{
+  split_empty_output_strings_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+      return do_chunked_pack(t);
+    },
+    [](cudf::packed_table const& t, int num_cols) { EXPECT_EQ(t.table.num_columns(), num_cols); },
+    {});
+}
+
+TEST_F(ContiguousSplitStringTableTest, NullStringColumn)
+{
+  split_null_input_strings_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected.view(), result.table);
+    });
+}
+
+// contiguous splits
+template <typename T>
+struct ContiguousSplitTableTest : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(ContiguousSplitTableTest, FixedWidthTypesWithoutChrono);
+
+TYPED_TEST(ContiguousSplitTableTest, SplitEndLessThanSize)
+{
+  split_end_less_than_size<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.table);
+    });
+}
+
+TYPED_TEST(ContiguousSplitTableTest, SplitEndToSize)
+{
+  split_end_to_size<TypeParam>(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::table_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.table);
+    });
+}
+
+struct ContiguousSplitTableCornerCases : public SplitTest<int8_t> {};
+
+TEST_F(ContiguousSplitTableCornerCases, EmptyTable)
+{
+  split_empty_table([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::contiguous_split(t, splits);
+  });
+}
+
+TEST_F(ContiguousSplitTableCornerCases, EmptyTableChunked)
+{
+  split_empty_table([](cudf::table_view const& t,
+                       std::vector<cudf::size_type> const&) { return do_chunked_pack(t); },
+                    {});
+}
+
+TEST_F(ContiguousSplitTableCornerCases, EmptyIndices)
+{
+  split_empty_indices([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::contiguous_split(t, splits);
+  });
+}
+
+TEST_F(ContiguousSplitTableCornerCases, InvalidSetOfIndices)
+{
+  split_invalid_indices([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::contiguous_split(t, splits);
+  });
+}
+
+TEST_F(ContiguousSplitTableCornerCases, ImproperRange)
+{
+  split_improper_range([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::contiguous_split(t, splits);
+  });
+}
+
+TEST_F(ContiguousSplitTableCornerCases, NegativeValue)
+{
+  split_negative_value([](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+    return cudf::contiguous_split(t, splits);
+  });
+}
+
+TEST_F(ContiguousSplitTableCornerCases, EmptyOutputColumn)
+{
+  split_empty_output_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const& splits) {
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::packed_table const& t, int num_cols) { EXPECT_EQ(t.table.num_columns(), num_cols); });
+}
+
+TEST_F(ContiguousSplitTableCornerCases, EmptyOutputColumnChunked)
+{
+  split_empty_output_column_value(
+    [](cudf::table_view const& t, std::vector<cudf::size_type> const&) {
+      return do_chunked_pack(t);
+    },
+    [](cudf::packed_table const& t, int num_cols) { EXPECT_EQ(t.table.num_columns(), num_cols); },
+    {});
+}
+
+TEST_F(ContiguousSplitTableCornerCases, MixedColumnTypes)
+{
+  cudf::size_type start = 0;
+  auto valids = cudf::detail::make_counting_transform_iterator(start, [](auto i) { return true; });
+
+  std::vector<std::string> strings[2] = {
+    {"", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"},
+    {"", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"}};
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+
+  auto iter0 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i); });
+  auto c0    = cudf::test::fixed_width_column_wrapper<int>(iter0, iter0 + 10, valids);
+  cols.push_back(c0.release());
+
+  auto iter1 = cudf::detail::make_counting_transform_iterator(10, [](auto i) { return (i); });
+  auto c1    = cudf::test::fixed_width_column_wrapper<int>(iter1, iter1 + 10, valids);
+  cols.push_back(c1.release());
+
+  auto c2 = cudf::test::strings_column_wrapper(strings[0].begin(), strings[0].end(), valids);
+  cols.push_back(c2.release());
+
+  auto c3 = cudf::test::strings_column_wrapper(strings[1].begin(), strings[1].end(), valids);
+  cols.push_back(c3.release());
+
+  auto iter4 = cudf::detail::make_counting_transform_iterator(20, [](auto i) { return (i); });
+  auto c4    = cudf::test::fixed_width_column_wrapper<int>(iter4, iter4 + 10, valids);
+  cols.push_back(c4.release());
+
+  auto tbl = cudf::table(std::move(cols));
+
+  std::vector<cudf::size_type> splits{5};
+
+  auto result   = cudf::contiguous_split(tbl, splits);
+  auto expected = cudf::split(tbl, splits);
+
+  for (unsigned long index = 0; index < expected.size(); index++) {
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected[index], result[index].table);
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, MixedColumnTypesChunked)
+{
+  cudf::size_type start = 0;
+  auto valids = cudf::detail::make_counting_transform_iterator(start, [](auto i) { return true; });
+
+  std::size_t num_rows = 1000000;
+
+  std::vector<std::string> strings1(num_rows);
+  std::vector<std::string> strings2(num_rows);
+  strings1[0] = "";
+  strings2[0] = "";
+  for (std::size_t i = 1; i < num_rows; ++i) {
+    auto str    = std::to_string(i);
+    strings1[i] = str;
+    strings2[i] = str;
+  }
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+
+  auto iter0 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i); });
+  auto c0    = cudf::test::fixed_width_column_wrapper<int>(iter0, iter0 + num_rows, valids);
+  cols.push_back(c0.release());
+
+  auto iter1 = cudf::detail::make_counting_transform_iterator(10, [](auto i) { return (i); });
+  auto c1    = cudf::test::fixed_width_column_wrapper<int>(iter1, iter1 + num_rows, valids);
+  cols.push_back(c1.release());
+
+  auto c2 = cudf::test::strings_column_wrapper(strings1.begin(), strings1.end(), valids);
+  cols.push_back(c2.release());
+
+  auto c3 = cudf::test::strings_column_wrapper(strings2.begin(), strings2.end(), valids);
+  cols.push_back(c3.release());
+
+  auto iter4 = cudf::detail::make_counting_transform_iterator(20, [](auto i) { return (i); });
+  auto c4    = cudf::test::fixed_width_column_wrapper<int>(iter4, iter4 + num_rows, valids);
+  cols.push_back(c4.release());
+
+  auto tbl     = cudf::table(std::move(cols));
+  auto results = do_chunked_pack(tbl.view());
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(tbl, results[0].table);
+}
+
+TEST_F(ContiguousSplitTableCornerCases, MixedColumnTypesSingleRowChunked)
+{
+  cudf::size_type start = 0;
+  auto valids = cudf::detail::make_counting_transform_iterator(start, [](auto i) { return true; });
+
+  std::size_t num_rows = 1;
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+
+  auto iter0 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i); });
+  auto c0    = cudf::test::fixed_width_column_wrapper<int32_t>(iter0, iter0 + num_rows, valids);
+  cols.push_back(c0.release());
+
+  auto iter1 = cudf::detail::make_counting_transform_iterator(1, [](auto i) { return (i); });
+  auto c1    = cudf::test::fixed_width_column_wrapper<int64_t>(iter1, iter1 + num_rows);
+  cols.push_back(c1.release());
+
+  auto tbl     = cudf::table(std::move(cols));
+  auto results = do_chunked_pack(tbl.view());
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(tbl, results[0].table);
+}
+
+TEST_F(ContiguousSplitTableCornerCases, PreSplitTable)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  cudf::test::lists_column_wrapper<int> col0{{{{1, 2, 3}, valids}, {4, 5}},
+                                             {{LCW{}, LCW{}, {7, 8}, LCW{}}, valids},
+                                             {{{6}}},
+                                             {{{7, 8}, {{9, 10, 11}, valids}, LCW{}}, valids},
+                                             {{LCW{}, {-1, -2, -3, -4, -5}}, valids},
+                                             {LCW{}},
+                                             {{-10}, {-100, -200}}};
+
+  cudf::test::strings_column_wrapper col1{
+    "Vimes", "Carrot", "Angua", "Cheery", "Detritus", "Slant", "Fred"};
+  cudf::test::fixed_width_column_wrapper<float> col2{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f};
+
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.push_back(std::make_unique<cudf::column>(col2));
+  children.push_back(std::make_unique<cudf::column>(col0));
+  children.push_back(std::make_unique<cudf::column>(col1));
+  auto col3 = cudf::make_structs_column(
+    static_cast<cudf::column_view>(col0).size(), std::move(children), 0, rmm::device_buffer{});
+
+  cudf::table_view t({col0, col1, col2, *col3});
+  auto pre_split = cudf::split(t, {1});
+
+  {
+    std::vector<cudf::size_type> splits{1, 4};
+
+    auto result   = cudf::contiguous_split(pre_split[1], splits);
+    auto expected = cudf::split(pre_split[1], splits);
+
+    for (size_t index = 0; index < expected.size(); index++) {
+      CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected[index], result[index].table);
+    }
+  }
+
+  {
+    std::vector<cudf::size_type> splits{0, 5};
+
+    auto result   = cudf::contiguous_split(pre_split[1], splits);
+    auto expected = cudf::split(pre_split[1], splits);
+
+    for (size_t index = 0; index < expected.size(); index++) {
+      CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected[index], result[index].table);
+    }
+  }
+
+  {
+    auto result = do_chunked_pack(pre_split[1]);
+    EXPECT_EQ(1, result.size());
+    auto expected = pre_split[1];
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected, result[0].table);
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, PreSplitTableLarge)
+{
+  // test splitting a table that is already split (has an offset)
+  cudf::size_type start        = 0;
+  cudf::size_type presplit_pos = 47;
+  cudf::size_type size         = 10002;
+
+  srand(824);
+  auto rvalids = cudf::detail::make_counting_transform_iterator(start, [](auto i) {
+    return static_cast<float>(rand()) / static_cast<float>(RAND_MAX) < 0.5f ? 0 : 1;
+  });
+
+  std::vector<bool> pre_split_valids{rvalids, rvalids + size};
+  cudf::test::fixed_width_column_wrapper<int> pre_split =
+    create_fixed_columns<int>(start, size, pre_split_valids.begin());
+
+  // pre-split this column
+  auto split_cols = cudf::split(pre_split, {47});
+
+  std::vector<cudf::size_type> splits{
+    2, 16, 31, 35, 64, 97, 158, 190, 638, 899, 900, 901, 996, 4200, 7131, 8111};
+
+  auto const post_split_start = start + presplit_pos;
+  auto const post_split_size  = size - presplit_pos;
+  auto el_iter                = thrust::make_counting_iterator(post_split_start);
+  std::vector<int> post_split_elements{el_iter, el_iter + post_split_size};
+  std::vector<bool> post_split_valids{
+    pre_split_valids.begin() + post_split_start,
+    pre_split_valids.begin() + post_split_start + post_split_size};
+
+  std::vector<cudf::test::fixed_width_column_wrapper<int>> expected =
+    create_expected_columns_for_splits<int>(splits, post_split_elements, post_split_valids);
+
+  cudf::table_view t({split_cols[1]});
+  auto result = cudf::contiguous_split(t, splits);
+
+  EXPECT_EQ(expected.size(), result.size());
+
+  for (unsigned long index = 0; index < result.size(); index++) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected[index], result[index].table.column(0));
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, PreSplitList)
+{
+  // list<list<int>>
+  {
+    cudf::test::lists_column_wrapper<int> list{{{1, 2}, {3, 4}},
+                                               {{5, 6}, {7}, {8, 9, 10}},
+                                               {{11, 12}, {13}},
+                                               {{14, 15, 16}, {17, 18}, {}},
+                                               {{-1, -2, -3}, {-4, -5, -6, -7}},
+                                               {{-8, -9}, {-10, -11}},
+                                               {{-12, -13}, {-14}, {-15, -16}},
+                                               {{-17, -18}, {}, {-19, -20}}};
+    auto pre_split = cudf::split(list, {2});
+
+    cudf::table_view t({pre_split[1]});
+    auto result   = cudf::contiguous_split(t, {3, 4});
+    auto expected = cudf::split(t, {3, 4});
+
+    auto iter = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + expected.size(), [&](cudf::size_type index) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(result[index].table, expected[index]);
+    });
+  }
+
+  // list<struct<float>>
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets{0, 2, 5, 7, 10, 12, 14, 17, 20};
+    cudf::test::fixed_width_column_wrapper<float> floats{1,  2,  3,  4,  5,  6,  7,  8,  9,  10,
+                                                         11, 12, 13, 14, 15, 16, 17, 18, 19, 20};
+    cudf::test::structs_column_wrapper data({floats});
+
+    auto list =
+      cudf::make_lists_column(8, offsets.release(), data.release(), 0, rmm::device_buffer{});
+
+    auto pre_split = cudf::split(*list, {2});
+
+    cudf::table_view t({pre_split[1]});
+    auto result   = cudf::contiguous_split(t, {3, 4});
+    auto expected = cudf::split(t, {3, 4});
+
+    auto iter = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + expected.size(), [&](cudf::size_type index) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(result[index].table, expected[index]);
+    });
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, PreSplitStructs)
+{
+  // includes struct<list>
+  {
+    cudf::test::fixed_width_column_wrapper<int> a{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+    cudf::test::fixed_width_column_wrapper<float> b{{0, -1, -2, -3, -4, -5, -6, -7, -8, -9},
+                                                    {1, 1, 1, 0, 0, 0, 0, 1, 1, 1}};
+    cudf::test::strings_column_wrapper c{
+      {"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx", "yy", "zzzz"},
+      {0, 0, 1, 1, 1, 1, 1, 1, 1, 1}};
+    std::vector<bool> list_validity{1, 0, 1, 0, 1, 0, 1, 1, 1, 1};
+    cudf::test::lists_column_wrapper<int16_t> d{
+      {{0, 1}, {2, 3, 4}, {5, 6}, {7}, {8, 9, 10}, {11, 12}, {}, {15, 16, 17}, {18, 19}, {20}},
+      list_validity.begin()};
+    cudf::test::fixed_width_column_wrapper<int> _a{10, 20, 30, 40, 50, 60, 70, 80, 90, 100};
+    cudf::test::fixed_width_column_wrapper<float> _b{
+      -10, -20, -30, -40, -50, -60, -70, -80, -90, -100};
+    cudf::test::strings_column_wrapper _c{
+      "aa", "", "ccc", "dddd", "eeeee", "f", "gg", "hhh", "i", "jjj"};
+    cudf::test::structs_column_wrapper e({_a, _b, _c}, {1, 1, 1, 0, 1, 1, 1, 0, 1, 1});
+    cudf::test::structs_column_wrapper s({a, b, c, d, e}, {1, 1, 0, 1, 1, 1, 1, 1, 1, 1});
+
+    auto pre_split = cudf::split(s, {4});
+
+    auto iter = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + pre_split.size(), [&](cudf::size_type index) {
+      cudf::table_view t({pre_split[index]});
+      auto result   = cudf::contiguous_split(t, {1});
+      auto expected = cudf::split(t, {1});
+
+      CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result[0].table, expected[0]);
+      CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result[1].table, expected[1]);
+    });
+  }
+
+  // struct<list<struct>>
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets{0, 2, 5, 7, 10, 12, 14, 17, 20};
+    cudf::test::fixed_width_column_wrapper<float> floats{1,  2,  3,  4,  5,  6,  7,  8,  9,  10,
+                                                         11, 12, 13, 14, 15, 16, 17, 18, 19, 20};
+    cudf::test::structs_column_wrapper data({floats});
+    auto list =
+      cudf::make_lists_column(8, offsets.release(), data.release(), 0, rmm::device_buffer{});
+    cudf::test::strings_column_wrapper strings{"a", "bb", "ccc", "dddd", "", "e", "ff", "ggg"};
+
+    std::vector<std::unique_ptr<cudf::column>> struct_children;
+    struct_children.push_back(std::move(list));
+    struct_children.push_back(strings.release());
+    cudf::test::structs_column_wrapper col(std::move(struct_children));
+
+    auto pre_split = cudf::split(col, {2});
+
+    cudf::table_view t({pre_split[1]});
+    auto result   = cudf::contiguous_split(t, {3, 4});
+    auto expected = cudf::split(t, {3, 4});
+
+    auto iter = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + expected.size(), [&](cudf::size_type index) {
+      CUDF_TEST_EXPECT_TABLES_EQUAL(result[index].table, expected[index]);
+    });
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, NestedEmpty)
+{
+  // this produces an empty strings column with no children,
+  // nested inside a list
+  {
+    auto empty_string = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+    auto offsets      = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list         = cudf::make_lists_column(
+      1, offsets.release(), std::move(empty_string), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+
+    std::vector<cudf::size_type> splits({0});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits));
+
+    std::vector<cudf::size_type> splits2({1});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits2));
+
+    EXPECT_NO_THROW(do_chunked_pack(src_table));
+  }
+
+  // this produces an empty strings column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::strings_column_wrapper str{"abc"};
+    auto empty_string = cudf::empty_like(str);
+    auto offsets      = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list         = cudf::make_lists_column(
+      1, offsets.release(), std::move(empty_string), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+
+    std::vector<cudf::size_type> splits({0});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits));
+
+    std::vector<cudf::size_type> splits2({1});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits2));
+
+    EXPECT_NO_THROW(do_chunked_pack(src_table));
+  }
+
+  // this produces an empty lists column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::lists_column_wrapper<float> listw{{1.0f, 2.0f}, {3.0f, 4.0f}};
+    auto empty_list = cudf::empty_like(listw);
+    auto offsets    = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list =
+      cudf::make_lists_column(1, offsets.release(), std::move(empty_list), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+
+    std::vector<cudf::size_type> splits({0});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits));
+
+    std::vector<cudf::size_type> splits2({1});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits2));
+
+    EXPECT_NO_THROW(do_chunked_pack(src_table));
+  }
+
+  // this produces an empty lists column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::lists_column_wrapper<float> listw{{1.0f, 2.0f}, {3.0f, 4.0f}};
+    auto empty_list = cudf::empty_like(listw);
+    auto offsets    = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list =
+      cudf::make_lists_column(1, offsets.release(), std::move(empty_list), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+
+    std::vector<cudf::size_type> splits({0});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits));
+
+    std::vector<cudf::size_type> splits2({1});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits2));
+
+    EXPECT_NO_THROW(do_chunked_pack(src_table));
+  }
+
+  // this produces an empty struct column with children that have no data,
+  // nested inside a list
+  {
+    cudf::test::fixed_width_column_wrapper<int> ints{0, 1, 2, 3, 4};
+    cudf::test::fixed_width_column_wrapper<float> floats{4, 3, 2, 1, 0};
+    auto struct_column = cudf::test::structs_column_wrapper({ints, floats});
+    auto empty_struct  = cudf::empty_like(struct_column);
+    auto offsets       = cudf::test::fixed_width_column_wrapper<int>({0, 0});
+    auto list          = cudf::make_lists_column(
+      1, offsets.release(), std::move(empty_struct), 0, rmm::device_buffer{});
+
+    cudf::table_view src_table({static_cast<cudf::column_view>(*list)});
+
+    std::vector<cudf::size_type> splits({0});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits));
+
+    std::vector<cudf::size_type> splits2({1});
+    EXPECT_NO_THROW(contiguous_split(src_table, splits2));
+
+    EXPECT_NO_THROW(do_chunked_pack(src_table));
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, SplitEmpty)
+{
+  // empty sliced column. this is specifically testing the corner case:
+  // - a sliced column of size 0
+  // - having children that are of size > 0
+  //
+  cudf::test::strings_column_wrapper a{"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx"};
+  cudf::test::lists_column_wrapper<int> b{
+    {0, 1}, {2}, {3, 4, 5}, {6, 7}, {8, 9}, {10}, {11, 12}, {13, 14}};
+  cudf::test::fixed_width_column_wrapper<float> c{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::strings_column_wrapper _a{"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx"};
+  cudf::test::lists_column_wrapper<float> _b{
+    {0, 1}, {2}, {3, 4, 5}, {6, 7}, {8, 9}, {10}, {11, 12}, {13, 14}};
+  cudf::test::fixed_width_column_wrapper<float> _c{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::structs_column_wrapper d({_a, _b, _c});
+
+  cudf::table_view t({a, b, c, d});
+
+  auto sliced = cudf::split(t, {0});
+
+  {
+    auto result = cudf::contiguous_split(sliced[0], {});
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(sliced[0], result[0].table);
+  }
+
+  {
+    auto result = do_chunked_pack(sliced[0]);
+    EXPECT_EQ(1, result.size());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(sliced[0], result[0].table);
+  }
+
+  {
+    auto result = cudf::contiguous_split(sliced[0], {0});
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(sliced[0], result[0].table);
+  }
+
+  {
+    EXPECT_THROW(cudf::contiguous_split(sliced[0], {1}), cudf::logic_error);
+  }
+}
+
+TEST_F(ContiguousSplitTableCornerCases, OutBufferToSmall)
+{
+  // internally, contiguous split chunks GPU work in 1MB contiguous copies
+  // so the output buffer must be 1MB or larger.
+  EXPECT_THROW(cudf::chunked_pack::create({}, 1 * 1024), cudf::logic_error);
+}
+
+TEST_F(ContiguousSplitTableCornerCases, ChunkSpanTooSmall)
+{
+  auto chunked_pack = cudf::chunked_pack::create({}, 1 * 1024 * 1024);
+  rmm::device_buffer buff(
+    1 * 1024, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+  cudf::device_span<uint8_t> too_small(static_cast<uint8_t*>(buff.data()), buff.size());
+  std::size_t copied = 0;
+  // throws because we created chunked_contig_split with 1MB, but we are giving
+  // it a 1KB span here
+  EXPECT_THROW(copied = chunked_pack->next(too_small), cudf::logic_error);
+  EXPECT_EQ(copied, 0);
+}
+
+TEST_F(ContiguousSplitTableCornerCases, EmptyTableHasNextFalse)
+{
+  auto chunked_pack = cudf::chunked_pack::create({}, 1 * 1024 * 1024);
+  rmm::device_buffer buff(
+    1 * 1024 * 1024, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+  cudf::device_span<uint8_t> bounce_buff(static_cast<uint8_t*>(buff.data()), buff.size());
+  EXPECT_EQ(chunked_pack->has_next(), false);  // empty input table
+  std::size_t copied = 0;
+  EXPECT_THROW(copied = chunked_pack->next(bounce_buff), cudf::logic_error);
+  EXPECT_EQ(copied, 0);
+}
+
+TEST_F(ContiguousSplitTableCornerCases, ExhaustedHasNextFalse)
+{
+  cudf::test::strings_column_wrapper a{"abc", "def", "ghi", "jkl", "mno", "", "st", "uvwx"};
+  cudf::table_view t({a});
+  rmm::device_buffer buff(
+    1 * 1024 * 1024, cudf::test::get_default_stream(), rmm::mr::get_current_device_resource());
+  cudf::device_span<uint8_t> bounce_buff(static_cast<uint8_t*>(buff.data()), buff.size());
+  auto chunked_pack = cudf::chunked_pack::create(t, buff.size());
+  EXPECT_EQ(chunked_pack->has_next(), true);
+  std::size_t copied = chunked_pack->next(bounce_buff);
+  EXPECT_EQ(copied, chunked_pack->get_total_contiguous_size());
+  EXPECT_EQ(chunked_pack->has_next(), false);
+  copied = 0;
+  EXPECT_THROW(copied = chunked_pack->next(bounce_buff), cudf::logic_error);
+  EXPECT_EQ(copied, 0);
+}
+
+struct ContiguousSplitNestedTypesTest : public cudf::test::BaseFixture {};
+
+TEST_F(ContiguousSplitNestedTypesTest, Lists)
+{
+  split_lists<int>(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, ListsChunked)
+{
+  split_lists<int>(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    /*split*/ false);
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, ListsWithNulls)
+{
+  split_lists_with_nulls<int>(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, ListsWithNullsChunked)
+{
+  split_lists_with_nulls<int>(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    /*split*/ false);
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, Structs)
+{
+  split_structs(
+    false,
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsChunked)
+{
+  split_structs(
+    false,
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    /*split*/ false);
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsWithNulls)
+{
+  split_structs(
+    true,
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsWithNullsChunked)
+{
+  split_structs(
+    true,
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    {});
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsNoChildren)
+{
+  split_structs_no_children(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsNoChildrenChunked)
+{
+  split_structs_no_children(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    /*split*/ false);
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsOfList)
+{
+  split_nested_struct_of_list(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, StructsOfListChunked)
+{
+  split_nested_struct_of_list(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    /*split*/ false);
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, ListOfStruct)
+{
+  split_nested_list_of_structs(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const& splits) {
+      cudf::table_view t({c});
+      return cudf::contiguous_split(t, splits);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    });
+}
+
+TEST_F(ContiguousSplitNestedTypesTest, ListOfStructChunked)
+{
+  split_nested_list_of_structs(
+    [](cudf::column_view const& c, std::vector<cudf::size_type> const&) {
+      cudf::table_view t({c});
+      return do_chunked_pack(t);
+    },
+    [](cudf::column_view const& expected, cudf::packed_table const& result) {
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, result.table.column(0));
+    },
+    /*split*/ false);
+}
diff --git a/cpp/tests/copying/utility_tests.cpp b/cpp/tests/copying/utility_tests.cpp
new file mode 100644
index 0000000..dadb8ea
--- /dev/null
+++ b/cpp/tests/copying/utility_tests.cpp
@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <string>
+
+template <typename T>
+struct EmptyLikeTest : public cudf::test::BaseFixture {};
+
+using numeric_types = cudf::test::NumericTypes;
+
+TYPED_TEST_SUITE(EmptyLikeTest, numeric_types);
+
+TYPED_TEST(EmptyLikeTest, ColumnNumericTests)
+{
+  cudf::size_type size   = 10;
+  cudf::mask_state state = cudf::mask_state::ALL_VALID;
+  auto input    = make_numeric_column(cudf::data_type{cudf::type_to_id<TypeParam>()}, size, state);
+  auto expected = make_numeric_column(cudf::data_type{cudf::type_to_id<TypeParam>()}, 0);
+  auto got      = cudf::empty_like(input->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *got);
+}
+
+struct EmptyLikeStringTest : public EmptyLikeTest<std::string> {};
+
+void check_empty_string_columns(cudf::column_view lhs, cudf::column_view rhs)
+{
+  EXPECT_EQ(lhs.type(), rhs.type());
+  EXPECT_EQ(lhs.size(), 0);
+  EXPECT_EQ(lhs.null_count(), 0);
+  EXPECT_EQ(lhs.nullable(), false);
+  EXPECT_EQ(lhs.has_nulls(), false);
+  // An empty column is not required to have children
+}
+
+TEST_F(EmptyLikeStringTest, ColumnStringTest)
+{
+  std::vector<char const*> h_strings{"the quick brown fox jumps over the lazy dog",
+                                     "thé result does not include the value in the sum in",
+                                     "",
+                                     nullptr,
+                                     "absent stop words"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto got = cudf::empty_like(strings);
+  check_empty_string_columns(got->view(), strings);
+}
+
+template <typename T>
+struct EmptyLikeScalarTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(EmptyLikeScalarTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(EmptyLikeScalarTest, FixedWidth)
+{
+  // make a column
+  auto input = make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, 1, rmm::device_buffer{}, 0);
+  // get a scalar out of it
+  std::unique_ptr<cudf::scalar> sc = cudf::get_element(*input, 0);
+
+  // empty_like(column) -> column
+  auto expected = cudf::empty_like(*input);
+  // empty_like(scalar) -> column
+  auto result = cudf::empty_like(*sc);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *result);
+}
+
+struct EmptyLikeScalarStringTest : public EmptyLikeScalarTest<std::string> {};
+
+TEST_F(EmptyLikeScalarStringTest, String)
+{
+  // make a column
+  cudf::test::strings_column_wrapper input{"abc"};
+
+  // get a scalar out of it
+  std::unique_ptr<cudf::scalar> sc = cudf::get_element(input, 0);
+
+  // empty_like(column) -> column
+  auto expected = cudf::empty_like(input);
+  // empty_like(scalar) -> column
+  auto result = cudf::empty_like(*sc);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *result);
+}
+
+struct EmptyLikeScalarListTest : public EmptyLikeScalarTest<cudf::list_view> {};
+
+TEST_F(EmptyLikeScalarListTest, List)
+{
+  // make a column
+  cudf::test::lists_column_wrapper<cudf::string_view> input{{{"abc", "def"}, {"h", "ijk"}},
+                                                            {{"123", "456"}, {"78"}}};
+  // get a scalar out of it
+  std::unique_ptr<cudf::scalar> sc = cudf::get_element(input, 0);
+
+  // empty_like(column) -> column
+  auto expected = cudf::empty_like(input);
+  // empty_like(scalar) -> column
+  auto result = cudf::empty_like(*sc);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *result);
+}
+
+struct EmptyLikeScalarStructTest : public EmptyLikeScalarTest<cudf::struct_view> {};
+
+TEST_F(EmptyLikeScalarStructTest, Struct)
+{
+  cudf::test::lists_column_wrapper<cudf::string_view> col0{{{"abc", "def"}, {"h", "ijk"}}};
+  cudf::test::strings_column_wrapper col1{"abc"};
+  cudf::test::fixed_width_column_wrapper<float> col2{1.0f};
+  // scalar. TODO:  make cudf::get_element() work for struct scalars
+  cudf::table_view tbl({col0, col1, col2});
+  cudf::struct_scalar sc(tbl);
+  // column
+  cudf::test::structs_column_wrapper input({col0, col1, col2});
+
+  // empty_like(column) -> column
+  auto expected = cudf::empty_like(input);
+  // empty_like(scalar) -> column
+  auto result = cudf::empty_like(sc);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *result);
+}
+
+std::unique_ptr<cudf::table> create_table(cudf::size_type size, cudf::mask_state state)
+{
+  auto num_column_1 = make_numeric_column(cudf::data_type{cudf::type_id::INT64}, size, state);
+  auto num_column_2 = make_numeric_column(cudf::data_type{cudf::type_id::INT32}, size, state);
+  auto num_column_3 = make_numeric_column(cudf::data_type{cudf::type_id::FLOAT64}, size, state);
+  auto num_column_4 = make_numeric_column(cudf::data_type{cudf::type_id::FLOAT32}, size, state);
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(std::move(num_column_1));
+  columns.push_back(std::move(num_column_2));
+  columns.push_back(std::move(num_column_3));
+  columns.push_back(std::move(num_column_4));
+
+  return std::make_unique<cudf::table>(std::move(columns));
+}
+
+struct EmptyLikeTableTest : public cudf::test::BaseFixture {};
+
+TEST_F(EmptyLikeTableTest, TableTest)
+{
+  cudf::mask_state state = cudf::mask_state::ALL_VALID;
+  cudf::size_type size   = 10;
+  auto input             = create_table(size, state);
+  auto expected          = create_table(0, cudf::mask_state::ALL_VALID);
+  auto got               = cudf::empty_like(input->view());
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(got->view(), expected->view());
+}
+
+template <typename T>
+struct AllocateLikeTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(AllocateLikeTest, numeric_types);
+
+TYPED_TEST(AllocateLikeTest, ColumnNumericTestSameSize)
+{
+  // For same size as input
+  cudf::size_type size = 10;
+
+  auto input = make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, size, cudf::mask_state::UNALLOCATED);
+  auto got = cudf::allocate_like(input->view());
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(*input, *got);
+
+  input = make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, size, cudf::mask_state::ALL_VALID);
+  got = cudf::allocate_like(input->view());
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(*input, *got);
+}
+
+TYPED_TEST(AllocateLikeTest, ColumnNumericTestSpecifiedSize)
+{
+  // For different size as input
+  cudf::size_type size           = 10;
+  cudf::size_type specified_size = 5;
+
+  auto state = cudf::mask_state::UNALLOCATED;
+  auto input = make_numeric_column(cudf::data_type{cudf::type_to_id<TypeParam>()}, size, state);
+  auto expected =
+    make_numeric_column(cudf::data_type{cudf::type_to_id<TypeParam>()}, specified_size, state);
+  auto got = cudf::allocate_like(input->view(), specified_size);
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(*expected, *got);
+
+  input = make_numeric_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, size, cudf::mask_state::ALL_VALID);
+  got = cudf::allocate_like(input->view(), specified_size);
+  // Can't use CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL because the sizes of
+  // the two columns are different.
+  EXPECT_EQ(input->type(), got->type());
+  EXPECT_EQ(specified_size, got->size());
+  EXPECT_EQ(0, got->null_count());
+  EXPECT_EQ(input->nullable(), got->nullable());
+  EXPECT_EQ(input->num_children(), got->num_children());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/datetime/datetime_ops_test.cpp b/cpp/tests/datetime/datetime_ops_test.cpp
new file mode 100644
index 0000000..6bae20e
--- /dev/null
+++ b/cpp/tests/datetime/datetime_ops_test.cpp
@@ -0,0 +1,1053 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/datetime.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/timestamp_utilities.cuh>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/transform.h>
+
+#define XXX false  // stub for null values
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+template <typename T>
+struct NonTimestampTest : public cudf::test::BaseFixture {
+  cudf::data_type type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+using NonTimestampTypes =
+  cudf::test::Concat<cudf::test::NumericTypes, cudf::test::StringTypes, cudf::test::DurationTypes>;
+
+TYPED_TEST_SUITE(NonTimestampTest, NonTimestampTypes);
+
+TYPED_TEST(NonTimestampTest, TestThrowsOnNonTimestamp)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  cudf::data_type dtype{cudf::type_to_id<T>()};
+  cudf::column col{dtype, 0, rmm::device_buffer{}, rmm::device_buffer{}, 0};
+
+  EXPECT_THROW(extract_year(col), cudf::logic_error);
+  EXPECT_THROW(extract_month(col), cudf::logic_error);
+  EXPECT_THROW(extract_day(col), cudf::logic_error);
+  EXPECT_THROW(extract_weekday(col), cudf::logic_error);
+  EXPECT_THROW(extract_hour(col), cudf::logic_error);
+  EXPECT_THROW(extract_minute(col), cudf::logic_error);
+  EXPECT_THROW(extract_second(col), cudf::logic_error);
+  EXPECT_THROW(extract_millisecond_fraction(col), cudf::logic_error);
+  EXPECT_THROW(extract_microsecond_fraction(col), cudf::logic_error);
+  EXPECT_THROW(extract_nanosecond_fraction(col), cudf::logic_error);
+  EXPECT_THROW(last_day_of_month(col), cudf::logic_error);
+  EXPECT_THROW(day_of_year(col), cudf::logic_error);
+  EXPECT_THROW(add_calendrical_months(col, *cudf::make_empty_column(cudf::type_id::INT16)),
+               cudf::logic_error);
+}
+
+struct BasicDatetimeOpsTest : public cudf::test::BaseFixture {};
+
+TEST_F(BasicDatetimeOpsTest, TestExtractingDatetimeComponents)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto timestamps_D =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      -1528,  // 1965-10-26 GMT
+      17716,  // 2018-07-04 GMT
+      19382   // 2023-01-25 GMT
+    };
+
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      -131968728,  // 1965-10-26 14:01:12 GMT
+      1530705600,  // 2018-07-04 12:00:00 GMT
+      1674631932   // 2023-01-25 07:32:12 GMT
+    };
+
+  auto timestamps_ms =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>{
+      -131968727238,  // 1965-10-26 14:01:12.762 GMT
+      1530705600000,  // 2018-07-04 12:00:00.000 GMT
+      1674631932929   // 2023-01-25 07:32:12.929 GMT
+    };
+
+  auto timestamps_ns =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep>{
+      -23324234,  // 1969-12-31 23:59:59.976675766 GMT
+      23432424,   // 1970-01-01 00:00:00.023432424 GMT
+      987234623   // 1970-01-01 00:00:00.987234623 GMT
+    };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{1965, 2018, 2023});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{1965, 2018, 2023});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{1965, 2018, 2023});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{1969, 1970, 1970});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{10, 7, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{10, 7, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{10, 7, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{12, 1, 1});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{26, 4, 25});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{26, 4, 25});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{26, 4, 25});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{31, 1, 1});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{2, 3, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{2, 3, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{2, 3, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{2, 3, 3});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{14, 12, 7});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{14, 12, 7});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{23, 0, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{1, 0, 32});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{1, 0, 32});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{59, 0, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_second(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_second(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{12, 0, 12});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_second(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{12, 0, 12});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{59, 0, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_millisecond_fraction(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_millisecond_fraction(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_millisecond_fraction(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{762, 0, 929});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_millisecond_fraction(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{976, 23, 987});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_microsecond_fraction(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_microsecond_fraction(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_microsecond_fraction(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_microsecond_fraction(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{675, 432, 234});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_nanosecond_fraction(timestamps_D),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_nanosecond_fraction(timestamps_s),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_nanosecond_fraction(timestamps_ms),
+                                 fixed_width_column_wrapper<int16_t>{0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_nanosecond_fraction(timestamps_ns),
+                                 fixed_width_column_wrapper<int16_t>{766, 424, 623});
+}
+
+template <typename T>
+struct TypedDatetimeOpsTest : public cudf::test::BaseFixture {
+  cudf::size_type size() { return cudf::size_type(10); }
+  cudf::data_type type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+TYPED_TEST_SUITE(TypedDatetimeOpsTest, cudf::test::TimestampTypes);
+
+TYPED_TEST(TypedDatetimeOpsTest, TestEmptyColumns)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto int16s_dtype     = cudf::data_type{cudf::type_to_id<int16_t>()};
+  auto timestamps_dtype = cudf::data_type{cudf::type_to_id<T>()};
+
+  cudf::column int16s{int16s_dtype, 0, rmm::device_buffer{}, rmm::device_buffer{}, 0};
+  cudf::column timestamps{timestamps_dtype, 0, rmm::device_buffer{}, rmm::device_buffer{}, 0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_second(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_millisecond_fraction(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_microsecond_fraction(timestamps), int16s);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_nanosecond_fraction(timestamps), int16s);
+}
+
+TYPED_TEST(TypedDatetimeOpsTest, TestExtractingGeneratedDatetimeComponents)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto start      = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto stop       = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+  auto timestamps = generate_timestamps<T>(this->size(), time_point_ms(start), time_point_ms(stop));
+
+  auto expected_years =
+    fixed_width_column_wrapper<int16_t>{1890, 1906, 1922, 1938, 1954, 1970, 1985, 2001, 2017, 2033};
+  auto expected_months   = fixed_width_column_wrapper<int16_t>{10, 8, 6, 4, 2, 1, 11, 9, 7, 5};
+  auto expected_days     = fixed_width_column_wrapper<int16_t>{11, 16, 20, 24, 26, 1, 5, 9, 14, 18};
+  auto expected_weekdays = fixed_width_column_wrapper<int16_t>{6, 4, 2, 7, 5, 4, 2, 7, 5, 3};
+  auto expected_hours    = fixed_width_column_wrapper<int16_t>{19, 20, 21, 22, 23, 0, 0, 1, 2, 3};
+  auto expected_minutes = fixed_width_column_wrapper<int16_t>{33, 26, 20, 13, 6, 0, 53, 46, 40, 33};
+  auto expected_seconds = fixed_width_column_wrapper<int16_t>{20, 40, 0, 20, 40, 0, 20, 40, 0, 20};
+
+  // Special cases for timestamp_D: zero out the expected hh/mm/ss cols
+  if (std::is_same_v<TypeParam, cudf::timestamp_D>) {
+    expected_hours   = fixed_width_column_wrapper<int16_t>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+    expected_minutes = fixed_width_column_wrapper<int16_t>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+    expected_seconds = fixed_width_column_wrapper<int16_t>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  }
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps), expected_years);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps), expected_months);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps), expected_days);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps), expected_weekdays);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps), expected_hours);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps), expected_minutes);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_second(timestamps), expected_seconds);
+}
+
+TYPED_TEST(TypedDatetimeOpsTest, TestExtractingGeneratedNullableDatetimeComponents)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto start = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto stop  = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+  auto timestamps =
+    generate_timestamps<T, true>(this->size(), time_point_ms(start), time_point_ms(stop));
+
+  auto expected_years = fixed_width_column_wrapper<int16_t>{
+    {1890, 1906, 1922, 1938, 1954, 1970, 1985, 2001, 2017, 2033},
+    {true, false, true, false, true, false, true, false, true, false}};
+  auto expected_months = fixed_width_column_wrapper<int16_t>{
+    {10, 8, 6, 4, 2, 1, 11, 9, 7, 5},
+    {true, false, true, false, true, false, true, false, true, false}};
+  auto expected_days = fixed_width_column_wrapper<int16_t>{
+    {11, 16, 20, 24, 26, 1, 5, 9, 14, 18},
+    {true, false, true, false, true, false, true, false, true, false}};
+  auto expected_weekdays = fixed_width_column_wrapper<int16_t>{
+    {6, 4, 2, 7, 5, 4, 2, 7, 5, 3},
+    {true, false, true, false, true, false, true, false, true, false}};
+  auto expected_hours = fixed_width_column_wrapper<int16_t>{
+    {19, 20, 21, 22, 23, 0, 0, 1, 2, 3},
+    {true, false, true, false, true, false, true, false, true, false}};
+  auto expected_minutes = fixed_width_column_wrapper<int16_t>{
+    {33, 26, 20, 13, 6, 0, 53, 46, 40, 33},
+    {true, false, true, false, true, false, true, false, true, false}};
+  auto expected_seconds = fixed_width_column_wrapper<int16_t>{
+    {20, 40, 0, 20, 40, 0, 20, 40, 0, 20},
+    {true, false, true, false, true, false, true, false, true, false}};
+
+  // Special cases for timestamp_D: zero out the expected hh/mm/ss cols
+  if (std::is_same_v<TypeParam, cudf::timestamp_D>) {
+    expected_hours = fixed_width_column_wrapper<int16_t>{
+      {0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+      {true, false, true, false, true, false, true, false, true, false}};
+    expected_minutes = fixed_width_column_wrapper<int16_t>{
+      {0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+      {true, false, true, false, true, false, true, false, true, false}};
+    expected_seconds = fixed_width_column_wrapper<int16_t>{
+      {0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+      {true, false, true, false, true, false, true, false, true, false}};
+  }
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_year(timestamps), expected_years);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_month(timestamps), expected_months);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_day(timestamps), expected_days);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_weekday(timestamps), expected_weekdays);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_hour(timestamps), expected_hours);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_minute(timestamps), expected_minutes);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_second(timestamps), expected_seconds);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestLastDayOfMonthWithSeconds)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s = fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+    662688000L,   // 1991-01-01 00:00:00 GMT
+    949496401L,   // 2000-02-02 13:00:01 GMT - leap year
+    4106854801L,  // 2100-02-21 01:00:01 GMT - not a leap year
+    1582391837L,  // 2020-02-22 17:17:17 GMT - leap year
+    1363046401L,  // 2013-03-12 00:00:01 GMT
+    1302696000L,  // 2011-04-13 12:00:00 GMT
+    1495800001L,  // 2017-05-26 12:00:01 GMT
+    1056931201L,  // 2003-06-30 00:00:01 GMT - already last day
+    1031961599L,  // 2002-09-13 23:59:59 GMT
+    0L,           // This is the UNIX epoch - 1970-01-01
+    -131968728L,  // 1965-10-26 14:01:12 GMT
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *last_day_of_month(timestamps_s),
+    fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      7700,   // 1991-01-31
+      11016,  // 2000-02-29
+      47540,  // 2100-02-28
+      18321,  // 2020-02-29
+      15795,  // 2013-03-31
+      15094,  // 2011-04-30
+      17317,  // 2017-05-31
+      12233,  // 2003-06-30
+      11960,  // 2002-09-30
+      30,     // This is the UNIX epoch - when rounded up becomes 1970-01-31
+      -1523   // 1965-10-31
+    },
+    verbosity);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestLastDayOfMonthWithDate)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in days since epoch
+  // Dates converted using epochconverter.com
+  // Make some nullable fields as well
+  auto timestamps_d = fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+    {
+      999,    // Random nullable field
+      0,      // This is the UNIX epoch - 1970-01-01
+      44376,  // 2091-07-01 00:00:00 GMT
+      47695,  // 2100-08-02 00:00:00 GMT
+      3,      // Random nullable field
+      66068,  // 2150-11-21 00:00:00 GMT
+      22270,  // 2030-12-22 00:00:00 GMT
+      111,    // Random nullable field
+    },
+    {false, true, true, true, false, true, true, false},
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *last_day_of_month(timestamps_d),
+    fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {
+        999,    // Random nullable field
+        30,     // This is the UNIX epoch - when rounded up becomes 1970-01-31
+        44406,  // 2091-07-31
+        47724,  // 2100-08-31
+        3,      // Random nullable field
+        66077,  // 2150-11-30
+        22279,  // 2030-12-31
+        111     // Random nullable field
+      },
+      {false, true, true, true, false, true, true, false}},
+    verbosity);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestDayOfYearWithDate)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Day number in the year
+  // Dates converted using epochconverter.com
+  // Make some nullable fields as well
+  auto timestamps_d =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      {
+        999L,         // Random nullable field
+        0L,           // This is the UNIX epoch - 1970-01-01
+        1577865600L,  // 2020-01-01 00:00:00 GMT
+        1581667200L,  // 2020-02-14 00:00:00 GMT
+        3L,           // Random nullable field
+        1609401600L,  // 2020-12-31 00:00:00 GMT
+        4133923200L,  // 2100-12-31 00:00:00 GMT
+        111L,         // Random nullable field
+        -2180188800L  // 1900-11-30 00:00:00 GMT
+      },
+      {false, true, true, true, false, true, true, false, true}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*day_of_year(timestamps_d),
+                                 fixed_width_column_wrapper<int16_t>{
+                                   {
+                                     999,  // Random nullable field
+                                     1,    // Number of year days until UNIX epoch time
+                                     1,    // Number of year days until 2020-01-01
+                                     45,   // Number of year days until 2020-02-14
+                                     3,    // Random nullable field
+                                     366,  // Number of year days until 2020-12-31
+                                     365,  // Number of year days until 2100-12-31
+                                     111,  // Random nullable field
+                                     334   // Number of year days until 1900-11-30
+                                   },
+                                   {false, true, true, true, false, true, true, false, true},
+                                 },
+                                 verbosity);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestDayOfYearWithEmptyColumn)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Create an empty column
+  auto timestamps_d = fixed_width_column_wrapper<cudf::timestamp_s>{};
+  auto out_col      = day_of_year(timestamps_d);
+  EXPECT_EQ(out_col->size(), 0);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestAddMonthsWithInvalidColType)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      662688000L  // 1991-01-01 00:00:00 GMT
+    };
+
+  // Months has to be an INT16 or INT32 type
+  EXPECT_NO_THROW(
+    add_calendrical_months(timestamps_s, cudf::test::fixed_width_column_wrapper<int32_t>{-2}));
+  EXPECT_NO_THROW(
+    add_calendrical_months(timestamps_s, cudf::test::fixed_width_column_wrapper<int16_t>{-2}));
+
+  EXPECT_THROW(
+    add_calendrical_months(timestamps_s, cudf::test::fixed_width_column_wrapper<int8_t>{-2}),
+    cudf::logic_error);
+  EXPECT_THROW(
+    add_calendrical_months(timestamps_s, cudf::test::fixed_width_column_wrapper<int64_t>{-2}),
+    cudf::logic_error);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestAddMonthsWithInvalidScalarType)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s = fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+    662688000L  // 1991-01-01 00:00:00 GMT
+  };
+
+  // Months has to be an INT16 or INT32 type
+  EXPECT_NO_THROW(add_calendrical_months(timestamps_s, *cudf::make_fixed_width_scalar<int32_t>(5)));
+  EXPECT_NO_THROW(
+    add_calendrical_months(timestamps_s, *cudf::make_fixed_width_scalar<int16_t>(-3)));
+
+  EXPECT_THROW(add_calendrical_months(timestamps_s, *cudf::make_fixed_width_scalar<int8_t>(-3)),
+               cudf::logic_error);
+  EXPECT_THROW(add_calendrical_months(timestamps_s, *cudf::make_fixed_width_scalar<int64_t>(-3)),
+               cudf::logic_error);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestAddMonthsWithIncorrectColSizes)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      662688000L  // 1991-01-01 00:00:00 GMT
+    };
+
+  // Provide more number of months rows than timestamp rows
+  auto months = cudf::test::fixed_width_column_wrapper<int16_t>{-2, 3};
+
+  EXPECT_THROW(add_calendrical_months(timestamps_s, months), cudf::logic_error);
+}
+
+using ValidMonthIntegerType = cudf::test::Types<int16_t, int32_t>;
+
+template <typename T>
+struct TypedAddMonthsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedAddMonthsTest, ValidMonthIntegerType);
+
+TYPED_TEST(TypedAddMonthsTest, TestAddMonthsWithSeconds)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      662688000L,   // 1991-01-01 00:00:00 GMT
+      949496401L,   // 2000-02-02 13:00:01 GMT - leap year
+      1056931201L,  // 2003-06-30 00:00:01 GMT - last day of month
+      1056964201L,  // 2003-06-30 09:10:01 GMT - last day of month
+      1056974401L,  // 2003-06-30 12:00:01 GMT - last day of month
+      1056994021L,  // 2003-06-30 17:27:01 GMT - last day of month
+      0L,           // This is the UNIX epoch - 1970-01-01
+      0L,           // This is the UNIX epoch - 1970-01-01
+      -131586588L,  // 1965-10-31 00:10:12 GMT
+      -131550590L,  // 1965-10-31 10:10:10 GMT
+      -131544000L,  // 1965-10-31 12:00:00 GMT
+      -131536728L   // 1965-10-31 14:01:12 GMT
+    };
+
+  auto const months =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{-2, 6, -1, 1, -4, 8, -2, 10, 4, -20, 1, 3};
+
+  auto const expected =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      657417600L,   // 1990-11-01 00:00:00 GMT
+      965221201L,   // 2000-08-02 13:00:01 GMT
+      1054252801L,  // 2003-05-30 00:00:01 GMT
+      1059556201L,  // 2003-07-30 09:10:01 GMT
+      1046433601L,  // 2003-02-28 12:00:01 GMT
+      1078075621L,  // 2004-02-29 17:27:01 GMT
+      -5270400L,    // 1969-11-01
+      26265600L,    // 1970-11-01
+      -121218588L,  // 1966-02-28 00:10:12 GMT
+      -184254590L,  // 1964-02-29 10:10:10 GMT
+      -128952000L,  // 1965-11-30 12:00:00 GMT
+      -123587928L   // 1966-01-31 14:01:12 GMT
+    };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *add_calendrical_months(timestamps_s, months), expected, verbosity);
+}
+
+TYPED_TEST(TypedAddMonthsTest, TestAddScalarMonthsWithSeconds)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s = fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+    662688000L,   // 1991-01-01 00:00:00 GMT
+    949496401L,   // 2000-02-02 13:00:01 GMT - leap year
+    1056964201L,  // 2003-06-30 09:10:01 GMT - last day of month
+    0L,           // This is the UNIX epoch - 1970-01-01
+    -131536728L   // 1965-10-31 14:01:12 GMT - last day of month
+  };
+
+  // add
+  auto const months1 = cudf::make_fixed_width_scalar<TypeParam>(11);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *add_calendrical_months(timestamps_s, *months1),
+    fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      691545600L,   // 1991-12-01 00:00:00 GMT
+      978440401L,   // 2001-01-02 13:00:01 GMT
+      1085908201L,  // 2004-05-30 09:10:01 GMT
+      28857600L,    // 1970-12-01 00:00:00 GMT
+      -102679128L,  // 1966-09-30 14:01:12 GMT
+    },
+    verbosity);
+
+  // subtract
+  auto const months2 = cudf::make_fixed_width_scalar<TypeParam>(-20);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *add_calendrical_months(timestamps_s, *months2),
+    fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      609984000L,   // 1989-05-01 00:00:00 GMT
+      896792401L,   // 1998-06-02 13:00:01 GMT
+      1004433001L,  // 2001-10-30 09:10:01 GMT
+      -52704000L,   // 1968-05-01 00:00:00 GMT
+      -184240728L,  // 1964-02-29 14:01:12 GMT - lands on a leap year february
+    },
+    verbosity);
+}
+
+TYPED_TEST(TypedAddMonthsTest, TestAddMonthsWithSecondsAndNullValues)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      {
+        662688000L,   // 1991-01-01 00:00:00 GMT
+        949496401L,   // 2000-02-02 13:00:01 GMT - leap year
+        1056931201L,  // 2003-06-30 00:00:01 GMT - last day of month
+        1056964201L,  // 2003-06-30 09:10:01 GMT - last day of month
+        1056974401L,  // 2003-06-30 12:00:01 GMT - last day of month
+        1056994021L,  // 2003-06-30 17:27:01 GMT - last day of month
+        0L,           // This is the UNIX epoch - 1970-01-01
+        0L,           // This is the UNIX epoch - 1970-01-01
+        -131586588L,  // 1965-10-31 00:10:12 GMT
+        -131550590L,  // 1965-10-31 10:10:10 GMT
+        -131544000L,  // 1965-10-31 12:00:00 GMT
+        -131536728L   // 1965-10-31 14:01:12 GMT
+      },
+      {true, false, true, false, true, false, true, false, true, true, true, true}};
+
+  auto const months = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {-2, 6, -1, 1, -4, 8, -2, 10, 4, -20, 1, 3},
+    {false, true, true, false, true, true, true, true, true, true, true, true}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *add_calendrical_months(timestamps_s, months),
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      {
+        0L,           // null value
+        0L,           // null value
+        1054252801L,  // 2003-05-30 00:00:01 GMT
+        0L,           // null value
+        1046433601L,  // 2003-02-28 12:00:01 GMT
+        0L,           // null value
+        -5270400L,    // 1969-11-01
+        0L,           // null value
+        -121218588L,  // 1966-02-28 00:10:12 GMT
+        -184254590L,  // 1964-02-29 10:10:10 GMT
+        -128952000L,  // 1965-11-30 12:00:00 GMT
+        -123587928L   // 1966-01-31 14:01:12 GMT
+      },
+      {false, false, true, false, true, false, true, false, true, true, true, true}},
+    verbosity);
+}
+
+TYPED_TEST(TypedAddMonthsTest, TestAddScalarMonthsWithSecondsWithNulls)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s = fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>(
+    {
+      662688000L,   // 1991-01-01 00:00:00 GMT
+      0L,           // NULL
+      1056964201L,  // 2003-06-30 09:10:01 GMT - last day of month
+      0L,           // This is the UNIX epoch - 1970-01-01
+      0L            // NULL
+    },
+    iterators::nulls_at({1, 4}));
+
+  // valid scalar
+  auto const months1 = cudf::make_fixed_width_scalar<TypeParam>(11);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *add_calendrical_months(timestamps_s, *months1),
+    fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>(
+      {
+        691545600L,   // 1991-12-01 00:00:00 GMT
+        0L,           // NULL
+        1085908201L,  // 2004-05-30 09:10:01 GMT
+        28857600L,    // 1970-12-01 00:00:00 GMT
+        0L,           // NULL
+      },
+      iterators::nulls_at({1, 4})),
+    verbosity);
+
+  // null scalar
+  auto const months2 =
+    cudf::make_default_constructed_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *add_calendrical_months(timestamps_s, *months2),
+    fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>({0L, 0L, 0L, 0L, 0L},
+                                                                          iterators::all_nulls()),
+    verbosity);
+}
+
+TEST_F(BasicDatetimeOpsTest, TestIsLeapYear)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      {
+        1594332839L,    // 2020-07-09 10:13:59 GMT - leap year
+        0L,             // null
+        915148800L,     // 1999-01-01 00:00:00 GMT - non leap year
+        -11663029161L,  // 1600-5-31 05:40:39 GMT - leap year
+        707904541L,     // 1992-06-07 08:09:01 GMT - leap year
+        -2181005247L,   // 1900-11-20 09:12:33 GMT - non leap year
+        0L,             // UNIX EPOCH 1970-01-01 00:00:00 GMT - non leap year
+        -12212553600L,  // First full year of Gregorian Calendar 1583-01-01 00:00:00 - non-leap-year
+        0L,             // null
+        13591632822L,   // 2400-09-13 13:33:42 GMT - leap year
+        4539564243L,    // 2113-11-08 06:04:03 GMT - non leap year
+        0L              // null
+      },
+      {true, false, true, true, true, true, true, true, false, true, true, false}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *is_leap_year(timestamps_s),
+    cudf::test::fixed_width_column_wrapper<bool>{
+      {true, XXX, false, true, true, false, false, false, XXX, true, false, XXX},
+      {true, false, true, true, true, true, true, true, false, true, true, false}});
+}
+
+TEST_F(BasicDatetimeOpsTest, TestDaysInMonths)
+
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      {
+        0L,            // NULL
+        -1887541682L,  // 1910-03-10 10:51:58
+        0L,            // NULL
+        -1251006943L,  // 1930-05-11 18:04:17
+        -932134638L,   // 1940-06-18 09:42:42
+        -614354877L,   // 1950-07-14 09:52:03
+        -296070394L,   // 1960-08-14 06:13:26
+        22840404L,     // 1970-09-22 08:33:24
+        339817190L,    // 1980-10-08 01:39:50
+        657928062L,    // 1990-11-06 21:47:42
+        976630837L,    // 2000-12-12 14:20:37
+        1294699018L,   // 2011-01-10 22:36:58
+        1613970182L,   // 2021-02-22 05:03:02 - non leap year February
+        1930963331L,   // 2031-03-11 02:42:11
+        2249867102L,   // 2041-04-18 03:05:02
+        951426858L,    // 2000-02-24 21:14:18 - leap year February
+      },
+      iterators::nulls_at({0, 2})};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*days_in_month(timestamps_s),
+                                 cudf::test::fixed_width_column_wrapper<int16_t>{
+                                   {-1, 31, -1, 31, 30, 31, 31, 30, 31, 30, 31, 31, 28, 31, 30, 29},
+                                   iterators::nulls_at({0, 2})});
+}
+
+TEST_F(BasicDatetimeOpsTest, TestQuarter)
+{
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+  using namespace cudf::test::iterators;
+
+  // Time in seconds since epoch
+  // Dates converted using epochconverter.com
+  auto timestamps_s =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{
+      {
+        1594332839L,    // 2020-07-09 10:13:59 GMT
+        0L,             // null
+        915148800L,     // 1999-01-01 00:00:00 GMT
+        -11663029161L,  // 1600-5-31 05:40:39 GMT
+        707904541L,     // 1992-06-07 08:09:01 GMT
+        -2181005247L,   // 1900-11-20 09:12:33 GMT
+        0L,             // UNIX EPOCH 1970-01-01 00:00:00 GMT
+        -12212553600L,  // First full year of Gregorian Calendar 1583-01-01 00:00:00
+        0L,             // null
+        13591632822L,   // 2400-09-13 13:33:42 GMT
+        4539564243L,    // 2113-11-08 06:04:03 GMT
+        0L,             // null
+        1608581568L,    // 2020-12-21 08:12:48 GMT
+        1584821568L,    // 2020-03-21 08:12:48 GMT
+      },
+      nulls_at({1, 8, 11})};
+
+  auto quarter = cudf::test::fixed_width_column_wrapper<int16_t>{
+    {3, 0 /*null*/, 1, 2, 2, 4, 1, 1, 0 /*null*/, 3, 4, 0 /*null*/, 4, 1}, nulls_at({1, 8, 11})};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*extract_quarter(timestamps_s), quarter);
+}
+
+TYPED_TEST(TypedDatetimeOpsTest, TestCeilDatetime)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto start = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto stop  = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+
+  auto const input =
+    generate_timestamps<T>(this->size(), time_point_ms(start), time_point_ms(stop));
+  auto const timestamps = to_host<T>(input).first;
+
+  std::vector<T> ceiled_day(timestamps.size());
+  thrust::transform(timestamps.begin(), timestamps.end(), ceiled_day.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<days>(i));
+  });
+  auto expected_day =
+    fixed_width_column_wrapper<T, typename T::duration::rep>(ceiled_day.begin(), ceiled_day.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::DAY), expected_day);
+
+  std::vector<T> ceiled_hour(timestamps.size());
+  thrust::transform(timestamps.begin(), timestamps.end(), ceiled_hour.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<hours>(i));
+  });
+  auto expected_hour = fixed_width_column_wrapper<T, typename T::duration::rep>(ceiled_hour.begin(),
+                                                                                ceiled_hour.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::HOUR), expected_hour);
+
+  std::vector<T> ceiled_minute(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), ceiled_minute.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<minutes>(i));
+  });
+  auto expected_minute = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    ceiled_minute.begin(), ceiled_minute.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::MINUTE),
+                                 expected_minute);
+
+  std::vector<T> ceiled_second(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), ceiled_second.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<seconds>(i));
+  });
+  auto expected_second = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    ceiled_second.begin(), ceiled_second.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::SECOND),
+                                 expected_second);
+
+  std::vector<T> ceiled_millisecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), ceiled_millisecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<milliseconds>(i));
+  });
+  auto expected_millisecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    ceiled_millisecond.begin(), ceiled_millisecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::MILLISECOND),
+                                 expected_millisecond);
+
+  std::vector<T> ceiled_microsecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), ceiled_microsecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<microseconds>(i));
+  });
+  auto expected_microsecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    ceiled_microsecond.begin(), ceiled_microsecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::MICROSECOND),
+                                 expected_microsecond);
+
+  std::vector<T> ceiled_nanosecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), ceiled_nanosecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(ceil<nanoseconds>(i));
+  });
+  auto expected_nanosecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    ceiled_nanosecond.begin(), ceiled_nanosecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*ceil_datetimes(input, rounding_frequency::NANOSECOND),
+                                 expected_nanosecond);
+}
+
+TYPED_TEST(TypedDatetimeOpsTest, TestFloorDatetime)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto start = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto stop  = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+
+  auto const input =
+    generate_timestamps<T>(this->size(), time_point_ms(start), time_point_ms(stop));
+  auto const timestamps = to_host<T>(input).first;
+
+  std::vector<T> floored_day(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_day.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<days>(i));
+  });
+  auto expected_day = fixed_width_column_wrapper<T, typename T::duration::rep>(floored_day.begin(),
+                                                                               floored_day.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::DAY), expected_day);
+
+  std::vector<T> floored_hour(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_hour.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<hours>(i));
+  });
+  auto expected_hour = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    floored_hour.begin(), floored_hour.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::HOUR), expected_hour);
+
+  std::vector<T> floored_minute(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_minute.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<minutes>(i));
+  });
+  auto expected_minute = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    floored_minute.begin(), floored_minute.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::MINUTE),
+                                 expected_minute);
+
+  std::vector<T> floored_second(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_second.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<seconds>(i));
+  });
+  auto expected_second = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    floored_second.begin(), floored_second.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::SECOND),
+                                 expected_second);
+
+  std::vector<T> floored_millisecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_millisecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<milliseconds>(i));
+  });
+  auto expected_millisecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    floored_millisecond.begin(), floored_millisecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::MILLISECOND),
+                                 expected_millisecond);
+
+  std::vector<T> floored_microsecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_microsecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<microseconds>(i));
+  });
+  auto expected_microsecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    floored_microsecond.begin(), floored_microsecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::MICROSECOND),
+                                 expected_microsecond);
+
+  std::vector<T> floored_nanosecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), floored_nanosecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(floor<nanoseconds>(i));
+  });
+  auto expected_nanosecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    floored_nanosecond.begin(), floored_nanosecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*floor_datetimes(input, rounding_frequency::NANOSECOND),
+                                 expected_nanosecond);
+}
+
+TYPED_TEST(TypedDatetimeOpsTest, TestRoundDatetime)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  using namespace cudf::datetime;
+  using namespace cuda::std::chrono;
+
+  auto start = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto stop  = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+
+  auto const input =
+    generate_timestamps<T>(this->size(), time_point_ms(start), time_point_ms(stop));
+  auto const timestamps = to_host<T>(input).first;
+
+  std::vector<T> rounded_day(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_day.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<days>(i));
+  });
+  auto expected_day = fixed_width_column_wrapper<T, typename T::duration::rep>(rounded_day.begin(),
+                                                                               rounded_day.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::DAY), expected_day);
+
+  std::vector<T> rounded_hour(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_hour.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<hours>(i));
+  });
+  auto expected_hour = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    rounded_hour.begin(), rounded_hour.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::HOUR), expected_hour);
+
+  std::vector<T> rounded_minute(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_minute.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<minutes>(i));
+  });
+  auto expected_minute = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    rounded_minute.begin(), rounded_minute.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::MINUTE),
+                                 expected_minute);
+
+  std::vector<T> rounded_second(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_second.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<seconds>(i));
+  });
+  auto expected_second = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    rounded_second.begin(), rounded_second.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::SECOND),
+                                 expected_second);
+
+  std::vector<T> rounded_millisecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_millisecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<milliseconds>(i));
+  });
+  auto expected_millisecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    rounded_millisecond.begin(), rounded_millisecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::MILLISECOND),
+                                 expected_millisecond);
+
+  std::vector<T> rounded_microsecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_microsecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<microseconds>(i));
+  });
+  auto expected_microsecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    rounded_microsecond.begin(), rounded_microsecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::MICROSECOND),
+                                 expected_microsecond);
+
+  std::vector<T> rounded_nanosecond(timestamps.size());
+  std::transform(timestamps.begin(), timestamps.end(), rounded_nanosecond.begin(), [](auto i) {
+    return time_point_cast<typename T::duration>(round<nanoseconds>(i));
+  });
+  auto expected_nanosecond = fixed_width_column_wrapper<T, typename T::duration::rep>(
+    rounded_nanosecond.begin(), rounded_nanosecond.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*round_datetimes(input, rounding_frequency::NANOSECOND),
+                                 expected_nanosecond);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/device_atomics/device_atomics_test.cu b/cpp/tests/device_atomics/device_atomics_test.cu
new file mode 100644
index 0000000..2419536
--- /dev/null
+++ b/cpp/tests/device_atomics/device_atomics_test.cu
@@ -0,0 +1,259 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/device_atomics.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/timestamp_utilities.cuh>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/host_vector.h>
+
+#include <algorithm>
+
+template <typename T>
+__global__ void gpu_atomic_test(T* result, T* data, size_t size)
+{
+  size_t id   = blockIdx.x * blockDim.x + threadIdx.x;
+  size_t step = blockDim.x * gridDim.x;
+
+  for (; id < size; id += step) {
+    atomicAdd(&result[0], data[id]);
+    atomicMin(&result[1], data[id]);
+    atomicMax(&result[2], data[id]);
+    cudf::genericAtomicOperation(&result[3], data[id], cudf::DeviceSum{});
+    cudf::genericAtomicOperation(&result[4], data[id], cudf::DeviceMin{});
+    cudf::genericAtomicOperation(&result[5], data[id], cudf::DeviceMax{});
+  }
+}
+
+template <typename T, typename BinaryOp>
+constexpr inline bool is_timestamp_sum()
+{
+  return cudf::is_timestamp<T>() && std::is_same_v<BinaryOp, cudf::DeviceSum>;
+}
+// Disable SUM of TIMESTAMP types
+template <typename T,
+          typename BinaryOp,
+          std::enable_if_t<is_timestamp_sum<T, BinaryOp>()>* = nullptr>
+__device__ T atomic_op(T* addr, T const& value, BinaryOp op)
+{
+  return {};
+}
+
+template <typename T,
+          typename BinaryOp,
+          std::enable_if_t<!is_timestamp_sum<T, BinaryOp>()>* = nullptr>
+__device__ T atomic_op(T* addr, T const& value, BinaryOp op)
+{
+  T old_value = *addr;
+  T assumed;
+
+  do {
+    assumed     = old_value;
+    T new_value = op(old_value, value);
+
+    old_value = atomicCAS(addr, assumed, new_value);
+  } while (assumed != old_value);
+
+  return old_value;
+}
+
+template <typename T>
+__global__ void gpu_atomicCAS_test(T* result, T* data, size_t size)
+{
+  size_t id   = blockIdx.x * blockDim.x + threadIdx.x;
+  size_t step = blockDim.x * gridDim.x;
+
+  for (; id < size; id += step) {
+    atomic_op(&result[0], data[id], cudf::DeviceSum{});
+    atomic_op(&result[1], data[id], cudf::DeviceMin{});
+    atomic_op(&result[2], data[id], cudf::DeviceMax{});
+    atomic_op(&result[3], data[id], cudf::DeviceSum{});
+    atomic_op(&result[4], data[id], cudf::DeviceMin{});
+    atomic_op(&result[5], data[id], cudf::DeviceMax{});
+  }
+}
+
+template <typename T>
+std::enable_if_t<!cudf::is_timestamp<T>(), T> accumulate(cudf::host_span<T const> xs)
+{
+  return std::accumulate(xs.begin(), xs.end(), T{0});
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_timestamp<T>(), T> accumulate(cudf::host_span<T const> xs)
+{
+  auto ys = std::vector<typename T::rep>(xs.size());
+  std::transform(
+    xs.begin(), xs.end(), ys.begin(), [](T const& ts) { return ts.time_since_epoch().count(); });
+  return T{typename T::duration{std::accumulate(ys.begin(), ys.end(), 0)}};
+}
+
+template <typename T>
+struct AtomicsTest : public cudf::test::BaseFixture {
+  void atomic_test(std::vector<int> const& v_input,
+                   bool is_cas_test,
+                   int block_size = 0,
+                   int grid_size  = 1)
+  {
+    size_t vec_size = v_input.size();
+
+    // use transform from thrust::host_vector<int> instead.
+    thrust::host_vector<T> v(vec_size);
+    std::transform(v_input.begin(), v_input.end(), v.begin(), [](int x) {
+      T t = cudf::test::make_type_param_scalar<T>(x);
+      return t;
+    });
+
+    T exact[3];
+    exact[0] = accumulate<T>(v);
+    exact[1] = *(std::min_element(v.begin(), v.end()));
+    exact[2] = *(std::max_element(v.begin(), v.end()));
+
+    thrust::host_vector<T> result_init(9);  // +3 padding for int8 tests
+    result_init[0] = cudf::test::make_type_param_scalar<T>(0);
+    if constexpr (cudf::is_chrono<T>()) {
+      result_init[1] = T::max();
+      result_init[2] = T::min();
+    } else {
+      result_init[1] = std::numeric_limits<T>::max();
+      result_init[2] = std::numeric_limits<T>::min();
+    }
+    result_init[3] = result_init[0];
+    result_init[4] = result_init[1];
+    result_init[5] = result_init[2];
+
+    auto dev_data = cudf::detail::make_device_uvector_sync(
+      v, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+    auto dev_result = cudf::detail::make_device_uvector_sync(
+      result_init, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+    if (block_size == 0) { block_size = vec_size; }
+
+    if (is_cas_test) {
+      gpu_atomicCAS_test<<<grid_size, block_size, 0, cudf::get_default_stream().value()>>>(
+        dev_result.data(), dev_data.data(), vec_size);
+    } else {
+      gpu_atomic_test<<<grid_size, block_size, 0, cudf::get_default_stream().value()>>>(
+        dev_result.data(), dev_data.data(), vec_size);
+    }
+
+    auto host_result = cudf::detail::make_host_vector_sync(dev_result, cudf::get_default_stream());
+
+    CUDF_CHECK_CUDA(cudf::get_default_stream().value());
+
+    if (!is_timestamp_sum<T, cudf::DeviceSum>()) {
+      EXPECT_EQ(host_result[0], exact[0]) << "atomicAdd test failed";
+    }
+    EXPECT_EQ(host_result[1], exact[1]) << "atomicMin test failed";
+    EXPECT_EQ(host_result[2], exact[2]) << "atomicMax test failed";
+    if (!is_timestamp_sum<T, cudf::DeviceSum>()) {
+      EXPECT_EQ(host_result[3], exact[0]) << "atomicAdd test(2) failed";
+    }
+    EXPECT_EQ(host_result[4], exact[1]) << "atomicMin test(2) failed";
+    EXPECT_EQ(host_result[5], exact[2]) << "atomicMax test(2) failed";
+  }
+};
+
+TYPED_TEST_SUITE(AtomicsTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+// tests for atomicAdd/Min/Max
+TYPED_TEST(AtomicsTest, atomicOps)
+{
+  bool is_cas_test = false;
+  std::vector<int> input_array({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  this->atomic_test(input_array, is_cas_test);
+
+  std::vector<int> input_array2({6, -6, 13, 62, -11, -20, 33});
+  this->atomic_test(input_array2, is_cas_test);
+}
+
+// tests for atomicCAS
+TYPED_TEST(AtomicsTest, atomicCAS)
+{
+  bool is_cas_test = true;
+  std::vector<int> input_array({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  this->atomic_test(input_array, is_cas_test);
+
+  std::vector<int> input_array2({6, -6, 13, 62, -11, -20, 33});
+  this->atomic_test(input_array2, is_cas_test);
+}
+
+// tests for atomicAdd/Min/Max
+TYPED_TEST(AtomicsTest, atomicOpsGrid)
+{
+  bool is_cas_test = false;
+  int block_size   = 3;
+  int grid_size    = 4;
+
+  std::vector<int> input_array({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  this->atomic_test(input_array, is_cas_test, block_size, grid_size);
+
+  std::vector<int> input_array2({6, -6, 13, 62, -11, -20, 33});
+  this->atomic_test(input_array2, is_cas_test, block_size, grid_size);
+}
+
+// tests for atomicCAS
+TYPED_TEST(AtomicsTest, atomicCASGrid)
+{
+  bool is_cas_test = true;
+  int block_size   = 3;
+  int grid_size    = 4;
+
+  std::vector<int> input_array({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  this->atomic_test(input_array, is_cas_test, block_size, grid_size);
+
+  std::vector<int> input_array2({6, -6, 13, 62, -11, -20, 33});
+  this->atomic_test(input_array2, is_cas_test, block_size, grid_size);
+}
+
+// tests for large array
+TYPED_TEST(AtomicsTest, atomicOpsRandom)
+{
+  bool is_cas_test = false;
+  int block_size   = 256;
+  int grid_size    = 64;
+
+  std::vector<int> input_array(grid_size * block_size);
+
+  std::default_random_engine engine;
+  std::uniform_int_distribution<> dist(-10, 10);
+  std::generate(input_array.begin(), input_array.end(), [&]() { return dist(engine); });
+
+  this->atomic_test(input_array, is_cas_test, block_size, grid_size);
+}
+
+TYPED_TEST(AtomicsTest, atomicCASRandom)
+{
+  bool is_cas_test = true;
+  int block_size   = 256;
+  int grid_size    = 64;
+
+  std::vector<int> input_array(grid_size * block_size);
+
+  std::default_random_engine engine;
+  std::uniform_int_distribution<> dist(-10, 10);
+  std::generate(input_array.begin(), input_array.end(), [&]() { return dist(engine); });
+
+  this->atomic_test(input_array, is_cas_test, block_size, grid_size);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/dictionary/add_keys_test.cpp b/cpp/tests/dictionary/add_keys_test.cpp
new file mode 100644
index 0000000..adbcf41
--- /dev/null
+++ b/cpp/tests/dictionary/add_keys_test.cpp
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <vector>
+
+struct DictionaryAddKeysTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryAddKeysTest, StringsColumn)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"fff", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "fff", "aaa"});
+  cudf::test::strings_column_wrapper new_keys({"ddd", "bbb", "eee"});
+
+  auto dictionary = cudf::dictionary::encode(strings);
+  auto result =
+    cudf::dictionary::add_keys(cudf::dictionary_column_view(dictionary->view()), new_keys);
+
+  cudf::dictionary_column_view view(result->view());
+
+  cudf::test::strings_column_wrapper keys_expected({"aaa", "bbb", "ccc", "ddd", "eee", "fff"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+
+  cudf::test::fixed_width_column_wrapper<uint8_t> indices_expected({5, 0, 3, 1, 2, 2, 2, 5, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), indices_expected);
+}
+
+TEST_F(DictionaryAddKeysTest, FloatColumn)
+{
+  cudf::test::fixed_width_column_wrapper<float> input{4.25, 7.125, 0.5, -11.75, 7.125, 0.5};
+  cudf::test::fixed_width_column_wrapper<float> new_keys{4.25, -11.75, 5.0};
+
+  auto dictionary = cudf::dictionary::encode(input);
+  auto result =
+    cudf::dictionary::add_keys(cudf::dictionary_column_view(dictionary->view()), new_keys);
+  cudf::dictionary_column_view view(result->view());
+
+  cudf::test::fixed_width_column_wrapper<float> keys_expected{-11.75, 0.5, 4.25, 5.0, 7.125};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+
+  cudf::test::fixed_width_column_wrapper<uint8_t> expected{2, 4, 1, 0, 4, 1};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), expected);
+}
+
+TEST_F(DictionaryAddKeysTest, WithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{{555, 0, 333, 111, 222, 222, 222, 555, 0},
+                                                        {1, 1, 1, 0, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int64_t> new_keys{0, 111, 444, 777};
+
+  auto dictionary = cudf::dictionary::encode(input);
+  auto result =
+    cudf::dictionary::add_keys(cudf::dictionary_column_view(dictionary->view()), new_keys);
+  auto decoded = cudf::dictionary::decode(result->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), input);  // new keys should not change anything
+}
+
+TEST_F(DictionaryAddKeysTest, Errors)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{1, 2, 3};
+  auto dictionary = cudf::dictionary::encode(input);
+
+  cudf::test::fixed_width_column_wrapper<float> new_keys{1.0, 2.0, 3.0};
+  EXPECT_THROW(cudf::dictionary::add_keys(dictionary->view(), new_keys), cudf::logic_error);
+  cudf::test::fixed_width_column_wrapper<int64_t> null_keys{{1, 2, 3}, {1, 0, 1}};
+  EXPECT_THROW(cudf::dictionary::add_keys(dictionary->view(), null_keys), cudf::logic_error);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/dictionary/decode_test.cpp b/cpp/tests/dictionary/decode_test.cpp
new file mode 100644
index 0000000..25ccb33
--- /dev/null
+++ b/cpp/tests/dictionary/decode_test.cpp
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <vector>
+
+struct DictionaryDecodeTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryDecodeTest, StringColumn)
+{
+  std::vector<char const*> h_strings{"eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+
+  auto dictionary = cudf::dictionary::encode(strings);
+  auto output     = cudf::dictionary::decode(cudf::dictionary_column_view(dictionary->view()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strings, *output);
+}
+
+TEST_F(DictionaryDecodeTest, FloatColumn)
+{
+  cudf::test::fixed_width_column_wrapper<float> input{4.25, 7.125, 0.5, -11.75, 7.125, 0.5};
+
+  auto dictionary = cudf::dictionary::encode(input);
+  auto output     = cudf::dictionary::decode(cudf::dictionary_column_view(dictionary->view()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *output);
+}
+
+TEST_F(DictionaryDecodeTest, ColumnWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{{444, 0, 333, 111, 222, 222, 222, 444, 000},
+                                                        {1, 1, 1, 1, 1, 0, 1, 1, 1}};
+
+  auto dictionary = cudf::dictionary::encode(input);
+  auto output     = cudf::dictionary::decode(cudf::dictionary_column_view(dictionary->view()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *output);
+}
+
+TEST_F(DictionaryDecodeTest, EmptyColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> input;
+  auto dictionary = cudf::dictionary::encode(input);
+  auto output     = cudf::dictionary::decode(cudf::dictionary_column_view(dictionary->view()));
+
+  // check empty
+  EXPECT_EQ(output->size(), 0);
+  EXPECT_EQ(output->type().id(), cudf::type_id::EMPTY);
+}
diff --git a/cpp/tests/dictionary/encode_test.cpp b/cpp/tests/dictionary/encode_test.cpp
new file mode 100644
index 0000000..6b0b33d
--- /dev/null
+++ b/cpp/tests/dictionary/encode_test.cpp
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <vector>
+
+struct DictionaryEncodeTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryEncodeTest, EncodeStringColumn)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"});
+
+  auto dictionary = cudf::dictionary::encode(strings);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  cudf::test::strings_column_wrapper keys_expected({"aaa", "bbb", "ccc", "ddd", "eee"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices_expected({4, 0, 3, 1, 2, 2, 2, 4, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), indices_expected);
+}
+
+TEST_F(DictionaryEncodeTest, EncodeFloat)
+{
+  cudf::test::fixed_width_column_wrapper<float> input{4.25, 7.125, 0.5, -11.75, 7.125, 0.5};
+
+  auto dictionary = cudf::dictionary::encode(input);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  cudf::test::fixed_width_column_wrapper<float> keys_expected{-11.75, 0.5, 4.25, 7.125};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected{2, 3, 1, 0, 3, 1};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), expected);
+}
+
+TEST_F(DictionaryEncodeTest, EncodeWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{{444, 0, 333, 111, 222, 222, 222, 444, 000},
+                                                        {1, 1, 1, 1, 1, 0, 1, 1, 1}};
+
+  auto dictionary = cudf::dictionary::encode(input);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  cudf::test::fixed_width_column_wrapper<int64_t> keys_expected{0, 111, 222, 333, 444};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected{4, 0, 3, 1, 2, 5, 2, 4, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), expected);
+}
+
+TEST_F(DictionaryEncodeTest, InvalidEncode)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> input{0, 1, 2, 3, -1, -2, -3};
+
+  EXPECT_THROW(cudf::dictionary::encode(input, cudf::data_type{cudf::type_id::INT16}),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/dictionary/factories_test.cpp b/cpp/tests/dictionary/factories_test.cpp
new file mode 100644
index 0000000..5db4bf9
--- /dev/null
+++ b/cpp/tests/dictionary/factories_test.cpp
@@ -0,0 +1,120 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+struct DictionaryFactoriesTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryFactoriesTest, CreateFromColumnViews)
+{
+  cudf::test::strings_column_wrapper keys({"aaa", "ccc", "ddd", "www"});
+  cudf::test::fixed_width_column_wrapper<uint32_t> values{2, 0, 3, 1, 2, 2, 2, 3, 0};
+
+  auto dictionary = cudf::make_dictionary_column(keys, values);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), values);
+}
+
+TEST_F(DictionaryFactoriesTest, ColumnViewsWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<float> keys{-11.75, 4.25, 7.125, 0.5, 12.0};
+  std::vector<uint32_t> h_values{1, 3, 2, 0, 1, 4, 1};
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices(
+    h_values.begin(), h_values.end(), thrust::make_transform_iterator(h_values.begin(), [](auto v) {
+      return v > 0;
+    }));
+  auto dictionary = cudf::make_dictionary_column(keys, indices);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys);
+  cudf::test::fixed_width_column_wrapper<uint32_t> values_expected(h_values.begin(),
+                                                                   h_values.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), values_expected);
+}
+
+TEST_F(DictionaryFactoriesTest, CreateFromColumns)
+{
+  std::vector<std::string> h_keys{"pear", "apple", "fruit", "macintosh"};
+  cudf::test::strings_column_wrapper keys(h_keys.begin(), h_keys.end());
+  std::vector<uint32_t> h_values{1, 2, 3, 1, 2, 3, 0};
+  cudf::test::fixed_width_column_wrapper<uint32_t> values(h_values.begin(), h_values.end());
+
+  auto dictionary =
+    cudf::make_dictionary_column(keys.release(), values.release(), rmm::device_buffer{}, 0);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  cudf::test::strings_column_wrapper keys_expected(h_keys.begin(), h_keys.end());
+  cudf::test::fixed_width_column_wrapper<uint32_t> values_expected(h_values.begin(),
+                                                                   h_values.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), values_expected);
+}
+
+TEST_F(DictionaryFactoriesTest, ColumnsWithNulls)
+{
+  std::vector<int64_t> h_keys{-1234567890, -987654321, 0, 19283714};
+  cudf::test::fixed_width_column_wrapper<int64_t> keys(h_keys.begin(), h_keys.end());
+  std::vector<uint32_t> h_values{1, 2, 3, 1, 2, 3, 0};
+  cudf::test::fixed_width_column_wrapper<uint32_t> values(h_values.begin(), h_values.end());
+  auto size                    = static_cast<cudf::size_type>(h_values.size());
+  rmm::device_buffer null_mask = create_null_mask(size, cudf::mask_state::ALL_NULL);
+  auto dictionary =
+    cudf::make_dictionary_column(keys.release(), values.release(), std::move(null_mask), size);
+  cudf::dictionary_column_view view(dictionary->view());
+  EXPECT_EQ(size, view.size());
+  EXPECT_EQ(size, view.null_count());
+
+  cudf::test::fixed_width_column_wrapper<int64_t> keys_expected(h_keys.begin(), h_keys.end());
+  cudf::test::fixed_width_column_wrapper<uint32_t> values_expected(h_values.begin(),
+                                                                   h_values.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.keys(), keys_expected);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.indices(), values_expected);
+}
+
+TEST_F(DictionaryFactoriesTest, KeysWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> keys{{0, 1, 2, 3, 4}, {1, 1, 1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices{5, 4, 3, 2, 1, 0};
+  EXPECT_THROW(cudf::make_dictionary_column(keys, indices), cudf::logic_error);
+}
+
+TEST_F(DictionaryFactoriesTest, IndicesWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> keys{0, 1, 2, 3, 4};
+  cudf::test::fixed_width_column_wrapper<uint32_t> indices{{5, 4, 3, 2, 1, 0}, {1, 1, 1, 0, 1, 0}};
+  EXPECT_THROW(
+    cudf::make_dictionary_column(keys.release(), indices.release(), rmm::device_buffer{}, 0),
+    cudf::logic_error);
+}
+
+TEST_F(DictionaryFactoriesTest, InvalidIndices)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> keys{0, 1, 2, 3, 4};
+  cudf::test::fixed_width_column_wrapper<int16_t> indices{5, 4, 3, 2, 1, 0};
+  EXPECT_THROW(cudf::make_dictionary_column(keys, indices), cudf::logic_error);
+  EXPECT_THROW(
+    cudf::make_dictionary_column(keys.release(), indices.release(), rmm::device_buffer{}, 0),
+    cudf::logic_error);
+}
diff --git a/cpp/tests/dictionary/fill_test.cpp b/cpp/tests/dictionary/fill_test.cpp
new file mode 100644
index 0000000..60e57d9
--- /dev/null
+++ b/cpp/tests/dictionary/fill_test.cpp
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <vector>
+
+struct DictionaryFillTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryFillTest, StringsColumn)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"fff", "aaa", "", "bbb", "ccc", "ccc", "ccc", "fff", "aaa", ""});
+  auto dictionary = cudf::dictionary::encode(strings);
+  cudf::string_scalar fv("___");
+  auto results = cudf::fill(dictionary->view(), 1, 4, fv);
+  auto decoded = cudf::dictionary::decode(results->view());
+  cudf::test::strings_column_wrapper expected(
+    {"fff", "___", "___", "___", "ccc", "ccc", "ccc", "fff", "aaa", ""});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+}
+
+TEST_F(DictionaryFillTest, WithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input({9, 8, 7, 6, 4}, {0, 1, 1, 0, 1});
+  auto dictionary = cudf::dictionary::encode(input);
+  cudf::numeric_scalar<int64_t> fv(-10);
+  auto results = cudf::fill(dictionary->view(), 0, 2, fv);
+  auto decoded = cudf::dictionary::decode(results->view());
+  cudf::test::fixed_width_column_wrapper<int64_t> expected({-10, -10, 7, 6, 4}, {1, 1, 1, 0, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+}
+
+TEST_F(DictionaryFillTest, FillWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<double> input({1.2, 8.5, 7.75, 6.25, 4.125},
+                                                       {1, 1, 1, 0, 1});
+  auto dictionary = cudf::dictionary::encode(input);
+  cudf::numeric_scalar<double> fv(0, false);
+  auto results = cudf::fill(dictionary->view(), 1, 3, fv);
+  auto decoded = cudf::dictionary::decode(results->view());
+  cudf::test::fixed_width_column_wrapper<double> expected({1.2, 0.0, 0.0, 0.0, 4.125},
+                                                          {1, 0, 0, 0, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+}
+
+TEST_F(DictionaryFillTest, Empty)
+{
+  auto dictionary = cudf::make_empty_column(cudf::data_type{cudf::type_id::DICTIONARY32});
+  cudf::numeric_scalar<int64_t> fv(-10);
+  auto results = cudf::fill(dictionary->view(), 0, 0, fv);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), dictionary->view());
+}
+
+TEST_F(DictionaryFillTest, Errors)
+{
+  cudf::test::strings_column_wrapper input{"this string intentionally left blank"};
+  auto dictionary = cudf::dictionary::encode(input);
+  cudf::numeric_scalar<int64_t> fv(-10);  // mismatched key
+  EXPECT_THROW(cudf::fill(dictionary->view(), 1, 2, fv), cudf::logic_error);
+}
diff --git a/cpp/tests/dictionary/gather_test.cpp b/cpp/tests/dictionary/gather_test.cpp
new file mode 100644
index 0000000..3267da7
--- /dev/null
+++ b/cpp/tests/dictionary/gather_test.cpp
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <vector>
+
+struct DictionaryGatherTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryGatherTest, Gather)
+{
+  cudf::test::strings_column_wrapper strings{
+    "eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"};
+  auto dictionary = cudf::dictionary::encode(strings);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map{0, 4, 3, 1};
+  auto table_result = cudf::gather(cudf::table_view{{view.parent()}}, gather_map)->release();
+  auto result       = cudf::dictionary_column_view(table_result.front()->view());
+
+  cudf::test::strings_column_wrapper expected{"eee", "ccc", "bbb", "aaa"};
+  auto decoded = cudf::dictionary::decode(result);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+TEST_F(DictionaryGatherTest, GatherWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> data{{1, 5, 5, 3, 7, 1}, {0, 1, 0, 1, 1, 1}};
+
+  auto dictionary = cudf::dictionary::encode(data);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  cudf::test::fixed_width_column_wrapper<int16_t> gather_map{{4, 1, 2, 4}};
+  auto table_result = cudf::gather(cudf::table_view{{dictionary->view()}}, gather_map);
+  auto result       = cudf::dictionary_column_view(table_result->view().column(0));
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{{7, 5, 5, 7}, {1, 1, 0, 1}};
+  auto result_decoded = cudf::dictionary::decode(result);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result_decoded->view());
+}
+
+TEST_F(DictionaryGatherTest, SortStrings)
+{
+  std::vector<std::string> h_strings{"eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+
+  auto dictionary = cudf::dictionary::encode(strings);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  auto result = cudf::sort(cudf::table_view{{dictionary->view()}},
+                           std::vector<cudf::order>{cudf::order::ASCENDING})
+                  ->release();
+
+  std::sort(h_strings.begin(), h_strings.end());
+  auto result_decoded = cudf::dictionary::decode(result.front()->view());
+  cudf::test::strings_column_wrapper expected(h_strings.begin(), h_strings.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result_decoded->view());
+}
+
+TEST_F(DictionaryGatherTest, SortFloat)
+{
+  std::vector<double> h_data{1.25, -5.75, 8.125, 1e9, 9.7};
+  cudf::test::fixed_width_column_wrapper<double> data(h_data.begin(), h_data.end());
+
+  auto dictionary = cudf::dictionary::encode(data);
+  cudf::dictionary_column_view view(dictionary->view());
+
+  auto result = cudf::sort(cudf::table_view{{dictionary->view()}},
+                           std::vector<cudf::order>{cudf::order::ASCENDING})
+                  ->release();
+
+  std::sort(h_data.begin(), h_data.end());
+  auto result_decoded = cudf::dictionary::decode(result.front()->view());
+  cudf::test::fixed_width_column_wrapper<double> expected(h_data.begin(), h_data.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result_decoded->view());
+}
diff --git a/cpp/tests/dictionary/remove_keys_test.cpp b/cpp/tests/dictionary/remove_keys_test.cpp
new file mode 100644
index 0000000..eb48c3e
--- /dev/null
+++ b/cpp/tests/dictionary/remove_keys_test.cpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct DictionaryRemoveKeysTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryRemoveKeysTest, StringsColumn)
+{
+  cudf::test::strings_column_wrapper strings{
+    "eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"};
+  cudf::test::strings_column_wrapper del_keys{"ddd", "bbb", "fff"};
+
+  auto const dictionary = cudf::dictionary::encode(strings);
+  // remove keys
+  {
+    auto const result =
+      cudf::dictionary::remove_keys(cudf::dictionary_column_view(dictionary->view()), del_keys);
+    std::vector<char const*> h_expected{
+      "eee", "aaa", nullptr, nullptr, "ccc", "ccc", "ccc", "eee", "aaa"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    auto const decoded = cudf::dictionary::decode(result->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+  }
+  // remove_unused_keys
+  {
+    cudf::test::fixed_width_column_wrapper<int32_t> gather_map{0, 4, 3, 1};
+    auto const table_result =
+      cudf::gather(cudf::table_view{{dictionary->view()}}, gather_map)->release();
+    auto const result  = cudf::dictionary::remove_unused_keys(table_result.front()->view());
+    auto const decoded = cudf::dictionary::decode(result->view());
+    cudf::test::strings_column_wrapper expected{"eee", "ccc", "bbb", "aaa"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+  }
+}
+
+TEST_F(DictionaryRemoveKeysTest, FloatColumn)
+{
+  cudf::test::fixed_width_column_wrapper<float> input{4.25, 7.125, 0.5, -11.75, 7.125, 0.5};
+  cudf::test::fixed_width_column_wrapper<float> del_keys{4.25, -11.75, 5.0};
+
+  auto const dictionary = cudf::dictionary::encode(input);
+
+  {
+    auto const result =
+      cudf::dictionary::remove_keys(cudf::dictionary_column_view(dictionary->view()), del_keys);
+    auto const decoded = cudf::dictionary::decode(result->view());
+    cudf::test::fixed_width_column_wrapper<float> expected{{0., 7.125, 0.5, 0., 7.125, 0.5},
+                                                           {0, 1, 1, 0, 1, 1}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<int32_t> gather_map{0, 2, 3, 1};
+    auto const table_result =
+      cudf::gather(cudf::table_view{{dictionary->view()}}, gather_map)->release();
+    auto const result  = cudf::dictionary::remove_unused_keys(table_result.front()->view());
+    auto const decoded = cudf::dictionary::decode(result->view());
+    cudf::test::fixed_width_column_wrapper<float> expected{{4.25, 0.5, -11.75, 7.125}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+  }
+}
+
+TEST_F(DictionaryRemoveKeysTest, WithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{{444, 0, 333, 111, 222, 222, 222, 444, 0},
+                                                        {1, 1, 1, 1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int64_t> del_keys{0, 111, 777};
+
+  auto const dictionary = cudf::dictionary::encode(input);
+  {
+    auto const result =
+      cudf::dictionary::remove_keys(cudf::dictionary_column_view(dictionary->view()), del_keys);
+    auto const decoded = cudf::dictionary::decode(result->view());
+    cudf::test::fixed_width_column_wrapper<int64_t> expected{{444, 0, 333, 0, 222, 0, 222, 444, 0},
+                                                             {1, 0, 1, 0, 1, 0, 1, 1, 0}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<int32_t> gather_map{0, 2, 3, 1};
+    auto const table_result =
+      cudf::gather(cudf::table_view{{dictionary->view()}}, gather_map)->release();
+    auto const result  = cudf::dictionary::remove_unused_keys(table_result.front()->view());
+    auto const decoded = cudf::dictionary::decode(result->view());
+    cudf::test::fixed_width_column_wrapper<int64_t> expected{{444, 333, 111, 0}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(decoded->view(), expected);
+  }
+}
+
+TEST_F(DictionaryRemoveKeysTest, Errors)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{1, 2, 3};
+  auto const dictionary = cudf::dictionary::encode(input);
+
+  cudf::test::fixed_width_column_wrapper<float> del_keys{1.0, 2.0, 3.0};
+  EXPECT_THROW(cudf::dictionary::remove_keys(dictionary->view(), del_keys), cudf::logic_error);
+  cudf::test::fixed_width_column_wrapper<int64_t> null_keys{{1, 2, 3}, {1, 0, 1}};
+  EXPECT_THROW(cudf::dictionary::remove_keys(dictionary->view(), null_keys), cudf::logic_error);
+}
diff --git a/cpp/tests/dictionary/scatter_test.cpp b/cpp/tests/dictionary/scatter_test.cpp
new file mode 100644
index 0000000..7030f1e
--- /dev/null
+++ b/cpp/tests/dictionary/scatter_test.cpp
@@ -0,0 +1,144 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <vector>
+
+struct DictionaryScatterTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryScatterTest, Scatter)
+{
+  cudf::test::strings_column_wrapper strings_source{"xxx", "bbb", "aaa", "ccc"};
+  auto source = cudf::dictionary::encode(strings_source);
+  cudf::test::strings_column_wrapper strings_target{
+    "eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"};
+  auto target = cudf::dictionary::encode(strings_target);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map{0, 2, 3, 7};
+  auto table_result =
+    cudf::scatter(
+      cudf::table_view{{source->view()}}, scatter_map, cudf::table_view{{target->view()}})
+      ->release();
+  auto decoded =
+    cudf::dictionary::decode(cudf::dictionary_column_view(table_result.front()->view()));
+  cudf::test::strings_column_wrapper expected{
+    "xxx", "aaa", "bbb", "aaa", "ccc", "ccc", "ccc", "ccc", "aaa"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+
+  // empty map test
+  cudf::test::fixed_width_column_wrapper<int32_t> empty_map{};
+  table_result =
+    cudf::scatter(cudf::table_view{{source->view()}}, empty_map, cudf::table_view{{target->view()}})
+      ->release();
+  decoded = cudf::dictionary::decode(cudf::dictionary_column_view(table_result.front()->view()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strings_target, decoded->view());
+
+  // empty target test
+  cudf::test::strings_column_wrapper empty_target;
+  auto empty_dictionary = cudf::dictionary::encode(empty_target);
+  table_result          = cudf::scatter(cudf::table_view{{empty_dictionary->view()}},
+                               empty_map,
+                               cudf::table_view{{empty_dictionary->view()}})
+                   ->release();
+  decoded = cudf::dictionary::decode(cudf::dictionary_column_view(table_result.front()->view()));
+  EXPECT_EQ(0, decoded->size());
+}
+
+TEST_F(DictionaryScatterTest, ScatterScalar)
+{
+  cudf::test::strings_column_wrapper strings_target{
+    "eee", "aaa", "ddd", "ccc", "ccc", "ccc", "eee", "aaa"};
+  auto target = cudf::dictionary::encode(strings_target);
+  std::vector<std::reference_wrapper<const cudf::scalar>> source;
+
+  const cudf::string_scalar source_scalar                       = cudf::string_scalar("bbb");
+  std::reference_wrapper<const cudf::string_scalar> slr_wrapper = std::ref(source_scalar);
+  source.emplace_back(slr_wrapper);
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map{0, 2, 3, 7};
+
+  auto table_result =
+    cudf::scatter(source, cudf::column_view{scatter_map}, cudf::table_view({target->view()}))
+      ->release();
+  auto decoded =
+    cudf::dictionary::decode(cudf::dictionary_column_view(table_result.front()->view()));
+
+  cudf::test::strings_column_wrapper expected{
+    "bbb", "aaa", "bbb", "bbb", "ccc", "ccc", "eee", "bbb"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+TEST_F(DictionaryScatterTest, WithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> data_source{{1, 5, 7, 9}, {0, 1, 1, 1}};
+  auto source = cudf::dictionary::encode(data_source);
+  cudf::test::fixed_width_column_wrapper<int64_t> data_target{{1, 5, 5, 3, 7, 1, 4, 2},
+                                                              {0, 1, 0, 1, 1, 1, 1, 1}};
+  auto target = cudf::dictionary::encode(data_target);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map{7, 2, 3, 1};
+  auto table_result =
+    cudf::scatter(
+      cudf::table_view{{source->view()}}, scatter_map, cudf::table_view{{target->view()}})
+      ->release();
+  auto decoded =
+    cudf::dictionary::decode(cudf::dictionary_column_view(table_result.front()->view()));
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{{1, 9, 5, 7, 7, 1, 4, 1},
+                                                           {0, 1, 1, 1, 1, 1, 1, 0}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+TEST_F(DictionaryScatterTest, ScalarWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> data_target{{1, 5, 5, 3, 7, 1, 4, 2},
+                                                              {0, 1, 0, 1, 1, 1, 1, 1}};
+  auto target = cudf::dictionary::encode(data_target);
+  std::vector<std::reference_wrapper<const cudf::scalar>> source;
+  const cudf::numeric_scalar<int64_t> source_slr = cudf::test::make_type_param_scalar<int64_t>(100);
+  std::reference_wrapper<const cudf::numeric_scalar<int64_t>> slr_wrapper = std::ref(source_slr);
+  source.emplace_back(slr_wrapper);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map{7, 2, 3, 1, -3};
+  auto table_result =
+    cudf::scatter(source, scatter_map, cudf::table_view{{target->view()}})->release();
+
+  auto decoded =
+    cudf::dictionary::decode(cudf::dictionary_column_view(table_result.front()->view()));
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{{1, 100, 100, 100, 7, 100, 4, 100},
+                                                           {0, 1, 1, 1, 1, 1, 1, 1}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+TEST_F(DictionaryScatterTest, Error)
+{
+  cudf::test::strings_column_wrapper strings_source{"this string intentionally left blank"};
+  auto source = cudf::dictionary::encode(strings_source);
+  cudf::test::fixed_width_column_wrapper<int64_t> integers_target({1, 2, 3});
+  auto target = cudf::dictionary::encode(integers_target);
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({0});
+  EXPECT_THROW(
+    cudf::scatter(
+      cudf::table_view{{source->view()}}, scatter_map, cudf::table_view{{target->view()}}),
+    cudf::logic_error);
+}
diff --git a/cpp/tests/dictionary/search_test.cpp b/cpp/tests/dictionary/search_test.cpp
new file mode 100644
index 0000000..35972ba
--- /dev/null
+++ b/cpp/tests/dictionary/search_test.cpp
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/detail/search.hpp>
+#include <cudf/dictionary/search.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+struct DictionarySearchTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionarySearchTest, StringsColumn)
+{
+  cudf::test::dictionary_column_wrapper<std::string> dictionary(
+    {"fff", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "", ""}, {1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  auto result = cudf::dictionary::get_index(dictionary, cudf::string_scalar("ccc"));
+  EXPECT_TRUE(result->is_valid());
+  auto n_result = dynamic_cast<cudf::numeric_scalar<uint32_t>*>(result.get());
+  EXPECT_EQ(uint32_t{3}, n_result->value());
+
+  result = cudf::dictionary::get_index(dictionary, cudf::string_scalar("eee"));
+  EXPECT_FALSE(result->is_valid());
+  result   = cudf::dictionary::detail::get_insert_index(dictionary,
+                                                      cudf::string_scalar("eee"),
+                                                      cudf::get_default_stream(),
+                                                      rmm::mr::get_current_device_resource());
+  n_result = dynamic_cast<cudf::numeric_scalar<uint32_t>*>(result.get());
+  EXPECT_EQ(uint32_t{5}, n_result->value());
+}
+
+TEST_F(DictionarySearchTest, WithNulls)
+{
+  cudf::test::dictionary_column_wrapper<int64_t> dictionary({9, 8, 7, 6, 4}, {0, 1, 1, 0, 1});
+
+  auto result = cudf::dictionary::get_index(dictionary, cudf::numeric_scalar<int64_t>(4));
+  EXPECT_TRUE(result->is_valid());
+  auto n_result = dynamic_cast<cudf::numeric_scalar<uint32_t>*>(result.get());
+  EXPECT_EQ(uint32_t{0}, n_result->value());
+
+  result = cudf::dictionary::get_index(dictionary, cudf::numeric_scalar<int64_t>(5));
+  EXPECT_FALSE(result->is_valid());
+  result   = cudf::dictionary::detail::get_insert_index(dictionary,
+                                                      cudf::numeric_scalar<int64_t>(5),
+                                                      cudf::get_default_stream(),
+                                                      rmm::mr::get_current_device_resource());
+  n_result = dynamic_cast<cudf::numeric_scalar<uint32_t>*>(result.get());
+  EXPECT_EQ(uint32_t{1}, n_result->value());
+}
+
+TEST_F(DictionarySearchTest, EmptyColumn)
+{
+  cudf::test::dictionary_column_wrapper<int64_t> dictionary{};
+  cudf::numeric_scalar<int64_t> key(7);
+  auto result = cudf::dictionary::get_index(dictionary, key);
+  EXPECT_FALSE(result->is_valid());
+  result = cudf::dictionary::detail::get_insert_index(
+    dictionary, key, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  EXPECT_FALSE(result->is_valid());
+}
+
+TEST_F(DictionarySearchTest, Errors)
+{
+  cudf::test::dictionary_column_wrapper<int64_t> dictionary({1, 2, 3});
+  cudf::numeric_scalar<double> key(7);
+  EXPECT_THROW(cudf::dictionary::get_index(dictionary, key), cudf::logic_error);
+  EXPECT_THROW(
+    cudf::dictionary::detail::get_insert_index(
+      dictionary, key, cudf::get_default_stream(), rmm::mr::get_current_device_resource()),
+    cudf::logic_error);
+}
diff --git a/cpp/tests/dictionary/set_keys_test.cpp b/cpp/tests/dictionary/set_keys_test.cpp
new file mode 100644
index 0000000..9eb4b43
--- /dev/null
+++ b/cpp/tests/dictionary/set_keys_test.cpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct DictionarySetKeysTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionarySetKeysTest, StringsKeys)
+{
+  cudf::test::strings_column_wrapper strings{
+    "eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"};
+  auto dictionary = cudf::dictionary::encode(strings);
+
+  cudf::test::strings_column_wrapper new_keys{"aaa", "ccc", "eee", "fff"};
+  auto result = cudf::dictionary::set_keys(dictionary->view(), new_keys);
+
+  std::vector<char const*> h_expected{
+    "eee", "aaa", nullptr, nullptr, "ccc", "ccc", "ccc", "eee", "aaa"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  auto decoded = cudf::dictionary::decode(result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+}
+
+TEST_F(DictionarySetKeysTest, FloatKeys)
+{
+  cudf::test::fixed_width_column_wrapper<float> input{4.25, 7.125, 0.5, -11.75, 7.125, 0.5};
+  auto dictionary = cudf::dictionary::encode(input);
+
+  cudf::test::fixed_width_column_wrapper<float> new_keys{0.5, 1.0, 4.25, 7.125};
+  auto result = cudf::dictionary::set_keys(dictionary->view(), new_keys);
+
+  cudf::test::fixed_width_column_wrapper<float> expected{{4.25, 7.125, 0.5, 0., 7.125, 0.5},
+                                                         {1, 1, 1, 0, 1, 1}};
+  auto decoded = cudf::dictionary::decode(result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+}
+
+TEST_F(DictionarySetKeysTest, WithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{{444, 0, 333, 111, 222, 222, 222, 444, 0},
+                                                        {1, 1, 1, 1, 1, 0, 1, 1, 1}};
+  auto dictionary = cudf::dictionary::encode(input);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> new_keys{0, 222, 333, 444};
+  auto result = cudf::dictionary::set_keys(dictionary->view(), new_keys);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{
+    {444, 0, 333, 111, 222, 222, 222, 444, 0}, {1, 1, 1, 0, 1, 0, 1, 1, 1}};
+  auto decoded = cudf::dictionary::decode(result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+}
+
+TEST_F(DictionarySetKeysTest, Errors)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{1, 2, 3};
+  auto dictionary = cudf::dictionary::encode(input);
+
+  cudf::test::fixed_width_column_wrapper<float> new_keys{1.0, 2.0, 3.0};
+  EXPECT_THROW(cudf::dictionary::set_keys(dictionary->view(), new_keys), cudf::logic_error);
+  cudf::test::fixed_width_column_wrapper<int64_t> null_keys{{1, 2, 3}, {1, 0, 1}};
+  EXPECT_THROW(cudf::dictionary::set_keys(dictionary->view(), null_keys), cudf::logic_error);
+}
+
+TEST_F(DictionarySetKeysTest, MatchDictionaries)
+{
+  cudf::test::dictionary_column_wrapper<int32_t> col1{5, 0, 4, 1, 2, 2, 2, 5, 0};
+  cudf::test::dictionary_column_wrapper<int32_t> col2{1, 0, 3, 1, 4, 5, 6, 5, 0};
+
+  auto input = std::vector<cudf::dictionary_column_view>(
+    {cudf::dictionary_column_view(col1), cudf::dictionary_column_view(col2)});
+
+  auto results = cudf::dictionary::match_dictionaries(input);
+  auto keys1   = cudf::dictionary_column_view(results[0]->view()).keys();
+  auto keys2   = cudf::dictionary_column_view(results[1]->view()).keys();
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys1, keys2);
+
+  auto result1 = cudf::dictionary::decode(cudf::dictionary_column_view(results[0]->view()));
+  auto result2 = cudf::dictionary::decode(cudf::dictionary_column_view(results[1]->view()));
+
+  auto expected1 = cudf::dictionary::decode(cudf::dictionary_column_view(col1));
+  auto expected2 = cudf::dictionary::decode(cudf::dictionary_column_view(col2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result1->view(), expected1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result2->view(), expected2->view());
+}
diff --git a/cpp/tests/dictionary/slice_test.cpp b/cpp/tests/dictionary/slice_test.cpp
new file mode 100644
index 0000000..1b95631
--- /dev/null
+++ b/cpp/tests/dictionary/slice_test.cpp
@@ -0,0 +1,88 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <vector>
+
+struct DictionarySliceTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionarySliceTest, SliceColumn)
+{
+  cudf::test::strings_column_wrapper strings{
+    {"eee", "aaa", "ddd", "bbb", "ccc", "ccc", "ccc", "eee", "aaa"}, {1, 1, 1, 1, 1, 0, 1, 1, 1}};
+  auto dictionary = cudf::dictionary::encode(strings);
+
+  std::vector<cudf::size_type> splits{1, 6};
+  auto result = cudf::slice(dictionary->view(), splits);
+
+  auto output = cudf::dictionary::decode(cudf::dictionary_column_view(result.front()));
+  cudf::test::strings_column_wrapper expected{{"aaa", "ddd", "bbb", "ccc", ""}, {1, 1, 1, 1, 0}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *output);
+
+  {
+    auto defragged =
+      cudf::dictionary::remove_unused_keys(cudf::dictionary_column_view(result.front()));
+    output = cudf::dictionary::decode(cudf::dictionary_column_view(*defragged));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *output);  // should be the same output
+  }
+  {
+    cudf::test::strings_column_wrapper new_keys{"000", "bbb"};
+    auto added = cudf::dictionary::add_keys(cudf::dictionary_column_view(result.front()), new_keys);
+    output     = cudf::dictionary::decode(cudf::dictionary_column_view(*added));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *output);
+  }
+  {
+    cudf::test::strings_column_wrapper new_keys{"aaa", "bbb", "ccc", "ddd", "000"};
+    auto added = cudf::dictionary::set_keys(cudf::dictionary_column_view(result.front()), new_keys);
+    output     = cudf::dictionary::decode(cudf::dictionary_column_view(*added));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *output);
+  }
+  {
+    // check new column is created correctly from sliced view (issue 5768)
+    cudf::column new_col(result.front());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.front(), new_col.view());
+  }
+}
+
+TEST_F(DictionarySliceTest, SplitColumn)
+{
+  cudf::test::fixed_width_column_wrapper<float> input{{4.25, 7.125, 0.5, 0., -11.75, 7.125, 0.5},
+                                                      {1, 1, 1, 0, 1, 1, 1}};
+  auto dictionary = cudf::dictionary::encode(input);
+
+  std::vector<cudf::size_type> splits{2, 6};
+  auto results = cudf::split(dictionary->view(), splits);
+
+  cudf::test::fixed_width_column_wrapper<float> expected1{{4.25, 7.125}, {1, 1}};
+  auto output1 = cudf::dictionary::decode(cudf::dictionary_column_view(results[0]));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, output1->view());
+
+  cudf::test::fixed_width_column_wrapper<float> expected2{{0.5, 0., -11.75, 7.125}, {1, 0, 1, 1}};
+  auto output2 = cudf::dictionary::decode(cudf::dictionary_column_view(results[1]));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, output2->view());
+
+  cudf::test::fixed_width_column_wrapper<float> expected3({0.5}, {1});
+  auto output3 = cudf::dictionary::decode(cudf::dictionary_column_view(results[2]));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, output3->view());
+}
diff --git a/cpp/tests/encode/encode_tests.cpp b/cpp/tests/encode/encode_tests.cpp
new file mode 100644
index 0000000..3638706
--- /dev/null
+++ b/cpp/tests/encode/encode_tests.cpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/table/table.hpp>
+#include <cudf/transform.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+template <typename T>
+class EncodeNumericTests : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(EncodeNumericTests, NumericTypesNotBool);
+
+TYPED_TEST(EncodeNumericTests, SingleNullEncode)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input({1}, {0});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect({0});
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TYPED_TEST(EncodeNumericTests, EmptyEncode)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input({});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect({});
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TYPED_TEST(EncodeNumericTests, SimpleNoNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 2, 3, 2, 3, 2, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{{0, 1, 2, 1, 2, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_keys{{1, 2, 3}};
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.first->view().column(0), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TYPED_TEST(EncodeNumericTests, SimpleWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 2, 3, 2, 3, 2, 1},
+                                                          {1, 1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{{0, 1, 2, 3, 2, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_keys{{1, 2, 3, 0}, {1, 1, 1, 0}};
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.first->view().column(0), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TYPED_TEST(EncodeNumericTests, UnorderedWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{2, 1, 5, 1, 1, 3, 2},
+                                                          {0, 1, 1, 1, 0, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{{4, 0, 3, 0, 4, 2, 1}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_keys{{1, 2, 3, 5, 0}, {1, 1, 1, 1, 0}};
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.first->view().column(0), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+struct EncodeStringTest : public cudf::test::BaseFixture {};
+
+TEST_F(EncodeStringTest, SimpleNoNulls)
+{
+  cudf::test::strings_column_wrapper input{"a", "b", "c", "d", "a"};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{0, 1, 2, 3, 0};
+  cudf::test::strings_column_wrapper expect_keys{"a", "b", "c", "d"};
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.first->view().column(0), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TEST_F(EncodeStringTest, SimpleWithNulls)
+{
+  cudf::test::strings_column_wrapper input{{"a", "b", "c", "d", "a"}, {1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{0, 3, 1, 2, 3};
+  cudf::test::strings_column_wrapper expect_keys{{"a", "c", "d", "0"}, {1, 1, 1, 0}};
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.first->view().column(0), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TEST_F(EncodeStringTest, UnorderedWithNulls)
+{
+  cudf::test::strings_column_wrapper input{{"ef", "a", "c", "d", "ef", "a"}, {1, 0, 1, 1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{3, 4, 1, 2, 4, 0};
+  cudf::test::strings_column_wrapper expect_keys{{"a", "c", "d", "ef", "0"}, {1, 1, 1, 1, 0}};
+  auto const result = cudf::encode(cudf::table_view({input}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.first->view().column(0), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+TYPED_TEST(EncodeNumericTests, TableEncodeWithNulls)
+{
+  auto col_1 = cudf::test::fixed_width_column_wrapper<TypeParam>({1, 0, 2, 0, 1}, {1, 0, 1, 0, 1});
+  auto col_2 = cudf::test::fixed_width_column_wrapper<TypeParam>({1, 3, 2, 0, 1}, {1, 1, 1, 0, 1});
+  auto input = cudf::table_view({col_1, col_2});
+
+  auto expect_keys_col1 =
+    cudf::test::fixed_width_column_wrapper<TypeParam>({1, 2, 0, 0}, {1, 1, 0, 0});
+  auto expect_keys_col2 =
+    cudf::test::fixed_width_column_wrapper<TypeParam>({1, 2, 3, 0}, {1, 1, 1, 0});
+  auto expect_keys = cudf::table_view({expect_keys_col1, expect_keys_col2});
+  auto expect      = cudf::test::fixed_width_column_wrapper<cudf::size_type>({0, 2, 1, 3, 0});
+
+  auto const result = cudf::encode(input);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result.first->view(), expect_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.second->view(), expect);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/error/error_handling_test.cu b/cpp/tests/error/error_handling_test.cu
new file mode 100644
index 0000000..5b84232
--- /dev/null
+++ b/cpp/tests/error/error_handling_test.cu
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/default_stream.hpp>
+#include <cudf_test/stream_checking_resource_adaptor.hpp>
+
+#include <cudf/filling.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <rmm/cuda_stream.hpp>
+
+TEST(ExpectsTest, FalseCondition)
+{
+  EXPECT_THROW(CUDF_EXPECTS(false, "condition is false"), cudf::logic_error);
+}
+
+TEST(ExpectsTest, TrueCondition) { EXPECT_NO_THROW(CUDF_EXPECTS(true, "condition is true")); }
+
+TEST(CudaTryTest, Error) { EXPECT_THROW(CUDF_CUDA_TRY(cudaErrorLaunchFailure), cudf::cuda_error); }
+
+TEST(CudaTryTest, Success) { EXPECT_NO_THROW(CUDF_CUDA_TRY(cudaSuccess)); }
+
+TEST(StreamCheck, success) { EXPECT_NO_THROW(CUDF_CHECK_CUDA(0)); }
+
+namespace {
+// Some silly kernel that will cause an error
+void __global__ test_kernel(int* data) { data[threadIdx.x] = threadIdx.x; }
+}  // namespace
+
+// In a release build and without explicit synchronization, CUDF_CHECK_CUDA may
+// or may not fail on erroneous asynchronous CUDA calls. Invoke
+// cudaStreamSynchronize to guarantee failure on error. In a non-release build,
+// CUDF_CHECK_CUDA deterministically fails on erroneous asynchronous CUDA
+// calls.
+TEST(StreamCheck, FailedKernel)
+{
+  rmm::cuda_stream stream;
+  int a;
+  test_kernel<<<0, 0, 0, stream.value()>>>(&a);
+#ifdef NDEBUG
+  stream.synchronize();
+#endif
+  EXPECT_THROW(CUDF_CHECK_CUDA(stream.value()), cudf::cuda_error);
+}
+
+TEST(StreamCheck, CatchFailedKernel)
+{
+  rmm::cuda_stream stream;
+  int a;
+  test_kernel<<<0, 0, 0, stream.value()>>>(&a);
+#ifndef NDEBUG
+  stream.synchronize();
+#endif
+  EXPECT_THROW(CUDF_CHECK_CUDA(stream.value()), cudf::cuda_error);
+}
+
+__global__ void kernel() { asm("trap;"); }
+
+TEST(DeathTest, CudaFatalError)
+{
+  testing::FLAGS_gtest_death_test_style = "threadsafe";
+  auto call_kernel                      = []() {
+    kernel<<<1, 1, 0, cudf::get_default_stream().value()>>>();
+    try {
+      CUDF_CUDA_TRY(cudaDeviceSynchronize());
+    } catch (const cudf::fatal_cuda_error& fe) {
+      std::abort();
+    }
+  };
+  ASSERT_DEATH(call_kernel(), "");
+}
+
+#ifndef NDEBUG
+
+__global__ void assert_false_kernel() { cudf_assert(false && "this kernel should die"); }
+
+__global__ void assert_true_kernel() { cudf_assert(true && "this kernel should live"); }
+
+TEST(DebugAssertDeathTest, cudf_assert_false)
+{
+  testing::FLAGS_gtest_death_test_style = "threadsafe";
+
+  auto call_kernel = []() {
+    assert_false_kernel<<<1, 1>>>();
+
+    // Kernel should fail with `cudaErrorAssert`
+    // This error invalidates the current device context, so we need to kill
+    // the current process. Running with EXPECT_DEATH spawns a new process for
+    // each attempted kernel launch
+    if (cudaErrorAssert == cudaDeviceSynchronize()) { std::abort(); }
+
+    // If we reach this point, the cudf_assert didn't work so we exit normally, which will cause
+    // EXPECT_DEATH to fail.
+  };
+
+  EXPECT_DEATH(call_kernel(), "this kernel should die");
+}
+
+TEST(DebugAssert, cudf_assert_true)
+{
+  assert_true_kernel<<<1, 1>>>();
+  ASSERT_EQ(cudaSuccess, cudaDeviceSynchronize());
+}
+
+#endif
+
+// These tests don't use CUDF_TEST_PROGRAM_MAIN because :
+// 1.) They don't need the RMM Pool
+// 2.) The RMM Pool interferes with the death test
+int main(int argc, char** argv)
+{
+  ::testing::InitGoogleTest(&argc, argv);
+  auto const cmd_opts    = parse_cudf_test_opts(argc, argv);
+  auto const stream_mode = cmd_opts["stream_mode"].as<std::string>();
+  if ((stream_mode == "new_cudf_default") || (stream_mode == "new_testing_default")) {
+    auto resource                      = rmm::mr::get_current_device_resource();
+    auto const stream_error_mode       = cmd_opts["stream_error_mode"].as<std::string>();
+    auto const error_on_invalid_stream = (stream_error_mode == "error");
+    auto const check_default_stream    = (stream_mode == "new_cudf_default");
+    auto adaptor                       = make_stream_checking_resource_adaptor(
+      resource, error_on_invalid_stream, check_default_stream);
+    rmm::mr::set_current_device_resource(&adaptor);
+  }
+  return RUN_ALL_TESTS();
+}
diff --git a/cpp/tests/filling/fill_tests.cpp b/cpp/tests/filling/fill_tests.cpp
new file mode 100644
index 0000000..564f554
--- /dev/null
+++ b/cpp/tests/filling/fill_tests.cpp
@@ -0,0 +1,407 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+auto all_valid   = [](cudf::size_type row) { return true; };
+auto odd_valid   = [](cudf::size_type row) { return row % 2 != 0; };
+auto all_invalid = [](cudf::size_type row) { return false; };
+
+template <typename T>
+class FillTypedTestFixture : public cudf::test::BaseFixture {
+ public:
+  static constexpr cudf::size_type column_size{1000};
+
+  template <typename BitInitializerType = decltype(all_valid)>
+  void test(cudf::size_type begin,
+            cudf::size_type end,
+            T value,
+            bool value_is_valid                     = true,
+            BitInitializerType destination_validity = all_valid)
+  {
+    static_assert(cudf::is_fixed_width<T>(), "this code assumes fixed-width types.");
+
+    cudf::size_type size{FillTypedTestFixture<T>::column_size};
+
+    cudf::test::fixed_width_column_wrapper<T, int32_t> destination(
+      thrust::make_counting_iterator(0),
+      thrust::make_counting_iterator(0) + size,
+      cudf::detail::make_counting_transform_iterator(0, destination_validity));
+
+    std::unique_ptr<cudf::scalar> p_val{nullptr};
+    cudf::data_type type{cudf::type_to_id<T>()};
+    if (cudf::is_numeric<T>()) {
+      p_val = cudf::make_numeric_scalar(type);
+    } else if (cudf::is_timestamp<T>()) {
+      p_val = cudf::make_timestamp_scalar(type);
+    } else if (cudf::is_duration<T>()) {
+      p_val = cudf::make_duration_scalar(type);
+    } else {
+      ASSERT_TRUE(false);  // should not be reached
+    }
+    using ScalarType = cudf::scalar_type_t<T>;
+    static_cast<ScalarType*>(p_val.get())->set_value(value);
+    static_cast<ScalarType*>(p_val.get())->set_valid_async(value_is_valid);
+
+    auto expected_elements =
+      cudf::detail::make_counting_transform_iterator(0, [begin, end, value](auto i) {
+        return (i >= begin && i < end) ? value : cudf::test::make_type_param_scalar<T>(i);
+      });
+    cudf::test::fixed_width_column_wrapper<T> expected(
+      expected_elements,
+      expected_elements + size,
+      cudf::detail::make_counting_transform_iterator(
+        0, [begin, end, value_is_valid, destination_validity](auto i) {
+          return (i >= begin && i < end) ? value_is_valid : destination_validity(i);
+        }));
+
+    // test out-of-place version first
+
+    auto p_ret = cudf::fill(destination, begin, end, *p_val);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*p_ret, expected);
+
+    // test in-place version second
+
+    cudf::mutable_column_view mutable_view{destination};
+    EXPECT_NO_THROW(cudf::fill_in_place(mutable_view, begin, end, *p_val));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(mutable_view, expected);
+  }
+};
+
+TYPED_TEST_SUITE(FillTypedTestFixture, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(FillTypedTestFixture, SetSingle)
+{
+  using T = TypeParam;
+
+  cudf::size_type index{9};
+  T value = cudf::test::make_type_param_scalar<TypeParam>(1);
+
+  // First set it as valid
+  this->test(index, index + 1, value, true);
+
+  // next set it as invalid
+  this->test(index, index + 1, value, false);
+}
+
+TYPED_TEST(FillTypedTestFixture, SetAll)
+{
+  using T = TypeParam;
+
+  cudf::size_type size{FillTypedTestFixture<T>::column_size};
+
+  T value = cudf::test::make_type_param_scalar<TypeParam>(1);
+
+  // First set it as valid
+  this->test(0, size, value, true);
+
+  // next set it as invalid
+  this->test(0, size, value, false);
+}
+
+TYPED_TEST(FillTypedTestFixture, SetRange)
+{
+  using T = TypeParam;
+
+  cudf::size_type begin{99};
+  cudf::size_type end{299};
+  T value = cudf::test::make_type_param_scalar<TypeParam>(1);
+
+  // First set it as valid
+  this->test(begin, end, value, true);
+
+  // Next set it as invalid
+  this->test(begin, end, value, false);
+}
+
+TYPED_TEST(FillTypedTestFixture, SetRangeNullCount)
+{
+  using T = TypeParam;
+
+  cudf::size_type size{FillTypedTestFixture<T>::column_size};
+
+  cudf::size_type begin{10};
+  cudf::size_type end{50};
+  T value = cudf::test::make_type_param_scalar<TypeParam>(1);
+
+  // First set it as valid value
+  this->test(begin, end, value, true, odd_valid);
+
+  // Next set it as invalid
+  this->test(begin, end, value, false, odd_valid);
+
+  // All invalid column should have some valid
+  this->test(begin, end, value, true, all_invalid);
+
+  // All should be invalid
+  this->test(begin, end, value, false, all_invalid);
+
+  // All should be valid
+  this->test(0, size, value, true, odd_valid);
+}
+
+class FillStringTestFixture : public cudf::test::BaseFixture {
+ public:
+  static constexpr cudf::size_type column_size{100};
+
+  template <typename BitInitializerType = decltype(all_valid)>
+  void test(cudf::size_type begin,
+            cudf::size_type end,
+            std::string value,
+            bool value_is_valid                     = true,
+            BitInitializerType destination_validity = all_valid)
+  {
+    cudf::size_type size{FillStringTestFixture::column_size};
+
+    auto destination_elements = cudf::detail::make_counting_transform_iterator(
+      0, [](auto i) { return "#" + std::to_string(i); });
+    auto destination = cudf::test::strings_column_wrapper(
+      destination_elements,
+      destination_elements + size,
+      cudf::detail::make_counting_transform_iterator(0, destination_validity));
+
+    auto p_val       = cudf::make_string_scalar(value);
+    using ScalarType = cudf::scalar_type_t<cudf::string_view>;
+    static_cast<ScalarType*>(p_val.get())->set_valid_async(value_is_valid);
+
+    auto p_chars   = value.c_str();
+    auto num_chars = value.length();
+    auto expected_elements =
+      cudf::detail::make_counting_transform_iterator(0, [begin, end, p_chars, num_chars](auto i) {
+        return (i >= begin && i < end) ? std::string(p_chars, num_chars) : "#" + std::to_string(i);
+      });
+    auto expected = cudf::test::strings_column_wrapper(
+      expected_elements,
+      expected_elements + size,
+      cudf::detail::make_counting_transform_iterator(
+        0, [begin, end, value_is_valid, destination_validity](auto i) {
+          return (i >= begin && i < end) ? value_is_valid : destination_validity(i);
+        }));
+
+    auto p_ret = cudf::fill(destination, begin, end, *p_val);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*p_ret, expected);
+  }
+};
+
+TEST_F(FillStringTestFixture, SetSingle)
+{
+  cudf::size_type size{FillStringTestFixture::column_size};
+
+  cudf::size_type index{9};
+  auto value = "#" + std::to_string(size * 2);
+
+  // First set it as valid
+  this->test(index, index + 1, value, true);
+
+  // next set it as invalid
+  this->test(index, index + 1, value, false);
+}
+
+TEST_F(FillStringTestFixture, SetAll)
+{
+  cudf::size_type size{FillStringTestFixture::column_size};
+
+  auto value = "#" + std::to_string(size * 2);
+
+  // First set it as valid
+  this->test(0, size, value, true);
+
+  // next set it as invalid
+  this->test(0, size, value, false);
+}
+
+TEST_F(FillStringTestFixture, SetRange)
+{
+  cudf::size_type size{FillStringTestFixture::column_size};
+
+  cudf::size_type begin{9};
+  cudf::size_type end{99};
+  auto value = "#" + std::to_string(size * 2);
+
+  // First set it as valid
+  this->test(begin, end, value, true);
+
+  // Next set it as invalid
+  this->test(begin, end, value, false);
+}
+
+TEST_F(FillStringTestFixture, SetRangeNullCount)
+{
+  cudf::size_type size{FillStringTestFixture::column_size};
+
+  cudf::size_type begin{10};
+  cudf::size_type end{50};
+  auto value = "#" + std::to_string(size * 2);
+
+  // First set it as valid value
+  this->test(begin, end, value, true, odd_valid);
+
+  // Next set it as invalid
+  this->test(begin, end, value, false, odd_valid);
+
+  // All invalid column should have some valid
+  this->test(begin, end, value, true, all_invalid);
+
+  // All should be invalid
+  this->test(begin, end, value, false, all_invalid);
+
+  // All should be valid
+  this->test(0, size, value, true, odd_valid);
+}
+
+class FillErrorTestFixture : public cudf::test::BaseFixture {};
+
+TEST_F(FillErrorTestFixture, InvalidInplaceCall)
+{
+  auto p_val_int   = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  using T_int      = cudf::id_to_type<cudf::type_id::INT32>;
+  using ScalarType = cudf::scalar_type_t<T_int>;
+  static_cast<ScalarType*>(p_val_int.get())->set_value(5);
+  static_cast<ScalarType*>(p_val_int.get())->set_valid_async(false);
+
+  auto destination = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + 100);
+
+  auto destination_view = cudf::mutable_column_view{destination};
+  EXPECT_THROW(cudf::fill_in_place(destination_view, 0, 100, *p_val_int), cudf::logic_error);
+
+  auto p_val_str = cudf::make_string_scalar("five");
+
+  std::vector<std::string> strings{"", "this", "is", "a", "column", "of", "strings"};
+  auto destination_string = cudf::test::strings_column_wrapper(strings.begin(), strings.end());
+
+  cudf::mutable_column_view destination_view_string{destination_string};
+  EXPECT_THROW(cudf::fill_in_place(destination_view_string, 0, 100, *p_val_str), cudf::logic_error);
+}
+
+TEST_F(FillErrorTestFixture, InvalidRange)
+{
+  auto p_val       = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  using T          = cudf::id_to_type<cudf::type_id::INT32>;
+  using ScalarType = cudf::scalar_type_t<T>;
+  static_cast<ScalarType*>(p_val.get())->set_value(5);
+
+  auto destination =
+    cudf::test::fixed_width_column_wrapper<int32_t>(thrust::make_counting_iterator(0),
+                                                    thrust::make_counting_iterator(0) + 100,
+                                                    thrust::make_constant_iterator(true));
+
+  cudf::mutable_column_view destination_view{destination};
+
+  // empty range == no-op, this is valid
+  EXPECT_NO_THROW(cudf::fill_in_place(destination_view, 0, 0, *p_val));
+  EXPECT_NO_THROW(auto p_ret = cudf::fill(destination, 0, 0, *p_val));
+
+  // out_begin is negative
+  EXPECT_THROW(cudf::fill_in_place(destination_view, -10, 0, *p_val), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::fill(destination, -10, 0, *p_val), cudf::logic_error);
+
+  // out_begin > out_end
+  EXPECT_THROW(cudf::fill_in_place(destination_view, 10, 5, *p_val), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::fill(destination, 10, 5, *p_val), cudf::logic_error);
+
+  // out_begin > destination.size()
+  EXPECT_THROW(cudf::fill_in_place(destination_view, 101, 100, *p_val), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::fill(destination, 101, 100, *p_val), cudf::logic_error);
+
+  // out_end > destination.size()
+  EXPECT_THROW(cudf::fill_in_place(destination_view, 99, 101, *p_val), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::fill(destination, 99, 101, *p_val), cudf::logic_error);
+
+  // Empty Column
+  destination      = cudf::test::fixed_width_column_wrapper<int32_t>{};
+  destination_view = destination;
+
+  // empty column, this is valid
+  EXPECT_NO_THROW(cudf::fill_in_place(destination_view, 0, destination_view.size(), *p_val));
+  EXPECT_NO_THROW(auto p_ret = cudf::fill(destination, 0, destination_view.size(), *p_val));
+}
+
+TEST_F(FillErrorTestFixture, DTypeMismatch)
+{
+  cudf::size_type size{100};
+
+  auto p_val       = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  using T          = cudf::id_to_type<cudf::type_id::INT32>;
+  using ScalarType = cudf::scalar_type_t<T>;
+  static_cast<ScalarType*>(p_val.get())->set_value(5);
+
+  auto destination = cudf::test::fixed_width_column_wrapper<float>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto destination_view = cudf::mutable_column_view{destination};
+
+  EXPECT_THROW(cudf::fill_in_place(destination_view, 0, 10, *p_val), cudf::logic_error);
+  EXPECT_THROW(auto p_ret = cudf::fill(destination, 0, 10, *p_val), cudf::logic_error);
+}
+
+template <typename T>
+class FixedPointAllReps : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointAllReps, OutOfPlaceFill)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3, -4}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{4104, 42, 1729, 55}, scale};
+    auto const expected = fp_wrapper{{42, 42, 42, 42}, scale};
+    auto const scalar   = cudf::make_fixed_point_scalar<decimalXX>(42, scale);
+
+    auto const result = cudf::fill(column, 0, 4, *scalar);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+}
+
+TYPED_TEST(FixedPointAllReps, InPlaceFill)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3, -4}) {
+    auto const scale    = scale_type{i};
+    auto column         = fp_wrapper{{4104, 42, 1729, 55}, scale};
+    auto const expected = fp_wrapper{{42, 42, 42, 42}, scale};
+    auto const scalar   = cudf::make_fixed_point_scalar<decimalXX>(42, scale);
+
+    auto mut_column = cudf::mutable_column_view{column};
+    cudf::fill_in_place(mut_column, 0, 4, *scalar);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(column, expected);
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/filling/repeat_tests.cpp b/cpp/tests/filling/repeat_tests.cpp
new file mode 100644
index 0000000..4f74523
--- /dev/null
+++ b/cpp/tests/filling/repeat_tests.cpp
@@ -0,0 +1,307 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <numeric>
+#include <random>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+template <typename T>
+class RepeatTypedTestFixture : public cudf::test::BaseFixture,
+                               cudf::test::UniformRandomGenerator<cudf::size_type> {
+ public:
+  RepeatTypedTestFixture() : cudf::test::UniformRandomGenerator<cudf::size_type>{0, 10} {}
+
+  cudf::size_type repeat_count() { return this->generate(); }
+};
+
+TYPED_TEST_SUITE(RepeatTypedTestFixture, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(RepeatTypedTestFixture, RepeatScalarCount)
+{
+  using T = TypeParam;
+  static_assert(cudf::is_fixed_width<T>(), "this code assumes fixed-width types.");
+
+  constexpr cudf::size_type num_values{10};
+  constexpr cudf::size_type repeat_count{10};
+
+  auto input = cudf::test::fixed_width_column_wrapper<T, int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + num_values);
+
+  static_assert(repeat_count > 0, "repeat_count should be larger than 0.");
+  auto expected_elements = cudf::detail::make_counting_transform_iterator(
+    0, [repeat_count](auto i) { return i / repeat_count; });
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T, typename decltype(expected_elements)::value_type>(
+      expected_elements, expected_elements + num_values * repeat_count);
+
+  auto input_table = cudf::table_view{{input}};
+  auto const p_ret = cudf::repeat(input_table, repeat_count);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected, verbosity);
+}
+
+TYPED_TEST(RepeatTypedTestFixture, RepeatColumnCount)
+{
+  using T = TypeParam;
+  static_assert(cudf::is_fixed_width<T>(), "this code assumes fixed-width types.");
+
+  constexpr cudf::size_type num_values{10};
+
+  std::vector<int64_t> inputs(num_values);
+  std::iota(inputs.begin(), inputs.end(), 0);
+
+  std::vector<cudf::size_type> counts(num_values);
+  std::transform(counts.begin(), counts.end(), counts.begin(), [&](cudf::size_type count) {
+    return this->repeat_count();
+  });
+
+  std::vector<T> expected_values;
+  for (size_t i{0}; i < counts.size(); i++) {
+    for (cudf::size_type j{0}; j < counts[i]; j++) {
+      expected_values.push_back(cudf::test::make_type_param_scalar<T>(inputs[i]));
+    }
+  }
+
+  cudf::test::fixed_width_column_wrapper<T, int64_t> input(inputs.begin(), inputs.end());
+
+  auto count =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(counts.begin(), counts.end());
+
+  cudf::test::fixed_width_column_wrapper<T> expected(expected_values.begin(),
+                                                     expected_values.end());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::repeat(input_table, count);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+TYPED_TEST(RepeatTypedTestFixture, RepeatNullable)
+{
+  using T = TypeParam;
+  static_assert(cudf::is_fixed_width<T>(), "this code assumes fixed-width types.");
+
+  constexpr cudf::size_type num_values{10};
+
+  std::vector<int64_t> input_values(num_values);
+  std::iota(input_values.begin(), input_values.end(), 0);
+  std::vector<bool> input_valids(num_values);
+  for (size_t i{0}; i < input_valids.size(); i++) {
+    input_valids[i] = (i % 2) == 0;
+  }
+
+  std::vector<cudf::size_type> counts(num_values);
+  std::transform(counts.begin(), counts.end(), counts.begin(), [&](cudf::size_type count) {
+    return this->repeat_count();
+  });
+
+  std::vector<T> expected_values;
+  std::vector<bool> expected_valids;
+  for (size_t i{0}; i < counts.size(); i++) {
+    for (cudf::size_type j{0}; j < counts[i]; j++) {
+      expected_values.push_back(cudf::test::make_type_param_scalar<T>(input_values[i]));
+      expected_valids.push_back(input_valids[i]);
+    }
+  }
+
+  cudf::test::fixed_width_column_wrapper<T, int64_t> input(
+    input_values.begin(), input_values.end(), input_valids.begin());
+
+  auto count =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(counts.begin(), counts.end());
+
+  cudf::test::fixed_width_column_wrapper<T> expected(
+    expected_values.begin(), expected_values.end(), expected_valids.begin());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::repeat(input_table, count);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+TYPED_TEST(RepeatTypedTestFixture, ZeroSizeInput)
+{
+  using T = TypeParam;
+  static_assert(cudf::is_fixed_width<T>(), "this code assumes fixed-width types.");
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> input(thrust::make_counting_iterator(0),
+                                                           thrust::make_counting_iterator(0));
+
+  auto count = cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0));
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected(thrust::make_counting_iterator(0),
+                                                              thrust::make_counting_iterator(0));
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::repeat(input_table, count);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+TYPED_TEST(RepeatTypedTestFixture, ZeroCount)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T, int32_t> input(thrust::make_counting_iterator(0),
+                                                           thrust::make_counting_iterator(10));
+
+  auto expected = cudf::make_empty_column(cudf::type_to_id<T>());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::repeat(input_table, 0);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected->view());
+}
+
+class RepeatStringTestFixture : public cudf::test::BaseFixture,
+                                cudf::test::UniformRandomGenerator<cudf::size_type> {
+ public:
+  RepeatStringTestFixture() : cudf::test::UniformRandomGenerator<cudf::size_type>{0, 10} {}
+
+  cudf::size_type repeat_count() { return this->generate(); }
+};
+
+TEST_F(RepeatStringTestFixture, RepeatNullable)
+{
+  constexpr cudf::size_type num_values{10};
+
+  std::vector<std::string> input_values(num_values);
+  std::vector<bool> input_valids(num_values);
+  for (size_t i{0}; i < num_values; i++) {
+    input_values[i] = "#" + std::to_string(i);
+    input_valids[i] = (i % 2) == 0;
+  }
+
+  std::vector<cudf::size_type> counts(num_values);
+  std::transform(counts.begin(), counts.end(), counts.begin(), [&](cudf::size_type count) {
+    return this->repeat_count();
+  });
+
+  std::vector<std::string> expected_values;
+  std::vector<bool> expected_valids;
+  for (size_t i{0}; i < counts.size(); i++) {
+    for (cudf::size_type j{0}; j < counts[i]; j++) {
+      expected_values.push_back(input_values[i]);
+      expected_valids.push_back(input_valids[i]);
+    }
+  }
+
+  auto input = cudf::test::strings_column_wrapper(
+    input_values.begin(), input_values.end(), input_valids.begin());
+
+  auto count =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(counts.begin(), counts.end());
+
+  auto expected = cudf::test::strings_column_wrapper(
+    expected_values.begin(), expected_values.end(), expected_valids.begin());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::repeat(input_table, count);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+TEST_F(RepeatStringTestFixture, ZeroSizeInput)
+{
+  std::vector<std::string> input_values{};
+  auto input = cudf::test::strings_column_wrapper(input_values.begin(), input_values.end());
+
+  auto count = cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0));
+
+  auto expected = cudf::test::strings_column_wrapper(input_values.begin(), input_values.end());
+
+  cudf::table_view input_table{{input}};
+  auto p_ret = cudf::repeat(input_table, count);
+
+  EXPECT_EQ(p_ret->num_columns(), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(p_ret->view().column(0), expected);
+}
+
+class RepeatErrorTestFixture : public cudf::test::BaseFixture {};
+
+TEST_F(RepeatErrorTestFixture, LengthMismatch)
+{
+  auto input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + 100);
+
+  auto count = cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + 200);
+
+  cudf::table_view input_table{{input}};
+
+  // input_table.num_rows() != count.size()
+  EXPECT_THROW(auto p_ret = cudf::repeat(input_table, count), cudf::logic_error);
+}
+
+TEST_F(RepeatErrorTestFixture, CountHasNulls)
+{
+  auto input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + 100);
+
+  auto count =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(thrust::make_counting_iterator(0),
+                                                            thrust::make_counting_iterator(0) + 100,
+                                                            thrust::make_constant_iterator(false));
+
+  cudf::table_view input_table{{input}};
+
+  // input_table.has_nulls() == true
+  EXPECT_THROW(auto ret = cudf::repeat(input_table, count), cudf::logic_error);
+}
+
+TEST_F(RepeatErrorTestFixture, Overflow)
+{
+  auto input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + 100);
+  cudf::table_view input_table{{input}};
+  // set the count such that (count * num_rows) > max(size_type);
+  // the extra divide by 2 ensures the max is exceeded despite truncation in integer division
+  auto count = std::numeric_limits<cudf::size_type>::max() / (input_table.num_rows() / 2);
+  EXPECT_THROW(cudf::repeat(input_table, count), std::overflow_error);
+}
+
+TEST_F(RepeatErrorTestFixture, NegativeCount)
+{
+  auto input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + 100);
+  cudf::table_view input_table{{input}};
+  EXPECT_THROW(cudf::repeat(input_table, -1), cudf::logic_error);
+}
diff --git a/cpp/tests/filling/sequence_tests.cpp b/cpp/tests/filling/sequence_tests.cpp
new file mode 100644
index 0000000..cf619aa
--- /dev/null
+++ b/cpp/tests/filling/sequence_tests.cpp
@@ -0,0 +1,179 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+template <typename T>
+class SequenceTypedTestFixture : public cudf::test::BaseFixture {};
+
+class SequenceTestFixture : public cudf::test::BaseFixture {};
+
+using NumericTypesNoBool = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+TYPED_TEST_SUITE(SequenceTypedTestFixture, NumericTypesNoBool);
+
+TYPED_TEST(SequenceTypedTestFixture, Incrementing)
+{
+  using T = TypeParam;
+
+  cudf::numeric_scalar<T> init(0);
+  cudf::numeric_scalar<T> step(1);
+
+  cudf::size_type num_els = 10;
+
+  T expected[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  cudf::test::fixed_width_column_wrapper<T> expected_w(expected, expected + num_els);
+
+  auto result = cudf::sequence(num_els, init, step);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TYPED_TEST(SequenceTypedTestFixture, Decrementing)
+{
+  using T = TypeParam;
+
+  cudf::numeric_scalar<T> init(0);
+  cudf::numeric_scalar<T> step(-5);
+
+  cudf::size_type num_els = 10;
+
+  T expected[] = {0, -5, -10, -15, -20, -25, -30, -35, -40, -45};
+  cudf::test::fixed_width_column_wrapper<T> expected_w(expected, expected + num_els);
+
+  auto result = cudf::sequence(num_els, init, step);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TYPED_TEST(SequenceTypedTestFixture, EmptyOutput)
+{
+  using T = TypeParam;
+
+  cudf::numeric_scalar<T> init(0);
+  cudf::numeric_scalar<T> step(-5);
+
+  cudf::size_type num_els = 0;
+
+  T expected[] = {};
+  cudf::test::fixed_width_column_wrapper<T> expected_w(expected, expected + num_els);
+
+  auto result = cudf::sequence(num_els, init, step);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TEST_F(SequenceTestFixture, BadTypes)
+{
+  cudf::string_scalar string_init("zero");
+  cudf::string_scalar string_step("???");
+  EXPECT_THROW(cudf::sequence(10, string_init, string_step), cudf::logic_error);
+
+  cudf::numeric_scalar<bool> bool_init(true);
+  cudf::numeric_scalar<bool> bool_step(false);
+  EXPECT_THROW(cudf::sequence(10, bool_init, bool_step), cudf::logic_error);
+
+  cudf::timestamp_scalar<cudf::timestamp_s> ts_init(cudf::duration_s{10}, true);
+  cudf::timestamp_scalar<cudf::timestamp_s> ts_step(cudf::duration_s{10}, true);
+  EXPECT_THROW(cudf::sequence(10, ts_init, ts_step), cudf::logic_error);
+}
+
+TEST_F(SequenceTestFixture, MismatchedInputs)
+{
+  cudf::numeric_scalar<int> init(0);
+  cudf::numeric_scalar<float> step(-5);
+  EXPECT_THROW(cudf::sequence(10, init, step), cudf::logic_error);
+
+  cudf::numeric_scalar<int> init2(0);
+  cudf::numeric_scalar<int8_t> step2(-5);
+  EXPECT_THROW(cudf::sequence(10, init2, step2), cudf::logic_error);
+
+  cudf::numeric_scalar<float> init3(0);
+  cudf::numeric_scalar<double> step3(-5);
+  EXPECT_THROW(cudf::sequence(10, init3, step3), cudf::logic_error);
+}
+
+TYPED_TEST(SequenceTypedTestFixture, DefaultStep)
+{
+  using T = TypeParam;
+
+  cudf::numeric_scalar<T> init(0);
+
+  cudf::size_type num_els = 10;
+
+  T expected[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  cudf::test::fixed_width_column_wrapper<T> expected_w(expected, expected + num_els);
+
+  auto result = cudf::sequence(num_els, init);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TEST_F(SequenceTestFixture, DateSequenceBasic)
+{
+  // Timestamp generated using https://www.epochconverter.com/
+  cudf::timestamp_scalar<cudf::timestamp_s> init(1629852896L, true);  // 2021-08-25 00:54:56 GMT
+  cudf::size_type size{5};
+  cudf::size_type months{1};
+
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, int64_t> expected{
+    1629852896L,  // 2021-08-25 00:54:56 GMT
+    1632531296L,  // 2021-09-25 00:54:56 GMT
+    1635123296L,  // 2021-10-25 00:54:56 GMT
+    1637801696L,  // 2021-11-25 00:54:56 GMT
+    1640393696L,  // 2021-12-25 00:54:56 GMT
+  };
+
+  auto got = calendrical_month_sequence(size, init, months);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *got);
+}
+
+TEST_F(SequenceTestFixture, DateSequenceLeapYear)
+{
+  // Timestamp generated using https://www.epochconverter.com/
+  cudf::timestamp_scalar<cudf::timestamp_s> init(951876379L, true);  // 2000-02-29 02:06:19 GMT
+  cudf::size_type size{5};
+  cudf::size_type months{12};
+
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, int64_t> expected{
+    951876379L,   // 2000-02-29 02:06:19 GMT Leap Year
+    983412379L,   // 2001-02-28 02:06:19 GMT
+    1014948379L,  // 2002-02-28 02:06:19 GMT
+    1046484379L,  // 2003-02-28 02:06:19 GMT
+    1078106779L,  // 2004-02-29 02:06:19 GMT Leap Year
+  };
+
+  auto got = calendrical_month_sequence(size, init, months);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *got);
+}
+
+TEST_F(SequenceTestFixture, DateSequenceBadTypes)
+{
+  cudf::numeric_scalar<int64_t> init(951876379, true);
+  cudf::size_type size   = 5;
+  cudf::size_type months = 12;
+
+  EXPECT_THROW(calendrical_month_sequence(size, init, months), cudf::logic_error);
+}
diff --git a/cpp/tests/fixed_point/fixed_point_tests.cpp b/cpp/tests/fixed_point/fixed_point_tests.cpp
new file mode 100644
index 0000000..227a75d
--- /dev/null
+++ b/cpp/tests/fixed_point/fixed_point_tests.cpp
@@ -0,0 +1,577 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/binaryop.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/null_mask.hpp>
+
+#include <algorithm>
+#include <limits>
+#include <numeric>
+#include <type_traits>
+#include <vector>
+
+using namespace numeric;
+
+struct FixedPointTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+using RepresentationTypes = ::testing::Types<int32_t, int64_t>;
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, RepresentationTypes);
+
+TYPED_TEST(FixedPointTestAllReps, SimpleDecimalXXConstruction)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX num0{1.234567, scale_type{0}};
+  decimalXX num1{1.234567, scale_type{-1}};
+  decimalXX num2{1.234567, scale_type{-2}};
+  decimalXX num3{1.234567, scale_type{-3}};
+  decimalXX num4{1.234567, scale_type{-4}};
+  decimalXX num5{1.234567, scale_type{-5}};
+  decimalXX num6{1.234567, scale_type{-6}};
+
+  EXPECT_EQ(1, static_cast<double>(num0));
+  EXPECT_EQ(1.2, static_cast<double>(num1));
+  EXPECT_EQ(1.23, static_cast<double>(num2));
+  EXPECT_EQ(1.234, static_cast<double>(num3));
+  EXPECT_EQ(1.2345, static_cast<double>(num4));
+  EXPECT_EQ(1.23456, static_cast<double>(num5));
+  EXPECT_EQ(1.234567, static_cast<double>(num6));
+}
+
+TYPED_TEST(FixedPointTestAllReps, SimpleNegativeDecimalXXConstruction)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX num0{-1.234567, scale_type{0}};
+  decimalXX num1{-1.234567, scale_type{-1}};
+  decimalXX num2{-1.234567, scale_type{-2}};
+  decimalXX num3{-1.234567, scale_type{-3}};
+  decimalXX num4{-1.234567, scale_type{-4}};
+  decimalXX num5{-1.234567, scale_type{-5}};
+  decimalXX num6{-1.234567, scale_type{-6}};
+
+  EXPECT_EQ(-1, static_cast<double>(num0));
+  EXPECT_EQ(-1.2, static_cast<double>(num1));
+  EXPECT_EQ(-1.23, static_cast<double>(num2));
+  EXPECT_EQ(-1.234, static_cast<double>(num3));
+  EXPECT_EQ(-1.2345, static_cast<double>(num4));
+  EXPECT_EQ(-1.23456, static_cast<double>(num5));
+  EXPECT_EQ(-1.234567, static_cast<double>(num6));
+}
+
+TYPED_TEST(FixedPointTestAllReps, PaddedDecimalXXConstruction)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX a{1.1, scale_type{-1}};
+  decimalXX b{1.01, scale_type{-2}};
+  decimalXX c{1.001, scale_type{-3}};
+  decimalXX d{1.0001, scale_type{-4}};
+  decimalXX e{1.00001, scale_type{-5}};
+  decimalXX f{1.000001, scale_type{-6}};
+
+  decimalXX x{1.000123, scale_type{-8}};
+  decimalXX y{0.000123, scale_type{-8}};
+
+  EXPECT_EQ(1.1, static_cast<double>(a));
+  EXPECT_EQ(1.01, static_cast<double>(b));
+  EXPECT_EQ(1, static_cast<double>(c));  // intentional (inherited problem from floating point)
+  EXPECT_EQ(1.0001, static_cast<double>(d));
+  EXPECT_EQ(1.00001, static_cast<double>(e));
+  EXPECT_EQ(1, static_cast<double>(f));  // intentional (inherited problem from floating point)
+
+  EXPECT_TRUE(1.000123 - static_cast<double>(x) < std::numeric_limits<double>::epsilon());
+  EXPECT_EQ(0.000123, static_cast<double>(y));
+}
+
+TYPED_TEST(FixedPointTestAllReps, SimpleBinaryFPConstruction)
+{
+  using binary_fp = fixed_point<TypeParam, Radix::BASE_2>;
+
+  binary_fp num0{10, scale_type{0}};
+  binary_fp num1{10, scale_type{1}};
+  binary_fp num2{10, scale_type{2}};
+  binary_fp num3{10, scale_type{3}};
+  binary_fp num4{10, scale_type{4}};
+
+  binary_fp num5{1.24, scale_type{0}};
+  binary_fp num6{1.24, scale_type{-1}};
+  binary_fp num7{1.32, scale_type{-2}};
+  binary_fp num8{1.41, scale_type{-3}};
+  binary_fp num9{1.45, scale_type{-4}};
+
+  EXPECT_EQ(10, static_cast<double>(num0));
+  EXPECT_EQ(10, static_cast<double>(num1));
+  EXPECT_EQ(8, static_cast<double>(num2));
+  EXPECT_EQ(8, static_cast<double>(num3));
+  EXPECT_EQ(0, static_cast<double>(num4));
+
+  EXPECT_EQ(1, static_cast<double>(num5));
+  EXPECT_EQ(1, static_cast<double>(num6));
+  EXPECT_EQ(1.25, static_cast<double>(num7));
+  EXPECT_EQ(1.375, static_cast<double>(num8));
+  EXPECT_EQ(1.4375, static_cast<double>(num9));
+}
+
+TYPED_TEST(FixedPointTestAllReps, MoreSimpleBinaryFPConstruction)
+{
+  using binary_fp = fixed_point<TypeParam, Radix::BASE_2>;
+
+  binary_fp num0{1.25, scale_type{-2}};
+  binary_fp num1{2.1, scale_type{-4}};
+
+  EXPECT_EQ(1.25, static_cast<double>(num0));
+  EXPECT_EQ(2.0625, static_cast<double>(num1));
+}
+
+TYPED_TEST(FixedPointTestAllReps, SimpleDecimalXXMath)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX ONE{1, scale_type{-2}};
+  decimalXX TWO{2, scale_type{-2}};
+  decimalXX THREE{3, scale_type{-2}};
+  decimalXX SIX{6, scale_type{-2}};
+
+  EXPECT_TRUE(ONE + ONE == TWO);
+
+  EXPECT_EQ(ONE + ONE, TWO);
+  EXPECT_EQ(ONE * TWO, TWO);
+  EXPECT_EQ(THREE * TWO, SIX);
+  EXPECT_EQ(THREE - TWO, ONE);
+  EXPECT_EQ(TWO / ONE, TWO);
+  EXPECT_EQ(SIX / TWO, THREE);
+
+  decimalXX a{1.23, scale_type{-2}};
+  decimalXX b{0, scale_type{0}};
+
+  EXPECT_EQ(a + b, a);
+  EXPECT_EQ(a - b, a);
+}
+
+TYPED_TEST(FixedPointTestAllReps, ComparisonOperators)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX ONE{1, scale_type{-1}};
+  decimalXX TWO{2, scale_type{-2}};
+  decimalXX THREE{3, scale_type{-3}};
+  decimalXX SIX{6, scale_type{-4}};
+
+  EXPECT_TRUE(ONE + ONE >= TWO);
+
+  EXPECT_TRUE(ONE + ONE <= TWO);
+  EXPECT_TRUE(ONE * TWO < THREE);
+  EXPECT_TRUE(THREE * TWO > THREE);
+  EXPECT_TRUE(THREE - TWO >= ONE);
+  EXPECT_TRUE(TWO / ONE < THREE);
+  EXPECT_TRUE(SIX / TWO >= ONE);
+}
+
+TYPED_TEST(FixedPointTestAllReps, DecimalXXTrickyDivision)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX ONE_1{1, scale_type{1}};
+  decimalXX SIX_0{6, scale_type{0}};
+  decimalXX SIX_1{6, scale_type{1}};
+  decimalXX TEN_0{10, scale_type{0}};
+  decimalXX TEN_1{10, scale_type{1}};
+  decimalXX SIXTY_1{60, scale_type{1}};
+
+  EXPECT_EQ(static_cast<int32_t>(ONE_1), 0);
+  EXPECT_EQ(static_cast<int32_t>(SIX_1), 0);
+  EXPECT_EQ(static_cast<int32_t>(TEN_0), 10);
+  EXPECT_EQ(static_cast<int32_t>(SIXTY_1), 60);
+
+  EXPECT_EQ(SIXTY_1 / TEN_0, ONE_1);
+  EXPECT_EQ(SIXTY_1 / TEN_1, SIX_0);
+
+  decimalXX A{34.56, scale_type{-2}};
+  decimalXX B{1.234, scale_type{-3}};
+  decimalXX C{1, scale_type{-2}};
+
+  EXPECT_EQ(static_cast<int32_t>(A / B), 20);
+  EXPECT_EQ(static_cast<int32_t>((A * C) / B), 28);
+
+  decimalXX n{28, scale_type{1}};
+  EXPECT_EQ(static_cast<int32_t>(n), 20);
+}
+
+TYPED_TEST(FixedPointTestAllReps, DecimalXXRounding)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX ZERO_0{0, scale_type{0}};
+  decimalXX ZERO_1{4, scale_type{1}};
+  decimalXX THREE_0{3, scale_type{0}};
+  decimalXX FOUR_0{4, scale_type{0}};
+  decimalXX FIVE_0{5, scale_type{0}};
+  decimalXX TEN_0{10, scale_type{0}};
+  decimalXX TEN_1{10, scale_type{1}};
+
+  decimalXX FOURTEEN_0{14, scale_type{0}};
+  decimalXX FIFTEEN_0{15, scale_type{0}};
+
+  EXPECT_EQ(ZERO_0, ZERO_1);
+  EXPECT_EQ(TEN_0, TEN_1);
+
+  EXPECT_EQ(ZERO_1 + TEN_1, TEN_1);
+  EXPECT_EQ(FOUR_0 + TEN_1, FOURTEEN_0);
+  EXPECT_TRUE(ZERO_0 == ZERO_1);
+  EXPECT_TRUE(FIVE_0 != TEN_1);
+  EXPECT_TRUE(FIVE_0 + FIVE_0 + FIVE_0 == FIFTEEN_0);
+  EXPECT_TRUE(FIVE_0 + FIVE_0 + FIVE_0 != TEN_1);
+  EXPECT_TRUE(FIVE_0 * THREE_0 == FIFTEEN_0);
+  EXPECT_TRUE(FIVE_0 * THREE_0 != TEN_1);
+}
+
+TYPED_TEST(FixedPointTestAllReps, ArithmeticWithDifferentScales)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX a{1, scale_type{0}};
+  decimalXX b{1.2, scale_type{-1}};
+  decimalXX c{1.23, scale_type{-2}};
+  decimalXX d{1.111, scale_type{-3}};
+
+  decimalXX x{2.2, scale_type{-1}};
+  decimalXX y{3.43, scale_type{-2}};
+  decimalXX z{4.541, scale_type{-3}};
+
+  decimalXX xx{0.2, scale_type{-1}};
+  decimalXX yy{0.03, scale_type{-2}};
+  decimalXX zz{0.119, scale_type{-3}};
+
+  EXPECT_EQ(a + b, x);
+  EXPECT_EQ(a + b + c, y);
+  EXPECT_EQ(a + b + c + d, z);
+  EXPECT_EQ(b - a, xx);
+  EXPECT_EQ(c - b, yy);
+  EXPECT_EQ(c - d, zz);
+}
+
+TYPED_TEST(FixedPointTestAllReps, RescaledTest)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX num0{1, scale_type{0}};
+  decimalXX num1{1.2, scale_type{-1}};
+  decimalXX num2{1.23, scale_type{-2}};
+  decimalXX num3{1.234, scale_type{-3}};
+  decimalXX num4{1.2345, scale_type{-4}};
+  decimalXX num5{1.23456, scale_type{-5}};
+  decimalXX num6{1.234567, scale_type{-6}};
+
+  EXPECT_EQ(num0, num6.rescaled(scale_type{0}));
+  EXPECT_EQ(num1, num6.rescaled(scale_type{-1}));
+  EXPECT_EQ(num2, num6.rescaled(scale_type{-2}));
+  EXPECT_EQ(num3, num6.rescaled(scale_type{-3}));
+  EXPECT_EQ(num4, num6.rescaled(scale_type{-4}));
+  EXPECT_EQ(num5, num6.rescaled(scale_type{-5}));
+}
+
+TYPED_TEST(FixedPointTestAllReps, RescaledRounding)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX num0{1500, scale_type{0}};
+  decimalXX num1{1499, scale_type{0}};
+  decimalXX num2{-1499, scale_type{0}};
+  decimalXX num3{-1500, scale_type{0}};
+
+  EXPECT_EQ(1000, static_cast<TypeParam>(num0.rescaled(scale_type{3})));
+  EXPECT_EQ(1000, static_cast<TypeParam>(num1.rescaled(scale_type{3})));
+  EXPECT_EQ(-1000, static_cast<TypeParam>(num2.rescaled(scale_type{3})));
+  EXPECT_EQ(-1000, static_cast<TypeParam>(num3.rescaled(scale_type{3})));
+}
+
+TYPED_TEST(FixedPointTestAllReps, BoolConversion)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  decimalXX truthy_value{1.234567, scale_type{0}};
+  decimalXX falsy_value{0, scale_type{0}};
+
+  // Test explicit conversions
+  EXPECT_EQ(static_cast<bool>(truthy_value), true);
+  EXPECT_EQ(static_cast<bool>(falsy_value), false);
+
+  // These operators also *explicitly* convert to bool
+  EXPECT_EQ(truthy_value && true, true);
+  EXPECT_EQ(true && truthy_value, true);
+  EXPECT_EQ(falsy_value || false, false);
+  EXPECT_EQ(false || falsy_value, false);
+  EXPECT_EQ(!truthy_value, false);
+  EXPECT_EQ(!falsy_value, true);
+}
+
+// These two overflow tests only work in a Debug build.
+// Unfortunately, in a full debug build, the test will each take about
+// an hour to run.
+// Therefore they are disabled here and can be enabled in an appropriate
+// debug build when required.
+TEST_F(FixedPointTest, DISABLED_OverflowDecimal32)
+{
+  // This flag is needed to avoid warnings with ASSERT_DEATH
+  ::testing::FLAGS_gtest_death_test_style = "threadsafe";
+
+  using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+
+  decimal32 num0{2, scale_type{-9}};
+  decimal32 num1{-2, scale_type{-9}};
+
+  ASSERT_DEATH(num0 + num0, ".*");
+  ASSERT_DEATH(num1 - num0, ".*");
+
+  decimal32 min{std::numeric_limits<int32_t>::min(), scale_type{0}};
+  decimal32 max{std::numeric_limits<int32_t>::max(), scale_type{0}};
+  decimal32 NEG_ONE{-1, scale_type{0}};
+  decimal32 ONE{1, scale_type{0}};
+  decimal32 TWO{2, scale_type{0}};
+
+  ASSERT_DEATH(min / NEG_ONE, ".*");
+  ASSERT_DEATH(max * TWO, ".*");
+  ASSERT_DEATH(min * TWO, ".*");
+  ASSERT_DEATH(max + ONE, ".*");
+  ASSERT_DEATH(max - NEG_ONE, ".*");
+  ASSERT_DEATH(min - ONE, ".*");
+  ASSERT_DEATH(max - NEG_ONE, ".*");
+}
+
+// See comment above for OverflowDecimal32 test.
+TEST_F(FixedPointTest, DISABLED_OverflowDecimal64)
+{
+  // This flag is needed to avoid warnings with ASSERT_DEATH
+  ::testing::FLAGS_gtest_death_test_style = "threadsafe";
+
+  using decimal64 = fixed_point<int64_t, Radix::BASE_10>;
+
+  decimal64 num0{5, scale_type{-18}};
+  decimal64 num1{-5, scale_type{-18}};
+
+  ASSERT_DEATH(num0 + num0, ".*");
+  ASSERT_DEATH(num1 - num0, ".*");
+
+  decimal64 min{std::numeric_limits<int64_t>::min(), scale_type{0}};
+  decimal64 max{std::numeric_limits<int64_t>::max(), scale_type{0}};
+  decimal64 NEG_ONE{-1, scale_type{0}};
+  decimal64 ONE{1, scale_type{0}};
+  decimal64 TWO{2, scale_type{0}};
+
+  ASSERT_DEATH(min / NEG_ONE, ".*");
+  ASSERT_DEATH(max * TWO, ".*");
+  ASSERT_DEATH(min * TWO, ".*");
+  ASSERT_DEATH(max + ONE, ".*");
+  ASSERT_DEATH(max - NEG_ONE, ".*");
+  ASSERT_DEATH(min - ONE, ".*");
+  ASSERT_DEATH(max - NEG_ONE, ".*");
+}
+
+template <typename ValueType, typename Binop>
+void integer_vector_test(ValueType const initial_value,
+                         int32_t const size,
+                         int32_t const scale,
+                         Binop binop)
+{
+  using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+
+  std::vector<decimal32> vec1(size);
+  std::vector<ValueType> vec2(size);
+
+  std::iota(std::begin(vec1), std::end(vec1), decimal32{initial_value, scale_type{scale}});
+  std::iota(std::begin(vec2), std::end(vec2), initial_value);
+
+  auto const res1 =
+    std::accumulate(std::cbegin(vec1), std::cend(vec1), decimal32{0, scale_type{scale}});
+
+  auto const res2 = std::accumulate(std::cbegin(vec2), std::cend(vec2), static_cast<ValueType>(0));
+
+  EXPECT_EQ(static_cast<int32_t>(res1), res2);
+
+  std::vector<ValueType> vec3(vec1.size());
+
+  std::transform(std::cbegin(vec1), std::cend(vec1), std::begin(vec3), [](auto const& e) {
+    return static_cast<int32_t>(e);
+  });
+
+  EXPECT_EQ(vec2, vec3);
+}
+
+TEST_F(FixedPointTest, Decimal32IntVector)
+{
+  integer_vector_test(0, 10, -2, std::plus<>());
+  integer_vector_test(0, 1000, -2, std::plus<>());
+
+  integer_vector_test(1, 10, 0, std::multiplies<>());
+  integer_vector_test(2, 20, 0, std::multiplies<>());
+}
+
+template <typename ValueType, typename Binop>
+void float_vector_test(ValueType const initial_value,
+                       int32_t const size,
+                       int32_t const scale,
+                       Binop binop)
+{
+  using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+
+  std::vector<decimal32> vec1(size);
+  std::vector<ValueType> vec2(size);
+
+  std::iota(std::begin(vec1), std::end(vec1), decimal32{initial_value, scale_type{scale}});
+  std::iota(std::begin(vec2), std::end(vec2), initial_value);
+
+  auto equal = std::equal(
+    std::cbegin(vec1), std::cend(vec1), std::cbegin(vec2), [](auto const& a, auto const& b) {
+      return static_cast<double>(a) - b <= std::numeric_limits<ValueType>::epsilon();
+    });
+
+  EXPECT_TRUE(equal);
+}
+
+TEST_F(FixedPointTest, Decimal32FloatVector)
+{
+  float_vector_test(0.1, 1000, -2, std::plus<>());
+  float_vector_test(0.15, 1000, -2, std::plus<>());
+
+  float_vector_test(0.1, 10, -2, std::multiplies<>());
+  float_vector_test(0.15, 20, -2, std::multiplies<>());
+}
+
+struct cast_to_int32_fn {
+  using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+  int32_t __host__ __device__ operator()(decimal32 fp) { return static_cast<int32_t>(fp); }
+};
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointColumnWrapper)
+{
+  using namespace numeric;
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+  using RepType   = TypeParam;
+
+  // fixed_point_column_wrapper
+  auto const w = cudf::test::fixed_point_column_wrapper<RepType>{{1, 2, 3, 4}, scale_type{0}};
+
+  // fixed_width_column_wrapper
+  auto const ONE   = decimalXX{1, scale_type{0}};
+  auto const TWO   = decimalXX{2, scale_type{0}};
+  auto const THREE = decimalXX{3, scale_type{0}};
+  auto const FOUR  = decimalXX{4, scale_type{0}};
+
+  auto const vec = std::vector<decimalXX>{ONE, TWO, THREE, FOUR};
+  auto const col = cudf::test::fixed_width_column_wrapper<decimalXX>(vec.begin(), vec.end());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col, w);
+}
+
+TYPED_TEST(FixedPointTestAllReps, NoScaleOrWrongTypeID)
+{
+  EXPECT_THROW(cudf::make_fixed_point_column(cudf::data_type{cudf::type_id::INT32}, 0),
+               cudf::logic_error);
+}
+
+TYPED_TEST(FixedPointTestAllReps, SimpleFixedPointColumnWrapper)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const a = cudf::test::fixed_point_column_wrapper<RepType>{{11, 22, 33}, scale_type{-1}};
+  auto const b = cudf::test::fixed_point_column_wrapper<RepType>{{110, 220, 330}, scale_type{-2}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(a, b);
+}
+
+TEST_F(FixedPointTest, PositiveScaleWithValuesOutsideUnderlyingType32)
+{
+  // This is testing fixed_point values outside the range of its underlying type.
+  // For example, 100,000,000 with scale of 6 is 100,000,000,000,000 (100 trillion) and this is
+  // outside the range of a int32_t
+
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<int32_t>;
+
+  auto const a = fp_wrapper{{100000000}, scale_type{6}};
+  auto const b = fp_wrapper{{5000000}, scale_type{7}};
+  auto const c = fp_wrapper{{2}, scale_type{0}};
+
+  auto const expected1 = fp_wrapper{{150000000}, scale_type{6}};
+  auto const expected2 = fp_wrapper{{50000000}, scale_type{6}};
+
+  auto const type    = cudf::data_type{cudf::type_id::DECIMAL32, 6};
+  auto const result1 = cudf::binary_operation(a, b, cudf::binary_operator::ADD, type);
+  auto const result2 = cudf::binary_operation(a, c, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, result1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result2->view());
+}
+
+TEST_F(FixedPointTest, PositiveScaleWithValuesOutsideUnderlyingType64)
+{
+  // This is testing fixed_point values outside the range of its underlying type.
+  // For example, 100,000,000 with scale of 100 is 10 ^ 108 and this is far outside the
+  // range of a int64_t
+
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<int64_t>;
+
+  auto const a = fp_wrapper{{100000000}, scale_type{100}};
+  auto const b = fp_wrapper{{5000000}, scale_type{101}};
+  auto const c = fp_wrapper{{2}, scale_type{0}};
+
+  auto const expected1 = fp_wrapper{{150000000}, scale_type{100}};
+  auto const expected2 = fp_wrapper{{50000000}, scale_type{100}};
+
+  auto const type    = cudf::data_type{cudf::type_id::DECIMAL64, 100};
+  auto const result1 = cudf::binary_operation(a, b, cudf::binary_operator::ADD, type);
+  auto const result2 = cudf::binary_operation(a, c, cudf::binary_operator::DIV, type);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, result1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result2->view());
+}
+
+TYPED_TEST(FixedPointTestAllReps, ExtremelyLargeNegativeScale)
+{
+  // This is testing fixed_point values with an extremely large negative scale. The fixed_point
+  // implementation should be able to handle any scale representable by an int32_t
+
+  using decimalXX  = fixed_point<TypeParam, Radix::BASE_10>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<TypeParam>;
+
+  auto const a = fp_wrapper{{10}, scale_type{-201}};
+  auto const b = fp_wrapper{{50}, scale_type{-202}};
+  auto const c = fp_wrapper{{2}, scale_type{0}};
+
+  auto const expected1 = fp_wrapper{{150}, scale_type{-202}};
+  auto const expected2 = fp_wrapper{{5}, scale_type{-201}};
+
+  auto const type1   = cudf::data_type{cudf::type_to_id<decimalXX>(), -202};
+  auto const result1 = cudf::binary_operation(a, b, cudf::binary_operator::ADD, type1);
+
+  auto const type2   = cudf::data_type{cudf::type_to_id<decimalXX>(), -201};
+  auto const result2 = cudf::binary_operation(a, c, cudf::binary_operator::DIV, type2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, result1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result2->view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/fixed_point/fixed_point_tests.cu b/cpp/tests/fixed_point/fixed_point_tests.cu
new file mode 100644
index 0000000..24b4e33
--- /dev/null
+++ b/cpp/tests/fixed_point/fixed_point_tests.cu
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/reduce.h>
+#include <thrust/scan.h>
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <limits>
+#include <vector>
+
+using namespace numeric;
+
+struct FixedPointTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+using RepresentationTypes = ::testing::Types<int32_t, int64_t, __int128_t>;
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, RepresentationTypes);
+
+TYPED_TEST(FixedPointTestAllReps, DecimalXXThrust)
+{
+  using decimalXX = fixed_point<TypeParam, Radix::BASE_10>;
+
+  std::vector<decimalXX> vec1(1000);
+  std::vector<int32_t> vec2(1000);
+
+  std::iota(std::begin(vec1), std::end(vec1), decimalXX{0, scale_type{-2}});
+  std::iota(std::begin(vec2), std::end(vec2), 0);
+
+  auto const res1 =
+    thrust::reduce(std::cbegin(vec1), std::cend(vec1), decimalXX{0, scale_type{-2}});
+
+  auto const res2 = std::accumulate(std::cbegin(vec2), std::cend(vec2), 0);
+
+  EXPECT_EQ(static_cast<int32_t>(res1), res2);
+
+  std::vector<int32_t> vec3(vec1.size());
+
+  thrust::transform(std::cbegin(vec1), std::cend(vec1), std::begin(vec3), [](auto const& e) {
+    return static_cast<int32_t>(e);
+  });
+
+  EXPECT_EQ(vec2, vec3);
+}
+
+struct cast_to_int32_fn {
+  using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+  int32_t __host__ __device__ operator()(decimal32 fp) { return static_cast<int32_t>(fp); }
+};
+
+TEST_F(FixedPointTest, DecimalXXThrustOnDevice)
+{
+  using decimal32 = fixed_point<int32_t, Radix::BASE_10>;
+
+  std::vector<decimal32> vec1(1000, decimal32{1, scale_type{-2}});
+  auto d_vec1 = cudf::detail::make_device_uvector_sync(
+    vec1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto const sum = thrust::reduce(rmm::exec_policy(cudf::get_default_stream()),
+                                  std::cbegin(d_vec1),
+                                  std::cend(d_vec1),
+                                  decimal32{0, scale_type{-2}});
+
+  EXPECT_EQ(static_cast<int32_t>(sum), 1000);
+
+  // TODO: Once nvbugs/1990211 is fixed (ExclusiveSum initial_value = 0 bug)
+  //       change inclusive scan to run on device (avoid copying to host)
+  thrust::inclusive_scan(std::cbegin(vec1), std::cend(vec1), std::begin(vec1));
+
+  d_vec1 = cudf::detail::make_device_uvector_sync(
+    vec1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  std::vector<int32_t> vec2(1000);
+  std::iota(std::begin(vec2), std::end(vec2), 1);
+
+  auto const res1 = thrust::reduce(rmm::exec_policy(cudf::get_default_stream()),
+                                   std::cbegin(d_vec1),
+                                   std::cend(d_vec1),
+                                   decimal32{0, scale_type{-2}});
+
+  auto const res2 = std::accumulate(std::cbegin(vec2), std::cend(vec2), 0);
+
+  EXPECT_EQ(static_cast<int32_t>(res1), res2);
+
+  rmm::device_uvector<int32_t> d_vec3(1000, cudf::get_default_stream());
+
+  thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                    std::cbegin(d_vec1),
+                    std::cend(d_vec1),
+                    std::begin(d_vec3),
+                    cast_to_int32_fn{});
+
+  auto vec3 = cudf::detail::make_std_vector_sync(d_vec3, cudf::get_default_stream());
+
+  EXPECT_EQ(vec2, vec3);
+}
diff --git a/cpp/tests/groupby/argmax_tests.cpp b/cpp/tests/groupby/argmax_tests.cpp
new file mode 100644
index 0000000..e0c5f37
--- /dev/null
+++ b/cpp/tests/groupby/argmax_tests.cpp
@@ -0,0 +1,256 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+template <typename V>
+struct groupby_argmax_test : public cudf::test::BaseFixture {};
+using K = int32_t;
+
+TYPED_TEST_SUITE(groupby_argmax_test, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(groupby_argmax_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMAX>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{0, 1, 2};
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_argmax_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMAX>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, cudf::test::iterators::all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_argmax_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMAX>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, cudf::test::iterators::all_nulls());
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_argmax_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMAX>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 4},
+                                                 {0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  //  {1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4},
+                                                        cudf::test::iterators::no_nulls());
+  //  {6, 3,     5, 4, 0,   2, 1,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3, 4, 7, 0}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_argmax_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_argmax_string_test, basic)
+{
+  using R = cudf::detail::target_type_t<cudf::string_view, cudf::aggregation::ARGMAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{
+    "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0, 4, 2});
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_argmax_string_test, zero_valid_values)
+{
+  using R = cudf::detail::target_type_t<cudf::string_view, cudf::aggregation::ARGMAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::strings_column_wrapper vals({"año", "bit", "₹1"}, cudf::test::iterators::all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, cudf::test::iterators::all_nulls());
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_dictionary_argmax_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_argmax_test, basic)
+{
+  using V = std::string;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMAX>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{     1,     2,    3,     1,     2,     2,     1,    3,    3,    2 };
+  cudf::test::dictionary_column_wrapper<V>  vals{ "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1, 2, 3 });
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({ 0, 4, 2 });
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_argmax_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_argmax_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+struct groupby_argmax_struct_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_argmax_struct_test, basic)
+{
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expect_keys    = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_indices = cudf::test::fixed_width_column_wrapper<int32_t>{0, 4, 2};
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_indices, std::move(agg));
+}
+
+TEST_F(groupby_argmax_struct_test, slice_input)
+{
+  constexpr int32_t dont_care{1};
+  auto const keys_original = cudf::test::fixed_width_column_wrapper<int32_t>{
+    dont_care, dont_care, 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, dont_care};
+  auto const vals_original = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"dont_care",
+                                                     "dont_care",
+                                                     "año",
+                                                     "bit",
+                                                     "₹1",
+                                                     "aaa",
+                                                     "zit",
+                                                     "bat",
+                                                     "aab",
+                                                     "$1",
+                                                     "€1",
+                                                     "wut",
+                                                     "dont_care"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{
+      dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const keys           = cudf::slice(keys_original, {2, 12})[0];
+  auto const vals           = cudf::slice(vals_original, {2, 12})[0];
+  auto const expect_keys    = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_indices = cudf::test::fixed_width_column_wrapper<int32_t>{0, 4, 2};
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_indices, std::move(agg));
+}
+
+TEST_F(groupby_argmax_struct_test, null_keys_and_values)
+{
+  constexpr int32_t null{0};
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, cudf::test::iterators::null_at(7)};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "" /*NULL*/, "" /*NULL*/, "$1", "€1", "wut", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 8, 7, 6, 5, null, null, 2, 1, 0, null};
+    return cudf::test::structs_column_wrapper{{child1, child2},
+                                              cudf::test::iterators::nulls_at({5, 6, 10})};
+  }();
+
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {1, 2, 3, 4}, cudf::test::iterators::no_nulls()};
+  auto const expect_indices = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {0, 4, 2, null}, cudf::test::iterators::null_at(3)};
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_indices, std::move(agg));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/groupby/argmin_tests.cpp b/cpp/tests/groupby/argmin_tests.cpp
new file mode 100644
index 0000000..0211bc5
--- /dev/null
+++ b/cpp/tests/groupby/argmin_tests.cpp
@@ -0,0 +1,255 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_argmin_test : public cudf::test::BaseFixture {};
+using K = int32_t;
+
+TYPED_TEST_SUITE(groupby_argmin_test, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(groupby_argmin_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMIN>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{6, 9, 8};
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_argmin_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMIN>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_argmin_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMIN>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_argmin_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMIN>;
+
+  if (std::is_same_v<V, bool>) return;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 4},
+                                                 {1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //  { 9, 6,     8, 5, 0,   7, 1,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3, 9, 8, 0}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  // TODO: explore making this a gtest parameter
+  auto agg2 = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_argmin_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_argmin_string_test, basic)
+{
+  using R = cudf::detail::target_type_t<cudf::string_view, cudf::aggregation::ARGMIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{
+    "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3, 5, 7});
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_argmin_string_test, zero_valid_values)
+{
+  using R = cudf::detail::target_type_t<cudf::string_view, cudf::aggregation::ARGMIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::strings_column_wrapper vals({"año", "bit", "₹1"}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_dictionary_argmin_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_argmin_test, basic)
+{
+  using V = std::string;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::ARGMIN>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{    1,     2,    3,     1,     2,     2,     1,    3,    3,    2 };
+  cudf::test::dictionary_column_wrapper<V>  vals{"año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1, 2, 3 });
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({ 3, 5, 7 });
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_argmin_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_argmin_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+struct groupby_argmin_struct_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_argmin_struct_test, basic)
+{
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expect_keys    = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_indices = cudf::test::fixed_width_column_wrapper<int32_t>{3, 5, 7};
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_indices, std::move(agg));
+}
+
+TEST_F(groupby_argmin_struct_test, slice_input)
+{
+  constexpr int32_t dont_care{1};
+  auto const keys_original = cudf::test::fixed_width_column_wrapper<int32_t>{
+    dont_care, dont_care, 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, dont_care};
+  auto const vals_original = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"dont_care",
+                                                     "dont_care",
+                                                     "año",
+                                                     "bit",
+                                                     "₹1",
+                                                     "aaa",
+                                                     "zit",
+                                                     "bat",
+                                                     "aab",
+                                                     "$1",
+                                                     "€1",
+                                                     "wut",
+                                                     "dont_care"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{
+      dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const keys           = cudf::slice(keys_original, {2, 12})[0];
+  auto const vals           = cudf::slice(vals_original, {2, 12})[0];
+  auto const expect_keys    = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_indices = cudf::test::fixed_width_column_wrapper<int32_t>{3, 5, 7};
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_indices, std::move(agg));
+}
+
+TEST_F(groupby_argmin_struct_test, null_keys_and_values)
+{
+  constexpr int32_t null{0};
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, null_at(7)};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "" /*NULL*/, "" /*NULL*/, "$1", "€1", "wut", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 8, 7, 6, 5, null, null, 2, 1, 0, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, nulls_at({5, 6, 10})};
+  }();
+
+  auto const expect_keys =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, 2, 3, 4}, no_nulls()};
+  auto const expect_indices =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{3, 1, 8, null}, null_at(3)};
+
+  auto agg = cudf::make_argmin_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_indices, std::move(agg));
+}
diff --git a/cpp/tests/groupby/collect_list_tests.cpp b/cpp/tests/groupby/collect_list_tests.cpp
new file mode 100644
index 0000000..749f401
--- /dev/null
+++ b/cpp/tests/groupby/collect_list_tests.cpp
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+template <typename V>
+struct groupby_collect_list_test : public cudf::test::BaseFixture {};
+
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(groupby_collect_list_test, FixedWidthTypesNotBool);
+
+TYPED_TEST(groupby_collect_list_test, CollectWithoutNulls)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{1, 1, 1, 2, 2, 2};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> values{1, 2, 3, 4, 5, 6};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{1, 2};
+  cudf::test::lists_column_wrapper<V, int32_t> expect_vals{{1, 2, 3}, {4, 5, 6}};
+
+  auto agg = cudf::make_collect_list_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, values, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectWithNulls)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{1, 1, 2, 2, 3, 3};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> values{
+    {1, 2, 3, 4, 5, 6}, {true, false, true, false, true, false}};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{1, 2, 3};
+
+  std::vector<int32_t> validity({true, false});
+  cudf::test::lists_column_wrapper<V, int32_t> expect_vals{
+    {{1, 2}, validity.begin()}, {{3, 4}, validity.begin()}, {{5, 6}, validity.begin()}};
+
+  auto agg = cudf::make_collect_list_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, values, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectWithNullExclusion)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{1, 1, 1, 2, 2, 3, 3, 4, 4};
+
+  cudf::test::fixed_width_column_wrapper<V, int32_t> values{
+    {1, 2, 3, 4, 5, 6, 7, 8, 9}, {false, true, false, true, false, false, false, true, true}};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{1, 2, 3, 4};
+
+  cudf::test::lists_column_wrapper<V, int32_t> expect_vals{{2}, {4}, {}, {8, 9}};
+
+  auto agg =
+    cudf::make_collect_list_aggregation<cudf::groupby_aggregation>(cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, values, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectOnEmptyInput)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> values{};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{};
+  cudf::test::lists_column_wrapper<V, int32_t> expect_vals{};
+
+  auto agg =
+    cudf::make_collect_list_aggregation<cudf::groupby_aggregation>(cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, values, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectLists)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{1, 1, 2, 2, 3, 3};
+  cudf::test::lists_column_wrapper<V, int32_t> values{
+    {1, 2}, {3, 4}, {5, 6, 7}, LCW{}, {9, 10}, {11}};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{1, 2, 3};
+
+  cudf::test::lists_column_wrapper<V, int32_t> expect_vals{
+    {{1, 2}, {3, 4}}, {{5, 6, 7}, LCW{}}, {{9, 10}, {11}}};
+
+  auto agg = cudf::make_collect_list_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, values, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectListsWithNullExclusion)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  using LCW = cudf::test::lists_column_wrapper<V, int32_t>;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{1, 1, 2, 2, 3, 3, 4, 4};
+  bool const validity_mask[] = {true, false, false, true, true, true, false, false};
+  LCW values{{{1, 2}, {3, 4}, {5, 6, 7}, LCW{}, {9, 10}, {11}, {20, 30, 40}, LCW{}}, validity_mask};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{1, 2, 3, 4};
+
+  LCW expect_vals{{{1, 2}}, {LCW{}}, {{9, 10}, {11}}, {}};
+
+  auto agg =
+    cudf::make_collect_list_aggregation<cudf::groupby_aggregation>(cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, values, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectOnEmptyInputLists)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  using LCW = cudf::test::lists_column_wrapper<V, int32_t>;
+
+  auto offsets = cudf::data_type{cudf::type_to_id<cudf::size_type>()};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{};
+  auto values =
+    cudf::make_lists_column(0, cudf::make_empty_column(offsets), LCW{}.release(), 0, {});
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{};
+
+  auto expect_child =
+    cudf::make_lists_column(0, cudf::make_empty_column(offsets), LCW{}.release(), 0, {});
+  auto expect_values =
+    cudf::make_lists_column(0, cudf::make_empty_column(offsets), std::move(expect_child), 0, {});
+
+  auto agg = cudf::make_collect_list_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, values->view(), expect_keys, expect_values->view(), std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, CollectOnEmptyInputListsOfStructs)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  using LCW = cudf::test::lists_column_wrapper<V, int32_t>;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{};
+  auto struct_child  = LCW{};
+  auto struct_column = cudf::test::structs_column_wrapper{{struct_child}};
+
+  auto values =
+    cudf::make_lists_column(0,
+                            cudf::make_empty_column(cudf::type_to_id<cudf::size_type>()),
+                            struct_column.release(),
+                            0,
+                            {});
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{};
+
+  auto expect_struct_child  = LCW{};
+  auto expect_struct_column = cudf::test::structs_column_wrapper{{expect_struct_child}};
+
+  auto expect_child =
+    cudf::make_lists_column(0,
+                            cudf::make_empty_column(cudf::type_to_id<cudf::size_type>()),
+                            expect_struct_column.release(),
+                            0,
+                            {});
+  auto expect_values =
+    cudf::make_lists_column(0,
+                            cudf::make_empty_column(cudf::type_to_id<cudf::size_type>()),
+                            std::move(expect_child),
+                            0,
+                            {});
+
+  auto agg = cudf::make_collect_list_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, values->view(), expect_keys, expect_values->view(), std::move(agg));
+}
+
+TYPED_TEST(groupby_collect_list_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> keys{1, 1, 1, 2, 2, 2};
+  cudf::test::dictionary_column_wrapper<V, int32_t> vals{1, 2, 3, 4, 5, 6};
+
+  cudf::test::fixed_width_column_wrapper<K, int32_t> expect_keys{1, 2};
+  cudf::test::lists_column_wrapper<V, int32_t> expect_vals_w{{1, 2, 3}, {4, 5, 6}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> offsets({0, 3, 6});
+  auto expect_vals = cudf::make_lists_column(cudf::column_view(offsets).size() - 1,
+                                             std::make_unique<cudf::column>(offsets),
+                                             std::make_unique<cudf::column>(vals),
+                                             0,
+                                             rmm::device_buffer{});
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals->view(),
+                  cudf::make_collect_list_aggregation<cudf::groupby_aggregation>());
+}
diff --git a/cpp/tests/groupby/collect_set_tests.cpp b/cpp/tests/groupby/collect_set_tests.cpp
new file mode 100644
index 0000000..518fec6
--- /dev/null
+++ b/cpp/tests/groupby/collect_set_tests.cpp
@@ -0,0 +1,399 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+
+namespace {
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+
+using keys_col      = cudf::test::fixed_width_column_wrapper<int32_t, int32_t>;
+using strings_col   = cudf::test::strings_column_wrapper;
+using strings_lists = cudf::test::lists_column_wrapper<cudf::string_view>;
+using validity_col  = std::initializer_list<bool>;
+
+auto groupby_collect_set(cudf::column_view const& keys,
+                         cudf::column_view const& values,
+                         std::unique_ptr<cudf::groupby_aggregation>&& agg)
+{
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = values;
+  requests[0].aggregations.emplace_back(std::move(agg));
+
+  auto const result      = cudf::groupby::groupby(cudf::table_view({keys})).aggregate(requests);
+  auto const result_keys = result.first->view();                 // <== table_view of 1 column
+  auto const result_vals = result.second[0].results[0]->view();  // <== column_view
+
+  // Sort the output columns based on the output keys.
+  // This is to facilitate comparison of the output with the expected columns.
+  auto keys_vals_sorted = cudf::sort_by_key(cudf::table_view{{result_keys.column(0), result_vals}},
+                                            result_keys,
+                                            {},
+                                            {cudf::null_order::AFTER})
+                            ->release();
+
+  // After the columns were reordered, individual rows of the output values column (which are lists)
+  // also need to be sorted.
+  auto out_values =
+    cudf::lists::sort_lists(cudf::lists_column_view{keys_vals_sorted.back()->view()},
+                            cudf::order::ASCENDING,
+                            cudf::null_order::AFTER);
+
+  return std::pair(std::move(keys_vals_sorted.front()), std::move(out_values));
+}
+
+}  // namespace
+
+struct CollectSetTest : public cudf::test::BaseFixture {
+  static auto collect_set()
+  {
+    return cudf::make_collect_set_aggregation<cudf::groupby_aggregation>();
+  }
+
+  static auto collect_set_null_unequal()
+  {
+    return cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL);
+  }
+
+  static auto collect_set_null_exclude()
+  {
+    return cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+      cudf::null_policy::EXCLUDE);
+  }
+};
+
+template <typename V>
+struct CollectSetTypedTest : public cudf::test::BaseFixture {};
+
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(CollectSetTypedTest, FixedWidthTypesNotBool);
+
+TYPED_TEST(CollectSetTypedTest, TrivialInput)
+{
+  using vals_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Empty input
+  {
+    keys_col keys{};
+    vals_col vals{};
+    keys_col keys_expected{};
+    lists_col vals_expected{};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // Single key input
+  {
+    keys_col keys{1};
+    vals_col vals{10};
+    keys_col keys_expected{1};
+    lists_col vals_expected{lists_col{10}};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // Non-repeated keys
+  {
+    keys_col keys{2, 1};
+    vals_col vals{20, 10};
+    keys_col keys_expected{1, 2};
+    lists_col vals_expected{lists_col{10}, lists_col{20}};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+}
+
+TYPED_TEST(CollectSetTypedTest, TypicalInput)
+{
+  using vals_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Pre-sorted keys
+  {
+    keys_col keys{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3};
+    vals_col vals{10, 11, 10, 10, 20, 21, 21, 20, 30, 33, 32, 31};
+    keys_col keys_expected{1, 2, 3};
+    lists_col vals_expected{{10, 11}, {20, 21}, {30, 31, 32, 33}};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // Expect the result keys to be sorted by sort-based groupby
+  {
+    keys_col keys{4, 1, 2, 4, 3, 3, 2, 1};
+    vals_col vals{40, 10, 20, 40, 30, 30, 20, 11};
+    keys_col keys_expected{1, 2, 3, 4};
+    lists_col vals_expected{{10, 11}, {20}, {30}, {40}};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+}
+
+// Keys and values columns are sliced columns
+TYPED_TEST(CollectSetTypedTest, SlicedColumnsInput)
+{
+  using vals_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  keys_col keys_original{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3};
+  vals_col vals_original{10, 11, 10, 10, 20, 21, 21, 20, 30, 33, 32, 31};
+  {
+    auto const keys          = cudf::slice(keys_original, {0, 4})[0];  // { 1, 1, 1, 1 }
+    auto const vals          = cudf::slice(vals_original, {0, 4})[0];  // { 10, 11, 10, 10 }
+    auto const keys_expected = keys_col{1};
+    auto const vals_expected = lists_col{{10, 11}};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+  {
+    auto const keys = cudf::slice(keys_original, {2, 10})[0];  // { 1, 1, 2, 2, 2, 2, 3, 3 }
+    auto const vals = cudf::slice(vals_original, {2, 10})[0];  // { 10, 10, 20, 21, 21, 20, 30, 33 }
+    auto const keys_expected = keys_col{1, 2, 3};
+    auto const vals_expected = lists_col{{10}, {20, 21}, {30, 33}};
+
+    auto const [out_keys, out_lists] =
+      groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+}
+
+TEST_F(CollectSetTest, StringInput)
+{
+  keys_col keys{1, 2, 3, 3, 2, 1, 2, 1, 2, 1, 1, 1, 1};
+  strings_col vals{
+    "String 1, first",
+    "String 2, first",
+    "String 3, first",
+    "String 3, second",
+    "String 2, second",
+    "String 1, second",
+    "String 2, second",  // repeated
+    "String 1, second",  // repeated
+    "String 2, second",  // repeated
+    "String 1, second",  // repeated
+    "String 1, second",  // repeated
+    "String 1, second",  // repeated
+    "String 1, second"   // repeated
+  };
+  keys_col keys_expected{1, 2, 3};
+  strings_lists vals_expected{{"String 1, first", "String 1, second"},
+                              {"String 2, first", "String 2, second"},
+                              {"String 3, first", "String 3, second"}};
+
+  auto const [out_keys, out_lists] = groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+}
+
+TEST_F(CollectSetTest, FloatsWithNaN)
+{
+  keys_col keys{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<float> vals{
+    {1.0f, 1.0f, -2.3e-5f, -2.3e-5f, 2.3e5f, 2.3e5f, -NAN, -NAN, NAN, NAN, 0.0f, 0.0f},
+    {true, true, true, true, true, true, true, true, true, true, false, false}};
+  keys_col keys_expected{1};
+  cudf::test::lists_column_wrapper<float> vals_expected;
+
+  // null equal with nan unequal
+  {
+    vals_expected                    = {{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, -NAN, NAN, NAN, 0.0f},
+                                         validity_col{true, true, true, true, true, true, true, false}}};
+    auto const [out_keys, out_lists] = groupby_collect_set(
+      keys,
+      vals,
+      cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+        cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // null unequal with nan unequal
+  {
+    vals_expected                    = {{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, -NAN, NAN, NAN, 0.0f, 0.0f},
+                                         validity_col{true, true, true, true, true, true, true, false, false}}};
+    auto const [out_keys, out_lists] = groupby_collect_set(
+      keys,
+      vals,
+      cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+        cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL, cudf::nan_equality::UNEQUAL));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // null exclude with nan unequal
+  {
+    vals_expected                    = {{-2.3e-5f, 1.0f, 2.3e5f, -NAN, -NAN, NAN, NAN}};
+    auto const [out_keys, out_lists] = groupby_collect_set(
+      keys,
+      vals,
+      cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+        cudf::null_policy::EXCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // null equal with nan equal
+  {
+    vals_expected = {
+      {{-2.3e-5f, 1.0f, 2.3e5f, NAN, 0.0f}, validity_col{true, true, true, true, false}}};
+    auto const [out_keys, out_lists] = groupby_collect_set(
+      keys,
+      vals,
+      cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+        cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+
+  // null unequal with nan equal
+  {
+    vals_expected                    = {{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, 0.0f, 0.0f},
+                                         validity_col{true, true, true, true, false, false}}};
+    auto const [out_keys, out_lists] = groupby_collect_set(
+      keys,
+      vals,
+      cudf::make_collect_set_aggregation<cudf::groupby_aggregation>(
+        cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL, cudf::nan_equality::ALL_EQUAL));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+  }
+}
+
+TYPED_TEST(CollectSetTypedTest, CollectWithNulls)
+{
+  using vals_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Just use an arbitrary value to store null entries
+  // Using this alias variable will make the code look cleaner
+  constexpr int32_t null = 0;
+
+  // Pre-sorted keys
+  {
+    keys_col keys{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3};
+    vals_col vals{{10, 10, null, null, 20, null, null, null, 30, 31, 30, 31},
+                  {true, true, false, false, true, false, false, false, true, true, true, true}};
+    keys_col keys_expected{1, 2, 3};
+    lists_col vals_expected;
+
+    // By default, nulls are consider equals, thus only one null is kept per key
+    {
+      vals_expected = {{{10, null}, validity_col{true, false}},
+                       {{20, null}, validity_col{true, false}},
+                       {{30, 31}, validity_col{true, true}}};
+      auto const [out_keys, out_lists] =
+        groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+    }
+
+    // All nulls per key are kept (nulls are put at the end of each list)
+    {
+      vals_expected = lists_col{{{10, null, null}, validity_col{true, false, false}},
+                                {{20, null, null, null}, validity_col{true, false, false, false}},
+                                {{30, 31}, validity_col{true, true}}};
+      auto const [out_keys, out_lists] =
+        groupby_collect_set(keys, vals, CollectSetTest::collect_set_null_unequal());
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+    }
+
+    // All nulls per key are excluded
+    {
+      vals_expected = lists_col{{10}, {20}, {30, 31}};
+      auto const [out_keys, out_lists] =
+        groupby_collect_set(keys, vals, CollectSetTest::collect_set_null_exclude());
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+    }
+  }
+
+  // Expect the result keys to be sorted by sort-based groupby
+  {
+    keys_col keys{4, 1, 2, 4, 3, 3, 3, 3, 2, 1};
+    vals_col vals{{40, 10, 20, 40, null, null, null, null, 21, null},
+                  {true, true, true, true, false, false, false, false, true, false}};
+    keys_col keys_expected{1, 2, 3, 4};
+    lists_col vals_expected;
+
+    // By default, nulls are consider equals, thus only one null is kept per key
+    {
+      vals_expected = {{{10, null}, validity_col{true, false}},
+                       {{20, 21}, validity_col{true, true}},
+                       {{null}, validity_col{false}},
+                       {{40}, validity_col{true}}};
+      auto const [out_keys, out_lists] =
+        groupby_collect_set(keys, vals, CollectSetTest::collect_set());
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+    }
+
+    // All nulls per key are kept (nulls are put at the end of each list)
+    {
+      vals_expected =
+        lists_col{{{10, null}, validity_col{true, false}},
+                  {{20, 21}, validity_col{true, true}},
+                  {{null, null, null, null}, validity_col{false, false, false, false}},
+                  {{40}, validity_col{true}}};
+      auto const [out_keys, out_lists] =
+        groupby_collect_set(keys, vals, CollectSetTest::collect_set_null_unequal());
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+    }
+
+    // All nulls per key are excluded
+    {
+      vals_expected = lists_col{{10}, {20, 21}, {}, {40}};
+      auto const [out_keys, out_lists] =
+        groupby_collect_set(keys, vals, CollectSetTest::collect_set_null_exclude());
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(keys_expected, *out_keys, verbosity);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(vals_expected, *out_lists, verbosity);
+    }
+  }
+}
diff --git a/cpp/tests/groupby/correlation_tests.cpp b/cpp/tests/groupby/correlation_tests.cpp
new file mode 100644
index 0000000..399ff9f
--- /dev/null
+++ b/cpp/tests/groupby/correlation_tests.cpp
@@ -0,0 +1,241 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+#include <limits>
+#include <vector>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_correlation_test : public cudf::test::BaseFixture {};
+
+using supported_types =
+  cudf::test::RemoveIf<cudf::test::ContainedIn<cudf::test::Types<bool>>, cudf::test::NumericTypes>;
+
+TYPED_TEST_SUITE(groupby_correlation_test, supported_types);
+using K = int32_t;
+
+TYPED_TEST(groupby_correlation_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  constexpr auto nan = std::numeric_limits<double>::quiet_NaN();
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 0, 3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals{{1.0, 0.6, nan}};
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_correlation_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> member_0{}, member_1{};
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_correlation_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> member_0{3, 4, 5}, member_1{6, 7, 8};
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_correlation_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> member_0({3, 4, 5}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> member_1({3, 4, 5}, all_nulls());
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_correlation_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  constexpr auto nan = std::numeric_limits<double>::quiet_NaN();
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val0({9, 1, 1, 2, 2, 3, 3,-1, 1, 4, 4},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val1({1, 1, 1, 2, 0, 3, 3,-1, 0, 2, 2});
+  // clang-format on
+  auto vals = cudf::test::structs_column_wrapper{{val0, val1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({1.0, 0.6, nan, 0.}, {1, 1, 1, 0});
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_correlation_test, null_values_same)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  constexpr auto nan = std::numeric_limits<double>::quiet_NaN();
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val0({9, 1, 1, 2, 2, 3, 3,-1, 1, 4, 4},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<V> val1({1, 1, 1, 2, 0, 3, 3,-1, 0, 2, 2},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  // clang-format on
+  auto vals = cudf::test::structs_column_wrapper{{val0, val1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({1.0, 0.6, nan, 0.}, {1, 1, 1, 0});
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+// keys=[1, 1, 1, 2, 2, 2, 2,   3, N, 3, 4]
+// val0=[N, 2, 3, 1, N, 3, 4,   1,-1, 1, 4]
+// val1=[N, 2, 3, 2,-1, 6,-6/1, 1,-1, 0, N]
+// corr=[    1.0,       -0.5/0, NAN,     NAN]
+TYPED_TEST(groupby_correlation_test, null_values_different)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  constexpr auto nan = std::numeric_limits<double>::quiet_NaN();
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val0({9, 1, 1, 2, 2, 3, 3,-1, 1, 4, 4},
+                                     {0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val1({1, 2, 1, 2,-1, 6, 3,-1, 0, 1, 2},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  // clang-format on
+  auto vals = cudf::test::structs_column_wrapper{{val0, val1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({1.0, 0., nan, 0.}, {1, 1, 1, 0});
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_correlation_test, min_periods)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  constexpr auto nan = std::numeric_limits<double>::quiet_NaN();
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 0, 3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals1{{1.0, 0.6, nan}};
+  auto agg1 = cudf::make_correlation_aggregation<cudf::groupby_aggregation>(
+    cudf::correlation_type::PEARSON, 3);
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg1), force_use_sort_impl::YES);
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals2{{1.0, 0.6, nan}, {0, 1, 0}};
+  auto agg2 = cudf::make_correlation_aggregation<cudf::groupby_aggregation>(
+    cudf::correlation_type::PEARSON, 4);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg2), force_use_sort_impl::YES);
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals3{{1.0, 0.6, nan}, {0, 0, 0}};
+  auto agg3 = cudf::make_correlation_aggregation<cudf::groupby_aggregation>(
+    cudf::correlation_type::PEARSON, 5);
+  test_single_agg(keys, vals, expect_keys, expect_vals3, std::move(agg3), force_use_sort_impl::YES);
+}
+
+struct groupby_dictionary_correlation_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_correlation_test, basic)
+{
+  using V = int16_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::CORRELATION>;
+
+  constexpr auto nan = std::numeric_limits<double>::quiet_NaN();
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::dictionary_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::dictionary_column_wrapper<V>{{1, 1, 1, 2, 0, 3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals{{1.0, 0.6, nan}};
+
+  auto agg =
+    cudf::make_correlation_aggregation<cudf::groupby_aggregation>(cudf::correlation_type::PEARSON);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
diff --git a/cpp/tests/groupby/count_scan_tests.cpp b/cpp/tests/groupby/count_scan_tests.cpp
new file mode 100644
index 0000000..fb80989
--- /dev/null
+++ b/cpp/tests/groupby/count_scan_tests.cpp
@@ -0,0 +1,208 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using key_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+template <typename T>
+struct groupby_count_scan_test : public cudf::test::BaseFixture {
+  using V              = T;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_ALL>;
+  using value_wrapper  = cudf::test::fixed_width_column_wrapper<V, int32_t>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+};
+
+TYPED_TEST_SUITE(groupby_count_scan_test, cudf::test::AllTypes);
+
+TYPED_TEST(groupby_count_scan_test, basic)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys  {1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  value_wrapper vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  key_wrapper expect_keys   {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  result_wrapper expect_vals{0, 1, 2, 0, 1, 2, 3, 0, 1, 2};
+  // clang-format on
+
+  // Count groupby aggregation is only supported with cudf::null_policy::EXCLUDE
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_scan_aggregation>();
+  EXPECT_THROW(test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg1)),
+               cudf::logic_error);
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_scan_test, empty_cols)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys;
+  value_wrapper vals;
+  key_wrapper expect_keys;
+  result_wrapper expect_vals;
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_scan_aggregation>();
+  EXPECT_NO_THROW(test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg1)));
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_scan_test, zero_valid_keys)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys({1, 2, 3}, cudf::test::iterators::all_nulls());
+  value_wrapper vals{3, 4, 5};
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_scan_test, zero_valid_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys{1, 1, 1};
+  value_wrapper vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+  key_wrapper expect_keys{1, 1, 1};
+  result_wrapper expect_vals{0, 1, 2};
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_scan_test, null_keys_and_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys(  {1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4}, {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  value_wrapper vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //                        {1, 1, 1, 2, 2, 2, 2, 3, _, 3, 4}
+  key_wrapper expect_keys(  {1, 1, 1, 2, 2, 2, 2, 3,    3, 4}, cudf::test::iterators::no_nulls());
+  //                        {0, 3, 6, 1, 4, _, 9, 2, 7, 8, -}
+  result_wrapper expect_vals{0, 1, 2, 0, 1,    2, 3, 0, 1, 0};
+  // clang-format on
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+struct groupby_count_scan_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_count_scan_string_test, basic)
+{
+  using V              = cudf::string_view;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_ALL>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+
+  // clang-format off
+  key_wrapper keys                      {  1,   3,   3,   5,   5,   0};
+  cudf::test::strings_column_wrapper vals{"1", "1", "1", "1", "1", "1"};
+  key_wrapper expect_keys   {0, 1, 3, 3, 5, 5};
+  result_wrapper expect_vals{0, 0, 0, 1, 0, 1};
+  // clang-format on
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+template <typename T>
+struct GroupByCountScanFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupByCountScanFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupByCountScanFixedPointTest, GroupByCountScan)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  using V              = decimalXX;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_ALL>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+
+  auto const scale       = scale_type{-1};
+  auto const keys        = key_wrapper{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals        = fp_wrapper{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+  auto const expect_keys = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  auto const expect_vals = result_wrapper{0, 1, 2, 0, 1, 2, 3, 0, 1, 2};
+
+  // Count groupby aggregation is only supported with cudf::null_policy::EXCLUDE
+  EXPECT_THROW(test_single_scan(keys,
+                                vals,
+                                expect_keys,
+                                expect_vals,
+                                cudf::make_count_aggregation<cudf::groupby_scan_aggregation>()),
+               cudf::logic_error);
+
+  auto agg2 =
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+struct groupby_dictionary_count_scan_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_count_scan_test, basic)
+{
+  using K              = int32_t;
+  using V              = std::string;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_ALL>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+
+  cudf::test::strings_column_wrapper keys{"1", "3", "3", "5", "5", "0"};
+  cudf::test::dictionary_column_wrapper<K> vals{1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expect_keys{"0", "1", "3", "3", "5", "5"};
+  result_wrapper expect_vals{0, 0, 0, 1, 0, 1};
+
+  // Count groupby aggregation is only supported with cudf::null_policy::EXCLUDE
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_scan_aggregation>();
+  EXPECT_THROW(test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg1)),
+               cudf::logic_error);
+  test_single_scan(
+    keys,
+    vals,
+    expect_keys,
+    expect_vals,
+    cudf::make_count_aggregation<cudf::groupby_scan_aggregation>(cudf::null_policy::INCLUDE));
+}
diff --git a/cpp/tests/groupby/count_tests.cpp b/cpp/tests/groupby/count_tests.cpp
new file mode 100644
index 0000000..4f0fdd5
--- /dev/null
+++ b/cpp/tests/groupby/count_tests.cpp
@@ -0,0 +1,222 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+template <typename V>
+struct groupby_count_test : public cudf::test::BaseFixture {};
+using K = int32_t;
+
+TYPED_TEST_SUITE(groupby_count_test, cudf::test::AllTypes);
+
+TYPED_TEST(groupby_count_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{3, 4, 3};
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+
+  auto agg2 = cudf::make_count_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals;
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals;
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_count_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, cudf::test::iterators::all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+
+  auto agg2 = cudf::make_count_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{0};
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+
+  cudf::test::fixed_width_column_wrapper<R> expect_vals2{3};
+  auto agg2 = cudf::make_count_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg2));
+}
+
+TYPED_TEST(groupby_count_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  // clang-format off
+  //                                                    {1, 1,  2, 2, 2,  3, 3,  4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,     2,        3,     4}, cudf::test::iterators::no_nulls());
+  //                                                    {3, 6,  1, 4, 9,  2, 8,  -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({2,     3,        2,     0});
+  // clang-format on
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+
+  cudf::test::fixed_width_column_wrapper<R> expect_vals2{3, 4, 2, 1};
+  auto agg2 = cudf::make_count_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg2));
+}
+
+struct groupby_count_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_count_string_test, basic)
+{
+  using V = cudf::string_view;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1,    3,  3,   5,   5,   0};
+  cudf::test::strings_column_wrapper        vals{"1", "1", "1", "1", "1", "1"};
+  // clang-format on
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{0, 1, 3, 5};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{1, 1, 2, 2};
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+}
+// clang-format on
+
+template <typename T>
+struct GroupByCountFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupByCountFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupByCountFixedPointTest, GroupByCount)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  using V = decimalXX;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  auto const scale = scale_type{-1};
+  auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals  = fp_wrapper{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+  auto const expect_vals = cudf::test::fixed_width_column_wrapper<R>{3, 4, 3};
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg1 = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg1), force_use_sort_impl::YES);
+
+  auto agg2 = cudf::make_count_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2));
+}
+
+struct groupby_dictionary_count_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_count_test, basic)
+{
+  using V = std::string;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  // clang-format off
+  cudf::test::strings_column_wrapper        keys{"1", "3", "3", "5", "5", "0"};
+  cudf::test::dictionary_column_wrapper<K>  vals{1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper        expect_keys{"0", "1", "3", "5"};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{1, 1, 2, 2};
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_count_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_count_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
diff --git a/cpp/tests/groupby/covariance_tests.cpp b/cpp/tests/groupby/covariance_tests.cpp
new file mode 100644
index 0000000..9600117
--- /dev/null
+++ b/cpp/tests/groupby/covariance_tests.cpp
@@ -0,0 +1,255 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <limits>
+#include <vector>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_covariance_test : public cudf::test::BaseFixture {};
+
+using supported_types =
+  cudf::test::RemoveIf<cudf::test::ContainedIn<cudf::test::Types<bool>>, cudf::test::NumericTypes>;
+
+TYPED_TEST_SUITE(groupby_covariance_test, supported_types);
+using K = int32_t;
+
+TYPED_TEST(groupby_covariance_test, invalid_types)
+{
+  using V = TypeParam;
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 2, 1}};
+  auto member_0 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2}};
+  // Covariance cudf::aggregations require all types are convertible to double, but
+  // duration_D cannot be converted to double.
+  auto member_1 =
+    cudf::test::fixed_width_column_wrapper<cudf::duration_D, cudf::duration_D::rep>{{0, 0, 1, 1}};
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  EXPECT_THROW(test_single_agg(keys, vals, keys, vals, std::move(agg), force_use_sort_impl::YES),
+               cudf::logic_error);
+}
+
+TYPED_TEST(groupby_covariance_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 0, 3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals{{1.0, 1.0, 0.0}};
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> member_0{}, member_1{};
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> member_0{3, 4, 5}, member_1{6, 7, 8};
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> member_0({3, 4, 5}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> member_1({3, 4, 5}, all_nulls());
+  auto vals = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val0({9, 1, 1, 2, 2, 3, 3,-1, 1, 4, 4},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val1({1, 1, 1, 2, 0, 3, 3,-1, 0, 2, 2});
+  // clang-format on
+  auto vals = cudf::test::structs_column_wrapper{{val0, val1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0.5, 1.0, 0.0, -0.}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, null_values_same)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val0({9, 1, 1, 2, 2, 3, 3,-1, 1, 4, 4},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<V> val1({1, 1, 1, 2, 0, 3, 3,-1, 0, 2, 2},
+                                     {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  // clang-format on
+  auto vals = cudf::test::structs_column_wrapper{{val0, val1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0.5, 1.0, 0.0, -0.}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, null_values_different)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val0({9, 1, 1, 2, 2, 3, 3,-1, 1, 4, 4},
+                                     {0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> val1({1, 2, 1, 2,-1, 3, 3,-1, 0, 4, 2},
+                                     {0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0});
+  // clang-format on
+  auto vals = cudf::test::structs_column_wrapper{{val0, val1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  cudf::test::fixed_width_column_wrapper<R> expect_vals(
+    {std::numeric_limits<double>::quiet_NaN(), 1.5, 0.0, -0.}, {0, 1, 1, 0});
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, min_periods)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 0, 3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals1{{1.0, 1.0, 0.0}};
+  auto agg1 = cudf::make_covariance_aggregation<cudf::groupby_aggregation>(3);
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg1), force_use_sort_impl::YES);
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals2{{1.0, 1.0, 0.0}, {0, 1, 0}};
+  auto agg2 = cudf::make_covariance_aggregation<cudf::groupby_aggregation>(4);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg2), force_use_sort_impl::YES);
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals3{{1.0, 1.0, 0.0}, {0, 0, 0}};
+  auto agg3 = cudf::make_covariance_aggregation<cudf::groupby_aggregation>(5);
+  test_single_agg(keys, vals, expect_keys, expect_vals3, std::move(agg3), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_covariance_test, ddof)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::fixed_width_column_wrapper<V>{{1, 1, 1, 2, 0, 3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals1{{2.0, 1.5, 0.0}};
+  auto agg1 = cudf::make_covariance_aggregation<cudf::groupby_aggregation>(1, 2);
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg1), force_use_sort_impl::YES);
+
+  auto const inf = std::numeric_limits<double>::infinity();
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals2{{inf, 3.0, 0.0}, {0, 1, 0}};
+  auto agg2 = cudf::make_covariance_aggregation<cudf::groupby_aggregation>(1, 3);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_dictionary_covariance_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_covariance_test, basic)
+{
+  using V = int16_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COVARIANCE>;
+
+  auto keys     = cudf::test::fixed_width_column_wrapper<K>{{1, 2, 3, 1, 2, 2, 1, 3, 3, 2}};
+  auto member_0 = cudf::test::dictionary_column_wrapper<V>{{1, 1, 1, 2, 2, 3, 3, 1, 1, 4}};
+  auto member_1 = cudf::test::dictionary_column_wrapper<V>{{1, 1, 1, 2, 3, -3, 3, 1, 1, 2}};
+  auto vals     = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals{{1.0, -0.5, 0.0}};
+
+  auto agg = cudf::make_covariance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), force_use_sort_impl::YES);
+}
diff --git a/cpp/tests/groupby/groupby_test_util.cpp b/cpp/tests/groupby/groupby_test_util.cpp
new file mode 100644
index 0000000..de51012
--- /dev/null
+++ b/cpp/tests/groupby/groupby_test_util.cpp
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "groupby_test_util.hpp"
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+
+#include <random>
+
+void test_single_agg(cudf::column_view const& keys,
+                     cudf::column_view const& values,
+                     cudf::column_view const& expect_keys,
+                     cudf::column_view const& expect_vals,
+                     std::unique_ptr<cudf::groupby_aggregation>&& agg,
+                     force_use_sort_impl use_sort,
+                     cudf::null_policy include_null_keys,
+                     cudf::sorted keys_are_sorted,
+                     std::vector<cudf::order> const& column_order,
+                     std::vector<cudf::null_order> const& null_precedence,
+                     cudf::sorted reference_keys_are_sorted)
+{
+  auto const [sorted_expect_keys, sorted_expect_vals] = [&]() {
+    if (reference_keys_are_sorted == cudf::sorted::NO) {
+      auto const sort_expect_order =
+        cudf::sorted_order(cudf::table_view{{expect_keys}}, column_order, null_precedence);
+      auto sorted_expect_keys = cudf::gather(cudf::table_view{{expect_keys}}, *sort_expect_order);
+      auto sorted_expect_vals = cudf::gather(cudf::table_view{{expect_vals}}, *sort_expect_order);
+      return std::make_pair(std::move(sorted_expect_keys), std::move(sorted_expect_vals));
+    } else {
+      auto sorted_expect_keys = std::make_unique<cudf::table>(cudf::table_view{{expect_keys}});
+      auto sorted_expect_vals = std::make_unique<cudf::table>(cudf::table_view{{expect_vals}});
+      return std::make_pair(std::move(sorted_expect_keys), std::move(sorted_expect_vals));
+    }
+  }();
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = values;
+
+  requests[0].aggregations.push_back(std::move(agg));
+
+  if (use_sort == force_use_sort_impl::YES) {
+    // WAR to force cudf::groupby to use sort implementation
+    requests[0].aggregations.push_back(
+      cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+  }
+
+  // since the default behavior of cudf::groupby(...) for an empty null_precedence vector is
+  // null_order::AFTER whereas for cudf::sorted_order(...) it's null_order::BEFORE
+  auto const precedence = null_precedence.empty()
+                            ? std::vector<cudf::null_order>(1, cudf::null_order::BEFORE)
+                            : null_precedence;
+
+  cudf::groupby::groupby gb_obj(
+    cudf::table_view({keys}), include_null_keys, keys_are_sorted, column_order, precedence);
+
+  auto result = gb_obj.aggregate(requests, cudf::test::get_default_stream());
+
+  if (use_sort == force_use_sort_impl::YES && keys_are_sorted == cudf::sorted::NO) {
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*sorted_expect_keys, result.first->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(sorted_expect_vals->get_column(0),
+                                        *result.second[0].results[0]);
+
+  } else {
+    auto const sort_order  = cudf::sorted_order(result.first->view(), column_order, precedence);
+    auto const sorted_keys = cudf::gather(result.first->view(), *sort_order);
+    auto const sorted_vals =
+      cudf::gather(cudf::table_view({result.second[0].results[0]->view()}), *sort_order);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*sorted_expect_keys, *sorted_keys);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(sorted_expect_vals->get_column(0),
+                                        sorted_vals->get_column(0));
+  }
+}
+
+void test_sum_agg(cudf::column_view const& keys,
+                  cudf::column_view const& values,
+                  cudf::column_view const& expected_keys,
+                  cudf::column_view const& expected_values)
+{
+  auto const do_test = [&](auto const use_sort_option) {
+    test_single_agg(keys,
+                    values,
+                    expected_keys,
+                    expected_values,
+                    cudf::make_sum_aggregation<cudf::groupby_aggregation>(),
+                    use_sort_option,
+                    cudf::null_policy::INCLUDE);
+  };
+  do_test(force_use_sort_impl::YES);
+  do_test(force_use_sort_impl::NO);
+}
+
+void test_single_scan(cudf::column_view const& keys,
+                      cudf::column_view const& values,
+                      cudf::column_view const& expect_keys,
+                      cudf::column_view const& expect_vals,
+                      std::unique_ptr<cudf::groupby_scan_aggregation>&& agg,
+                      cudf::null_policy include_null_keys,
+                      cudf::sorted keys_are_sorted,
+                      std::vector<cudf::order> const& column_order,
+                      std::vector<cudf::null_order> const& null_precedence)
+{
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = values;
+
+  requests[0].aggregations.push_back(std::move(agg));
+
+  cudf::groupby::groupby gb_obj(
+    cudf::table_view({keys}), include_null_keys, keys_are_sorted, column_order, null_precedence);
+
+  // cudf::groupby scan uses sort implementation
+  auto result = gb_obj.scan(requests);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view({expect_keys}), result.first->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expect_vals, *result.second[0].results[0]);
+}
diff --git a/cpp/tests/groupby/groupby_test_util.hpp b/cpp/tests/groupby/groupby_test_util.hpp
new file mode 100644
index 0000000..755b0c2
--- /dev/null
+++ b/cpp/tests/groupby/groupby_test_util.hpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+enum class force_use_sort_impl : bool { NO, YES };
+
+void test_single_agg(cudf::column_view const& keys,
+                     cudf::column_view const& values,
+                     cudf::column_view const& expect_keys,
+                     cudf::column_view const& expect_vals,
+                     std::unique_ptr<cudf::groupby_aggregation>&& agg,
+                     force_use_sort_impl use_sort                 = force_use_sort_impl::NO,
+                     cudf::null_policy include_null_keys          = cudf::null_policy::EXCLUDE,
+                     cudf::sorted keys_are_sorted                 = cudf::sorted::NO,
+                     std::vector<cudf::order> const& column_order = {},
+                     std::vector<cudf::null_order> const& null_precedence = {},
+                     cudf::sorted reference_keys_are_sorted               = cudf::sorted::NO);
+
+void test_sum_agg(cudf::column_view const& keys,
+                  cudf::column_view const& values,
+                  cudf::column_view const& expected_keys,
+                  cudf::column_view const& expected_values);
+
+void test_single_scan(cudf::column_view const& keys,
+                      cudf::column_view const& values,
+                      cudf::column_view const& expect_keys,
+                      cudf::column_view const& expect_vals,
+                      std::unique_ptr<cudf::groupby_scan_aggregation>&& agg,
+                      cudf::null_policy include_null_keys          = cudf::null_policy::EXCLUDE,
+                      cudf::sorted keys_are_sorted                 = cudf::sorted::NO,
+                      std::vector<cudf::order> const& column_order = {},
+                      std::vector<cudf::null_order> const& null_precedence = {});
diff --git a/cpp/tests/groupby/groups_tests.cpp b/cpp/tests/groupby/groups_tests.cpp
new file mode 100644
index 0000000..fb471e3
--- /dev/null
+++ b/cpp/tests/groupby/groups_tests.cpp
@@ -0,0 +1,118 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/groupby.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+void test_groups(cudf::column_view const& keys,
+                 cudf::column_view const& expect_grouped_keys,
+                 std::vector<cudf::size_type> const& expect_group_offsets,
+                 cudf::column_view const& values                = {},
+                 cudf::column_view const& expect_grouped_values = {})
+{
+  cudf::groupby::groupby gb(cudf::table_view({keys}));
+  cudf::groupby::groupby::groups gb_groups;
+
+  if (values.size()) {
+    gb_groups = gb.get_groups(cudf::table_view({values}));
+  } else {
+    gb_groups = gb.get_groups();
+  }
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view({expect_grouped_keys}), gb_groups.keys->view());
+
+  auto got_offsets = gb_groups.offsets;
+  EXPECT_EQ(expect_group_offsets.size(), got_offsets.size());
+  for (auto i = 0u; i != expect_group_offsets.size(); ++i) {
+    EXPECT_EQ(expect_group_offsets[i], got_offsets[i]);
+  }
+
+  if (values.size()) {
+    CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view({expect_grouped_values}),
+                                  gb_groups.values->view());
+  }
+}
+
+struct groupby_group_keys_test : public cudf::test::BaseFixture {};
+
+template <typename V>
+struct groupby_group_keys_and_values_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_group_keys_and_values_test, cudf::test::NumericTypes);
+
+TEST_F(groupby_group_keys_test, basic)
+{
+  using K = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 2, 1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<K> expect_grouped_keys{1, 1, 1, 2, 2, 3};
+  std::vector<cudf::size_type> expect_group_offsets = {0, 3, 5, 6};
+  test_groups(keys, expect_grouped_keys, expect_group_offsets);
+}
+
+TEST_F(groupby_group_keys_test, empty_keys)
+{
+  using K = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<K> expect_grouped_keys{};
+  std::vector<cudf::size_type> expect_group_offsets = {0};
+  test_groups(keys, expect_grouped_keys, expect_group_offsets);
+}
+
+TEST_F(groupby_group_keys_test, all_null_keys)
+{
+  using K = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 1, 2, 3, 1, 2},
+                                                 cudf::test::iterators::all_nulls());
+  cudf::test::fixed_width_column_wrapper<K> expect_grouped_keys{};
+  std::vector<cudf::size_type> expect_group_offsets = {0};
+  test_groups(keys, expect_grouped_keys, expect_group_offsets);
+}
+
+TYPED_TEST(groupby_group_keys_and_values_test, basic_with_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({5, 4, 3, 2, 1, 0});
+  cudf::test::fixed_width_column_wrapper<K> expect_grouped_keys{0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<V> values({0, 0, 1, 1, 2, 2});
+  cudf::test::fixed_width_column_wrapper<V> expect_grouped_values{2, 2, 1, 1, 0, 0};
+  std::vector<cudf::size_type> expect_group_offsets = {0, 1, 2, 3, 4, 5, 6};
+  test_groups(keys, expect_grouped_keys, expect_group_offsets, values, expect_grouped_values);
+}
+
+TYPED_TEST(groupby_group_keys_and_values_test, some_nulls)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 1, 3, 2, 1, 2}, {1, 0, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<K> expect_grouped_keys({1, 2, 3},
+                                                                cudf::test::iterators::no_nulls());
+  cudf::test::fixed_width_column_wrapper<V> values({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<V> expect_grouped_values({1, 6, 3});
+  std::vector<cudf::size_type> expect_group_offsets = {0, 1, 2, 3};
+  test_groups(keys, expect_grouped_keys, expect_group_offsets, values, expect_grouped_values);
+}
diff --git a/cpp/tests/groupby/histogram_tests.cpp b/cpp/tests/groupby/histogram_tests.cpp
new file mode 100644
index 0000000..c5833f4
--- /dev/null
+++ b/cpp/tests/groupby/histogram_tests.cpp
@@ -0,0 +1,396 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/sorting.hpp>
+
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using int64s_col  = cudf::test::fixed_width_column_wrapper<int64_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+
+auto groupby_histogram(cudf::column_view const& keys,
+                       cudf::column_view const& values,
+                       cudf::aggregation::Kind agg_kind)
+{
+  CUDF_EXPECTS(
+    agg_kind == cudf::aggregation::HISTOGRAM || agg_kind == cudf::aggregation::MERGE_HISTOGRAM,
+    "Aggregation must be either HISTOGRAM or MERGE_HISTOGRAM.");
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back();
+  requests[0].values = values;
+  if (agg_kind == cudf::aggregation::HISTOGRAM) {
+    requests[0].aggregations.push_back(
+      cudf::make_histogram_aggregation<cudf::groupby_aggregation>());
+  } else {
+    requests[0].aggregations.push_back(
+      cudf::make_merge_histogram_aggregation<cudf::groupby_aggregation>());
+  }
+
+  auto gb_obj              = cudf::groupby::groupby(cudf::table_view({keys}));
+  auto const agg_results   = gb_obj.aggregate(requests, cudf::test::get_default_stream());
+  auto const agg_histogram = agg_results.second[0].results[0]->view();
+  EXPECT_EQ(agg_histogram.type().id(), cudf::type_id::LIST);
+  EXPECT_EQ(agg_histogram.null_count(), 0);
+
+  auto const histograms = cudf::lists_column_view{agg_histogram}.child();
+  EXPECT_EQ(histograms.num_children(), 2);
+  EXPECT_EQ(histograms.null_count(), 0);
+  EXPECT_EQ(histograms.child(1).null_count(), 0);
+
+  auto const key_sort_order = cudf::sorted_order(agg_results.first->view(), {}, {});
+  auto sorted_keys =
+    std::move(cudf::gather(agg_results.first->view(), *key_sort_order)->release().front());
+  auto const sorted_vals =
+    std::move(cudf::gather(cudf::table_view{{agg_histogram}}, *key_sort_order)->release().front());
+  auto sorted_histograms = cudf::lists::sort_lists(cudf::lists_column_view{*sorted_vals},
+                                                   cudf::order::ASCENDING,
+                                                   cudf::null_order::BEFORE,
+                                                   rmm::mr::get_current_device_resource());
+
+  return std::pair{std::move(sorted_keys), std::move(sorted_histograms)};
+}
+
+template <typename T>
+struct GroupbyHistogramTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct GroupbyMergeHistogramTest : public cudf::test::BaseFixture {};
+
+// Avoid unsigned types, as the tests below have negative values in their input.
+using HistogramTestTypes = cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t>,
+                                              cudf::test::FloatingPointTypes,
+                                              cudf::test::FixedPointTypes,
+                                              cudf::test::ChronoTypes>;
+TYPED_TEST_SUITE(GroupbyHistogramTest, HistogramTestTypes);
+TYPED_TEST_SUITE(GroupbyMergeHistogramTest, HistogramTestTypes);
+
+TYPED_TEST(GroupbyHistogramTest, EmptyInput)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+
+  auto const keys   = int32s_col{};
+  auto const values = col_data{};
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::HISTOGRAM);
+
+  // The structure of the output is already verified in the function `groupby_histogram`.
+  ASSERT_EQ(res_histogram->size(), 0);
+}
+
+TYPED_TEST(GroupbyHistogramTest, SimpleInputNoNull)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+
+  // key = 0: values = [2, 2, -3, -2, 2]
+  // key = 1: values = [2, 0, 5, 2, 1]
+  // key = 2: values = [-3, 1, 1, 2, 2]
+  auto const keys   = int32s_col{2, 0, 2, 1, 1, 1, 0, 0, 0, 1, 2, 2, 1, 0, 2};
+  auto const values = col_data{-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1, 2, 1, 2, 2};
+
+  auto const expected_keys      = int32s_col{0, 1, 2};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{-3, -2, 2, 0, 1, 2, 5, -3, 1, 2};
+      auto counts = int64s_col{1, 1, 3, 1, 1, 2, 1, 1, 2, 2};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 7, 10}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyHistogramTest, SlicedInputNoNull)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+
+  auto const keys_original = int32s_col{2, 0, 2, 1, 0, 2, 0, 2, 1, 1, 1, 0, 0, 0, 1, 2, 2, 1, 0, 2};
+  auto const values_original =
+    col_data{1, 2, 0, 2, 1, -3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1, 2, 1, 2, 2};
+  // key = 0: values = [2, 2, -3, -2, 2]
+  // key = 1: values = [2, 0, 5, 2, 1]
+  // key = 2: values = [-3, 1, 1, 2, 2]
+  auto const keys   = cudf::slice(keys_original, {5, 20})[0];
+  auto const values = cudf::slice(values_original, {5, 20})[0];
+
+  auto const expected_keys      = int32s_col{0, 1, 2};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{-3, -2, 2, 0, 1, 2, 5, -3, 1, 2};
+      auto counts = int64s_col{1, 1, 3, 1, 1, 2, 1, 1, 2, 2};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 7, 10}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyHistogramTest, InputWithNulls)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+  using namespace cudf::test::iterators;
+  auto constexpr null{0};
+
+  // key = 0: values = [-3, null, 2, null, 2]
+  // key = 1: values = [1, 2, null, 5, 2, -3, 1, 1]
+  // key = 2: values = [null, 2, 0, -2, 2, null, 2]
+  auto const keys = int32s_col{2, 0, 2, 1, 1, 1, 2, 1, 1, 0, 1, 2, 0, 0, 1, 2, 2, 1, 0, 2};
+  auto const values =
+    col_data{{null, -3, 2, 1, 2, null, 0, 5, 2, null, -3, -2, 2, null, 1, 2, null, 1, 2, 2},
+             nulls_at({0, 5, 9, 13, 16})};
+
+  auto const expected_keys      = int32s_col{0, 1, 2};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{{null, -3, 2, null, -3, 1, 2, 5, null, -2, 0, 2}, nulls_at({0, 3, 8})};
+      auto counts = int64s_col{2, 1, 2, 1, 1, 3, 2, 1, 2, 1, 1, 3};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 8, 12}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyHistogramTest, SlicedInputWithNulls)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+  using namespace cudf::test::iterators;
+  auto constexpr null{0};
+
+  auto const keys_original =
+    int32s_col{1, 0, 2, 2, 0, 2, 0, 2, 1, 1, 1, 2, 1, 1, 0, 1, 2, 0, 0, 1, 2, 2, 1, 0, 2, 0, 1, 2};
+  auto const values_original =
+    col_data{{null, 1,  1,  2, 1,    null, -3, 2,    1, 2, null, 0,    5, 2,
+              null, -3, -2, 2, null, 1,    2,  null, 1, 2, 2,    null, 1, 2},
+             nulls_at({0, 5, 10, 14, 18, 21, 25})};
+
+  // key = 0: values = [-3, null, 2, null, 2]
+  // key = 1: values = [1, 2, null, 5, 2, -3, 1, 1]
+  // key = 2: values = [null, 2, 0, -2, 2, null, 2]
+  auto const keys   = cudf::slice(keys_original, {5, 25})[0];
+  auto const values = cudf::slice(values_original, {5, 25})[0];
+
+  auto const expected_keys      = int32s_col{0, 1, 2};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{{null, -3, 2, null, -3, 1, 2, 5, null, -2, 0, 2}, nulls_at({0, 3, 8})};
+      auto counts = int64s_col{2, 1, 2, 1, 1, 3, 2, 1, 2, 1, 1, 3};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 8, 12}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyMergeHistogramTest, EmptyInput)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+
+  auto const keys   = int32s_col{};
+  auto const values = [] {
+    auto structs = [] {
+      auto values = col_data{};
+      auto counts = int64s_col{};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      0, int32s_col{}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, *values, cudf::aggregation::MERGE_HISTOGRAM);
+
+  // The structure of the output is already verified in the function `groupby_histogram`.
+  ASSERT_EQ(res_histogram->size(), 0);
+}
+
+TYPED_TEST(GroupbyMergeHistogramTest, SimpleInputNoNull)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+
+  // key = 0: histograms = [[<-3, 1>, <-2, 1>, <2, 3>], [<0, 1>, <1, 1>], [<-3, 3>, <0, 1>, <1, 2>]]
+  // key = 1: histograms = [[<-2, 1>, <1, 3>, <2, 2>], [<0, 2>, <1, 1>, <2, 2>]]
+  auto const keys   = int32s_col{0, 1, 0, 1, 0};
+  auto const values = [] {
+    auto structs = [] {
+      auto values = col_data{-3, -2, 2, -2, 1, 2, 0, 1, 0, 1, 2, -3, 0, 1};
+      auto counts = int64s_col{1, 1, 3, 1, 3, 2, 1, 1, 2, 1, 2, 3, 1, 2};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      5, int32s_col{0, 3, 6, 8, 11, 14}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const expected_keys      = int32s_col{0, 1};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{-3, -2, 0, 1, 2, -2, 0, 1, 2};
+      auto counts = int64s_col{4, 1, 2, 3, 3, 1, 2, 4, 4};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      2, int32s_col{0, 5, 9}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, *values, cudf::aggregation::MERGE_HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyMergeHistogramTest, SlicedInputNoNull)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+
+  // key = 0: histograms = [[<-3, 1>, <-2, 1>, <2, 3>], [<0, 1>, <1, 1>], [<-3, 3>, <0, 1>, <1, 2>]]
+  // key = 1: histograms = [[<-2, 1>, <1, 3>, <2, 2>], [<0, 2>, <1, 1>, <2, 2>]]
+  auto const keys_original   = int32s_col{0, 1, 0, 1, 0, 1, 0};
+  auto const values_original = [] {
+    auto structs = [] {
+      auto values = col_data{0, 2, -3, 1, -3, -2, 2, -2, 1, 2, 0, 1, 0, 1, 2, -3, 0, 1};
+      auto counts = int64s_col{1, 2, 3, 1, 1, 1, 3, 1, 3, 2, 1, 1, 2, 1, 2, 3, 1, 2};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(7,
+                                   int32s_col{0, 2, 4, 7, 10, 12, 15, 18}.release(),
+                                   structs.release(),
+                                   0,
+                                   rmm::device_buffer{});
+  }();
+  auto const keys   = cudf::slice(keys_original, {2, 7})[0];
+  auto const values = cudf::slice(*values_original, {2, 7})[0];
+
+  auto const expected_keys      = int32s_col{0, 1};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{-3, -2, 0, 1, 2, -2, 0, 1, 2};
+      auto counts = int64s_col{4, 1, 2, 3, 3, 1, 2, 4, 4};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      2, int32s_col{0, 5, 9}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::MERGE_HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyMergeHistogramTest, InputWithNulls)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+  using namespace cudf::test::iterators;
+  auto constexpr null{0};
+
+  // key = 0: histograms = [[<null, 1>, <2, 3>], [<null, 2>, <1, 1>], [<0, 1>, <1, 2>]]
+  // key = 1: histograms = [[<null, 1>, <1, 3>, <2, 2>], [<0, 2>, <1, 1>, <2, 2>]]
+  auto const keys   = int32s_col{0, 1, 1, 0, 0};
+  auto const values = [] {
+    auto structs = [] {
+      auto values = col_data{{null, 2, null, 1, 2, 0, 1, 2, null, 1, 0, 1}, nulls_at({0, 2, 8})};
+      auto counts = int64s_col{1, 3, 1, 3, 2, 2, 1, 2, 2, 1, 1, 2};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      5, int32s_col{0, 2, 5, 8, 10, 12}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const expected_keys      = int32s_col{0, 1};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{{null, 0, 1, 2, null, 0, 1, 2}, nulls_at({0, 4})};
+      auto counts = int64s_col{3, 1, 3, 3, 1, 2, 4, 4};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      2, int32s_col{0, 4, 8}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, *values, cudf::aggregation::MERGE_HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
+
+TYPED_TEST(GroupbyMergeHistogramTest, SlicedInputWithNulls)
+{
+  using col_data = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+  using namespace cudf::test::iterators;
+  auto constexpr null{0};
+
+  // key = 0: histograms = [[<null, 1>, <2, 3>], [<null, 2>, <1, 1>], [<0, 1>, <1, 2>]]
+  // key = 1: histograms = [[<null, 1>, <1, 3>, <2, 2>], [<0, 2>, <1, 1>, <2, 2>]]
+  auto const keys_original   = int32s_col{0, 1, 0, 1, 1, 0, 0};
+  auto const values_original = [] {
+    auto structs = [] {
+      auto values = col_data{{null, 2, null, 1, null, 2, null, 1, 2, 0, 1, 2, null, 1, 0, 1},
+                             nulls_at({0, 2, 4, 6, 12})};
+      auto counts = int64s_col{1, 3, 2, 1, 1, 3, 1, 3, 2, 2, 1, 2, 2, 1, 1, 2};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(7,
+                                   int32s_col{0, 2, 4, 6, 9, 12, 14, 16}.release(),
+                                   structs.release(),
+                                   0,
+                                   rmm::device_buffer{});
+  }();
+  auto const keys   = cudf::slice(keys_original, {2, 7})[0];
+  auto const values = cudf::slice(*values_original, {2, 7})[0];
+
+  auto const expected_keys      = int32s_col{0, 1};
+  auto const expected_histogram = [] {
+    auto structs = [] {
+      auto values = col_data{{null, 0, 1, 2, null, 0, 1, 2}, nulls_at({0, 4})};
+      auto counts = int64s_col{3, 1, 3, 3, 1, 2, 4, 4};
+      return structs_col{{values, counts}};
+    }();
+    return cudf::make_lists_column(
+      2, int32s_col{0, 4, 8}.release(), structs.release(), 0, rmm::device_buffer{});
+  }();
+
+  auto const [res_keys, res_histogram] =
+    groupby_histogram(keys, values, cudf::aggregation::MERGE_HISTOGRAM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *res_keys);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_histogram, *res_histogram);
+}
diff --git a/cpp/tests/groupby/keys_tests.cpp b/cpp/tests/groupby/keys_tests.cpp
new file mode 100644
index 0000000..7064abc
--- /dev/null
+++ b/cpp/tests/groupby/keys_tests.cpp
@@ -0,0 +1,421 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_keys_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::
+  Types<int8_t, int16_t, int32_t, int64_t, float, double, numeric::decimal32, numeric::decimal64>;
+
+TYPED_TEST_SUITE(groupby_keys_test, supported_types);
+
+TYPED_TEST(groupby_keys_test, basic)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys        { 1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys { 1, 2, 3 };
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 3, 4, 3 };
+  // clang-format on
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_keys_test, zero_valid_keys)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys      ( { 1, 2, 3}, all_nulls() );
+  cudf::test::fixed_width_column_wrapper<V> vals        { 3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys { };
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { };
+  // clang-format on
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_keys_test, some_null_keys)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::COUNT_VALID>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys(       { 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                        { 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4};
+
+                                                    //  { 1, 1, 1,  2, 2, 2, 2,  3, 3,  4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,        2,           3,     4}, no_nulls() );
+                                                    //  { 0, 3, 6,  1, 4, 5, 9,  2, 8,  -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 3,        4,           2,     1};
+  // clang-format on
+
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_keys_test, include_null_keys)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys(       { 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                        { 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4};
+
+                                                    //  { 1, 1, 1,  2, 2, 2, 2,  3, 3,  4,  -}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,        2,           3,     4,  3},
+                                                        { 1,        1,           1,     1,  0});
+                                                    //  { 0, 3, 6,  1, 4, 5, 9,  2, 8,  -,  -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 9,        19,          10,    4,  7};
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::NO,
+                  cudf::null_policy::INCLUDE);
+}
+
+TYPED_TEST(groupby_keys_test, pre_sorted_keys)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys        { 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4};
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys { 1,       2,          3,       4};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 3,       18,         24,      4};
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::YES,
+                  cudf::null_policy::EXCLUDE,
+                  cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_keys_test, pre_sorted_keys_descending)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys        { 4, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys { 4, 3,       2,          1      };
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 0, 6,       22,        21      };
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::YES,
+                  cudf::null_policy::EXCLUDE,
+                  cudf::sorted::YES,
+                  {cudf::order::DESCENDING});
+}
+
+TYPED_TEST(groupby_keys_test, pre_sorted_keys_nullable)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys(       { 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4},
+                                                        { 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,       2,          3,       4}, no_nulls() );
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 3,       15,         17,      4};
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::YES,
+                  cudf::null_policy::EXCLUDE,
+                  cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_keys_test, pre_sorted_keys_nulls_before_include_nulls)
+{
+  using K = TypeParam;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys(       { 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4},
+                                                        { 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4};
+
+                                                    //  { 1, 1, 1,  -, -,  2, 2,  -,  3, 3,  4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,        2,     2,     3,  3,     4},
+                                                        { 1,        0,     1,     0,  1,     1});
+  cudf::test::fixed_width_column_wrapper<R> expect_vals { 3,        7,     11,    7,  17,    4};
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::YES,
+                  cudf::null_policy::INCLUDE,
+                  cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_keys_test, mismatch_num_rows)
+{
+  using K = TypeParam;
+  using V = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4};
+
+  // Verify that scan throws an error when given data of mismatched sizes.
+  auto agg = cudf::make_count_aggregation<cudf::groupby_aggregation>();
+  EXPECT_THROW(test_single_agg(keys, vals, keys, vals, std::move(agg)), cudf::logic_error);
+  auto agg2 = cudf::make_count_aggregation<cudf::groupby_scan_aggregation>();
+  EXPECT_THROW(test_single_scan(keys, vals, keys, vals, std::move(agg2)), cudf::logic_error);
+}
+
+template <typename T>
+using FWCW = cudf::test::fixed_width_column_wrapper<T>;
+
+TYPED_TEST(groupby_keys_test, structs)
+{
+  using V = TypeParam;
+
+  using R       = cudf::detail::target_type_t<int, cudf::aggregation::ARGMAX>;
+  using STRINGS = cudf::test::strings_column_wrapper;
+  using STRUCTS = cudf::test::structs_column_wrapper;
+
+  if (std::is_same_v<V, bool>) return;
+
+  /*
+    `@` indicates null
+       keys:                values:
+       /+----------------+
+       |s1{s2{a,b},   c}|
+       +-----------------+
+     0 |  { { 1, 1}, "a"}|  1
+     1 |  { { 1, 2}, "b"}|  2
+     2 |  {@{ 2, 1}, "c"}|  3
+     3 |  {@{ 2, 1}, "c"}|  4
+     4 | @{ { 2, 2}, "d"}|  5
+     5 | @{ { 2, 2}, "d"}|  6
+     6 |  { { 1, 1}, "a"}|  7
+     7 |  {@{ 2, 1}, "c"}|  8
+     8 |  { {@1, 1}, "a"}|  9
+       +-----------------+
+  */
+
+  // clang-format off
+  auto col_a = FWCW<V>{{ 1,   1,   2,   2,   2,   2,   1,   2,   1 }, null_at(8)};
+  auto col_b = FWCW<V> { 1,   2,   1,   1,   2,   2,   1,   1,   1 };
+  auto col_c = STRINGS {"a", "b", "c", "c", "d", "d", "a", "c", "a"};
+  // clang-format on
+  auto s2 = STRUCTS{{col_a, col_b}, nulls_at({2, 3, 7})};
+
+  auto keys = STRUCTS{{s2, col_c}, nulls_at({4, 5})};
+  auto vals = FWCW<int>{1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format off
+  auto expected_col_a = FWCW<V>{{1,   1,   1,   2 }, null_at(2)};
+  auto expected_col_b = FWCW<V>{ 1,   2,   1,   1 };
+  auto expected_col_c = STRINGS{"a", "b", "a", "c"};
+  // clang-format on
+  auto expected_s2 = STRUCTS{{expected_col_a, expected_col_b}, null_at(3)};
+
+  auto expect_keys = STRUCTS{{expected_s2, expected_col_c}, no_nulls()};
+  auto expect_vals = FWCW<R>{6, 1, 8, 7};
+
+  auto agg = cudf::make_argmax_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+template <typename T>
+using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+TYPED_TEST(groupby_keys_test, lists)
+{
+  using R = cudf::detail::target_type_t<int32_t, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto keys   = LCW<TypeParam> { {1,1}, {2,2}, {3,3}, {1,1}, {2,2} };
+  auto values = FWCW<int32_t>  {    0,     1,     2,     3,     4  };
+
+  auto expected_keys   = LCW<TypeParam> { {1,1}, {2,2}, {3,3} };
+  auto expected_values = FWCW<R>        {    3,     5,     2  };
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, values, expected_keys, expected_values, std::move(agg));
+}
+
+struct groupby_string_keys_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_string_keys_test, basic)
+{
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::strings_column_wrapper        keys        { "aaa", "año", "₹1", "aaa", "año", "año", "aaa", "₹1", "₹1", "año"};
+  cudf::test::fixed_width_column_wrapper<V> vals        {     0,     1,    2,     3,     4,     5,     6,    7,    8,     9};
+
+  cudf::test::strings_column_wrapper        expect_keys({ "aaa", "año", "₹1" });
+  cudf::test::fixed_width_column_wrapper<R> expect_vals {     9,    19,   17 };
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+// clang-format on
+
+struct groupby_dictionary_keys_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_keys_test, basic)
+{
+  using K = std::string;
+  using V = int32_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  cudf::test::dictionary_column_wrapper<K> keys { "aaa", "año", "₹1", "aaa", "año", "año", "aaa", "₹1", "₹1", "año"};
+  cudf::test::fixed_width_column_wrapper<V> vals{     0,     1,    2,     3,     4,     5,     6,    7,    8,     9};
+  cudf::test::dictionary_column_wrapper<K>expect_keys  ({ "aaa", "año", "₹1" });
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({     9,    19,   17 });
+  // clang-format on
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_sum_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+struct groupby_cache_test : public cudf::test::BaseFixture {};
+
+// To check if the cache doesn't insert multiple times to cache for the same aggregation on a
+// column in the same request. If this test fails, then insert happened and the key stored in the
+// cache map becomes a dangling reference. Any comparison with the same aggregation as the key will
+// fail.
+TEST_F(groupby_cache_test, duplicate_agggregations)
+{
+  using K = int32_t;
+  using V = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  cudf::groupby::groupby gb_obj(cudf::table_view({keys}));
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals;
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+
+  // hash groupby
+  EXPECT_NO_THROW(gb_obj.aggregate(requests));
+
+  // sort groupby
+  // WAR to force groupby to use sort implementation
+  requests[0].aggregations.push_back(
+    cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+  EXPECT_NO_THROW(gb_obj.aggregate(requests));
+}
+
+// To check if the cache doesn't insert multiple times to cache for the same aggregation on the same
+// column but in different requests. If this test fails, then insert happened and the key stored in
+// the cache map becomes a dangling reference. Any comparison with the same aggregation as the key
+// will fail.
+TEST_F(groupby_cache_test, duplicate_columns)
+{
+  using K = int32_t;
+  using V = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  cudf::groupby::groupby gb_obj(cudf::table_view({keys}));
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals;
+  requests[0].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[1].values = vals;
+  requests[1].aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+
+  // hash groupby
+  EXPECT_NO_THROW(gb_obj.aggregate(requests));
+
+  // sort groupby
+  // WAR to force groupby to use sort implementation
+  requests[0].aggregations.push_back(
+    cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+  EXPECT_NO_THROW(gb_obj.aggregate(requests));
+}
diff --git a/cpp/tests/groupby/lists_tests.cpp b/cpp/tests/groupby/lists_tests.cpp
new file mode 100644
index 0000000..1fe0b7d
--- /dev/null
+++ b/cpp/tests/groupby/lists_tests.cpp
@@ -0,0 +1,107 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+template <typename V>
+struct groupby_lists_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_lists_test, cudf::test::FixedWidthTypes);
+
+using namespace cudf::test::iterators;
+
+// Type of aggregation result.
+using agg_result_t = cudf::detail::target_type_t<int32_t, cudf::aggregation::SUM>;
+
+template <typename T>
+using fwcw = cudf::test::fixed_width_column_wrapper<T>;
+
+template <typename T>
+using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+
+namespace {
+static constexpr auto null = -1;
+
+// Checking with a single aggregation, and aggregation column.
+// This test is orthogonal to the aggregation type; it focuses on testing the grouping
+// with LISTS keys.
+
+}  // namespace
+
+TYPED_TEST(groupby_lists_test, basic)
+{
+  if (std::is_same_v<TypeParam, bool>) { return; }
+
+  // clang-format off
+  auto keys   = lcw<TypeParam> { {1,1}, {2,2}, {3,3}, {1,1}, {2,2} };
+  auto values = fwcw<int32_t>  {    0,     1,     2,     3,     4  };
+
+  auto expected_keys   = lcw<TypeParam>    { {1,1}, {2,2}, {3,3} };
+  auto expected_values = fwcw<agg_result_t>{    3,     5,     2  };
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_lists_test, all_null_input)
+{
+  // clang-format off
+  auto keys   = lcw<TypeParam> { {{1,1}, {2,2}, {3,3}, {1,1}, {2,2}}, all_nulls()};
+  auto values = fwcw<int32_t>  {     0,     1,     2,     3,     4 };
+
+  auto expected_keys   = lcw<TypeParam>    { {{null,null}}, all_nulls()};
+  auto expected_values = fwcw<agg_result_t>{          10 };
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_lists_test, lists_with_nulls)
+{
+  // clang-format off
+  auto keys   = lcw<TypeParam> { {{1,1}, {2,2}, {3,3}, {1,1}, {2,2}}, nulls_at({1,2,4})};
+  auto values = fwcw<int32_t>  {     0,     1,     2,     3,     4 };
+
+  auto expected_keys   = lcw<TypeParam>    { {{null,null}, {1,1}}, null_at(0)};
+  auto expected_values = fwcw<agg_result_t>{           7,     3 };
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_lists_test, lists_with_null_elements)
+{
+  auto keys =
+    lcw<TypeParam>{{lcw<TypeParam>{{{1, 2, 3}, {}, {4, 5}, {}, {6, 0}}, nulls_at({1, 3})},
+                    lcw<TypeParam>{{{1, 2, 3}, {}, {4, 5}, {}, {6, 0}}, nulls_at({1, 3})},
+                    lcw<TypeParam>{{{1, 2, 3}, {}, {4, 5}, {}, {6, 0}}, nulls_at({1, 3})},
+                    lcw<TypeParam>{{{1, 2, 3}, {}, {4, 5}, {}, {6, 0}}, nulls_at({1, 3})}},
+                   nulls_at({2, 3})};
+  auto values = fwcw<int32_t>{1, 2, 4, 5};
+
+  auto expected_keys = lcw<TypeParam>{
+    {{}, lcw<TypeParam>{{{1, 2, 3}, {}, {4, 5}, {}, {6, 0}}, nulls_at({1, 3})}}, null_at(0)};
+  auto expected_values = fwcw<agg_result_t>{9, 3};
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
diff --git a/cpp/tests/groupby/m2_tests.cpp b/cpp/tests/groupby/m2_tests.cpp
new file mode 100644
index 0000000..2947913
--- /dev/null
+++ b/cpp/tests/groupby/m2_tests.cpp
@@ -0,0 +1,241 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};                                       // Mark for null elements
+constexpr double NaN{std::numeric_limits<double>::quiet_NaN()};  // Mark for NaN double elements
+
+template <class T>
+using keys_col = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+template <class T>
+using vals_col = cudf::test::fixed_width_column_wrapper<T>;
+
+template <class T>
+using M2s_col = cudf::test::fixed_width_column_wrapper<T>;
+
+auto compute_M2(cudf::column_view const& keys, cudf::column_view const& values)
+{
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = values;
+  requests[0].aggregations.emplace_back(cudf::make_m2_aggregation<cudf::groupby_aggregation>());
+
+  auto gb_obj = cudf::groupby::groupby(cudf::table_view({keys}));
+  auto result = gb_obj.aggregate(requests);
+  return std::pair(std::move(result.first->release()[0]), std::move(result.second[0].results[0]));
+}
+}  // namespace
+
+template <class T>
+struct GroupbyM2TypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes = cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t>,
+                                     cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(GroupbyM2TypedTest, TestTypes);
+
+TYPED_TEST(GroupbyM2TypedTest, EmptyInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  auto const keys = keys_col<T>{};
+  auto const vals = vals_col<T>{};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_M2s        = M2s_col<R>{};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, AllNullKeysInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  auto const keys = keys_col<T>{{1, 2, 3}, all_nulls()};
+  auto const vals = vals_col<T>{3, 4, 5};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{};
+  auto const expected_M2s        = M2s_col<R>{};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, AllNullValuesInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  auto const keys = keys_col<T>{1, 2, 3};
+  auto const vals = vals_col<T>{{3, 4, 5}, all_nulls()};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_M2s        = M2s_col<R>{{null, null, null}, all_nulls()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, SimpleInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // key = 1: vals = [0, 3, 6]
+  // key = 2: vals = [1, 4, 5, 9]
+  // key = 3: vals = [2, 7, 8]
+  auto const keys = keys_col<T>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = vals_col<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{1, 2, 3};
+  auto const expected_M2s        = M2s_col<R>{18.0, 32.75, 20.0 + 2.0 / 3.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, SimpleInputHavingNegativeValues)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // key = 1: vals = [0,  3, -6]
+  // key = 2: vals = [1, -4, -5, 9]
+  // key = 3: vals = [-2, 7, -8]
+  auto const keys = keys_col<T>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = vals_col<T>{0, 1, -2, 3, -4, -5, -6, 7, -8, 9};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{1, 2, 3};
+  auto const expected_M2s        = M2s_col<R>{42.0, 122.75, 114.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, ValuesHaveNulls)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  auto const keys = keys_col<T>{1, 2, 3, 4, 5, 2, 3, 2};
+  auto const vals = vals_col<T>{{0, null, 2, 3, null, 5, 6, 7}, nulls_at({1, 4})};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{1, 2, 3, 4, 5};
+  auto const expected_M2s        = M2s_col<R>{{0.0, 2.0, 8.0, 0.0, 0.0 /*NULL*/}, null_at(4)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, KeysAndValuesHaveNulls)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // key = 1: vals = [null, 3, 6]
+  // key = 2: vals = [1, 4, null, 9]
+  // key = 3: vals = [2, 8]
+  // key = 4: vals = [null]
+  auto const keys = keys_col<T>{{1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, null_at(7)};
+  auto const vals = vals_col<T>{{null, 1, 2, 3, 4, null, 6, 7, 8, 9, null}, nulls_at({0, 5, 10})};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{1, 2, 3, 4};
+  auto const expected_M2s = M2s_col<R>{{4.5, 32.0 + 2.0 / 3.0, 18.0, 0.0 /*NULL*/}, null_at(3)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, InputHaveNullsAndNaNs)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // key = 1: vals = [0, 3, 6]
+  // key = 2: vals = [1, 4, NaN, 9]
+  // key = 3: vals = [null, 2, 8]
+  // key = 4: vals = [null, 10, NaN]
+  auto const keys = keys_col<T>{{4, 3, 1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4, 4}, null_at(9)};
+  auto const vals = vals_col<double>{
+    {0.0 /*NULL*/, 0.0 /*NULL*/, 0.0, 1.0, 2.0, 3.0, 4.0, NaN, 6.0, 7.0, 8.0, 9.0, 10.0, NaN},
+    nulls_at({0, 1})};
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{1, 2, 3, 4};
+  auto const expected_M2s        = M2s_col<R>{18.0, NaN, 18.0, NaN};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
+
+TYPED_TEST(GroupbyM2TypedTest, SlicedColumnsInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // This test should compute M2 aggregation on the same dataset as the InputHaveNullsAndNaNs test.
+  // i.e.:
+  //
+  // key = 1: vals = [0, 3, 6]
+  // key = 2: vals = [1, 4, NaN, 9]
+  // key = 3: vals = [null, 2, 8]
+  // key = 4: vals = [null, 10, NaN]
+
+  auto const keys_original =
+    keys_col<T>{{
+                  1, 2, 3, 4, 5, 1, 2, 3, 4, 5,                 // will not use, don't care
+                  4, 3, 1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4, 4,  // use this
+                  1, 2, 3, 4, 5, 1, 2, 3, 4, 5                  // will not use, don't care
+                },
+                null_at(19)};
+  auto const vals_original = vals_col<double>{
+    {
+      3.0, 2.0,  5.0,  4.0,  6.0, 9.0, 1.0, 0.0,  1.0,  7.0,  // will not use, don't care
+      0.0, 0.0,  0.0,  1.0,  2.0, 3.0, 4.0, NaN,  6.0,  7.0, 8.0, 9.0, 10.0, NaN,  // use this
+      9.0, 10.0, 11.0, 12.0, 0.0, 5.0, 1.0, 20.0, 19.0, 15.0  // will not use, don't care
+    },
+    nulls_at({10, 11})};
+
+  auto const keys = cudf::slice(keys_original, {10, 24})[0];
+  auto const vals = cudf::slice(vals_original, {10, 24})[0];
+
+  auto const [out_keys, out_M2s] = compute_M2(keys, vals);
+  auto const expected_keys       = keys_col<T>{1, 2, 3, 4};
+  auto const expected_M2s        = M2s_col<R>{18.0, NaN, 18.0, NaN};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, *out_M2s, verbosity);
+}
diff --git a/cpp/tests/groupby/max_scan_tests.cpp b/cpp/tests/groupby/max_scan_tests.cpp
new file mode 100644
index 0000000..2d8400e
--- /dev/null
+++ b/cpp/tests/groupby/max_scan_tests.cpp
@@ -0,0 +1,272 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+
+using namespace cudf::test::iterators;
+
+using key_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+template <typename T>
+struct groupby_max_scan_test : public cudf::test::BaseFixture {
+  using V              = T;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::MAX>;
+  using value_wrapper  = cudf::test::fixed_width_column_wrapper<V, int32_t>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+};
+
+TYPED_TEST_SUITE(groupby_max_scan_test, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(groupby_max_scan_test, basic)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys   {1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  value_wrapper vals({5, 6, 7, 8, 9, 0, 1, 2, 3, 4});
+
+  key_wrapper expect_keys    {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+                          // {5, 8, 1, 6, 9, 0, 4, 7, 2, 3}
+  result_wrapper expect_vals({5, 8, 8, 6, 9, 9, 9, 7, 7, 7});
+  // clang-format on
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_max_scan_test, pre_sorted)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys   {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  value_wrapper vals({5, 8, 1, 6, 9, 0, 4, 7, 2, 3});
+
+  key_wrapper expect_keys    {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  result_wrapper expect_vals({5, 8, 8, 6, 9, 9, 9, 7, 7, 7});
+  // clang-format on
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys,
+                   vals,
+                   expect_keys,
+                   expect_vals,
+                   std::move(agg),
+                   cudf::null_policy::EXCLUDE,
+                   cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_max_scan_test, empty_cols)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys{};
+  value_wrapper vals{};
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_max_scan_test, zero_valid_keys)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys({1, 2, 3}, all_nulls());
+  value_wrapper vals({3, 4, 5});
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_max_scan_test, zero_valid_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys{1, 1, 1};
+  value_wrapper vals({3, 4, 5}, all_nulls());
+  key_wrapper expect_keys{1, 1, 1};
+  result_wrapper expect_vals({-1, -1, -1}, all_nulls());
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_max_scan_test, null_keys_and_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys(  {1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4}, {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  value_wrapper vals({5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 4}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+                         //  {1, 1, 1, 2, 2, 2, 2, 3,   _, 3, 4}
+  key_wrapper expect_keys(   {1, 1, 1, 2, 2, 2, 2, 3,      3, 4}, no_nulls() );
+                         //  { -, 3, 6, 1, 4,  -, 9, 2, _, 8, -}
+  result_wrapper expect_vals({-1, 8, 8, 6, 9, -1, 9, 7,    7, -1},
+                             { 0, 1, 1, 1, 1,  0, 1, 1,    1, 0});
+  // clang-format on
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+struct groupby_max_scan_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_max_scan_string_test, basic)
+{
+  key_wrapper keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{
+    "año", "bit", "₹1", "aaa", "zit", "bat", "aaa", "$1", "₹1", "wut"};
+
+  key_wrapper expect_keys{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  cudf::test::strings_column_wrapper expect_vals(
+    {"año", "año", "año", "bit", "zit", "zit", "zit", "₹1", "₹1", "₹1"});
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+template <typename T>
+struct GroupByMaxScanFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupByMaxScanFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupByMaxScanFixedPointTest, GroupBySortMaxScanDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys = key_wrapper{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals = fp_wrapper{{5, 6, 7, 8, 9, 0, 1, 2, 3, 4}, scale};
+
+    //                                      {5, 8, 1, 6, 9, 0, 4, 7, 2, 3}
+    auto const expect_keys     = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+    auto const expect_vals_max = fp_wrapper{{5, 8, 8, 6, 9, 9, 9, 7, 7, 7}, scale};
+    // clang-format on
+
+    auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+    test_single_scan(keys, vals, expect_keys, expect_vals_max, std::move(agg));
+  }
+}
+
+struct groupby_max_scan_struct_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_max_scan_struct_test, basic)
+{
+  auto const keys = key_wrapper{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expect_keys = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "año", "año", "bit", "zit", "zit", "zit", "₹1", "₹1", "₹1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 2, 5, 5, 5, 3, 3, 3};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_max_scan_struct_test, slice_input)
+{
+  constexpr int32_t dont_care{1};
+  auto const keys_original =
+    key_wrapper{dont_care, dont_care, 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, dont_care};
+  auto const vals_original = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"dont_care",
+                                                     "dont_care",
+                                                     "año",
+                                                     "bit",
+                                                     "₹1",
+                                                     "aaa",
+                                                     "zit",
+                                                     "bat",
+                                                     "aab",
+                                                     "$1",
+                                                     "€1",
+                                                     "wut",
+                                                     "dont_care"};
+    auto child2 = key_wrapper{dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const keys        = cudf::slice(keys_original, {2, 12})[0];
+  auto const vals        = cudf::slice(vals_original, {2, 12})[0];
+  auto const expect_keys = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "año", "año", "bit", "zit", "zit", "zit", "₹1", "₹1", "₹1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 2, 5, 5, 5, 3, 3, 3};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_max_scan_struct_test, null_keys_and_values)
+{
+  constexpr int32_t null{0};
+  auto const keys = key_wrapper{{1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, null_at(7)};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "" /*NULL*/, "" /*NULL*/, "$1", "€1", "wut", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 8, 7, 6, 5, null, null, 2, 1, 0, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, nulls_at({5, 6, 10})};
+  }();
+
+  auto const expect_keys = key_wrapper{{1, 1, 1, 2, 2, 2, 2, 3, 3, 4}, no_nulls()};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "año", "" /*NULL*/, "bit", "zit", "" /*NULL*/, "zit", "₹1", "₹1", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 9, null, 8, 5, null, 5, 7, 7, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, nulls_at({2, 5, 9})};
+  }();
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
diff --git a/cpp/tests/groupby/max_tests.cpp b/cpp/tests/groupby/max_tests.cpp
new file mode 100644
index 0000000..9481770
--- /dev/null
+++ b/cpp/tests/groupby/max_tests.cpp
@@ -0,0 +1,551 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+
+#include <limits>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_max_test : public cudf::test::BaseFixture {};
+
+using K = int32_t;
+TYPED_TEST_SUITE(groupby_max_test, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(groupby_max_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({6, 9, 8});
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_max_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_max_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_max_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_max_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MAX>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //  { 0, 3,     1, 4, 5,   2, 8,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3, 5, 8, 0}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_max_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_max_string_test, basic)
+{
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{
+    "año", "bit", "₹1", "aaa", "zit", "bat", "aaa", "$1", "₹1", "wut"};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::strings_column_wrapper expect_vals({"año", "zit", "₹1"});
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_max_string_test, zero_valid_values)
+{
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::strings_column_wrapper vals({"año", "bit", "₹1"}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::strings_column_wrapper expect_vals({""}, all_nulls());
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_max_string_test, max_sorted_strings)
+{
+  // testcase replicated in issue #8717
+  cudf::test::strings_column_wrapper keys(
+    {"",   "",   "",   "",   "",   "",   "06", "06", "06", "06", "10", "10", "10", "10", "14", "14",
+     "14", "14", "18", "18", "18", "18", "22", "22", "22", "22", "26", "26", "26", "26", "30", "30",
+     "30", "30", "34", "34", "34", "34", "38", "38", "38", "38", "42", "42", "42", "42"},
+    {0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper vals(
+    {"", "", "",   "", "", "", "06", "", "", "", "10", "", "", "", "14", "",
+     "", "", "18", "", "", "", "22", "", "", "", "26", "", "", "", "30", "",
+     "", "", "34", "", "", "", "38", "", "", "", "42", "", "", ""},
+    {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
+     0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0});
+  cudf::test::strings_column_wrapper expect_keys(
+    {"06", "10", "14", "18", "22", "26", "30", "34", "38", "42", ""},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::strings_column_wrapper expect_vals(
+    {"06", "10", "14", "18", "22", "26", "30", "34", "38", "42", ""},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  // cudf::test::fixed_width_column_wrapper<size_type> expect_argmax(
+  // {6, 10, 14, 18, 22, 26, 30, 34, 38, 42, -1},
+  // {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::NO,
+                  cudf::null_policy::INCLUDE,
+                  cudf::sorted::YES);
+}
+
+struct groupby_dictionary_max_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_max_test, basic)
+{
+  using V = std::string;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{     1,     2,    3,     1,     2,     2,     1,    3,    3,    2 };
+  cudf::test::dictionary_column_wrapper<V>  vals{ "año", "bit", "₹1", "aaa", "zit", "bat", "aaa", "$1", "₹1", "wut"};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys   {     1,     2,    3 };
+  cudf::test::dictionary_column_wrapper<V>  expect_vals_w({ "año", "zit", "₹1" });
+  // clang-format on
+
+  auto expect_vals = cudf::dictionary::set_keys(expect_vals_w, vals.keys());
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals->view(),
+                  cudf::make_max_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals->view(),
+                  cudf::make_max_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_dictionary_max_test, fixed_width)
+{
+  using V = int64_t;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{     1,     2,    3,     1,     2,     2,     1,    3,    3,    2 };
+  cudf::test::dictionary_column_wrapper<V>  vals{ 0xABC, 0xBBB, 0xF1, 0xAAA, 0xFFF, 0xBAA, 0xAAA, 0x01, 0xF1, 0xEEE};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys    {     1,     2,    3 };
+  cudf::test::fixed_width_column_wrapper<V>  expect_vals_w({ 0xABC, 0xFFF, 0xF1 });
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals_w,
+                  cudf::make_max_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals_w,
+                  cudf::make_max_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+template <typename T>
+struct GroupByMaxFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupByMaxFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupByMaxFixedPointTest, GroupBySortMaxDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using K          = int32_t;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_max = fp_wrapper{{6, 9, 8}, scale};
+
+    auto agg3 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(
+      keys, vals, expect_keys, expect_vals_max, std::move(agg3), force_use_sort_impl::YES);
+  }
+}
+
+TYPED_TEST(GroupByMaxFixedPointTest, GroupByHashMaxDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using K          = int32_t;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_max = fp_wrapper{{6, 9, 8}, scale};
+
+    auto agg7 = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals_max, std::move(agg7));
+  }
+}
+
+struct groupby_max_struct_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_max_struct_test, basic)
+{
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"año", "zit", "₹1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 5, 3};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_max_struct_test, slice_input)
+{
+  constexpr int32_t dont_care{1};
+  auto const keys_original = cudf::test::fixed_width_column_wrapper<int32_t>{
+    dont_care, dont_care, 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, dont_care};
+  auto const vals_original = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"dont_care",
+                                                     "dont_care",
+                                                     "año",
+                                                     "bit",
+                                                     "₹1",
+                                                     "aaa",
+                                                     "zit",
+                                                     "bat",
+                                                     "aab",
+                                                     "$1",
+                                                     "€1",
+                                                     "wut",
+                                                     "dont_care"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{
+      dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const keys        = cudf::slice(keys_original, {2, 12})[0];
+  auto const vals        = cudf::slice(vals_original, {2, 12})[0];
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"año", "zit", "₹1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 5, 3};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_max_struct_test, null_keys_and_values)
+{
+  constexpr int32_t null{0};
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, null_at(7)};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "" /*NULL*/, "" /*NULL*/, "$1", "€1", "wut", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 8, 7, 6, 5, null, null, 2, 1, 0, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, nulls_at({5, 6, 10})};
+  }();
+
+  auto const expect_keys =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, 2, 3, 4}, no_nulls()};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"año", "zit", "₹1", "" /*NULL*/};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{9, 5, 7, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, null_at(3)};
+  }();
+
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_max_struct_test, values_with_null_child)
+{
+  constexpr int32_t null{0};
+  {
+    auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1};
+    auto const vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{-1, null}, null_at(1)};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1};
+    auto const expect_vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{1};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{-1};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+  }
+
+  {
+    auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1};
+    auto const vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{{-1, null}, null_at(1)};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{null, null}, nulls_at({0, 1})};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1};
+    auto const expect_vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{-1};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{null}, null_at(0)};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+  }
+}
+
+struct groupby_max_list_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_max_list_test, basic)
+{
+  using lists = cudf::test::lists_column_wrapper<int32_t>;
+
+  auto const keys        = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 1, 2};
+  auto const vals        = lists{{1, 2}, {3, 4}, {5, 6, 7}, {0, 8}, {9, 10}};
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3};
+  auto const expect_vals = lists{{1, 2}, {9, 10}, {5, 6, 7}};
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_max_aggregation<cudf::groupby_aggregation>());
+}
+
+TEST_F(groupby_max_list_test, slice_input)
+{
+  using lists = cudf::test::lists_column_wrapper<int32_t>;
+  constexpr int32_t dont_care{1};
+
+  auto const keys_original =
+    cudf::test::fixed_width_column_wrapper<int32_t>{dont_care, 1, 2, 3, 1, 2, dont_care};
+  auto const vals_original =
+    lists{{1, 2, 3, 4, 5} /*dont care*/, {1, 2}, {3, 4}, {5, 6, 7}, {0, 8}, {9, 10}};
+  auto const keys = cudf::slice(keys_original, {1, 6})[0];
+  auto const vals = cudf::slice(vals_original, {1, 6})[0];
+
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3};
+  auto const expect_vals = lists{{1, 2}, {9, 10}, {5, 6, 7}};
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_max_aggregation<cudf::groupby_aggregation>());
+}
+
+TEST_F(groupby_max_list_test, null_keys_and_values)
+{
+  using lists = cudf::test::lists_column_wrapper<int32_t>;
+  constexpr int32_t null{0};
+
+  auto const keys =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, 2, 3, null, 1, 2}, null_at(3)};
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int>{{1, 2, 3}, no_nulls()};
+
+  // Null list element.
+  {
+    auto const vals = lists{{{} /*null*/, {1, 2}, {3, 4}, {5, 6, 7}, {0, 8}, {9, 10}}, null_at(0)};
+    auto const expect_vals = lists{{0, 8}, {9, 10}, {3, 4}};
+    test_single_agg(keys,
+                    vals,
+                    expect_keys,
+                    expect_vals,
+                    cudf::make_max_aggregation<cudf::groupby_aggregation>());
+  }
+
+  // Null child element.
+  {
+    auto const vals        = lists{lists{{0, null}, null_at(1)},
+                            lists{1, 2},
+                            lists{3, 4},
+                            lists{5, 6, 7},
+                            lists{0, 8},
+                            lists{9, 10}};
+    auto const expect_vals = lists{{0, 8}, {9, 10}, {3, 4}};
+    test_single_agg(keys,
+                    vals,
+                    expect_keys,
+                    expect_vals,
+                    cudf::make_max_aggregation<cudf::groupby_aggregation>());
+  }
+}
+
+template <typename V>
+struct groupby_max_floating_point_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_max_floating_point_test, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(groupby_max_floating_point_test, values_with_infinity)
+{
+  using T          = TypeParam;
+  using int32s_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using floats_col = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto constexpr inf = std::numeric_limits<T>::infinity();
+
+  auto const keys = int32s_col{1, 2, 1, 2};
+  auto const vals = floats_col{static_cast<T>(1), static_cast<T>(1), inf, static_cast<T>(2)};
+
+  auto const expected_keys = int32s_col{1, 2};
+  auto const expected_vals = floats_col{inf, static_cast<T>(2)};
+
+  // Related issue: https://github.com/rapidsai/cudf/issues/11352
+  // The issue only occurs in sort-based cudf::aggregation.
+  auto agg = cudf::make_max_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(
+    keys, vals, expected_keys, expected_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_max_floating_point_test, values_with_nan)
+{
+  using T          = TypeParam;
+  using int32s_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using floats_col = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto constexpr nan = std::numeric_limits<T>::quiet_NaN();
+
+  auto const keys = int32s_col{1, 1};
+  auto const vals = floats_col{nan, nan};
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals;
+  requests[0].aggregations.emplace_back(cudf::make_max_aggregation<cudf::groupby_aggregation>());
+
+  // Without properly handling NaN, this will hang forever in hash-based aggregate (which is the
+  // default back-end for min/max in groupby context).
+  // This test is just to verify that the aggregate operation does not hang.
+  auto gb_obj       = cudf::groupby::groupby(cudf::table_view({keys}));
+  auto const result = gb_obj.aggregate(requests);
+
+  EXPECT_EQ(result.first->num_rows(), 1);
+}
diff --git a/cpp/tests/groupby/mean_tests.cpp b/cpp/tests/groupby/mean_tests.cpp
new file mode 100644
index 0000000..2d6ad24
--- /dev/null
+++ b/cpp/tests/groupby/mean_tests.cpp
@@ -0,0 +1,206 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+#include <initializer_list>
+#include <vector>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_mean_test : public cudf::test::BaseFixture {};
+
+template <typename Target, typename Source>
+std::vector<Target> convert(std::initializer_list<Source> in)
+{
+  std::vector<Target> out(std::cbegin(in), std::cend(in));
+  return out;
+}
+
+using supported_types =
+  cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>,
+                     cudf::test::DurationTypes>;
+TYPED_TEST_SUITE(groupby_mean_test, supported_types);
+using K = int32_t;
+
+TYPED_TEST(groupby_mean_test, basic)
+{
+  using V  = TypeParam;
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::MEAN>;
+  using RT = typename std::conditional<cudf::is_duration<R>(), int, double>::type;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1,       2,          3};
+  //                                       {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  std::vector<RT> expect_v = convert<RT>(  {3.,      19. / 4,    17. / 3});
+  cudf::test::fixed_width_column_wrapper<R, RT> expect_vals(expect_v.cbegin(), expect_v.cend());
+  // clang-format on
+
+  auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_mean_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_mean_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_mean_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_mean_test, null_keys_and_values)
+{
+  using V  = TypeParam;
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::MEAN>;
+  using RT = typename std::conditional<cudf::is_duration<R>(), int, double>::type;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  // clang-format off
+  //                                                    {1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,        2,         3,       4}, no_nulls());
+  //                                                    {3, 6,     1, 4, 9,   2, 8,    -}
+  std::vector<RT> expect_v = convert<RT>(               {4.5,      14. / 3,   5.,      0.});
+  // clang-format on
+  cudf::test::fixed_width_column_wrapper<R, RT> expect_vals(
+    expect_v.cbegin(), expect_v.cend(), {1, 1, 1, 0});
+
+  auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+// clang-format on
+
+struct groupby_dictionary_mean_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_mean_test, basic)
+{
+  using V = int16_t;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEAN>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys(        {1,      2,       3});
+  cudf::test::fixed_width_column_wrapper<R, double> expect_vals({9. / 3, 19. / 4, 17. / 3});
+  // clang-format on
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_mean_aggregation<cudf::groupby_aggregation>());
+}
+
+template <typename T>
+struct FixedPointTestBothReps : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTestBothReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestBothReps, GroupBySortMeanDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_min = fp_wrapper{{3, 4, 5}, scale};
+
+    auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(
+      keys, vals, expect_keys, expect_vals_min, std::move(agg), force_use_sort_impl::YES);
+  }
+}
+
+TYPED_TEST(FixedPointTestBothReps, GroupByHashMeanDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using K          = int32_t;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_min = fp_wrapper{{3, 4, 5}, scale};
+
+    auto agg = cudf::make_mean_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals_min, std::move(agg));
+  }
+}
diff --git a/cpp/tests/groupby/median_tests.cpp b/cpp/tests/groupby/median_tests.cpp
new file mode 100644
index 0000000..4b037c0
--- /dev/null
+++ b/cpp/tests/groupby/median_tests.cpp
@@ -0,0 +1,144 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_median_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+TYPED_TEST_SUITE(groupby_median_test, supported_types);
+
+TYPED_TEST(groupby_median_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEDIAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format off
+  //                                                   {1, 1, 1, 2, 2, 2, 2, 3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1,       2,          3};
+  //                                                    {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3.,     4.5,        7.}, no_nulls());
+  // clang-format on
+
+  auto agg = cudf::make_median_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_median_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEDIAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_median_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_median_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEDIAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_median_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_median_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEDIAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_median_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_median_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEDIAN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //  { 3, 6,     1, 4, 9,   2, 8,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({4.5, 4., 5., 0.}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_median_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_median_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MEDIAN>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{ 1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //                                                    {1, 1, 1, 2, 2, 2, 2, 3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,       2,          3      });
+  //                                                    {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3.,       4.5,       7.     }, no_nulls());
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_median_aggregation<cudf::groupby_aggregation>());
+}
diff --git a/cpp/tests/groupby/merge_lists_tests.cpp b/cpp/tests/groupby/merge_lists_tests.cpp
new file mode 100644
index 0000000..f2909f8
--- /dev/null
+++ b/cpp/tests/groupby/merge_lists_tests.cpp
@@ -0,0 +1,386 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/table/table_view.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};  // Mark for null elements
+
+using vcol_views = std::vector<cudf::column_view>;
+
+auto merge_lists(vcol_views const& keys_cols, vcol_views const& values_cols)
+{
+  // Append all the keys and lists together.
+  auto const keys   = cudf::concatenate(keys_cols);
+  auto const values = cudf::concatenate(values_cols);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = *values;
+  requests[0].aggregations.emplace_back(
+    cudf::make_merge_lists_aggregation<cudf::groupby_aggregation>());
+
+  auto gb_obj = cudf::groupby::groupby(cudf::table_view({*keys}));
+  auto result = gb_obj.aggregate(requests);
+  return std::pair(std::move(result.first->release()[0]), std::move(result.second[0].results[0]));
+}
+
+}  // namespace
+
+template <typename V>
+struct GroupbyMergeListsTypedTest : public cudf::test::BaseFixture {};
+
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(GroupbyMergeListsTypedTest, FixedWidthTypesNotBool);
+
+TYPED_TEST(GroupbyMergeListsTypedTest, InvalidInput)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys = keys_col{1, 2, 3};
+
+  // The input lists column must NOT be nullable.
+  auto const lists = lists_col{{lists_col{1}, lists_col{} /*NULL*/, lists_col{2}}, null_at(1)};
+  EXPECT_THROW(merge_lists({keys}, {lists}), cudf::logic_error);
+
+  // The input column must be a lists column.
+  auto const non_lists = keys_col{1, 2, 3, 4, 5};
+  EXPECT_THROW(merge_lists({keys}, {non_lists}), cudf::logic_error);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, EmptyInput)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Keys and lists columns are all empty.
+  auto const keys   = keys_col{};
+  auto const lists0 = lists_col{{1, 2, 3}, {4, 5, 6}};
+  auto const lists  = cudf::empty_like(lists0);
+
+  auto const [out_keys, out_lists] = merge_lists(vcol_views{keys}, vcol_views{*lists});
+  auto const expected_keys         = keys_col{};
+  auto const expected_lists        = cudf::empty_like(lists0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, InputWithoutNull)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    {1, 2, 3},  // key = 1
+    {4, 5, 6}   // key = 2
+  };
+  auto const lists2 = lists_col{
+    {10, 11},  // key = 1
+    {11, 12}   // key = 3
+  };
+  auto const lists3 = lists_col{
+    {20, 21, 22},  // key = 2
+    {23, 24, 25},  // key = 3
+    {24, 25, 26}   // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    {1, 2, 3, 10, 11},      // key = 1
+    {4, 5, 6, 20, 21, 22},  // key = 2
+    {11, 12, 23, 24, 25},   // key = 3
+    {24, 25, 26}            // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, InputHasNulls)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  // Note that the null elements here are not sorted, while the results from current collect_list
+  // are sorted.
+  auto const lists1 = lists_col{
+    lists_col{{1, null, 3}, null_at(1)},  // key = 1
+    lists_col{4, 5, 6}                    // key = 2
+  };
+  auto const lists2 = lists_col{
+    lists_col{10, 11},                          // key = 1
+    lists_col{{null, null, null}, all_nulls()}  // key = 3
+  };
+  auto const lists3 = lists_col{
+    lists_col{20, 21, 22},                          // key = 2
+    lists_col{{null, 24, null}, nulls_at({0, 2})},  // key = 3
+    lists_col{{24, 25, 26}, no_nulls()}             // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    lists_col{{1, null, 3, 10, 11}, null_at(1)},                               // key = 1
+    lists_col{4, 5, 6, 20, 21, 22},                                            // key = 2
+    lists_col{{null, null, null, null, 24, null}, nulls_at({0, 1, 2, 3, 5})},  // key = 3
+    lists_col{24, 25, 26}                                                      // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, InputHasEmptyLists)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    {1, 2, 3},  // key = 1
+    {}          // key = 2
+  };
+  auto const lists2 = lists_col{
+    {},       // key = 1
+    {11, 12}  // key = 3
+  };
+  auto const lists3 = lists_col{
+    {},           // key = 2
+    {},           // key = 3
+    {24, 25, 26}  // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    {1, 2, 3},    // key = 1
+    {},           // key = 2
+    {11, 12},     // key = 3
+    {24, 25, 26}  // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, InputHasNullsAndEmptyLists)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2, 3};
+  auto const keys2 = keys_col{1, 3, 4};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  // Note that the null elements here are not sorted, while the results from current collect_list
+  // are sorted.
+  auto const lists1 = lists_col{
+    lists_col{{1, null, 3}, null_at(1)},  // key = 1
+    lists_col{},                          // key = 2
+    lists_col{4, 5}                       // key = 3
+  };
+  auto const lists2 = lists_col{
+    lists_col{10, 11},                           // key = 1
+    lists_col{{null, null, null}, all_nulls()},  // key = 3
+    lists_col{}                                  // key = 4
+  };
+  auto const lists3 = lists_col{
+    lists_col{20, 21, 22},                          // key = 2
+    lists_col{{null, 24, null}, nulls_at({0, 2})},  // key = 3
+    lists_col{{24, 25, 26}, no_nulls()}             // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    lists_col{{1, null, 3, 10, 11}, null_at(1)},                                     // key = 1
+    lists_col{20, 21, 22},                                                           // key = 2
+    lists_col{{4, 5, null, null, null, null, 24, null}, nulls_at({2, 3, 4, 5, 7})},  // key = 3
+    lists_col{24, 25, 26}                                                            // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, InputHasListsOfLists)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    lists_col{lists_col{1, 2, 3}, lists_col{4}, lists_col{5, 6}},  // key = 1
+    lists_col{lists_col{}, lists_col{7}}                           // key = 2
+  };
+  auto const lists2 = lists_col{
+    lists_col{lists_col{}, lists_col{8, 9}},     // key = 1
+    lists_col{lists_col{11}, lists_col{12, 13}}  // key = 3
+  };
+  auto const lists3 = lists_col{
+    lists_col{lists_col{14}, lists_col{15, 16, 17, 18}},             // key = 2
+    lists_col{lists_col{}},                                          // key = 3
+    lists_col{lists_col{17, 18, 19, 20, 21}, lists_col{18, 19, 20}}  // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    lists_col{
+      lists_col{1, 2, 3}, lists_col{4}, lists_col{5, 6}, lists_col{}, lists_col{8, 9}},  // key = 1
+    lists_col{lists_col{}, lists_col{7}, lists_col{14}, lists_col{15, 16, 17, 18}},      // key = 2
+    lists_col{lists_col{11}, lists_col{12, 13}, lists_col{}},                            // key = 3
+    lists_col{lists_col{17, 18, 19, 20, 21}, lists_col{18, 19, 20}}                      // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeListsTypedTest, SlicedColumnsInput)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1_original = keys_col{1, 2, 4, 5, 6, 7, 8, 9, 10};
+  auto const keys2_original = keys_col{0, 0, 1, 1, 1, 3, 4, 5, 6};
+  auto const keys3_original = keys_col{0, 1, 2, 3, 4, 5, 6, 7, 8};
+
+  auto const keys1 = cudf::slice(keys1_original, {0, 2})[0];  // { 1, 2 }
+  auto const keys2 = cudf::slice(keys2_original, {4, 6})[0];  // { 1, 3 }
+  auto const keys3 = cudf::slice(keys3_original, {2, 5})[0];  // { 2, 3, 4 }
+
+  auto const lists1_original = lists_col{
+    {10, 11, 12},
+    {12, 13, 14},
+    {1, 2, 3},  // key = 1
+    {4, 5, 6}   // key = 2
+  };
+  auto const lists2_original = lists_col{{1, 2},
+                                         {10, 11},  // key = 1
+                                         {11, 12},  // key = 3
+                                         {13},
+                                         {14},
+                                         {15, 16}};
+  auto const lists3_original = lists_col{{20, 21, 22},  // key = 2
+                                         {23, 24, 25},  // key = 3
+                                         {24, 25, 26},  // key = 4
+                                         {1, 2, 3, 4, 5}};
+
+  auto const lists1 = cudf::slice(lists1_original, {2, 4})[0];
+  auto const lists2 = cudf::slice(lists2_original, {1, 3})[0];
+  auto const lists3 = cudf::slice(lists3_original, {0, 3})[0];
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    {1, 2, 3, 10, 11},      // key = 1
+    {4, 5, 6, 20, 21, 22},  // key = 2
+    {11, 12, 23, 24, 25},   // key = 3
+    {24, 25, 26}            // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+struct GroupbyMergeListsTest : public cudf::test::BaseFixture {};
+
+TEST_F(GroupbyMergeListsTest, StringsColumnInput)
+{
+  using strings_col = cudf::test::strings_column_wrapper;
+  using lists_col   = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const keys1 = strings_col{"dog", "unknown"};
+  auto const keys2 = strings_col{"banana", "unknown", "dog"};
+  auto const keys3 = strings_col{"apple", "dog", "water melon"};
+
+  auto const lists1 = lists_col{
+    lists_col{"Poodle", "Golden Retriever", "Corgi"},            // key = "dog"
+    lists_col{{"Whale", "" /*NULL*/, "Polar Bear"}, null_at(1)}  // key = "unknown"
+  };
+  auto const lists2 = lists_col{
+    lists_col{"Green", "Yellow"},                       // key = "banana"
+    lists_col{},                                        // key = "unknown"
+    lists_col{{"" /*NULL*/, "" /*NULL*/}, all_nulls()}  // key = "dog"
+  };
+  auto const lists3 = lists_col{
+    lists_col{"Fuji", "Red Delicious"},                                          // key = "apple"
+    lists_col{{"" /*NULL*/, "German Shepherd", "" /*NULL*/}, nulls_at({0, 2})},  // key = "dog"
+    lists_col{{"Seeedless", "Mini"}, no_nulls()}  // key = "water melon"
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_lists(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = strings_col{"apple", "banana", "dog", "unknown", "water melon"};
+  auto const expected_lists = lists_col{
+    lists_col{"Fuji", "Red Delicious"},  // key = "apple"
+    lists_col{"Green", "Yellow"},        // key = "banana"
+    lists_col{{
+                "Poodle",
+                "Golden Retriever",
+                "Corgi",
+                "" /*NULL*/,
+                "" /*NULL*/,
+                "" /*NULL*/,
+                "German Shepherd",
+                "" /*NULL*/
+              },
+              nulls_at({3, 4, 5, 7})},                            // key = "dog"
+    lists_col{{"Whale", "" /*NULL*/, "Polar Bear"}, null_at(1)},  // key = "unknown"
+    lists_col{{"Seeedless", "Mini"}, no_nulls()}                  // key = "water melon"
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
diff --git a/cpp/tests/groupby/merge_m2_tests.cpp b/cpp/tests/groupby/merge_m2_tests.cpp
new file mode 100644
index 0000000..1087410
--- /dev/null
+++ b/cpp/tests/groupby/merge_m2_tests.cpp
@@ -0,0 +1,477 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/table/table_view.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};                                       // Mark for null elements
+constexpr double NaN{std::numeric_limits<double>::quiet_NaN()};  // Mark for NaN double elements
+
+template <class T>
+using keys_col = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+template <class T>
+using vals_col = cudf::test::fixed_width_column_wrapper<T>;
+
+using counts_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+template <class T>
+using means_col = cudf::test::fixed_width_column_wrapper<T>;
+
+template <class T>
+using M2s_col = cudf::test::fixed_width_column_wrapper<T>;
+
+using structs_col = cudf::test::structs_column_wrapper;
+using vcol_views  = std::vector<cudf::column_view>;
+
+/**
+ * @brief Compute `COUNT_VALID`, `MEAN`, `M2` aggregations for the given values columns.
+ * @return A pair of unique keys column and a structs column containing the computed values of
+ *         (`COUNT_VALID`, `MEAN`, `M2`).
+ */
+auto compute_partial_results(cudf::column_view const& keys, cudf::column_view const& values)
+{
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = values;
+  requests[0].aggregations.emplace_back(cudf::make_count_aggregation<cudf::groupby_aggregation>());
+  requests[0].aggregations.emplace_back(cudf::make_mean_aggregation<cudf::groupby_aggregation>());
+  requests[0].aggregations.emplace_back(cudf::make_m2_aggregation<cudf::groupby_aggregation>());
+
+  auto gb_obj                  = cudf::groupby::groupby(cudf::table_view({keys}));
+  auto [out_keys, out_results] = gb_obj.aggregate(requests);
+
+  auto const num_output_rows = out_keys->num_rows();
+  return std::pair(std::move(out_keys->release()[0]),
+                   cudf::make_structs_column(
+                     num_output_rows, std::move(out_results[0].results), 0, rmm::device_buffer{}));
+}
+
+/**
+ * @brief Perform merging for partial results of M2 aggregations.
+ *
+ * @return A pair of unique keys column and a structs column containing the merged values of
+ *         (`COUNT_VALID`, `MEAN`, `M2`).
+ */
+auto merge_M2(vcol_views const& keys_cols, vcol_views const& values_cols)
+{
+  // Append all the keys and values together.
+  auto const keys   = cudf::concatenate(keys_cols);
+  auto const values = cudf::concatenate(values_cols);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = *values;
+  requests[0].aggregations.emplace_back(
+    cudf::make_merge_m2_aggregation<cudf::groupby_aggregation>());
+
+  auto gb_obj = cudf::groupby::groupby(cudf::table_view({*keys}));
+  auto result = gb_obj.aggregate(requests);
+  return std::pair(std::move(result.first->release()[0]), std::move(result.second[0].results[0]));
+}
+}  // namespace
+
+template <class T>
+struct GroupbyMergeM2TypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes = cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t>,
+                                     cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(GroupbyMergeM2TypedTest, TestTypes);
+
+TYPED_TEST(GroupbyMergeM2TypedTest, InvalidInput)
+{
+  using T = TypeParam;
+
+  auto const keys = keys_col<T>{1, 2, 3};
+
+  // The input column must be a structs column.
+  {
+    auto const values = keys_col<T>{1, 2, 3};
+    EXPECT_THROW(merge_M2({keys}, {values}), cudf::logic_error);
+  }
+
+  // The input column must be a structs column having 3 children.
+  {
+    auto vals1      = keys_col<T>{1, 2, 3};
+    auto vals2      = vals_col<double>{1.0, 2.0, 3.0};
+    auto const vals = structs_col{vals1, vals2};
+    EXPECT_THROW(merge_M2({keys}, {vals}), cudf::logic_error);
+  }
+
+  // The input column must be a structs column having types (int32_t, double, double).
+  {
+    auto vals1      = keys_col<T>{1, 2, 3};
+    auto vals2      = keys_col<T>{1, 2, 3};
+    auto vals3      = keys_col<T>{1, 2, 3};
+    auto const vals = structs_col{vals1, vals2, vals3};
+    EXPECT_THROW(merge_M2({keys}, {vals}), cudf::logic_error);
+  }
+}
+
+TYPED_TEST(GroupbyMergeM2TypedTest, EmptyInput)
+{
+  using T      = TypeParam;
+  using M2_t   = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+  using mean_t = cudf::detail::target_type_t<T, cudf::aggregation::MEAN>;
+
+  auto const keys = keys_col<T>{};
+  auto vals_count = counts_col{};
+  auto vals_mean  = means_col<mean_t>{};
+  auto vals_M2    = M2s_col<M2_t>{};
+  auto const vals = structs_col{vals_count, vals_mean, vals_M2};
+
+  auto const [out_keys, out_vals] = merge_M2({keys}, {vals});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(vals, *out_vals, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeM2TypedTest, SimpleInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // Full dataset:
+  //
+  // keys = [1, 2, 3, 1, 2, 2, 1, 3, 3, 2]
+  // vals = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  //
+  // key = 1: vals = [0, 3, 6]
+  // key = 2: vals = [1, 4, 5, 9]
+  // key = 3: vals = [2, 7, 8]
+
+  // Partitioned datasets:
+  auto const keys1 = keys_col<T>{1, 2, 3};
+  auto const keys2 = keys_col<T>{1, 2, 2};
+  auto const keys3 = keys_col<T>{1, 3, 3, 2};
+
+  auto const vals1 = vals_col<T>{0, 1, 2};
+  auto const vals2 = vals_col<T>{3, 4, 5};
+  auto const vals3 = vals_col<T>{6, 7, 8, 9};
+
+  // The expected results to validate.
+  auto const expected_keys = keys_col<T>{1, 2, 3};
+  auto const expected_M2s  = M2s_col<R>{18.0, 32.75, 20.0 + 2.0 / 3.0};
+
+  // Compute partial results (`COUNT_VALID`, `MEAN`, `M2`) of each dataset.
+  // The partial results are also assembled into a structs column.
+  auto const [out1_keys, out1_vals] = compute_partial_results(keys1, vals1);
+  auto const [out2_keys, out2_vals] = compute_partial_results(keys2, vals2);
+  auto const [out3_keys, out3_vals] = compute_partial_results(keys3, vals3);
+
+  // Merge the partial results to the final results.
+  // Merging can be done in just one merge step, or in multiple steps.
+
+  // Multiple steps merging:
+  {
+    auto const [out4_keys, out4_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys}, vcol_views{*out1_vals, *out2_vals});
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out3_keys, *out4_keys}, vcol_views{*out3_vals, *out4_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+
+  // One step merging:
+  {
+    auto const [final_keys, final_vals] = merge_M2(vcol_views{*out1_keys, *out2_keys, *out3_keys},
+                                                   vcol_views{*out1_vals, *out2_vals, *out3_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+}
+
+TYPED_TEST(GroupbyMergeM2TypedTest, SimpleInputHavingNegativeValues)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // Full dataset:
+  //
+  // keys = [1, 2,  3, 1,  2,  2,  1, 3,  3, 2]
+  // vals = [0, 1, -2, 3, -4, -5, -6, 7, -8, 9]
+  //
+  // key = 1: vals = [0,  3, -6]
+  // key = 2: vals = [1, -4, -5, 9]
+  // key = 3: vals = [-2, 7, -8]
+
+  // Partitioned datasets:
+  auto const keys1 = keys_col<T>{1, 2, 3};
+  auto const keys2 = keys_col<T>{1, 2, 2};
+  auto const keys3 = keys_col<T>{1, 3, 3, 2};
+
+  auto const vals1 = vals_col<T>{0, 1, -2};
+  auto const vals2 = vals_col<T>{3, -4, -5};
+  auto const vals3 = vals_col<T>{-6, 7, -8, 9};
+
+  // The expected results to validate.
+  auto const expected_keys = keys_col<T>{1, 2, 3};
+  auto const expected_M2s  = M2s_col<R>{42.0, 122.75, 114.0};
+
+  // Compute partial results (`COUNT_VALID`, `MEAN`, `M2`) of each dataset.
+  // The partial results are also assembled into a structs column.
+  auto const [out1_keys, out1_vals] = compute_partial_results(keys1, vals1);
+  auto const [out2_keys, out2_vals] = compute_partial_results(keys2, vals2);
+  auto const [out3_keys, out3_vals] = compute_partial_results(keys3, vals3);
+
+  // Merge the partial results to the final results.
+  // Merging can be done in just one merge step, or in multiple steps.
+
+  // Multiple steps merging:
+  {
+    auto const [out4_keys, out4_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys}, vcol_views{*out1_vals, *out2_vals});
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out3_keys, *out4_keys}, vcol_views{*out3_vals, *out4_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+
+  // One step merging:
+  {
+    auto const [final_keys, final_vals] = merge_M2(vcol_views{*out1_keys, *out2_keys, *out3_keys},
+                                                   vcol_views{*out1_vals, *out2_vals, *out3_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+}
+
+TYPED_TEST(GroupbyMergeM2TypedTest, InputHasNulls)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // Full dataset:
+  //
+  // keys = [1,    2, 3, 1, 2, 2,    1, null, 3, 2, 4]
+  // vals = [null, 1, 2, 3, 4, null, 6, 7,    8, 9, null]
+  //
+  // key = 1: vals = [null, 3, 6]
+  // key = 2: vals = [1, 4, null, 9]
+  // key = 3: vals = [2, 8]
+  // key = 4: vals = [null]
+
+  // Partitioned datasets:
+  auto const keys1 = keys_col<T>{1, 2, 3, 1};
+  auto const keys2 = keys_col<T>{{2, 2, 1, null}, null_at(3)};
+  auto const keys3 = keys_col<T>{3, 2, 4};
+
+  auto const vals1 = vals_col<T>{{null, 1, 2, 3}, null_at(0)};
+  auto const vals2 = vals_col<T>{{4, null, 6, 7}, null_at(1)};
+  auto const vals3 = vals_col<T>{{8, 9, null}, null_at(2)};
+
+  // The expected results to validate.
+  auto const expected_keys = keys_col<T>{1, 2, 3, 4};
+  auto const expected_M2s  = M2s_col<R>{{4.5, 32.0 + 2.0 / 3.0, 18.0, 0.0 /*NULL*/}, null_at(3)};
+
+  // Compute partial results (`COUNT_VALID`, `MEAN`, `M2`) of each dataset.
+  // The partial results are also assembled into a structs column.
+  auto const [out1_keys, out1_vals] = compute_partial_results(keys1, vals1);
+  auto const [out2_keys, out2_vals] = compute_partial_results(keys2, vals2);
+  auto const [out3_keys, out3_vals] = compute_partial_results(keys3, vals3);
+
+  // Merge the partial results to the final results.
+  // Merging can be done in just one merge step, or in multiple steps.
+
+  // Multiple steps merging:
+  {
+    auto const [out4_keys, out4_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys}, vcol_views{*out1_vals, *out2_vals});
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out3_keys, *out4_keys}, vcol_views{*out3_vals, *out4_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+
+  // One step merging:
+  {
+    auto const [final_keys, final_vals] = merge_M2(vcol_views{*out1_keys, *out2_keys, *out3_keys},
+                                                   vcol_views{*out1_vals, *out2_vals, *out3_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+}
+
+TYPED_TEST(GroupbyMergeM2TypedTest, InputHaveNullsAndNaNs)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // Full dataset:
+  //
+  // keys = [4, 3, 1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4, 4]
+  // vals = [null, null, 0.0, 1.0, 2.0, 3.0, 4.0, NaN, 6.0, 7.0, 8.0, 9.0, 10.0, NaN]
+  //
+  // key = 1: vals = [0, 3, 6]
+  // key = 2: vals = [1, 4, NaN, 9]
+  // key = 3: vals = [null, 2, 8]
+  // key = 4: vals = [null, 10, NaN]
+
+  // Partitioned datasets:
+  auto const keys1 = keys_col<T>{4, 3, 1, 2};
+  auto const keys2 = keys_col<T>{3, 1, 2};
+  auto const keys3 = keys_col<T>{{2, 1, null}, null_at(2)};
+  auto const keys4 = keys_col<T>{3, 2, 4, 4};
+
+  auto const vals1 = vals_col<double>{{0.0 /*NULL*/, 0.0 /*NULL*/, 0.0, 1.0}, nulls_at({0, 1})};
+  auto const vals2 = vals_col<double>{2.0, 3.0, 4.0};
+  auto const vals3 = vals_col<double>{NaN, 6.0, 7.0};
+  auto const vals4 = vals_col<double>{8.0, 9.0, 10.0, NaN};
+
+  // The expected results to validate.
+  auto const expected_keys = keys_col<T>{1, 2, 3, 4};
+  auto const expected_M2s  = M2s_col<R>{18.0, NaN, 18.0, NaN};
+
+  // Compute partial results (`COUNT_VALID`, `MEAN`, `M2`) of each dataset.
+  // The partial results are also assembled into a structs column.
+  auto const [out1_keys, out1_vals] = compute_partial_results(keys1, vals1);
+  auto const [out2_keys, out2_vals] = compute_partial_results(keys2, vals2);
+  auto const [out3_keys, out3_vals] = compute_partial_results(keys3, vals3);
+  auto const [out4_keys, out4_vals] = compute_partial_results(keys4, vals4);
+
+  // Merge the partial results to the final results.
+  // Merging can be done in just one merge step, or in multiple steps.
+
+  // Multiple steps merging:
+  {
+    auto const [out5_keys, out5_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys}, vcol_views{*out1_vals, *out2_vals});
+    auto const [out6_keys, out6_vals] =
+      merge_M2(vcol_views{*out3_keys, *out4_keys}, vcol_views{*out3_vals, *out4_vals});
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out5_keys, *out6_keys}, vcol_views{*out5_vals, *out6_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+
+  // One step merging:
+  {
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys, *out3_keys, *out4_keys},
+               vcol_views{*out1_vals, *out2_vals, *out3_vals, *out4_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+}
+
+TYPED_TEST(GroupbyMergeM2TypedTest, SlicedColumnsInput)
+{
+  using T = TypeParam;
+  using R = cudf::detail::target_type_t<T, cudf::aggregation::M2>;
+
+  // This test should compute M2 aggregation on the same dataset as the InputHaveNullsAndNaNs test.
+  // i.e.:
+  //
+  // keys = [4, 3, 1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4, 4]
+  // vals = [null, null, 0.0, 1.0, 2.0, 3.0, 4.0, NaN, 6.0, 7.0, 8.0, 9.0, 10.0, NaN]
+  //
+  // key = 1: vals = [0, 3, 6]
+  // key = 2: vals = [1, 4, NaN, 9]
+  // key = 3: vals = [null, 2, 8]
+  // key = 4: vals = [null, 10, NaN]
+
+  auto const keys_original =
+    keys_col<T>{{
+                  1, 2, 3, 4, 5, 1, 2, 3, 4, 5,                 // will not use, don't care
+                  4, 3, 1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4, 4,  // use this
+                  1, 2, 3, 4, 5, 1, 2, 3, 4, 5                  // will not use, don't care
+                },
+                null_at(19)};
+  auto const vals_original = vals_col<double>{
+    {
+      3.0, 2.0,  5.0,  4.0,  6.0, 9.0, 1.0, 0.0,  1.0,  7.0,  // will not use, don't care
+      0.0, 0.0,  0.0,  1.0,  2.0, 3.0, 4.0, NaN,  6.0,  7.0, 8.0, 9.0, 10.0, NaN,  // use this
+      9.0, 10.0, 11.0, 12.0, 0.0, 5.0, 1.0, 20.0, 19.0, 15.0  // will not use, don't care
+    },
+    nulls_at({10, 11})};
+
+  // Partitioned datasets, taken from the original dataset in the range [10, 24).
+  auto const keys1 = cudf::slice(keys_original, {10, 14})[0];  // {4, 3, 1, 2}
+  auto const keys2 = cudf::slice(keys_original, {14, 17})[0];  // {3, 1, 2}
+  auto const keys3 = cudf::slice(keys_original, {17, 20})[0];  // {2, 1, null}
+  auto const keys4 = cudf::slice(keys_original, {20, 24})[0];  // {3, 2, 4, 4}
+
+  auto const vals1 = cudf::slice(vals_original, {10, 14})[0];  // {null, null, 0.0, 1.0}
+  auto const vals2 = cudf::slice(vals_original, {14, 17})[0];  // {2.0, 3.0, 4.0}
+  auto const vals3 = cudf::slice(vals_original, {17, 20})[0];  // {NaN, 6.0, 7.0}
+  auto const vals4 = cudf::slice(vals_original, {20, 24})[0];  // {8.0, 9.0, 10.0, NaN}
+
+  // The expected results to validate.
+  auto const expected_keys = keys_col<T>{1, 2, 3, 4};
+  auto const expected_M2s  = M2s_col<R>{18.0, NaN, 18.0, NaN};
+
+  // Compute partial results (`COUNT_VALID`, `MEAN`, `M2`) of each dataset.
+  // The partial results are also assembled into a structs column.
+  auto const [out1_keys, out1_vals] = compute_partial_results(keys1, vals1);
+  auto const [out2_keys, out2_vals] = compute_partial_results(keys2, vals2);
+  auto const [out3_keys, out3_vals] = compute_partial_results(keys3, vals3);
+  auto const [out4_keys, out4_vals] = compute_partial_results(keys4, vals4);
+
+  // Merge the partial results to the final results.
+  // Merging can be done in just one merge step, or in multiple steps.
+
+  // Multiple steps merging:
+  {
+    auto const [out5_keys, out5_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys}, vcol_views{*out1_vals, *out2_vals});
+    auto const [out6_keys, out6_vals] =
+      merge_M2(vcol_views{*out3_keys, *out4_keys}, vcol_views{*out3_vals, *out4_vals});
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out5_keys, *out6_keys}, vcol_views{*out5_vals, *out6_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+
+  // One step merging:
+  {
+    auto const [final_keys, final_vals] =
+      merge_M2(vcol_views{*out1_keys, *out2_keys, *out3_keys, *out4_keys},
+               vcol_views{*out1_vals, *out2_vals, *out3_vals, *out4_vals});
+
+    auto const out_M2s = final_vals->child(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_keys, *final_keys, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_M2s, out_M2s, verbosity);
+  }
+}
diff --git a/cpp/tests/groupby/merge_sets_tests.cpp b/cpp/tests/groupby/merge_sets_tests.cpp
new file mode 100644
index 0000000..5fc7e68
--- /dev/null
+++ b/cpp/tests/groupby/merge_sets_tests.cpp
@@ -0,0 +1,358 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};  // Mark for null elements
+
+using vcol_views = std::vector<cudf::column_view>;
+
+auto merge_sets(vcol_views const& keys_cols, vcol_views const& values_cols)
+{
+  // Append all the keys and lists together.
+  auto const keys   = cudf::concatenate(keys_cols);
+  auto const values = cudf::concatenate(values_cols);
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = *values;
+  requests[0].aggregations.emplace_back(
+    cudf::make_merge_sets_aggregation<cudf::groupby_aggregation>());
+
+  auto const result      = cudf::groupby::groupby(cudf::table_view({*keys})).aggregate(requests);
+  auto const result_keys = result.first->view();                 // <== table_view of 1 column
+  auto const result_vals = result.second[0].results[0]->view();  // <== column_view
+
+  // Sort the output columns based on the output keys.
+  // This is to facilitate comparison of the output with the expected columns.
+  auto keys_vals_sorted = cudf::sort_by_key(cudf::table_view{{result_keys.column(0), result_vals}},
+                                            result_keys,
+                                            {},
+                                            {cudf::null_order::AFTER})
+                            ->release();
+
+  // After the columns were reordered, individual rows of the output values column (which are lists)
+  // also need to be sorted.
+  auto out_values =
+    cudf::lists::sort_lists(cudf::lists_column_view{keys_vals_sorted.back()->view()},
+                            cudf::order::ASCENDING,
+                            cudf::null_order::AFTER);
+
+  return std::pair(std::move(keys_vals_sorted.front()), std::move(out_values));
+}
+
+}  // namespace
+
+template <typename V>
+struct GroupbyMergeSetsTypedTest : public cudf::test::BaseFixture {};
+
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(GroupbyMergeSetsTypedTest, FixedWidthTypesNotBool);
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, InvalidInput)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys = keys_col{1, 2, 3};
+
+  // The input lists column must NOT be nullable.
+  auto const lists = lists_col{{lists_col{1}, lists_col{} /*NULL*/, lists_col{2}}, null_at(1)};
+  EXPECT_THROW(merge_sets({keys}, {lists}), cudf::logic_error);
+
+  // The input column must be a lists column.
+  auto const non_lists = keys_col{1, 2, 3, 4, 5};
+  EXPECT_THROW(merge_sets({keys}, {non_lists}), cudf::logic_error);
+}
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, EmptyInput)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Keys and lists columns are all empty.
+  auto const keys   = keys_col{};
+  auto const lists0 = lists_col{{1, 2, 3}, {4, 5, 6}};
+  auto const lists  = cudf::empty_like(lists0);
+
+  auto const [out_keys, out_lists] = merge_sets(vcol_views{keys}, vcol_views{*lists});
+  auto const expected_keys         = keys_col{};
+  auto const expected_lists        = cudf::empty_like(lists0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, InputWithoutNull)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    {1, 2, 3, 4, 5, 6},       // key = 1
+    {10, 11, 12, 13, 14, 15}  // key = 2
+  };
+  auto const lists2 = lists_col{
+    {4, 5, 6, 7, 8, 9},       // key = 1
+    {20, 21, 22, 23, 24, 25}  // key = 3
+  };
+  auto const lists3 = lists_col{
+    {11, 12},                  // key = 2
+    {23, 24, 25, 26, 27, 28},  // key = 3
+    {30, 31, 32}               // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_sets(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    {1, 2, 3, 4, 5, 6, 7, 8, 9},           // key = 1
+    {10, 11, 12, 13, 14, 15},              // key = 2
+    {20, 21, 22, 23, 24, 25, 26, 27, 28},  // key = 3
+    {30, 31, 32}                           // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, InputHasNulls)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    lists_col{{1, null, null, null, 5, 6}, nulls_at({1, 2, 3})},  // key = 1
+    lists_col{10, 11, 12, 13, 14, 15}                             // key = 2
+  };
+  auto const lists2 = lists_col{
+    lists_col{{null, null, 6, 7, 8, 9}, nulls_at({0, 1})},  // key = 1
+    lists_col{{null, 21, 22, 23, 24, 25}, null_at(0)}       // key = 3
+  };
+  auto const lists3 = lists_col{
+    lists_col{11, 12},                     // key = 2
+    lists_col{23, 24, 25, 26, 27, 28},     // key = 3
+    lists_col{{30, null, 32}, null_at(1)}  // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_sets(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    lists_col{{1, 5, 6, 7, 8, 9, null}, null_at(6)},                // key = 1
+    lists_col{10, 11, 12, 13, 14, 15},                              // key = 2
+    lists_col{{21, 22, 23, 24, 25, 26, 27, 28, null}, null_at(8)},  // key = 3
+    lists_col{{30, 32, null}, null_at(2)}                           // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, InputHasEmptyLists)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2};
+  auto const keys2 = keys_col{1, 3};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    {1, 2, 3},  // key = 1
+    {}          // key = 2
+  };
+  auto const lists2 = lists_col{
+    {0, 1, 2, 3, 4, 5},       // key = 1
+    {11, 12, 12, 12, 12, 12}  // key = 3
+  };
+  auto const lists3 = lists_col{
+    {},           // key = 2
+    {},           // key = 3
+    {24, 25, 26}  // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_sets(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    {0, 1, 2, 3, 4, 5},  // key = 1
+    {},                  // key = 2
+    {11, 12},            // key = 3
+    {24, 25, 26}         // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, InputHasNullsAndEmptyLists)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1 = keys_col{1, 2, 3};
+  auto const keys2 = keys_col{1, 3, 4};
+  auto const keys3 = keys_col{2, 3, 4};
+
+  auto const lists1 = lists_col{
+    lists_col{{null, 1, 2, 3}, null_at(0)},  // key = 1
+    lists_col{},                             // key = 2
+    lists_col{}                              // key = 3
+  };
+  auto const lists2 = lists_col{
+    lists_col{0, 1, 2, 3, 4, 5},                                        // key = 1
+    lists_col{{null, 11, null, 12, 12, 12, 12, 12}, nulls_at({0, 2})},  // key = 3
+    lists_col{20}                                                       // key = 4
+  };
+  auto const lists3 = lists_col{
+    lists_col{},                                                    // key = 2
+    lists_col{},                                                    // key = 3
+    lists_col{{24, 25, null, null, null, 26}, nulls_at({2, 3, 4})}  // key = 4
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_sets(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    lists_col{{0, 1, 2, 3, 4, 5, null}, null_at(6)},  // key = 1
+    lists_col{},                                      // key = 2
+    lists_col{{11, 12, null}, null_at(2)},            // key = 3
+    lists_col{{20, 24, 25, 26, null}, null_at(4)}     // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+TYPED_TEST(GroupbyMergeSetsTypedTest, SlicedColumnsInput)
+{
+  using keys_col  = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const keys1_original = keys_col{1, 2, 4, 5, 6, 7, 8, 9, 10};
+  auto const keys2_original = keys_col{0, 0, 1, 1, 1, 3, 4, 5, 6};
+  auto const keys3_original = keys_col{0, 1, 2, 3, 4, 5, 6, 7, 8};
+
+  auto const keys1 = cudf::slice(keys1_original, {0, 2})[0];  // { 1, 2 }
+  auto const keys2 = cudf::slice(keys2_original, {4, 6})[0];  // { 1, 3 }
+  auto const keys3 = cudf::slice(keys3_original, {2, 5})[0];  // { 2, 3, 4 }
+
+  auto const lists1_original = lists_col{
+    {10, 11, 12, 10, 11, 12, 10, 11, 12},
+    {12, 13, 12, 13, 12, 13, 12, 13, 14},
+    {1, 2, 3, 1, 2, 3, 1, 2, 3},  // key = 1
+    {4, 5, 6, 4, 5, 6, 4, 5, 6}   // key = 2
+  };
+  auto const lists2_original = lists_col{{1, 1, 1, 1, 1, 1, 1, 2},
+                                         {10, 11, 11, 11, 11, 11, 12},                  // key = 1
+                                         {11, 12, 13, 12, 13, 12, 13, 12, 13, 14, 15},  // key = 3
+                                         {13, 14, 15},
+                                         {14, 15, 16},
+                                         {15, 16}};
+  auto const lists3_original = lists_col{{20, 21, 20, 21, 20, 21, 20, 21, 22},  // key = 2
+                                         {23, 24, 25, 23, 24, 25},              // key = 3
+                                         {24, 25, 26},                          // key = 4
+                                         {1, 2, 3, 4, 5}};
+
+  auto const lists1 = cudf::slice(lists1_original, {2, 4})[0];
+  auto const lists2 = cudf::slice(lists2_original, {1, 3})[0];
+  auto const lists3 = cudf::slice(lists3_original, {0, 3})[0];
+
+  auto const [out_keys, out_lists] =
+    merge_sets(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = keys_col{1, 2, 3, 4};
+  auto const expected_lists = lists_col{
+    {1, 2, 3, 10, 11, 12},             // key = 1
+    {4, 5, 6, 20, 21, 22},             // key = 2
+    {11, 12, 13, 14, 15, 23, 24, 25},  // key = 3
+    {24, 25, 26}                       // key = 4
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
+
+struct GroupbyMergeSetsTest : public cudf::test::BaseFixture {};
+
+TEST_F(GroupbyMergeSetsTest, StringsColumnInput)
+{
+  using strings_col = cudf::test::strings_column_wrapper;
+  using lists_col   = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const keys1 = strings_col{"apple", "dog", "unknown"};
+  auto const keys2 = strings_col{"banana", "unknown", "dog"};
+  auto const keys3 = strings_col{"apple", "dog", "water melon"};
+
+  auto const lists1 = lists_col{
+    lists_col{"Fuji", "Honey Bee"},                              // key = "apple"
+    lists_col{"Poodle", "Golden Retriever", "Corgi"},            // key = "dog"
+    lists_col{{"Whale", "" /*NULL*/, "Polar Bear"}, null_at(1)}  // key = "unknown"
+  };
+  auto const lists2 = lists_col{
+    lists_col{"Green", "Yellow"},                                    // key = "banana"
+    lists_col{},                                                     // key = "unknown"
+    lists_col{{"" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, all_nulls()}  // key = "dog"
+  };
+  auto const lists3 = lists_col{
+    lists_col{"Fuji", "Red Delicious"},  // key = "apple"
+    lists_col{{"" /*NULL*/, "Corgi", "German Shepherd", "" /*NULL*/, "Golden Retriever"},
+              nulls_at({0, 3})},                  // key = "dog"
+    lists_col{{"Seeedless", "Mini"}, no_nulls()}  // key = "water melon"
+  };
+
+  auto const [out_keys, out_lists] =
+    merge_sets(vcol_views{keys1, keys2, keys3}, vcol_views{lists1, lists2, lists3});
+  auto const expected_keys  = strings_col{"apple", "banana", "dog", "unknown", "water melon"};
+  auto const expected_lists = lists_col{
+    lists_col{"Fuji", "Honey Bee", "Red Delicious"},  // key = "apple"
+    lists_col{"Green", "Yellow"},                     // key = "banana"
+    lists_col{{
+                "Corgi", "German Shepherd", "Golden Retriever", "Poodle", "" /*NULL*/
+              },
+              null_at(4)},                                        // key = "dog"
+    lists_col{{"Polar Bear", "Whale", "" /*NULL*/}, null_at(2)},  // key = "unknown"
+    lists_col{{"Mini", "Seeedless"}, no_nulls()}                  // key = "water melon"
+  };
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_keys, *out_keys, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lists, *out_lists, verbosity);
+}
diff --git a/cpp/tests/groupby/min_scan_tests.cpp b/cpp/tests/groupby/min_scan_tests.cpp
new file mode 100644
index 0000000..035f8e3
--- /dev/null
+++ b/cpp/tests/groupby/min_scan_tests.cpp
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using key_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+template <typename T>
+struct groupby_min_scan_test : public cudf::test::BaseFixture {
+  using V              = T;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::MIN>;
+  using value_wrapper  = cudf::test::fixed_width_column_wrapper<V, int32_t>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+};
+
+TYPED_TEST_SUITE(groupby_min_scan_test, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(groupby_min_scan_test, basic)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys   {1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  value_wrapper vals({5, 6, 7, 8, 9, 0, 1, 2, 3, 4});
+
+  key_wrapper expect_keys    {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  result_wrapper expect_vals({5, 5, 1, 6, 6, 0, 0, 7, 2, 2});
+  // clang-format on
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_min_scan_test, pre_sorted)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys   {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  value_wrapper vals({5, 8, 1, 6, 9, 0, 4, 7, 2, 3});
+
+  key_wrapper expect_keys    {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  result_wrapper expect_vals({5, 5, 1, 6, 6, 0, 0, 7, 2, 2});
+  // clang-format on
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys,
+                   vals,
+                   expect_keys,
+                   expect_vals,
+                   std::move(agg),
+                   cudf::null_policy::EXCLUDE,
+                   cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_min_scan_test, empty_cols)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys{};
+  value_wrapper vals{};
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_min_scan_test, zero_valid_keys)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys({1, 2, 3}, cudf::test::iterators::all_nulls());
+  value_wrapper vals({3, 4, 5});
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_min_scan_test, zero_valid_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys{1, 1, 1};
+  value_wrapper vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+  key_wrapper expect_keys{1, 1, 1};
+  result_wrapper expect_vals({-1, -1, -1}, cudf::test::iterators::all_nulls());
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_min_scan_test, null_keys_and_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys(  {1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4}, {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  value_wrapper vals({5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 4}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+                         //  { 1, 1, 1, 2, 2,  2, 2, 3, _, 3, 4}
+  key_wrapper expect_keys(   { 1, 1, 1, 2, 2,  2, 2, 3,    3, 4}, cudf::test::iterators::no_nulls());
+                         //  { _, 8, 1, 6, 9,  _, 4, 7, 2, 3, _}
+  result_wrapper expect_vals({-1, 8, 1, 6, 6, -1, 4, 7,    3, -1},
+                             { 0, 1, 1, 1, 1,  0, 1, 1,    1, 0});
+  // clang-format on
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+struct groupby_min_scan_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_min_scan_string_test, basic)
+{
+  key_wrapper keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{
+    "año", "bit", "₹1", "aaa", "zit", "bat", "aaa", "$1", "₹1", "wut"};
+
+  key_wrapper expect_keys{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  cudf::test::strings_column_wrapper expect_vals(
+    {"año", "aaa", "aaa", "bit", "bit", "bat", "bat", "₹1", "$1", "$1"});
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+template <typename T>
+struct GroupByMinScanFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupByMinScanFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupByMinScanFixedPointTest, GroupBySortMinScanDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+
+    // clang-format off
+    auto const keys = key_wrapper{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals = fp_wrapper{{5, 6, 7, 8, 9, 0, 1, 2, 3, 4}, scale};
+
+    // {5, 8, 1, 6, 9, 0, 4, 7, 2, 3}
+    auto const expect_keys     = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+    auto const expect_vals_min = fp_wrapper{{5, 5, 1, 6, 6, 0, 0, 7, 2, 2}, scale};
+    // clang-format on
+
+    auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+    test_single_scan(keys, vals, expect_keys, expect_vals_min, std::move(agg));
+  }
+}
+
+struct groupby_min_scan_struct_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_min_scan_struct_test, basic)
+{
+  auto const keys = key_wrapper{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expect_keys = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "aaa", "aaa", "bit", "bit", "bat", "bat", "₹1", "$1", "$1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 4, 4, 2, 2, 6, 6, 3, 8, 8};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_min_scan_struct_test, slice_input)
+{
+  constexpr int32_t dont_care{1};
+  auto const keys_original =
+    key_wrapper{dont_care, dont_care, 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, dont_care};
+  auto const vals_original = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"dont_care",
+                                                     "dont_care",
+                                                     "año",
+                                                     "bit",
+                                                     "₹1",
+                                                     "aaa",
+                                                     "zit",
+                                                     "bat",
+                                                     "aab",
+                                                     "$1",
+                                                     "€1",
+                                                     "wut",
+                                                     "dont_care"};
+    auto child2 = key_wrapper{dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const keys        = cudf::slice(keys_original, {2, 12})[0];
+  auto const vals        = cudf::slice(vals_original, {2, 12})[0];
+  auto const expect_keys = key_wrapper{1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "aaa", "aaa", "bit", "bit", "bat", "bat", "₹1", "$1", "$1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 4, 4, 2, 2, 6, 6, 3, 8, 8};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_min_scan_struct_test, null_keys_and_values)
+{
+  constexpr int32_t null{0};
+  auto const keys =
+    key_wrapper{{1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, cudf::test::iterators::null_at(7)};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "" /*NULL*/, "" /*NULL*/, "$1", "€1", "wut", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 8, 7, 6, 5, null, null, 2, 1, 0, null};
+    return cudf::test::structs_column_wrapper{{child1, child2},
+                                              cudf::test::iterators::nulls_at({5, 6, 10})};
+  }();
+
+  auto const expect_keys =
+    key_wrapper{{1, 1, 1, 2, 2, 2, 2, 3, 3, 4}, cudf::test::iterators::no_nulls()};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "aaa", "" /*NULL*/, "bit", "bit", "" /*NULL*/, "bit", "₹1", "€1", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 6, null, 8, 8, null, 8, 7, 1, null};
+    return cudf::test::structs_column_wrapper{{child1, child2},
+                                              cudf::test::iterators::nulls_at({2, 5, 9})};
+  }();
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
diff --git a/cpp/tests/groupby/min_tests.cpp b/cpp/tests/groupby/min_tests.cpp
new file mode 100644
index 0000000..44f9b70
--- /dev/null
+++ b/cpp/tests/groupby/min_tests.cpp
@@ -0,0 +1,547 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+
+#include <limits>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_min_test : public cudf::test::BaseFixture {};
+
+using K = int32_t;
+TYPED_TEST_SUITE(groupby_min_test, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(groupby_min_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0, 1, 2});
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_min_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_min_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_min_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_min_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::MIN>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //  { 3, 6,     1, 4, 9,   2, 8,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3, 1, 2, 0}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+struct groupby_min_string_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_min_string_test, basic)
+{
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{
+    "año", "bit", "₹1", "aaa", "zit", "bat", "aaa", "$1", "₹1", "wut"};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::strings_column_wrapper expect_vals({"aaa", "bat", "$1"});
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_min_string_test, zero_valid_values)
+{
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::strings_column_wrapper vals({"año", "bit", "₹1"}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::strings_column_wrapper expect_vals({""}, all_nulls());
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_min_string_test, min_sorted_strings)
+{
+  // testcase replicated in issue #8717
+  cudf::test::strings_column_wrapper keys(
+    {"",   "",   "",   "",   "",   "",   "06", "06", "06", "06", "10", "10", "10", "10", "14", "14",
+     "14", "14", "18", "18", "18", "18", "22", "22", "22", "22", "26", "26", "26", "26", "30", "30",
+     "30", "30", "34", "34", "34", "34", "38", "38", "38", "38", "42", "42", "42", "42"},
+    {0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper vals(
+    {"", "", "",   "", "", "", "06", "", "", "", "10", "", "", "", "14", "",
+     "", "", "18", "", "", "", "22", "", "", "", "26", "", "", "", "30", "",
+     "", "", "34", "", "", "", "38", "", "", "", "42", "", "", ""},
+    {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
+     0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0});
+  cudf::test::strings_column_wrapper expect_keys(
+    {"06", "10", "14", "18", "22", "26", "30", "34", "38", "42", ""},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::strings_column_wrapper expect_vals(
+    {"06", "10", "14", "18", "22", "26", "30", "34", "38", "42", ""},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::NO,
+                  cudf::null_policy::INCLUDE,
+                  cudf::sorted::YES);
+}
+
+struct groupby_dictionary_min_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_dictionary_min_test, basic)
+{
+  using V = std::string;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{     1,     2,    3,     1,     2,     2,     1,    3,    3,    2 };
+  cudf::test::dictionary_column_wrapper<V>  vals{ "año", "bit", "₹1", "aaa", "zit", "bat", "aaa", "$1", "₹1", "wut"};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys   {     1,     2,    3 };
+  cudf::test::dictionary_column_wrapper<V>  expect_vals_w({ "aaa", "bat", "$1" });
+  // clang-format on
+
+  auto expect_vals = cudf::dictionary::set_keys(expect_vals_w, vals.keys());
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals->view(),
+                  cudf::make_min_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals->view(),
+                  cudf::make_min_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+TEST_F(groupby_dictionary_min_test, fixed_width)
+{
+  using V = int64_t;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{     1,     2,    3,     1,     2,     2,     1,    3,    3,    2 };
+  cudf::test::dictionary_column_wrapper<V>  vals{ 0xABC, 0xBBB, 0xF1, 0xAAA, 0xFFF, 0xBAA, 0xAAA, 0x01, 0xF1, 0xEEE};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys    {     1,     2,    3 };
+  cudf::test::fixed_width_column_wrapper<V>  expect_vals_w({ 0xAAA, 0xBAA, 0x01 });
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals_w,
+                  cudf::make_min_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals_w,
+                  cudf::make_min_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+template <typename T>
+struct GroupByMinFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupByMinFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupByMinFixedPointTest, GroupBySortMinDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_min = fp_wrapper{{0, 1, 2}, scale};
+
+    auto agg2 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(
+      keys, vals, expect_keys, expect_vals_min, std::move(agg2), force_use_sort_impl::YES);
+  }
+}
+
+TYPED_TEST(GroupByMinFixedPointTest, GroupByHashMinDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using K          = int32_t;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_min = fp_wrapper{{0, 1, 2}, scale};
+
+    auto agg6 = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals_min, std::move(agg6));
+  }
+}
+
+struct groupby_min_struct_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_min_struct_test, basic)
+{
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"aaa", "bat", "$1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{4, 6, 8};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_min_struct_test, slice_input)
+{
+  constexpr int32_t dont_care{1};
+  auto const keys_original = cudf::test::fixed_width_column_wrapper<int32_t>{
+    dont_care, dont_care, 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, dont_care};
+  auto const vals_original = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"dont_care",
+                                                     "dont_care",
+                                                     "año",
+                                                     "bit",
+                                                     "₹1",
+                                                     "aaa",
+                                                     "zit",
+                                                     "bat",
+                                                     "aab",
+                                                     "$1",
+                                                     "€1",
+                                                     "wut",
+                                                     "dont_care"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{
+      dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const keys        = cudf::slice(keys_original, {2, 12})[0];
+  auto const vals        = cudf::slice(vals_original, {2, 12})[0];
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"aaa", "bat", "$1"};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{4, 6, 8};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_min_struct_test, null_keys_and_values)
+{
+  constexpr int32_t null{0};
+  auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4}, null_at(7)};
+  auto const vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{
+      "año", "bit", "₹1", "aaa", "zit", "" /*NULL*/, "" /*NULL*/, "$1", "€1", "wut", "" /*NULL*/};
+    auto child2 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{9, 8, 7, 6, 5, null, null, 2, 1, 0, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, nulls_at({5, 6, 10})};
+  }();
+
+  auto const expect_keys =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, 2, 3, 4}, no_nulls()};
+  auto const expect_vals = [] {
+    auto child1 = cudf::test::strings_column_wrapper{"aaa", "bit", "€1", "" /*NULL*/};
+    auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{6, 8, 1, null};
+    return cudf::test::structs_column_wrapper{{child1, child2}, null_at(3)};
+  }();
+
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TEST_F(groupby_min_struct_test, values_with_null_child)
+{
+  constexpr int32_t null{0};
+  {
+    auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1};
+    auto const vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{-1, null}, null_at(1)};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1};
+    auto const expect_vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{1};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{null}, null_at(0)};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+  }
+
+  {
+    auto const keys = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1};
+    auto const vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{{-1, null}, null_at(1)};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{null, null}, nulls_at({0, 1})};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto const expect_keys = cudf::test::fixed_width_column_wrapper<int32_t>{1};
+    auto const expect_vals = [] {
+      auto child1 = cudf::test::fixed_width_column_wrapper<int32_t>{{null}, null_at(0)};
+      auto child2 = cudf::test::fixed_width_column_wrapper<int32_t>{{null}, null_at(0)};
+      return cudf::test::structs_column_wrapper{child1, child2};
+    }();
+
+    auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+  }
+}
+
+struct groupby_min_list_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_min_list_test, basic)
+{
+  using lists = cudf::test::lists_column_wrapper<int32_t>;
+
+  auto const keys        = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 1, 2};
+  auto const vals        = lists{{1, 2}, {3, 4}, {5, 6, 7}, {0, 8}, {9, 10}};
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3};
+  auto const expect_vals = lists{{0, 8}, {3, 4}, {5, 6, 7}};
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_min_aggregation<cudf::groupby_aggregation>());
+}
+
+TEST_F(groupby_min_list_test, slice_input)
+{
+  using lists = cudf::test::lists_column_wrapper<int32_t>;
+  constexpr int32_t dont_care{1};
+
+  auto const keys_original =
+    cudf::test::fixed_width_column_wrapper<int32_t>{dont_care, 1, 2, 3, 1, 2, dont_care};
+  auto const vals_original =
+    lists{{1, 2, 3, 4, 5} /*dont care*/, {1, 2}, {3, 4}, {5, 6, 7}, {0, 8}, {9, 10}};
+  auto const keys = cudf::slice(keys_original, {1, 6})[0];
+  auto const vals = cudf::slice(vals_original, {1, 6})[0];
+
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3};
+  auto const expect_vals = lists{{0, 8}, {3, 4}, {5, 6, 7}};
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_min_aggregation<cudf::groupby_aggregation>());
+}
+
+TEST_F(groupby_min_list_test, null_keys_and_values)
+{
+  using lists = cudf::test::lists_column_wrapper<int32_t>;
+  constexpr int32_t null{0};
+
+  auto const keys =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, 2, 3, null, 1, 2}, null_at(3)};
+  auto const expect_keys = cudf::test::fixed_width_column_wrapper<int>{{1, 2, 3}, no_nulls()};
+
+  // Null list element.
+  {
+    auto const vals = lists{{{} /*null*/, {1, 2}, {3, 4}, {5, 6, 7}, {0, 8}, {9, 10}}, null_at(0)};
+    auto const expect_vals = lists{{0, 8}, {1, 2}, {3, 4}};
+    test_single_agg(keys,
+                    vals,
+                    expect_keys,
+                    expect_vals,
+                    cudf::make_min_aggregation<cudf::groupby_aggregation>());
+  }
+
+  // Null child element.
+  {
+    auto const vals        = lists{lists{{0, null}, null_at(1)},
+                            lists{1, 2},
+                            lists{3, 4},
+                            lists{5, 6, 7},
+                            lists{0, 8},
+                            lists{9, 10}};
+    auto const expect_vals = lists{lists{{0, null}, null_at(1)}, {1, 2}, {3, 4}};
+    test_single_agg(keys,
+                    vals,
+                    expect_keys,
+                    expect_vals,
+                    cudf::make_min_aggregation<cudf::groupby_aggregation>());
+  }
+}
+
+template <typename V>
+struct groupby_min_floating_point_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_min_floating_point_test, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(groupby_min_floating_point_test, values_with_infinity)
+{
+  using T          = TypeParam;
+  using int32s_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using floats_col = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto constexpr inf = std::numeric_limits<T>::infinity();
+
+  auto const keys = int32s_col{1, 2, 1, 2};
+  auto const vals = floats_col{static_cast<T>(1), static_cast<T>(1), -inf, static_cast<T>(2)};
+
+  auto const expected_keys = int32s_col{1, 2};
+  auto const expected_vals = floats_col{-inf, static_cast<T>(1)};
+
+  // Related issue: https://github.com/rapidsai/cudf/issues/11352
+  // The issue only occurs in sort-based aggregation.
+  auto agg = cudf::make_min_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(
+    keys, vals, expected_keys, expected_vals, std::move(agg), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_min_floating_point_test, values_with_nan)
+{
+  using T          = TypeParam;
+  using int32s_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using floats_col = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto constexpr nan = std::numeric_limits<T>::quiet_NaN();
+
+  auto const keys = int32s_col{1, 1};
+  auto const vals = floats_col{nan, nan};
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back(cudf::groupby::aggregation_request());
+  requests[0].values = vals;
+  requests[0].aggregations.emplace_back(cudf::make_min_aggregation<cudf::groupby_aggregation>());
+
+  // Without properly handling NaN, this will hang forever in hash-based aggregate (which is the
+  // default back-end for min/max in groupby context).
+  // This test is just to verify that the aggregate operation does not hang.
+  auto gb_obj       = cudf::groupby::groupby(cudf::table_view({keys}));
+  auto const result = gb_obj.aggregate(requests);
+
+  EXPECT_EQ(result.first->num_rows(), 1);
+}
diff --git a/cpp/tests/groupby/nth_element_tests.cpp b/cpp/tests/groupby/nth_element_tests.cpp
new file mode 100644
index 0000000..5fb911e
--- /dev/null
+++ b/cpp/tests/groupby/nth_element_tests.cpp
@@ -0,0 +1,515 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_nth_element_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_nth_element_test, cudf::test::AllTypes);
+
+// clang-format off
+TYPED_TEST(groupby_nth_element_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+  //keys                                                  {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  //vals                                                  {0, 3, 6, 1, 4, 5, 9, 2, 7, 8};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  //groupby.first()
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals0({0, 1, 2});
+  test_single_agg(keys, vals, expect_keys, expect_vals0, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(1);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals1({3, 4, 7});
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals2({6, 5, 8});
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, basic_out_of_bounds)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 3, 4, 5, 3, 2, 2, 9});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(3);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals({0, 9, 0}, {0, 1, 0});
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, negative)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+  //keys                            {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  //vals                            {0, 3, 6, 1, 4, 5, 9, 2, 7, 8};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  //groupby.last()
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-1);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals0({6, 9, 8});
+  test_single_agg(keys, vals, expect_keys, expect_vals0, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-2);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals1({3, 5, 7});
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-3);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals2({0, 4, 2});
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, negative_out_of_bounds)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 3, 4, 5, 3, 2, 2, 9});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-4);
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals({0, 1, 0}, {0, 1, 0});
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({3, 4, 5});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals({3}, all_nulls());
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                              {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //keys                                    {1, 1, 1   2,2,2,2    3, 3,    4}
+  //vals                                    {-,3,6,    1,4,-,9,  2,8,      -}
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals({-1, 1, 2, -1}, {0, 1, 1, 0});
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, null_keys_and_values_out_of_bounds)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                     {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                              {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+  //                                        {1, 1, 1    2, 2, 2,    3, 3,   4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //                                        {-,3,6,     1,4,-,9,    2,8,    -}
+  //                                         value,     null,       out,    out
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals({6, -1, -1, -1}, {1, 0, 0, 0});
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, exclude_nulls)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 3, 1, 2, 2, 1, 3, 3, 2, 4, 4, 2},
+                                     {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 4, 4, 2},
+                                              {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //keys                                    {1, 1, 1    2, 2, 2, 2      3, 3, 3    4}
+  //vals                                    {-, 3, 6    1, 4, -, 9, -   2, 2, 8,   4,-}
+  //                                      0  null,      value,          value,     null
+  //                                      1  value,     value,          value,     null
+  //                                      2  value,     null,           value,     out
+  //null_policy::INCLUDE
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_nuls0({-1, 1, 2, 4}, {0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_nuls1({3, 4, 2, -1}, {1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_nuls2({6, -1, 8, -1}, {1, 0, 1, 0});
+
+  //null_policy::EXCLUDE
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals0({3, 1, 2, 4});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals1({6, 4, 2, -1}, {1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals2({-1, 9, 8, -1}, {0, 1, 1, 0});
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0, cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_nuls0, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(1, cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_nuls1, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2, cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_nuls2, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0, cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals0, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(1, cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2, cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg));
+}
+
+TYPED_TEST(groupby_nth_element_test, exclude_nulls_negative_index)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NTH_ELEMENT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 3, 1, 2, 2, 1, 3, 3, 2, 4, 4, 2},
+                                     {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V, int32_t> vals({0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 4, 4, 2},
+                                              {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //keys                                    {1, 1, 1    2, 2, 2,        3, 3,       4}
+  //vals                                    {-, 3, 6    1, 4, -, 9, -   2, 2, 8,    4,-}
+  //                                      0  null,      value,          value,      value
+  //                                      1  value,     value,          value,      null
+  //                                      2  value,     null,           value,      out
+  //                                      3  out,       value,          out,        out
+  //                                      4  out,       null,           out,        out
+
+  //null_policy::INCLUDE
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_nuls0({6, -1, 8, -1}, {1, 0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_nuls1({3, 9, 2, 4});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_nuls2({-1, -1, 2, -1}, {0, 0, 1, 0});
+
+  //null_policy::EXCLUDE
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals0({6, 9, 8, 4});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals1({3, 4, 2, -1}, {1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<R, int32_t> expect_vals2({-1, 1, 2, -1}, {0, 1, 1, 0});
+
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-1, cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_nuls0, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-2, cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_nuls1, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-3, cudf::null_policy::INCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_nuls2, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-1, cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals0, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-2, cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg));
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-3, cudf::null_policy::EXCLUDE);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg));
+}
+
+struct groupby_nth_element_string_test : public cudf::test::BaseFixture {
+};
+
+TEST_F(groupby_nth_element_string_test, basic_string)
+{
+  using K = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::strings_column_wrapper vals{"ABCD", "1", "2", "3", "4", "5", "6", "7", "8", "9"};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  //groupby.first()
+  auto agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0);
+  cudf::test::strings_column_wrapper expect_vals0{"ABCD", "1", "2"};
+  test_single_agg(keys, vals, expect_keys, expect_vals0, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(1);
+  cudf::test::strings_column_wrapper expect_vals1{"3", "4", "7"};
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2);
+  cudf::test::strings_column_wrapper expect_vals2{"6", "5", "8"};
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg));
+
+  //+ve out of bounds
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(3);
+  cudf::test::strings_column_wrapper expect_vals3{{"", "9", ""}, {0, 1, 0}};
+  test_single_agg(keys, vals, expect_keys, expect_vals3, std::move(agg));
+
+  //groupby.last()
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-1);
+  cudf::test::strings_column_wrapper expect_vals4{"6", "9", "8"};
+  test_single_agg(keys, vals, expect_keys, expect_vals4, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-2);
+  cudf::test::strings_column_wrapper expect_vals5{"3", "5", "7"};
+  test_single_agg(keys, vals, expect_keys, expect_vals5, std::move(agg));
+
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-3);
+  cudf::test::strings_column_wrapper expect_vals6{"ABCD", "4", "2"};
+  test_single_agg(keys, vals, expect_keys, expect_vals6, std::move(agg));
+
+  //-ve out of bounds
+  agg = cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(-4);
+  cudf::test::strings_column_wrapper expect_vals7{{"", "1", ""}, {0, 1, 0}};
+  test_single_agg(keys, vals, expect_keys, expect_vals7, std::move(agg));
+}
+// clang-format on
+
+TEST_F(groupby_nth_element_string_test, dictionary)
+{
+  using K = int32_t;
+  using V = std::string;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V> vals{"AB", "1", "2", "3", "4", "5", "6", "7", "8", "9"};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::dictionary_column_wrapper<V> expect_vals_w{"6", "5", "8"};
+
+  auto expect_vals = cudf::dictionary::set_keys(expect_vals_w, vals.keys());
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals->view(),
+                  cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2));
+}
+
+template <typename T>
+struct groupby_nth_element_lists_test : cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_nth_element_lists_test, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(groupby_nth_element_lists_test, Basics)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  using lists = cudf::test::lists_column_wrapper<V, int32_t>;
+
+  auto keys   = cudf::test::fixed_width_column_wrapper<K, int32_t>{1, 1, 2, 2, 3, 3};
+  auto values = lists{{1, 2}, {3, 4}, {5, 6, 7}, lists{}, {9, 10}, {11}};
+
+  auto expected_keys   = cudf::test::fixed_width_column_wrapper<K, int32_t>{1, 2, 3};
+  auto expected_values = lists{{1, 2}, {5, 6, 7}, {9, 10}};
+
+  test_single_agg(keys,
+                  values,
+                  expected_keys,
+                  expected_values,
+                  cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+}
+
+TYPED_TEST(groupby_nth_element_lists_test, EmptyInput)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  using lists = cudf::test::lists_column_wrapper<V, int32_t>;
+
+  auto keys   = cudf::test::fixed_width_column_wrapper<K, int32_t>{};
+  auto values = lists{};
+
+  auto expected_keys   = cudf::test::fixed_width_column_wrapper<K, int32_t>{};
+  auto expected_values = lists{};
+
+  test_single_agg(keys,
+                  values,
+                  expected_keys,
+                  expected_values,
+                  cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(2));
+}
+
+struct groupby_nth_element_structs_test : cudf::test::BaseFixture {};
+
+TEST_F(groupby_nth_element_structs_test, Basics)
+{
+  using structs = cudf::test::structs_column_wrapper;
+  using ints    = cudf::test::fixed_width_column_wrapper<int>;
+  using doubles = cudf::test::fixed_width_column_wrapper<double>;
+  using strings = cudf::test::strings_column_wrapper;
+
+  auto keys   = ints{0, 0, 0, 1, 1, 1, 2, 2, 2, 3};
+  auto child0 = ints{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto child1 = doubles{0.1, 1.2, 2.3, 3.4, 4.51, 5.3e4, 6.3231, -0.07, 832.1, 9.999};
+  auto child2 = strings{"", "a", "b", "c", "d", "e", "f", "g", "HH", "JJJ"};
+  auto values = structs{{child0, child1, child2}, {1, 0, 1, 0, 1, 1, 1, 1, 0, 1}};
+
+  auto expected_keys   = ints{0, 1, 2, 3};
+  auto expected_ch0    = ints{1, 4, 7, 0};
+  auto expected_ch1    = doubles{1.2, 4.51, -0.07, 0.0};
+  auto expected_ch2    = strings{"a", "d", "g", ""};
+  auto expected_values = structs{{expected_ch0, expected_ch1, expected_ch2}, {0, 1, 1, 0}};
+  test_single_agg(keys,
+                  values,
+                  expected_keys,
+                  expected_values,
+                  cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(1));
+
+  expected_keys   = ints{0, 1, 2, 3};
+  expected_ch0    = ints{0, 4, 6, 9};
+  expected_ch1    = doubles{0.1, 4.51, 6.3231, 9.999};
+  expected_ch2    = strings{"", "d", "f", "JJJ"};
+  expected_values = structs{{expected_ch0, expected_ch1, expected_ch2}, {1, 1, 1, 1}};
+  test_single_agg(
+    keys,
+    values,
+    expected_keys,
+    expected_values,
+    cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0, cudf::null_policy::EXCLUDE));
+}
+
+TEST_F(groupby_nth_element_structs_test, NestedStructs)
+{
+  using structs = cudf::test::structs_column_wrapper;
+  using ints    = cudf::test::fixed_width_column_wrapper<int>;
+  using doubles = cudf::test::fixed_width_column_wrapper<double>;
+  using lists   = cudf::test::lists_column_wrapper<int>;
+
+  auto keys             = ints{0, 0, 0, 1, 1, 1, 2, 2, 2, 3};
+  auto child0           = ints{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto child0_of_child1 = ints{0, -1, -2, -3, -4, -5, -6, -7, -8, -9};
+  auto child1_of_child1 = doubles{0.1, 1.2, 2.3, 3.4, 4.51, 5.3e4, 6.3231, -0.07, 832.1, 9.999};
+  auto child1           = structs{child0_of_child1, child1_of_child1};
+  auto child2           = lists{{0}, {1, 2, 3}, {}, {4}, {5, 6}, {}, {}, {7}, {8, 9}, {}};
+  auto values           = structs{{child0, child1, child2}, {1, 0, 1, 0, 1, 1, 1, 1, 0, 1}};
+
+  auto expected_keys       = ints{0, 1, 2, 3};
+  auto expected_ch0        = ints{1, 4, 7, 0};
+  auto expected_ch0_of_ch1 = ints{-1, -4, -7, 0};
+  auto expected_ch1_of_ch1 = doubles{1.2, 4.51, -0.07, 0.0};
+  auto expected_ch1        = structs{expected_ch0_of_ch1, expected_ch1_of_ch1};
+  auto expected_ch2        = lists{{1, 2, 3}, {5, 6}, {7}, {}};
+  auto expected_values     = structs{{expected_ch0, expected_ch1, expected_ch2}, {0, 1, 1, 0}};
+  test_single_agg(keys,
+                  values,
+                  expected_keys,
+                  expected_values,
+                  cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(1));
+
+  expected_keys       = ints{0, 1, 2, 3};
+  expected_ch0        = ints{0, 4, 6, 9};
+  expected_ch0_of_ch1 = ints{0, -4, -6, -9};
+  expected_ch1_of_ch1 = doubles{0.1, 4.51, 6.3231, 9.999};
+  expected_ch1        = structs{expected_ch0_of_ch1, expected_ch1_of_ch1};
+  expected_ch2        = lists{{0}, {5, 6}, {}, {}};
+  expected_values     = structs{{expected_ch0, expected_ch1, expected_ch2}, {1, 1, 1, 1}};
+  test_single_agg(
+    keys,
+    values,
+    expected_keys,
+    expected_values,
+    cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0, cudf::null_policy::EXCLUDE));
+}
+
+TEST_F(groupby_nth_element_structs_test, EmptyInput)
+{
+  using structs = cudf::test::structs_column_wrapper;
+  using ints    = cudf::test::fixed_width_column_wrapper<int>;
+  using doubles = cudf::test::fixed_width_column_wrapper<double>;
+  using strings = cudf::test::strings_column_wrapper;
+
+  auto keys   = ints{};
+  auto child0 = ints{};
+  auto child1 = doubles{};
+  auto child2 = strings{};
+  auto values = structs{{child0, child1, child2}};
+
+  auto expected_keys   = ints{};
+  auto expected_ch0    = ints{};
+  auto expected_ch1    = doubles{};
+  auto expected_ch2    = strings{};
+  auto expected_values = structs{{expected_ch0, expected_ch1, expected_ch2}};
+  test_single_agg(keys,
+                  values,
+                  expected_keys,
+                  expected_values,
+                  cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+}
diff --git a/cpp/tests/groupby/nunique_tests.cpp b/cpp/tests/groupby/nunique_tests.cpp
new file mode 100644
index 0000000..c9156d8
--- /dev/null
+++ b/cpp/tests/groupby/nunique_tests.cpp
@@ -0,0 +1,228 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+template <typename V>
+struct groupby_nunique_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_nunique_test, cudf::test::AllTypes);
+
+TYPED_TEST(groupby_nunique_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format off
+  //                                                   {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1,        2,          3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{3,        4,          3};
+  cudf::test::fixed_width_column_wrapper<R> expect_bool_vals{2,   1,          1};
+  // clang-format on
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  if (std::is_same<V, bool>())
+    test_single_agg(keys, vals, expect_keys, expect_bool_vals, std::move(agg));
+  else
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, basic_duplicates)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 3, 2, 2, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{2, 4, 1};
+  cudf::test::fixed_width_column_wrapper<R> expect_bool_vals{2, 1, 1};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  if (std::is_same<V, bool>())
+    test_single_agg(keys, vals, expect_keys, expect_bool_vals, std::move(agg));
+  else
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({0, 0, 0}, cudf::test::iterators::all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 0, 0}, cudf::test::iterators::all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{0};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //                                        {1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4},
+                                                        cudf::test::iterators::no_nulls());
+  // all unique values only                 {3, 6,     1, 4, 9,   2, 8,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{2, 3, 2, 0};
+  cudf::test::fixed_width_column_wrapper<R> expect_bool_vals{1, 1, 1, 0};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  if (std::is_same<V, bool>())
+    test_single_agg(keys, vals, expect_keys, expect_bool_vals, std::move(agg));
+  else
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, null_keys_and_values_with_duplicates)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 3, 1, 2, 2, 1, 3, 3, 2, 4, 4, 2},
+                                                 {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 4, 4, 2},
+                                                 {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0});
+
+  //  { 1, 1,     2, 2, 2,    3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4},
+                                                        cudf::test::iterators::no_nulls());
+  //  { 3, 6,-    1, 4, 9,-   2*, 8,   -*}
+  //  unique,     with null,  dup,     dup null
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{2, 3, 2, 0};
+  cudf::test::fixed_width_column_wrapper<R> expect_bool_vals{1, 1, 1, 0};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>();
+  if (std::is_same<V, bool>())
+    test_single_agg(keys, vals, expect_keys, expect_bool_vals, std::move(agg));
+  else
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, include_nulls)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 3, 1, 2, 2, 1, 3, 3, 2, 4, 4, 2},
+                                                 {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 4, 4, 2},
+                                                 {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0});
+
+  //  { 1, 1,     2, 2, 2,    3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4},
+                                                        cudf::test::iterators::no_nulls());
+  //  { 3, 6,-    1, 4, 9,-   2*, 8,   -*}
+  //  unique,     with null,  dup,     dup null
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{3, 4, 2, 1};
+  cudf::test::fixed_width_column_wrapper<R> expect_bool_vals{2, 2, 1, 1};
+
+  auto agg = cudf::make_nunique_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE);
+  if (std::is_same<V, bool>())
+    test_single_agg(keys, vals, expect_keys, expect_bool_vals, std::move(agg));
+  else
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_nunique_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::NUNIQUE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 3, 1, 2, 2, 1, 0, 3, 2, 4, 4, 2},
+                                     {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<V>  vals({0, 1, 2, 2, 3, 4, 0, 6, 7, 8, 9, 0, 0, 0},
+                                     {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0});
+
+  // { 1, 1,   2, 2, 2,   3, 3,   4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, cudf::test::iterators::no_nulls());
+  // { 3, 6,-  1, 4, 9,-  2*, 8,  -*}
+  //  unique,  with null, dup,    dup null
+  cudf::test::fixed_width_column_wrapper<R> expect_fixed_vals({3, 4, 2, 1});
+  cudf::test::fixed_width_column_wrapper<R> expect_bool_vals{2, 2, 1, 1};
+  // clang-format on
+
+  cudf::column_view expect_vals = (std::is_same<V, bool>()) ? cudf::column_view{expect_bool_vals}
+                                                            : cudf::column_view{expect_fixed_vals};
+
+  test_single_agg(
+    keys,
+    vals,
+    expect_keys,
+    expect_vals,
+    cudf::make_nunique_aggregation<cudf::groupby_aggregation>(cudf::null_policy::INCLUDE));
+}
diff --git a/cpp/tests/groupby/product_tests.cpp b/cpp/tests/groupby/product_tests.cpp
new file mode 100644
index 0000000..0145293
--- /dev/null
+++ b/cpp/tests/groupby/product_tests.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_product_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+TYPED_TEST_SUITE(groupby_product_test, supported_types);
+
+TYPED_TEST(groupby_product_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys        { 1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals        { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+                                                    //  { 1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys { 1,        2,           3      };
+                                                    //  { 0, 3, 6,  1, 4, 5, 9,  2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({   0.,       180.,      112. }, no_nulls());
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
+
+TYPED_TEST(groupby_product_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
+
+TYPED_TEST(groupby_product_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({0, 0, 0}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
+
+TYPED_TEST(groupby_product_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 0, 0}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
+
+TYPED_TEST(groupby_product_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys(       { 1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                            { 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals(       { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3},
+                                            { 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+                                        //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,        2,         3,       4}, no_nulls());
+                                        //  { _, 3, 6,  1, 4, 9,   2, 8,    _}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({ 18.,      36.,       16.,     3.},
+                                            { 1,        1,         1,       0});
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
+
+TYPED_TEST(groupby_product_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{ 1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+                                                    //  { 1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,        2,           3      });
+                                                    //  { 0, 3, 6,  1, 4, 5, 9,  2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({  0.,     180.,        112. }, no_nulls());
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
+
+TYPED_TEST(groupby_product_test, dictionary_with_nulls)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::PRODUCT>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{ 1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                     {1, 0, 0, 1, 1, 1, 1, 1, 1, 1}};
+
+                                                    //  { 1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({ 1,        2,           3      });
+                                                    //  { 0, 3, 6,  @, 4, 5, 9,  @, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({  0.,     180.,        56. }, no_nulls());
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_product_aggregation<cudf::groupby_aggregation>());
+}
diff --git a/cpp/tests/groupby/quantile_tests.cpp b/cpp/tests/groupby/quantile_tests.cpp
new file mode 100644
index 0000000..8d134f3
--- /dev/null
+++ b/cpp/tests/groupby/quantile_tests.cpp
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_quantile_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+using K = int32_t;
+TYPED_TEST_SUITE(groupby_quantile_test, supported_types);
+
+TYPED_TEST(groupby_quantile_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format on
+  //                                       {1, 1, 1, 2, 2, 2, 2, 3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  //                                       {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3., 4.5, 7.}, no_nulls());
+  // clang-format on
+
+  auto agg =
+    cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.5}, cudf::interpolation::LINEAR);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_quantile_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg =
+    cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.5}, cudf::interpolation::LINEAR);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_quantile_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg =
+    cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.5}, cudf::interpolation::LINEAR);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_quantile_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg =
+    cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.5}, cudf::interpolation::LINEAR);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_quantile_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //  { 3, 6,     1, 4, 9,   2, 8,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({4.5, 4., 5., 0.}, {1, 1, 1, 0});
+
+  auto agg =
+    cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.5}, cudf::interpolation::LINEAR);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_quantile_test, multiple_quantile)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format off
+  //                                       {1, 1, 1,   2, 2, 2, 2, 3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  //                                        {0, 3, 6,  1, 4, 5, 9, 2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({1.5, 4.5, 3.25, 6.,   4.5, 7.5}, no_nulls());
+  // clang-format on
+
+  auto agg = cudf::make_quantile_aggregation<cudf::groupby_aggregation>(
+    {0.25, 0.75}, cudf::interpolation::LINEAR);
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  std::move(agg),
+                  force_use_sort_impl::YES,
+                  cudf::null_policy::EXCLUDE,
+                  cudf::sorted::NO,
+                  {},
+                  {},
+                  cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_quantile_test, interpolation_types)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 9};
+
+  // clang-format off
+  //                                                   {1, 1, 1,  2, 2, 2, 2,  3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+
+  //                                                     {0, 3, 6,  1, 4, 5, 9,  2, 7}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals1({2.4,      4.2,         4.}, no_nulls());
+  auto agg1 = cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.4}, cudf::interpolation::LINEAR);
+  test_single_agg(keys, vals, expect_keys, expect_vals1, std::move(agg1));
+
+  //                                                     {0, 3, 6,  1, 4, 5, 9,  2, 7}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals2({3,        4,           2}, no_nulls());
+  auto agg2 = cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.4}, cudf::interpolation::NEAREST);
+  test_single_agg(keys, vals, expect_keys, expect_vals2, std::move(agg2));
+
+  //                                                     {0, 3, 6,  1, 4, 5, 9,  2, 7}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals3({0,        4,          2}, no_nulls());
+  auto agg3 = cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.4}, cudf::interpolation::LOWER);
+  test_single_agg(keys, vals, expect_keys, expect_vals3, std::move(agg3));
+
+  //                                                     {0, 3, 6,  1, 4, 5, 9,  2, 7}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals4({3,        5,           7}, no_nulls());
+  auto agg4 = cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.4}, cudf::interpolation::HIGHER);
+  test_single_agg(keys, vals, expect_keys, expect_vals4, std::move(agg4));
+
+  //                                                     {0, 3, 6,  1, 4, 5, 9,  2, 7}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals5({1.5,      4.5,         4.5}, no_nulls());
+  auto agg5 = cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.4}, cudf::interpolation::MIDPOINT);
+  test_single_agg(keys, vals, expect_keys, expect_vals5, std::move(agg5));
+  // clang-format on
+}
+
+TYPED_TEST(groupby_quantile_test, dictionary)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::QUANTILE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //                                                    {1, 1, 1, 2, 2, 2, 2, 3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3});
+  //                                                    {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3.,      4.5,        7.}, no_nulls());
+  // clang-format on
+
+  test_single_agg(
+    keys,
+    vals,
+    expect_keys,
+    expect_vals,
+    cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.5}, cudf::interpolation::LINEAR));
+}
diff --git a/cpp/tests/groupby/rank_scan_tests.cpp b/cpp/tests/groupby/rank_scan_tests.cpp
new file mode 100644
index 0000000..76b0556
--- /dev/null
+++ b/cpp/tests/groupby/rank_scan_tests.cpp
@@ -0,0 +1,596 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename T>
+using input              = cudf::test::fixed_width_column_wrapper<T>;
+using rank_result_col    = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+using percent_result_col = cudf::test::fixed_width_column_wrapper<double>;
+using null_iter_t        = decltype(nulls_at({}));
+
+auto constexpr X     = int32_t{0};  // Placeholder for NULL rows.
+auto const all_valid = nulls_at({});
+
+void test_rank_scans(cudf::column_view const& keys,
+                     cudf::column_view const& order,
+                     cudf::column_view const& expected_dense,
+                     cudf::column_view const& expected_rank,
+                     cudf::column_view const& expected_percent_rank)
+{
+  test_single_scan(keys,
+                   order,
+                   keys,
+                   expected_dense,
+                   cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                     cudf::rank_method::DENSE, {}, cudf::null_policy::INCLUDE),
+                   cudf::null_policy::INCLUDE,
+                   cudf::sorted::YES);
+  test_single_scan(keys,
+                   order,
+                   keys,
+                   expected_rank,
+                   cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                     cudf::rank_method::MIN, {}, cudf::null_policy::INCLUDE),
+                   cudf::null_policy::INCLUDE,
+                   cudf::sorted::YES);
+  test_single_scan(keys,
+                   order,
+                   keys,
+                   expected_percent_rank,
+                   cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                     cudf::rank_method::MIN,
+                     {},
+                     cudf::null_policy::INCLUDE,
+                     {},
+                     cudf::rank_percentage::ONE_NORMALIZED),
+                   cudf::null_policy::INCLUDE,
+                   cudf::sorted::YES);
+}
+
+struct groupby_rank_scan_test : public cudf::test::BaseFixture {};
+
+struct groupby_rank_scan_test_failures : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct typed_groupby_rank_scan_test : public cudf::test::BaseFixture {};
+
+using testing_type_set = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                            cudf::test::FloatingPointTypes,
+                                            cudf::test::FixedPointTypes,
+                                            cudf::test::ChronoTypes>;
+
+TYPED_TEST_SUITE(typed_groupby_rank_scan_test, testing_type_set);
+
+TYPED_TEST(typed_groupby_rank_scan_test, empty_cols)
+{
+  using T = TypeParam;
+
+  auto const keys            = input<T>{};
+  auto const order_by        = input<T>{};
+  auto const order_by_struct = cudf::test::structs_column_wrapper{};
+
+  auto const expected_dense   = rank_result_col{};
+  auto const expected_rank    = rank_result_col{};
+  auto const expected_percent = percent_result_col{};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_struct, expected_dense, expected_rank, expected_percent);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, zero_valid_keys)
+{
+  using T = TypeParam;
+
+  auto const keys            = input<T>{{X, X, X}, all_nulls()};
+  auto const order_by        = input<T>{{3, 3, 1}};
+  auto const order_by_struct = [] {
+    auto member_1 = input<T>{{3, 3, 1}};
+    auto member_2 = input<T>{{3, 3, 1}};
+    return cudf::test::structs_column_wrapper{member_1, member_2};
+  }();
+
+  auto const dense_rank_results  = rank_result_col{1, 1, 2};
+  auto const rank_results        = rank_result_col{1, 1, 3};
+  auto const percent_rank_result = percent_result_col{0, 0, 1};
+
+  test_rank_scans(keys, order_by, dense_rank_results, rank_results, percent_rank_result);
+  test_rank_scans(keys, order_by_struct, dense_rank_results, rank_results, percent_rank_result);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, zero_valid_orders)
+{
+  using T           = TypeParam;
+  using null_iter_t = decltype(all_nulls());
+
+  auto const keys                 = input<T>{{1, 1, 3, 3}};
+  auto const make_order_by        = [&] { return input<T>{{X, X, X, X}, all_nulls()}; };
+  auto const make_struct_order_by = [&](null_iter_t const& null_iter = no_nulls()) {
+    auto member1 = make_order_by();
+    auto member2 = make_order_by();
+    return cudf::test::structs_column_wrapper{{member1, member2}, null_iter};
+  };
+  auto const order_by                  = make_order_by();
+  auto const order_by_struct           = make_struct_order_by();
+  auto const order_by_struct_all_nulls = make_struct_order_by(all_nulls());
+
+  auto const expected_dense   = rank_result_col{1, 1, 1, 1};
+  auto const expected_rank    = rank_result_col{1, 1, 1, 1};
+  auto const expected_percent = percent_result_col{0, 0, 0, 0};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_struct, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_struct_all_nulls, expected_dense, expected_rank, expected_percent);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, basic)
+{
+  using T = TypeParam;
+
+  auto const keys            = /*        */ input<T>{0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const make_order_by   = [&] { return input<T>{5, 5, 5, 4, 4, 4, 3, 3, 2, 2, 1, 1}; };
+  auto const order_by        = make_order_by();
+  auto const order_by_struct = [&] {
+    auto order2 = make_order_by();
+    auto order3 = make_order_by();
+    return cudf::test::structs_column_wrapper{order2, order3};
+  }();
+
+  auto const expected_dense   = rank_result_col{1, 1, 1, 2, 2, 2, 3, 1, 2, 2, 3, 3};
+  auto const expected_rank    = rank_result_col{1, 1, 1, 4, 4, 4, 7, 1, 2, 2, 4, 4};
+  auto const expected_percent = percent_result_col{
+    0.0, 0.0, 0.0, 3.0 / 6, 3.0 / 6, 3.0 / 6, 6.0 / 6, 0.0, 1.0 / 4, 1.0 / 4, 3.0 / 4, 3.0 / 4};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_struct, expected_dense, expected_rank, expected_percent);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, null_orders)
+{
+  using T = TypeParam;
+
+  auto const null_mask     = nulls_at({2, 8});
+  auto const keys          = input<T>{{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+  auto const make_order_by = [&] {
+    return input<T>{{-1, -2, X, -2, -3, -3, -4, -4, X, -5, -5, -5}, null_mask};
+  };
+  auto const make_struct_order_by = [&](null_iter_t const& null_iter = all_valid) {
+    auto member1 = make_order_by();
+    auto member2 = make_order_by();
+    return cudf::test::structs_column_wrapper{{member1, member2}, null_iter};
+  };
+  auto const order_by                   = make_order_by();
+  auto const order_by_struct            = make_struct_order_by();
+  auto const order_by_struct_with_nulls = make_struct_order_by(null_mask);
+
+  auto const expected_dense   = rank_result_col{1, 2, 3, 4, 5, 5, 1, 1, 2, 3, 3, 3};
+  auto const expected_rank    = rank_result_col{1, 2, 3, 4, 5, 5, 1, 1, 3, 4, 4, 4};
+  auto const expected_percent = percent_result_col{
+    0.0, 1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 4.0 / 5, 0.0, 0.0, 2.0 / 5, 3.0 / 5, 3.0 / 5, 3.0 / 5};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_struct, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(
+    keys, order_by_struct_with_nulls, expected_dense, expected_rank, expected_percent);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, null_orders_and_keys)
+{
+  using T = TypeParam;
+
+  auto const null_mask     = nulls_at({2, 8});
+  auto const keys          = input<T>{{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1}, nulls_at({9, 10, 11})};
+  auto const make_order_by = [&] {
+    return input<T>{{-1, -2, -2, -2, -3, -3, -4, -4, -4, -5, -5, -6}, null_mask};
+  };
+  auto const make_struct_order_by = [&](null_iter_t const& null_iter = all_valid) {
+    auto member1 = make_order_by();
+    auto member2 = make_order_by();
+    return cudf::test::structs_column_wrapper{{member1, member2}, null_iter};
+  };
+  auto const order_by                   = make_order_by();
+  auto const order_by_struct            = make_struct_order_by();
+  auto const order_by_struct_with_nulls = make_struct_order_by(null_mask);
+
+  auto const expected_dense   = rank_result_col{{1, 2, 3, 4, 5, 5, 1, 1, 2, 1, 1, 2}};
+  auto const expected_rank    = rank_result_col{{1, 2, 3, 4, 5, 5, 1, 1, 3, 1, 1, 3}};
+  auto const expected_percent = percent_result_col{
+    {0.0, 1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 4.0 / 5, 0.0, 0.0, 2.0 / 2, 0.0, 0.0, 2.0 / 2}};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_struct, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(
+    keys, order_by_struct_with_nulls, expected_dense, expected_rank, expected_percent);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, mixedStructs)
+{
+  auto const struct_col = [] {
+    auto nums    = input<TypeParam>{{0, 0, 7, 7, 7, X, 4, 4, 4, 9, 9, 9}, null_at(5)};
+    auto strings = cudf::test::strings_column_wrapper{
+      {"0a", "0a", "2a", "2a", "3b", "5", "6c", "6c", "XX", "9", "9", "10d"}, null_at(8)};
+    return cudf::test::structs_column_wrapper{{nums, strings}, null_at(11)}.release();
+  }();
+
+  auto const keys = cudf::test::strings_column_wrapper{
+    {"0", "0", "0", "0", "0", "0", "1", "1", "1", "X", "X", "X"}, nulls_at({9, 10, 11})};
+
+  auto const expected_dense   = rank_result_col{1, 1, 2, 2, 3, 4, 1, 1, 2, 1, 1, 2};
+  auto const expected_rank    = rank_result_col{1, 1, 3, 3, 5, 6, 1, 1, 3, 1, 1, 3};
+  auto const expected_percent = percent_result_col{
+    0.0, 0.0, 2.0 / 5, 2.0 / 5, 4.0 / 5, 5.0 / 5, 0.0, 0.0, 2.0 / 2, 0.0, 0.0, 2.0 / 2};
+
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = *struct_col;
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::DENSE, {}, cudf::null_policy::INCLUDE));
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN, {}, cudf::null_policy::INCLUDE));
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN,
+    {},
+    cudf::null_policy::INCLUDE,
+    {},
+    cudf::rank_percentage::ONE_NORMALIZED));
+
+  cudf::groupby::groupby gb_obj(
+    cudf::table_view({keys}), cudf::null_policy::INCLUDE, cudf::sorted::YES);
+  auto [result_keys, agg_results] = gb_obj.scan(requests);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view({keys}), result_keys->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[0], expected_dense);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[1], expected_rank);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[2], expected_percent);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, nestedStructs)
+{
+  using T = TypeParam;
+
+  auto nested_structs = [] {
+    auto structs_member = [] {
+      auto nums_member    = input<T>{{0, 0, 7, 7, 7, 5, 4, 4, 4, 9, 9, 9}, null_at(5)};
+      auto strings_member = cudf::test::strings_column_wrapper{
+        {"0a", "0a", "2a", "2a", "3b", "5", "6c", "6c", "6c", "9", "9", "10d"}, null_at(8)};
+      return cudf::test::structs_column_wrapper{nums_member, strings_member};
+    }();
+    auto nums_member = input<T>{{0, 0, 7, 7, 7, 5, 4, 4, 4, 9, 9, 9}, null_at(5)};
+    return cudf::test::structs_column_wrapper{structs_member, nums_member}.release();
+  }();
+
+  auto flat_struct = [] {
+    auto nums_member    = input<T>{{0, 0, 7, 7, 7, 5, 4, 4, 4, 9, 9, 9}, null_at(5)};
+    auto strings_member = cudf::test::strings_column_wrapper{
+      {"0a", "0a", "2a", "2a", "3b", "5", "6c", "6c", "6c", "9", "9", "10d"}, null_at(8)};
+    auto nuther_nums =
+      cudf::test::fixed_width_column_wrapper<T>{{0, 0, 7, 7, 7, 5, 4, 4, 4, 9, 9, 9}, null_at(5)};
+    return cudf::test::structs_column_wrapper{nums_member, strings_member, nuther_nums}.release();
+  }();
+
+  auto const keys = cudf::test::strings_column_wrapper{
+    {"0", "0", "0", "0", "0", "0", "1", "1", "1", "1", "0", "1"}, nulls_at({9, 10, 11})};
+
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = *nested_structs;
+  requests[0].aggregations.push_back(
+    cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(cudf::rank_method::DENSE));
+  requests[0].aggregations.push_back(
+    cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(cudf::rank_method::MIN));
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN,
+    {},
+    cudf::null_policy::INCLUDE,
+    {},
+    cudf::rank_percentage::ONE_NORMALIZED));
+  requests[1].values = *flat_struct;
+  requests[1].aggregations.push_back(
+    cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(cudf::rank_method::DENSE));
+  requests[1].aggregations.push_back(
+    cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(cudf::rank_method::MIN));
+  requests[1].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN,
+    {},
+    cudf::null_policy::INCLUDE,
+    {},
+    cudf::rank_percentage::ONE_NORMALIZED));
+
+  cudf::groupby::groupby gb_obj(
+    cudf::table_view({keys}), cudf::null_policy::INCLUDE, cudf::sorted::YES);
+  auto [result_keys, agg_results] = gb_obj.scan(requests);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view({keys}), result_keys->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[0], *agg_results[1].results[0]);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[1], *agg_results[1].results[1]);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[2], *agg_results[1].results[2]);
+}
+
+TYPED_TEST(typed_groupby_rank_scan_test, structsWithNullPushdown)
+{
+  using T = TypeParam;
+
+  auto constexpr num_rows = 12;
+
+  auto get_struct_column = [] {
+    auto nums_member =
+      cudf::test::fixed_width_column_wrapper<T>{{0, 0, 7, 7, 7, 5, 4, 4, 4, 9, 9, 9}, null_at(5)};
+    auto strings_member = cudf::test::strings_column_wrapper{
+      {"0a", "0a", "2a", "2a", "3b", "5", "6c", "6c", "6c", "9", "9", "10d"}, null_at(8)};
+    auto struct_column = cudf::test::structs_column_wrapper{nums_member, strings_member}.release();
+    // Reset null-mask, a posteriori. Nulls will not be pushed down to children.
+    auto const null_iter = nulls_at({1, 2, 11});
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_iter, null_iter + num_rows);
+    struct_column->set_null_mask(std::move(null_mask), null_count);
+    return struct_column;
+  };
+
+  auto const possibly_null_structs = get_struct_column();
+
+  auto const definitely_null_structs = [&] {
+    auto struct_column = get_struct_column();
+    struct_column->set_null_mask(cudf::create_null_mask(num_rows, cudf::mask_state::ALL_NULL),
+                                 num_rows);
+    return struct_column;
+  }();
+
+  cudf::test::strings_column_wrapper keys = {
+    {"0", "0", "0", "0", "0", "0", "1", "1", "1", "X", "X", "X"}, nulls_at({9, 10, 11})};
+
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests.emplace_back(cudf::groupby::scan_request());
+  requests[0].values = *possibly_null_structs;
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::DENSE, {}, cudf::null_policy::INCLUDE));
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN, {}, cudf::null_policy::INCLUDE));
+  requests[0].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN,
+    {},
+    cudf::null_policy::INCLUDE,
+    {},
+    cudf::rank_percentage::ONE_NORMALIZED));
+  requests[1].values = *definitely_null_structs;
+  requests[1].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::DENSE, {}, cudf::null_policy::INCLUDE));
+  requests[1].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN, {}, cudf::null_policy::INCLUDE));
+  requests[1].aggregations.push_back(cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+    cudf::rank_method::MIN,
+    {},
+    cudf::null_policy::INCLUDE,
+    {},
+    cudf::rank_percentage::ONE_NORMALIZED));
+
+  cudf::groupby::groupby gb_obj(
+    cudf::table_view({keys}), cudf::null_policy::INCLUDE, cudf::sorted::YES);
+  auto [result_keys, agg_results] = gb_obj.scan(requests);
+
+  auto expected_dense   = rank_result_col{1, 2, 2, 3, 4, 5, 1, 1, 2, 1, 1, 2};
+  auto expected_rank    = rank_result_col{1, 2, 2, 4, 5, 6, 1, 1, 3, 1, 1, 3};
+  auto expected_percent = percent_result_col{
+    0.0, 1.0 / 5, 1.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5, 0.0, 0.0, 2.0 / 2, 0.0, 0.0, 2.0 / 2};
+  auto expected_rank_for_null = rank_result_col{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  auto expected_percent_for_null =
+    percent_result_col{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[0], expected_dense);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[1], expected_rank);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[0].results[2], expected_percent);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[1].results[0], expected_rank_for_null);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[1].results[1], expected_rank_for_null);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*agg_results[1].results[2], expected_percent_for_null);
+}
+
+/* List support dependent on https://github.com/rapidsai/cudf/issues/8683
+template <typename T>
+struct list_groupby_rank_scan_test : public cudf::test::BaseFixture {
+};
+
+using list_test_type_set = Concat<IntegralTypesNotBool,
+                                              FloatingPointTypes,
+                                              FixedPointTypes>;
+
+TYPED_TEST_SUITE(list_groupby_rank_scan_test, list_test_type_set);
+
+TYPED_TEST(list_groupby_rank_scan_test, lists)
+{
+  using T = TypeParam;
+
+  auto list_col = lists_column_wrapper<T>{
+    {0, 0},
+    {0, 0},
+    {7, 2},
+    {7, 2},
+    {7, 3},
+    {5, 5},
+    {4, 6},
+    {4, 6},
+    {4, 6},
+    {9, 9},
+    {9, 9},
+    {9, 10}}.release();
+  cudf::test::fixed_width_column_wrapper<T> element1{0, 0, 7, 7, 7, 5, 4, 4, 4, 9, 9, 9};
+  cudf::test::fixed_width_column_wrapper<T> element2{0, 0, 2, 2, 3, 5, 6, 6, 6, 9, 9, 10};
+  auto struct_col =  cudf::test::structs_column_wrapper{element1, element2}.release();
+
+  cudf::test::fixed_width_column_wrapper<T> keys = {{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1},
+                                        {1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0}};
+
+  std::vector<cudf::groupby::scan_request> requests;
+  requests.emplace_back(groupby::aggregation_request());
+  requests.emplace_back(groupby::aggregation_request());
+  requests[0].values = list_col;
+  requests[0].aggregations.push_back(make_rank_aggregation<cudf::groupby_scan_aggregation>(rank_method::DENSE));
+  requests[0].aggregations.push_back(make_rank_aggregation<cudf::groupby_scan_aggregation>(rank_method::MIN));
+  requests[1].values = struct_col;
+  requests[1].aggregations.push_back(make_rank_aggregation<cudf::groupby_scan_aggregation>(rank_method::DENSE));
+  requests[1].aggregations.push_back(make_rank_aggregation<cudf::groupby_scan_aggregation>(rank_method::MIN));
+
+  cudf::groupby::groupby gb_obj(table_view({keys}), null_policy::INCLUDE, cudf::sorted::YES);
+  auto result = gb_obj.scan(requests);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(table_view({keys}), result.first->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *result.second[0].results[0], *result.second[1].results[0]);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *result.second[0].results[2], *result.second[1].results[2]);
+}
+*/
+
+TEST(groupby_rank_scan_test, bools)
+{
+  using bools       = cudf::test::fixed_width_column_wrapper<bool>;
+  using null_iter_t = decltype(nulls_at({}));
+
+  auto const keys          = bools{{0, 0, 0, 0, 0, 0, 1, 1, 1, X, X, X}, nulls_at({9, 10, 11})};
+  auto const nulls_6_8     = nulls_at({6, 8});
+  auto const make_order_by = [&] { return bools{{0, 0, 1, 1, 1, 1, X, 1, X, 0, 0, 1}, nulls_6_8}; };
+  auto const make_structs  = [&](null_iter_t const& null_iter = all_valid) {
+    auto member_1 = make_order_by();
+    auto member_2 = make_order_by();
+    return cudf::test::structs_column_wrapper{{member_1, member_2}, null_iter};
+  };
+
+  auto const order_by                    = make_order_by();
+  auto const order_by_structs            = make_structs();
+  auto const order_by_structs_with_nulls = make_structs(nulls_6_8);
+
+  auto const expected_dense   = rank_result_col{{1, 1, 2, 2, 2, 2, 1, 2, 3, 1, 1, 2}};
+  auto const expected_rank    = rank_result_col{{1, 1, 3, 3, 3, 3, 1, 2, 3, 1, 1, 3}};
+  auto const expected_percent = percent_result_col{
+    {0.0, 0.0, 2.0 / 5, 2.0 / 5, 2.0 / 5, 2.0 / 5, 0.0, 1.0 / 2, 2.0 / 2, 0.0, 0.0, 2.0 / 2}};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_structs, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(
+    keys, order_by_structs_with_nulls, expected_dense, expected_rank, expected_percent);
+}
+
+TEST(groupby_rank_scan_test, strings)
+{
+  using strings     = cudf::test::strings_column_wrapper;
+  using null_iter_t = decltype(nulls_at({}));
+
+  auto const keys =
+    strings{{"0", "0", "0", "0", "0", "0", "1", "1", "1", "X", "X", "X"}, nulls_at({9, 10, 11})};
+  auto const nulls_2_8     = nulls_at({2, 8});
+  auto const make_order_by = [&] {
+    return strings{{"-1", "-2", "X", "-2", "-3", "-3", "-4", "-4", "X", "-5", "-5", "-6"},
+                   nulls_2_8};
+  };
+  auto const make_structs = [&](null_iter_t const& null_iter = all_valid) {
+    auto member_1 = make_order_by();
+    auto member_2 = make_order_by();
+    return cudf::test::structs_column_wrapper{{member_1, member_2}, null_iter};
+  };
+
+  auto const order_by                    = make_order_by();
+  auto const order_by_structs            = make_structs();
+  auto const order_by_structs_with_nulls = make_structs(nulls_at({4, 5, 11}));
+
+  auto const expected_dense   = rank_result_col{{1, 2, 3, 4, 5, 5, 1, 1, 2, 1, 1, 2}};
+  auto const expected_rank    = rank_result_col{{1, 2, 3, 4, 5, 5, 1, 1, 3, 1, 1, 3}};
+  auto const expected_percent = percent_result_col{
+    {0.0, 1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 4.0 / 5, 0.0, 0.0, 2.0 / 2, 0.0, 0.0, 2.0 / 2}};
+
+  test_rank_scans(keys, order_by, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(keys, order_by_structs, expected_dense, expected_rank, expected_percent);
+  test_rank_scans(
+    keys, order_by_structs_with_nulls, expected_dense, expected_rank, expected_percent);
+}
+
+TEST_F(groupby_rank_scan_test_failures, DISABLED_test_exception_triggers)
+{
+  using T = uint32_t;
+
+  auto const keys = input<T>{{1, 2, 3}, null_at(2)};
+  auto const col  = input<T>{3, 3, 1};
+
+  // All of these aggregations raise exceptions unless provided presorted keys
+  EXPECT_THROW(test_single_scan(keys,
+                                col,
+                                keys,
+                                col,
+                                cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                                  cudf::rank_method::DENSE),
+                                cudf::null_policy::INCLUDE,
+                                cudf::sorted::NO),
+               cudf::logic_error);
+
+  EXPECT_THROW(test_single_scan(keys,
+                                col,
+                                keys,
+                                col,
+                                cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                                  cudf::rank_method::MIN),
+                                cudf::null_policy::INCLUDE,
+                                cudf::sorted::NO),
+               cudf::logic_error);
+
+  EXPECT_THROW(test_single_scan(keys,
+                                col,
+                                keys,
+                                col,
+                                cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                                  cudf::rank_method::DENSE),
+                                cudf::null_policy::EXCLUDE,
+                                cudf::sorted::YES),
+               cudf::logic_error);
+
+  EXPECT_THROW(test_single_scan(keys,
+                                col,
+                                keys,
+                                col,
+                                cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                                  cudf::rank_method::MIN),
+                                cudf::null_policy::EXCLUDE,
+                                cudf::sorted::YES),
+               cudf::logic_error);
+
+  EXPECT_THROW(test_single_scan(keys,
+                                col,
+                                keys,
+                                col,
+                                cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                                  cudf::rank_method::DENSE),
+                                cudf::null_policy::EXCLUDE,
+                                cudf::sorted::NO),
+               cudf::logic_error);
+
+  EXPECT_THROW(test_single_scan(keys,
+                                col,
+                                keys,
+                                col,
+                                cudf::make_rank_aggregation<cudf::groupby_scan_aggregation>(
+                                  cudf::rank_method::MIN),
+                                cudf::null_policy::EXCLUDE,
+                                cudf::sorted::NO),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/groupby/replace_nulls_tests.cpp b/cpp/tests/groupby/replace_nulls_tests.cpp
new file mode 100644
index 0000000..d4bd278
--- /dev/null
+++ b/cpp/tests/groupby/replace_nulls_tests.cpp
@@ -0,0 +1,369 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/groupby.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+using namespace cudf::test::iterators;
+
+using K = int32_t;
+
+template <typename T>
+struct GroupbyReplaceNullsFixedWidthTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupbyReplaceNullsFixedWidthTest, cudf::test::FixedWidthTypes);
+
+template <typename K, typename V>
+void TestReplaceNullsGroupbySingle(K const& key,
+                                   V const& input,
+                                   K const& expected_key,
+                                   V const& expected_val,
+                                   cudf::replace_policy policy)
+{
+  cudf::groupby::groupby gb_obj(cudf::table_view({key}));
+  std::vector<cudf::replace_policy> policies{policy};
+  auto p = gb_obj.replace_nulls(cudf::table_view({input}), policies);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*p.first, cudf::table_view({expected_key}));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*p.second, cudf::table_view({expected_val}));
+}
+
+TYPED_TEST(GroupbyReplaceNullsFixedWidthTest, PrecedingFill)
+{
+  // Group 0 value: {42, 24, null}  --> {42, 24, 24}
+  // Group 1 value: {7, null, null} --> {7, 7, 7}
+  cudf::test::fixed_width_column_wrapper<K> key{0, 1, 0, 1, 0, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> val({42, 7, 24, 10, 1, 1000},
+                                                        {1, 1, 1, 0, 0, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_val({42, 24, 24, 7, 7, 7}, no_nulls());
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(GroupbyReplaceNullsFixedWidthTest, FollowingFill)
+{
+  // Group 0 value: {2, null, 32}               --> {2, 32, 32}
+  // Group 1 value: {8, null, null, 128, 256}   --> {8, 128, 128, 128, 256}
+  cudf::test::fixed_width_column_wrapper<K> key{0, 0, 1, 1, 0, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> val({2, 4, 8, 16, 32, 64, 128, 256},
+                                                        {1, 0, 1, 0, 1, 0, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_val({2, 32, 32, 8, 128, 128, 128, 256},
+                                                               no_nulls());
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
+
+TYPED_TEST(GroupbyReplaceNullsFixedWidthTest, PrecedingFillLeadingNulls)
+{
+  // Group 0 value: {null, 24, null}    --> {null, 24, 24}
+  // Group 1 value: {null, null, null}  --> {null, null, null}
+  cudf::test::fixed_width_column_wrapper<K> key{0, 1, 0, 1, 0, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> val({42, 7, 24, 10, 1, 1000},
+                                                        {0, 0, 1, 0, 0, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_val({-1, 24, 24, -1, -1, -1},
+                                                               {0, 1, 1, 0, 0, 0});
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(GroupbyReplaceNullsFixedWidthTest, FollowingFillTrailingNulls)
+{
+  // Group 0 value: {2, null, null}                 --> {2, null, null}
+  // Group 1 value: {null, null, 64, null, null}    --> {64, 64, 64, null, null}
+  cudf::test::fixed_width_column_wrapper<K> key{0, 0, 1, 1, 0, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> val({2, 4, 8, 16, 32, 64, 128, 256},
+                                                        {1, 0, 0, 0, 0, 1, 0, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expect_val({2, -1, -1, 64, 64, 64, -1, -1},
+                                                               {1, 0, 0, 1, 1, 1, 0, 0});
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
+
+struct GroupbyReplaceNullsStringsTest : public cudf::test::BaseFixture {};
+
+TEST_F(GroupbyReplaceNullsStringsTest, PrecedingFill)
+{
+  // Group 0 value: {"y" "42"}  --> {"y", "42"}
+  // Group 1 value: {"xx" @ "zzz" @ "one"} --> {"xx" "xx" "zzz" "zzz" "one"}
+  cudf::test::fixed_width_column_wrapper<K> key{1, 1, 0, 1, 0, 1, 1};
+  cudf::test::strings_column_wrapper val({"xx", "", "y", "zzz", "42", "", "one"},
+                                         {true, false, true, true, true, false, true});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expect_val({"y", "42", "xx", "xx", "zzz", "zzz", "one"},
+                                                no_nulls());
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TEST_F(GroupbyReplaceNullsStringsTest, FollowingFill)
+{
+  // Group 0 value: {@ "42"}  --> {"42", "42"}
+  // Group 1 value: {"xx" @ "zzz" @ "one"} --> {"xx" "zzz" "zzz" "one" "one"}
+  cudf::test::fixed_width_column_wrapper<K> key{1, 1, 0, 1, 0, 1, 1};
+  cudf::test::strings_column_wrapper val({"xx", "", "", "zzz", "42", "", "one"},
+                                         {true, false, false, true, true, false, true});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expect_val({"42", "42", "xx", "zzz", "zzz", "one", "one"},
+                                                no_nulls());
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
+
+TEST_F(GroupbyReplaceNullsStringsTest, PrecedingFillPrecedingNull)
+{
+  // Group 0 value: {"y" "42"}  --> {"y", "42"}
+  // Group 1 value: {@ @ "zzz" "zzz" "zzz"} --> {@ @ "zzz" "zzz" "zzz"}
+  cudf::test::fixed_width_column_wrapper<K> key{1, 1, 0, 1, 0, 1, 1};
+  cudf::test::strings_column_wrapper val({"", "", "y", "zzz", "42", "", ""},
+                                         {false, false, true, true, true, false, false});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expect_val({"y", "42", "", "", "zzz", "zzz", "zzz"},
+                                                {true, true, false, false, true, true, true});
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TEST_F(GroupbyReplaceNullsStringsTest, FollowingFillTrailingNull)
+{
+  // Group 0 value: {@ "y"}  --> {"y", "y"}
+  // Group 1 value: {"xx" @ "zzz" @ @} --> {"xx" "zzz" "zzz" @ @}
+  cudf::test::fixed_width_column_wrapper<K> key{1, 1, 0, 1, 0, 1, 1};
+  cudf::test::strings_column_wrapper val({"xx", "", "", "zzz", "y", "", ""},
+                                         {true, false, false, true, true, false, false});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expect_val({"y", "y", "xx", "zzz", "zzz", "", ""},
+                                                {true, true, true, true, true, false, false});
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
+
+template <typename T>
+struct GroupbyReplaceNullsListsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupbyReplaceNullsListsTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(GroupbyReplaceNullsListsTest, PrecedingFillNonNested)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  // Group 0 value: {{1 2 3} @ {4 5} @} --> {{1 2 3} {1 2 3} {4 5} {4 5}}, w/o leading nulls
+  // Group 1 value: {@ {} @} --> {@ {} {}}, w/ leading nulls
+  cudf::test::fixed_width_column_wrapper<K> key{0, 1, 0, 0, 1, 1, 0};
+
+  std::vector<cudf::valid_type> mask{1, 0, 0, 1, 1, 0, 0};
+  LCW val({{1, 2, 3}, {}, {}, {4, 5}, {}, {}, {}}, mask.begin());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 0, 1, 1, 1};
+  std::vector<cudf::valid_type> expected_mask{1, 1, 1, 1, 0, 1, 1};
+  LCW expect_val({{1, 2, 3}, {1, 2, 3}, {4, 5}, {4, 5}, {-1}, {}, {}}, expected_mask.begin());
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(GroupbyReplaceNullsListsTest, FollowingFillNonNested)
+{
+  using LCW = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  // Group 0 value: {@ {5 6} @ {-1}} --> {{5 6} {5 6} {-1} {-1}}, w/o trailing nulls
+  // Group 1 value: {@ {} @} --> {{} {} @}}, w/ trailing nulls
+  cudf::test::fixed_width_column_wrapper<K> key{0, 1, 0, 0, 1, 1, 0};
+
+  std::vector<cudf::valid_type> mask{0, 0, 1, 0, 1, 0, 1};
+  LCW val({{}, {}, {5, 6}, {}, {}, {}, {-1}}, mask.begin());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 0, 1, 1, 1};
+  std::vector<cudf::valid_type> expected_mask{1, 1, 1, 1, 1, 1, 0};
+  LCW expect_val({{5, 6}, {5, 6}, {-1}, {-1}, {}, {}, {}}, expected_mask.begin());
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
+
+TYPED_TEST(GroupbyReplaceNullsListsTest, PrecedingFillNested)
+{
+  using LCW    = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using Mask_t = std::vector<cudf::valid_type>;
+  // Group 0 value: {{{1 @ 3} @}
+  //                 @
+  //                 {{@} {}}}} -->
+  //                {{{1 @ 3} @}
+  //                 {{1 @ 3} @}
+  //                 {{@} {}}}}, w/o leading nulls
+  // Group 1 value: {@
+  //                 {@ {102 @}}
+  //                 @
+  //                 {{@ 202} {}}}} -->
+  //                {@
+  //                 {@ {102 @}}
+  //                 {@ {102 @}}
+  //                 {{@ 202} {}}}}, w/ leading nulls
+  // Only top level nulls are replaced.
+  cudf::test::fixed_width_column_wrapper<K> key{1, 0, 1, 1, 0, 0, 1};
+
+  // clang-format off
+  LCW val({{},
+           LCW({LCW({1, -1, 3}, Mask_t{1, 0, 1}.begin()), {}}, Mask_t{1, 0}.begin()),
+           LCW({LCW{}, LCW({102, -1}, Mask_t{1, 0}.begin())}, Mask_t{0, 1}.begin()),
+           {},
+           {},
+           {LCW({{}}, Mask_t{0}.begin()), LCW{}},
+           {LCW({-1, 202}, Mask_t{0, 1}.begin()), LCW{}}},
+           Mask_t{0, 1, 1, 0, 0, 1, 1}.begin());
+  // clang-format on
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1, 1};
+
+  // clang-format off
+  LCW expect_val({LCW({LCW({1, -1, 3}, Mask_t{1, 0, 1}.begin()), {}}, Mask_t{1, 0}.begin()),
+                  LCW({LCW({1, -1, 3}, Mask_t{1, 0, 1}.begin()), {}}, Mask_t{1, 0}.begin()),
+                  {LCW({{}}, Mask_t{0}.begin()), LCW{}},
+                  {},
+                  LCW({LCW{}, LCW({102, -1}, Mask_t{1, 0}.begin())}, Mask_t{0, 1}.begin()),
+                  LCW({LCW{}, LCW({102, -1}, Mask_t{1, 0}.begin())}, Mask_t{0, 1}.begin()),
+                  {LCW({-1, 202}, Mask_t{0, 1}.begin()), LCW{}}},
+           Mask_t{1, 1, 1, 0, 1 ,1 ,1}.begin());
+  // clang-format on
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(GroupbyReplaceNullsListsTest, FollowingFillNested)
+{
+  using LCW    = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using Mask_t = std::vector<cudf::valid_type>;
+  // Group 0 value: {{{1 @ 3} @}
+  //                 @
+  //                 {{@} {}}}} -->
+  //                {{{1 @ 3} @}
+  //                 {{@} {}}}}
+  //                 {{@} {}}}}, w/o trailing nulls
+  // Group 1 value: {{@ {102 @}}
+  //                 @
+  //                 {{@ 202} {}}}}
+  //                 @ -->
+  //                {{@ {102 @}}
+  //                 {{@ 202} {}}}
+  //                 {{@ 202} {}}}
+  //                 @}, w/ trailing nulls
+  // Only top level nulls are replaced.
+  cudf::test::fixed_width_column_wrapper<K> key{1, 0, 1, 1, 0, 0, 1};
+
+  // clang-format off
+  LCW val({LCW({LCW{}, LCW({102, -1}, Mask_t{1, 0}.begin())}, Mask_t{0, 1}.begin()),
+           LCW({LCW({1, -1, 3}, Mask_t{1, 0, 1}.begin()), {}}, Mask_t{1, 0}.begin()),
+           {},
+           {LCW({-1, 202}, Mask_t{0, 1}.begin()), LCW{}},
+           {},
+           {LCW({{}}, Mask_t{0}.begin()), LCW{}},
+           {}},
+           Mask_t{1, 1, 0, 1, 0, 1, 0}.begin());
+  // clang-format on
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1, 1};
+
+  // clang-format off
+  LCW expect_val({LCW({LCW({1, -1, 3}, Mask_t{1, 0, 1}.begin()), {}}, Mask_t{1, 0}.begin()),
+                 {LCW({{}}, Mask_t{0}.begin()), LCW{}},
+                 {LCW({{}}, Mask_t{0}.begin()), LCW{}},
+                 LCW({LCW{}, LCW({102, -1}, Mask_t{1, 0}.begin())}, Mask_t{0, 1}.begin()),
+                 {LCW({-1, 202}, Mask_t{0, 1}.begin()), LCW{}},
+                 {LCW({-1, 202}, Mask_t{0, 1}.begin()), LCW{}},
+                 {}},
+           Mask_t{1, 1, 1, 1, 1, 1, 0}.begin());
+  // clang-format on
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
+
+struct GroupbyReplaceNullsStructsTest : public cudf::test::BaseFixture {
+  using SCW = cudf::test::structs_column_wrapper;
+
+  SCW data(cudf::test::fixed_width_column_wrapper<int32_t> field0,
+           cudf::test::strings_column_wrapper field1,
+           cudf::test::lists_column_wrapper<int32_t> field2,
+           std::initializer_list<cudf::valid_type> mask)
+  {
+    return SCW({field0, field1, field2}, mask.begin());
+  }
+};
+
+TEST_F(GroupbyReplaceNullsStructsTest, PrecedingFill)
+{
+  using LCW    = cudf::test::lists_column_wrapper<int32_t>;
+  using Mask_t = std::vector<cudf::valid_type>;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 0, 0, 1, 0, 1, 1};
+
+  // Only null rows are replaced.
+
+  SCW val =
+    this->data({{1, -1, 3, -1, -1, -1, 7}, {1, 0, 1, 0, 0, 0, 1}},
+               {{"x", "yy", "", "", "", "zz", ""}, {true, true, false, false, false, true, false}},
+               LCW({{1, 2, 3}, {-1}, {}, {}, {42}, {}, {}}, Mask_t{1, 1, 0, 0, 1, 0, 0}.begin()),
+               {1, 1, 0, 0, 1, 1, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1, 1};
+
+  SCW expect_val = this->data(
+    {{-1, -1, -1, 1, 1, -1, -1}, {0, 0, 0, 1, 1, 0, 0}},
+    {{"yy", "yy", "", "x", "x", "zz", "zz"}, {true, true, false, true, true, true, true}},
+    LCW({LCW{-1}, {-1}, {42}, {1, 2, 3}, {1, 2, 3}, {}, {}}, Mask_t{1, 1, 1, 1, 1, 0, 0}.begin()),
+    {1, 1, 1, 1, 1, 1, 1});
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::PRECEDING);
+}
+
+TEST_F(GroupbyReplaceNullsStructsTest, FollowingFill)
+{
+  using LCW    = cudf::test::lists_column_wrapper<int32_t>;
+  using Mask_t = std::vector<cudf::valid_type>;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 0, 0, 1, 0, 1, 1};
+
+  // Only null rows are replaced.
+
+  SCW val =
+    this->data({{1, -1, 3, -1, -1, -1, 7}, {1, 0, 1, 0, 0, 0, 1}},
+               {{"x", "yy", "", "", "", "zz", ""}, {true, true, false, false, false, true, false}},
+               LCW({{1, 2, 3}, {-1}, {}, {}, {42}, {}, {}}, Mask_t{1, 1, 0, 0, 1, 0, 0}.begin()),
+               {1, 1, 0, 0, 1, 1, 0});
+
+  cudf::test::fixed_width_column_wrapper<K> expect_key{0, 0, 0, 1, 1, 1, 1};
+
+  SCW expect_val = this->data(
+    {{-1, -1, -1, 1, -1, -1, -1}, {0, 0, 0, 1, 0, 0, 0}},
+    {{"yy", "", "", "x", "zz", "zz", ""}, {true, false, false, true, true, true, false}},
+    LCW({LCW{-1}, {42}, {42}, {1, 2, 3}, {}, {}, {}}, Mask_t{1, 1, 1, 1, 0, 0, 0}.begin()),
+    {1, 1, 1, 1, 1, 1, 0});
+
+  TestReplaceNullsGroupbySingle(key, val, expect_key, expect_val, cudf::replace_policy::FOLLOWING);
+}
diff --git a/cpp/tests/groupby/shift_tests.cpp b/cpp/tests/groupby/shift_tests.cpp
new file mode 100644
index 0000000..d2ecb66
--- /dev/null
+++ b/cpp/tests/groupby/shift_tests.cpp
@@ -0,0 +1,529 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+template <typename T>
+struct groupby_shift_fixed_width_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_shift_fixed_width_test, cudf::test::FixedWidthTypes);
+
+template <typename K, typename V>
+void test_groupby_shift_fixed_width_single(
+  cudf::test::fixed_width_column_wrapper<K> const& key,
+  cudf::test::fixed_width_column_wrapper<V> const& value,
+  cudf::size_type offset,
+  cudf::scalar const& fill_value,
+  cudf::test::fixed_width_column_wrapper<V> const& expected)
+{
+  cudf::groupby::groupby gb_obj(cudf::table_view({key}));
+  std::vector<cudf::size_type> offsets{offset};
+  auto got = gb_obj.shift(cudf::table_view{{value}}, offsets, {fill_value});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL((*got.second).view().column(0), expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, ForwardShiftWithoutNull_NullScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9};
+  cudf::test::fixed_width_column_wrapper<V> expected({-1, -1, 3, 5, -1, -1, 4},
+                                                     {0, 0, 1, 1, 0, 0, 1});
+  cudf::size_type offset = 2;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, ForwardShiftWithNull_NullScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> val({3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> expected({-1, -1, -1, -1, -1, -1, -1},
+                                                     {0, 0, 0, 0, 0, 0, 0});
+  cudf::size_type offset = 2;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, ForwardShiftWithoutNull_ValidScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val({3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5});
+  cudf::test::fixed_width_column_wrapper<V> expected({42, 42, 42, 3, 5, 8, 9, 42, 42, 42, 4, 6, 7});
+  cudf::size_type offset = 3;
+  auto slr =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(42), true);
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, ForwardShiftWithNull_ValidScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val({3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5},
+                                                {1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1});
+  cudf::test::fixed_width_column_wrapper<V> expected(
+    {42, 42, 42, 3, 5, -1, -1, 42, 42, 42, -1, -1, 7}, {1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1});
+  cudf::size_type offset = 3;
+  auto slr =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(42), true);
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, BackwardShiftWithoutNull_NullScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9};
+  cudf::test::fixed_width_column_wrapper<V> expected({5, 8, 9, -1, 6, 7, -1},
+                                                     {1, 1, 1, 0, 1, 1, 0});
+  cudf::size_type offset = -1;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, BackwardShiftWithNull_NullScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> val({3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> expected({-1, 8, 9, -1, 6, 7, -1},
+                                                     {0, 1, 1, 0, 1, 1, 0});
+  cudf::size_type offset = -1;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, BackwardShiftWithoutNull_ValidScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<V> expected(
+    {3, 5, 42, 42, 42, 42, 42, 4, 42, 42, 42, 42, 42});
+  cudf::size_type offset = -5;
+  auto slr =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(42), true);
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, BackwardShiftWithNull_ValidScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val({3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5},
+                                                {1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1});
+  cudf::test::fixed_width_column_wrapper<V> expected({5, -1, -1, -1, 3, 5, 42, -1, 7, 0, 2, -1, 42},
+                                                     {1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1});
+  cudf::size_type offset = -1;
+  auto slr =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(42), true);
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, ZeroShiftNullScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9};
+  cudf::test::fixed_width_column_wrapper<V> expected({3, 5, 8, 9, 4, 6, 7});
+  cudf::size_type offset = 0;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, ZeroShiftValidScalar)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<V> expected({3, 5, 8, 9, 1, 3, 5, 4, 6, 7, 0, 2, 4});
+  cudf::size_type offset = 0;
+  auto slr =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(42), true);
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, VeryLargeForwardOffset)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<V> expected(
+    {-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
+  cudf::size_type offset = 1024;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+TYPED_TEST(groupby_shift_fixed_width_test, VeryLargeBackwardOffset)
+{
+  using K = int32_t;
+  using V = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 1};
+  cudf::test::fixed_width_column_wrapper<V> val{3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<V> expected(
+    {-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
+  cudf::size_type offset = -1024;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_fixed_width_single<K, V>(key, val, offset, *slr, expected);
+}
+
+struct groupby_shift_string_test : public cudf::test::BaseFixture {};
+
+template <typename K>
+void test_groupby_shift_string_single(cudf::test::fixed_width_column_wrapper<K> const& key,
+                                      cudf::test::strings_column_wrapper const& value,
+                                      cudf::size_type offset,
+                                      cudf::scalar const& fill_value,
+                                      cudf::test::strings_column_wrapper const& expected)
+{
+  cudf::groupby::groupby gb_obj(cudf::table_view({key}));
+  std::vector<cudf::size_type> offsets{offset};
+  auto got = gb_obj.shift(cudf::table_view{{value}}, offsets, {fill_value});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL((*got.second).view().column(0), expected);
+}
+
+TEST_F(groupby_shift_string_test, ForwardShiftWithoutNull_NullScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"", "a", "cc", "f", "", "bb", "d"},
+                                              {0, 1, 1, 1, 0, 1, 1});
+  cudf::size_type offset = 1;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, ForwardShiftWithNull_NullScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val({"a", "bb", "cc", "d", "eee", "f", "gg"},
+                                         {1, 0, 1, 1, 0, 0, 0});
+  cudf::test::strings_column_wrapper expected({"", "", "a", "cc", "", "", ""},
+                                              {0, 0, 1, 1, 0, 0, 0});
+  cudf::size_type offset = 2;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, ForwardShiftWithoutNull_ValidScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"42", "42", "a", "cc", "42", "42", "bb"});
+
+  cudf::size_type offset = 2;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, ForwardShiftWithNull_ValidScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val({"a", "bb", "cc", "d", "eee", "f", "gg"},
+                                         {1, 1, 0, 0, 1, 0, 1});
+  cudf::test::strings_column_wrapper expected({"42", "a", "", "", "42", "bb", ""},
+                                              {1, 1, 0, 0, 1, 1, 0});
+
+  cudf::size_type offset = 1;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, BackwardShiftWithoutNull_NullScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"gg", "", "", "", "", "", ""},
+                                              {1, 0, 0, 0, 0, 0, 0});
+
+  cudf::size_type offset = -3;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, BackwardShiftWithNull_NullScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val({"a", "bb", "cc", "d", "eee", "f", "gg"},
+                                         {1, 0, 1, 1, 0, 0, 0});
+  cudf::test::strings_column_wrapper expected({"cc", "", "", "", "d", "", ""},
+                                              {1, 0, 0, 0, 1, 0, 0});
+
+  cudf::size_type offset = -1;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, BackwardShiftWithoutNull_ValidScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"42", "42", "42", "42", "42", "42", "42"});
+
+  cudf::size_type offset = -4;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, BackwardShiftWithNull_ValidScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val({"a", "bb", "cc", "d", "eee", "f", "gg"},
+                                         {1, 1, 0, 0, 1, 0, 1});
+  cudf::test::strings_column_wrapper expected({"", "gg", "42", "42", "eee", "42", "42"},
+                                              {0, 1, 1, 1, 1, 1, 1});
+
+  cudf::size_type offset = -2;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, ZeroShiftNullScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"a", "cc", "f", "gg", "bb", "d", "eee"});
+
+  cudf::size_type offset = 0;
+  auto slr               = cudf::make_default_constructed_scalar(cudf::column_view(val).type());
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, ZeroShiftValidScalar)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"a", "cc", "f", "gg", "bb", "d", "eee"});
+
+  cudf::size_type offset = 0;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, VeryLargeForwardOffset)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"42", "42", "42", "42", "42", "42", "42"});
+
+  cudf::size_type offset = 1024;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+TEST_F(groupby_shift_string_test, VeryLargeBackwardOffset)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper val{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::strings_column_wrapper expected({"42", "42", "42", "42", "42", "42", "42"});
+
+  cudf::size_type offset = -1024;
+  auto slr               = cudf::make_string_scalar("42");
+
+  test_groupby_shift_string_single(key, val, offset, *slr, expected);
+}
+
+template <typename T>
+struct groupby_shift_mixed_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_shift_mixed_test, cudf::test::FixedWidthTypes);
+
+template <typename K>
+void test_groupby_shift_multi(cudf::test::fixed_width_column_wrapper<K> const& key,
+                              cudf::table_view const& value,
+                              std::vector<cudf::size_type> offsets,
+                              std::vector<std::reference_wrapper<const cudf::scalar>> fill_values,
+                              cudf::table_view const& expected)
+{
+  cudf::groupby::groupby gb_obj(cudf::table_view({key}));
+  auto got = gb_obj.shift(value, offsets, fill_values);
+  CUDF_TEST_EXPECT_TABLES_EQUAL((*got.second).view(), expected);
+}
+
+TYPED_TEST(groupby_shift_mixed_test, NoFill)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper v1{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::fixed_width_column_wrapper<TypeParam> v2{1, 2, 3, 4, 5, 6, 7};
+  cudf::table_view value{{v1, v2}};
+
+  cudf::test::strings_column_wrapper e1({"", "", "a", "cc", "", "", "bb"}, {0, 0, 1, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<TypeParam> e2({-1, 1, 3, 6, -1, 2, 4},
+                                                       {0, 1, 1, 1, 0, 1, 1});
+  cudf::table_view expected{{e1, e2}};
+
+  std::vector<cudf::size_type> offset{2, 1};
+  auto slr1 = cudf::make_default_constructed_scalar(cudf::column_view(v1).type());
+  auto slr2 = cudf::make_default_constructed_scalar(cudf::column_view(v2).type());
+  std::vector<std::reference_wrapper<const cudf::scalar>> fill_values{*slr1, *slr2};
+
+  test_groupby_shift_multi(key, value, offset, fill_values, expected);
+}
+
+TYPED_TEST(groupby_shift_mixed_test, Fill)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{1, 2, 1, 2, 2, 1, 1};
+  cudf::test::strings_column_wrapper v1{"a", "bb", "cc", "d", "eee", "f", "gg"};
+  cudf::test::fixed_width_column_wrapper<TypeParam> v2{1, 2, 3, 4, 5, 6, 7};
+  cudf::table_view value{{v1, v2}};
+
+  cudf::test::strings_column_wrapper e1({"cc", "f", "gg", "42", "d", "eee", "42"});
+  cudf::test::fixed_width_column_wrapper<TypeParam> e2({6, 7, 42, 42, 5, 42, 42});
+  cudf::table_view expected{{e1, e2}};
+
+  std::vector<cudf::size_type> offset{-1, -2};
+
+  auto slr1 = cudf::make_string_scalar("42");
+  auto slr2 =
+    cudf::scalar_type_t<TypeParam>(cudf::test::make_type_param_scalar<TypeParam>(42), true);
+  std::vector<std::reference_wrapper<const cudf::scalar>> fill_values{*slr1, slr2};
+
+  test_groupby_shift_multi(key, value, offset, fill_values, expected);
+}
+
+struct groupby_shift_fixed_point_type_test : public cudf::test::BaseFixture {};
+
+TEST_F(groupby_shift_fixed_point_type_test, Matching)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{2, 3, 4, 4, 3, 2, 2, 4};
+  cudf::test::fixed_point_column_wrapper<int32_t> v1{{10, 10, 40, 40, 20, 20, 30, 40},
+                                                     numeric::scale_type{-1}};
+  cudf::test::fixed_point_column_wrapper<int64_t> v2{{5, 5, 8, 8, 6, 7, 9, 7},
+                                                     numeric::scale_type{3}};
+  cudf::table_view value{{v1, v2}};
+
+  std::vector<cudf::size_type> offset{-3, 1};
+  auto slr1 = cudf::make_fixed_point_scalar<numeric::decimal32>(-42, numeric::scale_type{-1});
+  auto slr2 = cudf::make_fixed_point_scalar<numeric::decimal64>(42, numeric::scale_type{3});
+  std::vector<std::reference_wrapper<const cudf::scalar>> fill_values{*slr1, *slr2};
+
+  cudf::test::fixed_point_column_wrapper<int32_t> e1{{-42, -42, -42, -42, -42, -42, -42, -42},
+                                                     numeric::scale_type{-1}};
+  cudf::test::fixed_point_column_wrapper<int64_t> e2{{42, 5, 7, 42, 5, 42, 8, 8},
+                                                     numeric::scale_type{3}};
+  cudf::table_view expected{{e1, e2}};
+
+  test_groupby_shift_multi(key, value, offset, fill_values, expected);
+}
+
+TEST_F(groupby_shift_fixed_point_type_test, MismatchScaleType)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{2, 3, 4, 4, 3, 2, 2, 4};
+  cudf::test::fixed_point_column_wrapper<int32_t> v1{{10, 10, 40, 40, 20, 20, 30, 40},
+                                                     numeric::scale_type{-1}};
+
+  std::vector<cudf::size_type> offset{-3};
+  auto slr1 = cudf::make_fixed_point_scalar<numeric::decimal32>(-42, numeric::scale_type{-4});
+
+  cudf::test::fixed_point_column_wrapper<int32_t> stub{{-42, -42, -42, -42, -42, -42, -42, -42},
+                                                       numeric::scale_type{-1}};
+
+  EXPECT_THROW(test_groupby_shift_multi(
+                 key, cudf::table_view{{v1}}, offset, {*slr1}, cudf::table_view{{stub}}),
+               cudf::logic_error);
+}
+
+TEST_F(groupby_shift_fixed_point_type_test, MismatchRepType)
+{
+  using K = int32_t;
+  cudf::test::fixed_width_column_wrapper<K> key{2, 3, 4, 4, 3, 2, 2, 4};
+  cudf::test::fixed_point_column_wrapper<int64_t> v1{{10, 10, 40, 40, 20, 20, 30, 40},
+                                                     numeric::scale_type{-1}};
+
+  std::vector<cudf::size_type> offset{-3};
+  auto slr1 = cudf::make_fixed_point_scalar<numeric::decimal32>(-42, numeric::scale_type{-1});
+
+  cudf::test::fixed_point_column_wrapper<int32_t> stub{{-42, -42, -42, -42, -42, -42, -42, -42},
+                                                       numeric::scale_type{-1}};
+
+  EXPECT_THROW(test_groupby_shift_multi(
+                 key, cudf::table_view{{v1}}, offset, {*slr1}, cudf::table_view{{stub}}),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/groupby/std_tests.cpp b/cpp/tests/groupby/std_tests.cpp
new file mode 100644
index 0000000..4c07e2e
--- /dev/null
+++ b/cpp/tests/groupby/std_tests.cpp
@@ -0,0 +1,163 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_std_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+TYPED_TEST_SUITE(groupby_std_test, supported_types);
+
+TYPED_TEST(groupby_std_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  // clang-format off
+  //                                                    {1, 1, 1,  2, 2, 2, 2,    3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K>  expect_keys{1,        2,             3};
+              //                                        {0, 3, 6,  1, 4, 5, 9,    2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3.,       sqrt(131./12), sqrt(31./3)}, no_nulls());
+  // clang-format on
+
+  auto agg = cudf::make_std_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_std_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_std_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_std_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_std_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_std_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_std_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_std_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+
+  //                                        { 1, 1,     2, 2, 2,   3, 3,       4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //                                        { 3, 6,     1, 4, 9,   2, 8,       3}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3 / sqrt(2), 7 / sqrt(3), 3 * sqrt(2), 0.},
+                                                        {1, 1, 1, 0});
+
+  auto agg = cudf::make_std_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_std_test, ddof_non_default)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+
+  //                                        { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //                                        { 3, 6,     1, 4, 9,   2, 8,    3}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0., 7 * sqrt(2. / 3), 0., 0.},
+                                                        {0, 1, 0, 0});
+
+  auto agg = cudf::make_std_aggregation<cudf::groupby_aggregation>(2);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_std_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::STD>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //                                        {1, 1, 1,  2, 2, 2, 2,    3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,        2,             3});
+  //                                        {0, 3, 6,  1, 4, 5, 9,    2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({3.,       sqrt(131./12), sqrt(31./3)}, no_nulls());
+  // clang-format on
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_std_aggregation<cudf::groupby_aggregation>());
+}
diff --git a/cpp/tests/groupby/structs_tests.cpp b/cpp/tests/groupby/structs_tests.cpp
new file mode 100644
index 0000000..f85fc63
--- /dev/null
+++ b/cpp/tests/groupby/structs_tests.cpp
@@ -0,0 +1,318 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_structs_test : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(groupby_structs_test, cudf::test::FixedWidthTypes);
+
+template <typename T>
+using fwcw = cudf::test::fixed_width_column_wrapper<T>;
+
+template <typename T>
+using lcw = cudf::test::lists_column_wrapper<T>;
+
+namespace {
+static constexpr auto null = -1;  // Signifies null value.
+
+// Checking with a single aggregation, and aggregation column.
+// This test is orthogonal to the aggregation type; it focuses on testing the grouping
+// with STRUCT keys.
+
+// Set this to true to enable printing, for debugging.
+auto constexpr print_enabled = false;
+
+void print_agg_results(cudf::column_view const& keys, cudf::column_view const& vals)
+{
+  if constexpr (print_enabled) {
+    auto requests = std::vector<cudf::groupby::aggregation_request>{};
+    requests.push_back(cudf::groupby::aggregation_request{});
+    requests.back().values = vals;
+    requests.back().aggregations.push_back(cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+    requests.back().aggregations.push_back(
+      cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+
+    auto gby = cudf::groupby::groupby{
+      cudf::table_view({keys}), cudf::null_policy::INCLUDE, cudf::sorted::NO, {}, {}};
+    auto result = gby.aggregate(requests);
+    std::cout << "Results: Keys: " << std::endl;
+    cudf::test::print(result.first->get_column(0).view());
+    std::cout << "Results: Values: " << std::endl;
+    cudf::test::print(result.second.front().results[0]->view());
+  }
+}
+
+}  // namespace
+
+TYPED_TEST(groupby_structs_test, basic)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {  0,    1,    2,    3,    4,    5,    6,    7,    8,    9};
+  auto member_0 = fwcw<M0>{  1,    2,    3,    1,    2,    2,    1,    3,    3,    2};
+  auto member_1 = fwcw<M1>{ 11,   22,   33,   11,   22,   22,   11,   33,   33,   22};
+  auto member_2 = cudf::test::strings_column_wrapper {"11", "22", "33", "11", "22", "22", "11", "33", "33", "22"};
+  auto keys     = cudf::test::structs_column_wrapper{member_0, member_1, member_2};
+
+  auto expected_values   = fwcw<R> {  9,   19,   17 };
+  auto expected_member_0 = fwcw<M0>{  1,    2,    3 };
+  auto expected_member_1 = fwcw<M1>{ 11,   22,   33 };
+  auto expected_member_2 = cudf::test::strings_column_wrapper {"11", "22", "33"};
+  auto expected_keys     = cudf::test::structs_column_wrapper{expected_member_0, expected_member_1, expected_member_2};
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, structs_with_nulls_in_members)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {  0,       1,    2,    3,    4,    5,    6,      7,    8,    9 };
+  auto member_0 = fwcw<M0>{{ 1,    null,    3,    1,    2,    2,    1,      3,    3,    2 }, null_at(1)};
+  auto member_1 = fwcw<M1>{{ 11,     22,   33,   11,   22,   22,   11,   null,   33,   22 }, null_at(7)};
+  auto member_2 = cudf::test::strings_column_wrapper { "11",   "22", "33", "11", "22", "22", "11",   "33", "33", "22"};
+  auto keys     = cudf::test::structs_column_wrapper{{member_0, member_1, member_2}};
+  // clang-format on
+
+  print_agg_results(keys, values);
+
+  // clang-format off
+  auto expected_values   = fwcw<R> {    9,   18,    10,     7,     1  };
+  auto expected_member_0 = fwcw<M0>{ {  1,    2,     3,     3,  null  }, null_at(4)};
+  auto expected_member_1 = fwcw<M1>{ { 11,   22,    33,  null,    22  }, null_at(3)};
+  auto expected_member_2 = cudf::test::strings_column_wrapper {  "11", "22",  "33",  "33",  "22" };
+  auto expected_keys     = cudf::test::structs_column_wrapper{expected_member_0, expected_member_1, expected_member_2};
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, structs_with_null_rows)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {  0,    1,    2,    3,    4,    5,    6,    7,    8,    9};
+  auto member_0 = fwcw<M0>{  1,    2,    3,    1,    2,    2,    1,    3,    3,    2};
+  auto member_1 = fwcw<M1>{ 11,   22,   33,   11,   22,   22,   11,   33,   33,   22};
+  auto member_2 = cudf::test::strings_column_wrapper {"11", "22", "33", "11", "22", "22", "11", "33", "33", "22"};
+  auto keys     = cudf::test::structs_column_wrapper{{member_0, member_1, member_2}, nulls_at({0, 3})};
+
+  auto expected_values   = fwcw<R> {    6,   19,   17,      3  };
+  auto expected_member_0 = fwcw<M0>{ {  1,    2,    3,   null  }, null_at(3)};
+  auto expected_member_1 = fwcw<M1>{ { 11,   22,   33,   null  }, null_at(3)};
+  auto expected_member_2 = cudf::test::strings_column_wrapper { {"11", "22", "33", "null" }, null_at(3)};
+  auto expected_keys     = cudf::test::structs_column_wrapper{{expected_member_0, expected_member_1, expected_member_2}, null_at(3)};
+  // clang-format on
+
+  print_agg_results(keys, values);
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, structs_with_nulls_in_rows_and_members)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {  0,    1,    2,    3,    4,    5,    6,    7,    8,    9  };
+  auto member_0 = fwcw<M0>{{ 1,    2,    3,    1,    2,    2,    1,    3,    3,    2  }, null_at(1)};
+  auto member_1 = fwcw<M1>{{ 11,   22,   33,   11,   22,   22,   11,   33,   33,   22 }, null_at(7)};
+  auto member_2 = cudf::test::strings_column_wrapper { "11", "22", "33", "11", "22", "22", "11", "33", "33", "22"};
+  auto keys     = cudf::test::structs_column_wrapper{{member_0, member_1, member_2}, null_at(4)};
+  // clang-format on
+
+  print_agg_results(keys, values);
+
+  // clang-format off
+  auto expected_values   = fwcw<R> {    9,   14,    10,     7,     1,      4  };
+  auto expected_member_0 = fwcw<M0>{{   1,    2,     3,     3,  null,   null  }, nulls_at({4,5})};
+  auto expected_member_1 = fwcw<M1>{{  11,   22,    33,  null,    22,   null  }, nulls_at({3,5})};
+  auto expected_member_2 = cudf::test::strings_column_wrapper {{ "11", "22",  "33",  "33",  "22", "null" }, null_at(5)};
+  auto expected_keys     = cudf::test::structs_column_wrapper{{expected_member_0, expected_member_1, expected_member_2}, null_at(5)};
+  // clang-format on
+
+  print_agg_results(keys, values);
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, null_members_differ_from_null_structs)
+{
+  // This test specifically confirms that a non-null STRUCT row `{null, null, null}` is grouped
+  // differently from a null STRUCT row (whose members are incidentally null).
+
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {    0,      1,    2,    3,    4,    5,    6,    7,    8,    9 };
+  auto member_0 = fwcw<M0>{{   1,   null,    3,    1,    2,    2,    1,    3,    3,    2 }, null_at(1)};
+  auto member_1 = fwcw<M1>{{  11,   null,   33,   11,   22,   22,   11,   33,   33,   22 }, null_at(1)};
+  auto member_2 = cudf::test::strings_column_wrapper {{ "11", "null", "33", "11", "22", "22", "11", "33", "33", "22"}, null_at(1)};
+  auto keys     = cudf::test::structs_column_wrapper{{member_0, member_1, member_2}, null_at(4)};
+  // clang-format on
+
+  print_agg_results(keys, values);
+
+  // Index-3 => Non-null Struct row, with nulls for all members.
+  // Index-4 => Null Struct row.
+
+  // clang-format off
+  auto expected_values   = fwcw<R> {    9,   14,    17,      1,      4  };
+  auto expected_member_0 = fwcw<M0>{ {  1,    2,     3,   null,   null  }, nulls_at({3,4})};
+  auto expected_member_1 = fwcw<M1>{ { 11,   22,    33,   null,   null  }, nulls_at({3,4})};
+  auto expected_member_2 = cudf::test::strings_column_wrapper { {"11", "22",  "33", "null", "null" }, nulls_at({3,4})};
+  auto expected_keys     = cudf::test::structs_column_wrapper{{expected_member_0, expected_member_1, expected_member_2}, null_at(4)};
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, structs_of_structs)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values            = fwcw<V> {    0,      1,    2,    3,    4,    5,    6,    7,    8,    9 };
+  auto struct_0_member_0 = fwcw<M0>{{   1,   null,    3,    1,    2,    2,    1,    3,    3,    2 }, null_at(1)};
+  auto struct_0_member_1 = fwcw<M1>{{  11,   null,   33,   11,   22,   22,   11,   33,   33,   22 }, null_at(1)};
+  auto struct_0_member_2 = cudf::test::strings_column_wrapper {{ "11", "null", "33", "11", "22", "22", "11", "33", "33", "22"}, null_at(1)};
+  // clang-format on
+
+  auto struct_0 = cudf::test::structs_column_wrapper{
+    {struct_0_member_0, struct_0_member_1, struct_0_member_2}, null_at(4)};
+  auto struct_1_member_1 = fwcw<M1>{8, 9, 6, 8, 0, 7, 8, 6, 6, 7};
+
+  auto keys = cudf::test::structs_column_wrapper{
+    {struct_0, struct_1_member_1}};  // Struct of cudf::test::structs_column_wrapper.
+
+  print_agg_results(keys, values);
+
+  // clang-format off
+  auto expected_values            = fwcw<R> {    9,   14,    17,      1,      4  };
+  auto expected_member_0          = fwcw<M0>{ {  1,    2,     3,   null,   null  }, nulls_at({3,4})};
+  auto expected_member_1          = fwcw<M1>{ { 11,   22,    33,   null,   null  }, nulls_at({3,4})};
+  auto expected_member_2          = cudf::test::strings_column_wrapper { {"11", "22",  "33", "null", "null" }, nulls_at({3,4})};
+  auto expected_structs           = cudf::test::structs_column_wrapper{{expected_member_0, expected_member_1, expected_member_2}, null_at(4)};
+  auto expected_struct_1_member_1 = fwcw<M1>{    8,    7,     6,      9,      0  };
+  auto expected_keys              = cudf::test::structs_column_wrapper{{expected_structs, expected_struct_1_member_1}};
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, empty_input)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {};
+  auto member_0 = fwcw<M0>{};
+  auto member_1 = fwcw<M1>{};
+  auto member_2 = cudf::test::strings_column_wrapper {};
+  auto keys     = cudf::test::structs_column_wrapper{member_0, member_1, member_2};
+
+  auto expected_values   = fwcw<R> {};
+  auto expected_member_0 = fwcw<M0>{};
+  auto expected_member_1 = fwcw<M1>{};
+  auto expected_member_2 = cudf::test::strings_column_wrapper {};
+  auto expected_keys     = cudf::test::structs_column_wrapper{expected_member_0, expected_member_1, expected_member_2};
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, all_null_input)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {  0,    1,    2,    3,    4,    5,    6,    7,    8,    9};
+  auto member_0 = fwcw<M0>{  1,    2,    3,    1,    2,    2,    1,    3,    3,    2};
+  auto member_1 = fwcw<M1>{ 11,   22,   33,   11,   22,   22,   11,   33,   33,   22};
+  auto member_2 = cudf::test::strings_column_wrapper {"11", "22", "33", "11", "22", "22", "11", "33", "33", "22"};
+  auto keys     = cudf::test::structs_column_wrapper{{member_0, member_1, member_2}, all_nulls()};
+
+  auto expected_values   = fwcw<R> {    45 };
+  auto expected_member_0 = fwcw<M0>{ null };
+  auto expected_member_1 = fwcw<M1>{ null };
+  auto expected_member_2 = cudf::test::strings_column_wrapper {"null"};
+  auto expected_keys     = cudf::test::structs_column_wrapper{{expected_member_0, expected_member_1, expected_member_2}, all_nulls()};
+  // clang-format on
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
+
+TYPED_TEST(groupby_structs_test, lists_as_keys)
+{
+  using V  = int32_t;    // Type of Aggregation Column.
+  using M0 = int32_t;    // Type of STRUCT's first (i.e. 0th) member.
+  using M1 = TypeParam;  // Type of STRUCT's second (i.e. 1th) member.
+  using R  = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  // clang-format off
+  auto values   = fwcw<V> {     0,      1,      2,      3,       4  };
+  auto member_0 = lcw<M0> { {1,1},  {2,2},  {3,3},   {1,1},   {2,2} };
+  auto member_1 = fwcw<M1>{     1,      2,      3,      1,       2  };
+  // clang-format on
+  auto keys = cudf::test::structs_column_wrapper{{member_0, member_1}};
+
+  // clang-format off
+  auto expected_values   = fwcw<R> {     3,      5,      2 };
+  auto expected_member_0 = lcw<M0> { {1,1},  {2,2},  {3,3} };
+  auto expected_member_1 = fwcw<M1>{     1,      2,      3 };
+  // clang-format on
+  auto expected_keys = cudf::test::structs_column_wrapper{{expected_member_0, expected_member_1}};
+
+  test_sum_agg(keys, values, expected_keys, expected_values);
+}
diff --git a/cpp/tests/groupby/sum_of_squares_tests.cpp b/cpp/tests/groupby/sum_of_squares_tests.cpp
new file mode 100644
index 0000000..2372411
--- /dev/null
+++ b/cpp/tests/groupby/sum_of_squares_tests.cpp
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_sum_of_squares_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+TYPED_TEST_SUITE(groupby_sum_of_squares_test, supported_types);
+
+TYPED_TEST(groupby_sum_of_squares_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM_OF_SQUARES>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //  { 1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  //  { 0, 3, 6,  1, 4, 5, 9,  2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({45., 123., 117.}, no_nulls());
+
+  auto agg = cudf::make_sum_of_squares_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_of_squares_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM_OF_SQUARES>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_sum_of_squares_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_of_squares_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM_OF_SQUARES>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_sum_of_squares_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_of_squares_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM_OF_SQUARES>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_sum_of_squares_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_of_squares_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM_OF_SQUARES>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4}, no_nulls());
+  //  { 3, 6,     1, 4, 9,   2, 8,    3}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({45., 98., 68., 9.}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_sum_of_squares_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_of_squares_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM_OF_SQUARES>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //                                                    {1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,        2,           3      });
+  //                                                    {0, 3, 6,  1, 4, 5, 9,  2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({45.,       123.,       117.   }, no_nulls());
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_sum_of_squares_aggregation<cudf::groupby_aggregation>());
+}
diff --git a/cpp/tests/groupby/sum_scan_tests.cpp b/cpp/tests/groupby/sum_scan_tests.cpp
new file mode 100644
index 0000000..b03212f
--- /dev/null
+++ b/cpp/tests/groupby/sum_scan_tests.cpp
@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using key_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+template <typename T>
+struct groupby_sum_scan_test : public cudf::test::BaseFixture {
+  using V              = T;
+  using R              = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+  using value_wrapper  = cudf::test::fixed_width_column_wrapper<V, int32_t>;
+  using result_wrapper = cudf::test::fixed_width_column_wrapper<R, int32_t>;
+};
+
+using supported_types =
+  cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>,
+                     cudf::test::DurationTypes>;
+
+TYPED_TEST_SUITE(groupby_sum_scan_test, supported_types);
+
+TYPED_TEST(groupby_sum_scan_test, basic)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys  {1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  value_wrapper vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  key_wrapper expect_keys   {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  //                        {0, 3, 6, 1, 4, 5, 9, 2, 7, 8}
+  result_wrapper expect_vals{0, 3, 9, 1, 5, 10, 19, 2, 9, 17};
+  // clang-format on
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_scan_test, pre_sorted)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys  {1, 1, 1, 2, 2, 2, 2, 3, 3, 3};
+  value_wrapper vals{0, 3, 6, 1, 4, 5, 9, 2, 7, 8};
+
+  key_wrapper expect_keys   {1, 1, 1, 2, 2,  2,  2, 3, 3, 3};
+  result_wrapper expect_vals{0, 3, 9, 1, 5, 10, 19, 2, 9, 17};
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys,
+                   vals,
+                   expect_keys,
+                   expect_vals,
+                   std::move(agg),
+                   cudf::null_policy::EXCLUDE,
+                   cudf::sorted::YES);
+}
+
+TYPED_TEST(groupby_sum_scan_test, empty_cols)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys{};
+  value_wrapper vals{};
+
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_scan_test, zero_valid_keys)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys({1, 2, 3}, cudf::test::iterators::all_nulls());
+  value_wrapper vals{3, 4, 5};
+  key_wrapper expect_keys{};
+  result_wrapper expect_vals{};
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_scan_test, zero_valid_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  key_wrapper keys{1, 1, 1};
+  value_wrapper vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+  key_wrapper expect_keys{1, 1, 1};
+  result_wrapper expect_vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_sum_scan_test, null_keys_and_values)
+{
+  using value_wrapper  = typename TestFixture::value_wrapper;
+  using result_wrapper = typename TestFixture::result_wrapper;
+
+  // clang-format off
+  key_wrapper keys(  {1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4}, {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  value_wrapper vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //                         { 1, 1, 1, 2, 2,  2,  2, 3, *, 3, 4};
+  key_wrapper expect_keys(   { 1, 1, 1, 2, 2,  2,  2, 3,    3, 4}, cudf::test::iterators::no_nulls());
+                          // { -, 3, 6, 1, 4,  -,  9, 2, _, 8, -}
+  result_wrapper expect_vals({-1, 3, 9, 1, 5, -1, 14, 2,   10, -1},
+                             { 0, 1, 1, 1, 1,  0,  1, 1,    1, 0});
+  // clang-format on
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+  test_single_scan(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+template <typename T>
+struct GroupBySumScanFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupBySumScanFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupBySumScanFixedPointTest, GroupBySortSumScanDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys = key_wrapper{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals = fp_wrapper{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+
+    auto const expect_keys     = key_wrapper{1, 1, 1, 2, 2,  2,  2, 3, 3,  3};
+    auto const expect_vals_sum = fp_wrapper{{0, 3, 9, 1, 5, 10, 19, 2, 9, 17}, scale};
+    // clang-format on
+
+    auto agg2 = cudf::make_sum_aggregation<cudf::groupby_scan_aggregation>();
+    test_single_scan(keys, vals, expect_keys, expect_vals_sum, std::move(agg2));
+  }
+}
diff --git a/cpp/tests/groupby/sum_tests.cpp b/cpp/tests/groupby/sum_tests.cpp
new file mode 100644
index 0000000..35e8fd1
--- /dev/null
+++ b/cpp/tests/groupby/sum_tests.cpp
@@ -0,0 +1,231 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_sum_test : public cudf::test::BaseFixture {};
+
+using K = int32_t;
+using supported_types =
+  cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>,
+                     cudf::test::DurationTypes>;
+
+TYPED_TEST_SUITE(groupby_sum_test, supported_types);
+
+TYPED_TEST(groupby_sum_test, basic)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{9, 19, 17};
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_sum_test, empty_cols)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_sum_test, zero_valid_keys)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, cudf::test::iterators::all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_sum_test, zero_valid_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, cudf::test::iterators::all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, cudf::test::iterators::all_nulls());
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_sum_test, null_keys_and_values)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 4},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0});
+
+  //  { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1, 2, 3, 4},
+                                                        cudf::test::iterators::no_nulls());
+  //  { 3, 6,     1, 4, 9,   2, 8,    -}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({9, 14, 10, 0}, {1, 1, 1, 0});
+
+  auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+
+  auto agg2 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg2), force_use_sort_impl::YES);
+}
+
+TYPED_TEST(groupby_sum_test, dictionary)
+{
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::SUM>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{9, 19, 17};
+
+  test_single_agg(
+    keys, vals, expect_keys, expect_vals, cudf::make_sum_aggregation<cudf::groupby_aggregation>());
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_sum_aggregation<cudf::groupby_aggregation>(),
+                  force_use_sort_impl::YES);
+}
+
+struct overflow_test : public cudf::test::BaseFixture {};
+TEST_F(overflow_test, overflow_integer)
+{
+  using int32_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using int64_col = cudf::test::fixed_width_column_wrapper<int64_t>;
+
+  auto const keys        = int32_col{0, 0};
+  auto const vals        = int32_col{-2147483648, -2147483648};
+  auto const expect_keys = int32_col{0};
+  auto const expect_vals = int64_col{-4294967296L};
+
+  auto test_sum = [&](auto const use_sort) {
+    auto agg = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg), use_sort);
+  };
+
+  test_sum(force_use_sort_impl::NO);
+  test_sum(force_use_sort_impl::YES);
+}
+
+template <typename T>
+struct GroupBySumFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(GroupBySumFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupBySumFixedPointTest, GroupBySortSumDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using K          = int32_t;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_sum = fp_wrapper{{9, 19, 17}, scale};
+
+    auto agg1 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(
+      keys, vals, expect_keys, expect_vals_sum, std::move(agg1), force_use_sort_impl::YES);
+
+    auto agg4 = cudf::make_product_aggregation<cudf::groupby_aggregation>();
+    EXPECT_THROW(
+      test_single_agg(keys, vals, expect_keys, {}, std::move(agg4), force_use_sort_impl::YES),
+      cudf::logic_error);
+  }
+}
+
+TYPED_TEST(GroupBySumFixedPointTest, GroupByHashSumDecimalAsValue)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using K          = int32_t;
+
+  for (auto const i : {2, 1, 0, -1, -2}) {
+    auto const scale = scale_type{i};
+    // clang-format off
+    auto const keys  = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+    auto const vals  = fp_wrapper{                              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, scale};
+    // clang-format on
+
+    auto const expect_keys     = cudf::test::fixed_width_column_wrapper<K>{1, 2, 3};
+    auto const expect_vals_sum = fp_wrapper{{9, 19, 17}, scale};
+
+    auto agg5 = cudf::make_sum_aggregation<cudf::groupby_aggregation>();
+    test_single_agg(keys, vals, expect_keys, expect_vals_sum, std::move(agg5));
+
+    auto agg8 = cudf::make_product_aggregation<cudf::groupby_aggregation>();
+    EXPECT_THROW(test_single_agg(keys, vals, expect_keys, {}, std::move(agg8)), cudf::logic_error);
+  }
+}
diff --git a/cpp/tests/groupby/tdigest_tests.cu b/cpp/tests/groupby/tdigest_tests.cu
new file mode 100644
index 0000000..97edc1c
--- /dev/null
+++ b/cpp/tests/groupby/tdigest_tests.cu
@@ -0,0 +1,508 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/tdigest_utilities.cuh>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/fill.h>
+#include <thrust/iterator/counting_iterator.h>
+
+/**
+ * @brief Functor to generate a tdigest by key.
+ *
+ */
+struct tdigest_gen_grouped {
+  template <
+    typename T,
+    typename std::enable_if_t<cudf::is_numeric<T>() || cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& keys,
+                                           cudf::column_view const& values,
+                                           int delta)
+  {
+    cudf::table_view t({keys});
+    cudf::groupby::groupby gb(t);
+    std::vector<cudf::groupby::aggregation_request> requests;
+    std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+    aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+    requests.push_back({values, std::move(aggregations)});
+    auto result = gb.aggregate(requests);
+    return std::move(result.second[0].results[0]);
+  }
+
+  template <
+    typename T,
+    typename std::enable_if_t<!cudf::is_numeric<T>() && !cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& keys,
+                                           cudf::column_view const& values,
+                                           int delta)
+  {
+    CUDF_FAIL("Invalid tdigest test type");
+  }
+};
+
+/**
+ * @brief Functor for generating a tdigest using groupby with a constant key.
+ *
+ */
+struct tdigest_groupby_simple_op {
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& values, int delta) const
+  {
+    // make a simple set of matching keys.
+    auto keys = cudf::make_fixed_width_column(
+      cudf::data_type{cudf::type_id::INT32}, values.size(), cudf::mask_state::UNALLOCATED);
+    thrust::fill(rmm::exec_policy(cudf::get_default_stream()),
+                 keys->mutable_view().template begin<int>(),
+                 keys->mutable_view().template end<int>(),
+                 0);
+
+    cudf::table_view t({*keys});
+    cudf::groupby::groupby gb(t);
+    std::vector<cudf::groupby::aggregation_request> requests;
+    std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+    aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+    requests.push_back({values, std::move(aggregations)});
+    auto result = gb.aggregate(requests);
+    return std::move(result.second[0].results[0]);
+  }
+};
+
+/**
+ * @brief Functor for merging tdigests using groupby with a constant key.
+ *
+ */
+struct tdigest_groupby_simple_merge_op {
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& merge_values,
+                                           int merge_delta) const
+  {
+    // make a simple set of matching keys.
+    auto merge_keys = cudf::make_fixed_width_column(
+      cudf::data_type{cudf::type_id::INT32}, merge_values.size(), cudf::mask_state::UNALLOCATED);
+    thrust::fill(rmm::exec_policy(cudf::get_default_stream()),
+                 merge_keys->mutable_view().template begin<int>(),
+                 merge_keys->mutable_view().template end<int>(),
+                 0);
+
+    cudf::table_view key_table({*merge_keys});
+    cudf::groupby::groupby gb(key_table);
+    std::vector<cudf::groupby::aggregation_request> requests;
+    std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+    aggregations.push_back(
+      cudf::make_merge_tdigest_aggregation<cudf::groupby_aggregation>(merge_delta));
+    requests.push_back({merge_values, std::move(aggregations)});
+    auto result = gb.aggregate(requests);
+    return std::move(result.second[0].results[0]);
+  }
+};
+
+template <typename T>
+struct TDigestAllTypes : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(TDigestAllTypes, cudf::test::NumericTypes);
+
+TYPED_TEST(TDigestAllTypes, Simple)
+{
+  using T = TypeParam;
+  cudf::test::tdigest_simple_aggregation<T>(tdigest_groupby_simple_op{});
+}
+
+TYPED_TEST(TDigestAllTypes, SimpleWithNulls)
+{
+  using T = TypeParam;
+  cudf::test::tdigest_simple_with_nulls_aggregation<T>(tdigest_groupby_simple_op{});
+}
+
+TYPED_TEST(TDigestAllTypes, AllNull)
+{
+  using T = TypeParam;
+  cudf::test::tdigest_simple_all_nulls_aggregation<T>(tdigest_groupby_simple_op{});
+}
+
+TYPED_TEST(TDigestAllTypes, LargeGroups)
+{
+  auto _values = cudf::test::generate_standardized_percentile_distribution(
+    cudf::data_type{cudf::type_id::FLOAT64});
+  int const delta = 1000;
+
+  // generate a random set of keys
+  std::vector<int> h_keys;
+  h_keys.reserve(_values->size());
+  auto iter = thrust::make_counting_iterator(0);
+  std::transform(iter, iter + _values->size(), std::back_inserter(h_keys), [](int i) {
+    return static_cast<int>(round(cudf::test::rand_range(0, 8)));
+  });
+  cudf::test::fixed_width_column_wrapper<int> _keys(h_keys.begin(), h_keys.end());
+
+  // group the input values together
+  cudf::table_view k({_keys});
+  cudf::groupby::groupby setup_gb(k);
+  cudf::table_view v({*_values});
+  auto groups = setup_gb.get_groups(v);
+
+  // slice it all up so we have keys/columns for everything.
+  std::vector<cudf::column_view> keys;
+  std::vector<cudf::column_view> values;
+  for (size_t idx = 0; idx < groups.offsets.size() - 1; idx++) {
+    auto k =
+      cudf::slice(groups.keys->get_column(0), {groups.offsets[idx], groups.offsets[idx + 1]});
+    keys.push_back(k[0]);
+
+    auto v =
+      cudf::slice(groups.values->get_column(0), {groups.offsets[idx], groups.offsets[idx + 1]});
+    values.push_back(v[0]);
+  }
+
+  // generate a separate tdigest for each group
+  std::vector<std::unique_ptr<cudf::column>> parts;
+  std::transform(
+    iter, iter + values.size(), std::back_inserter(parts), [&keys, &values, delta](int i) {
+      cudf::table_view t({keys[i]});
+      cudf::groupby::groupby gb(t);
+      std::vector<cudf::groupby::aggregation_request> requests;
+      std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+      aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+      requests.push_back({values[i], std::move(aggregations)});
+      auto result = gb.aggregate(requests);
+      return std::move(result.second[0].results[0]);
+    });
+  std::vector<cudf::column_view> part_views;
+  std::transform(parts.begin(),
+                 parts.end(),
+                 std::back_inserter(part_views),
+                 [](std::unique_ptr<cudf::column> const& col) { return col->view(); });
+  auto merged_parts = cudf::concatenate(part_views);
+
+  // generate a tdigest on the whole input set
+  cudf::table_view t({_keys});
+  cudf::groupby::groupby gb(t);
+  std::vector<cudf::groupby::aggregation_request> requests;
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+  requests.push_back({*_values, std::move(aggregations)});
+  auto result = gb.aggregate(requests);
+
+  // verify that they end up the same.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result.second[0].results[0], *merged_parts);
+}
+
+struct TDigestTest : public cudf::test::BaseFixture {};
+
+TEST_F(TDigestTest, EmptyMixed)
+{
+  cudf::test::fixed_width_column_wrapper<double> values{
+    {123456.78, 10.0, 20.0, 25.0, 30.0, 40.0, 50.0, 60.0, 70.0}, {1, 0, 0, 1, 0, 0, 1, 1, 0}};
+  cudf::test::strings_column_wrapper keys{"b", "a", "c", "c", "d", "d", "e", "e", "f"};
+
+  auto const delta = 1000;
+  cudf::table_view t({keys});
+  cudf::groupby::groupby gb(t);
+  std::vector<cudf::groupby::aggregation_request> requests;
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+  requests.push_back({values, std::move(aggregations)});
+  auto result = gb.aggregate(requests);
+
+  using FCW = cudf::test::fixed_width_column_wrapper<double>;
+  auto expected =
+    cudf::test::make_expected_tdigest_column({{FCW{}, FCW{}, 0, 0},
+                                              {FCW{123456.78}, FCW{1.0}, 123456.78, 123456.78},
+                                              {FCW{25.0}, FCW{1.0}, 25.0, 25.0},
+                                              {FCW{}, FCW{}, 0, 0},
+                                              {FCW{50.0, 60.0}, FCW{1.0, 1.0}, 50.0, 60.0},
+                                              {FCW{}, FCW{}, 0, 0}});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result.second[0].results[0], *expected);
+}
+
+TEST_F(TDigestTest, LargeInputDouble)
+{
+  cudf::test::tdigest_simple_large_input_double_aggregation(tdigest_groupby_simple_op{});
+}
+
+TEST_F(TDigestTest, LargeInputInt)
+{
+  cudf::test::tdigest_simple_large_input_int_aggregation(tdigest_groupby_simple_op{});
+}
+
+TEST_F(TDigestTest, LargeInputDecimal)
+{
+  cudf::test::tdigest_simple_large_input_decimal_aggregation(tdigest_groupby_simple_op{});
+}
+
+struct TDigestMergeTest : public cudf::test::BaseFixture {};
+
+// Note: there is no need to test different types here as the internals of a tdigest are always
+// the same regardless of input.
+TEST_F(TDigestMergeTest, Simple)
+{
+  cudf::test::tdigest_merge_simple(tdigest_groupby_simple_op{}, tdigest_groupby_simple_merge_op{});
+}
+
+struct key_groups {
+  __device__ cudf::size_type operator()(cudf::size_type i) { return i < 250000 ? 0 : 1; }
+};
+TEST_F(TDigestMergeTest, Grouped)
+{
+  auto values = cudf::test::generate_standardized_percentile_distribution(
+    cudf::data_type{cudf::type_id::FLOAT64});
+  ASSERT_EQ(values->size(), 750000);
+  // all in the same group
+  auto keys = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT32}, values->size(), cudf::mask_state::UNALLOCATED);
+  // 3 groups. 0-250000 in group 0.  250000-500000 in group 1 and 500000-750000 in group 1
+  auto key_iter = cudf::detail::make_counting_transform_iterator(0, key_groups{});
+  thrust::copy(rmm::exec_policy(cudf::get_default_stream()),
+               key_iter,
+               key_iter + keys->size(),
+               keys->mutable_view().template begin<int>());
+
+  auto split_values         = cudf::split(*values, {250000, 500000});
+  auto grouped_split_values = cudf::split(*values, {250000});
+  auto split_keys           = cudf::split(*keys, {250000, 500000});
+
+  int const delta = 1000;
+
+  // generate separate digests
+  std::vector<std::unique_ptr<cudf::column>> parts;
+  auto iter = thrust::make_counting_iterator(0);
+  std::transform(
+    iter,
+    iter + split_values.size(),
+    std::back_inserter(parts),
+    [&split_keys, &split_values, delta](int i) {
+      cudf::table_view t({split_keys[i]});
+      cudf::groupby::groupby gb(t);
+      std::vector<cudf::groupby::aggregation_request> requests;
+      std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+      aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+      requests.push_back({split_values[i], std::move(aggregations)});
+      auto result = gb.aggregate(requests);
+      return std::move(result.second[0].results[0]);
+    });
+  std::vector<cudf::column_view> part_views;
+  std::transform(parts.begin(),
+                 parts.end(),
+                 std::back_inserter(part_views),
+                 [](std::unique_ptr<cudf::column> const& col) { return col->view(); });
+
+  // merge delta = 1000
+  {
+    int const merge_delta = 1000;
+
+    // merge them
+    auto merge_input = cudf::concatenate(part_views);
+    cudf::test::fixed_width_column_wrapper<int> merge_keys{0, 1, 1};
+    cudf::table_view key_table({merge_keys});
+    cudf::groupby::groupby gb(key_table);
+    std::vector<cudf::groupby::aggregation_request> requests;
+    std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+    aggregations.push_back(
+      cudf::make_merge_tdigest_aggregation<cudf::groupby_aggregation>(merge_delta));
+    requests.push_back({*merge_input, std::move(aggregations)});
+    auto result = gb.aggregate(requests);
+
+    ASSERT_EQ(result.second[0].results[0]->size(), 2);
+    cudf::tdigest::tdigest_column_view tdv(*result.second[0].results[0]);
+
+    // verify centroids
+    std::vector<cudf::test::expected_value> expected{// group 0
+                                                     {0, 0.00013945158577498588, 2},
+                                                     {10, 0.04804393446447509375, 50},
+                                                     {66, 2.10089484962640948851, 316},
+                                                     {139, 8.92977366346101852912, 601},
+                                                     {243, 23.89152910016953867967, 784},
+                                                     {366, 41.62636569363655780762, 586},
+                                                     {432, 47.73085102980330418632, 326},
+                                                     {460, 49.20637897385523018556, 196},
+                                                     {501, 49.99998311512171511595, 1},
+                                                     // group 1
+                                                     {502 + 0, 50.00022508669655252334, 2},
+                                                     {502 + 15, 50.05415694538910287292, 74},
+                                                     {502 + 70, 51.21421484112906341579, 334},
+                                                     {502 + 150, 55.19367617848146778670, 635},
+                                                     {502 + 260, 63.24605285552920008740, 783},
+                                                     {502 + 380, 76.99522005804017510400, 1289},
+                                                     {502 + 440, 84.22673817294192133431, 758},
+                                                     {502 + 490, 88.11787981529532487457, 784},
+                                                     {502 + 555, 93.02766411136053648079, 704},
+                                                     {502 + 618, 96.91486035315536184953, 516},
+                                                     {502 + 710, 99.87755861436669135855, 110},
+                                                     {502 + 733, 99.99970905482754801596, 1}};
+    cudf::test::tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    auto split_results = cudf::split(*result.second[0].results[0], {1});
+    auto iter          = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + split_results.size(), [&](cudf::size_type i) {
+      auto copied = std::make_unique<cudf::column>(split_results[i]);
+      cudf::test::tdigest_minmax_compare<double>(cudf::tdigest::tdigest_column_view(*copied),
+                                                 grouped_split_values[i]);
+    });
+  }
+
+  // merge delta = 100
+  {
+    int const merge_delta = 100;
+
+    // merge them
+    auto merge_input = cudf::concatenate(part_views);
+    cudf::test::fixed_width_column_wrapper<int> merge_keys{0, 1, 1};
+    cudf::table_view key_table({merge_keys});
+    cudf::groupby::groupby gb(key_table);
+    std::vector<cudf::groupby::aggregation_request> requests;
+    std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+    aggregations.push_back(
+      cudf::make_merge_tdigest_aggregation<cudf::groupby_aggregation>(merge_delta));
+    requests.push_back({*merge_input, std::move(aggregations)});
+    auto result = gb.aggregate(requests);
+
+    ASSERT_EQ(result.second[0].results[0]->size(), 2);
+    cudf::tdigest::tdigest_column_view tdv(*result.second[0].results[0]);
+
+    // verify centroids
+    std::vector<cudf::test::expected_value> expected{// group 0
+                                                     {0, 0.02182479870203561656, 231},
+                                                     {3, 0.60625795002234528219, 1688},
+                                                     {13, 8.40462931740497687372, 5867},
+                                                     {27, 28.79997783486397722186, 7757},
+                                                     {35, 40.22391421196020644402, 6224},
+                                                     {45, 48.96506331299028857984, 2225},
+                                                     {50, 49.99979491345574444949, 4},
+                                                     // group 1
+                                                     {51 + 0, 50.02171921312970681583, 460},
+                                                     {51 + 5, 51.45308398121498072442, 5074},
+                                                     {51 + 11, 55.96880716301625113829, 10011},
+                                                     {51 + 22, 70.18029861315150697010, 15351},
+                                                     {51 + 38, 92.65943436519887654867, 10718},
+                                                     {51 + 47, 99.27745505225347244505, 3639}};
+    cudf::test::tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    auto split_results = cudf::split(*result.second[0].results[0], {1});
+    auto iter          = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + split_results.size(), [&](cudf::size_type i) {
+      auto copied = std::make_unique<cudf::column>(split_results[i]);
+      cudf::test::tdigest_minmax_compare<double>(cudf::tdigest::tdigest_column_view(*copied),
+                                                 grouped_split_values[i]);
+    });
+  }
+
+  // merge delta = 10
+  {
+    int const merge_delta = 10;
+
+    // merge them
+    auto merge_input = cudf::concatenate(part_views);
+    cudf::test::fixed_width_column_wrapper<int> merge_keys{0, 1, 1};
+    cudf::table_view key_table({merge_keys});
+    cudf::groupby::groupby gb(key_table);
+    std::vector<cudf::groupby::aggregation_request> requests;
+    std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+    aggregations.push_back(
+      cudf::make_merge_tdigest_aggregation<cudf::groupby_aggregation>(merge_delta));
+    requests.push_back({*merge_input, std::move(aggregations)});
+    auto result = gb.aggregate(requests);
+
+    ASSERT_EQ(result.second[0].results[0]->size(), 2);
+    cudf::tdigest::tdigest_column_view tdv(*result.second[0].results[0]);
+
+    // verify centroids
+    std::vector<cudf::test::expected_value> expected{// group 0
+                                                     {0, 2.34644806683495144028, 23623},
+                                                     {1, 10.95523693698660672169, 62290},
+                                                     {2, 24.90731657803452847588, 77208},
+                                                     {3, 38.88062495289155862110, 62658},
+                                                     {4, 47.56288303840698006297, 24217},
+                                                     {5, 49.99979491345574444949, 4},
+                                                     // group 1
+                                                     {6 + 0, 52.40174463129091719793, 47410},
+                                                     {6 + 1, 60.97025126481504031517, 124564},
+                                                     {6 + 2, 74.91722742839780835311, 154387},
+                                                     {6 + 3, 88.87559489177009197647, 124810},
+                                                     {6 + 4, 97.55823307073454486726, 48817},
+                                                     {6 + 5, 99.99901807905750672489, 12}};
+    cudf::test::tdigest_sample_compare(tdv, expected);
+
+    // verify min/max
+    auto split_results = cudf::split(*result.second[0].results[0], {1});
+    auto iter          = thrust::make_counting_iterator(0);
+    std::for_each(iter, iter + split_results.size(), [&](cudf::size_type i) {
+      auto copied = std::make_unique<cudf::column>(split_results[i]);
+      cudf::test::tdigest_minmax_compare<double>(cudf::tdigest::tdigest_column_view(*copied),
+                                                 grouped_split_values[i]);
+    });
+  }
+}
+
+TEST_F(TDigestMergeTest, Empty)
+{
+  cudf::test::tdigest_merge_empty(tdigest_groupby_simple_merge_op{});
+}
+
+TEST_F(TDigestMergeTest, EmptyGroups)
+{
+  cudf::test::fixed_width_column_wrapper<double> values_b{{126, 15, 1, 99, 67, 55, 2},
+                                                          {1, 0, 0, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> values_d{{100, 200, 300, 400, 500, 600, 700},
+                                                          {1, 1, 1, 1, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int> keys{0, 0, 0, 0, 0, 0, 0};
+  int const delta = 1000;
+
+  auto a = cudf::tdigest::detail::make_empty_tdigest_column(cudf::get_default_stream(),
+                                                            rmm::mr::get_current_device_resource());
+  auto b = cudf::type_dispatcher(
+    static_cast<cudf::column_view>(values_b).type(), tdigest_gen_grouped{}, keys, values_b, delta);
+  auto c = cudf::tdigest::detail::make_empty_tdigest_column(cudf::get_default_stream(),
+                                                            rmm::mr::get_current_device_resource());
+  auto d = cudf::type_dispatcher(
+    static_cast<cudf::column_view>(values_d).type(), tdigest_gen_grouped{}, keys, values_d, delta);
+  auto e = cudf::tdigest::detail::make_empty_tdigest_column(cudf::get_default_stream(),
+                                                            rmm::mr::get_current_device_resource());
+
+  std::vector<cudf::column_view> cols;
+  cols.push_back(*a);
+  cols.push_back(*b);
+  cols.push_back(*c);
+  cols.push_back(*d);
+  cols.push_back(*e);
+  auto values = cudf::concatenate(cols);
+
+  cudf::test::fixed_width_column_wrapper<int> merge_keys{0, 0, 1, 0, 2};
+
+  cudf::table_view t({merge_keys});
+  cudf::groupby::groupby gb(t);
+  std::vector<cudf::groupby::aggregation_request> requests;
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  aggregations.push_back(cudf::make_merge_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+  requests.push_back({*values, std::move(aggregations)});
+  auto result = gb.aggregate(requests);
+
+  using FCW = cudf::test::fixed_width_column_wrapper<double>;
+  cudf::test::fixed_width_column_wrapper<double> expected_means{
+    2, 55, 67, 99, 100, 126, 200, 300, 400, 500, 600};
+  cudf::test::fixed_width_column_wrapper<double> expected_weights{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  auto expected = cudf::test::make_expected_tdigest_column(
+    {{expected_means, expected_weights, 2, 600}, {FCW{}, FCW{}, 0, 0}, {FCW{}, FCW{}, 0, 0}});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *result.second[0].results[0]);
+}
diff --git a/cpp/tests/groupby/var_tests.cpp b/cpp/tests/groupby/var_tests.cpp
new file mode 100644
index 0000000..baebc45
--- /dev/null
+++ b/cpp/tests/groupby/var_tests.cpp
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+using namespace cudf::test::iterators;
+
+template <typename V>
+struct groupby_var_test : public cudf::test::BaseFixture {};
+
+using supported_types = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+TYPED_TEST_SUITE(groupby_var_test, supported_types);
+
+TYPED_TEST(groupby_var_test, basic)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //                                                   {1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1,        2,           3};
+  //                                                   {0, 3, 6,  1, 4, 5, 9,  2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({9.,      131. / 12,   31. / 3}, no_nulls());
+  // clang-format on
+
+  auto agg = cudf::make_variance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_var_test, empty_cols)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{};
+  cudf::test::fixed_width_column_wrapper<V> vals{};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_variance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_var_test, zero_valid_keys)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3}, all_nulls());
+  cudf::test::fixed_width_column_wrapper<V> vals{3, 4, 5};
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals{};
+
+  auto agg = cudf::make_variance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_var_test, zero_valid_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<V> vals({3, 4, 5}, all_nulls());
+
+  cudf::test::fixed_width_column_wrapper<K> expect_keys{1};
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0}, all_nulls());
+
+  auto agg = cudf::make_variance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_var_test, null_keys_and_values)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+
+  // clang-format off
+  //                                                    {1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,        2,         3,       4}, no_nulls());
+  //                                                    {3, 6,     1, 4, 9,   2, 8,    3}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({4.5,      49. / 3,   18.,     0.}, {1, 1, 1, 0});
+  // clang-format on
+
+  auto agg = cudf::make_variance_aggregation<cudf::groupby_aggregation>();
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_var_test, ddof_non_default)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  cudf::test::fixed_width_column_wrapper<K> keys({1, 2, 3, 1, 2, 2, 1, 3, 3, 2, 4},
+                                                 {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<V> vals({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3},
+                                                 {0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+
+  // clang-format off
+  //                                                    { 1, 1,     2, 2, 2,   3, 3,    4}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,         2,         3,       4}, no_nulls());
+  //                                                    { 3, 6,     1, 4, 9,   2, 8,    3}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({0.,        98. / 3,   0.,      0.},
+                                                        {0,         1,         0,       0});
+  // clang-format on
+
+  auto agg = cudf::make_variance_aggregation<cudf::groupby_aggregation>(2);
+  test_single_agg(keys, vals, expect_keys, expect_vals, std::move(agg));
+}
+
+TYPED_TEST(groupby_var_test, dictionary)
+{
+  using K = int32_t;
+  using V = TypeParam;
+  using R = cudf::detail::target_type_t<V, cudf::aggregation::VARIANCE>;
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::dictionary_column_wrapper<V>  vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  //                                                    {1, 1, 1,  2, 2, 2, 2,  3, 3, 3}
+  cudf::test::fixed_width_column_wrapper<K> expect_keys({1,        2,           3      });
+  //                                                    {0, 3, 6,  1, 4, 5, 9,  2, 7, 8}
+  cudf::test::fixed_width_column_wrapper<R> expect_vals({9.,      131./12,      31./3  }, no_nulls());
+  // clang-format on
+
+  test_single_agg(keys,
+                  vals,
+                  expect_keys,
+                  expect_vals,
+                  cudf::make_variance_aggregation<cudf::groupby_aggregation>());
+}
+
+// This test ensures that the same results are produced by the sort-based and
+// hash-based implementations of groupby-variance.
+TYPED_TEST(groupby_var_test, sort_vs_hash)
+{
+  using K = int32_t;
+  using V = double;
+
+  cudf::test::fixed_width_column_wrapper<K> keys{50, 30, 90, 80};
+  cudf::test::fixed_width_column_wrapper<V> vals{380.0, 370.0, 24.0, 26.0};
+
+  cudf::groupby::groupby gb_obj(cudf::table_view({keys}));
+
+  auto agg1 = cudf::make_variance_aggregation<cudf::groupby_aggregation>();
+
+  std::vector<cudf::groupby::aggregation_request> requests;
+  requests.emplace_back();
+  requests[0].values = vals;
+  requests[0].aggregations.push_back(std::move(agg1));
+
+  auto result1 = gb_obj.aggregate(requests);
+
+  // This agg forces a sort groupby.
+  auto agg2 = cudf::make_quantile_aggregation<cudf::groupby_aggregation>({0.25});
+  requests[0].aggregations.push_back(std::move(agg2));
+
+  auto result2 = gb_obj.aggregate(requests);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result1.second[0].results[0], *result2.second[0].results[0]);
+}
diff --git a/cpp/tests/hash_map/map_test.cu b/cpp/tests/hash_map/map_test.cu
new file mode 100644
index 0000000..8d71c51
--- /dev/null
+++ b/cpp/tests/hash_map/map_test.cu
@@ -0,0 +1,216 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <hash/concurrent_unordered_map.cuh>
+
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/logical.h>
+#include <thrust/pair.h>
+#include <thrust/tabulate.h>
+
+#include <cstdlib>
+#include <iostream>
+#include <limits>
+#include <random>
+#include <unordered_map>
+#include <vector>
+
+template <typename K, typename V>
+struct key_value_types {
+  using key_type   = K;
+  using value_type = V;
+  using pair_type  = thrust::pair<K, V>;
+  using map_type   = concurrent_unordered_map<key_type, value_type>;
+};
+
+template <typename T>
+struct InsertTest : public cudf::test::BaseFixture {
+  using key_type   = typename T::key_type;
+  using value_type = typename T::value_type;
+  using pair_type  = typename T::pair_type;
+  using map_type   = typename T::map_type;
+
+  InsertTest()
+  {
+    // prevent overflow of small types
+    const size_t input_size =
+      std::min(static_cast<key_type>(size), std::numeric_limits<key_type>::max());
+    pairs.resize(input_size, cudf::get_default_stream());
+    map = std::move(map_type::create(compute_hash_table_size(size), cudf::get_default_stream()));
+    cudf::get_default_stream().synchronize();
+  }
+
+  const cudf::size_type size{10000};
+  rmm::device_uvector<pair_type> pairs{static_cast<std::size_t>(size), cudf::get_default_stream()};
+  std::unique_ptr<map_type, std::function<void(map_type*)>> map;
+};
+
+using TestTypes = ::testing::Types<key_value_types<int32_t, int32_t>,
+                                   key_value_types<int64_t, int64_t>,
+                                   key_value_types<int16_t, int16_t>,
+                                   key_value_types<int32_t, float>,
+                                   key_value_types<int64_t, double>>;
+
+TYPED_TEST_SUITE(InsertTest, TestTypes);
+
+template <typename map_type, typename pair_type>
+struct insert_pair {
+  insert_pair(map_type _map) : map{_map} {}
+
+  __device__ bool operator()(pair_type const& pair)
+  {
+    auto result = map.insert(pair);
+    if (result.first == map.end()) { return false; }
+    return result.second;
+  }
+
+  map_type map;
+};
+
+template <typename map_type, typename pair_type>
+struct find_pair {
+  find_pair(map_type _map) : map{_map} {}
+
+  __device__ bool operator()(pair_type const& pair)
+  {
+    auto result = map.find(pair.first);
+    if (result == map.end()) { return false; }
+    return *result == pair;
+  }
+  map_type map;
+};
+
+template <typename pair_type,
+          typename key_type   = typename pair_type::first_type,
+          typename value_type = typename pair_type::second_type>
+struct unique_pair_generator {
+  __device__ pair_type operator()(cudf::size_type i)
+  {
+    return thrust::make_pair(key_type(i), value_type(i));
+  }
+};
+
+template <typename pair_type,
+          typename key_type   = typename pair_type::first_type,
+          typename value_type = typename pair_type::second_type>
+struct identical_pair_generator {
+  identical_pair_generator(key_type k = 42, value_type v = 42) : key{k}, value{v} {}
+  __device__ pair_type operator()(cudf::size_type i) { return thrust::make_pair(key, value); }
+  key_type key;
+  value_type value;
+};
+
+template <typename pair_type,
+          typename key_type   = typename pair_type::first_type,
+          typename value_type = typename pair_type::second_type>
+struct identical_key_generator {
+  identical_key_generator(key_type k = 42) : key{k} {}
+  __device__ pair_type operator()(cudf::size_type i)
+  {
+    return thrust::make_pair(key, value_type(i));
+  }
+  key_type key;
+};
+
+TYPED_TEST(InsertTest, UniqueKeysUniqueValues)
+{
+  using map_type  = typename TypeParam::map_type;
+  using pair_type = typename TypeParam::pair_type;
+  thrust::tabulate(rmm::exec_policy(cudf::get_default_stream()),
+                   this->pairs.begin(),
+                   this->pairs.end(),
+                   unique_pair_generator<pair_type>{});
+  // All pairs should be new inserts
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             this->pairs.begin(),
+                             this->pairs.end(),
+                             insert_pair<map_type, pair_type>{*this->map}));
+
+  // All pairs should be present in the map
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             this->pairs.begin(),
+                             this->pairs.end(),
+                             find_pair<map_type, pair_type>{*this->map}));
+}
+
+TYPED_TEST(InsertTest, IdenticalKeysIdenticalValues)
+{
+  using map_type  = typename TypeParam::map_type;
+  using pair_type = typename TypeParam::pair_type;
+  thrust::tabulate(rmm::exec_policy(cudf::get_default_stream()),
+                   this->pairs.begin(),
+                   this->pairs.end(),
+                   identical_pair_generator<pair_type>{});
+  // Insert a single pair
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             this->pairs.begin(),
+                             this->pairs.begin() + 1,
+                             insert_pair<map_type, pair_type>{*this->map}));
+  // Identical inserts should all return false (no new insert)
+  EXPECT_FALSE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                              this->pairs.begin(),
+                              this->pairs.end(),
+                              insert_pair<map_type, pair_type>{*this->map}));
+
+  // All pairs should be present in the map
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             this->pairs.begin(),
+                             this->pairs.end(),
+                             find_pair<map_type, pair_type>{*this->map}));
+}
+
+TYPED_TEST(InsertTest, IdenticalKeysUniqueValues)
+{
+  using map_type  = typename TypeParam::map_type;
+  using pair_type = typename TypeParam::pair_type;
+  thrust::tabulate(rmm::exec_policy(cudf::get_default_stream()),
+                   this->pairs.begin(),
+                   this->pairs.end(),
+                   identical_key_generator<pair_type>{});
+
+  // Insert a single pair
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             this->pairs.begin(),
+                             this->pairs.begin() + 1,
+                             insert_pair<map_type, pair_type>{*this->map}));
+
+  // Identical key inserts should all return false (no new insert)
+  EXPECT_FALSE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                              this->pairs.begin() + 1,
+                              this->pairs.end(),
+                              insert_pair<map_type, pair_type>{*this->map}));
+
+  // Only first pair is present in map
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             this->pairs.begin(),
+                             this->pairs.begin() + 1,
+                             find_pair<map_type, pair_type>{*this->map}));
+
+  EXPECT_FALSE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                              this->pairs.begin() + 1,
+                              this->pairs.end(),
+                              find_pair<map_type, pair_type>{*this->map}));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/hashing/md5_test.cpp b/cpp/tests/hashing/md5_test.cpp
new file mode 100644
index 0000000..52ca52e
--- /dev/null
+++ b/cpp/tests/hashing/md5_test.cpp
@@ -0,0 +1,290 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/hashing.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+class MD5HashTest : public cudf::test::BaseFixture {};
+
+TEST_F(MD5HashTest, MultiValue)
+{
+  cudf::test::strings_column_wrapper const strings_col(
+    {"",
+     "A 60 character string to test MD5's message padding algorithm",
+     "A very long (greater than 128 bytes/char string) to test a multi hash-step data point in the "
+     "MD5 hash function. This string needed to be longer.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"});
+
+  cudf::test::strings_column_wrapper const md5_string_results1(
+    {"d41d8cd98f00b204e9800998ecf8427e",
+     "682240021651ae166d08fe2a014d5c09",
+     "3669d5225fddbb34676312ca3b78bbd9",
+     "c61a4185135eda043f35e92c3505e180",
+     "52da74c75cb6575d25be29e66bd0adde"});
+
+  cudf::test::strings_column_wrapper const md5_string_results2(
+    {"d41d8cd98f00b204e9800998ecf8427e",
+     "e5a5682e82278e78dbaad9a689df7a73",
+     "4121ab1bb6e84172fd94822645862ae9",
+     "28970886501efe20164213855afe5850",
+     "6bc1b872103cc6a02d882245b8516e2e"});
+
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col(
+    {0, 100, -100, limits::min(), limits::max()});
+
+  // Different truth values should be equal
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 1, 2, 255, 0});
+
+  auto const string_input1      = cudf::table_view({strings_col});
+  auto const string_input2      = cudf::table_view({strings_col, strings_col});
+  auto const md5_string_output1 = cudf::hashing::md5(string_input1);
+  auto const md5_string_output2 = cudf::hashing::md5(string_input2);
+  EXPECT_EQ(string_input1.num_rows(), md5_string_output1->size());
+  EXPECT_EQ(string_input2.num_rows(), md5_string_output2->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(md5_string_output1->view(), md5_string_results1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(md5_string_output2->view(), md5_string_results2);
+
+  auto const input1      = cudf::table_view({strings_col, ints_col, bools_col1});
+  auto const input2      = cudf::table_view({strings_col, ints_col, bools_col2});
+  auto const md5_output1 = cudf::hashing::md5(input1);
+  auto const md5_output2 = cudf::hashing::md5(input2);
+  EXPECT_EQ(input1.num_rows(), md5_output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(md5_output1->view(), md5_output2->view());
+}
+
+TEST_F(MD5HashTest, MultiValueNulls)
+{
+  // Nulls with different values should be equal
+  cudf::test::strings_column_wrapper const strings_col1(
+    {"",
+     "Different but null!",
+     "A very long (greater than 128 bytes/char string) to test a multi hash-step data point in the "
+     "MD5 hash function. This string needed to be longer.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"},
+    {1, 0, 0, 1, 0});
+  cudf::test::strings_column_wrapper const strings_col2(
+    {"",
+     "A 60 character string to test MD5's message padding algorithm",
+     "Very different... but null",
+     "All work and no play makes Jack a dull boy",
+     ""},
+    {1, 0, 0, 1, 1});  // empty string is equivalent to null
+
+  // Nulls with different values should be equal
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col1(
+    {0, 100, -100, limits::min(), limits::max()}, {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col2(
+    {0, -200, 200, limits::min(), limits::max()}, {1, 0, 0, 1, 1});
+
+  // Nulls with different values should be equal
+  // Different truth values should be equal
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 0, 1, 1}, {1, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 2, 1, 0, 255}, {1, 1, 0, 0, 1});
+
+  auto const input1 = cudf::table_view({strings_col1, ints_col1, bools_col1});
+  auto const input2 = cudf::table_view({strings_col2, ints_col2, bools_col2});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+TEST_F(MD5HashTest, StringListsNulls)
+{
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0; });
+
+  cudf::test::strings_column_wrapper const strings_col(
+    {"",
+     "A 60 character string to test MD5's message padding algorithm",
+     "A very long (greater than 128 bytes/char string) to test a multi hash-step data point in the "
+     "MD5 hash function. This string needed to be longer. It needed to be even longer.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"});
+
+  cudf::test::lists_column_wrapper<cudf::string_view> strings_list_col(
+    {{""},
+     {{"NULL", "A 60 character string to test MD5's message padding algorithm"}, validity},
+     {"A very long (greater than 128 bytes/char string) to test a multi hash-step data point in "
+      "the "
+      "MD5 hash function. This string needed to be longer.",
+      " It needed to be even longer."},
+     {"All ", "work ", "and", " no", " play ", "makes Jack", " a dull boy"},
+     {"!\"#$%&\'()*+,-./0123456789:;<=>?@[\\]^_`", "{|}~"}});
+
+  auto const input1 = cudf::table_view({strings_col});
+  auto const input2 = cudf::table_view({strings_list_col});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+template <typename T>
+class MD5HashTestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MD5HashTestTyped, cudf::test::NumericTypes);
+
+TYPED_TEST(MD5HashTestTyped, Equality)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> const col({0, 127, 1, 2, 8});
+  auto const input = cudf::table_view({col});
+
+  // Hash of same input should be equal
+  auto const output1 = cudf::hashing::md5(input);
+  auto const output2 = cudf::hashing::md5(input);
+
+  EXPECT_EQ(input.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+TYPED_TEST(MD5HashTestTyped, EqualityNulls)
+{
+  using T = TypeParam;
+
+  // Nulls with different values should be equal
+  cudf::test::fixed_width_column_wrapper<T> const col1({0, 127, 1, 2, 8}, {0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T> const col2({1, 127, 1, 2, 8}, {0, 1, 1, 1, 1});
+
+  auto const input1 = cudf::table_view({col1});
+  auto const input2 = cudf::table_view({col2});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+TEST_F(MD5HashTest, TestBoolListsWithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<bool> const col1({0, 255, 255, 16, 27, 18, 100, 1, 2},
+                                                          {1, 0, 0, 0, 1, 1, 1, 0, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const col2({0, 255, 255, 32, 81, 68, 3, 101, 4},
+                                                          {1, 0, 0, 1, 0, 1, 0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const col3({0, 255, 255, 64, 49, 42, 5, 6, 102},
+                                                          {1, 0, 0, 1, 1, 0, 0, 0, 1});
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; });
+  cudf::test::lists_column_wrapper<bool> const list_col(
+    {{0, 0, 0}, {1}, {}, {{1, 1, 1}, validity}, {1, 1}, {1, 1}, {1}, {1}, {1}}, validity);
+
+  auto const input1 = cudf::table_view({col1, col2, col3});
+  auto const input2 = cudf::table_view({list_col});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+template <typename T>
+class MD5HashListTestTyped : public cudf::test::BaseFixture {};
+
+using NumericTypesNoBools =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(MD5HashListTestTyped, NumericTypesNoBools);
+
+TYPED_TEST(MD5HashListTestTyped, TestListsWithNulls)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> const col1({0, 255, 255, 16, 27, 18, 100, 1, 2},
+                                                       {1, 0, 0, 0, 1, 1, 1, 0, 0});
+  cudf::test::fixed_width_column_wrapper<T> const col2({0, 255, 255, 32, 81, 68, 3, 101, 4},
+                                                       {1, 0, 0, 1, 0, 1, 0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<T> const col3({0, 255, 255, 64, 49, 42, 5, 6, 102},
+                                                       {1, 0, 0, 1, 1, 0, 0, 0, 1});
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; });
+  cudf::test::lists_column_wrapper<T> const list_col(
+    {{0, 0, 0}, {127}, {}, {{32, 127, 64}, validity}, {27, 49}, {18, 68}, {100}, {101}, {102}},
+    validity);
+
+  auto const input1 = cudf::table_view({col1, col2, col3});
+  auto const input2 = cudf::table_view({list_col});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+template <typename T>
+class MD5HashTestFloatTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MD5HashTestFloatTyped, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(MD5HashTestFloatTyped, TestExtremes)
+{
+  using T = TypeParam;
+  T min   = std::numeric_limits<T>::min();
+  T max   = std::numeric_limits<T>::max();
+  T nan   = std::numeric_limits<T>::quiet_NaN();
+  T inf   = std::numeric_limits<T>::infinity();
+
+  cudf::test::fixed_width_column_wrapper<T> const col1(
+    {T(0.0), T(100.0), T(-100.0), min, max, nan, inf, -inf});
+  cudf::test::fixed_width_column_wrapper<T> const col2(
+    {T(-0.0), T(100.0), T(-100.0), min, max, -nan, inf, -inf});
+
+  auto const input1 = cudf::table_view({col1});
+  auto const input2 = cudf::table_view({col2});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view(), verbosity);
+}
+
+TYPED_TEST(MD5HashTestFloatTyped, TestListExtremes)
+{
+  using T = TypeParam;
+  T min   = std::numeric_limits<T>::min();
+  T max   = std::numeric_limits<T>::max();
+  T nan   = std::numeric_limits<T>::quiet_NaN();
+  T inf   = std::numeric_limits<T>::infinity();
+
+  cudf::test::lists_column_wrapper<T> const col1(
+    {{T(0.0)}, {T(100.0), T(-100.0)}, {min, max, nan}, {inf, -inf}});
+  cudf::test::lists_column_wrapper<T> const col2(
+    {{T(-0.0)}, {T(100.0), T(-100.0)}, {min, max, -nan}, {inf, -inf}});
+
+  auto const input1 = cudf::table_view({col1});
+  auto const input2 = cudf::table_view({col2});
+
+  auto const output1 = cudf::hashing::md5(input1);
+  auto const output2 = cudf::hashing::md5(input2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view(), verbosity);
+}
diff --git a/cpp/tests/hashing/murmurhash3_x64_128_test.cpp b/cpp/tests/hashing/murmurhash3_x64_128_test.cpp
new file mode 100644
index 0000000..4fb8f78
--- /dev/null
+++ b/cpp/tests/hashing/murmurhash3_x64_128_test.cpp
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/hashing.hpp>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+using NumericTypesNoBools =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+template <typename T>
+class MurmurHash3_x64_128_TestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MurmurHash3_x64_128_TestTyped, NumericTypesNoBools);
+
+TYPED_TEST(MurmurHash3_x64_128_TestTyped, TestNumeric)
+{
+  using T   = TypeParam;
+  auto col1 = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {-1, -1, 0, 2, 22, 0, 11, 12, 116, 32, 0, 42, 7, 62, 1, -22, 0, 0},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+  auto col2 = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {-1, -1, 0, 2, 22, 1, 11, 12, 116, 32, 0, 42, 7, 62, 1, -22, 1, -22},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+
+  auto output1 = cudf::hashing::murmurhash3_x64_128(cudf::table_view({col1}));
+  auto output2 = cudf::hashing::murmurhash3_x64_128(cudf::table_view({col2}));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(output1->view(), output2->view());
+
+  output1 = cudf::hashing::murmurhash3_x64_128(cudf::table_view({col1}), 7);
+  output2 = cudf::hashing::murmurhash3_x64_128(cudf::table_view({col2}), 7);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(output1->view(), output2->view());
+}
+
+class MurmurHash3_x64_128_Test : public cudf::test::BaseFixture {};
+
+TEST_F(MurmurHash3_x64_128_Test, StringType)
+{
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"The",
+     "quick",
+     "brown fox",
+     "jumps over the lazy dog.",
+     "I am Jack's complete lack of null value",
+     "A very long (greater than 128 bytes/characters) to test a very long string. "
+     "2nd half of the very long string to verify the long string hashing happening.",
+     "Some multi-byte characters here: ééé",
+     "ééé",
+     "ééé ééé",
+     "ééé ééé ééé ééé",
+     "",
+     "!@#$%^&*(())",
+     "0123456789",
+     "{}|:<>?,./;[]=-"});
+
+  auto output = cudf::hashing::murmurhash3_x64_128(cudf::table_view({col1}));
+  // these were generated using the CPU compiled
+  // https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp
+  auto expected = cudf::test::fixed_width_column_wrapper<uint64_t>({3481043174314896794ul,
+                                                                    1981901315483788749ul,
+                                                                    1418748153263580713ul,
+                                                                    11224732510765974842ul,
+                                                                    10813495276579975748ul,
+                                                                    8563282101401420087ul,
+                                                                    7289234017606107350ul,
+                                                                    225672801045596944ul,
+                                                                    14927688838032769435ul,
+                                                                    7513581995808204968ul,
+                                                                    0ul,
+                                                                    14163495587303857889ul,
+                                                                    4581940570640870180ul,
+                                                                    18164432652839101653ul});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view().column(0), expected);
+
+  auto const seed = uint64_t{7};
+
+  output   = cudf::hashing::murmurhash3_x64_128(cudf::table_view({col1}), seed);
+  expected = cudf::test::fixed_width_column_wrapper<uint64_t>({5091211404759866125ul,
+                                                               12948345853121693662ul,
+                                                               14974420008081159223ul,
+                                                               4475830656132398742ul,
+                                                               15724398074328467356ul,
+                                                               4091324140202743991ul,
+                                                               7130403777725115865ul,
+                                                               11087585763075301159ul,
+                                                               12568262854562899547ul,
+                                                               2679775340886828858ul,
+                                                               17582832888865278351ul,
+                                                               5264478748926531221ul,
+                                                               8863578460974333747ul,
+                                                               11176802453047055260ul});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view().column(0), expected);
+}
diff --git a/cpp/tests/hashing/murmurhash3_x86_32_test.cpp b/cpp/tests/hashing/murmurhash3_x86_32_test.cpp
new file mode 100644
index 0000000..a4b1655
--- /dev/null
+++ b/cpp/tests/hashing/murmurhash3_x86_32_test.cpp
@@ -0,0 +1,405 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/hashing.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+class MurmurHashTest : public cudf::test::BaseFixture {};
+
+TEST_F(MurmurHashTest, MultiValue)
+{
+  cudf::test::strings_column_wrapper const strings_col(
+    {"",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"});
+
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col(
+    {0, 100, -100, limits::min(), limits::max()});
+
+  // Different truth values should be equal
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 1, 2, 255, 0});
+
+  using ts = cudf::timestamp_s;
+  cudf::test::fixed_width_column_wrapper<ts, ts::duration> const secs_col(
+    {ts::duration::zero(),
+     static_cast<ts::duration>(100),
+     static_cast<ts::duration>(-100),
+     ts::duration::min(),
+     ts::duration::max()});
+
+  auto const input1 = cudf::table_view({strings_col, ints_col, bools_col1, secs_col});
+  auto const input2 = cudf::table_view({strings_col, ints_col, bools_col2, secs_col});
+
+  auto const output1 = cudf::hashing::murmurhash3_x86_32(input1);
+  auto const output2 = cudf::hashing::murmurhash3_x86_32(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+TEST_F(MurmurHashTest, MultiValueNulls)
+{
+  // Nulls with different values should be equal
+  cudf::test::strings_column_wrapper const strings_col1(
+    {"",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"},
+    {0, 1, 1, 0, 1});
+  cudf::test::strings_column_wrapper const strings_col2(
+    {"different but null",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "I am Jack's complete lack of null value",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"},
+    {0, 1, 1, 0, 1});
+
+  // Nulls with different values should be equal
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col1(
+    {0, 100, -100, limits::min(), limits::max()}, {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col2(
+    {0, -200, 200, limits::min(), limits::max()}, {1, 0, 0, 1, 1});
+
+  // Nulls with different values should be equal
+  // Different truth values should be equal
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 0, 1, 1}, {1, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 2, 1, 0, 255}, {1, 1, 0, 0, 1});
+
+  // Nulls with different values should be equal
+  using ts = cudf::timestamp_s;
+  cudf::test::fixed_width_column_wrapper<ts, ts::duration> const secs_col1(
+    {ts::duration::zero(),
+     static_cast<ts::duration>(100),
+     static_cast<ts::duration>(-100),
+     ts::duration::min(),
+     ts::duration::max()},
+    {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<ts, ts::duration> const secs_col2(
+    {ts::duration::zero(),
+     static_cast<ts::duration>(-200),
+     static_cast<ts::duration>(200),
+     ts::duration::min(),
+     ts::duration::max()},
+    {1, 0, 0, 1, 1});
+
+  auto const input1 = cudf::table_view({strings_col1, ints_col1, bools_col1, secs_col1});
+  auto const input2 = cudf::table_view({strings_col2, ints_col2, bools_col2, secs_col2});
+
+  auto const output1 = cudf::hashing::murmurhash3_x86_32(input1);
+  auto const output2 = cudf::hashing::murmurhash3_x86_32(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+TEST_F(MurmurHashTest, BasicList)
+{
+  using LCW = cudf::test::lists_column_wrapper<uint64_t>;
+  using ICW = cudf::test::fixed_width_column_wrapper<uint32_t>;
+
+  auto const col = LCW{{}, {}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+  auto const input  = cudf::table_view({col});
+  auto const expect = ICW{1607593296,
+                          1607593296,
+                          -636010097,
+                          -132459357,
+                          -636010097,
+                          -2008850957,
+                          -1023787369,
+                          761197503,
+                          761197503,
+                          1340177511,
+                          -1023787369,
+                          -1023787369};
+
+  auto const output = cudf::hashing::murmurhash3_x86_32(input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+
+  auto const expect_seeded = ICW{1607594268u,
+                                 1607594268u,
+                                 1576790066u,
+                                 1203671017u,
+                                 1576790066u,
+                                 2107478077u,
+                                 1756855002u,
+                                 2228938758u,
+                                 2228938758u,
+                                 3491134126u,
+                                 1756855002u,
+                                 1756855002u};
+
+  auto const seeded_output = cudf::hashing::murmurhash3_x86_32(input, 15);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_seeded, seeded_output->view(), verbosity);
+}
+
+TEST_F(MurmurHashTest, NullableList)
+{
+  using LCW = cudf::test::lists_column_wrapper<uint64_t>;
+  using ICW = cudf::test::fixed_width_column_wrapper<uint32_t>;
+
+  auto const valids = std::vector<bool>{1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0};
+  auto const col =
+    LCW{{{}, {}, {1}, {1}, {2, 2}, {2}, {2}, {}, {2, 2}, {2, 2}, {}}, valids.begin()};
+  auto expect = ICW{-2023148619,
+                    -2023148619,
+                    -31671896,
+                    -31671896,
+                    -1205248335,
+                    1865773848,
+                    1865773848,
+                    -2023148682,
+                    -1205248335,
+                    -1205248335,
+                    -2023148682};
+
+  auto const output = cudf::hashing::murmurhash3_x86_32(cudf::table_view({col}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+
+  auto const expect_seeded = ICW{2271820643u,
+                                 2271820643u,
+                                 1038318696u,
+                                 1038318696u,
+                                 595138041u,
+                                 3027840870u,
+                                 3027840870u,
+                                 2271820578u,
+                                 595138041u,
+                                 595138041u,
+                                 2271820578u};
+
+  auto const seeded_output = cudf::hashing::murmurhash3_x86_32(cudf::table_view({col}), 31);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_seeded, seeded_output->view(), verbosity);
+}
+
+TEST_F(MurmurHashTest, ListOfStruct)
+{
+  auto col1 = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {-1, -1, 0, 2, 2, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, 1, 2},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+  auto col2 = cudf::test::strings_column_wrapper{
+    {"x", "x", "a", "a", "b", "b", "a", "b", "a", "b", "a", "c", "a", "c", "a", "c", "b", "b"},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1}};
+  auto struct_col = cudf::test::structs_column_wrapper{
+    {col1, col2}, {0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+    0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+
+  auto list_nullmask = std::vector<bool>{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::make_lists_column(
+    17, offsets.release(), struct_col.release(), null_count, std::move(null_mask));
+
+  auto expect = cudf::test::fixed_width_column_wrapper<uint32_t>{83451479,
+                                                                 83451479,
+                                                                 83455332,
+                                                                 83455332,
+                                                                 -759684425,
+                                                                 -959632766,
+                                                                 -959632766,
+                                                                 -959632766,
+                                                                 -959636527,
+                                                                 -656998704,
+                                                                 613652814,
+                                                                 1902080426,
+                                                                 1902080426,
+                                                                 2061025592,
+                                                                 2061025592,
+                                                                 -319840811,
+                                                                 -319840811};
+
+  auto const output = cudf::hashing::murmurhash3_x86_32(cudf::table_view({*list_column}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+
+  auto expect_seeded = cudf::test::fixed_width_column_wrapper<uint32_t>{81710442u,
+                                                                        81710442u,
+                                                                        81729816u,
+                                                                        81729816u,
+                                                                        3532787573u,
+                                                                        3642097855u,
+                                                                        3642097855u,
+                                                                        3642097855u,
+                                                                        3642110391u,
+                                                                        3889855760u,
+                                                                        1494406307u,
+                                                                        103934081u,
+                                                                        103934081u,
+                                                                        3462063680u,
+                                                                        3462063680u,
+                                                                        1696730835u,
+                                                                        1696730835u};
+
+  auto const seeded_output =
+    cudf::hashing::murmurhash3_x86_32(cudf::table_view({*list_column}), 619);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect_seeded, seeded_output->view(), verbosity);
+}
+
+TEST_F(MurmurHashTest, ListOfEmptyStruct)
+{
+  // []
+  // []
+  // Null
+  // Null
+  // [Null, Null]
+  // [Null, Null]
+  // [Null, Null]
+  // [Null]
+  // [Null]
+  // [{}]
+  // [{}]
+  // [{}, {}]
+  // [{}, {}]
+
+  auto struct_validity = std::vector<bool>{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(struct_validity.begin(), struct_validity.end());
+  auto struct_col = cudf::make_structs_column(14, {}, null_count, std::move(null_mask));
+
+  auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+    0, 0, 0, 0, 0, 2, 4, 6, 7, 8, 9, 10, 12, 14};
+  auto list_nullmask = std::vector<bool>{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::make_lists_column(
+    13, offsets.release(), std::move(struct_col), null_count, std::move(null_mask));
+
+  auto expect = cudf::test::fixed_width_column_wrapper<uint32_t>{2271818677u,
+                                                                 2271818677u,
+                                                                 2271818614u,
+                                                                 2271818614u,
+                                                                 3954409013u,
+                                                                 3954409013u,
+                                                                 3954409013u,
+                                                                 2295666275u,
+                                                                 2295666275u,
+                                                                 2295666276u,
+                                                                 2295666276u,
+                                                                 3954409052u,
+                                                                 3954409052u};
+
+  auto output = cudf::hashing::murmurhash3_x86_32(cudf::table_view({*list_column}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+}
+
+TEST_F(MurmurHashTest, EmptyDeepList)
+{
+  // List<List<int>>, where all lists are empty
+  // []
+  // []
+  // Null
+  // Null
+
+  // Internal empty list
+  auto list1 = cudf::test::lists_column_wrapper<int>{};
+
+  auto offsets       = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 0, 0, 0};
+  auto list_nullmask = std::vector<bool>{1, 1, 0, 0};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::make_lists_column(
+    4, offsets.release(), list1.release(), null_count, std::move(null_mask));
+
+  auto expect = cudf::test::fixed_width_column_wrapper<uint32_t>{
+    2271818677u, 2271818677u, 2271818614u, 2271818614u};
+
+  auto output = cudf::hashing::murmurhash3_x86_32(cudf::table_view({*list_column}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+}
+
+template <typename T>
+class MurmurHashTestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MurmurHashTestTyped, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(MurmurHashTestTyped, Equality)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> const col{0, 127, 1, 2, 8};
+  auto const input = cudf::table_view({col});
+
+  // Hash of same input should be equal
+  auto const output1 = cudf::hashing::murmurhash3_x86_32(input);
+  auto const output2 = cudf::hashing::murmurhash3_x86_32(input);
+
+  EXPECT_EQ(input.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+TYPED_TEST(MurmurHashTestTyped, EqualityNulls)
+{
+  using T = TypeParam;
+
+  // Nulls with different values should be equal
+  cudf::test::fixed_width_column_wrapper<T, int32_t> const col1({0, 127, 1, 2, 8}, {0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> const col2({1, 127, 1, 2, 8}, {0, 1, 1, 1, 1});
+
+  auto const input1 = cudf::table_view({col1});
+  auto const input2 = cudf::table_view({col2});
+
+  auto const output1 = cudf::hashing::murmurhash3_x86_32(input1);
+  auto const output2 = cudf::hashing::murmurhash3_x86_32(input2);
+
+  EXPECT_EQ(input1.num_rows(), output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+template <typename T>
+class MurmurHashTestFloatTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MurmurHashTestFloatTyped, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(MurmurHashTestFloatTyped, TestExtremes)
+{
+  using T = TypeParam;
+  T min   = std::numeric_limits<T>::min();
+  T max   = std::numeric_limits<T>::max();
+  T nan   = std::numeric_limits<T>::quiet_NaN();
+  T inf   = std::numeric_limits<T>::infinity();
+
+  cudf::test::fixed_width_column_wrapper<T> const col(
+    {T(0.0), T(100.0), T(-100.0), min, max, nan, inf, -inf});
+  cudf::test::fixed_width_column_wrapper<T> const col_neg_zero(
+    {T(-0.0), T(100.0), T(-100.0), min, max, nan, inf, -inf});
+  cudf::test::fixed_width_column_wrapper<T> const col_neg_nan(
+    {T(0.0), T(100.0), T(-100.0), min, max, -nan, inf, -inf});
+
+  auto const table_col          = cudf::table_view({col});
+  auto const table_col_neg_zero = cudf::table_view({col_neg_zero});
+  auto const table_col_neg_nan  = cudf::table_view({col_neg_nan});
+
+  auto const hash_col          = cudf::hashing::murmurhash3_x86_32(table_col);
+  auto const hash_col_neg_zero = cudf::hashing::murmurhash3_x86_32(table_col_neg_zero);
+  auto const hash_col_neg_nan  = cudf::hashing::murmurhash3_x86_32(table_col_neg_nan);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_col, *hash_col_neg_zero, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_col, *hash_col_neg_nan, verbosity);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/hashing/spark_murmurhash3_x86_32_test.cpp b/cpp/tests/hashing/spark_murmurhash3_x86_32_test.cpp
new file mode 100644
index 0000000..c228c1e
--- /dev/null
+++ b/cpp/tests/hashing/spark_murmurhash3_x86_32_test.cpp
@@ -0,0 +1,576 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/hashing.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+template <typename T>
+class SparkMurmurHashTestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SparkMurmurHashTestTyped, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(SparkMurmurHashTestTyped, Equality)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> const col{0, 127, 1, 2, 8};
+  auto const input = cudf::table_view({col});
+
+  // Hash of same input should be equal
+  auto const spark_output1 = cudf::hashing::spark_murmurhash3_x86_32(input, 0);
+  auto const spark_output2 = cudf::hashing::spark_murmurhash3_x86_32(input);
+
+  EXPECT_EQ(input.num_rows(), spark_output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(spark_output1->view(), spark_output2->view());
+}
+
+TYPED_TEST(SparkMurmurHashTestTyped, EqualityNulls)
+{
+  using T = TypeParam;
+
+  // Nulls with different values should be equal
+  cudf::test::fixed_width_column_wrapper<T, int32_t> const col1({0, 127, 1, 2, 8}, {0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> const col2({1, 127, 1, 2, 8}, {0, 1, 1, 1, 1});
+
+  auto const input1 = cudf::table_view({col1});
+  auto const input2 = cudf::table_view({col2});
+
+  auto const spark_output1 = cudf::hashing::spark_murmurhash3_x86_32(input1, 0);
+  auto const spark_output2 = cudf::hashing::spark_murmurhash3_x86_32(input2);
+
+  EXPECT_EQ(input1.num_rows(), spark_output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(spark_output1->view(), spark_output2->view());
+}
+
+template <typename T>
+class SparkMurmurHashTestFloatTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SparkMurmurHashTestFloatTyped, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(SparkMurmurHashTestFloatTyped, TestExtremes)
+{
+  using T = TypeParam;
+  T min   = std::numeric_limits<T>::min();
+  T max   = std::numeric_limits<T>::max();
+  T nan   = std::numeric_limits<T>::quiet_NaN();
+  T inf   = std::numeric_limits<T>::infinity();
+
+  cudf::test::fixed_width_column_wrapper<T> const col(
+    {T(0.0), T(100.0), T(-100.0), min, max, nan, inf, -inf});
+  cudf::test::fixed_width_column_wrapper<T> const col_neg_zero(
+    {T(-0.0), T(100.0), T(-100.0), min, max, nan, inf, -inf});
+  cudf::test::fixed_width_column_wrapper<T> const col_neg_nan(
+    {T(0.0), T(100.0), T(-100.0), min, max, -nan, inf, -inf});
+
+  auto const table_col          = cudf::table_view({col});
+  auto const table_col_neg_zero = cudf::table_view({col_neg_zero});
+  auto const table_col_neg_nan  = cudf::table_view({col_neg_nan});
+
+  // Spark hash is sensitive to 0 and -0
+  auto const spark_col         = cudf::hashing::spark_murmurhash3_x86_32(table_col, 0);
+  auto const spark_col_neg_nan = cudf::hashing::spark_murmurhash3_x86_32(table_col_neg_nan);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*spark_col, *spark_col_neg_nan);
+}
+
+class SparkMurmurHashTest : public cudf::test::BaseFixture {};
+
+TEST_F(SparkMurmurHashTest, MultiValueNulls)
+{
+  // Nulls with different values should be equal
+  cudf::test::strings_column_wrapper const strings_col1(
+    {"",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"},
+    {0, 1, 1, 0, 1});
+  cudf::test::strings_column_wrapper const strings_col2(
+    {"different but null",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "I am Jack's complete lack of null value",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"},
+    {0, 1, 1, 0, 1});
+
+  // Nulls with different values should be equal
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col1(
+    {0, 100, -100, limits::min(), limits::max()}, {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col2(
+    {0, -200, 200, limits::min(), limits::max()}, {1, 0, 0, 1, 1});
+
+  // Nulls with different values should be equal
+  // Different truth values should be equal
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 0, 1, 1}, {1, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 2, 1, 0, 255}, {1, 1, 0, 0, 1});
+
+  // Nulls with different values should be equal
+  using ts = cudf::timestamp_s;
+  cudf::test::fixed_width_column_wrapper<ts, ts::duration> const secs_col1(
+    {ts::duration::zero(),
+     static_cast<ts::duration>(100),
+     static_cast<ts::duration>(-100),
+     ts::duration::min(),
+     ts::duration::max()},
+    {1, 0, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<ts, ts::duration> const secs_col2(
+    {ts::duration::zero(),
+     static_cast<ts::duration>(-200),
+     static_cast<ts::duration>(200),
+     ts::duration::min(),
+     ts::duration::max()},
+    {1, 0, 0, 1, 1});
+
+  auto const input1        = cudf::table_view({strings_col1, ints_col1, bools_col1, secs_col1});
+  auto const input2        = cudf::table_view({strings_col2, ints_col2, bools_col2, secs_col2});
+  auto const spark_output1 = cudf::hashing::spark_murmurhash3_x86_32(input1, 0);
+  auto const spark_output2 = cudf::hashing::spark_murmurhash3_x86_32(input2);
+
+  EXPECT_EQ(input1.num_rows(), spark_output1->size());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(spark_output1->view(), spark_output2->view());
+}
+
+TEST_F(SparkMurmurHashTest, MultiValueWithSeeds)
+{
+  // The hash values were determined by running the following Scala code in Apache Spark.
+  // Note that Spark >= 3.2 normalizes the float/double value of -0. to +0. and both values hash
+  // to the same result. This is normalized in the calling code (Spark RAPIDS plugin) for Spark
+  // >= 3.2. However, the reference values for -0. below must be obtained with Spark < 3.2 and
+  // libcudf will continue to implement the Spark < 3.2 behavior until Spark >= 3.2 is required and
+  // the workaround in the calling code is removed. This also affects the combined hash values.
+
+  /*
+  import org.apache.spark.sql.functions._
+  import org.apache.spark.sql.types._
+  import org.apache.spark.sql.Row
+  import org.apache.spark.sql.catalyst.util.DateTimeUtils
+
+  val schema = new StructType()
+      .add("structs", new StructType()
+          .add("a", IntegerType)
+          .add("b", StringType)
+          .add("c", new StructType()
+              .add("x", FloatType)
+              .add("y", LongType)))
+      .add("strings", StringType)
+      .add("doubles", DoubleType)
+      .add("timestamps", TimestampType)
+      .add("decimal64", DecimalType(18, 7))
+      .add("longs", LongType)
+      .add("floats", FloatType)
+      .add("dates", DateType)
+      .add("decimal32", DecimalType(9, 3))
+      .add("ints", IntegerType)
+      .add("shorts", ShortType)
+      .add("bytes", ByteType)
+      .add("bools", BooleanType)
+      .add("decimal128", DecimalType(38, 11))
+
+  val data = Seq(
+      Row(Row(0, "a", Row(0f, 0L)), "", 0.toDouble,
+          DateTimeUtils.toJavaTimestamp(0), BigDecimal(0), 0.toLong, 0.toFloat,
+          DateTimeUtils.toJavaDate(0), BigDecimal(0), 0, 0.toShort, 0.toByte,
+          false, BigDecimal(0)),
+      Row(Row(100, "bc", Row(100f, 100L)), "The quick brown fox", -(0.toDouble),
+          DateTimeUtils.toJavaTimestamp(100), BigDecimal("0.00001"), 100.toLong, -(0.toFloat),
+          DateTimeUtils.toJavaDate(100), BigDecimal("0.1"), 100, 100.toShort, 100.toByte,
+          true, BigDecimal("0.000000001")),
+      Row(Row(-100, "def", Row(-100f, -100L)), "jumps over the lazy dog.", -Double.NaN,
+          DateTimeUtils.toJavaTimestamp(-100), BigDecimal("-0.00001"), -100.toLong, -Float.NaN,
+          DateTimeUtils.toJavaDate(-100), BigDecimal("-0.1"), -100, -100.toShort, -100.toByte,
+          true, BigDecimal("-0.00000000001")),
+      Row(Row(0x12345678, "ghij", Row(Float.PositiveInfinity, 0x123456789abcdefL)),
+          "All work and no play makes Jack a dull boy", Double.MinValue,
+          DateTimeUtils.toJavaTimestamp(Long.MinValue/1000000), BigDecimal("-99999999999.9999999"),
+          Long.MinValue, Float.MinValue, DateTimeUtils.toJavaDate(Int.MinValue/100),
+          BigDecimal("-999999.999"), Int.MinValue, Short.MinValue, Byte.MinValue, true,
+          BigDecimal("-9999999999999999.99999999999")),
+      Row(Row(-0x76543210, "klmno", Row(Float.NegativeInfinity, -0x123456789abcdefL)),
+          "!\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~\ud720\ud721", Double.MaxValue,
+          DateTimeUtils.toJavaTimestamp(Long.MaxValue/1000000), BigDecimal("99999999999.9999999"),
+          Long.MaxValue, Float.MaxValue, DateTimeUtils.toJavaDate(Int.MaxValue/100),
+          BigDecimal("999999.999"), Int.MaxValue, Short.MaxValue, Byte.MaxValue, false,
+          BigDecimal("99999999999999999999999999.99999999999")))
+
+  val df = spark.createDataFrame(sc.parallelize(data), schema)
+  df.columns.foreach(c => println(s"$c => ${df.select(hash(col(c))).collect.mkString(",")}"))
+  println(s"combined => ${df.select(hash(col("*"))).collect.mkString(",")}")
+  */
+
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_structs_expected(
+    {-105406170, 90479889, -678041645, 1667387937, 301478567});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_strings_expected(
+    {142593372, 1217302703, -715697185, -2061143941, -111635966});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_doubles_expected(
+    {-1670924195, -853646085, -1281358385, 1897734433, -508695674});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_timestamps_expected(
+    {-1670924195, 1114849490, 904948192, -1832979433, 1752430209});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_decimal64_expected(
+    {-1670924195, 1114849490, 904948192, 1962370902, -1795328666});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_longs_expected(
+    {-1670924195, 1114849490, 904948192, -853646085, -1604625029});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_floats_expected(
+    {933211791, 723455942, -349261430, -1225560532, -338752985});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_dates_expected(
+    {933211791, 751823303, -1080202046, -1906567553, -1503850410});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_decimal32_expected(
+    {-1670924195, 1114849490, 904948192, -1454351396, -193774131});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_ints_expected(
+    {933211791, 751823303, -1080202046, 723455942, 133916647});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_shorts_expected(
+    {933211791, 751823303, -1080202046, -1871935946, 1249274084});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_bytes_expected(
+    {933211791, 751823303, -1080202046, 1110053733, 1135925485});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_bools_expected(
+    {933211791, -559580957, -559580957, -559580957, 933211791});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_decimal128_expected(
+    {-783713497, -295670906, 1398487324, -52622807, -1359749815});
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_combined_expected(
+    {401603227, 588162166, 552160517, 1132537411, -326043017});
+
+  using double_limits = std::numeric_limits<double>;
+  using long_limits   = std::numeric_limits<int64_t>;
+  using float_limits  = std::numeric_limits<float>;
+  using int_limits    = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> a_col{0, 100, -100, 0x1234'5678, -0x7654'3210};
+  cudf::test::strings_column_wrapper b_col{"a", "bc", "def", "ghij", "klmno"};
+  cudf::test::fixed_width_column_wrapper<float> x_col{
+    0.f, 100.f, -100.f, float_limits::infinity(), -float_limits::infinity()};
+  cudf::test::fixed_width_column_wrapper<int64_t> y_col{
+    0L, 100L, -100L, 0x0123'4567'89ab'cdefL, -0x0123'4567'89ab'cdefL};
+  cudf::test::structs_column_wrapper c_col{{x_col, y_col}};
+  cudf::test::structs_column_wrapper const structs_col{{a_col, b_col, c_col}};
+
+  cudf::test::strings_column_wrapper const strings_col(
+    {"",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "All work and no play makes Jack a dull boy",
+     "!\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~\ud720\ud721"});
+  cudf::test::fixed_width_column_wrapper<double> const doubles_col(
+    {0., -0., -double_limits::quiet_NaN(), double_limits::lowest(), double_limits::max()});
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> const
+    timestamps_col({0L, 100L, -100L, long_limits::min() / 1000000, long_limits::max() / 1000000});
+  cudf::test::fixed_point_column_wrapper<int64_t> const decimal64_col(
+    {0L, 100L, -100L, -999999999999999999L, 999999999999999999L}, numeric::scale_type{-7});
+  cudf::test::fixed_width_column_wrapper<int64_t> const longs_col(
+    {0L, 100L, -100L, long_limits::min(), long_limits::max()});
+  cudf::test::fixed_width_column_wrapper<float> const floats_col(
+    {0.f, -0.f, -float_limits::quiet_NaN(), float_limits::lowest(), float_limits::max()});
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> dates_col(
+    {0, 100, -100, int_limits::min() / 100, int_limits::max() / 100});
+  cudf::test::fixed_point_column_wrapper<int32_t> const decimal32_col(
+    {0, 100, -100, -999999999, 999999999}, numeric::scale_type{-3});
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col(
+    {0, 100, -100, int_limits::min(), int_limits::max()});
+  cudf::test::fixed_width_column_wrapper<int16_t> const shorts_col({0, 100, -100, -32768, 32767});
+  cudf::test::fixed_width_column_wrapper<int8_t> const bytes_col({0, 100, -100, -128, 127});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 1, 2, 255, 0});
+  cudf::test::fixed_point_column_wrapper<__int128_t> const decimal128_col(
+    {static_cast<__int128>(0),
+     static_cast<__int128>(100),
+     static_cast<__int128>(-1),
+     (static_cast<__int128>(0xFFFF'FFFF'FCC4'D1C3u) << 64 | 0x602F'7FC3'1800'0001u),
+     (static_cast<__int128>(0x0785'EE10'D5DA'46D9u) << 64 | 0x00F4'369F'FFFF'FFFFu)},
+    numeric::scale_type{-11});
+
+  auto const hash_structs =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({structs_col}), 42);
+  auto const hash_strings =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({strings_col}), 42);
+  auto const hash_doubles =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({doubles_col}), 42);
+  auto const hash_timestamps =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({timestamps_col}), 42);
+  auto const hash_decimal64 =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({decimal64_col}), 42);
+  auto const hash_longs =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({longs_col}), 42);
+  auto const hash_floats =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({floats_col}), 42);
+  auto const hash_dates =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({dates_col}), 42);
+  auto const hash_decimal32 =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({decimal32_col}), 42);
+  auto const hash_ints = cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({ints_col}), 42);
+  auto const hash_shorts =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({shorts_col}), 42);
+  auto const hash_bytes =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({bytes_col}), 42);
+  auto const hash_bools1 =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({bools_col1}), 42);
+  auto const hash_bools2 =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({bools_col2}), 42);
+  auto const hash_decimal128 =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({decimal128_col}), 42);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_structs, hash_structs_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_strings, hash_strings_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_doubles, hash_doubles_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_timestamps, hash_timestamps_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_decimal64, hash_decimal64_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_longs, hash_longs_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_floats, hash_floats_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_dates, hash_dates_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_decimal32, hash_decimal32_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_ints, hash_ints_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_shorts, hash_shorts_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_bytes, hash_bytes_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_bools1, hash_bools_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_bools2, hash_bools_expected, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_decimal128, hash_decimal128_expected, verbosity);
+
+  auto const combined_table = cudf::table_view({structs_col,
+                                                strings_col,
+                                                doubles_col,
+                                                timestamps_col,
+                                                decimal64_col,
+                                                longs_col,
+                                                floats_col,
+                                                dates_col,
+                                                decimal32_col,
+                                                ints_col,
+                                                shorts_col,
+                                                bytes_col,
+                                                bools_col2,
+                                                decimal128_col});
+  auto const hash_combined  = cudf::hashing::spark_murmurhash3_x86_32(combined_table, 42);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_combined, hash_combined_expected, verbosity);
+}
+
+TEST_F(SparkMurmurHashTest, StringsWithSeed)
+{
+  // The hash values were determined by running the following Scala code in Apache Spark:
+  // val strs = Seq("", "The quick brown fox",
+  //              "jumps over the lazy dog.",
+  //              "All work and no play makes Jack a dull boy",
+  //              "!\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~\ud720\ud721")
+  // println(strs.map(org.apache.spark.unsafe.types.UTF8String.fromString)
+  //   .map(org.apache.spark.sql.catalyst.expressions.Murmur3HashFunction.hash(
+  //     _, org.apache.spark.sql.types.StringType, 314)))
+
+  cudf::test::fixed_width_column_wrapper<int32_t> const hash_strings_expected_seed_314(
+    {1467149710, 723257560, -1620282500, -2001858707, 1588473657});
+
+  cudf::test::strings_column_wrapper const strings_col(
+    {"",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "All work and no play makes Jack a dull boy",
+     "!\"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~\ud720\ud721"});
+
+  auto const hash_strings =
+    cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({strings_col}), 314);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*hash_strings, hash_strings_expected_seed_314, verbosity);
+}
+
+TEST_F(SparkMurmurHashTest, ListValues)
+{
+  /*
+  import org.apache.spark.sql.functions._
+  import org.apache.spark.sql.types.{ArrayType, IntegerType, StructType}
+  import org.apache.spark.sql.Row
+
+  val schema = new StructType()
+    .add("lists",ArrayType(ArrayType(IntegerType)))
+
+  val data = Seq(
+    Row(null),
+    Row(List(null)),
+    Row(List(List())),
+    Row(List(List(1))),
+    Row(List(List(1, 2))),
+    Row(List(List(1, 2, 3))),
+    Row(List(List(1, 2), List(3))),
+    Row(List(List(1), List(2, 3))),
+    Row(List(List(1), List(null, 2, 3))),
+    Row(List(List(1, 2), List(3), List(null))),
+    Row(List(List(1, 2), null, List(3))),
+  )
+
+  val df = spark.createDataFrame(
+    spark.sparkContext.parallelize(data), schema)
+
+  val df2 = df.selectExpr("lists", "hash(lists) as hash")
+  df2.printSchema()
+  df2.show(false)
+  */
+
+  auto const null = -1;
+  auto nested_list =
+    cudf::test::lists_column_wrapper<int>({{},
+                                           {1},
+                                           {1, 2},
+                                           {1, 2, 3},
+                                           {1, 2},
+                                           {3},
+                                           {1},
+                                           {2, 3},
+                                           {1},
+                                           {{null, 2, 3}, cudf::test::iterators::nulls_at({0})},
+                                           {1, 2},
+                                           {3},
+                                           {{null}, cudf::test::iterators::nulls_at({0})},
+                                           {1, 2},
+                                           {},
+                                           {3}},
+                                          cudf::test::iterators::nulls_at({0, 14}));
+  auto offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 0, 1, 2, 3, 4, 6, 8, 10, 13, 16};
+  auto list_validity = cudf::test::iterators::nulls_at({0});
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_validity, list_validity + 11);
+  auto list_column = cudf::make_lists_column(
+    11, offsets.release(), nested_list.release(), null_count, std::move(null_mask));
+
+  auto expect = cudf::test::fixed_width_column_wrapper<int32_t>{42,
+                                                                42,
+                                                                42,
+                                                                -559580957,
+                                                                -222940379,
+                                                                -912918097,
+                                                                -912918097,
+                                                                -912918097,
+                                                                -912918097,
+                                                                -912918097,
+                                                                -912918097};
+
+  auto output = cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({*list_column}), 42);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+}
+
+TEST_F(SparkMurmurHashTest, StructOfListValues)
+{
+  /*
+  import org.apache.spark.sql.functions._
+  import org.apache.spark.sql.types.{ArrayType, IntegerType, StructType}
+  import org.apache.spark.sql.Row
+
+  val schema = new StructType()
+    .add("structs", new StructType()
+        .add("a", ArrayType(IntegerType))
+        .add("b", ArrayType(IntegerType)))
+
+  val data = Seq(
+    Row(Row(List(), List())),
+    Row(Row(List(0), List(0))),
+    Row(Row(List(1, null), null)),
+    Row(Row(List(1, null), List())),
+    Row(Row(List(), List(null, 1))),
+    Row(Row(null, List(1))),
+    Row(Row(List(2, 3), List(4, 5))),
+  )
+
+  val df = spark.createDataFrame(
+    spark.sparkContext.parallelize(data), schema)
+
+  val df2 = df.selectExpr("lists", "hash(lists) as hash")
+  df2.printSchema()
+  df2.show(false)
+  */
+
+  auto const null = -1;
+  auto col1 =
+    cudf::test::lists_column_wrapper<int>({{},
+                                           {0},
+                                           {{1, null}, cudf::test::iterators::nulls_at({1})},
+                                           {{1, null}, cudf::test::iterators::nulls_at({1})},
+                                           {},
+                                           {} /*NULL*/,
+                                           {2, 3}},
+                                          cudf::test::iterators::nulls_at({5}));
+  auto col2 = cudf::test::lists_column_wrapper<int>(
+    {{}, {0}, {} /*NULL*/, {}, {{null, 1}, cudf::test::iterators::nulls_at({0})}, {1}, {4, 5}},
+    cudf::test::iterators::nulls_at({2}));
+  auto struct_column = cudf::test::structs_column_wrapper{{col1, col2}};
+
+  auto expect = cudf::test::fixed_width_column_wrapper<int32_t>{
+    42, 59727262, -559580957, -559580957, -559580957, -559580957, 170038658};
+
+  auto output = cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({struct_column}), 42);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+}
+
+TEST_F(SparkMurmurHashTest, ListOfStructValues)
+{
+  /*
+  import org.apache.spark.sql.functions._
+  import org.apache.spark.sql.types.{ArrayType, IntegerType, StructType}
+  import org.apache.spark.sql.Row
+
+  val schema = new StructType()
+    .add("lists", ArrayType(new StructType()
+      .add("a", IntegerType)
+      .add("b", IntegerType)))
+
+  val data = Seq(
+    Row(List(Row(0, 0))),
+    Row(List(null)),
+    Row(List(Row(null, null))),
+    Row(List(Row(1, null))),
+    Row(List(Row(null, 1))),
+    Row(List(Row(null, 1), Row(2, 3))),
+    Row(List(Row(2, 3), null)),
+    Row(List(Row(2, 3), Row(4, 5))),
+  )
+
+  val df = spark.createDataFrame(
+    spark.sparkContext.parallelize(data), schema)
+
+  val df2 = df.selectExpr("lists", "hash(lists) as hash")
+  df2.printSchema()
+  df2.show(false)
+  */
+
+  auto const null = -1;
+  auto col1       = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {0, null, null, 1, null, null, 2, 2, null, 2, 4},
+    cudf::test::iterators::nulls_at({1, 2, 4, 5, 8}));
+  auto col2 = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {0, null, null, null, 1, 1, 3, 3, null, 3, 5}, cudf::test::iterators::nulls_at({1, 2, 3, 8}));
+  auto struct_column =
+    cudf::test::structs_column_wrapper{{col1, col2}, {1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1}};
+  auto offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 2, 3, 4, 5, 7, 9, 11};
+  auto list_nullmask = std::vector<bool>(1, 8);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::make_lists_column(
+    8, offsets.release(), struct_column.release(), null_count, std::move(null_mask));
+
+  // TODO: Lists of structs are not yet supported. Once support is added,
+  // remove this EXPECT_THROW and uncomment the rest of this test.
+  EXPECT_THROW(cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({*list_column}), 42),
+               cudf::logic_error);
+
+  /*
+  auto expect = cudf::test::fixed_width_column_wrapper<int32_t>{
+    59727262, 42, 42, -559580957, -559580957, -912918097, 1092624418, 170038658};
+
+  auto output = cudf::hashing::spark_murmurhash3_x86_32(cudf::table_view({*list_column}), 42);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, output->view(), verbosity);
+  */
+}
diff --git a/cpp/tests/hashing/xxhash_64_test.cpp b/cpp/tests/hashing/xxhash_64_test.cpp
new file mode 100644
index 0000000..5916c4c
--- /dev/null
+++ b/cpp/tests/hashing/xxhash_64_test.cpp
@@ -0,0 +1,177 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/hashing.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+using NumericTypesNoBools =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+template <typename T>
+class XXHash_64_TestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(XXHash_64_TestTyped, NumericTypesNoBools);
+
+TYPED_TEST(XXHash_64_TestTyped, TestAllNumeric)
+{
+  using T   = TypeParam;
+  auto col1 = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {-1, -1, 0, 2, 22, 0, 11, 12, 116, 32, 0, 42, 7, 62, 1, -22, 0, 0},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+  auto col2 = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {-1, -1, 0, 2, 22, 1, 11, 12, 116, 32, 0, 42, 7, 62, 1, -22, 1, -22},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+
+  auto output1 = cudf::hashing::xxhash_64(cudf::table_view({col1}));
+  auto output2 = cudf::hashing::xxhash_64(cudf::table_view({col2}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+
+  constexpr uint64_t seed = 7;
+
+  output1 = cudf::hashing::xxhash_64(cudf::table_view({col1}), seed);
+  output2 = cudf::hashing::xxhash_64(cudf::table_view({col2}), seed);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output1->view(), output2->view());
+}
+
+class XXHash_64_Test : public cudf::test::BaseFixture {};
+
+TEST_F(XXHash_64_Test, TestInteger)
+{
+  auto col1 =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{-127,
+                                                     -70000,
+                                                     0,
+                                                     200000,
+                                                     128,
+                                                     std::numeric_limits<int32_t>::max(),
+                                                     std::numeric_limits<int32_t>::min(),
+                                                     std::numeric_limits<int32_t>::lowest()}};
+
+  auto const output = cudf::hashing::xxhash_64(cudf::table_view({col1}));
+
+  // these were generated using the CPU compiled version of the cuco xxhash_64 source
+  // https://github.com/NVIDIA/cuCollections/blob/dev/include/cuco/detail/hash_functions/xxhash.cuh
+  auto expected = cudf::test::fixed_width_column_wrapper<uint64_t>({4827426872506142937ul,
+                                                                    13867166853951622683ul,
+                                                                    4246796580750024372ul,
+                                                                    17339819992360460003ul,
+                                                                    7292178400482025765ul,
+                                                                    2971168436322821236ul,
+                                                                    9380524276503839603ul,
+                                                                    9380524276503839603ul});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(), expected);
+}
+
+TEST_F(XXHash_64_Test, TestDouble)
+{
+  auto col1 =
+    cudf::test::fixed_width_column_wrapper<double>{{-127.,
+                                                    -70000.125,
+                                                    0.0,
+                                                    200000.5,
+                                                    128.5,
+                                                    -0.0,
+                                                    std::numeric_limits<double>::infinity(),
+                                                    std::numeric_limits<double>::quiet_NaN(),
+                                                    std::numeric_limits<double>::max(),
+                                                    std::numeric_limits<double>::min(),
+                                                    std::numeric_limits<double>::lowest()}};
+
+  auto const output = cudf::hashing::xxhash_64(cudf::table_view({col1}));
+
+  // these were generated using the CPU compiled version of the cuco xxhash_64 source
+  // https://github.com/NVIDIA/cuCollections/blob/dev/include/cuco/detail/hash_functions/xxhash.cuh
+  auto expected = cudf::test::fixed_width_column_wrapper<uint64_t>({16892115221677838993ul,
+                                                                    1686446903308179321ul,
+                                                                    3803688792395291579ul,
+                                                                    18250447068822614389ul,
+                                                                    3511911086082166358ul,
+                                                                    4558309869707674848ul,
+                                                                    18031741628920313605ul,
+                                                                    16838308782748609196ul,
+                                                                    3127544388062992779ul,
+                                                                    1692401401506680154ul,
+                                                                    13770442912356326755ul});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(), expected);
+}
+
+TEST_F(XXHash_64_Test, StringType)
+{
+  // clang-format off
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"The",
+     "quick",
+     "brown fox",
+     "jumps over the lazy dog.",
+     "I am Jack's complete lack of null value",
+     "A very long (greater than 128 bytes/characters) to test a very long string. "
+     "2nd half of the very long string to verify the long string hashing happening.",
+     "Some multi-byte characters here: ééé",
+     "ééé",
+     "ééé ééé",
+     "ééé ééé ééé ééé",
+     "",
+     "!@#$%^&*(())",
+     "0123456789",
+     "{}|:<>?,./;[]=-"});
+  // clang-format on
+
+  auto output = cudf::hashing::xxhash_64(cudf::table_view({col1}));
+
+  // these were generated using the CPU compiled version of the cuco xxhash_64 source
+  // https://github.com/NVIDIA/cuCollections/blob/dev/include/cuco/detail/hash_functions/xxhash.cuh
+  // Also verified these with https://pypi.org/project/xxhash/
+  // using xxhash.xxh64(bytes(s,'utf-8')).intdigest()
+  auto expected = cudf::test::fixed_width_column_wrapper<uint64_t>({4686269239494003989ul,
+                                                                    6715983472207430822ul,
+                                                                    8148134898123095730ul,
+                                                                    17291005374665645904ul,
+                                                                    2631835514925512071ul,
+                                                                    4181420602165187991ul,
+                                                                    8749004388517322364ul,
+                                                                    17701789113925815768ul,
+                                                                    8612485687958712810ul,
+                                                                    5148645515269989956ul,
+                                                                    17241709254077376921ul,
+                                                                    7379359170906687646ul,
+                                                                    4566581271137380327ul,
+                                                                    17962149534752128981ul});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(), expected);
+}
+
+TEST_F(XXHash_64_Test, TestFixedPoint)
+{
+  auto const col1 = cudf::test::fixed_point_column_wrapper<int32_t>(
+    {0, 100, -100, -999999999, 999999999}, numeric::scale_type{-3});
+  auto const output = cudf::hashing::xxhash_64(cudf::table_view({col1}));
+
+  // these were generated using the CPU compiled version of the cuco xxhash_64 source
+  // https://github.com/NVIDIA/cuCollections/blob/dev/include/cuco/detail/hash_functions/xxhash.cuh
+  // and passing the 'value' of each input (without the scale) as the decimal-type
+  auto expected = cudf::test::fixed_width_column_wrapper<uint64_t>({4246796580750024372ul,
+                                                                    5959467639951725378ul,
+                                                                    4122185689695768261ul,
+                                                                    3249245648192442585ul,
+                                                                    8009575895491381648ul});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(), expected);
+}
diff --git a/cpp/tests/identify_stream_usage/test_default_stream_identification.cu b/cpp/tests/identify_stream_usage/test_default_stream_identification.cu
new file mode 100644
index 0000000..28bb47a
--- /dev/null
+++ b/cpp/tests/identify_stream_usage/test_default_stream_identification.cu
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <stdexcept>
+
+__global__ void kernel() { printf("The kernel ran!\n"); }
+
+void test_cudaLaunchKernel()
+{
+  cudaStream_t stream;
+  cudaStreamCreate(&stream);
+  kernel<<<1, 1, 0, stream>>>();
+  cudaError_t err{cudaDeviceSynchronize()};
+  if (err != cudaSuccess) { throw std::runtime_error("Kernel failed on non-default stream!"); }
+  err = cudaGetLastError();
+  if (err != cudaSuccess) { throw std::runtime_error("Kernel failed on non-default stream!"); }
+
+  try {
+    kernel<<<1, 1>>>();
+  } catch (std::runtime_error&) {
+    return;
+  }
+  throw std::runtime_error("No exception raised for kernel on default stream!");
+}
+
+int main() { test_cudaLaunchKernel(); }
diff --git a/cpp/tests/interop/arrow_utils.hpp b/cpp/tests/interop/arrow_utils.hpp
new file mode 100644
index 0000000..fc8f5b3
--- /dev/null
+++ b/cpp/tests/interop/arrow_utils.hpp
@@ -0,0 +1,206 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <arrow/util/bitmap_builders.h>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/interop.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#pragma once
+
+template <typename T>
+std::enable_if_t<cudf::is_fixed_width<T>() and !std::is_same_v<T, bool>,
+                 std::shared_ptr<arrow::Array>>
+get_arrow_array(std::vector<T> const& data, std::vector<uint8_t> const& mask = {})
+{
+  std::shared_ptr<arrow::Buffer> data_buffer;
+  arrow::BufferBuilder buff_builder;
+  CUDF_EXPECTS(buff_builder.Append(data.data(), sizeof(T) * data.size()).ok(),
+               "Failed to append values");
+  CUDF_EXPECTS(buff_builder.Finish(&data_buffer).ok(), "Failed to allocate buffer");
+
+  std::shared_ptr<arrow::Buffer> mask_buffer =
+    mask.empty() ? nullptr : arrow::internal::BytesToBits(mask).ValueOrDie();
+
+  return cudf::detail::to_arrow_array(cudf::type_to_id<T>(), data.size(), data_buffer, mask_buffer);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_fixed_width<T>() and !std::is_same_v<T, bool>,
+                 std::shared_ptr<arrow::Array>>
+get_arrow_array(std::initializer_list<T> elements, std::initializer_list<uint8_t> validity = {})
+{
+  std::vector<T> data(elements);
+  std::vector<uint8_t> mask(validity);
+
+  return get_arrow_array<T>(data, mask);
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, std::shared_ptr<arrow::Array>> get_arrow_array(
+  std::vector<bool> const& data, std::vector<bool> const& mask = {})
+{
+  std::shared_ptr<arrow::BooleanArray> boolean_array;
+  arrow::BooleanBuilder boolean_builder;
+
+  if (mask.empty()) {
+    CUDF_EXPECTS(boolean_builder.AppendValues(data).ok(),
+                 "Failed to append values to boolean builder");
+  } else {
+    CUDF_EXPECTS(boolean_builder.AppendValues(data, mask).ok(),
+                 "Failed to append values to boolean builder");
+  }
+  CUDF_EXPECTS(boolean_builder.Finish(&boolean_array).ok(), "Failed to create arrow boolean array");
+
+  return boolean_array;
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, std::shared_ptr<arrow::Array>> get_arrow_array(
+  std::initializer_list<bool> elements, std::initializer_list<bool> validity = {})
+{
+  std::vector<bool> mask(validity);
+  std::vector<bool> data(elements);
+
+  return get_arrow_array<T>(data, mask);
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, std::shared_ptr<arrow::Array>>
+get_arrow_array(std::vector<std::string> const& data, std::vector<uint8_t> const& mask = {})
+{
+  std::shared_ptr<arrow::StringArray> string_array;
+  arrow::StringBuilder string_builder;
+
+  CUDF_EXPECTS(string_builder.AppendValues(data, mask.data()).ok(),
+               "Failed to append values to string builder");
+  CUDF_EXPECTS(string_builder.Finish(&string_array).ok(), "Failed to create arrow string array");
+
+  return string_array;
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, std::shared_ptr<arrow::Array>>
+get_arrow_array(std::initializer_list<std::string> elements,
+                std::initializer_list<uint8_t> validity = {})
+{
+  std::vector<uint8_t> mask(validity);
+  std::vector<std::string> data(elements);
+
+  return get_arrow_array<T>(data, mask);
+}
+
+template <typename KEY_TYPE, typename IND_TYPE>
+std::shared_ptr<arrow::Array> get_arrow_dict_array(std::vector<KEY_TYPE> const& keys,
+                                                   std::vector<IND_TYPE> const& ind,
+                                                   std::vector<uint8_t> const& validity = {})
+{
+  auto keys_array    = get_arrow_array<KEY_TYPE>(keys);
+  auto indices_array = get_arrow_array<IND_TYPE>(ind, validity);
+
+  return std::make_shared<arrow::DictionaryArray>(
+    arrow::dictionary(indices_array->type(), keys_array->type()), indices_array, keys_array);
+}
+
+template <typename KEY_TYPE, typename IND_TYPE>
+std::shared_ptr<arrow::Array> get_arrow_dict_array(std::initializer_list<KEY_TYPE> keys,
+                                                   std::initializer_list<IND_TYPE> ind,
+                                                   std::initializer_list<uint8_t> validity = {})
+{
+  auto keys_array    = get_arrow_array<KEY_TYPE>(keys);
+  auto indices_array = get_arrow_array<IND_TYPE>(ind, validity);
+
+  return std::make_shared<arrow::DictionaryArray>(
+    arrow::dictionary(indices_array->type(), keys_array->type()), indices_array, keys_array);
+}
+
+// Creates only single layered list
+template <typename T>
+std::shared_ptr<arrow::Array> get_arrow_list_array(std::vector<T> data,
+                                                   std::vector<int32_t> offsets,
+                                                   std::vector<uint8_t> data_validity = {},
+                                                   std::vector<uint8_t> list_validity = {})
+{
+  auto data_array = get_arrow_array<T>(data, data_validity);
+  std::shared_ptr<arrow::Buffer> offset_buffer;
+  arrow::BufferBuilder buff_builder;
+  CUDF_EXPECTS(buff_builder.Append(offsets.data(), sizeof(int32_t) * offsets.size()).ok(),
+               "Failed to append values to buffer builder");
+  CUDF_EXPECTS(buff_builder.Finish(&offset_buffer).ok(), "Failed to allocate buffer");
+
+  return std::make_shared<arrow::ListArray>(
+    arrow::list(data_array->type()),
+    offsets.size() - 1,
+    offset_buffer,
+    data_array,
+    list_validity.empty() ? nullptr : arrow::internal::BytesToBits(list_validity).ValueOrDie());
+}
+
+template <typename T>
+std::shared_ptr<arrow::Array> get_arrow_list_array(
+  std::initializer_list<T> data,
+  std::initializer_list<int32_t> offsets,
+  std::initializer_list<uint8_t> data_validity = {},
+  std::initializer_list<uint8_t> list_validity = {})
+{
+  std::vector<T> data_vector(data);
+  std::vector<int32_t> ofst(offsets);
+  std::vector<uint8_t> data_mask(data_validity);
+  std::vector<uint8_t> list_mask(list_validity);
+  return get_arrow_list_array<T>(data_vector, ofst, data_mask, list_mask);
+}
+
+std::pair<std::unique_ptr<cudf::table>, std::shared_ptr<arrow::Table>> get_tables(
+  cudf::size_type length = 10000);
+
+template <typename T>
+[[nodiscard]] auto make_decimal128_arrow_array(std::vector<T> const& data,
+                                               std::optional<std::vector<int>> const& validity,
+                                               int32_t scale) -> std::shared_ptr<arrow::Array>
+{
+  auto constexpr BIT_WIDTH_RATIO = sizeof(__int128_t) / sizeof(T);
+
+  std::shared_ptr<arrow::Array> arr;
+  arrow::Decimal128Builder decimal_builder(arrow::decimal(18, -scale),
+                                           arrow::default_memory_pool());
+
+  for (T i = 0; i < static_cast<T>(data.size() / BIT_WIDTH_RATIO); ++i) {
+    if (validity.has_value() and not validity.value()[i]) {
+      CUDF_EXPECTS(decimal_builder.AppendNull().ok(), "Failed to append");
+    } else {
+      CUDF_EXPECTS(
+        decimal_builder.Append(reinterpret_cast<uint8_t const*>(data.data() + BIT_WIDTH_RATIO * i))
+          .ok(),
+        "Failed to append");
+    }
+  }
+
+  CUDF_EXPECTS(decimal_builder.Finish(&arr).ok(), "Failed to build array");
+
+  return arr;
+}
diff --git a/cpp/tests/interop/dlpack_test.cpp b/cpp/tests/interop/dlpack_test.cpp
new file mode 100644
index 0000000..ed44727
--- /dev/null
+++ b/cpp/tests/interop/dlpack_test.cpp
@@ -0,0 +1,496 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/interop.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <dlpack/dlpack.h>
+
+#include <thrust/host_vector.h>
+
+struct dlpack_deleter {
+  void operator()(DLManagedTensor* tensor) { tensor->deleter(tensor); }
+};
+
+using unique_managed_tensor = std::unique_ptr<DLManagedTensor, dlpack_deleter>;
+
+template <typename T>
+DLDataType get_dtype()
+{
+  uint8_t const bits{sizeof(T) * 8};
+  uint16_t const lanes{1};
+  if (std::is_floating_point_v<T>) {
+    return DLDataType{kDLFloat, bits, lanes};
+  } else if (std::is_signed_v<T>) {
+    return DLDataType{kDLInt, bits, lanes};
+  } else if (std::is_unsigned_v<T>) {
+    return DLDataType{kDLUInt, bits, lanes};
+  } else {
+    static_assert(true, "unsupported type");
+  }
+}
+
+template <typename T>
+void validate_dtype(DLDataType const& dtype)
+{
+  switch (dtype.code) {
+    case kDLInt: EXPECT_TRUE(std::is_integral_v<T> && std::is_signed_v<T>); break;
+    case kDLUInt: EXPECT_TRUE(std::is_integral_v<T> && std::is_unsigned_v<T>); break;
+    case kDLFloat: EXPECT_TRUE(std::is_floating_point_v<T>); break;
+    default: FAIL();
+  }
+  EXPECT_EQ(1, dtype.lanes);
+  EXPECT_EQ(sizeof(T) * 8, dtype.bits);
+}
+
+class DLPackUntypedTests : public cudf::test::BaseFixture {};
+
+TEST_F(DLPackUntypedTests, EmptyTableToDlpack)
+{
+  cudf::table_view empty(std::vector<cudf::column_view>{});
+  // No type information to construct a correct empty dlpack object
+  EXPECT_EQ(nullptr, cudf::to_dlpack(empty));
+}
+
+TEST_F(DLPackUntypedTests, EmptyColsToDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1({});
+  cudf::test::fixed_width_column_wrapper<int32_t> col2({});
+  cudf::table_view input({col1, col2});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+  validate_dtype<int32_t>(tensor->dl_tensor.dtype);
+  EXPECT_NE(nullptr, tensor);
+  EXPECT_EQ(nullptr, tensor->dl_tensor.data);
+  EXPECT_EQ(2, tensor->dl_tensor.ndim);
+  EXPECT_EQ(0, tensor->dl_tensor.strides[0]);
+  EXPECT_EQ(0, tensor->dl_tensor.strides[1]);
+  EXPECT_EQ(0, tensor->dl_tensor.shape[0]);
+  EXPECT_EQ(2, tensor->dl_tensor.shape[1]);
+  EXPECT_EQ(kDLCUDA, tensor->dl_tensor.device.device_type);
+  auto result = cudf::from_dlpack(tensor.get());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, result->view());
+}
+
+TEST_F(DLPackUntypedTests, NullTensorFromDlpack)
+{
+  EXPECT_THROW(cudf::from_dlpack(nullptr), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, MultipleTypesToDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1({1, 2, 3, 4});
+  cudf::test::fixed_width_column_wrapper<int32_t> col2({1, 2, 3, 4});
+  cudf::table_view input({col1, col2});
+  EXPECT_THROW(cudf::to_dlpack(input), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, InvalidNullsToDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1({1, 2, 3, 4});
+  cudf::test::fixed_width_column_wrapper<int32_t> col2({1, 2, 3, 4}, {1, 0, 1, 1});
+  cudf::table_view input({col1, col2});
+  EXPECT_THROW(cudf::to_dlpack(input), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, StringTypeToDlpack)
+{
+  cudf::test::strings_column_wrapper col({"foo", "bar", "baz"});
+  cudf::table_view input({col});
+  EXPECT_THROW(cudf::to_dlpack(input), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedDeviceTypeFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof an unsupported device type
+  tensor->dl_tensor.device.device_type = kDLOpenCL;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, InvalidDeviceIdFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof the wrong device ID
+  tensor->dl_tensor.device.device_id += 1;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedDimsFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof an unsupported number of dims
+  tensor->dl_tensor.ndim = 3;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, TooManyRowsFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof too many rows
+  constexpr int64_t max_size_type{std::numeric_limits<int32_t>::max()};
+  tensor->dl_tensor.shape[0] = max_size_type + 1;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), std::overflow_error);
+}
+
+TEST_F(DLPackUntypedTests, TooManyColsFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1({1, 2, 3, 4});
+  cudf::test::fixed_width_column_wrapper<int32_t> col2({5, 6, 7, 8});
+  cudf::table_view input({col1, col2});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof too many cols
+  constexpr int64_t max_size_type{std::numeric_limits<int32_t>::max()};
+  tensor->dl_tensor.shape[1] = max_size_type + 1;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), std::overflow_error);
+}
+
+TEST_F(DLPackUntypedTests, InvalidTypeFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof an invalid data type
+  tensor->dl_tensor.dtype.code = 3;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedIntBitsizeFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof an unsupported bitsize
+  tensor->dl_tensor.dtype.bits = 7;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedFloatBitsizeFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<float> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof an unsupported bitsize
+  tensor->dl_tensor.dtype.bits = 7;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedLanesFromDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Spoof an unsupported number of lanes
+  tensor->dl_tensor.dtype.lanes = 2;
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedBroadcast1DTensorFromDlpack)
+{
+  using T            = float;
+  constexpr int ndim = 1;
+  // Broadcasted (stride-0) 1D tensor
+  auto const data       = cudf::test::make_type_param_vector<T>({1});
+  int64_t shape[ndim]   = {5};
+  int64_t strides[ndim] = {0};
+
+  DLManagedTensor tensor{};
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = ndim;
+  tensor.dl_tensor.byte_offset        = 0;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = strides;
+
+  thrust::host_vector<T> host_vector(data.begin(), data.end());
+  tensor.dl_tensor.data = host_vector.data();
+
+  EXPECT_THROW(cudf::from_dlpack(&tensor), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedStrided1DTensorFromDlpack)
+{
+  using T            = float;
+  constexpr int ndim = 1;
+  // Strided 1D tensor
+  auto const data       = cudf::test::make_type_param_vector<T>({1, 2, 3, 4});
+  int64_t shape[ndim]   = {2};
+  int64_t strides[ndim] = {2};
+
+  DLManagedTensor tensor{};
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = ndim;
+  tensor.dl_tensor.byte_offset        = 0;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = strides;
+
+  thrust::host_vector<T> host_vector(data.begin(), data.end());
+  tensor.dl_tensor.data = host_vector.data();
+
+  EXPECT_THROW(cudf::from_dlpack(&tensor), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedImplicitRowMajor2DTensorFromDlpack)
+{
+  using T            = float;
+  constexpr int ndim = 2;
+  // Row major 2D tensor
+  auto const data     = cudf::test::make_type_param_vector<T>({1, 2, 3, 4});
+  int64_t shape[ndim] = {2, 2};
+
+  DLManagedTensor tensor{};
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = ndim;
+  tensor.dl_tensor.byte_offset        = 0;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = nullptr;
+
+  thrust::host_vector<T> host_vector(data.begin(), data.end());
+  tensor.dl_tensor.data = host_vector.data();
+
+  EXPECT_THROW(cudf::from_dlpack(&tensor), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedExplicitRowMajor2DTensorFromDlpack)
+{
+  using T            = float;
+  constexpr int ndim = 2;
+  // Row major 2D tensor with explicit strides
+  auto const data       = cudf::test::make_type_param_vector<T>({1, 2, 3, 4});
+  int64_t shape[ndim]   = {2, 2};
+  int64_t strides[ndim] = {2, 1};
+
+  DLManagedTensor tensor{};
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = ndim;
+  tensor.dl_tensor.byte_offset        = 0;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = strides;
+
+  thrust::host_vector<T> host_vector(data.begin(), data.end());
+  tensor.dl_tensor.data = host_vector.data();
+
+  EXPECT_THROW(cudf::from_dlpack(&tensor), cudf::logic_error);
+}
+
+TEST_F(DLPackUntypedTests, UnsupportedStridedColMajor2DTensorFromDlpack)
+{
+  using T            = float;
+  constexpr int ndim = 2;
+  // Column major, but strided in fastest dimension
+  auto const data       = cudf::test::make_type_param_vector<T>({1, 2, 3, 4, 5, 6, 7, 8});
+  int64_t shape[ndim]   = {2, 2};
+  int64_t strides[ndim] = {2, 4};
+
+  DLManagedTensor tensor{};
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = ndim;
+  tensor.dl_tensor.byte_offset        = 0;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = strides;
+
+  thrust::host_vector<T> host_vector(data.begin(), data.end());
+  tensor.dl_tensor.data = host_vector.data();
+
+  EXPECT_THROW(cudf::from_dlpack(&tensor), cudf::logic_error);
+}
+
+template <typename T>
+class DLPackTimestampTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(DLPackTimestampTests, cudf::test::ChronoTypes);
+
+TYPED_TEST(DLPackTimestampTests, ChronoTypesToDlpack)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  EXPECT_THROW(cudf::to_dlpack(input), cudf::logic_error);
+}
+
+template <typename T>
+class DLPackNumericTests : public cudf::test::BaseFixture {};
+
+// The list of supported types comes from DLDataType_to_data_type() in cpp/src/dlpack/dlpack.cpp
+// TODO: Replace with `NumericTypes` when unsigned support is added. Issue #5353
+using SupportedTypes =
+  cudf::test::RemoveIf<cudf::test::ContainedIn<cudf::test::Types<bool>>, cudf::test::NumericTypes>;
+TYPED_TEST_SUITE(DLPackNumericTests, SupportedTypes);
+
+TYPED_TEST(DLPackNumericTests, ToDlpack1D)
+{
+  // Test nullable column with no nulls
+  cudf::test::fixed_width_column_wrapper<TypeParam> col({1, 2, 3, 4}, {1, 1, 1, 1});
+  auto const col_view = static_cast<cudf::column_view>(col);
+  EXPECT_FALSE(col_view.has_nulls());
+  EXPECT_TRUE(col_view.nullable());
+
+  cudf::table_view input({col});
+  unique_managed_tensor result(cudf::to_dlpack(input));
+
+  auto const& tensor = result->dl_tensor;
+  validate_dtype<TypeParam>(tensor.dtype);
+  EXPECT_EQ(kDLCUDA, tensor.device.device_type);
+  EXPECT_EQ(1, tensor.ndim);
+  EXPECT_EQ(uint64_t{0}, tensor.byte_offset);
+  EXPECT_EQ(nullptr, tensor.strides);
+
+  EXPECT_NE(nullptr, tensor.data);
+  EXPECT_NE(nullptr, tensor.shape);
+
+  // Verify that data matches input column
+  constexpr cudf::data_type type{cudf::type_to_id<TypeParam>()};
+  cudf::column_view const result_view(
+    type, tensor.shape[0], tensor.data, col_view.null_mask(), col_view.null_count());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_view, result_view);
+}
+
+TYPED_TEST(DLPackNumericTests, ToDlpack2D)
+{
+  using T             = TypeParam;
+  auto const col1_tmp = cudf::test::make_type_param_vector<T>({1, 2, 3, 4});
+  auto const col2_tmp = cudf::test::make_type_param_vector<T>({4, 5, 6, 7});
+  std::vector<cudf::test::fixed_width_column_wrapper<TypeParam>> cols;
+  cols.push_back(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(col1_tmp.cbegin(), col1_tmp.cend()));
+  cols.push_back(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(col2_tmp.cbegin(), col2_tmp.cend()));
+
+  std::vector<cudf::column_view> col_views;
+  std::transform(cols.begin(), cols.end(), std::back_inserter(col_views), [](auto const& col) {
+    return static_cast<cudf::column_view>(col);
+  });
+
+  cudf::table_view input(col_views);
+  unique_managed_tensor result(cudf::to_dlpack(input));
+
+  auto const& tensor = result->dl_tensor;
+  validate_dtype<TypeParam>(tensor.dtype);
+  EXPECT_EQ(kDLCUDA, tensor.device.device_type);
+  EXPECT_EQ(2, tensor.ndim);
+  EXPECT_EQ(uint64_t{0}, tensor.byte_offset);
+
+  EXPECT_NE(nullptr, tensor.data);
+  EXPECT_NE(nullptr, tensor.shape);
+  EXPECT_NE(nullptr, tensor.strides);
+
+  EXPECT_EQ(1, tensor.strides[0]);
+  EXPECT_EQ(tensor.shape[0], tensor.strides[1]);
+
+  // Verify that data matches input columns
+  cudf::size_type offset{0};
+  for (auto const& col : input) {
+    constexpr cudf::data_type type{cudf::type_to_id<TypeParam>()};
+    cudf::column_view const result_view(type, tensor.shape[0], tensor.data, nullptr, 0, offset);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(col, result_view);
+    offset += tensor.strides[1];
+  }
+}
+
+TYPED_TEST(DLPackNumericTests, FromDlpack1D)
+{
+  // Use to_dlpack to generate an input tensor
+  cudf::test::fixed_width_column_wrapper<TypeParam> col({1, 2, 3, 4});
+  cudf::table_view input({col});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Verify that from_dlpack(to_dlpack(input)) == input
+  auto result = cudf::from_dlpack(tensor.get());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, result->view());
+}
+
+TYPED_TEST(DLPackNumericTests, FromDlpack2D)
+{
+  // Use to_dlpack to generate an input tensor
+  using T         = TypeParam;
+  auto const col1 = cudf::test::make_type_param_vector<T>({1, 2, 3, 4});
+  auto const col2 = cudf::test::make_type_param_vector<T>({4, 5, 6, 7});
+  std::vector<cudf::test::fixed_width_column_wrapper<TypeParam>> cols;
+  cols.push_back(cudf::test::fixed_width_column_wrapper<T>(col1.cbegin(), col1.cend()));
+  cols.push_back(cudf::test::fixed_width_column_wrapper<T>(col2.cbegin(), col2.cend()));
+
+  std::vector<cudf::column_view> col_views;
+  std::transform(cols.begin(), cols.end(), std::back_inserter(col_views), [](auto const& col) {
+    return static_cast<cudf::column_view>(col);
+  });
+
+  cudf::table_view input(col_views);
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  // Verify that from_dlpack(to_dlpack(input)) == input
+  auto result = cudf::from_dlpack(tensor.get());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, result->view());
+}
+
+TYPED_TEST(DLPackNumericTests, FromDlpackCpu)
+{
+  // Host buffer with stride > rows and byte_offset > 0
+  using T         = TypeParam;
+  auto const data = cudf::test::make_type_param_vector<T>({0, 1, 2, 3, 4, 0, 5, 6, 7, 8, 0});
+  uint64_t const offset{sizeof(T)};
+  int64_t shape[2]   = {4, 2};
+  int64_t strides[2] = {1, 5};
+
+  DLManagedTensor tensor{};
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = 2;
+  tensor.dl_tensor.byte_offset        = offset;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = strides;
+
+  thrust::host_vector<T> host_vector(data.begin(), data.end());
+  tensor.dl_tensor.data = host_vector.data();
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> col1({1, 2, 3, 4});
+  cudf::test::fixed_width_column_wrapper<TypeParam> col2({5, 6, 7, 8});
+  cudf::table_view expected({col1, col2});
+
+  auto result = cudf::from_dlpack(&tensor);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(DLPackNumericTests, FromDlpackEmpty1D)
+{
+  // Use to_dlpack to generate an input tensor
+  cudf::table_view input(std::vector<cudf::column_view>{});
+  unique_managed_tensor tensor(cudf::to_dlpack(input));
+
+  EXPECT_EQ(nullptr, tensor.get());
+  EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
+}
diff --git a/cpp/tests/interop/from_arrow_test.cpp b/cpp/tests/interop/from_arrow_test.cpp
new file mode 100644
index 0000000..a898106
--- /dev/null
+++ b/cpp/tests/interop/from_arrow_test.cpp
@@ -0,0 +1,553 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/interop.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <tests/interop/arrow_utils.hpp>
+
+std::unique_ptr<cudf::table> get_cudf_table()
+{
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.emplace_back(
+    cudf::test::fixed_width_column_wrapper<int32_t>({1, 2, 5, 2, 7}, {1, 0, 1, 1, 1}).release());
+  columns.emplace_back(cudf::test::fixed_width_column_wrapper<int64_t>({1, 2, 3, 4, 5}).release());
+  columns.emplace_back(
+    cudf::test::strings_column_wrapper({"fff", "aaa", "", "fff", "ccc"}, {1, 1, 1, 0, 1})
+      .release());
+  auto col4 = cudf::test::fixed_width_column_wrapper<int32_t>({1, 2, 5, 2, 7}, {1, 0, 1, 1, 1});
+  columns.emplace_back(std::move(cudf::dictionary::encode(col4)));
+  columns.emplace_back(
+    cudf::test::fixed_width_column_wrapper<bool>({true, false, true, false, true}, {1, 0, 1, 1, 0})
+      .release());
+  // columns.emplace_back(cudf::test::lists_column_wrapper<int>({{1, 2}, {3, 4}, {}, {6}, {7, 8,
+  // 9}}).release());
+  return std::make_unique<cudf::table>(std::move(columns));
+}
+
+struct FromArrowTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct FromArrowTestDurationsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FromArrowTestDurationsTest, cudf::test::DurationTypes);
+
+TEST_F(FromArrowTest, EmptyTable)
+{
+  auto tables = get_tables(0);
+
+  auto expected_cudf_table = tables.first->view();
+  auto arrow_table         = tables.second;
+
+  auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_cudf_table, got_cudf_table->view());
+}
+
+TEST_F(FromArrowTest, DateTimeTable)
+{
+  auto data = std::vector<int64_t>{1, 2, 3, 4, 5, 6};
+
+  auto col = cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>(
+    data.begin(), data.end());
+
+  cudf::table_view expected_table_view({col});
+
+  std::shared_ptr<arrow::Array> arr;
+  arrow::TimestampBuilder timestamp_builder(arrow::timestamp(arrow::TimeUnit::type::MILLI),
+                                            arrow::default_memory_pool());
+  ASSERT_TRUE(timestamp_builder.AppendValues(data).ok());
+  ASSERT_TRUE(timestamp_builder.Finish(&arr).ok());
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector({arrow::field("a", arr->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto arrow_table = arrow::Table::Make(schema, {arr});
+
+  auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table_view, got_cudf_table->view());
+}
+
+TYPED_TEST(FromArrowTestDurationsTest, DurationTable)
+{
+  using T = TypeParam;
+
+  auto data = {T{1}, T{2}, T{3}, T{4}, T{5}, T{6}};
+  auto col  = cudf::test::fixed_width_column_wrapper<T>(data);
+
+  std::shared_ptr<arrow::Array> arr;
+  cudf::table_view expected_table_view({col});
+  arrow::TimeUnit::type arrow_unit;
+
+  switch (cudf::type_to_id<TypeParam>()) {
+    case cudf::type_id::DURATION_SECONDS: arrow_unit = arrow::TimeUnit::type::SECOND; break;
+    case cudf::type_id::DURATION_MILLISECONDS: arrow_unit = arrow::TimeUnit::type::MILLI; break;
+    case cudf::type_id::DURATION_MICROSECONDS: arrow_unit = arrow::TimeUnit::type::MICRO; break;
+    case cudf::type_id::DURATION_NANOSECONDS: arrow_unit = arrow::TimeUnit::type::NANO; break;
+    case cudf::type_id::DURATION_DAYS: return;
+    default: CUDF_FAIL("Unsupported duration unit in arrow");
+  }
+  arrow::DurationBuilder duration_builder(duration(arrow_unit), arrow::default_memory_pool());
+  ASSERT_TRUE(duration_builder.AppendValues(std::vector<int64_t>{1, 2, 3, 4, 5, 6}).ok());
+  ASSERT_TRUE(duration_builder.Finish(&arr).ok());
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector({arrow::field("a", arr->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto arrow_table = arrow::Table::Make(schema, {arr});
+
+  auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table_view, got_cudf_table->view());
+}
+
+TEST_F(FromArrowTest, NestedList)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3 != 0; });
+  auto col = cudf::test::lists_column_wrapper<int64_t>(
+    {{{{{1, 2}, valids}, {{3, 4}, valids}, {5}}, {{6}, {{7, 8, 9}, valids}}}, valids});
+  cudf::table_view expected_table_view({col});
+
+  auto list_arr = get_arrow_list_array<int64_t>({6, 7, 8, 9}, {0, 1, 4}, {1, 0, 1, 1});
+  std::vector<int32_t> offset{0, 0, 2};
+  auto mask_buffer     = arrow::internal::BytesToBits({0, 1}).ValueOrDie();
+  auto nested_list_arr = std::make_shared<arrow::ListArray>(arrow::list(list(arrow::int64())),
+                                                            offset.size() - 1,
+                                                            arrow::Buffer::Wrap(offset),
+                                                            list_arr,
+                                                            mask_buffer);
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector(
+    {arrow::field("a", nested_list_arr->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto arrow_table = arrow::Table::Make(schema, {nested_list_arr});
+
+  auto got_cudf_table = cudf::from_arrow(*arrow_table);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table_view, got_cudf_table->view());
+}
+
+TEST_F(FromArrowTest, StructColumn)
+{
+  using vector_of_columns = std::vector<std::unique_ptr<cudf::column>>;
+
+  // Create cudf table
+  auto nested_type_field_names =
+    std::vector<std::vector<std::string>>{{"string", "integral", "bool", "nested_list", "struct"}};
+  auto str_col =
+    cudf::test::strings_column_wrapper{
+      "Samuel Vimes", "Carrot Ironfoundersson", "Angua von Überwald"}
+      .release();
+  auto str_col2 =
+    cudf::test::strings_column_wrapper{{"CUDF", "ROCKS", "EVERYWHERE"}, {0, 1, 0}}.release();
+  int num_rows{str_col->size()};
+  auto int_col = cudf::test::fixed_width_column_wrapper<int32_t, int32_t>{{48, 27, 25}}.release();
+  auto int_col2 =
+    cudf::test::fixed_width_column_wrapper<int32_t, int32_t>{{12, 24, 47}, {1, 0, 1}}.release();
+  auto bool_col = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}}.release();
+  auto list_col =
+    cudf::test::lists_column_wrapper<int64_t>({{{1, 2}, {3, 4}, {5}}, {{{6}}}, {{7}, {8, 9}}})
+      .release();
+  vector_of_columns cols2;
+  cols2.push_back(std::move(str_col2));
+  cols2.push_back(std::move(int_col2));
+  auto [null_mask, null_count] =
+    cudf::bools_to_mask(cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}});
+  auto sub_struct_col =
+    cudf::make_structs_column(num_rows, std::move(cols2), null_count, std::move(*null_mask));
+  vector_of_columns cols;
+  cols.push_back(std::move(str_col));
+  cols.push_back(std::move(int_col));
+  cols.push_back(std::move(bool_col));
+  cols.push_back(std::move(list_col));
+  cols.push_back(std::move(sub_struct_col));
+
+  auto struct_col = cudf::make_structs_column(num_rows, std::move(cols), 0, {});
+  cudf::table_view expected_cudf_table({struct_col->view()});
+
+  // Create Arrow table
+  std::vector<std::string> str{"Samuel Vimes", "Carrot Ironfoundersson", "Angua von Überwald"};
+  std::vector<std::string> str2{"CUDF", "ROCKS", "EVERYWHERE"};
+  auto str_array  = get_arrow_array<cudf::string_view>(str);
+  auto int_array  = get_arrow_array<int32_t>({48, 27, 25});
+  auto str2_array = get_arrow_array<cudf::string_view>(str2, {0, 1, 0});
+  auto int2_array = get_arrow_array<int32_t>({12, 24, 47}, {1, 0, 1});
+  auto bool_array = get_arrow_array<bool>({true, true, false});
+  auto list_arr = get_arrow_list_array<int64_t>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 4, 5, 6, 7, 9});
+  std::vector<int32_t> offset{0, 3, 4, 6};
+  auto nested_list_arr = std::make_shared<arrow::ListArray>(
+    arrow::list(list(arrow::int64())), offset.size() - 1, arrow::Buffer::Wrap(offset), list_arr);
+
+  std::vector<std::shared_ptr<arrow::Array>> child_arrays2({str2_array, int2_array});
+  auto fields2 = std::vector<std::shared_ptr<arrow::Field>>{
+    std::make_shared<arrow::Field>("string2", str2_array->type(), str2_array->null_count() > 0),
+    std::make_shared<arrow::Field>("integral2", int2_array->type(), int2_array->null_count() > 0)};
+  std::shared_ptr<arrow::Buffer> mask_buffer = arrow::internal::BytesToBits({1, 1, 0}).ValueOrDie();
+  auto dtype2                                = std::make_shared<arrow::StructType>(fields2);
+  auto struct_array2                         = std::make_shared<arrow::StructArray>(
+    dtype2, static_cast<int64_t>(expected_cudf_table.num_rows()), child_arrays2, mask_buffer);
+
+  std::vector<std::shared_ptr<arrow::Array>> child_arrays(
+    {str_array, int_array, bool_array, nested_list_arr, struct_array2});
+  std::vector<std::shared_ptr<arrow::Field>> fields;
+  std::transform(child_arrays.cbegin(),
+                 child_arrays.cend(),
+                 nested_type_field_names[0].cbegin(),
+                 std::back_inserter(fields),
+                 [](auto const array, auto const name) {
+                   return std::make_shared<arrow::Field>(
+                     name, array->type(), array->null_count() > 0);
+                 });
+  auto dtype = std::make_shared<arrow::StructType>(fields);
+
+  auto struct_array = std::make_shared<arrow::StructArray>(
+    dtype, static_cast<int64_t>(expected_cudf_table.num_rows()), child_arrays);
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector(
+    {arrow::field("a", struct_array->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+  auto input  = arrow::Table::Make(schema, {struct_array});
+
+  auto got_cudf_table = cudf::from_arrow(*input);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_cudf_table, got_cudf_table->view());
+}
+
+TEST_F(FromArrowTest, DictionaryIndicesType)
+{
+  auto array1 =
+    get_arrow_dict_array<int64_t, int8_t>({1, 2, 5, 7}, {0, 1, 2, 1, 3}, {1, 0, 1, 1, 1});
+  auto array2 =
+    get_arrow_dict_array<int64_t, int16_t>({1, 2, 5, 7}, {0, 1, 2, 1, 3}, {1, 0, 1, 1, 1});
+  auto array3 =
+    get_arrow_dict_array<int64_t, int64_t>({1, 2, 5, 7}, {0, 1, 2, 1, 3}, {1, 0, 1, 1, 1});
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector({arrow::field("a", array1->type()),
+                                                            arrow::field("b", array2->type()),
+                                                            arrow::field("c", array3->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto arrow_table = arrow::Table::Make(schema, {array1, array2, array3});
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  auto col = cudf::test::fixed_width_column_wrapper<int64_t>({1, 2, 5, 2, 7}, {1, 0, 1, 1, 1});
+  columns.emplace_back(std::move(cudf::dictionary::encode(col)));
+  columns.emplace_back(std::move(cudf::dictionary::encode(col)));
+  columns.emplace_back(std::move(cudf::dictionary::encode(col)));
+
+  cudf::table expected_table(std::move(columns));
+
+  auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_table.view(), got_cudf_table->view());
+}
+
+TEST_F(FromArrowTest, ChunkedArray)
+{
+  auto int64array     = get_arrow_array<int64_t>({1, 2, 3, 4, 5});
+  auto int32array_1   = get_arrow_array<int32_t>({1, 2}, {1, 0});
+  auto int32array_2   = get_arrow_array<int32_t>({5, 2, 7}, {1, 1, 1});
+  auto string_array_1 = get_arrow_array<cudf::string_view>({
+    "fff",
+    "aaa",
+    "",
+  });
+  auto string_array_2 = get_arrow_array<cudf::string_view>(
+    {
+      "fff",
+      "ccc",
+    },
+    {0, 1});
+  auto dict_array1 = get_arrow_dict_array({1, 2, 5, 7}, {0, 1, 2}, {1, 0, 1});
+  auto dict_array2 = get_arrow_dict_array({1, 2, 5, 7}, {1, 3});
+
+  auto int64_chunked_array = std::make_shared<arrow::ChunkedArray>(int64array);
+  auto int32_chunked_array = std::make_shared<arrow::ChunkedArray>(
+    std::vector<std::shared_ptr<arrow::Array>>{int32array_1, int32array_2});
+  auto string_chunked_array = std::make_shared<arrow::ChunkedArray>(
+    std::vector<std::shared_ptr<arrow::Array>>{string_array_1, string_array_2});
+  auto dict_chunked_array = std::make_shared<arrow::ChunkedArray>(
+    std::vector<std::shared_ptr<arrow::Array>>{dict_array1, dict_array2});
+  auto boolean_array = get_arrow_array<bool>({true, false, true, false, true}, {1, 0, 1, 1, 0});
+  auto boolean_chunked_array = std::make_shared<arrow::ChunkedArray>(boolean_array);
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector(
+    {arrow::field("a", int32_chunked_array->type()),
+     arrow::field("b", int64array->type()),
+     arrow::field("c", string_array_1->type()),
+     arrow::field("d", dict_chunked_array->type()),
+     arrow::field("e", boolean_chunked_array->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto arrow_table = arrow::Table::Make(schema,
+                                        {int32_chunked_array,
+                                         int64_chunked_array,
+                                         string_chunked_array,
+                                         dict_chunked_array,
+                                         boolean_chunked_array});
+
+  auto expected_cudf_table = get_cudf_table();
+
+  auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_cudf_table->view(), got_cudf_table->view());
+}
+
+struct FromArrowTestSlice
+  : public FromArrowTest,
+    public ::testing::WithParamInterface<std::tuple<cudf::size_type, cudf::size_type>> {};
+
+TEST_P(FromArrowTestSlice, SliceTest)
+{
+  auto tables             = get_tables(10000);
+  auto cudf_table_view    = tables.first->view();
+  auto arrow_table        = tables.second;
+  auto const [start, end] = GetParam();
+
+  auto sliced_cudf_table   = cudf::slice(cudf_table_view, {start, end})[0];
+  auto expected_cudf_table = cudf::table{sliced_cudf_table};
+  auto sliced_arrow_table  = arrow_table->Slice(start, end - start);
+  auto got_cudf_table      = cudf::from_arrow(*sliced_arrow_table);
+
+  // This has been added to take-care of empty string column issue with no children
+  if (got_cudf_table->num_rows() == 0 and expected_cudf_table.num_rows() == 0) {
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected_cudf_table.view(), got_cudf_table->view());
+  } else {
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_cudf_table.view(), got_cudf_table->view());
+  }
+}
+
+template <typename T>
+using fp_wrapper = cudf::test::fixed_point_column_wrapper<T>;
+
+TEST_F(FromArrowTest, FixedPoint128Table)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const data     = std::vector<__int128_t>{1, 2, 3, 4, 5, 6};
+    auto const col      = fp_wrapper<__int128_t>(data.cbegin(), data.cend(), scale_type{scale});
+    auto const expected = cudf::table_view({col});
+
+    auto const arr = make_decimal128_arrow_array(data, std::nullopt, scale);
+
+    auto const field         = arrow::field("a", arr->type());
+    auto const schema_vector = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema        = std::make_shared<arrow::Schema>(schema_vector);
+    auto const arrow_table   = arrow::Table::Make(schema, {arr});
+
+    auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got_cudf_table->view());
+  }
+}
+
+TEST_F(FromArrowTest, FixedPoint128TableLarge)
+{
+  using namespace numeric;
+  auto constexpr NUM_ELEMENTS = 1000;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto iota           = thrust::make_counting_iterator(1);
+    auto const data     = std::vector<__int128_t>(iota, iota + NUM_ELEMENTS);
+    auto const col      = fp_wrapper<__int128_t>(iota, iota + NUM_ELEMENTS, scale_type{scale});
+    auto const expected = cudf::table_view({col});
+
+    auto const arr = make_decimal128_arrow_array(data, std::nullopt, scale);
+
+    auto const field         = arrow::field("a", arr->type());
+    auto const schema_vector = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema        = std::make_shared<arrow::Schema>(schema_vector);
+    auto const arrow_table   = arrow::Table::Make(schema, {arr});
+
+    auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got_cudf_table->view());
+  }
+}
+
+TEST_F(FromArrowTest, FixedPoint128TableNulls)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const data     = std::vector<__int128_t>{1, 2, 3, 4, 5, 6, 0, 0};
+    auto const validity = std::vector<int32_t>{1, 1, 1, 1, 1, 1, 0, 0};
+    auto const col =
+      fp_wrapper<__int128_t>({1, 2, 3, 4, 5, 6, 0, 0}, {1, 1, 1, 1, 1, 1, 0, 0}, scale_type{scale});
+    auto const expected = cudf::table_view({col});
+
+    auto const arr = make_decimal128_arrow_array(data, validity, scale);
+
+    auto const field         = arrow::field("a", arr->type());
+    auto const schema_vector = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema        = std::make_shared<arrow::Schema>(schema_vector);
+    auto const arrow_table   = arrow::Table::Make(schema, {arr});
+
+    auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got_cudf_table->view());
+  }
+}
+
+TEST_F(FromArrowTest, FixedPoint128TableNullsLarge)
+{
+  using namespace numeric;
+  auto constexpr NUM_ELEMENTS = 1000;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto every_other = [](auto i) { return i % 2 ? 0 : 1; };
+    auto validity    = cudf::detail::make_counting_transform_iterator(0, every_other);
+    auto iota        = thrust::make_counting_iterator(1);
+    auto const data  = std::vector<__int128_t>(iota, iota + NUM_ELEMENTS);
+    auto const col = fp_wrapper<__int128_t>(iota, iota + NUM_ELEMENTS, validity, scale_type{scale});
+    auto const expected = cudf::table_view({col});
+
+    auto const arr = make_decimal128_arrow_array(
+      data, std::vector<int32_t>(validity, validity + NUM_ELEMENTS), scale);
+
+    auto const field         = arrow::field("a", arr->type());
+    auto const schema_vector = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema        = std::make_shared<arrow::Schema>(schema_vector);
+    auto const arrow_table   = arrow::Table::Make(schema, {arr});
+
+    auto got_cudf_table = cudf::from_arrow(*arrow_table);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got_cudf_table->view());
+  }
+}
+
+INSTANTIATE_TEST_CASE_P(FromArrowTest,
+                        FromArrowTestSlice,
+                        ::testing::Values(std::make_tuple(0, 10000),
+                                          std::make_tuple(2912, 2915),
+                                          std::make_tuple(100, 3000),
+                                          std::make_tuple(0, 0),
+                                          std::make_tuple(0, 3000),
+                                          std::make_tuple(10000, 10000)));
+
+template <typename T>
+struct FromArrowNumericScalarTest : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(FromArrowNumericScalarTest, NumericTypesNotBool);
+
+TYPED_TEST(FromArrowNumericScalarTest, Basic)
+{
+  TypeParam const value{42};
+  auto const arrow_scalar = arrow::MakeScalar(value);
+  auto const cudf_scalar  = cudf::from_arrow(*arrow_scalar);
+  auto const cudf_numeric_scalar =
+    dynamic_cast<cudf::numeric_scalar<TypeParam>*>(cudf_scalar.get());
+  if (cudf_numeric_scalar == nullptr) { CUDF_FAIL("Attempted to test with a non-numeric type."); }
+  EXPECT_EQ(cudf_numeric_scalar->type(), cudf::data_type(cudf::type_to_id<TypeParam>()));
+  EXPECT_EQ(cudf_numeric_scalar->value(), value);
+}
+
+struct FromArrowDecimalScalarTest : public cudf::test::BaseFixture {};
+
+// Only testing Decimal128 because that's the only size cudf and arrow have in common.
+TEST_F(FromArrowDecimalScalarTest, Basic)
+{
+  auto const value{42};
+  auto const precision{8};
+  auto const scale{4};
+  auto arrow_scalar = arrow::Decimal128Scalar(value, arrow::decimal128(precision, -scale));
+  auto cudf_scalar  = cudf::from_arrow(arrow_scalar);
+
+  // Arrow offers a minimum of 128 bits for the Decimal type.
+  auto const cudf_decimal_scalar =
+    dynamic_cast<cudf::fixed_point_scalar<numeric::decimal128>*>(cudf_scalar.get());
+  EXPECT_EQ(cudf_decimal_scalar->type(),
+            cudf::data_type(cudf::type_to_id<numeric::decimal128>(), scale));
+  EXPECT_EQ(cudf_decimal_scalar->value(), value);
+}
+
+struct FromArrowStringScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(FromArrowStringScalarTest, Basic)
+{
+  auto const value        = std::string("hello world");
+  auto const arrow_scalar = arrow::StringScalar(value);
+  auto const cudf_scalar  = cudf::from_arrow(arrow_scalar);
+
+  auto const cudf_string_scalar = dynamic_cast<cudf::string_scalar*>(cudf_scalar.get());
+  EXPECT_EQ(cudf_string_scalar->type(), cudf::data_type(cudf::type_id::STRING));
+  EXPECT_EQ(cudf_string_scalar->to_string(), value);
+}
+
+struct FromArrowListScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(FromArrowListScalarTest, Basic)
+{
+  std::vector<int64_t> host_values = {1, 2, 3, 5, 6, 7, 8};
+  std::vector<bool> host_validity  = {true, true, true, false, true, true, true};
+
+  arrow::Int64Builder builder;
+  auto const status      = builder.AppendValues(host_values, host_validity);
+  auto const maybe_array = builder.Finish();
+  auto const array       = *maybe_array;
+
+  auto const arrow_scalar = arrow::ListScalar(array);
+  auto const cudf_scalar  = cudf::from_arrow(arrow_scalar);
+
+  auto const cudf_list_scalar = dynamic_cast<cudf::list_scalar*>(cudf_scalar.get());
+  EXPECT_EQ(cudf_list_scalar->type(), cudf::data_type(cudf::type_id::LIST));
+
+  cudf::test::fixed_width_column_wrapper<int64_t> const lhs(
+    host_values.begin(), host_values.end(), host_validity.begin());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(lhs, cudf_list_scalar->view());
+}
+
+struct FromArrowStructScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(FromArrowStructScalarTest, Basic)
+{
+  int64_t const value{42};
+  auto const underlying_arrow_scalar = arrow::MakeScalar(value);
+
+  auto const field        = arrow::field("", underlying_arrow_scalar->type);
+  auto const arrow_type   = arrow::struct_({field});
+  auto const arrow_scalar = arrow::StructScalar({underlying_arrow_scalar}, arrow_type);
+  auto const cudf_scalar  = cudf::from_arrow(arrow_scalar);
+
+  auto const cudf_struct_scalar = dynamic_cast<cudf::struct_scalar*>(cudf_scalar.get());
+  EXPECT_EQ(cudf_struct_scalar->type(), cudf::data_type(cudf::type_id::STRUCT));
+
+  cudf::test::fixed_width_column_wrapper<int64_t> const col({value});
+  cudf::table_view const lhs({col});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(lhs, cudf_struct_scalar->view());
+}
diff --git a/cpp/tests/interop/to_arrow_test.cpp b/cpp/tests/interop/to_arrow_test.cpp
new file mode 100644
index 0000000..6bb4cdf
--- /dev/null
+++ b/cpp/tests/interop/to_arrow_test.cpp
@@ -0,0 +1,684 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/interop.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <tests/interop/arrow_utils.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+using vector_of_columns = std::vector<std::unique_ptr<cudf::column>>;
+
+std::pair<std::unique_ptr<cudf::table>, std::shared_ptr<arrow::Table>> get_tables(
+  cudf::size_type length)
+{
+  std::vector<int64_t> int64_data(length);
+  std::vector<bool> bool_data(length);
+  std::vector<std::string> string_data(length);
+  std::vector<uint8_t> validity(length);
+  std::vector<bool> bool_validity(length);
+  std::vector<uint8_t> bool_data_validity;
+  cudf::size_type length_of_individual_list = 3;
+  cudf::size_type length_of_list            = length_of_individual_list * length;
+  std::vector<int64_t> list_int64_data(length_of_list);
+  std::vector<uint8_t> list_int64_data_validity(length_of_list);
+  std::vector<int32_t> list_offsets(length + 1);
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+
+  std::generate(int64_data.begin(), int64_data.end(), []() { return rand() % 500000; });
+  std::generate(list_int64_data.begin(), list_int64_data.end(), []() { return rand() % 500000; });
+  auto validity_generator = []() { return rand() % 7 != 0; };
+  std::generate(
+    list_int64_data_validity.begin(), list_int64_data_validity.end(), validity_generator);
+  // cudf::size_type n = 0;
+  std::generate(
+    list_offsets.begin(), list_offsets.end(), [length_of_individual_list, n = 0]() mutable {
+      return (n++) * length_of_individual_list;
+    });
+  std::generate(bool_data.begin(), bool_data.end(), validity_generator);
+  std::generate(
+    string_data.begin(), string_data.end(), []() { return rand() % 7 != 0 ? "CUDF" : "Rocks"; });
+  std::generate(validity.begin(), validity.end(), validity_generator);
+  std::generate(bool_validity.begin(), bool_validity.end(), validity_generator);
+
+  std::transform(bool_validity.cbegin(),
+                 bool_validity.cend(),
+                 std::back_inserter(bool_data_validity),
+                 [](auto val) { return static_cast<uint8_t>(val); });
+
+  columns.emplace_back(cudf::test::fixed_width_column_wrapper<int64_t>(
+                         int64_data.begin(), int64_data.end(), validity.begin())
+                         .release());
+  columns.emplace_back(
+    cudf::test::strings_column_wrapper(string_data.begin(), string_data.end(), validity.begin())
+      .release());
+  auto col4 = cudf::test::fixed_width_column_wrapper<int64_t>(
+    int64_data.begin(), int64_data.end(), validity.begin());
+  auto dict_col = cudf::dictionary::encode(col4);
+  columns.emplace_back(std::move(cudf::dictionary::encode(col4)));
+  columns.emplace_back(cudf::test::fixed_width_column_wrapper<bool>(
+                         bool_data.begin(), bool_data.end(), bool_validity.begin())
+                         .release());
+  auto list_child_column = cudf::test::fixed_width_column_wrapper<int64_t>(
+    list_int64_data.begin(), list_int64_data.end(), list_int64_data_validity.begin());
+  auto list_offsets_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>(list_offsets.begin(), list_offsets.end());
+  auto [list_mask, list_nulls] = cudf::bools_to_mask(cudf::test::fixed_width_column_wrapper<bool>(
+    bool_data_validity.begin(), bool_data_validity.end()));
+  columns.emplace_back(cudf::make_lists_column(length,
+                                               list_offsets_column.release(),
+                                               list_child_column.release(),
+                                               list_nulls,
+                                               std::move(*list_mask)));
+  auto int_column = cudf::test::fixed_width_column_wrapper<int64_t>(
+                      int64_data.begin(), int64_data.end(), validity.begin())
+                      .release();
+  auto str_column =
+    cudf::test::strings_column_wrapper(string_data.begin(), string_data.end(), validity.begin())
+      .release();
+  vector_of_columns cols;
+  cols.push_back(move(int_column));
+  cols.push_back(move(str_column));
+  auto [null_mask, null_count] = cudf::bools_to_mask(cudf::test::fixed_width_column_wrapper<bool>(
+    bool_data_validity.begin(), bool_data_validity.end()));
+  columns.emplace_back(
+    cudf::make_structs_column(length, std::move(cols), null_count, std::move(*null_mask)));
+
+  auto int64array = get_arrow_array<int64_t>(int64_data, validity);
+
+  auto string_array = get_arrow_array<cudf::string_view>(string_data, validity);
+  cudf::dictionary_column_view view(dict_col->view());
+  auto keys       = cudf::test::to_host<int64_t>(view.keys()).first;
+  auto indices    = cudf::test::to_host<uint32_t>(view.indices()).first;
+  auto dict_array = get_arrow_dict_array(std::vector<int64_t>(keys.begin(), keys.end()),
+                                         std::vector<int32_t>(indices.begin(), indices.end()),
+                                         validity);
+  auto boolarray  = get_arrow_array<bool>(bool_data, bool_validity);
+  auto list_array = get_arrow_list_array<int64_t>(
+    list_int64_data, list_offsets, list_int64_data_validity, bool_data_validity);
+
+  arrow::ArrayVector child_arrays({int64array, string_array});
+  std::vector<std::shared_ptr<arrow::Field>> fields = {
+    arrow::field("integral", int64array->type(), int64array->null_count() > 0),
+    arrow::field("string", string_array->type(), string_array->null_count() > 0)};
+  auto dtype = std::make_shared<arrow::StructType>(fields);
+  std::shared_ptr<arrow::Buffer> mask_buffer =
+    arrow::internal::BytesToBits(static_cast<std::vector<uint8_t>>(bool_data_validity))
+      .ValueOrDie();
+  auto struct_array =
+    std::make_shared<arrow::StructArray>(dtype, length, child_arrays, mask_buffer);
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector = {
+    arrow::field("a", int64array->type()),
+    arrow::field("b", string_array->type()),
+    arrow::field("c", dict_array->type()),
+    arrow::field("d", boolarray->type()),
+    arrow::field("e", list_array->type()),
+    arrow::field("f", struct_array->type())};
+
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  return std::pair(
+    std::make_unique<cudf::table>(std::move(columns)),
+    arrow::Table::Make(
+      schema, {int64array, string_array, dict_array, boolarray, list_array, struct_array}));
+}
+
+struct ToArrowTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct ToArrowTestDurationsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ToArrowTestDurationsTest, cudf::test::DurationTypes);
+
+TEST_F(ToArrowTest, EmptyTable)
+{
+  auto tables = get_tables(0);
+
+  auto cudf_table_view      = tables.first->view();
+  auto expected_arrow_table = tables.second;
+  auto struct_meta          = cudf::column_metadata{"f"};
+  struct_meta.children_meta = {{"integral"}, {"string"}};
+
+  auto got_arrow_table =
+    cudf::to_arrow(cudf_table_view, {{"a"}, {"b"}, {"c"}, {"d"}, {"e"}, struct_meta});
+
+  ASSERT_EQ(expected_arrow_table->Equals(*got_arrow_table, true), true);
+}
+
+TEST_F(ToArrowTest, DateTimeTable)
+{
+  auto data = {1, 2, 3, 4, 5, 6};
+
+  auto col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>(data);
+
+  cudf::table_view input_view({col});
+
+  std::shared_ptr<arrow::Array> arr;
+  arrow::TimestampBuilder timestamp_builder(timestamp(arrow::TimeUnit::type::MILLI),
+                                            arrow::default_memory_pool());
+  ASSERT_TRUE(timestamp_builder.AppendValues(std::vector<int64_t>{1, 2, 3, 4, 5, 6}).ok());
+  ASSERT_TRUE(timestamp_builder.Finish(&arr).ok());
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector({arrow::field("a", arr->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+  auto got_arrow_table = cudf::to_arrow(input_view, {{"a"}});
+
+  ASSERT_EQ(expected_arrow_table->Equals(*got_arrow_table, true), true);
+}
+
+TYPED_TEST(ToArrowTestDurationsTest, DurationTable)
+{
+  using T = TypeParam;
+
+  auto data = {T{1}, T{2}, T{3}, T{4}, T{5}, T{6}};
+  auto col  = cudf::test::fixed_width_column_wrapper<T>(data);
+
+  cudf::table_view input_view({col});
+
+  std::shared_ptr<arrow::Array> arr;
+  arrow::TimeUnit::type arrow_unit;
+  switch (cudf::type_to_id<TypeParam>()) {
+    case cudf::type_id::DURATION_SECONDS: arrow_unit = arrow::TimeUnit::type::SECOND; break;
+    case cudf::type_id::DURATION_MILLISECONDS: arrow_unit = arrow::TimeUnit::type::MILLI; break;
+    case cudf::type_id::DURATION_MICROSECONDS: arrow_unit = arrow::TimeUnit::type::MICRO; break;
+    case cudf::type_id::DURATION_NANOSECONDS: arrow_unit = arrow::TimeUnit::type::NANO; break;
+    case cudf::type_id::DURATION_DAYS: return;
+    default: CUDF_FAIL("Unsupported duration unit in arrow");
+  }
+  arrow::DurationBuilder duration_builder(duration(arrow_unit), arrow::default_memory_pool());
+  ASSERT_TRUE(duration_builder.AppendValues(std::vector<int64_t>{1, 2, 3, 4, 5, 6}).ok());
+  ASSERT_TRUE(duration_builder.Finish(&arr).ok());
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector({arrow::field("a", arr->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+  auto got_arrow_table = cudf::to_arrow(input_view, {{"a"}});
+
+  ASSERT_EQ(expected_arrow_table->Equals(*got_arrow_table, true), true);
+}
+
+TEST_F(ToArrowTest, NestedList)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3 != 0; });
+  auto col = cudf::test::lists_column_wrapper<int64_t>(
+    {{{{{1, 2}, valids}, {{3, 4}, valids}, {5}}, {{6}, {{7, 8, 9}, valids}}}, valids});
+  cudf::table_view input_view({col});
+
+  auto list_arr = get_arrow_list_array<int64_t>({6, 7, 8, 9}, {0, 1, 4}, {1, 0, 1, 1});
+  std::vector<int32_t> offset{0, 0, 2};
+  auto mask_buffer     = arrow::internal::BytesToBits({0, 1}).ValueOrDie();
+  auto nested_list_arr = std::make_shared<arrow::ListArray>(arrow::list(list(arrow::int64())),
+                                                            offset.size() - 1,
+                                                            arrow::Buffer::Wrap(offset),
+                                                            list_arr,
+                                                            mask_buffer);
+
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector(
+    {arrow::field("a", nested_list_arr->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto expected_arrow_table = arrow::Table::Make(schema, {nested_list_arr});
+  auto got_arrow_table      = cudf::to_arrow(input_view, {{"a"}});
+
+  ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+}
+
+TEST_F(ToArrowTest, StructColumn)
+{
+  // Create cudf table
+  auto nested_type_field_names =
+    std::vector<std::vector<std::string>>{{"string", "integral", "bool", "nested_list", "struct"}};
+  auto str_col =
+    cudf::test::strings_column_wrapper{
+      "Samuel Vimes", "Carrot Ironfoundersson", "Angua von Überwald"}
+      .release();
+  auto str_col2 =
+    cudf::test::strings_column_wrapper{{"CUDF", "ROCKS", "EVERYWHERE"}, {0, 1, 0}}.release();
+  int num_rows{str_col->size()};
+  auto int_col = cudf::test::fixed_width_column_wrapper<int32_t, int32_t>{{48, 27, 25}}.release();
+  auto int_col2 =
+    cudf::test::fixed_width_column_wrapper<int32_t, int32_t>{{12, 24, 47}, {1, 0, 1}}.release();
+  auto bool_col = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}}.release();
+  auto list_col =
+    cudf::test::lists_column_wrapper<int64_t>({{{1, 2}, {3, 4}, {5}}, {{{6}}}, {{7}, {8, 9}}})
+      .release();
+  vector_of_columns cols2;
+  cols2.push_back(std::move(str_col2));
+  cols2.push_back(std::move(int_col2));
+  auto [null_mask, null_count] =
+    cudf::bools_to_mask(cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}});
+  auto sub_struct_col =
+    cudf::make_structs_column(num_rows, std::move(cols2), null_count, std::move(*null_mask));
+  vector_of_columns cols;
+  cols.push_back(std::move(str_col));
+  cols.push_back(std::move(int_col));
+  cols.push_back(std::move(bool_col));
+  cols.push_back(std::move(list_col));
+  cols.push_back(std::move(sub_struct_col));
+
+  auto struct_col = cudf::make_structs_column(num_rows, std::move(cols), 0, {});
+  cudf::table_view input_view({struct_col->view()});
+
+  // Create name metadata
+  auto sub_metadata          = cudf::column_metadata{"struct"};
+  sub_metadata.children_meta = {{"string2"}, {"integral2"}};
+  auto metadata              = cudf::column_metadata{"a"};
+  metadata.children_meta     = {{"string"}, {"integral"}, {"bool"}, {"nested_list"}, sub_metadata};
+
+  // Create Arrow table
+  std::vector<std::string> str{"Samuel Vimes", "Carrot Ironfoundersson", "Angua von Überwald"};
+  std::vector<std::string> str2{"CUDF", "ROCKS", "EVERYWHERE"};
+  auto str_array  = get_arrow_array<cudf::string_view>(str);
+  auto int_array  = get_arrow_array<int32_t>({48, 27, 25});
+  auto str2_array = get_arrow_array<cudf::string_view>(str2, {0, 1, 0});
+  auto int2_array = get_arrow_array<int32_t>({12, 24, 47}, {1, 0, 1});
+  auto bool_array = get_arrow_array<bool>({true, true, false});
+  auto list_arr = get_arrow_list_array<int64_t>({1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 2, 4, 5, 6, 7, 9});
+  std::vector<int32_t> offset{0, 3, 4, 6};
+  auto nested_list_arr = std::make_shared<arrow::ListArray>(
+    arrow::list(list(arrow::int64())), offset.size() - 1, arrow::Buffer::Wrap(offset), list_arr);
+
+  std::vector<std::shared_ptr<arrow::Array>> child_arrays2({str2_array, int2_array});
+  auto fields2 = std::vector<std::shared_ptr<arrow::Field>>{
+    std::make_shared<arrow::Field>("string2", str2_array->type(), str2_array->null_count() > 0),
+    std::make_shared<arrow::Field>("integral2", int2_array->type(), int2_array->null_count() > 0)};
+  auto dtype2                                = std::make_shared<arrow::StructType>(fields2);
+  std::shared_ptr<arrow::Buffer> mask_buffer = arrow::internal::BytesToBits({1, 1, 0}).ValueOrDie();
+  auto struct_array2                         = std::make_shared<arrow::StructArray>(
+    dtype2, static_cast<int64_t>(input_view.num_rows()), child_arrays2, mask_buffer);
+
+  std::vector<std::shared_ptr<arrow::Array>> child_arrays(
+    {str_array, int_array, bool_array, nested_list_arr, struct_array2});
+  std::vector<std::shared_ptr<arrow::Field>> fields;
+  std::transform(child_arrays.cbegin(),
+                 child_arrays.cend(),
+                 nested_type_field_names[0].cbegin(),
+                 std::back_inserter(fields),
+                 [](auto const array, auto const name) {
+                   return std::make_shared<arrow::Field>(
+                     name, array->type(), array->null_count() > 0);
+                 });
+  auto dtype = std::make_shared<arrow::StructType>(fields);
+
+  auto struct_array = std::make_shared<arrow::StructArray>(
+    dtype, static_cast<int64_t>(input_view.num_rows()), child_arrays);
+  std::vector<std::shared_ptr<arrow::Field>> schema_vector(
+    {arrow::field("a", struct_array->type())});
+  auto schema = std::make_shared<arrow::Schema>(schema_vector);
+
+  auto expected_arrow_table = arrow::Table::Make(schema, {struct_array});
+
+  auto got_arrow_table = cudf::to_arrow(input_view, {metadata});
+
+  ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+}
+
+template <typename T>
+using fp_wrapper = cudf::test::fixed_point_column_wrapper<T>;
+
+TEST_F(ToArrowTest, FixedPoint64Table)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const col         = fp_wrapper<int64_t>({-1, 2, 3, 4, 5, 6}, scale_type{scale});
+    auto const input       = cudf::table_view({col});
+    auto const expect_data = std::vector<int64_t>{-1, -1, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0};
+
+    auto const arr = make_decimal128_arrow_array(expect_data, std::nullopt, scale);
+
+    auto const field                = arrow::field("a", arr->type());
+    auto const schema_vector        = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema               = std::make_shared<arrow::Schema>(schema_vector);
+    auto const expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint128Table)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const col         = fp_wrapper<__int128_t>({-1, 2, 3, 4, 5, 6}, scale_type{scale});
+    auto const input       = cudf::table_view({col});
+    auto const expect_data = std::vector<__int128_t>{-1, 2, 3, 4, 5, 6};
+
+    auto const arr = make_decimal128_arrow_array(expect_data, std::nullopt, scale);
+
+    auto const field                = arrow::field("a", arr->type());
+    auto const schema_vector        = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema               = std::make_shared<arrow::Schema>(schema_vector);
+    auto const expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint64TableLarge)
+{
+  using namespace numeric;
+  auto constexpr BIT_WIDTH_RATIO = 2;  // Array::Type:type::DECIMAL (128) / int64_t
+  auto constexpr NUM_ELEMENTS    = 1000;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const iota  = thrust::make_counting_iterator(1);
+    auto const col   = fp_wrapper<int64_t>(iota, iota + NUM_ELEMENTS, scale_type{scale});
+    auto const input = cudf::table_view({col});
+
+    auto const every_other = [](auto i) { return i % 2 == 0 ? i / 2 : 0; };
+    auto const transform   = cudf::detail::make_counting_transform_iterator(2, every_other);
+    auto const expect_data =
+      std::vector<int64_t>{transform, transform + NUM_ELEMENTS * BIT_WIDTH_RATIO};
+
+    auto const arr = make_decimal128_arrow_array(expect_data, std::nullopt, scale);
+
+    auto const field                = arrow::field("a", arr->type());
+    auto const schema_vector        = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema               = std::make_shared<arrow::Schema>(schema_vector);
+    auto const expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint128TableLarge)
+{
+  using namespace numeric;
+  auto constexpr NUM_ELEMENTS = 1000;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const iota        = thrust::make_counting_iterator(1);
+    auto const col         = fp_wrapper<__int128_t>(iota, iota + NUM_ELEMENTS, scale_type{scale});
+    auto const input       = cudf::table_view({col});
+    auto const expect_data = std::vector<__int128_t>{iota, iota + NUM_ELEMENTS};
+
+    auto const arr = make_decimal128_arrow_array(expect_data, std::nullopt, scale);
+
+    auto const field                = arrow::field("a", arr->type());
+    auto const schema_vector        = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema               = std::make_shared<arrow::Schema>(schema_vector);
+    auto const expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint64TableNullsSimple)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const data     = std::vector<int64_t>{1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 0, 0, 0, 0};
+    auto const validity = std::vector<int32_t>{1, 1, 1, 1, 1, 1, 0, 0};
+    auto const col =
+      fp_wrapper<int64_t>({1, 2, 3, 4, 5, 6, 0, 0}, {1, 1, 1, 1, 1, 1, 0, 0}, scale_type{scale});
+    auto const input = cudf::table_view({col});
+
+    auto const arr = make_decimal128_arrow_array(data, validity, scale);
+
+    auto const field         = arrow::field("a", arr->type());
+    auto const schema_vector = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema        = std::make_shared<arrow::Schema>(schema_vector);
+    auto const arrow_table   = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint128TableNullsSimple)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const data     = std::vector<__int128_t>{1, 2, 3, 4, 5, 6, 0, 0};
+    auto const validity = std::vector<int32_t>{1, 1, 1, 1, 1, 1, 0, 0};
+    auto const col =
+      fp_wrapper<__int128_t>({1, 2, 3, 4, 5, 6, 0, 0}, {1, 1, 1, 1, 1, 1, 0, 0}, scale_type{scale});
+    auto const input = cudf::table_view({col});
+
+    auto const arr = make_decimal128_arrow_array(data, validity, scale);
+
+    auto const field         = arrow::field("a", arr->type());
+    auto const schema_vector = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema        = std::make_shared<arrow::Schema>(schema_vector);
+    auto const arrow_table   = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint64TableNulls)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const col = fp_wrapper<int64_t>(
+      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 0, 1, 0, 1, 0, 1, 0, 1, 0}, scale_type{scale});
+    auto const input = cudf::table_view({col});
+
+    auto const expect_data =
+      std::vector<int64_t>{1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 9, 0, 10, 0};
+    auto const validity = std::vector<int32_t>{1, 0, 1, 0, 1, 0, 1, 0, 1, 0};
+
+    auto arr = make_decimal128_arrow_array(expect_data, validity, scale);
+
+    auto const field                = arrow::field("a", arr->type());
+    auto const schema_vector        = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema               = std::make_shared<arrow::Schema>(schema_vector);
+    auto const expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+    auto got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+TEST_F(ToArrowTest, FixedPoint128TableNulls)
+{
+  using namespace numeric;
+
+  for (auto const scale : {3, 2, 1, 0, -1, -2, -3}) {
+    auto const col = fp_wrapper<__int128_t>(
+      {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, {1, 0, 1, 0, 1, 0, 1, 0, 1, 0}, scale_type{scale});
+    auto const input = cudf::table_view({col});
+
+    auto const expect_data = std::vector<__int128_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    auto const validity    = std::vector<int32_t>{1, 0, 1, 0, 1, 0, 1, 0, 1, 0};
+
+    auto arr = make_decimal128_arrow_array(expect_data, validity, scale);
+
+    auto const field                = arrow::field("a", arr->type());
+    auto const schema_vector        = std::vector<std::shared_ptr<arrow::Field>>({field});
+    auto const schema               = std::make_shared<arrow::Schema>(schema_vector);
+    auto const expected_arrow_table = arrow::Table::Make(schema, {arr});
+
+    auto const got_arrow_table = cudf::to_arrow(input, {{"a"}});
+
+    ASSERT_TRUE(expected_arrow_table->Equals(*got_arrow_table, true));
+  }
+}
+
+struct ToArrowTestSlice
+  : public ToArrowTest,
+    public ::testing::WithParamInterface<std::tuple<cudf::size_type, cudf::size_type>> {};
+
+TEST_P(ToArrowTestSlice, SliceTest)
+{
+  auto tables             = get_tables(10000);
+  auto cudf_table_view    = tables.first->view();
+  auto arrow_table        = tables.second;
+  auto const [start, end] = GetParam();
+
+  auto sliced_cudf_table    = cudf::slice(cudf_table_view, {start, end})[0];
+  auto expected_arrow_table = arrow_table->Slice(start, end - start);
+  auto struct_meta          = cudf::column_metadata{"f"};
+  struct_meta.children_meta = {{"integral"}, {"string"}};
+  auto got_arrow_table =
+    cudf::to_arrow(sliced_cudf_table, {{"a"}, {"b"}, {"c"}, {"d"}, {"e"}, struct_meta});
+
+  ASSERT_EQ(expected_arrow_table->Equals(*got_arrow_table, true), true);
+}
+
+INSTANTIATE_TEST_CASE_P(ToArrowTest,
+                        ToArrowTestSlice,
+                        ::testing::Values(std::make_tuple(0, 10000),
+                                          std::make_tuple(100, 3000),
+                                          std::make_tuple(0, 0),
+                                          std::make_tuple(0, 3000)));
+
+template <typename T>
+struct ToArrowNumericScalarTest : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(ToArrowNumericScalarTest, NumericTypesNotBool);
+
+TYPED_TEST(ToArrowNumericScalarTest, Basic)
+{
+  TypeParam const value{42};
+  auto const cudf_scalar = cudf::make_fixed_width_scalar<TypeParam>(value);
+
+  cudf::column_metadata const metadata{""};
+  auto const arrow_scalar = cudf::to_arrow(*cudf_scalar, metadata);
+
+  auto const ref_arrow_scalar = arrow::MakeScalar(value);
+  EXPECT_TRUE(arrow_scalar->Equals(*ref_arrow_scalar));
+}
+
+struct ToArrowDecimalScalarTest : public cudf::test::BaseFixture {};
+
+// Only testing Decimal128 because that's the only size cudf and arrow have in common.
+TEST_F(ToArrowDecimalScalarTest, Basic)
+{
+  auto const value{42};
+  auto const precision{18};  // cudf will convert to the widest-precision Arrow scalar of the type
+  int32_t const scale{4};
+
+  auto const cudf_scalar =
+    cudf::make_fixed_point_scalar<numeric::decimal128>(value, numeric::scale_type{scale});
+
+  cudf::column_metadata const metadata{""};
+  auto const arrow_scalar = cudf::to_arrow(*cudf_scalar, metadata);
+
+  auto const maybe_ref_arrow_scalar =
+    arrow::MakeScalar(arrow::decimal128(precision, -scale), value);
+  if (!maybe_ref_arrow_scalar.ok()) { CUDF_FAIL("Failed to construct reference scalar"); }
+  auto const ref_arrow_scalar = *maybe_ref_arrow_scalar;
+  EXPECT_TRUE(arrow_scalar->Equals(*ref_arrow_scalar));
+}
+
+struct ToArrowStringScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(ToArrowStringScalarTest, Basic)
+{
+  std::string const value{"hello world"};
+  auto const cudf_scalar = cudf::make_string_scalar(value);
+  cudf::column_metadata const metadata{""};
+  auto const arrow_scalar = cudf::to_arrow(*cudf_scalar, metadata);
+
+  auto const ref_arrow_scalar = arrow::MakeScalar(value);
+  EXPECT_TRUE(arrow_scalar->Equals(*ref_arrow_scalar));
+}
+
+struct ToArrowListScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(ToArrowListScalarTest, Basic)
+{
+  std::vector<int64_t> const host_values = {1, 2, 3, 5, 6, 7, 8};
+  std::vector<bool> const host_validity  = {true, true, true, false, true, true, true};
+
+  cudf::test::fixed_width_column_wrapper<int64_t> const col(
+    host_values.begin(), host_values.end(), host_validity.begin());
+
+  auto const cudf_scalar = cudf::make_list_scalar(col);
+
+  cudf::column_metadata const metadata{""};
+  auto const arrow_scalar = cudf::to_arrow(*cudf_scalar, metadata);
+
+  arrow::Int64Builder builder;
+  auto const status      = builder.AppendValues(host_values, host_validity);
+  auto const maybe_array = builder.Finish();
+  auto const array       = *maybe_array;
+
+  auto const ref_arrow_scalar = arrow::ListScalar(array);
+
+  EXPECT_TRUE(arrow_scalar->Equals(ref_arrow_scalar));
+}
+
+struct ToArrowStructScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(ToArrowStructScalarTest, Basic)
+{
+  int64_t const value{42};
+  auto const field_name{"a"};
+
+  cudf::test::fixed_width_column_wrapper<int64_t> const col{value};
+  cudf::table_view const tbl({col});
+  auto const cudf_scalar = cudf::make_struct_scalar(tbl);
+
+  cudf::column_metadata metadata{""};
+  metadata.children_meta.emplace_back(field_name);
+  auto const arrow_scalar = cudf::to_arrow(*cudf_scalar, metadata);
+
+  auto const underlying_arrow_scalar = arrow::MakeScalar(value);
+  auto const field            = arrow::field(field_name, underlying_arrow_scalar->type, false);
+  auto const arrow_type       = arrow::struct_({field});
+  auto const ref_arrow_scalar = arrow::StructScalar({underlying_arrow_scalar}, arrow_type);
+
+  EXPECT_TRUE(arrow_scalar->Equals(ref_arrow_scalar));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/arrow_io_source_test.cpp b/cpp/tests/io/arrow_io_source_test.cpp
new file mode 100644
index 0000000..979f8e4
--- /dev/null
+++ b/cpp/tests/io/arrow_io_source_test.cpp
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/io/arrow_io_source.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/io/parquet.hpp>
+
+#include <arrow/filesystem/filesystem.h>
+#include <arrow/filesystem/s3fs.h>
+#include <arrow/io/api.h>
+#include <arrow/util/config.h>
+
+#include <fstream>
+#include <memory>
+#include <string>
+
+// Global environment for temporary files
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+// Base test fixture for tests
+struct ArrowIOTest : public cudf::test::BaseFixture {};
+
+TEST_F(ArrowIOTest, URIFileSystem)
+{
+  const std::string file_name = temp_env->get_temp_dir() + "JsonLinesFileTest.json";
+  std::ofstream outfile(file_name, std::ofstream::out);
+  outfile << "{\"a\":11, \"b\":1.1}\n{\"a\":22, \"b\":2.2}";
+  outfile.close();
+
+  std::string file_uri = "file://" + file_name;
+  auto datasource      = std::make_unique<cudf::io::arrow_io_source>(file_uri);
+
+  // Populate the JSON Reader Options
+  cudf::io::json_reader_options options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info(datasource.get())).lines(true);
+
+  // Read the JSON file from the LocalFileSystem
+  cudf::io::table_with_metadata tbl = cudf::io::read_json(options);
+
+  ASSERT_EQ(2, tbl.tbl->num_columns());
+  ASSERT_EQ(2, tbl.tbl->num_rows());
+}
+
+TEST_F(ArrowIOTest, S3FileSystem)
+{
+  std::string s3_uri = "s3://rapidsai-data/cudf/test/tips.parquet?region=us-east-2";
+
+  // Check to see if Arrow was built with support for S3. If not, ensure this
+  // test throws. If so, validate the S3 file contents.
+  auto const s3_unsupported = arrow::fs::FileSystemFromUri(s3_uri).status().IsNotImplemented();
+  if (s3_unsupported) {
+    EXPECT_THROW(std::make_unique<cudf::io::arrow_io_source>(s3_uri), cudf::logic_error);
+  } else {
+    auto datasource = std::make_unique<cudf::io::arrow_io_source>(s3_uri);
+
+    // Populate the Parquet Reader Options
+    cudf::io::source_info src(datasource.get());
+    std::vector<std::string> single_column;
+    single_column.insert(single_column.begin(), "total_bill");
+    cudf::io::parquet_reader_options_builder builder(src);
+    cudf::io::parquet_reader_options options = builder.columns(single_column).build();
+
+    // Read the Parquet file from S3
+    cudf::io::table_with_metadata tbl = cudf::io::read_parquet(options);
+
+    ASSERT_EQ(1, tbl.tbl->num_columns());  // Only single column specified in reader_options
+    ASSERT_EQ(244, tbl.tbl->num_rows());   // known number of rows from the S3 file
+  }
+
+#ifdef ARROW_S3
+  if (!s3_unsupported) {
+    // Verify that we are using Arrow with S3, and call finalize
+    // https://github.com/apache/arrow/issues/36974
+    // This needs to be in a separate conditional to ensure we call
+    // finalize after all arrow_io_source instances have been deleted.
+    [[maybe_unused]] auto _ = arrow::fs::EnsureS3Finalized();
+  }
+#endif
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/comp/decomp_test.cpp b/cpp/tests/io/comp/decomp_test.cpp
new file mode 100644
index 0000000..35176c7
--- /dev/null
+++ b/cpp/tests/io/comp/decomp_test.cpp
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/comp/gpuinflate.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+#include <src/io/comp/nvcomp_adapter.hpp>
+
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <vector>
+
+using cudf::device_span;
+
+/**
+ * @brief Base test fixture for decompression
+ *
+ * Calls into Decompressor fixture to dispatch actual decompression work,
+ * whose interface and setup is different for each codec.
+ */
+template <typename Decompressor>
+struct DecompressTest : public cudf::test::BaseFixture {
+  std::vector<uint8_t> vector_from_string(char const* str) const
+  {
+    return std::vector<uint8_t>(reinterpret_cast<uint8_t const*>(str),
+                                reinterpret_cast<uint8_t const*>(str) + strlen(str));
+  }
+
+  void Decompress(std::vector<uint8_t>* decompressed,
+                  uint8_t const* compressed,
+                  size_t compressed_size)
+  {
+    auto stream = cudf::get_default_stream();
+    rmm::device_buffer src{compressed, compressed_size, stream};
+    rmm::device_uvector<uint8_t> dst{decompressed->size(), stream};
+
+    cudf::detail::hostdevice_vector<device_span<uint8_t const>> inf_in(1, stream);
+    inf_in[0] = {static_cast<uint8_t const*>(src.data()), src.size()};
+    inf_in.host_to_device_async(stream);
+
+    cudf::detail::hostdevice_vector<device_span<uint8_t>> inf_out(1, stream);
+    inf_out[0] = dst;
+    inf_out.host_to_device_async(stream);
+
+    cudf::detail::hostdevice_vector<cudf::io::compression_result> inf_stat(1, stream);
+    inf_stat[0] = {};
+    inf_stat.host_to_device_async(stream);
+
+    static_cast<Decompressor*>(this)->dispatch(inf_in, inf_out, inf_stat);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(
+      decompressed->data(), dst.data(), dst.size(), cudaMemcpyDefault, stream.value()));
+    inf_stat.device_to_host_sync(stream);
+    ASSERT_EQ(inf_stat[0].status, cudf::io::compression_status::SUCCESS);
+  }
+};
+
+/**
+ * @brief Derived fixture for GZIP decompression
+ */
+struct GzipDecompressTest : public DecompressTest<GzipDecompressTest> {
+  void dispatch(device_span<device_span<uint8_t const>> d_inf_in,
+                device_span<device_span<uint8_t>> d_inf_out,
+                device_span<cudf::io::compression_result> d_inf_stat)
+  {
+    cudf::io::gpuinflate(d_inf_in,
+                         d_inf_out,
+                         d_inf_stat,
+                         cudf::io::gzip_header_included::YES,
+                         cudf::get_default_stream());
+  }
+};
+
+/**
+ * @brief Derived fixture for Snappy decompression
+ */
+struct SnappyDecompressTest : public DecompressTest<SnappyDecompressTest> {
+  void dispatch(device_span<device_span<uint8_t const>> d_inf_in,
+                device_span<device_span<uint8_t>> d_inf_out,
+                device_span<cudf::io::compression_result> d_inf_stat)
+  {
+    cudf::io::gpu_unsnap(d_inf_in, d_inf_out, d_inf_stat, cudf::get_default_stream());
+  }
+};
+
+/**
+ * @brief Derived fixture for Brotli decompression
+ */
+struct BrotliDecompressTest : public DecompressTest<BrotliDecompressTest> {
+  void dispatch(device_span<device_span<uint8_t const>> d_inf_in,
+                device_span<device_span<uint8_t>> d_inf_out,
+                device_span<cudf::io::compression_result> d_inf_stat)
+  {
+    rmm::device_buffer d_scratch{cudf::io::get_gpu_debrotli_scratch_size(1),
+                                 cudf::get_default_stream()};
+
+    cudf::io::gpu_debrotli(d_inf_in,
+                           d_inf_out,
+                           d_inf_stat,
+                           d_scratch.data(),
+                           d_scratch.size(),
+                           cudf::get_default_stream());
+  }
+};
+
+struct NvcompConfigTest : public cudf::test::BaseFixture {};
+
+TEST_F(GzipDecompressTest, HelloWorld)
+{
+  constexpr char uncompressed[]  = "hello world";
+  constexpr uint8_t compressed[] = {
+    0x1f, 0x8b, 0x8,  0x0,  0x9,  0x63, 0x99, 0x5c, 0x2,  0xff, 0xcb, 0x48, 0xcd, 0xc9, 0xc9, 0x57,
+    0x28, 0xcf, 0x2f, 0xca, 0x49, 0x1,  0x0,  0x85, 0x11, 0x4a, 0xd,  0xb,  0x0,  0x0,  0x0};
+
+  std::vector<uint8_t> input = vector_from_string(uncompressed);
+  std::vector<uint8_t> output(input.size());
+  Decompress(&output, compressed, sizeof(compressed));
+  EXPECT_EQ(output, input);
+}
+
+TEST_F(SnappyDecompressTest, HelloWorld)
+{
+  constexpr char uncompressed[]  = "hello world";
+  constexpr uint8_t compressed[] = {
+    0xb, 0x28, 0x68, 0x65, 0x6c, 0x6c, 0x6f, 0x20, 0x77, 0x6f, 0x72, 0x6c, 0x64};
+
+  std::vector<uint8_t> input = vector_from_string(uncompressed);
+  std::vector<uint8_t> output(input.size());
+  Decompress(&output, compressed, sizeof(compressed));
+  EXPECT_EQ(output, input);
+}
+
+TEST_F(SnappyDecompressTest, ShortLiteralAfterLongCopyAtStartup)
+{
+  constexpr char uncompressed[]  = "Aaaaaaaaaaaah!";
+  constexpr uint8_t compressed[] = {14, 0x0, 'A', 0x0, 'a', (10 - 4) * 4 + 1, 1, 0x4, 'h', '!'};
+
+  std::vector<uint8_t> input = vector_from_string(uncompressed);
+  std::vector<uint8_t> output(input.size());
+  Decompress(&output, compressed, sizeof(compressed));
+  EXPECT_EQ(output, input);
+}
+
+TEST_F(BrotliDecompressTest, HelloWorld)
+{
+  constexpr char uncompressed[]  = "hello world";
+  constexpr uint8_t compressed[] = {
+    0xb, 0x5, 0x80, 0x68, 0x65, 0x6c, 0x6c, 0x6f, 0x20, 0x77, 0x6f, 0x72, 0x6c, 0x64, 0x3};
+
+  std::vector<uint8_t> input = vector_from_string(uncompressed);
+  std::vector<uint8_t> output(input.size());
+  Decompress(&output, compressed, sizeof(compressed));
+  EXPECT_EQ(output, input);
+}
+
+TEST_F(NvcompConfigTest, Compression)
+{
+  using cudf::io::nvcomp::compression_type;
+  auto const& comp_disabled = cudf::io::nvcomp::is_compression_disabled;
+
+  EXPECT_FALSE(comp_disabled(compression_type::DEFLATE, {2, 5, 0, true, true, 0}));
+  // version 2.5 required
+  EXPECT_TRUE(comp_disabled(compression_type::DEFLATE, {2, 4, 0, true, true, 0}));
+  // all integrations enabled required
+  EXPECT_TRUE(comp_disabled(compression_type::DEFLATE, {2, 5, 0, false, true, 0}));
+
+  EXPECT_FALSE(comp_disabled(compression_type::ZSTD, {2, 4, 0, true, true, 0}));
+  EXPECT_FALSE(comp_disabled(compression_type::ZSTD, {2, 4, 0, false, true, 0}));
+  // 2.4 version required
+  EXPECT_TRUE(comp_disabled(compression_type::ZSTD, {2, 3, 1, false, true, 0}));
+  // stable integrations enabled required
+  EXPECT_TRUE(comp_disabled(compression_type::ZSTD, {2, 4, 0, false, false, 0}));
+
+  EXPECT_FALSE(comp_disabled(compression_type::SNAPPY, {2, 5, 0, true, true, 0}));
+  EXPECT_FALSE(comp_disabled(compression_type::SNAPPY, {2, 4, 0, false, true, 0}));
+  // stable integrations enabled required
+  EXPECT_TRUE(comp_disabled(compression_type::SNAPPY, {2, 3, 0, false, false, 0}));
+}
+
+TEST_F(NvcompConfigTest, Decompression)
+{
+  using cudf::io::nvcomp::compression_type;
+  auto const& decomp_disabled = cudf::io::nvcomp::is_decompression_disabled;
+
+  EXPECT_FALSE(decomp_disabled(compression_type::DEFLATE, {2, 5, 0, true, true, 7}));
+  // version 2.5 required
+  EXPECT_TRUE(decomp_disabled(compression_type::DEFLATE, {2, 4, 0, true, true, 7}));
+  // all integrations enabled required
+  EXPECT_TRUE(decomp_disabled(compression_type::DEFLATE, {2, 5, 0, false, true, 7}));
+
+  EXPECT_FALSE(decomp_disabled(compression_type::ZSTD, {2, 4, 0, true, true, 7}));
+  EXPECT_FALSE(decomp_disabled(compression_type::ZSTD, {2, 3, 2, false, true, 6}));
+  EXPECT_FALSE(decomp_disabled(compression_type::ZSTD, {2, 3, 0, true, true, 6}));
+  // 2.3.1 and earlier requires all integrations to be enabled
+  EXPECT_TRUE(decomp_disabled(compression_type::ZSTD, {2, 3, 1, false, true, 7}));
+  // 2.3 version required
+  EXPECT_TRUE(decomp_disabled(compression_type::ZSTD, {2, 2, 0, true, true, 7}));
+  // stable integrations enabled required
+  EXPECT_TRUE(decomp_disabled(compression_type::ZSTD, {2, 4, 0, false, false, 7}));
+  // 2.4.0 disabled on Pascal
+  EXPECT_TRUE(decomp_disabled(compression_type::ZSTD, {2, 4, 0, true, true, 6}));
+
+  EXPECT_FALSE(decomp_disabled(compression_type::SNAPPY, {2, 4, 0, true, true, 7}));
+  EXPECT_FALSE(decomp_disabled(compression_type::SNAPPY, {2, 3, 0, false, true, 7}));
+  EXPECT_FALSE(decomp_disabled(compression_type::SNAPPY, {2, 2, 0, false, true, 7}));
+  // stable integrations enabled required
+  EXPECT_TRUE(decomp_disabled(compression_type::SNAPPY, {2, 2, 0, false, false, 7}));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/csv_test.cpp b/cpp/tests/io/csv_test.cpp
new file mode 100644
index 0000000..8922658
--- /dev/null
+++ b/cpp/tests/io/csv_test.cpp
@@ -0,0 +1,2500 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/io/arrow_io_source.hpp>
+#include <cudf/io/csv.hpp>
+#include <cudf/strings/convert/convert_datetime.hpp>
+#include <cudf/strings/convert/convert_fixed_point.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/unary.hpp>
+
+#include <arrow/io/api.h>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/find.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <fstream>
+#include <iostream>
+#include <iterator>
+#include <limits>
+#include <numeric>
+#include <sstream>
+#include <string>
+#include <vector>
+
+using cudf::data_type;
+using cudf::type_id;
+using cudf::type_to_id;
+
+template <typename T>
+auto dtype()
+{
+  return data_type{type_to_id<T>()};
+}
+
+template <typename T, typename SourceElementT = T>
+using column_wrapper =
+  typename std::conditional<std::is_same_v<T, cudf::string_view>,
+                            cudf::test::strings_column_wrapper,
+                            cudf::test::fixed_width_column_wrapper<T, SourceElementT>>::type;
+using column     = cudf::column;
+using table      = cudf::table;
+using table_view = cudf::table_view;
+
+// Global environment for temporary files
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+// Base test fixture for tests
+struct CsvWriterTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct CsvFixedPointWriterTest : public CsvWriterTest {};
+
+TYPED_TEST_SUITE(CsvFixedPointWriterTest, cudf::test::FixedPointTypes);
+
+// Base test fixture for tests
+struct CsvReaderTest : public cudf::test::BaseFixture {};
+
+// Typed test fixture for timestamp type tests
+template <typename T>
+struct CsvReaderNumericTypeTest : public CsvReaderTest {};
+
+// Declare typed test cases
+using SupportedNumericTypes = cudf::test::Types<int64_t, double>;
+TYPED_TEST_SUITE(CsvReaderNumericTypeTest, SupportedNumericTypes);
+
+template <typename DecimalType>
+struct CsvFixedPointReaderTest : public CsvReaderTest {
+  void run_tests(std::vector<std::string> const& reference_strings, numeric::scale_type scale)
+  {
+    cudf::test::strings_column_wrapper const strings(reference_strings.begin(),
+                                                     reference_strings.end());
+    auto const expected = cudf::strings::to_fixed_point(
+      cudf::strings_column_view(strings), data_type{type_to_id<DecimalType>(), scale});
+
+    auto const buffer = std::accumulate(reference_strings.begin(),
+                                        reference_strings.end(),
+                                        std::string{},
+                                        [](std::string const& acc, std::string const& rhs) {
+                                          return acc.empty() ? rhs : (acc + "\n" + rhs);
+                                        });
+
+    cudf::io::csv_reader_options const in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .dtypes({data_type{type_to_id<DecimalType>(), scale}})
+        .header(-1);
+
+    auto const result      = cudf::io::read_csv(in_opts);
+    auto const result_view = result.tbl->view();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, result_view.column(0));
+    EXPECT_EQ(result_view.num_columns(), 1);
+  }
+};
+
+TYPED_TEST_SUITE(CsvFixedPointReaderTest, cudf::test::FixedPointTypes);
+
+namespace {
+// Generates a vector of uniform random values of type T
+template <typename T>
+inline auto random_values(size_t size)
+{
+  std::vector<T> values(size);
+
+  using T1 = T;
+  using uniform_distribution =
+    typename std::conditional_t<std::is_same_v<T1, bool>,
+                                std::bernoulli_distribution,
+                                std::conditional_t<std::is_floating_point_v<T1>,
+                                                   std::uniform_real_distribution<T1>,
+                                                   std::uniform_int_distribution<T1>>>;
+
+  static constexpr auto seed = 0xf00d;
+  static std::mt19937 engine{seed};
+  static uniform_distribution dist{};
+  std::generate_n(values.begin(), size, [&]() { return T{dist(engine)}; });
+
+  return values;
+}
+
+MATCHER_P(FloatNearPointwise, tolerance, "Out-of-range")
+{
+  return (std::get<0>(arg) > std::get<1>(arg) - tolerance &&
+          std::get<0>(arg) < std::get<1>(arg) + tolerance);
+}
+
+template <typename T>
+using wrapper = cudf::test::fixed_width_column_wrapper<T>;
+
+// temporary method to verify the float columns until
+// CUDF_TEST_EXPECT_COLUMNS_EQUAL supports floating point
+template <typename T, typename valid_t>
+void check_float_column(cudf::column_view const& col_lhs,
+                        cudf::column_view const& col_rhs,
+                        T tol,
+                        valid_t const& validity)
+{
+  auto h_data = cudf::test::to_host<T>(col_rhs).first;
+
+  std::vector<T> data(h_data.size());
+  std::copy(h_data.begin(), h_data.end(), data.begin());
+
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT(col_lhs,
+                                                (wrapper<T>{data.begin(), data.end(), validity}));
+  EXPECT_TRUE(col_lhs.null_count() == 0 and col_rhs.null_count() == 0);
+  EXPECT_THAT(cudf::test::to_host<T>(col_lhs).first,
+              ::testing::Pointwise(FloatNearPointwise(tol), data));
+}
+
+// timestamp column checker within tolerance
+// given by `tol_ms` (milliseconds)
+void check_timestamp_column(cudf::column_view const& col_lhs,
+                            cudf::column_view const& col_rhs,
+                            long tol_ms = 1000l)
+{
+  using T = cudf::timestamp_ms;
+  using namespace cuda::std::chrono;
+
+  auto h_lhs = cudf::test::to_host<T>(col_lhs).first;
+  auto h_rhs = cudf::test::to_host<T>(col_rhs).first;
+
+  cudf::size_type nrows = h_lhs.size();
+  EXPECT_TRUE(nrows == static_cast<cudf::size_type>(h_rhs.size()));
+
+  auto begin_count = thrust::make_counting_iterator<cudf::size_type>(0);
+  auto end_count   = thrust::make_counting_iterator<cudf::size_type>(nrows);
+
+  auto* ptr_lhs = h_lhs.data();  // cannot capture host_vector in thrust,
+                                 // not even in host lambda
+  auto* ptr_rhs = h_rhs.data();
+
+  auto found = thrust::find_if(
+    thrust::host, begin_count, end_count, [ptr_lhs, ptr_rhs, tol_ms](auto row_index) {
+      auto delta_ms = cuda::std::chrono::duration_cast<cuda::std::chrono::milliseconds>(
+        ptr_lhs[row_index] - ptr_rhs[row_index]);
+      return delta_ms.count() >= tol_ms;
+    });
+
+  EXPECT_TRUE(found == end_count);  // not found...
+}
+
+// helper to replace in `str`  _all_ occurrences of `from` with `to`
+std::string replace_all_helper(std::string str, std::string const& from, std::string const& to)
+{
+  size_t start_pos = 0;
+  while ((start_pos = str.find(from, start_pos)) != std::string::npos) {
+    str.replace(start_pos, from.length(), to);
+    start_pos += to.length();
+  }
+  return str;
+}
+
+// compare string columns accounting for special character
+// treatment: double double quotes ('\"')
+// and surround whole string by double quotes if it contains:
+// newline '\n', <delimiter>, and double quotes;
+void check_string_column(cudf::column_view const& col_lhs,
+                         cudf::column_view const& col_rhs,
+                         std::string const& delimiter = ",")
+{
+  auto h_lhs = cudf::test::to_host<std::string>(col_lhs).first;
+  auto h_rhs = cudf::test::to_host<std::string>(col_rhs).first;
+
+  std::string newline("\n");
+  std::string quotes("\"");
+  std::string quotes_repl("\"\"");
+
+  std::vector<std::string> v_lhs;
+  std::transform(h_lhs.begin(),
+                 h_lhs.end(),
+                 std::back_inserter(v_lhs),
+                 [delimiter, newline, quotes, quotes_repl](std::string const& str_row) {
+                   auto found_quote = str_row.find(quotes);
+                   auto found_newl  = str_row.find(newline);
+                   auto found_delim = str_row.find(delimiter);
+
+                   bool flag_found_quotes = (found_quote != std::string::npos);
+                   bool need_surround = flag_found_quotes || (found_newl != std::string::npos) ||
+                                        (found_delim != std::string::npos);
+
+                   std::string str_repl;
+                   if (flag_found_quotes) {
+                     str_repl = replace_all_helper(str_row, quotes, quotes_repl);
+                   } else {
+                     str_repl = str_row;
+                   }
+                   return need_surround ? quotes + str_repl + quotes : str_row;
+                 });
+  EXPECT_TRUE(std::equal(v_lhs.begin(), v_lhs.end(), h_rhs.begin()));
+}
+
+// Helper function to compare two floating-point column contents
+template <typename T, std::enable_if_t<std::is_floating_point_v<T>>* = nullptr>
+void expect_column_data_equal(std::vector<T> const& lhs, cudf::column_view const& rhs)
+{
+  EXPECT_THAT(cudf::test::to_host<T>(rhs).first,
+              ::testing::Pointwise(FloatNearPointwise(1e-6), lhs));
+}
+
+// Helper function to compare two column contents
+template <typename T, std::enable_if_t<!std::is_floating_point_v<T>>* = nullptr>
+void expect_column_data_equal(std::vector<T> const& lhs, cudf::column_view const& rhs)
+{
+  EXPECT_THAT(cudf::test::to_host<T>(rhs).first, ::testing::ElementsAreArray(lhs));
+}
+
+void write_csv_helper(std::string const& filename,
+                      cudf::table_view const& table,
+                      std::vector<std::string> const& names = {})
+{
+  cudf::io::csv_writer_options writer_options =
+    cudf::io::csv_writer_options::builder(cudf::io::sink_info(filename), table)
+      .include_header(not names.empty())
+      .names(names);
+
+  cudf::io::write_csv(writer_options);
+}
+
+template <typename T>
+std::string assign(T input)
+{
+  return std::to_string(input);
+}
+
+std::string assign(std::string input) { return input; }
+
+template <typename T>
+std::vector<std::string> prepend_zeros(std::vector<T> const& input,
+                                       int zero_count         = 0,
+                                       bool add_positive_sign = false)
+{
+  std::vector<std::string> output(input.size());
+  std::transform(input.begin(), input.end(), output.begin(), [=](T const& num) {
+    auto str         = assign(num);
+    bool is_negative = (str[0] == '-');
+    if (is_negative) {
+      str.insert(1, zero_count, '0');
+      return str;
+    } else if (add_positive_sign) {
+      return "+" + std::string(zero_count, '0') + str;
+    } else {
+      str.insert(0, zero_count, '0');
+      return str;
+    }
+  });
+  return output;
+}
+
+}  // namespace
+
+TYPED_TEST(CsvReaderNumericTypeTest, SingleColumn)
+{
+  constexpr auto num_rows = 10;
+  auto sequence           = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return static_cast<TypeParam>(i + 1000.50f); });
+
+  auto filepath = temp_env->get_temp_filepath("SingleColumn.csv");
+  {
+    std::ofstream out_file{filepath, std::ofstream::out};
+    std::ostream_iterator<TypeParam> output_iterator(out_file, "\n");
+    std::copy(sequence, sequence + num_rows, output_iterator);
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath}).header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  expect_column_data_equal(std::vector<TypeParam>(sequence, sequence + num_rows), view.column(0));
+}
+
+TYPED_TEST(CsvFixedPointReaderTest, SingleColumnNegativeScale)
+{
+  this->run_tests({"1.23", "876e-2", "5.43e1", "-0.12", "0.25", "-0.23", "-0.27", "0.00", "0.00"},
+                  numeric::scale_type{-2});
+}
+
+TYPED_TEST(CsvFixedPointReaderTest, SingleColumnNoScale)
+{
+  this->run_tests({"123", "-87600e-2", "54.3e1", "-12", "25", "-23", "-27", "0", "0"},
+                  numeric::scale_type{0});
+}
+
+TYPED_TEST(CsvFixedPointReaderTest, SingleColumnPositiveScale)
+{
+  this->run_tests(
+    {"123000", "-87600000e-2", "54300e1", "-12000", "25000", "-23000", "-27000", "0000", "0000"},
+    numeric::scale_type{3});
+}
+
+TYPED_TEST(CsvFixedPointWriterTest, SingleColumnNegativeScale)
+{
+  std::vector<std::string> reference_strings = {
+    "1.23", "-8.76", "5.43", "-0.12", "0.25", "-0.23", "-0.27", "0.00", "0.00"};
+
+  auto validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2 == 0); });
+  cudf::test::strings_column_wrapper strings(
+    reference_strings.begin(), reference_strings.end(), validity);
+
+  std::vector<std::string> valid_reference_strings;
+  thrust::copy_if(thrust::host,
+                  reference_strings.begin(),
+                  reference_strings.end(),
+                  thrust::make_counting_iterator(0),
+                  std::back_inserter(valid_reference_strings),
+                  validity.functor());
+  reference_strings = valid_reference_strings;
+
+  using DecimalType = TypeParam;
+  auto input_column =
+    cudf::strings::to_fixed_point(cudf::strings_column_view(strings),
+                                  data_type{type_to_id<DecimalType>(), numeric::scale_type{-2}});
+
+  auto input_table = cudf::table_view{std::vector<cudf::column_view>{*input_column}};
+
+  auto filepath = temp_env->get_temp_dir() + "FixedPointSingleColumnNegativeScale.csv";
+
+  cudf::io::csv_writer_options writer_options =
+    cudf::io::csv_writer_options::builder(cudf::io::sink_info(filepath), input_table)
+      .include_header(false);
+
+  cudf::io::write_csv(writer_options);
+
+  std::vector<std::string> result_strings;
+  result_strings.reserve(reference_strings.size());
+
+  std::ifstream read_result_file(filepath);
+  ASSERT_TRUE(read_result_file.is_open());
+
+  std::copy(std::istream_iterator<std::string>(read_result_file),
+            std::istream_iterator<std::string>(),
+            std::back_inserter(result_strings));
+
+  EXPECT_EQ(result_strings, reference_strings);
+}
+
+TYPED_TEST(CsvFixedPointWriterTest, SingleColumnPositiveScale)
+{
+  std::vector<std::string> reference_strings = {
+    "123000", "-876000", "543000", "-12000", "25000", "-23000", "-27000", "0000", "0000"};
+
+  auto validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2 == 0); });
+  cudf::test::strings_column_wrapper strings(
+    reference_strings.begin(), reference_strings.end(), validity);
+
+  std::vector<std::string> valid_reference_strings;
+  thrust::copy_if(thrust::host,
+                  reference_strings.begin(),
+                  reference_strings.end(),
+                  thrust::make_counting_iterator(0),
+                  std::back_inserter(valid_reference_strings),
+                  validity.functor());
+  reference_strings = valid_reference_strings;
+
+  using DecimalType = TypeParam;
+  auto input_column =
+    cudf::strings::to_fixed_point(cudf::strings_column_view(strings),
+                                  data_type{type_to_id<DecimalType>(), numeric::scale_type{3}});
+
+  auto input_table = cudf::table_view{std::vector<cudf::column_view>{*input_column}};
+
+  auto filepath = temp_env->get_temp_dir() + "FixedPointSingleColumnPositiveScale.csv";
+
+  cudf::io::csv_writer_options writer_options =
+    cudf::io::csv_writer_options::builder(cudf::io::sink_info(filepath), input_table)
+      .include_header(false);
+
+  cudf::io::write_csv(writer_options);
+
+  std::vector<std::string> result_strings;
+  result_strings.reserve(reference_strings.size());
+
+  std::ifstream read_result_file(filepath);
+  ASSERT_TRUE(read_result_file.is_open());
+
+  std::copy(std::istream_iterator<std::string>(read_result_file),
+            std::istream_iterator<std::string>(),
+            std::back_inserter(result_strings));
+
+  EXPECT_EQ(result_strings, reference_strings);
+}
+
+void test_quoting_disabled_with_delimiter(char delimiter_char)
+{
+  auto const delimiter     = std::string{delimiter_char};
+  auto const input_strings = cudf::test::strings_column_wrapper{
+    std::string{"All"} + delimiter + "the" + delimiter + "leaves",
+    "are\"brown",
+    "and\nthe\nsky\nis\ngrey"};
+  auto const input_table = table_view{{input_strings}};
+
+  auto const filepath = temp_env->get_temp_dir() + "unquoted.csv";
+  auto w_options = cudf::io::csv_writer_options::builder(cudf::io::sink_info{filepath}, input_table)
+                     .include_header(false)
+                     .inter_column_delimiter(delimiter_char)
+                     .quoting(cudf::io::quote_style::NONE);
+  cudf::io::write_csv(w_options.build());
+
+  auto r_options = cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+                     .header(-1)
+                     .delimiter(delimiter_char)
+                     .quoting(cudf::io::quote_style::NONE);
+  auto r_table = cudf::io::read_csv(r_options.build());
+
+  auto const expected =
+    cudf::test::strings_column_wrapper{"All", "are\"brown", "and", "the", "sky", "is", "grey"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(r_table.tbl->view().column(0), expected);
+}
+
+TEST_F(CsvWriterTest, QuotingDisabled)
+{
+  test_quoting_disabled_with_delimiter(',');
+  test_quoting_disabled_with_delimiter('\u0001');
+}
+
+TEST_F(CsvReaderTest, MultiColumn)
+{
+  constexpr auto num_rows = 10;
+  auto int8_values        = random_values<int8_t>(num_rows);
+  auto int16_values       = random_values<int16_t>(num_rows);
+  auto int32_values       = random_values<int32_t>(num_rows);
+  auto int64_values       = random_values<int64_t>(num_rows);
+  auto uint8_values       = random_values<uint8_t>(num_rows);
+  auto uint16_values      = random_values<uint16_t>(num_rows);
+  auto uint32_values      = random_values<uint32_t>(num_rows);
+  auto uint64_values      = random_values<uint64_t>(num_rows);
+  auto float32_values     = random_values<float>(num_rows);
+  auto float64_values     = random_values<double>(num_rows);
+
+  auto filepath = temp_env->get_temp_dir() + "MultiColumn.csv";
+  {
+    std::ostringstream line;
+    for (int i = 0; i < num_rows; ++i) {
+      line << std::to_string(int8_values[i]) << "," << int16_values[i] << "," << int32_values[i]
+           << "," << int64_values[i] << "," << std::to_string(uint8_values[i]) << ","
+           << uint16_values[i] << "," << uint32_values[i] << "," << uint64_values[i] << ","
+           << float32_values[i] << "," << float64_values[i] << "\n";
+    }
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << line.str();
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .header(-1)
+      .dtypes({dtype<int8_t>(),
+               dtype<int16_t>(),
+               dtype<int32_t>(),
+               dtype<int64_t>(),
+               dtype<uint8_t>(),
+               dtype<uint16_t>(),
+               dtype<uint32_t>(),
+               dtype<uint64_t>(),
+               dtype<float>(),
+               dtype<double>()});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  expect_column_data_equal(int8_values, view.column(0));
+  expect_column_data_equal(int16_values, view.column(1));
+  expect_column_data_equal(int32_values, view.column(2));
+  expect_column_data_equal(int64_values, view.column(3));
+  expect_column_data_equal(uint8_values, view.column(4));
+  expect_column_data_equal(uint16_values, view.column(5));
+  expect_column_data_equal(uint32_values, view.column(6));
+  expect_column_data_equal(uint64_values, view.column(7));
+  expect_column_data_equal(float32_values, view.column(8));
+  expect_column_data_equal(float64_values, view.column(9));
+}
+
+TEST_F(CsvReaderTest, RepeatColumn)
+{
+  constexpr auto num_rows = 10;
+  auto int16_values       = random_values<int16_t>(num_rows);
+  auto int64_values       = random_values<int64_t>(num_rows);
+  auto uint64_values      = random_values<uint64_t>(num_rows);
+  auto float32_values     = random_values<float>(num_rows);
+
+  auto filepath = temp_env->get_temp_dir() + "RepeatColumn.csv";
+  {
+    std::ostringstream line;
+    for (int i = 0; i < num_rows; ++i) {
+      line << int16_values[i] << "," << int64_values[i] << "," << uint64_values[i] << ","
+           << float32_values[i] << "\n";
+    }
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << line.str();
+  }
+
+  // repeats column in indexes and names, misses 1 column.
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({dtype<int16_t>(), dtype<int64_t>(), dtype<uint64_t>(), dtype<float>()})
+      .names({"A", "B", "C", "D"})
+      .use_cols_indexes({1, 0, 0})
+      .use_cols_names({"D", "B", "B"})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(3, view.num_columns());
+  expect_column_data_equal(int16_values, view.column(0));
+  expect_column_data_equal(int64_values, view.column(1));
+  expect_column_data_equal(float32_values, view.column(2));
+}
+
+TEST_F(CsvReaderTest, Booleans)
+{
+  auto filepath = temp_env->get_temp_dir() + "Booleans.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "YES,1,bar,true\nno,2,FOO,true\nBar,3,yes,false\nNo,4,NO,"
+               "true\nYes,5,foo,false\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A", "B", "C", "D"})
+      .dtypes({dtype<int32_t>(), dtype<int32_t>(), dtype<int16_t>(), dtype<bool>()})
+      .true_values({"yes", "Yes", "YES", "foo", "FOO"})
+      .false_values({"no", "No", "NO", "Bar", "bar"})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  // Booleans are the same (integer) data type, but valued at 0 or 1
+  auto const view = result.tbl->view();
+  EXPECT_EQ(4, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+  ASSERT_EQ(type_id::INT32, view.column(1).type().id());
+  ASSERT_EQ(type_id::INT16, view.column(2).type().id());
+  ASSERT_EQ(type_id::BOOL8, view.column(3).type().id());
+
+  expect_column_data_equal(std::vector<int32_t>{1, 0, 0, 0, 1}, view.column(0));
+  expect_column_data_equal(std::vector<int16_t>{0, 1, 1, 0, 1}, view.column(2));
+  expect_column_data_equal(std::vector<bool>{true, true, false, true, false}, view.column(3));
+}
+
+TEST_F(CsvReaderTest, Dates)
+{
+  auto filepath = temp_env->get_temp_dir() + "Dates.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "05/03/2001\n31/10/2010\n20/10/1994\n18/10/1990\n1/1/1970\n";
+    outfile << "18/04/1995\n14/07/1994\n07/06/2006 11:20:30.400\n";
+    outfile << "16/09/2005T1:2:30.400PM\n2/2/1970\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_MILLISECONDS}})
+      .dayfirst(true)
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_MILLISECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  expect_column_data_equal(std::vector<cudf::timestamp_ms>{cudf::timestamp_ms{983750400000ms},
+                                                           cudf::timestamp_ms{1288483200000ms},
+                                                           cudf::timestamp_ms{782611200000ms},
+                                                           cudf::timestamp_ms{656208000000ms},
+                                                           cudf::timestamp_ms{0ms},
+                                                           cudf::timestamp_ms{798163200000ms},
+                                                           cudf::timestamp_ms{774144000000ms},
+                                                           cudf::timestamp_ms{1149679230400ms},
+                                                           cudf::timestamp_ms{1126875750400ms},
+                                                           cudf::timestamp_ms{2764800000ms}},
+                           view.column(0));
+}
+
+TEST_F(CsvReaderTest, DatesCastToTimestampSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "DatesCastToTimestampS.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "05/03/2001\n31/10/2010\n20/10/1994\n18/10/1990\n1/1/1970\n";
+    outfile << "18/04/1995\n14/07/1994\n07/06/2006 11:20:30.400\n";
+    outfile << "16/09/2005T1:2:30.400PM\n2/2/1970\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_SECONDS}})
+      .dayfirst(true)
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_SECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  expect_column_data_equal(std::vector<cudf::timestamp_s>{cudf::timestamp_s{983750400s},
+                                                          cudf::timestamp_s{1288483200s},
+                                                          cudf::timestamp_s{782611200s},
+                                                          cudf::timestamp_s{656208000s},
+                                                          cudf::timestamp_s{0s},
+                                                          cudf::timestamp_s{798163200s},
+                                                          cudf::timestamp_s{774144000s},
+                                                          cudf::timestamp_s{1149679230s},
+                                                          cudf::timestamp_s{1126875750s},
+                                                          cudf::timestamp_s{2764800s}},
+                           view.column(0));
+}
+
+TEST_F(CsvReaderTest, DatesCastToTimestampMilliSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "DatesCastToTimestampMs.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "05/03/2001\n31/10/2010\n20/10/1994\n18/10/1990\n1/1/1970\n";
+    outfile << "18/04/1995\n14/07/1994\n07/06/2006 11:20:30.400\n";
+    outfile << "16/09/2005T1:2:30.400PM\n2/2/1970\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_MILLISECONDS}})
+      .dayfirst(true)
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_MILLISECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  expect_column_data_equal(std::vector<cudf::timestamp_ms>{cudf::timestamp_ms{983750400000ms},
+                                                           cudf::timestamp_ms{1288483200000ms},
+                                                           cudf::timestamp_ms{782611200000ms},
+                                                           cudf::timestamp_ms{656208000000ms},
+                                                           cudf::timestamp_ms{0ms},
+                                                           cudf::timestamp_ms{798163200000ms},
+                                                           cudf::timestamp_ms{774144000000ms},
+                                                           cudf::timestamp_ms{1149679230400ms},
+                                                           cudf::timestamp_ms{1126875750400ms},
+                                                           cudf::timestamp_ms{2764800000ms}},
+                           view.column(0));
+}
+
+TEST_F(CsvReaderTest, DatesCastToTimestampMicroSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "DatesCastToTimestampUs.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "05/03/2001\n31/10/2010\n20/10/1994\n18/10/1990\n1/1/1970\n";
+    outfile << "18/04/1995\n14/07/1994\n07/06/2006 11:20:30.400\n";
+    outfile << "16/09/2005T1:2:30.400PM\n2/2/1970\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_MICROSECONDS}})
+      .dayfirst(true)
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_MICROSECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  expect_column_data_equal(std::vector<cudf::timestamp_us>{cudf::timestamp_us{983750400000000us},
+                                                           cudf::timestamp_us{1288483200000000us},
+                                                           cudf::timestamp_us{782611200000000us},
+                                                           cudf::timestamp_us{656208000000000us},
+                                                           cudf::timestamp_us{0us},
+                                                           cudf::timestamp_us{798163200000000us},
+                                                           cudf::timestamp_us{774144000000000us},
+                                                           cudf::timestamp_us{1149679230400000us},
+                                                           cudf::timestamp_us{1126875750400000us},
+                                                           cudf::timestamp_us{2764800000000us}},
+                           view.column(0));
+}
+
+TEST_F(CsvReaderTest, DatesCastToTimestampNanoSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "DatesCastToTimestampNs.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "05/03/2001\n31/10/2010\n20/10/1994\n18/10/1990\n1/1/1970\n";
+    outfile << "18/04/1995\n14/07/1994\n07/06/2006 11:20:30.400\n";
+    outfile << "16/09/2005T1:2:30.400PM\n2/2/1970\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_NANOSECONDS}})
+      .dayfirst(true)
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_NANOSECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  expect_column_data_equal(
+    std::vector<cudf::timestamp_ns>{cudf::timestamp_ns{983750400000000000ns},
+                                    cudf::timestamp_ns{1288483200000000000ns},
+                                    cudf::timestamp_ns{782611200000000000ns},
+                                    cudf::timestamp_ns{656208000000000000ns},
+                                    cudf::timestamp_ns{0ns},
+                                    cudf::timestamp_ns{798163200000000000ns},
+                                    cudf::timestamp_ns{774144000000000000ns},
+                                    cudf::timestamp_ns{1149679230400000000ns},
+                                    cudf::timestamp_ns{1126875750400000000ns},
+                                    cudf::timestamp_ns{2764800000000000ns}},
+    view.column(0));
+}
+
+TEST_F(CsvReaderTest, IntegersCastToTimestampSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "IntegersCastToTimestampS.csv";
+  std::vector<int64_t> input_vals{1, 10, 111, 2, 11, 112, 3, 12, 113, 43432423, 13342, 13243214};
+  auto expected_column =
+    column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>(input_vals.begin(), input_vals.end());
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    for (auto v : input_vals) {
+      outfile << v << "\n";
+    }
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_SECONDS}})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_SECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_column, view.column(0));
+}
+
+TEST_F(CsvReaderTest, IntegersCastToTimestampMilliSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "IntegersCastToTimestampMs.csv";
+  std::vector<int64_t> input_vals{1, 10, 111, 2, 11, 112, 3, 12, 113, 43432423, 13342, 13243214};
+  auto expected_column = column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>(
+    input_vals.begin(), input_vals.end());
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    for (auto v : input_vals) {
+      outfile << v << "\n";
+    }
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_MILLISECONDS}})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_MILLISECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_column, view.column(0));
+}
+
+TEST_F(CsvReaderTest, IntegersCastToTimestampMicroSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "IntegersCastToTimestampUs.csv";
+  std::vector<int64_t> input_vals{1, 10, 111, 2, 11, 112, 3, 12, 113, 43432423, 13342, 13243214};
+  auto expected_column = column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep>(
+    input_vals.begin(), input_vals.end());
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    for (auto v : input_vals) {
+      outfile << v << "\n";
+    }
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_MICROSECONDS}})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_MICROSECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_column, view.column(0));
+}
+
+TEST_F(CsvReaderTest, IntegersCastToTimestampNanoSeconds)
+{
+  auto filepath = temp_env->get_temp_dir() + "IntegersCastToTimestampNs.csv";
+  std::vector<int64_t> input_vals{1, 10, 111, 2, 11, 112, 3, 12, 113, 43432423, 13342, 13243214};
+  auto expected_column = column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep>(
+    input_vals.begin(), input_vals.end());
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    for (auto v : input_vals) {
+      outfile << v << "\n";
+    }
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_NANOSECONDS}})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::TIMESTAMP_NANOSECONDS, view.column(0).type().id());
+
+  using namespace cuda::std::chrono_literals;
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_column, view.column(0));
+}
+
+TEST_F(CsvReaderTest, FloatingPoint)
+{
+  auto filepath = temp_env->get_temp_dir() + "FloatingPoint.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "5.6;0.5679e2;1.2e10;0.07e1;3000e-3;12.34e0;3.1e-001;-73."
+               "98007199999998;";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({dtype<float>()})
+      .lineterminator(';')
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::FLOAT32, view.column(0).type().id());
+
+  auto const ref_vals =
+    std::vector<float>{5.6, 56.79, 12000000000, 0.7, 3.000, 12.34, 0.31, -73.98007199999998};
+  expect_column_data_equal(ref_vals, view.column(0));
+
+  auto const bitmask = cudf::test::bitmask_to_host(view.column(0));
+  ASSERT_EQ((1u << ref_vals.size()) - 1, bitmask[0]);
+}
+
+TEST_F(CsvReaderTest, Strings)
+{
+  std::vector<std::string> names{"line", "verse"};
+
+  auto filepath = temp_env->get_temp_dir() + "Strings.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << names[0] << ',' << names[1] << '\n';
+    outfile << "10,abc def ghi" << '\n';
+    outfile << "20,\"jkl mno pqr\"" << '\n';
+    outfile << "30,stu \"\"vwx\"\" yz" << '\n';
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names(names)
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()})
+      .quoting(cudf::io::quote_style::NONE);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(2, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+  ASSERT_EQ(type_id::STRING, view.column(1).type().id());
+
+  expect_column_data_equal(
+    std::vector<std::string>{"abc def ghi", "\"jkl mno pqr\"", "stu \"\"vwx\"\" yz"},
+    view.column(1));
+}
+
+TEST_F(CsvReaderTest, StringsQuotes)
+{
+  std::vector<std::string> names{"line", "verse"};
+
+  auto filepath = temp_env->get_temp_dir() + "StringsQuotes.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << names[0] << ',' << names[1] << '\n';
+    outfile << "10,`abc,\ndef, ghi`" << '\n';
+    outfile << "20,`jkl, ``mno``, pqr`" << '\n';
+    outfile << "30,stu `vwx` yz" << '\n';
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names(names)
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()})
+      .quotechar('`');
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(2, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+  ASSERT_EQ(type_id::STRING, view.column(1).type().id());
+
+  expect_column_data_equal(
+    std::vector<std::string>{"abc,\ndef, ghi", "jkl, `mno`, pqr", "stu `vwx` yz"}, view.column(1));
+}
+
+TEST_F(CsvReaderTest, StringsQuotesIgnored)
+{
+  std::vector<std::string> names{"line", "verse"};
+
+  auto filepath = temp_env->get_temp_dir() + "StringsQuotesIgnored.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << names[0] << ',' << names[1] << '\n';
+    outfile << "10,\"abcdef ghi\"" << '\n';
+    outfile << "20,\"jkl \"\"mno\"\" pqr\"" << '\n';
+    outfile << "30,stu \"vwx\" yz" << '\n';
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names(names)
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()})
+      .quoting(cudf::io::quote_style::NONE)
+      .doublequote(false);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(2, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+  ASSERT_EQ(type_id::STRING, view.column(1).type().id());
+
+  expect_column_data_equal(
+    std::vector<std::string>{"\"abcdef ghi\"", "\"jkl \"\"mno\"\" pqr\"", "stu \"vwx\" yz"},
+    view.column(1));
+}
+
+TEST_F(CsvReaderTest, SkiprowsNrows)
+{
+  auto filepath = temp_env->get_temp_dir() + "SkiprowsNrows.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "1\n2\n3\n4\n5\n6\n7\n8\n9\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({dtype<int32_t>()})
+      .header(1)
+      .skiprows(2)
+      .nrows(2);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+
+  expect_column_data_equal(std::vector<int32_t>{5, 6}, view.column(0));
+}
+
+TEST_F(CsvReaderTest, ByteRange)
+{
+  auto filepath = temp_env->get_temp_dir() + "ByteRange.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "1000\n2000\n3000\n4000\n5000\n6000\n7000\n8000\n9000\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({dtype<int32_t>()})
+      .header(-1)
+      .byte_range_offset(11)
+      .byte_range_size(15);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+
+  expect_column_data_equal(std::vector<int32_t>{4000, 5000, 6000}, view.column(0));
+}
+
+TEST_F(CsvReaderTest, ByteRangeStrings)
+{
+  std::string input = "\"a\"\n\"b\"\n\"c\"";
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{input.c_str(), input.size()})
+      .names({"A"})
+      .dtypes({dtype<cudf::string_view>()})
+      .header(-1)
+      .byte_range_offset(4);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::STRING, view.column(0).type().id());
+
+  expect_column_data_equal(std::vector<std::string>{"c"}, view.column(0));
+}
+
+TEST_F(CsvReaderTest, BlanksAndComments)
+{
+  auto filepath = temp_env->get_temp_dir() + "BlanksAndComments.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "1\n#blank\n3\n4\n5\n#blank\n\n\n8\n9\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({dtype<int32_t>()})
+      .header(-1)
+      .comment('#');
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::INT32, view.column(0).type().id());
+
+  expect_column_data_equal(std::vector<int32_t>{1, 3, 4, 5, 8, 9}, view.column(0));
+}
+
+TEST_F(CsvReaderTest, EmptyFile)
+{
+  auto filepath = temp_env->get_temp_dir() + "EmptyFile.csv";
+  {
+    std::ofstream outfile{filepath, std::ofstream::out};
+    outfile << "";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(0, view.num_columns());
+}
+
+TEST_F(CsvReaderTest, NoDataFile)
+{
+  auto filepath = temp_env->get_temp_dir() + "NoDataFile.csv";
+  {
+    std::ofstream outfile{filepath, std::ofstream::out};
+    outfile << "\n\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(0, view.num_columns());
+}
+
+TEST_F(CsvReaderTest, HeaderOnlyFile)
+{
+  auto filepath = temp_env->get_temp_dir() + "HeaderOnlyFile.csv";
+  {
+    std::ofstream outfile{filepath, std::ofstream::out};
+    outfile << "\"a\",\"b\",\"c\"\n\n";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(0, view.num_rows());
+  EXPECT_EQ(3, view.num_columns());
+}
+
+TEST_F(CsvReaderTest, ArrowFileSource)
+{
+  auto filepath = temp_env->get_temp_dir() + "ArrowFileSource.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "A\n9\n8\n7\n6\n5\n4\n3\n2\n";
+  }
+
+  std::shared_ptr<arrow::io::ReadableFile> infile;
+  ASSERT_TRUE(arrow::io::ReadableFile::Open(filepath).Value(&infile).ok());
+
+  auto arrow_source = cudf::io::arrow_io_source{infile};
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{&arrow_source})
+      .dtypes({dtype<int8_t>()});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::INT8, view.column(0).type().id());
+
+  expect_column_data_equal(std::vector<int8_t>{9, 8, 7, 6, 5, 4, 3, 2}, view.column(0));
+}
+
+TEST_F(CsvReaderTest, InvalidFloatingPoint)
+{
+  auto const filepath = temp_env->get_temp_dir() + "InvalidFloatingPoint.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "1.2e1+\n3.4e2-\n5.6e3e\n7.8e3A\n9.0Be1\n1C.2";
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({dtype<float>()})
+      .header(-1);
+  auto const result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(1, view.num_columns());
+  ASSERT_EQ(type_id::FLOAT32, view.column(0).type().id());
+
+  // ignore all data because it is all nulls.
+  ASSERT_EQ(6u, result.tbl->view().column(0).null_count());
+}
+
+TEST_F(CsvReaderTest, StringInference)
+{
+  std::string buffer = "\"-1\"\n";
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .header(-1);
+  auto const result = cudf::io::read_csv(in_opts);
+
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), type_id::STRING);
+}
+
+TEST_F(CsvReaderTest, TypeInferenceThousands)
+{
+  std::string buffer = "1`400,123,1`234.56\n123`456,123456,12.34";
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .header(-1)
+      .thousands('`');
+  auto const result      = cudf::io::read_csv(in_opts);
+  auto const result_view = result.tbl->view();
+
+  EXPECT_EQ(result_view.num_columns(), 3);
+  EXPECT_EQ(result_view.column(0).type().id(), type_id::INT64);
+  EXPECT_EQ(result_view.column(1).type().id(), type_id::INT64);
+  EXPECT_EQ(result_view.column(2).type().id(), type_id::FLOAT64);
+
+  auto tsnd_sep_col = std::vector<int64_t>{1400L, 123456L};
+  auto int_col      = std::vector<int64_t>{123L, 123456L};
+  auto dbl_col      = std::vector<double>{1234.56, 12.34};
+  expect_column_data_equal(tsnd_sep_col, result_view.column(0));
+  expect_column_data_equal(int_col, result_view.column(1));
+  expect_column_data_equal(dbl_col, result_view.column(2));
+}
+
+TEST_F(CsvReaderTest, TypeInferenceWithDecimal)
+{
+  // Given that thousands:'`' and decimal(';'), we expect:
+  // col#0 => INT64 (column contains only digits & thousands sep)
+  // col#1 => STRING (contains digits and period character, which is NOT the decimal point here)
+  // col#2 => FLOAT64 (column contains digits and decimal point (i.e., ';'))
+  std::string buffer = "1`400,1.23,1`234;56\n123`456,123.456,12;34";
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .header(-1)
+      .thousands('`')
+      .decimal(';');
+  auto const result      = cudf::io::read_csv(in_opts);
+  auto const result_view = result.tbl->view();
+
+  EXPECT_EQ(result_view.num_columns(), 3);
+  EXPECT_EQ(result_view.column(0).type().id(), type_id::INT64);
+  EXPECT_EQ(result_view.column(1).type().id(), type_id::STRING);
+  EXPECT_EQ(result_view.column(2).type().id(), type_id::FLOAT64);
+
+  auto int_col = std::vector<int64_t>{1400L, 123456L};
+  auto str_col = std::vector<std::string>{"1.23", "123.456"};
+  auto dbl_col = std::vector<double>{1234.56, 12.34};
+  expect_column_data_equal(int_col, result_view.column(0));
+  expect_column_data_equal(str_col, result_view.column(1));
+  expect_column_data_equal(dbl_col, result_view.column(2));
+}
+
+TEST_F(CsvReaderTest, SkipRowsXorSkipFooter)
+{
+  std::string buffer = "1,2,3";
+
+  cudf::io::csv_reader_options skiprows_options =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .header(-1)
+      .skiprows(1);
+  EXPECT_NO_THROW(cudf::io::read_csv(skiprows_options));
+
+  cudf::io::csv_reader_options skipfooter_options =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .header(-1)
+      .skipfooter(1);
+  EXPECT_NO_THROW(cudf::io::read_csv(skipfooter_options));
+}
+
+TEST_F(CsvReaderTest, nullHandling)
+{
+  auto const filepath = temp_env->get_temp_dir() + "NullValues.csv";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "NULL\n\nnull\nn/a\nNull\nNA\nnan";
+  }
+
+  // Test disabling na_filter
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .na_filter(false)
+        .dtypes({dtype<cudf::string_view>()})
+        .header(-1)
+        .skip_blank_lines(false);
+    auto const result = cudf::io::read_csv(in_opts);
+    auto const view   = result.tbl->view();
+    auto expect =
+      cudf::test::strings_column_wrapper({"NULL", "", "null", "n/a", "Null", "NA", "nan"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, view.column(0));
+  }
+
+  // Test enabling na_filter
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .dtypes({dtype<cudf::string_view>()})
+        .header(-1)
+        .skip_blank_lines(false);
+    auto const result = cudf::io::read_csv(in_opts);
+    auto const view   = result.tbl->view();
+    auto expect =
+      cudf::test::strings_column_wrapper({"NULL", "", "null", "n/a", "Null", "NA", "nan"},
+                                         {false, false, false, false, true, false, false});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, view.column(0));
+  }
+
+  // Setting na_values with default values
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .na_values({"Null"})
+        .dtypes({dtype<cudf::string_view>()})
+        .header(-1)
+        .skip_blank_lines(false);
+    auto const result = cudf::io::read_csv(in_opts);
+    auto const view   = result.tbl->view();
+    auto expect =
+      cudf::test::strings_column_wrapper({"NULL", "", "null", "n/a", "Null", "NA", "nan"},
+                                         {false, false, false, false, false, false, false});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, view.column(0));
+  }
+
+  // Setting na_values without default values
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .keep_default_na(false)
+        .na_values({"Null"})
+        .dtypes({dtype<cudf::string_view>()})
+        .header(-1)
+        .skip_blank_lines(false);
+    auto const result = cudf::io::read_csv(in_opts);
+    auto const view   = result.tbl->view();
+    auto expect =
+      cudf::test::strings_column_wrapper({"NULL", "", "null", "n/a", "Null", "NA", "nan"},
+                                         {true, true, true, true, false, true, true, true});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, view.column(0));
+  }
+
+  // Filter enabled, but no NA values
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .keep_default_na(false)
+        .dtypes({dtype<cudf::string_view>()})
+        .header(-1)
+        .skip_blank_lines(false);
+    auto const result = cudf::io::read_csv(in_opts);
+    auto const view   = result.tbl->view();
+    auto expect =
+      cudf::test::strings_column_wrapper({"NULL", "", "null", "n/a", "Null", "NA", "nan"});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, view.column(0));
+  }
+}
+
+TEST_F(CsvReaderTest, FailCases)
+{
+  std::string buffer = "1,2,3";
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .byte_range_offset(4)
+        .skiprows(1),
+      std::invalid_argument);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .byte_range_offset(4)
+        .skipfooter(1),
+      std::invalid_argument);
+  }
+
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .byte_range_offset(4)
+        .nrows(1),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .byte_range_size(4)
+        .skiprows(1),
+      std::invalid_argument);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .byte_range_size(4)
+        .skipfooter(1),
+      std::invalid_argument);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .byte_range_size(4)
+        .nrows(1),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .skiprows(1)
+        .byte_range_offset(4),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .skipfooter(1)
+        .byte_range_offset(4),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .nrows(1)
+        .byte_range_offset(4),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .skiprows(1)
+        .byte_range_size(4),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .skipfooter(1)
+        .byte_range_size(4),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .nrows(1)
+        .byte_range_size(4),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .nrows(1)
+        .skipfooter(1),
+      std::invalid_argument);
+    ;
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .skipfooter(1)
+        .nrows(1),
+      cudf::logic_error);
+  }
+  {
+    EXPECT_THROW(
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .na_filter(false)
+        .na_values({"Null"}),
+      cudf::logic_error);
+  }
+}
+
+TEST_F(CsvReaderTest, HexTest)
+{
+  auto filepath = temp_env->get_temp_filepath("Hexadecimal.csv");
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << "0x0\n-0x1000\n0xfedcba\n0xABCDEF\n0xaBcDeF\n9512c20b\n";
+  }
+  // specify hex columns by name
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .dtypes({dtype<int64_t>()})
+        .header(-1)
+        .parse_hex({"A"});
+    auto result = cudf::io::read_csv(in_opts);
+
+    expect_column_data_equal(
+      std::vector<int64_t>{0, -4096, 16702650, 11259375, 11259375, 2501034507},
+      result.tbl->view().column(0));
+  }
+
+  // specify hex columns by index
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .dtypes({dtype<int64_t>()})
+        .header(-1)
+        .parse_hex(std::vector<int>{0});
+    auto result = cudf::io::read_csv(in_opts);
+
+    expect_column_data_equal(
+      std::vector<int64_t>{0, -4096, 16702650, 11259375, 11259375, 2501034507},
+      result.tbl->view().column(0));
+  }
+}
+
+TYPED_TEST(CsvReaderNumericTypeTest, SingleColumnWithWriter)
+{
+  constexpr auto num_rows = 10;
+  auto sequence           = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return static_cast<TypeParam>(i + 1000.50f); });
+  auto input_column = column_wrapper<TypeParam>(sequence, sequence + num_rows);
+  auto input_table  = cudf::table_view{std::vector<cudf::column_view>{input_column}};
+
+  auto filepath = temp_env->get_temp_filepath("SingleColumnWithWriter.csv");
+
+  write_csv_helper(filepath, input_table);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath}).header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_table, result_table);
+}
+
+TEST_F(CsvReaderTest, MultiColumnWithWriter)
+{
+  constexpr auto num_rows = 10;
+  auto int8_column        = []() {
+    auto values = random_values<int8_t>(num_rows);
+    return column_wrapper<int8_t>(values.begin(), values.end());
+  }();
+  auto int16_column = []() {
+    auto values = random_values<int16_t>(num_rows);
+    return column_wrapper<int16_t>(values.begin(), values.end());
+  }();
+  auto int32_column = []() {
+    auto values = random_values<int32_t>(num_rows);
+    return column_wrapper<int32_t>(values.begin(), values.end());
+  }();
+  auto int64_column = []() {
+    auto values = random_values<int64_t>(num_rows);
+    return column_wrapper<int64_t>(values.begin(), values.end());
+  }();
+  auto uint8_column = []() {
+    auto values = random_values<uint8_t>(num_rows);
+    return column_wrapper<uint8_t>(values.begin(), values.end());
+  }();
+  auto uint16_column = []() {
+    auto values = random_values<uint16_t>(num_rows);
+    return column_wrapper<uint16_t>(values.begin(), values.end());
+  }();
+  auto uint32_column = []() {
+    auto values = random_values<uint32_t>(num_rows);
+    return column_wrapper<uint32_t>(values.begin(), values.end());
+  }();
+  auto uint64_column = []() {
+    auto values = random_values<uint64_t>(num_rows);
+    return column_wrapper<uint64_t>(values.begin(), values.end());
+  }();
+  auto float32_column = []() {
+    auto values = random_values<float>(num_rows);
+    return column_wrapper<float>(values.begin(), values.end());
+  }();
+  auto float64_column = []() {
+    auto values = random_values<double>(num_rows);
+    return column_wrapper<double>(values.begin(), values.end());
+  }();
+
+  std::vector<cudf::column_view> input_columns{int8_column,
+                                               int16_column,
+                                               int32_column,
+                                               int64_column,
+                                               uint8_column,
+                                               uint16_column,
+                                               uint32_column,
+                                               uint64_column,
+                                               float32_column,
+                                               float64_column};
+  cudf::table_view input_table{input_columns};
+
+  auto filepath = temp_env->get_temp_dir() + "MultiColumnWithWriter.csv";
+
+  write_csv_helper(filepath, input_table);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .header(-1)
+      .dtypes({dtype<int8_t>(),
+               dtype<int16_t>(),
+               dtype<int32_t>(),
+               dtype<int64_t>(),
+               dtype<uint8_t>(),
+               dtype<uint16_t>(),
+               dtype<uint32_t>(),
+               dtype<uint64_t>(),
+               dtype<float>(),
+               dtype<double>()});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+
+  std::vector<cudf::size_type> non_float64s{0, 1, 2, 3, 4, 5, 6, 7, 8};
+  auto const input_sliced_view  = input_table.select(non_float64s);
+  auto const result_sliced_view = result_table.select(non_float64s);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_sliced_view, result_sliced_view);
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  double tol{1.0e-6};
+  auto float64_col_idx = non_float64s.size();
+  check_float_column(
+    input_table.column(float64_col_idx), result_table.column(float64_col_idx), tol, validity);
+}
+
+TEST_F(CsvReaderTest, DatesWithWriter)
+{
+  auto filepath = temp_env->get_temp_dir() + "DatesWithWriter.csv";
+
+  auto input_column = column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>{983750400000,
+                                                                                  1288483200000,
+                                                                                  782611200000,
+                                                                                  656208000000,
+                                                                                  0L,
+                                                                                  798163200000,
+                                                                                  774144000000,
+                                                                                  1149679230400,
+                                                                                  1126875750400,
+                                                                                  2764800000};
+  cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+  // TODO need to add a dayfirst flag?
+  write_csv_helper(filepath, input_table);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({data_type{type_id::TIMESTAMP_MILLISECONDS}})
+      .dayfirst(true)
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+
+  check_timestamp_column(input_table.column(0), result_table.column(0));
+}
+
+TEST_F(CsvReaderTest, DatesStringWithWriter)
+{
+  {
+    auto filepath = temp_env->get_temp_dir() + "DatesStringWithWriter_D.csv";
+
+    auto input_column = column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{-106751, 106751};
+    auto expected_column = column_wrapper<cudf::string_view>{"1677-09-22", "2262-04-11"};
+
+    cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+    write_csv_helper(filepath, input_table);
+
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .header(-1);
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const result_table = result.tbl->view();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column, result_table.column(0));
+  }
+
+  {
+    auto filepath = temp_env->get_temp_dir() + "DatesStringWithWriter_s.csv";
+
+    auto input_column =
+      column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep>{-9223372036, 9223372036};
+    auto expected_column =
+      column_wrapper<cudf::string_view>{"1677-09-21T00:12:44Z", "2262-04-11T23:47:16Z"};
+
+    cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+    write_csv_helper(filepath, input_table);
+
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .header(-1);
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const result_table = result.tbl->view();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column, result_table.column(0));
+  }
+
+  {
+    auto filepath = temp_env->get_temp_dir() + "DatesStringWithWriter_ms.csv";
+
+    auto input_column =
+      column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>{-9223372036854, 9223372036854};
+    auto expected_column =
+      column_wrapper<cudf::string_view>{"1677-09-21T00:12:43.146Z", "2262-04-11T23:47:16.854Z"};
+
+    cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+    write_csv_helper(filepath, input_table);
+
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .header(-1);
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const result_table = result.tbl->view();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column, result_table.column(0));
+  }
+
+  {
+    auto filepath = temp_env->get_temp_dir() + "DatesStringWithWriter_us.csv";
+
+    auto input_column = column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep>{
+      -9223372036854775, 9223372036854775};
+    auto cast_column     = cudf::strings::from_timestamps(input_column, "%Y-%m-%dT%H:%M:%S.%fZ");
+    auto expected_column = column_wrapper<cudf::string_view>{"1677-09-21T00:12:43.145225Z",
+                                                             "2262-04-11T23:47:16.854775Z"};
+
+    cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+    write_csv_helper(filepath, input_table);
+
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .header(-1);
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const result_table = result.tbl->view();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column, result_table.column(0));
+  }
+
+  {
+    auto filepath = temp_env->get_temp_dir() + "DatesStringWithWriter_ns.csv";
+
+    auto input_column = column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep>{
+      -9223372036854775807, 9223372036854775807};
+    auto expected_column = column_wrapper<cudf::string_view>{"1677-09-21T00:12:43.145224193Z",
+                                                             "2262-04-11T23:47:16.854775807Z"};
+
+    cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+    write_csv_helper(filepath, input_table);
+
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+        .names({"A"})
+        .header(-1);
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const result_table = result.tbl->view();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column, result_table.column(0));
+  }
+}
+
+TEST_F(CsvReaderTest, FloatingPointWithWriter)
+{
+  auto filepath = temp_env->get_temp_dir() + "FloatingPointWithWriter.csv";
+
+  auto input_column =
+    column_wrapper<double>{5.6, 56.79, 12000000000., 0.7, 3.000, 12.34, 0.31, -73.98007199999998};
+  cudf::table_view input_table(std::vector<cudf::column_view>{input_column});
+
+  // TODO add lineterminator=";"
+  write_csv_helper(filepath, input_table);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names({"A"})
+      .dtypes({dtype<double>()})
+      .header(-1);
+  // in_opts.lineterminator = ';';
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_table, result_table);
+}
+
+TEST_F(CsvReaderTest, StringsWithWriter)
+{
+  std::vector<std::string> names{"line", "verse"};
+
+  auto filepath = temp_env->get_temp_dir() + "StringsWithWriter.csv";
+
+  auto int_column = column_wrapper<int32_t>{10, 20, 30};
+  auto string_column =
+    column_wrapper<cudf::string_view>{"abc def ghi", "\"jkl mno pqr\"", "stu \"\"vwx\"\" yz"};
+  cudf::table_view input_table(std::vector<cudf::column_view>{int_column, string_column});
+
+  // TODO add quoting style flag?
+  write_csv_helper(filepath, input_table, names);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()})
+      .quoting(cudf::io::quote_style::NONE);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_table.column(0), result_table.column(0));
+  check_string_column(input_table.column(1), result_table.column(1));
+  ASSERT_EQ(result.metadata.schema_info.size(), names.size());
+  for (auto i = 0ul; i < names.size(); ++i)
+    EXPECT_EQ(names[i], result.metadata.schema_info[i].name);
+}
+
+TEST_F(CsvReaderTest, StringsWithWriterSimple)
+{
+  std::vector<std::string> names{"line", "verse"};
+
+  auto filepath = temp_env->get_temp_dir() + "StringsWithWriterSimple.csv";
+
+  auto int_column    = column_wrapper<int32_t>{10, 20, 30};
+  auto string_column = column_wrapper<cudf::string_view>{"abc def ghi", "jkl mno pq", "stu vwx y"};
+  cudf::table_view input_table(std::vector<cudf::column_view>{int_column, string_column});
+
+  // TODO add quoting style flag?
+  write_csv_helper(filepath, input_table, names);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()})
+      .quoting(cudf::io::quote_style::NONE);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_table.column(0), result_table.column(0));
+  check_string_column(input_table.column(1), result_table.column(1));
+  ASSERT_EQ(result.metadata.schema_info.size(), names.size());
+  for (auto i = 0ul; i < names.size(); ++i)
+    EXPECT_EQ(names[i], result.metadata.schema_info[i].name);
+}
+
+TEST_F(CsvReaderTest, StringsEmbeddedDelimiter)
+{
+  std::vector<std::string> names{"line", "verse"};
+
+  auto filepath = temp_env->get_temp_dir() + "StringsWithWriterSimple.csv";
+
+  auto int_column    = column_wrapper<int32_t>{10, 20, 30};
+  auto string_column = column_wrapper<cudf::string_view>{"abc def ghi", "jkl,mno,pq", "stu vwx y"};
+  cudf::table_view input_table(std::vector<cudf::column_view>{int_column, string_column});
+
+  write_csv_helper(filepath, input_table, names);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()});
+  auto result = cudf::io::read_csv(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_table, result.tbl->view());
+  ASSERT_EQ(result.metadata.schema_info.size(), names.size());
+  for (auto i = 0ul; i < names.size(); ++i)
+    EXPECT_EQ(names[i], result.metadata.schema_info[i].name);
+}
+
+TEST_F(CsvReaderTest, HeaderEmbeddedDelimiter)
+{
+  std::vector<std::string> names{
+    "header1", "header,2", "quote\"embedded", "new\nline", "\"quoted\""};
+
+  auto filepath = temp_env->get_temp_dir() + "HeaderEmbeddedDelimiter.csv";
+
+  auto int_column    = column_wrapper<int32_t>{10, 20, 30};
+  auto string_column = column_wrapper<cudf::string_view>{"abc", "jkl,mno", "xyz"};
+  cudf::table_view input_table(
+    std::vector<cudf::column_view>{int_column, string_column, int_column, int_column, int_column});
+
+  write_csv_helper(filepath, input_table, names);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names(names)
+      .dtypes({dtype<int32_t>(),
+               dtype<cudf::string_view>(),
+               dtype<int32_t>(),
+               dtype<int32_t>(),
+               dtype<int32_t>()});
+  auto result = cudf::io::read_csv(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_table, result.tbl->view());
+  ASSERT_EQ(result.metadata.schema_info.size(), names.size());
+  for (auto i = 0ul; i < names.size(); ++i)
+    EXPECT_EQ(names[i], result.metadata.schema_info[i].name);
+}
+
+TEST_F(CsvReaderTest, EmptyFileWithWriter)
+{
+  auto filepath = temp_env->get_temp_dir() + "EmptyFileWithWriter.csv";
+
+  cudf::table_view empty_table;
+  write_csv_helper(filepath, empty_table);
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_csv(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty_table, result.tbl->view());
+}
+
+class TestSource : public cudf::io::datasource {
+ public:
+  std::string const str;
+
+  TestSource(std::string s) : str(std::move(s)) {}
+  std::unique_ptr<buffer> host_read(size_t offset, size_t size) override
+  {
+    size = std::min(size, str.size() - offset);
+    return std::make_unique<non_owning_buffer>((uint8_t*)str.data() + offset, size);
+  }
+
+  size_t host_read(size_t offset, size_t size, uint8_t* dst) override
+  {
+    auto const read_size = std::min(size, str.size() - offset);
+    memcpy(dst, str.data() + offset, size);
+    return read_size;
+  }
+
+  [[nodiscard]] size_t size() const override { return str.size(); }
+};
+
+TEST_F(CsvReaderTest, UserImplementedSource)
+{
+  constexpr auto num_rows = 10;
+  auto int8_values        = random_values<int8_t>(num_rows);
+  auto int16_values       = random_values<int16_t>(num_rows);
+  auto int32_values       = random_values<int32_t>(num_rows);
+
+  std::ostringstream csv_data;
+  for (int i = 0; i < num_rows; ++i) {
+    csv_data << std::to_string(int8_values[i]) << "," << int16_values[i] << "," << int32_values[i]
+             << "\n";
+  }
+  TestSource source{csv_data.str()};
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{&source})
+      .dtypes({dtype<int8_t>(), dtype<int16_t>(), dtype<int32_t>()})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  expect_column_data_equal(int8_values, view.column(0));
+  expect_column_data_equal(int16_values, view.column(1));
+  expect_column_data_equal(int32_values, view.column(2));
+}
+
+TEST_F(CsvReaderTest, DurationsWithWriter)
+{
+  auto filepath = temp_env->get_temp_dir() + "DurationsWithWriter.csv";
+
+  constexpr long max_value_d  = std::numeric_limits<cudf::duration_D::rep>::max();
+  constexpr long min_value_d  = std::numeric_limits<cudf::duration_D::rep>::min();
+  constexpr long max_value_ns = std::numeric_limits<cudf::duration_s::rep>::max();
+  constexpr long min_value_ns = std::numeric_limits<cudf::duration_s::rep>::min();
+  column_wrapper<cudf::duration_D, cudf::duration_D::rep> durations_D{
+    {-86400L, -3600L, -2L, -1L, 0L, 1L, 2L, min_value_d, max_value_d}};
+  column_wrapper<cudf::duration_s, int64_t> durations_s{{-86400L,
+                                                         -3600L,
+                                                         -2L,
+                                                         -1L,
+                                                         0L,
+                                                         1L,
+                                                         2L,
+                                                         min_value_ns / 1000000000 + 1,
+                                                         max_value_ns / 1000000000}};
+  column_wrapper<cudf::duration_ms, int64_t> durations_ms{
+    {-86400L, -3600L, -2L, -1L, 0L, 1L, 2L, min_value_ns / 1000000 + 1, max_value_ns / 1000000}};
+  column_wrapper<cudf::duration_us, int64_t> durations_us{
+    {-86400L, -3600L, -2L, -1L, 0L, 1L, 2L, min_value_ns / 1000 + 1, max_value_ns / 1000}};
+  column_wrapper<cudf::duration_ns, int64_t> durations_ns{
+    {-86400L, -3600L, -2L, -1L, 0L, 1L, 2L, min_value_ns, max_value_ns}};
+
+  cudf::table_view input_table(std::vector<cudf::column_view>{
+    durations_D, durations_s, durations_ms, durations_us, durations_ns});
+  std::vector<std::string> names{"D", "s", "ms", "us", "ns"};
+
+  write_csv_helper(filepath, input_table, names);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .names(names)
+      .dtypes({data_type{type_id::DURATION_DAYS},
+               data_type{type_id::DURATION_SECONDS},
+               data_type{type_id::DURATION_MILLISECONDS},
+               data_type{type_id::DURATION_MICROSECONDS},
+               data_type{type_id::DURATION_NANOSECONDS}});
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_table, result_table);
+  ASSERT_EQ(result.metadata.schema_info.size(), names.size());
+  for (auto i = 0ul; i < names.size(); ++i)
+    EXPECT_EQ(names[i], result.metadata.schema_info[i].name);
+}
+
+TEST_F(CsvReaderTest, ParseInRangeIntegers)
+{
+  std::vector<int64_t> small_int               = {0, -10, 20, -30};
+  std::vector<int64_t> less_equal_int64_max    = {std::numeric_limits<int64_t>::max() - 3,
+                                                  std::numeric_limits<int64_t>::max() - 2,
+                                                  std::numeric_limits<int64_t>::max() - 1,
+                                                  std::numeric_limits<int64_t>::max()};
+  std::vector<int64_t> greater_equal_int64_min = {std::numeric_limits<int64_t>::min() + 3,
+                                                  std::numeric_limits<int64_t>::min() + 2,
+                                                  std::numeric_limits<int64_t>::min() + 1,
+                                                  std::numeric_limits<int64_t>::min()};
+  std::vector<uint64_t> greater_int64_max      = {uint64_t{std::numeric_limits<int64_t>::max()} - 1,
+                                                  uint64_t{std::numeric_limits<int64_t>::max()},
+                                                  uint64_t{std::numeric_limits<int64_t>::max()} + 1,
+                                                  uint64_t{std::numeric_limits<int64_t>::max()} + 2};
+  std::vector<uint64_t> less_equal_uint64_max  = {std::numeric_limits<uint64_t>::max() - 3,
+                                                  std::numeric_limits<uint64_t>::max() - 2,
+                                                  std::numeric_limits<uint64_t>::max() - 1,
+                                                  std::numeric_limits<uint64_t>::max()};
+  auto input_small_int = column_wrapper<int64_t>(small_int.begin(), small_int.end());
+  auto input_less_equal_int64_max =
+    column_wrapper<int64_t>(less_equal_int64_max.begin(), less_equal_int64_max.end());
+  auto input_greater_equal_int64_min =
+    column_wrapper<int64_t>(greater_equal_int64_min.begin(), greater_equal_int64_min.end());
+  auto input_greater_int64_max =
+    column_wrapper<uint64_t>(greater_int64_max.begin(), greater_int64_max.end());
+  auto input_less_equal_uint64_max =
+    column_wrapper<uint64_t>(less_equal_uint64_max.begin(), less_equal_uint64_max.end());
+
+  auto small_int_append_zeros               = prepend_zeros(small_int, 32, true);
+  auto less_equal_int64_max_append_zeros    = prepend_zeros(less_equal_int64_max, 32, true);
+  auto greater_equal_int64_min_append_zeros = prepend_zeros(greater_equal_int64_min, 17);
+  auto greater_int64_max_append_zeros       = prepend_zeros(greater_int64_max, 5);
+  auto less_equal_uint64_max_append_zeros   = prepend_zeros(less_equal_uint64_max, 8, true);
+
+  auto input_small_int_append =
+    column_wrapper<cudf::string_view>(small_int_append_zeros.begin(), small_int_append_zeros.end());
+  auto input_less_equal_int64_max_append = column_wrapper<cudf::string_view>(
+    less_equal_int64_max_append_zeros.begin(), less_equal_int64_max_append_zeros.end());
+  auto input_greater_equal_int64_min_append = column_wrapper<cudf::string_view>(
+    greater_equal_int64_min_append_zeros.begin(), greater_equal_int64_min_append_zeros.end());
+  auto input_greater_int64_max_append = column_wrapper<cudf::string_view>(
+    greater_int64_max_append_zeros.begin(), greater_int64_max_append_zeros.end());
+  auto input_less_equal_uint64_max_append = column_wrapper<cudf::string_view>(
+    less_equal_uint64_max_append_zeros.begin(), less_equal_uint64_max_append_zeros.end());
+
+  std::vector<cudf::column_view> input_columns{input_small_int,
+                                               input_less_equal_int64_max,
+                                               input_greater_equal_int64_min,
+                                               input_greater_int64_max,
+                                               input_less_equal_uint64_max,
+                                               input_small_int_append,
+                                               input_less_equal_int64_max_append,
+                                               input_greater_equal_int64_min_append,
+                                               input_greater_int64_max_append,
+                                               input_less_equal_uint64_max_append};
+  cudf::table_view input_table{input_columns};
+
+  auto filepath = temp_env->get_temp_filepath("ParseInRangeIntegers.csv");
+
+  write_csv_helper(filepath, input_table);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath}).header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_small_int, view.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_int64_max, view.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_equal_int64_min, view.column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_int64_max, view.column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_uint64_max, view.column(4));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_small_int, view.column(5));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_int64_max, view.column(6));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_equal_int64_min, view.column(7));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_int64_max, view.column(8));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_uint64_max, view.column(9));
+}
+
+TEST_F(CsvReaderTest, ParseOutOfRangeIntegers)
+{
+  std::vector<std::string> out_of_range_positive = {"111111111111111111111",
+                                                    "2222222222222222222222",
+                                                    "33333333333333333333333",
+                                                    "444444444444444444444444"};
+  std::vector<std::string> out_of_range_negative = {"-111111111111111111111",
+                                                    "-2222222222222222222222",
+                                                    "-33333333333333333333333",
+                                                    "-444444444444444444444444"};
+  std::vector<std::string> greater_uint64_max    = {
+    "18446744073709551615", "18446744073709551616", "18446744073709551617", "18446744073709551618"};
+  std::vector<std::string> less_int64_min = {
+    "-9223372036854775807", "-9223372036854775808", "-9223372036854775809", "-9223372036854775810"};
+  std::vector<std::string> mixed_range = {
+    "18446744073709551613", "18446744073709551614", "18446744073709551615", "-5"};
+  auto input_out_of_range_positive =
+    column_wrapper<cudf::string_view>(out_of_range_positive.begin(), out_of_range_positive.end());
+  auto input_out_of_range_negative =
+    column_wrapper<cudf::string_view>(out_of_range_negative.begin(), out_of_range_negative.end());
+  auto input_greater_uint64_max =
+    column_wrapper<cudf::string_view>(greater_uint64_max.begin(), greater_uint64_max.end());
+  auto input_less_int64_min =
+    column_wrapper<cudf::string_view>(less_int64_min.begin(), less_int64_min.end());
+  auto input_mixed_range =
+    column_wrapper<cudf::string_view>(mixed_range.begin(), mixed_range.end());
+
+  auto out_of_range_positive_append_zeros = prepend_zeros(out_of_range_positive, 32, true);
+  auto out_of_range_negative_append_zeros = prepend_zeros(out_of_range_negative, 5);
+  auto greater_uint64_max_append_zeros    = prepend_zeros(greater_uint64_max, 8, true);
+  auto less_int64_min_append_zeros        = prepend_zeros(less_int64_min, 17);
+  auto mixed_range_append_zeros           = prepend_zeros(mixed_range, 2, true);
+
+  auto input_out_of_range_positive_append = column_wrapper<cudf::string_view>(
+    out_of_range_positive_append_zeros.begin(), out_of_range_positive_append_zeros.end());
+  auto input_out_of_range_negative_append = column_wrapper<cudf::string_view>(
+    out_of_range_negative_append_zeros.begin(), out_of_range_negative_append_zeros.end());
+  auto input_greater_uint64_max_append = column_wrapper<cudf::string_view>(
+    greater_uint64_max_append_zeros.begin(), greater_uint64_max_append_zeros.end());
+  auto input_less_int64_min_append = column_wrapper<cudf::string_view>(
+    less_int64_min_append_zeros.begin(), less_int64_min_append_zeros.end());
+  auto input_mixed_range_append = column_wrapper<cudf::string_view>(
+    mixed_range_append_zeros.begin(), mixed_range_append_zeros.end());
+
+  std::vector<cudf::column_view> input_columns{input_out_of_range_positive,
+                                               input_out_of_range_negative,
+                                               input_greater_uint64_max,
+                                               input_less_int64_min,
+                                               input_mixed_range,
+                                               input_out_of_range_positive_append,
+                                               input_out_of_range_negative_append,
+                                               input_greater_uint64_max_append,
+                                               input_less_int64_min_append,
+                                               input_mixed_range_append};
+  cudf::table_view input_table{input_columns};
+
+  auto filepath = temp_env->get_temp_filepath("ParseOutOfRangeIntegers.csv");
+
+  write_csv_helper(filepath, input_table);
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath}).header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_positive, view.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_negative, view.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_uint64_max, view.column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_int64_min, view.column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_mixed_range, view.column(4));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_positive_append, view.column(5));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_negative_append, view.column(6));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_uint64_max_append, view.column(7));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_int64_min_append, view.column(8));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_mixed_range_append, view.column(9));
+}
+
+TEST_F(CsvReaderTest, ReadMaxNumericValue)
+{
+  constexpr auto num_rows = 10;
+  auto sequence           = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return std::numeric_limits<uint64_t>::max() - i; });
+
+  auto filepath = temp_env->get_temp_filepath("ReadMaxNumericValue.csv");
+  {
+    std::ofstream out_file{filepath, std::ofstream::out};
+    std::ostream_iterator<uint64_t> output_iterator(out_file, "\n");
+    std::copy(sequence, sequence + num_rows, output_iterator);
+  }
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath}).header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const view = result.tbl->view();
+  expect_column_data_equal(std::vector<uint64_t>(sequence, sequence + num_rows), view.column(0));
+}
+
+TEST_F(CsvReaderTest, DefaultWriteChunkSize)
+{
+  for (auto num_rows : {1, 20, 100, 1000}) {
+    auto sequence = cudf::detail::make_counting_transform_iterator(
+      0, [](auto i) { return static_cast<int32_t>(i + 1000.50f); });
+    auto input_column = column_wrapper<int32_t>(sequence, sequence + num_rows);
+    auto input_table  = cudf::table_view{std::vector<cudf::column_view>{input_column}};
+
+    cudf::io::csv_writer_options opts =
+      cudf::io::csv_writer_options::builder(cudf::io::sink_info{"unused.path"}, input_table);
+    ASSERT_EQ(num_rows, opts.get_rows_per_chunk());
+  }
+}
+
+TEST_F(CsvReaderTest, DtypesMap)
+{
+  std::string csv_in{"12,9\n34,8\n56,7"};
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+      .names({"A", "B"})
+      .dtypes({{"B", dtype<int16_t>()}, {"A", dtype<int32_t>()}})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  ASSERT_EQ(result_table.num_columns(), 2);
+  ASSERT_EQ(result_table.column(0).type(), data_type{type_id::INT32});
+  ASSERT_EQ(result_table.column(1).type(), data_type{type_id::INT16});
+  expect_column_data_equal(std::vector<int32_t>{12, 34, 56}, result_table.column(0));
+  expect_column_data_equal(std::vector<int16_t>{9, 8, 7}, result_table.column(1));
+}
+
+TEST_F(CsvReaderTest, DtypesMapPartial)
+{
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{nullptr, 0})
+      .names({"A", "B"})
+      .dtypes({{"A", dtype<int16_t>()}});
+  {
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const view = result.tbl->view();
+    ASSERT_EQ(type_id::INT16, view.column(0).type().id());
+    // Default to String if there's no data
+    ASSERT_EQ(type_id::STRING, view.column(1).type().id());
+  }
+
+  in_opts.set_dtypes({{"B", dtype<uint32_t>()}});
+  {
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const view = result.tbl->view();
+    ASSERT_EQ(type_id::STRING, view.column(0).type().id());
+    ASSERT_EQ(type_id::UINT32, view.column(1).type().id());
+  }
+}
+
+TEST_F(CsvReaderTest, DtypesArrayInvalid)
+{
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{nullptr, 0})
+      .names({"A", "B", "C"})
+      .dtypes(std::vector<cudf::data_type>{dtype<int16_t>(), dtype<int8_t>()});
+
+  EXPECT_THROW(cudf::io::read_csv(in_opts), cudf::logic_error);
+}
+
+TEST_F(CsvReaderTest, CsvDefaultOptionsWriteReadMatch)
+{
+  auto const filepath = temp_env->get_temp_dir() + "issue.csv";
+
+  // make up some kind of dataframe
+  auto int_column = column_wrapper<int32_t>{10, 20, 30};
+  auto str_column = column_wrapper<cudf::string_view>{"abc", "mno", "xyz"};
+  cudf::table_view input_table(std::vector<cudf::column_view>{int_column, str_column});
+
+  // write that dataframe to a csv using default options to some temporary file
+  cudf::io::csv_writer_options writer_options =
+    cudf::io::csv_writer_options::builder(cudf::io::sink_info{filepath}, input_table);
+  cudf::io::write_csv(writer_options);
+
+  // read the temp csv file using default options
+  cudf::io::csv_reader_options read_options =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<cudf::string_view>()});
+
+  cudf::io::table_with_metadata new_table_and_metadata = cudf::io::read_csv(read_options);
+
+  // verify that the tables are identical, or as identical as expected.
+  auto const new_table_view = new_table_and_metadata.tbl->view();
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(input_table, new_table_view);
+  EXPECT_EQ(new_table_and_metadata.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(new_table_and_metadata.metadata.schema_info[1].name, "1");
+}
+
+TEST_F(CsvReaderTest, UseColsValidation)
+{
+  const std::string buffer = "1,2,3";
+
+  const cudf::io::csv_reader_options idx_cnt_options =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .names({"a", "b"})
+      .use_cols_indexes({0});
+  EXPECT_THROW(cudf::io::read_csv(idx_cnt_options), cudf::logic_error);
+
+  cudf::io::csv_reader_options unique_idx_cnt_options =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .names({"a", "b"})
+      .use_cols_indexes({0, 0});
+  EXPECT_THROW(cudf::io::read_csv(unique_idx_cnt_options), cudf::logic_error);
+
+  cudf::io::csv_reader_options bad_name_options =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .names({"a", "b", "c"})
+      .use_cols_names({"nonexistent_name"});
+  EXPECT_THROW(cudf::io::read_csv(bad_name_options), cudf::logic_error);
+}
+
+TEST_F(CsvReaderTest, CropColumns)
+{
+  const std::string csv_in{"12,9., 10\n34,8., 20\n56,7., 30"};
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<float>()})
+      .names({"a", "b"})
+      .header(-1);
+  auto const result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  ASSERT_EQ(result_table.num_columns(), 2);
+  ASSERT_EQ(result_table.column(0).type(), data_type{type_id::INT32});
+  ASSERT_EQ(result_table.column(1).type(), data_type{type_id::FLOAT32});
+  expect_column_data_equal(std::vector<int32_t>{12, 34, 56}, result_table.column(0));
+  expect_column_data_equal(std::vector<float>{9., 8., 7.}, result_table.column(1));
+}
+
+TEST_F(CsvReaderTest, CropColumnsUseColsNames)
+{
+  std::string csv_in{"12,9., 10\n34,8., 20\n56,7., 30"};
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<float>()})
+      .names({"a", "b"})
+      .use_cols_names({"b"})
+      .header(-1);
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  ASSERT_EQ(result_table.num_columns(), 1);
+  ASSERT_EQ(result_table.column(0).type(), data_type{type_id::FLOAT32});
+  expect_column_data_equal(std::vector<float>{9., 8., 7.}, result_table.column(0));
+}
+
+TEST_F(CsvReaderTest, ExtraColumns)
+{
+  std::string csv_in{"12,9., 10\n34,8., 20\n56,7., 30"};
+  {
+    cudf::io::csv_reader_options opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+        .names({"a", "b", "c", "d"})
+        .header(-1);
+    auto result = cudf::io::read_csv(opts);
+
+    auto const result_table = result.tbl->view();
+    ASSERT_EQ(result_table.num_columns(), 4);
+    ASSERT_EQ(result_table.column(3).type(), data_type{type_id::INT8});
+    ASSERT_EQ(result_table.column(3).null_count(), 3);
+  }
+  {
+    cudf::io::csv_reader_options with_dtypes_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+        .names({"a", "b", "c", "d"})
+        .dtypes({dtype<int32_t>(), dtype<int32_t>(), dtype<int32_t>(), dtype<float>()})
+        .header(-1);
+    auto result = cudf::io::read_csv(with_dtypes_opts);
+
+    auto const result_table = result.tbl->view();
+    ASSERT_EQ(result_table.num_columns(), 4);
+    ASSERT_EQ(result_table.column(3).type(), data_type{type_id::FLOAT32});
+    ASSERT_EQ(result_table.column(3).null_count(), 3);
+  }
+}
+
+TEST_F(CsvReaderTest, ExtraColumnsUseCols)
+{
+  std::string csv_in{"12,9., 10\n34,8., 20\n56,7., 30"};
+
+  {
+    cudf::io::csv_reader_options in_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+        .names({"a", "b", "c", "d"})
+        .use_cols_names({"b", "d"})
+        .header(-1);
+    auto result = cudf::io::read_csv(in_opts);
+
+    auto const result_table = result.tbl->view();
+    ASSERT_EQ(result_table.num_columns(), 2);
+    ASSERT_EQ(result_table.column(1).type(), data_type{type_id::INT8});
+    ASSERT_EQ(result_table.column(1).null_count(), 3);
+  }
+  {
+    cudf::io::csv_reader_options with_dtypes_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+        .names({"a", "b", "c", "d"})
+        .use_cols_names({"b", "d"})
+        .dtypes({dtype<int32_t>(), dtype<int32_t>(), dtype<int32_t>(), dtype<cudf::string_view>()})
+        .header(-1);
+    auto result = cudf::io::read_csv(with_dtypes_opts);
+
+    auto const result_table = result.tbl->view();
+    ASSERT_EQ(result_table.num_columns(), 2);
+    ASSERT_EQ(result_table.column(1).type(), data_type{type_id::STRING});
+    ASSERT_EQ(result_table.column(1).null_count(), 3);
+  }
+}
+
+TEST_F(CsvReaderTest, EmptyColumns)
+{
+  // First column only has empty fields. second column contains only "null" literals
+  std::string csv_in{",null\n,null"};
+
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+      .names({"a", "b", "c", "d"})
+      .header(-1);
+  // More elements in `names` than in the file; additional columns are filled with nulls
+  auto result = cudf::io::read_csv(in_opts);
+
+  auto const result_table = result.tbl->view();
+  EXPECT_EQ(result_table.num_columns(), 4);
+  // All columns should contain only nulls; expect INT8 type to use as little memory as possible
+  for (auto& column : result_table) {
+    EXPECT_EQ(column.type(), data_type{type_id::INT8});
+    EXPECT_EQ(column.null_count(), 2);
+  }
+}
+
+TEST_F(CsvReaderTest, BlankLineAfterFirstRow)
+{
+  std::string csv_in{"12,9., 10\n\n"};
+
+  {
+    cudf::io::csv_reader_options no_header_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()})
+        .header(-1);
+    // No header, getting column names/count from first row
+    auto result = cudf::io::read_csv(no_header_opts);
+
+    auto const result_table = result.tbl->view();
+    ASSERT_EQ(result_table.num_columns(), 3);
+  }
+  {
+    cudf::io::csv_reader_options header_opts =
+      cudf::io::csv_reader_options::builder(cudf::io::source_info{csv_in.c_str(), csv_in.size()});
+    // Getting column names/count from header
+    auto result = cudf::io::read_csv(header_opts);
+
+    auto const result_table = result.tbl->view();
+    ASSERT_EQ(result_table.num_columns(), 3);
+  }
+}
+
+TEST_F(CsvReaderTest, NullCount)
+{
+  std::string buffer = "0,,\n1,1.,\n2,,\n3,,\n4,4.,\n5,5.,\n6,6.,\n7,7.,\n";
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .header(-1);
+  auto const result      = cudf::io::read_csv(in_opts);
+  auto const result_view = result.tbl->view();
+
+  EXPECT_EQ(result_view.num_rows(), 8);
+  EXPECT_EQ(result_view.column(0).null_count(), 0);
+  EXPECT_EQ(result_view.column(1).null_count(), 3);
+  EXPECT_EQ(result_view.column(2).null_count(), 8);
+}
+
+TEST_F(CsvReaderTest, UTF8BOM)
+{
+  std::string buffer = "\xEF\xBB\xBFMonth,Day,Year\nJune,6,2023\nAugust,25,1990\nMay,1,2000\n";
+  cudf::io::csv_reader_options in_opts =
+    cudf::io::csv_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()});
+  auto const result      = cudf::io::read_csv(in_opts);
+  auto const result_view = result.tbl->view();
+  EXPECT_EQ(result_view.num_rows(), 3);
+  EXPECT_EQ(result.metadata.schema_info.front().name, "Month");
+
+  auto col1     = cudf::test::strings_column_wrapper({"June", "August", "May"});
+  auto col2     = cudf::test::fixed_width_column_wrapper<int64_t>({6, 25, 1});
+  auto col3     = cudf::test::fixed_width_column_wrapper<int64_t>({2023, 1990, 2000});
+  auto expected = cudf::table_view({col1, col2, col3});
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result_view, expected);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/file_io_test.cpp b/cpp/tests/io/file_io_test.cpp
new file mode 100644
index 0000000..c9a1718
--- /dev/null
+++ b/cpp/tests/io/file_io_test.cpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <src/io/utilities/file_io_utilities.hpp>
+
+#include <type_traits>
+
+// Base test fixture for tests
+struct CuFileIOTest : public cudf::test::BaseFixture {};
+
+TEST_F(CuFileIOTest, SliceSize)
+{
+  std::vector<std::pair<size_t, size_t>> test_cases{
+    {1 << 20, 1 << 18}, {1 << 18, 1 << 20}, {1 << 20, 3333}, {0, 1 << 18}, {0, 0}, {1 << 20, 0}};
+  for (auto const& test_case : test_cases) {
+    auto const slices = cudf::io::detail::make_file_io_slices(test_case.first, test_case.second);
+    if (slices.empty()) {
+      ASSERT_EQ(test_case.first, 0);
+    } else {
+      ASSERT_EQ(slices.front().offset, 0);
+      ASSERT_EQ(slices.back().offset + slices.back().size, test_case.first);
+      for (auto i = 1u; i < slices.size(); ++i) {
+        ASSERT_EQ(slices[i].offset, slices[i - 1].offset + slices[i - 1].size);
+      }
+    }
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/fst/common.hpp b/cpp/tests/io/fst/common.hpp
new file mode 100644
index 0000000..382d21f
--- /dev/null
+++ b/cpp/tests/io/fst/common.hpp
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <array>
+#include <string>
+#include <vector>
+
+//------------------------------------------------------------------------------
+// TEST FST SPECIFICATIONS
+//------------------------------------------------------------------------------
+// FST to check for brackets and braces outside of pairs of quotes
+enum class dfa_states : char {
+  // The state being active while being outside of a string. When encountering an opening bracket or
+  // curly brace, we push it onto the stack. When encountering a closing bracket or brace, we pop it
+  // from the stack.
+  TT_OOS = 0U,
+  // The state being active while being within a string (e.g., field name or a string value). We do
+  // not push or pop from the stack while being in this state.
+  TT_STR,
+  // The state being active after encountering an escape symbol (e.g., '\') while being in the
+  // TT_STR state.
+  TT_ESC,
+  // Total number of states
+  TT_NUM_STATES
+};
+
+/**
+ * @brief Definition of the symbol groups
+ */
+enum class dfa_symbol_group_id : uint32_t {
+  OPENING_BRACE,     ///< Opening brace SG: {
+  OPENING_BRACKET,   ///< Opening bracket SG: [
+  CLOSING_BRACE,     ///< Closing brace SG: }
+  CLOSING_BRACKET,   ///< Closing bracket SG: ]
+  QUOTE_CHAR,        ///< Quote character SG: "
+  ESCAPE_CHAR,       ///< Escape character SG: '\'
+  OTHER_SYMBOLS,     ///< SG implicitly matching all other characters
+  NUM_SYMBOL_GROUPS  ///< Total number of symbol groups
+};
+
+// Aliases for readability of the transition table
+constexpr auto TT_OOS = dfa_states::TT_OOS;
+constexpr auto TT_STR = dfa_states::TT_STR;
+constexpr auto TT_ESC = dfa_states::TT_ESC;
+
+constexpr auto TT_NUM_STATES     = static_cast<char>(dfa_states::TT_NUM_STATES);
+constexpr auto NUM_SYMBOL_GROUPS = static_cast<uint32_t>(dfa_symbol_group_id::NUM_SYMBOL_GROUPS);
+
+// Transition table
+std::array<std::array<dfa_states, NUM_SYMBOL_GROUPS>, TT_NUM_STATES> const pda_state_tt{
+  {/* IN_STATE          {       [       }       ]       "       \    OTHER */
+   /* TT_OOS    */ {{TT_OOS, TT_OOS, TT_OOS, TT_OOS, TT_STR, TT_OOS, TT_OOS}},
+   /* TT_STR    */ {{TT_STR, TT_STR, TT_STR, TT_STR, TT_OOS, TT_ESC, TT_STR}},
+   /* TT_ESC    */ {{TT_STR, TT_STR, TT_STR, TT_STR, TT_STR, TT_STR, TT_STR}}}};
+
+// Translation table (i.e., for each transition, what are the symbols that we output)
+std::array<std::array<std::vector<char>, NUM_SYMBOL_GROUPS>, TT_NUM_STATES> const pda_out_tt{
+  {/* IN_STATE         {      [      }      ]      "      \    OTHER */
+   /* TT_OOS    */ {{{'{'}, {'['}, {'}'}, {']'}, {'x'}, {'x'}, {'x'}}},
+   /* TT_STR    */ {{{'x'}, {'x'}, {'x'}, {'x'}, {'x'}, {'x'}, {'x'}}},
+   /* TT_ESC    */ {{{'x'}, {'x'}, {'x'}, {'x'}, {'x'}, {'x'}, {'x'}}}}};
+
+// The i-th string representing all the characters of a symbol group
+std::array<std::string, NUM_SYMBOL_GROUPS - 1> const pda_sgs{"{", "[", "}", "]", "\"", "\\"};
+
+// The DFA's starting state
+constexpr char start_state = static_cast<char>(dfa_states::TT_OOS);
diff --git a/cpp/tests/io/fst/fst_test.cu b/cpp/tests/io/fst/fst_test.cu
new file mode 100644
index 0000000..fd69251
--- /dev/null
+++ b/cpp/tests/io/fst/fst_test.cu
@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/fst/lookup_tables.cuh>
+#include <io/utilities/hostdevice_vector.hpp>
+#include <tests/io/fst/common.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cstdlib>
+#include <vector>
+
+namespace {
+
+//------------------------------------------------------------------------------
+// CPU-BASED IMPLEMENTATIONS FOR VERIFICATION
+//------------------------------------------------------------------------------
+/**
+ * @brief CPU-based implementation of a finite-state transducer (FST).
+ *
+ * @tparam InputItT Forward input iterator type to symbols fed into the FST
+ * @tparam StateT Type representing states of the finite-state machine
+ * @tparam SymbolGroupLutT Sequence container of symbol groups. Each symbol group is a sequence
+ * container to symbols within that group.
+ * @tparam TransitionTableT Two-dimensional container type
+ * @tparam TransducerTableT Two-dimensional container type
+ * @tparam OutputItT Forward output iterator type
+ * @tparam IndexOutputItT Forward output iterator type
+ * @param[in] begin Forward iterator to the beginning of the symbol sequence
+ * @param[in] end Forward iterator to one past the last element of the symbol sequence
+ * @param[in] init_state The starting state of the finite-state machine
+ * @param[in] symbol_group_lut Sequence container of symbol groups. Each symbol group is a sequence
+ * container to symbols within that group. The index of the symbol group containing a symbol being
+ * read will be used as symbol_gid of the transition and translation tables.
+ * @param[in] transition_table The two-dimensional transition table, i.e.,
+ * transition_table[state][symbol_gid] -> new_state
+ * @param[in] translation_table The two-dimensional transducer table, i.e.,
+ * translation_table[state][symbol_gid] -> range_of_output_symbols
+ * @param[out] out_tape A forward output iterator to which the transduced input will be written
+ * @param[out] out_index_tape A forward output iterator to which indexes of the symbols that
+ * actually caused some output are written to
+ * @return A pair of iterators to one past the last element of (1) the transduced output symbol
+ * sequence and (2) the indexes of
+ */
+template <typename InputItT,
+          typename StateT,
+          typename SymbolGroupLutT,
+          typename TransitionTableT,
+          typename TransducerTableT,
+          typename OutputItT,
+          typename IndexOutputItT>
+static std::pair<OutputItT, IndexOutputItT> fst_baseline(InputItT begin,
+                                                         InputItT end,
+                                                         StateT const& init_state,
+                                                         SymbolGroupLutT symbol_group_lut,
+                                                         TransitionTableT transition_table,
+                                                         TransducerTableT translation_table,
+                                                         OutputItT out_tape,
+                                                         IndexOutputItT out_index_tape)
+{
+  // Initialize "FSM" with starting state
+  StateT state = init_state;
+
+  // To track the symbol offset within the input that caused the FST to output
+  std::size_t in_offset = 0;
+  for (auto it = begin; it < end; it++) {
+    // The symbol currently being read
+    auto const& symbol = *it;
+
+    // Iterate over symbol groups and search for the first symbol group containing the current
+    // symbol, if no match is found we use cend(symbol_group_lut) as the "catch-all" symbol group
+    auto symbol_group_it =
+      std::find_if(std::cbegin(symbol_group_lut), std::cend(symbol_group_lut), [symbol](auto& sg) {
+        return std::find(std::cbegin(sg), std::cend(sg), symbol) != std::cend(sg);
+      });
+    auto symbol_group = std::distance(std::cbegin(symbol_group_lut), symbol_group_it);
+
+    // Output the translated symbols to the output tape
+    out_tape = std::copy(std::cbegin(translation_table[state][symbol_group]),
+                         std::cend(translation_table[state][symbol_group]),
+                         out_tape);
+
+    auto out_size = std::distance(std::cbegin(translation_table[state][symbol_group]),
+                                  std::cend(translation_table[state][symbol_group]));
+
+    out_index_tape = std::fill_n(out_index_tape, out_size, in_offset);
+
+    // Transition the state of the finite-state machine
+    state = static_cast<char>(transition_table[state][symbol_group]);
+
+    // Continue with next symbol from input tape
+    in_offset++;
+  }
+  return {out_tape, out_index_tape};
+}
+}  // namespace
+
+// Base test fixture for tests
+struct FstTest : public cudf::test::BaseFixture {};
+
+TEST_F(FstTest, GroundTruth)
+{
+  // Type used to represent the atomic symbol type used within the finite-state machine
+  using SymbolT = char;
+
+  // Type sufficiently large to index symbols within the input and output (may be unsigned)
+  using SymbolOffsetT = uint32_t;
+
+  // Prepare cuda stream for data transfers & kernels
+  rmm::cuda_stream stream{};
+  rmm::cuda_stream_view stream_view(stream);
+
+  // Test input
+  std::string input = R"(  {)"
+                      R"("category": "reference",)"
+                      R"("index:" [4,12,42],)"
+                      R"("author": "Nigel Rees",)"
+                      R"("title": "Sayings of the Century",)"
+                      R"("price": 8.95)"
+                      R"(}  )"
+                      R"({)"
+                      R"("category": "reference",)"
+                      R"("index:" [4,{},null,{"a":[]}],)"
+                      R"("author": "Nigel Rees",)"
+                      R"("title": "Sayings of the Century",)"
+                      R"("price": 8.95)"
+                      R"(}  {} [] [ ])";
+
+  size_t string_size                 = input.size() * (1 << 10);
+  auto d_input_scalar                = cudf::make_string_scalar(input);
+  auto& d_string_scalar              = static_cast<cudf::string_scalar&>(*d_input_scalar);
+  const cudf::size_type repeat_times = string_size / input.size();
+  auto d_input_string                = cudf::strings::repeat_string(d_string_scalar, repeat_times);
+  auto& d_input = static_cast<cudf::scalar_type_t<std::string>&>(*d_input_string);
+  input         = d_input.to_string(stream);
+
+  // Prepare input & output buffers
+  constexpr std::size_t single_item = 1;
+  cudf::detail::hostdevice_vector<SymbolT> output_gpu(input.size(), stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> output_gpu_size(single_item, stream_view);
+  cudf::detail::hostdevice_vector<SymbolOffsetT> out_indexes_gpu(input.size(), stream_view);
+
+  // Run algorithm
+  auto parser = cudf::io::fst::detail::make_fst(
+    cudf::io::fst::detail::make_symbol_group_lut(pda_sgs),
+    cudf::io::fst::detail::make_transition_table(pda_state_tt),
+    cudf::io::fst::detail::make_translation_table<TT_NUM_STATES * NUM_SYMBOL_GROUPS>(pda_out_tt),
+    stream);
+
+  // Allocate device-side temporary storage & run algorithm
+  parser.Transduce(d_input.data(),
+                   static_cast<SymbolOffsetT>(d_input.size()),
+                   output_gpu.device_ptr(),
+                   out_indexes_gpu.device_ptr(),
+                   output_gpu_size.device_ptr(),
+                   start_state,
+                   stream.value());
+
+  // Async copy results from device to host
+  output_gpu.device_to_host_async(stream.view());
+  out_indexes_gpu.device_to_host_async(stream.view());
+  output_gpu_size.device_to_host_async(stream.view());
+
+  // Prepare CPU-side results for verification
+  std::string output_cpu{};
+  std::vector<SymbolOffsetT> out_index_cpu{};
+  output_cpu.reserve(input.size());
+  out_index_cpu.reserve(input.size());
+
+  // Run CPU-side algorithm
+  fst_baseline(std::begin(input),
+               std::end(input),
+               start_state,
+               pda_sgs,
+               pda_state_tt,
+               pda_out_tt,
+               std::back_inserter(output_cpu),
+               std::back_inserter(out_index_cpu));
+
+  // Make sure results have been copied back to host
+  stream.synchronize();
+
+  // Verify results
+  ASSERT_EQ(output_gpu_size[0], output_cpu.size());
+  CUDF_TEST_EXPECT_VECTOR_EQUAL(output_gpu, output_cpu, output_cpu.size());
+  CUDF_TEST_EXPECT_VECTOR_EQUAL(out_indexes_gpu, out_index_cpu, output_cpu.size());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/fst/logical_stack_test.cu b/cpp/tests/io/fst/logical_stack_test.cu
new file mode 100644
index 0000000..3d67437
--- /dev/null
+++ b/cpp/tests/io/fst/logical_stack_test.cu
@@ -0,0 +1,249 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+
+#include <cudf/types.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+#include <src/io/fst/logical_stack.cuh>
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <cstdlib>
+#include <iostream>
+#include <iterator>
+#include <stack>
+#include <vector>
+
+namespace {
+namespace fst = cudf::io::fst;
+
+/**
+ * @brief Generates the sparse representation of stack operations to feed into the logical
+ * stack
+ *
+ * @param begin Forward input iterator to the first item of symbols that are checked for whether
+ * they push or pop
+ * @param end Forward input iterator to one one past the last item of symbols that are checked for
+ * whether they push or pop
+ * @param to_stack_op A function object that takes an instance of InputItT's value type and
+ * returns the kind of stack operation such item represents (i.e., of type stack_op_type)
+ * @param stack_symbol_out Forward output iterator to which symbols that either push or pop are
+ * assigned
+ * @param stack_op_index_out Forward output iterator to which the indexes of symbols that either
+ * push or pop are assigned
+ * @return Pair of iterators to one past the last item of the items written to \p stack_symbol_out
+ * and \p stack_op_index_out, respectively
+ */
+template <typename InputItT,
+          typename ToStackOpTypeT,
+          typename StackSymbolOutItT,
+          typename StackOpIndexOutItT>
+std::pair<StackSymbolOutItT, StackOpIndexOutItT> to_sparse_stack_symbols(
+  InputItT begin,
+  InputItT end,
+  ToStackOpTypeT to_stack_op,
+  StackSymbolOutItT stack_symbol_out,
+  StackOpIndexOutItT stack_op_index_out)
+{
+  std::size_t index = 0;
+  for (auto it = begin; it < end; it++) {
+    fst::stack_op_type op_type = to_stack_op(*it);
+    if (op_type == fst::stack_op_type::PUSH || op_type == fst::stack_op_type::POP) {
+      *stack_symbol_out   = *it;
+      *stack_op_index_out = index;
+      stack_symbol_out++;
+      stack_op_index_out++;
+    }
+    index++;
+  }
+  return std::make_pair(stack_symbol_out, stack_op_index_out);
+}
+
+/**
+ * @brief Reads in a sequence of items that represent stack operations, applies these operations to
+ * a stack, and, for every operation being read in, outputs what was the symbol on top of the stack
+ * before the operations was applied. In case the stack is empty before any operation,
+ * \p empty_stack will be output instead.
+ *
+ * @tparam InputItT Forward input iterator type to items representing stack operations
+ * @tparam ToStackOpTypeT A transform function object class that maps an item representing a stack
+ * operation to the stack_op_type of such item
+ * @tparam StackSymbolT Type representing items being pushed onto the stack
+ * @tparam TopOfStackOutItT A forward output iterator type being assigned items of StackSymbolT
+ * @param[in] begin Forward iterator to the beginning of the items representing stack operations
+ * @param[in] end Iterator to one past the last item representing the stack operation
+ * @param[in] to_stack_op A function object that takes an instance of InputItT's value type and
+ * returns the kind of stack operation such item represents (i.e., of type stack_op_type)
+ * @param[in] empty_stack A symbol that will be written to top_of_stack_out_it whenever the stack
+ * was empty
+ * @param[out] top_of_stack The output iterator to which the item will be written to
+ * @return TopOfStackOutItT Iterators to one past the last element that was written
+ */
+template <typename InputItT,
+          typename ToStackOpTypeT,
+          typename StackSymbolT,
+          typename TopOfStackOutItT>
+TopOfStackOutItT to_top_of_stack(InputItT begin,
+                                 InputItT end,
+                                 ToStackOpTypeT to_stack_op,
+                                 StackSymbolT empty_stack,
+                                 TopOfStackOutItT top_of_stack_out_it)
+{
+  // This is the data structure that keeps track of the full stack state for each input symbol
+  std::stack<StackSymbolT> stack_state;
+
+  for (auto it = begin; it < end; it++) {
+    // Write what is currently on top of the stack when reading in the current symbol
+    *top_of_stack_out_it = stack_state.empty() ? empty_stack : stack_state.top();
+    top_of_stack_out_it++;
+
+    auto const& current        = *it;
+    fst::stack_op_type op_type = to_stack_op(current);
+
+    // Check whether this symbol corresponds to a push or pop operation and modify the stack
+    // accordingly
+    if (op_type == fst::stack_op_type::PUSH) {
+      stack_state.push(current);
+    } else if (op_type == fst::stack_op_type::POP) {
+      stack_state.pop();
+    }
+  }
+  return top_of_stack_out_it;
+}
+
+/**
+ * @brief Function object used to filter for brackets and braces that represent push and pop
+ * operations
+ *
+ */
+struct JSONToStackOp {
+  template <typename StackSymbolT>
+  constexpr CUDF_HOST_DEVICE fst::stack_op_type operator()(StackSymbolT const& stack_symbol) const
+  {
+    return (stack_symbol == '{' || stack_symbol == '[')   ? fst::stack_op_type::PUSH
+           : (stack_symbol == '}' || stack_symbol == ']') ? fst::stack_op_type::POP
+                                                          : fst::stack_op_type::READ;
+  }
+};
+}  // namespace
+
+// Base test fixture for tests
+struct LogicalStackTest : public cudf::test::BaseFixture {};
+
+TEST_F(LogicalStackTest, GroundTruth)
+{
+  // Type sufficient to cover any stack level (must be a signed type)
+  using StackLevelT   = int8_t;
+  using SymbolT       = char;
+  using SymbolOffsetT = uint32_t;
+
+  // The stack symbol that we'll fill everywhere where there's nothing on the stack
+  constexpr SymbolT empty_stack_symbol = '_';
+
+  // This just has to be a stack symbol that may not be confused with a symbol that would push
+  constexpr SymbolT read_symbol = 'x';
+
+  // Prepare cuda stream for data transfers & kernels
+  rmm::cuda_stream stream{};
+  rmm::cuda_stream_view stream_view(stream);
+
+  // Test input,
+  std::string input = R"(  {)"
+                      R"(category": "reference",)"
+                      R"("index:" [4,12,42],)"
+                      R"("author": "Nigel Rees",)"
+                      R"("title": "Sayings of the Century",)"
+                      R"("price": 8.95)"
+                      R"(}  )"
+                      R"({)"
+                      R"("category": "reference",)"
+                      R"("index:" [4,{},null,{"a":[]}],)"
+                      R"("author": "Nigel Rees",)"
+                      R"("title": "Sayings of the Century",)"
+                      R"("price": 8.95)"
+                      R"(}  {} [] [ ])";
+
+  // Repeat input sample 1024x
+  for (std::size_t i = 0; i < 10; i++)
+    input += input;
+
+  // Input's size
+  std::size_t string_size = input.size();
+
+  // Getting the symbols that actually modify the stack (i.e., symbols that push or pop)
+  std::string stack_symbols{};
+  std::vector<SymbolOffsetT> stack_op_indexes;
+  stack_op_indexes.reserve(string_size);
+
+  // Get the sparse representation of stack operations
+  to_sparse_stack_symbols(std::cbegin(input),
+                          std::cend(input),
+                          JSONToStackOp{},
+                          std::back_inserter(stack_symbols),
+                          std::back_inserter(stack_op_indexes));
+
+  rmm::device_uvector<SymbolT> d_stack_ops{stack_symbols.size(), stream_view};
+  rmm::device_uvector<SymbolOffsetT> d_stack_op_indexes{stack_op_indexes.size(), stream_view};
+  cudf::detail::hostdevice_vector<SymbolT> top_of_stack_gpu{string_size, stream_view};
+  cudf::device_span<SymbolOffsetT> d_stack_op_idx_span{d_stack_op_indexes};
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(d_stack_ops.data(),
+                                stack_symbols.data(),
+                                stack_symbols.size() * sizeof(SymbolT),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  CUDF_CUDA_TRY(cudaMemcpyAsync(d_stack_op_indexes.data(),
+                                stack_op_indexes.data(),
+                                stack_op_indexes.size() * sizeof(SymbolOffsetT),
+                                cudaMemcpyDefault,
+                                stream.value()));
+
+  // Run algorithm
+  fst::sparse_stack_op_to_top_of_stack<StackLevelT>(d_stack_ops.data(),
+                                                    d_stack_op_idx_span,
+                                                    JSONToStackOp{},
+                                                    top_of_stack_gpu.device_ptr(),
+                                                    empty_stack_symbol,
+                                                    read_symbol,
+                                                    string_size,
+                                                    stream.value());
+
+  // Async copy results from device to host
+  top_of_stack_gpu.device_to_host_async(stream_view);
+
+  // Get CPU-side results for verification
+  std::string top_of_stack_cpu{};
+  top_of_stack_cpu.reserve(string_size);
+  to_top_of_stack(std::cbegin(input),
+                  std::cend(input),
+                  JSONToStackOp{},
+                  empty_stack_symbol,
+                  std::back_inserter(top_of_stack_cpu));
+
+  // Make sure results have been copied back to host
+  stream.synchronize();
+
+  // Verify results
+  ASSERT_EQ(string_size, top_of_stack_cpu.size());
+  ASSERT_EQ(top_of_stack_gpu.size(), top_of_stack_cpu.size());
+  CUDF_TEST_EXPECT_VECTOR_EQUAL(top_of_stack_gpu.host_ptr(), top_of_stack_cpu, string_size);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/json_chunked_reader.cpp b/cpp/tests/io/json_chunked_reader.cpp
new file mode 100644
index 0000000..e2d5959
--- /dev/null
+++ b/cpp/tests/io/json_chunked_reader.cpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <io/json/read_json.hpp>
+
+/**
+ * @brief Base test fixture for JSON reader tests
+ */
+struct JsonReaderTest : public cudf::test::BaseFixture {};
+
+// function to extract first delimiter in the string in each chunk,
+// collate together and form byte_range for each chunk,
+// parse separately.
+std::vector<cudf::io::table_with_metadata> skeleton_for_parellel_chunk_reader(
+  cudf::host_span<std::unique_ptr<cudf::io::datasource>> sources,
+  cudf::io::json_reader_options const& reader_opts,
+  int32_t chunk_size,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  using namespace cudf::io::json::detail;
+  using cudf::size_type;
+  // assuming single source.
+  size_t total_source_size = 0;
+  for (auto const& source : sources) {
+    total_source_size += source->size();
+  }
+  size_t num_chunks                = (total_source_size + chunk_size - 1) / chunk_size;
+  constexpr size_type no_min_value = -1;
+
+  // Get the first delimiter in each chunk.
+  std::vector<size_type> first_delimiter_index(num_chunks);
+  auto reader_opts_chunk = reader_opts;
+  for (size_t i = 0; i < num_chunks; i++) {
+    auto const chunk_start = i * chunk_size;
+    reader_opts_chunk.set_byte_range_offset(chunk_start);
+    reader_opts_chunk.set_byte_range_size(chunk_size);
+    first_delimiter_index[i] =
+      find_first_delimiter_in_chunk(sources, reader_opts_chunk, '\n', stream);
+    if (first_delimiter_index[i] != no_min_value) { first_delimiter_index[i] += chunk_start; }
+  }
+
+  // Process and allocate record start, end for each worker.
+  using record_range = std::pair<size_type, size_type>;
+  std::vector<record_range> record_ranges;
+  record_ranges.reserve(num_chunks);
+  first_delimiter_index[0] = 0;
+  auto prev                = first_delimiter_index[0];
+  for (size_t i = 1; i < num_chunks; i++) {
+    if (first_delimiter_index[i] == no_min_value) continue;
+    record_ranges.push_back({prev, first_delimiter_index[i]});
+    prev = first_delimiter_index[i];
+  }
+  record_ranges.push_back({prev, total_source_size});
+
+  std::vector<cudf::io::table_with_metadata> tables;
+  // Process each chunk in parallel.
+  for (auto const& [chunk_start, chunk_end] : record_ranges) {
+    if (chunk_start == -1 or chunk_end == -1) continue;
+    reader_opts_chunk.set_byte_range_offset(chunk_start);
+    reader_opts_chunk.set_byte_range_size(chunk_end - chunk_start);
+    tables.push_back(read_json(sources, reader_opts_chunk, stream, mr));
+  }
+  // assume all records have same number of columns, and inferred same type. (or schema is passed)
+  // TODO a step before to merge all columns, types and infer final schema.
+  return tables;
+}
+
+TEST_F(JsonReaderTest, ByteRange)
+{
+  std::string const json_string = R"(
+    { "a": { "y" : 6}, "b" : [1, 2, 3], "c": 11 }
+    { "a": { "y" : 6}, "b" : [4, 5   ], "c": 12 }
+    { "a": { "y" : 6}, "b" : [6      ], "c": 13 }
+    { "a": { "y" : 6}, "b" : [7      ], "c": 14 })";
+
+  // Initialize parsing options (reading json lines)
+  cudf::io::json_reader_options json_lines_options =
+    cudf::io::json_reader_options::builder(
+      cudf::io::source_info{json_string.c_str(), json_string.size()})
+      .compression(cudf::io::compression_type::NONE)
+      .lines(true);
+
+  // Read full test data via existing, nested JSON lines reader
+  cudf::io::table_with_metadata current_reader_table = cudf::io::read_json(json_lines_options);
+
+  auto datasources = cudf::io::datasource::create(json_lines_options.get_source().host_buffers());
+
+  // Test for different chunk sizes
+  for (auto chunk_size : {7, 10, 15, 20, 40, 50, 100, 200, 500}) {
+    auto const tables = skeleton_for_parellel_chunk_reader(datasources,
+                                                           json_lines_options,
+                                                           chunk_size,
+                                                           cudf::get_default_stream(),
+                                                           rmm::mr::get_current_device_resource());
+
+    auto table_views = std::vector<cudf::table_view>(tables.size());
+    std::transform(tables.begin(), tables.end(), table_views.begin(), [](auto& table) {
+      return table.tbl->view();
+    });
+    auto result = cudf::concatenate(table_views);
+
+    // Verify that the data read via chunked reader matches the data read via nested JSON reader
+    // cannot use EQUAL due to concatenate removing null mask
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(current_reader_table.tbl->view(), result->view());
+  }
+}
diff --git a/cpp/tests/io/json_test.cpp b/cpp/tests/io/json_test.cpp
new file mode 100644
index 0000000..7c911ac
--- /dev/null
+++ b/cpp/tests/io/json_test.cpp
@@ -0,0 +1,1994 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/io/arrow_io_source.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/strings/convert/convert_fixed_point.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <limits>
+#include <thrust/iterator/constant_iterator.h>
+
+#include <arrow/io/api.h>
+
+#include <fstream>
+#include <type_traits>
+
+#define wrapper cudf::test::fixed_width_column_wrapper
+using float_wrapper        = wrapper<float>;
+using float64_wrapper      = wrapper<double>;
+using int_wrapper          = wrapper<int>;
+using int8_wrapper         = wrapper<int8_t>;
+using int16_wrapper        = wrapper<int16_t>;
+using int64_wrapper        = wrapper<int64_t>;
+using timestamp_ms_wrapper = wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep>;
+using bool_wrapper         = wrapper<bool>;
+
+using cudf::data_type;
+using cudf::type_id;
+using cudf::type_to_id;
+
+template <typename T>
+auto dtype()
+{
+  return data_type{type_to_id<T>()};
+}
+
+template <typename T, typename SourceElementT = T>
+using column_wrapper =
+  typename std::conditional<std::is_same_v<T, cudf::string_view>,
+                            cudf::test::strings_column_wrapper,
+                            cudf::test::fixed_width_column_wrapper<T, SourceElementT>>::type;
+
+cudf::test::TempDirTestEnvironment* const temp_env =
+  static_cast<cudf::test::TempDirTestEnvironment*>(
+    ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+template <typename T>
+std::vector<std::string> prepend_zeros(std::vector<T> const& input,
+                                       int zero_count         = 0,
+                                       bool add_positive_sign = false)
+{
+  std::vector<std::string> output(input.size());
+  std::transform(input.begin(), input.end(), output.begin(), [=](T const& num) {
+    auto str         = std::to_string(num);
+    bool is_negative = (str[0] == '-');
+    if (is_negative) {
+      str.insert(1, zero_count, '0');
+      return str;
+    } else if (add_positive_sign) {
+      return "+" + std::string(zero_count, '0') + str;
+    } else {
+      str.insert(0, zero_count, '0');
+      return str;
+    }
+  });
+  return output;
+}
+
+template <>
+std::vector<std::string> prepend_zeros<std::string>(std::vector<std::string> const& input,
+                                                    int zero_count,
+                                                    bool add_positive_sign)
+{
+  std::vector<std::string> output(input.size());
+  std::transform(input.begin(), input.end(), output.begin(), [=](std::string const& num) {
+    auto str         = num;
+    bool is_negative = (str[0] == '-');
+    if (is_negative) {
+      str.insert(1, zero_count, '0');
+      return str;
+    } else if (add_positive_sign) {
+      return "+" + std::string(zero_count, '0') + str;
+    } else {
+      str.insert(0, zero_count, '0');
+      return str;
+    }
+  });
+  return output;
+}
+
+// Generates a vector of uniform random values of type T
+template <typename T>
+inline auto random_values(size_t size)
+{
+  std::vector<T> values(size);
+
+  using T1 = T;
+  using uniform_distribution =
+    typename std::conditional_t<std::is_same_v<T1, bool>,
+                                std::bernoulli_distribution,
+                                std::conditional_t<std::is_floating_point_v<T1>,
+                                                   std::uniform_real_distribution<T1>,
+                                                   std::uniform_int_distribution<T1>>>;
+
+  static constexpr auto seed = 0xf00d;
+  static std::mt19937 engine{seed};
+  static uniform_distribution dist{};
+  std::generate_n(values.begin(), size, [&]() { return T{dist(engine)}; });
+
+  return values;
+}
+
+MATCHER_P(FloatNearPointwise, tolerance, "Out-of-range")
+{
+  return (std::get<0>(arg) > std::get<1>(arg) - tolerance &&
+          std::get<0>(arg) < std::get<1>(arg) + tolerance);
+}
+
+// temporary method to verify the float columns until
+// CUDF_TEST_EXPECT_COLUMNS_EQUAL supports floating point
+template <typename T, typename valid_t>
+void check_float_column(cudf::column_view const& col,
+                        std::vector<T> const& data,
+                        valid_t const& validity)
+{
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(col, (wrapper<T>{data.begin(), data.end(), validity}));
+  EXPECT_EQ(col.null_count(), 0);
+  EXPECT_THAT(cudf::test::to_host<T>(col).first,
+              ::testing::Pointwise(FloatNearPointwise(1e-6), data));
+}
+
+/**
+ * @brief Base test fixture for JSON reader tests
+ */
+struct JsonReaderTest : public cudf::test::BaseFixture {};
+
+/**
+ * @brief Enum class to be used to specify the test case of parametrized tests
+ */
+enum class json_test_t {
+  // Run test with the existing JSON lines reader using row-orient input data
+  legacy_lines_row_orient,
+  // Run test with the existing JSON lines reader using record-orient input data
+  legacy_lines_record_orient,
+  // Run test with the nested JSON lines reader using record-orient input data
+  json_experimental_record_orient,
+  // Run test with the nested JSON lines reader using row-orient input data
+  json_experimental_row_orient
+};
+
+constexpr bool is_legacy_test(json_test_t test_opt)
+{
+  return test_opt == json_test_t::legacy_lines_row_orient or
+         test_opt == json_test_t::legacy_lines_record_orient;
+}
+
+constexpr bool is_row_orient_test(json_test_t test_opt)
+{
+  return test_opt == json_test_t::legacy_lines_row_orient or
+         test_opt == json_test_t::json_experimental_row_orient;
+}
+
+/**
+ * @brief Test fixture for parametrized JSON reader tests
+ */
+struct JsonReaderParamTest : public cudf::test::BaseFixture,
+                             public testing::WithParamInterface<json_test_t> {};
+
+/**
+ * @brief Test fixture for parametrized JSON reader tests, testing record orient-only for legacy
+ * JSON lines reader and the nested reader
+ */
+struct JsonReaderDualTest : public cudf::test::BaseFixture,
+                            public testing::WithParamInterface<json_test_t> {};
+
+/**
+ * @brief Test fixture for parametrized JSON reader tests that only tests the new nested JSON reader
+ */
+struct JsonReaderNoLegacy : public cudf::test::BaseFixture,
+                            public testing::WithParamInterface<json_test_t> {};
+
+/**
+ * @brief Generates a JSON lines string that uses the record orient
+ *
+ * @param records An array of a map of key-value pairs
+ * @param record_delimiter The delimiter to be used to delimit a record
+ * @param prefix The prefix prepended to the whole string
+ * @param suffix The suffix to be appended after the whole string
+ * @return The JSON lines string that uses the record orient
+ */
+std::string to_records_orient(std::vector<std::map<std::string, std::string>> const& records,
+                              std::string record_delimiter,
+                              std::string prefix = "",
+                              std::string suffix = "")
+{
+  std::string result = prefix;
+  for (auto record_it = std::cbegin(records); record_it != std::cend(records); record_it++) {
+    result += "{";
+    for (auto kv_pair_it = std::cbegin(*record_it); kv_pair_it != std::cend(*record_it);
+         kv_pair_it++) {
+      auto const& [key, value] = *kv_pair_it;
+      result += "\"" + key + "\":" + value;
+      result += (kv_pair_it != std::prev(std::end(*record_it))) ? ", " : "";
+    }
+    result += "}";
+    if (record_it != std::prev(std::end(records))) { result += record_delimiter; }
+  }
+  return (result + suffix);
+}
+
+template <typename DecimalType>
+struct JsonFixedPointReaderTest : public JsonReaderTest {};
+
+template <typename DecimalType>
+struct JsonValidFixedPointReaderTest : public JsonFixedPointReaderTest<DecimalType> {
+  void run_test(std::vector<std::string> const& reference_strings,
+                numeric::scale_type scale,
+                bool use_legacy_parser)
+  {
+    cudf::test::strings_column_wrapper const strings(reference_strings.begin(),
+                                                     reference_strings.end());
+    auto const expected = cudf::strings::to_fixed_point(
+      cudf::strings_column_view(strings), data_type{type_to_id<DecimalType>(), scale});
+
+    auto const buffer =
+      std::accumulate(reference_strings.begin(),
+                      reference_strings.end(),
+                      std::string{},
+                      [](std::string const& acc, std::string const& rhs) {
+                        return acc + (acc.empty() ? "" : "\n") + "{\"col0\":" + rhs + "}";
+                      });
+    cudf::io::json_reader_options const in_opts =
+      cudf::io::json_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+        .dtypes({data_type{type_to_id<DecimalType>(), scale}})
+        .lines(true)
+        .legacy(use_legacy_parser);
+
+    auto const result      = cudf::io::read_json(in_opts);
+    auto const result_view = result.tbl->view();
+
+    ASSERT_EQ(result_view.num_columns(), 1);
+    EXPECT_EQ(result.metadata.schema_info[0].name, "col0");
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, result_view.column(0));
+  }
+
+  void run_tests(std::vector<std::string> const& reference_strings, numeric::scale_type scale)
+  {
+    // Test both parsers
+    run_test(reference_strings, scale, false);
+    run_test(reference_strings, scale, true);
+  }
+};
+
+TYPED_TEST_SUITE(JsonFixedPointReaderTest, cudf::test::FixedPointTypes);
+TYPED_TEST_SUITE(JsonValidFixedPointReaderTest, cudf::test::FixedPointTypes);
+
+// Parametrize qualifying JSON tests for executing both nested reader and legacy JSON lines reader
+INSTANTIATE_TEST_CASE_P(JsonReaderParamTest,
+                        JsonReaderParamTest,
+                        ::testing::Values(json_test_t::legacy_lines_row_orient,
+                                          json_test_t::legacy_lines_record_orient,
+                                          json_test_t::json_experimental_record_orient,
+                                          json_test_t::json_experimental_row_orient));
+
+// Parametrize qualifying JSON tests for executing both nested reader and legacy JSON lines reader
+INSTANTIATE_TEST_CASE_P(JsonReaderDualTest,
+                        JsonReaderDualTest,
+                        ::testing::Values(json_test_t::legacy_lines_record_orient,
+                                          json_test_t::json_experimental_record_orient));
+
+// Parametrize qualifying JSON tests for executing nested reader only
+INSTANTIATE_TEST_CASE_P(JsonReaderNoLegacy,
+                        JsonReaderNoLegacy,
+                        ::testing::Values(json_test_t::json_experimental_row_orient,
+                                          json_test_t::json_experimental_record_orient));
+
+TEST_P(JsonReaderParamTest, BasicJsonLines)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[1, 1.1]\n[2, 2.2]\n[3, 3.3]\n";
+  std::string record_orient = to_records_orient(
+    {{{"0", "1"}, {"1", "1.1"}}, {{"0", "2"}, {"1", "2.2"}}, {{"0", "3"}, {"1", "3.3"}}}, "\n");
+  std::string data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .dtypes(std::vector<data_type>{dtype<int32_t>(), dtype<double>()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 3);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT32);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int_wrapper{{1, 2, 3}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 float64_wrapper{{1.1, 2.2, 3.3}, validity});
+}
+
+TEST_P(JsonReaderParamTest, FloatingPoint)
+{
+  auto const test_opt = GetParam();
+  std::string row_orient =
+    "[5.6]\n[0.5679e2]\n[1.2e10]\n[0.07e1]\n[3000e-3]\n[12.34e0]\n[3.1e-001]\n[-73."
+    "98007199999998]\n";
+  std::string record_orient = to_records_orient({{{"0", "5.6"}},
+                                                 {{"0", "0.5679e2"}},
+                                                 {{"0", "1.2e10"}},
+                                                 {{"0", "0.07e1"}},
+                                                 {{"0", "3000e-3"}},
+                                                 {{"0", "12.34e0"}},
+                                                 {{"0", "3.1e-001"}},
+                                                 {{"0", "-73.98007199999998"}}},
+                                                "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  auto filepath = temp_env->get_temp_dir() + "FloatingPoint.json";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << data;
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({dtype<float>()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::FLOAT32);
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    result.tbl->get_column(0),
+    float_wrapper{{5.6, 56.79, 12000000000., 0.7, 3.000, 12.34, 0.31, -73.98007199999998},
+                  validity});
+
+  auto const bitmask = cudf::test::bitmask_to_host(result.tbl->get_column(0));
+  ASSERT_EQ((1u << result.tbl->get_column(0).size()) - 1, bitmask[0]);
+}
+
+TEST_P(JsonReaderParamTest, JsonLinesStrings)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[1, 1.1, \"aa \"]\n[2, 2.2, \"  bbb\"]";
+  std::string record_orient = to_records_orient({{{"0", "1"}, {"1", "1.1"}, {"2", R"("aa ")"}},
+                                                 {{"0", "2"}, {"1", "2.2"}, {"2", R"("  bbb")"}}},
+                                                "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .dtypes({{"2", dtype<cudf::string_view>()}, {"0", dtype<int32_t>()}, {"1", dtype<double>()}})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 3);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT32);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+  EXPECT_EQ(result.tbl->get_column(2).type().id(), cudf::type_id::STRING);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+  EXPECT_EQ(result.metadata.schema_info[2].name, "2");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int_wrapper{{1, 2}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1), float64_wrapper{{1.1, 2.2}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(2),
+                                 cudf::test::strings_column_wrapper({"aa ", "  bbb"}));
+}
+
+TEST_P(JsonReaderParamTest, MultiColumn)
+{
+  auto const test_opt   = GetParam();
+  bool const row_orient = is_row_orient_test(test_opt);
+
+  constexpr auto num_rows = 10;
+  auto int8_values        = random_values<int8_t>(num_rows);
+  auto int16_values       = random_values<int16_t>(num_rows);
+  auto int32_values       = random_values<int32_t>(num_rows);
+  auto int64_values       = random_values<int64_t>(num_rows);
+  auto float32_values     = random_values<float>(num_rows);
+  auto float64_values     = random_values<double>(num_rows);
+
+  auto filepath = temp_env->get_temp_dir() + "MultiColumn.json";
+  {
+    std::ostringstream line;
+    if (row_orient) {
+      for (int i = 0; i < num_rows; ++i) {
+        line << "[" << std::to_string(int8_values[i]) << "," << int16_values[i] << ","
+             << int32_values[i] << "," << int64_values[i] << "," << float32_values[i] << ","
+             << float64_values[i] << "]\n";
+      }
+    } else {
+      std::vector<std::map<std::string, std::string>> records;
+      for (int i = 0; i < num_rows; ++i) {
+        records.push_back({
+          {"0", std::to_string(int8_values[i])},     //
+          {"1", std::to_string(int16_values[i])},    //
+          {"2", std::to_string(int32_values[i])},    //
+          {"3", std::to_string(int64_values[i])},    //
+          {"4", std::to_string(float32_values[i])},  //
+          {"5", std::to_string(float64_values[i])},  //
+        });
+      }
+      line << to_records_orient(records, "\n");
+    }
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << line.str();
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({dtype<int8_t>(),
+               dtype<int16_t>(),
+               dtype<int32_t>(),
+               dtype<int64_t>(),
+               dtype<float>(),
+               dtype<double>()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  auto const view = result.tbl->view();
+
+  EXPECT_EQ(view.num_columns(), 6);
+  EXPECT_EQ(view.column(0).type().id(), cudf::type_id::INT8);
+  EXPECT_EQ(view.column(1).type().id(), cudf::type_id::INT16);
+  EXPECT_EQ(view.column(2).type().id(), cudf::type_id::INT32);
+  EXPECT_EQ(view.column(3).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(view.column(4).type().id(), cudf::type_id::FLOAT32);
+  EXPECT_EQ(view.column(5).type().id(), cudf::type_id::FLOAT64);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.column(0),
+                                 int8_wrapper{int8_values.begin(), int8_values.end(), validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.column(1),
+                                 int16_wrapper{int16_values.begin(), int16_values.end(), validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.column(2),
+                                 int_wrapper{int32_values.begin(), int32_values.end(), validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view.column(3),
+                                 int64_wrapper{int64_values.begin(), int64_values.end(), validity});
+  check_float_column(view.column(4), float32_values, validity);
+  check_float_column(view.column(5), float64_values, validity);
+}
+
+TEST_P(JsonReaderParamTest, Booleans)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[true]\n[true]\n[false]\n[false]\n[true]";
+  std::string record_orient = to_records_orient(
+    {
+      {{"0", "true"}},
+      {{"0", "true"}},
+      {{"0", "false"}},
+      {{"0", "false"}},
+      {{"0", "true"}},
+    },
+    "\n");
+  std::string data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  auto filepath = temp_env->get_temp_dir() + "Booleans.json";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << data;
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({dtype<bool>()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  // Booleans are the same (integer) data type, but valued at 0 or 1
+  auto const view = result.tbl->view();
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::BOOL8);
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 bool_wrapper{{true, true, false, false, true}, validity});
+}
+
+TEST_P(JsonReaderParamTest, Dates)
+{
+  auto const test_opt = GetParam();
+  std::string row_orient =
+    "[05/03/2001]\n[31/10/2010]\n[20/10/1994]\n[18/10/1990]\n[1/1/1970]\n"
+    "[18/04/1995]\n[14/07/1994]\n[\"07/06/2006 11:20:30.400\"]\n"
+    "[\"16/09/2005T1:2:30.400PM\"]\n[2/2/1970]\n[null]";
+  std::string record_orient = to_records_orient({{{"0", R"("05/03/2001")"}},
+                                                 {{"0", R"("31/10/2010")"}},
+                                                 {{"0", R"("20/10/1994")"}},
+                                                 {{"0", R"("18/10/1990")"}},
+                                                 {{"0", R"("1/1/1970")"}},
+                                                 {{"0", R"("18/04/1995")"}},
+                                                 {{"0", R"("14/07/1994")"}},
+                                                 {{"0", R"("07/06/2006 11:20:30.400")"}},
+                                                 {{"0", R"("16/09/2005T1:2:30.400PM")"}},
+                                                 {{"0", R"("2/2/1970")"}},
+                                                 {{"0", R"(null)"}}},
+                                                "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  auto filepath = temp_env->get_temp_dir() + "Dates.json";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << data;
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({data_type{type_id::TIMESTAMP_MILLISECONDS}})
+      .lines(true)
+      .dayfirst(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::TIMESTAMP_MILLISECONDS);
+
+  auto validity = cudf::test::iterators::nulls_at({10});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 timestamp_ms_wrapper{{983750400000,
+                                                       1288483200000,
+                                                       782611200000,
+                                                       656208000000,
+                                                       0L,
+                                                       798163200000,
+                                                       774144000000,
+                                                       1149679230400,
+                                                       1126875750400,
+                                                       2764800000,
+                                                       0L},
+                                                      validity});
+}
+
+TEST_P(JsonReaderParamTest, Durations)
+{
+  auto const test_opt = GetParam();
+  std::string row_orient =
+    "[-2]\n[-1]\n[0]\n"
+    "[\"1 days\"]\n[\"0 days 23:01:00\"]\n[\"0 days 00:00:00.000000123\"]\n"
+    "[\"0:0:0.000123\"]\n[\"0:0:0.000123000\"]\n[\"00:00:00.100000001\"]\n"
+    "[-2147483648]\n[2147483647]\n[null]";
+  std::string record_orient = to_records_orient({{{"0", "-2"}},
+                                                 {{"0", "-1"}},
+                                                 {{"0", "0"}},
+                                                 {{"0", R"("1 days")"}},
+                                                 {{"0", R"("0 days 23:01:00")"}},
+                                                 {{"0", R"("0 days 00:00:00.000000123")"}},
+                                                 {{"0", R"("0:0:0.000123")"}},
+                                                 {{"0", R"("0:0:0.000123000")"}},
+                                                 {{"0", R"("00:00:00.100000001")"}},
+                                                 {{"0", R"(-2147483648)"}},
+                                                 {{"0", R"(2147483647)"}},
+                                                 {{"0", R"(null)"}}},
+                                                "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+  auto filepath             = temp_env->get_temp_dir() + "Durations.json";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << data;
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({data_type{type_id::DURATION_NANOSECONDS}})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::DURATION_NANOSECONDS);
+
+  auto validity = cudf::test::iterators::nulls_at({11});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    result.tbl->get_column(0),
+    wrapper<cudf::duration_ns, cudf::duration_ns::rep>{{-2L,
+                                                        -1L,
+                                                        0L,
+                                                        1L * 60 * 60 * 24 * 1000000000L,
+                                                        (23 * 60 + 1) * 60 * 1000000000L,
+                                                        123L,
+                                                        123000L,
+                                                        123000L,
+                                                        100000001L,
+                                                        -2147483648L,
+                                                        2147483647L,
+                                                        0L},
+                                                       validity});
+}
+
+TEST_P(JsonReaderParamTest, JsonLinesDtypeInference)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[100, 1.1, \"aa \"]\n[200, 2.2, \"  bbb\"]";
+  std::string record_orient = to_records_orient({{{"0", "100"}, {"1", "1.1"}, {"2", R"("aa ")"}},
+                                                 {{"0", "200"}, {"1", "2.2"}, {"2", R"("  bbb")"}}},
+                                                "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 3);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+  EXPECT_EQ(result.tbl->get_column(2).type().id(), cudf::type_id::STRING);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+  EXPECT_EQ(result.metadata.schema_info[2].name, "2");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int64_wrapper{{100, 200}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1), float64_wrapper{{1.1, 2.2}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(2),
+                                 cudf::test::strings_column_wrapper({"aa ", "  bbb"}));
+}
+
+TEST_P(JsonReaderParamTest, JsonLinesFileInput)
+{
+  auto const test_opt    = GetParam();
+  std::string row_orient = "[11, 1.1]\n[22, 2.2]";
+  std::string record_orient =
+    to_records_orient({{{"0", "11"}, {"1", "1.1"}}, {{"0", "22"}, {"1", "2.2"}}}, "\n");
+  std::string data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  const std::string fname = temp_env->get_temp_dir() + "JsonLinesFileTest.json";
+  std::ofstream outfile(fname, std::ofstream::out);
+  outfile << data;
+  outfile.close();
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{fname})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int64_wrapper{{11, 22}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1), float64_wrapper{{1.1, 2.2}, validity});
+}
+
+TEST_F(JsonReaderTest, JsonLinesByteRange)
+{
+  const std::string fname = temp_env->get_temp_dir() + "JsonLinesByteRangeTest.json";
+  std::ofstream outfile(fname, std::ofstream::out);
+  outfile << "[1000]\n[2000]\n[3000]\n[4000]\n[5000]\n[6000]\n[7000]\n[8000]\n[9000]\n";
+  outfile.close();
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{fname})
+      .lines(true)
+      .legacy(true)  // Support in new reader coming in https://github.com/rapidsai/cudf/pull/12498
+      .byte_range_offset(11)
+      .byte_range_size(20);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->num_rows(), 3);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 int64_wrapper{{3000, 4000, 5000}, validity});
+}
+
+TEST_P(JsonReaderDualTest, JsonLinesObjects)
+{
+  auto const test_opt     = GetParam();
+  const std::string fname = temp_env->get_temp_dir() + "JsonLinesObjectsTest.json";
+  std::ofstream outfile(fname, std::ofstream::out);
+  outfile << " {\"co\\\"l1\" : 1, \"col2\" : 2.0} \n";
+  outfile.close();
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{fname})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 1);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.metadata.schema_info[0].name, is_legacy_test(test_opt) ? "co\\\"l1" : "co\"l1");
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+  EXPECT_EQ(result.metadata.schema_info[1].name, "col2");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int64_wrapper{{1}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1), float64_wrapper{{2.0}, validity});
+}
+
+TEST_P(JsonReaderDualTest, JsonLinesObjectsStrings)
+{
+  auto const test_opt    = GetParam();
+  auto test_json_objects = [test_opt](std::string const& data) {
+    cudf::io::json_reader_options in_options =
+      cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+        .lines(true)
+        .legacy(is_legacy_test(test_opt));
+
+    cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+    EXPECT_EQ(result.tbl->num_columns(), 3);
+    EXPECT_EQ(result.tbl->num_rows(), 2);
+
+    EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+    EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+    EXPECT_EQ(result.tbl->get_column(2).type().id(), cudf::type_id::STRING);
+
+    EXPECT_EQ(result.metadata.schema_info[0].name, "col1");
+    EXPECT_EQ(result.metadata.schema_info[1].name, "col2");
+    EXPECT_EQ(result.metadata.schema_info[2].name, "col3");
+
+    auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int64_wrapper{{100, 200}, validity});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                   float64_wrapper{{1.1, 2.2}, validity});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(2),
+                                   cudf::test::strings_column_wrapper({"aaa", "bbb"}));
+  };
+  // simple case
+  test_json_objects(
+    "{\"col1\":100, \"col2\":1.1, \"col3\":\"aaa\"}\n"
+    "{\"col1\":200, \"col2\":2.2, \"col3\":\"bbb\"}\n");
+  // out of order fields
+  test_json_objects(
+    "{\"col1\":100, \"col2\":1.1, \"col3\":\"aaa\"}\n"
+    "{\"col3\":\"bbb\", \"col1\":200, \"col2\":2.2}\n");
+}
+
+TEST_P(JsonReaderDualTest, JsonLinesObjectsMissingData)
+{
+  auto const test_opt = GetParam();
+  // Note: columns will be ordered based on which fields appear first
+  std::string const data =
+    "{              \"col2\":1.1, \"col3\":\"aaa\"}\n"
+    "{\"col1\":200,               \"col3\":\"bbb\"}\n";
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 3);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::FLOAT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::STRING);
+  EXPECT_EQ(result.tbl->get_column(2).type().id(), cudf::type_id::INT64);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "col2");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "col3");
+  EXPECT_EQ(result.metadata.schema_info[2].name, "col1");
+
+  auto col1_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0; });
+  auto col2_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i == 0; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(2), int64_wrapper{{0, 200}, col1_validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 float64_wrapper{{1.1, 0.}, col2_validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 cudf::test::strings_column_wrapper({"aaa", "bbb"}));
+}
+
+TEST_P(JsonReaderDualTest, JsonLinesObjectsOutOfOrder)
+{
+  auto const test_opt = GetParam();
+  std::string const data =
+    "{\"col1\":100, \"col2\":1.1, \"col3\":\"aaa\"}\n"
+    "{\"col3\":\"bbb\", \"col1\":200, \"col2\":2.2}\n";
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 3);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "col1");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "col2");
+  EXPECT_EQ(result.metadata.schema_info[2].name, "col3");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int64_wrapper{{100, 200}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1), float64_wrapper{{1.1, 2.2}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(2),
+                                 cudf::test::strings_column_wrapper({"aaa", "bbb"}));
+}
+
+TEST_F(JsonReaderTest, EmptyFile)
+{
+  auto filepath = temp_env->get_temp_dir() + "EmptyFile.json";
+  {
+    std::ofstream outfile{filepath, std::ofstream::out};
+    outfile << "";
+  }
+
+  // New reader only - legacy reader is strict about having non-empty input
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath}).lines(true);
+  auto result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(0, view.num_columns());
+}
+
+TEST_F(JsonReaderTest, NoDataFile)
+{
+  auto filepath = temp_env->get_temp_dir() + "NoDataFile.json";
+  {
+    std::ofstream outfile{filepath, std::ofstream::out};
+    outfile << "{}\n";
+  }
+
+  // New reader only - legacy reader is strict about having non-empty input
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath}).lines(true);
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(0, view.num_columns());
+}
+
+// empty input in values orient
+TEST_F(JsonReaderTest, NoDataFileValues)
+{
+  auto filepath = temp_env->get_temp_dir() + "NoDataFileValues.csv";
+  {
+    std::ofstream outfile{filepath, std::ofstream::out};
+    outfile << "[]\n";
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath}).lines(true);
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+  EXPECT_EQ(0, view.num_columns());
+}
+
+TEST_F(JsonReaderTest, ArrowFileSource)
+{
+  const std::string fname = temp_env->get_temp_dir() + "ArrowFileSource.csv";
+
+  std::ofstream outfile(fname, std::ofstream::out);
+  outfile << "[9]\n[8]\n[7]\n[6]\n[5]\n[4]\n[3]\n[2]\n";
+  outfile.close();
+
+  std::shared_ptr<arrow::io::ReadableFile> infile;
+  ASSERT_TRUE(arrow::io::ReadableFile::Open(fname).Value(&infile).ok());
+
+  auto arrow_source = cudf::io::arrow_io_source{infile};
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{&arrow_source})
+      .dtypes({dtype<int8_t>()})
+      .lines(true)
+      .legacy(true);  // Support in new reader coming in https://github.com/rapidsai/cudf/pull/12498
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT8);
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 int8_wrapper{{9, 8, 7, 6, 5, 4, 3, 2}, validity});
+}
+
+TEST_P(JsonReaderParamTest, InvalidFloatingPoint)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[1.2e1+]\n[3.4e2-]\n[5.6e3e]\n[7.8e3A]\n[9.0Be1]\n[1C.2]";
+  std::string record_orient = to_records_orient({{{"0", "1.2e1+"}},
+                                                 {{"0", "3.4e2-"}},
+                                                 {{"0", "5.6e3e"}},
+                                                 {{"0", "7.8e3A"}},
+                                                 {{"0", "9.0Be1"}},
+                                                 {{"0", "1C.2"}}},
+                                                "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  auto const filepath = temp_env->get_temp_dir() + "InvalidFloatingPoint.json";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << data;
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .dtypes({dtype<float>()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::FLOAT32);
+
+  // ignore all data because it is all nulls.
+  ASSERT_EQ(6u, result.tbl->view().column(0).null_count());
+}
+
+TEST_P(JsonReaderParamTest, StringInference)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[\"-1\"]";
+  std::string record_orient = to_records_orient({{{"0", R"("-1")"}}}, "\n");
+  std::string data          = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.c_str(), data.size()})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 1);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::STRING);
+}
+
+TEST_P(JsonReaderParamTest, ParseInRangeIntegers)
+{
+  auto const test_opt   = GetParam();
+  bool const row_orient = is_row_orient_test(test_opt);
+
+  constexpr auto num_rows                      = 4;
+  std::vector<int64_t> small_int               = {0, -10, 20, -30};
+  std::vector<int64_t> less_equal_int64_max    = {std::numeric_limits<int64_t>::max() - 3,
+                                                  std::numeric_limits<int64_t>::max() - 2,
+                                                  std::numeric_limits<int64_t>::max() - 1,
+                                                  std::numeric_limits<int64_t>::max()};
+  std::vector<int64_t> greater_equal_int64_min = {std::numeric_limits<int64_t>::min() + 3,
+                                                  std::numeric_limits<int64_t>::min() + 2,
+                                                  std::numeric_limits<int64_t>::min() + 1,
+                                                  std::numeric_limits<int64_t>::min()};
+  std::vector<uint64_t> greater_int64_max      = {uint64_t{std::numeric_limits<int64_t>::max()} - 1,
+                                                  uint64_t{std::numeric_limits<int64_t>::max()},
+                                                  uint64_t{std::numeric_limits<int64_t>::max()} + 1,
+                                                  uint64_t{std::numeric_limits<int64_t>::max()} + 2};
+  std::vector<uint64_t> less_equal_uint64_max  = {std::numeric_limits<uint64_t>::max() - 3,
+                                                  std::numeric_limits<uint64_t>::max() - 2,
+                                                  std::numeric_limits<uint64_t>::max() - 1,
+                                                  std::numeric_limits<uint64_t>::max()};
+  auto input_small_int = column_wrapper<int64_t>(small_int.begin(), small_int.end());
+  auto input_less_equal_int64_max =
+    column_wrapper<int64_t>(less_equal_int64_max.begin(), less_equal_int64_max.end());
+  auto input_greater_equal_int64_min =
+    column_wrapper<int64_t>(greater_equal_int64_min.begin(), greater_equal_int64_min.end());
+  auto input_greater_int64_max =
+    column_wrapper<uint64_t>(greater_int64_max.begin(), greater_int64_max.end());
+  auto input_less_equal_uint64_max =
+    column_wrapper<uint64_t>(less_equal_uint64_max.begin(), less_equal_uint64_max.end());
+
+  auto small_int_append_zeros               = prepend_zeros(small_int, 32, true);
+  auto less_equal_int64_max_append_zeros    = prepend_zeros(less_equal_int64_max, 32, true);
+  auto greater_equal_int64_min_append_zeros = prepend_zeros(greater_equal_int64_min, 17);
+  auto greater_int64_max_append_zeros       = prepend_zeros(greater_int64_max, 5);
+  auto less_equal_uint64_max_append_zeros   = prepend_zeros(less_equal_uint64_max, 8, true);
+
+  auto filepath = temp_env->get_temp_dir() + "ParseInRangeIntegers.json";
+  {
+    std::ostringstream line;
+    if (row_orient) {
+      for (int i = 0; i < num_rows; ++i) {
+        line << "[" << small_int[i] << "," << less_equal_int64_max[i] << ","
+             << greater_equal_int64_min[i] << "," << greater_int64_max[i] << ","
+             << less_equal_uint64_max[i] << "," << small_int_append_zeros[i] << ","
+             << less_equal_int64_max_append_zeros[i] << ","
+             << greater_equal_int64_min_append_zeros[i] << "," << greater_int64_max_append_zeros[i]
+             << "," << less_equal_uint64_max_append_zeros[i] << "]\n";
+      }
+    } else {
+      std::vector<std::map<std::string, std::string>> records;
+      for (int i = 0; i < num_rows; ++i) {
+        records.push_back({
+          {"0", std::to_string(small_int[i])},                //
+          {"1", std::to_string(less_equal_int64_max[i])},     //
+          {"2", std::to_string(greater_equal_int64_min[i])},  //
+          {"3", std::to_string(greater_int64_max[i])},        //
+          {"4", std::to_string(less_equal_uint64_max[i])},    //
+          {"5", small_int_append_zeros[i]},                   //
+          {"6", less_equal_int64_max_append_zeros[i]},        //
+          {"7", greater_equal_int64_min_append_zeros[i]},     //
+          {"8", greater_int64_max_append_zeros[i]},           //
+          {"9", less_equal_uint64_max_append_zeros[i]},       //
+        });
+      }
+      line << to_records_orient(records, "\n");
+    }
+
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << line.str();
+  }
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_small_int, view.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_int64_max, view.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_equal_int64_min, view.column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_int64_max, view.column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_uint64_max, view.column(4));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_small_int, view.column(5));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_int64_max, view.column(6));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_equal_int64_min, view.column(7));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_int64_max, view.column(8));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_equal_uint64_max, view.column(9));
+}
+
+TEST_P(JsonReaderParamTest, ParseOutOfRangeIntegers)
+{
+  auto const test_opt   = GetParam();
+  bool const row_orient = is_row_orient_test(test_opt);
+
+  constexpr auto num_rows                        = 4;
+  std::vector<std::string> out_of_range_positive = {"111111111111111111111",
+                                                    "2222222222222222222222",
+                                                    "33333333333333333333333",
+                                                    "444444444444444444444444"};
+  std::vector<std::string> out_of_range_negative = {"-111111111111111111111",
+                                                    "-2222222222222222222222",
+                                                    "-33333333333333333333333",
+                                                    "-444444444444444444444444"};
+  std::vector<std::string> greater_uint64_max    = {
+    "18446744073709551615", "18446744073709551616", "18446744073709551617", "18446744073709551618"};
+  std::vector<std::string> less_int64_min = {
+    "-9223372036854775807", "-9223372036854775808", "-9223372036854775809", "-9223372036854775810"};
+  std::vector<std::string> mixed_range = {
+    "18446744073709551613", "18446744073709551614", "18446744073709551615", "-5"};
+  auto input_out_of_range_positive =
+    column_wrapper<cudf::string_view>(out_of_range_positive.begin(), out_of_range_positive.end());
+  auto input_out_of_range_negative =
+    column_wrapper<cudf::string_view>(out_of_range_negative.begin(), out_of_range_negative.end());
+  auto input_greater_uint64_max =
+    column_wrapper<cudf::string_view>(greater_uint64_max.begin(), greater_uint64_max.end());
+  auto input_less_int64_min =
+    column_wrapper<cudf::string_view>(less_int64_min.begin(), less_int64_min.end());
+  auto input_mixed_range =
+    column_wrapper<cudf::string_view>(mixed_range.begin(), mixed_range.end());
+
+  auto out_of_range_positive_append_zeros = prepend_zeros(out_of_range_positive, 32, true);
+  auto out_of_range_negative_append_zeros = prepend_zeros(out_of_range_negative, 5);
+  auto greater_uint64_max_append_zeros    = prepend_zeros(greater_uint64_max, 8, true);
+  auto less_int64_min_append_zeros        = prepend_zeros(less_int64_min, 17);
+  auto mixed_range_append_zeros           = prepend_zeros(mixed_range, 2, true);
+
+  auto input_out_of_range_positive_append = column_wrapper<cudf::string_view>(
+    out_of_range_positive_append_zeros.begin(), out_of_range_positive_append_zeros.end());
+  auto input_out_of_range_negative_append = column_wrapper<cudf::string_view>(
+    out_of_range_negative_append_zeros.begin(), out_of_range_negative_append_zeros.end());
+  auto input_greater_uint64_max_append = column_wrapper<cudf::string_view>(
+    greater_uint64_max_append_zeros.begin(), greater_uint64_max_append_zeros.end());
+  auto input_less_int64_min_append = column_wrapper<cudf::string_view>(
+    less_int64_min_append_zeros.begin(), less_int64_min_append_zeros.end());
+  auto input_mixed_range_append = column_wrapper<cudf::string_view>(
+    mixed_range_append_zeros.begin(), mixed_range_append_zeros.end());
+
+  auto filepath = temp_env->get_temp_dir() + "ParseOutOfRangeIntegers.json";
+  {
+    std::ostringstream line;
+    if (row_orient) {
+      for (int i = 0; i < num_rows; ++i) {
+        line << "[" << out_of_range_positive[i] << "," << out_of_range_negative[i] << ","
+             << greater_uint64_max[i] << "," << less_int64_min[i] << "," << mixed_range[i] << ","
+             << out_of_range_positive_append_zeros[i] << ","
+             << out_of_range_negative_append_zeros[i] << "," << greater_uint64_max_append_zeros[i]
+             << "," << less_int64_min_append_zeros[i] << "," << mixed_range_append_zeros[i]
+             << "]\n";
+      }
+    } else {
+      std::vector<std::map<std::string, std::string>> records;
+      for (int i = 0; i < num_rows; ++i) {
+        records.push_back({
+          {"0", out_of_range_positive[i]},               //
+          {"1", out_of_range_negative[i]},               //
+          {"2", greater_uint64_max[i]},                  //
+          {"3", less_int64_min[i]},                      //
+          {"4", mixed_range[i]},                         //
+          {"5", out_of_range_positive_append_zeros[i]},  //
+          {"6", out_of_range_negative_append_zeros[i]},  //
+          {"7", greater_uint64_max_append_zeros[i]},     //
+          {"8", less_int64_min_append_zeros[i]},         //
+          {"9", mixed_range_append_zeros[i]},            //
+        });
+      }
+      line << to_records_orient(records, "\n");
+    }
+
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << line.str();
+  }
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  auto const view = result.tbl->view();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_positive, view.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_negative, view.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_uint64_max, view.column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_int64_min, view.column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_mixed_range, view.column(4));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_positive_append, view.column(5));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_out_of_range_negative_append, view.column(6));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_greater_uint64_max_append, view.column(7));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_less_int64_min_append, view.column(8));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(input_mixed_range_append, view.column(9));
+}
+
+TEST_P(JsonReaderParamTest, JsonLinesMultipleFileInputs)
+{
+  auto const test_opt = GetParam();
+  std::vector<std::string> row_orient{"[11, 1.1]\n[22, 2.2]\n", "[33, 3.3]\n[44, 4.4]"};
+  std::vector<std::string> record_orient{
+    to_records_orient({{{"0", "11"}, {"1", "1.1"}}, {{"0", "22"}, {"1", "2.2"}}}, "\n") + "\n",
+    to_records_orient({{{"0", "33"}, {"1", "3.3"}}, {{"0", "44"}, {"1", "4.4"}}}, "\n") + "\n"};
+  auto const& data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  const std::string file1 = temp_env->get_temp_dir() + "JsonLinesFileTest1.json";
+  std::ofstream outfile(file1, std::ofstream::out);
+  outfile << data[0];
+  outfile.close();
+
+  const std::string file2 = temp_env->get_temp_dir() + "JsonLinesFileTest2.json";
+  std::ofstream outfile2(file2, std::ofstream::out);
+  outfile2 << data[1];
+  outfile2.close();
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{{file1, file2}})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 4);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 int64_wrapper{{11, 22, 33, 44}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 float64_wrapper{{1.1, 2.2, 3.3, 4.4}, validity});
+}
+
+TEST_P(JsonReaderNoLegacy, JsonLinesMultipleFileInputsNoNL)
+{
+  auto const test_opt = GetParam();
+  // Strings for the two separate input files in row-orient that do not end with a newline
+  std::vector<std::string> row_orient{"[11, 1.1]\n[22, 2.2]", "[33, 3.3]\n[44, 4.4]"};
+  // Strings for the two separate input files in record-orient that do not end with a newline
+  std::vector<std::string> record_orient{
+    to_records_orient({{{"0", "11"}, {"1", "1.1"}}, {{"0", "22"}, {"1", "2.2"}}}, "\n"),
+    to_records_orient({{{"0", "33"}, {"1", "3.3"}}, {{"0", "44"}, {"1", "4.4"}}}, "\n")};
+  auto const& data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  const std::string file1 = temp_env->get_temp_dir() + "JsonLinesFileTest1.json";
+  std::ofstream outfile(file1, std::ofstream::out);
+  outfile << data[0];
+  outfile.close();
+
+  const std::string file2 = temp_env->get_temp_dir() + "JsonLinesFileTest2.json";
+  std::ofstream outfile2(file2, std::ofstream::out);
+  outfile2 << data[1];
+  outfile2.close();
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{{file1, file2}})
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 4);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 int64_wrapper{{11, 22, 33, 44}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 float64_wrapper{{1.1, 2.2, 3.3, 4.4}, validity});
+}
+
+TEST_F(JsonReaderTest, BadDtypeParams)
+{
+  std::string buffer = "[1,2,3,4]";
+
+  cudf::io::json_reader_options options_vec =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .lines(true)
+      .dtypes({dtype<int8_t>()})
+      .legacy(true);
+
+  // should throw because there are four columns and only one dtype
+  EXPECT_THROW(cudf::io::read_json(options_vec), cudf::logic_error);
+
+  cudf::io::json_reader_options options_map =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .lines(true)
+      .legacy(true)
+      .dtypes(std::map<std::string, cudf::data_type>{{"0", dtype<int8_t>()},
+                                                     {"1", dtype<int8_t>()},
+                                                     {"2", dtype<int8_t>()},
+                                                     {"wrong_name", dtype<int8_t>()}});
+  // should throw because one of the columns is not in the dtype map
+  EXPECT_THROW(cudf::io::read_json(options_map), cudf::logic_error);
+}
+
+TEST_F(JsonReaderTest, JsonExperimentalBasic)
+{
+  std::string const fname = temp_env->get_temp_dir() + "JsonExperimentalBasic.json";
+  std::ofstream outfile(fname, std::ofstream::out);
+  outfile << R"([{"a":"11", "b":"1.1"},{"a":"22", "b":"2.2"}])";
+  outfile.close();
+
+  cudf::io::json_reader_options options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{fname});
+  auto result = cudf::io::read_json(options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::STRING);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::STRING);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "a");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "b");
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 cudf::test::strings_column_wrapper({"11", "22"}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 cudf::test::strings_column_wrapper({"1.1", "2.2"}));
+}
+
+TEST_F(JsonReaderTest, JsonExperimentalLines)
+{
+  std::string const json_string =
+    R"({"a":"a0"}
+    {"a":"a1"}
+    {"a":"a2", "b":"b2"}
+    {"a":"a3", "c":"c3"}
+    {"a":"a4"})";
+
+  // Initialize parsing options (reading json lines)
+  cudf::io::json_reader_options json_lines_options =
+    cudf::io::json_reader_options::builder(
+      cudf::io::source_info{json_string.c_str(), json_string.size()})
+      .lines(true);
+
+  // Read test data via nested JSON reader
+  auto const table = cudf::io::read_json(json_lines_options);
+
+  // Read test data via legacy, non-nested JSON lines reader
+  json_lines_options.enable_legacy(true);
+  auto const legacy_reader_table = cudf::io::read_json(json_lines_options);
+
+  // Verify that the data read via non-nested JSON lines reader matches the data read via nested
+  // JSON reader
+  CUDF_TEST_EXPECT_TABLES_EQUAL(legacy_reader_table.tbl->view(), table.tbl->view());
+}
+
+TEST_F(JsonReaderTest, JsonLongString)
+{
+  // Unicode
+  // 0000-FFFF     Basic Multilingual Plane
+  // 10000-10FFFF  Supplementary Plane
+  cudf::test::strings_column_wrapper col1{
+    {
+      "\"\\/\b\f\n\r\t",
+      "\"",
+      "\\",
+      "/",
+      "\b",
+      "\f\n",
+      "\r\t",
+      "$€",
+      "ராபிட்ஸ்",
+      "C𝞵𝓓𝒻",
+      "",  // null
+      "",  // null
+      "கார்த்தி",
+      "CႮ≪ㇳ䍏凹沦王辿龸ꁗ믜스폶ﴠ",  //  0000-FFFF
+      "𐀀𑿪𒐦𓃰𔙆 𖦆𗿿𘳕𚿾[↳] 𜽆𝓚𞤁🄰",                            // 10000-1FFFF
+      "𠘨𡥌𢗉𣇊𤊩𥅽𦉱𧴱𨁲𩁹𪐢𫇭𬬭𭺷𮊦屮",                // 20000-2FFFF
+      "𰾑𱔈𲍉",                                          // 30000-3FFFF
+      R"("$€ \u0024\u20ac \\u0024\\u20ac  \\\u0024\\\u20ac \\\\u0024\\\\u20ac)",
+      R"(        \\\\\\\\\\\\\\\\)",
+      R"(\\\\\\\\\\\\\\\\)",
+      R"(\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\)",
+      R"( \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\)",
+      R"(                      \\abcd)",
+      R"(                 \\\\\\\\\\\\\\\\                 \\\\\\\\\\\\\\\\)",
+      R"(                \\\\\\\\\\\\\\\\                 \\\\\\\\\\\\\\\\)",
+    },
+    cudf::test::iterators::nulls_at({10, 11})};
+
+  cudf::test::fixed_width_column_wrapper<int16_t> repeat_times{
+    {1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 19, 37, 81, 161, 323, 631, 1279, 10, 1, 2, 1, 100, 1000, 1, 3},
+    cudf::test::iterators::no_nulls()};
+  auto d_col2 = cudf::strings::repeat_strings(cudf::strings_column_view{col1}, repeat_times);
+  auto col2   = d_col2->view();
+  cudf::table_view const tbl_view{{col1, col2, repeat_times}};
+  cudf::io::table_metadata mt{{{"col1"}, {"col2"}, {"int16"}}};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(true)
+                           .metadata(mt)
+                           .lines(true)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+
+  cudf::table_view const expected = tbl_view;
+  std::map<std::string, data_type> types;
+  types["col1"]  = data_type{type_id::STRING};
+  types["col2"]  = data_type{type_id::STRING};
+  types["int16"] = data_type{type_id::INT16};
+
+  // Initialize parsing options (reading json lines)
+  cudf::io::json_reader_options json_lines_options =
+    cudf::io::json_reader_options::builder(
+      cudf::io::source_info{out_buffer.data(), out_buffer.size()})
+      .lines(true)
+      .dtypes(types);
+
+  // Read test data via nested JSON reader
+  auto const table = cudf::io::read_json(json_lines_options);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, table.tbl->view());
+}
+
+TEST_F(JsonReaderTest, ErrorStrings)
+{
+  // cases of invalid escape characters, invalid unicode encodings.
+  // Error strings will decode to nulls
+  auto const buffer = std::string{R"(
+    {"col0": "\"\a"}
+    {"col0": "\u"}
+    {"col0": "\u0"}
+    {"col0": "\u0b"}
+    {"col0": "\u00b"}
+    {"col0": "\u00bz"}
+    {"col0": "\t34567890123456\t9012345678901\ug0bc"}
+    {"col0": "\t34567890123456\t90123456789012\u0hbc"}
+    {"col0": "\t34567890123456\t90123456789012\u00ic"}
+    {"col0": "\u0b95\u0bbe\u0bb0\u0bcd\u0ba4\u0bcd\u0ba4\u0bbfகார்த்தி"}
+)"};
+  // Last one is not an error case, but shows that unicode in json is copied string column output.
+
+  cudf::io::json_reader_options const in_opts =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .dtypes({data_type{cudf::type_id::STRING}})
+      .lines(true)
+      .legacy(false);
+
+  auto const result      = cudf::io::read_json(in_opts);
+  auto const result_view = result.tbl->view().column(0);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "col0");
+  EXPECT_EQ(result_view.null_count(), 9);
+  cudf::test::strings_column_wrapper expected{
+    {"",
+     "",
+     "",
+     "",
+     "",
+     "",
+     "",
+     "",
+     "",
+     "கார்த்தி\xe0\xae\x95\xe0\xae\xbe\xe0\xae\xb0\xe0\xaf\x8d\xe0\xae\xa4\xe0\xaf\x8d\xe0\xae\xa4"
+     "\xe0\xae\xbf"},
+    // unicode hex 0xe0 0xae 0x95 0xe0 0xae 0xbe 0xe0 0xae 0xb0 0xe0 0xaf 0x8d
+    //             0xe0 0xae 0xa4 0xe0 0xaf 0x8d 0xe0 0xae 0xa4 0xe0 0xae 0xbf
+    cudf::test::iterators::nulls_at({0, 1, 2, 3, 4, 5, 6, 7, 8})};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result_view, expected);
+}
+
+TEST_F(JsonReaderTest, TokenAllocation)
+{
+  std::array<std::string const, 3> const json_inputs{
+    R"({"":1})",
+    "{}\n{}\n{}",
+    R"({"":{"":{"":{"":{"":{"":{"":{"":{"":{"":{"":{"":1}}}}}}}}}}}})",
+  };
+
+  for (auto const& json_string : json_inputs) {
+    // Initialize parsing options (reading json lines)
+    cudf::io::json_reader_options json_lines_options =
+      cudf::io::json_reader_options::builder(
+        cudf::io::source_info{json_string.c_str(), json_string.size()})
+        .lines(true);
+
+    EXPECT_NO_THROW(cudf::io::read_json(json_lines_options));
+  }
+}
+
+TEST_F(JsonReaderTest, ExperimentalLinesNoOmissions)
+{
+  std::array<std::string const, 4> const json_inputs
+    // single column
+    {R"({"a":"a0"}
+    {"a":"a1"}
+    {"a":"a2"}
+    {"a":"a3"}
+    {"a":"a4"})",
+     // single column, single row
+     R"({"a":"a0"})",
+     // single row
+     R"({"a":"a0", "b":"b0"})",
+     // two column, two rows
+     R"({"a":"a0", "b":"b0"}
+    {"a":"a1", "b":"b1"})"};
+
+  for (auto const& json_string : json_inputs) {
+    // Initialize parsing options (reading json lines)
+    cudf::io::json_reader_options json_lines_options =
+      cudf::io::json_reader_options::builder(
+        cudf::io::source_info{json_string.c_str(), json_string.size()})
+        .lines(true);
+
+    // Read test data via nested JSON reader
+    auto const table = cudf::io::read_json(json_lines_options);
+
+    // Read test data via legacy, non-nested JSON lines reader
+    json_lines_options.enable_legacy(true);
+    auto const legacy_reader_table = cudf::io::read_json(json_lines_options);
+
+    // Verify that the data read via non-nested JSON lines reader matches the data read via
+    // nested JSON reader
+    CUDF_TEST_EXPECT_TABLES_EQUAL(legacy_reader_table.tbl->view(), table.tbl->view());
+  }
+}
+
+TEST_F(JsonReaderTest, TestColumnOrder)
+{
+  std::string const json_string =
+    // Expected order:
+    // root: b, c, a, d
+    // a: 2, 0, 1
+    {R"({"b":"b0"}
+    {"c":"c1","a":{"2":null}}
+    {"d":"d2","a":{"0":"a2.0", "2":"a2.2"}}
+    {"b":"b3","a":{"1":null, "2":"a3.2"}})"};
+
+  std::vector<std::string> const root_col_names{"b", "c", "a", "d"};
+  std::vector<std::string> const a_child_col_names{"2", "0", "1"};
+
+  // Initialize parsing options (reading json lines)
+  cudf::io::json_reader_options json_lines_options =
+    cudf::io::json_reader_options::builder(
+      cudf::io::source_info{json_string.c_str(), json_string.size()})
+      .lines(true);
+
+  // Read in data using nested JSON reader
+  cudf::io::table_with_metadata new_reader_table = cudf::io::read_json(json_lines_options);
+
+  // Verify root column order (assert to avoid OOB access)
+  ASSERT_EQ(new_reader_table.metadata.schema_info.size(), root_col_names.size());
+
+  for (std::size_t i = 0; i < a_child_col_names.size(); i++) {
+    auto const& root_col_name = root_col_names[i];
+    EXPECT_EQ(new_reader_table.metadata.schema_info[i].name, root_col_name);
+  }
+
+  // Verify nested child column order (assert to avoid OOB access)
+  ASSERT_EQ(new_reader_table.metadata.schema_info[2].children.size(), a_child_col_names.size());
+  for (std::size_t i = 0; i < a_child_col_names.size(); i++) {
+    auto const& a_child_col_name = a_child_col_names[i];
+    EXPECT_EQ(new_reader_table.metadata.schema_info[2].children[i].name, a_child_col_name);
+  }
+
+  // Verify data of root columns
+  ASSERT_EQ(root_col_names.size(), new_reader_table.tbl->num_columns());
+  column_wrapper<cudf::string_view> root_col_data_b{{"b0", "", "", "b3"},
+                                                    {true, false, false, true}};
+  column_wrapper<cudf::string_view> root_col_data_c{{"", "c1", "", ""},
+                                                    {false, true, false, false}};
+  column_wrapper<cudf::string_view> root_col_data_d{{"", "", "d2", ""},
+                                                    {false, false, true, false}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(root_col_data_b, new_reader_table.tbl->get_column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(root_col_data_c, new_reader_table.tbl->get_column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(root_col_data_d, new_reader_table.tbl->get_column(3));
+
+  // Verify data of child columns of column 'a'
+  auto const col_a = new_reader_table.tbl->get_column(2);
+  ASSERT_EQ(a_child_col_names.size(), col_a.num_children());
+  column_wrapper<cudf::string_view> col_a2{{"", "", "a2.2", "a3.2"}, {false, false, true, true}};
+  column_wrapper<cudf::string_view> col_a0{{"", "", "a2.0", ""}, {false, false, true, false}};
+  // col a.1 is inferred as all-null
+  int8_wrapper col_a1{{0, 0, 0, 0}, {false, false, false, false}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_a2, col_a.child(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_a0, col_a.child(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_a1, col_a.child(2));
+}
+
+TEST_P(JsonReaderParamTest, JsonDtypeSchema)
+{
+  auto const test_opt       = GetParam();
+  std::string row_orient    = "[1, 1.1, \"aa \"]\n[2, 2.2, \"  bbb\"]";
+  std::string record_orient = to_records_orient({{{"0", "1"}, {"1", "1.1"}, {"2", R"("aa ")"}},
+                                                 {{"0", "2"}, {"1", "2.2"}, {"2", R"("  bbb")"}}},
+                                                "\n");
+
+  std::string data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  std::map<std::string, cudf::io::schema_element> dtype_schema{
+    {"2", {dtype<cudf::string_view>()}}, {"0", {dtype<int32_t>()}}, {"1", {dtype<double>()}}};
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .dtypes(dtype_schema)
+      .lines(true)
+      .legacy(is_legacy_test(test_opt));
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 3);
+  EXPECT_EQ(result.tbl->num_rows(), 2);
+
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT32);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+  EXPECT_EQ(result.tbl->get_column(2).type().id(), cudf::type_id::STRING);
+
+  EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "1");
+  EXPECT_EQ(result.metadata.schema_info[2].name, "2");
+
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), int_wrapper{{1, 2}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1), float64_wrapper{{1.1, 2.2}, validity});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(2),
+                                 cudf::test::strings_column_wrapper({"aa ", "  bbb"}));
+}
+
+TEST_F(JsonReaderTest, JsonNestedDtypeSchema)
+{
+  std::string json_string = R"( [{"a":[123, {"0": 123}], "b":1.0}, {"b":1.1}, {"b":2.1}])";
+
+  std::map<std::string, cudf::io::schema_element> dtype_schema{
+    {"a",
+     {
+       data_type{cudf::type_id::LIST},
+       {{"element", {data_type{cudf::type_id::STRUCT}, {{"0", {dtype<float>()}}}}}},
+     }},
+    {"b", {dtype<int32_t>()}},
+  };
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(
+      cudf::io::source_info{json_string.data(), json_string.size()})
+      .dtypes(dtype_schema)
+      .lines(false);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  // Make sure we have columns "a" and "b"
+  ASSERT_EQ(result.tbl->num_columns(), 2);
+  ASSERT_EQ(result.metadata.schema_info.size(), 2);
+  EXPECT_EQ(result.metadata.schema_info[0].name, "a");
+  EXPECT_EQ(result.metadata.schema_info[1].name, "b");
+  // Make sure column "a" is a list column (offsets and elements)
+  ASSERT_EQ(result.tbl->get_column(0).num_children(), 2);
+  ASSERT_EQ(result.metadata.schema_info[0].children.size(), 2);
+  // Make sure column "b" is a leaf column
+  ASSERT_EQ(result.tbl->get_column(1).num_children(), 0);
+  ASSERT_EQ(result.metadata.schema_info[1].children.size(), 0);
+  // Offsets child with no other child columns
+  ASSERT_EQ(result.tbl->get_column(0).child(0).num_children(), 0);
+  ASSERT_EQ(result.metadata.schema_info[0].children[0].children.size(), 0);
+  EXPECT_EQ(result.metadata.schema_info[0].children[0].name, "offsets");
+  // Elements is the struct column with a single child column "0"
+  ASSERT_EQ(result.tbl->get_column(0).child(1).num_children(), 1);
+  ASSERT_EQ(result.metadata.schema_info[0].children[1].children.size(), 1);
+  EXPECT_EQ(result.metadata.schema_info[0].children[1].name, "element");
+
+  // Verify column "a" being a list column
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::LIST);
+  // Verify column "a->element->0" is a float column
+  EXPECT_EQ(result.tbl->get_column(0).child(1).child(0).type().id(), cudf::type_id::FLOAT32);
+  // Verify column "b" is an int column
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::INT32);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0).child(0), int_wrapper{{0, 2, 2, 2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0).child(1).child(0),
+                                 float_wrapper{{0.0, 123.0}, {false, true}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 int_wrapper{{1, 1, 2}, {true, true, true}});
+  // List column expected
+  auto leaf_child     = float_wrapper{{0.0, 123.0}, {false, true}};
+  auto const validity = {1, 0, 0};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(validity.begin(), validity.end());
+  auto expected = cudf::make_lists_column(
+    3,
+    int_wrapper{{0, 2, 2, 2}}.release(),
+    cudf::test::structs_column_wrapper{{leaf_child}, {false, true}}.release(),
+    null_count,
+    std::move(null_mask));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), *expected);
+}
+
+TEST_P(JsonReaderParamTest, JsonDtypeParsing)
+{
+  auto const test_opt = GetParam();
+  // All corner cases of dtype parsing
+  //  0, "0", " 0", 1, "1", " 1", "a", "z", null, true, false,  "null", "true", "false", nan, "nan"
+  // Test for dtypes: bool, int, float, str, duration, timestamp
+  std::string row_orient =
+    "[0]\n[\"0\"]\n[\" 0\"]\n[1]\n[\"1\"]\n[\" 1\"]\n[\"a\"]\n[\"z\"]\n"
+    "[null]\n[true]\n[false]\n[\"null\"]\n[\"true\"]\n[\"false\"]\n[nan]\n[\"nan\"]\n";
+  std::string record_orient = to_records_orient({{{"0", "0"}},
+                                                 {{"0", "\"0\""}},
+                                                 {{"0", "\" 0\""}},
+                                                 {{"0", "1"}},
+                                                 {{"0", "\"1\""}},
+                                                 {{"0", "\" 1\""}},
+                                                 {{"0", "\"a\""}},
+                                                 {{"0", "\"z\""}},
+                                                 {{"0", "null"}},
+                                                 {{"0", "true"}},
+                                                 {{"0", "false"}},
+                                                 {{"0", "\"null\""}},
+                                                 {{"0", "\"true\""}},
+                                                 {{"0", "\"false\""}},
+                                                 {{"0", "nan"}},
+                                                 {{"0", "\"nan\""}}},
+                                                "\n");
+
+  std::string data = is_row_orient_test(test_opt) ? row_orient : record_orient;
+
+  auto make_validity = [](std::vector<int> const& validity) {
+    return cudf::detail::make_counting_transform_iterator(
+      0, [&](auto i) -> bool { return static_cast<bool>(validity[i]); });
+  };
+
+  constexpr int int_ignore{};
+  constexpr double double_ignore{};
+  constexpr bool bool_ignore{};
+
+  std::vector<int> const validity = {1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0};
+
+  auto int_col   = int_wrapper{{0,
+                                0,
+                                int_ignore,
+                                1,
+                                1,
+                                int_ignore,
+                                int_ignore,
+                                int_ignore,
+                                int_ignore,
+                                1,
+                                0,
+                                int_ignore,
+                                1,
+                                0,
+                                int_ignore,
+                                int_ignore},
+                             make_validity(validity)};
+  auto float_col = float_wrapper{{0.0,
+                                  0.0,
+                                  double_ignore,
+                                  1.0,
+                                  1.0,
+                                  double_ignore,
+                                  double_ignore,
+                                  double_ignore,
+                                  double_ignore,
+                                  1.0,
+                                  0.0,
+                                  double_ignore,
+                                  1.0,
+                                  0.0,
+                                  double_ignore,
+                                  double_ignore},
+                                 make_validity(validity)};
+  auto str_col =
+    cudf::test::strings_column_wrapper{// clang-format off
+    {"0", "0", " 0", "1", "1", " 1", "a", "z", "", "true", "false", "null", "true", "false", "nan", "nan"},
+     cudf::test::iterators::nulls_at(std::vector<int>{8})};
+  // clang-format on
+  auto bool_col = bool_wrapper{{false,
+                                false,
+                                bool_ignore,
+                                true,
+                                true,
+                                bool_ignore,
+                                bool_ignore,
+                                bool_ignore,
+                                bool_ignore,
+                                true,
+                                false,
+                                bool_ignore,
+                                true,
+                                false,
+                                bool_ignore,
+                                bool_ignore},
+                               make_validity(validity)};
+
+  // Types to test
+  const std::vector<data_type> dtypes = {
+    dtype<int32_t>(), dtype<float>(), dtype<cudf::string_view>(), dtype<bool>()};
+  const std::vector<cudf::column_view> cols{cudf::column_view(int_col),
+                                            cudf::column_view(float_col),
+                                            cudf::column_view(str_col),
+                                            cudf::column_view(bool_col)};
+  for (size_t col_type = 0; col_type < cols.size(); col_type++) {
+    std::map<std::string, cudf::io::schema_element> dtype_schema{{"0", {dtypes[col_type]}}};
+    cudf::io::json_reader_options in_options =
+      cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+        .dtypes(dtype_schema)
+        .lines(true)
+        .legacy(is_legacy_test(test_opt));
+
+    cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+    EXPECT_EQ(result.tbl->num_columns(), 1);
+    EXPECT_EQ(result.tbl->num_rows(), 16);
+    EXPECT_EQ(result.metadata.schema_info[0].name, "0");
+
+    EXPECT_EQ(result.tbl->get_column(0).type().id(), dtypes[col_type].id());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), cols[col_type]);
+  }
+}
+
+TYPED_TEST(JsonValidFixedPointReaderTest, SingleColumnNegativeScale)
+{
+  this->run_tests({"1.23", "876e-2", "5.43e1", "-0.12", "0.25", "-0.23", "-0.27", "0.00", "0.00"},
+                  numeric::scale_type{-2});
+}
+
+TYPED_TEST(JsonValidFixedPointReaderTest, SingleColumnNoScale)
+{
+  this->run_tests({"123", "-87600e-2", "54.3e1", "-12", "25", "-23", "-27", "0", "0"},
+                  numeric::scale_type{0});
+}
+
+TYPED_TEST(JsonValidFixedPointReaderTest, SingleColumnPositiveScale)
+{
+  this->run_tests(
+    {"123000", "-87600000e-2", "54300e1", "-12000", "25000", "-23000", "-27000", "0000", "0000"},
+    numeric::scale_type{3});
+}
+
+TYPED_TEST(JsonFixedPointReaderTest, EmptyValues)
+{
+  auto const buffer = std::string{"{\"col0\":}"};
+
+  cudf::io::json_reader_options const in_opts =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{buffer.c_str(), buffer.size()})
+      .dtypes({data_type{type_to_id<TypeParam>(), 0}})
+      .lines(true)
+      .legacy(true);  // Legacy behavior; not aligned with JSON specs
+
+  auto const result      = cudf::io::read_json(in_opts);
+  auto const result_view = result.tbl->view();
+
+  ASSERT_EQ(result_view.num_columns(), 1);
+  EXPECT_EQ(result_view.num_rows(), 1);
+  EXPECT_EQ(result.metadata.schema_info[0].name, "col0");
+  EXPECT_EQ(result_view.column(0).null_count(), 1);
+}
+
+TEST_F(JsonReaderTest, UnsupportedMultipleFileInputs)
+{
+  std::string const data = "{\"col\":0}";
+  auto const buffer      = cudf::io::host_buffer{data.data(), data.size()};
+  auto const src         = cudf::io::source_info{{buffer, buffer}};
+
+  cudf::io::json_reader_options const not_lines_opts = cudf::io::json_reader_options::builder(src);
+  EXPECT_THROW(cudf::io::read_json(not_lines_opts), cudf::logic_error);
+
+  cudf::io::json_reader_options const comp_exp_opts =
+    cudf::io::json_reader_options::builder(src).compression(cudf::io::compression_type::GZIP);
+  EXPECT_THROW(cudf::io::read_json(comp_exp_opts), cudf::logic_error);
+
+  cudf::io::json_reader_options const comp_opts =
+    cudf::io::json_reader_options::builder(src).compression(cudf::io::compression_type::GZIP);
+  EXPECT_THROW(cudf::io::read_json(comp_opts), cudf::logic_error);
+}
+
+TEST_F(JsonReaderTest, TrailingCommas)
+{
+  std::vector<std::string> const json_lines_valid{
+    R"({"a":"a0",}
+    {"a":"a2", "b":"b2",}
+    {"a":"a4",})",
+    R"({"a":"a0"}
+    {"a":"a2", "b": [1, 2,]})",
+    R"({"a":"a0",}
+    {"a":"a2", "b": [1, 2,],})",
+  };
+  for (size_t i = 0; i < json_lines_valid.size(); i++) {
+    auto const& json_string = json_lines_valid[i];
+    // Initialize parsing options (reading json lines)
+    cudf::io::json_reader_options json_parser_options =
+      cudf::io::json_reader_options::builder(
+        cudf::io::source_info{json_string.c_str(), json_string.size()})
+        .lines(true);
+    EXPECT_NO_THROW(cudf::io::read_json(json_parser_options)) << "Failed on test case " << i;
+  }
+
+  std::vector<std::string> const json_valid{
+    R"([{"a":"a0",},  {"a":"a2", "b":"b2",}, {"a":"a4"},])",
+    R"([{"a":"a0"},  {"a":"a2", "b": [1, 2,]}])",
+    R"([{"a":"a0",}, {"a":"a2", "b": [1, 2,],}])",
+    R"([{"a": 1,}, {"a": null, "b": [null,],}])",
+  };
+  for (size_t i = 0; i < json_valid.size(); i++) {
+    auto const& json_string                           = json_valid[i];
+    cudf::io::json_reader_options json_parser_options = cudf::io::json_reader_options::builder(
+      cudf::io::source_info{json_string.c_str(), json_string.size()});
+    EXPECT_NO_THROW(cudf::io::read_json(json_parser_options)) << "Failed on test case " << i;
+  }
+
+  std::vector<std::string> const json_invalid{
+    R"([{"a":"a0",,}])",
+    R"([{"a":"a0"},,])",
+    R"([,{"a":"a0"}])",
+    R"([{,"a":"a0"}])",
+    R"([{,}])",
+    R"([,])",
+    R"([,,])",
+    R"([{,,}])",
+  };
+  for (size_t i = 0; i < json_invalid.size(); i++) {
+    auto const& json_string                           = json_invalid[i];
+    cudf::io::json_reader_options json_parser_options = cudf::io::json_reader_options::builder(
+      cudf::io::source_info{json_string.c_str(), json_string.size()});
+    EXPECT_THROW(cudf::io::read_json(json_parser_options), cudf::logic_error)
+      << "Failed on test case " << i;
+  }
+}
+
+TEST_F(JsonReaderTest, JSONLinesRecovering)
+{
+  std::string data =
+    // 0 -> a: -2 (valid)
+    R"({"a":-2})"
+    "\n"
+    // 1 -> (invalid)
+    R"({"a":])"
+    "\n"
+    // 2 -> (invalid)
+    R"({"b":{"a":[321})"
+    "\n"
+    // 3 -> c: [1] (valid)
+    R"({"c":1.2})"
+    "\n"
+    "\n"
+    // 4 -> a: 123 (valid)
+    R"({"a":123})";
+
+  auto filepath = temp_env->get_temp_dir() + "RecoveringLines.json";
+  {
+    std::ofstream outfile(filepath, std::ofstream::out);
+    outfile << data;
+  }
+
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{filepath})
+      .lines(true)
+      .recovery_mode(cudf::io::json_recovery_mode_t::RECOVER_WITH_NULL);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+
+  EXPECT_EQ(result.tbl->num_columns(), 2);
+  EXPECT_EQ(result.tbl->num_rows(), 5);
+  EXPECT_EQ(result.tbl->get_column(0).type().id(), cudf::type_id::INT64);
+  EXPECT_EQ(result.tbl->get_column(1).type().id(), cudf::type_id::FLOAT64);
+
+  std::vector<bool> a_validity{true, false, false, false, true};
+  std::vector<bool> c_validity{false, false, false, true, false};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0),
+                                 int64_wrapper{{-2, 0, 0, 0, 123}, a_validity.cbegin()});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(1),
+                                 float64_wrapper{{0.0, 0.0, 0.0, 1.2, 0.0}, c_validity.cbegin()});
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/json_tree.cpp b/cpp/tests/io/json_tree.cpp
new file mode 100644
index 0000000..16c2271
--- /dev/null
+++ b/cpp/tests/io/json_tree.cpp
@@ -0,0 +1,895 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/json/nested_json.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing/detail/hashing.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+#include <string>
+#include <unordered_map>
+
+namespace cuio_json = cudf::io::json;
+
+// Host copy of tree_meta_t
+struct tree_meta_t2 {
+  std::vector<cuio_json::NodeT> node_categories;
+  std::vector<cuio_json::NodeIndexT> parent_node_ids;
+  std::vector<cuio_json::TreeDepthT> node_levels;
+  std::vector<cuio_json::SymbolOffsetT> node_range_begin;
+  std::vector<cuio_json::SymbolOffsetT> node_range_end;
+};
+
+namespace {
+std::string get_node_string(std::size_t const node_id,
+                            tree_meta_t2 const& tree_rep,
+                            std::string const& json_input)
+{
+  auto node_to_str = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::NC_STRUCT: return "STRUCT";
+      case cuio_json::NC_LIST: return "LIST";
+      case cuio_json::NC_FN: return "FN";
+      case cuio_json::NC_STR: return "STR";
+      case cuio_json::NC_VAL: return "VAL";
+      case cuio_json::NC_ERR: return "ERR";
+      default: return "N/A";
+    };
+  };
+
+  return "<" + std::to_string(node_id) + ":" + node_to_str(tree_rep.node_categories[node_id]) +
+         ":[" + std::to_string(tree_rep.node_range_begin[node_id]) + ", " +
+         std::to_string(tree_rep.node_range_end[node_id]) + ") '" +
+         json_input.substr(tree_rep.node_range_begin[node_id],
+                           tree_rep.node_range_end[node_id] - tree_rep.node_range_begin[node_id]) +
+         "'>";
+}
+
+void print_tree_representation(std::string const& json_input, tree_meta_t2 const& tree_rep)
+{
+  for (std::size_t i = 0; i < tree_rep.node_categories.size(); i++) {
+    auto parent_id = tree_rep.parent_node_ids[i];
+    std::stack<std::size_t> path;
+    path.push(i);
+    while (parent_id != cuio_json::parent_node_sentinel) {
+      path.push(parent_id);
+      parent_id = tree_rep.parent_node_ids[parent_id];
+    }
+
+    while (path.size()) {
+      auto const node_id = path.top();
+      std::cout << get_node_string(node_id, tree_rep, json_input)
+                << (path.size() > 1 ? " -> " : "");
+      path.pop();
+    }
+    std::cout << "\n";
+  }
+}
+
+tree_meta_t2 to_cpu_tree(cuio_json::tree_meta_t const& d_value, rmm::cuda_stream_view stream)
+{
+  return {cudf::detail::make_std_vector_async(d_value.node_categories, stream),
+          cudf::detail::make_std_vector_async(d_value.parent_node_ids, stream),
+          cudf::detail::make_std_vector_async(d_value.node_levels, stream),
+          cudf::detail::make_std_vector_async(d_value.node_range_begin, stream),
+          cudf::detail::make_std_vector_async(d_value.node_range_end, stream)};
+}
+
+// DEBUG prints
+auto to_cat = [](auto v) -> std::string {
+  switch (v) {
+    case cuio_json::NC_STRUCT: return " S";
+    case cuio_json::NC_LIST: return " L";
+    case cuio_json::NC_STR: return " \"";
+    case cuio_json::NC_VAL: return " V";
+    case cuio_json::NC_FN: return " F";
+    case cuio_json::NC_ERR: return "ER";
+    default: return "UN";
+  };
+};
+auto to_int    = [](auto v) { return std::to_string(static_cast<int>(v)); };
+auto print_vec = [](auto const& cpu, auto const name, auto converter) {
+  for (auto const& v : cpu)
+    printf("%3s,", converter(v).c_str());
+  std::cout << name << std::endl;
+};
+void print_tree(tree_meta_t2 const& cpu_tree)
+{
+  print_vec(cpu_tree.node_categories, "node_categories", to_cat);
+  print_vec(cpu_tree.parent_node_ids, "parent_node_ids", to_int);
+  print_vec(cpu_tree.node_levels, "node_levels", to_int);
+  print_vec(cpu_tree.node_range_begin, "node_range_begin", to_int);
+  print_vec(cpu_tree.node_range_end, "node_range_end", to_int);
+}
+void print_tree(cuio_json::tree_meta_t const& d_gpu_tree)
+{
+  auto const cpu_tree = to_cpu_tree(d_gpu_tree, cudf::get_default_stream());
+  print_tree(cpu_tree);
+}
+
+template <typename T>
+bool compare_vector(std::vector<T> const& cpu_vec,
+                    std::vector<T> const& gpu_vec,
+                    std::string const& name)
+{
+  EXPECT_EQ(cpu_vec.size(), gpu_vec.size());
+  bool mismatch = false;
+  if (!std::equal(cpu_vec.begin(), cpu_vec.end(), gpu_vec.begin())) {
+    print_vec(cpu_vec, name + "(cpu)", to_int);
+    print_vec(gpu_vec, name + "(gpu)", to_int);
+    for (auto i = 0lu; i < cpu_vec.size(); i++) {
+      mismatch |= (cpu_vec[i] != gpu_vec[i]);
+      printf("%3s,", (cpu_vec[i] == gpu_vec[i] ? " " : "x"));
+    }
+    printf("\n");
+  }
+  EXPECT_FALSE(mismatch);
+  return mismatch;
+}
+
+template <typename T>
+bool compare_vector(std::vector<T> const& cpu_vec,
+                    rmm::device_uvector<T> const& d_vec,
+                    std::string const& name)
+{
+  auto gpu_vec = cudf::detail::make_std_vector_async(d_vec, cudf::get_default_stream());
+  return compare_vector(cpu_vec, gpu_vec, name);
+}
+
+void compare_trees(tree_meta_t2 const& cpu_tree,
+                   cuio_json::tree_meta_t const& d_gpu_tree,
+                   bool print = false)
+{
+  auto cpu_num_nodes = cpu_tree.node_categories.size();
+  EXPECT_EQ(cpu_num_nodes, d_gpu_tree.node_categories.size());
+  EXPECT_EQ(cpu_num_nodes, d_gpu_tree.parent_node_ids.size());
+  EXPECT_EQ(cpu_num_nodes, d_gpu_tree.node_levels.size());
+  EXPECT_EQ(cpu_num_nodes, d_gpu_tree.node_range_begin.size());
+  EXPECT_EQ(cpu_num_nodes, d_gpu_tree.node_range_end.size());
+  auto gpu_tree = to_cpu_tree(d_gpu_tree, cudf::get_default_stream());
+  bool mismatch = false;
+
+#define COMPARE_MEMBER(member)                                                       \
+  for (std::size_t i = 0; i < cpu_num_nodes; i++) {                                  \
+    EXPECT_EQ(cpu_tree.member[i], gpu_tree.member[i]) << #member << "[" << i << "]"; \
+  }
+  COMPARE_MEMBER(node_categories);
+  COMPARE_MEMBER(parent_node_ids);
+  COMPARE_MEMBER(node_levels);
+  COMPARE_MEMBER(node_range_begin);
+  COMPARE_MEMBER(node_range_end);
+#undef COMPARE_MEMBER
+
+#define PRINT_VEC(vec, conv) print_vec(vec, #vec, conv);
+#define PRINT_COMPARISON(vec, conv)                                                  \
+  PRINT_VEC(cpu_tree.vec, conv);                                                     \
+  PRINT_VEC(gpu_tree.vec, conv);                                                     \
+  if (!std::equal(cpu_tree.vec.begin(), cpu_tree.vec.end(), gpu_tree.vec.begin())) { \
+    for (auto i = 0lu; i < cpu_tree.vec.size(); i++) {                               \
+      mismatch |= (gpu_tree.vec[i] != cpu_tree.vec[i]);                              \
+      printf("%3s,", (gpu_tree.vec[i] == cpu_tree.vec[i] ? " " : "x"));              \
+    }                                                                                \
+    printf("\n");                                                                    \
+  }
+  if (print) {
+    for (int i = 0; i < int(cpu_num_nodes); i++)
+      printf("%3d,", i);
+    printf(" node_id\n");
+    PRINT_COMPARISON(node_categories, to_cat);   // Works
+    PRINT_COMPARISON(node_levels, to_int);       // Works
+    PRINT_COMPARISON(node_range_begin, to_int);  // Works
+    PRINT_COMPARISON(node_range_end, to_int);    // Works
+    PRINT_COMPARISON(parent_node_ids, to_int);   // Works
+    EXPECT_FALSE(mismatch);
+  }
+#undef PRINT_VEC
+#undef PRINT_COMPARISON
+}
+
+template <typename T>
+auto translate_col_id(T const& col_id)
+{
+  using value_type = typename T::value_type;
+  std::unordered_map<value_type, value_type> col_id_map;
+  std::vector<value_type> new_col_ids(col_id.size());
+  value_type unique_id = 0;
+  for (auto id : col_id) {
+    if (col_id_map.count(id) == 0) { col_id_map[id] = unique_id++; }
+  }
+  for (size_t i = 0; i < col_id.size(); i++) {
+    new_col_ids[i] = col_id_map[col_id[i]];
+  }
+  return new_col_ids;
+}
+
+tree_meta_t2 get_tree_representation_cpu(
+  cudf::device_span<cuio_json::PdaTokenT const> tokens_gpu,
+  cudf::device_span<cuio_json::SymbolOffsetT const> token_indices_gpu1,
+  cudf::io::json_reader_options const& options,
+  rmm::cuda_stream_view stream)
+{
+  constexpr bool include_quote_char = true;
+  // Copy the JSON tokens to the host
+  thrust::host_vector<cuio_json::PdaTokenT> tokens =
+    cudf::detail::make_host_vector_async(tokens_gpu, stream);
+  thrust::host_vector<cuio_json::SymbolOffsetT> token_indices =
+    cudf::detail::make_host_vector_async(token_indices_gpu1, stream);
+
+  // Make sure tokens have been copied to the host
+  stream.synchronize();
+
+  // DEBUG print
+  [[maybe_unused]] auto to_token_str = [](cuio_json::PdaTokenT token) {
+    switch (token) {
+      case cuio_json::token_t::StructBegin: return " {";
+      case cuio_json::token_t::StructEnd: return " }";
+      case cuio_json::token_t::ListBegin: return " [";
+      case cuio_json::token_t::ListEnd: return " ]";
+      case cuio_json::token_t::FieldNameBegin: return "FB";
+      case cuio_json::token_t::FieldNameEnd: return "FE";
+      case cuio_json::token_t::StringBegin: return "SB";
+      case cuio_json::token_t::StringEnd: return "SE";
+      case cuio_json::token_t::ErrorBegin: return "er";
+      case cuio_json::token_t::ValueBegin: return "VB";
+      case cuio_json::token_t::ValueEnd: return "VE";
+      case cuio_json::token_t::StructMemberBegin: return " <";
+      case cuio_json::token_t::StructMemberEnd: return " >";
+      case cuio_json::token_t::LineEnd: return ";";
+      default: return ".";
+    }
+  };
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) {
+    std::cout << "Tokens: \n";
+    for (auto i = 0u; i < tokens.size(); i++) {
+      std::cout << to_token_str(tokens[i]) << " ";
+    }
+    std::cout << std::endl;
+  }
+
+  // Whether a token does represent a node in the tree representation
+  auto is_node = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::token_t::StructBegin:
+      case cuio_json::token_t::ListBegin:
+      case cuio_json::token_t::StringBegin:
+      case cuio_json::token_t::ValueBegin:
+      case cuio_json::token_t::FieldNameBegin:
+      case cuio_json::token_t::ErrorBegin: return true;
+      default: return false;
+    };
+  };
+
+  // The node that a token represents
+  auto token_to_node = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::token_t::StructBegin: return cuio_json::NC_STRUCT;
+      case cuio_json::token_t::ListBegin: return cuio_json::NC_LIST;
+      case cuio_json::token_t::StringBegin: return cuio_json::NC_STR;
+      case cuio_json::token_t::ValueBegin: return cuio_json::NC_STR;  // NC_VAL;
+      case cuio_json::token_t::FieldNameBegin: return cuio_json::NC_FN;
+      default: return cuio_json::NC_ERR;
+    };
+  };
+
+  // Includes quote char for end-of-string token or Skips the quote char for beginning-of-field-name
+  auto get_token_index = [include_quote_char](cuio_json::PdaTokenT const token,
+                                              cuio_json::SymbolOffsetT const token_index) {
+    constexpr cuio_json::SymbolOffsetT quote_char_size = 1;
+    switch (token) {
+      // Strip off or include quote char for StringBegin
+      case cuio_json::token_t::StringBegin:
+        return token_index + (include_quote_char ? 0 : quote_char_size);
+      // Strip off or Include trailing quote char for string values for StringEnd
+      case cuio_json::token_t::StringEnd:
+        return token_index + (include_quote_char ? quote_char_size : 0);
+      // Strip off quote char included for FieldNameBegin
+      case cuio_json::token_t::FieldNameBegin: return token_index + quote_char_size;
+      default: return token_index;
+    };
+  };
+
+  // Whether a token expects to be followed by its respective end-of-* token partner
+  auto is_begin_of_section = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::token_t::StringBegin:
+      case cuio_json::token_t::ValueBegin:
+      case cuio_json::token_t::FieldNameBegin: return true;
+      default: return false;
+    };
+  };
+
+  // The end-of-* partner token for a given beginning-of-* token
+  auto end_of_partner = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::token_t::StringBegin: return cuio_json::token_t::StringEnd;
+      case cuio_json::token_t::ValueBegin: return cuio_json::token_t::ValueEnd;
+      case cuio_json::token_t::FieldNameBegin: return cuio_json::token_t::FieldNameEnd;
+      default: return cuio_json::token_t::ErrorBegin;
+    };
+  };
+
+  // Whether the token pops from the parent node stack
+  auto does_pop = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::token_t::StructEnd:
+      case cuio_json::token_t::ListEnd: return true;
+      default: return false;
+    };
+  };
+
+  // Whether the token pushes onto the parent node stack
+  auto does_push = [](cuio_json::PdaTokenT const token) {
+    switch (token) {
+      case cuio_json::token_t::StructBegin:
+      case cuio_json::token_t::ListBegin: return true;
+      default: return false;
+    };
+  };
+
+  // The node id sitting on top of the stack becomes the node's parent
+  // The full stack represents the path from the root to the current node
+  std::stack<std::pair<cuio_json::NodeIndexT, bool>> parent_stack;
+
+  constexpr bool field_name_node    = true;
+  constexpr bool no_field_name_node = false;
+
+  std::vector<cuio_json::NodeT> node_categories;
+  std::vector<cuio_json::NodeIndexT> parent_node_ids;
+  std::vector<cuio_json::TreeDepthT> node_levels;
+  std::vector<cuio_json::SymbolOffsetT> node_range_begin;
+  std::vector<cuio_json::SymbolOffsetT> node_range_end;
+
+  std::size_t node_id = 0;
+  for (std::size_t i = 0; i < tokens.size(); i++) {
+    auto token = tokens[i];
+
+    // The section from the original JSON input that this token demarcates
+    std::size_t range_begin = get_token_index(token, token_indices[i]);
+    std::size_t range_end   = range_begin + 1;
+
+    // Identify this node's parent node id
+    std::size_t parent_node_id =
+      (parent_stack.size() > 0) ? parent_stack.top().first : cuio_json::parent_node_sentinel;
+
+    // If this token is the beginning-of-{value, string, field name}, also consume the next end-of-*
+    // token
+    if (is_begin_of_section(token)) {
+      if ((i + 1) < tokens.size() && end_of_partner(token) == tokens[i + 1]) {
+        // Update the range_end for this pair of tokens
+        range_end = get_token_index(tokens[i + 1], token_indices[i + 1]);
+        // We can skip the subsequent end-of-* token
+        i++;
+      }
+    }
+
+    // Emit node if this token becomes a node in the tree
+    if (is_node(token)) {
+      node_categories.push_back(token_to_node(token));
+      parent_node_ids.push_back(parent_node_id);
+      node_levels.push_back(parent_stack.size());
+      node_range_begin.push_back(range_begin);
+      node_range_end.push_back(range_end);
+    }
+
+    // Modify the stack if needed
+    if (token == cuio_json::token_t::FieldNameBegin) {
+      parent_stack.push({node_id, field_name_node});
+    } else {
+      if (does_push(token)) {
+        parent_stack.push({node_id, no_field_name_node});
+      } else if (does_pop(token)) {
+        CUDF_EXPECTS(parent_stack.size() >= 1, "Invalid JSON input.");
+        parent_stack.pop();
+      }
+
+      // If what we're left with is a field name on top of stack, we need to pop it
+      if (parent_stack.size() >= 1 && parent_stack.top().second == field_name_node) {
+        parent_stack.pop();
+      }
+    }
+
+    // Update node_id
+    if (is_node(token)) { node_id++; }
+  }
+
+  return {std::move(node_categories),
+          std::move(parent_node_ids),
+          std::move(node_levels),
+          std::move(node_range_begin),
+          std::move(node_range_end)};
+}
+
+std::tuple<std::vector<cuio_json::NodeIndexT>, std::vector<cudf::size_type>>
+records_orient_tree_traversal_cpu(cudf::host_span<cuio_json::SymbolT const> input,
+                                  tree_meta_t2 const& tree,
+                                  bool is_array_of_arrays,
+                                  bool is_enabled_lines,
+                                  rmm::cuda_stream_view stream)
+{
+  std::vector<cuio_json::NodeIndexT> node_ids(tree.parent_node_ids.size());
+  std::iota(node_ids.begin(), node_ids.end(), 0);
+
+  const cuio_json::NodeIndexT row_array_children_level = is_enabled_lines ? 1 : 2;
+  std::unordered_map<cuio_json::NodeIndexT, cuio_json::NodeIndexT> list_indices;
+  if (is_array_of_arrays) {
+    cuio_json::NodeIndexT parent_node = -1, child_index = 0;
+    for (size_t i = 0; i < tree.node_levels.size(); i++) {
+      if (tree.node_levels[i] == row_array_children_level) {
+        if (tree.parent_node_ids[i] != parent_node) {
+          parent_node = tree.parent_node_ids[i];
+          child_index = 0;
+        }
+        list_indices[i] = child_index++;
+      }
+    }
+  }
+
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) {
+    for (int i = 0; i < int(tree.node_range_begin.size()); i++) {
+      printf("%3s ",
+             std::string(input.data() + tree.node_range_begin[i],
+                         tree.node_range_end[i] - tree.node_range_begin[i])
+               .c_str());
+    }
+    printf(" (JSON)\n");
+    print_vec(tree.node_categories, "node_categories", to_cat);
+    print_vec(node_ids, "cpu.node_ids", to_int);
+  }
+
+  // print_vec(tree.parent_node_ids, "tree.parent_node_ids (before)");
+  constexpr cuio_json::NodeIndexT top_node = -1;
+  // CPU version of the algorithm
+  // Calculate row offsets too.
+  auto hash_path = [&](auto node_id) {
+    size_t seed = 0;
+    while (node_id != top_node) {
+      seed = cudf::hashing::detail::hash_combine(
+        seed, std::hash<cuio_json::TreeDepthT>{}(tree.node_levels[node_id]));
+      seed = cudf::hashing::detail::hash_combine(
+        seed, std::hash<cuio_json::NodeT>{}(tree.node_categories[node_id]));
+      if (tree.node_categories[node_id] == cuio_json::node_t::NC_FN) {
+        auto field_name =
+          std::string_view(input.data() + tree.node_range_begin[node_id],
+                           tree.node_range_end[node_id] - tree.node_range_begin[node_id]);
+        seed = cudf::hashing::detail::hash_combine(seed, std::hash<std::string_view>{}(field_name));
+      }
+      if (is_array_of_arrays and tree.node_levels[node_id] == row_array_children_level)
+        seed = cudf::hashing::detail::hash_combine(seed, list_indices[node_id]);
+      node_id = tree.parent_node_ids[node_id];
+    }
+    return seed;
+  };
+  auto equal_path = [&](auto node_id1, auto node_id2) {
+    bool is_equal = true;
+    while (is_equal and node_id1 != top_node and node_id2 != top_node) {
+      is_equal &= tree.node_levels[node_id1] == tree.node_levels[node_id2];
+      is_equal &= tree.node_categories[node_id1] == tree.node_categories[node_id2];
+      if (is_equal and tree.node_categories[node_id1] == cuio_json::node_t::NC_FN) {
+        auto field_name1 =
+          std::string_view(input.data() + tree.node_range_begin[node_id1],
+                           tree.node_range_end[node_id1] - tree.node_range_begin[node_id1]);
+        auto field_name2 =
+          std::string_view(input.data() + tree.node_range_begin[node_id2],
+                           tree.node_range_end[node_id2] - tree.node_range_begin[node_id2]);
+        is_equal &= field_name1 == field_name2;
+      }
+      if (is_array_of_arrays and is_equal and
+          tree.node_levels[node_id1] == row_array_children_level) {
+        is_equal &= list_indices[node_id1] == list_indices[node_id2];
+      }
+      node_id1 = tree.parent_node_ids[node_id1];
+      node_id2 = tree.parent_node_ids[node_id2];
+    }
+    return is_equal and node_id1 == top_node and node_id2 == top_node;
+  };
+  std::unordered_map<cuio_json::NodeIndexT, int, decltype(hash_path), decltype(equal_path)>
+    node_id_map(10, hash_path, equal_path);
+  auto unique_col_id = 0;
+  for (auto& node_idx : node_ids) {
+    if (node_id_map.count(node_idx) == 0) {
+      node_id_map[node_idx] = unique_col_id++;  // node_idx;
+      node_idx              = node_id_map[node_idx];
+    } else {
+      node_idx = node_id_map[node_idx];
+    }
+  }
+  // Translate parent_node_ids
+  auto parent_col_ids(tree.parent_node_ids);
+  for (auto& parent_node_id : parent_col_ids) {
+    if (parent_node_id != top_node) parent_node_id = node_ids[parent_node_id];
+  }
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) {
+    print_vec(node_ids, "cpu.node_ids (after)", to_int);
+    print_vec(tree.parent_node_ids, "cpu.parent_node_ids (after)", to_int);
+  }
+  // row_offsets
+  std::vector<int> row_offsets(tree.parent_node_ids.size(), 0);
+  std::unordered_map<int, int> col_id_current_offset;
+  for (std::size_t i = 0; i < tree.parent_node_ids.size(); i++) {
+    auto current_col_id = node_ids[i];
+    auto parent_col_id  = parent_col_ids[i];
+    auto parent_node_id = tree.parent_node_ids[i];
+    if (parent_col_id == top_node) {
+      // row_offsets[current_col_id] = 0; // JSON lines treats top node as list.
+      col_id_current_offset[current_col_id]++;
+      row_offsets[i] = col_id_current_offset[current_col_id] - 1;
+    } else {
+      if (tree.node_categories[parent_node_id] == cuio_json::node_t::NC_LIST and
+          !(is_array_of_arrays and tree.node_levels[i] == row_array_children_level)) {
+        col_id_current_offset[current_col_id]++;
+        row_offsets[i] = col_id_current_offset[current_col_id] - 1;
+      } else {
+        row_offsets[i]                        = col_id_current_offset[parent_col_id] - 1;
+        col_id_current_offset[current_col_id] = col_id_current_offset[parent_col_id];
+      }
+    }
+  }
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) {
+    print_vec(row_offsets, "cpu.row_offsets (generated)", to_int);
+  }
+  return {std::move(node_ids), std::move(row_offsets)};
+}
+
+}  // namespace
+
+// Base test fixture for tests
+struct JsonTest : public cudf::test::BaseFixture {};
+
+TEST_F(JsonTest, TreeRepresentation)
+{
+  auto const stream = cudf::get_default_stream();
+
+  // Test input
+  std::string const input = R"(  [{)"
+                            R"("category": "reference",)"
+                            R"("index:": [4,12,42],)"
+                            R"("author": "Nigel Rees",)"
+                            R"("title": "[Sayings of the Century]",)"
+                            R"("price": 8.95)"
+                            R"(},  )"
+                            R"({)"
+                            R"("category": "reference",)"
+                            R"("index": [4,{},null,{"a":[{ }, {}] } ],)"
+                            R"("author": "Nigel Rees",)"
+                            R"("title": "{}[], <=semantic-symbols-string",)"
+                            R"("price": 8.95)"
+                            R"(}] )";
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input = cudf::device_span<cuio_json::SymbolT const>{
+    d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+
+  cudf::io::json_reader_options const options{};
+
+  // Parse the JSON and get the token stream
+  auto const [tokens_gpu, token_indices_gpu] = cudf::io::json::detail::get_token_stream(
+    d_input, options, stream, rmm::mr::get_current_device_resource());
+
+  // Get the JSON's tree representation
+  auto gpu_tree = cuio_json::detail::get_tree_representation(
+    tokens_gpu, token_indices_gpu, stream, rmm::mr::get_current_device_resource());
+  // host tree generation
+  auto cpu_tree = get_tree_representation_cpu(tokens_gpu, token_indices_gpu, options, stream);
+  compare_trees(cpu_tree, gpu_tree);
+
+  // Print tree representation
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) { print_tree_representation(input, cpu_tree); }
+
+  // Golden sample of node categories
+  std::vector<cuio_json::node_t> golden_node_categories = {
+    cuio_json::NC_LIST, cuio_json::NC_STRUCT, cuio_json::NC_FN,     cuio_json::NC_STR,
+    cuio_json::NC_FN,   cuio_json::NC_LIST,   cuio_json::NC_STR,    cuio_json::NC_STR,
+    cuio_json::NC_STR,  cuio_json::NC_FN,     cuio_json::NC_STR,    cuio_json::NC_FN,
+    cuio_json::NC_STR,  cuio_json::NC_FN,     cuio_json::NC_STR,    cuio_json::NC_STRUCT,
+    cuio_json::NC_FN,   cuio_json::NC_STR,    cuio_json::NC_FN,     cuio_json::NC_LIST,
+    cuio_json::NC_STR,  cuio_json::NC_STRUCT, cuio_json::NC_STR,    cuio_json::NC_STRUCT,
+    cuio_json::NC_FN,   cuio_json::NC_LIST,   cuio_json::NC_STRUCT, cuio_json::NC_STRUCT,
+    cuio_json::NC_FN,   cuio_json::NC_STR,    cuio_json::NC_FN,     cuio_json::NC_STR,
+    cuio_json::NC_FN,   cuio_json::NC_STR};
+
+  // Golden sample of node ids
+  // clang-format off
+  std::vector<cuio_json::NodeIndexT> golden_parent_node_ids = {
+    cuio_json::parent_node_sentinel, 0, 1, 2,
+    1, 4, 5, 5,
+    5, 1, 9, 1,
+    11, 1, 13, 0,
+    15, 16, 15, 18,
+    19, 19, 19, 19,
+    23, 24, 25, 25,
+    15, 28, 15, 30,
+    15, 32};
+  // clang-format on
+
+  // Golden sample of node levels
+  std::vector<cuio_json::TreeDepthT> golden_node_levels = {0, 1, 2, 3, 2, 3, 4, 4, 4, 2, 3, 2,
+                                                           3, 2, 3, 1, 2, 3, 2, 3, 4, 4, 4, 4,
+                                                           5, 6, 7, 7, 2, 3, 2, 3, 2, 3};
+
+  // Golden sample of the character-ranges from the original input that each node demarcates
+  std::vector<std::size_t> golden_node_range_begin = {
+    2,   3,   5,   16,  29,  38,  39,  41,  44,  49,  58,  72,  80,  108, 116, 124, 126,
+    137, 150, 158, 159, 161, 164, 169, 171, 174, 175, 180, 189, 198, 212, 220, 255, 263};
+
+  // Golden sample of the character-ranges from the original input that each node demarcates
+  std::vector<std::size_t> golden_node_range_end = {
+    3,   4,   13,  27,  35,  39,  40,  43,  46,  55,  70,  77,  106, 113, 120, 125, 134,
+    148, 155, 159, 160, 162, 168, 170, 172, 175, 176, 181, 195, 210, 217, 253, 260, 267};
+
+  // Check results against golden samples
+  ASSERT_EQ(golden_node_categories.size(), cpu_tree.node_categories.size());
+  ASSERT_EQ(golden_parent_node_ids.size(), cpu_tree.parent_node_ids.size());
+  ASSERT_EQ(golden_node_levels.size(), cpu_tree.node_levels.size());
+  ASSERT_EQ(golden_node_range_begin.size(), cpu_tree.node_range_begin.size());
+  ASSERT_EQ(golden_node_range_end.size(), cpu_tree.node_range_end.size());
+
+  for (std::size_t i = 0; i < golden_node_categories.size(); i++) {
+    ASSERT_EQ(golden_node_categories[i], cpu_tree.node_categories[i]) << "[" << i << "]";
+    ASSERT_EQ(golden_parent_node_ids[i], cpu_tree.parent_node_ids[i]) << "[" << i << "]";
+    ASSERT_EQ(golden_node_levels[i], cpu_tree.node_levels[i]) << "[" << i << "]";
+    ASSERT_EQ(golden_node_range_begin[i], cpu_tree.node_range_begin[i]) << "[" << i << "]";
+    ASSERT_EQ(golden_node_range_end[i], cpu_tree.node_range_end[i]) << "[" << i << "]";
+  }
+}
+
+TEST_F(JsonTest, TreeRepresentation2)
+{
+  auto const stream = cudf::get_default_stream();
+  // Test input: value end with comma, space, close-brace ", }"
+  std::string const input =
+    // 0         1         2         3         4         5         6         7         8         9
+    // 0123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890
+    R"([ {}, { "a": { "y" : 6, "z": [] }}, { "a" : { "x" : 8, "y": 9 }, "b" : {"x": 10 , "z": 11)"
+    "\n}}]";
+  // Prepare input & output buffers
+  cudf::string_scalar d_scalar(input, true, stream);
+  auto d_input = cudf::device_span<cuio_json::SymbolT const>{d_scalar.data(),
+                                                             static_cast<size_t>(d_scalar.size())};
+
+  cudf::io::json_reader_options const options{};
+
+  // Parse the JSON and get the token stream
+  auto const [tokens_gpu, token_indices_gpu] = cudf::io::json::detail::get_token_stream(
+    d_input, options, stream, rmm::mr::get_current_device_resource());
+
+  // Get the JSON's tree representation
+  auto gpu_tree = cuio_json::detail::get_tree_representation(
+    tokens_gpu, token_indices_gpu, stream, rmm::mr::get_current_device_resource());
+  // host tree generation
+  auto cpu_tree = get_tree_representation_cpu(tokens_gpu, token_indices_gpu, options, stream);
+  compare_trees(cpu_tree, gpu_tree);
+
+  // Print tree representation
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) { print_tree_representation(input, cpu_tree); }
+
+  // Golden sample of node categories
+  // clang-format off
+  std::vector<cuio_json::node_t> golden_node_categories = {
+    cuio_json::NC_LIST, cuio_json::NC_STRUCT,
+    cuio_json::NC_STRUCT, cuio_json::NC_FN, cuio_json::NC_STRUCT,  cuio_json::NC_FN,  cuio_json::NC_STR, cuio_json::NC_FN,  cuio_json::NC_LIST,
+    cuio_json::NC_STRUCT, cuio_json::NC_FN, cuio_json::NC_STRUCT,  cuio_json::NC_FN,  cuio_json::NC_STR, cuio_json::NC_FN,  cuio_json::NC_STR,
+                          cuio_json::NC_FN, cuio_json::NC_STRUCT,  cuio_json::NC_FN,  cuio_json::NC_STR, cuio_json::NC_FN,  cuio_json::NC_STR};
+
+  // Golden sample of node ids
+  std::vector<cuio_json::NodeIndexT> golden_parent_node_ids = {
+    cuio_json::parent_node_sentinel, 0,
+    0, 2,  3,  4,  5,  4, 7,
+    0, 9, 10, 11, 12, 11, 14,
+       9, 16, 17, 18, 17, 20};
+  // clang-format on
+
+  // Golden sample of node levels
+  std::vector<cuio_json::TreeDepthT> golden_node_levels = {
+    0, 1, 1, 2, 3, 4, 5, 4, 5, 1, 2, 3, 4, 5, 4, 5, 2, 3, 4, 5, 4, 5,
+  };
+
+  // Golden sample of the character-ranges from the original input that each node demarcates
+  std::vector<std::size_t> golden_node_range_begin = {0,  2,  6,  9,  13, 16, 21, 25, 29, 36, 39,
+                                                      44, 47, 52, 56, 60, 66, 71, 73, 77, 83, 87};
+
+  // Golden sample of the character-ranges from the original input that each node demarcates
+  std::vector<std::size_t> golden_node_range_end = {1,  3,  7,  10, 14, 17, 22, 26, 30, 37, 40,
+                                                    45, 48, 53, 57, 61, 67, 72, 74, 79, 84, 89};
+
+  // Check results against golden samples
+  ASSERT_EQ(golden_node_categories.size(), cpu_tree.node_categories.size());
+  ASSERT_EQ(golden_parent_node_ids.size(), cpu_tree.parent_node_ids.size());
+  ASSERT_EQ(golden_node_levels.size(), cpu_tree.node_levels.size());
+  ASSERT_EQ(golden_node_range_begin.size(), cpu_tree.node_range_begin.size());
+  ASSERT_EQ(golden_node_range_end.size(), cpu_tree.node_range_end.size());
+
+  for (std::size_t i = 0; i < golden_node_categories.size(); i++) {
+    ASSERT_EQ(golden_node_categories[i], cpu_tree.node_categories[i]);
+    ASSERT_EQ(golden_parent_node_ids[i], cpu_tree.parent_node_ids[i]);
+    ASSERT_EQ(golden_node_levels[i], cpu_tree.node_levels[i]);
+    ASSERT_EQ(golden_node_range_begin[i], cpu_tree.node_range_begin[i]);
+    ASSERT_EQ(golden_node_range_end[i], cpu_tree.node_range_end[i]);
+  }
+}
+
+TEST_F(JsonTest, TreeRepresentation3)
+{
+  auto const stream = cudf::get_default_stream();
+  // Test input: Json lines with same TreeRepresentation2 input
+  std::string const input =
+    R"(  {}
+ { "a": { "y" : 6, "z": [] }}
+ { "a" : { "x" : 8, "y": 9 }, "b" : {"x": 10 , "z": 11 }} )";  // Prepare input & output buffers
+  cudf::string_scalar d_scalar(input, true, stream);
+  auto d_input = cudf::device_span<cuio_json::SymbolT const>{d_scalar.data(),
+                                                             static_cast<size_t>(d_scalar.size())};
+
+  cudf::io::json_reader_options options{};
+  options.enable_lines(true);
+
+  // Parse the JSON and get the token stream
+  auto const [tokens_gpu, token_indices_gpu] = cudf::io::json::detail::get_token_stream(
+    d_input, options, stream, rmm::mr::get_current_device_resource());
+
+  // Get the JSON's tree representation
+  auto gpu_tree = cuio_json::detail::get_tree_representation(
+    tokens_gpu, token_indices_gpu, stream, rmm::mr::get_current_device_resource());
+  // host tree generation
+  auto cpu_tree = get_tree_representation_cpu(tokens_gpu, token_indices_gpu, options, stream);
+  compare_trees(cpu_tree, gpu_tree);
+
+  // Print tree representation
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) { print_tree_representation(input, cpu_tree); }
+}
+
+TEST_F(JsonTest, TreeRepresentationError)
+{
+  auto const stream       = cudf::get_default_stream();
+  std::string const input = R"([ {}, }{])";
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input = cudf::device_span<cuio_json::SymbolT const>{
+    d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+  cudf::io::json_reader_options const options{};
+
+  // Parse the JSON and get the token stream
+  auto const [tokens_gpu, token_indices_gpu] = cudf::io::json::detail::get_token_stream(
+    d_input, options, stream, rmm::mr::get_current_device_resource());
+
+  // Get the JSON's tree representation
+  // This JSON is invalid and will raise an exception.
+  EXPECT_THROW(cuio_json::detail::get_tree_representation(
+                 tokens_gpu, token_indices_gpu, stream, rmm::mr::get_current_device_resource()),
+               cudf::logic_error);
+}
+
+/**
+ * @brief Test fixture for parametrized JSON tree traversal tests
+ */
+struct JsonTreeTraversalTest : public cudf::test::BaseFixture,
+                               public testing::WithParamInterface<std::tuple<bool, std::string>> {};
+
+//
+std::vector<std::string> json_list = {
+  "[]",
+  "value",
+  "\"string\"",
+  "[1, 2, 3]",
+  R"({"a": 1, "b": 2, "c": 3})",
+  // input a: {x:i, y:i, z:[]}, b: {x:i, z:i}
+  R"([ {}, { "a": { "y" : 6, "z": [] }}, { "a" : { "x" : 8, "y": 9}, "b" : {"x": 10, "z": 11}}])",
+  // input a: {x:i, y:i, z:[]}, b: {x:i, z: {p: i, q: i}}
+  R"([ {}, { "a": { "y" : 1, "z": [] }},
+             { "a": { "x" : 2, "y": 3}, "b" : {"x": 4, "z": [ {"p": 1, "q": 2}]}},
+             { "a": { "y" : 6, "z": [7, 8, 9]}, "b": {"x": 10, "z": [{}, {"q": 3}, {"p": 4}]}},
+             { "a": { "z": [12, 13, 14, 15]}},
+             { "a": { "z": [16], "x": 2}}
+        ])",
+  //^row offset a a.x a.y a.z   b b.x b.z
+  //            1       1   1
+  //            2   2   2       2   2   2                     b.z[] 0        b.z.p 0, b.z.q 0
+  //            3       3   3   3   3   3   a.z[] 0, 1, 2     b.z[] 1, 2, 3  b.z.q 2, b.z.p 3
+  //            4           4               a.z[] 3, 4, 5, 6
+  //            5   5       5               a.z[] 7
+  R"([[1, 2, 3], [4, 5, 6], [7, 8, 9]])",
+  R"([[1, 2, 3], [4, 5], [7]])",
+  R"([[1], [4, 5, 6], [7, 8]])",
+};
+
+std::vector<std::string> json_lines_list = {
+  // Test input a: {x:i, y:i, z:[]}, b: {x:i, z:i} with JSON-lines
+  "",
+  R"(  {}
+ { "a": { "y" : 6, "z": [] }}
+ { "a": { "y" : 6, "z": [2, 3, 4, 5] }}
+ { "a": { "z": [4], "y" : 6 }}
+ { "a" : { "x" : 8, "y": 9 }, "b" : {"x": 10 , "z": 11 }} )",
+  // empty list, row.
+  R"( {"a" : [], "b" : {}}
+ {"a" : []}
+ {"b" : {}})",
+  R"([1, 2, 3]
+     [4, 5, 6])",
+  R"([1]
+     [4, [5], 6]
+     [7, [8]])"};
+INSTANTIATE_TEST_SUITE_P(Mixed_And_Records,
+                         JsonTreeTraversalTest,
+                         ::testing::Combine(::testing::Values(false),
+                                            ::testing::ValuesIn(json_list)));
+INSTANTIATE_TEST_SUITE_P(JsonLines,
+                         JsonTreeTraversalTest,
+                         ::testing::Combine(::testing::Values(true),
+                                            ::testing::ValuesIn(json_lines_list)));
+
+TEST_P(JsonTreeTraversalTest, CPUvsGPUTraversal)
+{
+  auto [json_lines, input] = GetParam();
+  auto stream              = cudf::get_default_stream();
+  cudf::io::json_reader_options options{};
+  options.enable_lines(json_lines);
+
+  // std::cout << json_lines << input << std::endl;
+  cudf::string_scalar d_scalar(input, true, stream);
+  auto d_input = cudf::device_span<cuio_json::SymbolT const>{d_scalar.data(),
+                                                             static_cast<size_t>(d_scalar.size())};
+
+  // Parse the JSON and get the token stream
+  auto const [tokens_gpu, token_indices_gpu] = cudf::io::json::detail::get_token_stream(
+    d_input, options, stream, rmm::mr::get_current_device_resource());
+  // host tree generation
+  auto cpu_tree = get_tree_representation_cpu(tokens_gpu, token_indices_gpu, options, stream);
+  bool const is_array_of_arrays =
+    (cpu_tree.node_categories.size() > 0 and
+     cpu_tree.node_categories[0] == cudf::io::json::NC_LIST) and
+    (json_lines or (cpu_tree.node_categories.size() > 1 and
+                    cpu_tree.node_categories[1] == cudf::io::json::NC_LIST));
+  // host tree traversal
+  auto [cpu_col_id, cpu_row_offsets] =
+    records_orient_tree_traversal_cpu(input, cpu_tree, is_array_of_arrays, json_lines, stream);
+  // gpu tree generation
+  auto gpu_tree = cuio_json::detail::get_tree_representation(
+    tokens_gpu, token_indices_gpu, stream, rmm::mr::get_current_device_resource());
+  // Print tree representation
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) {
+    printf("BEFORE traversal (gpu_tree):\n");
+    print_tree(gpu_tree);
+  }
+  // gpu tree traversal
+  auto [gpu_col_id, gpu_row_offsets] =
+    cuio_json::detail::records_orient_tree_traversal(d_input,
+                                                     gpu_tree,
+                                                     is_array_of_arrays,
+                                                     json_lines,
+                                                     stream,
+                                                     rmm::mr::get_current_device_resource());
+  // Print tree representation
+  if (std::getenv("NJP_DEBUG_DUMP") != nullptr) {
+    printf("AFTER  traversal (gpu_tree):\n");
+    print_tree(gpu_tree);
+  }
+
+  // convert to sequence because gpu col id might be have random id
+  auto gpu_col_id2 = translate_col_id(cudf::detail::make_std_vector_async(gpu_col_id, stream));
+  EXPECT_FALSE(compare_vector(cpu_col_id, gpu_col_id2, "col_id"));
+  EXPECT_FALSE(compare_vector(cpu_row_offsets, gpu_row_offsets, "row_offsets"));
+}
diff --git a/cpp/tests/io/json_type_cast_test.cu b/cpp/tests/io/json_type_cast_test.cu
new file mode 100644
index 0000000..9eb5e8f
--- /dev/null
+++ b/cpp/tests/io/json_type_cast_test.cu
@@ -0,0 +1,253 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <io/utilities/string_parsing.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <algorithm>
+#include <iterator>
+#include <type_traits>
+
+using namespace cudf::test::iterators;
+
+struct JSONTypeCastTest : public cudf::test::BaseFixture {};
+
+namespace {
+struct offsets_to_length {
+  __device__ cudf::size_type operator()(thrust::tuple<cudf::size_type, cudf::size_type> const& p)
+  {
+    return thrust::get<1>(p) - thrust::get<0>(p);
+  }
+};
+
+/// Returns length of each string in the column
+auto string_offset_to_length(cudf::strings_column_view const& column, rmm::cuda_stream_view stream)
+{
+  auto offsets_begin = column.offsets_begin();
+  auto offsets_pair =
+    thrust::make_zip_iterator(thrust::make_tuple(offsets_begin, thrust::next(offsets_begin)));
+  rmm::device_uvector<cudf::size_type> svs_length(column.size(), stream);
+  thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                    offsets_pair,
+                    offsets_pair + column.size(),
+                    svs_length.begin(),
+                    offsets_to_length{});
+  return svs_length;
+}
+}  // namespace
+
+auto default_json_options()
+{
+  auto parse_opts = cudf::io::parse_options{',', '\n', '\"', '.'};
+
+  auto const stream     = cudf::get_default_stream();
+  parse_opts.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  parse_opts.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  parse_opts.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+  return parse_opts;
+}
+
+TEST_F(JSONTypeCastTest, String)
+{
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+  auto const type   = cudf::data_type{cudf::type_id::STRING};
+
+  auto in_valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 4; });
+  std::vector<char const*> input_values{"this", "is", "null", "of", "", "strings", R"("null")"};
+  cudf::test::strings_column_wrapper input(input_values.begin(), input_values.end(), in_valids);
+
+  auto column                                     = cudf::strings_column_view(input);
+  rmm::device_uvector<cudf::size_type> svs_length = string_offset_to_length(column, stream);
+
+  auto null_mask_it = no_nulls();
+  auto null_mask =
+    std::get<0>(cudf::test::detail::make_null_mask(null_mask_it, null_mask_it + column.size()));
+
+  auto str_col = cudf::io::json::detail::parse_data(
+    column.chars().data<char>(),
+    thrust::make_zip_iterator(thrust::make_tuple(column.offsets_begin(), svs_length.begin())),
+    column.size(),
+    type,
+    std::move(null_mask),
+    0,
+    default_json_options().view(),
+    stream,
+    mr);
+
+  auto out_valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2 and i != 4; });
+  std::vector<char const*> expected_values{"this", "is", "", "of", "", "strings", "null"};
+  cudf::test::strings_column_wrapper expected(
+    expected_values.begin(), expected_values.end(), out_valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(str_col->view(), expected);
+}
+
+TEST_F(JSONTypeCastTest, Int)
+{
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+  auto const type   = cudf::data_type{cudf::type_id::INT64};
+
+  cudf::test::strings_column_wrapper data({"1", "null", "3", "true", "5", "false"});
+  auto column                                     = cudf::strings_column_view(data);
+  rmm::device_uvector<cudf::size_type> svs_length = string_offset_to_length(column, stream);
+
+  auto null_mask_it = no_nulls();
+  auto null_mask =
+    std::get<0>(cudf::test::detail::make_null_mask(null_mask_it, null_mask_it + column.size()));
+
+  auto col = cudf::io::json::detail::parse_data(
+    column.chars().data<char>(),
+    thrust::make_zip_iterator(thrust::make_tuple(column.offsets_begin(), svs_length.begin())),
+    column.size(),
+    type,
+    std::move(null_mask),
+    0,
+    default_json_options().view(),
+    stream,
+    mr);
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<int64_t>{{1, 2, 3, 1, 5, 0}, {1, 0, 1, 1, 1, 1}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(col->view(), expected);
+}
+
+TEST_F(JSONTypeCastTest, StringEscapes)
+{
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+  auto const type   = cudf::data_type{cudf::type_id::STRING};
+
+  cudf::test::strings_column_wrapper data({
+    R"("\uD83D\uDE80")",
+    R"("\uff21\ud83d\ude80\uff21\uff21")",
+    R"("invalid char being escaped escape char\-")",
+    R"("too few hex digits \u12")",
+    R"("too few hex digits for surrogate pair \uD83D\uDE")",
+    R"("\u005C")",
+    R"("\u27A9")",
+    R"("escape with nothing to escape \")",
+    R"("\"\\\/\b\f\n\r\t")",
+  });
+  auto column                                     = cudf::strings_column_view(data);
+  rmm::device_uvector<cudf::size_type> svs_length = string_offset_to_length(column, stream);
+
+  auto null_mask_it = no_nulls();
+  auto null_mask =
+    std::get<0>(cudf::test::detail::make_null_mask(null_mask_it, null_mask_it + column.size()));
+
+  auto col = cudf::io::json::detail::parse_data(
+    column.chars().data<char>(),
+    thrust::make_zip_iterator(thrust::make_tuple(column.offsets_begin(), svs_length.begin())),
+    column.size(),
+    type,
+    std::move(null_mask),
+    0,
+    default_json_options().view(),
+    stream,
+    mr);
+
+  auto expected = cudf::test::strings_column_wrapper{
+    {"🚀", "Ａ🚀ＡＡ", "", "", "", "\\", "➩", "", "\"\\/\b\f\n\r\t"},
+    {true, true, false, false, false, true, true, false, true}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(col->view(), expected);
+}
+
+TEST_F(JSONTypeCastTest, ErrorNulls)
+{
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+  auto const type   = cudf::data_type{cudf::type_id::STRING};
+
+  // error in decoding
+  std::vector<char const*> input_values{R"("\"\a")",
+                                        R"("\u")",
+                                        R"("\u0")",
+                                        R"("\u0b")",
+                                        R"("\u00b")",
+                                        R"("\u00bz")",
+                                        R"("\t34567890123456\t9012345678901\ug0bc")",
+                                        R"("\t34567890123456\t90123456789012\u0hbc")",
+                                        R"("\t34567890123456\t90123456789012\u00ic")",
+                                        R"("\t34567890123456\t9012345678901\")",
+                                        R"("\t34567890123456\t90123456789012\")",
+                                        R"(null)"};
+  // Note: without quotes are copied without decoding
+  cudf::test::strings_column_wrapper input(input_values.begin(), input_values.end());
+
+  auto column        = cudf::strings_column_view(input);
+  auto space_length  = 128;
+  auto prepend_space = [&space_length](auto const& s) {
+    if (s[0] == '"') return "\"" + std::string(space_length, ' ') + std::string(s + 1);
+    return std::string(s);
+  };
+  std::vector<std::string> small_input;
+  std::transform(
+    input_values.begin(), input_values.end(), std::back_inserter(small_input), prepend_space);
+  cudf::test::strings_column_wrapper small_col(small_input.begin(), small_input.end());
+
+  std::vector<std::string> large_input;
+  space_length = 128 * 128;
+  std::transform(
+    input_values.begin(), input_values.end(), std::back_inserter(large_input), prepend_space);
+  cudf::test::strings_column_wrapper large_col(large_input.begin(), large_input.end());
+
+  std::vector<char const*> expected_values{"", "", "", "", "", "", "", "", "", "", "", ""};
+  cudf::test::strings_column_wrapper expected(
+    expected_values.begin(), expected_values.end(), cudf::test::iterators::all_nulls());
+
+  // single threads, warp, block.
+  for (auto const& column :
+       {column, cudf::strings_column_view(small_col), cudf::strings_column_view(large_col)}) {
+    rmm::device_uvector<cudf::size_type> svs_length = string_offset_to_length(column, stream);
+
+    auto null_mask_it = no_nulls();
+    auto null_mask =
+      std::get<0>(cudf::test::detail::make_null_mask(null_mask_it, null_mask_it + column.size()));
+
+    auto str_col = cudf::io::json::detail::parse_data(
+      column.chars().data<char>(),
+      thrust::make_zip_iterator(thrust::make_tuple(column.offsets_begin(), svs_length.begin())),
+      column.size(),
+      type,
+      std::move(null_mask),
+      0,
+      default_json_options().view(),
+      stream,
+      mr);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(str_col->view(), expected);
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/json_writer.cpp b/cpp/tests/io/json_writer.cpp
new file mode 100644
index 0000000..3a4074c
--- /dev/null
+++ b/cpp/tests/io/json_writer.cpp
@@ -0,0 +1,556 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/io/json.hpp>
+#include <cudf/io/types.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+
+#include <string>
+#include <vector>
+
+struct JsonWriterTest : public cudf::test::BaseFixture {};
+
+TEST_F(JsonWriterTest, EmptyInput)
+{
+  cudf::test::strings_column_wrapper col1;
+  cudf::test::strings_column_wrapper col2;
+  cudf::test::fixed_width_column_wrapper<int> col3;
+  cudf::test::fixed_width_column_wrapper<float> col4;
+  cudf::test::fixed_width_column_wrapper<int16_t> col5;
+  cudf::table_view tbl_view{{col1, col2, col3, col4}};
+  cudf::io::table_metadata mt{{{"col1"}, {"col2"}, {"int"}, {"float"}, {"int16"}}};
+
+  std::vector<char> out_buffer;
+  auto destination = cudf::io::sink_info(&out_buffer);
+  auto out_options = cudf::io::json_writer_options_builder(destination, tbl_view)
+                       .include_nulls(true)
+                       .metadata(mt)
+                       .lines(false)
+                       .na_rep("null")
+                       .build();
+
+  // Empty columns in table
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+  std::string const expected = R"([])";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+
+  // Empty columns in table - JSON Lines
+  out_buffer.clear();
+  out_options.enable_lines(true);
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+  std::string const expected_lines = "\n";
+  EXPECT_EQ(expected_lines, std::string(out_buffer.data(), out_buffer.size()));
+
+  // Empty table - JSON Lines
+  cudf::table_view tbl_view2{};
+  out_options.set_table(tbl_view2);
+  out_buffer.clear();
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+  EXPECT_EQ(expected_lines, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, ErrorCases)
+{
+  cudf::test::strings_column_wrapper col1{"a", "b", "c"};
+  cudf::test::strings_column_wrapper col2{"d", "e", "f"};
+  cudf::test::fixed_width_column_wrapper<int> col3{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<float> col4{1.5, 2.5, 3.5};
+  cudf::test::fixed_width_column_wrapper<int16_t> col5{{1, 2, 3},
+                                                       cudf::test::iterators::nulls_at({0, 2})};
+  cudf::table_view tbl_view{{col1, col2, col3, col4, col5}};
+  cudf::io::table_metadata mt{{{"col1"}, {"col2"}, {"int"}, {"float"}}};
+
+  std::vector<char> out_buffer;
+  auto destination = cudf::io::sink_info(&out_buffer);
+  auto out_options = cudf::io::json_writer_options_builder(destination, tbl_view)
+                       .include_nulls(true)
+                       .metadata(mt)
+                       .lines(false)
+                       .na_rep("null")
+                       .build();
+
+  // not enough column names
+  EXPECT_THROW(cudf::io::write_json(out_options, rmm::mr::get_current_device_resource()),
+               cudf::logic_error);
+
+  mt.schema_info.emplace_back("int16");
+  out_options.set_metadata(mt);
+  EXPECT_NO_THROW(cudf::io::write_json(out_options, rmm::mr::get_current_device_resource()));
+
+  // chunk_rows must be at least 8
+  out_options.set_rows_per_chunk(0);
+  EXPECT_THROW(cudf::io::write_json(out_options, rmm::mr::get_current_device_resource()),
+               cudf::logic_error);
+}
+
+TEST_F(JsonWriterTest, PlainTable)
+{
+  cudf::test::strings_column_wrapper col1{"a", "b", "c"};
+  cudf::test::strings_column_wrapper col2{"d", "e", "f"};
+  cudf::test::fixed_width_column_wrapper<int> col3{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<float> col4{1.5, 2.5, 3.5};
+  cudf::test::fixed_width_column_wrapper<int16_t> col5{{1, 2, 3},
+                                                       cudf::test::iterators::nulls_at({0, 2})};
+  cudf::table_view tbl_view{{col1, col2, col3, col4, col5}};
+  cudf::io::table_metadata mt{{{"col1"}, {"col2"}, {"int"}, {"float"}, {"int16"}}};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(true)
+                           .metadata(mt)
+                           .lines(false)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+
+  std::string const expected =
+    R"([{"col1":"a","col2":"d","int":1,"float":1.5,"int16":null},{"col1":"b","col2":"e","int":2,"float":2.5,"int16":2},{"col1":"c","col2":"f","int":3,"float":3.5,"int16":null}])";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, SimpleNested)
+{
+  std::string const data = R"(
+{"a": 1, "b": 2, "c": {"d": 3        }, "f": 5.5,  "g": [1]}
+{"a": 6, "b": 7, "c": {"d": 8        }, "f": 10.5, "g": null}
+{"a": 1, "b": 2, "c": {        "e": 4}, "f": 5.5,  "g": [2, null]}
+{"a": 6, "b": 7, "c": {        "e": 9}, "f": 10.5, "g": [3, 4, 5]} )";
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+  cudf::table_view tbl_view            = result.tbl->view();
+  cudf::io::table_metadata mt{result.metadata};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(false)
+                           .metadata(mt)
+                           .lines(true)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+  std::string const expected = R"({"a":1,"b":2,"c":{"d":3},"f":5.5,"g":[1]}
+{"a":6,"b":7,"c":{"d":8},"f":10.5}
+{"a":1,"b":2,"c":{"e":4},"f":5.5,"g":[2,null]}
+{"a":6,"b":7,"c":{"e":9},"f":10.5,"g":[3,4,5]}
+)";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, MixedNested)
+{
+  std::string const data = R"(
+{"a": 1, "b": 2, "c": {"d": [3]      }, "f": 5.5,  "g": [ {"h": 1}]}
+{"a": 6, "b": 7, "c": {"d": [8]      }, "f": 10.5, "g": null}
+{"a": 1, "b": 2, "c": {        "e": 4}, "f": 5.5,  "g": [{"h": 2}, null]}
+{"a": 6, "b": 7, "c": {        "e": 9}, "f": 10.5, "g": [{"h": 3}, {"h": 4}, {"h": 5}]} )";
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+  cudf::table_view tbl_view            = result.tbl->view();
+  cudf::io::table_metadata mt{result.metadata};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(false)
+                           .metadata(mt)
+                           .lines(false)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+  std::string const expected =
+    R"([{"a":1,"b":2,"c":{"d":[3]},"f":5.5,"g":[{"h":1}]},)"
+    R"({"a":6,"b":7,"c":{"d":[8]},"f":10.5},)"
+    R"({"a":1,"b":2,"c":{"e":4},"f":5.5,"g":[{"h":2},null]},)"
+    R"({"a":6,"b":7,"c":{"e":9},"f":10.5,"g":[{"h":3},{"h":4},{"h":5}]}])";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, WriteReadNested)
+{
+  using namespace cudf::test::iterators;
+  using LCW = cudf::test::lists_column_wrapper<int64_t>;
+  cudf::test::fixed_width_column_wrapper<int> a{1, 6, 1, 6};
+  cudf::test::fixed_width_column_wrapper<uint8_t> b{2, 7, 2, 7};
+  cudf::test::fixed_width_column_wrapper<int64_t> d{{3, 8, 0, 0}, nulls_at({2, 3})};
+  cudf::test::fixed_width_column_wrapper<int64_t> e{{0, 0, 4, 9}, nulls_at({0, 1})};
+  cudf::test::structs_column_wrapper c{{d, e}};
+  cudf::test::fixed_width_column_wrapper<float> f{5.5, 10.5, 5.5, 10.5};
+  LCW g{{LCW{1}, LCW{0}, LCW{{2, 0}, null_at(1)}, LCW{3, 4, 5}}, null_at(1)};
+  cudf::table_view tbl_view{{a, b, c, f, g}};
+  cudf::io::table_metadata mt{{{"a"}, {"b"}, {"c"}, {"f"}, {"g"}}};
+  mt.schema_info[2].children = {{"d"}, {"e"}};
+
+  std::vector<char> out_buffer;
+  auto destination = cudf::io::sink_info(&out_buffer);
+  auto out_options = cudf::io::json_writer_options_builder(destination, tbl_view)
+                       .include_nulls(false)
+                       .metadata(mt)
+                       .lines(true)
+                       .na_rep("null")
+                       .build();
+
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+  std::string const expected = R"({"a":1,"b":2,"c":{"d":3},"f":5.5,"g":[1]}
+{"a":6,"b":7,"c":{"d":8},"f":10.5}
+{"a":1,"b":2,"c":{"e":4},"f":5.5,"g":[2,null]}
+{"a":6,"b":7,"c":{"e":9},"f":10.5,"g":[3,4,5]}
+)";
+  auto const output_string   = std::string(out_buffer.data(), out_buffer.size());
+  EXPECT_EQ(expected, output_string);
+
+  // Read back the written JSON, and compare with the original table
+  // Without type information
+  auto in_options = cudf::io::json_reader_options::builder(
+                      cudf::io::source_info{output_string.data(), output_string.size()})
+                      .lines(true)
+                      .build();
+
+  auto result             = cudf::io::read_json(in_options);
+  auto tbl_out            = result.tbl->view();
+  auto const int64_dtype  = cudf::data_type{cudf::type_id::INT64};
+  auto const double_dtype = cudf::data_type{cudf::type_id::FLOAT64};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*cudf::cast(a, int64_dtype), tbl_out.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*cudf::cast(b, int64_dtype), tbl_out.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(c, tbl_out.column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*cudf::cast(f, double_dtype), tbl_out.column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(g, tbl_out.column(4));
+
+  mt.schema_info[4].children = {{"offsets"}, {"element"}};  // list child column names
+  EXPECT_EQ(mt.schema_info.size(), result.metadata.schema_info.size());
+  for (auto i = 0UL; i < mt.schema_info.size(); i++) {
+    EXPECT_EQ(mt.schema_info[i].name, result.metadata.schema_info[i].name) << "[" << i << "]";
+    EXPECT_EQ(mt.schema_info[i].children.size(), result.metadata.schema_info[i].children.size())
+      << "[" << i << "]";
+    for (auto j = 0UL; j < mt.schema_info[i].children.size(); j++) {
+      EXPECT_EQ(mt.schema_info[i].children[j].name, result.metadata.schema_info[i].children[j].name)
+        << "[" << i << "][" << j << "]";
+    }
+  }
+
+  // Read with type information
+  std::map<std::string, cudf::io::schema_element> types;
+  types["a"]                  = cudf::io::schema_element{cudf::data_type{cudf::type_id::INT32}};
+  types["b"]                  = cudf::io::schema_element{cudf::data_type{cudf::type_id::UINT8}};
+  types["c"]                  = cudf::io::schema_element{cudf::data_type{cudf::type_id::STRUCT}};
+  types["c"].child_types["d"] = cudf::io::schema_element{cudf::data_type{cudf::type_id::INT64}};
+  types["c"].child_types["e"] = cudf::io::schema_element{cudf::data_type{cudf::type_id::INT64}};
+  types["f"]                  = cudf::io::schema_element{cudf::data_type{cudf::type_id::FLOAT32}};
+  types["g"]                  = cudf::io::schema_element{cudf::data_type{cudf::type_id::LIST}};
+  types["g"].child_types["element"] =
+    cudf::io::schema_element{cudf::data_type{cudf::type_id::INT64}};
+
+  in_options.set_dtypes(types);
+  result  = cudf::io::read_json(in_options);
+  tbl_out = result.tbl->view();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(a, tbl_out.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(b, tbl_out.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(c, tbl_out.column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(f, tbl_out.column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(g, tbl_out.column(4));
+  EXPECT_EQ(mt.schema_info.size(), result.metadata.schema_info.size());
+  for (auto i = 0UL; i < mt.schema_info.size(); i++) {
+    EXPECT_EQ(mt.schema_info[i].name, result.metadata.schema_info[i].name) << "[" << i << "]";
+    EXPECT_EQ(mt.schema_info[i].children.size(), result.metadata.schema_info[i].children.size())
+      << "[" << i << "]";
+    for (auto j = 0UL; j < mt.schema_info[i].children.size(); j++) {
+      EXPECT_EQ(mt.schema_info[i].children[j].name, result.metadata.schema_info[i].children[j].name)
+        << "[" << i << "][" << j << "]";
+    }
+  }
+
+  // Without children column names
+  mt.schema_info[2].children.clear();
+  out_options.set_metadata(mt);
+  out_buffer.clear();
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+
+  in_options = cudf::io::json_reader_options::builder(
+                 cudf::io::source_info{out_buffer.data(), out_buffer.size()})
+                 .lines(true)
+                 .build();
+  result = cudf::io::read_json(in_options);
+
+  mt.schema_info[2].children = {{"0"}, {"1"}};
+  EXPECT_EQ(mt.schema_info.size(), result.metadata.schema_info.size());
+  for (auto i = 0UL; i < mt.schema_info.size(); i++) {
+    EXPECT_EQ(mt.schema_info[i].name, result.metadata.schema_info[i].name) << "[" << i << "]";
+    EXPECT_EQ(mt.schema_info[i].children.size(), result.metadata.schema_info[i].children.size())
+      << "[" << i << "]";
+    for (auto j = 0UL; j < mt.schema_info[i].children.size(); j++) {
+      EXPECT_EQ(mt.schema_info[i].children[j].name, result.metadata.schema_info[i].children[j].name)
+        << "[" << i << "][" << j << "]";
+    }
+  }
+
+  // without column names
+  out_options.set_metadata(cudf::io::table_metadata{});
+  out_buffer.clear();
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+  in_options = cudf::io::json_reader_options::builder(
+                 cudf::io::source_info{out_buffer.data(), out_buffer.size()})
+                 .lines(true)
+                 .build();
+  result = cudf::io::read_json(in_options);
+
+  mt.schema_info             = {{"0"}, {"1"}, {"2"}, {"3"}, {"4"}};
+  mt.schema_info[2].children = {{"0"}, {"1"}};
+  mt.schema_info[4].children = {{"offsets"}, {"element"}};  // list child column names
+  EXPECT_EQ(mt.schema_info.size(), result.metadata.schema_info.size());
+  for (auto i = 0UL; i < mt.schema_info.size(); i++) {
+    EXPECT_EQ(mt.schema_info[i].name, result.metadata.schema_info[i].name) << "[" << i << "]";
+    EXPECT_EQ(mt.schema_info[i].children.size(), result.metadata.schema_info[i].children.size())
+      << "[" << i << "]";
+    for (auto j = 0UL; j < mt.schema_info[i].children.size(); j++) {
+      EXPECT_EQ(mt.schema_info[i].children[j].name, result.metadata.schema_info[i].children[j].name)
+        << "[" << i << "][" << j << "]";
+    }
+  }
+}
+
+TEST_F(JsonWriterTest, SpecialChars)
+{
+  cudf::test::fixed_width_column_wrapper<int> a{1, 6, 1, 6};
+  cudf::test::strings_column_wrapper b{"abcd", "b\b\f\n\r\t", "\"c\"", "/\\"};
+  cudf::table_view tbl_view{{a, b}};
+  cudf::io::table_metadata mt{{{"\"a\""}, {"\'b\'"}}};
+
+  std::vector<char> out_buffer;
+  auto destination = cudf::io::sink_info(&out_buffer);
+  auto out_options = cudf::io::json_writer_options_builder(destination, tbl_view)
+                       .include_nulls(false)
+                       .metadata(mt)
+                       .lines(true)
+                       .na_rep("null")
+                       .build();
+
+  cudf::io::write_json(out_options, rmm::mr::get_current_device_resource());
+  std::string const expected = R"({"\"a\"":1,"'b'":"abcd"}
+{"\"a\"":6,"'b'":"b\b\f\n\r\t"}
+{"\"a\"":1,"'b'":"\"c\""}
+{"\"a\"":6,"'b'":"\/\\"}
+)";
+  auto const output_string   = std::string(out_buffer.data(), out_buffer.size());
+  EXPECT_EQ(expected, output_string);
+}
+
+TEST_F(JsonWriterTest, NullList)
+{
+  std::string const data = R"(
+{"a": [null], "b": [[1, 2, 3], [null], [null, null, null], [4, null, 5]]}
+{"a": [2, null, null, 3] , "b": null}
+{"a": [null, null, 4], "b": [[2, null], null]}
+{"a": [5, null, null], "b": [null, [3, 4, 5]]} )";
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+  cudf::table_view tbl_view            = result.tbl->view();
+  cudf::io::table_metadata mt{result.metadata};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(true)
+                           .metadata(mt)
+                           .lines(true)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+  std::string const expected = R"({"a":[null],"b":[[1,2,3],[null],[null,null,null],[4,null,5]]}
+{"a":[2,null,null,3],"b":null}
+{"a":[null,null,4],"b":[[2,null],null]}
+{"a":[5,null,null],"b":[null,[3,4,5]]}
+)";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, ChunkedNested)
+{
+  std::string const data = R"(
+{"a": 1, "b": -2, "c": {      }, "e": [{"f": 1}]}
+{"a": 2, "b": -2, "c": {      }, "e": null}
+{"a": 3, "b": -2, "c": {"d": 9}, "e": [{"f": 2}, null]}
+{"a": 4, "b": -2, "c": {"d": 16}, "e": [{"f": 3}, {"f": 4}, {"f": 5}]}
+{"a": 5, "b": -2, "c": {      }, "e": []}
+{"a": 6, "b": -2, "c": {"d": 36}, "e": [{"f": 6}]}
+{"a": 7, "b": -2, "c": {"d": 49}, "e": [{"f": 7}]}
+{"a": 8, "b": -2, "c": {"d": 64}, "e": [{"f": 8}]}
+{"a": 9, "b": -2, "c": {"d": 81}, "e": [{"f": 9}]}
+)";
+  cudf::io::json_reader_options in_options =
+    cudf::io::json_reader_options::builder(cudf::io::source_info{data.data(), data.size()})
+      .lines(true);
+
+  cudf::io::table_with_metadata result = cudf::io::read_json(in_options);
+  cudf::table_view tbl_view            = result.tbl->view();
+  cudf::io::table_metadata mt{result.metadata};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(false)
+                           .metadata(mt)
+                           .lines(true)
+                           .na_rep("null")
+                           .rows_per_chunk(8);
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+  std::string const expected =
+    R"({"a":1,"b":-2,"c":{},"e":[{"f":1}]}
+{"a":2,"b":-2,"c":{}}
+{"a":3,"b":-2,"c":{"d":9},"e":[{"f":2},null]}
+{"a":4,"b":-2,"c":{"d":16},"e":[{"f":3},{"f":4},{"f":5}]}
+{"a":5,"b":-2,"c":{},"e":[]}
+{"a":6,"b":-2,"c":{"d":36},"e":[{"f":6}]}
+{"a":7,"b":-2,"c":{"d":49},"e":[{"f":7}]}
+{"a":8,"b":-2,"c":{"d":64},"e":[{"f":8}]}
+{"a":9,"b":-2,"c":{"d":81},"e":[{"f":9}]}
+)";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, StructAllNullCombinations)
+{
+  auto const_1_iter = thrust::make_constant_iterator(1);
+
+  auto col_a = cudf::test::fixed_width_column_wrapper<int>(
+    const_1_iter, const_1_iter + 32, cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i / 16;
+    }));
+
+  auto col_b = cudf::test::fixed_width_column_wrapper<int>(
+    const_1_iter, const_1_iter + 32, cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return (i / 8) % 2;
+    }));
+
+  auto col_c = cudf::test::fixed_width_column_wrapper<int>(
+    const_1_iter, const_1_iter + 32, cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return (i / 4) % 2;
+    }));
+
+  auto col_d = cudf::test::fixed_width_column_wrapper<int>(
+    const_1_iter, const_1_iter + 32, cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return (i / 2) % 2;
+    }));
+
+  auto col_e = cudf::test::fixed_width_column_wrapper<int>(
+    const_1_iter, const_1_iter + 32, cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i % 2;
+    }));
+
+  // The table has 32 rows with validity from 00000 to 11111
+  cudf::table_view tbl_view = cudf::table_view({col_a, col_b, col_c, col_d, col_e});
+  cudf::io::table_metadata mt{{{"a"}, {"b"}, {"c"}, {"d"}, {"e"}}};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(false)
+                           .metadata(mt)
+                           .lines(true)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+  std::string const expected = R"({}
+{"e":1}
+{"d":1}
+{"d":1,"e":1}
+{"c":1}
+{"c":1,"e":1}
+{"c":1,"d":1}
+{"c":1,"d":1,"e":1}
+{"b":1}
+{"b":1,"e":1}
+{"b":1,"d":1}
+{"b":1,"d":1,"e":1}
+{"b":1,"c":1}
+{"b":1,"c":1,"e":1}
+{"b":1,"c":1,"d":1}
+{"b":1,"c":1,"d":1,"e":1}
+{"a":1}
+{"a":1,"e":1}
+{"a":1,"d":1}
+{"a":1,"d":1,"e":1}
+{"a":1,"c":1}
+{"a":1,"c":1,"e":1}
+{"a":1,"c":1,"d":1}
+{"a":1,"c":1,"d":1,"e":1}
+{"a":1,"b":1}
+{"a":1,"b":1,"e":1}
+{"a":1,"b":1,"d":1}
+{"a":1,"b":1,"d":1,"e":1}
+{"a":1,"b":1,"c":1}
+{"a":1,"b":1,"c":1,"e":1}
+{"a":1,"b":1,"c":1,"d":1}
+{"a":1,"b":1,"c":1,"d":1,"e":1}
+)";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+TEST_F(JsonWriterTest, Unicode)
+{
+  //                                       UTF-8,                      UTF-16
+  cudf::test::strings_column_wrapper col1{"\"\\/\b\f\n\r\t", "ராபிட்ஸ்", "$€𐐷𤭢", "C𝞵𝓓𝒻"};
+  // Unicode
+  // 0000-FFFF     Basic Multilingual Plane
+  // 10000-10FFFF  Supplementary Plane
+  cudf::test::strings_column_wrapper col2{
+    "CႮ≪ㇳ䍏凹沦王辿龸ꁗ믜스폶ﴠ",  //  0000-FFFF
+    "𐀀𑿪𒐦𓃰𔙆 𖦆𗿿𘳕𚿾[↳] 𜽆𝓚𞤁🄰",                            // 10000-1FFFF
+    "𠘨𡥌𢗉𣇊𤊩𥅽𦉱𧴱𨁲𩁹𪐢𫇭𬬭𭺷𮊦屮",                // 20000-2FFFF
+    "𰾑𱔈𲍉"};                                         // 30000-3FFFF
+  cudf::test::fixed_width_column_wrapper<int16_t> col3{{1, 2, 3, 4},
+                                                       cudf::test::iterators::nulls_at({0, 2})};
+  cudf::table_view tbl_view{{col1, col2, col3}};
+  cudf::io::table_metadata mt{{{"col1"}, {"col2"}, {"int16"}}};
+
+  std::vector<char> out_buffer;
+  auto destination     = cudf::io::sink_info(&out_buffer);
+  auto options_builder = cudf::io::json_writer_options_builder(destination, tbl_view)
+                           .include_nulls(true)
+                           .metadata(mt)
+                           .lines(true)
+                           .na_rep("null");
+
+  cudf::io::write_json(options_builder.build(), rmm::mr::get_current_device_resource());
+
+  std::string const expected =
+    R"({"col1":"\"\\\/\b\f\n\r\t","col2":"C\u10ae\u226a\u31f3\u434f\u51f9\u6ca6\u738b\u8fbf\u9fb8\ua057\ubbdc\uc2a4\ud3f6\ue4fe\ufd20","int16":null}
+{"col1":"\u0bb0\u0bbe\u0baa\u0bbf\u0b9f\u0bcd\u0bb8\u0bcd","col2":"\ud800\udc00\ud807\udfea\ud809\udc26\ud80c\udcf0\ud811\ude46 \ud81a\udd86\ud81f\udfff\ud823\udcd5\ud82b\udffe[\u21b3] \ud833\udf46\ud835\udcda\ud83a\udd01\ud83c\udd30","int16":2}
+{"col1":"$\u20ac\ud801\udc37\ud852\udf62","col2":"\ud841\ude28\ud846\udd4c\ud849\uddc9\ud84c\uddca\ud850\udea9\ud854\udd7d\ud858\ude71\ud85f\udd31\ud860\udc72\ud864\udc79\ud869\udc22\ud86c\udded\ud872\udf2d\ud877\udeb7\ud878\udea6\u5c6e","int16":null}
+{"col1":"C\ud835\udfb5\ud835\udcd3\ud835\udcbb","col2":"\ud883\udf91\ud885\udd08\ud888\udf49","int16":4}
+)";
+  EXPECT_EQ(expected, std::string(out_buffer.data(), out_buffer.size()));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/metadata_utilities.cpp b/cpp/tests/io/metadata_utilities.cpp
new file mode 100644
index 0000000..84f04f6
--- /dev/null
+++ b/cpp/tests/io/metadata_utilities.cpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/io_metadata_utilities.hpp>
+
+#include <gmock/gmock.h>
+
+namespace cudf::test {
+
+void expect_metadata_equal(cudf::io::table_input_metadata in_meta,
+                           cudf::io::table_metadata out_meta)
+{
+  std::function<void(cudf::io::column_name_info, cudf::io::column_in_metadata)> compare_names =
+    [&](cudf::io::column_name_info out_col, cudf::io::column_in_metadata in_col) {
+      if (not in_col.get_name().empty()) { EXPECT_EQ(out_col.name, in_col.get_name()); }
+      ASSERT_EQ(out_col.children.size(), in_col.num_children());
+      for (size_t i = 0; i < out_col.children.size(); ++i) {
+        compare_names(out_col.children[i], in_col.child(i));
+      }
+    };
+
+  ASSERT_EQ(out_meta.schema_info.size(), in_meta.column_metadata.size());
+
+  for (size_t i = 0; i < out_meta.schema_info.size(); ++i) {
+    compare_names(out_meta.schema_info[i], in_meta.column_metadata[i]);
+  }
+}
+
+void expect_metadata_equal(cudf::io::table_metadata lhs_meta, cudf::io::table_metadata rhs_meta)
+{
+  std::function<void(cudf::io::column_name_info, cudf::io::column_name_info)> compare_names =
+    [&](cudf::io::column_name_info lhs, cudf::io::column_name_info rhs) {
+      // Ensure column names match
+      EXPECT_EQ(lhs.name, rhs.name);
+
+      // Ensure number of child columns match
+      ASSERT_EQ(lhs.children.size(), rhs.children.size());
+      for (size_t i = 0; i < lhs.children.size(); ++i) {
+        compare_names(lhs.children[i], rhs.children[i]);
+      }
+    };
+
+  // Ensure the number of columns at the root level matches
+  ASSERT_EQ(lhs_meta.schema_info.size(), rhs_meta.schema_info.size());
+
+  // Recurse for each column making sure their names and descendants match
+  for (size_t i = 0; i < rhs_meta.schema_info.size(); ++i) {
+    compare_names(lhs_meta.schema_info[i], rhs_meta.schema_info[i]);
+  }
+}
+
+}  // namespace cudf::test
diff --git a/cpp/tests/io/nested_json_test.cpp b/cpp/tests/io/nested_json_test.cpp
new file mode 100644
index 0000000..00d6571
--- /dev/null
+++ b/cpp/tests/io/nested_json_test.cpp
@@ -0,0 +1,926 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/json/nested_json.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/io_metadata_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/iterator/zip_iterator.h>
+
+#include <string>
+
+namespace cuio_json = cudf::io::json;
+
+namespace {
+// Forward declaration
+void print_column(std::string const& input,
+                  cuio_json::json_column const& column,
+                  uint32_t indent = 0);
+
+/**
+ * @brief Helper to generate indentation
+ */
+std::string pad(uint32_t indent = 0)
+{
+  std::string pad{};
+  if (indent > 0) pad.insert(pad.begin(), indent, ' ');
+  return pad;
+}
+
+/**
+ * @brief Prints a string column.
+ */
+void print_json_string_col(std::string const& input,
+                           cuio_json::json_column const& column,
+                           uint32_t indent = 0)
+{
+  for (std::size_t i = 0; i < column.string_offsets.size(); i++) {
+    std::cout << pad(indent) << i << ": [" << (column.validity[i] ? "1" : "0") << "] '"
+              << input.substr(column.string_offsets[i], column.string_lengths[i]) << "'\n";
+  }
+}
+
+/**
+ * @brief Prints a list column.
+ */
+void print_json_list_col(std::string const& input,
+                         cuio_json::json_column const& column,
+                         uint32_t indent = 0)
+{
+  std::cout << pad(indent) << " [LIST]\n";
+  std::cout << pad(indent) << " -> num. child-columns: " << column.child_columns.size() << "\n";
+  std::cout << pad(indent) << " -> num. rows: " << column.current_offset << "\n";
+  std::cout << pad(indent) << " -> num. valid: " << column.valid_count << "\n";
+  std::cout << pad(indent) << " offsets[]: "
+            << "\n";
+  for (std::size_t i = 0; i < column.child_offsets.size() - 1; i++) {
+    std::cout << pad(indent + 2) << i << ": [" << (column.validity[i] ? "1" : "0") << "] ["
+              << column.child_offsets[i] << ", " << column.child_offsets[i + 1] << ")\n";
+  }
+  if (column.child_columns.size() > 0) {
+    std::cout << pad(indent) << column.child_columns.begin()->first << "[]: "
+              << "\n";
+    print_column(input, column.child_columns.begin()->second, indent + 2);
+  }
+}
+
+/**
+ * @brief Prints a struct column.
+ */
+void print_json_struct_col(std::string const& input,
+                           cuio_json::json_column const& column,
+                           uint32_t indent = 0)
+{
+  std::cout << pad(indent) << " [STRUCT]\n";
+  std::cout << pad(indent) << " -> num. child-columns: " << column.child_columns.size() << "\n";
+  std::cout << pad(indent) << " -> num. rows: " << column.current_offset << "\n";
+  std::cout << pad(indent) << " -> num. valid: " << column.valid_count << "\n";
+  std::cout << pad(indent) << " -> validity[]: "
+            << "\n";
+  for (decltype(column.current_offset) i = 0; i < column.current_offset; i++) {
+    std::cout << pad(indent + 2) << i << ": [" << (column.validity[i] ? "1" : "0") << "]\n";
+  }
+  auto it = std::begin(column.child_columns);
+  for (std::size_t i = 0; i < column.child_columns.size(); i++) {
+    std::cout << pad(indent + 2) << "child #" << i << " '" << it->first << "'[] \n";
+    print_column(input, it->second, indent + 2);
+    it++;
+  }
+}
+
+/**
+ * @brief Prints the column's data and recurses through and prints all the child columns.
+ */
+void print_column(std::string const& input, cuio_json::json_column const& column, uint32_t indent)
+{
+  switch (column.type) {
+    case cuio_json::json_col_t::StringColumn: print_json_string_col(input, column, indent); break;
+    case cuio_json::json_col_t::ListColumn: print_json_list_col(input, column, indent); break;
+    case cuio_json::json_col_t::StructColumn: print_json_struct_col(input, column, indent); break;
+    case cuio_json::json_col_t::Unknown: std::cout << pad(indent) << "[UNKNOWN]\n"; break;
+    default: break;
+  }
+}
+}  // namespace
+
+// Base test fixture for tests
+struct JsonTest : public cudf::test::BaseFixture {};
+
+TEST_F(JsonTest, StackContext)
+{
+  // Type used to represent the atomic symbol type used within the finite-state machine
+  using SymbolT      = char;
+  using StackSymbolT = char;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+
+  // Test input
+  std::string const input = R"(  [{)"
+                            R"("category": "reference",)"
+                            R"("index:": [4,12,42],)"
+                            R"("author": "Nigel Rees",)"
+                            R"("title": "[Sayings of the Century]",)"
+                            R"("price": 8.95)"
+                            R"(},  )"
+                            R"({)"
+                            R"("category": "reference",)"
+                            R"("index": [4,{},null,{"a":[{ }, {}] } ],)"
+                            R"("author": "Nigel Rees",)"
+                            R"("title": "{}\\\"[], <=semantic-symbols-string\\\\",)"
+                            R"("price": 8.95)"
+                            R"(}] )";
+
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input =
+    cudf::device_span<SymbolT const>{d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+  cudf::detail::hostdevice_vector<StackSymbolT> stack_context(input.size(), stream);
+
+  // Run algorithm
+  constexpr auto stack_behavior = cuio_json::stack_behavior_t::PushPopWithoutReset;
+  cuio_json::detail::get_stack_context(d_input, stack_context.device_ptr(), stack_behavior, stream);
+
+  // Copy back the results
+  stack_context.device_to_host_async(stream);
+
+  // Make sure we copied back the stack context
+  stream.synchronize();
+
+  std::vector<char> const golden_stack_context{
+    '_', '_', '_', '[', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '[', '[', '[', '[', '[', '[', '[', '[', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '[', '[', '[', '[', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '[', '[', '[', '{', '[', '[', '[', '[', '[', '[', '[', '{',
+    '{', '{', '{', '{', '[', '{', '{', '[', '[', '[', '{', '[', '{', '{', '[', '[', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '[', '_'};
+
+  ASSERT_EQ(golden_stack_context.size(), stack_context.size());
+  CUDF_TEST_EXPECT_VECTOR_EQUAL(golden_stack_context, stack_context, stack_context.size());
+}
+
+TEST_F(JsonTest, StackContextUtf8)
+{
+  // Type used to represent the atomic symbol type used within the finite-state machine
+  using SymbolT      = char;
+  using StackSymbolT = char;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+
+  // Test input
+  std::string const input = R"([{"a":{"year":1882,"author": "Bharathi"}, {"a":"filip ʒakotɛ"}}])";
+
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input =
+    cudf::device_span<SymbolT const>{d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+  cudf::detail::hostdevice_vector<StackSymbolT> stack_context(input.size(), stream);
+
+  // Run algorithm
+  constexpr auto stack_behavior = cuio_json::stack_behavior_t::PushPopWithoutReset;
+  cuio_json::detail::get_stack_context(d_input, stack_context.device_ptr(), stack_behavior, stream);
+
+  // Copy back the results
+  stack_context.device_to_host_async(stream);
+
+  // Make sure we copied back the stack context
+  stream.synchronize();
+
+  std::vector<char> const golden_stack_context{
+    '_', '[', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{',
+    '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '{', '['};
+
+  ASSERT_EQ(golden_stack_context.size(), stack_context.size());
+  CUDF_TEST_EXPECT_VECTOR_EQUAL(golden_stack_context, stack_context, stack_context.size());
+}
+
+TEST_F(JsonTest, StackContextRecovering)
+{
+  // Type used to represent the atomic symbol type used within the finite-state machine
+  using SymbolT      = char;
+  using StackSymbolT = char;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+
+  // JSON lines input that recovers on invalid lines
+  std::string const input = R"({"a":-2},
+  {"a":
+  {"a":{"a":[321
+  {"a":[1]}
+
+  {"b":123}
+  )";
+
+  // Expected stack context (including stack context of the newline characters)
+  std::string const golden_stack_context =
+    "_{{{{{{{__"
+    "___{{{{{"
+    "___{{{{{{{{{{[[[["
+    "___{{{{{[[{_"
+    "_"
+    "___{{{{{{{{_"
+    "__";
+
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input =
+    cudf::device_span<SymbolT const>{d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+  cudf::detail::hostdevice_vector<StackSymbolT> stack_context(input.size(), stream);
+
+  // Run algorithm
+  constexpr auto stack_behavior = cuio_json::stack_behavior_t::ResetOnDelimiter;
+  cuio_json::detail::get_stack_context(d_input, stack_context.device_ptr(), stack_behavior, stream);
+
+  // Copy back the results
+  stack_context.device_to_host_async(stream);
+
+  // Make sure we copied back the stack context
+  stream.synchronize();
+
+  // Verify results
+  ASSERT_EQ(golden_stack_context.size(), stack_context.size());
+  CUDF_TEST_EXPECT_VECTOR_EQUAL(golden_stack_context, stack_context, stack_context.size());
+}
+
+TEST_F(JsonTest, TokenStream)
+{
+  using cuio_json::PdaTokenT;
+  using cuio_json::SymbolOffsetT;
+  using cuio_json::SymbolT;
+  // Test input
+  std::string const input = R"(  [{)"
+                            R"("category": "reference",)"
+                            R"("index:": [4,12,42],)"
+                            R"("author": "Nigel Rees",)"
+                            R"("title": "[Sayings of the Century]",)"
+                            R"("price": 8.95)"
+                            R"(},  )"
+                            R"({)"
+                            R"("category": "reference",)"
+                            R"("index": [4,{},null,{"a":[{ }, {}] } ],)"
+                            R"("author": "Nigel Rees",)"
+                            R"("title": "{}[], <=semantic-symbols-string",)"
+                            R"("price": 8.95)"
+                            R"(}] )";
+
+  auto const stream = cudf::get_default_stream();
+
+  // Default parsing options
+  cudf::io::json_reader_options default_options{};
+
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input =
+    cudf::device_span<SymbolT const>{d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+
+  // Parse the JSON and get the token stream
+  auto [d_tokens_gpu, d_token_indices_gpu] = cuio_json::detail::get_token_stream(
+    d_input, default_options, stream, rmm::mr::get_current_device_resource());
+  // Copy back the number of tokens that were written
+  auto const tokens_gpu        = cudf::detail::make_std_vector_async(d_tokens_gpu, stream);
+  auto const token_indices_gpu = cudf::detail::make_std_vector_async(d_token_indices_gpu, stream);
+
+  // Golden token stream sample
+  using token_t = cuio_json::token_t;
+  std::vector<std::pair<std::size_t, cuio_json::PdaTokenT>> const golden_token_stream = {
+    {2, token_t::ListBegin},
+    {3, token_t::StructBegin},
+    {4, token_t::StructMemberBegin},
+    {4, token_t::FieldNameBegin},
+    {13, token_t::FieldNameEnd},
+    {16, token_t::StringBegin},
+    {26, token_t::StringEnd},
+    {27, token_t::StructMemberEnd},
+    {28, token_t::StructMemberBegin},
+    {28, token_t::FieldNameBegin},
+    {35, token_t::FieldNameEnd},
+    {38, token_t::ListBegin},
+    {39, token_t::ValueBegin},
+    {40, token_t::ValueEnd},
+    {41, token_t::ValueBegin},
+    {43, token_t::ValueEnd},
+    {44, token_t::ValueBegin},
+    {46, token_t::ValueEnd},
+    {46, token_t::ListEnd},
+    {47, token_t::StructMemberEnd},
+    {48, token_t::StructMemberBegin},
+    {48, token_t::FieldNameBegin},
+    {55, token_t::FieldNameEnd},
+    {58, token_t::StringBegin},
+    {69, token_t::StringEnd},
+    {70, token_t::StructMemberEnd},
+    {71, token_t::StructMemberBegin},
+    {71, token_t::FieldNameBegin},
+    {77, token_t::FieldNameEnd},
+    {80, token_t::StringBegin},
+    {105, token_t::StringEnd},
+    {106, token_t::StructMemberEnd},
+    {107, token_t::StructMemberBegin},
+    {107, token_t::FieldNameBegin},
+    {113, token_t::FieldNameEnd},
+    {116, token_t::ValueBegin},
+    {120, token_t::ValueEnd},
+    {120, token_t::StructMemberEnd},
+    {120, token_t::StructEnd},
+    {124, token_t::StructBegin},
+    {125, token_t::StructMemberBegin},
+    {125, token_t::FieldNameBegin},
+    {134, token_t::FieldNameEnd},
+    {137, token_t::StringBegin},
+    {147, token_t::StringEnd},
+    {148, token_t::StructMemberEnd},
+    {149, token_t::StructMemberBegin},
+    {149, token_t::FieldNameBegin},
+    {155, token_t::FieldNameEnd},
+    {158, token_t::ListBegin},
+    {159, token_t::ValueBegin},
+    {160, token_t::ValueEnd},
+    {161, token_t::StructBegin},
+    {162, token_t::StructEnd},
+    {164, token_t::ValueBegin},
+    {168, token_t::ValueEnd},
+    {169, token_t::StructBegin},
+    {170, token_t::StructMemberBegin},
+    {170, token_t::FieldNameBegin},
+    {172, token_t::FieldNameEnd},
+    {174, token_t::ListBegin},
+    {175, token_t::StructBegin},
+    {177, token_t::StructEnd},
+    {180, token_t::StructBegin},
+    {181, token_t::StructEnd},
+    {182, token_t::ListEnd},
+    {184, token_t::StructMemberEnd},
+    {184, token_t::StructEnd},
+    {186, token_t::ListEnd},
+    {187, token_t::StructMemberEnd},
+    {188, token_t::StructMemberBegin},
+    {188, token_t::FieldNameBegin},
+    {195, token_t::FieldNameEnd},
+    {198, token_t::StringBegin},
+    {209, token_t::StringEnd},
+    {210, token_t::StructMemberEnd},
+    {211, token_t::StructMemberBegin},
+    {211, token_t::FieldNameBegin},
+    {217, token_t::FieldNameEnd},
+    {220, token_t::StringBegin},
+    {252, token_t::StringEnd},
+    {253, token_t::StructMemberEnd},
+    {254, token_t::StructMemberBegin},
+    {254, token_t::FieldNameBegin},
+    {260, token_t::FieldNameEnd},
+    {263, token_t::ValueBegin},
+    {267, token_t::ValueEnd},
+    {267, token_t::StructMemberEnd},
+    {267, token_t::StructEnd},
+    {268, token_t::ListEnd}};
+
+  // Verify the number of tokens matches
+  ASSERT_EQ(golden_token_stream.size(), tokens_gpu.size());
+  ASSERT_EQ(golden_token_stream.size(), token_indices_gpu.size());
+
+  for (std::size_t i = 0; i < tokens_gpu.size(); i++) {
+    // Ensure the index the tokens are pointing to do match
+    EXPECT_EQ(golden_token_stream[i].first, token_indices_gpu[i]) << "Mismatch at #" << i;
+
+    // Ensure the token category is correct
+    EXPECT_EQ(golden_token_stream[i].second, tokens_gpu[i]) << "Mismatch at #" << i;
+  }
+}
+
+TEST_F(JsonTest, TokenStream2)
+{
+  using cuio_json::PdaTokenT;
+  using cuio_json::SymbolOffsetT;
+  using cuio_json::SymbolT;
+  // value end with comma, space, close-brace ", }"
+  std::string const input =
+    R"([ {}, { "a": { "y" : 6, "z": [] }}, { "a" : { "x" : 8, "y": 9}, "b" : {"x": 10 , "z": 11)"
+    "\n}}]";
+
+  auto const stream = cudf::get_default_stream();
+
+  // Default parsing options
+  cudf::io::json_reader_options default_options{};
+
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input =
+    cudf::device_span<SymbolT const>{d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+
+  // Parse the JSON and get the token stream
+  auto [d_tokens_gpu, d_token_indices_gpu] = cuio_json::detail::get_token_stream(
+    d_input, default_options, stream, rmm::mr::get_current_device_resource());
+  // Copy back the number of tokens that were written
+  auto const tokens_gpu        = cudf::detail::make_std_vector_async(d_tokens_gpu, stream);
+  auto const token_indices_gpu = cudf::detail::make_std_vector_async(d_token_indices_gpu, stream);
+
+  // Golden token stream sample
+  using token_t = cuio_json::token_t;
+  // clang-format off
+  std::vector<std::pair<std::size_t, cuio_json::PdaTokenT>> const golden_token_stream = {
+    {0, token_t::ListBegin},
+    {2, token_t::StructBegin}, {3, token_t::StructEnd}, //{}
+    {6, token_t::StructBegin},
+        {8, token_t::StructMemberBegin}, {8, token_t::FieldNameBegin}, {10, token_t::FieldNameEnd}, //a
+            {13, token_t::StructBegin},
+                {15, token_t::StructMemberBegin}, {15, token_t::FieldNameBegin}, {17, token_t::FieldNameEnd}, {21, token_t::ValueBegin}, {22, token_t::ValueEnd}, {22, token_t::StructMemberEnd}, //a.y
+                {24, token_t::StructMemberBegin}, {24, token_t::FieldNameBegin},  {26, token_t::FieldNameEnd},  {29, token_t::ListBegin}, {30, token_t::ListEnd}, {32, token_t::StructMemberEnd}, //a.z
+            {32, token_t::StructEnd},
+        {33, token_t::StructMemberEnd},
+    {33, token_t::StructEnd},
+    {36, token_t::StructBegin},
+        {38, token_t::StructMemberBegin}, {38, token_t::FieldNameBegin}, {40, token_t::FieldNameEnd}, //a
+            {44, token_t::StructBegin},
+                {46, token_t::StructMemberBegin}, {46, token_t::FieldNameBegin}, {48, token_t::FieldNameEnd}, {52, token_t::ValueBegin}, {53, token_t::ValueEnd}, {53, token_t::StructMemberEnd}, //a.x
+                {55, token_t::StructMemberBegin}, {55, token_t::FieldNameBegin}, {57, token_t::FieldNameEnd}, {60, token_t::ValueBegin}, {61, token_t::ValueEnd}, {61, token_t::StructMemberEnd}, //a.y
+            {61, token_t::StructEnd},
+        {62, token_t::StructMemberEnd},
+        {64, token_t::StructMemberBegin}, {64, token_t::FieldNameBegin}, {66, token_t::FieldNameEnd}, //b
+            {70, token_t::StructBegin},
+                {71, token_t::StructMemberBegin}, {71, token_t::FieldNameBegin}, {73, token_t::FieldNameEnd}, {76, token_t::ValueBegin}, {78, token_t::ValueEnd}, {79, token_t::StructMemberEnd}, //b.x
+                {81, token_t::StructMemberBegin}, {81, token_t::FieldNameBegin}, {83, token_t::FieldNameEnd}, {86, token_t::ValueBegin}, {88, token_t::ValueEnd}, {89, token_t::StructMemberEnd}, //b.z
+            {89, token_t::StructEnd},
+        {90, token_t::StructMemberEnd},
+    {90, token_t::StructEnd},
+    {91, token_t::ListEnd}};
+  // clang-format on
+
+  // Verify the number of tokens matches
+  ASSERT_EQ(golden_token_stream.size(), tokens_gpu.size());
+  ASSERT_EQ(golden_token_stream.size(), token_indices_gpu.size());
+
+  for (std::size_t i = 0; i < tokens_gpu.size(); i++) {
+    // Ensure the index the tokens are pointing to do match
+    EXPECT_EQ(golden_token_stream[i].first, token_indices_gpu[i]) << "Mismatch at #" << i;
+
+    // Ensure the token category is correct
+    EXPECT_EQ(golden_token_stream[i].second, tokens_gpu[i]) << "Mismatch at #" << i;
+  }
+}
+
+struct JsonParserTest : public cudf::test::BaseFixture, public testing::WithParamInterface<bool> {};
+INSTANTIATE_TEST_SUITE_P(Experimental, JsonParserTest, testing::Bool());
+
+TEST_P(JsonParserTest, ExtractColumn)
+{
+  using cuio_json::SymbolT;
+  bool const is_full_gpu = GetParam();
+  auto json_parser       = is_full_gpu ? cuio_json::detail::device_parse_nested_json
+                                       : cuio_json::detail::host_parse_nested_json;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // Default parsing options
+  cudf::io::json_reader_options default_options{};
+
+  std::string const input = R"( [{"a":0.0, "b":1.0}, {"a":0.1, "b":1.1}, {"a":0.2, "b":1.2}] )";
+  auto const d_input      = cudf::detail::make_device_uvector_async(
+    cudf::host_span<char const>{input.c_str(), input.size()},
+    stream,
+    rmm::mr::get_current_device_resource());
+  // Get the JSON's tree representation
+  auto const cudf_table = json_parser(d_input, default_options, stream, mr);
+
+  auto const expected_col_count = 2;
+  EXPECT_EQ(cudf_table.tbl->num_columns(), expected_col_count);
+
+  auto expected_col1 =
+    cudf::test::fixed_width_column_wrapper<double>({0.0, 0.1, 0.2}, {true, true, true});
+  auto expected_col2 =
+    cudf::test::fixed_width_column_wrapper<double>({1.0, 1.1, 1.2}, {true, true, true});
+  cudf::column_view parsed_col1 = cudf_table.tbl->get_column(0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_col1, parsed_col1);
+  cudf::column_view parsed_col2 = cudf_table.tbl->get_column(1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_col2, parsed_col2);
+}
+
+TEST_F(JsonTest, RecoveringTokenStream)
+{
+  // Test input. Inline comments used to indicate character indexes
+  //                           012345678 <= line 0
+  std::string const input = R"({"a":-2},)"
+                            // 9
+                            "\n"
+                            // 01234 <= line 1
+                            R"({"a":)"
+                            // 5
+                            "\n"
+                            // 67890123456789 <= line 2
+                            R"({"a":{"a":[321)"
+                            // 0
+                            "\n"
+                            // 123456789 <= line 3
+                            R"({"a":[1]})"
+                            // 0
+                            "\n"
+                            // 1  <= line 4
+                            "\n"
+                            // 23456789 <= line 5
+                            R"({"b":123})";
+
+  // Golden token stream sample
+  using token_t = cuio_json::token_t;
+  std::vector<std::pair<std::size_t, cuio_json::PdaTokenT>> const golden_token_stream = {
+    // Line 0 (invalid)
+    {0, token_t::StructBegin},
+    {0, token_t::StructEnd},
+    // Line 1 (valid)
+    {10, token_t::StructBegin},
+    {11, token_t::StructMemberBegin},
+    {11, token_t::FieldNameBegin},
+    {13, token_t::FieldNameEnd},
+    // Line 2 (valid)
+    {16, token_t::StructBegin},
+    {17, token_t::StructMemberBegin},
+    {17, token_t::FieldNameBegin},
+    {19, token_t::FieldNameEnd},
+    {21, token_t::StructBegin},
+    {22, token_t::StructMemberBegin},
+    {22, token_t::FieldNameBegin},
+    {24, token_t::FieldNameEnd},
+    {26, token_t::ListBegin},
+    {27, token_t::ValueBegin},
+    {30, token_t::ValueEnd},
+    // Line 3 (valid)
+    {31, token_t::StructBegin},
+    {32, token_t::StructMemberBegin},
+    {32, token_t::FieldNameBegin},
+    {34, token_t::FieldNameEnd},
+    {36, token_t::ListBegin},
+    {37, token_t::ValueBegin},
+    {38, token_t::ValueEnd},
+    {38, token_t::ListEnd},
+    {39, token_t::StructMemberEnd},
+    {39, token_t::StructEnd},
+    // Line 4 (empty)
+    // Line 5 (valid)
+    {42, token_t::StructBegin},
+    {43, token_t::StructMemberBegin},
+    {43, token_t::FieldNameBegin},
+    {45, token_t::FieldNameEnd},
+    {47, token_t::ValueBegin},
+    {50, token_t::ValueEnd},
+    {50, token_t::StructMemberEnd},
+    {50, token_t::StructEnd}};
+
+  auto const stream = cudf::get_default_stream();
+
+  // Default parsing options
+  cudf::io::json_reader_options default_options{};
+  default_options.set_recovery_mode(cudf::io::json_recovery_mode_t::RECOVER_WITH_NULL);
+  default_options.enable_lines(true);
+
+  // Prepare input & output buffers
+  cudf::string_scalar const d_scalar(input, true, stream);
+  auto const d_input = cudf::device_span<cuio_json::SymbolT const>{
+    d_scalar.data(), static_cast<size_t>(d_scalar.size())};
+
+  // Parse the JSON and get the token stream
+  auto [d_tokens_gpu, d_token_indices_gpu] = cuio_json::detail::get_token_stream(
+    d_input, default_options, stream, rmm::mr::get_current_device_resource());
+  // Copy back the number of tokens that were written
+  auto const tokens_gpu        = cudf::detail::make_std_vector_async(d_tokens_gpu, stream);
+  auto const token_indices_gpu = cudf::detail::make_std_vector_async(d_token_indices_gpu, stream);
+
+  // Verify the number of tokens matches
+  ASSERT_EQ(golden_token_stream.size(), tokens_gpu.size());
+  ASSERT_EQ(golden_token_stream.size(), token_indices_gpu.size());
+
+  for (std::size_t i = 0; i < tokens_gpu.size(); i++) {
+    // Ensure the index the tokens are pointing to do match
+    EXPECT_EQ(golden_token_stream[i].first, token_indices_gpu[i]) << "Mismatch at #" << i;
+    // Ensure the token category is correct
+    EXPECT_EQ(golden_token_stream[i].second, tokens_gpu[i]) << "Mismatch at #" << i;
+  }
+}
+
+TEST_F(JsonTest, PostProcessTokenStream)
+{
+  // Golden token stream sample
+  using token_t       = cuio_json::token_t;
+  using token_index_t = cuio_json::SymbolOffsetT;
+  using tuple_t       = thrust::tuple<token_index_t, cuio_json::PdaTokenT>;
+
+  std::vector<tuple_t> const input = {// Line 0 (invalid)
+                                      {0, token_t::LineEnd},
+                                      {0, token_t::StructBegin},
+                                      {1, token_t::StructMemberBegin},
+                                      {1, token_t::FieldNameBegin},
+                                      {3, token_t::FieldNameEnd},
+                                      {5, token_t::ValueBegin},
+                                      {7, token_t::ValueEnd},
+                                      {7, token_t::StructMemberEnd},
+                                      {7, token_t::StructEnd},
+                                      {8, token_t::ErrorBegin},
+                                      {9, token_t::LineEnd},
+                                      // Line 1
+                                      {10, token_t::StructBegin},
+                                      {11, token_t::StructMemberBegin},
+                                      {11, token_t::FieldNameBegin},
+                                      {13, token_t::FieldNameEnd},
+                                      {15, token_t::LineEnd},
+                                      // Line 2 (invalid)
+                                      {16, token_t::StructBegin},
+                                      {17, token_t::StructMemberBegin},
+                                      {17, token_t::FieldNameBegin},
+                                      {19, token_t::FieldNameEnd},
+                                      {21, token_t::StructBegin},
+                                      {22, token_t::StructMemberBegin},
+                                      {22, token_t::FieldNameBegin},
+                                      {24, token_t::FieldNameEnd},
+                                      {26, token_t::ListBegin},
+                                      {27, token_t::ValueBegin},
+                                      {29, token_t::ErrorBegin},
+                                      {30, token_t::LineEnd},
+                                      // Line 3 (invalid)
+                                      {31, token_t::StructBegin},
+                                      {32, token_t::StructMemberBegin},
+                                      {32, token_t::FieldNameBegin},
+                                      {34, token_t::FieldNameEnd},
+                                      {36, token_t::ListBegin},
+                                      {37, token_t::ValueBegin},
+                                      {38, token_t::ValueEnd},
+                                      {38, token_t::ListEnd},
+                                      {39, token_t::StructMemberEnd},
+                                      {39, token_t::StructEnd},
+                                      {40, token_t::ErrorBegin},
+                                      {40, token_t::LineEnd},
+                                      // Line 4
+                                      {41, token_t::LineEnd},
+                                      // Line 5
+                                      {42, token_t::StructBegin},
+                                      {43, token_t::StructMemberBegin},
+                                      {43, token_t::FieldNameBegin},
+                                      {45, token_t::FieldNameEnd},
+                                      {47, token_t::ValueBegin},
+                                      {50, token_t::ValueEnd},
+                                      {50, token_t::StructMemberEnd},
+                                      {50, token_t::StructEnd}};
+
+  std::vector<tuple_t> const expected_output = {// Line 0 (invalid)
+                                                {0, token_t::StructBegin},
+                                                {0, token_t::StructEnd},
+                                                // Line 1
+                                                {10, token_t::StructBegin},
+                                                {11, token_t::StructMemberBegin},
+                                                {11, token_t::FieldNameBegin},
+                                                {13, token_t::FieldNameEnd},
+                                                // Line 2 (invalid)
+                                                {0, token_t::StructBegin},
+                                                {0, token_t::StructEnd},
+                                                // Line 3 (invalid)
+                                                {0, token_t::StructBegin},
+                                                {0, token_t::StructEnd},
+                                                // Line 4 (empty)
+                                                // Line 5
+                                                {42, token_t::StructBegin},
+                                                {43, token_t::StructMemberBegin},
+                                                {43, token_t::FieldNameBegin},
+                                                {45, token_t::FieldNameEnd},
+                                                {47, token_t::ValueBegin},
+                                                {50, token_t::ValueEnd},
+                                                {50, token_t::StructMemberEnd},
+                                                {50, token_t::StructEnd}};
+
+  // Decompose tuples
+  auto const stream = cudf::get_default_stream();
+  std::vector<token_index_t> offsets(input.size());
+  std::vector<cuio_json::PdaTokenT> tokens(input.size());
+  auto token_tuples = thrust::make_zip_iterator(offsets.begin(), tokens.begin());
+  thrust::copy(input.cbegin(), input.cend(), token_tuples);
+
+  // Initialize device-side test data
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    cudf::host_span<token_index_t const>{offsets.data(), offsets.size()},
+    stream,
+    rmm::mr::get_current_device_resource());
+  auto const d_tokens =
+    cudf::detail::make_device_uvector_async(tokens, stream, rmm::mr::get_current_device_resource());
+
+  // Run system-under-test
+  auto [d_filtered_tokens, d_filtered_indices] =
+    cuio_json::detail::process_token_stream(d_tokens, d_offsets, stream);
+
+  auto const filtered_tokens  = cudf::detail::make_std_vector_async(d_filtered_tokens, stream);
+  auto const filtered_indices = cudf::detail::make_std_vector_async(d_filtered_indices, stream);
+
+  // Verify the number of tokens matches
+  ASSERT_EQ(filtered_tokens.size(), expected_output.size());
+  ASSERT_EQ(filtered_indices.size(), expected_output.size());
+
+  for (std::size_t i = 0; i < filtered_tokens.size(); i++) {
+    // Ensure the index the tokens are pointing to do match
+    EXPECT_EQ(thrust::get<0>(expected_output[i]), filtered_indices[i]) << "Mismatch at #" << i;
+    // Ensure the token category is correct
+    EXPECT_EQ(thrust::get<1>(expected_output[i]), filtered_tokens[i]) << "Mismatch at #" << i;
+  }
+}
+
+TEST_P(JsonParserTest, UTF_JSON)
+{
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream      = cudf::get_default_stream();
+  auto mr                = rmm::mr::get_current_device_resource();
+  bool const is_full_gpu = GetParam();
+  auto json_parser       = is_full_gpu ? cuio_json::detail::device_parse_nested_json
+                                       : cuio_json::detail::host_parse_nested_json;
+
+  // Default parsing options
+  cudf::io::json_reader_options default_options{};
+
+  // Only ASCII string
+  std::string const ascii_pass = R"([
+  {"a":1,"b":2,"c":[3], "d": {}},
+  {"a":1,"b":4.0,"c":[], "d": {"year":1882,"author": "Bharathi"}},
+  {"a":1,"b":6.0,"c":[5, 7], "d": null},
+  {"a":1,"b":8.0,"c":null, "d": {}},
+  {"a":1,"b":null,"c":null},
+  {"a":1,"b":Infinity,"c":[null], "d": {"year":-600,"author": "Kaniyan"}}])";
+  auto const d_ascii_pass      = cudf::detail::make_device_uvector_sync(
+    cudf::host_span<char const>{ascii_pass.c_str(), ascii_pass.size()},
+    stream,
+    rmm::mr::get_current_device_resource());
+
+  CUDF_EXPECT_NO_THROW(json_parser(d_ascii_pass, default_options, stream, mr));
+
+  // utf-8 string that fails parsing.
+  std::string const utf_failed = R"([
+  {"a":1,"b":2,"c":[3], "d": {}},
+  {"a":1,"b":4.0,"c":[], "d": {"year":1882,"author": "Bharathi"}},
+  {"a":1,"b":6.0,"c":[5, 7], "d": null},
+  {"a":1,"b":8.0,"c":null, "d": {}},
+  {"a":1,"b":null,"c":null},
+  {"a":1,"b":Infinity,"c":[null], "d": {"year":-600,"author": "filip ʒakotɛ"}}])";
+  auto const d_utf_failed      = cudf::detail::make_device_uvector_sync(
+    cudf::host_span<char const>{utf_failed.c_str(), utf_failed.size()},
+    stream,
+    rmm::mr::get_current_device_resource());
+  CUDF_EXPECT_NO_THROW(json_parser(d_utf_failed, default_options, stream, mr));
+
+  // utf-8 string that passes parsing.
+  std::string const utf_pass = R"([
+  {"a":1,"b":2,"c":[3], "d": {}},
+  {"a":1,"b":4.0,"c":[], "d": {"year":1882,"author": "Bharathi"}},
+  {"a":1,"b":6.0,"c":[5, 7], "d": null},
+  {"a":1,"b":8.0,"c":null, "d": {}},
+  {"a":1,"b":null,"c":null},
+  {"a":1,"b":Infinity,"c":[null], "d": {"year":-600,"author": "Kaniyan"}},
+  {"a":1,"b":NaN,"c":[null, null], "d": {"year": 2, "author": "filip ʒakotɛ"}}])";
+  auto const d_utf_pass      = cudf::detail::make_device_uvector_sync(
+    cudf::host_span<char const>{utf_pass.c_str(), utf_pass.size()},
+    stream,
+    rmm::mr::get_current_device_resource());
+  CUDF_EXPECT_NO_THROW(json_parser(d_utf_pass, default_options, stream, mr));
+}
+
+TEST_P(JsonParserTest, ExtractColumnWithQuotes)
+{
+  using cuio_json::SymbolT;
+  bool const is_full_gpu = GetParam();
+  auto json_parser       = is_full_gpu ? cuio_json::detail::device_parse_nested_json
+                                       : cuio_json::detail::host_parse_nested_json;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // Default parsing options
+  cudf::io::json_reader_options options{};
+  options.enable_keep_quotes(true);
+
+  std::string const input = R"( [{"a":"0.0", "b":1.0}, {"b":1.1}, {"b":2.1, "a":"2.0"}] )";
+  auto const d_input      = cudf::detail::make_device_uvector_async(
+    cudf::host_span<char const>{input.c_str(), input.size()},
+    stream,
+    rmm::mr::get_current_device_resource());
+  // Get the JSON's tree representation
+  auto const cudf_table = json_parser(d_input, options, stream, mr);
+
+  auto constexpr expected_col_count = 2;
+  EXPECT_EQ(cudf_table.tbl->num_columns(), expected_col_count);
+
+  auto expected_col1 =
+    cudf::test::strings_column_wrapper({R"("0.0")", R"()", R"("2.0")"}, {true, false, true});
+  auto expected_col2 =
+    cudf::test::fixed_width_column_wrapper<double>({1.0, 1.1, 2.1}, {true, true, true});
+  cudf::column_view parsed_col1 = cudf_table.tbl->get_column(0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_col1, parsed_col1);
+  cudf::column_view parsed_col2 = cudf_table.tbl->get_column(1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_col2, parsed_col2);
+}
+
+TEST_P(JsonParserTest, ExpectFailMixStructAndList)
+{
+  using cuio_json::SymbolT;
+  bool const is_full_gpu = GetParam();
+  auto json_parser       = is_full_gpu ? cuio_json::detail::device_parse_nested_json
+                                       : cuio_json::detail::host_parse_nested_json;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // Default parsing options
+  cudf::io::json_reader_options options{};
+  options.enable_keep_quotes(true);
+
+  std::vector<std::string> const inputs_fail{
+    R"( [{"a":[123], "b":1.0}, {"b":1.1}, {"b":2.1, "a":{"0":123}}] )",
+    R"( [{"a":{"0":"foo"}, "b":1.0}, {"b":1.1}, {"b":2.1, "a":[123]}] )",
+    R"( [{"a":{"0":null}, "b":1.0}, {"b":1.1}, {"b":2.1, "a":[123]}] )"};
+
+  std::vector<std::string> const inputs_succeed{
+    R"( [{"a":[123, {"0": 123}], "b":1.0}, {"b":1.1}, {"b":2.1}] )",
+    R"( [{"a":[123, "123"], "b":1.0}, {"b":1.1}, {"b":2.1}] )"};
+
+  // libcudf does not currently support a mix of lists and structs.
+  for (auto const& input : inputs_fail) {
+    auto const d_input = cudf::detail::make_device_uvector_async(
+      cudf::host_span<char const>{input.c_str(), input.size()},
+      stream,
+      rmm::mr::get_current_device_resource());
+    EXPECT_THROW(auto const cudf_table = json_parser(d_input, options, stream, mr),
+                 cudf::logic_error);
+  }
+
+  for (auto const& input : inputs_succeed) {
+    auto const d_input = cudf::detail::make_device_uvector_async(
+      cudf::host_span<char const>{input.c_str(), input.size()},
+      stream,
+      rmm::mr::get_current_device_resource());
+    CUDF_EXPECT_NO_THROW(auto const cudf_table = json_parser(d_input, options, stream, mr));
+  }
+}
+
+TEST_P(JsonParserTest, EmptyString)
+{
+  using cuio_json::SymbolT;
+  bool const is_full_gpu = GetParam();
+  auto json_parser       = is_full_gpu ? cuio_json::detail::device_parse_nested_json
+                                       : cuio_json::detail::host_parse_nested_json;
+
+  // Prepare cuda stream for data transfers & kernels
+  auto const stream = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // Default parsing options
+  cudf::io::json_reader_options default_options{};
+
+  std::string const input = R"([])";
+  auto const d_input =
+    cudf::detail::make_device_uvector_sync(cudf::host_span<char const>{input.c_str(), input.size()},
+                                           stream,
+                                           rmm::mr::get_current_device_resource());
+  // Get the JSON's tree representation
+  auto const cudf_table = json_parser(d_input, default_options, stream, mr);
+
+  auto const expected_col_count = 0;
+  EXPECT_EQ(cudf_table.tbl->num_columns(), expected_col_count);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/orc_test.cpp b/cpp/tests/io/orc_test.cpp
new file mode 100644
index 0000000..890ef91
--- /dev/null
+++ b/cpp/tests/io/orc_test.cpp
@@ -0,0 +1,1937 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/io_metadata_utilities.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/io/orc.hpp>
+#include <cudf/io/orc_metadata.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <src/io/comp/nvcomp_adapter.hpp>
+
+#include <type_traits>
+
+template <typename T, typename SourceElementT = T>
+using column_wrapper =
+  typename std::conditional<std::is_same_v<T, cudf::string_view>,
+                            cudf::test::strings_column_wrapper,
+                            cudf::test::fixed_width_column_wrapper<T, SourceElementT>>::type;
+
+using str_col     = column_wrapper<cudf::string_view>;
+using bool_col    = column_wrapper<bool>;
+using int8_col    = column_wrapper<int8_t>;
+using int16_col   = column_wrapper<int16_t>;
+using int32_col   = column_wrapper<int32_t>;
+using int64_col   = column_wrapper<int64_t>;
+using float32_col = column_wrapper<float>;
+using float64_col = column_wrapper<double>;
+using dec32_col   = column_wrapper<numeric::decimal32>;
+using dec64_col   = column_wrapper<numeric::decimal64>;
+using dec128_col  = column_wrapper<numeric::decimal128>;
+using struct_col  = cudf::test::structs_column_wrapper;
+template <typename T>
+using list_col = cudf::test::lists_column_wrapper<T>;
+
+using column     = cudf::column;
+using table      = cudf::table;
+using table_view = cudf::table_view;
+
+// Global environment for temporary files
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+template <typename T>
+std::unique_ptr<cudf::table> create_random_fixed_table(cudf::size_type num_columns,
+                                                       cudf::size_type num_rows,
+                                                       bool include_validity)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  std::vector<column_wrapper<T>> src_cols(num_columns);
+  for (int idx = 0; idx < num_columns; idx++) {
+    auto rand_elements =
+      cudf::detail::make_counting_transform_iterator(0, [](T i) { return rand(); });
+    if (include_validity) {
+      src_cols[idx] = column_wrapper<T>(rand_elements, rand_elements + num_rows, valids);
+    } else {
+      src_cols[idx] = column_wrapper<T>(rand_elements, rand_elements + num_rows);
+    }
+  }
+  std::vector<std::unique_ptr<cudf::column>> columns(num_columns);
+  std::transform(src_cols.begin(), src_cols.end(), columns.begin(), [](column_wrapper<T>& in) {
+    auto ret                    = in.release();
+    [[maybe_unused]] auto nulls = ret->has_nulls();  // pre-cache the null count
+    return ret;
+  });
+  return std::make_unique<cudf::table>(std::move(columns));
+}
+
+// Base test fixture for tests
+struct OrcWriterTest : public cudf::test::BaseFixture {};
+
+// Typed test fixture for numeric type tests
+template <typename T>
+struct OrcWriterNumericTypeTest : public OrcWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Typed test fixture for timestamp type tests
+template <typename T>
+struct OrcWriterTimestampTypeTest : public OrcWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Declare typed test cases
+// TODO: Replace with `NumericTypes` when unsigned support is added. Issue #5351
+using SupportedTypes = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, bool, float, double>;
+TYPED_TEST_SUITE(OrcWriterNumericTypeTest, SupportedTypes);
+using SupportedTimestampTypes =
+  cudf::test::RemoveIf<cudf::test::ContainedIn<cudf::test::Types<cudf::timestamp_D>>,
+                       cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(OrcWriterTimestampTypeTest, SupportedTimestampTypes);
+
+// Base test fixture for chunked writer tests
+struct OrcChunkedWriterTest : public cudf::test::BaseFixture {};
+
+// Typed test fixture for numeric type tests
+template <typename T>
+struct OrcChunkedWriterNumericTypeTest : public OrcChunkedWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Declare typed test cases
+TYPED_TEST_SUITE(OrcChunkedWriterNumericTypeTest, SupportedTypes);
+
+// Test fixture for reader tests
+struct OrcReaderTest : public cudf::test::BaseFixture {};
+
+// Test fixture for statistics tests
+struct OrcStatisticsTest : public cudf::test::BaseFixture {};
+
+// Test fixture for metadata tests
+struct OrcMetadataReaderTest : public cudf::test::BaseFixture {};
+
+namespace {
+// Generates a vector of uniform random values of type T
+template <typename T>
+inline auto random_values(size_t size)
+{
+  std::vector<T> values(size);
+
+  using T1 = T;
+  using uniform_distribution =
+    typename std::conditional_t<std::is_same_v<T1, bool>,
+                                std::bernoulli_distribution,
+                                std::conditional_t<std::is_floating_point_v<T1>,
+                                                   std::uniform_real_distribution<T1>,
+                                                   std::uniform_int_distribution<T1>>>;
+
+  static constexpr auto seed = 0xf00d;
+  static std::mt19937 engine{seed};
+  static uniform_distribution dist{};
+  std::generate_n(values.begin(), size, [&]() { return T{dist(engine)}; });
+
+  return values;
+}
+
+struct SkipRowTest {
+  int test_calls{0};
+  SkipRowTest() {}
+
+  std::unique_ptr<table> get_expected_result(std::string const& filepath,
+                                             int skip_rows,
+                                             int file_num_rows,
+                                             int read_num_rows)
+  {
+    auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+    column_wrapper<int32_t, typename decltype(sequence)::value_type> input_col(
+      sequence, sequence + file_num_rows);
+    table_view input_table({input_col});
+
+    cudf::io::orc_writer_options out_opts =
+      cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, input_table);
+    cudf::io::write_orc(out_opts);
+
+    auto begin_sequence = sequence, end_sequence = sequence;
+    if (skip_rows < file_num_rows) {
+      begin_sequence += skip_rows;
+      end_sequence += std::min(skip_rows + read_num_rows, file_num_rows);
+    }
+    column_wrapper<int32_t, typename decltype(sequence)::value_type> output_col(begin_sequence,
+                                                                                end_sequence);
+    std::vector<std::unique_ptr<column>> output_cols;
+    output_cols.push_back(output_col.release());
+    return std::make_unique<table>(std::move(output_cols));
+  }
+
+  void test(int skip_rows, int file_num_rows, int read_num_rows)
+  {
+    auto filepath =
+      temp_env->get_temp_filepath("SkipRowTest" + std::to_string(test_calls++) + ".orc");
+    auto expected_result = get_expected_result(filepath, skip_rows, file_num_rows, read_num_rows);
+    cudf::io::orc_reader_options in_opts =
+      cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+        .use_index(false)
+        .skip_rows(skip_rows)
+        .num_rows(read_num_rows);
+    auto result = cudf::io::read_orc(in_opts);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_result->view(), result.tbl->view());
+  }
+
+  void test(int skip_rows, int file_num_rows)
+  {
+    auto filepath =
+      temp_env->get_temp_filepath("SkipRowTest" + std::to_string(test_calls++) + ".orc");
+    auto expected_result =
+      get_expected_result(filepath, skip_rows, file_num_rows, file_num_rows - skip_rows);
+    cudf::io::orc_reader_options in_opts =
+      cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+        .use_index(false)
+        .skip_rows(skip_rows);
+    auto result = cudf::io::read_orc(in_opts);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_result->view(), result.tbl->view());
+  }
+};
+
+}  // namespace
+
+TYPED_TEST(OrcWriterNumericTypeTest, SingleColumn)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(sequence,
+                                                                         sequence + num_rows);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcSingleColumn.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(OrcWriterNumericTypeTest, SingleColumnWithNulls)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2); });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + num_rows, validity);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcSingleColumnWithNulls.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(OrcWriterTimestampTypeTest, Timestamps)
+{
+  auto sequence =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (std::rand() / 10); });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(sequence,
+                                                                         sequence + num_rows);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcTimestamps.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .use_index(false)
+      .timestamp_type(this->type());
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(OrcWriterTimestampTypeTest, TimestampsWithNulls)
+{
+  auto sequence =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (std::rand() / 10); });
+  auto validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i > 30) && (i < 60); });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + num_rows, validity);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcTimestampsWithNulls.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .use_index(false)
+      .timestamp_type(this->type());
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(OrcWriterTimestampTypeTest, TimestampOverflow)
+{
+  constexpr int64_t max = std::numeric_limits<int64_t>::max();
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return max - i; });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(sequence,
+                                                                         sequence + num_rows);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcTimestampOverflow.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .use_index(false)
+      .timestamp_type(this->type());
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(OrcWriterTest, MultiColumn)
+{
+  constexpr auto num_rows = 10;
+
+  auto col0_data = random_values<bool>(num_rows);
+  auto col1_data = random_values<int8_t>(num_rows);
+  auto col2_data = random_values<int16_t>(num_rows);
+  auto col3_data = random_values<int32_t>(num_rows);
+  auto col4_data = random_values<float>(num_rows);
+  auto col5_data = random_values<double>(num_rows);
+  auto col6_vals = random_values<int64_t>(num_rows);
+  auto col6_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal128{col6_vals[i], numeric::scale_type{12}};
+  });
+  auto col7_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal128{col6_vals[i], numeric::scale_type{-12}};
+  });
+
+  bool_col col0(col0_data.begin(), col0_data.end());
+  int8_col col1(col1_data.begin(), col1_data.end());
+  int16_col col2(col2_data.begin(), col2_data.end());
+  int32_col col3(col3_data.begin(), col3_data.end());
+  float32_col col4(col4_data.begin(), col4_data.end());
+  float64_col col5(col5_data.begin(), col5_data.end());
+  dec128_col col6(col6_data, col6_data + num_rows);
+  dec128_col col7(col7_data, col7_data + num_rows);
+
+  list_col<int64_t> col8{
+    {9, 8}, {7, 6, 5}, {}, {4}, {3, 2, 1, 0}, {20, 21, 22, 23, 24}, {}, {66, 666}, {}, {-1, -2}};
+
+  int32_col child_col{48, 27, 25, 31, 351, 351, 29, 15, -1, -99};
+  struct_col col9{child_col};
+
+  table_view expected({col0, col1, col2, col3, col4, col5, col6, col7, col8, col9});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("bools");
+  expected_metadata.column_metadata[1].set_name("int8s");
+  expected_metadata.column_metadata[2].set_name("int16s");
+  expected_metadata.column_metadata[3].set_name("int32s");
+  expected_metadata.column_metadata[4].set_name("floats");
+  expected_metadata.column_metadata[5].set_name("doubles");
+  expected_metadata.column_metadata[6].set_name("decimal_pos_scale");
+  expected_metadata.column_metadata[7].set_name("decimal_neg_scale");
+  expected_metadata.column_metadata[8].set_name("lists");
+  expected_metadata.column_metadata[9].set_name("structs");
+
+  auto filepath = temp_env->get_temp_filepath("OrcMultiColumn.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcWriterTest, MultiColumnWithNulls)
+{
+  constexpr auto num_rows = 10;
+
+  auto col0_data = random_values<bool>(num_rows);
+  auto col1_data = random_values<int8_t>(num_rows);
+  auto col2_data = random_values<int16_t>(num_rows);
+  auto col3_data = random_values<int32_t>(num_rows);
+  auto col4_data = random_values<float>(num_rows);
+  auto col5_data = random_values<double>(num_rows);
+  auto col6_vals = random_values<int32_t>(num_rows);
+  auto col6_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal64{col6_vals[i], numeric::scale_type{2}};
+  });
+  auto col0_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2); });
+  auto col1_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i < 2); });
+  auto col3_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i == (num_rows - 1)); });
+  auto col4_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i >= 4 && i <= 6); });
+  auto col5_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i > 8); });
+  auto col6_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 3); });
+
+  bool_col col0{col0_data.begin(), col0_data.end(), col0_mask};
+  int8_col col1{col1_data.begin(), col1_data.end(), col1_mask};
+  int16_col col2(col2_data.begin(), col2_data.end());
+  int32_col col3{col3_data.begin(), col3_data.end(), col3_mask};
+  float32_col col4{col4_data.begin(), col4_data.end(), col4_mask};
+  float64_col col5{col5_data.begin(), col5_data.end(), col5_mask};
+  dec64_col col6{col6_data, col6_data + num_rows, col6_mask};
+  list_col<int32_t> col7{
+    {{9, 8}, {7, 6, 5}, {}, {4}, {3, 2, 1, 0}, {20, 21, 22, 23, 24}, {}, {66, 666}, {}, {-1, -2}},
+    col0_mask};
+  auto ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {48, 27, 25, 31, 351, 351, 29, 15, -1, -99}, {1, 0, 1, 1, 0, 1, 1, 1, 0, 1}};
+  struct_col col8{{ages_col}, {0, 1, 1, 0, 1, 1, 0, 1, 1, 0}};
+  table_view expected({col0, col1, col2, col3, col4, col5, col6, col7, col8});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("bools");
+  expected_metadata.column_metadata[1].set_name("int8s");
+  expected_metadata.column_metadata[2].set_name("int16s");
+  expected_metadata.column_metadata[3].set_name("int32s");
+  expected_metadata.column_metadata[4].set_name("floats");
+  expected_metadata.column_metadata[5].set_name("doubles");
+  expected_metadata.column_metadata[6].set_name("decimal");
+  expected_metadata.column_metadata[7].set_name("lists");
+  expected_metadata.column_metadata[8].set_name("structs");
+
+  auto filepath = temp_env->get_temp_filepath("OrcMultiColumnWithNulls.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcWriterTest, ReadZeroRows)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  constexpr auto num_rows = 10;
+  column_wrapper<int64_t, typename decltype(sequence)::value_type> col(sequence,
+                                                                       sequence + num_rows);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcSingleColumn.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .use_index(false)
+      .num_rows(0);
+  auto result = cudf::io::read_orc(in_opts);
+
+  EXPECT_EQ(0, result.tbl->num_rows());
+  EXPECT_EQ(1, result.tbl->num_columns());
+}
+
+TEST_F(OrcWriterTest, Strings)
+{
+  std::vector<char const*> strings{
+    "Monday", "Monday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+  auto const num_rows = strings.size();
+
+  auto seq_col0 = random_values<int>(num_rows);
+  auto seq_col2 = random_values<float>(num_rows);
+
+  int32_col col0(seq_col0.begin(), seq_col0.end());
+  str_col col1(strings.begin(), strings.end());
+  float32_col col2(seq_col2.begin(), seq_col2.end());
+
+  table_view expected({col0, col1, col2});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_other");
+  expected_metadata.column_metadata[1].set_name("col_string");
+  expected_metadata.column_metadata[2].set_name("col_another");
+
+  auto filepath = temp_env->get_temp_filepath("OrcStrings.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcWriterTest, SlicedTable)
+{
+  // This test checks for writing zero copy, offsetted views into existing cudf tables
+
+  std::vector<char const*> strings{
+    "Monday", "Monday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+  auto const num_rows = strings.size();
+
+  auto seq_col0  = random_values<int32_t>(num_rows);
+  auto seq_col2  = random_values<float>(num_rows);
+  auto vals_col3 = random_values<int32_t>(num_rows);
+  auto seq_col3  = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal64{vals_col3[i], numeric::scale_type{2}};
+  });
+
+  int32_col col0(seq_col0.begin(), seq_col0.end());
+  str_col col1(strings.begin(), strings.end());
+  float32_col col2(seq_col2.begin(), seq_col2.end());
+  float32_col col3(seq_col3, seq_col3 + num_rows);
+
+  list_col<int64_t> col4{
+    {9, 8}, {7, 6, 5}, {}, {4}, {3, 2, 1, 0}, {20, 21, 22, 23, 24}, {}, {66, 666}};
+
+  int16_col ages_col{{48, 27, 25, 31, 351, 351, 29, 15}, cudf::test::iterators::null_at(5)};
+  struct_col col5{{ages_col}, cudf::test::iterators::null_at(4)};
+
+  table_view expected({col0, col1, col2, col3, col4, col5});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_other");
+  expected_metadata.column_metadata[1].set_name("col_string");
+  expected_metadata.column_metadata[2].set_name("col_another");
+  expected_metadata.column_metadata[3].set_name("col_decimal");
+  expected_metadata.column_metadata[4].set_name("lists");
+  expected_metadata.column_metadata[5].set_name("structs");
+
+  auto expected_slice = cudf::slice(expected, {2, static_cast<cudf::size_type>(num_rows)});
+
+  auto filepath = temp_env->get_temp_filepath("SlicedTable.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected_slice)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_slice, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcWriterTest, HostBuffer)
+{
+  constexpr auto num_rows = 100 << 10;
+  auto const seq_col      = random_values<int>(num_rows);
+  int32_col col(seq_col.begin(), seq_col.end());
+
+  table_view expected{{col}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_other");
+
+  std::vector<char> out_buffer;
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info(&out_buffer), expected)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(
+      cudf::io::source_info(out_buffer.data(), out_buffer.size()))
+      .use_index(false);
+  auto const result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcWriterTest, negTimestampsNano)
+{
+  // This is a separate test because ORC format has a bug where writing a timestamp between -1 and 0
+  // seconds from UNIX epoch is read as that timestamp + 1 second. We mimic that behavior and so
+  // this test has to hardcode test values which are < -1 second.
+  // Details: https://github.com/rapidsai/cudf/pull/5529#issuecomment-648768925
+  auto timestamps_ns =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep>{
+      -131968727238000000,
+      -1530705634500000000,
+      -1674638741932929000,
+    };
+  cudf::table_view expected({timestamps_ns});
+
+  auto filepath = temp_env->get_temp_filepath("OrcNegTimestamp.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    expected.column(0), result.tbl->view().column(0), cudf::test::debug_output_level::ALL_ERRORS);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(OrcWriterTest, Slice)
+{
+  int32_col col{{1, 2, 3, 4, 5}, cudf::test::iterators::null_at(3)};
+  std::vector<cudf::size_type> indices{2, 5};
+  std::vector<cudf::column_view> result = cudf::slice(col, indices);
+  cudf::table_view tbl{result};
+
+  auto filepath = temp_env->get_temp_filepath("Slice.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto read_table = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(read_table.tbl->view(), tbl);
+}
+
+TEST_F(OrcChunkedWriterTest, SingleTable)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedSingle.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(*table1);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *table1);
+}
+
+TEST_F(OrcChunkedWriterTest, SimpleTable)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+  auto table2 = create_random_fixed_table<int>(5, 5, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedSimple.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(*table1).write(*table2);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(OrcChunkedWriterTest, LargeTables)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(512, 4096, true);
+  auto table2 = create_random_fixed_table<int>(512, 8192, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedLarge.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(*table1).write(*table2);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(OrcChunkedWriterTest, ManyTables)
+{
+  srand(31337);
+  std::vector<std::unique_ptr<table>> tables;
+  std::vector<table_view> table_views;
+  constexpr int num_tables = 96;
+  for (int idx = 0; idx < num_tables; idx++) {
+    auto tbl = create_random_fixed_table<int>(16, 64, true);
+    table_views.push_back(*tbl);
+    tables.push_back(std::move(tbl));
+  }
+
+  auto expected = cudf::concatenate(table_views);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedManyTables.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer writer(opts);
+  std::for_each(table_views.begin(), table_views.end(), [&writer](table_view const& tbl) {
+    writer.write(tbl);
+  });
+  writer.close();
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(OrcChunkedWriterTest, Metadata)
+{
+  std::vector<char const*> strings{
+    "Monday", "Tuesday", "THURSDAY", "Wednesday", "Friday", "Sunday", "Saturday"};
+  auto const num_rows = strings.size();
+
+  auto seq_col0 = random_values<int>(num_rows);
+  auto seq_col2 = random_values<float>(num_rows);
+
+  int32_col col0(seq_col0.begin(), seq_col0.end());
+  str_col col1{strings.begin(), strings.end()};
+  float32_col col2(seq_col2.begin(), seq_col2.end());
+
+  table_view expected({col0, col1, col2});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_other");
+  expected_metadata.column_metadata[1].set_name("col_string");
+  expected_metadata.column_metadata[2].set_name("col_another");
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedMetadata.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath})
+      .metadata(expected_metadata);
+  cudf::io::orc_chunked_writer(opts).write(expected).write(expected);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcChunkedWriterTest, Strings)
+{
+  bool mask1[] = {true, true, false, true, true, true, true};
+  std::vector<char const*> h_strings1{"four", "score", "and", "seven", "years", "ago", "abcdefgh"};
+  str_col strings1(h_strings1.begin(), h_strings1.end(), mask1);
+  table_view tbl1({strings1});
+
+  bool mask2[] = {false, true, true, true, true, true, true};
+  std::vector<char const*> h_strings2{"ooooo", "ppppppp", "fff", "j", "cccc", "bbb", "zzzzzzzzzzz"};
+  str_col strings2(h_strings2.begin(), h_strings2.end(), mask2);
+  table_view tbl2({strings2});
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl1, tbl2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedStrings.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(tbl1).write(tbl2);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(OrcChunkedWriterTest, MismatchedTypes)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(4, 4, true);
+  auto table2 = create_random_fixed_table<float>(4, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedMismatchedTypes.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer writer(opts);
+  writer.write(*table1);
+  EXPECT_THROW(writer.write(*table2), cudf::logic_error);
+}
+
+TEST_F(OrcChunkedWriterTest, ChunkedWritingAfterClosing)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(4, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedWritingAfterClosing.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer writer(opts);
+  writer.write(*table1);
+  writer.close();
+  EXPECT_THROW(writer.write(*table1), cudf::logic_error);
+}
+
+TEST_F(OrcChunkedWriterTest, MismatchedStructure)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(4, 4, true);
+  auto table2 = create_random_fixed_table<int>(3, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedMismatchedStructure.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer writer(opts);
+  writer.write(*table1);
+  EXPECT_THROW(writer.write(*table2), cudf::logic_error);
+}
+
+TEST_F(OrcChunkedWriterTest, ReadStripes)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+  auto table2 = create_random_fixed_table<int>(5, 5, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table2, *table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedStripes.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(*table1).write(*table2);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).stripes({{1, 0, 1}});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(OrcChunkedWriterTest, ReadStripesError)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedStripesError.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(*table1);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).stripes({{0, 1}});
+  EXPECT_THROW(cudf::io::read_orc(read_opts), cudf::logic_error);
+  read_opts.set_stripes({{-1}});
+  EXPECT_THROW(cudf::io::read_orc(read_opts), cudf::logic_error);
+}
+
+TYPED_TEST(OrcChunkedWriterNumericTypeTest, UnalignedSize)
+{
+  // write out two 31 row tables and make sure they get
+  // read back with all their validity bits in the right place
+
+  using T = TypeParam;
+
+  int num_els = 31;
+
+  bool mask[] = {false, true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true};
+
+  T c1a[num_els];
+  std::fill(c1a, c1a + num_els, static_cast<T>(5));
+  T c1b[num_els];
+  std::fill(c1b, c1b + num_els, static_cast<T>(6));
+  column_wrapper<T> c1a_w(c1a, c1a + num_els, mask);
+  column_wrapper<T> c1b_w(c1b, c1b + num_els, mask);
+  table_view tbl1({c1a_w, c1b_w});
+
+  T c2a[num_els];
+  std::fill(c2a, c2a + num_els, static_cast<T>(8));
+  T c2b[num_els];
+  std::fill(c2b, c2b + num_els, static_cast<T>(9));
+  column_wrapper<T> c2a_w(c2a, c2a + num_els, mask);
+  column_wrapper<T> c2b_w(c2b, c2b + num_els, mask);
+  table_view tbl2({c2a_w, c2b_w});
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl1, tbl2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedUnalignedSize.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(tbl1).write(tbl2);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TYPED_TEST(OrcChunkedWriterNumericTypeTest, UnalignedSize2)
+{
+  // write out two 33 row tables and make sure they get
+  // read back with all their validity bits in the right place
+
+  using T = TypeParam;
+
+  int num_els = 33;
+
+  bool mask[] = {false, true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true, true, true};
+
+  T c1a[num_els];
+  std::fill(c1a, c1a + num_els, static_cast<T>(5));
+  T c1b[num_els];
+  std::fill(c1b, c1b + num_els, static_cast<T>(6));
+  column_wrapper<T> c1a_w(c1a, c1a + num_els, mask);
+  column_wrapper<T> c1b_w(c1b, c1b + num_els, mask);
+  table_view tbl1({c1a_w, c1b_w});
+
+  T c2a[num_els];
+  std::fill(c2a, c2a + num_els, static_cast<T>(8));
+  T c2b[num_els];
+  std::fill(c2b, c2b + num_els, static_cast<T>(9));
+  column_wrapper<T> c2a_w(c2a, c2a + num_els, mask);
+  column_wrapper<T> c2b_w(c2b, c2b + num_els, mask);
+  table_view tbl2({c2a_w, c2b_w});
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl1, tbl2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedUnalignedSize2.orc");
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::orc_chunked_writer(opts).write(tbl1).write(tbl2);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(OrcReaderTest, CombinedSkipRowTest)
+{
+  SkipRowTest skip_row;
+  skip_row.test(50, 75);
+  skip_row.test(2, 100);
+  skip_row.test(2, 100, 50);
+  skip_row.test(2, 100, 98);
+  skip_row.test(2, 100, 99);
+  skip_row.test(2, 100, 100);
+  skip_row.test(2, 100, 110);
+}
+
+TEST_F(OrcStatisticsTest, Basic)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto ts_sequence =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i - 4) * 1000002; });
+  auto dec_sequence =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return i * 1001; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  std::vector<char const*> strings{
+    "Monday", "Monday", "Friday", "Monday", "Friday", "Friday", "Friday", "Wednesday", "Tuesday"};
+  int num_rows = strings.size();
+
+  column_wrapper<int32_t, typename decltype(sequence)::value_type> col1(
+    sequence, sequence + num_rows, validity);
+  column_wrapper<float, typename decltype(sequence)::value_type> col2(
+    sequence, sequence + num_rows, validity);
+  str_col col3{strings.begin(), strings.end()};
+  column_wrapper<cudf::timestamp_ns, typename decltype(sequence)::value_type> col4(
+    ts_sequence, ts_sequence + num_rows, validity);
+  column_wrapper<cudf::timestamp_us, typename decltype(sequence)::value_type> col5(
+    ts_sequence, ts_sequence + num_rows, validity);
+  bool_col col6({true, true, true, true, true, false, false, false, false}, validity);
+
+  cudf::test::fixed_point_column_wrapper<int64_t> col7(
+    dec_sequence, dec_sequence + num_rows, numeric::scale_type{-1});
+
+  table_view expected({col1, col2, col3, col4, col5, col6, col7});
+
+  auto filepath = temp_env->get_temp_filepath("OrcStatsMerge.orc");
+
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  auto const stats = cudf::io::read_parsed_orc_statistics(cudf::io::source_info{filepath});
+
+  auto expected_column_names = std::vector<std::string>{""};
+  std::generate_n(
+    std::back_inserter(expected_column_names),
+    expected.num_columns(),
+    [starting_index = 0]() mutable { return "_col" + std::to_string(starting_index++); });
+  EXPECT_EQ(stats.column_names, expected_column_names);
+
+  auto validate_statistics = [&](std::vector<cudf::io::column_statistics> const& stats) {
+    ASSERT_EQ(stats.size(), expected.num_columns() + 1);
+    auto& s0 = stats[0];
+    EXPECT_EQ(*s0.number_of_values, 9ul);
+
+    auto& s1 = stats[1];
+    EXPECT_EQ(*s1.number_of_values, 4ul);
+    EXPECT_TRUE(*s1.has_null);
+    auto& ts1 = std::get<cudf::io::integer_statistics>(s1.type_specific_stats);
+    EXPECT_EQ(*ts1.minimum, 1);
+    EXPECT_EQ(*ts1.maximum, 7);
+    EXPECT_EQ(*ts1.sum, 16);
+
+    auto& s2 = stats[2];
+    EXPECT_EQ(*s2.number_of_values, 4ul);
+    EXPECT_TRUE(*s2.has_null);
+    auto& ts2 = std::get<cudf::io::double_statistics>(s2.type_specific_stats);
+    EXPECT_EQ(*ts2.minimum, 1.);
+    EXPECT_EQ(*ts2.maximum, 7.);
+    EXPECT_EQ(*ts2.sum, 16.);
+
+    auto& s3 = stats[3];
+    EXPECT_EQ(*s3.number_of_values, 9ul);
+    EXPECT_FALSE(*s3.has_null);
+    auto& ts3 = std::get<cudf::io::string_statistics>(s3.type_specific_stats);
+    EXPECT_EQ(*ts3.minimum, "Friday");
+    EXPECT_EQ(*ts3.maximum, "Wednesday");
+    EXPECT_EQ(*ts3.sum, 58ul);
+
+    auto& s4 = stats[4];
+    EXPECT_EQ(*s4.number_of_values, 4ul);
+    EXPECT_TRUE(*s4.has_null);
+    auto& ts4 = std::get<cudf::io::timestamp_statistics>(s4.type_specific_stats);
+    EXPECT_EQ(*ts4.minimum, -4);
+    EXPECT_EQ(*ts4.maximum, 3);
+    EXPECT_EQ(*ts4.minimum_utc, -4);
+    EXPECT_EQ(*ts4.maximum_utc, 3);
+    EXPECT_EQ(*ts4.minimum_nanos, 999994);
+    EXPECT_EQ(*ts4.maximum_nanos, 6);
+
+    auto& s5 = stats[5];
+    EXPECT_EQ(*s5.number_of_values, 4ul);
+    EXPECT_TRUE(*s5.has_null);
+    auto& ts5 = std::get<cudf::io::timestamp_statistics>(s5.type_specific_stats);
+    EXPECT_EQ(*ts5.minimum, -3001);
+    EXPECT_EQ(*ts5.maximum, 3000);
+    EXPECT_EQ(*ts5.minimum_utc, -3001);
+    EXPECT_EQ(*ts5.maximum_utc, 3000);
+    EXPECT_EQ(*ts5.minimum_nanos, 994000);
+    EXPECT_EQ(*ts5.maximum_nanos, 6000);
+
+    auto& s6 = stats[6];
+    EXPECT_EQ(*s6.number_of_values, 4ul);
+    EXPECT_TRUE(*s6.has_null);
+    auto& ts6 = std::get<cudf::io::bucket_statistics>(s6.type_specific_stats);
+    EXPECT_EQ(ts6.count[0], 2);
+
+    auto& s7 = stats[7];
+    EXPECT_EQ(*s7.number_of_values, 9ul);
+    EXPECT_FALSE(*s7.has_null);
+    auto& ts7 = std::get<cudf::io::decimal_statistics>(s7.type_specific_stats);
+    EXPECT_EQ(*ts7.minimum, "0.0");
+    EXPECT_EQ(*ts7.maximum, "800.8");
+    EXPECT_EQ(*ts7.sum, "3603.6");
+  };
+
+  validate_statistics(stats.file_stats);
+  // There's only one stripe, so column stats are the same as stripe stats
+  validate_statistics(stats.stripes_stats[0]);
+}
+
+TEST_F(OrcWriterTest, SlicedValidMask)
+{
+  std::vector<char const*> strings;
+  // Need more than 32 elements to reproduce the issue
+  for (int i = 0; i < 34; ++i)
+    strings.emplace_back("a long string to make sure overflow affects the output");
+  // An element is null only to enforce the output column to be nullable
+  str_col col{strings.begin(), strings.end(), cudf::test::iterators::null_at(32)};
+
+  // Bug tested here is easiest to reproduce when column_offset % 32 is 31
+  std::vector<cudf::size_type> indices{31, 34};
+  auto sliced_col = cudf::slice(static_cast<cudf::column_view>(col), indices);
+  cudf::table_view tbl{sliced_col};
+
+  cudf::io::table_input_metadata expected_metadata(tbl);
+  expected_metadata.column_metadata[0].set_name("col_string");
+
+  auto filepath = temp_env->get_temp_filepath("OrcStrings.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, tbl)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(tbl, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcReaderTest, SingleInputs)
+{
+  srand(31533);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+
+  auto filepath1 = temp_env->get_temp_filepath("SimpleTable1.orc");
+  cudf::io::orc_writer_options write_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath1}, table1->view());
+  cudf::io::write_orc(write_opts);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{{filepath1}});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *table1);
+}
+
+TEST_F(OrcReaderTest, zstdCompressionRegression)
+{
+  if (cudf::io::nvcomp::is_decompression_disabled(cudf::io::nvcomp::compression_type::ZSTD)) {
+    GTEST_SKIP() << "Newer nvCOMP version is required";
+  }
+
+  // Test with zstd compressed orc file with high compression ratio.
+  constexpr uint8_t input_buffer[] = {
+    0x4f, 0x52, 0x43, 0x5a, 0x00, 0x00, 0x28, 0xb5, 0x2f, 0xfd, 0xa4, 0x34, 0xc7, 0x03, 0x00, 0x74,
+    0x00, 0x00, 0x18, 0x41, 0xff, 0xaa, 0x02, 0x00, 0xbb, 0xff, 0x45, 0xc8, 0x01, 0x25, 0x30, 0x04,
+    0x65, 0x00, 0x00, 0x10, 0xaa, 0x1f, 0x02, 0x00, 0x01, 0x29, 0x0b, 0xc7, 0x39, 0xb8, 0x02, 0xcb,
+    0xaf, 0x38, 0xc0, 0x07, 0x00, 0x00, 0x40, 0x01, 0xc0, 0x05, 0x00, 0x00, 0x46, 0x4d, 0x45, 0x00,
+    0x00, 0x0a, 0x06, 0x08, 0x01, 0x10, 0x01, 0x18, 0x30, 0x0a, 0x06, 0x08, 0x02, 0x10, 0x01, 0x18,
+    0x06, 0x0a, 0x06, 0x08, 0x03, 0x10, 0x01, 0x18, 0x05, 0x12, 0x02, 0x08, 0x00, 0x12, 0x04, 0x08,
+    0x03, 0x10, 0x02, 0x59, 0x00, 0x00, 0x08, 0x03, 0x10, 0x63, 0x1a, 0x0c, 0x08, 0x03, 0x10, 0x00,
+    0x18, 0x3b, 0x20, 0x25, 0x28, 0xa0, 0x9e, 0x75, 0x22, 0x10, 0x08, 0x0c, 0x12, 0x01, 0x01, 0x1a,
+    0x09, 0x63, 0x64, 0x5f, 0x67, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x22, 0x02, 0x08, 0x07, 0x30, 0xa0,
+    0x9e, 0x75, 0x08, 0x2f, 0x10, 0x05, 0x18, 0x80, 0x80, 0x10, 0x22, 0x02, 0x00, 0x0c, 0x28, 0x00,
+    0x30, 0x09, 0x82, 0xf4, 0x03, 0x03, 0x4f, 0x52, 0x43, 0x17};
+
+  auto source =
+    cudf::io::source_info(reinterpret_cast<char const*>(input_buffer), sizeof(input_buffer));
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(source).use_index(false);
+
+  cudf::io::table_with_metadata result;
+  CUDF_EXPECT_NO_THROW(result = cudf::io::read_orc(in_opts));
+  EXPECT_EQ(1920800, result.tbl->num_rows());
+}
+
+TEST_F(OrcReaderTest, MultipleInputs)
+{
+  srand(31537);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+  auto table2 = create_random_fixed_table<int>(5, 5, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto const filepath1 = temp_env->get_temp_filepath("SimpleTable1.orc");
+  {
+    cudf::io::orc_writer_options out_opts =
+      cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath1}, table1->view());
+    cudf::io::write_orc(out_opts);
+  }
+
+  auto const filepath2 = temp_env->get_temp_filepath("SimpleTable2.orc");
+  {
+    cudf::io::orc_writer_options out_opts =
+      cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath2}, table2->view());
+    cudf::io::write_orc(out_opts);
+  }
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{{filepath1, filepath2}});
+  auto result = cudf::io::read_orc(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+struct OrcWriterTestDecimal : public OrcWriterTest,
+                              public ::testing::WithParamInterface<std::tuple<int, int>> {};
+
+TEST_P(OrcWriterTestDecimal, Decimal64)
+{
+  auto const [num_rows, scale] = GetParam();
+
+  // Using int16_t because scale causes values to overflow if they already require 32 bits
+  auto const vals = random_values<int32_t>(num_rows);
+  auto data       = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal64{vals[i], numeric::scale_type{scale}};
+  });
+  auto mask = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 7 == 0; });
+  dec64_col col{data, data + num_rows, mask};
+  cudf::table_view tbl({static_cast<cudf::column_view>(col)});
+
+  auto filepath = temp_env->get_temp_filepath("Decimal64.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(tbl.column(0), result.tbl->view().column(0));
+}
+
+INSTANTIATE_TEST_CASE_P(OrcWriterTest,
+                        OrcWriterTestDecimal,
+                        ::testing::Combine(::testing::Values(1, 10000, 10001, 34567),
+                                           ::testing::Values(-2, 0, 2)));
+
+TEST_F(OrcWriterTest, Decimal32)
+{
+  constexpr auto num_rows = 12000;
+
+  // Using int16_t because scale causes values to overflow if they already require 32 bits
+  auto const vals = random_values<int16_t>(num_rows);
+  auto data       = cudf::detail::make_counting_transform_iterator(0, [&vals](auto i) {
+    return numeric::decimal32{vals[i], numeric::scale_type{2}};
+  });
+  auto mask = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 13; });
+  dec32_col col{data, data + num_rows, mask};
+  cudf::table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("Decimal32.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col, result.tbl->view().column(0));
+}
+
+TEST_F(OrcStatisticsTest, Overflow)
+{
+  int num_rows       = 10;
+  auto too_large_seq = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i * (std::numeric_limits<int64_t>::max() / 20); });
+  auto too_small_seq = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i * (std::numeric_limits<int64_t>::min() / 20); });
+  auto not_too_large_seq = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i * (std::numeric_limits<int64_t>::max() / 200); });
+  auto not_too_small_seq = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i * (std::numeric_limits<int64_t>::min() / 200); });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  column_wrapper<int64_t, typename decltype(too_large_seq)::value_type> col1(
+    too_large_seq, too_large_seq + num_rows, validity);
+  column_wrapper<int64_t, typename decltype(too_small_seq)::value_type> col2(
+    too_small_seq, too_small_seq + num_rows, validity);
+  column_wrapper<int64_t, typename decltype(not_too_large_seq)::value_type> col3(
+    not_too_large_seq, not_too_large_seq + num_rows, validity);
+  column_wrapper<int64_t, typename decltype(not_too_small_seq)::value_type> col4(
+    not_too_small_seq, not_too_small_seq + num_rows, validity);
+  table_view tbl({col1, col2, col3, col4});
+
+  auto filepath = temp_env->get_temp_filepath("OrcStatsOverflow.orc");
+
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_orc(out_opts);
+
+  auto const stats = cudf::io::read_parsed_orc_statistics(cudf::io::source_info{filepath});
+
+  auto check_sum_exist = [&](int idx, bool expected) {
+    auto const& s  = stats.file_stats[idx];
+    auto const& ts = std::get<cudf::io::integer_statistics>(s.type_specific_stats);
+    EXPECT_EQ(ts.sum.has_value(), expected);
+  };
+  check_sum_exist(1, false);
+  check_sum_exist(2, false);
+  check_sum_exist(3, true);
+  check_sum_exist(4, true);
+}
+
+TEST_F(OrcStatisticsTest, HasNull)
+{
+  // This test can now be implemented with libcudf; keeping the pyorc version to keep the test
+  // inputs diversified
+  // Method to create file:
+  // >>> import pyorc
+  // >>> output = open("./temp.orc", "wb")
+  // >>> writer = pyorc.Writer(output, pyorc.Struct(a=pyorc.BigInt(), b=pyorc.BigInt()))
+  // >>> writer.write((1, 3))
+  // >>> writer.write((2, 4))
+  // >>> writer.write((None, 5))
+  // >>> writer.close()
+  //
+  // Contents of file:
+  // >>> import pyarrow.orc as po
+  // >>> po.ORCFile('new.orc').read()
+  // pyarrow.Table
+  // a: int64
+  // b: int64
+  // ----
+  // a: [[1,2,null]]
+  // b: [[3,4,5]]
+  auto nulls_orc = std::array<uint8_t, 308>{
+    0x4F, 0x52, 0x43, 0x1D, 0x00, 0x00, 0x0A, 0x0C, 0x0A, 0x04, 0x00, 0x00, 0x00, 0x00, 0x12, 0x04,
+    0x08, 0x03, 0x50, 0x00, 0x2C, 0x00, 0x00, 0xE3, 0x12, 0xE7, 0x62, 0x67, 0x80, 0x00, 0x21, 0x1E,
+    0x0E, 0x26, 0x21, 0x36, 0x0E, 0x26, 0x01, 0x16, 0x09, 0xB6, 0x00, 0x46, 0x00, 0x2C, 0x00, 0x00,
+    0xE3, 0x12, 0xE7, 0x62, 0x67, 0x80, 0x00, 0x21, 0x1E, 0x0E, 0x66, 0x21, 0x36, 0x0E, 0x36, 0x01,
+    0x2E, 0x09, 0x89, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0xFF, 0xE0, 0x05, 0x00, 0x00, 0xFF, 0xC0,
+    0x07, 0x00, 0x00, 0x46, 0x01, 0x24, 0x05, 0x00, 0x00, 0xFF, 0xE0, 0x09, 0x00, 0x00, 0x46, 0x02,
+    0x68, 0xA0, 0x68, 0x00, 0x00, 0xE3, 0x62, 0xE3, 0x60, 0x13, 0x60, 0x90, 0x10, 0xE4, 0x02, 0xD1,
+    0x8C, 0x12, 0x92, 0x60, 0x9A, 0x09, 0x4C, 0x33, 0x00, 0xC5, 0x59, 0xC1, 0x34, 0x23, 0x98, 0x66,
+    0x04, 0xD2, 0x6C, 0x60, 0x3E, 0x13, 0x94, 0xCF, 0x24, 0xC1, 0x2E, 0xC4, 0x02, 0x52, 0x07, 0x24,
+    0x99, 0x60, 0xA4, 0x14, 0x73, 0x68, 0x88, 0x33, 0x00, 0x46, 0x00, 0x00, 0xE3, 0x52, 0xE2, 0x62,
+    0xE1, 0x60, 0x0E, 0x60, 0xE0, 0xE2, 0xE1, 0x60, 0x12, 0x62, 0xE3, 0x60, 0x12, 0x60, 0x91, 0x60,
+    0x0B, 0x60, 0x04, 0xF2, 0x98, 0x81, 0x3C, 0x36, 0x01, 0x2E, 0x09, 0x89, 0x00, 0x06, 0x00, 0xB4,
+    0x00, 0x00, 0xE3, 0x60, 0x16, 0x98, 0xC6, 0x28, 0xC5, 0xC5, 0xC1, 0x2C, 0xE0, 0x2C, 0x21, 0xA3,
+    0x60, 0xAE, 0xC1, 0xAC, 0x24, 0xC4, 0xC1, 0x23, 0xC4, 0xC4, 0xC8, 0x24, 0xC5, 0x98, 0x28, 0xC5,
+    0x98, 0xA4, 0xC0, 0xA0, 0xC1, 0x60, 0xC0, 0xA0, 0xC4, 0xC1, 0xC1, 0x82, 0xCE, 0x32, 0x60, 0xB6,
+    0x62, 0xE1, 0x60, 0x0E, 0x60, 0xB0, 0xE2, 0xE1, 0x60, 0x12, 0x62, 0xE3, 0x60, 0x12, 0x60, 0x91,
+    0x60, 0x0B, 0x60, 0x04, 0xF2, 0x98, 0x81, 0x3C, 0x36, 0x01, 0x2E, 0x09, 0x89, 0x00, 0x06, 0x87,
+    0x09, 0x7E, 0x1E, 0x8C, 0x49, 0xAC, 0x86, 0x7A, 0xE6, 0x7A, 0xA6, 0x00, 0x08, 0x5D, 0x10, 0x01,
+    0x18, 0x80, 0x80, 0x04, 0x22, 0x02, 0x00, 0x0C, 0x28, 0x26, 0x30, 0x06, 0x82, 0xF4, 0x03, 0x03,
+    0x4F, 0x52, 0x43, 0x17,
+  };
+
+  auto const stats = cudf::io::read_parsed_orc_statistics(
+    cudf::io::source_info{reinterpret_cast<char const*>(nulls_orc.data()), nulls_orc.size()});
+
+  EXPECT_EQ(stats.file_stats[1].has_null, true);
+  EXPECT_EQ(stats.file_stats[2].has_null, false);
+
+  EXPECT_EQ(stats.stripes_stats[0][1].has_null, true);
+  EXPECT_EQ(stats.stripes_stats[0][2].has_null, false);
+}
+
+struct OrcWriterTestStripes
+  : public OrcWriterTest,
+    public ::testing::WithParamInterface<std::tuple<size_t, cudf::size_type>> {};
+
+TEST_P(OrcWriterTestStripes, StripeSize)
+{
+  constexpr auto num_rows            = 1000000;
+  auto const [size_bytes, size_rows] = GetParam();
+
+  auto const seq_col = random_values<int>(num_rows);
+  auto const validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  column_wrapper<int64_t> col{seq_col.begin(), seq_col.end(), validity};
+
+  std::vector<std::unique_ptr<column>> cols;
+  cols.push_back(col.release());
+  auto const expected = std::make_unique<table>(std::move(cols));
+
+  auto validate = [&](std::vector<char> const& orc_buffer) {
+    auto const expected_stripe_num =
+      std::max<cudf::size_type>(num_rows / size_rows, (num_rows * sizeof(int64_t)) / size_bytes);
+    auto const stats = cudf::io::read_parsed_orc_statistics(
+      cudf::io::source_info(orc_buffer.data(), orc_buffer.size()));
+    EXPECT_EQ(stats.stripes_stats.size(), expected_stripe_num);
+
+    cudf::io::orc_reader_options in_opts =
+      cudf::io::orc_reader_options::builder(
+        cudf::io::source_info(orc_buffer.data(), orc_buffer.size()))
+        .use_index(false);
+    auto result = cudf::io::read_orc(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result.tbl->view());
+  };
+
+  {
+    std::vector<char> out_buffer_chunked;
+    cudf::io::chunked_orc_writer_options opts =
+      cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info(&out_buffer_chunked))
+        .stripe_size_rows(size_rows)
+        .stripe_size_bytes(size_bytes);
+    cudf::io::orc_chunked_writer(opts).write(expected->view());
+    validate(out_buffer_chunked);
+  }
+  {
+    std::vector<char> out_buffer;
+    cudf::io::orc_writer_options out_opts =
+      cudf::io::orc_writer_options::builder(cudf::io::sink_info(&out_buffer), expected->view())
+        .stripe_size_rows(size_rows)
+        .stripe_size_bytes(size_bytes);
+    cudf::io::write_orc(out_opts);
+    validate(out_buffer);
+  }
+}
+
+INSTANTIATE_TEST_CASE_P(OrcWriterTest,
+                        OrcWriterTestStripes,
+                        ::testing::Values(std::make_tuple(800000ul, 1000000),
+                                          std::make_tuple(2000000ul, 1000000),
+                                          std::make_tuple(4000000ul, 1000000),
+                                          std::make_tuple(8000000ul, 1000000),
+                                          std::make_tuple(8000000ul, 500000),
+                                          std::make_tuple(8000000ul, 250000),
+                                          std::make_tuple(8000000ul, 100000)));
+
+TEST_F(OrcWriterTest, StripeSizeInvalid)
+{
+  auto const unused_table = std::make_unique<table>();
+  std::vector<char> out_buffer;
+
+  EXPECT_THROW(
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info(&out_buffer), unused_table->view())
+      .stripe_size_rows(511),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info(&out_buffer), unused_table->view())
+      .stripe_size_bytes(63 << 10),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info(&out_buffer), unused_table->view())
+      .row_index_stride(511),
+    cudf::logic_error);
+}
+
+TEST_F(OrcWriterTest, TestMap)
+{
+  auto const num_rows       = 1200000;
+  auto const lists_per_row  = 4;
+  auto const num_child_rows = (num_rows * lists_per_row) / 2;  // half due to validity
+
+  auto keys      = random_values<int>(num_child_rows);
+  auto vals      = random_values<float>(num_child_rows);
+  auto vals_mask = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3; });
+  int32_col keys_col(keys.begin(), keys.end());
+  float32_col vals_col{vals.begin(), vals.end(), vals_mask};
+  auto s_col = struct_col({keys_col, vals_col}).release();
+
+  auto valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  std::vector<int> row_offsets(num_rows + 1);
+  int offset = 0;
+  for (int idx = 0; idx < (num_rows) + 1; ++idx) {
+    row_offsets[idx] = offset;
+    if (valids[idx]) { offset += lists_per_row; }
+  }
+  int32_col offsets(row_offsets.begin(), row_offsets.end());
+
+  auto num_list_rows           = static_cast<cudf::column_view>(offsets).size() - 1;
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(valids, valids + num_list_rows);
+  auto list_col                = cudf::make_lists_column(
+    num_list_rows, offsets.release(), std::move(s_col), null_count, std::move(null_mask));
+
+  table_view expected({*list_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_list_column_as_map();
+
+  auto filepath = temp_env->get_temp_filepath("MapColumn.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(expected_metadata);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(OrcReaderTest, NestedColumnSelection)
+{
+  auto const num_rows  = 1000;
+  auto child_col1_data = random_values<int32_t>(num_rows);
+  auto child_col2_data = random_values<int64_t>(num_rows);
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3; });
+  int32_col child_col1{child_col1_data.begin(), child_col1_data.end(), validity};
+  int64_col child_col2{child_col2_data.begin(), child_col2_data.end(), validity};
+  struct_col s_col{child_col1, child_col2};
+  table_view expected({s_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("struct_s");
+  expected_metadata.column_metadata[0].child(0).set_name("field_a");
+  expected_metadata.column_metadata[0].child(1).set_name("field_b");
+
+  auto filepath = temp_env->get_temp_filepath("OrcNestedSelection.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .use_index(false)
+      .columns({"struct_s.field_b"});
+  auto result = cudf::io::read_orc(in_opts);
+
+  // Verify that only one child column is included in the output table
+  ASSERT_EQ(1, result.tbl->view().column(0).num_children());
+  // Verify that the first child column is `field_b`
+  int64_col expected_col{child_col2_data.begin(), child_col2_data.end(), validity};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_col, result.tbl->view().column(0).child(0));
+  ASSERT_EQ("field_b", result.metadata.schema_info[0].children[0].name);
+}
+
+TEST_F(OrcReaderTest, DecimalOptions)
+{
+  constexpr auto num_rows = 10;
+  auto col_vals           = random_values<int64_t>(num_rows);
+  auto col_data           = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal128{col_vals[i], numeric::scale_type{2}};
+  });
+  auto mask = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3 == 0; });
+
+  dec128_col col{col_data, col_data + num_rows, mask};
+  table_view expected({col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("dec");
+
+  auto filepath = temp_env->get_temp_filepath("OrcDecimalOptions.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options valid_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .decimal128_columns({"dec", "fake_name"});
+  // Should not throw, even with "fake name"
+  EXPECT_NO_THROW(cudf::io::read_orc(valid_opts));
+}
+
+TEST_F(OrcWriterTest, DecimalOptionsNested)
+{
+  auto const num_rows = 100;
+
+  auto dec_vals  = random_values<int32_t>(num_rows);
+  auto dec1_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal64{dec_vals[i], numeric::scale_type{2}};
+  });
+  auto dec2_data = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return numeric::decimal128{dec_vals[i], numeric::scale_type{2}};
+  });
+  dec64_col dec1_col(dec1_data, dec1_data + num_rows);
+  dec128_col dec2_col(dec2_data, dec2_data + num_rows);
+  auto child_struct_col = cudf::test::structs_column_wrapper{dec1_col, dec2_col};
+
+  auto int_vals = random_values<int32_t>(num_rows);
+  int32_col int_col(int_vals.begin(), int_vals.end());
+  auto map_struct_col = struct_col({child_struct_col, int_col}).release();
+
+  std::vector<int> row_offsets(num_rows + 1);
+  std::iota(row_offsets.begin(), row_offsets.end(), 0);
+  int32_col offsets(row_offsets.begin(), row_offsets.end());
+
+  auto map_list_col = cudf::make_lists_column(
+    num_rows, offsets.release(), std::move(map_struct_col), 0, rmm::device_buffer{});
+
+  table_view expected({*map_list_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("maps");
+  expected_metadata.column_metadata[0].set_list_column_as_map();
+  expected_metadata.column_metadata[0].child(1).child(0).child(0).set_name("dec64");
+  expected_metadata.column_metadata[0].child(1).child(0).child(1).set_name("dec128");
+
+  auto filepath = temp_env->get_temp_filepath("OrcMultiColumn.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath})
+      .use_index(false)
+      // One less level of nesting because children of map columns are the child struct's children
+      .decimal128_columns({"maps.0.dec64"});
+  auto result = cudf::io::read_orc(in_opts);
+
+  // Both columns should be read as decimal128
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result.tbl->view().column(0).child(1).child(0).child(0),
+                                      result.tbl->view().column(0).child(1).child(0).child(1));
+}
+
+TEST_F(OrcReaderTest, EmptyColumnsParam)
+{
+  srand(31337);
+  auto const expected = create_random_fixed_table<int>(2, 4, false);
+
+  std::vector<char> out_buffer;
+  cudf::io::orc_writer_options args =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{&out_buffer}, *expected);
+  cudf::io::write_orc(args);
+
+  cudf::io::orc_reader_options read_opts =
+    cudf::io::orc_reader_options::builder(
+      cudf::io::source_info{out_buffer.data(), out_buffer.size()})
+      .columns({});
+  auto const result = cudf::io::read_orc(read_opts);
+
+  EXPECT_EQ(result.tbl->num_columns(), 0);
+  EXPECT_EQ(result.tbl->num_rows(), 0);
+}
+
+TEST_F(OrcMetadataReaderTest, TestBasic)
+{
+  auto const num_rows = 1'200'000;
+
+  auto ints   = random_values<int>(num_rows);
+  auto floats = random_values<float>(num_rows);
+  int32_col int_col(ints.begin(), ints.end());
+  float32_col float_col(floats.begin(), floats.end());
+
+  table_view expected({int_col, float_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("int_col");
+  expected_metadata.column_metadata[1].set_name("float_col");
+
+  auto filepath = temp_env->get_temp_filepath("MetadataTest.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_orc(out_opts);
+
+  auto meta = read_orc_metadata(cudf::io::source_info{filepath});
+  EXPECT_EQ(meta.num_rows(), num_rows);
+
+  EXPECT_EQ(meta.schema().root().name(), "");
+  EXPECT_EQ(meta.schema().root().type_kind(), cudf::io::orc::STRUCT);
+  ASSERT_EQ(meta.schema().root().num_children(), 2);
+
+  EXPECT_EQ(meta.schema().root().child(0).name(), "int_col");
+  EXPECT_EQ(meta.schema().root().child(1).name(), "float_col");
+}
+
+TEST_F(OrcMetadataReaderTest, TestNested)
+{
+  auto const num_rows       = 1'200'000;
+  auto const lists_per_row  = 4;
+  auto const num_child_rows = num_rows * lists_per_row;
+
+  auto keys = random_values<int>(num_child_rows);
+  auto vals = random_values<float>(num_child_rows);
+  int32_col keys_col(keys.begin(), keys.end());
+  float32_col vals_col(vals.begin(), vals.end());
+  auto s_col = struct_col({keys_col, vals_col}).release();
+
+  std::vector<int> row_offsets(num_rows + 1);
+  for (int idx = 0; idx < num_rows + 1; ++idx) {
+    row_offsets[idx] = idx * lists_per_row;
+  }
+  int32_col offsets(row_offsets.begin(), row_offsets.end());
+
+  auto list_col =
+    cudf::make_lists_column(num_rows, offsets.release(), std::move(s_col), 0, rmm::device_buffer{});
+
+  table_view expected({*list_col, *list_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("maps");
+  expected_metadata.column_metadata[0].set_list_column_as_map();
+  expected_metadata.column_metadata[1].set_name("lists");
+  expected_metadata.column_metadata[1].child(1).child(0).set_name("int_field");
+  expected_metadata.column_metadata[1].child(1).child(1).set_name("float_field");
+
+  auto filepath = temp_env->get_temp_filepath("MetadataTest.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_orc(out_opts);
+
+  auto meta = read_orc_metadata(cudf::io::source_info{filepath});
+  EXPECT_EQ(meta.num_rows(), num_rows);
+
+  EXPECT_EQ(meta.schema().root().name(), "");
+  EXPECT_EQ(meta.schema().root().type_kind(), cudf::io::orc::STRUCT);
+  ASSERT_EQ(meta.schema().root().num_children(), 2);
+
+  auto const& out_map_col = meta.schema().root().child(0);
+  EXPECT_EQ(out_map_col.name(), "maps");
+  EXPECT_EQ(out_map_col.type_kind(), cudf::io::orc::MAP);
+  ASSERT_EQ(out_map_col.num_children(), 2);
+  EXPECT_EQ(out_map_col.child(0).name(), "");  // keys (no name in ORC)
+  EXPECT_EQ(out_map_col.child(1).name(), "");  // values (no name in ORC)
+
+  auto const& out_list_col = meta.schema().root().child(1);
+  EXPECT_EQ(out_list_col.name(), "lists");
+  EXPECT_EQ(out_list_col.type_kind(), cudf::io::orc::LIST);
+  ASSERT_EQ(out_list_col.num_children(), 1);
+
+  auto const& out_list_struct_col = out_list_col.child(0);
+  EXPECT_EQ(out_list_struct_col.name(), "");  // elements (no name in ORC)
+  EXPECT_EQ(out_list_struct_col.type_kind(), cudf::io::orc::STRUCT);
+  ASSERT_EQ(out_list_struct_col.num_children(), 2);
+
+  auto const& out_int_col = out_list_struct_col.child(0);
+  EXPECT_EQ(out_int_col.name(), "int_field");
+  EXPECT_EQ(out_int_col.type_kind(), cudf::io::orc::INT);
+
+  auto const& out_float_col = out_list_struct_col.child(1);
+  EXPECT_EQ(out_float_col.name(), "float_field");
+  EXPECT_EQ(out_float_col.type_kind(), cudf::io::orc::FLOAT);
+}
+
+TEST_F(OrcReaderTest, ZstdMaxCompressionRate)
+{
+  if (cudf::io::nvcomp::is_decompression_disabled(cudf::io::nvcomp::compression_type::ZSTD) or
+      cudf::io::nvcomp::is_compression_disabled(cudf::io::nvcomp::compression_type::ZSTD)) {
+    GTEST_SKIP() << "Newer nvCOMP version is required";
+  }
+
+  // Encodes as 64KB of zeros, which compresses to 18 bytes with ZSTD
+  std::vector<float> const h_data(8 * 1024);
+  float32_col col(h_data.begin(), h_data.end());
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcHugeCompRatio.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::ZSTD);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(OrcWriterTest, CompStats)
+{
+  auto table = create_random_fixed_table<int>(1, 100000, true);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::orc_writer_options opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{&unused_buffer}, table->view())
+      .compression_statistics(stats);
+  cudf::io::write_orc(opts);
+
+  EXPECT_NE(stats->num_compressed_bytes(), 0);
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+  EXPECT_FALSE(std::isnan(stats->compression_ratio()));
+}
+
+TEST_F(OrcChunkedWriterTest, CompStats)
+{
+  auto table = create_random_fixed_table<int>(1, 100000, true);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{&unused_buffer})
+      .compression_statistics(stats);
+  cudf::io::orc_chunked_writer(opts).write(*table);
+
+  EXPECT_NE(stats->num_compressed_bytes(), 0);
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+  EXPECT_FALSE(std::isnan(stats->compression_ratio()));
+
+  auto const single_table_comp_stats = *stats;
+  cudf::io::orc_chunked_writer(opts).write(*table);
+
+  EXPECT_EQ(stats->compression_ratio(), single_table_comp_stats.compression_ratio());
+  EXPECT_EQ(stats->num_compressed_bytes(), 2 * single_table_comp_stats.num_compressed_bytes());
+
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+}
+
+void expect_compression_stats_empty(std::shared_ptr<cudf::io::writer_compression_statistics> stats)
+{
+  EXPECT_EQ(stats->num_compressed_bytes(), 0);
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+  EXPECT_TRUE(std::isnan(stats->compression_ratio()));
+}
+
+TEST_F(OrcWriterTest, CompStatsEmptyTable)
+{
+  auto table_no_rows = create_random_fixed_table<int>(20, 0, false);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::orc_writer_options opts = cudf::io::orc_writer_options::builder(
+                                        cudf::io::sink_info{&unused_buffer}, table_no_rows->view())
+                                        .compression_statistics(stats);
+  cudf::io::write_orc(opts);
+
+  expect_compression_stats_empty(stats);
+}
+
+TEST_F(OrcChunkedWriterTest, CompStatsEmptyTable)
+{
+  auto table_no_rows = create_random_fixed_table<int>(20, 0, false);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::chunked_orc_writer_options opts =
+    cudf::io::chunked_orc_writer_options::builder(cudf::io::sink_info{&unused_buffer})
+      .compression_statistics(stats);
+  cudf::io::orc_chunked_writer(opts).write(*table_no_rows);
+
+  expect_compression_stats_empty(stats);
+}
+
+TEST_F(OrcWriterTest, EmptyRowGroup)
+{
+  std::vector<int> ints(10000 + 5, -1);
+  auto mask = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i >= 10000; });
+  int32_col col{ints.begin(), ints.end(), mask};
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcEmptyRowGroup.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(in_opts);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(OrcWriterTest, NoNullsAsNonNullable)
+{
+  auto valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  column_wrapper<int32_t> col{{1, 2, 3}, valids};
+  table_view expected({col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_nullability(false);
+
+  auto filepath = temp_env->get_temp_filepath("NonNullable.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  // Writer should be able to write a column without nulls as non-nullable
+  EXPECT_NO_THROW(cudf::io::write_orc(out_opts));
+}
+
+TEST_F(OrcWriterTest, SlicedStringColumn)
+{
+  std::vector<char const*> strings{"a", "bc", "def", "longer", "strings", "at the end"};
+  str_col col(strings.begin(), strings.end());
+  table_view expected({col});
+
+  // Slice the table to include the longer strings
+  auto expected_slice = cudf::slice(expected, {2, 6});
+
+  auto filepath = temp_env->get_temp_filepath("SlicedTable.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected_slice);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_slice, result.tbl->view());
+}
+
+TEST_F(OrcWriterTest, EmptyChildStringColumn)
+{
+  list_col<cudf::string_view> col{{}, {}};
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("OrcEmptyChildStringColumn.orc");
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_orc(out_opts);
+
+  cudf::io::orc_reader_options in_opts =
+    cudf::io::orc_reader_options::builder(cudf::io::source_info{filepath}).use_index(false);
+  auto result = cudf::io::read_orc(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+template <typename T>
+void check_all_null_stats(cudf::io::column_statistics const& stats)
+{
+  EXPECT_EQ(stats.number_of_values, 0);
+  EXPECT_TRUE(stats.has_null);
+
+  auto const ts = std::get<T>(stats.type_specific_stats);
+  EXPECT_FALSE(ts.minimum.has_value());
+  EXPECT_FALSE(ts.maximum.has_value());
+  EXPECT_TRUE(ts.sum.has_value());
+  EXPECT_EQ(*ts.sum, 0);
+}
+
+TEST_F(OrcStatisticsTest, AllNulls)
+{
+  float64_col double_col({0., 0., 0.}, cudf::test::iterators::all_nulls());
+  int32_col int_col({0, 0, 0}, cudf::test::iterators::all_nulls());
+  str_col string_col({"", "", ""}, cudf::test::iterators::all_nulls());
+
+  cudf::table_view expected({int_col, double_col, string_col});
+
+  std::vector<char> out_buffer;
+  cudf::io::orc_writer_options out_opts =
+    cudf::io::orc_writer_options::builder(cudf::io::sink_info{&out_buffer}, expected);
+  cudf::io::write_orc(out_opts);
+
+  auto const stats = cudf::io::read_parsed_orc_statistics(
+    cudf::io::source_info{out_buffer.data(), out_buffer.size()});
+
+  check_all_null_stats<cudf::io::integer_statistics>(stats.file_stats[1]);
+  check_all_null_stats<cudf::io::double_statistics>(stats.file_stats[2]);
+  check_all_null_stats<cudf::io::string_statistics>(stats.file_stats[3]);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/parquet_chunked_reader_test.cpp b/cpp/tests/io/parquet_chunked_reader_test.cpp
new file mode 100644
index 0000000..05fb9a3
--- /dev/null
+++ b/cpp/tests/io/parquet_chunked_reader_test.cpp
@@ -0,0 +1,1016 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/io_metadata_utilities.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <src/io/parquet/compact_protocol_reader.hpp>
+#include <src/io/parquet/parquet.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <fstream>
+#include <type_traits>
+
+namespace {
+// Global environment for temporary files
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+using int32s_col       = cudf::test::fixed_width_column_wrapper<int32_t>;
+using int64s_col       = cudf::test::fixed_width_column_wrapper<int64_t>;
+using strings_col      = cudf::test::strings_column_wrapper;
+using structs_col      = cudf::test::structs_column_wrapper;
+using int32s_lists_col = cudf::test::lists_column_wrapper<int32_t>;
+
+auto write_file(std::vector<std::unique_ptr<cudf::column>>& input_columns,
+                std::string const& filename,
+                bool nullable,
+                std::size_t max_page_size_bytes = cudf::io::default_max_page_size_bytes,
+                std::size_t max_page_size_rows  = cudf::io::default_max_page_size_rows)
+{
+  // Just shift nulls of the next column by one position to avoid having all nulls in the same
+  // table rows.
+  if (nullable) {
+    // Generate deterministic bitmask instead of random bitmask for easy computation of data size.
+    auto const valid_iter = cudf::detail::make_counting_transform_iterator(
+      0, [](cudf::size_type i) { return i % 4 != 3; });
+
+    cudf::size_type offset{0};
+    for (auto& col : input_columns) {
+      auto const [null_mask, null_count] =
+        cudf::test::detail::make_null_mask(valid_iter + offset, valid_iter + col->size() + offset);
+      col = cudf::structs::detail::superimpose_nulls(
+        static_cast<cudf::bitmask_type const*>(null_mask.data()),
+        null_count,
+        std::move(col),
+        cudf::get_default_stream(),
+        rmm::mr::get_current_device_resource());
+    }
+  }
+
+  auto input_table = std::make_unique<cudf::table>(std::move(input_columns));
+  auto filepath =
+    temp_env->get_temp_filepath(nullable ? filename + "_nullable.parquet" : filename + ".parquet");
+
+  auto const write_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *input_table)
+      .max_page_size_bytes(max_page_size_bytes)
+      .max_page_size_rows(max_page_size_rows)
+      .max_page_fragment_size(cudf::io::default_max_page_fragment_size)
+      .build();
+  cudf::io::write_parquet(write_opts);
+
+  return std::pair{std::move(input_table), std::move(filepath)};
+}
+
+auto chunked_read(std::string const& filepath,
+                  std::size_t output_limit,
+                  std::size_t input_limit = 0)
+{
+  auto const read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}).build();
+  auto reader = cudf::io::chunked_parquet_reader(output_limit, input_limit, read_opts);
+
+  auto num_chunks = 0;
+  auto out_tables = std::vector<std::unique_ptr<cudf::table>>{};
+
+  do {
+    auto chunk = reader.read_chunk();
+    // If the input file is empty, the first call to `read_chunk` will return an empty table.
+    // Thus, we only check for non-empty output table from the second call.
+    if (num_chunks > 0) {
+      CUDF_EXPECTS(chunk.tbl->num_rows() != 0, "Number of rows in the new chunk is zero.");
+    }
+    ++num_chunks;
+    out_tables.emplace_back(std::move(chunk.tbl));
+  } while (reader.has_next());
+
+  auto out_tviews = std::vector<cudf::table_view>{};
+  for (auto const& tbl : out_tables) {
+    out_tviews.emplace_back(tbl->view());
+  }
+
+  return std::pair(cudf::concatenate(out_tviews), num_chunks);
+}
+
+}  // namespace
+
+struct ParquetChunkedReaderTest : public cudf::test::BaseFixture {};
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadNoData)
+{
+  std::vector<std::unique_ptr<cudf::column>> input_columns;
+  input_columns.emplace_back(int32s_col{}.release());
+  input_columns.emplace_back(int64s_col{}.release());
+
+  auto const [expected, filepath] = write_file(input_columns, "chunked_read_empty", false);
+  auto const [result, num_chunks] = chunked_read(filepath, 1'000);
+  EXPECT_EQ(num_chunks, 1);
+  EXPECT_EQ(result->num_rows(), 0);
+  EXPECT_EQ(result->num_columns(), 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadSimpleData)
+{
+  auto constexpr num_rows = 40'000;
+
+  auto const generate_input = [num_rows](bool nullable) {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    auto const value_iter = thrust::make_counting_iterator(0);
+    input_columns.emplace_back(int32s_col(value_iter, value_iter + num_rows).release());
+    input_columns.emplace_back(int64s_col(value_iter, value_iter + num_rows).release());
+
+    return write_file(input_columns, "chunked_read_simple", nullable);
+  };
+
+  {
+    auto const [expected, filepath] = generate_input(false);
+    auto const [result, num_chunks] = chunked_read(filepath, 240'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  {
+    auto const [expected, filepath] = generate_input(true);
+    auto const [result, num_chunks] = chunked_read(filepath, 240'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadBoundaryCases)
+{
+  // Tests some specific boundary conditions in the split calculations.
+
+  auto constexpr num_rows = 40'000;
+
+  auto const [expected, filepath] = [num_rows]() {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    auto const value_iter = thrust::make_counting_iterator(0);
+    input_columns.emplace_back(int32s_col(value_iter, value_iter + num_rows).release());
+    return write_file(input_columns, "chunked_read_simple_boundary", false /*nullable*/);
+  }();
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 1);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit slightly less than one page of data
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 79'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit exactly the size one page of data
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 80'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit slightly more the size one page of data
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 81'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit slightly less than two pages of data
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 159'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit exactly the size of two pages of data minus one byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 159'999);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit exactly the size of two pages of data
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 160'000);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a limit slightly more the size two pages of data
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 161'000);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithString)
+{
+  auto constexpr num_rows = 60'000;
+
+  auto const generate_input = [num_rows](bool nullable) {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    auto const value_iter = thrust::make_counting_iterator(0);
+
+    // ints                                            Page    total bytes   cumulative bytes
+    // 20000 rows of 4 bytes each                    = A0      80000         80000
+    // 20000 rows of 4 bytes each                    = A1      80000         160000
+    // 20000 rows of 4 bytes each                    = A2      80000         240000
+    input_columns.emplace_back(int32s_col(value_iter, value_iter + num_rows).release());
+
+    // strings                                         Page    total bytes   cumulative bytes
+    // 20000 rows of 1 char each    (20000  + 80004) = B0      100004        100004
+    // 20000 rows of 4 chars each   (80000  + 80004) = B1      160004        260008
+    // 20000 rows of 16 chars each  (320000 + 80004) = B2      400004        660012
+    auto const strings  = std::vector<std::string>{"a", "bbbb", "cccccccccccccccc"};
+    auto const str_iter = cudf::detail::make_counting_transform_iterator(0, [&](int32_t i) {
+      if (i < 20000) { return strings[0]; }
+      if (i < 40000) { return strings[1]; }
+      return strings[2];
+    });
+    input_columns.emplace_back(strings_col(str_iter, str_iter + num_rows).release());
+
+    // Cumulative sizes:
+    // A0 + B0 :  180004
+    // A1 + B1 :  420008
+    // A2 + B2 :  900012
+    //                                    skip_rows / num_rows
+    // byte_limit==500000  should give 2 chunks: {0, 40000}, {40000, 20000}
+    // byte_limit==1000000 should give 1 chunks: {0, 60000},
+    return write_file(input_columns,
+                      "chunked_read_with_strings",
+                      nullable,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  };
+
+  auto const [expected_no_null, filepath_no_null]       = generate_input(false);
+  auto const [expected_with_nulls, filepath_with_nulls] = generate_input(true);
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Other tests:
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 500'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 500'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1'000'000);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1'000'000);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithStringPrecise)
+{
+  auto constexpr num_rows = 60'000;
+
+  auto const generate_input = [num_rows](bool nullable) {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+
+    // strings                                                 Page    total bytes   cumulative
+    // 20000 rows alternating 1-4 chars each (50000 + 80004)   A0      130004        130004
+    // 20000 rows alternating 1-4 chars each (50000 + 80004)   A1      130004        260008
+    // ...
+    auto const strings = std::vector<std::string>{"a", "bbbb"};
+    auto const str_iter =
+      cudf::detail::make_counting_transform_iterator(0, [&](int32_t i) { return strings[i % 2]; });
+    input_columns.emplace_back(strings_col(str_iter, str_iter + num_rows).release());
+
+    // Cumulative sizes:
+    // A0 :  130004
+    // A1 :  260008
+    // A2 :  390012
+    return write_file(input_columns,
+                      "chunked_read_with_strings_precise",
+                      nullable,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  };
+
+  auto const [expected_no_null, filepath_no_null] = generate_input(false);
+
+  // a chunk limit of 1 byte less than 2 pages should force it to produce 3 chunks:
+  // each 1 page in size
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 260'007);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  // a chunk limit of exactly equal to 2 pages should force it to produce 2 chunks
+  // pages 0-1 and page 2
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 260'008);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithStructs)
+{
+  auto constexpr num_rows = 100'000;
+
+  auto const generate_input = [num_rows](bool nullable) {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    auto const int_iter = thrust::make_counting_iterator(0);
+    input_columns.emplace_back(int32s_col(int_iter, int_iter + num_rows).release());
+    input_columns.emplace_back([=] {
+      auto child1 = int32s_col(int_iter, int_iter + num_rows);
+      auto child2 = int32s_col(int_iter + num_rows, int_iter + num_rows * 2);
+
+      auto const str_iter = cudf::detail::make_counting_transform_iterator(
+        0, [&](int32_t i) { return std::to_string(i); });
+      auto child3 = strings_col{str_iter, str_iter + num_rows};
+
+      return structs_col{{child1, child2, child3}}.release();
+    }());
+
+    return write_file(input_columns,
+                      "chunked_read_with_structs",
+                      nullable,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  };
+
+  auto const [expected_no_null, filepath_no_null]       = generate_input(false);
+  auto const [expected_with_nulls, filepath_with_nulls] = generate_input(true);
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Other tests:
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 500'000);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 500'000);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithListsNoNulls)
+{
+  auto constexpr num_rows = 100'000;
+
+  auto const [expected, filepath] = [num_rows]() {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    // 20000 rows in 1 page consist of:
+    //
+    // 20001 offsets :   80004  bytes
+    // 30000 ints    :   120000 bytes
+    // total         :   200004 bytes
+    auto const template_lists = int32s_lists_col{
+      int32s_lists_col{}, int32s_lists_col{0}, int32s_lists_col{1, 2}, int32s_lists_col{3, 4, 5}};
+
+    auto const gather_iter =
+      cudf::detail::make_counting_transform_iterator(0, [&](int32_t i) { return i % 4; });
+    auto const gather_map = int32s_col(gather_iter, gather_iter + num_rows);
+    input_columns.emplace_back(
+      std::move(cudf::gather(cudf::table_view{{template_lists}}, gather_map)->release().front()));
+
+    return write_file(input_columns,
+                      "chunked_read_with_lists_no_null",
+                      false /*nullable*/,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  }();
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 1);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size slightly less than 1 page (forcing it to be at least 1 page per read)
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 200'000);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size exactly 1 page
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 200'004);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size 2 pages. 3 chunks (2 pages + 2 pages + 1 page)
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 400'008);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size 2 pages minus one byte: each chunk will be just one page
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 400'007);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithListsHavingNulls)
+{
+  auto constexpr num_rows = 100'000;
+
+  auto const [expected, filepath] = [num_rows]() {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    // 20000 rows in 1 page consist of:
+    //
+    // 625 validity words :   2500 bytes   (a null every 4 rows: null at indices [3, 7, 11, ...])
+    // 20001 offsets      :   80004  bytes
+    // 15000 ints         :   60000 bytes
+    // total              :   142504 bytes
+    auto const template_lists =
+      int32s_lists_col{// these will all be null
+                       int32s_lists_col{},
+                       int32s_lists_col{0},
+                       int32s_lists_col{1, 2},
+                       int32s_lists_col{3, 4, 5, 6, 7, 8, 9} /* this list will be nullified out */};
+    auto const gather_iter =
+      cudf::detail::make_counting_transform_iterator(0, [&](int32_t i) { return i % 4; });
+    auto const gather_map = int32s_col(gather_iter, gather_iter + num_rows);
+    input_columns.emplace_back(
+      std::move(cudf::gather(cudf::table_view{{template_lists}}, gather_map)->release().front()));
+
+    return write_file(input_columns,
+                      "chunked_read_with_lists_nulls",
+                      true /*nullable*/,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  }();
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 1);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size slightly less than 1 page (forcing it to be at least 1 page per read)
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 142'500);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size exactly 1 page
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 142'504);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size 2 pages. 3 chunks (2 pages + 2 pages + 1 page)
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 285'008);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+
+  // chunk size 2 pages minus 1 byte: each chunk will be just one page
+  {
+    auto const [result, num_chunks] = chunked_read(filepath, 285'007);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithStructsOfLists)
+{
+  auto constexpr num_rows = 100'000;
+
+  auto const generate_input = [num_rows](bool nullable) {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    auto const int_iter = thrust::make_counting_iterator(0);
+    input_columns.emplace_back(int32s_col(int_iter, int_iter + num_rows).release());
+    input_columns.emplace_back([=] {
+      std::vector<std::unique_ptr<cudf::column>> child_columns;
+      child_columns.emplace_back(int32s_col(int_iter, int_iter + num_rows).release());
+      child_columns.emplace_back(
+        int32s_col(int_iter + num_rows, int_iter + num_rows * 2).release());
+
+      auto const str_iter = cudf::detail::make_counting_transform_iterator(0, [&](int32_t i) {
+        return std::to_string(i) + "++++++++++++++++++++" + std::to_string(i);
+      });
+      child_columns.emplace_back(strings_col{str_iter, str_iter + num_rows}.release());
+
+      auto const template_lists = int32s_lists_col{
+        int32s_lists_col{}, int32s_lists_col{0}, int32s_lists_col{0, 1}, int32s_lists_col{0, 1, 2}};
+      auto const gather_iter =
+        cudf::detail::make_counting_transform_iterator(0, [&](int32_t i) { return i % 4; });
+      auto const gather_map = int32s_col(gather_iter, gather_iter + num_rows);
+      child_columns.emplace_back(
+        std::move(cudf::gather(cudf::table_view{{template_lists}}, gather_map)->release().front()));
+
+      return structs_col(std::move(child_columns)).release();
+    }());
+
+    return write_file(input_columns,
+                      "chunked_read_with_structs_of_lists",
+                      nullable,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  };
+
+  auto const [expected_no_null, filepath_no_null]       = generate_input(false);
+  auto const [expected_with_nulls, filepath_with_nulls] = generate_input(true);
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1);
+    EXPECT_EQ(num_chunks, 10);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Other tests:
+
+  // for these tests, different columns get written to different numbers of pages so it's a
+  // little tricky to describe the expected results by page counts. To get an idea of how
+  // these values are chosen, see the debug output from the call to print_cumulative_row_info() in
+  // reader_impl_preprocess.cu -> find_splits()
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1'000'000);
+    EXPECT_EQ(num_chunks, 7);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1'500'000);
+    EXPECT_EQ(num_chunks, 4);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 2'000'000);
+    EXPECT_EQ(num_chunks, 4);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 5'000'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1'000'000);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1'500'000);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 2'000'000);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 5'000'000);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadWithListsOfStructs)
+{
+  auto constexpr num_rows = 100'000;
+
+  auto const generate_input = [num_rows](bool nullable) {
+    std::vector<std::unique_ptr<cudf::column>> input_columns;
+    auto const int_iter = thrust::make_counting_iterator(0);
+    input_columns.emplace_back(int32s_col(int_iter, int_iter + num_rows).release());
+
+    auto offsets = std::vector<cudf::size_type>{};
+    offsets.reserve(num_rows * 2);
+    cudf::size_type num_structs = 0;
+    for (int i = 0; i < num_rows; ++i) {
+      offsets.push_back(num_structs);
+      auto const new_list_size = i % 4;
+      num_structs += new_list_size;
+    }
+    offsets.push_back(num_structs);
+
+    auto const make_structs_col = [=] {
+      auto child1 = int32s_col(int_iter, int_iter + num_structs);
+      auto child2 = int32s_col(int_iter + num_structs, int_iter + num_structs * 2);
+
+      auto const str_iter = cudf::detail::make_counting_transform_iterator(
+        0, [&](int32_t i) { return std::to_string(i) + std::to_string(i) + std::to_string(i); });
+      auto child3 = strings_col{str_iter, str_iter + num_structs};
+
+      return structs_col{{child1, child2, child3}}.release();
+    };
+
+    input_columns.emplace_back(
+      cudf::make_lists_column(static_cast<cudf::size_type>(offsets.size() - 1),
+                              int32s_col(offsets.begin(), offsets.end()).release(),
+                              make_structs_col(),
+                              0,
+                              rmm::device_buffer{}));
+
+    return write_file(input_columns,
+                      "chunked_read_with_lists_of_structs",
+                      nullable,
+                      512 * 1024,  // 512KB per page
+                      20000        // 20k rows per page
+    );
+  };
+
+  auto const [expected_no_null, filepath_no_null]       = generate_input(false);
+  auto const [expected_with_nulls, filepath_with_nulls] = generate_input(true);
+
+  // Test with zero limit: everything will be read in one chunk
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very small limit: 1 byte
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1);
+    EXPECT_EQ(num_chunks, 10);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // Test with a very large limit
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 2L << 40);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  // for these tests, different columns get written to different numbers of pages so it's a
+  // little tricky to describe the expected results by page counts. To get an idea of how
+  // these values are chosen, see the debug output from the call to print_cumulative_row_info() in
+  // reader_impl_preprocess.cu -> find_splits()
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1'000'000);
+    EXPECT_EQ(num_chunks, 7);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 1'500'000);
+    EXPECT_EQ(num_chunks, 4);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 2'000'000);
+    EXPECT_EQ(num_chunks, 4);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_no_null, 5'000'000);
+    EXPECT_EQ(num_chunks, 2);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_no_null, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1'000'000);
+    EXPECT_EQ(num_chunks, 5);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 1'500'000);
+    EXPECT_EQ(num_chunks, 4);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 2'000'000);
+    EXPECT_EQ(num_chunks, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+
+  {
+    auto const [result, num_chunks] = chunked_read(filepath_with_nulls, 5'000'000);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expected_with_nulls, *result);
+  }
+}
+
+TEST_F(ParquetChunkedReaderTest, TestChunkedReadNullCount)
+{
+  auto constexpr num_rows = 100'000;
+
+  auto const sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 1; });
+  auto const validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 4 != 3; });
+  cudf::test::fixed_width_column_wrapper<int32_t> col{sequence, sequence + num_rows, validity};
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  cols.push_back(col.release());
+  auto const expected = std::make_unique<cudf::table>(std::move(cols));
+
+  auto const filepath        = temp_env->get_temp_filepath("chunked_reader_null_count.parquet");
+  auto const page_limit_rows = num_rows / 5;
+  auto const write_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected)
+      .max_page_size_rows(page_limit_rows)  // 20k rows per page
+      .build();
+  cudf::io::write_parquet(write_opts);
+
+  auto const byte_limit = page_limit_rows * sizeof(int);
+  auto const read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}).build();
+  auto reader = cudf::io::chunked_parquet_reader(byte_limit, read_opts);
+
+  do {
+    // Every fourth row is null
+    EXPECT_EQ(reader.read_chunk().tbl->get_column(0).null_count(), page_limit_rows / 4);
+  } while (reader.has_next());
+}
+
+TEST_F(ParquetChunkedReaderTest, InputLimitSimple)
+{
+  auto const filepath = temp_env->get_temp_filepath("input_limit_10_rowgroups.parquet");
+
+  // This results in 10 grow groups, at 4001150 bytes per row group
+  constexpr int num_rows = 25'000'000;
+  auto value_iter = cudf::detail::make_counting_transform_iterator(0, [](int i) { return i; });
+  cudf::test::fixed_width_column_wrapper<int> expected(value_iter, value_iter + num_rows);
+  cudf::io::parquet_writer_options opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath},
+                                              cudf::table_view{{expected}})
+      // note: it is unnecessary to force compression to NONE here because the size we are using in
+      // the row group is the uncompressed data size. But forcing the dictionary policy to
+      // dictionary_policy::NEVER is necessary to prevent changes in the
+      // decompressed-but-not-yet-decoded data.
+      .dictionary_policy(cudf::io::dictionary_policy::NEVER);
+
+  cudf::io::write_parquet(opts);
+
+  {
+    // no chunking
+    auto const [result, num_chunks] = chunked_read(filepath, 0, 0);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->get_column(0));
+  }
+
+  {
+    // 25 chunks of 100k rows each
+    auto const [result, num_chunks] = chunked_read(filepath, 0, 1);
+    EXPECT_EQ(num_chunks, 25);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->get_column(0));
+  }
+
+  {
+    // 25 chunks of 100k rows each
+    auto const [result, num_chunks] = chunked_read(filepath, 0, 4000000);
+    EXPECT_EQ(num_chunks, 25);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->get_column(0));
+  }
+
+  {
+    // 25 chunks of 100k rows each
+    auto const [result, num_chunks] = chunked_read(filepath, 0, 4100000);
+    EXPECT_EQ(num_chunks, 25);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->get_column(0));
+  }
+
+  {
+    // 12 chunks of 200k rows each, plus 1 final chunk of 100k rows.
+    auto const [result, num_chunks] = chunked_read(filepath, 0, 8002301);
+    EXPECT_EQ(num_chunks, 13);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->get_column(0));
+  }
+
+  {
+    // 1 big chunk
+    auto const [result, num_chunks] = chunked_read(filepath, 0, size_t{1} * 1024 * 1024 * 1024);
+    EXPECT_EQ(num_chunks, 1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->get_column(0));
+  }
+}
diff --git a/cpp/tests/io/parquet_test.cpp b/cpp/tests/io/parquet_test.cpp
new file mode 100644
index 0000000..81e0e12
--- /dev/null
+++ b/cpp/tests/io/parquet_test.cpp
@@ -0,0 +1,6735 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/io_metadata_utilities.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/datasource.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/io/parquet_metadata.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <src/io/parquet/compact_protocol_reader.hpp>
+#include <src/io/parquet/parquet.hpp>
+#include <src/io/parquet/parquet_gpu.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <fstream>
+#include <random>
+#include <type_traits>
+
+template <typename T, typename SourceElementT = T>
+using column_wrapper =
+  typename std::conditional<std::is_same_v<T, cudf::string_view>,
+                            cudf::test::strings_column_wrapper,
+                            cudf::test::fixed_width_column_wrapper<T, SourceElementT>>::type;
+using column     = cudf::column;
+using table      = cudf::table;
+using table_view = cudf::table_view;
+
+// Global environment for temporary files
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+template <typename T, typename Elements>
+std::unique_ptr<cudf::table> create_fixed_table(cudf::size_type num_columns,
+                                                cudf::size_type num_rows,
+                                                bool include_validity,
+                                                Elements elements)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  std::vector<cudf::test::fixed_width_column_wrapper<T>> src_cols(num_columns);
+  for (int idx = 0; idx < num_columns; idx++) {
+    if (include_validity) {
+      src_cols[idx] =
+        cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_rows, valids);
+    } else {
+      src_cols[idx] = cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_rows);
+    }
+  }
+  std::vector<std::unique_ptr<cudf::column>> columns(num_columns);
+  std::transform(src_cols.begin(),
+                 src_cols.end(),
+                 columns.begin(),
+                 [](cudf::test::fixed_width_column_wrapper<T>& in) {
+                   auto ret = in.release();
+                   // pre-cache the null count
+                   [[maybe_unused]] auto const nulls = ret->has_nulls();
+                   return ret;
+                 });
+  return std::make_unique<cudf::table>(std::move(columns));
+}
+
+template <typename T>
+std::unique_ptr<cudf::table> create_random_fixed_table(cudf::size_type num_columns,
+                                                       cudf::size_type num_rows,
+                                                       bool include_validity)
+{
+  auto rand_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](T i) { return rand(); });
+  return create_fixed_table<T>(num_columns, num_rows, include_validity, rand_elements);
+}
+
+template <typename T>
+std::unique_ptr<cudf::table> create_compressible_fixed_table(cudf::size_type num_columns,
+                                                             cudf::size_type num_rows,
+                                                             cudf::size_type period,
+                                                             bool include_validity)
+{
+  auto compressible_elements =
+    cudf::detail::make_counting_transform_iterator(0, [period](T i) { return i / period; });
+  return create_fixed_table<T>(num_columns, num_rows, include_validity, compressible_elements);
+}
+
+// this function replicates the "list_gen" function in
+// python/cudf/cudf/tests/test_parquet.py
+template <typename T>
+std::unique_ptr<cudf::column> make_parquet_list_list_col(
+  int skip_rows, int num_rows, int lists_per_row, int list_size, bool include_validity)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0 ? 1 : 0; });
+
+  // root list
+  std::vector<int> row_offsets(num_rows + 1);
+  int row_offset_count = 0;
+  {
+    int offset = 0;
+    for (int idx = 0; idx < (num_rows) + 1; idx++) {
+      row_offsets[row_offset_count] = offset;
+      if (!include_validity || valids[idx]) { offset += lists_per_row; }
+      row_offset_count++;
+    }
+  }
+  cudf::test::fixed_width_column_wrapper<int> offsets(row_offsets.begin(),
+                                                      row_offsets.begin() + row_offset_count);
+
+  // child list
+  std::vector<int> child_row_offsets((num_rows * lists_per_row) + 1);
+  int child_row_offset_count = 0;
+  {
+    int offset = 0;
+    for (int idx = 0; idx < (num_rows * lists_per_row); idx++) {
+      int row_index = idx / lists_per_row;
+      if (include_validity && !valids[row_index]) { continue; }
+
+      child_row_offsets[child_row_offset_count] = offset;
+      offset += list_size;
+      child_row_offset_count++;
+    }
+    child_row_offsets[child_row_offset_count++] = offset;
+  }
+  cudf::test::fixed_width_column_wrapper<int> child_offsets(
+    child_row_offsets.begin(), child_row_offsets.begin() + child_row_offset_count);
+
+  // child values
+  std::vector<T> child_values(num_rows * lists_per_row * list_size);
+  T first_child_value_index = skip_rows * lists_per_row * list_size;
+  int child_value_count     = 0;
+  {
+    for (int idx = 0; idx < (num_rows * lists_per_row * list_size); idx++) {
+      int row_index = idx / (lists_per_row * list_size);
+
+      int val = first_child_value_index;
+      first_child_value_index++;
+
+      if (include_validity && !valids[row_index]) { continue; }
+
+      child_values[child_value_count] = val;
+      child_value_count++;
+    }
+  }
+  // validity by value instead of index
+  auto valids2 = cudf::detail::make_counting_transform_iterator(
+    0, [list_size](auto i) { return (i % list_size) % 2 == 0 ? 1 : 0; });
+  auto child_data = include_validity
+                      ? cudf::test::fixed_width_column_wrapper<T>(
+                          child_values.begin(), child_values.begin() + child_value_count, valids2)
+                      : cudf::test::fixed_width_column_wrapper<T>(
+                          child_values.begin(), child_values.begin() + child_value_count);
+
+  int child_offsets_size = static_cast<cudf::column_view>(child_offsets).size() - 1;
+  auto child             = cudf::make_lists_column(
+    child_offsets_size, child_offsets.release(), child_data.release(), 0, rmm::device_buffer{});
+
+  int offsets_size             = static_cast<cudf::column_view>(offsets).size() - 1;
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(valids, valids + offsets_size);
+  return include_validity
+           ? cudf::make_lists_column(
+               offsets_size, offsets.release(), std::move(child), null_count, std::move(null_mask))
+           : cudf::make_lists_column(
+               offsets_size, offsets.release(), std::move(child), 0, rmm::device_buffer{});
+}
+
+// given a datasource pointing to a parquet file, read the footer
+// of the file to populate the FileMetaData pointed to by file_meta_data.
+// throws cudf::logic_error if the file or metadata is invalid.
+void read_footer(std::unique_ptr<cudf::io::datasource> const& source,
+                 cudf::io::parquet::FileMetaData* file_meta_data)
+{
+  constexpr auto header_len = sizeof(cudf::io::parquet::file_header_s);
+  constexpr auto ender_len  = sizeof(cudf::io::parquet::file_ender_s);
+
+  auto const len           = source->size();
+  auto const header_buffer = source->host_read(0, header_len);
+  auto const header =
+    reinterpret_cast<cudf::io::parquet::file_header_s const*>(header_buffer->data());
+  auto const ender_buffer = source->host_read(len - ender_len, ender_len);
+  auto const ender = reinterpret_cast<cudf::io::parquet::file_ender_s const*>(ender_buffer->data());
+
+  // checks for valid header, footer, and file length
+  ASSERT_GT(len, header_len + ender_len);
+  ASSERT_TRUE(header->magic == cudf::io::parquet::parquet_magic &&
+              ender->magic == cudf::io::parquet::parquet_magic);
+  ASSERT_TRUE(ender->footer_len != 0 && ender->footer_len <= (len - header_len - ender_len));
+
+  // parquet files end with 4-byte footer_length and 4-byte magic == "PAR1"
+  // seek backwards from the end of the file (footer_length + 8 bytes of ender)
+  auto const footer_buffer =
+    source->host_read(len - ender->footer_len - ender_len, ender->footer_len);
+  cudf::io::parquet::CompactProtocolReader cp(footer_buffer->data(), ender->footer_len);
+
+  // returns true on success
+  bool res = cp.read(file_meta_data);
+  ASSERT_TRUE(res);
+}
+
+// returns the number of bits used for dictionary encoding data at the given page location.
+// this assumes the data is uncompressed.
+// throws cudf::logic_error if the page_loc data is invalid.
+int read_dict_bits(std::unique_ptr<cudf::io::datasource> const& source,
+                   cudf::io::parquet::PageLocation const& page_loc)
+{
+  CUDF_EXPECTS(page_loc.offset > 0, "Cannot find page header");
+  CUDF_EXPECTS(page_loc.compressed_page_size > 0, "Invalid page header length");
+
+  cudf::io::parquet::PageHeader page_hdr;
+  auto const page_buf = source->host_read(page_loc.offset, page_loc.compressed_page_size);
+  cudf::io::parquet::CompactProtocolReader cp(page_buf->data(), page_buf->size());
+  bool res = cp.read(&page_hdr);
+  CUDF_EXPECTS(res, "Cannot parse page header");
+
+  // cp should be pointing at the start of page data now. the first byte
+  // should be the encoding bit size
+  return cp.getb();
+}
+
+// read column index from datasource at location indicated by chunk,
+// parse and return as a ColumnIndex struct.
+// throws cudf::logic_error if the chunk data is invalid.
+cudf::io::parquet::ColumnIndex read_column_index(
+  std::unique_ptr<cudf::io::datasource> const& source, cudf::io::parquet::ColumnChunk const& chunk)
+{
+  CUDF_EXPECTS(chunk.column_index_offset > 0, "Cannot find column index");
+  CUDF_EXPECTS(chunk.column_index_length > 0, "Invalid column index length");
+
+  cudf::io::parquet::ColumnIndex colidx;
+  auto const ci_buf = source->host_read(chunk.column_index_offset, chunk.column_index_length);
+  cudf::io::parquet::CompactProtocolReader cp(ci_buf->data(), ci_buf->size());
+  bool res = cp.read(&colidx);
+  CUDF_EXPECTS(res, "Cannot parse column index");
+  return colidx;
+}
+
+// read offset index from datasource at location indicated by chunk,
+// parse and return as an OffsetIndex struct.
+// throws cudf::logic_error if the chunk data is invalid.
+cudf::io::parquet::OffsetIndex read_offset_index(
+  std::unique_ptr<cudf::io::datasource> const& source, cudf::io::parquet::ColumnChunk const& chunk)
+{
+  CUDF_EXPECTS(chunk.offset_index_offset > 0, "Cannot find offset index");
+  CUDF_EXPECTS(chunk.offset_index_length > 0, "Invalid offset index length");
+
+  cudf::io::parquet::OffsetIndex offidx;
+  auto const oi_buf = source->host_read(chunk.offset_index_offset, chunk.offset_index_length);
+  cudf::io::parquet::CompactProtocolReader cp(oi_buf->data(), oi_buf->size());
+  bool res = cp.read(&offidx);
+  CUDF_EXPECTS(res, "Cannot parse offset index");
+  return offidx;
+}
+
+// Return as a Statistics from the column chunk
+cudf::io::parquet::Statistics const& get_statistics(cudf::io::parquet::ColumnChunk const& chunk)
+{
+  return chunk.meta_data.statistics;
+}
+
+// read page header from datasource at location indicated by page_loc,
+// parse and return as a PageHeader struct.
+// throws cudf::logic_error if the page_loc data is invalid.
+cudf::io::parquet::PageHeader read_page_header(std::unique_ptr<cudf::io::datasource> const& source,
+                                               cudf::io::parquet::PageLocation const& page_loc)
+{
+  CUDF_EXPECTS(page_loc.offset > 0, "Cannot find page header");
+  CUDF_EXPECTS(page_loc.compressed_page_size > 0, "Invalid page header length");
+
+  cudf::io::parquet::PageHeader page_hdr;
+  auto const page_buf = source->host_read(page_loc.offset, page_loc.compressed_page_size);
+  cudf::io::parquet::CompactProtocolReader cp(page_buf->data(), page_buf->size());
+  bool res = cp.read(&page_hdr);
+  CUDF_EXPECTS(res, "Cannot parse page header");
+  return page_hdr;
+}
+
+// Base test fixture for tests
+struct ParquetWriterTest : public cudf::test::BaseFixture {};
+
+// Base test fixture for tests
+struct ParquetReaderTest : public cudf::test::BaseFixture {};
+
+// Base test fixture for "stress" tests
+struct ParquetWriterStressTest : public cudf::test::BaseFixture {};
+
+// Typed test fixture for numeric type tests
+template <typename T>
+struct ParquetWriterNumericTypeTest : public ParquetWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Typed test fixture for comparable type tests
+template <typename T>
+struct ParquetWriterComparableTypeTest : public ParquetWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Typed test fixture for timestamp type tests
+template <typename T>
+struct ParquetWriterChronoTypeTest : public ParquetWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Typed test fixture for timestamp type tests
+template <typename T>
+struct ParquetWriterTimestampTypeTest : public ParquetWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Typed test fixture for all types
+template <typename T>
+struct ParquetWriterSchemaTest : public ParquetWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+template <typename T>
+struct ParquetReaderSourceTest : public ParquetReaderTest {};
+
+// Declare typed test cases
+// TODO: Replace with `NumericTypes` when unsigned support is added. Issue #5352
+using SupportedTypes = cudf::test::Types<int8_t, int16_t, int32_t, int64_t, bool, float, double>;
+TYPED_TEST_SUITE(ParquetWriterNumericTypeTest, SupportedTypes);
+using ComparableAndFixedTypes =
+  cudf::test::Concat<cudf::test::ComparableTypes, cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(ParquetWriterComparableTypeTest, ComparableAndFixedTypes);
+TYPED_TEST_SUITE(ParquetWriterChronoTypeTest, cudf::test::ChronoTypes);
+using SupportedTimestampTypes =
+  cudf::test::Types<cudf::timestamp_ms, cudf::timestamp_us, cudf::timestamp_ns>;
+TYPED_TEST_SUITE(ParquetWriterTimestampTypeTest, SupportedTimestampTypes);
+TYPED_TEST_SUITE(ParquetWriterSchemaTest, cudf::test::AllTypes);
+using ByteLikeTypes = cudf::test::Types<int8_t, char, uint8_t, unsigned char, std::byte>;
+TYPED_TEST_SUITE(ParquetReaderSourceTest, ByteLikeTypes);
+
+// Base test fixture for chunked writer tests
+struct ParquetChunkedWriterTest : public cudf::test::BaseFixture {};
+
+// Typed test fixture for numeric type tests
+template <typename T>
+struct ParquetChunkedWriterNumericTypeTest : public ParquetChunkedWriterTest {
+  auto type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+// Declare typed test cases
+TYPED_TEST_SUITE(ParquetChunkedWriterNumericTypeTest, SupportedTypes);
+
+// Base test fixture for size-parameterized tests
+class ParquetSizedTest : public ::cudf::test::BaseFixtureWithParam<int> {};
+
+// test the allowed bit widths for dictionary encoding
+// values chosen to trigger 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 20, and 24 bit dictionaries
+INSTANTIATE_TEST_SUITE_P(ParquetDictionaryTest,
+                         ParquetSizedTest,
+                         testing::Range(1, 25),
+                         testing::PrintToStringParamName());
+
+// Base test fixture for V2 header tests
+class ParquetV2Test : public ::cudf::test::BaseFixtureWithParam<bool> {};
+INSTANTIATE_TEST_SUITE_P(ParquetV2ReadWriteTest,
+                         ParquetV2Test,
+                         testing::Bool(),
+                         testing::PrintToStringParamName());
+
+namespace {
+// Generates a vector of uniform random values of type T
+template <typename T>
+inline auto random_values(size_t size)
+{
+  std::vector<T> values(size);
+
+  using T1 = T;
+  using uniform_distribution =
+    typename std::conditional_t<std::is_same_v<T1, bool>,
+                                std::bernoulli_distribution,
+                                std::conditional_t<std::is_floating_point_v<T1>,
+                                                   std::uniform_real_distribution<T1>,
+                                                   std::uniform_int_distribution<T1>>>;
+
+  static constexpr auto seed = 0xf00d;
+  static std::mt19937 engine{seed};
+  static uniform_distribution dist{};
+  std::generate_n(values.begin(), size, [&]() { return T{dist(engine)}; });
+
+  return values;
+}
+
+}  // namespace
+
+TYPED_TEST(ParquetWriterNumericTypeTest, SingleColumn)
+{
+  auto sequence =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return TypeParam(i % 400); });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  constexpr auto num_rows = 800;
+  column_wrapper<TypeParam> col(sequence, sequence + num_rows, validity);
+
+  auto expected = table_view{{col}};
+
+  auto filepath = temp_env->get_temp_filepath("SingleColumn.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(ParquetWriterNumericTypeTest, SingleColumnWithNulls)
+{
+  auto sequence =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return TypeParam(i); });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 2); });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam> col(sequence, sequence + num_rows, validity);
+
+  auto expected = table_view{{col}};
+
+  auto filepath = temp_env->get_temp_filepath("SingleColumnWithNulls.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+template <typename mask_op_t>
+void test_durations(mask_op_t mask_op)
+{
+  std::default_random_engine generator;
+  std::uniform_int_distribution<int> distribution_d(0, 30);
+  auto sequence_d = cudf::detail::make_counting_transform_iterator(
+    0, [&](auto i) { return distribution_d(generator); });
+
+  std::uniform_int_distribution<int> distribution_s(0, 86400);
+  auto sequence_s = cudf::detail::make_counting_transform_iterator(
+    0, [&](auto i) { return distribution_s(generator); });
+
+  std::uniform_int_distribution<int> distribution(0, 86400 * 1000);
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [&](auto i) { return distribution(generator); });
+
+  auto mask = cudf::detail::make_counting_transform_iterator(0, mask_op);
+
+  constexpr auto num_rows = 100;
+  // Durations longer than a day are not exactly valid, but cudf should be able to round trip
+  auto durations_d = cudf::test::fixed_width_column_wrapper<cudf::duration_D, int64_t>(
+    sequence_d, sequence_d + num_rows, mask);
+  auto durations_s = cudf::test::fixed_width_column_wrapper<cudf::duration_s, int64_t>(
+    sequence_s, sequence_s + num_rows, mask);
+  auto durations_ms = cudf::test::fixed_width_column_wrapper<cudf::duration_ms, int64_t>(
+    sequence, sequence + num_rows, mask);
+  auto durations_us = cudf::test::fixed_width_column_wrapper<cudf::duration_us, int64_t>(
+    sequence, sequence + num_rows, mask);
+  auto durations_ns = cudf::test::fixed_width_column_wrapper<cudf::duration_ns, int64_t>(
+    sequence, sequence + num_rows, mask);
+
+  auto expected = table_view{{durations_d, durations_s, durations_ms, durations_us, durations_ns}};
+
+  auto filepath = temp_env->get_temp_filepath("Durations.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  auto durations_d_got =
+    cudf::cast(result.tbl->view().column(0), cudf::data_type{cudf::type_id::DURATION_DAYS});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(durations_d, durations_d_got->view());
+
+  auto durations_s_got =
+    cudf::cast(result.tbl->view().column(1), cudf::data_type{cudf::type_id::DURATION_SECONDS});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(durations_s, durations_s_got->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(durations_ms, result.tbl->view().column(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(durations_us, result.tbl->view().column(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(durations_ns, result.tbl->view().column(4));
+}
+
+TEST_F(ParquetWriterTest, Durations)
+{
+  test_durations([](auto i) { return true; });
+  test_durations([](auto i) { return (i % 2) != 0; });
+  test_durations([](auto i) { return (i % 3) != 0; });
+  test_durations([](auto i) { return false; });
+}
+
+TYPED_TEST(ParquetWriterTimestampTypeTest, Timestamps)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return ((std::rand() / 10000) * 1000); });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + num_rows, validity);
+
+  auto expected = table_view{{col}};
+
+  auto filepath = temp_env->get_temp_filepath("Timestamps.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .timestamp_type(this->type());
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(ParquetWriterTimestampTypeTest, TimestampsWithNulls)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return ((std::rand() / 10000) * 1000); });
+  auto validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i > 30) && (i < 60); });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + num_rows, validity);
+
+  auto expected = table_view{{col}};
+
+  auto filepath = temp_env->get_temp_filepath("TimestampsWithNulls.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .timestamp_type(this->type());
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TYPED_TEST(ParquetWriterTimestampTypeTest, TimestampOverflow)
+{
+  constexpr int64_t max = std::numeric_limits<int64_t>::max();
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return max - i; });
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  constexpr auto num_rows = 100;
+  column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + num_rows, validity);
+  table_view expected({col});
+
+  auto filepath = temp_env->get_temp_filepath("ParquetTimestampOverflow.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .timestamp_type(this->type());
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_P(ParquetV2Test, MultiColumn)
+{
+  constexpr auto num_rows = 100000;
+  auto const is_v2        = GetParam();
+
+  // auto col0_data = random_values<bool>(num_rows);
+  auto col1_data = random_values<int8_t>(num_rows);
+  auto col2_data = random_values<int16_t>(num_rows);
+  auto col3_data = random_values<int32_t>(num_rows);
+  auto col4_data = random_values<float>(num_rows);
+  auto col5_data = random_values<double>(num_rows);
+  auto col6_vals = random_values<int16_t>(num_rows);
+  auto col7_vals = random_values<int32_t>(num_rows);
+  auto col8_vals = random_values<int64_t>(num_rows);
+  auto col6_data = cudf::detail::make_counting_transform_iterator(0, [col6_vals](auto i) {
+    return numeric::decimal32{col6_vals[i], numeric::scale_type{5}};
+  });
+  auto col7_data = cudf::detail::make_counting_transform_iterator(0, [col7_vals](auto i) {
+    return numeric::decimal64{col7_vals[i], numeric::scale_type{-5}};
+  });
+  auto col8_data = cudf::detail::make_counting_transform_iterator(0, [col8_vals](auto i) {
+    return numeric::decimal128{col8_vals[i], numeric::scale_type{-6}};
+  });
+  auto validity  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  // column_wrapper<bool> col0{
+  //    col0_data.begin(), col0_data.end(), validity};
+  column_wrapper<int8_t> col1{col1_data.begin(), col1_data.end(), validity};
+  column_wrapper<int16_t> col2{col2_data.begin(), col2_data.end(), validity};
+  column_wrapper<int32_t> col3{col3_data.begin(), col3_data.end(), validity};
+  column_wrapper<float> col4{col4_data.begin(), col4_data.end(), validity};
+  column_wrapper<double> col5{col5_data.begin(), col5_data.end(), validity};
+  column_wrapper<numeric::decimal32> col6{col6_data, col6_data + num_rows, validity};
+  column_wrapper<numeric::decimal64> col7{col7_data, col7_data + num_rows, validity};
+  column_wrapper<numeric::decimal128> col8{col8_data, col8_data + num_rows, validity};
+
+  auto expected = table_view{{col1, col2, col3, col4, col5, col6, col7, col8}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  // expected_metadata.column_metadata[0].set_name( "bools");
+  expected_metadata.column_metadata[0].set_name("int8s");
+  expected_metadata.column_metadata[1].set_name("int16s");
+  expected_metadata.column_metadata[2].set_name("int32s");
+  expected_metadata.column_metadata[3].set_name("floats");
+  expected_metadata.column_metadata[4].set_name("doubles");
+  expected_metadata.column_metadata[5].set_name("decimal32s").set_decimal_precision(10);
+  expected_metadata.column_metadata[6].set_name("decimal64s").set_decimal_precision(20);
+  expected_metadata.column_metadata[7].set_name("decimal128s").set_decimal_precision(40);
+
+  auto filepath = temp_env->get_temp_filepath("MultiColumn.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .write_v2_headers(is_v2)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_P(ParquetV2Test, MultiColumnWithNulls)
+{
+  constexpr auto num_rows = 100;
+  auto const is_v2        = GetParam();
+
+  // auto col0_data = random_values<bool>(num_rows);
+  auto col1_data = random_values<int8_t>(num_rows);
+  auto col2_data = random_values<int16_t>(num_rows);
+  auto col3_data = random_values<int32_t>(num_rows);
+  auto col4_data = random_values<float>(num_rows);
+  auto col5_data = random_values<double>(num_rows);
+  auto col6_vals = random_values<int32_t>(num_rows);
+  auto col7_vals = random_values<int64_t>(num_rows);
+  auto col6_data = cudf::detail::make_counting_transform_iterator(0, [col6_vals](auto i) {
+    return numeric::decimal32{col6_vals[i], numeric::scale_type{-2}};
+  });
+  auto col7_data = cudf::detail::make_counting_transform_iterator(0, [col7_vals](auto i) {
+    return numeric::decimal64{col7_vals[i], numeric::scale_type{-8}};
+  });
+  // auto col0_mask = cudf::detail::make_counting_transform_iterator(
+  //    0, [](auto i) { return (i % 2); });
+  auto col1_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i < 10); });
+  auto col2_mask = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  auto col3_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i == (num_rows - 1)); });
+  auto col4_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i >= 40 && i <= 60); });
+  auto col5_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i > 80); });
+  auto col6_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 5); });
+  auto col7_mask =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i != 55); });
+
+  // column_wrapper<bool> col0{
+  //    col0_data.begin(), col0_data.end(), col0_mask};
+  column_wrapper<int8_t> col1{col1_data.begin(), col1_data.end(), col1_mask};
+  column_wrapper<int16_t> col2{col2_data.begin(), col2_data.end(), col2_mask};
+  column_wrapper<int32_t> col3{col3_data.begin(), col3_data.end(), col3_mask};
+  column_wrapper<float> col4{col4_data.begin(), col4_data.end(), col4_mask};
+  column_wrapper<double> col5{col5_data.begin(), col5_data.end(), col5_mask};
+  column_wrapper<numeric::decimal32> col6{col6_data, col6_data + num_rows, col6_mask};
+  column_wrapper<numeric::decimal64> col7{col7_data, col7_data + num_rows, col7_mask};
+
+  auto expected = table_view{{/*col0, */ col1, col2, col3, col4, col5, col6, col7}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  // expected_metadata.column_names.emplace_back("bools");
+  expected_metadata.column_metadata[0].set_name("int8s");
+  expected_metadata.column_metadata[1].set_name("int16s");
+  expected_metadata.column_metadata[2].set_name("int32s");
+  expected_metadata.column_metadata[3].set_name("floats");
+  expected_metadata.column_metadata[4].set_name("doubles");
+  expected_metadata.column_metadata[5].set_name("decimal32s").set_decimal_precision(9);
+  expected_metadata.column_metadata[6].set_name("decimal64s").set_decimal_precision(20);
+
+  auto filepath = temp_env->get_temp_filepath("MultiColumnWithNulls.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .write_v2_headers(is_v2)
+      .metadata(expected_metadata);
+
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  // TODO: Need to be able to return metadata in tree form from reader so they can be compared.
+  // Unfortunately the closest thing to a hierarchical schema is column_name_info which does not
+  // have any tests for it c++ or python.
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_P(ParquetV2Test, Strings)
+{
+  auto const is_v2 = GetParam();
+
+  std::vector<char const*> strings{
+    "Monday", "Wȅdnȅsday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+  auto const num_rows = strings.size();
+
+  auto seq_col0 = random_values<int>(num_rows);
+  auto seq_col2 = random_values<float>(num_rows);
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  column_wrapper<int> col0{seq_col0.begin(), seq_col0.end(), validity};
+  column_wrapper<cudf::string_view> col1{strings.begin(), strings.end()};
+  column_wrapper<float> col2{seq_col2.begin(), seq_col2.end(), validity};
+
+  auto expected = table_view{{col0, col1, col2}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_other");
+  expected_metadata.column_metadata[1].set_name("col_string");
+  expected_metadata.column_metadata[2].set_name("col_another");
+
+  auto filepath = temp_env->get_temp_filepath("Strings.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .write_v2_headers(is_v2)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(ParquetWriterTest, StringsAsBinary)
+{
+  std::vector<char const*> unicode_strings{
+    "Monday", "Wȅdnȅsday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+  std::vector<char const*> ascii_strings{
+    "Monday", "Wednesday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+
+  column_wrapper<cudf::string_view> col0{ascii_strings.begin(), ascii_strings.end()};
+  column_wrapper<cudf::string_view> col1{unicode_strings.begin(), unicode_strings.end()};
+  column_wrapper<cudf::string_view> col2{ascii_strings.begin(), ascii_strings.end()};
+  cudf::test::lists_column_wrapper<uint8_t> col3{{'M', 'o', 'n', 'd', 'a', 'y'},
+                                                 {'W', 'e', 'd', 'n', 'e', 's', 'd', 'a', 'y'},
+                                                 {'F', 'r', 'i', 'd', 'a', 'y'},
+                                                 {'M', 'o', 'n', 'd', 'a', 'y'},
+                                                 {'F', 'r', 'i', 'd', 'a', 'y'},
+                                                 {'F', 'r', 'i', 'd', 'a', 'y'},
+                                                 {'F', 'r', 'i', 'd', 'a', 'y'},
+                                                 {'F', 'u', 'n', 'd', 'a', 'y'}};
+  cudf::test::lists_column_wrapper<uint8_t> col4{
+    {'M', 'o', 'n', 'd', 'a', 'y'},
+    {'W', 200, 133, 'd', 'n', 200, 133, 's', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'M', 'o', 'n', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'F', 'u', 'n', 'd', 'a', 'y'}};
+
+  auto write_tbl = table_view{{col0, col1, col2, col3, col4}};
+
+  cudf::io::table_input_metadata expected_metadata(write_tbl);
+  expected_metadata.column_metadata[0].set_name("col_single").set_output_as_binary(true);
+  expected_metadata.column_metadata[1].set_name("col_string").set_output_as_binary(true);
+  expected_metadata.column_metadata[2].set_name("col_another").set_output_as_binary(true);
+  expected_metadata.column_metadata[3].set_name("col_binary");
+  expected_metadata.column_metadata[4].set_name("col_binary");
+
+  auto filepath = temp_env->get_temp_filepath("BinaryStrings.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, write_tbl)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .set_column_schema(
+        {cudf::io::reader_column_schema().set_convert_binary_to_strings(false),
+         cudf::io::reader_column_schema().set_convert_binary_to_strings(false),
+         cudf::io::reader_column_schema().set_convert_binary_to_strings(false),
+         cudf::io::reader_column_schema().add_child(cudf::io::reader_column_schema()),
+         cudf::io::reader_column_schema().add_child(cudf::io::reader_column_schema())});
+  auto result   = cudf::io::read_parquet(in_opts);
+  auto expected = table_view{{col3, col4, col3, col3, col4}};
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_P(ParquetV2Test, SlicedTable)
+{
+  // This test checks for writing zero copy, offsetted views into existing cudf tables
+
+  std::vector<char const*> strings{
+    "Monday", "Wȅdnȅsday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+  auto const num_rows = strings.size();
+  auto const is_v2    = GetParam();
+
+  auto seq_col0 = random_values<int>(num_rows);
+  auto seq_col2 = random_values<float>(num_rows);
+  auto validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3 != 0; });
+
+  column_wrapper<int> col0{seq_col0.begin(), seq_col0.end(), validity};
+  column_wrapper<cudf::string_view> col1{strings.begin(), strings.end()};
+  column_wrapper<float> col2{seq_col2.begin(), seq_col2.end(), validity};
+
+  using lcw = cudf::test::lists_column_wrapper<uint64_t>;
+  lcw col3{{9, 8}, {7, 6, 5}, {}, {4}, {3, 2, 1, 0}, {20, 21, 22, 23, 24}, {}, {66, 666}};
+
+  // [[[NULL,2,NULL,4]], [[NULL,6,NULL], [8,9]]]
+  // [NULL, [[13],[14,15,16]],  NULL]
+  // [NULL, [], NULL, [[]]]
+  // NULL
+  // [[[NULL,2,NULL,4]], [[NULL,6,NULL], [8,9]]]
+  // [NULL, [[13],[14,15,16]],  NULL]
+  // [[[]]]
+  // [NULL, [], NULL, [[]]]
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+  lcw col4{{
+             {{{{1, 2, 3, 4}, valids}}, {{{5, 6, 7}, valids}, {8, 9}}},
+             {{{{10, 11}, {12}}, {{13}, {14, 15, 16}}, {{17, 18}}}, valids},
+             {{lcw{lcw{}}, lcw{}, lcw{}, lcw{lcw{}}}, valids},
+             lcw{lcw{lcw{}}},
+             {{{{1, 2, 3, 4}, valids}}, {{{5, 6, 7}, valids}, {8, 9}}},
+             {{{{10, 11}, {12}}, {{13}, {14, 15, 16}}, {{17, 18}}}, valids},
+             lcw{lcw{lcw{}}},
+             {{lcw{lcw{}}, lcw{}, lcw{}, lcw{lcw{}}}, valids},
+           },
+           valids2};
+
+  // Struct column
+  auto ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {48, 27, 25, 31, 351, 351, 29, 15}, {1, 1, 1, 1, 1, 0, 1, 1}};
+
+  auto col5 = cudf::test::structs_column_wrapper{{ages_col}, {1, 1, 1, 1, 0, 1, 1, 1}};
+
+  // Struct/List mixed column
+
+  // []
+  // [NULL, 2, NULL]
+  // [4, 5]
+  // NULL
+  // []
+  // [7, 8, 9]
+  // [10]
+  // [11, 12]
+  lcw land{{{}, {{1, 2, 3}, valids}, {4, 5}, {}, {}, {7, 8, 9}, {10}, {11, 12}}, valids2};
+
+  // []
+  // [[1, 2, 3], [], [4, 5], [], [0, 6, 0]]
+  // [[7, 8], []]
+  // [[]]
+  // [[]]
+  // [[], [], []]
+  // [[10]]
+  // [[13, 14], [15]]
+  lcw flats{lcw{},
+            {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},
+            {{7, 8}, {}},
+            lcw{lcw{}},
+            lcw{lcw{}},
+            lcw{lcw{}, lcw{}, lcw{}},
+            {lcw{10}},
+            {{13, 14}, {15}}};
+
+  auto struct_1 = cudf::test::structs_column_wrapper{land, flats};
+  auto is_human = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, true, false, true, false}};
+  auto col6 = cudf::test::structs_column_wrapper{{is_human, struct_1}};
+
+  auto expected = table_view({col0, col1, col2, col3, col4, col5, col6});
+
+  // auto expected_slice = expected;
+  auto expected_slice = cudf::slice(expected, {2, static_cast<cudf::size_type>(num_rows) - 1});
+
+  cudf::io::table_input_metadata expected_metadata(expected_slice);
+  expected_metadata.column_metadata[0].set_name("col_other");
+  expected_metadata.column_metadata[1].set_name("col_string");
+  expected_metadata.column_metadata[2].set_name("col_another");
+  expected_metadata.column_metadata[3].set_name("col_list");
+  expected_metadata.column_metadata[4].set_name("col_multi_level_list");
+  expected_metadata.column_metadata[5].set_name("col_struct");
+  expected_metadata.column_metadata[5].set_name("col_struct_list");
+  expected_metadata.column_metadata[6].child(0).set_name("human?");
+  expected_metadata.column_metadata[6].child(1).set_name("particulars");
+  expected_metadata.column_metadata[6].child(1).child(0).set_name("land");
+  expected_metadata.column_metadata[6].child(1).child(1).set_name("flats");
+
+  auto filepath = temp_env->get_temp_filepath("SlicedTable.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected_slice)
+      .write_v2_headers(is_v2)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected_slice, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_P(ParquetV2Test, ListColumn)
+{
+  auto const is_v2 = GetParam();
+
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // [NULL, 2, NULL]
+  // []
+  // [4, 5]
+  // NULL
+  lcw col0{{{{1, 2, 3}, valids}, {}, {4, 5}, {}}, valids2};
+
+  // [[1, 2, 3], [], [4, 5], [], [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  lcw col1{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, {{7, 8}}, lcw{}, lcw{lcw{}}};
+
+  // [[1, 2, 3], [], [4, 5], NULL, [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  lcw col2{{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, valids2}, {{7, 8}}, lcw{}, lcw{lcw{}}};
+
+  // [[1, 2, 3], [], [4, 5], NULL, [NULL, 6, NULL]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  using dlcw = cudf::test::lists_column_wrapper<double>;
+  dlcw col3{{{{1., 2., 3.}, {}, {4., 5.}, {}, {{0., 6., 0.}, valids}}, valids2},
+            {{7., 8.}},
+            dlcw{},
+            dlcw{dlcw{}}};
+
+  // TODO: uint16_t lists are not read properly in parquet reader
+  // [[1, 2, 3], [], [4, 5], NULL, [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // NULL
+  // using ui16lcw = cudf::test::lists_column_wrapper<uint16_t>;
+  // cudf::test::lists_column_wrapper<uint16_t> col4{
+  //   {{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, valids2}, {{7, 8}}, ui16lcw{}, ui16lcw{ui16lcw{}}},
+  //   valids2};
+
+  // [[1, 2, 3], [], [4, 5], NULL, [NULL, 6, NULL]]
+  // [[7, 8]]
+  // []
+  // NULL
+  lcw col5{
+    {{{{1, 2, 3}, {}, {4, 5}, {}, {{0, 6, 0}, valids}}, valids2}, {{7, 8}}, lcw{}, lcw{lcw{}}},
+    valids2};
+
+  using strlcw = cudf::test::lists_column_wrapper<cudf::string_view>;
+  cudf::test::lists_column_wrapper<cudf::string_view> col6{
+    {{"Monday", "Monday", "Friday"}, {}, {"Monday", "Friday"}, {}, {"Sunday", "Funday"}},
+    {{"bee", "sting"}},
+    strlcw{},
+    strlcw{strlcw{}}};
+
+  // [[[NULL,2,NULL,4]], [[NULL,6,NULL], [8,9]]]
+  // [NULL, [[13],[14,15,16]],  NULL]
+  // [NULL, [], NULL, [[]]]
+  // NULL
+  lcw col7{{
+             {{{{1, 2, 3, 4}, valids}}, {{{5, 6, 7}, valids}, {8, 9}}},
+             {{{{10, 11}, {12}}, {{13}, {14, 15, 16}}, {{17, 18}}}, valids},
+             {{lcw{lcw{}}, lcw{}, lcw{}, lcw{lcw{}}}, valids},
+             lcw{lcw{lcw{}}},
+           },
+           valids2};
+
+  table_view expected({col0, col1, col2, col3, /* col4, */ col5, col6, col7});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_list_int_0");
+  expected_metadata.column_metadata[1].set_name("col_list_list_int_1");
+  expected_metadata.column_metadata[2].set_name("col_list_list_int_nullable_2");
+  expected_metadata.column_metadata[3].set_name("col_list_list_nullable_double_nullable_3");
+  // expected_metadata.column_metadata[0].set_name("col_list_list_uint16_4");
+  expected_metadata.column_metadata[4].set_name("col_list_nullable_list_nullable_int_nullable_5");
+  expected_metadata.column_metadata[5].set_name("col_list_list_string_6");
+  expected_metadata.column_metadata[6].set_name("col_list_list_list_7");
+
+  auto filepath = temp_env->get_temp_filepath("ListColumn.parquet");
+  auto out_opts = cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+                    .write_v2_headers(is_v2)
+                    .metadata(expected_metadata)
+                    .compression(cudf::io::compression_type::NONE);
+
+  cudf::io::write_parquet(out_opts);
+
+  auto in_opts = cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result  = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(ParquetWriterTest, MultiIndex)
+{
+  constexpr auto num_rows = 100;
+
+  auto col0_data = random_values<int8_t>(num_rows);
+  auto col1_data = random_values<int16_t>(num_rows);
+  auto col2_data = random_values<int32_t>(num_rows);
+  auto col3_data = random_values<float>(num_rows);
+  auto col4_data = random_values<double>(num_rows);
+  auto validity  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  column_wrapper<int8_t> col0{col0_data.begin(), col0_data.end(), validity};
+  column_wrapper<int16_t> col1{col1_data.begin(), col1_data.end(), validity};
+  column_wrapper<int32_t> col2{col2_data.begin(), col2_data.end(), validity};
+  column_wrapper<float> col3{col3_data.begin(), col3_data.end(), validity};
+  column_wrapper<double> col4{col4_data.begin(), col4_data.end(), validity};
+
+  auto expected = table_view{{col0, col1, col2, col3, col4}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("int8s");
+  expected_metadata.column_metadata[1].set_name("int16s");
+  expected_metadata.column_metadata[2].set_name("int32s");
+  expected_metadata.column_metadata[3].set_name("floats");
+  expected_metadata.column_metadata[4].set_name("doubles");
+
+  auto filepath = temp_env->get_temp_filepath("MultiIndex.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(expected_metadata)
+      .key_value_metadata(
+        {{{"pandas", "\"index_columns\": [\"int8s\", \"int16s\"], \"column1\": [\"int32s\"]"}}});
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .use_pandas_metadata(true)
+      .columns({"int32s", "floats", "doubles"});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(ParquetWriterTest, BufferSource)
+{
+  constexpr auto num_rows = 100 << 10;
+  auto const seq_col      = random_values<int>(num_rows);
+  auto const validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  column_wrapper<int> col{seq_col.begin(), seq_col.end(), validity};
+
+  auto const expected = table_view{{col}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_other");
+
+  std::vector<char> out_buffer;
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer), expected)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(out_opts);
+
+  // host buffer
+  {
+    cudf::io::parquet_reader_options in_opts = cudf::io::parquet_reader_options::builder(
+      cudf::io::source_info(out_buffer.data(), out_buffer.size()));
+    auto const result = cudf::io::read_parquet(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+    cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+  }
+
+  // device buffer
+  {
+    auto const d_input = cudf::detail::make_device_uvector_sync(
+      cudf::host_span<uint8_t const>{reinterpret_cast<uint8_t const*>(out_buffer.data()),
+                                     out_buffer.size()},
+      cudf::get_default_stream(),
+      rmm::mr::get_current_device_resource());
+    auto const d_buffer = cudf::device_span<std::byte const>(
+      reinterpret_cast<std::byte const*>(d_input.data()), d_input.size());
+    cudf::io::parquet_reader_options in_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info(d_buffer));
+    auto const result = cudf::io::read_parquet(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+    cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+  }
+}
+
+TEST_F(ParquetWriterTest, ManyFragments)
+{
+  srand(31337);
+  auto const expected = create_random_fixed_table<int>(10, 6'000'000, false);
+
+  auto const filepath = temp_env->get_temp_filepath("ManyFragments.parquet");
+  cudf::io::parquet_writer_options const args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected)
+      .max_page_size_bytes(8 * 1024);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options const read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(ParquetWriterTest, NonNullable)
+{
+  srand(31337);
+  auto expected = create_random_fixed_table<int>(9, 9, false);
+
+  auto filepath = temp_env->get_temp_filepath("NonNullable.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(ParquetWriterTest, Struct)
+{
+  // Struct<is_human:bool, Struct<names:string, ages:int>>
+
+  auto names = {"Samuel Vimes",
+                "Carrot Ironfoundersson",
+                "Angua von Uberwald",
+                "Cheery Littlebottom",
+                "Detritus",
+                "Mr Slant"};
+
+  // `Name` column has all valid values.
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+
+  auto ages_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto struct_1 = cudf::test::structs_column_wrapper{{names_col, ages_col}, {1, 1, 1, 1, 0, 1}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_2 =
+    cudf::test::structs_column_wrapper{{is_human_col, struct_1}, {0, 1, 1, 1, 1, 1}}.release();
+
+  auto expected = table_view({*struct_2});
+
+  auto filepath = temp_env->get_temp_filepath("Struct.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options read_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath));
+  cudf::io::read_parquet(read_args);
+}
+
+TEST_P(ParquetV2Test, StructOfList)
+{
+  auto const is_v2 = GetParam();
+
+  // Struct<is_human:bool,
+  //        Struct<weight:float,
+  //               ages:int,
+  //               land_unit:List<int>>,
+  //               flats:List<List<int>>
+  //              >
+  //       >
+
+  auto weights_col = cudf::test::fixed_width_column_wrapper<float>{1.1, 2.4, 5.3, 8.0, 9.6, 6.9};
+
+  auto ages_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // []
+  // [NULL, 2, NULL]
+  // [4, 5]
+  // NULL
+  // []
+  // [7, 8, 9]
+  lcw land_unit{{{}, {{1, 2, 3}, valids}, {4, 5}, {}, {}, {7, 8, 9}}, valids2};
+
+  // []
+  // [[1, 2, 3], [], [4, 5], [], [0, 6, 0]]
+  // [[7, 8], []]
+  // [[]]
+  // [[]]
+  // [[], [], []]
+  lcw flats{lcw{},
+            {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},
+            {{7, 8}, {}},
+            lcw{lcw{}},
+            lcw{lcw{}},
+            lcw{lcw{}, lcw{}, lcw{}}};
+
+  auto struct_1 = cudf::test::structs_column_wrapper{{weights_col, ages_col, land_unit, flats},
+                                                     {1, 1, 1, 1, 0, 1}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_2 =
+    cudf::test::structs_column_wrapper{{is_human_col, struct_1}, {0, 1, 1, 1, 1, 1}}.release();
+
+  auto expected = table_view({*struct_2});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("being");
+  expected_metadata.column_metadata[0].child(0).set_name("human?");
+  expected_metadata.column_metadata[0].child(1).set_name("particulars");
+  expected_metadata.column_metadata[0].child(1).child(0).set_name("weight");
+  expected_metadata.column_metadata[0].child(1).child(1).set_name("age");
+  expected_metadata.column_metadata[0].child(1).child(2).set_name("land_unit");
+  expected_metadata.column_metadata[0].child(1).child(3).set_name("flats");
+
+  auto filepath = temp_env->get_temp_filepath("StructOfList.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .write_v2_headers(is_v2)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options read_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath));
+  auto const result = cudf::io::read_parquet(read_args);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_P(ParquetV2Test, ListOfStruct)
+{
+  auto const is_v2 = GetParam();
+
+  // List<Struct<is_human:bool,
+  //             Struct<weight:float,
+  //                    ages:int,
+  //                   >
+  //            >
+  //     >
+
+  auto weight_col = cudf::test::fixed_width_column_wrapper<float>{1.1, 2.4, 5.3, 8.0, 9.6, 6.9};
+
+  auto ages_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto struct_1 = cudf::test::structs_column_wrapper{{weight_col, ages_col}, {1, 1, 1, 1, 0, 1}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_2 =
+    cudf::test::structs_column_wrapper{{is_human_col, struct_1}, {0, 1, 1, 1, 1, 1}}.release();
+
+  auto list_offsets_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 5, 5, 6}.release();
+  auto num_list_rows = list_offsets_column->size() - 1;
+
+  auto list_col = cudf::make_lists_column(
+    num_list_rows, std::move(list_offsets_column), std::move(struct_2), 0, {});
+
+  auto expected = table_view({*list_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("family");
+  expected_metadata.column_metadata[0].child(1).child(0).set_name("human?");
+  expected_metadata.column_metadata[0].child(1).child(1).set_name("particulars");
+  expected_metadata.column_metadata[0].child(1).child(1).child(0).set_name("weight");
+  expected_metadata.column_metadata[0].child(1).child(1).child(1).set_name("age");
+
+  auto filepath = temp_env->get_temp_filepath("ListOfStruct.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .write_v2_headers(is_v2)
+      .metadata(expected_metadata);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options read_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath));
+  auto const result = cudf::io::read_parquet(read_args);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+// custom data sink that supports device writes. uses plain file io.
+class custom_test_data_sink : public cudf::io::data_sink {
+ public:
+  explicit custom_test_data_sink(std::string const& filepath)
+  {
+    outfile_.open(filepath, std::ios::out | std::ios::binary | std::ios::trunc);
+    CUDF_EXPECTS(outfile_.is_open(), "Cannot open output file");
+  }
+
+  virtual ~custom_test_data_sink() { flush(); }
+
+  void host_write(void const* data, size_t size) override
+  {
+    outfile_.write(static_cast<char const*>(data), size);
+  }
+
+  [[nodiscard]] bool supports_device_write() const override { return true; }
+
+  void device_write(void const* gpu_data, size_t size, rmm::cuda_stream_view stream) override
+  {
+    this->device_write_async(gpu_data, size, stream).get();
+  }
+
+  std::future<void> device_write_async(void const* gpu_data,
+                                       size_t size,
+                                       rmm::cuda_stream_view stream) override
+  {
+    return std::async(std::launch::deferred, [=] {
+      char* ptr = nullptr;
+      CUDF_CUDA_TRY(cudaMallocHost(&ptr, size));
+      CUDF_CUDA_TRY(cudaMemcpyAsync(ptr, gpu_data, size, cudaMemcpyDefault, stream.value()));
+      stream.synchronize();
+      outfile_.write(ptr, size);
+      CUDF_CUDA_TRY(cudaFreeHost(ptr));
+    });
+  }
+
+  void flush() override { outfile_.flush(); }
+
+  size_t bytes_written() override { return outfile_.tellp(); }
+
+ private:
+  std::ofstream outfile_;
+};
+
+TEST_F(ParquetWriterTest, CustomDataSink)
+{
+  auto filepath = temp_env->get_temp_filepath("CustomDataSink.parquet");
+  custom_test_data_sink custom_sink(filepath);
+
+  srand(31337);
+  auto expected = create_random_fixed_table<int>(5, 10, false);
+
+  // write out using the custom sink
+  {
+    cudf::io::parquet_writer_options args =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+    cudf::io::write_parquet(args);
+  }
+
+  // write out using a memmapped sink
+  std::vector<char> buf_sink;
+  {
+    cudf::io::parquet_writer_options args =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&buf_sink}, *expected);
+    cudf::io::write_parquet(args);
+  }
+
+  // read them back in and make sure everything matches
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+
+  cudf::io::parquet_reader_options buf_args = cudf::io::parquet_reader_options::builder(
+    cudf::io::source_info{buf_sink.data(), buf_sink.size()});
+  auto buf_tbl = cudf::io::read_parquet(buf_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(buf_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterTest, DeviceWriteLargeishFile)
+{
+  auto filepath = temp_env->get_temp_filepath("DeviceWriteLargeishFile.parquet");
+  custom_test_data_sink custom_sink(filepath);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_random_fixed_table<int>(4, 4 * 1024 * 1024, false);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterTest, PartitionedWrite)
+{
+  auto source = create_compressible_fixed_table<int>(16, 4 * 1024 * 1024, 1000, false);
+
+  auto filepath1 = temp_env->get_temp_filepath("PartitionedWrite1.parquet");
+  auto filepath2 = temp_env->get_temp_filepath("PartitionedWrite2.parquet");
+
+  auto partition1 = cudf::io::partition_info{10, 1024 * 1024};
+  auto partition2 = cudf::io::partition_info{20 * 1024 + 7, 3 * 1024 * 1024};
+
+  auto expected1 =
+    cudf::slice(*source, {partition1.start_row, partition1.start_row + partition1.num_rows});
+  auto expected2 =
+    cudf::slice(*source, {partition2.start_row, partition2.start_row + partition2.num_rows});
+
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(
+      cudf::io::sink_info(std::vector<std::string>{filepath1, filepath2}), *source)
+      .partitions({partition1, partition2})
+      .compression(cudf::io::compression_type::NONE);
+  cudf::io::write_parquet(args);
+
+  auto result1 = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath1)));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected1, result1.tbl->view());
+
+  auto result2 = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath2)));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected2, result2.tbl->view());
+}
+
+TEST_P(ParquetV2Test, PartitionedWriteEmptyPartitions)
+{
+  auto const is_v2 = GetParam();
+
+  auto source = create_random_fixed_table<int>(4, 4, false);
+
+  auto filepath1 = temp_env->get_temp_filepath("PartitionedWrite1.parquet");
+  auto filepath2 = temp_env->get_temp_filepath("PartitionedWrite2.parquet");
+
+  auto partition1 = cudf::io::partition_info{1, 0};
+  auto partition2 = cudf::io::partition_info{1, 0};
+
+  auto expected1 =
+    cudf::slice(*source, {partition1.start_row, partition1.start_row + partition1.num_rows});
+  auto expected2 =
+    cudf::slice(*source, {partition2.start_row, partition2.start_row + partition2.num_rows});
+
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(
+      cudf::io::sink_info(std::vector<std::string>{filepath1, filepath2}), *source)
+      .partitions({partition1, partition2})
+      .write_v2_headers(is_v2)
+      .compression(cudf::io::compression_type::NONE);
+  cudf::io::write_parquet(args);
+
+  auto result1 = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath1)));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected1, result1.tbl->view());
+
+  auto result2 = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath2)));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected2, result2.tbl->view());
+}
+
+TEST_P(ParquetV2Test, PartitionedWriteEmptyColumns)
+{
+  auto const is_v2 = GetParam();
+
+  auto source = create_random_fixed_table<int>(0, 4, false);
+
+  auto filepath1 = temp_env->get_temp_filepath("PartitionedWrite1.parquet");
+  auto filepath2 = temp_env->get_temp_filepath("PartitionedWrite2.parquet");
+
+  auto partition1 = cudf::io::partition_info{1, 0};
+  auto partition2 = cudf::io::partition_info{1, 0};
+
+  auto expected1 =
+    cudf::slice(*source, {partition1.start_row, partition1.start_row + partition1.num_rows});
+  auto expected2 =
+    cudf::slice(*source, {partition2.start_row, partition2.start_row + partition2.num_rows});
+
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(
+      cudf::io::sink_info(std::vector<std::string>{filepath1, filepath2}), *source)
+      .partitions({partition1, partition2})
+      .write_v2_headers(is_v2)
+      .compression(cudf::io::compression_type::NONE);
+  cudf::io::write_parquet(args);
+
+  auto result1 = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath1)));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected1, result1.tbl->view());
+
+  auto result2 = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath2)));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected2, result2.tbl->view());
+}
+
+template <typename T>
+std::string create_parquet_file(int num_cols)
+{
+  srand(31337);
+  auto const table = create_random_fixed_table<T>(num_cols, 10, true);
+  auto const filepath =
+    temp_env->get_temp_filepath(typeid(T).name() + std::to_string(num_cols) + ".parquet");
+  cudf::io::parquet_writer_options const out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, table->view());
+  cudf::io::write_parquet(out_opts);
+  return filepath;
+}
+
+TEST_F(ParquetWriterTest, MultipleMismatchedSources)
+{
+  auto const int5file = create_parquet_file<int>(5);
+  {
+    auto const float5file = create_parquet_file<float>(5);
+    std::vector<std::string> files{int5file, float5file};
+    cudf::io::parquet_reader_options const read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{files});
+    EXPECT_THROW(cudf::io::read_parquet(read_opts), cudf::logic_error);
+  }
+  {
+    auto const int10file = create_parquet_file<int>(10);
+    std::vector<std::string> files{int5file, int10file};
+    cudf::io::parquet_reader_options const read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{files});
+    EXPECT_THROW(cudf::io::read_parquet(read_opts), cudf::logic_error);
+  }
+}
+
+TEST_F(ParquetWriterTest, Slice)
+{
+  auto col =
+    cudf::test::fixed_width_column_wrapper<int>{{1, 2, 3, 4, 5}, {true, true, true, false, true}};
+  std::vector<cudf::size_type> indices{2, 5};
+  std::vector<cudf::column_view> result = cudf::slice(col, indices);
+  cudf::table_view tbl{result};
+
+  auto filepath = temp_env->get_temp_filepath("Slice.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto read_table = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(read_table.tbl->view(), tbl);
+}
+
+TEST_F(ParquetChunkedWriterTest, SingleTable)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedSingle.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(*table1);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *table1);
+}
+
+TEST_F(ParquetChunkedWriterTest, SimpleTable)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+  auto table2 = create_random_fixed_table<int>(5, 5, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedSimple.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(*table1).write(*table2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(ParquetChunkedWriterTest, LargeTables)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(512, 4096, true);
+  auto table2 = create_random_fixed_table<int>(512, 8192, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedLarge.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  auto md = cudf::io::parquet_chunked_writer(args).write(*table1).write(*table2).close();
+  ASSERT_EQ(md, nullptr);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(ParquetChunkedWriterTest, ManyTables)
+{
+  srand(31337);
+  std::vector<std::unique_ptr<table>> tables;
+  std::vector<table_view> table_views;
+  constexpr int num_tables = 96;
+  for (int idx = 0; idx < num_tables; idx++) {
+    auto tbl = create_random_fixed_table<int>(16, 64, true);
+    table_views.push_back(*tbl);
+    tables.push_back(std::move(tbl));
+  }
+
+  auto expected = cudf::concatenate(table_views);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedManyTables.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer writer(args);
+  std::for_each(table_views.begin(), table_views.end(), [&writer](table_view const& tbl) {
+    writer.write(tbl);
+  });
+  auto md = writer.close({"dummy/path"});
+  ASSERT_NE(md, nullptr);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(ParquetChunkedWriterTest, Strings)
+{
+  std::vector<std::unique_ptr<cudf::column>> cols;
+
+  bool mask1[] = {true, true, false, true, true, true, true};
+  std::vector<char const*> h_strings1{"four", "score", "and", "seven", "years", "ago", "abcdefgh"};
+  cudf::test::strings_column_wrapper strings1(h_strings1.begin(), h_strings1.end(), mask1);
+  cols.push_back(strings1.release());
+  cudf::table tbl1(std::move(cols));
+
+  bool mask2[] = {false, true, true, true, true, true, true};
+  std::vector<char const*> h_strings2{"ooooo", "ppppppp", "fff", "j", "cccc", "bbb", "zzzzzzzzzzz"};
+  cudf::test::strings_column_wrapper strings2(h_strings2.begin(), h_strings2.end(), mask2);
+  cols.push_back(strings2.release());
+  cudf::table tbl2(std::move(cols));
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl1, tbl2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedStrings.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(tbl1).write(tbl2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(ParquetChunkedWriterTest, ListColumn)
+{
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // COL0 (Same nullability) ====================
+  // [NULL, 2, NULL]
+  // []
+  // [4, 5]
+  // NULL
+  lcw col0_tbl0{{{{1, 2, 3}, valids}, {}, {4, 5}, {}}, valids2};
+
+  // [7, 8, 9]
+  // []
+  // [NULL, 11]
+  // NULL
+  lcw col0_tbl1{{{7, 8, 9}, {}, {{10, 11}, valids}, {}}, valids2};
+
+  // COL1 (Nullability different in different chunks, test of merging nullability in writer)
+  // [NULL, 2, NULL]
+  // []
+  // [4, 5]
+  // []
+  lcw col1_tbl0{{{1, 2, 3}, valids}, {}, {4, 5}, {}};
+
+  // [7, 8, 9]
+  // []
+  // [10, 11]
+  // NULL
+  lcw col1_tbl1{{{7, 8, 9}, {}, {10, 11}, {}}, valids2};
+
+  // COL2 (non-nested columns to test proper schema construction)
+  size_t num_rows_tbl0 = static_cast<cudf::column_view>(col0_tbl0).size();
+  size_t num_rows_tbl1 = static_cast<cudf::column_view>(col0_tbl1).size();
+  auto seq_col0        = random_values<int>(num_rows_tbl0);
+  auto seq_col1        = random_values<int>(num_rows_tbl1);
+
+  column_wrapper<int> col2_tbl0{seq_col0.begin(), seq_col0.end(), valids};
+  column_wrapper<int> col2_tbl1{seq_col1.begin(), seq_col1.end(), valids2};
+
+  auto tbl0 = table_view({col0_tbl0, col1_tbl0, col2_tbl0});
+  auto tbl1 = table_view({col0_tbl1, col1_tbl1, col2_tbl1});
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl0, tbl1}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedLists.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(tbl0).write(tbl1);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(ParquetChunkedWriterTest, ListOfStruct)
+{
+  // Table 1
+  auto weight_1   = cudf::test::fixed_width_column_wrapper<float>{{57.5, 51.1, 15.3}};
+  auto ages_1     = cudf::test::fixed_width_column_wrapper<int32_t>{{30, 27, 5}};
+  auto struct_1_1 = cudf::test::structs_column_wrapper{weight_1, ages_1};
+  auto is_human_1 = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}};
+  auto struct_2_1 = cudf::test::structs_column_wrapper{{is_human_1, struct_1_1}};
+
+  auto list_offsets_column_1 =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 3, 3}.release();
+  auto num_list_rows_1 = list_offsets_column_1->size() - 1;
+
+  auto list_col_1 = cudf::make_lists_column(
+    num_list_rows_1, std::move(list_offsets_column_1), struct_2_1.release(), 0, {});
+
+  auto table_1 = table_view({*list_col_1});
+
+  // Table 2
+  auto weight_2   = cudf::test::fixed_width_column_wrapper<float>{{1.1, -1.0, -1.0}};
+  auto ages_2     = cudf::test::fixed_width_column_wrapper<int32_t>{{31, 351, 351}, {1, 1, 0}};
+  auto struct_1_2 = cudf::test::structs_column_wrapper{{weight_2, ages_2}, {1, 0, 1}};
+  auto is_human_2 = cudf::test::fixed_width_column_wrapper<bool>{{false, false, false}, {1, 1, 0}};
+  auto struct_2_2 = cudf::test::structs_column_wrapper{{is_human_2, struct_1_2}};
+
+  auto list_offsets_column_2 =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 2, 3}.release();
+  auto num_list_rows_2 = list_offsets_column_2->size() - 1;
+
+  auto list_col_2 = cudf::make_lists_column(
+    num_list_rows_2, std::move(list_offsets_column_2), struct_2_2.release(), 0, {});
+
+  auto table_2 = table_view({*list_col_2});
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({table_1, table_2}));
+
+  cudf::io::table_input_metadata expected_metadata(table_1);
+  expected_metadata.column_metadata[0].set_name("family");
+  expected_metadata.column_metadata[0].child(1).set_nullability(false);
+  expected_metadata.column_metadata[0].child(1).child(0).set_name("human?");
+  expected_metadata.column_metadata[0].child(1).child(1).set_name("particulars");
+  expected_metadata.column_metadata[0].child(1).child(1).child(0).set_name("weight");
+  expected_metadata.column_metadata[0].child(1).child(1).child(1).set_name("age");
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedListOfStruct.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  args.set_metadata(expected_metadata);
+  cudf::io::parquet_chunked_writer(args).write(table_1).write(table_2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result.tbl, *full_table);
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(ParquetChunkedWriterTest, ListOfStructOfStructOfListOfList)
+{
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // Table 1 ===========================
+
+  // []
+  // [NULL, 2, NULL]
+  // [4, 5]
+  // NULL
+  lcw land_1{{{}, {{1, 2, 3}, valids}, {4, 5}, {}}, valids2};
+
+  // []
+  // [[1, 2, 3], [], [4, 5], [], [0, 6, 0]]
+  // [[7, 8], []]
+  // [[]]
+  lcw flats_1{lcw{}, {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, {{7, 8}, {}}, lcw{lcw{}}};
+
+  auto weight_1   = cudf::test::fixed_width_column_wrapper<float>{{57.5, 51.1, 15.3, 1.1}};
+  auto ages_1     = cudf::test::fixed_width_column_wrapper<int32_t>{{30, 27, 5, 31}};
+  auto struct_1_1 = cudf::test::structs_column_wrapper{weight_1, ages_1, land_1, flats_1};
+  auto is_human_1 = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false, false}};
+  auto struct_2_1 = cudf::test::structs_column_wrapper{{is_human_1, struct_1_1}};
+
+  auto list_offsets_column_1 =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 3, 4}.release();
+  auto num_list_rows_1 = list_offsets_column_1->size() - 1;
+
+  auto list_col_1 = cudf::make_lists_column(
+    num_list_rows_1, std::move(list_offsets_column_1), struct_2_1.release(), 0, {});
+
+  auto table_1 = table_view({*list_col_1});
+
+  // Table 2 ===========================
+
+  // []
+  // [7, 8, 9]
+  lcw land_2{{}, {7, 8, 9}};
+
+  // [[]]
+  // [[], [], []]
+  lcw flats_2{lcw{lcw{}}, lcw{lcw{}, lcw{}, lcw{}}};
+
+  auto weight_2   = cudf::test::fixed_width_column_wrapper<float>{{-1.0, -1.0}};
+  auto ages_2     = cudf::test::fixed_width_column_wrapper<int32_t>{{351, 351}, {1, 0}};
+  auto struct_1_2 = cudf::test::structs_column_wrapper{{weight_2, ages_2, land_2, flats_2}, {0, 1}};
+  auto is_human_2 = cudf::test::fixed_width_column_wrapper<bool>{{false, false}, {1, 0}};
+  auto struct_2_2 = cudf::test::structs_column_wrapper{{is_human_2, struct_1_2}};
+
+  auto list_offsets_column_2 =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 2}.release();
+  auto num_list_rows_2 = list_offsets_column_2->size() - 1;
+
+  auto list_col_2 = cudf::make_lists_column(
+    num_list_rows_2, std::move(list_offsets_column_2), struct_2_2.release(), 0, {});
+
+  auto table_2 = table_view({*list_col_2});
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({table_1, table_2}));
+
+  cudf::io::table_input_metadata expected_metadata(table_1);
+  expected_metadata.column_metadata[0].set_name("family");
+  expected_metadata.column_metadata[0].child(1).set_nullability(false);
+  expected_metadata.column_metadata[0].child(1).child(0).set_name("human?");
+  expected_metadata.column_metadata[0].child(1).child(1).set_name("particulars");
+  expected_metadata.column_metadata[0].child(1).child(1).child(0).set_name("weight");
+  expected_metadata.column_metadata[0].child(1).child(1).child(1).set_name("age");
+  expected_metadata.column_metadata[0].child(1).child(1).child(2).set_name("land_unit");
+  expected_metadata.column_metadata[0].child(1).child(1).child(3).set_name("flats");
+
+  auto filepath = temp_env->get_temp_filepath("ListOfStructOfStructOfListOfList.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  args.set_metadata(expected_metadata);
+  cudf::io::parquet_chunked_writer(args).write(table_1).write(table_2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result.tbl, *full_table);
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+
+  // We specifically mentioned in input schema that struct_2 is non-nullable across chunked calls.
+  auto result_parent_list = result.tbl->get_column(0);
+  auto result_struct_2    = result_parent_list.child(cudf::lists_column_view::child_column_index);
+  EXPECT_EQ(result_struct_2.nullable(), false);
+}
+
+TEST_F(ParquetChunkedWriterTest, MismatchedTypes)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(4, 4, true);
+  auto table2 = create_random_fixed_table<float>(4, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedMismatchedTypes.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer writer(args);
+  writer.write(*table1);
+  EXPECT_THROW(writer.write(*table2), cudf::logic_error);
+  writer.close();
+}
+
+TEST_F(ParquetChunkedWriterTest, ChunkedWriteAfterClosing)
+{
+  srand(31337);
+  auto table = create_random_fixed_table<int>(4, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedWriteAfterClosing.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer writer(args);
+  writer.write(*table).close();
+  EXPECT_THROW(writer.write(*table), cudf::logic_error);
+}
+
+TEST_F(ParquetChunkedWriterTest, ReadingUnclosedFile)
+{
+  srand(31337);
+  auto table = create_random_fixed_table<int>(4, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ReadingUnclosedFile.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer writer(args);
+  writer.write(*table);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  EXPECT_THROW(cudf::io::read_parquet(read_opts), cudf::logic_error);
+}
+
+TEST_F(ParquetChunkedWriterTest, MismatchedStructure)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(4, 4, true);
+  auto table2 = create_random_fixed_table<float>(3, 4, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedMismatchedStructure.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer writer(args);
+  writer.write(*table1);
+  EXPECT_THROW(writer.write(*table2), cudf::logic_error);
+  writer.close();
+}
+
+TEST_F(ParquetChunkedWriterTest, MismatchedStructureList)
+{
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // COL0 (mismatched depth) ====================
+  // [NULL, 2, NULL]
+  // []
+  // [4, 5]
+  // NULL
+  lcw col00{{{{1, 2, 3}, valids}, {}, {4, 5}, {}}, valids2};
+
+  // [[1, 2, 3], [], [4, 5], [], [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  lcw col01{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, {{7, 8}}, lcw{}, lcw{lcw{}}};
+
+  // COL2 (non-nested columns to test proper schema construction)
+  size_t num_rows = static_cast<cudf::column_view>(col00).size();
+  auto seq_col0   = random_values<int>(num_rows);
+  auto seq_col1   = random_values<int>(num_rows);
+
+  column_wrapper<int> col10{seq_col0.begin(), seq_col0.end(), valids};
+  column_wrapper<int> col11{seq_col1.begin(), seq_col1.end(), valids2};
+
+  auto tbl0 = table_view({col00, col10});
+  auto tbl1 = table_view({col01, col11});
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedLists.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer writer(args);
+  writer.write(tbl0);
+  EXPECT_THROW(writer.write(tbl1), cudf::logic_error);
+}
+
+TEST_F(ParquetChunkedWriterTest, DifferentNullability)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+  auto table2 = create_random_fixed_table<int>(5, 5, false);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedNullable.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(*table1).write(*table2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(ParquetChunkedWriterTest, DifferentNullabilityStruct)
+{
+  // Struct<is_human:bool (non-nullable),
+  //        Struct<weight:float>,
+  //               age:int
+  //              > (nullable)
+  //       > (non-nullable)
+
+  // Table 1: is_human and struct_1 are non-nullable but should be nullable when read back.
+  auto weight_1   = cudf::test::fixed_width_column_wrapper<float>{{57.5, 51.1, 15.3}};
+  auto ages_1     = cudf::test::fixed_width_column_wrapper<int32_t>{{30, 27, 5}};
+  auto struct_1_1 = cudf::test::structs_column_wrapper{weight_1, ages_1};
+  auto is_human_1 = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}};
+  auto struct_2_1 = cudf::test::structs_column_wrapper{{is_human_1, struct_1_1}};
+  auto table_1    = cudf::table_view({struct_2_1});
+
+  // Table 2: struct_1 and is_human are nullable now so if we hadn't assumed worst case (nullable)
+  // when writing table_1, we would have wrong pages for it.
+  auto weight_2   = cudf::test::fixed_width_column_wrapper<float>{{1.1, -1.0, -1.0}};
+  auto ages_2     = cudf::test::fixed_width_column_wrapper<int32_t>{{31, 351, 351}, {1, 1, 0}};
+  auto struct_1_2 = cudf::test::structs_column_wrapper{{weight_2, ages_2}, {1, 0, 1}};
+  auto is_human_2 = cudf::test::fixed_width_column_wrapper<bool>{{false, false, false}, {1, 1, 0}};
+  auto struct_2_2 = cudf::test::structs_column_wrapper{{is_human_2, struct_1_2}};
+  auto table_2    = cudf::table_view({struct_2_2});
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({table_1, table_2}));
+
+  cudf::io::table_input_metadata expected_metadata(table_1);
+  expected_metadata.column_metadata[0].set_name("being");
+  expected_metadata.column_metadata[0].child(0).set_name("human?");
+  expected_metadata.column_metadata[0].child(1).set_name("particulars");
+  expected_metadata.column_metadata[0].child(1).child(0).set_name("weight");
+  expected_metadata.column_metadata[0].child(1).child(1).set_name("age");
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedNullableStruct.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  args.set_metadata(expected_metadata);
+  cudf::io::parquet_chunked_writer(args).write(table_1).write(table_2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result.tbl, *full_table);
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(ParquetChunkedWriterTest, ForcedNullability)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, false);
+  auto table2 = create_random_fixed_table<int>(5, 5, false);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedNoNullable.parquet");
+
+  cudf::io::table_input_metadata metadata(*table1);
+
+  // In the absence of prescribed per-column nullability in metadata, the writer assumes the worst
+  // and considers all columns nullable. However cudf::concatenate will not force nulls in case no
+  // columns are nullable. To get the expected result, we tell the writer the nullability of all
+  // columns in advance.
+  for (auto& col_meta : metadata.column_metadata) {
+    col_meta.set_nullability(false);
+  }
+
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath})
+      .metadata(std::move(metadata));
+  cudf::io::parquet_chunked_writer(args).write(*table1).write(*table2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(ParquetChunkedWriterTest, ForcedNullabilityList)
+{
+  srand(31337);
+
+  auto valids  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  auto valids2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; });
+
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // COL0 ====================
+  // [1, 2, 3]
+  // []
+  // [4, 5]
+  // NULL
+  lcw col00{{{1, 2, 3}, {}, {4, 5}, {}}, valids2};
+
+  // [7]
+  // []
+  // [8, 9, 10, 11]
+  // NULL
+  lcw col01{{{7}, {}, {8, 9, 10, 11}, {}}, valids2};
+
+  // COL1 (non-nested columns to test proper schema construction)
+  size_t num_rows = static_cast<cudf::column_view>(col00).size();
+  auto seq_col0   = random_values<int>(num_rows);
+  auto seq_col1   = random_values<int>(num_rows);
+
+  column_wrapper<int> col10{seq_col0.begin(), seq_col0.end(), valids};
+  column_wrapper<int> col11{seq_col1.begin(), seq_col1.end(), valids2};
+
+  auto table1 = table_view({col00, col10});
+  auto table2 = table_view({col01, col11});
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({table1, table2}));
+
+  cudf::io::table_input_metadata metadata(table1);
+  metadata.column_metadata[0].set_nullability(true);  // List is nullable at first (root) level
+  metadata.column_metadata[0].child(1).set_nullability(
+    false);  // non-nullable at second (leaf) level
+  metadata.column_metadata[1].set_nullability(true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedListNullable.parquet");
+
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath})
+      .metadata(std::move(metadata));
+  cudf::io::parquet_chunked_writer(args).write(table1).write(table2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(ParquetChunkedWriterTest, ForcedNullabilityStruct)
+{
+  // Struct<is_human:bool (non-nullable),
+  //        Struct<weight:float>,
+  //               age:int
+  //              > (nullable)
+  //       > (non-nullable)
+
+  // Table 1: is_human and struct_2 are non-nullable and should stay that way when read back.
+  auto weight_1   = cudf::test::fixed_width_column_wrapper<float>{{57.5, 51.1, 15.3}};
+  auto ages_1     = cudf::test::fixed_width_column_wrapper<int32_t>{{30, 27, 5}};
+  auto struct_1_1 = cudf::test::structs_column_wrapper{weight_1, ages_1};
+  auto is_human_1 = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}};
+  auto struct_2_1 = cudf::test::structs_column_wrapper{{is_human_1, struct_1_1}};
+  auto table_1    = cudf::table_view({struct_2_1});
+
+  auto weight_2   = cudf::test::fixed_width_column_wrapper<float>{{1.1, -1.0, -1.0}};
+  auto ages_2     = cudf::test::fixed_width_column_wrapper<int32_t>{{31, 351, 351}, {1, 1, 0}};
+  auto struct_1_2 = cudf::test::structs_column_wrapper{{weight_2, ages_2}, {1, 0, 1}};
+  auto is_human_2 = cudf::test::fixed_width_column_wrapper<bool>{{false, false, false}};
+  auto struct_2_2 = cudf::test::structs_column_wrapper{{is_human_2, struct_1_2}};
+  auto table_2    = cudf::table_view({struct_2_2});
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({table_1, table_2}));
+
+  cudf::io::table_input_metadata expected_metadata(table_1);
+  expected_metadata.column_metadata[0].set_name("being").set_nullability(false);
+  expected_metadata.column_metadata[0].child(0).set_name("human?").set_nullability(false);
+  expected_metadata.column_metadata[0].child(1).set_name("particulars");
+  expected_metadata.column_metadata[0].child(1).child(0).set_name("weight");
+  expected_metadata.column_metadata[0].child(1).child(1).set_name("age");
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedNullableStruct.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  args.set_metadata(expected_metadata);
+  cudf::io::parquet_chunked_writer(args).write(table_1).write(table_2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+  cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+}
+
+TEST_F(ParquetChunkedWriterTest, ReadRowGroups)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+  auto table2 = create_random_fixed_table<int>(5, 5, true);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table2, *table1, *table2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedRowGroups.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  {
+    cudf::io::parquet_chunked_writer(args).write(*table1).write(*table2);
+  }
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .row_groups({{1, 0, 1}});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *full_table);
+}
+
+TEST_F(ParquetChunkedWriterTest, ReadRowGroupsError)
+{
+  srand(31337);
+  auto table1 = create_random_fixed_table<int>(5, 5, true);
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedRowGroupsError.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(*table1);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}).row_groups({{0, 1}});
+  EXPECT_THROW(cudf::io::read_parquet(read_opts), cudf::logic_error);
+  read_opts.set_row_groups({{-1}});
+  EXPECT_THROW(cudf::io::read_parquet(read_opts), cudf::logic_error);
+  read_opts.set_row_groups({{0}, {0}});
+  EXPECT_THROW(cudf::io::read_parquet(read_opts), cudf::logic_error);
+}
+
+TEST_F(ParquetWriterTest, DecimalWrite)
+{
+  constexpr cudf::size_type num_rows = 500;
+  auto seq_col0                      = random_values<int32_t>(num_rows);
+  auto seq_col1                      = random_values<int64_t>(num_rows);
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  auto col0 = cudf::test::fixed_point_column_wrapper<int32_t>{
+    seq_col0.begin(), seq_col0.end(), valids, numeric::scale_type{5}};
+  auto col1 = cudf::test::fixed_point_column_wrapper<int64_t>{
+    seq_col1.begin(), seq_col1.end(), valids, numeric::scale_type{-9}};
+
+  auto table = table_view({col0, col1});
+
+  auto filepath = temp_env->get_temp_filepath("DecimalWrite.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, table);
+
+  cudf::io::table_input_metadata expected_metadata(table);
+
+  // verify failure if too small a precision is given
+  expected_metadata.column_metadata[0].set_decimal_precision(7);
+  expected_metadata.column_metadata[1].set_decimal_precision(1);
+  args.set_metadata(expected_metadata);
+  EXPECT_THROW(cudf::io::write_parquet(args), cudf::logic_error);
+
+  // verify success if equal precision is given
+  expected_metadata.column_metadata[0].set_decimal_precision(7);
+  expected_metadata.column_metadata[1].set_decimal_precision(9);
+  args.set_metadata(std::move(expected_metadata));
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, table);
+}
+
+TYPED_TEST(ParquetChunkedWriterNumericTypeTest, UnalignedSize)
+{
+  // write out two 31 row tables and make sure they get
+  // read back with all their validity bits in the right place
+
+  using T = TypeParam;
+
+  int num_els = 31;
+  std::vector<std::unique_ptr<cudf::column>> cols;
+
+  bool mask[] = {false, true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true, true, true,
+
+                 true,  true, true, true, true, true, true, true, true};
+  T c1a[num_els];
+  std::fill(c1a, c1a + num_els, static_cast<T>(5));
+  T c1b[num_els];
+  std::fill(c1b, c1b + num_els, static_cast<T>(6));
+  column_wrapper<T> c1a_w(c1a, c1a + num_els, mask);
+  column_wrapper<T> c1b_w(c1b, c1b + num_els, mask);
+  cols.push_back(c1a_w.release());
+  cols.push_back(c1b_w.release());
+  cudf::table tbl1(std::move(cols));
+
+  T c2a[num_els];
+  std::fill(c2a, c2a + num_els, static_cast<T>(8));
+  T c2b[num_els];
+  std::fill(c2b, c2b + num_els, static_cast<T>(9));
+  column_wrapper<T> c2a_w(c2a, c2a + num_els, mask);
+  column_wrapper<T> c2b_w(c2b, c2b + num_els, mask);
+  cols.push_back(c2a_w.release());
+  cols.push_back(c2b_w.release());
+  cudf::table tbl2(std::move(cols));
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl1, tbl2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedUnalignedSize.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(tbl1).write(tbl2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TYPED_TEST(ParquetChunkedWriterNumericTypeTest, UnalignedSize2)
+{
+  // write out two 33 row tables and make sure they get
+  // read back with all their validity bits in the right place
+
+  using T = TypeParam;
+
+  int num_els = 33;
+  std::vector<std::unique_ptr<cudf::column>> cols;
+
+  bool mask[] = {false, true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true, true, true,
+                 true,  true, true, true, true, true, true, true, true, true, true};
+
+  T c1a[num_els];
+  std::fill(c1a, c1a + num_els, static_cast<T>(5));
+  T c1b[num_els];
+  std::fill(c1b, c1b + num_els, static_cast<T>(6));
+  column_wrapper<T> c1a_w(c1a, c1a + num_els, mask);
+  column_wrapper<T> c1b_w(c1b, c1b + num_els, mask);
+  cols.push_back(c1a_w.release());
+  cols.push_back(c1b_w.release());
+  cudf::table tbl1(std::move(cols));
+
+  T c2a[num_els];
+  std::fill(c2a, c2a + num_els, static_cast<T>(8));
+  T c2b[num_els];
+  std::fill(c2b, c2b + num_els, static_cast<T>(9));
+  column_wrapper<T> c2a_w(c2a, c2a + num_els, mask);
+  column_wrapper<T> c2b_w(c2b, c2b + num_els, mask);
+  cols.push_back(c2a_w.release());
+  cols.push_back(c2b_w.release());
+  cudf::table tbl2(std::move(cols));
+
+  auto expected = cudf::concatenate(std::vector<table_view>({tbl1, tbl2}));
+
+  auto filepath = temp_env->get_temp_filepath("ChunkedUnalignedSize2.parquet");
+  cudf::io::chunked_parquet_writer_options args =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{filepath});
+  cudf::io::parquet_chunked_writer(args).write(tbl1).write(tbl2);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+// custom mem mapped data sink that supports device writes
+template <bool supports_device_writes>
+class custom_test_memmap_sink : public cudf::io::data_sink {
+ public:
+  explicit custom_test_memmap_sink(std::vector<char>* mm_writer_buf)
+  {
+    mm_writer = cudf::io::data_sink::create(mm_writer_buf);
+  }
+
+  virtual ~custom_test_memmap_sink() { mm_writer->flush(); }
+
+  void host_write(void const* data, size_t size) override { mm_writer->host_write(data, size); }
+
+  [[nodiscard]] bool supports_device_write() const override { return supports_device_writes; }
+
+  void device_write(void const* gpu_data, size_t size, rmm::cuda_stream_view stream) override
+  {
+    this->device_write_async(gpu_data, size, stream).get();
+  }
+
+  std::future<void> device_write_async(void const* gpu_data,
+                                       size_t size,
+                                       rmm::cuda_stream_view stream) override
+  {
+    return std::async(std::launch::deferred, [=] {
+      char* ptr = nullptr;
+      CUDF_CUDA_TRY(cudaMallocHost(&ptr, size));
+      CUDF_CUDA_TRY(cudaMemcpyAsync(ptr, gpu_data, size, cudaMemcpyDefault, stream.value()));
+      stream.synchronize();
+      mm_writer->host_write(ptr, size);
+      CUDF_CUDA_TRY(cudaFreeHost(ptr));
+    });
+  }
+
+  void flush() override { mm_writer->flush(); }
+
+  size_t bytes_written() override { return mm_writer->bytes_written(); }
+
+ private:
+  std::unique_ptr<data_sink> mm_writer;
+};
+
+TEST_F(ParquetWriterStressTest, LargeTableWeakCompression)
+{
+  std::vector<char> mm_buf;
+  mm_buf.reserve(4 * 1024 * 1024 * 16);
+  custom_test_memmap_sink<false> custom_sink(&mm_buf);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_random_fixed_table<int>(16, 4 * 1024 * 1024, false);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{mm_buf.data(), mm_buf.size()});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterStressTest, LargeTableGoodCompression)
+{
+  std::vector<char> mm_buf;
+  mm_buf.reserve(4 * 1024 * 1024 * 16);
+  custom_test_memmap_sink<false> custom_sink(&mm_buf);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_compressible_fixed_table<int>(16, 4 * 1024 * 1024, 128 * 1024, false);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{mm_buf.data(), mm_buf.size()});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterStressTest, LargeTableWithValids)
+{
+  std::vector<char> mm_buf;
+  mm_buf.reserve(4 * 1024 * 1024 * 16);
+  custom_test_memmap_sink<false> custom_sink(&mm_buf);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_compressible_fixed_table<int>(16, 4 * 1024 * 1024, 6, true);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{mm_buf.data(), mm_buf.size()});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterStressTest, DeviceWriteLargeTableWeakCompression)
+{
+  std::vector<char> mm_buf;
+  mm_buf.reserve(4 * 1024 * 1024 * 16);
+  custom_test_memmap_sink<true> custom_sink(&mm_buf);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_random_fixed_table<int>(16, 4 * 1024 * 1024, false);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{mm_buf.data(), mm_buf.size()});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterStressTest, DeviceWriteLargeTableGoodCompression)
+{
+  std::vector<char> mm_buf;
+  mm_buf.reserve(4 * 1024 * 1024 * 16);
+  custom_test_memmap_sink<true> custom_sink(&mm_buf);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_compressible_fixed_table<int>(16, 4 * 1024 * 1024, 128 * 1024, false);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{mm_buf.data(), mm_buf.size()});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetWriterStressTest, DeviceWriteLargeTableWithValids)
+{
+  std::vector<char> mm_buf;
+  mm_buf.reserve(4 * 1024 * 1024 * 16);
+  custom_test_memmap_sink<true> custom_sink(&mm_buf);
+
+  // exercises multiple rowgroups
+  srand(31337);
+  auto expected = create_compressible_fixed_table<int>(16, 4 * 1024 * 1024, 6, true);
+
+  // write out using the custom sink (which uses device writes)
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&custom_sink}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options custom_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{mm_buf.data(), mm_buf.size()});
+  auto custom_tbl = cudf::io::read_parquet(custom_args);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(custom_tbl.tbl->view(), expected->view());
+}
+
+TEST_F(ParquetReaderTest, UserBounds)
+{
+  // trying to read more rows than there are should result in
+  // receiving the properly capped # of rows
+  {
+    srand(31337);
+    auto expected = create_random_fixed_table<int>(4, 4, false);
+
+    auto filepath = temp_env->get_temp_filepath("TooManyRows.parquet");
+    cudf::io::parquet_writer_options args =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected);
+    cudf::io::write_parquet(args);
+
+    // attempt to read more rows than there actually are
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}).num_rows(16);
+    auto result = cudf::io::read_parquet(read_opts);
+
+    // we should only get back 4 rows
+    EXPECT_EQ(result.tbl->view().column(0).size(), 4);
+  }
+
+  // trying to read past the end of the # of actual rows should result
+  // in empty columns.
+  {
+    srand(31337);
+    auto expected = create_random_fixed_table<int>(4, 4, false);
+
+    auto filepath = temp_env->get_temp_filepath("PastBounds.parquet");
+    cudf::io::parquet_writer_options args =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected);
+    cudf::io::write_parquet(args);
+
+    // attempt to read more rows than there actually are
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}).skip_rows(4);
+    auto result = cudf::io::read_parquet(read_opts);
+
+    // we should get empty columns back
+    EXPECT_EQ(result.tbl->view().num_columns(), 4);
+    EXPECT_EQ(result.tbl->view().column(0).size(), 0);
+  }
+
+  // trying to read 0 rows should result in empty columns
+  {
+    srand(31337);
+    auto expected = create_random_fixed_table<int>(4, 4, false);
+
+    auto filepath = temp_env->get_temp_filepath("ZeroRows.parquet");
+    cudf::io::parquet_writer_options args =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected);
+    cudf::io::write_parquet(args);
+
+    // attempt to read more rows than there actually are
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath}).num_rows(0);
+    auto result = cudf::io::read_parquet(read_opts);
+
+    EXPECT_EQ(result.tbl->view().num_columns(), 4);
+    EXPECT_EQ(result.tbl->view().column(0).size(), 0);
+  }
+
+  // trying to read 0 rows past the end of the # of actual rows should result
+  // in empty columns.
+  {
+    srand(31337);
+    auto expected = create_random_fixed_table<int>(4, 4, false);
+
+    auto filepath = temp_env->get_temp_filepath("ZeroRowsPastBounds.parquet");
+    cudf::io::parquet_writer_options args =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *expected);
+    cudf::io::write_parquet(args);
+
+    // attempt to read more rows than there actually are
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+        .skip_rows(4)
+        .num_rows(0);
+    auto result = cudf::io::read_parquet(read_opts);
+
+    // we should get empty columns back
+    EXPECT_EQ(result.tbl->view().num_columns(), 4);
+    EXPECT_EQ(result.tbl->view().column(0).size(), 0);
+  }
+}
+
+TEST_F(ParquetReaderTest, UserBoundsWithNulls)
+{
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<float> col{{1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2, 3,3,3,3,3,3,3,3, 4,4,4,4,4,4,4,4,  5,5,5,5,5,5,5,5, 6,6,6,6,6,6,6,6, 7,7,7,7,7,7,7,7, 8,8,8,8,8,8,8,8}
+                                                   ,{1,1,1,0,0,0,1,1, 1,1,1,1,1,1,1,1, 0,0,0,0,0,0,0,0, 1,1,1,1,1,1,0,0,  1,0,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,0}};
+  // clang-format on
+  cudf::table_view tbl({col});
+  auto filepath = temp_env->get_temp_filepath("UserBoundsWithNulls.parquet");
+  cudf::io::parquet_writer_options out_args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_parquet(out_args);
+
+  // skip_rows / num_rows
+  // clang-format off
+  std::vector<std::pair<int, int>> params{ {-1, -1}, {1, 3}, {3, -1},
+                                           {31, -1}, {32, -1}, {33, -1},
+                                           {31, 5}, {32, 5}, {33, 5},
+                                           {-1, 7}, {-1, 31}, {-1, 32}, {-1, 33},
+                                           {62, -1}, {63, -1},
+                                           {62, 2}, {63, 1}};
+  // clang-format on
+  for (auto p : params) {
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+    if (p.first >= 0) { read_args.set_skip_rows(p.first); }
+    if (p.second >= 0) { read_args.set_num_rows(p.second); }
+    auto result = cudf::io::read_parquet(read_args);
+
+    p.first  = p.first < 0 ? 0 : p.first;
+    p.second = p.second < 0 ? static_cast<cudf::column_view>(col).size() - p.first : p.second;
+    std::vector<cudf::size_type> slice_indices{p.first, p.first + p.second};
+    auto expected = cudf::slice(col, slice_indices);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), expected[0]);
+  }
+}
+
+TEST_F(ParquetReaderTest, UserBoundsWithNullsMixedTypes)
+{
+  constexpr int num_rows = 32 * 1024;
+
+  std::mt19937 gen(6542);
+  std::bernoulli_distribution bn(0.7f);
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [&](int index) { return bn(gen); });
+  auto values = thrust::make_counting_iterator(0);
+
+  // int64
+  cudf::test::fixed_width_column_wrapper<int64_t> c0(values, values + num_rows, valids);
+
+  // list<float>
+  constexpr int floats_per_row = 4;
+  auto c1_offset_iter          = cudf::detail::make_counting_transform_iterator(
+    0, [floats_per_row](cudf::size_type idx) { return idx * floats_per_row; });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> c1_offsets(c1_offset_iter,
+                                                                     c1_offset_iter + num_rows + 1);
+  cudf::test::fixed_width_column_wrapper<float> c1_floats(
+    values, values + (num_rows * floats_per_row), valids);
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(valids, valids + num_rows);
+
+  auto _c1 = cudf::make_lists_column(
+    num_rows, c1_offsets.release(), c1_floats.release(), null_count, std::move(null_mask));
+  auto c1 = cudf::purge_nonempty_nulls(*_c1);
+
+  // list<list<int>>
+  auto c2 = make_parquet_list_list_col<int>(0, num_rows, 5, 8, true);
+
+  // struct<list<string>, int, float>
+  std::vector<std::string> strings{
+    "abc", "x", "bananas", "gpu", "minty", "backspace", "", "cayenne", "turbine", "soft"};
+  std::uniform_int_distribution<int> uni(0, strings.size() - 1);
+  auto string_iter = cudf::detail::make_counting_transform_iterator(
+    0, [&](cudf::size_type idx) { return strings[uni(gen)]; });
+  constexpr int string_per_row  = 3;
+  constexpr int num_string_rows = num_rows * string_per_row;
+  cudf::test::strings_column_wrapper string_col{string_iter, string_iter + num_string_rows};
+  auto offset_iter = cudf::detail::make_counting_transform_iterator(
+    0, [string_per_row](cudf::size_type idx) { return idx * string_per_row; });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets(offset_iter,
+                                                                  offset_iter + num_rows + 1);
+
+  auto _c3_valids =
+    cudf::detail::make_counting_transform_iterator(0, [&](int index) { return index % 200; });
+  std::vector<bool> c3_valids(num_rows);
+  std::copy(_c3_valids, _c3_valids + num_rows, c3_valids.begin());
+  std::tie(null_mask, null_count) = cudf::test::detail::make_null_mask(valids, valids + num_rows);
+  auto _c3_list                   = cudf::make_lists_column(
+    num_rows, offsets.release(), string_col.release(), null_count, std::move(null_mask));
+  auto c3_list = cudf::purge_nonempty_nulls(*_c3_list);
+  cudf::test::fixed_width_column_wrapper<int> c3_ints(values, values + num_rows, valids);
+  cudf::test::fixed_width_column_wrapper<float> c3_floats(values, values + num_rows, valids);
+  std::vector<std::unique_ptr<cudf::column>> c3_children;
+  c3_children.push_back(std::move(c3_list));
+  c3_children.push_back(c3_ints.release());
+  c3_children.push_back(c3_floats.release());
+  cudf::test::structs_column_wrapper _c3(std::move(c3_children), c3_valids);
+  auto c3 = cudf::purge_nonempty_nulls(_c3);
+
+  // write it out
+  cudf::table_view tbl({c0, *c1, *c2, *c3});
+  auto filepath = temp_env->get_temp_filepath("UserBoundsWithNullsMixedTypes.parquet");
+  cudf::io::parquet_writer_options out_args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_parquet(out_args);
+
+  // read it back
+  std::vector<std::pair<int, int>> params{
+    {-1, -1}, {0, num_rows}, {1, num_rows - 1}, {num_rows - 1, 1}, {517, 22000}};
+  for (auto p : params) {
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+    if (p.first >= 0) { read_args.set_skip_rows(p.first); }
+    if (p.second >= 0) { read_args.set_num_rows(p.second); }
+    auto result = cudf::io::read_parquet(read_args);
+
+    p.first  = p.first < 0 ? 0 : p.first;
+    p.second = p.second < 0 ? num_rows - p.first : p.second;
+    std::vector<cudf::size_type> slice_indices{p.first, p.first + p.second};
+    auto expected = cudf::slice(tbl, slice_indices);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, expected[0]);
+  }
+}
+
+TEST_F(ParquetReaderTest, UserBoundsWithNullsLarge)
+{
+  constexpr int num_rows = 30 * 1000000;
+
+  std::mt19937 gen(6747);
+  std::bernoulli_distribution bn(0.7f);
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [&](int index) { return bn(gen); });
+  auto values = thrust::make_counting_iterator(0);
+
+  cudf::test::fixed_width_column_wrapper<int> col(values, values + num_rows, valids);
+
+  // this file will have row groups of 1,000,000 each
+  cudf::table_view tbl({col});
+  auto filepath = temp_env->get_temp_filepath("UserBoundsWithNullsLarge.parquet");
+  cudf::io::parquet_writer_options out_args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_parquet(out_args);
+
+  // skip_rows / num_rows
+  // clang-format off
+  std::vector<std::pair<int, int>> params{ {-1, -1}, {31, -1}, {32, -1}, {33, -1}, {1613470, -1}, {1999999, -1},
+                                           {31, 1}, {32, 1}, {33, 1},
+                                           // deliberately span some row group boundaries
+                                           {999000, 1001}, {999000, 2000}, {2999999, 2}, {13999997, -1},
+                                           {16785678, 3}, {22996176, 31},
+                                           {24001231, 17}, {29000001, 989999}, {29999999, 1} };
+  // clang-format on
+  for (auto p : params) {
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+    if (p.first >= 0) { read_args.set_skip_rows(p.first); }
+    if (p.second >= 0) { read_args.set_num_rows(p.second); }
+    auto result = cudf::io::read_parquet(read_args);
+
+    p.first  = p.first < 0 ? 0 : p.first;
+    p.second = p.second < 0 ? static_cast<cudf::column_view>(col).size() - p.first : p.second;
+    std::vector<cudf::size_type> slice_indices{p.first, p.first + p.second};
+    auto expected = cudf::slice(col, slice_indices);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), expected[0]);
+  }
+}
+
+TEST_F(ParquetReaderTest, ListUserBoundsWithNullsLarge)
+{
+  constexpr int num_rows = 5 * 1000000;
+  auto colp              = make_parquet_list_list_col<int>(0, num_rows, 5, 8, true);
+  cudf::column_view col  = *colp;
+
+  // this file will have row groups of 1,000,000 each
+  cudf::table_view tbl({col});
+  auto filepath = temp_env->get_temp_filepath("ListUserBoundsWithNullsLarge.parquet");
+  cudf::io::parquet_writer_options out_args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl);
+  cudf::io::write_parquet(out_args);
+
+  // skip_rows / num_rows
+  // clang-format off
+  std::vector<std::pair<int, int>> params{ {-1, -1}, {31, -1}, {32, -1}, {33, -1}, {161470, -1}, {4499997, -1},
+                                           {31, 1}, {32, 1}, {33, 1},
+                                           // deliberately span some row group boundaries
+                                           {999000, 1001}, {999000, 2000}, {2999999, 2},
+                                           {1678567, 3}, {4299676, 31},
+                                           {4001231, 17}, {1900000, 989999}, {4999999, 1} };
+  // clang-format on
+  for (auto p : params) {
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+    if (p.first >= 0) { read_args.set_skip_rows(p.first); }
+    if (p.second >= 0) { read_args.set_num_rows(p.second); }
+    auto result = cudf::io::read_parquet(read_args);
+
+    p.first  = p.first < 0 ? 0 : p.first;
+    p.second = p.second < 0 ? static_cast<cudf::column_view>(col).size() - p.first : p.second;
+    std::vector<cudf::size_type> slice_indices{p.first, p.first + p.second};
+    auto expected = cudf::slice(col, slice_indices);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->get_column(0), expected[0]);
+  }
+}
+
+TEST_F(ParquetReaderTest, ReorderedColumns)
+{
+  {
+    auto a = cudf::test::strings_column_wrapper{{"a", "", "c"}, {true, false, true}};
+    auto b = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3};
+
+    cudf::table_view tbl{{a, b}};
+    auto filepath = temp_env->get_temp_filepath("ReorderedColumns.parquet");
+    cudf::io::table_input_metadata md(tbl);
+    md.column_metadata[0].set_name("a");
+    md.column_metadata[1].set_name("b");
+    cudf::io::parquet_writer_options opts =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl).metadata(md);
+    cudf::io::write_parquet(opts);
+
+    // read them out of order
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+        .columns({"b", "a"});
+    auto result = cudf::io::read_parquet(read_opts);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), b);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), a);
+  }
+
+  {
+    auto a = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3};
+    auto b = cudf::test::strings_column_wrapper{{"a", "", "c"}, {true, false, true}};
+
+    cudf::table_view tbl{{a, b}};
+    auto filepath = temp_env->get_temp_filepath("ReorderedColumns2.parquet");
+    cudf::io::table_input_metadata md(tbl);
+    md.column_metadata[0].set_name("a");
+    md.column_metadata[1].set_name("b");
+    cudf::io::parquet_writer_options opts =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl).metadata(md);
+    cudf::io::write_parquet(opts);
+
+    // read them out of order
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+        .columns({"b", "a"});
+    auto result = cudf::io::read_parquet(read_opts);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), b);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), a);
+  }
+
+  auto a = cudf::test::fixed_width_column_wrapper<int>{1, 2, 3, 10, 20, 30};
+  auto b = cudf::test::strings_column_wrapper{{"a", "", "c", "cats", "dogs", "owls"},
+                                              {true, false, true, true, false, true}};
+  auto c = cudf::test::fixed_width_column_wrapper<int>{{15, 16, 17, 25, 26, 32},
+                                                       {false, true, true, true, true, false}};
+  auto d = cudf::test::strings_column_wrapper{"ducks", "sheep", "cows", "fish", "birds", "ants"};
+
+  cudf::table_view tbl{{a, b, c, d}};
+  auto filepath = temp_env->get_temp_filepath("ReorderedColumns3.parquet");
+  cudf::io::table_input_metadata md(tbl);
+  md.column_metadata[0].set_name("a");
+  md.column_metadata[1].set_name("b");
+  md.column_metadata[2].set_name("c");
+  md.column_metadata[3].set_name("d");
+  cudf::io::parquet_writer_options opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, tbl)
+      .metadata(std::move(md));
+  cudf::io::write_parquet(opts);
+
+  {
+    // read them out of order
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+        .columns({"d", "a", "b", "c"});
+    auto result = cudf::io::read_parquet(read_opts);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), d);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), a);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(2), b);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(3), c);
+  }
+
+  {
+    // read them out of order
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+        .columns({"c", "d", "a", "b"});
+    auto result = cudf::io::read_parquet(read_opts);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), c);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), d);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(2), a);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(3), b);
+  }
+
+  {
+    // read them out of order
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+        .columns({"d", "c", "b", "a"});
+    auto result = cudf::io::read_parquet(read_opts);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), d);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), c);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(2), b);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(3), a);
+  }
+}
+
+TEST_F(ParquetReaderTest, SelectNestedColumn)
+{
+  // Struct<is_human:bool,
+  //        Struct<weight:float,
+  //               ages:int,
+  //               land_unit:List<int>>,
+  //               flats:List<List<int>>
+  //              >
+  //       >
+
+  auto weights_col = cudf::test::fixed_width_column_wrapper<float>{1.1, 2.4, 5.3, 8.0, 9.6, 6.9};
+
+  auto ages_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto struct_1 = cudf::test::structs_column_wrapper{{weights_col, ages_col}, {1, 1, 1, 1, 0, 1}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_2 =
+    cudf::test::structs_column_wrapper{{is_human_col, struct_1}, {0, 1, 1, 1, 1, 1}}.release();
+
+  auto input = table_view({*struct_2});
+
+  cudf::io::table_input_metadata input_metadata(input);
+  input_metadata.column_metadata[0].set_name("being");
+  input_metadata.column_metadata[0].child(0).set_name("human?");
+  input_metadata.column_metadata[0].child(1).set_name("particulars");
+  input_metadata.column_metadata[0].child(1).child(0).set_name("weight");
+  input_metadata.column_metadata[0].child(1).child(1).set_name("age");
+
+  auto filepath = temp_env->get_temp_filepath("SelectNestedColumn.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, input)
+      .metadata(std::move(input_metadata));
+  cudf::io::write_parquet(args);
+
+  {  // Test selecting a single leaf from the table
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath))
+        .columns({"being.particulars.age"});
+    auto const result = cudf::io::read_parquet(read_args);
+
+    auto expect_ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+      {48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+    auto expect_s_1 = cudf::test::structs_column_wrapper{{expect_ages_col}, {1, 1, 1, 1, 0, 1}};
+    auto expect_s_2 =
+      cudf::test::structs_column_wrapper{{expect_s_1}, {0, 1, 1, 1, 1, 1}}.release();
+    auto expected = table_view({*expect_s_2});
+
+    cudf::io::table_input_metadata expected_metadata(expected);
+    expected_metadata.column_metadata[0].set_name("being");
+    expected_metadata.column_metadata[0].child(0).set_name("particulars");
+    expected_metadata.column_metadata[0].child(0).child(0).set_name("age");
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+    cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+  }
+
+  {  // Test selecting a non-leaf and expecting all hierarchy from that node onwards
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath))
+        .columns({"being.particulars"});
+    auto const result = cudf::io::read_parquet(read_args);
+
+    auto expected_weights_col =
+      cudf::test::fixed_width_column_wrapper<float>{1.1, 2.4, 5.3, 8.0, 9.6, 6.9};
+
+    auto expected_ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+      {48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+    auto expected_s_1 = cudf::test::structs_column_wrapper{
+      {expected_weights_col, expected_ages_col}, {1, 1, 1, 1, 0, 1}};
+
+    auto expect_s_2 =
+      cudf::test::structs_column_wrapper{{expected_s_1}, {0, 1, 1, 1, 1, 1}}.release();
+    auto expected = table_view({*expect_s_2});
+
+    cudf::io::table_input_metadata expected_metadata(expected);
+    expected_metadata.column_metadata[0].set_name("being");
+    expected_metadata.column_metadata[0].child(0).set_name("particulars");
+    expected_metadata.column_metadata[0].child(0).child(0).set_name("weight");
+    expected_metadata.column_metadata[0].child(0).child(1).set_name("age");
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+    cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+  }
+
+  {  // Test selecting struct children out of order
+    cudf::io::parquet_reader_options read_args =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info(filepath))
+        .columns({"being.particulars.age", "being.particulars.weight", "being.human?"});
+    auto const result = cudf::io::read_parquet(read_args);
+
+    auto expected_weights_col =
+      cudf::test::fixed_width_column_wrapper<float>{1.1, 2.4, 5.3, 8.0, 9.6, 6.9};
+
+    auto expected_ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+      {48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+    auto expected_is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+      {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+    auto expect_s_1 = cudf::test::structs_column_wrapper{{expected_ages_col, expected_weights_col},
+                                                         {1, 1, 1, 1, 0, 1}};
+
+    auto expect_s_2 =
+      cudf::test::structs_column_wrapper{{expect_s_1, expected_is_human_col}, {0, 1, 1, 1, 1, 1}}
+        .release();
+
+    auto expected = table_view({*expect_s_2});
+
+    cudf::io::table_input_metadata expected_metadata(expected);
+    expected_metadata.column_metadata[0].set_name("being");
+    expected_metadata.column_metadata[0].child(0).set_name("particulars");
+    expected_metadata.column_metadata[0].child(0).child(0).set_name("age");
+    expected_metadata.column_metadata[0].child(0).child(1).set_name("weight");
+    expected_metadata.column_metadata[0].child(1).set_name("human?");
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+    cudf::test::expect_metadata_equal(expected_metadata, result.metadata);
+  }
+}
+
+TEST_F(ParquetReaderTest, DecimalRead)
+{
+  {
+    /* We could add a dataset to include this file, but we don't want tests in cudf to have data.
+       This test is a temporary test until python gains the ability to write decimal, so we're
+       embedding
+       a parquet file directly into the code here to prevent issues with finding the file */
+    unsigned char const decimals_parquet[] = {
+      0x50, 0x41, 0x52, 0x31, 0x15, 0x00, 0x15, 0xb0, 0x03, 0x15, 0xb8, 0x03, 0x2c, 0x15, 0x6a,
+      0x15, 0x00, 0x15, 0x06, 0x15, 0x08, 0x1c, 0x36, 0x02, 0x28, 0x04, 0x7f, 0x96, 0x98, 0x00,
+      0x18, 0x04, 0x81, 0x69, 0x67, 0xff, 0x00, 0x00, 0x00, 0xd8, 0x01, 0xf0, 0xd7, 0x04, 0x00,
+      0x00, 0x00, 0x64, 0x01, 0x03, 0x06, 0x68, 0x12, 0xdc, 0xff, 0xbd, 0x18, 0xfd, 0xff, 0x64,
+      0x13, 0x80, 0x00, 0xb3, 0x5d, 0x62, 0x00, 0x90, 0x35, 0xa9, 0xff, 0xa2, 0xde, 0xe3, 0xff,
+      0xe9, 0xbf, 0x96, 0xff, 0x1f, 0x8a, 0x98, 0xff, 0xb1, 0x50, 0x34, 0x00, 0x88, 0x24, 0x59,
+      0x00, 0x2a, 0x33, 0xbe, 0xff, 0xd5, 0x16, 0xbc, 0xff, 0x13, 0x50, 0x8d, 0xff, 0xcb, 0x63,
+      0x2d, 0x00, 0x80, 0x8f, 0xbe, 0xff, 0x82, 0x40, 0x10, 0x00, 0x84, 0x68, 0x70, 0xff, 0x9b,
+      0x69, 0x78, 0x00, 0x14, 0x6c, 0x10, 0x00, 0x50, 0xd9, 0xe1, 0xff, 0xaa, 0xcd, 0x6a, 0x00,
+      0xcf, 0xb1, 0x28, 0x00, 0x77, 0x57, 0x8d, 0x00, 0xee, 0x05, 0x79, 0x00, 0xf0, 0x15, 0xeb,
+      0xff, 0x02, 0xe2, 0x06, 0x00, 0x87, 0x43, 0x86, 0x00, 0xf8, 0x2d, 0x2e, 0x00, 0xee, 0x2e,
+      0x98, 0xff, 0x39, 0xcb, 0x4d, 0x00, 0x1e, 0x6b, 0xea, 0xff, 0x80, 0x8e, 0x6c, 0xff, 0x97,
+      0x25, 0x26, 0x00, 0x4d, 0x0d, 0x0a, 0x00, 0xca, 0x64, 0x7f, 0x00, 0xf4, 0xbe, 0xa1, 0xff,
+      0xe2, 0x12, 0x6c, 0xff, 0xbd, 0x77, 0xae, 0xff, 0xf9, 0x4b, 0x36, 0x00, 0xb0, 0xe3, 0x79,
+      0xff, 0xa2, 0x2a, 0x29, 0x00, 0xcd, 0x06, 0xbc, 0xff, 0x2d, 0xa3, 0x7e, 0x00, 0xa9, 0x08,
+      0xa1, 0xff, 0xbf, 0x81, 0xd0, 0xff, 0x4f, 0x03, 0x73, 0x00, 0xb0, 0x99, 0x0c, 0x00, 0xbd,
+      0x6f, 0xf8, 0xff, 0x6b, 0x02, 0x05, 0x00, 0xc1, 0xe1, 0xba, 0xff, 0x81, 0x69, 0x67, 0xff,
+      0x7f, 0x96, 0x98, 0x00, 0x15, 0x00, 0x15, 0xd0, 0x06, 0x15, 0xda, 0x06, 0x2c, 0x15, 0x6a,
+      0x15, 0x00, 0x15, 0x06, 0x15, 0x08, 0x1c, 0x36, 0x02, 0x28, 0x08, 0xff, 0x3f, 0x7a, 0x10,
+      0xf3, 0x5a, 0x00, 0x00, 0x18, 0x08, 0x01, 0xc0, 0x85, 0xef, 0x0c, 0xa5, 0xff, 0xff, 0x00,
+      0x00, 0x00, 0xa8, 0x03, 0xf4, 0xa7, 0x01, 0x04, 0x00, 0x00, 0x00, 0x64, 0x01, 0x03, 0x06,
+      0x55, 0x6f, 0xc5, 0xe4, 0x9f, 0x1a, 0x00, 0x00, 0x47, 0x89, 0x0a, 0xe8, 0x58, 0xf0, 0xff,
+      0xff, 0x63, 0xee, 0x21, 0xdd, 0xdd, 0xca, 0xff, 0xff, 0xbe, 0x6f, 0x3b, 0xaa, 0xe9, 0x3d,
+      0x00, 0x00, 0xd6, 0x91, 0x2a, 0xb7, 0x08, 0x02, 0x00, 0x00, 0x75, 0x45, 0x2c, 0xd7, 0x76,
+      0x0c, 0x00, 0x00, 0x54, 0x49, 0x92, 0x44, 0x9c, 0xbf, 0xff, 0xff, 0x41, 0xa9, 0x6d, 0xec,
+      0x7a, 0xd0, 0xff, 0xff, 0x27, 0xa0, 0x23, 0x41, 0x44, 0xc1, 0xff, 0xff, 0x18, 0xd4, 0xe1,
+      0x30, 0xd3, 0xe0, 0xff, 0xff, 0x59, 0xac, 0x14, 0xf4, 0xec, 0x58, 0x00, 0x00, 0x2c, 0x17,
+      0x29, 0x57, 0x44, 0x13, 0x00, 0x00, 0xa2, 0x0d, 0x4a, 0xcc, 0x63, 0xff, 0xff, 0xff, 0x81,
+      0x33, 0xbc, 0xda, 0xd5, 0xda, 0xff, 0xff, 0x4c, 0x05, 0xf4, 0x78, 0x19, 0xea, 0xff, 0xff,
+      0x06, 0x71, 0x25, 0xde, 0x5a, 0xaf, 0xff, 0xff, 0x95, 0x32, 0x5f, 0x76, 0x98, 0xb3, 0xff,
+      0xff, 0xf1, 0x34, 0x3c, 0xbf, 0xa8, 0xbe, 0xff, 0xff, 0x27, 0x73, 0x40, 0x0c, 0x7d, 0xcd,
+      0xff, 0xff, 0x68, 0xa9, 0xc2, 0xe9, 0x2c, 0x03, 0x00, 0x00, 0x3f, 0x79, 0xd9, 0x04, 0x8c,
+      0xe5, 0xff, 0xff, 0x91, 0xb4, 0x9b, 0xe3, 0x8f, 0x21, 0x00, 0x00, 0xb8, 0x20, 0xc8, 0xc2,
+      0x4d, 0xa6, 0xff, 0xff, 0x47, 0xfa, 0xde, 0x36, 0x4a, 0xf3, 0xff, 0xff, 0x72, 0x80, 0x94,
+      0x59, 0xdd, 0x4e, 0x00, 0x00, 0x29, 0xe4, 0xd6, 0x43, 0xb0, 0xf0, 0xff, 0xff, 0x68, 0x36,
+      0xbc, 0x2d, 0xd1, 0xa9, 0xff, 0xff, 0xbc, 0xe4, 0xbe, 0xd7, 0xed, 0x1b, 0x00, 0x00, 0x02,
+      0x8b, 0xcb, 0xd7, 0xed, 0x47, 0x00, 0x00, 0x3c, 0x06, 0xe4, 0xda, 0xc7, 0x47, 0x00, 0x00,
+      0xf3, 0x39, 0x55, 0x28, 0x97, 0xba, 0xff, 0xff, 0x07, 0x79, 0x38, 0x4e, 0xe0, 0x21, 0x00,
+      0x00, 0xde, 0xed, 0x1c, 0x23, 0x09, 0x49, 0x00, 0x00, 0x49, 0x46, 0x49, 0x5d, 0x8f, 0x34,
+      0x00, 0x00, 0x38, 0x18, 0x50, 0xf6, 0xa1, 0x11, 0x00, 0x00, 0xdf, 0xb8, 0x19, 0x14, 0xd1,
+      0xe1, 0xff, 0xff, 0x2c, 0x56, 0x72, 0x93, 0x64, 0x3f, 0x00, 0x00, 0x1c, 0xe0, 0xbe, 0x87,
+      0x7d, 0xf9, 0xff, 0xff, 0x73, 0x0e, 0x3c, 0x01, 0x91, 0xf9, 0xff, 0xff, 0xb2, 0x37, 0x85,
+      0x81, 0x5f, 0x54, 0x00, 0x00, 0x58, 0x44, 0xb0, 0x1a, 0xac, 0xbb, 0xff, 0xff, 0x36, 0xbf,
+      0xbe, 0x5e, 0x22, 0xff, 0xff, 0xff, 0x06, 0x20, 0xa0, 0x23, 0x0d, 0x3b, 0x00, 0x00, 0x19,
+      0xc6, 0x49, 0x0a, 0x00, 0xcf, 0xff, 0xff, 0x4f, 0xcd, 0xc6, 0x95, 0x4b, 0xf1, 0xff, 0xff,
+      0xa3, 0x59, 0xaf, 0x65, 0xec, 0xe9, 0xff, 0xff, 0x58, 0xef, 0x05, 0x50, 0x63, 0xe4, 0xff,
+      0xff, 0xc7, 0x6a, 0x9e, 0xf1, 0x69, 0x20, 0x00, 0x00, 0xd1, 0xb3, 0xc9, 0x14, 0xb2, 0x29,
+      0x00, 0x00, 0x1d, 0x48, 0x16, 0x70, 0xf0, 0x40, 0x00, 0x00, 0x01, 0xc0, 0x85, 0xef, 0x0c,
+      0xa5, 0xff, 0xff, 0xff, 0x3f, 0x7a, 0x10, 0xf3, 0x5a, 0x00, 0x00, 0x15, 0x00, 0x15, 0x90,
+      0x0d, 0x15, 0x9a, 0x0d, 0x2c, 0x15, 0x6a, 0x15, 0x00, 0x15, 0x06, 0x15, 0x08, 0x1c, 0x36,
+      0x02, 0x28, 0x10, 0x4b, 0x3b, 0x4c, 0xa8, 0x5a, 0x86, 0xc4, 0x7a, 0x09, 0x8a, 0x22, 0x3f,
+      0xff, 0xff, 0xff, 0xff, 0x18, 0x10, 0xb4, 0xc4, 0xb3, 0x57, 0xa5, 0x79, 0x3b, 0x85, 0xf6,
+      0x75, 0xdd, 0xc0, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xc8, 0x06, 0xf4, 0x47, 0x03,
+      0x04, 0x00, 0x00, 0x00, 0x64, 0x01, 0x03, 0x06, 0x05, 0x49, 0xf7, 0xfc, 0x89, 0x3d, 0x3e,
+      0x20, 0x07, 0x72, 0x3e, 0xa1, 0x66, 0x81, 0x67, 0x80, 0x23, 0x78, 0x06, 0x68, 0x0e, 0x78,
+      0xf5, 0x08, 0xed, 0x20, 0xcd, 0x0e, 0x7f, 0x9c, 0x70, 0xa0, 0xb9, 0x16, 0x44, 0xb2, 0x41,
+      0x62, 0xba, 0x82, 0xad, 0xe1, 0x12, 0x9b, 0xa6, 0x53, 0x8d, 0x20, 0x27, 0xd5, 0x84, 0x63,
+      0xb8, 0x07, 0x4b, 0x5b, 0xa4, 0x1c, 0xa4, 0x1c, 0x17, 0xbf, 0x4b, 0x00, 0x24, 0x04, 0x56,
+      0xa8, 0x52, 0xaf, 0x33, 0xf7, 0xad, 0x7c, 0xc8, 0x83, 0x25, 0x13, 0xaf, 0x80, 0x25, 0x6f,
+      0xbd, 0xd1, 0x15, 0x69, 0x64, 0x20, 0x7b, 0xd7, 0x33, 0xba, 0x66, 0x29, 0x8a, 0x00, 0xda,
+      0x42, 0x07, 0x2c, 0x6c, 0x39, 0x76, 0x9f, 0xdc, 0x17, 0xad, 0xb6, 0x58, 0xdf, 0x5f, 0x00,
+      0x18, 0x3a, 0xae, 0x1c, 0xd6, 0x5f, 0x9d, 0x78, 0x8d, 0x73, 0xdd, 0x3e, 0xd6, 0x18, 0x33,
+      0x40, 0xe4, 0x36, 0xde, 0xb0, 0xb7, 0x33, 0x2a, 0x6b, 0x08, 0x03, 0x6c, 0x6d, 0x8f, 0x13,
+      0x93, 0xd0, 0xd7, 0x87, 0x62, 0x63, 0x53, 0xfb, 0xd8, 0xbb, 0xc9, 0x54, 0x90, 0xd6, 0xa9,
+      0x8f, 0xc8, 0x60, 0xbd, 0xec, 0x75, 0x23, 0x9a, 0x21, 0xec, 0xe4, 0x86, 0x43, 0xd7, 0xc1,
+      0x88, 0xdc, 0x82, 0x00, 0x32, 0x79, 0xc9, 0x2b, 0x70, 0x85, 0xb7, 0x25, 0xa1, 0xcc, 0x7d,
+      0x0b, 0x29, 0x03, 0xea, 0x80, 0xff, 0x9b, 0xf3, 0x24, 0x7f, 0xd1, 0xff, 0xf0, 0x22, 0x65,
+      0x85, 0x99, 0x17, 0x63, 0xc2, 0xc0, 0xb7, 0x62, 0x05, 0xda, 0x7a, 0xa0, 0xc3, 0x2a, 0x6f,
+      0x1f, 0xee, 0x1f, 0x31, 0xa8, 0x42, 0x80, 0xe4, 0xb7, 0x6c, 0xf6, 0xac, 0x47, 0xb0, 0x17,
+      0x69, 0xcb, 0xff, 0x66, 0x8a, 0xd6, 0x25, 0x00, 0xf3, 0xcf, 0x0a, 0xaf, 0xf8, 0x92, 0x8a,
+      0xa0, 0xdf, 0x71, 0x13, 0x8d, 0x9d, 0xff, 0x7e, 0xe0, 0x0a, 0x52, 0xf1, 0x97, 0x01, 0xa9,
+      0x73, 0x27, 0xfd, 0x63, 0x58, 0x00, 0x32, 0xa6, 0xf6, 0x78, 0xb8, 0xe4, 0xfd, 0x20, 0x7c,
+      0x90, 0xee, 0xad, 0x8c, 0xc9, 0x71, 0x35, 0x66, 0x71, 0x3c, 0xe0, 0xe4, 0x0b, 0xbb, 0xa0,
+      0x50, 0xe9, 0xf2, 0x81, 0x1d, 0x3a, 0x95, 0x94, 0x00, 0xd5, 0x49, 0x00, 0x07, 0xdf, 0x21,
+      0x53, 0x36, 0x8d, 0x9e, 0xd9, 0xa5, 0x52, 0x4d, 0x0d, 0x29, 0x74, 0xf0, 0x40, 0xbd, 0xda,
+      0x63, 0x4e, 0xdd, 0x91, 0x8e, 0xa6, 0xa7, 0xf6, 0x78, 0x58, 0x3b, 0x0a, 0x5c, 0x60, 0x3c,
+      0x15, 0x34, 0xf8, 0x2c, 0x21, 0xe3, 0x56, 0x1b, 0x9e, 0xd9, 0x56, 0xd3, 0x13, 0x2e, 0x80,
+      0x2c, 0x36, 0xda, 0x1d, 0xc8, 0xfb, 0x52, 0xee, 0x17, 0xb3, 0x2b, 0xf3, 0xd2, 0xeb, 0x29,
+      0xa0, 0x37, 0xa0, 0x12, 0xce, 0x1c, 0x50, 0x6a, 0xf4, 0x11, 0xcd, 0x96, 0x88, 0x3f, 0x43,
+      0x78, 0xc0, 0x2c, 0x53, 0x6c, 0xa6, 0xdf, 0xb9, 0x9e, 0x93, 0xd4, 0x1e, 0xa9, 0x7f, 0x67,
+      0xa6, 0xc1, 0x80, 0x46, 0x0f, 0x63, 0x7d, 0x15, 0xf2, 0x4c, 0xc5, 0xda, 0x11, 0x9a, 0x20,
+      0x67, 0x27, 0xe8, 0x00, 0xec, 0x03, 0x1d, 0x15, 0xa7, 0x92, 0xb3, 0x1f, 0xda, 0x20, 0x92,
+      0xd8, 0x00, 0xfb, 0x06, 0x80, 0xeb, 0x4b, 0x0c, 0xc1, 0x1f, 0x49, 0x40, 0x06, 0x8d, 0x8a,
+      0xf8, 0x34, 0xb1, 0x0c, 0x1d, 0x20, 0xd0, 0x47, 0xe5, 0xb1, 0x7e, 0xf7, 0xe4, 0xb4, 0x7e,
+      0x9c, 0x84, 0x18, 0x61, 0x32, 0x4f, 0xc0, 0xc2, 0xb2, 0xcc, 0x63, 0xf6, 0xe1, 0x16, 0xd6,
+      0xd9, 0x4b, 0x74, 0x13, 0x01, 0xa1, 0xe2, 0x00, 0xb7, 0x9e, 0xc1, 0x3a, 0xc5, 0xaf, 0xe8,
+      0x54, 0x07, 0x2a, 0x20, 0xfd, 0x2c, 0x6f, 0xb9, 0x80, 0x18, 0x92, 0x87, 0xa0, 0x81, 0x24,
+      0x60, 0x47, 0x17, 0x4f, 0xbc, 0xbe, 0xf5, 0x03, 0x69, 0x80, 0xe3, 0x10, 0x54, 0xd6, 0x68,
+      0x7d, 0x75, 0xd3, 0x0a, 0x45, 0x38, 0x9e, 0xa9, 0xfd, 0x05, 0x40, 0xd2, 0x1e, 0x6f, 0x5c,
+      0x30, 0x10, 0xfe, 0x9b, 0x9f, 0x6d, 0xc0, 0x9d, 0x6c, 0x17, 0x7d, 0x00, 0x09, 0xb6, 0x8a,
+      0x31, 0x8e, 0x1b, 0x6b, 0x84, 0x1e, 0x79, 0xce, 0x10, 0x55, 0x59, 0x6a, 0x40, 0x16, 0xdc,
+      0x9a, 0xcf, 0x4d, 0xb0, 0x8f, 0xac, 0xe3, 0x8d, 0xee, 0xd2, 0xef, 0x01, 0x8c, 0xe0, 0x2b,
+      0x24, 0xe5, 0xb4, 0xe1, 0x86, 0x72, 0x00, 0x30, 0x07, 0xce, 0x02, 0x23, 0x41, 0x33, 0x40,
+      0xf0, 0x9b, 0xc2, 0x2d, 0x30, 0xec, 0x3b, 0x17, 0xb2, 0x8f, 0x64, 0x7d, 0xcd, 0x70, 0x9e,
+      0x80, 0x22, 0xb5, 0xdf, 0x6d, 0x2a, 0x43, 0xd4, 0x2b, 0x5a, 0xf6, 0x96, 0xa6, 0xea, 0x91,
+      0x62, 0x80, 0x39, 0xf2, 0x5a, 0x8e, 0xc0, 0xb9, 0x29, 0x99, 0x17, 0xe7, 0x35, 0x2c, 0xf6,
+      0x4d, 0x18, 0x00, 0x48, 0x10, 0x85, 0xb4, 0x3f, 0x89, 0x60, 0x49, 0x6e, 0xf0, 0xcd, 0x9d,
+      0x92, 0xeb, 0x96, 0x80, 0xcf, 0xf9, 0xf1, 0x46, 0x1d, 0xc0, 0x49, 0xb3, 0x36, 0x2e, 0x24,
+      0xc8, 0xdb, 0x41, 0x72, 0x20, 0xf5, 0xde, 0x5c, 0xf9, 0x4a, 0x6e, 0xa0, 0x0b, 0x13, 0xfc,
+      0x2d, 0x17, 0x07, 0x16, 0x5e, 0x00, 0x3c, 0x54, 0x41, 0x0e, 0xa2, 0x0d, 0xf3, 0x48, 0x12,
+      0x2e, 0x7c, 0xab, 0x3c, 0x59, 0x1c, 0x40, 0xca, 0xb0, 0x71, 0xc7, 0x29, 0xf0, 0xbb, 0x9f,
+      0xf4, 0x3f, 0x25, 0x49, 0xad, 0xc2, 0x8f, 0x80, 0x04, 0x38, 0x6d, 0x35, 0x02, 0xca, 0xe6,
+      0x02, 0x83, 0x89, 0x4e, 0x74, 0xdb, 0x08, 0x5a, 0x80, 0x13, 0x99, 0xd4, 0x26, 0xc1, 0x27,
+      0xce, 0xb0, 0x98, 0x99, 0xca, 0xf6, 0x3e, 0x50, 0x49, 0xd0, 0xbf, 0xcb, 0x6f, 0xbe, 0x5b,
+      0x92, 0x63, 0xde, 0x94, 0xd3, 0x8f, 0x07, 0x06, 0x0f, 0x2b, 0x80, 0x36, 0xf1, 0x77, 0xf6,
+      0x29, 0x33, 0x13, 0xa9, 0x4a, 0x55, 0x3d, 0x6c, 0xca, 0xdb, 0x4e, 0x40, 0xc4, 0x95, 0x54,
+      0xf4, 0xe2, 0x8c, 0x1b, 0xa0, 0xfe, 0x30, 0x50, 0x9d, 0x62, 0xbc, 0x5c, 0x00, 0xb4, 0xc4,
+      0xb3, 0x57, 0xa5, 0x79, 0x3b, 0x85, 0xf6, 0x75, 0xdd, 0xc0, 0x00, 0x00, 0x00, 0x01, 0x4b,
+      0x3b, 0x4c, 0xa8, 0x5a, 0x86, 0xc4, 0x7a, 0x09, 0x8a, 0x22, 0x3f, 0xff, 0xff, 0xff, 0xff,
+      0x15, 0x02, 0x19, 0x4c, 0x48, 0x0c, 0x73, 0x70, 0x61, 0x72, 0x6b, 0x5f, 0x73, 0x63, 0x68,
+      0x65, 0x6d, 0x61, 0x15, 0x06, 0x00, 0x15, 0x02, 0x25, 0x02, 0x18, 0x06, 0x64, 0x65, 0x63,
+      0x37, 0x70, 0x34, 0x25, 0x0a, 0x15, 0x08, 0x15, 0x0e, 0x00, 0x15, 0x04, 0x25, 0x02, 0x18,
+      0x07, 0x64, 0x65, 0x63, 0x31, 0x34, 0x70, 0x35, 0x25, 0x0a, 0x15, 0x0a, 0x15, 0x1c, 0x00,
+      0x15, 0x0e, 0x15, 0x20, 0x15, 0x02, 0x18, 0x08, 0x64, 0x65, 0x63, 0x33, 0x38, 0x70, 0x31,
+      0x38, 0x25, 0x0a, 0x15, 0x24, 0x15, 0x4c, 0x00, 0x16, 0x6a, 0x19, 0x1c, 0x19, 0x3c, 0x26,
+      0x08, 0x1c, 0x15, 0x02, 0x19, 0x35, 0x06, 0x08, 0x00, 0x19, 0x18, 0x06, 0x64, 0x65, 0x63,
+      0x37, 0x70, 0x34, 0x15, 0x02, 0x16, 0x6a, 0x16, 0xf6, 0x03, 0x16, 0xfe, 0x03, 0x26, 0x08,
+      0x3c, 0x36, 0x02, 0x28, 0x04, 0x7f, 0x96, 0x98, 0x00, 0x18, 0x04, 0x81, 0x69, 0x67, 0xff,
+      0x00, 0x19, 0x1c, 0x15, 0x00, 0x15, 0x00, 0x15, 0x02, 0x00, 0x00, 0x00, 0x26, 0x86, 0x04,
+      0x1c, 0x15, 0x04, 0x19, 0x35, 0x06, 0x08, 0x00, 0x19, 0x18, 0x07, 0x64, 0x65, 0x63, 0x31,
+      0x34, 0x70, 0x35, 0x15, 0x02, 0x16, 0x6a, 0x16, 0xa6, 0x07, 0x16, 0xb0, 0x07, 0x26, 0x86,
+      0x04, 0x3c, 0x36, 0x02, 0x28, 0x08, 0xff, 0x3f, 0x7a, 0x10, 0xf3, 0x5a, 0x00, 0x00, 0x18,
+      0x08, 0x01, 0xc0, 0x85, 0xef, 0x0c, 0xa5, 0xff, 0xff, 0x00, 0x19, 0x1c, 0x15, 0x00, 0x15,
+      0x00, 0x15, 0x02, 0x00, 0x00, 0x00, 0x26, 0xb6, 0x0b, 0x1c, 0x15, 0x0e, 0x19, 0x35, 0x06,
+      0x08, 0x00, 0x19, 0x18, 0x08, 0x64, 0x65, 0x63, 0x33, 0x38, 0x70, 0x31, 0x38, 0x15, 0x02,
+      0x16, 0x6a, 0x16, 0x86, 0x0e, 0x16, 0x90, 0x0e, 0x26, 0xb6, 0x0b, 0x3c, 0x36, 0x02, 0x28,
+      0x10, 0x4b, 0x3b, 0x4c, 0xa8, 0x5a, 0x86, 0xc4, 0x7a, 0x09, 0x8a, 0x22, 0x3f, 0xff, 0xff,
+      0xff, 0xff, 0x18, 0x10, 0xb4, 0xc4, 0xb3, 0x57, 0xa5, 0x79, 0x3b, 0x85, 0xf6, 0x75, 0xdd,
+      0xc0, 0x00, 0x00, 0x00, 0x01, 0x00, 0x19, 0x1c, 0x15, 0x00, 0x15, 0x00, 0x15, 0x02, 0x00,
+      0x00, 0x00, 0x16, 0xa2, 0x19, 0x16, 0x6a, 0x00, 0x19, 0x2c, 0x18, 0x18, 0x6f, 0x72, 0x67,
+      0x2e, 0x61, 0x70, 0x61, 0x63, 0x68, 0x65, 0x2e, 0x73, 0x70, 0x61, 0x72, 0x6b, 0x2e, 0x76,
+      0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x18, 0x05, 0x33, 0x2e, 0x30, 0x2e, 0x31, 0x00, 0x18,
+      0x29, 0x6f, 0x72, 0x67, 0x2e, 0x61, 0x70, 0x61, 0x63, 0x68, 0x65, 0x2e, 0x73, 0x70, 0x61,
+      0x72, 0x6b, 0x2e, 0x73, 0x71, 0x6c, 0x2e, 0x70, 0x61, 0x72, 0x71, 0x75, 0x65, 0x74, 0x2e,
+      0x72, 0x6f, 0x77, 0x2e, 0x6d, 0x65, 0x74, 0x61, 0x64, 0x61, 0x74, 0x61, 0x18, 0xf4, 0x01,
+      0x7b, 0x22, 0x74, 0x79, 0x70, 0x65, 0x22, 0x3a, 0x22, 0x73, 0x74, 0x72, 0x75, 0x63, 0x74,
+      0x22, 0x2c, 0x22, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x22, 0x3a, 0x5b, 0x7b, 0x22, 0x6e,
+      0x61, 0x6d, 0x65, 0x22, 0x3a, 0x22, 0x64, 0x65, 0x63, 0x37, 0x70, 0x34, 0x22, 0x2c, 0x22,
+      0x74, 0x79, 0x70, 0x65, 0x22, 0x3a, 0x22, 0x64, 0x65, 0x63, 0x69, 0x6d, 0x61, 0x6c, 0x28,
+      0x37, 0x2c, 0x34, 0x29, 0x22, 0x2c, 0x22, 0x6e, 0x75, 0x6c, 0x6c, 0x61, 0x62, 0x6c, 0x65,
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+      0x22, 0x3a, 0x22, 0x64, 0x65, 0x63, 0x69, 0x6d, 0x61, 0x6c, 0x28, 0x31, 0x34, 0x2c, 0x35,
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+      0x7b, 0x7d, 0x7d, 0x2c, 0x7b, 0x22, 0x6e, 0x61, 0x6d, 0x65, 0x22, 0x3a, 0x22, 0x64, 0x65,
+      0x63, 0x33, 0x38, 0x70, 0x31, 0x38, 0x22, 0x2c, 0x22, 0x74, 0x79, 0x70, 0x65, 0x22, 0x3a,
+      0x22, 0x64, 0x65, 0x63, 0x69, 0x6d, 0x61, 0x6c, 0x28, 0x33, 0x38, 0x2c, 0x31, 0x38, 0x29,
+      0x22, 0x2c, 0x22, 0x6e, 0x75, 0x6c, 0x6c, 0x61, 0x62, 0x6c, 0x65, 0x22, 0x3a, 0x74, 0x72,
+      0x75, 0x65, 0x2c, 0x22, 0x6d, 0x65, 0x74, 0x61, 0x64, 0x61, 0x74, 0x61, 0x22, 0x3a, 0x7b,
+      0x7d, 0x7d, 0x5d, 0x7d, 0x00, 0x18, 0x4a, 0x70, 0x61, 0x72, 0x71, 0x75, 0x65, 0x74, 0x2d,
+      0x6d, 0x72, 0x20, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x31, 0x2e, 0x31, 0x30,
+      0x2e, 0x31, 0x20, 0x28, 0x62, 0x75, 0x69, 0x6c, 0x64, 0x20, 0x61, 0x38, 0x39, 0x64, 0x66,
+      0x38, 0x66, 0x39, 0x39, 0x33, 0x32, 0x62, 0x36, 0x65, 0x66, 0x36, 0x36, 0x33, 0x33, 0x64,
+      0x30, 0x36, 0x30, 0x36, 0x39, 0x65, 0x35, 0x30, 0x63, 0x39, 0x62, 0x37, 0x39, 0x37, 0x30,
+      0x62, 0x65, 0x62, 0x64, 0x31, 0x29, 0x19, 0x3c, 0x1c, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x1c,
+      0x00, 0x00, 0x00, 0xd3, 0x02, 0x00, 0x00, 0x50, 0x41, 0x52, 0x31};
+    unsigned int decimals_parquet_len = 2366;
+
+    cudf::io::parquet_reader_options read_opts = cudf::io::parquet_reader_options::builder(
+      cudf::io::source_info{reinterpret_cast<char const*>(decimals_parquet), decimals_parquet_len});
+    auto result = cudf::io::read_parquet(read_opts);
+
+    auto validity =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 50; });
+
+    EXPECT_EQ(result.tbl->view().num_columns(), 3);
+
+    int32_t col0_data[] = {
+      -2354584, -190275,  8393572,  6446515,  -5687920, -1843550, -6897687, -6780385, 3428529,
+      5842056,  -4312278, -4450603, -7516141, 2974667,  -4288640, 1065090,  -9410428, 7891355,
+      1076244,  -1975984, 6999466,  2666959,  9262967,  7931374,  -1370640, 451074,   8799111,
+      3026424,  -6803730, 5098297,  -1414370, -9662848, 2499991,  658765,   8348874,  -6177036,
+      -9694494, -5343299, 3558393,  -8789072, 2697890,  -4454707, 8299309,  -6223703, -3112513,
+      7537487,  825776,   -495683,  328299,   -4529727, 0,        -9999999, 9999999};
+
+    EXPECT_EQ(static_cast<std::size_t>(result.tbl->view().column(0).size()),
+              sizeof(col0_data) / sizeof(col0_data[0]));
+    cudf::test::fixed_point_column_wrapper<int32_t> col0(
+      std::begin(col0_data), std::end(col0_data), validity, numeric::scale_type{-4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), col0);
+
+    int64_t col1_data[] = {29274040266581,  -17210335917753, -58420730139037,
+                           68073792696254,  2236456014294,   13704555677045,
+                           -70797090469548, -52248605513407, -68976081919961,
+                           -34277313883112, 97774730521689,  21184241014572,
+                           -670882460254,   -40862944054399, -24079852370612,
+                           -88670167797498, -84007574359403, -71843004533519,
+                           -55538016554201, 3491435293032,   -29085437167297,
+                           36901882672273,  -98622066122568, -13974902998457,
+                           86712597643378,  -16835133643735, -94759096142232,
+                           30708340810940,  79086853262082,  78923696440892,
+                           -76316597208589, 37247268714759,  80303592631774,
+                           57790350050889,  19387319851064,  -33186875066145,
+                           69701203023404,  -7157433049060,  -7073790423437,
+                           92769171617714,  -75127120182184, -951893180618,
+                           64927618310150,  -53875897154023, -16168039035569,
+                           -24273449166429, -30359781249192, 35639397345991,
+                           45844829680593,  71401416837149,  0,
+                           -99999999999999, 99999999999999};
+
+    EXPECT_EQ(static_cast<std::size_t>(result.tbl->view().column(1).size()),
+              sizeof(col1_data) / sizeof(col1_data[0]));
+    cudf::test::fixed_point_column_wrapper<int64_t> col1(
+      std::begin(col1_data), std::end(col1_data), validity, numeric::scale_type{-5});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), col1);
+
+    cudf::io::parquet_reader_options read_strict_opts = read_opts;
+    read_strict_opts.set_columns({"dec7p4", "dec14p5"});
+    EXPECT_NO_THROW(cudf::io::read_parquet(read_strict_opts));
+  }
+  {
+    // dec7p3: Decimal(precision=7, scale=3) backed by FIXED_LENGTH_BYTE_ARRAY(length = 4)
+    // dec12p11: Decimal(precision=12, scale=11) backed by FIXED_LENGTH_BYTE_ARRAY(length = 6)
+    // dec20p1: Decimal(precision=20, scale=1) backed by FIXED_LENGTH_BYTE_ARRAY(length = 9)
+    unsigned char const fixed_len_bytes_decimal_parquet[] = {
+      0x50, 0x41, 0x52, 0x31, 0x15, 0x00, 0x15, 0xA8, 0x01, 0x15, 0xAE, 0x01, 0x2C, 0x15, 0x28,
+      0x15, 0x00, 0x15, 0x06, 0x15, 0x08, 0x1C, 0x36, 0x02, 0x28, 0x04, 0x00, 0x97, 0x45, 0x72,
+      0x18, 0x04, 0x00, 0x01, 0x81, 0x3B, 0x00, 0x00, 0x00, 0x54, 0xF0, 0x53, 0x04, 0x00, 0x00,
+      0x00, 0x26, 0x01, 0x03, 0x00, 0x00, 0x61, 0x10, 0xCF, 0x00, 0x0A, 0xA9, 0x08, 0x00, 0x77,
+      0x58, 0x6F, 0x00, 0x6B, 0xEE, 0xA4, 0x00, 0x92, 0xF8, 0x94, 0x00, 0x2E, 0x18, 0xD4, 0x00,
+      0x4F, 0x45, 0x33, 0x00, 0x97, 0x45, 0x72, 0x00, 0x0D, 0xC2, 0x75, 0x00, 0x76, 0xAA, 0xAA,
+      0x00, 0x30, 0x9F, 0x86, 0x00, 0x4B, 0x9D, 0xB1, 0x00, 0x4E, 0x4B, 0x3B, 0x00, 0x01, 0x81,
+      0x3B, 0x00, 0x22, 0xD4, 0x53, 0x00, 0x72, 0xC4, 0xAF, 0x00, 0x43, 0x9B, 0x72, 0x00, 0x1D,
+      0x91, 0xC3, 0x00, 0x45, 0x27, 0x48, 0x15, 0x00, 0x15, 0xF4, 0x01, 0x15, 0xFA, 0x01, 0x2C,
+      0x15, 0x28, 0x15, 0x00, 0x15, 0x06, 0x15, 0x08, 0x1C, 0x36, 0x02, 0x28, 0x06, 0x00, 0xD5,
+      0xD7, 0x31, 0x99, 0xA6, 0x18, 0x06, 0xFF, 0x17, 0x2B, 0x5A, 0xF0, 0x01, 0x00, 0x00, 0x00,
+      0x7A, 0xF0, 0x79, 0x04, 0x00, 0x00, 0x00, 0x24, 0x01, 0x03, 0x02, 0x00, 0x54, 0x23, 0xCF,
+      0x13, 0x0A, 0x00, 0x07, 0x22, 0xB1, 0x21, 0x7E, 0x00, 0x64, 0x19, 0xD6, 0xD2, 0xA5, 0x00,
+      0x61, 0x7F, 0xF6, 0xB9, 0xB0, 0x00, 0xD0, 0x7F, 0x9C, 0xA9, 0xE9, 0x00, 0x65, 0x58, 0xF0,
+      0xAD, 0xFB, 0x00, 0xBC, 0x61, 0xE2, 0x03, 0xDA, 0xFF, 0x17, 0x2B, 0x5A, 0xF0, 0x01, 0x00,
+      0x63, 0x4B, 0x4C, 0xFE, 0x45, 0x00, 0x7A, 0xA0, 0xD8, 0xD1, 0xC0, 0x00, 0xC0, 0x63, 0xF7,
+      0x9D, 0x0A, 0x00, 0x88, 0x22, 0x0F, 0x1B, 0x25, 0x00, 0x1A, 0x80, 0x56, 0x34, 0xC7, 0x00,
+      0x5F, 0x48, 0x61, 0x09, 0x7C, 0x00, 0x61, 0xEF, 0x92, 0x42, 0x2F, 0x00, 0xD5, 0xD7, 0x31,
+      0x99, 0xA6, 0xFF, 0x17, 0x2B, 0x5A, 0xF0, 0x01, 0x00, 0x71, 0xDD, 0xE2, 0x22, 0x7B, 0x00,
+      0x54, 0xBF, 0xAE, 0xE9, 0x3C, 0x15, 0x00, 0x15, 0xD4, 0x02, 0x15, 0xDC, 0x02, 0x2C, 0x15,
+      0x28, 0x15, 0x00, 0x15, 0x06, 0x15, 0x08, 0x1C, 0x36, 0x04, 0x28, 0x09, 0x00, 0x7D, 0xFE,
+      0x02, 0xDA, 0xB2, 0x62, 0xA3, 0xFB, 0x18, 0x09, 0x00, 0x03, 0x9C, 0xCD, 0x5A, 0xAC, 0xBB,
+      0xF1, 0xE3, 0x00, 0x00, 0x00, 0xAA, 0x01, 0xF0, 0xA9, 0x04, 0x00, 0x00, 0x00, 0x07, 0xBF,
+      0xBF, 0x0F, 0x00, 0x7D, 0xFE, 0x02, 0xDA, 0xB2, 0x62, 0xA3, 0xFB, 0x00, 0x7D, 0x9A, 0xCB,
+      0xDA, 0x4B, 0x10, 0x8B, 0xAC, 0x00, 0x20, 0xBA, 0x97, 0x87, 0x2E, 0x3B, 0x4E, 0x04, 0x00,
+      0x15, 0xBB, 0xC2, 0xDF, 0x2D, 0x25, 0x08, 0xB6, 0x00, 0x5C, 0x67, 0x0E, 0x36, 0x30, 0xF1,
+      0xAC, 0xA4, 0x00, 0x44, 0xF1, 0x8E, 0xFB, 0x17, 0x5E, 0xE1, 0x96, 0x00, 0x64, 0x69, 0xF9,
+      0x66, 0x3F, 0x11, 0xED, 0xB9, 0x00, 0x45, 0xB5, 0xDA, 0x14, 0x9C, 0xA3, 0xFA, 0x64, 0x00,
+      0x26, 0x5F, 0xDE, 0xD7, 0x67, 0x95, 0xEF, 0xB1, 0x00, 0x35, 0xDB, 0x9B, 0x88, 0x46, 0xD0,
+      0xA1, 0x0E, 0x00, 0x45, 0xA9, 0x92, 0x8E, 0x89, 0xD1, 0xAC, 0x4C, 0x00, 0x4C, 0xF1, 0xCB,
+      0x27, 0x82, 0x3A, 0x7D, 0xB7, 0x00, 0x64, 0xD3, 0xD2, 0x2F, 0x9C, 0x83, 0x16, 0x75, 0x00,
+      0x15, 0xDF, 0xC2, 0xA9, 0x63, 0xB8, 0x33, 0x65, 0x00, 0x27, 0x40, 0x28, 0x97, 0x05, 0x8E,
+      0xE3, 0x46, 0x00, 0x03, 0x9C, 0xCD, 0x5A, 0xAC, 0xBB, 0xF1, 0xE3, 0x00, 0x22, 0x23, 0xF5,
+      0xE8, 0x9D, 0x55, 0xD4, 0x9C, 0x00, 0x25, 0xB9, 0xD8, 0x87, 0x2D, 0xF1, 0xF2, 0x17, 0x15,
+      0x02, 0x19, 0x4C, 0x48, 0x0C, 0x73, 0x70, 0x61, 0x72, 0x6B, 0x5F, 0x73, 0x63, 0x68, 0x65,
+      0x6D, 0x61, 0x15, 0x06, 0x00, 0x15, 0x0E, 0x15, 0x08, 0x15, 0x02, 0x18, 0x06, 0x64, 0x65,
+      0x63, 0x37, 0x70, 0x33, 0x25, 0x0A, 0x15, 0x06, 0x15, 0x0E, 0x00, 0x15, 0x0E, 0x15, 0x0C,
+      0x15, 0x02, 0x18, 0x08, 0x64, 0x65, 0x63, 0x31, 0x32, 0x70, 0x31, 0x31, 0x25, 0x0A, 0x15,
+      0x16, 0x15, 0x18, 0x00, 0x15, 0x0E, 0x15, 0x12, 0x15, 0x02, 0x18, 0x07, 0x64, 0x65, 0x63,
+      0x32, 0x30, 0x70, 0x31, 0x25, 0x0A, 0x15, 0x02, 0x15, 0x28, 0x00, 0x16, 0x28, 0x19, 0x1C,
+      0x19, 0x3C, 0x26, 0x08, 0x1C, 0x15, 0x0E, 0x19, 0x35, 0x06, 0x08, 0x00, 0x19, 0x18, 0x06,
+      0x64, 0x65, 0x63, 0x37, 0x70, 0x33, 0x15, 0x02, 0x16, 0x28, 0x16, 0xEE, 0x01, 0x16, 0xF4,
+      0x01, 0x26, 0x08, 0x3C, 0x36, 0x02, 0x28, 0x04, 0x00, 0x97, 0x45, 0x72, 0x18, 0x04, 0x00,
+      0x01, 0x81, 0x3B, 0x00, 0x19, 0x1C, 0x15, 0x00, 0x15, 0x00, 0x15, 0x02, 0x00, 0x00, 0x00,
+      0x26, 0xFC, 0x01, 0x1C, 0x15, 0x0E, 0x19, 0x35, 0x06, 0x08, 0x00, 0x19, 0x18, 0x08, 0x64,
+      0x65, 0x63, 0x31, 0x32, 0x70, 0x31, 0x31, 0x15, 0x02, 0x16, 0x28, 0x16, 0xC2, 0x02, 0x16,
+      0xC8, 0x02, 0x26, 0xFC, 0x01, 0x3C, 0x36, 0x02, 0x28, 0x06, 0x00, 0xD5, 0xD7, 0x31, 0x99,
+      0xA6, 0x18, 0x06, 0xFF, 0x17, 0x2B, 0x5A, 0xF0, 0x01, 0x00, 0x19, 0x1C, 0x15, 0x00, 0x15,
+      0x00, 0x15, 0x02, 0x00, 0x00, 0x00, 0x26, 0xC4, 0x04, 0x1C, 0x15, 0x0E, 0x19, 0x35, 0x06,
+      0x08, 0x00, 0x19, 0x18, 0x07, 0x64, 0x65, 0x63, 0x32, 0x30, 0x70, 0x31, 0x15, 0x02, 0x16,
+      0x28, 0x16, 0xAE, 0x03, 0x16, 0xB6, 0x03, 0x26, 0xC4, 0x04, 0x3C, 0x36, 0x04, 0x28, 0x09,
+      0x00, 0x7D, 0xFE, 0x02, 0xDA, 0xB2, 0x62, 0xA3, 0xFB, 0x18, 0x09, 0x00, 0x03, 0x9C, 0xCD,
+      0x5A, 0xAC, 0xBB, 0xF1, 0xE3, 0x00, 0x19, 0x1C, 0x15, 0x00, 0x15, 0x00, 0x15, 0x02, 0x00,
+      0x00, 0x00, 0x16, 0xDE, 0x07, 0x16, 0x28, 0x00, 0x19, 0x2C, 0x18, 0x18, 0x6F, 0x72, 0x67,
+      0x2E, 0x61, 0x70, 0x61, 0x63, 0x68, 0x65, 0x2E, 0x73, 0x70, 0x61, 0x72, 0x6B, 0x2E, 0x76,
+      0x65, 0x72, 0x73, 0x69, 0x6F, 0x6E, 0x18, 0x05, 0x33, 0x2E, 0x30, 0x2E, 0x31, 0x00, 0x18,
+      0x29, 0x6F, 0x72, 0x67, 0x2E, 0x61, 0x70, 0x61, 0x63, 0x68, 0x65, 0x2E, 0x73, 0x70, 0x61,
+      0x72, 0x6B, 0x2E, 0x73, 0x71, 0x6C, 0x2E, 0x70, 0x61, 0x72, 0x71, 0x75, 0x65, 0x74, 0x2E,
+      0x72, 0x6F, 0x77, 0x2E, 0x6D, 0x65, 0x74, 0x61, 0x64, 0x61, 0x74, 0x61, 0x18, 0xF4, 0x01,
+      0x7B, 0x22, 0x74, 0x79, 0x70, 0x65, 0x22, 0x3A, 0x22, 0x73, 0x74, 0x72, 0x75, 0x63, 0x74,
+      0x22, 0x2C, 0x22, 0x66, 0x69, 0x65, 0x6C, 0x64, 0x73, 0x22, 0x3A, 0x5B, 0x7B, 0x22, 0x6E,
+      0x61, 0x6D, 0x65, 0x22, 0x3A, 0x22, 0x64, 0x65, 0x63, 0x37, 0x70, 0x33, 0x22, 0x2C, 0x22,
+      0x74, 0x79, 0x70, 0x65, 0x22, 0x3A, 0x22, 0x64, 0x65, 0x63, 0x69, 0x6D, 0x61, 0x6C, 0x28,
+      0x37, 0x2C, 0x33, 0x29, 0x22, 0x2C, 0x22, 0x6E, 0x75, 0x6C, 0x6C, 0x61, 0x62, 0x6C, 0x65,
+      0x22, 0x3A, 0x74, 0x72, 0x75, 0x65, 0x2C, 0x22, 0x6D, 0x65, 0x74, 0x61, 0x64, 0x61, 0x74,
+      0x61, 0x22, 0x3A, 0x7B, 0x7D, 0x7D, 0x2C, 0x7B, 0x22, 0x6E, 0x61, 0x6D, 0x65, 0x22, 0x3A,
+      0x22, 0x64, 0x65, 0x63, 0x31, 0x32, 0x70, 0x31, 0x31, 0x22, 0x2C, 0x22, 0x74, 0x79, 0x70,
+      0x65, 0x22, 0x3A, 0x22, 0x64, 0x65, 0x63, 0x69, 0x6D, 0x61, 0x6C, 0x28, 0x31, 0x32, 0x2C,
+      0x31, 0x31, 0x29, 0x22, 0x2C, 0x22, 0x6E, 0x75, 0x6C, 0x6C, 0x61, 0x62, 0x6C, 0x65, 0x22,
+      0x3A, 0x74, 0x72, 0x75, 0x65, 0x2C, 0x22, 0x6D, 0x65, 0x74, 0x61, 0x64, 0x61, 0x74, 0x61,
+      0x22, 0x3A, 0x7B, 0x7D, 0x7D, 0x2C, 0x7B, 0x22, 0x6E, 0x61, 0x6D, 0x65, 0x22, 0x3A, 0x22,
+      0x64, 0x65, 0x63, 0x32, 0x30, 0x70, 0x31, 0x22, 0x2C, 0x22, 0x74, 0x79, 0x70, 0x65, 0x22,
+      0x3A, 0x22, 0x64, 0x65, 0x63, 0x69, 0x6D, 0x61, 0x6C, 0x28, 0x32, 0x30, 0x2C, 0x31, 0x29,
+      0x22, 0x2C, 0x22, 0x6E, 0x75, 0x6C, 0x6C, 0x61, 0x62, 0x6C, 0x65, 0x22, 0x3A, 0x74, 0x72,
+      0x75, 0x65, 0x2C, 0x22, 0x6D, 0x65, 0x74, 0x61, 0x64, 0x61, 0x74, 0x61, 0x22, 0x3A, 0x7B,
+      0x7D, 0x7D, 0x5D, 0x7D, 0x00, 0x18, 0x4A, 0x70, 0x61, 0x72, 0x71, 0x75, 0x65, 0x74, 0x2D,
+      0x6D, 0x72, 0x20, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6F, 0x6E, 0x20, 0x31, 0x2E, 0x31, 0x30,
+      0x2E, 0x31, 0x20, 0x28, 0x62, 0x75, 0x69, 0x6C, 0x64, 0x20, 0x61, 0x38, 0x39, 0x64, 0x66,
+      0x38, 0x66, 0x39, 0x39, 0x33, 0x32, 0x62, 0x36, 0x65, 0x66, 0x36, 0x36, 0x33, 0x33, 0x64,
+      0x30, 0x36, 0x30, 0x36, 0x39, 0x65, 0x35, 0x30, 0x63, 0x39, 0x62, 0x37, 0x39, 0x37, 0x30,
+      0x62, 0x65, 0x62, 0x64, 0x31, 0x29, 0x19, 0x3C, 0x1C, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x1C,
+      0x00, 0x00, 0x00, 0xC5, 0x02, 0x00, 0x00, 0x50, 0x41, 0x52, 0x31,
+    };
+
+    unsigned int parquet_len = 1226;
+
+    cudf::io::parquet_reader_options read_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info{
+        reinterpret_cast<char const*>(fixed_len_bytes_decimal_parquet), parquet_len});
+    auto result = cudf::io::read_parquet(read_opts);
+    EXPECT_EQ(result.tbl->view().num_columns(), 3);
+
+    auto validity_c0    = cudf::test::iterators::nulls_at({19});
+    int32_t col0_data[] = {6361295, 698632,  7821423, 7073444, 9631892, 3021012, 5195059,
+                           9913714, 901749,  7776938, 3186566, 4955569, 5131067, 98619,
+                           2282579, 7521455, 4430706, 1937859, 4532040, 0};
+
+    EXPECT_EQ(static_cast<std::size_t>(result.tbl->view().column(0).size()),
+              sizeof(col0_data) / sizeof(col0_data[0]));
+    cudf::test::fixed_point_column_wrapper<int32_t> col0(
+      std::begin(col0_data), std::end(col0_data), validity_c0, numeric::scale_type{-3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), col0);
+
+    auto validity_c1    = cudf::test::iterators::nulls_at({18});
+    int64_t col1_data[] = {361378026250,
+                           30646804862,
+                           429930238629,
+                           418758703536,
+                           895494171113,
+                           435283865083,
+                           809096053722,
+                           -999999999999,
+                           426465099333,
+                           526684574144,
+                           826310892810,
+                           584686967589,
+                           113822282951,
+                           409236212092,
+                           420631167535,
+                           918438386086,
+                           -999999999999,
+                           489053889147,
+                           0,
+                           363993164092};
+
+    EXPECT_EQ(static_cast<std::size_t>(result.tbl->view().column(1).size()),
+              sizeof(col1_data) / sizeof(col1_data[0]));
+    cudf::test::fixed_point_column_wrapper<int64_t> col1(
+      std::begin(col1_data), std::end(col1_data), validity_c1, numeric::scale_type{-11});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(1), col1);
+
+    auto validity_c2       = cudf::test::iterators::nulls_at({6, 14});
+    __int128_t col2_data[] = {9078697037144433659,
+                              9050770539577117612,
+                              2358363961733893636,
+                              1566059559232276662,
+                              6658306200002735268,
+                              4967909073046397334,
+                              0,
+                              7235588493887532473,
+                              5023160741463849572,
+                              2765173712965988273,
+                              3880866513515749646,
+                              5019704400576359500,
+                              5544435986818825655,
+                              7265381725809874549,
+                              0,
+                              1576192427381240677,
+                              2828305195087094598,
+                              260308667809395171,
+                              2460080200895288476,
+                              2718441925197820439};
+
+    EXPECT_EQ(static_cast<std::size_t>(result.tbl->view().column(2).size()),
+              sizeof(col2_data) / sizeof(col2_data[0]));
+    cudf::test::fixed_point_column_wrapper<__int128_t> col2(
+      std::begin(col2_data), std::end(col2_data), validity_c2, numeric::scale_type{-1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(2), col2);
+  }
+}
+
+TEST_F(ParquetReaderTest, EmptyOutput)
+{
+  cudf::test::fixed_width_column_wrapper<int> c0;
+  cudf::test::strings_column_wrapper c1;
+  cudf::test::fixed_point_column_wrapper<int> c2({}, numeric::scale_type{2});
+  cudf::test::lists_column_wrapper<float> _c3{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}};
+  auto c3 = cudf::empty_like(_c3);
+
+  cudf::test::fixed_width_column_wrapper<int> sc0;
+  cudf::test::strings_column_wrapper sc1;
+  cudf::test::lists_column_wrapper<int> _sc2{{1, 2}};
+  std::vector<std::unique_ptr<cudf::column>> struct_children;
+  struct_children.push_back(sc0.release());
+  struct_children.push_back(sc1.release());
+  struct_children.push_back(cudf::empty_like(_sc2));
+  cudf::test::structs_column_wrapper c4(std::move(struct_children));
+
+  table_view expected({c0, c1, c2, *c3, c4});
+
+  // set precision on the decimal column
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[2].set_decimal_precision(1);
+
+  auto filepath = temp_env->get_temp_filepath("EmptyOutput.parquet");
+  cudf::io::parquet_writer_options out_args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  out_args.set_metadata(std::move(expected_metadata));
+  cudf::io::write_parquet(out_args);
+
+  cudf::io::parquet_reader_options read_args =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_args);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(ParquetWriterTest, RowGroupSizeInvalid)
+{
+  auto const unused_table = std::make_unique<table>();
+  std::vector<char> out_buffer;
+
+  EXPECT_THROW(cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer),
+                                                         unused_table->view())
+                 .row_group_size_rows(0),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer),
+                                                         unused_table->view())
+                 .max_page_size_rows(0),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer),
+                                                         unused_table->view())
+                 .row_group_size_bytes(3 << 8),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer),
+                                                         unused_table->view())
+                 .max_page_size_bytes(3 << 8),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer),
+                                                         unused_table->view())
+                 .max_page_size_bytes(0xFFFF'FFFFUL),
+               cudf::logic_error);
+
+  EXPECT_THROW(cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info(&out_buffer))
+                 .row_group_size_rows(0),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info(&out_buffer))
+                 .max_page_size_rows(0),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info(&out_buffer))
+                 .row_group_size_bytes(3 << 8),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info(&out_buffer))
+                 .max_page_size_bytes(3 << 8),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info(&out_buffer))
+                 .max_page_size_bytes(0xFFFF'FFFFUL),
+               cudf::logic_error);
+}
+
+TEST_F(ParquetWriterTest, RowGroupPageSizeMatch)
+{
+  auto const unused_table = std::make_unique<table>();
+  std::vector<char> out_buffer;
+
+  auto options = cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer),
+                                                           unused_table->view())
+                   .row_group_size_bytes(128 * 1024)
+                   .max_page_size_bytes(512 * 1024)
+                   .row_group_size_rows(10000)
+                   .max_page_size_rows(20000)
+                   .build();
+  EXPECT_EQ(options.get_row_group_size_bytes(), options.get_max_page_size_bytes());
+  EXPECT_EQ(options.get_row_group_size_rows(), options.get_max_page_size_rows());
+}
+
+TEST_F(ParquetChunkedWriterTest, RowGroupPageSizeMatch)
+{
+  std::vector<char> out_buffer;
+
+  auto options = cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info(&out_buffer))
+                   .row_group_size_bytes(128 * 1024)
+                   .max_page_size_bytes(512 * 1024)
+                   .row_group_size_rows(10000)
+                   .max_page_size_rows(20000)
+                   .build();
+  EXPECT_EQ(options.get_row_group_size_bytes(), options.get_max_page_size_bytes());
+  EXPECT_EQ(options.get_row_group_size_rows(), options.get_max_page_size_rows());
+}
+
+TEST_F(ParquetWriterTest, EmptyList)
+{
+  auto L1 = cudf::make_lists_column(0,
+                                    cudf::make_empty_column(cudf::data_type(cudf::type_id::INT32)),
+                                    cudf::make_empty_column(cudf::data_type{cudf::type_id::INT64}),
+                                    0,
+                                    {});
+  auto L0 = cudf::make_lists_column(
+    3, cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0}.release(), std::move(L1), 0, {});
+
+  auto filepath = temp_env->get_temp_filepath("EmptyList.parquet");
+  cudf::io::write_parquet(cudf::io::parquet_writer_options_builder(cudf::io::sink_info(filepath),
+                                                                   cudf::table_view({*L0})));
+
+  auto result = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options_builder(cudf::io::source_info(filepath)));
+
+  using lcw     = cudf::test::lists_column_wrapper<int64_t>;
+  auto expected = lcw{lcw{}, lcw{}, lcw{}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), expected);
+}
+
+TEST_F(ParquetWriterTest, DeepEmptyList)
+{
+  // Make a list column LLLi st only L is valid and LLi are all null. This tests whether we can
+  // handle multiple nullptr offsets
+
+  auto L2 = cudf::make_lists_column(0,
+                                    cudf::make_empty_column(cudf::data_type(cudf::type_id::INT32)),
+                                    cudf::make_empty_column(cudf::data_type{cudf::type_id::INT64}),
+                                    0,
+                                    {});
+  auto L1 = cudf::make_lists_column(
+    0, cudf::make_empty_column(cudf::data_type(cudf::type_id::INT32)), std::move(L2), 0, {});
+  auto L0 = cudf::make_lists_column(
+    3, cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0}.release(), std::move(L1), 0, {});
+
+  auto filepath = temp_env->get_temp_filepath("DeepEmptyList.parquet");
+  cudf::io::write_parquet(cudf::io::parquet_writer_options_builder(cudf::io::sink_info(filepath),
+                                                                   cudf::table_view({*L0})));
+
+  auto result = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options_builder(cudf::io::source_info(filepath)));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), *L0);
+}
+
+TEST_F(ParquetWriterTest, EmptyListWithStruct)
+{
+  auto L2 = cudf::make_lists_column(0,
+                                    cudf::make_empty_column(cudf::data_type(cudf::type_id::INT32)),
+                                    cudf::make_empty_column(cudf::data_type{cudf::type_id::INT64}),
+                                    0,
+                                    {});
+
+  auto children = std::vector<std::unique_ptr<cudf::column>>{};
+  children.push_back(std::move(L2));
+  auto S2 = cudf::make_structs_column(0, std::move(children), 0, {});
+  auto L1 = cudf::make_lists_column(
+    0, cudf::make_empty_column(cudf::data_type(cudf::type_id::INT32)), std::move(S2), 0, {});
+  auto L0 = cudf::make_lists_column(
+    3, cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0}.release(), std::move(L1), 0, {});
+
+  auto filepath = temp_env->get_temp_filepath("EmptyListWithStruct.parquet");
+  cudf::io::write_parquet(cudf::io::parquet_writer_options_builder(cudf::io::sink_info(filepath),
+                                                                   cudf::table_view({*L0})));
+  auto result = cudf::io::read_parquet(
+    cudf::io::parquet_reader_options_builder(cudf::io::source_info(filepath)));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tbl->view().column(0), *L0);
+}
+
+TEST_F(ParquetWriterTest, CheckPageRows)
+{
+  auto sequence = thrust::make_counting_iterator(0);
+  auto validity = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  constexpr auto page_rows = 5000;
+  constexpr auto num_rows  = 2 * page_rows;
+  column_wrapper<int> col(sequence, sequence + num_rows, validity);
+
+  auto expected = table_view{{col}};
+
+  auto const filepath = temp_env->get_temp_filepath("CheckPageRows.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .max_page_size_rows(page_rows);
+  cudf::io::write_parquet(out_opts);
+
+  // check first page header and make sure it has only page_rows values
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  ASSERT_GT(fmd.row_groups.size(), 0);
+  ASSERT_EQ(fmd.row_groups[0].columns.size(), 1);
+  auto const& first_chunk = fmd.row_groups[0].columns[0].meta_data;
+  ASSERT_GT(first_chunk.data_page_offset, 0);
+
+  // read first data page header.  sizeof(PageHeader) is not exact, but the thrift encoded
+  // version should be smaller than size of the struct.
+  auto const ph = read_page_header(
+    source, {first_chunk.data_page_offset, sizeof(cudf::io::parquet::PageHeader), 0});
+
+  EXPECT_EQ(ph.data_page_header.num_values, page_rows);
+}
+
+TEST_F(ParquetWriterTest, CheckPageRowsAdjusted)
+{
+  // enough for a few pages with the default 20'000 rows/page
+  constexpr auto rows_per_page = 20'000;
+  constexpr auto num_rows      = 3 * rows_per_page;
+  const std::string s1(32, 'a');
+  auto col0_elements =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return s1; });
+  auto col0 = cudf::test::strings_column_wrapper(col0_elements, col0_elements + num_rows);
+
+  auto const expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("CheckPageRowsAdjusted.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .max_page_size_rows(rows_per_page);
+  cudf::io::write_parquet(out_opts);
+
+  // check first page header and make sure it has only page_rows values
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  ASSERT_GT(fmd.row_groups.size(), 0);
+  ASSERT_EQ(fmd.row_groups[0].columns.size(), 1);
+  auto const& first_chunk = fmd.row_groups[0].columns[0].meta_data;
+  ASSERT_GT(first_chunk.data_page_offset, 0);
+
+  // read first data page header.  sizeof(PageHeader) is not exact, but the thrift encoded
+  // version should be smaller than size of the struct.
+  auto const ph = read_page_header(
+    source, {first_chunk.data_page_offset, sizeof(cudf::io::parquet::PageHeader), 0});
+
+  EXPECT_LE(ph.data_page_header.num_values, rows_per_page);
+}
+
+TEST_F(ParquetWriterTest, CheckPageRowsTooSmall)
+{
+  constexpr auto rows_per_page = 1'000;
+  constexpr auto fragment_size = 5'000;
+  constexpr auto num_rows      = 3 * rows_per_page;
+  const std::string s1(32, 'a');
+  auto col0_elements =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return s1; });
+  auto col0 = cudf::test::strings_column_wrapper(col0_elements, col0_elements + num_rows);
+
+  auto const expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("CheckPageRowsTooSmall.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .max_page_fragment_size(fragment_size)
+      .max_page_size_rows(rows_per_page);
+  cudf::io::write_parquet(out_opts);
+
+  // check that file is written correctly when rows/page < fragment size
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  ASSERT_TRUE(fmd.row_groups.size() > 0);
+  ASSERT_TRUE(fmd.row_groups[0].columns.size() == 1);
+  auto const& first_chunk = fmd.row_groups[0].columns[0].meta_data;
+  ASSERT_TRUE(first_chunk.data_page_offset > 0);
+
+  // read first data page header.  sizeof(PageHeader) is not exact, but the thrift encoded
+  // version should be smaller than size of the struct.
+  auto const ph = read_page_header(
+    source, {first_chunk.data_page_offset, sizeof(cudf::io::parquet::PageHeader), 0});
+
+  // there should be only one page since the fragment size is larger than rows_per_page
+  EXPECT_EQ(ph.data_page_header.num_values, num_rows);
+}
+
+TEST_F(ParquetWriterTest, Decimal128Stats)
+{
+  // check that decimal128 min and max statistics are written in network byte order
+  // this is negative, so should be the min
+  std::vector<uint8_t> expected_min{
+    0xa1, 0xb2, 0xc3, 0xd4, 0xe5, 0xf6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  std::vector<uint8_t> expected_max{
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0xa1, 0xb2, 0xc3, 0xd4, 0xe5, 0xf6};
+
+  __int128_t val0 = 0xa1b2'c3d4'e5f6ULL;
+  __int128_t val1 = val0 << 80;
+  column_wrapper<numeric::decimal128> col0{{numeric::decimal128(val0, numeric::scale_type{0}),
+                                            numeric::decimal128(val1, numeric::scale_type{0})}};
+
+  auto expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("Decimal128Stats.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  auto const stats = get_statistics(fmd.row_groups[0].columns[0]);
+
+  EXPECT_EQ(expected_min, stats.min_value);
+  EXPECT_EQ(expected_max, stats.max_value);
+}
+
+// =============================================================================
+// ---- test data for stats sort order tests
+// need at least 3 pages, and min page count is 5000, so need at least 15000 values.
+// use 20000 to be safe.
+static constexpr int num_ordered_rows            = 20000;
+static constexpr int page_size_for_ordered_tests = 5000;
+
+namespace {
+namespace testdata {
+// ----- most numerics. scale by 100 so all values fit in a single byte
+
+template <typename T>
+std::enable_if_t<std::is_arithmetic_v<T> && !std::is_same_v<T, bool>,
+                 cudf::test::fixed_width_column_wrapper<T>>
+ascending()
+{
+  int start = std::is_signed_v<T> ? -num_ordered_rows / 2 : 0;
+  auto elements =
+    cudf::detail::make_counting_transform_iterator(start, [](auto i) { return i / 100; });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<std::is_arithmetic_v<T> && !std::is_same_v<T, bool>,
+                 cudf::test::fixed_width_column_wrapper<T>>
+descending()
+{
+  if (std::is_signed_v<T>) {
+    auto elements = cudf::detail::make_counting_transform_iterator(-num_ordered_rows / 2,
+                                                                   [](auto i) { return -i / 100; });
+    return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+  } else {
+    auto elements = cudf::detail::make_counting_transform_iterator(
+      0, [](auto i) { return (num_ordered_rows - i) / 100; });
+    return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+  }
+}
+
+template <typename T>
+std::enable_if_t<std::is_arithmetic_v<T> && !std::is_same_v<T, bool>,
+                 cudf::test::fixed_width_column_wrapper<T>>
+unordered()
+{
+  if (std::is_signed_v<T>) {
+    auto elements = cudf::detail::make_counting_transform_iterator(
+      -num_ordered_rows / 2, [](auto i) { return (i % 2 ? i : -i) / 100; });
+    return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+  } else {
+    auto elements = cudf::detail::make_counting_transform_iterator(
+      0, [](auto i) { return (i % 2 ? i : num_ordered_rows - i) / 100; });
+    return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+  }
+}
+
+// ----- bool
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, cudf::test::fixed_width_column_wrapper<bool>> ascending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i >= num_ordered_rows / 2; });
+  return cudf::test::fixed_width_column_wrapper<bool>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, cudf::test::fixed_width_column_wrapper<bool>> descending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i < num_ordered_rows / 2; });
+  return cudf::test::fixed_width_column_wrapper<bool>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, cudf::test::fixed_width_column_wrapper<bool>> unordered()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    switch (i / page_size_for_ordered_tests) {
+      case 0: return true;
+      case 1: return false;
+      case 2: return true;
+      default: return false;
+    }
+  });
+  return cudf::test::fixed_width_column_wrapper<bool>(elements, elements + num_ordered_rows);
+}
+
+// ----- fixed point types
+
+template <typename T>
+std::enable_if_t<cudf::is_fixed_point<T>(), cudf::test::fixed_width_column_wrapper<T>> ascending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    -num_ordered_rows / 2, [](auto i) { return T(i, numeric::scale_type{0}); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_fixed_point<T>(), cudf::test::fixed_width_column_wrapper<T>> descending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    -num_ordered_rows / 2, [](auto i) { return T(-i, numeric::scale_type{0}); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_fixed_point<T>(), cudf::test::fixed_width_column_wrapper<T>> unordered()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    -num_ordered_rows / 2, [](auto i) { return T(i % 2 ? i : -i, numeric::scale_type{0}); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+// ----- chrono types
+// ----- timstamp
+
+template <typename T>
+std::enable_if_t<cudf::is_timestamp<T>(), cudf::test::fixed_width_column_wrapper<T>> ascending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return T(typename T::duration(i)); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_timestamp<T>(), cudf::test::fixed_width_column_wrapper<T>> descending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return T(typename T::duration(num_ordered_rows - i)); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_timestamp<T>(), cudf::test::fixed_width_column_wrapper<T>> unordered()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return T(typename T::duration(i % 2 ? i : num_ordered_rows - i)); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+// ----- duration
+
+template <typename T>
+std::enable_if_t<cudf::is_duration<T>(), cudf::test::fixed_width_column_wrapper<T>> ascending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return T(i); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_duration<T>(), cudf::test::fixed_width_column_wrapper<T>> descending()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return T(num_ordered_rows - i); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_duration<T>(), cudf::test::fixed_width_column_wrapper<T>> unordered()
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return T(i % 2 ? i : num_ordered_rows - i); });
+  return cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_ordered_rows);
+}
+
+// ----- string_view
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, cudf::test::strings_column_wrapper>
+ascending()
+{
+  char buf[10];
+  auto elements = cudf::detail::make_counting_transform_iterator(0, [&buf](auto i) {
+    sprintf(buf, "%09d", i);
+    return std::string(buf);
+  });
+  return cudf::test::strings_column_wrapper(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, cudf::test::strings_column_wrapper>
+descending()
+{
+  char buf[10];
+  auto elements = cudf::detail::make_counting_transform_iterator(0, [&buf](auto i) {
+    sprintf(buf, "%09d", num_ordered_rows - i);
+    return std::string(buf);
+  });
+  return cudf::test::strings_column_wrapper(elements, elements + num_ordered_rows);
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, cudf::test::strings_column_wrapper>
+unordered()
+{
+  char buf[10];
+  auto elements = cudf::detail::make_counting_transform_iterator(0, [&buf](auto i) {
+    sprintf(buf, "%09d", (i % 2 == 0) ? i : (num_ordered_rows - i));
+    return std::string(buf);
+  });
+  return cudf::test::strings_column_wrapper(elements, elements + num_ordered_rows);
+}
+
+}  // namespace testdata
+}  // anonymous namespace
+
+TYPED_TEST(ParquetWriterComparableTypeTest, ThreeColumnSorted)
+{
+  using T = TypeParam;
+
+  auto col0 = testdata::ascending<T>();
+  auto col1 = testdata::descending<T>();
+  auto col2 = testdata::unordered<T>();
+
+  auto const expected = table_view{{col0, col1, col2}};
+
+  auto const filepath = temp_env->get_temp_filepath("ThreeColumnSorted.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .max_page_size_rows(page_size_for_ordered_tests)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  ASSERT_GT(fmd.row_groups.size(), 0);
+
+  auto const& columns = fmd.row_groups[0].columns;
+  ASSERT_EQ(columns.size(), static_cast<size_t>(expected.num_columns()));
+
+  // now check that the boundary order for chunk 1 is ascending,
+  // chunk 2 is descending, and chunk 3 is unordered
+  cudf::io::parquet::BoundaryOrder expected_orders[] = {
+    cudf::io::parquet::BoundaryOrder::ASCENDING,
+    cudf::io::parquet::BoundaryOrder::DESCENDING,
+    cudf::io::parquet::BoundaryOrder::UNORDERED};
+
+  for (std::size_t i = 0; i < columns.size(); i++) {
+    auto const ci = read_column_index(source, columns[i]);
+    EXPECT_EQ(ci.boundary_order, expected_orders[i]);
+  }
+}
+
+// utility functions for column index tests
+
+// compare two values.  return -1 if v1 < v2,
+// 0 if v1 == v2, and 1 if v1 > v2.
+template <typename T>
+int32_t compare(T& v1, T& v2)
+{
+  return (v1 > v2) - (v1 < v2);
+}
+
+// compare two binary statistics blobs based on their physical
+// and converted types. returns -1 if v1 < v2, 0 if v1 == v2, and
+// 1 if v1 > v2.
+int32_t compare_binary(std::vector<uint8_t> const& v1,
+                       std::vector<uint8_t> const& v2,
+                       cudf::io::parquet::Type ptype,
+                       cudf::io::parquet::ConvertedType ctype)
+{
+  switch (ptype) {
+    case cudf::io::parquet::INT32:
+      switch (ctype) {
+        case cudf::io::parquet::UINT_8:
+        case cudf::io::parquet::UINT_16:
+        case cudf::io::parquet::UINT_32:
+          return compare(*(reinterpret_cast<uint32_t const*>(v1.data())),
+                         *(reinterpret_cast<uint32_t const*>(v2.data())));
+        default:
+          return compare(*(reinterpret_cast<int32_t const*>(v1.data())),
+                         *(reinterpret_cast<int32_t const*>(v2.data())));
+      }
+
+    case cudf::io::parquet::INT64:
+      if (ctype == cudf::io::parquet::UINT_64) {
+        return compare(*(reinterpret_cast<uint64_t const*>(v1.data())),
+                       *(reinterpret_cast<uint64_t const*>(v2.data())));
+      }
+      return compare(*(reinterpret_cast<int64_t const*>(v1.data())),
+                     *(reinterpret_cast<int64_t const*>(v2.data())));
+
+    case cudf::io::parquet::FLOAT:
+      return compare(*(reinterpret_cast<float const*>(v1.data())),
+                     *(reinterpret_cast<float const*>(v2.data())));
+
+    case cudf::io::parquet::DOUBLE:
+      return compare(*(reinterpret_cast<double const*>(v1.data())),
+                     *(reinterpret_cast<double const*>(v2.data())));
+
+    case cudf::io::parquet::BYTE_ARRAY: {
+      int32_t v1sz = v1.size();
+      int32_t v2sz = v2.size();
+      int32_t ret  = memcmp(v1.data(), v2.data(), std::min(v1sz, v2sz));
+      if (ret != 0 or v1sz == v2sz) { return ret; }
+      return v1sz - v2sz;
+    }
+
+    default: CUDF_FAIL("Invalid type in compare_binary");
+  }
+
+  return 0;
+}
+
+TEST_P(ParquetV2Test, LargeColumnIndex)
+{
+  // create a file large enough to be written in 2 batches (currently 1GB per batch)
+  // pick fragment size that num_rows is divisible by, so we'll get equal sized row groups
+  const std::string s1(1000, 'a');
+  const std::string s2(1000, 'b');
+  constexpr auto num_rows  = 512 * 1024;
+  constexpr auto frag_size = num_rows / 128;
+  auto const is_v2         = GetParam();
+
+  auto col0_elements = cudf::detail::make_counting_transform_iterator(
+    0, [&](auto i) { return (i < num_rows) ? s1 : s2; });
+  auto col0 = cudf::test::strings_column_wrapper(col0_elements, col0_elements + 2 * num_rows);
+
+  auto const expected = table_view{{col0, col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("LargeColumnIndex.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .compression(cudf::io::compression_type::NONE)
+      .dictionary_policy(cudf::io::dictionary_policy::NEVER)
+      .write_v2_headers(is_v2)
+      .max_page_fragment_size(frag_size)
+      .row_group_size_bytes(1024 * 1024 * 1024)
+      .row_group_size_rows(num_rows);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (auto const& rg : fmd.row_groups) {
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      // check trunc(page.min) <= stats.min && trun(page.max) >= stats.max
+      auto const ptype = fmd.schema[c + 1].type;
+      auto const ctype = fmd.schema[c + 1].converted_type;
+      EXPECT_TRUE(compare_binary(ci.min_values[0], stats.min_value, ptype, ctype) <= 0);
+      EXPECT_TRUE(compare_binary(ci.max_values[0], stats.max_value, ptype, ctype) >= 0);
+    }
+  }
+}
+
+TEST_P(ParquetV2Test, CheckColumnOffsetIndex)
+{
+  constexpr auto num_rows = 100000;
+  auto const is_v2        = GetParam();
+  auto const expected_hdr_type =
+    is_v2 ? cudf::io::parquet::PageType::DATA_PAGE_V2 : cudf::io::parquet::PageType::DATA_PAGE;
+
+  // fixed length strings
+  auto str1_elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    char buf[30];
+    sprintf(buf, "%012d", i);
+    return std::string(buf);
+  });
+  auto col0          = cudf::test::strings_column_wrapper(str1_elements, str1_elements + num_rows);
+
+  auto col1_data = random_values<int8_t>(num_rows);
+  auto col2_data = random_values<int16_t>(num_rows);
+  auto col3_data = random_values<int32_t>(num_rows);
+  auto col4_data = random_values<uint64_t>(num_rows);
+  auto col5_data = random_values<float>(num_rows);
+  auto col6_data = random_values<double>(num_rows);
+
+  auto col1 = cudf::test::fixed_width_column_wrapper<int8_t>(col1_data.begin(), col1_data.end());
+  auto col2 = cudf::test::fixed_width_column_wrapper<int16_t>(col2_data.begin(), col2_data.end());
+  auto col3 = cudf::test::fixed_width_column_wrapper<int32_t>(col3_data.begin(), col3_data.end());
+  auto col4 = cudf::test::fixed_width_column_wrapper<uint64_t>(col4_data.begin(), col4_data.end());
+  auto col5 = cudf::test::fixed_width_column_wrapper<float>(col5_data.begin(), col5_data.end());
+  auto col6 = cudf::test::fixed_width_column_wrapper<double>(col6_data.begin(), col6_data.end());
+
+  // mixed length strings
+  auto str2_elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    char buf[30];
+    sprintf(buf, "%d", i);
+    return std::string(buf);
+  });
+  auto col7          = cudf::test::strings_column_wrapper(str2_elements, str2_elements + num_rows);
+
+  auto const expected = table_view{{col0, col1, col2, col3, col4, col5, col6, col7}};
+
+  auto const filepath = temp_env->get_temp_filepath("CheckColumnOffsetIndex.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .write_v2_headers(is_v2)
+      .max_page_size_rows(20000);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      // loop over offsets, read each page header, make sure it's a data page and that
+      // the first row index is correct
+      auto const oi = read_offset_index(source, chunk);
+
+      int64_t num_vals = 0;
+      for (size_t o = 0; o < oi.page_locations.size(); o++) {
+        auto const& page_loc = oi.page_locations[o];
+        auto const ph        = read_page_header(source, page_loc);
+        EXPECT_EQ(ph.type, expected_hdr_type);
+        EXPECT_EQ(page_loc.first_row_index, num_vals);
+        num_vals += is_v2 ? ph.data_page_header_v2.num_rows : ph.data_page_header.num_values;
+      }
+
+      // loop over page stats from the column index. check that stats.min <= page.min
+      // and stats.max >= page.max for each page.
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      // schema indexing starts at 1
+      auto const ptype = fmd.schema[c + 1].type;
+      auto const ctype = fmd.schema[c + 1].converted_type;
+      for (size_t p = 0; p < ci.min_values.size(); p++) {
+        // null_pages should always be false
+        EXPECT_FALSE(ci.null_pages[p]);
+        // null_counts should always be 0
+        EXPECT_EQ(ci.null_counts[p], 0);
+        EXPECT_TRUE(compare_binary(stats.min_value, ci.min_values[p], ptype, ctype) <= 0);
+      }
+      for (size_t p = 0; p < ci.max_values.size(); p++)
+        EXPECT_TRUE(compare_binary(stats.max_value, ci.max_values[p], ptype, ctype) >= 0);
+    }
+  }
+}
+
+TEST_P(ParquetV2Test, CheckColumnOffsetIndexNulls)
+{
+  constexpr auto num_rows = 100000;
+  auto const is_v2        = GetParam();
+  auto const expected_hdr_type =
+    is_v2 ? cudf::io::parquet::PageType::DATA_PAGE_V2 : cudf::io::parquet::PageType::DATA_PAGE;
+
+  // fixed length strings
+  auto str1_elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    char buf[30];
+    sprintf(buf, "%012d", i);
+    return std::string(buf);
+  });
+  auto col0          = cudf::test::strings_column_wrapper(str1_elements, str1_elements + num_rows);
+
+  auto col1_data = random_values<int8_t>(num_rows);
+  auto col2_data = random_values<int16_t>(num_rows);
+  auto col3_data = random_values<int32_t>(num_rows);
+  auto col4_data = random_values<uint64_t>(num_rows);
+  auto col5_data = random_values<float>(num_rows);
+  auto col6_data = random_values<double>(num_rows);
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // add null values for all but first column
+  auto col1 =
+    cudf::test::fixed_width_column_wrapper<int8_t>(col1_data.begin(), col1_data.end(), valids);
+  auto col2 =
+    cudf::test::fixed_width_column_wrapper<int16_t>(col2_data.begin(), col2_data.end(), valids);
+  auto col3 =
+    cudf::test::fixed_width_column_wrapper<int32_t>(col3_data.begin(), col3_data.end(), valids);
+  auto col4 =
+    cudf::test::fixed_width_column_wrapper<uint64_t>(col4_data.begin(), col4_data.end(), valids);
+  auto col5 =
+    cudf::test::fixed_width_column_wrapper<float>(col5_data.begin(), col5_data.end(), valids);
+  auto col6 =
+    cudf::test::fixed_width_column_wrapper<double>(col6_data.begin(), col6_data.end(), valids);
+
+  // mixed length strings
+  auto str2_elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    char buf[30];
+    sprintf(buf, "%d", i);
+    return std::string(buf);
+  });
+  auto col7 = cudf::test::strings_column_wrapper(str2_elements, str2_elements + num_rows, valids);
+
+  auto expected = table_view{{col0, col1, col2, col3, col4, col5, col6, col7}};
+
+  auto const filepath = temp_env->get_temp_filepath("CheckColumnOffsetIndexNulls.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .write_v2_headers(is_v2)
+      .max_page_size_rows(20000);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      // loop over offsets, read each page header, make sure it's a data page and that
+      // the first row index is correct
+      auto const oi = read_offset_index(source, chunk);
+
+      int64_t num_vals = 0;
+      for (size_t o = 0; o < oi.page_locations.size(); o++) {
+        auto const& page_loc = oi.page_locations[o];
+        auto const ph        = read_page_header(source, page_loc);
+        EXPECT_EQ(ph.type, expected_hdr_type);
+        EXPECT_EQ(page_loc.first_row_index, num_vals);
+        num_vals += is_v2 ? ph.data_page_header_v2.num_rows : ph.data_page_header.num_values;
+      }
+
+      // loop over page stats from the column index. check that stats.min <= page.min
+      // and stats.max >= page.max for each page.
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      // should be half nulls, except no nulls in column 0
+      EXPECT_EQ(stats.null_count, c == 0 ? 0 : num_rows / 2);
+
+      // schema indexing starts at 1
+      auto const ptype = fmd.schema[c + 1].type;
+      auto const ctype = fmd.schema[c + 1].converted_type;
+      for (size_t p = 0; p < ci.min_values.size(); p++) {
+        EXPECT_FALSE(ci.null_pages[p]);
+        if (c > 0) {  // first column has no nulls
+          EXPECT_GT(ci.null_counts[p], 0);
+        } else {
+          EXPECT_EQ(ci.null_counts[p], 0);
+        }
+        EXPECT_TRUE(compare_binary(stats.min_value, ci.min_values[p], ptype, ctype) <= 0);
+      }
+      for (size_t p = 0; p < ci.max_values.size(); p++) {
+        EXPECT_TRUE(compare_binary(stats.max_value, ci.max_values[p], ptype, ctype) >= 0);
+      }
+    }
+  }
+}
+
+TEST_P(ParquetV2Test, CheckColumnOffsetIndexNullColumn)
+{
+  constexpr auto num_rows = 100000;
+  auto const is_v2        = GetParam();
+  auto const expected_hdr_type =
+    is_v2 ? cudf::io::parquet::PageType::DATA_PAGE_V2 : cudf::io::parquet::PageType::DATA_PAGE;
+
+  // fixed length strings
+  auto str1_elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    char buf[30];
+    sprintf(buf, "%012d", i);
+    return std::string(buf);
+  });
+  auto col0          = cudf::test::strings_column_wrapper(str1_elements, str1_elements + num_rows);
+
+  auto col1_data = random_values<int32_t>(num_rows);
+  auto col2_data = random_values<int32_t>(num_rows);
+
+  // col1 is all nulls
+  auto valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return false; });
+  auto col1 =
+    cudf::test::fixed_width_column_wrapper<int32_t>(col1_data.begin(), col1_data.end(), valids);
+  auto col2 = cudf::test::fixed_width_column_wrapper<int32_t>(col2_data.begin(), col2_data.end());
+
+  // mixed length strings
+  auto str2_elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    char buf[30];
+    sprintf(buf, "%d", i);
+    return std::string(buf);
+  });
+  auto col3          = cudf::test::strings_column_wrapper(str2_elements, str2_elements + num_rows);
+
+  auto expected = table_view{{col0, col1, col2, col3}};
+
+  auto const filepath = temp_env->get_temp_filepath("CheckColumnOffsetIndexNullColumn.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .write_v2_headers(is_v2)
+      .max_page_size_rows(20000);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      // loop over offsets, read each page header, make sure it's a data page and that
+      // the first row index is correct
+      auto const oi = read_offset_index(source, chunk);
+
+      int64_t num_vals = 0;
+      for (size_t o = 0; o < oi.page_locations.size(); o++) {
+        auto const& page_loc = oi.page_locations[o];
+        auto const ph        = read_page_header(source, page_loc);
+        EXPECT_EQ(ph.type, expected_hdr_type);
+        EXPECT_EQ(page_loc.first_row_index, num_vals);
+        num_vals += is_v2 ? ph.data_page_header_v2.num_rows : ph.data_page_header.num_values;
+      }
+
+      // loop over page stats from the column index. check that stats.min <= page.min
+      // and stats.max >= page.max for each non-empty page.
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      // there should be no nulls except column 1 which is all nulls
+      EXPECT_EQ(stats.null_count, c == 1 ? num_rows : 0);
+
+      // schema indexing starts at 1
+      auto const ptype = fmd.schema[c + 1].type;
+      auto const ctype = fmd.schema[c + 1].converted_type;
+      for (size_t p = 0; p < ci.min_values.size(); p++) {
+        // check tnat null_pages is true for column 1
+        if (c == 1) {
+          EXPECT_TRUE(ci.null_pages[p]);
+          EXPECT_GT(ci.null_counts[p], 0);
+        }
+        if (not ci.null_pages[p]) {
+          EXPECT_EQ(ci.null_counts[p], 0);
+          EXPECT_TRUE(compare_binary(stats.min_value, ci.min_values[p], ptype, ctype) <= 0);
+        }
+      }
+      for (size_t p = 0; p < ci.max_values.size(); p++) {
+        if (not ci.null_pages[p]) {
+          EXPECT_TRUE(compare_binary(stats.max_value, ci.max_values[p], ptype, ctype) >= 0);
+        }
+      }
+    }
+  }
+}
+
+TEST_P(ParquetV2Test, CheckColumnOffsetIndexStruct)
+{
+  auto const is_v2 = GetParam();
+  auto const expected_hdr_type =
+    is_v2 ? cudf::io::parquet::PageType::DATA_PAGE_V2 : cudf::io::parquet::PageType::DATA_PAGE;
+
+  auto c0 = testdata::ascending<uint32_t>();
+
+  auto sc0 = testdata::ascending<cudf::string_view>();
+  auto sc1 = testdata::descending<int32_t>();
+  auto sc2 = testdata::unordered<int64_t>();
+
+  std::vector<std::unique_ptr<cudf::column>> struct_children;
+  struct_children.push_back(sc0.release());
+  struct_children.push_back(sc1.release());
+  struct_children.push_back(sc2.release());
+  cudf::test::structs_column_wrapper c1(std::move(struct_children));
+
+  auto listgen = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return i % 2 == 0 ? i / 2 : num_ordered_rows - (i / 2); });
+  auto list =
+    cudf::test::fixed_width_column_wrapper<int32_t>(listgen, listgen + 2 * num_ordered_rows);
+  auto offgen = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  auto offsets =
+    cudf::test::fixed_width_column_wrapper<int32_t>(offgen, offgen + num_ordered_rows + 1);
+
+  auto c2 = cudf::make_lists_column(num_ordered_rows, offsets.release(), list.release(), 0, {});
+
+  table_view expected({c0, c1, *c2});
+
+  auto const filepath = temp_env->get_temp_filepath("CheckColumnOffsetIndexStruct.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .write_v2_headers(is_v2)
+      .max_page_size_rows(page_size_for_ordered_tests);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  // hard coded schema indices.
+  // TODO find a way to do this without magic
+  size_t const colidxs[] = {1, 3, 4, 5, 8};
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      size_t colidx     = colidxs[c];
+      auto const& chunk = rg.columns[c];
+
+      // loop over offsets, read each page header, make sure it's a data page and that
+      // the first row index is correct
+      auto const oi = read_offset_index(source, chunk);
+
+      int64_t num_vals = 0;
+      for (size_t o = 0; o < oi.page_locations.size(); o++) {
+        auto const& page_loc = oi.page_locations[o];
+        auto const ph        = read_page_header(source, page_loc);
+        EXPECT_EQ(ph.type, expected_hdr_type);
+        EXPECT_EQ(page_loc.first_row_index, num_vals);
+        // last column has 2 values per row
+        num_vals += is_v2 ? ph.data_page_header_v2.num_rows
+                          : ph.data_page_header.num_values / (c == rg.columns.size() - 1 ? 2 : 1);
+      }
+
+      // loop over page stats from the column index. check that stats.min <= page.min
+      // and stats.max >= page.max for each page.
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      auto const ptype = fmd.schema[colidx].type;
+      auto const ctype = fmd.schema[colidx].converted_type;
+      for (size_t p = 0; p < ci.min_values.size(); p++) {
+        EXPECT_TRUE(compare_binary(stats.min_value, ci.min_values[p], ptype, ctype) <= 0);
+      }
+      for (size_t p = 0; p < ci.max_values.size(); p++) {
+        EXPECT_TRUE(compare_binary(stats.max_value, ci.max_values[p], ptype, ctype) >= 0);
+      }
+    }
+  }
+}
+
+TEST_P(ParquetV2Test, CheckColumnOffsetIndexStructNulls)
+{
+  auto const is_v2 = GetParam();
+  auto const expected_hdr_type =
+    is_v2 ? cudf::io::parquet::PageType::DATA_PAGE_V2 : cudf::io::parquet::PageType::DATA_PAGE;
+
+  auto validity2 =
+    cudf::detail::make_counting_transform_iterator(0, [](cudf::size_type i) { return i % 2; });
+  auto validity3 = cudf::detail::make_counting_transform_iterator(
+    0, [](cudf::size_type i) { return (i % 3) != 0; });
+  auto validity4 = cudf::detail::make_counting_transform_iterator(
+    0, [](cudf::size_type i) { return (i % 4) != 0; });
+  auto validity5 = cudf::detail::make_counting_transform_iterator(
+    0, [](cudf::size_type i) { return (i % 5) != 0; });
+
+  auto c0 = testdata::ascending<uint32_t>();
+
+  auto col1_data = random_values<int32_t>(num_ordered_rows);
+  auto col2_data = random_values<int32_t>(num_ordered_rows);
+  auto col3_data = random_values<int32_t>(num_ordered_rows);
+
+  // col1 is all nulls
+  auto col1 =
+    cudf::test::fixed_width_column_wrapper<int32_t>(col1_data.begin(), col1_data.end(), validity2);
+  auto col2 =
+    cudf::test::fixed_width_column_wrapper<int32_t>(col2_data.begin(), col2_data.end(), validity3);
+  auto col3 =
+    cudf::test::fixed_width_column_wrapper<int32_t>(col2_data.begin(), col2_data.end(), validity4);
+
+  std::vector<std::unique_ptr<cudf::column>> struct_children;
+  struct_children.push_back(col1.release());
+  struct_children.push_back(col2.release());
+  struct_children.push_back(col3.release());
+  auto struct_validity = std::vector<bool>(validity5, validity5 + num_ordered_rows);
+  cudf::test::structs_column_wrapper c1(std::move(struct_children), struct_validity);
+  table_view expected({c0, c1});
+
+  auto const filepath = temp_env->get_temp_filepath("CheckColumnOffsetIndexStructNulls.parquet");
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .write_v2_headers(is_v2)
+      .max_page_size_rows(page_size_for_ordered_tests);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      // loop over offsets, read each page header, make sure it's a data page and that
+      // the first row index is correct
+      auto const oi = read_offset_index(source, chunk);
+      auto const ci = read_column_index(source, chunk);
+
+      int64_t num_vals = 0;
+      for (size_t o = 0; o < oi.page_locations.size(); o++) {
+        auto const& page_loc = oi.page_locations[o];
+        auto const ph        = read_page_header(source, page_loc);
+        EXPECT_EQ(ph.type, expected_hdr_type);
+        EXPECT_EQ(page_loc.first_row_index, num_vals);
+        num_vals += is_v2 ? ph.data_page_header_v2.num_rows : ph.data_page_header.num_values;
+        // check that null counts match
+        if (is_v2) { EXPECT_EQ(ci.null_counts[o], ph.data_page_header_v2.num_nulls); }
+      }
+    }
+  }
+}
+
+TEST_P(ParquetV2Test, CheckColumnIndexListWithNulls)
+{
+  auto const is_v2 = GetParam();
+  auto const expected_hdr_type =
+    is_v2 ? cudf::io::parquet::PageType::DATA_PAGE_V2 : cudf::io::parquet::PageType::DATA_PAGE;
+
+  using cudf::test::iterators::null_at;
+  using cudf::test::iterators::nulls_at;
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  // 4 nulls
+  // [NULL, 2, NULL]
+  // []
+  // [4, 5]
+  // NULL
+  lcw col0{{{{1, 2, 3}, nulls_at({0, 2})}, {}, {4, 5}, {}}, null_at(3)};
+
+  // 4 nulls
+  // [[1, 2, 3], [], [4, 5], [], [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  lcw col1{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, {{7, 8}}, lcw{}, lcw{lcw{}}};
+
+  // 4 nulls
+  // [[1, 2, 3], [], [4, 5], NULL, [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  lcw col2{{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, null_at(3)}, {{7, 8}}, lcw{}, lcw{lcw{}}};
+
+  // 6 nulls
+  // [[1, 2, 3], [], [4, 5], NULL, [NULL, 6, NULL]]
+  // [[7, 8]]
+  // []
+  // [[]]
+  using dlcw = cudf::test::lists_column_wrapper<double>;
+  dlcw col3{{{{1., 2., 3.}, {}, {4., 5.}, {}, {{0., 6., 0.}, nulls_at({0, 2})}}, null_at(3)},
+            {{7., 8.}},
+            dlcw{},
+            dlcw{dlcw{}}};
+
+  // 4 nulls
+  // [[1, 2, 3], [], [4, 5], NULL, [0, 6, 0]]
+  // [[7, 8]]
+  // []
+  // NULL
+  using ui16lcw = cudf::test::lists_column_wrapper<uint16_t>;
+  cudf::test::lists_column_wrapper<uint16_t> col4{
+    {{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, null_at(3)}, {{7, 8}}, ui16lcw{}, ui16lcw{ui16lcw{}}},
+    null_at(3)};
+
+  // 6 nulls
+  // [[1, 2, 3], [], [4, 5], NULL, [NULL, 6, NULL]]
+  // [[7, 8]]
+  // []
+  // NULL
+  lcw col5{{{{{1, 2, 3}, {}, {4, 5}, {}, {{0, 6, 0}, nulls_at({0, 2})}}, null_at(3)},
+            {{7, 8}},
+            lcw{},
+            lcw{lcw{}}},
+           null_at(3)};
+
+  // 4 nulls
+  using strlcw = cudf::test::lists_column_wrapper<cudf::string_view>;
+  cudf::test::lists_column_wrapper<cudf::string_view> col6{
+    {{"Monday", "Monday", "Friday"}, {}, {"Monday", "Friday"}, {}, {"Sunday", "Funday"}},
+    {{"bee", "sting"}},
+    strlcw{},
+    strlcw{strlcw{}}};
+
+  // 11 nulls
+  // [[[NULL,2,NULL,4]], [[NULL,6,NULL], [8,9]]]
+  // [NULL, [[13],[14,15,16]],  NULL]
+  // [NULL, [], NULL, [[]]]
+  // NULL
+  lcw col7{{
+             {{{{1, 2, 3, 4}, nulls_at({0, 2})}}, {{{5, 6, 7}, nulls_at({0, 2})}, {8, 9}}},
+             {{{{10, 11}, {12}}, {{13}, {14, 15, 16}}, {{17, 18}}}, nulls_at({0, 2})},
+             {{lcw{lcw{}}, lcw{}, lcw{}, lcw{lcw{}}}, nulls_at({0, 2})},
+             lcw{lcw{lcw{}}},
+           },
+           null_at(3)};
+
+  table_view expected({col0, col1, col2, col3, col4, col5, col6, col7});
+
+  int64_t const expected_null_counts[] = {4, 4, 4, 6, 4, 6, 4, 11};
+
+  auto const filepath = temp_env->get_temp_filepath("ColumnIndexListWithNulls.parquet");
+  auto out_opts = cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+                    .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+                    .write_v2_headers(is_v2)
+                    .compression(cudf::io::compression_type::NONE);
+
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      // loop over offsets, read each page header, make sure it's a data page and that
+      // the first row index is correct
+      auto const oi = read_offset_index(source, chunk);
+
+      for (size_t o = 0; o < oi.page_locations.size(); o++) {
+        auto const& page_loc = oi.page_locations[o];
+        auto const ph        = read_page_header(source, page_loc);
+        EXPECT_EQ(ph.type, expected_hdr_type);
+        // check null counts in V2 header
+        if (is_v2) { EXPECT_EQ(ph.data_page_header_v2.num_nulls, expected_null_counts[c]); }
+      }
+
+      // check null counts in column chunk stats and page indexes
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+      EXPECT_EQ(stats.null_count, expected_null_counts[c]);
+
+      // should only be one page
+      EXPECT_FALSE(ci.null_pages[0]);
+      EXPECT_EQ(ci.null_counts[0], expected_null_counts[c]);
+    }
+  }
+}
+
+TEST_F(ParquetWriterTest, CheckColumnIndexTruncation)
+{
+  char const* coldata[] = {
+    // in-range 7 bit.  should truncate to "yyyyyyyz"
+    "yyyyyyyyy",
+    // max 7 bit. should truncate to "x7fx7fx7fx7fx7fx7fx7fx80", since it's
+    // considered binary, not UTF-8.  If UTF-8 it should not truncate.
+    "\x7f\x7f\x7f\x7f\x7f\x7f\x7f\x7f\x7f",
+    // max binary.  this should not truncate
+    "\xff\xff\xff\xff\xff\xff\xff\xff\xff",
+    // in-range 2-byte UTF8 (U+00E9). should truncate to "éééê"
+    "ééééé",
+    // max 2-byte UTF8 (U+07FF). should not truncate
+    "߿߿߿߿߿",
+    // in-range 3-byte UTF8 (U+0800). should truncate to "ࠀࠁ"
+    "ࠀࠀࠀ",
+    // max 3-byte UTF8 (U+FFFF). should not truncate
+    "\xef\xbf\xbf\xef\xbf\xbf\xef\xbf\xbf",
+    // in-range 4-byte UTF8 (U+10000). should truncate to "𐀀𐀁"
+    "𐀀𐀀𐀀",
+    // max unicode (U+10FFFF). should truncate to \xf4\x8f\xbf\xbf\xf4\x90\x80\x80,
+    // which is no longer valid unicode, but is still ok UTF-8???
+    "\xf4\x8f\xbf\xbf\xf4\x8f\xbf\xbf\xf4\x8f\xbf\xbf",
+    // max 4-byte UTF8 (U+1FFFFF). should not truncate
+    "\xf7\xbf\xbf\xbf\xf7\xbf\xbf\xbf\xf7\xbf\xbf\xbf"};
+
+  // NOTE: UTF8 min is initialized with 0xf7bfbfbf. Binary values larger
+  // than that will not become minimum value (when written as UTF-8).
+  char const* truncated_min[] = {"yyyyyyyy",
+                                 "\x7f\x7f\x7f\x7f\x7f\x7f\x7f\x7f",
+                                 "\xf7\xbf\xbf\xbf",
+                                 "éééé",
+                                 "߿߿߿߿",
+                                 "ࠀࠀ",
+                                 "\xef\xbf\xbf\xef\xbf\xbf",
+                                 "𐀀𐀀",
+                                 "\xf4\x8f\xbf\xbf\xf4\x8f\xbf\xbf",
+                                 "\xf7\xbf\xbf\xbf"};
+
+  char const* truncated_max[] = {"yyyyyyyz",
+                                 "\x7f\x7f\x7f\x7f\x7f\x7f\x7f\x80",
+                                 "\xff\xff\xff\xff\xff\xff\xff\xff\xff",
+                                 "éééê",
+                                 "߿߿߿߿߿",
+                                 "ࠀࠁ",
+                                 "\xef\xbf\xbf\xef\xbf\xbf\xef\xbf\xbf",
+                                 "𐀀𐀁",
+                                 "\xf4\x8f\xbf\xbf\xf4\x90\x80\x80",
+                                 "\xf7\xbf\xbf\xbf\xf7\xbf\xbf\xbf\xf7\xbf\xbf\xbf"};
+
+  auto cols = [&]() {
+    using string_wrapper = column_wrapper<cudf::string_view>;
+    std::vector<std::unique_ptr<column>> cols;
+    for (auto const str : coldata) {
+      cols.push_back(string_wrapper{str}.release());
+    }
+    return cols;
+  }();
+  auto expected = std::make_unique<table>(std::move(cols));
+
+  auto const filepath = temp_env->get_temp_filepath("CheckColumnIndexTruncation.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected->view())
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .column_index_truncate_length(8);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      // check trunc(page.min) <= stats.min && trun(page.max) >= stats.max
+      auto const ptype = fmd.schema[c + 1].type;
+      auto const ctype = fmd.schema[c + 1].converted_type;
+      EXPECT_TRUE(compare_binary(ci.min_values[0], stats.min_value, ptype, ctype) <= 0);
+      EXPECT_TRUE(compare_binary(ci.max_values[0], stats.max_value, ptype, ctype) >= 0);
+
+      // check that truncated values == expected
+      EXPECT_EQ(memcmp(ci.min_values[0].data(), truncated_min[c], ci.min_values[0].size()), 0);
+      EXPECT_EQ(memcmp(ci.max_values[0].data(), truncated_max[c], ci.max_values[0].size()), 0);
+    }
+  }
+}
+
+TEST_F(ParquetWriterTest, BinaryColumnIndexTruncation)
+{
+  std::vector<uint8_t> truncated_min[] = {{0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe},
+                                          {0xfe, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff},
+                                          {0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}};
+
+  std::vector<uint8_t> truncated_max[] = {{0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xff},
+                                          {0xff},
+                                          {0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}};
+
+  cudf::test::lists_column_wrapper<uint8_t> col0{
+    {0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe, 0xfe}};
+  cudf::test::lists_column_wrapper<uint8_t> col1{
+    {0xfe, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}};
+  cudf::test::lists_column_wrapper<uint8_t> col2{
+    {0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}};
+
+  auto expected = table_view{{col0, col1, col2}};
+
+  cudf::io::table_input_metadata output_metadata(expected);
+  output_metadata.column_metadata[0].set_name("col_binary0").set_output_as_binary(true);
+  output_metadata.column_metadata[1].set_name("col_binary1").set_output_as_binary(true);
+  output_metadata.column_metadata[2].set_name("col_binary2").set_output_as_binary(true);
+
+  auto const filepath = temp_env->get_temp_filepath("BinaryColumnIndexTruncation.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(output_metadata))
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .column_index_truncate_length(8);
+  cudf::io::write_parquet(out_opts);
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  for (size_t r = 0; r < fmd.row_groups.size(); r++) {
+    auto const& rg = fmd.row_groups[r];
+    for (size_t c = 0; c < rg.columns.size(); c++) {
+      auto const& chunk = rg.columns[c];
+
+      auto const ci    = read_column_index(source, chunk);
+      auto const stats = get_statistics(chunk);
+
+      // check trunc(page.min) <= stats.min && trun(page.max) >= stats.max
+      auto const ptype = fmd.schema[c + 1].type;
+      auto const ctype = fmd.schema[c + 1].converted_type;
+      EXPECT_TRUE(compare_binary(ci.min_values[0], stats.min_value, ptype, ctype) <= 0);
+      EXPECT_TRUE(compare_binary(ci.max_values[0], stats.max_value, ptype, ctype) >= 0);
+
+      // check that truncated values == expected
+      EXPECT_EQ(ci.min_values[0], truncated_min[c]);
+      EXPECT_EQ(ci.max_values[0], truncated_max[c]);
+    }
+  }
+}
+
+TEST_F(ParquetReaderTest, EmptyColumnsParam)
+{
+  srand(31337);
+  auto const expected = create_random_fixed_table<int>(2, 4, false);
+
+  std::vector<char> out_buffer;
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&out_buffer}, *expected);
+  cudf::io::write_parquet(args);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(
+      cudf::io::source_info{out_buffer.data(), out_buffer.size()})
+      .columns({});
+  auto const result = cudf::io::read_parquet(read_opts);
+
+  EXPECT_EQ(result.tbl->num_columns(), 0);
+  EXPECT_EQ(result.tbl->num_rows(), 0);
+}
+
+TEST_F(ParquetReaderTest, BinaryAsStrings)
+{
+  std::vector<char const*> strings{
+    "Monday", "Wednesday", "Friday", "Monday", "Friday", "Friday", "Friday", "Funday"};
+  auto const num_rows = strings.size();
+
+  auto seq_col0 = random_values<int>(num_rows);
+  auto seq_col2 = random_values<float>(num_rows);
+  auto seq_col3 = random_values<uint8_t>(num_rows);
+  auto validity = cudf::test::iterators::no_nulls();
+
+  column_wrapper<int> int_col{seq_col0.begin(), seq_col0.end(), validity};
+  column_wrapper<cudf::string_view> string_col{strings.begin(), strings.end()};
+  column_wrapper<float> float_col{seq_col2.begin(), seq_col2.end(), validity};
+  cudf::test::lists_column_wrapper<uint8_t> list_int_col{
+    {'M', 'o', 'n', 'd', 'a', 'y'},
+    {'W', 'e', 'd', 'n', 'e', 's', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'M', 'o', 'n', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'F', 'r', 'i', 'd', 'a', 'y'},
+    {'F', 'u', 'n', 'd', 'a', 'y'}};
+
+  auto output = table_view{{int_col, string_col, float_col, string_col, list_int_col}};
+  cudf::io::table_input_metadata output_metadata(output);
+  output_metadata.column_metadata[0].set_name("col_other");
+  output_metadata.column_metadata[1].set_name("col_string");
+  output_metadata.column_metadata[2].set_name("col_float");
+  output_metadata.column_metadata[3].set_name("col_string2").set_output_as_binary(true);
+  output_metadata.column_metadata[4].set_name("col_binary").set_output_as_binary(true);
+
+  auto filepath = temp_env->get_temp_filepath("BinaryReadStrings.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, output)
+      .metadata(std::move(output_metadata));
+  cudf::io::write_parquet(out_opts);
+
+  auto expected_string = table_view{{int_col, string_col, float_col, string_col, string_col}};
+  auto expected_mixed  = table_view{{int_col, string_col, float_col, list_int_col, list_int_col}};
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .set_column_schema({{}, {}, {}, {}, {}});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_string, result.tbl->view());
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_string, result.tbl->view());
+
+  std::vector<cudf::io::reader_column_schema> md{
+    {},
+    {},
+    {},
+    cudf::io::reader_column_schema().set_convert_binary_to_strings(false),
+    cudf::io::reader_column_schema().set_convert_binary_to_strings(false)};
+
+  cudf::io::parquet_reader_options mixed_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .set_column_schema(md);
+  result = cudf::io::read_parquet(mixed_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_mixed, result.tbl->view());
+}
+
+TEST_F(ParquetReaderTest, NestedByteArray)
+{
+  constexpr auto num_rows = 8;
+
+  auto seq_col0       = random_values<int>(num_rows);
+  auto seq_col2       = random_values<float>(num_rows);
+  auto seq_col3       = random_values<uint8_t>(num_rows);
+  auto const validity = cudf::test::iterators::no_nulls();
+
+  column_wrapper<int> int_col{seq_col0.begin(), seq_col0.end(), validity};
+  column_wrapper<float> float_col{seq_col2.begin(), seq_col2.end(), validity};
+  cudf::test::lists_column_wrapper<uint8_t> list_list_int_col{
+    {{'M', 'o', 'n', 'd', 'a', 'y'},
+     {'W', 'e', 'd', 'n', 'e', 's', 'd', 'a', 'y'},
+     {'F', 'r', 'i', 'd', 'a', 'y'}},
+    {{'M', 'o', 'n', 'd', 'a', 'y'}, {'F', 'r', 'i', 'd', 'a', 'y'}},
+    {{'M', 'o', 'n', 'd', 'a', 'y'},
+     {'W', 'e', 'd', 'n', 'e', 's', 'd', 'a', 'y'},
+     {'F', 'r', 'i', 'd', 'a', 'y'}},
+    {{'F', 'r', 'i', 'd', 'a', 'y'},
+     {'F', 'r', 'i', 'd', 'a', 'y'},
+     {'F', 'u', 'n', 'd', 'a', 'y'}},
+    {{'M', 'o', 'n', 'd', 'a', 'y'},
+     {'W', 'e', 'd', 'n', 'e', 's', 'd', 'a', 'y'},
+     {'F', 'r', 'i', 'd', 'a', 'y'}},
+    {{'F', 'r', 'i', 'd', 'a', 'y'},
+     {'F', 'r', 'i', 'd', 'a', 'y'},
+     {'F', 'u', 'n', 'd', 'a', 'y'}},
+    {{'M', 'o', 'n', 'd', 'a', 'y'},
+     {'W', 'e', 'd', 'n', 'e', 's', 'd', 'a', 'y'},
+     {'F', 'r', 'i', 'd', 'a', 'y'}},
+    {{'M', 'o', 'n', 'd', 'a', 'y'}, {'F', 'r', 'i', 'd', 'a', 'y'}}};
+
+  auto const expected = table_view{{int_col, float_col, list_list_int_col}};
+  cudf::io::table_input_metadata output_metadata(expected);
+  output_metadata.column_metadata[0].set_name("col_other");
+  output_metadata.column_metadata[1].set_name("col_float");
+  output_metadata.column_metadata[2].set_name("col_binary").child(1).set_output_as_binary(true);
+
+  auto filepath = temp_env->get_temp_filepath("NestedByteArray.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(output_metadata));
+  cudf::io::write_parquet(out_opts);
+
+  auto source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  EXPECT_EQ(fmd.schema[5].type, cudf::io::parquet::Type::BYTE_ARRAY);
+
+  std::vector<cudf::io::reader_column_schema> md{
+    {},
+    {},
+    cudf::io::reader_column_schema().add_child(
+      cudf::io::reader_column_schema().set_convert_binary_to_strings(false))};
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .set_column_schema(md);
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(ParquetWriterTest, ByteArrayStats)
+{
+  // check that byte array min and max statistics are written as expected. If a byte array is
+  // written as a string, max utf8 is 0xf7bfbfbf and so the minimum value will be set to that value
+  // instead of a potential minimum higher than that.
+  std::vector<uint8_t> expected_col0_min{0xf0};
+  std::vector<uint8_t> expected_col0_max{0xf0, 0xf5, 0xf5};
+  std::vector<uint8_t> expected_col1_min{0xfe, 0xfe, 0xfe};
+  std::vector<uint8_t> expected_col1_max{0xfe, 0xfe, 0xfe};
+
+  cudf::test::lists_column_wrapper<uint8_t> list_int_col0{
+    {0xf0}, {0xf0, 0xf5, 0xf3}, {0xf0, 0xf5, 0xf5}};
+  cudf::test::lists_column_wrapper<uint8_t> list_int_col1{
+    {0xfe, 0xfe, 0xfe}, {0xfe, 0xfe, 0xfe}, {0xfe, 0xfe, 0xfe}};
+
+  auto expected = table_view{{list_int_col0, list_int_col1}};
+  cudf::io::table_input_metadata output_metadata(expected);
+  output_metadata.column_metadata[0].set_name("col_binary0").set_output_as_binary(true);
+  output_metadata.column_metadata[1].set_name("col_binary1").set_output_as_binary(true);
+
+  auto filepath = temp_env->get_temp_filepath("ByteArrayStats.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(output_metadata));
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .set_column_schema({{}, {}});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  auto source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+
+  EXPECT_EQ(fmd.schema[1].type, cudf::io::parquet::Type::BYTE_ARRAY);
+  EXPECT_EQ(fmd.schema[2].type, cudf::io::parquet::Type::BYTE_ARRAY);
+
+  auto const stats0 = get_statistics(fmd.row_groups[0].columns[0]);
+  auto const stats1 = get_statistics(fmd.row_groups[0].columns[1]);
+
+  EXPECT_EQ(expected_col0_min, stats0.min_value);
+  EXPECT_EQ(expected_col0_max, stats0.max_value);
+  EXPECT_EQ(expected_col1_min, stats1.min_value);
+  EXPECT_EQ(expected_col1_max, stats1.max_value);
+}
+
+TEST_F(ParquetReaderTest, StructByteArray)
+{
+  constexpr auto num_rows = 100;
+
+  auto seq_col0       = random_values<uint8_t>(num_rows);
+  auto const validity = cudf::test::iterators::no_nulls();
+
+  column_wrapper<uint8_t> int_col{seq_col0.begin(), seq_col0.end(), validity};
+  cudf::test::lists_column_wrapper<uint8_t> list_of_int{{seq_col0.begin(), seq_col0.begin() + 50},
+                                                        {seq_col0.begin() + 50, seq_col0.end()}};
+  auto struct_col = cudf::test::structs_column_wrapper{{list_of_int}, validity};
+
+  auto const expected = table_view{{struct_col}};
+  EXPECT_EQ(1, expected.num_columns());
+  cudf::io::table_input_metadata output_metadata(expected);
+  output_metadata.column_metadata[0]
+    .set_name("struct_binary")
+    .child(0)
+    .set_name("a")
+    .set_output_as_binary(true);
+
+  auto filepath = temp_env->get_temp_filepath("StructByteArray.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(output_metadata));
+  cudf::io::write_parquet(out_opts);
+
+  std::vector<cudf::io::reader_column_schema> md{cudf::io::reader_column_schema().add_child(
+    cudf::io::reader_column_schema().set_convert_binary_to_strings(false))};
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .set_column_schema(md);
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(ParquetReaderTest, NestingOptimizationTest)
+{
+  // test nesting levels > cudf::io::parquet::gpu::max_cacheable_nesting_decode_info deep.
+  constexpr cudf::size_type num_nesting_levels = 16;
+  static_assert(num_nesting_levels > cudf::io::parquet::gpu::max_cacheable_nesting_decode_info);
+  constexpr cudf::size_type rows_per_level = 2;
+
+  constexpr cudf::size_type num_values = (1 << num_nesting_levels) * rows_per_level;
+  auto value_iter                      = thrust::make_counting_iterator(0);
+  auto validity =
+    cudf::detail::make_counting_transform_iterator(0, [](cudf::size_type i) { return i % 2; });
+  cudf::test::fixed_width_column_wrapper<int> values(value_iter, value_iter + num_values, validity);
+
+  // ~256k values with num_nesting_levels = 16
+  int total_values_produced = num_values;
+  auto prev_col             = values.release();
+  for (int idx = 0; idx < num_nesting_levels; idx++) {
+    auto const depth    = num_nesting_levels - idx;
+    auto const num_rows = (1 << (num_nesting_levels - idx));
+
+    auto offsets_iter = cudf::detail::make_counting_transform_iterator(
+      0, [depth, rows_per_level](cudf::size_type i) { return i * rows_per_level; });
+    total_values_produced += (num_rows + 1);
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets(offsets_iter,
+                                                                    offsets_iter + num_rows + 1);
+    auto c   = cudf::make_lists_column(num_rows, offsets.release(), std::move(prev_col), 0, {});
+    prev_col = std::move(c);
+  }
+  auto const& expect = prev_col;
+
+  auto filepath = temp_env->get_temp_filepath("NestingDecodeCache.parquet");
+  cudf::io::parquet_writer_options opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, table_view{{*expect}});
+  cudf::io::write_parquet(opts);
+
+  cudf::io::parquet_reader_options in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expect, result.tbl->get_column(0));
+}
+
+TEST_F(ParquetWriterTest, SingleValueDictionaryTest)
+{
+  constexpr unsigned int expected_bits = 1;
+  constexpr unsigned int nrows         = 1'000'000U;
+
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "a unique string value suffixed with 1"; });
+  auto const col0     = cudf::test::strings_column_wrapper(elements, elements + nrows);
+  auto const expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("SingleValueDictionaryTest.parquet");
+  // set row group size so that there will be only one row group
+  // no compression so we can easily read page data
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::NONE)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .row_group_size_rows(nrows);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+
+  // make sure dictionary was used
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto used_dict = [&fmd]() {
+    for (auto enc : fmd.row_groups[0].columns[0].meta_data.encodings) {
+      if (enc == cudf::io::parquet::Encoding::PLAIN_DICTIONARY or
+          enc == cudf::io::parquet::Encoding::RLE_DICTIONARY) {
+        return true;
+      }
+    }
+    return false;
+  };
+  EXPECT_TRUE(used_dict());
+
+  // and check that the correct number of bits was used
+  auto const oi    = read_offset_index(source, fmd.row_groups[0].columns[0]);
+  auto const nbits = read_dict_bits(source, oi.page_locations[0]);
+  EXPECT_EQ(nbits, expected_bits);
+}
+
+TEST_F(ParquetWriterTest, DictionaryNeverTest)
+{
+  constexpr unsigned int nrows = 1'000U;
+
+  // only one value, so would normally use dictionary
+  auto elements = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "a unique string value suffixed with 1"; });
+  auto const col0     = cudf::test::strings_column_wrapper(elements, elements + nrows);
+  auto const expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("DictionaryNeverTest.parquet");
+  // no compression so we can easily read page data
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::NONE)
+      .dictionary_policy(cudf::io::dictionary_policy::NEVER);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+
+  // make sure dictionary was not used
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto used_dict = [&fmd]() {
+    for (auto enc : fmd.row_groups[0].columns[0].meta_data.encodings) {
+      if (enc == cudf::io::parquet::Encoding::PLAIN_DICTIONARY or
+          enc == cudf::io::parquet::Encoding::RLE_DICTIONARY) {
+        return true;
+      }
+    }
+    return false;
+  };
+  EXPECT_FALSE(used_dict());
+}
+
+TEST_F(ParquetWriterTest, DictionaryAdaptiveTest)
+{
+  constexpr unsigned int nrows = 65'536U;
+  // cardinality is chosen to result in a dictionary > 1MB in size
+  constexpr unsigned int cardinality = 32'768U;
+
+  // single value will have a small dictionary
+  auto elements0 = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "a unique string value suffixed with 1"; });
+  auto const col0 = cudf::test::strings_column_wrapper(elements0, elements0 + nrows);
+
+  // high cardinality will have a large dictionary
+  auto elements1  = cudf::detail::make_counting_transform_iterator(0, [cardinality](auto i) {
+    return "a unique string value suffixed with " + std::to_string(i % cardinality);
+  });
+  auto const col1 = cudf::test::strings_column_wrapper(elements1, elements1 + nrows);
+
+  auto const expected = table_view{{col0, col1}};
+
+  auto const filepath = temp_env->get_temp_filepath("DictionaryAdaptiveTest.parquet");
+  // no compression so we can easily read page data
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::ZSTD)
+      .dictionary_policy(cudf::io::dictionary_policy::ADAPTIVE);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+
+  // make sure dictionary was used as expected. col0 should use one,
+  // col1 should not.
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto used_dict = [&fmd](int col) {
+    for (auto enc : fmd.row_groups[0].columns[col].meta_data.encodings) {
+      if (enc == cudf::io::parquet::Encoding::PLAIN_DICTIONARY or
+          enc == cudf::io::parquet::Encoding::RLE_DICTIONARY) {
+        return true;
+      }
+    }
+    return false;
+  };
+  EXPECT_TRUE(used_dict(0));
+  EXPECT_FALSE(used_dict(1));
+}
+
+TEST_F(ParquetWriterTest, DictionaryAlwaysTest)
+{
+  constexpr unsigned int nrows = 65'536U;
+  // cardinality is chosen to result in a dictionary > 1MB in size
+  constexpr unsigned int cardinality = 32'768U;
+
+  // single value will have a small dictionary
+  auto elements0 = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "a unique string value suffixed with 1"; });
+  auto const col0 = cudf::test::strings_column_wrapper(elements0, elements0 + nrows);
+
+  // high cardinality will have a large dictionary
+  auto elements1  = cudf::detail::make_counting_transform_iterator(0, [cardinality](auto i) {
+    return "a unique string value suffixed with " + std::to_string(i % cardinality);
+  });
+  auto const col1 = cudf::test::strings_column_wrapper(elements1, elements1 + nrows);
+
+  auto const expected = table_view{{col0, col1}};
+
+  auto const filepath = temp_env->get_temp_filepath("DictionaryAlwaysTest.parquet");
+  // no compression so we can easily read page data
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::ZSTD)
+      .dictionary_policy(cudf::io::dictionary_policy::ALWAYS);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+
+  // make sure dictionary was used for both columns
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto used_dict = [&fmd](int col) {
+    for (auto enc : fmd.row_groups[0].columns[col].meta_data.encodings) {
+      if (enc == cudf::io::parquet::Encoding::PLAIN_DICTIONARY or
+          enc == cudf::io::parquet::Encoding::RLE_DICTIONARY) {
+        return true;
+      }
+    }
+    return false;
+  };
+  EXPECT_TRUE(used_dict(0));
+  EXPECT_TRUE(used_dict(1));
+}
+
+TEST_F(ParquetWriterTest, DictionaryPageSizeEst)
+{
+  // one page
+  constexpr unsigned int nrows = 20'000U;
+
+  // this test is creating a pattern of repeating then non-repeating values to trigger
+  // a "worst-case" for page size estimation in the presence of a dictionary. have confirmed
+  // that this fails for values over 16 in the final term of `max_RLE_page_size()`.
+  // The output of the iterator will be 'CCCCCRRRRRCCCCCRRRRR...` where 'C' is a changing
+  // value, and 'R' repeats. The encoder will turn this into a literal run of 8 values
+  // (`CCCCCRRR`) followed by a repeated run of 2 (`RR`). This pattern then repeats, getting
+  // as close as possible to a condition of repeated 8 value literal runs.
+  auto elements0  = cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+    if ((i / 5) % 2 == 1) {
+      return std::string("non-unique string");
+    } else {
+      return "a unique string value suffixed with " + std::to_string(i);
+    }
+  });
+  auto const col0 = cudf::test::strings_column_wrapper(elements0, elements0 + nrows);
+
+  auto const expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("DictionaryPageSizeEst.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::ZSTD)
+      .dictionary_policy(cudf::io::dictionary_policy::ALWAYS);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_P(ParquetSizedTest, DictionaryTest)
+{
+  unsigned int const cardinality = (1 << (GetParam() - 1)) + 1;
+  unsigned int const nrows       = std::max(cardinality * 3 / 2, 3'000'000U);
+
+  auto elements       = cudf::detail::make_counting_transform_iterator(0, [cardinality](auto i) {
+    return "a unique string value suffixed with " + std::to_string(i % cardinality);
+  });
+  auto const col0     = cudf::test::strings_column_wrapper(elements, elements + nrows);
+  auto const expected = table_view{{col0}};
+
+  auto const filepath = temp_env->get_temp_filepath("DictionaryTest.parquet");
+  // set row group size so that there will be only one row group
+  // no compression so we can easily read page data
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .compression(cudf::io::compression_type::NONE)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_COLUMN)
+      .dictionary_policy(cudf::io::dictionary_policy::ALWAYS)
+      .row_group_size_rows(nrows)
+      .row_group_size_bytes(512 * 1024 * 1024);
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options default_in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result = cudf::io::read_parquet(default_in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+
+  // make sure dictionary was used
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto used_dict = [&fmd]() {
+    for (auto enc : fmd.row_groups[0].columns[0].meta_data.encodings) {
+      if (enc == cudf::io::parquet::Encoding::PLAIN_DICTIONARY or
+          enc == cudf::io::parquet::Encoding::RLE_DICTIONARY) {
+        return true;
+      }
+    }
+    return false;
+  };
+  EXPECT_TRUE(used_dict());
+
+  // and check that the correct number of bits was used
+  auto const oi    = read_offset_index(source, fmd.row_groups[0].columns[0]);
+  auto const nbits = read_dict_bits(source, oi.page_locations[0]);
+  EXPECT_EQ(nbits, GetParam());
+}
+
+TYPED_TEST(ParquetReaderSourceTest, BufferSourceTypes)
+{
+  using T = TypeParam;
+
+  srand(31337);
+  auto table = create_random_fixed_table<int>(5, 5, true);
+
+  std::vector<char> out_buffer;
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer), *table);
+  cudf::io::write_parquet(out_opts);
+
+  {
+    cudf::io::parquet_reader_options in_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info(
+        cudf::host_span<T>(reinterpret_cast<T*>(out_buffer.data()), out_buffer.size())));
+    auto const result = cudf::io::read_parquet(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*table, result.tbl->view());
+  }
+
+  {
+    cudf::io::parquet_reader_options in_opts =
+      cudf::io::parquet_reader_options::builder(cudf::io::source_info(cudf::host_span<T const>(
+        reinterpret_cast<T const*>(out_buffer.data()), out_buffer.size())));
+    auto const result = cudf::io::read_parquet(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*table, result.tbl->view());
+  }
+}
+
+TYPED_TEST(ParquetReaderSourceTest, BufferSourceArrayTypes)
+{
+  using T = TypeParam;
+
+  srand(31337);
+  auto table = create_random_fixed_table<int>(5, 5, true);
+
+  std::vector<char> out_buffer;
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info(&out_buffer), *table);
+  cudf::io::write_parquet(out_opts);
+
+  auto full_table = cudf::concatenate(std::vector<table_view>({*table, *table}));
+
+  {
+    auto spans = std::vector<cudf::host_span<T>>{
+      cudf::host_span<T>(reinterpret_cast<T*>(out_buffer.data()), out_buffer.size()),
+      cudf::host_span<T>(reinterpret_cast<T*>(out_buffer.data()), out_buffer.size())};
+    cudf::io::parquet_reader_options in_opts = cudf::io::parquet_reader_options::builder(
+      cudf::io::source_info(cudf::host_span<cudf::host_span<T>>(spans.data(), spans.size())));
+    auto const result = cudf::io::read_parquet(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*full_table, result.tbl->view());
+  }
+
+  {
+    auto spans = std::vector<cudf::host_span<T const>>{
+      cudf::host_span<T const>(reinterpret_cast<T const*>(out_buffer.data()), out_buffer.size()),
+      cudf::host_span<T const>(reinterpret_cast<T const*>(out_buffer.data()), out_buffer.size())};
+    cudf::io::parquet_reader_options in_opts = cudf::io::parquet_reader_options::builder(
+      cudf::io::source_info(cudf::host_span<cudf::host_span<T const>>(spans.data(), spans.size())));
+    auto const result = cudf::io::read_parquet(in_opts);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*full_table, result.tbl->view());
+  }
+}
+
+TEST_F(ParquetWriterTest, UserNullability)
+{
+  auto weight_col = cudf::test::fixed_width_column_wrapper<float>{{57.5, 51.1, 15.3}};
+  auto ages_col   = cudf::test::fixed_width_column_wrapper<int32_t>{{30, 27, 5}};
+  auto struct_col = cudf::test::structs_column_wrapper{weight_col, ages_col};
+
+  auto expected = table_view({struct_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_nullability(false);
+  expected_metadata.column_metadata[0].child(0).set_nullability(true);
+
+  auto filepath = temp_env->get_temp_filepath("SingleWriteNullable.parquet");
+  cudf::io::parquet_writer_options write_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_parquet(write_opts);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result = cudf::io::read_parquet(read_opts);
+
+  EXPECT_FALSE(result.tbl->view().column(0).nullable());
+  EXPECT_TRUE(result.tbl->view().column(0).child(0).nullable());
+  EXPECT_FALSE(result.tbl->view().column(0).child(1).nullable());
+}
+
+TEST_F(ParquetWriterTest, UserNullabilityInvalid)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [&](int index) { return index % 2; });
+  auto col      = cudf::test::fixed_width_column_wrapper<double>{{57.5, 51.1, 15.3}, valids};
+  auto expected = table_view({col});
+
+  auto filepath = temp_env->get_temp_filepath("SingleWriteNullableInvalid.parquet");
+  cudf::io::parquet_writer_options write_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected);
+  // Should work without the nullability option
+  EXPECT_NO_THROW(cudf::io::write_parquet(write_opts));
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_nullability(false);
+  write_opts.set_metadata(std::move(expected_metadata));
+  // Can't write a column with nulls as not nullable
+  EXPECT_THROW(cudf::io::write_parquet(write_opts), cudf::logic_error);
+}
+
+TEST_F(ParquetReaderTest, SingleLevelLists)
+{
+  unsigned char list_bytes[] = {
+    0x50, 0x41, 0x52, 0x31, 0x15, 0x00, 0x15, 0x28, 0x15, 0x28, 0x15, 0xa7, 0xce, 0x91, 0x8c, 0x06,
+    0x1c, 0x15, 0x04, 0x15, 0x00, 0x15, 0x06, 0x15, 0x06, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03,
+    0x02, 0x02, 0x00, 0x00, 0x00, 0x03, 0x03, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x15,
+    0x02, 0x19, 0x3c, 0x48, 0x0c, 0x73, 0x70, 0x61, 0x72, 0x6b, 0x5f, 0x73, 0x63, 0x68, 0x65, 0x6d,
+    0x61, 0x15, 0x02, 0x00, 0x35, 0x00, 0x18, 0x01, 0x66, 0x15, 0x02, 0x15, 0x06, 0x4c, 0x3c, 0x00,
+    0x00, 0x00, 0x15, 0x02, 0x25, 0x04, 0x18, 0x05, 0x61, 0x72, 0x72, 0x61, 0x79, 0x00, 0x16, 0x02,
+    0x19, 0x1c, 0x19, 0x1c, 0x26, 0x08, 0x1c, 0x15, 0x02, 0x19, 0x25, 0x00, 0x06, 0x19, 0x28, 0x01,
+    0x66, 0x05, 0x61, 0x72, 0x72, 0x61, 0x79, 0x15, 0x00, 0x16, 0x04, 0x16, 0x56, 0x16, 0x56, 0x26,
+    0x08, 0x3c, 0x18, 0x04, 0x01, 0x00, 0x00, 0x00, 0x18, 0x04, 0x00, 0x00, 0x00, 0x00, 0x16, 0x00,
+    0x28, 0x04, 0x01, 0x00, 0x00, 0x00, 0x18, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0x1c, 0x15,
+    0x00, 0x15, 0x00, 0x15, 0x02, 0x00, 0x00, 0x00, 0x16, 0x56, 0x16, 0x02, 0x26, 0x08, 0x16, 0x56,
+    0x14, 0x00, 0x00, 0x28, 0x13, 0x52, 0x41, 0x50, 0x49, 0x44, 0x53, 0x20, 0x53, 0x70, 0x61, 0x72,
+    0x6b, 0x20, 0x50, 0x6c, 0x75, 0x67, 0x69, 0x6e, 0x19, 0x1c, 0x1c, 0x00, 0x00, 0x00, 0x9f, 0x00,
+    0x00, 0x00, 0x50, 0x41, 0x52, 0x31};
+
+  // read single level list reproducing parquet file
+  cudf::io::parquet_reader_options read_opts = cudf::io::parquet_reader_options::builder(
+    cudf::io::source_info{reinterpret_cast<char const*>(list_bytes), sizeof(list_bytes)});
+  auto table = cudf::io::read_parquet(read_opts);
+
+  auto const c0 = table.tbl->get_column(0);
+  EXPECT_TRUE(c0.type().id() == cudf::type_id::LIST);
+
+  auto const lc    = cudf::lists_column_view(c0);
+  auto const child = lc.child();
+  EXPECT_TRUE(child.type().id() == cudf::type_id::INT32);
+}
+
+TEST_F(ParquetReaderTest, ChunkedSingleLevelLists)
+{
+  unsigned char list_bytes[] = {
+    0x50, 0x41, 0x52, 0x31, 0x15, 0x00, 0x15, 0x28, 0x15, 0x28, 0x15, 0xa7, 0xce, 0x91, 0x8c, 0x06,
+    0x1c, 0x15, 0x04, 0x15, 0x00, 0x15, 0x06, 0x15, 0x06, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03,
+    0x02, 0x02, 0x00, 0x00, 0x00, 0x03, 0x03, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x15,
+    0x02, 0x19, 0x3c, 0x48, 0x0c, 0x73, 0x70, 0x61, 0x72, 0x6b, 0x5f, 0x73, 0x63, 0x68, 0x65, 0x6d,
+    0x61, 0x15, 0x02, 0x00, 0x35, 0x00, 0x18, 0x01, 0x66, 0x15, 0x02, 0x15, 0x06, 0x4c, 0x3c, 0x00,
+    0x00, 0x00, 0x15, 0x02, 0x25, 0x04, 0x18, 0x05, 0x61, 0x72, 0x72, 0x61, 0x79, 0x00, 0x16, 0x02,
+    0x19, 0x1c, 0x19, 0x1c, 0x26, 0x08, 0x1c, 0x15, 0x02, 0x19, 0x25, 0x00, 0x06, 0x19, 0x28, 0x01,
+    0x66, 0x05, 0x61, 0x72, 0x72, 0x61, 0x79, 0x15, 0x00, 0x16, 0x04, 0x16, 0x56, 0x16, 0x56, 0x26,
+    0x08, 0x3c, 0x18, 0x04, 0x01, 0x00, 0x00, 0x00, 0x18, 0x04, 0x00, 0x00, 0x00, 0x00, 0x16, 0x00,
+    0x28, 0x04, 0x01, 0x00, 0x00, 0x00, 0x18, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0x1c, 0x15,
+    0x00, 0x15, 0x00, 0x15, 0x02, 0x00, 0x00, 0x00, 0x16, 0x56, 0x16, 0x02, 0x26, 0x08, 0x16, 0x56,
+    0x14, 0x00, 0x00, 0x28, 0x13, 0x52, 0x41, 0x50, 0x49, 0x44, 0x53, 0x20, 0x53, 0x70, 0x61, 0x72,
+    0x6b, 0x20, 0x50, 0x6c, 0x75, 0x67, 0x69, 0x6e, 0x19, 0x1c, 0x1c, 0x00, 0x00, 0x00, 0x9f, 0x00,
+    0x00, 0x00, 0x50, 0x41, 0x52, 0x31};
+
+  auto reader = cudf::io::chunked_parquet_reader(
+    1L << 31,
+    cudf::io::parquet_reader_options::builder(
+      cudf::io::source_info{reinterpret_cast<char const*>(list_bytes), sizeof(list_bytes)}));
+  int iterations = 0;
+  while (reader.has_next() && iterations < 10) {
+    auto chunk = reader.read_chunk();
+  }
+  EXPECT_TRUE(iterations < 10);
+}
+
+TEST_F(ParquetWriterTest, CompStats)
+{
+  auto table = create_random_fixed_table<int>(1, 100000, true);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::parquet_writer_options opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&unused_buffer}, table->view())
+      .compression_statistics(stats);
+  cudf::io::write_parquet(opts);
+
+  EXPECT_NE(stats->num_compressed_bytes(), 0);
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+  EXPECT_FALSE(std::isnan(stats->compression_ratio()));
+}
+
+TEST_F(ParquetChunkedWriterTest, CompStats)
+{
+  auto table = create_random_fixed_table<int>(1, 100000, true);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::chunked_parquet_writer_options opts =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{&unused_buffer})
+      .compression_statistics(stats);
+  cudf::io::parquet_chunked_writer(opts).write(*table);
+
+  EXPECT_NE(stats->num_compressed_bytes(), 0);
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+  EXPECT_FALSE(std::isnan(stats->compression_ratio()));
+
+  auto const single_table_comp_stats = *stats;
+  cudf::io::parquet_chunked_writer(opts).write(*table);
+
+  EXPECT_EQ(stats->compression_ratio(), single_table_comp_stats.compression_ratio());
+  EXPECT_EQ(stats->num_compressed_bytes(), 2 * single_table_comp_stats.num_compressed_bytes());
+
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+}
+
+void expect_compression_stats_empty(std::shared_ptr<cudf::io::writer_compression_statistics> stats)
+{
+  EXPECT_EQ(stats->num_compressed_bytes(), 0);
+  EXPECT_EQ(stats->num_failed_bytes(), 0);
+  EXPECT_EQ(stats->num_skipped_bytes(), 0);
+  EXPECT_TRUE(std::isnan(stats->compression_ratio()));
+}
+
+TEST_F(ParquetWriterTest, CompStatsEmptyTable)
+{
+  auto table_no_rows = create_random_fixed_table<int>(20, 0, false);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::parquet_writer_options opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{&unused_buffer},
+                                              table_no_rows->view())
+      .compression_statistics(stats);
+  cudf::io::write_parquet(opts);
+
+  expect_compression_stats_empty(stats);
+}
+
+TEST_F(ParquetChunkedWriterTest, CompStatsEmptyTable)
+{
+  auto table_no_rows = create_random_fixed_table<int>(20, 0, false);
+
+  auto const stats = std::make_shared<cudf::io::writer_compression_statistics>();
+
+  std::vector<char> unused_buffer;
+  cudf::io::chunked_parquet_writer_options opts =
+    cudf::io::chunked_parquet_writer_options::builder(cudf::io::sink_info{&unused_buffer})
+      .compression_statistics(stats);
+  cudf::io::parquet_chunked_writer(opts).write(*table_no_rows);
+
+  expect_compression_stats_empty(stats);
+}
+
+TEST_F(ParquetReaderTest, ReorderedReadMultipleFiles)
+{
+  constexpr auto num_rows    = 50'000;
+  constexpr auto cardinality = 20'000;
+
+  // table 1
+  auto str1 = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "cat " + std::to_string(i % cardinality); });
+  auto cols1 = cudf::test::strings_column_wrapper(str1, str1 + num_rows);
+
+  auto int1 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % cardinality; });
+  auto coli1 = cudf::test::fixed_width_column_wrapper<int>(int1, int1 + num_rows);
+
+  auto const expected1 = table_view{{cols1, coli1}};
+  auto const swapped1  = table_view{{coli1, cols1}};
+
+  auto const filepath1 = temp_env->get_temp_filepath("LargeReorderedRead1.parquet");
+  auto out_opts1 =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath1}, expected1)
+      .compression(cudf::io::compression_type::NONE);
+  cudf::io::write_parquet(out_opts1);
+
+  // table 2
+  auto str2 = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return "dog " + std::to_string(i % cardinality); });
+  auto cols2 = cudf::test::strings_column_wrapper(str2, str2 + num_rows);
+
+  auto int2 = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return (i % cardinality) + cardinality; });
+  auto coli2 = cudf::test::fixed_width_column_wrapper<int>(int2, int2 + num_rows);
+
+  auto const expected2 = table_view{{cols2, coli2}};
+  auto const swapped2  = table_view{{coli2, cols2}};
+
+  auto const filepath2 = temp_env->get_temp_filepath("LargeReorderedRead2.parquet");
+  auto out_opts2 =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath2}, expected2)
+      .compression(cudf::io::compression_type::NONE);
+  cudf::io::write_parquet(out_opts2);
+
+  // read in both files swapping the columns
+  auto read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{{filepath1, filepath2}})
+      .columns({"_col1", "_col0"});
+  auto result = cudf::io::read_parquet(read_opts);
+  auto sliced = cudf::slice(result.tbl->view(), {0, num_rows, num_rows, 2 * num_rows});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(sliced[0], swapped1);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(sliced[1], swapped2);
+}
+
+// Test fixture for metadata tests
+struct ParquetMetadataReaderTest : public cudf::test::BaseFixture {
+  std::string print(cudf::io::parquet_column_schema schema, int depth = 0)
+  {
+    std::string child_str;
+    for (auto const& child : schema.children()) {
+      child_str += print(child, depth + 1);
+    }
+    return std::string(depth, ' ') + schema.name() + "\n" + child_str;
+  }
+};
+
+TEST_F(ParquetMetadataReaderTest, TestBasic)
+{
+  auto const num_rows = 1200;
+
+  auto ints   = random_values<int>(num_rows);
+  auto floats = random_values<float>(num_rows);
+  column_wrapper<int> int_col(ints.begin(), ints.end());
+  column_wrapper<float> float_col(floats.begin(), floats.end());
+
+  table_view expected({int_col, float_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("int_col");
+  expected_metadata.column_metadata[1].set_name("float_col");
+
+  auto filepath = temp_env->get_temp_filepath("MetadataTest.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_parquet(out_opts);
+
+  auto meta = read_parquet_metadata(cudf::io::source_info{filepath});
+  EXPECT_EQ(meta.num_rows(), num_rows);
+
+  std::string expected_schema = R"(schema
+ int_col
+ float_col
+)";
+  EXPECT_EQ(expected_schema, print(meta.schema().root()));
+
+  EXPECT_EQ(meta.schema().root().name(), "schema");
+  EXPECT_EQ(meta.schema().root().type_kind(), cudf::io::parquet::TypeKind::UNDEFINED_TYPE);
+  ASSERT_EQ(meta.schema().root().num_children(), 2);
+
+  EXPECT_EQ(meta.schema().root().child(0).name(), "int_col");
+  EXPECT_EQ(meta.schema().root().child(1).name(), "float_col");
+}
+
+TEST_F(ParquetMetadataReaderTest, TestNested)
+{
+  auto const num_rows       = 1200;
+  auto const lists_per_row  = 4;
+  auto const num_child_rows = num_rows * lists_per_row;
+
+  auto keys = random_values<int>(num_child_rows);
+  auto vals = random_values<float>(num_child_rows);
+  column_wrapper<int> keys_col(keys.begin(), keys.end());
+  column_wrapper<float> vals_col(vals.begin(), vals.end());
+  auto s_col = cudf::test::structs_column_wrapper({keys_col, vals_col}).release();
+
+  std::vector<int> row_offsets(num_rows + 1);
+  for (int idx = 0; idx < num_rows + 1; ++idx) {
+    row_offsets[idx] = idx * lists_per_row;
+  }
+  column_wrapper<int> offsets(row_offsets.begin(), row_offsets.end());
+
+  auto list_col =
+    cudf::make_lists_column(num_rows, offsets.release(), std::move(s_col), 0, rmm::device_buffer{});
+
+  table_view expected({*list_col, *list_col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("maps");
+  expected_metadata.column_metadata[0].set_list_column_as_map();
+  expected_metadata.column_metadata[1].set_name("lists");
+  expected_metadata.column_metadata[1].child(1).child(0).set_name("int_field");
+  expected_metadata.column_metadata[1].child(1).child(1).set_name("float_field");
+
+  auto filepath = temp_env->get_temp_filepath("MetadataTest.orc");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  cudf::io::write_parquet(out_opts);
+
+  auto meta = read_parquet_metadata(cudf::io::source_info{filepath});
+  EXPECT_EQ(meta.num_rows(), num_rows);
+
+  std::string expected_schema = R"(schema
+ maps
+  key_value
+   key
+   value
+ lists
+  list
+   element
+    int_field
+    float_field
+)";
+  EXPECT_EQ(expected_schema, print(meta.schema().root()));
+
+  EXPECT_EQ(meta.schema().root().name(), "schema");
+  EXPECT_EQ(meta.schema().root().type_kind(),
+            cudf::io::parquet::TypeKind::UNDEFINED_TYPE);  // struct
+  ASSERT_EQ(meta.schema().root().num_children(), 2);
+
+  auto const& out_map_col = meta.schema().root().child(0);
+  EXPECT_EQ(out_map_col.name(), "maps");
+  EXPECT_EQ(out_map_col.type_kind(), cudf::io::parquet::TypeKind::UNDEFINED_TYPE);  // map
+
+  ASSERT_EQ(out_map_col.num_children(), 1);
+  EXPECT_EQ(out_map_col.child(0).name(), "key_value");  // key_value (named in parquet writer)
+  ASSERT_EQ(out_map_col.child(0).num_children(), 2);
+  EXPECT_EQ(out_map_col.child(0).child(0).name(), "key");    // key (named in parquet writer)
+  EXPECT_EQ(out_map_col.child(0).child(1).name(), "value");  // value (named in parquet writer)
+  EXPECT_EQ(out_map_col.child(0).child(0).type_kind(), cudf::io::parquet::TypeKind::INT32);  // int
+  EXPECT_EQ(out_map_col.child(0).child(1).type_kind(),
+            cudf::io::parquet::TypeKind::FLOAT);  // float
+
+  auto const& out_list_col = meta.schema().root().child(1);
+  EXPECT_EQ(out_list_col.name(), "lists");
+  EXPECT_EQ(out_list_col.type_kind(), cudf::io::parquet::TypeKind::UNDEFINED_TYPE);  // list
+  // TODO repetition type?
+  ASSERT_EQ(out_list_col.num_children(), 1);
+  EXPECT_EQ(out_list_col.child(0).name(), "list");  // list (named in parquet writer)
+  ASSERT_EQ(out_list_col.child(0).num_children(), 1);
+
+  auto const& out_list_struct_col = out_list_col.child(0).child(0);
+  EXPECT_EQ(out_list_struct_col.name(), "element");  // elements (named in parquet writer)
+  EXPECT_EQ(out_list_struct_col.type_kind(),
+            cudf::io::parquet::TypeKind::UNDEFINED_TYPE);  // struct
+  ASSERT_EQ(out_list_struct_col.num_children(), 2);
+
+  auto const& out_int_col = out_list_struct_col.child(0);
+  EXPECT_EQ(out_int_col.name(), "int_field");
+  EXPECT_EQ(out_int_col.type_kind(), cudf::io::parquet::TypeKind::INT32);
+
+  auto const& out_float_col = out_list_struct_col.child(1);
+  EXPECT_EQ(out_float_col.name(), "float_field");
+  EXPECT_EQ(out_float_col.type_kind(), cudf::io::parquet::TypeKind::FLOAT);
+}
+
+TEST_F(ParquetWriterTest, NoNullsAsNonNullable)
+{
+  auto valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  column_wrapper<int32_t> col{{1, 2, 3}, valids};
+  table_view expected({col});
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_nullability(false);
+
+  auto filepath = temp_env->get_temp_filepath("NonNullable.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata));
+  // Writer should be able to write a column without nulls as non-nullable
+  EXPECT_NO_THROW(cudf::io::write_parquet(out_opts));
+}
+
+TEST_F(ParquetReaderTest, FilterSimple)
+{
+  srand(31337);
+  auto written_table = create_random_fixed_table<int>(9, 9, false);
+
+  auto filepath = temp_env->get_temp_filepath("FilterSimple.parquet");
+  cudf::io::parquet_writer_options args =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, *written_table);
+  cudf::io::write_parquet(args);
+
+  // Filtering AST - table[0] < RAND_MAX/2
+  auto literal_value     = cudf::numeric_scalar<decltype(RAND_MAX)>(RAND_MAX / 2);
+  auto literal           = cudf::ast::literal(literal_value);
+  auto col_ref_0         = cudf::ast::column_reference(0);
+  auto filter_expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, literal);
+
+  auto predicate = cudf::compute_column(*written_table, filter_expression);
+  EXPECT_EQ(predicate->view().type().id(), cudf::type_id::BOOL8)
+    << "Predicate filter should return a boolean";
+  auto expected = cudf::apply_boolean_mask(*written_table, *predicate);
+  // To make sure AST filters out some elements
+  EXPECT_LT(expected->num_rows(), written_table->num_rows());
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .filter(filter_expression);
+  auto result = cudf::io::read_parquet(read_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+auto create_parquet_with_stats(std::string const& filename)
+{
+  auto col0 = testdata::ascending<uint32_t>();
+  auto col1 = testdata::descending<int64_t>();
+  auto col2 = testdata::unordered<double>();
+
+  auto const expected = table_view{{col0, col1, col2}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("col_uint32");
+  expected_metadata.column_metadata[1].set_name("col_int64");
+  expected_metadata.column_metadata[2].set_name("col_double");
+
+  auto const filepath = temp_env->get_temp_filepath(filename);
+  const cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(std::move(expected_metadata))
+      .row_group_size_rows(8000)
+      .stats_level(cudf::io::statistics_freq::STATISTICS_ROWGROUP);
+  cudf::io::write_parquet(out_opts);
+
+  std::vector<std::unique_ptr<column>> columns;
+  columns.push_back(col0.release());
+  columns.push_back(col1.release());
+  columns.push_back(col2.release());
+
+  return std::pair{cudf::table{std::move(columns)}, filepath};
+}
+
+TEST_F(ParquetReaderTest, FilterIdentity)
+{
+  auto [src, filepath] = create_parquet_with_stats("FilterIdentity.parquet");
+
+  // Filtering AST - identity function, always true.
+  auto literal_value     = cudf::numeric_scalar<bool>(true);
+  auto literal           = cudf::ast::literal(literal_value);
+  auto filter_expression = cudf::ast::operation(cudf::ast::ast_operator::IDENTITY, literal);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .filter(filter_expression);
+  auto result = cudf::io::read_parquet(read_opts);
+
+  cudf::io::parquet_reader_options read_opts2 =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto result2 = cudf::io::read_parquet(read_opts2);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *result2.tbl);
+}
+
+TEST_F(ParquetReaderTest, FilterReferenceExpression)
+{
+  auto [src, filepath] = create_parquet_with_stats("FilterReferenceExpression.parquet");
+  // Filtering AST - table[0] < 150
+  auto literal_value     = cudf::numeric_scalar<uint32_t>(150);
+  auto literal           = cudf::ast::literal(literal_value);
+  auto col_ref_0         = cudf::ast::column_reference(0);
+  auto filter_expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, literal);
+
+  // Expected result
+  auto predicate = cudf::compute_column(src, filter_expression);
+  auto expected  = cudf::apply_boolean_mask(src, *predicate);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .filter(filter_expression);
+  auto result = cudf::io::read_parquet(read_opts);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+TEST_F(ParquetReaderTest, FilterNamedExpression)
+{
+  auto [src, filepath] = create_parquet_with_stats("NamedExpression.parquet");
+  // Filtering AST - table["col_uint32"] < 150
+  auto literal_value  = cudf::numeric_scalar<uint32_t>(150);
+  auto literal        = cudf::ast::literal(literal_value);
+  auto col_name_0     = cudf::ast::column_name_reference("col_uint32");
+  auto parquet_filter = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_name_0, literal);
+  auto col_ref_0      = cudf::ast::column_reference(0);
+  auto table_filter   = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, literal);
+
+  // Expected result
+  auto predicate = cudf::compute_column(src, table_filter);
+  auto expected  = cudf::apply_boolean_mask(src, *predicate);
+
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .filter(parquet_filter);
+  auto result = cudf::io::read_parquet(read_opts);
+
+  // tests
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*result.tbl, *expected);
+}
+
+// Test for Types - numeric, chrono, string.
+template <typename T>
+struct ParquetReaderPredicatePushdownTest : public ParquetReaderTest {};
+
+// These chrono types are not supported because parquet writer does not have a type to represent
+// them.
+using UnsupportedChronoTypes =
+  cudf::test::Types<cudf::timestamp_s, cudf::duration_D, cudf::duration_s>;
+// Also fixed point types unsupported, because AST does not support them yet.
+using SupportedTestTypes = cudf::test::RemoveIf<cudf::test::ContainedIn<UnsupportedChronoTypes>,
+                                                cudf::test::ComparableTypes>;
+
+TYPED_TEST_SUITE(ParquetReaderPredicatePushdownTest, SupportedTestTypes);
+
+template <typename T>
+auto create_parquet_typed_with_stats(std::string const& filename)
+{
+  auto col0 = testdata::ascending<T>();
+  auto col1 = testdata::descending<T>();
+  auto col2 = testdata::unordered<T>();
+
+  auto const written_table = table_view{{col0, col1, col2}};
+  auto const filepath      = temp_env->get_temp_filepath("FilterTyped.parquet");
+  {
+    cudf::io::table_input_metadata expected_metadata(written_table);
+    expected_metadata.column_metadata[0].set_name("col0");
+    expected_metadata.column_metadata[1].set_name("col1");
+    expected_metadata.column_metadata[2].set_name("col2");
+
+    const cudf::io::parquet_writer_options out_opts =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, written_table)
+        .metadata(std::move(expected_metadata))
+        .row_group_size_rows(8000);
+    cudf::io::write_parquet(out_opts);
+  }
+
+  std::vector<std::unique_ptr<column>> columns;
+  columns.push_back(col0.release());
+  columns.push_back(col1.release());
+  columns.push_back(col2.release());
+
+  return std::pair{cudf::table{std::move(columns)}, filepath};
+}
+
+TYPED_TEST(ParquetReaderPredicatePushdownTest, FilterTyped)
+{
+  using T = TypeParam;
+
+  auto const [src, filepath] = create_parquet_typed_with_stats<T>("FilterTyped.parquet");
+  auto const written_table   = src.view();
+
+  // Filtering AST
+  auto literal_value = []() {
+    if constexpr (cudf::is_timestamp<T>()) {
+      // table[0] < 10000 timestamp days/seconds/milliseconds/microseconds/nanoseconds
+      return cudf::timestamp_scalar<T>(T(typename T::duration(10000)));  // i (0-20,000)
+    } else if constexpr (cudf::is_duration<T>()) {
+      // table[0] < 10000 day/seconds/milliseconds/microseconds/nanoseconds
+      return cudf::duration_scalar<T>(T(10000));  // i (0-20,000)
+    } else if constexpr (std::is_same_v<T, cudf::string_view>) {
+      // table[0] < "000010000"
+      return cudf::string_scalar("000010000");  // i (0-20,000)
+    } else {
+      // table[0] < 0 or 100u
+      return cudf::numeric_scalar<T>((100 - 100 * std::is_signed_v<T>));  // i/100 (-100-100/ 0-200)
+    }
+  }();
+  auto literal           = cudf::ast::literal(literal_value);
+  auto col_name_0        = cudf::ast::column_name_reference("col0");
+  auto filter_expression = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_name_0, literal);
+  auto col_ref_0         = cudf::ast::column_reference(0);
+  auto ref_filter        = cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, literal);
+
+  // Expected result
+  auto predicate = cudf::compute_column(written_table, ref_filter);
+  EXPECT_EQ(predicate->view().type().id(), cudf::type_id::BOOL8)
+    << "Predicate filter should return a boolean";
+  auto expected = cudf::apply_boolean_mask(written_table, *predicate);
+
+  // Reading with Predicate Pushdown
+  cudf::io::parquet_reader_options read_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath})
+      .filter(filter_expression);
+  auto result       = cudf::io::read_parquet(read_opts);
+  auto result_table = result.tbl->view();
+
+  // tests
+  EXPECT_EQ(int(written_table.column(0).type().id()), int(result_table.column(0).type().id()))
+    << "col0 type mismatch";
+  // To make sure AST filters out some elements
+  EXPECT_LT(expected->num_rows(), written_table.num_rows());
+  EXPECT_EQ(result_table.num_rows(), expected->num_rows());
+  EXPECT_EQ(result_table.num_columns(), expected->num_columns());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result_table);
+}
+
+TEST_F(ParquetReaderTest, FilterMultiple1)
+{
+  using T = cudf::string_view;
+
+  auto const [src, filepath] = create_parquet_typed_with_stats<T>("FilterMultiple1.parquet");
+  auto const written_table   = src.view();
+
+  // Filtering AST - 10000 < table[0] < 12000
+  std::string const low  = "000010000";
+  std::string const high = "000012000";
+  auto lov               = cudf::string_scalar(low, true);
+  auto hiv               = cudf::string_scalar(high, true);
+  auto filter_col        = cudf::ast::column_reference(0);
+  auto lo_lit            = cudf::ast::literal(lov);
+  auto hi_lit            = cudf::ast::literal(hiv);
+  auto expr_1 = cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col, lo_lit);
+  auto expr_2 = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col, hi_lit);
+  auto expr_3 = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_1, expr_2);
+
+  // Expected result
+  auto predicate = cudf::compute_column(written_table, expr_3);
+  auto expected  = cudf::apply_boolean_mask(written_table, *predicate);
+
+  auto si                  = cudf::io::source_info(filepath);
+  auto builder             = cudf::io::parquet_reader_options::builder(si).filter(expr_3);
+  auto table_with_metadata = cudf::io::read_parquet(builder);
+  auto result              = table_with_metadata.tbl->view();
+
+  // tests
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result);
+}
+
+TEST_F(ParquetReaderTest, FilterMultiple2)
+{
+  // multiple conditions on same column.
+  using T = cudf::string_view;
+
+  auto const [src, filepath] = create_parquet_typed_with_stats<T>("FilterMultiple2.parquet");
+  auto const written_table   = src.view();
+  // 0-8000, 8001-16000, 16001-20000
+
+  // Filtering AST
+  // (table[0] >= "000010000" AND table[0] < "000012000") OR
+  // (table[0] >= "000017000" AND table[0] < "000019000")
+  std::string const low1  = "000010000";
+  std::string const high1 = "000012000";
+  auto lov                = cudf::string_scalar(low1, true);
+  auto hiv                = cudf::string_scalar(high1, true);
+  auto filter_col         = cudf::ast::column_reference(0);
+  auto lo_lit             = cudf::ast::literal(lov);
+  auto hi_lit             = cudf::ast::literal(hiv);
+  auto expr_1 = cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col, lo_lit);
+  auto expr_2 = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col, hi_lit);
+  auto expr_3 = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_1, expr_2);
+  std::string const low2  = "000017000";
+  std::string const high2 = "000019000";
+  auto lov2               = cudf::string_scalar(low2, true);
+  auto hiv2               = cudf::string_scalar(high2, true);
+  auto lo_lit2            = cudf::ast::literal(lov2);
+  auto hi_lit2            = cudf::ast::literal(hiv2);
+  auto expr_4 = cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col, lo_lit2);
+  auto expr_5 = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col, hi_lit2);
+  auto expr_6 = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_4, expr_5);
+  auto expr_7 = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_OR, expr_3, expr_6);
+
+  // Expected result
+  auto predicate = cudf::compute_column(written_table, expr_7);
+  auto expected  = cudf::apply_boolean_mask(written_table, *predicate);
+
+  auto si                  = cudf::io::source_info(filepath);
+  auto builder             = cudf::io::parquet_reader_options::builder(si).filter(expr_7);
+  auto table_with_metadata = cudf::io::read_parquet(builder);
+  auto result              = table_with_metadata.tbl->view();
+
+  // tests
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result);
+}
+
+TEST_F(ParquetReaderTest, FilterMultiple3)
+{
+  // multiple conditions with reference to multiple columns.
+  // index and name references mixed.
+  using T                    = uint32_t;
+  auto const [src, filepath] = create_parquet_typed_with_stats<T>("FilterMultiple3.parquet");
+  auto const written_table   = src.view();
+
+  // Filtering AST - (table[0] >= 70 AND table[0] < 90) OR (table[1] >= 100 AND table[1] < 120)
+  // row groups min, max:
+  // table[0] 0-80, 81-160, 161-200.
+  // table[1] 200-121, 120-41, 40-0.
+  auto filter_col1  = cudf::ast::column_reference(0);
+  auto filter_col2  = cudf::ast::column_name_reference("col1");
+  T constexpr low1  = 70;
+  T constexpr high1 = 90;
+  T constexpr low2  = 100;
+  T constexpr high2 = 120;
+  auto lov          = cudf::numeric_scalar(low1, true);
+  auto hiv          = cudf::numeric_scalar(high1, true);
+  auto lo_lit1      = cudf::ast::literal(lov);
+  auto hi_lit1      = cudf::ast::literal(hiv);
+  auto expr_1  = cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col1, lo_lit1);
+  auto expr_2  = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col1, hi_lit1);
+  auto expr_3  = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_1, expr_2);
+  auto lov2    = cudf::numeric_scalar(low2, true);
+  auto hiv2    = cudf::numeric_scalar(high2, true);
+  auto lo_lit2 = cudf::ast::literal(lov2);
+  auto hi_lit2 = cudf::ast::literal(hiv2);
+  auto expr_4  = cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col2, lo_lit2);
+  auto expr_5  = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col2, hi_lit2);
+  auto expr_6  = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_4, expr_5);
+  // expression to test
+  auto expr_7 = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_OR, expr_3, expr_6);
+
+  // Expected result
+  auto filter_col2_ref = cudf::ast::column_reference(1);
+  auto expr_4_ref =
+    cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col2_ref, lo_lit2);
+  auto expr_5_ref = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col2_ref, hi_lit2);
+  auto expr_6_ref =
+    cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_4_ref, expr_5_ref);
+  auto expr_7_ref = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_OR, expr_3, expr_6_ref);
+  auto predicate  = cudf::compute_column(written_table, expr_7_ref);
+  auto expected   = cudf::apply_boolean_mask(written_table, *predicate);
+
+  auto si                  = cudf::io::source_info(filepath);
+  auto builder             = cudf::io::parquet_reader_options::builder(si).filter(expr_7);
+  auto table_with_metadata = cudf::io::read_parquet(builder);
+  auto result              = table_with_metadata.tbl->view();
+
+  // tests
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result);
+}
+
+TEST_F(ParquetReaderTest, FilterSupported)
+{
+  using T                    = uint32_t;
+  auto const [src, filepath] = create_parquet_typed_with_stats<T>("FilterSupported.parquet");
+  auto const written_table   = src.view();
+
+  // Filtering AST - ((table[0] > 70 AND table[0] <= 90) OR (table[1] >= 100 AND table[1] < 120))
+  //              AND (table[1] != 110)
+  // row groups min, max:
+  // table[0] 0-80, 81-160, 161-200.
+  // table[1] 200-121, 120-41, 40-0.
+  auto filter_col1       = cudf::ast::column_reference(0);
+  auto filter_col2       = cudf::ast::column_reference(1);
+  T constexpr low1       = 70;
+  T constexpr high1      = 90;
+  T constexpr low2       = 100;
+  T constexpr high2      = 120;
+  T constexpr skip_value = 110;
+  auto lov               = cudf::numeric_scalar(low1, true);
+  auto hiv               = cudf::numeric_scalar(high1, true);
+  auto lo_lit1           = cudf::ast::literal(lov);
+  auto hi_lit1           = cudf::ast::literal(hiv);
+  auto expr_1  = cudf::ast::operation(cudf::ast::ast_operator::GREATER, filter_col1, lo_lit1);
+  auto expr_2  = cudf::ast::operation(cudf::ast::ast_operator::LESS_EQUAL, filter_col1, hi_lit1);
+  auto expr_3  = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_1, expr_2);
+  auto lov2    = cudf::numeric_scalar(low2, true);
+  auto hiv2    = cudf::numeric_scalar(high2, true);
+  auto lo_lit2 = cudf::ast::literal(lov2);
+  auto hi_lit2 = cudf::ast::literal(hiv2);
+  auto expr_4  = cudf::ast::operation(cudf::ast::ast_operator::GREATER_EQUAL, filter_col2, lo_lit2);
+  auto expr_5  = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col2, hi_lit2);
+  auto expr_6  = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_4, expr_5);
+  auto expr_7  = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_OR, expr_3, expr_6);
+  auto skip_ov = cudf::numeric_scalar(skip_value, true);
+  auto skip_lit = cudf::ast::literal(skip_ov);
+  auto expr_8   = cudf::ast::operation(cudf::ast::ast_operator::NOT_EQUAL, filter_col2, skip_lit);
+  auto expr_9   = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, expr_7, expr_8);
+
+  // Expected result
+  auto predicate = cudf::compute_column(written_table, expr_9);
+  auto expected  = cudf::apply_boolean_mask(written_table, *predicate);
+
+  auto si                  = cudf::io::source_info(filepath);
+  auto builder             = cudf::io::parquet_reader_options::builder(si).filter(expr_9);
+  auto table_with_metadata = cudf::io::read_parquet(builder);
+  auto result              = table_with_metadata.tbl->view();
+
+  // tests
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result);
+}
+
+TEST_F(ParquetReaderTest, FilterSupported2)
+{
+  using T                 = uint32_t;
+  constexpr auto num_rows = 4000;
+  auto elements0 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i / 2000; });
+  auto elements1 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i / 1000; });
+  auto elements2 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i / 500; });
+  auto col0 = cudf::test::fixed_width_column_wrapper<T>(elements0, elements0 + num_rows);
+  auto col1 = cudf::test::fixed_width_column_wrapper<T>(elements1, elements1 + num_rows);
+  auto col2 = cudf::test::fixed_width_column_wrapper<T>(elements2, elements2 + num_rows);
+  auto const written_table = table_view{{col0, col1, col2}};
+  auto const filepath      = temp_env->get_temp_filepath("FilterSupported2.parquet");
+  {
+    const cudf::io::parquet_writer_options out_opts =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, written_table)
+        .row_group_size_rows(1000);
+    cudf::io::write_parquet(out_opts);
+  }
+  auto si          = cudf::io::source_info(filepath);
+  auto filter_col0 = cudf::ast::column_reference(0);
+  auto filter_col1 = cudf::ast::column_reference(1);
+  auto filter_col2 = cudf::ast::column_reference(2);
+  auto s_value     = cudf::numeric_scalar<T>(1, true);
+  auto lit_value   = cudf::ast::literal(s_value);
+
+  auto test_expr = [&](auto& expr) {
+    // Expected result
+    auto predicate = cudf::compute_column(written_table, expr);
+    auto expected  = cudf::apply_boolean_mask(written_table, *predicate);
+
+    // tests
+    auto builder             = cudf::io::parquet_reader_options::builder(si).filter(expr);
+    auto table_with_metadata = cudf::io::read_parquet(builder);
+    auto result              = table_with_metadata.tbl->view();
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result);
+  };
+
+  // row groups min, max:
+  // table[0] 0-0, 0-0, 1-1, 1-1
+  // table[1] 0-0, 1-1, 2-2, 3-3
+  // table[2] 0-1, 2-3, 4-5, 6-7
+
+  // Filtering AST -   table[i] == 1
+  {
+    auto expr0 = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, filter_col0, lit_value);
+    test_expr(expr0);
+
+    auto expr1 = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, filter_col1, lit_value);
+    test_expr(expr1);
+
+    auto expr2 = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, filter_col2, lit_value);
+    test_expr(expr2);
+  }
+  // Filtering AST -   table[i] != 1
+  {
+    auto expr0 = cudf::ast::operation(cudf::ast::ast_operator::NOT_EQUAL, filter_col0, lit_value);
+    test_expr(expr0);
+
+    auto expr1 = cudf::ast::operation(cudf::ast::ast_operator::NOT_EQUAL, filter_col1, lit_value);
+    test_expr(expr1);
+
+    auto expr2 = cudf::ast::operation(cudf::ast::ast_operator::NOT_EQUAL, filter_col2, lit_value);
+    test_expr(expr2);
+  }
+}
+
+// Error types - type mismatch, invalid column name, invalid literal type, invalid operator,
+// non-bool filter output type.
+TEST_F(ParquetReaderTest, FilterErrors)
+{
+  using T                    = uint32_t;
+  auto const [src, filepath] = create_parquet_typed_with_stats<T>("FilterErrors.parquet");
+  auto const written_table   = src.view();
+  auto si                    = cudf::io::source_info(filepath);
+
+  // Filtering AST - invalid column index
+  {
+    auto filter_col1 = cudf::ast::column_reference(3);
+    T constexpr low  = 100;
+    auto lov         = cudf::numeric_scalar(low, true);
+    auto low_lot     = cudf::ast::literal(lov);
+    auto expr        = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col1, low_lot);
+
+    auto builder = cudf::io::parquet_reader_options::builder(si).filter(expr);
+    EXPECT_THROW(cudf::io::read_parquet(builder), cudf::logic_error);
+  }
+
+  // Filtering AST - invalid column name
+  {
+    auto filter_col1 = cudf::ast::column_name_reference("col3");
+    T constexpr low  = 100;
+    auto lov         = cudf::numeric_scalar(low, true);
+    auto low_lot     = cudf::ast::literal(lov);
+    auto expr        = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col1, low_lot);
+    auto builder     = cudf::io::parquet_reader_options::builder(si).filter(expr);
+    EXPECT_THROW(cudf::io::read_parquet(builder), cudf::logic_error);
+  }
+
+  // Filtering AST - incompatible literal type
+  {
+    auto filter_col1      = cudf::ast::column_name_reference("col0");
+    auto filter_col2      = cudf::ast::column_reference(1);
+    int64_t constexpr low = 100;
+    auto lov              = cudf::numeric_scalar(low, true);
+    auto low_lot          = cudf::ast::literal(lov);
+    auto expr1    = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col1, low_lot);
+    auto expr2    = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col2, low_lot);
+    auto builder1 = cudf::io::parquet_reader_options::builder(si).filter(expr1);
+    EXPECT_THROW(cudf::io::read_parquet(builder1), cudf::logic_error);
+
+    auto builder2 = cudf::io::parquet_reader_options::builder(si).filter(expr2);
+    EXPECT_THROW(cudf::io::read_parquet(builder2), cudf::logic_error);
+  }
+
+  // Filtering AST - "table[0] + 110" is invalid filter expression
+  {
+    auto filter_col1      = cudf::ast::column_reference(0);
+    T constexpr add_value = 110;
+    auto add_v            = cudf::numeric_scalar(add_value, true);
+    auto add_lit          = cudf::ast::literal(add_v);
+    auto expr_8 = cudf::ast::operation(cudf::ast::ast_operator::ADD, filter_col1, add_lit);
+
+    auto si      = cudf::io::source_info(filepath);
+    auto builder = cudf::io::parquet_reader_options::builder(si).filter(expr_8);
+    EXPECT_THROW(cudf::io::read_parquet(builder), cudf::logic_error);
+
+    // Expected result throw to show that the filter expression is invalid,
+    // not a limitation of the parquet predicate pushdown.
+    auto predicate = cudf::compute_column(written_table, expr_8);
+    EXPECT_THROW(cudf::apply_boolean_mask(written_table, *predicate), cudf::logic_error);
+  }
+
+  // Filtering AST - INT64(table[0] < 100) non-bool expression
+  {
+    auto filter_col1 = cudf::ast::column_reference(0);
+    T constexpr low  = 100;
+    auto lov         = cudf::numeric_scalar(low, true);
+    auto low_lot     = cudf::ast::literal(lov);
+    auto bool_expr   = cudf::ast::operation(cudf::ast::ast_operator::LESS, filter_col1, low_lot);
+    auto cast        = cudf::ast::operation(cudf::ast::ast_operator::CAST_TO_INT64, bool_expr);
+
+    auto builder = cudf::io::parquet_reader_options::builder(si).filter(cast);
+    EXPECT_THROW(cudf::io::read_parquet(builder), cudf::logic_error);
+    EXPECT_NO_THROW(cudf::compute_column(written_table, cast));
+    auto predicate = cudf::compute_column(written_table, cast);
+    EXPECT_NE(predicate->view().type().id(), cudf::type_id::BOOL8);
+  }
+}
+
+// Filter without stats information in file.
+TEST_F(ParquetReaderTest, FilterNoStats)
+{
+  using T                 = uint32_t;
+  constexpr auto num_rows = 16000;
+  auto elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i / 1000; });
+  auto col0 = cudf::test::fixed_width_column_wrapper<T>(elements, elements + num_rows);
+  auto const written_table = table_view{{col0}};
+  auto const filepath      = temp_env->get_temp_filepath("FilterNoStats.parquet");
+  {
+    const cudf::io::parquet_writer_options out_opts =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, written_table)
+        .row_group_size_rows(8000)
+        .stats_level(cudf::io::statistics_freq::STATISTICS_NONE);
+    cudf::io::write_parquet(out_opts);
+  }
+  auto si          = cudf::io::source_info(filepath);
+  auto filter_col0 = cudf::ast::column_reference(0);
+  auto s_value     = cudf::numeric_scalar<T>(1, true);
+  auto lit_value   = cudf::ast::literal(s_value);
+
+  // row groups min, max:
+  // table[0] 0-0, 1-1, 2-2, 3-3
+  // Filtering AST - table[0] > 1
+  auto expr = cudf::ast::operation(cudf::ast::ast_operator::GREATER, filter_col0, lit_value);
+
+  // Expected result
+  auto predicate = cudf::compute_column(written_table, expr);
+  auto expected  = cudf::apply_boolean_mask(written_table, *predicate);
+
+  // tests
+  auto builder             = cudf::io::parquet_reader_options::builder(si).filter(expr);
+  auto table_with_metadata = cudf::io::read_parquet(builder);
+  auto result              = table_with_metadata.tbl->view();
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), result);
+}
+
+// Filter for float column with NaN values
+TEST_F(ParquetReaderTest, FilterFloatNAN)
+{
+  constexpr auto num_rows = 24000;
+  auto elements           = cudf::detail::make_counting_transform_iterator(
+    0, [num_rows](auto i) { return i > num_rows / 2 ? NAN : i; });
+  auto col0 = cudf::test::fixed_width_column_wrapper<float>(elements, elements + num_rows);
+  auto col1 = cudf::test::fixed_width_column_wrapper<double>(elements, elements + num_rows);
+
+  auto const written_table = table_view{{col0, col1}};
+  auto const filepath      = temp_env->get_temp_filepath("FilterFloatNAN.parquet");
+  {
+    const cudf::io::parquet_writer_options out_opts =
+      cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, written_table)
+        .row_group_size_rows(8000);
+    cudf::io::write_parquet(out_opts);
+  }
+  auto si          = cudf::io::source_info(filepath);
+  auto filter_col0 = cudf::ast::column_reference(0);
+  auto filter_col1 = cudf::ast::column_reference(1);
+  auto s0_value    = cudf::numeric_scalar<float>(NAN, true);
+  auto lit0_value  = cudf::ast::literal(s0_value);
+  auto s1_value    = cudf::numeric_scalar<double>(NAN, true);
+  auto lit1_value  = cudf::ast::literal(s1_value);
+
+  // row groups min, max:
+  // table[0] 0-0, 1-1, 2-2, 3-3
+  // Filtering AST - table[0] == NAN, table[1] != NAN
+  auto expr_eq  = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, filter_col0, lit0_value);
+  auto expr_neq = cudf::ast::operation(cudf::ast::ast_operator::NOT_EQUAL, filter_col1, lit1_value);
+
+  // Expected result
+  auto predicate0 = cudf::compute_column(written_table, expr_eq);
+  auto expected0  = cudf::apply_boolean_mask(written_table, *predicate0);
+  auto predicate1 = cudf::compute_column(written_table, expr_neq);
+  auto expected1  = cudf::apply_boolean_mask(written_table, *predicate1);
+
+  // tests
+  auto builder0             = cudf::io::parquet_reader_options::builder(si).filter(expr_eq);
+  auto table_with_metadata0 = cudf::io::read_parquet(builder0);
+  auto result0              = table_with_metadata0.tbl->view();
+  auto builder1             = cudf::io::parquet_reader_options::builder(si).filter(expr_neq);
+  auto table_with_metadata1 = cudf::io::read_parquet(builder1);
+  auto result1              = table_with_metadata1.tbl->view();
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected0->view(), result0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected1->view(), result1);
+}
+
+TEST_F(ParquetWriterTest, TimestampMicrosINT96NoOverflow)
+{
+  using namespace cuda::std::chrono;
+  using namespace cudf::io;
+
+  column_wrapper<cudf::timestamp_us> big_ts_col{
+    sys_days{year{3023} / month{7} / day{14}} + 7h + 38min + 45s + 418688us,
+    sys_days{year{723} / month{3} / day{21}} + 14h + 20min + 13s + microseconds{781ms}};
+
+  table_view expected({big_ts_col});
+  auto filepath = temp_env->get_temp_filepath("BigINT96Timestamp.parquet");
+
+  auto const out_opts =
+    parquet_writer_options::builder(sink_info{filepath}, expected).int96_timestamps(true).build();
+  write_parquet(out_opts);
+
+  auto const in_opts = parquet_reader_options::builder(source_info(filepath))
+                         .timestamp_type(cudf::data_type(cudf::type_id::TIMESTAMP_MICROSECONDS))
+                         .build();
+  auto const result = read_parquet(in_opts);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result.tbl->view());
+}
+
+TEST_F(ParquetWriterTest, PreserveNullability)
+{
+  constexpr auto num_rows = 100;
+
+  auto const col0_data = random_values<int32_t>(num_rows);
+  auto const col1_data = random_values<int32_t>(num_rows);
+
+  auto const col0_validity = cudf::test::iterators::no_nulls();
+  auto const col1_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  column_wrapper<int32_t> col0{col0_data.begin(), col0_data.end(), col0_validity};
+  column_wrapper<int32_t> col1{col1_data.begin(), col1_data.end(), col1_validity};
+  auto const col2 = make_parquet_list_list_col<int>(0, num_rows, 5, 8, true);
+
+  auto const expected = table_view{{col0, col1, *col2}};
+
+  cudf::io::table_input_metadata expected_metadata(expected);
+  expected_metadata.column_metadata[0].set_name("mandatory");
+  expected_metadata.column_metadata[0].set_nullability(false);
+  expected_metadata.column_metadata[1].set_name("optional");
+  expected_metadata.column_metadata[1].set_nullability(true);
+  expected_metadata.column_metadata[2].set_name("lists");
+  expected_metadata.column_metadata[2].set_nullability(true);
+  // offsets is a cudf thing that's not part of the parquet schema so it won't have nullability set
+  expected_metadata.column_metadata[2].child(0).set_name("offsets");
+  expected_metadata.column_metadata[2].child(1).set_name("element");
+  expected_metadata.column_metadata[2].child(1).set_nullability(false);
+  expected_metadata.column_metadata[2].child(1).child(0).set_name("offsets");
+  expected_metadata.column_metadata[2].child(1).child(1).set_name("element");
+  expected_metadata.column_metadata[2].child(1).child(1).set_nullability(true);
+
+  auto const filepath = temp_env->get_temp_filepath("PreserveNullability.parquet");
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .metadata(expected_metadata);
+
+  cudf::io::write_parquet(out_opts);
+
+  cudf::io::parquet_reader_options const in_opts =
+    cudf::io::parquet_reader_options::builder(cudf::io::source_info{filepath});
+  auto const result        = cudf::io::read_parquet(in_opts);
+  auto const read_metadata = cudf::io::table_input_metadata{result.metadata};
+
+  // test that expected_metadata matches read_metadata
+  std::function<void(cudf::io::column_in_metadata, cudf::io::column_in_metadata)>
+    compare_names_and_nullability = [&](auto lhs, auto rhs) {
+      EXPECT_EQ(lhs.get_name(), rhs.get_name());
+      ASSERT_EQ(lhs.is_nullability_defined(), rhs.is_nullability_defined());
+      if (lhs.is_nullability_defined()) { EXPECT_EQ(lhs.nullable(), rhs.nullable()); }
+      ASSERT_EQ(lhs.num_children(), rhs.num_children());
+      for (int i = 0; i < lhs.num_children(); ++i) {
+        compare_names_and_nullability(lhs.child(i), rhs.child(i));
+      }
+    };
+
+  ASSERT_EQ(expected_metadata.column_metadata.size(), read_metadata.column_metadata.size());
+
+  for (size_t i = 0; i < expected_metadata.column_metadata.size(); ++i) {
+    compare_names_and_nullability(expected_metadata.column_metadata[i],
+                                  read_metadata.column_metadata[i]);
+  }
+}
+
+TEST_P(ParquetV2Test, CheckEncodings)
+{
+  using cudf::io::parquet::Encoding;
+  constexpr auto num_rows = 100'000;
+  auto const is_v2        = GetParam();
+
+  auto const validity = cudf::test::iterators::no_nulls();
+  // data should be PLAIN for v1, RLE for V2
+  auto col0_data =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) -> bool { return i % 2 == 0; });
+  // data should be PLAIN for both
+  auto col1_data = random_values<int32_t>(num_rows);
+  // data should be PLAIN_DICTIONARY for v1, PLAIN and RLE_DICTIONARY for v2
+  auto col2_data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 1; });
+
+  cudf::test::fixed_width_column_wrapper<bool> col0{col0_data, col0_data + num_rows, validity};
+  column_wrapper<int32_t> col1{col1_data.begin(), col1_data.end(), validity};
+  column_wrapper<int32_t> col2{col2_data, col2_data + num_rows, validity};
+
+  auto expected = table_view{{col0, col1, col2}};
+
+  auto const filename = is_v2 ? "CheckEncodingsV2.parquet" : "CheckEncodingsV1.parquet";
+  auto filepath       = temp_env->get_temp_filepath(filename);
+  cudf::io::parquet_writer_options out_opts =
+    cudf::io::parquet_writer_options::builder(cudf::io::sink_info{filepath}, expected)
+      .max_page_size_rows(num_rows)
+      .write_v2_headers(is_v2);
+  cudf::io::write_parquet(out_opts);
+
+  // make sure the expected encodings are present
+  auto contains = [](auto const& vec, auto const& enc) {
+    return std::find(vec.begin(), vec.end(), enc) != vec.end();
+  };
+
+  auto const source = cudf::io::datasource::create(filepath);
+  cudf::io::parquet::FileMetaData fmd;
+
+  read_footer(source, &fmd);
+  auto const& chunk0_enc = fmd.row_groups[0].columns[0].meta_data.encodings;
+  auto const& chunk1_enc = fmd.row_groups[0].columns[1].meta_data.encodings;
+  auto const& chunk2_enc = fmd.row_groups[0].columns[2].meta_data.encodings;
+  if (is_v2) {
+    // col0 should have RLE for rep/def and data
+    EXPECT_TRUE(chunk0_enc.size() == 1);
+    EXPECT_TRUE(contains(chunk0_enc, Encoding::RLE));
+    // col1 should have RLE for rep/def and PLAIN for data
+    EXPECT_TRUE(chunk1_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::PLAIN));
+    // col2 should have RLE for rep/def, PLAIN for dict, and RLE_DICTIONARY for data
+    EXPECT_TRUE(chunk2_enc.size() == 3);
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::PLAIN));
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::RLE_DICTIONARY));
+  } else {
+    // col0 should have RLE for rep/def and PLAIN for data
+    EXPECT_TRUE(chunk0_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk0_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk0_enc, Encoding::PLAIN));
+    // col1 should have RLE for rep/def and PLAIN for data
+    EXPECT_TRUE(chunk1_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk1_enc, Encoding::PLAIN));
+    // col2 should have RLE for rep/def and PLAIN_DICTIONARY for data and dict
+    EXPECT_TRUE(chunk2_enc.size() == 2);
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::RLE));
+    EXPECT_TRUE(contains(chunk2_enc, Encoding::PLAIN_DICTIONARY));
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/row_selection_test.cpp b/cpp/tests/io/row_selection_test.cpp
new file mode 100644
index 0000000..b4583ac
--- /dev/null
+++ b/cpp/tests/io/row_selection_test.cpp
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <src/io/utilities/row_selection.hpp>
+
+#include <limits>
+
+using cudf::io::detail::skip_rows_num_rows_from_options;
+
+// Base test fixture for tests
+struct FromOptsTest : public cudf::test::BaseFixture {};
+
+TEST_F(FromOptsTest, PassThrough)
+{
+  // select all rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(0, 100, 100);
+    EXPECT_EQ(out_skip, 0);
+    EXPECT_EQ(out_num, 100);
+  }
+
+  // select all except first skip_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(10, 90, 100);
+    EXPECT_EQ(out_skip, 10);
+    EXPECT_EQ(out_num, 90);
+  }
+
+  // select first num_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(0, 60, 100);
+    EXPECT_EQ(out_skip, 0);
+    EXPECT_EQ(out_num, 60);
+  }
+}
+
+TEST_F(FromOptsTest, DefaultNumRows)
+{
+  // no skip_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(0, std::nullopt, 100);
+    EXPECT_EQ(out_skip, 0);
+    EXPECT_EQ(out_num, 100);
+  }
+
+  // with skip_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(20, std::nullopt, 100);
+    EXPECT_EQ(out_skip, 20);
+    EXPECT_EQ(out_num, 80);
+  }
+}
+
+TEST_F(FromOptsTest, InputSize32BitOverflow)
+{
+  // Input number of rows too large to fit into cudf::size_type
+  // Test that we can still select rows from such input
+  auto const too_large_for_32bit = std::numeric_limits<int64_t>::max();
+
+  // no num_rows
+  {
+    auto [out_skip, out_num] =
+      skip_rows_num_rows_from_options(too_large_for_32bit - 10, std::nullopt, too_large_for_32bit);
+    EXPECT_EQ(out_skip, too_large_for_32bit - 10);
+    EXPECT_EQ(out_num, 10);
+  }
+
+  // with num_rows
+  {
+    auto [out_skip, out_num] =
+      skip_rows_num_rows_from_options(too_large_for_32bit - 100, 30, too_large_for_32bit);
+    EXPECT_EQ(out_skip, too_large_for_32bit - 100);
+    EXPECT_EQ(out_num, 30);
+  }
+}
+
+TEST_F(FromOptsTest, LimitOptionsToFileRows)
+{
+  // limit skip_rows without num_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(1000, std::nullopt, 100);
+    EXPECT_EQ(out_skip, 100);
+    EXPECT_EQ(out_num, 0);
+  }
+
+  // limit skip_rows with num_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(1000, 2, 100);
+    EXPECT_EQ(out_skip, 100);
+    EXPECT_EQ(out_num, 0);
+  }
+
+  // limit num_rows without skip_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(0, 1000, 100);
+    EXPECT_EQ(out_skip, 0);
+    EXPECT_EQ(out_num, 100);
+  }
+
+  // limit num_rows with skip_rows
+  {
+    auto [out_skip, out_num] = skip_rows_num_rows_from_options(10, 1000, 100);
+    EXPECT_EQ(out_skip, 10);
+    EXPECT_EQ(out_num, 90);
+  }
+}
+
+TEST_F(FromOptsTest, OverFlowDetection)
+{
+  auto const too_large_for_32bit = std::numeric_limits<int64_t>::max();
+
+  // Too many rows to read until the end of the file
+  EXPECT_THROW(skip_rows_num_rows_from_options(0, std::nullopt, too_large_for_32bit),
+               std::overflow_error);
+
+  // Should work fine with num_rows
+  EXPECT_NO_THROW(
+    skip_rows_num_rows_from_options(1000, too_large_for_32bit - 100, too_large_for_32bit));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/text/data_chunk_source_test.cpp b/cpp/tests/io/text/data_chunk_source_test.cpp
new file mode 100644
index 0000000..e186dcd
--- /dev/null
+++ b/cpp/tests/io/text/data_chunk_source_test.cpp
@@ -0,0 +1,389 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <cudf/io/text/data_chunk_source_factories.hpp>
+#include <cudf/io/text/detail/bgzip_utils.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <fstream>
+#include <random>
+
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+struct DataChunkSourceTest : public cudf::test::BaseFixture {};
+
+std::string chunk_to_host(cudf::io::text::device_data_chunk const& chunk)
+{
+  std::string result(chunk.size(), '\0');
+  CUDF_CUDA_TRY(cudaMemcpy(result.data(), chunk.data(), chunk.size(), cudaMemcpyDefault));
+  return result;
+}
+
+void test_source(std::string const& content, cudf::io::text::data_chunk_source const& source)
+{
+  {
+    // full contents
+    auto reader      = source.create_reader();
+    auto const chunk = reader->get_next_chunk(content.size(), cudf::get_default_stream());
+    ASSERT_EQ(chunk->size(), content.size());
+    ASSERT_EQ(chunk_to_host(*chunk), content);
+  }
+  {
+    // skipping contents
+    auto reader = source.create_reader();
+    reader->skip_bytes(4);
+    auto const chunk = reader->get_next_chunk(content.size(), cudf::get_default_stream());
+    ASSERT_EQ(chunk->size(), content.size() - 4);
+    ASSERT_EQ(chunk_to_host(*chunk), content.substr(4));
+  }
+  {
+    // reading multiple chunks, starting with a small one
+    auto reader       = source.create_reader();
+    auto const chunk1 = reader->get_next_chunk(5, cudf::get_default_stream());
+    auto const chunk2 = reader->get_next_chunk(content.size() - 5, cudf::get_default_stream());
+    ASSERT_EQ(chunk1->size(), 5);
+    ASSERT_EQ(chunk2->size(), content.size() - 5);
+    ASSERT_EQ(chunk_to_host(*chunk1), content.substr(0, 5));
+    ASSERT_EQ(chunk_to_host(*chunk2), content.substr(5));
+  }
+  {
+    // reading multiple chunks
+    auto reader       = source.create_reader();
+    auto const chunk1 = reader->get_next_chunk(content.size() / 2, cudf::get_default_stream());
+    auto const chunk2 =
+      reader->get_next_chunk(content.size() - content.size() / 2, cudf::get_default_stream());
+    ASSERT_EQ(chunk1->size(), content.size() / 2);
+    ASSERT_EQ(chunk2->size(), content.size() - content.size() / 2);
+    ASSERT_EQ(chunk_to_host(*chunk1), content.substr(0, content.size() / 2));
+    ASSERT_EQ(chunk_to_host(*chunk2), content.substr(content.size() / 2));
+  }
+  {
+    // reading too many bytes
+    auto reader      = source.create_reader();
+    auto const chunk = reader->get_next_chunk(content.size() + 10, cudf::get_default_stream());
+    ASSERT_EQ(chunk->size(), content.size());
+    ASSERT_EQ(chunk_to_host(*chunk), content);
+    auto next_chunk = reader->get_next_chunk(1, cudf::get_default_stream());
+    ASSERT_EQ(next_chunk->size(), 0);
+  }
+  {
+    // skipping past the end
+    auto reader = source.create_reader();
+    reader->skip_bytes(content.size() + 10);
+    auto const next_chunk = reader->get_next_chunk(1, cudf::get_default_stream());
+    ASSERT_EQ(next_chunk->size(), 0);
+  }
+}
+
+TEST_F(DataChunkSourceTest, DataSourceHost)
+{
+  std::string const content = "host buffer source";
+  auto const datasource =
+    cudf::io::datasource::create(cudf::io::host_buffer{content.data(), content.size()});
+  auto const source = cudf::io::text::make_source(*datasource);
+
+  test_source(content, *source);
+}
+
+TEST_F(DataChunkSourceTest, DataSourceFile)
+{
+  std::string content = "file datasource";
+  // make it big enough to have is_device_read_preferred return true
+  content.reserve(content.size() << 20);
+  for (int i = 0; i < 20; i++) {
+    content += content;
+  }
+  auto const filename = temp_env->get_temp_filepath("file_source");
+  {
+    std::ofstream file{filename};
+    file << content;
+  }
+  auto const datasource = cudf::io::datasource::create(filename);
+  auto const source     = cudf::io::text::make_source(*datasource);
+
+  test_source(content, *source);
+}
+
+TEST_F(DataChunkSourceTest, Device)
+{
+  std::string const content = "device buffer source";
+  cudf::string_scalar scalar(content);
+  auto const source = cudf::io::text::make_source(scalar);
+
+  test_source(content, *source);
+}
+
+TEST_F(DataChunkSourceTest, File)
+{
+  std::string const content = "file source";
+  auto const filename       = temp_env->get_temp_filepath("file_source");
+  {
+    std::ofstream file{filename};
+    file << content;
+  }
+  auto const source = cudf::io::text::make_source_from_file(filename);
+
+  test_source(content, *source);
+}
+
+TEST_F(DataChunkSourceTest, Host)
+{
+  std::string const content = "host buffer source";
+  auto const source         = cudf::io::text::make_source(content);
+
+  test_source(content, *source);
+}
+
+enum class compression { ENABLED, DISABLED };
+
+enum class eof { ADD_EOF_BLOCK, NO_EOF_BLOCK };
+
+uint64_t virtual_offset(std::size_t block_offset, std::size_t local_offset)
+{
+  return (block_offset << 16) | local_offset;
+}
+
+void write_bgzip(std::ostream& output_stream,
+                 cudf::host_span<char const> data,
+                 std::default_random_engine& rng,
+                 compression compress,
+                 eof add_eof)
+{
+  std::vector<char> const extra_garbage_fields1{{13,  // magic number
+                                                 37,  // magic number
+                                                 7,   // field length
+                                                 0,   // field length
+                                                 1,
+                                                 2,
+                                                 3,
+                                                 4,
+                                                 5,
+                                                 6,
+                                                 7}};
+  std::vector<char> const extra_garbage_fields2{{12,  // magic number
+                                                 34,  // magic number
+                                                 2,   // field length
+                                                 0,   // field length
+                                                 1,  2,
+                                                 56,  // magic number
+                                                 78,  // magic number
+                                                 1,   // field length
+                                                 0,   // field length
+                                                 3,   //
+                                                 90,  // magic number
+                                                 12,  // magic number
+                                                 8,   // field length
+                                                 0,   // field length
+                                                 1,  2, 3, 4, 5, 6, 7, 8}};
+  // make sure the block size with header stays below 65536
+  std::uniform_int_distribution<std::size_t> block_size_dist{1, 65000};
+  auto begin     = data.begin();
+  auto const end = data.end();
+  int i          = 0;
+  while (begin < end) {
+    using cudf::host_span;
+    auto len = std::min<std::size_t>(end - begin, block_size_dist(rng));
+    host_span<char const> const garbage_before =
+      i & 1 ? extra_garbage_fields1 : host_span<char const>{};
+    host_span<char const> const garbage_after =
+      i & 2 ? extra_garbage_fields2 : host_span<char const>{};
+    if (compress == compression::ENABLED) {
+      cudf::io::text::detail::bgzip::write_compressed_block(
+        output_stream, {begin, len}, garbage_before, garbage_after);
+    } else {
+      cudf::io::text::detail::bgzip::write_uncompressed_block(
+        output_stream, {begin, len}, garbage_before, garbage_after);
+    }
+    begin += len;
+    i++;
+  }
+  if (add_eof == eof::ADD_EOF_BLOCK) {
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+  }
+}
+
+TEST_F(DataChunkSourceTest, BgzipSource)
+{
+  auto const filename = temp_env->get_temp_filepath("bgzip_source");
+  std::string input{"bananarama"};
+  input.reserve(input.size() << 25);
+  for (int i = 0; i < 24; i++) {
+    input = input + input;
+  }
+  {
+    std::ofstream output_stream{filename};
+    std::default_random_engine rng{};
+    write_bgzip(output_stream, input, rng, compression::DISABLED, eof::ADD_EOF_BLOCK);
+  }
+
+  auto const source = cudf::io::text::make_source_from_bgzip_file(filename);
+
+  test_source(input, *source);
+}
+
+TEST_F(DataChunkSourceTest, BgzipSourceVirtualOffsets)
+{
+  auto const filename = temp_env->get_temp_filepath("bgzip_source_offsets");
+  std::string input{"bananarama"};
+  input.reserve(input.size() << 25);
+  for (int i = 0; i < 24; i++) {
+    input = input + input;
+  }
+  std::string const padding_garbage(10000, 'g');
+  std::string const data_garbage{"GARBAGE"};
+  std::string const begininput{"begin of bananarama"};
+  std::string const endinput{"end of bananarama"};
+  std::size_t begin_compressed_offset{};
+  std::size_t end_compressed_offset{};
+  std::size_t const begin_local_offset{data_garbage.size()};
+  std::size_t const end_local_offset{endinput.size()};
+  {
+    std::ofstream output_stream{filename};
+    output_stream.write(padding_garbage.data(), padding_garbage.size());
+    std::default_random_engine rng{};
+    begin_compressed_offset = output_stream.tellp();
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream,
+                                                            data_garbage + begininput);
+    write_bgzip(output_stream, input, rng, compression::DISABLED, eof::NO_EOF_BLOCK);
+    end_compressed_offset = output_stream.tellp();
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream,
+                                                            endinput + data_garbage + data_garbage);
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+    output_stream.write(padding_garbage.data(), padding_garbage.size());
+  }
+  input = begininput + input + endinput;
+
+  auto const source = cudf::io::text::make_source_from_bgzip_file(
+    filename,
+    virtual_offset(begin_compressed_offset, begin_local_offset),
+    virtual_offset(end_compressed_offset, end_local_offset));
+
+  test_source(input, *source);
+}
+
+TEST_F(DataChunkSourceTest, BgzipSourceVirtualOffsetsSingleGZipBlock)
+{
+  auto const filename = temp_env->get_temp_filepath("bgzip_source_offsets_single_block");
+  std::string const input{"collection unit brings"};
+  std::string const head_garbage{"garbage"};
+  std::string const tail_garbage{"GARBAGE"};
+  std::size_t const begin_local_offset{head_garbage.size()};
+  std::size_t const end_local_offset{head_garbage.size() + input.size()};
+  {
+    std::ofstream output_stream{filename};
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream,
+                                                            head_garbage + input + tail_garbage);
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+  }
+
+  auto const source = cudf::io::text::make_source_from_bgzip_file(
+    filename, virtual_offset(0, begin_local_offset), virtual_offset(0, end_local_offset));
+
+  test_source(input, *source);
+}
+
+TEST_F(DataChunkSourceTest, BgzipSourceVirtualOffsetsSingleChunk)
+{
+  auto const filename = temp_env->get_temp_filepath("bgzip_source_offsets_single_chunk");
+  std::string const input{"collection unit brings"};
+  std::string const head_garbage{"garbage"};
+  std::string const tail_garbage{"GARBAGE"};
+  std::size_t end_compressed_offset{};
+  std::size_t const begin_local_offset{head_garbage.size()};
+  std::size_t const end_local_offset{input.size() - 10};
+  {
+    std::ofstream output_stream{filename};
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream,
+                                                            head_garbage + input.substr(0, 10));
+    end_compressed_offset = output_stream.tellp();
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream,
+                                                            input.substr(10) + tail_garbage);
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+  }
+
+  auto const source = cudf::io::text::make_source_from_bgzip_file(
+    filename,
+    virtual_offset(0, begin_local_offset),
+    virtual_offset(end_compressed_offset, end_local_offset));
+
+  test_source(input, *source);
+}
+
+TEST_F(DataChunkSourceTest, BgzipCompressedSourceVirtualOffsets)
+{
+  auto const filename = temp_env->get_temp_filepath("bgzip_source_compressed_offsets");
+  std::string input{"bananarama"};
+  input.reserve(input.size() << 25);
+  for (int i = 0; i < 24; i++) {
+    input = input + input;
+  }
+  std::string const padding_garbage(10000, 'g');
+  std::string const data_garbage{"GARBAGE"};
+  std::string const begininput{"begin of bananarama"};
+  std::string const endinput{"end of bananarama"};
+  std::size_t begin_compressed_offset{};
+  std::size_t end_compressed_offset{};
+  std::size_t const begin_local_offset{data_garbage.size()};
+  std::size_t const end_local_offset{endinput.size()};
+  {
+    std::ofstream output_stream{filename};
+    output_stream.write(padding_garbage.data(), padding_garbage.size());
+    std::default_random_engine rng{};
+    begin_compressed_offset = output_stream.tellp();
+    cudf::io::text::detail::bgzip::write_compressed_block(output_stream, data_garbage + begininput);
+    write_bgzip(output_stream, input, rng, compression::ENABLED, eof::NO_EOF_BLOCK);
+    end_compressed_offset = output_stream.tellp();
+    cudf::io::text::detail::bgzip::write_compressed_block(output_stream,
+                                                          endinput + data_garbage + data_garbage);
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+    output_stream.write(padding_garbage.data(), padding_garbage.size());
+  }
+  input = begininput + input + endinput;
+
+  auto source = cudf::io::text::make_source_from_bgzip_file(
+    filename,
+    virtual_offset(begin_compressed_offset, begin_local_offset),
+    virtual_offset(end_compressed_offset, end_local_offset));
+  test_source(input, *source);
+}
+
+TEST_F(DataChunkSourceTest, BgzipSourceVirtualOffsetsSingleCompressedGZipBlock)
+{
+  auto const filename = temp_env->get_temp_filepath("bgzip_source_offsets_single_compressed_block");
+  std::string const input{"collection unit brings"};
+  std::string const head_garbage(10000, 'g');
+  std::string const tail_garbage{"GARBAGE"};
+  std::size_t const begin_local_offset{head_garbage.size()};
+  std::size_t const end_local_offset{head_garbage.size() + input.size()};
+  {
+    std::ofstream output_stream{filename};
+    cudf::io::text::detail::bgzip::write_compressed_block(output_stream,
+                                                          head_garbage + input + tail_garbage);
+    cudf::io::text::detail::bgzip::write_uncompressed_block(output_stream, {});
+  }
+
+  auto const source = cudf::io::text::make_source_from_bgzip_file(
+    filename, virtual_offset(0, begin_local_offset), virtual_offset(0, end_local_offset));
+
+  test_source(input, *source);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/text/multibyte_split_test.cpp b/cpp/tests/io/text/multibyte_split_test.cpp
new file mode 100644
index 0000000..afa0274
--- /dev/null
+++ b/cpp/tests/io/text/multibyte_split_test.cpp
@@ -0,0 +1,561 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/utilities/output_builder.cuh>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/concatenate.hpp>
+#include <cudf/io/text/byte_range_info.hpp>
+#include <cudf/io/text/data_chunk_source_factories.hpp>
+#include <cudf/io/text/multibyte_split.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+using cudf::test::strings_column_wrapper;
+// 😀 | F0 9F 98 80 | 11110000 10011111 10011000 10000000
+// 😎 | F0 9F 98 8E | 11110000 10011111 10011000 10001110
+
+struct MultibyteSplitTest : public cudf::test::BaseFixture {};
+
+TEST_F(MultibyteSplitTest, Simple)
+{
+  auto delimiter  = std::string(":");
+  auto host_input = std::string("abc:def");
+
+  auto expected = strings_column_wrapper{"abc:", "def"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, NondeterministicMatching)
+{
+  auto delimiter  = std::string("abac");
+  auto host_input = std::string("ababacabacab");
+
+  auto expected = strings_column_wrapper{"ababac", "abac", "ab"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, NoDelimiter)
+{
+  auto delimiter  = std::string(":");
+  auto host_input = std::string("abcdefg");
+
+  auto expected = strings_column_wrapper{"abcdefg"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, DelimiterAtEnd)
+{
+  auto delimiter  = std::string(":");
+  auto host_input = std::string("abcdefg:");
+
+  auto expected = strings_column_wrapper{"abcdefg:"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, DelimiterAtEndByteRange)
+{
+  auto delimiter  = std::string(":");
+  auto host_input = std::string("abcdefg:");
+
+  auto expected = strings_column_wrapper{"abcdefg:"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(
+    *source,
+    delimiter,
+    cudf::io::text::byte_range_info{0, static_cast<int64_t>(host_input.size())});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, DelimiterAtEndByteRange2)
+{
+  auto delimiter  = std::string(":");
+  auto host_input = std::string("abcdefg:");
+
+  auto expected = strings_column_wrapper{"abcdefg:"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(
+    *source,
+    delimiter,
+    cudf::io::text::byte_range_info{0, static_cast<int64_t>(host_input.size() - 1)});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, LargeInputSparse)
+{
+  auto host_input    = std::string(1024 * 1024 * 32, '.');
+  auto host_expected = std::vector<std::string>();
+
+  host_input[host_input.size() / 2] = '|';
+
+  host_expected.emplace_back(host_input.substr(0, host_input.size() / 2 + 1));
+  host_expected.emplace_back(host_input.substr(host_input.size() / 2 + 1));
+
+  auto expected = strings_column_wrapper{host_expected.begin(), host_expected.end()};
+
+  auto delimiter = std::string("|");
+  auto source    = cudf::io::text::make_source(host_input);
+  auto out       = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, LargeInput)
+{
+  auto host_input    = std::string();
+  auto host_expected = std::vector<std::string>();
+
+  for (auto i = 0; i < (2 * 32 * 128 * 1024); i++) {
+    host_input += "...:|";
+    host_expected.emplace_back(std::string("...:|"));
+  }
+
+  auto expected = strings_column_wrapper{host_expected.begin(), host_expected.end()};
+
+  auto delimiter = std::string("...:|");
+  auto source    = cudf::io::text::make_source(host_input);
+  auto out       = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, OverlappingMatchErasure)
+{
+  auto delimiter = "::";
+
+  auto host_input = std::string(
+    ":::::"
+    ":::::");
+  auto expected = strings_column_wrapper{":::::", ":::::"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(*source, delimiter);
+
+  // CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out); // this use case it not yet supported.
+}
+
+TEST_F(MultibyteSplitTest, DelimiterErasure)
+{
+  auto delimiter = "\r\n";
+
+  auto host_input = std::string("line\r\nanother line\r\nthird line\r\n");
+  auto expected   = strings_column_wrapper{"line", "another line", "third line"};
+
+  cudf::io::text::parse_options options;
+  options.strip_delimiters = true;
+  auto source              = cudf::io::text::make_source(host_input);
+  auto out                 = cudf::io::text::multibyte_split(*source, delimiter, options);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, DelimiterErasureByteRange)
+{
+  auto delimiter = "\r\n";
+
+  auto host_input = std::string("line\r\nanother line\r\nthird line\r\n");
+  auto expected   = strings_column_wrapper{"line", "another line", "third line"};
+
+  cudf::io::text::parse_options options;
+  options.strip_delimiters = true;
+  options.byte_range       = cudf::io::text::byte_range_info(0, host_input.size() - 1);
+  auto source              = cudf::io::text::make_source(host_input);
+  auto out                 = cudf::io::text::multibyte_split(*source, delimiter, options);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, DelimiterErasureOverlap)
+{
+  auto delimiter = "::";
+
+  auto host_input = std::string("::a:::b::c::::d");
+  auto expected   = strings_column_wrapper{"", "a", "", "b", "c", "", "", "d"};
+
+  cudf::io::text::parse_options options;
+  options.strip_delimiters = true;
+  auto source              = cudf::io::text::make_source(host_input);
+  auto out                 = cudf::io::text::multibyte_split(*source, delimiter, options);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out);
+}
+
+TEST_F(MultibyteSplitTest, HandpickedInput)
+{
+  auto delimiters = "::|";
+  auto host_input = std::string(
+    "aaa::|"
+    "bbb::|"
+    "ccc::|"
+    "ddd::|"
+    "eee::|"
+    "fff::|"
+    "ggg::|"
+    "hhh::|"
+    "___::|"
+    "here::|"
+    "is::|"
+    "another::|"
+    "simple::|"
+    "text::|"
+    "separated::|"
+    "by::|"
+    "emojis::|"
+    "which::|"
+    "are::|"
+    "multiple::|"
+    "bytes::|"
+    "and::|"
+    "used::|"
+    "as::|"
+    "delimiters.::|"
+    "::|"
+    "::|"
+    "::|");
+
+  auto expected = strings_column_wrapper{
+    "aaa::|",         "bbb::|",      "ccc::|",       "ddd::|",  "eee::|",    "fff::|",
+    "ggg::|",         "hhh::|",      "___::|",       "here::|", "is::|",     "another::|",
+    "simple::|",      "text::|",     "separated::|", "by::|",   "emojis::|", "which::|",
+    "are::|",         "multiple::|", "bytes::|",     "and::|",  "used::|",   "as::|",
+    "delimiters.::|", "::|",         "::|",          "::|"};
+
+  auto source = cudf::io::text::make_source(host_input);
+  auto out    = cudf::io::text::multibyte_split(*source, delimiters);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, LargeInputMultipleRange)
+{
+  auto host_input = std::string();
+  for (auto i = 0; i < (2 * 32 * 128 * 1024); i++) {
+    host_input += "...:|";
+  }
+
+  auto delimiter = std::string("...:|");
+  auto source    = cudf::io::text::make_source(host_input);
+
+  auto byte_ranges = cudf::io::text::create_byte_range_infos_consecutive(host_input.size(), 3);
+  auto out0        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[0]);
+  auto out1        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[1]);
+  auto out2        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[2]);
+
+  auto out_views = std::vector<cudf::column_view>({out0->view(), out1->view(), out2->view()});
+  auto out       = cudf::concatenate(out_views);
+
+  auto expected = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    expected->view(), *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, LargeInputSparseMultipleRange)
+{
+  auto host_input = std::string();
+  for (auto i = 0; i < (2 * 32 * 128 * 1024); i++) {
+    host_input += ".....";
+  }
+
+  auto delimiter                        = std::string("...:|");
+  host_input[host_input.size() / 2]     = ':';
+  host_input[host_input.size() / 2 + 1] = '|';
+  auto source                           = cudf::io::text::make_source(host_input);
+
+  auto byte_ranges = cudf::io::text::create_byte_range_infos_consecutive(host_input.size(), 3);
+  auto out0        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[0]);
+  auto out1        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[1]);
+  auto out2        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[2]);
+
+  auto out_views = std::vector<cudf::column_view>({out0->view(), out1->view(), out2->view()});
+  auto out       = cudf::concatenate(out_views);
+
+  auto expected = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    expected->view(), *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, LargeInputMultipleRangeSingleByte)
+{
+  auto host_input = std::string();
+  for (auto i = 0; i < (2 * 32 * 128 * 1024); i++) {
+    host_input += "...:|";
+  }
+
+  auto delimiter = std::string("|");
+  auto source    = cudf::io::text::make_source(host_input);
+
+  auto byte_ranges = cudf::io::text::create_byte_range_infos_consecutive(host_input.size(), 3);
+  auto out0        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[0]);
+  auto out1        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[1]);
+  auto out2        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[2]);
+
+  auto out_views = std::vector<cudf::column_view>({out0->view(), out1->view(), out2->view()});
+  auto out       = cudf::concatenate(out_views);
+
+  auto expected = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    expected->view(), *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, LargeInputSparseMultipleRangeSingleByte)
+{
+  auto host_input = std::string();
+  for (auto i = 0; i < (2 * 32 * 128 * 1024); i++) {
+    host_input += ".....";
+  }
+
+  auto delimiter                    = std::string("|");
+  host_input[host_input.size() / 2] = '|';
+  auto source                       = cudf::io::text::make_source(host_input);
+
+  auto byte_ranges = cudf::io::text::create_byte_range_infos_consecutive(host_input.size(), 3);
+  auto out0        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[0]);
+  auto out1        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[1]);
+  auto out2        = cudf::io::text::multibyte_split(*source, delimiter, byte_ranges[2]);
+
+  auto out_views = std::vector<cudf::column_view>({out0->view(), out1->view(), out2->view()});
+  auto out       = cudf::concatenate(out_views);
+
+  auto expected = cudf::io::text::multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    expected->view(), *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, SmallInputAllPossibleRanges)
+{
+  using namespace cudf::io::text;
+
+  auto host_input = std::string();
+  for (auto i = 0; i < 5; i++) {
+    host_input += "::";
+  }
+
+  auto delimiter = std::string("::");
+  auto source    = make_source(host_input);
+
+  // for all possible ways to split the input, check that each field is only output once
+  int size = static_cast<int>(host_input.size());
+  for (int split1 = 1; split1 < size; split1++) {
+    SCOPED_TRACE(split1);
+    for (int split2 = split1 + 1; split2 < size; split2++) {
+      SCOPED_TRACE(split2);
+      auto out1 = multibyte_split(*source, delimiter, byte_range_info{0, split1});
+      auto out2 = multibyte_split(*source, delimiter, byte_range_info{split1, split2 - split1});
+      auto out3 = multibyte_split(*source, delimiter, byte_range_info{split2, size - split2});
+
+      auto out_views = std::vector<cudf::column_view>({out1->view(), out2->view(), out3->view()});
+      auto out       = cudf::concatenate(out_views);
+
+      auto expected = multibyte_split(*source, delimiter);
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+        expected->view(), *out, cudf::test::debug_output_level::ALL_ERRORS);
+    }
+  }
+}
+
+TEST_F(MultibyteSplitTest, SmallInputAllPossibleRangesSingleByte)
+{
+  using namespace cudf::io::text;
+
+  auto host_input = std::string();
+  for (auto i = 0; i < 5; i++) {
+    host_input += std::to_string(i) + ":";
+  }
+
+  auto delimiter = std::string(":");
+  auto source    = make_source(host_input);
+
+  // for all possible ways to split the input, check that each field is only output once
+  int size = static_cast<int>(host_input.size());
+  for (int split1 = 1; split1 < size; split1++) {
+    SCOPED_TRACE(split1);
+    for (int split2 = split1 + 1; split2 < size; split2++) {
+      SCOPED_TRACE(split2);
+      auto out1 = multibyte_split(*source, delimiter, byte_range_info{0, split1});
+      auto out2 = multibyte_split(*source, delimiter, byte_range_info{split1, split2 - split1});
+      auto out3 = multibyte_split(*source, delimiter, byte_range_info{split2, size - split2});
+
+      auto out_views = std::vector<cudf::column_view>({out1->view(), out2->view(), out3->view()});
+      auto out       = cudf::concatenate(out_views);
+
+      auto expected = multibyte_split(*source, delimiter);
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+        expected->view(), *out, cudf::test::debug_output_level::ALL_ERRORS);
+    }
+  }
+}
+
+TEST_F(MultibyteSplitTest, SingletonRangeAtEnd)
+{
+  // we want a delimiter at the end of the file to not create a new empty row even if it is the only
+  // character in the byte range
+  using namespace cudf::io::text;
+  auto host_input = std::string("ab:cd:");
+  auto delimiter  = std::string(":");
+  auto source     = make_source(host_input);
+  auto expected   = strings_column_wrapper{};
+
+  auto out = multibyte_split(*source, delimiter, byte_range_info{5, 1});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, EmptyInput)
+{
+  using namespace cudf::io::text;
+  auto host_input = std::string();
+  auto delimiter  = std::string("::");
+  auto source     = make_source(host_input);
+  auto expected   = strings_column_wrapper{};
+
+  auto out = multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, EmptyInputSingleByte)
+{
+  using namespace cudf::io::text;
+  auto host_input = std::string();
+  auto delimiter  = std::string(":");
+  auto source     = make_source(host_input);
+  auto expected   = strings_column_wrapper{};
+
+  auto out = multibyte_split(*source, delimiter);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, EmptyRange)
+{
+  using namespace cudf::io::text;
+  auto host_input = std::string("ab::cd");
+  auto delimiter  = std::string("::");
+  auto source     = make_source(host_input);
+  auto expected   = strings_column_wrapper{};
+
+  auto out = multibyte_split(*source, delimiter, byte_range_info{4, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, EmptyRangeSingleByte)
+{
+  using namespace cudf::io::text;
+  auto host_input = std::string("ab:cd");
+  auto delimiter  = std::string(":");
+  auto source     = make_source(host_input);
+  auto expected   = strings_column_wrapper{};
+
+  auto out = multibyte_split(*source, delimiter, byte_range_info{3, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *out, cudf::test::debug_output_level::ALL_ERRORS);
+}
+
+TEST_F(MultibyteSplitTest, EmptySplitDeviceSpan)
+{
+  cudf::split_device_span<int> span;
+  ASSERT_EQ(span.size(), 0);
+  ASSERT_EQ(span.head().size(), 0);
+  ASSERT_EQ(span.head().data(), nullptr);
+  ASSERT_EQ(span.tail().size(), 0);
+  ASSERT_EQ(span.tail().data(), nullptr);
+}
+
+TEST_F(MultibyteSplitTest, SplitDeviceSpan)
+{
+  int i = 0;
+  int j = 1;
+  cudf::split_device_span<int> span{{&i, 1}, {&j, 1}};
+  ASSERT_EQ(span.size(), 2);
+  ASSERT_EQ(span.head().size(), 1);
+  ASSERT_EQ(span.head().data(), &i);
+  ASSERT_EQ(span.tail().size(), 1);
+  ASSERT_EQ(span.tail().data(), &j);
+  ASSERT_EQ(&span[0], &i);
+  ASSERT_EQ(&span[1], &j);
+  ASSERT_EQ(&*span.begin(), &i);
+  ASSERT_EQ(&*(span.begin() + 1), &j);
+  ASSERT_NE(span.begin() + 1, span.end());
+  ASSERT_EQ(span.begin() + 2, span.end());
+}
+
+TEST_F(MultibyteSplitTest, OutputBuilder)
+{
+  auto const stream = cudf::get_default_stream();
+  cudf::output_builder<char> builder{10, 4, stream};
+  auto const output = builder.next_output(stream);
+  ASSERT_GE(output.size(), 10);
+  ASSERT_EQ(output.tail().size(), 0);
+  ASSERT_EQ(output.tail().data(), nullptr);
+  ASSERT_EQ(builder.size(), 0);
+  builder.advance_output(1, stream);
+  ASSERT_EQ(builder.size(), 1);
+  auto const output2 = builder.next_output(stream);
+  ASSERT_EQ(output2.head().data(), output.head().data() + 1);
+  builder.advance_output(10, stream);
+  ASSERT_EQ(builder.size(), 11);
+  auto const output3 = builder.next_output(stream);
+  ASSERT_EQ(output3.head().size(), 9);
+  ASSERT_EQ(output3.head().data(), output.head().data() + 11);
+  ASSERT_EQ(output3.tail().size(), 40);
+  builder.advance_output(9, stream);
+  ASSERT_EQ(builder.size(), 20);
+  auto const output4 = builder.next_output(stream);
+  ASSERT_EQ(output4.head().size(), 0);
+  ASSERT_EQ(output4.tail().size(), output3.tail().size());
+  ASSERT_EQ(output4.tail().data(), output3.tail().data());
+  builder.advance_output(1, stream);
+  auto const output5 = builder.next_output(stream);
+  ASSERT_EQ(output5.head().size(), 39);
+  ASSERT_EQ(output5.head().data(), output4.tail().data() + 1);
+  ASSERT_EQ(output5.tail().size(), 0);
+  ASSERT_EQ(output5.tail().data(), nullptr);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/io/type_inference_test.cu b/cpp/tests/io/type_inference_test.cu
new file mode 100644
index 0000000..a14e7ec
--- /dev/null
+++ b/cpp/tests/io/type_inference_test.cu
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <io/utilities/string_parsing.hpp>
+#include <io/utilities/trie.cuh>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf_test/base_fixture.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/tuple.h>
+
+#include <cstddef>
+#include <string>
+#include <vector>
+
+using cudf::io::parse_options;
+using cudf::io::detail::infer_data_type;
+
+// Base test fixture for tests
+struct TypeInference : public cudf::test::BaseFixture {};
+
+TEST_F(TypeInference, Basic)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json([42,52,5])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 4, 7};
+  auto const string_length   = std::vector<cudf::size_type>{2, 2, 1};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::INT64});
+}
+
+TEST_F(TypeInference, Null)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json([52,5])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 1, 4};
+  auto const string_length   = std::vector<cudf::size_type>{0, 2, 1};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::INT64});
+}
+
+TEST_F(TypeInference, AllNull)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json([null])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 1, 1};
+  auto const string_length   = std::vector<cudf::size_type>{0, 0, 4};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::INT8});  // INT8 if all nulls
+}
+
+TEST_F(TypeInference, String)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json(["1990","8","25"])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 8, 12};
+  auto const string_length   = std::vector<cudf::size_type>{6, 3, 4};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::STRING});
+}
+
+TEST_F(TypeInference, Bool)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json([true,false,false])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 6, 12};
+  auto const string_length   = std::vector<cudf::size_type>{4, 5, 5};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::BOOL8});
+}
+
+TEST_F(TypeInference, Timestamp)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json([1970/2/5,1970/8/25])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 10};
+  auto const string_length   = std::vector<cudf::size_type>{8, 9};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  // All data time (quoted and unquoted) is inferred as string for now
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::STRING});
+}
+
+TEST_F(TypeInference, InvalidInput)
+{
+  auto const stream = cudf::get_default_stream();
+
+  auto options       = parse_options{',', '\n', '\"'};
+  options.trie_true  = cudf::detail::create_serialized_trie({"true"}, stream);
+  options.trie_false = cudf::detail::create_serialized_trie({"false"}, stream);
+  options.trie_na    = cudf::detail::create_serialized_trie({"", "null"}, stream);
+
+  std::string data      = R"json([1,2,3,a,5])json";
+  auto d_data           = cudf::make_string_scalar(data);
+  auto& d_string_scalar = static_cast<cudf::string_scalar&>(*d_data);
+
+  auto const string_offset   = std::vector<cudf::size_type>{1, 3, 5, 7, 9};
+  auto const string_length   = std::vector<cudf::size_type>{1, 1, 1, 1, 1};
+  auto const d_string_offset = cudf::detail::make_device_uvector_async(
+    string_offset, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const d_string_length = cudf::detail::make_device_uvector_async(
+    string_length, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_col_strings =
+    thrust::make_zip_iterator(thrust::make_tuple(d_string_offset.begin(), d_string_length.begin()));
+
+  auto res_type =
+    infer_data_type(options.json_view(),
+                    {d_string_scalar.data(), static_cast<std::size_t>(d_string_scalar.size())},
+                    d_col_strings,
+                    string_offset.size(),
+                    stream);
+
+  // Invalid input is inferred as string for now
+  EXPECT_EQ(res_type, cudf::data_type{cudf::type_id::STRING});
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/iterator/README.md b/cpp/tests/iterator/README.md
new file mode 100644
index 0000000..93c3dd6
--- /dev/null
+++ b/cpp/tests/iterator/README.md
@@ -0,0 +1,18 @@
+
+# Iterator Test decomposition
+
+The Iterator tests have been decomposed across different types to
+make sure that no single test file takes too long to compile.
+
+The decomposition is that each of the following
+categorizes of types should be placed in a separate file:
+ - numeric
+ - chrono ( timestamp, duration )
+ - fixed point ( numeric::decimal32, numeric::decimal64 )
+ - string
+
+The `numeric` and `chrono` type lists have roughly the same
+number of entries allowing for a balanced compile time between
+those two. We follow the same pattern for `fixed point` and
+`string` so it is clear where to test those types, even though
+they have a smaller set of entries and will compile quickly.
diff --git a/cpp/tests/iterator/indexalator_test.cu b/cpp/tests/iterator/indexalator_test.cu
new file mode 100644
index 0000000..1ff7f4c
--- /dev/null
+++ b/cpp/tests/iterator/indexalator_test.cu
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+#include <tests/iterator/iterator_tests.cuh>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/indexalator.cuh>
+
+#include <thrust/host_vector.h>
+#include <thrust/optional.h>
+#include <thrust/pair.h>
+
+using TestingTypes = cudf::test::IntegralTypesNotBool;
+
+template <typename T>
+struct IndexalatorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(IndexalatorTest, TestingTypes);
+
+TYPED_TEST(IndexalatorTest, input_iterator)
+{
+  using T = TypeParam;
+
+  auto host_values = cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+
+  auto d_col = cudf::test::fixed_width_column_wrapper<T>(host_values.begin(), host_values.end());
+
+  auto expected_values = thrust::host_vector<cudf::size_type>(host_values.size());
+  std::transform(host_values.begin(), host_values.end(), expected_values.begin(), [](auto v) {
+    return static_cast<cudf::size_type>(v);
+  });
+
+  auto it_dev = cudf::detail::indexalator_factory::make_input_iterator(d_col);
+  this->iterator_test_thrust(expected_values, it_dev, host_values.size());
+}
+
+TYPED_TEST(IndexalatorTest, pair_iterator)
+{
+  using T = TypeParam;
+
+  auto host_values = cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -120, 115});
+  auto validity    = std::vector<bool>({0, 1, 1, 1, 1, 1, 0, 1, 1});
+
+  auto d_col = cudf::test::fixed_width_column_wrapper<T>(
+    host_values.begin(), host_values.end(), validity.begin());
+
+  auto expected_values =
+    thrust::host_vector<thrust::pair<cudf::size_type, bool>>(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 validity.begin(),
+                 expected_values.begin(),
+                 [](T v, bool b) { return thrust::make_pair(static_cast<cudf::size_type>(v), b); });
+
+  auto it_dev = cudf::detail::indexalator_factory::make_input_pair_iterator(d_col);
+  this->iterator_test_thrust(expected_values, it_dev, host_values.size());
+}
+
+TYPED_TEST(IndexalatorTest, optional_iterator)
+{
+  using T = TypeParam;
+
+  auto host_values = cudf::test::make_type_param_vector<T>({0, 6, 0, -104, 103, 64, -13, -20, 45});
+  auto validity    = std::vector<bool>({0, 1, 1, 1, 1, 1, 0, 1, 1});
+
+  auto d_col = cudf::test::fixed_width_column_wrapper<T>(
+    host_values.begin(), host_values.end(), validity.begin());
+
+  auto expected_values = thrust::host_vector<thrust::optional<cudf::size_type>>(host_values.size());
+
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 validity.begin(),
+                 expected_values.begin(),
+                 [](T v, bool b) {
+                   return (b) ? thrust::make_optional(static_cast<cudf::size_type>(v))
+                              : thrust::nullopt;
+                 });
+
+  auto it_dev = cudf::detail::indexalator_factory::make_input_optional_iterator(d_col);
+  this->iterator_test_thrust(expected_values, it_dev, host_values.size());
+}
diff --git a/cpp/tests/iterator/iterator_tests.cuh b/cpp/tests/iterator/iterator_tests.cuh
new file mode 100644
index 0000000..7a5a9ea
--- /dev/null
+++ b/cpp/tests/iterator/iterator_tests.cuh
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#pragma once
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/transform_unary_functions.cuh>  // for meanvar
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/distance.h>
+#include <thrust/equal.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#include <thrust/host_vector.h>
+#include <thrust/logical.h>
+#include <thrust/transform.h>
+
+#include <cub/device/device_reduce.cuh>
+
+#include <bitset>
+#include <cstdint>
+#include <iostream>
+#include <numeric>
+#include <random>
+
+// Base Typed test fixture for iterator test
+template <typename T>
+struct IteratorTest : public cudf::test::BaseFixture {
+  // iterator test case which uses cub
+  template <typename InputIterator, typename T_output>
+  void iterator_test_cub(T_output expected, InputIterator d_in, int num_items)
+  {
+    T_output init = cudf::test::make_type_param_scalar<T_output>(0);
+    rmm::device_uvector<T_output> dev_result(1, cudf::get_default_stream());
+
+    // Get temporary storage size
+    size_t temp_storage_bytes = 0;
+    cub::DeviceReduce::Reduce(nullptr,
+                              temp_storage_bytes,
+                              d_in,
+                              dev_result.begin(),
+                              num_items,
+                              thrust::minimum{},
+                              init,
+                              cudf::get_default_stream().value());
+
+    // Allocate temporary storage
+    rmm::device_buffer d_temp_storage(temp_storage_bytes, cudf::get_default_stream());
+
+    // Run reduction
+    cub::DeviceReduce::Reduce(d_temp_storage.data(),
+                              temp_storage_bytes,
+                              d_in,
+                              dev_result.begin(),
+                              num_items,
+                              thrust::minimum{},
+                              init,
+                              cudf::get_default_stream().value());
+
+    evaluate(expected, dev_result, "cub test");
+  }
+
+  // iterator test case which uses thrust
+  template <typename InputIterator, typename T_output>
+  void iterator_test_thrust(thrust::host_vector<T_output> const& expected,
+                            InputIterator d_in,
+                            int num_items)
+  {
+    InputIterator d_in_last = d_in + num_items;
+    EXPECT_EQ(thrust::distance(d_in, d_in_last), num_items);
+    auto dev_expected = cudf::detail::make_device_uvector_sync(
+      expected, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+    // using a temporary vector and calling transform and all_of separately is
+    // equivalent to thrust::equal but compiles ~3x faster
+    auto dev_results = rmm::device_uvector<bool>(num_items, cudf::get_default_stream());
+    thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                      d_in,
+                      d_in_last,
+                      dev_expected.begin(),
+                      dev_results.begin(),
+                      thrust::equal_to{});
+    auto result = thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                                 dev_results.begin(),
+                                 dev_results.end(),
+                                 thrust::identity<bool>{});
+    EXPECT_TRUE(result) << "thrust test";
+  }
+
+  template <typename T_output>
+  void evaluate(T_output expected,
+                rmm::device_uvector<T_output> const& dev_result,
+                char const* msg = nullptr)
+  {
+    auto host_result = cudf::detail::make_host_vector_sync(dev_result, cudf::get_default_stream());
+
+    EXPECT_EQ(expected, host_result[0]) << msg;
+  }
+
+  template <typename T_output>
+  void values_equal_test(thrust::host_vector<T_output> const& expected,
+                         cudf::column_device_view const& col)
+  {
+    if (col.nullable()) {
+      auto it_dev = cudf::detail::make_null_replacement_iterator(
+        col, cudf::test::make_type_param_scalar<T_output>(0));
+      iterator_test_thrust(expected, it_dev, col.size());
+    } else {
+      auto it_dev = col.begin<T_output>();
+      iterator_test_thrust(expected, it_dev, col.size());
+    }
+  }
+};
diff --git a/cpp/tests/iterator/optional_iterator_test.cuh b/cpp/tests/iterator/optional_iterator_test.cuh
new file mode 100644
index 0000000..6a264ce
--- /dev/null
+++ b/cpp/tests/iterator/optional_iterator_test.cuh
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#pragma once
+
+#include <tests/iterator/iterator_tests.cuh>
+
+#include <thrust/host_vector.h>
+#include <thrust/optional.h>
+
+template <typename T>
+void nonull_optional_iterator(IteratorTest<T>& testFixture)
+{
+  // data and valid arrays
+  auto host_values_std =
+    cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  thrust::host_vector<T> host_values(host_values_std);
+
+  // create a column
+  cudf::test::fixed_width_column_wrapper<T> w_col(host_values.begin(), host_values.end());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::optional<T>> replaced_array(host_values.size());
+  std::transform(host_values.begin(), host_values.end(), replaced_array.begin(), [](auto s) {
+    return thrust::optional<T>{s};
+  });
+
+  // GPU test
+  testFixture.iterator_test_thrust(
+    replaced_array,
+    cudf::detail::make_optional_iterator<T>(*d_col, cudf::nullate::DYNAMIC{false}),
+    host_values.size());
+  testFixture.iterator_test_thrust(
+    replaced_array,
+    cudf::detail::make_optional_iterator<T>(*d_col, cudf::nullate::NO{}),
+    host_values.size());
+}
+
+template <typename T>
+void null_optional_iterator(IteratorTest<T>& testFixture)
+{
+  // data and valid arrays
+  auto host_values = cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  thrust::host_vector<bool> host_bools(std::vector<bool>({1, 1, 0, 1, 1, 1, 0, 1, 1}));
+
+  // create a column with bool vector
+  cudf::test::fixed_width_column_wrapper<T> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::optional<T>> optional_values(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 optional_values.begin(),
+                 [](auto s, bool b) { return b ? thrust::optional<T>{s} : thrust::optional<T>{}; });
+
+  thrust::host_vector<thrust::optional<T>> value_all_valid(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 value_all_valid.begin(),
+                 [](auto s, bool b) { return thrust::optional<T>{s}; });
+
+  // GPU test for correct null mapping
+  testFixture.iterator_test_thrust(
+    optional_values, d_col->optional_begin<T>(cudf::nullate::DYNAMIC{true}), host_values.size());
+
+  testFixture.iterator_test_thrust(
+    optional_values, d_col->optional_begin<T>(cudf::nullate::YES{}), host_values.size());
+  testFixture.iterator_test_thrust(
+    optional_values, d_col->optional_begin<T>(cudf::nullate::YES{}), host_values.size());
+
+  // GPU test for ignoring null mapping
+  testFixture.iterator_test_thrust(
+    value_all_valid, d_col->optional_begin<T>(cudf::nullate::DYNAMIC{false}), host_values.size());
+
+  testFixture.iterator_test_thrust(
+    value_all_valid, d_col->optional_begin<T>(cudf::nullate::NO{}), host_values.size());
+  testFixture.iterator_test_thrust(
+    value_all_valid, d_col->optional_begin<T>(cudf::nullate::NO{}), host_values.size());
+}
diff --git a/cpp/tests/iterator/optional_iterator_test_chrono.cu b/cpp/tests/iterator/optional_iterator_test_chrono.cu
new file mode 100644
index 0000000..eba9bb1
--- /dev/null
+++ b/cpp/tests/iterator/optional_iterator_test_chrono.cu
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <tests/iterator/optional_iterator_test.cuh>
+
+using TestingTypes = cudf::test::ChronoTypes;
+
+template <typename T>
+struct ChronoOptionalIteratorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(ChronoOptionalIteratorTest, TestingTypes);
+TYPED_TEST(ChronoOptionalIteratorTest, nonull_optional_iterator)
+{
+  nonull_optional_iterator(*this);
+}
+TYPED_TEST(ChronoOptionalIteratorTest, null_optional_iterator) { null_optional_iterator(*this); }
diff --git a/cpp/tests/iterator/optional_iterator_test_numeric.cu b/cpp/tests/iterator/optional_iterator_test_numeric.cu
new file mode 100644
index 0000000..d70a074
--- /dev/null
+++ b/cpp/tests/iterator/optional_iterator_test_numeric.cu
@@ -0,0 +1,134 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <tests/iterator/optional_iterator_test.cuh>
+
+#include <cudf/utilities/default_stream.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/optional.h>
+#include <thrust/reduce.h>
+#include <thrust/transform.h>
+
+using TestingTypes = cudf::test::NumericTypes;
+
+namespace cudf {
+// To print meanvar for debug.
+// Needs to be in the cudf namespace for ADL
+template <typename T>
+std::ostream& operator<<(std::ostream& os, cudf::meanvar<T> const& rhs)
+{
+  return os << "[" << rhs.value << ", " << rhs.value_squared << ", " << rhs.count << "] ";
+};
+}  // namespace cudf
+
+template <typename T>
+struct NumericOptionalIteratorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(NumericOptionalIteratorTest, TestingTypes);
+TYPED_TEST(NumericOptionalIteratorTest, nonull_optional_iterator)
+{
+  nonull_optional_iterator(*this);
+}
+TYPED_TEST(NumericOptionalIteratorTest, null_optional_iterator) { null_optional_iterator(*this); }
+
+// Transformers and Operators for optional_iterator test
+template <typename ElementType>
+struct transformer_optional_meanvar {
+  using ResultType = thrust::optional<cudf::meanvar<ElementType>>;
+
+  CUDF_HOST_DEVICE inline ResultType operator()(thrust::optional<ElementType> const& optional)
+  {
+    if (optional.has_value()) {
+      auto v = *optional;
+      return cudf::meanvar<ElementType>{v, static_cast<ElementType>(v * v), 1};
+    }
+    return thrust::nullopt;
+  }
+};
+
+template <typename T>
+struct optional_to_meanvar {
+  CUDF_HOST_DEVICE inline T operator()(thrust::optional<T> const& v) { return v.value_or(T{0}); }
+};
+
+// TODO: enable this test also at __CUDACC_DEBUG__
+// This test causes fatal compilation error only at device debug mode.
+// Workaround: exclude this test only at device debug mode.
+#if !defined(__CUDACC_DEBUG__)
+TYPED_TEST(NumericOptionalIteratorTest, mean_var_output)
+{
+  using T        = TypeParam;
+  using T_output = cudf::meanvar<T>;
+  transformer_optional_meanvar<T> transformer{};
+
+  int const column_size{50};
+  const T init{0};
+
+  // data and valid arrays
+  std::vector<T> host_values(column_size);
+  std::vector<bool> host_bools(column_size);
+
+  cudf::test::UniformRandomGenerator<T> rng;
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(host_values.begin(), host_values.end(), [&rng]() { return rng.generate(); });
+  std::generate(host_bools.begin(), host_bools.end(), [&rbg]() { return rbg.generate(); });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate expected values by CPU
+  T_output expected_value;
+
+  expected_value.count = d_col->size() - static_cast<cudf::column_view>(w_col).null_count();
+
+  std::vector<T> replaced_array(d_col->size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_array.begin(),
+                 [&](T x, bool b) { return (b) ? static_cast<T>(x) : init; });
+
+  expected_value.count = d_col->size() - static_cast<cudf::column_view>(w_col).null_count();
+  expected_value.value = std::accumulate(replaced_array.begin(), replaced_array.end(), T{0});
+  expected_value.value_squared = std::accumulate(
+    replaced_array.begin(), replaced_array.end(), T{0}, [](T acc, T i) { return acc + i * i; });
+
+  // GPU test
+  auto it_dev         = d_col->optional_begin<T>(cudf::nullate::YES{});
+  auto it_dev_squared = thrust::make_transform_iterator(it_dev, transformer);
+
+  // this can be computed with a single reduce and without a temporary output vector
+  // but the approach increases the compile time by ~2x
+  auto results = rmm::device_uvector<T_output>(d_col->size(), cudf::get_default_stream());
+  thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                    it_dev_squared,
+                    it_dev_squared + d_col->size(),
+                    results.begin(),
+                    optional_to_meanvar<T_output>{});
+  auto result = thrust::reduce(
+    rmm::exec_policy(cudf::get_default_stream()), results.begin(), results.end(), T_output{});
+
+  if (not std::is_floating_point<T>()) {
+    EXPECT_EQ(expected_value, result) << "optional iterator reduction sum";
+  } else {
+    EXPECT_NEAR(expected_value.value, result.value, 1e-3) << "optional iterator reduction sum";
+    EXPECT_NEAR(expected_value.value_squared, result.value_squared, 1e-3)
+      << "optional iterator reduction sum squared";
+    EXPECT_EQ(expected_value.count, result.count) << "optional iterator reduction count";
+  }
+}
+#endif
diff --git a/cpp/tests/iterator/pair_iterator_test.cuh b/cpp/tests/iterator/pair_iterator_test.cuh
new file mode 100644
index 0000000..7dfa704
--- /dev/null
+++ b/cpp/tests/iterator/pair_iterator_test.cuh
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#pragma once
+
+#include <tests/iterator/iterator_tests.cuh>
+
+#include <thrust/host_vector.h>
+#include <thrust/pair.h>
+
+template <typename T>
+void nonull_pair_iterator(IteratorTest<T>& testFixture)
+{
+  // data and valid arrays
+  auto host_values_std =
+    cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  thrust::host_vector<T> host_values(host_values_std);
+
+  // create a column
+  cudf::test::fixed_width_column_wrapper<T> w_col(host_values.begin(), host_values.end());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::pair<T, bool>> replaced_array(host_values.size());
+  std::transform(host_values.begin(), host_values.end(), replaced_array.begin(), [](auto s) {
+    return thrust::make_pair(s, true);
+  });
+
+  // GPU test
+  auto it_dev = d_col->pair_begin<T, false>();
+  testFixture.iterator_test_thrust(replaced_array, it_dev, host_values.size());
+}
+
+template <typename T>
+void null_pair_iterator(IteratorTest<T>& testFixture)
+{
+  // data and valid arrays
+  auto host_values = cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  thrust::host_vector<bool> host_bools(std::vector<bool>({1, 1, 0, 1, 1, 1, 0, 1, 1}));
+
+  // create a column with bool vector
+  cudf::test::fixed_width_column_wrapper<T> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::pair<T, bool>> value_and_validity(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 value_and_validity.begin(),
+                 [](auto s, auto b) {
+                   return thrust::pair<T, bool>{s, b};
+                 });
+  thrust::host_vector<thrust::pair<T, bool>> value_all_valid(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 value_all_valid.begin(),
+                 [](auto s, auto b) {
+                   return thrust::pair<T, bool>{s, true};
+                 });
+
+  // GPU test
+  auto it_dev = d_col->pair_begin<T, true>();
+  testFixture.iterator_test_thrust(value_and_validity, it_dev, host_values.size());
+
+  auto it_hasnonull_dev = d_col->pair_begin<T, false>();
+  testFixture.iterator_test_thrust(value_all_valid, it_hasnonull_dev, host_values.size());
+
+  auto itb_dev = cudf::detail::make_validity_iterator(*d_col);
+  testFixture.iterator_test_thrust(host_bools, itb_dev, host_values.size());
+}
diff --git a/cpp/tests/iterator/pair_iterator_test_chrono.cu b/cpp/tests/iterator/pair_iterator_test_chrono.cu
new file mode 100644
index 0000000..f96820a
--- /dev/null
+++ b/cpp/tests/iterator/pair_iterator_test_chrono.cu
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <tests/iterator/pair_iterator_test.cuh>
+
+using TestingTypes = cudf::test::ChronoTypes;
+
+template <typename T>
+struct ChronoPairIteratorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(ChronoPairIteratorTest, TestingTypes);
+TYPED_TEST(ChronoPairIteratorTest, nonull_pair_iterator) { nonull_pair_iterator(*this); }
+TYPED_TEST(ChronoPairIteratorTest, null_pair_iterator) { null_pair_iterator(*this); }
diff --git a/cpp/tests/iterator/pair_iterator_test_numeric.cu b/cpp/tests/iterator/pair_iterator_test_numeric.cu
new file mode 100644
index 0000000..420b3d4
--- /dev/null
+++ b/cpp/tests/iterator/pair_iterator_test_numeric.cu
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <tests/iterator/pair_iterator_test.cuh>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/pair.h>
+#include <thrust/reduce.h>
+
+using TestingTypes = cudf::test::NumericTypes;
+
+namespace cudf {
+// To print meanvar for debug.
+// Needs to be in the cudf namespace for ADL
+template <typename T>
+std::ostream& operator<<(std::ostream& os, cudf::meanvar<T> const& rhs)
+{
+  return os << "[" << rhs.value << ", " << rhs.value_squared << ", " << rhs.count << "] ";
+};
+}  // namespace cudf
+
+template <typename T>
+struct NumericPairIteratorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(NumericPairIteratorTest, TestingTypes);
+TYPED_TEST(NumericPairIteratorTest, nonull_pair_iterator) { nonull_pair_iterator(*this); }
+TYPED_TEST(NumericPairIteratorTest, null_pair_iterator) { null_pair_iterator(*this); }
+
+// Transformers and Operators for pair_iterator test
+template <typename ElementType>
+struct transformer_pair_meanvar {
+  using ResultType = thrust::pair<cudf::meanvar<ElementType>, bool>;
+
+  CUDF_HOST_DEVICE inline ResultType operator()(thrust::pair<ElementType, bool> const& pair)
+  {
+    ElementType v = pair.first;
+    return {{v, static_cast<ElementType>(v * v), (pair.second) ? 1 : 0}, pair.second};
+  };
+};
+
+struct sum_if_not_null {
+  template <typename T>
+  CUDF_HOST_DEVICE inline thrust::pair<T, bool> operator()(thrust::pair<T, bool> const& lhs,
+                                                           thrust::pair<T, bool> const& rhs)
+  {
+    if (lhs.second & rhs.second)
+      return {lhs.first + rhs.first, true};
+    else if (lhs.second)
+      return {lhs};
+    else
+      return {rhs};
+  }
+};
+
+// TODO: enable this test also at __CUDACC_DEBUG__
+// This test causes fatal compilation error only at device debug mode.
+// Workaround: exclude this test only at device debug mode.
+#if !defined(__CUDACC_DEBUG__)
+// This test computes `count`, `sum`, `sum_of_squares` at a single reduction call.
+// It would be useful for `var`, `std` operation
+TYPED_TEST(NumericPairIteratorTest, mean_var_output)
+{
+  using T        = TypeParam;
+  using T_output = cudf::meanvar<T>;
+  transformer_pair_meanvar<T> transformer{};
+
+  int const column_size{5000};
+  const T init{0};
+
+  // data and valid arrays
+  std::vector<T> host_values(column_size);
+  std::vector<bool> host_bools(column_size);
+
+  if constexpr (std::is_floating_point<T>()) {
+    cudf::test::UniformRandomGenerator<int32_t> rng;
+    std::generate(host_values.begin(), host_values.end(), [&rng]() {
+      return static_cast<T>(rng.generate() % 10);  // reduces float-op errors
+    });
+  } else {
+    cudf::test::UniformRandomGenerator<T> rng;
+    std::generate(host_values.begin(), host_values.end(), [&rng]() { return rng.generate(); });
+  }
+
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(host_bools.begin(), host_bools.end(), [&rbg]() { return rbg.generate(); });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate expected values by CPU
+  T_output expected_value;
+
+  expected_value.count = d_col->size() - static_cast<cudf::column_view>(w_col).null_count();
+
+  std::vector<T> replaced_array(d_col->size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_array.begin(),
+                 [&](T x, bool b) { return (b) ? static_cast<T>(x) : init; });
+
+  expected_value.count = d_col->size() - static_cast<cudf::column_view>(w_col).null_count();
+  expected_value.value = std::accumulate(replaced_array.begin(), replaced_array.end(), T{0});
+  expected_value.value_squared = std::accumulate(
+    replaced_array.begin(), replaced_array.end(), T{0}, [](T acc, T i) { return acc + i * i; });
+
+  // GPU test
+  auto it_dev         = d_col->pair_begin<T, true>();
+  auto it_dev_squared = thrust::make_transform_iterator(it_dev, transformer);
+  auto result         = thrust::reduce(rmm::exec_policy(cudf::get_default_stream()),
+                               it_dev_squared,
+                               it_dev_squared + d_col->size(),
+                               thrust::make_pair(T_output{}, true),
+                               sum_if_not_null{});
+  if constexpr (not std::is_floating_point<T>()) {
+    EXPECT_EQ(expected_value, result.first) << "pair iterator reduction sum";
+  } else {
+    EXPECT_NEAR(expected_value.value, result.first.value, 1e-3) << "pair iterator reduction sum";
+    EXPECT_NEAR(expected_value.value_squared, result.first.value_squared, 1e-3)
+      << "pair iterator reduction sum squared";
+    EXPECT_EQ(expected_value.count, result.first.count) << "pair iterator reduction count";
+  }
+}
+#endif
diff --git a/cpp/tests/iterator/scalar_iterator_test.cu b/cpp/tests/iterator/scalar_iterator_test.cu
new file mode 100644
index 0000000..b867703
--- /dev/null
+++ b/cpp/tests/iterator/scalar_iterator_test.cu
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <tests/iterator/iterator_tests.cuh>
+
+#include <thrust/host_vector.h>
+#include <thrust/pair.h>
+
+using TestingTypes = cudf::test::FixedWidthTypesWithoutFixedPoint;
+
+TYPED_TEST_SUITE(IteratorTest, TestingTypes);
+
+TYPED_TEST(IteratorTest, scalar_iterator)
+{
+  using T = TypeParam;
+  T init  = cudf::test::make_type_param_scalar<T>(
+    cudf::test::UniformRandomGenerator<int>(-128, 128).generate());
+  // data and valid arrays
+  thrust::host_vector<T> host_values(100, init);
+  std::vector<bool> host_bools(100, true);
+
+  // create a scalar
+  using ScalarType = cudf::scalar_type_t<T>;
+  std::unique_ptr<cudf::scalar> s(new ScalarType{init, true});
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::pair<T, bool>> value_and_validity(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 value_and_validity.begin(),
+                 [](auto v, auto b) {
+                   return thrust::pair<T, bool>{v, b};
+                 });
+
+  // GPU test
+  auto it_dev = cudf::detail::make_scalar_iterator<T>(*s);
+  this->iterator_test_thrust(host_values, it_dev, host_values.size());
+
+  auto it_pair_dev = cudf::detail::make_pair_iterator<T>(*s);
+  this->iterator_test_thrust(value_and_validity, it_pair_dev, host_values.size());
+}
+
+TYPED_TEST(IteratorTest, null_scalar_iterator)
+{
+  using T = TypeParam;
+  T init  = cudf::test::make_type_param_scalar<T>(
+    cudf::test::UniformRandomGenerator<int>(-128, 128).generate());
+  // data and valid arrays
+  std::vector<T> host_values(100, init);
+  std::vector<bool> host_bools(100, true);
+
+  // create a scalar
+  using ScalarType = cudf::scalar_type_t<T>;
+  std::unique_ptr<cudf::scalar> s(new ScalarType{init, true});
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::pair<T, bool>> value_and_validity(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 value_and_validity.begin(),
+                 [](auto v, auto b) {
+                   return thrust::pair<T, bool>{v, b};
+                 });
+
+  // GPU test
+  auto it_pair_dev = cudf::detail::make_pair_iterator<T>(*s);
+  this->iterator_test_thrust(value_and_validity, it_pair_dev, host_values.size());
+}
diff --git a/cpp/tests/iterator/sizes_to_offsets_iterator_test.cu b/cpp/tests/iterator/sizes_to_offsets_iterator_test.cu
new file mode 100644
index 0000000..3b412b7
--- /dev/null
+++ b/cpp/tests/iterator/sizes_to_offsets_iterator_test.cu
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/sizes_to_offsets_iterator.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_scalar.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/scan.h>
+
+#include <algorithm>
+
+using TestingTypes = cudf::test::IntegralTypesNotBool;
+
+template <typename T>
+struct SizesToOffsetsIteratorTestTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SizesToOffsetsIteratorTestTyped, TestingTypes);
+
+TYPED_TEST(SizesToOffsetsIteratorTestTyped, ExclusiveScan)
+{
+  using T        = TypeParam;
+  using LastType = int64_t;
+
+  auto stream = cudf::get_default_stream();
+
+  auto sizes = std::vector<T>({0, 6, 0, 14, 13, 64, 10, 20, 41});
+
+  auto d_col  = cudf::test::fixed_width_column_wrapper<T>(sizes.begin(), sizes.end());
+  auto d_view = cudf::column_view(d_col);
+
+  auto last   = rmm::device_scalar<LastType>(0, stream);
+  auto result = rmm::device_uvector<T>(d_view.size(), stream);
+  auto output_itr =
+    cudf::detail::make_sizes_to_offsets_iterator(result.begin(), result.end(), last.data());
+
+  thrust::exclusive_scan(
+    rmm::exec_policy(stream), d_view.begin<T>(), d_view.end<T>(), output_itr, LastType{0});
+
+  auto expected_values = std::vector<T>(sizes.size());
+  std::exclusive_scan(sizes.begin(), sizes.end(), expected_values.begin(), T{0});
+  auto expected_reduce =
+    static_cast<LastType>(std::reduce(sizes.begin(), sizes.begin() + sizes.size() - 1, T{0}));
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T>(expected_values.begin(), expected_values.end());
+  auto result_col = cudf::column_view(
+    cudf::data_type(cudf::type_to_id<T>()), d_view.size(), result.data(), nullptr, 0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result_col, expected);
+  EXPECT_EQ(last.value(stream), expected_reduce);
+}
+
+struct SizesToOffsetsIteratorTest : public cudf::test::BaseFixture {};
+
+TEST_F(SizesToOffsetsIteratorTest, ScanWithOverflow)
+{
+  auto stream = cudf::get_default_stream();
+
+  std::vector<int32_t> values(30000, 100000);
+  auto d_col  = cudf::test::fixed_width_column_wrapper<int32_t>(values.begin(), values.end());
+  auto d_view = cudf::column_view(d_col);
+
+  auto last   = rmm::device_scalar<int64_t>(0, stream);
+  auto result = rmm::device_uvector<int32_t>(d_view.size(), stream);
+  auto output_itr =
+    cudf::detail::make_sizes_to_offsets_iterator(result.begin(), result.end(), last.data());
+
+  thrust::exclusive_scan(rmm::exec_policy(stream),
+                         d_view.begin<int32_t>(),
+                         d_view.end<int32_t>(),
+                         output_itr,
+                         int64_t{0});
+
+  auto expected = static_cast<int64_t>(
+    std::reduce(values.begin(), values.begin() + values.size() - 1, int64_t{0}));
+  EXPECT_EQ(last.value(stream), expected);
+}
diff --git a/cpp/tests/iterator/value_iterator.cpp b/cpp/tests/iterator/value_iterator.cpp
new file mode 100644
index 0000000..a25b19e
--- /dev/null
+++ b/cpp/tests/iterator/value_iterator.cpp
@@ -0,0 +1,18 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/iterator/value_iterator_test.cuh b/cpp/tests/iterator/value_iterator_test.cuh
new file mode 100644
index 0000000..8252ce8
--- /dev/null
+++ b/cpp/tests/iterator/value_iterator_test.cuh
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#pragma once
+
+#include <tests/iterator/iterator_tests.cuh>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+
+#include <thrust/host_vector.h>
+
+// tests for non-null iterator (pointer of device array)
+template <typename T>
+void non_null_iterator(IteratorTest<T>& testFixture)
+{
+  auto host_array = cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  auto dev_array  = cudf::detail::make_device_uvector_sync(
+    host_array, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<T> replaced_array(host_array);
+
+  // driven by iterator as a pointer of device array.
+  auto it_dev      = dev_array.begin();
+  T expected_value = *std::min_element(replaced_array.begin(), replaced_array.end());
+  testFixture.iterator_test_thrust(replaced_array, it_dev, dev_array.size());
+  testFixture.iterator_test_cub(expected_value, it_dev, dev_array.size());
+
+  // test column input
+  cudf::test::fixed_width_column_wrapper<T> w_col(host_array.begin(), host_array.end());
+  testFixture.values_equal_test(replaced_array, *cudf::column_device_view::create(w_col));
+}
+
+// Tests for null input iterator (column with null bitmap)
+// Actually, we can use cub for reduction with nulls without creating custom
+// kernel or multiple steps. We may accelerate the reduction for a column using
+// cub
+template <typename T>
+void null_iterator(IteratorTest<T>& testFixture)
+{
+  T init = cudf::test::make_type_param_scalar<T>(0);
+  // data and valid arrays
+  auto host_values = cudf::test::make_type_param_vector<T>({0, 6, 0, -14, 13, 64, -13, -20, 45});
+  std::vector<bool> host_bools({1, 1, 0, 1, 1, 1, 0, 1, 1});
+
+  // create a column with bool vector
+  cudf::test::fixed_width_column_wrapper<T> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<T> replaced_array(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_array.begin(),
+                 [&](T x, bool b) { return (b) ? x : init; });
+  T expected_value = *std::min_element(replaced_array.begin(), replaced_array.end());
+  // TODO uncomment after time_point ostream operator<<
+  // std::cout << "expected <null_iterator> = " << expected_value << std::endl;
+
+  // GPU test
+  auto it_dev =
+    cudf::detail::make_null_replacement_iterator(*d_col, cudf::test::make_type_param_scalar<T>(0));
+  testFixture.iterator_test_cub(expected_value, it_dev, d_col->size());
+  testFixture.values_equal_test(replaced_array, *d_col);
+}
diff --git a/cpp/tests/iterator/value_iterator_test_chrono.cu b/cpp/tests/iterator/value_iterator_test_chrono.cu
new file mode 100644
index 0000000..73796f5
--- /dev/null
+++ b/cpp/tests/iterator/value_iterator_test_chrono.cu
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <tests/iterator/value_iterator_test.cuh>
+
+using TestingTypes = cudf::test::ChronoTypes;
+
+template <typename T>
+struct ChronoValueIteratorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(ChronoValueIteratorTest, TestingTypes);
+TYPED_TEST(ChronoValueIteratorTest, non_null_iterator) { non_null_iterator(*this); }
+TYPED_TEST(ChronoValueIteratorTest, null_iterator) { null_iterator(*this); }
diff --git a/cpp/tests/iterator/value_iterator_test_numeric.cu b/cpp/tests/iterator/value_iterator_test_numeric.cu
new file mode 100644
index 0000000..0d5ab6a
--- /dev/null
+++ b/cpp/tests/iterator/value_iterator_test_numeric.cu
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <tests/iterator/value_iterator_test.cuh>
+
+using TestingTypes = cudf::test::NumericTypes;
+
+template <typename T>
+struct NumericValueIteratorTest : public IteratorTest<T> {};
+
+TYPED_TEST_SUITE(NumericValueIteratorTest, TestingTypes);
+TYPED_TEST(NumericValueIteratorTest, non_null_iterator) { non_null_iterator(*this); }
+TYPED_TEST(NumericValueIteratorTest, null_iterator) { null_iterator(*this); }
diff --git a/cpp/tests/iterator/value_iterator_test_strings.cu b/cpp/tests/iterator/value_iterator_test_strings.cu
new file mode 100644
index 0000000..10bb3f2
--- /dev/null
+++ b/cpp/tests/iterator/value_iterator_test_strings.cu
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include "iterator_tests.cuh"
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/pair.h>
+
+auto strings_to_string_views(std::vector<std::string>& input_strings)
+{
+  auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  std::vector<char> chars;
+  std::vector<int32_t> offsets;
+  std::tie(chars, offsets) = cudf::test::detail::make_chars_and_offsets(
+    input_strings.begin(), input_strings.end(), all_valid);
+  auto dev_chars = cudf::detail::make_device_uvector_sync(
+    chars, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // calculate the expected value by CPU. (but contains device pointers)
+  thrust::host_vector<cudf::string_view> replaced_array(input_strings.size());
+  std::transform(thrust::counting_iterator<size_t>(0),
+                 thrust::counting_iterator<size_t>(replaced_array.size()),
+                 replaced_array.begin(),
+                 [c_start = dev_chars.begin(), offsets](auto i) {
+                   return cudf::string_view(c_start + offsets[i], offsets[i + 1] - offsets[i]);
+                 });
+  return std::make_tuple(std::move(dev_chars), replaced_array);
+}
+
+struct StringIteratorTest : public IteratorTest<cudf::string_view> {};
+
+TEST_F(StringIteratorTest, string_view_null_iterator)
+{
+  using T = cudf::string_view;
+  std::string zero("zero");
+  // the char data has to be in GPU
+  auto initmsg = cudf::detail::make_device_uvector_sync(
+    zero, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  T init = T{initmsg.data(), int(initmsg.size())};
+
+  // data and valid arrays
+  std::vector<std::string> host_values(
+    {"one", "two", "three", "four", "five", "six", "eight", "nine"});
+  std::vector<bool> host_bools({1, 1, 0, 1, 1, 1, 0, 1, 1});
+
+  // replace nulls in CPU
+  std::vector<std::string> replaced_strings(host_values.size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_strings.begin(),
+                 [zero](auto s, auto b) { return b ? s : zero; });
+
+  auto [dev_chars, replaced_array] = strings_to_string_views(replaced_strings);
+
+  // create a column with bool vector
+  cudf::test::strings_column_wrapper w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // GPU test
+  auto it_dev = cudf::detail::make_null_replacement_iterator(*d_col, init);
+  this->iterator_test_thrust(replaced_array, it_dev, host_values.size());
+  // this->values_equal_test(replaced_array, *d_col); //string_view{0} is invalid
+}
+
+TEST_F(StringIteratorTest, string_view_no_null_iterator)
+{
+  using T = cudf::string_view;
+  // T init = T{"", 0};
+  std::string zero("zero");
+  // the char data has to be in GPU
+  auto initmsg = cudf::detail::make_device_uvector_sync(
+    zero, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  T init = T{initmsg.data(), int(initmsg.size())};
+
+  // data array
+  std::vector<std::string> host_values(
+    {"one", "two", "three", "four", "five", "six", "eight", "nine"});
+
+  auto [dev_chars, all_array] = strings_to_string_views(host_values);
+
+  // create a column with bool vector
+  cudf::test::strings_column_wrapper w_col(host_values.begin(), host_values.end());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // GPU test
+  auto it_dev = d_col->begin<T>();
+  this->iterator_test_thrust(all_array, it_dev, host_values.size());
+}
+
+TEST_F(StringIteratorTest, string_scalar_iterator)
+{
+  using T = cudf::string_view;
+  // T init = T{"", 0};
+  std::string zero("zero");
+  // the char data has to be in GPU
+  auto initmsg = cudf::detail::make_device_uvector_sync(
+    zero, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  T init = T{initmsg.data(), int(initmsg.size())};
+
+  // data array
+  std::vector<std::string> host_values(100, zero);
+
+  auto [dev_chars, all_array] = strings_to_string_views(host_values);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<thrust::pair<T, bool>> value_and_validity(host_values.size());
+  std::transform(all_array.begin(), all_array.end(), value_and_validity.begin(), [](auto v) {
+    return thrust::pair<T, bool>{v, true};
+  });
+
+  // create a scalar
+  using ScalarType = cudf::scalar_type_t<T>;
+  std::unique_ptr<cudf::scalar> s(new ScalarType{zero, true});
+
+  // GPU test
+  auto it_dev = cudf::detail::make_scalar_iterator<T>(*s);
+  this->iterator_test_thrust(all_array, it_dev, host_values.size());
+
+  auto it_pair_dev = cudf::detail::make_pair_iterator<T>(*s);
+  this->iterator_test_thrust(value_and_validity, it_pair_dev, host_values.size());
+}
diff --git a/cpp/tests/iterator/value_iterator_test_transform.cu b/cpp/tests/iterator/value_iterator_test_transform.cu
new file mode 100644
index 0000000..a309cfd
--- /dev/null
+++ b/cpp/tests/iterator/value_iterator_test_transform.cu
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under the License
+ * is distributed on an "AS IS" BASIS,  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+ * or implied. See the License for the specific language governing permissions and limitations under
+ * the License.
+ */
+#include <tests/iterator/iterator_tests.cuh>
+
+#include <thrust/functional.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/transform_iterator.h>
+
+struct TransformedIteratorTest : public IteratorTest<int8_t> {};
+
+// Tests up cast reduction with null iterator.
+// The up cast iterator will be created by transform_iterator and
+// cudf::detail::make_null_replacement_iterator(col, T{0})
+TEST_F(TransformedIteratorTest, null_iterator_upcast)
+{
+  int const column_size{1000};
+  using T        = int8_t;
+  using T_upcast = int64_t;
+  T init{0};
+
+  // data and valid arrays
+  std::vector<T> host_values(column_size);
+  std::vector<bool> host_bools(column_size);
+
+  cudf::test::UniformRandomGenerator<T> rng(-128, 127);
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(host_values.begin(), host_values.end(), [&rng]() { return rng.generate(); });
+  std::generate(host_bools.begin(), host_bools.end(), [&rbg]() { return rbg.generate(); });
+
+  cudf::test::fixed_width_column_wrapper<T> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<T> replaced_array(d_col->size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_array.begin(),
+                 [&](T x, bool b) { return (b) ? x : init; });
+  T_upcast expected_value = *std::min_element(replaced_array.begin(), replaced_array.end());
+  // std::cout << "expected <null_iterator> = " << expected_value << std::endl;
+
+  // GPU test
+  auto it_dev        = cudf::detail::make_null_replacement_iterator(*d_col, T{0});
+  auto it_dev_upcast = thrust::make_transform_iterator(it_dev, thrust::identity<T_upcast>());
+  this->iterator_test_thrust(replaced_array, it_dev_upcast, d_col->size());
+  this->iterator_test_cub(expected_value, it_dev, d_col->size());
+}
+
+// Tests for square input iterator using helper strcut
+// `cudf::transformer_squared<T, T_upcast>` The up cast iterator will be created
+// by make_transform_iterator(
+//        cudf::detail::make_null_replacement_iterator(col, T{0}),
+//        cudf::detail::transformer_squared<T_upcast>)
+TEST_F(TransformedIteratorTest, null_iterator_square)
+{
+  int const column_size{1000};
+  using T        = int8_t;
+  using T_upcast = int64_t;
+  T init{0};
+  cudf::transformer_squared<T_upcast> transformer{};
+
+  // data and valid arrays
+  std::vector<T> host_values(column_size);
+  std::vector<bool> host_bools(column_size);
+
+  cudf::test::UniformRandomGenerator<T> rng(-128, 127);
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(host_values.begin(), host_values.end(), [&rng]() { return rng.generate(); });
+  std::generate(host_bools.begin(), host_bools.end(), [&rbg]() { return rbg.generate(); });
+
+  cudf::test::fixed_width_column_wrapper<T> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate the expected value by CPU.
+  thrust::host_vector<T_upcast> replaced_array(d_col->size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_array.begin(),
+                 [&](T x, bool b) { return (b) ? x * x : init; });
+  T_upcast expected_value = *std::min_element(replaced_array.begin(), replaced_array.end());
+  // std::cout << "expected <null_iterator> = " << expected_value << std::endl;
+
+  // GPU test
+  auto it_dev         = cudf::detail::make_null_replacement_iterator(*d_col, T{0});
+  auto it_dev_upcast  = thrust::make_transform_iterator(it_dev, thrust::identity<T_upcast>());
+  auto it_dev_squared = thrust::make_transform_iterator(it_dev_upcast, transformer);
+  this->iterator_test_thrust(replaced_array, it_dev_squared, d_col->size());
+  this->iterator_test_cub(expected_value, it_dev_squared, d_col->size());
+}
+
+// TODO only few types
+TEST_F(TransformedIteratorTest, large_size_reduction)
+{
+  using T = int64_t;
+
+  int const column_size{1000000};
+  const T init{0};
+
+  // data and valid arrays
+  std::vector<T> host_values(column_size);
+  std::vector<bool> host_bools(column_size);
+
+  cudf::test::UniformRandomGenerator<T> rng(-128, 128);
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(host_values.begin(), host_values.end(), [&rng]() { return rng.generate(); });
+  std::generate(host_bools.begin(), host_bools.end(), [&rbg]() { return rbg.generate(); });
+
+  cudf::test::fixed_width_column_wrapper<T> w_col(
+    host_values.begin(), host_values.end(), host_bools.begin());
+  auto d_col = cudf::column_device_view::create(w_col);
+
+  // calculate by cudf::reduce
+  thrust::host_vector<T> replaced_array(d_col->size());
+  std::transform(host_values.begin(),
+                 host_values.end(),
+                 host_bools.begin(),
+                 replaced_array.begin(),
+                 [&](T x, bool b) { return (b) ? x : init; });
+  T expected_value = *std::min_element(replaced_array.begin(), replaced_array.end());
+  // std::cout << "expected <null_iterator> = " << expected_value << std::endl;
+
+  // GPU test
+  auto it_dev = cudf::detail::make_null_replacement_iterator(*d_col, init);
+  this->iterator_test_thrust(replaced_array, it_dev, d_col->size());
+  this->iterator_test_cub(expected_value, it_dev, d_col->size());
+}
diff --git a/cpp/tests/join/conditional_join_tests.cu b/cpp/tests/join/conditional_join_tests.cu
new file mode 100644
index 0000000..ad5a331
--- /dev/null
+++ b/cpp/tests/join/conditional_join_tests.cu
@@ -0,0 +1,891 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/join.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/equal.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+
+#include <algorithm>
+#include <iostream>
+#include <random>
+#include <stdexcept>
+#include <tuple>
+#include <utility>
+#include <vector>
+
+namespace {
+using PairJoinReturn   = std::pair<std::unique_ptr<rmm::device_uvector<cudf::size_type>>,
+                                 std::unique_ptr<rmm::device_uvector<cudf::size_type>>>;
+using SingleJoinReturn = std::unique_ptr<rmm::device_uvector<cudf::size_type>>;
+using NullMaskVector   = std::vector<bool>;
+
+template <typename T>
+using ColumnVector = std::vector<std::vector<T>>;
+
+template <typename T>
+using NullableColumnVector = std::vector<std::pair<std::vector<T>, NullMaskVector>>;
+
+constexpr cudf::size_type JoinNoneValue =
+  std::numeric_limits<cudf::size_type>::min();  // TODO: how to test if this isn't public?
+
+// Common column references.
+auto const col_ref_left_0  = cudf::ast::column_reference(0, cudf::ast::table_reference::LEFT);
+auto const col_ref_right_0 = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+
+// Common expressions.
+auto left_zero_eq_right_zero =
+  cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_left_0, col_ref_right_0);
+
+// Generate a single pair of left/right non-nullable columns of random data
+// suitable for testing a join against a reference join implementation.
+template <typename T>
+std::pair<std::vector<T>, std::vector<T>> gen_random_repeated_columns(
+  unsigned int N_left            = 10000,
+  unsigned int num_repeats_left  = 10,
+  unsigned int N_right           = 10000,
+  unsigned int num_repeats_right = 10)
+{
+  // Generate columns of num_repeats repeats of the integer range [0, num_unique),
+  // then merge a shuffled version and compare to hash join.
+  unsigned int num_unique_left  = N_left / num_repeats_left;
+  unsigned int num_unique_right = N_right / num_repeats_right;
+
+  std::vector<T> left(N_left);
+  std::vector<T> right(N_right);
+
+  for (unsigned int i = 0; i < num_repeats_left; ++i) {
+    std::iota(std::next(left.begin(), num_unique_left * i),
+              std::next(left.begin(), num_unique_left * (i + 1)),
+              0);
+  }
+  for (unsigned int i = 0; i < num_repeats_right; ++i) {
+    std::iota(std::next(right.begin(), num_unique_right * i),
+              std::next(right.begin(), num_unique_right * (i + 1)),
+              0);
+  }
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::shuffle(left.begin(), left.end(), gen);
+  std::shuffle(right.begin(), right.end(), gen);
+  return std::pair(std::move(left), std::move(right));
+}
+
+// Generate a single pair of left/right nullable columns of random data
+// suitable for testing a join against a reference join implementation.
+template <typename T>
+std::pair<std::pair<std::vector<T>, std::vector<bool>>,
+          std::pair<std::vector<T>, std::vector<bool>>>
+gen_random_nullable_repeated_columns(unsigned int N = 10000, unsigned int num_repeats = 10)
+{
+  auto [left, right] = gen_random_repeated_columns<T>(N, num_repeats);
+
+  std::vector<bool> left_nulls(N);
+  std::vector<bool> right_nulls(N);
+
+  // Seed with a real random value, if available
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::uniform_real_distribution<> uniform_dist(0, 1);
+
+  std::generate(left_nulls.begin(), left_nulls.end(), [&uniform_dist, &gen]() {
+    return uniform_dist(gen) > 0.5;
+  });
+  std::generate(right_nulls.begin(), right_nulls.end(), [&uniform_dist, &gen]() {
+    return uniform_dist(gen) > 0.5;
+  });
+
+  return std::pair(std::pair(std::move(left), std::move(left_nulls)),
+                   std::pair(std::move(right), std::move(right_nulls)));
+}
+
+// `rmm::device_uvector<T>` requires that T be trivially copyable. `thrust::pair` does
+// not satisfy this requirement because it defines nontrivial copy/move
+// constructors. Therefore, we need a simple, trivially copyable pair-like
+// object. `index_pair` is a minimal implementation suitable for use in the
+// tests in this file.
+struct index_pair {
+  cudf::size_type first{};
+  cudf::size_type second{};
+  __device__ index_pair(){};
+  __device__ index_pair(cudf::size_type const& first, cudf::size_type const& second)
+    : first(first), second(second){};
+};
+
+__device__ inline bool operator<(index_pair const& lhs, index_pair const& rhs)
+{
+  if (lhs.first > rhs.first) return false;
+  return (lhs.first < rhs.first) || (lhs.second < rhs.second);
+}
+
+__device__ inline bool operator==(index_pair const& lhs, index_pair const& rhs)
+{
+  return lhs.first == rhs.first && lhs.second == rhs.second;
+}
+
+}  // namespace
+
+/**
+ * Fixture for all nested loop conditional joins.
+ */
+template <typename T>
+struct ConditionalJoinTest : public cudf::test::BaseFixture {
+  /**
+   * Convenience utility for parsing initializer lists of input data into
+   * suitable inputs for tables.
+   */
+  template <typename U>
+  std::tuple<std::vector<cudf::test::fixed_width_column_wrapper<T>>,
+             std::vector<cudf::test::fixed_width_column_wrapper<T>>,
+             std::vector<cudf::column_view>,
+             std::vector<cudf::column_view>,
+             cudf::table_view,
+             cudf::table_view>
+  parse_input(std::vector<U> left_data, std::vector<U> right_data)
+  {
+    auto wrapper_generator = [](U& v) {
+      if constexpr (std::is_same_v<U, std::vector<T>>) {
+        return cudf::test::fixed_width_column_wrapper<T>(v.begin(), v.end());
+      } else if constexpr (std::is_same_v<U, std::pair<std::vector<T>, std::vector<bool>>>) {
+        return cudf::test::fixed_width_column_wrapper<T>(
+          v.first.begin(), v.first.end(), v.second.begin());
+      }
+      throw std::runtime_error("Invalid input to parse_input.");
+      return cudf::test::fixed_width_column_wrapper<T>();
+    };
+
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    std::vector<cudf::test::fixed_width_column_wrapper<T>> left_wrappers;
+    std::vector<cudf::column_view> left_columns;
+    for (auto v : left_data) {
+      left_wrappers.push_back(wrapper_generator(v));
+      left_columns.push_back(left_wrappers.back());
+    }
+
+    std::vector<cudf::test::fixed_width_column_wrapper<T>> right_wrappers;
+    std::vector<cudf::column_view> right_columns;
+    for (auto v : right_data) {
+      right_wrappers.push_back(wrapper_generator(v));
+      right_columns.push_back(right_wrappers.back());
+    }
+
+    return std::make_tuple(std::move(left_wrappers),
+                           std::move(right_wrappers),
+                           std::move(left_columns),
+                           std::move(right_columns),
+                           cudf::table_view(left_columns),
+                           cudf::table_view(right_columns));
+  }
+};
+
+/**
+ * Fixture for join types that return both left and right indices (inner, left,
+ * and full joins).
+ */
+template <typename T>
+struct ConditionalJoinPairReturnTest : public ConditionalJoinTest<T> {
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void _test(cudf::table_view left,
+             cudf::table_view right,
+             cudf::ast::operation predicate,
+             std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs)
+  {
+    auto result_size = this->join_size(left, right, predicate);
+    EXPECT_TRUE(result_size == expected_outputs.size());
+
+    auto result = this->join(left, right, predicate);
+    std::vector<std::pair<cudf::size_type, cudf::size_type>> result_pairs;
+    for (size_t i = 0; i < result.first->size(); ++i) {
+      // Note: Not trying to be terribly efficient here since these tests are
+      // small, otherwise a batch copy to host before constructing the tuples
+      // would be important.
+      result_pairs.push_back({result.first->element(i, cudf::get_default_stream()),
+                              result.second->element(i, cudf::get_default_stream())});
+    }
+    std::sort(result_pairs.begin(), result_pairs.end());
+    std::sort(expected_outputs.begin(), expected_outputs.end());
+
+    EXPECT_TRUE(std::equal(expected_outputs.begin(), expected_outputs.end(), result_pairs.begin()));
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void test(ColumnVector<T> left_data,
+            ColumnVector<T> right_data,
+            cudf::ast::operation predicate,
+            std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+    this->_test(left, right, predicate, expected_outputs);
+  }
+
+  void test_nulls(NullableColumnVector<T> left_data,
+                  NullableColumnVector<T> right_data,
+                  cudf::ast::operation predicate,
+                  std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+    this->_test(left, right, predicate, expected_outputs);
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * an equality predicate on all corresponding columns and verify that the outputs match the
+   * expected outputs (up to order).
+   */
+  void _compare_to_hash_join(PairJoinReturn const& result, PairJoinReturn const& reference)
+  {
+    auto result_pairs =
+      rmm::device_uvector<index_pair>(result.first->size(), cudf::get_default_stream());
+    auto reference_pairs =
+      rmm::device_uvector<index_pair>(reference.first->size(), cudf::get_default_stream());
+
+    thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                      result.first->begin(),
+                      result.first->end(),
+                      result.second->begin(),
+                      result_pairs.begin(),
+                      [] __device__(cudf::size_type first, cudf::size_type second) {
+                        return index_pair{first, second};
+                      });
+    thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                      reference.first->begin(),
+                      reference.first->end(),
+                      reference.second->begin(),
+                      reference_pairs.begin(),
+                      [] __device__(cudf::size_type first, cudf::size_type second) {
+                        return index_pair{first, second};
+                      });
+
+    thrust::sort(
+      rmm::exec_policy(cudf::get_default_stream()), result_pairs.begin(), result_pairs.end());
+    thrust::sort(
+      rmm::exec_policy(cudf::get_default_stream()), reference_pairs.begin(), reference_pairs.end());
+
+    EXPECT_TRUE(thrust::equal(rmm::exec_policy(cudf::get_default_stream()),
+                              reference_pairs.begin(),
+                              reference_pairs.end(),
+                              result_pairs.begin()));
+  }
+
+  void compare_to_hash_join(ColumnVector<T> left_data, ColumnVector<T> right_data)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+    auto result    = this->join(left, right, left_zero_eq_right_zero);
+    auto reference = this->reference_join(left, right);
+    this->_compare_to_hash_join(result, reference);
+  }
+
+  void compare_to_hash_join_nulls(NullableColumnVector<T> left_data,
+                                  NullableColumnVector<T> right_data)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+
+    // Test comparing nulls as equal (the default for ref joins, uses NULL_EQUAL for AST
+    // expression).
+    auto predicate =
+      cudf::ast::operation(cudf::ast::ast_operator::NULL_EQUAL, col_ref_left_0, col_ref_right_0);
+    auto result    = this->join(left, right, predicate);
+    auto reference = this->reference_join(left, right);
+    this->_compare_to_hash_join(result, reference);
+
+    // Test comparing nulls as equal (null_equality::UNEQUAL for ref joins, uses EQUAL for AST
+    // expression).
+    result    = this->join(left, right, left_zero_eq_right_zero);
+    reference = this->reference_join(left, right, cudf::null_equality::UNEQUAL);
+    this->_compare_to_hash_join(result, reference);
+  }
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * conditional join API.
+   */
+  virtual PairJoinReturn join(cudf::table_view left,
+                              cudf::table_view right,
+                              cudf::ast::operation predicate) = 0;
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * conditional join size computation API.
+   */
+  virtual std::size_t join_size(cudf::table_view left,
+                                cudf::table_view right,
+                                cudf::ast::operation predicate) = 0;
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * hash join API for comparison with conditional joins.
+   */
+  virtual PairJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) = 0;
+};
+
+/**
+ * Tests of conditional inner joins.
+ */
+template <typename T>
+struct ConditionalInnerJoinTest : public ConditionalJoinPairReturnTest<T> {
+  PairJoinReturn join(cudf::table_view left,
+                      cudf::table_view right,
+                      cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_inner_join(left, right, predicate);
+  }
+
+  std::size_t join_size(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_inner_join_size(left, right, predicate);
+  }
+
+  PairJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::inner_join(left, right, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(ConditionalInnerJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(ConditionalInnerJoinTest, TestOneColumnOneRowAllEqual)
+{
+  this->test({{0}}, {{0}}, left_zero_eq_right_zero, {{0, 0}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestOneColumnLeftEmpty)
+{
+  this->test({{}}, {{3, 4, 5}}, left_zero_eq_right_zero, {});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestOneColumnTwoRowAllEqual)
+{
+  this->test({{0, 1}}, {{0, 0}}, left_zero_eq_right_zero, {{0, 0}, {0, 1}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestTwoColumnOneRowAllEqual)
+{
+  this->test({{0}, {0}}, {{0}, {0}}, left_zero_eq_right_zero, {{0, 0}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestTwoColumnThreeRowAllEqual)
+{
+  this->test({{0, 1, 2}, {10, 20, 30}},
+             {{0, 1, 2}, {30, 40, 50}},
+             left_zero_eq_right_zero,
+             {{0, 0}, {1, 1}, {2, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestTwoColumnThreeRowSomeEqual)
+{
+  this->test({{0, 1, 2}, {10, 20, 30}},
+             {{0, 1, 3}, {30, 40, 50}},
+             left_zero_eq_right_zero,
+             {{0, 0}, {1, 1}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestNotComparison)
+{
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::NOT, col_ref_0);
+
+  this->test({{0, 1, 2}}, {{3, 4, 5}}, expression, {{0, 0}, {0, 1}, {0, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestGreaterComparison)
+{
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_0, col_ref_1);
+
+  this->test({{0, 1, 2}}, {{1, 0, 0}}, expression, {{1, 1}, {1, 2}, {2, 0}, {2, 1}, {2, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestGreaterTwoColumnComparison)
+{
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_0, col_ref_1);
+
+  this->test({{0, 1, 2}, {0, 0, 0}},
+             {{0, 0, 0}, {1, 0, 0}},
+             expression,
+             {{1, 1}, {1, 2}, {2, 0}, {2, 1}, {2, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestGreaterDifferentNumberColumnComparison)
+{
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_0, col_ref_1);
+
+  this->test(
+    {{0, 1, 2}}, {{0, 0, 0}, {1, 0, 0}}, expression, {{1, 1}, {1, 2}, {2, 0}, {2, 1}, {2, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestGreaterDifferentNumberColumnDifferentSizeComparison)
+{
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_0, col_ref_1);
+
+  this->test({{0, 1}}, {{0, 0, 0}, {1, 0, 0}}, expression, {{1, 1}, {1, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestComplexConditionMultipleColumns)
+{
+  // LEFT is implicit, but specifying explicitly to validate that it works.
+  auto col_ref_0      = cudf::ast::column_reference(0, cudf::ast::table_reference::LEFT);
+  auto scalar_1       = cudf::numeric_scalar<TypeParam>(1);
+  auto literal_1      = cudf::ast::literal(scalar_1);
+  auto left_0_equal_1 = cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_0, literal_1);
+
+  auto col_ref_1 = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+  auto comparison_filter =
+    cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_1, col_ref_0);
+
+  auto expression =
+    cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, left_0_equal_1, comparison_filter);
+
+  this->test({{0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}},
+             {{0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2},
+              {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
+              {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}},
+             expression,
+             {{4, 0}, {5, 0}, {6, 0}, {7, 0}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestSymmetry)
+{
+  auto col_ref_0  = cudf::ast::column_reference(0);
+  auto col_ref_1  = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+  auto expression = cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_1, col_ref_0);
+  auto expression_reverse =
+    cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_0, col_ref_1);
+
+  this->test(
+    {{0, 1, 2}}, {{1, 2, 3}}, expression, {{0, 0}, {0, 1}, {0, 2}, {1, 1}, {1, 2}, {2, 2}});
+  this->test(
+    {{0, 1, 2}}, {{1, 2, 3}}, expression_reverse, {{0, 0}, {0, 1}, {0, 2}, {1, 1}, {1, 2}, {2, 2}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestCompareRandomToHash)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestCompareRandomToHashNulls)
+{
+  auto [left, right] = gen_random_nullable_repeated_columns<TypeParam>();
+  this->compare_to_hash_join_nulls({left}, {right});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestCompareRandomToHashNullsLargerLeft)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>(2000, 10, 1000, 10);
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestCompareRandomToHashNullsLargerRight)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>(1000, 10, 2000, 10);
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestOneColumnTwoNullsRowAllEqual)
+{
+  this->test_nulls(
+    {{{0, 1}, {1, 0}}}, {{{0, 0}, {1, 1}}}, left_zero_eq_right_zero, {{0, 0}, {0, 1}});
+};
+
+TYPED_TEST(ConditionalInnerJoinTest, TestOneColumnTwoNullsNoOutputRowAllEqual)
+{
+  this->test_nulls({{{0, 1}, {0, 1}}}, {{{0, 0}, {1, 1}}}, left_zero_eq_right_zero, {});
+};
+
+/**
+ * Tests of conditional left joins.
+ */
+template <typename T>
+struct ConditionalLeftJoinTest : public ConditionalJoinPairReturnTest<T> {
+  PairJoinReturn join(cudf::table_view left,
+                      cudf::table_view right,
+                      cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_left_join(left, right, predicate);
+  }
+
+  std::size_t join_size(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_left_join_size(left, right, predicate);
+  }
+
+  PairJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::left_join(left, right, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(ConditionalLeftJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(ConditionalLeftJoinTest, TestTwoColumnThreeRowSomeEqual)
+{
+  this->test({{0, 1, 2}, {10, 20, 30}},
+             {{0, 1, 3}, {30, 40, 50}},
+             left_zero_eq_right_zero,
+             {{0, 0}, {1, 1}, {2, JoinNoneValue}});
+};
+
+TYPED_TEST(ConditionalLeftJoinTest, TestOneColumnLeftEmpty)
+{
+  this->test({{}}, {{3, 4, 5}}, left_zero_eq_right_zero, {});
+};
+
+TYPED_TEST(ConditionalLeftJoinTest, TestCompareRandomToHash)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalLeftJoinTest, TestCompareRandomToHashNulls)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+/**
+ * Tests of conditional full joins.
+ */
+template <typename T>
+struct ConditionalFullJoinTest : public ConditionalJoinPairReturnTest<T> {
+  PairJoinReturn join(cudf::table_view left,
+                      cudf::table_view right,
+                      cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_full_join(left, right, predicate);
+  }
+
+  std::size_t join_size(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    // Full joins don't actually support size calculations, but to support a
+    // uniform testing framework we just calculate it from the result of doing
+    // the join.
+    return cudf::conditional_full_join(left, right, predicate).first->size();
+  }
+
+  PairJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::full_join(left, right, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(ConditionalFullJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(ConditionalFullJoinTest, TestOneColumnNoneEqual)
+{
+  this->test({{0, 1, 2}},
+             {{3, 4, 5}},
+             left_zero_eq_right_zero,
+             {{0, JoinNoneValue},
+              {1, JoinNoneValue},
+              {2, JoinNoneValue},
+              {JoinNoneValue, 0},
+              {JoinNoneValue, 1},
+              {JoinNoneValue, 2}});
+};
+
+TYPED_TEST(ConditionalFullJoinTest, TestOneColumnLeftEmpty)
+{
+  this->test({{}},
+             {{3, 4, 5}},
+             left_zero_eq_right_zero,
+             {{JoinNoneValue, 0}, {JoinNoneValue, 1}, {JoinNoneValue, 2}});
+};
+
+TYPED_TEST(ConditionalFullJoinTest, TestTwoColumnThreeRowSomeEqual)
+{
+  this->test({{0, 1, 2}, {10, 20, 30}},
+             {{0, 1, 3}, {30, 40, 50}},
+             left_zero_eq_right_zero,
+             {{0, 0}, {1, 1}, {2, JoinNoneValue}, {JoinNoneValue, 2}});
+};
+
+TYPED_TEST(ConditionalFullJoinTest, TestCompareRandomToHash)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalFullJoinTest, TestCompareRandomToHashNulls)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+/**
+ * Fixture for join types that return both only left indices (left semi and
+ * left anti).
+ */
+template <typename T>
+struct ConditionalJoinSingleReturnTest : public ConditionalJoinTest<T> {
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void test(ColumnVector<T> left_data,
+            ColumnVector<T> right_data,
+            cudf::ast::operation predicate,
+            std::vector<cudf::size_type> expected_outputs)
+  {
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+    auto result_size = this->join_size(left, right, predicate);
+    EXPECT_TRUE(result_size == expected_outputs.size());
+
+    auto result = this->join(left, right, predicate);
+    std::vector<cudf::size_type> resulting_indices;
+    for (size_t i = 0; i < result->size(); ++i) {
+      // Note: Not trying to be terribly efficient here since these tests are
+      // small, otherwise a batch copy to host before constructing the tuples
+      // would be important.
+      resulting_indices.push_back(result->element(i, cudf::get_default_stream()));
+    }
+    std::sort(resulting_indices.begin(), resulting_indices.end());
+    std::sort(expected_outputs.begin(), expected_outputs.end());
+    EXPECT_TRUE(
+      std::equal(resulting_indices.begin(), resulting_indices.end(), expected_outputs.begin()));
+  }
+
+  void _compare_to_hash_join(std::unique_ptr<rmm::device_uvector<cudf::size_type>> const& result,
+                             std::unique_ptr<rmm::device_uvector<cudf::size_type>> const& reference)
+  {
+    thrust::sort(rmm::exec_policy(cudf::get_default_stream()), result->begin(), result->end());
+    thrust::sort(
+      rmm::exec_policy(cudf::get_default_stream()), reference->begin(), reference->end());
+    EXPECT_TRUE(thrust::equal(rmm::exec_policy(cudf::get_default_stream()),
+                              result->begin(),
+                              result->end(),
+                              reference->begin()));
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * an equality predicate on all corresponding columns and verify that the outputs match the
+   * expected outputs (up to order).
+   */
+  void compare_to_hash_join(ColumnVector<T> left_data, ColumnVector<T> right_data)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+    auto result    = this->join(left, right, left_zero_eq_right_zero);
+    auto reference = this->reference_join(left, right);
+    this->_compare_to_hash_join(result, reference);
+  }
+
+  void compare_to_hash_join_nulls(NullableColumnVector<T> left_data,
+                                  NullableColumnVector<T> right_data)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers, right_wrappers, left_columns, right_columns, left, right] =
+      this->parse_input(left_data, right_data);
+    auto predicate =
+      cudf::ast::operation(cudf::ast::ast_operator::NULL_EQUAL, col_ref_left_0, col_ref_right_0);
+    auto result    = this->join(left, right, predicate);
+    auto reference = this->reference_join(left, right);
+    this->_compare_to_hash_join(result, reference);
+
+    result    = this->join(left, right, left_zero_eq_right_zero);
+    reference = this->reference_join(left, right, cudf::null_equality::UNEQUAL);
+    this->_compare_to_hash_join(result, reference);
+  }
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * conditional join API.
+   */
+  virtual SingleJoinReturn join(cudf::table_view left,
+                                cudf::table_view right,
+                                cudf::ast::operation predicate) = 0;
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * conditional join size computation API.
+   */
+  virtual std::size_t join_size(cudf::table_view left,
+                                cudf::table_view right,
+                                cudf::ast::operation predicate) = 0;
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * hash join API for comparison with conditional joins.
+   */
+  virtual SingleJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) = 0;
+};
+
+/**
+ * Tests of conditional left semi joins.
+ */
+template <typename T>
+struct ConditionalLeftSemiJoinTest : public ConditionalJoinSingleReturnTest<T> {
+  SingleJoinReturn join(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_left_semi_join(left, right, predicate);
+  }
+
+  std::size_t join_size(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_left_semi_join_size(left, right, predicate);
+  }
+
+  SingleJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::left_semi_join(left, right, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(ConditionalLeftSemiJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(ConditionalLeftSemiJoinTest, TestTwoColumnThreeRowSomeEqual)
+{
+  this->test({{0, 1, 2}, {10, 20, 30}}, {{0, 1, 3}, {30, 40, 50}}, left_zero_eq_right_zero, {0, 1});
+};
+
+TYPED_TEST(ConditionalLeftSemiJoinTest, TestCompareRandomToHash)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalLeftSemiJoinTest, TestCompareRandomToHashNulls)
+{
+  auto [left, right] = gen_random_nullable_repeated_columns<TypeParam>();
+  this->compare_to_hash_join_nulls({left}, {right});
+};
+
+/**
+ * Tests of conditional left anti joins.
+ */
+template <typename T>
+struct ConditionalLeftAntiJoinTest : public ConditionalJoinSingleReturnTest<T> {
+  SingleJoinReturn join(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_left_anti_join(left, right, predicate);
+  }
+
+  std::size_t join_size(cudf::table_view left,
+                        cudf::table_view right,
+                        cudf::ast::operation predicate) override
+  {
+    return cudf::conditional_left_anti_join_size(left, right, predicate);
+  }
+
+  SingleJoinReturn reference_join(
+    cudf::table_view left,
+    cudf::table_view right,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::left_anti_join(left, right, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(ConditionalLeftAntiJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(ConditionalLeftAntiJoinTest, TestTwoColumnThreeRowSomeEqual)
+{
+  this->test({{0, 1, 2}, {10, 20, 30}}, {{0, 1, 3}, {30, 40, 50}}, left_zero_eq_right_zero, {2});
+};
+
+TYPED_TEST(ConditionalLeftAntiJoinTest, TestCompareRandomToHash)
+{
+  auto [left, right] = gen_random_repeated_columns<TypeParam>();
+  this->compare_to_hash_join({left}, {right});
+};
+
+TYPED_TEST(ConditionalLeftAntiJoinTest, TestCompareRandomToHashNulls)
+{
+  auto [left, right] = gen_random_nullable_repeated_columns<TypeParam>();
+  this->compare_to_hash_join_nulls({left}, {right});
+};
diff --git a/cpp/tests/join/cross_join_tests.cpp b/cpp/tests/join/cross_join_tests.cpp
new file mode 100644
index 0000000..8fe8c44
--- /dev/null
+++ b/cpp/tests/join/cross_join_tests.cpp
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/join.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+template <typename T, typename SourceT = T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T, SourceT>;
+
+template <typename T>
+class CrossJoinTypeTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(CrossJoinTypeTests, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(CrossJoinTypeTests, CrossJoin)
+{
+  auto a_0 = column_wrapper<int32_t>{10, 20, 20, 50};
+  auto a_1 = column_wrapper<float>{5.0, .5, .5, .7};
+  auto a_2 = column_wrapper<TypeParam, int32_t>{0, 0, 0, 0};
+  auto a_3 = cudf::test::strings_column_wrapper({"quick", "accénted", "turtlé", "composéd"});
+
+  auto b_0 = column_wrapper<int32_t>{10, 20, 20};
+  auto b_1 = column_wrapper<float>{5.0, .7, .7};
+  auto b_2 = column_wrapper<TypeParam, int32_t>{0, 0, 0};
+  auto b_3 = cudf::test::strings_column_wrapper({"result", "", "words"});
+
+  auto expect_0 = column_wrapper<int32_t>{10, 10, 10, 20, 20, 20, 20, 20, 20, 50, 50, 50};
+  auto expect_1 = column_wrapper<float>{5.0, 5.0, 5.0, .5, .5, .5, .5, .5, .5, .7, .7, .7};
+  auto expect_2 = column_wrapper<TypeParam, int32_t>({0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
+  auto expect_3 = cudf::test::strings_column_wrapper({"quick",
+                                                      "quick",
+                                                      "quick",
+                                                      "accénted",
+                                                      "accénted",
+                                                      "accénted",
+                                                      "turtlé",
+                                                      "turtlé",
+                                                      "turtlé",
+                                                      "composéd",
+                                                      "composéd",
+                                                      "composéd"});
+  auto expect_4 = column_wrapper<int32_t>{10, 20, 20, 10, 20, 20, 10, 20, 20, 10, 20, 20};
+  auto expect_5 = column_wrapper<float>{5.0, .7, .7, 5.0, .7, .7, 5.0, .7, .7, 5.0, .7, .7};
+  auto expect_6 = column_wrapper<TypeParam, int32_t>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto expect_7 = cudf::test::strings_column_wrapper(
+    {"result", "", "words", "result", "", "words", "result", "", "words", "result", "", "words"});
+
+  auto table_a      = cudf::table_view{{a_0, a_1, a_2, a_3}};
+  auto table_b      = cudf::table_view{{b_0, b_1, b_2, b_3}};
+  auto table_expect = cudf::table_view{
+    {expect_0, expect_1, expect_2, expect_3, expect_4, expect_5, expect_6, expect_7}};
+
+  auto join_table = cudf::cross_join(table_a, table_b);
+
+  EXPECT_EQ(join_table->num_columns(), table_a.num_columns() + table_b.num_columns());
+  EXPECT_EQ(join_table->num_rows(), table_a.num_rows() * table_b.num_rows());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(join_table->view(), table_expect);
+}
+
+class CrossJoinInvalidInputs : public cudf::test::BaseFixture {};
+
+TEST_F(CrossJoinInvalidInputs, EmptyTable)
+{
+  auto b_0 = column_wrapper<int32_t>{10, 20, 20, 50};
+  auto b_1 = column_wrapper<float>{5.0, .7, .7, .7};
+  auto b_2 = column_wrapper<int8_t>{90, 75, 62, 41};
+  auto b_3 = cudf::test::strings_column_wrapper({"quick", "words", "result", ""});
+
+  auto column_a = std::vector<std::unique_ptr<cudf::column>>{};
+  auto table_a  = cudf::table(std::move(column_a));
+  auto table_b  = cudf::table_view{{b_0, b_1, b_2, b_3}};
+
+  //
+  //  table_a has no columns, table_b has columns
+  //  Let's check different permutations of passing table
+  //  with no columns to verify that exceptions are thrown
+  //
+  EXPECT_THROW(cudf::cross_join(table_a, table_b), cudf::logic_error);
+  EXPECT_THROW(cudf::cross_join(table_b, table_a), cudf::logic_error);
+}
+
+class CrossJoinEmptyResult : public cudf::test::BaseFixture {};
+
+TEST_F(CrossJoinEmptyResult, NoRows)
+{
+  auto a_0           = column_wrapper<int32_t>{};
+  auto a_1           = column_wrapper<float>{};
+  auto a_2           = column_wrapper<int8_t>{};
+  auto empty_strings = std::vector<std::string>();
+  auto a_3 = cudf::test::strings_column_wrapper(empty_strings.begin(), empty_strings.end());
+
+  auto b_0 = column_wrapper<int32_t>{10, 20, 20, 50};
+  auto b_1 = column_wrapper<float>{5.0, .7, .7, .7};
+  auto b_2 = column_wrapper<int8_t>{90, 75, 62, 41};
+  auto b_3 = cudf::test::strings_column_wrapper({"quick", "words", "result", ""});
+
+  auto expect_0 = column_wrapper<int32_t>{};
+  auto expect_1 = column_wrapper<float>{};
+  auto expect_2 = column_wrapper<int8_t>{};
+  auto expect_3 = cudf::test::strings_column_wrapper(empty_strings.begin(), empty_strings.end());
+  auto expect_4 = column_wrapper<int32_t>{};
+  auto expect_5 = column_wrapper<float>{};
+  auto expect_6 = column_wrapper<int8_t>{};
+  auto expect_7 = cudf::test::strings_column_wrapper(empty_strings.begin(), empty_strings.end());
+
+  auto table_a      = cudf::table_view{{a_0, a_1, a_2, a_3}};
+  auto table_b      = cudf::table_view{{b_0, b_1, b_2, b_3}};
+  auto table_expect = cudf::table_view{
+    {expect_0, expect_1, expect_2, expect_3, expect_4, expect_5, expect_6, expect_7}};
+
+  auto join_table         = cudf::cross_join(table_a, table_b);
+  auto join_table_reverse = cudf::cross_join(table_b, table_a);
+
+  EXPECT_EQ(join_table->num_columns(), table_a.num_columns() + table_b.num_columns());
+  EXPECT_EQ(join_table->num_rows(), 0);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(join_table->view(), table_expect);
+  EXPECT_EQ(join_table_reverse->num_columns(), table_a.num_columns() + table_b.num_columns());
+  EXPECT_EQ(join_table_reverse->num_rows(), 0);
+}
diff --git a/cpp/tests/join/join_tests.cpp b/cpp/tests/join/join_tests.cpp
new file mode 100644
index 0000000..089db31
--- /dev/null
+++ b/cpp/tests/join/join_tests.cpp
@@ -0,0 +1,2147 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/join.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <limits>
+
+template <typename T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T>;
+using strcol_wrapper = cudf::test::strings_column_wrapper;
+using CVector        = std::vector<std::unique_ptr<cudf::column>>;
+using Table          = cudf::table;
+constexpr cudf::size_type NoneValue =
+  std::numeric_limits<cudf::size_type>::min();  // TODO: how to test if this isn't public?
+
+// This function is a wrapper around cudf's join APIs that takes the gather map
+// from join APIs and materializes the table that would be created by gathering
+// from the joined tables. Join APIs originally returned tables like this, but
+// they were modified in https://github.com/rapidsai/cudf/pull/7454. This
+// helper function allows us to avoid rewriting all our tests in terms of
+// gather maps.
+template <std::pair<std::unique_ptr<rmm::device_uvector<cudf::size_type>>,
+                    std::unique_ptr<rmm::device_uvector<cudf::size_type>>> (*join_impl)(
+            cudf::table_view const& left_keys,
+            cudf::table_view const& right_keys,
+            cudf::null_equality compare_nulls,
+            rmm::mr::device_memory_resource* mr),
+          cudf::out_of_bounds_policy oob_policy = cudf::out_of_bounds_policy::DONT_CHECK>
+std::unique_ptr<cudf::table> join_and_gather(
+  cudf::table_view const& left_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& left_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  auto left_selected  = left_input.select(left_on);
+  auto right_selected = right_input.select(right_on);
+  auto const [left_join_indices, right_join_indices] =
+    join_impl(left_selected, right_selected, compare_nulls, mr);
+
+  auto left_indices_span  = cudf::device_span<cudf::size_type const>{*left_join_indices};
+  auto right_indices_span = cudf::device_span<cudf::size_type const>{*right_join_indices};
+
+  auto left_indices_col  = cudf::column_view{left_indices_span};
+  auto right_indices_col = cudf::column_view{right_indices_span};
+
+  auto left_result  = cudf::gather(left_input, left_indices_col, oob_policy);
+  auto right_result = cudf::gather(right_input, right_indices_col, oob_policy);
+
+  auto joined_cols = left_result->release();
+  auto right_cols  = right_result->release();
+  joined_cols.insert(joined_cols.end(),
+                     std::make_move_iterator(right_cols.begin()),
+                     std::make_move_iterator(right_cols.end()));
+  return std::make_unique<cudf::table>(std::move(joined_cols));
+}
+
+std::unique_ptr<cudf::table> inner_join(
+  cudf::table_view const& left_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& left_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+{
+  return join_and_gather<cudf::inner_join>(
+    left_input, right_input, left_on, right_on, compare_nulls);
+}
+
+std::unique_ptr<cudf::table> left_join(
+  cudf::table_view const& left_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& left_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+{
+  return join_and_gather<cudf::left_join, cudf::out_of_bounds_policy::NULLIFY>(
+    left_input, right_input, left_on, right_on, compare_nulls);
+}
+
+std::unique_ptr<cudf::table> full_join(
+  cudf::table_view const& full_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& full_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+{
+  return join_and_gather<cudf::full_join, cudf::out_of_bounds_policy::NULLIFY>(
+    full_input, right_input, full_on, right_on, compare_nulls);
+}
+
+struct JoinTest : public cudf::test::BaseFixture {
+  std::pair<std::unique_ptr<cudf::table>, std::unique_ptr<cudf::table>> gather_maps_as_tables(
+    cudf::column_view const& expected_left_map,
+    cudf::column_view const& expected_right_map,
+    std::pair<std::unique_ptr<rmm::device_uvector<cudf::size_type>>,
+              std::unique_ptr<rmm::device_uvector<cudf::size_type>>> const& result)
+  {
+    auto result_table =
+      cudf::table_view({cudf::column_view{cudf::data_type{cudf::type_id::INT32},
+                                          static_cast<cudf::size_type>(result.first->size()),
+                                          result.first->data(),
+                                          nullptr,
+                                          0},
+                        cudf::column_view{cudf::data_type{cudf::type_id::INT32},
+                                          static_cast<cudf::size_type>(result.second->size()),
+                                          result.second->data(),
+                                          nullptr,
+                                          0}});
+    auto result_sort_order = cudf::sorted_order(result_table);
+    auto sorted_result     = cudf::gather(result_table, *result_sort_order);
+
+    cudf::table_view gold({expected_left_map, expected_right_map});
+    auto gold_sort_order = cudf::sorted_order(gold);
+    auto sorted_gold     = cudf::gather(gold, *gold_sort_order);
+
+    return std::pair(std::move(sorted_gold), std::move(sorted_result));
+  }
+};
+
+TEST_F(JoinTest, EmptySentinelRepro)
+{
+  // This test reproduced an implementation specific behavior where the combination of these
+  // particular values ended up hashing to the empty key sentinel value used by the hash join
+  // This test may no longer be relevant if the implementation ever changes.
+  auto const left_first_col  = cudf::test::fixed_width_column_wrapper<int32_t>{1197};
+  auto const left_second_col = cudf::test::strings_column_wrapper{"201812"};
+  auto const left_third_col  = cudf::test::fixed_width_column_wrapper<int64_t>{2550000371};
+
+  auto const right_first_col  = cudf::test::fixed_width_column_wrapper<int32_t>{1197};
+  auto const right_second_col = cudf::test::strings_column_wrapper{"201812"};
+  auto const right_third_col  = cudf::test::fixed_width_column_wrapper<int64_t>{2550000371};
+
+  cudf::table_view left({left_first_col, left_second_col, left_third_col});
+  cudf::table_view right({right_first_col, right_second_col, right_third_col});
+
+  auto result = inner_join(left, right, {0, 1, 2}, {0, 1, 2});
+
+  EXPECT_EQ(result->num_rows(), 1);
+}
+
+TEST_F(JoinTest, LeftJoinNoNullsWithNoCommon)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 3}};
+  strcol_wrapper col0_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1{{"s1", "s0", "s1", "s2", "s1"}};
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = left_join(t0, t1, {0}, {0});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 1, 2, 2, 0, 3}, {1, 1, 1, 1, 1, 1}};
+  strcol_wrapper col_gold_1({"s0", "s1", "s2", "s2", "s4", "s1"}, {1, 1, 1, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 1, 2, 2, 4, 1}, {1, 1, 1, 1, 1, 1}};
+  column_wrapper<int32_t> col_gold_3{{3, -1, 2, 2, 0, 3}, {1, 0, 1, 1, 1, 1}};
+  strcol_wrapper col_gold_4({"s1", "", "s1", "s0", "s1", "s1"}, {1, 0, 1, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_5{{1, -1, 1, 0, 1, 1}, {1, 0, 1, 1, 1, 1}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, FullJoinNoNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 3}};
+  strcol_wrapper col0_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1{{"s1", "s0", "s1", "s2", "s1"}};
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = full_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 1, 2, 0, 3, -1, -1, -1, -1}, {1, 1, 1, 1, 1, 0, 0, 0, 0}};
+  strcol_wrapper col_gold_1({"s0", "s1", "s2", "s4", "s1", "", "", "", ""},
+                            {1, 1, 1, 1, 1, 0, 0, 0, 0});
+  column_wrapper<int32_t> col_gold_2{{0, 1, 2, 4, 1, -1, -1, -1, -1}, {1, 1, 1, 1, 1, 0, 0, 0, 0}};
+  column_wrapper<int32_t> col_gold_3{{-1, -1, -1, -1, 3, 2, 2, 0, 4}, {0, 0, 0, 0, 1, 1, 1, 1, 1}};
+  strcol_wrapper col_gold_4({"", "", "", "", "s1", "s1", "s0", "s1", "s2"},
+                            {0, 0, 0, 0, 1, 1, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_5{{-1, -1, -1, -1, 1, 1, 0, 1, 2}, {0, 0, 0, 0, 1, 1, 1, 1, 1}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, FullJoinWithNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 3}};
+  strcol_wrapper col0_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}, {1, 1, 1, 0, 1}};
+  strcol_wrapper col1_1{{"s1", "s0", "s1", "s2", "s1"}};
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = full_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 1, 2, 0, 3, -1, -1, -1, -1}, {1, 1, 1, 1, 1, 0, 0, 0, 0}};
+  strcol_wrapper col_gold_1({"s0", "s1", "s2", "s4", "s1", "", "", "", ""},
+                            {1, 1, 1, 1, 1, 0, 0, 0, 0});
+  column_wrapper<int32_t> col_gold_2{{0, 1, 2, 4, 1, -1, -1, -1, -1}, {1, 1, 1, 1, 1, 0, 0, 0, 0}};
+  column_wrapper<int32_t> col_gold_3{{-1, -1, -1, -1, 3, 2, 2, 0, 4}, {0, 0, 0, 0, 1, 1, 1, 1, 0}};
+  strcol_wrapper col_gold_4({"", "", "", "", "s1", "s1", "s0", "s1", "s2"},
+                            {0, 0, 0, 0, 1, 1, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_5{{-1, -1, -1, -1, 1, 1, 0, 1, 2}, {0, 0, 0, 0, 1, 1, 1, 1, 1}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, FullJoinOnNulls)
+{
+  // clang-format off
+  column_wrapper<int32_t> col0_0{{  3,    1 },
+                                 {  1,    0  }};
+  strcol_wrapper          col0_1({"s0", "s1" });
+  column_wrapper<int32_t> col0_2{{  0,    1 }};
+
+  column_wrapper<int32_t> col1_0{{  2,    5,    3,    7 },
+                                 {  1,    1,    1,    0 }};
+  strcol_wrapper          col1_1({"s1", "s0", "s0", "s1" });
+  column_wrapper<int32_t> col1_2{{  1,    4,    2,    8 }};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = full_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{   3,   -1,   -1,    -1},
+                                     {   1,    0,    0,     0}};
+  strcol_wrapper          col_gold_1{{ "s0", "s1",  "",    ""},
+                                     {   1,    1,    0,     0}};
+  column_wrapper<int32_t> col_gold_2{{   0,    1,   -1,    -1},
+                                     {   1,    1,    0,     0}};
+  column_wrapper<int32_t> col_gold_3{{   3,   -1,    2,     5},
+                                     {   1,    0,    1,     1}};
+  strcol_wrapper          col_gold_4{{ "s0", "s1", "s1",  "s0"}};
+  column_wrapper<int32_t> col_gold_5{{   2,    8,    1,     4}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+
+  // Repeat test with compare_nulls_equal=false,
+  // as per SQL standard.
+
+  result            = full_join(t0, t1, {0, 1}, {0, 1}, cudf::null_equality::UNEQUAL);
+  result_sort_order = cudf::sorted_order(result->view());
+  sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  col_gold_0 =               {{   3,   -1,   -1,    -1,   -1},
+                              {   1,    0,    0,     0,    0}};
+  col_gold_1 = strcol_wrapper{{ "s0", "s1",   "",    "",   ""},
+                              {   1,    1,    0,     0,    0}};
+  col_gold_2 =               {{   0,    1,   -1,    -1,   -1},
+                              {   1,    1,    0,     0,    0}};
+  col_gold_3 =               {{   3,   -1,    2,     5,   -1},
+                              {   1,    0,    1,     1,    0}};
+  col_gold_4 = strcol_wrapper{{ "s0",  "",  "s1",  "s0",  "s1"},
+                              {   1,    0,    1,     1,    1}};
+  col_gold_5 =               {{   2,   -1,    1,     4,    8},
+                              {   1,    0,    1,     1,    1}};
+
+  // clang-format on
+
+  CVector cols_gold_nulls_unequal;
+  cols_gold_nulls_unequal.push_back(col_gold_0.release());
+  cols_gold_nulls_unequal.push_back(col_gold_1.release());
+  cols_gold_nulls_unequal.push_back(col_gold_2.release());
+  cols_gold_nulls_unequal.push_back(col_gold_3.release());
+  cols_gold_nulls_unequal.push_back(col_gold_4.release());
+  cols_gold_nulls_unequal.push_back(col_gold_5.release());
+
+  Table gold_nulls_unequal{std::move(cols_gold_nulls_unequal)};
+
+  gold_sort_order = cudf::sorted_order(gold_nulls_unequal.view());
+  sorted_gold     = cudf::gather(gold_nulls_unequal.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, LeftJoinNoNulls)
+{
+  column_wrapper<int32_t> col0_0({3, 1, 2, 0, 3});
+  strcol_wrapper col0_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col0_2({0, 1, 2, 4, 1});
+
+  column_wrapper<int32_t> col1_0({2, 2, 0, 4, 3});
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2({1, 0, 1, 2, 1});
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = left_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0({3, 1, 2, 0, 3});
+  strcol_wrapper col_gold_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col_gold_2({0, 1, 2, 4, 1});
+  column_wrapper<int32_t> col_gold_3{{-1, -1, -1, -1, 3}, {0, 0, 0, 0, 1}};
+  strcol_wrapper col_gold_4{{"", "", "", "", "s1"}, {0, 0, 0, 0, 1}};
+  column_wrapper<int32_t> col_gold_5{{-1, -1, -1, -1, 1}, {0, 0, 0, 0, 1}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, LeftJoinWithNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = left_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 1, 2, 0, 2}, {1, 1, 1, 1, 1}};
+  strcol_wrapper col_gold_1({"s1", "s1", "", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 1, 2, 4, 1}, {1, 1, 1, 1, 1}};
+  column_wrapper<int32_t> col_gold_3{{3, -1, -1, -1, 2}, {1, 0, 0, 0, 1}};
+  strcol_wrapper col_gold_4{{"s1", "", "", "", "s0"}, {1, 0, 0, 0, 1}};
+  column_wrapper<int32_t> col_gold_5{{1, -1, -1, -1, -1}, {1, 0, 0, 0, 0}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, LeftJoinWithStructsAndNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+  auto col0_names_col = strcol_wrapper{
+    "Samuel Vimes", "Carrot Ironfoundersson", "Detritus", "Samuel Vimes", "Angua von Überwald"};
+  auto col0_ages_col = column_wrapper<int32_t>{{48, 27, 351, 31, 25}};
+
+  auto col0_is_human_col = column_wrapper<bool>{{true, true, false, false, false}, {1, 1, 0, 1, 0}};
+
+  auto col0_3 =
+    cudf::test::structs_column_wrapper{{col0_names_col, col0_ages_col, col0_is_human_col}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+  auto col1_names_col = strcol_wrapper{
+    "Samuel Vimes", "Detritus", "Detritus", "Carrot Ironfoundersson", "Angua von Überwald"};
+  auto col1_ages_col = column_wrapper<int32_t>{{48, 35, 351, 22, 25}};
+
+  auto col1_is_human_col = column_wrapper<bool>{{true, true, false, false, true}, {1, 1, 0, 1, 1}};
+
+  auto col1_3 =
+    cudf::test::structs_column_wrapper{{col1_names_col, col1_ages_col, col1_is_human_col}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols0.push_back(col0_3.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+  cols1.push_back(col1_3.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = left_join(t0, t1, {3}, {3});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 2, 1, 0, 2}, {1, 1, 1, 1, 1}};
+  strcol_wrapper col_gold_1({"s1", "", "s1", "s4", "s0"}, {1, 0, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 2, 1, 4, 1}, {1, 1, 1, 1, 1}};
+  auto col0_gold_names_col = strcol_wrapper{
+    "Samuel Vimes", "Detritus", "Carrot Ironfoundersson", "Samuel Vimes", "Angua von Überwald"};
+  auto col0_gold_ages_col = column_wrapper<int32_t>{{48, 351, 27, 31, 25}};
+
+  auto col0_gold_is_human_col =
+    column_wrapper<bool>{{true, false, true, false, false}, {1, 0, 1, 1, 0}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {col0_gold_names_col, col0_gold_ages_col, col0_gold_is_human_col}};
+
+  column_wrapper<int32_t> col_gold_4{{2, 0, -1, -1, -1}, {1, 1, 0, 0, 0}};
+  strcol_wrapper col_gold_5{{"s1", "s1", "", "", ""}, {1, 1, 0, 0, 0}};
+  column_wrapper<int32_t> col_gold_6{{1, 1, -1, -1, -1}, {1, 1, 0, 0, 0}};
+  auto col1_gold_names_col = strcol_wrapper{{
+                                              "Samuel Vimes",
+                                              "Detritus",
+                                              "",
+                                              "",
+                                              "",
+                                            },
+                                            {1, 1, 0, 0, 0}};
+  auto col1_gold_ages_col  = column_wrapper<int32_t>{{48, 351, -1, -1, -1}, {1, 1, 0, 0, 0}};
+
+  auto col1_gold_is_human_col =
+    column_wrapper<bool>{{true, false, false, false, false}, {1, 0, 0, 0, 0}};
+
+  auto col_gold_7 = cudf::test::structs_column_wrapper{
+    {col1_gold_names_col, col1_gold_ages_col, col1_gold_is_human_col}, {1, 1, 0, 0, 0}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  cols_gold.push_back(col_gold_6.release());
+  cols_gold.push_back(col_gold_7.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, LeftJoinOnNulls)
+{
+  // clang-format off
+  column_wrapper<int32_t> col0_0{{  3,    1,    2},
+                                 {  1,    0,    1}};
+  strcol_wrapper          col0_1({"s0", "s1", "s2" });
+  column_wrapper<int32_t> col0_2{{  0,    1,    2 }};
+
+  column_wrapper<int32_t> col1_0{{  2,    5,    3,    7 },
+                                 {  1,    1,    1,    0 }};
+  strcol_wrapper          col1_1({"s1", "s0", "s0", "s1" });
+  column_wrapper<int32_t> col1_2{{  1,    4,    2,    8 }};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = left_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{   3,    -1,    2},
+                                     {   1,     0,    1}};
+  strcol_wrapper          col_gold_1({ "s0",  "s1", "s2"},
+                                     {   1,     1,    1});
+  column_wrapper<int32_t> col_gold_2{{   0,     1,    2},
+                                     {   1,     1,    1}};
+  column_wrapper<int32_t> col_gold_3{{   3,    -1,   -1},
+                                     {   1,     0,    0}};
+  strcol_wrapper          col_gold_4({ "s0",  "s1",  ""},
+                                     {   1,     1,    0});
+  column_wrapper<int32_t> col_gold_5{{   2,     8,   -1},
+                                     {   1,     1,    0}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+
+  // Repeat test with compare_nulls_equal=false,
+  // as per SQL standard.
+
+  result            = left_join(t0, t1, {0, 1}, {0, 1}, cudf::null_equality::UNEQUAL);
+  result_sort_order = cudf::sorted_order(result->view());
+  sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+
+  col_gold_0 = {{   3,    -1,    2},
+                {   1,     0,    1}};
+  col_gold_1 = {{ "s0",  "s1", "s2"},
+                {   1,     1,    1}};
+  col_gold_2 = {{   0,     1,    2},
+                {   1,     1,    1}};
+  col_gold_3 = {{   3,    -1,   -1},
+                {   1,     0,    0}};
+  col_gold_4 = {{ "s0",   "",   ""},
+                {   1,     0,    0}};
+  col_gold_5 = {{   2,    -1,   -1},
+                {   1,     0,    0}};
+
+  // clang-format on
+  CVector cols_gold_nulls_unequal;
+  cols_gold_nulls_unequal.push_back(col_gold_0.release());
+  cols_gold_nulls_unequal.push_back(col_gold_1.release());
+  cols_gold_nulls_unequal.push_back(col_gold_2.release());
+  cols_gold_nulls_unequal.push_back(col_gold_3.release());
+  Table gold_nulls_unequal{std::move(cols_gold_nulls_unequal)};
+
+  gold_sort_order = cudf::sorted_order(gold_nulls_unequal.view());
+  sorted_gold     = cudf::gather(gold_nulls_unequal.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, InnerJoinNoNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "s0", "s4", "s0"});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = inner_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 2, 2}};
+  strcol_wrapper col_gold_1({"s1", "s0", "s0"});
+  column_wrapper<int32_t> col_gold_2{{0, 2, 1}};
+  column_wrapper<int32_t> col_gold_3{{3, 2, 2}};
+  strcol_wrapper col_gold_4({"s1", "s0", "s0"});
+  column_wrapper<int32_t> col_gold_5{{1, 0, 0}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, InnerJoinWithNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "s0", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = inner_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 2}};
+  strcol_wrapper col_gold_1({"s1", "s0"}, {1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 1}};
+  column_wrapper<int32_t> col_gold_3{{3, 2}};
+  strcol_wrapper col_gold_4({"s1", "s0"}, {1, 1});
+  column_wrapper<int32_t> col_gold_5{{1, -1}, {1, 0}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, InnerJoinWithStructsAndNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "s0", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+  std::initializer_list<std::string> col0_names = {
+    "Samuel Vimes", "Carrot Ironfoundersson", "Detritus", "Samuel Vimes", "Angua von Überwald"};
+  auto col0_names_col = strcol_wrapper{col0_names.begin(), col0_names.end()};
+  auto col0_ages_col  = column_wrapper<int32_t>{{48, 27, 351, 31, 25}};
+
+  auto col0_is_human_col = column_wrapper<bool>{{true, true, false, false, false}, {1, 1, 0, 1, 0}};
+
+  auto col0_3 =
+    cudf::test::structs_column_wrapper{{col0_names_col, col0_ages_col, col0_is_human_col}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+  std::initializer_list<std::string> col1_names = {"Carrot Ironfoundersson",
+                                                   "Angua von Überwald",
+                                                   "Detritus",
+                                                   "Carrot Ironfoundersson",
+                                                   "Samuel Vimes"};
+  auto col1_names_col = strcol_wrapper{col1_names.begin(), col1_names.end()};
+  auto col1_ages_col  = column_wrapper<int32_t>{{351, 25, 27, 31, 48}};
+
+  auto col1_is_human_col = column_wrapper<bool>{{true, false, false, false, true}, {1, 0, 0, 1, 1}};
+
+  auto col1_3 =
+    cudf::test::structs_column_wrapper{{col1_names_col, col1_ages_col, col1_is_human_col}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols0.push_back(col0_3.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+  cols1.push_back(col1_3.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = inner_join(t0, t1, {0, 1, 3}, {0, 1, 3});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 2}};
+  strcol_wrapper col_gold_1({"s1", "s0"}, {1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 1}};
+  auto col_gold_3_names_col = strcol_wrapper{"Samuel Vimes", "Angua von Überwald"};
+  auto col_gold_3_ages_col  = column_wrapper<int32_t>{{48, 25}};
+
+  auto col_gold_3_is_human_col = column_wrapper<bool>{{true, false}, {1, 0}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {col_gold_3_names_col, col_gold_3_ages_col, col_gold_3_is_human_col}};
+
+  column_wrapper<int32_t> col_gold_4{{3, 2}};
+  strcol_wrapper col_gold_5({"s1", "s0"}, {1, 1});
+  column_wrapper<int32_t> col_gold_6{{1, -1}, {1, 0}};
+  auto col_gold_7_names_col = strcol_wrapper{"Samuel Vimes", "Angua von Überwald"};
+  auto col_gold_7_ages_col  = column_wrapper<int32_t>{{48, 25}};
+
+  auto col_gold_7_is_human_col = column_wrapper<bool>{{true, false}, {1, 0}};
+
+  auto col_gold_7 = cudf::test::structs_column_wrapper{
+    {col_gold_7_names_col, col_gold_7_ages_col, col_gold_7_is_human_col}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  cols_gold.push_back(col_gold_6.release());
+  cols_gold.push_back(col_gold_7.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+// // Test to check join behavior when join keys are null.
+TEST_F(JoinTest, InnerJoinOnNulls)
+{
+  // clang-format off
+  column_wrapper<int32_t> col0_0{{  3,    1,    2,    0,    2}};
+  strcol_wrapper          col0_1({"s1", "s1", "s8", "s4", "s0"},
+                                 {  1,    1,    0,    1,    1});
+  column_wrapper<int32_t> col0_2{{  0,    1,    2,    4,    1}};
+
+  column_wrapper<int32_t> col1_0{{  2,    2,    0,    4,    3}};
+  strcol_wrapper          col1_1({"s1", "s0", "s1", "s2", "s1"},
+                                 {  1,    0,    1,    1,    1});
+  column_wrapper<int32_t> col1_2{{  1,    0,    1,    2,    1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = inner_join(t0, t1, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0 {{  3,    2}};
+  strcol_wrapper          col_gold_1 ({"s1", "s0"},
+                                      {  1,    0});
+  column_wrapper<int32_t> col_gold_2{{   0,    2}};
+  column_wrapper<int32_t> col_gold_3 {{  3,    2}};
+  strcol_wrapper          col_gold_4 ({"s1", "s0"},
+                                      {  1,    0});
+  column_wrapper<int32_t> col_gold_5{{   1,    0}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+
+  // Repeat test with compare_nulls_equal=false,
+  // as per SQL standard.
+
+  result            = inner_join(t0, t1, {0, 1}, {0, 1},  cudf::null_equality::UNEQUAL);
+  result_sort_order = cudf::sorted_order(result->view());
+  sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  col_gold_0 =               {{  3}};
+  col_gold_1 = strcol_wrapper({"s1"},
+                              {  1});
+  col_gold_2 =               {{  0}};
+  col_gold_3 =               {{  3}};
+  col_gold_4 = strcol_wrapper({"s1"},
+                              {  1});
+  col_gold_5 =               {{  1}};
+
+  // clang-format on
+
+  CVector cols_gold_sql;
+  cols_gold_sql.push_back(col_gold_0.release());
+  cols_gold_sql.push_back(col_gold_1.release());
+  cols_gold_sql.push_back(col_gold_2.release());
+  cols_gold_sql.push_back(col_gold_3.release());
+  cols_gold_sql.push_back(col_gold_4.release());
+  cols_gold_sql.push_back(col_gold_5.release());
+  Table gold_sql(std::move(cols_gold_sql));
+
+  gold_sort_order = cudf::sorted_order(gold_sql.view());
+  sorted_gold     = cudf::gather(gold_sql.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+// Empty Left Table
+TEST_F(JoinTest, EmptyLeftTableInnerJoin)
+{
+  column_wrapper<int32_t> col0_0;
+  column_wrapper<int32_t> col0_1;
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  column_wrapper<int32_t> col1_1{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table empty0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result = inner_join(empty0, t1, {0, 1}, {0, 1});
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty0, *result);
+}
+
+TEST_F(JoinTest, EmptyLeftTableLeftJoin)
+{
+  column_wrapper<int32_t> col0_0;
+  column_wrapper<int32_t> col0_1;
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  column_wrapper<int32_t> col1_1{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table empty0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result = left_join(empty0, t1, {0, 1}, {0, 1});
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty0, *result);
+}
+
+TEST_F(JoinTest, EmptyLeftTableFullJoin)
+{
+  column_wrapper<int32_t> col0_0;
+  column_wrapper<int32_t> col0_1;
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  column_wrapper<int32_t> col1_1{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table lhs(std::move(cols0));
+  Table rhs(std::move(cols1));
+
+  auto result            = full_join(lhs, rhs, {0, 1}, {0, 1});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}};
+  column_wrapper<int32_t> col_gold_1{{-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}};
+  column_wrapper<int32_t> col_gold_2{{2, 2, 0, 4, 3}};
+  column_wrapper<int32_t> col_gold_3{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+// Empty Right Table
+TEST_F(JoinTest, EmptyRightTableInnerJoin)
+{
+  column_wrapper<int32_t> col0_0{{2, 2, 0, 4, 3}};
+  column_wrapper<int32_t> col0_1{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  column_wrapper<int32_t> col1_0;
+  column_wrapper<int32_t> col1_1;
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table empty1(std::move(cols1));
+
+  {
+    auto result = inner_join(t0, empty1, {0, 1}, {0, 1});
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty1, *result);
+  }
+
+  {
+    cudf::hash_join hash_join(empty1, cudf::null_equality::EQUAL);
+
+    auto output_size                         = hash_join.inner_join_size(t0);
+    std::optional<std::size_t> optional_size = output_size;
+
+    std::size_t const size_gold = 0;
+    EXPECT_EQ(output_size, size_gold);
+
+    auto result = hash_join.inner_join(t0, optional_size);
+    column_wrapper<int32_t> col_gold_0{};
+    column_wrapper<int32_t> col_gold_1{};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+}
+
+TEST_F(JoinTest, EmptyRightTableLeftJoin)
+{
+  column_wrapper<int32_t> col0_0{{2, 2, 0, 4, 3}, {1, 1, 1, 1, 1}};
+  column_wrapper<int32_t> col0_1{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  column_wrapper<int32_t> col1_0;
+  column_wrapper<int32_t> col1_1;
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table empty1(std::move(cols1));
+
+  {
+    auto result = left_join(t0, empty1, {0, 1}, {0, 1});
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(t0, *result);
+  }
+
+  {
+    cudf::hash_join hash_join(empty1, cudf::null_equality::EQUAL);
+
+    auto output_size                         = hash_join.left_join_size(t0);
+    std::optional<std::size_t> optional_size = output_size;
+
+    std::size_t const size_gold = 5;
+    EXPECT_EQ(output_size, size_gold);
+
+    auto result = hash_join.left_join(t0, optional_size);
+    column_wrapper<int32_t> col_gold_0{{0, 1, 2, 3, 4}};
+    column_wrapper<int32_t> col_gold_1{{NoneValue, NoneValue, NoneValue, NoneValue, NoneValue}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+}
+
+TEST_F(JoinTest, EmptyRightTableFullJoin)
+{
+  column_wrapper<int32_t> col0_0{{2, 2, 0, 4, 3}};
+  column_wrapper<int32_t> col0_1{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+
+  column_wrapper<int32_t> col1_0;
+  column_wrapper<int32_t> col1_1;
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table empty1(std::move(cols1));
+
+  {
+    auto result = full_join(t0, empty1, {0, 1}, {0, 1});
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(t0, *result);
+  }
+
+  {
+    cudf::hash_join hash_join(empty1, cudf::null_equality::EQUAL);
+
+    auto output_size                         = hash_join.full_join_size(t0);
+    std::optional<std::size_t> optional_size = output_size;
+
+    std::size_t const size_gold = 5;
+    EXPECT_EQ(output_size, size_gold);
+
+    auto result = hash_join.full_join(t0, optional_size);
+    column_wrapper<int32_t> col_gold_0{{0, 1, 2, 3, 4}};
+    column_wrapper<int32_t> col_gold_1{{NoneValue, NoneValue, NoneValue, NoneValue, NoneValue}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+}
+
+// Both tables empty
+TEST_F(JoinTest, BothEmptyInnerJoin)
+{
+  column_wrapper<int32_t> col0_0;
+  column_wrapper<int32_t> col0_1;
+
+  column_wrapper<int32_t> col1_0;
+  column_wrapper<int32_t> col1_1;
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table empty1(std::move(cols1));
+
+  auto result = inner_join(t0, empty1, {0, 1}, {0, 1});
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty1, *result);
+}
+
+TEST_F(JoinTest, BothEmptyLeftJoin)
+{
+  column_wrapper<int32_t> col0_0;
+  column_wrapper<int32_t> col0_1;
+
+  column_wrapper<int32_t> col1_0;
+  column_wrapper<int32_t> col1_1;
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table empty1(std::move(cols1));
+
+  auto result = left_join(t0, empty1, {0, 1}, {0, 1});
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty1, *result);
+}
+
+TEST_F(JoinTest, BothEmptyFullJoin)
+{
+  column_wrapper<int32_t> col0_0;
+  column_wrapper<int32_t> col0_1;
+
+  column_wrapper<int32_t> col1_0;
+  column_wrapper<int32_t> col1_1;
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table empty1(std::move(cols1));
+
+  auto result = full_join(t0, empty1, {0, 1}, {0, 1});
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(empty1, *result);
+}
+
+// // EqualValues X Inner,Left,Full
+
+TEST_F(JoinTest, EqualValuesInnerJoin)
+{
+  column_wrapper<int32_t> col0_0{{0, 0}};
+  strcol_wrapper col0_1({"s0", "s0"});
+
+  column_wrapper<int32_t> col1_0{{0, 0}};
+  strcol_wrapper col1_1({"s0", "s0"});
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result = inner_join(t0, t1, {0, 1}, {0, 1});
+
+  column_wrapper<int32_t> col_gold_0{{0, 0, 0, 0}};
+  strcol_wrapper col_gold_1({"s0", "s0", "s0", "s0"});
+  column_wrapper<int32_t> col_gold_2{{0, 0, 0, 0}};
+  strcol_wrapper col_gold_3({"s0", "s0", "s0", "s0"});
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+
+  Table gold(std::move(cols_gold));
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(gold, *result);
+}
+
+TEST_F(JoinTest, EqualValuesLeftJoin)
+{
+  column_wrapper<int32_t> col0_0{{0, 0}};
+  strcol_wrapper col0_1({"s0", "s0"});
+
+  column_wrapper<int32_t> col1_0{{0, 0}};
+  strcol_wrapper col1_1({"s0", "s0"});
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result = left_join(t0, t1, {0, 1}, {0, 1});
+
+  column_wrapper<int32_t> col_gold_0{{0, 0, 0, 0}, {1, 1, 1, 1}};
+  strcol_wrapper col_gold_1({"s0", "s0", "s0", "s0"}, {1, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 0, 0, 0}, {1, 1, 1, 1}};
+  strcol_wrapper col_gold_3({"s0", "s0", "s0", "s0"}, {1, 1, 1, 1});
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(gold, *result);
+}
+
+TEST_F(JoinTest, EqualValuesFullJoin)
+{
+  column_wrapper<int32_t> col0_0{{0, 0}};
+  strcol_wrapper col0_1({"s0", "s0"});
+
+  column_wrapper<int32_t> col1_0{{0, 0}};
+  strcol_wrapper col1_1({"s0", "s0"});
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result = full_join(t0, t1, {0, 1}, {0, 1});
+
+  column_wrapper<int32_t> col_gold_0{{0, 0, 0, 0}};
+  strcol_wrapper col_gold_1({"s0", "s0", "s0", "s0"});
+  column_wrapper<int32_t> col_gold_2{{0, 0, 0, 0}};
+  strcol_wrapper col_gold_3({"s0", "s0", "s0", "s0"});
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(gold, *result);
+}
+
+TEST_F(JoinTest, InnerJoinCornerCase)
+{
+  column_wrapper<int64_t> col0_0{{4, 1, 3, 2, 2, 2, 2}};
+  column_wrapper<int64_t> col1_0{{2}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols1.push_back(col1_0.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = inner_join(t0, t1, {0}, {0});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int64_t> col_gold_0{{2, 2, 2, 2}};
+  column_wrapper<int64_t> col_gold_1{{2, 2, 2, 2}};
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, HashJoinSequentialProbes)
+{
+  CVector cols1;
+  cols1.emplace_back(column_wrapper<int32_t>{{2, 2, 0, 4, 3}}.release());
+  cols1.emplace_back(strcol_wrapper{{"s1", "s0", "s1", "s2", "s1"}}.release());
+
+  Table t1(std::move(cols1));
+
+  cudf::hash_join hash_join(t1, cudf::nullable_join::NO, cudf::null_equality::EQUAL);
+
+  {
+    CVector cols0;
+    cols0.emplace_back(column_wrapper<int32_t>{{3, 1, 2, 0, 3}}.release());
+    cols0.emplace_back(strcol_wrapper({"s0", "s1", "s2", "s4", "s1"}).release());
+
+    Table t0(std::move(cols0));
+
+    auto output_size                         = hash_join.full_join_size(t0);
+    std::optional<std::size_t> optional_size = output_size;
+
+    std::size_t const size_gold = 9;
+    EXPECT_EQ(output_size, size_gold);
+
+    auto result = hash_join.full_join(t0, optional_size);
+    column_wrapper<int32_t> col_gold_0{{NoneValue, NoneValue, NoneValue, NoneValue, 4, 0, 1, 2, 3}};
+    column_wrapper<int32_t> col_gold_1{{0, 1, 2, 3, 4, NoneValue, NoneValue, NoneValue, NoneValue}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+
+  {
+    CVector cols0;
+    cols0.emplace_back(column_wrapper<int32_t>{{3, 1, 2, 0, 3}}.release());
+    cols0.emplace_back(strcol_wrapper({"s0", "s1", "s2", "s4", "s1"}).release());
+
+    Table t0(std::move(cols0));
+
+    auto output_size                         = hash_join.left_join_size(t0);
+    std::optional<std::size_t> optional_size = output_size;
+
+    std::size_t const size_gold = 5;
+    EXPECT_EQ(output_size, size_gold);
+
+    auto result = hash_join.left_join(t0, optional_size);
+    column_wrapper<int32_t> col_gold_0{{0, 1, 2, 3, 4}};
+    column_wrapper<int32_t> col_gold_1{{NoneValue, NoneValue, NoneValue, NoneValue, 4}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+
+  {
+    CVector cols0;
+    cols0.emplace_back(column_wrapper<int32_t>{{3, 1, 2, 0, 2}}.release());
+    cols0.emplace_back(strcol_wrapper({"s1", "s1", "s0", "s4", "s0"}).release());
+
+    Table t0(std::move(cols0));
+
+    auto output_size                         = hash_join.inner_join_size(t0);
+    std::optional<std::size_t> optional_size = output_size;
+
+    std::size_t const size_gold = 3;
+    EXPECT_EQ(output_size, size_gold);
+
+    auto result = hash_join.inner_join(t0, optional_size);
+    column_wrapper<int32_t> col_gold_0{{2, 4, 0}};
+    column_wrapper<int32_t> col_gold_1{{1, 1, 4}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+}
+
+TEST_F(JoinTest, HashJoinWithStructsAndNulls)
+{
+  auto col0_names_col = strcol_wrapper{
+    "Samuel Vimes", "Carrot Ironfoundersson", "Detritus", "Samuel Vimes", "Angua von Überwald"};
+  auto col0_ages_col = column_wrapper<int32_t>{{48, 27, 351, 31, 25}};
+
+  auto col0_is_human_col = column_wrapper<bool>{{true, true, false, false, false}, {1, 1, 0, 1, 0}};
+
+  auto col0 =
+    cudf::test::structs_column_wrapper{{col0_names_col, col0_ages_col, col0_is_human_col}};
+
+  auto col1_names_col = strcol_wrapper{
+    "Samuel Vimes", "Detritus", "Detritus", "Carrot Ironfoundersson", "Angua von Überwald"};
+  auto col1_ages_col = column_wrapper<int32_t>{{48, 35, 351, 22, 25}};
+
+  auto col1_is_human_col = column_wrapper<bool>{{true, true, false, false, true}, {1, 1, 0, 1, 1}};
+
+  auto col1 =
+    cudf::test::structs_column_wrapper{{col1_names_col, col1_ages_col, col1_is_human_col}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0.release());
+  cols1.push_back(col1.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+  auto const has_nulls = cudf::has_nested_nulls(t0) || cudf::has_nested_nulls(t1)
+                           ? cudf::nullable_join::YES
+                           : cudf::nullable_join::NO;
+
+  auto hash_join = cudf::hash_join(t1, has_nulls, cudf::null_equality::EQUAL);
+
+  {
+    auto output_size = hash_join.left_join_size(t0);
+    EXPECT_EQ(5, output_size);
+    auto result = hash_join.left_join(t0, output_size);
+    column_wrapper<int32_t> col_gold_0{{0, 1, 2, 3, 4}};
+    column_wrapper<int32_t> col_gold_1{{0, NoneValue, 2, NoneValue, NoneValue}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+
+  {
+    auto output_size = hash_join.inner_join_size(t0);
+    EXPECT_EQ(2, output_size);
+    auto result = hash_join.inner_join(t0, output_size);
+    column_wrapper<int32_t> col_gold_0{{0, 2}};
+    column_wrapper<int32_t> col_gold_1{{0, 2}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+
+  {
+    auto output_size = hash_join.full_join_size(t0);
+    EXPECT_EQ(8, output_size);
+    auto result = hash_join.full_join(t0, output_size);
+    column_wrapper<int32_t> col_gold_0{{NoneValue, NoneValue, NoneValue, 0, 1, 2, 3, 4}};
+    column_wrapper<int32_t> col_gold_1{{1, 3, 4, 0, NoneValue, 2, NoneValue, NoneValue}};
+    auto const [sorted_gold, sorted_result] = gather_maps_as_tables(col_gold_0, col_gold_1, result);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+  }
+}
+
+TEST_F(JoinTest, HashJoinWithNullsOneSide)
+{
+  auto const t0 = [] {
+    column_wrapper<int32_t> col0{2, 2, 0, 4, 3};
+    column_wrapper<int32_t> col1{1, 10, 1, 2, 1};
+    CVector cols;
+    cols.emplace_back(col0.release());
+    cols.emplace_back(col1.release());
+    return Table{std::move(cols)};
+  }();
+
+  auto const t1 = [] {
+    column_wrapper<int32_t> col0{1, 2, 3, 4, 5, 2, 2, 0, 4, 3, 1, 2, 3, 4, 5};
+    column_wrapper<int32_t> col1{{1, 2, 3, 4, 5, 1, 0, 1, 2, 1, 1, 2, 3, 4, 5},
+                                 cudf::test::iterators::null_at(6)};
+    CVector cols;
+    cols.emplace_back(col0.release());
+    cols.emplace_back(col1.release());
+    return Table{std::move(cols)};
+  }();
+
+  auto const hash_join   = cudf::hash_join(t0, cudf::null_equality::EQUAL);
+  auto constexpr invalid = std::numeric_limits<int32_t>::min();  // invalid index sentinel
+
+  auto const sort_result = [](auto const& result) {
+    auto const left_cv  = cudf::column_view{cudf::data_type{cudf::type_id::INT32},
+                                           static_cast<cudf::size_type>(result.first->size()),
+                                           result.first->data(),
+                                           nullptr,
+                                           0};
+    auto const right_cv = cudf::column_view{cudf::data_type{cudf::type_id::INT32},
+                                            static_cast<cudf::size_type>(result.second->size()),
+                                            result.second->data(),
+                                            nullptr,
+                                            0};
+    auto sorted_left    = cudf::sort(cudf::table_view{{left_cv}});
+    auto sorted_right   = cudf::sort(cudf::table_view{{right_cv}});
+    return std::pair{std::move(sorted_left), std::move(sorted_right)};
+  };
+
+  {
+    auto const output_size = hash_join.left_join_size(t1);
+    auto const result      = hash_join.left_join(t1, std::optional<std::size_t>{output_size});
+    auto const [sorted_left_indices, sorted_right_indices] = sort_result(result);
+
+    auto const expected_left_indices =
+      column_wrapper<int32_t>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14};
+    auto const expected_right_indices = column_wrapper<int32_t>{invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                0,
+                                                                2,
+                                                                3,
+                                                                4};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_left_indices, sorted_left_indices->get_column(0));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_right_indices, sorted_right_indices->get_column(0));
+  }
+
+  {
+    auto const output_size = hash_join.inner_join_size(t1);
+    auto const result      = hash_join.inner_join(t1, std::optional<std::size_t>{output_size});
+    auto const [sorted_left_indices, sorted_right_indices] = sort_result(result);
+
+    auto const expected_left_indices  = column_wrapper<int32_t>{5, 7, 8, 9};
+    auto const expected_right_indices = column_wrapper<int32_t>{0, 2, 3, 4};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_left_indices, sorted_left_indices->get_column(0));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_right_indices, sorted_right_indices->get_column(0));
+  }
+
+  {
+    auto const output_size = hash_join.full_join_size(t1);
+    auto const result      = hash_join.full_join(t1, std::optional<std::size_t>{output_size});
+    auto const [sorted_left_indices, sorted_right_indices] = sort_result(result);
+
+    auto const expected_left_indices =
+      column_wrapper<int32_t>{invalid, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14};
+    auto const expected_right_indices = column_wrapper<int32_t>{invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                invalid,
+                                                                0,
+                                                                1,
+                                                                2,
+                                                                3,
+                                                                4};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_left_indices, sorted_left_indices->get_column(0));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_right_indices, sorted_right_indices->get_column(0));
+  }
+}
+
+TEST_F(JoinTest, HashJoinLargeOutputSize)
+{
+  // self-join a table of zeroes to generate an output row count that would overflow int32_t
+  std::size_t col_size = 65567;
+  rmm::device_buffer zeroes(col_size * sizeof(int32_t), cudf::get_default_stream());
+  CUDF_CUDA_TRY(
+    cudaMemsetAsync(zeroes.data(), 0, zeroes.size(), cudf::get_default_stream().value()));
+  cudf::column_view col_zeros(
+    cudf::data_type{cudf::type_id::INT32}, col_size, zeroes.data(), nullptr, 0);
+  cudf::table_view tview{{col_zeros}};
+  cudf::hash_join hash_join(tview, cudf::nullable_join::NO, cudf::null_equality::UNEQUAL);
+  std::size_t output_size = hash_join.inner_join_size(tview);
+  EXPECT_EQ(col_size * col_size, output_size);
+}
+
+struct JoinDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(JoinDictionaryTest, LeftJoinNoNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 3}};
+  strcol_wrapper col0_1_w({"s0", "s1", "s2", "s4", "s1"});
+  auto col0_1 = cudf::dictionary::encode(col0_1_w);
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1_w{{"s1", "s0", "s1", "s2", "s1"}};
+  auto col1_1 = cudf::dictionary::encode(col1_1_w);
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  auto t0 = cudf::table_view({col0_0, col0_1->view(), col0_2});
+  auto t1 = cudf::table_view({col1_0, col1_1->view(), col1_2});
+  auto g0 = cudf::table_view({col0_0, col0_1_w, col0_2});
+  auto g1 = cudf::table_view({col1_0, col1_1_w, col1_2});
+
+  auto result      = left_join(t0, t1, {0}, {0});
+  auto result_view = result->view();
+  auto decoded1    = cudf::dictionary::decode(result_view.column(1));
+  auto decoded4    = cudf::dictionary::decode(result_view.column(4));
+  std::vector<cudf::column_view> result_decoded({result_view.column(0),
+                                                 decoded1->view(),
+                                                 result_view.column(2),
+                                                 result_view.column(3),
+                                                 decoded4->view(),
+                                                 result_view.column(5)});
+  auto result_sort_order = cudf::sorted_order(cudf::table_view(result_decoded));
+  auto sorted_result     = cudf::gather(cudf::table_view(result_decoded), *result_sort_order);
+
+  auto gold            = left_join(g0, g1, {0}, {0});
+  auto gold_sort_order = cudf::sorted_order(gold->view());
+  auto sorted_gold     = cudf::gather(gold->view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinDictionaryTest, LeftJoinWithNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "s0", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2_w{{0, 1, 2, 4, 1}};
+  auto col0_2 = cudf::dictionary::encode(col0_2_w);
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2_w{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+  auto col1_2 = cudf::dictionary::encode(col1_2_w);
+
+  auto t0 = cudf::table_view({col0_0, col0_1, col0_2->view()});
+  auto t1 = cudf::table_view({col1_0, col1_1, col1_2->view()});
+
+  auto result      = left_join(t0, t1, {0, 1}, {0, 1});
+  auto result_view = result->view();
+  auto decoded2    = cudf::dictionary::decode(result_view.column(2));
+  auto decoded5    = cudf::dictionary::decode(result_view.column(5));
+  std::vector<cudf::column_view> result_decoded({result_view.column(0),
+                                                 result_view.column(1),
+                                                 decoded2->view(),
+                                                 result_view.column(3),
+                                                 result_view.column(4),
+                                                 decoded5->view()});
+  auto result_sort_order = cudf::sorted_order(cudf::table_view(result_decoded));
+  auto sorted_result     = cudf::gather(cudf::table_view(result_decoded), *result_sort_order);
+
+  auto g0              = cudf::table_view({col0_0, col0_1, col0_2_w});
+  auto g1              = cudf::table_view({col1_0, col1_1, col1_2_w});
+  auto gold            = left_join(g0, g1, {0, 1}, {0, 1});
+  auto gold_sort_order = cudf::sorted_order(gold->view());
+  auto sorted_gold     = cudf::gather(gold->view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinDictionaryTest, InnerJoinNoNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1_w({"s1", "s1", "s0", "s4", "s0"});
+  auto col0_1 = cudf::dictionary::encode(col0_1_w);
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1_w({"s1", "s0", "s1", "s2", "s1"});
+  auto col1_1 = cudf::dictionary::encode(col1_1_w);
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  auto t0 = cudf::table_view({col0_0, col0_1->view(), col0_2});
+  auto t1 = cudf::table_view({col1_0, col1_1->view(), col1_2});
+
+  auto result      = inner_join(t0, t1, {0, 1}, {0, 1});
+  auto result_view = result->view();
+  auto decoded1    = cudf::dictionary::decode(result_view.column(1));
+  auto decoded4    = cudf::dictionary::decode(result_view.column(4));
+  std::vector<cudf::column_view> result_decoded({result_view.column(0),
+                                                 decoded1->view(),
+                                                 result_view.column(2),
+                                                 result_view.column(3),
+                                                 decoded4->view(),
+                                                 result_view.column(5)});
+  auto result_sort_order = cudf::sorted_order(cudf::table_view(result_decoded));
+  auto sorted_result     = cudf::gather(cudf::table_view(result_decoded), *result_sort_order);
+
+  auto g0              = cudf::table_view({col0_0, col0_1_w, col0_2});
+  auto g1              = cudf::table_view({col1_0, col1_1_w, col1_2});
+  auto gold            = inner_join(g0, g1, {0, 1}, {0, 1});
+  auto gold_sort_order = cudf::sorted_order(gold->view());
+  auto sorted_gold     = cudf::gather(gold->view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinDictionaryTest, InnerJoinWithNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 2}};
+  strcol_wrapper col0_1({"s1", "s1", "s0", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2_w{{0, 1, 2, 4, 1}};
+  auto col0_2 = cudf::dictionary::encode(col0_2_w);
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2_w{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+  auto col1_2 = cudf::dictionary::encode(col1_2_w);
+
+  auto t0 = cudf::table_view({col0_0, col0_1, col0_2->view()});
+  auto t1 = cudf::table_view({col1_0, col1_1, col1_2->view()});
+
+  auto result      = inner_join(t0, t1, {0, 1}, {0, 1});
+  auto result_view = result->view();
+  auto decoded2    = cudf::dictionary::decode(result_view.column(2));
+  auto decoded5    = cudf::dictionary::decode(result_view.column(5));
+  std::vector<cudf::column_view> result_decoded({result_view.column(0),
+                                                 result_view.column(1),
+                                                 decoded2->view(),
+                                                 result_view.column(3),
+                                                 result_view.column(4),
+                                                 decoded5->view()});
+  auto result_sort_order = cudf::sorted_order(cudf::table_view(result_decoded));
+  auto sorted_result     = cudf::gather(cudf::table_view(result_decoded), *result_sort_order);
+
+  auto g0              = cudf::table_view({col0_0, col0_1, col0_2_w});
+  auto g1              = cudf::table_view({col1_0, col1_1, col1_2_w});
+  auto gold            = inner_join(g0, g1, {0, 1}, {0, 1});
+  auto gold_sort_order = cudf::sorted_order(gold->view());
+  auto sorted_gold     = cudf::gather(gold->view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinDictionaryTest, FullJoinNoNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 3}};
+  strcol_wrapper col0_1_w({"s0", "s1", "s2", "s4", "s1"});
+  auto col0_1 = cudf::dictionary::encode(col0_1_w);
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}};
+  strcol_wrapper col1_1_w{{"s1", "s0", "s1", "s2", "s1"}};
+  auto col1_1 = cudf::dictionary::encode(col1_1_w);
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  auto t0 = cudf::table_view({col0_0, col0_1->view(), col0_2});
+  auto t1 = cudf::table_view({col1_0, col1_1->view(), col1_2});
+
+  auto result      = full_join(t0, t1, {0, 1}, {0, 1});
+  auto result_view = result->view();
+  auto decoded1    = cudf::dictionary::decode(result_view.column(1));
+  auto decoded4    = cudf::dictionary::decode(result_view.column(4));
+  std::vector<cudf::column_view> result_decoded({result_view.column(0),
+                                                 decoded1->view(),
+                                                 result_view.column(2),
+                                                 result_view.column(3),
+                                                 decoded4->view(),
+                                                 result_view.column(5)});
+  auto result_sort_order = cudf::sorted_order(cudf::table_view(result_decoded));
+  auto sorted_result     = cudf::gather(cudf::table_view(result_decoded), *result_sort_order);
+
+  auto g0              = cudf::table_view({col0_0, col0_1_w, col0_2});
+  auto g1              = cudf::table_view({col1_0, col1_1_w, col1_2});
+  auto gold            = full_join(g0, g1, {0, 1}, {0, 1});
+  auto gold_sort_order = cudf::sorted_order(gold->view());
+  auto sorted_gold     = cudf::gather(gold->view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinDictionaryTest, FullJoinWithNulls)
+{
+  column_wrapper<int32_t> col0_0_w{{3, 1, 2, 0, 3}};
+  auto col0_0 = cudf::dictionary::encode(col0_0_w);
+  strcol_wrapper col0_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  column_wrapper<int32_t> col1_0_w{{2, 2, 0, 4, 3}, {1, 1, 1, 0, 1}};
+  auto col1_0 = cudf::dictionary::encode(col1_0_w);
+  strcol_wrapper col1_1{{"s1", "s0", "s1", "s2", "s1"}};
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  auto t0 = cudf::table_view({col0_0->view(), col0_1, col0_2});
+  auto t1 = cudf::table_view({col1_0->view(), col1_1, col1_2});
+
+  auto result      = full_join(t0, t1, {0, 1}, {0, 1});
+  auto result_view = result->view();
+  auto decoded0    = cudf::dictionary::decode(result_view.column(0));
+  auto decoded3    = cudf::dictionary::decode(result_view.column(3));
+  std::vector<cudf::column_view> result_decoded({decoded0->view(),
+                                                 result_view.column(1),
+                                                 result_view.column(2),
+                                                 decoded3->view(),
+                                                 result_view.column(4),
+                                                 result_view.column(5)});
+  auto result_sort_order = cudf::sorted_order(cudf::table_view(result_decoded));
+  auto sorted_result     = cudf::gather(cudf::table_view(result_decoded), *result_sort_order);
+
+  auto g0              = cudf::table_view({col0_0_w, col0_1, col0_2});
+  auto g1              = cudf::table_view({col1_0_w, col1_1, col1_2});
+  auto gold            = full_join(g0, g1, {0, 1}, {0, 1});
+  auto gold_sort_order = cudf::sorted_order(gold->view());
+  auto sorted_gold     = cudf::gather(gold->view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, FullJoinWithStructsAndNulls)
+{
+  column_wrapper<int32_t> col0_0{{3, 1, 2, 0, 3}};
+  strcol_wrapper col0_1({"s0", "s1", "s2", "s4", "s1"});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+
+  std::initializer_list<std::string> col0_names = {"Samuel Vimes",
+                                                   "Carrot Ironfoundersson",
+                                                   "Angua von Überwald",
+                                                   "Detritus",
+                                                   "Carrot Ironfoundersson"};
+  auto col0_names_col = strcol_wrapper{col0_names.begin(), col0_names.end()};
+  auto col0_ages_col  = column_wrapper<int32_t>{{48, 27, 25, 31, 351}};
+
+  auto col0_is_human_col = column_wrapper<bool>{{true, true, false, false, false}, {1, 1, 0, 1, 1}};
+
+  auto col0_3 = cudf::test::structs_column_wrapper{
+    {col0_names_col, col0_ages_col, col0_is_human_col}, {1, 1, 1, 1, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, 3}, {1, 1, 1, 0, 1}};
+  strcol_wrapper col1_1{{"s1", "s0", "s1", "s2", "s1"}};
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}};
+
+  std::initializer_list<std::string> col1_names = {"Carrot Ironfoundersson",
+                                                   "Samuel Vimes",
+                                                   "Carrot Ironfoundersson",
+                                                   "Angua von Überwald",
+                                                   "Carrot Ironfoundersson"};
+  auto col1_names_col = strcol_wrapper{col1_names.begin(), col1_names.end()};
+  auto col1_ages_col  = column_wrapper<int32_t>{{27, 48, 27, 25, 27}};
+
+  auto col1_is_human_col = column_wrapper<bool>{{true, true, true, false, true}, {1, 1, 1, 0, 1}};
+
+  auto col1_3 =
+    cudf::test::structs_column_wrapper{{col1_names_col, col1_ages_col, col1_is_human_col}};
+
+  CVector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols0.push_back(col0_3.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+  cols1.push_back(col1_3.release());
+
+  Table t0(std::move(cols0));
+  Table t1(std::move(cols1));
+
+  auto result            = full_join(t0, t1, {0, 1, 3}, {0, 1, 3});
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{3, 1, 2, 0, 3, -1, -1, -1, -1, -1},
+                                     {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+  strcol_wrapper col_gold_1({"s0", "s1", "s2", "s4", "s1", "", "", "", "", ""},
+                            {1, 1, 1, 1, 1, 0, 0, 0, 0, 0});
+  column_wrapper<int32_t> col_gold_2{{0, 1, 2, 4, 1, -1, -1, -1, -1, -1},
+                                     {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+  auto gold_names0_col = strcol_wrapper{{"Samuel Vimes",
+                                         "Carrot Ironfoundersson",
+                                         "Angua von Überwald",
+                                         "Detritus",
+                                         "Carrot Ironfoundersson",
+                                         "",
+                                         "",
+                                         "",
+                                         "",
+                                         ""},
+                                        {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+  auto gold_ages0_col  = column_wrapper<int32_t>{{48, 27, 25, 31, 351, -1, -1, -1, -1, -1},
+                                                 {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+
+  auto gold_is_human0_col =
+    column_wrapper<bool>{{true, true, false, false, false, false, false, false, false, false},
+                         {1, 1, 0, 1, 1, 0, 0, 0, 0, 0}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {gold_names0_col, gold_ages0_col, gold_is_human0_col}, {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+
+  column_wrapper<int32_t> col_gold_4{{-1, -1, -1, -1, -1, 3, 2, 2, 0, 4},
+                                     {0, 0, 0, 0, 0, 1, 1, 1, 1, 0}};
+  strcol_wrapper col_gold_5({"", "", "", "", "", "s1", "s1", "s0", "s1", "s2"},
+                            {0, 0, 0, 0, 0, 1, 1, 1, 1, 1});
+  column_wrapper<int32_t> col_gold_6{{-1, -1, -1, -1, -1, 1, 1, 0, 1, 2},
+                                     {0, 0, 0, 0, 0, 1, 1, 1, 1, 1}};
+  auto gold_names1_col = strcol_wrapper{{"",
+                                         "",
+                                         "",
+                                         "",
+                                         "",
+                                         "Carrot Ironfoundersson",
+                                         "Carrot Ironfoundersson",
+                                         "Samuel Vimes",
+                                         "Carrot Ironfoundersson",
+                                         "Angua von Überwald"},
+                                        {0, 0, 0, 0, 0, 1, 1, 1, 1, 1}};
+  auto gold_ages1_col  = column_wrapper<int32_t>{{-1, -1, -1, -1, -1, 27, 27, 48, 27, 25},
+                                                 {0, 0, 0, 0, 0, 1, 1, 1, 1, 1}};
+
+  auto gold_is_human1_col =
+    column_wrapper<bool>{{false, false, false, false, false, true, true, true, true, false},
+                         {0, 0, 0, 0, 0, 1, 1, 1, 1, 0}};
+
+  auto col_gold_7 = cudf::test::structs_column_wrapper{
+    {gold_names1_col, gold_ages1_col, gold_is_human1_col}, {0, 0, 0, 0, 0, 1, 1, 1, 1, 1}};
+
+  CVector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  cols_gold.push_back(col_gold_4.release());
+  cols_gold.push_back(col_gold_5.release());
+  cols_gold.push_back(col_gold_6.release());
+  cols_gold.push_back(col_gold_7.release());
+
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, Repro_StructsWithoutNullsPushedDown)
+{
+  // When joining on a STRUCT column, if the parent nulls are not reflected in
+  // the children, the join might produce incorrect results.
+  //
+  // In this test, a fact table of structs is joined against a dimension table.
+  // Both tables must match (only) on the NULL row. This will fail if the fact table's
+  // nulls are not pushed down into its children.
+  using ints    = column_wrapper<int32_t>;
+  using structs = cudf::test::structs_column_wrapper;
+  using namespace cudf::test::iterators;
+
+  auto make_table = [](auto&& col) {
+    auto columns = CVector{};
+    columns.push_back(std::move(col));
+    return cudf::table{std::move(columns)};
+  };
+
+  auto const fact_table = [make_table] {
+    auto fact_ints    = ints{0, 1, 2, 3, 4};
+    auto fact_structs = structs{{fact_ints}, no_nulls()}.release();
+    // Now set struct validity to invalidate index#3.
+    cudf::detail::set_null_mask(
+      fact_structs->mutable_view().null_mask(), 3, 4, false, cudf::get_default_stream());
+    // Struct row#3 is null, but Struct.child has a non-null value.
+    return make_table(std::move(fact_structs));
+  }();
+
+  auto const dimension_table = [make_table] {
+    auto dim_ints    = ints{999};
+    auto dim_structs = structs{{dim_ints}, null_at(0)};
+    return make_table(dim_structs.release());
+  }();
+
+  auto const result = inner_join(fact_table.view(), dimension_table.view(), {0}, {0});
+  EXPECT_EQ(result->num_rows(), 1);  // The null STRUCT rows should match.
+
+  // Note: Join result might not have nulls pushed down, since it's an output of gather().
+  // Must superimpose parent nulls before comparisons.
+  auto [superimposed_results, _] = cudf::structs::detail::push_down_nulls(
+    *result, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto const expected = [] {
+    auto fact_ints    = ints{0};
+    auto fact_structs = structs{{fact_ints}, null_at(0)};
+    auto dim_ints     = ints{0};
+    auto dim_structs  = structs{{dim_ints}, null_at(0)};
+    auto columns      = CVector{};
+    columns.push_back(fact_structs.release());
+    columns.push_back(dim_structs.release());
+    return cudf::table{std::move(columns)};
+  }();
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(superimposed_results, expected);
+}
+
+using lcw = cudf::test::lists_column_wrapper<int32_t>;
+using cudf::test::iterators::null_at;
+
+struct JoinTestLists : public cudf::test::BaseFixture {
+  /*
+    [
+      NULL,      0
+      [1],       1
+      [2, NULL], 2
+      [],        3
+      [5, 6]     4
+  */
+  lcw build{{{0}, {1}, {{2, 0}, null_at(1)}, {}, {5, 6}}, null_at(0)};
+
+  /*
+    [
+      [1],       0
+      [3],       1
+      NULL,      2
+      [],        3
+      [2, NULL], 4
+      [5],       5
+      [6]        6
+    ]
+  */
+  lcw probe{{{1}, {3}, {0}, {}, {{2, 0}, null_at(1)}, {5}, {6}}, null_at(2)};
+
+  auto column_view_from_device_uvector(rmm::device_uvector<cudf::size_type> const& vector)
+  {
+    auto const indices_span = cudf::device_span<cudf::size_type const>{vector};
+    return cudf::column_view{indices_span};
+  }
+
+  auto sort_and_gather(
+    cudf::table_view table,
+    cudf::column_view gather_map,
+    cudf::out_of_bounds_policy oob_policy = cudf::out_of_bounds_policy::DONT_CHECK)
+  {
+    auto const gather_table = cudf::gather(table, gather_map, oob_policy);
+    auto const sort_order   = cudf::sorted_order(*gather_table);
+    return cudf::gather(*gather_table, *sort_order);
+  }
+
+  template <typename JoinFunc>
+  void join(cudf::column_view left_gold_map,
+            cudf::column_view right_gold_map,
+            cudf::null_equality nulls_equal,
+            JoinFunc join_func,
+            cudf::out_of_bounds_policy oob_policy)
+  {
+    auto const build_tv = cudf::table_view{{build}};
+    auto const probe_tv = cudf::table_view{{probe}};
+
+    auto const [left_result_map, right_result_map] =
+      join_func(build_tv, probe_tv, nulls_equal, rmm::mr::get_current_device_resource());
+
+    auto const left_result_table =
+      sort_and_gather(build_tv, column_view_from_device_uvector(*left_result_map), oob_policy);
+    auto const right_result_table =
+      sort_and_gather(probe_tv, column_view_from_device_uvector(*right_result_map), oob_policy);
+
+    auto const left_gold_table  = sort_and_gather(build_tv, left_gold_map, oob_policy);
+    auto const right_gold_table = sort_and_gather(probe_tv, right_gold_map, oob_policy);
+
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*left_result_table, *left_gold_table);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*right_result_table, *right_gold_table);
+  }
+
+  void inner_join(cudf::column_view left_gold_map,
+                  cudf::column_view right_gold_map,
+                  cudf::null_equality nulls_equal)
+  {
+    join(left_gold_map,
+         right_gold_map,
+         nulls_equal,
+         cudf::inner_join,
+         cudf::out_of_bounds_policy::DONT_CHECK);
+  }
+
+  void full_join(cudf::column_view left_gold_map,
+                 cudf::column_view right_gold_map,
+                 cudf::null_equality nulls_equal)
+  {
+    join(left_gold_map,
+         right_gold_map,
+         nulls_equal,
+         cudf::full_join,
+         cudf::out_of_bounds_policy::NULLIFY);
+  }
+
+  void left_join(cudf::column_view left_gold_map,
+                 cudf::column_view right_gold_map,
+                 cudf::null_equality nulls_equal)
+  {
+    join(left_gold_map,
+         right_gold_map,
+         nulls_equal,
+         cudf::left_join,
+         cudf::out_of_bounds_policy::NULLIFY);
+  }
+};
+
+TEST_F(JoinTestLists, ListWithNullsEqualInnerJoin)
+{
+  auto const left_gold_map  = column_wrapper<int32_t>({0, 1, 2, 3});
+  auto const right_gold_map = column_wrapper<int32_t>({0, 2, 3, 4});
+  this->inner_join(left_gold_map, right_gold_map, cudf::null_equality::EQUAL);
+}
+
+TEST_F(JoinTestLists, ListWithNullsUnequalInnerJoin)
+{
+  auto const left_gold_map  = column_wrapper<int32_t>({1, 3});
+  auto const right_gold_map = column_wrapper<int32_t>({0, 3});
+  this->inner_join(left_gold_map, right_gold_map, cudf::null_equality::UNEQUAL);
+}
+
+TEST_F(JoinTestLists, ListWithNullsEqualFullJoin)
+{
+  auto const left_gold_map =
+    column_wrapper<int32_t>({0, 1, 2, 3, 4, NoneValue, NoneValue, NoneValue});
+  auto const right_gold_map = column_wrapper<int32_t>({2, 0, 4, 3, NoneValue, 1, 5, 6});
+  this->full_join(left_gold_map, right_gold_map, cudf::null_equality::EQUAL);
+}
+
+TEST_F(JoinTestLists, ListWithNullsUnequalFullJoin)
+{
+  auto const left_gold_map =
+    column_wrapper<int32_t>({0, 1, 2, 3, 4, NoneValue, NoneValue, NoneValue, NoneValue, NoneValue});
+  auto const right_gold_map =
+    column_wrapper<int32_t>({NoneValue, 0, NoneValue, 3, NoneValue, 1, 5, 6, 2, 4});
+  this->full_join(left_gold_map, right_gold_map, cudf::null_equality::UNEQUAL);
+}
+
+TEST_F(JoinTestLists, ListWithNullsEqualLeftJoin)
+{
+  auto const left_gold_map  = column_wrapper<int32_t>({0, 1, 2, 3, 4});
+  auto const right_gold_map = column_wrapper<int32_t>({2, 0, 4, 3, NoneValue});
+  this->left_join(left_gold_map, right_gold_map, cudf::null_equality::EQUAL);
+}
+
+TEST_F(JoinTestLists, ListWithNullsUnequalLeftJoin)
+{
+  auto const left_gold_map  = column_wrapper<int32_t>({0, 1, 2, 3, 4});
+  auto const right_gold_map = column_wrapper<int32_t>({NoneValue, 0, NoneValue, 3, NoneValue});
+  this->left_join(left_gold_map, right_gold_map, cudf::null_equality::UNEQUAL);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/join/mixed_join_tests.cu b/cpp/tests/join/mixed_join_tests.cu
new file mode 100644
index 0000000..eb450d4
--- /dev/null
+++ b/cpp/tests/join/mixed_join_tests.cu
@@ -0,0 +1,943 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/join.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/host_vector.h>
+#include <thrust/pair.h>
+#include <thrust/sort.h>
+
+#include <algorithm>
+#include <iostream>
+#include <random>
+#include <stdexcept>
+#include <tuple>
+#include <utility>
+#include <vector>
+
+namespace {
+using PairJoinReturn   = std::pair<std::unique_ptr<rmm::device_uvector<cudf::size_type>>,
+                                 std::unique_ptr<rmm::device_uvector<cudf::size_type>>>;
+using SingleJoinReturn = std::unique_ptr<rmm::device_uvector<cudf::size_type>>;
+using NullMaskVector   = std::vector<bool>;
+
+template <typename T>
+using ColumnVector = std::vector<std::vector<T>>;
+
+template <typename T>
+using NullableColumnVector = std::vector<std::pair<std::vector<T>, NullMaskVector>>;
+
+constexpr cudf::size_type JoinNoneValue =
+  std::numeric_limits<cudf::size_type>::min();  // TODO: how to test if this isn't public?
+
+// Common column references.
+auto const col_ref_left_0  = cudf::ast::column_reference(0, cudf::ast::table_reference::LEFT);
+auto const col_ref_right_0 = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+
+// Common expressions.
+auto left_zero_eq_right_zero =
+  cudf::ast::operation(cudf::ast::ast_operator::EQUAL, col_ref_left_0, col_ref_right_0);
+
+// Generate a single pair of left/right non-nullable columns of random data
+// suitable for testing a join against a reference join implementation.
+template <typename T>
+std::pair<std::vector<T>, std::vector<T>> gen_random_repeated_columns(
+  unsigned int N_left            = 10000,
+  unsigned int num_repeats_left  = 10,
+  unsigned int N_right           = 10000,
+  unsigned int num_repeats_right = 10)
+{
+  // Generate columns of num_repeats repeats of the integer range [0, num_unique),
+  // then merge a shuffled version and compare to hash join.
+  unsigned int num_unique_left  = N_left / num_repeats_left;
+  unsigned int num_unique_right = N_right / num_repeats_right;
+
+  std::vector<T> left(N_left);
+  std::vector<T> right(N_right);
+
+  for (unsigned int i = 0; i < num_repeats_left; ++i) {
+    std::iota(std::next(left.begin(), num_unique_left * i),
+              std::next(left.begin(), num_unique_left * (i + 1)),
+              0);
+  }
+  for (unsigned int i = 0; i < num_repeats_right; ++i) {
+    std::iota(std::next(right.begin(), num_unique_right * i),
+              std::next(right.begin(), num_unique_right * (i + 1)),
+              0);
+  }
+
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::shuffle(left.begin(), left.end(), gen);
+  std::shuffle(right.begin(), right.end(), gen);
+  return std::pair(std::move(left), std::move(right));
+}
+
+// Generate a single pair of left/right nullable columns of random data
+// suitable for testing a join against a reference join implementation.
+template <typename T>
+std::pair<std::pair<std::vector<T>, std::vector<bool>>,
+          std::pair<std::vector<T>, std::vector<bool>>>
+gen_random_nullable_repeated_columns(unsigned int N = 10000, unsigned int num_repeats = 10)
+{
+  auto [left, right] = gen_random_repeated_columns<T>(N, num_repeats);
+
+  std::vector<bool> left_nulls(N);
+  std::vector<bool> right_nulls(N);
+
+  // Seed with a real random value, if available
+  std::random_device rd;
+  std::mt19937 gen(rd());
+  std::uniform_real_distribution<> uniform_dist(0, 1);
+
+  std::generate(left_nulls.begin(), left_nulls.end(), [&uniform_dist, &gen]() {
+    return uniform_dist(gen) > 0.5;
+  });
+  std::generate(right_nulls.begin(), right_nulls.end(), [&uniform_dist, &gen]() {
+    return uniform_dist(gen) > 0.5;
+  });
+
+  return std::pair(std::pair(std::move(left), std::move(left_nulls)),
+                   std::pair(std::move(right), std::move(right_nulls)));
+}
+
+}  // namespace
+
+/**
+ * Fixture for all mixed hash + conditional joins.
+ */
+template <typename T>
+struct MixedJoinTest : public cudf::test::BaseFixture {
+  /**
+   * Convenience utility for parsing initializer lists of input data into
+   * suitable inputs for tables.
+   */
+  template <typename U>
+  std::tuple<std::vector<cudf::test::fixed_width_column_wrapper<T>>,
+             std::vector<cudf::test::fixed_width_column_wrapper<T>>,
+             std::vector<cudf::column_view>,
+             std::vector<cudf::column_view>,
+             cudf::table_view,
+             cudf::table_view,
+             cudf::table_view,
+             cudf::table_view>
+  parse_input(std::vector<U> left_data,
+              std::vector<U> right_data,
+              std::vector<cudf::size_type> equality_columns,
+              std::vector<cudf::size_type> conditional_columns)
+  {
+    auto wrapper_generator = [](U& v) {
+      if constexpr (std::is_same_v<U, std::vector<T>>) {
+        return cudf::test::fixed_width_column_wrapper<T>(v.begin(), v.end());
+      } else if constexpr (std::is_same_v<U, std::pair<std::vector<T>, std::vector<bool>>>) {
+        return cudf::test::fixed_width_column_wrapper<T>(
+          v.first.begin(), v.first.end(), v.second.begin());
+      }
+      throw std::runtime_error("Invalid input to parse_input.");
+      return cudf::test::fixed_width_column_wrapper<T>();
+    };
+
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    std::vector<cudf::test::fixed_width_column_wrapper<T>> left_wrappers;
+    std::vector<cudf::column_view> left_columns;
+    for (auto v : left_data) {
+      left_wrappers.push_back(wrapper_generator(v));
+      left_columns.push_back(left_wrappers.back());
+    }
+
+    std::vector<cudf::test::fixed_width_column_wrapper<T>> right_wrappers;
+    std::vector<cudf::column_view> right_columns;
+    for (auto v : right_data) {
+      right_wrappers.push_back(wrapper_generator(v));
+      right_columns.push_back(right_wrappers.back());
+    }
+
+    auto left  = cudf::table_view(left_columns);
+    auto right = cudf::table_view(right_columns);
+
+    return std::make_tuple(std::move(left_wrappers),
+                           std::move(right_wrappers),
+                           std::move(left_columns),
+                           std::move(right_columns),
+                           left.select(equality_columns),
+                           right.select(equality_columns),
+                           left.select(conditional_columns),
+                           right.select(conditional_columns));
+  }
+};
+
+/**
+ * Fixture for join types that return both left and right indices (inner, left,
+ * and full joins).
+ */
+template <typename T>
+struct MixedJoinPairReturnTest : public MixedJoinTest<T> {
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * verify that the outputs match the expected outputs (up to order).
+   */
+  virtual void _test(cudf::table_view left_equality,
+                     cudf::table_view right_equality,
+                     cudf::table_view left_conditional,
+                     cudf::table_view right_conditional,
+                     cudf::ast::operation predicate,
+                     std::vector<cudf::size_type> expected_counts,
+                     std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs,
+                     cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+  {
+    auto [result_size, actual_counts] = this->join_size(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+    EXPECT_TRUE(result_size == expected_outputs.size());
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_counts_cw(
+      expected_counts.begin(), expected_counts.end());
+    auto const actual_counts_view =
+      cudf::column_view(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                        actual_counts->size(),
+                        actual_counts->data(),
+                        nullptr,
+                        0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_counts_cw, actual_counts_view);
+
+    auto result = this->join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+    std::vector<std::pair<cudf::size_type, cudf::size_type>> result_pairs;
+    for (size_t i = 0; i < result.first->size(); ++i) {
+      // Note: Not trying to be terribly efficient here since these tests are
+      // small, otherwise a batch copy to host before constructing the tuples
+      // would be important.
+      result_pairs.push_back({result.first->element(i, cudf::get_default_stream()),
+                              result.second->element(i, cudf::get_default_stream())});
+    }
+    std::sort(result_pairs.begin(), result_pairs.end());
+    std::sort(expected_outputs.begin(), expected_outputs.end());
+
+    EXPECT_TRUE(std::equal(expected_outputs.begin(), expected_outputs.end(), result_pairs.begin()));
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void test(ColumnVector<T> left_data,
+            ColumnVector<T> right_data,
+            std::vector<cudf::size_type> equality_columns,
+            std::vector<cudf::size_type> conditional_columns,
+            cudf::ast::operation predicate,
+            std::vector<cudf::size_type> expected_counts,
+            std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers,
+          right_wrappers,
+          left_columns,
+          right_columns,
+          left_equality,
+          right_equality,
+          left_conditional,
+          right_conditional] =
+      this->parse_input(left_data, right_data, equality_columns, conditional_columns);
+    this->_test(left_equality,
+                right_equality,
+                left_conditional,
+                right_conditional,
+                predicate,
+                expected_counts,
+                expected_outputs);
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void test_nulls(NullableColumnVector<T> left_data,
+                  NullableColumnVector<T> right_data,
+                  std::vector<cudf::size_type> equality_columns,
+                  std::vector<cudf::size_type> conditional_columns,
+                  cudf::ast::operation predicate,
+                  std::vector<cudf::size_type> expected_counts,
+                  std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs,
+                  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers,
+          right_wrappers,
+          left_columns,
+          right_columns,
+          left_equality,
+          right_equality,
+          left_conditional,
+          right_conditional] =
+      this->parse_input(left_data, right_data, equality_columns, conditional_columns);
+    this->_test(left_equality,
+                right_equality,
+                left_conditional,
+                right_conditional,
+                predicate,
+                expected_counts,
+                expected_outputs,
+                compare_nulls);
+  }
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * mixed join API.
+   */
+  virtual PairJoinReturn join(cudf::table_view left_equality,
+                              cudf::table_view right_equality,
+                              cudf::table_view left_conditional,
+                              cudf::table_view right_conditional,
+                              cudf::ast::operation predicate,
+                              cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) = 0;
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * mixed join size computation API.
+   */
+  virtual std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) = 0;
+};
+
+/**
+ * Tests of mixed inner joins.
+ */
+template <typename T>
+struct MixedInnerJoinTest : public MixedJoinPairReturnTest<T> {
+  PairJoinReturn join(cudf::table_view left_equality,
+                      cudf::table_view right_equality,
+                      cudf::table_view left_conditional,
+                      cudf::table_view right_conditional,
+                      cudf::ast::operation predicate,
+                      cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_inner_join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+
+  std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_inner_join_size(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(MixedInnerJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(MixedInnerJoinTest, Empty)
+{
+  this->test({}, {}, {}, {}, left_zero_eq_right_zero, {}, {});
+}
+
+TYPED_TEST(MixedInnerJoinTest, BasicEquality)
+{
+  this->test({{0, 1, 2}, {3, 4, 5}, {10, 20, 30}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 1, 0},
+             {{1, 1}});
+}
+
+TYPED_TEST(MixedInnerJoinTest, BasicNullEqualityEqual)
+{
+  this->test_nulls({{{0, 1, 2}, {1, 1, 0}}, {{3, 4, 5}, {1, 1, 1}}, {{10, 20, 30}, {1, 1, 1}}},
+                   {{{0, 1, 3}, {1, 1, 0}}, {{5, 4, 5}, {1, 1, 1}}, {{30, 40, 30}, {1, 1, 1}}},
+                   {0},
+                   {1, 2},
+                   left_zero_eq_right_zero,
+                   {0, 1, 1},
+                   {{1, 1}, {2, 2}},
+                   cudf::null_equality::EQUAL);
+};
+
+TYPED_TEST(MixedInnerJoinTest, BasicNullEqualityUnequal)
+{
+  this->test_nulls({{{0, 1, 2}, {1, 1, 0}}, {{3, 4, 5}, {1, 1, 1}}, {{10, 20, 30}, {1, 1, 1}}},
+                   {{{0, 1, 3}, {1, 1, 0}}, {{5, 4, 5}, {1, 1, 1}}, {{30, 40, 30}, {1, 1, 1}}},
+                   {0},
+                   {1, 2},
+                   left_zero_eq_right_zero,
+                   {0, 1, 0},
+                   {{1, 1}},
+                   cudf::null_equality::UNEQUAL);
+};
+
+TYPED_TEST(MixedInnerJoinTest, AsymmetricEquality)
+{
+  this->test({{0, 2, 1}, {3, 5, 4}, {10, 30, 20}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 0, 1},
+             {{2, 1}});
+}
+
+TYPED_TEST(MixedInnerJoinTest, AsymmetricLeftLargerEquality)
+{
+  this->test({{0, 2, 1, 4}, {3, 5, 4, 10}, {10, 30, 20, 100}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 0, 1, 0},
+             {{2, 1}});
+}
+
+TYPED_TEST(MixedInnerJoinTest, AsymmetricLeftLargerGreater)
+{
+  auto col_ref_left_1  = cudf::ast::column_reference(1, cudf::ast::table_reference::LEFT);
+  auto col_ref_right_1 = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+  auto condition =
+    cudf::ast::operation(cudf::ast::ast_operator::GREATER, col_ref_left_1, col_ref_right_1);
+  this->test({{2, 3, 9, 0, 1, 7, 4, 6, 5, 8}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 0}},
+             {{6, 5, 9, 8, 10, 32}, {0, 1, 2, 3, 4, 5}, {7, 8, 9, 0, 1, 2}},
+             {0},
+             {0, 1},
+             condition,
+             {0, 0, 1, 0, 0, 0, 0, 1, 1, 0},
+             {{2, 2}, {7, 0}, {8, 1}});
+}
+
+TYPED_TEST(MixedInnerJoinTest, AsymmetricRightLargerEquality)
+{
+  this->test({{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {{0, 2, 1, 4}, {3, 5, 4, 10}, {10, 30, 20, 100}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 0, 1, 0},
+             {{1, 2}});
+}
+
+TYPED_TEST(MixedInnerJoinTest, BasicInequality)
+{
+  auto const col_ref_left_1  = cudf::ast::column_reference(0, cudf::ast::table_reference::LEFT);
+  auto const col_ref_right_1 = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+  auto const col_ref_left_2  = cudf::ast::column_reference(1, cudf::ast::table_reference::LEFT);
+  auto const col_ref_right_2 = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+
+  auto scalar_1        = cudf::numeric_scalar<TypeParam>(35);
+  auto const literal_1 = cudf::ast::literal(scalar_1);
+
+  auto const op1 =
+    cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_left_1, col_ref_right_1);
+  auto const op2 = cudf::ast::operation(cudf::ast::ast_operator::LESS, literal_1, col_ref_right_2);
+
+  auto const predicate = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, op1, op2);
+
+  this->test({{0, 1, 2, 4}, {3, 4, 5, 6}, {10, 20, 30, 40}},
+             {{0, 1, 3, 4}, {5, 4, 5, 7}, {30, 40, 50, 60}},
+             {0},
+             {1, 2},
+             predicate,
+             {0, 0, 0, 1},
+             {{3, 3}});
+}
+
+/**
+ * Tests of mixed left joins.
+ */
+template <typename T>
+struct MixedLeftJoinTest : public MixedJoinPairReturnTest<T> {
+  PairJoinReturn join(cudf::table_view left_equality,
+                      cudf::table_view right_equality,
+                      cudf::table_view left_conditional,
+                      cudf::table_view right_conditional,
+                      cudf::ast::operation predicate,
+                      cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_left_join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+
+  std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_left_join_size(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(MixedLeftJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(MixedLeftJoinTest, Basic)
+{
+  this->test({{0, 1, 2}, {3, 4, 5}, {10, 20, 30}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {1, 1, 1},
+             {{0, JoinNoneValue}, {1, 1}, {2, JoinNoneValue}});
+}
+
+TYPED_TEST(MixedLeftJoinTest, Basic2)
+{
+  auto const col_ref_left_1  = cudf::ast::column_reference(0, cudf::ast::table_reference::LEFT);
+  auto const col_ref_right_1 = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+  auto const col_ref_left_2  = cudf::ast::column_reference(1, cudf::ast::table_reference::LEFT);
+  auto const col_ref_right_2 = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+
+  auto scalar_1        = cudf::numeric_scalar<TypeParam>(35);
+  auto const literal_1 = cudf::ast::literal(scalar_1);
+
+  auto const op1 =
+    cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_left_1, col_ref_right_1);
+  auto const op2 = cudf::ast::operation(cudf::ast::ast_operator::LESS, literal_1, col_ref_right_2);
+
+  auto const predicate = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, op1, op2);
+
+  this->test({{0, 1, 2, 4}, {3, 4, 5, 6}, {10, 20, 30, 40}},
+             {{0, 1, 3, 4}, {5, 4, 5, 7}, {30, 40, 50, 60}},
+             {0},
+             {1, 2},
+             predicate,
+             {1, 1, 1, 1},
+             {{0, JoinNoneValue}, {1, JoinNoneValue}, {2, JoinNoneValue}, {3, 3}});
+}
+
+/**
+ * Tests of mixed full joins.
+ */
+template <typename T>
+struct MixedFullJoinTest : public MixedJoinPairReturnTest<T> {
+  PairJoinReturn join(cudf::table_view left_equality,
+                      cudf::table_view right_equality,
+                      cudf::table_view left_conditional,
+                      cudf::table_view right_conditional,
+                      cudf::ast::operation predicate,
+                      cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_full_join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+
+  std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    // Full joins don't actually support size calculations, and there's no easy way to spoof it.
+    CUDF_FAIL("Size calculation not supported for full joins.");
+  }
+
+  /*
+   * Override method to remove size calculation testing since it's not possible for full joins.
+   */
+  void _test(cudf::table_view left_equality,
+             cudf::table_view right_equality,
+             cudf::table_view left_conditional,
+             cudf::table_view right_conditional,
+             cudf::ast::operation predicate,
+             std::vector<cudf::size_type> expected_counts,
+             std::vector<std::pair<cudf::size_type, cudf::size_type>> expected_outputs,
+             cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    auto result = this->join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+    std::vector<std::pair<cudf::size_type, cudf::size_type>> result_pairs;
+    for (size_t i = 0; i < result.first->size(); ++i) {
+      result_pairs.push_back({result.first->element(i, cudf::get_default_stream()),
+                              result.second->element(i, cudf::get_default_stream())});
+    }
+    std::sort(result_pairs.begin(), result_pairs.end());
+    std::sort(expected_outputs.begin(), expected_outputs.end());
+
+    EXPECT_TRUE(std::equal(expected_outputs.begin(), expected_outputs.end(), result_pairs.begin()));
+  }
+};
+
+TYPED_TEST_SUITE(MixedFullJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(MixedFullJoinTest, Basic)
+{
+  this->test(
+    {{0, 1, 2}, {3, 4, 5}, {10, 20, 30}},
+    {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+    {0},
+    {1, 2},
+    left_zero_eq_right_zero,
+    {1, 1, 1},
+    {{0, JoinNoneValue}, {1, 1}, {2, JoinNoneValue}, {JoinNoneValue, 0}, {JoinNoneValue, 2}});
+}
+
+TYPED_TEST(MixedFullJoinTest, Basic2)
+{
+  auto const col_ref_left_1  = cudf::ast::column_reference(0, cudf::ast::table_reference::LEFT);
+  auto const col_ref_right_1 = cudf::ast::column_reference(0, cudf::ast::table_reference::RIGHT);
+  auto const col_ref_left_2  = cudf::ast::column_reference(1, cudf::ast::table_reference::LEFT);
+  auto const col_ref_right_2 = cudf::ast::column_reference(1, cudf::ast::table_reference::RIGHT);
+
+  auto scalar_1        = cudf::numeric_scalar<TypeParam>(35);
+  auto const literal_1 = cudf::ast::literal(scalar_1);
+
+  auto const op1 =
+    cudf::ast::operation(cudf::ast::ast_operator::LESS, col_ref_left_1, col_ref_right_1);
+  auto const op2 = cudf::ast::operation(cudf::ast::ast_operator::LESS, literal_1, col_ref_right_2);
+
+  auto const predicate = cudf::ast::operation(cudf::ast::ast_operator::LOGICAL_AND, op1, op2);
+
+  this->test({{0, 1, 2, 4}, {3, 4, 5, 6}, {10, 20, 30, 40}},
+             {{0, 1, 3, 4}, {5, 4, 5, 7}, {30, 40, 50, 60}},
+             {0},
+             {1, 2},
+             predicate,
+             {1, 1, 1, 1},
+             {{0, JoinNoneValue},
+              {1, JoinNoneValue},
+              {2, JoinNoneValue},
+              {3, 3},
+              {JoinNoneValue, 0},
+              {JoinNoneValue, 1},
+              {JoinNoneValue, 2}});
+}
+
+template <typename T>
+struct MixedJoinSingleReturnTest : public MixedJoinTest<T> {
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * verify that the outputs match the expected outputs (up to order).
+   */
+  virtual void _test(cudf::table_view left_equality,
+                     cudf::table_view right_equality,
+                     cudf::table_view left_conditional,
+                     cudf::table_view right_conditional,
+                     cudf::ast::operation predicate,
+                     std::vector<cudf::size_type> expected_outputs,
+                     cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+  {
+    auto [result_size, actual_counts] = this->join_size(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+    EXPECT_TRUE(result_size == expected_outputs.size());
+
+    auto result = this->join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+    std::vector<cudf::size_type> resulting_indices;
+    for (size_t i = 0; i < result->size(); ++i) {
+      // Note: Not trying to be terribly efficient here since these tests are
+      // small, otherwise a batch copy to host before constructing the tuples
+      // would be important.
+      resulting_indices.push_back(result->element(i, cudf::get_default_stream()));
+    }
+    std::sort(resulting_indices.begin(), resulting_indices.end());
+    std::sort(expected_outputs.begin(), expected_outputs.end());
+    EXPECT_TRUE(
+      std::equal(resulting_indices.begin(), resulting_indices.end(), expected_outputs.begin()));
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void test(ColumnVector<T> left_data,
+            ColumnVector<T> right_data,
+            std::vector<cudf::size_type> equality_columns,
+            std::vector<cudf::size_type> conditional_columns,
+            cudf::ast::operation predicate,
+            std::vector<cudf::size_type> expected_outputs)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers,
+          right_wrappers,
+          left_columns,
+          right_columns,
+          left_equality,
+          right_equality,
+          left_conditional,
+          right_conditional] =
+      this->parse_input(left_data, right_data, equality_columns, conditional_columns);
+    this->_test(left_equality,
+                right_equality,
+                left_conditional,
+                right_conditional,
+                predicate,
+                expected_outputs);
+  }
+
+  /*
+   * Perform a join of tables constructed from two input data sets according to
+   * the provided predicate and verify that the outputs match the expected
+   * outputs (up to order).
+   */
+  void test_nulls(NullableColumnVector<T> left_data,
+                  NullableColumnVector<T> right_data,
+                  std::vector<cudf::size_type> equality_columns,
+                  std::vector<cudf::size_type> conditional_columns,
+                  cudf::ast::operation predicate,
+                  std::vector<cudf::size_type> expected_outputs,
+                  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+  {
+    // Note that we need to maintain the column wrappers otherwise the
+    // resulting column views will be referencing potentially invalid memory.
+    auto [left_wrappers,
+          right_wrappers,
+          left_columns,
+          right_columns,
+          left_equality,
+          right_equality,
+          left_conditional,
+          right_conditional] =
+      this->parse_input(left_data, right_data, equality_columns, conditional_columns);
+    this->_test(left_equality,
+                right_equality,
+                left_conditional,
+                right_conditional,
+                predicate,
+                expected_outputs,
+                compare_nulls);
+  }
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * mixed join API.
+   */
+  virtual SingleJoinReturn join(cudf::table_view left_equality,
+                                cudf::table_view right_equality,
+                                cudf::table_view left_conditional,
+                                cudf::table_view right_conditional,
+                                cudf::ast::operation predicate,
+                                cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) = 0;
+
+  /**
+   * This method must be implemented by subclasses for specific types of joins.
+   * It should be a simply forwarding of arguments to the appropriate cudf
+   * mixed join size computation API.
+   */
+  virtual std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) = 0;
+};
+
+/**
+ * Tests of mixed left semi joins.
+ */
+template <typename T>
+struct MixedLeftSemiJoinTest : public MixedJoinSingleReturnTest<T> {
+  SingleJoinReturn join(cudf::table_view left_equality,
+                        cudf::table_view right_equality,
+                        cudf::table_view left_conditional,
+                        cudf::table_view right_conditional,
+                        cudf::ast::operation predicate,
+                        cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_left_semi_join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+
+  std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_left_semi_join_size(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(MixedLeftSemiJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(MixedLeftSemiJoinTest, BasicEquality)
+{
+  this->test({{0, 1, 2}, {3, 4, 5}, {10, 20, 30}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {1});
+}
+
+TYPED_TEST(MixedLeftSemiJoinTest, BasicEqualityDuplicates)
+{
+  this->test({{0, 1, 2, 1}, {3, 4, 5, 6}, {10, 20, 30, 40}},
+             {{0, 1, 3, 1}, {5, 4, 5, 6}, {30, 40, 50, 40}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {1, 3});
+}
+
+TYPED_TEST(MixedLeftSemiJoinTest, BasicNullEqualityEqual)
+{
+  this->test_nulls({{{0, 1, 2}, {1, 1, 0}}, {{3, 4, 5}, {1, 1, 1}}, {{10, 20, 30}, {1, 1, 1}}},
+                   {{{0, 1, 3}, {1, 1, 0}}, {{5, 4, 5}, {1, 1, 1}}, {{30, 40, 30}, {1, 1, 1}}},
+                   {0},
+                   {1, 2},
+                   left_zero_eq_right_zero,
+                   {1, 2},
+                   cudf::null_equality::EQUAL);
+};
+
+TYPED_TEST(MixedLeftSemiJoinTest, BasicNullEqualityUnequal)
+{
+  this->test_nulls({{{0, 1, 2}, {1, 1, 0}}, {{3, 4, 5}, {1, 1, 1}}, {{10, 20, 30}, {1, 1, 1}}},
+                   {{{0, 1, 3}, {1, 1, 0}}, {{5, 4, 5}, {1, 1, 1}}, {{30, 40, 30}, {1, 1, 1}}},
+                   {0},
+                   {1, 2},
+                   left_zero_eq_right_zero,
+                   {1},
+                   cudf::null_equality::UNEQUAL);
+};
+
+TYPED_TEST(MixedLeftSemiJoinTest, AsymmetricEquality)
+{
+  this->test({{0, 2, 1}, {3, 5, 4}, {10, 30, 20}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {2});
+}
+
+TYPED_TEST(MixedLeftSemiJoinTest, AsymmetricLeftLargerEquality)
+{
+  this->test({{0, 2, 1, 4}, {3, 5, 4, 10}, {10, 30, 20, 100}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {2});
+}
+
+/**
+ * Tests of mixed left semi joins.
+ */
+template <typename T>
+struct MixedLeftAntiJoinTest : public MixedJoinSingleReturnTest<T> {
+  SingleJoinReturn join(cudf::table_view left_equality,
+                        cudf::table_view right_equality,
+                        cudf::table_view left_conditional,
+                        cudf::table_view right_conditional,
+                        cudf::ast::operation predicate,
+                        cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_left_anti_join(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+
+  std::pair<std::size_t, std::unique_ptr<rmm::device_uvector<cudf::size_type>>> join_size(
+    cudf::table_view left_equality,
+    cudf::table_view right_equality,
+    cudf::table_view left_conditional,
+    cudf::table_view right_conditional,
+    cudf::ast::operation predicate,
+    cudf::null_equality compare_nulls = cudf::null_equality::EQUAL) override
+  {
+    return cudf::mixed_left_anti_join_size(
+      left_equality, right_equality, left_conditional, right_conditional, predicate, compare_nulls);
+  }
+};
+
+TYPED_TEST_SUITE(MixedLeftAntiJoinTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(MixedLeftAntiJoinTest, BasicEquality)
+{
+  this->test({{0, 1, 2}, {3, 4, 5}, {10, 20, 30}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 2});
+}
+
+TYPED_TEST(MixedLeftAntiJoinTest, BasicNullEqualityEqual)
+{
+  this->test_nulls({{{0, 1, 2}, {1, 1, 0}}, {{3, 4, 5}, {1, 1, 1}}, {{10, 20, 30}, {1, 1, 1}}},
+                   {{{0, 1, 3}, {1, 1, 0}}, {{5, 4, 5}, {1, 1, 1}}, {{30, 40, 30}, {1, 1, 1}}},
+                   {0},
+                   {1, 2},
+                   left_zero_eq_right_zero,
+                   {0},
+                   cudf::null_equality::EQUAL);
+};
+
+TYPED_TEST(MixedLeftAntiJoinTest, BasicNullEqualityUnequal)
+{
+  this->test_nulls({{{0, 1, 2}, {1, 1, 0}}, {{3, 4, 5}, {1, 1, 1}}, {{10, 20, 30}, {1, 1, 1}}},
+                   {{{0, 1, 3}, {1, 1, 0}}, {{5, 4, 5}, {1, 1, 1}}, {{30, 40, 30}, {1, 1, 1}}},
+                   {0},
+                   {1, 2},
+                   left_zero_eq_right_zero,
+                   {0, 2},
+                   cudf::null_equality::UNEQUAL);
+};
+
+TYPED_TEST(MixedLeftAntiJoinTest, AsymmetricEquality)
+{
+  this->test({{0, 2, 1}, {3, 5, 4}, {10, 30, 20}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 1});
+}
+
+TYPED_TEST(MixedLeftAntiJoinTest, AsymmetricLeftLargerEquality)
+{
+  this->test({{0, 2, 1, 4}, {3, 5, 4, 10}, {10, 30, 20, 100}},
+             {{0, 1, 3}, {5, 4, 5}, {30, 40, 50}},
+             {0},
+             {1, 2},
+             left_zero_eq_right_zero,
+             {0, 1, 3});
+}
diff --git a/cpp/tests/join/semi_anti_join_tests.cpp b/cpp/tests/join/semi_anti_join_tests.cpp
new file mode 100644
index 0000000..0e0c92b
--- /dev/null
+++ b/cpp/tests/join/semi_anti_join_tests.cpp
@@ -0,0 +1,311 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/join.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+template <typename T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T>;
+using strcol_wrapper = cudf::test::strings_column_wrapper;
+using column_vector  = std::vector<std::unique_ptr<cudf::column>>;
+using Table          = cudf::table;
+
+struct JoinTest : public cudf::test::BaseFixture {};
+
+namespace {
+// This function is a wrapper around cudf's join APIs that takes the gather map
+// from join APIs and materializes the table that would be created by gathering
+// from the joined tables. Join APIs originally returned tables like this, but
+// they were modified in https://github.com/rapidsai/cudf/pull/7454. This
+// helper function allows us to avoid rewriting all our tests in terms of
+// gather maps.
+template <std::unique_ptr<rmm::device_uvector<cudf::size_type>> (*join_impl)(
+  cudf::table_view const& left_keys,
+  cudf::table_view const& right_keys,
+  cudf::null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr)>
+std::unique_ptr<cudf::table> join_and_gather(
+  cudf::table_view const& left_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& left_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource())
+{
+  auto left_selected      = left_input.select(left_on);
+  auto right_selected     = right_input.select(right_on);
+  auto const join_indices = join_impl(left_selected, right_selected, compare_nulls, mr);
+
+  auto left_indices_span = cudf::device_span<cudf::size_type const>{*join_indices};
+  auto left_indices_col  = cudf::column_view{left_indices_span};
+  return cudf::gather(left_input, left_indices_col);
+}
+}  // namespace
+
+std::unique_ptr<cudf::table> left_semi_join(
+  cudf::table_view const& left_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& left_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+{
+  return join_and_gather<cudf::left_semi_join>(
+    left_input, right_input, left_on, right_on, compare_nulls);
+}
+
+std::unique_ptr<cudf::table> left_anti_join(
+  cudf::table_view const& left_input,
+  cudf::table_view const& right_input,
+  std::vector<cudf::size_type> const& left_on,
+  std::vector<cudf::size_type> const& right_on,
+  cudf::null_equality compare_nulls = cudf::null_equality::EQUAL)
+{
+  return join_and_gather<cudf::left_anti_join>(
+    left_input, right_input, left_on, right_on, compare_nulls);
+}
+
+TEST_F(JoinTest, TestSimple)
+{
+  column_wrapper<int32_t> left_col0{0, 1, 2};
+  column_wrapper<int32_t> right_col0{0, 1, 3};
+
+  auto left  = cudf::table_view{{left_col0}};
+  auto right = cudf::table_view{{right_col0}};
+
+  auto result    = left_semi_join(left, right);
+  auto result_cv = cudf::column_view(cudf::data_type{cudf::type_to_id<cudf::size_type>()},
+                                     result->size(),
+                                     result->data(),
+                                     nullptr,
+                                     0);
+  column_wrapper<cudf::size_type> expected{0, 1};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result_cv);
+}
+
+std::pair<std::unique_ptr<cudf::table>, std::unique_ptr<cudf::table>> get_saj_tables(
+  std::vector<bool> const& left_is_human_nulls, std::vector<bool> const& right_is_human_nulls)
+{
+  column_wrapper<int32_t> col0_0{{99, 1, 2, 0, 2}, {0, 1, 1, 1, 1}};
+  strcol_wrapper col0_1({"s1", "s1", "s0", "s4", "s0"}, {1, 1, 0, 1, 1});
+  column_wrapper<int32_t> col0_2{{0, 1, 2, 4, 1}};
+  auto col0_names_col = strcol_wrapper{
+    "Samuel Vimes", "Carrot Ironfoundersson", "Detritus", "Samuel Vimes", "Angua von Überwald"};
+  auto col0_ages_col = column_wrapper<int32_t>{{48, 27, 351, 31, 25}};
+
+  auto col0_is_human_col =
+    column_wrapper<bool>{{true, true, false, false, false}, left_is_human_nulls.begin()};
+
+  auto col0_3 = cudf::test::structs_column_wrapper{
+    {col0_names_col, col0_ages_col, col0_is_human_col}, {1, 1, 1, 1, 1}};
+
+  column_wrapper<int32_t> col1_0{{2, 2, 0, 4, -99}, {1, 1, 1, 1, 0}};
+  strcol_wrapper col1_1({"s1", "s0", "s1", "s2", "s1"});
+  column_wrapper<int32_t> col1_2{{1, 0, 1, 2, 1}, {1, 0, 1, 1, 1}};
+  auto col1_names_col = strcol_wrapper{"Carrot Ironfoundersson",
+                                       "Angua von Überwald",
+                                       "Detritus",
+                                       "Carrot Ironfoundersson",
+                                       "Samuel Vimes"};
+  auto col1_ages_col  = column_wrapper<int32_t>{{351, 25, 27, 31, 48}};
+
+  auto col1_is_human_col =
+    column_wrapper<bool>{{true, false, false, false, true}, right_is_human_nulls.begin()};
+
+  auto col1_3 =
+    cudf::test::structs_column_wrapper{{col1_names_col, col1_ages_col, col1_is_human_col}};
+
+  column_vector cols0, cols1;
+  cols0.push_back(col0_0.release());
+  cols0.push_back(col0_1.release());
+  cols0.push_back(col0_2.release());
+  cols0.push_back(col0_3.release());
+  cols1.push_back(col1_0.release());
+  cols1.push_back(col1_1.release());
+  cols1.push_back(col1_2.release());
+  cols1.push_back(col1_3.release());
+
+  return {std::make_unique<Table>(std::move(cols0)), std::make_unique<Table>(std::move(cols1))};
+}
+
+TEST_F(JoinTest, SemiJoinWithStructsAndNulls)
+{
+  auto tables = get_saj_tables({1, 1, 0, 1, 0}, {1, 0, 0, 1, 1});
+
+  auto result =
+    left_semi_join(*tables.first, *tables.second, {0, 1, 3}, {0, 1, 3}, cudf::null_equality::EQUAL);
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{99, 2}, {0, 1}};
+  strcol_wrapper col_gold_1({"s1", "s0"}, {1, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 1}};
+  auto col_gold_3_names_col = strcol_wrapper{"Samuel Vimes", "Angua von Überwald"};
+  auto col_gold_3_ages_col  = column_wrapper<int32_t>{{48, 25}};
+
+  auto col_gold_3_is_human_col = column_wrapper<bool>{{true, false}, {1, 0}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {col_gold_3_names_col, col_gold_3_ages_col, col_gold_3_is_human_col}};
+
+  column_vector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, SemiJoinWithStructsAndNullsNotEqual)
+{
+  auto tables = get_saj_tables({1, 1, 0, 1, 1}, {1, 1, 0, 1, 1});
+
+  auto result = left_semi_join(
+    *tables.first, *tables.second, {0, 1, 3}, {0, 1, 3}, cudf::null_equality::UNEQUAL);
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{2}, {1}};
+  strcol_wrapper col_gold_1({"s0"}, {1});
+  column_wrapper<int32_t> col_gold_2{{1}};
+  auto col_gold_3_names_col = strcol_wrapper{"Angua von Überwald"};
+  auto col_gold_3_ages_col  = column_wrapper<int32_t>{{25}};
+
+  auto col_gold_3_is_human_col = column_wrapper<bool>{{false}, {1}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {col_gold_3_names_col, col_gold_3_ages_col, col_gold_3_is_human_col}};
+
+  column_vector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, AntiJoinWithStructsAndNulls)
+{
+  auto tables = get_saj_tables({1, 1, 0, 1, 0}, {1, 0, 0, 1, 1});
+
+  auto result =
+    left_anti_join(*tables.first, *tables.second, {0, 1, 3}, {0, 1, 3}, cudf::null_equality::EQUAL);
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{1, 2, 0}, {1, 1, 1}};
+  strcol_wrapper col_gold_1({"s1", "s0", "s4"}, {1, 0, 1});
+  column_wrapper<int32_t> col_gold_2{{1, 2, 4}};
+  auto col_gold_3_names_col = strcol_wrapper{"Carrot Ironfoundersson", "Detritus", "Samuel Vimes"};
+  auto col_gold_3_ages_col  = column_wrapper<int32_t>{{27, 351, 31}};
+
+  auto col_gold_3_is_human_col = column_wrapper<bool>{{true, false, false}, {1, 0, 1}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {col_gold_3_names_col, col_gold_3_ages_col, col_gold_3_is_human_col}};
+
+  column_vector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, AntiJoinWithStructsAndNullsNotEqual)
+{
+  auto tables = get_saj_tables({1, 1, 0, 1, 1}, {1, 1, 0, 1, 1});
+
+  auto result = left_anti_join(
+    *tables.first, *tables.second, {0, 1, 3}, {0, 1, 3}, cudf::null_equality::UNEQUAL);
+  auto result_sort_order = cudf::sorted_order(result->view());
+  auto sorted_result     = cudf::gather(result->view(), *result_sort_order);
+
+  column_wrapper<int32_t> col_gold_0{{99, 1, 2, 0}, {0, 1, 1, 1}};
+  strcol_wrapper col_gold_1({"s1", "s1", "s0", "s4"}, {1, 1, 0, 1});
+  column_wrapper<int32_t> col_gold_2{{0, 1, 2, 4}};
+  auto col_gold_3_names_col =
+    strcol_wrapper{"Samuel Vimes", "Carrot Ironfoundersson", "Detritus", "Samuel Vimes"};
+  auto col_gold_3_ages_col = column_wrapper<int32_t>{{48, 27, 351, 31}};
+
+  auto col_gold_3_is_human_col = column_wrapper<bool>{{true, true, false, false}, {1, 1, 0, 1}};
+
+  auto col_gold_3 = cudf::test::structs_column_wrapper{
+    {col_gold_3_names_col, col_gold_3_ages_col, col_gold_3_is_human_col}};
+
+  column_vector cols_gold;
+  cols_gold.push_back(col_gold_0.release());
+  cols_gold.push_back(col_gold_1.release());
+  cols_gold.push_back(col_gold_2.release());
+  cols_gold.push_back(col_gold_3.release());
+  Table gold(std::move(cols_gold));
+
+  auto gold_sort_order = cudf::sorted_order(gold.view());
+  auto sorted_gold     = cudf::gather(gold.view(), *gold_sort_order);
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*sorted_gold, *sorted_result);
+}
+
+TEST_F(JoinTest, AntiJoinWithStructsAndNullsOnOneSide)
+{
+  auto constexpr null{0};
+  auto left_col0 = [] {
+    column_wrapper<int32_t> child1{{1, null}, cudf::test::iterators::null_at(1)};
+    column_wrapper<int32_t> child2{11, 12};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+  auto right_col0 = [] {
+    column_wrapper<int32_t> child1{1, 2, 3, 4};
+    column_wrapper<int32_t> child2{11, 12, 13, 14};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto left  = cudf::table_view{{left_col0}};
+  auto right = cudf::table_view{{right_col0}};
+
+  auto result      = cudf::left_anti_join(left, right);
+  auto result_span = cudf::device_span<cudf::size_type const>{*result};
+  auto result_col  = cudf::column_view{result_span};
+  auto expected    = column_wrapper<cudf::size_type>{1};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result_col);
+}
diff --git a/cpp/tests/labeling/label_bins_tests.cpp b/cpp/tests/labeling/label_bins_tests.cpp
new file mode 100644
index 0000000..91aa13a
--- /dev/null
+++ b/cpp/tests/labeling/label_bins_tests.cpp
@@ -0,0 +1,436 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/labeling/label_bins.hpp>
+#include <cudf/types.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <limits>
+#include <numeric>
+#include <vector>
+
+namespace {
+
+template <typename T>
+using fwc_wrapper = cudf::test::fixed_width_column_wrapper<T>;
+
+template <typename T>
+using fpc_wrapper = cudf::test::fixed_point_column_wrapper<T>;
+
+// TODO: Should we move these into type_lists? They seem generally useful.
+using cudf::test::FixedPointTypes;
+using cudf::test::FloatingPointTypes;
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, FloatingPointTypes>;
+using SignedNumericTypesNotBool =
+  cudf::test::Types<int8_t, int16_t, int32_t, int64_t, float, double>;
+
+struct BinTestFixture : public cudf::test::BaseFixture {};
+
+/*
+ * Test error cases.
+ *
+ * Most of these are not parameterized by type to avoid unnecessary test overhead.
+ */
+
+// Left edges type check.
+TEST(BinColumnErrorTests, TestInvalidLeft)
+{
+  fwc_wrapper<double> left_edges{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<float> right_edges{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  fwc_wrapper<float> input{0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+  EXPECT_THROW(
+    cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO),
+    cudf::logic_error);
+};
+
+// Right edges type check.
+TEST(BinColumnErrorTests, TestInvalidRight)
+{
+  fwc_wrapper<float> left_edges{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<double> right_edges{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  fwc_wrapper<float> input{0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+  EXPECT_THROW(
+    cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO),
+    cudf::logic_error);
+};
+
+// Input type check.
+TEST(BinColumnErrorTests, TestInvalidInput)
+{
+  fwc_wrapper<float> left_edges{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<float> right_edges{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  fwc_wrapper<double> input{0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+  EXPECT_THROW(
+    cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO),
+    cudf::logic_error);
+};
+
+// Number of left and right edges must match.
+TEST(BinColumnErrorTests, TestMismatchedEdges)
+{
+  fwc_wrapper<float> left_edges{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<float> right_edges{1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<float> input{0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+  EXPECT_THROW(
+    cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO),
+    cudf::logic_error);
+};
+
+// Left edges with nulls.
+TEST(BinColumnErrorTests, TestLeftEdgesWithNullsBefore)
+{
+  fwc_wrapper<float> left_edges{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fwc_wrapper<float> right_edges{1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<float> input{0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+  EXPECT_THROW(
+    cudf::label_bins(input, left_edges, cudf::inclusive::NO, right_edges, cudf::inclusive::NO),
+    cudf::logic_error);
+};
+
+// Right edges with nulls.
+TEST(BinColumnErrorTests, TestRightEdgesWithNullsBefore)
+{
+  fwc_wrapper<float> left_edges{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  fwc_wrapper<float> right_edges{{1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fwc_wrapper<float> input{0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+  EXPECT_THROW(
+    cudf::label_bins(input, left_edges, cudf::inclusive::NO, right_edges, cudf::inclusive::NO),
+    cudf::logic_error);
+};
+
+/*
+ * Valid exceptional cases.
+ */
+
+template <typename T>
+struct GenericExceptionCasesBinTestFixture : public BinTestFixture {
+  void test(fwc_wrapper<T> input,
+            fwc_wrapper<cudf::size_type> expected,
+            fwc_wrapper<T> left_edges,
+            fwc_wrapper<T> right_edges)
+  {
+    auto result =
+      cudf::label_bins(input, left_edges, cudf::inclusive::NO, right_edges, cudf::inclusive::NO);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+};
+
+template <typename T>
+struct ExceptionCasesBinTestFixture : public GenericExceptionCasesBinTestFixture<T> {};
+
+TYPED_TEST_SUITE(ExceptionCasesBinTestFixture, NumericTypesNotBool);
+
+// Empty input must return an empty output.
+TYPED_TEST(ExceptionCasesBinTestFixture, TestEmptyInput)
+{
+  this->test({}, {}, {0, 2, 4, 6, 8}, {2, 4, 6, 8, 10});
+};
+
+// If no edges are provided, the bin for all inputs is null.
+TYPED_TEST(ExceptionCasesBinTestFixture, TestEmptyEdges)
+{
+  this->test({1, 1}, {{0, 0}, {0, 0}}, {}, {});
+};
+
+// Values outside the bounds should be labeled NULL.
+TYPED_TEST(ExceptionCasesBinTestFixture, TestOutOfBoundsInput)
+{
+  this->test({7, 9, 11, 13}, {{3, 4, 0, 0}, {1, 1, 0, 0}}, {0, 2, 4, 6, 8}, {2, 4, 6, 8, 10});
+};
+
+// Null inputs must map to nulls.
+TYPED_TEST(ExceptionCasesBinTestFixture, TestInputWithNulls)
+{
+  this->test(
+    {{1, 3, 5, 7}, {0, 1, 0, 1}}, {{0, 1, 0, 3}, {0, 1, 0, 1}}, {0, 2, 4, 6, 8}, {2, 4, 6, 8, 10});
+};
+
+// Test that nan values are assigned the NULL label.
+template <typename T>
+struct NaNBinTestFixture : public GenericExceptionCasesBinTestFixture<T> {};
+
+TYPED_TEST_SUITE(NaNBinTestFixture, FloatingPointTypes);
+
+TYPED_TEST(NaNBinTestFixture, TestNaN)
+{
+  if (std::numeric_limits<TypeParam>::has_quiet_NaN) {
+    this->test(
+      {std::numeric_limits<TypeParam>::quiet_NaN()}, {{0}, {0}}, {0, 2, 4, 6, 8}, {2, 4, 6, 8, 10});
+  }
+}
+
+/*
+ * Test inclusion options.
+ */
+
+template <typename T>
+struct BoundaryExclusionBinTestFixture : public BinTestFixture {
+  void test(cudf::inclusive left_inc,
+            cudf::inclusive right_inc,
+            fwc_wrapper<cudf::size_type> expected)
+  {
+    fwc_wrapper<T> left_edges{0, 2, 4, 6, 8};
+    fwc_wrapper<T> right_edges{2, 4, 6, 8, 10};
+    fwc_wrapper<T> input{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+
+    auto result = cudf::label_bins(input, left_edges, left_inc, right_edges, right_inc);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+};
+
+TYPED_TEST_SUITE(BoundaryExclusionBinTestFixture, NumericTypesNotBool);
+
+// Boundary points when both bounds are excluded should be labeled null.
+TYPED_TEST(BoundaryExclusionBinTestFixture, TestNoIncludes)
+{
+  this->test(cudf::inclusive::NO,
+             cudf::inclusive::NO,
+             {{0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5}, {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0}});
+};
+
+// Boundary point 1 should be in bin 1 [1, 2).
+TYPED_TEST(BoundaryExclusionBinTestFixture, TestIncludeLeft)
+{
+  this->test(cudf::inclusive::YES,
+             cudf::inclusive::NO,
+             {{0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 0}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0}});
+};
+
+// Boundary point 1 should be in bin 0 (0, 1].
+TYPED_TEST(BoundaryExclusionBinTestFixture, TestIncludeRight)
+{
+  this->test(cudf::inclusive::NO,
+             cudf::inclusive::YES,
+             {{0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4}, {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+};
+
+/*
+ * Test real data.
+ */
+
+// Test numeric data of reasonable size with noncontiguous bins.
+template <typename T>
+struct RealDataBinTestFixture : public BinTestFixture {
+  void test(unsigned int num_elements   = 512,
+            unsigned int inputs_per_bin = 4,
+            T left_edge_start_val       = 0)
+  {
+    // Avoid testing numbers that are larger than the current type supports.
+    const T largest_value = (num_elements / inputs_per_bin) * 4;
+    num_elements          = std::min(std::numeric_limits<T>::max(), largest_value);
+
+    unsigned int num_edges = num_elements / inputs_per_bin;
+
+    std::vector<T> left_edge_vector(num_edges);
+    std::vector<T> right_edge_vector(num_edges);
+    std::vector<T> partial_input_vector(num_edges);
+    std::vector<T> input_vector;
+    std::vector<cudf::size_type> partial_expected_vector(num_edges);
+    std::vector<cudf::size_type> expected_vector;
+    std::vector<unsigned int> expected_validity(num_elements, 1);
+
+    std::iota(left_edge_vector.begin(), left_edge_vector.end(), left_edge_start_val);
+
+    // Create noncontiguous bins of width 2 separate by 2, and place inputs in the middle of each
+    // bin.
+    std::transform(
+      left_edge_vector.begin(), left_edge_vector.end(), left_edge_vector.begin(), [](T val) {
+        return val * 4;
+      });
+    std::transform(
+      left_edge_vector.begin(), left_edge_vector.end(), right_edge_vector.begin(), [](T val) {
+        return val + 2;
+      });
+    std::transform(
+      left_edge_vector.begin(), left_edge_vector.end(), partial_input_vector.begin(), [](T val) {
+        return val + 1;
+      });
+    std::iota(partial_expected_vector.begin(), partial_expected_vector.end(), 0);
+
+    // Create vector containing duplicates of all the inputs.
+    input_vector.reserve(num_elements);
+    expected_vector.reserve(num_elements);
+    for (unsigned int i = 0; i < inputs_per_bin; ++i) {
+      input_vector.insert(
+        input_vector.end(), partial_input_vector.begin(), partial_input_vector.end());
+      expected_vector.insert(
+        expected_vector.end(), partial_expected_vector.begin(), partial_expected_vector.end());
+    }
+
+    // Column wrappers are necessary inputs for the function.
+    fwc_wrapper<T> left_edges(left_edge_vector.begin(), left_edge_vector.end());
+    fwc_wrapper<T> right_edges(right_edge_vector.begin(), right_edge_vector.end());
+    fwc_wrapper<T> input(input_vector.begin(), input_vector.end());
+    fwc_wrapper<cudf::size_type> expected(
+      expected_vector.begin(), expected_vector.end(), expected_validity.begin());
+
+    auto result =
+      cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+};
+
+TYPED_TEST_SUITE(RealDataBinTestFixture, NumericTypesNotBool);
+
+TYPED_TEST(RealDataBinTestFixture, TestRealData256) { this->test(256); };
+TYPED_TEST(RealDataBinTestFixture, TestRealData512) { this->test(512); };
+TYPED_TEST(RealDataBinTestFixture, TestRealData1024) { this->test(1024); };
+
+// Test negative numbers for signed types.
+template <typename T>
+struct NegativeNumbersBinTestFixture : public RealDataBinTestFixture<T> {
+  void test(unsigned int num_elements = 512, unsigned int inputs_per_bin = 4)
+  {
+    RealDataBinTestFixture<T>::test(
+      num_elements, inputs_per_bin, -static_cast<T>(num_elements / 2));
+  }
+};
+
+TYPED_TEST_SUITE(NegativeNumbersBinTestFixture, SignedNumericTypesNotBool);
+
+TYPED_TEST(NegativeNumbersBinTestFixture, TestNegativeNumbers256) { this->test(256); };
+TYPED_TEST(NegativeNumbersBinTestFixture, TestNegativeNumbers512) { this->test(512); };
+TYPED_TEST(NegativeNumbersBinTestFixture, TestNegativeNumbers1024) { this->test(1024); };
+
+/*
+ * Test fixed point types.
+ */
+
+template <typename T>
+struct FixedPointBinTestFixture : public BinTestFixture {};
+
+TYPED_TEST_SUITE(FixedPointBinTestFixture, FixedPointTypes);
+
+TYPED_TEST(FixedPointBinTestFixture, TestFixedPointData)
+{
+  using fpc_type_wrapper = fpc_wrapper<cudf::device_storage_type_t<TypeParam>>;
+
+  fpc_type_wrapper left_edges{{0, 10, 20, 30, 40, 50, 60, 70, 80, 90}, numeric::scale_type{0}};
+  fpc_type_wrapper right_edges{{10, 20, 30, 40, 50, 60, 70, 80, 90, 100}, numeric::scale_type{0}};
+  fpc_type_wrapper input{{25, 25, 25, 25, 25, 25, 25, 25, 25, 25}, numeric::scale_type{0}};
+
+  auto result =
+    cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO);
+
+  // Check that every element is placed in bin 2.
+  fwc_wrapper<cudf::size_type> expected{{2, 2, 2, 2, 2, 2, 2, 2, 2, 2},
+                                        {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+};
+
+/*
+ * Test strings.
+ */
+
+// Basic test of strings of lowercase alphanumerics.
+TEST(TestStringData, SimpleStringTest)
+{
+  cudf::test::strings_column_wrapper left_edges{"a", "b", "c", "d", "e"};
+  cudf::test::strings_column_wrapper right_edges{"b", "c", "d", "e", "f"};
+  cudf::test::strings_column_wrapper input{"abc", "bcd", "cde", "def", "efg"};
+
+  auto result =
+    cudf::label_bins(input, left_edges, cudf::inclusive::YES, right_edges, cudf::inclusive::NO);
+
+  fwc_wrapper<cudf::size_type> expected{{0, 1, 2, 3, 4}, {1, 1, 1, 1, 1}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+};
+
+// Test non-ASCII characters.
+TEST(TestStringData, NonAsciiStringTest)
+{
+  cudf::test::strings_column_wrapper left_edges{"A"};
+  cudf::test::strings_column_wrapper right_edges{"z"};
+  cudf::test::strings_column_wrapper input{"Héllo",
+                                           "thesé",
+                                           "HERE",
+                                           "tést strings",
+                                           "",
+                                           "1.75",
+                                           "-34",
+                                           "+9.8",
+                                           "17¼",
+                                           "x³",
+                                           "2³",
+                                           " 12⅝",
+                                           "1234567890",
+                                           "de",
+                                           "\t\r\n\f "};
+
+  auto result =
+    cudf::label_bins(input, left_edges, cudf::inclusive::NO, right_edges, cudf::inclusive::NO);
+
+  fwc_wrapper<cudf::size_type> expected{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
+                                        {1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+// Test sliced non-ASCII characters.
+TEST(TestStringData, SlicedNonAsciiStringTest)
+{
+  cudf::test::strings_column_wrapper left_edges{"A"};
+  cudf::test::strings_column_wrapper right_edges{"z"};
+  cudf::test::strings_column_wrapper input{"Héllo",
+                                           "thesé",
+                                           "HERE",
+                                           "tést strings",
+                                           "",
+                                           "1.75",
+                                           "-34",
+                                           "+9.8",
+                                           "17¼",
+                                           "x³",
+                                           "2³",
+                                           " 12⅝",
+                                           "1234567890",
+                                           "de",
+                                           "\t\r\n\f "};
+
+  auto sliced_inputs = cudf::slice(input, {1, 5, 5, 11});
+
+  {
+    auto result = cudf::label_bins(
+      sliced_inputs[0], left_edges, cudf::inclusive::NO, right_edges, cudf::inclusive::NO);
+    fwc_wrapper<cudf::size_type> expected{{0, 0, 0, 0}, {1, 1, 1, 0}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto result = cudf::label_bins(
+      sliced_inputs[1], left_edges, cudf::inclusive::NO, right_edges, cudf::inclusive::NO);
+    fwc_wrapper<cudf::size_type> expected{{0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 1, 0}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+}  // anonymous namespace
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/lists/combine/concatenate_list_elements_tests.cpp b/cpp/tests/lists/combine/concatenate_list_elements_tests.cpp
new file mode 100644
index 0000000..f62d2a0
--- /dev/null
+++ b/cpp/tests/lists/combine/concatenate_list_elements_tests.cpp
@@ -0,0 +1,809 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/lists/combine.hpp>
+
+#include <stdexcept>
+
+using namespace cudf::test::iterators;
+
+namespace {
+using StrListsCol = cudf::test::lists_column_wrapper<cudf::string_view>;
+using IntListsCol = cudf::test::lists_column_wrapper<int32_t>;
+using IntCol      = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};
+
+template <class T, class... Ts>
+auto build_lists_col(T& list, Ts&... lists)
+{
+  return T(std::initializer_list<T>{std::move(list), std::move(lists)...});
+}
+
+}  // namespace
+
+struct ConcatenateListElementsTest : public cudf::test::BaseFixture {};
+
+TEST_F(ConcatenateListElementsTest, InvalidInput)
+{
+  // Input lists is not a 2-level depth lists column.
+  {
+    auto const col = IntCol{};
+    EXPECT_THROW(cudf::lists::concatenate_list_elements(col), std::invalid_argument);
+  }
+
+  // Input lists is not at least 2-level depth lists column.
+  {
+    auto const col = IntListsCol{1, 2, 3};
+    EXPECT_THROW(cudf::lists::concatenate_list_elements(col), std::invalid_argument);
+  }
+}
+
+template <typename T>
+struct ConcatenateListElementsTypedTest : public cudf::test::BaseFixture {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(ConcatenateListElementsTypedTest, TypesForTest);
+
+TYPED_TEST(ConcatenateListElementsTypedTest, SimpleInputNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto row0           = ListsCol{{1, 2}, {3}, {4, 5, 6}};
+  auto row1           = ListsCol{ListsCol{}};
+  auto row2           = ListsCol{{7, 8}, {9, 10}};
+  auto const col      = build_lists_col(row0, row1, row2);
+  auto const results  = cudf::lists::concatenate_list_elements(col);
+  auto const expected = ListsCol{{1, 2, 3, 4, 5, 6}, {}, {7, 8, 9, 10}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+}
+
+TYPED_TEST(ConcatenateListElementsTypedTest, SimpleInputNestedManyLevelsNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto row00 = ListsCol{{1, 2}, {3}, {4, 5, 6}};
+  auto row01 = ListsCol{ListsCol{}};
+  auto row02 = ListsCol{{7, 8}, {9, 10}};
+  auto row0  = build_lists_col(row00, row01, row02);
+
+  auto row10 = ListsCol{{1, 2}, {3}, {4, 5, 6}};
+  auto row11 = ListsCol{ListsCol{}};
+  auto row12 = ListsCol{{7, 8}, {9, 10}};
+  auto row1  = build_lists_col(row10, row11, row12);
+
+  auto row20 = ListsCol{{1, 2}, {3}, {4, 5, 6}};
+  auto row21 = ListsCol{ListsCol{}};
+  auto row22 = ListsCol{{7, 8}, {9, 10}};
+  auto row2  = build_lists_col(row20, row21, row22);
+
+  auto const col      = build_lists_col(row0, row1, row2);
+  auto const results  = cudf::lists::concatenate_list_elements(col);
+  auto const expected = ListsCol{ListsCol{{1, 2}, {3}, {4, 5, 6}, {}, {7, 8}, {9, 10}},
+                                 ListsCol{{1, 2}, {3}, {4, 5, 6}, {}, {7, 8}, {9, 10}},
+                                 ListsCol{{1, 2}, {3}, {4, 5, 6}, {}, {7, 8}, {9, 10}}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+}
+
+TEST_F(ConcatenateListElementsTest, SimpleInputStringsColumnNoNull)
+{
+  auto row0 = StrListsCol{StrListsCol{"Tomato", "Apple"}, StrListsCol{"Orange"}};
+  auto row1 = StrListsCol{StrListsCol{"Banana", "Kiwi", "Cherry"}, StrListsCol{"Lemon", "Peach"}};
+  auto row2 = StrListsCol{StrListsCol{"Coconut"}, StrListsCol{}};
+  auto const col      = build_lists_col(row0, row1, row2);
+  auto const results  = cudf::lists::concatenate_list_elements(col);
+  auto const expected = StrListsCol{StrListsCol{"Tomato", "Apple", "Orange"},
+                                    StrListsCol{"Banana", "Kiwi", "Cherry", "Lemon", "Peach"},
+                                    StrListsCol{"Coconut"}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+}
+
+TYPED_TEST(ConcatenateListElementsTypedTest, SimpleInputWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+  auto row0      = ListsCol{{ListsCol{{1, null, 3, 4}, null_at(1)},
+                             ListsCol{{10, 11, 12, null}, null_at(3)},
+                             ListsCol{} /*NULL*/},
+                       null_at(2)};
+  auto row1      = ListsCol{ListsCol{{null, 2, 3, 4}, null_at(0)},
+                       ListsCol{{13, 14, 15, 16, 17, null}, null_at(5)},
+                       ListsCol{{20, null}, null_at(1)}};
+  auto row2      = ListsCol{{ListsCol{{null, 2, 3, 4}, null_at(0)},
+                             ListsCol{} /*NULL*/,
+                             ListsCol{{null, 21, null, null}, nulls_at({0, 2, 3})}},
+                       null_at(1)};
+  auto row3      = ListsCol{{ListsCol{} /*NULL*/, ListsCol{{null, 18}, null_at(0)}}, null_at(0)};
+  auto row4      = ListsCol{ListsCol{{1, 2, null, 4}, null_at(2)},
+                       ListsCol{{19, 20, null}, null_at(2)},
+                       ListsCol{22, 23, 24, 25}};
+  auto row5      = ListsCol{ListsCol{{1, 2, 3, null}, null_at(3)},
+                       ListsCol{{null}, null_at(0)},
+                       ListsCol{{null, null, null, null, null}, all_nulls()}};
+  auto row6 =
+    ListsCol{{ListsCol{} /*NULL*/, ListsCol{} /*NULL*/, ListsCol{} /*NULL*/}, all_nulls()};
+  auto const col = build_lists_col(row0, row1, row2, row3, row4, row5, row6);
+
+  // Ignore null list elements.
+  {
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{{ListsCol{{1, null, 3, 4, 10, 11, 12, null}, nulls_at({1, 7})},
+                ListsCol{{null, 2, 3, 4, 13, 14, 15, 16, 17, null, 20, null}, nulls_at({0, 9, 11})},
+                ListsCol{{null, 2, 3, 4, null, 21, null, null}, nulls_at({0, 4, 6, 7})},
+                ListsCol{{null, 18}, null_at(0)},
+                ListsCol{{1, 2, null, 4, 19, 20, null, 22, 23, 24, 25}, nulls_at({2, 6})},
+                ListsCol{{1, 2, 3, null, null, null, null, null, null, null},
+                         nulls_at({3, 4, 5, 6, 7, 8, 9})},
+                ListsCol{} /*NULL*/},
+               null_at(6)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+
+  // Null lists result in null rows.
+  {
+    auto const results = cudf::lists::concatenate_list_elements(
+      col, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected =
+      ListsCol{{ListsCol{} /*NULL*/,
+                ListsCol{{null, 2, 3, 4, 13, 14, 15, 16, 17, null, 20, null}, nulls_at({0, 9, 11})},
+                ListsCol{} /*NULL*/,
+                ListsCol{} /*NULL*/,
+                ListsCol{{1, 2, null, 4, 19, 20, null, 22, 23, 24, 25}, nulls_at({2, 6})},
+                ListsCol{{1, 2, 3, null, null, null, null, null, null, null},
+                         nulls_at({3, 4, 5, 6, 7, 8, 9})},
+                ListsCol{} /*NULL*/},
+               nulls_at({0, 2, 3, 6})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+
+TYPED_TEST(ConcatenateListElementsTypedTest, SimpleInputNestedManyLevelsWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto row00 = ListsCol{{1, 2}, {3}, {4, 5, 6}};
+  auto row01 = ListsCol{ListsCol{}}; /*NULL*/
+  auto row02 = ListsCol{{7, 8}, {9, 10}};
+  auto row0  = ListsCol{{std::move(row00), std::move(row01), std::move(row02)}, null_at(1)};
+
+  auto row10 = ListsCol{{{1, 2}, {3}, {4, 5, 6} /*NULL*/}, null_at(2)};
+  auto row11 = ListsCol{ListsCol{}};
+  auto row12 = ListsCol{{7, 8}, {9, 10}};
+  auto row1  = build_lists_col(row10, row11, row12);
+
+  auto row20 = ListsCol{{1, 2}, {3}, {4, 5, 6}};
+  auto row21 = ListsCol{ListsCol{}};
+  auto row22 = ListsCol{ListsCol{{null, 8}, null_at(0)}, {9, 10}};
+  auto row2  = build_lists_col(row20, row21, row22);
+
+  auto const col = build_lists_col(row0, row1, row2);
+
+  // Ignore null list elements.
+  {
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{ListsCol{{1, 2}, {3}, {4, 5, 6}, {7, 8}, {9, 10}},
+               ListsCol{{{1, 2}, {3}, {} /*NULL*/, {}, {7, 8}, {9, 10}}, null_at(2)},
+               ListsCol{{1, 2}, {3}, {4, 5, 6}, {}, ListsCol{{null, 8}, null_at(0)}, {9, 10}}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+
+  // Null lists result in null rows.
+  {
+    auto const results = cudf::lists::concatenate_list_elements(
+      col, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected =
+      ListsCol{{ListsCol{ListsCol{}}, /*NULL*/
+                ListsCol{{{1, 2}, {3}, {} /*NULL*/, {}, {7, 8}, {9, 10}}, null_at(2)},
+                ListsCol{{1, 2}, {3}, {4, 5, 6}, {}, ListsCol{{null, 8}, null_at(0)}, {9, 10}}},
+               null_at(0)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+
+TEST_F(ConcatenateListElementsTest, SimpleInputStringsColumnWithNulls)
+{
+  auto row0 = StrListsCol{
+    StrListsCol{{"Tomato", "Bear" /*NULL*/, "Apple"}, null_at(1)},
+    StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/}, nulls_at({1, 2, 3})}};
+  auto row1 = StrListsCol{
+    StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/}, nulls_at({1, 4})},
+    StrListsCol{"Lemon", "Peach"}};
+  auto row2      = StrListsCol{{StrListsCol{"Coconut"}, StrListsCol{} /*NULL*/}, null_at(1)};
+  auto const col = build_lists_col(row0, row1, row2);
+
+  // Ignore null list elements.
+  {
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = StrListsCol{
+      StrListsCol{{"Tomato", "" /*NULL*/, "Apple", "Orange", "" /*NULL*/, "" /*NULL*/, ""
+                   /*NULL*/},
+                  nulls_at({1, 4, 5, 6})},
+      StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/, "Lemon", "Peach"},
+                  nulls_at({1, 4})},
+      StrListsCol{"Coconut"}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+
+  // Null lists result in null rows.
+  {
+    auto const results = cudf::lists::concatenate_list_elements(
+      col, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected = StrListsCol{
+      {StrListsCol{
+         {"Tomato", "" /*NULL*/, "Apple", "Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/},
+         nulls_at({1, 4, 5, 6})},
+       StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/, "Lemon", "Peach"},
+                   nulls_at({1, 4})},
+       StrListsCol{} /*NULL*/},
+      null_at(2)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+TEST_F(ConcatenateListElementsTest, SimpleInputStringsColumnWithEmptyStringsAndNulls)
+{
+  auto row0 = StrListsCol{
+    StrListsCol{"", "", ""},
+    StrListsCol{{"Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2, 3})}};
+  auto row1 = StrListsCol{
+    StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/}, nulls_at({1, 4})},
+    StrListsCol{""}};
+  auto row2      = StrListsCol{{StrListsCol{"Coconut"}, StrListsCol{} /*NULL*/}, null_at(1)};
+  auto const col = build_lists_col(row0, row1, row2);
+
+  // Ignore null list elements.
+  {
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = StrListsCol{
+      StrListsCol{{"", "", "", "Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/},
+                  nulls_at({4, 5, 6})},
+      StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/, ""}, nulls_at({1, 4})},
+      StrListsCol{"Coconut"}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+
+  // Null lists result in null rows.
+  {
+    auto const results = cudf::lists::concatenate_list_elements(
+      col, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected = StrListsCol{
+      {StrListsCol{{"", "", "", "Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/},
+                   nulls_at({4, 5, 6})},
+       StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/, ""}, nulls_at({1, 4})},
+       StrListsCol{} /*NULL*/},
+      null_at(2)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+
+TYPED_TEST(ConcatenateListElementsTypedTest, SlicedColumnsInputNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col_original = ListsCol{ListsCol{{1, 2, 3}, {2, 3}},
+                                     ListsCol{{3, 4, 5, 6}, {5, 6}, {}, {7}},
+                                     ListsCol{{7, 7, 7}, {7, 8, 1, 0}, {1}},
+                                     ListsCol{{9, 10, 11}},
+                                     ListsCol{},
+                                     ListsCol{{12, 13, 14, 15}, {16}, {17}}};
+
+  {
+    auto const col     = cudf::slice(col_original, {0, 3})[0];
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{{1, 2, 3, 2, 3}, {3, 4, 5, 6, 5, 6, 7}, {7, 7, 7, 7, 8, 1, 0, 1}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col      = cudf::slice(col_original, {1, 4})[0];
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = ListsCol{{3, 4, 5, 6, 5, 6, 7}, {7, 7, 7, 7, 8, 1, 0, 1}, {9, 10, 11}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col      = cudf::slice(col_original, {2, 5})[0];
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = ListsCol{{7, 7, 7, 7, 8, 1, 0, 1}, {9, 10, 11}, {}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col      = cudf::slice(col_original, {3, 6})[0];
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = ListsCol{{9, 10, 11}, {}, {12, 13, 14, 15, 16, 17}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+
+TYPED_TEST(ConcatenateListElementsTypedTest, SlicedColumnsInputWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto row0 = ListsCol{ListsCol{{null, 2, 3}, null_at(0)}, ListsCol{2, 3}};
+  auto row1 = ListsCol{ListsCol{{3, null, null, 6}, nulls_at({1, 2})},
+                       ListsCol{{5, 6, null}, null_at(2)},
+                       ListsCol{},
+                       ListsCol{{7, null}, null_at(1)}};
+  auto row2 = ListsCol{ListsCol{7, 7, 7}, ListsCol{{7, 8, null, 0}, null_at(2)}, ListsCol{1}};
+  auto row3 = ListsCol{ListsCol{9, 10, 11}};
+  auto row4 = ListsCol{ListsCol{}};
+  auto row5 = ListsCol{ListsCol{{12, null, 14, 15}, null_at(1)}, ListsCol{16}, ListsCol{17}};
+  auto const col_original = build_lists_col(row0, row1, row2, row3, row4, row5);
+
+  {
+    auto const col     = cudf::slice(col_original, {0, 3})[0];
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{ListsCol{{null, 2, 3, 2, 3}, null_at(0)},
+               ListsCol{{3, null, null, 6, 5, 6, null, 7, null}, nulls_at({1, 2, 6, 8})},
+               ListsCol{{7, 7, 7, 7, 8, null, 0, 1}, null_at(5)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col     = cudf::slice(col_original, {1, 4})[0];
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{ListsCol{{3, null, null, 6, 5, 6, null, 7, null}, nulls_at({1, 2, 6, 8})},
+               ListsCol{{7, 7, 7, 7, 8, null, 0, 1}, null_at(5)},
+               ListsCol{9, 10, 11}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col     = cudf::slice(col_original, {2, 5})[0];
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{ListsCol{{7, 7, 7, 7, 8, null, 0, 1}, null_at(5)}, ListsCol{9, 10, 11}, ListsCol{}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col     = cudf::slice(col_original, {3, 6})[0];
+    auto const results = cudf::lists::concatenate_list_elements(col);
+    auto const expected =
+      ListsCol{ListsCol{9, 10, 11}, ListsCol{}, ListsCol{{12, null, 14, 15, 16, 17}, null_at(1)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+
+TEST_F(ConcatenateListElementsTest, SlicedStringsColumnsInputWithNulls)
+{
+  auto row0 = StrListsCol{
+    StrListsCol{{"Tomato", "Bear" /*NULL*/, "Apple"}, null_at(1)},
+    StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/}, nulls_at({1, 4})},
+    StrListsCol{"Coconut"}};
+  auto row1 = StrListsCol{
+    StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/}, nulls_at({1, 4})},
+    StrListsCol{"Coconut"},
+    StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/}, nulls_at({1, 2, 3})}};
+  auto row2 = StrListsCol{
+    StrListsCol{"Coconut"},
+    StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/}, nulls_at({1, 2, 3})},
+    StrListsCol{"Lemon", "Peach"}};
+  auto row3 = StrListsCol{
+    {StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/}, nulls_at({1, 2, 3})},
+     StrListsCol{"Lemon", "Peach"},
+     StrListsCol{} /*NULL*/},
+    null_at(2)};
+  auto const col_original = build_lists_col(row0, row1, row2, row3);
+
+  {
+    auto const col      = cudf::slice(col_original, {0, 2})[0];
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = StrListsCol{StrListsCol{{"Tomato",
+                                                   "" /*NULL*/,
+                                                   "Apple",
+                                                   "Banana",
+                                                   "" /*NULL*/,
+                                                   "Kiwi",
+                                                   "Cherry",
+                                                   "" /*NULL*/,
+                                                   "Coconut"},
+                                                  nulls_at({1, 4, 7})},
+                                      StrListsCol{{"Banana",
+                                                   "" /*NULL*/,
+                                                   "Kiwi",
+                                                   "Cherry",
+                                                   "" /*NULL*/,
+                                                   "Coconut",
+                                                   "Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/},
+                                                  nulls_at({1, 4, 7, 8, 9})}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col      = cudf::slice(col_original, {1, 3})[0];
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = StrListsCol{StrListsCol{{"Banana",
+                                                   "" /*NULL*/,
+                                                   "Kiwi",
+                                                   "Cherry",
+                                                   "" /*NULL*/,
+                                                   "Coconut",
+                                                   "Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/},
+                                                  nulls_at({1, 4, 7, 8, 9})},
+                                      StrListsCol{{"Coconut",
+                                                   "Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "", /*NULL*/
+                                                   "Lemon",
+                                                   "Peach"},
+                                                  nulls_at({2, 3, 4})}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col      = cudf::slice(col_original, {2, 4})[0];
+    auto const results  = cudf::lists::concatenate_list_elements(col);
+    auto const expected = StrListsCol{StrListsCol{{"Coconut",
+                                                   "Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "", /*NULL*/
+                                                   "Lemon",
+                                                   "Peach"},
+                                                  nulls_at({2, 3, 4})},
+                                      StrListsCol{{"Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "", /*NULL*/
+                                                   "Lemon",
+                                                   "Peach"},
+                                                  nulls_at({1, 2, 3})}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+  {
+    auto const col     = cudf::slice(col_original, {2, 4})[0];
+    auto const results = cudf::lists::concatenate_list_elements(
+      col, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected = StrListsCol{{StrListsCol{{"Coconut",
+                                                    "Orange",
+                                                    "" /*NULL*/,
+                                                    "" /*NULL*/,
+                                                    "", /*NULL*/
+                                                    "Lemon",
+                                                    "Peach"},
+                                                   nulls_at({2, 3, 4})},
+                                       StrListsCol{} /*NULL*/},
+                                      null_at(1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results, verbosity);
+  }
+}
+
+TEST_F(ConcatenateListElementsTest, ListsOfListsOfStructsNoNull)
+{
+  using structs_col = cudf::test::structs_column_wrapper;
+  using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using strings_col = cudf::test::strings_column_wrapper;
+
+  // Input:
+  // [ [{1, 11, "1"}, {2, 12, "2"}], [{3, 13, "3"}], [{4, 14, "4"}, {5, 15, "5"}, {6, 16, "6"}] ]
+  // [ [] ]
+  // [ [{7, 17, "7"}, {8, 18, "8"}], [{9, 19, "9"}, {10, 110, "10"}] ]
+  auto const input = [] {
+    auto child = [] {
+      auto child1  = int32s_col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+      auto child2  = int32s_col{11, 12, 13, 14, 15, 16, 17, 18, 19, 110};
+      auto child3  = strings_col{"1", "2", "3", "4", "5", "6", "7", "8", "9", "10"};
+      auto structs = structs_col{{child1, child2, child3}};
+      auto offsets = int32s_col{0, 2, 3, 6, 6, 8, 10};
+      return cudf::make_lists_column(6, offsets.release(), structs.release(), 0, {});
+    }();
+
+    auto offsets = int32s_col{0, 3, 4, 6};
+    return cudf::make_lists_column(3, offsets.release(), std::move(child), 0, {});
+  }();
+
+  // Output:
+  // [{1, 11, "1"}, {2, 12, "2"}, {3, 13, "3"}, {4, 14, "4"}, {5, 15, "5"}, {6, 16, "6"}]
+  // []
+  // [{7, 17, "7"}, {8, 18, "8"}, {9, 19, "9"}, {10, 110, "10"}]
+  auto const expected = [] {
+    auto child1  = int32s_col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    auto child2  = int32s_col{11, 12, 13, 14, 15, 16, 17, 18, 19, 110};
+    auto child3  = strings_col{"1", "2", "3", "4", "5", "6", "7", "8", "9", "10"};
+    auto structs = structs_col{{child1, child2, child3}};
+    auto offsets = int32s_col{0, 6, 6, 10};
+    return cudf::make_lists_column(3, offsets.release(), structs.release(), 0, {});
+  }();
+
+  auto const results = cudf::lists::concatenate_list_elements(*input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+}
+
+TEST_F(ConcatenateListElementsTest, ListsOfListsOfStructsWithNull)
+{
+  using structs_col = cudf::test::structs_column_wrapper;
+  using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using strings_col = cudf::test::strings_column_wrapper;
+
+  // Input:
+  // [ [{1, 11, "1"}, {null, null, null}], [{3, 13, "3"}], NULL ]
+  // [ [{4, 14, "4"}, {5, 15, "5"}, {null, null, null}] ]
+  // [ [{7, 17, "7"}, {null, null, null}], [{9, 19, "9"}, {10, 110, "10"}] ]
+  auto const input = [] {
+    auto child = [] {
+      auto child1                  = int32s_col{1, null, 3, 4, 5, null, 7, null, 9, 10};
+      auto child2                  = int32s_col{11, null, 13, 14, 15, null, 17, null, 19, 110};
+      auto child3                  = strings_col{"1", "", "3", "4", "5", "", "7", "", "9", "10"};
+      auto structs                 = structs_col{{child1, child2, child3}, nulls_at({1, 5, 7})};
+      auto offsets                 = int32s_col{0, 2, 3, 3, 6, 8, 10};
+      auto const null_it           = null_at(2);  // null list
+      auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 6);
+      return cudf::make_lists_column(
+        6, offsets.release(), structs.release(), null_count, std::move(null_mask));
+    }();
+
+    auto offsets = int32s_col{0, 3, 4, 6};
+    return cudf::make_lists_column(3, offsets.release(), std::move(child), 0, {});
+  }();
+
+  // Concatenate with ignoring null lists.
+  {
+    // Output:
+    // [{1, 11, "1"}, {null, null, null}, {3, 13, "3"}]
+    // [{4, 14, "4"}, {5, 15, "5"}, {null, null, null}]
+    // [{7, 17, "7"}, {null, null, null}, {9, 19, "9"}, {10, 110, "10"}]
+    auto const expected = [] {
+      auto child1  = int32s_col{1, null, 3, 4, 5, null, 7, null, 9, 10};
+      auto child2  = int32s_col{11, null, 13, 14, 15, null, 17, null, 19, 110};
+      auto child3  = strings_col{"1", "", "3", "4", "5", "", "7", "", "9", "10"};
+      auto structs = structs_col{{child1, child2, child3}, nulls_at({1, 5, 7})};
+      auto offsets = int32s_col{0, 3, 6, 10};
+      return cudf::make_lists_column(3, offsets.release(), structs.release(), 0, {});
+    }();
+
+    auto const results = cudf::lists::concatenate_list_elements(*input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+
+  // Concatenate with ignoring null lists and sliced input.
+  {
+    // Output:
+    // [{4, 14, "4"}, {5, 15, "5"}, {null, null, null}]
+    auto const expected = [] {
+      auto child1  = int32s_col{4, 5, null};
+      auto child2  = int32s_col{14, 15, null};
+      auto child3  = strings_col{"4", "5", ""};
+      auto structs = structs_col{{child1, child2, child3}, null_at(2)};
+      auto offsets = int32s_col{0, 3};
+      return cudf::make_lists_column(1, offsets.release(), structs.release(), 0, {});
+    }();
+
+    auto const sliced_input = cudf::slice(*input, {1, 2})[0];
+    auto const results      = cudf::lists::concatenate_list_elements(sliced_input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+
+  // Concatenate with `concatenate_null_policy::NULLIFY_OUTPUT_ROW`.
+  {
+    // Output:
+    // NULL
+    // [{4, 14, "4"}, {5, 15, "5"}, {null, null, null}]
+    // [{7, 17, "7"}, {null, null, null}, {9, 19, "9"}, {10, 110, "10"}]
+    auto const expected = [] {
+      auto child1                  = int32s_col{4, 5, null, 7, null, 9, 10};
+      auto child2                  = int32s_col{14, 15, null, 17, null, 19, 110};
+      auto child3                  = strings_col{"4", "5", "", "7", "", "9", "10"};
+      auto structs                 = structs_col{{child1, child2, child3}, nulls_at({2, 4})};
+      auto offsets                 = int32s_col{0, 0, 3, 7};
+      auto const null_it           = null_at(0);  // null row
+      auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 3);
+      return cudf::make_lists_column(
+        3, offsets.release(), structs.release(), null_count, std::move(null_mask));
+    }();
+
+    auto const results = cudf::lists::concatenate_list_elements(
+      *input, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+
+  // Concatenate with `concatenate_null_policy::NULLIFY_OUTPUT_ROW` and sliced input.
+  {
+    // Output:
+    // NULL
+    // [{4, 14, "4"}, {5, 15, "5"}, {null, null, null}]
+    auto const expected = [] {
+      auto child1                  = int32s_col{4, 5, null};
+      auto child2                  = int32s_col{14, 15, null};
+      auto child3                  = strings_col{"4", "5", ""};
+      auto structs                 = structs_col{{child1, child2, child3}, null_at(2)};
+      auto offsets                 = int32s_col{0, 0, 3};
+      auto const null_it           = null_at(0);  // null row
+      auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 2);
+      return cudf::make_lists_column(
+        2, offsets.release(), structs.release(), null_count, std::move(null_mask));
+    }();
+
+    auto const sliced_input = cudf::slice(*input, {0, 2})[0];
+    auto const results      = cudf::lists::concatenate_list_elements(
+      sliced_input, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+}
+
+TEST_F(ConcatenateListElementsTest, ListsOfListsOfStructsHavingListsNoNull)
+{
+  using structs_col = cudf::test::structs_column_wrapper;
+  using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+
+  // clang-format off
+  // Input:
+  // [ [{1, 11, [1, 1]}, {2, 12, [2]}], [{3, 13, [3, 3]}], [{4, 14, []}, {5, 15, [5, 5, 5]}, {6, 16, [6, 6]}] ]
+  // [ [] ]
+  // [ [{7, 17, [7]}, {8, 18, [8]}], [{9, 19, [9, 9]}, {10, 110, [10, 10, 10, 10]}] ]
+  // clang-format on
+  auto const input = [] {
+    auto child = [] {
+      auto child1 = int32s_col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+      auto child2 = int32s_col{11, 12, 13, 14, 15, 16, 17, 18, 19, 110};
+      auto child3 =
+        lists_col{{1, 1}, {2}, {3, 3}, {}, {5, 5, 5}, {6, 6}, {7}, {8}, {9, 9}, {10, 10, 10, 10}};
+      auto structs = structs_col{{child1, child2, child3}};
+      auto offsets = int32s_col{0, 2, 3, 6, 6, 8, 10};
+      return cudf::make_lists_column(6, offsets.release(), structs.release(), 0, {});
+    }();
+
+    auto offsets = int32s_col{0, 3, 4, 6};
+    return cudf::make_lists_column(3, offsets.release(), std::move(child), 0, {});
+  }();
+
+  // clang-format off
+  // Output:
+  // [{1, 11, [1, 1]}, {2, 12, [2]}, {3, 13, [3, 3]}, {4, 14, []}, {5, 15, [5, 5, 5]}, {6, 16, [6, 6]}]
+  // []
+  // [{7, 17, [7]}, {8, 18, [8]}, {9, 19, [9, 9]}, {10, 110, [10, 10, 10, 10]}]
+  // clang-format on
+  auto const expected = [] {
+    auto child1 = int32s_col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    auto child2 = int32s_col{11, 12, 13, 14, 15, 16, 17, 18, 19, 110};
+    auto child3 =
+      lists_col{{1, 1}, {2}, {3, 3}, {}, {5, 5, 5}, {6, 6}, {7}, {8}, {9, 9}, {10, 10, 10, 10}};
+    auto structs = structs_col{{child1, child2, child3}};
+    auto offsets = int32s_col{0, 6, 6, 10};
+    return cudf::make_lists_column(3, offsets.release(), structs.release(), 0, {});
+  }();
+
+  auto const results = cudf::lists::concatenate_list_elements(*input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+}
+
+TEST_F(ConcatenateListElementsTest, ListsOfListsOfStructsHavingListsWithNulls)
+{
+  using structs_col = cudf::test::structs_column_wrapper;
+  using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+
+  // Input:
+  // [ [{1, 11, [1, 1]}, {2, 12, [2]}], [{3, 13, [3, 3]}] ]
+  // [ [{4, 14, null}, {5, 15, [5, 5, 5]}, {6, 16, [6, 6]}], NULL ]
+  // [ [{7, 17, [7]}, {8, 18, [8]}], [{9, 19, [9, 9]}, {10, 110, [10, 10, 10, 10]}] ]
+  auto const input = [] {
+    auto child = [] {
+      auto child1 = int32s_col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+      auto child2 = int32s_col{11, 12, 13, 14, 15, 16, 17, 18, 19, 110};
+      auto child3 =
+        lists_col{{{1, 1}, {2}, {3, 3}, {}, {5, 5, 5}, {6, 6}, {7}, {8}, {9, 9}, {10, 10, 10, 10}},
+                  null_at(3)};
+      auto structs                 = structs_col{{child1, child2, child3}};
+      auto offsets                 = int32s_col{0, 2, 3, 6, 6, 8, 10};
+      auto const null_it           = null_at(3);  // null list
+      auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 6);
+      return cudf::make_lists_column(
+        6, offsets.release(), structs.release(), null_count, std::move(null_mask));
+    }();
+
+    auto offsets = int32s_col{0, 2, 4, 6};
+    return cudf::make_lists_column(3, offsets.release(), std::move(child), 0, {});
+  }();
+
+  // Concatenate with ignoring null lists.
+  {
+    // Output:
+    // [{1, 11, [1, 1]}, {2, 12, [2]}, {3, 13, [3, 3]}]
+    // [{4, 14, null}, {5, 15, [5, 5, 5]}, {6, 16, [6, 6]}]
+    // [{7, 17, [7]}, {8, 18, [8]}, {9, 19, [9, 9]}, {10, 110, [10, 10, 10, 10]}]
+    auto const expected = [] {
+      auto child1 = int32s_col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+      auto child2 = int32s_col{11, 12, 13, 14, 15, 16, 17, 18, 19, 110};
+      auto child3 =
+        lists_col{{{1, 1}, {2}, {3, 3}, {}, {5, 5, 5}, {6, 6}, {7}, {8}, {9, 9}, {10, 10, 10, 10}},
+                  null_at(3)};
+      auto structs = structs_col{{child1, child2, child3}};
+      auto offsets = int32s_col{0, 3, 6, 10};
+      return cudf::make_lists_column(3, offsets.release(), structs.release(), 0, {});
+    }();
+
+    auto const results = cudf::lists::concatenate_list_elements(*input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+
+  // Concatenate with ignoring null lists and sliced input.
+  {
+    // Output:
+    // [{4, 14, null}, {5, 15, [5, 5, 5]}, {6, 16, [6, 6]}]
+    auto const expected = [] {
+      auto child1  = int32s_col{4, 5, 6};
+      auto child2  = int32s_col{14, 15, 16};
+      auto child3  = lists_col{{{}, {5, 5, 5}, {6, 6}}, null_at(0)};
+      auto structs = structs_col{{child1, child2, child3}};
+      auto offsets = int32s_col{0, 3};
+      return cudf::make_lists_column(1, offsets.release(), structs.release(), 0, {});
+    }();
+
+    auto const sliced_input = cudf::slice(*input, {1, 2})[0];
+    auto const results      = cudf::lists::concatenate_list_elements(sliced_input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+
+  // Concatenate with `concatenate_null_policy::NULLIFY_OUTPUT_ROW`.
+  {
+    // Output:
+    // [{1, 11, [1, 1]}, {2, 12, [2]}, {3, 13, [3, 3]}]
+    // NULL
+    // [{7, 17, [7]}, {8, 18, [8]}, {9, 19, [9, 9]}, {10, 110, [10, 10, 10, 10]}]
+    auto const expected = [] {
+      auto child1 = int32s_col{1, 2, 3, 7, 8, 9, 10};
+      auto child2 = int32s_col{11, 12, 13, 17, 18, 19, 110};
+      auto child3 =
+        lists_col{{{1, 1}, {2}, {3, 3}, {7}, {8}, {9, 9}, {10, 10, 10, 10}}, no_nulls()};
+      auto structs                 = structs_col{{child1, child2, child3}};
+      auto offsets                 = int32s_col{0, 3, 3, 7};
+      auto const null_it           = null_at(1);  // null row
+      auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 3);
+      return cudf::make_lists_column(
+        3, offsets.release(), structs.release(), null_count, std::move(null_mask));
+    }();
+
+    auto const results = cudf::lists::concatenate_list_elements(
+      *input, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+
+  // Concatenate with `concatenate_null_policy::NULLIFY_OUTPUT_ROW` and sliced input.
+  {
+    // Output:
+    // NULL
+    // [{7, 17, [7]}, {8, 18, [8]}, {9, 19, [9, 9]}, {10, 110, [10, 10, 10, 10]}]
+    auto const expected = [] {
+      auto child1                  = int32s_col{7, 8, 9, 10};
+      auto child2                  = int32s_col{17, 18, 19, 110};
+      auto child3                  = lists_col{{{7}, {8}, {9, 9}, {10, 10, 10, 10}}, no_nulls()};
+      auto structs                 = structs_col{{child1, child2, child3}};
+      auto offsets                 = int32s_col{0, 0, 4};
+      auto const null_it           = null_at(0);  // null row
+      auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 2);
+      return cudf::make_lists_column(
+        2, offsets.release(), structs.release(), null_count, std::move(null_mask));
+    }();
+
+    auto const sliced_input = cudf::slice(*input, {1, 3})[0];
+    auto const results      = cudf::lists::concatenate_list_elements(
+      sliced_input, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results, verbosity);
+  }
+}
diff --git a/cpp/tests/lists/combine/concatenate_rows_tests.cpp b/cpp/tests/lists/combine/concatenate_rows_tests.cpp
new file mode 100644
index 0000000..008003a
--- /dev/null
+++ b/cpp/tests/lists/combine/concatenate_rows_tests.cpp
@@ -0,0 +1,961 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/lists/combine.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+using StrListsCol = cudf::test::lists_column_wrapper<cudf::string_view>;
+using IntListsCol = cudf::test::lists_column_wrapper<int32_t>;
+using IntCol      = cudf::test::fixed_width_column_wrapper<int32_t>;
+using TView       = cudf::table_view;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};
+}  // namespace
+
+struct ListConcatenateRowsTest : public cudf::test::BaseFixture {};
+
+TEST_F(ListConcatenateRowsTest, InvalidInput)
+{
+  // Empty input table
+  EXPECT_THROW(cudf::lists::concatenate_rows(TView{}), cudf::logic_error);
+
+  // Input table contains non-list column
+  {
+    auto const col1 = IntCol{}.release();
+    auto const col2 = IntListsCol{}.release();
+    EXPECT_THROW(cudf::lists::concatenate_rows(TView{{col1->view(), col2->view()}}),
+                 cudf::logic_error);
+  }
+
+  // Types mismatch
+  {
+    auto const col1 = IntListsCol{}.release();
+    auto const col2 = StrListsCol{}.release();
+    EXPECT_THROW(cudf::lists::concatenate_rows(TView{{col1->view(), col2->view()}}),
+                 cudf::logic_error);
+  }
+}
+
+template <typename T>
+struct ListConcatenateRowsTypedTest : public cudf::test::BaseFixture {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(ListConcatenateRowsTypedTest, TypesForTest);
+
+TYPED_TEST(ListConcatenateRowsTypedTest, ConcatenateEmptyColumns)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col     = ListsCol{}.release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col->view(), col->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *results, verbosity);
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, ConcatenateOneColumnNotNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col     = ListsCol{{1, 2}, {3, 4}, {5, 6}}.release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *results, verbosity);
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, ConcatenateOneColumnWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col = ListsCol{{ListsCol{{1, 2, null}, null_at(2)},
+                             ListsCol{} /*NULL*/,
+                             ListsCol{{null, 3, 4, 4, 4, 4}, null_at(0)},
+                             ListsCol{5, 6}},
+                            null_at(1)}
+                     .release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *results, verbosity);
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, SimpleInputNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1        = ListsCol{{1, 2}, {3, 4}, {5, 6}}.release();
+  auto const empty_lists = ListsCol{ListsCol{}, ListsCol{}, ListsCol{}}.release();
+  auto const col2        = ListsCol{{7, 8}, {9, 10}, {11, 12}}.release();
+  auto const expected    = ListsCol{{1, 2, 7, 8}, {3, 4, 9, 10}, {5, 6, 11, 12}}.release();
+  auto const results =
+    cudf::lists::concatenate_rows(TView{{col1->view(), empty_lists->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, SimpleInputWithNullableChild)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1        = ListsCol{{1, 2}, ListsCol{{null}, null_at(0)}, {5, 6}}.release();
+  auto const empty_lists = ListsCol{{ListsCol{}, ListsCol{}, ListsCol{}}, null_at(2)}.release();
+  auto const col2        = ListsCol{{7, 8}, {9, 10}, {11, 12}}.release();
+  auto const expected =
+    ListsCol{{1, 2, 7, 8}, ListsCol{{null, 9, 10}, null_at(0)}, {5, 6, 11, 12}}.release();
+  auto const results =
+    cudf::lists::concatenate_rows(TView{{col1->view(), empty_lists->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListConcatenateRowsTest, SimpleInputStringsColumnsNoNull)
+{
+  auto const col1 = StrListsCol{
+    StrListsCol{"Tomato", "Apple"},
+    StrListsCol{"Banana", "Kiwi", "Cherry"},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const col2 =
+    StrListsCol{StrListsCol{"Orange"}, StrListsCol{"Lemon", "Peach"}, StrListsCol{}}.release();
+  auto const expected = StrListsCol{
+    StrListsCol{"Tomato", "Apple", "Orange"},
+    StrListsCol{"Banana", "Kiwi", "Cherry", "Lemon", "Peach"},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListConcatenateRowsTest, SimpleInputStringsColumnsWithNullableChild)
+{
+  auto const col1 = StrListsCol{
+    StrListsCol{"Tomato", "Apple"},
+    StrListsCol{"Banana", "Kiwi", "Cherry"},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const col2 = StrListsCol{
+    StrListsCol{"Orange"},
+    StrListsCol{{"Lemon", "Peach"}, null_at(1)},
+    StrListsCol{}}.release();
+  auto const expected = StrListsCol{
+    StrListsCol{"Tomato", "Apple", "Orange"},
+    StrListsCol{{"Banana", "Kiwi", "Cherry", "Lemon", "Peach"}, null_at(4)},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, SimpleInputWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1 = ListsCol{{ListsCol{{1, null, 3, 4}, null_at(1)},
+                              ListsCol{{null, 2, 3, 4}, null_at(0)},
+                              ListsCol{{null, 2, 3, 4}, null_at(0)},
+                              ListsCol{} /*NULL*/,
+                              ListsCol{{1, 2, null, 4}, null_at(2)},
+                              ListsCol{{1, 2, 3, null}, null_at(3)},
+                              ListsCol{} /*NULL*/},
+                             nulls_at({3, 6})}
+                      .release();
+  auto const col2 = ListsCol{{ListsCol{{10, 11, 12, null}, null_at(3)},
+                              ListsCol{{13, 14, 15, 16, 17, null}, null_at(5)},
+                              ListsCol{} /*NULL*/,
+                              ListsCol{{null, 18}, null_at(0)},
+                              ListsCol{{19, 20, null}, null_at(2)},
+                              ListsCol{{null}, null_at(0)},
+                              ListsCol{} /*NULL*/},
+                             nulls_at({2, 6})}
+                      .release();
+  auto const col3 = ListsCol{{ListsCol{} /*NULL*/,
+                              ListsCol{{20, null}, null_at(1)},
+                              ListsCol{{null, 21, null, null}, nulls_at({0, 2, 3})},
+                              ListsCol{},
+                              ListsCol{22, 23, 24, 25},
+                              ListsCol{{null, null, null, null, null}, all_nulls()},
+                              ListsCol{} /*NULL*/},
+                             nulls_at({0, 6})}
+                      .release();
+
+  // Ignore null list elements
+  {
+    auto const results =
+      cudf::lists::concatenate_rows(TView{{col1->view(), col2->view(), col3->view()}});
+    auto const expected =
+      ListsCol{{ListsCol{{1, null, 3, 4, 10, 11, 12, null}, nulls_at({1, 7})},
+                ListsCol{{null, 2, 3, 4, 13, 14, 15, 16, 17, null, 20, null}, nulls_at({0, 9, 11})},
+                ListsCol{{null, 2, 3, 4, null, 21, null, null}, nulls_at({0, 4, 6, 7})},
+                ListsCol{{null, 18}, null_at(0)},
+                ListsCol{{1, 2, null, 4, 19, 20, null, 22, 23, 24, 25}, nulls_at({2, 6})},
+                ListsCol{{1, 2, 3, null, null, null, null, null, null, null},
+                         nulls_at({3, 4, 5, 6, 7, 8, 9})},
+                ListsCol{} /*NULL*/},
+               null_at(6)}
+        .release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+
+  // Null list rows result in null list rows
+  {
+    auto const results =
+      cudf::lists::concatenate_rows(TView{{col1->view(), col2->view(), col3->view()}},
+                                    cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected =
+      ListsCol{{ListsCol{} /*NULL*/,
+                ListsCol{{null, 2, 3, 4, 13, 14, 15, 16, 17, null, 20, null}, nulls_at({0, 9, 11})},
+                ListsCol{} /*NULL*/,
+                ListsCol{} /*NULL*/,
+                ListsCol{{1, 2, null, 4, 19, 20, null, 22, 23, 24, 25}, nulls_at({2, 6})},
+                ListsCol{{1, 2, 3, null, null, null, null, null, null, null},
+                         nulls_at({3, 4, 5, 6, 7, 8, 9})},
+                ListsCol{} /*NULL*/},
+               nulls_at({0, 2, 3, 6})}
+        .release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+}
+
+TEST_F(ListConcatenateRowsTest, SimpleInputStringsColumnsWithNulls)
+{
+  auto const col1 = StrListsCol{
+    StrListsCol{{"Tomato", "Bear" /*NULL*/, "Apple"}, null_at(1)},
+    StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/}, nulls_at({1, 4})},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const col2 =
+    StrListsCol{
+      {StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/},
+                   nulls_at({1, 2, 3})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{{"Deer" /*NULL*/, "Snake" /*NULL*/, "Horse" /*NULL*/}, all_nulls()}}, /*NULL*/
+      null_at(2)}
+      .release();
+
+  // Ignore null list elements
+  {
+    auto const results  = cudf::lists::concatenate_rows(TView{{col1->view(), col2->view()}});
+    auto const expected = StrListsCol{
+      StrListsCol{{"Tomato", "" /*NULL*/, "Apple", "Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/},
+                  nulls_at({1, 4, 5, 6})},
+      StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/, "Lemon", "Peach"},
+                  nulls_at({1, 4})},
+      StrListsCol{
+        "Coconut"}}.release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+
+  // Null list rows result in null list rows
+  {
+    auto const results =
+      cudf::lists::concatenate_rows(TView{{col1->view(), col2->view()}},
+                                    cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected =
+      StrListsCol{
+        {StrListsCol{
+           {"Tomato", "" /*NULL*/, "Apple", "Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/},
+           nulls_at({1, 4, 5, 6})},
+         StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/, "Lemon", "Peach"},
+                     nulls_at({1, 4})},
+         StrListsCol{""} /*NULL*/},
+        null_at(2)}
+        .release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+}
+
+TEST_F(ListConcatenateRowsTest, SimpleInputStringsColumnsWithEmptyLists)
+{
+  auto const col1 =
+    StrListsCol{StrListsCol{{"" /*NULL*/}, null_at(0)}, StrListsCol{"One"}}.release();
+  auto const col2 = StrListsCol{
+    StrListsCol{{"Tomato", "" /*NULL*/, "Apple"}, null_at(1)},
+    StrListsCol{
+      "Two"}}.release();
+  auto const col3 =
+    StrListsCol{{StrListsCol{"Lemon", "Peach"}, StrListsCol{"Three"} /*NULL*/}, null_at(1)}
+      .release();
+
+  // Ignore null list elements
+  {
+    auto const results =
+      cudf::lists::concatenate_rows(TView{{col1->view(), col2->view(), col3->view()}});
+    auto const expected = StrListsCol{
+      StrListsCol{{"" /*NULL*/, "Tomato", "" /*NULL*/, "Apple", "Lemon", "Peach"},
+                  nulls_at({0, 2})},
+      StrListsCol{"One",
+                  "Two"}}.release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+
+  // Null list rows result in null list rows
+  {
+    auto const results =
+      cudf::lists::concatenate_rows(TView{{col1->view(), col2->view(), col3->view()}},
+                                    cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected =
+      StrListsCol{{StrListsCol{{"" /*NULL*/, "Tomato", "" /*NULL*/, "Apple", "Lemon", "Peach"},
+                               nulls_at({0, 2})},
+                   StrListsCol{""} /*NULL*/},
+                  null_at(1)}
+        .release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, SlicedColumnsInputNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col_original = ListsCol{{1, 2, 3}, {2, 3}, {3, 4, 5, 6}, {5, 6}, {}, {7}}.release();
+  auto const col1         = cudf::slice(col_original->view(), {0, 3})[0];
+  auto const col2         = cudf::slice(col_original->view(), {1, 4})[0];
+  auto const col3         = cudf::slice(col_original->view(), {2, 5})[0];
+  auto const col4         = cudf::slice(col_original->view(), {3, 6})[0];
+  auto const expected     = ListsCol{
+        {1, 2, 3, 2, 3, 3, 4, 5, 6, 5, 6},
+        {2, 3, 3, 4, 5, 6, 5, 6},
+        {3, 4, 5, 6, 5, 6, 7}}.release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col1, col2, col3, col4}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListConcatenateRowsTypedTest, SlicedColumnsInputWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col_original = ListsCol{{ListsCol{{null, 2, 3}, null_at(0)},
+                                      ListsCol{2, 3}, /*NULL*/
+                                      ListsCol{{3, null, 5, 6}, null_at(1)},
+                                      ListsCol{5, 6}, /*NULL*/
+                                      ListsCol{},     /*NULL*/
+                                      ListsCol{7},
+                                      ListsCol{8, 9, 10}},
+                                     nulls_at({1, 3, 4})}
+                              .release();
+  auto const col1     = cudf::slice(col_original->view(), {0, 3})[0];
+  auto const col2     = cudf::slice(col_original->view(), {1, 4})[0];
+  auto const col3     = cudf::slice(col_original->view(), {2, 5})[0];
+  auto const col4     = cudf::slice(col_original->view(), {3, 6})[0];
+  auto const col5     = cudf::slice(col_original->view(), {4, 7})[0];
+  auto const expected = ListsCol{
+    ListsCol{{null, 2, 3, 3, null, 5, 6}, nulls_at({0, 4})},
+    ListsCol{{3, null, 5, 6, 7}, null_at(1)},
+    ListsCol{{3, null, 5, 6, 7, 8, 9, 10},
+             null_at(1)}}.release();
+  auto const results = cudf::lists::concatenate_rows(TView{{col1, col2, col3, col4, col5}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListConcatenateRowsTest, SlicedStringsColumnsInputWithNulls)
+{
+  auto const col =
+    StrListsCol{
+      {StrListsCol{{"Tomato", "Bear" /*NULL*/, "Apple"}, null_at(1)},
+       StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/},
+                   nulls_at({1, 4})},
+       StrListsCol{"Coconut"},
+       StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/},
+                   nulls_at({1, 2, 3})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{{"Deer" /*NULL*/, "Snake" /*NULL*/, "Horse" /*NULL*/}, all_nulls()}}, /*NULL*/
+      null_at(5)}
+      .release();
+  auto const col1 = cudf::slice(col->view(), {0, 3})[0];
+  auto const col2 = cudf::slice(col->view(), {1, 4})[0];
+  auto const col3 = cudf::slice(col->view(), {2, 5})[0];
+  auto const col4 = cudf::slice(col->view(), {3, 6})[0];
+
+  {
+    auto const results  = cudf::lists::concatenate_rows(TView{{col1, col2, col3, col4}});
+    auto const expected = StrListsCol{StrListsCol{{"Tomato",
+                                                   "" /*NULL*/,
+                                                   "Apple",
+                                                   "Banana",
+                                                   "" /*NULL*/,
+                                                   "Kiwi",
+                                                   "Cherry",
+                                                   "" /*NULL*/,
+                                                   "Coconut",
+                                                   "Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/},
+                                                  nulls_at({1, 4, 7, 10, 11, 12})},
+                                      StrListsCol{{"Banana",
+                                                   "" /*NULL*/,
+                                                   "Kiwi",
+                                                   "Cherry",
+                                                   "" /*NULL*/,
+                                                   "Coconut",
+                                                   "Orange",
+                                                   "" /*NULL*/,
+                                                   "" /*NULL*/,
+                                                   "", /*NULL*/
+                                                   "Lemon",
+                                                   "Peach"},
+                                                  nulls_at({1, 4, 7, 8, 9})},
+                                      StrListsCol{{
+                                                    "Coconut",
+                                                    "Orange",
+                                                    "" /*NULL*/,
+                                                    "" /*NULL*/,
+                                                    "", /*NULL*/
+                                                    "Lemon",
+                                                    "Peach",
+                                                  },
+                                                  nulls_at({2, 3, 4})}}
+                            .release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+
+  {
+    auto const results = cudf::lists::concatenate_rows(
+      TView{{col1, col2, col3, col4}}, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+    auto const expected = StrListsCol{{StrListsCol{{"Tomato",
+                                                    "" /*NULL*/,
+                                                    "Apple",
+                                                    "Banana",
+                                                    "" /*NULL*/,
+                                                    "Kiwi",
+                                                    "Cherry",
+                                                    "" /*NULL*/,
+                                                    "Coconut",
+                                                    "Orange",
+                                                    "" /*NULL*/,
+                                                    "" /*NULL*/,
+                                                    "" /*NULL*/},
+                                                   nulls_at({1, 4, 7, 10, 11, 12})},
+                                       StrListsCol{{"Banana",
+                                                    "" /*NULL*/,
+                                                    "Kiwi",
+                                                    "Cherry",
+                                                    "" /*NULL*/,
+                                                    "Coconut",
+                                                    "Orange",
+                                                    "" /*NULL*/,
+                                                    "" /*NULL*/,
+                                                    "", /*NULL*/
+                                                    "Lemon",
+                                                    "Peach"},
+                                                   nulls_at({1, 4, 7, 8, 9})},
+                                       StrListsCol{} /*NULL*/},
+                                      null_at(2)}
+                            .release();
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+  }
+}
+
+TEST_F(ListConcatenateRowsTest, StringsColumnsWithEmptyListTest)
+{
+  auto const col1 = StrListsCol{{"1", "2", "3", "4"}}.release();
+  auto const col2 = StrListsCol{{"a", "b", "c"}}.release();
+  auto const col3 = StrListsCol{StrListsCol{}}.release();
+  auto const col4 = StrListsCol{{"x", "y", "" /*NULL*/, "z"}, null_at(2)}.release();
+  auto const col5 = StrListsCol{{StrListsCol{}}, null_at(0)}.release();
+  auto const expected =
+    StrListsCol{{"1", "2", "3", "4", "a", "b", "c", "x", "y", "" /*NULL*/, "z"}, null_at(9)}
+      .release();
+  auto const results = cudf::lists::concatenate_rows(
+    TView{{col1->view(), col2->view(), col3->view(), col4->view(), col5->view()}});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+struct ListConcatenateRowsNestedTypesTest : public cudf::test::BaseFixture {};
+
+TEST_F(ListConcatenateRowsNestedTypesTest, Identity)
+{
+  // list<list<string>>
+
+  // col 0
+  cudf::test::lists_column_wrapper<cudf::string_view> l0{
+    {{{{"whee", "yay", "bananas"}, nulls_at({1})}, {}},
+     {{}},
+     {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"}}, nulls_at({0, 2})},
+     {{"f", "tesla"}},
+     {{"phone"}, {"hack", "table", "car"}}},
+    nulls_at({3, 4})};
+
+  // perform the concatenate
+  cudf::table_view t({l0});
+  auto result = cudf::lists::concatenate_rows(t);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, l0);
+}
+
+TEST_F(ListConcatenateRowsNestedTypesTest, List)
+{
+  // list<list<string>>
+
+  // col 0
+  cudf::test::lists_column_wrapper<cudf::string_view> l0{
+    {{"whee", "yay", "bananas"}, {}},
+    {{}},
+    {{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"}},
+    {{"f", "tesla"}},
+    {{"phone"}, {"hack", "table", "car"}}};
+
+  // col1
+  cudf::test::lists_column_wrapper<cudf::string_view> l1{
+    {{}},
+    {{"arg"}, {"mno", "ampere"}, {"gpu"}, {"def"}},
+    {{"", "hhh"}},
+    {{"warp", "donuts", "parking"}, {"", "apply", "twelve", "mouse", "bbb"}, {"bbb", "pom"}, {}},
+    {{}}};
+
+  // perform the concatenate
+  cudf::table_view t({l0, l1});
+  auto result = cudf::lists::concatenate_rows(t);
+
+  // expected
+  cudf::test::lists_column_wrapper<cudf::string_view> expected{
+    {{"whee", "yay", "bananas"}, {}, {}},
+    {{}, {"arg"}, {"mno", "ampere"}, {"gpu"}, {"def"}},
+    {{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"}, {"", "hhh"}},
+    {{"f", "tesla"},
+     {"warp", "donuts", "parking"},
+     {"", "apply", "twelve", "mouse", "bbb"},
+     {"bbb", "pom"},
+     {}},
+    {{"phone"}, {"hack", "table", "car"}, {}}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+}
+
+TEST_F(ListConcatenateRowsNestedTypesTest, ListWithNulls)
+{
+  // list<list<string>>
+
+  // clang-format off
+
+  // col 0
+  cudf::test::lists_column_wrapper<cudf::string_view>
+    l0{ {
+          {{{"whee", "yay", "bananas"}, nulls_at({1})}, {}},
+          {{}},
+          {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"}},       nulls_at({0, 2})},
+          {{"f", "tesla"}},
+          {{"phone"}, {"hack", "table", "car"}}
+        }, nulls_at({3, 4}) };
+
+  // col1
+  cudf::test::lists_column_wrapper<cudf::string_view>
+    l1{ {
+          {{}},
+          {{"arg"}, {"mno", "ampere"}, {"gpu"}, {"def"}},
+          {{{{"", "hhh"}, nulls_at({0})}, {"www"}},                               nulls_at({1})},
+          {{"warp", "donuts", "parking"}, { "", "apply", "twelve", "mouse", "bbb"}, {"bbb", "pom"}, {}},
+          {{}}
+        }, nulls_at({4}) };
+
+  // col2
+  cudf::test::lists_column_wrapper<cudf::string_view>
+    l2{ {
+          {{"monitor", "sugar"}},
+          {{"spurs", "garlic"}, {"onion", "shallot", "carrot"}},
+          {{"cars", "trucks", "planes"}, {"abc"}, {"mno", "pqr"}},
+          {{}, {"ram", "cpu", "disk"}, {}},
+          {{"round"}, {"square"}}
+        }, nulls_at({0, 4}) };
+
+  // concatenate_policy::IGNORE_NULLS
+  {
+    // perform the concatenate
+    cudf::table_view t({l0, l1, l2});
+    auto result = cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::IGNORE);
+
+    // expected
+    cudf::test::lists_column_wrapper<cudf::string_view>
+      expected{ {
+                  {{{"whee", "yay", "bananas"}, nulls_at({1})}, {}, {}},
+                  {{}, {"arg"}, {"mno", "ampere"}, {"gpu"}, {"def"}, {"spurs", "garlic"}, {"onion", "shallot", "carrot"}},
+                  {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"},
+                    {{"", "hhh"}, nulls_at({0})}, {"www"}, {"cars", "trucks", "planes"}, {"abc"}, {"mno", "pqr"}},
+                      nulls_at({0, 2, 4}) },
+                  {{"warp", "donuts", "parking"}, { "", "apply", "twelve", "mouse", "bbb"}, {"bbb", "pom"}, {}, {}, {"ram", "cpu", "disk"}, {}},
+                  {{}}
+                }, nulls_at({4}) };
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  // concatenate_policy::NULLIFY_OUTPUT_ROW
+  {
+    // perform the concatenate
+    cudf::table_view t({l0, l1, l2});
+    auto result = cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+
+    // expected
+    cudf::test::lists_column_wrapper<cudf::string_view>
+      expected{ {
+                  {{}},
+                  {{}, {"arg"}, {"mno", "ampere"}, {"gpu"}, {"def"}, {"spurs", "garlic"}, {"onion", "shallot", "carrot"}},
+                  {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"},
+                    {{"", "hhh"}, nulls_at({0})}, {"www"}, {"cars", "trucks", "planes"}, {"abc"}, {"mno", "pqr"}},
+                      nulls_at({0, 2, 4}) },
+                  {{}},
+                  {{}}
+                }, nulls_at({0, 3, 4}) };
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  // clang-format on
+}
+
+TEST_F(ListConcatenateRowsNestedTypesTest, ListWithNullsSliced)
+{
+  // list<list<string>>
+
+  // clang-format off
+
+  // col 0
+  cudf::test::lists_column_wrapper<cudf::string_view>
+    unsliced_l0{ {
+          {{{"whee", "yay", "bananas"}, nulls_at({1})}, {}},
+          {{}},
+          {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"}},       nulls_at({0, 2})},
+          {{"f", "tesla"}},
+          {{"phone"}, {"hack", "table", "car"}}
+        }, nulls_at({3, 4}) };
+  auto l0 = cudf::split(unsliced_l0, {2})[1];
+
+  // col1
+  cudf::test::lists_column_wrapper<cudf::string_view>
+    unsliced_l1{ {
+          {{}},
+          {{"arg"}, {"mno", "ampere"}, {"gpu"}, {"def"}},
+          {{{{"", "hhh"}, nulls_at({0})}, {"www"}},                               nulls_at({1})},
+          {{"warp", "donuts", "parking"}, { "", "apply", "twelve", "mouse", "bbb"}, {"bbb", "pom"}, {}},
+          {{}}
+        }, nulls_at({4}) };
+  auto l1 = cudf::split(unsliced_l1, {2})[1];
+
+  // concatenate_policy::IGNORE_NULLS
+  {
+    // perform the concatenate
+    cudf::table_view t({l0, l1});
+    auto result = cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::IGNORE);
+
+    // expected
+    cudf::test::lists_column_wrapper<cudf::string_view>
+      expected{ { {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"},
+                    {{"", "hhh"}, nulls_at({0})}, {"www"}},                           nulls_at({0, 2, 4}) },
+                  {{"warp", "donuts", "parking"}, { "", "apply", "twelve", "mouse", "bbb"}, {"bbb", "pom"}, {}},
+                  {{}}
+                }, nulls_at({2}) };
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  // concatenate_policy::NULLIFY_OUTPUT_ROW
+  {
+    // perform the concatenate
+    cudf::table_view t({l0, l1});
+    auto result = cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+
+    // expected
+    cudf::test::lists_column_wrapper<cudf::string_view>
+      expected{ { {{{"abc"}, {"def", "g", "xyw", "ijk"}, {"x", "y", "", "column"},
+                    {{"", "hhh"}, nulls_at({0})}, {"www"}},                           nulls_at({0, 2, 4}) },
+                  {{}},
+                  {{}}
+                }, nulls_at({1, 2}) };
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+  }
+
+  // clang-format on
+}
+
+TEST_F(ListConcatenateRowsNestedTypesTest, Struct)
+{
+  // list<struct<int, string>>
+
+  // col 0
+  cudf::test::fixed_width_column_wrapper<int> s0_0{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::strings_column_wrapper s0_1{
+    "whee", "yay", "bananas", "abc", "def", "g", "xyw", "ijk"};
+  std::vector<std::unique_ptr<cudf::column>> s0_children;
+  s0_children.push_back(s0_0.release());
+  s0_children.push_back(s0_1.release());
+  cudf::test::structs_column_wrapper s0(std::move(s0_children));
+  cudf::test::fixed_width_column_wrapper<int> l0_offsets{0, 2, 2, 5, 6, 8};
+  auto const l0_size = static_cast<cudf::column_view>(l0_offsets).size() - 1;
+  auto l0            = cudf::make_lists_column(l0_size, l0_offsets.release(), s0.release(), 0, {});
+
+  // col1
+  cudf::test::fixed_width_column_wrapper<int> s1_0{
+    10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24};
+  cudf::test::strings_column_wrapper s1_1{"arg",
+                                          "mno",
+                                          "ampere",
+                                          "gpu",
+                                          "",
+                                          "hhh",
+                                          "warp",
+                                          "donuts",
+                                          "parking",
+                                          "",
+                                          "apply",
+                                          "twelve",
+                                          "mouse",
+                                          "bbb",
+                                          "pom"};
+  std::vector<std::unique_ptr<cudf::column>> s1_children;
+  s1_children.push_back(s1_0.release());
+  s1_children.push_back(s1_1.release());
+  cudf::test::structs_column_wrapper s1(std::move(s1_children));
+  cudf::test::fixed_width_column_wrapper<int> l1_offsets{0, 0, 4, 7, 15, 15};
+  auto const l1_size = static_cast<cudf::column_view>(l1_offsets).size() - 1;
+  auto l1            = cudf::make_lists_column(l1_size, l1_offsets.release(), s1.release(), 0, {});
+
+  // perform the concatenate
+  cudf::table_view t({*l0, *l1});
+  auto result = cudf::lists::concatenate_rows(t);
+
+  // expected
+  cudf::test::fixed_width_column_wrapper<int> se_0{0, 1,  10, 11, 12, 13, 2,  3,  4,  14, 15, 16,
+                                                   5, 17, 18, 19, 20, 21, 22, 23, 24, 6,  7};
+  cudf::test::strings_column_wrapper se_1{"whee",    "yay",    "arg",     "mno", "ampere", "gpu",
+                                          "bananas", "abc",    "def",     "",    "hhh",    "warp",
+                                          "g",       "donuts", "parking", "",    "apply",  "twelve",
+                                          "mouse",   "bbb",    "pom",     "xyw", "ijk"};
+  std::vector<std::unique_ptr<cudf::column>> se_children;
+  se_children.push_back(se_0.release());
+  se_children.push_back(se_1.release());
+  cudf::test::structs_column_wrapper se(std::move(se_children));
+  cudf::test::fixed_width_column_wrapper<int> le_offsets{0, 2, 6, 12, 21, 23};
+  auto const le_size = static_cast<cudf::column_view>(le_offsets).size() - 1;
+  auto expected      = cudf::make_lists_column(le_size, le_offsets.release(), se.release(), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
+
+TEST_F(ListConcatenateRowsNestedTypesTest, StructWithNulls)
+{
+  // list<struct<int, string>>
+
+  // col 0
+  cudf::test::fixed_width_column_wrapper<int> s0_0{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::strings_column_wrapper s0_1{
+    {"whee", "yay", "bananas", "abc", "def", "g", "xyw", "ijk"}, nulls_at({1, 3, 4})};
+  std::vector<std::unique_ptr<cudf::column>> s0_children;
+  s0_children.push_back(s0_0.release());
+  s0_children.push_back(s0_1.release());
+  cudf::test::structs_column_wrapper s0(std::move(s0_children));
+  cudf::test::fixed_width_column_wrapper<int> l0_offsets{0, 2, 2, 5, 6, 8};
+  auto const l0_size = static_cast<cudf::column_view>(l0_offsets).size() - 1;
+  std::vector<bool> l0_validity{false, true, true, false, true};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(l0_validity.begin(), l0_validity.end());
+  auto l0 = cudf::make_lists_column(
+    l0_size, l0_offsets.release(), s0.release(), null_count, std::move(null_mask));
+
+  // col1
+  cudf::test::fixed_width_column_wrapper<int> s1_0{
+    {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}, nulls_at({14})};
+  cudf::test::strings_column_wrapper s1_1{"arg",
+                                          "mno",
+                                          "ampere",
+                                          "gpu",
+                                          "",
+                                          "hhh",
+                                          "warp",
+                                          "donuts",
+                                          "parking",
+                                          "",
+                                          "apply",
+                                          "twelve",
+                                          "mouse",
+                                          "bbb",
+                                          "pom"};
+  std::vector<std::unique_ptr<cudf::column>> s1_children;
+  s1_children.push_back(s1_0.release());
+  s1_children.push_back(s1_1.release());
+  cudf::test::structs_column_wrapper s1(std::move(s1_children));
+  cudf::test::fixed_width_column_wrapper<int> l1_offsets{0, 0, 4, 7, 15, 15};
+  auto const l1_size = static_cast<cudf::column_view>(l1_offsets).size() - 1;
+  std::vector<bool> l1_validity{false, true, true, true, true};
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(l1_validity.begin(), l1_validity.end());
+  auto l1 = cudf::make_lists_column(
+    l1_size, l1_offsets.release(), s1.release(), null_count, std::move(null_mask));
+
+  // concatenate_policy::IGNORE_NULLS
+  {
+    // perform the concatenate
+    cudf::table_view t({*l0, *l1});
+    auto result = cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::IGNORE);
+
+    // expected
+    cudf::test::fixed_width_column_wrapper<int> se_0{
+      {10, 11, 12, 13, 2, 3, 4, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 6, 7}, nulls_at({17})};
+    cudf::test::strings_column_wrapper se_1{
+      {"arg",    "mno",     "ampere", "gpu",   "bananas", "",      "",    "",    "hhh", "warp",
+       "donuts", "parking", "",       "apply", "twelve",  "mouse", "bbb", "pom", "xyw", "ijk"},
+      nulls_at({5, 6})};
+    std::vector<std::unique_ptr<cudf::column>> se_children;
+    se_children.push_back(se_0.release());
+    se_children.push_back(se_1.release());
+    cudf::test::structs_column_wrapper se(std::move(se_children));
+    cudf::test::fixed_width_column_wrapper<int> le_offsets{0, 0, 4, 10, 18, 20};
+    auto const le_size = static_cast<cudf::column_view>(le_offsets).size() - 1;
+    std::vector<bool> le_validity{false, true, true, true, true};
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(le_validity.begin(), le_validity.end());
+    auto expected = cudf::make_lists_column(
+      le_size, le_offsets.release(), se.release(), null_count, std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+  }
+
+  // concatenate_policy::NULLIFY_OUTPUT_ROW
+  {
+    // perform the concatenate
+    cudf::table_view t({*l0, *l1});
+    auto result =
+      cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+
+    // expected
+    cudf::test::fixed_width_column_wrapper<int> se_0{{10, 11, 12, 13, 2, 3, 4, 14, 15, 16, 6, 7},
+                                                     nulls_at({})};
+    cudf::test::strings_column_wrapper se_1{
+      {"arg", "mno", "ampere", "gpu", "bananas", "", "", "", "hhh", "warp", "xyw", "ijk"},
+      nulls_at({5, 6})};
+    std::vector<std::unique_ptr<cudf::column>> se_children;
+    se_children.push_back(se_0.release());
+    se_children.push_back(se_1.release());
+    cudf::test::structs_column_wrapper se(std::move(se_children));
+    cudf::test::fixed_width_column_wrapper<int> le_offsets{0, 0, 4, 10, 10, 12};
+    auto const le_size = static_cast<cudf::column_view>(le_offsets).size() - 1;
+    std::vector<bool> le_validity{false, true, true, false, true};
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(le_validity.begin(), le_validity.end());
+    auto expected = cudf::make_lists_column(
+      le_size, le_offsets.release(), se.release(), null_count, std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+  }
+}
+
+TEST_F(ListConcatenateRowsNestedTypesTest, StructWithNullsSliced)
+{
+  // list<struct<int, string>>
+
+  // col 0
+  cudf::test::fixed_width_column_wrapper<int> s0_0{0, 1, 2, 3, 4, 5, 6, 7};
+  cudf::test::strings_column_wrapper s0_1{
+    {"whee", "yay", "bananas", "abc", "def", "g", "xyw", "ijk"}, nulls_at({1, 3, 4})};
+  std::vector<std::unique_ptr<cudf::column>> s0_children;
+  s0_children.push_back(s0_0.release());
+  s0_children.push_back(s0_1.release());
+  cudf::test::structs_column_wrapper s0(std::move(s0_children));
+  cudf::test::fixed_width_column_wrapper<int> l0_offsets{0, 2, 2, 5, 6, 8};
+  auto const l0_size = static_cast<cudf::column_view>(l0_offsets).size() - 1;
+  std::vector<bool> l0_validity{false, true, false, false, true};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(l0_validity.begin(), l0_validity.end());
+  auto l0_unsliced = cudf::make_lists_column(
+    l0_size, l0_offsets.release(), s0.release(), null_count, std::move(null_mask));
+  auto l0 = cudf::split(*l0_unsliced, {2})[1];
+
+  // col1
+  cudf::test::fixed_width_column_wrapper<int> s1_0{
+    {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}, nulls_at({14})};
+  cudf::test::strings_column_wrapper s1_1{"arg",
+                                          "mno",
+                                          "ampere",
+                                          "gpu",
+                                          "",
+                                          "hhh",
+                                          "warp",
+                                          "donuts",
+                                          "parking",
+                                          "",
+                                          "apply",
+                                          "twelve",
+                                          "mouse",
+                                          "bbb",
+                                          "pom"};
+  std::vector<std::unique_ptr<cudf::column>> s1_children;
+  s1_children.push_back(s1_0.release());
+  s1_children.push_back(s1_1.release());
+  cudf::test::structs_column_wrapper s1(std::move(s1_children));
+  cudf::test::fixed_width_column_wrapper<int> l1_offsets{0, 0, 4, 7, 15, 15};
+  auto const l1_size = static_cast<cudf::column_view>(l1_offsets).size() - 1;
+  std::vector<bool> l1_validity{false, true, false, true, true};
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(l1_validity.begin(), l1_validity.end());
+  auto l1_unsliced = cudf::make_lists_column(
+    l1_size, l1_offsets.release(), s1.release(), null_count, std::move(null_mask));
+  auto l1 = cudf::split(*l1_unsliced, {2})[1];
+
+  // concatenate_policy::IGNORE_NULLS
+  {
+    // perform the concatenate
+    cudf::table_view t({l0, l1});
+    auto result = cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::IGNORE);
+
+    // expected
+    cudf::test::fixed_width_column_wrapper<int> se_0{{17, 18, 19, 20, 21, 22, 23, 24, 6, 7},
+                                                     nulls_at({7})};
+    cudf::test::strings_column_wrapper se_1{
+      {"donuts", "parking", "", "apply", "twelve", "mouse", "bbb", "pom", "xyw", "ijk"}};
+    std::vector<std::unique_ptr<cudf::column>> se_children;
+    se_children.push_back(se_0.release());
+    se_children.push_back(se_1.release());
+    cudf::test::structs_column_wrapper se(std::move(se_children));
+    cudf::test::fixed_width_column_wrapper<int> le_offsets{0, 0, 8, 10};
+    auto const le_size = static_cast<cudf::column_view>(le_offsets).size() - 1;
+    std::vector<bool> le_validity{false, true, true};
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(le_validity.begin(), le_validity.end());
+    auto expected = cudf::make_lists_column(
+      le_size, le_offsets.release(), se.release(), null_count, std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+  }
+
+  // concatenate_policy::NULLIFY_OUTPUT_ROW
+  {
+    // perform the concatenate
+    cudf::table_view t({l0, l1});
+    auto result =
+      cudf::lists::concatenate_rows(t, cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW);
+
+    // expected
+    cudf::test::fixed_width_column_wrapper<int> se_0{{6, 7}, nulls_at({})};
+    cudf::test::strings_column_wrapper se_1{"xyw", "ijk"};
+    std::vector<std::unique_ptr<cudf::column>> se_children;
+    se_children.push_back(se_0.release());
+    se_children.push_back(se_1.release());
+    cudf::test::structs_column_wrapper se(std::move(se_children));
+    cudf::test::fixed_width_column_wrapper<int> le_offsets{0, 0, 0, 2};
+    auto const le_size = static_cast<cudf::column_view>(le_offsets).size() - 1;
+    std::vector<bool> le_validity{false, false, true};
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(le_validity.begin(), le_validity.end());
+    auto expected = cudf::make_lists_column(
+      le_size, le_offsets.release(), se.release(), null_count, std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+  }
+}
diff --git a/cpp/tests/lists/contains_tests.cpp b/cpp/tests/lists/contains_tests.cpp
new file mode 100644
index 0000000..62e6653
--- /dev/null
+++ b/cpp/tests/lists/contains_tests.cpp
@@ -0,0 +1,1841 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/lists/contains.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+namespace {
+template <typename T, std::enable_if_t<cudf::is_numeric<T>(), void>* = nullptr>
+auto create_scalar_search_key(T const& value)
+{
+  auto search_key = cudf::make_numeric_scalar(cudf::data_type{cudf::type_to_id<T>()});
+  search_key->set_valid_async(true);
+  static_cast<cudf::scalar_type_t<T>*>(search_key.get())->set_value(value);
+  return search_key;
+}
+
+template <typename T, std::enable_if_t<std::is_same_v<T, std::string>, void>* = nullptr>
+auto create_scalar_search_key(std::string const& value)
+{
+  return cudf::make_string_scalar(value);
+}
+
+template <typename T, std::enable_if_t<cudf::is_timestamp<T>(), void>* = nullptr>
+auto create_scalar_search_key(typename T::rep const& value)
+{
+  auto search_key = cudf::make_timestamp_scalar(cudf::data_type{cudf::type_to_id<T>()});
+  search_key->set_valid_async(true);
+  static_cast<cudf::scalar_type_t<typename T::rep>*>(search_key.get())->set_value(value);
+  return search_key;
+}
+
+template <typename T, std::enable_if_t<cudf::is_duration<T>(), void>* = nullptr>
+auto create_scalar_search_key(typename T::rep const& value)
+{
+  auto search_key = cudf::make_duration_scalar(cudf::data_type{cudf::type_to_id<T>()});
+  search_key->set_valid_async(true);
+  static_cast<cudf::scalar_type_t<typename T::rep>*>(search_key.get())->set_value(value);
+  return search_key;
+}
+
+template <typename... Args>
+auto make_struct_scalar(Args&&... args)
+{
+  return cudf::struct_scalar(std::vector<cudf::column_view>{std::forward<Args>(args)...});
+}
+
+template <typename T, std::enable_if_t<cudf::is_numeric<T>(), void>* = nullptr>
+auto create_null_search_key()
+{
+  auto search_key = cudf::make_numeric_scalar(cudf::data_type{cudf::type_to_id<T>()});
+  search_key->set_valid_async(false);
+  return search_key;
+}
+
+template <typename T, std::enable_if_t<cudf::is_timestamp<T>(), void>* = nullptr>
+auto create_null_search_key()
+{
+  auto search_key = cudf::make_timestamp_scalar(cudf::data_type{cudf::type_to_id<T>()});
+  search_key->set_valid_async(false);
+  return search_key;
+}
+
+template <typename T, std::enable_if_t<cudf::is_duration<T>(), void>* = nullptr>
+auto create_null_search_key()
+{
+  auto search_key = cudf::make_duration_scalar(cudf::data_type{cudf::type_to_id<T>()});
+  search_key->set_valid_async(false);
+  return search_key;
+}
+
+}  // namespace
+
+auto constexpr X          = int32_t{0};           // Placeholder for nulls.
+auto constexpr ABSENT     = cudf::size_type{-1};  // Index when key is not found in a list.
+auto constexpr FIND_FIRST = cudf::lists::duplicate_find_option::FIND_FIRST;
+auto constexpr FIND_LAST  = cudf::lists::duplicate_find_option::FIND_LAST;
+
+using bools_col   = cudf::test::fixed_width_column_wrapper<bool, int32_t>;
+using indices_col = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+using cudf::test::iterators::all_nulls;
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+using ContainsTestTypes = cudf::test::
+  Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes, cudf::test::ChronoTypes>;
+
+struct ContainsTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedContainsTest : public ContainsTest {};
+
+TYPED_TEST_SUITE(TypedContainsTest, ContainsTestTypes);
+
+TYPED_TEST(TypedContainsTest, ScalarKeyWithNoNulls)
+{
+  using T = TypeParam;
+
+  auto const search_space_col = cudf::test::lists_column_wrapper<T, int32_t>{{0, 1, 2, 1},
+                                                                             {3, 4, 5},
+                                                                             {6, 7, 8},
+                                                                             {9, 0, 1, 3, 1},
+                                                                             {2, 3, 4},
+                                                                             {5, 6, 7},
+                                                                             {8, 9, 0},
+                                                                             {},
+                                                                             {1, 2, 1, 3},
+                                                                             {}};
+  auto const search_space     = cudf::lists_column_view{search_space_col};
+  auto search_key_one         = create_scalar_search_key<T>(1);
+
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space, *search_key_one);
+    auto expected = bools_col{1, 0, 0, 1, 0, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto result   = cudf::lists::contains_nulls(search_space);
+    auto expected = bools_col{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space, *search_key_one, FIND_FIRST);
+    auto expected = indices_col{1, ABSENT, ABSENT, 2, ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space, *search_key_one, FIND_LAST);
+    auto expected = indices_col{3, ABSENT, ABSENT, 4, ABSENT, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedContainsTest, ScalarKeyWithNullLists)
+{
+  // Test List columns that have NULL list rows.
+  using T = TypeParam;
+
+  auto const search_space_col = cudf::test::lists_column_wrapper<T, int32_t>{{{0, 1, 2, 1},
+                                                                              {3, 4, 5},
+                                                                              {6, 7, 8},
+                                                                              {},
+                                                                              {9, 0, 1, 3, 1},
+                                                                              {2, 3, 4},
+                                                                              {5, 6, 7},
+                                                                              {8, 9, 0},
+                                                                              {},
+                                                                              {1, 2, 2, 3},
+                                                                              {}},
+                                                                             nulls_at({3, 10})};
+  auto const search_space     = cudf::lists_column_view{search_space_col};
+  auto search_key_one         = create_scalar_search_key<T>(1);
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space, *search_key_one);
+    auto expected = bools_col{{1, 0, 0, X, 1, 0, 0, 0, 0, 1, X}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto result   = cudf::lists::contains_nulls(search_space);
+    auto expected = bools_col{{0, 0, 0, X, 0, 0, 0, 0, 0, 0, X}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space, *search_key_one, FIND_FIRST);
+    auto expected = indices_col{{1, ABSENT, ABSENT, X, 2, ABSENT, ABSENT, ABSENT, ABSENT, 0, X},
+                                nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space, *search_key_one, FIND_LAST);
+    auto expected = indices_col{{3, ABSENT, ABSENT, X, 4, ABSENT, ABSENT, ABSENT, ABSENT, 0, X},
+                                nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedContainsTest, SlicedLists)
+{
+  // Test sliced List columns.
+  using T = TypeParam;
+
+  auto search_space = cudf::test::lists_column_wrapper<T, int32_t>{{{0, 1, 2, 1},
+                                                                    {3, 4, 5},
+                                                                    {6, 7, 8},
+                                                                    {},
+                                                                    {9, 0, 1, 3, 1},
+                                                                    {2, 3, 4},
+                                                                    {5, 6, 7},
+                                                                    {8, 9, 0},
+                                                                    {},
+                                                                    {1, 2, 1, 3},
+                                                                    {}},
+                                                                   nulls_at({3, 10})};
+
+  {
+    // First Slice.
+    auto sliced_column_1 =
+      cudf::detail::slice(search_space, {1, 8}, cudf::get_default_stream()).front();
+    auto search_key_one = create_scalar_search_key<T>(1);
+    {
+      // CONTAINS
+      auto result          = cudf::lists::contains(sliced_column_1, *search_key_one);
+      auto expected_result = bools_col{{0, 0, X, 1, 0, 0, 0}, null_at(2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+    {
+      // CONTAINS NULLS
+      auto result          = cudf::lists::contains_nulls(sliced_column_1);
+      auto expected_result = bools_col{{0, 0, X, 0, 0, 0, 0}, null_at(2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+    {
+      // FIND_FIRST
+      auto result = cudf::lists::index_of(sliced_column_1, *search_key_one, FIND_FIRST);
+      auto expected_result =
+        indices_col{{ABSENT, ABSENT, 0, 2, ABSENT, ABSENT, ABSENT}, null_at(2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+    {
+      // FIND_LAST
+      auto result = cudf::lists::index_of(sliced_column_1, *search_key_one, FIND_LAST);
+      auto expected_result =
+        indices_col{{ABSENT, ABSENT, 0, 4, ABSENT, ABSENT, ABSENT}, null_at(2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+  }
+
+  {
+    // Second Slice.
+    auto sliced_column_2 =
+      cudf::detail::slice(search_space, {3, 10}, cudf::get_default_stream()).front();
+    auto search_key_one = create_scalar_search_key<T>(1);
+    {
+      // CONTAINS
+      auto result          = cudf::lists::contains(sliced_column_2, *search_key_one);
+      auto expected_result = bools_col{{X, 1, 0, 0, 0, 0, 1}, null_at(0)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+    {
+      // CONTAINS NULLS
+      auto result          = cudf::lists::contains_nulls(sliced_column_2);
+      auto expected_result = bools_col{{X, 0, 0, 0, 0, 0, 0}, null_at(0)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+    {
+      // FIND_FIRST
+      auto result          = cudf::lists::index_of(sliced_column_2, *search_key_one, FIND_FIRST);
+      auto expected_result = indices_col{{0, 2, ABSENT, ABSENT, ABSENT, ABSENT, 0}, null_at(0)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+    {
+      // FIND_LAST
+      auto result          = cudf::lists::index_of(sliced_column_2, *search_key_one, FIND_LAST);
+      auto expected_result = indices_col{{0, 4, ABSENT, ABSENT, ABSENT, ABSENT, 2}, null_at(0)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result, result->view());
+    }
+  }
+}
+
+TYPED_TEST(TypedContainsTest, ScalarKeyNonNullListsWithNullValues)
+{
+  // Test List columns that have no NULL list rows, but NULL elements in some list rows.
+  using T = TypeParam;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 1, 2, X, 4, 5, X, 7, 8, X, X, 1, 2, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+  auto search_space = cudf::make_lists_column(
+    8, indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(), numerals.release(), 0, {});
+  // Search space: [ [x], [1,2], [x,4,5,x], [], [], [7,8,x], [x], [1,2,x,1] ]
+  auto search_key_one = create_scalar_search_key<T>(1);
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_one);
+    auto expected = bools_col{0, 1, 0, 0, 0, 0, 0, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto result   = cudf::lists::contains_nulls(search_space->view());
+    auto expected = bools_col{1, 0, 1, 0, 0, 1, 1, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_FIRST);
+    auto expected = indices_col{ABSENT, 0, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_LAST);
+    auto expected = indices_col{ABSENT, 0, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 3};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedContainsTest, ScalarKeysWithNullsInLists)
+{
+  using T = TypeParam;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 1, 2, X, 4, 5, X, 7, 8, X, X, 1, 2, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+  auto input_null_mask_iter = null_at(4);
+
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(8,
+                                              indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+                                              numerals.release(),
+                                              null_count,
+                                              std::move(null_mask));
+
+  // Search space: [ [x], [1,2], [x,4,5,x], [], x, [7,8,x], [x], [1,2,x,1] ]
+  auto search_key_one = create_scalar_search_key<T>(1);
+  {
+    // CONTAINS.
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_one);
+    auto expected = bools_col{{0, 1, 0, 0, X, 0, 0, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS.
+    auto result   = cudf::lists::contains_nulls(search_space->view());
+    auto expected = bools_col{{1, 0, 1, 0, X, 1, 1, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST.
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 0, ABSENT, ABSENT, X, ABSENT, ABSENT, 0}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST.
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 0, ABSENT, ABSENT, X, ABSENT, ABSENT, 3}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TEST_F(ContainsTest, BoolScalarWithNullsInLists)
+{
+  using T = bool;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 1, 1, X, 1, 1, X, 1, 1, X, X, 1, 1, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+  auto input_null_mask_iter = null_at(4);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(
+    8,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+    numerals.release(),
+    null_count,
+    std::move(null_mask));
+
+  // Search space: [ [x], [1,1], [x,1,1,x], [], x, [1,1,x], [x], [1,1,x,1] ]
+  auto search_key_one = create_scalar_search_key<T>(1);
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_one);
+    auto expected = bools_col{{0, 1, 1, 0, X, 1, 0, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto result   = cudf::lists::contains_nulls(search_space->view());
+    auto expected = bools_col{{1, 0, 1, 0, X, 1, 1, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST.
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 0, 1, ABSENT, X, 0, ABSENT, 0}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST.
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 1, 2, ABSENT, X, 1, ABSENT, 3}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TEST_F(ContainsTest, StringScalarWithNullsInLists)
+{
+  using T = std::string;
+
+  auto strings = cudf::test::strings_column_wrapper{
+    {"X", "1", "2", "X", "4", "5", "X", "7", "8", "X", "X", "1", "2", "X", "1"},
+    nulls_at({0, 3, 6, 9, 10, 13})};
+  auto input_null_mask_iter = null_at(4);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(8,
+                                              indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+                                              strings.release(),
+                                              null_count,
+                                              std::move(null_mask));
+
+  // Search space: [ [x], [1,2], [x,4,5,x], [], x, [7,8,x], [x], [1,2,x,1] ]
+  auto search_key_one = create_scalar_search_key<T>("1");
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_one);
+    auto expected = bools_col{{0, 1, 0, 0, X, 0, 0, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto result   = cudf::lists::contains_nulls(search_space->view());
+    auto expected = bools_col{{1, 0, 1, 0, X, 1, 1, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST.
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 0, ABSENT, ABSENT, X, ABSENT, ABSENT, 0}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST.
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 0, ABSENT, ABSENT, X, ABSENT, ABSENT, 3}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedContainsTest, ScalarNullSearchKey)
+{
+  using T = TypeParam;
+
+  auto search_space = cudf::test::lists_column_wrapper<T, int32_t>{{{0, 1, 2},
+                                                                    {3, 4, 5},
+                                                                    {6, 7, 8},
+                                                                    {},
+                                                                    {9, 0, 1},
+                                                                    {2, 3, 4},
+                                                                    {5, 6, 7},
+                                                                    {8, 9, 0},
+                                                                    {},
+                                                                    {1, 2, 3},
+                                                                    {}},
+                                                                   nulls_at({3, 10})}
+                        .release();
+  auto search_key_null = create_null_search_key<T>();
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_null);
+    auto expected = bools_col{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, all_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_null, FIND_FIRST);
+    auto expected = indices_col{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, all_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_null, FIND_LAST);
+    auto expected = indices_col{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, all_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TEST_F(ContainsTest, ScalarTypeRelatedExceptions)
+{
+  {
+    // Nested types unsupported.
+    auto list_of_lists = cudf::test::lists_column_wrapper<int32_t>{
+      {{1, 2, 3}, {4, 5, 6}},
+      {{1, 2, 3}, {4, 5, 6}},
+      {{1, 2, 3},
+       {4, 5, 6}}}.release();
+    auto skey = create_scalar_search_key<int32_t>(10);
+    EXPECT_THROW(cudf::lists::contains(list_of_lists->view(), *skey), cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_lists->view(), *skey, FIND_FIRST),
+                 cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_lists->view(), *skey, FIND_LAST),
+                 cudf::data_type_error);
+  }
+  {
+    // Search key must match list elements in type.
+    auto list_of_ints =
+      cudf::test::lists_column_wrapper<int32_t>{
+        {0, 1, 2},
+        {3, 4, 5},
+      }
+        .release();
+    auto skey = create_scalar_search_key<std::string>("Hello, World!");
+    EXPECT_THROW(cudf::lists::contains(list_of_ints->view(), *skey), cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_ints->view(), *skey, FIND_FIRST),
+                 cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_ints->view(), *skey, FIND_LAST),
+                 cudf::data_type_error);
+  }
+}
+
+template <typename T>
+struct TypedVectorContainsTest : public ContainsTest {};
+
+using VectorTestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(TypedVectorContainsTest, VectorTestTypes);
+
+TYPED_TEST(TypedVectorContainsTest, VectorKeysWithNoNulls)
+{
+  using T = TypeParam;
+
+  auto search_space = cudf::test::lists_column_wrapper<T, int32_t>{
+    {0, 1, 2, 1},
+    {3, 4, 5},
+    {6, 7, 8},
+    {9, 0, 1, 3, 1},
+    {2, 3, 4},
+    {5, 6, 7},
+    {8, 9, 0},
+    {},
+    {1, 2, 3, 3},
+    {}}.release();
+
+  auto search_key =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 1, 2, 3, 1, 2, 3, 1};
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_key);
+    auto expected = bools_col{1, 0, 0, 1, 1, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_key, FIND_FIRST);
+    auto expected = indices_col{1, ABSENT, ABSENT, 2, 0, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_key, FIND_LAST);
+    auto expected = indices_col{3, ABSENT, ABSENT, 4, 0, ABSENT, ABSENT, ABSENT, 3, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedVectorContainsTest, VectorWithNullLists)
+{
+  // Test List columns that have NULL list rows.
+
+  using T = TypeParam;
+
+  auto search_space = cudf::test::lists_column_wrapper<T, int32_t>{{{0, 1, 2, 1},
+                                                                    {3, 4, 5},
+                                                                    {6, 7, 8},
+                                                                    {},
+                                                                    {9, 0, 1, 3, 1},
+                                                                    {2, 3, 4},
+                                                                    {5, 6, 7},
+                                                                    {8, 9, 0},
+                                                                    {},
+                                                                    {1, 2, 3, 3},
+                                                                    {}},
+                                                                   nulls_at({3, 10})}
+                        .release();
+
+  auto search_keys =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2};
+
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_keys);
+    auto expected = bools_col{{1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_FIRST);
+    auto expected = indices_col{{1, ABSENT, ABSENT, X, ABSENT, 1, ABSENT, ABSENT, ABSENT, 0, X},
+                                nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_LAST);
+    auto expected = indices_col{{3, ABSENT, ABSENT, X, ABSENT, 1, ABSENT, ABSENT, ABSENT, 0, X},
+                                nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedVectorContainsTest, VectorNonNullListsWithNullValues)
+{
+  // Test List columns that have no NULL list rows, but NULL elements in some list rows.
+  using T = TypeParam;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 1, 2, X, 4, 5, X, 7, 8, X, X, 1, 2, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+
+  auto search_space = cudf::make_lists_column(
+    8, indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(), numerals.release(), 0, {});
+  // Search space: [ [x], [1,2], [x,4,5,x], [], [], [7,8,x], [x], [1,2,x,1] ]
+  auto search_keys = cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 1, 2, 3, 1, 1};
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_keys);
+    auto expected = bools_col{0, 1, 0, 0, 0, 0, 0, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_FIRST);
+    auto expected = indices_col{ABSENT, 1, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_LAST);
+    auto expected = indices_col{ABSENT, 1, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 3};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedVectorContainsTest, VectorWithNullsInLists)
+{
+  using T = TypeParam;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 1, 2, X, 4, 5, X, 7, 8, X, X, 1, 2, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+
+  auto input_null_mask_iter = null_at(4);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(8,
+                                              indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+                                              numerals.release(),
+                                              null_count,
+                                              std::move(null_mask));
+
+  // Search space: [ [x], [1,2], [x,4,5,x], [], x, [7,8,x], [x], [1,2,x,1] ]
+
+  auto search_keys = cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 1, 2, 3, 1, 1};
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_keys);
+    auto expected = bools_col{{0, 1, 0, 0, X, 0, 0, 1}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, ABSENT, X, ABSENT, ABSENT, 0}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, ABSENT, X, ABSENT, ABSENT, 3}, null_at(4)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedVectorContainsTest, ListContainsVectorWithNullsInListsAndInSearchKeys)
+{
+  using T = TypeParam;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 1, 2, X, 4, 5, X, 7, 8, X, X, 1, 2, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+
+  auto input_null_mask_iter = null_at(4);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(8,
+                                              indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+                                              numerals.release(),
+                                              null_count,
+                                              std::move(null_mask));
+
+  // Search space: [ [x], [1,2], [x,4,5,x], [], x, [7,8,x], [x], [1,2,x,1] ]
+
+  auto search_keys =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{{1, 2, 3, X, 2, 3, 1, 1}, null_at(3)};
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_keys);
+    auto expected = bools_col{{0, 1, 0, X, X, 0, 0, 1}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, X, X, ABSENT, ABSENT, 0}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, X, X, ABSENT, ABSENT, 3}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TEST_F(ContainsTest, BoolKeyVectorWithNullsInListsAndInSearchKeys)
+{
+  using T = bool;
+
+  auto numerals = cudf::test::fixed_width_column_wrapper<T>{
+    {X, 0, 1, X, 1, 1, X, 1, 1, X, X, 0, 1, X, 1}, nulls_at({0, 3, 6, 9, 10, 13})};
+
+  auto input_null_mask_iter = null_at(4);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(8,
+                                              indices_col{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+                                              numerals.release(),
+                                              null_count,
+                                              std::move(null_mask));
+
+  auto search_keys =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{{0, 1, 0, X, 0, 0, 1, 1}, null_at(3)};
+  // Search space: [ [x], [0,1], [x,1,1,x], [], x, [1,1,x], [x], [0,1,x,1] ]
+  // Search keys : [  0,   1,     0,         x, 0,  0,       1,   1        ]
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_keys);
+    auto expected = bools_col{{0, 1, 0, X, X, 0, 0, 1}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, X, X, ABSENT, ABSENT, 1}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, X, X, ABSENT, ABSENT, 3}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TEST_F(ContainsTest, StringKeyVectorWithNullsInListsAndInSearchKeys)
+{
+  auto strings = cudf::test::strings_column_wrapper{
+    {"X", "1", "2", "X", "4", "5", "X", "7", "8", "X", "X", "1", "2", "X", "1"},
+    nulls_at({0, 3, 6, 9, 10, 13})};
+  auto input_null_mask_iter = null_at(4);
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(input_null_mask_iter, input_null_mask_iter + 8);
+  auto search_space = cudf::make_lists_column(
+    8,
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 3, 7, 7, 7, 10, 11, 15}.release(),
+    strings.release(),
+    null_count,
+    std::move(null_mask));
+
+  auto search_keys =
+    cudf::test::strings_column_wrapper{{"1", "2", "3", "X", "2", "3", "1", "1"}, null_at(3)};
+
+  // Search space: [ [x], [1,2], [x,4,5,x], [], x, [7,8,x], [x], [1,2,x,1] ]
+  // Search keys:  [  1,   2,     3,         X, 2,  3,       1,   1]
+
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_keys);
+    auto expected = bools_col{{0, 1, 0, X, X, 0, 0, 1}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_FIRST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, X, X, ABSENT, ABSENT, 0}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_keys, FIND_LAST);
+    auto expected = indices_col{{ABSENT, 1, ABSENT, X, X, ABSENT, ABSENT, 3}, nulls_at({3, 4})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TEST_F(ContainsTest, VectorTypeRelatedExceptions)
+{
+  {
+    // Nested types unsupported.
+    auto list_of_lists = cudf::test::lists_column_wrapper<int32_t>{
+      {{1, 2, 3}, {4, 5, 6}},
+      {{1, 2, 3}, {4, 5, 6}},
+      {{1, 2, 3},
+       {4, 5, 6}}}.release();
+    auto skey = cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2};
+    EXPECT_THROW(cudf::lists::contains(list_of_lists->view(), skey), cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_lists->view(), skey, FIND_FIRST),
+                 cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_lists->view(), skey, FIND_LAST),
+                 cudf::data_type_error);
+  }
+  {
+    // Search key must match list elements in type.
+    auto list_of_ints =
+      cudf::test::lists_column_wrapper<int32_t>{
+        {0, 1, 2},
+        {3, 4, 5},
+      }
+        .release();
+    auto skey = cudf::test::strings_column_wrapper{"Hello", "World"};
+    EXPECT_THROW(cudf::lists::contains(list_of_ints->view(), skey), cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_ints->view(), skey, FIND_FIRST),
+                 cudf::data_type_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_ints->view(), skey, FIND_LAST),
+                 cudf::data_type_error);
+  }
+  {
+    // Search key column size must match lists column size.
+    auto list_of_ints =
+      cudf::test::lists_column_wrapper<int32_t>{{0, 1, 2}, {3, 4, 5}, {6, 7, 8}}.release();
+    auto skey = cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2, 3};
+    EXPECT_THROW(cudf::lists::contains(list_of_ints->view(), skey), cudf::logic_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_ints->view(), skey, FIND_FIRST), cudf::logic_error);
+    EXPECT_THROW(cudf::lists::index_of(list_of_ints->view(), skey, FIND_LAST), cudf::logic_error);
+  }
+}
+
+template <typename T>
+struct TypedContainsNaNsTest : public ContainsTest {};
+
+TYPED_TEST_SUITE(TypedContainsNaNsTest, cudf::test::FloatingPointTypes);
+
+namespace {
+template <typename T>
+T get_nan(char const* nan_contents)
+{
+  return std::nan(nan_contents);
+}
+
+template <>
+float get_nan<float>(char const* nan_contents)
+{
+  return std::nanf(nan_contents);
+}
+}  // namespace
+
+TYPED_TEST(TypedContainsNaNsTest, ListWithNaNsScalar)
+{
+  using T = TypeParam;
+
+  auto nan_1 = get_nan<T>("1");
+  auto nan_2 = get_nan<T>("2");
+  auto nan_3 = get_nan<T>("3");
+
+  auto search_space = cudf::test::lists_column_wrapper<T>{
+    {0.0, 1.0, 2.0},
+    {3, 4, 5},
+    {6, 7, 8},
+    {9, 0, 1},
+    {nan_1, 3.0, 4.0},
+    {5, 6, 7},
+    {8, nan_2, 0},
+    {},
+    {1, 2, 3},
+    {}}.release();
+
+  auto search_key_nan = create_scalar_search_key<T>(nan_3);
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_nan);
+    auto expected = bools_col{0, 0, 0, 0, 1, 0, 1, 0, 0, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result = cudf::lists::index_of(search_space->view(), *search_key_nan, FIND_FIRST);
+    auto expected =
+      indices_col{ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT, 1, ABSENT, ABSENT, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result = cudf::lists::index_of(search_space->view(), *search_key_nan, FIND_LAST);
+    auto expected =
+      indices_col{ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT, 1, ABSENT, ABSENT, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedContainsNaNsTest, ListWithNaNsContainsVector)
+{
+  // Test that different bit representations of NaN values
+  // are recognized as NaN.
+  // Also checks that a null handling is not broken by the
+  // presence of NaN values:
+  //   1. If the search key is null, null is still returned.
+  //   2. If the list contains a null, and the non-null search
+  //      key is not found:
+  //      a) contains() returns `null`.
+  //      b) index_of() returns -1.
+  using T = TypeParam;
+
+  auto nan_1 = get_nan<T>("1");
+  auto nan_2 = get_nan<T>("2");
+  auto nan_3 = get_nan<T>("3");
+
+  auto search_space = cudf::test::lists_column_wrapper<T>{
+    {0.0, 1.0, 2.0},
+    {{3, 4, 5}, null_at(2)},  // i.e. {3, 4, ∅}.
+    {6, 7, 8},
+    {9, 0, 1},
+    {nan_1, 3.0, 4.0},
+    {5, 6, 7},
+    {8, nan_2, 0},
+    {},
+    {1, 2, 3},
+    {}}.release();
+
+  auto search_key_values = std::vector<T>{1.0, 2.0, 3.0, nan_3, nan_3, nan_3, 0.0, nan_3, 2.0, 0.0};
+
+  {
+    // With nulls in the search key rows. (At index 2.)
+    auto search_keys =
+      cudf::test::fixed_width_column_wrapper<T>{
+        search_key_values.begin(), search_key_values.end(), null_at(2)}
+        .release();
+
+    {
+      // CONTAINS
+      auto result   = cudf::lists::contains(search_space->view(), search_keys->view());
+      auto expected = bools_col{{1, 0, 0, 0, 1, 0, 1, 0, 1, 0}, null_at(2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_FIRST
+      auto result = cudf::lists::index_of(search_space->view(), search_keys->view(), FIND_FIRST);
+      auto expected =
+        indices_col{{1, ABSENT, X, ABSENT, 0, ABSENT, 2, ABSENT, 1, ABSENT}, nulls_at({2})};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_LAST
+      auto result = cudf::lists::index_of(search_space->view(), search_keys->view(), FIND_LAST);
+      auto expected =
+        indices_col{{1, ABSENT, X, ABSENT, 0, ABSENT, 2, ABSENT, 1, ABSENT}, nulls_at({2})};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+  }
+  {
+    // No nulls in the search key rows.
+    auto search_keys =
+      cudf::test::fixed_width_column_wrapper<T>(search_key_values.begin(), search_key_values.end())
+        .release();
+    {
+      // CONTAINS
+      auto result   = cudf::lists::contains(search_space->view(), search_keys->view());
+      auto expected = bools_col{1, 0, 0, 0, 1, 0, 1, 0, 1, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_FIRST
+      auto result   = cudf::lists::index_of(search_space->view(), search_keys->view(), FIND_FIRST);
+      auto expected = indices_col{1, ABSENT, ABSENT, ABSENT, 0, ABSENT, 2, ABSENT, 1, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_LAST
+      auto result   = cudf::lists::index_of(search_space->view(), search_keys->view(), FIND_LAST);
+      auto expected = indices_col{1, ABSENT, ABSENT, ABSENT, 0, ABSENT, 2, ABSENT, 1, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+  }
+}
+
+template <typename T>
+struct TypedContainsDecimalsTest : public ContainsTest {};
+
+TYPED_TEST_SUITE(TypedContainsDecimalsTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(TypedContainsDecimalsTest, ScalarKey)
+{
+  using T = TypeParam;
+
+  auto const search_space = [] {
+    auto const values = std::vector<typename T::rep>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1,
+                                                     2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3};
+    auto decimals     = cudf::test::fixed_point_column_wrapper<typename T::rep>{
+      values.begin(), values.end(), numeric::scale_type{0}};
+    auto list_offsets = indices_col{0, 3, 6, 9, 12, 15, 18, 21, 21, 24, 24};
+    return cudf::make_lists_column(10, list_offsets.release(), decimals.release(), 0, {});
+  }();
+  auto search_key_one =
+    cudf::make_fixed_point_scalar<T>(typename T::rep{1}, numeric::scale_type{0});
+
+  // Search space: [[0,1,2], [3,4,5], [6,7,8], [9,0,1], [2,3,4], [5,6,7], [8,9,0], [], [1,2,3], []]
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), *search_key_one);
+    auto expected = bools_col{1, 0, 0, 1, 0, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_FIRST);
+    auto expected = indices_col{1, ABSENT, ABSENT, 2, ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), *search_key_one, FIND_LAST);
+    auto expected = indices_col{1, ABSENT, ABSENT, 2, ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedContainsDecimalsTest, VectorKey)
+{
+  using T = TypeParam;
+
+  auto const search_space = [] {
+    auto const values = std::vector<typename T::rep>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1,
+                                                     2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3};
+    auto decimals     = cudf::test::fixed_point_column_wrapper<typename T::rep>{
+      values.begin(), values.end(), numeric::scale_type{0}};
+    auto list_offsets = indices_col{0, 3, 6, 9, 12, 15, 18, 21, 21, 24, 24};
+    return cudf::make_lists_column(10, list_offsets.release(), decimals.release(), 0, {});
+  }();
+
+  auto search_key = cudf::test::fixed_point_column_wrapper<typename T::rep>{
+    {1, 2, 3, 1, 2, 3, 1, 2, 3, 1},
+    numeric::scale_type{
+      0}}.release();
+
+  // Search space: [ [0,1,2], [3,4,5], [6,7,8], [9,0,1], [2,3,4], [5,6,7], [8,9,0], [], [1,2,3], []
+  // ] Search keys:  [  1,       2,       3,       1,       2,       3,       1,       2,  3, 1 ]
+  {
+    // CONTAINS
+    auto result   = cudf::lists::contains(search_space->view(), search_key->view());
+    auto expected = bools_col{1, 0, 0, 1, 1, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto result   = cudf::lists::index_of(search_space->view(), search_key->view(), FIND_FIRST);
+    auto expected = indices_col{1, ABSENT, ABSENT, 2, 0, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto result   = cudf::lists::index_of(search_space->view(), search_key->view(), FIND_LAST);
+    auto expected = indices_col{1, ABSENT, ABSENT, 2, 0, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+template <typename T>
+struct TypedStructContainsTest : public ContainsTest {};
+TYPED_TEST_SUITE(TypedStructContainsTest, ContainsTestTypes);
+
+TYPED_TEST(TypedStructContainsTest, EmptyInputTest)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists = [] {
+    auto offsets = indices_col{};
+    auto data    = tdata_col{};
+    auto child   = cudf::test::structs_column_wrapper{{data}};
+    return cudf::make_lists_column(0, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const scalar_key = [] {
+    auto child = tdata_col{0};
+    return make_struct_scalar(child);
+  }();
+  auto const column_key = [] {
+    auto child = tdata_col{};
+    return cudf::test::structs_column_wrapper{{child}};
+  }();
+
+  auto const result1  = cudf::lists::contains(lists->view(), scalar_key);
+  auto const result2  = cudf::lists::contains(lists->view(), column_key);
+  auto const expected = bools_col{};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result2);
+}
+
+TYPED_TEST(TypedStructContainsTest, ScalarKeyNoNullLists)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, 1, 2, 1,
+                              3, 4, 5,
+                              6, 7, 8,
+                              9, 0, 1, 3, 1,
+                              2, 3, 4,
+                              5, 6, 7,
+                              8, 9, 0,
+                              1, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, 1, 2, 3,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 1, 2, 2, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 0, 1, 1
+    };
+    // clang-format on
+    auto child = cudf::test::structs_column_wrapper{{data1, data2}};
+    return cudf::make_lists_column(10, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const key = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{1};
+    return make_struct_scalar(child1, child2);
+  }();
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists->view(), key);
+    auto const expected = bools_col{1, 0, 0, 0, 0, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto const result   = cudf::lists::contains_nulls(lists->view());
+    auto const expected = bools_col{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result = cudf::lists::index_of(lists->view(), key, FIND_FIRST);
+    auto const expected =
+      indices_col{1, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result = cudf::lists::index_of(lists->view(), key, FIND_LAST);
+    auto const expected =
+      indices_col{1, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTest, ScalarKeyWithNullLists)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, 1, 2, 1,
+                              3, 4, 5,
+                              6, 7, 8,
+                              9, 0, 1, 3, 1,
+                              2, 3, 4,
+                              5, 6, 7,
+                              8, 9, 0,
+                              1, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, 1, 2, 3,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 1, 2, 2, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 0, 1, 1
+    };
+    // clang-format on
+    auto child               = cudf::test::structs_column_wrapper{{data1, data2}};
+    auto const validity_iter = nulls_at({3, 10});
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(validity_iter, validity_iter + 11);
+    return cudf::make_lists_column(
+      11, offsets.release(), child.release(), null_count, std::move(null_mask));
+  }();
+
+  auto const key = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{1};
+    return make_struct_scalar(child1, child2);
+  }();
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists->view(), key);
+    auto const expected = bools_col{{1, 0, 0, X, 0, 0, 0, 0, 0, 1, X}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto const result   = cudf::lists::contains_nulls(lists->view());
+    auto const expected = bools_col{{0, 0, 0, X, 0, 0, 0, 0, 0, 0, X}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result   = cudf::lists::index_of(lists->view(), key, FIND_FIRST);
+    auto const expected = indices_col{
+      {1, ABSENT, ABSENT, X, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 0, X}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result   = cudf::lists::index_of(lists->view(), key, FIND_LAST);
+    auto const expected = indices_col{
+      {1, ABSENT, ABSENT, X, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 2, X}, nulls_at({3, 10})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTest, SlicedListsColumnNoNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists_original = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, 1, 2, 1,
+                              3, 4, 5,
+                              6, 7, 8,
+                              9, 0, 1, 3, 1,
+                              2, 3, 4,
+                              5, 6, 7,
+                              8, 9, 0,
+                              1, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, 1, 2, 3,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 1, 2, 2, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 0, 1, 1
+    };
+    // clang-format on
+    auto child = cudf::test::structs_column_wrapper{{data1, data2}};
+    return cudf::make_lists_column(10, offsets.release(), child.release(), 0, {});
+  }();
+  auto const lists = cudf::slice(lists_original->view(), {3, 10})[0];
+
+  auto const key = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{1};
+    return make_struct_scalar(child1, child2);
+  }();
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists, key);
+    auto const expected = bools_col{0, 0, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto const result   = cudf::lists::contains_nulls(lists);
+    auto const expected = bools_col{0, 0, 0, 0, 0, 0, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result   = cudf::lists::index_of(lists, key, FIND_FIRST);
+    auto const expected = indices_col{ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result   = cudf::lists::index_of(lists, key, FIND_LAST);
+    auto const expected = indices_col{ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTest, ScalarKeyNoNullListsWithNullStructs)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, X, 2, 1,
+                              3, 4, 5,
+                              6, 7, 8,
+                              X, 0, 1, 3, 1,
+                              X, 3, 4,
+                              5, 6, 7,
+                              8, 9, 0,
+                              X, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, X, 2, 1,
+                              0, 1, 2,
+                              0, 1, 2,
+                              X, 1, 2, 2, 2,
+                              X, 1, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              X, 0, 1, 1
+    };
+    // clang-format on
+    auto child = cudf::test::structs_column_wrapper{{data1, data2}, nulls_at({1, 10, 15, 24})};
+    return cudf::make_lists_column(10, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const key = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{1};
+    return make_struct_scalar(child1, child2);
+  }();
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists->view(), key);
+    auto const expected = bools_col{1, 0, 0, 0, 0, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // CONTAINS NULLS
+    auto const result   = cudf::lists::contains_nulls(lists->view());
+    auto const expected = bools_col{1, 0, 0, 1, 1, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result = cudf::lists::index_of(lists->view(), key, FIND_FIRST);
+    auto const expected =
+      indices_col{3, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result = cudf::lists::index_of(lists->view(), key, FIND_LAST);
+    auto const expected =
+      indices_col{3, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTest, ColumnKeyNoNullLists)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, 1, 2, 1,
+                              3, 4, 3,
+                              6, 7, 8,
+                              9, 0, 1, 3, 1,
+                              2, 3, 4,
+                              5, 6, 7,
+                              8, 9, 0,
+                              1, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, 1, 2, 3,
+                              0, 0, 0,
+                              0, 1, 2,
+                              1, 1, 2, 2, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 0, 1, 1
+    };
+    // clang-format on
+    auto child = cudf::test::structs_column_wrapper{{data1, data2}};
+    return cudf::make_lists_column(10, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const keys = [] {
+    auto child1 = tdata_col{1, 3, 1, 1, 2, 1, 0, 0, 1, 0};
+    auto child2 = tdata_col{1, 0, 1, 1, 2, 1, 0, 0, 1, 0};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists->view(), keys);
+    auto const expected = bools_col{1, 1, 0, 0, 0, 0, 0, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result = cudf::lists::index_of(lists->view(), keys, FIND_FIRST);
+    auto const expected =
+      indices_col{1, 0, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 0, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result = cudf::lists::index_of(lists->view(), keys, FIND_LAST);
+    auto const expected =
+      indices_col{1, 2, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, ABSENT, 2, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTest, ColumnKeyWithSlicedListsNoNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists_original = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, 1, 2, 1,
+                              3, 4, 3,
+                              6, 7, 8,
+                              9, 0, 1, 3, 1,
+                              2, 3, 4,
+                              5, 6, 7,
+                              8, 9, 0,
+                              1, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, 1, 2, 3,
+                              0, 0, 0,
+                              0, 1, 2,
+                              1, 1, 2, 2, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              0, 1, 2,
+                              1, 0, 1, 1
+    };
+    // clang-format on
+    auto child = cudf::test::structs_column_wrapper{{data1, data2}};
+    return cudf::make_lists_column(10, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const keys_original = [] {
+    auto child1 = tdata_col{1, 9, 1, 6, 2, 1, 0, 0, 1, 0};
+    auto child2 = tdata_col{1, 1, 1, 1, 2, 1, 0, 0, 1, 0};
+    return cudf::test::structs_column_wrapper{{child1, child2}};
+  }();
+
+  auto const lists = cudf::slice(lists_original->view(), {3, 7})[0];
+  auto const keys  = cudf::slice(keys_original, {1, 5})[0];
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists, keys);
+    auto const expected = bools_col{1, 0, 1, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result   = cudf::lists::index_of(lists, keys, FIND_FIRST);
+    auto const expected = indices_col{0, ABSENT, 1, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result   = cudf::lists::index_of(lists, keys, FIND_LAST);
+    auto const expected = indices_col{0, ABSENT, 1, ABSENT};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTest, ColumnKeyWithSlicedListsHavingNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists_original = [] {
+    auto offsets = indices_col{0, 4, 7, 10, 10, 15, 18, 21, 24, 24, 28, 28};
+    // clang-format off
+    auto data1    = tdata_col{0, X, 2, 1,
+                              3, 4, 5,
+                              6, 7, 8,
+                              X, 0, 1, 3, 1,
+                              X, 3, 4,
+                              5, 6, 6,
+                              8, 9, 0,
+                              X, 2, 1, 3
+    };
+    auto data2    = tdata_col{0, X, 2, 1,
+                              0, 1, 2,
+                              0, 1, 2,
+                              X, 1, 2, 2, 2,
+                              X, 1, 2,
+                              0, 1, 1,
+                              0, 1, 2,
+                              X, 0, 1, 1
+    };
+    // clang-format on
+    auto child = cudf::test::structs_column_wrapper{{data1, data2}, nulls_at({1, 10, 15, 24})};
+    auto const validity_iter = nulls_at({3, 10});
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(validity_iter, validity_iter + 11);
+    return cudf::make_lists_column(
+      11, offsets.release(), child.release(), null_count, std::move(null_mask));
+  }();
+
+  auto const keys_original = [] {
+    auto child1 = tdata_col{{1, X, 1, 6, X, 1, 0, 0, 1, 0, 1}, null_at(4)};
+    auto child2 = tdata_col{{1, X, 1, 1, X, 1, 0, 0, 1, 0, 1}, null_at(4)};
+    return cudf::test::structs_column_wrapper{{child1, child2}, null_at(1)};
+  }();
+
+  auto const lists = cudf::slice(lists_original->view(), {4, 8})[0];
+  auto const keys  = cudf::slice(keys_original, {1, 5})[0];
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(lists, keys);
+    auto const expected = bools_col{{X, 0, 1, 0}, null_at(0)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result   = cudf::lists::index_of(lists, keys, FIND_FIRST);
+    auto const expected = indices_col{{X, ABSENT, 1, ABSENT}, null_at(0)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result   = cudf::lists::index_of(lists, keys, FIND_LAST);
+    auto const expected = indices_col{{X, ABSENT, 2, ABSENT}, null_at(0)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+template <typename T>
+struct TypedListContainsTest : public ContainsTest {};
+TYPED_TEST_SUITE(TypedListContainsTest, ContainsTestTypes);
+
+TYPED_TEST(TypedListContainsTest, ScalarKeyLists)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists_no_nulls = lists_col{lists_col{{0, 1, 2},  // list0
+                                                  {3, 4, 5},
+                                                  {0, 1, 2},
+                                                  {9, 0, 1, 3, 1}},
+                                        lists_col{{2, 3, 4},  // list1
+                                                  {3, 4, 5},
+                                                  {8, 9, 0},
+                                                  {}},
+                                        lists_col{{0, 2, 1},  // list2
+                                                  {}}};
+
+  auto const lists_have_nulls = lists_col{lists_col{{{0, 1, 2},  // list0
+                                                     {} /*NULL*/,
+                                                     {0, 1, 2},
+                                                     {9, 0, 1, 3, 1}},
+                                                    null_at(1)},
+                                          lists_col{{{} /*NULL*/,  // list1
+                                                     {3, 4, 5},
+                                                     {8, 9, 0},
+                                                     {}},
+                                                    null_at(0)},
+                                          lists_col{{0, 2, 1},  // list2
+                                                    {}}};
+
+  auto const key = [] {
+    auto const child = tdata_col{0, 1, 2};
+    return cudf::list_scalar(child);
+  }();
+
+  auto const do_test = [&](auto const& lists, bool has_nulls) {
+    {
+      // CONTAINS
+      auto const result   = cudf::lists::contains(cudf::lists_column_view{lists}, key);
+      auto const expected = bools_col{1, 0, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // CONTAINS NULLS
+      auto const result   = cudf::lists::contains_nulls(cudf::lists_column_view{lists});
+      auto const expected = has_nulls ? bools_col{1, 1, 0} : bools_col{0, 0, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_FIRST
+      auto const result   = cudf::lists::index_of(cudf::lists_column_view{lists}, key, FIND_FIRST);
+      auto const expected = indices_col{0, ABSENT, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_LAST
+      auto const result   = cudf::lists::index_of(cudf::lists_column_view{lists}, key, FIND_LAST);
+      auto const expected = indices_col{2, ABSENT, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+  };
+
+  do_test(lists_no_nulls, false);
+  do_test(lists_have_nulls, true);
+}
+
+TYPED_TEST(TypedListContainsTest, SlicedListsColumn)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists_no_nulls_original = lists_col{lists_col{{0, 0, 0},  // list-2 (don't care)
+                                                           {0, 1, 2},
+                                                           {0, 1, 2},
+                                                           {0, 0, 0}},
+                                                 lists_col{{0, 0, 0},  // list-1 (don't care)
+                                                           {0, 1, 2},
+                                                           {0, 1, 2},
+                                                           {0, 0, 0}},
+                                                 lists_col{{0, 1, 2},  // list0
+                                                           {3, 4, 5},
+                                                           {0, 1, 2},
+                                                           {9, 0, 1, 3, 1}},
+                                                 lists_col{{2, 3, 4},  // list1
+                                                           {3, 4, 5},
+                                                           {8, 9, 0},
+                                                           {}},
+                                                 lists_col{{0, 2, 1},  // list2
+                                                           {}},
+                                                 lists_col{{0, 0, 0},  // list3 (don't care)
+                                                           {0, 1, 2},
+                                                           {0, 1, 2},
+                                                           {0, 0, 0}},
+                                                 lists_col{{0, 0, 0},  // list4 (don't care)
+                                                           {0, 1, 2},
+                                                           {0, 1, 2},
+                                                           {0, 0, 0}}};
+
+  auto const lists_have_nulls_original = lists_col{lists_col{{0, 0, 0},  // list-1 (don't care)
+                                                             {0, 1, 2},
+                                                             {0, 1, 2},
+                                                             {0, 0, 0}},
+                                                   lists_col{{{0, 1, 2},  // list0
+                                                              {} /*NULL*/,
+                                                              {0, 1, 2},
+                                                              {9, 0, 1, 3, 1}},
+                                                             null_at(1)},
+                                                   lists_col{{{} /*NULL*/,  // list1
+                                                              {3, 4, 5},
+                                                              {8, 9, 0},
+                                                              {}},
+                                                             null_at(0)},
+                                                   lists_col{{0, 2, 1},  // list2
+                                                             {}},
+                                                   lists_col{{0, 0, 0},  // list3 (don't care)
+                                                             {0, 1, 2},
+                                                             {0, 1, 2},
+                                                             {0, 0, 0}}};
+
+  auto const lists_no_nulls   = cudf::slice(lists_no_nulls_original, {2, 5})[0];
+  auto const lists_have_nulls = cudf::slice(lists_have_nulls_original, {1, 4})[0];
+
+  auto const key = [] {
+    auto const child = tdata_col{0, 1, 2};
+    return cudf::list_scalar(child);
+  }();
+
+  auto const do_test = [&](auto const& lists, bool has_nulls) {
+    {
+      // CONTAINS
+      auto const result   = cudf::lists::contains(cudf::lists_column_view{lists}, key);
+      auto const expected = bools_col{1, 0, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // CONTAINS NULLS
+      auto const result   = cudf::lists::contains_nulls(cudf::lists_column_view{lists});
+      auto const expected = has_nulls ? bools_col{1, 1, 0} : bools_col{0, 0, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_FIRST
+      auto const result   = cudf::lists::index_of(cudf::lists_column_view{lists}, key, FIND_FIRST);
+      auto const expected = indices_col{0, ABSENT, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_LAST
+      auto const result   = cudf::lists::index_of(cudf::lists_column_view{lists}, key, FIND_LAST);
+      auto const expected = indices_col{2, ABSENT, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+  };
+
+  do_test(lists_no_nulls, false);
+  do_test(lists_have_nulls, true);
+}
+
+TYPED_TEST(TypedListContainsTest, ColumnKeyLists)
+{
+  using lists_col     = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  auto constexpr null = int32_t{0};
+
+  auto const lists_no_nulls = lists_col{lists_col{{0, 0, 2},  // list0
+                                                  {3, 4, 5},
+                                                  {0, 0, 2},
+                                                  {9, 0, 1, 3, 1}},
+                                        lists_col{{2, 3, 4},  // list1
+                                                  {3, 4, 5},
+                                                  {2, 3, 4},
+                                                  {}},
+                                        lists_col{{0, 2, 0},  // list2
+                                                  {0, 2, 0},
+                                                  {3, 4, 5},
+                                                  {}}};
+
+  auto const lists_have_nulls = lists_col{lists_col{{lists_col{{0, null, 2}, null_at(1)},  // list0
+                                                     lists_col{} /*NULL*/,
+                                                     lists_col{{0, null, 2}, null_at(1)},
+                                                     lists_col{9, 0, 1, 3, 1}},
+                                                    null_at(1)},
+                                          lists_col{{lists_col{} /*NULL*/,  // list1
+                                                     lists_col{3, 4, 5},
+                                                     lists_col{2, 3, 4},
+                                                     lists_col{}},
+                                                    null_at(0)},
+                                          lists_col{lists_col{0, 2, 1},  // list2
+                                                    lists_col{{0, 2, null}, null_at(2)},
+                                                    lists_col{3, 4, 5},
+                                                    lists_col{}}};
+
+  auto const key = lists_col{
+    lists_col{{0, null, 2}, null_at(1)}, lists_col{2, 3, 4}, lists_col{{0, 2, null}, null_at(2)}};
+
+  auto const do_test = [&](auto const& lists, bool has_nulls) {
+    {
+      // CONTAINS
+      auto const result   = cudf::lists::contains(cudf::lists_column_view{lists}, key);
+      auto const expected = has_nulls ? bools_col{1, 1, 1} : bools_col{0, 1, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // CONTAINS NULLS
+      auto const result   = cudf::lists::contains_nulls(cudf::lists_column_view{lists});
+      auto const expected = has_nulls ? bools_col{1, 1, 0} : bools_col{0, 0, 0};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_FIRST
+      auto const result   = cudf::lists::index_of(cudf::lists_column_view{lists}, key, FIND_FIRST);
+      auto const expected = has_nulls ? indices_col{0, 2, 1} : indices_col{ABSENT, 0, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+    {
+      // FIND_LAST
+      auto const result   = cudf::lists::index_of(cudf::lists_column_view{lists}, key, FIND_LAST);
+      auto const expected = has_nulls ? indices_col{2, 2, 1} : indices_col{ABSENT, 2, ABSENT};
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+    }
+  };
+
+  do_test(lists_no_nulls, false);
+  do_test(lists_have_nulls, true);
+}
+
+TYPED_TEST(TypedListContainsTest, ColumnKeyWithListsOfStructsNoNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const lists = [] {
+    auto child_offsets = indices_col{0, 3, 6, 9, 14, 17, 20, 23, 23};
+    // clang-format off
+    auto data1 = tdata_col{0, 0, 2,
+                           3, 4, 5,
+                           0, 0, 2,
+                           9, 0, 1, 3, 1,
+                           0, 2, 0,
+                           0, 0, 2,
+                           3, 4, 5
+
+    };
+    auto data2 = tdata_col{10, 10, 12,
+                           13, 14, 15,
+                           10, 10, 12,
+                           19, 10, 11, 13, 11,
+                           10, 12, 10,
+                           10, 10, 12,
+                           13, 14, 15
+
+    };
+    // clang-format on
+    auto structs = cudf::test::structs_column_wrapper{{data1, data2}};
+    auto child   = cudf::make_lists_column(8, child_offsets.release(), structs.release(), 0, {});
+
+    auto offsets = indices_col{0, 4, 8};
+    return cudf::make_lists_column(2, offsets.release(), std::move(child), 0, {});
+  }();
+
+  auto const key = [] {
+    auto data1       = tdata_col{0, 0, 2};
+    auto data2       = tdata_col{10, 10, 12};
+    auto const child = cudf::test::structs_column_wrapper{{data1, data2}};
+    return cudf::list_scalar(child);
+  }();
+
+  {
+    // CONTAINS
+    auto const result   = cudf::lists::contains(cudf::lists_column_view{lists->view()}, key);
+    auto const expected = bools_col{1, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_FIRST
+    auto const result =
+      cudf::lists::index_of(cudf::lists_column_view{lists->view()}, key, FIND_FIRST);
+    auto const expected = indices_col{0, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    // FIND_LAST
+    auto const result =
+      cudf::lists::index_of(cudf::lists_column_view{lists->view()}, key, FIND_LAST);
+    auto const expected = indices_col{2, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
diff --git a/cpp/tests/lists/count_elements_tests.cpp b/cpp/tests/lists/count_elements_tests.cpp
new file mode 100644
index 0000000..e099139
--- /dev/null
+++ b/cpp/tests/lists/count_elements_tests.cpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/lists/count_elements.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+struct ListsElementsTest : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+template <typename T>
+class ListsElementsNumericsTest : public ListsElementsTest {};
+
+TYPED_TEST_SUITE(ListsElementsNumericsTest, NumericTypesNotBool);
+
+TYPED_TEST(ListsElementsNumericsTest, CountElements)
+{
+  auto validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 1; });
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+  LCW input({LCW{3, 2, 1}, LCW{}, LCW{30, 20, 10, 50}, LCW{100, 120}, LCW{0}}, validity);
+
+  auto result = cudf::lists::count_elements(cudf::lists_column_view(input));
+  cudf::test::fixed_width_column_wrapper<int32_t> expected({3, 0, 4, 2, 1}, {1, 0, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(ListsElementsTest, CountElementsStrings)
+{
+  auto validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 1; });
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW input(
+    {LCW{"", "Héllo", "thesé"}, LCW{}, LCW{"are", "some", "", "z"}, LCW{"tést", "String"}, LCW{""}},
+    validity);
+
+  auto result = cudf::lists::count_elements(cudf::lists_column_view(input));
+  cudf::test::fixed_width_column_wrapper<int32_t> expected({3, 0, 4, 2, 1}, {1, 0, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(ListsElementsTest, CountElementsSliced)
+{
+  auto validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 1; });
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW input(
+    {LCW{"", "Héllo", "thesé"}, LCW{}, LCW{"are", "some", "", "z"}, LCW{"tést", "String"}, LCW{""}},
+    validity);
+
+  auto sliced = cudf::slice(input, {1, 4}).front();
+  auto result = cudf::lists::count_elements(cudf::lists_column_view(sliced));
+  cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 4, 2}, {0, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TYPED_TEST(ListsElementsNumericsTest, CountElementsNestedLists)
+{
+  std::vector<int32_t> validity{1, 0, 1, 1};
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+  LCW list({LCW{LCW{2, 3}, LCW{4, 5}},
+            LCW{LCW{}},
+            LCW{LCW{6, 7, 8}, LCW{9, 10, 11}, LCW({12, 13, 14}, validity.begin())},
+            LCW{LCW{15, 16}, LCW{17, 18}, LCW{19, 20}, LCW{21, 22}, LCW{23, 24}}},
+           validity.begin());
+
+  auto result = cudf::lists::count_elements(cudf::lists_column_view(list));
+  cudf::test::fixed_width_column_wrapper<int32_t> expected({2, 1, 3, 5}, {1, 0, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(ListsElementsTest, CountElementsEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  LCW empty{};
+  auto result = cudf::lists::count_elements(cudf::lists_column_view(empty));
+  EXPECT_EQ(0, result->size());
+}
diff --git a/cpp/tests/lists/explode_tests.cpp b/cpp/tests/lists/explode_tests.cpp
new file mode 100644
index 0000000..4ba7a77
--- /dev/null
+++ b/cpp/tests/lists/explode_tests.cpp
@@ -0,0 +1,1177 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/explode.hpp>
+
+using FCW = cudf::test::fixed_width_column_wrapper<int32_t>;
+using LCW = cudf::test::lists_column_wrapper<int32_t>;
+
+class ExplodeTest : public cudf::test::BaseFixture {};
+
+class ExplodeOuterTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+class ExplodeTypedTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+class ExplodeOuterTypedTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ExplodeTypedTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST_SUITE(ExplodeOuterTypedTest, cudf::test::FixedPointTypes);
+
+TEST_F(ExplodeTest, Empty)
+{
+  cudf::table_view t({LCW{}, FCW{}});
+
+  auto ret = cudf::explode(t, 0);
+
+  cudf::table_view expected({FCW{}, FCW{}});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  auto pos_ret = cudf::explode_position(t, 0);
+
+  cudf::table_view pos_expected({FCW{}, FCW{}, FCW{}});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, NonList)
+{
+  cudf::table_view t({FCW{100, 200, 300}, FCW{100, 200, 300}});
+
+  EXPECT_THROW(cudf::explode(t, 1), cudf::logic_error);
+  EXPECT_THROW(cudf::explode_position(t, 1), cudf::logic_error);
+}
+
+TEST_F(ExplodeTest, Basics)
+{
+  //    a                   b                  c
+  //    100                [1, 2, 7]           string0
+  //    200                [5, 6]              string1
+  //    300                [0, 3]              string2
+
+  FCW a{100, 200, 300};
+  LCW b{LCW{1, 2, 7}, LCW{5, 6}, LCW{0, 3}};
+  cudf::test::strings_column_wrapper c{"string0", "string1", "string2"};
+
+  FCW expected_a{100, 100, 100, 200, 200, 300, 300};
+  FCW expected_b{1, 2, 7, 5, 6, 0, 3};
+  cudf::test::strings_column_wrapper expected_c{
+    "string0", "string0", "string0", "string1", "string1", "string2", "string2"};
+
+  cudf::table_view t({a, b, c});
+  cudf::table_view expected({expected_a, expected_b, expected_c});
+
+  auto ret = cudf::explode(t, 1);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 2, 0, 1, 0, 1};
+  cudf::table_view pos_expected({expected_a, expected_pos_col, expected_b, expected_c});
+
+  auto pos_ret = cudf::explode_position(t, 1);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, SingleNull)
+{
+  //    a                   b
+  //    null                100
+  //    [5, 6]              200
+  //    []                  300
+  //    [0, 3]              400
+
+  constexpr auto null = 0;
+
+  auto first_invalid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0; });
+
+  LCW a({LCW{null}, LCW{5, 6}, LCW{}, LCW{0, 3}}, first_invalid);
+  FCW b({100, 200, 300, 400});
+
+  FCW expected_a{5, 6, 0, 3};
+  FCW expected_b{200, 200, 400, 400};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 1};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, Nulls)
+{
+  //    a                   b
+  //    [1, 2, 7]           100
+  //    null                200
+  //    [0, 3]              300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  auto always_valid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  LCW a({LCW{1, 2, 7}, LCW{null}, LCW{0, 3}}, valids);
+  FCW b({100, 200, 300}, valids);
+
+  FCW expected_a({1, 2, 7, 0, 3});
+  FCW expected_b({100, 100, 100, 300, 300}, always_valid);
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 2, 0, 1};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, NullsInList)
+{
+  //    a                   b
+  //    [1, null, 7]        100
+  //    [5, null, 0, null]  200
+  //    []                  300
+  //    [0, null, 8]        400
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a{
+    LCW({1, null, 7}, valids), LCW({5, null, 0, null}, valids), LCW{}, LCW({0, null, 8}, valids)};
+  FCW b{100, 200, 300, 400};
+
+  FCW expected_a({1, null, 7, 5, null, 0, null, 0, null, 8}, {1, 0, 1, 1, 0, 1, 0, 1, 0, 1});
+  FCW expected_b{100, 100, 100, 200, 200, 200, 200, 400, 400, 400};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 2, 0, 1, 2, 3, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, Nested)
+{
+  //    a                      b
+  //    [[1, 2], [7, 6, 5]]    100
+  //    [[5, 6]]               200
+  //    [[0, 3],[],[5],[2, 1]] 300
+
+  LCW a{LCW{LCW{1, 2}, LCW{7, 6, 5}}, LCW{LCW{5, 6}}, LCW{LCW{0, 3}, LCW{}, LCW{5}, LCW{2, 1}}};
+  FCW b{100, 200, 300};
+
+  LCW expected_a{LCW{1, 2}, LCW{7, 6, 5}, LCW{5, 6}, LCW{0, 3}, LCW{}, LCW{5}, LCW{2, 1}};
+  FCW expected_b{100, 100, 200, 300, 300, 300, 300};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 0, 1, 2, 3};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, NestedNulls)
+{
+  //    a                   b
+  //    [[1, 2], [7, 6, 5]] 100
+  //    null                null
+  //    [[0, 3],[5],[2, 1]] 300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  auto always_valid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  LCW a({LCW{LCW{1, 2}, LCW{7, 6, 5}}, LCW{LCW{null}}, LCW{LCW{0, 3}, LCW{5}, LCW{2, 1}}}, valids);
+  FCW b({100, null, 300}, valids);
+
+  LCW expected_a{LCW{1, 2}, LCW{7, 6, 5}, LCW{0, 3}, LCW{5}, LCW{2, 1}};
+  FCW expected_b({100, 100, 300, 300, 300}, always_valid);
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, NullsInNested)
+{
+  //    a                      b
+  //    [[1, null], [7, 6, 5]] 100
+  //    [[5, 6]]               200
+  //    [[0, 3],[5],[2, null]] 300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW({1, null}, valids), LCW{7, 6, 5}},
+         LCW{LCW{5, 6}},
+         LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)}});
+  FCW b({100, 200, 300});
+
+  LCW expected_a{
+    LCW({1, null}, valids), LCW{7, 6, 5}, LCW{5, 6}, LCW{0, 3}, LCW{5}, LCW({2, null}, valids)};
+  FCW expected_b{100, 100, 200, 300, 300, 300};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, NullsInNestedDoubleExplode)
+{
+  //    a                          b
+  //    [[1, null], [], [7, 6, 5]] 100
+  //    [[5, 6]]                   200
+  //    [[0, 3],[5],[2, null]]     300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a{LCW{LCW({1, null}, valids), LCW{}, LCW{7, 6, 5}},
+        LCW{LCW{5, 6}},
+        LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)}};
+  FCW b{100, 200, 300};
+
+  FCW expected_a({1, null, 7, 6, 5, 5, 6, 0, 3, 5, 2, null}, {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  FCW expected_b{100, 100, 100, 100, 100, 200, 200, 300, 300, 300, 300, 300};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto first_explode_ret = cudf::explode(t, 0);
+  auto ret               = cudf::explode(first_explode_ret->view(), 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 1, 2, 0, 1, 0, 1, 0, 0, 1};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(first_explode_ret->view(), 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, NestedStructs)
+{
+  //    a                      b
+  //    [[1, null], [7, 6, 5]] {100, "100"}
+  //    [[5, 6]]               {200, "200"}
+  //    [[0, 3],[5],[2, null]] {300, "300"}
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW({1, null}, valids), LCW{7, 6, 5}},
+         LCW{LCW{5, 6}},
+         LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)}});
+  FCW b1({100, 200, 300});
+  cudf::test::strings_column_wrapper b2{"100", "200", "300"};
+  cudf::test::structs_column_wrapper b({b1, b2});
+
+  LCW expected_a{
+    LCW({1, null}, valids), LCW{7, 6, 5}, LCW{5, 6}, LCW{0, 3}, LCW{5}, LCW({2, null}, valids)};
+  FCW expected_b1{100, 100, 200, 300, 300, 300};
+  cudf::test::strings_column_wrapper expected_b2{"100", "100", "200", "300", "300", "300"};
+  cudf::test::structs_column_wrapper expected_b({expected_b1, expected_b2});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, ListOfStructsWithEmpties)
+{
+  //  a           b
+  //  [{1}}]      "a"
+  //  [{null}]    "b"
+  //  [null]      "c"
+  //  []          "d"
+  //  null        "e"
+
+  constexpr auto null = 0;
+
+  // row 0.  1 struct that contains a single int
+  cudf::test::fixed_width_column_wrapper<int32_t> i0{1};
+  std::vector<std::unique_ptr<cudf::column>> s0_cols;
+  s0_cols.push_back(i0.release());
+  cudf::test::structs_column_wrapper s0(std::move(s0_cols));
+  cudf::test::fixed_width_column_wrapper<int32_t> off0{0, 1};
+  auto row0 = cudf::make_lists_column(1, off0.release(), s0.release(), 0, rmm::device_buffer{});
+
+  // row 1.  1 struct that contains a null value
+  cudf::test::fixed_width_column_wrapper<int32_t> i1{{1}, {false}};
+  std::vector<std::unique_ptr<cudf::column>> s1_cols;
+  s1_cols.push_back(i1.release());
+  cudf::test::structs_column_wrapper s1(std::move(s1_cols));
+  cudf::test::fixed_width_column_wrapper<int32_t> off1{0, 1};
+  auto row1 = cudf::make_lists_column(1, off1.release(), s1.release(), 0, rmm::device_buffer{});
+
+  // row 2.  1 null struct
+  cudf::test::fixed_width_column_wrapper<int32_t> i2{0};
+  std::vector<std::unique_ptr<cudf::column>> s2_cols;
+  s2_cols.push_back(i2.release());
+  std::vector<bool> r2_valids{false};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(r2_valids.begin(), r2_valids.end());
+  auto s2 = cudf::make_structs_column(1, std::move(s2_cols), null_count, std::move(null_mask));
+  cudf::test::fixed_width_column_wrapper<int32_t> off2{0, 1};
+  auto row2 = cudf::make_lists_column(1, off2.release(), std::move(s2), 0, rmm::device_buffer{});
+
+  // row 3.  empty list.
+  cudf::test::fixed_width_column_wrapper<int32_t> i3{};
+  std::vector<std::unique_ptr<cudf::column>> s3_cols;
+  s3_cols.push_back(i3.release());
+  auto s3 = cudf::make_structs_column(0, std::move(s3_cols), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<int32_t> off3{0, 0};
+  auto row3 = cudf::make_lists_column(1, off3.release(), std::move(s3), 0, rmm::device_buffer{});
+
+  // row 4.  null list
+  cudf::test::fixed_width_column_wrapper<int32_t> i4{};
+  std::vector<std::unique_ptr<cudf::column>> s4_cols;
+  s4_cols.push_back(i4.release());
+  auto s4 = cudf::make_structs_column(0, std::move(s4_cols), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<int32_t> off4{0, 0};
+  std::vector<bool> r4_valids{false};
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(r4_valids.begin(), r4_valids.end());
+  auto row4 =
+    cudf::make_lists_column(1, off4.release(), std::move(s4), null_count, std::move(null_mask));
+
+  // concatenated
+  auto final_col =
+    cudf::concatenate(std::vector<cudf::column_view>({*row0, *row1, *row2, *row3, *row4}));
+  auto s = cudf::test::strings_column_wrapper({"a", "b", "c", "d", "e"}).release();
+
+  cudf::table_view t({final_col->view(), s->view()});
+
+  auto ret = cudf::explode(t, 0);
+  auto expected_numeric_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, null, null}, {1, 0, 0}};
+
+  auto expected_a = cudf::test::structs_column_wrapper{{expected_numeric_col}, {1, 1, 0}}.release();
+  auto expected_b = cudf::test::strings_column_wrapper({"a", "b", "c"}).release();
+
+  cudf::table_view expected({expected_a->view(), expected_b->view()});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+  FCW expected_pos_col{0, 0, 0};
+  cudf::table_view pos_expected({expected_pos_col, expected_a->view(), expected_b->view()});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TYPED_TEST(ExplodeTypedTest, ListOfStructs)
+{
+  //  a                        b
+  //  [{70, "70"}, {75, "75"}] 100
+  //  [{50, "50"}, {55, "55"}] 200
+  //  [{35, "35"}, {45, "45"}] 300
+  //  [{25, "25"}, {30, "30"}] 400
+  //  [{15, "15"}, {20, "20"}] 500
+
+  auto numeric_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {70, 75, 50, 55, 35, 45, 25, 30, 15, 20}};
+  cudf::test::strings_column_wrapper string_col{
+    "70", "75", "50", "55", "35", "45", "25", "30", "15", "20"};
+  auto struct_col = cudf::test::structs_column_wrapper{{numeric_col, string_col}}.release();
+  auto a =
+    cudf::make_lists_column(5, FCW{0, 2, 4, 6, 8, 10}.release(), std::move(struct_col), 0, {});
+
+  FCW b{100, 200, 300, 400, 500};
+
+  cudf::table_view t({a->view(), b});
+  auto ret = cudf::explode(t, 0);
+
+  auto expected_numeric_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {70, 75, 50, 55, 35, 45, 25, 30, 15, 20}};
+  cudf::test::strings_column_wrapper expected_string_col{
+    "70", "75", "50", "55", "35", "45", "25", "30", "15", "20"};
+
+  auto expected_a =
+    cudf::test::structs_column_wrapper{{expected_numeric_col, expected_string_col}}.release();
+  FCW expected_b{100, 100, 200, 200, 300, 300, 400, 400, 500, 500};
+
+  cudf::table_view expected({expected_a->view(), expected_b});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
+  cudf::table_view pos_expected({expected_pos_col, expected_a->view(), expected_b});
+
+  auto pos_ret = cudf::explode_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeTest, SlicedList)
+{
+  //    a                              b
+  //    [[1, null],[7, 6, 5]]          100
+  //    [[5, 6]]                       200
+  //    [[0, 3],[5],[2, null]]         300
+  //    [[8, 3],[],[4, null, 1, null]] 400
+  //    [[2, 3, 4],[9, 8]]             500
+
+  //    slicing the top 2 rows and the bottom row off
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW({1, 2}, valids), LCW{7, 6, 5}},
+         LCW{LCW{5, 6}},
+         LCW{LCW{0, 3}, LCW{5}, LCW({2, 1}, valids)},
+         LCW{LCW{8, 3}, LCW{}, LCW({4, 3, 1, 2}, valids)},
+         LCW{LCW{2, 3, 4}, LCW{9, 8}}});
+  FCW b({100, 200, 300, 400, 500});
+
+  LCW expected_a{
+    LCW{0, 3}, LCW{5}, LCW({2, null}, valids), LCW{8, 3}, LCW{}, LCW({4, null, 1, null}, valids)};
+  FCW expected_b{300, 300, 300, 400, 400, 400};
+
+  cudf::table_view t({a, b});
+  auto sliced_t = cudf::slice(t, {2, 4});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode(sliced_t[0], 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 2, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_position(sliced_t[0], 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, Empty)
+{
+  LCW a{};
+  FCW b{};
+
+  cudf::table_view t({LCW{}, FCW{}});
+
+  auto ret = cudf::explode_outer(t, 0);
+
+  FCW expected_a{};
+  FCW expected_b{};
+  cudf::table_view expected({FCW{}, FCW{}});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+}
+
+TEST_F(ExplodeOuterTest, NonList)
+{
+  cudf::table_view t({FCW{100, 200, 300}, FCW{100, 200, 300}});
+
+  EXPECT_THROW(cudf::explode_outer(t, 1), cudf::logic_error);
+  EXPECT_THROW(cudf::explode_outer_position(t, 1), cudf::logic_error);
+}
+
+TEST_F(ExplodeOuterTest, Basics)
+{
+  //    a                   b                  c
+  //    100                [1, 2, 7]           string0
+  //    200                [5, 6]              string1
+  //    300                [0, 3]              string2
+
+  FCW a{100, 200, 300};
+  LCW b{LCW{1, 2, 7}, LCW{5, 6}, LCW{0, 3}};
+  cudf::test::strings_column_wrapper c{"string0", "string1", "string2"};
+
+  FCW expected_a{100, 100, 100, 200, 200, 300, 300};
+  FCW expected_b{1, 2, 7, 5, 6, 0, 3};
+  cudf::test::strings_column_wrapper expected_c{
+    "string0", "string0", "string0", "string1", "string1", "string2", "string2"};
+
+  cudf::table_view t({a, b, c});
+  cudf::table_view expected({expected_a, expected_b, expected_c});
+
+  auto ret = cudf::explode_outer(t, 1);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 2, 0, 1, 0, 1};
+  cudf::table_view pos_expected({expected_a, expected_pos_col, expected_b, expected_c});
+
+  auto pos_ret = cudf::explode_outer_position(t, 1);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, SingleNull)
+{
+  //    a              b
+  //    null           100
+  //    [5, 6]         200
+  //    []             300
+  //    [0, 3]         400
+
+  constexpr auto null = 0;
+
+  auto first_invalid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0; });
+
+  LCW a({LCW{null}, LCW{5, 6}, LCW{}, LCW{0, 3}}, first_invalid);
+  FCW b({100, 200, 300, 400});
+
+  FCW expected_a{{null, 5, 6, 0, 0, 3}, {0, 1, 1, 0, 1, 1}};
+  FCW expected_b{100, 200, 200, 300, 400, 400};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 0, 1, 0, 0, 1}, {0, 1, 1, 0, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, Nulls)
+{
+  //    a                   b
+  //    [1, 2, 7]           100
+  //    null                null
+  //    [0, 3]              300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{1, 2, 7}, LCW{null}, LCW{0, 3}}, valids);
+  FCW b({100, null, 300}, valids);
+
+  FCW expected_a({1, 2, 7, null, 0, 3}, {1, 1, 1, 0, 1, 1});
+  FCW expected_b({100, 100, 100, null, 300, 300}, {1, 1, 1, 0, 1, 1});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 2, 0, 0, 1}, {1, 1, 1, 0, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, AllNulls)
+{
+  //    a            b
+  //    null         100
+  //    null         200
+  //    null         300
+
+  constexpr auto null = 0;
+
+  auto non_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return false; });
+
+  LCW a({LCW{null}, LCW{null}, LCW{null}}, non_valid);
+  FCW b({100, 200, 300});
+
+  FCW expected_a({null, null, null}, {0, 0, 0});
+  FCW expected_b({100, 200, 300});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 0, 0}, {0, 0, 0}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, SequentialNulls)
+{
+  //    a               b
+  //    [1, 2, null]    100
+  //    [3, 4]          200
+  //    []              300
+  //    []              400
+  //    [5, 6, 7]       500
+
+  constexpr auto null = 0;
+
+  auto third_invalid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2; });
+
+  LCW a{LCW({1, 2, null}, third_invalid), LCW{3, 4}, LCW{}, LCW{}, LCW{5, 6, 7}};
+  FCW b{100, 200, 300, 400, 500};
+
+  FCW expected_a({1, 2, null, 3, 4, null, null, 5, 6, 7}, {1, 1, 0, 1, 1, 0, 0, 1, 1, 1});
+  FCW expected_b({100, 100, 100, 200, 200, 300, 400, 500, 500, 500});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 2, 0, 1, 0, 0, 0, 1, 2}, {1, 1, 1, 1, 1, 0, 0, 1, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, MoreEmptyThanData)
+{
+  //    a               b
+  //    [1, 2]          100
+  //    []              200
+  //    []              300
+  //    []              400
+  //    []              500
+  //    [3]             600
+
+  constexpr auto null = 0;
+
+  LCW a{LCW{1, 2}, LCW{}, LCW{}, LCW{}, LCW{}, LCW{3}};
+  FCW b{100, 200, 300, 400, 500, 600};
+
+  FCW expected_a({1, 2, null, null, null, null, 3}, {1, 1, 0, 0, 0, 0, 1});
+  FCW expected_b({100, 100, 200, 300, 400, 500, 600});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 0, 0, 0, 0, 0}, {1, 1, 0, 0, 0, 0, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, TrailingEmptys)
+{
+  //    a               b
+  //    [1, 2]          100
+  //    []              200
+  //    []              300
+  //    []              400
+  //    []              500
+
+  constexpr auto null = 0;
+
+  LCW a{LCW{1, 2}, LCW{}, LCW{}, LCW{}, LCW{}};
+  FCW b{100, 200, 300, 400, 500};
+
+  FCW expected_a({1, 2, null, null, null, null}, {1, 1, 0, 0, 0, 0});
+  FCW expected_b({100, 100, 200, 300, 400, 500});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 0, 0, 0, 0}, {1, 1, 0, 0, 0, 0}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, LeadingNulls)
+{
+  //    a               b
+  //    null            100
+  //    null            200
+  //    null            300
+  //    null            400
+  //    [1, 2]          500
+
+  constexpr auto null = 0;
+
+  auto valids = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i == 4; });
+
+  LCW a({LCW{null}, LCW{null}, LCW{null}, LCW{null}, LCW{1, 2}}, valids);
+  FCW b{100, 200, 300, 400, 500};
+
+  FCW expected_a({null, null, null, null, 1, 2}, {0, 0, 0, 0, 1, 1});
+  FCW expected_b({100, 200, 300, 400, 500, 500});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 0, 0, 0, 0, 1}, {0, 0, 0, 0, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, NullsInList)
+{
+  //    a                   b
+  //    [1, null, 7]        100
+  //    [5, null, 0, null]  200
+  //    []                  300
+  //    [0, null, 8]        400
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a{
+    LCW({1, null, 7}, valids), LCW({5, null, 0, null}, valids), LCW{}, LCW({0, null, 8}, valids)};
+  FCW b{100, 200, 300, 400};
+
+  FCW expected_a({1, null, 7, 5, null, 0, null, null, 0, null, 8},
+                 {1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1});
+  FCW expected_b{100, 100, 100, 200, 200, 200, 200, 300, 400, 400, 400};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 2, 0, 1, 2, 3, 0, 0, 1, 2}, {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, Nested)
+{
+  //    a                      b
+  //    [[1, 2], [7, 6, 5]]    100
+  //    [[5, 6]]               200
+  //    [[0, 3],[],[5],[2, 1]] 300
+
+  LCW a{LCW{LCW{1, 2}, LCW{7, 6, 5}}, LCW{LCW{5, 6}}, LCW{LCW{0, 3}, LCW{}, LCW{5}, LCW{2, 1}}};
+  FCW b{100, 200, 300};
+
+  LCW expected_a{LCW{1, 2}, LCW{7, 6, 5}, LCW{5, 6}, LCW{0, 3}, LCW{}, LCW{5}, LCW{2, 1}};
+  FCW expected_b{100, 100, 200, 300, 300, 300, 300};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 0, 1, 2, 3};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, NestedNulls)
+{
+  //    a                   b
+  //    [[1, 2], [7, 6, 5]] 100
+  //    [[5, 6]]            200
+  //    [[0, 3],[5],[2, 1]] 300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW{1, 2}, LCW{7, 6, 5}}, LCW{LCW{null}}, LCW{LCW{0, 3}, LCW{5}, LCW{2, 1}}}, valids);
+  FCW b({100, 200, 300});
+
+  auto expected_valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2; });
+  LCW expected_a({LCW{1, 2}, LCW{7, 6, 5}, LCW{null}, LCW{0, 3}, LCW{5}, LCW{2, 1}},
+                 expected_valids);
+  FCW expected_b({100, 100, 200, 300, 300, 300});
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 0, 0, 1, 2}, {1, 1, 0, 1, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, NullsInNested)
+{
+  //    a                      b
+  //    [[1, null], [7, 6, 5]] 100
+  //    [[5, 6]]               200
+  //    [[0, 3],[5],[2, null]] 300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW({1, null}, valids), LCW{7, 6, 5}},
+         LCW{LCW{5, 6}},
+         LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)}});
+  FCW b({100, 200, 300});
+
+  LCW expected_a{
+    LCW({1, null}, valids), LCW{7, 6, 5}, LCW{5, 6}, LCW{0, 3}, LCW{5}, LCW({2, null}, valids)};
+  FCW expected_b{100, 100, 200, 300, 300, 300};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, NullsInNestedDoubleExplode)
+{
+  //    a                          b
+  //    [[1, null], [], [7, 6, 5]] 100
+  //    [[5, 6]]                   200
+  //    [[0, 3],[5],[2, null]]     300
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a{LCW{LCW({1, null}, valids), LCW{}, LCW{7, 6, 5}},
+        LCW{LCW{5, 6}},
+        LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)}};
+  FCW b{100, 200, 300};
+
+  FCW expected_a({1, null, null, 7, 6, 5, 5, 6, 0, 3, 5, 2, null},
+                 {1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+  FCW expected_b{100, 100, 100, 100, 100, 100, 200, 200, 300, 300, 300, 300, 300};
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto first_explode_ret = cudf::explode_outer(t, 0);
+  auto ret               = cudf::explode_outer(first_explode_ret->view(), 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{{0, 1, 0, 0, 1, 2, 0, 1, 0, 1, 0, 0, 1},
+                       {1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(first_explode_ret->view(), 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, NestedStructs)
+{
+  //    a                      b
+  //    [[1, null], [7, 6, 5]] {100, "100"}
+  //    [[5, 6]]               {200, "200"}
+  //    [[0, 3],[5],[2, null]] {300, "300"}
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW({1, null}, valids), LCW{7, 6, 5}},
+         LCW{LCW{5, 6}},
+         LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)}});
+  FCW b1({100, 200, 300});
+  cudf::test::strings_column_wrapper b2{"100", "200", "300"};
+  cudf::test::structs_column_wrapper b({b1, b2});
+
+  LCW expected_a{
+    LCW({1, null}, valids), LCW{7, 6, 5}, LCW{5, 6}, LCW{0, 3}, LCW{5}, LCW({2, null}, valids)};
+  FCW expected_b1{100, 100, 200, 300, 300, 300};
+  cudf::test::strings_column_wrapper expected_b2{"100", "100", "200", "300", "300", "300"};
+  cudf::test::structs_column_wrapper expected_b({expected_b1, expected_b2});
+
+  cudf::table_view t({a, b});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(t, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, ListOfStructsWithEmpties)
+{
+  //  a           b
+  //  [{1}}]      "a"
+  //  [{null}]    "b"
+  //  [null]      "c"
+  //  []          "d"
+  //  null        "e"
+
+  constexpr auto null = 0;
+
+  // row 0.  1 struct that contains a single int
+  cudf::test::fixed_width_column_wrapper<int32_t> i0{1};
+  std::vector<std::unique_ptr<cudf::column>> s0_cols;
+  s0_cols.push_back(i0.release());
+  cudf::test::structs_column_wrapper s0(std::move(s0_cols));
+  cudf::test::fixed_width_column_wrapper<int32_t> off0{0, 1};
+  auto row0 = cudf::make_lists_column(1, off0.release(), s0.release(), 0, rmm::device_buffer{});
+
+  // row 1.  1 struct that contains a null value
+  cudf::test::fixed_width_column_wrapper<int32_t> i1{{1}, {false}};
+  std::vector<std::unique_ptr<cudf::column>> s1_cols;
+  s1_cols.push_back(i1.release());
+  cudf::test::structs_column_wrapper s1(std::move(s1_cols));
+  cudf::test::fixed_width_column_wrapper<int32_t> off1{0, 1};
+  auto row1 = cudf::make_lists_column(1, off1.release(), s1.release(), 0, rmm::device_buffer{});
+
+  // row 2.  1 null struct
+  cudf::test::fixed_width_column_wrapper<int32_t> i2{0};
+  std::vector<std::unique_ptr<cudf::column>> s2_cols;
+  s2_cols.push_back(i2.release());
+  std::vector<bool> r2_valids{false};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(r2_valids.begin(), r2_valids.end());
+  auto s2 = cudf::make_structs_column(1, std::move(s2_cols), null_count, std::move(null_mask));
+  cudf::test::fixed_width_column_wrapper<int32_t> off2{0, 1};
+  auto row2 = cudf::make_lists_column(1, off2.release(), std::move(s2), 0, rmm::device_buffer{});
+
+  // row 3.  empty list.
+  cudf::test::fixed_width_column_wrapper<int32_t> i3{};
+  std::vector<std::unique_ptr<cudf::column>> s3_cols;
+  s3_cols.push_back(i3.release());
+  auto s3 = cudf::make_structs_column(0, std::move(s3_cols), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<int32_t> off3{0, 0};
+  auto row3 = cudf::make_lists_column(1, off3.release(), std::move(s3), 0, rmm::device_buffer{});
+
+  // row 4.  null list
+  cudf::test::fixed_width_column_wrapper<int32_t> i4{};
+  std::vector<std::unique_ptr<cudf::column>> s4_cols;
+  s4_cols.push_back(i4.release());
+  auto s4 = cudf::make_structs_column(0, std::move(s4_cols), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<int32_t> off4{0, 0};
+  std::vector<bool> r4_valids{false};
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(r4_valids.begin(), r4_valids.end());
+  auto row4 =
+    cudf::make_lists_column(1, off4.release(), std::move(s4), null_count, std::move(null_mask));
+
+  // concatenated
+  auto final_col =
+    cudf::concatenate(std::vector<cudf::column_view>({*row0, *row1, *row2, *row3, *row4}));
+  auto s = cudf::test::strings_column_wrapper({"a", "b", "c", "d", "e"}).release();
+
+  cudf::table_view t({final_col->view(), s->view()});
+
+  auto ret = cudf::explode_outer(t, 0);
+
+  auto expected_numeric_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, null, null, null, null}, {1, 0, 0, 0, 0}};
+
+  auto expected_a =
+    cudf::test::structs_column_wrapper{{expected_numeric_col}, {1, 1, 0, 0, 0}}.release();
+  auto expected_b = cudf::test::strings_column_wrapper({"a", "b", "c", "d", "e"}).release();
+
+  cudf::table_view expected({expected_a->view(), expected_b->view()});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+  FCW expected_pos_col{{0, 0, 0, null, null}, {1, 1, 1, 0, 0}};
+  cudf::table_view pos_expected({expected_pos_col, expected_a->view(), expected_b->view()});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TYPED_TEST(ExplodeOuterTypedTest, ListOfStructs)
+{
+  //  a                        b
+  //  [{70, "70"}, {75, "75"}] 100
+  //  [{50, "50"}, {55, "55"}] 200
+  //  [{35, "35"}, {45, "45"}] 300
+  //  [{25, "25"}, {30, "30"}] 400
+  //  [{15, "15"}, {20, "20"}] 500
+
+  auto numeric_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {70, 75, 50, 55, 35, 45, 25, 30, 15, 20}};
+  cudf::test::strings_column_wrapper string_col{
+    "70", "75", "50", "55", "35", "45", "25", "30", "15", "20"};
+  auto struct_col = cudf::test::structs_column_wrapper{{numeric_col, string_col}}.release();
+  auto a =
+    cudf::make_lists_column(5, FCW{0, 2, 4, 6, 8, 10}.release(), std::move(struct_col), 0, {});
+
+  FCW b{100, 200, 300, 400, 500};
+
+  cudf::table_view t({a->view(), b});
+  auto ret = cudf::explode_outer(t, 0);
+
+  auto expected_numeric_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {70, 75, 50, 55, 35, 45, 25, 30, 15, 20}};
+  cudf::test::strings_column_wrapper expected_string_col{
+    "70", "75", "50", "55", "35", "45", "25", "30", "15", "20"};
+
+  auto expected_a =
+    cudf::test::structs_column_wrapper{{expected_numeric_col, expected_string_col}}.release();
+  FCW expected_b{100, 100, 200, 200, 300, 300, 400, 400, 500, 500};
+
+  cudf::table_view expected({expected_a->view(), expected_b});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
+  cudf::table_view pos_expected({expected_pos_col, expected_a->view(), expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(t, 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
+
+TEST_F(ExplodeOuterTest, SlicedList)
+{
+  //    a                              b
+  //    [[1, null],[7, 6, 5]]          100
+  //    [[5, 6]]                       200
+  //    [[0, 3],[5],[2, null]]         300
+  //    [[8, 3],[],[4, null, 1, null]] 400
+  //    [[2, 3, 4],[9, 8]]             500
+
+  //    slicing the top 2 rows and the bottom row off
+
+  constexpr auto null = 0;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  LCW a({LCW{LCW({1, null}, valids), LCW{7, 6, 5}},
+         LCW{LCW{5, 6}},
+         LCW{LCW{0, 3}, LCW{5}, LCW({2, null}, valids)},
+         LCW{LCW{8, 3}, LCW{}, LCW({4, null, 1, null}, valids)},
+         LCW{LCW{2, 3, 4}, LCW{9, 8}}});
+  FCW b({100, 200, 300, 400, 500});
+
+  LCW expected_a{
+    LCW{0, 3}, LCW{5}, LCW({2, null}, valids), LCW{8, 3}, LCW{}, LCW({4, null, 1, null}, valids)};
+  FCW expected_b{300, 300, 300, 400, 400, 400};
+
+  cudf::table_view t({a, b});
+  auto sliced_t = cudf::slice(t, {2, 4});
+  cudf::table_view expected({expected_a, expected_b});
+
+  auto ret = cudf::explode_outer(sliced_t[0], 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(ret->view(), expected);
+
+  FCW expected_pos_col{0, 1, 2, 0, 1, 2};
+  cudf::table_view pos_expected({expected_pos_col, expected_a, expected_b});
+
+  auto pos_ret = cudf::explode_outer_position(sliced_t[0], 0);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(pos_ret->view(), pos_expected);
+}
diff --git a/cpp/tests/lists/extract_tests.cpp b/cpp/tests/lists/extract_tests.cpp
new file mode 100644
index 0000000..017cd47
--- /dev/null
+++ b/cpp/tests/lists/extract_tests.cpp
@@ -0,0 +1,428 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/lists/extract.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct ListsExtractTest : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+template <typename T>
+class ListsExtractNumericsTest : public ListsExtractTest {};
+
+TYPED_TEST_SUITE(ListsExtractNumericsTest, NumericTypesNotBool);
+
+TYPED_TEST(ListsExtractNumericsTest, ExtractElement)
+{
+  auto validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 1; });
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+  LCW input({LCW{3, 2, 1}, LCW{}, LCW{30, 20, 10, 50}, LCW{100, 120}, LCW{0}}, validity);
+
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 0);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({3, 0, 30, 100, 0}, {1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 1);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({2, 0, 20, 120, 0}, {1, 0, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 2);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({1, 0, 10, 0, 0}, {1, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 3);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({0, 0, 50, 0, 0}, {0, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 4);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -1);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({1, 0, 50, 120, 0}, {1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -2);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({2, 0, 10, 100, 0}, {1, 0, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -3);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({3, 0, 20, 0, 0}, {1, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -4);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({0, 0, 30, 0, 0}, {0, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -5);
+    cudf::test::fixed_width_column_wrapper<TypeParam> expected({0, 0, 0, 0, 0}, {0, 0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(ListsExtractTest, ExtractElementStrings)
+{
+  auto validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 1; });
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW input(
+    {LCW{"", "Héllo", "thesé"}, LCW{}, LCW{"are", "some", "", "z"}, LCW{"tést", "String"}, LCW{""}},
+    validity);
+
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 0);
+    cudf::test::strings_column_wrapper expected({"", "", "are", "tést", ""}, {1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 1);
+    cudf::test::strings_column_wrapper expected({"Héllo", "", "some", "String", ""},
+                                                {1, 0, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 2);
+    cudf::test::strings_column_wrapper expected({"thesé", "", "", "", ""}, {1, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 3);
+    cudf::test::strings_column_wrapper expected({"", "", "z", "", ""}, {0, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 4);
+    cudf::test::strings_column_wrapper expected({"", "", "", "", ""}, {0, 0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -1);
+    cudf::test::strings_column_wrapper expected({"thesé", "", "z", "String", ""}, {1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -2);
+    cudf::test::strings_column_wrapper expected({"Héllo", "", "", "tést", ""}, {1, 0, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -3);
+    cudf::test::strings_column_wrapper expected({"", "", "some", "", ""}, {1, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -4);
+    cudf::test::strings_column_wrapper expected({"", "", "are", "", ""}, {0, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -5);
+    cudf::test::strings_column_wrapper expected({"", "", "", "", ""}, {0, 0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TYPED_TEST(ListsExtractNumericsTest, ExtractElementNestedLists)
+{
+  std::vector<int32_t> validity{1, 0, 1, 1};
+  using LCW = cudf::test::lists_column_wrapper<TypeParam>;
+  LCW list({LCW{LCW{2, 3}, LCW{4, 5}},
+            LCW{LCW{}},
+            LCW{LCW{6, 7, 8}, LCW{9, 10, 11}, LCW{12, 13, 14}},
+            LCW{LCW{15, 16}, LCW{17, 18}, LCW{19, 20}, LCW{21, 22}, LCW{23, 24}}},
+           validity.begin());
+
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), 0);
+    LCW expected({LCW{2, 3}, LCW{}, LCW{6, 7, 8}, LCW{15, 16}}, validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), 1);
+    LCW expected({LCW{4, 5}, LCW{}, LCW{9, 10, 11}, LCW{17, 18}}, validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), 2);
+    std::vector<int32_t> expected_validity{0, 0, 1, 1};
+    LCW expected({LCW{}, LCW{}, LCW{12, 13, 14}, LCW{19, 20}}, expected_validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), 3);
+    std::vector<int32_t> expected_validity{0, 0, 0, 1};
+    LCW expected({LCW{}, LCW{}, LCW{}, LCW{21, 22}}, expected_validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), -1);
+    LCW expected({LCW{4, 5}, LCW{}, LCW{12, 13, 14}, LCW{23, 24}}, validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), -2);
+    LCW expected({LCW{2, 3}, LCW{}, LCW{9, 10, 11}, LCW{21, 22}}, validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(list), -3);
+    std::vector<int32_t> expected_validity{0, 0, 1, 1};
+    LCW expected({LCW{}, LCW{}, LCW{6, 7, 8}, LCW{19, 20}}, expected_validity.begin());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(ListsExtractTest, ExtractElementEmpty)
+{
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  LCW empty{};
+  auto result = cudf::lists::extract_list_element(cudf::lists_column_view(empty), 1);
+  EXPECT_EQ(cudf::data_type{cudf::type_id::STRING}, result->type());
+  EXPECT_EQ(0, result->size());
+
+  LCW empty_strings({LCW{"", "", ""}});
+  result = cudf::lists::extract_list_element(cudf::lists_column_view(empty_strings), 1);
+  cudf::test::strings_column_wrapper expected({""});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+
+  LCW null_strings({LCW{"", "", ""}}, thrust::make_constant_iterator<int32_t>(0));
+  result = cudf::lists::extract_list_element(cudf::lists_column_view(null_strings), 1);
+  cudf::test::strings_column_wrapper expected_null({""}, {0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_null, *result);
+}
+
+TEST_F(ListsExtractTest, ExtractElementWithNulls)
+{
+  auto validity = thrust::make_transform_iterator(
+    thrust::make_counting_iterator<cudf::size_type>(0), [](auto i) { return i != 1; });
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW input{
+    {{"Héllo", "", "thesé"}, validity}, {"are"}, {{"some", ""}, validity}, {"tést", "strings"}};
+
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 0);
+    cudf::test::strings_column_wrapper expected({"Héllo", "are", "some", "tést"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), 1);
+    cudf::test::strings_column_wrapper expected({"", "", "", "strings"}, {0, 0, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+  {
+    auto result = cudf::lists::extract_list_element(cudf::lists_column_view(input), -1);
+    cudf::test::strings_column_wrapper expected({"thesé", "are", "", "strings"}, {1, 1, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *result);
+  }
+}
+
+struct ListsExtractColumnIndicesTest : ListsExtractTest {};
+
+template <typename T>
+struct ListsExtractColumnIndicesTypedTest : ListsExtractColumnIndicesTest {};
+
+TYPED_TEST_SUITE(ListsExtractColumnIndicesTypedTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ListsExtractColumnIndicesTypedTest, ExtractElement)
+{
+  using LCW     = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using FWCW    = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using indices = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto input_column = LCW({LCW{3, 2, 1}, LCW{}, LCW{30, 20, 10, 50}, LCW{100, 120}, LCW{0}, LCW{}},
+                          cudf::test::iterators::null_at(1));
+  auto input        = cudf::lists_column_view(input_column);
+
+  {
+    // Test fetching first element.
+    auto result   = cudf::lists::extract_list_element(input, indices{0, 0, 0, 0, 0, 0});
+    auto expected = FWCW({3, 0, 30, 100, 0, 0}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching last element.
+    auto result   = cudf::lists::extract_list_element(input, indices{2, 0, 3, 1, 0, 0});
+    auto expected = FWCW({1, 0, 50, 120, 0, 0}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching *all* out of bounds.
+    auto result   = cudf::lists::extract_list_element(input, indices{9, 9, 9, 9, 9, 9});
+    auto expected = FWCW({0, 0, 0, 0, 0, 0}, cudf::test::iterators::all_nulls());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching first from the end.
+    auto result   = cudf::lists::extract_list_element(input, indices{-1, -1, -1, -1, -1, -1});
+    auto expected = FWCW({1, 0, 50, 120, 0, 0}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching last from the end.
+    auto result   = cudf::lists::extract_list_element(input, indices{-3, 0, -4, -2, -1, 0});
+    auto expected = FWCW({3, 0, 30, 100, 0, 0}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching *all* negative out of bounds.
+    auto result   = cudf::lists::extract_list_element(input, indices{-9, -9, -9, -9, -9, -9});
+    auto expected = FWCW({0, 0, 0, 0, 0, 0}, cudf::test::iterators::all_nulls());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test mixed indices.
+    auto result   = cudf::lists::extract_list_element(input, indices{-2, 0, 3, -1, 0, 0});
+    auto expected = FWCW({2, 0, 50, 120, 0, 0}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test possibly null indices.
+    auto result = cudf::lists::extract_list_element(
+      input, indices{{-2, 0, 3, -1, 0, 0}, cudf::test::iterators::nulls_at({2, 4})});
+    auto expected = FWCW({2, 0, 50, 120, 0, 0}, cudf::test::iterators::nulls_at({1, 2, 4, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TYPED_TEST(ListsExtractColumnIndicesTypedTest, FailureCases)
+{
+  using LCW     = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+  using indices = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  {
+    // Non-empty input, with mismatched size of indices.
+    auto input_column =
+      LCW({LCW{3, 2, 1}, LCW{}, LCW{30, 20, 10, 50}, LCW{100, 120}, LCW{0}, LCW{}},
+          cudf::test::iterators::null_at(1));
+    auto input = cudf::lists_column_view(input_column);
+
+    EXPECT_THROW(cudf::lists::extract_list_element(input, indices{0, 1, 2}), cudf::logic_error);
+  }
+  {
+    // Non-empty input, with empty indices.
+    auto input_column =
+      LCW({LCW{3, 2, 1}, LCW{}, LCW{30, 20, 10, 50}, LCW{100, 120}, LCW{0}, LCW{}},
+          cudf::test::iterators::null_at(1));
+    auto input = cudf::lists_column_view(input_column);
+
+    EXPECT_THROW(cudf::lists::extract_list_element(input, indices{}), cudf::logic_error);
+  }
+  {
+    // Empty input, with mismatched size of indices.
+    auto input_column = LCW{};
+    auto input        = cudf::lists_column_view(input_column);
+    EXPECT_THROW(cudf::lists::extract_list_element(input, indices{0, 1, 2}), cudf::logic_error);
+  }
+}
+
+TEST_F(ListsExtractColumnIndicesTest, ExtractStrings)
+{
+  using LCW     = cudf::test::lists_column_wrapper<cudf::string_view>;
+  using strings = cudf::test::strings_column_wrapper;
+  using indices = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto input_column = LCW(
+    {LCW{"3", "2", "1"}, LCW{}, LCW{"30", "20", "10", "50"}, LCW{"100", "120"}, LCW{"0"}, LCW{}},
+    cudf::test::iterators::null_at(1));
+  auto input = cudf::lists_column_view(input_column);
+
+  {
+    // Test fetching first element.
+    auto result = cudf::lists::extract_list_element(input, indices{0, 0, 0, 0, 0, 0});
+    auto expected =
+      strings({"3", "", "30", "100", "0", ""}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching last element.
+    auto result = cudf::lists::extract_list_element(input, indices{2, 0, 3, 1, 0, 0});
+    auto expected =
+      strings({"1", "", "50", "120", "0", ""}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching *all* out of bounds.
+    auto result   = cudf::lists::extract_list_element(input, indices{9, 9, 9, 9, 9, 9});
+    auto expected = strings({"", "", "", "", "", ""}, cudf::test::iterators::all_nulls());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching first from the end.
+    auto result = cudf::lists::extract_list_element(input, indices{-1, -1, -1, -1, -1, -1});
+    auto expected =
+      strings({"1", "", "50", "120", "0", ""}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching last from the end.
+    auto result = cudf::lists::extract_list_element(input, indices{-3, 0, -4, -2, -1, 0});
+    auto expected =
+      strings({"3", "", "30", "100", "0", ""}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test fetching *all* negative out of bounds.
+    auto result   = cudf::lists::extract_list_element(input, indices{-9, -9, -9, -9, -9, -9});
+    auto expected = strings({"", "", "", "", "", ""}, cudf::test::iterators::all_nulls());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test mixed indices.
+    auto result = cudf::lists::extract_list_element(input, indices{-2, 0, 3, -1, 0, 0});
+    auto expected =
+      strings({"2", "", "50", "120", "0", ""}, cudf::test::iterators::nulls_at({1, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    // Test possibly null indices.
+    auto result = cudf::lists::extract_list_element(
+      input, indices{{-2, 0, 3, -1, 0, 0}, cudf::test::iterators::nulls_at({2, 4})});
+    auto expected =
+      strings({"2", "", "50", "120", "", ""}, cudf::test::iterators::nulls_at({1, 2, 4, 5}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/lists/reverse_tests.cpp b/cpp/tests/lists/reverse_tests.cpp
new file mode 100644
index 0000000..00dc13c
--- /dev/null
+++ b/cpp/tests/lists/reverse_tests.cpp
@@ -0,0 +1,472 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/lists/reverse.hpp>
+#include <cudf/null_mask.hpp>
+
+using namespace cudf::test::iterators;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+
+using ints_lists  = cudf::test::lists_column_wrapper<int32_t>;
+using ints_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+using strings_col = cudf::test::strings_column_wrapper;
+using structs_col = cudf::test::structs_column_wrapper;
+
+struct ListsReverseTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct ListsReverseTypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(ListsReverseTypedTest, TestTypes);
+
+TEST_F(ListsReverseTest, EmptyInput)
+{
+  // Empty column.
+  {
+    auto const input   = ints_lists{};
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *results);
+  }
+
+  // Empty lists.
+  {
+    auto const input   = ints_lists{ints_lists{}, ints_lists{}, ints_lists{}};
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *results);
+  }
+
+  // Empty nested lists.
+  {
+    auto const input   = ints_lists{ints_lists{ints_lists{}}, ints_lists{}, ints_lists{}};
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *results);
+  }
+}
+
+TYPED_TEST(ListsReverseTypedTest, SimpleInputNoNulls)
+{
+  using lists_col           = cudf::test::lists_column_wrapper<TypeParam>;
+  auto const input_original = lists_col{{}, {1, 2, 3}, {}, {4, 5}, {6, 7, 8}, {9}};
+
+  {
+    auto const expected = lists_col{{}, {3, 2, 1}, {}, {5, 4}, {8, 7, 6}, {9}};
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input_original));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const expected = lists_col{{3, 2, 1}, {}, {5, 4}};
+    auto const input    = cudf::slice(input_original, {1, 4})[0];
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const expected = lists_col{lists_col{}};
+    auto const input    = cudf::slice(input_original, {2, 3})[0];
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const expected = lists_col{{}, {5, 4}};
+    auto const input    = cudf::slice(input_original, {2, 4})[0];
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TYPED_TEST(ListsReverseTypedTest, SimpleInputWithNulls)
+{
+  using lists_col           = cudf::test::lists_column_wrapper<TypeParam>;
+  auto const input_original = lists_col{{lists_col{},
+                                         lists_col{1, 2, 3},
+                                         lists_col{} /*null*/,
+                                         lists_col{{4, 5, null}, null_at(2)},
+                                         lists_col{6, 7, 8},
+                                         lists_col{9}},
+                                        null_at(2)};
+
+  {
+    auto const expected = lists_col{{lists_col{},
+                                     lists_col{3, 2, 1},
+                                     lists_col{} /*null*/,
+                                     lists_col{{null, 5, 4}, null_at(0)},
+                                     lists_col{8, 7, 6},
+                                     lists_col{9}},
+                                    null_at(2)};
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input_original));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const expected = lists_col{
+      {lists_col{3, 2, 1}, lists_col{} /*null*/, lists_col{{null, 5, 4}, null_at(0)}}, null_at(1)};
+    auto const input   = cudf::slice(input_original, {1, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const expected = lists_col{{lists_col{} /*null*/}, null_at(0)};
+    auto const input    = cudf::slice(input_original, {2, 3})[0];
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const expected =
+      lists_col{{lists_col{} /*null*/, lists_col{{null, 5, 4}, null_at(0)}}, null_at(0)};
+    auto const input   = cudf::slice(input_original, {2, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const input    = cudf::slice(input_original, {4, 6})[0];
+    auto const expected = lists_col{{8, 7, 6}, {9}};
+    auto const results  = cudf::lists::reverse(cudf::lists_column_view(input));
+    // The result doesn't have nulls, but it is nullable.
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results);
+  }
+}
+
+TYPED_TEST(ListsReverseTypedTest, InputListsOfListsNoNulls)
+{
+  using lists_col           = cudf::test::lists_column_wrapper<TypeParam>;
+  auto const input_original = [] {
+    auto child =
+      lists_col{{1, 2, 3}, {4, 5, 6}, {7}, {4, 5}, {}, {4, 5, 6}, {}, {6, 7, 8}, {}, {9}}.release();
+    auto offsets = ints_col{0, 0, 3, 3, 6, 9, 10, 10, 10}.release();
+    return cudf::make_lists_column(8, std::move(offsets), std::move(child), 0, {});
+  }();
+
+  {
+    auto const expected = [] {
+      auto child =
+        lists_col{{7}, {4, 5, 6}, {1, 2, 3}, {4, 5, 6}, {}, {4, 5}, {}, {6, 7, 8}, {}, {9}}
+          .release();
+      auto offsets = ints_col{0, 0, 3, 3, 6, 9, 10, 10, 10}.release();
+      return cudf::make_lists_column(8, std::move(offsets), std::move(child), 0, {});
+    }();
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(*input_original));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+  }
+
+  {
+    auto const expected = [] {
+      auto child   = lists_col{{7}, {4, 5, 6}, {1, 2, 3}, {4, 5, 6}, {}, {4, 5}}.release();
+      auto offsets = ints_col{0, 3, 3, 6}.release();
+      return cudf::make_lists_column(3, std::move(offsets), std::move(child), 0, {});
+    }();
+    auto const input   = cudf::slice(*input_original, {1, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+  }
+
+  {
+    auto const input   = cudf::slice(*input_original, {2, 3})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *results);
+  }
+
+  {
+    auto const expected = [] {
+      auto child   = lists_col{{4, 5, 6}, {}, {4, 5}}.release();
+      auto offsets = ints_col{0, 0, 3}.release();
+      return cudf::make_lists_column(2, std::move(offsets), std::move(child), 0, {});
+    }();
+    auto const input   = cudf::slice(*input_original, {2, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+  }
+}
+
+TYPED_TEST(ListsReverseTypedTest, InputListsOfListsWithNulls)
+{
+  using lists_col           = cudf::test::lists_column_wrapper<TypeParam>;
+  auto const input_original = [] {
+    auto child = lists_col{{{1, 2, 3},
+                            {4, 5, 6},
+                            {7},
+                            {4, 5},
+                            {} /*null*/,
+                            {4, 5, 6},
+                            {},
+                            {6, 7, 8},
+                            {} /*null*/,
+                            {9}},
+                           nulls_at({4, 8})}
+                   .release();
+    auto offsets   = ints_col{0, 0, 3, 3, 6, 9, 10, 10, 10}.release();
+    auto null_mask = cudf::create_null_mask(8, cudf::mask_state::ALL_VALID);
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 2, 3, false);
+
+    return cudf::make_lists_column(
+      8, std::move(offsets), std::move(child), 1, std::move(null_mask));
+  }();
+
+  {
+    auto const expected = [] {
+      auto child = lists_col{{{7},
+                              {4, 5, 6},
+                              {1, 2, 3},
+                              {4, 5, 6},
+                              {} /*null*/,
+                              {4, 5},
+                              {} /*null*/,
+                              {6, 7, 8},
+                              {},
+                              {9}},
+                             nulls_at({4, 6})}
+                     .release();
+      auto offsets   = ints_col{0, 0, 3, 3, 6, 9, 10, 10, 10}.release();
+      auto null_mask = cudf::create_null_mask(8, cudf::mask_state::ALL_VALID);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 2, 3, false);
+
+      return cudf::make_lists_column(
+        8, std::move(offsets), std::move(child), 1, std::move(null_mask));
+    }();
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(*input_original));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+  }
+
+  {
+    auto const expected = [] {
+      auto child   = lists_col{{7}, {4, 5, 6}, {1, 2, 3}}.release();
+      auto offsets = ints_col{0, 3}.release();
+      return cudf::make_lists_column(1, std::move(offsets), std::move(child), 0, {});
+    }();
+    auto const input   = cudf::slice(*input_original, {0, 1})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *results);
+  }
+
+  {
+    auto const expected = [] {
+      auto child =
+        lists_col{{{7}, {4, 5, 6}, {1, 2, 3}, {4, 5, 6}, {} /*null*/, {4, 5}}, null_at(4)}
+          .release();
+      auto offsets   = ints_col{0, 3, 3, 6}.release();
+      auto null_mask = cudf::create_null_mask(3, cudf::mask_state::ALL_VALID);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 1, 2, false);
+
+      return cudf::make_lists_column(
+        3, std::move(offsets), std::move(child), 1, std::move(null_mask));
+    }();
+    auto const input   = cudf::slice(*input_original, {1, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+  }
+
+  {
+    auto const expected = [] {
+      auto child     = lists_col{{{4, 5, 6}, {} /*null*/, {4, 5}}, null_at(1)}.release();
+      auto offsets   = ints_col{0, 0, 3}.release();
+      auto null_mask = cudf::create_null_mask(2, cudf::mask_state::ALL_VALID);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 0, 1, false);
+
+      return cudf::make_lists_column(
+        2, std::move(offsets), std::move(child), 1, std::move(null_mask));
+    }();
+    auto const input   = cudf::slice(*input_original, {2, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    // The result doesn't have nulls, but it is nullable.
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results);
+  }
+}
+
+TYPED_TEST(ListsReverseTypedTest, InputListsOfStructsWithNulls)
+{
+  using data_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input_original = [] {
+    auto child = [] {
+      auto grandchild1 = data_col{{
+                                    1,    XXX,  null, XXX, XXX, 2,  3,    4,   // list1
+                                    5,    6,    7,    8,   9,   10, null, 11,  // list2
+                                    null, null, 12,   13,  14,  15, 16,   17   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+      auto grandchild2 = strings_col{{
+                                       // begin list1
+                                       "Banana",
+                                       "YYY", /*NULL*/
+                                       "Apple",
+                                       "XXX", /*NULL*/
+                                       "YYY", /*NULL*/
+                                       "Banana",
+                                       "Cherry",
+                                       "Kiwi",  // end list1
+                                                // begin list2
+                                       "Bear",
+                                       "Duck",
+                                       "Cat",
+                                       "Dog",
+                                       "Panda",
+                                       "Bear",
+                                       "" /*NULL*/,
+                                       "Panda",  // end list2
+                                                 // begin list3
+                                       "ÁÁÁ",
+                                       "ÉÉÉÉÉ",
+                                       "ÍÍÍÍÍ",
+                                       "ÁBC",
+                                       "" /*NULL*/,
+                                       "ÁÁÁ",
+                                       "ÁBC",
+                                       "XYZ"  // end list3
+                                     },
+                                     nulls_at({14, 20})};
+      return structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})}.release();
+    }();
+    auto offsets   = ints_col{0, 0, 8, 16, 16, 16, 24}.release();
+    auto null_mask = cudf::create_null_mask(6, cudf::mask_state::ALL_VALID);
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 0, 1, false);
+    cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 4, 5, false);
+
+    return cudf::make_lists_column(
+      6, std::move(offsets), std::move(child), 2, std::move(null_mask));
+  }();
+
+  {
+    auto const expected = [] {
+      auto child = [] {
+        auto grandchild1 = data_col{{
+                                      4,  3,    2,  null, null, null, null, 1,     // list1
+                                      11, null, 10, 9,    8,    7,    6,    5,     // list2
+                                      17, 16,   15, 14,   13,   12,   null, null,  // list3
+                                    },
+                                    nulls_at({3, 4, 5, 6, 9, 22, 23})};
+        auto grandchild2 = strings_col{{
+                                         // begin list1
+                                         "Kiwi",
+                                         "Cherry",
+                                         "Banana",
+                                         "", /*NULL*/
+                                         "", /*NULL*/
+                                         "Apple",
+                                         "",        /*NULL*/
+                                         "Banana",  // end list1
+                                                    // begin list2
+                                         "Panda",
+                                         "" /*NULL*/,
+                                         "Bear",
+                                         "Panda",
+                                         "Dog",
+                                         "Cat",
+                                         "Duck",
+                                         "Bear",  // end list2
+                                                  // begin list3
+                                         "XYZ",
+                                         "ÁBC",
+                                         "ÁÁÁ",
+                                         "" /*NULL*/,
+                                         "ÁBC",
+                                         "ÍÍÍÍÍ",
+                                         "ÉÉÉÉÉ",
+                                         "ÁÁÁ"  // end list3
+                                       },
+                                       nulls_at({3, 4, 6, 9, 19})};
+        return structs_col{{grandchild1, grandchild2}, nulls_at({3, 4, 6})}.release();
+      }();
+      auto offsets   = ints_col{0, 0, 8, 16, 16, 16, 24}.release();
+      auto null_mask = cudf::create_null_mask(6, cudf::mask_state::ALL_VALID);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 0, 1, false);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(null_mask.data()), 4, 5, false);
+
+      return cudf::make_lists_column(
+        6, std::move(offsets), std::move(child), 2, std::move(null_mask));
+    }();
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(*input_original));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+  }
+
+  {
+    auto const expected = [] {
+      auto child = [] {
+        auto grandchild1 = data_col{{
+                                      4,
+                                      3,
+                                      2,
+                                      null,
+                                      null,
+                                      null,
+                                      null,
+                                      1,  // end list1
+                                      11,
+                                      null,
+                                      10,
+                                      9,
+                                      8,
+                                      7,
+                                      6,
+                                      5  // end list2
+                                    },
+                                    nulls_at({3, 4, 5, 6, 9})};
+        auto grandchild2 = strings_col{{
+                                         // begin list1
+                                         "Kiwi",
+                                         "Cherry",
+                                         "Banana",
+                                         "", /*NULL*/
+                                         "", /*NULL*/
+                                         "Apple",
+                                         "",        /*NULL*/
+                                         "Banana",  // end list1
+                                                    // begin list2
+                                         "Panda",
+                                         "" /*NULL*/,
+                                         "Bear",
+                                         "Panda",
+                                         "Dog",
+                                         "Cat",
+                                         "Duck",
+                                         "Bear"  // end list2
+                                       },
+                                       nulls_at({3, 4, 6, 9})};
+        return structs_col{{grandchild1, grandchild2}, nulls_at({3, 4, 6})}.release();
+      }();
+
+      auto offsets = ints_col{0, 8, 16, 16}.release();
+      return cudf::make_lists_column(3, std::move(offsets), std::move(child), 0, {});
+    }();
+    auto const input   = cudf::slice(*input_original, {1, 4})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    // The result doesn't have nulls, but it is nullable.
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results);
+  }
+
+  {
+    auto const input   = cudf::slice(*input_original, {4, 5})[0];
+    auto const results = cudf::lists::reverse(cudf::lists_column_view(input));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, *results);
+  }
+}
diff --git a/cpp/tests/lists/sequences_tests.cpp b/cpp/tests/lists/sequences_tests.cpp
new file mode 100644
index 0000000..9aed342
--- /dev/null
+++ b/cpp/tests/lists/sequences_tests.cpp
@@ -0,0 +1,248 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/lists/filling.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+template <typename T, typename U = int32_t>
+using ListsCol = cudf::test::lists_column_wrapper<T, U>;
+
+template <typename T, typename U = int32_t>
+using FWDCol = cudf::test::fixed_width_column_wrapper<T, U>;
+
+using IntsCol = cudf::test::fixed_width_column_wrapper<int32_t>;
+}  // namespace
+
+/*-----------------------------------------------------------------------------------------------*/
+template <typename T>
+class NumericSequencesTypedTest : public cudf::test::BaseFixture {};
+using NumericTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(NumericSequencesTypedTest, NumericTypes);
+
+TYPED_TEST(NumericSequencesTypedTest, SimpleTestNoNull)
+{
+  using T = TypeParam;
+
+  auto const starts = FWDCol<T>{1, 2, 3};
+  auto const sizes  = IntsCol{5, 3, 4};
+
+  // Sequences with step == 1.
+  {
+    auto const expected =
+      ListsCol<T>{ListsCol<T>{1, 2, 3, 4, 5}, ListsCol<T>{2, 3, 4}, ListsCol<T>{3, 4, 5, 6}};
+    auto const result = cudf::lists::sequences(starts, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Sequences with various steps.
+  {
+    auto const steps = FWDCol<T>{1, 3, 2};
+    auto const expected =
+      ListsCol<T>{ListsCol<T>{1, 2, 3, 4, 5}, ListsCol<T>{2, 5, 8}, ListsCol<T>{3, 5, 7, 9}};
+    auto const result = cudf::lists::sequences(starts, steps, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TYPED_TEST(NumericSequencesTypedTest, ZeroSizesTest)
+{
+  using T = TypeParam;
+
+  auto const starts = FWDCol<T>{1, 2, 3};
+  auto const sizes  = IntsCol{0, 3, 0};
+
+  // Sequences with step == 1.
+  {
+    auto const expected = ListsCol<T>{ListsCol<T>{}, ListsCol<T>{2, 3, 4}, ListsCol<T>{}};
+    auto const result   = cudf::lists::sequences(starts, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Sequences with various steps.
+  {
+    auto const steps    = FWDCol<T>{1, 3, 2};
+    auto const expected = ListsCol<T>{ListsCol<T>{}, ListsCol<T>{2, 5, 8}, ListsCol<T>{}};
+    auto const result   = cudf::lists::sequences(starts, steps, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TYPED_TEST(NumericSequencesTypedTest, SlicedInputTestNoNulls)
+{
+  using T = TypeParam;
+  constexpr int32_t dont_care{123};
+
+  auto const starts_original =
+    FWDCol<T>{dont_care, dont_care, dont_care, 1, 2, 3, 4, 5, dont_care, dont_care};
+  auto const sizes_original = IntsCol{dont_care, 5, 3, 4, 1, 2, dont_care, dont_care};
+
+  auto const starts = cudf::slice(starts_original, {3, 8})[0];
+  auto const sizes  = cudf::slice(sizes_original, {1, 6})[0];
+
+  // Sequences with step == 1.
+  {
+    auto const expected = ListsCol<T>{ListsCol<T>{1, 2, 3, 4, 5},
+                                      ListsCol<T>{2, 3, 4},
+                                      ListsCol<T>{3, 4, 5, 6},
+                                      ListsCol<T>{4},
+                                      ListsCol<T>{5, 6}
+
+    };
+    auto const result   = cudf::lists::sequences(starts, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Sequences with various steps.
+  {
+    auto const steps_original = FWDCol<T>{dont_care, dont_care, 1, 3, 2, 2, 3, dont_care};
+    auto const steps          = cudf::slice(steps_original, {2, 7})[0];
+
+    auto const expected = ListsCol<T>{ListsCol<T>{1, 2, 3, 4, 5},
+                                      ListsCol<T>{2, 5, 8},
+                                      ListsCol<T>{3, 5, 7, 9},
+                                      ListsCol<T>{4},
+                                      ListsCol<T>{5, 8}
+
+    };
+    auto const result   = cudf::lists::sequences(starts, steps, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+/*-----------------------------------------------------------------------------------------------*/
+// Data generated using https://www.epochconverter.com/
+template <typename T>
+class DurationSequencesTypedTest : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(DurationSequencesTypedTest, cudf::test::DurationTypes);
+
+// Start time is 1638477473L - Thursday, December 2, 2021 8:37:53 PM.
+constexpr int64_t start_time = 1638477473L;
+
+TYPED_TEST(DurationSequencesTypedTest, SequencesNoNull)
+{
+  using T = TypeParam;
+
+  auto const starts = FWDCol<T, int64_t>{start_time, start_time, start_time};
+  auto const sizes  = IntsCol{1, 2, 3};
+
+  // Sequences with step == 1.
+  {
+    auto const expected_h = std::vector<int64_t>{start_time, start_time + 1L, start_time + 2L};
+    auto const expected =
+      ListsCol<T, int64_t>{ListsCol<T, int64_t>{expected_h.begin(), expected_h.begin() + 1},
+                           ListsCol<T, int64_t>{expected_h.begin(), expected_h.begin() + 2},
+                           ListsCol<T, int64_t>{expected_h.begin(), expected_h.begin() + 3}};
+    auto const result = cudf::lists::sequences(starts, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Sequences with various steps, including negative.
+  {
+    auto const steps    = FWDCol<T, int64_t>{10L, -155L, -13L};
+    auto const expected = ListsCol<T, int64_t>{
+      ListsCol<T, int64_t>{start_time},
+      ListsCol<T, int64_t>{start_time, start_time - 155L},
+      ListsCol<T, int64_t>{start_time, start_time - 13L, start_time - 13L * 2L}};
+    auto const result = cudf::lists::sequences(starts, steps, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+/*-----------------------------------------------------------------------------------------------*/
+class NumericSequencesTest : public cudf::test::BaseFixture {};
+
+TEST_F(NumericSequencesTest, EmptyInput)
+{
+  auto const starts   = IntsCol{};
+  auto const sizes    = IntsCol{};
+  auto const steps    = IntsCol{};
+  auto const expected = ListsCol<int32_t>{};
+
+  // Sequences with step == 1.
+  {
+    auto const result = cudf::lists::sequences(starts, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Sequences with given steps.
+  {
+    auto const result = cudf::lists::sequences(starts, steps, sizes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(NumericSequencesTest, InvalidSizesInput)
+{
+  auto const starts = IntsCol{};
+  auto const steps  = IntsCol{};
+  auto const sizes  = FWDCol<float>{};
+
+  EXPECT_THROW(cudf::lists::sequences(starts, sizes), cudf::logic_error);
+  EXPECT_THROW(cudf::lists::sequences(starts, steps, sizes), cudf::logic_error);
+}
+
+TEST_F(NumericSequencesTest, MismatchedColumnSizesInput)
+{
+  auto const starts = IntsCol{1, 2, 3};
+  auto const steps  = IntsCol{1, 2};
+  auto const sizes  = IntsCol{1, 2, 3, 4};
+
+  EXPECT_THROW(cudf::lists::sequences(starts, sizes), cudf::logic_error);
+  EXPECT_THROW(cudf::lists::sequences(starts, steps, sizes), cudf::logic_error);
+}
+
+TEST_F(NumericSequencesTest, MismatchedColumnTypesInput)
+{
+  auto const starts = IntsCol{1, 2, 3};
+  auto const steps  = FWDCol<float>{1, 2, 3};
+  auto const sizes  = IntsCol{1, 2, 3};
+
+  EXPECT_THROW(cudf::lists::sequences(starts, steps, sizes), cudf::logic_error);
+}
+
+TEST_F(NumericSequencesTest, InputHasNulls)
+{
+  constexpr int32_t null{0};
+
+  {
+    auto const starts = IntsCol{{null, 2, 3}, null_at(0)};
+    auto const sizes  = IntsCol{1, 2, 3};
+    EXPECT_THROW(cudf::lists::sequences(starts, sizes), cudf::logic_error);
+  }
+
+  {
+    auto const starts = IntsCol{1, 2, 3};
+    auto const sizes  = IntsCol{{null, 2, 3}, null_at(0)};
+    EXPECT_THROW(cudf::lists::sequences(starts, sizes), cudf::logic_error);
+  }
+
+  {
+    auto const starts = IntsCol{1, 2, 3};
+    auto const steps  = IntsCol{{null, 2, 3}, null_at(0)};
+    auto const sizes  = IntsCol{1, 2, 3};
+    EXPECT_THROW(cudf::lists::sequences(starts, steps, sizes), cudf::logic_error);
+  }
+}
diff --git a/cpp/tests/lists/set_operations/difference_distinct_tests.cpp b/cpp/tests/lists/set_operations/difference_distinct_tests.cpp
new file mode 100644
index 0000000..84c51f2
--- /dev/null
+++ b/cpp/tests/lists/set_operations/difference_distinct_tests.cpp
@@ -0,0 +1,670 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/lists/set_operations.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/null_mask.hpp>
+
+#include <limits>
+#include <string>
+
+using float_type = double;
+using namespace cudf::test::iterators;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr neg_NaN      = -std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr neg_Inf      = -std::numeric_limits<float_type>::infinity();
+auto constexpr NaN          = std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr Inf          = std::numeric_limits<float_type>::infinity();
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using bools_col     = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_lists  = cudf::test::lists_column_wrapper<float_type>;
+using strings_lists = cudf::test::lists_column_wrapper<cudf::string_view>;
+using strings_col   = cudf::test::strings_column_wrapper;
+using structs_col   = cudf::test::structs_column_wrapper;
+using lists_cv      = cudf::lists_column_view;
+
+namespace {
+auto set_difference_sorted(cudf::column_view const& lhs,
+                           cudf::column_view const& rhs,
+                           cudf::null_equality nulls_equal = NULL_EQUAL,
+                           cudf::nan_equality nans_equal   = NAN_EQUAL)
+{
+  auto const results =
+    cudf::lists::difference_distinct(lists_cv{lhs}, lists_cv{rhs}, nulls_equal, nans_equal);
+  return cudf::lists::sort_lists(
+    lists_cv{*results}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+}
+}  // namespace
+
+struct SetDifferenceTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct SetDifferenceTypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(SetDifferenceTypedTest, TestTypes);
+
+TEST_F(SetDifferenceTest, TrivialTest)
+{
+  auto const lhs =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+  auto const rhs =
+    floats_lists{{floats_lists{{1.0, 0.5, null, 0.0, 0.0, null, NaN}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  {} /*NULL*/},
+                 null_at(3)};
+  auto const expected = floats_lists{
+    {floats_lists{5.0}, floats_lists{5.0, NaN}, floats_lists{} /*NULL*/, floats_lists{} /*NULL*/},
+    nulls_at({2, 3})};
+
+  auto const results_sorted = set_difference_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+}
+
+TEST_F(SetDifferenceTest, TrivialIdentityTest)
+{
+  auto const input =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+
+  auto const expected =
+    floats_lists{{floats_lists{}, floats_lists{}, {} /*NULL*/, floats_lists{}}, null_at(2)};
+
+  auto const results_sorted = set_difference_sorted(input, input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+}
+
+TEST_F(SetDifferenceTest, FloatingPointTestsWithSignedZero)
+{
+  // -0.0 and 0.0 should be considered equal.
+  auto const lhs      = floats_lists{{0.0, 0.0, 0.0, 0.0, 0.0}, {-0.0, 1.0}, {0.0}};
+  auto const rhs      = floats_lists{{-0.0, -0.0, -0.0, -0.0, -0.0}, {0.0, 2.0}, {1.0}};
+  auto const expected = floats_lists{floats_lists{}, floats_lists{1.0}, floats_lists{0.0}};
+
+  auto const results_sorted = set_difference_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(SetDifferenceTest, FloatingPointTestsWithInf)
+{
+  auto const lhs      = floats_lists{{Inf, Inf, Inf}, {Inf, 0.0, neg_Inf}};
+  auto const rhs      = floats_lists{{neg_Inf, neg_Inf}, {0.0}};
+  auto const expected = floats_lists{floats_lists{Inf}, floats_lists{neg_Inf, Inf}};
+
+  auto const results_sorted = set_difference_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(SetDifferenceTest, FloatingPointTestsWithNaNs)
+{
+  auto const lhs =
+    floats_lists{{0, -1, 1, NaN}, {2, 0, neg_NaN}, {1, -2, 2, 0, 1, 2}, {NaN, NaN, NaN, NaN, NaN}};
+  auto const rhs =
+    floats_lists{{2, 3, 4, neg_NaN}, {2, 0}, {neg_NaN, 1, -2, 2, 0, 1, 2}, {neg_NaN, neg_NaN}};
+
+  // NaNs are equal.
+  {
+    auto const expected       = floats_lists{{-1, 0, 1}, {neg_NaN}, {}, {}};
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // NaNs are unequal.
+  {
+    auto const expected = floats_lists{{-1, 0, 1, NaN}, {neg_NaN}, {}, {NaN, NaN, NaN, NaN, NaN}};
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetDifferenceTest, StringTestsNonNull)
+{
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{};
+    auto const rhs      = strings_lists{};
+    auto const expected = strings_lists{};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{strings_lists{}};
+    auto const rhs      = strings_lists{strings_lists{}};
+    auto const expected = strings_lists{strings_lists{}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // No overlap.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"aha", "bear", "blow", "heat"};
+    auto const expected = strings_lists{"a", "is", "string", "this"};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // One list column.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"a", "delicious", "banana"};
+    auto const expected = strings_lists{"is", "string", "this"};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column.
+  {
+    auto const lhs      = strings_lists{strings_lists{"one", "two", "three"},
+                                   strings_lists{"four", "five", "six"},
+                                   strings_lists{"1", "2", "3"}};
+    auto const rhs      = strings_lists{strings_lists{"one", "banana"},
+                                   strings_lists{"apple", "kiwi", "cherry"},
+                                   strings_lists{"two", "and", "1"}};
+    auto const expected = strings_lists{
+      strings_lists{"three", "two"}, strings_lists{"five", "four", "six"}, strings_lists{"2", "3"}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetDifferenceTest, StringTestsWithNullsEqual)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected = strings_lists{"a", "is", "string", "this"};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = [] {
+      auto str_lists = strings_lists{{strings_lists{"a", "is", "string", "this"},
+                                      strings_lists{} /*NULL*/,
+                                      strings_lists{"a", "is", "string"}},
+                                     null_at(1)}
+                         .release();
+      auto& child = str_lists->child(cudf::lists_column_view::child_column_index);
+      child.set_null_mask(cudf::create_null_mask(child.size(), cudf::mask_state::ALL_VALID), 0);
+      return str_lists;
+    }();
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+}
+
+TEST_F(SetDifferenceTest, StringTestsWithNullsUnequal)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected =
+      strings_lists{{null, null, null, "a", "is", "string", "this"}, nulls_at({0, 1, 2})};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = strings_lists{
+      {strings_lists{{null, null, null, null, "a", "is", "string", "this"}, nulls_at({0, 1, 2, 3})},
+       strings_lists{} /*NULL*/,
+       strings_lists{"a", "is", "string"}},
+      null_at(1)};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetDifferenceTypedTest, TrivialInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Empty input.
+  {
+    auto const lhs      = lists_col{};
+    auto const rhs      = lists_col{};
+    auto const expected = lists_col{};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // All input lists are empty.
+  {
+    auto const lhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const rhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const expected = lists_col{lists_col{}, lists_col{}, lists_col{}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple empty lists.
+  {
+    auto const lhs      = lists_col{{}, {1, 2}, {}, {5, 4, 3, 2, 1, 0}, {}, {6}, {}};
+    auto const rhs      = lists_col{{}, {}, {0}, {0, 1, 2, 3, 4, 5}, {}, {6, 7}, {}};
+    auto const expected = lists_col{{}, {1, 2}, {}, {}, {}, {}, {}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetDifferenceTypedTest, SlicedNonNullInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const lhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {3, 2, 1, 4, 1}, {5}, {10, 8, 9}, {6, 7}};
+  auto const rhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {5, 6, 7, 8, 7, 5}, {}, {1, 2, 3}, {6, 7}};
+
+  {
+    auto const expected = lists_col{{}, {1, 2, 3, 4}, {5}, {8, 9, 10}, {}};
+
+    auto const results_sorted = set_difference_sorted(lhs_original, rhs_original);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 5})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 5})[0];
+    auto const expected = lists_col{{1, 2, 3, 4}, {5}, {8, 9, 10}, {}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 3})[0];
+    auto const expected = lists_col{{1, 2, 3, 4}, {5}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {0, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {0, 3})[0];
+    auto const expected = lists_col{{}, {1, 2, 3, 4}, {5}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetDifferenceTypedTest, InputHaveNullsTests)
+{
+  using lists_col     = cudf::test::lists_column_wrapper<TypeParam>;
+  auto constexpr null = TypeParam{0};
+
+  // Nullable lists.
+  {
+    auto const lhs = lists_col{{{3, 2, 1, 4, 1}, {5}, {} /*NULL*/, {} /*NULL*/, {10, 8, 9}, {6, 7}},
+                               nulls_at({2, 3})};
+    auto const rhs =
+      lists_col{{{1, 2}, {} /*NULL*/, {3}, {} /*NULL*/, {10, 11, 12}, {1, 2}}, nulls_at({1, 3})};
+    auto const expected = lists_col{{{3, 4}, {} /*NULL*/, {} /*NULL*/, {} /*NULL*/, {8, 9}, {6, 7}},
+                                    nulls_at({1, 2, 3})};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are equal.
+  {
+    auto const lhs      = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs      = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected = lists_col{lists_col{1, 3}, lists_col{}, lists_col{{null}, null_at(0)}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are unequal.
+  {
+    auto const lhs      = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs      = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected = lists_col{lists_col{{null, null, 1, 3}, nulls_at({0, 1})},
+                                    lists_col{{null}, null_at(0)},
+                                    lists_col{{null, null}, nulls_at({0, 1})}};
+
+    auto const results_sorted = set_difference_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetDifferenceTest, InputListsOfNestedStructsHaveNull)
+{
+  auto const get_structs_lhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    1,    XXX,  null, XXX, XXX, 1, 1,    1,  // list1
+                                    1,    1,    1,    1,   2,   1, null, 2,  // list2
+                                    null, null, 2,    2,   3,   2, 3,    3   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Only grandchild1 of rhs is different from lhs'. The rest is exactly the same.
+  auto const get_structs_rhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    2,    XXX,  null, XXX, XXX, 2, 2,    2,  // list1
+                                    3,    3,    3,    3,   3,   3, null, 3,  // list2
+                                    null, null, 4,    4,   4,   4, 4,    4   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Nulls are equal.
+  {
+    auto const get_structs_expected = [] {
+      auto grandchild1 = int32s_col{
+        1,
+        1,
+        1,  // end list1
+        1,
+        1,
+        1,
+        1,
+        2,  // end list2
+        2,
+        2,
+        2,
+        3,
+        3,
+        3  // end list3
+      };
+      auto grandchild2 = strings_col{{
+                                       "Banana",
+                                       "Cherry",
+                                       "Kiwi",  // end list1
+                                       "Bear",
+                                       "Cat",
+                                       "Dog",
+                                       "Duck",
+                                       "Panda",  // end list2
+                                       "ÁBC",
+                                       "ÁÁÁ",
+                                       "ÍÍÍÍÍ",
+                                       "" /*NULL*/,
+                                       "XYZ",
+                                       "ÁBC"  // end list3
+                                     },
+                                     null_at(11)};
+      auto child1      = structs_col{grandchild1, grandchild2};
+      return structs_col{{child1}};
+    };
+
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 3, 8, 14}.release(), get_structs_expected().release(), 0, {});
+
+    auto const results_sorted = set_difference_sorted(*lhs, *rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results_sorted);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const get_structs_expected = [] {
+      auto grandchild1 = int32s_col{{
+                                      null, null, null, null, 1, 1, 1,    // end list1
+                                      null, 1,    1,    1,    1, 2,       // end list2
+                                      null, null, 2,    2,    2, 3, 3, 3  // end list3
+                                    },
+                                    nulls_at({0, 1, 2, 3, 7, 13, 14})};
+      auto grandchild2 = strings_col{{
+                                       "" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "Apple", "Banana",
+                                       "Cherry",    "Kiwi",  // end list1
+                                       "" /*NULL*/, "Bear",      "Cat",       "Dog",   "Duck",
+                                       "Panda",  // end list2
+                                       "ÁÁÁ",       "ÉÉÉÉÉ",     "ÁBC",       "ÁÁÁ",   "ÍÍÍÍÍ",
+                                       "" /*NULL*/, "XYZ",
+                                       "ÁBC"  // end list3
+                                     },
+                                     nulls_at({0, 1, 2, 7, 18})};
+      auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({0, 1, 2})};
+      return structs_col{{child1}};
+    };
+
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 7, 13, 21}.release(), get_structs_expected().release(), 0, {});
+
+    auto const results_sorted = set_difference_sorted(*lhs, *rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+}
+
+TEST_F(SetDifferenceTest, InputListsOfStructsOfLists)
+{
+  auto const lhs = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               1,
+                               2,  // end list1
+                                   // begin list2
+                               3,  // end list2
+                                   // begin list3
+                               4,
+                               5,
+                               6};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{0, 1},
+                                 floats_lists{0, 2},
+                                 floats_lists{1, 1},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{6, 7},
+                                 floats_lists{6, 8},
+                                 floats_lists{6, 7, 8}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 4, 7}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const rhs = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               1,
+                               2,  // end list1
+                                   // begin list2
+                               3,  // end list2
+                                   // begin list3
+                               4,
+                               5,
+                               6};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{1, 1},
+                                 floats_lists{0, 2},
+                                 floats_lists{1, 2},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{6, 7, 8, 9},
+                                 floats_lists{6, 8},
+                                 floats_lists{3, 4, 5}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 4, 7}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const expected = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{0, 2, 4, 6};
+      auto child2 = floats_lists{
+        floats_lists{0, 1}, floats_lists{1, 1}, floats_lists{6, 7}, floats_lists{6, 7, 8}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 2, 2, 4}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const results = cudf::lists::difference_distinct(lists_cv{*lhs}, lists_cv{*rhs});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+}
diff --git a/cpp/tests/lists/set_operations/have_overlap_tests.cpp b/cpp/tests/lists/set_operations/have_overlap_tests.cpp
new file mode 100644
index 0000000..0815280
--- /dev/null
+++ b/cpp/tests/lists/set_operations/have_overlap_tests.cpp
@@ -0,0 +1,551 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/lists/set_operations.hpp>
+
+#include <limits>
+#include <string>
+
+using float_type = double;
+using namespace cudf::test::iterators;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr neg_NaN      = -std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr neg_Inf      = -std::numeric_limits<float_type>::infinity();
+auto constexpr NaN          = std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr Inf          = std::numeric_limits<float_type>::infinity();
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using bools_col     = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_lists  = cudf::test::lists_column_wrapper<float_type>;
+using strings_lists = cudf::test::lists_column_wrapper<cudf::string_view>;
+using strings_col   = cudf::test::strings_column_wrapper;
+using structs_col   = cudf::test::structs_column_wrapper;
+using lists_cv      = cudf::lists_column_view;
+
+struct ListOverlapTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct ListOverlapTypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(ListOverlapTypedTest, TestTypes);
+
+TEST_F(ListOverlapTest, TrivialTest)
+{
+  auto const lhs =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+  auto const rhs =
+    floats_lists{{floats_lists{{1.0, 0.5, null, 0.0, 0.0, null, NaN}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  {} /*NULL*/},
+                 null_at(3)};
+  auto const expected = bools_col{{1, 1, null, null}, nulls_at({2, 3})};
+
+  auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+}
+
+TEST_F(ListOverlapTest, FloatingPointTestsWithSignedZero)
+{
+  // -0.0 and 0.0 should be considered equal.
+  auto const lhs      = floats_lists{{0.0, 0.0, 0.0, 0.0, 0.0}, {-0.0, 1.0}, {0.0}};
+  auto const rhs      = floats_lists{{-0.0, -0.0, -0.0, -0.0, -0.0}, {0.0, 2.0}, {1.0}};
+  auto const expected = bools_col{1, 1, 0};
+
+  auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+}
+
+TEST_F(ListOverlapTest, FloatingPointTestsWithInf)
+{
+  auto const lhs      = floats_lists{{Inf, Inf, Inf}, {Inf, 0.0, neg_Inf}};
+  auto const rhs      = floats_lists{{neg_Inf, neg_Inf}, {0.0}};
+  auto const expected = bools_col{0, 1};
+
+  auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+}
+
+TEST_F(ListOverlapTest, FloatingPointTestsWithNaNs)
+{
+  auto const lhs =
+    floats_lists{{0, -1, 1, NaN}, {2, 0, neg_NaN}, {1, -2, 2, 0, 1, 2}, {NaN, NaN, NaN, NaN, NaN}};
+  auto const rhs =
+    floats_lists{{2, 3, 4, neg_NaN}, {2, 0}, {neg_NaN, 1, -2, 2, 0, 1, 2}, {neg_NaN, neg_NaN}};
+
+  // NaNs are equal.
+  {
+    auto const expected = bools_col{1, 1, 1, 1};
+    auto const results =
+      cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // NaNs are unequal.
+  {
+    auto const expected = bools_col{0, 1, 1, 0};
+    auto const results =
+      cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TEST_F(ListOverlapTest, StringTestsNonNull)
+{
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{};
+    auto const rhs      = strings_lists{};
+    auto const expected = bools_col{};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{strings_lists{}};
+    auto const rhs      = strings_lists{strings_lists{}};
+    auto const expected = bools_col{0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // No overlap.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"aha", "bear", "blow", "heat"};
+    auto const expected = bools_col{0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // One list column.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"a", "delicious", "banana"};
+    auto const expected = bools_col{1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Multiple lists column.
+  {
+    auto const lhs      = strings_lists{strings_lists{"one", "two", "three"},
+                                   strings_lists{"four", "five", "six"},
+                                   strings_lists{"1", "2", "3"}};
+    auto const rhs      = strings_lists{strings_lists{"one", "banana"},
+                                   strings_lists{"apple", "kiwi", "cherry"},
+                                   strings_lists{"two", "and", "1"}};
+    auto const expected = bools_col{1, 0, 1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TEST_F(ListOverlapTest, StringTestsWithNullsEqual)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected = bools_col{1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = bools_col{{1, 0 /*null*/, 1}, null_at(1)};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TEST_F(ListOverlapTest, StringTestsWithNullsUnequal)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected = bools_col{0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = bools_col{{0, 0 /*null*/, 1}, null_at(1)};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TYPED_TEST(ListOverlapTypedTest, TrivialInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Empty input.
+  {
+    auto const lhs      = lists_col{};
+    auto const rhs      = lists_col{};
+    auto const expected = bools_col{};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // All input lists are empty.
+  {
+    auto const lhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const rhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const expected = bools_col{0, 0, 0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Multiple empty lists.
+  {
+    auto const lhs      = lists_col{{}, {1, 2}, {}, {5, 4, 3, 2, 1, 0}, {}, {6}, {}};
+    auto const rhs      = lists_col{{}, {}, {0}, {0, 1, 2, 3, 4, 5}, {}, {6, 7}, {}};
+    auto const expected = bools_col{0, 0, 0, 1, 0, 1, 0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TYPED_TEST(ListOverlapTypedTest, SlicedNonNullInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const lhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {3, 2, 1, 4, 1}, {5}, {10, 8, 9}, {6, 7}};
+  auto const rhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {5, 6, 7, 8, 7, 5}, {}, {1, 2, 3}, {6, 7}};
+
+  {
+    auto const expected = bools_col{1, 0, 0, 0, 1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs_original}, lists_cv{rhs_original});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 5})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 5})[0];
+    auto const expected = bools_col{0, 0, 0, 1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 3})[0];
+    auto const expected = bools_col{0, 0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {0, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {0, 3})[0];
+    auto const expected = bools_col{1, 0, 0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TYPED_TEST(ListOverlapTypedTest, InputHaveNullsTests)
+{
+  using lists_col     = cudf::test::lists_column_wrapper<TypeParam>;
+  auto constexpr null = TypeParam{0};
+
+  // Nullable lists.
+  {
+    auto const lhs = lists_col{{{3, 2, 1, 4, 1}, {5}, {} /*NULL*/, {} /*NULL*/, {10, 8, 9}, {6, 7}},
+                               nulls_at({2, 3})};
+    auto const rhs =
+      lists_col{{{1, 2}, {} /*NULL*/, {3}, {} /*NULL*/, {10, 11, 12}, {1, 2}}, nulls_at({1, 3})};
+    auto const expected =
+      bools_col{{1, 0 /*null*/, 0 /*null*/, 0 /*null*/, 1, 0}, nulls_at({1, 2, 3})};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Nullable child and nulls are equal.
+  {
+    auto const lhs      = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs      = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected = bools_col{1, 1, 1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Nullable child and nulls are unequal.
+  {
+    auto const lhs      = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs      = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected = bools_col{0, 1, 1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{lhs}, lists_cv{rhs}, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TEST_F(ListOverlapTest, InputListsOfNestedStructsHaveNull)
+{
+  auto const get_structs_lhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    1,    XXX,  null, XXX, XXX, 1, 1,    1,  // list1
+                                    1,    1,    1,    1,   2,   1, null, 2,  // list2
+                                    null, null, 2,    2,   3,   2, 3,    3   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "XXX", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "XXX", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Only grandchild1 of rhs is different from lhs'. The rest is exactly the same.
+  auto const get_structs_rhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    2,    XXX,  null, XXX, XXX, 2, 2,    2,  // list1
+                                    3,    3,    3,    3,   3,   3, null, 3,  // list2
+                                    null, null, 4,    4,   4,   4, 4,    4   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "XXX", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "XXX", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Nulls are equal.
+  {
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = bools_col{1, 1, 1};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{*lhs}, lists_cv{*rhs}, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = bools_col{0, 0, 0};
+
+    auto const results = cudf::lists::have_overlap(lists_cv{*lhs}, lists_cv{*rhs}, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+  }
+}
+
+TEST_F(ListOverlapTest, InputListsOfStructsOfLists)
+{
+  auto const lhs = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               1,
+                               2,  // end list1
+                                   // begin list2
+                               3,  // end list2
+                                   // begin list3
+                               4,
+                               5,
+                               6};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{0, 1},
+                                 floats_lists{0, 2},
+                                 floats_lists{1, 1},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{6, 7},
+                                 floats_lists{6, 8},
+                                 floats_lists{6, 7, 8}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 4, 7}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const rhs = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               1,
+                               2,  // end list1
+                                   // begin list2
+                               3,  // end list2
+                                   // begin list3
+                               4,
+                               5,
+                               6};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{1, 1},
+                                 floats_lists{1, 2},
+                                 floats_lists{1, 2},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{6, 7, 8, 9},
+                                 floats_lists{6, 8},
+                                 floats_lists{3, 4, 5}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 4, 7}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const expected = bools_col{0, 1, 1};
+
+  auto const results = cudf::lists::have_overlap(lists_cv{*lhs}, lists_cv{*rhs});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results);
+}
diff --git a/cpp/tests/lists/set_operations/intersect_distinct_tests.cpp b/cpp/tests/lists/set_operations/intersect_distinct_tests.cpp
new file mode 100644
index 0000000..11f98af
--- /dev/null
+++ b/cpp/tests/lists/set_operations/intersect_distinct_tests.cpp
@@ -0,0 +1,637 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/lists/set_operations.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/lists/stream_compaction.hpp>
+#include <cudf/null_mask.hpp>
+
+#include <limits>
+#include <string>
+
+using float_type = double;
+using namespace cudf::test::iterators;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr neg_NaN      = -std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr neg_Inf      = -std::numeric_limits<float_type>::infinity();
+auto constexpr NaN          = std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr Inf          = std::numeric_limits<float_type>::infinity();
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using bools_col     = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_lists  = cudf::test::lists_column_wrapper<float_type>;
+using strings_lists = cudf::test::lists_column_wrapper<cudf::string_view>;
+using strings_col   = cudf::test::strings_column_wrapper;
+using structs_col   = cudf::test::structs_column_wrapper;
+using lists_cv      = cudf::lists_column_view;
+
+namespace {
+auto set_intersect_sorted(cudf::column_view const& lhs,
+                          cudf::column_view const& rhs,
+                          cudf::null_equality nulls_equal = NULL_EQUAL,
+                          cudf::nan_equality nans_equal   = NAN_EQUAL)
+{
+  auto const results =
+    cudf::lists::intersect_distinct(lists_cv{lhs}, lists_cv{rhs}, nulls_equal, nans_equal);
+  return cudf::lists::sort_lists(
+    lists_cv{*results}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+}
+}  // namespace
+
+struct SetIntersectTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct SetIntersectTypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(SetIntersectTypedTest, TestTypes);
+
+TEST_F(SetIntersectTest, TrivialTest)
+{
+  auto const lhs =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+  auto const rhs =
+    floats_lists{{floats_lists{{1.0, 0.5, null, 0.0, 0.0, null, NaN}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  {} /*NULL*/},
+                 null_at(3)};
+  auto const expected = floats_lists{{floats_lists{{null, 0.0, NaN}, null_at(0)},
+                                      floats_lists{{null, 0.0, 1.0}, null_at(0)},
+                                      floats_lists{} /*NULL*/,
+                                      floats_lists{} /*NULL*/},
+                                     nulls_at({2, 3})};
+
+  auto const results_sorted = set_intersect_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+}
+
+TEST_F(SetIntersectTest, TrivialIdentityTest)
+{
+  auto const input =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+
+  // `intersect_distinct(input, input) <==> lists::distinct(input)`.
+  auto const input_distinct        = cudf::lists::distinct(lists_cv{input});
+  auto const input_distinct_sorted = cudf::lists::sort_lists(
+    lists_cv{*input_distinct}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+
+  auto const results_sorted = set_intersect_sorted(input, input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*input_distinct_sorted, *results_sorted);
+}
+
+TEST_F(SetIntersectTest, FloatingPointTestsWithSignedZero)
+{
+  // -0.0 and 0.0 should be considered equal.
+  auto const lhs      = floats_lists{{0.0, 0.0, 0.0, 0.0, 0.0}, {-0.0, 1.0}, {0.0}};
+  auto const rhs      = floats_lists{{-0.0, -0.0, -0.0, -0.0, -0.0}, {0.0, 2.0}, {1.0}};
+  auto const expected = floats_lists{floats_lists{0}, floats_lists{0}, floats_lists{}};
+
+  auto const results_sorted = set_intersect_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(SetIntersectTest, FloatingPointTestsWithInf)
+{
+  auto const lhs      = floats_lists{{Inf, Inf, Inf}, {Inf, 0.0, neg_Inf}};
+  auto const rhs      = floats_lists{{neg_Inf, neg_Inf}, {0.0, Inf}};
+  auto const expected = floats_lists{floats_lists{}, floats_lists{0.0, Inf}};
+
+  auto const results_sorted = set_intersect_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(SetIntersectTest, FloatingPointTestsWithNaNs)
+{
+  auto const lhs =
+    floats_lists{{0, -1, 1, NaN}, {2, 0, neg_NaN}, {1, -2, 2, 0, 1, 2}, {NaN, NaN, NaN, NaN, NaN}};
+  auto const rhs =
+    floats_lists{{2, 3, 4, neg_NaN}, {2, 0}, {neg_NaN, 1, -2, 2, 0, 1, 2}, {neg_NaN, neg_NaN}};
+
+  // NaNs are equal.
+  {
+    auto const expected       = floats_lists{{NaN}, {0, 2}, {-2, 0, 1, 2}, {NaN}};
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // NaNs are unequal.
+  {
+    auto const expected       = floats_lists{{}, {0, 2}, {-2, 0, 1, 2}, {}};
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetIntersectTest, StringTestsNonNull)
+{
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{};
+    auto const rhs      = strings_lists{};
+    auto const expected = strings_lists{};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{strings_lists{}};
+    auto const rhs      = strings_lists{strings_lists{}};
+    auto const expected = strings_lists{strings_lists{}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // No overlap.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"aha", "bear", "blow", "heat"};
+    auto const expected = strings_lists{strings_lists{}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // One list column.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"a", "delicious", "banana"};
+    auto const expected = strings_lists{"a"};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column.
+  {
+    auto const lhs      = strings_lists{strings_lists{"one", "two", "three"},
+                                   strings_lists{"four", "five", "six"},
+                                   strings_lists{"1", "2", "3"}};
+    auto const rhs      = strings_lists{strings_lists{"one", "banana"},
+                                   strings_lists{"apple", "kiwi", "cherry"},
+                                   strings_lists{"two", "and", "1"}};
+    auto const expected = strings_lists{strings_lists{"one"}, strings_lists{}, strings_lists{"1"}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetIntersectTest, StringTestsWithNullsEqual)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected = strings_lists{{null}, null_at(0)};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = strings_lists{
+      {strings_lists{{null}, null_at(0)}, strings_lists{} /*NULL*/, strings_lists{"this"}},
+      null_at(1)};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetIntersectTest, StringTestsWithNullsUnequal)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected = strings_lists{strings_lists{}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = [] {
+      auto str_lists =
+        strings_lists{{strings_lists{}, strings_lists{} /*NULL*/, strings_lists{"this"}},
+                      null_at(1)}
+          .release();
+      auto& child = str_lists->child(cudf::lists_column_view::child_column_index);
+      child.set_null_mask(cudf::create_null_mask(child.size(), cudf::mask_state::ALL_VALID), 0);
+      return str_lists;
+    }();
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetIntersectTypedTest, TrivialInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Empty input.
+  {
+    auto const lhs      = lists_col{};
+    auto const rhs      = lists_col{};
+    auto const expected = lists_col{};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // All input lists are empty.
+  {
+    auto const lhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const rhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const expected = lists_col{lists_col{}, lists_col{}, lists_col{}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple empty lists.
+  {
+    auto const lhs      = lists_col{{}, {1, 2}, {}, {5, 4, 3, 2, 1, 0}, {}, {6}, {}};
+    auto const rhs      = lists_col{{}, {}, {0}, {0, 1, 2, 3, 4, 5}, {}, {6, 7}, {}};
+    auto const expected = lists_col{{}, {}, {}, {0, 1, 2, 3, 4, 5}, {}, {6}, {}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetIntersectTypedTest, SlicedNonNullInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const lhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {3, 2, 1, 4, 1}, {5}, {10, 8, 9}, {6, 7}};
+  auto const rhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {5, 6, 7, 8, 7, 5}, {}, {1, 2, 3}, {6, 7}};
+
+  {
+    auto const expected = lists_col{{1, 2, 3}, {}, {}, {}, {6, 7}};
+
+    auto const results_sorted = set_intersect_sorted(lhs_original, rhs_original);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 5})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 5})[0];
+    auto const expected = lists_col{{}, {}, {}, {6, 7}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 3})[0];
+    auto const expected = lists_col{lists_col{}, lists_col{}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {0, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {0, 3})[0];
+    auto const expected = lists_col{{1, 2, 3}, {}, {}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetIntersectTypedTest, InputHaveNullsTests)
+{
+  using lists_col     = cudf::test::lists_column_wrapper<TypeParam>;
+  auto constexpr null = TypeParam{0};
+
+  // Nullable lists.
+  {
+    auto const lhs = lists_col{{{3, 2, 1, 4, 1}, {5}, {} /*NULL*/, {} /*NULL*/, {10, 8, 9}, {6, 7}},
+                               nulls_at({2, 3})};
+    auto const rhs =
+      lists_col{{{1, 2}, {} /*NULL*/, {3}, {} /*NULL*/, {10, 11, 12}, {1, 2}}, nulls_at({1, 3})};
+    auto const expected =
+      lists_col{{{1, 2}, {} /*NULL*/, {} /*NULL*/, {} /*NULL*/, {10}, {}}, nulls_at({1, 2, 3})};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are equal.
+  {
+    auto const lhs = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected =
+      lists_col{lists_col{{null}, null_at(0)}, lists_col{{null, 5}, null_at(0)}, lists_col{7, 9}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are unequal.
+  {
+    auto const lhs      = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs      = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected = lists_col{lists_col{}, lists_col{5}, lists_col{7, 9}};
+
+    auto const results_sorted = set_intersect_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetIntersectTest, InputListsOfNestedStructsHaveNull)
+{
+  auto const get_structs_lhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    1,    XXX,  null, XXX, XXX, 1, 1,    1,  // list1
+                                    1,    1,    1,    1,   2,   1, null, 2,  // list2
+                                    null, null, 2,    2,   3,   2, 3,    3   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Only grandchild1 of rhs is different from lhs'. The rest is exactly the same.
+  auto const get_structs_rhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    2,    XXX,  null, XXX, XXX, 2, 2,    2,  // list1
+                                    3,    3,    3,    3,   3,   3, null, 3,  // list2
+                                    null, null, 4,    4,   4,   4, 4,    4   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Nulls are equal.
+  {
+    auto const get_structs_expected = [] {
+      auto grandchild1 = int32s_col{{
+                                      null,
+                                      null,  // end list1
+                                      null,  // end list2
+                                      null,
+                                      null  // end list3
+                                    },
+                                    all_nulls()};
+      auto grandchild2 = strings_col{{
+                                       "" /*NULL*/,
+                                       "Apple",      // end list1
+                                       "" /*NULL*/,  // end list2
+                                       "ÁÁÁ",
+                                       "ÉÉÉÉÉ"  // end list3
+                                     },
+                                     nulls_at({0, 2})};
+      auto child1      = structs_col{{grandchild1, grandchild2}, null_at(0)};
+      return structs_col{{child1}};
+    };
+
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 2, 3, 5}.release(), get_structs_expected().release(), 0, {});
+
+    auto const results_sorted = set_intersect_sorted(*lhs, *rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results_sorted);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const get_structs_expected = [] {
+      auto grandchild1 = int32s_col{};
+      auto grandchild2 = strings_col{};
+      auto child1      = structs_col{{grandchild1, grandchild2}};
+      return structs_col{{child1}};
+    };
+
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 0, 0, 0}.release(), get_structs_expected().release(), 0, {});
+
+    auto const results_sorted = set_intersect_sorted(*lhs, *rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+}
+
+TEST_F(SetIntersectTest, InputListsOfStructsOfLists)
+{
+  auto const lhs = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               1,
+                               2,  // end list1
+                                   // begin list2
+                               3,  // end list2
+                                   // begin list3
+                               4,
+                               5,
+                               6};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{0, 1},
+                                 floats_lists{0, 2},
+                                 floats_lists{1, 1},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{6, 7},
+                                 floats_lists{6, 8},
+                                 floats_lists{6, 7, 8}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 4, 7}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const rhs = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               1,
+                               2,  // end list1
+                                   // begin list2
+                               3,  // end list2
+                                   // begin list3
+                               4,
+                               5,
+                               6};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{1, 1},
+                                 floats_lists{0, 2},
+                                 floats_lists{1, 2},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{6, 7, 8, 9},
+                                 floats_lists{6, 8},
+                                 floats_lists{3, 4, 5}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 3, 4, 7}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const expected = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{1, 3, 5};
+      auto child2 = floats_lists{floats_lists{0, 2}, floats_lists{3, 4, 5}, floats_lists{6, 8}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      3, int32s_col{0, 1, 2, 3}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const results = cudf::lists::intersect_distinct(lists_cv{*lhs}, lists_cv{*rhs});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+}
diff --git a/cpp/tests/lists/set_operations/union_distinct_tests.cpp b/cpp/tests/lists/set_operations/union_distinct_tests.cpp
new file mode 100644
index 0000000..e33ea31
--- /dev/null
+++ b/cpp/tests/lists/set_operations/union_distinct_tests.cpp
@@ -0,0 +1,622 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/lists/set_operations.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/lists/stream_compaction.hpp>
+
+#include <limits>
+#include <string>
+
+using float_type = double;
+using namespace cudf::test::iterators;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr neg_NaN      = -std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr neg_Inf      = -std::numeric_limits<float_type>::infinity();
+auto constexpr NaN          = std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr Inf          = std::numeric_limits<float_type>::infinity();
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using bools_col     = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_lists  = cudf::test::lists_column_wrapper<float_type>;
+using strings_lists = cudf::test::lists_column_wrapper<cudf::string_view>;
+using strings_col   = cudf::test::strings_column_wrapper;
+using structs_col   = cudf::test::structs_column_wrapper;
+using lists_cv      = cudf::lists_column_view;
+
+namespace {
+auto set_union_sorted(cudf::column_view const& lhs,
+                      cudf::column_view const& rhs,
+                      cudf::null_equality nulls_equal = NULL_EQUAL,
+                      cudf::nan_equality nans_equal   = NAN_EQUAL)
+{
+  auto const results =
+    cudf::lists::union_distinct(lists_cv{lhs}, lists_cv{rhs}, nulls_equal, nans_equal);
+  return cudf::lists::sort_lists(
+    lists_cv{*results}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+}
+}  // namespace
+
+struct SetUnionTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct SetUnionTypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(SetUnionTypedTest, TestTypes);
+
+TEST_F(SetUnionTest, TrivialTest)
+{
+  auto const lhs =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+  auto const rhs =
+    floats_lists{{floats_lists{{1.0, 0.5, null, 0.0, 0.0, null, NaN}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  floats_lists{{2.0, 1.0, null, 0.0, 0.0, null}, nulls_at({2, 5})},
+                  {} /*NULL*/},
+                 null_at(3)};
+  auto const expected = floats_lists{{floats_lists{{null, 0.0, 0.5, 1.0, 5.0, NaN}, null_at(0)},
+                                      floats_lists{{null, 0.0, 1.0, 2.0, 5.0, NaN}, null_at(0)},
+                                      floats_lists{} /*NULL*/,
+                                      floats_lists{} /*NULL*/},
+                                     nulls_at({2, 3})};
+
+  auto const results_sorted = set_union_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+}
+
+TEST_F(SetUnionTest, TrivialIdentityTest)
+{
+  auto const input =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+
+  // `union_distinct(input, input) <==> lists::distinct(input)`.
+  auto const input_distinct        = cudf::lists::distinct(lists_cv{input});
+  auto const input_distinct_sorted = cudf::lists::sort_lists(
+    lists_cv{*input_distinct}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+
+  auto const results_sorted = set_union_sorted(input, input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*input_distinct_sorted, *results_sorted);
+}
+
+TEST_F(SetUnionTest, FloatingPointTestsWithSignedZero)
+{
+  // -0.0 and 0.0 should be considered equal.
+  auto const lhs      = floats_lists{{0.0, 0.0, 0.0, 0.0, 0.0}, {-0.0, 1.0}, {0.0}};
+  auto const rhs      = floats_lists{{-0.0, -0.0, -0.0, -0.0, -0.0}, {0.0, 2.0}, {1.0}};
+  auto const expected = floats_lists{floats_lists{0}, floats_lists{0, 1, 2}, floats_lists{0, 1}};
+
+  auto const results_sorted = set_union_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(SetUnionTest, FloatingPointTestsWithInf)
+{
+  auto const lhs      = floats_lists{{Inf, Inf, Inf}, {Inf, 0.0, neg_Inf}};
+  auto const rhs      = floats_lists{{neg_Inf, neg_Inf}, {0.0, Inf}};
+  auto const expected = floats_lists{floats_lists{neg_Inf, Inf}, floats_lists{neg_Inf, 0.0, Inf}};
+
+  auto const results_sorted = set_union_sorted(lhs, rhs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(SetUnionTest, FloatingPointTestsWithNaNs)
+{
+  auto const lhs =
+    floats_lists{{0, -1, 1, NaN}, {2, 0, neg_NaN}, {1, -2, 2, 0, 1, 2}, {NaN, NaN, NaN, NaN, NaN}};
+  auto const rhs =
+    floats_lists{{2, 3, 4, neg_NaN}, {2, 0}, {neg_NaN, 1, -2, 2, 0, 1, 2}, {neg_NaN, neg_NaN}};
+
+  // NaNs are equal.
+  {
+    auto const expected =
+      floats_lists{{-1, 0, 1, 2, 3, 4, NaN}, {0, 2, neg_NaN}, {-2, 0, 1, 2, neg_NaN}, {NaN}};
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // NaNs are unequal.
+  {
+    auto const expected       = floats_lists{{-1, 0, 1, 2, 3, 4, NaN, neg_NaN},
+                                             {0, 2, neg_NaN},
+                                             {-2, 0, 1, 2, neg_NaN},
+                                             {NaN, NaN, NaN, NaN, NaN, neg_NaN, neg_NaN}};
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetUnionTest, StringTestsNonNull)
+{
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{};
+    auto const rhs      = strings_lists{};
+    auto const expected = strings_lists{};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Trivial cases - empty input.
+  {
+    auto const lhs      = strings_lists{strings_lists{}};
+    auto const rhs      = strings_lists{strings_lists{}};
+    auto const expected = strings_lists{strings_lists{}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // No overlap.
+  {
+    auto const lhs = strings_lists{"this", "is", "a", "string"};
+    auto const rhs = strings_lists{"aha", "bear", "blow", "heat"};
+    auto const expected =
+      strings_lists{strings_lists{"a", "aha", "bear", "blow", "heat", "is", "string", "this"}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // One list column.
+  {
+    auto const lhs      = strings_lists{"this", "is", "a", "string"};
+    auto const rhs      = strings_lists{"a", "delicious", "banana"};
+    auto const expected = strings_lists{"a", "banana", "delicious", "is", "string", "this"};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column.
+  {
+    auto const lhs = strings_lists{strings_lists{"one", "two", "three"},
+                                   strings_lists{"four", "five", "six"},
+                                   strings_lists{"1", "2", "3"}};
+    auto const rhs = strings_lists{strings_lists{"one", "banana"},
+                                   strings_lists{"apple", "kiwi", "cherry"},
+                                   strings_lists{"two", "and", "1"}};
+    auto const expected =
+      strings_lists{strings_lists{"banana", "one", "three", "two"},
+                    strings_lists{"apple", "cherry", "five", "four", "kiwi", "six"},
+                    strings_lists{"1", "2", "3", "and", "two"}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetUnionTest, StringTestsWithNullsEqual)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected =
+      strings_lists{{null, "1111", "2222", "a", "abc", "aha", "is", "string", "this"}, null_at(0)};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected = strings_lists{
+      {strings_lists{{null, "1111", "2222", "a", "abc", "aha", "is", "string", "this"}, null_at(0)},
+       strings_lists{} /*NULL*/,
+       strings_lists{"a", "aha", "is", "is another", "string", "string???", "this"}},
+      null_at(1)};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetUnionTest, StringTestsWithNullsUnequal)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const lhs = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const rhs =
+      strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})};
+    auto const expected = strings_lists{{null,
+                                         null,
+                                         null,
+                                         null,
+                                         null,
+                                         null,
+                                         "1111",
+                                         "2222",
+                                         "a",
+                                         "abc",
+                                         "aha",
+                                         "is",
+                                         "string",
+                                         "this"},
+                                        nulls_at({0, 1, 2, 3, 4, 5})};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const lhs = strings_lists{
+      strings_lists{{"this", null, "is", null, "a", null, null, "string"}, nulls_at({1, 3, 5, 6})},
+      strings_lists{},
+      strings_lists{"this", "is", "a", "string"}};
+    auto const rhs = strings_lists{
+      {strings_lists{{"aha", null, "abc", null, "1111", null, "2222"}, nulls_at({1, 3, 5})},
+       strings_lists{}, /* NULL */
+       strings_lists{"aha", "this", "is another", "string???"}},
+      null_at(1)};
+    auto const expected =
+      strings_lists{{strings_lists{{null,
+                                    null,
+                                    null,
+                                    null,
+                                    null,
+                                    null,
+                                    null,
+                                    "1111",
+                                    "2222",
+                                    "a",
+                                    "abc",
+                                    "aha",
+                                    "is",
+                                    "string",
+                                    "this"},
+                                   nulls_at({0, 1, 2, 3, 4, 5, 6})},
+                     strings_lists{} /*NULL*/,
+                     strings_lists{"a", "aha", "is", "is another", "string", "string???", "this"}},
+                    null_at(1)};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetUnionTypedTest, TrivialInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Empty input.
+  {
+    auto const lhs      = lists_col{};
+    auto const rhs      = lists_col{};
+    auto const expected = lists_col{};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // All input lists are empty.
+  {
+    auto const lhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const rhs      = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const expected = lists_col{lists_col{}, lists_col{}, lists_col{}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple empty lists.
+  {
+    auto const lhs      = lists_col{{}, {1, 2}, {}, {5, 4, 3, 2, 1, 0}, {}, {6}, {}};
+    auto const rhs      = lists_col{{}, {}, {0}, {0, 1, 2, 3, 4, 5}, {}, {6, 7}, {}};
+    auto const expected = lists_col{{}, {1, 2}, {0}, {0, 1, 2, 3, 4, 5}, {}, {6, 7}, {}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetUnionTypedTest, SlicedNonNullInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const lhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {3, 2, 1, 4, 1}, {5}, {10, 8, 9}, {6, 7}};
+  auto const rhs_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {5, 6, 7, 8, 7, 5}, {}, {1, 2, 3}, {6, 7}};
+
+  {
+    auto const expected =
+      lists_col{{1, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8}, {5}, {1, 2, 3, 8, 9, 10}, {6, 7}};
+
+    auto const results_sorted = set_union_sorted(lhs_original, rhs_original);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 5})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 5})[0];
+    auto const expected = lists_col{{1, 2, 3, 4, 5, 6, 7, 8}, {5}, {1, 2, 3, 8, 9, 10}, {6, 7}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {1, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {1, 3})[0];
+    auto const expected = lists_col{{1, 2, 3, 4, 5, 6, 7, 8}, {5}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const lhs      = cudf::slice(lhs_original, {0, 3})[0];
+    auto const rhs      = cudf::slice(rhs_original, {0, 3})[0];
+    auto const expected = lists_col{{1, 2, 3}, {1, 2, 3, 4, 5, 6, 7, 8}, {5}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(SetUnionTypedTest, InputHaveNullsTests)
+{
+  using lists_col     = cudf::test::lists_column_wrapper<TypeParam>;
+  auto constexpr null = TypeParam{0};
+
+  // Nullable lists.
+  {
+    auto const lhs = lists_col{{{3, 2, 1, 4, 1}, {5}, {} /*NULL*/, {} /*NULL*/, {10, 8, 9}, {6, 7}},
+                               nulls_at({2, 3})};
+    auto const rhs =
+      lists_col{{{1, 2}, {} /*NULL*/, {3}, {} /*NULL*/, {10, 11, 12}, {1, 2}}, nulls_at({1, 3})};
+    auto const expected = lists_col{
+      {{1, 2, 3, 4}, {} /*NULL*/, {} /*NULL*/, {} /*NULL*/, {8, 9, 10, 11, 12}, {1, 2, 6, 7}},
+      nulls_at({1, 2, 3})};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are equal.
+  {
+    auto const lhs      = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs      = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected = lists_col{lists_col{{null, 1, 3, 5}, null_at(0)},
+                                    lists_col{{null, 5}, null_at(0)},
+                                    lists_col{{null, 7, 8, 9}, null_at(0)}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are unequal.
+  {
+    auto const lhs = lists_col{lists_col{{null, 1, null, 3}, nulls_at({0, 2})},
+                               lists_col{{null, 5}, null_at(0)},
+                               lists_col{{null, 7, null, 9}, nulls_at({0, 2})}};
+    auto const rhs = lists_col{lists_col{{null, null, 5}, nulls_at({0, 1})},
+                               lists_col{{5, null}, null_at(1)},
+                               lists_col{7, 8, 9}};
+    auto const expected =
+      lists_col{lists_col{{null, null, null, null, 1, 3, 5}, nulls_at({0, 1, 2, 3})},
+                lists_col{{null, null, 5}, nulls_at({0, 1})},
+                lists_col{{null, null, 7, 8, 9}, nulls_at({0, 1})}};
+
+    auto const results_sorted = set_union_sorted(lhs, rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results_sorted);
+  }
+}
+
+TEST_F(SetUnionTest, InputListsOfNestedStructsHaveNull)
+{
+  auto const get_structs_lhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    1,    XXX,  null, XXX, XXX, 1, 1,    1,  // list1
+                                    1,    1,    1,    1,   2,   1, null, 2,  // list2
+                                    null, null, 2,    2,   3,   2, 3,    3   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Only grandchild1 of rhs is different from lhs'. The rest is exactly the same.
+  auto const get_structs_rhs = [] {
+    auto grandchild1 = int32s_col{{
+                                    2,    XXX,  null, XXX, XXX, 2, 2,    2,  // list1
+                                    3,    3,    3,    3,   3,   3, null, 3,  // list2
+                                    null, null, 4,    4,   4,   4, 4,    4   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  // Nulls are equal.
+  {
+    auto const get_structs_expected = [] {
+      auto grandchild1 = int32s_col{{
+                                      null, null, 1, 1, 1, 2, 2,
+                                      2,                                           // end list1
+                                      null, 1,    1, 1, 1, 2, 3, 3, 3, 3, 3,       // end list2
+                                      null, null, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4  // end list3
+                                    },
+                                    nulls_at({0, 1, 8, 19, 20})};
+      auto grandchild2 =
+        strings_col{{
+                      "" /*NULL*/, "Apple",     "Banana", "Cherry", "Kiwi",  "Banana",    "Cherry",
+                      "Kiwi",  // end list1
+                      "" /*NULL*/, "Bear",      "Cat",    "Dog",    "Duck",  "Panda",     "Bear",
+                      "Cat",       "Dog",       "Duck",   "Panda",  // end list2
+
+                      "ÁÁÁ",       "ÉÉÉÉÉ",     "ÁBC",    "ÁÁÁ",    "ÍÍÍÍÍ", "" /*NULL*/, "XYZ",
+                      "ÁBC",       "" /*NULL*/, "XYZ",    "ÁBC",    "ÁÁÁ",   "ÍÍÍÍÍ"  // end list3
+                    },
+                    nulls_at({0, 8, 24, 27})};
+      auto child1 = structs_col{{grandchild1, grandchild2}, null_at(0)};
+      return structs_col{{child1}};
+    };
+
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 8, 19, 32}.release(), get_structs_expected().release(), 0, {});
+
+    auto const results_sorted = set_union_sorted(*lhs, *rhs, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results_sorted);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const get_structs_expected = [] {
+      auto grandchild1 = int32s_col{
+        {
+          null, null, null, null, null, null, null, null, 1, 1, 1, 2, 2, 2,    // end list1
+          null, null, 1,    1,    1,    1,    2,    3,    3, 3, 3, 3,          // end list2
+          null, null, null, null, 2,    2,    2,    3,    3, 3, 4, 4, 4, 4, 4  // end list3
+        },
+        nulls_at({0, 1, 2, 3, 4, 5, 6, 7, 14, 15, 26, 27, 28, 29})};
+      auto grandchild2 = strings_col{
+        {
+          "" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "Apple",
+          "Apple",     "Banana",    "Cherry",    "Kiwi",      "Banana",    "Cherry",
+          "Kiwi",  // end list1
+          "" /*NULL*/, "" /*NULL*/, "Bear",      "Cat",       "Dog",       "Duck",      "Panda",
+          "Bear",      "Cat",       "Dog",       "Duck",      "Panda",  // end list2
+          "ÁÁÁ",       "ÁÁÁ",       "ÉÉÉÉÉ",     "ÉÉÉÉÉ",     "ÁBC",       "ÁÁÁ",       "ÍÍÍÍÍ",
+          "" /*NULL*/, "XYZ",       "ÁBC",       "" /*NULL*/, "XYZ",       "ÁBC",       "ÁÁÁ",
+          "ÍÍÍÍÍ"  // end list3
+        },
+        nulls_at({0, 1, 2, 3, 4, 5, 14, 15, 33, 36})};
+      auto child1 = structs_col{{grandchild1, grandchild2}, nulls_at({0, 1, 2, 3, 4, 5})};
+      return structs_col{{child1}};
+    };
+
+    auto const lhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_lhs().release(), 0, {});
+    auto const rhs = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs_rhs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 14, 26, 41}.release(), get_structs_expected().release(), 0, {});
+
+    auto const results_sorted = set_union_sorted(*lhs, *rhs, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+}
diff --git a/cpp/tests/lists/sort_lists_tests.cpp b/cpp/tests/lists/sort_lists_tests.cpp
new file mode 100644
index 0000000..7d925da
--- /dev/null
+++ b/cpp/tests/lists/sort_lists_tests.cpp
@@ -0,0 +1,291 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/lists/sorting.hpp>
+
+template <typename T>
+using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+auto generate_sorted_lists(cudf::lists_column_view const& input,
+                           cudf::order column_order,
+                           cudf::null_order null_precedence)
+{
+  return std::pair{cudf::lists::sort_lists(input, column_order, null_precedence),
+                   cudf::lists::stable_sort_lists(input, column_order, null_precedence)};
+}
+
+template <typename T>
+struct SortLists : public cudf::test::BaseFixture {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::NumericTypes, cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(SortLists, TypesForTest);
+
+TYPED_TEST(SortLists, NoNull)
+{
+  using T = TypeParam;
+
+  // List<T>
+  LCW<T> list{{3, 2, 1, 4}, {5}, {10, 8, 9}, {6, 7}};
+
+  // Ascending
+  // LCW<int>  order{{2, 1, 0, 3}, {0}, {1, 2, 0},  {0, 1}};
+  LCW<T> expected{{1, 2, 3, 4}, {5}, {8, 9, 10}, {6, 7}};
+  {
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::ASCENDING, cudf::null_order::AFTER);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+  {
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  // Descending
+  // LCW<int>  order{{3, 0, 1, 2}, {0}, {0, 1, 2},  {1, 0}};
+  LCW<T> expected2{{4, 3, 2, 1}, {5}, {10, 9, 8}, {7, 6}};
+  {
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::DESCENDING, cudf::null_order::AFTER);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected2);
+  }
+  {
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::DESCENDING, cudf::null_order::BEFORE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected2);
+  }
+}
+
+TYPED_TEST(SortLists, Null)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) return;
+  std::vector<bool> valids_o{1, 1, 0, 1};
+  std::vector<bool> valids_a{1, 1, 1, 0};
+  std::vector<bool> valids_b{0, 1, 1, 1};
+
+  // List<T>
+  LCW<T> list{{{3, 2, 4, 1}, valids_o.begin()}, {5}, {10, 8, 9}, {6, 7}};
+  // LCW<int>  order{{2, 1, 3, 0}, {0}, {1, 2, 0},  {0, 1}};
+
+  {
+    LCW<T> expected{{{1, 2, 3, 4}, valids_a.begin()}, {5}, {8, 9, 10}, {6, 7}};
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::ASCENDING, cudf::null_order::AFTER);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  {
+    LCW<T> expected{{{4, 1, 2, 3}, valids_b.begin()}, {5}, {8, 9, 10}, {6, 7}};
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  // Descending
+  // LCW<int>  order{{3, 0, 1, 2}, {0}, {0, 1, 2},  {1, 0}};
+  {
+    LCW<T> expected{{{4, 3, 2, 1}, valids_b.begin()}, {5}, {10, 9, 8}, {7, 6}};
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::DESCENDING, cudf::null_order::AFTER);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  {
+    LCW<T> expected{{{3, 2, 1, 4}, valids_a.begin()}, {5}, {10, 9, 8}, {7, 6}};
+    auto const [sorted_lists, stable_sorted_lists] = generate_sorted_lists(
+      cudf::lists_column_view{list}, cudf::order::DESCENDING, cudf::null_order::BEFORE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+}
+
+using SortListsInt = SortLists<int>;
+
+TEST_F(SortListsInt, Empty)
+{
+  using T = int;
+
+  {
+    LCW<T> l{};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{l}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), l);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), l);
+  }
+  {
+    LCW<T> l{LCW<T>{}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{l}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), l);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), l);
+  }
+  {
+    LCW<T> l{LCW<T>{}, LCW<T>{}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{l}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), l);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), l);
+  }
+}
+
+TEST_F(SortListsInt, Single)
+{
+  using T = int;
+
+  {
+    LCW<T> l{1};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{l}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), l);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), l);
+  }
+  {
+    LCW<T> l{{1, 2, 3}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{l}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), l);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), l);
+  }
+}
+
+TEST_F(SortListsInt, NullRows)
+{
+  using T = int;
+  std::vector<int> valids{0, 1, 0};
+  LCW<T> l{{{1, 2, 3}, {4, 5, 6}, {7}}, valids.begin()};  // offset 0, 0, 3, 3
+
+  auto const [sorted_lists, stable_sorted_lists] =
+    generate_sorted_lists(cudf::lists_column_view{l}, {}, {});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), l);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), l);
+}
+
+// Disabling this test.
+// Reason: After this exception "cudaErrorAssert device-side assert triggered", further tests fail
+TEST_F(SortListsInt, DISABLED_Depth)
+{
+  using T = int;
+  LCW<T> l1{LCW<T>{{1, 2}, {3}}, LCW<T>{{4, 5}}};
+  // device exception
+  EXPECT_THROW(cudf::lists::sort_lists(cudf::lists_column_view{l1}, {}, {}), std::exception);
+}
+
+TEST_F(SortListsInt, Sliced)
+{
+  using T = int;
+  LCW<T> l{{3, 2, 1, 4}, {7, 5, 6}, {8, 9}, {10}};
+
+  {
+    auto const sliced_list = cudf::slice(l, {0, 4})[0];
+    auto const expected    = LCW<T>{{1, 2, 3, 4}, {5, 6, 7}, {8, 9}, {10}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{sliced_list}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  {
+    auto const sliced_list = cudf::slice(l, {1, 4})[0];
+    auto const expected    = LCW<T>{{5, 6, 7}, {8, 9}, {10}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{sliced_list}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  {
+    auto const sliced_list = cudf::slice(l, {1, 2})[0];
+    auto const expected    = LCW<T>{{5, 6, 7}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{sliced_list}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+
+  {
+    auto const sliced_list = cudf::slice(l, {0, 2})[0];
+    auto const expected    = LCW<T>{{1, 2, 3, 4}, {5, 6, 7}};
+    auto const [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{sliced_list}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(stable_sorted_lists->view(), expected);
+  }
+}
+
+using SortListsDouble = SortLists<double>;
+TEST_F(SortListsDouble, InfinityAndNaN)
+{
+  auto constexpr NaN = std::numeric_limits<double>::quiet_NaN();
+  auto constexpr Inf = std::numeric_limits<double>::infinity();
+
+  using LCW = cudf::test::lists_column_wrapper<double>;
+  {
+    LCW input{-0.0, -NaN, -NaN, NaN, Inf, -Inf, 7, 5, 6, NaN, Inf, -Inf, -NaN, -NaN, -0.0};
+    auto [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{input}, {}, {});
+    LCW expected{-Inf, -Inf, -0, -0, 5, 6, 7, Inf, Inf, -NaN, -NaN, NaN, NaN, -NaN, -NaN};
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(stable_sorted_lists->view(), expected);
+  }
+  // This data includes a row with over 200 elements to test the
+  // radix sort is not used in the logic path in segmented_sort.
+  // Technically radix sort is not expected to be used in either case.
+  {
+    // clang-format off
+    LCW input{0.0, -0.0, -NaN, -NaN, NaN, Inf, -Inf,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+               1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,
+              NaN, Inf, -Inf, -NaN, -NaN, -0.0, 0.0};
+    LCW expected{-Inf, -Inf, 0.0, -0.0, 0, 0,
+               0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0, 0,
+               1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+               2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
+               4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
+               5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+               6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
+               7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
+               8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
+               9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
+              Inf, Inf, -NaN, -NaN, NaN, NaN, -NaN, -NaN};
+    // clang-format on
+    auto [sorted_lists, stable_sorted_lists] =
+      generate_sorted_lists(cudf::lists_column_view{input}, {}, {});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(sorted_lists->view(), expected);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(stable_sorted_lists->view(), expected);
+  }
+}
diff --git a/cpp/tests/lists/stream_compaction/apply_boolean_mask_tests.cpp b/cpp/tests/lists/stream_compaction/apply_boolean_mask_tests.cpp
new file mode 100644
index 0000000..4d38dbc
--- /dev/null
+++ b/cpp/tests/lists/stream_compaction/apply_boolean_mask_tests.cpp
@@ -0,0 +1,232 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/lists/extract.hpp>
+#include <cudf/lists/stream_compaction.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+namespace cudf::test {
+
+using namespace iterators;
+using cudf::lists_column_view;
+using cudf::lists::apply_boolean_mask;
+
+template <typename T>
+using lists    = lists_column_wrapper<T, int32_t>;
+using filter_t = lists_column_wrapper<bool, int32_t>;
+
+template <typename T>
+using fwcw    = fixed_width_column_wrapper<T, int32_t>;
+using offsets = fwcw<int32_t>;
+using strings = strings_column_wrapper;
+
+auto constexpr X = int32_t{0};  // Placeholder for NULL.
+
+struct ApplyBooleanMaskTest : public BaseFixture {};
+
+template <typename T>
+struct ApplyBooleanMaskTypedTest : ApplyBooleanMaskTest {};
+
+TYPED_TEST_SUITE(ApplyBooleanMaskTypedTest, cudf::test::NumericTypes);
+
+TYPED_TEST(ApplyBooleanMaskTypedTest, StraightLine)
+{
+  using T    = TypeParam;
+  auto input = lists<T>{{0, 1, 2, 3}, {4, 5}, {6, 7, 8, 9}, {0, 1}, {2, 3, 4, 5}, {6, 7}}.release();
+  auto filter = filter_t{{1, 0, 1, 0}, {1, 0}, {1, 0, 1, 0}, {1, 0}, {1, 0, 1, 0}, {1, 0}};
+
+  {
+    // Unsliced.
+    auto filtered = apply_boolean_mask(lists_column_view{*input}, lists_column_view{filter});
+    auto expected = lists<T>{{0, 2}, {4}, {6, 8}, {0}, {2, 4}, {6}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+  {
+    // Sliced input: Remove the first row.
+    auto sliced = cudf::slice(*input, {1, input->size()}).front();
+    //           == lists_t {{4, 5}, {6, 7, 8, 9}, {0, 1}, {2, 3, 4, 5}, {6, 7}};
+    auto filter   = filter_t{{0, 1}, {0, 1, 0, 1}, {1, 1}, {0, 1, 0, 1}, {0, 0}};
+    auto filtered = apply_boolean_mask(lists_column_view{sliced}, lists_column_view{filter});
+    auto expected = lists<T>{{5}, {7, 9}, {0, 1}, {3, 5}, {}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+}
+
+TYPED_TEST(ApplyBooleanMaskTypedTest, NullElementsInTheListRows)
+{
+  using T = TypeParam;
+  auto input =
+    lists<T>{
+      {0, 1, 2, 3},
+      lists<T>{{X, 5}, null_at(0)},
+      {6, 7, 8, 9},
+      {0, 1},
+      lists<T>{{X, 3, 4, X}, nulls_at({0, 3})},
+      lists<T>{{X, X}, nulls_at({0, 1})},
+    }
+      .release();
+  auto filter = filter_t{{1, 0, 1, 0}, {1, 0}, {1, 0, 1, 0}, {1, 0}, {1, 0, 1, 0}, {1, 0}};
+
+  {
+    // Unsliced.
+    auto filtered = apply_boolean_mask(lists_column_view{*input}, lists_column_view{filter});
+    auto expected = lists<T>{{0, 2},
+                             lists<T>{{X}, null_at(0)},
+                             {6, 8},
+                             {0},
+                             lists<T>{{X, 4}, null_at(0)},
+                             lists<T>{{X}, null_at(0)}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+  {
+    // Sliced input: Remove the first row.
+    auto sliced = cudf::slice(*input, {1, input->size()}).front();
+    //           == lists_t {{X, 5}, {6, 7, 8, 9}, {0, 1}, {X, 3, 4, X}, {X, X}};
+    auto filter   = filter_t{{0, 1}, {0, 1, 0, 1}, {1, 1}, {0, 1, 0, 1}, {0, 0}};
+    auto filtered = apply_boolean_mask(lists_column_view{sliced}, lists_column_view{filter});
+    auto expected = lists<T>{{5}, {7, 9}, {0, 1}, lists<T>{{3, X}, null_at(1)}, {}};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+}
+
+TYPED_TEST(ApplyBooleanMaskTypedTest, NullListRowsInTheInputColumn)
+{
+  using T = TypeParam;
+  auto input =
+    lists<T>{{{0, 1, 2, 3}, {}, {6, 7, 8, 9}, {}, {2, 3, 4, 5}, {6, 7}}, nulls_at({1, 3})}
+      .release();
+  auto filter = filter_t{{1, 0, 1, 0}, {}, {1, 0, 1, 0}, {}, {1, 0, 1, 0}, {1, 0}};
+
+  {
+    // Unsliced.
+    auto filtered = apply_boolean_mask(lists_column_view{*input}, lists_column_view{filter});
+    auto expected = lists<T>{{{0, 2}, {}, {6, 8}, {}, {2, 4}, {6}}, nulls_at({1, 3})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+  {
+    // Sliced input: Remove the first row.
+    auto sliced = cudf::slice(*input, {1, input->size()}).front();
+    //           == lists_t{{{}, {6, 7, 8, 9}, {}, {2, 3, 4, 5}, {6, 7}}, nulls_at({0,2})};
+    auto filter   = filter_t{{}, {0, 1, 0, 1}, {}, {0, 1, 0, 1}, {0, 0}};
+    auto filtered = apply_boolean_mask(lists_column_view{sliced}, lists_column_view{filter});
+    auto expected = lists<T>{{{}, {7, 9}, {}, {3, 5}, {}}, nulls_at({0, 2})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+  {
+    // Sliced input: Remove the first two rows.
+    auto sliced = cudf::slice(*input, {2, input->size()}).front();
+    //           == lists_t{{{6, 7, 8, 9}, {}, {2, 3, 4, 5}, {6, 7}}, null_at(1)};
+    auto filter   = filter_t{{0, 1, 0, 1}, {}, {0, 1, 0, 1}, {0, 0}};
+    auto filtered = apply_boolean_mask(lists_column_view{sliced}, lists_column_view{filter});
+    auto expected = lists<T>{{{7, 9}, {}, {3, 5}, {}}, null_at(1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*filtered, expected);
+  }
+}
+
+TYPED_TEST(ApplyBooleanMaskTypedTest, StructInput)
+{
+  using T    = TypeParam;
+  using fwcw = fwcw<T>;
+
+  auto constexpr num_input_rows = 7;
+  auto const input              = [] {
+    auto child_num               = fwcw{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+    auto child_str               = strings{"0", "1", "2", "3", "4", "5", "6", "7", "8", "9"};
+    auto const null_mask_begin   = null_at(5);
+    auto const null_mask_end     = null_mask_begin + num_input_rows;
+    auto [null_mask, null_count] = detail::make_null_mask(null_mask_begin, null_mask_end);
+    return cudf::make_lists_column(num_input_rows,
+                                   offsets{0, 2, 3, 6, 6, 8, 8, 10}.release(),
+                                   structs_column_wrapper{{child_num, child_str}}.release(),
+                                   null_count,
+                                   std::move(null_mask));
+  }();
+  {
+    // Unsliced.
+    // The input should now look as follows: (String child dropped for brevity.)
+    // Input:                     {[0, 1], [2], [3, 4, 5], [], [6, 7], [], [8, 9]}
+    auto const filter   = filter_t{{1, 1}, {0}, {0, 1, 0}, {}, {1, 0}, {}, {0, 1}};
+    auto const result   = apply_boolean_mask(lists_column_view{*input}, lists_column_view{filter});
+    auto const expected = [] {
+      auto child_num               = fwcw{0, 1, 4, 6, 9};
+      auto child_str               = strings{"0", "1", "4", "6", "9"};
+      auto const null_mask_begin   = null_at(5);
+      auto const null_mask_end     = null_mask_begin + num_input_rows;
+      auto [null_mask, null_count] = detail::make_null_mask(null_mask_begin, null_mask_end);
+      return cudf::make_lists_column(num_input_rows,
+                                     offsets{0, 2, 2, 3, 3, 4, 4, 5}.release(),
+                                     structs_column_wrapper{{child_num, child_str}}.release(),
+                                     null_count,
+                                     std::move(null_mask));
+    }();
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+  }
+  {
+    // Sliced. Remove the first row.
+    auto const sliced_input = cudf::slice(*input, {1, input->size()}).front();
+    // The input should now look as follows: (String child dropped for brevity.)
+    // Input:                   {[2], [3, 4, 5], [], [6, 7], [], [8, 9]}
+    auto const filter = filter_t{{0}, {0, 1, 0}, {}, {1, 0}, {}, {0, 1}};
+    auto const result =
+      apply_boolean_mask(lists_column_view{sliced_input}, lists_column_view{filter});
+    auto const expected = [] {
+      auto child_num               = fwcw{4, 6, 9};
+      auto child_str               = strings{"4", "6", "9"};
+      auto const null_mask_begin   = null_at(4);
+      auto const null_mask_end     = null_mask_begin + num_input_rows;
+      auto [null_mask, null_count] = detail::make_null_mask(null_mask_begin, null_mask_end);
+      return cudf::make_lists_column(num_input_rows - 1,
+                                     offsets{0, 0, 1, 1, 2, 2, 3}.release(),
+                                     structs_column_wrapper{{child_num, child_str}}.release(),
+                                     null_count,
+                                     std::move(null_mask));
+    }();
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+  }
+}
+
+TEST_F(ApplyBooleanMaskTest, Trivial)
+{
+  auto const input  = lists<int32_t>{};
+  auto const filter = filter_t{};
+  auto const result = apply_boolean_mask(lists_column_view{input}, lists_column_view{filter});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, lists<int32_t>{});
+}
+
+TEST_F(ApplyBooleanMaskTest, Failure)
+{
+  {
+    // Invalid mask type.
+    auto const input  = lists<int32_t>{{1, 2, 3}, {4, 5, 6}};
+    auto const filter = lists<int32_t>{{0, 0, 0}};
+    EXPECT_THROW(apply_boolean_mask(lists_column_view{input}, lists_column_view{filter}),
+                 cudf::logic_error);
+  }
+  {
+    // Mismatched number of rows.
+    auto const input  = lists<int32_t>{{1, 2, 3}, {4, 5, 6}};
+    auto const filter = filter_t{{0, 0, 0}};
+    EXPECT_THROW(apply_boolean_mask(lists_column_view{input}, lists_column_view{filter}),
+                 cudf::logic_error);
+  }
+}
+}  // namespace cudf::test
diff --git a/cpp/tests/lists/stream_compaction/distinct_tests.cpp b/cpp/tests/lists/stream_compaction/distinct_tests.cpp
new file mode 100644
index 0000000..fbc637f
--- /dev/null
+++ b/cpp/tests/lists/stream_compaction/distinct_tests.cpp
@@ -0,0 +1,758 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/lists/stream_compaction.hpp>
+
+using float_type = double;
+using namespace cudf::test::iterators;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr neg_NaN      = -std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr neg_Inf      = -std::numeric_limits<float_type>::infinity();
+auto constexpr NaN          = std::numeric_limits<float_type>::quiet_NaN();
+auto constexpr Inf          = std::numeric_limits<float_type>::infinity();
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using int32s_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_lists  = cudf::test::lists_column_wrapper<float_type>;
+using strings_lists = cudf::test::lists_column_wrapper<cudf::string_view>;
+using strings_col   = cudf::test::strings_column_wrapper;
+using structs_col   = cudf::test::structs_column_wrapper;
+using lists_cv      = cudf::lists_column_view;
+
+namespace {
+
+auto distinct_sorted(cudf::column_view const& input,
+                     cudf::null_equality nulls_equal = NULL_EQUAL,
+                     cudf::nan_equality nans_equal   = NAN_EQUAL)
+{
+  auto const results = cudf::lists::distinct(lists_cv{input}, nulls_equal, nans_equal);
+
+  // The sorted result will have nulls first and NaNs last.
+  // In addition, row equality comparisons in tests just ignore NaN sign thus the expected values
+  // can be just NaN while the input can be mixed of NaN and neg_NaN.
+  return cudf::lists::sort_lists(
+    lists_cv{*results}, cudf::order::ASCENDING, cudf::null_order::BEFORE);
+}
+
+}  // namespace
+
+struct ListDistinctTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct ListDistinctTypedTest : public cudf::test::BaseFixture {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(ListDistinctTypedTest, TestTypes);
+
+TEST_F(ListDistinctTest, TrivialTest)
+{
+  auto const input =
+    floats_lists{{floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0}, null_at(6)},
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)},
+                  {} /*NULL*/,
+                  floats_lists{{NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0}, null_at(6)}},
+                 null_at(2)};
+
+  auto const expected = floats_lists{{floats_lists{{null, 0.0, 5.0, NaN}, null_at(0)},
+                                      floats_lists{{null, 0.0, 1.0, 5.0, NaN}, null_at(0)},
+                                      floats_lists{} /*NULL*/,
+                                      floats_lists{{null, 0.0, 1.0, 5.0, NaN}, null_at(0)}},
+                                     null_at(2)};
+
+  auto const results_sorted = distinct_sorted(input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(ListDistinctTest, FloatingPointTestsWithSignedZero)
+{
+  // -0.0 and 0.0 should be considered equal.
+  auto const input    = floats_lists{0.0, 1, 2, -0.0, 1, 2, 0.0, 1, 2, -0.0, -0.0, 0.0, 0.0, 3};
+  auto const expected = floats_lists{0, 1, 2, 3};
+
+  auto const results_sorted = distinct_sorted(input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(ListDistinctTest, FloatingPointTestsWithInf)
+{
+  auto const input    = floats_lists{Inf, 0, neg_Inf, 0, Inf, 0, neg_Inf, 0, Inf, 0, neg_Inf};
+  auto const expected = floats_lists{neg_Inf, 0, Inf};
+
+  auto const results_sorted = distinct_sorted(input);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+}
+
+TEST_F(ListDistinctTest, FloatingPointTestsWithNaNs)
+{
+  auto const input =
+    floats_lists{0, -1, 1, NaN, 2, 0, neg_NaN, 1, -2, 2, 0, 1, 2, neg_NaN, NaN, NaN, NaN, neg_NaN};
+
+  // NaNs are equal.
+  {
+    auto const expected = floats_lists{-2, -1, 0, 1, 2, NaN};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // NaNs are unequal.
+  {
+    auto const expected = floats_lists{-2, -1, 0, 1, 2, NaN, NaN, NaN, NaN, NaN, NaN, NaN};
+
+    auto const results_sorted = distinct_sorted(input, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, StringTestsNonNull)
+{
+  // Trivial cases - empty input.
+  {
+    auto const input    = strings_lists{{}};
+    auto const expected = strings_lists{{}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // No duplicate.
+  {
+    auto const input    = strings_lists{"this", "is", "a", "string"};
+    auto const expected = strings_lists{"a", "is", "string", "this"};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // One list column.
+  {
+    auto const input    = strings_lists{"this", "is", "is", "is", "a", "string", "string"};
+    auto const expected = strings_lists{"a", "is", "string", "this"};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column.
+  {
+    auto const input = strings_lists{
+      strings_lists{"this", "is", "a", "no duplicate", "string"},
+      strings_lists{"this", "is", "is", "a", "one duplicate", "string"},
+      strings_lists{"this", "is", "is", "is", "a", "two duplicates", "string"},
+      strings_lists{"this", "is", "is", "is", "is", "a", "three duplicates", "string"}};
+    auto const expected =
+      strings_lists{strings_lists{"a", "is", "no duplicate", "string", "this"},
+                    strings_lists{"a", "is", "one duplicate", "string", "this"},
+                    strings_lists{"a", "is", "string", "this", "two duplicates"},
+                    strings_lists{"a", "is", "string", "this", "three duplicates"}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, StringTestsWithNullsEqual)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const input = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const expected = strings_lists{{null, "a", "is", "string", "this"}, null_at(0)};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const input = strings_lists{
+      {strings_lists{{"this", null, "is", null, "a", null, "no duplicate", null, "string"},
+                     nulls_at({1, 3, 5, 7})},
+       strings_lists{}, /* NULL */
+       strings_lists{"this", "is", "is", "a", "one duplicate", "string"}},
+      null_at(1)};
+    auto const expected =
+      strings_lists{{strings_lists{{null, "a", "is", "no duplicate", "string", "this"}, null_at(0)},
+                     strings_lists{}, /* NULL */
+                     strings_lists{"a", "is", "one duplicate", "string", "this"}},
+                    null_at(1)};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, StringTestsWithNullsUnequal)
+{
+  auto const null = std::string("");
+
+  // One list column with null entries.
+  {
+    auto const input = strings_lists{
+      {"this", null, "is", "is", "is", "a", null, "string", null, "string"}, nulls_at({1, 6, 8})};
+    auto const expected =
+      strings_lists{{null, null, null, "a", "is", "string", "this"}, nulls_at({0, 1, 2})};
+
+    auto const results_sorted = distinct_sorted(input, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple lists column with null lists and null entries.
+  {
+    auto const input = strings_lists{
+      {strings_lists{{"this", null, "is", null, "a", null, "no duplicate", null, "string"},
+                     nulls_at({1, 3, 5, 7})},
+       strings_lists{}, /* NULL */
+       strings_lists{"this", "is", "is", "a", "one duplicate", "string"}},
+      null_at(1)};
+    auto const expected = strings_lists{
+      {strings_lists{{null, null, null, null, "a", "is", "no duplicate", "string", "this"},
+                     nulls_at({0, 1, 2, 3})},
+       strings_lists{}, /* NULL */
+       strings_lists{"a", "is", "one duplicate", "string", "this"}},
+      null_at(1)};
+
+    auto const results_sorted = distinct_sorted(input, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(ListDistinctTypedTest, TrivialInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  // Empty input.
+  {
+    auto const input    = lists_col{};
+    auto const expected = lists_col{};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // All input lists are empty.
+  {
+    auto const input    = lists_col{lists_col{}, lists_col{}, lists_col{}};
+    auto const expected = lists_col{lists_col{}, lists_col{}, lists_col{}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Trivial cases.
+  {
+    auto const input    = lists_col{0, 1, 2, 3, 4, 5};
+    auto const expected = lists_col{0, 1, 2, 3, 4, 5};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Multiple empty lists.
+  {
+    auto const input    = lists_col{{}, {}, {5, 4, 3, 2, 1, 0}, {}, {6}, {}};
+    auto const expected = lists_col{{}, {}, {0, 1, 2, 3, 4, 5}, {}, {6}, {}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(ListDistinctTypedTest, SlicedNonNullInputTests)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const input_original =
+    lists_col{{1, 2, 3, 2, 3, 2, 3, 2, 3}, {3, 2, 1, 4, 1}, {5}, {10, 8, 9}, {6, 7}};
+
+  {
+    auto const expected = lists_col{{1, 2, 3}, {1, 2, 3, 4}, {5}, {8, 9, 10}, {6, 7}};
+
+    auto const results_sorted = distinct_sorted(input_original);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const input    = cudf::slice(input_original, {0, 5})[0];
+    auto const expected = lists_col{{1, 2, 3}, {1, 2, 3, 4}, {5}, {8, 9, 10}, {6, 7}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const input    = cudf::slice(input_original, {1, 5})[0];
+    auto const expected = lists_col{{1, 2, 3, 4}, {5}, {8, 9, 10}, {6, 7}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const input    = cudf::slice(input_original, {1, 3})[0];
+    auto const expected = lists_col{{1, 2, 3, 4}, {5}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  {
+    auto const input    = cudf::slice(input_original, {0, 3})[0];
+    auto const expected = lists_col{{1, 2, 3}, {1, 2, 3, 4}, {5}};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TYPED_TEST(ListDistinctTypedTest, InputHaveNullsTests)
+{
+  using lists_col     = cudf::test::lists_column_wrapper<TypeParam>;
+  auto constexpr null = TypeParam{0};
+
+  // Nullable lists.
+  {
+    auto const input = lists_col{
+      {{3, 2, 1, 4, 1}, {5}, {} /*NULL*/, {} /*NULL*/, {10, 8, 9}, {6, 7}}, nulls_at({2, 3})};
+    auto const expected = lists_col{
+      {{1, 2, 3, 4}, {5}, {} /*NULL*/, {} /*NULL*/, {8, 9, 10}, {6, 7}}, nulls_at({2, 3})};
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are equal.
+  {
+    auto const input =
+      lists_col{{null, 1, null, 3, null, 5, null, 7, null, 9}, nulls_at({0, 2, 4, 6, 8})};
+    auto const expected = lists_col{{null, 1, 3, 5, 7, 9}, null_at(0)};
+
+    auto const results_sorted = distinct_sorted(input, NULL_EQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+
+  // Nullable child and nulls are unequal.
+  {
+    auto const input = lists_col{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, nulls_at({0, 2, 4, 6, 8})};
+    auto const expected =
+      lists_col{{null, null, null, null, null, 1, 3, 5, 7, 9}, nulls_at({0, 1, 2, 3, 4})};
+
+    auto const results_sorted = distinct_sorted(input, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, InputListsOfStructsNoNull)
+{
+  auto const get_structs = [] {
+    auto child1 = int32s_col{
+      1, 1, 1, 1, 1, 1, 1, 1,  // list1
+      1, 1, 1, 1, 2, 1, 2, 2,  // list2
+      2, 2, 2, 2, 3, 2, 3, 3   // list3
+    };
+    auto child2 = strings_col{
+      // begin list1
+      "Banana",
+      "Mango",
+      "Apple",
+      "Cherry",
+      "Kiwi",
+      "Banana",
+      "Cherry",
+      "Kiwi",  // end list1
+      // begin list2
+      "Bear",
+      "Duck",
+      "Cat",
+      "Dog",
+      "Panda",
+      "Bear",
+      "Cat",
+      "Panda",  // end list2
+      // begin list3
+      "ÁÁÁ",
+      "ÉÉÉÉÉ",
+      "ÍÍÍÍÍ",
+      "ÁBC",
+      "XYZ",
+      "ÁÁÁ",
+      "ÁBC",
+      "XYZ"  // end list3
+    };
+    return structs_col{{child1, child2}};
+  };
+
+  auto const get_expected = [] {
+    auto child1 = int32s_col{1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3};
+    auto child2 = strings_col{
+      // begin list1
+      "Apple",
+      "Banana",
+      "Cherry",
+      "Kiwi",
+      "Mango",  // end list1
+      // begin list2
+      "Bear",
+      "Cat",
+      "Dog",
+      "Duck",
+      "Cat",
+      "Panda",  // end list2
+      // begin list3
+      "ÁBC",
+      "ÁÁÁ",
+      "ÉÉÉÉÉ",
+      "ÍÍÍÍÍ",
+      "XYZ",
+      "ÁBC"  // end list3
+    };
+    return structs_col{{child1, child2}};
+  };
+
+  // Test full columns.
+  {
+    auto const input = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 5, 11, 17}.release(), get_expected().release(), 0, {});
+
+    auto const results_sorted = distinct_sorted(*input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+
+  // Test sliced columns.
+  {
+    auto const input_original = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs().release(), 0, {});
+    auto const expected_original = cudf::make_lists_column(
+      3, int32s_col{0, 5, 11, 17}.release(), get_expected().release(), 0, {});
+    auto const input    = cudf::slice(*input_original, {1, 3})[0];
+    auto const expected = cudf::slice(*expected_original, {1, 3})[0];
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, InputListsOfStructsHaveNull)
+{
+  auto const get_structs = [] {
+    auto child1 = int32s_col{{
+                               1,    1,    null, XXX, XXX, 1, 1,    1,  // list1
+                               1,    1,    1,    1,   2,   1, null, 2,  // list2
+                               null, null, 2,    2,   3,   2, 3,    3   // list3
+                             },
+                             nulls_at({2, 14, 16, 17})};
+    auto child2 = strings_col{{
+                                // begin list1
+                                "Banana",
+                                "Mango",
+                                "Apple",
+                                "XXX", /*NULL*/
+                                "XXX", /*NULL*/
+                                "Banana",
+                                "Cherry",
+                                "Kiwi",  // end list1
+                                         // begin list2
+                                "Bear",
+                                "Duck",
+                                "Cat",
+                                "Dog",
+                                "Panda",
+                                "Bear",
+                                "" /*NULL*/,
+                                "Panda",  // end list2
+                                          // begin list3
+                                "ÁÁÁ",
+                                "ÉÉÉÉÉ",
+                                "ÍÍÍÍÍ",
+                                "ÁBC",
+                                "" /*NULL*/,
+                                "ÁÁÁ",
+                                "ÁBC",
+                                "XYZ"  // end list3
+                              },
+                              nulls_at({14, 20})};
+    return structs_col{{child1, child2}, nulls_at({3, 4})};
+  };
+
+  auto const get_expected = [] {
+    auto child1 = int32s_col{{      // begin list1
+                              XXX,  // end list1
+                              null,
+                              1,
+                              1,
+                              1,
+                              1,
+                              // begin list2
+                              null,  // end list2
+                              1,
+                              1,
+                              1,
+                              1,
+                              2,
+                              // begin list3
+                              null,
+                              null,
+                              2,
+                              2,
+                              2,
+                              3,
+                              3,
+                              3},  // end list3
+                             nulls_at({1, 6, 12, 13})};
+    auto child2 = strings_col{{       // begin list1
+                               "XXX", /*NULL*/
+                               "Apple",
+                               "Banana",
+                               "Cherry",
+                               "Kiwi",
+                               "Mango",  // end list1
+                                         // begin list2
+                               "",       /*NULL*/
+                               "Bear",
+                               "Cat",
+                               "Dog",
+                               "Duck",
+                               "Panda",  // end list2
+                                         // begin list3
+                               "ÁÁÁ",
+                               "ÉÉÉÉÉ",
+                               "ÁBC",
+                               "ÁÁÁ",
+                               "ÍÍÍÍÍ",
+                               "", /*NULL*/
+                               "XYZ",
+                               "ÁBC"},  // end list3
+                              nulls_at({6, 17})};
+    return structs_col{{child1, child2}, null_at(0)};
+  };
+
+  // Test full columns.
+  {
+    auto const input = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 6, 12, 20}.release(), get_expected().release(), 0, {});
+
+    auto const results_sorted = distinct_sorted(*input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+
+  // Test sliced columns.
+  {
+    auto const input_original = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs().release(), 0, {});
+    auto const expected_original = cudf::make_lists_column(
+      3, int32s_col{0, 6, 12, 20}.release(), get_expected().release(), 0, {});
+    auto const input    = cudf::slice(*input_original, {1, 3})[0];
+    auto const expected = cudf::slice(*expected_original, {1, 3})[0];
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, InputListsOfNestedStructsHaveNull)
+{
+  auto const get_structs = [] {
+    auto grandchild1 = int32s_col{{
+                                    1,    XXX,  null, XXX, XXX, 1, 1,    1,  // list1
+                                    1,    1,    1,    1,   2,   1, null, 2,  // list2
+                                    null, null, 2,    2,   3,   2, 3,    3   // list3
+                                  },
+                                  nulls_at({2, 14, 16, 17})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "Banana",
+                                     "YYY", /*NULL*/
+                                     "Apple",
+                                     "XXX", /*NULL*/
+                                     "YYY", /*NULL*/
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "Bear",
+                                     "Duck",
+                                     "Cat",
+                                     "Dog",
+                                     "Panda",
+                                     "Bear",
+                                     "" /*NULL*/,
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÍÍÍÍÍ",
+                                     "ÁBC",
+                                     "" /*NULL*/,
+                                     "ÁÁÁ",
+                                     "ÁBC",
+                                     "XYZ"  // end list3
+                                   },
+                                   nulls_at({14, 20})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({1, 3, 4})};
+    return structs_col{{child1}};
+  };
+
+  auto const get_expected = [] {
+    auto grandchild1 = int32s_col{{// begin list1
+                                   XXX,
+                                   null,
+                                   1,
+                                   1,
+                                   1,  // end list1
+                                       // begin list2
+                                   null,
+                                   1,
+                                   1,
+                                   1,
+                                   1,
+                                   2,  // end list2
+                                       // begin list3
+                                   null,
+                                   null,
+                                   2,
+                                   2,
+                                   2,
+                                   3,
+                                   3,
+                                   3},
+                                  nulls_at({1, 5, 11, 12})};
+    auto grandchild2 = strings_col{{
+                                     // begin list1
+                                     "XXX" /*NULL*/,
+                                     "Apple",
+                                     "Banana",
+                                     "Cherry",
+                                     "Kiwi",  // end list1
+                                              // begin list2
+                                     "" /*NULL*/,
+                                     "Bear",
+                                     "Cat",
+                                     "Dog",
+                                     "Duck",
+                                     "Panda",  // end list2
+                                               // begin list3
+                                     "ÁÁÁ",
+                                     "ÉÉÉÉÉ",
+                                     "ÁBC",
+                                     "ÁÁÁ",
+                                     "ÍÍÍÍÍ",
+                                     "", /*NULL*/
+                                     "XYZ",
+                                     "ÁBC"  // end list3
+                                   },
+                                   nulls_at({5, 16})};
+    auto child1      = structs_col{{grandchild1, grandchild2}, nulls_at({0})};
+    return structs_col{{child1}};
+  };
+
+  // Test full columns.
+  {
+    auto const input = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs().release(), 0, {});
+    auto const expected = cudf::make_lists_column(
+      3, int32s_col{0, 5, 11, 19}.release(), get_expected().release(), 0, {});
+
+    auto const results_sorted = distinct_sorted(*input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results_sorted);
+  }
+
+  // Test sliced columns.
+  {
+    auto const input_original = cudf::make_lists_column(
+      3, int32s_col{0, 8, 16, 24}.release(), get_structs().release(), 0, {});
+    auto const expected_original = cudf::make_lists_column(
+      3, int32s_col{0, 5, 11, 19}.release(), get_expected().release(), 0, {});
+    auto const input    = cudf::slice(*input_original, {1, 3})[0];
+    auto const expected = cudf::slice(*expected_original, {1, 3})[0];
+
+    auto const results_sorted = distinct_sorted(input);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *results_sorted);
+  }
+}
+
+TEST_F(ListDistinctTest, InputListsOfStructsOfLists)
+{
+  auto const input = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{// begin list1
+                               0,
+                               0,
+                               0,  // end list1
+                                   // begin list2
+                               1,  // end list2
+                                   // begin list3
+                               2,
+                               2,  // end list3
+                                   // begin list4
+                               3,
+                               3,
+                               3};
+      auto child2 = floats_lists{// begin list1
+                                 floats_lists{0, 1},
+                                 floats_lists{0, 1},
+                                 floats_lists{0, 1},     // end list1
+                                                         // begin list2
+                                 floats_lists{3, 4, 5},  // end list2
+                                                         // begin list3
+                                 floats_lists{},
+                                 floats_lists{},  // end list3
+                                                  // begin list4
+                                 floats_lists{6, 7},
+                                 floats_lists{6, 7},
+                                 floats_lists{6, 7}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      4, int32s_col{0, 3, 4, 6, 9}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const expected = [] {
+    auto const get_structs = [] {
+      auto child1 = int32s_col{0, 1, 2, 3};
+      auto child2 =
+        floats_lists{floats_lists{0, 1}, floats_lists{3, 4, 5}, floats_lists{}, floats_lists{6, 7}};
+      return structs_col{{child1, child2}};
+    };
+
+    return cudf::make_lists_column(
+      4, int32s_col{0, 1, 2, 3, 4}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const results = cudf::lists::distinct(lists_cv{*input});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *results);
+}
diff --git a/cpp/tests/merge/merge_dictionary_test.cpp b/cpp/tests/merge/merge_dictionary_test.cpp
new file mode 100644
index 0000000..5a5655e
--- /dev/null
+++ b/cpp/tests/merge/merge_dictionary_test.cpp
@@ -0,0 +1,150 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/merge.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+#include <vector>
+
+struct MergeDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(MergeDictionaryTest, Merge1Column)
+{
+  cudf::test::strings_column_wrapper left_w({"ab", "ab", "cd", "de", "de", "fg", "gh", "gh"});
+  auto left = cudf::dictionary::encode(left_w);
+  cudf::test::strings_column_wrapper right_w({"ab", "cd", "de", "fg", "gh"});
+  auto right = cudf::dictionary::encode(right_w);
+
+  cudf::table_view left_view{{left->view()}};
+  cudf::table_view right_view{{right->view()}};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto result = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+
+  cudf::test::strings_column_wrapper expected_w(
+    {"ab", "ab", "ab", "cd", "cd", "de", "de", "de", "fg", "fg", "gh", "gh", "gh"});
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected->view(), result->get_column(0).view());
+}
+
+TEST_F(MergeDictionaryTest, Merge2Columns)
+{
+  cudf::test::strings_column_wrapper left_w1({"ab", "bc", "cd", "de", "de", "fg", "fg"});
+  auto left1 = cudf::dictionary::encode(left_w1);
+  cudf::test::strings_column_wrapper left_w2({"zy", "zy", "xw", "xw", "vu", "vu", "ts"});
+  auto left2 = cudf::dictionary::encode(left_w2);
+  cudf::table_view left_view{{left1->view(), left2->view()}};
+
+  cudf::test::strings_column_wrapper right_w1({"ab", "ab", "bc", "cd", "de", "fg"});
+  auto right1 = cudf::dictionary::encode(right_w1);
+  cudf::test::strings_column_wrapper right_w2({"zy", "xw", "xw", "vu", "ts", "ts"});
+  auto right2 = cudf::dictionary::encode(right_w2);
+  cudf::table_view right_view{{right1->view(), right2->view()}};
+
+  std::vector<cudf::size_type> key_cols{0, 1};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto result   = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+  auto decoded1 = cudf::dictionary::decode(result->get_column(0).view());
+  auto decoded2 = cudf::dictionary::decode(result->get_column(1).view());
+
+  cudf::test::strings_column_wrapper expected_1(
+    {"ab", "ab", "ab", "bc", "bc", "cd", "cd", "de", "de", "de", "fg", "fg", "fg"});
+  cudf::test::strings_column_wrapper expected_2(
+    {"zy", "zy", "xw", "zy", "xw", "xw", "vu", "xw", "vu", "ts", "vu", "ts", "ts"});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_1, decoded1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_2, decoded2->view());
+
+  left_view  = cudf::table_view{{left1->view(), left_w2}};
+  right_view = cudf::table_view{{right1->view(), right_w2}};
+  result     = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+  decoded1   = cudf::dictionary::decode(result->get_column(0).view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_1, decoded1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_2, result->get_column(1).view());
+
+  left_view  = cudf::table_view{{left_w1, left2->view()}};
+  right_view = cudf::table_view{{right_w1, right2->view()}};
+  result     = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+  decoded2   = cudf::dictionary::decode(result->get_column(1).view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_1, result->get_column(0).view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_2, decoded2->view());
+}
+
+TEST_F(MergeDictionaryTest, WithNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int8_t> left_w1({1, 2, 2, 4, 4, 5, 0},
+                                                         {1, 1, 1, 1, 1, 1, 0});
+  auto left1 = cudf::dictionary::encode(left_w1);
+  cudf::test::fixed_width_column_wrapper<int64_t> left_w2({1000, 1000, 800, 500, 500, 100, 0},
+                                                          {1, 1, 1, 1, 1, 1, 0});
+  auto left2 = cudf::dictionary::encode(left_w2);
+  cudf::table_view left_view{{left1->view(), left2->view()}};
+
+  cudf::test::fixed_width_column_wrapper<int8_t> right_w1({1, 1, 2, 4, 5, 0}, {1, 1, 1, 1, 1, 0});
+  auto right1 = cudf::dictionary::encode(right_w1);
+  cudf::test::fixed_width_column_wrapper<int64_t> right_w2({1000, 800, 800, 400, 100, 0},
+                                                           {1, 1, 1, 1, 1, 0});
+  auto right2 = cudf::dictionary::encode(right_w2);
+  cudf::table_view right_view{{right1->view(), right2->view()}};
+
+  std::vector<cudf::size_type> key_cols{0, 1};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER, cudf::null_order::BEFORE};
+
+  auto result   = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+  auto decoded1 = cudf::dictionary::decode(result->get_column(0).view());
+  auto decoded2 = cudf::dictionary::decode(result->get_column(1).view());
+
+  cudf::test::fixed_width_column_wrapper<int8_t> expected_1(
+    {1, 1, 1, 2, 2, 2, 4, 4, 4, 5, 5, 0, 0}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0});
+  cudf::test::fixed_width_column_wrapper<int64_t> expected_2(
+    {1000, 1000, 800, 1000, 800, 800, 500, 500, 400, 100, 100, 0, 0},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_1, decoded1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_2, decoded2->view());
+
+  left_view  = cudf::table_view{{left1->view(), left_w2}};
+  right_view = cudf::table_view{{right1->view(), right_w2}};
+  result     = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+  decoded1   = cudf::dictionary::decode(result->get_column(0).view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_1, decoded1->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_2, result->get_column(1).view());
+
+  left_view  = cudf::table_view{{left_w1, left2->view()}};
+  right_view = cudf::table_view{{right_w1, right2->view()}};
+  result     = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+  decoded2   = cudf::dictionary::decode(result->get_column(1).view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_1, result->get_column(0).view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_2, decoded2->view());
+}
diff --git a/cpp/tests/merge/merge_string_test.cpp b/cpp/tests/merge/merge_string_test.cpp
new file mode 100644
index 0000000..291167e
--- /dev/null
+++ b/cpp/tests/merge/merge_string_test.cpp
@@ -0,0 +1,413 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/merge.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <cassert>
+#include <initializer_list>
+#include <limits>
+#include <memory>
+#include <vector>
+
+#include <gtest/gtest.h>
+
+using cudf::test::fixed_width_column_wrapper;
+using cudf::test::strings_column_wrapper;
+
+template <typename T>
+class MergeStringTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MergeStringTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(MergeStringTest, Merge1StringKeyColumns)
+{
+  strings_column_wrapper leftColWrap1({"ab", "bc", "cd", "de", "ef", "fg", "gh", "hi"});
+  cudf::size_type inputRows1 = static_cast<cudf::column_view const&>(leftColWrap1).size();
+
+  auto sequence0 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row;
+  });
+
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type> leftColWrap2(
+    sequence0, sequence0 + inputRows1);
+
+  strings_column_wrapper rightColWrap1({"ac", "bd", "ce", "df", "eg", "fh", "gi", "hj"});
+  cudf::size_type inputRows2 = static_cast<cudf::column_view const&>(rightColWrap1).size();
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type> rightColWrap2(
+    sequence0, sequence0 + inputRows2);
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2}};
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  EXPECT_NO_THROW(p_outputTable =
+                    cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+
+  strings_column_wrapper expectedDataWrap1({"ab",
+                                            "ac",
+                                            "bc",
+                                            "bd",
+                                            "cd",
+                                            "ce",
+                                            "de",
+                                            "df",
+                                            "ef",
+                                            "eg",
+                                            "fg",
+                                            "fh",
+                                            "gh",
+                                            "gi",
+                                            "hi",
+                                            "hj"});
+
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row / 2;
+  });
+  fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type> expectedDataWrap2(
+    seq_out2, seq_out2 + outputRows);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+}
+
+// rename test <TestName> as DISABLED_<TestName> to disable:
+// Example: TYPED_TEST(MergeStringTest, DISABLED_Merge2StringKeyColumns)
+//
+TYPED_TEST(MergeStringTest, Merge2StringKeyColumns)
+{
+  strings_column_wrapper leftColWrap1({"ab", "bc", "cd", "de", "ef", "fg", "gh", "hi"});
+  strings_column_wrapper leftColWrap3({"zy", "yx", "xw", "wv", "vu", "ut", "ts", "sr"});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(leftColWrap1).size();
+
+  EXPECT_EQ(inputRows, static_cast<cudf::column_view const&>(leftColWrap3).size());
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 1;
+    else
+      return 2 * row;
+  });
+
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type> leftColWrap2(
+    sequence_l, sequence_l + inputRows);
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2, leftColWrap3}};
+
+  strings_column_wrapper rightColWrap1({"ac", "bd", "ce", "df", "eg", "fh", "gi", "hj"});
+
+  EXPECT_EQ(inputRows, static_cast<cudf::column_view const&>(rightColWrap1).size());
+
+  auto sequence_r = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return 2 * row + 1;
+  });
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence_r)::value_type> rightColWrap2(
+    sequence_r, sequence_r + inputRows);
+
+  strings_column_wrapper rightColWrap3({"zx", "yw", "xv", "wu", "vt", "us", "tr", "sp"});
+
+  EXPECT_EQ(inputRows, static_cast<cudf::column_view const&>(rightColWrap3).size());
+
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2, rightColWrap3}};
+
+  std::vector<cudf::size_type> key_cols{0, 2};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  EXPECT_NO_THROW(p_outputTable =
+                    cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+  strings_column_wrapper expectedDataWrap1({"ab",
+                                            "ac",
+                                            "bc",
+                                            "bd",
+                                            "cd",
+                                            "ce",
+                                            "de",
+                                            "df",
+                                            "ef",
+                                            "eg",
+                                            "fg",
+                                            "fh",
+                                            "gh",
+                                            "gi",
+                                            "hi",
+                                            "hj"});
+
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+  fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type> expectedDataWrap2(
+    seq_out2, seq_out2 + outputRows);
+
+  strings_column_wrapper expectedDataWrap3({"zy",
+                                            "zx",
+                                            "yx",
+                                            "yw",
+                                            "xw",
+                                            "xv",
+                                            "wv",
+                                            "wu",
+                                            "vu",
+                                            "vt",
+                                            "ut",
+                                            "us",
+                                            "ts",
+                                            "tr",
+                                            "sr",
+                                            "sp"});
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+  auto expected_column_view3{static_cast<cudf::column_view const&>(expectedDataWrap3)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+  auto output_column_view3{p_outputTable->view().column(2)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view3, output_column_view3);
+}
+
+TYPED_TEST(MergeStringTest, Merge1StringKeyNullColumns)
+{
+  // data: "ab", "bc", "cd", "de" | valid: 1 1 1 0
+  strings_column_wrapper leftColWrap1({"ab", "bc", "cd", "de", "ef", "fg", "gh", "hi"},
+                                      {1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(leftColWrap1).size();
+
+  auto sequence0 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row;
+  });
+
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type> leftColWrap2(
+    sequence0, sequence0 + inputRows);
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2}};
+
+  // data: "ac", "bd", "ce", "df" | valid: 1 1 1 0
+  strings_column_wrapper rightColWrap1({"ac", "bd", "ce", "df", "eg", "fh", "gi", "hj"},
+                                       {1, 1, 1, 1, 1, 1, 1, 0});
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type> rightColWrap2(
+    sequence0, sequence0 + inputRows);
+
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  EXPECT_NO_THROW(p_outputTable =
+                    cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+
+  // data: "ab", "ac", "bc", "bd", "cd", "ce", "de", "df" | valid: 1 1 1 1 1 1 0 0
+  strings_column_wrapper expectedDataWrap1({"ab",
+                                            "ac",
+                                            "bc",
+                                            "bd",
+                                            "cd",
+                                            "ce",
+                                            "de",
+                                            "df",
+                                            "ef",
+                                            "eg",
+                                            "fg",
+                                            "fh",
+                                            "gh",
+                                            "gi",
+                                            "hi",
+                                            "hj"},
+                                           {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0});
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row / 2;
+  });
+  fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type> expectedDataWrap2(
+    seq_out2, seq_out2 + outputRows);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+}
+
+TYPED_TEST(MergeStringTest, Merge2StringKeyNullColumns)
+{
+  strings_column_wrapper leftColWrap1({"ab", "bc", "cd", "de", "ef", "fg", "gh", "hi"},
+                                      {1, 1, 1, 1, 1, 1, 1, 0});
+  strings_column_wrapper leftColWrap3({"zy", "yx", "xw", "wv", "vu", "ut", "ts", "sr"},
+                                      {1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(leftColWrap1).size();
+
+  EXPECT_EQ(inputRows, static_cast<cudf::column_view const&>(leftColWrap3).size());
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 1;
+    else
+      return 2 * row;
+  });
+
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type> leftColWrap2(
+    sequence_l, sequence_l + inputRows);
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2, leftColWrap3}};
+
+  strings_column_wrapper rightColWrap1({"ac", "bd", "ce", "df", "eg", "fh", "gi", "hj"},
+                                       {1, 1, 1, 1, 1, 1, 1, 0});
+
+  EXPECT_EQ(inputRows, static_cast<cudf::column_view const&>(rightColWrap1).size());
+
+  auto sequence_r = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return 2 * row + 1;
+  });
+  fixed_width_column_wrapper<TypeParam, typename decltype(sequence_r)::value_type> rightColWrap2(
+    sequence_r, sequence_r + inputRows);
+
+  strings_column_wrapper rightColWrap3({"zx", "yw", "xv", "wu", "vt", "us", "tr", "sp"},
+                                       {1, 1, 1, 1, 1, 1, 1, 0});
+
+  EXPECT_EQ(inputRows, static_cast<cudf::column_view const&>(rightColWrap3).size());
+
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2, rightColWrap3}};
+
+  std::vector<cudf::size_type> key_cols{0, 2};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER, cudf::null_order::BEFORE};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  EXPECT_NO_THROW(p_outputTable =
+                    cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+  strings_column_wrapper expectedDataWrap1({"ab",
+                                            "ac",
+                                            "bc",
+                                            "bd",
+                                            "cd",
+                                            "ce",
+                                            "de",
+                                            "df",
+                                            "ef",
+                                            "eg",
+                                            "fg",
+                                            "fh",
+                                            "gh",
+                                            "gi",
+                                            "hi",
+                                            "hj"},
+                                           {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0});
+
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type> expectedDataWrap2(
+    seq_out2, seq_out2 + outputRows);
+
+  strings_column_wrapper expectedDataWrap3({"zy",
+                                            "zx",
+                                            "yx",
+                                            "yw",
+                                            "xw",
+                                            "xv",
+                                            "wv",
+                                            "wu",
+                                            "vu",
+                                            "vt",
+                                            "ut",
+                                            "us",
+                                            "ts",
+                                            "tr",
+                                            "sr",
+                                            "sp"},
+                                           {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0});
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+  auto expected_column_view3{static_cast<cudf::column_view const&>(expectedDataWrap3)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+  auto output_column_view3{p_outputTable->view().column(2)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view3, output_column_view3);
+}
diff --git a/cpp/tests/merge/merge_test.cpp b/cpp/tests/merge/merge_test.cpp
new file mode 100644
index 0000000..3a61c07
--- /dev/null
+++ b/cpp/tests/merge/merge_test.cpp
@@ -0,0 +1,908 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/merge.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/merge.h>
+
+#include <vector>
+
+template <typename T>
+class MergeTest_ : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(MergeTest_, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(MergeTest_, MergeIsZeroWhenShouldNotBeZero)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1({1, 2, 3, 4, 5});
+  cudf::test::fixed_width_column_wrapper<TypeParam> rightColWrap1{};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order;
+  column_order.push_back(cudf::order::ASCENDING);
+  std::vector<cudf::null_order> null_precedence(column_order.size(), cudf::null_order::AFTER);
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+  cudf::table_view expected{{leftColWrap1}};
+
+  auto result = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence);
+
+  int expected_len = 5;
+  ASSERT_EQ(result->num_rows(), expected_len);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(MergeTest_, MismatchedNumColumns)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1({0, 1, 2, 3});
+  columnFactoryT rightColWrap1({0, 1, 2, 3});
+  columnFactoryT rightColWrap2({0, 1, 2, 3});
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, MismatchedColumnDypes)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> leftColWrap1{{0, 1, 2, 3}};
+  cudf::test::fixed_width_column_wrapper<double> rightColWrap1{{0, 1, 2, 3}};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, EmptyKeyColumns)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1({0, 1, 2, 3});
+  columnFactoryT rightColWrap1({0, 1, 2, 3});
+
+  std::vector<cudf::size_type> key_cols{};  // empty! this should trigger exception
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, TooManyKeyColumns)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1{0, 1, 2, 3};
+  columnFactoryT rightColWrap1{0, 1, 2, 3};
+
+  std::vector<cudf::size_type> key_cols{
+    0, 1};  // more keys than columns: this should trigger exception
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, EmptyOrderTypes)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1{0, 1, 2, 3};
+  columnFactoryT rightColWrap1{0, 1, 2, 3};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{};  // empty! this should trigger exception
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, TooManyOrderTypes)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1{0, 1, 2, 3};
+  columnFactoryT rightColWrap1{0, 1, 2, 3};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING,
+    cudf::order::DESCENDING};  // more order types than columns: this should trigger exception
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, MismatchedKeyColumnsAndOrderTypes)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  columnFactoryT leftColWrap1{0, 1, 2, 3};
+  columnFactoryT leftColWrap2{0, 1, 2, 3};
+  columnFactoryT rightColWrap1{0, 1, 2, 3};
+  columnFactoryT rightColWrap2{0, 1, 2, 3};
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2}};
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  std::vector<cudf::size_type> key_cols{0, 1};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  EXPECT_THROW(cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(MergeTest_, NoInputTables)
+{
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(p_outputTable = cudf::merge({}, {}, {}, {}));
+  EXPECT_EQ(p_outputTable->num_columns(), 0);
+}
+
+TYPED_TEST(MergeTest_, SingleTableInput)
+{
+  cudf::size_type inputRows = 40;
+
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type>
+    colWrap1(sequence, sequence + inputRows);
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{colWrap1}};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(p_outputTable =
+                         cudf::merge({left_view}, key_cols, column_order, null_precedence));
+
+  auto input_column_view{left_view.column(0)};
+  auto output_column_view{p_outputTable->view().column(0)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input_column_view, output_column_view);
+}
+
+TYPED_TEST(MergeTest_, MergeTwoEmptyTables)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  columnFactoryT leftColWrap1{};
+  columnFactoryT rightColWrap1{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(
+    p_outputTable = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(left_view, p_outputTable->view());
+}
+
+TYPED_TEST(MergeTest_, MergeWithEmptyColumn)
+{
+  using columnFactoryT = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  cudf::size_type inputRows = 40;
+
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type>
+    leftColWrap1(sequence, sequence + inputRows);
+  columnFactoryT rightColWrap1{};  // wrapper of empty column <- this might require a (sequence,
+                                   // sequence) generator
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(
+    p_outputTable = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type>
+    expectedDataWrap1(
+      sequence,
+      sequence +
+        outputRows);  //<- confirmed I can reuse a sequence, wo/ creating overlapping columns!
+
+  auto expected_column_view{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto output_column_view{p_outputTable->view().column(0)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view, output_column_view);
+}
+
+TYPED_TEST(MergeTest_, Merge1KeyColumns)
+{
+  cudf::size_type inputRows = 40;
+
+  auto sequence0 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row;
+  });
+
+  auto sequence1 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 1;
+    else
+      return 2 * row;
+  });
+
+  auto sequence2 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return 2 * row + 1;
+  });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>
+    leftColWrap1(sequence1, sequence1 + inputRows);
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type>
+    leftColWrap2(sequence0, sequence0 + inputRows);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence2)::value_type>
+    rightColWrap1(sequence2, sequence2 + inputRows);
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type>
+    rightColWrap2(
+      sequence0,
+      sequence0 +
+        inputRows);  //<- confirmed I can reuse a sequence, wo/ creating overlapping columns!
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2}};
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(
+    p_outputTable = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+
+  auto seq_out1 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row >= outputRows / 2) ? 1 : 0;
+    } else
+      return row;
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out1)::value_type>
+    expectedDataWrap1(seq_out1, seq_out1 + outputRows);
+
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row / 2;
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type>
+    expectedDataWrap2(seq_out2, seq_out2 + outputRows);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+}
+
+TYPED_TEST(MergeTest_, Merge2KeyColumns)
+{
+  cudf::size_type inputRows = 40;
+
+  auto sequence1 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row >= inputRows / 2) ? 1 : 0;
+    } else
+      return row;
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>
+    leftColWrap1(sequence1, sequence1 + inputRows);
+
+  auto sequence2 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return ((row / (inputRows / 4)) % 2 == 0) ? 1 : 0;
+    } else {
+      return row * 2;
+    }
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence2)::value_type>
+    leftColWrap2(sequence2, sequence2 + inputRows);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>
+    rightColWrap1(sequence1, sequence1 + inputRows);
+
+  auto sequence3 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return ((row / (inputRows / 4)) % 2 == 0) ? 1 : 0;
+    } else
+      return (2 * row + 1);
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence3)::value_type>
+    rightColWrap2(sequence3, sequence3 + inputRows);
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2}};
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  std::vector<cudf::size_type> key_cols{0, 1};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(
+    p_outputTable = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+
+  auto seq_out1 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row >= outputRows / 2) ? 1 : 0;
+    } else
+      return (row / 2);
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out1)::value_type>
+    expectedDataWrap1(seq_out1, seq_out1 + outputRows);
+
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return ((row / (outputRows / 4)) % 2 == 0) ? 1 : 0;
+    } else {
+      return (row % 2 == 0 ? row + 1 : row - 1);
+    }
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type>
+    expectedDataWrap2(seq_out2, seq_out2 + outputRows);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+}
+
+TYPED_TEST(MergeTest_, Merge1KeyNullColumns)
+{
+  cudf::size_type inputRows = 40;
+
+  // data: 0  2  4  6 | valid: 1 1 1 0
+  auto sequence1       = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return 0;  // <- no shortcut to this can avoid compiler errors
+    } else {
+      return row * 2;
+    }
+  });
+  auto valid_sequence1 = cudf::detail::make_counting_transform_iterator(
+    0, [inputRows](auto row) { return (row < inputRows - 1); });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>
+    leftColWrap1(sequence1, sequence1 + inputRows, valid_sequence1);
+
+  // data: 1  3  5  7 | valid: 1 1 1 0
+  auto sequence2 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return 1;
+    } else
+      return (2 * row + 1);
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence2)::value_type>
+    rightColWrap1(sequence2,
+                  sequence2 + inputRows,
+                  valid_sequence1);  // <- recycle valid_seq1, confirmed okay...
+
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+
+  /*Note: default behavior semantics for null_precedence has changed
+   *      wrt legacy code:
+   *
+   * in legacy code missing (default) nulls argument
+   * meant nulls are greatest; i.e., null_order::AFTER (not null_order::BEFORE)
+   *
+   * While new semantics is (see row_operators.cuh: row_lexicographic_comparator::operator() ):
+   * null_order null_precedence = _null_precedence == nullptr ?
+   *                  null_order::BEFORE: _null_precedence[i];
+   *
+   * hence missing (default) value meant nulls are smallest
+   * null_order::BEFORE (not  null_order::AFTER) (!)
+   */
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER};
+
+  cudf::table_view left_view{{leftColWrap1}};
+  cudf::table_view right_view{{rightColWrap1}};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(
+    p_outputTable = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+  const cudf::size_type column1TotalNulls =
+    a_left_tbl_cview.null_count() + a_right_tbl_cview.null_count();
+
+  // data: 0 1 2 3 4 5 6 7 | valid: 1 1 1 1 1 1 0 0
+  auto seq_out1 =
+    cudf::detail::make_counting_transform_iterator(0, [outputRows, column1TotalNulls](auto row) {
+      if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+        return (row >= (outputRows - column1TotalNulls) / 2) ? 1 : 0;
+      } else
+        return (row);
+    });
+  auto valid_seq_out = cudf::detail::make_counting_transform_iterator(
+    0,
+    [outputRows, column1TotalNulls](auto row) { return (row < (outputRows - column1TotalNulls)); });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out1)::value_type>
+    expectedDataWrap1(seq_out1, seq_out1 + outputRows, valid_seq_out);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto output_column_view1{p_outputTable->view().column(0)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+}
+
+TYPED_TEST(MergeTest_, Merge2KeyNullColumns)
+{
+  cudf::size_type inputRows = 40;
+
+  // data: 0 1 2 3 | valid: 1 1 1 1
+  auto sequence1 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row >= inputRows / 2) ? 1 : 0;
+    } else
+      return (row);
+  });
+  auto valid_sequence1 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto row) { return true; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>
+    leftColWrap1(sequence1,
+                 sequence1 + inputRows,
+                 valid_sequence1);  // if left out: valid_sequence defaults to `false`;
+
+  // data: 0 2 4 6 | valid: 1 1 1 1
+  auto sequence2 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return ((row / (inputRows / 4)) % 2 == 0) ? 1 : 0;
+    } else {
+      return row * 2;
+    }
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence2)::value_type>
+    leftColWrap2(sequence2, sequence2 + inputRows, valid_sequence1);
+
+  // data: 0 1 2 3 | valid: 1 1 1 1
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>
+    rightColWrap1(sequence1,
+                  sequence1 + inputRows,
+                  valid_sequence1);  // if left out: valid_sequence defaults to `false`;
+
+  // data: 0 1 2 3 | valid: 0 0 0 0
+  auto sequence3 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return ((row / (inputRows / 4)) % 2 == 0) ? 1 : 0;
+    } else
+      return (row);
+  });
+  auto valid_sequence0 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto row) { return false; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence3)::value_type>
+    rightColWrap2(sequence3, sequence3 + inputRows, valid_sequence0);
+
+  cudf::table_view left_view{{leftColWrap1, leftColWrap2}};
+  cudf::table_view right_view{{rightColWrap1, rightColWrap2}};
+
+  std::vector<cudf::size_type> key_cols{0, 1};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER, cudf::null_order::AFTER};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  CUDF_EXPECT_NO_THROW(
+    p_outputTable = cudf::merge({left_view, right_view}, key_cols, column_order, null_precedence));
+
+  cudf::column_view const& a_left_tbl_cview{static_cast<cudf::column_view const&>(leftColWrap1)};
+  cudf::column_view const& a_right_tbl_cview{static_cast<cudf::column_view const&>(rightColWrap1)};
+  const cudf::size_type outputRows = a_left_tbl_cview.size() + a_right_tbl_cview.size();
+
+  // data: 0 0 1 1 2 2 3 3 | valid: 1 1 1 1 1 1 1 1
+  auto seq_out1 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row >= outputRows / 2) ? 1 : 0;
+    } else
+      return (row / 2);
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out1)::value_type>
+    expectedDataWrap1(seq_out1, seq_out1 + outputRows, valid_sequence1);
+
+  // data: 0 0 2 1 4 2 6 3 | valid: 0 1 0 1 0 1 0 1
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return ((row / (outputRows / 8)) % 2 == 0) ? 1 : 0;
+    } else {
+      return (row % 2 != 0 ? 2 * (row / 2) : (row / 2));
+    }
+  });
+  auto valid_sequence_out =
+    cudf::detail::make_counting_transform_iterator(0, [outputRows](auto row) {
+      if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+        return ((row / (outputRows / 4)) % 2 == 1) ? 1 : 0;
+      } else {
+        return (row % 2 != 0) ? 1 : 0;
+      }
+    });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type>
+    expectedDataWrap2(seq_out2, seq_out2 + outputRows, valid_sequence_out);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+}
+
+TYPED_TEST(MergeTest_, NMerge1KeyColumns)
+{
+  cudf::size_type inputRows = 64;
+
+  auto sequence0 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 0;
+    else
+      return row;
+  });
+
+  auto sequence1 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 1;
+    else
+      return inputRows - row;
+  });
+
+  constexpr int num_tables = 63;
+  using PairT0 =
+    cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence0)::value_type>;
+  using PairT1 =
+    cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence1)::value_type>;
+  std::vector<std::pair<PairT0, PairT1>> facts{};
+  std::vector<cudf::table_view> tables{};
+  for (int i = 0; i < num_tables; ++i) {
+    facts.emplace_back(std::pair(PairT0(sequence0, sequence0 + inputRows),
+                                 PairT1(sequence1, sequence1 + inputRows)));
+    tables.push_back(cudf::table_view{{facts.back().first, facts.back().second}});
+  }
+  std::vector<cudf::size_type> key_cols{0};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  std::unique_ptr<cudf::table> p_outputTable;
+  EXPECT_NO_THROW(p_outputTable = cudf::merge(tables, key_cols, column_order, null_precedence));
+
+  const cudf::size_type outputRows = inputRows * num_tables;
+
+  auto seq_out1 = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (0);
+    } else
+      return (row / num_tables);
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out1)::value_type>
+    expectedDataWrap1(seq_out1, seq_out1 + outputRows);
+
+  auto seq_out2 = cudf::detail::make_counting_transform_iterator(0, [inputRows](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)
+      return 1;
+    else
+      return inputRows - row / num_tables;
+  });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(seq_out2)::value_type>
+    expectedDataWrap2(seq_out2, seq_out2 + outputRows);
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+}
+
+class MergeTest : public cudf::test::BaseFixture {};
+
+TEST_F(MergeTest, KeysWithNulls)
+{
+  cudf::size_type nrows = 13200;  // Ensures that thrust::merge uses more than one tile/block
+  auto data_iter        = thrust::make_counting_iterator<int32_t>(0);
+  auto valids1 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto row) { return row % 10 != 0; });
+  cudf::test::fixed_width_column_wrapper<int32_t> data1(data_iter, data_iter + nrows, valids1);
+  auto valids2 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto row) { return row % 15 != 0; });
+  cudf::test::fixed_width_column_wrapper<int32_t> data2(data_iter, data_iter + nrows, valids2);
+  auto all_data = cudf::concatenate(std::vector<cudf::column_view>{{data1, data2}});
+
+  std::vector<cudf::order> column_orders{cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedences{cudf::null_order::AFTER, cudf::null_order::BEFORE};
+
+  for (auto co : column_orders)
+    for (auto np : null_precedences) {
+      std::vector<cudf::order> column_order{co};
+      std::vector<cudf::null_order> null_precedence{np};
+      auto sorted1 =
+        cudf::sort(cudf::table_view({data1}), column_order, null_precedence)->release();
+      auto col1 = sorted1.front()->view();
+      auto sorted2 =
+        cudf::sort(cudf::table_view({data2}), column_order, null_precedence)->release();
+      auto col2 = sorted2.front()->view();
+
+      auto result = cudf::merge(
+        {cudf::table_view({col1}), cudf::table_view({col2})}, {0}, column_order, null_precedence);
+      auto sorted_all =
+        cudf::sort(cudf::table_view({all_data->view()}), column_order, null_precedence);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorted_all->view().column(0), result->view().column(0));
+    }
+}
+
+TEST_F(MergeTest, Structs)
+{
+  // clang-format off
+
+  cudf::test::fixed_width_column_wrapper<int> t0_col0{0, 2, 4, 6, 8};
+    cudf::test::strings_column_wrapper t0_scol0{"abc", "def", "ghi", "jkl", "mno"};
+    cudf::test::fixed_width_column_wrapper<float> t0_scol1{1, 2, 3, 4, 5};
+  cudf::test::structs_column_wrapper t0_col1({t0_scol0, t0_scol1});
+
+  cudf::test::fixed_width_column_wrapper<int> t1_col0{1, 3, 5, 7, 9};
+    cudf::test::strings_column_wrapper t1_scol0{"pqr", "stu", "vwx", "yzz", "000"};
+    cudf::test::fixed_width_column_wrapper<float> t1_scol1{-1, -2, -3, -4, -5};
+  cudf::test::structs_column_wrapper t1_col1({t1_scol0, t1_scol1});
+
+  cudf::table_view t0({t0_col0, t0_col1});
+  cudf::table_view t1({t1_col0, t1_col1});
+
+  auto result = cudf::merge({t0, t1}, {0}, {cudf::order::ASCENDING});
+
+  cudf::test::fixed_width_column_wrapper<int> e_col0{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+    cudf::test::strings_column_wrapper e_scol0{"abc", "pqr", "def", "stu", "ghi", "vwx", "jkl", "yzz", "mno", "000"};
+    cudf::test::fixed_width_column_wrapper<float> e_scol1{1, -1, 2, -2, 3, -3, 4, -4, 5, -5};
+  cudf::test::structs_column_wrapper e_col1({e_scol0, e_scol1});
+
+  cudf::table_view expected({e_col0, e_col1});
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected, *result);
+
+  // clang-format on
+}
+
+TEST_F(MergeTest, StructsWithNulls)
+{
+  // clang-format off
+
+  cudf::test::fixed_width_column_wrapper<int> t0_col0{0, 2, 4, 6, 8};
+    cudf::test::strings_column_wrapper t0_scol0{{"abc", "def", "ghi", "jkl", "mno"}, {1, 1, 0, 0, 1}};
+    cudf::test::fixed_width_column_wrapper<float> t0_scol1{{1, 2, 3, 4, 5}, {0, 1, 0, 0, 1}};
+  cudf::test::structs_column_wrapper t0_col1({t0_scol0, t0_scol1}, {1, 0, 1, 0, 0});
+
+  cudf::test::fixed_width_column_wrapper<int> t1_col0{1, 3, 5, 7, 9};
+    cudf::test::strings_column_wrapper t1_scol0{"pqr", "stu", "vwx", "yzz", "000"};
+    cudf::test::fixed_width_column_wrapper<float> t1_scol1{{-1, -2, -3, -4, -5}, {1, 1, 1, 0, 0}};
+  cudf::test::structs_column_wrapper t1_col1({t1_scol0, t1_scol1}, {1, 1, 1, 1, 0});
+
+  cudf::table_view t0({t0_col0, t0_col1});
+  cudf::table_view t1({t1_col0, t1_col1});
+
+  auto result = cudf::merge({t0, t1}, {0}, {cudf::order::ASCENDING});
+
+  cudf::test::fixed_width_column_wrapper<int> e_col0{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+    cudf::test::strings_column_wrapper e_scol0{{"abc", "pqr", "def", "stu", "ghi", "vwx", "jkl", "yzz", "mno", "000"},
+                                               {1,     1,     0,      1,    0,     1,     0,     1,     0,     1}};
+    cudf::test::fixed_width_column_wrapper<float> e_scol1{{1, -1, 2, -2, 3, -3, 4, -4, 5, -5},
+                                                          {0,  1, 0,  1, 0,  1, 0,  0, 0,  0}};
+  cudf::test::structs_column_wrapper e_col1({e_scol0, e_scol1}, {1, 1, 0, 1, 1, 1, 0, 1, 0, 0});
+
+  cudf::table_view expected({e_col0, e_col1});
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected, *result);
+
+  // clang-format on
+}
+
+TEST_F(MergeTest, StructsNested)
+{
+  // clang-format off
+
+  cudf::test::fixed_width_column_wrapper<int> t0_col0{8, 6, 4, 2, 0};
+    cudf::test::strings_column_wrapper t0_scol0{"mno", "jkl", "ghi", "def", "abc"};
+    cudf::test::fixed_width_column_wrapper<float> t0_scol1{5, 4, 3, 2, 1};
+      cudf::test::strings_column_wrapper t0_sscol0{"5555", "4444", "333", "22", "1"};
+      cudf::test::fixed_width_column_wrapper<float> t0_sscol1{50, 40, 30, 20, 10};
+    cudf::test::structs_column_wrapper t0_scol2({t0_sscol0, t0_sscol1});
+  cudf::test::structs_column_wrapper t0_col1({t0_scol0, t0_scol1, t0_scol2});
+
+  cudf::test::fixed_width_column_wrapper<int> t1_col0{9, 7, 5, 3, 1};
+    cudf::test::strings_column_wrapper t1_scol0{"000", "yzz", "vwx", "stu", "pqr"};
+    cudf::test::fixed_width_column_wrapper<float> t1_scol1{-5, -4, -3, -2, -1};
+      cudf::test::strings_column_wrapper t1_sscol0{"-5555", "-4444", "-333", "-22", "-1"};
+      cudf::test::fixed_width_column_wrapper<float> t1_sscol1{-50, -40, -30, -20, -10};
+    cudf::test::structs_column_wrapper t1_scol2({t1_sscol0, t1_sscol1});
+  cudf::test::structs_column_wrapper t1_col1({t1_scol0, t1_scol1, t1_scol2});
+
+  cudf::table_view t0({t0_col0 , t0_col1});
+  cudf::table_view t1({t1_col0 , t1_col1});
+
+  auto result = cudf::merge({t0, t1}, {0}, {cudf::order::DESCENDING});
+
+  cudf::test::fixed_width_column_wrapper<int> e_col0{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+    cudf::test::strings_column_wrapper e_scol0{"000", "mno", "yzz", "jkl", "vwx", "ghi", "stu", "def", "pqr", "abc"};
+    cudf::test::fixed_width_column_wrapper<float> e_scol1{-5, 5, -4, 4, -3, 3, -2, 2, -1, 1};
+      cudf::test::strings_column_wrapper e_sscol0{"-5555", "5555", "-4444", "4444", "-333", "333", "-22", "22", "-1", "1"};
+      cudf::test::fixed_width_column_wrapper<float> e_sscol1{-50, 50, -40, 40, -30, 30, -20, 20, -10, 10};
+    cudf::test::structs_column_wrapper e_scol2({e_sscol0, e_sscol1});
+  cudf::test::structs_column_wrapper e_col1({e_scol0, e_scol1, e_scol2});
+
+  cudf::table_view expected({e_col0, e_col1});
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected, *result);
+
+  // clang-format on
+}
+
+TEST_F(MergeTest, StructsNestedWithNulls)
+{
+  // clang-format off
+
+  cudf::test::fixed_width_column_wrapper<int> t0_col0{8, 6, 4, 2, 0};
+    cudf::test::strings_column_wrapper t0_scol0{"mno", "jkl", "ghi", "def", "abc"};
+    cudf::test::fixed_width_column_wrapper<float> t0_scol1{{5, 4, 3, 2, 1}, {1, 1, 0, 1, 1}};
+      cudf::test::strings_column_wrapper t0_sscol0{{"5555", "4444", "333", "22", "1"}, {1, 0, 1, 1, 0}};
+      cudf::test::fixed_width_column_wrapper<float> t0_sscol1{50, 40, 30, 20, 10};
+    cudf::test::structs_column_wrapper t0_scol2({t0_sscol0, t0_sscol1}, {0, 0, 1, 1, 1});
+  cudf::test::structs_column_wrapper t0_col1({t0_scol0, t0_scol1, t0_scol2}, {0, 0, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<int> t1_col0{9, 7, 5, 3, 1};
+    cudf::test::strings_column_wrapper t1_scol0{"000", "yzz", "vwx", "stu", "pqr"};
+    cudf::test::fixed_width_column_wrapper<float> t1_scol1{{-5, -4, -3, -2, -1}, {1, 1, 1, 0, 1}};
+      cudf::test::strings_column_wrapper t1_sscol0{{"-5555", "-4444", "-333", "-22", "-1"}, {1, 1, 1, 1, 1}};
+      cudf::test::fixed_width_column_wrapper<float> t1_sscol1{-50, -40, -30, -20, -10};
+    cudf::test::structs_column_wrapper t1_scol2({t1_sscol0, t1_sscol1}, {1, 1, 1, 1, 0});
+  cudf::test::structs_column_wrapper t1_col1({t1_scol0, t1_scol1, t1_scol2});
+
+  cudf::table_view t0({t0_col0 , t0_col1});
+  cudf::table_view t1({t1_col0 , t1_col1});
+
+  auto result = cudf::merge({t0, t1}, {0}, {cudf::order::DESCENDING});
+
+  cudf::test::fixed_width_column_wrapper<int> e_col0{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+    cudf::test::strings_column_wrapper e_scol0{"000", "mno", "yzz", "jkl", "vwx", "ghi", "stu", "def", "pqr", "abc"};
+    cudf::test::fixed_width_column_wrapper<float> e_scol1{{-5, 5, -4, 4, -3, 3, -2, 2, -1, 1},
+                                                          { 1, 1,  1, 1,  1, 0,  0, 1,  1, 1}};
+      cudf::test::strings_column_wrapper e_sscol0{{"-5555", "5555", "-4444", "4444", "-333", "333", "-22", "22", "-1", "1"},
+                                                  {  1,      0,       1,      0,       1,     1,      1,    1,     0,   0}};
+      cudf::test::fixed_width_column_wrapper<float> e_sscol1{-50, 50, -40, 40, -30, 30, -20, 20, -10, 10};
+    cudf::test::structs_column_wrapper e_scol2({e_sscol0, e_sscol1}, {1, 0, 1, 0, 1, 1, 1, 1, 0, 1});
+  cudf::test::structs_column_wrapper e_col1({e_scol0, e_scol1, e_scol2}, {1, 0, 1, 0, 1, 1, 1, 1, 1, 1});
+
+  cudf::table_view expected({e_col0, e_col1});
+
+  CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected, *result);
+
+  // clang-format on
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+template <typename T>
+using fp_wrapper = cudf::test::fixed_point_column_wrapper<T>;
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointMerge)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using RepType   = cudf::device_storage_type_t<decimalXX>;
+
+  auto const a       = fp_wrapper<RepType>{{4, 22, 33, 44, 55}, scale_type{-1}};
+  auto const b       = fp_wrapper<RepType>{{5, 7, 10}, scale_type{-1}};
+  auto const table_a = cudf::table_view(std::vector<cudf::column_view>{a});
+  auto const table_b = cudf::table_view(std::vector<cudf::column_view>{b});
+  auto const tables  = std::vector<cudf::table_view>{table_a, table_b};
+
+  auto const key_cols = std::vector<cudf::size_type>{0};
+  auto const order    = std::vector<cudf::order>{cudf::order::ASCENDING};
+
+  auto const exp       = fp_wrapper<RepType>{{4, 5, 7, 10, 22, 33, 44, 55}, scale_type{-1}};
+  auto const exp_table = cudf::table_view(std::vector<cudf::column_view>{exp});
+
+  auto const result = cudf::merge(tables, key_cols, order);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(exp_table.column(0), result->view().column(0));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/partitioning/hash_partition_test.cpp b/cpp/tests/partitioning/hash_partition_test.cpp
new file mode 100644
index 0000000..f3d131a
--- /dev/null
+++ b/cpp/tests/partitioning/hash_partition_test.cpp
@@ -0,0 +1,435 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/partitioning.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+using cudf::test::fixed_width_column_wrapper;
+using cudf::test::strings_column_wrapper;
+using structs_col = cudf::test::structs_column_wrapper;
+
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+// Transform vector of column wrappers to vector of column views
+template <typename T>
+auto make_view_vector(std::vector<T> const& columns)
+{
+  std::vector<cudf::column_view> views(columns.size());
+  std::transform(columns.begin(), columns.end(), views.begin(), [](auto const& col) {
+    return static_cast<cudf::column_view>(col);
+  });
+  return views;
+}
+
+class HashPartition : public cudf::test::BaseFixture {};
+
+TEST_F(HashPartition, InvalidColumnsToHash)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({-1});
+
+  cudf::size_type const num_partitions = 3;
+  EXPECT_THROW(cudf::hash_partition(input, columns_to_hash, num_partitions), std::out_of_range);
+}
+
+TEST_F(HashPartition, ZeroPartitions)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({2});
+
+  cudf::size_type const num_partitions = 0;
+  auto [output, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  // Expect empty table with same number of columns and zero partitions
+  EXPECT_EQ(input.num_columns(), output->num_columns());
+  EXPECT_EQ(0, output->num_rows());
+  EXPECT_EQ(std::size_t{num_partitions}, offsets.size());
+}
+
+TEST_F(HashPartition, ZeroRows)
+{
+  fixed_width_column_wrapper<float> floats({});
+  fixed_width_column_wrapper<int16_t> integers({});
+  strings_column_wrapper strings;
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({2});
+
+  cudf::size_type const num_partitions = 3;
+  auto [output, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  // Expect empty table with same number of columns and same number of partitions
+  EXPECT_EQ(input.num_columns(), output->num_columns());
+  EXPECT_EQ(0, output->num_rows());
+  EXPECT_EQ(std::size_t{num_partitions}, offsets.size());
+}
+
+TEST_F(HashPartition, ZeroColumns)
+{
+  auto input = cudf::table_view(std::vector<cudf::column_view>{});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({});
+
+  cudf::size_type const num_partitions = 3;
+  auto [output, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  // Expect empty table with same number of columns and same number of partitions
+  EXPECT_EQ(input.num_columns(), output->num_columns());
+  EXPECT_EQ(0, output->num_rows());
+  EXPECT_EQ(std::size_t{num_partitions}, offsets.size());
+}
+
+TEST_F(HashPartition, MixedColumnTypes)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({0, 2});
+
+  cudf::size_type const num_partitions = 3;
+  auto [output1, offsets1] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output2, offsets2] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  // Expect output to have size num_partitions
+  EXPECT_EQ(static_cast<size_t>(num_partitions), offsets1.size());
+  EXPECT_EQ(offsets1.size(), offsets2.size());
+
+  // Expect output to have same shape as input
+  CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(input, output1->view());
+
+  // Expect deterministic result from hashing the same input
+  CUDF_TEST_EXPECT_TABLES_EQUAL(output1->view(), output2->view());
+}
+
+TEST_F(HashPartition, NullableStrings)
+{
+  strings_column_wrapper strings({"a", "bb", "ccc", "d"}, {1, 1, 1, 1});
+  cudf::table_view input({strings});
+
+  std::vector<cudf::size_type> const columns_to_hash({0});
+  cudf::size_type const num_partitions = 3;
+
+  auto [result, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  auto const& col = result->get_column(0);
+  EXPECT_EQ(0, col.null_count());
+}
+
+TEST_F(HashPartition, ColumnsToHash)
+{
+  fixed_width_column_wrapper<int32_t> to_hash({1, 2, 3, 4, 5, 6});
+  fixed_width_column_wrapper<int32_t> first_col({7, 8, 9, 10, 11, 12});
+  fixed_width_column_wrapper<int32_t> second_col({13, 14, 15, 16, 17, 18});
+  auto first_input  = cudf::table_view({to_hash, first_col});
+  auto second_input = cudf::table_view({to_hash, second_col});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({0});
+
+  cudf::size_type const num_partitions = 3;
+  auto [first_result, first_offsets] =
+    cudf::hash_partition(first_input, columns_to_hash, num_partitions);
+  auto [second_result, second_offsets] =
+    cudf::hash_partition(second_input, columns_to_hash, num_partitions);
+
+  // Expect offsets to be equal and num_partitions in length
+  EXPECT_EQ(static_cast<size_t>(num_partitions), first_offsets.size());
+  EXPECT_TRUE(std::equal(
+    first_offsets.begin(), first_offsets.end(), second_offsets.begin(), second_offsets.end()));
+
+  // Expect same result for the hashed columns
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(first_result->get_column(0).view(),
+                                 second_result->get_column(0).view());
+}
+
+TEST_F(HashPartition, IdentityHashFailure)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({2});
+
+  cudf::size_type const num_partitions = 3;
+  EXPECT_THROW(
+    cudf::hash_partition(input, columns_to_hash, num_partitions, cudf::hash_id::HASH_IDENTITY),
+    cudf::logic_error);
+}
+
+TEST_F(HashPartition, UnsupportedHashFunction)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({2});
+
+  cudf::size_type const num_partitions = 3;
+  EXPECT_THROW(
+    cudf::hash_partition(input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MD5),
+    cudf::logic_error);
+}
+
+TEST_F(HashPartition, CustomSeedValue)
+{
+  fixed_width_column_wrapper<float> floats({1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f});
+  fixed_width_column_wrapper<int16_t> integers({1, 2, 3, 4, 5, 6, 7, 8});
+  strings_column_wrapper strings({"a", "bb", "ccc", "d", "ee", "fff", "gg", "h"});
+  auto input = cudf::table_view({floats, integers, strings});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({0, 2});
+
+  cudf::size_type const num_partitions = 3;
+  auto [output1, offsets1]             = cudf::hash_partition(
+    input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MURMUR3, 12345);
+  auto [output2, offsets2] = cudf::hash_partition(
+    input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MURMUR3, 12345);
+
+  // Expect output to have size num_partitions
+  EXPECT_EQ(static_cast<size_t>(num_partitions), offsets1.size());
+  EXPECT_EQ(offsets1.size(), offsets2.size());
+
+  // Expect output to have same shape as input
+  CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(input, output1->view());
+
+  // Expect deterministic result from hashing the same input
+  CUDF_TEST_EXPECT_TABLES_EQUAL(output1->view(), output2->view());
+}
+
+template <typename T>
+class HashPartitionFixedWidth : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(HashPartitionFixedWidth, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(HashPartitionFixedWidth, NullableFixedWidth)
+{
+  fixed_width_column_wrapper<TypeParam, int32_t> fixed({1, 2, 3, 4}, {1, 1, 1, 1});
+  cudf::table_view input({fixed});
+
+  std::vector<cudf::size_type> const columns_to_hash({0});
+  cudf::size_type const num_partitions = 3;
+
+  auto [result, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  auto const& col = result->get_column(0);
+  EXPECT_EQ(0, col.null_count());
+}
+
+template <typename T>
+void run_fixed_width_test(size_t cols,
+                          size_t rows,
+                          cudf::size_type num_partitions,
+                          cudf::hash_id hash_function,
+                          bool has_nulls = false)
+{
+  std::vector<fixed_width_column_wrapper<T, int32_t>> columns;
+  columns.reserve(cols);
+  if (has_nulls) {
+    std::generate_n(std::back_inserter(columns), cols, [rows]() {
+      auto iter   = thrust::make_counting_iterator(0);
+      auto valids = thrust::make_transform_iterator(iter, [](auto i) { return i % 4 != 0; });
+      return fixed_width_column_wrapper<T, int32_t>(iter, iter + rows, valids);
+    });
+  } else {
+    std::generate_n(std::back_inserter(columns), cols, [rows]() {
+      auto iter = thrust::make_counting_iterator(0);
+      return fixed_width_column_wrapper<T, int32_t>(iter, iter + rows);
+    });
+  }
+  auto input = cudf::table_view(make_view_vector(columns));
+
+  auto columns_to_hash = std::vector<cudf::size_type>(cols);
+  std::iota(columns_to_hash.begin(), columns_to_hash.end(), 0);
+
+  auto [output1, offsets1] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output2, offsets2] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  // Expect output to have size num_partitions
+  EXPECT_EQ(static_cast<size_t>(num_partitions), offsets1.size());
+  EXPECT_TRUE(std::equal(offsets1.begin(), offsets1.end(), offsets2.begin()));
+
+  // Expect output to have same shape as input
+  CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(input, output1->view());
+  CUDF_TEST_EXPECT_TABLE_PROPERTIES_EQUAL(output1->view(), output2->view());
+
+  // Compute number of rows in each partition
+  EXPECT_EQ(0, offsets1[0]);
+  offsets1.push_back(rows);
+  std::adjacent_difference(offsets1.begin() + 1, offsets1.end(), offsets1.begin() + 1);
+
+  // Compute the partition number for each row
+  cudf::size_type partition = 0;
+  std::vector<cudf::size_type> partitions;
+  std::for_each(offsets1.begin() + 1, offsets1.end(), [&](cudf::size_type const& count) {
+    std::fill_n(std::back_inserter(partitions), count, partition++);
+  });
+
+  // Make a table view of the partition numbers
+  constexpr cudf::data_type dtype{cudf::type_id::INT32};
+  auto d_partitions = cudf::detail::make_device_uvector_sync(
+    partitions, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  cudf::column_view partitions_col(dtype, rows, d_partitions.data(), nullptr, 0);
+  cudf::table_view partitions_table({partitions_col});
+
+  // Sort partition numbers by the corresponding row hashes of each output
+  auto hash1 = cudf::hash(output1->view());
+  cudf::table_view hash1_table({hash1->view()});
+  auto sorted_partitions1 = cudf::sort_by_key(partitions_table, hash1_table);
+
+  auto hash2 = cudf::hash(output2->view());
+  cudf::table_view hash2_table({hash2->view()});
+  auto sorted_partitions2 = cudf::sort_by_key(partitions_table, hash2_table);
+
+  // After sorting by row hashes, the corresponding partition numbers should be
+  // equal
+  CUDF_TEST_EXPECT_TABLES_EQUAL(sorted_partitions1->view(), sorted_partitions2->view());
+}
+
+TYPED_TEST(HashPartitionFixedWidth, MorePartitionsThanRows)
+{
+  run_fixed_width_test<TypeParam>(5, 10, 50, cudf::hash_id::HASH_MURMUR3);
+  run_fixed_width_test<TypeParam>(5, 10, 50, cudf::hash_id::HASH_IDENTITY);
+}
+
+TYPED_TEST(HashPartitionFixedWidth, LargeInput)
+{
+  run_fixed_width_test<TypeParam>(10, 1000, 10, cudf::hash_id::HASH_MURMUR3);
+  run_fixed_width_test<TypeParam>(10, 1000, 10, cudf::hash_id::HASH_IDENTITY);
+}
+
+TYPED_TEST(HashPartitionFixedWidth, HasNulls)
+{
+  run_fixed_width_test<TypeParam>(10, 1000, 10, cudf::hash_id::HASH_MURMUR3, true);
+  run_fixed_width_test<TypeParam>(10, 1000, 10, cudf::hash_id::HASH_IDENTITY, true);
+}
+
+TEST_F(HashPartition, FixedPointColumnsToHash)
+{
+  fixed_width_column_wrapper<int32_t> to_hash({1});
+  cudf::test::fixed_point_column_wrapper<int64_t> first_col({7}, numeric::scale_type{-1});
+  cudf::test::fixed_point_column_wrapper<__int128_t> second_col({77}, numeric::scale_type{0});
+
+  auto input = cudf::table_view({to_hash, first_col, second_col});
+
+  auto columns_to_hash = std::vector<cudf::size_type>({0});
+
+  cudf::size_type const num_partitions = 1;
+  auto [result, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+
+  // Expect offsets to be equal and num_partitions in length
+  EXPECT_EQ(static_cast<size_t>(num_partitions), offsets.size());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->get_column(0).view(), input.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->get_column(1).view(), input.column(1));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->get_column(2).view(), input.column(2));
+}
+
+TEST_F(HashPartition, ListWithNulls)
+{
+  using lcw = cudf::test::lists_column_wrapper<int32_t>;
+
+  lcw to_hash{{{1}, {2, 2}, {0}, {1}, {2, 2}, {0}}, nulls_at({2, 5})};
+  fixed_width_column_wrapper<int32_t> first_col({7, 8, 9, 10, 11, 12});
+  fixed_width_column_wrapper<int32_t> second_col({13, 14, 15, 16, 17, 18});
+  auto const first_input  = cudf::table_view({to_hash, first_col});
+  auto const second_input = cudf::table_view({to_hash, second_col});
+
+  auto const columns_to_hash = std::vector<cudf::size_type>({0});
+
+  cudf::size_type const num_partitions = 3;
+  auto const [first_result, first_offsets] =
+    cudf::hash_partition(first_input, columns_to_hash, num_partitions);
+  auto const [second_result, second_offsets] =
+    cudf::hash_partition(second_input, columns_to_hash, num_partitions);
+
+  // Expect offsets to be equal and num_partitions in length
+  EXPECT_EQ(static_cast<size_t>(num_partitions), first_offsets.size());
+  EXPECT_TRUE(std::equal(
+    first_offsets.begin(), first_offsets.end(), second_offsets.begin(), second_offsets.end()));
+
+  // Expect same result for the hashed columns
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(first_result->get_column(0).view(),
+                                 second_result->get_column(0).view());
+}
+
+TEST_F(HashPartition, StructofStructWithNulls)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 1}, 5}  |
+  // 2 |  { {1, 2}, 4}  |
+  // 3 |  Null          |
+  // 4 |  { Null,   4}  |
+  // 5 |  { Null,   4}  |
+
+  auto const to_hash = [&] {
+    auto a  = fixed_width_column_wrapper<int32_t>{1, 1, 1, 0, 0, 0};
+    auto b  = fixed_width_column_wrapper<int32_t>{1, 1, 2, 0, 0, 0};
+    auto s2 = structs_col{{a, b}, nulls_at({4, 5})};
+
+    auto c = fixed_width_column_wrapper<int32_t>{5, 5, 4, 0, 4, 4};
+    return structs_col({s2, c}, null_at(3));
+  }();
+
+  fixed_width_column_wrapper<int32_t> first_col({7, 8, 9, 10, 11, 12});
+  fixed_width_column_wrapper<int32_t> second_col({13, 14, 15, 16, 17, 18});
+  auto const first_input  = cudf::table_view({to_hash, first_col});
+  auto const second_input = cudf::table_view({to_hash, second_col});
+
+  auto const columns_to_hash = std::vector<cudf::size_type>({0});
+
+  cudf::size_type const num_partitions = 3;
+  auto const [first_result, first_offsets] =
+    cudf::hash_partition(first_input, columns_to_hash, num_partitions);
+  auto const [second_result, second_offsets] =
+    cudf::hash_partition(second_input, columns_to_hash, num_partitions);
+
+  // Expect offsets to be equal and num_partitions in length
+  EXPECT_EQ(static_cast<size_t>(num_partitions), first_offsets.size());
+  EXPECT_TRUE(std::equal(
+    first_offsets.begin(), first_offsets.end(), second_offsets.begin(), second_offsets.end()));
+
+  // Expect same result for the hashed columns
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(first_result->get_column(0).view(),
+                                 second_result->get_column(0).view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/partitioning/partition_test.cpp b/cpp/tests/partitioning/partition_test.cpp
new file mode 100644
index 0000000..8ea224e
--- /dev/null
+++ b/cpp/tests/partitioning/partition_test.cpp
@@ -0,0 +1,347 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/partitioning.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table.hpp>
+
+template <typename T>
+class PartitionTest : public cudf::test::BaseFixture {
+  using value_type = cudf::test::GetType<T, 0>;
+  using map_type   = cudf::test::GetType<T, 1>;
+};
+
+using types =
+  cudf::test::CrossProduct<cudf::test::FixedWidthTypes, cudf::test::IntegralTypesNotBool>;
+
+// using types = cudf::test::Types<cudf::test::Types<int32_t, int32_t> >;
+
+TYPED_TEST_SUITE(PartitionTest, types);
+
+using cudf::test::fixed_width_column_wrapper;
+using cudf::test::strings_column_wrapper;
+
+// Exceptional cases
+TYPED_TEST(PartitionTest, EmptyInputs)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type> empty_column{};
+  fixed_width_column_wrapper<map_type> empty_map{};
+
+  auto result = cudf::partition(cudf::table_view{{empty_column}}, empty_map, 10);
+
+  auto result_offsets = result.second;
+
+  EXPECT_EQ(result_offsets.size(), std::size_t{11});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(empty_column, result.first->get_column(0));
+}
+
+TYPED_TEST(PartitionTest, MapInputSizeMismatch)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> input({1, 2, 3});
+  fixed_width_column_wrapper<map_type> map{1, 2};
+
+  EXPECT_THROW(cudf::partition(cudf::table_view{{input}}, map, 3), cudf::logic_error);
+}
+
+TYPED_TEST(PartitionTest, MapWithNullsThrows)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> input({1, 2, 3});
+  fixed_width_column_wrapper<map_type> map{{1, 2}, {1, 0}};
+
+  EXPECT_THROW(cudf::partition(cudf::table_view{{input}}, map, 3), cudf::logic_error);
+}
+
+/**
+ * @brief Verifies that partitions indicated by `offsets` are equal between
+ * `expected` and `actual`.
+ *
+ * The order of rows within each partition may be different, so each partition
+ * is first sorted before being compared for equality.
+ */
+void expect_equal_partitions(cudf::table_view expected,
+                             cudf::table_view actual,
+                             std::vector<cudf::size_type> const& offsets)
+{
+  // Need to convert partition offsets into split points by dropping the first
+  // and last element
+  std::vector<cudf::size_type> split_points;
+  std::copy(std::next(offsets.begin()), std::prev(offsets.end()), std::back_inserter(split_points));
+
+  // Split the partitions, sort each partition, then compare for equality
+  auto actual_split   = cudf::split(actual, split_points);
+  auto expected_split = cudf::split(expected, split_points);
+  std::equal(expected_split.begin(),
+             expected_split.end(),
+             actual_split.begin(),
+             [](cudf::table_view expected, cudf::table_view actual) {
+               auto sorted_expected = cudf::sort(expected);
+               auto sorted_actual   = cudf::sort(actual);
+               CUDF_TEST_EXPECT_TABLES_EQUAL(*sorted_expected, *sorted_actual);
+               return true;
+             });
+}
+
+void run_partition_test(cudf::table_view table_to_partition,
+                        cudf::column_view partition_map,
+                        cudf::size_type num_partitions,
+                        cudf::table_view expected_partitioned_table,
+                        std::vector<cudf::size_type> const& expected_offsets)
+{
+  auto result = cudf::partition(table_to_partition, partition_map, num_partitions);
+  auto const& actual_partitioned_table = result.first;
+  auto const& actual_offsets           = result.second;
+  EXPECT_EQ(actual_offsets, expected_offsets);
+
+  expect_equal_partitions(expected_partitioned_table, *actual_partitioned_table, expected_offsets);
+}
+
+// Normal cases
+TYPED_TEST(PartitionTest, Identity)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> first({0, 1, 2, 3, 4, 5});
+  strings_column_wrapper strings{"this", "is", "a", "column", "of", "strings"};
+  auto table_to_partition = cudf::table_view{{first, strings}};
+
+  fixed_width_column_wrapper<map_type> map{0, 1, 2, 3, 4, 5};
+
+  std::vector<cudf::size_type> expected_offsets{0, 1, 2, 3, 4, 5, 6};
+
+  run_partition_test(table_to_partition, map, 6, table_to_partition, expected_offsets);
+}
+
+TYPED_TEST(PartitionTest, Struct)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> A({1, 2}, {0, 1});
+  auto struct_col         = cudf::test::structs_column_wrapper({A}, {0, 1}).release();
+  auto table_to_partition = cudf::table_view{{*struct_col}};
+
+  fixed_width_column_wrapper<map_type> map{9, 2};
+
+  fixed_width_column_wrapper<value_type, int32_t> A_expected({2, 1}, {1, 0});
+  auto struct_expected = cudf::test::structs_column_wrapper({A_expected}, {1, 0}).release();
+  auto expected        = cudf::table_view{{*struct_expected}};
+
+  std::vector<cudf::size_type> expected_offsets{0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2};
+
+  // This does not work because we cannot sort a struct right now...
+  // run_partition_test(table_to_partition, map, 12, expected, expected_offsets);
+  // But there is no ambiguity in the ordering so I'll just copy it all here for now.
+  auto num_partitions                  = 12;
+  auto result                          = cudf::partition(table_to_partition, map, num_partitions);
+  auto const& actual_partitioned_table = result.first;
+  auto const& actual_offsets           = result.second;
+  EXPECT_EQ(actual_offsets, expected_offsets);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *actual_partitioned_table);
+}
+
+TYPED_TEST(PartitionTest, Reverse)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> first({0, 1, 3, 7, 5, 13});
+  strings_column_wrapper strings{"this", "is", "a", "column", "of", "strings"};
+  auto table_to_partition = cudf::table_view{{first, strings}};
+
+  fixed_width_column_wrapper<map_type> map{5, 4, 3, 2, 1, 0};
+
+  std::vector<cudf::size_type> expected_offsets{0, 1, 2, 3, 4, 5, 6};
+
+  fixed_width_column_wrapper<value_type, int32_t> expected_first({13, 5, 7, 3, 1, 0});
+  strings_column_wrapper expected_strings{"strings", "of", "column", "a", "is", "this"};
+  auto expected_partitioned_table = cudf::table_view{{expected_first, expected_strings}};
+
+  run_partition_test(table_to_partition, map, 6, expected_partitioned_table, expected_offsets);
+}
+
+TYPED_TEST(PartitionTest, SinglePartition)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> first({0, 1, 3, 7, 5, 13});
+  strings_column_wrapper strings{"this", "is", "a", "column", "of", "strings"};
+  auto table_to_partition = cudf::table_view{{first, strings}};
+
+  fixed_width_column_wrapper<map_type> map{0, 0, 0, 0, 0, 0};
+
+  std::vector<cudf::size_type> expected_offsets{0, 6};
+
+  fixed_width_column_wrapper<value_type, int32_t> expected_first({13, 5, 7, 3, 1, 0});
+  strings_column_wrapper expected_strings{"strings", "of", "column", "a", "is", "this"};
+  auto expected_partitioned_table = cudf::table_view{{expected_first, expected_strings}};
+
+  run_partition_test(table_to_partition, map, 1, expected_partitioned_table, expected_offsets);
+}
+
+TYPED_TEST(PartitionTest, EmptyPartitions)
+{
+  using value_type = cudf::test::GetType<TypeParam, 0>;
+  using map_type   = cudf::test::GetType<TypeParam, 1>;
+
+  fixed_width_column_wrapper<value_type, int32_t> first({0, 1, 3, 7, 5, 13});
+  strings_column_wrapper strings{"this", "is", "a", "column", "of", "strings"};
+  auto table_to_partition = cudf::table_view{{first, strings}};
+
+  fixed_width_column_wrapper<map_type> map{2, 2, 0, 0, 4, 4};
+
+  std::vector<cudf::size_type> expected_offsets{0, 2, 2, 4, 4, 6};
+
+  fixed_width_column_wrapper<value_type, int32_t> expected_first({3, 7, 0, 1, 5, 13});
+  strings_column_wrapper expected_strings{"a", "column", "this", "is", "of", "strings"};
+  auto expected_partitioned_table = cudf::table_view{{expected_first, expected_strings}};
+
+  run_partition_test(table_to_partition, map, 5, expected_partitioned_table, expected_offsets);
+}
+
+template <typename T>
+class PartitionTestFixedPoint : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(PartitionTestFixedPoint, cudf::test::FixedPointTypes);
+
+TYPED_TEST(PartitionTestFixedPoint, Partition)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper({11, 22, 33, 44, 55, 66}, scale_type{-1});
+  auto const map      = fixed_width_column_wrapper<int32_t>{0, 1, 2, 3, 4, 5};
+  auto const expected = cudf::table_view{{input}};
+  auto const offsets  = std::vector<cudf::size_type>{0, 1, 2, 3, 4, 5, 6};
+
+  run_partition_test(expected, map, 6, expected, offsets);
+}
+
+TYPED_TEST(PartitionTestFixedPoint, Partition1)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper({11, 22, 33, 44, 55, 66}, scale_type{-1});
+  auto const map      = fixed_width_column_wrapper<int32_t>{5, 4, 3, 2, 1, 0};
+  auto const expected = fp_wrapper({66, 55, 44, 33, 22, 11}, scale_type{-1});
+  auto const offsets  = std::vector<cudf::size_type>{0, 1, 2, 3, 4, 5, 6};
+
+  run_partition_test(cudf::table_view{{input}}, map, 6, cudf::table_view{{expected}}, offsets);
+}
+
+TYPED_TEST(PartitionTestFixedPoint, Partition2)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper({11, 22, 33, 44, 55, 66}, scale_type{-1});
+  auto const map      = fixed_width_column_wrapper<int32_t>{2, 1, 0, 2, 1, 0};
+  auto const expected = fp_wrapper({33, 66, 22, 55, 11, 44}, scale_type{-1});
+  auto const offsets  = std::vector<cudf::size_type>{0, 2, 4, 6};
+
+  run_partition_test(cudf::table_view{{input}}, map, 3, cudf::table_view{{expected}}, offsets);
+}
+
+struct PartitionTestNotTyped : public cudf::test::BaseFixture {};
+
+TEST_F(PartitionTestNotTyped, ListOfStringsEmpty)
+{
+  cudf::test::lists_column_wrapper<cudf::string_view> list{{}, {}};
+  auto table_to_partition = cudf::table_view{{list}};
+  fixed_width_column_wrapper<int32_t> map{0, 0};
+
+  auto result = cudf::partition(table_to_partition, map, 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(table_to_partition, result.first->view());
+  EXPECT_EQ(3, result.second.size());
+}
+
+TEST_F(PartitionTestNotTyped, ListOfListOfIntEmpty)
+{
+  cudf::test::lists_column_wrapper<int32_t> level_2_list;
+
+  fixed_width_column_wrapper<int32_t> level_1_offsets{0, 0, 0};
+  std::unique_ptr<cudf::column> level_1_list =
+    cudf::make_lists_column(2, level_1_offsets.release(), level_2_list.release(), 0, {});
+
+  auto table_to_partition = cudf::table_view{{*level_1_list}};
+  fixed_width_column_wrapper<int32_t> map{0, 0};
+
+  auto result = cudf::partition(table_to_partition, map, 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(table_to_partition, result.first->view());
+  EXPECT_EQ(3, result.second.size());
+}
+
+TEST_F(PartitionTestNotTyped, ListOfListOfListOfIntEmpty)
+{
+  cudf::test::lists_column_wrapper<int32_t> level_3_list{};
+
+  fixed_width_column_wrapper<int32_t> level_2_offsets{};
+  std::unique_ptr<cudf::column> level_2_list =
+    cudf::make_lists_column(0, level_2_offsets.release(), level_3_list.release(), 0, {});
+
+  fixed_width_column_wrapper<int32_t> level_1_offsets{0, 0};
+  std::unique_ptr<cudf::column> level_1_list =
+    cudf::make_lists_column(1, level_1_offsets.release(), std::move(level_2_list), 0, {});
+
+  auto table_to_partition = cudf::table_view{{*level_1_list}};
+  fixed_width_column_wrapper<int32_t> map{0};
+
+  auto result = cudf::partition(table_to_partition, map, 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(table_to_partition, result.first->view());
+  EXPECT_EQ(3, result.second.size());
+}
+
+TEST_F(PartitionTestNotTyped, NoIntegerOverflow)
+{
+  auto elements = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+  fixed_width_column_wrapper<int8_t> map(elements, elements + 129);
+  auto table_to_partition = cudf::table_view{{map}};
+
+  std::vector<cudf::size_type> expected_offsets{0, 65, 129};
+
+  auto expected_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i / 65; });
+  fixed_width_column_wrapper<int8_t> expected(expected_elements, expected_elements + 129);
+  auto expected_table = cudf::table_view{{expected}};
+
+  run_partition_test(table_to_partition, map, 2, expected_table, expected_offsets);
+}
diff --git a/cpp/tests/partitioning/round_robin_test.cpp b/cpp/tests/partitioning/round_robin_test.cpp
new file mode 100644
index 0000000..7f83b5d
--- /dev/null
+++ b/cpp/tests/partitioning/round_robin_test.cpp
@@ -0,0 +1,744 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/partitioning.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <cassert>
+#include <initializer_list>
+#include <limits>
+#include <memory>
+#include <numeric>
+#include <vector>
+
+#include <gtest/gtest.h>
+
+using cudf::test::fixed_width_column_wrapper;
+using cudf::test::strings_column_wrapper;
+
+template <typename T>
+class RoundRobinTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(RoundRobinTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(RoundRobinTest, EmptyInput)
+{
+  auto const empty_column    = fixed_width_column_wrapper<TypeParam>{};
+  auto const num_partitions  = 5;
+  auto const start_partition = 0;
+  auto const [out_table, out_offsets] =
+    cudf::round_robin_partition(cudf::table_view{{empty_column}}, num_partitions, start_partition);
+
+  EXPECT_EQ(out_table->num_rows(), 0);
+  EXPECT_EQ(out_offsets.size(), std::size_t{num_partitions});
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinPartitions13_3)
+{
+  strings_column_wrapper rrColWrap1(
+    {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions = 3;
+
+  cudf::size_type start_partition = 0;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"a", "d", "g", "j", "m", "b", "e", "h", "k", "c", "f", "i", "l"},
+      {1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {0, 3, 6, 9, 12, 1, 4, 7, 10, 2, 5, 8, 11});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 5, 9};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  start_partition = 1;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"c", "f", "i", "l", "a", "d", "g", "j", "m", "b", "e", "h", "k"},
+      {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {2, 5, 8, 11, 0, 3, 6, 9, 12, 1, 4, 7, 10});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 4, 9};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  start_partition = 2;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"b", "e", "h", "k", "c", "f", "i", "l", "a", "d", "g", "j", "m"},
+      {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {1, 4, 7, 10, 2, 5, 8, 11, 0, 3, 6, 9, 12});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 4, 8};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinPartitions11_3)
+{
+  strings_column_wrapper rrColWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions = 3;
+
+  cudf::size_type start_partition = 0;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"a", "d", "g", "j", "b", "e", "h", "k", "c", "f", "i"}, {1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 4, 8};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  start_partition = 1;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"c", "f", "i", "a", "d", "g", "j", "b", "e", "h", "k"}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {2, 5, 8, 0, 3, 6, 9, 1, 4, 7, 10});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 3, 7};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  start_partition = 2;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"b", "e", "h", "k", "c", "f", "i", "a", "d", "g", "j"}, {1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {1, 4, 7, 10, 2, 5, 8, 0, 3, 6, 9});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 4, 7};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinDegeneratePartitions11_15)
+{
+  strings_column_wrapper rrColWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions = 15;
+
+  cudf::size_type start_partition = 2;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{
+      0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 11};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  start_partition = 10;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"f", "g", "h", "i", "j", "k", "a", "b", "c", "d", "e"}, {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {5, 6, 7, 8, 9, 10, 0, 1, 2, 3, 4});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{
+      0, 1, 2, 3, 4, 5, 6, 6, 6, 6, 6, 7, 8, 9, 10};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  start_partition = 14;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "a"}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{
+      0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10, 10};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinDegeneratePartitions11_11)
+{
+  strings_column_wrapper rrColWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions = 11;
+
+  cudf::size_type start_partition = 2;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"j", "k", "a", "b", "c", "d", "e", "f", "g", "h", "i"}, {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2({0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {9, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinNPartitionsDivideNRows)
+{
+  // test the case when nrows `mod` npartitions = 0
+  //
+  // input:
+  // strings: ["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u"],
+  // bools:   [1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1],
+  // nulls:   [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0])
+  //
+  strings_column_wrapper rrColWrap1(
+    {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k",
+     "l", "m", "n", "o", "p", "q", "r", "s", "t", "u"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions = 3;
+
+  // expected:
+  // indxs:   [0,3,6,9,12,15,18,1,4,7,10,13,16,19,2,5,8,11,14,17,20],
+  // strings: ["a","d","g","j","m","p","s","b","e","h","k","n","q","t","c","f","i","l","o","r","u"],
+  // bools:   [1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1],
+  // nulls:   [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0],
+  // offsets: [0,7,14]
+  //
+  cudf::size_type start_partition = 0;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"a", "d", "g", "j", "m", "p", "s", "b", "e", "h", "k",
+       "n", "q", "t", "c", "f", "i", "l", "o", "r", "u"},
+      {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2(
+        {1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {0, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19, 2, 5, 8, 11, 14, 17, 20});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 7, 14};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+
+  // expected:
+  // indxs:   [2,5,8,11,14,17,20,0,3,6,9,12,15,18,1,4,7,10,13,16,19],
+  // strings: ["c","f","i","l","o","r","u","a","d","g","j","m","p","s","b","e","h","k","n","q","t"],
+  // bools:   [1,0,1,0,1,0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0],
+  // nulls:   [1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1],
+  // offsets: [0,7,14]
+  //
+  start_partition = 1;
+  {
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+    EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+    auto p_outputTable = std::move(result.first);
+
+    auto output_column_view1{p_outputTable->view().column(0)};
+    auto output_column_view2{p_outputTable->view().column(1)};
+
+    strings_column_wrapper expectedDataWrap1(
+      {"c", "f", "i", "l", "o", "r", "u", "a", "d", "g", "j",
+       "m", "p", "s", "b", "e", "h", "k", "n", "q", "t"},
+      {1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+    auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      fixed_width_column_wrapper<bool> expectedDataWrap2(
+        {1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+      EXPECT_EQ(inputRows, expected_column_view2.size());
+      EXPECT_EQ(inputRows, output_column_view2.size());
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    } else {
+      fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+        {2, 5, 8, 11, 14, 17, 20, 0, 3, 6, 9, 12, 15, 18, 1, 4, 7, 10, 13, 16, 19});
+      auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+    }
+
+    std::vector<cudf::size_type> expected_partition_offsets{0, 7, 14};
+    EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+
+    EXPECT_EQ(expected_partition_offsets, result.second);
+  }
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinSinglePartition)
+{
+  strings_column_wrapper rrColWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(
+    0, [bool8 = (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8)](auto row) {
+      return bool8 ? static_cast<decltype(row)>(row % 2 == 0) : row;
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions  = 1;
+  cudf::size_type start_partition = 0;
+  std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::size_type>> result;
+  EXPECT_NO_THROW(result = cudf::round_robin_partition(rr_view, num_partitions, start_partition));
+
+  auto p_outputTable = std::move(result.first);
+
+  auto output_column_view1{p_outputTable->view().column(0)};
+  auto output_column_view2{p_outputTable->view().column(1)};
+
+  strings_column_wrapper expectedDataWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                           {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  auto expected_column_view1{static_cast<cudf::column_view const&>(expectedDataWrap1)};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view1, output_column_view1);
+
+  if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+    fixed_width_column_wrapper<bool> expectedDataWrap2({1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1});
+    auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+
+    EXPECT_EQ(inputRows, expected_column_view2.size());
+    EXPECT_EQ(inputRows, output_column_view2.size());
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+  } else {
+    fixed_width_column_wrapper<TypeParam, int32_t> expectedDataWrap2(
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+    auto expected_column_view2{static_cast<cudf::column_view const&>(expectedDataWrap2)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_column_view2, output_column_view2);
+  }
+
+  std::vector<cudf::size_type> expected_partition_offsets{0};
+  EXPECT_EQ(static_cast<std::size_t>(num_partitions), expected_partition_offsets.size());
+  EXPECT_EQ(expected_partition_offsets, result.second);
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinIncorrectNumPartitions)
+{
+  strings_column_wrapper rrColWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row % 2 == 0) ? 1 : 0;
+    } else
+      return row;
+  });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions  = 0;
+  cudf::size_type start_partition = 0;
+
+  EXPECT_THROW(cudf::round_robin_partition(rr_view, num_partitions, start_partition),
+               cudf::logic_error);
+}
+
+TYPED_TEST(RoundRobinTest, RoundRobinIncorrectStartPartition)
+{
+  strings_column_wrapper rrColWrap1({"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"},
+                                    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0});
+
+  cudf::size_type inputRows = static_cast<cudf::column_view const&>(rrColWrap1).size();
+
+  auto sequence_l = cudf::detail::make_counting_transform_iterator(0, [](auto row) {
+    if (cudf::type_to_id<TypeParam>() == cudf::type_id::BOOL8) {
+      return (row % 2 == 0) ? 1 : 0;
+    } else
+      return row;
+  });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence_l)::value_type>
+    rrColWrap2(sequence_l, sequence_l + inputRows);
+
+  cudf::table_view rr_view{{rrColWrap1, rrColWrap2}};
+
+  cudf::size_type num_partitions  = 4;
+  cudf::size_type start_partition = 5;
+
+  EXPECT_THROW(cudf::round_robin_partition(rr_view, num_partitions, start_partition),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/quantiles/percentile_approx_test.cpp b/cpp/tests/quantiles/percentile_approx_test.cpp
new file mode 100644
index 0000000..46d4066
--- /dev/null
+++ b/cpp/tests/quantiles/percentile_approx_test.cpp
@@ -0,0 +1,455 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/tdigest_utilities.cuh>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/quantiles.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <arrow/util/tdigest.h>
+
+std::unique_ptr<cudf::column> arrow_percentile_approx(cudf::column_view const& _values,
+                                                      int delta,
+                                                      std::vector<double> const& percentages)
+{
+  // sort the incoming values using the same settings that groupby does.
+  // this is a little weak because null_order::AFTER is hardcoded internally to groupby.
+  cudf::table_view t({_values});
+  auto sorted_t      = cudf::sort(t, {}, {cudf::null_order::AFTER});
+  auto sorted_values = sorted_t->get_column(0).view();
+
+  std::vector<double> h_values(sorted_values.size());
+  CUDF_CUDA_TRY(cudaMemcpy(h_values.data(),
+                           sorted_values.data<double>(),
+                           sizeof(double) * sorted_values.size(),
+                           cudaMemcpyDefault));
+  std::vector<char> h_validity(sorted_values.size());
+  if (sorted_values.null_mask() != nullptr) {
+    auto validity = cudf::mask_to_bools(sorted_values.null_mask(), 0, sorted_values.size());
+    CUDF_CUDA_TRY(cudaMemcpy(h_validity.data(),
+                             (validity->view().data<char>()),
+                             sizeof(char) * sorted_values.size(),
+                             cudaMemcpyDefault));
+  }
+
+  // generate the tdigest
+  arrow::internal::TDigest atd(delta, sorted_values.size() * 2);
+  for (size_t idx = 0; idx < h_values.size(); idx++) {
+    if (sorted_values.null_mask() == nullptr || h_validity[idx]) { atd.Add(h_values[idx]); }
+  }
+
+  // generate the percentiles and stuff them into a list column
+  std::vector<double> h_result;
+  h_result.reserve(percentages.size());
+  std::transform(
+    percentages.begin(), percentages.end(), std::back_inserter(h_result), [&atd](double p) {
+      return atd.Quantile(p);
+    });
+  cudf::test::fixed_width_column_wrapper<double> result(h_result.begin(), h_result.end());
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets{
+    0, static_cast<cudf::size_type>(percentages.size())};
+  return cudf::make_lists_column(1, offsets.release(), result.release(), 0, {});
+}
+
+struct percentile_approx_dispatch {
+  template <
+    typename T,
+    typename Func,
+    typename std::enable_if_t<cudf::is_numeric<T>() || cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(Func op,
+                                           cudf::column_view const& values,
+                                           int delta,
+                                           std::vector<double> const& percentages,
+                                           cudf::size_type ulps)
+  {
+    // arrow implementation.
+    auto expected = [&]() {
+      // we're explicitly casting back to doubles here but this is ok because that is
+      // exactly what happens inside of the cudf implementation as values are processed as well. so
+      // this should not affect results.
+      auto as_doubles = cudf::cast(values, cudf::data_type{cudf::type_id::FLOAT64});
+      return arrow_percentile_approx(*as_doubles, delta, percentages);
+    }();
+
+    // gpu implementation
+    auto agg_result = op(values, delta);
+
+    cudf::test::fixed_width_column_wrapper<double> g_percentages(percentages.begin(),
+                                                                 percentages.end());
+    cudf::tdigest::tdigest_column_view tdv(*agg_result);
+    auto result = cudf::percentile_approx(tdv, g_percentages);
+
+    cudf::test::detail::expect_columns_equivalent(
+      *expected, *result, cudf::test::debug_output_level::FIRST_ERROR, ulps);
+
+    return result;
+  }
+
+  template <
+    typename T,
+    typename Func,
+    typename std::enable_if_t<!cudf::is_numeric<T>() && !cudf::is_fixed_point<T>()>* = nullptr>
+  std::unique_ptr<cudf::column> operator()(Func op,
+                                           cudf::column_view const& values,
+                                           int delta,
+                                           std::vector<double> const& percentages,
+                                           cudf::size_type ulps)
+  {
+    CUDF_FAIL("Invalid input type for percentile_approx test");
+  }
+};
+
+void percentile_approx_test(cudf::column_view const& _keys,
+                            cudf::column_view const& _values,
+                            int delta,
+                            std::vector<double> const& percentages,
+                            cudf::size_type ulps)
+{
+  // first pass:  validate the actual percentages we get per group.
+
+  // produce the groups.
+  cudf::table_view k({_keys});
+  cudf::groupby::groupby pass1_gb(k);
+  cudf::table_view v({_values});
+  auto groups = pass1_gb.get_groups(v);
+  // slice it all up so we have keys/columns for everything.
+  std::vector<cudf::column_view> keys;
+  std::vector<cudf::column_view> values;
+  for (size_t idx = 0; idx < groups.offsets.size() - 1; idx++) {
+    auto k =
+      cudf::slice(groups.keys->get_column(0), {groups.offsets[idx], groups.offsets[idx + 1]});
+    keys.push_back(k[0]);
+
+    auto v =
+      cudf::slice(groups.values->get_column(0), {groups.offsets[idx], groups.offsets[idx + 1]});
+    values.push_back(v[0]);
+  }
+
+  std::vector<std::unique_ptr<cudf::column>> groupby_parts;
+  std::vector<std::unique_ptr<cudf::column>> reduce_parts;
+  for (size_t idx = 0; idx < values.size(); idx++) {
+    // via groupby
+    auto groupby = [&](cudf::column_view const& values, int delta) {
+      cudf::table_view t({keys[idx]});
+      cudf::groupby::groupby gb(t);
+      std::vector<cudf::groupby::aggregation_request> requests;
+      std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+      aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+      requests.push_back({values, std::move(aggregations)});
+      return std::move(gb.aggregate(requests).second[0].results[0]);
+    };
+    groupby_parts.push_back(cudf::type_dispatcher(values[idx].type(),
+                                                  percentile_approx_dispatch{},
+                                                  groupby,
+                                                  values[idx],
+                                                  delta,
+                                                  percentages,
+                                                  ulps));
+
+    // via reduce
+    auto reduce = [](cudf::column_view const& values, int delta) {
+      // result is a scalar, but we want to extract out the underlying column
+      auto scalar_result =
+        cudf::reduce(values,
+                     *cudf::make_tdigest_aggregation<cudf::reduce_aggregation>(delta),
+                     cudf::data_type{cudf::type_id::STRUCT});
+      auto tbl = static_cast<cudf::struct_scalar const*>(scalar_result.get())->view();
+      std::vector<std::unique_ptr<cudf::column>> cols;
+      std::transform(
+        tbl.begin(), tbl.end(), std::back_inserter(cols), [](cudf::column_view const& col) {
+          return std::make_unique<cudf::column>(col);
+        });
+      return cudf::make_structs_column(tbl.num_rows(), std::move(cols), 0, rmm::device_buffer());
+    };
+    // groupby path
+    reduce_parts.push_back(cudf::type_dispatcher(values[idx].type(),
+                                                 percentile_approx_dispatch{},
+                                                 reduce,
+                                                 values[idx],
+                                                 delta,
+                                                 percentages,
+                                                 ulps));
+  }
+
+  // second pass. run the percentile_approx with all the keys in one pass and make sure we get the
+  // same results as the concatenated by-key results.
+  std::vector<cudf::column_view> part_views;
+  std::transform(groupby_parts.begin(),
+                 groupby_parts.end(),
+                 std::back_inserter(part_views),
+                 [](std::unique_ptr<cudf::column> const& c) { return c->view(); });
+  auto expected = cudf::concatenate(part_views);
+
+  cudf::groupby::groupby gb(k);
+  std::vector<cudf::groupby::aggregation_request> requests;
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+  requests.push_back({_values, std::move(aggregations)});
+  auto gb_result = gb.aggregate(requests);
+
+  cudf::test::fixed_width_column_wrapper<double> g_percentages(percentages.begin(),
+                                                               percentages.end());
+  cudf::tdigest::tdigest_column_view tdv(*(gb_result.second[0].results[0]));
+  auto result = cudf::percentile_approx(tdv, g_percentages);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *result);
+}
+
+void simple_test(cudf::data_type input_type, std::vector<std::pair<int, int>> params)
+{
+  auto values = cudf::test::generate_standardized_percentile_distribution(input_type);
+  // all in the same group
+  auto keys = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT32}, values->size(), cudf::mask_state::UNALLOCATED);
+  CUDF_CUDA_TRY(
+    cudaMemset(keys->mutable_view().data<int32_t>(), 0, values->size() * sizeof(int32_t)));
+
+  // runs both groupby and reduce paths
+  std::for_each(params.begin(), params.end(), [&](std::pair<int, int> const& params) {
+    percentile_approx_test(
+      *keys, *values, params.first, {0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0}, params.second);
+  });
+}
+
+struct group_index {
+  int32_t operator()(int32_t i) { return i / 150000; }
+};
+
+void grouped_test(cudf::data_type input_type, std::vector<std::pair<int, int>> params)
+{
+  auto values = cudf::test::generate_standardized_percentile_distribution(input_type);
+  // all in the same group
+  auto keys = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT32}, values->size(), cudf::mask_state::UNALLOCATED);
+  auto i      = thrust::make_counting_iterator(0);
+  auto h_keys = std::vector<int32_t>(values->size());
+  std::transform(i, i + values->size(), h_keys.begin(), group_index{});
+  CUDF_CUDA_TRY(cudaMemcpy(keys->mutable_view().data<int32_t>(),
+                           h_keys.data(),
+                           h_keys.size() * sizeof(int32_t),
+                           cudaMemcpyDefault));
+
+  std::for_each(params.begin(), params.end(), [&](std::pair<int, int> const& params) {
+    percentile_approx_test(
+      *keys, *values, params.first, {0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0}, params.second);
+  });
+}
+
+std::pair<rmm::device_buffer, cudf::size_type> make_null_mask(cudf::column_view const& col)
+{
+  auto itr = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+  return cudf::test::detail::make_null_mask(itr, itr + col.size());
+}
+
+void simple_with_nulls_test(cudf::data_type input_type, std::vector<std::pair<int, int>> params)
+{
+  auto values = cudf::test::generate_standardized_percentile_distribution(input_type);
+  // all in the same group
+  auto keys = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT32}, values->size(), cudf::mask_state::UNALLOCATED);
+  CUDF_CUDA_TRY(
+    cudaMemset(keys->mutable_view().data<int32_t>(), 0, values->size() * sizeof(int32_t)));
+
+  // add a null mask
+  auto mask = make_null_mask(*values);
+  values->set_null_mask(mask.first, mask.second);
+
+  std::for_each(params.begin(), params.end(), [&](std::pair<int, int> const& params) {
+    percentile_approx_test(
+      *keys, *values, params.first, {0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0}, params.second);
+  });
+}
+
+void grouped_with_nulls_test(cudf::data_type input_type, std::vector<std::pair<int, int>> params)
+{
+  auto values = cudf::test::generate_standardized_percentile_distribution(input_type);
+  // all in the same group
+  auto keys = cudf::make_fixed_width_column(
+    cudf::data_type{cudf::type_id::INT32}, values->size(), cudf::mask_state::UNALLOCATED);
+  auto i      = thrust::make_counting_iterator(0);
+  auto h_keys = std::vector<int32_t>(values->size());
+  std::transform(i, i + values->size(), h_keys.begin(), group_index{});
+  CUDF_CUDA_TRY(cudaMemcpy(keys->mutable_view().data<int32_t>(),
+                           h_keys.data(),
+                           h_keys.size() * sizeof(int32_t),
+                           cudaMemcpyDefault));
+
+  // add a null mask
+  auto mask = make_null_mask(*values);
+  values->set_null_mask(mask.first, mask.second);
+
+  std::for_each(params.begin(), params.end(), [&](std::pair<int, int> const& params) {
+    percentile_approx_test(
+      *keys, *values, params.first, {0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0}, params.second);
+  });
+}
+
+template <typename T>
+cudf::data_type get_appropriate_type()
+{
+  if constexpr (cudf::is_fixed_point<T>()) { return cudf::data_type{cudf::type_to_id<T>(), -7}; }
+  return cudf::data_type{cudf::type_to_id<T>()};
+}
+
+using PercentileApproxTypes =
+  cudf::test::Concat<cudf::test::NumericTypes, cudf::test::FixedPointTypes>;
+
+template <typename T>
+struct PercentileApproxInputTypesTest : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(PercentileApproxInputTypesTest, PercentileApproxTypes);
+
+TYPED_TEST(PercentileApproxInputTypesTest, Simple)
+{
+  using T               = TypeParam;
+  auto const input_type = get_appropriate_type<T>();
+
+  simple_test(input_type,
+              {{1000, cudf::test::default_ulp},
+               {100, cudf::test::default_ulp * 4},
+               {10, cudf::test::default_ulp * 11}});
+}
+
+TYPED_TEST(PercentileApproxInputTypesTest, Grouped)
+{
+  using T               = TypeParam;
+  auto const input_type = get_appropriate_type<T>();
+
+  grouped_test(input_type,
+               {{1000, cudf::test::default_ulp},
+                {100, cudf::test::default_ulp * 2},
+                {10, cudf::test::default_ulp * 10}});
+}
+
+TYPED_TEST(PercentileApproxInputTypesTest, SimpleWithNulls)
+{
+  using T               = TypeParam;
+  auto const input_type = get_appropriate_type<T>();
+
+  simple_with_nulls_test(input_type,
+                         {{1000, cudf::test::default_ulp},
+                          {100, cudf::test::default_ulp * 2},
+                          {10, cudf::test::default_ulp * 11}});
+}
+
+TYPED_TEST(PercentileApproxInputTypesTest, GroupedWithNulls)
+{
+  using T               = TypeParam;
+  auto const input_type = get_appropriate_type<T>();
+
+  grouped_with_nulls_test(input_type,
+                          {{1000, cudf::test::default_ulp},
+                           {100, cudf::test::default_ulp * 2},
+                           {10, cudf::test::default_ulp * 6}});
+}
+
+struct PercentileApproxTest : public cudf::test::BaseFixture {};
+
+TEST_F(PercentileApproxTest, EmptyInput)
+{
+  auto empty_ = cudf::tdigest::detail::make_empty_tdigest_column(
+    cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  cudf::test::fixed_width_column_wrapper<double> percentiles{0.0, 0.25, 0.3};
+
+  std::vector<cudf::column_view> input;
+  input.push_back(*empty_);
+  input.push_back(*empty_);
+  input.push_back(*empty_);
+  auto empty = cudf::concatenate(input);
+
+  cudf::tdigest::tdigest_column_view tdv(*empty);
+  auto result = cudf::percentile_approx(tdv, percentiles);
+
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets{0, 0, 0, 0};
+  std::vector<bool> nulls{0, 0, 0};
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(nulls.begin(), nulls.end());
+
+  auto expected = cudf::make_lists_column(3,
+                                          offsets.release(),
+                                          cudf::make_empty_column(cudf::type_id::FLOAT64),
+                                          null_count,
+                                          std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
+
+TEST_F(PercentileApproxTest, EmptyPercentiles)
+{
+  auto const delta = 1000;
+
+  cudf::test::fixed_width_column_wrapper<double> values{0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<int> keys{0, 0, 0, 1, 1, 1};
+  cudf::table_view t({keys});
+  cudf::groupby::groupby gb(t);
+  std::vector<cudf::groupby::aggregation_request> requests;
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+  requests.push_back({values, std::move(aggregations)});
+  auto tdigest_column = gb.aggregate(requests);
+
+  cudf::test::fixed_width_column_wrapper<double> percentiles{};
+
+  cudf::tdigest::tdigest_column_view tdv(*tdigest_column.second[0].results[0]);
+  auto result = cudf::percentile_approx(tdv, percentiles);
+
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets{0, 0, 0};
+  std::vector<bool> nulls{0, 0};
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(nulls.begin(), nulls.end());
+
+  auto expected = cudf::make_lists_column(2,
+                                          offsets.release(),
+                                          cudf::make_empty_column(cudf::type_id::FLOAT64),
+                                          null_count,
+                                          std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
+
+TEST_F(PercentileApproxTest, NullPercentiles)
+{
+  auto const delta = 1000;
+
+  cudf::test::fixed_width_column_wrapper<double> values{1, 1, 2, 3, 4, 5, 6, 7, 8};
+  cudf::test::fixed_width_column_wrapper<int> keys{0, 0, 0, 0, 0, 1, 1, 1, 1};
+  cudf::table_view t({keys});
+  cudf::groupby::groupby gb(t);
+  std::vector<cudf::groupby::aggregation_request> requests;
+  std::vector<std::unique_ptr<cudf::groupby_aggregation>> aggregations;
+  aggregations.push_back(cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta));
+  requests.push_back({values, std::move(aggregations)});
+  auto tdigest_column = gb.aggregate(requests);
+
+  cudf::tdigest::tdigest_column_view tdv(*tdigest_column.second[0].results[0]);
+
+  cudf::test::fixed_width_column_wrapper<double> npercentiles{{0.5, 0.5, 1.0, 1.0}, {0, 0, 1, 1}};
+  auto result = cudf::percentile_approx(tdv, npercentiles);
+
+  std::vector<bool> valids{0, 0, 1, 1};
+  cudf::test::lists_column_wrapper<double> expected{{{99, 99, 4, 4}, valids.begin()},
+                                                    {{99, 99, 8, 8}, valids.begin()}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+}
diff --git a/cpp/tests/quantiles/quantile_test.cpp b/cpp/tests/quantiles/quantile_test.cpp
new file mode 100644
index 0000000..a607376
--- /dev/null
+++ b/cpp/tests/quantiles/quantile_test.cpp
@@ -0,0 +1,475 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/quantiles.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <limits>
+#include <memory>
+#include <type_traits>
+#include <vector>
+
+namespace {
+struct q_res {
+  q_res(double value, bool is_valid = true) : is_valid(is_valid), value(value) {}
+
+  bool is_valid;
+  double value;
+};
+
+// ----- test data -------------------------------------------------------------
+
+namespace testdata {
+struct q_expect {
+  q_expect(double quantile)
+    : quantile(quantile),
+      higher(0, false),
+      lower(0, false),
+      linear(0, false),
+      midpoint(0, false),
+      nearest(0, false)
+  {
+  }
+
+  q_expect(
+    double quantile, double higher, double lower, double linear, double midpoint, double nearest)
+    : quantile(quantile),
+      higher(higher),
+      lower(lower),
+      linear(linear),
+      midpoint(midpoint),
+      nearest(nearest)
+  {
+  }
+
+  double quantile;
+  q_res higher;
+  q_res lower;
+  q_res linear;
+  q_res midpoint;
+  q_res nearest;
+};
+
+template <typename T>
+struct test_case {
+  cudf::test::fixed_width_column_wrapper<T> column;
+  std::vector<q_expect> expectations;
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> ordered_indices;
+};
+
+// interpolate_center
+
+template <typename T>
+test_case<T> interpolate_center()
+{
+  auto low     = std::numeric_limits<T>::lowest();
+  auto max     = std::numeric_limits<T>::max();
+  double mid_d = [] {
+    if (std::is_floating_point_v<T>) return 0.0;
+    if (std::is_signed_v<T>) return -0.5;
+    return static_cast<double>(std::numeric_limits<T>::max()) / 2.0;
+  }();
+
+  // int64_t is internally casted to a double, meaning the lerp center point
+  // is float-like.
+  double lin_d = [] {
+    if (std::is_floating_point_v<T> || std::is_same_v<T, int64_t>) return 0.0;
+    if (std::is_signed_v<T>) return -0.5;
+    return static_cast<double>(std::numeric_limits<T>::max()) / 2.0;
+  }();
+  auto max_d = static_cast<double>(max);
+  auto low_d = static_cast<double>(low);
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({low, max}),
+                      {q_expect{0.50, max_d, low_d, lin_d, mid_d, low_d}}};
+}
+
+template <>
+test_case<bool> interpolate_center()
+{
+  auto low   = std::numeric_limits<bool>::lowest();
+  auto max   = std::numeric_limits<bool>::max();
+  auto mid_d = 0.5;
+  auto low_d = static_cast<double>(low);
+  auto max_d = static_cast<double>(max);
+  return test_case<bool>{cudf::test::fixed_width_column_wrapper<bool>({low, max}),
+                         {q_expect{0.5, max_d, low_d, mid_d, mid_d, low_d}}};
+}
+
+// interpolate_extrema_high
+
+template <typename T>
+test_case<T> interpolate_extrema_high()
+{
+  T max        = std::numeric_limits<T>::max();
+  T low        = max - 2;
+  auto low_d   = static_cast<double>(low);
+  auto max_d   = static_cast<double>(max);
+  auto exact_d = static_cast<double>(max - 1);
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({low, max}),
+                      {q_expect{0.50, max_d, low_d, exact_d, exact_d, low_d}}};
+}
+
+template <>
+test_case<bool> interpolate_extrema_high<bool>()
+{
+  return interpolate_center<bool>();
+}
+
+// interpolate_extrema_low
+
+template <typename T>
+test_case<T> interpolate_extrema_low()
+{
+  T lowest     = std::numeric_limits<T>::lowest();
+  T a          = lowest;
+  T b          = lowest + 2;
+  auto a_d     = static_cast<double>(a);
+  auto b_d     = static_cast<double>(b);
+  auto exact_d = static_cast<double>(a + 1);
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({a, b}),
+                      {q_expect{0.50, b_d, a_d, exact_d, exact_d, a_d}}};
+}
+
+template <>
+test_case<bool> interpolate_extrema_low<bool>()
+{
+  return interpolate_center<bool>();
+}
+
+// single
+
+template <typename T>
+std::enable_if_t<std::is_floating_point_v<T>, test_case<T>> single()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({7.309999942779541}),
+                      {
+                        q_expect{
+                          -1.0,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                        },
+                        q_expect{
+                          0.0,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                        },
+                        q_expect{
+                          1.0,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                          7.309999942779541,
+                        },
+                      }};
+}
+
+template <typename T>
+std::enable_if_t<std::is_integral_v<T> and not cudf::is_boolean<T>(), test_case<T>> single()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({1}),
+                      {q_expect{0.7, 1, 1, 1, 1, 1}}};
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_boolean<T>(), test_case<T>> single()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({1}),
+                      {q_expect{0.7, 1.0, 1.0, 1.0, 1.0, 1.0}}};
+}
+
+// all_invalid
+
+template <typename T>
+std::enable_if_t<std::is_floating_point_v<T>, test_case<T>> all_invalid()
+{
+  return test_case<T>{
+    cudf::test::fixed_width_column_wrapper<T>({6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.01, 0.8, 5.7},
+                                              {0, 0, 0, 0, 0, 0, 0, 0, 0}),
+    {q_expect{-1.0}, q_expect{0.0}, q_expect{0.5}, q_expect{1.0}, q_expect{2.0}}};
+}
+
+template <typename T>
+std::enable_if_t<std::is_integral_v<T> and not cudf::is_boolean<T>(), test_case<T>> all_invalid()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({6, 0, 3, 4, 2, 1, -1, 1, 6},
+                                                                {0, 0, 0, 0, 0, 0, 0, 0, 0}),
+                      {q_expect{0.7}}};
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_boolean<T>(), test_case<T>> all_invalid()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({1, 0, 1, 1, 0, 1, 0, 1, 1},
+                                                                {0, 0, 0, 0, 0, 0, 0, 0, 0}),
+                      {q_expect{0.7}}};
+}
+
+// some invalid
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, double>, test_case<T>> some_invalid()
+{
+  T high = 0.16;
+  T low  = -1.024;
+  T mid  = -0.432;
+  T lin  = -0.432;
+  return test_case<T>{
+    cudf::test::fixed_width_column_wrapper<T>({6.8, high, 3.4, 4.17, 2.13, 1.11, low, 0.8, 5.7},
+                                              {0, 1, 0, 0, 0, 0, 1, 0, 0}),
+    {q_expect{-1.0, low, low, low, low, low},
+     q_expect{0.0, low, low, low, low, low},
+     q_expect{0.5, high, low, lin, mid, low},
+     q_expect{1.0, high, high, high, high, high},
+     q_expect{2.0, high, high, high, high, high}},
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>({6, 1})};
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, float>, test_case<T>> some_invalid()
+{
+  T high     = 0.16;
+  T low      = -1.024;
+  double mid = -0.43200002610683441;
+  double lin = -0.43200002610683441;
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>(
+                        {T(6.8), high, T(3.4), T(4.17), T(2.13), T(1.11), low, T(0.8), T(5.7)},
+                        {0, 1, 0, 0, 0, 0, 1, 0, 0}),
+                      {q_expect{-1.0, low, low, low, low, low},
+                       q_expect{0.0, low, low, low, low, low},
+                       q_expect{0.5, high, low, lin, mid, low},
+                       q_expect{1.0, high, high, high, high, high},
+                       q_expect{2.0, high, high, high, high, high}},
+                      cudf::test::fixed_width_column_wrapper<cudf::size_type>({6, 1})};
+}
+
+template <typename T>
+std::enable_if_t<std::is_integral_v<T> and not cudf::is_boolean<T>(), test_case<T>> some_invalid()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({6, 0, 3, 4, 2, 1, -1, 1, 6},
+                                                                {0, 0, 1, 0, 0, 0, 0, 0, 1}),
+                      {q_expect{0.0, 3.0, 3.0, 3.0, 3.0, 3.0},
+                       q_expect{0.5, 6.0, 3.0, 4.5, 4.5, 3.0},
+                       q_expect{1.0, 6.0, 6.0, 6.0, 6.0, 6.0}},
+                      cudf::test::fixed_width_column_wrapper<cudf::size_type>({2, 8})};
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_boolean<T>(), test_case<T>> some_invalid()
+{
+  return test_case<T>{cudf::test::fixed_width_column_wrapper<T>({1, 0, 1, 1, 0, 1, 0, 1, 1},
+                                                                {0, 0, 1, 0, 1, 0, 0, 0, 0}),
+                      {q_expect{0.0, 0.0, 0.0, 0.0, 0.0, 0.0},
+                       q_expect{0.5, 1.0, 0.0, 0.5, 0.5, 0.0},
+                       q_expect{1.0, 1.0, 1.0, 1.0, 1.0, 1.0}},
+                      cudf::test::fixed_width_column_wrapper<cudf::size_type>({4, 2})};
+}
+
+// unsorted
+
+template <typename T>
+std::enable_if_t<std::is_floating_point_v<T>, test_case<T>> unsorted()
+{
+  return test_case<T>{
+    cudf::test::fixed_width_column_wrapper<T>({6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.00, 0.8, 5.7}),
+    {
+      q_expect{0.0, -1.00, -1.00, -1.00, -1.00, -1.00},
+    },
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>({6, 1, 7, 5, 4, 2, 3, 8, 0})};
+}
+
+template <typename T>
+std::enable_if_t<std::is_integral_v<T> and not cudf::is_boolean<T>(), test_case<T>> unsorted()
+{
+  return std::is_signed<T>()
+           ? test_case<T>{cudf::test::fixed_width_column_wrapper<T>({6, 0, 3, 4, 2, 1, -1, 1, 6}),
+                          {q_expect{0.0, -1, -1, -1, -1, -1}},
+                          cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+                            {6, 1, 7, 5, 4, 2, 3, 8, 0})}
+           : test_case<T>{cudf::test::fixed_width_column_wrapper<T>({6, 0, 3, 4, 2, 1, 1, 1, 6}),
+                          {q_expect{0.0, 1, 1, 1, 1, 1}},
+                          cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+                            {6, 1, 7, 5, 4, 2, 3, 8, 0})};
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_boolean<T>(), test_case<T>> unsorted()
+{
+  return test_case<T>{
+    cudf::test::fixed_width_column_wrapper<T>({0, 0, 1, 1, 0, 1, 1, 0, 1}),
+    {q_expect{
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+    }},
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>({0, 1, 4, 7, 2, 3, 5, 6, 9})};
+}
+
+}  // namespace testdata
+
+// =============================================================================
+// ----- helper functions ------------------------------------------------------
+
+template <typename T>
+void test(testdata::test_case<T> test_case)
+{
+  for (auto& expected : test_case.expectations) {
+    auto q = std::vector<double>{expected.quantile};
+
+    auto nullable = static_cast<cudf::column_view>(test_case.column).nullable();
+
+    auto make_expected_column = [nullable](q_res expected) {
+      return nullable ? cudf::test::fixed_width_column_wrapper<double>({expected.value},
+                                                                       {expected.is_valid})
+                      : cudf::test::fixed_width_column_wrapper<double>({expected.value});
+    };
+
+    auto actual_higher =
+      cudf::quantile(test_case.column, q, cudf::interpolation::HIGHER, test_case.ordered_indices);
+    auto expected_higher_col = make_expected_column(expected.higher);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_higher_col, actual_higher->view());
+
+    auto actual_lower =
+      cudf::quantile(test_case.column, q, cudf::interpolation::LOWER, test_case.ordered_indices);
+    auto expected_lower_col = make_expected_column(expected.lower);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_col, actual_lower->view());
+
+    auto actual_linear =
+      cudf::quantile(test_case.column, q, cudf::interpolation::LINEAR, test_case.ordered_indices);
+    auto expected_linear_col = make_expected_column(expected.linear);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_linear_col, actual_linear->view());
+
+    auto actual_midpoint =
+      cudf::quantile(test_case.column, q, cudf::interpolation::MIDPOINT, test_case.ordered_indices);
+    auto expected_midpoint_col = make_expected_column(expected.midpoint);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_midpoint_col, actual_midpoint->view());
+
+    auto actual_nearest =
+      cudf::quantile(test_case.column, q, cudf::interpolation::NEAREST, test_case.ordered_indices);
+    auto expected_nearest_col = make_expected_column(expected.nearest);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_nearest_col, actual_nearest->view());
+  }
+}
+
+// =============================================================================
+// ----- tests -----------------------------------------------------------------
+
+template <typename T>
+struct QuantileTest : public cudf::test::BaseFixture {};
+
+using TestTypes = cudf::test::NumericTypes;
+TYPED_TEST_SUITE(QuantileTest, TestTypes);
+
+TYPED_TEST(QuantileTest, TestSingle) { test(testdata::single<TypeParam>()); }
+
+TYPED_TEST(QuantileTest, TestSomeElementsInvalid) { test(testdata::some_invalid<TypeParam>()); }
+
+TYPED_TEST(QuantileTest, TestAllElementsInvalid) { test(testdata::all_invalid<TypeParam>()); }
+
+TYPED_TEST(QuantileTest, TestUnsorted) { test(testdata::unsorted<TypeParam>()); }
+
+TYPED_TEST(QuantileTest, TestInterpolateCenter) { test(testdata::interpolate_center<TypeParam>()); }
+
+TYPED_TEST(QuantileTest, TestInterpolateExtremaHigh)
+{
+  test(testdata::interpolate_extrema_high<TypeParam>());
+}
+
+TYPED_TEST(QuantileTest, TestInterpolateExtremaLow)
+{
+  test(testdata::interpolate_extrema_low<TypeParam>());
+}
+
+TYPED_TEST(QuantileTest, TestEmpty)
+{
+  auto input    = cudf::test::fixed_width_column_wrapper<TypeParam>({});
+  auto expected = cudf::test::fixed_width_column_wrapper<double>({0, 0}, {0, 0});
+  auto actual   = cudf::quantile(input, {0.5, 0.25});
+}
+
+template <typename T>
+struct QuantileUnsupportedTypesTest : public cudf::test::BaseFixture {};
+
+// TODO add tests for FixedPointTypes
+using UnsupportedTestTypes = cudf::test::RemoveIf<
+  cudf::test::ContainedIn<cudf::test::Concat<TestTypes, cudf::test::FixedPointTypes>>,
+  cudf::test::AllTypes>;
+TYPED_TEST_SUITE(QuantileUnsupportedTypesTest, UnsupportedTestTypes);
+
+TYPED_TEST(QuantileUnsupportedTypesTest, TestZeroElements)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input({});
+
+  EXPECT_THROW(cudf::quantile(input, {0}), cudf::logic_error);
+}
+
+TYPED_TEST(QuantileUnsupportedTypesTest, TestOneElements)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> input({0});
+
+  EXPECT_THROW(cudf::quantile(input, {0}), cudf::logic_error);
+}
+
+TYPED_TEST(QuantileUnsupportedTypesTest, TestMultipleElements)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> input({0, 1, 2});
+
+  EXPECT_THROW(cudf::quantile(input, {0}), cudf::logic_error);
+}
+
+struct QuantileDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(QuantileDictionaryTest, TestValid)
+{
+  cudf::test::dictionary_column_wrapper<int32_t> col{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  cudf::test::fixed_width_column_wrapper<int32_t> indices{0, 2, 4, 6, 8, 1, 3, 5, 7, 9};
+
+  auto result = cudf::quantile(col, {0.5}, cudf::interpolation::LINEAR);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(),
+                                      cudf::test::fixed_width_column_wrapper<double>{5.5});
+
+  result = cudf::quantile(col, {0.5}, cudf::interpolation::LINEAR, indices);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(),
+                                      cudf::test::fixed_width_column_wrapper<double>{5.5});
+
+  result = cudf::quantile(col, {0.1, 0.2}, cudf::interpolation::HIGHER);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(),
+                                      cudf::test::fixed_width_column_wrapper<double>{2.0, 3.0});
+
+  result = cudf::quantile(col, {0.25, 0.5, 0.75}, cudf::interpolation::MIDPOINT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    result->view(), cudf::test::fixed_width_column_wrapper<double>{3.5, 5.5, 7.5});
+};
+
+}  // anonymous namespace
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/quantiles/quantiles_test.cpp b/cpp/tests/quantiles/quantiles_test.cpp
new file mode 100644
index 0000000..5b7b6dd
--- /dev/null
+++ b/cpp/tests/quantiles/quantiles_test.cpp
@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/quantiles.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+template <typename T>
+struct QuantilesTest : public cudf::test::BaseFixture {};
+
+using TestTypes = cudf::test::AllTypes;
+
+TYPED_TEST_SUITE(QuantilesTest, TestTypes);
+
+TYPED_TEST(QuantilesTest, TestZeroColumns)
+{
+  auto input = cudf::table_view(std::vector<cudf::column_view>{});
+
+  EXPECT_THROW(cudf::quantiles(input, {0.0f}), cudf::logic_error);
+}
+
+TYPED_TEST(QuantilesTest, TestMultiColumnZeroRows)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> input_a({});
+  auto input = cudf::table_view({input_a});
+
+  EXPECT_THROW(cudf::quantiles(input, {0.0f}), cudf::logic_error);
+}
+
+TYPED_TEST(QuantilesTest, TestZeroRequestedQuantiles)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> input_a({1}, {1});
+  auto input = cudf::table_view(std::vector<cudf::column_view>{input_a});
+
+  auto actual   = cudf::quantiles(input, {});
+  auto expected = cudf::empty_like(input);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected->view(), actual->view());
+}
+
+TYPED_TEST(QuantilesTest, TestMultiColumnOrderCountMismatch)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> input_a({});
+  cudf::test::fixed_width_column_wrapper<T> input_b({});
+  auto input = cudf::table_view({input_a});
+
+  EXPECT_THROW(cudf::quantiles(input,
+                               {0.0f},
+                               cudf::interpolation::NEAREST,
+                               cudf::sorted::NO,
+                               {cudf::order::ASCENDING},
+                               {cudf::null_order::AFTER, cudf::null_order::AFTER}),
+               cudf::logic_error);
+}
+
+TYPED_TEST(QuantilesTest, TestMultiColumnNullOrderCountMismatch)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> input_a({});
+  cudf::test::fixed_width_column_wrapper<T> input_b({});
+  auto input = cudf::table_view({input_a});
+
+  EXPECT_THROW(cudf::quantiles(input,
+                               {0.0f},
+                               cudf::interpolation::NEAREST,
+                               cudf::sorted::NO,
+                               {cudf::order::ASCENDING, cudf::order::ASCENDING},
+                               {cudf::null_order::AFTER}),
+               cudf::logic_error);
+}
+
+TYPED_TEST(QuantilesTest, TestMultiColumnArithmeticInterpolation)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> input_a({});
+  cudf::test::fixed_width_column_wrapper<T> input_b({});
+  auto input = cudf::table_view({input_a});
+
+  EXPECT_THROW(cudf::quantiles(input, {0.0f}, cudf::interpolation::LINEAR), cudf::logic_error);
+
+  EXPECT_THROW(cudf::quantiles(input, {0.0f}, cudf::interpolation::MIDPOINT), cudf::logic_error);
+}
+
+TYPED_TEST(QuantilesTest, TestMultiColumnUnsorted)
+{
+  using T = TypeParam;
+
+  auto input_a = cudf::test::strings_column_wrapper(
+    {"C", "B", "A", "A", "D", "B", "D", "B", "D", "C", "C", "C",
+     "D", "B", "D", "B", "C", "C", "A", "D", "B", "A", "A", "A"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> input_b(
+    {4, 3, 5, 0, 1, 0, 4, 1, 5, 3, 0, 5, 2, 4, 3, 2, 1, 2, 3, 0, 5, 1, 4, 2},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto input = cudf::table_view({input_a, input_b});
+
+  auto actual = cudf::quantiles(input,
+                                {0.0f, 0.5f, 0.7f, 0.25f, 1.0f},
+                                cudf::interpolation::NEAREST,
+                                cudf::sorted::NO,
+                                {cudf::order::ASCENDING, cudf::order::DESCENDING});
+
+  auto expected_a = cudf::test::strings_column_wrapper({"A", "C", "C", "B", "D"}, {1, 1, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected_b({5, 5, 1, 5, 0}, {1, 1, 1, 1, 1});
+
+  auto expected = cudf::table_view({expected_a, expected_b});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(QuantilesTest, TestMultiColumnAssumedSorted)
+{
+  using T = TypeParam;
+
+  auto input_a = cudf::test::strings_column_wrapper(
+    {"C", "B", "A", "A", "D", "B", "D", "B", "D", "C", "C", "C",
+     "D", "B", "D", "B", "C", "C", "A", "D", "B", "A", "A", "A"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> input_b(
+    {4, 3, 5, 0, 1, 0, 4, 1, 5, 3, 0, 5, 2, 4, 3, 2, 1, 2, 3, 0, 5, 1, 4, 2},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto input = cudf::table_view({input_a, input_b});
+
+  auto actual = cudf::quantiles(
+    input, {0.0f, 0.5f, 0.7f, 0.25f, 1.0f}, cudf::interpolation::NEAREST, cudf::sorted::YES);
+
+  auto expected_a = cudf::test::strings_column_wrapper({"C", "D", "C", "D", "A"}, {1, 1, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected_b({4, 2, 1, 4, 2}, {1, 1, 1, 1, 1});
+
+  auto expected = cudf::table_view({expected_a, expected_b});
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
diff --git a/cpp/tests/reductions/collect_ops_tests.cpp b/cpp/tests/reductions/collect_ops_tests.cpp
new file mode 100644
index 0000000..70aa735
--- /dev/null
+++ b/cpp/tests/reductions/collect_ops_tests.cpp
@@ -0,0 +1,369 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/sorting.hpp>
+
+namespace {
+
+auto collect_set(cudf::column_view const& input,
+                 std::unique_ptr<cudf::reduce_aggregation> const& agg)
+{
+  auto const result_scalar = cudf::reduce(input, *agg, cudf::data_type{cudf::type_id::LIST});
+
+  // The results of `collect_set` are unordered thus we need to sort them for comparison.
+  auto const result_sorted_table =
+    cudf::sort(cudf::table_view{{dynamic_cast<cudf::list_scalar*>(result_scalar.get())->view()}},
+               {},
+               {cudf::null_order::AFTER});
+
+  return std::make_unique<cudf::list_scalar>(std::move(result_sorted_table->get_column(0)));
+}
+
+}  // namespace
+
+template <typename T>
+struct CollectTestFixedWidth : public cudf::test::BaseFixture {};
+
+using CollectFixedWidthTypes = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::ChronoTypes,
+                                                  cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(CollectTestFixedWidth, CollectFixedWidthTypes);
+
+// ------------------------------------------------------------------------
+TYPED_TEST(CollectTestFixedWidth, CollectList)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  std::vector<int> values({5, 0, -120, -111, 0, 64, 63, 99, 123, -16});
+  std::vector<bool> null_mask({1, 1, 0, 1, 1, 1, 0, 1, 0, 1});
+
+  // null_include without nulls
+  fw_wrapper col(values.begin(), values.end());
+  auto const ret = cudf::reduce(col,
+                                *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(),
+                                cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col, dynamic_cast<cudf::list_scalar*>(ret.get())->view());
+
+  // null_include with nulls
+  fw_wrapper col_with_null(values.begin(), values.end(), null_mask.begin());
+  auto const ret1 = cudf::reduce(col_with_null,
+                                 *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(),
+                                 cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_with_null,
+                                 dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // null_exclude with nulls
+  fw_wrapper col_null_filtered{{5, 0, -111, 0, 64, 99, -16}};
+  auto const ret2 = cudf::reduce(
+    col_with_null,
+    *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(cudf::null_policy::EXCLUDE),
+    cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col_null_filtered,
+                                 dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+}
+
+TYPED_TEST(CollectTestFixedWidth, CollectSet)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  std::vector<int> values({5, 0, 120, 0, 0, 64, 64, 99, 120, 99});
+  std::vector<bool> null_mask({1, 1, 0, 1, 1, 1, 0, 1, 0, 1});
+
+  fw_wrapper col(values.begin(), values.end());
+  fw_wrapper col_with_null(values.begin(), values.end(), null_mask.begin());
+
+  auto null_exclude = cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+    cudf::null_policy::EXCLUDE, cudf::null_equality::UNEQUAL, cudf::nan_equality::ALL_EQUAL);
+  auto null_eq = cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+    cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL);
+  auto null_unequal = cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+    cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL, cudf::nan_equality::ALL_EQUAL);
+
+  // test without nulls
+  auto const ret = collect_set(col, null_eq);
+  fw_wrapper expected{{0, 5, 64, 99, 120}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, dynamic_cast<cudf::list_scalar*>(ret.get())->view());
+
+  // null exclude
+  auto const ret1 = collect_set(col_with_null, null_exclude);
+  fw_wrapper expected1{{0, 5, 64, 99}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // null equal
+  auto const ret2 = collect_set(col_with_null, null_eq);
+  fw_wrapper expected2{{0, 5, 64, 99, -1}, {1, 1, 1, 1, 0}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+
+  // null unequal
+  auto const ret3 = collect_set(col_with_null, null_unequal);
+  fw_wrapper expected3{{0, 5, 64, 99, -1, -1, -1}, {1, 1, 1, 1, 0, 0, 0}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, dynamic_cast<cudf::list_scalar*>(ret3.get())->view());
+}
+
+TYPED_TEST(CollectTestFixedWidth, MergeLists)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col  = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // test without nulls
+  auto const lists1    = lists_col{{1, 2, 3}, {}, {}, {4}, {5, 6, 7}, {8, 9}, {}};
+  auto const expected1 = fw_wrapper{{1, 2, 3, 4, 5, 6, 7, 8, 9}};
+  auto const ret1      = cudf::reduce(lists1,
+                                 *cudf::make_merge_lists_aggregation<cudf::reduce_aggregation>(),
+                                 cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // test with nulls
+  auto const lists2    = lists_col{{
+                                  lists_col{1, 2, 3},
+                                  lists_col{},
+                                  lists_col{{0, 4, 0, 5}, cudf::test::iterators::nulls_at({0, 2})},
+                                  lists_col{{0, 0, 0}, cudf::test::iterators::all_nulls()},
+                                  lists_col{6},
+                                  lists_col{-1, -1},  // null_list
+                                  lists_col{7, 8, 9},
+                                },
+                                cudf::test::iterators::null_at(5)};
+  auto const expected2 = fw_wrapper{{1, 2, 3, 0, 4, 0, 5, 0, 0, 0, 6, 7, 8, 9},
+                                    {1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1}};
+  auto const ret2      = cudf::reduce(lists2,
+                                 *cudf::make_merge_lists_aggregation<cudf::reduce_aggregation>(),
+                                 cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+}
+
+TYPED_TEST(CollectTestFixedWidth, MergeSets)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col  = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // test without nulls
+  auto const lists1    = lists_col{{1, 2, 3}, {}, {}, {4}, {1, 3, 4}, {0, 3, 10}, {}};
+  auto const expected1 = fw_wrapper{{0, 1, 2, 3, 4, 10}};
+  auto const ret1 =
+    collect_set(lists1, cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // test with null_equal
+  auto const lists2    = lists_col{{
+                                  lists_col{1, 2, 3},
+                                  lists_col{},
+                                  lists_col{{0, 4, 0, 5}, cudf::test::iterators::nulls_at({0, 2})},
+                                  lists_col{{0, 0, 0}, cudf::test::iterators::all_nulls()},
+                                  lists_col{5},
+                                  lists_col{-1, -1},  // null_list
+                                  lists_col{1, 3, 5},
+                                },
+                                cudf::test::iterators::null_at(5)};
+  auto const expected2 = fw_wrapper{{1, 2, 3, 4, 5, 0}, {1, 1, 1, 1, 1, 0}};
+  auto const ret2 =
+    collect_set(lists2, cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+
+  // test with null_unequal
+  auto const& lists3   = lists2;
+  auto const expected3 = fw_wrapper{{1, 2, 3, 4, 5, 0, 0, 0, 0, 0}, {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+  auto const ret3      = collect_set(
+    lists3,
+    cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>(cudf::null_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, dynamic_cast<cudf::list_scalar*>(ret3.get())->view());
+}
+
+struct CollectTest : public cudf::test::BaseFixture {};
+
+TEST_F(CollectTest, CollectSetWithNaN)
+{
+  using fp_wrapper = cudf::test::fixed_width_column_wrapper<float>;
+
+  fp_wrapper col{{1.0f, 1.0f, -2.3e-5f, -2.3e-5f, 2.3e5f, 2.3e5f, -NAN, -NAN, NAN, NAN, 0.0f, 0.0f},
+                 {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+
+  // nan unequal with null equal
+  fp_wrapper expected1{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, -NAN, NAN, NAN, 0.0f},
+                       {1, 1, 1, 1, 1, 1, 1, 0}};
+  auto const ret1 = collect_set(
+    col,
+    cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // nan unequal with null unequal
+  fp_wrapper expected2{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, -NAN, NAN, NAN, 0.0f, 0.0f},
+                       {1, 1, 1, 1, 1, 1, 1, 0, 0}};
+  auto const ret2 = collect_set(
+    col,
+    cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL, cudf::nan_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+
+  // nan equal with null equal
+  fp_wrapper expected3{{-2.3e-5f, 1.0f, 2.3e5f, NAN, 0.0f}, {1, 1, 1, 1, 0}};
+  auto const ret3 = collect_set(
+    col,
+    cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, dynamic_cast<cudf::list_scalar*>(ret3.get())->view());
+
+  // nan equal with null unequal
+  fp_wrapper expected4{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, 0.0f, 0.0f}, {1, 1, 1, 1, 0, 0}};
+  auto const ret4 = collect_set(
+    col,
+    cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL, cudf::nan_equality::ALL_EQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, dynamic_cast<cudf::list_scalar*>(ret4.get())->view());
+}
+
+TEST_F(CollectTest, MergeSetsWithNaN)
+{
+  using fp_wrapper = cudf::test::fixed_width_column_wrapper<float>;
+  using lists_col  = cudf::test::lists_column_wrapper<float>;
+
+  auto const col = lists_col{
+    lists_col{1.0f, -2.3e-5f, NAN},
+    lists_col{},
+    lists_col{{-2.3e-5f, 2.3e5f, NAN, 0.0f}, cudf::test::iterators::nulls_at({3})},
+    lists_col{{0.0f, 0.0f}, cudf::test::iterators::all_nulls()},
+    lists_col{-NAN},
+  };
+
+  // nan unequal with null equal
+  fp_wrapper expected1{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, NAN, NAN, 0.0f}, {1, 1, 1, 1, 1, 1, 0}};
+  auto const ret1 = collect_set(col,
+                                cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>(
+                                  cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // nan unequal with null unequal
+  fp_wrapper expected2{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, NAN, NAN, 0.0f, 0.0f, 0.0f},
+                       {1, 1, 1, 1, 1, 1, 0, 0, 0}};
+  auto const ret2 = collect_set(col,
+                                cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>(
+                                  cudf::null_equality::UNEQUAL, cudf::nan_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+
+  // nan equal with null equal
+  fp_wrapper expected3{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, 0.0f}, {1, 1, 1, 1, 0}};
+  auto const ret3 = collect_set(col,
+                                cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>(
+                                  cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, dynamic_cast<cudf::list_scalar*>(ret3.get())->view());
+
+  // nan equal with null unequal
+  fp_wrapper expected4{{-2.3e-5f, 1.0f, 2.3e5f, -NAN, 0.0f, 0.0f, 0.0f}, {1, 1, 1, 1, 0, 0, 0}};
+  auto const ret4 = collect_set(col,
+                                cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>(
+                                  cudf::null_equality::UNEQUAL, cudf::nan_equality::ALL_EQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, dynamic_cast<cudf::list_scalar*>(ret4.get())->view());
+}
+
+TEST_F(CollectTest, CollectStrings)
+{
+  using str_col   = cudf::test::strings_column_wrapper;
+  using lists_col = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const s_col =
+    str_col{{"a", "a", "b", "b", "b", "c", "c", "d", "e", "e"}, {1, 1, 1, 0, 1, 1, 0, 1, 1, 1}};
+
+  // collect_list including nulls
+  auto const ret1 = cudf::reduce(s_col,
+                                 *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(),
+                                 cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(s_col, dynamic_cast<cudf::list_scalar*>(ret1.get())->view());
+
+  // collect_list excluding nulls
+  auto const expected2 = str_col{"a", "a", "b", "b", "c", "d", "e", "e"};
+  auto const ret2      = cudf::reduce(
+    s_col,
+    *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(cudf::null_policy::EXCLUDE),
+    cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, dynamic_cast<cudf::list_scalar*>(ret2.get())->view());
+
+  // collect_set with null_equal
+  auto const expected3 = str_col{{"a", "b", "c", "d", "e", ""}, cudf::test::iterators::null_at(5)};
+  auto const ret3 =
+    collect_set(s_col, cudf::make_collect_set_aggregation<cudf::reduce_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, dynamic_cast<cudf::list_scalar*>(ret3.get())->view());
+
+  // collect_set with null_unequal
+  auto const expected4 = str_col{{"a", "b", "c", "d", "e", "", ""}, {1, 1, 1, 1, 1, 0, 0}};
+  auto const ret4      = collect_set(s_col,
+                                cudf::make_collect_set_aggregation<cudf::reduce_aggregation>(
+                                  cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, dynamic_cast<cudf::list_scalar*>(ret4.get())->view());
+
+  lists_col strings{{"a"},
+                    {},
+                    {"a", "b"},
+                    lists_col{{"b", "null", "c"}, cudf::test::iterators::null_at(1)},
+                    lists_col{{"null", "d"}, cudf::test::iterators::null_at(0)},
+                    lists_col{{"null"}, cudf::test::iterators::null_at(0)},
+                    {"e"}};
+
+  // merge_lists
+  auto const expected5 = str_col{{"a", "a", "b", "b", "null", "c", "null", "d", "null", "e"},
+                                 {1, 1, 1, 1, 0, 1, 0, 1, 0, 1}};
+  auto const ret5      = cudf::reduce(strings,
+                                 *cudf::make_merge_lists_aggregation<cudf::reduce_aggregation>(),
+                                 cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected5, dynamic_cast<cudf::list_scalar*>(ret5.get())->view());
+
+  // merge_sets with null_equal
+  auto const expected6 = str_col{{"a", "b", "c", "d", "e", "null"}, {1, 1, 1, 1, 1, 0}};
+  auto const ret6 =
+    collect_set(strings, cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected6, dynamic_cast<cudf::list_scalar*>(ret6.get())->view());
+
+  // merge_sets with null_unequal
+  auto const expected7 =
+    str_col{{"a", "b", "c", "d", "e", "null", "null", "null"}, {1, 1, 1, 1, 1, 0, 0, 0}};
+  auto const ret7 = collect_set(
+    strings,
+    cudf::make_merge_sets_aggregation<cudf::reduce_aggregation>(cudf::null_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected7, dynamic_cast<cudf::list_scalar*>(ret7.get())->view());
+}
+
+TEST_F(CollectTest, CollectEmptys)
+{
+  using int_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  // test collect empty columns
+  auto empty = int_col{};
+  auto ret   = cudf::reduce(empty,
+                          *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(),
+                          cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(int_col{}, dynamic_cast<cudf::list_scalar*>(ret.get())->view());
+
+  ret = collect_set(empty, cudf::make_collect_set_aggregation<cudf::reduce_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(int_col{}, dynamic_cast<cudf::list_scalar*>(ret.get())->view());
+
+  // test collect all null columns
+  auto all_nulls = int_col{{1, 2, 3, 4, 5}, {0, 0, 0, 0, 0}};
+  ret            = cudf::reduce(all_nulls,
+                     *cudf::make_collect_list_aggregation<cudf::reduce_aggregation>(),
+                     cudf::data_type{cudf::type_id::LIST});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(int_col{}, dynamic_cast<cudf::list_scalar*>(ret.get())->view());
+
+  ret = collect_set(all_nulls, cudf::make_collect_set_aggregation<cudf::reduce_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(int_col{}, dynamic_cast<cudf::list_scalar*>(ret.get())->view());
+}
diff --git a/cpp/tests/reductions/list_rank_test.cpp b/cpp/tests/reductions/list_rank_test.cpp
new file mode 100644
index 0000000..68da95f
--- /dev/null
+++ b/cpp/tests/reductions/list_rank_test.cpp
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <benchmarks/common/generate_input.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/reduction.hpp>
+
+struct ListRankScanTest : public cudf::test::BaseFixture {
+  inline void test_ungrouped_rank_scan(cudf::column_view const& input,
+                                       cudf::column_view const& expect_vals,
+                                       cudf::scan_aggregation const& agg,
+                                       cudf::null_policy null_handling)
+  {
+    auto col_out = cudf::scan(input, agg, cudf::scan_type::INCLUSIVE, null_handling);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      expect_vals, col_out->view(), cudf::test::debug_output_level::ALL_ERRORS);
+  }
+};
+
+TEST_F(ListRankScanTest, BasicList)
+{
+  using lcw      = cudf::test::lists_column_wrapper<uint64_t>;
+  auto const col = lcw{{}, {}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+
+  auto const expected_dense_vals =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 2, 3, 4, 5, 6, 7, 7, 8, 9, 9};
+  this->test_ungrouped_rank_scan(
+    col,
+    expected_dense_vals,
+    *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+    cudf::null_policy::INCLUDE);
+}
+
+TEST_F(ListRankScanTest, DeepList)
+{
+  using lcw = cudf::test::lists_column_wrapper<uint64_t>;
+  lcw col{
+    {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},
+    {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},
+    {{1, 2, 3}, {}, {4, 5}, {0, 6, 0}},
+    {{7, 8}, {}},
+    lcw{lcw{}, lcw{}, lcw{}},
+    lcw{lcw{}},
+    lcw{lcw{}},
+    lcw{lcw{}},
+    lcw{lcw{}, lcw{}, lcw{}},
+    lcw{lcw{}, lcw{}, lcw{}},
+    {lcw{10}},
+    {lcw{10}},
+    {{13, 14}, {15}},
+    {{13, 14}, {16}},
+    lcw{},
+    lcw{lcw{}},
+  };
+
+  {  // Non-sliced
+    auto const expected_dense_vals = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+      1, 1, 2, 3, 4, 5, 5, 5, 6, 6, 7, 7, 8, 9, 10, 11};
+    this->test_ungrouped_rank_scan(
+      col,
+      expected_dense_vals,
+      *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+      cudf::null_policy::INCLUDE);
+  }
+
+  {  // sliced
+    auto sliced_col = cudf::slice(col, {3, 12})[0];
+    auto const expected_dense_vals =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 3, 3, 3, 4, 4, 5, 5};
+    this->test_ungrouped_rank_scan(
+      sliced_col,
+      expected_dense_vals,
+      *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+      cudf::null_policy::INCLUDE);
+  }
+}
+
+TEST_F(ListRankScanTest, ListOfStruct)
+{
+  // Constructing a list of struct of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 0}]         ==
+  // 16.  [{Null, 0}]
+
+  auto col1 = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {-1, -1, 0, 2, 2, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, 1, 2},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0}};
+  auto col2 = cudf::test::strings_column_wrapper{
+    {"x", "x", "a", "a", "b", "b", "a", "b", "a", "b", "a", "c", "a", "c", "a", "c", "b", "b"},
+    {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1}};
+  auto struct_col = cudf::test::structs_column_wrapper{
+    {col1, col2}, {0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+    0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+
+  auto list_nullmask = std::vector<bool>{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                       17,
+                                       nullptr,
+                                       static_cast<cudf::bitmask_type*>(null_mask.data()),
+                                       null_count,
+                                       0,
+                                       {offsets, struct_col});
+
+  {  // Non-sliced
+    auto expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+      1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9, 9, 10, 10};
+
+    this->test_ungrouped_rank_scan(
+      list_column,
+      expect,
+      *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+      cudf::null_policy::INCLUDE);
+  }
+
+  {  // Sliced
+    auto sliced_col = cudf::slice(list_column, {3, 15})[0];
+    auto expect =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 3, 3, 3, 4, 5, 6, 7, 7, 8, 8};
+
+    this->test_ungrouped_rank_scan(
+      sliced_col,
+      expect,
+      *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+      cudf::null_policy::INCLUDE);
+  }
+}
+
+TEST_F(ListRankScanTest, ListOfEmptyStruct)
+{
+  // []
+  // []
+  // Null
+  // Null
+  // [Null, Null]
+  // [Null, Null]
+  // [Null, Null]
+  // [Null]
+  // [Null]
+  // [{}]
+  // [{}]
+  // [{}, {}]
+  // [{}, {}]
+
+  auto struct_validity = std::vector<bool>{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(struct_validity.begin(), struct_validity.end());
+  auto struct_col = cudf::make_structs_column(14, {}, null_count, std::move(null_mask));
+
+  auto offsets = cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+    0, 0, 0, 0, 0, 2, 4, 6, 7, 8, 9, 10, 12, 14};
+  auto list_nullmask = std::vector<bool>{1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  std::tie(null_mask, null_count) =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::make_lists_column(
+    13, offsets.release(), std::move(struct_col), null_count, std::move(null_mask));
+
+  auto expect =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6};
+
+  this->test_ungrouped_rank_scan(
+    *list_column,
+    expect,
+    *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+    cudf::null_policy::INCLUDE);
+}
+
+TEST_F(ListRankScanTest, EmptyDeepList)
+{
+  // List<List<int>>, where all lists are empty
+  // []
+  // []
+  // Null
+  // Null
+
+  // Internal empty list
+  auto list1 = cudf::test::lists_column_wrapper<int>{};
+
+  auto offsets       = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 0, 0, 0};
+  auto list_nullmask = std::vector<bool>{1, 1, 0, 0};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_nullmask.begin(), list_nullmask.end());
+  auto list_column = cudf::make_lists_column(
+    4, offsets.release(), list1.release(), null_count, std::move(null_mask));
+
+  auto expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 2, 2};
+
+  this->test_ungrouped_rank_scan(
+    *list_column,
+    expect,
+    *cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE),
+    cudf::null_policy::INCLUDE);
+}
diff --git a/cpp/tests/reductions/rank_tests.cpp b/cpp/tests/reductions/rank_tests.cpp
new file mode 100644
index 0000000..4cc8d7e
--- /dev/null
+++ b/cpp/tests/reductions/rank_tests.cpp
@@ -0,0 +1,328 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "scan_tests.hpp"
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/host_vector.h>
+
+using rank_result_col    = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+using percent_result_col = cudf::test::fixed_width_column_wrapper<double>;
+
+auto const rank = cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::MIN);
+auto const dense_rank =
+  cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::DENSE);
+auto const percent_rank =
+  cudf::make_rank_aggregation<cudf::scan_aggregation>(cudf::rank_method::MIN,
+                                                      {},
+                                                      cudf::null_policy::INCLUDE,
+                                                      {},
+                                                      cudf::rank_percentage::ONE_NORMALIZED);
+
+auto constexpr INCLUSIVE_SCAN = cudf::scan_type::INCLUSIVE;
+auto constexpr INCLUDE_NULLS  = cudf::null_policy::INCLUDE;
+
+template <typename T>
+struct TypedRankScanTest : BaseScanTest<T> {
+  inline void test_ungrouped_rank_scan(cudf::column_view const& input,
+                                       cudf::column_view const& expect_vals,
+                                       cudf::scan_aggregation const& agg)
+  {
+    auto col_out = cudf::scan(input, agg, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expect_vals, col_out->view());
+  }
+};
+
+using RankTypes = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                     cudf::test::FloatingPointTypes,
+                                     cudf::test::FixedPointTypes,
+                                     cudf::test::ChronoTypes,
+                                     cudf::test::StringTypes>;
+
+TYPED_TEST_SUITE(TypedRankScanTest, RankTypes);
+
+TYPED_TEST(TypedRankScanTest, Rank)
+{
+  auto const v = [] {
+    if (std::is_signed_v<TypeParam>)
+      return make_vector<TypeParam>({-120, -120, -120, -16, -16, 5, 6, 6, 6, 6, 34, 113});
+    return make_vector<TypeParam>({5, 5, 5, 6, 6, 9, 11, 11, 11, 11, 14, 34});
+  }();
+  auto const col = this->make_column(v);
+
+  auto const expected_dense   = rank_result_col{1, 1, 1, 2, 2, 3, 4, 4, 4, 4, 5, 6};
+  auto const expected_rank    = rank_result_col{1, 1, 1, 4, 4, 6, 7, 7, 7, 7, 11, 12};
+  auto const expected_percent = percent_result_col{0.0,
+                                                   0.0,
+                                                   0.0,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   5.0 / 11,
+                                                   6.0 / 11,
+                                                   6.0 / 11,
+                                                   6.0 / 11,
+                                                   6.0 / 11,
+                                                   10.0 / 11,
+                                                   11.0 / 11};
+  this->test_ungrouped_rank_scan(*col, expected_dense, *dense_rank);
+  this->test_ungrouped_rank_scan(*col, expected_rank, *rank);
+  this->test_ungrouped_rank_scan(*col, expected_percent, *percent_rank);
+}
+
+TYPED_TEST(TypedRankScanTest, RankWithNulls)
+{
+  auto const v = [] {
+    if (std::is_signed_v<TypeParam>)
+      return make_vector<TypeParam>({-120, -120, -120, -16, -16, 5, 6, 6, 6, 6, 34, 113});
+    return make_vector<TypeParam>({5, 5, 5, 6, 6, 9, 11, 11, 11, 11, 14, 34});
+  }();
+  auto const null_iter = cudf::test::iterators::nulls_at({3, 6, 7, 11});
+  auto const b         = thrust::host_vector<bool>(null_iter, null_iter + v.size());
+  auto col             = this->make_column(v, b);
+
+  auto const expected_dense   = rank_result_col{1, 1, 1, 2, 3, 4, 5, 5, 6, 6, 7, 8};
+  auto const expected_rank    = rank_result_col{1, 1, 1, 4, 5, 6, 7, 7, 9, 9, 11, 12};
+  auto const expected_percent = percent_result_col{0.0,
+                                                   0.0,
+                                                   0.0,
+                                                   3.0 / 11,
+                                                   4.0 / 11,
+                                                   5.0 / 11,
+                                                   6.0 / 11,
+                                                   6.0 / 11,
+                                                   8.0 / 11,
+                                                   8.0 / 11,
+                                                   10.0 / 11,
+                                                   11.0 / 11};
+  this->test_ungrouped_rank_scan(*col, expected_dense, *dense_rank);
+  this->test_ungrouped_rank_scan(*col, expected_rank, *rank);
+  this->test_ungrouped_rank_scan(*col, expected_percent, *percent_rank);
+}
+
+namespace {
+template <typename TypeParam>
+auto make_input_column()
+{
+  if constexpr (std::is_same_v<TypeParam, cudf::string_view>) {
+    return cudf::test::strings_column_wrapper{
+      {"0", "0", "4", "4", "4", "5", "7", "7", "7", "9", "9", "9"},
+      cudf::test::iterators::null_at(5)};
+  } else {
+    using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+    return (std::is_signed_v<TypeParam>)
+             ? fw_wrapper{{-1, -1, -4, -4, -4, 5, 7, 7, 7, 9, 9, 9},
+                          cudf::test::iterators::null_at(5)}
+             : fw_wrapper{{0, 0, 4, 4, 4, 5, 7, 7, 7, 9, 9, 9}, cudf::test::iterators::null_at(5)};
+  }
+}
+
+auto make_strings_column()
+{
+  return cudf::test::strings_column_wrapper{
+    {"0a", "0a", "2a", "2a", "3b", "5", "6c", "6c", "6c", "9", "9", "10d"},
+    cudf::test::iterators::null_at(8)};
+}
+
+template <typename TypeParam>
+auto make_mixed_structs_column()
+{
+  auto col     = make_input_column<TypeParam>();
+  auto strings = make_strings_column();
+  return cudf::test::structs_column_wrapper{{col, strings}};
+}
+}  // namespace
+
+TYPED_TEST(TypedRankScanTest, MixedStructs)
+{
+  auto const struct_col       = make_mixed_structs_column<TypeParam>();
+  auto const expected_dense   = rank_result_col{1, 1, 2, 2, 3, 4, 5, 5, 6, 7, 7, 8};
+  auto const expected_rank    = rank_result_col{1, 1, 3, 3, 5, 6, 7, 7, 9, 10, 10, 12};
+  auto const expected_percent = percent_result_col{0.0,
+                                                   0.0,
+                                                   2.0 / 11,
+                                                   2.0 / 11,
+                                                   4.0 / 11,
+                                                   5.0 / 11,
+                                                   6.0 / 11,
+                                                   6.0 / 11,
+                                                   8.0 / 11,
+                                                   9.0 / 11,
+                                                   9.0 / 11,
+                                                   11.0 / 11};
+
+  this->test_ungrouped_rank_scan(struct_col, expected_dense, *dense_rank);
+  this->test_ungrouped_rank_scan(struct_col, expected_rank, *rank);
+  this->test_ungrouped_rank_scan(struct_col, expected_percent, *percent_rank);
+}
+
+TYPED_TEST(TypedRankScanTest, NestedStructs)
+{
+  auto const nested_col = [&] {
+    auto struct_col = [&] {
+      auto col     = make_input_column<TypeParam>();
+      auto strings = make_strings_column();
+      return cudf::test::structs_column_wrapper{{col, strings}};
+    }();
+    auto col = make_input_column<TypeParam>();
+    return cudf::test::structs_column_wrapper{{struct_col, col}};
+  }();
+
+  auto const flat_col = [&] {
+    auto col         = make_input_column<TypeParam>();
+    auto strings_col = make_strings_column();
+    auto nuther_col  = make_input_column<TypeParam>();
+    return cudf::test::structs_column_wrapper{{col, strings_col, nuther_col}};
+  }();
+
+  auto const dense_out      = cudf::scan(nested_col, *dense_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto const dense_expected = cudf::scan(flat_col, *dense_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(dense_out->view(), dense_expected->view());
+
+  auto const rank_out      = cudf::scan(nested_col, *rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto const rank_expected = cudf::scan(flat_col, *rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(rank_out->view(), rank_expected->view());
+
+  auto const percent_out = cudf::scan(nested_col, *percent_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto const percent_expected = cudf::scan(flat_col, *percent_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(percent_out->view(), percent_expected->view());
+}
+
+TYPED_TEST(TypedRankScanTest, StructsWithNullPushdown)
+{
+  auto struct_col = make_mixed_structs_column<TypeParam>().release();
+
+  // First, verify that if the structs column has only nulls, all output rows are ranked 1.
+  {
+    // Null mask not pushed down to members.
+    struct_col->set_null_mask(create_null_mask(struct_col->size(), cudf::mask_state::ALL_NULL),
+                              struct_col->size());
+    auto const expected_null_result = rank_result_col{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+    auto const expected_percent_rank_null_result =
+      percent_result_col{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
+    auto const dense_out   = cudf::scan(*struct_col, *dense_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    auto const rank_out    = cudf::scan(*struct_col, *rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    auto const percent_out = cudf::scan(*struct_col, *percent_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(dense_out->view(), expected_null_result);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(rank_out->view(), expected_null_result);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(percent_out->view(), expected_percent_rank_null_result);
+  }
+
+  // Next, verify that if the structs column a null mask that is NOT pushed down to members,
+  // the ranks are still correct.
+  {
+    auto const null_iter = cudf::test::iterators::nulls_at({1, 2});
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_iter, null_iter + struct_col->size());
+    struct_col->set_null_mask(std::move(null_mask), null_count);
+    auto const expected_dense   = rank_result_col{1, 2, 2, 3, 4, 5, 6, 6, 7, 8, 8, 9};
+    auto const expected_rank    = rank_result_col{1, 2, 2, 4, 5, 6, 7, 7, 9, 10, 10, 12};
+    auto const expected_percent = percent_result_col{0.0,
+                                                     1.0 / 11,
+                                                     1.0 / 11,
+                                                     3.0 / 11,
+                                                     4.0 / 11,
+                                                     5.0 / 11,
+                                                     6.0 / 11,
+                                                     6.0 / 11,
+                                                     8.0 / 11,
+                                                     9.0 / 11,
+                                                     9.0 / 11,
+                                                     11.0 / 11};
+    auto const dense_out   = cudf::scan(*struct_col, *dense_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    auto const rank_out    = cudf::scan(*struct_col, *rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    auto const percent_out = cudf::scan(*struct_col, *percent_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(dense_out->view(), expected_dense);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(rank_out->view(), expected_rank);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(percent_out->view(), expected_percent);
+  }
+}
+
+struct RankScanTest : public cudf::test::BaseFixture {};
+
+TEST(RankScanTest, BoolRank)
+{
+  auto const vals =
+    cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+  auto const expected_dense   = rank_result_col{1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2};
+  auto const expected_rank    = rank_result_col{1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4};
+  auto const expected_percent = percent_result_col{0.0,
+                                                   0.0,
+                                                   0.0,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11};
+
+  auto const dense_out   = cudf::scan(vals, *dense_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto const rank_out    = cudf::scan(vals, *rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto const percent_out = cudf::scan(vals, *percent_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_dense, dense_out->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_rank, rank_out->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_percent, percent_out->view());
+}
+
+TEST(RankScanTest, BoolRankWithNull)
+{
+  auto const vals = cudf::test::fixed_width_column_wrapper<bool>{
+    {0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}, cudf::test::iterators::nulls_at({8, 9, 10, 11})};
+  auto const expected_dense   = rank_result_col{1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3};
+  auto const expected_rank    = rank_result_col{1, 1, 1, 4, 4, 4, 4, 4, 9, 9, 9, 9};
+  auto const expected_percent = percent_result_col{0.0,
+                                                   0.0,
+                                                   0.0,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   3.0 / 11,
+                                                   8.0 / 11,
+                                                   8.0 / 11,
+                                                   8.0 / 11,
+                                                   8.0 / 11};
+
+  auto nullable_dense_out   = cudf::scan(vals, *dense_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto nullable_rank_out    = cudf::scan(vals, *rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  auto nullable_percent_out = cudf::scan(vals, *percent_rank, INCLUSIVE_SCAN, INCLUDE_NULLS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_dense, nullable_dense_out->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_rank, nullable_rank_out->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_percent, nullable_percent_out->view());
+}
+
+TEST(RankScanTest, ExclusiveScan)
+{
+  auto const vals = cudf::test::fixed_width_column_wrapper<uint32_t>{3, 4, 5};
+
+  // Only inclusive scans are supported, so these should all raise exceptions.
+  EXPECT_THROW(cudf::scan(vals, *dense_rank, cudf::scan_type::EXCLUSIVE, INCLUDE_NULLS),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::scan(vals, *rank, cudf::scan_type::EXCLUSIVE, INCLUDE_NULLS),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::scan(vals, *percent_rank, cudf::scan_type::EXCLUSIVE, INCLUDE_NULLS),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/reductions/reduction_tests.cpp b/cpp/tests/reductions/reduction_tests.cpp
new file mode 100644
index 0000000..7644ac4
--- /dev/null
+++ b/cpp/tests/reductions/reduction_tests.cpp
@@ -0,0 +1,3147 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/types.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <iostream>
+#include <vector>
+
+using aggregation        = cudf::aggregation;
+using reduce_aggregation = cudf::reduce_aggregation;
+
+template <typename T>
+auto convert_int(int value)
+{
+  if (std::is_unsigned_v<T>) value = std::abs(value);
+  if constexpr (cudf::is_timestamp_t<T>::value) {
+    return T{typename T::duration(value)};
+  } else {
+    return static_cast<T>(value);
+  }
+}
+
+template <typename T>
+auto convert_values(std::vector<int> const& int_values)
+{
+  std::vector<T> v(int_values.size());
+  std::transform(
+    int_values.begin(), int_values.end(), v.begin(), [](int x) { return convert_int<T>(x); });
+  return v;
+}
+
+template <typename T>
+cudf::test::fixed_width_column_wrapper<T> construct_null_column(std::vector<T> const& values,
+                                                                std::vector<bool> const& bools)
+{
+  if (values.size() > bools.size()) { throw std::logic_error("input vector size mismatch."); }
+  return cudf::test::fixed_width_column_wrapper<T>(values.begin(), values.end(), bools.begin());
+}
+
+template <typename T>
+std::vector<T> replace_nulls(std::vector<T> const& values,
+                             std::vector<bool> const& bools,
+                             T identity)
+{
+  std::vector<T> v(values.size());
+  std::transform(values.begin(), values.end(), bools.begin(), v.begin(), [identity](T x, bool b) {
+    return (b) ? x : identity;
+  });
+  return v;
+}
+
+// ------------------------------------------------------------------------
+
+// This is the main test feature
+template <typename T>
+struct ReductionTest : public cudf::test::BaseFixture {
+  // Sum/Prod/SumOfSquare never support non arithmetics
+  static constexpr bool ret_non_arithmetic = (std::is_arithmetic_v<T> || std::is_same_v<T, bool>);
+
+  ReductionTest() {}
+
+  ~ReductionTest() {}
+
+  template <typename T_out>
+  std::pair<T_out, bool> reduction_test(cudf::column_view const& underlying_column,
+                                        reduce_aggregation const& agg,
+                                        std::optional<cudf::data_type> _output_dtype = {})
+  {
+    auto const output_dtype                 = _output_dtype.value_or(underlying_column.type());
+    std::unique_ptr<cudf::scalar> reduction = cudf::reduce(underlying_column, agg, output_dtype);
+    using ScalarType                        = cudf::scalar_type_t<T_out>;
+    auto result                             = static_cast<ScalarType*>(reduction.get());
+    return {result->value(), result->is_valid()};
+  }
+
+  // Test with initial value
+  template <typename T_out>
+  std::pair<T_out, bool> reduction_test(cudf::column_view const& underlying_column,
+                                        cudf::scalar const& initial_value,
+                                        reduce_aggregation const& agg,
+                                        std::optional<cudf::data_type> _output_dtype = {})
+  {
+    auto const output_dtype = _output_dtype.value_or(underlying_column.type());
+    std::unique_ptr<cudf::scalar> reduction =
+      cudf::reduce(underlying_column, agg, output_dtype, initial_value);
+    using ScalarType = cudf::scalar_type_t<T_out>;
+    auto result      = static_cast<ScalarType*>(reduction.get());
+    return {result->value(), result->is_valid()};
+  }
+};
+
+template <typename T>
+struct MinMaxReductionTest : public ReductionTest<T> {};
+
+using MinMaxTypes = cudf::test::Types<int16_t, int32_t, float, double>;
+TYPED_TEST_SUITE(MinMaxReductionTest, MinMaxTypes);
+
+// ------------------------------------------------------------------------
+TYPED_TEST(MinMaxReductionTest, MinMaxTypes)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({5, 0, -120, -111, 0, 64, 63, 99, 123, -16});
+  std::vector<bool> host_bools({1, 1, 0, 1, 1, 1, 0, 1, 0, 1});
+  std::vector<bool> all_null({0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
+  std::vector<T> v       = convert_values<T>(int_values);
+  T init_value           = convert_int<T>(100);
+  auto const init_scalar = cudf::make_fixed_width_scalar<T>(init_value);
+
+  // Min/Max succeeds for any gdf types including
+  // non-arithmetic types (date32, date64, timestamp, category)
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+
+  T expected_min_result      = *(std::min_element(v.begin(), v.end()));
+  T expected_max_result      = *(std::max_element(v.begin(), v.end()));
+  T expected_min_init_result = std::accumulate(
+    v.begin(), v.end(), init_value, [](T const& a, T const& b) { return std::min<T>(a, b); });
+  T expected_max_init_result = std::accumulate(
+    v.begin(), v.end(), init_value, [](T const& a, T const& b) { return std::max<T>(a, b); });
+
+  EXPECT_EQ(
+    this->template reduction_test<T>(col, *cudf::make_min_aggregation<reduce_aggregation>()).first,
+    expected_min_result);
+  EXPECT_EQ(
+    this->template reduction_test<T>(col, *cudf::make_max_aggregation<reduce_aggregation>()).first,
+    expected_max_result);
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *init_scalar, *cudf::make_min_aggregation<reduce_aggregation>())
+              .first,
+            expected_min_init_result);
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *init_scalar, *cudf::make_max_aggregation<reduce_aggregation>())
+              .first,
+            expected_max_init_result);
+
+  auto res = cudf::minmax(col);
+
+  using ScalarType = cudf::scalar_type_t<T>;
+  auto min_result  = static_cast<ScalarType*>(res.first.get());
+  auto max_result  = static_cast<ScalarType*>(res.second.get());
+  EXPECT_EQ(T{min_result->value()}, expected_min_result);
+  EXPECT_EQ(T{max_result->value()}, expected_max_result);
+
+  // test with some nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+
+  auto r_min = replace_nulls(v, host_bools, std::numeric_limits<T>::max());
+  auto r_max = replace_nulls(v, host_bools, std::numeric_limits<T>::lowest());
+
+  T expected_min_null_result = *(std::min_element(r_min.begin(), r_min.end()));
+  T expected_max_null_result = *(std::max_element(r_max.begin(), r_max.end()));
+  T expected_min_init_null_result =
+    std::accumulate(r_min.begin(), r_min.end(), init_value, [](T const& a, T const& b) {
+      return std::min<T>(a, b);
+    });
+  T expected_max_init_null_result =
+    std::accumulate(r_max.begin(), r_max.end(), init_value, [](T const& a, T const& b) {
+      return std::max<T>(a, b);
+    });
+
+  EXPECT_EQ(
+    this->template reduction_test<T>(col_nulls, *cudf::make_min_aggregation<reduce_aggregation>())
+      .first,
+    expected_min_null_result);
+  EXPECT_EQ(
+    this->template reduction_test<T>(col_nulls, *cudf::make_max_aggregation<reduce_aggregation>())
+      .first,
+    expected_max_null_result);
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col_nulls, *init_scalar, *cudf::make_min_aggregation<reduce_aggregation>())
+              .first,
+            expected_min_init_null_result);
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col_nulls, *init_scalar, *cudf::make_max_aggregation<reduce_aggregation>())
+              .first,
+            expected_max_init_null_result);
+
+  auto null_res = cudf::minmax(col_nulls);
+
+  using ScalarType     = cudf::scalar_type_t<T>;
+  auto min_null_result = static_cast<ScalarType*>(null_res.first.get());
+  auto max_null_result = static_cast<ScalarType*>(null_res.second.get());
+  EXPECT_EQ(T{min_null_result->value()}, expected_min_null_result);
+  EXPECT_EQ(T{max_null_result->value()}, expected_max_null_result);
+
+  // test with all null
+  cudf::test::fixed_width_column_wrapper<T> col_all_nulls = construct_null_column(v, all_null);
+  init_scalar->set_valid_async(false);
+
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<T>(col_all_nulls, *cudf::make_min_aggregation<reduce_aggregation>())
+      .second);
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<T>(col_all_nulls, *cudf::make_max_aggregation<reduce_aggregation>())
+      .second);
+  EXPECT_FALSE(this
+                 ->template reduction_test<T>(
+                   col_all_nulls, *init_scalar, *cudf::make_min_aggregation<reduce_aggregation>())
+                 .second);
+  EXPECT_FALSE(this
+                 ->template reduction_test<T>(
+                   col_all_nulls, *init_scalar, *cudf::make_max_aggregation<reduce_aggregation>())
+                 .second);
+
+  auto all_null_res = cudf::minmax(col_all_nulls);
+
+  using ScalarType         = cudf::scalar_type_t<T>;
+  auto min_all_null_result = static_cast<ScalarType*>(all_null_res.first.get());
+  auto max_all_null_result = static_cast<ScalarType*>(all_null_res.second.get());
+  EXPECT_EQ(min_all_null_result->is_valid(), false);
+  EXPECT_EQ(max_all_null_result->is_valid(), false);
+}
+
+template <typename T>
+struct SumReductionTest : public ReductionTest<T> {};
+using SumTypes = cudf::test::Types<int16_t, int32_t, float, double>;
+TYPED_TEST_SUITE(SumReductionTest, SumTypes);
+
+TYPED_TEST(SumReductionTest, Sum)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({6, -14, 13, 64, 0, -13, -20, 45});
+  std::vector<bool> host_bools({1, 1, 0, 0, 1, 1, 1, 1});
+  std::vector<T> v       = convert_values<T>(int_values);
+  T init_value           = convert_int<T>(100);
+  auto const init_scalar = cudf::make_fixed_width_scalar<T>(init_value);
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  T expected_value      = std::accumulate(v.begin(), v.end(), T{0});
+  T expected_value_init = std::accumulate(v.begin(), v.end(), init_value);
+
+  EXPECT_EQ(
+    this->template reduction_test<T>(col, *cudf::make_sum_aggregation<reduce_aggregation>()).first,
+    expected_value);
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *init_scalar, *cudf::make_sum_aggregation<reduce_aggregation>())
+              .first,
+            expected_value_init);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  auto r                                              = replace_nulls(v, host_bools, T{0});
+  T expected_null_value                               = std::accumulate(r.begin(), r.end(), T{0});
+  init_scalar->set_valid_async(false);
+
+  EXPECT_EQ(
+    this->template reduction_test<T>(col_nulls, *cudf::make_sum_aggregation<reduce_aggregation>())
+      .first,
+    expected_null_value);
+  EXPECT_FALSE(this
+                 ->template reduction_test<T>(
+                   col_nulls, *init_scalar, *cudf::make_sum_aggregation<reduce_aggregation>())
+                 .second);
+}
+
+TYPED_TEST_SUITE(ReductionTest, cudf::test::NumericTypes);
+
+TYPED_TEST(ReductionTest, Product)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({5, -1, 1, 0, 3, 2, 4});
+  std::vector<bool> host_bools({1, 1, 0, 0, 1, 1, 1});
+  std::vector<TypeParam> v = convert_values<TypeParam>(int_values);
+  T init_value             = convert_int<T>(4);
+  auto const init_scalar   = cudf::make_fixed_width_scalar<T>(init_value);
+
+  auto calc_prod = [](std::vector<T>& v) {
+    T expected_value = std::accumulate(v.begin(), v.end(), T{1}, std::multiplies<T>());
+    return expected_value;
+  };
+
+  auto calc_prod_init = [](std::vector<T>& v, T init) {
+    T expected_value = std::accumulate(v.begin(), v.end(), init, std::multiplies<T>());
+    return expected_value;
+  };
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  TypeParam expected_value      = calc_prod(v);
+  TypeParam expected_value_init = calc_prod_init(v, init_value);
+
+  EXPECT_EQ(
+    this->template reduction_test<T>(col, *cudf::make_product_aggregation<reduce_aggregation>())
+      .first,
+    expected_value);
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *init_scalar, *cudf::make_product_aggregation<reduce_aggregation>())
+              .first,
+            expected_value_init);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  auto r                                              = replace_nulls(v, host_bools, T{1});
+  TypeParam expected_null_value                       = calc_prod(r);
+  init_scalar->set_valid_async(false);
+
+  EXPECT_EQ(
+    this
+      ->template reduction_test<T>(col_nulls, *cudf::make_product_aggregation<reduce_aggregation>())
+      .first,
+    expected_null_value);
+  EXPECT_FALSE(this
+                 ->template reduction_test<T>(
+                   col_nulls, *init_scalar, *cudf::make_product_aggregation<reduce_aggregation>())
+                 .second);
+}
+
+TYPED_TEST(ReductionTest, SumOfSquare)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2});
+  std::vector<bool> host_bools({1, 1, 0, 0, 1, 1, 1, 1});
+  std::vector<T> v = convert_values<T>(int_values);
+
+  auto calc_reduction = [](std::vector<T>& v) {
+    T value = std::accumulate(v.begin(), v.end(), T{0}, [](T acc, T i) { return acc + i * i; });
+    return value;
+  };
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  T expected_value = calc_reduction(v);
+
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *cudf::make_sum_of_squares_aggregation<reduce_aggregation>())
+              .first,
+            expected_value);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  auto r                                              = replace_nulls(v, host_bools, T{0});
+  T expected_null_value                               = calc_reduction(r);
+
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col_nulls, *cudf::make_sum_of_squares_aggregation<reduce_aggregation>())
+              .first,
+            expected_null_value);
+}
+
+auto histogram_reduction(cudf::column_view const& input,
+                         std::unique_ptr<cudf::reduce_aggregation> const& agg)
+{
+  CUDF_EXPECTS(
+    agg->kind == cudf::aggregation::HISTOGRAM || agg->kind == cudf::aggregation::MERGE_HISTOGRAM,
+    "Aggregation must be either HISTOGRAM or MERGE_HISTOGRAM.");
+
+  auto const result_scalar = cudf::reduce(input, *agg, cudf::data_type{cudf::type_id::INT64});
+  EXPECT_EQ(result_scalar->is_valid(), true);
+
+  auto const result_list_scalar = dynamic_cast<cudf::list_scalar*>(result_scalar.get());
+  EXPECT_NE(result_list_scalar, nullptr);
+
+  auto const histogram = result_list_scalar->view();
+  EXPECT_EQ(histogram.num_children(), 2);
+  EXPECT_EQ(histogram.null_count(), 0);
+  EXPECT_EQ(histogram.child(1).null_count(), 0);
+
+  // Sort the histogram based on the first column (unique input values).
+  auto const sort_order = cudf::sorted_order(cudf::table_view{{histogram.child(0)}}, {}, {});
+  return std::move(cudf::gather(cudf::table_view{{histogram}}, *sort_order)->release().front());
+}
+
+template <typename T>
+struct ReductionHistogramTest : public cudf::test::BaseFixture {};
+
+// Avoid unsigned types, as the tests below have negative values in their input.
+using HistogramTestTypes = cudf::test::Concat<cudf::test::Types<int8_t, int16_t, int32_t, int64_t>,
+                                              cudf::test::FloatingPointTypes,
+                                              cudf::test::FixedPointTypes,
+                                              cudf::test::ChronoTypes>;
+TYPED_TEST_SUITE(ReductionHistogramTest, HistogramTestTypes);
+
+TYPED_TEST(ReductionHistogramTest, Histogram)
+{
+  using data_col    = cudf::test::fixed_width_column_wrapper<TypeParam, int>;
+  using int64_col   = cudf::test::fixed_width_column_wrapper<int64_t>;
+  using structs_col = cudf::test::structs_column_wrapper;
+
+  auto const agg = cudf::make_histogram_aggregation<reduce_aggregation>();
+
+  // Empty input.
+  {
+    auto const input    = data_col{};
+    auto const expected = [] {
+      auto child1 = data_col{};
+      auto child2 = int64_col{};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  {
+    auto const input    = data_col{-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1};
+    auto const expected = [] {
+      auto child1 = data_col{-3, -2, 0, 1, 2, 5};
+      auto child2 = int64_col{2, 1, 1, 2, 4, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test without nulls, sliced input.
+  {
+    auto const input_original = data_col{-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1};
+    auto const input          = cudf::slice(input_original, {0, 7})[0];
+    auto const expected       = [] {
+      auto child1 = data_col{-3, 0, 1, 2, 5};
+      auto child2 = int64_col{1, 1, 1, 3, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test with nulls.
+  using namespace cudf::test::iterators;
+  auto constexpr null{0};
+  {
+    auto const input    = data_col{{null, -3, 2, 1, 2, 0, null, 5, 2, null, -3, -2, null, 2, 1},
+                                nulls_at({0, 6, 9, 12})};
+    auto const expected = [] {
+      auto child1 = data_col{{null, -3, -2, 0, 1, 2, 5}, null_at(0)};
+      auto child2 = int64_col{4, 2, 1, 1, 2, 4, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test with nulls, sliced input.
+  {
+    auto const input_original = data_col{
+      {null, -3, 2, 1, 2, 0, null, 5, 2, null, -3, -2, null, 2, 1}, nulls_at({0, 6, 9, 12})};
+    auto const input    = cudf::slice(input_original, {0, 9})[0];
+    auto const expected = [] {
+      auto child1 = data_col{{null, -3, 0, 1, 2, 5}, null_at(0)};
+      auto child2 = int64_col{2, 1, 1, 1, 3, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+TYPED_TEST(ReductionHistogramTest, MergeHistogram)
+{
+  using data_col    = cudf::test::fixed_width_column_wrapper<TypeParam>;
+  using int64_col   = cudf::test::fixed_width_column_wrapper<int64_t>;
+  using structs_col = cudf::test::structs_column_wrapper;
+
+  auto const agg = cudf::make_merge_histogram_aggregation<reduce_aggregation>();
+
+  // Empty input.
+  {
+    auto const input = [] {
+      auto child1 = data_col{};
+      auto child2 = int64_col{};
+      return structs_col{{child1, child2}};
+    }();
+    auto const expected = [] {
+      auto child1 = data_col{};
+      auto child2 = int64_col{};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test without nulls.
+  {
+    auto const input = [] {
+      auto child1 = data_col{-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1};
+      auto child2 = int64_col{2, 1, 1, 2, 4, 1, 2, 3, 5, 3, 4};
+      return structs_col{{child1, child2}};
+    }();
+
+    auto const expected = [] {
+      auto child1 = data_col{-3, -2, 0, 1, 2, 5};
+      auto child2 = int64_col{5, 5, 4, 5, 8, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test without nulls, sliced input.
+  {
+    auto const input_original = [] {
+      auto child1 = data_col{-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1};
+      auto child2 = int64_col{2, 1, 1, 2, 4, 1, 2, 3, 5, 3, 4};
+      return structs_col{{child1, child2}};
+    }();
+    auto const input = cudf::slice(input_original, {0, 7})[0];
+
+    auto const expected = [] {
+      auto child1 = data_col{-3, 0, 1, 2, 5};
+      auto child2 = int64_col{2, 4, 1, 5, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test with nulls.
+  using namespace cudf::test::iterators;
+  auto constexpr null{0};
+  {
+    auto const input = [] {
+      auto child1 = data_col{{-3, 2, null, 1, 2, null, 0, 5, null, 2, -3, null, -2, 2, 1, null},
+                             nulls_at({2, 5, 8, 11, 15})};
+      auto child2 = int64_col{2, 1, 12, 1, 2, 11, 4, 1, 10, 2, 3, 15, 5, 3, 4, 19};
+      return structs_col{{child1, child2}};
+    }();
+
+    auto const expected = [] {
+      auto child1 = data_col{{null, -3, -2, 0, 1, 2, 5}, null_at(0)};
+      auto child2 = int64_col{67, 5, 5, 4, 5, 8, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  // Test with nulls, sliced input.
+  {
+    auto const input_original = [] {
+      auto child1 = data_col{{-3, 2, null, 1, 2, null, 0, 5, null, 2, -3, null, -2, 2, 1, null},
+                             nulls_at({2, 5, 8, 11, 15})};
+      auto child2 = int64_col{2, 1, 12, 1, 2, 11, 4, 1, 10, 2, 3, 15, 5, 3, 4, 19};
+      return structs_col{{child1, child2}};
+    }();
+    auto const input = cudf::slice(input_original, {0, 9})[0];
+
+    auto const expected = [] {
+      auto child1 = data_col{{null, -3, 0, 1, 2, 5}, null_at(0)};
+      auto child2 = int64_col{33, 2, 4, 1, 3, 1};
+      return structs_col{{child1, child2}};
+    }();
+    auto const result = histogram_reduction(input, agg);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+}
+
+template <typename T>
+struct ReductionAnyAllTest : public ReductionTest<bool> {};
+using AnyAllTypes = cudf::test::Types<int32_t, float, bool>;
+TYPED_TEST_SUITE(ReductionAnyAllTest, AnyAllTypes);
+
+TYPED_TEST(ReductionAnyAllTest, AnyAllTrueTrue)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({true, true, true, true});
+  std::vector<bool> host_bools({1, 1, 0, 1});
+  std::vector<T> v       = convert_values<T>(int_values);
+  auto const init_scalar = cudf::make_fixed_width_scalar<T>(convert_int<T>(true));
+
+  // Min/Max succeeds for any gdf types including
+  // non-arithmetic types (date32, date64, timestamp, category)
+  bool expected = true;
+  cudf::data_type output_dtype(cudf::type_id::BOOL8);
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *init_scalar, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *init_scalar, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  init_scalar->set_valid_async(false);
+
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col_nulls, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col_nulls, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<bool>(
+        col_nulls, *init_scalar, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+      .second);
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<bool>(
+        col_nulls, *init_scalar, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+      .second);
+}
+
+TYPED_TEST(ReductionAnyAllTest, AnyAllFalseFalse)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({false, false, false, false});
+  std::vector<bool> host_bools({1, 1, 0, 1});
+  std::vector<T> v       = convert_values<T>(int_values);
+  auto const init_scalar = cudf::make_fixed_width_scalar<T>(convert_int<T>(false));
+
+  // Min/Max succeeds for any gdf types including
+  // non-arithmetic types (date32, date64, timestamp, category)
+  bool expected = false;
+  cudf::data_type output_dtype(cudf::type_id::BOOL8);
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *init_scalar, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col, *init_scalar, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  init_scalar->set_valid_async(false);
+
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col_nulls, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_EQ(this
+              ->template reduction_test<bool>(
+                col_nulls, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+              .first,
+            expected);
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<bool>(
+        col_nulls, *init_scalar, *cudf::make_any_aggregation<reduce_aggregation>(), output_dtype)
+      .second);
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<bool>(
+        col_nulls, *init_scalar, *cudf::make_all_aggregation<reduce_aggregation>(), output_dtype)
+      .second);
+}
+
+// ----------------------------------------------------------------------------
+
+template <typename T>
+struct MultiStepReductionTest : public ReductionTest<T> {};
+using MultiStepReductionTypes = cudf::test::Types<int16_t, int32_t, float, double>;
+TYPED_TEST_SUITE(MultiStepReductionTest, MultiStepReductionTypes);
+
+TYPED_TEST(MultiStepReductionTest, Mean)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2, 28});
+  std::vector<bool> host_bools({1, 1, 0, 1, 1, 1, 0, 1});
+
+  auto calc_mean = [](std::vector<T>& v, cudf::size_type valid_count) {
+    double sum = std::accumulate(v.begin(), v.end(), double{0});
+    return sum / valid_count;
+  };
+
+  // test without nulls
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  double expected_value = calc_mean(v, v.size());
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(col,
+                                                *cudf::make_mean_aggregation<reduce_aggregation>(),
+                                                cudf::data_type(cudf::type_id::FLOAT64))
+              .first,
+            expected_value);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  cudf::size_type valid_count =
+    cudf::column_view(col_nulls).size() - cudf::column_view(col_nulls).null_count();
+  auto replaced_array = replace_nulls(v, host_bools, T{0});
+
+  double expected_value_nulls = calc_mean(replaced_array, valid_count);
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(col_nulls,
+                                                *cudf::make_mean_aggregation<reduce_aggregation>(),
+                                                cudf::data_type(cudf::type_id::FLOAT64))
+              .first,
+            expected_value_nulls);
+}
+
+// This test is disabled for only a Debug build because a compiler error
+// documented in cpp/src/reductions/std.cu and cpp/src/reductions/var.cu
+#ifdef NDEBUG
+TYPED_TEST(MultiStepReductionTest, var_std)
+#else
+TYPED_TEST(MultiStepReductionTest, DISABLED_var_std)
+#endif
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2, 28});
+  std::vector<bool> host_bools({1, 1, 0, 1, 1, 1, 0, 1});
+
+  auto calc_var = [](std::vector<T>& v, cudf::size_type valid_count, int ddof) {
+    double mean = std::accumulate(v.begin(), v.end(), double{0});
+    mean /= valid_count;
+
+    double sum_of_sq = std::accumulate(
+      v.begin(), v.end(), double{0}, [](double acc, TypeParam i) { return acc + i * i; });
+
+    cudf::size_type div = valid_count - ddof;
+
+    double var = sum_of_sq / div - ((mean * mean) * valid_count) / div;
+    return var;
+  };
+
+  // test without nulls
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+
+  auto const ddof = 1;
+  double var      = calc_var(v, v.size(), ddof);
+  double std      = std::sqrt(var);
+  auto var_agg    = cudf::make_variance_aggregation<reduce_aggregation>(ddof);
+  auto std_agg    = cudf::make_std_aggregation<reduce_aggregation>(ddof);
+
+  EXPECT_EQ(
+    this->template reduction_test<double>(col, *var_agg, cudf::data_type(cudf::type_id::FLOAT64))
+      .first,
+    var);
+  EXPECT_EQ(
+    this->template reduction_test<double>(col, *std_agg, cudf::data_type(cudf::type_id::FLOAT64))
+      .first,
+    std);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  cudf::size_type valid_count =
+    cudf::column_view(col_nulls).size() - cudf::column_view(col_nulls).null_count();
+  auto replaced_array = replace_nulls(v, host_bools, T{0});
+
+  double var_nulls = calc_var(replaced_array, valid_count, ddof);
+  double std_nulls = std::sqrt(var_nulls);
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *var_agg, cudf::data_type(cudf::type_id::FLOAT64))
+              .first,
+            var_nulls);
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *std_agg, cudf::data_type(cudf::type_id::FLOAT64))
+              .first,
+            std_nulls);
+}
+
+// ----------------------------------------------------------------------------
+
+template <typename T>
+struct ReductionMultiStepErrorCheck : public ReductionTest<T> {
+  void reduction_error_check(cudf::test::fixed_width_column_wrapper<T>& col,
+                             bool succeeded_condition,
+                             reduce_aggregation const& agg,
+                             cudf::data_type output_dtype)
+  {
+    const cudf::column_view underlying_column = col;
+    auto statement = [&]() { cudf::reduce(underlying_column, agg, output_dtype); };
+
+    if (succeeded_condition) {
+      CUDF_EXPECT_NO_THROW(statement());
+    } else {
+      EXPECT_ANY_THROW(statement());
+    }
+  }
+};
+
+TYPED_TEST_SUITE(ReductionMultiStepErrorCheck, cudf::test::AllTypes);
+
+// This test is disabled for only a Debug build because a compiler error
+// documented in cpp/src/reductions/std.cu and cpp/src/reductions/var.cu
+#ifdef NDEBUG
+TYPED_TEST(ReductionMultiStepErrorCheck, ErrorHandling)
+#else
+TYPED_TEST(ReductionMultiStepErrorCheck, DISABLED_ErrorHandling)
+#endif
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2});
+  std::vector<bool> host_bools({1, 0});
+
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+
+  bool is_input_acceptable = this->ret_non_arithmetic;
+
+  std::vector<cudf::data_type> dtypes(static_cast<int32_t>(cudf::type_id::NUM_TYPE_IDS) + 1);
+  int i = 0;
+  std::generate(dtypes.begin(), dtypes.end(), [&]() {
+    return cudf::data_type(static_cast<cudf::type_id>(i++));
+  });
+
+  auto is_supported_outdtype = [](cudf::data_type dtype) {
+    if (dtype == cudf::data_type(cudf::type_id::FLOAT32)) return true;
+    if (dtype == cudf::data_type(cudf::type_id::FLOAT64)) return true;
+    return false;
+  };
+
+  auto evaluate = [&](cudf::data_type dtype) mutable {
+    bool expect_succeed = is_input_acceptable & is_supported_outdtype(dtype);
+    auto const ddof     = 1;
+    auto var_agg        = cudf::make_variance_aggregation<reduce_aggregation>(ddof);
+    auto std_agg        = cudf::make_std_aggregation<reduce_aggregation>(ddof);
+    this->reduction_error_check(
+      col, expect_succeed, *cudf::make_mean_aggregation<reduce_aggregation>(), dtype);
+    this->reduction_error_check(col, expect_succeed, *var_agg, dtype);
+    this->reduction_error_check(col, expect_succeed, *std_agg, dtype);
+
+    this->reduction_error_check(
+      col_nulls, expect_succeed, *cudf::make_mean_aggregation<reduce_aggregation>(), dtype);
+    this->reduction_error_check(col_nulls, expect_succeed, *var_agg, dtype);
+    this->reduction_error_check(col_nulls, expect_succeed, *std_agg, dtype);
+    return;
+  };
+
+  std::for_each(dtypes.begin(), dtypes.end(), evaluate);
+}
+
+// ----------------------------------------------------------------------------
+
+struct ReductionDtypeTest : public cudf::test::BaseFixture {
+  template <typename T_in, typename T_out>
+  void reduction_test(std::vector<int>& int_values,
+                      T_out expected_value,
+                      bool succeeded_condition,
+                      reduce_aggregation const& agg,
+                      cudf::data_type out_dtype,
+                      bool expected_overflow = false)
+  {
+    std::vector<T_in> input_values = convert_values<T_in>(int_values);
+    cudf::test::fixed_width_column_wrapper<T_in> const col(input_values.begin(),
+                                                           input_values.end());
+
+    auto statement = [&]() {
+      std::unique_ptr<cudf::scalar> result = cudf::reduce(col, agg, out_dtype);
+      using ScalarType                     = cudf::scalar_type_t<T_out>;
+      auto result1                         = static_cast<ScalarType*>(result.get());
+      if (result1->is_valid() && !expected_overflow) {
+        EXPECT_EQ(expected_value, result1->value());
+      }
+    };
+
+    if (succeeded_condition) {
+      CUDF_EXPECT_NO_THROW(statement());
+    } else {
+      EXPECT_ANY_THROW(statement());
+    }
+  }
+};
+
+TEST_F(ReductionDtypeTest, all_null_output)
+{
+  auto sum_agg = cudf::make_sum_aggregation<reduce_aggregation>();
+
+  auto const col =
+    cudf::test::fixed_point_column_wrapper<int32_t>{{0, 0, 0}, {0, 0, 0}, numeric::scale_type{-2}}
+      .release();
+
+  std::unique_ptr<cudf::scalar> result = cudf::reduce(*col, *sum_agg, col->type());
+  EXPECT_EQ(result->is_valid(), false);
+  EXPECT_EQ(result->type().id(), col->type().id());
+  EXPECT_EQ(result->type().scale(), col->type().scale());
+}
+
+// test case for different output precision
+TEST_F(ReductionDtypeTest, different_precision)
+{
+  constexpr bool expected_overflow = true;
+  std::vector<int> int_values({6, -14, 13, 109, -13, -20, 0, 98, 122, 123});
+  int expected_value = std::accumulate(int_values.begin(), int_values.end(), 0);
+  auto sum_agg       = cudf::make_sum_aggregation<reduce_aggregation>();
+
+  // over flow
+  this->reduction_test<int8_t, int8_t>(int_values,
+                                       static_cast<int8_t>(expected_value),
+                                       true,
+                                       *sum_agg,
+                                       cudf::data_type(cudf::type_id::INT8),
+                                       expected_overflow);
+
+  this->reduction_test<int8_t, int64_t>(int_values,
+                                        static_cast<int64_t>(expected_value),
+                                        true,
+                                        *sum_agg,
+                                        cudf::data_type(cudf::type_id::INT64));
+
+  this->reduction_test<int8_t, double>(int_values,
+                                       static_cast<double>(expected_value),
+                                       true,
+                                       *sum_agg,
+                                       cudf::data_type(cudf::type_id::FLOAT64));
+
+  // down cast (over flow)
+  this->reduction_test<double, int8_t>(int_values,
+                                       static_cast<int8_t>(expected_value),
+                                       true,
+                                       *sum_agg,
+                                       cudf::data_type(cudf::type_id::INT8),
+                                       expected_overflow);
+
+  // down cast (no over flow)
+  this->reduction_test<double, int16_t>(int_values,
+                                        static_cast<int16_t>(expected_value),
+                                        true,
+                                        *sum_agg,
+                                        cudf::data_type(cudf::type_id::INT16));
+
+  // not supported case:
+  // wrapper classes other than bool are not convertible
+  this->reduction_test<cudf::timestamp_D, cudf::timestamp_s>(
+    int_values,
+    cudf::timestamp_s{cudf::duration_s(expected_value)},
+    false,
+    *sum_agg,
+    cudf::data_type(cudf::type_id::TIMESTAMP_SECONDS));
+
+  this->reduction_test<cudf::timestamp_s, cudf::timestamp_ns>(
+    int_values,
+    cudf::timestamp_ns{cudf::duration_ns(expected_value)},
+    false,
+    *sum_agg,
+    cudf::data_type(cudf::type_id::TIMESTAMP_NANOSECONDS));
+
+  this->reduction_test<int8_t, cudf::timestamp_us>(
+    int_values,
+    cudf::timestamp_us{cudf::duration_us(expected_value)},
+    false,
+    *sum_agg,
+    cudf::data_type(cudf::type_id::TIMESTAMP_MICROSECONDS));
+
+  std::vector<bool> v = convert_values<bool>(int_values);
+
+  // When summing bool values into an non-bool arithmetic type,
+  // it's an integer/float sum of ones and zeros.
+  int expected = std::accumulate(v.begin(), v.end(), int{0});
+
+  this->reduction_test<bool, int8_t>(int_values,
+                                     static_cast<int8_t>(expected),
+                                     true,
+                                     *sum_agg,
+                                     cudf::data_type(cudf::type_id::INT8));
+  this->reduction_test<bool, int16_t>(int_values,
+                                      static_cast<int16_t>(expected),
+                                      true,
+                                      *sum_agg,
+                                      cudf::data_type(cudf::type_id::INT16));
+  this->reduction_test<bool, int32_t>(int_values,
+                                      static_cast<int32_t>(expected),
+                                      true,
+                                      *sum_agg,
+                                      cudf::data_type(cudf::type_id::INT32));
+  this->reduction_test<bool, int64_t>(int_values,
+                                      static_cast<int64_t>(expected),
+                                      true,
+                                      *sum_agg,
+                                      cudf::data_type(cudf::type_id::INT64));
+  this->reduction_test<bool, float>(int_values,
+                                    static_cast<float>(expected),
+                                    true,
+                                    *sum_agg,
+                                    cudf::data_type(cudf::type_id::FLOAT32));
+  this->reduction_test<bool, double>(int_values,
+                                     static_cast<double>(expected),
+                                     true,
+                                     *sum_agg,
+                                     cudf::data_type(cudf::type_id::FLOAT64));
+
+  // make sure boolean arithmetic semantics are obeyed when reducing to a bool
+  this->reduction_test<bool, bool>(
+    int_values, true, true, *sum_agg, cudf::data_type(cudf::type_id::BOOL8));
+
+  this->reduction_test<int32_t, bool>(
+    int_values, true, true, *sum_agg, cudf::data_type(cudf::type_id::BOOL8));
+
+  // cudf::timestamp_s and int64_t are not convertible types.
+  this->reduction_test<cudf::timestamp_s, int64_t>(int_values,
+                                                   static_cast<int64_t>(expected_value),
+                                                   false,
+                                                   *sum_agg,
+                                                   cudf::data_type(cudf::type_id::INT64));
+}
+
+struct ReductionEmptyTest : public cudf::test::BaseFixture {};
+
+// test case for empty input cases
+TEST_F(ReductionEmptyTest, empty_column)
+{
+  using T        = int32_t;
+  auto statement = [](cudf::column_view const& col) {
+    std::unique_ptr<cudf::scalar> result =
+      cudf::reduce(col,
+                   *cudf::make_sum_aggregation<reduce_aggregation>(),
+                   cudf::data_type(cudf::type_id::INT64));
+    EXPECT_EQ(result->is_valid(), false);
+  };
+
+  // default column_view{} is an empty column
+  // empty column_view
+  CUDF_EXPECT_NO_THROW(statement(cudf::column_view{}));
+
+  // test if the size of input column is zero
+  // expect result.is_valid() is false
+  std::vector<T> empty_data(0);
+  cudf::test::fixed_width_column_wrapper<T> const col0(empty_data.begin(), empty_data.end());
+  CUDF_EXPECT_NO_THROW(statement(col0));
+
+  // test if null count is equal or greater than size of input
+  // expect result.is_valid() is false
+  int col_size = 5;
+  std::vector<T> col_data(col_size);
+  std::vector<bool> valids(col_size, 0);
+
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(col_data, valids);
+  CUDF_EXPECT_NO_THROW(statement(col_nulls));
+
+  auto any_agg   = cudf::make_any_aggregation<cudf::reduce_aggregation>();
+  auto all_agg   = cudf::make_all_aggregation<cudf::reduce_aggregation>();
+  auto bool_type = cudf::data_type{cudf::type_id::BOOL8};
+
+  auto result = cudf::reduce(col0, *any_agg, bool_type);
+  EXPECT_EQ(result->is_valid(), true);
+  EXPECT_EQ(dynamic_cast<cudf::numeric_scalar<bool>*>(result.get())->value(), false);
+  result = cudf::reduce(col_nulls, *any_agg, bool_type);
+  EXPECT_EQ(result->is_valid(), true);
+  EXPECT_EQ(dynamic_cast<cudf::numeric_scalar<bool>*>(result.get())->value(), false);
+
+  result = cudf::reduce(col0, *all_agg, bool_type);
+  EXPECT_EQ(result->is_valid(), true);
+  EXPECT_EQ(dynamic_cast<cudf::numeric_scalar<bool>*>(result.get())->value(), true);
+  result = cudf::reduce(col_nulls, *all_agg, bool_type);
+  EXPECT_EQ(result->is_valid(), true);
+  EXPECT_EQ(dynamic_cast<cudf::numeric_scalar<bool>*>(result.get())->value(), true);
+}
+
+// ----------------------------------------------------------------------------
+
+struct ReductionParamTest : public ReductionTest<double>,
+                            public ::testing::WithParamInterface<cudf::size_type> {};
+
+INSTANTIATE_TEST_CASE_P(ddofParam, ReductionParamTest, ::testing::Range(1, 5));
+
+// This test is disabled for only a Debug build because a compiler error
+// documented in cpp/src/reductions/std.cu and cpp/src/reductions/var.cu
+#ifdef NDEBUG
+TEST_P(ReductionParamTest, std_var)
+#else
+TEST_P(ReductionParamTest, DISABLED_std_var)
+#endif
+{
+  int ddof = GetParam();
+  std::vector<double> int_values({-3, 2, 1, 0, 5, -3, -2, 28});
+  std::vector<bool> host_bools({1, 1, 0, 1, 1, 1, 0, 1});
+
+  auto calc_var = [ddof](std::vector<double>& v, cudf::size_type valid_count) {
+    double mean = std::accumulate(v.begin(), v.end(), double{0});
+    mean /= valid_count;
+
+    double sum_of_sq = std::accumulate(
+      v.begin(), v.end(), double{0}, [](double acc, double i) { return acc + i * i; });
+
+    cudf::size_type div = valid_count - ddof;
+
+    double var = sum_of_sq / div - ((mean * mean) * valid_count) / div;
+    return var;
+  };
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<double> col(int_values.begin(), int_values.end());
+
+  double var   = calc_var(int_values, int_values.size());
+  double std   = std::sqrt(var);
+  auto var_agg = cudf::make_variance_aggregation<reduce_aggregation>(ddof);
+  auto std_agg = cudf::make_std_aggregation<reduce_aggregation>(ddof);
+
+  EXPECT_EQ(
+    this->template reduction_test<double>(col, *var_agg, cudf::data_type(cudf::type_id::FLOAT64))
+      .first,
+    var);
+  EXPECT_EQ(
+    this->template reduction_test<double>(col, *std_agg, cudf::data_type(cudf::type_id::FLOAT64))
+      .first,
+    std);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<double> col_nulls =
+    construct_null_column(int_values, host_bools);
+  cudf::size_type valid_count =
+    cudf::column_view(col_nulls).size() - cudf::column_view(col_nulls).null_count();
+  auto replaced_array = replace_nulls<double>(int_values, host_bools, int{0});
+
+  double var_nulls = calc_var(replaced_array, valid_count);
+  double std_nulls = std::sqrt(var_nulls);
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *var_agg, cudf::data_type(cudf::type_id::FLOAT64))
+              .first,
+            var_nulls);
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *std_agg, cudf::data_type(cudf::type_id::FLOAT64))
+              .first,
+            std_nulls);
+}
+
+//-------------------------------------------------------------------
+struct StringReductionTest : public cudf::test::BaseFixture,
+                             public testing::WithParamInterface<std::vector<std::string>> {
+  // Min/Max
+
+  void reduction_test(cudf::column_view const& underlying_column,
+                      std::string expected_value,
+                      bool succeeded_condition,
+                      reduce_aggregation const& agg,
+                      cudf::data_type output_dtype = cudf::data_type{})
+  {
+    if (cudf::data_type{} == output_dtype) output_dtype = underlying_column.type();
+
+    auto statement = [&]() {
+      std::unique_ptr<cudf::scalar> result = cudf::reduce(underlying_column, agg, output_dtype);
+      using ScalarType                     = cudf::scalar_type_t<cudf::string_view>;
+      auto result1                         = static_cast<ScalarType*>(result.get());
+      EXPECT_TRUE(result1->is_valid());
+      if (result1->is_valid()) {
+        EXPECT_EQ(expected_value, result1->to_string())
+          << (agg.kind == aggregation::MIN ? "MIN" : "MAX");
+      }
+    };
+
+    if (succeeded_condition) {
+      CUDF_EXPECT_NO_THROW(statement());
+    } else {
+      EXPECT_ANY_THROW(statement());
+    }
+  }
+
+  void reduction_test(cudf::column_view const& underlying_column,
+                      std::string initial_value,
+                      std::string expected_value,
+                      bool succeeded_condition,
+                      reduce_aggregation const& agg,
+                      cudf::data_type output_dtype = cudf::data_type{})
+  {
+    if (cudf::data_type{} == output_dtype) output_dtype = underlying_column.type();
+    auto string_scalar = cudf::make_string_scalar(initial_value);
+
+    auto statement = [&]() {
+      std::unique_ptr<cudf::scalar> result =
+        cudf::reduce(underlying_column, agg, output_dtype, *string_scalar);
+      using ScalarType = cudf::scalar_type_t<cudf::string_view>;
+      auto result1     = static_cast<ScalarType*>(result.get());
+      EXPECT_TRUE(result1->is_valid());
+      if (result1->is_valid()) {
+        EXPECT_EQ(expected_value, result1->to_string())
+          << (agg.kind == aggregation::MIN ? "MIN" : "MAX");
+      }
+    };
+
+    if (succeeded_condition) {
+      CUDF_EXPECT_NO_THROW(statement());
+    } else {
+      EXPECT_ANY_THROW(statement());
+    }
+  }
+};
+
+// ------------------------------------------------------------------------
+std::vector<std::string> string_list[] = {
+  {"one", "two", "three", "four", "five", "six", "seven", "eight", "nine"},
+  {"", "two", "three", "four", "five", "six", "seven", "eight", "nine"},
+  {"one", "", "three", "four", "five", "six", "seven", "eight", "nine"},
+  {"", "", "", "four", "five", "six", "seven", "eight", "nine"},
+  {"", "", "", "", "", "", "", "", ""},
+  // DeviceMin identity sentinel test cases
+  {"\xF7\xBF\xBF\xBF", "", "", "", "", "", "", "", ""},
+  {"one", "two", "three", "four", "\xF7\xBF\xBF\xBF", "six", "seven", "eight", "nine"},
+  {"one", "two", "\xF7\xBF\xBF\xBF", "four", "five", "six", "seven", "eight", "nine"},
+};
+INSTANTIATE_TEST_CASE_P(string_cases, StringReductionTest, testing::ValuesIn(string_list));
+TEST_P(StringReductionTest, MinMax)
+{
+  // data and valid arrays
+  std::vector<std::string> host_strings(GetParam());
+  std::vector<bool> host_bools({1, 0, 1, 1, 1, 1, 0, 0, 1});
+  bool succeed(true);
+  std::string initial_value = "init";
+
+  // all valid string column
+  cudf::test::strings_column_wrapper col(host_strings.begin(), host_strings.end());
+
+  std::string expected_min_result = *(std::min_element(host_strings.begin(), host_strings.end()));
+  std::string expected_max_result = *(std::max_element(host_strings.begin(), host_strings.end()));
+  std::string expected_min_init_result = std::min(expected_min_result, initial_value);
+  std::string expected_max_init_result = std::max(expected_max_result, initial_value);
+
+  // string column with nulls
+  cudf::test::strings_column_wrapper col_nulls(
+    host_strings.begin(), host_strings.end(), host_bools.begin());
+
+  std::vector<std::string> r_strings;
+  std::copy_if(host_strings.begin(),
+               host_strings.end(),
+               std::back_inserter(r_strings),
+               [host_bools, i = 0](auto s) mutable { return host_bools[i++]; });
+
+  std::string expected_min_null_result = *(std::min_element(r_strings.begin(), r_strings.end()));
+  std::string expected_max_null_result = *(std::max_element(r_strings.begin(), r_strings.end()));
+  std::string expected_min_init_null_result = std::min(expected_min_null_result, initial_value);
+  std::string expected_max_init_null_result = std::max(expected_max_null_result, initial_value);
+
+  // MIN
+  this->reduction_test(
+    col, expected_min_result, succeed, *cudf::make_min_aggregation<reduce_aggregation>());
+  this->reduction_test(col_nulls,
+                       expected_min_null_result,
+                       succeed,
+                       *cudf::make_min_aggregation<reduce_aggregation>());
+  this->reduction_test(col,
+                       initial_value,
+                       expected_min_init_result,
+                       succeed,
+                       *cudf::make_min_aggregation<reduce_aggregation>());
+  this->reduction_test(col_nulls,
+                       initial_value,
+                       expected_min_init_null_result,
+                       succeed,
+                       *cudf::make_min_aggregation<reduce_aggregation>());
+  // MAX
+  this->reduction_test(
+    col, expected_max_result, succeed, *cudf::make_max_aggregation<reduce_aggregation>());
+  this->reduction_test(col_nulls,
+                       expected_max_null_result,
+                       succeed,
+                       *cudf::make_max_aggregation<reduce_aggregation>());
+  this->reduction_test(col,
+                       initial_value,
+                       expected_max_init_result,
+                       succeed,
+                       *cudf::make_max_aggregation<reduce_aggregation>());
+  this->reduction_test(col_nulls,
+                       initial_value,
+                       expected_max_init_null_result,
+                       succeed,
+                       *cudf::make_max_aggregation<reduce_aggregation>());
+
+  // MINMAX
+  auto result = cudf::minmax(col);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.first.get())->to_string(),
+            expected_min_result);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.second.get())->to_string(),
+            expected_max_result);
+  result = cudf::minmax(col_nulls);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.first.get())->to_string(),
+            expected_min_null_result);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.second.get())->to_string(),
+            expected_max_null_result);
+}
+
+TEST_P(StringReductionTest, DictionaryMinMax)
+{
+  // data and valid arrays
+  std::vector<std::string> host_strings(GetParam());
+  cudf::test::dictionary_column_wrapper<std::string> col(host_strings.begin(), host_strings.end());
+
+  std::string expected_min_result = *(std::min_element(host_strings.begin(), host_strings.end()));
+  std::string expected_max_result = *(std::max_element(host_strings.begin(), host_strings.end()));
+
+  auto result = cudf::minmax(col);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.first.get())->to_string(),
+            expected_min_result);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.second.get())->to_string(),
+            expected_max_result);
+
+  // column with nulls
+  std::vector<bool> validity({1, 0, 1, 1, 1, 1, 0, 0, 1});
+  cudf::test::dictionary_column_wrapper<std::string> col_nulls(
+    host_strings.begin(), host_strings.end(), validity.begin());
+
+  std::vector<std::string> r_strings;
+  std::copy_if(host_strings.begin(),
+               host_strings.end(),
+               std::back_inserter(r_strings),
+               [validity, i = 0](auto s) mutable { return validity[i++]; });
+
+  expected_min_result = *(std::min_element(r_strings.begin(), r_strings.end()));
+  expected_max_result = *(std::max_element(r_strings.begin(), r_strings.end()));
+
+  result = cudf::minmax(col_nulls);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.first.get())->to_string(),
+            expected_min_result);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.second.get())->to_string(),
+            expected_max_result);
+
+  // test sliced column
+  result = cudf::minmax(cudf::slice(col_nulls, {3, 7}).front());
+  // 3->2 and 7->5 because r_strings contains no null entries
+  expected_min_result = *(std::min_element(r_strings.begin() + 2, r_strings.begin() + 5));
+  expected_max_result = *(std::max_element(r_strings.begin() + 2, r_strings.begin() + 5));
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.first.get())->to_string(),
+            expected_min_result);
+  EXPECT_EQ(static_cast<cudf::string_scalar*>(result.second.get())->to_string(),
+            expected_max_result);
+}
+
+TEST_F(StringReductionTest, AllNull)
+{
+  // data and all null arrays
+  std::vector<std::string> host_strings(
+    {"one", "two", "three", "four", "five", "six", "seven", "eight", "nine"});
+  std::vector<bool> host_bools(host_strings.size(), false);
+  auto initial_value = cudf::make_string_scalar("init");
+  initial_value->set_valid_async(false);
+
+  // string column with nulls
+  cudf::test::strings_column_wrapper col_nulls(
+    host_strings.begin(), host_strings.end(), host_bools.begin());
+  cudf::data_type output_dtype = cudf::column_view(col_nulls).type();
+
+  // MIN
+  auto result =
+    cudf::reduce(col_nulls, *cudf::make_min_aggregation<reduce_aggregation>(), output_dtype);
+  EXPECT_FALSE(result->is_valid());
+  result = cudf::reduce(
+    col_nulls, *cudf::make_min_aggregation<reduce_aggregation>(), output_dtype, *initial_value);
+  EXPECT_FALSE(result->is_valid());
+  // MAX
+  result = cudf::reduce(col_nulls, *cudf::make_max_aggregation<reduce_aggregation>(), output_dtype);
+  EXPECT_FALSE(result->is_valid());
+  result = cudf::reduce(
+    col_nulls, *cudf::make_max_aggregation<reduce_aggregation>(), output_dtype, *initial_value);
+  EXPECT_FALSE(result->is_valid());
+  // MINMAX
+  auto mm_result = cudf::minmax(col_nulls);
+  EXPECT_FALSE(mm_result.first->is_valid());
+  EXPECT_FALSE(mm_result.second->is_valid());
+}
+
+TYPED_TEST(ReductionTest, Median)
+{
+  using T = TypeParam;
+  //{-20, -14, -13,  0, 6, 13, 45, 64/None} =  3.0, 0.0
+  std::vector<int> int_values({6, -14, 13, 64, 0, -13, -20, 45});
+  std::vector<bool> host_bools({1, 1, 1, 0, 1, 1, 1, 1});
+  std::vector<T> v = convert_values<T>(int_values);
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  double expected_value = [] {
+    if (std::is_same_v<T, bool>) return 1.0;
+    if (std::is_signed_v<T>) return 3.0;
+    return 13.5;
+  }();
+  EXPECT_EQ(
+    this->template reduction_test<double>(col, *cudf::make_median_aggregation<reduce_aggregation>())
+      .first,
+    expected_value);
+
+  auto col_odd              = cudf::split(col, {1})[1];
+  double expected_value_odd = [] {
+    if (std::is_same_v<T, bool>) return 1.0;
+    if (std::is_signed_v<T>) return 0.0;
+    return 14.0;
+  }();
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_odd, *cudf::make_median_aggregation<reduce_aggregation>())
+              .first,
+            expected_value_odd);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  double expected_null_value                          = [] {
+    if (std::is_same_v<T, bool>) return 1.0;
+    if (std::is_signed_v<T>) return 0.0;
+    return 13.0;
+  }();
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_median_aggregation<reduce_aggregation>())
+              .first,
+            expected_null_value);
+
+  auto col_nulls_odd             = cudf::split(col_nulls, {1})[1];
+  double expected_null_value_odd = [] {
+    if (std::is_same_v<T, bool>) return 1.0;
+    if (std::is_signed_v<T>) return -6.5;
+    return 13.5;
+  }();
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls_odd, *cudf::make_median_aggregation<reduce_aggregation>())
+              .first,
+            expected_null_value_odd);
+}
+
+TYPED_TEST(ReductionTest, Quantile)
+{
+  using T = TypeParam;
+  //{-20, -14, -13,  0, 6, 13, 45, 64/None}
+  std::vector<int> int_values({6, -14, 13, 64, 0, -13, -20, 45});
+  std::vector<bool> host_bools({1, 1, 1, 0, 1, 1, 1, 1});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::interpolation interp{cudf::interpolation::LINEAR};
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  double expected_value0 = std::is_same_v<T, bool> || std::is_unsigned_v<T> ? v[4] : v[6];
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col, *cudf::make_quantile_aggregation<reduce_aggregation>({0.0}, interp))
+              .first,
+            expected_value0);
+
+  double expected_value1 = v[3];
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col, *cudf::make_quantile_aggregation<reduce_aggregation>({1.0}, interp))
+              .first,
+            expected_value1);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  double expected_null_value1                         = v[7];
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_quantile_aggregation<reduce_aggregation>({0}, interp))
+              .first,
+            expected_value0);
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_quantile_aggregation<reduce_aggregation>({1}, interp))
+              .first,
+            expected_null_value1);
+}
+
+TYPED_TEST(ReductionTest, UniqueCount)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({1, -3, 1, 2, 0, 2, -4, 45});  // 6 unique values
+  std::vector<bool> host_bools({1, 1, 1, 0, 1, 1, 1, 1});
+  std::vector<T> v = convert_values<T>(int_values);
+
+  // test without nulls
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  cudf::size_type expected_value = std::is_same_v<T, bool> ? 2 : 6;
+  EXPECT_EQ(
+    this
+      ->template reduction_test<cudf::size_type>(
+        col, *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::INCLUDE))
+      .first,
+    expected_value);
+  EXPECT_EQ(
+    this
+      ->template reduction_test<cudf::size_type>(
+        col, *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::EXCLUDE))
+      .first,
+    expected_value);
+
+  // test with nulls
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  cudf::size_type expected_null_value0                = std::is_same_v<T, bool> ? 3 : 7;
+  cudf::size_type expected_null_value1                = std::is_same_v<T, bool> ? 2 : 6;
+
+  EXPECT_EQ(
+    this
+      ->template reduction_test<cudf::size_type>(
+        col_nulls, *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::INCLUDE))
+      .first,
+    expected_null_value0);
+  EXPECT_EQ(
+    this
+      ->template reduction_test<cudf::size_type>(
+        col_nulls, *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::EXCLUDE))
+      .first,
+    expected_null_value1);
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionProductZeroScale)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const ONE   = decimalXX{1, scale_type{0}};
+  auto const TWO   = decimalXX{2, scale_type{0}};
+  auto const THREE = decimalXX{3, scale_type{0}};
+  auto const FOUR  = decimalXX{4, scale_type{0}};
+  auto const _24   = decimalXX{24, scale_type{0}};
+  auto const _48   = decimalXX{48, scale_type{0}};
+
+  auto const in       = std::vector<decimalXX>{ONE, TWO, THREE, FOUR};
+  auto const column   = cudf::test::fixed_width_column_wrapper<decimalXX>(in.cbegin(), in.cend());
+  auto const expected = std::accumulate(in.cbegin(), in.cend(), ONE, std::multiplies<decimalXX>());
+  auto const out_type = static_cast<cudf::column_view>(column).type();
+
+  auto const result =
+    cudf::reduce(column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type);
+  auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+  auto const result_fp     = decimalXX{result_scalar->value()};
+
+  EXPECT_EQ(result_fp, expected);
+  EXPECT_EQ(result_fp, _24);
+
+  // Test with initial value
+  auto const init_expected =
+    std::accumulate(in.cbegin(), in.cend(), TWO, std::multiplies<decimalXX>());
+  auto const init_scalar = cudf::make_fixed_point_scalar<decimalXX>(2, scale_type{0});
+
+  auto const init_result = cudf::reduce(
+    column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+  auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+  auto const init_result_fp     = decimalXX{init_result_scalar->value()};
+
+  EXPECT_EQ(init_result_fp, init_expected);
+  EXPECT_EQ(init_result_fp, _48);
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionProduct)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{1, 2, 3, 1, 2, 3}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{36, scale_type{i * 6}}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{72, scale_type{i * 7}}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(2, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionProductWithNulls)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{1, 2, 3, 1, 2, 3}, {1, 1, 1, 0, 0, 0}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{6, scale_type{i * 3}}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{12, scale_type{i * 4}}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(2, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionSum)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale = scale_type{i};
+
+    auto const column   = fp_wrapper{{1, 2, 3, 4}, scale};
+    auto const expected = decimalXX{scaled_integer<RepType>{10, scale}};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{12, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(2, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionSumAlternate)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const ZERO   = decimalXX{0, scale_type{0}};
+  auto const ONE    = decimalXX{1, scale_type{0}};
+  auto const TWO    = decimalXX{2, scale_type{0}};
+  auto const THREE  = decimalXX{3, scale_type{0}};
+  auto const FOUR   = decimalXX{4, scale_type{0}};
+  auto const TEN    = decimalXX{10, scale_type{0}};
+  auto const TWELVE = decimalXX{12, scale_type{0}};
+
+  auto const in       = std::vector<decimalXX>{ONE, TWO, THREE, FOUR};
+  auto const column   = cudf::test::fixed_width_column_wrapper<decimalXX>(in.cbegin(), in.cend());
+  auto const expected = std::accumulate(in.cbegin(), in.cend(), ZERO, std::plus<decimalXX>());
+  auto const out_type = static_cast<cudf::column_view>(column).type();
+
+  auto const result =
+    cudf::reduce(column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type);
+  auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+  EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+  EXPECT_EQ(result_scalar->fixed_point_value(), TEN);
+
+  // Test with initial value
+  auto const init_expected = std::accumulate(in.cbegin(), in.cend(), TWO, std::plus<decimalXX>());
+  auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(2, scale_type{0});
+
+  auto const init_result =
+    cudf::reduce(column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+  auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+  EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  EXPECT_EQ(init_result_scalar->fixed_point_value(), TWELVE);
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionSumFractional)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{111, 222, 333}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{666, scale}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{668, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(2, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionSumLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2}) {
+    auto const scale          = scale_type{i};
+    auto f                    = thrust::make_counting_iterator(0);
+    auto const values         = std::vector<RepType>(f, f + 1000);
+    auto const column         = fp_wrapper{values.cbegin(), values.cend(), scale};
+    auto const out_type       = static_cast<cudf::column_view>(column).type();
+    auto const expected_value = std::accumulate(values.cbegin(), values.cend(), RepType{0});
+    auto const expected       = decimalXX{scaled_integer<RepType>{expected_value, scale}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    int const init_value = 2;
+    auto const init_expected_value =
+      std::accumulate(values.cbegin(), values.cend(), RepType{init_value});
+    auto const init_expected = decimalXX{scaled_integer<RepType>{init_expected_value, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(init_value, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_sum_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionMin)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale    = scale_type{i};
+    auto const ONE      = decimalXX{scaled_integer<RepType>{1, scale}};
+    auto const column   = fp_wrapper{{1, 2, 3, 4}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_min_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), ONE);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{0, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(0, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_min_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionMinLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale = scale_type{i};
+    auto f = cudf::detail::make_counting_transform_iterator(0, [](auto e) { return e % 43; });
+    auto const column   = fp_wrapper{f, f + 5000, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{0, scale};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_min_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{0, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(0, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_min_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionMax)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale    = scale_type{i};
+    auto const FOUR     = decimalXX{scaled_integer<RepType>{4, scale}};
+    auto const column   = fp_wrapper{{1, 2, 3, 4}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_max_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), FOUR);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{5, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(5, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_max_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionMaxLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale = scale_type{i};
+    auto f = cudf::detail::make_counting_transform_iterator(0, [](auto e) { return e % 43; });
+    auto const column   = fp_wrapper{f, f + 5000, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{42, scale}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_max_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimalXX{scaled_integer<RepType>{43, scale}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimalXX>(43, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_max_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionNUnique)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{1, 1, 2, 2, 3, 3, 4, 4}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_nunique_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<cudf::size_type>*>(result.get());
+
+    EXPECT_EQ(result_scalar->value(), 4);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionSumOfSquares)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{1, 2, 3, 4}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{30, scale_type{i * 2}}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_sum_of_squares_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionMedianOddNumberOfElements)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const s : {0, -1, -2, -3, -4}) {
+    auto const scale    = scale_type{s};
+    auto const column   = fp_wrapper{{1, 2, 2, 3, 4}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{2, scale}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_median_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionMedianEvenNumberOfElements)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const s : {0, -1, -2, -3, -4}) {
+    auto const scale    = scale_type{s};
+    auto const column   = fp_wrapper{{10, 20, 20, 30, 30, 40}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+    auto const expected = decimalXX{scaled_integer<RepType>{25, scale}};
+
+    auto const result =
+      cudf::reduce(column, *cudf::make_median_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionQuantile)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const s : {0, -1, -2, -3, -4}) {
+    auto const scale    = scale_type{s};
+    auto const column   = fp_wrapper{{1, 2, 3, 4, 5}, scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+
+    for (auto const i : {0, 1, 2, 3, 4}) {
+      auto const expected      = decimalXX{scaled_integer<RepType>{i + 1, scale}};
+      auto const result        = cudf::reduce(column,
+                                       *cudf::make_quantile_aggregation<reduce_aggregation>(
+                                         {i / 4.0}, cudf::interpolation::LINEAR),
+                                       out_type);
+      auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+      EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+    }
+  }
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReductionNthElement)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const s : {0, -1, -2, -3, -4}) {
+    auto const scale    = scale_type{s};
+    auto const values   = std::vector<RepType>{4104, 42, 1729, 55};
+    auto const column   = fp_wrapper{values.cbegin(), values.cend(), scale};
+    auto const out_type = static_cast<cudf::column_view>(column).type();
+
+    for (auto const i : {0, 1, 2, 3}) {
+      auto const expected = decimalXX{scaled_integer<RepType>{values[i], scale}};
+      auto const result   = cudf::reduce(
+        column,
+        *cudf::make_nth_element_aggregation<reduce_aggregation>(i, cudf::null_policy::INCLUDE),
+        out_type);
+      auto const result_scalar = static_cast<cudf::scalar_type_t<decimalXX>*>(result.get());
+      EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+    }
+  }
+}
+
+struct Decimal128Only : public cudf::test::BaseFixture {};
+
+TEST_F(Decimal128Only, Decimal128ProductReduction)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{2, 2, 2, 2, 2, 2, 2, 2, 2}, scale};
+    auto const expected = decimal128{scaled_integer<RepType>{512, scale_type{i * 9}}};
+
+    auto const out_type = cudf::data_type{cudf::type_id::DECIMAL128, scale};
+    auto const result =
+      cudf::reduce(column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimal128>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimal128{scaled_integer<RepType>{1024, scale_type{i * 10}}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimal128>(2, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar =
+      static_cast<cudf::scalar_type_t<decimal128>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TEST_F(Decimal128Only, Decimal128ProductReduction2)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  for (auto const i : {0, -1, -2, -3, -4, -5, -6}) {
+    auto const scale    = scale_type{i};
+    auto const column   = fp_wrapper{{1, 2, 3, 4, 5, 6}, scale};
+    auto const expected = decimal128{scaled_integer<RepType>{720, scale_type{i * 6}}};
+
+    auto const out_type = cudf::data_type{cudf::type_id::DECIMAL128, scale};
+    auto const result =
+      cudf::reduce(column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type);
+    auto const result_scalar = static_cast<cudf::scalar_type_t<decimal128>*>(result.get());
+
+    EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+    // Test with initial value
+    auto const init_expected = decimal128{scaled_integer<RepType>{2160, scale_type{i * 7}}};
+    auto const init_scalar   = cudf::make_fixed_point_scalar<decimal128>(3, scale);
+
+    auto const init_result = cudf::reduce(
+      column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+    auto const init_result_scalar =
+      static_cast<cudf::scalar_type_t<decimal128>*>(init_result.get());
+
+    EXPECT_EQ(init_result_scalar->fixed_point_value(), init_expected);
+  }
+}
+
+TEST_F(Decimal128Only, Decimal128ProductReduction3)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const values   = std::vector(127, -2);
+  auto const scale    = scale_type{0};
+  auto const column   = fp_wrapper{values.cbegin(), values.cend(), scale};
+  auto const lowest   = cuda::std::numeric_limits<RepType>::lowest();
+  auto const expected = decimal128{scaled_integer<RepType>{lowest, scale}};
+
+  auto const out_type = cudf::data_type{cudf::type_id::DECIMAL128, scale};
+  auto const result =
+    cudf::reduce(column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type);
+  auto const result_scalar = static_cast<cudf::scalar_type_t<decimal128>*>(result.get());
+
+  EXPECT_EQ(result_scalar->fixed_point_value(), expected);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_point_scalar<decimal128>(5, scale);
+
+  auto const init_result = cudf::reduce(
+    column, *cudf::make_product_aggregation<reduce_aggregation>(), out_type, *init_scalar);
+  auto const init_result_scalar = static_cast<cudf::scalar_type_t<decimal128>*>(init_result.get());
+
+  EXPECT_EQ(init_result_scalar->fixed_point_value(), expected);
+}
+
+TYPED_TEST(ReductionTest, NthElement)
+{
+  using T = TypeParam;
+  std::vector<int> int_values(4000);
+  std::iota(int_values.begin(), int_values.end(), 0);
+  std::vector<bool> host_bools(int_values.size());
+  auto valid_condition = [](auto i) { return (i % 3 and i % 7); };
+  std::transform(int_values.begin(), int_values.end(), host_bools.begin(), valid_condition);
+
+  cudf::size_type valid_count = std::count(host_bools.begin(), host_bools.end(), true);
+  std::vector<int> int_values_valid(valid_count);
+  std::copy_if(int_values.begin(), int_values.end(), int_values_valid.begin(), valid_condition);
+
+  std::vector<T> v           = convert_values<T>(int_values);
+  std::vector<T> v_valid     = convert_values<T>(int_values_valid);
+  cudf::size_type input_size = v.size();
+
+  auto mod = [](int a, int b) { return (a % b + b) % b; };
+  cudf::test::fixed_width_column_wrapper<T> col(v.begin(), v.end());
+  cudf::test::fixed_width_column_wrapper<T> col_nulls = construct_null_column(v, host_bools);
+  // without nulls
+  for (cudf::size_type n :
+       {-input_size, -input_size / 2, -2, -1, 0, 1, 2, input_size / 2, input_size - 1}) {
+    auto const index         = mod(n, v.size());
+    T expected_value_nonull  = v[index];
+    bool const expected_null = host_bools[index];
+    EXPECT_EQ(
+      this
+        ->template reduction_test<T>(
+          col,
+          *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::INCLUDE))
+        .first,
+      expected_value_nonull);
+    EXPECT_EQ(
+      this
+        ->template reduction_test<T>(
+          col,
+          *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::EXCLUDE))
+        .first,
+      expected_value_nonull);
+    auto res = this->template reduction_test<T>(
+      col_nulls,
+      *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::INCLUDE));
+    EXPECT_EQ(res.first, expected_value_nonull);
+    EXPECT_EQ(res.second, expected_null);
+  }
+  // valid only
+  for (cudf::size_type n :
+       {-valid_count, -valid_count / 2, -2, -1, 0, 1, 2, valid_count / 2, valid_count - 1}) {
+    T expected_value_null = v_valid[mod(n, v_valid.size())];
+    EXPECT_EQ(
+      this
+        ->template reduction_test<T>(
+          col_nulls,
+          *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::EXCLUDE))
+        .first,
+      expected_value_null);
+  }
+  // error cases
+  for (cudf::size_type n : {-input_size - 1, input_size}) {
+    EXPECT_ANY_THROW(this->template reduction_test<T>(
+      col, *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::INCLUDE)));
+    EXPECT_ANY_THROW(this->template reduction_test<T>(
+      col_nulls,
+      *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::INCLUDE)));
+    EXPECT_ANY_THROW(this->template reduction_test<T>(
+      col, *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::EXCLUDE)));
+    EXPECT_ANY_THROW(this->template reduction_test<T>(
+      col_nulls,
+      *cudf::make_nth_element_aggregation<reduce_aggregation>(n, cudf::null_policy::EXCLUDE)));
+  }
+}
+
+struct DictionaryStringReductionTest : public StringReductionTest {};
+
+std::vector<std::string> data_list[] = {
+  {"nine", "two", "five", "three", "five", "six", "two", "eight", "nine"},
+};
+INSTANTIATE_TEST_CASE_P(dictionary_cases,
+                        DictionaryStringReductionTest,
+                        testing::ValuesIn(data_list));
+TEST_P(DictionaryStringReductionTest, MinMax)
+{
+  std::vector<std::string> host_strings(GetParam());
+  cudf::data_type output_type{cudf::type_id::STRING};
+
+  cudf::test::dictionary_column_wrapper<std::string> col(host_strings.begin(), host_strings.end());
+
+  // MIN
+  this->reduction_test(col,
+                       *(std::min_element(host_strings.begin(), host_strings.end())),
+                       true,
+                       *cudf::make_min_aggregation<reduce_aggregation>(),
+                       output_type);
+  // sliced
+  this->reduction_test(cudf::slice(col, {1, 7}).front(),
+                       *(std::min_element(host_strings.begin() + 1, host_strings.begin() + 7)),
+                       true,
+                       *cudf::make_min_aggregation<reduce_aggregation>(),
+                       output_type);
+  // MAX
+  this->reduction_test(col,
+                       *(std::max_element(host_strings.begin(), host_strings.end())),
+                       true,
+                       *cudf::make_max_aggregation<reduce_aggregation>(),
+                       output_type);
+  // sliced
+  this->reduction_test(cudf::slice(col, {1, 7}).front(),
+                       *(std::max_element(host_strings.begin() + 1, host_strings.begin() + 7)),
+                       true,
+                       *cudf::make_max_aggregation<reduce_aggregation>(),
+                       output_type);
+}
+
+template <typename T>
+struct DictionaryAnyAllTest : public ReductionTest<bool> {};
+using DictionaryAnyAllTypes = cudf::test::Types<int16_t, int32_t, float, double, bool>;
+TYPED_TEST_SUITE(DictionaryAnyAllTest, cudf::test::NumericTypes);
+TYPED_TEST(DictionaryAnyAllTest, AnyAll)
+{
+  using T = TypeParam;
+  std::vector<int> all_values({true, true, true, true});
+  std::vector<T> v_all = convert_values<T>(all_values);
+  std::vector<int> none_values({false, false, false, false});
+  std::vector<T> v_none = convert_values<T>(none_values);
+  std::vector<int> some_values({false, true, false, true});
+  std::vector<T> v_some = convert_values<T>(some_values);
+  cudf::data_type output_dtype(cudf::type_id::BOOL8);
+
+  auto any_agg = cudf::make_any_aggregation<reduce_aggregation>();
+  auto all_agg = cudf::make_all_aggregation<reduce_aggregation>();
+
+  // without nulls
+  {
+    cudf::test::dictionary_column_wrapper<T> all_col(v_all.begin(), v_all.end());
+    EXPECT_TRUE(this->template reduction_test<bool>(all_col, *any_agg, output_dtype).first);
+    EXPECT_TRUE(this->template reduction_test<bool>(all_col, *all_agg, output_dtype).first);
+    cudf::test::dictionary_column_wrapper<T> none_col(v_none.begin(), v_none.end());
+    EXPECT_FALSE(this->template reduction_test<bool>(none_col, *any_agg, output_dtype).first);
+    EXPECT_FALSE(this->template reduction_test<bool>(none_col, *all_agg, output_dtype).first);
+    cudf::test::dictionary_column_wrapper<T> some_col(v_some.begin(), v_some.end());
+    EXPECT_TRUE(this->template reduction_test<bool>(some_col, *any_agg, output_dtype).first);
+    EXPECT_FALSE(this->template reduction_test<bool>(some_col, *all_agg, output_dtype).first);
+    // sliced test
+    auto slice1 = cudf::slice(some_col, {1, 3}).front();
+    auto slice2 = cudf::slice(some_col, {1, 2}).front();
+    EXPECT_TRUE(this->template reduction_test<bool>(slice1, *any_agg, output_dtype).first);
+    EXPECT_TRUE(this->template reduction_test<bool>(slice2, *all_agg, output_dtype).first);
+  }
+  // with nulls
+  {
+    std::vector<bool> valid({1, 1, 0, 1});
+    cudf::test::dictionary_column_wrapper<T> all_col(v_all.begin(), v_all.end(), valid.begin());
+    EXPECT_TRUE(this->template reduction_test<bool>(all_col, *any_agg, output_dtype).first);
+    EXPECT_TRUE(this->template reduction_test<bool>(all_col, *all_agg, output_dtype).first);
+    cudf::test::dictionary_column_wrapper<T> none_col(v_none.begin(), v_none.end(), valid.begin());
+    EXPECT_FALSE(this->template reduction_test<bool>(none_col, *any_agg, output_dtype).first);
+    EXPECT_FALSE(this->template reduction_test<bool>(none_col, *all_agg, output_dtype).first);
+    cudf::test::dictionary_column_wrapper<T> some_col(v_some.begin(), v_some.end(), valid.begin());
+    EXPECT_TRUE(this->template reduction_test<bool>(some_col, *any_agg, output_dtype).first);
+    EXPECT_FALSE(this->template reduction_test<bool>(some_col, *all_agg, output_dtype).first);
+    // sliced test
+    auto slice1 = cudf::slice(some_col, {0, 3}).front();
+    auto slice2 = cudf::slice(some_col, {1, 4}).front();
+    EXPECT_TRUE(this->template reduction_test<bool>(slice1, *any_agg, output_dtype).first);
+    EXPECT_TRUE(this->template reduction_test<bool>(slice2, *all_agg, output_dtype).first);
+  }
+}
+
+template <typename T>
+struct DictionaryReductionTest : public ReductionTest<T> {};
+
+using DictionaryTypes = cudf::test::Types<int16_t, int32_t, float, double>;
+TYPED_TEST_SUITE(DictionaryReductionTest, DictionaryTypes);
+TYPED_TEST(DictionaryReductionTest, Sum)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({6, -14, 13, 64, 0, -13, -20, 45});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<T>()};
+
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+
+  T expected_value = std::accumulate(v.begin(), v.end(), T{0});
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *cudf::make_sum_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            expected_value);
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 0, 0, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+  expected_value = [v, validity] {
+    auto const r = replace_nulls(v, validity, T{0});
+    return std::accumulate(r.begin(), r.end(), T{0});
+  }();
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col_nulls, *cudf::make_sum_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            expected_value);
+}
+
+TYPED_TEST(DictionaryReductionTest, Product)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({5, -1, 1, 0, 3, 2, 4});
+  std::vector<TypeParam> v = convert_values<TypeParam>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<T>()};
+
+  auto calc_prod = [](std::vector<T> const& v) {
+    return std::accumulate(v.cbegin(), v.cend(), T{1}, std::multiplies<T>());
+  };
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *cudf::make_product_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            calc_prod(v));
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 0, 0, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col_nulls, *cudf::make_product_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            calc_prod(replace_nulls(v, validity, T{1})));
+}
+
+TYPED_TEST(DictionaryReductionTest, SumOfSquare)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<T>()};
+
+  auto calc_reduction = [](std::vector<T> const& v) {
+    return std::accumulate(v.cbegin(), v.cend(), T{0}, [](T acc, T i) { return acc + i * i; });
+  };
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+
+  EXPECT_EQ(this
+              ->template reduction_test<T>(
+                col, *cudf::make_sum_of_squares_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            calc_reduction(v));
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 0, 0, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  EXPECT_EQ(
+    this
+      ->template reduction_test<T>(
+        col_nulls, *cudf::make_sum_of_squares_aggregation<reduce_aggregation>(), output_type)
+      .first,
+    calc_reduction(replace_nulls(v, validity, T{0})));
+}
+
+TYPED_TEST(DictionaryReductionTest, Mean)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2, 28});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<double>()};
+
+  auto calc_mean = [](std::vector<T> const& v, cudf::size_type valid_count) {
+    double sum = std::accumulate(v.cbegin(), v.cend(), double{0});
+    return sum / valid_count;
+  };
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col, *cudf::make_mean_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            calc_mean(v, v.size()));
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 0, 1, 1, 1, 0, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  cudf::size_type valid_count = std::count(validity.begin(), validity.end(), true);
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_mean_aggregation<reduce_aggregation>(), output_type)
+              .first,
+            calc_mean(replace_nulls(v, validity, T{0}), valid_count));
+}
+
+#ifdef NDEBUG
+TYPED_TEST(DictionaryReductionTest, VarStd)
+#else
+TYPED_TEST(DictionaryReductionTest, DISABLED_VarStd)
+#endif
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2, 28});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<double>()};
+
+  auto calc_var = [](std::vector<T> const& v, cudf::size_type valid_count, cudf::size_type ddof) {
+    double mean = std::accumulate(v.cbegin(), v.cend(), double{0});
+    mean /= valid_count;
+    double sum_of_sq = std::accumulate(
+      v.cbegin(), v.cend(), double{0}, [](double acc, TypeParam i) { return acc + i * i; });
+    auto const div = valid_count - ddof;
+    double var     = sum_of_sq / div - ((mean * mean) * valid_count) / div;
+    return var;
+  };
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+
+  cudf::size_type const ddof = 1;
+  double var                 = calc_var(v, v.size(), ddof);
+  double std                 = std::sqrt(var);
+  auto var_agg               = cudf::make_variance_aggregation<reduce_aggregation>(ddof);
+  auto std_agg               = cudf::make_std_aggregation<reduce_aggregation>(ddof);
+
+  EXPECT_EQ(this->template reduction_test<double>(col, *var_agg, output_type).first, var);
+  EXPECT_EQ(this->template reduction_test<double>(col, *std_agg, output_type).first, std);
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 0, 1, 1, 1, 0, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  cudf::size_type const valid_count = std::count(validity.begin(), validity.end(), true);
+
+  double var_nulls = calc_var(replace_nulls(v, validity, T{0}), valid_count, ddof);
+  double std_nulls = std::sqrt(var_nulls);
+
+  EXPECT_EQ(this->template reduction_test<double>(col_nulls, *var_agg, output_type).first,
+            var_nulls);
+  EXPECT_EQ(this->template reduction_test<double>(col_nulls, *std_agg, output_type).first,
+            std_nulls);
+}
+
+TYPED_TEST(DictionaryReductionTest, NthElement)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({-3, 2, 1, 0, 5, -3, -2, 28});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<T>()};
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+  cudf::size_type n = 5;
+  EXPECT_EQ(this
+              ->template reduction_test<T>(col,
+                                           *cudf::make_nth_element_aggregation<reduce_aggregation>(
+                                             n, cudf::null_policy::INCLUDE),
+                                           output_type)
+              .first,
+            v[n]);
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 0, 1, 1, 1, 0, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  EXPECT_EQ(this
+              ->template reduction_test<T>(col_nulls,
+                                           *cudf::make_nth_element_aggregation<reduce_aggregation>(
+                                             n, cudf::null_policy::INCLUDE),
+                                           output_type)
+              .first,
+            v[n]);
+  EXPECT_FALSE(
+    this
+      ->template reduction_test<T>(
+        col_nulls,
+        *cudf::make_nth_element_aggregation<reduce_aggregation>(2, cudf::null_policy::INCLUDE),
+        output_type)
+      .second);
+}
+
+TYPED_TEST(DictionaryReductionTest, UniqueCount)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({1, -3, 1, 2, 0, 2, -4, 45});  // 6 unique values
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::data_type output_type{cudf::type_to_id<cudf::size_type>()};
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+  EXPECT_EQ(this
+              ->template reduction_test<int>(
+                col,
+                *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::INCLUDE),
+                output_type)
+              .first,
+            6);
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 1, 0, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  EXPECT_EQ(this
+              ->template reduction_test<int>(
+                col_nulls,
+                *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::INCLUDE),
+                output_type)
+              .first,
+            7);
+  EXPECT_EQ(this
+              ->template reduction_test<int>(
+                col_nulls,
+                *cudf::make_nunique_aggregation<reduce_aggregation>(cudf::null_policy::EXCLUDE),
+                output_type)
+              .first,
+            6);
+}
+
+TYPED_TEST(DictionaryReductionTest, Median)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({6, -14, 13, 64, 0, -13, -20, 45});
+  std::vector<T> v = convert_values<T>(int_values);
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+  EXPECT_EQ(
+    this->template reduction_test<double>(col, *cudf::make_median_aggregation<reduce_aggregation>())
+      .first,
+    (std::is_signed_v<T>) ? 3.0 : 13.5);
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 1, 0, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_median_aggregation<reduce_aggregation>())
+              .first,
+            (std::is_signed_v<T>) ? 0.0 : 13.0);
+}
+
+TYPED_TEST(DictionaryReductionTest, Quantile)
+{
+  using T = TypeParam;
+  std::vector<int> int_values({6, -14, 13, 64, 0, -13, -20, 45});
+  std::vector<T> v = convert_values<T>(int_values);
+  cudf::interpolation interp{cudf::interpolation::LINEAR};
+
+  // test without nulls
+  cudf::test::dictionary_column_wrapper<T> col(v.begin(), v.end());
+  double expected_value = std::is_same_v<T, bool> || std::is_unsigned_v<T> ? 0.0 : -20.0;
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col, *cudf::make_quantile_aggregation<reduce_aggregation>({0.0}, interp))
+              .first,
+            expected_value);
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col, *cudf::make_quantile_aggregation<reduce_aggregation>({1.0}, interp))
+              .first,
+            64.0);
+
+  // test with nulls
+  std::vector<bool> validity({1, 1, 1, 0, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<T> col_nulls(v.begin(), v.end(), validity.begin());
+
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_quantile_aggregation<reduce_aggregation>({0}, interp))
+              .first,
+            expected_value);
+  EXPECT_EQ(this
+              ->template reduction_test<double>(
+                col_nulls, *cudf::make_quantile_aggregation<reduce_aggregation>({1}, interp))
+              .first,
+            45.0);
+}
+
+struct ListReductionTest : public cudf::test::BaseFixture {
+  void reduction_test(cudf::column_view const& input_data,
+                      cudf::column_view const& expected_value,
+                      bool succeeded_condition,
+                      bool is_valid,
+                      reduce_aggregation const& agg)
+  {
+    auto statement = [&]() {
+      std::unique_ptr<cudf::scalar> result =
+        cudf::reduce(input_data, agg, cudf::data_type(cudf::type_id::LIST));
+      auto list_result = dynamic_cast<cudf::list_scalar*>(result.get());
+      EXPECT_EQ(is_valid, list_result->is_valid());
+      if (is_valid) {
+        CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_value, list_result->view());
+      } else {
+        CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_value, list_result->view());
+      }
+    };
+
+    if (succeeded_condition) {
+      CUDF_EXPECT_NO_THROW(statement());
+    } else {
+      EXPECT_ANY_THROW(statement());
+    }
+  }
+};
+
+TEST_F(ListReductionTest, ListReductionNthElement)
+{
+  using LCW        = cudf::test::lists_column_wrapper<int>;
+  using ElementCol = cudf::test::fixed_width_column_wrapper<int>;
+
+  // test without nulls
+  LCW col{{-3}, {2, 1}, {0, 5, -3}, {-2}, {}, {28}};
+  this->reduction_test(
+    col,
+    ElementCol{0, 5, -3},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(2, cudf::null_policy::INCLUDE));
+
+  // test with null-exclude
+  std::vector<bool> validity{1, 0, 0, 1, 1, 0};
+  LCW col_nulls({{-3}, {2, 1}, {0, 5, -3}, {-2}, {}, {28}}, validity.begin());
+  this->reduction_test(
+    col_nulls,
+    ElementCol{-2},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(1, cudf::null_policy::EXCLUDE));
+
+  // test with null-include
+  this->reduction_test(
+    col_nulls,
+    ElementCol{},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(1, cudf::null_policy::INCLUDE));
+}
+
+TEST_F(ListReductionTest, NestedListReductionNthElement)
+{
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  // test without nulls
+  auto validity    = std::vector<bool>{1, 0, 0, 1, 1};
+  auto nested_list = LCW(
+    {{LCW{}, LCW{2, 3, 4}}, {}, {LCW{5}, LCW{6}, LCW{7, 8}}, {LCW{9, 10}}, {LCW{11}, LCW{12, 13}}},
+    validity.begin());
+  this->reduction_test(
+    nested_list,
+    LCW{{}, {2, 3, 4}},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(0, cudf::null_policy::INCLUDE));
+
+  // test with null-include
+  this->reduction_test(
+    nested_list,
+    LCW{},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(2, cudf::null_policy::INCLUDE));
+
+  // test with null-exclude
+  this->reduction_test(
+    nested_list,
+    LCW{{11}, {12, 13}},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(2, cudf::null_policy::EXCLUDE));
+}
+
+TEST_F(ListReductionTest, NonValidListReductionNthElement)
+{
+  using LCW        = cudf::test::lists_column_wrapper<int>;
+  using ElementCol = cudf::test::fixed_width_column_wrapper<int>;
+
+  // test against col.size() <= col.null_count()
+  std::vector<bool> validity{0};
+  this->reduction_test(
+    LCW{{{1, 2}}, validity.begin()},
+    ElementCol{},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(0, cudf::null_policy::INCLUDE));
+
+  // test against empty input
+  this->reduction_test(
+    LCW{},
+    ElementCol{},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(0, cudf::null_policy::INCLUDE));
+}
+
+TEST_F(ListReductionTest, ReductionMinMaxNoNull)
+{
+  using INTS_CW          = cudf::test::fixed_width_column_wrapper<int>;
+  using LISTS_CW         = cudf::test::lists_column_wrapper<int>;
+  using STRINGS_CW       = cudf::test::strings_column_wrapper;
+  using LISTS_STRINGS_CW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  {
+    auto const input = LISTS_CW{{3, 4}, {1, 2}, {5, 6, 7}, {0, 8}, {9, 10}, {1, 0}};
+    this->reduction_test(
+      input, INTS_CW{0, 8}, true, true, *cudf::make_min_aggregation<reduce_aggregation>());
+    this->reduction_test(
+      input, INTS_CW{9, 10}, true, true, *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+  {
+    auto const input = LISTS_STRINGS_CW{
+      {"34", "43"}, {"12", "21"}, {"567", "6", "765"}, {"08", "8"}, {"109", "10"}, {"10", "00"}};
+    this->reduction_test(
+      input, STRINGS_CW{"08", "8"}, true, true, *cudf::make_min_aggregation<reduce_aggregation>());
+    this->reduction_test(input,
+                         STRINGS_CW{"567", "6", "765"},
+                         true,
+                         true,
+                         *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+}
+
+TEST_F(ListReductionTest, ReductionMinMaxSlicedInput)
+{
+  using INTS_CW          = cudf::test::fixed_width_column_wrapper<int>;
+  using LISTS_CW         = cudf::test::lists_column_wrapper<int>;
+  using STRINGS_CW       = cudf::test::strings_column_wrapper;
+  using LISTS_STRINGS_CW = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  {
+    auto const input_original = LISTS_CW{{9, 9} /*don't care*/,
+                                         {0, 0} /*don't care*/,
+                                         {3, 4},
+                                         {1, 2},
+                                         {5, 6, 7},
+                                         {0, 8},
+                                         {9, 10},
+                                         {1, 0},
+                                         {0, 7} /*don't care*/};
+    auto const input          = cudf::slice(input_original, {2, 8})[0];
+    this->reduction_test(
+      input, INTS_CW{0, 8}, true, true, *cudf::make_min_aggregation<reduce_aggregation>());
+    this->reduction_test(
+      input, INTS_CW{9, 10}, true, true, *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+  {
+    auto const input_original = LISTS_STRINGS_CW{{"08", "8"} /*don't care*/,
+                                                 {"999", "8"} /*don't care*/,
+                                                 {"34", "43"},
+                                                 {"12", "21"},
+                                                 {"567", "6", "765"},
+                                                 {"08", "8"},
+                                                 {"109", "10"},
+                                                 {"10", "00"}};
+    auto const input          = cudf::slice(input_original, {2, 8})[0];
+    this->reduction_test(
+      input, STRINGS_CW{"08", "8"}, true, true, *cudf::make_min_aggregation<reduce_aggregation>());
+    this->reduction_test(input,
+                         STRINGS_CW{"567", "6", "765"},
+                         true,
+                         true,
+                         *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+}
+
+TEST_F(ListReductionTest, ReductionMinMaxWithNulls)
+{
+  using INTS_CW  = cudf::test::fixed_width_column_wrapper<int>;
+  using LISTS_CW = cudf::test::lists_column_wrapper<int>;
+  using cudf::test::iterators::null_at;
+  using cudf::test::iterators::nulls_at;
+  constexpr int null{0};
+
+  auto const input = LISTS_CW{{LISTS_CW{3, 4},
+                               LISTS_CW{1, 2},
+                               LISTS_CW{{1, null}, null_at(1)},
+                               LISTS_CW{} /*null*/,
+                               LISTS_CW{5, 6, 7},
+                               LISTS_CW{1, 8},
+                               LISTS_CW{{9, null}, null_at(1)},
+                               LISTS_CW{} /*null*/},
+                              nulls_at({3, 7})};
+  this->reduction_test(input,
+                       INTS_CW{{1, null}, null_at(1)},
+                       true,
+                       true,
+                       *cudf::make_min_aggregation<reduce_aggregation>());
+  this->reduction_test(input,
+                       INTS_CW{{9, null}, null_at(1)},
+                       true,
+                       true,
+                       *cudf::make_max_aggregation<reduce_aggregation>());
+}
+
+struct StructReductionTest : public cudf::test::BaseFixture {
+  using SCW = cudf::test::structs_column_wrapper;
+
+  void reduction_test(cudf::column_view const& struct_column,
+                      cudf::table_view const& expected_value,
+                      bool succeeded_condition,
+                      bool is_valid,
+                      reduce_aggregation const& agg)
+  {
+    auto statement = [&]() {
+      std::unique_ptr<cudf::scalar> result =
+        cudf::reduce(struct_column, agg, cudf::data_type(cudf::type_id::STRUCT));
+      auto struct_result = dynamic_cast<cudf::struct_scalar*>(result.get());
+      EXPECT_EQ(is_valid, struct_result->is_valid());
+      if (is_valid) { CUDF_TEST_EXPECT_TABLES_EQUIVALENT(expected_value, struct_result->view()); }
+    };
+
+    if (succeeded_condition) {
+      CUDF_EXPECT_NO_THROW(statement());
+    } else {
+      EXPECT_ANY_THROW(statement());
+    }
+  }
+};
+
+TEST_F(StructReductionTest, StructReductionNthElement)
+{
+  using ICW = cudf::test::fixed_width_column_wrapper<int>;
+
+  // test without nulls
+  auto child0 = *ICW{-3, 2, 1, 0, 5, -3, -2, 28}.release();
+  auto child1 = *ICW{0, 1, 2, 3, 4, 5, 6, 7}.release();
+  auto child2 =
+    *ICW{{-10, 10, -100, 100, -1000, 1000, -10000, 10000}, {1, 0, 0, 1, 1, 1, 0, 1}}.release();
+  std::vector<std::unique_ptr<cudf::column>> input_vector;
+  input_vector.push_back(std::make_unique<cudf::column>(child0));
+  input_vector.push_back(std::make_unique<cudf::column>(child1));
+  input_vector.push_back(std::make_unique<cudf::column>(child2));
+  auto struct_col  = SCW(std::move(input_vector));
+  auto result_col0 = ICW{1};
+  auto result_col1 = ICW{2};
+  auto result_col2 = ICW{{0}, {0}};
+  this->reduction_test(
+    struct_col,
+    cudf::table_view{{result_col0, result_col1, result_col2}},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(2, cudf::null_policy::INCLUDE));
+
+  // test with null-include
+  std::vector<bool> validity{1, 1, 1, 0, 1, 0, 0, 1};
+  input_vector.clear();
+  input_vector.push_back(std::make_unique<cudf::column>(child0));
+  input_vector.push_back(std::make_unique<cudf::column>(child1));
+  input_vector.push_back(std::make_unique<cudf::column>(child2));
+  struct_col  = SCW(std::move(input_vector), validity);
+  result_col0 = ICW{{0}, {0}};
+  result_col1 = ICW{{0}, {0}};
+  result_col2 = ICW{{0}, {0}};
+  this->reduction_test(
+    struct_col,
+    cudf::table_view{{result_col0, result_col1, result_col2}},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(6, cudf::null_policy::INCLUDE));
+
+  // test with null-exclude
+  result_col0 = ICW{{28}, {1}};
+  result_col1 = ICW{{7}, {1}};
+  result_col2 = ICW{{10000}, {1}};
+  this->reduction_test(
+    struct_col,
+    cudf::table_view{{result_col0, result_col1, result_col2}},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(4, cudf::null_policy::EXCLUDE));
+}
+
+TEST_F(StructReductionTest, NestedStructReductionNthElement)
+{
+  using ICW = cudf::test::fixed_width_column_wrapper<int>;
+  using LCW = cudf::test::lists_column_wrapper<int>;
+
+  auto int_col0      = ICW{-4, -3, -2, -1, 0};
+  auto struct_col0   = SCW({int_col0}, std::vector<bool>{1, 0, 0, 1, 1});
+  auto int_col1      = ICW{0, 1, 2, 3, 4};
+  auto list_col      = LCW{{0}, {}, {1, 2}, {3}, {4}};
+  auto struct_col1   = SCW({struct_col0, int_col1, list_col}, std::vector<bool>{1, 1, 1, 0, 1});
+  auto result_child0 = ICW{0};
+  auto result_col0   = SCW({result_child0}, std::vector<bool>{0});
+  auto result_col1   = ICW{{1}, {1}};
+  auto result_col2   = LCW({LCW{}}, std::vector<bool>{1}.begin());
+  // test without nulls
+  this->reduction_test(
+    struct_col1,
+    cudf::table_view{{result_col0, result_col1, result_col2}},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(1, cudf::null_policy::INCLUDE));
+
+  // test with null-include
+  result_child0 = ICW{0};
+  result_col0   = SCW({result_child0}, std::vector<bool>{0});
+  result_col1   = ICW{{0}, {0}};
+  result_col2   = LCW({LCW{3}}, std::vector<bool>{0}.begin());
+  this->reduction_test(
+    struct_col1,
+    cudf::table_view{{result_col0, result_col1, result_col2}},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(3, cudf::null_policy::INCLUDE));
+
+  // test with null-exclude
+  result_child0 = ICW{0};
+  result_col0   = SCW({result_child0}, std::vector<bool>{1});
+  result_col1   = ICW{{4}, {1}};
+  result_col2   = LCW({LCW{4}}, std::vector<bool>{1}.begin());
+  this->reduction_test(
+    struct_col1,
+    cudf::table_view{{result_col0, result_col1, result_col2}},  // expected_value,
+    true,
+    true,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(3, cudf::null_policy::EXCLUDE));
+}
+
+TEST_F(StructReductionTest, NonValidStructReductionNthElement)
+{
+  using ICW = cudf::test::fixed_width_column_wrapper<int>;
+
+  // test against col.size() <= col.null_count()
+  auto child0     = ICW{-3, 3};
+  auto child1     = ICW{0, 0};
+  auto child2     = ICW{{-10, 10}, {0, 1}};
+  auto struct_col = SCW{{child0, child1, child2}, {0, 0}};
+  auto ret_col0   = ICW{{0}, {0}};
+  auto ret_col1   = ICW{{0}, {0}};
+  auto ret_col2   = ICW{{0}, {0}};
+  this->reduction_test(
+    struct_col,
+    cudf::table_view{{ret_col0, ret_col1, ret_col2}},  // expected_value,
+    true,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(0, cudf::null_policy::INCLUDE));
+
+  // test against empty input (would fail because we can not create empty struct scalar)
+  child0     = ICW{};
+  child1     = ICW{};
+  child2     = ICW{};
+  struct_col = SCW{{child0, child1, child2}};
+  ret_col0   = ICW{};
+  ret_col1   = ICW{};
+  ret_col2   = ICW{};
+  this->reduction_test(
+    struct_col,
+    cudf::table_view{{ret_col0, ret_col1, ret_col2}},  // expected_value,
+    false,
+    false,
+    *cudf::make_nth_element_aggregation<reduce_aggregation>(0, cudf::null_policy::INCLUDE));
+}
+
+TEST_F(StructReductionTest, StructReductionMinMaxNoNull)
+{
+  using INTS_CW    = cudf::test::fixed_width_column_wrapper<int>;
+  using STRINGS_CW = cudf::test::strings_column_wrapper;
+  using STRUCTS_CW = cudf::test::structs_column_wrapper;
+
+  auto const input = [] {
+    auto child1 = STRINGS_CW{"año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = INTS_CW{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return STRUCTS_CW{{child1, child2}};
+  }();
+
+  {
+    auto const expected_child1 = STRINGS_CW{"$1"};
+    auto const expected_child2 = INTS_CW{8};
+    this->reduction_test(input,
+                         cudf::table_view{{expected_child1, expected_child2}},
+                         true,
+                         true,
+                         *cudf::make_min_aggregation<reduce_aggregation>());
+  }
+
+  {
+    auto const expected_child1 = STRINGS_CW{"₹1"};
+    auto const expected_child2 = INTS_CW{3};
+    this->reduction_test(input,
+                         cudf::table_view{{expected_child1, expected_child2}},
+                         true,
+                         true,
+                         *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+}
+
+TEST_F(StructReductionTest, StructReductionMinMaxSlicedInput)
+{
+  using INTS_CW    = cudf::test::fixed_width_column_wrapper<int>;
+  using STRINGS_CW = cudf::test::strings_column_wrapper;
+  using STRUCTS_CW = cudf::test::structs_column_wrapper;
+  constexpr int32_t dont_care{1};
+
+  auto const input_original = [] {
+    auto child1 = STRINGS_CW{"$dont_care",
+                             "$dont_care",
+                             "año",
+                             "bit",
+                             "₹1",
+                             "aaa",
+                             "zit",
+                             "bat",
+                             "aab",
+                             "$1",
+                             "€1",
+                             "wut",
+                             "₹dont_care"};
+    auto child2 = INTS_CW{dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return STRUCTS_CW{{child1, child2}};
+  }();
+
+  auto const input = cudf::slice(input_original, {2, 12})[0];
+
+  {
+    auto const expected_child1 = STRINGS_CW{"$1"};
+    auto const expected_child2 = INTS_CW{8};
+    this->reduction_test(input,
+                         cudf::table_view{{expected_child1, expected_child2}},
+                         true,
+                         true,
+                         *cudf::make_min_aggregation<reduce_aggregation>());
+  }
+
+  {
+    auto const expected_child1 = STRINGS_CW{"₹1"};
+    auto const expected_child2 = INTS_CW{3};
+    this->reduction_test(input,
+                         cudf::table_view{{expected_child1, expected_child2}},
+                         true,
+                         true,
+                         *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+}
+
+TEST_F(StructReductionTest, StructReductionMinMaxWithNulls)
+{
+  using INTS_CW    = cudf::test::fixed_width_column_wrapper<int>;
+  using STRINGS_CW = cudf::test::strings_column_wrapper;
+  using STRUCTS_CW = cudf::test::structs_column_wrapper;
+  using cudf::test::iterators::null_at;
+  using cudf::test::iterators::nulls_at;
+
+  // `null` means null at child column.
+  // `NULL` means null at parent column.
+  auto const input = [] {
+    auto child1 = STRINGS_CW{{"año",
+                              "bit",
+                              "₹1" /*null*/,
+                              "aaa" /*NULL*/,
+                              "zit",
+                              "bat",
+                              "aab",
+                              "$1" /*null*/,
+                              "€1" /*NULL*/,
+                              "wut"},
+                             nulls_at({2, 7})};
+    auto child2 = INTS_CW{{1, 2, 3 /*null*/, 4 /*NULL*/, 5, 6, 7, 8 /*null*/, 9 /*NULL*/, 10},
+                          nulls_at({2, 7})};
+    return STRUCTS_CW{{child1, child2}, nulls_at({3, 8})};
+  }();
+
+  {
+    // In the structs column, the min struct is {null, null}.
+    auto const expected_child1 = STRINGS_CW{{""}, null_at(0)};
+    auto const expected_child2 = INTS_CW{{8}, null_at(0)};
+    this->reduction_test(input,
+                         cudf::table_view{{expected_child1, expected_child2}},
+                         true,
+                         true,
+                         *cudf::make_min_aggregation<reduce_aggregation>());
+  }
+
+  {
+    auto const expected_child1 = STRINGS_CW{"zit"};
+    auto const expected_child2 = INTS_CW{5};
+    this->reduction_test(input,
+                         cudf::table_view{{expected_child1, expected_child2}},
+                         true,
+                         true,
+                         *cudf::make_max_aggregation<reduce_aggregation>());
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/reductions/scan_tests.cpp b/cpp/tests/reductions/scan_tests.cpp
new file mode 100644
index 0000000..161b1ee
--- /dev/null
+++ b/cpp/tests/reductions/scan_tests.cpp
@@ -0,0 +1,755 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/reductions/scan_tests.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/reduction.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/zip_iterator.h>
+#include <thrust/tuple.h>
+
+#include <algorithm>
+#include <numeric>
+
+using aggregation      = cudf::aggregation;
+using scan_aggregation = cudf::scan_aggregation;
+using cudf::null_policy;
+using cudf::scan_type;
+
+// This is the main test feature
+template <typename T>
+struct ScanTest : public BaseScanTest<T> {
+  using HostType = typename BaseScanTest<T>::HostType;
+
+  void scan_test(cudf::host_span<HostType const> v,
+                 cudf::host_span<bool const> b,
+                 scan_aggregation const& agg,
+                 scan_type inclusive,
+                 null_policy null_handling,
+                 numeric::scale_type scale)
+  {
+    auto col_in = this->make_column(v, b, scale);
+
+    if (not this->params_supported(agg, inclusive)) {
+      EXPECT_THROW(scan(*col_in, agg, inclusive, null_handling), cudf::logic_error);
+    } else {
+      auto expected_col_out = this->make_expected(v, b, agg, inclusive, null_handling, scale);
+      auto col_out          = scan(*col_in, agg, inclusive, null_handling);
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_col_out, *col_out);
+      EXPECT_FALSE(cudf::has_nonempty_nulls(col_out->view()));
+    }
+  }
+
+  // Overload to iterate the test over a few different scales for fixed-point tests
+  void scan_test(cudf::host_span<HostType const> v,
+                 cudf::host_span<bool const> b,
+                 scan_aggregation const& agg,
+                 scan_type inclusive,
+                 null_policy null_handling = null_policy::EXCLUDE)
+  {
+    if constexpr (cudf::is_fixed_point<T>()) {
+      for (auto scale : {0, -1, -2, -3}) {
+        scan_test(v, b, agg, inclusive, null_handling, numeric::scale_type{scale});
+      }
+    } else {
+      scan_test(v, b, agg, inclusive, null_handling, numeric::scale_type{0});
+    }
+  }
+
+  bool params_supported(scan_aggregation const& agg, scan_type inclusive)
+  {
+    bool supported = [&] {
+      switch (agg.kind) {
+        case aggregation::SUM: return std::is_invocable_v<cudf::DeviceSum, T, T>;
+        case aggregation::PRODUCT: return std::is_invocable_v<cudf::DeviceProduct, T, T>;
+        case aggregation::MIN: return std::is_invocable_v<cudf::DeviceMin, T, T>;
+        case aggregation::MAX: return std::is_invocable_v<cudf::DeviceMax, T, T>;
+        case aggregation::RANK: return std::is_invocable_v<cudf::DeviceMax, T, T>;  // comparable
+        default: return false;
+      }
+      return false;
+    }();
+
+    // special cases for individual types
+    if constexpr (cudf::is_fixed_point<T>()) return supported && (agg.kind != aggregation::PRODUCT);
+    if constexpr (std::is_same_v<T, cudf::string_view> || cudf::is_timestamp<T>())
+      return supported && (inclusive == scan_type::INCLUSIVE);
+    return supported;
+  }
+
+  std::function<HostType(HostType, HostType)> make_agg(scan_aggregation const& agg)
+  {
+    if constexpr (std::is_same_v<T, cudf::string_view>) {
+      switch (agg.kind) {
+        case aggregation::MIN: return [](HostType a, HostType b) { return std::min(a, b); };
+        case aggregation::MAX: return [](HostType a, HostType b) { return std::max(a, b); };
+        default: {
+          CUDF_FAIL("Unsupported aggregation");
+          return [](HostType a, HostType b) { return std::min(a, b); };
+        }
+      }
+    } else {
+      switch (agg.kind) {
+        case aggregation::SUM: return std::plus<HostType>{};
+        case aggregation::PRODUCT: return std::multiplies<HostType>{};
+        case aggregation::MIN: return [](HostType a, HostType b) { return std::min(a, b); };
+        case aggregation::MAX: return [](HostType a, HostType b) { return std::max(a, b); };
+        default: {
+          CUDF_FAIL("Unsupported aggregation");
+          return [](HostType a, HostType b) { return std::min(a, b); };
+        }
+      }
+    }
+  }
+
+  HostType make_identity(scan_aggregation const& agg)
+  {
+    if constexpr (std::is_same_v<T, cudf::string_view>) {
+      switch (agg.kind) {
+        case aggregation::MIN: return std::string{"\xF7\xBF\xBF\xBF"};
+        case aggregation::MAX: return std::string{};
+        default: CUDF_FAIL("Unsupported aggregation");
+      }
+    } else {
+      switch (agg.kind) {
+        case aggregation::SUM: return HostType{0};
+        case aggregation::PRODUCT: return HostType{1};
+        case aggregation::MIN:
+          if constexpr (std::numeric_limits<HostType>::has_infinity) {
+            return std::numeric_limits<HostType>::infinity();
+          } else {
+            return std::numeric_limits<HostType>::max();
+          }
+        case aggregation::MAX:
+          if constexpr (std::numeric_limits<HostType>::has_infinity) {
+            return -std::numeric_limits<HostType>::infinity();
+          } else {
+            return std::numeric_limits<HostType>::lowest();
+          }
+        default: CUDF_FAIL("Unsupported aggregation");
+      }
+    }
+  }
+
+  std::unique_ptr<cudf::column> make_expected(cudf::host_span<HostType const> v,
+                                              cudf::host_span<bool const> b,
+                                              scan_aggregation const& agg,
+                                              scan_type inclusive,
+                                              null_policy null_handling,
+                                              numeric::scale_type scale = numeric::scale_type{0})
+  {
+    auto op       = this->make_agg(agg);
+    auto identity = this->make_identity(agg);
+
+    thrust::host_vector<HostType> expected(v.size());
+    thrust::host_vector<bool> b_out(b.begin(), b.end());
+
+    bool const nullable = (b.size() > 0);
+
+    auto masked_value = [identity](auto const& z) {
+      return thrust::get<1>(z) ? thrust::get<0>(z) : identity;
+    };
+
+    if (inclusive == scan_type::INCLUSIVE) {
+      if (nullable) {
+        std::transform_inclusive_scan(
+          thrust::make_zip_iterator(thrust::make_tuple(v.begin(), b.begin())),
+          thrust::make_zip_iterator(thrust::make_tuple(v.end(), b.end())),
+          expected.begin(),
+          op,
+          masked_value);
+
+        if (null_handling == null_policy::INCLUDE) {
+          std::inclusive_scan(b.begin(), b.end(), b_out.begin(), std::logical_and<bool>{});
+        }
+      } else {
+        std::inclusive_scan(v.begin(), v.end(), expected.begin(), op);
+      }
+    } else {
+      if (nullable) {
+        std::transform_exclusive_scan(
+          thrust::make_zip_iterator(thrust::make_tuple(v.begin(), b.begin())),
+          thrust::make_zip_iterator(thrust::make_tuple(v.end(), b.end())),
+          expected.begin(),
+          identity,
+          op,
+          masked_value);
+
+        if (null_handling == null_policy::INCLUDE) {
+          std::exclusive_scan(b.begin(), b.end(), b_out.begin(), true, std::logical_and<bool>{});
+        }
+      } else {
+        std::exclusive_scan(v.begin(), v.end(), expected.begin(), identity, op);
+      }
+    }
+
+    return nullable ? this->make_column(expected, b_out, scale)
+                    : this->make_column(expected, {}, scale);
+  }
+};
+
+using TestTypes = cudf::test::
+  Concat<cudf::test::NumericTypes, cudf::test::FixedPointTypes, cudf::test::StringTypes>;
+
+TYPED_TEST_SUITE(ScanTest, TestTypes);
+
+TYPED_TEST(ScanTest, Min)
+{
+  auto const v = make_vector<TypeParam>({123, 64, 63, 99, -5, 123, -16, -120, -111});
+  auto const b = thrust::host_vector<bool>(std::vector<bool>{1, 0, 1, 1, 1, 1, 0, 0, 1});
+
+  // no nulls
+  this->scan_test(v, {}, *cudf::make_min_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v, {}, *cudf::make_min_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  // skipna = true (default)
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+  // skipna = false
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::INCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::INCLUDE);
+}
+
+TYPED_TEST(ScanTest, Max)
+{
+  auto const v = make_vector<TypeParam>({-120, 5, 0, -120, -111, 64, 63, 99, 123, -16});
+  auto const b = thrust::host_vector<bool>(std::vector<bool>{1, 0, 1, 1, 1, 1, 0, 1, 0, 1});
+
+  // inclusive
+  // no nulls
+  this->scan_test(v, {}, *cudf::make_max_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v, {}, *cudf::make_max_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  // skipna = true (default)
+  this->scan_test(v,
+                  b,
+                  *cudf::make_max_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_max_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+  // skipna = false
+  this->scan_test(v,
+                  b,
+                  *cudf::make_max_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::INCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_max_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::INCLUDE);
+}
+
+TYPED_TEST(ScanTest, Product)
+{
+  auto const v = make_vector<TypeParam>({5, -1, 1, 3, -2, 4});
+  auto const b = thrust::host_vector<bool>(std::vector<bool>{1, 1, 1, 0, 1, 1});
+
+  // no nulls
+  this->scan_test(v, {}, *cudf::make_product_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v, {}, *cudf::make_product_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  // skipna = true (default)
+  this->scan_test(v,
+                  b,
+                  *cudf::make_product_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_product_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+  // skipna = false
+  this->scan_test(v,
+                  b,
+                  *cudf::make_product_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::INCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_product_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::INCLUDE);
+}
+
+TYPED_TEST(ScanTest, Sum)
+{
+  auto const v = [] {
+    if (std::is_signed_v<TypeParam>)
+      return make_vector<TypeParam>({-120, 5, 6, 113, -111, 64, -63, 9, 34, -16});
+    return make_vector<TypeParam>({12, 5, 6, 13, 11, 14, 3, 9, 34, 16});
+  }();
+  auto const b = thrust::host_vector<bool>(std::vector<bool>{1, 0, 1, 1, 0, 0, 1, 1, 1, 1});
+
+  // no nulls
+  this->scan_test(v, {}, *cudf::make_sum_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v, {}, *cudf::make_sum_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  // skipna = true (default)
+  this->scan_test(v,
+                  b,
+                  *cudf::make_sum_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_sum_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+  // skipna = false
+  this->scan_test(v,
+                  b,
+                  *cudf::make_sum_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::INCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_sum_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::INCLUDE);
+}
+
+TYPED_TEST(ScanTest, EmptyColumn)
+{
+  auto const v = thrust::host_vector<typename TypeParam_to_host_type<TypeParam>::type>{};
+  auto const b = thrust::host_vector<bool>{};
+
+  // skipna = true (default)
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+  // skipna = false
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::INCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::INCLUDE);
+}
+
+TYPED_TEST(ScanTest, LeadingNulls)
+{
+  auto const v = make_vector<TypeParam>({100, 200, 300});
+  auto const b = thrust::host_vector<bool>(std::vector<bool>{0, 1, 1});
+
+  // skipna = true (default)
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+  // skipna = false
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::INCLUDE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::INCLUDE);
+}
+
+class ScanStringsTest : public ScanTest<cudf::string_view> {};
+
+TEST_F(ScanStringsTest, MoreStringsMinMax)
+{
+  int row_count = 512;
+
+  auto data_begin = cudf::detail::make_counting_transform_iterator(0, [](auto idx) {
+    char const s[] = {static_cast<char>('a' + (idx % 26)), 0};
+    return std::string(s);
+  });
+  auto validity   = cudf::detail::make_counting_transform_iterator(
+    0, [](auto idx) -> bool { return (idx % 23) != 22; });
+  cudf::test::strings_column_wrapper col(data_begin, data_begin + row_count, validity);
+
+  thrust::host_vector<std::string> v(data_begin, data_begin + row_count);
+  thrust::host_vector<bool> b(validity, validity + row_count);
+
+  this->scan_test(v, {}, *cudf::make_min_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v, b, *cudf::make_min_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+
+  this->scan_test(v, {}, *cudf::make_min_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  this->scan_test(v, b, *cudf::make_min_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_min_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+
+  this->scan_test(v, {}, *cudf::make_max_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v, b, *cudf::make_max_aggregation<scan_aggregation>(), scan_type::INCLUSIVE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_max_aggregation<scan_aggregation>(),
+                  scan_type::INCLUSIVE,
+                  null_policy::EXCLUDE);
+
+  this->scan_test(v, {}, *cudf::make_max_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  this->scan_test(v, b, *cudf::make_max_aggregation<scan_aggregation>(), scan_type::EXCLUSIVE);
+  this->scan_test(v,
+                  b,
+                  *cudf::make_max_aggregation<scan_aggregation>(),
+                  scan_type::EXCLUSIVE,
+                  null_policy::EXCLUDE);
+}
+
+template <typename T>
+struct ScanChronoTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ScanChronoTest, cudf::test::ChronoTypes);
+
+TYPED_TEST(ScanChronoTest, ChronoMinMax)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({5, 4, 6, 0, 1, 6, 5, 3},
+                                                                 {1, 1, 1, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected_min({5, 4, 4, 0, 1, 1, 1, 1},
+                                                                          {1, 1, 1, 0, 1, 1, 1, 1});
+
+  auto result =
+    cudf::scan(col, *cudf::make_min_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected_min);
+
+  result = cudf::scan(col,
+                      *cudf::make_min_aggregation<scan_aggregation>(),
+                      cudf::scan_type::INCLUSIVE,
+                      cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected_min);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected_max({5, 5, 6, 0, 6, 6, 6, 6},
+                                                                          {1, 1, 1, 0, 1, 1, 1, 1});
+  result =
+    cudf::scan(col, *cudf::make_max_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected_max);
+
+  result = cudf::scan(col,
+                      *cudf::make_max_aggregation<scan_aggregation>(),
+                      cudf::scan_type::INCLUSIVE,
+                      cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected_max);
+
+  EXPECT_THROW(
+    cudf::scan(col, *cudf::make_max_aggregation<scan_aggregation>(), cudf::scan_type::EXCLUSIVE),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::scan(col, *cudf::make_min_aggregation<scan_aggregation>(), cudf::scan_type::EXCLUSIVE),
+    cudf::logic_error);
+}
+
+template <typename T>
+struct ScanDurationTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ScanDurationTest, cudf::test::DurationTypes);
+
+TYPED_TEST(ScanDurationTest, Sum)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({5, 4, 6, 0, 1, 6, 5, 3},
+                                                                 {1, 1, 1, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> expected({5, 9, 15, 0, 16, 22, 27, 30},
+                                                                      {1, 1, 1, 0, 1, 1, 1, 1});
+
+  auto result =
+    cudf::scan(col, *cudf::make_sum_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+
+  result = cudf::scan(col,
+                      *cudf::make_sum_aggregation<scan_aggregation>(),
+                      cudf::scan_type::INCLUSIVE,
+                      cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+
+  EXPECT_THROW(
+    cudf::scan(col, *cudf::make_sum_aggregation<scan_aggregation>(), cudf::scan_type::EXCLUSIVE),
+    cudf::logic_error);
+}
+
+struct StructScanTest : public cudf::test::BaseFixture {};
+
+TEST_F(StructScanTest, StructScanMinMaxNoNull)
+{
+  using INTS_CW    = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using STRINGS_CW = cudf::test::strings_column_wrapper;
+  using STRUCTS_CW = cudf::test::structs_column_wrapper;
+
+  auto const input = [] {
+    auto child1 = STRINGS_CW{"año", "bit", "₹1", "aaa", "zit", "bat", "aab", "$1", "€1", "wut"};
+    auto child2 = INTS_CW{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+    return STRUCTS_CW{{child1, child2}};
+  }();
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{"año", "año", "año", "aaa", "aaa", "aaa", "aaa", "$1", "$1", "$1"};
+      auto child2 = INTS_CW{1, 1, 1, 4, 4, 4, 4, 8, 8, 8};
+      return STRUCTS_CW{{child1, child2}};
+    }();
+    auto const result = cudf::scan(
+      input, *cudf::make_min_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{"año", "bit", "₹1", "₹1", "₹1", "₹1", "₹1", "₹1", "₹1", "₹1"};
+      auto child2 = INTS_CW{1, 2, 3, 3, 3, 3, 3, 3, 3, 3};
+      return STRUCTS_CW{{child1, child2}};
+    }();
+    auto const result = cudf::scan(
+      input, *cudf::make_max_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(StructScanTest, StructScanMinMaxSlicedInput)
+{
+  using INTS_CW    = cudf::test::fixed_width_column_wrapper<int>;
+  using STRINGS_CW = cudf::test::strings_column_wrapper;
+  using STRUCTS_CW = cudf::test::structs_column_wrapper;
+  constexpr int32_t dont_care{1};
+
+  auto const input_original = [] {
+    auto child1 = STRINGS_CW{"$dont_care",
+                             "$dont_care",
+                             "año",
+                             "bit",
+                             "₹1",
+                             "aaa",
+                             "zit",
+                             "bat",
+                             "aab",
+                             "$1",
+                             "€1",
+                             "wut",
+                             "₹dont_care"};
+    auto child2 = INTS_CW{dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, dont_care};
+    return STRUCTS_CW{{child1, child2}};
+  }();
+
+  auto const input = cudf::slice(input_original, {2, 12})[0];
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{"año", "año", "año", "aaa", "aaa", "aaa", "aaa", "$1", "$1", "$1"};
+      auto child2 = INTS_CW{1, 1, 1, 4, 4, 4, 4, 8, 8, 8};
+      return STRUCTS_CW{{child1, child2}};
+    }();
+    auto const result = cudf::scan(
+      input, *cudf::make_min_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{"año", "bit", "₹1", "₹1", "₹1", "₹1", "₹1", "₹1", "₹1", "₹1"};
+      auto child2 = INTS_CW{1, 2, 3, 3, 3, 3, 3, 3, 3, 3};
+      return STRUCTS_CW{{child1, child2}};
+    }();
+    auto const result = cudf::scan(
+      input, *cudf::make_max_aggregation<scan_aggregation>(), cudf::scan_type::INCLUSIVE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(StructScanTest, StructScanMinMaxWithNulls)
+{
+  using INTS_CW    = cudf::test::fixed_width_column_wrapper<int>;
+  using STRINGS_CW = cudf::test::strings_column_wrapper;
+  using STRUCTS_CW = cudf::test::structs_column_wrapper;
+  using cudf::test::iterators::null_at;
+  using cudf::test::iterators::nulls_at;
+
+  // `null` means null at child column.
+  // `NULL` means null at parent column.
+  auto const input = [] {
+    auto child1 = STRINGS_CW{{"año",
+                              "bit",
+                              "₹1" /*null*/,
+                              "aaa" /*NULL*/,
+                              "zit",
+                              "bat",
+                              "aab",
+                              "$1" /*null*/,
+                              "€1" /*NULL*/,
+                              "wut"},
+                             nulls_at({2, 7})};
+    auto child2 = INTS_CW{{1, 2, 3 /*null*/, 4 /*NULL*/, 5, 6, 7, 8 /*null*/, 9 /*NULL*/, 10},
+                          nulls_at({2, 7})};
+    return STRUCTS_CW{{child1, child2}, nulls_at({3, 8})};
+  }();
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{{"año",
+                                "año",
+                                "" /*null*/,
+                                "" /*null*/,
+                                "" /*null*/,
+                                "" /*null*/,
+                                "" /*null*/,
+                                "" /*null*/,
+                                "" /*null*/,
+                                "" /*null*/},
+                               nulls_at({2, 3, 4, 5, 6, 7, 8, 9})};
+      auto child2 = INTS_CW{{1,
+                             1,
+                             0 /*null*/,
+                             0 /*null*/,
+                             0 /*null*/,
+                             0 /*null*/,
+                             0 /*null*/,
+                             0 /*null*/,
+                             0 /*null*/,
+                             0 /*null*/},
+                            nulls_at({2, 3, 4, 5, 6, 7, 8, 9})};
+      return STRUCTS_CW{{child1, child2}, nulls_at({3, 8})};
+    }();
+
+    auto const result = cudf::scan(input,
+                                   *cudf::make_min_aggregation<scan_aggregation>(),
+                                   cudf::scan_type::INCLUSIVE,
+                                   null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{
+        "año", "bit", "bit", "" /*NULL*/, "zit", "zit", "zit", "zit", "" /*NULL*/, "zit"};
+      auto child2 = INTS_CW{1, 2, 2, 0 /*NULL*/, 5, 5, 5, 5, 0 /*NULL*/, 5};
+      return STRUCTS_CW{{child1, child2}, nulls_at({3, 8})};
+    }();
+
+    auto const result = cudf::scan(input,
+                                   *cudf::make_max_aggregation<scan_aggregation>(),
+                                   cudf::scan_type::INCLUSIVE,
+                                   null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{{"año",
+                                "año",
+                                "" /*null*/,
+                                "" /*NULL*/,
+                                "" /*NULL*/,
+                                "" /*NULL*/,
+                                "" /*NULL*/,
+                                "" /*NULL*/,
+                                "" /*NULL*/,
+                                "" /*NULL*/},
+                               null_at(2)};
+      auto child2 = INTS_CW{{1,
+                             1,
+                             0 /*null*/,
+                             0 /*NULL*/,
+                             0 /*NULL*/,
+                             0 /*NULL*/,
+                             0 /*NULL*/,
+                             0 /*NULL*/,
+                             0 /*NULL*/,
+                             0 /*NULL*/},
+                            null_at(2)};
+      return STRUCTS_CW{{child1, child2}, nulls_at({3, 4, 5, 6, 7, 8, 9})};
+    }();
+
+    auto const result = cudf::scan(input,
+                                   *cudf::make_min_aggregation<scan_aggregation>(),
+                                   cudf::scan_type::INCLUSIVE,
+                                   null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const expected = [] {
+      auto child1 = STRINGS_CW{"año",
+                               "bit",
+                               "bit",
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/};
+      auto child2 = INTS_CW{1,
+                            2,
+                            2,
+                            0 /*NULL*/,
+                            0 /*NULL*/,
+                            0 /*NULL*/,
+                            0 /*NULL*/,
+                            0 /*NULL*/,
+                            0 /*NULL*/,
+                            0 /*NULL*/};
+      return STRUCTS_CW{{child1, child2}, nulls_at({3, 4, 5, 6, 7, 8, 9})};
+    }();
+
+    auto const result = cudf::scan(input,
+                                   *cudf::make_max_aggregation<scan_aggregation>(),
+                                   cudf::scan_type::INCLUSIVE,
+                                   null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
diff --git a/cpp/tests/reductions/scan_tests.hpp b/cpp/tests/reductions/scan_tests.hpp
new file mode 100644
index 0000000..858697d
--- /dev/null
+++ b/cpp/tests/reductions/scan_tests.hpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/strings/string_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <cudf/utilities/traits.hpp>
+
+#include <thrust/host_vector.h>
+
+#include <initializer_list>
+#include <type_traits>
+#include <vector>
+
+template <typename T>
+struct TypeParam_to_host_type {
+  using type = T;
+};
+
+template <>
+struct TypeParam_to_host_type<cudf::string_view> {
+  using type = std::string;
+};
+
+template <>
+struct TypeParam_to_host_type<numeric::decimal32> {
+  using type = numeric::decimal32::rep;
+};
+
+template <>
+struct TypeParam_to_host_type<numeric::decimal64> {
+  using type = numeric::decimal64::rep;
+};
+
+template <>
+struct TypeParam_to_host_type<numeric::decimal128> {
+  using type = numeric::decimal128::rep;
+};
+
+template <typename TypeParam, typename T>
+std::enable_if_t<std::is_same_v<TypeParam, cudf::string_view>, thrust::host_vector<std::string>>
+make_vector(std::initializer_list<T> const& init)
+{
+  return cudf::test::make_type_param_vector<std::string, T>(init);
+}
+
+template <typename TypeParam, typename T>
+std::enable_if_t<cudf::is_fixed_point<TypeParam>(), thrust::host_vector<typename TypeParam::rep>>
+make_vector(std::initializer_list<T> const& init)
+{
+  return cudf::test::make_type_param_vector<typename TypeParam::rep, T>(init);
+}
+
+template <typename TypeParam, typename T>
+std::enable_if_t<not(std::is_same_v<TypeParam, cudf::string_view> ||
+                     cudf::is_fixed_point<TypeParam>()),
+                 thrust::host_vector<TypeParam>>
+make_vector(std::initializer_list<T> const& init)
+{
+  return cudf::test::make_type_param_vector<TypeParam, T>(init);
+}
+
+// This is the base test feature
+template <typename T>
+struct BaseScanTest : public cudf::test::BaseFixture {
+  using HostType = typename TypeParam_to_host_type<T>::type;
+
+  std::unique_ptr<cudf::column> make_column(cudf::host_span<HostType const> v,
+                                            cudf::host_span<bool const> b = {},
+                                            numeric::scale_type scale     = numeric::scale_type{0})
+  {
+    if constexpr (std::is_same_v<T, cudf::string_view>) {
+      auto col = (b.size() > 0) ? cudf::test::strings_column_wrapper(v.begin(), v.end(), b.begin())
+                                : cudf::test::strings_column_wrapper(v.begin(), v.end());
+      return col.release();
+    } else if constexpr (cudf::is_fixed_point<T>()) {
+      auto col = (b.size() > 0) ? cudf::test::fixed_point_column_wrapper<typename T::rep>(
+                                    v.begin(), v.end(), b.begin(), scale)
+                                : cudf::test::fixed_point_column_wrapper<typename T::rep>(
+                                    v.begin(), v.end(), scale);
+      return col.release();
+    } else {
+      auto col = (b.size() > 0)
+                   ? cudf::test::fixed_width_column_wrapper<T>(v.begin(), v.end(), b.begin())
+                   : cudf::test::fixed_width_column_wrapper<T>(v.begin(), v.end());
+      return col.release();
+    }
+  }
+};
diff --git a/cpp/tests/reductions/segmented_reduction_tests.cpp b/cpp/tests/reductions/segmented_reduction_tests.cpp
new file mode 100644
index 0000000..fde587f
--- /dev/null
+++ b/cpp/tests/reductions/segmented_reduction_tests.cpp
@@ -0,0 +1,1494 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+
+#include <limits>
+#include <utility>
+#include <vector>
+
+#define XXX 0  // null placeholder
+
+template <typename T>
+struct SegmentedReductionTest : public cudf::test::BaseFixture {};
+
+struct SegmentedReductionTestUntyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_CASE(SegmentedReductionTest, cudf::test::NumericTypes);
+
+TYPED_TEST(SegmentedReductionTest, SumExcludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [1], [null], [null, null], []
+  // values:   {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}
+  // offsets:  {0, 3, 6, 7, 8, 10, 10}
+  // nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}
+  // outputs:  {6, 4, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 1, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{6, 4, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(3);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{9, 7, 4, 3, 3, 3}, {1, 1, 1, 1, 1, 1}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, ProductExcludeNulls)
+{
+  // [1, 3, 5], [null, 3, 5], [1], [null], [null, null], []
+  // values:    {1, 3, 5, XXX, 3, 5, 1, XXX, XXX, XXX}
+  // offsets:   {0, 3, 6, 7, 8, 10, 10}
+  // nullmask:  {1, 1, 1, 0, 1, 1, 1, 0, 0, 0}
+  // outputs:   {15, 15, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 1, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 3, 5, XXX, 3, 5, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 1, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<TypeParam>{{15, 15, 1, XXX, XXX, XXX},
+                                                                        {1, 1, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(3);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{45, 45, 3, 3, 3, 3}, {1, 1, 1, 1, 1, 1}};
+
+  res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::EXCLUDE,
+                           *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::EXCLUDE,
+                           *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, MaxExcludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [1], [null], [null, null], []
+  // values:    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}
+  // offsets:   {0, 3, 6, 7, 8, 10, 10}
+  // nullmask:  {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}
+  // outputs:   {3, 3, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 1, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{3, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(2);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{3, 3, 2, 2, 2, 2}, {1, 1, 1, 1, 1, 1}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, MinExcludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [1], [null], [null, null], []
+  // values:   {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}
+  // offsets:  {0, 3, 6, 7, 8, 10, 10}
+  // nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}
+  // outputs:  {1, 1, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 1, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{1, 1, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(2);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{1, 1, 1, 2, 2, 2}, {1, 1, 1, 1, 1, 1}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, AnyExcludeNulls)
+{
+  // [0, 0, 0], [0, null, 0], [0, 1, 0], [1, null, 0], [], [0], [1], [null], [null, null]
+  // values:  {0, 0, 0, 0, XXX, 0, 0, 1, 0, 1, XXX, 0, 0, 1, XXX, XXX, XXX}
+  // offsets: {0, 3, 6, 9, 12, 12, 13, 14, 15, 17}
+  // nullmask:{1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0}
+  // outputs: {0, 0, 1, 1, XXX, 0, 1, XXX, XXX}
+  // output nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {0, 0, 0, 0, XXX, 0, 0, 1, 0, 1, XXX, 0, 0, 1, XXX, XXX, XXX},
+    {1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 9, 12, 12, 13, 14, 15, 17};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {false, false, true, true, bool{XXX}, false, true, bool{XXX}, bool{XXX}},
+    {true, true, true, true, false, true, true, false, false}};
+
+  auto const agg         = cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::BOOL8};
+  auto const policy      = cudf::null_policy::EXCLUDE;
+
+  auto res = cudf::segmented_reduce(input, d_offsets, *agg, output_type, policy);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(0);
+  auto const init_expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {false, false, true, true, false, false, true, false, false},
+    {true, true, true, true, true, true, true, true, true}};
+
+  res = cudf::segmented_reduce(input, d_offsets, *agg, output_type, policy, *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res = cudf::segmented_reduce(input, d_offsets, *agg, output_type, policy, *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, AllExcludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [], [1], [null], [null, null], [1, 0, 3], [1, null, 0], [0]
+  // values: {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX, 1, 0, 3, 1, XXX, 0, 0}
+  // offsets: {0, 3, 6, 6, 7, 8, 10, 13, 16, 17}
+  // nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1}
+  // outputs: {true, true, XXX, true, XXX, XXX, false, false, false}
+  // output nullmask: {1, 1, 0, 1, 0, 0, 1, 1, 1}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX, 1, 0, 3, 1, XXX, 0, 0},
+    {1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 6, 7, 8, 10, 13, 16, 17};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, bool{XXX}, true, bool{XXX}, bool{XXX}, false, false, false},
+    {true, true, false, true, false, false, true, true, true}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::BOOL8},
+                           cudf::null_policy::EXCLUDE);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(1);
+  auto const init_expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, true, true, true, true, false, false, false},
+    {true, true, true, true, true, true, true, true, true}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::BOOL8},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::BOOL8},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, SumIncludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [1], [null], [null, null], []
+  // values:   {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}
+  // offsets:  {0, 3, 6, 7, 8, 10, 10}
+  // nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}
+  // outputs:  {6, XXX, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 0, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<TypeParam>{{6, XXX, 1, XXX, XXX, XXX},
+                                                                        {1, 0, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(3);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{9, XXX, 4, XXX, XXX, 3}, {1, 0, 1, 0, 0, 1}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {XXX, XXX, XXX, XXX, XXX, XXX}, {0, 0, 0, 0, 0, 0}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, ProductIncludeNulls)
+{
+  // [1, 3, 5], [null, 3, 5], [1], [null], [null, null], []
+  // values:    {1, 3, 5, XXX, 3, 5, 1, XXX, XXX, XXX}
+  // offsets:   {0, 3, 6, 7, 8, 10, 10}
+  // nullmask:  {1, 1, 1, 0, 1, 1, 1, 0, 0, 0}
+  // outputs:   {15, XXX, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 0, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 3, 5, XXX, 3, 5, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 1, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<TypeParam>{{15, XXX, 1, XXX, XXX, XXX},
+                                                                        {1, 0, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(3);
+  auto const init_expect = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {45, XXX, 3, XXX, XXX, 3}, {1, 0, 1, 0, 0, 1}};
+
+  res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::INCLUDE,
+                           *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {XXX, XXX, XXX, XXX, XXX, XXX}, {0, 0, 0, 0, 0, 0}};
+
+  res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::INCLUDE,
+                           *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, MaxIncludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [1], [null], [null, null], []
+  // values:    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}
+  // offsets:   {0, 3, 6, 7, 8, 10, 10}
+  // nullmask:  {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}
+  // outputs:   {3, XXX, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 0, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 1, 0, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<TypeParam>{{3, XXX, 1, XXX, XXX, XXX},
+                                                                        {1, 0, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(2);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{3, XXX, 2, XXX, XXX, 2}, {1, 0, 1, 0, 0, 1}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {XXX, XXX, XXX, XXX, XXX, XXX}, {0, 0, 0, 0, 0, 0}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, MinIncludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [1], [null], [null, null], []
+  // values:   {1, 2, 3, 1, XXX, 3, 1, XXX, XXX}
+  // offsets:  {0, 3, 6, 7, 8, 10, 10}
+  // nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0}
+  // outputs:  {1, XXX, 1, XXX, XXX, XXX}
+  // output nullmask: {1, 0, 1, 0, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 1, 0, 1, 1, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<TypeParam>{{1, XXX, 1, XXX, XXX, XXX},
+                                                                        {1, 0, 1, 0, 0, 0}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(2);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<TypeParam>{{1, XXX, 1, XXX, XXX, 2}, {1, 0, 1, 0, 0, 1}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {XXX, XXX, XXX, XXX, XXX, XXX}, {0, 0, 0, 0, 0, 0}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<TypeParam>()},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, AnyIncludeNulls)
+{
+  // [0, 0, 0], [0, null, 0], [0, 1, 0], [1, null, 0], [], [0], [1], [null], [null, null]
+  // values:  {0, 0, 0, 0, XXX, 0, 0, 1, 0, 1, XXX, 0, 0, 1, XXX, XXX, XXX}
+  // offsets: {0, 3, 6, 9, 12, 12, 13, 14, 15, 17}
+  // nullmask:{1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0}
+  // outputs: {0, XXX, 1, XXX, XXX, 0, 1, XXX, XXX}
+  // output nullmask: {1, 0, 1, 0, 0, 1, 1, 0, 0}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {0, 0, 0, 0, XXX, 0, 0, 1, 0, 1, XXX, 0, 0, 1, XXX, XXX, XXX},
+    {1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 9, 12, 12, 13, 14, 15, 17};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {false, bool{XXX}, true, bool{XXX}, bool{XXX}, false, true, bool{XXX}, bool{XXX}},
+    {true, false, true, false, false, true, true, false, false}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::BOOL8},
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(1);
+  auto const init_expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, bool{XXX}, true, bool{XXX}, true, true, true, bool{XXX}, bool{XXX}},
+    {true, false, true, false, true, true, true, false, false}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::BOOL8},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX}},
+    {false, false, false, false, false, false, false, false, false}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::BOOL8},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TYPED_TEST(SegmentedReductionTest, AllIncludeNulls)
+{
+  // [1, 2, 3], [1, null, 3], [], [1], [null], [null, null], [1, 0, 3], [1, null, 0], [0]
+  // values: {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX, 1, 0, 3, 1, XXX, 0, 0}
+  // offsets: {0, 3, 6, 6, 7, 8, 10, 13, 16, 17}
+  // nullmask: {1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1}
+  // outputs: {true, XXX, XXX, true, XXX, XXX, false, XXX, false}
+  // output nullmask: {1, 0, 0, 1, 0, 0, 1, 0, 1}
+  auto const input = cudf::test::fixed_width_column_wrapper<TypeParam>{
+    {1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX, 1, 0, 3, 1, XXX, 0, 0},
+    {1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 6, 7, 8, 10, 13, 16, 17};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, bool{XXX}, bool{XXX}, true, bool{XXX}, bool{XXX}, false, bool{XXX}, false},
+    {true, false, false, true, false, false, true, false, true}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::BOOL8},
+                           cudf::null_policy::INCLUDE);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<TypeParam>(1);
+  auto const init_expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, bool{XXX}, true, true, bool{XXX}, bool{XXX}, false, bool{XXX}, false},
+    {true, false, true, true, false, false, true, false, true}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::BOOL8},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<bool>{
+    {bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX},
+     bool{XXX}},
+    {false, false, false, false, false, false, false, false, false}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::BOOL8},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TEST_F(SegmentedReductionTestUntyped, PartialSegmentReduction)
+{
+  // Segmented reduction allows offsets only specify part of the input columns.
+  // [1], [2, 3], [4]
+  // values: {1, 2, 3, 4, 5, 6, 7}
+  // offsets: {0, 1, 3, 4}
+  // nullmask: {1, 1, 1, 1, 1, 1, 1}
+  // outputs: {1, 5, 4}
+  // output nullmask: {1, 1, 1}
+
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {1, 2, 3, 4, 5, 6, 7}, {true, true, true, true, true, true, true}};
+  auto const offsets   = std::vector<cudf::size_type>{1, 3, 4};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<int32_t>{{5, 4}, {true, true}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::INT32},
+                           cudf::null_policy::INCLUDE);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<int32_t>(3);
+  auto const init_expect = cudf::test::fixed_width_column_wrapper<int32_t>{{8, 7}, {true, true}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::INT32},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{XXX, XXX}, {false, false}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::INT32},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TEST_F(SegmentedReductionTestUntyped, NonNullableInput)
+{
+  // Segmented reduction allows offsets only specify part of the input columns.
+  // [1], [], [2, 3], [4, 5, 6, 7]
+  // values: {1, 2, 3, 4, 5, 6, 7}
+  // offsets: {0, 1, 1, 3, 7}
+  // nullmask: nullptr
+  // outputs: {1, 5, 4}
+  // output nullmask: {1, 1, 1}
+
+  auto const input     = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5, 6, 7};
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 3, 7};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{1, XXX, 5, 22}, {true, false, true, true}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::INT32},
+                           cudf::null_policy::INCLUDE);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<int32_t>(3);
+  auto const init_expect =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{4, 3, 8, 25}, {true, true, true, true}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::INT32},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, init_expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  auto null_init_expect = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {XXX, XXX, XXX, XXX}, {false, false, false, false}};
+
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_id::INT32},
+                               cudf::null_policy::INCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, null_init_expect);
+}
+
+TEST_F(SegmentedReductionTestUntyped, Mean)
+{
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>{10, 20, 30, 40, 50, 60, 70, 80, 90};
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_mean_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::FLOAT32};
+
+  auto const expected =
+    cudf::test::fixed_width_column_wrapper<float>{{10, 0, 30, 70}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, MeanNulls)
+{
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {10, 20, 30, 40, 50, 60, 0, 80, 90}, {1, 1, 1, 1, 1, 1, 0, 1, 1});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_mean_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::FLOAT64};
+
+  auto expected = cudf::test::fixed_width_column_wrapper<double>{{10, 0, 30, 70}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+
+  expected = cudf::test::fixed_width_column_wrapper<double>{{10, 0, 30, 0}, {1, 0, 1, 0}};
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, SumOfSquares)
+{
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>{10, 20, 30, 40, 50, 60, 70, 80, 90};
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_sum_of_squares_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::INT32};
+
+  auto const expected =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{100, 0, 2900, 25500}, {1, 0, 1, 1}};
+
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, SumOfSquaresNulls)
+{
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {10, 20, 30, 40, 50, 60, 0, 80, 90}, {1, 1, 1, 1, 1, 1, 0, 1, 1});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_sum_of_squares_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::INT64};
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<int64_t>{{100, 0, 2900, 20600}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+
+  expected = cudf::test::fixed_width_column_wrapper<int64_t>{{100, 0, 2900, 0}, {1, 0, 1, 0}};
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, StandardDeviation)
+{
+  constexpr float NaN{std::numeric_limits<float>::quiet_NaN()};
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>{10, 20, 30, 40, 50, 60, 70, 80, 90};
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_std_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::FLOAT32};
+
+  auto expected = cudf::test::fixed_width_column_wrapper<float>{
+    {NaN, 0.f, 10.f, static_cast<float>(std::sqrt(250.))}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, StandardDeviationNulls)
+{
+  constexpr double NaN{std::numeric_limits<double>::quiet_NaN()};
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {10, 0, 20, 30, 54, 63, 0, 72, 81}, {1, 0, 1, 1, 1, 1, 0, 1, 1});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_std_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::FLOAT64};
+
+  auto expected = cudf::test::fixed_width_column_wrapper<double>{
+    {NaN, 0., std::sqrt(50.), std::sqrt(135.)}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+
+  expected = cudf::test::fixed_width_column_wrapper<double>{{NaN, 0., 0., 0.}, {1, 0, 0, 0}};
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, Variance)
+{
+  constexpr float NaN{std::numeric_limits<float>::quiet_NaN()};
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>{10, 20, 30, 40, 50, 60, 70, 80, 90};
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_variance_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::FLOAT32};
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<float>{{NaN, 0.f, 100.f, 250.f}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, VarianceNulls)
+{
+  constexpr double NaN{std::numeric_limits<double>::quiet_NaN()};
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {10, 0, 20, 30, 54, 63, 0, 72, 81}, {1, 0, 1, 1, 1, 1, 0, 1, 1});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_variance_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::FLOAT64};
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<double>{{NaN, 0., 50., 135.}, {1, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+
+  expected = cudf::test::fixed_width_column_wrapper<double>{{NaN, 0., 0., 0.}, {1, 0, 0, 0}};
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, NUnique)
+{
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>({10, 15, 20, 30, 60, 60, 70, 70, 80});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 2, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_nunique_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::INT32};
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{{1, 0, 1, 2, 3}, {1, 0, 1, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, NUniqueNulls)
+{
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {10, 0, 20, 30, 60, 60, 70, 70, 0}, {1, 0, 1, 1, 1, 1, 1, 1, 0});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 2, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg         = cudf::make_nunique_aggregation<cudf::segmented_reduce_aggregation>();
+  auto const output_type = cudf::data_type{cudf::type_id::INT32};
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{{1, 0, 0, 2, 2}, {1, 0, 0, 1, 1}};
+  auto result =
+    cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+
+  expected =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{{1, 0, 1, 2, 3}, {1, 0, 1, 1, 1}};
+  result = cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+TEST_F(SegmentedReductionTestUntyped, Errors)
+{
+  auto const input = cudf::test::fixed_width_column_wrapper<int32_t>(
+    {10, 0, 20, 30, 54, 63, 0, 72, 81}, {1, 0, 1, 1, 1, 1, 0, 1, 1});
+  auto const offsets   = std::vector<cudf::size_type>{0, 1, 1, 4, 9};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const null_policy = cudf::null_policy::EXCLUDE;
+  auto const output_type = cudf::data_type{cudf::type_id::TIMESTAMP_DAYS};
+  auto const str_input =
+    cudf::test::strings_column_wrapper({"10", "0", "20", "30", "54", "63", "", "72", "81"});
+
+  auto const sum_agg = cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *sum_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *sum_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const product_agg = cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *product_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *product_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const min_agg = cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *min_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const max_agg = cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *max_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const any_agg = cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *any_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *any_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const all_agg = cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *all_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *all_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const mean_agg = cudf::make_mean_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *mean_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *mean_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const std_agg = cudf::make_std_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *std_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *std_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const var_agg = cudf::make_variance_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *var_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *var_agg, output_type, null_policy),
+               cudf::logic_error);
+
+  auto const squares_agg =
+    cudf::make_sum_of_squares_aggregation<cudf::segmented_reduce_aggregation>();
+  EXPECT_THROW(cudf::segmented_reduce(input, d_offsets, *squares_agg, output_type, null_policy),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_reduce(str_input, d_offsets, *squares_agg, output_type, null_policy),
+               cudf::logic_error);
+}
+
+TEST_F(SegmentedReductionTestUntyped, ReduceEmptyColumn)
+{
+  auto const input     = cudf::test::fixed_width_column_wrapper<int32_t>{};
+  auto const offsets   = std::vector<cudf::size_type>{0};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::fixed_width_column_wrapper<int32_t>{};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_to_id<int32_t>()},
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with initial value
+  auto const init_scalar = cudf::make_fixed_width_scalar<int32_t>(3);
+  res                    = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<int32_t>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+
+  // Test with null initial value
+  init_scalar->set_valid_async(false);
+  res = cudf::segmented_reduce(input,
+                               d_offsets,
+                               *cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>(),
+                               cudf::data_type{cudf::type_to_id<int32_t>()},
+                               cudf::null_policy::EXCLUDE,
+                               *init_scalar);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TEST_F(SegmentedReductionTestUntyped, EmptyInputWithOffsets)
+{
+  auto const input     = cudf::test::fixed_width_column_wrapper<int32_t>{};
+  auto const offsets   = std::vector<cudf::size_type>{0, 0, 0, 0, 0, 0};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{XXX, XXX, XXX, XXX, XXX}, {0, 0, 0, 0, 0}};
+
+  auto aggregates =
+    std::vector<std::unique_ptr<cudf::segmented_reduce_aggregation,
+                                std::default_delete<cudf::segmented_reduce_aggregation>>>();
+  aggregates.push_back(std::move(cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>()));
+  aggregates.push_back(std::move(cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>()));
+  aggregates.push_back(std::move(cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>()));
+  aggregates.push_back(
+    std::move(cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>()));
+
+  auto output_type = cudf::data_type{cudf::type_to_id<int32_t>()};
+  for (auto&& agg : aggregates) {
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, output_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+
+  auto const expect_bool =
+    cudf::test::fixed_width_column_wrapper<bool>{{XXX, XXX, XXX, XXX, XXX}, {0, 0, 0, 0, 0}};
+
+  auto result =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_any_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::BOOL8},
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect_bool);
+  result = cudf::segmented_reduce(input,
+                                  d_offsets,
+                                  *cudf::make_all_aggregation<cudf::segmented_reduce_aggregation>(),
+                                  cudf::data_type{cudf::type_id::BOOL8},
+                                  cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect_bool);
+}
+
+template <typename T>
+struct SegmentedReductionFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SegmentedReductionFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(SegmentedReductionFixedPointTest, MaxWithNulls)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto const input =
+      cudf::test::fixed_point_column_wrapper<RepType>({1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX},
+                                                      {1, 1, 1, 1, 0, 1, 1, 0, 0, 0},
+                                                      numeric::scale_type{scale});
+    auto out_type = cudf::column_view(input).type();
+    auto expect   = cudf::test::fixed_point_column_wrapper<RepType>(
+      {3, XXX, 1, XXX, XXX, XXX}, {1, 0, 1, 0, 0, 0}, numeric::scale_type{scale});
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {3, 3, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}, numeric::scale_type{scale});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+TYPED_TEST(SegmentedReductionFixedPointTest, MinWithNulls)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto const input =
+      cudf::test::fixed_point_column_wrapper<RepType>({1, 2, 3, 1, XXX, 3, 1, XXX, XXX, XXX},
+                                                      {1, 1, 1, 1, 0, 1, 1, 0, 0, 0},
+                                                      numeric::scale_type{scale});
+    auto out_type = cudf::column_view(input).type();
+    auto expect   = cudf::test::fixed_point_column_wrapper<RepType>(
+      {1, XXX, 1, XXX, XXX, XXX}, {1, 0, 1, 0, 0, 0}, numeric::scale_type{scale});
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {1, 1, 1, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}, numeric::scale_type{scale});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+TYPED_TEST(SegmentedReductionFixedPointTest, MaxNonNullableInput)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 4, 4};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto const input =
+      cudf::test::fixed_point_column_wrapper<RepType>({1, 2, 3, 1}, numeric::scale_type{scale});
+    auto out_type     = cudf::column_view(input).type();
+    auto const expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {3, 1, XXX}, {1, 1, 0}, numeric::scale_type{scale});
+
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+TYPED_TEST(SegmentedReductionFixedPointTest, MinNonNullableInput)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 4, 4};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto const input =
+      cudf::test::fixed_point_column_wrapper<RepType>({1, 2, 3, 1}, numeric::scale_type{scale});
+    auto out_type     = cudf::column_view(input).type();
+    auto const expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {1, 1, XXX}, {1, 1, 0}, numeric::scale_type{scale});
+
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+TYPED_TEST(SegmentedReductionFixedPointTest, Sum)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_sum_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto input =
+      cudf::test::fixed_point_column_wrapper<RepType>({-10, 0, 33, 100, XXX, 53, 11, XXX, XXX, XXX},
+                                                      {1, 1, 1, 1, 0, 1, 1, 0, 0, 0},
+                                                      numeric::scale_type{scale});
+    auto const out_type = cudf::column_view(input).type();
+
+    auto expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {23, XXX, 11, XXX, XXX, XXX}, {1, 0, 1, 0, 0, 0}, numeric::scale_type{scale});
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {23, 153, 11, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}, numeric::scale_type{scale});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    input = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-10, 0, 33, 100, 123, 53, 11, 0, -120, 88}, numeric::scale_type{scale});
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {23, 276, 11, 0, -32, XXX}, {1, 1, 1, 1, 1, 0}, numeric::scale_type{scale});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+TYPED_TEST(SegmentedReductionFixedPointTest, Product)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 12, 12};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_product_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto input = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-10, 1, 33, 40, XXX, 50, 11000, XXX, XXX, XXX, XXX, XXX},
+      {1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0},
+      numeric::scale_type{scale});
+    auto const out_type = cudf::column_view(input).type();
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    auto expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-330, XXX, 11000, XXX, XXX, XXX}, {1, 0, 1, 0, 0, 0}, numeric::scale_type{scale * 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-330, 2000, 11000, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}, numeric::scale_type{scale * 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    input = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-10, 1, 33, 3, 40, 50, 11000, 0, -3, 50, 10, 4}, numeric::scale_type{scale});
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-330, 6000, 11000, 0, -6000, XXX}, {1, 1, 1, 1, 1, 0}, numeric::scale_type{scale * 4});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+TYPED_TEST(SegmentedReductionFixedPointTest, SumOfSquares)
+{
+  using RepType = cudf::device_storage_type_t<TypeParam>;
+
+  auto const offsets   = std::vector<cudf::size_type>{0, 3, 6, 7, 8, 10, 10};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const agg = cudf::make_sum_of_squares_aggregation<cudf::segmented_reduce_aggregation>();
+
+  for (auto scale : {-2, 0, 5}) {
+    auto input =
+      cudf::test::fixed_point_column_wrapper<RepType>({-10, 0, 33, 100, XXX, 53, 11, XXX, XXX, XXX},
+                                                      {1, 1, 1, 1, 0, 1, 1, 0, 0, 0},
+                                                      numeric::scale_type{scale});
+    auto const out_type = cudf::column_view(input).type();
+
+    auto expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {1189, XXX, 121, XXX, XXX, XXX}, {1, 0, 1, 0, 0, 0}, numeric::scale_type{scale * 2});
+    auto result =
+      cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {1189, 12809, 121, XXX, XXX, XXX}, {1, 1, 1, 0, 0, 0}, numeric::scale_type{scale * 2});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+
+    input = cudf::test::fixed_point_column_wrapper<RepType>(
+      {-10, 0, 33, 100, 123, 53, 11, 0, -120, 88}, numeric::scale_type{scale});
+    expect = cudf::test::fixed_point_column_wrapper<RepType>(
+      {1189, 27938, 121, 0, 22144, XXX}, {1, 1, 1, 1, 1, 0}, numeric::scale_type{scale * 2});
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::INCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+    result = cudf::segmented_reduce(input, d_offsets, *agg, out_type, cudf::null_policy::EXCLUDE);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  }
+}
+
+// String min/max test grid
+// Segment: Length 0, length 1, length 2
+// Element nulls: No nulls, all nulls, some nulls
+// String: Empty string,
+// Position of the min/max: start of segment, end of segment
+// Include null, exclude null
+
+#undef XXX
+#define XXX ""  // null placeholder
+
+struct SegmentedReductionStringTest : public cudf::test::BaseFixture {
+  std::pair<cudf::test::strings_column_wrapper,
+            cudf::test::fixed_width_column_wrapper<cudf::size_type>>
+  input()
+  {
+    return std::pair(
+      cudf::test::strings_column_wrapper{
+        {"world", "cudf", XXX, "", "rapids", "i am", "ai", "apples", "zebras", XXX, XXX, XXX},
+        {true, true, false, true, true, true, true, true, true, false, false, false}},
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 4, 7, 9, 9, 10, 12});
+  }
+};
+
+TEST_F(SegmentedReductionStringTest, MaxIncludeNulls)
+{
+  // data: ['world'], ['cudf', NULL, ''], ['rapids', 'i am', 'ai'], ['apples', 'zebras'],
+  //       [], [NULL], [NULL, NULL]
+  // values:  {"world", "cudf", XXX, "", "rapids", "i am", "ai", "apples", "zebras", XXX, XXX, XXX}
+  // nullmask:{1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0}
+  // offsets: {0, 1, 4, 7, 9, 9, 10, 12}
+  // output_dtype: string dtype
+  // outputs: {"world", XXX, "rapids", "zebras", XXX, XXX, XXX}
+  // output nullmask: {1, 0, 1, 1, 0, 0, 0}
+
+  auto const [input, offsets] = this->input();
+  cudf::data_type output_dtype{cudf::type_id::STRING};
+
+  cudf::test::strings_column_wrapper expect{{"world", XXX, "rapids", "zebras", XXX, XXX, XXX},
+                                            {true, false, true, true, false, false, false}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           cudf::column_view(offsets),
+                           *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                           output_dtype,
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TEST_F(SegmentedReductionStringTest, MaxExcludeNulls)
+{
+  // data: ['world'], ['cudf', NULL, ''], ['rapids', 'i am', 'ai'], ['apples', 'zebras'],
+  //       [], [NULL], [NULL, NULL]
+  // values:  {"world", "cudf", XXX, "", "rapids", "i am", "ai", "apples", "zebras", XXX, XXX, XXX}
+  // nullmask:{1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0}
+  // offsets: {0, 1, 4, 7, 9, 9, 10, 12}
+  // output_dtype: string dtype
+  // outputs: {"world", "cudf", "rapids", "zebras", XXX, XXX, XXX}
+  // output nullmask: {1, 1, 1, 1, 0, 0, 0}
+
+  auto const [input, offsets] = this->input();
+  cudf::data_type output_dtype{cudf::type_id::STRING};
+
+  cudf::test::strings_column_wrapper expect{{"world", "cudf", "rapids", "zebras", XXX, XXX, XXX},
+                                            {true, true, true, true, false, false, false}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           cudf::column_view(offsets),
+                           *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                           output_dtype,
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TEST_F(SegmentedReductionStringTest, MinIncludeNulls)
+{
+  // data: ['world'], ['cudf', NULL, ''], ['rapids', 'i am', 'ai'], ['apples', 'zebras'],
+  //       [], [NULL], [NULL, NULL]
+  // values:  {"world", "cudf", XXX, "", "rapids", "i am", "ai", "apples", "zebras", XXX, XXX, XXX}
+  // nullmask:{1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0}
+  // offsets: {0, 1, 4, 7, 9, 9, 10, 12}
+  // output_dtype: string dtype
+  // outputs: {"world", XXX, "ai", "apples", XXX, XXX, XXX}
+  // output nullmask: {1, 0, 1, 1, 0, 0, 0}
+
+  auto const [input, offsets] = this->input();
+  cudf::data_type output_dtype{cudf::type_id::STRING};
+
+  cudf::test::strings_column_wrapper expect{{"world", XXX, "ai", "apples", XXX, XXX, XXX},
+                                            {true, false, true, true, false, false, false}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           cudf::column_view(offsets),
+                           *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                           output_dtype,
+                           cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TEST_F(SegmentedReductionStringTest, MinExcludeNulls)
+{
+  // data: ['world'], ['cudf', NULL, ''], ['rapids', 'i am', 'ai'], ['apples', 'zebras'],
+  //       [], [NULL], [NULL, NULL]
+  // values:  {"world", "cudf", XXX, "", "rapids", "i am", "ai", "apples", "zebras", XXX, XXX, XXX}
+  // nullmask:{1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0}
+  // offsets: {0, 1, 4, 7, 9, 9, 10, 12}
+  // output_dtype: string dtype
+  // outputs: {"world", "", "ai", "apples", XXX, XXX, XXX}
+  // output nullmask: {1, 1, 1, 1, 0, 0, 0}
+
+  auto const [input, offsets] = this->input();
+  cudf::data_type output_dtype{cudf::type_id::STRING};
+
+  cudf::test::strings_column_wrapper expect{{"world", "", "ai", "apples", XXX, XXX, XXX},
+                                            {true, true, true, true, false, false, false}};
+
+  auto res =
+    cudf::segmented_reduce(input,
+                           cudf::column_view(offsets),
+                           *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                           output_dtype,
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*res, expect);
+}
+
+TEST_F(SegmentedReductionStringTest, EmptyInputWithOffsets)
+{
+  auto const input     = cudf::test::strings_column_wrapper{};
+  auto const offsets   = std::vector<cudf::size_type>{0, 0, 0, 0};
+  auto const d_offsets = cudf::detail::make_device_uvector_async(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const expect = cudf::test::strings_column_wrapper({XXX, XXX, XXX}, {0, 0, 0});
+
+  auto result =
+    cudf::segmented_reduce(input,
+                           d_offsets,
+                           *cudf::make_max_aggregation<cudf::segmented_reduce_aggregation>(),
+                           cudf::data_type{cudf::type_id::STRING},
+                           cudf::null_policy::EXCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+  result = cudf::segmented_reduce(input,
+                                  d_offsets,
+                                  *cudf::make_min_aggregation<cudf::segmented_reduce_aggregation>(),
+                                  cudf::data_type{cudf::type_id::STRING},
+                                  cudf::null_policy::INCLUDE);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+#undef XXX
diff --git a/cpp/tests/reductions/tdigest_tests.cu b/cpp/tests/reductions/tdigest_tests.cu
new file mode 100644
index 0000000..c8fec51
--- /dev/null
+++ b/cpp/tests/reductions/tdigest_tests.cu
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/tdigest_utilities.cuh>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/reduction.hpp>
+
+template <typename T>
+struct ReductionTDigestAllTypes : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(ReductionTDigestAllTypes, cudf::test::NumericTypes);
+
+struct reduce_op {
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& values, int delta) const
+  {
+    // result is a scalar, but we want to extract out the underlying column
+    auto scalar_result =
+      cudf::reduce(values,
+                   *cudf::make_tdigest_aggregation<cudf::reduce_aggregation>(delta),
+                   cudf::data_type{cudf::type_id::STRUCT});
+    auto tbl = static_cast<cudf::struct_scalar const*>(scalar_result.get())->view();
+    std::vector<std::unique_ptr<cudf::column>> cols;
+    std::transform(
+      tbl.begin(), tbl.end(), std::back_inserter(cols), [](cudf::column_view const& col) {
+        return std::make_unique<cudf::column>(col);
+      });
+    return cudf::make_structs_column(tbl.num_rows(), std::move(cols), 0, rmm::device_buffer());
+  }
+};
+
+struct reduce_merge_op {
+  std::unique_ptr<cudf::column> operator()(cudf::column_view const& values, int delta) const
+  {
+    // result is a scalar, but we want to extract out the underlying column
+    auto scalar_result =
+      cudf::reduce(values,
+                   *cudf::make_merge_tdigest_aggregation<cudf::reduce_aggregation>(delta),
+                   cudf::data_type{cudf::type_id::STRUCT});
+    auto tbl = static_cast<cudf::struct_scalar const*>(scalar_result.get())->view();
+    std::vector<std::unique_ptr<cudf::column>> cols;
+    std::transform(
+      tbl.begin(), tbl.end(), std::back_inserter(cols), [](cudf::column_view const& col) {
+        return std::make_unique<cudf::column>(col);
+      });
+    return cudf::make_structs_column(tbl.num_rows(), std::move(cols), 0, rmm::device_buffer());
+  }
+};
+
+TYPED_TEST(ReductionTDigestAllTypes, Simple)
+{
+  using T = TypeParam;
+  cudf::test::tdigest_simple_aggregation<T>(reduce_op{});
+}
+
+TYPED_TEST(ReductionTDigestAllTypes, SimpleWithNulls)
+{
+  using T = TypeParam;
+  cudf::test::tdigest_simple_with_nulls_aggregation<T>(reduce_op{});
+}
+
+TYPED_TEST(ReductionTDigestAllTypes, AllNull)
+{
+  using T = TypeParam;
+  cudf::test::tdigest_simple_all_nulls_aggregation<T>(reduce_op{});
+}
+
+struct ReductionTDigestMerge : public cudf::test::BaseFixture {};
+
+TEST_F(ReductionTDigestMerge, Simple)
+{
+  cudf::test::tdigest_merge_simple(reduce_op{}, reduce_merge_op{});
+}
+
+// tests an issue with the cluster generating code with a small number of centroids that have large
+// weights
+TEST_F(ReductionTDigestMerge, FewHeavyCentroids)
+{
+  // digest 1
+  cudf::test::fixed_width_column_wrapper<double> c0c{1.0, 2.0};
+  cudf::test::fixed_width_column_wrapper<double> c0w{100.0, 50.0};
+  cudf::test::structs_column_wrapper c0s({c0c, c0w});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> c0_offsets{0, 2};
+  auto c0l =
+    cudf::make_lists_column(1, c0_offsets.release(), c0s.release(), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<double> c0min{1.0};
+  cudf::test::fixed_width_column_wrapper<double> c0max{2.0};
+  std::vector<std::unique_ptr<cudf::column>> c0_children;
+  c0_children.push_back(std::move(c0l));
+  c0_children.push_back(c0min.release());
+  c0_children.push_back(c0max.release());
+  // tdigest struct
+  auto c0 = cudf::make_structs_column(1, std::move(c0_children), 0, {});
+  cudf::tdigest::tdigest_column_view tdv0(*c0);
+
+  // digest 2
+  cudf::test::fixed_width_column_wrapper<double> c1c{3.0, 4.0};
+  cudf::test::fixed_width_column_wrapper<double> c1w{200.0, 50.0};
+  cudf::test::structs_column_wrapper c1s({c1c, c1w});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> c1_offsets{0, 2};
+  auto c1l =
+    cudf::make_lists_column(1, c1_offsets.release(), c1s.release(), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<double> c1min{3.0};
+  cudf::test::fixed_width_column_wrapper<double> c1max{4.0};
+  std::vector<std::unique_ptr<cudf::column>> c1_children;
+  c1_children.push_back(std::move(c1l));
+  c1_children.push_back(c1min.release());
+  c1_children.push_back(c1max.release());
+  // tdigest struct
+  auto c1 = cudf::make_structs_column(1, std::move(c1_children), 0, {});
+
+  std::vector<cudf::column_view> views;
+  views.push_back(*c0);
+  views.push_back(*c1);
+  auto values = cudf::concatenate(views);
+
+  // merge
+  auto scalar_result =
+    cudf::reduce(*values,
+                 *cudf::make_merge_tdigest_aggregation<cudf::reduce_aggregation>(1000),
+                 cudf::data_type{cudf::type_id::STRUCT});
+
+  // convert to a table
+  auto tbl = static_cast<cudf::struct_scalar const*>(scalar_result.get())->view();
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  std::transform(
+    tbl.begin(), tbl.end(), std::back_inserter(cols), [](cudf::column_view const& col) {
+      return std::make_unique<cudf::column>(col);
+    });
+  auto result = cudf::make_structs_column(tbl.num_rows(), std::move(cols), 0, rmm::device_buffer());
+
+  // we expect to see exactly 4 centroids (the same inputs) with properly computed min/max.
+  cudf::test::fixed_width_column_wrapper<double> ec{1.0, 2.0, 3.0, 4.0};
+  cudf::test::fixed_width_column_wrapper<double> ew{100.0, 50.0, 200.0, 50.0};
+  cudf::test::structs_column_wrapper es({ec, ew});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets{0, 4};
+  auto el = cudf::make_lists_column(1, e_offsets.release(), es.release(), 0, rmm::device_buffer{});
+  cudf::test::fixed_width_column_wrapper<double> emin{1.0};
+  cudf::test::fixed_width_column_wrapper<double> emax{4.0};
+  std::vector<std::unique_ptr<cudf::column>> e_children;
+  e_children.push_back(std::move(el));
+  e_children.push_back(emin.release());
+  e_children.push_back(emax.release());
+  // tdigest struct
+  auto expected = cudf::make_structs_column(1, std::move(e_children), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *expected);
+}
diff --git a/cpp/tests/replace/clamp_test.cpp b/cpp/tests/replace/clamp_test.cpp
new file mode 100644
index 0000000..5b27666
--- /dev/null
+++ b/cpp/tests/replace/clamp_test.cpp
@@ -0,0 +1,660 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <gtest/gtest.h>
+
+#include <thrust/iterator/counting_iterator.h>
+
+struct ClampErrorTest : public cudf::test::BaseFixture {};
+
+TEST_F(ClampErrorTest, MisMatchingScalarTypes)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(true);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT64));
+  hi->set_valid_async(true);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> input({1, 2, 3, 4, 5, 6});
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *hi), cudf::logic_error);
+}
+
+TEST_F(ClampErrorTest, MisMatchingInputAndScalarTypes)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(true);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi->set_valid_async(true);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> input({1, 2, 3, 4, 5, 6});
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *hi), cudf::logic_error);
+}
+
+TEST_F(ClampErrorTest, MisMatchingReplaceScalarTypes)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(true);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi->set_valid_async(true);
+  auto lo_replace = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT64));
+  lo_replace->set_valid_async(true);
+  auto hi_replace = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi_replace->set_valid_async(true);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> input({1, 2, 3, 4, 5, 6});
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *lo_replace, *hi, *hi_replace), cudf::logic_error);
+}
+
+TEST_F(ClampErrorTest, InValidCase1)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(true);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi->set_valid_async(true);
+  auto lo_replace = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo_replace->set_valid_async(false);
+  auto hi_replace = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi_replace->set_valid_async(true);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> input({1, 2, 3, 4, 5, 6});
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *lo_replace, *hi, *hi_replace), cudf::logic_error);
+}
+
+TEST_F(ClampErrorTest, InValidCase2)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(true);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi->set_valid_async(true);
+  auto lo_replace = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo_replace->set_valid_async(true);
+  auto hi_replace = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi_replace->set_valid_async(false);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> input({1, 2, 3, 4, 5, 6});
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *lo_replace, *hi, *hi_replace), cudf::logic_error);
+}
+
+struct ClampEmptyCaseTest : public cudf::test::BaseFixture {};
+
+TEST_F(ClampEmptyCaseTest, BothScalarEmptyInvalid)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(false);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi->set_valid_async(false);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> input({1, 2, 3, 4, 5, 6});
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, got->view());
+}
+
+TEST_F(ClampEmptyCaseTest, EmptyInput)
+{
+  auto lo = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  lo->set_valid_async(true);
+  auto hi = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+  hi->set_valid_async(true);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> input({});
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, got->view());
+}
+
+template <class T>
+struct ClampTestNumeric : public cudf::test::BaseFixture {
+  std::unique_ptr<cudf::column> run_clamp(cudf::host_span<T const> input,
+                                          cudf::host_span<cudf::size_type const> input_validity,
+                                          T lo,
+                                          bool lo_validity,
+                                          T hi,
+                                          bool hi_validity,
+                                          T lo_replace,
+                                          bool lo_replace_validity,
+                                          T hi_replace,
+                                          bool hi_replace_validity)
+  {
+    using ScalarType = cudf::scalar_type_t<T>;
+    std::unique_ptr<cudf::scalar> lo_scalar{nullptr};
+    std::unique_ptr<cudf::scalar> hi_scalar{nullptr};
+    std::unique_ptr<cudf::scalar> lo_replace_scalar{nullptr};
+    std::unique_ptr<cudf::scalar> hi_replace_scalar{nullptr};
+    if (cudf::is_numeric<T>()) {
+      lo_scalar =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      hi_scalar =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      lo_replace_scalar =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      hi_replace_scalar =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+    } else if (cudf::is_timestamp<T>()) {
+      lo_scalar =
+        cudf::make_timestamp_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      hi_scalar =
+        cudf::make_timestamp_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      lo_replace_scalar =
+        cudf::make_timestamp_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      hi_replace_scalar =
+        cudf::make_timestamp_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+    } else if (cudf::is_duration<T>()) {
+      lo_scalar =
+        cudf::make_duration_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      hi_scalar =
+        cudf::make_duration_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      lo_replace_scalar =
+        cudf::make_duration_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+      hi_replace_scalar =
+        cudf::make_duration_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+    }
+
+    static_cast<ScalarType*>(lo_scalar.get())->set_value(lo);
+    static_cast<ScalarType*>(lo_scalar.get())->set_valid_async(lo_validity);
+    static_cast<ScalarType*>(lo_replace_scalar.get())->set_value(lo_replace);
+    static_cast<ScalarType*>(lo_replace_scalar.get())->set_valid_async(lo_replace_validity);
+    static_cast<ScalarType*>(hi_scalar.get())->set_value(hi);
+    static_cast<ScalarType*>(hi_scalar.get())->set_valid_async(hi_validity);
+    static_cast<ScalarType*>(hi_replace_scalar.get())->set_value(hi_replace);
+    static_cast<ScalarType*>(hi_replace_scalar.get())->set_valid_async(hi_replace_validity);
+
+    if (input.size() == input_validity.size()) {
+      cudf::test::fixed_width_column_wrapper<T> input_column(
+        input.begin(), input.end(), input_validity.begin());
+
+      return cudf::clamp(
+        input_column, *lo_scalar, *lo_replace_scalar, *hi_scalar, *hi_replace_scalar);
+    } else {
+      cudf::test::fixed_width_column_wrapper<T> input_column(input.begin(), input.end());
+      return cudf::clamp(
+        input_column, *lo_scalar, *lo_replace_scalar, *hi_scalar, *hi_replace_scalar);
+    }
+  }
+};
+using Types = cudf::test::FixedWidthTypesWithoutFixedPoint;
+
+TYPED_TEST_SUITE(ClampTestNumeric, Types);
+
+TYPED_TEST(ClampTestNumeric, WithNoNull)
+{
+  using T = TypeParam;
+
+  T lo(cudf::test::make_type_param_scalar<T>(2));
+  T hi(cudf::test::make_type_param_scalar<T>(8));
+  auto input = cudf::test::make_type_param_vector<T>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+
+  auto got = this->run_clamp(input, {}, lo, true, hi, true, lo, true, hi, true);
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({2, 2, 2, 3, 4, 5, 6, 7, 8, 8, 8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(ClampTestNumeric, LowerNull)
+{
+  using T = TypeParam;
+
+  T lo(cudf::test::make_type_param_scalar<T>(2));
+  T hi(cudf::test::make_type_param_scalar<T>(8));
+  auto input = cudf::test::make_type_param_vector<T>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+
+  auto got = this->run_clamp(input, {}, lo, false, hi, true, lo, false, hi, true);
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(ClampTestNumeric, UpperNull)
+{
+  using T = TypeParam;
+
+  T lo(cudf::test::make_type_param_scalar<T>(2));
+  T hi(cudf::test::make_type_param_scalar<T>(8));
+  auto input = cudf::test::make_type_param_vector<T>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+
+  auto got = this->run_clamp(input, {}, lo, true, hi, false, lo, true, hi, false);
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({2, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(ClampTestNumeric, InputNull)
+{
+  using T = TypeParam;
+
+  T lo(cudf::test::make_type_param_scalar<T>(2));
+  T hi(cudf::test::make_type_param_scalar<T>(8));
+  auto input = cudf::test::make_type_param_vector<T>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+  std::vector<cudf::size_type> input_validity({0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0});
+
+  auto got = this->run_clamp(input, input_validity, lo, true, hi, true, lo, true, hi, true);
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({2, 2, 2, 3, 4, 5, 6, 7, 8, 8, 8},
+                                                              {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(ClampTestNumeric, InputNulliWithReplace)
+{
+  using T = TypeParam;
+
+  T lo(cudf::test::make_type_param_scalar<T>(2));
+  T hi(cudf::test::make_type_param_scalar<T>(8));
+  T lo_replace(cudf::test::make_type_param_scalar<T>(16));
+  T hi_replace(cudf::test::make_type_param_scalar<T>(32));
+  auto input = cudf::test::make_type_param_vector<T>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
+  std::vector<cudf::size_type> input_validity({0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0});
+
+  auto got =
+    this->run_clamp(input, input_validity, lo, true, hi, true, lo_replace, true, hi_replace, true);
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected({16, 16, 2, 3, 4, 5, 6, 7, 8, 32, 32},
+                                                              {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+template <typename T>
+struct ClampFloatTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ClampFloatTest, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(ClampFloatTest, WithNANandNoNull)
+{
+  using T          = TypeParam;
+  using ScalarType = cudf::scalar_type_t<T>;
+
+  cudf::test::fixed_width_column_wrapper<T> input(
+    {T(8.0), T(6.0), T(NAN), T(3.0), T(4.0), T(5.0), T(1.0), T(NAN), T(2.0), T(9.0)});
+  auto lo_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+  auto hi_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+
+  static_cast<ScalarType*>(lo_scalar.get())->set_value(2.0);
+  static_cast<ScalarType*>(lo_scalar.get())->set_valid_async(true);
+  static_cast<ScalarType*>(hi_scalar.get())->set_value(6.0);
+  static_cast<ScalarType*>(hi_scalar.get())->set_valid_async(true);
+
+  auto got = cudf::clamp(input, *lo_scalar, *hi_scalar);
+  cudf::test::fixed_width_column_wrapper<T> expected(
+    {T(6.0), T(6.0), T(NAN), T(3.0), T(4.0), T(5.0), T(2.0), T(NAN), T(2.0), T(6.0)});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(ClampFloatTest, WithNANandNull)
+{
+  using T          = TypeParam;
+  using ScalarType = cudf::scalar_type_t<T>;
+
+  cudf::test::fixed_width_column_wrapper<T> input(
+    {T(8.0), T(6.0), T(NAN), T(3.0), T(4.0), T(5.0), T(1.0), T(NAN), T(2.0), T(9.0)},
+    {1, 1, 1, 0, 1, 1, 1, 0, 1, 1});
+  auto lo_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+  auto hi_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+
+  static_cast<ScalarType*>(lo_scalar.get())->set_value(2.0);
+  static_cast<ScalarType*>(lo_scalar.get())->set_valid_async(true);
+  static_cast<ScalarType*>(hi_scalar.get())->set_value(6.0);
+  static_cast<ScalarType*>(hi_scalar.get())->set_valid_async(true);
+
+  auto got = cudf::clamp(input, *lo_scalar, *hi_scalar);
+  cudf::test::fixed_width_column_wrapper<T> expected(
+    {T(6.0), T(6.0), T(NAN), T(3.0), T(4.0), T(5.0), T(2.0), T(NAN), T(2.0), T(6.0)},
+    {1, 1, 1, 0, 1, 1, 1, 0, 1, 1});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(ClampFloatTest, SignOfAFloat)
+{
+  using T          = TypeParam;
+  using ScalarType = cudf::scalar_type_t<T>;
+
+  cudf::test::fixed_width_column_wrapper<T> input(
+    {T(2.0), T(0.0), T(NAN), T(4.0), T(-0.5), T(-1.0), T(1.0), T(NAN), T(0.5), T(9.0)},
+    {1, 1, 1, 0, 1, 1, 1, 0, 1, 1});
+  auto lo_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+  auto lo_replace_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+  auto hi_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+  auto hi_replace_scalar =
+    cudf::make_numeric_scalar(cudf::data_type(cudf::data_type{cudf::type_to_id<T>()}));
+
+  static_cast<ScalarType*>(lo_scalar.get())->set_value(0.0);
+  static_cast<ScalarType*>(lo_scalar.get())->set_valid_async(true);
+  static_cast<ScalarType*>(hi_scalar.get())->set_value(0.0);
+  static_cast<ScalarType*>(hi_scalar.get())->set_valid_async(true);
+  static_cast<ScalarType*>(lo_replace_scalar.get())->set_value(-1.0);
+  static_cast<ScalarType*>(lo_replace_scalar.get())->set_valid_async(true);
+  static_cast<ScalarType*>(hi_replace_scalar.get())->set_value(1.0);
+  static_cast<ScalarType*>(hi_replace_scalar.get())->set_valid_async(true);
+
+  auto got = cudf::clamp(input, *lo_scalar, *lo_replace_scalar, *hi_scalar, *hi_replace_scalar);
+  cudf::test::fixed_width_column_wrapper<T> expected(
+    {T(1.0), T(0.0), T(NAN), T(4.0), T(-1.0), T(-1.0), T(1.0), T(NAN), T(1.0), T(1.0)},
+    {1, 1, 1, 0, 1, 1, 1, 0, 1, 1});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+struct ClampStringTest : public cudf::test::BaseFixture {};
+
+TEST_F(ClampStringTest, WithNullableColumn)
+{
+  std::vector<std::string> strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+  std::vector<bool> valids{1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1};
+
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end(), valids.begin());
+
+  auto lo = cudf::make_string_scalar("B");
+  auto hi = cudf::make_string_scalar("e");
+  lo->set_valid_async(true);
+  hi->set_valid_async(true);
+
+  std::vector<std::string> expected_strings{"B", "b", "c", "D", "e", "F", "G", "H", "i", "e", "B"};
+
+  cudf::test::strings_column_wrapper expected(
+    expected_strings.begin(), expected_strings.end(), valids.begin());
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TEST_F(ClampStringTest, WithNonNullableColumn)
+{
+  std::vector<std::string> strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end());
+
+  auto lo = cudf::make_string_scalar("B");
+  auto hi = cudf::make_string_scalar("e");
+  lo->set_valid_async(true);
+  hi->set_valid_async(true);
+
+  std::vector<std::string> expected_strings{"B", "b", "c", "D", "e", "F", "G", "H", "e", "e", "B"};
+
+  cudf::test::strings_column_wrapper expected(expected_strings.begin(), expected_strings.end());
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TEST_F(ClampStringTest, WithNullableColumnNullLow)
+{
+  std::vector<std::string> strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+  std::vector<bool> valids{1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1};
+
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end(), valids.begin());
+
+  auto lo = cudf::make_string_scalar("B");
+  auto hi = cudf::make_string_scalar("e");
+  lo->set_valid_async(false);
+  hi->set_valid_async(true);
+
+  std::vector<std::string> expected_strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "e", "B"};
+
+  cudf::test::strings_column_wrapper expected(
+    expected_strings.begin(), expected_strings.end(), valids.begin());
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TEST_F(ClampStringTest, WithNullableColumnNullHigh)
+{
+  std::vector<std::string> strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+  std::vector<bool> valids{1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1};
+
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end(), valids.begin());
+
+  auto lo = cudf::make_string_scalar("B");
+  auto hi = cudf::make_string_scalar("e");
+  lo->set_valid_async(true);
+  hi->set_valid_async(false);
+
+  std::vector<std::string> expected_strings{"B", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+
+  cudf::test::strings_column_wrapper expected(
+    expected_strings.begin(), expected_strings.end(), valids.begin());
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TEST_F(ClampStringTest, WithNullableColumnBothLoAndHiNull)
+{
+  std::vector<std::string> strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+  std::vector<bool> valids{1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1};
+
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end(), valids.begin());
+
+  auto lo = cudf::make_string_scalar("B");
+  auto hi = cudf::make_string_scalar("e");
+  lo->set_valid_async(false);
+  hi->set_valid_async(false);
+
+  auto got = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, got->view());
+}
+
+TEST_F(ClampStringTest, WithReplaceString)
+{
+  std::vector<std::string> strings{"A", "b", "c", "D", "e", "F", "G", "H", "i", "j", "B"};
+  std::vector<bool> valids{1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1};
+
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end(), valids.begin());
+
+  auto lo         = cudf::make_string_scalar("B");
+  auto lo_replace = cudf::make_string_scalar("Z");
+  auto hi         = cudf::make_string_scalar("e");
+  auto hi_replace = cudf::make_string_scalar("z");
+  lo->set_valid_async(true);
+  lo_replace->set_valid_async(true);
+  hi->set_valid_async(true);
+  hi_replace->set_valid_async(true);
+
+  std::vector<std::string> expected_strings{"Z", "b", "c", "D", "e", "F", "G", "H", "z", "z", "B"};
+
+  cudf::test::strings_column_wrapper expected(
+    expected_strings.begin(), expected_strings.end(), valids.begin());
+
+  auto got = cudf::clamp(input, *lo, *lo_replace, *hi, *hi_replace);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+struct ClampDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(ClampDictionaryTest, WithNullableColumn)
+{
+  cudf::test::strings_column_wrapper input_s({"a", "b", "c", "d", "", "d", "c", "b", "a"},
+                                             {1, 1, 1, 1, 0, 1, 1, 1, 1});
+  auto input = cudf::dictionary::encode(input_s);
+
+  auto results = cudf::clamp(input->view(), cudf::string_scalar("b"), cudf::string_scalar("c"));
+  auto decoded = cudf::dictionary::decode(results->view());
+
+  cudf::test::strings_column_wrapper expected({"b", "b", "c", "c", "", "c", "c", "b", "b"},
+                                              {1, 1, 1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+TEST_F(ClampDictionaryTest, WithNonNullableColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int8_t> input_s({3, 3, 1, 1, 2, 2, 4, 4});
+  auto input = cudf::dictionary::encode(input_s);
+
+  auto results =
+    cudf::clamp(input->view(), cudf::numeric_scalar<int8_t>(2), cudf::numeric_scalar<int8_t>(3));
+  auto decoded = cudf::dictionary::decode(results->view());
+
+  cudf::test::fixed_width_column_wrapper<int8_t> expected({3, 3, 2, 2, 2, 2, 3, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+TEST_F(ClampDictionaryTest, NullLowHi)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> input_s({200, 100, 0, 300, 300, 400, 100, 200, 0},
+                                                          {1, 1, 0, 1, 1, 1, 1, 1, 0});
+  auto input = cudf::dictionary::encode(input_s);
+  {
+    auto results = cudf::clamp(
+      input->view(), cudf::numeric_scalar<int16_t>(0, false), cudf::numeric_scalar<int16_t>(300));
+    auto decoded = cudf::dictionary::decode(results->view());
+    cudf::test::fixed_width_column_wrapper<int16_t> expected(
+      {200, 100, 0, 300, 300, 300, 100, 200, 0}, {1, 1, 0, 1, 1, 1, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+  }
+  {
+    auto results = cudf::clamp(
+      input->view(), cudf::numeric_scalar<int16_t>(200), cudf::numeric_scalar<int16_t>(0, false));
+    auto decoded = cudf::dictionary::decode(results->view());
+    cudf::test::fixed_width_column_wrapper<int16_t> expected(
+      {200, 200, 0, 300, 300, 400, 200, 200, 0}, {1, 1, 0, 1, 1, 1, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+  }
+  {
+    auto results = cudf::clamp(input->view(),
+                               cudf::numeric_scalar<int16_t>(0, false),
+                               cudf::numeric_scalar<int16_t>(0, false));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(input->view(), results->view());
+  }
+}
+
+TEST_F(ClampDictionaryTest, WithReplace)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input_s({1, 2, 3, 4, 0, 4, 3, 2, 1},
+                                                          {1, 1, 1, 1, 0, 1, 1, 1, 1});
+  auto input = cudf::dictionary::encode(input_s);
+
+  auto results = cudf::clamp(input->view(),
+                             cudf::numeric_scalar<int64_t>(2),
+                             cudf::numeric_scalar<int64_t>(2000),
+                             cudf::numeric_scalar<int64_t>(3),
+                             cudf::numeric_scalar<int64_t>(3000));
+  auto decoded = cudf::dictionary::decode(results->view());
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected({2000, 2, 3, 3000, 0, 3000, 3, 2, 2000},
+                                                           {1, 1, 1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, decoded->view());
+}
+
+template <typename T>
+struct FixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTest, ZeroScale)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const scale    = scale_type{0};
+  auto const lo       = cudf::make_fixed_point_scalar<decimalXX>(2, scale);
+  auto const hi       = cudf::make_fixed_point_scalar<decimalXX>(8, scale);
+  auto const input    = fp_wrapper{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, scale};
+  auto const expected = fp_wrapper{{2, 2, 2, 3, 4, 5, 6, 7, 8, 8, 8}, scale};
+  auto const result   = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTest, LargeTest)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const scale = scale_type{-3};
+  auto const lo    = cudf::make_fixed_point_scalar<decimalXX>(1000, scale);
+  auto const hi    = cudf::make_fixed_point_scalar<decimalXX>(2000, scale);
+
+  auto begin          = thrust::make_counting_iterator(-1000);
+  auto clamp          = [](int e) { return e < 1000 ? 1000 : e > 2000 ? 2000 : e; };
+  auto begin2         = cudf::detail::make_counting_transform_iterator(-1000, clamp);
+  auto const input    = fp_wrapper{begin, begin + 5000, scale};
+  auto const expected = fp_wrapper{begin2, begin2 + 5000, scale};
+  auto const result   = cudf::clamp(input, *lo, *hi);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTest, MismatchedScalarScales)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const scale = scale_type{0};
+  auto const lo    = cudf::make_fixed_point_scalar<decimalXX>(2, scale_type{-1});
+  auto const hi    = cudf::make_fixed_point_scalar<decimalXX>(8, scale);
+  auto const input = fp_wrapper{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, scale};
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *hi), cudf::logic_error);
+}
+
+TYPED_TEST(FixedPointTest, MismatchedColumnScalarScale)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const scale = scale_type{0};
+  auto const lo    = cudf::make_fixed_point_scalar<decimalXX>(2, scale);
+  auto const hi    = cudf::make_fixed_point_scalar<decimalXX>(8, scale);
+  auto const input = fp_wrapper{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, scale_type{-4}};
+
+  EXPECT_THROW(cudf::clamp(input, *lo, *hi), cudf::logic_error);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/replace/normalize_replace_tests.cpp b/cpp/tests/replace/normalize_replace_tests.cpp
new file mode 100644
index 0000000..80b9416
--- /dev/null
+++ b/cpp/tests/replace/normalize_replace_tests.cpp
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2018-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/replace.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+// This is the main test fixture
+struct ReplaceTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+void normalize_nans_and_zeros_test_internal(
+  cudf::test::fixed_width_column_wrapper<T> const& test_data_in,
+  cudf::column_view const& test_data_comp)
+{
+  // mutable overload
+  {
+    cudf::column test_data(test_data_in);
+    cudf::mutable_column_view mutable_view = test_data;
+    cudf::normalize_nans_and_zeros(mutable_view);
+
+    // compare bitwise
+    CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+      mutable_view.head(), test_data_comp.head(), mutable_view.size() * sizeof(T));
+  }
+
+  // returned column overload
+  {
+    cudf::column test_data(test_data_in);
+    cudf::column_view view = test_data;
+
+    auto out      = cudf::normalize_nans_and_zeros(view);
+    auto out_view = out->view();
+
+    // compare bitwise
+    CUDF_TEST_EXPECT_EQUAL_BUFFERS(
+      out_view.head(), test_data_comp.head(), out_view.size() * sizeof(T));
+  }
+}
+
+// Test for normalize_nans_and_nulls
+TEST_F(ReplaceTest, NormalizeNansAndZerosFloat)
+{
+  // bad data
+  cudf::test::fixed_width_column_wrapper<float> f_test_data{
+    32.5f, -0.0f, 111.0f, -NAN, NAN, 1.0f, 0.0f, 54.3f};
+  // good data
+  cudf::test::fixed_width_column_wrapper<float> f_test_data_comp{
+    32.5f, 0.0f, 111.0f, NAN, NAN, 1.0f, 0.0f, 54.3f};
+  //
+  normalize_nans_and_zeros_test_internal<float>(f_test_data, f_test_data_comp);
+}
+
+// Test for normalize_nans_and_nulls
+TEST_F(ReplaceTest, NormalizeNansAndZerosDouble)
+{
+  // bad data
+  cudf::test::fixed_width_column_wrapper<double> d_test_data{
+    32.5, -0.0, 111.0, double(-NAN), double(NAN), 1.0, 0.0, 54.3};
+  // good data
+  cudf::test::fixed_width_column_wrapper<double> d_test_data_comp{
+    32.5, 0.0, 111.0, double(NAN), double(NAN), 1.0, 0.0, 54.3};
+  //
+  normalize_nans_and_zeros_test_internal<double>(d_test_data, d_test_data_comp);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/replace/replace_nans_tests.cpp b/cpp/tests/replace/replace_nans_tests.cpp
new file mode 100644
index 0000000..c74f6d6
--- /dev/null
+++ b/cpp/tests/replace/replace_nans_tests.cpp
@@ -0,0 +1,195 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+struct ReplaceNaNsErrorTest : public cudf::test::BaseFixture {};
+
+// Error: old-values and new-values size mismatch
+TEST_F(ReplaceNaNsErrorTest, SizeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<float> input_column{7, 5, 6, 3, 1, 8, 4};
+  cudf::test::fixed_width_column_wrapper<float> replacement_column{{10, 11, 12, 13}};
+
+  EXPECT_THROW(cudf::replace_nans(input_column, replacement_column), cudf::logic_error);
+}
+
+// Error : column type mismatch
+TEST_F(ReplaceNaNsErrorTest, TypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<float> input_column{7, 5, 6, 3, 1, 2, 8, 4};
+  cudf::test::fixed_width_column_wrapper<double> replacement_column{10, 11, 12, 13, 14, 15, 16, 17};
+
+  EXPECT_THROW(cudf::replace_nans(input_column, replacement_column), cudf::logic_error);
+}
+
+// Error: column type mismatch
+TEST_F(ReplaceNaNsErrorTest, TypeMismatchScalar)
+{
+  cudf::test::fixed_width_column_wrapper<double> input_column{7, 5, 6, 3, 1, 2, 8, 4};
+  cudf::numeric_scalar<float> replacement(1);
+
+  EXPECT_THROW(cudf::replace_nans(input_column, replacement), cudf::logic_error);
+}
+
+// Error: column type mismatch
+TEST_F(ReplaceNaNsErrorTest, NonFloatType)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{7, 5, 6, 3, 1, 2, 8, 4};
+  cudf::numeric_scalar<float> replacement(1);
+
+  EXPECT_THROW(cudf::replace_nans(input_column, replacement), cudf::logic_error);
+}
+
+template <typename T>
+struct ReplaceNaNsTest : public cudf::test::BaseFixture {};
+
+using test_types = cudf::test::Types<float, double>;
+
+TYPED_TEST_SUITE(ReplaceNaNsTest, test_types);
+
+template <typename T>
+void ReplaceNaNsColumn(cudf::test::fixed_width_column_wrapper<T> input,
+                       cudf::test::fixed_width_column_wrapper<T> replacement_values,
+                       cudf::test::fixed_width_column_wrapper<T> expected)
+{
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nans(input, replacement_values));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+template <typename T>
+void ReplaceNaNsScalar(cudf::test::fixed_width_column_wrapper<T> input,
+                       cudf::scalar const& replacement_value,
+                       cudf::test::fixed_width_column_wrapper<T> expected)
+{
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nans(input, replacement_value));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TYPED_TEST(ReplaceNaNsTest, ReplaceColumn)
+{
+  using T = TypeParam;
+
+  auto nan = std::numeric_limits<double>::quiet_NaN();
+  auto inputColumn =
+    cudf::test::make_type_param_vector<T>({nan, 1.0, nan, 3.0, 4.0, nan, nan, 7.0, 8.0, 9.0});
+  auto replacement =
+    cudf::test::make_type_param_vector<T>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0});
+
+  ReplaceNaNsColumn<T>(
+    cudf::test::fixed_width_column_wrapper<T>(inputColumn.begin(), inputColumn.end()),
+    cudf::test::fixed_width_column_wrapper<T>(replacement.begin(), replacement.end()),
+    cudf::test::fixed_width_column_wrapper<T>(replacement.begin(), replacement.end()));
+}
+
+TYPED_TEST(ReplaceNaNsTest, ReplaceColumnNullable)
+{
+  using T = TypeParam;
+
+  auto nan = std::numeric_limits<double>::quiet_NaN();
+  auto inputColumn =
+    cudf::test::make_type_param_vector<T>({nan, 1.0, nan, 3.0, 4.0, nan, nan, 7.0, 8.0, 9.0});
+  auto inputValid = std::vector<int>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto replacement =
+    cudf::test::make_type_param_vector<T>({0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0});
+
+  // Nulls should be untouched as they are considered not nan.
+  ReplaceNaNsColumn<T>(
+    cudf::test::fixed_width_column_wrapper<T>(
+      inputColumn.begin(), inputColumn.end(), inputValid.begin()),
+    cudf::test::fixed_width_column_wrapper<T>(replacement.begin(), replacement.end()),
+    cudf::test::fixed_width_column_wrapper<T>(
+      replacement.begin(), replacement.end(), inputValid.begin()));
+}
+
+TYPED_TEST(ReplaceNaNsTest, ReplacementHasNulls)
+{
+  using T = TypeParam;
+
+  auto nan = std::numeric_limits<double>::quiet_NaN();
+  auto input_column =
+    cudf::test::make_type_param_vector<T>({7.0, nan, 6.0, 3.0, nan, 2.0, 8.0, 4.0});
+  auto replace_data =
+    cudf::test::make_type_param_vector<T>({4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 0.0, 1.0});
+  auto replace_valid = std::vector<int>{1, 0, 1, 1, 1, 1, 1, 1};
+  auto result_data =
+    cudf::test::make_type_param_vector<T>({7.0, 5.0, 6.0, 3.0, 8.0, 2.0, 8.0, 4.0});
+  auto result_valid = std::vector<int>{1, 0, 1, 1, 1, 1, 1, 1};
+
+  ReplaceNaNsColumn<T>(
+    cudf::test::fixed_width_column_wrapper<T>(input_column.begin(), input_column.end()),
+    cudf::test::fixed_width_column_wrapper<T>(
+      replace_data.begin(), replace_data.end(), replace_valid.begin()),
+    cudf::test::fixed_width_column_wrapper<T>(
+      result_data.begin(), result_data.end(), result_valid.begin()));
+}
+
+TYPED_TEST(ReplaceNaNsTest, ReplaceColumn_Empty)
+{
+  ReplaceNaNsColumn<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>{},
+                               cudf::test::fixed_width_column_wrapper<TypeParam>{},
+                               cudf::test::fixed_width_column_wrapper<TypeParam>{});
+}
+
+TYPED_TEST(ReplaceNaNsTest, ReplaceScalar)
+{
+  using T = TypeParam;
+
+  auto nan = std::numeric_limits<double>::quiet_NaN();
+  auto input_data =
+    cudf::test::make_type_param_vector<T>({nan, 1.0, nan, 3.0, 4.0, nan, nan, 7.0, 8.0, 9.0});
+  auto input_valid = std::vector<int>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto expect_data =
+    cudf::test::make_type_param_vector<T>({0.0, 1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 7.0, 8.0, 9.0});
+  cudf::numeric_scalar<T> replacement(1);
+
+  ReplaceNaNsScalar<T>(cudf::test::fixed_width_column_wrapper<T>(
+                         input_data.begin(), input_data.end(), input_valid.begin()),
+                       replacement,
+                       cudf::test::fixed_width_column_wrapper<T>(
+                         expect_data.begin(), expect_data.end(), input_valid.begin()));
+}
+
+TYPED_TEST(ReplaceNaNsTest, ReplaceNullScalar)
+{
+  using T = TypeParam;
+
+  auto nan = std::numeric_limits<double>::quiet_NaN();
+  auto input_data =
+    cudf::test::make_type_param_vector<T>({nan, 1.0, nan, 3.0, 4.0, nan, nan, 7.0, 8.0, 9.0});
+  auto input_valid = std::vector<int>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto expect_data =
+    cudf::test::make_type_param_vector<T>({0.0, 1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 7.0, 8.0, 9.0});
+  auto expect_valid = std::vector<int>{0, 0, 0, 0, 0, 0, 0, 1, 1, 1};
+  cudf::numeric_scalar<T> replacement(1, false);
+
+  ReplaceNaNsScalar<T>(cudf::test::fixed_width_column_wrapper<T>(
+                         input_data.begin(), input_data.end(), input_valid.begin()),
+                       replacement,
+                       cudf::test::fixed_width_column_wrapper<T>(
+                         expect_data.begin(), expect_data.end(), expect_valid.begin()));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/replace/replace_nulls_tests.cpp b/cpp/tests/replace/replace_nulls_tests.cpp
new file mode 100644
index 0000000..a7c5414
--- /dev/null
+++ b/cpp/tests/replace/replace_nulls_tests.cpp
@@ -0,0 +1,752 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018 BlazingDB, Inc.
+ *     Copyright 2018 Alexander Ocsa <cristhian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+using namespace cudf::test::iterators;
+
+struct ReplaceErrorTest : public cudf::test::BaseFixture {};
+
+// Error: old-values and new-values size mismatch
+TEST_F(ReplaceErrorTest, SizeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{{7, 5, 6, 3, 1, 2, 8, 4},
+                                                               {0, 0, 1, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> values_to_replace_column{{10, 11, 12, 13}};
+
+  ASSERT_THROW(cudf::replace_nulls(input_column, values_to_replace_column), cudf::logic_error);
+}
+
+// Error: column type mismatch
+TEST_F(ReplaceErrorTest, TypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{{7, 5, 6, 3, 1, 2, 8, 4},
+                                                               {0, 0, 1, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<float> values_to_replace_column{
+    {10, 11, 12, 13, 14, 15, 16, 17}};
+
+  EXPECT_THROW(cudf::replace_nulls(input_column, values_to_replace_column), cudf::logic_error);
+}
+
+// Error: column type mismatch
+TEST_F(ReplaceErrorTest, TypeMismatchScalar)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{{7, 5, 6, 3, 1, 2, 8, 4},
+                                                               {0, 0, 1, 1, 1, 1, 1, 1}};
+  cudf::numeric_scalar<float> replacement(1);
+
+  EXPECT_THROW(cudf::replace_nulls(input_column, replacement), cudf::logic_error);
+}
+
+struct ReplaceNullsStringsTest : public cudf::test::BaseFixture {};
+
+TEST_F(ReplaceNullsStringsTest, SimpleReplace)
+{
+  std::vector<std::string> input{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> input_v{0, 0, 0, 0, 0, 0, 0, 0};
+  std::vector<std::string> replacement{"a", "b", "c", "d", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> replacement_v{1, 1, 1, 1, 1, 1, 1, 1};
+
+  cudf::test::strings_column_wrapper input_w{input.begin(), input.end(), input_v.begin()};
+  cudf::test::strings_column_wrapper replacement_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+  cudf::test::strings_column_wrapper expected_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input_w, replacement_w));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TEST_F(ReplaceNullsStringsTest, ReplaceWithNulls)
+{
+  std::vector<std::string> input{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> input_v{0, 0, 0, 0, 0, 0, 0, 0};
+  std::vector<std::string> replacement{"", "", "c", "d", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> replacement_v{0, 0, 1, 1, 1, 1, 1, 1};
+
+  cudf::test::strings_column_wrapper input_w{input.begin(), input.end(), input_v.begin()};
+  cudf::test::strings_column_wrapper replacement_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+  cudf::test::strings_column_wrapper expected_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input_w, replacement_w));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TEST_F(ReplaceNullsStringsTest, ReplaceWithAllNulls)
+{
+  std::vector<std::string> input{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> input_v{0, 0, 0, 0, 0, 0, 0, 0};
+  std::vector<std::string> replacement{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> replacement_v{0, 0, 0, 0, 0, 0, 0, 0};
+
+  cudf::test::strings_column_wrapper input_w{input.begin(), input.end(), input_v.begin()};
+  cudf::test::strings_column_wrapper replacement_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+  cudf::test::strings_column_wrapper expected_w{input.begin(), input.end(), input_v.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input_w, replacement_w));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TEST_F(ReplaceNullsStringsTest, ReplaceWithAllEmpty)
+{
+  std::vector<std::string> input{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> input_v{0, 0, 0, 0, 0, 0, 0, 0};
+  std::vector<std::string> replacement{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> replacement_v{1, 1, 1, 1, 1, 1, 1, 1};
+
+  cudf::test::strings_column_wrapper input_w{input.begin(), input.end(), input_v.begin()};
+  cudf::test::strings_column_wrapper replacement_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+  cudf::test::strings_column_wrapper expected_w{input.begin(), input.end(), replacement_v.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input_w, replacement_w));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+TEST_F(ReplaceNullsStringsTest, ReplaceNone)
+{
+  std::vector<std::string> input{"a", "b", "c", "d", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> input_v{1, 1, 1, 1, 1, 1, 1, 1};
+  std::vector<std::string> replacement{"z", "a", "c", "d", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> replacement_v{0, 0, 1, 1, 1, 1, 1, 1};
+
+  cudf::test::strings_column_wrapper input_w{input.begin(), input.end(), input_v.begin()};
+  cudf::test::strings_column_wrapper replacement_w{
+    replacement.begin(), replacement.end(), replacement_v.begin()};
+  cudf::test::strings_column_wrapper expected_w{input.begin(), input.end()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input_w, replacement_w));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected_w);
+}
+
+TEST_F(ReplaceNullsStringsTest, SimpleReplaceScalar)
+{
+  std::vector<std::string> input{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> input_v{0, 0, 0, 0, 0, 0, 0, 0};
+  std::unique_ptr<cudf::scalar> repl = cudf::make_string_scalar("rep");
+  repl->set_valid_async(true, cudf::get_default_stream());
+  std::vector<std::string> expected{"rep", "rep", "rep", "rep", "rep", "rep", "rep", "rep"};
+
+  cudf::test::strings_column_wrapper input_w{input.begin(), input.end(), input_v.begin()};
+  cudf::test::strings_column_wrapper expected_w{expected.begin(), expected.end()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input_w, *repl));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+struct ReplaceNullsPolicyStringTest : public cudf::test::BaseFixture {};
+
+TEST_F(ReplaceNullsPolicyStringTest, PrecedingFill)
+{
+  cudf::test::strings_column_wrapper input({"head", "", "", "mid", "mid", "", "tail"},
+                                           {1, 0, 0, 1, 1, 0, 1});
+
+  cudf::test::strings_column_wrapper expected({"head", "head", "head", "mid", "mid", "mid", "tail"},
+                                              no_nulls());
+
+  auto result = cudf::replace_nulls(input, cudf::replace_policy::PRECEDING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(ReplaceNullsPolicyStringTest, FollowingFill)
+{
+  cudf::test::strings_column_wrapper input({"head", "", "", "mid", "mid", "", "tail"},
+                                           {1, 0, 0, 1, 1, 0, 1});
+
+  cudf::test::strings_column_wrapper expected({"head", "mid", "mid", "mid", "mid", "tail", "tail"},
+                                              no_nulls());
+
+  auto result = cudf::replace_nulls(input, cudf::replace_policy::FOLLOWING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(ReplaceNullsPolicyStringTest, PrecedingFillLeadingNulls)
+{
+  cudf::test::strings_column_wrapper input({"", "", "", "mid", "mid", "", "tail"},
+                                           {0, 0, 0, 1, 1, 0, 1});
+
+  cudf::test::strings_column_wrapper expected({"", "", "", "mid", "mid", "mid", "tail"},
+                                              {0, 0, 0, 1, 1, 1, 1});
+
+  auto result = cudf::replace_nulls(input, cudf::replace_policy::PRECEDING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(ReplaceNullsPolicyStringTest, FollowingFillTrailingNulls)
+{
+  cudf::test::strings_column_wrapper input({"head", "", "", "mid", "mid", "", ""},
+                                           {1, 0, 0, 1, 1, 0, 0});
+
+  cudf::test::strings_column_wrapper expected({"head", "mid", "mid", "mid", "mid", "", ""},
+                                              {1, 1, 1, 1, 1, 0, 0});
+
+  auto result = cudf::replace_nulls(input, cudf::replace_policy::FOLLOWING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+template <typename T>
+struct ReplaceNullsTest : public cudf::test::BaseFixture {};
+
+using test_types = cudf::test::NumericTypes;
+
+TYPED_TEST_SUITE(ReplaceNullsTest, test_types);
+
+template <typename T>
+void ReplaceNullsColumn(cudf::test::fixed_width_column_wrapper<T> input,
+                        cudf::test::fixed_width_column_wrapper<T> replacement_values,
+                        cudf::test::fixed_width_column_wrapper<T> expected)
+{
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input, replacement_values));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+template <typename T>
+void ReplaceNullsScalar(cudf::test::fixed_width_column_wrapper<T> input,
+                        cudf::scalar const& replacement_value,
+                        cudf::test::fixed_width_column_wrapper<T> expected)
+{
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::replace_nulls(input, replacement_value));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TYPED_TEST(ReplaceNullsTest, ReplaceColumn)
+{
+  auto const inputColumn =
+    cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+  auto const inputValid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({0, 0, 0, 0, 0, 1, 1, 1, 1, 1});
+  auto const replacementColumn =
+    cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+
+  ReplaceNullsColumn<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  inputColumn.begin(), inputColumn.end(), inputValid.begin()),
+                                cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  replacementColumn.begin(), replacementColumn.end()),
+                                cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  replacementColumn.begin(), replacementColumn.end()));
+}
+
+TYPED_TEST(ReplaceNullsTest, ReplaceColumn_Empty)
+{
+  ReplaceNullsColumn<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>{},
+                                cudf::test::fixed_width_column_wrapper<TypeParam>{},
+                                cudf::test::fixed_width_column_wrapper<TypeParam>{});
+}
+
+TYPED_TEST(ReplaceNullsTest, ReplaceScalar)
+{
+  auto const inputColumn =
+    cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+  auto const inputValid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({0, 0, 0, 0, 0, 1, 1, 1, 1, 1});
+  auto const expectedColumn =
+    cudf::test::make_type_param_vector<TypeParam>({1, 1, 1, 1, 1, 5, 6, 7, 8, 9});
+  cudf::numeric_scalar<TypeParam> replacement(1);
+
+  ReplaceNullsScalar<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  inputColumn.begin(), inputColumn.end(), inputValid.begin()),
+                                replacement,
+                                cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  expectedColumn.begin(), expectedColumn.end()));
+}
+
+TYPED_TEST(ReplaceNullsTest, ReplacementHasNulls)
+{
+  using T = TypeParam;
+
+  auto const input_column   = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  auto const replace_column = cudf::test::make_type_param_vector<T>({4, 5, 6, 7, 8, 9, 0, 1});
+  auto const result_column  = cudf::test::make_type_param_vector<T>({4, 5, 6, 3, 1, 2, 8, 4});
+
+  auto const input_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({0, 0, 1, 1, 1, 1, 1, 1});
+  auto const replace_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({1, 0, 1, 1, 1, 1, 1, 1});
+  auto const result_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({1, 0, 1, 1, 1, 1, 1, 1});
+
+  ReplaceNullsColumn<T>(cudf::test::fixed_width_column_wrapper<T>(
+                          input_column.begin(), input_column.end(), input_valid.begin()),
+                        cudf::test::fixed_width_column_wrapper<T>(
+                          replace_column.begin(), replace_column.end(), replace_valid.begin()),
+                        cudf::test::fixed_width_column_wrapper<T>(
+                          result_column.begin(), result_column.end(), result_valid.begin()));
+}
+
+TYPED_TEST(ReplaceNullsTest, LargeScale)
+{
+  std::vector<TypeParam> inputColumn(10000);
+  for (size_t i = 0; i < inputColumn.size(); i++)
+    inputColumn[i] = i % 2;
+  std::vector<cudf::valid_type> inputValid(10000);
+  for (size_t i = 0; i < inputValid.size(); i++)
+    inputValid[i] = i % 2;
+  std::vector<TypeParam> expectedColumn(10000);
+  for (size_t i = 0; i < expectedColumn.size(); i++)
+    expectedColumn[i] = 1;
+
+  ReplaceNullsColumn<TypeParam>(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      inputColumn.begin(), inputColumn.end(), inputValid.begin()),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(expectedColumn.begin(), expectedColumn.end()),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(expectedColumn.begin(),
+                                                      expectedColumn.end()));
+}
+
+TYPED_TEST(ReplaceNullsTest, LargeScaleScalar)
+{
+  std::vector<TypeParam> inputColumn(10000);
+  for (size_t i = 0; i < inputColumn.size(); i++)
+    inputColumn[i] = i % 2;
+  std::vector<cudf::valid_type> inputValid(10000);
+  for (size_t i = 0; i < inputValid.size(); i++)
+    inputValid[i] = i % 2;
+  std::vector<TypeParam> expectedColumn(10000);
+  for (size_t i = 0; i < expectedColumn.size(); i++)
+    expectedColumn[i] = 1;
+  cudf::numeric_scalar<TypeParam> replacement(1);
+
+  ReplaceNullsScalar<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  inputColumn.begin(), inputColumn.end(), inputValid.begin()),
+                                replacement,
+                                cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  expectedColumn.begin(), expectedColumn.end()));
+}
+
+template <typename T>
+struct ReplaceNullsPolicyTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ReplaceNullsPolicyTest, test_types);
+
+template <typename T>
+void TestReplaceNullsWithPolicy(cudf::test::fixed_width_column_wrapper<T> input,
+                                cudf::test::fixed_width_column_wrapper<T> expected,
+                                cudf::replace_policy policy)
+{
+  auto result = cudf::replace_nulls(input, policy);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected);
+}
+
+TYPED_TEST(ReplaceNullsPolicyTest, PrecedingFill)
+{
+  auto const col  = cudf::test::make_type_param_vector<TypeParam>({42, 2, 1, -10, 20, -30});
+  auto const mask = cudf::test::make_type_param_vector<cudf::valid_type>({1, 0, 0, 1, 0, 1});
+  auto const expect_col =
+    cudf::test::make_type_param_vector<TypeParam>({42, 42, 42, -10, -10, -30});
+
+  TestReplaceNullsWithPolicy(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(col.begin(), col.end(), mask.begin()),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      expect_col.begin(), expect_col.end(), no_nulls()),
+    cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyTest, FollowingFill)
+{
+  auto const col  = cudf::test::make_type_param_vector<TypeParam>({42, 2, 1, -10, 20, -30});
+  auto const mask = cudf::test::make_type_param_vector<cudf::valid_type>({1, 0, 0, 1, 0, 1});
+  auto const expect_col =
+    cudf::test::make_type_param_vector<TypeParam>({42, -10, -10, -10, -30, -30});
+
+  TestReplaceNullsWithPolicy(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(col.begin(), col.end(), mask.begin()),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      expect_col.begin(), expect_col.end(), no_nulls()),
+    cudf::replace_policy::FOLLOWING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyTest, PrecedingFillLeadingNulls)
+{
+  auto const col         = cudf::test::make_type_param_vector<TypeParam>({1, 2, 3, 4, 5});
+  auto const mask        = cudf::test::make_type_param_vector<cudf::valid_type>({0, 0, 1, 0, 1});
+  auto const expect_col  = cudf::test::make_type_param_vector<TypeParam>({1, 2, 3, 3, 5});
+  auto const expect_mask = cudf::test::make_type_param_vector<cudf::valid_type>({0, 0, 1, 1, 1});
+
+  TestReplaceNullsWithPolicy(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(col.begin(), col.end(), mask.begin()),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      expect_col.begin(), expect_col.end(), expect_mask.begin()),
+    cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyTest, FollowingFillTrailingNulls)
+{
+  auto const col         = cudf::test::make_type_param_vector<TypeParam>({1, 2, 3, 4, 5});
+  auto const mask        = cudf::test::make_type_param_vector<cudf::valid_type>({1, 0, 1, 0, 0});
+  auto const expect_col  = cudf::test::make_type_param_vector<TypeParam>({1, 3, 3, 4, 5});
+  auto const expect_mask = cudf::test::make_type_param_vector<cudf::valid_type>({1, 1, 1, 0, 0});
+
+  TestReplaceNullsWithPolicy(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(col.begin(), col.end(), mask.begin()),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      expect_col.begin(), expect_col.end(), expect_mask.begin()),
+    cudf::replace_policy::FOLLOWING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyTest, PrecedingFillLargeArray)
+{
+  cudf::size_type const sz = 1000;
+
+  // Source: 0, null, null...
+  auto src_begin       = thrust::make_counting_iterator(0);
+  auto src_end         = src_begin + sz;
+  auto nulls_idx_begin = thrust::make_counting_iterator(1);
+  auto nulls_idx_end   = nulls_idx_begin + sz - 1;
+
+  // Expected: 0, 0, 0, ...
+  auto expected_begin = thrust::make_constant_iterator(0);
+  auto expected_end   = expected_begin + sz;
+
+  TestReplaceNullsWithPolicy(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      src_begin, src_end, nulls_at(nulls_idx_begin, nulls_idx_end)),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(expected_begin, expected_end, no_nulls()),
+    cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyTest, FollowingFillLargeArray)
+{
+  cudf::size_type const sz = 1000;
+
+  // Source: null, ... null, 999
+  auto src_begin       = thrust::make_counting_iterator(0);
+  auto src_end         = src_begin + sz;
+  auto nulls_idx_begin = thrust::make_counting_iterator(0);
+  auto nulls_idx_end   = nulls_idx_begin + sz - 1;
+
+  // Expected: 999, 999, 999, ...
+  auto expected_begin = thrust::make_constant_iterator(sz - 1);
+  auto expected_end   = expected_begin + sz;
+
+  TestReplaceNullsWithPolicy(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(
+      src_begin, src_end, nulls_at(nulls_idx_begin, nulls_idx_end)),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(expected_begin, expected_end, no_nulls()),
+    cudf::replace_policy::FOLLOWING);
+}
+
+template <typename T>
+struct ReplaceNullsFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ReplaceNullsFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(ReplaceNullsFixedPointTest, ReplaceColumn)
+{
+  auto const scale = numeric::scale_type{0};
+  auto const sz    = std::size_t{1000};
+  auto data_begin  = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return TypeParam{i, scale};
+  });
+  auto valid_begin =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return i % 3 ? 1 : 0; });
+  auto replace_begin  = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return TypeParam{-2, scale};
+  });
+  auto expected_begin = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    int val = i % 3 ? static_cast<int>(i) : -2;
+    return TypeParam{val, scale};
+  });
+
+  ReplaceNullsColumn<TypeParam>(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(data_begin, data_begin + sz, valid_begin),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(replace_begin, replace_begin + sz),
+    cudf::test::fixed_width_column_wrapper<TypeParam>(expected_begin, expected_begin + sz));
+}
+
+TYPED_TEST(ReplaceNullsFixedPointTest, ReplaceColumn_Empty)
+{
+  ReplaceNullsColumn<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>{},
+                                cudf::test::fixed_width_column_wrapper<TypeParam>{},
+                                cudf::test::fixed_width_column_wrapper<TypeParam>{});
+}
+
+TYPED_TEST(ReplaceNullsFixedPointTest, ReplaceScalar)
+{
+  auto const scale = numeric::scale_type{0};
+  auto const sz    = std::size_t{1000};
+  auto data_begin  = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return TypeParam{i, scale};
+  });
+  auto valid_begin =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return i % 3 ? 1 : 0; });
+  auto expected_begin = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    int val = i % 3 ? static_cast<int>(i) : -2;
+    return TypeParam{val, scale};
+  });
+
+  cudf::fixed_point_scalar<TypeParam> replacement{-2, scale};
+
+  ReplaceNullsScalar<TypeParam>(
+    cudf::test::fixed_width_column_wrapper<TypeParam>(data_begin, data_begin + sz, valid_begin),
+    replacement,
+    cudf::test::fixed_width_column_wrapper<TypeParam>(expected_begin, expected_begin + sz));
+}
+
+TYPED_TEST(ReplaceNullsFixedPointTest, ReplacementHasNulls)
+{
+  auto const scale = numeric::scale_type{0};
+  auto const sz    = std::size_t{1000};
+  auto data_begin  = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return TypeParam{i, scale};
+  });
+  auto data_valid_begin =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return i % 3 ? 1 : 0; });
+  auto replace_begin = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    return TypeParam{-2, scale};
+  });
+  auto replace_valid_begin =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return i % 2 ? 1 : 0; });
+  auto expected_begin = cudf::detail::make_counting_transform_iterator(0, [&](auto i) {
+    int val = i % 3 ? static_cast<int>(i) : -2;
+    return TypeParam{val, scale};
+  });
+  auto expected_valid_begin =
+    cudf::detail::make_counting_transform_iterator(0, [&](auto i) { return i % 6 ? 1 : 0; });
+
+  ReplaceNullsColumn<TypeParam>(cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  data_begin, data_begin + sz, data_valid_begin),
+                                cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  replace_begin, replace_begin + sz, replace_valid_begin),
+                                cudf::test::fixed_width_column_wrapper<TypeParam>(
+                                  expected_begin, expected_begin + sz, expected_valid_begin));
+}
+
+template <typename T>
+struct ReplaceNullsPolicyFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ReplaceNullsPolicyFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(ReplaceNullsPolicyFixedPointTest, PrecedingFill)
+{
+  using fp     = TypeParam;
+  auto const s = numeric::scale_type{0};
+  auto col     = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{42, s}, fp{2, s}, fp{1, s}, fp{-10, s}, fp{20, s}, fp{-30, s}}, {1, 0, 0, 1, 0, 1});
+  auto expect_col = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{42, s}, fp{42, s}, fp{42, s}, fp{-10, s}, fp{-10, s}, fp{-30, s}}, {1, 1, 1, 1, 1, 1});
+
+  TestReplaceNullsWithPolicy(
+    std::move(col), std::move(expect_col), cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyFixedPointTest, FollowingFill)
+{
+  using fp     = TypeParam;
+  auto const s = numeric::scale_type{0};
+  auto col     = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{42, s}, fp{2, s}, fp{1, s}, fp{-10, s}, fp{20, s}, fp{-30, s}}, {1, 0, 0, 1, 0, 1});
+  auto expect_col = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{42, s}, fp{-10, s}, fp{-10, s}, fp{-10, s}, fp{-30, s}, fp{-30, s}}, {1, 1, 1, 1, 1, 1});
+
+  TestReplaceNullsWithPolicy(
+    std::move(col), std::move(expect_col), cudf::replace_policy::FOLLOWING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyFixedPointTest, PrecedingFillLeadingNulls)
+{
+  using fp     = TypeParam;
+  auto const s = numeric::scale_type{0};
+  auto col     = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{1, s}, fp{2, s}, fp{3, s}, fp{4, s}, fp{5, s}}, {0, 0, 1, 0, 1});
+  auto expect_col = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{1, s}, fp{2, s}, fp{3, s}, fp{3, s}, fp{5, s}}, {0, 0, 1, 1, 1});
+
+  TestReplaceNullsWithPolicy(
+    std::move(col), std::move(expect_col), cudf::replace_policy::PRECEDING);
+}
+
+TYPED_TEST(ReplaceNullsPolicyFixedPointTest, FollowingFillTrailingNulls)
+{
+  using fp     = TypeParam;
+  auto const s = numeric::scale_type{0};
+  auto col     = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{1, s}, fp{2, s}, fp{3, s}, fp{4, s}, fp{5, s}}, {1, 0, 1, 0, 0});
+  auto expect_col = cudf::test::fixed_width_column_wrapper<TypeParam>(
+    {fp{1, s}, fp{3, s}, fp{3, s}, fp{4, s}, fp{5, s}}, {1, 1, 1, 0, 0});
+
+  TestReplaceNullsWithPolicy(
+    std::move(col), std::move(expect_col), cudf::replace_policy::FOLLOWING);
+}
+
+struct ReplaceDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(ReplaceDictionaryTest, ReplaceNulls)
+{
+  cudf::test::strings_column_wrapper input_w({"c", "", "", "a", "d", "d", "", ""},
+                                             {1, 0, 0, 1, 1, 1, 0, 0});
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::strings_column_wrapper replacement_w({"c", "c", "", "a", "d", "d", "b", ""},
+                                                   {1, 1, 0, 1, 1, 1, 1, 0});
+  auto replacement = cudf::dictionary::encode(replacement_w);
+  cudf::test::strings_column_wrapper expected_w({"c", "c", "", "a", "d", "d", "b", ""},
+                                                {1, 1, 0, 1, 1, 1, 1, 0});
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  auto result = cudf::replace_nulls(input->view(), replacement->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected->view());
+}
+
+TEST_F(ReplaceDictionaryTest, ReplaceNullsWithScalar)
+{
+  cudf::test::strings_column_wrapper input_w({"c", "", "", "a", "d", "d", "", ""},
+                                             {1, 0, 0, 1, 1, 1, 0, 0});
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::strings_column_wrapper expected_w({"c", "b", "b", "a", "d", "d", "b", "b"});
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  auto result = cudf::replace_nulls(input->view(), cudf::string_scalar("b"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected->view());
+}
+
+TEST_F(ReplaceDictionaryTest, ReplaceNullsError)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_w({1, 1, 2, 2}, {1, 0, 0, 1});
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<int64_t> replacement_w({1, 2, 3, 4});
+  auto replacement = cudf::dictionary::encode(replacement_w);
+
+  EXPECT_THROW(cudf::replace_nulls(input->view(), replacement->view()), cudf::logic_error);
+  EXPECT_THROW(cudf::replace_nulls(input->view(), cudf::string_scalar("x")), cudf::logic_error);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> input_one_w({1}, {0});
+  auto input_one  = cudf::dictionary::encode(input_one_w);
+  auto dict_input = cudf::dictionary_column_view(input_one->view());
+  auto dict_repl  = cudf::dictionary_column_view(replacement->view());
+  EXPECT_THROW(cudf::replace_nulls(input->view(), replacement->view()), cudf::logic_error);
+}
+
+TEST_F(ReplaceDictionaryTest, ReplaceNullsEmpty)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input_empty_w({});
+  auto input_empty = cudf::dictionary::encode(input_empty_w);
+  auto result      = cudf::replace_nulls(input_empty->view(), input_empty->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), input_empty->view());
+}
+
+TEST_F(ReplaceDictionaryTest, ReplaceNullsNoNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int8_t> input_w({1, 1, 1});
+  auto input  = cudf::dictionary::encode(input_w);
+  auto result = cudf::replace_nulls(input->view(), input->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), input->view());
+
+  result = cudf::replace_nulls(input->view(), cudf::numeric_scalar<int8_t>(0, false));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), input->view());
+}
+
+struct ReplaceNullsPolicyDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(ReplaceNullsPolicyDictionaryTest, PrecedingFill)
+{
+  cudf::test::strings_column_wrapper input_w({"head", "", "", "mid1", "mid2", "tail", "", ""},
+                                             {1, 0, 0, 1, 1, 1, 0, 0});
+  auto input = cudf::dictionary::encode(input_w);
+
+  cudf::test::strings_column_wrapper expected_w(
+    {"head", "head", "head", "mid1", "mid2", "tail", "tail", "tail"}, no_nulls());
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  auto result = cudf::replace_nulls(*input, cudf::replace_policy::PRECEDING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected->view());
+}
+
+TEST_F(ReplaceNullsPolicyDictionaryTest, FollowingFill)
+{
+  cudf::test::strings_column_wrapper input_w({"head", "", "", "mid1", "mid2", "", "", "tail"},
+                                             {1, 0, 0, 1, 1, 0, 0, 1});
+  auto input = cudf::dictionary::encode(input_w);
+
+  cudf::test::strings_column_wrapper expected_w(
+    {"head", "mid1", "mid1", "mid1", "mid2", "tail", "tail", "tail"}, no_nulls());
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  auto result = cudf::replace_nulls(*input, cudf::replace_policy::FOLLOWING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected->view());
+}
+
+TEST_F(ReplaceNullsPolicyDictionaryTest, PrecedingFillLeadingNulls)
+{
+  cudf::test::strings_column_wrapper input_w({"", "", "", "mid1", "mid2", "", "", "tail"},
+                                             {0, 0, 0, 1, 1, 0, 0, 1});
+  auto input = cudf::dictionary::encode(input_w);
+
+  cudf::test::strings_column_wrapper expected_w(
+    {"", "", "", "mid1", "mid2", "mid2", "mid2", "tail"}, {0, 0, 0, 1, 1, 1, 1, 1});
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  auto result = cudf::replace_nulls(*input, cudf::replace_policy::PRECEDING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected->view());
+}
+
+TEST_F(ReplaceNullsPolicyDictionaryTest, FollowingFillTrailingNulls)
+{
+  cudf::test::strings_column_wrapper input_w({"head", "", "", "mid", "tail", "", "", ""},
+                                             {1, 0, 0, 1, 1, 0, 0, 0});
+  auto input = cudf::dictionary::encode(input_w);
+
+  cudf::test::strings_column_wrapper expected_w({"head", "mid", "mid", "mid", "tail", "", "", ""},
+                                                {1, 1, 1, 1, 1, 0, 0, 0});
+  auto expected = cudf::dictionary::encode(expected_w);
+
+  auto result = cudf::replace_nulls(*input, cudf::replace_policy::FOLLOWING);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected->view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/replace/replace_tests.cpp b/cpp/tests/replace/replace_tests.cpp
new file mode 100644
index 0000000..63460b0
--- /dev/null
+++ b/cpp/tests/replace/replace_tests.cpp
@@ -0,0 +1,624 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018 BlazingDB, Inc.
+ *     Copyright 2018 Cristhian Alberto Gonzales Castillo <cristhian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/replace.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <cstdlib>
+#include <cudf/types.hpp>
+#include <gtest/gtest.h>
+#include <iostream>
+#include <vector>
+
+struct ReplaceErrorTest : public cudf::test::BaseFixture {};
+
+// Error: old-values and new-values size mismatch
+TEST_F(ReplaceErrorTest, SizeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{{7, 5, 6, 3, 1, 2, 8, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> values_to_replace_column{{10, 11, 12, 13}};
+  cudf::test::fixed_width_column_wrapper<int32_t> replacement_values_column{{15, 16, 17}};
+
+  EXPECT_THROW(
+    cudf::find_and_replace_all(input_column, values_to_replace_column, replacement_values_column),
+    cudf::logic_error);
+}
+
+// Error: column type mismatch
+TEST_F(ReplaceErrorTest, TypeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{{7, 5, 6, 3, 1, 2, 8, 4}};
+  cudf::test::fixed_width_column_wrapper<float> values_to_replace_column{{10, 11, 12}};
+  cudf::test::fixed_width_column_wrapper<int32_t> replacement_values_column{{15, 16, 17}};
+
+  EXPECT_THROW(
+    cudf::find_and_replace_all(input_column, values_to_replace_column, replacement_values_column),
+    cudf::logic_error);
+}
+
+// Error: nulls in old-values
+TEST_F(ReplaceErrorTest, NullInOldValues)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column{{7, 5, 6, 3, 1, 2, 8, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> values_to_replace_column{{10, 11, 12, 13},
+                                                                           {0, 1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> replacement_values_column{{15, 16, 17, 18}};
+
+  EXPECT_THROW(
+    cudf::find_and_replace_all(input_column, values_to_replace_column, replacement_values_column),
+    cudf::logic_error);
+}
+
+struct ReplaceStringsTest : public cudf::test::BaseFixture {};
+
+// Strings test
+TEST_F(ReplaceStringsTest, Strings)
+{
+  std::vector<std::string> input{"a", "b", "c", "d", "e", "f", "g", "h"};
+  std::vector<std::string> values_to_replace{"a"};
+  std::vector<std::string> replacement{"z"};
+
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{replacement.begin(), replacement.end()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  std::vector<std::string> expected{"z", "b", "c", "d", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> ex_valid{1, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsReplacementNulls)
+{
+  std::vector<std::string> input{"a", "b", "c", "d", "e", "f", "g", "h"};
+  std::vector<std::string> values_to_replace{"a", "b"};
+  std::vector<std::string> replacement{"z", ""};
+  std::vector<cudf::valid_type> replacement_valid{1, 0};
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{
+    replacement.begin(), replacement.end(), replacement_valid.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  std::vector<std::string> expected{"z", "", "c", "d", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> ex_valid{1, 0, 1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsResultAllNulls)
+{
+  std::vector<std::string> input{"b", "b", "b", "b", "b", "b", "b", "b"};
+  std::vector<std::string> values_to_replace{"a", "b"};
+  std::vector<std::string> replacement{"a", ""};
+  std::vector<cudf::valid_type> replacement_valid{1, 0};
+  std::vector<std::string> expected{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> ex_valid{0, 0, 0, 0, 0, 0, 0, 0};
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{
+    replacement.begin(), replacement.end(), replacement_valid.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsResultAllEmpty)
+{
+  std::vector<std::string> input{"b", "b", "b", "b", "b", "b", "b", "b"};
+  std::vector<std::string> values_to_replace{"a", "b"};
+  std::vector<std::string> replacement{"a", ""};
+  std::vector<cudf::valid_type> replacement_valid{1, 1};
+  std::vector<std::string> expected{"", "", "", "", "", "", "", ""};
+  std::vector<cudf::valid_type> ex_valid{1, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{
+    replacement.begin(), replacement.end(), replacement_valid.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsInputNulls)
+{
+  std::vector<std::string> input{"a", "b", "", "", "e", "f", "g", "h"};
+  std::vector<std::string> values_to_replace{"a", "b"};
+  std::vector<std::string> replacement{"z", "y"};
+  std::vector<cudf::valid_type> input_valid{1, 1, 0, 0, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end(), input_valid.begin()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{replacement.begin(), replacement.end()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  std::vector<std::string> expected{"z", "y", "", "", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> ex_valid{1, 1, 0, 0, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsInputAndReplacementNulls)
+{
+  std::vector<std::string> input{"a", "b", "", "", "e", "f", "g", "h"};
+  std::vector<std::string> values_to_replace{"a", "b"};
+  std::vector<std::string> replacement{"z", ""};
+  std::vector<cudf::valid_type> replacement_valid{1, 0};
+  std::vector<cudf::valid_type> input_valid{1, 1, 0, 0, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end(), input_valid.begin()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{
+    replacement.begin(), replacement.end(), replacement_valid.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  std::vector<std::string> expected{"z", "", "", "", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> ex_valid{1, 0, 0, 0, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsEmptyReplacement)
+{
+  std::vector<std::string> input{"a", "b", "", "", "e", "f", "g", "h"};
+  std::vector<std::string> values_to_replace{};
+  std::vector<std::string> replacement{};
+  std::vector<cudf::valid_type> input_valid{1, 1, 0, 0, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper input_wrapper{input.begin(), input.end(), input_valid.begin()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{replacement.begin(), replacement.end()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+  std::vector<std::string> expected{"a", "b", "", "", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> ex_valid{1, 1, 0, 0, 1, 1, 1, 1};
+  cudf::test::strings_column_wrapper expected_wrapper{
+    expected.begin(), expected.end(), ex_valid.begin()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+// Strings test
+TEST_F(ReplaceStringsTest, StringsLargeScale)
+{
+  std::vector<std::string> input{"a", "b", "", "", "e", "f", "g", "h"};
+  std::vector<std::string> values_to_replace{"a", "b"};
+  std::vector<std::string> replacement{"z", ""};
+  std::vector<cudf::valid_type> replacement_valid{1, 0};
+  std::vector<cudf::valid_type> input_valid{1, 1, 0, 0, 1, 1, 1, 1};
+  std::vector<std::string> expected{"z", "", "", "", "e", "f", "g", "h"};
+  std::vector<cudf::valid_type> ex_valid{1, 0, 0, 0, 1, 1, 1, 1};
+
+  std::vector<std::string> big_input{};
+  std::vector<cudf::valid_type> big_input_valid{};
+  std::vector<std::string> big_expected{};
+  std::vector<cudf::valid_type> big_ex_valid{};
+
+  for (int i = 0; i < 10000; i++) {
+    int ind = i % input.size();
+    big_input.push_back(input[ind]);
+    big_input_valid.push_back(input_valid[ind]);
+    big_expected.push_back(expected[ind]);
+    big_ex_valid.push_back(ex_valid[ind]);
+  }
+
+  cudf::test::strings_column_wrapper expected_wrapper{
+    big_expected.begin(), big_expected.end(), big_ex_valid.begin()};
+
+  cudf::test::strings_column_wrapper input_wrapper{
+    big_input.begin(), big_input.end(), big_input_valid.begin()};
+  cudf::test::strings_column_wrapper values_to_replace_wrapper{values_to_replace.begin(),
+                                                               values_to_replace.end()};
+  cudf::test::strings_column_wrapper replacement_wrapper{
+    replacement.begin(), replacement.end(), replacement_valid.begin()};
+
+  std::unique_ptr<cudf::column> result;
+  ASSERT_NO_THROW(result = cudf::find_and_replace_all(
+                    input_wrapper, values_to_replace_wrapper, replacement_wrapper));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_wrapper);
+}
+
+//// This is the main test feature
+template <class T>
+struct ReplaceTest : cudf::test::BaseFixture {
+  ReplaceTest()
+  {
+    // Use constant seed so the pseudo-random order is the same each time
+    // Each time the class is constructed a new constant seed is used
+    static size_t number_of_instantiations{0};
+    std::srand(number_of_instantiations++);
+  }
+
+  ~ReplaceTest() override {}
+};
+
+/**
+ * @brief Main method for testing.
+ * Initializes the input columns with the given values. Then compute the actual
+ * resultant column by invoking `cudf::find_and_replace_all()` and then
+ * compute the expected column.
+ *
+ * @param input_column The original values
+ * @param values_to_replace_column The values that will be replaced
+ * @param replacement_values_column The new values
+ * @param input_column_valid The mask for replace column
+ * @param replacement_values_valid The mask for new values
+ * @param print Optionally print the set of columns for debug
+ */
+template <typename T>
+void test_replace(cudf::host_span<T const> input_column,
+                  cudf::host_span<T const> values_to_replace_column,
+                  cudf::host_span<T const> replacement_values_column,
+                  cudf::host_span<cudf::valid_type const> input_column_valid       = {},
+                  cudf::host_span<cudf::valid_type const> replacement_values_valid = {},
+                  bool print                                                       = false)
+{
+  cudf::test::fixed_width_column_wrapper<T> _input_column(input_column.begin(), input_column.end());
+  if (input_column_valid.size() > 0) {
+    _input_column = cudf::test::fixed_width_column_wrapper<T>(
+      input_column.begin(), input_column.end(), input_column_valid.begin());
+  }
+
+  cudf::test::fixed_width_column_wrapper<T> _values_to_replace_column(
+    values_to_replace_column.begin(), values_to_replace_column.end());
+  cudf::test::fixed_width_column_wrapper<T> _replacement_values_column(
+    replacement_values_column.begin(), replacement_values_column.end());
+  if (replacement_values_valid.size() > 0) {
+    _replacement_values_column =
+      cudf::test::fixed_width_column_wrapper<T>(replacement_values_column.begin(),
+                                                replacement_values_column.end(),
+                                                replacement_values_valid.begin());
+  }
+
+  /* getting the actual result*/
+  std::unique_ptr<cudf::column> actual_result;
+  ASSERT_NO_THROW(actual_result = cudf::find_and_replace_all(
+                    _input_column, _values_to_replace_column, _replacement_values_column));
+
+  /* computing the expected result */
+  thrust::host_vector<T> reference_result(input_column.begin(), input_column.end());
+  thrust::host_vector<bool> isReplaced(reference_result.size(), false);
+  thrust::host_vector<cudf::valid_type> expected_valid(input_column_valid.begin(),
+                                                       input_column_valid.end());
+  if (replacement_values_valid.size() > 0 && 0 == input_column_valid.size()) {
+    expected_valid.assign(input_column.size(), true);
+  }
+
+  bool const input_has_nulls       = (input_column_valid.size() > 0);
+  bool const replacement_has_nulls = (replacement_values_valid.size() > 0);
+
+  for (size_t i = 0; i < values_to_replace_column.size(); i++) {
+    size_t k  = 0;
+    auto pred = [=, &k, &reference_result, &expected_valid, &isReplaced](T element) {
+      bool toBeReplaced = false;
+      if (!isReplaced[k]) {
+        if (!input_has_nulls || expected_valid[k]) {
+          if (element == values_to_replace_column[i]) {
+            toBeReplaced  = true;
+            isReplaced[k] = toBeReplaced;
+            if (replacement_has_nulls && !replacement_values_valid[i]) {
+              if (print) std::cout << "clearing bit at: " << k << "\n";
+              expected_valid[k] = false;
+            }
+          }
+        }
+      }
+
+      ++k;
+      return toBeReplaced;
+    };
+    std::replace_if(
+      reference_result.begin(), reference_result.end(), pred, replacement_values_column[i]);
+  }
+
+  cudf::test::fixed_width_column_wrapper<T> expected(reference_result.begin(),
+                                                     reference_result.end());
+  if (expected_valid.size() > 0)
+    expected = cudf::test::fixed_width_column_wrapper<T>(
+      reference_result.begin(), reference_result.end(), expected_valid.begin());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual_result);
+}
+
+using Types = cudf::test::NumericTypes;
+
+TYPED_TEST_SUITE(ReplaceTest, Types);
+
+// Simple test, replacing all even replacement_values_column
+TYPED_TEST(ReplaceTest, ReplaceEvenPosition)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({1, 2, 3, 4, 5, 6, 7, 8});
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({2, 6, 4, 8});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({0, 4, 2, 6});
+
+  test_replace<T>(input_column, values_to_replace_column, replacement_values_column);
+}
+
+// Similar test as ReplaceEvenPosition, but with unordered data
+TYPED_TEST(ReplaceTest, Unordered)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({2, 6, 4, 8});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({0, 4, 2, 6});
+
+  test_replace<T>(input_column, values_to_replace_column, replacement_values_column);
+}
+
+// Testing with Nothing To Replace
+TYPED_TEST(ReplaceTest, NothingToReplace)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({10, 11, 12});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({15, 16, 17});
+
+  test_replace<T>(input_column, values_to_replace_column, replacement_values_column);
+}
+
+// Testing with empty Data
+TYPED_TEST(ReplaceTest, EmptyData)
+{
+  using T = TypeParam;
+  thrust::host_vector<T> input_column{{}};
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({10, 11, 12});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({15, 16, 17});
+
+  test_replace<T>(input_column, values_to_replace_column, replacement_values_column);
+}
+
+// Testing with empty Replace
+TYPED_TEST(ReplaceTest, EmptyReplace)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  thrust::host_vector<T> values_to_replace_column{};
+  thrust::host_vector<T> replacement_values_column{};
+
+  test_replace<T>(input_column, values_to_replace_column, replacement_values_column);
+}
+
+// Testing with input column containing nulls
+TYPED_TEST(ReplaceTest, NullsInData)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  auto const input_column_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({1, 1, 1, 0, 0, 1, 1, 1});
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({2, 6, 4, 8});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({0, 4, 2, 6});
+
+  test_replace<T>(
+    input_column, values_to_replace_column, replacement_values_column, input_column_valid);
+}
+
+// Testing with replacement column containing nulls
+TYPED_TEST(ReplaceTest, NullsInNewValues)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({2, 6, 4, 8});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({0, 4, 2, 6});
+  auto const replacement_values_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({0, 1, 1, 1});
+
+  test_replace<TypeParam>(input_column,
+                          values_to_replace_column,
+                          replacement_values_column,
+                          {},
+                          replacement_values_valid);
+}
+
+// Testing with both replacement and input column containing nulls
+TYPED_TEST(ReplaceTest, NullsInBoth)
+{
+  using T                 = TypeParam;
+  auto const input_column = cudf::test::make_type_param_vector<T>({7, 5, 6, 3, 1, 2, 8, 4});
+  auto const input_column_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({1, 1, 1, 0, 0, 1, 1, 1});
+  auto const values_to_replace_column  = cudf::test::make_type_param_vector<T>({2, 6, 4, 8});
+  auto const replacement_values_column = cudf::test::make_type_param_vector<T>({0, 4, 2, 6});
+  auto const replacement_values_valid =
+    cudf::test::make_type_param_vector<cudf::valid_type>({1, 1, 0, 1});
+
+  test_replace<TypeParam>(input_column,
+                          values_to_replace_column,
+                          replacement_values_column,
+                          input_column_valid,
+                          replacement_values_valid);
+}
+
+// Test with much larger data sets
+TYPED_TEST(ReplaceTest, LargeScaleReplaceTest)
+{
+  const size_t DATA_SIZE    = 1000000;
+  const size_t REPLACE_SIZE = 10000;
+
+  thrust::host_vector<TypeParam> input_column(DATA_SIZE);
+  std::generate(std::begin(input_column), std::end(input_column), [REPLACE_SIZE]() {
+    return std::rand() % (REPLACE_SIZE);
+  });
+
+  std::vector<TypeParam> values_to_replace_column(REPLACE_SIZE);
+  std::vector<TypeParam> replacement_values_column(REPLACE_SIZE);
+  size_t count = 0;
+  for (size_t i = 0; i < 7; i++) {
+    for (size_t j = 0; j < REPLACE_SIZE; j += 7) {
+      if (i + j < REPLACE_SIZE) {
+        values_to_replace_column[i + j] = count;
+        count++;
+        replacement_values_column[i + j] = count;
+      }
+    }
+  }
+  cudf::test::fixed_width_column_wrapper<TypeParam> _input_column(input_column.begin(),
+                                                                  input_column.end());
+  cudf::test::fixed_width_column_wrapper<TypeParam> _values_to_replace_column(
+    values_to_replace_column.begin(), values_to_replace_column.end());
+  cudf::test::fixed_width_column_wrapper<TypeParam> _replacement_values_column(
+    replacement_values_column.begin(), replacement_values_column.end());
+
+  std::unique_ptr<cudf::column> actual_result;
+  ASSERT_NO_THROW(actual_result = cudf::find_and_replace_all(
+                    _input_column, _values_to_replace_column, _replacement_values_column));
+
+  std::for_each(input_column.begin(), input_column.end(), [](TypeParam& d) { d += 1; });
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected(input_column.begin(),
+                                                             input_column.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *actual_result);
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+template <typename T>
+using wrapper = cudf::test::fixed_width_column_wrapper<T>;
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointReplace)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const ONE = decimalXX{1, scale_type{0}};
+  auto const TWO = decimalXX{2, scale_type{0}};
+  auto const sz  = std::size_t{1000};
+
+  auto mod2            = [&](auto e) { return e % 2 ? ONE : TWO; };
+  auto transform_begin = cudf::detail::make_counting_transform_iterator(0, mod2);
+  auto const vec1      = std::vector<decimalXX>(transform_begin, transform_begin + sz);
+  auto const vec2      = std::vector<decimalXX>(sz, TWO);
+
+  auto const to_replace  = std::vector<decimalXX>{ONE};
+  auto const replacement = std::vector<decimalXX>{TWO};
+
+  auto const input_w       = wrapper<decimalXX>(vec1.begin(), vec1.end());
+  auto const to_replace_w  = wrapper<decimalXX>(to_replace.begin(), to_replace.end());
+  auto const replacement_w = wrapper<decimalXX>(replacement.begin(), replacement.end());
+  auto const expected_w    = wrapper<decimalXX>(vec2.begin(), vec2.end());
+
+  auto const result = cudf::find_and_replace_all(input_w, to_replace_w, replacement_w);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expected_w);
+}
+
+struct ReplaceDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(ReplaceDictionaryTest, StringsKeys)
+{
+  cudf::test::strings_column_wrapper input_w({"a", "b", "a", "c", "b", "a", "c", "b"});
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::strings_column_wrapper values_to_replace_w({"a"});
+  auto values_to_replace = cudf::dictionary::encode(values_to_replace_w);
+  cudf::test::strings_column_wrapper replacements_w({"z"});
+  auto replacements = cudf::dictionary::encode(replacements_w);
+
+  auto result =
+    cudf::find_and_replace_all(input->view(), values_to_replace->view(), replacements->view());
+  auto decoded = cudf::dictionary::decode(result->view());
+  cudf::test::strings_column_wrapper expected({"z", "b", "z", "c", "b", "z", "c", "b"});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+}
+
+TEST_F(ReplaceDictionaryTest, InputAndReplacementNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_w({1, 2, 1, 2, 0, 3, 4, 4, 3},
+                                                          {1, 1, 1, 1, 0, 1, 1, 1, 1});
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<int32_t> values_to_replace_w({2, 3});
+  auto values_to_replace = cudf::dictionary::encode(values_to_replace_w);
+  cudf::test::fixed_width_column_wrapper<int32_t> replacements_w({5, 0}, {1, 0});
+  auto replacements = cudf::dictionary::encode(replacements_w);
+
+  auto result =
+    cudf::find_and_replace_all(input->view(), values_to_replace->view(), replacements->view());
+  auto decoded = cudf::dictionary::decode(result->view());
+  cudf::test::fixed_width_column_wrapper<int32_t> expected({1, 5, 1, 5, 0, 0, 4, 4, 0},
+                                                           {1, 1, 1, 1, 0, 0, 1, 1, 0});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*decoded, expected);
+}
+
+TEST_F(ReplaceDictionaryTest, EmptyReplacement)
+{
+  cudf::test::fixed_width_column_wrapper<double> input_w(
+    {1.0, 2.0, 1.0, 2.0, 0.0, 3.0, 4.0, 4.0, 3.0}, {1, 1, 1, 1, 0, 1, 1, 1, 1});
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<double> empty_w({});
+  auto empty  = cudf::dictionary::encode(empty_w);
+  auto result = cudf::find_and_replace_all(input->view(), empty->view(), empty->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, *input);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/reshape/byte_cast_tests.cpp b/cpp/tests/reshape/byte_cast_tests.cpp
new file mode 100644
index 0000000..0b4cd0c
--- /dev/null
+++ b/cpp/tests/reshape/byte_cast_tests.cpp
@@ -0,0 +1,408 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/reshape.hpp>
+
+class ByteCastTest : public cudf::test::BaseFixture {};
+
+TEST_F(ByteCastTest, int16ValuesWithSplit)
+{
+  using limits = std::numeric_limits<int16_t>;
+  cudf::test::fixed_width_column_wrapper<int16_t> const int16_col(
+    {short(0), short(100), short(-100), limits::min(), limits::max()});
+  cudf::test::lists_column_wrapper<uint8_t> const int16_expected(
+    {{0x00, 0x00}, {0x64, 0x00}, {0x9c, 0xff}, {0x00, 0x80}, {0xff, 0x7f}});
+  cudf::test::lists_column_wrapper<uint8_t> const int16_expected_slice1(
+    {{0x00, 0x00}, {0x00, 0x64}, {0xff, 0x9c}});
+  cudf::test::lists_column_wrapper<uint8_t> const int16_expected_slice2(
+    {{0x80, 0x00}, {0x7f, 0xff}});
+
+  std::vector<cudf::size_type> splits({3});
+  std::vector<cudf::column_view> split_column = cudf::split(int16_col, splits);
+
+  auto const output_int16        = cudf::byte_cast(int16_col, cudf::flip_endianness::NO);
+  auto const output_int16_slice1 = cudf::byte_cast(split_column.at(0), cudf::flip_endianness::YES);
+  auto const output_int16_slice2 = cudf::byte_cast(split_column.at(1), cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int16->view(), int16_expected);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int16_slice1->view(), int16_expected_slice1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int16_slice2->view(), int16_expected_slice2);
+}
+
+TEST_F(ByteCastTest, int16ValuesWithNulls)
+{
+  using limits = std::numeric_limits<int16_t>;
+  auto odd_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  cudf::test::fixed_width_column_wrapper<int16_t> const int16_col(
+    {short(0), short(100), short(-100), limits::min(), limits::max()}, {0, 1, 0, 1, 0});
+
+  auto int16_data = cudf::test::fixed_width_column_wrapper<uint8_t>{0x00, 0x64, 0x80, 0x00};
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(odd_validity, odd_validity + 5);
+  auto int16_expected          = cudf::make_lists_column(
+    5,
+    std::move(cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 2, 2, 4, 4}.release()),
+    std::move(int16_data.release()),
+    null_count,
+    std::move(null_mask));
+
+  auto const output_int16 = cudf::byte_cast(int16_col, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output_int16->view(), int16_expected->view());
+}
+
+TEST_F(ByteCastTest, int32Values)
+{
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const int32_col(
+    {0, 100, -100, limits::min(), limits::max()});
+  cudf::test::lists_column_wrapper<uint8_t> const int32_expected_flipped(
+    {{0x00, 0x00, 0x00, 0x00},
+     {0x00, 0x00, 0x00, 0x64},
+     {0xff, 0xff, 0xff, 0x9c},
+     {0x80, 0x00, 0x00, 0x00},
+     {0x7f, 0xff, 0xff, 0xff}});
+  cudf::test::lists_column_wrapper<uint8_t> const int32_expected({{0x00, 0x00, 0x00, 0x00},
+                                                                  {0x64, 0x00, 0x00, 0x00},
+                                                                  {0x9c, 0xff, 0xff, 0xff},
+                                                                  {0x00, 0x00, 0x00, 0x80},
+                                                                  {0xff, 0xff, 0xff, 0x7f}});
+
+  auto const output_int32_flipped = cudf::byte_cast(int32_col, cudf::flip_endianness::YES);
+  auto const output_int32         = cudf::byte_cast(int32_col, cudf::flip_endianness::NO);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int32_flipped->view(), int32_expected_flipped);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int32->view(), int32_expected);
+}
+
+TEST_F(ByteCastTest, int32ValuesWithNulls)
+{
+  using limits = std::numeric_limits<int32_t>;
+  auto even_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i + 1) % 2; });
+
+  cudf::test::fixed_width_column_wrapper<int32_t> const int32_col(
+    {0, 100, -100, limits::min(), limits::max()}, {1, 0, 1, 0, 1});
+
+  auto int32_data = cudf::test::fixed_width_column_wrapper<uint8_t>{
+    0x00, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0x9c, 0x7f, 0xff, 0xff, 0xff};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(even_validity, even_validity + 5);
+
+  auto int32_expected = cudf::make_lists_column(
+    5,
+    std::move(cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 4, 8, 8, 12}.release()),
+    std::move(int32_data.release()),
+    null_count,
+    std::move(null_mask));
+
+  auto const output_int32 = cudf::byte_cast(int32_col, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output_int32->view(), int32_expected->view());
+}
+
+TEST_F(ByteCastTest, int64ValuesWithSplit)
+{
+  using limits = std::numeric_limits<int64_t>;
+  cudf::test::fixed_width_column_wrapper<int64_t> const int64_col(
+    {long(0), long(100), long(-100), limits::min(), limits::max()});
+  cudf::test::lists_column_wrapper<uint8_t> const int64_expected_flipped(
+    {{0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64},
+     {0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x9c},
+     {0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x7f, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}});
+  cudf::test::lists_column_wrapper<uint8_t> const int64_expected_slice1(
+    {{0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x9c, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff}});
+  cudf::test::lists_column_wrapper<uint8_t> const int64_expected_slice2(
+    {{0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80},
+     {0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x7f}});
+
+  std::vector<cudf::size_type> splits({3});
+  std::vector<cudf::column_view> split_column = cudf::split(int64_col, splits);
+
+  auto const output_int64_flipped = cudf::byte_cast(int64_col, cudf::flip_endianness::YES);
+  auto const output_int64_slice1  = cudf::byte_cast(split_column.at(0), cudf::flip_endianness::NO);
+  auto const output_int64_slice2  = cudf::byte_cast(split_column.at(1), cudf::flip_endianness::NO);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int64_flipped->view(), int64_expected_flipped);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int64_slice1->view(), int64_expected_slice1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_int64_slice2->view(), int64_expected_slice2);
+}
+
+TEST_F(ByteCastTest, int64ValuesWithNulls)
+{
+  using limits = std::numeric_limits<int64_t>;
+  auto odd_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  cudf::test::fixed_width_column_wrapper<int64_t> const int64_col(
+    {long(0), long(100), long(-100), limits::min(), limits::max()}, {0, 1, 0, 1, 0});
+
+  auto int64_data = cudf::test::fixed_width_column_wrapper<uint8_t>{
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x64, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(odd_validity, odd_validity + 5);
+  auto int64_expected          = cudf::make_lists_column(
+    5,
+    std::move(
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 8, 8, 16, 16}.release()),
+    std::move(int64_data.release()),
+    null_count,
+    std::move(null_mask));
+
+  auto const output_int64 = cudf::byte_cast(int64_col, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output_int64->view(), int64_expected->view());
+}
+
+TEST_F(ByteCastTest, fp32ValuesWithSplit)
+{
+  using limits = std::numeric_limits<float>;
+  float nan    = limits::quiet_NaN();
+  float inf    = limits::infinity();
+  cudf::test::fixed_width_column_wrapper<float> const fp32_col(
+    {float(0.0), float(100.0), float(-100.0), limits::min(), limits::max(), nan, -nan, inf, -inf});
+  cudf::test::lists_column_wrapper<uint8_t> const fp32_expected({{0x00, 0x00, 0x00, 0x00},
+                                                                 {0x00, 0x00, 0xc8, 0x42},
+                                                                 {0x00, 0x00, 0xc8, 0xc2},
+                                                                 {0x00, 0x00, 0x80, 0x00},
+                                                                 {0xff, 0xff, 0x7f, 0x7f},
+                                                                 {0x00, 0x00, 0xc0, 0x7f},
+                                                                 {0x00, 0x00, 0xc0, 0xff},
+                                                                 {0x00, 0x00, 0x80, 0x7f},
+                                                                 {0x00, 0x00, 0x80, 0xff}});
+  cudf::test::lists_column_wrapper<uint8_t> const fp32_expected_slice1({{0x00, 0x00, 0x00, 0x00},
+                                                                        {0x42, 0xc8, 0x00, 0x00},
+                                                                        {0xc2, 0xc8, 0x00, 0x00},
+                                                                        {0x00, 0x80, 0x00, 0x00},
+                                                                        {0x7f, 0x7f, 0xff, 0xff}});
+  cudf::test::lists_column_wrapper<uint8_t> const fp32_expected_slice2({{0x7f, 0xc0, 0x00, 0x00},
+                                                                        {0xff, 0xc0, 0x00, 0x00},
+                                                                        {0x7f, 0x80, 0x00, 0x00},
+                                                                        {0xff, 0x80, 0x00, 0x00}});
+
+  std::vector<cudf::size_type> splits({5});
+  std::vector<cudf::column_view> split_column = cudf::split(fp32_col, splits);
+
+  auto const output_fp32        = cudf::byte_cast(fp32_col, cudf::flip_endianness::NO);
+  auto const output_fp32_slice1 = cudf::byte_cast(split_column.at(0), cudf::flip_endianness::YES);
+  auto const output_fp32_slice2 = cudf::byte_cast(split_column.at(1), cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_fp32->view(), fp32_expected);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_fp32_slice1->view(), fp32_expected_slice1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_fp32_slice2->view(), fp32_expected_slice2);
+}
+
+TEST_F(ByteCastTest, fp32ValuesWithNulls)
+{
+  using limits = std::numeric_limits<float>;
+  auto even_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i + 1) % 2; });
+
+  cudf::test::fixed_width_column_wrapper<float> const fp32_col(
+    {float(0.0), float(100.0), float(-100.0), limits::min(), limits::max()}, {1, 0, 1, 0, 1});
+
+  auto fp32_data = cudf::test::fixed_width_column_wrapper<uint8_t>{
+    0x00, 0x00, 0x00, 0x00, 0xc2, 0xc8, 0x00, 0x00, 0x7f, 0x7f, 0xff, 0xff};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(even_validity, even_validity + 5);
+  auto fp32_expected = cudf::make_lists_column(
+    5,
+    std::move(cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 4, 8, 8, 12}.release()),
+    std::move(fp32_data.release()),
+    null_count,
+    std::move(null_mask));
+
+  auto const output_fp32 = cudf::byte_cast(fp32_col, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output_fp32->view(), fp32_expected->view());
+}
+
+TEST_F(ByteCastTest, fp64ValuesWithSplit)
+{
+  using limits = std::numeric_limits<double>;
+  double nan   = limits::quiet_NaN();
+  double inf   = limits::infinity();
+  cudf::test::fixed_width_column_wrapper<double> const fp64_col({double(0.0),
+                                                                 double(100.0),
+                                                                 double(-100.0),
+                                                                 limits::min(),
+                                                                 limits::max(),
+                                                                 nan,
+                                                                 -nan,
+                                                                 inf,
+                                                                 -inf});
+  cudf::test::lists_column_wrapper<uint8_t> const fp64_flipped_expected(
+    {{0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x40, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0xc0, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x7f, 0xef, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff},
+     {0x7f, 0xf8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0xff, 0xf8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x7f, 0xf0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0xff, 0xf0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00}});
+  cudf::test::lists_column_wrapper<uint8_t> const fp64_expected_slice1(
+    {{0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0x40},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x59, 0xc0},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00},
+     {0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xef, 0x7f}});
+  cudf::test::lists_column_wrapper<uint8_t> const fp64_expected_slice2(
+    {{0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf8, 0x7f},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf8, 0xff},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf0, 0x7f},
+     {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf0, 0xff}});
+
+  std::vector<cudf::size_type> splits({5});
+  std::vector<cudf::column_view> split_column = cudf::split(fp64_col, splits);
+
+  auto const output_fp64_flipped = cudf::byte_cast(fp64_col, cudf::flip_endianness::YES);
+  auto const output_fp64_slice1  = cudf::byte_cast(split_column.at(0), cudf::flip_endianness::NO);
+  auto const output_fp64_slice2  = cudf::byte_cast(split_column.at(1), cudf::flip_endianness::NO);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_fp64_flipped->view(), fp64_flipped_expected);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_fp64_slice1->view(), fp64_expected_slice1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_fp64_slice2->view(), fp64_expected_slice2);
+}
+
+TEST_F(ByteCastTest, fp64ValuesWithNulls)
+{
+  using limits = std::numeric_limits<double>;
+  auto odd_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; });
+
+  cudf::test::fixed_width_column_wrapper<double> const fp64_col(
+    {double(0.0), double(100.0), double(-100.0), limits::min(), limits::max()}, {0, 1, 0, 1, 0});
+
+  auto fp64_data = cudf::test::fixed_width_column_wrapper<uint8_t>{
+    0x40, 0x59, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(odd_validity, odd_validity + 5);
+  auto fp64_expected           = cudf::make_lists_column(
+    5,
+    std::move(
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 8, 8, 16, 16}.release()),
+    std::move(fp64_data.release()),
+    null_count,
+    std::move(null_mask));
+
+  auto const output_fp64 = cudf::byte_cast(fp64_col, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(output_fp64->view(), fp64_expected->view());
+}
+
+TEST_F(ByteCastTest, StringValuesNoNulls)
+{
+  cudf::test::strings_column_wrapper const strings_col(
+    {"", "The quick", " brown fox...", "!\"#$%&\'()*+,-./", "0123456789:;<=>?@", "[\\]^_`{|}~"});
+  cudf::test::lists_column_wrapper<uint8_t> const strings_expected(
+    {{},
+     {0x54, 0x68, 0x65, 0x20, 0x71, 0x75, 0x69, 0x63, 0x6b},
+     {0x20, 0x62, 0x72, 0x6f, 0x77, 0x6e, 0x20, 0x66, 0x6f, 0x78, 0x2e, 0x2e, 0x2e},
+     {0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27, 0x28, 0x29, 0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f},
+     {0x30,
+      0x31,
+      0x32,
+      0x33,
+      0x34,
+      0x35,
+      0x36,
+      0x37,
+      0x38,
+      0x39,
+      0x3a,
+      0x3b,
+      0x3c,
+      0x3d,
+      0x3e,
+      0x3f,
+      0x40},
+     {0x5b, 0x5c, 0x5d, 0x5e, 0x5f, 0x60, 0x7b, 0x7c, 0x7d, 0x7e}});
+
+  auto const output_strings = cudf::byte_cast(strings_col, cudf::flip_endianness::YES);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_strings->view(), strings_expected);
+}
+
+TEST_F(ByteCastTest, StringValuesWithNulls)
+{
+  auto const strings_col = [] {
+    auto output =
+      cudf::test::strings_column_wrapper(
+        {"", "The quick", " brown fox...", "!\"#$%&\'()*+,-./", "0123456789:;<=>?@", "[\\]^_`{|}~"})
+        .release();
+
+    // Set nulls by `set_null_mask` so the output column will have non-empty nulls.
+    // This is intentional.
+    auto const null_iter = cudf::test::iterators::nulls_at({2, 4});
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_iter, null_iter + output->size());
+    output->set_null_mask(std::move(null_mask), null_count);
+    return output;
+  }();
+
+  auto const strings_expected = cudf::test::lists_column_wrapper<uint8_t>{
+    {{},
+     {0x54, 0x68, 0x65, 0x20, 0x71, 0x75, 0x69, 0x63, 0x6b},
+     {} /*NULL*/,
+     {0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27, 0x28, 0x29, 0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f},
+     {} /*NULL*/,
+     {0x5b, 0x5c, 0x5d, 0x5e, 0x5f, 0x60, 0x7b, 0x7c, 0x7d, 0x7e}},
+    cudf::test::iterators::nulls_at({2, 4})};
+
+  auto const output_strings = cudf::byte_cast(*strings_col, cudf::flip_endianness::YES);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output_strings->view(), strings_expected);
+}
+
+TEST_F(ByteCastTest, int32Empty)
+{
+  auto const input    = cudf::test::fixed_width_column_wrapper<int32_t>{};
+  auto const expected = cudf::test::lists_column_wrapper<uint8_t>{};
+  auto const output   = cudf::byte_cast(input, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *output);
+}
+
+TEST_F(ByteCastTest, int32sAllNulls)
+{
+  auto const input =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{0, 0, 0}, cudf::test::iterators::all_nulls()};
+  auto const output     = cudf::byte_cast(input, cudf::flip_endianness::YES);
+  auto const& out_child = output->child(cudf::lists_column_view::child_column_index);
+  EXPECT_EQ(output->size(), 3);
+  EXPECT_EQ(output->null_count(), 3);
+  EXPECT_EQ(out_child.size(), 0);
+  EXPECT_EQ(out_child.type().id(), cudf::type_id::UINT8);
+}
+
+TEST_F(ByteCastTest, StringEmpty)
+{
+  auto const input    = cudf::test::strings_column_wrapper{};
+  auto const expected = cudf::test::lists_column_wrapper<uint8_t>{};
+  auto const output   = cudf::byte_cast(input, cudf::flip_endianness::YES);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *output);
+}
+
+TEST_F(ByteCastTest, StringsAllNulls)
+{
+  auto const input =
+    cudf::test::strings_column_wrapper{{"", "", ""}, cudf::test::iterators::all_nulls()};
+  auto const output     = cudf::byte_cast(input, cudf::flip_endianness::YES);
+  auto const& out_child = output->child(cudf::lists_column_view::child_column_index);
+  EXPECT_EQ(output->size(), 3);
+  EXPECT_EQ(output->null_count(), 3);
+  EXPECT_EQ(out_child.size(), 0);
+  EXPECT_EQ(out_child.type().id(), cudf::type_id::UINT8);
+}
diff --git a/cpp/tests/reshape/interleave_columns_tests.cpp b/cpp/tests/reshape/interleave_columns_tests.cpp
new file mode 100644
index 0000000..e8ea9d6
--- /dev/null
+++ b/cpp/tests/reshape/interleave_columns_tests.cpp
@@ -0,0 +1,1365 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/reshape.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+using TView       = cudf::table_view;
+using IntCol      = cudf::test::fixed_width_column_wrapper<int32_t>;
+using StructsCol  = cudf::test::structs_column_wrapper;
+using StringsCol  = cudf::test::strings_column_wrapper;
+using StrListsCol = cudf::test::lists_column_wrapper<cudf::string_view>;
+using IntListsCol = cudf::test::lists_column_wrapper<int32_t>;
+
+constexpr int32_t null{0};      // mark for null elements
+constexpr int32_t NOT_USE{-1};  // mark for elements that we don't care
+}  // namespace
+
+template <typename T>
+struct InterleaveColumnsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(InterleaveColumnsTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(InterleaveColumnsTest, NoColumns)
+{
+  cudf::table_view in(std::vector<cudf::column_view>{});
+
+  EXPECT_THROW(cudf::interleave_columns(in), cudf::logic_error);
+}
+
+TYPED_TEST(InterleaveColumnsTest, OneColumn)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> a({-1, 0, 1});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T, int32_t>({-1, 0, 1});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, TwoColumns)
+{
+  using T = TypeParam;
+
+  auto a = cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 2});
+  auto b = cudf::test::fixed_width_column_wrapper<T, int32_t>({1, 3});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a, b});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 1, 2, 3});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, ThreeColumns)
+{
+  using T = TypeParam;
+
+  auto a = cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 3, 6});
+  auto b = cudf::test::fixed_width_column_wrapper<T, int32_t>({1, 4, 7});
+  auto c = cudf::test::fixed_width_column_wrapper<T, int32_t>({2, 5, 8});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a, b, c});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, OneColumnEmpty)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> a({});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T>({});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, ThreeColumnsEmpty)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> a({});
+  cudf::test::fixed_width_column_wrapper<T> b({});
+  cudf::test::fixed_width_column_wrapper<T> c({});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a, b, c});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T>({});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, OneColumnNullable)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> a({1, 2, 3}, {0, 1, 0});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 2, 0}, {0, 1, 0});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, TwoColumnNullable)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> a({1, 2, 3}, {0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> b({4, 5, 6}, {1, 0, 1});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a, b});
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 4, 2, 0, 0, 6}, {0, 1, 1, 0, 0, 1});
+  auto actual = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, ThreeColumnsNullable)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> a({1, 4, 7}, {1, 0, 1});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> b({2, 5, 8}, {0, 1, 0});
+  cudf::test::fixed_width_column_wrapper<T, int32_t> c({3, 6, 9}, {1, 0, 1});
+
+  cudf::table_view in(std::vector<cudf::column_view>{a, b, c});
+
+  auto expected = cudf::test::fixed_width_column_wrapper<T, int32_t>({1, 0, 3, 0, 5, 0, 7, 0, 9},
+                                                                     {1, 0, 1, 0, 1, 0, 1, 0, 1});
+  auto actual   = cudf::interleave_columns(in);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(InterleaveColumnsTest, MismatchedDtypes)
+{
+  using T = TypeParam;
+
+  if (not std::is_same_v<int, T> and not cudf::is_fixed_point<T>()) {
+    cudf::test::fixed_width_column_wrapper<int32_t> input_a({1, 4, 7}, {1, 0, 1});
+    cudf::test::fixed_width_column_wrapper<T, int32_t> input_b({2, 5, 8}, {0, 1, 0});
+
+    cudf::table_view input(std::vector<cudf::column_view>{input_a, input_b});
+
+    EXPECT_THROW(cudf::interleave_columns(input), cudf::logic_error);
+  }
+}
+
+struct InterleaveStringsColumnsTest : public cudf::test::BaseFixture {};
+
+TEST_F(InterleaveStringsColumnsTest, ZeroSizedColumns)
+{
+  auto const col0 = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0}});
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(InterleaveStringsColumnsTest, SingleColumn)
+{
+  auto col0 = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, col0, verbosity);
+}
+
+TEST_F(InterleaveStringsColumnsTest, MultiColumnNullAndEmpty)
+{
+  auto col0 = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+  auto col1 = cudf::test::strings_column_wrapper({"", "", "", ""}, {true, false, true, false});
+
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"", "", "", "", "", "", "", ""}, {false, true, true, false, true, true, false, false});
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0, col1}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(InterleaveStringsColumnsTest, MultiColumnEmptyNonNullable)
+{
+  auto col0 = cudf::test::strings_column_wrapper({"", "", "", ""});
+  auto col1 = cudf::test::strings_column_wrapper({"", "", "", ""});
+
+  auto exp_results = cudf::test::strings_column_wrapper({"", "", "", "", "", "", "", ""});
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0, col1}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(InterleaveStringsColumnsTest, MultiColumnStringMix)
+{
+  auto col0 = cudf::test::strings_column_wrapper({"null", "null", "", "valid", "", "valid"},
+                                                 {false, false, true, true, true, true});
+  auto col1 = cudf::test::strings_column_wrapper({"", "valid", "null", "null", "valid", ""},
+                                                 {true, true, false, false, true, true});
+  auto col2 = cudf::test::strings_column_wrapper({"valid", "", "valid", "", "null", "null"},
+                                                 {true, true, true, true, false, false});
+
+  auto exp_results = cudf::test::strings_column_wrapper({"null",
+                                                         "",
+                                                         "valid",
+                                                         "null",
+                                                         "valid",
+                                                         "",
+                                                         "",
+                                                         "null",
+                                                         "valid",
+                                                         "valid",
+                                                         "null",
+                                                         "",
+                                                         "",
+                                                         "valid",
+                                                         "null",
+                                                         "valid",
+                                                         "",
+                                                         "null"},
+                                                        {false,
+                                                         true,
+                                                         true,
+                                                         false,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         false,
+                                                         true,
+                                                         true,
+                                                         false,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         false,
+                                                         true,
+                                                         true,
+                                                         false});
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0, col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(InterleaveStringsColumnsTest, MultiColumnStringMixNonNullable)
+{
+  auto col0 = cudf::test::strings_column_wrapper({"c00", "c01", "", "valid", "", "valid"});
+  auto col1 = cudf::test::strings_column_wrapper({"", "valid", "c13", "c14", "valid", ""});
+  auto col2 = cudf::test::strings_column_wrapper({"valid", "", "valid", "", "c24", "c25"});
+
+  auto exp_results = cudf::test::strings_column_wrapper({"c00",
+                                                         "",
+                                                         "valid",
+                                                         "c01",
+                                                         "valid",
+                                                         "",
+                                                         "",
+                                                         "c13",
+                                                         "valid",
+                                                         "valid",
+                                                         "c14",
+                                                         "",
+                                                         "",
+                                                         "valid",
+                                                         "c24",
+                                                         "valid",
+                                                         "",
+                                                         "c25"});
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0, col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(InterleaveStringsColumnsTest, MultiColumnStringMixNullableMix)
+{
+  auto col0 = cudf::test::strings_column_wrapper({"c00", "c01", "", "valid", "", "valid"});
+  auto col1 = cudf::test::strings_column_wrapper({"", "valid", "null", "null", "valid", ""},
+                                                 {true, true, false, false, true, true});
+  auto col2 = cudf::test::strings_column_wrapper({"valid", "", "valid", "", "c24", "c25"});
+
+  auto exp_results = cudf::test::strings_column_wrapper({"c00",
+                                                         "",
+                                                         "valid",
+                                                         "c01",
+                                                         "valid",
+                                                         "",
+                                                         "",
+                                                         "null",
+                                                         "valid",
+                                                         "valid",
+                                                         "null",
+                                                         "",
+                                                         "",
+                                                         "valid",
+                                                         "c24",
+                                                         "valid",
+                                                         "",
+                                                         "c25"},
+                                                        {true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         false,
+                                                         true,
+                                                         true,
+                                                         false,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true,
+                                                         true});
+
+  auto results = cudf::interleave_columns(cudf::table_view{{col0, col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointInterleave)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  for (int i = 0; i > -4; --i) {
+    auto const ONE  = decimalXX{1, scale_type{i}};
+    auto const TWO  = decimalXX{2, scale_type{i}};
+    auto const FOUR = decimalXX{4, scale_type{i}};
+    auto const FIVE = decimalXX{5, scale_type{i}};
+
+    auto const a = cudf::test::fixed_width_column_wrapper<decimalXX>({ONE, FOUR});
+    auto const b = cudf::test::fixed_width_column_wrapper<decimalXX>({TWO, FIVE});
+
+    auto const input    = cudf::table_view{std::vector<cudf::column_view>{a, b}};
+    auto const expected = cudf::test::fixed_width_column_wrapper<decimalXX>({ONE, TWO, FOUR, FIVE});
+    auto const actual   = cudf::interleave_columns(input);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, actual->view());
+  }
+}
+
+struct ListsColumnsInterleaveTest : public cudf::test::BaseFixture {};
+
+TEST_F(ListsColumnsInterleaveTest, InvalidInput)
+{
+  // Input table contains non-list column
+  {
+    auto const col1 = IntCol{}.release();
+    auto const col2 = IntListsCol{}.release();
+    EXPECT_THROW(cudf::interleave_columns(TView{{col1->view(), col2->view()}}), cudf::logic_error);
+  }
+
+  // Types mismatch
+  {
+    auto const col1 = IntListsCol{}.release();
+    auto const col2 = StrListsCol{}.release();
+    EXPECT_THROW(cudf::interleave_columns(TView{{col1->view(), col2->view()}}), cudf::logic_error);
+  }
+}
+
+template <typename T>
+struct ListsColumnsInterleaveTypedTest : public cudf::test::BaseFixture {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(ListsColumnsInterleaveTypedTest, TypesForTest);
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InterleaveEmptyColumns)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col     = ListsCol{}.release();
+  auto const results = cudf::interleave_columns(TView{{col->view(), col->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InterleaveOneColumnNotNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col     = ListsCol{{1, 2}, {3, 4}, {5, 6}}.release();
+  auto const results = cudf::interleave_columns(TView{{col->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InterleaveOneColumnWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col = ListsCol{{ListsCol{{1, 2, null}, null_at(2)},
+                             ListsCol{} /*NULL*/,
+                             ListsCol{{null, 3, 4, 4, 4, 4}, null_at(0)},
+                             ListsCol{5, 6}},
+                            null_at(1)}
+                     .release();
+  auto const results = cudf::interleave_columns(TView{{col->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*col, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SimpleInputNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1     = ListsCol{{1, 2}, {3, 4}, {5, 6}}.release();
+  auto const col2     = ListsCol{{7, 8}, {9, 10}, {11, 12}}.release();
+  auto const expected = ListsCol{{1, 2}, {7, 8}, {3, 4}, {9, 10}, {5, 6}, {11, 12}}.release();
+  auto const results  = cudf::interleave_columns(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListsColumnsInterleaveTest, SimpleInputStringsColumnsNoNull)
+{
+  auto const col1 = StrListsCol{
+    StrListsCol{"Tomato", "Apple"},
+    StrListsCol{"Banana", "Kiwi", "Cherry"},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const col2 =
+    StrListsCol{StrListsCol{"Orange"}, StrListsCol{"Lemon", "Peach"}, StrListsCol{}}.release();
+  auto const expected = StrListsCol{
+    StrListsCol{"Tomato", "Apple"},
+    StrListsCol{"Orange"},
+    StrListsCol{"Banana", "Kiwi", "Cherry"},
+    StrListsCol{"Lemon", "Peach"},
+    StrListsCol{"Coconut"},
+    StrListsCol{}}.release();
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SimpleInputWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1 = ListsCol{{ListsCol{{1, null, 3, 4}, null_at(1)},
+                              ListsCol{{null, 2, 3, 4}, null_at(0)},
+                              ListsCol{{null, 2, 3, 4}, null_at(0)},
+                              ListsCol{} /*NULL*/,
+                              ListsCol{{1, 2, null, 4}, null_at(2)},
+                              ListsCol{{1, 2, 3, null}, null_at(3)}},
+                             null_at(3)}
+                      .release();
+  auto const col2 = ListsCol{{ListsCol{{10, 11, 12, null}, null_at(3)},
+                              ListsCol{{13, 14, 15, 16, 17, null}, null_at(5)},
+                              ListsCol{} /*NULL*/,
+                              ListsCol{{null, 18}, null_at(0)},
+                              ListsCol{{19, 20, null}, null_at(2)},
+                              ListsCol{{null}, null_at(0)}},
+                             null_at(2)}
+                      .release();
+  auto const col3 = ListsCol{{ListsCol{} /*NULL*/,
+                              ListsCol{{20, null}, null_at(1)},
+                              ListsCol{{null, 21, null, null}, nulls_at({0, 2, 3})},
+                              ListsCol{},
+                              ListsCol{22, 23, 24, 25},
+                              ListsCol{{null, null, null, null, null}, all_nulls()}},
+                             null_at(0)}
+                      .release();
+  auto const expected = ListsCol{{ListsCol{{1, null, 3, 4}, null_at(1)},
+                                  ListsCol{{10, 11, 12, null}, null_at(3)},
+                                  ListsCol{} /*NULL*/,
+                                  ListsCol{{null, 2, 3, 4}, null_at(0)},
+                                  ListsCol{{13, 14, 15, 16, 17, null}, null_at(5)},
+                                  ListsCol{{20, null}, null_at(1)},
+                                  ListsCol{{null, 2, 3, 4}, null_at(0)},
+                                  ListsCol{} /*NULL*/,
+                                  ListsCol{{null, 21, null, null}, nulls_at({0, 2, 3})},
+                                  ListsCol{} /*NULL*/,
+                                  ListsCol{{null, 18}, null_at(0)},
+                                  ListsCol{},
+                                  ListsCol{{1, 2, null, 4}, null_at(2)},
+                                  ListsCol{{19, 20, null}, null_at(2)},
+                                  ListsCol{22, 23, 24, 25},
+                                  ListsCol{{1, 2, 3, null}, null_at(3)},
+                                  ListsCol{{null}, null_at(0)},
+                                  ListsCol{{null, null, null, null, null}, all_nulls()}},
+                                 nulls_at({2, 7, 9})}
+                          .release();
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view(), col3->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SimpleInputWithNullableChild)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1 = ListsCol{{1, 2}, {3, 4}}.release();
+  auto const col2 = ListsCol{{5, 6}, {7, 8}}.release();
+  auto const col3 = ListsCol{{9, 10}, ListsCol{{null, 12}, null_at(0)}}.release();
+  auto const expected =
+    ListsCol{{1, 2}, {5, 6}, {9, 10}, {3, 4}, {7, 8}, ListsCol{{null, 12}, null_at(0)}}.release();
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view(), col3->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListsColumnsInterleaveTest, SimpleInputStringsColumnsWithNulls)
+{
+  auto const col1 = StrListsCol{
+    StrListsCol{{"Tomato", "Bear" /*NULL*/, "Apple"}, null_at(1)},
+    StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/}, nulls_at({1, 4})},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const col2 =
+    StrListsCol{
+      {StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/},
+                   nulls_at({1, 2, 3})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{{"Deer" /*NULL*/, "Snake" /*NULL*/, "Horse" /*NULL*/}, all_nulls()}}, /*NULL*/
+      null_at(2)}
+      .release();
+
+  auto const expected =
+    StrListsCol{
+      {StrListsCol{{"Tomato", "" /*NULL*/, "Apple"}, null_at(1)},
+       StrListsCol{{"Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2, 3})},
+       StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/}, nulls_at({1, 4})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{"Coconut"},
+       StrListsCol{}}, /*NULL*/
+      null_at(5)}
+      .release();
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListsColumnsInterleaveTest, SimpleInputStringsColumnsWithNullableChild)
+{
+  auto const col1 = StrListsCol{
+    StrListsCol{"Tomato", "Bear", "Apple"},
+    StrListsCol{"Banana", "Pig", "Kiwi", "Cherry", "Whale"},
+    StrListsCol{
+      "Coconut"}}.release();
+  auto const col2 = StrListsCol{
+    StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/}, nulls_at({1, 2, 3})},
+    StrListsCol{"Lemon", "Peach"},
+    StrListsCol{
+      {"Deer" /*NULL*/, "Snake" /*NULL*/, "Horse" /*NULL*/},
+      all_nulls()}}.release();
+
+  auto const expected = StrListsCol{
+    StrListsCol{"Tomato", "Bear", "Apple"},
+    StrListsCol{{"Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2, 3})},
+    StrListsCol{"Banana", "Pig", "Kiwi", "Cherry", "Whale"},
+    StrListsCol{"Lemon", "Peach"},
+    StrListsCol{"Coconut"},
+    StrListsCol{
+      {"Deer" /*NULL*/, "Snake" /*NULL*/, "Horse" /*NULL*/},
+      all_nulls()}}.release();
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedColumnsInputNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col      = ListsCol{{1, 2, 3}, {2, 3}, {3, 4, 5, 6}, {5, 6}, {}, {7}}.release();
+  auto const col1     = cudf::slice(col->view(), {0, 3})[0];
+  auto const col2     = cudf::slice(col->view(), {1, 4})[0];
+  auto const col3     = cudf::slice(col->view(), {2, 5})[0];
+  auto const col4     = cudf::slice(col->view(), {3, 6})[0];
+  auto const expected = ListsCol{
+    ListsCol{1, 2, 3},
+    ListsCol{2, 3},
+    ListsCol{3, 4, 5, 6},
+    ListsCol{5, 6},
+    ListsCol{2, 3},
+    ListsCol{3, 4, 5, 6},
+    ListsCol{5, 6},
+    ListsCol{},
+    ListsCol{3, 4, 5, 6},
+    ListsCol{5, 6},
+    ListsCol{},
+    ListsCol{7}}.release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2, col3, col4}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedColumnsInputWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col = ListsCol{{ListsCol{{null, 2, 3}, null_at(0)},
+                             ListsCol{2, 3}, /*NULL*/
+                             ListsCol{{3, null, 5, 6}, null_at(1)},
+                             ListsCol{5, 6}, /*NULL*/
+                             ListsCol{},     /*NULL*/
+                             ListsCol{7},
+                             ListsCol{8, 9, 10}},
+                            nulls_at({1, 3, 4})}
+                     .release();
+  auto const col1     = cudf::slice(col->view(), {0, 3})[0];
+  auto const col2     = cudf::slice(col->view(), {1, 4})[0];
+  auto const col3     = cudf::slice(col->view(), {2, 5})[0];
+  auto const col4     = cudf::slice(col->view(), {3, 6})[0];
+  auto const col5     = cudf::slice(col->view(), {4, 7})[0];
+  auto const expected = ListsCol{{ListsCol{{null, 2, 3}, null_at(0)},
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{{3, null, 5, 6}, null_at(1)},
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{{3, null, 5, 6}, null_at(1)},
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{7},
+                                  ListsCol{{3, null, 5, 6}, null_at(1)},
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{}, /*NULL*/
+                                  ListsCol{7},
+                                  ListsCol{8, 9, 10}},
+                                 nulls_at({1, 3, 4, 5, 7, 8, 11, 12})}
+                          .release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2, col3, col4, col5}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedColumnsInputNullableChild)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col =
+    ListsCol{{1, 2, 3}, ListsCol{{null, 3}, null_at(0)}, {3, 4, 5, 6}, {5, 6}, {}, {7}}.release();
+  auto const col1     = cudf::slice(col->view(), {0, 3})[0];
+  auto const col2     = cudf::slice(col->view(), {1, 4})[0];
+  auto const col3     = cudf::slice(col->view(), {2, 5})[0];
+  auto const col4     = cudf::slice(col->view(), {3, 6})[0];
+  auto const expected = ListsCol{
+    ListsCol{1, 2, 3},
+    ListsCol{{null, 3}, null_at(0)},
+    ListsCol{3, 4, 5, 6},
+    ListsCol{5, 6},
+    ListsCol{{null, 3}, null_at(0)},
+    ListsCol{3, 4, 5, 6},
+    ListsCol{5, 6},
+    ListsCol{},
+    ListsCol{3, 4, 5, 6},
+    ListsCol{5, 6},
+    ListsCol{},
+    ListsCol{7}}.release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2, col3, col4}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InputListsOfListsNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1 = ListsCol{ListsCol{ListsCol{1, 2, 3}, ListsCol{4, 5, 6}},
+                             ListsCol{ListsCol{7, 8}, ListsCol{9, 10}},
+                             ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15}, ListsCol{16, 17}}};
+  auto const col2 =
+    ListsCol{ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15, 16}},
+             ListsCol{ListsCol{17, 18}, ListsCol{19, 110}},
+             ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}}};
+  auto const expected =
+    ListsCol{ListsCol{ListsCol{1, 2, 3}, ListsCol{4, 5, 6}},
+             ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15, 16}},
+             ListsCol{ListsCol{7, 8}, ListsCol{9, 10}},
+             ListsCol{ListsCol{17, 18}, ListsCol{19, 110}},
+             ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15}, ListsCol{16, 17}},
+             ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}}}
+      .release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InputListsOfListsWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1 = ListsCol{
+    ListsCol{ListsCol{{null, 2, 3}, null_at(0)}, ListsCol{{4, null, null}, nulls_at({1, 2})}},
+    ListsCol{{ListsCol{7, 8}, ListsCol{9, 10}, ListsCol{null, null, null} /*NULL*/}, null_at(2)},
+    ListsCol{ListsCol{11, 12, 13}, ListsCol{{14, null}, null_at(1)}, ListsCol{16, 17}}};
+  auto const col2 =
+    ListsCol{ListsCol{{ListsCol{11, 12, 13}, ListsCol{null, null} /*NULL*/}, null_at(1)},
+             ListsCol{ListsCol{17, 18}, ListsCol{{19, 110, null}, null_at(2)}},
+             ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}}};
+  auto const expected = ListsCol{
+    ListsCol{ListsCol{{null, 2, 3}, null_at(0)}, ListsCol{{4, null, null}, nulls_at({1, 2})}},
+    ListsCol{{ListsCol{11, 12, 13}, ListsCol{null, null} /*NULL*/}, null_at(1)},
+    ListsCol{{ListsCol{7, 8}, ListsCol{9, 10}, ListsCol{null, null, null} /*NULL*/}, null_at(2)},
+    ListsCol{ListsCol{17, 18}, ListsCol{{19, 110, null}, null_at(2)}},
+    ListsCol{ListsCol{11, 12, 13}, ListsCol{{14, null}, null_at(1)}, ListsCol{16, 17}},
+    ListsCol{
+      ListsCol{111, 112, 13},
+      ListsCol{114, 115},
+      ListsCol{116, 117}}}.release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedInputListsOfListsNoNull)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1_original = ListsCol{
+    ListsCol{ListsCol{11, 11, 11}, ListsCol{22}, ListsCol{33, 33, 33}},  // don't care
+    ListsCol{ListsCol{11, 11, 11}, ListsCol{22}, ListsCol{33, 33, 33}},  // don't care
+    //
+    ListsCol{ListsCol{1, 2, 3}, ListsCol{4, 5, 6}},
+    ListsCol{ListsCol{7, 8}, ListsCol{9, 10}},
+    ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15}, ListsCol{16, 17}},
+    //
+    ListsCol{ListsCol{11, 11, 11}, ListsCol{22}, ListsCol{33, 33, 33}},  // don't care
+    ListsCol{ListsCol{11, 11, 11}, ListsCol{22}, ListsCol{33, 33, 33}}   // don't care
+  };
+  auto const col2_original = ListsCol{
+    ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15, 16}},
+    ListsCol{ListsCol{17, 18}, ListsCol{19, 110}},
+    ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}},
+    //
+    ListsCol{ListsCol{11, 11, 11}, ListsCol{22}, ListsCol{33, 33, 33}}  // don't care
+  };
+
+  auto const col1 = cudf::slice(col1_original, {2, 5})[0];
+  auto const col2 = cudf::slice(col2_original, {0, 3})[0];
+  auto const expected =
+    ListsCol{ListsCol{ListsCol{1, 2, 3}, ListsCol{4, 5, 6}},
+             ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15, 16}},
+             ListsCol{ListsCol{7, 8}, ListsCol{9, 10}},
+             ListsCol{ListsCol{17, 18}, ListsCol{19, 110}},
+             ListsCol{ListsCol{11, 12, 13}, ListsCol{14, 15}, ListsCol{16, 17}},
+             ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}}}
+      .release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedInputListsOfListsWithNulls)
+{
+  using ListsCol = cudf::test::lists_column_wrapper<TypeParam>;
+
+  auto const col1_original = ListsCol{
+    {
+      ListsCol{ListsCol{{null, 11}, null_at(0)},
+               ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+      ListsCol{ListsCol{{null, 11}, null_at(0)},
+               ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+      ListsCol{ListsCol{{null, 11}, null_at(0)},
+               ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+      //
+      ListsCol{ListsCol{{null, 2, 3}, null_at(0)}, ListsCol{{4, null, null}, nulls_at({1, 2})}},
+      ListsCol{{ListsCol{7, 8}, ListsCol{9, 10}, ListsCol{null, null, null} /*NULL*/}, null_at(2)},
+      ListsCol{ListsCol{11, 12, 13}, ListsCol{{14, null}, null_at(1)}, ListsCol{16, 17}},
+      //
+      ListsCol{ListsCol{{null, 11}, null_at(0)},
+               ListsCol{{22, null, null}, nulls_at({1, 2})}}  // don't care
+    },
+    nulls_at({0, 2, 3})};
+  auto const col2_original = ListsCol{
+    ListsCol{ListsCol{{null, 11}, null_at(0)},
+             ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+    ListsCol{ListsCol{{null, 11}, null_at(0)},
+             ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+                                                             //
+    ListsCol{{ListsCol{11, 12, 13}, ListsCol{null, null} /*NULL*/}, null_at(1)},
+    ListsCol{ListsCol{17, 18}, ListsCol{{19, 110, null}, null_at(2)}},
+    ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}},
+    ListsCol{ListsCol{{null, 11}, null_at(0)},
+             //
+             ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+    ListsCol{ListsCol{{null, 11}, null_at(0)},
+             ListsCol{{22, null, null}, nulls_at({1, 2})}},  // don't care
+    ListsCol{ListsCol{{null, 11}, null_at(0)},
+             ListsCol{{22, null, null}, nulls_at({1, 2})}}  // don't care
+  };
+
+  auto const col1 = cudf::slice(col1_original, {3, 6})[0];
+  auto const col2 = cudf::slice(col2_original, {2, 5})[0];
+  auto const expected =
+    ListsCol{
+      {ListsCol{ListsCol{{null, 2, 3}, null_at(0)}, ListsCol{{4, null, null}, nulls_at({1, 2})}},
+       ListsCol{{ListsCol{11, 12, 13}, ListsCol{null, null} /*NULL*/}, null_at(1)},
+       ListsCol{{ListsCol{7, 8}, ListsCol{9, 10}, ListsCol{null, null, null} /*NULL*/}, null_at(2)},
+       ListsCol{ListsCol{17, 18}, ListsCol{{19, 110, null}, null_at(2)}},
+       ListsCol{ListsCol{11, 12, 13}, ListsCol{{14, null}, null_at(1)}, ListsCol{16, 17}},
+       ListsCol{ListsCol{111, 112, 13}, ListsCol{114, 115}, ListsCol{116, 117}}},
+      null_at(0)}
+      .release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InputListsOfStructsNoNull)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto structs1 = [] {
+    auto child1 = ColWrapper{1, 2, 3, 4, 5};
+    auto child2 = ColWrapper{6, 7, 8, 9, 10};
+    auto child3 = StringsCol{"Banana", "Mango", "Apple", "Cherry", "Kiwi"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto structs2 = [] {
+    auto child1 = ColWrapper{11, 12, 13, 14, 15};
+    auto child2 = ColWrapper{16, 17, 18, 19, 110};
+    auto child3 = StringsCol{"Bear", "Duck", "Cat", "Dog", "Panda"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto structs_expected = [] {
+    auto child1 = ColWrapper{1, 11, 12, 13, 2, 3, 14, 4, 5, 15};
+    auto child2 = ColWrapper{6, 16, 17, 18, 7, 8, 19, 9, 10, 110};
+    auto child3 = StringsCol{
+      "Banana", "Bear", "Duck", "Cat", "Mango", "Apple", "Dog", "Cherry", "Kiwi", "Panda"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const col1 =
+    cudf::make_lists_column(3, IntCol{0, 1, 3, 5}.release(), structs1.release(), 0, {});
+  auto const col2 =
+    cudf::make_lists_column(3, IntCol{0, 3, 4, 5}.release(), structs2.release(), 0, {});
+  auto const expected = cudf::make_lists_column(
+    6, IntCol{0, 1, 4, 6, 7, 9, 10}.release(), structs_expected.release(), 0, {});
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, InputListsOfStructsWithNulls)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto structs1 = [] {
+    auto child1 = ColWrapper{{1, 2, null, 4, 5}, null_at(2)};
+    auto child2 = ColWrapper{{6, 7, 8, 9, null}, null_at(4)};
+    auto child3 = StringsCol{"Banana", "Mango", "Apple", "Cherry", "Kiwi"};
+    return StructsCol{{child1, child2, child3}, null_at(0)};
+  }();
+
+  auto structs2 = [] {
+    auto child1 = ColWrapper{11, 12, 13, 14, 15};
+    auto child2 = ColWrapper{{null, 17, 18, 19, 110}, null_at(0)};
+    auto child3 = StringsCol{{"" /*NULL*/, "Duck", "Cat", "Dog", "" /*NULL*/}, nulls_at({0, 4})};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto structs_expected = [] {
+    auto child1 = ColWrapper{{1, 11, 12, 13, 2, null, 14, 4, 5, 15}, null_at(5)};
+    auto child2 = ColWrapper{{6, null, 17, 18, 7, 8, 19, 9, null, 110}, nulls_at({1, 8})};
+    auto child3 = StringsCol{{"Banana",
+                              "" /*NULL*/,
+                              "Duck",
+                              "Cat",
+                              "Mango",
+                              "Apple",
+                              "Dog",
+                              "Cherry",
+                              "Kiwi",
+                              "" /*NULL*/},
+                             nulls_at({1, 9})};
+    return StructsCol{{child1, child2, child3}, null_at(0)};
+  }();
+
+  auto const col1 =
+    cudf::make_lists_column(3, IntCol{0, 1, 3, 5}.release(), structs1.release(), 0, {});
+  auto const col2 =
+    cudf::make_lists_column(3, IntCol{0, 3, 4, 5}.release(), structs2.release(), 0, {});
+  auto const expected = cudf::make_lists_column(
+    6, IntCol{0, 1, 4, 6, 7, 9, 10}.release(), structs_expected.release(), 0, {});
+  auto const results = cudf::interleave_columns(TView{{col1->view(), col2->view()}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedInputListsOfStructsNoNull)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto structs1 = [] {
+    auto child1 = ColWrapper{NOT_USE, NOT_USE, 1, 2, 3, 4, 5, NOT_USE};
+    auto child2 = ColWrapper{NOT_USE, NOT_USE, 6, 7, 8, 9, 10, NOT_USE};
+    auto child3 =
+      StringsCol{"NOT_USE", "NOT_USE", "Banana", "Mango", "Apple", "Cherry", "Kiwi", "NOT_USE"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto structs2 = [] {
+    auto child1 = ColWrapper{11, 12, 13, 14, 15, NOT_USE, NOT_USE};
+    auto child2 = ColWrapper{16, 17, 18, 19, 110, NOT_USE, NOT_USE};
+    auto child3 = StringsCol{"Bear", "Duck", "Cat", "Dog", "Panda", "NOT_USE", "NOT_USE"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto structs_expected = [] {
+    auto child1 = ColWrapper{1, 11, 12, 13, 2, 3, 14, 4, 5, 15};
+    auto child2 = ColWrapper{6, 16, 17, 18, 7, 8, 19, 9, 10, 110};
+    auto child3 = StringsCol{
+      "Banana", "Bear", "Duck", "Cat", "Mango", "Apple", "Dog", "Cherry", "Kiwi", "Panda"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const col1_original =
+    cudf::make_lists_column(5, IntCol{0, 2, 3, 5, 7, 8}.release(), structs1.release(), 0, {});
+  auto const col2_original =
+    cudf::make_lists_column(4, IntCol{0, 3, 4, 5, 7}.release(), structs2.release(), 0, {});
+  auto const expected = cudf::make_lists_column(
+    6, IntCol{0, 1, 4, 6, 7, 9, 10}.release(), structs_expected.release(), 0, {});
+
+  auto const col1    = cudf::slice(col1_original->view(), {1, 4})[0];
+  auto const col2    = cudf::slice(col2_original->view(), {0, 3})[0];
+  auto const results = cudf::interleave_columns(TView{{col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TYPED_TEST(ListsColumnsInterleaveTypedTest, SlicedInputListsOfStructsWithNulls)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto structs1 = [] {
+    auto child1 = ColWrapper{{NOT_USE, 1, 2, null, 4, 5, NOT_USE}, nulls_at({0, 3})};
+    auto child2 = ColWrapper{{NOT_USE, 6, 7, 8, 9, null, NOT_USE}, null_at(5)};
+    auto child3 = StringsCol{"NOT_USE", "Banana", "Mango", "Apple", "Cherry", "Kiwi", "NOT_USE"};
+    return StructsCol{{child1, child2, child3}, nulls_at({1, 6})};
+  }();
+
+  auto structs2 = [] {
+    auto child1 = ColWrapper{{NOT_USE, 11, 12, 13, 14, 15}, null_at(0)};
+    auto child2 = ColWrapper{{NOT_USE, null, 17, 18, 19, 110}, null_at(1)};
+    auto child3 =
+      StringsCol{{"NOT_USE", "" /*NULL*/, "Duck", "Cat", "Dog", "" /*NULL*/}, nulls_at({0, 1, 5})};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto structs_expected = [] {
+    auto child1 = ColWrapper{{1, 11, 12, 13, 2, null, 14, 4, 5, 15}, null_at(5)};
+    auto child2 = ColWrapper{{6, null, 17, 18, 7, 8, 19, 9, null, 110}, nulls_at({1, 8})};
+    auto child3 = StringsCol{{"Banana",
+                              "" /*NULL*/,
+                              "Duck",
+                              "Cat",
+                              "Mango",
+                              "Apple",
+                              "Dog",
+                              "Cherry",
+                              "Kiwi",
+                              "" /*NULL*/},
+                             nulls_at({1, 9})};
+    return StructsCol{{child1, child2, child3}, null_at(0)};
+  }();
+
+  auto const col1_original =
+    cudf::make_lists_column(5, IntCol{0, 1, 2, 4, 6, 7}.release(), structs1.release(), 0, {});
+  auto const col2_original =
+    cudf::make_lists_column(4, IntCol{0, 1, 4, 5, 6}.release(), structs2.release(), 0, {});
+
+  auto const col1     = cudf::slice(col1_original->view(), {1, 4})[0];
+  auto const col2     = cudf::slice(col2_original->view(), {1, 4})[0];
+  auto const expected = cudf::make_lists_column(
+    6, IntCol{0, 1, 4, 6, 7, 9, 10}.release(), structs_expected.release(), 0, {});
+  auto const results = cudf::interleave_columns(TView{{col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+TEST_F(ListsColumnsInterleaveTest, SlicedStringsColumnsInputWithNulls)
+{
+  auto const col =
+    StrListsCol{
+      {StrListsCol{{"Tomato", "Bear" /*NULL*/, "Apple"}, null_at(1)},
+       StrListsCol{{"Banana", "Pig" /*NULL*/, "Kiwi", "Cherry", "Whale" /*NULL*/},
+                   nulls_at({1, 4})},
+       StrListsCol{"Coconut"},
+       StrListsCol{{"Orange", "Dog" /*NULL*/, "Fox" /*NULL*/, "Duck" /*NULL*/},
+                   nulls_at({1, 2, 3})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{{"Deer" /*NULL*/, "Snake" /*NULL*/, "Horse" /*NULL*/}, all_nulls()}}, /*NULL*/
+      null_at(5)}
+      .release();
+  auto const col1 = cudf::slice(col->view(), {0, 3})[0];
+  auto const col2 = cudf::slice(col->view(), {1, 4})[0];
+  auto const col3 = cudf::slice(col->view(), {2, 5})[0];
+  auto const col4 = cudf::slice(col->view(), {3, 6})[0];
+  auto const expected =
+    StrListsCol{
+      {StrListsCol{{"Tomato", "" /*NULL*/, "Apple"}, null_at(1)},
+       StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/}, nulls_at({1, 4})},
+       StrListsCol{"Coconut"},
+       StrListsCol{{"Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2, 3})},
+       StrListsCol{{"Banana", "" /*NULL*/, "Kiwi", "Cherry", "" /*NULL*/}, nulls_at({1, 4})},
+       StrListsCol{"Coconut"},
+       StrListsCol{{"Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2, 3})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{"Coconut"},
+       StrListsCol{{"Orange", "" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2, 3})},
+       StrListsCol{"Lemon", "Peach"},
+       StrListsCol{}}, /*NULL*/
+      null_at(11)}
+      .release();
+  auto const results = cudf::interleave_columns(TView{{col1, col2, col3, col4}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected, *results, verbosity);
+}
+
+struct StructsColumnsInterleaveTest : public cudf::test::BaseFixture {};
+
+TEST_F(StructsColumnsInterleaveTest, InvalidInput)
+{
+  // Input table contains non-structs column
+  {
+    auto const col1 = IntCol{};
+    auto const col2 = StructsCol{};
+    EXPECT_THROW(cudf::interleave_columns(TView{{col1, col2}}), cudf::logic_error);
+  }
+
+  // Types mismatch
+  {
+    auto const structs1 = [] {
+      auto child1 = IntCol{1, 2, 3};
+      auto child2 = IntCol{4, 5, 6};
+      return StructsCol{{child1, child2}};
+    }();
+
+    auto const structs2 = [] {
+      auto child1 = IntCol{7, 8, 9};
+      auto child2 = StringsCol{"", "abc", "123"};
+      return StructsCol{{child1, child2}};
+    }();
+
+    EXPECT_THROW(cudf::interleave_columns(TView{{structs1, structs2}}), cudf::logic_error);
+  }
+
+  // Numbers of children mismatch
+  {
+    auto const structs1 = [] {
+      auto child1 = IntCol{1, 2, 3};
+      auto child2 = IntCol{4, 5, 6};
+      return StructsCol{{child1, child2}};
+    }();
+
+    auto const structs2 = [] {
+      auto child1 = IntCol{7, 8, 9};
+      auto child2 = IntCol{10, 11, 12};
+      auto child3 = IntCol{13, 14, 15};
+      return StructsCol{{child1, child2, child3}};
+    }();
+
+    EXPECT_THROW(cudf::interleave_columns(TView{{structs1, structs2}}), cudf::logic_error);
+  }
+}
+
+TEST_F(StructsColumnsInterleaveTest, InterleaveEmptyColumns)
+{
+  auto const structs = StructsCol{};
+  auto const results = cudf::interleave_columns(TView{{structs, structs}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(structs, *results, verbosity);
+}
+
+template <typename T>
+struct StructsColumnsInterleaveTypedTest : public cudf::test::BaseFixture {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::FixedPointTypes>;
+TYPED_TEST_SUITE(StructsColumnsInterleaveTypedTest, TypesForTest);
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, InterleaveOneColumnNotNull)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const structs = [] {
+    auto child1 = ColWrapper{1, 2, 3};
+    auto child2 = ColWrapper{4, 5, 6};
+    auto child3 = StringsCol{"Banana", "Mango", "Apple"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+  auto const results = cudf::interleave_columns(TView{{structs}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(structs, *results, verbosity);
+}
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, InterleaveOneColumnWithNulls)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const structs = [] {
+    auto child1 = ColWrapper{{1, 2, null, 3}, null_at(2)};
+    auto child2 = ColWrapper{{4, null, 5, 6}, null_at(1)};
+    auto child3 = StringsCol{{"" /*NULL*/, "Banana", "Mango", "Apple"}, null_at(0)};
+    return StructsCol{{child1, child2, child3}, null_at(3)};
+  }();
+  auto const results = cudf::interleave_columns(TView{{structs}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(structs, *results, verbosity);
+}
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, SimpleInputNoNull)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const structs1 = [] {
+    auto child1 = ColWrapper{1, 2, 3};
+    auto child2 = ColWrapper{4, 5, 6};
+    auto child3 = StringsCol{"Banana", "Mango", "Apple"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const structs2 = [] {
+    auto child1 = ColWrapper{7, 8, 9};
+    auto child2 = ColWrapper{10, 11, 12};
+    auto child3 = StringsCol{"Bear", "Duck", "Cat"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const expected = [] {
+    auto child1 = ColWrapper{1, 7, 2, 8, 3, 9};
+    auto child2 = ColWrapper{4, 10, 5, 11, 6, 12};
+    auto child3 = StringsCol{"Banana", "Bear", "Mango", "Duck", "Apple", "Cat"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const results = cudf::interleave_columns(TView{{structs1, structs2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results, verbosity);
+}
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, SimpleInputWithNulls)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const structs1 = [] {
+    auto child1 = ColWrapper{{1, 2, null, 3, 4}, null_at(2)};
+    auto child2 = ColWrapper{{4, null, 5, 6, 7}, null_at(1)};
+    auto child3 = StringsCol{{"" /*NULL*/, "Banana", "Mango", "Apple", "Cherry"}, null_at(0)};
+    return StructsCol{{child1, child2, child3}, null_at(0)};
+  }();
+
+  auto const structs2 = [] {
+    auto child1 = ColWrapper{{7, null, null, 8, 9}, nulls_at({1, 2})};
+    auto child2 = ColWrapper{{10, 11, 12, null, 14}, null_at(3)};
+    auto child3 = StringsCol{"Bear", "Duck", "Cat", "Dog", "Panda"};
+    return StructsCol{{child1, child2, child3}, null_at(4)};
+  }();
+
+  auto const structs3 = [] {
+    auto child1 = ColWrapper{{-1, -2, -3, 0, null}, null_at(4)};
+    auto child2 = ColWrapper{{-5, 0, null, -1, -10}, null_at(2)};
+    auto child3 = StringsCol{"111", "Bànànà", "abcxyz", "é á í", "zzz"};
+    return StructsCol{{child1, child2, child3}, null_at(1)};
+  }();
+
+  auto const expected = [] {
+    auto child1 = ColWrapper{{1, 7, -1, 2, null, -2, null, null, -3, 3, 8, 0, 4, 9, null},
+                             nulls_at({4, 6, 7, 14})};
+    auto child2 = ColWrapper{{4, 10, -5, null, 11, 0, 5, 12, null, 6, null, -1, 7, 14, -10},
+                             nulls_at({3, 8, 10})};
+    auto child3 = StringsCol{{"" /*NULL*/,
+                              "Bear",
+                              "111",
+                              "Banana",
+                              "Duck",
+                              "Bànànà",
+                              "Mango",
+                              "Cat",
+                              "abcxyz",
+                              "Apple",
+                              "Dog",
+                              "é á í",
+                              "Cherry",
+                              "Panda",
+                              "zzz"},
+                             null_at(0)};
+    return StructsCol{{child1, child2, child3}, nulls_at({0, 5, 13})};
+  }();
+
+  auto const results = cudf::interleave_columns(TView{{structs1, structs2, structs3}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results, verbosity);
+}
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, NestedInputStructsColumns)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const structs1 = [] {
+    auto child_structs1 = [] {
+      auto child1 = ColWrapper{{null, 2, 3, 4, 5}, null_at(0)};
+      auto child2 = ColWrapper{{6, 7, 8, null, 10}, null_at(3)};
+      return StructsCol{{child1, child2}, null_at(0)};
+    }();
+
+    auto child_structs2 = [] {
+      auto child1 = ColWrapper{{11, null, 13, 14, 15}, null_at(1)};
+      auto child2 = ColWrapper{{null, 17, 18, 19, 20}, null_at(0)};
+      return StructsCol{{child1, child2}, nulls_at({0, 1})};
+    }();
+
+    auto child_strings = [] { return StringsCol{"Banana", "Mango", "Apple", "Cherry", "Kiwi"}; }();
+
+    return StructsCol{{child_structs1, child_structs2, child_strings}, null_at(0)};
+  }();
+
+  auto const structs2 = [] {
+    auto child_structs1 = [] {
+      auto child1 = ColWrapper{{-1, null, -3, -4, -5}, null_at(1)};
+      auto child2 = ColWrapper{{-6, -7, -8, null, -10}, null_at(3)};
+      return StructsCol{{child1, child2}};
+    }();
+
+    auto child_structs2 = [] {
+      auto child1 = ColWrapper{{-11, -12, null, -14, -15}, null_at(2)};
+      auto child2 = ColWrapper{{-16, -17, -18, -19, null}, null_at(4)};
+      return StructsCol{{child1, child2}, null_at(2)};
+    }();
+
+    auto child_strings = [] { return StringsCol{"Bear", "Duck", "Cat", "Dog", "Rabbit"}; }();
+
+    return StructsCol{{child_structs1, child_structs2, child_strings}, null_at(2)};
+  }();
+
+  auto const expected = [] {
+    auto child_structs1 = [] {
+      auto child1 = ColWrapper{{null, -1, 2, null, 3, -3, 4, -4, 5, -5}, nulls_at({0, 3})};
+      auto child2 = ColWrapper{{6, -6, 7, -7, 8, -8, null, null, 10, -10}, nulls_at({6, 7})};
+      return StructsCol{{child1, child2}, null_at(0)};
+    }();
+
+    auto child_structs2 = [] {
+      auto child1 = ColWrapper{{11, -11, null, -12, 13, null, 14, -14, 15, -15}, nulls_at({2, 5})};
+      auto child2 = ColWrapper{{null, -16, 17, -17, 18, -18, 19, -19, 20, null}, nulls_at({0, 9})};
+      return StructsCol{{child1, child2}, nulls_at({0, 2, 5})};
+    }();
+
+    auto child_strings = [] {
+      return StringsCol{
+        "Banana", "Bear", "Mango", "Duck", "Apple", "Cat", "Cherry", "Dog", "Kiwi", "Rabbit"};
+    }();
+
+    return StructsCol{{child_structs1, child_structs2, child_strings}, nulls_at({0, 5})};
+  }();
+
+  auto const results = cudf::interleave_columns(TView{{structs1, structs2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results, verbosity);
+}
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, SlicedColumnsInputNoNull)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const structs1_original = [] {
+    auto child1 = ColWrapper{NOT_USE, NOT_USE, 1, 2, 3, NOT_USE};
+    auto child2 = ColWrapper{NOT_USE, NOT_USE, 4, 5, 6, NOT_USE};
+    auto child3 = StringsCol{"NOT_USE", "NOT_USE", "Banana", "Mango", "Apple", "NOT_USE"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  // structs2 has more rows than structs1
+  auto const structs2_original = [] {
+    auto child1 = ColWrapper{NOT_USE, 7, 8, 9, NOT_USE, NOT_USE, NOT_USE};
+    auto child2 = ColWrapper{NOT_USE, 10, 11, 12, NOT_USE, NOT_USE, NOT_USE};
+    auto child3 = StringsCol{"NOT_USE", "Bear", "Duck", "Cat", "NOT_USE", "NOT_USE", "NOT_USE"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const expected = [] {
+    auto child1 = ColWrapper{1, 7, 2, 8, 3, 9};
+    auto child2 = ColWrapper{4, 10, 5, 11, 6, 12};
+    auto child3 = StringsCol{"Banana", "Bear", "Mango", "Duck", "Apple", "Cat"};
+    return StructsCol{{child1, child2, child3}};
+  }();
+
+  auto const structs1 = cudf::slice(structs1_original, {2, 5})[0];
+  auto const structs2 = cudf::slice(structs2_original, {1, 4})[0];
+  auto const results  = cudf::interleave_columns(TView{{structs1, structs2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results, verbosity);
+}
+
+TYPED_TEST(StructsColumnsInterleaveTypedTest, SlicedColumnsInputWithNulls)
+{
+  using ColWrapper = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  constexpr int32_t NOT_USE{-1};  // mark for elements that we don't care
+
+  auto const structs1_original = [] {
+    auto child1 = ColWrapper{{NOT_USE, NOT_USE, 1, 2, null, 3, 4, NOT_USE}, null_at(4)};
+    auto child2 = ColWrapper{{NOT_USE, NOT_USE, 4, null, 5, 6, 7, NOT_USE}, null_at(3)};
+    auto child3 = StringsCol{
+      {"NOT_USE", "NOT_USE", "" /*NULL*/, "Banana", "Mango", "Apple", "Cherry", "NOT_USE"},
+      null_at(2)};
+    return StructsCol{{child1, child2, child3}, null_at(2)};
+  }();
+
+  auto const structs2_original = [] {
+    auto child1 = ColWrapper{{7, null, null, 8, 9, NOT_USE, NOT_USE}, nulls_at({1, 2})};
+    auto child2 = ColWrapper{{10, 11, 12, null, 14, NOT_USE, NOT_USE}, null_at(3)};
+    auto child3 = StringsCol{"Bear", "Duck", "Cat", "Dog", "Panda", "NOT_USE", "NOT_USE"};
+    return StructsCol{{child1, child2, child3}, null_at(4)};
+  }();
+
+  auto const structs3_original = [] {
+    auto child1 = ColWrapper{{NOT_USE, NOT_USE, NOT_USE, -1, -2, -3, 0, null}, null_at(7)};
+    auto child2 = ColWrapper{{NOT_USE, NOT_USE, NOT_USE, -5, 0, null, -1, -10}, null_at(5)};
+    auto child3 =
+      StringsCol{"NOT_USE", "NOT_USE", "NOT_USE", "111", "Bànànà", "abcxyz", "é á í", "zzz"};
+    return StructsCol{{child1, child2, child3}, null_at(4)};
+  }();
+
+  auto const expected = [] {
+    auto child1 = ColWrapper{{1, 7, -1, 2, null, -2, null, null, -3, 3, 8, 0, 4, 9, null},
+                             nulls_at({4, 6, 7, 14})};
+    auto child2 = ColWrapper{{4, 10, -5, null, 11, 0, 5, 12, null, 6, null, -1, 7, 14, -10},
+                             nulls_at({3, 8, 10})};
+    auto child3 = StringsCol{{"" /*NULL*/,
+                              "Bear",
+                              "111",
+                              "Banana",
+                              "Duck",
+                              "Bànànà",
+                              "Mango",
+                              "Cat",
+                              "abcxyz",
+                              "Apple",
+                              "Dog",
+                              "é á í",
+                              "Cherry",
+                              "Panda",
+                              "zzz"},
+                             null_at(0)};
+    return StructsCol{{child1, child2, child3}, nulls_at({0, 5, 13})};
+  }();
+
+  auto const structs1 = cudf::slice(structs1_original, {2, 7})[0];
+  auto const structs2 = cudf::slice(structs2_original, {0, 5})[0];
+  auto const structs3 = cudf::slice(structs3_original, {3, 8})[0];
+  auto const results  = cudf::interleave_columns(TView{{structs1, structs2, structs3}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, *results, verbosity);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/reshape/tile_tests.cpp b/cpp/tests/reshape/tile_tests.cpp
new file mode 100644
index 0000000..ed76b9d
--- /dev/null
+++ b/cpp/tests/reshape/tile_tests.cpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/reshape.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/error.hpp>
+
+template <typename T>
+struct TileTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TileTest, cudf::test::AllTypes);
+
+TYPED_TEST(TileTest, NoColumns)
+{
+  cudf::table_view in(std::vector<cudf::column_view>{});
+
+  auto expected = in;
+
+  auto actual = cudf::tile(in, 10);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(TileTest, NoRows)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> in_a({});
+  cudf::table_view in(std::vector<cudf::column_view>{in_a});
+
+  auto expected = in;
+
+  auto actual = cudf::tile(in, 10);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(TileTest, OneColumn)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> in_a({-1, 0, 1});
+  cudf::table_view in(std::vector<cudf::column_view>{in_a});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected_a({-1, 0, 1, -1, 0, 1});
+  cudf::table_view expected(std::vector<cudf::column_view>{expected_a});
+
+  auto actual = cudf::tile(in, 2);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(TileTest, OneColumnNullable)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> in_a({-1, 0, 1}, {1, 0, 0});
+  cudf::table_view in(std::vector<cudf::column_view>{in_a});
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> expected_a({-1, 0, 1, -1, 0, 1},
+                                                                {1, 0, 0, 1, 0, 0});
+  cudf::table_view expected(std::vector<cudf::column_view>{expected_a});
+
+  auto actual = cudf::tile(in, 2);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
+
+TYPED_TEST(TileTest, OneColumnNegativeCount)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> in_a({-1, 0, 1}, {1, 0, 0});
+  cudf::table_view in(std::vector<cudf::column_view>{in_a});
+
+  EXPECT_THROW(cudf::tile(in, -1), cudf::logic_error);
+}
+
+TYPED_TEST(TileTest, OneColumnZeroCount)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T, int32_t> in_a({-1, 0, 1}, {1, 0, 0});
+  cudf::table_view in(std::vector<cudf::column_view>{in_a});
+
+  std::vector<T> vals{};
+  std::vector<bool> mask{};
+
+  cudf::test::fixed_width_column_wrapper<T> expected_a(vals.begin(), vals.end(), mask.begin());
+
+  cudf::table_view expected(std::vector<cudf::column_view>{expected_a});
+
+  auto actual = cudf::tile(in, 0);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, actual->view());
+}
diff --git a/cpp/tests/rolling/collect_ops_test.cpp b/cpp/tests/rolling/collect_ops_test.cpp
new file mode 100644
index 0000000..a6fa5b3
--- /dev/null
+++ b/cpp/tests/rolling/collect_ops_test.cpp
@@ -0,0 +1,2288 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <thrust/functional.h>
+
+#include <vector>
+
+struct CollectListTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedCollectListTest : public CollectListTest {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypes,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::DurationTypes,
+                                        cudf::test::FixedPointTypes>;
+
+TYPED_TEST_SUITE(TypedCollectListTest, TypesForTest);
+
+TYPED_TEST(TypedCollectListTest, BasicRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14};
+
+  auto const prev_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 2, 2};
+  auto const foll_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 1, 0};
+
+  EXPECT_EQ(static_cast<cudf::column_view>(prev_column).size(),
+            static_cast<cudf::column_view>(foll_column).size());
+
+  auto const result_column_based_window =
+    cudf::rolling_window(input_column,
+                         prev_column,
+                         foll_column,
+                         1,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {10, 11},
+      {10, 11, 12},
+      {11, 12, 13},
+      {12, 13, 14},
+      {13, 14},
+    }
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column_based_window->view());
+
+  auto const result_fixed_window = cudf::rolling_window(
+    input_column, 2, 1, 1, *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_fixed_window->view());
+
+  auto const result_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    2,
+    1,
+    1,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, RollingWindowWithEmptyOutputLists)
+{
+  using T = TypeParam;
+
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 15};
+
+  auto const prev_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 0, 2, 2};
+  auto const foll_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 0, 1, 0};
+
+  EXPECT_EQ(static_cast<cudf::column_view>(prev_column).size(),
+            static_cast<cudf::column_view>(foll_column).size());
+
+  auto const result_column_based_window =
+    cudf::rolling_window(input_column,
+                         prev_column,
+                         foll_column,
+                         0,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {10, 11},
+      {10, 11, 12},
+      {11, 12, 13},
+      {},
+      {13, 14, 15},
+      {14, 15},
+    }
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column_based_window->view());
+
+  auto const result_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    prev_column,
+    foll_column,
+    0,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, RollingWindowWithEmptyOutputListsAtEnds)
+{
+  using T = TypeParam;
+
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{0, 1, 2, 3, 4, 5};
+
+  auto const prev_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 2, 2, 2, 0};
+  auto foll_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 1, 1, 1, 0};
+
+  auto const result =
+    cudf::rolling_window(input_column,
+                         prev_column,
+                         foll_column,
+                         0,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{{}, {0, 1, 2}, {1, 2, 3}, {2, 3, 4}, {3, 4, 5}, {}}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    prev_column,
+    foll_column,
+    0,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, RollingWindowHonoursMinPeriods)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+  using T = TypeParam;
+
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{0, 1, 2, 3, 4, 5};
+  auto const num_elements = static_cast<cudf::column_view>(input_column).size();
+
+  auto preceding   = 2;
+  auto following   = 1;
+  auto min_periods = 3;
+  auto const result =
+    cudf::rolling_window(input_column,
+                         preceding,
+                         following,
+                         min_periods,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{}, {0, 1, 2}, {1, 2, 3}, {2, 3, 4}, {3, 4, 5}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i != (num_elements - 1);
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+
+  preceding   = 2;
+  following   = 2;
+  min_periods = 4;
+
+  auto result_2 =
+    cudf::rolling_window(input_column,
+                         preceding,
+                         following,
+                         min_periods,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+  auto expected_result_2 = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{}, {0, 1, 2, 3}, {1, 2, 3, 4}, {2, 3, 4, 5}, {}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i < 4;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(), result_2->view());
+
+  auto result_2_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(),
+                                      result_2_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, RollingWindowWithNullInputsHonoursMinPeriods)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+  // Input column has null inputs.
+  using T = TypeParam;
+
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{{0, 1, 2, 3, 4, 5}, {1, 0, 1, 1, 0, 1}};
+
+  {
+    // One result row at each end should be null.
+    auto preceding   = 2;
+    auto following   = 1;
+    auto min_periods = 3;
+    auto const result =
+      cudf::rolling_window(input_column,
+                           preceding,
+                           following,
+                           min_periods,
+                           *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_result_child_values   = std::vector<int32_t>{0, 1, 2, 1, 2, 3, 2, 3, 4, 3, 4, 5};
+    auto expected_result_child_validity = std::vector<bool>{1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1};
+    auto expected_result_child =
+      cudf::test::fixed_width_column_wrapper<T, int32_t>(expected_result_child_values.begin(),
+                                                         expected_result_child_values.end(),
+                                                         expected_result_child_validity.begin());
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 3, 6, 9, 12, 12}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0},
+      [expected_num_rows](auto i) { return i != 0 && i != (expected_num_rows - 1); });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+
+  {
+    // One result row at each end should be null.
+    // Exclude nulls: No nulls elements for any output list rows.
+    auto preceding    = 2;
+    auto following    = 1;
+    auto min_periods  = 3;
+    auto const result = cudf::rolling_window(
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    auto expected_result_child_values = std::vector<int32_t>{0, 2, 2, 3, 2, 3, 3, 5};
+    auto expected_result_child        = cudf::test::fixed_width_column_wrapper<T, int32_t>(
+      expected_result_child_values.begin(), expected_result_child_values.end());
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 2, 4, 6, 8, 8}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0},
+      [expected_num_rows](auto i) { return i != 0 && i != (expected_num_rows - 1); });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+
+  {
+    // First result row, and the last two result rows should be null.
+    auto preceding   = 2;
+    auto following   = 2;
+    auto min_periods = 4;
+    auto const result =
+      cudf::rolling_window(input_column,
+                           preceding,
+                           following,
+                           min_periods,
+                           *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_result_child_values   = std::vector<int32_t>{0, 1, 2, 3, 1, 2, 3, 4, 2, 3, 4, 5};
+    auto expected_result_child_validity = std::vector<bool>{1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1};
+    auto expected_result_child =
+      cudf::test::fixed_width_column_wrapper<T, int32_t>(expected_result_child_values.begin(),
+                                                         expected_result_child_values.end(),
+                                                         expected_result_child_validity.begin());
+
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 4, 8, 12, 12, 12}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0}, [expected_num_rows](auto i) { return i > 0 && i < 4; });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+
+  {
+    // First result row, and the last two result rows should be null.
+    // Exclude nulls: No nulls elements for any output list rows.
+    auto preceding    = 2;
+    auto following    = 2;
+    auto min_periods  = 4;
+    auto const result = cudf::rolling_window(
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    auto expected_result_child_values = std::vector<int32_t>{0, 2, 3, 2, 3, 2, 3, 5};
+    auto expected_result_child        = cudf::test::fixed_width_column_wrapper<T, int32_t>(
+      expected_result_child_values.begin(), expected_result_child_values.end());
+
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 3, 5, 8, 8, 8}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0}, [expected_num_rows](auto i) { return i > 0 && i < 4; });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+}
+
+TEST_F(CollectListTest, RollingWindowHonoursMinPeriodsOnStrings)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+  auto const input_column = cudf::test::strings_column_wrapper{"0", "1", "2", "3", "4", "5"};
+  auto const num_elements = static_cast<cudf::column_view>(input_column).size();
+
+  auto preceding   = 2;
+  auto following   = 1;
+  auto min_periods = 3;
+  auto const result =
+    cudf::rolling_window(input_column,
+                         preceding,
+                         following,
+                         min_periods,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{}, {"0", "1", "2"}, {"1", "2", "3"}, {"2", "3", "4"}, {"3", "4", "5"}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i != (num_elements - 1);
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+
+  preceding   = 2;
+  following   = 2;
+  min_periods = 4;
+
+  auto result_2 =
+    cudf::rolling_window(input_column,
+                         preceding,
+                         following,
+                         min_periods,
+                         *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+  auto expected_result_2 = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{}, {"0", "1", "2", "3"}, {"1", "2", "3", "4"}, {"2", "3", "4", "5"}, {}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i < 4;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(), result_2->view());
+
+  auto result_2_with_nulls_excluded = cudf::rolling_window(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(),
+                                      result_2_with_nulls_excluded->view());
+}
+
+TEST_F(CollectListTest, RollingWindowHonoursMinPeriodsWithDecimal)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+  auto const input_iter =
+    cudf::detail::make_counting_transform_iterator(0, thrust::identity<int32_t>{});
+  auto const input_column = cudf::test::fixed_point_column_wrapper<int32_t>{
+    input_iter, input_iter + 6, numeric::scale_type{0}};
+
+  {
+    // One result row at each end should be null.
+    auto preceding   = 2;
+    auto following   = 1;
+    auto min_periods = 3;
+    auto const result =
+      cudf::rolling_window(input_column,
+                           preceding,
+                           following,
+                           min_periods,
+                           *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_result_child_values = std::vector<int32_t>{0, 1, 2, 1, 2, 3, 2, 3, 4, 3, 4, 5};
+    auto expected_result_child =
+      cudf::test::fixed_point_column_wrapper<int32_t>{expected_result_child_values.begin(),
+                                                      expected_result_child_values.end(),
+                                                      numeric::scale_type{0}};
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 3, 6, 9, 12, 12}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0},
+      [expected_num_rows](auto i) { return i != 0 && i != (expected_num_rows - 1); });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+    auto const result_with_nulls_excluded = cudf::rolling_window(
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(),
+                                        result_with_nulls_excluded->view());
+  }
+
+  {
+    // First result row, and the last two result rows should be null.
+    auto preceding   = 2;
+    auto following   = 2;
+    auto min_periods = 4;
+    auto const result =
+      cudf::rolling_window(input_column,
+                           preceding,
+                           following,
+                           min_periods,
+                           *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_result_child_values = std::vector<int32_t>{0, 1, 2, 3, 1, 2, 3, 4, 2, 3, 4, 5};
+    auto expected_result_child =
+      cudf::test::fixed_point_column_wrapper<int32_t>{expected_result_child_values.begin(),
+                                                      expected_result_child_values.end(),
+                                                      numeric::scale_type{0}};
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 4, 8, 12, 12, 12}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0}, [expected_num_rows](auto i) { return i > 0 && i < 4; });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+    auto const result_with_nulls_excluded = cudf::rolling_window(
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(),
+                                        result_with_nulls_excluded->view());
+  }
+}
+
+TYPED_TEST(TypedCollectListTest, BasicGroupedRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 20, 21, 22, 23};
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result =
+    grouped_rolling_window(cudf::table_view{std::vector<cudf::column_view>{group_column}},
+                           input_column,
+                           preceding,
+                           following,
+                           min_periods,
+                           *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {10, 11},
+    {10, 11, 12},
+    {11, 12, 13},
+    {12, 13, 14},
+    {13, 14},
+    {20, 21},
+    {20, 21, 22},
+    {21, 22, 23},
+    {22, 23}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = grouped_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, BasicGroupedRollingWindowWithNulls)
+{
+  using T = TypeParam;
+
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {10, 11, 12, 13, 14, 20, 21, 22, 23}, {1, 0, 1, 1, 1, 1, 0, 1, 1}};
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+
+  {
+    // Nulls included.
+    auto const result =
+      grouped_rolling_window(cudf::table_view{std::vector<cudf::column_view>{group_column}},
+                             input_column,
+                             preceding,
+                             following,
+                             min_periods,
+                             *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+      {10, 11, 10, 11, 12, 11, 12, 13, 12, 13, 14, 13, 14, 20, 21, 20, 21, 22, 21, 22, 23, 22, 23},
+      {1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1}};
+
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<int32_t>{0, 2, 5, 8, 11, 13, 15, 18, 21, 23};
+
+    auto expected_result =
+      cudf::make_lists_column(static_cast<cudf::column_view>(group_column).size(),
+                              expected_offsets.release(),
+                              expected_child.release(),
+                              0,
+                              {});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+
+  {
+    // Nulls excluded.
+    auto const result = grouped_rolling_window(
+      cudf::table_view{std::vector<cudf::column_view>{group_column}},
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    auto expected_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+      10, 10, 12, 12, 13, 12, 13, 14, 13, 14, 20, 20, 22, 22, 23, 22, 23};
+
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 3, 5, 8, 10, 11, 13, 15, 17};
+
+    auto expected_result =
+      cudf::make_lists_column(static_cast<cudf::column_view>(group_column).size(),
+                              expected_offsets.release(),
+                              expected_child.release(),
+                              0,
+                              {});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+}
+
+TYPED_TEST(TypedCollectListTest, BasicGroupedTimeRangeRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 20, 21, 22, 23};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {10, 11, 12, 13},
+    {10, 11, 12, 13},
+    {10, 11, 12, 13, 14},
+    {10, 11, 12, 13, 14},
+    {10, 11, 12, 13, 14},
+    {20},
+    {21, 22},
+    {21, 22, 23},
+    {21, 22, 23}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, GroupedTimeRangeRollingWindowWithNulls)
+{
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {10, 11, 12, 13, 14, 20, 21, 22, 23}, {1, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto null_at_0 = cudf::test::iterators::null_at(0);
+  auto null_at_1 = cudf::test::iterators::null_at(1);
+
+  // In the results, `11` and `21` should be nulls.
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{10, 11, 12, 13}, null_at_1},
+    {{10, 11, 12, 13}, null_at_1},
+    {{10, 11, 12, 13, 14}, null_at_1},
+    {{10, 11, 12, 13, 14}, null_at_1},
+    {{10, 11, 12, 13, 14}, null_at_1},
+    {{20}, null_at_1},
+    {{21, 22}, null_at_0},
+    {{21, 22, 23}, null_at_0},
+    {{21, 22, 23}, null_at_0}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  // After null exclusion, `11`, `21`, and `null` should not appear.
+  auto const expected_result_with_nulls_excluded = cudf::test::lists_column_wrapper<T, int32_t>{
+    {10, 12, 13},
+    {10, 12, 13},
+    {10, 12, 13, 14},
+    {10, 12, 13, 14},
+    {10, 12, 13, 14},
+    {20},
+    {22},
+    {22, 23},
+    {22, 23}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_with_nulls_excluded->view(),
+                                      result_with_nulls_excluded->view());
+}
+
+TEST_F(CollectListTest, BasicGroupedTimeRangeRollingWindowOnStrings)
+{
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::strings_column_wrapper{"10", "11", "12", "13", "14", "20", "21", "22", "23"};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {"10", "11", "12", "13"},
+    {"10", "11", "12", "13"},
+    {"10", "11", "12", "13", "14"},
+    {"10", "11", "12", "13", "14"},
+    {"10", "11", "12", "13", "14"},
+    {"20"},
+    {"21", "22"},
+    {"21", "22", "23"},
+    {"21", "22", "23"}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TEST_F(CollectListTest, GroupedTimeRangeRollingWindowOnStringsWithNulls)
+{
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::strings_column_wrapper{
+    {"10", "11", "12", "13", "14", "20", "21", "22", "23"}, {1, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto null_at_0 = cudf::test::iterators::null_at(0);
+  auto null_at_1 = cudf::test::iterators::null_at(1);
+
+  // In the results, `11` and `21` should be nulls.
+  auto const expected_result = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{"10", "11", "12", "13"}, null_at_1},
+    {{"10", "11", "12", "13"}, null_at_1},
+    {{"10", "11", "12", "13", "14"}, null_at_1},
+    {{"10", "11", "12", "13", "14"}, null_at_1},
+    {{"10", "11", "12", "13", "14"}, null_at_1},
+    {"20"},
+    {{"21", "22"}, null_at_0},
+    {{"21", "22", "23"}, null_at_0},
+    {{"21", "22", "23"},
+     null_at_0}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  // After null exclusion, `11`, `21`, and `null` should not appear.
+  auto const expected_result_with_nulls_excluded =
+    cudf::test::lists_column_wrapper<cudf::string_view>{{"10", "12", "13"},
+                                                        {"10", "12", "13"},
+                                                        {"10", "12", "13", "14"},
+                                                        {"10", "12", "13", "14"},
+                                                        {"10", "12", "13", "14"},
+                                                        {"20"},
+                                                        {"22"},
+                                                        {"22", "23"},
+                                                        {"22", "23"}}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_with_nulls_excluded->view(),
+                                      result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, BasicGroupedTimeRangeRollingWindowOnStructs)
+{
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto numeric_member_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 20, 21, 22, 23};
+  auto string_member_column =
+    cudf::test::strings_column_wrapper{"10", "11", "12", "13", "14", "20", "21", "22", "23"};
+  auto struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  struct_members.emplace_back(numeric_member_column.release());
+  struct_members.emplace_back(string_member_column.release());
+  auto const struct_column = cudf::make_structs_column(9, std::move(struct_members), 0, {});
+  auto const preceding     = 2;
+  auto const following     = 1;
+  auto const min_periods   = 1;
+  auto const result        = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    struct_column->view(),
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto expected_numeric_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    10, 11, 12, 13, 10, 11, 12, 13, 10, 11, 12, 13, 14, 10, 11, 12,
+    13, 14, 10, 11, 12, 13, 14, 20, 21, 22, 21, 22, 23, 21, 22, 23};
+
+  auto expected_string_column = cudf::test::strings_column_wrapper{
+    "10", "11", "12", "13", "10", "11", "12", "13", "10", "11", "12", "13", "14", "10", "11", "12",
+    "13", "14", "10", "11", "12", "13", "14", "20", "21", "22", "21", "22", "23", "21", "22", "23"};
+
+  auto expected_struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  expected_struct_members.emplace_back(expected_numeric_column.release());
+  expected_struct_members.emplace_back(expected_string_column.release());
+
+  auto expected_structs_column =
+    cudf::make_structs_column(32, std::move(expected_struct_members), 0, {});
+  auto expected_offsets_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 8, 13, 18, 23, 24, 26, 29, 32}
+      .release();
+  auto expected_result = cudf::make_lists_column(
+    9, std::move(expected_offsets_column), std::move(expected_structs_column), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    struct_column->view(),
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, GroupedTimeRangeRollingWindowWithMinPeriods)
+{
+  // Test that min_periods is honoured.
+  // i.e. output row is null when min_periods exceeds number of observations.
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 20, 21, 22, 23};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 4;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{10, 11, 12, 13},
+     {10, 11, 12, 13},
+     {10, 11, 12, 13, 14},
+     {10, 11, 12, 13, 14},
+     {10, 11, 12, 13, 14},
+     {},
+     {},
+     {},
+     {}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i < 5;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, GroupedTimeRangeRollingWindowWithNullsAndMinPeriods)
+{
+  // Test that min_periods is honoured.
+  // i.e. output row is null when min_periods exceeds number of observations.
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {10, 11, 12, 13, 14, 20, 21, 22, 23}, {1, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 4;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto null_at_1 = cudf::test::iterators::null_at(1);
+
+  // In the results, `11` and `21` should be nulls.
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{{10, 11, 12, 13}, null_at_1},
+     {{10, 11, 12, 13}, null_at_1},
+     {{10, 11, 12, 13, 14}, null_at_1},
+     {{10, 11, 12, 13, 14}, null_at_1},
+     {{10, 11, 12, 13, 14}, null_at_1},
+     {},
+     {},
+     {},
+     {}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i < 5;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  // After null exclusion, `11`, `21`, and `null` should not appear.
+  auto const expected_result_with_nulls_excluded = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{10, 12, 13},
+     {10, 12, 13},
+     {10, 12, 13, 14},
+     {10, 12, 13, 14},
+     {10, 12, 13, 14},
+     {},
+     {},
+     {},
+     {}},
+    cudf::detail::make_counting_transform_iterator(
+      0, [](auto i) { return i < 5; })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_with_nulls_excluded->view(),
+                                      result_with_nulls_excluded->view());
+}
+
+TEST_F(CollectListTest, GroupedTimeRangeRollingWindowOnStringsWithMinPeriods)
+{
+  // Test that min_periods is honoured.
+  // i.e. output row is null when min_periods exceeds number of observations.
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::strings_column_wrapper{"10", "11", "12", "13", "14", "20", "21", "22", "23"};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 4;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{"10", "11", "12", "13"},
+     {"10", "11", "12", "13"},
+     {"10", "11", "12", "13", "14"},
+     {"10", "11", "12", "13", "14"},
+     {"10", "11", "12", "13", "14"},
+     {},
+     {},
+     {},
+     {}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i < 5;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TEST_F(CollectListTest, GroupedTimeRangeRollingWindowOnStringsWithNullsAndMinPeriods)
+{
+  // Test that min_periods is honoured.
+  // i.e. output row is null when min_periods exceeds number of observations.
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::strings_column_wrapper{
+    {"10", "11", "12", "13", "14", "20", "21", "22", "23"}, {1, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 4;
+  auto const result      = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto null_at_1 = cudf::test::iterators::null_at(1);
+
+  // In the results, `11` and `21` should be nulls.
+  auto const expected_result = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{{"10", "11", "12", "13"}, null_at_1},
+     {{"10", "11", "12", "13"}, null_at_1},
+     {{"10", "11", "12", "13", "14"}, null_at_1},
+     {{"10", "11", "12", "13", "14"}, null_at_1},
+     {{"10", "11", "12", "13", "14"}, null_at_1},
+     {},
+     {},
+     {},
+     {}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i < 5;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  // After null exclusion, `11`, `21`, and `null` should not appear.
+  auto const expected_result_with_nulls_excluded =
+    cudf::test::lists_column_wrapper<cudf::string_view>{
+      {{"10", "12", "13"},
+       {"10", "12", "13"},
+       {"10", "12", "13", "14"},
+       {"10", "12", "13", "14"},
+       {"10", "12", "13", "14"},
+       {},
+       {},
+       {},
+       {}},
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i < 5; })}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_with_nulls_excluded->view(),
+                                      result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectListTest, GroupedTimeRangeRollingWindowOnStructsWithMinPeriods)
+{
+  // Test that min_periods is honoured.
+  // i.e. output row is null when min_periods exceeds number of observations.
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto numeric_member_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 20, 21, 22, 23};
+  auto string_member_column =
+    cudf::test::strings_column_wrapper{"10", "11", "12", "13", "14", "20", "21", "22", "23"};
+  auto struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  struct_members.emplace_back(numeric_member_column.release());
+  struct_members.emplace_back(string_member_column.release());
+  auto const struct_column = cudf::make_structs_column(9, std::move(struct_members), 0, {});
+  auto const preceding     = 2;
+  auto const following     = 1;
+  auto const min_periods   = 4;
+  auto const result        = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    struct_column->view(),
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>());
+
+  auto expected_numeric_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    10, 11, 12, 13, 10, 11, 12, 13, 10, 11, 12, 13, 14, 10, 11, 12, 13, 14, 10, 11, 12, 13, 14};
+
+  auto expected_string_column = cudf::test::strings_column_wrapper{
+    "10", "11", "12", "13", "10", "11", "12", "13", "10", "11", "12", "13",
+    "14", "10", "11", "12", "13", "14", "10", "11", "12", "13", "14"};
+
+  auto expected_struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  expected_struct_members.emplace_back(expected_numeric_column.release());
+  expected_struct_members.emplace_back(expected_string_column.release());
+
+  auto expected_structs_column =
+    cudf::make_structs_column(23, std::move(expected_struct_members), 0, {});
+  auto expected_offsets_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 4, 8, 13, 18, 23, 23, 23, 23, 23}
+      .release();
+  auto expected_validity_iter =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i < 5; });
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(expected_validity_iter, expected_validity_iter + 9);
+  auto expected_result = cudf::make_lists_column(9,
+                                                 std::move(expected_offsets_column),
+                                                 std::move(expected_structs_column),
+                                                 null_count,
+                                                 std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = cudf::grouped_time_range_rolling_window(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    struct_column->view(),
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+// The results of `collect_set` are unordered lists.
+// Thus, we have to sort the lists for comparison.
+namespace {
+
+template <typename WindowType>
+auto rolling_collect_set(cudf::column_view const& input,
+                         WindowType const& preceding_window,
+                         WindowType const& following_window,
+                         cudf::size_type min_periods,
+                         cudf::rolling_aggregation const& agg)
+{
+  auto const result =
+    cudf::rolling_window(input, preceding_window, following_window, min_periods, agg);
+  EXPECT_EQ(result->type().id(), cudf::type_id::LIST);
+
+  return cudf::lists::sort_lists(
+    cudf::lists_column_view{result->view()}, cudf::order::ASCENDING, cudf::null_order::AFTER);
+}
+
+template <typename WindowType>
+auto grouped_rolling_collect_set(cudf::table_view const& group_keys,
+                                 cudf::column_view const& input,
+                                 WindowType const& preceding_window,
+                                 WindowType const& following_window,
+                                 cudf::size_type min_periods,
+                                 cudf::rolling_aggregation const& agg)
+{
+  auto const result = cudf::grouped_rolling_window(
+    group_keys, input, preceding_window, following_window, min_periods, agg);
+  EXPECT_EQ(result->type().id(), cudf::type_id::LIST);
+
+  return cudf::lists::sort_lists(
+    cudf::lists_column_view{result->view()}, cudf::order::ASCENDING, cudf::null_order::AFTER);
+}
+
+template <typename WindowType>
+auto grouped_time_range_rolling_collect_set(cudf::table_view const& group_keys,
+                                            cudf::column_view const& timestamp_column,
+                                            cudf::order const& timestamp_order,
+                                            cudf::column_view const& input,
+                                            WindowType const& preceding_window_in_days,
+                                            WindowType const& following_window_in_days,
+                                            cudf::size_type min_periods,
+                                            cudf::rolling_aggregation const& agg)
+{
+  auto const result = cudf::grouped_time_range_rolling_window(group_keys,
+                                                              timestamp_column,
+                                                              timestamp_order,
+                                                              input,
+                                                              preceding_window_in_days,
+                                                              following_window_in_days,
+                                                              min_periods,
+                                                              agg);
+  EXPECT_EQ(result->type().id(), cudf::type_id::LIST);
+
+  return cudf::lists::sort_lists(
+    cudf::lists_column_view{result->view()}, cudf::order::ASCENDING, cudf::null_order::AFTER);
+}
+
+}  // namespace
+
+struct CollectSetTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedCollectSetTest : public CollectSetTest {};
+
+using TypesForSetTest = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                           cudf::test::FloatingPointTypes,
+                                           cudf::test::DurationTypes,
+                                           cudf::test::FixedPointTypes>;
+
+TYPED_TEST_SUITE(TypedCollectSetTest, TypesForSetTest);
+
+TYPED_TEST(TypedCollectSetTest, BasicRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 10, 11, 12, 11};
+
+  auto const prev_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 2, 2};
+  auto const foll_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 1, 0};
+
+  EXPECT_EQ(static_cast<cudf::column_view>(prev_column).size(),
+            static_cast<cudf::column_view>(foll_column).size());
+
+  auto const result_column_based_window =
+    rolling_collect_set(input_column,
+                        prev_column,
+                        foll_column,
+                        1,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {10},
+      {10, 11},
+      {10, 11, 12},
+      {11, 12},
+      {11, 12},
+    }
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column_based_window->view());
+
+  auto const result_fixed_window = rolling_collect_set(
+    input_column, 2, 1, 1, *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_fixed_window->view());
+
+  auto const result_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    2,
+    1,
+    1,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectSetTest, RollingWindowWithEmptyOutputLists)
+{
+  using T = TypeParam;
+
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 11, 11, 14, 15};
+
+  auto const prev_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 0, 2, 2};
+  auto const foll_column =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 0, 1, 0};
+
+  EXPECT_EQ(static_cast<cudf::column_view>(prev_column).size(),
+            static_cast<cudf::column_view>(foll_column).size());
+
+  auto const result_column_based_window =
+    rolling_collect_set(input_column,
+                        prev_column,
+                        foll_column,
+                        0,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {10, 11},
+      {10, 11},
+      {11},
+      {},
+      {11, 14, 15},
+      {14, 15},
+    }
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column_based_window->view());
+
+  auto const result_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    prev_column,
+    foll_column,
+    0,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectSetTest, RollingWindowHonoursMinPeriods)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+  using T = TypeParam;
+
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{0, 1, 2, 2, 4, 5};
+  auto const num_elements = static_cast<cudf::column_view>(input_column).size();
+
+  auto preceding   = 2;
+  auto following   = 1;
+  auto min_periods = 3;
+  auto const result =
+    rolling_collect_set(input_column,
+                        preceding,
+                        following,
+                        min_periods,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{}, {0, 1, 2}, {1, 2}, {2, 4}, {2, 4, 5}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i != (num_elements - 1);
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+
+  preceding   = 2;
+  following   = 2;
+  min_periods = 4;
+
+  auto result_2 =
+    rolling_collect_set(input_column,
+                        preceding,
+                        following,
+                        min_periods,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+  auto expected_result_2 = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{}, {0, 1, 2}, {1, 2, 4}, {2, 4, 5}, {}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i < 4;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(), result_2->view());
+
+  auto result_2_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(),
+                                      result_2_with_nulls_excluded->view());
+}
+
+TEST_F(CollectSetTest, RollingWindowHonoursMinPeriodsOnStrings)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+  auto const input_column = cudf::test::strings_column_wrapper{"0", "1", "2", "2", "4", "4"};
+  auto const num_elements = static_cast<cudf::column_view>(input_column).size();
+
+  auto preceding   = 2;
+  auto following   = 1;
+  auto min_periods = 3;
+  auto const result =
+    rolling_collect_set(input_column,
+                        preceding,
+                        following,
+                        min_periods,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{}, {"0", "1", "2"}, {"1", "2"}, {"2", "4"}, {"2", "4"}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i != (num_elements - 1);
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+
+  preceding   = 2;
+  following   = 2;
+  min_periods = 4;
+
+  auto result_2 =
+    rolling_collect_set(input_column,
+                        preceding,
+                        following,
+                        min_periods,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+  auto expected_result_2 = cudf::test::lists_column_wrapper<cudf::string_view>{
+    {{}, {"0", "1", "2"}, {"1", "2", "4"}, {"2", "4"}, {}, {}},
+    cudf::detail::make_counting_transform_iterator(0, [num_elements](auto i) {
+      return i != 0 && i < 4;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(), result_2->view());
+
+  auto result_2_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_2->view(),
+                                      result_2_with_nulls_excluded->view());
+}
+
+TEST_F(CollectSetTest, RollingWindowHonoursMinPeriodsWithDecimal)
+{
+  // Test that when the number of observations is fewer than min_periods,
+  // the result is null.
+
+  auto const input_column =
+    cudf::test::fixed_point_column_wrapper<int32_t>{{0, 0, 1, 2, 3, 3}, numeric::scale_type{0}};
+
+  {
+    // One result row at each end should be null.
+    auto preceding   = 2;
+    auto following   = 1;
+    auto min_periods = 3;
+    auto const result =
+      rolling_collect_set(input_column,
+                          preceding,
+                          following,
+                          min_periods,
+                          *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_result_child_values = std::vector<int32_t>{0, 1, 0, 1, 2, 1, 2, 3, 2, 3};
+    auto expected_result_child =
+      cudf::test::fixed_point_column_wrapper<int32_t>{expected_result_child_values.begin(),
+                                                      expected_result_child_values.end(),
+                                                      numeric::scale_type{0}};
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 2, 5, 8, 10, 10}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0},
+      [expected_num_rows](auto i) { return i != 0 && i != (expected_num_rows - 1); });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+    auto const result_with_nulls_excluded = rolling_collect_set(
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(),
+                                        result_with_nulls_excluded->view());
+  }
+
+  {
+    // First result row, and the last two result rows should be null.
+    auto preceding   = 2;
+    auto following   = 2;
+    auto min_periods = 4;
+    auto const result =
+      rolling_collect_set(input_column,
+                          preceding,
+                          following,
+                          min_periods,
+                          *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_result_child_values = std::vector<int32_t>{0, 1, 2, 0, 1, 2, 3, 1, 2, 3};
+    auto expected_result_child =
+      cudf::test::fixed_point_column_wrapper<int32_t>{expected_result_child_values.begin(),
+                                                      expected_result_child_values.end(),
+                                                      numeric::scale_type{0}};
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 3, 7, 10, 10, 10}.release();
+    auto expected_num_rows = expected_offsets->size() - 1;
+    auto null_mask_iter    = cudf::detail::make_counting_transform_iterator(
+      cudf::size_type{0}, [expected_num_rows](auto i) { return i > 0 && i < 4; });
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(null_mask_iter, null_mask_iter + expected_num_rows);
+
+    auto expected_result = cudf::make_lists_column(expected_num_rows,
+                                                   std::move(expected_offsets),
+                                                   expected_result_child.release(),
+                                                   null_count,
+                                                   std::move(null_mask));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+    auto const result_with_nulls_excluded = rolling_collect_set(
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(),
+                                        result_with_nulls_excluded->view());
+  }
+}
+
+TYPED_TEST(TypedCollectSetTest, BasicGroupedRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 11, 13, 13, 20, 21, 20, 23};
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result =
+    grouped_rolling_collect_set(cudf::table_view{std::vector<cudf::column_view>{group_column}},
+                                input_column,
+                                preceding,
+                                following,
+                                min_periods,
+                                *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{
+      {10, 11}, {10, 11}, {11, 13}, {11, 13}, {13}, {20, 21}, {20, 21}, {20, 21, 23}, {20, 23}}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = grouped_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectSetTest, BasicGroupedRollingWindowWithNulls)
+{
+  using T = TypeParam;
+
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {10, 0, 0, 13, 13, 20, 21, 0, 0, 23}, {1, 0, 0, 1, 1, 1, 1, 0, 0, 1}};
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+
+  {
+    // Nulls included and nulls are equal.
+    auto const result =
+      grouped_rolling_collect_set(cudf::table_view{std::vector<cudf::column_view>{group_column}},
+                                  input_column,
+                                  preceding,
+                                  following,
+                                  min_periods,
+                                  *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+    // Null values are sorted to the tails of lists (sets)
+    auto expected_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{{
+                                                                               10, 0,      // row 0
+                                                                               10, 0,      // row 1
+                                                                               13, 0,      // row 2
+                                                                               13, 0,      // row 3
+                                                                               13,         // row 4
+                                                                               20, 21,     // row 5
+                                                                               20, 21, 0,  // row 6
+                                                                               21, 0,      // row 7
+                                                                               23, 0,      // row 8
+                                                                               23, 0,      // row 9
+                                                                             },
+                                                                             {
+                                                                               1, 0,     // row 0
+                                                                               1, 0,     // row 1
+                                                                               1, 0,     // row 2
+                                                                               1, 0,     // row 3
+                                                                               1,        // row 4
+                                                                               1, 1,     // row 5
+                                                                               1, 1, 0,  // row 6
+                                                                               1, 0,     // row 7
+                                                                               1, 0,     // row 8
+                                                                               1, 0      // row 9
+                                                                             }};
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<int32_t>{0, 2, 4, 6, 8, 9, 11, 14, 16, 18, 20};
+
+    auto expected_result =
+      cudf::make_lists_column(static_cast<cudf::column_view>(group_column).size(),
+                              expected_offsets.release(),
+                              expected_child.release(),
+                              0,
+                              {});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+
+  {
+    // Nulls included and nulls are NOT equal.
+    auto const result =
+      grouped_rolling_collect_set(cudf::table_view{std::vector<cudf::column_view>{group_column}},
+                                  input_column,
+                                  preceding,
+                                  following,
+                                  min_periods,
+                                  *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+                                    cudf::null_policy::INCLUDE, cudf::null_equality::UNEQUAL));
+    // Null values are sorted to the tails of lists (sets)
+    auto expected_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{{
+                                                                               10, 0,      // row 0
+                                                                               10, 0,  0,  // row 1
+                                                                               13, 0,  0,  // row 2
+                                                                               13, 0,      // row 3
+                                                                               13,         // row 4
+                                                                               20, 21,     // row 5
+                                                                               20, 21, 0,  // row 6
+                                                                               21, 0,  0,  // row 7
+                                                                               23, 0,  0,  // row 8
+                                                                               23, 0       // row 9
+                                                                             },
+                                                                             {
+                                                                               1, 0,     // row 0
+                                                                               1, 0, 0,  // row 1
+                                                                               1, 0, 0,  // row 2
+                                                                               1, 0,     // row 3
+                                                                               1,        // row 4
+                                                                               1, 1,     // row 5
+                                                                               1, 1, 0,  // row 6
+                                                                               1, 0, 0,  // row 7
+                                                                               1, 0, 0,  // row 8
+                                                                               1, 0      // row 9
+                                                                             }};
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<int32_t>{0, 2, 5, 8, 10, 11, 13, 16, 19, 22, 24};
+
+    auto expected_result =
+      cudf::make_lists_column(static_cast<cudf::column_view>(group_column).size(),
+                              expected_offsets.release(),
+                              expected_child.release(),
+                              0,
+                              {});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+
+  {
+    // Nulls excluded.
+    auto const result = grouped_rolling_collect_set(
+      cudf::table_view{std::vector<cudf::column_view>{group_column}},
+      input_column,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+    auto expected_child = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+      10,  // row 0
+      10,  // row 1
+      13,  // row 2
+      13,  // row 3
+      13,  // row 4
+      20,
+      21,  // row 5
+      20,
+      21,  // row 6
+      21,  // row 7
+      23,  // row 8
+      23   // row 9
+    };
+
+    auto expected_offsets =
+      cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2, 3, 4, 5, 7, 9, 10, 11, 12};
+
+    auto expected_result =
+      cudf::make_lists_column(static_cast<cudf::column_view>(group_column).size(),
+                              expected_offsets.release(),
+                              expected_child.release(),
+                              0,
+                              {});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+  }
+}
+
+TYPED_TEST(TypedCollectSetTest, BasicGroupedTimeRangeRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 12, 13, 14, 20, 21, 22, 23};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result      = grouped_time_range_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {10, 11, 12, 13},
+    {10, 11, 12, 13},
+    {10, 11, 12, 13, 14},
+    {10, 11, 12, 13, 14},
+    {10, 11, 12, 13, 14},
+    {20},
+    {21, 22},
+    {21, 22, 23},
+    {21, 22, 23}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = grouped_time_range_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectSetTest, GroupedTimeRangeRollingWindowWithNulls)
+{
+  using T = TypeParam;
+
+  auto const time_column =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      1, 1, 2, 2, 3, 1, 4, 5, 6};
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {10, 10, 12, 13, 14, 20, 21, 22, 22}, {1, 0, 1, 1, 1, 1, 0, 1, 1}};
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result      = grouped_time_range_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto null_at_1 = cudf::test::iterators::null_at(1);
+  auto null_at_3 = cudf::test::iterators::null_at(3);
+  auto null_at_4 = cudf::test::iterators::null_at(4);
+
+  // In the results, `11` and `21` should be nulls.
+  auto const expected_result = cudf::test::lists_column_wrapper<T, int32_t>{
+    {{10, 12, 13, 10}, null_at_3},
+    {{10, 12, 13, 10}, null_at_3},
+    {{10, 12, 13, 14, 10}, null_at_4},
+    {{10, 12, 13, 14, 10}, null_at_4},
+    {{10, 12, 13, 14, 10}, null_at_4},
+    {{20}, null_at_1},
+    {{22, 21}, null_at_1},
+    {{22, 21}, null_at_1},
+    {{22, 21}, null_at_1}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = grouped_time_range_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    time_column,
+    cudf::order::ASCENDING,
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  // After null exclusion, `11`, `21`, and `null` should not appear.
+  auto const expected_result_with_nulls_excluded = cudf::test::lists_column_wrapper<T, int32_t>{
+    {10, 12, 13},
+    {10, 12, 13},
+    {10, 12, 13, 14},
+    {10, 12, 13, 14},
+    {10, 12, 13, 14},
+    {20},
+    {22},
+    {22},
+    {22}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_with_nulls_excluded->view(),
+                                      result_with_nulls_excluded->view());
+}
+
+TYPED_TEST(TypedCollectSetTest, SlicedGroupedRollingWindow)
+{
+  using T = TypeParam;
+
+  auto const group_original =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_original =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{10, 11, 11, 13, 13, 20, 21, 21, 23};
+  auto const group_col = cudf::slice(group_original, {2, 7})[0];  // { 1, 1, 1, 2, 2 }
+  auto const input_col = cudf::slice(input_original, {2, 7})[0];  // { 11, 13, 13, 20, 21 }
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result =
+    grouped_rolling_collect_set(cudf::table_view{std::vector<cudf::column_view>{group_col}},
+                                input_col,
+                                preceding,
+                                following,
+                                min_periods,
+                                *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<T, int32_t>{{11, 13}, {11, 13}, {13}, {20, 21}, {20, 21}}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+}
+
+TEST_F(CollectSetTest, BoolRollingWindow)
+{
+  auto const input_column =
+    cudf::test::fixed_width_column_wrapper<bool>{false, false, true, true, true};
+
+  auto const prev_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 2, 2};
+  auto const foll_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 1, 0};
+
+  EXPECT_EQ(static_cast<cudf::column_view>(prev_column).size(),
+            static_cast<cudf::column_view>(foll_column).size());
+
+  auto const result_column_based_window =
+    rolling_collect_set(input_column,
+                        prev_column,
+                        foll_column,
+                        1,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<bool>{
+      {false},
+      {false, true},
+      {false, true},
+      {true},
+      {true},
+    }
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column_based_window->view());
+
+  auto const result_fixed_window = rolling_collect_set(
+    input_column, 2, 1, 1, *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_fixed_window->view());
+
+  auto const result_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    2,
+    1,
+    1,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TEST_F(CollectSetTest, BoolGroupedRollingWindow)
+{
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<bool>{
+    false, true, false, true, false, false, false, true, true};
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  auto const result =
+    grouped_rolling_collect_set(cudf::table_view{std::vector<cudf::column_view>{group_column}},
+                                input_column,
+                                preceding,
+                                following,
+                                min_periods,
+                                *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  auto const expected_result = cudf::test::lists_column_wrapper<bool>{
+    {false, true},
+    {false, true},
+    {false, true},
+    {false, true},
+    {false, true},
+    {false},
+    {false, true},
+    {false, true},
+    {true}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  auto const result_with_nulls_excluded = grouped_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+}
+
+TEST_F(CollectSetTest, FloatGroupedRollingWindowWithNaNs)
+{
+  auto const group_column =
+    cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, 1, 1, 2, 2, 2, 2};
+  auto const input_column = cudf::test::fixed_width_column_wrapper<double>{
+    {1.23, 0.2341, 0.2341, -5.23e9, std::nan("1"), 1.1, std::nan("1"), std::nan("1"), 0.0},
+    {true, true, true, true, true, true, true, true, false}};
+
+  auto const preceding   = 2;
+  auto const following   = 1;
+  auto const min_periods = 1;
+  // test on nan_equality::UNEQUAL
+  auto const result = grouped_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+
+  auto const expected_result = cudf::test::lists_column_wrapper<double>{
+    {{0.2341, 1.23}, std::initializer_list<bool>{true, true}},
+    {{0.2341, 1.23}, std::initializer_list<bool>{true, true}},
+    {{-5.23e9, 0.2341}, std::initializer_list<bool>{true, true}},
+    {{-5.23e9, 0.2341, std::nan("1")}, std::initializer_list<bool>{true, true, true}},
+    {{-5.23e9, std::nan("1")}, std::initializer_list<bool>{true, true}},
+    {{1.1, std::nan("1")}, std::initializer_list<bool>{true, true}},
+    {{1.1, std::nan("1"), std::nan("1")}, std::initializer_list<bool>{true, true, true}},
+    {{std::nan("1"), std::nan("1"), 0.0}, std::initializer_list<bool>{true, true, false}},
+    {{std::nan("1"), 0.0},
+     std::initializer_list<bool>{
+       true, false}}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result->view());
+
+  // test on nan_equality::ALL_EQUAL
+  auto const result_nan_equal = grouped_rolling_collect_set(
+    cudf::table_view{std::vector<cudf::column_view>{group_column}},
+    input_column,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL));
+
+  auto const expected_result_nan_equal = cudf::test::lists_column_wrapper<double>{
+    {{0.2341, 1.23}, std::initializer_list<bool>{true, true}},
+    {{0.2341, 1.23}, std::initializer_list<bool>{true, true}},
+    {{-5.23e9, 0.2341}, std::initializer_list<bool>{true, true}},
+    {{-5.23e9, 0.2341, std::nan("1")}, std::initializer_list<bool>{true, true, true}},
+    {{-5.23e9, std::nan("1")}, std::initializer_list<bool>{true, true}},
+    {{1.1, std::nan("1")}, std::initializer_list<bool>{true, true}},
+    {{1.1, std::nan("1")}, std::initializer_list<bool>{true, true}},
+    {{std::nan("1"), 0.0}, std::initializer_list<bool>{true, false}},
+    {{std::nan("1"), 0.0},
+     std::initializer_list<bool>{true,
+                                 false}}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_nan_equal->view(), result_nan_equal->view());
+}
+
+TEST_F(CollectSetTest, BasicRollingWindowWithNaNs)
+{
+  auto const input_column = cudf::test::fixed_width_column_wrapper<double>{
+    1.23, 0.2341, std::nan("1"), std::nan("1"), -5.23e9};
+
+  auto const prev_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 2, 2};
+  auto const foll_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 1, 0};
+
+  EXPECT_EQ(static_cast<cudf::column_view>(prev_column).size(),
+            static_cast<cudf::column_view>(foll_column).size());
+
+  auto const result_column_based_window = rolling_collect_set(
+    input_column,
+    prev_column,
+    foll_column,
+    1,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+
+  auto const expected_result =
+    cudf::test::lists_column_wrapper<double>{
+      {0.2341, 1.23},
+      {0.2341, 1.23, std::nan("1")},
+      {0.2341, std::nan("1"), std::nan("1")},
+      {-5.23e9, std::nan("1"), std::nan("1")},
+      {-5.23e9, std::nan("1")},
+    }
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_column_based_window->view());
+
+  auto const result_fixed_window = rolling_collect_set(
+    input_column,
+    2,
+    1,
+    1,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_fixed_window->view());
+
+  auto const result_with_nulls_excluded = rolling_collect_set(
+    input_column,
+    2,
+    1,
+    1,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+      cudf::null_policy::EXCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::UNEQUAL));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result->view(), result_with_nulls_excluded->view());
+
+  auto const expected_result_for_nan_equal =
+    cudf::test::lists_column_wrapper<double>{
+      {0.2341, 1.23},
+      {0.2341, 1.23, std::nan("1")},
+      {0.2341, std::nan("1")},
+      {-5.23e9, std::nan("1")},
+      {-5.23e9, std::nan("1")},
+    }
+      .release();
+
+  auto const result_with_nan_equal = rolling_collect_set(
+    input_column,
+    2,
+    1,
+    1,
+    *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>(
+      cudf::null_policy::INCLUDE, cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_result_for_nan_equal->view(),
+                                      result_with_nan_equal->view());
+}
+
+TEST_F(CollectSetTest, StructTypeRollingWindow)
+{
+  auto col1               = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5};
+  auto col2               = cudf::test::strings_column_wrapper{"a", "b", "c", "d", "e"};
+  auto const input_column = cudf::test::structs_column_wrapper{{col1, col2}};
+  auto const prev_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 2, 2};
+  auto const foll_column  = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 1, 0};
+
+  auto const expected = [] {
+    auto child1 =
+      cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 1, 2, 3, 2, 3, 4, 3, 4, 5, 4, 5};
+    auto child2 = cudf::test::strings_column_wrapper{
+      "a", "b", "a", "b", "c", "b", "c", "d", "c", "d", "e", "d", "e"};
+    return cudf::make_lists_column(
+      5,
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 5, 8, 11, 13}.release(),
+      cudf::test::structs_column_wrapper{{child1, child2}}.release(),
+      0,
+      {});
+  }();
+  auto const result =
+    rolling_collect_set(input_column,
+                        prev_column,
+                        foll_column,
+                        1,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), result->view());
+}
+
+TEST_F(CollectSetTest, ListTypeRollingWindow)
+{
+  auto const input_column =
+    cudf::test::lists_column_wrapper<int32_t>{{1, 2, 3}, {4, 5}, {6}, {7, 8, 9}, {10}};
+
+  auto const prev_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 2, 2, 2, 2};
+  auto const foll_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>{1, 1, 1, 1, 0};
+
+  auto const expected = [] {
+    auto data = cudf::test::fixed_width_column_wrapper<int32_t>{
+      1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 4, 5, 6, 7, 8, 9, 6, 7, 8, 9, 10, 7, 8, 9, 10};
+    auto inner_offsets = cudf::test::fixed_width_column_wrapper<int32_t>{
+      0, 3, 5, 8, 10, 11, 13, 14, 17, 18, 21, 22, 25, 26};
+    auto outer_offsets =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 5, 8, 11, 13};
+
+    auto inner_list = cudf::make_lists_column(13, inner_offsets.release(), data.release(), 0, {});
+
+    return cudf::make_lists_column(5, outer_offsets.release(), std::move(inner_list), 0, {});
+  }();
+
+  auto const result =
+    rolling_collect_set(input_column,
+                        prev_column,
+                        foll_column,
+                        1,
+                        *cudf::make_collect_set_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), result->view());
+}
diff --git a/cpp/tests/rolling/empty_input_test.cpp b/cpp/tests/rolling/empty_input_test.cpp
new file mode 100644
index 0000000..e7d1e3f
--- /dev/null
+++ b/cpp/tests/rolling/empty_input_test.cpp
@@ -0,0 +1,411 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+namespace {
+// Helper functions to construct rolling window operators.
+auto count_valid()
+{
+  return cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE);
+}
+
+auto count_all()
+{
+  return cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE);
+}
+
+auto sum() { return cudf::make_sum_aggregation<cudf::rolling_aggregation>(); }
+
+auto mean() { return cudf::make_mean_aggregation<cudf::rolling_aggregation>(); }
+
+auto min() { return cudf::make_min_aggregation<cudf::rolling_aggregation>(); }
+
+auto max() { return cudf::make_max_aggregation<cudf::rolling_aggregation>(); }
+
+auto lead() { return cudf::make_lead_aggregation<cudf::rolling_aggregation>(3); }
+
+auto lag() { return cudf::make_lag_aggregation<cudf::rolling_aggregation>(3); }
+
+auto row_number() { return cudf::make_row_number_aggregation<cudf::rolling_aggregation>(); }
+
+auto collect_list() { return cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(); }
+
+auto udf()
+{
+  return cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+    cudf::udf_type::CUDA, "", cudf::data_type{cudf::type_id::INT32});
+}
+
+// Constants for rolling_window.
+auto constexpr min_periods = 1;
+auto constexpr preceding   = 2;
+auto constexpr following   = 2;
+
+auto preceding_scalar() { return cudf::numeric_scalar<cudf::size_type>(preceding); }
+auto following_scalar() { return cudf::numeric_scalar<cudf::size_type>(following); }
+auto preceding_column()
+{
+  return cudf::test::fixed_width_column_wrapper<cudf::size_type>{}.release();
+}
+auto following_column()
+{
+  return cudf::test::fixed_width_column_wrapper<cudf::size_type>{}.release();
+}
+
+}  // namespace
+
+struct RollingEmptyInputTest : cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedRollingEmptyInputTest : RollingEmptyInputTest {};
+
+TYPED_TEST_SUITE(TypedRollingEmptyInputTest, cudf::test::FixedWidthTypes);
+
+using agg_vector_t = std::vector<std::unique_ptr<cudf::rolling_aggregation>>;
+
+void rolling_output_type_matches(cudf::column_view const& result,
+                                 cudf::type_id expected_type,
+                                 cudf::type_id expected_child_type)
+{
+  EXPECT_EQ(result.type().id(), expected_type);
+  EXPECT_EQ(result.size(), 0);
+  if (expected_type == cudf::type_id::LIST) {
+    EXPECT_EQ(result.child(cudf::lists_column_view::child_column_index).type().id(),
+              expected_child_type);
+  }
+  if (expected_type == cudf::type_id::STRUCT) {
+    EXPECT_EQ(result.child(0).type().id(), expected_child_type);
+  }
+}
+
+void rolling_output_type_matches(cudf::column_view const& empty_input,
+                                 agg_vector_t const& aggs,
+                                 cudf::type_id expected_type,
+                                 cudf::type_id expected_child_type = cudf::type_id::EMPTY)
+{
+  auto const preceding_col = preceding_column();
+  auto const following_col = following_column();
+
+  for (auto const& agg : aggs) {
+    auto rolling_output_numeric_bounds =
+      cudf::rolling_window(empty_input, preceding, following, min_periods, *agg);
+    rolling_output_type_matches(
+      rolling_output_numeric_bounds->view(), expected_type, expected_child_type);
+
+    auto rolling_output_columnar_bounds =
+      cudf::rolling_window(empty_input, *preceding_col, *following_col, min_periods, *agg);
+    rolling_output_type_matches(
+      rolling_output_columnar_bounds->view(), expected_type, expected_child_type);
+
+    auto grouped_rolling_output =
+      cudf::grouped_rolling_window(cudf::table_view{std::vector{empty_input}},
+                                   empty_input,
+                                   preceding,
+                                   following,
+                                   min_periods,
+                                   *agg);
+    rolling_output_type_matches(grouped_rolling_output->view(), expected_type, expected_child_type);
+
+    auto grouped_range_rolling_output =
+      cudf::grouped_range_rolling_window(cudf::table_view{std::vector{empty_input}},
+                                         empty_input,
+                                         cudf::order::ASCENDING,
+                                         empty_input,
+                                         cudf::range_window_bounds::get(preceding_scalar()),
+                                         cudf::range_window_bounds::get(following_scalar()),
+                                         min_periods,
+                                         *agg);
+    rolling_output_type_matches(
+      grouped_range_rolling_output->view(), expected_type, expected_child_type);
+  }
+}
+
+void rolling_window_throws(cudf::column_view const& empty_input, agg_vector_t const& aggs)
+{
+  for (auto const& agg : aggs) {
+    EXPECT_THROW(cudf::rolling_window(empty_input, 2, 2, 1, *agg), cudf::logic_error);
+  }
+}
+
+TYPED_TEST(TypedRollingEmptyInputTest, EmptyFixedWidthInputs)
+{
+  using InputType = TypeParam;
+
+  auto input_col   = cudf::test::fixed_width_column_wrapper<InputType>{}.release();
+  auto empty_input = input_col->view();
+
+  /// Test aggregations that yield columns of type `size_type`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(count_valid());
+    aggs.emplace_back(count_all());
+    aggs.emplace_back(row_number());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<cudf::size_type>());
+  }
+
+  /// Test aggregations that yield columns of same type as input.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(min());
+    aggs.emplace_back(max());
+    aggs.emplace_back(lead());
+    aggs.emplace_back(lag());
+    aggs.emplace_back(udf());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<InputType>());
+  }
+
+  /// `SUM` returns 64-bit promoted types for integral/decimal input.
+  /// For other fixed-width input types, the same type is returned.
+  /// Timestamp types are not supported.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(sum());
+
+    using expected_type = cudf::detail::target_type_t<InputType, cudf::aggregation::SUM>;
+    if constexpr (cudf::is_timestamp<InputType>()) {
+      EXPECT_THROW(
+        rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<expected_type>()),
+        cudf::logic_error);
+    } else {
+      rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<expected_type>());
+    }
+  }
+
+  /// `MEAN` returns float64 for all numeric types,
+  /// except for duration-types, which yield the same duration-type.
+  /// Timestamp types are not supported.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(mean());
+
+    using expected_type = cudf::detail::target_type_t<InputType, cudf::aggregation::MEAN>;
+    if constexpr (cudf::is_timestamp<InputType>()) {
+      EXPECT_THROW(
+        rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<expected_type>()),
+        cudf::logic_error);
+    } else {
+      rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<expected_type>());
+    }
+  }
+
+  /// For an input type `T`, `COLLECT_LIST` returns a column of type `list<T>`.
+  {
+    auto aggs = std::vector<std::unique_ptr<cudf::rolling_aggregation>>{};
+    aggs.emplace_back(collect_list());
+
+    rolling_output_type_matches(
+      empty_input, aggs, cudf::type_to_id<cudf::list_view>(), cudf::type_to_id<InputType>());
+  }
+}
+
+TEST_F(RollingEmptyInputTest, Strings)
+{
+  auto input_col   = cudf::test::strings_column_wrapper{}.release();
+  auto empty_input = input_col->view();
+
+  /// Test aggregations that yield columns of type `size_type`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(count_valid());
+    aggs.emplace_back(count_all());
+    aggs.emplace_back(row_number());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<cudf::size_type>());
+  }
+
+  /// Test aggregations that yield columns of same type as input.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(min());
+    aggs.emplace_back(max());
+    aggs.emplace_back(lead());
+    aggs.emplace_back(lag());
+    aggs.emplace_back(udf());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_id::STRING);
+  }
+
+  /// For an input type `T`, `COLLECT_LIST` returns a column of type `list<T>`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(collect_list());
+
+    rolling_output_type_matches(
+      empty_input, aggs, cudf::type_to_id<cudf::list_view>(), cudf::type_id::STRING);
+  }
+
+  /// All other aggregations are unsupported.
+  {
+    auto unsupported_aggs = agg_vector_t{};
+    unsupported_aggs.emplace_back(sum());
+    unsupported_aggs.emplace_back(mean());
+
+    rolling_window_throws(empty_input, unsupported_aggs);
+  }
+}
+
+TEST_F(RollingEmptyInputTest, Dictionaries)
+{
+  auto input_col   = cudf::test::dictionary_column_wrapper<std::string>{}.release();
+  auto empty_input = input_col->view();
+
+  /// Test aggregations that yield columns of type `size_type`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(count_valid());
+    aggs.emplace_back(count_all());
+    aggs.emplace_back(row_number());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<cudf::size_type>());
+  }
+
+  /// Test aggregations that yield columns of same type as input.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(min());
+    aggs.emplace_back(max());
+    aggs.emplace_back(lead());
+    aggs.emplace_back(lag());
+    aggs.emplace_back(udf());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_id::DICTIONARY32);
+  }
+
+  /// For an input type `T`, `COLLECT_LIST` returns a column of type `list<T>`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(collect_list());
+
+    rolling_output_type_matches(
+      empty_input, aggs, cudf::type_to_id<cudf::list_view>(), cudf::type_id::DICTIONARY32);
+  }
+
+  /// All other aggregations are unsupported.
+  {
+    auto unsupported_aggs = agg_vector_t{};
+    unsupported_aggs.emplace_back(sum());
+    unsupported_aggs.emplace_back(mean());
+
+    rolling_window_throws(empty_input, unsupported_aggs);
+  }
+}
+
+TYPED_TEST(TypedRollingEmptyInputTest, Lists)
+{
+  using T = TypeParam;
+
+  auto input_col   = cudf::test::lists_column_wrapper<T>{}.release();
+  auto empty_input = input_col->view();
+
+  /// Test aggregations that yield columns of type `size_type`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(count_valid());
+    aggs.emplace_back(count_all());
+    aggs.emplace_back(row_number());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<cudf::size_type>());
+  }
+
+  /// Test aggregations that yield columns of same type as input.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(min());
+    aggs.emplace_back(max());
+    aggs.emplace_back(lead());
+    aggs.emplace_back(lag());
+    aggs.emplace_back(udf());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_id::LIST, cudf::type_to_id<T>());
+  }
+
+  /// For an input type `T`, `COLLECT_LIST` returns a column of type `list<T>`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(collect_list());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_id::LIST, cudf::type_id::LIST);
+  }
+
+  /// All other aggregations are unsupported.
+  {
+    auto unsupported_aggs = agg_vector_t{};
+    unsupported_aggs.emplace_back(sum());
+    unsupported_aggs.emplace_back(mean());
+
+    rolling_window_throws(empty_input, unsupported_aggs);
+  }
+}
+
+TYPED_TEST(TypedRollingEmptyInputTest, Structs)
+{
+  using T = TypeParam;
+
+  auto member_col  = cudf::test::fixed_width_column_wrapper<T>{};
+  auto input_col   = cudf::test::structs_column_wrapper{{member_col}}.release();
+  auto empty_input = input_col->view();
+
+  /// Test aggregations that yield columns of type `size_type`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(count_valid());
+    aggs.emplace_back(count_all());
+    aggs.emplace_back(row_number());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_to_id<cudf::size_type>());
+  }
+
+  /// Test aggregations that yield columns of same type as input.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(min());
+    aggs.emplace_back(max());
+    aggs.emplace_back(lead());
+    aggs.emplace_back(lag());
+    aggs.emplace_back(udf());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_id::STRUCT, cudf::type_to_id<T>());
+  }
+
+  /// For an input type `T`, `COLLECT_LIST` returns a column of type `list<T>`.
+  {
+    auto aggs = agg_vector_t{};
+    aggs.emplace_back(collect_list());
+
+    rolling_output_type_matches(empty_input, aggs, cudf::type_id::LIST, cudf::type_id::STRUCT);
+  }
+
+  /// All other aggregations are unsupported.
+  {
+    auto unsupported_aggs = agg_vector_t{};
+    unsupported_aggs.emplace_back(sum());
+    unsupported_aggs.emplace_back(mean());
+
+    rolling_window_throws(empty_input, unsupported_aggs);
+  }
+}
diff --git a/cpp/tests/rolling/grouped_rolling_range_test.cpp b/cpp/tests/rolling/grouped_rolling_range_test.cpp
new file mode 100644
index 0000000..fcfbd0e
--- /dev/null
+++ b/cpp/tests/rolling/grouped_rolling_range_test.cpp
@@ -0,0 +1,941 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/bit.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <algorithm>
+#include <vector>
+
+template <typename T>
+using fwcw = cudf::test::fixed_width_column_wrapper<T>;
+template <typename T>
+using decimals_column = cudf::test::fixed_point_column_wrapper<T>;
+using ints_column     = fwcw<int32_t>;
+using bigints_column  = fwcw<int64_t>;
+using strings_column  = cudf::test::strings_column_wrapper;
+using column_ptr      = std::unique_ptr<cudf::column>;
+
+template <typename T>
+struct BaseGroupedRollingRangeOrderByTest : cudf::test::BaseFixture {
+  // Stand-in for std::pow(10, n), but for integral return.
+  static constexpr std::array<int32_t, 6> pow10{1, 10, 100, 1000, 10000, 100000};
+
+  // Test data.
+  column_ptr const grouping_keys = ints_column{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2}.release();
+  column_ptr const agg_values    = ints_column{1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3}.release();
+  cudf::size_type const num_rows = grouping_keys->size();
+
+  /**
+   * @brief Get grouped rolling results for specified order-by column and range bounds.
+   */
+  [[nodiscard]] column_ptr get_grouped_range_rolling_result(
+    cudf::range_window_bounds const& preceding,
+    cudf::range_window_bounds const& following,
+    cudf::column_view const& order_by_column,
+    cudf::rolling_aggregation const& agg,
+    cudf::order const& order = cudf::order::ASCENDING) const
+  {
+    return cudf::grouped_range_rolling_window(cudf::table_view{{grouping_keys->view()}},
+                                              order_by_column,
+                                              order,
+                                              agg_values->view(),
+                                              preceding,
+                                              following,
+                                              1,  // min_periods
+                                              agg);
+  }
+
+  [[nodiscard]] column_ptr get_grouped_range_rolling_sum_result(
+    cudf::range_window_bounds const& preceding,
+    cudf::range_window_bounds const& following,
+    cudf::column_view const& order_by_column,
+    cudf::order const& order = cudf::order::ASCENDING) const
+  {
+    return get_grouped_range_rolling_result(
+      preceding,
+      following,
+      order_by_column,
+      *cudf::make_sum_aggregation<cudf::rolling_aggregation>(),
+      order);
+  }
+};
+
+template <typename T>
+struct GroupedRollingRangeOrderByNumericTest : public BaseGroupedRollingRangeOrderByTest<T> {
+  using base = BaseGroupedRollingRangeOrderByTest<T>;
+
+  using base::agg_values;
+  using base::get_grouped_range_rolling_sum_result;
+  using base::grouping_keys;
+  using base::num_rows;
+
+  static auto constexpr inf = std::numeric_limits<T>::infinity();
+  static auto constexpr nan = std::numeric_limits<T>::quiet_NaN();
+
+  [[nodiscard]] auto make_range_bounds(T const& value) const
+  {
+    return cudf::range_window_bounds::get(*cudf::make_fixed_width_scalar(value));
+  }
+
+  [[nodiscard]] auto make_unbounded_range_bounds() const
+  {
+    return cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<T>()});
+  }
+
+  /// Generate order-by column with values: [0, 100,   200,   300,   ... 1100,   1200,   1300]
+  [[nodiscard]] column_ptr generate_order_by_column() const
+  {
+    auto const begin = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<cudf::size_type>(0), [&](T const& i) -> T { return i * 100; });
+
+    return fwcw<T>(begin, begin + num_rows).release();
+  }
+
+  /// Generate order-by column with values: [-1400, -1300, -1200 ... -300, -200, -100]
+  [[nodiscard]] column_ptr generate_negative_order_by_column() const
+  {
+    auto const begin =
+      thrust::make_transform_iterator(thrust::make_counting_iterator<cudf::size_type>(0),
+                                      [&](T const& i) -> T { return (i - num_rows) * 100; });
+
+    return fwcw<T>(begin, begin + num_rows).release();
+  }
+
+  /**
+   * @brief Run grouped_rolling test with no nulls in the order-by column
+   */
+  void run_test_no_null_oby() const
+  {
+    auto const preceding = make_range_bounds(T{200});
+    auto const following = make_range_bounds(T{100});
+    auto const order_by  = generate_order_by_column();
+    auto const results   = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+    auto const expected_results = bigints_column{{2, 3, 4, 4, 4, 3, 4, 6, 8, 6, 6, 9, 12, 9},
+                                                 cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  /**
+   * @brief Run grouped_rolling test with no nulls in the order-by column
+   */
+  void run_test_negative_oby() const
+  {
+    auto const preceding = make_range_bounds(T{200});
+    auto const following = make_range_bounds(T{100});
+    auto const order_by  = generate_negative_order_by_column();
+    auto const results   = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+    auto const expected_results = bigints_column{{2, 3, 4, 4, 4, 3, 4, 6, 8, 6, 6, 9, 12, 9},
+                                                 cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  /**
+   * @brief Run grouped_rolling test with nulls in the order-by column
+   * (i.e. 2 nulls at the beginning of each group)
+   *
+   */
+  void run_test_nulls_in_oby() const
+  {
+    auto const preceding = make_range_bounds(T{200});
+    auto const following = make_range_bounds(T{100});
+
+    // Nullify the first two rows of each group in the order_by column.
+    auto const nulled_order_by = [&] {
+      auto col           = generate_order_by_column();
+      auto new_null_mask = create_null_mask(col->size(), cudf::mask_state::ALL_VALID);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(new_null_mask.data()),
+                          0,
+                          2,
+                          false);  // Nulls in first group.
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(new_null_mask.data()),
+                          6,
+                          8,
+                          false);  // Nulls in second group.
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(new_null_mask.data()),
+                          10,
+                          12,
+                          false);  // Nulls in third group.
+      col->set_null_mask(std::move(new_null_mask), 6);
+      return col;
+    }();
+
+    auto const results =
+      get_grouped_range_rolling_sum_result(preceding, following, *nulled_order_by);
+    auto const expected_results =
+      bigints_column{{2, 2, 2, 3, 4, 3, 4, 4, 4, 4, 6, 6, 6, 6}, cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  /**
+   * @brief Run grouped_rolling test with unbounded preceding and unbounded following.
+   */
+  void run_test_unbounded_preceding_to_unbounded_following()
+  {
+    auto const order_by  = generate_order_by_column();
+    auto const preceding = make_unbounded_range_bounds();
+    auto const following = make_unbounded_range_bounds();
+    auto const results   = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+
+    auto const expected_results = bigints_column{6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 12, 12, 12, 12};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  /**
+   * @brief Run grouped_rolling test with unbounded preceding and current row.
+   */
+  void run_test_unbounded_preceding_to_current_row()
+  {
+    auto const order_by            = generate_order_by_column();
+    auto const unbounded_preceding = make_unbounded_range_bounds();
+    auto const current_row         = make_range_bounds(T{0});
+    auto const results =
+      get_grouped_range_rolling_sum_result(unbounded_preceding, current_row, *order_by);
+
+    auto const expected_results = bigints_column{{1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 3, 6, 9, 12},
+                                                 cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  /**
+   * @brief Run grouped_rolling test with current row and unbounded following.
+   */
+  void run_test_current_row_to_unbounded_following()
+  {
+    auto const order_by            = generate_order_by_column();
+    auto const unbounded_following = make_unbounded_range_bounds();
+
+    auto const current_row = make_range_bounds(T{0});
+    auto const results =
+      get_grouped_range_rolling_sum_result(current_row, unbounded_following, *order_by);
+
+    auto const expected_results = bigints_column{{6, 5, 4, 3, 2, 1, 8, 6, 4, 2, 12, 9, 6, 3},
+                                                 cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  [[nodiscard]] column_ptr generate_ascending_order_by_NaNs_infinity()
+  {
+    auto const vec =
+      std::vector<T>{-inf, -inf, -50, 0, 7, nan, -inf, 0, inf, nan, 0, inf, nan, nan};
+    return fwcw<T>(vec.begin(), vec.end()).release();
+  }
+
+  void run_test_bounded_ascending_order_by_NaNs_infinity()
+  {
+    auto const order_by  = generate_ascending_order_by_NaNs_infinity();
+    auto const preceding = make_range_bounds(T{200});
+    auto const following = make_range_bounds(T{100});
+
+    auto const results = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+
+    auto const expected_results =
+      bigints_column{{2, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 6, 6}, cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  void run_test_unbounded_ascending_order_by_NaNs_infinity()
+  {
+    auto const order_by = generate_ascending_order_by_NaNs_infinity();
+
+    {
+      // UNBOUNDED PRECEDING to CURRENT ROW.
+      auto const preceding = make_unbounded_range_bounds();
+      auto const following = make_range_bounds(T{0});
+
+      auto const results = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+
+      auto const expected_results = bigints_column{{2, 2, 3, 4, 5, 6, 2, 4, 6, 8, 3, 6, 12, 12},
+                                                   cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+    {
+      // CURRENT ROW to UNBOUNDED FOLLOWING
+      auto const preceding = make_range_bounds(T{0});
+      auto const following = make_unbounded_range_bounds();
+
+      auto const results = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+
+      auto const expected_results = bigints_column{{6, 6, 4, 3, 2, 1, 8, 6, 4, 2, 12, 9, 6, 6},
+                                                   cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+    {
+      // UNBOUNDED PRECEDING to UNBOUNDED FOLLOWING
+      auto const preceding = make_unbounded_range_bounds();
+      auto const following = make_unbounded_range_bounds();
+
+      auto const results = get_grouped_range_rolling_sum_result(preceding, following, *order_by);
+
+      auto const expected_results = bigints_column{6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 12, 12, 12, 12};
+
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+  }
+
+  [[nodiscard]] column_ptr generate_descending_order_by_NaNs_infinity()
+  {
+    auto const vec =
+      std::vector<T>{nan, 7, 0, -50, -inf, -inf, nan, inf, 0, -inf, nan, nan, inf, 0};
+    return fwcw<T>(vec.begin(), vec.end()).release();
+  }
+
+  void run_test_bounded_descending_order_by_NaNs_infinity()
+  {
+    auto const order_by  = generate_descending_order_by_NaNs_infinity();
+    auto const preceding = make_range_bounds(T{200});
+    auto const following = make_range_bounds(T{100});
+
+    auto const results = get_grouped_range_rolling_sum_result(
+      preceding, following, *order_by, cudf::order::DESCENDING);
+
+    auto const expected_results =
+      bigints_column{{1, 3, 3, 3, 2, 2, 2, 2, 2, 2, 6, 6, 3, 3}, cudf::test::iterators::no_nulls()};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  void run_test_unbounded_descending_order_by_NaNs_infinity()
+  {
+    auto const order_by = generate_descending_order_by_NaNs_infinity();
+
+    {
+      // UNBOUNDED PRECEDING to CURRENT ROW.
+      auto const preceding = make_unbounded_range_bounds();
+      auto const following = make_range_bounds(T{0});
+
+      auto const results = get_grouped_range_rolling_sum_result(
+        preceding, following, *order_by, cudf::order::DESCENDING);
+
+      auto const expected_results = bigints_column{{1, 2, 3, 4, 6, 6, 2, 4, 6, 8, 6, 6, 9, 12},
+                                                   cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+    {
+      // CURRENT ROW to UNBOUNDED FOLLOWING
+      auto const preceding = make_range_bounds(T{0});
+      auto const following = make_unbounded_range_bounds();
+
+      auto const results = get_grouped_range_rolling_sum_result(
+        preceding, following, *order_by, cudf::order::DESCENDING);
+
+      auto const expected_results = bigints_column{{6, 5, 4, 3, 2, 2, 8, 6, 4, 2, 12, 12, 6, 3},
+                                                   cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+    {
+      // UNBOUNDED PRECEDING to UNBOUNDED FOLLOWING
+      auto const preceding = make_unbounded_range_bounds();
+      auto const following = make_unbounded_range_bounds();
+
+      auto const results = get_grouped_range_rolling_sum_result(
+        preceding, following, *order_by, cudf::order::DESCENDING);
+
+      auto const expected_results = bigints_column{6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 12, 12, 12, 12};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+  }
+};
+
+template <typename FloatingPointType>
+struct GroupedRollingRangeOrderByFloatingPointTest
+  : GroupedRollingRangeOrderByNumericTest<FloatingPointType> {};
+
+TYPED_TEST_SUITE(GroupedRollingRangeOrderByFloatingPointTest, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(GroupedRollingRangeOrderByFloatingPointTest, BoundedRanges)
+{
+  this->run_test_no_null_oby();
+  this->run_test_negative_oby();
+  this->run_test_nulls_in_oby();
+  this->run_test_bounded_ascending_order_by_NaNs_infinity();
+  this->run_test_bounded_descending_order_by_NaNs_infinity();
+}
+
+TYPED_TEST(GroupedRollingRangeOrderByFloatingPointTest, UnboundedRanges)
+{
+  this->run_test_unbounded_preceding_to_unbounded_following();
+  this->run_test_unbounded_preceding_to_current_row();
+  this->run_test_current_row_to_unbounded_following();
+  this->run_test_unbounded_ascending_order_by_NaNs_infinity();
+  this->run_test_unbounded_descending_order_by_NaNs_infinity();
+}
+
+template <typename DecimalT>
+struct GroupedRollingRangeOrderByDecimalTypedTest
+  : BaseGroupedRollingRangeOrderByTest<typename DecimalT::rep> {
+  using Rep  = typename DecimalT::rep;
+  using base = BaseGroupedRollingRangeOrderByTest<Rep>;
+
+  using base::agg_values;
+  using base::grouping_keys;
+  using base::num_rows;
+
+  [[nodiscard]] auto make_fixed_point_range_bounds(typename DecimalT::rep value,
+                                                   numeric::scale_type scale) const
+  {
+    return cudf::range_window_bounds::get(*cudf::make_fixed_point_scalar<DecimalT>(value, scale));
+  }
+
+  [[nodiscard]] auto make_unbounded_fixed_point_range_bounds() const
+  {
+    return cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<DecimalT>()});
+  }
+
+  /// For different scales, generate order_by column with
+  /// the same effective values:           [0, 100,   200,   300,   ... 1100,   1200,   1300]
+  /// For scale == -2, the rep values are: [0, 10000, 20000, 30000, ... 110000, 120000, 130000]
+  /// For scale ==  2, the rep values are: [0, 1,     2,     3,     ... 11,     12,     13]
+  [[nodiscard]] column_ptr generate_order_by_column(numeric::scale_type scale) const
+  {
+    auto const begin = thrust::make_transform_iterator(
+      thrust::make_counting_iterator<Rep>(0),
+      [&](auto i) -> Rep { return (i * 10000) / base::pow10[scale + 2]; });
+
+    return decimals_column<Rep>{begin, begin + num_rows, numeric::scale_type{scale}}.release();
+  }
+
+  /**
+   * @brief Scale the range bounds value to new scale, so that effective
+   * value remains identical.
+   *
+   * Keeping the effective range bounds value identical ensures that
+   * the expected result from grouped_rolling remains the same.
+   */
+  [[nodiscard]] Rep rescale_range_value(Rep const& value_at_scale_0,
+                                        numeric::scale_type new_scale) const
+  {
+    // Scale  ->   Rep (for value == 200)
+    //  -2    ->       20000
+    //  -1    ->       2000
+    //   0    ->       200
+    //   1    ->       20
+    //   2    ->       2
+    return (value_at_scale_0 * 100) / base::pow10[new_scale + 2];
+  }
+
+  /**
+   * @brief Get grouped rolling results for specified order-by column and range scale
+   *
+   */
+  [[nodiscard]] column_ptr get_grouped_range_rolling_result(
+    cudf::column_view const& order_by_column, numeric::scale_type const& range_scale) const
+  {
+    auto const preceding =
+      this->make_fixed_point_range_bounds(rescale_range_value(Rep{200}, range_scale), range_scale);
+    auto const following =
+      this->make_fixed_point_range_bounds(rescale_range_value(Rep{100}, range_scale), range_scale);
+
+    return base::get_grouped_range_rolling_sum_result(preceding, following, order_by_column);
+  }
+
+  /**
+   * @brief Run grouped_rolling test for specified order-by column scale with
+   * no nulls in the order-by column
+   *
+   */
+  void run_test_no_null_oby(numeric::scale_type const& order_by_column_scale) const
+  {
+    auto const order_by = generate_order_by_column(order_by_column_scale);
+    // Run tests for range bounds generated for all scales >= oby_column_scale.
+    for (int32_t range_scale = order_by_column_scale; range_scale <= 2; ++range_scale) {
+      auto const results =
+        get_grouped_range_rolling_result(*order_by, numeric::scale_type{range_scale});
+      auto const expected_results = bigints_column{{2, 3, 4, 4, 4, 3, 4, 6, 8, 6, 6, 9, 12, 9},
+                                                   cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+  }
+
+  /**
+   * @brief Run grouped_rolling test for specified order-by column scale with
+   * nulls in the order-by column (i.e. 2 nulls at the beginning of each group)
+   *
+   */
+  void run_test_nulls_in_oby(numeric::scale_type const& order_by_column_scale) const
+  {
+    // Nullify the first two rows of each group in the order_by column.
+    auto const nulled_order_by = [&] {
+      auto col           = generate_order_by_column(order_by_column_scale);
+      auto new_null_mask = create_null_mask(col->size(), cudf::mask_state::ALL_VALID);
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(new_null_mask.data()),
+                          0,
+                          2,
+                          false);  // Nulls in first group.
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(new_null_mask.data()),
+                          6,
+                          8,
+                          false);  // Nulls in second group.
+      cudf::set_null_mask(static_cast<cudf::bitmask_type*>(new_null_mask.data()),
+                          10,
+                          12,
+                          false);  // Nulls in third group.
+      col->set_null_mask(std::move(new_null_mask), 6);
+      return col;
+    }();
+
+    // Run tests for range bounds generated for all scales >= oby_column_scale.
+    for (auto range_scale = int32_t{order_by_column_scale}; range_scale <= 2; ++range_scale) {
+      auto const results =
+        get_grouped_range_rolling_result(*nulled_order_by, numeric::scale_type{range_scale});
+      auto const expected_results = bigints_column{{2, 2, 2, 3, 4, 3, 4, 4, 4, 4, 6, 6, 6, 6},
+                                                   cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+  }
+
+  /**
+   * @brief Run grouped_rolling test for specified order-by column scale with
+   * unbounded preceding and unbounded following.
+   *
+   */
+  void run_test_unbounded_preceding_to_unbounded_following(numeric::scale_type oby_column_scale)
+  {
+    auto const order_by  = generate_order_by_column(oby_column_scale);
+    auto const preceding = make_unbounded_fixed_point_range_bounds();
+    auto const following = make_unbounded_fixed_point_range_bounds();
+    auto results =
+      cudf::grouped_range_rolling_window(cudf::table_view{{grouping_keys->view()}},
+                                         order_by->view(),
+                                         cudf::order::ASCENDING,
+                                         agg_values->view(),
+                                         preceding,
+                                         following,
+                                         1,  // min_periods
+                                         *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+
+    auto expected_results = bigints_column{6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 12, 12, 12, 12};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+  }
+
+  /**
+   * @brief Run grouped_rolling test for specified order-by column scale with
+   * unbounded preceding and current row.
+   *
+   */
+  void run_test_unbounded_preceding_to_current_row(numeric::scale_type oby_column_scale)
+  {
+    auto const order_by            = generate_order_by_column(oby_column_scale);
+    auto const unbounded_preceding = make_unbounded_fixed_point_range_bounds();
+
+    for (int32_t range_scale = oby_column_scale; range_scale <= 2; ++range_scale) {
+      auto const current_row =
+        make_fixed_point_range_bounds(rescale_range_value(Rep{0}, numeric::scale_type{range_scale}),
+                                      numeric::scale_type{range_scale});
+      auto const results = cudf::grouped_range_rolling_window(
+        cudf::table_view{{grouping_keys->view()}},
+        order_by->view(),
+        cudf::order::ASCENDING,
+        agg_values->view(),
+        unbounded_preceding,
+        current_row,
+        1,  // min_periods
+        *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+
+      auto expected_results = bigints_column{{1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 3, 6, 9, 12},
+                                             cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+  }
+
+  /**
+   * @brief Run grouped_rolling test for specified order-by column scale with
+   * current row and unbounded following.
+   *
+   */
+  void run_test_current_row_to_unbounded_following(numeric::scale_type oby_column_scale)
+  {
+    auto const order_by            = generate_order_by_column(oby_column_scale);
+    auto const unbounded_following = make_unbounded_fixed_point_range_bounds();
+
+    for (int32_t range_scale = oby_column_scale; range_scale <= 2; ++range_scale) {
+      auto const current_row =
+        make_fixed_point_range_bounds(rescale_range_value(Rep{0}, numeric::scale_type{range_scale}),
+                                      numeric::scale_type{range_scale});
+      auto const results = cudf::grouped_range_rolling_window(
+        cudf::table_view{{grouping_keys->view()}},
+        order_by->view(),
+        cudf::order::ASCENDING,
+        agg_values->view(),
+        current_row,
+        unbounded_following,
+        1,  // min_periods
+        *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+
+      auto expected_results = bigints_column{{6, 5, 4, 3, 2, 1, 8, 6, 4, 2, 12, 9, 6, 3},
+                                             cudf::test::iterators::no_nulls()};
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_results);
+    }
+  }
+};
+
+TYPED_TEST_SUITE(GroupedRollingRangeOrderByDecimalTypedTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(GroupedRollingRangeOrderByDecimalTypedTest, BoundedRanges)
+{
+  for (auto const order_by_column_scale : {-2, -1, 0, 1, 2}) {
+    auto const oby_scale = numeric::scale_type{order_by_column_scale};
+    this->run_test_no_null_oby(oby_scale);
+    this->run_test_nulls_in_oby(oby_scale);
+  }
+}
+
+TYPED_TEST(GroupedRollingRangeOrderByDecimalTypedTest, UnboundedRanges)
+{
+  for (auto const order_by_scale : {-2, -1, 0, 1, 2}) {
+    auto const order_by_column_scale = numeric::scale_type{order_by_scale};
+    this->run_test_unbounded_preceding_to_unbounded_following(order_by_column_scale);
+    this->run_test_unbounded_preceding_to_current_row(order_by_column_scale);
+    this->run_test_current_row_to_unbounded_following(order_by_column_scale);
+  }
+}
+
+struct GroupedRollingRangeOrderByStringTest : public cudf::test::BaseFixture {
+  // Test data.
+  column_ptr const grouping_keys = ints_column{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2}.release();
+  column_ptr const agg_values    = ints_column{1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3}.release();
+  cudf::size_type const num_rows = grouping_keys->size();
+  cudf::size_type const min_periods = 1;
+
+  cudf::data_type const string_type{cudf::type_id::STRING};
+  cudf::range_window_bounds const unbounded_preceding =
+    cudf::range_window_bounds::unbounded(string_type);
+  cudf::range_window_bounds const unbounded_following =
+    cudf::range_window_bounds::unbounded(string_type);
+  cudf::range_window_bounds const current_row = cudf::range_window_bounds::current_row(string_type);
+
+  [[nodiscard]] static auto nullable_ints_column(std::initializer_list<int> const& ints)
+  {
+    return ints_column{ints, cudf::test::iterators::no_nulls()};
+  }
+
+  [[nodiscard]] auto get_count_over_partitioned_window(
+    cudf::column_view const& order_by,
+    cudf::order const& order,
+    cudf::range_window_bounds const& preceding,
+    cudf::range_window_bounds const& following) const
+  {
+    return cudf::grouped_range_rolling_window(
+      cudf::table_view{{*grouping_keys}},
+      order_by,
+      order,
+      *agg_values,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  }
+
+  [[nodiscard]] auto get_count_over_unpartitioned_window(
+    cudf::column_view const& order_by,
+    cudf::order const& order,
+    cudf::range_window_bounds const& preceding,
+    cudf::range_window_bounds const& following) const
+  {
+    return cudf::grouped_range_rolling_window(
+      cudf::table_view{std::vector<cudf::column_view>{}},
+      order_by,
+      order,
+      *agg_values,
+      preceding,
+      following,
+      min_periods,
+      *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  }
+};
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Ascending_Partitioned_NoNulls)
+{
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          "A", "A", "A", "B", "B", "B", // Group 0.
+          "C", "C", "C", "C",           // Group 1.
+          "D", "D", "E", "E"            // Group 2.
+      }.release();
+  // clang-format on
+
+  // Partitioned cases.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 4, 4, 4, 4, 2, 2, 4, 4}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::ASCENDING, current_row, unbounded_following),
+    nullable_ints_column({6, 6, 6, 3, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, unbounded_following),
+    ints_column{6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(*orderby, cudf::order::ASCENDING, current_row, current_row),
+    nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Ascending_NoParts_NoNulls)
+{
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          "A", "A", "A", "B", "B", "B",
+          "C", "C", "C", "C",
+          "D", "D", "E", "E"
+      }.release();
+  // clang-format on
+
+  // Un-partitioned cases.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 10, 10, 10, 10, 12, 12, 14, 14}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::ASCENDING, current_row, unbounded_following),
+    nullable_ints_column({14, 14, 14, 11, 11, 11, 8, 8, 8, 8, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, unbounded_following),
+    ints_column{14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*get_count_over_unpartitioned_window(
+                                   *orderby, cudf::order::ASCENDING, current_row, current_row),
+                                 nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Ascending_Partitioned_WithNulls)
+{
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          {
+              "X", "X", "X", "B", "B", "B", // Group 0.
+              "C", "C", "C", "C",           // Group 1.
+              "X", "X", "E", "E"            // Group 2.
+          },
+          cudf::test::iterators::nulls_at({0, 1, 2, 10, 11})
+      }.release();
+  // clang-format on
+
+  // Partitioned cases.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 4, 4, 4, 4, 2, 2, 4, 4}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::ASCENDING, current_row, unbounded_following),
+    nullable_ints_column({6, 6, 6, 3, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, unbounded_following),
+    ints_column{6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(*orderby, cudf::order::ASCENDING, current_row, current_row),
+    nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Ascending_NoParts_WithNulls)
+{
+  // Un-partitioned cases. Null values have to be clustered together.
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          {
+              "X", "X", "X", "B", "B", "B",
+              "C", "C", "C", "C",
+              "D", "D", "E", "E"
+          },
+          cudf::test::iterators::nulls_at({0, 1, 2})
+      }.release();
+  // clang-format on
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 10, 10, 10, 10, 12, 12, 14, 14}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::ASCENDING, current_row, unbounded_following),
+    nullable_ints_column({14, 14, 14, 11, 11, 11, 8, 8, 8, 8, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::ASCENDING, unbounded_preceding, unbounded_following),
+    ints_column{14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*get_count_over_unpartitioned_window(
+                                   *orderby, cudf::order::ASCENDING, current_row, current_row),
+                                 nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Descending_Partitioned_NoNulls)
+{
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          "B", "B", "B", "A", "A", "A", // Group 0.
+          "C", "C", "C", "C",           // Group 1.
+          "E", "E", "D", "D"            // Group 2.
+      }.release();
+  // clang-format on
+
+  // Partitioned cases.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 4, 4, 4, 4, 2, 2, 4, 4}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::DESCENDING, current_row, unbounded_following),
+    nullable_ints_column({6, 6, 6, 3, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, unbounded_following),
+    ints_column{6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(*orderby, cudf::order::DESCENDING, current_row, current_row),
+    nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Descending_NoParts_NoNulls)
+{
+  // Un-partitioned cases. Order-by column must be entirely in descending order.
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          "E", "E", "E", "D", "D", "D",
+          "C", "C", "C", "C",
+          "B", "B", "A", "A"
+      }.release();
+  // clang-format on
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 10, 10, 10, 10, 12, 12, 14, 14}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::DESCENDING, current_row, unbounded_following),
+    nullable_ints_column({14, 14, 14, 11, 11, 11, 8, 8, 8, 8, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, unbounded_following),
+    ints_column{14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*get_count_over_unpartitioned_window(
+                                   *orderby, cudf::order::DESCENDING, current_row, current_row),
+                                 nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Descending_Partitioned_WithNulls)
+{
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          {
+              "X", "X", "X", "A", "A", "A", // Group 0.
+              "C", "C", "C", "C",           // Group 1.
+              "X", "X", "D", "D"            // Group 2.
+          },
+          cudf::test::iterators::nulls_at({0, 1, 2, 10, 11})
+      }.release();
+  // clang-format on
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 4, 4, 4, 4, 2, 2, 4, 4}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::DESCENDING, current_row, unbounded_following),
+    nullable_ints_column({6, 6, 6, 3, 3, 3, 4, 4, 4, 4, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, unbounded_following),
+    ints_column{6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_partitioned_window(*orderby, cudf::order::DESCENDING, current_row, current_row),
+    nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
+
+TEST_F(GroupedRollingRangeOrderByStringTest, Descending_NoParts_WithNulls)
+{
+  // Order-by column must be ordered completely in descending.
+  // clang-format off
+  auto const orderby =
+      strings_column{
+          {
+              "X", "X", "X", "D", "D", "D",
+              "C", "C", "C", "C",
+              "B", "B", "A", "A"
+          },
+          cudf::test::iterators::nulls_at({0, 1, 2})
+      }.release();
+  // clang-format on
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, current_row),
+    nullable_ints_column({3, 3, 3, 6, 6, 6, 10, 10, 10, 10, 12, 12, 14, 14}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::DESCENDING, current_row, unbounded_following),
+    nullable_ints_column({14, 14, 14, 11, 11, 11, 8, 8, 8, 8, 4, 4, 2, 2}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *get_count_over_unpartitioned_window(
+      *orderby, cudf::order::DESCENDING, unbounded_preceding, unbounded_following),
+    ints_column{14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*get_count_over_unpartitioned_window(
+                                   *orderby, cudf::order::DESCENDING, current_row, current_row),
+                                 nullable_ints_column({3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 2, 2, 2, 2}));
+}
diff --git a/cpp/tests/rolling/grouped_rolling_test.cpp b/cpp/tests/rolling/grouped_rolling_test.cpp
new file mode 100644
index 0000000..7dd72ac
--- /dev/null
+++ b/cpp/tests/rolling/grouped_rolling_test.cpp
@@ -0,0 +1,2472 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "rolling_test.hpp"
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <src/rolling/detail/rolling.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+
+const std::string cuda_func{
+  R"***(
+    template <typename OutType, typename InType>
+    __device__ void CUDA_GENERIC_AGGREGATOR(OutType *ret, InType *in_col, cudf::size_type start,
+                                            cudf::size_type count) {
+      OutType val = 0;
+      for (cudf::size_type i = 0; i < count; i++) {
+        val += in_col[start + i];
+      }
+      *ret = val;
+    }
+  )***"};
+
+const std::string ptx_func{
+  R"***(
+  //
+  // Generated by NVIDIA NVVM Compiler
+  //
+  // Compiler Build ID: CL-24817639
+  // Cuda compilation tools, release 10.0, V10.0.130
+  // Based on LLVM 3.4svn
+  //
+
+  .version 6.3
+  .target sm_70
+  .address_size 64
+
+  // .globl	_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE
+  .common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE;
+
+  .visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE(
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_0,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_1,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_2,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_3,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_4,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_5,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_6,
+  .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_7
+  )
+  {
+  .reg .pred 	%p<3>;
+  .reg .b32 	%r<6>;
+  .reg .b64 	%rd<18>;
+
+
+  ld.param.u64 	%rd6, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_0];
+  ld.param.u64 	%rd7, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_5];
+  ld.param.u64 	%rd8, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_6];
+  ld.param.u64 	%rd9, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_7];
+  mov.u64 	%rd15, 0;
+  mov.u64 	%rd16, %rd15;
+
+  BB0_1:
+  mov.u64 	%rd2, %rd16;
+  mov.u32 	%r5, 0;
+  setp.ge.s64	%p1, %rd15, %rd8;
+  mov.u64 	%rd17, %rd15;
+  @%p1 bra 	BB0_3;
+
+  mul.lo.s64 	%rd12, %rd15, %rd9;
+  add.s64 	%rd13, %rd12, %rd7;
+  ld.u32 	%r5, [%rd13];
+  add.s64 	%rd17, %rd15, 1;
+
+  BB0_3:
+  cvt.s64.s32	%rd14, %r5;
+  add.s64 	%rd16, %rd14, %rd2;
+  setp.lt.s64	%p2, %rd15, %rd8;
+  mov.u64 	%rd15, %rd17;
+  @%p2 bra 	BB0_1;
+
+  st.u64 	[%rd6], %rd2;
+  mov.u32 	%r4, 0;
+  st.param.b32	[func_retval0+0], %r4;
+  ret;
+  }
+  )***"};
+
+template <typename T>
+class GroupedRollingTest : public cudf::test::BaseFixture {
+ protected:
+  // input as column_wrapper
+  void run_test_col(cudf::table_view const& keys,
+                    cudf::column_view const& input,
+                    std::vector<cudf::size_type> const& expected_grouping,
+                    cudf::size_type const& preceding_window,
+                    cudf::size_type const& following_window,
+                    cudf::size_type min_periods,
+                    cudf::rolling_aggregation const& op)
+  {
+    std::unique_ptr<cudf::column> output;
+
+    // wrap windows
+    EXPECT_NO_THROW(output = cudf::grouped_rolling_window(
+                      keys, input, preceding_window, following_window, min_periods, op));
+
+    auto reference = create_reference_output(
+      op, input, expected_grouping, preceding_window, following_window, min_periods);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, *reference);
+  }
+
+  void run_test_col_agg(cudf::table_view const& keys,
+                        cudf::column_view const& input,
+                        std::vector<cudf::size_type> const& expected_grouping,
+                        cudf::size_type preceding_window,
+                        cudf::size_type following_window,
+                        cudf::size_type min_periods)
+  {
+    // Skip grouping-tests on bool8 keys. sort_helper does not support this.
+    if (keys.num_columns() > 0 && cudf::is_boolean(keys.column(0).type())) { return; }
+
+    // test all supported aggregators
+    run_test_col(keys,
+                 input,
+                 expected_grouping,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+    run_test_col(keys,
+                 input,
+                 expected_grouping,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+    run_test_col(
+      keys,
+      input,
+      expected_grouping,
+      preceding_window,
+      following_window,
+      min_periods,
+      *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+    run_test_col(keys,
+                 input,
+                 expected_grouping,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+
+    if (!cudf::is_timestamp(input.type())) {
+      run_test_col(keys,
+                   input,
+                   expected_grouping,
+                   preceding_window,
+                   following_window,
+                   min_periods,
+                   *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+      run_test_col(keys,
+                   input,
+                   expected_grouping,
+                   preceding_window,
+                   following_window,
+                   min_periods,
+                   *cudf::make_mean_aggregation<cudf::rolling_aggregation>());
+    }
+    run_test_col(keys,
+                 input,
+                 expected_grouping,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
+
+    // >>> test UDFs <<<
+    if (input.type() == cudf::data_type{cudf::type_id::INT32} && !input.has_nulls()) {
+      auto cuda_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+        cudf::udf_type::CUDA, cuda_func, cudf::data_type{cudf::type_id::INT64});
+      run_test_col(keys,
+                   input,
+                   expected_grouping,
+                   preceding_window,
+                   following_window,
+                   min_periods,
+                   *cuda_udf_agg);
+
+      auto ptx_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+        cudf::udf_type::PTX, ptx_func, cudf::data_type{cudf::type_id::INT64});
+      run_test_col(keys,
+                   input,
+                   expected_grouping,
+                   preceding_window,
+                   following_window,
+                   min_periods,
+                   *ptx_udf_agg);
+    }
+  }
+
+ private:
+  // use SFINAE to only instantiate for supported combinations
+
+  // specialization for COUNT_VALID, COUNT_ALL
+  template <bool include_nulls>
+  std::unique_ptr<cudf::column> create_count_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window,
+    cudf::size_type const& following_window,
+    cudf::size_type min_periods)
+  {
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<cudf::size_type> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+
+    std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
+    cudf::bitmask_type* valid_mask           = in_valid.data();
+
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto group_end_index   = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
+      auto group_start_index = group_end_index - 1;
+
+      cudf::size_type start       = std::min(num_rows, std::max(0, i - preceding_window + 1));
+      cudf::size_type end         = std::min(num_rows, std::max(0, i + following_window + 1));
+      cudf::size_type start_index = std::max(*group_start_index, std::min(start, end));
+      cudf::size_type end_index   = std::min(*group_end_index, std::max(start, end));
+
+      // aggregate
+      cudf::size_type count = 0;
+      for (cudf::size_type j = start_index; j < end_index; j++) {
+        if (include_nulls || !input.nullable() || cudf::bit_is_set(valid_mask, j)) count++;
+      }
+
+      ref_valid[i] = ((end_index - start_index) >= min_periods);
+      if (ref_valid[i]) ref_data[i] = count;
+    }
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  std::unique_ptr<cudf::column> create_row_number_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window,
+    cudf::size_type const& following_window,
+    cudf::size_type min_periods)
+  {
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<cudf::size_type> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+
+    std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
+
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto group_end_index   = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
+      auto group_start_index = group_end_index - 1;
+
+      cudf::size_type start       = std::min(num_rows, std::max(0, i - preceding_window + 1));
+      cudf::size_type end         = std::min(num_rows, std::max(0, i + following_window + 1));
+      cudf::size_type start_index = std::max(*group_start_index, std::min(start, end));
+      cudf::size_type end_index   = std::min(*group_end_index, std::max(start, end));
+
+      // aggregate
+      cudf::size_type count{end_index - start_index};
+      cudf::size_type row_number{i - start_index + 1};
+
+      ref_valid[i] = (count >= min_periods);
+      ref_data[i]  = row_number;
+    }
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  template <typename agg_op,
+            cudf::aggregation::Kind k,
+            typename OutputType,
+            bool is_mean,
+            std::enable_if_t<is_rolling_supported<T, k>()>* = nullptr>
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window,
+    cudf::size_type const& following_window,
+    cudf::size_type min_periods)
+  {
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<OutputType> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+    auto [in_col, in_valid]        = cudf::test::to_host<T>(input);
+    cudf::bitmask_type* valid_mask = in_valid.data();
+
+    agg_op op;
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      OutputType val = agg_op::template identity<OutputType>();
+
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto group_end_index   = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
+      auto group_start_index = group_end_index - 1;
+
+      cudf::size_type start = std::min(
+        num_rows, std::max(0, i - preceding_window + 1));  // Preceding window includes current row.
+      cudf::size_type end         = std::min(num_rows, std::max(0, i + following_window + 1));
+      cudf::size_type start_index = std::max(*group_start_index, std::min(start, end));
+      cudf::size_type end_index   = std::min(*group_end_index, std::max(start, end));
+
+      // aggregate
+      cudf::size_type count = 0;
+      for (cudf::size_type j = start_index; j < end_index; j++) {
+        if (!input.nullable() || cudf::bit_is_set(valid_mask, j)) {
+          val = op(static_cast<OutputType>(in_col[j]), val);
+          count++;
+        }
+      }
+
+      ref_valid[i] = (count >= min_periods);
+      if (ref_valid[i]) {
+        cudf::detail::rolling_store_output_functor<OutputType, is_mean>{}(ref_data[i], val, count);
+      }
+    }
+
+    cudf::test::fixed_width_column_wrapper<OutputType> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  template <typename agg_op,
+            cudf::aggregation::Kind k,
+            typename OutputType,
+            bool is_mean,
+            std::enable_if_t<!is_rolling_supported<T, k>()>* = nullptr>
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window_col,
+    cudf::size_type const& following_window_col,
+    cudf::size_type min_periods)
+  {
+    CUDF_FAIL("Unsupported combination of type and aggregation");
+  }
+
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::rolling_aggregation const& op,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window,
+    cudf::size_type const& following_window,
+    cudf::size_type min_periods)
+  {
+    // unroll aggregation types
+    switch (op.kind) {
+      case cudf::aggregation::SUM:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::SUM,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
+                                       false>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::MIN:
+        return create_reference_output<cudf::DeviceMin,
+                                       cudf::aggregation::MIN,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MIN>,
+                                       false>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::MAX:
+        return create_reference_output<cudf::DeviceMax,
+                                       cudf::aggregation::MAX,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MAX>,
+                                       false>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::COUNT_VALID:
+        return create_count_reference_output<false>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::COUNT_ALL:
+        return create_count_reference_output<true>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::ROW_NUMBER:
+        return create_row_number_reference_output(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::MEAN:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::MEAN,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MEAN>,
+                                       true>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      // >>> UDFs <<<
+      case cudf::aggregation::CUDA:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::SUM,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
+                                       false>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      case cudf::aggregation::PTX:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::SUM,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
+                                       false>(
+          input, group_offsets, preceding_window, following_window, min_periods);
+      default: return cudf::test::fixed_width_column_wrapper<T>({}).release();
+    }
+  }
+};
+
+// // ------------- expected failures --------------------
+
+class GroupedRollingErrorTest : public cudf::test::BaseFixture {};
+
+// negative sizes
+TEST_F(GroupedRollingErrorTest, NegativeMinPeriods)
+{
+  // Construct agg column.
+  const std::vector<cudf::size_type> col_data{0, 1, 2, 0, 4};
+  const std::vector<bool> col_valid{1, 1, 1, 0, 1};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> input{
+    col_data.begin(), col_data.end(), col_valid.begin()};
+
+  // Construct Grouping keys table-view.
+  auto const N_ELEMENTS{col_data.size()};
+  const std::vector<cudf::size_type> grouping_key_vec(N_ELEMENTS, 0);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> grouping_keys_col(
+    grouping_key_vec.begin(), grouping_key_vec.end(), col_valid.begin());
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{grouping_keys_col}};
+
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      grouping_keys, input, 2, 2, -2, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+}
+
+TEST_F(GroupedRollingErrorTest, EmptyInput)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> empty_col{};
+  std::unique_ptr<cudf::column> output;
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{}};
+  EXPECT_NO_THROW(
+    output = cudf::grouped_rolling_window(
+      grouping_keys, empty_col, 2, 0, 2, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()));
+  EXPECT_EQ(output->size(), 0);
+}
+
+// incorrect type/aggregation combo: sum of timestamps
+TEST_F(GroupedRollingErrorTest, SumTimestampNotSupported)
+{
+  constexpr cudf::size_type size{10};
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> input_D(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> input_s(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> input_ms(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep> input_us(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> input_ns(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+
+  // Construct table-view of grouping keys.
+  std::vector<cudf::size_type> grouping_keys_vec(size, 0);  // `size` elements, each == 0.
+  const cudf::table_view grouping_keys{
+    std::vector<cudf::column_view>{cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+      grouping_keys_vec.begin(), grouping_keys_vec.end())}};
+
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      grouping_keys, input_D, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      grouping_keys, input_s, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      grouping_keys, input_ms, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      grouping_keys, input_us, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      grouping_keys, input_ns, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+}
+
+TYPED_TEST_SUITE(GroupedRollingTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(GroupedRollingTest, SimplePartitionedStaticWindowsWithGroupKeys)
+{
+  auto const col_data = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90};
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
+  const std::vector<bool> col_mask(DATA_SIZE, true);
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  // 2 grouping keys, with effectively 3 groups of at most 4 rows each:
+  //   1. key_0 {0, 0, 0, ...0}
+  //   2. key_1 {0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
+  std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
+  std::vector<int64_t> key_1_vec;
+  int i{0};
+  std::generate_n(
+    std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 4; });  // Groups of 4.
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
+                                                                         key_0_vec.end());
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
+                                                                         key_1_vec.end());
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
+
+  cudf::size_type preceding_window = 2;
+  cudf::size_type following_window = 1;
+  std::vector<cudf::size_type> expected_group_offsets{0, 4, 8, DATA_SIZE};
+
+  this->run_test_col_agg(
+    grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
+}
+
+TYPED_TEST(GroupedRollingTest, SimplePartitionedStaticWindowWithNoGroupKeys)
+{
+  auto const col_data =
+    cudf::test::make_type_param_vector<TypeParam>({0, 10, 20, 30, 40, 50, 60, 70, 80, 90});
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
+  const std::vector<bool> col_mask(DATA_SIZE, true);
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{}};
+
+  cudf::size_type preceding_window = 2;
+  cudf::size_type following_window = 1;
+  std::vector<cudf::size_type> expected_group_offsets{0, DATA_SIZE};
+
+  this->run_test_col_agg(
+    grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
+}
+
+// all rows are invalid
+TYPED_TEST(GroupedRollingTest, AllInvalid)
+{
+  auto const col_data =
+    cudf::test::make_type_param_vector<TypeParam>({0, 10, 20, 30, 40, 50, 60, 70, 80, 90});
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
+  const std::vector<bool> col_mask(DATA_SIZE, false);
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  // 2 grouping keys, with effectively 3 groups of at most 4 rows each:
+  //   1. key_0 {0, 0, 0, ...0}
+  //   2. key_1 {0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
+  std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
+  std::vector<int64_t> key_1_vec;
+  int i{0};
+  std::generate_n(
+    std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 4; });  // Groups of 4.
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
+                                                                         key_0_vec.end());
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
+                                                                         key_1_vec.end());
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
+
+  cudf::size_type preceding_window = 2;
+  cudf::size_type following_window = 1;
+  std::vector<cudf::size_type> expected_group_offsets{0, 4, 8, DATA_SIZE};
+
+  this->run_test_col_agg(
+    grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
+}
+
+// window = following_window = 0
+TYPED_TEST(GroupedRollingTest, ZeroWindow)
+{
+  auto const col_data = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90};
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(col_data.size())};
+  const std::vector<bool> col_mask(DATA_SIZE, true);
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  // 2 grouping keys, with effectively 3 groups of at most 4 rows each:
+  //   1. key_0 {0, 0, 0, ...0}
+  //   2. key_1 {0, 0, 0, 0, 1, 1, 1, 1, 2, 2}
+  std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
+  std::vector<int64_t> key_1_vec;
+  int i{0};
+  std::generate_n(
+    std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 4; });  // Groups of 4.
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
+                                                                         key_0_vec.end());
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
+                                                                         key_1_vec.end());
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
+
+  cudf::size_type preceding_window = 1;
+  cudf::size_type following_window = 0;
+  std::vector<cudf::size_type> expected_group_offsets{0, 4, 8, DATA_SIZE};
+
+  this->run_test_col_agg(
+    grouping_keys, input, expected_group_offsets, preceding_window, following_window, 1);
+}
+
+using GroupedRollingTestInts = GroupedRollingTest<int32_t>;
+
+TEST_F(GroupedRollingTestInts, SumLargeWindow)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> input({1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  cudf::size_type preceding_window = 2147483640;
+  cudf::size_type following_window = 2147483642;
+
+  cudf::table_view groupby_keys;
+
+  auto result =
+    cudf::grouped_rolling_window(groupby_keys,
+                                 input,
+                                 preceding_window,
+                                 following_window,
+                                 1,
+                                 *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+
+  cudf::test::fixed_width_column_wrapper<int64_t, int32_t> expected(
+    {10, 10, 10, 10, 10, 10, 10, 10, 10, 10});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+}
+
+// ------------- non-fixed-width types --------------------
+
+using GroupedRollingTestStrings = GroupedRollingTest<cudf::string_view>;
+
+TEST_F(GroupedRollingTestStrings, StringsUnsupportedOperators)
+{
+  cudf::test::strings_column_wrapper input{{"This", "is", "not", "a", "string", "type"},
+                                           {1, 1, 1, 0, 1, 0}};
+
+  const cudf::size_type DATA_SIZE{static_cast<cudf::column_view>(input).size()};
+  const std::vector<cudf::size_type> key_col_vec(DATA_SIZE, 0);
+  const cudf::table_view key_cols{
+    std::vector<cudf::column_view>{cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+      key_col_vec.begin(), key_col_vec.end())}};
+
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      key_cols, input, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::grouped_rolling_window(
+      key_cols, input, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+}
+
+template <typename T>
+class GroupedTimeRangeRollingTest : public cudf::test::BaseFixture {
+ protected:
+  // input as column_wrapper
+  void run_test_col(cudf::table_view const& keys,
+                    cudf::column_view const& timestamp_column,
+                    cudf::order const& timestamp_order,
+                    cudf::column_view const& input,
+                    std::vector<cudf::size_type> const& expected_grouping,
+                    cudf::size_type const& preceding_window_in_days,
+                    cudf::size_type const& following_window_in_days,
+                    cudf::size_type min_periods,
+                    cudf::rolling_aggregation const& op)
+  {
+    std::unique_ptr<cudf::column> output;
+
+    // wrap windows
+    EXPECT_NO_THROW(output = cudf::grouped_time_range_rolling_window(keys,
+                                                                     timestamp_column,
+                                                                     timestamp_order,
+                                                                     input,
+                                                                     preceding_window_in_days,
+                                                                     following_window_in_days,
+                                                                     min_periods,
+                                                                     op));
+
+    auto reference = create_reference_output(op,
+                                             timestamp_column,
+                                             timestamp_order,
+                                             input,
+                                             expected_grouping,
+                                             preceding_window_in_days,
+                                             following_window_in_days,
+                                             min_periods);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, *reference);
+  }
+
+  void run_test_col_agg(cudf::table_view const& keys,
+                        cudf::column_view const& timestamp_column,
+                        cudf::order const& timestamp_order,
+                        cudf::column_view const& input,
+                        std::vector<cudf::size_type> const& expected_grouping,
+                        cudf::size_type preceding_window_in_days,
+                        cudf::size_type following_window_in_days,
+                        cudf::size_type min_periods)
+  {
+    // Skip grouping-tests on bool8 keys. sort_helper does not support this.
+    if (keys.num_columns() > 0 && cudf::is_boolean(keys.column(0).type())) { return; }
+
+    // test all supported aggregators
+    run_test_col(keys,
+                 timestamp_column,
+                 timestamp_order,
+                 input,
+                 expected_grouping,
+                 preceding_window_in_days,
+                 following_window_in_days,
+                 min_periods,
+                 *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+    run_test_col(keys,
+                 timestamp_column,
+                 timestamp_order,
+                 input,
+                 expected_grouping,
+                 preceding_window_in_days,
+                 following_window_in_days,
+                 min_periods,
+                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+    run_test_col(
+      keys,
+      timestamp_column,
+      timestamp_order,
+      input,
+      expected_grouping,
+      preceding_window_in_days,
+      following_window_in_days,
+      min_periods,
+      *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+    run_test_col(keys,
+                 timestamp_column,
+                 timestamp_order,
+                 input,
+                 expected_grouping,
+                 preceding_window_in_days,
+                 following_window_in_days,
+                 min_periods,
+                 *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+    if (!cudf::is_timestamp(input.type())) {
+      run_test_col(keys,
+                   timestamp_column,
+                   timestamp_order,
+                   input,
+                   expected_grouping,
+                   preceding_window_in_days,
+                   following_window_in_days,
+                   min_periods,
+                   *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+      run_test_col(keys,
+                   timestamp_column,
+                   timestamp_order,
+                   input,
+                   expected_grouping,
+                   preceding_window_in_days,
+                   following_window_in_days,
+                   min_periods,
+                   *cudf::make_mean_aggregation<cudf::rolling_aggregation>());
+    }
+    run_test_col(keys,
+                 timestamp_column,
+                 timestamp_order,
+                 input,
+                 expected_grouping,
+                 preceding_window_in_days,
+                 following_window_in_days,
+                 min_periods,
+                 *cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
+  }
+
+ private:
+  // use SFINAE to only instantiate for supported combinations
+
+  // specialization for COUNT_VALID, COUNT_ALL
+  template <bool include_nulls>
+  std::unique_ptr<cudf::column> create_count_reference_output(
+    cudf::column_view const& timestamp_column,
+    cudf::order const& timestamp_order,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window_in_days,
+    cudf::size_type const& following_window_in_days,
+    cudf::size_type min_periods)
+  {
+    assert(timestamp_column.type().id() == cudf::type_id::TIMESTAMP_DAYS);  // Testing with DAYS.
+
+    auto timestamp_vec = cudf::test::to_host<int32_t>(timestamp_column).first;
+
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<cudf::size_type> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+    std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
+    cudf::bitmask_type* valid_mask           = in_valid.data();
+
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto group_end_index   = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
+      auto group_start_index = group_end_index - 1;
+
+      cudf::size_type start_index = i;
+      cudf::size_type end_index   = i;
+
+      if (timestamp_order == cudf::order::ASCENDING) {
+        while ((start_index - 1) >= *group_start_index &&
+               timestamp_vec[start_index - 1] >= (timestamp_vec[i] - preceding_window_in_days)) {
+          --start_index;
+        }
+
+        while ((end_index + 1) < *group_end_index &&
+               timestamp_vec[end_index + 1] <= (timestamp_vec[i] + following_window_in_days)) {
+          ++end_index;
+        }
+        ++end_index;  // One past the last.
+      } else {
+        while ((start_index - 1) >= *group_start_index &&
+               timestamp_vec[start_index - 1] <= (timestamp_vec[i] + preceding_window_in_days)) {
+          --start_index;
+        }
+
+        while ((end_index + 1) < *group_end_index &&
+               timestamp_vec[end_index + 1] >= (timestamp_vec[i] - following_window_in_days)) {
+          ++end_index;
+        }
+        ++end_index;  // One past the last.
+      }
+
+      // aggregate
+      cudf::size_type count = 0;
+      for (cudf::size_type j = start_index; j < end_index; j++) {
+        if (include_nulls || !input.nullable() || cudf::bit_is_set(valid_mask, j)) count++;
+      }
+
+      ref_valid[i] = ((end_index - start_index) >= min_periods);
+      if (ref_valid[i]) ref_data[i] = count;
+    }
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  std::unique_ptr<cudf::column> create_row_number_reference_output(
+    cudf::column_view const& timestamp_column,
+    cudf::order const& timestamp_order,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window_in_days,
+    cudf::size_type const& following_window_in_days,
+    cudf::size_type min_periods)
+  {
+    assert(timestamp_column.type().id() == cudf::type_id::TIMESTAMP_DAYS);  // Testing with DAYS.
+
+    auto timestamp_vec = cudf::test::to_host<int32_t>(timestamp_column).first;
+
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<cudf::size_type> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+
+    std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
+
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto group_end_index   = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
+      auto group_start_index = group_end_index - 1;
+
+      cudf::size_type start_index = i;
+      cudf::size_type end_index   = i;
+
+      if (timestamp_order == cudf::order::ASCENDING) {
+        while ((start_index - 1) >= *group_start_index &&
+               timestamp_vec[start_index - 1] >= (timestamp_vec[i] - preceding_window_in_days)) {
+          --start_index;
+        }
+
+        while ((end_index + 1) < *group_end_index &&
+               timestamp_vec[end_index + 1] <= (timestamp_vec[i] + following_window_in_days)) {
+          ++end_index;
+        }
+        ++end_index;  // One past the last.
+      } else {
+        while ((start_index - 1) >= *group_start_index &&
+               timestamp_vec[start_index - 1] <= (timestamp_vec[i] + preceding_window_in_days)) {
+          --start_index;
+        }
+
+        while ((end_index + 1) < *group_end_index &&
+               timestamp_vec[end_index + 1] >= (timestamp_vec[i] - following_window_in_days)) {
+          ++end_index;
+        }
+        ++end_index;  // One past the last.
+      }
+
+      // aggregate
+      cudf::size_type count{end_index - start_index};
+      cudf::size_type row_number{i - start_index + 1};
+
+      ref_valid[i] = (count >= min_periods);
+      ref_data[i]  = row_number;
+    }
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  template <typename agg_op,
+            cudf::aggregation::Kind k,
+            typename OutputType,
+            bool is_mean,
+            std::enable_if_t<is_rolling_supported<T, k>()>* = nullptr>
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::column_view const& timestamp_column,
+    cudf::order const& timestamp_order,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window_in_days,
+    cudf::size_type const& following_window_in_days,
+    cudf::size_type min_periods)
+  {
+    assert(timestamp_column.type().id() == cudf::type_id::TIMESTAMP_DAYS);  // Testing with DAYS.
+
+    auto timestamp_vec = cudf::test::to_host<int32_t>(timestamp_column).first;
+
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<OutputType> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+    auto [in_col, in_valid]        = cudf::test::to_host<T>(input);
+    cudf::bitmask_type* valid_mask = in_valid.data();
+
+    agg_op op;
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      OutputType val = agg_op::template identity<OutputType>();
+
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto group_end_index   = std::upper_bound(group_offsets.begin(), group_offsets.end(), i);
+      auto group_start_index = group_end_index - 1;
+
+      cudf::size_type start_index = i;
+      cudf::size_type end_index   = i;
+
+      if (timestamp_order == cudf::order::ASCENDING) {
+        while ((start_index - 1) >= *group_start_index &&
+               timestamp_vec[start_index - 1] >= (timestamp_vec[i] - preceding_window_in_days)) {
+          --start_index;
+        }
+
+        while ((end_index + 1) < *group_end_index &&
+               timestamp_vec[end_index + 1] <= (timestamp_vec[i] + following_window_in_days)) {
+          ++end_index;
+        }
+        ++end_index;  // One past the last.
+      } else {
+        while ((start_index - 1) >= *group_start_index &&
+               timestamp_vec[start_index - 1] <= (timestamp_vec[i] + preceding_window_in_days)) {
+          --start_index;
+        }
+
+        while ((end_index + 1) < *group_end_index &&
+               timestamp_vec[end_index + 1] >= (timestamp_vec[i] - following_window_in_days)) {
+          ++end_index;
+        }
+        ++end_index;
+      }
+
+      // aggregate
+      cudf::size_type count = 0;
+      for (cudf::size_type j = start_index; j < end_index; j++) {
+        if (!input.nullable() || cudf::bit_is_set(valid_mask, j)) {
+          val = op(static_cast<OutputType>(in_col[j]), val);
+          count++;
+        }
+      }
+
+      ref_valid[i] = (count >= min_periods);
+      if (ref_valid[i]) {
+        cudf::detail::rolling_store_output_functor<OutputType, is_mean>{}(ref_data[i], val, count);
+      }
+    }
+
+    cudf::test::fixed_width_column_wrapper<OutputType> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  template <typename agg_op,
+            cudf::aggregation::Kind k,
+            typename OutputType,
+            bool is_mean,
+            std::enable_if_t<!is_rolling_supported<T, k>()>* = nullptr>
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::column_view const& timestamp_column,
+    cudf::order const& timestamp_order,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window_col,
+    cudf::size_type const& following_window_col,
+    cudf::size_type min_periods)
+  {
+    CUDF_FAIL("Unsupported combination of type and aggregation");
+  }
+
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::rolling_aggregation const& op,
+    cudf::column_view const& timestamp_column,
+    cudf::order const& timestamp_order,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& group_offsets,
+    cudf::size_type const& preceding_window,
+    cudf::size_type const& following_window,
+    cudf::size_type min_periods)
+  {
+    // unroll aggregation types
+    switch (op.kind) {
+      case cudf::aggregation::SUM:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::SUM,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
+                                       false>(timestamp_column,
+                                              timestamp_order,
+                                              input,
+                                              group_offsets,
+                                              preceding_window,
+                                              following_window,
+                                              min_periods);
+      case cudf::aggregation::MIN:
+        return create_reference_output<cudf::DeviceMin,
+                                       cudf::aggregation::MIN,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MIN>,
+                                       false>(timestamp_column,
+                                              timestamp_order,
+                                              input,
+                                              group_offsets,
+                                              preceding_window,
+                                              following_window,
+                                              min_periods);
+      case cudf::aggregation::MAX:
+        return create_reference_output<cudf::DeviceMax,
+                                       cudf::aggregation::MAX,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MAX>,
+                                       false>(timestamp_column,
+                                              timestamp_order,
+                                              input,
+                                              group_offsets,
+                                              preceding_window,
+                                              following_window,
+                                              min_periods);
+      case cudf::aggregation::COUNT_VALID:
+        return create_count_reference_output<false>(timestamp_column,
+                                                    timestamp_order,
+                                                    input,
+                                                    group_offsets,
+                                                    preceding_window,
+                                                    following_window,
+                                                    min_periods);
+      case cudf::aggregation::COUNT_ALL:
+        return create_count_reference_output<true>(timestamp_column,
+                                                   timestamp_order,
+                                                   input,
+                                                   group_offsets,
+                                                   preceding_window,
+                                                   following_window,
+                                                   min_periods);
+      case cudf::aggregation::ROW_NUMBER:
+        return create_row_number_reference_output(timestamp_column,
+                                                  timestamp_order,
+                                                  input,
+                                                  group_offsets,
+                                                  preceding_window,
+                                                  following_window,
+                                                  min_periods);
+      case cudf::aggregation::MEAN:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::MEAN,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MEAN>,
+                                       true>(timestamp_column,
+                                             timestamp_order,
+                                             input,
+                                             group_offsets,
+                                             preceding_window,
+                                             following_window,
+                                             min_periods);
+      default: return cudf::test::fixed_width_column_wrapper<T>({}).release();
+    }
+  }
+};
+
+TYPED_TEST_SUITE(GroupedTimeRangeRollingTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(GroupedTimeRangeRollingTest,
+           SimplePartitionedStaticWindowsWithGroupKeysAndTimeRangesAscending)
+{
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(18)};
+  const std::vector<int> col_data(DATA_SIZE, 1);
+  const std::vector<bool> col_mask(DATA_SIZE, true);
+  cudf::test::fixed_width_column_wrapper<TypeParam, int> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  // 2 grouping keys, with effectively 3 groups of at most 6 rows each:
+  //   1. key_0 {0, 0, 0, ...0}
+  //   2. key_1 {0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2}
+  std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
+  std::vector<int64_t> key_1_vec;
+  int i{0};
+  std::generate_n(
+    std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 6; });  // Groups of 6.
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
+                                                                         key_0_vec.end());
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
+                                                                         key_1_vec.end());
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
+
+  cudf::size_type preceding_window_in_days = 1;
+  cudf::size_type following_window_in_days = 1;
+  std::vector<cudf::size_type> expected_group_offsets{0, 6, 12, DATA_SIZE};
+
+  // Timestamp column.
+  std::vector<int32_t> timestamp_days_vec{0, 2, 3, 4, 5, 7, 0, 0, 1, 2, 3, 3, 0, 1, 2, 3, 3, 3};
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>
+    timestamp_days_ascending(timestamp_days_vec.begin(), timestamp_days_vec.end());
+
+  this->run_test_col_agg(grouping_keys,
+                         timestamp_days_ascending,
+                         cudf::order::ASCENDING,
+                         input,
+                         expected_group_offsets,
+                         preceding_window_in_days,
+                         following_window_in_days,
+                         1);
+}
+
+TYPED_TEST(GroupedTimeRangeRollingTest,
+           SimplePartitionedStaticWindowsWithGroupKeysAndTimeRangesDescending)
+{
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(18)};
+  const std::vector<int> col_data(DATA_SIZE, 1);
+  const std::vector<bool> col_mask(DATA_SIZE, true);
+  cudf::test::fixed_width_column_wrapper<TypeParam, int> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  // 2 grouping keys, with effectively 3 groups of at most 6 rows each:
+  //   1. key_0 {0, 0, 0, ...0}
+  //   2. key_1 {0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2}
+  std::vector<int64_t> key_0_vec(DATA_SIZE, 0);
+  std::vector<int64_t> key_1_vec;
+  int i{0};
+  std::generate_n(
+    std::back_inserter(key_1_vec), DATA_SIZE, [&i]() { return i++ / 6; });  // Groups of 6.
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_0(key_0_vec.begin(),
+                                                                         key_0_vec.end());
+  const cudf::test::fixed_width_column_wrapper<TypeParam, int64_t> key_1(key_1_vec.begin(),
+                                                                         key_1_vec.end());
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{key_0, key_1}};
+
+  cudf::size_type preceding_window_in_days = 1;
+  cudf::size_type following_window_in_days = 2;
+  std::vector<cudf::size_type> expected_group_offsets{0, 6, 12, DATA_SIZE};
+
+  // Timestamp column.
+  std::vector<int32_t> timestamp_days_vec{0, 2, 3, 4, 5, 7, 0, 0, 1, 2, 3, 3, 0, 1, 2, 3, 3, 3};
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>
+    timestamp_days_descending(timestamp_days_vec.rbegin(), timestamp_days_vec.rend());
+  this->run_test_col_agg(grouping_keys,
+                         timestamp_days_descending,
+                         cudf::order::DESCENDING,
+                         input,
+                         expected_group_offsets,
+                         preceding_window_in_days,
+                         following_window_in_days,
+                         1);
+}
+
+TYPED_TEST(GroupedTimeRangeRollingTest, SimplePartitionedStaticWindowsWithNoGroupingKeys)
+{
+  const cudf::size_type DATA_SIZE{static_cast<cudf::size_type>(6)};
+  const std::vector<int> col_data(DATA_SIZE, 1);
+  const std::vector<bool> col_mask(DATA_SIZE, true);
+  cudf::test::fixed_width_column_wrapper<TypeParam, int> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  const cudf::table_view grouping_keys{std::vector<cudf::column_view>{}};
+
+  cudf::size_type preceding_window_in_days = 1;
+  cudf::size_type following_window_in_days = 1;
+  std::vector<cudf::size_type> expected_group_offsets{0, DATA_SIZE};
+
+  // Timestamp column.
+  std::vector<int32_t> timestamp_days_vec{0, 2, 3, 4, 5, 7};
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>
+    timestamp_days_ascending(timestamp_days_vec.begin(), timestamp_days_vec.end());
+
+  this->run_test_col_agg(grouping_keys,
+                         timestamp_days_ascending,
+                         cudf::order::ASCENDING,
+                         input,
+                         expected_group_offsets,
+                         preceding_window_in_days,
+                         following_window_in_days,
+                         1);
+}
+
+template <typename T>
+struct TypedNullTimestampTestForRangeQueries : public cudf::test::BaseFixture {};
+
+struct NullTimestampTestForRangeQueries : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedNullTimestampTestForRangeQueries, cudf::test::IntegralTypes);
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountSingleGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 1, 2, 2, 3, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountSingleGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 3, 2, 1, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountMultiGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 2, 1, 2, 3, 4, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 2, 2, 2, 2, 2, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountMultiGroupTimestampASCNullsLast)
+{
+  using T = int32_t;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 3, 1, 2, 3, 4, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 2, 2, 2, 3, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountSingleGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 1, 2, 2, 3, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountSingleGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 3, 2, 1, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountMultiGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 2, 2, 2, 2, 2, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountMultiGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 2, 2, 2, 2, 3, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountSingleGroupAllNullTimestamps)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 9, 9, 9, 9, 9, 9, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedNullTimestampTestForRangeQueries, CountMultiGroupAllNullTimestamps)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const preceding     = 1L;
+  auto const following     = 1L;
+  auto const min_periods   = 1L;
+  auto const output        = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    preceding,
+    following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 3, 2, 4, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+template <typename T>
+struct TypedUnboundedWindowTest : public cudf::test::BaseFixture {};
+
+struct UnboundedWindowTest : public cudf::test::BaseFixture {};
+
+using FixedWidthTypes = cudf::test::Concat<cudf::test::IntegralTypes,
+                                           cudf::test::FloatingPointTypes,
+                                           cudf::test::DurationTypes,
+                                           cudf::test::TimestampTypes>;
+
+TYPED_TEST_SUITE(TypedUnboundedWindowTest, FixedWidthTypes);
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingWindowSingleGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 5, 6, 7, 8, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingWindowSingleGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 9, 9, 5, 5, 4, 4, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingWindowSingleGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{9, 9, 9, 9, 9, 9, 9, 9, 9, 9});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingWindowSingleGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 4, 5, 5, 5, 9, 9, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingWindowSingleGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 8, 7, 6, 5, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingWindowSingleGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{9, 9, 9, 9, 9, 9, 9, 9, 9, 9});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingWindowSingleGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 5, 6, 7, 8, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingWindowSingleGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 9, 9, 5, 5, 4, 4, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingWindowSingleGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{9, 9, 9, 9, 9, 9, 9, 9, 9, 9});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingWindowSingleGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 4, 5, 5, 5, 9, 9, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingWindowSingleGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 8, 7, 6, 5, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingWindowSingleGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {9, 8, 7, 6, 5, 4, 3, 2, 1, 0}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{9, 9, 9, 9, 9, 9, 9, 9, 9, 9});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingCountMultiGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 2, 1, 2, 3, 4, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 5, 5, 2, 2, 4, 5, 5}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingCountMultiGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 2, 1, 2, 3, 4, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {5, 5, 5, 2, 2, 5, 5, 3, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingCountMultiGroupTimestampASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 2, 1, 2, 3, 4, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{5, 5, 5, 5, 5, 5, 5, 5, 5, 5});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingCountMultiGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 3, 1, 2, 3, 4, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 5, 5, 2, 3, 3, 5, 5}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingCountMultiGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 3, 1, 2, 3, 4, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {5, 5, 5, 2, 2, 5, 5, 4, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingCountMultiGroupTimestampASCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {1, 2, 2, 1, 3, 1, 2, 3, 4, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{5, 5, 5, 5, 5, 5, 5, 5, 5, 5});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingCountMultiGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 5, 5, 2, 2, 4, 5, 5}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingCountMultiGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {5, 5, 5, 2, 2, 5, 5, 3, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingCountMultiGroupTimestampDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{5, 5, 5, 5, 5, 5, 5, 5, 5, 5});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingCountMultiGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_day_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    one_day_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 5, 5, 2, 3, 3, 5, 5}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingCountMultiGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_day_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    one_day_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {5, 5, 4, 2, 2, 5, 5, 4, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest,
+           UnboundedPrecedingAndFollowingCountMultiGroupTimestampDESCNullsLast)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const time_col =
+    cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+      {4, 3, 2, 1, 0, 9, 8, 7, 6, 5}, {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output              = cudf::grouped_time_range_rolling_window(
+    grouping_keys,
+    time_col,
+    cudf::order::DESCENDING,
+    agg_col,
+    unbounded_preceding,
+    unbounded_following,
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{5, 5, 5, 5, 5, 5, 5, 5, 5, 5});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingWindowSingleGroup)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_row_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 unbounded_preceding,
+                                 one_row_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 4, 5, 5, 6, 7, 8, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingWindowSingleGroup)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_row_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 one_row_preceding,
+                                 unbounded_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 8, 7, 6, 5, 4, 4, 3, 2, 1}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingAndFollowingWindowSingleGroup)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 unbounded_preceding,
+                                 unbounded_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{9, 9, 9, 9, 9, 9, 9, 9, 9, 9});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingWindowMultiGroup)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 1, 1, 0, 1, 0, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const one_row_following   = cudf::window_bounds::get(1L);
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 unbounded_preceding,
+                                 one_row_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {1, 2, 2, 3, 3, 1, 2, 3, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedFollowingWindowMultiGroup)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 1, 1, 0, 1, 0, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const one_row_preceding   = cudf::window_bounds::get(1L);
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 one_row_preceding,
+                                 unbounded_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 2, 1, 1, 4, 4, 3, 2, 1}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingAndFollowingWindowMultiGroup)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 1, 1, 0, 1, 0, 1, 1, 1, 1}};
+
+  auto const grouping_keys       = cudf::table_view{std::vector<cudf::column_view>{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 unbounded_preceding,
+                                 unbounded_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    output->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{3, 3, 3, 3, 3, 4, 4, 4, 4, 4});
+}
+
+TYPED_TEST(TypedUnboundedWindowTest, UnboundedPrecedingAndFollowingStructGroup)
+{
+  // Test that grouping on STRUCT keys produces is possible.
+
+  using T        = TypeParam;
+  using numerics = cudf::test::fixed_width_column_wrapper<T>;
+  using result_t = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto const grp_col = [] {
+    auto grp_col_inner = numerics{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+    return cudf::test::structs_column_wrapper{{grp_col_inner}};
+  }();
+
+  auto const agg_col =
+    numerics{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, cudf::test::iterators::nulls_at({0, 3, 5})};
+
+  auto const grouping_keys       = cudf::table_view{{grp_col}};
+  auto const unbounded_preceding = cudf::window_bounds::unbounded();
+  auto const unbounded_following = cudf::window_bounds::unbounded();
+  auto const min_periods         = 1L;
+  auto const output =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 agg_col,
+                                 unbounded_preceding,
+                                 unbounded_following,
+                                 min_periods,
+                                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(), result_t{3, 3, 3, 3, 3, 4, 4, 4, 4, 4});
+}
diff --git a/cpp/tests/rolling/lead_lag_test.cpp b/cpp/tests/rolling/lead_lag_test.cpp
new file mode 100644
index 0000000..de057e9
--- /dev/null
+++ b/cpp/tests/rolling/lead_lag_test.cpp
@@ -0,0 +1,1131 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <initializer_list>
+#include <memory>
+#include <string>
+
+struct LeadLagWindowTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedLeadLagWindowTest : public cudf::test::BaseFixture {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypes,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::DurationTypes,
+                                        cudf::test::TimestampTypes>;
+
+TYPED_TEST_SUITE(TypedLeadLagWindowTest, TypesForTest);
+
+TYPED_TEST(TypedLeadLagWindowTest, LeadLagBasics)
+{
+  using T = int32_t;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50}.release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{3, 4, 5, -1, -1, -1, 30, 40, 50, -1, -1, -1},
+                                              {1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0}}
+      .release()
+      ->view());
+
+  auto lag_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{-1, -1, 0, 1, 2, 3, -1, -1, 0, 10, 20, 30},
+                                              {0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, LeadLagWithNulls)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+                                              {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1}}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{3, 4, 5, -1, -1, -1, 30, 40, 50, -1, -1, -1},
+                                              {1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0}}
+      .release()
+      ->view());
+
+  auto const lag_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{-1, -1, 0, 1, -1, 3, -1, -1, 0, 10, -1, 30},
+                                              {0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithDefaults)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+                                              {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1}}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto const default_value =
+    cudf::make_fixed_width_scalar(cudf::test::detail::fixed_width_type_converter<int32_t, T>{}(99));
+  auto const default_outputs = cudf::make_column_from_scalar(*default_value, input_col->size());
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{3, 4, 5, 99, 99, 99, 30, 40, 50, 99, 99, 99},
+                                              {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}
+      .release()
+      ->view());
+
+  auto const lag_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{99, 99, 0, 1, -1, 3, 99, 99, 0, 10, -1, 30},
+                                              {1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithDefaultsContainingNulls)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+                                              {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1}}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const default_outputs =
+    cudf::test::fixed_width_column_wrapper<T>{{-1, 99, -1, 99, 99, -1, 99, 99, -1, 99, 99, -1},
+                                              {0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0}}
+      .release();
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{3, 4, 5, 99, 99, -1, 30, 40, 50, 99, 99, -1},
+                                              {1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0}}
+      .release()
+      ->view());
+
+  auto const lag_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{-1, 99, 0, 1, -1, 3, 99, 99, 0, 10, -1, 30},
+                                              {0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithOutOfRangeOffsets)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+                                              {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1}}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const default_value =
+    cudf::make_fixed_width_scalar(cudf::test::detail::fixed_width_type_converter<int32_t, T>{}(99));
+  auto const default_outputs = cudf::make_column_from_scalar(*default_value, input_col->size());
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_30_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(30));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_30_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1},
+                                              {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}
+      .release()
+      ->view());
+
+  auto const lag_20_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(20));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_20_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99},
+                                              {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithZeroOffsets)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+                                              {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1}}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_0_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*lead_0_output_col, *input_col);
+
+  auto const lag_0_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*lag_0_output_col, *input_col);
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithNegativeOffsets)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+                                              {1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1}}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const default_value =
+    cudf::make_fixed_width_scalar(cudf::test::detail::fixed_width_type_converter<int32_t, T>{}(99));
+  auto const default_outputs = cudf::make_column_from_scalar(*default_value, input_col->size());
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lag_minus_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(-3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_minus_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{3, 4, 5, 99, 99, 99, 30, 40, 50, 99, 99, 99},
+                                              {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}
+      .release()
+      ->view());
+
+  auto const lead_minus_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(-2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_minus_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{99, 99, 0, 1, -1, 3, 99, 99, 0, 10, -1, 30},
+                                              {1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithNoGrouping)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5}, {1, 1, 0, 1, 1, 1}}.release();
+
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{}};
+
+  auto const default_value =
+    cudf::make_fixed_width_scalar(cudf::test::detail::fixed_width_type_converter<int32_t, T>{}(99));
+  auto const default_outputs = cudf::make_column_from_scalar(*default_value, input_col->size());
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{3, 4, 5, 99, 99, 99}, {1, 1, 1, 1, 1, 1}}
+      .release()
+      ->view());
+
+  auto const lag_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{99, 99, 0, 1, -1, 3}, {1, 1, 1, 1, 0, 1}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, TestLeadLagWithAllNullInput)
+{
+  using T = TypeParam;
+
+  auto const input_col = cudf::test::fixed_width_column_wrapper<T>{
+    {0, 1, 2, 3, 4, 5, 0, 10, 20, 30, 40, 50},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return false;
+    })}.release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const default_value =
+    cudf::make_fixed_width_scalar(cudf::test::detail::fixed_width_type_converter<int32_t, T>{}(99));
+  auto const default_outputs = cudf::make_column_from_scalar(*default_value, input_col->size());
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lead_3_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{-1, -1, -1, 99, 99, 99, -1, -1, -1, 99, 99, 99},
+                                              {0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1}}
+      .release()
+      ->view());
+
+  auto const lag_2_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 *default_outputs,
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    *lag_2_output_col,
+    cudf::test::fixed_width_column_wrapper<T>{{99, 99, -1, -1, -1, -1, 99, 99, -1, -1, -1, -1},
+                                              {1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0}}
+      .release()
+      ->view());
+}
+
+TYPED_TEST(TypedLeadLagWindowTest, DefaultValuesWithoutLeadLag)
+{
+  // Test that passing default values for window-functions
+  // other than lead/lag lead to cudf::logic_error.
+
+  using T = TypeParam;
+
+  auto const input_col = cudf::test::fixed_width_column_wrapper<T>{
+    {0, 1, 2, 3, 4, 5}, cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return true;
+    })}.release();
+
+  auto const grouping_key  = cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const default_value =
+    cudf::make_fixed_width_scalar(cudf::test::detail::fixed_width_type_converter<int32_t, T>{}(99));
+  auto const default_outputs = cudf::make_column_from_scalar(*default_value, input_col->size());
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto const assert_aggregation_fails = [&](auto&& aggr) {
+    EXPECT_THROW(
+      cudf::grouped_rolling_window(grouping_keys,
+                                   input_col->view(),
+                                   default_outputs->view(),
+                                   preceding,
+                                   following,
+                                   min_periods,
+                                   *cudf::make_count_aggregation<cudf::rolling_aggregation>()),
+      cudf::logic_error);
+  };
+
+  auto aggs = {cudf::make_count_aggregation<cudf::rolling_aggregation>(),
+               cudf::make_min_aggregation<cudf::rolling_aggregation>()};
+  std::for_each(
+    aggs.begin(), aggs.end(), [&](auto& agg) { assert_aggregation_fails(std::move(agg)); });
+}
+
+template <typename T>
+struct TypedNestedLeadLagWindowTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedNestedLeadLagWindowTest, TypesForTest);
+
+TYPED_TEST(TypedNestedLeadLagWindowTest, NumericListsWithNullsAllOver)
+{
+  using T   = TypeParam;
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto null_at_2       = cudf::test::iterators::null_at(2);
+  auto const input_col = lcw{{{0, 0},
+                              {1, 1},
+                              {2, 2},
+                              {3, 3, 3},
+                              {{4, 4, 4, 4}, null_at_2},
+                              {5, 5, 5, 5, 5},
+                              {0, 0},
+                              {10, 10},
+                              {20, 20},
+                              {30, 30, 30},
+                              {40, 40, 40, 40},
+                              {{50, 50, 50, 50, 50}, null_at_2}},
+                             null_at_2}
+                           .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lead_3_output_col->view(),
+                                      lcw{{{3, 3, 3},
+                                           {{4, 4, 4, 4}, null_at_2},
+                                           {5, 5, 5, 5, 5},
+                                           {},
+                                           {},
+                                           {},
+                                           {30, 30, 30},
+                                           {40, 40, 40, 40},
+                                           {{50, 50, 50, 50, 50}, null_at_2},
+                                           {},
+                                           {},
+                                           {}},
+                                          cudf::test::iterators::nulls_at({3, 4, 5, 9, 10, 11})}
+                                        .release()
+                                        ->view());
+
+  auto lag_1_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lag_1_output_col->view(),
+                                      lcw{{{},
+                                           {0, 0},
+                                           {1, 1},
+                                           {2, 2},
+                                           {3, 3, 3},
+                                           {{4, 4, 4, 4}, null_at_2},
+                                           {},
+                                           {0, 0},
+                                           {10, 10},
+                                           {20, 20},
+                                           {30, 30, 30},
+                                           {40, 40, 40, 40}},
+                                          cudf::test::iterators::nulls_at({0, 3, 6})}
+                                        .release()
+                                        ->view());
+}
+
+TYPED_TEST(TypedNestedLeadLagWindowTest, NumericListsWithDefaults)
+{
+  using T   = TypeParam;
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto null_at_2       = cudf::test::iterators::null_at(2);
+  auto const input_col = lcw{{{0, 0},
+                              {1, 1},
+                              {2, 2},
+                              {3, 3, 3},
+                              {{4, 4, 4, 4}, null_at_2},
+                              {5, 5, 5, 5, 5},
+                              {0, 0},
+                              {10, 10},
+                              {20, 20},
+                              {30, 30, 30},
+                              {40, 40, 40, 40},
+                              {{50, 50, 50, 50, 50}, null_at_2}},
+                             null_at_2}
+                           .release();
+
+  auto const defaults_col =
+    lcw{
+      {
+        {},
+        {91, 91},
+        {92, 92},
+        {},  // null!
+        {94, 94, 94},
+        {95, 95},
+        {},
+        {91, 91},
+        {92, 92},
+        {},  // null!
+        {94, 94, 94},
+        {95, 95},
+      },
+    }
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_3_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lead_3_output_col->view(),
+                                      lcw{{{3, 3, 3},
+                                           {{4, 4, 4, 4}, null_at_2},
+                                           {5, 5, 5, 5, 5},
+                                           {},
+                                           {},
+                                           {},
+                                           {30, 30, 30},
+                                           {40, 40, 40, 40},
+                                           {{50, 50, 50, 50, 50}, null_at_2},
+                                           {},
+                                           {},
+                                           {}},
+                                          cudf::test::iterators::nulls_at({3, 4, 5, 9, 10, 11})}
+                                        .release()
+                                        ->view());
+
+  auto lag_1_output_col =
+    cudf::grouped_rolling_window(grouping_keys,
+                                 input_col->view(),
+                                 preceding,
+                                 following,
+                                 min_periods,
+                                 *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lag_1_output_col->view(),
+                                      lcw{{{},
+                                           {0, 0},
+                                           {1, 1},
+                                           {2, 2},
+                                           {3, 3, 3},
+                                           {{4, 4, 4, 4}, null_at_2},
+                                           {},
+                                           {0, 0},
+                                           {10, 10},
+                                           {20, 20},
+                                           {30, 30, 30},
+                                           {40, 40, 40, 40}},
+                                          cudf::test::iterators::nulls_at({0, 3, 6})}
+                                        .release()
+                                        ->view());
+}
+
+TYPED_TEST(TypedNestedLeadLagWindowTest, Structs)
+{
+  using T   = TypeParam;
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto null_at_2 = cudf::test::iterators::null_at(2);
+  auto lists_col = lcw{{{0, 0},
+                        {1, 1},
+                        {2, 2},
+                        {3, 3, 3},
+                        {{4, 4, 4, 4}, null_at_2},
+                        {5, 5, 5, 5, 5},
+                        {0, 0},
+                        {10, 10},
+                        {20, 20},
+                        {30, 30, 30},
+                        {40, 40, 40, 40},
+                        {{50, 50, 50, 50, 50}, null_at_2}},
+                       null_at_2};
+
+  auto strings_col = cudf::test::strings_column_wrapper{{"00",
+                                                         "11",
+                                                         "22",
+                                                         "333",
+                                                         "4444",
+                                                         "55555",
+                                                         "00",
+                                                         "1010",
+                                                         "2020",
+                                                         "303030",
+                                                         "40404040",
+                                                         "5050505050"},
+                                                        cudf::test::iterators::null_at(9)};
+
+  auto structs_col = cudf::test::structs_column_wrapper{lists_col, strings_col}.release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  // Test LEAD().
+  {
+    auto lead_3_output_col =
+      cudf::grouped_rolling_window(grouping_keys,
+                                   structs_col->view(),
+                                   preceding,
+                                   following,
+                                   min_periods,
+                                   *cudf::make_lead_aggregation<cudf::rolling_aggregation>(3));
+    auto expected_lists_col   = lcw{{{3, 3, 3},
+                                     {{4, 4, 4, 4}, null_at_2},
+                                     {5, 5, 5, 5, 5},
+                                     {},
+                                     {},
+                                     {},
+                                     {30, 30, 30},
+                                     {40, 40, 40, 40},
+                                     {{50, 50, 50, 50, 50}, null_at_2},
+                                     {},
+                                     {},
+                                     {}},
+                                  cudf::test::iterators::nulls_at({3, 4, 5, 9, 10, 11})};
+    auto expected_strings_col = cudf::test::strings_column_wrapper{
+      {"333", "4444", "55555", "", "", "", "", "40404040", "5050505050", "", "", ""},
+      cudf::test::iterators::nulls_at({3, 4, 5, 6, 9, 10, 11})};
+
+    auto expected_structs_col =
+      cudf::test::structs_column_wrapper{{expected_lists_col, expected_strings_col},
+                                         cudf::test::iterators::nulls_at({3, 4, 5, 9, 10, 11})}
+        .release();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lead_3_output_col->view(), expected_structs_col->view());
+  }
+
+  // Test LAG()
+  {
+    auto lag_1_output_col =
+      cudf::grouped_rolling_window(grouping_keys,
+                                   structs_col->view(),
+                                   preceding,
+                                   following,
+                                   min_periods,
+                                   *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+    auto expected_lists_col = lcw{{{},  // null.
+                                   {0, 0},
+                                   {1, 1},
+                                   {},  // null.
+                                   {3, 3, 3},
+                                   {{4, 4, 4, 4}, null_at_2},
+                                   {},  // null.
+                                   {0, 0},
+                                   {10, 10},
+                                   {20, 20},
+                                   {30, 30, 30},
+                                   {40, 40, 40, 40}},
+                                  cudf::test::iterators::nulls_at({0, 3, 6})};
+    auto expected_strings_col =
+      cudf::test::strings_column_wrapper{{"",  // null.
+                                          "00",
+                                          "11",
+                                          "22",
+                                          "333",
+                                          "4444",
+                                          "",  // null.
+                                          "00",
+                                          "1010",
+                                          "2020",
+                                          "",  // null.
+                                          "40404040"},
+                                         cudf::test::iterators::nulls_at({0, 6, 10})};
+
+    auto expected_structs_col =
+      cudf::test::structs_column_wrapper{{expected_lists_col, expected_strings_col},
+                                         cudf::test::iterators::nulls_at({0, 6})}
+        .release();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lag_1_output_col->view(), expected_structs_col->view());
+  }
+}
+
+struct LeadLagNonFixedWidthTest : cudf::test::BaseFixture {};
+
+TEST_F(LeadLagNonFixedWidthTest, StringsNoDefaults)
+{
+  auto input_col =
+    cudf::test::strings_column_wrapper{{"",
+                                        "A_1",
+                                        "A_22",
+                                        "A_333",
+                                        "A_4444",
+                                        "A_55555",
+                                        "B_0",
+                                        "",
+                                        "B_22",
+                                        "B_333",
+                                        "B_4444",
+                                        "B_55555"},
+                                       cudf::test::iterators::nulls_at(std::vector{0, 7})}
+      .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_2 = grouped_rolling_window(grouping_keys,
+                                       input_col->view(),
+                                       preceding,
+                                       following,
+                                       min_periods,
+                                       *cudf::make_lead_aggregation<cudf::rolling_aggregation>(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    lead_2->view(),
+    cudf::test::strings_column_wrapper{
+      {"A_22", "A_333", "A_4444", "A_55555", "", "", "B_22", "B_333", "B_4444", "B_55555", "", ""},
+      cudf::test::iterators::nulls_at(std::vector{4, 5, 10, 11})});
+
+  auto lag_1 = grouped_rolling_window(grouping_keys,
+                                      input_col->view(),
+                                      preceding,
+                                      following,
+                                      min_periods,
+                                      *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    lag_1->view(),
+    cudf::test::strings_column_wrapper{
+      {"", "", "A_1", "A_22", "A_333", "A_4444", "", "B_0", "", "B_22", "B_333", "B_4444"},
+      cudf::test::iterators::nulls_at(std::vector{0, 1, 6, 8})});
+}
+
+TEST_F(LeadLagNonFixedWidthTest, StringsWithDefaults)
+{
+  auto input_col =
+    cudf::test::strings_column_wrapper{{"",
+                                        "A_1",
+                                        "A_22",
+                                        "A_333",
+                                        "A_4444",
+                                        "A_55555",
+                                        "B_0",
+                                        "",
+                                        "B_22",
+                                        "B_333",
+                                        "B_4444",
+                                        "B_55555"},
+                                       cudf::test::iterators::nulls_at(std::vector{0, 7})}
+      .release();
+
+  auto defaults_col = cudf::test::strings_column_wrapper{"9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999"}
+                        .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_2 = grouped_rolling_window(grouping_keys,
+                                       input_col->view(),
+                                       defaults_col->view(),
+                                       preceding,
+                                       following,
+                                       min_periods,
+                                       *cudf::make_lead_aggregation<cudf::rolling_aggregation>(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lead_2->view(),
+                                      cudf::test::strings_column_wrapper{"A_22",
+                                                                         "A_333",
+                                                                         "A_4444",
+                                                                         "A_55555",
+                                                                         "9999",
+                                                                         "9999",
+                                                                         "B_22",
+                                                                         "B_333",
+                                                                         "B_4444",
+                                                                         "B_55555",
+                                                                         "9999",
+                                                                         "9999"});
+
+  auto lag_1 = grouped_rolling_window(grouping_keys,
+                                      input_col->view(),
+                                      defaults_col->view(),
+                                      preceding,
+                                      following,
+                                      min_periods,
+                                      *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    lag_1->view(),
+    cudf::test::strings_column_wrapper{
+      {"9999", "", "A_1", "A_22", "A_333", "A_4444", "9999", "B_0", "", "B_22", "B_333", "B_4444"},
+      cudf::test::iterators::nulls_at(std::vector{1, 8})});
+}
+
+TEST_F(LeadLagNonFixedWidthTest, StringsWithDefaultsNoGroups)
+{
+  auto input_col =
+    cudf::test::strings_column_wrapper{{"",
+                                        "A_1",
+                                        "A_22",
+                                        "A_333",
+                                        "A_4444",
+                                        "A_55555",
+                                        "B_0",
+                                        "",
+                                        "B_22",
+                                        "B_333",
+                                        "B_4444",
+                                        "B_55555"},
+                                       cudf::test::iterators::nulls_at(std::vector{0, 7})}
+      .release();
+
+  auto defaults_col = cudf::test::strings_column_wrapper{"9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999",
+                                                         "9999"}
+                        .release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  auto lead_2 = grouped_rolling_window(grouping_keys,
+                                       input_col->view(),
+                                       defaults_col->view(),
+                                       preceding,
+                                       following,
+                                       min_periods,
+                                       *cudf::make_lead_aggregation<cudf::rolling_aggregation>(2));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    lead_2->view(),
+    cudf::test::strings_column_wrapper{{"A_22",
+                                        "A_333",
+                                        "A_4444",
+                                        "A_55555",
+                                        "B_0",
+                                        "",
+                                        "B_22",
+                                        "B_333",
+                                        "B_4444",
+                                        "B_55555",
+                                        "9999",
+                                        "9999"},
+                                       cudf::test::iterators::null_at(5)});
+
+  auto lag_1 = grouped_rolling_window(grouping_keys,
+                                      input_col->view(),
+                                      defaults_col->view(),
+                                      preceding,
+                                      following,
+                                      min_periods,
+                                      *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    lag_1->view(),
+    cudf::test::strings_column_wrapper{{"9999",
+                                        "",
+                                        "A_1",
+                                        "A_22",
+                                        "A_333",
+                                        "A_4444",
+                                        "A_55555",
+                                        "B_0",
+                                        "",
+                                        "B_22",
+                                        "B_333",
+                                        "B_4444"},
+                                       cudf::test::iterators::nulls_at(std::vector{1, 8})});
+}
+
+TEST_F(LeadLagNonFixedWidthTest, Dictionary)
+{
+  using dictionary = cudf::test::dictionary_column_wrapper<std::string>;
+
+  auto input_strings = std::initializer_list<std::string>{"",
+                                                          "A_1",
+                                                          "A_22",
+                                                          "A_333",
+                                                          "A_4444",
+                                                          "A_55555",
+                                                          "B_0",
+                                                          "",
+                                                          "B_22",
+                                                          "B_333",
+                                                          "B_4444",
+                                                          "B_55555"};
+  auto input_col     = dictionary{input_strings}.release();
+
+  auto const grouping_key =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_key}};
+
+  auto const preceding   = 4;
+  auto const following   = 3;
+  auto const min_periods = 1;
+
+  {
+    auto lead_2 =
+      grouped_rolling_window(grouping_keys,
+                             input_col->view(),
+                             preceding,
+                             following,
+                             min_periods,
+                             *cudf::make_lead_aggregation<cudf::rolling_aggregation>(2));
+
+    auto expected_keys = cudf::test::strings_column_wrapper{input_strings}.release();
+    auto expected_values =
+      cudf::test::fixed_width_column_wrapper<uint32_t>{
+        {2, 3, 4, 5, 0, 0, 7, 8, 9, 10, 0, 0},
+        cudf::test::iterators::nulls_at(std::vector{4, 5, 10, 11})}
+        .release();
+    auto expected_output =
+      make_dictionary_column(expected_keys->view(), expected_values->view()).release();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lead_2->view(), expected_output->view());
+  }
+
+  {
+    auto lag_1 = grouped_rolling_window(grouping_keys,
+                                        input_col->view(),
+                                        preceding,
+                                        following,
+                                        min_periods,
+                                        *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+
+    auto expected_keys = cudf::test::strings_column_wrapper{input_strings}.release();
+    auto expected_values =
+      cudf::test::fixed_width_column_wrapper<uint32_t>{
+        {0, 0, 1, 2, 3, 4, 0, 6, 0, 7, 8, 9}, cudf::test::iterators::nulls_at(std::vector{0, 6})}
+        .release();
+    auto expected_output =
+      make_dictionary_column(expected_keys->view(), expected_values->view()).release();
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(lag_1->view(), expected_output->view());
+  }
+}
diff --git a/cpp/tests/rolling/nth_element_test.cpp b/cpp/tests/rolling/nth_element_test.cpp
new file mode 100644
index 0000000..4edbcf0
--- /dev/null
+++ b/cpp/tests/rolling/nth_element_test.cpp
@@ -0,0 +1,632 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/rolling.hpp>
+
+#include <gtest/gtest-typed-test.h>
+
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <memory>
+#include <optional>
+
+auto constexpr X = int32_t{0};  // Placeholder for null.
+
+template <typename T>
+using fwcw                 = cudf::test::fixed_width_column_wrapper<T>;
+using grouping_keys_column = fwcw<int32_t>;
+
+/// Rolling test executor with fluent interface.
+class rolling_exec {
+  cudf::size_type _preceding{1};
+  cudf::size_type _following{0};
+  cudf::size_type _min_periods{1};
+  cudf::column_view _grouping;
+  cudf::column_view _input;
+  cudf::null_policy _null_handling = cudf::null_policy::INCLUDE;
+
+ public:
+  rolling_exec& preceding(cudf::size_type preceding)
+  {
+    _preceding = preceding;
+    return *this;
+  }
+  rolling_exec& following(cudf::size_type following)
+  {
+    _following = following;
+    return *this;
+  }
+  rolling_exec& min_periods(cudf::size_type min_periods)
+  {
+    _min_periods = min_periods;
+    return *this;
+  }
+  rolling_exec& grouping(cudf::column_view grouping)
+  {
+    _grouping = grouping;
+    return *this;
+  }
+  rolling_exec& input(cudf::column_view input)
+  {
+    _input = input;
+    return *this;
+  }
+  rolling_exec& null_handling(cudf::null_policy null_handling)
+  {
+    _null_handling = null_handling;
+    return *this;
+  }
+
+  std::unique_ptr<cudf::column> test_grouped_nth_element(
+    cudf::size_type n, std::optional<cudf::null_policy> null_handling = std::nullopt) const
+  {
+    return cudf::grouped_rolling_window(
+      cudf::table_view{{_grouping}},
+      _input,
+      _preceding,
+      _following,
+      _min_periods,
+      *cudf::make_nth_element_aggregation<cudf::rolling_aggregation>(
+        n, null_handling.value_or(_null_handling)));
+  }
+
+  std::unique_ptr<cudf::column> test_nth_element(
+    cudf::size_type n, std::optional<cudf::null_policy> null_handling = std::nullopt) const
+  {
+    return cudf::rolling_window(_input,
+                                _preceding,
+                                _following,
+                                _min_periods,
+                                *cudf::make_nth_element_aggregation<cudf::rolling_aggregation>(
+                                  n, null_handling.value_or(_null_handling)));
+  }
+};
+
+struct NthElementTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct NthElementTypedTest : public NthElementTest {};
+
+using TypesForTest = cudf::test::Concat<cudf::test::IntegralTypes,
+                                        cudf::test::FloatingPointTypes,
+                                        cudf::test::DurationTypes,
+                                        cudf::test::TimestampTypes>;
+
+TYPED_TEST_SUITE(NthElementTypedTest, TypesForTest);
+
+TYPED_TEST(NthElementTypedTest, RollingWindow)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    fwcw<T>{{0, 1, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, 20}, cudf::test::iterators::null_at(5)};
+  auto tester = rolling_exec{}.input(input_col);
+  {
+    // Window of 5 elements, min-periods == 1.
+    tester.preceding(3).following(2).min_periods(1);
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element,
+      fwcw<T>{{0, 0, 0, 1, 2, 3, 4, X, 10, 11, 12, 13, 14, 15}, cudf::test::iterators::null_at(7)});
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element,
+      fwcw<T>{{2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, 20, 20, 20},
+              cudf::test::iterators::null_at(3)});
+    auto const third_element = tester.test_nth_element(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*third_element,
+                                        fwcw<T>{{2, 2, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, 20},
+                                                cudf::test::iterators::null_at(5)});
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element,
+                                        fwcw<T>{{1, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, 16, 16},
+                                                cudf::test::iterators::null_at(4)});
+  }
+  {
+    // Window of 3 elements, min-periods == 3. Expect null elements at column margins.
+    tester.preceding(2).following(1).min_periods(3);
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element,
+                                        fwcw<T>{{X, 0, 1, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, X},
+                                                cudf::test::iterators::nulls_at({0, 6, 13})});
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element,
+                                        fwcw<T>{{X, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, 20, X},
+                                                cudf::test::iterators::nulls_at({0, 4, 13})});
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element,
+                                        fwcw<T>{{X, 1, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, X},
+                                                cudf::test::iterators::nulls_at({0, 5, 13})});
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element,
+                                        fwcw<T>{{X, 1, 2, 3, 4, X, 10, 11, 12, 13, 14, 15, 16, X},
+                                                cudf::test::iterators::nulls_at({0, 5, 13})});
+  }
+  {
+    // Too large values for `min_periods`. No window has enough periods.
+    tester.preceding(2).following(1).min_periods(4);
+    auto const all_null_values =
+      fwcw<T>{{X, X, X, X, X, X, X, X, X, X, X, X, X, X}, cudf::test::iterators::all_nulls()};
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element, all_null_values);
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element, all_null_values);
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element, all_null_values);
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element, all_null_values);
+  }
+}
+
+TYPED_TEST(NthElementTypedTest, RollingWindowExcludeNulls)
+{
+  using T = TypeParam;
+
+  auto const input_col =
+    fwcw<T>{{0, X, X, X, 4, X, 6, 7}, cudf::test::iterators::nulls_at({1, 2, 3, 5})};
+  auto tester = rolling_exec{}.input(input_col);
+
+  {
+    // Window of 5 elements, min-periods == 2.
+    tester.preceding(3).following(2).min_periods(1).null_handling(cudf::null_policy::EXCLUDE);
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element, fwcw<T>{{0, 0, 0, 4, 4, 4, 4, 6}, cudf::test::iterators::no_nulls()});
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element, fwcw<T>{{0, 0, 4, 4, 6, 7, 7, 7}, cudf::test::iterators::no_nulls()});
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_element,
+      fwcw<T>{{X, X, 4, X, 6, 6, 6, 7}, cudf::test::iterators::nulls_at({0, 1, 3})});
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_last_element,
+      fwcw<T>{{X, X, 0, X, 4, 6, 6, 6}, cudf::test::iterators::nulls_at({0, 1, 3})});
+  }
+  {
+    // Window of 3 elements, min-periods == 1.
+    tester.preceding(2).following(1).min_periods(1).null_handling(cudf::null_policy::EXCLUDE);
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element, fwcw<T>{{0, 0, X, 4, 4, 4, 6, 6}, cudf::test::iterators::null_at(2)});
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element, fwcw<T>{{0, 0, X, 4, 4, 6, 7, 7}, cudf::test::iterators::null_at(2)});
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_element,
+      fwcw<T>{{X, X, X, X, X, 6, 7, 7}, cudf::test::iterators::nulls_at({0, 1, 2, 3, 4})});
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_last_element,
+      fwcw<T>{{X, X, X, X, X, 4, 6, 6}, cudf::test::iterators::nulls_at({0, 1, 2, 3, 4})});
+  }
+  {
+    // Too large values for `min_periods`. No window has enough periods.
+    tester.preceding(2).following(1).min_periods(4);
+    auto const all_null_values =
+      fwcw<T>{{X, X, X, X, X, X, X, X}, cudf::test::iterators::all_nulls()};
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element, all_null_values);
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element, all_null_values);
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element, all_null_values);
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element, all_null_values);
+  }
+}
+
+TYPED_TEST(NthElementTypedTest, GroupedRollingWindow)
+{
+  using T = TypeParam;
+
+  // clang-format off
+  auto const group_col = fwcw<int32_t>{0, 0, 0, 0, 0, 0,
+                                       10, 10, 10, 10, 10, 10, 10,
+                                       20};
+  auto const input_col = fwcw<T> {0, 1, 2, 3, 4, 5,           // Group 0
+                                  10, 11, 12, 13, 14, 15, 16, // Group 10
+                                  20};                        // Group 20
+  // clang-format on
+  auto tester = rolling_exec{}.grouping(group_col).input(input_col);
+
+  {
+    // Window of 5 elements, min-periods == 1.
+    tester.preceding(3).following(2).min_periods(1);
+    auto const first_element = tester.test_grouped_nth_element(0);
+    // clang-format off
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element,
+                                        fwcw<T>{{0, 0, 0, 1, 2, 3,           // Group 0
+                                                 10, 10, 10, 11, 12, 13, 14, // Group 10
+                                                 20},                        // Group 20
+                                                cudf::test::iterators::no_nulls()});
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element,
+                                        fwcw<T>{{2, 3, 4, 5, 5, 5,           // Group 0
+                                                 12, 13, 14, 15, 16, 16, 16, // Group 10
+                                                 20},                        // Group 20
+                                                cudf::test::iterators::no_nulls()});
+    auto const third_element = tester.test_grouped_nth_element(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*third_element,
+                                        fwcw<T>{{2, 2, 2, 3, 4, 5,           // Group 0
+                                                 12, 12, 12, 13, 14, 15, 16, // Group 10
+                                                 X},                         // Group 20
+                                                cudf::test::iterators::null_at(13)});
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element,
+                                        fwcw<T>{{1, 2, 3, 4, 4, 4,           // Group 0
+                                                 11, 12, 13, 14, 15, 15, 15, // Group 10
+                                                 X},                         // Group 20
+                                                cudf::test::iterators::null_at(13)});
+    // clang-format on
+  }
+  {
+    // Window of 3 elements, min-periods == 3. Expect null elements at group margins.
+    tester.preceding(2).following(1).min_periods(3);
+    auto const first_element = tester.test_grouped_nth_element(0);
+    // clang-format off
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element,
+                                        fwcw<T>{{X, 0, 1, 2, 3, X,         // Group 0
+                                                 X, 10, 11, 12, 13, 14, X, // Group 10
+                                                 X},                       // Group 20
+                                                cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element,
+                                        fwcw<T>{{X, 2, 3, 4, 5, X,         // Group 0
+                                                 X, 12, 13, 14, 15, 16, X, // Group 10
+                                                 X},                       // Group 20
+                                                cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    auto const second_element = tester.test_grouped_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element,
+                                        fwcw<T>{{X, 1, 2, 3, 4, X,         // Group 0
+                                                 X, 11, 12, 13, 14, 15, X, // Group 10
+                                                 X},                       // Group 20
+                                                cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element,
+                                        fwcw<T>{{X, 1, 2, 3, 4, X,         // Group 0
+                                                 X, 11, 12, 13, 14, 15, X, // Group 10
+                                                 X},                       // Group 20
+                                                cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    // clang-format on
+  }
+  {
+    // Too large values for `min_periods`. No window has enough periods.
+    tester.preceding(2).following(1).min_periods(4);
+    auto const all_null_values =
+      fwcw<T>{{X, X, X, X, X, X, X, X, X, X, X, X, X, X}, cudf::test::iterators::all_nulls()};
+
+    auto const first_element = tester.test_grouped_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element, all_null_values);
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element, all_null_values);
+    auto const second_element = tester.test_grouped_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element, all_null_values);
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element, all_null_values);
+  }
+}
+
+TYPED_TEST(NthElementTypedTest, GroupedRollingWindowExcludeNulls)
+{
+  using T = TypeParam;
+
+  // clang-format off
+  auto const group_col = fwcw<int32_t>{0, 0, 0, 0, 0, 0,
+                                       10, 10, 10, 10, 10, 10, 10,
+                                       20,
+                                       30};
+  auto const input_col = fwcw<T> {{0, 1, X, 3, X, 5,         // Group 0
+                                   10, X, X, 13, 14, 15, 16, // Group 10
+                                   20,                       // Group 20
+                                   X},                       // Group 30
+                                  cudf::test::iterators::nulls_at({2, 4, 7, 8, 14})};
+  // clang-format on
+  auto tester = rolling_exec{}.grouping(group_col).input(input_col);
+
+  {
+    // Window of 5 elements, min-periods == 1.
+    tester.preceding(3).following(2).min_periods(1).null_handling(cudf::null_policy::EXCLUDE);
+    auto const first_element = tester.test_grouped_nth_element(0);
+    // clang-format off
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element,
+                                        fwcw<T>{{0, 0, 0, 1, 3, 3,           // Group 0
+                                                 10, 10, 10, 13, 13, 13, 14, // Group 10
+                                                 20,                         // Group 20
+                                                 X},                         // Group 30
+                                                cudf::test::iterators::null_at(14)});
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element,
+                                        fwcw<T>{{1, 3, 3, 5, 5, 5,           // Group 0
+                                                 10, 13, 14, 15, 16, 16, 16, // Group 10
+                                                 20,                         // Group 20
+                                                 X},                         // Group 30
+                                                cudf::test::iterators::null_at(14)});
+    auto const third_element = tester.test_grouped_nth_element(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*third_element,
+                                        fwcw<T>{{X, 3, 3, 5, X, X,          // Group 0
+                                                 X, X, 14, 15, 15, 15, 16,  // Group 10
+                                                 X,                         // Group 20
+                                                 X},                        // Group 30
+                                                cudf::test::iterators::nulls_at({0, 4, 5, 6, 7, 13, 14})});
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element,
+                                        fwcw<T>{{0, 1, 1, 3, 3, 3,          // Group 0
+                                                 X, 10, 13, 14, 15, 15, 15, // Group 10
+                                                 X,                         // Group 20
+                                                 X},                        // Group 30
+                                                cudf::test::iterators::nulls_at({6, 13, 14})});
+    // clang-format on
+  }
+  {
+    // Window of 3 elements, min-periods == 3. Expect null elements at group margins.
+    tester.preceding(2).following(1).min_periods(3);
+    auto const first_element = tester.test_grouped_nth_element(0);
+    // clang-format off
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element,
+                                        fwcw<T>{{X, 0, 1, 3, 3, X,         // Group 0
+                                                 X, 10, 13, 13, 13, 14, X, // Group 10
+                                                 X,                        // Group 20
+                                                 X},                       // Group 30
+                                                cudf::test::iterators::nulls_at({0, 5, 6, 12, 13, 14})});
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element,
+                                        fwcw<T>{{X, 1, 3, 3, 5, X,         // Group 0
+                                                 X, 10, 13, 14, 15, 16, X, // Group 10
+                                                 X,                        // Group 20
+                                                 X},                       // Group 30
+                                                cudf::test::iterators::nulls_at({0, 5, 6, 12, 13, 14})});
+    auto const second_element = tester.test_grouped_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element,
+                                        fwcw<T>{{X, 1, 3, X, 5, X,       // Group 0
+                                                 X, X, X, 14, 14, 15, X, // Group 10
+                                                 X,                      // Group 20
+                                                 X},                     // Group 30
+                                                cudf::test::iterators::nulls_at({0, 3, 5, 6, 7, 8, 12, 13, 14})});
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element,
+                                        fwcw<T>{{X, 0, 1, X, 3, X,       // Group 0
+                                                 X, X, X, 13, 14, 15, X, // Group 10
+                                                 X,                      // Group 20
+                                                 X},                     // Group 30
+                                                cudf::test::iterators::nulls_at({0, 3, 5, 6, 7, 8, 12, 13, 14})});
+    // clang-format on
+  }
+  {
+    // Too large values for `min_periods`. No window has enough periods.
+    tester.preceding(2).following(1).min_periods(4);
+    auto const all_null_values =
+      fwcw<T>{{X, X, X, X, X, X, X, X, X, X, X, X, X, X, X}, cudf::test::iterators::all_nulls()};
+
+    auto const first_element = tester.test_grouped_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element, all_null_values);
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element, all_null_values);
+    auto const second_element = tester.test_grouped_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element, all_null_values);
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element, all_null_values);
+  }
+}
+
+TYPED_TEST(NthElementTypedTest, EmptyInput)
+{
+  using T = TypeParam;
+
+  auto const group_col = fwcw<int32_t>{};
+  auto const input_col = fwcw<T>{};
+  auto tester = rolling_exec{}.grouping(group_col).input(input_col).preceding(3).following(1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*tester.test_grouped_nth_element(0), fwcw<T>{});
+}
+
+TEST_F(NthElementTest, RollingWindowOnStrings)
+{
+  using strings = cudf::test::strings_column_wrapper;
+
+  auto constexpr X = "";  // Placeholder for null string.
+
+  auto const input_col =
+    strings{{"", "1", "22", "333", "4444", "", "10", "11", "12", "13", "14", "15", "16", "20"},
+            cudf::test::iterators::null_at(5)};
+  auto tester = rolling_exec{}.input(input_col);
+
+  {
+    // Window of 5 elements, min-periods == 1.
+    tester.preceding(3).following(2).min_periods(1);
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element,
+      strings{{"", "", "", "1", "22", "333", "4444", X, "10", "11", "12", "13", "14", "15"},
+              cudf::test::iterators::null_at(7)});
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element,
+      strings{{"22", "333", "4444", X, "10", "11", "12", "13", "14", "15", "16", "20", "20", "20"},
+              cudf::test::iterators::null_at(3)});
+    auto const third_element = tester.test_nth_element(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *third_element,
+      strings{{"22", "22", "22", "333", "4444", X, "10", "11", "12", "13", "14", "15", "16", "20"},
+              cudf::test::iterators::null_at(5)});
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_last_element,
+      strings{{"1", "22", "333", "4444", X, "10", "11", "12", "13", "14", "15", "16", "16", "16"},
+              cudf::test::iterators::null_at(4)});
+  }
+  {
+    // Window of 3 elements, min-periods == 3. Expect null elements at column margins.
+    tester.preceding(2).following(1).min_periods(3);
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element,
+      strings{{X, "", "1", "22", "333", "4444", X, "10", "11", "12", "13", "14", "15", X},
+              cudf::test::iterators::nulls_at({0, 6, 13})});
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element,
+      strings{{X, "22", "333", "4444", X, "10", "11", "12", "13", "14", "15", "16", "20", X},
+              cudf::test::iterators::nulls_at({0, 4, 13})});
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_element,
+      strings{{X, "1", "22", "333", "4444", X, "10", "11", "12", "13", "14", "15", "16", X},
+              cudf::test::iterators::nulls_at({0, 5, 13})});
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_last_element,
+      strings{{X, "1", "22", "333", "4444", X, "10", "11", "12", "13", "14", "15", "16", X},
+              cudf::test::iterators::nulls_at({0, 5, 13})});
+  }
+  {
+    // Too large values for `min_periods`. No window has enough periods.
+    tester.preceding(2).following(1).min_periods(4);
+    auto const all_null_values =
+      strings{{X, X, X, X, X, X, X, X, X, X, X, X, X, X}, cudf::test::iterators::all_nulls()};
+
+    auto const first_element = tester.test_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element, all_null_values);
+    auto const last_element = tester.test_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element, all_null_values);
+    auto const second_element = tester.test_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element, all_null_values);
+    auto const second_last_element = tester.test_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element, all_null_values);
+  }
+}
+
+TEST_F(NthElementTest, GroupedRollingWindowForStrings)
+{
+  using strings    = cudf::test::strings_column_wrapper;
+  auto constexpr X = "";  // Placeholder for null strings.
+
+  // clang-format off
+  auto const group_col = fwcw<int32_t>{0, 0, 0, 0, 0, 0,
+                                       10, 10, 10, 10, 10, 10, 10,
+                                       20};
+  auto const input_col = strings{{"", "1", "22", "333", "4444", X,          // Group 0
+                                  "10", "11", "12", "13", "14", "15", "16", // Group 10
+                                  "20"},                                    // Group 20
+                                 cudf::test::iterators::null_at(5)};
+  // clang-format on
+  auto tester = rolling_exec{}.grouping(group_col).input(input_col);
+
+  {
+    // Window of 5 elements, min-periods == 1.
+    tester.preceding(3).following(2).min_periods(1);
+
+    auto const first_element = tester.test_grouped_nth_element(0);
+    // clang-format off
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element,
+      strings{{"", "", "", "1", "22", "333",             // Group 0
+               "10", "10", "10", "11", "12", "13", "14", // Group 10
+               "20"},                                    // Group 20
+              cudf::test::iterators::no_nulls()});
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element,
+      strings{{"22", "333", "4444", X, X, X,             // Group 0
+               "12", "13", "14", "15", "16", "16", "16", // Group 10
+               "20"},                                    // Group 20
+              cudf::test::iterators::nulls_at({3, 4, 5})});
+    auto const third_element = tester.test_grouped_nth_element(2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *third_element,
+      strings{{"22", "22", "22", "333", "4444", X,       // Group 0
+               "12", "12", "12", "13", "14", "15", "16", // Group 10
+               X},                                       // Group 20
+              cudf::test::iterators::nulls_at({5, 13})});
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_last_element,
+      strings{{"1", "22", "333", "4444", "4444", "4444", // Group 0
+               "11", "12", "13", "14", "15", "15", "15", // Group 10
+               X},                                       // Group 20
+              cudf::test::iterators::null_at(13)});
+    // clang-format on
+  }
+  {
+    // Window of 3 elements, min-periods == 3. Expect null elements at group margins.
+    tester.preceding(2).following(1).min_periods(3);
+    auto const first_element = tester.test_grouped_nth_element(0);
+    // clang-format off
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *first_element,
+      strings{{X, "", "1", "22", "333", X,         // Group 0
+               X, "10", "11", "12", "13", "14", X, // Group 10
+               X},                                 // Group 20
+              cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *last_element,
+      strings{{X, "22", "333", "4444", X, X,       // Group 0
+               X, "12", "13", "14", "15", "16", X, // Group 10
+               X},                                 // Group 20
+              cudf::test::iterators::nulls_at({0, 4, 5, 6, 12, 13})});
+    auto const second_element = tester.test_grouped_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_element,
+      strings{{X, "1", "22", "333", "4444", X,     // Group 0
+               X, "11", "12", "13", "14", "15", X, // Group 10
+               X},                                 // Group 20
+              cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+      *second_last_element,
+      strings{{X, "1", "22", "333", "4444", X,     // Group 0
+               X, "11", "12", "13", "14", "15", X, // Group 10
+               X},                                 // Group 20
+              cudf::test::iterators::nulls_at({0, 5, 6, 12, 13})});
+    // clang-format on
+  }
+  {
+    // Too large values for `min_periods`. No window has enough periods.
+    tester.preceding(2).following(1).min_periods(4);
+    auto const all_null_strings =
+      strings{{X, X, X, X, X, X, X, X, X, X, X, X, X, X}, cudf::test::iterators::all_nulls()};
+
+    auto const first_element = tester.test_grouped_nth_element(0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*first_element, all_null_strings);
+    auto const last_element = tester.test_grouped_nth_element(-1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*last_element, all_null_strings);
+    auto const second_element = tester.test_grouped_nth_element(1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_element, all_null_strings);
+    auto const second_last_element = tester.test_grouped_nth_element(-2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*second_last_element, all_null_strings);
+  }
+}
diff --git a/cpp/tests/rolling/offset_row_window_test.cpp b/cpp/tests/rolling/offset_row_window_test.cpp
new file mode 100644
index 0000000..ec72687
--- /dev/null
+++ b/cpp/tests/rolling/offset_row_window_test.cpp
@@ -0,0 +1,343 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/lists/explode.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+template <typename T>
+using fwcw = cudf::test::fixed_width_column_wrapper<T>;
+template <typename T>
+using decimals_column = cudf::test::fixed_point_column_wrapper<T>;
+using ints_column     = fwcw<int32_t>;
+using bigints_column  = fwcw<int64_t>;
+using strings_column  = cudf::test::strings_column_wrapper;
+using lists_column    = cudf::test::lists_column_wrapper<int32_t>;
+using column_ptr      = std::unique_ptr<cudf::column>;
+using cudf::test::iterators::all_nulls;
+using cudf::test::iterators::no_nulls;
+using cudf::test::iterators::nulls_at;
+
+auto constexpr null = int32_t{0};  // NULL representation for int32_t;
+
+struct OffsetRowWindowTest : public cudf::test::BaseFixture {
+  static ints_column const _keys;    // {0, 0, 0, 0, 0, 0, 1, 1, 1, 1};
+  static ints_column const _values;  // {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  struct rolling_runner {
+    cudf::window_bounds _preceding, _following;
+    cudf::size_type _min_periods;
+    bool _grouped = true;
+
+    rolling_runner(cudf::window_bounds const& preceding,
+                   cudf::window_bounds const& following,
+                   cudf::size_type min_periods_ = 1)
+      : _preceding{preceding}, _following{following}, _min_periods{min_periods_}
+    {
+    }
+
+    rolling_runner& min_periods(cudf::size_type min_periods_)
+    {
+      _min_periods = min_periods_;
+      return *this;
+    }
+
+    rolling_runner& grouped(bool grouped_)
+    {
+      _grouped = grouped_;
+      return *this;
+    }
+
+    std::unique_ptr<cudf::column> operator()(cudf::rolling_aggregation const& agg) const
+    {
+      auto const grouping_keys =
+        _grouped ? std::vector<cudf::column_view>{_keys} : std::vector<cudf::column_view>{};
+      return cudf::grouped_rolling_window(
+        cudf::table_view{grouping_keys}, _values, _preceding, _following, _min_periods, agg);
+    }
+  };
+};
+
+ints_column const OffsetRowWindowTest::_keys{0, 0, 0, 0, 0, 0, 1, 1, 1, 1};
+ints_column const OffsetRowWindowTest::_values{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+auto const AGG_COUNT_NON_NULL =
+  cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE);
+auto const AGG_COUNT_ALL =
+  cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE);
+auto const AGG_MIN          = cudf::make_min_aggregation<cudf::rolling_aggregation>();
+auto const AGG_MAX          = cudf::make_max_aggregation<cudf::rolling_aggregation>();
+auto const AGG_SUM          = cudf::make_sum_aggregation<cudf::rolling_aggregation>();
+auto const AGG_COLLECT_LIST = cudf::make_collect_list_aggregation<cudf::rolling_aggregation>();
+
+TEST_F(OffsetRowWindowTest, OffsetRowWindow_Grouped_3_to_Minus_1)
+{
+  auto const preceding = cudf::window_bounds::get(3);
+  auto const following = cudf::window_bounds::get(-1);
+  auto run_rolling     = rolling_runner{preceding, following}.min_periods(1).grouped(true);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 0, 1, 2, 2}, nulls_at({0, 6})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 0, 1, 2, 2}, nulls_at({0, 6})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_MIN), ints_column{{null, 0, 0, 1, 2, 3, null, 6, 6, 7}, nulls_at({0, 6})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_MAX), ints_column{{null, 0, 1, 2, 3, 4, null, 6, 7, 8}, nulls_at({0, 6})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_SUM),
+    bigints_column{{null, 0, 1, 3, 5, 7, null, 6, 13, 15}, nulls_at({0, 6})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{}, {0}, {0, 1}, {1, 2}, {2, 3}, {3, 4}, {}, {6}, {6, 7}, {7, 8}},
+                 nulls_at({0, 6})});
+
+  run_rolling.min_periods(0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 0, 1, 2, 2}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 0, 1, 2, 2}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{}, {0}, {0, 1}, {1, 2}, {2, 3}, {3, 4}, {}, {6}, {6, 7}, {7, 8}}, no_nulls()});
+}
+
+TEST_F(OffsetRowWindowTest, OffsetRowWindow_Ungrouped_3_to_Minus_1)
+{
+  auto const preceding = cudf::window_bounds::get(3);
+  auto const following = cudf::window_bounds::get(-1);
+  auto run_rolling     = rolling_runner{preceding, following}.min_periods(1).grouped(false);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 2, 2, 2, 2}, nulls_at({0})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 2, 2, 2, 2}, nulls_at({0})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_MIN),
+                                 ints_column{{null, 0, 0, 1, 2, 3, 4, 5, 6, 7}, nulls_at({0})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_MAX),
+                                 ints_column{{null, 0, 1, 2, 3, 4, 5, 6, 7, 8}, nulls_at({0})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_SUM), bigints_column{{null, 0, 1, 3, 5, 7, 9, 11, 13, 15}, nulls_at({0})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{}, {0}, {0, 1}, {1, 2}, {2, 3}, {3, 4}, {4, 5}, {5, 6}, {6, 7}, {7, 8}},
+                 nulls_at({0})});
+
+  run_rolling.min_periods(0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 2, 2, 2, 2}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{0, 1, 2, 2, 2, 2, 2, 2, 2, 2}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{}, {0}, {0, 1}, {1, 2}, {2, 3}, {3, 4}, {4, 5}, {5, 6}, {6, 7}, {7, 8}},
+                 no_nulls()});
+}
+
+TEST_F(OffsetRowWindowTest, OffsetRowWindow_Grouped_0_to_2)
+{
+  auto const preceding = cudf::window_bounds::get(0);
+  auto const following = cudf::window_bounds::get(2);
+  auto run_rolling     = rolling_runner{preceding, following}.min_periods(1).grouped(true);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COUNT_NON_NULL),
+    ints_column{{2, 2, 2, 2, 1, null, 2, 2, 1, null}, nulls_at({5, 9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COUNT_ALL),
+    ints_column{{2, 2, 2, 2, 1, null, 2, 2, 1, null}, nulls_at({5, 9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_MIN), ints_column{{1, 2, 3, 4, 5, null, 7, 8, 9, null}, nulls_at({5, 9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_MAX), ints_column{{2, 3, 4, 5, 5, null, 8, 9, 9, null}, nulls_at({5, 9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_SUM),
+    bigints_column{{3, 5, 7, 9, 5, null, 15, 17, 9, null}, nulls_at({5, 9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{1, 2}, {2, 3}, {3, 4}, {4, 5}, {5}, {}, {7, 8}, {8, 9}, {9}, {}},
+                 nulls_at({5, 9})});
+
+  run_rolling.min_periods(0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{2, 2, 2, 2, 1, 0, 2, 2, 1, 0}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{2, 2, 2, 2, 1, 0, 2, 2, 1, 0}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{1, 2}, {2, 3}, {3, 4}, {4, 5}, {5}, {}, {7, 8}, {8, 9}, {9}, {}}, no_nulls});
+}
+
+TEST_F(OffsetRowWindowTest, OffsetRowWindow_Ungrouped_0_to_2)
+{
+  auto const preceding = cudf::window_bounds::get(0);
+  auto const following = cudf::window_bounds::get(2);
+  auto run_rolling     = rolling_runner{preceding, following}.min_periods(1).grouped(false);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{2, 2, 2, 2, 2, 2, 2, 2, 1, null}, nulls_at({9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{2, 2, 2, 2, 2, 2, 2, 2, 1, null}, nulls_at({9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_MIN),
+                                 ints_column{{1, 2, 3, 4, 5, 6, 7, 8, 9, null}, nulls_at({9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_MAX),
+                                 ints_column{{2, 3, 4, 5, 6, 7, 8, 9, 9, null}, nulls_at({9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_SUM), bigints_column{{3, 5, 7, 9, 11, 13, 15, 17, 9, null}, nulls_at({9})});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{1, 2}, {2, 3}, {3, 4}, {4, 5}, {5, 6}, {6, 7}, {7, 8}, {8, 9}, {9}, {}},
+                 nulls_at({9})});
+
+  run_rolling.min_periods(0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_NON_NULL),
+                                 ints_column{{2, 2, 2, 2, 2, 2, 2, 2, 1, 0}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*run_rolling(*AGG_COUNT_ALL),
+                                 ints_column{{2, 2, 2, 2, 2, 2, 2, 2, 1, 0}, no_nulls()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *run_rolling(*AGG_COLLECT_LIST),
+    lists_column{{{1, 2}, {2, 3}, {3, 4}, {4, 5}, {5, 6}, {6, 7}, {7, 8}, {8, 9}, {9}, {}},
+                 no_nulls});
+}
+
+// To test that preceding bounds are clamped correctly at group boundaries.
+TEST_F(OffsetRowWindowTest, TestNegativeBoundsClamp)
+{
+  auto const grp_iter =
+    thrust::make_transform_iterator(thrust::make_counting_iterator(0), [](auto const& i) {
+      return i / 10;  // 0-9 in the first group, 10-19 in the second, etc.
+    });
+  auto const agg_iter = thrust::make_constant_iterator(1);
+
+  auto const grp = ints_column(grp_iter, grp_iter + 30);
+  auto const agg = ints_column(agg_iter, agg_iter + 30);
+
+  auto const min_periods = 0;
+  auto const rolling_sum = [&](auto const preceding, auto const following) {
+    return cudf::grouped_rolling_window(
+      cudf::table_view{{grp}}, agg, preceding, following, min_periods, *AGG_SUM);
+  };
+
+  // Testing negative preceding.
+  for (auto const preceding : {0, -1, -2, -5, -10, -20, -50}) {
+    auto const results      = rolling_sum(preceding, 100);
+    auto const expected_fun = [&](auto const& i) {
+      assert(preceding < 1);
+      auto const index_in_group = i % 10;
+      auto const start          = std::min(-(preceding - 1) + index_in_group, 10);
+      return int64_t{10 - start};
+    };
+    auto const expected_iter =
+      thrust::make_transform_iterator(thrust::make_counting_iterator(0), expected_fun);
+    auto const expected = bigints_column(expected_iter, expected_iter + 30, no_nulls());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  // Testing negative following.
+  for (auto const following : {-1, -2, -5, -10, -20, -50}) {
+    auto const results      = rolling_sum(100, following);
+    auto const expected_fun = [&](auto const& i) {
+      assert(following < 0);
+      auto const index_in_group = i % 10;
+      auto const end            = std::max(index_in_group + following, -1);
+      return int64_t{end + 1};
+    };
+    auto const expected_iter =
+      thrust::make_transform_iterator(thrust::make_counting_iterator(0), expected_fun);
+    auto const expected = bigints_column(expected_iter, expected_iter + 30, no_nulls());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(OffsetRowWindowTest, CheckGroupBoundaries)
+{
+  auto grp_iter =
+    thrust::make_transform_iterator(thrust::make_counting_iterator(0), [](auto const& i) {
+      if (i < 10) return 1;
+      if (i < 20) return 2;
+      return 3;
+    });
+  auto const grp = ints_column(grp_iter, grp_iter + 30);
+  auto const agg = ints_column(grp_iter, grp_iter + 30);
+  {
+    auto const results =
+      cudf::grouped_rolling_window(cudf::table_view{{grp}},
+                                   agg,
+                                   -80,
+                                   100,
+                                   1,
+                                   *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+    auto const null_iter = thrust::make_constant_iterator<int32_t>(null);
+    auto const expected  = ints_column(null_iter, null_iter + 30, all_nulls());
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+  {
+    auto const results =
+      cudf::grouped_rolling_window(cudf::table_view{{grp}},
+                                   agg,
+                                   -1,
+                                   4,
+                                   1,
+                                   *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+    auto const expected =
+      ints_column{{1, 1, 1, 1,    1,    1, 1, 1, null, null, 2, 2, 2, 2,    2,
+                   2, 2, 2, null, null, 3, 3, 3, 3,    3,    3, 3, 3, null, null},
+                  nulls_at({8, 9, 18, 19, 28, 29})};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+}
diff --git a/cpp/tests/rolling/range_comparator_test.cu b/cpp/tests/rolling/range_comparator_test.cu
new file mode 100644
index 0000000..96c3fe7
--- /dev/null
+++ b/cpp/tests/rolling/range_comparator_test.cu
@@ -0,0 +1,147 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <src/rolling/detail/range_comparator_utils.cuh>
+
+struct RangeComparatorTest : cudf::test::BaseFixture {};
+
+template <typename T>
+struct RangeComparatorTypedTest : RangeComparatorTest {};
+
+using TestTypes =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+
+TYPED_TEST_SUITE(RangeComparatorTypedTest, TestTypes);
+
+TYPED_TEST(RangeComparatorTypedTest, TestLessComparator)
+{
+  auto const less     = cudf::detail::nan_aware_less{};
+  auto constexpr nine = TypeParam{9};
+  auto constexpr ten  = TypeParam{10};
+
+  EXPECT_TRUE(less(nine, ten));
+  EXPECT_FALSE(less(ten, nine));
+  EXPECT_FALSE(less(ten, ten));
+
+  if constexpr (std::is_floating_point_v<TypeParam>) {
+    auto constexpr NaN = std::numeric_limits<TypeParam>::quiet_NaN();
+    auto constexpr Inf = std::numeric_limits<TypeParam>::infinity();
+    // NaN.
+    EXPECT_FALSE(less(NaN, ten));
+    EXPECT_FALSE(less(NaN, NaN));
+    EXPECT_FALSE(less(NaN, Inf));
+    EXPECT_FALSE(less(NaN, -Inf));
+    // Infinity.
+    EXPECT_TRUE(less(Inf, NaN));
+    EXPECT_FALSE(less(Inf, Inf));
+    EXPECT_FALSE(less(Inf, ten));
+    EXPECT_FALSE(less(Inf, -Inf));
+    // -Infinity.
+    EXPECT_TRUE(less(-Inf, NaN));
+    EXPECT_TRUE(less(-Inf, Inf));
+    EXPECT_TRUE(less(-Inf, ten));
+    EXPECT_FALSE(less(-Inf, -Inf));
+    // Finite.
+    EXPECT_TRUE(less(ten, NaN));
+    EXPECT_TRUE(less(ten, Inf));
+    EXPECT_FALSE(less(ten, -Inf));
+  }
+}
+
+TYPED_TEST(RangeComparatorTypedTest, TestGreaterComparator)
+{
+  auto const greater  = cudf::detail::nan_aware_greater{};
+  auto constexpr nine = TypeParam{9};
+  auto constexpr ten  = TypeParam{10};
+
+  EXPECT_FALSE(greater(nine, ten));
+  EXPECT_TRUE(greater(ten, nine));
+  EXPECT_FALSE(greater(ten, ten));
+
+  if constexpr (std::is_floating_point_v<TypeParam>) {
+    auto constexpr NaN = std::numeric_limits<TypeParam>::quiet_NaN();
+    auto constexpr Inf = std::numeric_limits<TypeParam>::infinity();
+    // NaN.
+    EXPECT_TRUE(greater(NaN, ten));
+    EXPECT_FALSE(greater(NaN, NaN));
+    EXPECT_TRUE(greater(NaN, Inf));
+    EXPECT_TRUE(greater(NaN, -Inf));
+    // Infinity.
+    EXPECT_FALSE(greater(Inf, NaN));
+    EXPECT_FALSE(greater(Inf, Inf));
+    EXPECT_TRUE(greater(Inf, ten));
+    EXPECT_TRUE(greater(Inf, -Inf));
+    // -Infinity.
+    EXPECT_FALSE(greater(-Inf, NaN));
+    EXPECT_FALSE(greater(-Inf, Inf));
+    EXPECT_FALSE(greater(-Inf, ten));
+    EXPECT_FALSE(greater(-Inf, -Inf));
+    // Finite.
+    EXPECT_FALSE(greater(ten, NaN));
+    EXPECT_FALSE(greater(ten, Inf));
+    EXPECT_TRUE(greater(ten, -Inf));
+  }
+}
+
+TYPED_TEST(RangeComparatorTypedTest, TestAddSafe)
+{
+  using T = TypeParam;
+  EXPECT_EQ(cudf::detail::add_safe(T{3}, T{4}), T{7});
+
+  if constexpr (cuda::std::numeric_limits<T>::is_signed) {
+    EXPECT_EQ(cudf::detail::add_safe(T{-3}, T{4}), T{1});
+  }
+
+  auto constexpr max = cuda::std::numeric_limits<T>::max();
+  EXPECT_EQ(cudf::detail::add_safe(T{max - 5}, T{4}), max - 1);
+  EXPECT_EQ(cudf::detail::add_safe(T{max - 4}, T{4}), max);
+  EXPECT_EQ(cudf::detail::add_safe(T{max - 3}, T{4}), max);
+  EXPECT_EQ(cudf::detail::add_safe(max, T{4}), max);
+
+  if constexpr (std::is_floating_point_v<T>) {
+    auto const NaN = std::numeric_limits<T>::quiet_NaN();
+    auto const Inf = std::numeric_limits<T>::infinity();
+    EXPECT_TRUE(std::isnan(cudf::detail::add_safe(NaN, T{4})));
+    EXPECT_EQ(cudf::detail::add_safe(Inf, T{4}), Inf);
+  }
+}
+
+TYPED_TEST(RangeComparatorTypedTest, TestSubtractSafe)
+{
+  using T = TypeParam;
+  EXPECT_EQ(cudf::detail::subtract_safe(T{4}, T{3}), T{1});
+
+  if constexpr (cuda::std::numeric_limits<T>::is_signed) {
+    EXPECT_EQ(cudf::detail::subtract_safe(T{3}, T{4}), T{-1});
+  }
+
+  auto constexpr min = cuda::std::numeric_limits<T>::lowest();
+  EXPECT_EQ(cudf::detail::subtract_safe(T{min + 5}, T{4}), min + 1);
+  EXPECT_EQ(cudf::detail::subtract_safe(T{min + 4}, T{4}), min);
+  EXPECT_EQ(cudf::detail::subtract_safe(T{min + 3}, T{4}), min);
+  EXPECT_EQ(cudf::detail::subtract_safe(min, T{4}), min);
+
+  if constexpr (std::is_floating_point_v<T>) {
+    auto const NaN = std::numeric_limits<T>::quiet_NaN();
+    auto const Inf = std::numeric_limits<T>::infinity();
+    EXPECT_TRUE(std::isnan(cudf::detail::subtract_safe(NaN, T{4})));
+    EXPECT_EQ(cudf::detail::subtract_safe(-Inf, T{4}), -Inf);
+  }
+}
diff --git a/cpp/tests/rolling/range_rolling_window_test.cpp b/cpp/tests/rolling/range_rolling_window_test.cpp
new file mode 100644
index 0000000..eed9db1
--- /dev/null
+++ b/cpp/tests/rolling/range_rolling_window_test.cpp
@@ -0,0 +1,596 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <src/rolling/detail/range_window_bounds.hpp>
+#include <src/rolling/detail/rolling.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+template <typename T, typename R = int32_t>
+using fwcw = cudf::test::fixed_width_column_wrapper<T>;
+
+using int_col  = fwcw<int32_t>;
+using size_col = fwcw<cudf::size_type>;
+
+template <typename T, typename R = typename T::rep>
+using time_col = fwcw<T, R>;
+
+using lists_col = cudf::test::lists_column_wrapper<int32_t>;
+
+template <typename ScalarT>
+struct window_exec {
+ public:
+  window_exec(cudf::column_view gby,
+              cudf::column_view oby,
+              cudf::order ordering,
+              cudf::column_view agg,
+              ScalarT preceding_scalar,
+              ScalarT following_scalar,
+              cudf::size_type min_periods = 1)
+    : gby_column(gby),
+      oby_column(oby),
+      order(ordering),
+      agg_column(agg),
+      preceding(preceding_scalar),
+      following(following_scalar),
+      min_periods(min_periods)
+  {
+  }
+
+  cudf::size_type num_rows() { return gby_column.size(); }
+
+  std::unique_ptr<cudf::column> operator()(
+    std::unique_ptr<cudf::rolling_aggregation> const& agg) const
+  {
+    auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{gby_column}};
+
+    return cudf::grouped_range_rolling_window(grouping_keys,
+                                              oby_column,
+                                              order,
+                                              agg_column,
+                                              cudf::range_window_bounds::get(preceding),
+                                              cudf::range_window_bounds::get(following),
+                                              min_periods,
+                                              *agg);
+  }
+
+ private:
+  cudf::column_view gby_column;  // Groupby column.
+  cudf::column_view oby_column;  // Orderby column.
+  cudf::order order;             // Ordering for `oby_column`.
+  cudf::column_view agg_column;  // Aggregation column.
+  ScalarT preceding;             // Preceding window scalar.
+  ScalarT following;             // Following window scalar.
+  cudf::size_type min_periods = 1;
+};  // struct window_exec;
+
+struct RangeRollingTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedTimeRangeRollingTest : RangeRollingTest {};
+
+TYPED_TEST_SUITE(TypedTimeRangeRollingTest, cudf::test::TimestampTypes);
+
+template <typename WindowExecT>
+void verify_results_for_ascending(WindowExecT exec)
+{
+  auto const n_rows       = exec.num_rows();
+  auto const all_valid    = thrust::make_constant_iterator<bool>(true);
+  auto const all_invalid  = thrust::make_constant_iterator<bool>(false);
+  auto const last_invalid = thrust::make_transform_iterator(
+    thrust::make_counting_iterator(0), [&n_rows](auto i) { return i != (n_rows - 1); });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE))
+      ->view(),
+    size_col{{1, 2, 2, 3, 2, 3, 3, 4, 4, 1}, all_valid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_count_aggregation<cudf::rolling_aggregation>())->view(),
+    size_col{{1, 2, 2, 3, 2, 3, 3, 4, 4, 0}, all_valid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_sum_aggregation<cudf::rolling_aggregation>())->view(),
+    fwcw<int64_t>{{0, 12, 12, 12, 8, 17, 17, 18, 18, 1}, last_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_min_aggregation<cudf::rolling_aggregation>())->view(),
+    int_col{{0, 4, 4, 2, 2, 3, 3, 1, 1, 1}, last_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_max_aggregation<cudf::rolling_aggregation>())->view(),
+    int_col{{0, 8, 8, 6, 6, 9, 9, 9, 9, 1}, last_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_mean_aggregation<cudf::rolling_aggregation>())->view(),
+    fwcw<double>{{0.0, 6.0, 6.0, 4.0, 4.0, 17.0 / 3, 17.0 / 3, 4.5, 4.5, 1.0}, last_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    exec(cudf::make_collect_list_aggregation<cudf::rolling_aggregation>())->view(),
+    lists_col{{{0},
+               {8, 4},
+               {8, 4},
+               {4, 6, 2},
+               {6, 2},
+               {9, 3, 5},
+               {9, 3, 5},
+               {9, 3, 5, 1},
+               {9, 3, 5, 1},
+               {{0}, all_invalid}},
+              all_valid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    exec(cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE))
+      ->view(),
+    lists_col{{{0},
+               {8, 4},
+               {8, 4},
+               {4, 6, 2},
+               {6, 2},
+               {9, 3, 5},
+               {9, 3, 5},
+               {9, 3, 5, 1},
+               {9, 3, 5, 1},
+               {}},
+              all_valid});
+}
+
+TYPED_TEST(TypedTimeRangeRollingTest, TimestampASC)
+{
+  // Confirm that timestamp columns can be used in range queries
+  // at all resolutions, given the right duration column type.
+
+  using TimeT     = TypeParam;
+  using DurationT = cudf::detail::range_type<TimeT>;
+  using time_col  = fwcw<TimeT>;
+
+  // clang-format off
+  auto gby_column  = int_col { 0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto agg_column  = int_col {{0, 8, 4, 6, 2, 9, 3, 5, 1, 7},
+                              {1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  auto time_column = time_col{ 1, 5, 6, 8, 9, 2, 2, 3, 4, 9};
+  // clang-format on
+
+  auto exec =
+    window_exec(gby_column,
+                time_column,
+                cudf::order::ASCENDING,
+                agg_column,
+                cudf::duration_scalar<DurationT>{DurationT{2}, true},   // 2 "durations" preceding.
+                cudf::duration_scalar<DurationT>{DurationT{1}, true});  // 1 "durations" following.
+
+  verify_results_for_ascending(exec);
+}
+
+template <typename WindowExecT>
+void verify_results_for_descending(WindowExecT exec)
+{
+  auto const all_valid     = thrust::make_constant_iterator<bool>(true);
+  auto const all_invalid   = thrust::make_constant_iterator<bool>(false);
+  auto const first_invalid = thrust::make_transform_iterator(thrust::make_counting_iterator(0),
+                                                             [](auto i) { return i != 0; });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE))
+      ->view(),
+    size_col{{1, 4, 4, 3, 3, 2, 3, 2, 2, 1}, all_valid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_count_aggregation<cudf::rolling_aggregation>())->view(),
+    size_col{{0, 4, 4, 3, 3, 2, 3, 2, 2, 1}, all_valid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_sum_aggregation<cudf::rolling_aggregation>())->view(),
+    fwcw<int64_t>{{1, 18, 18, 17, 17, 8, 12, 12, 12, 0}, first_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_min_aggregation<cudf::rolling_aggregation>())->view(),
+    int_col{{1, 1, 1, 3, 3, 2, 2, 4, 4, 0}, first_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_max_aggregation<cudf::rolling_aggregation>())->view(),
+    int_col{{1, 9, 9, 9, 9, 6, 6, 8, 8, 0}, first_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    exec(cudf::make_mean_aggregation<cudf::rolling_aggregation>())->view(),
+    fwcw<double>{{1.0, 4.5, 4.5, 17.0 / 3, 17.0 / 3, 4.0, 4.0, 6.0, 6.0, 0.0}, first_invalid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    exec(cudf::make_collect_list_aggregation<cudf::rolling_aggregation>())->view(),
+    lists_col{{{{0}, all_invalid},
+               {1, 5, 3, 9},
+               {1, 5, 3, 9},
+               {5, 3, 9},
+               {5, 3, 9},
+               {2, 6},
+               {2, 6, 4},
+               {4, 8},
+               {4, 8},
+               {0}},
+              all_valid});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    exec(cudf::make_collect_list_aggregation<cudf::rolling_aggregation>(cudf::null_policy::EXCLUDE))
+      ->view(),
+    lists_col{{{},
+               {1, 5, 3, 9},
+               {1, 5, 3, 9},
+               {5, 3, 9},
+               {5, 3, 9},
+               {2, 6},
+               {2, 6, 4},
+               {4, 8},
+               {4, 8},
+               {0}},
+              all_valid});
+}
+
+TYPED_TEST(TypedTimeRangeRollingTest, TimestampDESC)
+{
+  // Confirm that timestamp columns can be used in range queries
+  // at all resolutions, given the right duration column type.
+  using TimeT     = TypeParam;
+  using DurationT = cudf::detail::range_type<TimeT>;
+  using time_col  = fwcw<TimeT>;
+
+  // clang-format off
+  auto gby_column  = int_col { 5, 5, 5, 5, 5, 1, 1, 1, 1, 1};
+  auto agg_column  = int_col {{7, 1, 5, 3, 9, 2, 6, 4, 8, 0},
+                              {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  auto time_column = time_col{ 9, 4, 3, 2, 2, 9, 8, 6, 5, 1};
+  // clang-format on
+
+  auto exec =
+    window_exec(gby_column,
+                time_column,
+                cudf::order::DESCENDING,
+                agg_column,
+                cudf::duration_scalar<DurationT>{DurationT{1}, true},   // 1 "durations" preceding.
+                cudf::duration_scalar<DurationT>{DurationT{2}, true});  // 2 "durations" following.
+
+  verify_results_for_descending(exec);
+}
+
+template <typename T>
+struct TypedIntegralRangeRollingTest : RangeRollingTest {};
+
+TYPED_TEST_SUITE(TypedIntegralRangeRollingTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(TypedIntegralRangeRollingTest, OrderByASC)
+{
+  // Confirm that integral ranges work with integral orderby columns,
+  // in ascending order.
+  using T = TypeParam;
+
+  // clang-format off
+  auto gby_column = int_col { 0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  auto agg_column = int_col {{0, 8, 4, 6, 2, 9, 3, 5, 1, 7},
+                             {1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  auto oby_column = fwcw<T>{  1, 5, 6, 8, 9, 2, 2, 3, 4, 9};
+  // clang-format on
+
+  auto exec = window_exec(gby_column,
+                          oby_column,
+                          cudf::order::ASCENDING,
+                          agg_column,
+                          cudf::numeric_scalar<T>(2),   // 2 preceding.
+                          cudf::numeric_scalar<T>(1));  // 1 following.
+
+  verify_results_for_ascending(exec);
+}
+
+TYPED_TEST(TypedIntegralRangeRollingTest, OrderByDesc)
+{
+  // Confirm that integral ranges work with integral orderby columns,
+  // in descending order.
+  using T = TypeParam;
+
+  // clang-format off
+  auto gby_column  = int_col { 5, 5, 5, 5, 5, 1, 1, 1, 1, 1};
+  auto agg_column  = int_col {{7, 1, 5, 3, 9, 2, 6, 4, 8, 0},
+                              {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  auto oby_column  = fwcw<T>{  9, 4, 3, 2, 2, 9, 8, 6, 5, 1};
+  // clang-format on
+
+  auto exec = window_exec(gby_column,
+                          oby_column,
+                          cudf::order::DESCENDING,
+                          agg_column,
+                          cudf::numeric_scalar<T>(1),   // 1 preceding.
+                          cudf::numeric_scalar<T>(2));  // 2 following.
+
+  verify_results_for_descending(exec);
+}
+
+template <typename T>
+struct TypedRangeRollingNullsTest : public RangeRollingTest {};
+
+using TypesUnderTest = cudf::test::IntegralTypesNotBool;
+
+TYPED_TEST_SUITE(TypedRangeRollingNullsTest, TypesUnderTest);
+
+template <typename T>
+auto do_count_over_window(cudf::column_view grouping_col,
+                          cudf::column_view order_by,
+                          cudf::order order,
+                          cudf::column_view aggregation_col,
+                          cudf::range_window_bounds&& preceding =
+                            cudf::range_window_bounds::get(cudf::numeric_scalar<T>{T{1}, true}),
+                          cudf::range_window_bounds&& following =
+                            cudf::range_window_bounds::get(cudf::numeric_scalar<T>{T{1}, true}))
+{
+  auto const min_periods   = cudf::size_type{1};
+  auto const grouping_keys = cudf::table_view{std::vector<cudf::column_view>{grouping_col}};
+
+  return cudf::grouped_range_rolling_window(
+    grouping_keys,
+    order_by,
+    order,
+    aggregation_col,
+    std::move(preceding),
+    std::move(following),
+    min_periods,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountSingleGroupOrderByASCNullsFirst)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::ASCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 1, 2, 2, 3, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountSingleGroupOrderByASCNullsLast)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::ASCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 3, 2, 1, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountMultiGroupOrderByASCNullsFirst)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{1, 2, 2, 1, 2, 1, 2, 3, 4, 5},
+                                                                 {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::ASCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 2, 2, 2, 2, 2, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountMultiGroupOrderByASCNullsLast)
+{
+  using T = int32_t;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{1, 2, 2, 1, 3, 1, 2, 3, 4, 5},
+                                                                 {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::ASCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 2, 2, 2, 3, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountSingleGroupOrderByDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{9, 8, 7, 6, 5, 4, 3, 2, 1, 0},
+                                                                 {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::DESCENDING, agg_col);
+  ;
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 1, 2, 2, 3, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountSingleGroupOrderByDESCNullsLast)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{9, 8, 7, 6, 5, 4, 3, 2, 1, 0},
+                                                                 {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::DESCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 3, 2, 1, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountMultiGroupOrderByDESCNullsFirst)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{4, 3, 2, 1, 0, 9, 8, 7, 6, 5},
+                                                                 {0, 0, 0, 1, 1, 0, 0, 1, 1, 1}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::DESCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {3, 3, 3, 2, 2, 2, 2, 2, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountMultiGroupOrderByDESCNullsLast)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{4, 3, 2, 1, 0, 9, 8, 7, 6, 5},
+                                                                 {1, 1, 1, 0, 0, 1, 1, 1, 0, 0}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::DESCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 2, 2, 2, 2, 3, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountSingleGroupAllNullOrderBys)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::ASCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 9, 9, 9, 9, 9, 9, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, CountMultiGroupAllNullOrderBys)
+{
+  using T = TypeParam;
+
+  // Groupby column.
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
+  // Aggregation column.
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  // OrderBy column.
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+
+  auto const output = do_count_over_window<T>(grp_col, oby_col, cudf::order::ASCENDING, agg_col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {2, 3, 3, 3, 2, 4, 4, 4, 4, 4}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, UnboundedPrecedingWindowSingleGroupOrderByASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const output = do_count_over_window<T>(
+    grp_col,
+    oby_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<T>()}),
+    cudf::range_window_bounds::get(cudf::numeric_scalar<T>{1, true}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {4, 4, 4, 4, 5, 6, 7, 8, 9, 9}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
+
+TYPED_TEST(TypedRangeRollingNullsTest, UnboundedFollowingWindowSingleGroupOrderByASCNullsFirst)
+{
+  using T = TypeParam;
+
+  auto const grp_col = cudf::test::fixed_width_column_wrapper<T>{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+  auto const agg_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {1, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+  auto const oby_col = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                                 {0, 0, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  auto const output = do_count_over_window<T>(
+    grp_col,
+    oby_col,
+    cudf::order::ASCENDING,
+    agg_col,
+    cudf::range_window_bounds::get(cudf::numeric_scalar<T>{1, true}),
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<T>()}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output->view(),
+                                 cudf::test::fixed_width_column_wrapper<cudf::size_type>{
+                                   {9, 9, 9, 9, 5, 5, 4, 4, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}});
+}
diff --git a/cpp/tests/rolling/range_window_bounds_test.cpp b/cpp/tests/rolling/range_window_bounds_test.cpp
new file mode 100644
index 0000000..ceedda7
--- /dev/null
+++ b/cpp/tests/rolling/range_window_bounds_test.cpp
@@ -0,0 +1,217 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/rolling/range_window_bounds.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <src/rolling/detail/range_window_bounds.hpp>
+
+#include <vector>
+
+struct RangeWindowBoundsTest : public cudf::test::BaseFixture {};
+
+template <typename Timestamp>
+struct TimestampRangeWindowBoundsTest : RangeWindowBoundsTest {};
+
+TYPED_TEST_SUITE(TimestampRangeWindowBoundsTest, cudf::test::TimestampTypes);
+
+TEST_F(RangeWindowBoundsTest, TestBasicTimestampRangeTypeMapping)
+{
+  // Explicitly check that the programmatic mapping of orderby column types
+  // to their respective range and range_rep types is accurate.
+
+  static_assert(std::is_same_v<cudf::detail::range_type<cudf::timestamp_D>, cudf::duration_D>);
+  static_assert(std::is_same_v<cudf::detail::range_type<cudf::timestamp_s>, cudf::duration_s>);
+  static_assert(std::is_same_v<cudf::detail::range_type<cudf::timestamp_ms>, cudf::duration_ms>);
+  static_assert(std::is_same_v<cudf::detail::range_type<cudf::timestamp_us>, cudf::duration_us>);
+  static_assert(std::is_same_v<cudf::detail::range_type<cudf::timestamp_ns>, cudf::duration_ns>);
+
+  static_assert(std::is_same_v<cudf::detail::range_rep_type<cudf::timestamp_D>, int32_t>);
+  static_assert(std::is_same_v<cudf::detail::range_rep_type<cudf::timestamp_s>, int64_t>);
+  static_assert(std::is_same_v<cudf::detail::range_rep_type<cudf::timestamp_ms>, int64_t>);
+  static_assert(std::is_same_v<cudf::detail::range_rep_type<cudf::timestamp_us>, int64_t>);
+  static_assert(std::is_same_v<cudf::detail::range_rep_type<cudf::timestamp_ns>, int64_t>);
+}
+
+TYPED_TEST(TimestampRangeWindowBoundsTest, BoundsConstruction)
+{
+  using OrderByType = TypeParam;
+  using range_type  = cudf::detail::range_type<OrderByType>;
+  using rep_type    = cudf::detail::range_rep_type<OrderByType>;
+  auto const dtype  = cudf::data_type{cudf::type_to_id<OrderByType>()};
+
+  static_assert(cudf::is_duration<range_type>());
+  auto range_3 = cudf::range_window_bounds::get(cudf::duration_scalar<range_type>{3, true});
+  EXPECT_FALSE(range_3.is_unbounded() &&
+               "range_window_bounds constructed from scalar cannot be unbounded.");
+  EXPECT_EQ(
+    cudf::detail::range_comparable_value<OrderByType>(range_3, dtype, cudf::get_default_stream()),
+    rep_type{3});
+
+  auto range_unbounded =
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<range_type>()});
+  EXPECT_TRUE(range_unbounded.is_unbounded() &&
+              "range_window_bounds::unbounded() must return an unbounded range.");
+  EXPECT_EQ(cudf::detail::range_comparable_value<OrderByType>(
+              range_unbounded, dtype, cudf::get_default_stream()),
+            rep_type{});
+}
+
+TYPED_TEST(TimestampRangeWindowBoundsTest, WrongRangeType)
+{
+  using OrderByType = TypeParam;
+  auto const dtype  = cudf::data_type{cudf::type_to_id<OrderByType>()};
+
+  using wrong_range_type = std::conditional_t<std::is_same_v<OrderByType, cudf::timestamp_D>,
+                                              cudf::duration_ns,
+                                              cudf::duration_D>;
+  auto range_3 = cudf::range_window_bounds::get(cudf::duration_scalar<wrong_range_type>{3, true});
+
+  EXPECT_THROW(
+    cudf::detail::range_comparable_value<OrderByType>(range_3, dtype, cudf::get_default_stream()),
+    cudf::logic_error);
+
+  auto range_unbounded =
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<wrong_range_type>()});
+  EXPECT_THROW(cudf::detail::range_comparable_value<OrderByType>(
+                 range_unbounded, dtype, cudf::get_default_stream()),
+               cudf::logic_error);
+}
+
+template <typename T>
+struct NumericRangeWindowBoundsTest : RangeWindowBoundsTest {};
+
+using TypesForTest = cudf::test::IntegralTypesNotBool;
+
+TYPED_TEST_SUITE(NumericRangeWindowBoundsTest, TypesForTest);
+
+TYPED_TEST(NumericRangeWindowBoundsTest, BasicNumericRangeTypeMapping)
+{
+  using T = TypeParam;
+
+  using range_type     = cudf::detail::range_type<T>;
+  using range_rep_type = cudf::detail::range_rep_type<T>;
+
+  static_assert(std::is_same_v<T, range_type>);
+  static_assert(std::is_same_v<T, range_rep_type>);
+}
+
+TYPED_TEST(NumericRangeWindowBoundsTest, BoundsConstruction)
+{
+  using OrderByType = TypeParam;
+  using range_type  = cudf::detail::range_type<OrderByType>;
+  using rep_type    = cudf::detail::range_rep_type<OrderByType>;
+  auto const dtype  = cudf::data_type{cudf::type_to_id<OrderByType>()};
+
+  static_assert(std::is_integral_v<range_type>);
+  auto range_3 = cudf::range_window_bounds::get(cudf::numeric_scalar<range_type>{3, true});
+  EXPECT_FALSE(range_3.is_unbounded() &&
+               "range_window_bounds constructed from scalar cannot be unbounded.");
+  EXPECT_EQ(
+    cudf::detail::range_comparable_value<OrderByType>(range_3, dtype, cudf::get_default_stream()),
+    rep_type{3});
+
+  auto range_unbounded =
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<range_type>()});
+  EXPECT_TRUE(range_unbounded.is_unbounded() &&
+              "range_window_bounds::unbounded() must return an unbounded range.");
+  EXPECT_EQ(cudf::detail::range_comparable_value<OrderByType>(
+              range_unbounded, dtype, cudf::get_default_stream()),
+            rep_type{});
+}
+
+TYPED_TEST(NumericRangeWindowBoundsTest, WrongRangeType)
+{
+  using OrderByType = TypeParam;
+  auto const dtype  = cudf::data_type{cudf::type_to_id<OrderByType>()};
+
+  using wrong_range_type =
+    std::conditional_t<std::is_same_v<OrderByType, int32_t>, int16_t, int32_t>;
+  auto range_3 = cudf::range_window_bounds::get(cudf::numeric_scalar<wrong_range_type>{3, true});
+
+  EXPECT_THROW(
+    cudf::detail::range_comparable_value<OrderByType>(range_3, dtype, cudf::get_default_stream()),
+    cudf::logic_error);
+
+  auto range_unbounded =
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<wrong_range_type>()});
+  EXPECT_THROW(cudf::detail::range_comparable_value<OrderByType>(
+                 range_unbounded, dtype, cudf::get_default_stream()),
+               cudf::logic_error);
+}
+
+template <typename T>
+struct DecimalRangeBoundsTest : RangeWindowBoundsTest {};
+
+TYPED_TEST_SUITE(DecimalRangeBoundsTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(DecimalRangeBoundsTest, BoundsConstruction)
+{
+  using DecimalT   = TypeParam;
+  using Rep        = cudf::detail::range_rep_type<DecimalT>;
+  auto const dtype = cudf::data_type{cudf::type_to_id<DecimalT>()};
+
+  // Interval type must match the decimal type.
+  static_assert(std::is_same_v<cudf::detail::range_type<DecimalT>, DecimalT>);
+
+  auto const range_3 = cudf::range_window_bounds::get(
+    cudf::fixed_point_scalar<DecimalT>{Rep{3}, numeric::scale_type{0}});
+  EXPECT_FALSE(range_3.is_unbounded() &&
+               "range_window_bounds constructed from scalar cannot be unbounded.");
+  EXPECT_EQ(
+    cudf::detail::range_comparable_value<DecimalT>(range_3, dtype, cudf::get_default_stream()),
+    Rep{3});
+
+  auto const range_unbounded =
+    cudf::range_window_bounds::unbounded(cudf::data_type{cudf::type_to_id<DecimalT>()});
+  EXPECT_TRUE(range_unbounded.is_unbounded() &&
+              "range_window_bounds::unbounded() must return an unbounded range.");
+}
+
+TYPED_TEST(DecimalRangeBoundsTest, Rescale)
+{
+  using DecimalT = TypeParam;
+  using RepT     = typename DecimalT::rep;
+
+  // Powers of 10.
+  auto constexpr pow10 = std::array{1, 10, 100, 1000, 10000, 100000};
+
+  // Check that the rep has expected values at different range scales.
+  auto const order_by_scale     = -2;
+  auto const order_by_data_type = cudf::data_type{cudf::type_to_id<DecimalT>(), order_by_scale};
+
+  for (auto const range_scale : {-2, -1, 0, 1, 2}) {
+    auto const decimal_range_bounds = cudf::range_window_bounds::get(
+      cudf::fixed_point_scalar<DecimalT>{RepT{20}, numeric::scale_type{range_scale}});
+    auto const rescaled_range_rep = cudf::detail::range_comparable_value<DecimalT>(
+      decimal_range_bounds, order_by_data_type, cudf::get_default_stream());
+    EXPECT_EQ(rescaled_range_rep, RepT{20} * pow10[range_scale - order_by_scale]);
+  }
+
+  // Order By column scale cannot exceed range scale:
+  {
+    auto const decimal_range_bounds = cudf::range_window_bounds::get(
+      cudf::fixed_point_scalar<DecimalT>{RepT{200}, numeric::scale_type{-3}});
+    EXPECT_THROW(cudf::detail::range_comparable_value<DecimalT>(
+                   decimal_range_bounds, order_by_data_type, cudf::get_default_stream()),
+                 cudf::logic_error);
+  }
+}
diff --git a/cpp/tests/rolling/rolling_test.cpp b/cpp/tests/rolling/rolling_test.cpp
new file mode 100644
index 0000000..d018197
--- /dev/null
+++ b/cpp/tests/rolling/rolling_test.cpp
@@ -0,0 +1,1628 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "rolling_test.hpp"
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <src/rolling/detail/rolling.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <limits>
+#include <type_traits>
+#include <vector>
+
+class RollingStringTest : public cudf::test::BaseFixture {};
+
+TEST_F(RollingStringTest, NoNullStringMinMaxCount)
+{
+  cudf::test::strings_column_wrapper input(
+    {"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"});
+  std::vector<cudf::size_type> window{2};
+  cudf::test::strings_column_wrapper expected_min(
+    {"This", "This", "being", "being", "being", "being", "column", "column", "column"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper expected_max(
+    {"rolling", "test", "test", "test", "test", "string", "string", "string", "string"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count(
+    {3, 4, 4, 4, 4, 4, 4, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto got_min = cudf::rolling_window(
+    input, window[0], window[0], 1, *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+  auto got_max = cudf::rolling_window(
+    input, window[0], window[0], 1, *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+  auto got_count_valid = cudf::rolling_window(
+    input, window[0], window[0], 1, *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto got_count_all = cudf::rolling_window(
+    input,
+    window[0],
+    window[0],
+    1,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, got_min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, got_max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count, got_count_valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count, got_count_all->view());
+}
+
+TEST_F(RollingStringTest, NullStringMinMaxCount)
+{
+  cudf::test::strings_column_wrapper input(
+    {"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"},
+    {1, 0, 0, 1, 0, 1, 1, 1, 0});
+  std::vector<cudf::size_type> window{2};
+  cudf::test::strings_column_wrapper expected_min(
+    {"This", "This", "test", "operated", "on", "on", "on", "on", "string"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper expected_max(
+    {"This", "test", "test", "test", "test", "string", "string", "string", "string"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count_val(
+    {1, 2, 1, 2, 3, 3, 3, 2, 1}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count_all(
+    {3, 4, 4, 4, 4, 4, 4, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto got_min = cudf::rolling_window(
+    input, window[0], window[0], 1, *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+  auto got_max = cudf::rolling_window(
+    input, window[0], window[0], 1, *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+  auto got_count_valid = cudf::rolling_window(
+    input, window[0], window[0], 1, *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto got_count_all = cudf::rolling_window(
+    input,
+    window[0],
+    window[0],
+    1,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, got_min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, got_max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_val, got_count_valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_all, got_count_all->view());
+}
+
+TEST_F(RollingStringTest, MinPeriods)
+{
+  cudf::test::strings_column_wrapper input(
+    {"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"},
+    {1, 0, 0, 1, 0, 1, 1, 1, 0});
+  std::vector<cudf::size_type> window{2};
+  cudf::test::strings_column_wrapper expected_min(
+    {"This", "This", "This", "operated", "on", "on", "on", "on", "on"},
+    {0, 0, 0, 0, 1, 1, 1, 0, 0});
+  cudf::test::strings_column_wrapper expected_max(
+    {"This", "test", "test", "test", "test", "string", "string", "string", "string"},
+    {0, 0, 0, 0, 1, 1, 1, 0, 0});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count_val(
+    {1, 2, 1, 2, 3, 3, 3, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count_all(
+    {3, 4, 4, 4, 4, 4, 4, 3, 2}, {0, 1, 1, 1, 1, 1, 1, 0, 0});
+
+  auto got_min = cudf::rolling_window(
+    input, window[0], window[0], 3, *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+  auto got_max = cudf::rolling_window(
+    input, window[0], window[0], 3, *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+  auto got_count_valid = cudf::rolling_window(
+    input, window[0], window[0], 3, *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto got_count_all = cudf::rolling_window(
+    input,
+    window[0],
+    window[0],
+    4,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, got_min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, got_max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_val, got_count_valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_all, got_count_all->view());
+}
+
+// =========================================================================================
+class RollingStructTest : public cudf::test::BaseFixture {};
+
+TEST_F(RollingStructTest, NoNullStructsMinMaxCount)
+{
+  using strings_col = cudf::test::strings_column_wrapper;
+  using ints_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using structs_col = cudf::test::structs_column_wrapper;
+
+  auto const do_test = [](auto const& input) {
+    auto const expected_min = [] {
+      auto child1 = strings_col{
+        "This", "This", "being", "being", "being", "being", "column", "column", "column"};
+      auto child2 = ints_col{1, 1, 5, 5, 5, 5, 9, 9, 9};
+      return structs_col{{child1, child2}, cudf::test::iterators::no_nulls()};
+    }();
+
+    auto const expected_max = [] {
+      auto child1 = strings_col{
+        "rolling", "test", "test", "test", "test", "string", "string", "string", "string"};
+      auto child2 = ints_col{3, 4, 4, 4, 4, 8, 8, 8, 8};
+      return structs_col{{child1, child2}, cudf::test::iterators::no_nulls()};
+    }();
+
+    auto const expected_count =
+      ints_col{{3, 4, 4, 4, 4, 4, 4, 3, 2}, cudf::test::iterators::no_nulls()};
+    auto constexpr preceding  = 2;
+    auto constexpr following  = 2;
+    auto constexpr min_period = 1;
+
+    auto const result_min =
+      cudf::rolling_window(input,
+                           preceding,
+                           following,
+                           min_period,
+                           *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+    auto const result_max =
+      cudf::rolling_window(input,
+                           preceding,
+                           following,
+                           min_period,
+                           *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+    auto const result_count_valid =
+      cudf::rolling_window(input,
+                           preceding,
+                           following,
+                           min_period,
+                           *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+    auto const result_count_all = cudf::rolling_window(
+      input,
+      preceding,
+      following,
+      min_period,
+      *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, result_min->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, result_max->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count, result_count_valid->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count, result_count_all->view());
+  };
+
+  auto const input_no_sliced = [] {
+    auto child1 =
+      strings_col{"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"};
+    auto child2 = ints_col{1, 2, 3, 4, 5, 6, 7, 8, 9};
+    return structs_col{{child1, child2}};
+  }();
+
+  auto const input_before_sliced = [] {
+    auto constexpr dont_care{0};
+    auto child1 = strings_col{"1dont_care",
+                              "1dont_care",
+                              "@dont_care",
+                              "This",
+                              "is",
+                              "rolling",
+                              "test",
+                              "being",
+                              "operated",
+                              "on",
+                              "string",
+                              "column",
+                              "1dont_care",
+                              "1dont_care",
+                              "@dont_care"};
+    auto child2 = ints_col{
+      dont_care, dont_care, dont_care, 1, 2, 3, 4, 5, 6, 7, 8, 9, dont_care, dont_care, dont_care};
+    return structs_col{{child1, child2}};
+  }();
+  auto const input_sliced = cudf::slice(input_before_sliced, {3, 12})[0];
+
+  do_test(input_no_sliced);
+  do_test(input_sliced);
+}
+
+TEST_F(RollingStructTest, NullChildrenMinMaxCount)
+{
+  using strings_col = cudf::test::strings_column_wrapper;
+  using ints_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using structs_col = cudf::test::structs_column_wrapper;
+
+  auto const input = [] {
+    auto child1 = strings_col{
+      {"This", "" /*NULL*/, "" /*NULL*/, "test", "" /*NULL*/, "operated", "on", "string", "column"},
+      cudf::test::iterators::nulls_at({1, 2, 4})};
+    auto child2 = ints_col{1, 2, 3, 4, 5, 6, 7, 8, 9};
+    return structs_col{{child1, child2}};
+  }();
+
+  auto const expected_min = [] {
+    auto child1 = strings_col{{"" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "" /*NULL*/,
+                               "column",
+                               "column",
+                               "column"},
+                              cudf::test::iterators::nulls_at({0, 1, 2, 3, 4, 5})};
+    auto child2 = ints_col{2, 2, 2, 3, 5, 5, 9, 9, 9};
+    return structs_col{{child1, child2}, cudf::test::iterators::no_nulls()};
+  }();
+
+  auto const expected_max = [] {
+    auto child1 =
+      strings_col{"This", "test", "test", "test", "test", "string", "string", "string", "string"};
+    auto child2 = ints_col{1, 4, 4, 4, 4, 8, 8, 8, 8};
+    return structs_col{{child1, child2}, cudf::test::iterators::no_nulls()};
+  }();
+
+  auto const expected_count =
+    ints_col{{3, 4, 4, 4, 4, 4, 4, 3, 2}, cudf::test::iterators::no_nulls()};
+  auto constexpr preceding  = 2;
+  auto constexpr following  = 2;
+  auto constexpr min_period = 1;
+
+  auto const result_min =
+    cudf::rolling_window(input,
+                         preceding,
+                         following,
+                         min_period,
+                         *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+
+  auto const result_max =
+    cudf::rolling_window(input,
+                         preceding,
+                         following,
+                         min_period,
+                         *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+
+  auto const result_count_valid =
+    cudf::rolling_window(input,
+                         preceding,
+                         following,
+                         min_period,
+                         *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto const result_count_all = cudf::rolling_window(
+    input,
+    preceding,
+    following,
+    min_period,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, result_min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, result_max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count, result_count_valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count, result_count_all->view());
+}
+
+TEST_F(RollingStructTest, NullParentMinMaxCount)
+{
+  using strings_col = cudf::test::strings_column_wrapper;
+  using ints_col    = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using structs_col = cudf::test::structs_column_wrapper;
+
+  auto constexpr null{0};
+  auto const input = [] {
+    auto child1 = strings_col{"This",
+                              "" /*NULL*/,
+                              "" /*NULL*/,
+                              "test",
+                              "" /*NULL*/,
+                              "operated",
+                              "on",
+                              "string",
+                              "" /*NULL*/};
+    auto child2 = ints_col{1, null, null, 4, null, 6, 7, 8, null};
+    return structs_col{{child1, child2}, cudf::test::iterators::nulls_at({1, 2, 4, 8})};
+  }();
+
+  auto const expected_min = [] {
+    auto child1 = strings_col{"This", "This", "test", "operated", "on", "on", "on", "on", "string"};
+    auto child2 = ints_col{1, 1, 4, 6, 7, 7, 7, 7, 8};
+    return structs_col{{child1, child2}, cudf::test::iterators::no_nulls()};
+  }();
+
+  auto const expected_max = [] {
+    auto child1 =
+      strings_col{"This", "test", "test", "test", "test", "string", "string", "string", "string"};
+    auto child2 = ints_col{1, 4, 4, 4, 4, 8, 8, 8, 8};
+    return structs_col{{child1, child2}, cudf::test::iterators::no_nulls()};
+  }();
+
+  auto const expected_count_valid =
+    ints_col{{1, 2, 1, 2, 3, 3, 3, 2, 1}, cudf::test::iterators::no_nulls()};
+  auto const expected_count_all =
+    ints_col{{3, 4, 4, 4, 4, 4, 4, 3, 2}, cudf::test::iterators::no_nulls()};
+  auto constexpr preceding  = 2;
+  auto constexpr following  = 2;
+  auto constexpr min_period = 1;
+
+  auto const result_min =
+    cudf::rolling_window(input,
+                         preceding,
+                         following,
+                         min_period,
+                         *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+
+  auto const result_max =
+    cudf::rolling_window(input,
+                         preceding,
+                         following,
+                         min_period,
+                         *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+
+  auto const result_count_valid =
+    cudf::rolling_window(input,
+                         preceding,
+                         following,
+                         min_period,
+                         *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto const result_count_all = cudf::rolling_window(
+    input,
+    preceding,
+    following,
+    min_period,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, result_min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, result_max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_valid, result_count_valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_all, result_count_all->view());
+}
+
+// =========================================================================================
+template <typename T>
+class RollingTest : public cudf::test::BaseFixture {
+ protected:
+  // input as column_wrapper
+  void run_test_col(cudf::column_view const& input,
+                    std::vector<cudf::size_type> const& preceding_window,
+                    std::vector<cudf::size_type> const& following_window,
+                    cudf::size_type min_periods,
+                    cudf::rolling_aggregation const& op)
+  {
+    std::unique_ptr<cudf::column> output;
+
+    // wrap windows
+    if (preceding_window.size() > 1) {
+      cudf::test::fixed_width_column_wrapper<cudf::size_type> preceding_window_wrapper(
+        preceding_window.begin(), preceding_window.end());
+      cudf::test::fixed_width_column_wrapper<cudf::size_type> following_window_wrapper(
+        following_window.begin(), following_window.end());
+
+      EXPECT_NO_THROW(
+        output = cudf::rolling_window(
+          input, preceding_window_wrapper, following_window_wrapper, min_periods, op));
+    } else {
+      EXPECT_NO_THROW(output = cudf::rolling_window(
+                        input, preceding_window[0], following_window[0], min_periods, op));
+    }
+
+    auto reference =
+      create_reference_output(op, input, preceding_window, following_window, min_periods);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, *reference);
+  }
+
+  // helper function to test all aggregators
+  void run_test_col_agg(cudf::column_view const& input,
+                        std::vector<cudf::size_type> const& preceding_window,
+                        std::vector<cudf::size_type> const& following_window,
+                        cudf::size_type min_periods)
+  {
+    // test all supported aggregators
+    run_test_col(input,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+    run_test_col(input,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+    run_test_col(
+      input,
+      preceding_window,
+      following_window,
+      min_periods,
+      *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+    run_test_col(input,
+                 preceding_window,
+                 following_window,
+                 min_periods,
+                 *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+
+    if (not cudf::is_timestamp(input.type())) {
+      run_test_col(input,
+                   preceding_window,
+                   following_window,
+                   min_periods,
+                   *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+      run_test_col(input,
+                   preceding_window,
+                   following_window,
+                   min_periods,
+                   *cudf::make_mean_aggregation<cudf::rolling_aggregation>());
+    }
+  }
+
+ private:
+  // use SFINAE to only instantiate for supported combinations
+
+  // specialization for COUNT_VALID, COUNT_ALL
+  template <bool include_nulls>
+  std::unique_ptr<cudf::column> create_count_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& preceding_window_col,
+    std::vector<cudf::size_type> const& following_window_col,
+    cudf::size_type min_periods)
+  {
+    cudf::size_type num_rows = input.size();
+    std::vector<cudf::size_type> ref_data(num_rows);
+    std::vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+
+    std::vector<cudf::bitmask_type> in_valid = cudf::test::bitmask_to_host(input);
+    cudf::bitmask_type* valid_mask           = in_valid.data();
+
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto preceding_window       = preceding_window_col[i % preceding_window_col.size()];
+      auto following_window       = following_window_col[i % following_window_col.size()];
+      cudf::size_type start       = std::min(num_rows, std::max(0, i - preceding_window + 1));
+      cudf::size_type end         = std::min(num_rows, std::max(0, i + following_window + 1));
+      cudf::size_type start_index = std::min(start, end);
+      cudf::size_type end_index   = std::max(start, end);
+
+      // aggregate
+      cudf::size_type count = 0;
+      for (cudf::size_type j = start_index; j < end_index; j++) {
+        if (include_nulls || !input.nullable() || cudf::bit_is_set(valid_mask, j)) count++;
+      }
+
+      ref_valid[i] = ((end_index - start_index) >= min_periods);
+      if (ref_valid[i]) ref_data[i] = count;
+    }
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  template <typename agg_op,
+            cudf::aggregation::Kind k,
+            typename OutputType,
+            bool is_mean,
+            std::enable_if_t<is_rolling_supported<T, k>()>* = nullptr>
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& preceding_window_col,
+    std::vector<cudf::size_type> const& following_window_col,
+    cudf::size_type min_periods)
+  {
+    cudf::size_type num_rows = input.size();
+    thrust::host_vector<OutputType> ref_data(num_rows);
+    thrust::host_vector<bool> ref_valid(num_rows);
+
+    // input data and mask
+    auto [in_col, in_valid]        = cudf::test::to_host<T>(input);
+    cudf::bitmask_type* valid_mask = in_valid.data();
+
+    agg_op op;
+    for (cudf::size_type i = 0; i < num_rows; i++) {
+      OutputType val = agg_op::template identity<OutputType>();
+
+      // load sizes
+      min_periods = std::max(min_periods, 1);  // at least one observation is required
+
+      // compute bounds
+      auto preceding_window       = preceding_window_col[i % preceding_window_col.size()];
+      auto following_window       = following_window_col[i % following_window_col.size()];
+      cudf::size_type start       = std::min(num_rows, std::max(0, i - preceding_window + 1));
+      cudf::size_type end         = std::min(num_rows, std::max(0, i + following_window + 1));
+      cudf::size_type start_index = std::min(start, end);
+      cudf::size_type end_index   = std::max(start, end);
+
+      // aggregate
+      cudf::size_type count = 0;
+      for (cudf::size_type j = start_index; j < end_index; j++) {
+        if (!input.nullable() || cudf::bit_is_set(valid_mask, j)) {
+          val = op(static_cast<OutputType>(in_col[j]), val);
+          count++;
+        }
+      }
+
+      ref_valid[i] = (count >= min_periods);
+      if (ref_valid[i]) {
+        cudf::detail::rolling_store_output_functor<OutputType, is_mean>{}(ref_data[i], val, count);
+      }
+    }
+
+    cudf::test::fixed_width_column_wrapper<OutputType> col(
+      ref_data.begin(), ref_data.end(), ref_valid.begin());
+    return col.release();
+  }
+
+  template <typename agg_op,
+            cudf::aggregation::Kind k,
+            typename OutputType,
+            bool is_mean,
+            std::enable_if_t<!is_rolling_supported<T, k>()>* = nullptr>
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& preceding_window_col,
+    std::vector<cudf::size_type> const& following_window_col,
+    cudf::size_type min_periods)
+  {
+    CUDF_FAIL("Unsupported combination of type and aggregation");
+  }
+
+  std::unique_ptr<cudf::column> create_reference_output(
+    cudf::rolling_aggregation const& op,
+    cudf::column_view const& input,
+    std::vector<cudf::size_type> const& preceding_window,
+    std::vector<cudf::size_type> const& following_window,
+    cudf::size_type min_periods)
+  {
+    // unroll aggregation types
+    switch (op.kind) {
+      case cudf::aggregation::SUM:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::SUM,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::SUM>,
+                                       false>(
+          input, preceding_window, following_window, min_periods);
+      case cudf::aggregation::MIN:
+        return create_reference_output<cudf::DeviceMin,
+                                       cudf::aggregation::MIN,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MIN>,
+                                       false>(
+          input, preceding_window, following_window, min_periods);
+      case cudf::aggregation::MAX:
+        return create_reference_output<cudf::DeviceMax,
+                                       cudf::aggregation::MAX,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MAX>,
+                                       false>(
+          input, preceding_window, following_window, min_periods);
+      case cudf::aggregation::COUNT_VALID:
+        return create_count_reference_output<false>(
+          input, preceding_window, following_window, min_periods);
+      case cudf::aggregation::COUNT_ALL:
+        return create_count_reference_output<true>(
+          input, preceding_window, following_window, min_periods);
+      case cudf::aggregation::MEAN:
+        return create_reference_output<cudf::DeviceSum,
+                                       cudf::aggregation::MEAN,
+                                       cudf::detail::target_type_t<T, cudf::aggregation::MEAN>,
+                                       true>(
+          input, preceding_window, following_window, min_periods);
+      default: return cudf::test::fixed_width_column_wrapper<T>({}).release();
+    }
+  }
+};
+
+template <typename T>
+class RollingVarStdTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(RollingVarStdTest, cudf::test::FixedWidthTypesWithoutChrono);
+
+class RollingtVarStdTestUntyped : public cudf::test::BaseFixture {};
+
+class RollingErrorTest : public cudf::test::BaseFixture {};
+
+// negative sizes
+TEST_F(RollingErrorTest, NegativeMinPeriods)
+{
+  const std::vector<cudf::size_type> col_data = {0, 1, 2, 0, 4};
+  const std::vector<bool> col_valid           = {1, 1, 1, 0, 1};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> input(
+    col_data.begin(), col_data.end(), col_valid.begin());
+
+  EXPECT_THROW(
+    cudf::rolling_window(input, 2, 2, -2, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+}
+
+// window array size mismatch
+TEST_F(RollingErrorTest, WindowArraySizeMismatch)
+{
+  const std::vector<cudf::size_type> col_data = {0, 1, 2, 0, 4};
+  const std::vector<bool> col_valid           = {1, 1, 1, 0, 1};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> input(
+    col_data.begin(), col_data.end(), col_valid.begin());
+
+  std::vector<cudf::size_type> five({2, 1, 2, 1, 4});
+  std::vector<cudf::size_type> four({1, 2, 3, 4});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> five_elements(five.begin(), five.end());
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> four_elements(four.begin(), four.end());
+
+  // this runs ok
+  EXPECT_NO_THROW(cudf::rolling_window(input,
+                                       five_elements,
+                                       five_elements,
+                                       1,
+                                       *cudf::make_sum_aggregation<cudf::rolling_aggregation>()));
+
+  // mismatch for the window array
+  EXPECT_THROW(cudf::rolling_window(input,
+                                    four_elements,
+                                    five_elements,
+                                    1,
+                                    *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+
+  // mismatch for the forward window array
+  EXPECT_THROW(cudf::rolling_window(input,
+                                    five_elements,
+                                    four_elements,
+                                    1,
+                                    *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+}
+
+TEST_F(RollingErrorTest, EmptyInput)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> empty_col{};
+  std::unique_ptr<cudf::column> output;
+  EXPECT_NO_THROW(output = cudf::rolling_window(
+                    empty_col, 2, 0, 2, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()));
+  EXPECT_EQ(output->size(), 0);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> preceding_window{};
+  cudf::test::fixed_width_column_wrapper<int32_t> following_window{};
+  EXPECT_NO_THROW(output =
+                    cudf::rolling_window(empty_col,
+                                         preceding_window,
+                                         following_window,
+                                         2,
+                                         *cudf::make_sum_aggregation<cudf::rolling_aggregation>()));
+  EXPECT_EQ(output->size(), 0);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> nonempty_col{{1, 2, 3}};
+  EXPECT_NO_THROW(output =
+                    cudf::rolling_window(nonempty_col,
+                                         preceding_window,
+                                         following_window,
+                                         2,
+                                         *cudf::make_sum_aggregation<cudf::rolling_aggregation>()));
+  EXPECT_EQ(output->size(), 0);
+}
+
+TEST_F(RollingErrorTest, SizeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> nonempty_col{{1, 2, 3}};
+  std::unique_ptr<cudf::column> output;
+
+  {
+    cudf::test::fixed_width_column_wrapper<int32_t> preceding_window{{1, 1}};  // wrong size
+    cudf::test::fixed_width_column_wrapper<int32_t> following_window{{1, 1, 1}};
+    EXPECT_THROW(
+      output = cudf::rolling_window(nonempty_col,
+                                    preceding_window,
+                                    following_window,
+                                    2,
+                                    *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+      cudf::logic_error);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<int32_t> preceding_window{{1, 1, 1}};
+    cudf::test::fixed_width_column_wrapper<int32_t> following_window{{1, 2}};  // wrong size
+    EXPECT_THROW(
+      output = cudf::rolling_window(nonempty_col,
+                                    preceding_window,
+                                    following_window,
+                                    2,
+                                    *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+      cudf::logic_error);
+  }
+}
+
+TEST_F(RollingErrorTest, WindowWrongDtype)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> nonempty_col{{1, 2, 3}};
+  std::unique_ptr<cudf::column> output;
+
+  cudf::test::fixed_width_column_wrapper<float> preceding_window{{1.0f, 1.0f, 1.0f}};
+  cudf::test::fixed_width_column_wrapper<float> following_window{{1.0f, 1.0f, 1.0f}};
+  EXPECT_THROW(
+    output = cudf::rolling_window(nonempty_col,
+                                  preceding_window,
+                                  following_window,
+                                  2,
+                                  *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+}
+
+// incorrect type/aggregation combo: sum of timestamps
+TEST_F(RollingErrorTest, SumTimestampNotSupported)
+{
+  constexpr cudf::size_type size{10};
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> input_D(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> input_s(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> input_ms(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep> input_us(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> input_ns(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+
+  EXPECT_THROW(cudf::rolling_window(
+                 input_D, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_s, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_ms, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_us, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_ns, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+}
+
+// incorrect type/aggregation combo: mean of timestamps
+TEST_F(RollingErrorTest, MeanTimestampNotSupported)
+{
+  constexpr cudf::size_type size{10};
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> input_D(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> input_s(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> input_ms(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep> input_us(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> input_ns(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(size));
+
+  EXPECT_THROW(cudf::rolling_window(
+                 input_D, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_s, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_ms, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_us, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(
+                 input_ns, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+               cudf::logic_error);
+}
+
+TYPED_TEST_SUITE(RollingTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+// simple example from Pandas docs
+TYPED_TEST(RollingTest, SimpleStatic)
+{
+  // https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
+  auto const col_data              = cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 0, 4});
+  const std::vector<bool> col_mask = {1, 1, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  std::vector<cudf::size_type> window{2};
+
+  // static sizes
+  this->run_test_col_agg(input, window, window, 1);
+}
+
+TYPED_TEST(RollingVarStdTest, SimpleStaticVarianceStd)
+{
+#define XXX 0  // NULL stub
+
+  using ResultType = double;
+
+  double const nan = std::numeric_limits<double>::signaling_NaN();
+
+  cudf::size_type const ddof = 1, min_periods = 0, preceding_window = 2, following_window = 1;
+
+  auto const col_data =
+    cudf::test::make_type_param_vector<TypeParam>({XXX, XXX, 9, 5, XXX, XXX, XXX, 0, 8, 5, 8});
+  const std::vector<bool> col_mask = {0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1};
+
+  auto const expected_var =
+    cudf::is_boolean<TypeParam>()
+      ? std::vector<ResultType>{XXX, nan, 0, 0, nan, XXX, nan, 0.5, 0.3333333333333333, 0, 0}
+      : std::vector<ResultType>{XXX, nan, 8, 8, nan, XXX, nan, 32, 16.33333333333333, 3, 4.5};
+  std::vector<ResultType> expected_std(expected_var.size());
+  std::transform(expected_var.begin(), expected_var.end(), expected_std.begin(), [](auto const& x) {
+    return std::sqrt(x);
+  });
+
+  const std::vector<bool> expected_mask = {0, /* all null window */
+                                           1, /* 0 div 0, nan */
+                                           1,
+                                           1,
+                                           1, /* 0 div 0, nan */
+                                           0, /* all null window */
+                                           1, /* 0 div 0, nan */
+                                           1,
+                                           1,
+                                           1,
+                                           1};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  cudf::test::fixed_width_column_wrapper<ResultType> var_expect(
+    expected_var.begin(), expected_var.end(), expected_mask.begin());
+  cudf::test::fixed_width_column_wrapper<ResultType> std_expect(
+    expected_std.begin(), expected_std.end(), expected_mask.begin());
+
+  std::unique_ptr<cudf::column> var_result, std_result;
+  // static sizes
+  EXPECT_NO_THROW(var_result = cudf::rolling_window(input,
+                                                    preceding_window,
+                                                    following_window,
+                                                    min_periods,
+                                                    dynamic_cast<cudf::rolling_aggregation const&>(
+                                                      *cudf::make_variance_aggregation(ddof))););
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*var_result, var_expect);
+
+  EXPECT_NO_THROW(std_result = cudf::rolling_window(input,
+                                                    preceding_window,
+                                                    following_window,
+                                                    min_periods,
+                                                    dynamic_cast<cudf::rolling_aggregation const&>(
+                                                      *cudf::make_std_aggregation(ddof))););
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*std_result, std_expect);
+
+#undef XXX
+}
+
+TEST_F(RollingtVarStdTestUntyped, SimpleStaticVarianceStdInfNaN)
+{
+#define XXX 0.  // NULL stub
+
+  using ResultType = double;
+
+  double const inf           = std::numeric_limits<double>::infinity();
+  double const nan           = std::numeric_limits<double>::signaling_NaN();
+  cudf::size_type const ddof = 1, min_periods = 1, preceding_window = 3, following_window = 0;
+
+  auto const col_data =
+    cudf::test::make_type_param_vector<double>({5., 4., XXX, inf, 4., 8., 0., nan, XXX, 5.});
+  const std::vector<bool> col_mask = {1, 1, 0, 1, 1, 1, 1, 1, 0, 1};
+
+  auto const expected_var =
+    std::vector<ResultType>{nan, 0.5, 0.5, nan, nan, nan, 16, nan, nan, nan};
+  std::vector<ResultType> expected_std(expected_var.size());
+  std::transform(expected_var.begin(), expected_var.end(), expected_std.begin(), [](auto const& x) {
+    return std::sqrt(x);
+  });
+
+  const std::vector<bool> expected_mask = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+
+  cudf::test::fixed_width_column_wrapper<double> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  cudf::test::fixed_width_column_wrapper<ResultType> var_expect(
+    expected_var.begin(), expected_var.end(), expected_mask.begin());
+  cudf::test::fixed_width_column_wrapper<ResultType> std_expect(
+    expected_std.begin(), expected_std.end(), expected_mask.begin());
+
+  std::unique_ptr<cudf::column> var_result, std_result;
+  // static sizes
+  EXPECT_NO_THROW(var_result = cudf::rolling_window(input,
+                                                    preceding_window,
+                                                    following_window,
+                                                    min_periods,
+                                                    dynamic_cast<cudf::rolling_aggregation const&>(
+                                                      *cudf::make_variance_aggregation(ddof))););
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*var_result, var_expect);
+
+  EXPECT_NO_THROW(std_result = cudf::rolling_window(input,
+                                                    preceding_window,
+                                                    following_window,
+                                                    min_periods,
+                                                    dynamic_cast<cudf::rolling_aggregation const&>(
+                                                      *cudf::make_std_aggregation(ddof))););
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*std_result, std_expect);
+
+#undef XXX
+}
+
+/*
+// negative sizes
+TYPED_TEST(RollingTest, NegativeWindowSizes)
+{
+  auto const col_data  = cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 0, 4});
+  auto const col_valid = std::vector<bool>{1, 1, 1, 0, 1};
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_valid.begin());
+  std::vector<cudf::size_type> window{3};
+  std::vector<cudf::size_type> negative_window{-2};
+
+
+  this->run_test_col_agg(input, negative_window, window, 1);
+  this->run_test_col_agg(input, window, negative_window, 1);
+  this->run_test_col_agg(input, negative_window, negative_window, 1);
+}
+ */
+
+// simple example from Pandas docs:
+TYPED_TEST(RollingTest, SimpleDynamic)
+{
+  // https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
+  auto const col_data              = cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 0, 4});
+  const std::vector<bool> col_mask = {1, 1, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  std::vector<cudf::size_type> preceding_window({1, 2, 3, 4, 2});
+  std::vector<cudf::size_type> following_window({2, 1, 2, 1, 2});
+
+  // dynamic sizes
+  this->run_test_col_agg(input, preceding_window, following_window, 1);
+}
+
+// this is a special test to check the volatile count variable issue (see rolling.cu for detail)
+TYPED_TEST(RollingTest, VolatileCount)
+{
+  auto const col_data = cudf::test::make_type_param_vector<TypeParam>({8, 70, 45, 20, 59, 80});
+  const std::vector<bool> col_mask = {1, 1, 0, 0, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  std::vector<cudf::size_type> preceding_window({5, 9, 4, 8, 3, 3});
+  std::vector<cudf::size_type> following_window({1, 1, 9, 2, 8, 9});
+
+  // dynamic sizes
+  this->run_test_col_agg(input, preceding_window, following_window, 1);
+}
+
+// all rows are invalid
+TYPED_TEST(RollingTest, AllInvalid)
+{
+  cudf::size_type num_rows = 1000;
+
+  std::vector<TypeParam> col_data(num_rows);
+  std::vector<bool> col_mask(num_rows, 0);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  std::vector<cudf::size_type> window({100});
+  cudf::size_type periods = 100;
+
+  this->run_test_col_agg(input, window, window, periods);
+}
+
+// window = following_window = 0
+// Note: Preceding includes current row, so its value is set to 1.
+TYPED_TEST(RollingTest, ZeroWindow)
+{
+  cudf::size_type num_rows = 1000;
+
+  std::vector<int> col_data(num_rows, 1);
+  std::vector<bool> col_mask(num_rows, 1);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+  std::vector<cudf::size_type> preceding({0});
+  std::vector<cudf::size_type> following({1});
+  cudf::size_type periods = num_rows;
+
+  this->run_test_col_agg(input, preceding, following, periods);
+}
+
+// min_periods = 0
+TYPED_TEST(RollingTest, ZeroPeriods)
+{
+  cudf::size_type num_rows = 1000;
+
+  std::vector<int> col_data(num_rows, 1);
+  std::vector<bool> col_mask(num_rows, 1);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  std::vector<cudf::size_type> window({num_rows});
+  cudf::size_type periods = 0;
+
+  this->run_test_col_agg(input, window, window, periods);
+}
+
+// window in one direction is not large enough to collect enough samples,
+//   but if using both directions we should get == min_periods,
+// also tests out of boundary accesses
+TYPED_TEST(RollingTest, BackwardForwardWindow)
+{
+  cudf::size_type num_rows = 1000;
+
+  std::vector<int> col_data(num_rows, 1);
+  std::vector<bool> col_mask(num_rows, 1);
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int> input(
+    col_data.begin(), col_data.end(), col_mask.begin());
+
+  std::vector<cudf::size_type> window({num_rows});
+  cudf::size_type periods = num_rows;
+
+  this->run_test_col_agg(input, window, window, periods);
+}
+
+// random input data, static parameters, no nulls
+TYPED_TEST(RollingTest, RandomStaticAllValid)
+{
+  cudf::size_type num_rows = 10000;
+
+  // random input
+  std::vector<TypeParam> col_data(num_rows);
+  cudf::test::UniformRandomGenerator<TypeParam> rng;
+  std::generate(col_data.begin(), col_data.end(), [&rng]() { return rng.generate(); });
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(col_data.begin(), col_data.end());
+
+  std::vector<cudf::size_type> window({50});
+  cudf::size_type periods = 50;
+
+  this->run_test_col_agg(input, window, window, periods);
+}
+
+// random input data, static parameters, with nulls
+TYPED_TEST(RollingTest, RandomStaticWithInvalid)
+{
+  cudf::size_type num_rows = 10000;
+
+  // random input
+  std::vector<TypeParam> col_data(num_rows);
+  std::vector<bool> col_valid(num_rows);
+  cudf::test::UniformRandomGenerator<TypeParam> rng;
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(col_data.begin(), col_data.end(), [&rng]() { return rng.generate(); });
+  std::generate(col_valid.begin(), col_valid.end(), [&rbg]() { return rbg.generate(); });
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_valid.begin());
+
+  std::vector<cudf::size_type> window({50});
+  cudf::size_type periods = 50;
+
+  this->run_test_col_agg(input, window, window, periods);
+}
+
+// random input data, dynamic parameters, no nulls
+TYPED_TEST(RollingTest, RandomDynamicAllValid)
+{
+  cudf::size_type num_rows        = 50000;
+  cudf::size_type max_window_size = 50;
+
+  // random input
+  std::vector<TypeParam> col_data(num_rows);
+  cudf::test::UniformRandomGenerator<TypeParam> rng;
+  std::generate(col_data.begin(), col_data.end(), [&rng]() { return rng.generate(); });
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(col_data.begin(), col_data.end());
+
+  // random parameters
+  cudf::test::UniformRandomGenerator<cudf::size_type> window_rng(0, max_window_size);
+  auto generator = [&]() { return window_rng.generate(); };
+
+  std::vector<cudf::size_type> preceding_window(num_rows);
+  std::vector<cudf::size_type> following_window(num_rows);
+
+  std::generate(preceding_window.begin(), preceding_window.end(), generator);
+  std::generate(following_window.begin(), following_window.end(), generator);
+
+  this->run_test_col_agg(input, preceding_window, following_window, max_window_size);
+}
+
+// random input data, dynamic parameters, with nulls
+TYPED_TEST(RollingTest, RandomDynamicWithInvalid)
+{
+  cudf::size_type num_rows        = 50000;
+  cudf::size_type max_window_size = 50;
+
+  // random input with nulls
+  std::vector<TypeParam> col_data(num_rows);
+  std::vector<bool> col_valid(num_rows);
+  cudf::test::UniformRandomGenerator<TypeParam> rng;
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(col_data.begin(), col_data.end(), [&rng]() { return rng.generate(); });
+  std::generate(col_valid.begin(), col_valid.end(), [&rbg]() { return rbg.generate(); });
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(
+    col_data.begin(), col_data.end(), col_valid.begin());
+
+  // random parameters
+  cudf::test::UniformRandomGenerator<cudf::size_type> window_rng(0, max_window_size);
+  auto generator = [&]() { return window_rng.generate(); };
+
+  std::vector<cudf::size_type> preceding_window(num_rows);
+  std::vector<cudf::size_type> following_window(num_rows);
+
+  std::generate(preceding_window.begin(), preceding_window.end(), generator);
+  std::generate(following_window.begin(), following_window.end(), generator);
+
+  this->run_test_col_agg(input, preceding_window, following_window, max_window_size);
+}
+
+// ------------- non-fixed-width types --------------------
+
+using RollingTestStrings = RollingTest<cudf::string_view>;
+
+TEST_F(RollingTestStrings, StringsUnsupportedOperators)
+{
+  cudf::test::strings_column_wrapper input{{"This", "is", "not", "a", "string", "type"},
+                                           {1, 1, 1, 0, 1, 0}};
+
+  std::vector<cudf::size_type> window{1};
+
+  EXPECT_THROW(
+    cudf::rolling_window(input, 2, 2, 0, *cudf::make_sum_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::rolling_window(input, 2, 2, 0, *cudf::make_mean_aggregation<cudf::rolling_aggregation>()),
+    cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(input,
+                                    2,
+                                    2,
+                                    0,
+                                    *cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+                                      cudf::udf_type::PTX, std::string{}, cudf::data_type{})),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::rolling_window(input,
+                                    2,
+                                    2,
+                                    0,
+                                    *cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+                                      cudf::udf_type::CUDA, std::string{}, cudf::data_type{})),
+               cudf::logic_error);
+}
+
+/*TEST_F(RollingTestStrings, SimpleStatic)
+{
+  cudf::test::strings_column_wrapper input{{"This", "is", "not", "a", "string", "type"},
+                                           {1, 1, 1, 0, 1, 0}};
+
+  std::vector<cudf::size_type> window{1};
+
+  EXPECT_NO_THROW(this->run_test_col(input, window, window, 0, rolling_operator::MIN));
+  EXPECT_NO_THROW(this->run_test_col(input, window, window, 0, rolling_operator::MAX));
+  EXPECT_NO_THROW(this->run_test_col(input, window, window, 0, rolling_operator::COUNT_VALID));
+  EXPECT_NO_THROW(this->run_test_col(input, window, window, 0, rolling_operator::COUNT_ALL));
+}*/
+
+struct RollingTestUdf : public cudf::test::BaseFixture {
+  const std::string cuda_func{
+    R"***(
+      template <typename OutType, typename InType>
+      __device__ void CUDA_GENERIC_AGGREGATOR(OutType *ret, InType *in_col, cudf::size_type start,
+                                              cudf::size_type count) {
+        OutType val = 0;
+        for (cudf::size_type i = 0; i < count; i++) {
+          val += in_col[start + i];
+        }
+        *ret = val;
+      }
+    )***"};
+
+  const std::string ptx_func{
+    R"***(
+    //
+    // Generated by NVIDIA NVVM Compiler
+    //
+    // Compiler Build ID: CL-24817639
+    // Cuda compilation tools, release 10.0, V10.0.130
+    // Based on LLVM 3.4svn
+    //
+
+    .version 6.3
+    .target sm_70
+    .address_size 64
+
+    // .globl	_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE
+    .common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE;
+
+    .visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE(
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_0,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_1,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_2,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_3,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_4,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_5,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_6,
+    .param .b64 _ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_7
+    )
+    {
+    .reg .pred 	%p<3>;
+    .reg .b32 	%r<6>;
+    .reg .b64 	%rd<18>;
+
+
+    ld.param.u64 	%rd6, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_0];
+    ld.param.u64 	%rd7, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_5];
+    ld.param.u64 	%rd8, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_6];
+    ld.param.u64 	%rd9, [_ZN8__main__7add$241E5ArrayIiLi1E1A7mutable7alignedE_paam_7];
+    mov.u64 	%rd15, 0;
+    mov.u64 	%rd16, %rd15;
+
+    BB0_1:
+    mov.u64 	%rd2, %rd16;
+    mov.u32 	%r5, 0;
+    setp.ge.s64	%p1, %rd15, %rd8;
+    mov.u64 	%rd17, %rd15;
+    @%p1 bra 	BB0_3;
+
+    mul.lo.s64 	%rd12, %rd15, %rd9;
+    add.s64 	%rd13, %rd12, %rd7;
+    ld.u32 	%r5, [%rd13];
+    add.s64 	%rd17, %rd15, 1;
+
+    BB0_3:
+    cvt.s64.s32	%rd14, %r5;
+    add.s64 	%rd16, %rd14, %rd2;
+    setp.lt.s64	%p2, %rd15, %rd8;
+    mov.u64 	%rd15, %rd17;
+    @%p2 bra 	BB0_1;
+
+    st.u64 	[%rd6], %rd2;
+    mov.u32 	%r4, 0;
+    st.param.b32	[func_retval0+0], %r4;
+    ret;
+    }
+    )***"};
+};
+
+TEST_F(RollingTestUdf, StaticWindow)
+{
+  cudf::size_type size = 1000;
+
+  cudf::test::fixed_width_column_wrapper<int32_t> input(thrust::make_counting_iterator(0),
+                                                        thrust::make_counting_iterator(size),
+                                                        thrust::make_constant_iterator(true));
+
+  std::unique_ptr<cudf::column> output;
+
+  auto start = cudf::detail::make_counting_transform_iterator(0, [size](cudf::size_type row) {
+    return std::accumulate(thrust::make_counting_iterator(std::max(0, row - 2 + 1)),
+                           thrust::make_counting_iterator(std::min(size, row + 2 + 1)),
+                           0);
+  });
+
+  auto valid = cudf::detail::make_counting_transform_iterator(
+    0, [size](cudf::size_type row) { return (row != 0 && row != size - 2 && row != size - 1); });
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{start, start + size, valid};
+
+  // Test CUDA UDF
+  auto cuda_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+    cudf::udf_type::CUDA, this->cuda_func, cudf::data_type{cudf::type_id::INT64});
+
+  output = cudf::rolling_window(input, 2, 2, 4, *cuda_udf_agg);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, expected);
+
+  // Test NUMBA UDF
+  auto ptx_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+    cudf::udf_type::PTX, this->ptx_func, cudf::data_type{cudf::type_id::INT64});
+
+  output = cudf::rolling_window(input, 2, 2, 4, *ptx_udf_agg);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, expected);
+}
+
+TEST_F(RollingTestUdf, DynamicWindow)
+{
+  cudf::size_type size = 1000;
+
+  cudf::test::fixed_width_column_wrapper<int32_t> input(thrust::make_counting_iterator(0),
+                                                        thrust::make_counting_iterator(size),
+                                                        thrust::make_constant_iterator(true));
+
+  auto prec = cudf::detail::make_counting_transform_iterator(
+    0, [] __device__(cudf::size_type row) { return row % 2 + 2; });
+
+  auto follow = cudf::detail::make_counting_transform_iterator(
+    0, [] __device__(cudf::size_type row) { return row % 2; });
+
+  cudf::test::fixed_width_column_wrapper<int32_t> preceding(prec, prec + size);
+  cudf::test::fixed_width_column_wrapper<int32_t> following(follow, follow + size);
+  std::unique_ptr<cudf::column> output;
+
+  auto start =
+    cudf::detail::make_counting_transform_iterator(0, [size] __device__(cudf::size_type row) {
+      return std::accumulate(thrust::make_counting_iterator(std::max(0, row - (row % 2 + 2) + 1)),
+                             thrust::make_counting_iterator(std::min(size, row + (row % 2) + 1)),
+                             0);
+    });
+
+  auto valid = cudf::detail::make_counting_transform_iterator(
+    0, [] __device__(cudf::size_type row) { return row != 0; });
+
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{start, start + size, valid};
+
+  // Test CUDA UDF
+  auto cuda_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+    cudf::udf_type::CUDA, this->cuda_func, cudf::data_type{cudf::type_id::INT64});
+
+  output = cudf::rolling_window(input, preceding, following, 2, *cuda_udf_agg);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, expected);
+
+  // Test PTX UDF
+  auto ptx_udf_agg = cudf::make_udf_aggregation<cudf::rolling_aggregation>(
+    cudf::udf_type::PTX, this->ptx_func, cudf::data_type{cudf::type_id::INT64});
+
+  output = cudf::rolling_window(input, preceding, following, 2, *ptx_udf_agg);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*output, expected);
+}
+
+template <typename T>
+struct FixedPointTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTests, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTests, MinMaxCountLagLead)
+{
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto const scale         = numeric::scale_type{-1};
+  auto const input         = fp_wrapper{{42, 1729, 55, 3, 1, 2}, {1, 1, 1, 1, 1, 1}, scale};
+  auto const expected_min  = fp_wrapper{{42, 42, 3, 1, 1, 1}, {1, 1, 1, 1, 1, 1}, scale};
+  auto const expected_max  = fp_wrapper{{1729, 1729, 1729, 55, 3, 2}, {1, 1, 1, 1, 1, 1}, scale};
+  auto const expected_lag  = fp_wrapper{{0, 42, 1729, 55, 3, 1}, {0, 1, 1, 1, 1, 1}, scale};
+  auto const expected_lead = fp_wrapper{{1729, 55, 3, 1, 2, 0}, {1, 1, 1, 1, 1, 0}, scale};
+  auto const expected_count_val = fw_wrapper{{2, 3, 3, 3, 3, 2}, {1, 1, 1, 1, 1, 1}};
+  auto const expected_count_all = fw_wrapper{{2, 3, 3, 3, 3, 2}, {1, 1, 1, 1, 1, 1}};
+  auto const expected_rowno     = fw_wrapper{{1, 2, 2, 2, 2, 2}, {1, 1, 1, 1, 1, 1}};
+  auto const expected_rowno1    = fw_wrapper{{1, 1, 1, 1, 1, 1}, {1, 1, 1, 1, 1, 1}};
+
+  auto const min =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+  auto const max =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+  auto const lag =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+  auto const lead = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_lead_aggregation<cudf::rolling_aggregation>(1));
+  auto const valid = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto const all = cudf::rolling_window(
+    input,
+    2,
+    1,
+    1,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+  auto const rowno = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lag, lag->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lead, lead->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_val, valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_all, all->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_rowno, rowno->view());
+
+  // ROW_NUMBER will always return row 1 if the preceding window is set to a constant 1
+  for (int following = 1; following < 5; ++following) {
+    auto const rowno1 = cudf::rolling_window(
+      input, 1, following, 1, *cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_rowno1, rowno1->view());
+  }
+}
+
+TYPED_TEST(FixedPointTests, MinMaxCountLagLeadNulls)
+{
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  auto const scale              = numeric::scale_type{-1};
+  auto const input              = fp_wrapper{{42, 1729, 55, 343, 1, 2}, {1, 0, 1, 0, 1, 1}, scale};
+  auto const expected_sum       = fp_wrapper{{42, 97, 55, 56, 3, 3}, {1, 1, 1, 1, 1, 1}, scale};
+  auto const expected_min       = fp_wrapper{{42, 42, 55, 1, 1, 1}, {1, 1, 1, 1, 1, 1}, scale};
+  auto const expected_max       = fp_wrapper{{42, 55, 55, 55, 2, 2}, {1, 1, 1, 1, 1, 1}, scale};
+  auto const expected_lag       = fp_wrapper{{0, 42, 1729, 55, 343, 1}, {0, 1, 0, 1, 0, 1}, scale};
+  auto const expected_lead      = fp_wrapper{{1729, 55, 343, 1, 2, 0}, {0, 1, 0, 1, 1, 0}, scale};
+  auto const expected_count_val = fw_wrapper{{1, 2, 1, 2, 2, 2}, {1, 1, 1, 1, 1, 1}};
+  auto const expected_count_all = fw_wrapper{{2, 3, 3, 3, 3, 2}, {1, 1, 1, 1, 1, 1}};
+  auto const expected_rowno     = fw_wrapper{{1, 2, 2, 2, 2, 2}, {1, 1, 1, 1, 1, 1}};
+
+  auto const sum =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_sum_aggregation<cudf::rolling_aggregation>());
+  auto const min =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+  auto const max =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+  auto const lag =
+    cudf::rolling_window(input, 2, 1, 1, *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+  auto const lead = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_lead_aggregation<cudf::rolling_aggregation>(1));
+  auto const valid = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto const all = cudf::rolling_window(
+    input,
+    2,
+    1,
+    1,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+  auto const rowno = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_sum, sum->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, max->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lag, lag->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lead, lead->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_val, valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_all, all->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_rowno, rowno->view());
+}
+
+TYPED_TEST(FixedPointTests, VarStd)
+{
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  double const nan = std::numeric_limits<double>::signaling_NaN();
+  double const inf = std::numeric_limits<double>::infinity();
+  cudf::size_type preceding_window{3}, following_window{0}, min_periods{1}, ddof{2};
+
+  // The variance of `input` given `scale` == 0
+  std::vector<double> result_base_v{
+    nan, inf, 1882804.66666666667, 1928018.666666666667, 1874.6666666666667, 2.0};
+  std::vector<bool> result_mask_v{1, 1, 1, 1, 1, 1};
+
+  // var tests
+  for (int32_t s = -2; s <= 2; s++) {
+    auto const scale = numeric::scale_type{s};
+    auto const input = fp_wrapper{{42, 1729, 55, 3, 1, 2}, {1, 1, 1, 1, 1, 1}, scale};
+
+    auto got = cudf::rolling_window(
+      input,
+      preceding_window,
+      following_window,
+      min_periods,
+      dynamic_cast<cudf::rolling_aggregation const&>(*cudf::make_variance_aggregation(ddof)));
+
+    std::vector<double> result_scaled_v(result_base_v.size());
+    std::transform(
+      result_base_v.begin(), result_base_v.end(), result_scaled_v.begin(), [&s](auto x) {
+        // When values are scaled by 10^n, the variance is scaled by 10^2n.
+        return x * exp10(s) * exp10(s);
+      });
+    fw_wrapper expect(result_scaled_v.begin(), result_scaled_v.end(), result_mask_v.begin());
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expect, *got);
+  }
+
+  // std tests
+  for (int32_t s = -2; s <= 2; s++) {
+    auto const scale = numeric::scale_type{s};
+    auto const input = fp_wrapper{{42, 1729, 55, 3, 1, 2}, {1, 1, 1, 1, 1, 1}, scale};
+
+    auto got = cudf::rolling_window(
+      input,
+      preceding_window,
+      following_window,
+      min_periods,
+      dynamic_cast<cudf::rolling_aggregation const&>(*cudf::make_std_aggregation(ddof)));
+
+    std::vector<double> result_scaled_v(result_base_v.size());
+    std::transform(
+      result_base_v.begin(), result_base_v.end(), result_scaled_v.begin(), [&s](auto x) {
+        // When values are scaled by 10^n, the variance is scaled by 10^2n.
+        return std::sqrt(x * exp10(s) * exp10(s));
+      });
+    fw_wrapper expect(result_scaled_v.begin(), result_scaled_v.end(), result_mask_v.begin());
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expect, *got);
+  }
+}
+
+class RollingDictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(RollingDictionaryTest, Count)
+{
+  cudf::test::dictionary_column_wrapper<std::string> input(
+    {"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"},
+    {1, 0, 0, 1, 0, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count_val(
+    {1, 2, 1, 2, 3, 3, 3, 2, 1}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_count_all(
+    {3, 4, 4, 4, 4, 4, 4, 3, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_row_number(
+    {1, 2, 2, 2, 2, 2, 2, 2, 2}, {1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto got_count_valid = cudf::rolling_window(
+    input, 2, 2, 1, *cudf::make_count_aggregation<cudf::rolling_aggregation>());
+  auto got_count_all = cudf::rolling_window(
+    input,
+    2,
+    2,
+    1,
+    *cudf::make_count_aggregation<cudf::rolling_aggregation>(cudf::null_policy::INCLUDE));
+  auto got_row_number = cudf::rolling_window(
+    input, 2, 2, 1, *cudf::make_row_number_aggregation<cudf::rolling_aggregation>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_val, got_count_valid->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_count_all, got_count_all->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_row_number, got_row_number->view());
+}
+
+TEST_F(RollingDictionaryTest, MinMax)
+{
+  cudf::test::dictionary_column_wrapper<std::string> input(
+    {"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"},
+    {1, 0, 0, 1, 0, 1, 1, 1, 0});
+  cudf::test::strings_column_wrapper expected_min(
+    {"This", "This", "test", "operated", "on", "on", "on", "on", "string"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper expected_max(
+    {"This", "test", "test", "test", "test", "string", "string", "string", "string"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto got_min_dict =
+    cudf::rolling_window(input, 2, 2, 1, *cudf::make_min_aggregation<cudf::rolling_aggregation>());
+  auto got_min = cudf::dictionary::decode(cudf::dictionary_column_view(got_min_dict->view()));
+
+  auto got_max_dict =
+    cudf::rolling_window(input, 2, 2, 1, *cudf::make_max_aggregation<cudf::rolling_aggregation>());
+  auto got_max = cudf::dictionary::decode(cudf::dictionary_column_view(got_max_dict->view()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_min, got_min->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_max, got_max->view());
+}
+
+TEST_F(RollingDictionaryTest, LeadLag)
+{
+  cudf::test::dictionary_column_wrapper<std::string> input(
+    {"This", "is", "rolling", "test", "being", "operated", "on", "string", "column"},
+    {1, 0, 0, 1, 0, 1, 1, 1, 0});
+  cudf::test::strings_column_wrapper expected_lead(
+    {"", "", "test", "", "operated", "on", "string", "", ""}, {0, 0, 1, 0, 1, 1, 1, 0, 0});
+  cudf::test::strings_column_wrapper expected_lag(
+    {"", "This", "", "", "test", "", "operated", "on", "string"}, {0, 1, 0, 0, 1, 0, 1, 1, 1});
+
+  auto got_lead_dict = cudf::rolling_window(
+    input, 2, 1, 1, *cudf::make_lead_aggregation<cudf::rolling_aggregation>(1));
+  auto got_lead = cudf::dictionary::decode(cudf::dictionary_column_view(got_lead_dict->view()));
+
+  auto got_lag_dict =
+    cudf::rolling_window(input, 2, 2, 1, *cudf::make_lag_aggregation<cudf::rolling_aggregation>(1));
+  auto got_lag = cudf::dictionary::decode(cudf::dictionary_column_view(got_lag_dict->view()));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lead, got_lead->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lag, got_lag->view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/rolling/rolling_test.hpp b/cpp/tests/rolling/rolling_test.hpp
new file mode 100644
index 0000000..ae632aa
--- /dev/null
+++ b/cpp/tests/rolling/rolling_test.hpp
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/aggregation.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/utilities/traits.hpp>
+
+// return true if the aggregation is valid for the specified ColumnType
+// valid aggregations may still be further specialized (eg, is_string_specialized)
+template <typename ColumnType, cudf::aggregation::Kind op>
+static constexpr bool is_rolling_supported()
+{
+  if (!cudf::detail::is_valid_aggregation<ColumnType, op>()) {
+    return false;
+  } else if (cudf::is_numeric<ColumnType>() or cudf::is_duration<ColumnType>()) {
+    return (op == cudf::aggregation::SUM) or (op == cudf::aggregation::MIN) or
+           (op == cudf::aggregation::MAX) or (op == cudf::aggregation::COUNT_VALID) or
+           (op == cudf::aggregation::COUNT_ALL) or (op == cudf::aggregation::MEAN) or
+           (op == cudf::aggregation::ROW_NUMBER) or (op == cudf::aggregation::LEAD) or
+           (op == cudf::aggregation::LAG) or (op == cudf::aggregation::COLLECT_LIST);
+  } else if (cudf::is_timestamp<ColumnType>()) {
+    return (op == cudf::aggregation::MIN) or (op == cudf::aggregation::MAX) or
+           (op == cudf::aggregation::COUNT_VALID) or (op == cudf::aggregation::COUNT_ALL) or
+           (op == cudf::aggregation::ROW_NUMBER) or (op == cudf::aggregation::LEAD) or
+           (op == cudf::aggregation::LAG) or (op == cudf::aggregation::COLLECT_LIST);
+  } else if (cudf::is_fixed_point<ColumnType>()) {
+    return (op == cudf::aggregation::SUM) or (op == cudf::aggregation::MIN) or
+           (op == cudf::aggregation::MAX) or (op == cudf::aggregation::COUNT_VALID) or
+           (op == cudf::aggregation::COUNT_ALL) or (op == cudf::aggregation::ROW_NUMBER) or
+           (op == cudf::aggregation::LEAD) or (op == cudf::aggregation::LAG) or
+           (op == cudf::aggregation::COLLECT_LIST);
+  } else if (std::is_same<ColumnType, cudf::string_view>()) {
+    return (op == cudf::aggregation::MIN) or (op == cudf::aggregation::MAX) or
+           (op == cudf::aggregation::COUNT_VALID) or (op == cudf::aggregation::COUNT_ALL) or
+           (op == cudf::aggregation::ROW_NUMBER) or (op == cudf::aggregation::COLLECT_LIST);
+
+  } else if (std::is_same<ColumnType, cudf::list_view>()) {
+    return (op == cudf::aggregation::COUNT_VALID) or (op == cudf::aggregation::COUNT_ALL) or
+           (op == cudf::aggregation::ROW_NUMBER) or (op == cudf::aggregation::COLLECT_LIST);
+  } else if (std::is_same<ColumnType, cudf::struct_view>()) {
+    // TODO: Add support for COUNT_VALID, COUNT_ALL, ROW_NUMBER.
+    return op == cudf::aggregation::COLLECT_LIST;
+  } else {
+    return false;
+  }
+}
diff --git a/cpp/tests/round/round_tests.cpp b/cpp/tests/round/round_tests.cpp
new file mode 100644
index 0000000..f97bb7a
--- /dev/null
+++ b/cpp/tests/round/round_tests.cpp
@@ -0,0 +1,785 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/round.hpp>
+
+#include <limits>
+
+struct RoundTests : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct RoundTestsIntegerTypes : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct RoundTestsFixedPointTypes : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct RoundTestsFloatingPointTypes : public cudf::test::BaseFixture {};
+
+using IntegerTypes = cudf::test::Types<int16_t, int32_t, int64_t>;
+
+TYPED_TEST_SUITE(RoundTestsIntegerTypes, IntegerTypes);
+TYPED_TEST_SUITE(RoundTestsFixedPointTypes, cudf::test::FixedPointTypes);
+TYPED_TEST_SUITE(RoundTestsFloatingPointTypes, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfUpZero)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input = fp_wrapper{
+    {1140, 1150, 1160, 1240, 1250, 1260, -1140, -1150, -1160, -1240, -1250, -1260}, scale_type{-2}};
+  auto const expected =
+    fp_wrapper{{11, 12, 12, 12, 13, 13, -11, -12, -12, -12, -13, -13}, scale_type{0}};
+  auto const result = cudf::round(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfUpZeroNoOp)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input = fp_wrapper{
+    {1125, 1150, 1160, 1240, 1250, 1260, -1125, -1150, -1160, -1240, -1250, -1260}, scale_type{0}};
+  auto const result = cudf::round(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfUpZeroNullMask)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const null_mask = std::vector<int>{1, 1, 0, 1};
+  auto const input     = fp_wrapper{{1150, 1160, 1240, 1250}, null_mask.cbegin(), scale_type{-2}};
+  auto const expected  = fp_wrapper{{12, 12, 1240, 13}, null_mask.cbegin(), scale_type{0}};
+  auto const result    = cudf::round(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfEvenZero)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input = fp_wrapper{
+    {1140, 1150, 1160, 1240, 1250, 1260, -1140, -1150, -1160, -1240, -1250, -1260}, scale_type{-2}};
+  auto const expected =
+    fp_wrapper{{11, 12, 12, 12, 12, 13, -11, -12, -12, -12, -12, -13}, scale_type{0}};
+  auto const result = cudf::round(input, 0, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfUp)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1140, 1150, 1160, -1140, -1150, -1160}, scale_type{-3}};
+  auto const expected = fp_wrapper{{11, 12, 12, -11, -12, -12}, scale_type{-1}};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfUp2)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{114, 115, 116, -114, -115, -116}, scale_type{-2}};
+  auto const expected = fp_wrapper{{11, 12, 12, -11, -12, -12}, scale_type{-1}};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfUp3)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1, 2, 3, -1, -2, -3}, scale_type{1}};
+  auto const expected = fp_wrapper{{100, 200, 300, -100, -200, -300}, scale_type{-1}};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfEven)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input = fp_wrapper{
+    {1140, 1150, 1160, 1240, 1250, 1260, -1140, -1150, -1160, -1240, -1250, -1260}, scale_type{-3}};
+  auto const expected =
+    fp_wrapper{{11, 12, 12, 12, 12, 13, -11, -12, -12, -12, -12, -13}, scale_type{-1}};
+  auto const result = cudf::round(input, 1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfEven2)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input =
+    fp_wrapper{{114, 115, 116, 124, 125, 126, -114, -115, -116, -124, -125, -126}, scale_type{-2}};
+  auto const expected =
+    fp_wrapper{{11, 12, 12, 12, 12, 13, -11, -12, -12, -12, -12, -13}, scale_type{-1}};
+  auto const result = cudf::round(input, 1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfEven3)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1, 2, 3, -1, -2, -3}, scale_type{1}};
+  auto const expected = fp_wrapper{{100, 200, 300, -100, -200, -300}, scale_type{-1}};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, EmptyFixedPointTypeTest)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input         = fp_wrapper{{}, scale_type{1}};
+  auto const expected      = fp_wrapper{{}, scale_type{-1}};
+  auto const expected_type = cudf::data_type{cudf::type_to_id<decimalXX>(), scale_type{-1}};
+  auto const result        = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  EXPECT_EQ(result->view().type(), expected_type);
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestNegHalfUp)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, scale_type{2}};
+  auto const expected = fp_wrapper{{1, 2, 2, 2, 3, 3, -1, -2, -2, -2, -3, -3}, scale_type{3}};
+  auto const result   = cudf::round(input, -3, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestNegHalfUp2)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, scale_type{3}};
+  auto const expected = fp_wrapper{{1, 2, 2, 2, 3, 3, -1, -2, -2, -2, -3, -3}, scale_type{4}};
+  auto const result   = cudf::round(input, -4, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfNegUp3)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1, 2, 3, -1, -2, -3}, scale_type{2}};
+  auto const expected = fp_wrapper{{10, 20, 30, -10, -20, -30}, scale_type{1}};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestNegHalfEven)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, scale_type{2}};
+  auto const expected = fp_wrapper{{1, 2, 2, 2, 2, 3, -1, -2, -2, -2, -2, -3}, scale_type{3}};
+  auto const result   = cudf::round(input, -3, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestNegHalfEven2)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, scale_type{3}};
+  auto const expected = fp_wrapper{{1, 2, 2, 2, 2, 3, -1, -2, -2, -2, -2, -3}, scale_type{4}};
+  auto const result   = cudf::round(input, -4, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, SimpleFixedPointTestHalfNegEven3)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1, 2, 3, -1, -2, -3}, scale_type{2}};
+  auto const expected = fp_wrapper{{10, 20, 30, -10, -20, -30}, scale_type{1}};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, TestForBlog)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{25649999}, scale_type{-5}};
+  auto const expected = fp_wrapper{{256}, scale_type{0}};
+  auto const result   = cudf::round(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFixedPointTypes, TestScaleMovementExceedingMaxPrecision)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  // max precision of int32 = 9
+  // scale movement = -(-11) -1 = 10 > 9
+  // max precision of int64 = 18
+  // scale movement = -(-20) -1 = 19 > 18
+  // max precision of int128 = 38
+  // scale movement = -(-40) -1 = 39 > 38
+  auto const target_scale = cuda::std::numeric_limits<RepType>::digits10 + 1 + 1;
+
+  auto const input =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, scale_type{1}};
+  auto const expected = fp_wrapper{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, scale_type{target_scale}};
+  auto const result   = cudf::round(input, -target_scale, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+
+  auto const input_even =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, scale_type{1}};
+  auto const expected_even =
+    fp_wrapper{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, scale_type{target_scale}};
+  auto const result_even = cudf::round(input, -target_scale, cudf::rounding_method::HALF_EVEN);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_even, result_even->view());
+
+  const std::initializer_list<bool> validity = {1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0};
+  auto const input_null =
+    fp_wrapper{{14, 15, 16, 24, 25, 26, -14, -15, -16, -24, -25, -26}, validity, scale_type{1}};
+  auto const expected_null =
+    fp_wrapper{{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, validity, scale_type{target_scale}};
+  auto const result_null = cudf::round(input_null, -target_scale, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_null, result_null->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestHalfUp0)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{1.4, 1.5, 1.6};
+  auto const expected = fw_wrapper{1, 2, 2};
+  auto const result   = cudf::round(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestHalfUp1)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{1.24, 1.25, 1.26};
+  auto const expected = fw_wrapper{1.2, 1.3, 1.3};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestHalfEven0)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{1.4, 1.5, 1.6, 2.4, 2.5, 2.6};
+  auto const expected = fw_wrapper{1, 2, 2, 2, 2, 3};
+  auto const result   = cudf::round(input, 0, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestHalfEven1)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{1.24, 1.25, 1.26, 1.34, 1.35, 1.36};
+  auto const expected = fw_wrapper{1.2, 1.2, 1.3, 1.3, 1.4, 1.4};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_EVEN);
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestWithNullsHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{{1.24, 1.25, 1.26}, {1, 0, 1}};
+  auto const expected = fw_wrapper{{1.2, 1.3, 1.3}, {1, 0, 1}};
+  auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestNegHalfUp1)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{5, 12, 15, 135, 1454, 1455, 1456};
+  auto const expected = fw_wrapper{10, 10, 20, 140, 1450, 1460, 1460};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestNegHalfEven1)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{5, 12, 15, 135, 1454, 1455, 1456};
+  auto const expected = fw_wrapper{0, 10, 20, 140, 1450, 1460, 1460};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SimpleFloatingPointTestNegHalfUp2)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{12, 135, 1454, 1455, 1500};
+  auto const expected = fw_wrapper{0, 100, 1500, 1500, 1500};
+  auto const result   = cudf::round(input, -2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, LargeFloatingPointHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto transform   = [](int i) -> float { return i % 2 == 0 ? i + 0.44 : i + 0.56; };
+  auto begin       = cudf::detail::make_counting_transform_iterator(0, transform);
+  auto const input = fw_wrapper(begin, begin + 2000);
+
+  auto transform2     = [](int i) { return i % 2 == 0 ? i + 0.4 : i + 0.6; };
+  auto begin2         = cudf::detail::make_counting_transform_iterator(0, transform2);
+  auto const expected = fw_wrapper(begin2, begin2 + 2000);
+
+  auto const result = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsIntegerTypes, LargeIntegerHalfEven)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto transform   = [](int i) -> float { return 10 * i + 5; };
+  auto begin       = cudf::detail::make_counting_transform_iterator(1, transform);
+  auto const input = fw_wrapper(begin, begin + 2000);
+
+  auto transform2     = [](int i) { return i % 2 == 0 ? 10 * i : 10 + 10 * i; };
+  auto begin2         = cudf::detail::make_counting_transform_iterator(1, transform2);
+  auto const expected = fw_wrapper(begin2, begin2 + 2000);
+
+  auto const result = cudf::round(input, -1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsFloatingPointTypes, SameSignificatDigitsHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{9.87654321};
+  auto const expected = fw_wrapper{9.88};
+  auto const result   = cudf::round(input, 2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+
+  auto const input2    = fw_wrapper{987.654321};
+  auto const expected2 = fw_wrapper{988};
+  auto const result2   = cudf::round(input2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result2->view());
+
+  auto const input3    = fw_wrapper{987654.321};
+  auto const expected3 = fw_wrapper{988000};
+  auto const result3   = cudf::round(input3, -3, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, result3->view());
+
+  auto const input4    = fw_wrapper{9876543.21};
+  auto const expected4 = fw_wrapper{9880000};
+  auto const result4   = cudf::round(input4, -4, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, result4->view());
+
+  auto const input5    = fw_wrapper{0.0000987654321};
+  auto const expected5 = fw_wrapper{0.0000988};
+  auto const result5   = cudf::round(input5, 7, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected5, result5->view());
+}
+
+TEST_F(RoundTests, FloatMaxTestHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<float>;
+
+  auto const input    = fw_wrapper{std::numeric_limits<float>::max()};  // 3.40282e+38
+  auto const expected = fw_wrapper{3.4e+38};
+  auto const result   = cudf::round(input, -37, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(RoundTests, DoubleMaxTestHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  auto const input    = fw_wrapper{std::numeric_limits<double>::max() - 5e+306};  // 1.74769e+308
+  auto const expected = fw_wrapper{1.7e+308};
+  auto const result   = cudf::round(input, -307, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(RoundTests, AvoidOverFlowTestHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  auto const input    = fw_wrapper{std::numeric_limits<double>::max()};
+  auto const expected = fw_wrapper{std::numeric_limits<double>::max()};
+  auto const result   = cudf::round(input, 2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsIntegerTypes, SimpleIntegerTestNegHalfUp2)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{12, 135, 1454, 1455, 1500};
+  auto const expected = fw_wrapper{0, 100, 1500, 1500, 1500};
+  auto const result   = cudf::round(input, -2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsIntegerTypes, SimpleIntegerTestNegHalfEven)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{12, 135, 1450, 1550, 1650};
+  auto const expected = fw_wrapper{0, 100, 1400, 1600, 1600};
+  auto const result   = cudf::round(input, -2, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsIntegerTypes, SimpleNegativeIntegerHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{-12, -135, -1454, -1455, -1500, -140, -150, -160};
+  auto const expected = fw_wrapper{0, -100, -1500, -1500, -1500, -100, -200, -200};
+  auto const result   = cudf::round(input, -2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsIntegerTypes, SimpleNegativeIntegerHalfEven)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{-12, -135, -145, -146, -1454, -1455, -1500};
+  auto const expected = fw_wrapper{-10, -140, -140, -150, -1450, -1460, -1500};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(RoundTests, SimpleNegativeIntegerWithUnsignedNumbersHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<uint32_t>;
+
+  auto const input    = fw_wrapper{12, 135, 1454, 1455, 1500, 140, 150, 160};
+  auto const expected = fw_wrapper{0, 100, 1500, 1500, 1500, 100, 200, 200};
+  auto const result   = cudf::round(input, -2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(RoundTests, SimpleNegativeInt8HalfEven)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int8_t>;
+
+  auto const input    = fw_wrapper{12, 35, 36, 15, 16, 24, 25, 26};
+  auto const expected = fw_wrapper{10, 40, 40, 20, 20, 20, 20, 30};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_EVEN);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(RoundTests, SimpleNegativeInt8HalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int8_t>;
+
+  auto const input    = fw_wrapper{12, 35, 36, 15, 16, 24, 25, 26};
+  auto const expected = fw_wrapper{10, 40, 40, 20, 20, 20, 30, 30};
+  auto const result   = cudf::round(input, -1, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(RoundTestsIntegerTypes, SimplePositiveIntegerHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const input    = fw_wrapper{-12, -135, -1454, -1455, -1500};
+  auto const expected = fw_wrapper{-12, -135, -1454, -1455, -1500};
+  auto const result   = cudf::round(input, 2, cudf::rounding_method::HALF_UP);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(RoundTests, Int64AtBoundaryHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int64_t>;
+
+  auto const m     = std::numeric_limits<int64_t>::max();  // 9223372036854775807
+  auto const input = fw_wrapper{m};
+
+  auto const expected = fw_wrapper{9223372036854775800};
+  auto const result   = cudf::round(input, -2, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+
+  auto const expected2 = fw_wrapper{9223372036850000000};
+  auto const result2   = cudf::round(input, -7, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, result2->view());
+
+  auto const expected3 = fw_wrapper{9223372000000000000};
+  auto const result3   = cudf::round(input, -11, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, result3->view());
+
+  auto const result4 = cudf::round(input, -12, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, result4->view());
+
+  auto const expected5 = fw_wrapper{9000000000000000000};
+  auto const result5   = cudf::round(input, -18, cudf::rounding_method::HALF_UP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected5, result5->view());
+}
+
+TEST_F(RoundTests, FixedPoint128HalfUp)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  {
+    auto const input    = fp_wrapper{{-160714515306}, scale_type{-13}};
+    auto const expected = fp_wrapper{{-16071451531}, scale_type{-12}};
+    auto const result   = cudf::round(input, 12, cudf::rounding_method::HALF_UP);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(RoundTests, FixedPointAtBoundaryTestHalfUp)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const m = std::numeric_limits<RepType>::max();  // 170141183460469231731687303715884105727
+
+  {
+    auto const input    = fp_wrapper{{m}, scale_type{0}};
+    auto const expected = fp_wrapper{{m / 100000}, scale_type{5}};
+    auto const result   = cudf::round(input, -5, cudf::rounding_method::HALF_UP);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const input    = fp_wrapper{{m}, scale_type{0}};
+    auto const expected = fp_wrapper{{m / 100000000000}, scale_type{11}};
+    auto const result   = cudf::round(input, -11, cudf::rounding_method::HALF_UP);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+
+  {
+    auto const input    = fp_wrapper{{m}, scale_type{0}};
+    auto const expected = fp_wrapper{{m / 1000000000000000}, scale_type{15}};
+    auto const result   = cudf::round(input, -15, cudf::rounding_method::HALF_UP);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(RoundTests, BoolTestHalfUp)
+{
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<bool>;
+
+  auto const input = fw_wrapper{0, 1, 0};
+  EXPECT_THROW(cudf::round(input, -2, cudf::rounding_method::HALF_UP), cudf::logic_error);
+}
+
+// Use __uint128_t for demonstration.
+constexpr __uint128_t operator""_uint128_t(const char* s)
+{
+  __uint128_t ret = 0;
+  for (int i = 0; s[i] != '\0'; ++i) {
+    ret *= 10;
+    if ('0' <= s[i] && s[i] <= '9') { ret += s[i] - '0'; }
+  }
+  return ret;
+}
+
+TEST_F(RoundTests, HalfEvenErrorsA)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  {
+    // 0.5 at scale -37 should round HALF_EVEN to 0, because 0 is an even number
+    auto const input =
+      fp_wrapper{{5000000000000000000000000000000000000_uint128_t}, scale_type{-37}};
+    auto const expected = fp_wrapper{{0}, scale_type{0}};
+    auto const result   = cudf::round(input, 0, cudf::rounding_method::HALF_EVEN);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(RoundTests, HalfEvenErrorsB)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  {
+    // 0.125 at scale -37 should round HALF_EVEN to 0.12, because 2 is an even number
+    auto const input =
+      fp_wrapper{{1250000000000000000000000000000000000_uint128_t}, scale_type{-37}};
+    auto const expected = fp_wrapper{{12}, scale_type{-2}};
+    auto const result   = cudf::round(input, 2, cudf::rounding_method::HALF_EVEN);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(RoundTests, HalfEvenErrorsC)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  {
+    // 0.0625 at scale -37 should round HALF_EVEN to 0.062, because 2 is an even number
+    auto const input =
+      fp_wrapper{{0625000000000000000000000000000000000_uint128_t}, scale_type{-37}};
+    auto const expected = fp_wrapper{{62}, scale_type{-3}};
+    auto const result   = cudf::round(input, 3, cudf::rounding_method::HALF_EVEN);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+TEST_F(RoundTests, HalfUpErrorsA)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  {
+    // 0.25 at scale -37 should round HALF_UP to 0.3
+    auto const input =
+      fp_wrapper{{2500000000000000000000000000000000000_uint128_t}, scale_type{-37}};
+    auto const expected = fp_wrapper{{3}, scale_type{-1}};
+    auto const result   = cudf::round(input, 1, cudf::rounding_method::HALF_UP);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/scalar/factories_test.cpp b/cpp/tests/scalar/factories_test.cpp
new file mode 100644
index 0000000..7da5c40
--- /dev/null
+++ b/cpp/tests/scalar/factories_test.cpp
@@ -0,0 +1,182 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+class ScalarFactoryTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct NumericScalarFactory : public ScalarFactoryTest {};
+
+TYPED_TEST_SUITE(NumericScalarFactory, cudf::test::NumericTypes);
+
+TYPED_TEST(NumericScalarFactory, FactoryDefault)
+{
+  std::unique_ptr<cudf::scalar> s =
+    cudf::make_numeric_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  EXPECT_EQ(s->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_FALSE(s->is_valid());
+}
+
+TYPED_TEST(NumericScalarFactory, TypeCast)
+{
+  std::unique_ptr<cudf::scalar> s =
+    cudf::make_numeric_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  auto numeric_s = static_cast<cudf::scalar_type_t<TypeParam>*>(s.get());
+
+  TypeParam value(37);
+  numeric_s->set_value(value);
+  EXPECT_EQ(numeric_s->value(), value);
+  EXPECT_TRUE(numeric_s->is_valid());
+  EXPECT_TRUE(s->is_valid());
+}
+
+template <typename T>
+struct TimestampScalarFactory : public ScalarFactoryTest {};
+
+TYPED_TEST_SUITE(TimestampScalarFactory, cudf::test::TimestampTypes);
+
+TYPED_TEST(TimestampScalarFactory, FactoryDefault)
+{
+  std::unique_ptr<cudf::scalar> s =
+    cudf::make_timestamp_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  EXPECT_EQ(s->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_FALSE(s->is_valid());
+}
+
+TYPED_TEST(TimestampScalarFactory, TypeCast)
+{
+  std::unique_ptr<cudf::scalar> s =
+    cudf::make_timestamp_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  auto numeric_s = static_cast<cudf::scalar_type_t<TypeParam>*>(s.get());
+
+  TypeParam value(typename TypeParam::duration{37});
+  numeric_s->set_value(value);
+  EXPECT_EQ(numeric_s->value(), value);
+  EXPECT_TRUE(numeric_s->is_valid());
+  EXPECT_TRUE(s->is_valid());
+}
+
+template <typename T>
+struct DefaultScalarFactory : public ScalarFactoryTest {};
+
+using MixedTypes = cudf::test::Concat<cudf::test::AllTypes, cudf::test::StringTypes>;
+TYPED_TEST_SUITE(DefaultScalarFactory, MixedTypes);
+
+TYPED_TEST(DefaultScalarFactory, FactoryDefault)
+{
+  std::unique_ptr<cudf::scalar> s =
+    cudf::make_default_constructed_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  EXPECT_EQ(s->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_FALSE(s->is_valid());
+}
+
+TYPED_TEST(DefaultScalarFactory, TypeCast)
+{
+  std::unique_ptr<cudf::scalar> s =
+    cudf::make_default_constructed_scalar(cudf::data_type{cudf::type_to_id<TypeParam>()});
+
+  auto numeric_s = static_cast<cudf::scalar_type_t<TypeParam>*>(s.get());
+
+  EXPECT_NO_THROW((void)numeric_s->value());
+  EXPECT_FALSE(numeric_s->is_valid());
+  EXPECT_FALSE(s->is_valid());
+}
+
+template <typename T>
+struct FixedWidthScalarFactory : public ScalarFactoryTest {};
+
+TYPED_TEST_SUITE(FixedWidthScalarFactory, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(FixedWidthScalarFactory, ValueProvided)
+{
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(54);
+
+  std::unique_ptr<cudf::scalar> s = cudf::make_fixed_width_scalar<TypeParam>(value);
+
+  auto numeric_s = static_cast<cudf::scalar_type_t<TypeParam>*>(s.get());
+
+  EXPECT_EQ(s->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(numeric_s->value(), value);
+  EXPECT_TRUE(numeric_s->is_valid());
+  EXPECT_TRUE(s->is_valid());
+}
+
+template <typename T>
+struct FixedPointScalarFactory : public ScalarFactoryTest {};
+
+TYPED_TEST_SUITE(FixedPointScalarFactory, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointScalarFactory, ValueProvided)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const rep_value      = static_cast<typename decimalXX::rep>(123);
+  auto const s              = cudf::make_fixed_point_scalar<decimalXX>(123, scale_type{-2});
+  auto const fp_s           = static_cast<cudf::scalar_type_t<decimalXX>*>(s.get());
+  auto const expected_dtype = cudf::data_type{cudf::type_to_id<decimalXX>(), -2};
+
+  EXPECT_EQ(s->type(), expected_dtype);
+  EXPECT_EQ(fp_s->value(), rep_value);
+  EXPECT_TRUE(fp_s->is_valid());
+  EXPECT_TRUE(s->is_valid());
+}
+
+struct StructScalarFactory : public ScalarFactoryTest {};
+
+TEST_F(StructScalarFactory, Basic)
+{
+  cudf::test::fixed_width_column_wrapper<int> col0{1};
+  cudf::test::strings_column_wrapper col1{"abc"};
+  cudf::test::lists_column_wrapper<int> col2{{1, 2, 3}};
+  cudf::test::structs_column_wrapper struct_col({col0, col1, col2});
+  cudf::column_view cv = static_cast<cudf::column_view>(struct_col);
+  std::vector<cudf::column_view> children(cv.child_begin(), cv.child_end());
+
+  // table_view constructor
+  {
+    auto sc = cudf::make_struct_scalar(cudf::table_view{children});
+    auto s  = static_cast<cudf::scalar_type_t<cudf::struct_view>*>(sc.get());
+    EXPECT_TRUE(s->is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{children}, s->view());
+  }
+
+  // host_span constructor
+  {
+    auto sc = cudf::make_struct_scalar(cudf::host_span<cudf::column_view const>{children});
+    auto s  = static_cast<cudf::scalar_type_t<cudf::struct_view>*>(sc.get());
+    EXPECT_TRUE(s->is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{children}, s->view());
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/scalar/scalar_device_view_test.cu b/cpp/tests/scalar/scalar_device_view_test.cu
new file mode 100644
index 0000000..8d0e54f
--- /dev/null
+++ b/cpp/tests/scalar/scalar_device_view_test.cu
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_device_view.cuh>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <random>
+#include <thrust/sequence.h>
+
+template <typename T>
+struct TypedScalarDeviceViewTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedScalarDeviceViewTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+template <typename ScalarDeviceViewType>
+__global__ void test_set_value(ScalarDeviceViewType s, ScalarDeviceViewType s1)
+{
+  s1.set_value(s.value());
+  s1.set_valid(true);
+}
+
+template <typename ScalarDeviceViewType>
+__global__ void test_value(ScalarDeviceViewType s, ScalarDeviceViewType s1, bool* result)
+{
+  *result = (s.value() == s1.value());
+}
+
+TYPED_TEST(TypedScalarDeviceViewTest, Value)
+{
+  TypeParam value  = cudf::test::make_type_param_scalar<TypeParam>(7);
+  TypeParam value1 = cudf::test::make_type_param_scalar<TypeParam>(11);
+  cudf::scalar_type_t<TypeParam> s(value);
+  cudf::scalar_type_t<TypeParam> s1{value1};
+
+  auto scalar_device_view  = cudf::get_scalar_device_view(s);
+  auto scalar_device_view1 = cudf::get_scalar_device_view(s1);
+  rmm::device_scalar<bool> result{cudf::get_default_stream()};
+
+  test_set_value<<<1, 1, 0, cudf::get_default_stream().value()>>>(scalar_device_view,
+                                                                  scalar_device_view1);
+  CUDF_CHECK_CUDA(0);
+
+  EXPECT_EQ(s1.value(), value);
+  EXPECT_TRUE(s1.is_valid());
+
+  test_value<<<1, 1, 0, cudf::get_default_stream().value()>>>(
+    scalar_device_view, scalar_device_view1, result.data());
+  CUDF_CHECK_CUDA(0);
+
+  EXPECT_TRUE(result.value(cudf::get_default_stream()));
+}
+
+template <typename ScalarDeviceViewType>
+__global__ void test_null(ScalarDeviceViewType s, bool* result)
+{
+  *result = s.is_valid();
+}
+
+TYPED_TEST(TypedScalarDeviceViewTest, ConstructNull)
+{
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(5);
+  cudf::scalar_type_t<TypeParam> s(value, false);
+  auto scalar_device_view = cudf::get_scalar_device_view(s);
+  rmm::device_scalar<bool> result{cudf::get_default_stream()};
+
+  test_null<<<1, 1, 0, cudf::get_default_stream().value()>>>(scalar_device_view, result.data());
+  CUDF_CHECK_CUDA(0);
+
+  EXPECT_FALSE(result.value(cudf::get_default_stream()));
+}
+
+template <typename ScalarDeviceViewType>
+__global__ void test_setnull(ScalarDeviceViewType s)
+{
+  s.set_valid(false);
+}
+
+TYPED_TEST(TypedScalarDeviceViewTest, SetNull)
+{
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(5);
+  cudf::scalar_type_t<TypeParam> s{value};
+  auto scalar_device_view = cudf::get_scalar_device_view(s);
+  s.set_valid_async(true);
+  EXPECT_TRUE(s.is_valid());
+
+  test_setnull<<<1, 1, 0, cudf::get_default_stream().value()>>>(scalar_device_view);
+  CUDF_CHECK_CUDA(0);
+
+  EXPECT_FALSE(s.is_valid());
+}
+
+struct StringScalarDeviceViewTest : public cudf::test::BaseFixture {};
+
+__global__ void test_string_value(cudf::string_scalar_device_view s,
+                                  char const* value,
+                                  cudf::size_type size,
+                                  bool* result)
+{
+  *result = (s.value() == cudf::string_view(value, size));
+}
+
+TEST_F(StringScalarDeviceViewTest, Value)
+{
+  std::string value("test string");
+  cudf::string_scalar s(value);
+
+  auto scalar_device_view = cudf::get_scalar_device_view(s);
+  rmm::device_scalar<bool> result{cudf::get_default_stream()};
+  auto value_v = cudf::detail::make_device_uvector_sync(
+    value, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  test_string_value<<<1, 1, 0, cudf::get_default_stream().value()>>>(
+    scalar_device_view, value_v.data(), value.size(), result.data());
+  CUDF_CHECK_CUDA(0);
+
+  EXPECT_TRUE(result.value(cudf::get_default_stream()));
+}
diff --git a/cpp/tests/scalar/scalar_test.cpp b/cpp/tests/scalar/scalar_test.cpp
new file mode 100644
index 0000000..d2f2b5d
--- /dev/null
+++ b/cpp/tests/scalar/scalar_test.cpp
@@ -0,0 +1,295 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+
+template <typename T>
+struct TypedScalarTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedScalarTestWithoutFixedPoint : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedScalarTest, cudf::test::FixedWidthTypes);
+TYPED_TEST_SUITE(TypedScalarTestWithoutFixedPoint, cudf::test::FixedWidthTypesWithoutFixedPoint);
+
+TYPED_TEST(TypedScalarTest, DefaultValidity)
+{
+  using Type = cudf::device_storage_type_t<TypeParam>;
+  Type value = static_cast<Type>(cudf::test::make_type_param_scalar<TypeParam>(7));
+  cudf::scalar_type_t<TypeParam> s(value);
+
+  EXPECT_TRUE(s.is_valid());
+  EXPECT_EQ(value, s.value());
+}
+
+TYPED_TEST(TypedScalarTest, ConstructNull)
+{
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(5);
+  cudf::scalar_type_t<TypeParam> s(value, false);
+
+  EXPECT_FALSE(s.is_valid());
+}
+
+TYPED_TEST(TypedScalarTestWithoutFixedPoint, SetValue)
+{
+  TypeParam init  = cudf::test::make_type_param_scalar<TypeParam>(0);
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(9);
+  cudf::scalar_type_t<TypeParam> s(init, true);
+  s.set_value(value);
+
+  EXPECT_TRUE(s.is_valid());
+  EXPECT_EQ(value, s.value());
+}
+
+TYPED_TEST(TypedScalarTestWithoutFixedPoint, SetNull)
+{
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(6);
+  cudf::scalar_type_t<TypeParam> s(value, true);
+  s.set_valid_async(false);
+
+  EXPECT_FALSE(s.is_valid());
+}
+
+TYPED_TEST(TypedScalarTest, CopyConstructor)
+{
+  using Type = cudf::device_storage_type_t<TypeParam>;
+  Type value = static_cast<Type>(cudf::test::make_type_param_scalar<TypeParam>(8));
+  cudf::scalar_type_t<TypeParam> s(value);
+  auto s2 = s;
+
+  EXPECT_TRUE(s2.is_valid());
+  EXPECT_EQ(value, s2.value());
+}
+
+TYPED_TEST(TypedScalarTest, MoveConstructor)
+{
+  TypeParam value = cudf::test::make_type_param_scalar<TypeParam>(8);
+  cudf::scalar_type_t<TypeParam> s(value);
+  auto data_ptr = s.data();
+  auto mask_ptr = s.validity_data();
+  decltype(s) s2(std::move(s));
+
+  EXPECT_EQ(mask_ptr, s2.validity_data());
+  EXPECT_EQ(data_ptr, s2.data());
+}
+
+struct StringScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringScalarTest, DefaultValidity)
+{
+  std::string value = "test string";
+  auto s            = cudf::string_scalar(value);
+
+  EXPECT_TRUE(s.is_valid());
+  EXPECT_EQ(value, s.to_string());
+}
+
+TEST_F(StringScalarTest, CopyConstructor)
+{
+  std::string value = "test_string";
+  auto s            = cudf::string_scalar(value);
+  auto s2           = s;
+
+  EXPECT_TRUE(s2.is_valid());
+  EXPECT_EQ(value, s2.to_string());
+}
+
+TEST_F(StringScalarTest, MoveConstructor)
+{
+  std::string value = "another test string";
+  auto s            = cudf::string_scalar(value);
+  auto data_ptr     = s.data();
+  auto mask_ptr     = s.validity_data();
+  decltype(s) s2(std::move(s));
+
+  EXPECT_EQ(mask_ptr, s2.validity_data());
+  EXPECT_EQ(data_ptr, s2.data());
+}
+
+struct ListScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(ListScalarTest, DefaultValidityNonNested)
+{
+  auto data = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto s    = cudf::list_scalar(data);
+
+  EXPECT_TRUE(s.is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(data, s.view());
+}
+
+TEST_F(ListScalarTest, DefaultValidityNested)
+{
+  auto data = cudf::test::lists_column_wrapper<int32_t>{{1, 2}, {2}, {}, {4, 5}};
+  auto s    = cudf::list_scalar(data);
+
+  EXPECT_TRUE(s.is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(data, s.view());
+}
+
+TEST_F(ListScalarTest, MoveColumnConstructor)
+{
+  auto data = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto col  = cudf::column(data);
+  auto ptr  = col.view().data<int32_t>();
+  auto s    = cudf::list_scalar(std::move(col));
+
+  EXPECT_TRUE(s.is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(data, s.view());
+  EXPECT_EQ(ptr, s.view().data<int32_t>());
+}
+
+TEST_F(ListScalarTest, CopyConstructorNonNested)
+{
+  auto data = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto s    = cudf::list_scalar(data);
+  auto s2   = s;
+
+  EXPECT_TRUE(s2.is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(data, s2.view());
+  EXPECT_NE(s.view().data<int32_t>(), s2.view().data<int32_t>());
+}
+
+TEST_F(ListScalarTest, CopyConstructorNested)
+{
+  auto data = cudf::test::lists_column_wrapper<int32_t>{{1, 2}, {2}, {}, {4, 5}};
+  auto s    = cudf::list_scalar(data);
+  auto s2   = s;
+
+  EXPECT_TRUE(s2.is_valid());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(data, s2.view());
+  EXPECT_NE(s.view().child(0).data<int32_t>(), s2.view().child(0).data<int32_t>());
+  EXPECT_NE(s.view().child(1).data<int32_t>(), s2.view().child(1).data<int32_t>());
+}
+
+TEST_F(ListScalarTest, MoveConstructorNonNested)
+{
+  auto data     = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3};
+  auto s        = cudf::list_scalar(data);
+  auto data_ptr = s.view().data<int32_t>();
+  auto mask_ptr = s.validity_data();
+  decltype(s) s2(std::move(s));
+
+  EXPECT_EQ(mask_ptr, s2.validity_data());
+  EXPECT_EQ(data_ptr, s2.view().data<int32_t>());
+  EXPECT_EQ(s.view().data<int32_t>(), nullptr);
+}
+
+TEST_F(ListScalarTest, MoveConstructorNested)
+{
+  auto data       = cudf::test::lists_column_wrapper<int32_t>{{1, 2}, {2}, {}, {4, 5}};
+  auto s          = cudf::list_scalar(data);
+  auto offset_ptr = s.view().child(0).data<cudf::size_type>();
+  auto data_ptr   = s.view().child(1).data<int32_t>();
+  auto mask_ptr   = s.validity_data();
+  decltype(s) s2(std::move(s));
+
+  EXPECT_EQ(mask_ptr, s2.validity_data());
+  EXPECT_EQ(offset_ptr, s2.view().child(0).data<cudf::size_type>());
+  EXPECT_EQ(data_ptr, s2.view().child(1).data<int32_t>());
+  EXPECT_EQ(s.view().data<int32_t>(), nullptr);
+  EXPECT_EQ(s.view().num_children(), 0);
+}
+
+struct StructScalarTest : public cudf::test::BaseFixture {};
+
+TEST_F(StructScalarTest, Basic)
+{
+  cudf::test::fixed_width_column_wrapper<int> col0{1};
+  cudf::test::strings_column_wrapper col1{"abc"};
+  cudf::test::lists_column_wrapper<int> col2{{1, 2, 3}};
+  cudf::test::structs_column_wrapper struct_col({col0, col1, col2});
+  cudf::column_view cv = static_cast<cudf::column_view>(struct_col);
+  std::vector<cudf::column_view> children(cv.child_begin(), cv.child_end());
+
+  // table_view constructor
+  {
+    auto s = cudf::struct_scalar(children, true);
+    EXPECT_TRUE(s.is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{children}, s.view());
+  }
+
+  // host_span constructor
+  {
+    auto s = cudf::struct_scalar(cudf::host_span<cudf::column_view const>{children}, true);
+    EXPECT_TRUE(s.is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{children}, s.view());
+  }
+}
+
+TEST_F(StructScalarTest, BasicNulls)
+{
+  cudf::test::fixed_width_column_wrapper<int> col0{1};
+  cudf::test::strings_column_wrapper col1{"abc"};
+  cudf::test::lists_column_wrapper<int> col2{{1, 2, 3}};
+  std::vector<cudf::column_view> src_children({col0, col1, col2});
+
+  std::vector<std::unique_ptr<cudf::column>> src_columns;
+
+  // structs_column_wrapper takes ownership of the incoming columns, so make a copy
+  src_columns.push_back(std::make_unique<cudf::column>(src_children[0]));
+  src_columns.push_back(std::make_unique<cudf::column>(src_children[1]));
+  src_columns.push_back(std::make_unique<cudf::column>(src_children[2]));
+  cudf::test::structs_column_wrapper valid_struct_col(std::move(src_columns), {1});
+  cudf::column_view vcv = static_cast<cudf::column_view>(valid_struct_col);
+  std::vector<cudf::column_view> valid_children(vcv.child_begin(), vcv.child_end());
+
+  // structs_column_wrapper takes ownership of the incoming columns, so make a copy
+  src_columns.push_back(std::make_unique<cudf::column>(src_children[0]));
+  src_columns.push_back(std::make_unique<cudf::column>(src_children[1]));
+  src_columns.push_back(std::make_unique<cudf::column>(src_children[2]));
+  cudf::test::structs_column_wrapper invalid_struct_col(std::move(src_columns), {0});
+  cudf::column_view icv = static_cast<cudf::column_view>(invalid_struct_col);
+  std::vector<cudf::column_view> invalid_children(icv.child_begin(), icv.child_end());
+
+  // table_view constructor
+  {
+    auto s = cudf::struct_scalar(cudf::table_view{src_children}, true);
+    EXPECT_TRUE(s.is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{valid_children}, s.view());
+  }
+  // host_span constructor
+  {
+    auto s = cudf::struct_scalar(cudf::host_span<cudf::column_view const>{src_children}, true);
+    EXPECT_TRUE(s.is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{valid_children}, s.view());
+  }
+
+  // with nulls, we expect the incoming children to get nullified by passing false to
+  // the scalar constructor itself. so we use the unmodified `children` as the input, but
+  // we compare against the modified `invalid_children` produced by the source column as
+  // proof that the scalar did the validity pushdown.
+
+  // table_view constructor
+  {
+    auto s = cudf::struct_scalar(cudf::table_view{src_children}, false);
+    EXPECT_TRUE(!s.is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{invalid_children}, s.view());
+  }
+
+  // host_span constructor
+  {
+    auto s = cudf::struct_scalar(cudf::host_span<cudf::column_view const>{src_children}, false);
+    EXPECT_TRUE(!s.is_valid());
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(cudf::table_view{invalid_children}, s.view());
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/search/search_dictionary_test.cpp b/cpp/tests/search/search_dictionary_test.cpp
new file mode 100644
index 0000000..79a3d3b
--- /dev/null
+++ b/cpp/tests/search/search_dictionary_test.cpp
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/search.hpp>
+
+struct DictionarySearchTest : public cudf::test::BaseFixture {};
+
+using cudf::numeric_scalar;
+using cudf::size_type;
+using cudf::string_scalar;
+using cudf::test::fixed_width_column_wrapper;
+
+TEST_F(DictionarySearchTest, search_dictionary)
+{
+  cudf::test::dictionary_column_wrapper<std::string> input(
+    {"", "", "10", "10", "20", "20", "30", "40"}, {0, 0, 1, 1, 1, 1, 1, 1});
+  cudf::test::dictionary_column_wrapper<std::string> values(
+    {"", "08", "10", "11", "30", "32", "90"}, {0, 1, 1, 1, 1, 1, 1});
+
+  auto result = cudf::upper_bound({cudf::table_view{{input}}},
+                                  {cudf::table_view{{values}}},
+                                  {cudf::order::ASCENDING},
+                                  {cudf::null_order::BEFORE});
+  fixed_width_column_wrapper<size_type> expect_upper{2, 2, 4, 4, 7, 7, 8};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect_upper);
+
+  result = cudf::lower_bound({cudf::table_view{{input}}},
+                             {cudf::table_view{{values}}},
+                             {cudf::order::ASCENDING},
+                             {cudf::null_order::BEFORE});
+  fixed_width_column_wrapper<size_type> expect_lower{0, 2, 2, 4, 6, 7, 8};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect_lower);
+}
+
+TEST_F(DictionarySearchTest, search_table_dictionary)
+{
+  fixed_width_column_wrapper<int32_t> column_0{{10, 10, 20, 20, 20, 20, 20, 20, 20, 50, 30},
+                                               {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  fixed_width_column_wrapper<float> column_1{{5.0, 6.0, .5, .5, .5, .5, .7, .7, .7, .7, .5},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  cudf::test::dictionary_column_wrapper<int16_t> column_2{
+    {90, 95, 77, 78, 79, 76, 61, 62, 63, 41, 50}, {1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1}};
+  cudf::table_view input({column_0, column_1, column_2});
+
+  fixed_width_column_wrapper<int32_t> values_0{{10, 40, 20}, {1, 0, 1}};
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+  cudf::test::dictionary_column_wrapper<int16_t> values_2{{95, 50, 77}, {1, 1, 0}};
+  cudf::table_view values({values_0, values_1, values_2});
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER}};
+
+  auto result = cudf::lower_bound(input, values, order_flags, null_order_flags);
+  fixed_width_column_wrapper<size_type> expect_lower{1, 10, 2};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect_lower);
+
+  result = cudf::upper_bound(input, values, order_flags, null_order_flags);
+  fixed_width_column_wrapper<size_type> expect_upper{2, 11, 6};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect_upper);
+}
+
+TEST_F(DictionarySearchTest, contains_dictionary)
+{
+  cudf::test::dictionary_column_wrapper<std::string> column(
+    {"00", "00", "17", "17", "23", "23", "29"});
+  EXPECT_TRUE(cudf::contains(column, string_scalar{"23"}));
+  EXPECT_FALSE(cudf::contains(column, string_scalar{"28"}));
+
+  cudf::test::dictionary_column_wrapper<std::string> needles({"00", "17", "23", "27"});
+  fixed_width_column_wrapper<bool> expect{1, 1, 1, 0};
+  auto result = cudf::contains(column, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(DictionarySearchTest, contains_nullable_dictionary)
+{
+  cudf::test::dictionary_column_wrapper<int64_t> column({0, 0, 17, 17, 23, 23, 29},
+                                                        {1, 0, 1, 1, 1, 1, 1});
+  EXPECT_TRUE(cudf::contains(column, numeric_scalar<int64_t>{23}));
+  EXPECT_FALSE(cudf::contains(column, numeric_scalar<int64_t>{28}));
+
+  cudf::test::dictionary_column_wrapper<int64_t> needles({0, 17, 23, 27});
+  fixed_width_column_wrapper<bool> expect{1, 1, 1, 0};
+  auto result = cudf::contains(column, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
diff --git a/cpp/tests/search/search_list_test.cpp b/cpp/tests/search/search_list_test.cpp
new file mode 100644
index 0000000..48711c2
--- /dev/null
+++ b/cpp/tests/search/search_list_test.cpp
@@ -0,0 +1,670 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/search.hpp>
+#include <cudf/table/table_view.hpp>
+
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+using bools_col   = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+using strings_col = cudf::test::strings_column_wrapper;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};  // Mark for null child elements at the current level
+constexpr int32_t XXX{0};   // Mark for null elements at all levels
+
+using TestTypes = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                     cudf::test::FloatingPointTypes,
+                                     cudf::test::DurationTypes,
+                                     cudf::test::TimestampTypes>;
+
+template <typename T>
+struct TypedListsContainsTestScalarNeedle : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(TypedListsContainsTestScalarNeedle, TestTypes);
+
+TYPED_TEST(TypedListsContainsTestScalarNeedle, EmptyInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = lists_col{};
+
+  auto const needle1 = [] {
+    auto child = tdata_col{};
+    return cudf::list_scalar(child);
+  }();
+  auto const needle2 = [] {
+    auto child = tdata_col{1, 2, 3};
+    return cudf::list_scalar(child);
+  }();
+
+  EXPECT_FALSE(cudf::contains(haystack, needle1));
+  EXPECT_FALSE(cudf::contains(haystack, needle2));
+}
+
+TYPED_TEST(TypedListsContainsTestScalarNeedle, TrivialInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = lists_col{{1, 2}, {1}, {}, {1, 3}, {4}, {1, 1}};
+
+  auto const needle1 = [] {
+    auto child = tdata_col{1, 2};
+    return cudf::list_scalar(child);
+  }();
+  auto const needle2 = [] {
+    auto child = tdata_col{2, 1};
+    return cudf::list_scalar(child);
+  }();
+
+  EXPECT_TRUE(cudf::contains(haystack, needle1));
+
+  // Lists are order-sensitive.
+  EXPECT_FALSE(cudf::contains(haystack, needle2));
+}
+
+TYPED_TEST(TypedListsContainsTestScalarNeedle, SlicedColumnInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original = lists_col{{0, 0}, {0}, {1, 2}, {1}, {}, {1, 3}, {0, 0}};
+  auto const haystack          = cudf::slice(haystack_original, {2, 6})[0];
+
+  auto const needle1 = [] {
+    auto child = tdata_col{1, 2};
+    return cudf::list_scalar(child);
+  }();
+  auto const needle2 = [] {
+    auto child = tdata_col{};
+    return cudf::list_scalar(child);
+  }();
+  auto const needle3 = [] {
+    auto child = tdata_col{0, 0};
+    return cudf::list_scalar(child);
+  }();
+
+  EXPECT_TRUE(cudf::contains(haystack, needle1));
+  EXPECT_TRUE(cudf::contains(haystack, needle2));
+  EXPECT_FALSE(cudf::contains(haystack, needle3));
+}
+
+TYPED_TEST(TypedListsContainsTestScalarNeedle, SimpleInputWithNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  // Test with invalid scalar.
+  {
+    auto const haystack = lists_col{{1, 2}, {1}, {}, {1, 3}, {4}, {}, {1, 1}};
+    auto const needle   = [] {
+      auto child = tdata_col{};
+      return cudf::list_scalar(child, false);
+    }();
+
+    EXPECT_FALSE(cudf::contains(haystack, needle));
+  }
+
+  // Test with nulls at the top level.
+  {
+    auto const haystack =
+      lists_col{{{1, 2}, {1}, {} /*NULL*/, {1, 3}, {4}, {} /*NULL*/, {1, 1}}, nulls_at({2, 5})};
+
+    auto const needle1 = [] {
+      auto child = tdata_col{1, 2};
+      return cudf::list_scalar(child);
+    }();
+    auto const needle2 = [] {
+      auto child = tdata_col{};
+      return cudf::list_scalar(child, false);
+    }();
+
+    EXPECT_TRUE(cudf::contains(haystack, needle1));
+    EXPECT_TRUE(cudf::contains(haystack, needle2));
+  }
+
+  // Test with nulls at the children level.
+  {
+    auto const haystack = lists_col{{lists_col{1, 2},
+                                     lists_col{1},
+                                     lists_col{{1, null}, null_at(1)},
+                                     lists_col{} /*NULL*/,
+                                     lists_col{1, 3},
+                                     lists_col{1, 4},
+                                     lists_col{4},
+                                     lists_col{} /*NULL*/,
+                                     lists_col{1, 1}},
+                                    nulls_at({3, 7})};
+
+    auto const needle1 = [] {
+      auto child = tdata_col{{1, null}, null_at(1)};
+      return cudf::list_scalar(child);
+    }();
+    auto const needle2 = [] {
+      auto child = tdata_col{{null, 1}, null_at(0)};
+      return cudf::list_scalar(child);
+    }();
+    auto const needle3 = [] {
+      auto child = tdata_col{1, 0};
+      return cudf::list_scalar(child);
+    }();
+
+    EXPECT_TRUE(cudf::contains(haystack, needle1));
+    EXPECT_FALSE(cudf::contains(haystack, needle2));
+    EXPECT_FALSE(cudf::contains(haystack, needle3));
+  }
+}
+
+TYPED_TEST(TypedListsContainsTestScalarNeedle, SlicedInputHavingNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original = lists_col{{{0, 0},
+                                            {0} /*NULL*/,
+                                            lists_col{{1, null}, null_at(1)},
+                                            {1},
+                                            {} /*NULL*/,
+                                            {1, 3},
+                                            {4},
+                                            {} /*NULL*/,
+                                            {1, 1},
+                                            {0}},
+                                           nulls_at({1, 4, 7})};
+  auto const haystack          = cudf::slice(haystack_original, {2, 9})[0];
+
+  auto const needle1 = [] {
+    auto child = tdata_col{{1, null}, null_at(1)};
+    return cudf::list_scalar(child);
+  }();
+  auto const needle2 = [] {
+    auto child = tdata_col{};
+    return cudf::list_scalar(child);
+  }();
+  auto const needle3 = [] {
+    auto child = tdata_col{0, 0};
+    return cudf::list_scalar(child);
+  }();
+
+  EXPECT_TRUE(cudf::contains(haystack, needle1));
+  EXPECT_FALSE(cudf::contains(haystack, needle2));
+  EXPECT_FALSE(cudf::contains(haystack, needle3));
+}
+
+template <typename T>
+struct TypedListContainsTestColumnNeedles : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedListContainsTestColumnNeedles, TestTypes);
+
+TYPED_TEST(TypedListContainsTestColumnNeedles, EmptyInput)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = lists_col{};
+  auto const needles  = lists_col{};
+  auto const expected = bools_col{};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedListContainsTestColumnNeedles, TrivialInput)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = lists_col{{0, 1}, {2}, {3, 4, 5}, {2, 3, 4}, {}, {0, 2, 0}};
+  auto const needles  = lists_col{{0, 1}, {1}, {3, 5, 4}, {}};
+
+  auto const expected = bools_col{1, 0, 0, 1};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedListContainsTestColumnNeedles, SlicedInputNoNulls)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original =
+    lists_col{{0, 0}, {0}, {0, 1}, {2}, {3, 4, 5}, {2, 3, 4}, {}, {0, 2, 0}};
+  auto const haystack = cudf::slice(haystack_original, {2, 8})[0];
+
+  auto const needles_original = lists_col{{0}, {0, 1}, {0, 0}, {3, 5, 4}, {}, {0, 0}, {} /*0*/};
+  auto const needles          = cudf::slice(needles_original, {1, 5})[0];
+
+  auto const expected = bools_col{1, 0, 0, 1};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedListContainsTestColumnNeedles, SlicedInputHavingNulls)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original = lists_col{{{0, 0},
+                                            {0} /*NULL*/,
+                                            lists_col{{1, null}, null_at(1)},
+                                            {1},
+                                            {} /*NULL*/,
+                                            {1, 3},
+                                            {4},
+                                            {} /*NULL*/,
+                                            {1, 1},
+                                            {0}},
+                                           nulls_at({1, 4, 7})};
+  auto const haystack          = cudf::slice(haystack_original, {2, 9})[0];
+
+  auto const needles_original = lists_col{{{0, 0},
+                                           {0} /*NULL*/,
+                                           lists_col{{1, null}, null_at(1)},
+                                           {1},
+                                           {} /*NULL*/,
+                                           {1, 3, 1},
+                                           {4},
+                                           {} /*NULL*/,
+                                           {},
+                                           {0}},
+                                          nulls_at({1, 4, 7})};
+  auto const needles          = cudf::slice(needles_original, {2, 9})[0];
+
+  auto const expected = bools_col{{1, 1, null, 0, 1, null, 0}, nulls_at({2, 5})};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedListContainsTestColumnNeedles, ListsOfStructs)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = [] {
+    auto offsets = int32s_col{0, 2, 3, 5, 8, 10};
+    // clang-format off
+    auto data1 = tdata_col{1, 2,     //
+                           1,        //
+                           0, 1,     //
+                           1, 3, 4,  //
+                           0, 0      //
+    };
+    auto data2 = tdata_col{1, 3,     //
+                           2,        //
+                           1, 1,     //
+                           0, 2, 0,  //
+                           1, 2      //
+    };
+    // clang-format on
+    auto child = structs_col{{data1, data2}};
+    return cudf::make_lists_column(5, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const needles = [] {
+    auto offsets = int32s_col{0, 3, 4, 6, 9, 11};
+    // clang-format off
+    auto data1 = tdata_col{1, 2, 1,  //
+                           1,        //
+                           0, 1,     //
+                           1, 3, 4,  //
+                           0, 0      //
+    };
+    auto data2 = tdata_col{1, 3, 0,  //
+                           2,        //
+                           1, 1,     //
+                           0, 2, 2,  //
+                           1, 1      //
+    };
+    // clang-format on
+    auto child = structs_col{{data1, data2}};
+    return cudf::make_lists_column(5, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const expected = bools_col{0, 1, 1, 0, 0};
+  auto const result   = cudf::contains(*haystack, *needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+auto search_bounds(cudf::table_view const& t,
+                   cudf::table_view const& values,
+                   std::vector<cudf::order> const& column_order,
+                   std::vector<cudf::null_order> const& null_precedence = {
+                     cudf::null_order::BEFORE})
+{
+  auto result_lower_bound = cudf::lower_bound(t, values, column_order, null_precedence);
+  auto result_upper_bound = cudf::upper_bound(t, values, column_order, null_precedence);
+  return std::pair(std::move(result_lower_bound), std::move(result_upper_bound));
+}
+
+struct ListBinarySearch : public cudf::test::BaseFixture {};
+
+TEST_F(ListBinarySearch, ListWithNulls)
+{
+  {
+    using lcw           = cudf::test::lists_column_wrapper<double>;
+    auto const haystack = lcw{
+      lcw{-3.45967821e+12},  // 0
+      lcw{-3.6912186e-32},   // 1
+      lcw{9.721175},         // 2
+    };
+
+    auto const needles = lcw{
+      lcw{{null, 4.22671e+32}, null_at(0)},
+    };
+
+    auto const expected = int32s_col{0};
+    auto const [result_lower_bound, result_upper_bound] =
+      search_bounds(cudf::table_view{{haystack}},
+                    cudf::table_view{{needles}},
+                    {cudf::order::ASCENDING},
+                    {cudf::null_order::BEFORE});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result_lower_bound, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result_upper_bound, verbosity);
+  }
+
+  {
+    using lcw       = cudf::test::lists_column_wrapper<int32_t, int32_t>;
+    auto const col1 = lcw{
+      lcw{{null}, null_at(0)},  // 0
+      lcw{-80},                 // 1
+      lcw{-17},                 // 2
+    };
+
+    auto const col2 = lcw{
+      lcw{27},                  // 0
+      lcw{{null}, null_at(0)},  // 1
+      lcw{},                    // 2
+    };
+
+    auto const val1 = lcw{
+      lcw{87},
+    };
+
+    auto const val2 = lcw{
+      lcw{},
+    };
+
+    cudf::table_view input{{col1, col2}};
+    cudf::table_view values{{val1, val2}};
+    std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+    std::vector<cudf::null_order> null_order_flags{cudf::null_order::BEFORE,
+                                                   cudf::null_order::BEFORE};
+
+    auto const expected                                 = int32s_col{3};
+    auto const [result_lower_bound, result_upper_bound] = search_bounds(
+      cudf::table_view{{input}}, cudf::table_view{{values}}, column_order, null_order_flags);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result_lower_bound, verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result_upper_bound, verbosity);
+  }
+}
+
+TEST_F(ListBinarySearch, ListsOfStructs)
+{
+  // Haystack must be pre-sorted.
+  auto const haystack = [] {
+    auto offsets = int32s_col{0, 2, 3, 4, 5, 7, 10, 13, 16, 18};
+    // clang-format off
+    auto data1 = int32s_col{1, 2,
+                            3,
+                            3,
+                            3,
+                            4, 5,
+                            4, 5, 4,
+                            4, 5, 4,
+                            4, 5, 4,
+                            4, 6
+    };
+    auto data2 = int32s_col{1, 2,
+                            3,
+                            3,
+                            3,
+                            4, 5,
+                            4, 5, 4,
+                            4, 5, 4,
+                            4, 5, 4,
+                            5, 1
+    };
+    // clang-format on
+    auto child = structs_col{{data1, data2}};
+    return cudf::make_lists_column(9, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const needles = [] {
+    auto offsets = int32s_col{0, 3, 4, 6, 8, 10, 13, 14, 15};
+    // clang-format off
+    auto data1 = int32s_col{1, 2, 1,
+                            3,
+                            4, 1,
+                            0, 1,
+                            1, 0,
+                            1, 3, 5,
+                            3,
+                            3
+    };
+    auto data2 = int32s_col{1, 3, 0,
+                            3,
+                            1, 2,
+                            1, 1,
+                            1, 2,
+                            0, 2, 2,
+                            3,
+                            3
+    };
+    // clang-format on
+    auto child = structs_col{{data1, data2}};
+    return cudf::make_lists_column(8, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const [result_lower_bound, result_upper_bound] = search_bounds(
+    cudf::table_view{{*haystack}}, cudf::table_view{{*needles}}, {cudf::order::ASCENDING});
+  auto const expected_lower_bound = int32s_col{1, 1, 4, 0, 0, 0, 1, 1};
+  auto const expected_upper_bound = int32s_col{1, 4, 4, 0, 0, 0, 4, 4};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, *result_lower_bound, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, *result_upper_bound, verbosity);
+}
+
+TEST_F(ListBinarySearch, ListsOfEqualStructsInTwoTables)
+{
+  // Haystack must be pre-sorted.
+  auto const haystack = [] {
+    auto offsets = int32s_col{0, 2, 3, 4, 5, 7, 10, 13, 16, 18};
+    // clang-format off
+    auto data1 = int32s_col{1, 2,
+                            3,
+                            3,
+                            3,
+                            4, 5,
+                            4, 5, 4,
+                            4, 5, 4,
+                            4, 5, 4,
+                            4, 6
+    };
+    auto data2 = int32s_col{1, 2,
+                            3,
+                            3,
+                            3,
+                            4, 5,
+                            4, 5, 4,
+                            4, 5, 4,
+                            4, 5, 4,
+                            5, 1
+    };
+    // clang-format on
+    auto child = structs_col{{data1, data2}};
+    return cudf::make_lists_column(9, offsets.release(), child.release(), 0, {});
+  }();
+
+  auto const needles = [] {
+    auto offsets = int32s_col{0, 2, 3, 4, 5, 7, 10, 13, 15, 17};
+    // clang-format off
+    auto data1 = int32s_col{1, 2,
+                            3,
+                            4,
+                            5,
+                            4, 5,
+                            5, 5, 4,
+                            4, 5, 4,
+                            4, 4,
+                            4, 6
+    };
+    auto data2 = int32s_col{1, 2,
+                            3,
+                            4,
+                            5,
+                            4, 5,
+                            5, 5, 4,
+                            4, 5, 4,
+                            4, 4,
+                            5, 1
+    };
+    // clang-format on
+    auto child = structs_col{{data1, data2}};
+    return cudf::make_lists_column(9, offsets.release(), child.release(), 0, {});
+  }();
+
+  // In this search, the two table have many equal structs.
+  // This help to verify the internal implementation of two-table lex comparator in which the
+  // structs column of two input tables are concatenated, ranked, then split.
+  auto const [result_lower_bound, result_upper_bound] = search_bounds(
+    cudf::table_view{{*haystack}}, cudf::table_view{{*needles}}, {cudf::order::ASCENDING});
+  auto const expected_lower_bound = int32s_col{0, 1, 4, 9, 4, 9, 5, 4, 8};
+  auto const expected_upper_bound = int32s_col{1, 4, 4, 9, 5, 9, 8, 4, 9};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, *result_lower_bound, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, *result_upper_bound, verbosity);
+}
+
+TEST_F(ListBinarySearch, CrazyListTest)
+{
+  // Data type: List<List<Struct<Struct<List<Struct<int, int>>>>>>
+
+  // Haystack must be pre-sorted.
+  auto const haystack = [] {
+    auto lists_of_structs_of_ints = [] {
+      auto offsets = int32s_col{0, 2, 3, 4, 5, 7, 10, 13, 16, 18};
+      // clang-format off
+      auto data1 = int32s_col{1, 2,
+        3,
+        3,
+        3,
+        4, 5,
+        4, 5, 4,
+        4, 5, 4,
+        4, 5, 4,
+        4, 6
+      };
+      auto data2 = int32s_col{1, 2,
+        3,
+        3,
+        3,
+        4, 5,
+        4, 5, 4,
+        4, 5, 4,
+        4, 5, 4,
+        5, 1
+      };
+      // clang-format on
+      auto child = structs_col{{data1, data2}};
+      return cudf::make_lists_column(9, offsets.release(), child.release(), 0, {});
+    }();
+
+    auto struct_nested0 = [&] {
+      std::vector<std::unique_ptr<cudf::column>> child_columns;
+      child_columns.emplace_back(std::move(lists_of_structs_of_ints));
+      return cudf::make_structs_column(9, std::move(child_columns), 0, {});
+    }();
+
+    auto struct_nested1 = [&] {
+      std::vector<std::unique_ptr<cudf::column>> child_columns;
+      child_columns.emplace_back(std::move(struct_nested0));
+      return cudf::make_structs_column(9, std::move(child_columns), 0, {});
+    }();
+
+    auto list_nested0 = [&] {
+      auto offsets = int32s_col{0, 3, 3, 4, 6, 9};
+      return cudf::make_lists_column(5, offsets.release(), std::move(struct_nested1), 0, {});
+    }();
+
+    auto offsets = int32s_col{0, 0, 2, 4, 5, 5};
+    return cudf::make_lists_column(5, offsets.release(), std::move(list_nested0), 0, {});
+  }();
+
+  auto const needles = [] {
+    auto lists_of_structs_of_ints = [] {
+      auto offsets = int32s_col{0, 2, 3, 4, 5, 7, 10, 13, 15, 17};
+      // clang-format off
+      auto data1 = int32s_col{1, 2,
+        3,
+        4,
+        5,
+        4, 5,
+        5, 5, 4,
+        4, 5, 4,
+        4, 4,
+        4, 6
+      };
+      auto data2 = int32s_col{1, 2,
+        3,
+        4,
+        5,
+        4, 5,
+        5, 5, 4,
+        4, 5, 4,
+        4, 4,
+        5, 1
+      };
+      // clang-format on
+      auto child = structs_col{{data1, data2}};
+      return cudf::make_lists_column(9, offsets.release(), child.release(), 0, {});
+    }();
+
+    auto struct_nested0 = [&] {
+      std::vector<std::unique_ptr<cudf::column>> child_columns;
+      child_columns.emplace_back(std::move(lists_of_structs_of_ints));
+      return cudf::make_structs_column(9, std::move(child_columns), 0, {});
+    }();
+
+    auto struct_nested1 = [&] {
+      std::vector<std::unique_ptr<cudf::column>> child_columns;
+      child_columns.emplace_back(std::move(struct_nested0));
+      return cudf::make_structs_column(9, std::move(child_columns), 0, {});
+    }();
+
+    auto list_nested0 = [&] {
+      auto offsets = int32s_col{0, 3, 3, 4, 6, 9};
+      return cudf::make_lists_column(5, offsets.release(), std::move(struct_nested1), 0, {});
+    }();
+
+    auto offsets = int32s_col{0, 2, 2, 4, 4, 5};
+    return cudf::make_lists_column(5, offsets.release(), std::move(list_nested0), 0, {});
+  }();
+
+  // In this search, the two table have many equal structs.
+  // This help to verify the internal implementation of two-table lex comparator in which the
+  // structs column of two input tables are concatenated, ranked, then split.
+  auto const [result_lower_bound, result_upper_bound] = search_bounds(
+    cudf::table_view{{*haystack}}, cudf::table_view{{*needles}}, {cudf::order::ASCENDING});
+
+  auto const expected_lower_bound = int32s_col{2, 0, 5, 0, 5};
+  auto const expected_upper_bound = int32s_col{2, 1, 5, 1, 5};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, *result_lower_bound, verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, *result_upper_bound, verbosity);
+}
diff --git a/cpp/tests/search/search_struct_test.cpp b/cpp/tests/search/search_struct_test.cpp
new file mode 100644
index 0000000..c35d359
--- /dev/null
+++ b/cpp/tests/search/search_struct_test.cpp
@@ -0,0 +1,749 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/search.hpp>
+#include <cudf/table/table_view.hpp>
+
+using namespace cudf::test::iterators;
+
+using bools_col   = cudf::test::fixed_width_column_wrapper<bool>;
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+using strings_col = cudf::test::strings_column_wrapper;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+constexpr int32_t null{0};       // Mark for null child elements at the current level
+constexpr int32_t XXX{0};        // Mark for null elements at all levels
+constexpr int32_t dont_care{0};  // Mark for elements that will be sliced off
+
+using TestTypes = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                     cudf::test::FloatingPointTypes,
+                                     cudf::test::DurationTypes,
+                                     cudf::test::TimestampTypes>;
+
+template <typename T>
+struct TypedStructSearchTest : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(TypedStructSearchTest, TestTypes);
+
+namespace {
+auto search_bounds(cudf::column_view const& t_col_view,
+                   std::unique_ptr<cudf::column> const& values_col,
+                   std::vector<cudf::order> const& column_orders        = {cudf::order::ASCENDING},
+                   std::vector<cudf::null_order> const& null_precedence = {
+                     cudf::null_order::BEFORE})
+{
+  auto const t            = cudf::table_view{std::vector<cudf::column_view>{t_col_view}};
+  auto const values       = cudf::table_view{std::vector<cudf::column_view>{values_col->view()}};
+  auto result_lower_bound = cudf::lower_bound(t, values, column_orders, null_precedence);
+  auto result_upper_bound = cudf::upper_bound(t, values, column_orders, null_precedence);
+  return std::pair(std::move(result_lower_bound), std::move(result_upper_bound));
+}
+
+auto search_bounds(std::unique_ptr<cudf::column> const& t_col,
+                   std::unique_ptr<cudf::column> const& values_col,
+                   std::vector<cudf::order> const& column_orders        = {cudf::order::ASCENDING},
+                   std::vector<cudf::null_order> const& null_precedence = {
+                     cudf::null_order::BEFORE})
+{
+  return search_bounds(t_col->view(), values_col, column_orders, null_precedence);
+}
+
+template <typename... Args>
+auto make_struct_scalar(Args&&... args)
+{
+  return cudf::struct_scalar(std::vector<cudf::column_view>{std::forward<Args>(args)...});
+}
+
+}  // namespace
+
+// Test case when all input columns are empty
+TYPED_TEST(TypedStructSearchTest, EmptyInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_t     = tdata_col{};
+  auto const structs_t = structs_col{{child_col_t}, std::vector<bool>{}}.release();
+
+  auto child_col_values     = tdata_col{};
+  auto const structs_values = structs_col{{child_col_values}, std::vector<bool>{}}.release();
+
+  auto const results  = search_bounds(structs_t, structs_values);
+  auto const expected = int32s_col{};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, results.second->view(), verbosity);
+}
+
+TYPED_TEST(TypedStructSearchTest, TrivialInputTests)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_t     = tdata_col{10, 20, 30, 40, 50};
+  auto const structs_t = structs_col{{child_col_t}}.release();
+
+  auto child_col_values1     = tdata_col{0, 1, 2, 3, 4};
+  auto const structs_values1 = structs_col{{child_col_values1}}.release();
+
+  auto child_col_values2     = tdata_col{100, 101, 102, 103, 104};
+  auto const structs_values2 = structs_col{{child_col_values2}}.release();
+
+  auto const results1  = search_bounds(structs_t, structs_values1);
+  auto const expected1 = int32s_col{0, 0, 0, 0, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, results1.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, results1.second->view(), verbosity);
+
+  auto const results2  = search_bounds(structs_t, structs_values2);
+  auto const expected2 = int32s_col{5, 5, 5, 5, 5};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, results2.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, results2.second->view(), verbosity);
+}
+
+TYPED_TEST(TypedStructSearchTest, SlicedColumnInputTests)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_values     = tdata_col{0, 1, 2, 3, 4, 5};
+  auto const structs_values = structs_col{child_col_values}.release();
+
+  auto child_col_t              = tdata_col{0, 1, 2, 2, 2, 2, 3, 3, 4, 4};
+  auto const structs_t_original = structs_col{child_col_t}.release();
+
+  auto structs_t = cudf::slice(structs_t_original->view(), {0, 10})[0];  // the entire column t
+  auto results   = search_bounds(structs_t, structs_values);
+  auto expected_lower_bound = int32s_col{0, 1, 2, 6, 8, 10};
+  auto expected_upper_bound = int32s_col{1, 2, 6, 8, 10, 10};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  structs_t            = cudf::slice(structs_t_original->view(), {0, 5})[0];
+  results              = search_bounds(structs_t, structs_values);
+  expected_lower_bound = int32s_col{0, 1, 2, 5, 5, 5};
+  expected_upper_bound = int32s_col{1, 2, 5, 5, 5, 5};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  structs_t            = cudf::slice(structs_t_original->view(), {5, 10})[0];
+  results              = search_bounds(structs_t, structs_values);
+  expected_lower_bound = int32s_col{0, 0, 0, 1, 3, 5};
+  expected_upper_bound = int32s_col{0, 0, 1, 3, 5, 5};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+}
+
+TYPED_TEST(TypedStructSearchTest, SimpleInputWithNullsTests)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_values     = tdata_col{{1, null, 70, XXX, 2, 100}, null_at(1)};
+  auto const structs_values = structs_col{{child_col_values}, null_at(3)}.release();
+
+  // Sorted asc, nulls first
+  auto child_col_t = tdata_col{{XXX, null, 0, 1, 2, 2, 2, 2, 3, 3, 4}, null_at(1)};
+  auto structs_t   = structs_col{{child_col_t}, null_at(0)}.release();
+
+  auto results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::BEFORE});
+  auto expected_lower_bound = int32s_col{3, 1, 11, 0, 4, 11};
+  auto expected_upper_bound = int32s_col{4, 2, 11, 1, 8, 11};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted asc, nulls last
+  child_col_t = tdata_col{{0, 1, 2, 2, 2, 2, 3, 3, 4, null, XXX}, null_at(9)};
+  structs_t   = structs_col{{child_col_t}, null_at(10)}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::AFTER});
+  expected_lower_bound = int32s_col{1, 9, 9, 10, 2, 9};
+  expected_upper_bound = int32s_col{2, 10, 9, 11, 6, 9};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted dsc, nulls first
+  child_col_t = tdata_col{{XXX, null, 4, 3, 3, 2, 2, 2, 2, 1, 0}, null_at(1)};
+  structs_t   = structs_col{{child_col_t}, null_at(0)}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::DESCENDING}, {cudf::null_order::BEFORE});
+  expected_lower_bound = int32s_col{9, 11, 0, 11, 5, 0};
+  expected_upper_bound = int32s_col{10, 11, 0, 11, 9, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted dsc, nulls last
+  child_col_t = tdata_col{{4, 3, 3, 2, 2, 2, 2, 1, 0, null, XXX}, null_at(9)};
+  structs_t   = structs_col{{child_col_t}, null_at(10)}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::DESCENDING}, {cudf::null_order::AFTER});
+  expected_lower_bound = int32s_col{7, 0, 0, 0, 3, 0};
+  expected_upper_bound = int32s_col{8, 0, 0, 0, 7, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+}
+
+TYPED_TEST(TypedStructSearchTest, SimpleInputWithValuesHavingNullsTests)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_values     = tdata_col{{1, null, 70, XXX, 2, 100}, null_at(1)};
+  auto const structs_values = structs_col{{child_col_values}, null_at(3)}.release();
+
+  // Sorted asc, search nulls first
+  auto child_col_t = tdata_col{0, 0, 0, 1, 2, 2, 2, 2, 3, 3, 4};
+  auto structs_t   = structs_col{{child_col_t}}.release();
+
+  auto results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::BEFORE});
+  auto expected_lower_bound = int32s_col{3, 0, 11, 0, 4, 11};
+  auto expected_upper_bound = int32s_col{4, 0, 11, 0, 8, 11};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted asc, search nulls last
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::AFTER});
+  expected_lower_bound = int32s_col{3, 11, 11, 11, 4, 11};
+  expected_upper_bound = int32s_col{4, 11, 11, 11, 8, 11};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted dsc, search nulls first
+  child_col_t = tdata_col{4, 3, 3, 2, 2, 2, 2, 1, 0, 0, 0};
+  structs_t   = structs_col{{child_col_t}}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::DESCENDING}, {cudf::null_order::BEFORE});
+  expected_lower_bound = int32s_col{7, 11, 0, 11, 3, 0};
+  expected_upper_bound = int32s_col{8, 11, 0, 11, 7, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted dsc, search nulls last
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::DESCENDING}, {cudf::null_order::AFTER});
+  expected_lower_bound = int32s_col{7, 0, 0, 0, 3, 0};
+  expected_upper_bound = int32s_col{8, 0, 0, 0, 7, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+}
+
+TYPED_TEST(TypedStructSearchTest, SimpleInputWithTargetHavingNullsTests)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col_values     = tdata_col{1, 0, 70, 0, 2, 100};
+  auto const structs_values = structs_col{{child_col_values}}.release();
+
+  // Sorted asc, nulls first
+  auto child_col_t = tdata_col{{XXX, null, 0, 1, 2, 2, 2, 2, 3, 3, 4}, null_at(1)};
+  auto structs_t   = structs_col{{child_col_t}, null_at(0)}.release();
+
+  auto results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::BEFORE});
+  auto expected_lower_bound = int32s_col{3, 2, 11, 2, 4, 11};
+  auto expected_upper_bound = int32s_col{4, 3, 11, 3, 8, 11};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted asc, nulls last
+  child_col_t = tdata_col{{0, 1, 2, 2, 2, 2, 3, 3, 4, null, XXX}, null_at(9)};
+  structs_t   = structs_col{{child_col_t}, null_at(10)}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::AFTER});
+  expected_lower_bound = int32s_col{1, 0, 9, 0, 2, 9};
+  expected_upper_bound = int32s_col{2, 1, 9, 1, 6, 9};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted dsc, nulls first
+  child_col_t = tdata_col{{XXX, null, 4, 3, 3, 2, 2, 2, 2, 1, 0}, null_at(1)};
+  structs_t   = structs_col{{child_col_t}, null_at(0)}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::DESCENDING}, {cudf::null_order::BEFORE});
+  expected_lower_bound = int32s_col{9, 10, 0, 10, 5, 0};
+  expected_upper_bound = int32s_col{10, 11, 0, 11, 9, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+
+  // Sorted dsc, nulls last
+  child_col_t = tdata_col{{4, 3, 3, 2, 2, 2, 2, 1, 0, null, XXX}, null_at(9)};
+  structs_t   = structs_col{{child_col_t}, null_at(10)}.release();
+  results =
+    search_bounds(structs_t, structs_values, {cudf::order::DESCENDING}, {cudf::null_order::AFTER});
+  expected_lower_bound = int32s_col{7, 8, 0, 8, 3, 0};
+  expected_upper_bound = int32s_col{8, 11, 0, 11, 7, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+}
+
+TYPED_TEST(TypedStructSearchTest, OneColumnHasNullMaskButNoNullElementTest)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto child_col1         = tdata_col{1, 20, 30};
+  auto const structs_col1 = structs_col{{child_col1}}.release();
+
+  auto child_col2         = tdata_col{0, 10, 10};
+  auto const structs_col2 = structs_col{child_col2}.release();
+
+  // structs_col3 (and its child column) will have a null mask but no null element
+  auto child_col3         = tdata_col{{0, 10, 10}, no_nulls()};
+  auto const structs_col3 = structs_col{{child_col3}, no_nulls()}.release();
+
+  // Search struct elements of structs_col2 and structs_col3 in the column structs_col1
+  {
+    auto const results1             = search_bounds(structs_col1, structs_col2);
+    auto const results2             = search_bounds(structs_col1, structs_col3);
+    auto const expected_lower_bound = int32s_col{0, 1, 1};
+    auto const expected_upper_bound = int32s_col{0, 1, 1};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results1.first->view(), verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results2.first->view(), verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results1.second->view(), verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results2.second->view(), verbosity);
+  }
+
+  // Search struct elements of structs_col1 in the columns structs_col2 and structs_col3
+  {
+    auto const results1             = search_bounds(structs_col2, structs_col1);
+    auto const results2             = search_bounds(structs_col3, structs_col1);
+    auto const expected_lower_bound = int32s_col{1, 3, 3};
+    auto const expected_upper_bound = int32s_col{1, 3, 3};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results1.first->view(), verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results2.first->view(), verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results1.second->view(), verbosity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results2.second->view(), verbosity);
+  }
+}
+
+TYPED_TEST(TypedStructSearchTest, ComplexStructTest)
+{
+  // Testing on struct<string, numeric, bool>.
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto names_column_t =
+    strings_col{"Cherry", "Kiwi", "Lemon", "Newton", "Tomato", /*NULL*/ "Washington"};
+  auto ages_column_t  = tdata_col{{5, 10, 15, 20, null, XXX}, null_at(4)};
+  auto is_human_col_t = bools_col{false, false, false, false, false, /*NULL*/ true};
+
+  auto const structs_t =
+    structs_col{{names_column_t, ages_column_t, is_human_col_t}, null_at(5)}.release();
+
+  auto names_column_values = strings_col{"Bagel", "Tomato", "Lemonade", /*NULL*/ "Donut", "Butter"};
+  auto ages_column_values  = tdata_col{{10, null, 15, XXX, 17}, null_at(1)};
+  auto is_human_col_values = bools_col{false, false, true, /*NULL*/ true, true};
+  auto const structs_values =
+    structs_col{{names_column_values, ages_column_values, is_human_col_values}, null_at(3)}
+      .release();
+
+  auto const results =
+    search_bounds(structs_t, structs_values, {cudf::order::ASCENDING}, {cudf::null_order::AFTER});
+  auto const expected_lower_bound = int32s_col{0, 4, 3, 5, 0};
+  auto const expected_upper_bound = int32s_col{0, 5, 3, 6, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_lower_bound, results.first->view(), verbosity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_upper_bound, results.second->view(), verbosity);
+}
+
+template <typename T>
+struct TypedStructContainsTestScalarNeedle : public cudf::test::BaseFixture {};
+TYPED_TEST_SUITE(TypedStructContainsTestScalarNeedle, TestTypes);
+
+TYPED_TEST(TypedStructContainsTestScalarNeedle, EmptyInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = [] {
+    auto child = tdata_col{};
+    return structs_col{{child}};
+  }();
+
+  auto const needle1 = [] {
+    auto child = tdata_col{1};
+    return make_struct_scalar(child);
+  }();
+  auto const needle2 = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{1};
+    return make_struct_scalar(child1, child2);
+  }();
+
+  EXPECT_FALSE(cudf::contains(haystack, needle1));
+  EXPECT_FALSE(cudf::contains(haystack, needle2));
+}
+
+TYPED_TEST(TypedStructContainsTestScalarNeedle, TrivialInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = [] {
+    auto child1 = tdata_col{1, 2, 3};
+    auto child2 = tdata_col{4, 5, 6};
+    auto child3 = strings_col{"x", "y", "z"};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const needle1 = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{4};
+    auto child3 = strings_col{"x"};
+    return make_struct_scalar(child1, child2, child3);
+  }();
+  auto const needle2 = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{4};
+    auto child3 = strings_col{"a"};
+    return make_struct_scalar(child1, child2, child3);
+  }();
+
+  EXPECT_TRUE(cudf::contains(haystack, needle1));
+  EXPECT_FALSE(cudf::contains(haystack, needle2));
+}
+
+TYPED_TEST(TypedStructContainsTestScalarNeedle, SlicedColumnInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original = [] {
+    auto child1 = tdata_col{dont_care, dont_care, 1, 2, 3, dont_care};
+    auto child2 = tdata_col{dont_care, dont_care, 4, 5, 6, dont_care};
+    auto child3 = strings_col{"dont_care", "dont_care", "x", "y", "z", "dont_care"};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const haystack = cudf::slice(haystack_original, {2, 5})[0];
+
+  auto const needle1 = [] {
+    auto child1 = tdata_col{1};
+    auto child2 = tdata_col{4};
+    auto child3 = strings_col{"x"};
+    return make_struct_scalar(child1, child2, child3);
+  }();
+  auto const needle2 = [] {
+    auto child1 = tdata_col{dont_care};
+    auto child2 = tdata_col{dont_care};
+    auto child3 = strings_col{"dont_care"};
+    return make_struct_scalar(child1, child2, child3);
+  }();
+
+  EXPECT_TRUE(cudf::contains(haystack, needle1));
+  EXPECT_FALSE(cudf::contains(haystack, needle2));
+}
+
+TYPED_TEST(TypedStructContainsTestScalarNeedle, SimpleInputWithNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  constexpr int32_t null{0};
+
+  // Test with nulls at the top level.
+  {
+    auto const col1 = [] {
+      auto child1 = tdata_col{1, null, 3};
+      auto child2 = tdata_col{4, null, 6};
+      auto child3 = strings_col{"x", "" /*NULL*/, "z"};
+      return structs_col{{child1, child2, child3}, null_at(1)};
+    }();
+
+    auto const needle1 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{"x"};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle2 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{"a"};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle3 = [] {
+      auto child1 = tdata_col{{null}, null_at(0)};
+      auto child2 = tdata_col{{null}, null_at(0)};
+      auto child3 = strings_col{{""}, null_at(0)};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+
+    EXPECT_TRUE(cudf::contains(col1, needle1));
+    EXPECT_FALSE(cudf::contains(col1, needle2));
+    EXPECT_FALSE(cudf::contains(col1, needle3));
+  }
+
+  // Test with nulls at the children level.
+  {
+    auto const col = [] {
+      auto child1 = tdata_col{{1, null, 3}, null_at(1)};
+      auto child2 = tdata_col{{4, null, 6}, null_at(1)};
+      auto child3 = strings_col{{"" /*NULL*/, "" /*NULL*/, "z"}, nulls_at({0, 1})};
+      return structs_col{{child1, child2, child3}};
+    }();
+
+    auto const needle1 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{{"" /*NULL*/}, null_at(0)};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle2 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{""};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle3 = [] {
+      auto child1 = tdata_col{{null}, null_at(0)};
+      auto child2 = tdata_col{{null}, null_at(0)};
+      auto child3 = strings_col{{""}, null_at(0)};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+
+    EXPECT_TRUE(cudf::contains(col, needle1));
+    EXPECT_FALSE(cudf::contains(col, needle2));
+    EXPECT_TRUE(cudf::contains(col, needle3));
+  }
+
+  // Test with nulls in the input scalar.
+  {
+    auto const haystack = [] {
+      auto child1 = tdata_col{1, 2, 3};
+      auto child2 = tdata_col{4, 5, 6};
+      auto child3 = strings_col{"x", "y", "z"};
+      return structs_col{{child1, child2, child3}};
+    }();
+
+    auto const needle1 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{"x"};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle2 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{{"" /*NULL*/}, null_at(0)};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+
+    EXPECT_TRUE(cudf::contains(haystack, needle1));
+    EXPECT_FALSE(cudf::contains(haystack, needle2));
+  }
+}
+
+TYPED_TEST(TypedStructContainsTestScalarNeedle, SlicedInputWithNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  // Test with nulls at the top level.
+  {
+    auto const haystack_original = [] {
+      auto child1 = tdata_col{dont_care, dont_care, 1, null, 3, dont_care};
+      auto child2 = tdata_col{dont_care, dont_care, 4, null, 6, dont_care};
+      auto child3 = strings_col{"dont_care", "dont_care", "x", "" /*NULL*/, "z", "dont_care"};
+      return structs_col{{child1, child2, child3}, null_at(3)};
+    }();
+    auto const col = cudf::slice(haystack_original, {2, 5})[0];
+
+    auto const needle1 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{"x"};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle2 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{"a"};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+
+    EXPECT_TRUE(cudf::contains(col, needle1));
+    EXPECT_FALSE(cudf::contains(col, needle2));
+  }
+
+  // Test with nulls at the children level.
+  {
+    auto const haystack_original = [] {
+      auto child1 =
+        tdata_col{{dont_care, dont_care /*also NULL*/, 1, null, 3, dont_care}, null_at(3)};
+      auto child2 =
+        tdata_col{{dont_care, dont_care /*also NULL*/, 4, null, 6, dont_care}, null_at(3)};
+      auto child3 = strings_col{
+        {"dont_care", "dont_care" /*also NULL*/, "" /*NULL*/, "y", "z", "dont_care"}, null_at(2)};
+      return structs_col{{child1, child2, child3}, null_at(1)};
+    }();
+    auto const haystack = cudf::slice(haystack_original, {2, 5})[0];
+
+    auto const needle1 = [] {
+      auto child1 = tdata_col{1};
+      auto child2 = tdata_col{4};
+      auto child3 = strings_col{{"x"}, null_at(0)};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+    auto const needle2 = [] {
+      auto child1 = tdata_col{dont_care};
+      auto child2 = tdata_col{dont_care};
+      auto child3 = strings_col{"dont_care"};
+      return make_struct_scalar(child1, child2, child3);
+    }();
+
+    EXPECT_TRUE(cudf::contains(haystack, needle1));
+    EXPECT_FALSE(cudf::contains(haystack, needle2));
+  }
+}
+
+template <typename T>
+struct TypedStructContainsTestColumnNeedles : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedStructContainsTestColumnNeedles, TestTypes);
+
+TYPED_TEST(TypedStructContainsTestColumnNeedles, EmptyInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = [] {
+    auto child1 = tdata_col{};
+    auto child2 = tdata_col{};
+    auto child3 = strings_col{};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  {
+    auto const needles = [] {
+      auto child1 = tdata_col{};
+      auto child2 = tdata_col{};
+      auto child3 = strings_col{};
+      return structs_col{{child1, child2, child3}};
+    }();
+    auto const expected = bools_col{};
+    auto const result   = cudf::contains(haystack, needles);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+  }
+
+  {
+    auto const needles = [] {
+      auto child1 = tdata_col{1, 2};
+      auto child2 = tdata_col{0, 2};
+      auto child3 = strings_col{"x", "y"};
+      return structs_col{{child1, child2, child3}};
+    }();
+    auto const result   = cudf::contains(haystack, needles);
+    auto const expected = bools_col{0, 0};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+  }
+}
+
+TYPED_TEST(TypedStructContainsTestColumnNeedles, TrivialInput)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = [] {
+    auto child1 = tdata_col{1, 3, 1, 1, 2, 1, 2, 2, 1, 2};
+    auto child2 = tdata_col{1, 0, 0, 0, 1, 0, 1, 2, 1, 1};
+    return structs_col{{child1, child2}};
+  }();
+
+  auto const needles = [] {
+    auto child1 = tdata_col{1, 3, 1, 1, 2, 1, 0, 0, 1, 0};
+    auto child2 = tdata_col{1, 0, 2, 3, 2, 1, 0, 0, 1, 0};
+    return structs_col{{child1, child2}};
+  }();
+
+  auto const expected = bools_col{1, 1, 0, 0, 1, 1, 0, 0, 1, 0};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedStructContainsTestColumnNeedles, SlicedInputNoNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original = [] {
+    auto child1 = tdata_col{dont_care, dont_care, 1, 3, 1, 1, 2, dont_care};
+    auto child2 = tdata_col{dont_care, dont_care, 1, 0, 0, 0, 1, dont_care};
+    auto child3 = strings_col{"dont_care", "dont_care", "x", "y", "z", "a", "b", "dont_care"};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const haystack = cudf::slice(haystack_original, {2, 7})[0];
+
+  auto const needles_original = [] {
+    auto child1 = tdata_col{dont_care, 1, 1, 1, 1, 2, dont_care, dont_care};
+    auto child2 = tdata_col{dont_care, 0, 1, 2, 3, 1, dont_care, dont_care};
+    auto child3 = strings_col{"dont_care", "z", "x", "z", "a", "b", "dont_care", "dont_care"};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const needles = cudf::slice(needles_original, {1, 6})[0];
+
+  auto const expected = bools_col{1, 1, 0, 0, 1};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedStructContainsTestColumnNeedles, SlicedInputHavingNulls)
+{
+  using tdata_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack_original = [] {
+    auto child1 =
+      tdata_col{{dont_care /*null*/, dont_care, 1, null, XXX, 1, 2, null, 2, 2, null, 2, dont_care},
+                nulls_at({0, 3, 7, 10})};
+    auto child2 =
+      tdata_col{{dont_care /*null*/, dont_care, 1, null, XXX, 0, null, 0, 1, 2, 1, 1, dont_care},
+                nulls_at({0, 3, 6})};
+    return structs_col{{child1, child2}, nulls_at({1, 4})};
+  }();
+  auto const haystack = cudf::slice(haystack_original, {2, 12})[0];
+
+  auto const needles_original = [] {
+    auto child1 =
+      tdata_col{{dont_care, XXX, null, 1, 1, 2, XXX, null, 1, 1, null, dont_care, dont_care},
+                nulls_at({2, 7, 10})};
+    auto child2 =
+      tdata_col{{dont_care, XXX, null, 2, 3, 2, XXX, null, null, 1, 0, dont_care, dont_care},
+                nulls_at({2, 7, 8})};
+    return structs_col{{child1, child2}, nulls_at({1, 6})};
+  }();
+  auto const needles = cudf::slice(needles_original, {1, 11})[0];
+
+  auto const expected = bools_col{{null, 1, 0, 0, 1, null, 1, 0, 1, 1}, nulls_at({0, 5})};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
+
+TYPED_TEST(TypedStructContainsTestColumnNeedles, StructOfLists)
+{
+  using lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>;
+
+  auto const haystack = [] {
+    // clang-format off
+    auto child1 = lists_col{{1, 2},    {1},       {}, {1, 3}};
+    auto child2 = lists_col{{1, 3, 4}, {2, 3, 4}, {},     {}};
+    // clang-format on
+    return structs_col{{child1, child2}};
+  }();
+
+  auto const needles = [] {
+    // clang-format off
+    auto child1 = lists_col{{1, 2},    {1},    {},     {1, 3}, {}};
+    auto child2 = lists_col{{1, 3, 4}, {2, 3}, {1, 2}, {},     {}};
+    // clang-format on
+    return structs_col{{child1, child2}};
+  }();
+
+  auto const expected = bools_col{1, 0, 0, 1, 1};
+  auto const result   = cudf::contains(haystack, needles);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result, verbosity);
+}
diff --git a/cpp/tests/search/search_test.cpp b/cpp/tests/search/search_test.cpp
new file mode 100644
index 0000000..efff7cd
--- /dev/null
+++ b/cpp/tests/search/search_test.cpp
@@ -0,0 +1,1873 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/search.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+struct SearchTest : public cudf::test::BaseFixture {};
+
+using cudf::numeric_scalar;
+using cudf::size_type;
+using cudf::string_scalar;
+using cudf::test::fixed_width_column_wrapper;
+
+TEST_F(SearchTest, empty_table)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{};
+  fixed_width_column_wrapper<element_type> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, empty_values)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{10, 20, 30, 40, 50};
+  fixed_width_column_wrapper<element_type> values{};
+  fixed_width_column_wrapper<size_type> expect{};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column__find_first)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{10, 20, 30, 40, 50};
+  fixed_width_column_wrapper<element_type> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 1, 2, 3, 3, 4, 4, 5};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column__find_last)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{10, 20, 30, 40, 50};
+  fixed_width_column_wrapper<element_type> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  fixed_width_column_wrapper<size_type> expect{0, 0, 1, 1, 3, 3, 4, 4, 5, 5};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column_desc__find_first)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{50, 40, 30, 20, 10};
+  fixed_width_column_wrapper<element_type> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  fixed_width_column_wrapper<size_type> expect{5, 5, 4, 4, 2, 2, 1, 1, 0, 0};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::DESCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column_desc__find_last)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{50, 40, 30, 20, 10};
+  fixed_width_column_wrapper<element_type> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  fixed_width_column_wrapper<size_type> expect{5, 5, 5, 4, 3, 2, 2, 1, 1, 0};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::DESCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_last__nulls_as_smallest)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{{10, 60, 10, 20, 30, 40, 50},
+                                                  {0, 0, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<element_type> values{{8, 8, 10, 11, 30, 32, 40, 47, 50, 90},
+                                                  {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<size_type> expect{2, 2, 3, 3, 5, 5, 6, 6, 7, 7};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_first__nulls_as_smallest)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{{10, 60, 10, 20, 30, 40, 50},
+                                                  {0, 0, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<element_type> values{{8, 8, 10, 11, 30, 32, 40, 47, 50, 90},
+                                                  {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<size_type> expect{0, 2, 2, 3, 4, 5, 5, 6, 6, 7};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_last__nulls_as_largest)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{{10, 20, 30, 40, 50, 10, 60},
+                                                  {1, 1, 1, 1, 1, 0, 0}};
+  fixed_width_column_wrapper<element_type> values{{8, 10, 11, 30, 32, 40, 47, 50, 90, 8},
+                                                  {1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  fixed_width_column_wrapper<size_type> expect{0, 1, 1, 3, 3, 4, 4, 5, 5, 7};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::AFTER}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_first__nulls_as_largest)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> column{{10, 20, 30, 40, 50, 10, 60},
+                                                  {1, 1, 1, 1, 1, 0, 0}};
+  fixed_width_column_wrapper<element_type> values{{8, 10, 11, 30, 32, 40, 47, 50, 90, 8},
+                                                  {1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  fixed_width_column_wrapper<size_type> expect{0, 0, 1, 2, 3, 3, 4, 4, 5, 5};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::AFTER}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_first)
+{
+  fixed_width_column_wrapper<int32_t> column_0{10, 20, 20, 20, 20, 20, 50};
+  fixed_width_column_wrapper<float> column_1{5.0, .5, .5, .7, .7, .7, .7};
+  fixed_width_column_wrapper<int8_t> column_2{90, 77, 78, 61, 62, 63, 41};
+
+  fixed_width_column_wrapper<int32_t> values_0{0,  0,  0,  0,  10, 10, 10, 10, 10,
+                                               10, 10, 10, 11, 20, 20, 20, 20, 20,
+                                               20, 20, 20, 20, 20, 20, 30, 50, 60};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+  fixed_width_column_wrapper<int8_t> values_2{0,  91, 0,  91, 0,  79, 90, 91, 77, 80, 90, 91, 91, 0,
+                                              76, 77, 78, 30, 65, 77, 78, 80, 62, 78, 64, 41, 20};
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,
+                                               1, 1, 2, 1, 3, 3, 3, 6, 4, 6, 6, 6, 7};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_last)
+{
+  fixed_width_column_wrapper<int32_t> column_0{10, 20, 20, 20, 20, 20, 50};
+  fixed_width_column_wrapper<float> column_1{5.0, .5, .5, .7, .7, .7, .7};
+  fixed_width_column_wrapper<int8_t> column_2{90, 77, 78, 61, 62, 63, 41};
+
+  fixed_width_column_wrapper<int32_t> values_0{0,  0,  0,  0,  10, 10, 10, 10, 10,
+                                               10, 10, 10, 11, 20, 20, 20, 20, 20,
+                                               20, 20, 20, 20, 20, 20, 30, 50, 60};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+  fixed_width_column_wrapper<int8_t> values_2{0,  91, 0,  91, 0,  79, 90, 91, 77, 80, 90, 91, 91, 0,
+                                              76, 77, 78, 30, 65, 77, 78, 80, 62, 78, 64, 41, 20};
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,
+                                               1, 2, 3, 1, 3, 3, 3, 6, 5, 6, 6, 7, 7};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table_partial_desc__find_first)
+{
+  fixed_width_column_wrapper<int32_t> column_0{50, 20, 20, 20, 20, 20, 10};
+  fixed_width_column_wrapper<float> column_1{.7, .5, .5, .7, .7, .7, 5.0};
+  fixed_width_column_wrapper<int8_t> column_2{41, 78, 77, 63, 62, 61, 90};
+
+  fixed_width_column_wrapper<int32_t> values_0{0,  0,  0,  0,  10, 10, 10, 10, 10,
+                                               10, 10, 10, 11, 20, 20, 20, 20, 20,
+                                               20, 20, 20, 20, 20, 20, 30, 50, 60};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+  fixed_width_column_wrapper<int8_t> values_2{0,  91, 0,  91, 0,  79, 90, 91, 77, 80, 90, 91, 91, 0,
+                                              76, 77, 78, 30, 65, 77, 78, 80, 62, 78, 64, 41, 20};
+
+  fixed_width_column_wrapper<size_type> expect{7, 7, 7, 7, 6, 7, 6, 6, 7, 7, 7, 7, 6, 1,
+                                               3, 2, 1, 3, 3, 3, 3, 3, 4, 3, 1, 0, 0};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::DESCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table_partial_desc__find_last)
+{
+  fixed_width_column_wrapper<int32_t> column_0{50, 20, 20, 20, 20, 20, 10};
+  fixed_width_column_wrapper<float> column_1{.7, .5, .5, .7, .7, .7, 5.0};
+  fixed_width_column_wrapper<int8_t> column_2{41, 78, 77, 63, 62, 61, 90};
+
+  fixed_width_column_wrapper<int32_t> values_0{0,  0,  0,  0,  10, 10, 10, 10, 10,
+                                               10, 10, 10, 11, 20, 20, 20, 20, 20,
+                                               20, 20, 20, 20, 20, 20, 30, 50, 60};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+  fixed_width_column_wrapper<int8_t> values_2{0,  91, 0,  91, 0,  79, 90, 91, 77, 80, 90, 91, 91, 0,
+                                              76, 77, 78, 30, 65, 77, 78, 80, 62, 78, 64, 41, 20};
+
+  fixed_width_column_wrapper<size_type> expect{7, 7, 7, 7, 6, 7, 7, 6, 7, 7, 7, 7, 6, 1,
+                                               3, 3, 2, 3, 3, 3, 3, 3, 5, 3, 1, 1, 0};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::DESCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_first__nulls_as_smallest)
+{
+  fixed_width_column_wrapper<int32_t> column_0{{30, 10, 10, 20, 20, 20, 20, 20, 20, 20, 50},
+                                               {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<float> column_1{{.5, 6.0, 5.0, .5, .5, .5, .5, .7, .7, .7, .7},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<int8_t> column_2{{50, 95, 90, 79, 76, 77, 78, 61, 62, 63, 41},
+                                              {1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<int32_t> values_0{{10, 40, 20}, {1, 0, 1}};
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+  fixed_width_column_wrapper<int8_t> values_2{{95, 50, 77}, {1, 1, 0}};
+
+  fixed_width_column_wrapper<size_type> expect{1, 0, 3};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_last__nulls_as_smallest)
+{
+  fixed_width_column_wrapper<int32_t> column_0{{30, 10, 10, 20, 20, 20, 20, 20, 20, 20, 50},
+                                               {0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<float> column_1{{.5, 6.0, 5.0, .5, .5, .5, .5, .7, .7, .7, .7},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<int8_t> column_2{{50, 90, 95, 79, 76, 77, 78, 61, 62, 63, 41},
+                                              {1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<int32_t> values_0{{10, 40, 20}, {1, 0, 1}};
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+  fixed_width_column_wrapper<int8_t> values_2{{95, 50, 77}, {1, 1, 0}};
+
+  fixed_width_column_wrapper<size_type> expect{2, 1, 5};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_first__nulls_as_largest)
+{
+  fixed_width_column_wrapper<int32_t> column_0{{10, 10, 20, 20, 20, 20, 20, 20, 20, 50, 30},
+                                               {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  fixed_width_column_wrapper<float> column_1{{5.0, 6.0, .5, .5, .5, .5, .7, .7, .7, .7, .5},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<int8_t> column_2{{90, 95, 77, 78, 79, 76, 61, 62, 63, 41, 50},
+                                              {1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<int32_t> values_0{{10, 40, 20}, {1, 0, 1}};
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+  fixed_width_column_wrapper<int8_t> values_2{{95, 50, 77}, {1, 1, 0}};
+
+  fixed_width_column_wrapper<size_type> expect{1, 10, 4};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_last__nulls_as_largest)
+{
+  fixed_width_column_wrapper<int32_t> column_0{{10, 10, 20, 20, 20, 20, 20, 20, 20, 50, 30},
+                                               {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0}};
+  fixed_width_column_wrapper<float> column_1{{5.0, 6.0, .5, .5, .5, .5, .7, .7, .7, .7, .5},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<int8_t> column_2{{90, 95, 77, 78, 79, 76, 61, 62, 63, 41, 50},
+                                              {1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<int32_t> values_0{{10, 40, 20}, {1, 0, 1}};
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+  fixed_width_column_wrapper<int8_t> values_2{{95, 50, 77}, {1, 1, 0}};
+
+  fixed_width_column_wrapper<size_type> expect{2, 11, 6};
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, contains_true)
+{
+  using element_type = int64_t;
+  bool expect        = true;
+  bool result        = false;
+
+  fixed_width_column_wrapper<element_type> column{0, 1, 17, 19, 23, 29, 71};
+  numeric_scalar<element_type> scalar{23};
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_false)
+{
+  using element_type = int64_t;
+  bool expect        = false;
+  bool result        = false;
+
+  fixed_width_column_wrapper<element_type> column{0, 1, 17, 19, 23, 29, 71};
+  numeric_scalar<element_type> scalar{24};
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_empty_value)
+{
+  using element_type = int64_t;
+  bool expect        = false;
+  bool result        = false;
+
+  fixed_width_column_wrapper<element_type> column{0, 1, 17, 19, 23, 29, 71};
+  numeric_scalar<element_type> scalar{23, false};
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_empty_column)
+{
+  using element_type = int64_t;
+  bool expect        = false;
+  bool result        = false;
+
+  fixed_width_column_wrapper<element_type> column{};
+  numeric_scalar<element_type> scalar{24};
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_nullable_column_true)
+{
+  using element_type = int64_t;
+  bool result        = false;
+  bool expect        = true;
+
+  fixed_width_column_wrapper<element_type> column{{0, 1, 17, 19, 23, 29, 71},
+                                                  {0, 0, 1, 1, 1, 1, 1}};
+  numeric_scalar<element_type> scalar{23};
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_nullable_column_false)
+{
+  using element_type = int64_t;
+  bool result        = false;
+  bool expect        = false;
+
+  fixed_width_column_wrapper<element_type> column{{0, 1, 17, 19, 23, 29, 71},
+                                                  {0, 0, 1, 1, 0, 1, 1}};
+  numeric_scalar<element_type> scalar{23};
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, empty_table_string)
+{
+  std::vector<char const*> h_col_strings{};
+  std::vector<char const*> h_val_strings{"0", "10", "11", "30", "32", "40", "47", "50", "7", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, empty_values_string)
+{
+  std::vector<char const*> h_col_strings{"10", "20", "30", "40", "50"};
+  std::vector<char const*> h_val_strings{};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column__find_first_string)
+{
+  std::vector<char const*> h_col_strings{"10", "20", "30", "40", "50"};
+  std::vector<char const*> h_val_strings{
+    "00", "07", "10", "11", "30", "32", "40", "47", "50", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 1, 2, 3, 3, 4, 4, 5};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column__find_last_string)
+{
+  std::vector<char const*> h_col_strings{"10", "20", "30", "40", "50"};
+  std::vector<char const*> h_val_strings{
+    "00", "07", "10", "11", "30", "32", "40", "47", "50", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 1, 1, 3, 3, 4, 4, 5, 5};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column_desc__find_first_string)
+{
+  std::vector<char const*> h_col_strings{"50", "40", "30", "20", "10"};
+  std::vector<char const*> h_val_strings{
+    "00", "07", "10", "11", "30", "32", "40", "47", "50", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{5, 5, 4, 4, 2, 2, 1, 1, 0, 0};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::DESCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column_desc__find_last_string)
+{
+  std::vector<char const*> h_col_strings{"50", "40", "30", "20", "10"};
+  std::vector<char const*> h_val_strings{
+    "00", "07", "10", "11", "30", "32", "40", "47", "50", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{5, 5, 5, 4, 3, 2, 2, 1, 1, 0};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::DESCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_last__nulls_as_smallest_string)
+{
+  std::vector<char const*> h_col_strings{nullptr, nullptr, "10", "20", "30", "40", "50"};
+  std::vector<char const*> h_val_strings{
+    nullptr, "08", "10", "11", "30", "32", "40", "47", "50", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{2, 2, 3, 3, 5, 5, 6, 6, 7, 7};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_first__nulls_as_smallest_string)
+{
+  std::vector<char const*> h_col_strings{nullptr, nullptr, "10", "20", "30", "40", "50"};
+  std::vector<char const*> h_val_strings{
+    nullptr, "08", "10", "11", "30", "32", "40", "47", "50", "90"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{0, 2, 2, 3, 4, 5, 5, 6, 6, 7};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::BEFORE}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_last__nulls_as_largest_string)
+{
+  std::vector<char const*> h_col_strings{"10", "20", "30", "40", "50", nullptr, nullptr};
+  std::vector<char const*> h_val_strings{
+    "08", "10", "11", "30", "32", "40", "47", "50", "90", nullptr};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{0, 1, 1, 3, 3, 4, 4, 5, 5, 7};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::AFTER}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, non_null_column__nullable_values__find_last__nulls_as_largest_string)
+{
+  cudf::test::strings_column_wrapper column({"N", "N", "N", "N", "Y", "Y", "Y", "Y"},
+                                            {1, 1, 1, 1, 1, 1, 1, 1});
+
+  cudf::test::strings_column_wrapper values({"Y", "Z", "N"}, {1, 0, 1});
+
+  fixed_width_column_wrapper<size_type> expect{8, 8, 4};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::upper_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::AFTER}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, nullable_column__find_first__nulls_as_largest_string)
+{
+  std::vector<char const*> h_col_strings{"10", "20", "30", "40", "50", nullptr, nullptr};
+  std::vector<char const*> h_val_strings{
+    "08", "10", "11", "30", "32", "40", "47", "50", "90", nullptr};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values(
+    h_val_strings.begin(),
+    h_val_strings.end(),
+    thrust::make_transform_iterator(h_val_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 1, 2, 3, 3, 4, 4, 5, 5};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result = cudf::lower_bound({cudf::table_view{{column}}},
+                                             {cudf::table_view{{values}}},
+                                             {cudf::order::ASCENDING},
+                                             {cudf::null_order::AFTER}));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_first_string)
+{
+  std::vector<char const*> h_col_0_strings{"10", "20", "20", "20", "20", "20", "50"};
+  std::vector<char const*> h_col_2_strings{"90", "77", "78", "61", "62", "63", "41"};
+
+  std::vector<char const*> h_val_0_strings{"0",  "0",  "0",  "0",  "10", "10", "10", "10", "10",
+                                           "10", "10", "10", "11", "20", "20", "20", "20", "20",
+                                           "20", "20", "20", "20", "20", "20", "30", "50", "60"};
+  std::vector<char const*> h_val_2_strings{"0",  "91", "0",  "91", "0",  "79", "90", "91", "77",
+                                           "80", "90", "91", "91", "00", "76", "77", "78", "30",
+                                           "65", "77", "78", "80", "62", "78", "64", "41", "20"};
+
+  fixed_width_column_wrapper<float> column_1{5.0, .5, .5, .7, .7, .7, .7};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,
+                                               1, 1, 2, 1, 3, 3, 3, 6, 4, 6, 6, 6, 7};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_last_string)
+{
+  std::vector<char const*> h_col_0_strings{"10", "20", "20", "20", "20", "20", "50"};
+  std::vector<char const*> h_col_2_strings{"90", "77", "78", "61", "62", "63", "41"};
+
+  std::vector<char const*> h_val_0_strings{"0",  "0",  "0",  "0",  "10", "10", "10", "10", "10",
+                                           "10", "10", "10", "11", "20", "20", "20", "20", "20",
+                                           "20", "20", "20", "20", "20", "20", "30", "50", "60"};
+  std::vector<char const*> h_val_2_strings{"0",  "91", "0",  "91", "0",  "79", "90", "91", "77",
+                                           "80", "90", "91", "91", "00", "76", "77", "78", "30",
+                                           "65", "77", "78", "80", "62", "78", "64", "41", "20"};
+
+  fixed_width_column_wrapper<float> column_1{5.0, .5, .5, .7, .7, .7, .7};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+
+  fixed_width_column_wrapper<size_type> expect{0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,
+                                               1, 2, 3, 1, 3, 3, 3, 6, 5, 6, 6, 7, 7};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table_partial_desc__find_first_string)
+{
+  std::vector<char const*> h_col_0_strings{"50", "20", "20", "20", "20", "20", "10"};
+  std::vector<char const*> h_col_2_strings{"41", "78", "77", "63", "62", "61", "90"};
+
+  std::vector<char const*> h_val_0_strings{"0",  "0",  "0",  "0",  "10", "10", "10", "10", "10",
+                                           "10", "10", "10", "11", "20", "20", "20", "20", "20",
+                                           "20", "20", "20", "20", "20", "20", "30", "50", "60"};
+  std::vector<char const*> h_val_2_strings{"0",  "91", "0",  "91", "0",  "79", "90", "91", "77",
+                                           "80", "90", "91", "91", "00", "76", "77", "78", "30",
+                                           "65", "77", "78", "80", "62", "78", "64", "41", "20"};
+
+  fixed_width_column_wrapper<float> column_1{.7, .5, .5, .7, .7, .7, 5.0};
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+
+  fixed_width_column_wrapper<size_type> expect{7, 7, 7, 7, 6, 7, 6, 6, 7, 7, 7, 7, 6, 1,
+                                               3, 2, 1, 3, 3, 3, 3, 3, 4, 3, 1, 0, 0};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::DESCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table_partial_desc__find_last_string)
+{
+  std::vector<char const*> h_col_0_strings{"50", "20", "20", "20", "20", "20", "10"};
+  std::vector<char const*> h_col_2_strings{"41", "78", "77", "63", "62", "61", "90"};
+
+  std::vector<char const*> h_val_0_strings{"0",  "0",  "0",  "0",  "10", "10", "10", "10", "10",
+                                           "10", "10", "10", "11", "20", "20", "20", "20", "20",
+                                           "20", "20", "20", "20", "20", "20", "30", "50", "60"};
+  std::vector<char const*> h_val_2_strings{"0",  "91", "0",  "91", "0",  "79", "90", "91", "77",
+                                           "80", "90", "91", "91", "00", "76", "77", "78", "30",
+                                           "65", "77", "78", "80", "62", "78", "64", "41", "20"};
+
+  fixed_width_column_wrapper<float> column_1{.7, .5, .5, .7, .7, .7, 5.0};
+
+  fixed_width_column_wrapper<float> values_1{0., 0., 6., 5., 0., 5., 5., 5., 5., 6., 6., 6., 9., 0.,
+                                             .5, .5, .5, .5, .6, .6, .6, .7, .7, .7, .7, .7, .5};
+
+  fixed_width_column_wrapper<size_type> expect{7, 7, 7, 7, 6, 7, 7, 6, 7, 7, 7, 7, 6, 1,
+                                               3, 3, 2, 3, 3, 3, 3, 3, 5, 3, 1, 1, 0};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::DESCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_first__nulls_as_smallest_string)
+{
+  std::vector<char const*> h_col_0_strings{
+    nullptr, "10", "10", "20", "20", "20", "20", "20", "20", "20", "50"};
+  std::vector<char const*> h_col_2_strings{
+    "50", "95", "90", nullptr, nullptr, "77", "78", "61", "62", "63", "41"};
+
+  std::vector<char const*> h_val_0_strings{"10", nullptr, "20"};
+  std::vector<char const*> h_val_2_strings{"95", "50", nullptr};
+
+  fixed_width_column_wrapper<float> column_1{{.5, 6.0, 5.0, .5, .5, .5, .5, .7, .7, .7, .7},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+
+  fixed_width_column_wrapper<size_type> expect{1, 0, 3};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_last__nulls_as_smallest_string)
+{
+  std::vector<char const*> h_col_0_strings{
+    nullptr, "10", "10", "20", "20", "20", "20", "20", "20", "20", "50"};
+  std::vector<char const*> h_col_2_strings{
+    "50", "90", "95", nullptr, nullptr, "77", "78", "61", "62", "63", "41"};
+
+  std::vector<char const*> h_val_0_strings{"10", nullptr, "20"};
+  std::vector<char const*> h_val_2_strings{"95", "50", nullptr};
+
+  fixed_width_column_wrapper<float> column_1{{.5, 6.0, 5.0, .5, .5, .5, .5, .7, .7, .7, .7},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+
+  fixed_width_column_wrapper<size_type> expect{2, 1, 5};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::BEFORE, cudf::null_order::BEFORE, cudf::null_order::BEFORE}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_first__nulls_as_largest_string)
+{
+  std::vector<char const*> h_col_0_strings{
+    "10", "10", "20", "20", "20", "20", "20", "20", "20", "50", nullptr};
+  std::vector<char const*> h_col_2_strings{
+    "90", "95", "77", "78", nullptr, nullptr, "61", "62", "63", "41", "50"};
+
+  std::vector<char const*> h_val_0_strings{"10", nullptr, "20"};
+  std::vector<char const*> h_val_2_strings{"95", "50", nullptr};
+
+  fixed_width_column_wrapper<float> column_1{{5.0, 6.0, .5, .5, .5, .5, .7, .7, .7, .7, .5},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+
+  fixed_width_column_wrapper<size_type> expect{1, 10, 4};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::lower_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, table__find_last__nulls_as_largest_string)
+{
+  std::vector<char const*> h_col_0_strings{
+    "10", "10", "20", "20", "20", "20", "20", "20", "20", "50", nullptr};
+  std::vector<char const*> h_col_2_strings{
+    "90", "95", "77", "78", nullptr, nullptr, "61", "62", "63", "41", "50"};
+
+  std::vector<char const*> h_val_0_strings{"10", nullptr, "20"};
+  std::vector<char const*> h_val_2_strings{"95", "50", nullptr};
+
+  fixed_width_column_wrapper<float> column_1{{5.0, 6.0, .5, .5, .5, .5, .7, .7, .7, .7, .5},
+                                             {1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  fixed_width_column_wrapper<float> values_1{{6., .5, .5}, {0, 1, 1}};
+
+  fixed_width_column_wrapper<size_type> expect{2, 11, 6};
+
+  cudf::test::strings_column_wrapper column_0(
+    h_col_0_strings.begin(),
+    h_col_0_strings.end(),
+    thrust::make_transform_iterator(h_col_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper column_2(
+    h_col_2_strings.begin(),
+    h_col_2_strings.end(),
+    thrust::make_transform_iterator(h_col_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_0(
+    h_val_0_strings.begin(),
+    h_val_0_strings.end(),
+    thrust::make_transform_iterator(h_val_0_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper values_2(
+    h_val_2_strings.begin(),
+    h_val_2_strings.end(),
+    thrust::make_transform_iterator(h_val_2_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(column_0.release());
+  columns.push_back(column_1.release());
+  columns.push_back(column_2.release());
+
+  std::vector<std::unique_ptr<cudf::column>> values;
+  values.push_back(values_0.release());
+  values.push_back(values_1.release());
+  values.push_back(values_2.release());
+
+  cudf::table input_table(std::move(columns));
+  cudf::table values_table(std::move(values));
+
+  std::vector<cudf::order> order_flags{
+    {cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING}};
+  std::vector<cudf::null_order> null_order_flags{
+    {cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER}};
+
+  std::unique_ptr<cudf::column> result{};
+
+  EXPECT_NO_THROW(result =
+                    cudf::upper_bound(input_table, values_table, order_flags, null_order_flags));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, contains_true_string)
+{
+  std::vector<char const*> h_col_strings{"00", "01", "17", "19", "23", "29", "71"};
+  string_scalar scalar{"23"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  bool expect = true;
+  bool result = false;
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_false_string)
+{
+  std::vector<char const*> h_col_strings{"0", "1", "17", "19", "23", "29", "71"};
+  string_scalar scalar{"24"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  bool expect = false;
+  bool result = false;
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_empty_value_string)
+{
+  std::vector<char const*> h_col_strings{"0", "1", "17", "19", "23", "29", "71"};
+  string_scalar scalar{"23", false};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  bool expect = false;
+  bool result = false;
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_empty_column_string)
+{
+  std::vector<char const*> h_col_strings{};
+  string_scalar scalar{"24"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  bool expect = false;
+  bool result = false;
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_nullable_column_true_string)
+{
+  std::vector<char const*> h_col_strings{nullptr, nullptr, "17", "19", "23", "29", "71"};
+  string_scalar scalar{"23"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  bool result = false;
+  bool expect = true;
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, contains_nullable_column_false_string)
+{
+  std::vector<char const*> h_col_strings{nullptr, nullptr, "17", "19", nullptr, "29", "71"};
+  string_scalar scalar{"23"};
+
+  cudf::test::strings_column_wrapper column(
+    h_col_strings.begin(),
+    h_col_strings.end(),
+    thrust::make_transform_iterator(h_col_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  bool result = false;
+  bool expect = false;
+
+  result = cudf::contains(column, scalar);
+
+  ASSERT_EQ(result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_some)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> haystack{0, 1, 17, 19, 23, 29, 71};
+  fixed_width_column_wrapper<element_type> needles{17, 19, 45, 72};
+
+  fixed_width_column_wrapper<bool> expect{1, 1, 0, 0};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_none)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> haystack{0, 1, 17, 19, 23, 29, 71};
+  fixed_width_column_wrapper<element_type> needles{2, 3};
+
+  fixed_width_column_wrapper<bool> expect{0, 0};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_some_string)
+{
+  std::vector<char const*> h_haystack_strings{"0", "1", "17", "19", "23", "29", "71"};
+  std::vector<char const*> h_needles_strings{"17", "19", "45", "72"};
+
+  cudf::test::strings_column_wrapper haystack(h_haystack_strings.begin(), h_haystack_strings.end());
+
+  cudf::test::strings_column_wrapper needles(h_needles_strings.begin(), h_needles_strings.end());
+
+  fixed_width_column_wrapper<bool> expect{1, 1, 0, 0};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_none_string)
+{
+  std::vector<char const*> h_haystack_strings{"0", "1", "17", "19", "23", "29", "71"};
+  std::vector<char const*> h_needles_strings{"2", "3"};
+
+  cudf::test::strings_column_wrapper haystack(h_haystack_strings.begin(), h_haystack_strings.end());
+
+  cudf::test::strings_column_wrapper needles(h_needles_strings.begin(), h_needles_strings.end());
+
+  fixed_width_column_wrapper<bool> expect{0, 0};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_some_with_nulls)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> haystack{{0, 1, 17, 19, 23, 29, 71},
+                                                    {1, 1, 0, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<element_type> needles{{17, 19, 23, 72}, {1, 0, 1, 1}};
+
+  fixed_width_column_wrapper<bool> expect{{0, 0, 1, 0}, {1, 0, 1, 1}};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_none_with_nulls)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> haystack{{0, 1, 17, 19, 23, 29, 71},
+                                                    {1, 1, 0, 1, 1, 1, 1}};
+  fixed_width_column_wrapper<element_type> needles{{17, 19, 24, 72}, {1, 0, 1, 1}};
+
+  fixed_width_column_wrapper<bool> expect{{0, 0, 0, 0}, {1, 0, 1, 1}};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_some_string_with_nulls)
+{
+  std::vector<char const*> h_haystack_strings{"0", "1", nullptr, "19", "23", "29", "71"};
+  std::vector<char const*> h_needles_strings{"17", "23", nullptr, "72"};
+
+  fixed_width_column_wrapper<bool> expect{{0, 1, 0, 0}, {1, 1, 0, 1}};
+
+  cudf::test::strings_column_wrapper haystack(
+    h_haystack_strings.begin(),
+    h_haystack_strings.end(),
+    thrust::make_transform_iterator(h_haystack_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper needles(
+    h_needles_strings.begin(),
+    h_needles_strings.end(),
+    thrust::make_transform_iterator(h_needles_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_none_string_with_nulls)
+{
+  std::vector<char const*> h_haystack_strings{"0", "1", nullptr, "19", "23", "29", "71"};
+  std::vector<char const*> h_needles_strings{"2", nullptr};
+
+  fixed_width_column_wrapper<bool> expect{{0, 0}, {1, 0}};
+
+  cudf::test::strings_column_wrapper haystack(
+    h_haystack_strings.begin(),
+    h_haystack_strings.end(),
+    thrust::make_transform_iterator(h_haystack_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper needles(
+    h_needles_strings.begin(),
+    h_needles_strings.end(),
+    thrust::make_transform_iterator(h_needles_strings.begin(),
+                                    [](auto str) { return str != nullptr; }));
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_empty_column)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> haystack{};
+  fixed_width_column_wrapper<element_type> needles{2, 3};
+
+  fixed_width_column_wrapper<bool> expect{0, 0};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_empty_column_string)
+{
+  std::vector<char const*> h_haystack_strings{};
+  std::vector<char const*> h_needles_strings{"17", "19", "45", "72"};
+
+  cudf::test::strings_column_wrapper haystack(h_haystack_strings.begin(), h_haystack_strings.end());
+
+  cudf::test::strings_column_wrapper needles(h_needles_strings.begin(), h_needles_strings.end());
+
+  fixed_width_column_wrapper<bool> expect{0, 0, 0, 0};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_empty_input_set)
+{
+  using element_type = int64_t;
+
+  fixed_width_column_wrapper<element_type> haystack{0, 1, 17, 19, 23, 29, 71};
+  fixed_width_column_wrapper<element_type> needles{};
+
+  fixed_width_column_wrapper<bool> expect{};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TEST_F(SearchTest, multi_contains_empty_input_set_string)
+{
+  std::vector<char const*> h_haystack_strings{"0", "1", "17", "19", "23", "29", "71"};
+  std::vector<char const*> h_needles_strings{};
+
+  cudf::test::strings_column_wrapper haystack(h_haystack_strings.begin(), h_haystack_strings.end());
+
+  cudf::test::strings_column_wrapper needles(h_needles_strings.begin(), h_needles_strings.end());
+
+  fixed_width_column_wrapper<bool> expect{};
+
+  auto result = cudf::contains(haystack, needles);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+template <typename T>
+struct FixedPointTestAllReps : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTestAllReps, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointLowerBound)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto vec = std::vector<decimalXX>(1000);
+  std::iota(std::begin(vec), std::end(vec), decimalXX{});
+
+  auto const values =
+    cudf::test::fixed_width_column_wrapper<decimalXX>{decimalXX{200, scale_type{0}},
+                                                      decimalXX{400, scale_type{0}},
+                                                      decimalXX{600, scale_type{0}},
+                                                      decimalXX{800, scale_type{0}}};
+  auto const expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{200, 400, 600, 800};
+  auto const column = cudf::test::fixed_width_column_wrapper<decimalXX>(vec.begin(), vec.end());
+
+  auto result = cudf::lower_bound({cudf::table_view{{column}}},
+                                  {cudf::table_view{{values}}},
+                                  {cudf::order::ASCENDING},
+                                  {cudf::null_order::BEFORE});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+TYPED_TEST(FixedPointTestAllReps, FixedPointUpperBound)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto vec = std::vector<decimalXX>(1000);
+  std::iota(std::begin(vec), std::end(vec), decimalXX{});
+
+  auto const values =
+    cudf::test::fixed_width_column_wrapper<decimalXX>{decimalXX{200, scale_type{0}},
+                                                      decimalXX{400, scale_type{0}},
+                                                      decimalXX{600, scale_type{0}},
+                                                      decimalXX{800, scale_type{0}}};
+  auto const expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{201, 401, 601, 801};
+  auto const column = cudf::test::fixed_width_column_wrapper<decimalXX>(vec.begin(), vec.end());
+
+  auto result = cudf::upper_bound({cudf::table_view{{column}}},
+                                  {cudf::table_view{{values}}},
+                                  {cudf::order::ASCENDING},
+                                  {cudf::null_order::BEFORE});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*result, expect);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/sort/is_sorted_tests.cpp b/cpp/tests/sort/is_sorted_tests.cpp
new file mode 100644
index 0000000..ba7c9d8
--- /dev/null
+++ b/cpp/tests/sort/is_sorted_tests.cpp
@@ -0,0 +1,500 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <vector>
+
+namespace testdata {
+// ----- most numerics
+
+template <typename T>
+std::enable_if_t<std::is_arithmetic_v<T> && !std::is_same_v<T, bool>,
+                 cudf::test::fixed_width_column_wrapper<T>>
+ascending()
+{
+  return std::is_signed_v<T>
+           ? cudf::test::fixed_width_column_wrapper<T>({std::numeric_limits<T>::lowest(),
+                                                        T(-100),
+                                                        T(-10),
+                                                        T(-1),
+                                                        T(0),
+                                                        T(1),
+                                                        T(10),
+                                                        T(100),
+                                                        std::numeric_limits<T>::max()})
+           : cudf::test::fixed_width_column_wrapper<T>({std::numeric_limits<T>::lowest(),
+                                                        T(0),
+                                                        T(1),
+                                                        T(10),
+                                                        T(100),
+                                                        std::numeric_limits<T>::max()});
+}
+
+template <typename T>
+std::enable_if_t<std::is_arithmetic_v<T> && !std::is_same_v<T, bool>,
+                 cudf::test::fixed_width_column_wrapper<T>>
+descending()
+{
+  return std::is_signed_v<T>
+           ? cudf::test::fixed_width_column_wrapper<T>({std::numeric_limits<T>::max(),
+                                                        T(100),
+                                                        T(10),
+                                                        T(1),
+                                                        T(0),
+                                                        T(-1),
+                                                        T(-10),
+                                                        T(-100),
+                                                        std::numeric_limits<T>::lowest()})
+           : cudf::test::fixed_width_column_wrapper<T>({std::numeric_limits<T>::max(),
+                                                        T(100),
+                                                        T(10),
+                                                        T(1),
+                                                        T(0),
+                                                        std::numeric_limits<T>::lowest()});
+}
+
+template <typename T>
+auto empty()
+{
+  return cudf::test::fixed_width_column_wrapper<T>();
+}
+
+template <typename T>
+auto nulls_after()
+{
+  return cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 0}, {1, 0});
+}
+
+template <typename T>
+auto nulls_before()
+{
+  return cudf::test::fixed_width_column_wrapper<T, int32_t>({0, 0}, {0, 1});
+}
+
+// ----- bool
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, cudf::test::fixed_width_column_wrapper<bool>> ascending()
+{
+  return cudf::test::fixed_width_column_wrapper<bool>({false, false, true, true});
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, bool>, cudf::test::fixed_width_column_wrapper<bool>> descending()
+{
+  return cudf::test::fixed_width_column_wrapper<bool>({true, true, false, false});
+}
+
+// ----- chrono types
+
+template <typename T>
+std::enable_if_t<cudf::is_chrono<T>(), cudf::test::fixed_width_column_wrapper<T>> ascending()
+{
+  return cudf::test::fixed_width_column_wrapper<T>({T::min(), T::max()});
+}
+
+template <typename T>
+std::enable_if_t<cudf::is_chrono<T>(), cudf::test::fixed_width_column_wrapper<T>> descending()
+{
+  return cudf::test::fixed_width_column_wrapper<T>({T::max(), T::min()});
+}
+
+// ----- string_view
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, cudf::test::strings_column_wrapper>
+ascending()
+{
+  return cudf::test::strings_column_wrapper({"A", "B"});
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::string_view>, cudf::test::strings_column_wrapper>
+descending()
+{
+  return cudf::test::strings_column_wrapper({"B", "A"});
+}
+
+template <>
+auto empty<cudf::string_view>()
+{
+  return cudf::test::strings_column_wrapper();
+}
+
+template <>
+auto nulls_after<cudf::string_view>()
+{
+  return cudf::test::strings_column_wrapper({"identical", "identical"}, {1, 0});
+}
+
+template <>
+auto nulls_before<cudf::string_view>()
+{
+  return cudf::test::strings_column_wrapper({"identical", "identical"}, {0, 1});
+}
+
+// ----- struct_view {"nestedInt" : {"Int" : 0 }, "float" : 1}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::struct_view>, cudf::test::structs_column_wrapper>
+ascending()
+{
+  using T1     = int32_t;
+  auto int_col = cudf::test::fixed_width_column_wrapper<int32_t>({std::numeric_limits<T1>::lowest(),
+                                                                  T1(-100),
+                                                                  T1(-10),
+                                                                  T1(-10),
+                                                                  T1(0),
+                                                                  T1(10),
+                                                                  T1(10),
+                                                                  T1(100),
+                                                                  std::numeric_limits<T1>::max()});
+  auto nestedInt_col = cudf::test::structs_column_wrapper{{int_col}};
+  auto float_col     = ascending<float>();
+  return cudf::test::structs_column_wrapper{{nestedInt_col, float_col}};
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::struct_view>, cudf::test::structs_column_wrapper>
+descending()
+{
+  using T1 = int32_t;
+  auto int_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>({std::numeric_limits<T1>::max(),
+                                                     T1(100),
+                                                     T1(10),
+                                                     T1(10),
+                                                     T1(0),
+                                                     T1(-10),
+                                                     T1(-10),
+                                                     T1(-100),
+                                                     std::numeric_limits<T1>::lowest()});
+  auto nestedInt_col = cudf::test::structs_column_wrapper{{int_col}};
+  auto float_col     = descending<float>();
+  return cudf::test::structs_column_wrapper{{nestedInt_col, float_col}};
+}
+
+template <>
+auto empty<cudf::struct_view>()
+{
+  auto int_col = cudf::test::fixed_width_column_wrapper<int32_t>();
+  auto col1    = cudf::test::structs_column_wrapper{{int_col}};
+  auto col2    = cudf::test::fixed_width_column_wrapper<float>();
+  return cudf::test::structs_column_wrapper{{col1, col2}};
+}
+
+template <>
+auto nulls_after<cudf::struct_view>()
+{
+  auto int_col = cudf::test::fixed_width_column_wrapper<int32_t>({1, 1});
+  auto col1    = cudf::test::structs_column_wrapper{{int_col}};
+  auto col2    = cudf::test::fixed_width_column_wrapper<float>({1, 1});
+  return cudf::test::structs_column_wrapper{{col1, col2}, {1, 0}};
+}
+
+template <>
+auto nulls_before<cudf::struct_view>()
+{
+  auto int_col = cudf::test::fixed_width_column_wrapper<int32_t>({1, 1});
+  auto col1    = cudf::test::structs_column_wrapper{{int_col}};
+  auto col2    = cudf::test::fixed_width_column_wrapper<float>({1, 1});
+  return cudf::test::structs_column_wrapper{{col1, col2}, {0, 1}};
+}
+
+using lcw = cudf::test::lists_column_wrapper<int32_t>;
+using cudf::test::iterators::null_at;
+/*
+List<List<List<int>
+[
+  [[[0]], [[0]], [[0]]],                        0
+  [[[0], [0], [0]]],                            1
+  [[[0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0]], [[0]]] 2
+  [[[0, 0, 0]]],                                3
+  [[[0, 0, 0]], [[0]], [[0]]],                  4
+]
+*/
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::list_view>, lcw> ascending()
+{
+  return lcw{lcw{lcw{lcw{0}}, lcw{lcw{0}}, lcw{lcw{0}}},
+             lcw{lcw{lcw{0}, lcw{0}, lcw{0}}},
+             lcw{lcw{lcw{0, 0}}, lcw{lcw{0, 0, 0, 0, 0, 0, 0, 0}}, lcw{lcw{0}}},
+             lcw{lcw{lcw{0, 0, 0}}},
+             lcw{lcw{lcw{0, 0, 0}}, lcw{lcw{0}}, lcw{lcw{0}}}};
+}
+
+template <typename T>
+std::enable_if_t<std::is_same_v<T, cudf::list_view>, lcw> descending()
+{
+  return lcw{lcw{lcw{lcw{0, 0, 0}}, lcw{lcw{0}}, lcw{lcw{0}}},
+             lcw{lcw{lcw{0, 0, 0}}},
+             lcw{lcw{lcw{0, 0}}, lcw{lcw{0, 0, 0, 0, 0, 0, 0, 0}}, lcw{lcw{0}}},
+
+             lcw{lcw{lcw{0}, lcw{0}, lcw{0}}},
+             lcw{lcw{lcw{0}}, lcw{lcw{0}}, lcw{lcw{0}}}};
+}
+
+template <>
+auto empty<cudf::list_view>()
+{
+  return lcw{};
+}
+
+template <>
+auto nulls_after<cudf::list_view>()
+{
+  return lcw{{{1}, {2, 2}, {0}}, null_at(2)};
+}
+
+template <>
+auto nulls_before<cudf::list_view>()
+{
+  return lcw{{{0}, {1}, {2, 2}}, null_at(0)};
+}
+
+}  // namespace testdata
+
+// =============================================================================
+// ---- tests ------------------------------------------------------------------
+
+template <typename T>
+struct IsSortedTest : public cudf::test::BaseFixture {};
+
+using SupportedTypes = cudf::test::
+  Concat<cudf::test::ComparableTypes, cudf::test::Types<cudf::struct_view>, cudf::test::ListTypes>;
+TYPED_TEST_SUITE(IsSortedTest, SupportedTypes);
+
+TYPED_TEST(IsSortedTest, NoColumns)
+{
+  cudf::table_view in{std::vector<cudf::table_view>{}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(true, actual);
+}
+
+TYPED_TEST(IsSortedTest, NoRows)
+{
+  using T = TypeParam;
+
+  if (std::is_same_v<T, cudf::string_view>) {
+    // cudf::test::strings_column_wrapper does not yet support empty columns.
+    return;
+  } else {
+    auto col1 = testdata::empty<T>();
+    auto col2 = testdata::empty<T>();
+
+    cudf::table_view in{{col1, col2}};
+    std::vector<cudf::order> order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+    std::vector<cudf::null_order> null_precedence{};
+
+    auto actual = cudf::is_sorted(in, order, null_precedence);
+
+    EXPECT_EQ(true, actual);
+  }
+}
+
+TYPED_TEST(IsSortedTest, Ascending)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::ascending<T>();
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(true, actual);
+}
+
+TYPED_TEST(IsSortedTest, AscendingFalse)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::descending<T>();
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto actual = cudf::is_sorted(in, order, {});
+
+  EXPECT_EQ(false, actual);
+}
+
+TYPED_TEST(IsSortedTest, Descending)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::descending<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(true, actual);
+}
+
+TYPED_TEST(IsSortedTest, DescendingFalse)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::ascending<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(false, actual);
+}
+
+TYPED_TEST(IsSortedTest, NullsAfter)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::nulls_after<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(true, actual);
+}
+
+TYPED_TEST(IsSortedTest, NullsAfterFalse)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::nulls_before<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(false, actual);
+}
+
+TYPED_TEST(IsSortedTest, NullsBefore)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::nulls_before<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::BEFORE};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(true, actual);
+}
+
+TYPED_TEST(IsSortedTest, NullsBeforeFalse)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::nulls_after<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::BEFORE};
+
+  auto actual = cudf::is_sorted(in, order, null_precedence);
+
+  EXPECT_EQ(false, actual);
+}
+
+TYPED_TEST(IsSortedTest, OrderArgsTooFew)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::ascending<T>();
+  auto col2 = testdata::ascending<T>();
+
+  cudf::table_view in{{col1, col2}};
+  std::vector<cudf::order> order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  EXPECT_THROW(cudf::is_sorted(in, order, null_precedence), cudf::logic_error);
+}
+
+TYPED_TEST(IsSortedTest, OrderArgsTooMany)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::ascending<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{cudf::order::ASCENDING, cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{};
+
+  EXPECT_THROW(cudf::is_sorted(in, order, null_precedence), cudf::logic_error);
+}
+
+TYPED_TEST(IsSortedTest, NullOrderArgsTooFew)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::nulls_before<T>();
+  auto col2 = testdata::nulls_before<T>();
+
+  cudf::table_view in{{col1, col2}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::BEFORE};
+
+  EXPECT_THROW(cudf::is_sorted(in, order, null_precedence), cudf::logic_error);
+}
+
+TYPED_TEST(IsSortedTest, NullOrderArgsTooMany)
+{
+  using T = TypeParam;
+
+  auto col1 = testdata::nulls_before<T>();
+
+  cudf::table_view in{{col1}};
+  std::vector<cudf::order> order{};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::BEFORE, cudf::null_order::BEFORE};
+
+  EXPECT_THROW(cudf::is_sorted(in, order, null_precedence), cudf::logic_error);
+}
+
+template <typename T>
+struct IsSortedFixedWidthOnly : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(IsSortedFixedWidthOnly, cudf::test::FixedWidthTypes);
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/sort/rank_test.cpp b/cpp/tests/sort/rank_test.cpp
new file mode 100644
index 0000000..47a1ba3
--- /dev/null
+++ b/cpp/tests/sort/rank_test.cpp
@@ -0,0 +1,921 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <tuple>
+#include <vector>
+
+template <typename T>
+using lists_col   = cudf::test::lists_column_wrapper<T, int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+namespace {
+void run_rank_test(cudf::table_view input,
+                   cudf::table_view expected,
+                   cudf::rank_method method,
+                   cudf::order column_order,
+                   cudf::null_policy null_handling,
+                   cudf::null_order null_precedence,
+                   bool percentage)
+{
+  int i = 0;
+  for (auto&& input_column : input) {
+    // Rank
+    auto got_rank_column =
+      cudf::rank(input_column, method, column_order, null_handling, null_precedence, percentage);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected.column(i), got_rank_column->view());
+    i++;
+  }
+}
+
+using input_arg_t = std::tuple<cudf::order, cudf::null_policy, cudf::null_order>;
+input_arg_t asc_keep{cudf::order::ASCENDING, cudf::null_policy::EXCLUDE, cudf::null_order::AFTER};
+input_arg_t asc_top{cudf::order::ASCENDING, cudf::null_policy::INCLUDE, cudf::null_order::BEFORE};
+input_arg_t asc_bottom{cudf::order::ASCENDING, cudf::null_policy::INCLUDE, cudf::null_order::AFTER};
+
+input_arg_t desc_keep{
+  cudf::order::DESCENDING, cudf::null_policy::EXCLUDE, cudf::null_order::BEFORE};
+input_arg_t desc_top{cudf::order::DESCENDING, cudf::null_policy::INCLUDE, cudf::null_order::AFTER};
+input_arg_t desc_bottom{
+  cudf::order::DESCENDING, cudf::null_policy::INCLUDE, cudf::null_order::BEFORE};
+using test_case_t = std::tuple<cudf::table_view, cudf::table_view>;
+}  // namespace
+
+template <typename T>
+struct Rank : public cudf::test::BaseFixture {
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8, 5}};
+  cudf::test::fixed_width_column_wrapper<T> col2{{5, 4, 3, 5, 8, 5}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col3{{"d", "e", "a", "d", "k", "d"}, {1, 1, 1, 1, 1, 1}};
+
+  void run_all_tests(cudf::rank_method method,
+                     input_arg_t input_arg,
+                     cudf::column_view const col1_rank,
+                     cudf::column_view const col2_rank,
+                     cudf::column_view const col3_rank,
+                     bool percentage = false)
+  {
+    if (std::is_same_v<T, bool>) return;
+    for (auto const& test_case : {
+           // Non-null column
+           test_case_t{cudf::table_view{{col1}}, cudf::table_view{{col1_rank}}},
+           // Null column
+           test_case_t{cudf::table_view{{col2}}, cudf::table_view{{col2_rank}}},
+           // Table
+           test_case_t{cudf::table_view{{col1, col2}}, cudf::table_view{{col1_rank, col2_rank}}},
+           // Table with String column
+           test_case_t{cudf::table_view{{col1, col2, col3}},
+                       cudf::table_view{{col1_rank, col2_rank, col3_rank}}},
+         }) {
+      auto [input, output] = test_case;
+
+      run_rank_test(input,
+                    output,
+                    method,
+                    std::get<0>(input_arg),
+                    std::get<1>(input_arg),
+                    std::get<2>(input_arg),
+                    percentage);
+    }
+  }
+};
+
+TYPED_TEST_SUITE(Rank, cudf::test::NumericTypes);
+
+// fixed_width_column_wrapper<T>   col1{{  5,   4,   3,   5,   8,   5}};
+//                                        3,   2,   1,   4,   6,   5
+TYPED_TEST(Rank, first_asc_keep)
+{
+  // ASCENDING
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 4, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 1, -1, 3, 5, 4},
+                                                                    {1, 1, 0, 1, 1, 1}};  // KEEP
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 5, 1, 3, 6, 4},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::FIRST, asc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, first_asc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 4, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{
+    {3, 2, 1, 4, 6, 5}};  // BEFORE = TOP
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 5, 1, 3, 6, 4}};
+  this->run_all_tests(cudf::rank_method::FIRST, asc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, first_asc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 4, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{
+    {2, 1, 6, 3, 5, 4}};  // AFTER  = BOTTOM
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 5, 1, 3, 6, 4}};
+  this->run_all_tests(cudf::rank_method::FIRST, asc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, first_desc_keep)
+{
+  // DESCENDING
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 5, 6, 3, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 5, -1, 3, 1, 4},
+                                                                    {1, 1, 0, 1, 1, 1}};  // KEEP
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 6, 4, 1, 5},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::FIRST, desc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, first_desc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 5, 6, 3, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{
+    {3, 6, 1, 4, 2, 5}};  // AFTER  = TOP
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 6, 4, 1, 5}};
+  this->run_all_tests(cudf::rank_method::FIRST, desc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, first_desc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 5, 6, 3, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{
+    {2, 5, 6, 3, 1, 4}};  // BEFORE = BOTTOM
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 6, 4, 1, 5}};
+  this->run_all_tests(cudf::rank_method::FIRST, desc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, dense_asc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 3, 4, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 1, -1, 2, 3, 2},
+                                                                    {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 3, 1, 2, 4, 2},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, dense_asc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 3, 4, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{3, 2, 1, 3, 4, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 3, 1, 2, 4, 2}};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, dense_asc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 3, 4, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 1, 4, 2, 3, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 3, 1, 2, 4, 2}};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, dense_desc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 3, 4, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 3, -1, 2, 1, 2},
+                                                                    {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 4, 3, 1, 3},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::DENSE, desc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, dense_desc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 3, 4, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{3, 4, 1, 3, 2, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 4, 3, 1, 3}};
+  this->run_all_tests(cudf::rank_method::DENSE, desc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, dense_desc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 3, 4, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 3, 4, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 4, 3, 1, 3}};
+  this->run_all_tests(cudf::rank_method::DENSE, desc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, min_asc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 3, 6, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 1, -1, 2, 5, 2},
+                                                                    {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 5, 1, 2, 6, 2},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::MIN, asc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, min_asc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 3, 6, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{3, 2, 1, 3, 6, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 5, 1, 2, 6, 2}};
+  this->run_all_tests(cudf::rank_method::MIN, asc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, min_asc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{3, 2, 1, 3, 6, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 1, 6, 2, 5, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{2, 5, 1, 2, 6, 2}};
+  this->run_all_tests(cudf::rank_method::MIN, asc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, min_desc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 5, 6, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 5, -1, 2, 1, 2},
+                                                                    {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 6, 3, 1, 3},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::MIN, desc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, min_desc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 5, 6, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{3, 6, 1, 3, 2, 3}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 6, 3, 1, 3}};
+  this->run_all_tests(cudf::rank_method::MIN, desc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, min_desc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{2, 5, 6, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{2, 5, 6, 2, 1, 2}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{3, 2, 6, 3, 1, 3}};
+  this->run_all_tests(cudf::rank_method::MIN, desc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, max_asc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{5, 2, 1, 5, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{4, 1, -1, 4, 5, 4},
+                                                                    {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{4, 5, 1, 4, 6, 4},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::MAX, asc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, max_asc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{5, 2, 1, 5, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{5, 2, 1, 5, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{4, 5, 1, 4, 6, 4}};
+  this->run_all_tests(cudf::rank_method::MAX, asc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, max_asc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{5, 2, 1, 5, 6, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{4, 1, 6, 4, 5, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{4, 5, 1, 4, 6, 4}};
+  this->run_all_tests(cudf::rank_method::MAX, asc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, max_desc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{4, 5, 6, 4, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{4, 5, -1, 4, 1, 4},
+                                                                    {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{5, 2, 6, 5, 1, 5},
+                                                                    {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::MAX, desc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, max_desc_top)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{4, 5, 6, 4, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{5, 6, 1, 5, 2, 5}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{5, 2, 6, 5, 1, 5}};
+  this->run_all_tests(cudf::rank_method::MAX, desc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, max_desc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col1_rank{{4, 5, 6, 4, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col2_rank{{4, 5, 6, 4, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col3_rank{{5, 2, 6, 5, 1, 5}};
+  this->run_all_tests(cudf::rank_method::MAX, desc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, average_asc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{4, 2, 1, 4, 6, 4}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{3, 1, -1, 3, 5, 3}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{3, 5, 1, 3, 6, 3}, {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::AVERAGE, asc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, average_asc_top)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{4, 2, 1, 4, 6, 4}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{4, 2, 1, 4, 6, 4}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{3, 5, 1, 3, 6, 3}};
+  this->run_all_tests(cudf::rank_method::AVERAGE, asc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, average_asc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{4, 2, 1, 4, 6, 4}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{3, 1, 6, 3, 5, 3}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{3, 5, 1, 3, 6, 3}};
+  this->run_all_tests(cudf::rank_method::AVERAGE, asc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, average_desc_keep)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{3, 5, 6, 3, 1, 3}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{3, 5, -1, 3, 1, 3}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{4, 2, 6, 4, 1, 4}, {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::AVERAGE, desc_keep, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, average_desc_top)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{3, 5, 6, 3, 1, 3}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{4, 6, 1, 4, 2, 4}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{4, 2, 6, 4, 1, 4}};
+  this->run_all_tests(cudf::rank_method::AVERAGE, desc_top, col1_rank, col2_rank, col3_rank);
+}
+
+TYPED_TEST(Rank, average_desc_bottom)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{3, 5, 6, 3, 1, 3}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{3, 5, 6, 3, 1, 3}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{4, 2, 6, 4, 1, 4}};
+  this->run_all_tests(cudf::rank_method::AVERAGE, desc_bottom, col1_rank, col2_rank, col3_rank);
+}
+
+// percentage==true (dense, not-dense)
+TYPED_TEST(Rank, dense_asc_keep_pct)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{0.75, 0.5, 0.25, 0.75, 1., 0.75}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{
+    {2.0 / 3.0, 1.0 / 3.0, -1., 2.0 / 3.0, 1., 2.0 / 3.0}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{0.5, 0.75, 0.25, 0.5, 1., 0.5},
+                                                           {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_keep, col1_rank, col2_rank, col3_rank, true);
+}
+
+TYPED_TEST(Rank, dense_asc_top_pct)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{0.75, 0.5, 0.25, 0.75, 1., 0.75}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{0.75, 0.5, 0.25, 0.75, 1., 0.75}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{0.5, 0.75, 0.25, 0.5, 1., 0.5}};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_top, col1_rank, col2_rank, col3_rank, true);
+}
+
+TYPED_TEST(Rank, dense_asc_bottom_pct)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{{0.75, 0.5, 0.25, 0.75, 1., 0.75}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{0.5, 0.25, 1., 0.5, 0.75, 0.5}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{{0.5, 0.75, 0.25, 0.5, 1., 0.5}};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_bottom, col1_rank, col2_rank, col3_rank, true);
+}
+
+TYPED_TEST(Rank, min_desc_keep_pct)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{
+    {1.0 / 3.0, 5.0 / 6.0, 1., 1.0 / 3.0, 1.0 / 6.0, 1.0 / 3.0}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{{0.4, 1., -1., 0.4, 0.2, 0.4},
+                                                           {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{
+    {0.5, 1.0 / 3.0, 1., 0.5, 1.0 / 6.0, 0.5}, {1, 1, 1, 1, 1, 1}};
+  this->run_all_tests(cudf::rank_method::MIN, desc_keep, col1_rank, col2_rank, col3_rank, true);
+}
+
+TYPED_TEST(Rank, min_desc_top_pct)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{
+    {1.0 / 3.0, 5.0 / 6.0, 1., 1.0 / 3.0, 1.0 / 6.0, 1.0 / 3.0}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{
+    {0.5, 1., 1.0 / 6.0, 0.5, 1.0 / 3.0, 0.5}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{
+    {0.5, 1.0 / 3.0, 1., 0.5, 1.0 / 6.0, 0.5}};
+  this->run_all_tests(cudf::rank_method::MIN, desc_top, col1_rank, col2_rank, col3_rank, true);
+}
+
+TYPED_TEST(Rank, min_desc_bottom_pct)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1_rank{
+    {1.0 / 3.0, 5.0 / 6.0, 1., 1.0 / 3.0, 1.0 / 6.0, 1.0 / 3.0}};
+  cudf::test::fixed_width_column_wrapper<double> col2_rank{
+    {1.0 / 3.0, 5.0 / 6.0, 1., 1.0 / 3.0, 1.0 / 6.0, 1.0 / 3.0}};
+  cudf::test::fixed_width_column_wrapper<double> col3_rank{
+    {0.5, 1.0 / 3.0, 1., 0.5, 1.0 / 6.0, 0.5}};
+  this->run_all_tests(cudf::rank_method::MIN, desc_bottom, col1_rank, col2_rank, col3_rank, true);
+}
+
+struct RankLarge : public cudf::test::BaseFixture {};
+
+TEST_F(RankLarge, average_large)
+{
+  // testcase of https://github.com/rapidsai/cudf/issues/9703
+  auto iter = thrust::counting_iterator<int64_t>(0);
+  cudf::test::fixed_width_column_wrapper<int64_t> col1(iter, iter + 10558);
+  auto result = cudf::rank(col1,
+                           cudf::rank_method::AVERAGE,
+                           {},
+                           cudf::null_policy::EXCLUDE,
+                           cudf::null_order::AFTER,
+                           false);
+  cudf::test::fixed_width_column_wrapper<double, int> expected(iter + 1, iter + 10559);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+template <typename T>
+struct RankListAndStruct : public cudf::test::BaseFixture {
+  void run_all_tests(cudf::rank_method method,
+                     input_arg_t input_arg,
+                     cudf::column_view const list_rank,
+                     cudf::column_view const struct_rank,
+                     bool percentage = false)
+  {
+    if constexpr (std::is_same_v<T, bool>) { return; }
+    /*
+    [
+      [],
+      [1],
+      [2, 2],
+      [2, 3],
+      [2, 2],
+      [1],
+      [],
+      NULL
+      [2],
+      NULL,
+      [1]
+    ]
+    */
+    auto list_col =
+      lists_col<T>{{{}, {1}, {2, 2}, {2, 3}, {2, 2}, {1}, {}, {} /*NULL*/, {2}, {} /*NULL*/, {1}},
+                   nulls_at({7, 9})};
+
+    // clang-format off
+    /*
+      +------------+
+      |           s|
+      +------------+
+    0 |   {0, null}|
+    1 |   {1, null}|
+    2 |        null|
+    3 |{null, null}|
+    4 |        null|
+    5 |{null, null}|
+    6 |   {null, 1}|
+    7 |   {null, 0}|
+      +------------+
+    */
+    std::vector<bool>                           struct_valids{1, 1, 0, 1, 0, 1, 1, 1};
+    auto col1       = cudf::test::fixed_width_column_wrapper<T>{{ 0,  1,  9, -1,  9, -1, -1, -1}, {1, 1, 1, 0, 1, 0, 0, 0}};
+    auto col2       = cudf::test::fixed_width_column_wrapper<T>{{-1, -1,  9, -1,  9, -1,  1,  0}, {0, 0, 1, 0, 1, 0, 1, 1}};
+    auto struct_col = cudf::test::structs_column_wrapper{{col1, col2}, struct_valids}.release();
+    // clang-format on
+
+    for (auto const& test_case : {
+           // Non-null column
+           test_case_t{cudf::table_view{{list_col}}, cudf::table_view{{list_rank}}},
+           // Null column
+           test_case_t{cudf::table_view{{struct_col->view()}}, cudf::table_view{{struct_rank}}},
+         }) {
+      auto [input, output] = test_case;
+
+      run_rank_test(input,
+                    output,
+                    method,
+                    std::get<0>(input_arg),
+                    std::get<1>(input_arg),
+                    std::get<2>(input_arg),
+                    percentage);
+    }
+  }
+};
+
+TYPED_TEST_SUITE(RankListAndStruct, cudf::test::NumericTypes);
+
+TYPED_TEST(RankListAndStruct, first_asc_keep)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> list_rank{
+    {1, 3, 7, 9, 8, 4, 2, -1, 6, -1, 5}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{1, 2, -1, 5, -1, 6, 4, 3},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::FIRST, asc_keep, list_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, first_asc_top)
+{
+  // ASCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    3, 5, 9, 11, 10, 6, 4, 1, 8, 2, 7};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{7, 8, 1, 3, 2, 4, 6, 5};
+  this->run_all_tests(cudf::rank_method::FIRST, asc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, first_asc_bottom)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    1, 3, 7, 9, 8, 4, 2, 10, 6, 11, 5};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{1, 2, 7, 5, 8, 6, 4, 3};
+  this->run_all_tests(cudf::rank_method::FIRST, asc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, first_desc_keep)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {8, 5, 2, 1, 3, 6, 9, -1, 4, -1, 7}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{2, 1, -1, 5, -1, 6, 3, 4},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::FIRST, desc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, first_desc_top)
+{
+  // DESCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    10, 7, 4, 3, 5, 8, 11, 1, 6, 2, 9};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{8, 7, 1, 3, 2, 4, 5, 6};
+  this->run_all_tests(cudf::rank_method::FIRST, desc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, first_desc_bottom)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    8, 5, 2, 1, 3, 6, 9, 10, 4, 11, 7};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{2, 1, 7, 5, 8, 6, 3, 4};
+  this->run_all_tests(cudf::rank_method::FIRST, desc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_asc_keep)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {1, 2, 4, 5, 4, 2, 1, -1, 3, -1, 2}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{1, 2, -1, 5, -1, 5, 4, 3},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_asc_top)
+{
+  // ASCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{2, 3, 5, 6, 5, 3, 2, 1, 4, 1, 3};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{5, 6, 1, 2, 1, 2, 4, 3};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_asc_bottom)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{1, 2, 4, 5, 4, 2, 1, 6, 3, 6, 2};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{1, 2, 6, 5, 6, 5, 4, 3};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_desc_keep)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {5, 4, 2, 1, 2, 4, 5, -1, 3, -1, 4}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{2, 1, -1, 5, -1, 5, 3, 4},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::DENSE, desc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_desc_top)
+{
+  // DESCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{6, 5, 3, 2, 3, 5, 6, 1, 4, 1, 5};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{6, 5, 1, 2, 1, 2, 3, 4};
+  this->run_all_tests(cudf::rank_method::DENSE, desc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_desc_bottom)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{5, 4, 2, 1, 2, 4, 5, 6, 3, 6, 4};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{2, 1, 6, 5, 6, 5, 3, 4};
+  this->run_all_tests(cudf::rank_method::DENSE, desc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, min_asc_keep)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {1, 3, 7, 9, 7, 3, 1, -1, 6, -1, 3}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{1, 2, -1, 5, -1, 5, 4, 3},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::MIN, asc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, min_asc_top)
+{
+  // ASCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    3, 5, 9, 11, 9, 5, 3, 1, 8, 1, 5};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{7, 8, 1, 3, 1, 3, 6, 5};
+  this->run_all_tests(cudf::rank_method::MIN, asc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, min_asc_bottom)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    1, 3, 7, 9, 7, 3, 1, 10, 6, 10, 3};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{1, 2, 7, 5, 7, 5, 4, 3};
+  this->run_all_tests(cudf::rank_method::MIN, asc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, min_desc_keep)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {8, 5, 2, 1, 2, 5, 8, -1, 4, -1, 5}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{2, 1, -1, 5, -1, 5, 3, 4},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::MIN, desc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, min_desc_top)
+{
+  // DESCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    10, 7, 4, 3, 4, 7, 10, 1, 6, 1, 7};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{8, 7, 1, 3, 1, 3, 5, 6};
+  this->run_all_tests(cudf::rank_method::MIN, desc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, min_desc_bottom)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    8, 5, 2, 1, 2, 5, 8, 10, 4, 10, 5};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{2, 1, 7, 5, 7, 5, 3, 4};
+  this->run_all_tests(cudf::rank_method::MIN, desc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, max_asc_keep)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {2, 5, 8, 9, 8, 5, 2, -1, 6, -1, 5}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{1, 2, -1, 6, -1, 6, 4, 3},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::MAX, asc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, max_asc_top)
+{
+  // ASCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    4, 7, 10, 11, 10, 7, 4, 2, 8, 2, 7};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{7, 8, 2, 4, 2, 4, 6, 5};
+  this->run_all_tests(cudf::rank_method::MAX, asc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, max_asc_bottom)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    2, 5, 8, 9, 8, 5, 2, 11, 6, 11, 5};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{1, 2, 8, 6, 8, 6, 4, 3};
+  this->run_all_tests(cudf::rank_method::MAX, asc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, max_desc_keep)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    {9, 7, 3, 1, 3, 7, 9, -1, 4, -1, 7}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{{2, 1, -1, 6, -1, 6, 3, 4},
+                                                                      nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::MAX, desc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, max_desc_top)
+{
+  // DESCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    11, 9, 5, 3, 5, 9, 11, 2, 6, 2, 9};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{8, 7, 2, 4, 2, 4, 5, 6};
+  this->run_all_tests(cudf::rank_method::MAX, desc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, max_desc_bottom)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> col_rank{
+    9, 7, 3, 1, 3, 7, 9, 11, 4, 11, 7};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> struct_rank{2, 1, 8, 6, 8, 6, 3, 4};
+  this->run_all_tests(cudf::rank_method::MAX, desc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, average_asc_keep)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<double> col_rank{
+    {1.5, 4.0, 7.5, 9.0, 7.5, 4.0, 1.5, -1.0, 6.0, -1.0, 4.0}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    {1.0, 2.0, -1.0, 5.5, -1.0, 5.5, 4.0, 3.0}, nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::AVERAGE, asc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, average_asc_top)
+{
+  // ASCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<double> col_rank{
+    3.5, 6.0, 9.5, 11.0, 9.5, 6.0, 3.5, 1.5, 8.0, 1.5, 6.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    7.0, 8.0, 1.5, 3.5, 1.5, 3.5, 6.0, 5.0};
+  this->run_all_tests(cudf::rank_method::AVERAGE, asc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, average_asc_bottom)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<double> col_rank{
+    1.5, 4.0, 7.5, 9.0, 7.5, 4.0, 1.5, 10.5, 6.0, 10.5, 4.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    1.0, 2.0, 7.5, 5.5, 7.5, 5.5, 4.0, 3.0};
+  this->run_all_tests(cudf::rank_method::AVERAGE, asc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, average_desc_keep)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<double> col_rank{
+    {8.5, 6.0, 2.5, 1.0, 2.5, 6.0, 8.5, -1.0, 4.0, -1.0, 6.0}, nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    {2.0, 1.0, -1.0, 5.5, -1.0, 5.5, 3.0, 4.0}, nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::AVERAGE, desc_keep, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, average_desc_top)
+{
+  // DESCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<double> col_rank{
+    10.5, 8.0, 4.5, 3.0, 4.5, 8.0, 10.5, 1.5, 6.0, 1.5, 8.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    8.0, 7.0, 1.5, 3.5, 1.5, 3.5, 5.0, 6.0};
+  this->run_all_tests(cudf::rank_method::AVERAGE, desc_top, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, average_desc_bottom)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<double> col_rank{
+    8.5, 6.0, 2.5, 1.0, 2.5, 6.0, 8.5, 10.5, 4.0, 10.5, 6.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    2.0, 1.0, 7.5, 5.5, 7.5, 5.5, 3.0, 4.0};
+  this->run_all_tests(cudf::rank_method::AVERAGE, desc_bottom, col_rank, struct_rank);
+}
+
+TYPED_TEST(RankListAndStruct, dense_asc_keep_pct)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<double> col_rank{{1.0 / 5.0,
+                                                           2.0 / 5.0,
+                                                           4.0 / 5.0,
+                                                           1.0,
+                                                           4.0 / 5.0,
+                                                           2.0 / 5.0,
+                                                           1.0 / 5.0,
+                                                           -1.0,
+                                                           3.0 / 5.0,
+                                                           -1.0,
+                                                           2.0 / 5.0},
+                                                          nulls_at({7, 9})};
+
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    {1.0 / 5.0, 2.0 / 5.0, -1.0, 1.0, -1.0, 1.0, 4.0 / 5.0, 3.0 / 5.0}, nulls_at({2, 4})};
+
+  this->run_all_tests(cudf::rank_method::DENSE, asc_keep, col_rank, struct_rank, true);
+}
+
+TYPED_TEST(RankListAndStruct, dense_asc_top_pct)
+{
+  // ASCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<double> col_rank{1.0 / 3.0,
+                                                          1.0 / 2.0,
+                                                          5.0 / 6.0,
+                                                          1.0,
+                                                          5.0 / 6.0,
+                                                          1.0 / 2.0,
+                                                          1.0 / 3.0,
+                                                          1.0 / 6.0,
+                                                          2.0 / 3.0,
+                                                          1.0 / 6.0,
+                                                          1.0 / 2.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    5.0 / 6.0, 1.0, 1.0 / 6.0, 2.0 / 6.0, 1.0 / 6.0, 2.0 / 6.0, 4.0 / 6.0, 3.0 / 6.0};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_top, col_rank, struct_rank, true);
+}
+
+TYPED_TEST(RankListAndStruct, dense_asc_bottom_pct)
+{
+  // ASCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<double> col_rank{1.0 / 6.0,
+                                                          1.0 / 3.0,
+                                                          2.0 / 3.0,
+                                                          5.0 / 6.0,
+                                                          2.0 / 3.0,
+                                                          1.0 / 3.0,
+                                                          1.0 / 6.0,
+                                                          1.0,
+                                                          1.0 / 2.0,
+                                                          1.0,
+                                                          1.0 / 3.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    1.0 / 6.0, 2.0 / 6.0, 1.0, 5.0 / 6.0, 1.0, 5.0 / 6.0, 4.0 / 6.0, 3.0 / 6.0};
+  this->run_all_tests(cudf::rank_method::DENSE, asc_bottom, col_rank, struct_rank, true);
+}
+
+TYPED_TEST(RankListAndStruct, min_desc_keep_pct)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<double> col_rank{{8.0 / 9.0,
+                                                           5.0 / 9.0,
+                                                           2.0 / 9.0,
+                                                           1.0 / 9.0,
+                                                           2.0 / 9.0,
+                                                           5.0 / 9.0,
+                                                           8.0 / 9.0,
+                                                           -1.0,
+                                                           4.0 / 9.0,
+                                                           -1.0,
+                                                           5.0 / 9.0},
+                                                          nulls_at({7, 9})};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    {2.0 / 6.0, 1.0 / 6.0, -1.0, 5.0 / 6.0, -1.0, 5.0 / 6.0, 3.0 / 6.0, 4.0 / 6.0},
+    nulls_at({2, 4})};
+  this->run_all_tests(cudf::rank_method::MIN, desc_keep, col_rank, struct_rank, true);
+}
+
+TYPED_TEST(RankListAndStruct, min_desc_top_pct)
+{
+  // DESCENDING and null_order::AFTER
+  cudf::test::fixed_width_column_wrapper<double> col_rank{10.0 / 11.0,
+                                                          7.0 / 11.0,
+                                                          4.0 / 11.0,
+                                                          3.0 / 11.0,
+                                                          4.0 / 11.0,
+                                                          7.0 / 11.0,
+                                                          10.0 / 11.0,
+                                                          1.0 / 11.0,
+                                                          6.0 / 11.0,
+                                                          1.0 / 11.0,
+                                                          7.0 / 11.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    1.0, 7.0 / 8.0, 1.0 / 8.0, 3.0 / 8.0, 1.0 / 8.0, 3.0 / 8.0, 5.0 / 8.0, 6.0 / 8.0};
+  this->run_all_tests(cudf::rank_method::MIN, desc_top, col_rank, struct_rank, true);
+}
+
+TYPED_TEST(RankListAndStruct, min_desc_bottom_pct)
+{
+  // DESCENDING and null_order::BEFORE
+  cudf::test::fixed_width_column_wrapper<double> col_rank{8.0 / 11.0,
+                                                          5.0 / 11.0,
+                                                          2.0 / 11.0,
+                                                          1.0 / 11.0,
+                                                          2.0 / 11.0,
+                                                          5.0 / 11.0,
+                                                          8.0 / 11.0,
+                                                          10.0 / 11.0,
+                                                          4.0 / 11.0,
+                                                          10.0 / 11.0,
+                                                          5.0 / 11.0};
+  cudf::test::fixed_width_column_wrapper<double> struct_rank{
+    2.0 / 8.0, 1.0 / 8.0, 7.0 / 8.0, 5.0 / 8.0, 7.0 / 8.0, 5.0 / 8.0, 3.0 / 8.0, 4.0 / 8.0};
+  this->run_all_tests(cudf::rank_method::MIN, desc_bottom, col_rank, struct_rank, true);
+}
diff --git a/cpp/tests/sort/segmented_sort_tests.cpp b/cpp/tests/sort/segmented_sort_tests.cpp
new file mode 100644
index 0000000..da9666c
--- /dev/null
+++ b/cpp/tests/sort/segmented_sort_tests.cpp
@@ -0,0 +1,340 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+
+#include <type_traits>
+#include <vector>
+
+template <typename T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T, int>;
+
+template <typename T>
+struct SegmentedSort : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SegmentedSort, cudf::test::NumericTypes);
+using SegmentedSortInt = SegmentedSort<int>;
+
+TEST_F(SegmentedSortInt, Empty)
+{
+  using T = int;
+  column_wrapper<T> col_empty{};
+  // clang-format off
+  column_wrapper<T>       col1{{8, 9, 2, 3, 2, 2, 4, 1, 7, 5, 6}};
+  column_wrapper<int> segments{{0,    2,       5,       8,      11}};
+  // clang-format on
+  cudf::table_view table_empty{{col_empty}};
+  cudf::table_view table_valid{{col1}};
+
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_valid, table_valid, segments));
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_valid, table_valid, col_empty));
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_empty, table_empty, segments));
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_empty, table_empty, col_empty));
+
+  // Swapping "empty" and "valid" tables is invalid because the keys and values will be of different
+  // sizes.
+  EXPECT_THROW(cudf::segmented_sort_by_key(table_empty, table_valid, segments), cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_sort_by_key(table_empty, table_valid, col_empty), cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_sort_by_key(table_valid, table_empty, segments), cudf::logic_error);
+  EXPECT_THROW(cudf::segmented_sort_by_key(table_valid, table_empty, col_empty), cudf::logic_error);
+}
+
+TEST_F(SegmentedSortInt, Single)
+{
+  using T = int;
+  column_wrapper<T> col1{{1}};
+  column_wrapper<T> col3{{8, 9, 2}};
+  column_wrapper<int> segments1{{0}};
+  column_wrapper<int> segments2{{0, 3}};
+  cudf::table_view table_1elem{{col1}};
+  cudf::table_view table_1segm{{col3}};
+
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_1elem, table_1elem, segments1));
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_1segm, table_1segm, segments2));
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(table_1segm, table_1segm, segments1));
+}
+
+TYPED_TEST(SegmentedSort, NoNull)
+{
+  using T = TypeParam;
+
+  // segments             {0   1   2} {3   4} {5} {6   7   8   9  10}{11  12}{13}{14  15}
+  column_wrapper<T> col1{{10, 36, 14, 32, 49, 23, 10, 34, 12, 45, 12, 37, 43, 26, 21, 16}};
+  column_wrapper<T> col2{{10, 63, 41, 23, 94, 32, 10, 43, 21, 54, 22, 73, 34, 62, 12, 61}};
+  // segment sorted order {0   2   1} {3   4} {5}  {6   8  10   7  9}{11  12}{13}{15  16}
+  column_wrapper<int> segments{0, 3, 5, 5, 5, 6, 11, 13, 14, 16};
+  cudf::table_view input1{{col1}};
+  cudf::table_view input2{{col1, col2}};
+
+  // Ascending
+  column_wrapper<T> col1_asc{{10, 14, 36, 32, 49, 23, 10, 12, 12, 34, 45, 37, 43, 26, 16, 21}};
+
+  auto results = cudf::segmented_sort_by_key(input1, input1, segments, {cudf::order::ASCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col1_asc}});
+
+  column_wrapper<T> col1_des{{36, 14, 10, 49, 32, 23, 45, 34, 12, 12, 10, 43, 37, 26, 21, 16}};
+  results = cudf::segmented_sort_by_key(input1, input1, segments, {cudf::order::DESCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col1_des}});
+
+  column_wrapper<T> col1_12_asc{{10, 14, 36, 32, 49, 23, 10, 12, 12, 34, 45, 37, 43, 26, 16, 21}};
+  column_wrapper<T> col2_12_asc{{10, 41, 63, 23, 94, 32, 10, 21, 22, 43, 54, 73, 34, 62, 61, 12}};
+  column_wrapper<T> col2_12_des{{10, 41, 63, 23, 94, 32, 10, 22, 21, 43, 54, 73, 34, 62, 61, 12}};
+
+  cudf::table_view expected12_aa{{col1_12_asc, col2_12_asc}};
+  results = cudf::segmented_sort_by_key(input2, input2, segments, {});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), expected12_aa);
+
+  cudf::table_view expected12_ad{{col1_12_asc, col2_12_des}};
+  results = cudf::segmented_sort_by_key(
+    input2, input2, segments, {cudf::order::ASCENDING, cudf::order::DESCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), expected12_ad);
+}
+
+TYPED_TEST(SegmentedSort, Null)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) return;
+
+  // segments            {0  1  2}{3  4} {5}{6  7  8  9 10}{11  12}{13}{14  15}
+  column_wrapper<T> col1{{1, 3, 2, 4, 5, 23, 6, 8, 7, 9, 7, 37, 43, 26, 21, 16},
+                         {1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1}};
+  column_wrapper<T> col2{{0, 0, 0, 1, 1, 4, 5, 5, 21, 5, 22, 6, 6, 7, 8, 8},
+                         {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1}};
+  column_wrapper<int> segments{0, 3, 5, 5, 5, 6, 11, 13, 14, 16};
+  cudf::table_view input1{{col1}};
+  cudf::table_view input2{{col1, col2}};
+
+  // Ascending
+  column_wrapper<T> col1_aa{{1, 3, 2, 4, 5, 23, 6, 7, 7, 8, 9, 37, 43, 26, 16, 21},
+                            {1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1}};
+  column_wrapper<T> col1_ab{{2, 1, 3, 4, 5, 23, 9, 6, 7, 7, 8, 37, 43, 26, 16, 21},
+                            {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+
+  auto results =
+    cudf::segmented_sort_by_key(input1, input1, segments, {}, {cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col1_aa}});
+  results = cudf::segmented_sort_by_key(input1, input1, segments, {}, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col1_ab}});
+
+  // Descending
+  column_wrapper<T> col1_da{{2, 3, 1, 5, 4, 23, 9, 8, 7, 7, 6, 43, 37, 26, 21, 16},
+                            {0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  column_wrapper<T> col1_db{{3, 1, 2, 5, 4, 23, 8, 7, 7, 6, 9, 43, 37, 26, 21, 16},
+                            {1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1}};
+  results = cudf::segmented_sort_by_key(
+    input1, input1, segments, {cudf::order::DESCENDING}, {cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col1_da}});
+  results = cudf::segmented_sort_by_key(
+    input1, input1, segments, {cudf::order::DESCENDING}, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col1_db}});
+
+  // second row null order.
+  column_wrapper<T> col2_12_aa{{0, 0, 0, 1, 1, 4, 5, 22, 21, 5, 5, 6, 6, 7, 8, 8},
+                               {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1}};
+  column_wrapper<T> col2_12_ab{{0, 0, 0, 1, 1, 4, 5, 5, 21, 22, 5, 6, 6, 7, 8, 8},
+                               {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1}};
+  cudf::table_view expected12_aa{{col1_aa, col2_12_aa}};
+  cudf::table_view expected12_ab{{col1_ab, col2_12_ab}};
+  results = cudf::segmented_sort_by_key(
+    input2, input2, segments, {}, {cudf::null_order::AFTER, cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), expected12_aa);
+  results = cudf::segmented_sort_by_key(
+    input2, input2, segments, {}, {cudf::null_order::BEFORE, cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), expected12_ab);
+}
+
+TYPED_TEST(SegmentedSort, StableNoNulls)
+{
+  using T = TypeParam;
+
+  // segments             {0   1   2} {3   4} {5} {6   7   8   9  10}{11  12}{13}{14  15}
+  column_wrapper<T> col1{{10, 36, 14, 32, 49, 23, 10, 34, 12, 45, 11, 37, 43, 26, 21, 16}};
+  column_wrapper<T> col2{{10, 63, 10, 23, 94, 32, 10, 43, 22, 43, 22, 34, 34, 62, 62, 61}};
+  // stable sorted order  {0   2   1} {3   4} {5} {6   8  10   7   9}{11  12}{13}{16  15}
+  column_wrapper<int> segments{0, 3, 5, 5, 5, 6, 11, 13, 14, 16};
+  auto values = cudf::table_view{{col1}};
+  auto keys   = cudf::table_view{{col2}};
+
+  // Ascending
+  column_wrapper<T> col_asc{{10, 14, 36, 32, 49, 23, 10, 12, 11, 34, 45, 37, 43, 26, 16, 21}};
+  auto results =
+    cudf::stable_segmented_sort_by_key(values, keys, segments, {cudf::order::ASCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col_asc}});
+  // Descending
+  column_wrapper<T> col_des{{36, 10, 14, 49, 32, 23, 34, 45, 12, 11, 10, 37, 43, 26, 21, 16}};
+  results = cudf::stable_segmented_sort_by_key(values, keys, segments, {cudf::order::DESCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col_des}});
+}
+
+TYPED_TEST(SegmentedSort, StableWithNulls)
+{
+  using T = TypeParam;
+
+  // segments             {0   1   2} {3   4} {5} {6   7   8   9  10}{11  12}{13}{14  15}
+  column_wrapper<T> col1{{10, 36, 0, 32, 49, 23, 10, 0, 12, 45, 11, 37, 43, 0, 21, 16},
+                         {1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1}};
+  column_wrapper<T> col2{{10, 0, 10, 23, 94, 32, 0, 43, 0, 43, 0, 34, 34, 62, 62, 61},
+                         {1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1}};
+  // stable sorted order  {0   2   1} {3   4} {5} {6   8  10   7   9}{11  12}{13}{16  15}
+  column_wrapper<int> segments{0, 3, 5, 5, 5, 6, 11, 13, 14, 16};
+  auto values = cudf::table_view{{col1}};
+  auto keys   = cudf::table_view{{col2}};
+
+  // Ascending
+  column_wrapper<T> col_asc{{36, 10, 0, 32, 49, 23, 10, 12, 11, 0, 45, 37, 43, 0, 16, 21},
+                            {1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1}};
+  auto results =
+    cudf::stable_segmented_sort_by_key(values, keys, segments, {cudf::order::ASCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col_asc}});
+
+  // Descending
+  column_wrapper<T> col_des{{10, 0, 36, 49, 32, 23, 0, 45, 12, 11, 10, 37, 43, 0, 21, 16},
+                            {1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1}};
+  results = cudf::stable_segmented_sort_by_key(values, keys, segments, {cudf::order::DESCENDING});
+  CUDF_TEST_EXPECT_TABLES_EQUAL(results->view(), cudf::table_view{{col_des}});
+}
+
+TEST_F(SegmentedSortInt, NonZeroSegmentsStart)
+{
+  using T = int;
+  // clang-format off
+  column_wrapper<T>        col1{{8, 9, 2, 3, 2, 2, 4, 1, 7, 5, 6}};
+  column_wrapper<int> segments1{{0,    2,       5,       8,     11}};
+  column_wrapper<int> segments2{{      2,       5,       8,      11}};
+  column_wrapper<int> segments3{{                  6,    8,      11}};
+  column_wrapper<int> segments4{{                  6,    8}};
+  column_wrapper<int> segments5{{0,       3,       6}};
+  column_wrapper<int> expected1{{0, 1, 2, 4, 3, 7, 5, 6, 9, 10, 8}};
+  column_wrapper<int> expected2{{0, 1, 2, 4, 3, 7, 5, 6, 9, 10, 8}};
+  column_wrapper<int> expected3{{0, 1, 2, 3, 4, 5, 7, 6, 9, 10, 8}};
+  column_wrapper<int> expected4{{0, 1, 2, 3, 4, 5, 7, 6, 8, 9, 10}};
+  column_wrapper<int> expected5{{2, 0, 1, 4, 5, 3, 6, 7, 8, 9, 10}};
+  // clang-format on
+  cudf::table_view input{{col1}};
+  auto results = cudf::segmented_sorted_order(input, segments1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected1);
+  results = cudf::stable_segmented_sorted_order(input, segments1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected1);
+
+  results = cudf::segmented_sorted_order(input, segments2);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected2);
+  results = cudf::stable_segmented_sorted_order(input, segments2);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected2);
+
+  results = cudf::segmented_sorted_order(input, segments3);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected3);
+  results = cudf::stable_segmented_sorted_order(input, segments3);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected3);
+
+  results = cudf::segmented_sorted_order(input, segments4);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected4);
+  results = cudf::stable_segmented_sorted_order(input, segments4);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected4);
+
+  results = cudf::segmented_sorted_order(input, segments5);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected5);
+  results = cudf::stable_segmented_sorted_order(input, segments5);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected5);
+}
+
+TEST_F(SegmentedSortInt, Sliced)
+{
+  using T = int;
+  // clang-format off
+  column_wrapper<T>        col1{{8, 9, 2, 3, 2, 2, 4, 1, 7, 5, 6}};
+  // sliced                                  2, 2, 4, 1, 7, 5, 6
+  column_wrapper<int> segments1{{0,    2,       5}};
+  column_wrapper<int> segments2{{-4,   0,      2,       5}};
+  column_wrapper<int> segments3{{                 7}};
+  column_wrapper<int> expected1{{0, 1, 3, 2, 4, 5, 6}};
+  column_wrapper<int> expected2{{0, 1, 3, 2, 4, 5, 6}};
+  column_wrapper<int> expected3{{0, 1, 2, 3, 4, 5, 6}};
+  // clang-format on
+  auto slice = cudf::slice(col1, {4, 11})[0];  // 7 elements
+  cudf::table_view input{{slice}};
+  auto seg_slice = cudf::slice(segments2, {2, 4})[0];  // 2 elements
+
+  // sliced input
+  auto results = cudf::segmented_sorted_order(input, segments1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected1);
+  results = cudf::stable_segmented_sorted_order(input, segments1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected1);
+  // sliced input and sliced segment
+  results = cudf::segmented_sorted_order(input, seg_slice);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected2);
+  results = cudf::stable_segmented_sorted_order(input, seg_slice);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected2);
+  // sliced input, segment end.
+  results = cudf::segmented_sorted_order(input, segments3);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected3);
+  results = cudf::stable_segmented_sorted_order(input, segments3);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected3);
+}
+
+TEST_F(SegmentedSortInt, ErrorsMismatchArgSizes)
+{
+  using T = int;
+  column_wrapper<T> col1{{5, 6, 7, 8, 9}};
+  column_wrapper<T> segments{{1, 2, 3, 4}};
+  cudf::table_view input1{{col1}};
+  std::vector<cudf::order> order{cudf::order::ASCENDING, cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_order{cudf::null_order::AFTER, cudf::null_order::AFTER};
+
+  // Mismatch order sizes
+  EXPECT_THROW(cudf::segmented_sort_by_key(input1, input1, segments, order, {}), cudf::logic_error);
+  EXPECT_THROW(cudf::stable_segmented_sorted_order(input1, segments, order, {}), cudf::logic_error);
+  // Mismatch null precedence sizes
+  EXPECT_THROW(cudf::segmented_sorted_order(input1, segments, {}, null_order), cudf::logic_error);
+  EXPECT_THROW(cudf::stable_segmented_sort_by_key(input1, input1, segments, {}, null_order),
+               cudf::logic_error);
+  // Both
+  EXPECT_THROW(cudf::segmented_sort_by_key(input1, input1, segments, order, null_order),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::stable_segmented_sort_by_key(input1, input1, segments, order, null_order),
+               cudf::logic_error);
+  // segmented_offsets beyond num_rows - undefined behavior, no throw.
+  CUDF_EXPECT_NO_THROW(cudf::segmented_sort_by_key(input1, input1, segments));
+  CUDF_EXPECT_NO_THROW(cudf::stable_segmented_sort_by_key(input1, input1, segments));
+}
+
+// Test specifically verifies the patch added in https://github.com/rapidsai/cudf/pull/12234
+// This test will fail if the CUB bug fix is not available or the patch has not been applied.
+TEST_F(SegmentedSortInt, Bool)
+{
+  cudf::test::fixed_width_column_wrapper<bool> col1{
+    {true,  false, false, true, true,  true,  true, true, true,  true, true,  true, true, false,
+     false, false, false, true, false, false, true, true, true,  true, true,  true, true, false,
+     true,  false, true,  true, true,  true,  true, true, false, true, false, false}};
+
+  cudf::test::fixed_width_column_wrapper<int> segments{{0, 5, 10, 15, 20, 25, 30, 40}};
+
+  cudf::test::fixed_width_column_wrapper<int> expected(
+    {1,  2,  0,  3,  4,  5,  6,  7,  8,  9,  13, 14, 10, 11, 12, 15, 16, 18, 19, 17,
+     20, 21, 22, 23, 24, 27, 29, 25, 26, 28, 36, 38, 39, 30, 31, 32, 33, 34, 35, 37});
+
+  auto test_col = cudf::column_view{col1};
+  auto result   = cudf::segmented_sorted_order(cudf::table_view({test_col}), segments);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+  result = cudf::stable_segmented_sorted_order(cudf::table_view({test_col}), segments);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+}
diff --git a/cpp/tests/sort/sort_nested_types_tests.cpp b/cpp/tests/sort/sort_nested_types_tests.cpp
new file mode 100644
index 0000000..8ab2393
--- /dev/null
+++ b/cpp/tests/sort/sort_nested_types_tests.cpp
@@ -0,0 +1,463 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+
+using int32s_lists = cudf::test::lists_column_wrapper<int32_t>;
+using int32s_col   = cudf::test::fixed_width_column_wrapper<int32_t>;
+using strings_col  = cudf::test::strings_column_wrapper;
+using structs_col  = cudf::test::structs_column_wrapper;
+
+using namespace cudf::test::iterators;
+
+constexpr auto null{0};
+
+struct NestedStructTest : public cudf::test::BaseFixture {};
+
+TEST_F(NestedStructTest, SimpleStructsOfListsNoNulls)
+{
+  auto const input = [] {
+    auto child = int32s_lists{{4, 2, 0}, {2}, {0, 5}, {1, 5}, {4, 1}};
+    return structs_col{{child}};
+  }();
+
+  {
+    auto const expected_order = int32s_col{2, 3, 1, 4, 0};
+    auto const order          = cudf::sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{0, 4, 1, 3, 2};
+    auto const order = cudf::sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, SimpleStructsOfListsWithNulls)
+{
+  auto const input = [] {
+    auto child =
+      int32s_lists{{{4, 2, null}, null_at(2)}, {2}, {{null, 5}, null_at(0)}, {0, 5}, {4, 1}};
+    return structs_col{{child}};
+  }();
+
+  {
+    auto const expected_order = int32s_col{2, 3, 1, 4, 0};
+    auto const order          = cudf::sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{0, 4, 1, 3, 2};
+    auto const order = cudf::sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, StructsHaveListsNoNulls)
+{
+  // Input has equal elements, thus needs to be tested by stable sort.
+  auto const input = [] {
+    auto child0 = int32s_lists{{4, 2, 0}, {}, {5}, {4, 1}, {4, 0}, {}, {}};
+    auto child1 = int32s_col{1, 2, 5, 0, 3, 3, 4};
+    return structs_col{{child0, child1}};
+  }();
+
+  {
+    auto const expected_order = int32s_col{1, 5, 6, 4, 3, 0, 2};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{2, 0, 3, 4, 6, 5, 1};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, StructsHaveListsWithNulls)
+{
+  // Input has equal elements, thus needs to be tested by stable sort.
+  auto const input = [] {
+    auto child0 =
+      int32s_lists{{{4, 2, null}, null_at(2)}, {}, {} /*NULL*/, {5}, {4, 1}, {4, 0}, {}, {}};
+    auto child1 = int32s_col{{1, 2, null, 5, null, 3, 3, 4}, nulls_at({2, 4})};
+    return structs_col{{child0, child1}, null_at(2)};
+  }();
+
+  {
+    auto const expected_order = int32s_col{2, 1, 6, 7, 5, 4, 0, 3};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{3, 0, 4, 5, 7, 6, 1, 2};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, SlicedStructsHaveListsNoNulls)
+{
+  // Input has equal elements, thus needs to be tested by stable sort.
+  // The original input has 3 first elements repeated at the beginning and the end.
+  auto const input_original = [] {
+    auto child0 = int32s_lists{
+      {4, 2, 0}, {}, {5}, {4, 2, 0}, {}, {5}, {4, 1}, {4, 0}, {}, {}, {4, 2, 0}, {}, {5}};
+    auto child1 = int32s_col{1, 2, 5, 1, 2, 5, 0, 3, 3, 4, 1, 2, 5};
+    return structs_col{{child0, child1}};
+  }();
+
+  auto const input = cudf::slice(input_original, {3, 10})[0];
+
+  {
+    auto const expected_order = int32s_col{1, 5, 6, 4, 3, 0, 2};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{2, 0, 3, 4, 6, 5, 1};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, SlicedStructsHaveListsWithNulls)
+{
+  // Input has equal elements, thus needs to be tested by stable sort.
+  // The original input has 2 first elements repeated at the beginning and the end.
+  auto const input_original = [] {
+    auto child0 = int32s_lists{{{4, 2, null}, null_at(2)},
+                               {},
+                               {{4, 2, null}, null_at(2)},
+                               {},
+                               {} /*NULL*/,
+                               {5},
+                               {4, 1},
+                               {4, 0},
+                               {},
+                               {},
+                               {{4, 2, null}, null_at(2)},
+                               {}};
+    auto child1 = int32s_col{{1, 2, 1, 2, null, 5, null, 3, 3, 4, 1, 2}, nulls_at({4, 6})};
+    return structs_col{{child0, child1}, null_at(4)};
+  }();
+
+  auto const input = cudf::slice(input_original, {2, 10})[0];
+
+  {
+    auto const expected_order = int32s_col{2, 1, 6, 7, 5, 4, 0, 3};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{3, 0, 4, 5, 7, 6, 1, 2};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, StructsOfStructsHaveListsNoNulls)
+{
+  // Input has equal elements, thus needs to be tested by stable sort.
+  auto const input = [] {
+    auto child0 = [] {
+      auto child0 = int32s_lists{{4, 2, 0}, {}, {5}, {4, 1}, {4, 0}, {}, {}};
+      auto child1 = int32s_col{1, 2, 5, 0, 3, 3, 4};
+      return structs_col{{child0, child1}};
+    }();
+    auto child1 = int32s_lists{{4, 2, 0}, {}, {5}, {4, 1}, {4, 0}, {}, {}};
+    auto child2 = int32s_col{1, 2, 5, 0, 3, 3, 4};
+    return structs_col{{child0, child1, child2}};
+  }();
+
+  {
+    auto const expected_order = int32s_col{1, 5, 6, 4, 3, 0, 2};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{2, 0, 3, 4, 6, 5, 1};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, StructsOfStructsHaveListsWithNulls)
+{
+  // Input has equal elements, thus needs to be tested by stable sort.
+  auto const input = [] {
+    auto child0 = [] {
+      auto child0 =
+        int32s_lists{{{4, 2, null}, null_at(2)}, {}, {} /*NULL*/, {5}, {4, 1}, {4, 0}, {}, {}};
+      auto child1 = int32s_col{{1, 2, null, 5, null, 3, 3, 4}, nulls_at({2, 4})};
+      return structs_col{{child0, child1}, null_at(2)};
+    }();
+    auto child1 =
+      int32s_lists{{{4, 2, null}, null_at(2)}, {}, {} /*NULL*/, {5}, {4, 1}, {4, 0}, {}, {}};
+    auto child2 = int32s_col{{1, 2, null, 5, null, 3, 3, 4}, nulls_at({2, 4})};
+    return structs_col{{child0, child1, child2}, null_at(2)};
+  }();
+
+  {
+    auto const expected_order = int32s_col{2, 1, 6, 7, 5, 4, 0, 3};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{3, 0, 4, 5, 7, 6, 1, 2};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedStructTest, SimpleStructsOfListsOfStructsNoNulls)
+{
+  auto const input = [] {
+    auto const make_lists_of_structs = [] {
+      auto const get_structs = [] {
+        auto child0 = int32s_col{3, 2, 3, 3, 4, 2, 4, 4, 1, 0, 3, 0, 2, 5, 4};
+        auto child1 = int32s_col{0, 4, 3, 2, 1, 1, 5, 1, 5, 5, 4, 2, 4, 1, 3};
+        return structs_col{{child0, child1}};
+      };
+      return cudf::make_lists_column(
+        8, int32s_col{0, 3, 5, 6, 6, 8, 10, 12, 15}.release(), get_structs().release(), 0, {});
+    };
+
+    std::vector<std::unique_ptr<cudf::column>> children;
+    children.emplace_back(make_lists_of_structs());
+    children.emplace_back(make_lists_of_structs());
+
+    return cudf::make_structs_column(8, std::move(children), 0, {});
+  }();
+
+  {
+    auto const expected_order = int32s_col{3, 5, 2, 7, 0, 1, 6, 4};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{*input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{4, 6, 1, 0, 7, 2, 5, 3};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{*input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+struct NestedListTest : public cudf::test::BaseFixture {};
+
+TEST_F(NestedListTest, SimpleListsOfStructsNoNulls)
+{
+  auto const input = [] {
+    auto const get_structs = [] {
+      auto child0 = int32s_col{3, 2, 3, 3, 4, 2, 4, 4, 1, 0, 3, 0, 2, 5, 4};
+      auto child1 = int32s_col{0, 4, 3, 2, 1, 1, 5, 1, 5, 5, 4, 2, 4, 1, 3};
+      return structs_col{{child0, child1}};
+    };
+    return cudf::make_lists_column(
+      8, int32s_col{0, 3, 5, 6, 6, 8, 10, 12, 15}.release(), get_structs().release(), 0, {});
+  }();
+
+  {
+    auto const expected_order = int32s_col{3, 5, 2, 7, 0, 1, 6, 4};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{*input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{4, 6, 1, 0, 7, 2, 5, 3};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{*input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedListTest, SlicedListsOfStructsNoNulls)
+{
+  auto const input_original = [] {
+    auto const get_structs = [] {
+      auto child0 = int32s_col{0, 0, 3, 2, 3, 3, 4, 2, 4, 4, 1, 0, 3, 0, 2, 5, 4, 0};
+      auto child1 = int32s_col{0, 0, 0, 4, 3, 2, 1, 1, 5, 1, 5, 5, 4, 2, 4, 1, 3, 0};
+      return structs_col{{child0, child1}};
+    };
+    return cudf::make_lists_column(11,
+                                   int32s_col{0, 1, 2, 5, 7, 8, 8, 10, 12, 14, 17, 18}.release(),
+                                   get_structs().release(),
+                                   0,
+                                   {});
+  }();
+  auto const input = cudf::slice(*input_original, {2, 10})[0];
+
+  {
+    auto const expected_order = int32s_col{3, 5, 2, 7, 0, 1, 6, 4};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{4, 6, 1, 0, 7, 2, 5, 3};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedListTest, ListsOfEqualStructsNoNulls)
+{
+  auto const input = [] {
+    auto const get_structs = [] {
+      auto child0 = int32s_col{0, 3, 0, 1};
+      auto child1 = strings_col{"a", "c", "a", "b"};
+      return structs_col{{child0, child1}};
+    };
+    return cudf::make_lists_column(
+      2, int32s_col{0, 2, 4}.release(), get_structs().release(), 0, {});
+  }();
+
+  {
+    auto const expected_order = int32s_col{1, 0};
+    auto const order          = cudf::sorted_order(cudf::table_view{{*input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{0, 1};
+    auto const order = cudf::sorted_order(cudf::table_view{{*input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedListTest, SimpleListsOfStructsWithNulls)
+{
+  // [ {null, 2},    {null, null}, {1, 2} ]     | 0
+  // []                                         | 1
+  // [ {null, null}, {4, 2} ]                   | 2
+  // []                                         | 3
+  // [ {3, 5},       {null, 4}            ]     | 4
+  // []                                         | 5
+  // [ {5, 3},       {5, 0},       {1, 1} ]     | 6
+  // [ {null, 3},    {5, 2},       {4, 2} ]     | 7
+  auto const input = [] {
+    auto const get_structs = [] {
+      auto child0 = int32s_col{{null, null, 1, null, 4, 3, null, 5, 5, 1, null, 5, 4},
+                               nulls_at({0, 1, 3, 6, 10})};
+      auto child1 = int32s_col{{2, null, 2, null, 2, 5, 4, 3, 0, 1, 3, 2, 2}, nulls_at({1, 3})};
+      return structs_col{{child0, child1}, nulls_at({1, 3})};
+    };
+    return cudf::make_lists_column(
+      8, int32s_col{0, 3, 3, 5, 5, 7, 7, 10, 13}.release(), get_structs().release(), 0, {});
+  }();
+
+  {
+    auto const expected_order = int32s_col{1, 3, 5, 2, 0, 7, 4, 6};
+    auto const order          = cudf::stable_sorted_order(
+      cudf::table_view{{*input}}, {cudf::order::ASCENDING}, {cudf::null_order::BEFORE});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{6, 4, 7, 0, 2, 1, 3, 5};
+    auto const order          = cudf::stable_sorted_order(
+      cudf::table_view{{*input}}, {cudf::order::DESCENDING}, {cudf::null_order::BEFORE});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{1, 3, 5, 2, 4, 6, 0, 7};
+    auto const order          = cudf::stable_sorted_order(
+      cudf::table_view{{*input}}, {cudf::order::ASCENDING}, {cudf::null_order::AFTER});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{7, 0, 6, 4, 2, 1, 3, 5};
+    auto const order          = cudf::stable_sorted_order(
+      cudf::table_view{{*input}}, {cudf::order::DESCENDING}, {cudf::null_order::AFTER});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedListTest, ListsOfListsOfStructsNoNulls)
+{
+  auto const input = [] {
+    auto const get_structs = [] {
+      auto child0 = int32s_col{0, 7, 4, 9, 2, 9, 4, 1, 5, 5, 3, 7, 0, 6, 3, 1, 9};
+      auto child1 = int32s_col{4, 6, 7, 3, 1, 2, 1, 10, 7, 9, 8, 7, 1, 10, 5, 3, 3};
+      return structs_col{{child0, child1}};
+    };
+    auto lists_of_structs =
+      cudf::make_lists_column(13,
+                              int32s_col{0, 1, 3, 4, 5, 7, 9, 10, 12, 12, 14, 15, 17, 17}.release(),
+                              get_structs().release(),
+                              0,
+                              {});
+    return cudf::make_lists_column(
+      8, int32s_col{0, 3, 4, 6, 6, 8, 10, 10, 13}.release(), std::move(lists_of_structs), 0, {});
+  }();
+
+  {
+    auto const expected_order = int32s_col{3, 6, 5, 0, 1, 7, 4, 2};
+    auto const order          = cudf::stable_sorted_order(cudf::table_view{{*input}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+
+  {
+    auto const expected_order = int32s_col{2, 4, 7, 1, 0, 5, 3, 6};
+    auto const order =
+      cudf::stable_sorted_order(cudf::table_view{{*input}}, {cudf::order::DESCENDING});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+  }
+}
+
+TEST_F(NestedListTest, MultipleListsColumnsWithNulls)
+{
+  // A STRUCT<LIST<INT>> column with all nulls.
+  auto const col0 = [] {
+    auto child = int32s_lists{{int32s_lists{}, int32s_lists{}}, all_nulls()};
+    return structs_col{{child}, all_nulls()};
+  }();
+
+  auto const col1 = [] {
+    auto child = int32s_lists{{0, 1, 2}, {10, 11, 12}};
+    return structs_col{{child}};
+  }();
+
+  auto const col2 = int32s_col{1, 0};
+
+  auto const expected_order = int32s_col{0, 1};
+  auto const order          = cudf::sorted_order(cudf::table_view{{col0, col1, col2}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_order, order->view());
+}
diff --git a/cpp/tests/sort/sort_test.cpp b/cpp/tests/sort/sort_test.cpp
new file mode 100644
index 0000000..e0828b7
--- /dev/null
+++ b/cpp/tests/sort/sort_test.cpp
@@ -0,0 +1,1116 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/sort.h>
+
+#include <type_traits>
+#include <vector>
+
+void run_sort_test(cudf::table_view input,
+                   cudf::column_view expected_sorted_indices,
+                   std::vector<cudf::order> column_order         = {},
+                   std::vector<cudf::null_order> null_precedence = {})
+{
+  // Sorted table
+  auto got_sorted_table      = cudf::sort(input, column_order, null_precedence);
+  auto expected_sorted_table = cudf::gather(input, expected_sorted_indices);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sorted_table->view(), got_sorted_table->view());
+
+  // Sorted by key
+  auto got_sort_by_key_table      = cudf::sort_by_key(input, input, column_order, null_precedence);
+  auto expected_sort_by_key_table = cudf::gather(input, expected_sorted_indices);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort_by_key_table->view(), got_sort_by_key_table->view());
+}
+
+using TestTypes = cudf::test::Concat<cudf::test::NumericTypes,  // include integers, floats and bool
+                                     cudf::test::ChronoTypes>;  // include timestamps and durations
+
+template <typename T>
+struct Sort : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(Sort, TestTypes);
+
+TYPED_TEST(Sort, WithNullMax)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8, 5}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k", "d"}, {1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{1, 0, 5, 3, 4, 2}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{
+    cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER};
+
+  // Sorted order
+  auto got = cudf::sorted_order(input, column_order, null_precedence);
+
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    // Run test for sort and sort_by_key
+    run_sort_test(input, expected, column_order, null_precedence);
+  } else {
+    // for bools only validate that the null element landed at the back, since
+    // the rest of the values are equivalent and yields random sorted order.
+    auto to_host = [](cudf::column_view const& col) {
+      thrust::host_vector<int32_t> h_data(col.size());
+      CUDF_CUDA_TRY(cudaMemcpy(
+        h_data.data(), col.data<int32_t>(), h_data.size() * sizeof(int32_t), cudaMemcpyDefault));
+      return h_data;
+    };
+    thrust::host_vector<int32_t> h_exp = to_host(expected);
+    thrust::host_vector<int32_t> h_got = to_host(got->view());
+    EXPECT_EQ(h_exp[h_exp.size() - 1], h_got[h_got.size() - 1]);
+
+    // Run test for sort and sort_by_key
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{0, 3, 5, 1, 4, 2}};
+    run_sort_test(input, expected_for_bool, column_order, null_precedence);
+  }
+}
+
+TYPED_TEST(Sort, WithNullMin)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}, {1, 1, 0, 1, 1}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"}, {1, 1, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}, {1, 1, 0, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 1, 0, 3, 4}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING};
+
+  auto got = cudf::sorted_order(input, column_order);
+
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    // Run test for sort and sort_by_key
+    run_sort_test(input, expected, column_order);
+  } else {
+    // for bools only validate that the null element landed at the front, since
+    // the rest of the values are equivalent and yields random sorted order.
+    auto to_host = [](cudf::column_view const& col) {
+      thrust::host_vector<int32_t> h_data(col.size());
+      CUDF_CUDA_TRY(cudaMemcpy(
+        h_data.data(), col.data<int32_t>(), h_data.size() * sizeof(int32_t), cudaMemcpyDefault));
+      return h_data;
+    };
+    thrust::host_vector<int32_t> h_exp = to_host(expected);
+    thrust::host_vector<int32_t> h_got = to_host(got->view());
+    EXPECT_EQ(h_exp.front(), h_got.front());
+
+    // Run test for sort and sort_by_key
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{2, 0, 3, 1, 4}};
+    run_sort_test(input, expected_for_bool, column_order);
+  }
+}
+
+TYPED_TEST(Sort, WithMixedNullOrder)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}, {0, 0, 1, 1, 0}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"}, {0, 1, 0, 0, 1});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}, {1, 0, 1, 0, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 3, 0, 1, 4}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence{
+    cudf::null_order::AFTER, cudf::null_order::BEFORE, cudf::null_order::AFTER};
+
+  auto got = cudf::sorted_order(input, column_order, null_precedence);
+
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+  } else {
+    // for bools only validate that the null element landed at the front, since
+    // the rest of the values are equivalent and yields random sorted order.
+    auto to_host = [](cudf::column_view const& col) {
+      thrust::host_vector<int32_t> h_data(col.size());
+      CUDF_CUDA_TRY(cudaMemcpy(
+        h_data.data(), col.data<int32_t>(), h_data.size() * sizeof(int32_t), cudaMemcpyDefault));
+      return h_data;
+    };
+    thrust::host_vector<int32_t> h_exp = to_host(expected);
+    thrust::host_vector<int32_t> h_got = to_host(got->view());
+    EXPECT_EQ(h_exp.front(), h_got.front());
+  }
+
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected, column_order, null_precedence);
+}
+
+TYPED_TEST(Sort, WithAllValid)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 1, 0, 3, 4}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING};
+
+  auto got = cudf::sorted_order(input, column_order);
+
+  // Skip validating bools order. Valid true bools are all
+  // equivalent, and yield random order after thrust::sort
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    // Run test for sort and sort_by_key
+    run_sort_test(input, expected, column_order);
+  } else {
+    // Run test for sort and sort_by_key
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{2, 0, 3, 1, 4}};
+    run_sort_test(input, expected_for_bool, column_order);
+  }
+}
+
+TYPED_TEST(Sort, WithStructColumn)
+{
+  using T = TypeParam;
+
+  std::initializer_list<std::string> names = {"Samuel Vimes",
+                                              "Carrot Ironfoundersson",
+                                              "Angua von Überwald",
+                                              "Cheery Littlebottom",
+                                              "Detritus",
+                                              "Mr Slant"};
+  auto num_rows{std::distance(names.begin(), names.end())};
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+  auto ages_col  = cudf::test::fixed_width_column_wrapper<T, int32_t>{{48, 27, 25, 31, 351, 351}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_col =
+    cudf::test::structs_column_wrapper{{names_col, ages_col, is_human_col}}.release();
+  auto struct_col_view{struct_col->view()};
+  EXPECT_EQ(num_rows, struct_col->size());
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8, 9}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k", "a"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2, 20}};
+  cudf::table_view input{{col1, col2, col3, struct_col_view}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 1, 0, 3, 4, 5}};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING,
+                                        cudf::order::ASCENDING,
+                                        cudf::order::DESCENDING,
+                                        cudf::order::ASCENDING};
+
+  auto got = cudf::sorted_order(input, column_order);
+
+  // Skip validating bools order. Valid true bools are all
+  // equivalent, and yield random order after thrust::sort
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    // Run test for sort and sort_by_key
+    run_sort_test(input, expected, column_order);
+  } else {
+    // Run test for sort and sort_by_key
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{2, 5, 3, 0, 1, 4}};
+    run_sort_test(input, expected_for_bool, column_order);
+  }
+}
+
+TYPED_TEST(Sort, WithNestedStructColumn)
+{
+  using T = TypeParam;
+
+  std::initializer_list<std::string> names = {"Samuel Vimes",
+                                              "Carrot Ironfoundersson",
+                                              "Angua von Überwald",
+                                              "Cheery Littlebottom",
+                                              "Detritus",
+                                              "Mr Slant"};
+  std::vector<bool> v{1, 1, 0, 1, 1, 0};
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+  auto ages_col  = cudf::test::fixed_width_column_wrapper<T, int32_t>{{48, 27, 25, 31, 351, 351}};
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+  auto struct_col1 = cudf::test::structs_column_wrapper{{names_col, ages_col, is_human_col}, v};
+
+  auto ages_col2   = cudf::test::fixed_width_column_wrapper<T, int32_t>{{48, 27, 25, 31, 351, 351}};
+  auto struct_col2 = cudf::test::structs_column_wrapper{{ages_col2, struct_col1}}.release();
+
+  auto struct_col_view{struct_col2->view()};
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{6, 6, 6, 6, 6, 6}};
+  cudf::test::fixed_width_column_wrapper<T> col2{{1, 1, 1, 2, 2, 2}};
+  cudf::table_view input{{col1, col2, struct_col_view}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{3, 5, 4, 2, 1, 0}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::DESCENDING, cudf::order::ASCENDING};
+
+  auto got = cudf::sorted_order(input, column_order);
+
+  // Skip validating bools order. Valid true bools are all
+  // equivalent, and yield random order after thrust::sort
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    // Run test for sort and sort_by_key
+    run_sort_test(input, expected, column_order);
+  } else {
+    // Run test for sort and sort_by_key
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{2, 5, 1, 3, 4, 0}};
+    run_sort_test(input, expected_for_bool, column_order);
+  }
+}
+
+TYPED_TEST(Sort, WithNullableStructColumn)
+{
+  // Test for a struct column that has nulls on struct layer but not pushed down on the child
+  using T    = int;
+  using fwcw = cudf::test::fixed_width_column_wrapper<T>;
+  using mask = std::vector<bool>;
+
+  auto make_struct = [&](std::vector<std::unique_ptr<cudf::column>> child_cols,
+                         std::vector<bool> nulls) {
+    cudf::test::structs_column_wrapper struct_col(std::move(child_cols));
+    auto struct_                 = struct_col.release();
+    auto [null_mask, null_count] = cudf::test::detail::make_null_mask(nulls.begin(), nulls.end());
+    struct_->set_null_mask(std::move(null_mask), null_count);
+    return struct_;
+  };
+
+  {
+    /*
+         /+-------------+
+         |s1{s2{a,b}, c}|
+         +--------------+
+       0 |  { {1, 1}, 5}|
+       1 |  { {1, 2}, 4}|
+       2 |  {@{2, 1}, 6}|
+       3 |  {@{2, 2}, 5}|
+       4 | @{ {2, 2}, 3}|
+       5 | @{ {1, 1}, 3}|
+       6 |  { {1, 2}, 3}|
+       7 |  {@{1, 1}, 4}|
+       8 |  { {2, 1}, 5}|
+         +--------------+
+
+      Intermediate representation:
+      s1{s2{a}}, b, c
+    */
+
+    auto col_a   = fwcw{1, 1, 2, 2, 2, 1, 1, 1, 2};
+    auto col_b   = fwcw{1, 2, 1, 2, 2, 1, 2, 1, 1};
+    auto s2_mask = mask{1, 1, 0, 0, 1, 1, 1, 0, 1};
+    auto col_c   = fwcw{5, 4, 6, 5, 3, 3, 3, 4, 5};
+    auto s1_mask = mask{1, 1, 1, 1, 0, 0, 1, 1, 1};
+
+    std::vector<std::unique_ptr<cudf::column>> s2_children;
+    s2_children.push_back(col_a.release());
+    s2_children.push_back(col_b.release());
+    auto s2 = make_struct(std::move(s2_children), s2_mask);
+
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.push_back(std::move(s2));
+    s1_children.push_back(col_c.release());
+    auto s1 = make_struct(std::move(s1_children), s1_mask);
+
+    auto expect = fwcw{4, 5, 7, 3, 2, 0, 6, 1, 8};
+    run_sort_test(cudf::table_view({s1->view()}), expect);
+  }
+  { /*
+        /+-------------+
+        |s1{a,s2{b, c}}|
+        +--------------+
+      0 |  {1,  {1, 5}}|
+      1 |  {1,  {2, 4}}|
+      2 |  {2, @{2, 6}}|
+      3 |  {2, @{1, 5}}|
+      4 | @{2,  {2, 3}}|
+      5 | @{1,  {1, 3}}|
+      6 |  {1,  {2, 3}}|
+      7 |  {1, @{1, 4}}|
+      8 |  {2,  {1, 5}}|
+        +--------------+
+
+     Intermediate representation:
+     s1{a}, s2{b}, c
+   */
+
+    auto s1_mask = mask{1, 1, 1, 1, 0, 0, 1, 1, 1};
+    auto col_a   = fwcw{1, 1, 2, 2, 2, 1, 1, 1, 2};
+    auto s2_mask = mask{1, 1, 0, 0, 1, 1, 1, 0, 1};
+    auto col_b   = fwcw{1, 2, 1, 2, 2, 1, 2, 1, 1};
+    auto col_c   = fwcw{5, 4, 6, 5, 3, 3, 3, 4, 5};
+
+    std::vector<std::unique_ptr<cudf::column>> s22_children;
+    s22_children.push_back(col_b.release());
+    s22_children.push_back(col_c.release());
+    auto s22 = make_struct(std::move(s22_children), s2_mask);
+
+    std::vector<std::unique_ptr<cudf::column>> s12_children;
+    s12_children.push_back(col_a.release());
+    s12_children.push_back(std::move(s22));
+    auto s12 = make_struct(std::move(s12_children), s1_mask);
+
+    auto expect = fwcw{4, 5, 7, 0, 6, 1, 2, 3, 8};
+    run_sort_test(cudf::table_view({s12->view()}), expect);
+  }
+}
+
+TYPED_TEST(Sort, WithSingleStructColumn)
+{
+  using T = TypeParam;
+
+  std::initializer_list<std::string> names = {"Samuel Vimes",
+                                              "Carrot Ironfoundersson",
+                                              "Angua von Überwald",
+                                              "Cheery Littlebottom",
+                                              "Detritus",
+                                              "Mr Slant"};
+  std::vector<bool> v{1, 1, 0, 1, 1, 0};
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+  auto ages_col  = cudf::test::fixed_width_column_wrapper<T, int32_t>{{48, 27, 25, 31, 351, 351}};
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+  auto struct_col =
+    cudf::test::structs_column_wrapper{{names_col, ages_col, is_human_col}, v}.release();
+  auto struct_col_view{struct_col->view()};
+  cudf::table_view input{{struct_col_view}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 5, 1, 3, 4, 0}};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+
+  auto got = cudf::sorted_order(input, column_order);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected, column_order);
+}
+
+TYPED_TEST(Sort, WithSlicedStructColumn)
+{
+  using T = TypeParam;
+  /*
+       /+-------------+
+       |             s|
+       +--------------+
+     0 | {"bbe", 1, 7}|
+     1 | {"bbe", 1, 8}|
+     2 | {"aaa", 0, 1}|
+     3 | {"abc", 0, 1}|
+     4 | {"ab",  0, 9}|
+     5 | {"za",  2, 5}|
+     6 | {"b",   1, 7}|
+     7 | { @,    3, 3}|
+       +--------------+
+  */
+  // clang-format off
+  using FWCW = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+  std::vector<bool>             string_valids{    1,     1,     1,     1,    1,    1,   1,   0};
+  std::initializer_list<std::string> names = {"bbe", "bbe", "aaa", "abc", "ab", "za", "b", "x"};
+  auto col2 =                           FWCW{{    1,     1,     0,     0,    0,    2,   1,   3}};
+  auto col3 =                           FWCW{{    7,     8,     1,     1,    9,    5,   7,   3}};
+  auto col1 = cudf::test::strings_column_wrapper{names.begin(), names.end(), string_valids.begin()};
+  auto struct_col = cudf::test::structs_column_wrapper{{col1, col2, col3}}.release();
+  // clang-format on
+  auto struct_col_view{struct_col->view()};
+  cudf::table_view input{{struct_col_view}};
+  auto sliced_columns = cudf::split(struct_col_view, std::vector<cudf::size_type>{3});
+  auto sliced_tables  = cudf::split(input, std::vector<cudf::size_type>{3});
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  /*
+        asce_null_first   sliced[3:]
+      /+-------------+
+      |             s|
+      +--------------+
+    7 | { @,    3, 3}|   7=4
+    2 | {"aaa", 0, 1}|
+    4 | {"ab",  0, 9}|   4=1
+    3 | {"abc", 0, 1}|   3=0
+    6 | {"b",   1, 7}|   6=3
+    0 | {"bbe", 1, 7}|
+    1 | {"bbe", 1, 8}|
+    5 | {"za",  2, 5}|   5=2
+      +--------------+
+  */
+
+  // normal
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{7, 2, 4, 3, 6, 0, 1, 5}};
+  auto got = cudf::sorted_order(input, column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected, column_order);
+
+  // table with sliced column
+  cudf::table_view input2{{sliced_columns[1]}};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected2{{4, 1, 0, 3, 2}};
+  got = cudf::sorted_order(input2, column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input2, expected2, column_order);
+
+  // sliced table[1]
+  cudf::test::fixed_width_column_wrapper<int32_t> expected3{{4, 1, 0, 3, 2}};
+  got = cudf::sorted_order(sliced_tables[1], column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(sliced_tables[1], expected3, column_order);
+
+  // sliced table[0]
+  cudf::test::fixed_width_column_wrapper<int32_t> expected4{{2, 0, 1}};
+  got = cudf::sorted_order(sliced_tables[0], column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(sliced_tables[0], expected4, column_order);
+}
+
+TYPED_TEST(Sort, SlicedColumns)
+{
+  using T    = TypeParam;
+  using FWCW = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  // clang-format off
+  std::vector<bool>             string_valids{    1,     1,     1,     1,    1,    1,   1,   0};
+  std::initializer_list<std::string> names = {"bbe", "bbe", "aaa", "abc", "ab", "za", "b", "x"};
+  auto col2 =                           FWCW{{    7,     8,     1,     1,    9,    5,   7,   3}};
+  auto col1 = cudf::test::strings_column_wrapper{names.begin(), names.end(), string_valids.begin()};
+  // clang-format on
+  cudf::table_view input{{col1, col2}};
+  auto sliced_columns1 = cudf::split(col1, std::vector<cudf::size_type>{3});
+  auto sliced_columns2 = cudf::split(col1, std::vector<cudf::size_type>{3});
+  auto sliced_tables   = cudf::split(input, std::vector<cudf::size_type>{3});
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::ASCENDING};
+
+  // normal
+  // cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 3, 7, 5, 0, 6, 1, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{7, 2, 4, 3, 6, 0, 1, 5}};
+  auto got = cudf::sorted_order(input, column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected, column_order);
+
+  // table with sliced column
+  cudf::table_view input2{{sliced_columns1[1], sliced_columns2[1]}};
+  // cudf::test::fixed_width_column_wrapper<int32_t> expected2{{0, 4, 2, 3, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected2{{4, 1, 0, 3, 2}};
+  got = cudf::sorted_order(input2, column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input2, expected2, column_order);
+}
+
+TYPED_TEST(Sort, WithStructColumnCombinations)
+{
+  using T    = TypeParam;
+  using FWCW = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  // clang-format off
+  /*
+    +------------+
+    |           s|
+    +------------+
+  0 |   {0, null}|
+  1 |   {1, null}|
+  2 |        null|
+  3 |{null, null}|
+  4 |        null|
+  5 |{null, null}|
+  6 |   {null, 1}|
+  7 |   {null, 0}|
+    +------------+
+  */
+  std::vector<bool>                           struct_valids{1, 1, 0, 1, 0, 1, 1, 1};
+  auto col1       = FWCW{{ 0,  1,  9, -1,  9, -1, -1, -1}, {1, 1, 1, 0, 1, 0, 0, 0}};
+  auto col2       = FWCW{{-1, -1,  9, -1,  9, -1,  1,  0}, {0, 0, 1, 0, 1, 0, 1, 1}};
+  auto struct_col = cudf::test::structs_column_wrapper{{col1, col2}, struct_valids}.release();
+  /*
+    desc_nulls_first     desc_nulls_last     asce_nulls_first     asce_nulls_last
+    +------------+       +------------+      +------------+       +------------+
+    |           s|       |           s|      |           s|       |           s|
+    +------------+       +------------+      +------------+       +------------+
+  2 |        null|     1 |   {1, null}|    2 |        null|     0 |   {0, null}|
+  4 |        null|     0 |   {0, null}|    4 |        null|     1 |   {1, null}|
+  3 |{null, null}|     6 |   {null, 1}|    3 |{null, null}|     7 |   {null, 0}|
+  5 |{null, null}|     7 |   {null, 0}|    5 |{null, null}|     6 |   {null, 1}|
+  6 |   {null, 1}|     3 |{null, null}|    7 |   {null, 0}|     3 |{null, null}|
+  7 |   {null, 0}|     5 |{null, null}|    6 |   {null, 1}|     5 |{null, null}|
+  1 |   {1, null}|     2 |        null|    0 |   {0, null}|     2 |        null|
+  0 |   {0, null}|     4 |        null|    1 |   {1, null}|     4 |        null|
+    +------------+       +------------+      +------------+       +------------+
+  */
+  // clang-format on
+  auto struct_col_view{struct_col->view()};
+  cudf::table_view input{{struct_col_view}};
+  std::vector<cudf::order> column_order1{cudf::order::DESCENDING};
+
+  // desc_nulls_first
+  cudf::test::fixed_width_column_wrapper<int32_t> expected1{{2, 4, 3, 5, 6, 7, 1, 0}};
+  auto got = cudf::sorted_order(input, column_order1, {cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected1, column_order1, {cudf::null_order::AFTER});
+
+  // desc_nulls_last
+  cudf::test::fixed_width_column_wrapper<int32_t> expected2{{1, 0, 6, 7, 3, 5, 2, 4}};
+  got = cudf::sorted_order(input, column_order1, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected2, column_order1, {cudf::null_order::BEFORE});
+
+  // asce_nulls_first
+  std::vector<cudf::order> column_order2{cudf::order::ASCENDING};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected3{{2, 4, 3, 5, 7, 6, 0, 1}};
+  got = cudf::sorted_order(input, column_order2, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected3, column_order2, {cudf::null_order::BEFORE});
+
+  // asce_nulls_last
+  cudf::test::fixed_width_column_wrapper<int32_t> expected4{{0, 1, 7, 6, 3, 5, 2, 4}};
+  got = cudf::sorted_order(input, column_order2, {cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected4, column_order2, {cudf::null_order::AFTER});
+}
+
+TYPED_TEST(Sort, WithStructColumnCombinationsWithoutNulls)
+{
+  using T    = TypeParam;
+  using FWCW = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  // clang-format off
+  /*
+    +------------+
+    |           s|
+    +------------+
+  0 |   {0, null}|
+  1 |   {1, null}|
+  2 |      {9, 9}|
+  3 |{null, null}|
+  4 |      {9, 9}|
+  5 |{null, null}|
+  6 |   {null, 1}|
+  7 |   {null, 0}|
+    +------------+
+  */
+  auto col1       = FWCW{{ 0,  1,  9, -1,  9, -1, -1, -1}, {1, 1, 1, 0, 1, 0, 0, 0}};
+  auto col2       = FWCW{{-1, -1,  9, -1,  9, -1,  1,  0}, {0, 0, 1, 0, 1, 0, 1, 1}};
+  auto struct_col = cudf::test::structs_column_wrapper{{col1, col2}}.release();
+  /* (nested columns are always nulls_first, spark requirement)
+    desc_nulls_*        asce_nulls_*
+    +------------+      +------------+
+    |           s|      |           s|
+    +------------+      +------------+
+  3 |{null, null}|    0 |   {0, null}|
+  5 |{null, null}|    1 |   {1, null}|
+  6 |   {null, 1}|    2 |      {9, 9}|
+  7 |   {null, 0}|    4 |      {9, 9}|
+  2 |      {9, 9}|    7 |   {null, 0}|
+  4 |      {9, 9}|    6 |   {null, 1}|
+  1 |   {1, null}|    3 |{null, null}|
+  0 |   {0, null}|    5 |{null, null}|
+    +------------+      +------------+
+  */
+  // clang-format on
+  auto struct_col_view{struct_col->view()};
+  cudf::table_view input{{struct_col_view}};
+  std::vector<cudf::order> column_order{cudf::order::DESCENDING};
+
+  // desc_nulls_first
+  auto const expected1 = []() {
+    if constexpr (std::is_same_v<T, bool>) {
+      return cudf::test::fixed_width_column_wrapper<int32_t>{{3, 5, 6, 7, 1, 2, 4, 0}};
+    }
+    return cudf::test::fixed_width_column_wrapper<int32_t>{{3, 5, 6, 7, 2, 4, 1, 0}};
+  }();
+  auto got = cudf::sorted_order(input, column_order, {cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected1, column_order, {cudf::null_order::AFTER});
+
+  // desc_nulls_last
+  cudf::test::fixed_width_column_wrapper<int32_t> expected2{{2, 4, 1, 0, 6, 7, 3, 5}};
+  got = cudf::sorted_order(input, column_order, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected2, column_order, {cudf::null_order::BEFORE});
+
+  // asce_nulls_first
+  std::vector<cudf::order> column_order2{cudf::order::ASCENDING};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected3{{3, 5, 7, 6, 0, 1, 2, 4}};
+  got = cudf::sorted_order(input, column_order2, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected3, column_order2, {cudf::null_order::BEFORE});
+
+  // asce_nulls_last
+  auto const expected4 = []() {
+    if constexpr (std::is_same_v<T, bool>) {
+      return cudf::test::fixed_width_column_wrapper<int32_t>{{0, 2, 4, 1, 7, 6, 3, 5}};
+    }
+    return cudf::test::fixed_width_column_wrapper<int32_t>{{0, 1, 2, 4, 7, 6, 3, 5}};
+  }();
+  got = cudf::sorted_order(input, column_order2, {cudf::null_order::AFTER});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected4, got->view());
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected4, column_order2, {cudf::null_order::AFTER});
+}
+
+TYPED_TEST(Sort, MismatchInColumnOrderSize)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+
+  EXPECT_THROW(cudf::sorted_order(input, column_order), cudf::logic_error);
+  EXPECT_THROW(cudf::sort(input, column_order), cudf::logic_error);
+  EXPECT_THROW(cudf::sort_by_key(input, input, column_order), cudf::logic_error);
+}
+
+TYPED_TEST(Sort, MismatchInNullPrecedenceSize)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::DESCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER, cudf::null_order::BEFORE};
+
+  EXPECT_THROW(cudf::sorted_order(input, column_order, null_precedence), cudf::logic_error);
+  EXPECT_THROW(cudf::sort(input, column_order, null_precedence), cudf::logic_error);
+  EXPECT_THROW(cudf::sort_by_key(input, input, column_order, null_precedence), cudf::logic_error);
+}
+
+TYPED_TEST(Sort, ZeroSizedColumns)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{};
+  cudf::table_view input{{col1}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+
+  auto got = cudf::sorted_order(input, column_order);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  // Run test for sort and sort_by_key
+  run_sort_test(input, expected, column_order);
+}
+
+TYPED_TEST(Sort, WithListColumn)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) { GTEST_SKIP(); }
+
+  /*
+  [
+    [[1, 2, 3], [], [4, 5], [], [0, 6, 0]], 0
+    [[1, 2, 3], [], [4, 5], [], [0, 6, 0]], 1
+    [[1, 2, 3], [], [4, 5], [0, 6, 0]],     2
+    [[1, 2], [3], [4, 5], [0, 6, 0]],       3
+    [[7, 8], []],                           4
+    [[], [], []],                           5
+    [[]],                                   6
+    [[10]],                                 7
+    []                                      8
+  ]
+  */
+
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+  lcw col{{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},
+          {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},
+          {{1, 2, 3}, {}, {4, 5}, {0, 6, 0}},
+          {{1, 2}, {3}, {4, 5}, {0, 6, 0}},
+          {{7, 8}, {}},
+          lcw{lcw{}, lcw{}, lcw{}},
+          lcw{lcw{}},
+          {lcw{10}},
+          lcw{}};
+
+  auto expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{8, 6, 5, 3, 0, 1, 2, 4, 7};
+  auto result = cudf::sorted_order(cudf::table_view({col}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, *result);
+}
+
+TYPED_TEST(Sort, WithNullableListColumn)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) { GTEST_SKIP(); }
+
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+  using cudf::test::iterators::nulls_at;
+
+  /*
+  [
+    [[1, 2, 3], [], [4, 5], [], [0, 6, 0]],   0
+    [[1, 2, 3], [], [4, 5], NULL, [0, 6, 0]], 1
+    [[1, 2, 3], [], [4, 5], [0, 6, 0]],       2
+    [[1, 2], [3], [4, 5], [0, 6, 0]],         3
+    [[1, 2], [3], [4, 5], [NULL, 6, 0]],      4
+    [[7, 8], []],                             5
+    [[], [], []],                             6
+    [[]],                                     7
+    [[10]],                                   8
+    [],                                       9
+    [[1, 2], [3], [4, 5], [NULL, 6, NULL]],   10
+    [[1, 2], [3], [4, 5], [NULL, 7]]          11
+  ]
+  */
+
+  lcw col{
+    {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},                   // 0
+    {{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, nulls_at({3})},  // 1
+    {{1, 2, 3}, {}, {4, 5}, {0, 6, 0}},                       // 2
+    {{1, 2}, {3}, {4, 5}, {0, 6, 0}},                         // 3
+    {{1, 2}, {3}, {4, 5}, {{0, 6, 0}, nulls_at({0})}},        // 4
+    {{7, 8}, {}},                                             // 5
+    lcw{lcw{}, lcw{}, lcw{}},                                 // 6
+    lcw{lcw{}},                                               // 7
+    {lcw{10}},                                                // 8
+    lcw{},                                                    // 9
+    {{1, 2}, {3}, {4, 5}, {{0, 6, 0}, nulls_at({0, 2})}},     // 10
+    {{1, 2}, {3}, {4, 5}, {{0, 7}, nulls_at({0})}},           // 11
+  };
+
+  auto expect =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{9, 7, 6, 10, 4, 11, 3, 1, 0, 2, 5, 8};
+  auto result = cudf::sorted_order(cudf::table_view({col}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, *result);
+}
+
+TYPED_TEST(Sort, MoreLists)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) { GTEST_SKIP(); }
+
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+  using cudf::test::iterators::null_at;
+  using cudf::test::iterators::nulls_at;
+
+  {
+    /*
+    [
+      [[NULL], [-21827]], 0
+      [[NULL, NULL]]      1
+    ]
+    */
+    lcw col{
+      lcw{lcw{{0}, nulls_at({0})}, lcw{-21827}},  // 0
+      lcw{lcw{{0, 0}, nulls_at({0, 1})}}          // 1
+    };
+    cudf::test::fixed_width_column_wrapper<int32_t> expected{{0, 1}};
+    auto result = cudf::sorted_order(cudf::table_view({col}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  {
+    /*
+    [
+      [[1], [2]],         0
+      [[1, NULL], [2, 3]] 1
+    ]
+    */
+    auto const col = lcw{lcw{lcw{1}, lcw{2}}, lcw{lcw{{1, 0}, nulls_at({1})}, lcw{2, 3}}};
+    cudf::test::fixed_width_column_wrapper<int32_t> expected{{0, 1}};
+    auto result = cudf::sorted_order(cudf::table_view({col}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+
+  {
+    /*
+    List<List<List<int>
+    [
+      [[[0, 0, 0]]],                                0
+      [[[0], [0], [0]]],                            1
+      [[[0]], [[0]], [[0]]],                        2
+      [[[0, 0, 0]], [[0]], [[0]]],                  3
+      [[[0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0]], [[0]]] 4
+    ]
+    */
+    lcw col{lcw{lcw{lcw{0, 0, 0}}},
+            lcw{lcw{lcw{0}, lcw{0}, lcw{0}}},
+            lcw{lcw{lcw{0}}, lcw{lcw{0}}, lcw{lcw{0}}},
+            lcw{lcw{lcw{0, 0, 0}}, lcw{lcw{0}}, lcw{lcw{0}}},
+            lcw{lcw{lcw{0, 0}}, lcw{lcw{0, 0, 0, 0, 0, 0, 0, 0}}, lcw{lcw{0}}}};
+    cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 1, 4, 0, 3}};
+    auto result = cudf::sorted_order(cudf::table_view({col}));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+  }
+  {
+    /*
+    [
+      [],    0
+      [1],   1
+      [2, 2] 2
+      [2, 3] 3
+      []     4
+      NULL   5
+      [2]    6
+      NULL   7
+      [NULL] 8
+    ]
+    */
+    lcw col{{{}, {1}, {2, 2}, {2, 3}, {}, {} /*NULL*/, {2}, {} /*NULL*/, {{0}, null_at(0)}},
+            nulls_at({5, 7})};
+
+    // ASCENDING, null_order::BEFORE
+    {
+      cudf::test::fixed_width_column_wrapper<int32_t> expected{{5, 7, 0, 4, 8, 1, 6, 2, 3}};
+      auto result = cudf::sorted_order(cudf::table_view({col}));
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+    }
+    // ASCENDING, null_order::AFTER
+    {
+      cudf::test::fixed_width_column_wrapper<int32_t> expected{{0, 4, 8, 1, 6, 2, 3, 5, 7}};
+      auto result = cudf::sorted_order(cudf::table_view({col}), {}, {cudf::null_order::AFTER});
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+    }
+    // DESCENDING, null_order::BEFORE
+    {
+      cudf::test::fixed_width_column_wrapper<int32_t> expected{{3, 2, 6, 1, 8, 0, 4, 5, 7}};
+      auto result = cudf::sorted_order(cudf::table_view({col}), {cudf::order::DESCENDING});
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+    }
+    // DESCENDING, null_order::AFTER
+    {
+      cudf::test::fixed_width_column_wrapper<int32_t> expected{{5, 7, 3, 2, 6, 1, 8, 0, 4}};
+      auto result = cudf::sorted_order(
+        cudf::table_view({col}), {cudf::order::DESCENDING}, {cudf::null_order::AFTER});
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+    }
+  }
+  {
+    lcw col{lcw{lcw{}, lcw{-729297378, -627961465}},
+            lcw{lcw{{0}, null_at(0)}, lcw{881899016, -1415270016}}};
+
+    {
+      cudf::test::fixed_width_column_wrapper<int32_t> expected{{0, 1}};
+      auto result = cudf::sorted_order(cudf::table_view({col}));
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+    }
+  }
+}
+
+TYPED_TEST(Sort, WithSlicedListColumn)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) { GTEST_SKIP(); }
+
+  using lcw = cudf::test::lists_column_wrapper<T, int32_t>;
+  using cudf::test::iterators::nulls_at;
+  lcw col{
+    {{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}},                   //
+    {{{1, 2, 3}, {}, {4, 5}, {}, {0, 6, 0}}, nulls_at({3})},  // 0
+    {{1, 2, 3}, {}, {4, 5}, {0, 6, 0}},                       // 1
+    {{1, 2}, {3}, {4, 5}, {0, 6, 0}},                         // 2
+    {{1, 2}, {3}, {4, 5}, {{0, 6, 0}, nulls_at({0})}},        // 3
+    {{7, 8}, {}},                                             // 4
+    lcw{lcw{}, lcw{}, lcw{}},                                 // 5
+    lcw{lcw{}},                                               // 6
+    {lcw{10}},                                                // 7
+    lcw{},                                                    // 8
+    {{1, 2}, {3}, {4, 5}, {{0, 6, 0}, nulls_at({0, 2})}},     // 9
+    {{1, 2}, {3}, {4, 5}, {{0, 7}, nulls_at({0})}},           //
+  };
+
+  auto sliced_col = cudf::slice(col, {1, 10});
+
+  auto expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{8, 6, 5, 3, 2, 0, 1, 4, 7};
+  auto result = cudf::sorted_order(cudf::table_view({sliced_col}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, *result);
+}
+
+TYPED_TEST(Sort, WithEmptyListColumn)
+{
+  using T = TypeParam;
+  if (std::is_same_v<T, bool>) { GTEST_SKIP(); }
+
+  auto L1 = cudf::make_lists_column(0,
+                                    cudf::make_empty_column(cudf::data_type(cudf::type_id::INT32)),
+                                    cudf::make_empty_column(cudf::data_type{cudf::type_id::INT64}),
+                                    0,
+                                    {});
+  auto L0 = cudf::make_lists_column(
+    3, cudf::test::fixed_width_column_wrapper<int32_t>{0, 0, 0, 0}.release(), std::move(L1), 0, {});
+
+  auto expect = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 1, 2};
+  auto result = cudf::sorted_order(cudf::table_view({*L0}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expect, *result);
+}
+
+struct SortByKey : public cudf::test::BaseFixture {};
+
+TEST_F(SortByKey, ValueKeysSizeMismatch)
+{
+  using T = int64_t;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view values{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<T> key_col{{5, 4, 3, 5}};
+  cudf::table_view keys{{key_col}};
+
+  EXPECT_THROW(cudf::sort_by_key(values, keys), cudf::logic_error);
+}
+
+template <typename T>
+struct SortFixedPointTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(SortFixedPointTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(SortFixedPointTest, SortedOrderGather)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const ZERO  = decimalXX{0, scale_type{0}};
+  auto const ONE   = decimalXX{1, scale_type{0}};
+  auto const TWO   = decimalXX{2, scale_type{0}};
+  auto const THREE = decimalXX{3, scale_type{0}};
+  auto const FOUR  = decimalXX{4, scale_type{0}};
+
+  auto const input_vec  = std::vector<decimalXX>{TWO, ONE, ZERO, FOUR, THREE};
+  auto const index_vec  = std::vector<cudf::size_type>{2, 1, 0, 4, 3};
+  auto const sorted_vec = std::vector<decimalXX>{ZERO, ONE, TWO, THREE, FOUR};
+
+  auto const input_col =
+    cudf::test::fixed_width_column_wrapper<decimalXX>(input_vec.begin(), input_vec.end());
+  auto const index_col =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(index_vec.begin(), index_vec.end());
+  auto const sorted_col =
+    cudf::test::fixed_width_column_wrapper<decimalXX>(sorted_vec.begin(), sorted_vec.end());
+
+  auto const sorted_table = cudf::table_view{{sorted_col}};
+  auto const input_table  = cudf::table_view{{input_col}};
+
+  auto const indices = cudf::sorted_order(input_table);
+  auto const sorted  = cudf::gather(input_table, indices->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(index_col, indices->view());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(sorted_table, sorted->view());
+}
+
+struct SortCornerTest : public cudf::test::BaseFixture {};
+
+TEST_F(SortCornerTest, WithEmptyStructColumn)
+{
+  using int_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  // struct{}, int, int
+  int_col col_for_mask{{0, 0, 0, 0, 0, 0}, {1, 0, 1, 1, 1, 1}};
+  auto null_mask  = cudf::copy_bitmask(col_for_mask);
+  auto struct_col = cudf::make_structs_column(
+    6, {}, cudf::column_view(col_for_mask).null_count(), std::move(null_mask));
+
+  int_col col1{{1, 2, 3, 1, 2, 3}};
+  int_col col2{{1, 1, 1, 2, 2, 2}};
+  cudf::table_view input{{struct_col->view(), col1, col2}};
+
+  int_col expected{{1, 0, 3, 4, 2, 5}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::ASCENDING};
+  auto got = cudf::sorted_order(input, column_order);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  // struct{struct{}, int}
+  int_col col3{{0, 1, 2, 3, 4, 5}};
+  std::vector<std::unique_ptr<cudf::column>> child_columns;
+  child_columns.push_back(std::move(struct_col));
+  child_columns.push_back(col3.release());
+  auto struct_col2 =
+    cudf::make_structs_column(6, std::move(child_columns), 0, rmm::device_buffer{});
+  cudf::table_view input2{{struct_col2->view()}};
+
+  int_col expected2{{5, 4, 3, 2, 0, 1}};
+  auto got2 = cudf::sorted_order(input2, {cudf::order::DESCENDING});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, got2->view());
+
+  // struct{struct{}, struct{int}}
+  int_col col_for_mask2{{0, 0, 0, 0, 0, 0}, {1, 0, 1, 1, 0, 1}};
+  auto null_mask2 = cudf::copy_bitmask(col_for_mask2);
+  std::vector<std::unique_ptr<cudf::column>> child_columns2;
+  auto child_col_1 = cudf::make_structs_column(
+    6, {}, cudf::column_view(col_for_mask2).null_count(), std::move(null_mask2));
+  child_columns2.push_back(std::move(child_col_1));
+  int_col col4{{5, 4, 3, 2, 1, 0}};
+  std::vector<std::unique_ptr<cudf::column>> grand_child;
+  grand_child.push_back(std::move(col4.release()));
+  auto child_col_2 = cudf::make_structs_column(6, std::move(grand_child), 0, rmm::device_buffer{});
+  child_columns2.push_back(std::move(child_col_2));
+  auto struct_col3 =
+    cudf::make_structs_column(6, std::move(child_columns2), 0, rmm::device_buffer{});
+  cudf::table_view input3{{struct_col3->view()}};
+
+  int_col expected3{{4, 1, 5, 3, 2, 0}};
+  auto got3 = cudf::sorted_order(input3, {cudf::order::ASCENDING});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected3, got3->view());
+};
+
+using SortDouble = Sort<double>;
+TEST_F(SortDouble, InfinityAndNan)
+{
+  auto constexpr NaN = std::numeric_limits<double>::quiet_NaN();
+  auto constexpr Inf = std::numeric_limits<double>::infinity();
+
+  auto input = cudf::test::fixed_width_column_wrapper<double>(
+    {-0.0, -NaN, -NaN, NaN, Inf, -Inf, 7.0, 5.0, 6.0, NaN, Inf, -Inf, -NaN, -NaN, -0.0});
+  auto expected =  // -inf,-inf,-0,-0,5,6,7,inf,inf,-nan,-nan,nan,nan,-nan,-nan
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+      {5, 11, 0, 14, 7, 8, 6, 4, 10, 1, 2, 3, 9, 12, 13});
+  auto results = cudf::sorted_order(cudf::table_view({input}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/sort/stable_sort_tests.cpp b/cpp/tests/sort/stable_sort_tests.cpp
new file mode 100644
index 0000000..71520ef
--- /dev/null
+++ b/cpp/tests/sort/stable_sort_tests.cpp
@@ -0,0 +1,291 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/sort.h>
+
+#include <type_traits>
+#include <vector>
+
+void run_stable_sort_test(cudf::table_view input,
+                          cudf::column_view expected_sorted_indices,
+                          std::vector<cudf::order> column_order         = {},
+                          std::vector<cudf::null_order> null_precedence = {})
+{
+  auto got_sort_by_key_table      = cudf::sort_by_key(input, input, column_order, null_precedence);
+  auto expected_sort_by_key_table = cudf::gather(input, expected_sorted_indices);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort_by_key_table->view(), got_sort_by_key_table->view());
+}
+
+using TestTypes = cudf::test::Concat<cudf::test::NumericTypes,  // include integers, floats and bool
+                                     cudf::test::ChronoTypes>;  // include timestamps and durations
+
+template <typename T>
+struct StableSort : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(StableSort, TestTypes);
+
+TYPED_TEST(StableSort, MixedNullOrder)
+{
+  using T = TypeParam;
+  using R = int32_t;
+
+  cudf::test::fixed_width_column_wrapper<T> col1({0, 1, 1, 0, 0, 1, 0, 1},
+                                                 {0, 1, 1, 1, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper col2({"2", "a", "b", "x", "k", "a", "x", "a"},
+                                          {1, 1, 1, 1, 0, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<R> expected{{4, 3, 6, 1, 5, 7, 2, 0}};
+
+  auto got = cudf::stable_sorted_order(cudf::table_view({col1, col2}),
+                                       {cudf::order::ASCENDING, cudf::order::ASCENDING},
+                                       {cudf::null_order::AFTER, cudf::null_order::BEFORE});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(StableSort, WithNullMax)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8, 5}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k", "d"}, {1, 1, 0, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 10, 2, 10}, {1, 1, 0, 1, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{1, 0, 3, 5, 4, 2}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{
+    cudf::null_order::AFTER, cudf::null_order::AFTER, cudf::null_order::AFTER};
+
+  auto got = cudf::stable_sorted_order(input, column_order, null_precedence);
+
+  if (not std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    run_stable_sort_test(input, expected, column_order, null_precedence);
+  } else {
+    // for bools only validate that the null element landed at the back, since
+    // the rest of the values are equivalent and yields random sorted order.
+    auto to_host = [](cudf::column_view const& col) {
+      thrust::host_vector<int32_t> h_data(col.size());
+      CUDF_CUDA_TRY(cudaMemcpy(
+        h_data.data(), col.data<int32_t>(), h_data.size() * sizeof(int32_t), cudaMemcpyDefault));
+      return h_data;
+    };
+    thrust::host_vector<int32_t> h_exp = to_host(expected);
+    thrust::host_vector<int32_t> h_got = to_host(got->view());
+    EXPECT_EQ(h_exp[h_exp.size() - 1], h_got[h_got.size() - 1]);
+
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{0, 3, 5, 1, 4, 2}};
+    run_stable_sort_test(input, expected_for_bool, column_order, null_precedence);
+  }
+}
+
+TYPED_TEST(StableSort, WithNullMin)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}, {1, 1, 0, 1, 1}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"}, {1, 1, 0, 1, 1});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 10, 2}, {1, 1, 0, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 1, 0, 3, 4}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING};
+
+  auto got = cudf::stable_sorted_order(input, column_order);
+
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    run_stable_sort_test(input, expected, column_order);
+  } else {
+    // for bools only validate that the null element landed at the front, since
+    // the rest of the values are equivalent and yields random sorted order.
+    auto to_host = [](cudf::column_view const& col) {
+      thrust::host_vector<int32_t> h_data(col.size());
+      CUDF_CUDA_TRY(cudaMemcpy(
+        h_data.data(), col.data<int32_t>(), h_data.size() * sizeof(int32_t), cudaMemcpyDefault));
+      return h_data;
+    };
+    thrust::host_vector<int32_t> h_exp = to_host(expected);
+    thrust::host_vector<int32_t> h_got = to_host(got->view());
+    EXPECT_EQ(h_exp.front(), h_got.front());
+
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{2, 0, 3, 1, 4}};
+    run_stable_sort_test(input, expected_for_bool, column_order);
+  }
+}
+
+TYPED_TEST(StableSort, WithAllValid)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 10, 2}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{{2, 1, 0, 3, 4}};
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::ASCENDING, cudf::order::DESCENDING};
+
+  auto got = cudf::stable_sorted_order(input, column_order);
+
+  // Skip validating bools order. Valid true bools are all
+  // equivalent, and yield random order after thrust::sort
+  if (!std::is_same_v<T, bool>) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+    run_stable_sort_test(input, expected, column_order);
+  } else {
+    cudf::test::fixed_width_column_wrapper<int32_t> expected_for_bool{{2, 0, 3, 1, 4}};
+    run_stable_sort_test(input, expected_for_bool, column_order);
+  }
+}
+
+TYPED_TEST(StableSort, MisMatchInColumnOrderSize)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING, cudf::order::DESCENDING};
+
+  EXPECT_THROW(cudf::stable_sorted_order(input, column_order), cudf::logic_error);
+  EXPECT_THROW(cudf::stable_sort_by_key(input, input, column_order), cudf::logic_error);
+}
+
+TYPED_TEST(StableSort, MisMatchInNullPrecedenceSize)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  std::vector<cudf::order> column_order{
+    cudf::order::ASCENDING, cudf::order::DESCENDING, cudf::order::DESCENDING};
+  std::vector<cudf::null_order> null_precedence{cudf::null_order::AFTER, cudf::null_order::BEFORE};
+
+  EXPECT_THROW(cudf::stable_sorted_order(input, column_order, null_precedence), cudf::logic_error);
+  EXPECT_THROW(cudf::stable_sort_by_key(input, input, column_order, null_precedence),
+               cudf::logic_error);
+}
+
+TYPED_TEST(StableSort, ZeroSizedColumns)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{};
+  cudf::table_view input{{col1}};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected{};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+
+  auto got = cudf::stable_sorted_order(input, column_order);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  run_stable_sort_test(input, expected, column_order);
+}
+
+struct StableSortByKey : public cudf::test::BaseFixture {};
+
+TEST_F(StableSortByKey, ValueKeysSizeMismatch)
+{
+  using T = int64_t;
+
+  cudf::test::fixed_width_column_wrapper<T> col1{{5, 4, 3, 5, 8}};
+  cudf::test::strings_column_wrapper col2({"d", "e", "a", "d", "k"});
+  cudf::test::fixed_width_column_wrapper<T> col3{{10, 40, 70, 5, 2}};
+  cudf::table_view values{{col1, col2, col3}};
+
+  cudf::test::fixed_width_column_wrapper<T> key_col{{5, 4, 3, 5}};
+  cudf::table_view keys{{key_col}};
+
+  EXPECT_THROW(cudf::stable_sort_by_key(values, keys), cudf::logic_error);
+}
+
+template <typename T>
+struct StableSortFixedPoint : public cudf::test::BaseFixture {};
+
+template <typename T>
+using wrapper = cudf::test::fixed_width_column_wrapper<T>;
+TYPED_TEST_SUITE(StableSortFixedPoint, cudf::test::FixedPointTypes);
+
+TYPED_TEST(StableSortFixedPoint, FixedPointSortedOrderGather)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+
+  auto const ZERO  = decimalXX{0, scale_type{0}};
+  auto const ONE   = decimalXX{1, scale_type{0}};
+  auto const TWO   = decimalXX{2, scale_type{0}};
+  auto const THREE = decimalXX{3, scale_type{0}};
+  auto const FOUR  = decimalXX{4, scale_type{0}};
+
+  auto const input_vec  = std::vector<decimalXX>{THREE, TWO, ONE, ZERO, FOUR, THREE};
+  auto const index_vec  = std::vector<cudf::size_type>{3, 2, 1, 0, 5, 4};
+  auto const sorted_vec = std::vector<decimalXX>{ZERO, ONE, TWO, THREE, THREE, FOUR};
+
+  auto const input_col  = wrapper<decimalXX>(input_vec.begin(), input_vec.end());
+  auto const index_col  = wrapper<cudf::size_type>(index_vec.begin(), index_vec.end());
+  auto const sorted_col = wrapper<decimalXX>(sorted_vec.begin(), sorted_vec.end());
+
+  auto const sorted_table = cudf::table_view{{sorted_col}};
+  auto const input_table  = cudf::table_view{{input_col}};
+
+  auto const indices = cudf::sorted_order(input_table);
+  auto const sorted  = cudf::gather(input_table, indices->view());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(index_col, indices->view());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(sorted_table, sorted->view());
+}
+
+using StableSortDouble = StableSort<double>;
+TEST_F(StableSortDouble, InfinityAndNaN)
+{
+  auto constexpr NaN = std::numeric_limits<double>::quiet_NaN();
+  auto constexpr Inf = std::numeric_limits<double>::infinity();
+
+  auto input = cudf::test::fixed_width_column_wrapper<double>(
+    {-0.0, -NaN, -NaN, NaN, Inf, -Inf, 7.0, 5.0, 6.0, NaN, Inf, -Inf, -NaN, -NaN, -0.0});
+  auto expected =  // -inf,-inf,-0,-0,5,6,7,inf,inf,-nan,-nan,nan,nan,-nan,-nan
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+      {5, 11, 0, 14, 7, 8, 6, 4, 10, 1, 2, 3, 9, 12, 13});
+  auto results = stable_sorted_order(cudf::table_view({input}));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
diff --git a/cpp/tests/stream_compaction/apply_boolean_mask_tests.cpp b/cpp/tests/stream_compaction/apply_boolean_mask_tests.cpp
new file mode 100644
index 0000000..7ee090c
--- /dev/null
+++ b/cpp/tests/stream_compaction/apply_boolean_mask_tests.cpp
@@ -0,0 +1,375 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+
+struct ApplyBooleanMask : public cudf::test::BaseFixture {};
+
+TEST_F(ApplyBooleanMask, NonNullBooleanMask)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  cudf::test::fixed_width_column_wrapper<bool> boolean_mask{
+    {true, false, true, false, true, false}};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{{true, true, true}, {1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{10, 70, 2}, {1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{10, 70, 2}, {1, 0, 1}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::apply_boolean_mask(input, boolean_mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(ApplyBooleanMask, NullBooleanMask)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  cudf::test::fixed_width_column_wrapper<bool> boolean_mask{{true, false, true, false, true, false},
+                                                            {0, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{{true, true}, {0, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{70, 2}, {0, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{70, 2}, {0, 1}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::apply_boolean_mask(input, boolean_mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(ApplyBooleanMask, EmptyMask)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  cudf::test::fixed_width_column_wrapper<bool> boolean_mask{};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::apply_boolean_mask(input, boolean_mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(ApplyBooleanMask, WrongMaskType)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  cudf::test::fixed_width_column_wrapper<int16_t> boolean_mask{
+    {true, false, true, false, true, false}};
+
+  EXPECT_THROW(cudf::apply_boolean_mask(input, boolean_mask), cudf::logic_error);
+}
+
+TEST_F(ApplyBooleanMask, MaskAndInputSizeMismatch)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  cudf::test::fixed_width_column_wrapper<bool> boolean_mask{{true, false, true, false, true}};
+
+  EXPECT_THROW(cudf::apply_boolean_mask(input, boolean_mask), cudf::logic_error);
+}
+
+TEST_F(ApplyBooleanMask, StringColumnTest)
+{
+  cudf::test::strings_column_wrapper col1{
+    {"This", "is", "the", "a", "k12", "string", "table", "column"}, {1, 1, 1, 1, 1, 0, 1, 1}};
+  cudf::table_view input{{col1}};
+  cudf::test::fixed_width_column_wrapper<bool> boolean_mask{
+    {true, true, true, true, false, true, false, true}, {1, 1, 0, 1, 1, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col1_expected{{"This", "is", "a", "string", "column"},
+                                                   {1, 1, 1, 0, 1}};
+  cudf::table_view expected{{col1_expected}};
+
+  auto got = cudf::apply_boolean_mask(input, boolean_mask);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(ApplyBooleanMask, withoutNullString)
+{
+  cudf::test::strings_column_wrapper col1({"d", "e", "a", "d", "k", "d", "l"});
+  cudf::table_view cudf_table_in_view{{col1}};
+
+  cudf::test::fixed_width_column_wrapper<bool> bool_filter{{1, 1, 0, 0, 1, 0, 0}};
+  cudf::column_view bool_filter_col(bool_filter);
+
+  std::unique_ptr<cudf::table> filteredTable =
+    cudf::apply_boolean_mask(cudf_table_in_view, bool_filter_col);
+  cudf::table_view tableView = filteredTable->view();
+
+  cudf::test::strings_column_wrapper expect_col1({"d", "e", "k"});
+  cudf::table_view expect_cudf_table_view{{expect_col1}};
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expect_cudf_table_view, tableView);
+}
+
+TEST_F(ApplyBooleanMask, FixedPointColumnTest)
+{
+  using namespace numeric;
+  using decimal32_wrapper = cudf::test::fixed_point_column_wrapper<int32_t>;
+  using decimal64_wrapper = cudf::test::fixed_point_column_wrapper<int64_t>;
+
+  auto const col1 = decimal32_wrapper{{10, 40, 70, 5, 2, 10, -123}, scale_type{-1}};
+  auto const col2 = decimal64_wrapper{{10, 40, 70, 5, 2, 10, -123}, scale_type{-10}};
+  cudf::table_view cudf_table_in_view{{col1, col2}};
+
+  cudf::test::fixed_width_column_wrapper<bool> bool_filter{{1, 1, 0, 0, 1, 0, 0}};
+  cudf::column_view bool_filter_col(bool_filter);
+
+  std::unique_ptr<cudf::table> filteredTable =
+    cudf::apply_boolean_mask(cudf_table_in_view, bool_filter_col);
+  cudf::table_view tableView = filteredTable->view();
+
+  auto const expect_col1 = decimal32_wrapper{{10, 40, 2}, scale_type{-1}};
+  auto const expect_col2 = decimal64_wrapper{{10, 40, 2}, scale_type{-10}};
+  cudf::table_view expect_cudf_table_view{{expect_col1, expect_col2}};
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expect_cudf_table_view, tableView);
+}
+
+TEST_F(ApplyBooleanMask, FixedPointLargeColumnTest)
+{
+  cudf::size_type const num_rows = 10000;
+
+  using decimal32_wrapper = cudf::test::fixed_point_column_wrapper<int32_t>;
+  using decimal64_wrapper = cudf::test::fixed_point_column_wrapper<int64_t>;
+
+  std::vector<int32_t> dec32_data(num_rows);
+  std::vector<int64_t> dec64_data(num_rows);
+  std::vector<bool> mask_data(num_rows);
+
+  cudf::test::UniformRandomGenerator<int32_t> rng32(-10000000, 10000000);
+  cudf::test::UniformRandomGenerator<int64_t> rng64(-1000000000000, 1000000000000);
+  cudf::test::UniformRandomGenerator<bool> rbg;
+  std::generate(dec32_data.begin(), dec32_data.end(), [&rng32]() { return rng32.generate(); });
+  std::generate(dec64_data.begin(), dec64_data.end(), [&rng64]() { return rng64.generate(); });
+  std::generate(mask_data.begin(), mask_data.end(), [&rbg]() { return rbg.generate(); });
+
+  decimal32_wrapper col32(dec32_data.begin(), dec32_data.end(), numeric::scale_type{-3});
+  decimal64_wrapper col64(dec64_data.begin(), dec64_data.end(), numeric::scale_type{-10});
+  cudf::table_view cudf_table_in_view{{col32, col64}};
+
+  cudf::test::fixed_width_column_wrapper<bool> bool_filter(mask_data.begin(), mask_data.end());
+  cudf::column_view bool_filter_col(bool_filter);
+
+  std::unique_ptr<cudf::table> filteredTable =
+    cudf::apply_boolean_mask(cudf_table_in_view, bool_filter_col);
+  cudf::table_view tableView = filteredTable->view();
+
+  std::vector<int32_t> expect_dec32_data;
+  std::vector<int64_t> expect_dec64_data;
+  thrust::copy_if(thrust::seq,
+                  dec32_data.cbegin(),
+                  dec32_data.cend(),
+                  mask_data.cbegin(),
+                  std::back_inserter(expect_dec32_data),
+                  thrust::identity{});
+  thrust::copy_if(thrust::seq,
+                  dec64_data.cbegin(),
+                  dec64_data.cend(),
+                  mask_data.cbegin(),
+                  std::back_inserter(expect_dec64_data),
+                  thrust::identity{});
+
+  decimal32_wrapper expect_col32(
+    expect_dec32_data.begin(), expect_dec32_data.end(), numeric::scale_type{-3});
+  decimal64_wrapper expect_col64(
+    expect_dec64_data.begin(), expect_dec64_data.end(), numeric::scale_type{-10});
+  cudf::table_view expect_cudf_table_view{{expect_col32, expect_col64}};
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expect_cudf_table_view, tableView);
+}
+
+TEST_F(ApplyBooleanMask, NoNullInput)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col(
+    {9668, 9590, 9526, 9205, 9434, 9347, 9160, 9569, 9143, 9807, 9606, 9446, 9279, 9822, 9691});
+  cudf::test::fixed_width_column_wrapper<bool> mask({false,
+                                                     false,
+                                                     true,
+                                                     false,
+                                                     false,
+                                                     true,
+                                                     false,
+                                                     true,
+                                                     false,
+                                                     true,
+                                                     false,
+                                                     false,
+                                                     true,
+                                                     false,
+                                                     true});
+  cudf::table_view input({col});
+  cudf::test::fixed_width_column_wrapper<int32_t> col_expected(
+    {9526, 9347, 9569, 9807, 9279, 9691});
+  cudf::table_view expected({col_expected});
+  auto got = cudf::apply_boolean_mask(input, mask);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(ApplyBooleanMask, CorrectNullCount)
+{
+  cudf::size_type inputRows = 471234;
+
+  auto seq1 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  auto valid_seq1 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto row) { return true; });
+  cudf::test::fixed_width_column_wrapper<int64_t, typename decltype(seq1)::value_type> col1(
+    seq1, seq1 + inputRows, valid_seq1);
+
+  cudf::table_view input{{col1}};
+
+  auto seq3 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return (i % 277) == 0; });
+  cudf::test::fixed_width_column_wrapper<bool> boolean_mask(seq3, seq3 + inputRows);
+
+  auto got     = cudf::apply_boolean_mask(input, boolean_mask);
+  auto out_col = got->get_column(0).view();
+  auto expected_null_count =
+    cudf::detail::null_count(out_col.null_mask(), 0, out_col.size(), cudf::get_default_stream());
+
+  ASSERT_EQ(out_col.null_count(), expected_null_count);
+}
+
+TEST_F(ApplyBooleanMask, StructFiltering)
+{
+  using namespace cudf::test;
+
+  auto int_member = fixed_width_column_wrapper<int32_t>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                        {1, 1, 1, 1, 0, 1, 1, 1, 1, 0}};
+
+  auto struct_column = structs_column_wrapper{{int_member}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto filter_mask = fixed_width_column_wrapper<bool>{{1, 1, 1, 1, 1, 0, 0, 0, 0, 0}};
+
+  auto filtered_table = cudf::apply_boolean_mask(cudf::table_view({struct_column}), filter_mask);
+  auto filtered_struct_column = filtered_table->get_column(0);
+
+  // Compare against expected results.
+  auto expected_int_member =
+    fixed_width_column_wrapper<int32_t>{{-1, 1, 2, 3, -1}, {0, 1, 1, 1, 0}};
+
+  auto expected_struct_column = structs_column_wrapper{{expected_int_member}, {1, 1, 1, 1, 0}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(filtered_struct_column, expected_struct_column);
+}
+
+TEST_F(ApplyBooleanMask, ListOfStructsFiltering)
+{
+  using namespace cudf::test;
+
+  auto key_member = fixed_width_column_wrapper<int32_t>{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+                                                        {1, 1, 1, 1, 0, 1, 1, 1, 1, 0}};
+
+  auto value_member = fixed_width_column_wrapper<int32_t>{{0, 10, 20, 30, 40, 50, 60, 70, 80, 90},
+                                                          {1, 1, 1, 1, 0, 1, 1, 1, 1, 0}};
+
+  auto struct_column =
+    structs_column_wrapper{{key_member, value_member}, {0, 1, 1, 1, 1, 0, 1, 1, 1, 1}};
+
+  auto list_of_structs_column =
+    cudf::make_lists_column(5,
+                            fixed_width_column_wrapper<int32_t>{0, 2, 4, 6, 8, 10}.release(),
+                            struct_column.release(),
+                            0,
+                            {});
+
+  auto filter_mask = fixed_width_column_wrapper<bool>{{1, 0, 1, 0, 1}};
+
+  auto filtered_table =
+    cudf::apply_boolean_mask(cudf::table_view({list_of_structs_column->view()}), filter_mask);
+  auto filtered_list_column = filtered_table->get_column(0);
+
+  // Compare against expected values.
+  auto expected_key_column =
+    fixed_width_column_wrapper<int32_t>{{0, 1, 4, 5, 8, 9}, {0, 1, 0, 0, 1, 0}};
+  auto expected_value_column =
+    fixed_width_column_wrapper<int32_t>{{0, 10, 40, 50, 80, 90}, {0, 1, 0, 0, 1, 0}};
+
+  auto expected_struct_column =
+    structs_column_wrapper{{expected_key_column, expected_value_column}, {0, 1, 1, 0, 1, 1}};
+
+  auto expected_list_of_structs_column =
+    cudf::make_lists_column(3,
+                            fixed_width_column_wrapper<int32_t>{0, 2, 4, 6}.release(),
+                            expected_struct_column.release(),
+                            0,
+                            {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(filtered_list_column,
+                                      expected_list_of_structs_column->view());
+}
+
+TEST_F(ApplyBooleanMask, StructOfListsFiltering)
+{
+  using namespace cudf::test;
+
+  auto lists_column = lists_column_wrapper<int32_t>{
+    {{0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 2; })};
+
+  auto structs_column = structs_column_wrapper{{lists_column}};
+
+  auto filter_mask    = fixed_width_column_wrapper<bool>{{1, 0, 1, 0, 1}};
+  auto filtered_table = cudf::apply_boolean_mask(cudf::table_view({structs_column}), filter_mask);
+
+  auto filtered_lists_column = filtered_table->get_column(0);
+
+  // Compare against expected values;
+
+  auto expected_lists_column = lists_column_wrapper<int32_t>{
+    {{0, 0}, {2, 2}, {4, 4}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 1; })};
+
+  auto expected_structs_column = structs_column_wrapper{{expected_lists_column}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(filtered_lists_column, expected_structs_column);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/stream_compaction/distinct_count_tests.cpp b/cpp/tests/stream_compaction/distinct_count_tests.cpp
new file mode 100644
index 0000000..864ac8f
--- /dev/null
+++ b/cpp/tests/stream_compaction/distinct_count_tests.cpp
@@ -0,0 +1,367 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <cmath>
+
+using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+
+using cudf::test::iterators::nulls_at;
+
+using cudf::nan_policy;
+using cudf::null_equality;
+using cudf::null_policy;
+
+constexpr int32_t XXX{70};  // Mark for null elements
+constexpr int32_t YYY{3};   // Mark for null elements
+
+template <typename T>
+struct TypedDistinctCount : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedDistinctCount, cudf::test::NumericTypes);
+
+TYPED_TEST(TypedDistinctCount, NoNull)
+{
+  using T = TypeParam;
+
+  auto const input = cudf::test::make_type_param_vector<T>(
+    {1, 3, 3, 4, 31, 1, 8, 2, 0, 4, 1, 4, 10, 40, 31, 42, 0, 42, 8, 5, 4});
+
+  cudf::test::fixed_width_column_wrapper<T> input_col(input.begin(), input.end());
+
+  // explicit instantiation to one particular type (`double`) to reduce build time
+  auto const expected =
+    static_cast<cudf::size_type>(std::set<double>(input.begin(), input.end()).size());
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TYPED_TEST(TypedDistinctCount, TableNoNull)
+{
+  using T = TypeParam;
+
+  auto const input1 = cudf::test::make_type_param_vector<T>(
+    {1, 3, 3, 3, 4, 31, 1, 8, 2, 0, 4, 1, 4, 10, 40, 31, 42, 0, 42, 8, 5, 4});
+  auto const input2 = cudf::test::make_type_param_vector<T>(
+    {3, 3, 3, 4, 31, 1, 8, 5, 0, 4, 1, 4, 10, 40, 31, 42, 0, 42, 8, 5, 4, 1});
+
+  std::vector<std::pair<T, T>> pair_input;
+  std::transform(
+    input1.begin(), input1.end(), input2.begin(), std::back_inserter(pair_input), [](T a, T b) {
+      return std::pair(a, b);
+    });
+
+  cudf::test::fixed_width_column_wrapper<T> input_col1(input1.begin(), input1.end());
+  cudf::test::fixed_width_column_wrapper<T> input_col2(input2.begin(), input2.end());
+  cudf::table_view input_table({input_col1, input_col2});
+
+  auto const expected = static_cast<cudf::size_type>(
+    std::set<std::pair<T, T>>(pair_input.begin(), pair_input.end()).size());
+  EXPECT_EQ(expected, cudf::distinct_count(input_table, null_equality::EQUAL));
+}
+
+struct DistinctCount : public cudf::test::BaseFixture {};
+
+TEST_F(DistinctCount, WithNull)
+{
+  using T = int32_t;
+
+  std::vector<T> input               = {1,   3,  3,  XXX, 31, 1, 8,  2, 0, XXX, XXX,
+                                        XXX, 10, 40, 31,  42, 0, 42, 8, 5, XXX};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,
+                                        0, 1, 1, 1, 1, 1, 1, 1, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col(input.begin(), input.end(), valid.begin());
+
+  // explicit instantiation to one particular type (`double`) to reduce build time
+  auto const expected =
+    static_cast<cudf::size_type>(std::set<double>(input.begin(), input.end()).size());
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(DistinctCount, IgnoringNull)
+{
+  using T = int32_t;
+
+  std::vector<T> input               = {1,   YYY, YYY, XXX, 31, 1, 8,  2, 0, XXX, 1,
+                                        XXX, 10,  40,  31,  42, 0, 42, 8, 5, XXX};
+  std::vector<cudf::size_type> valid = {1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,
+                                        0, 1, 1, 1, 1, 1, 1, 1, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col(input.begin(), input.end(), valid.begin());
+
+  auto const expected =
+    static_cast<cudf::size_type>(std::set<T>(input.begin(), input.end()).size());
+  // Removing 2 from expected to remove count for `XXX` and `YYY`
+  EXPECT_EQ(expected - 2,
+            cudf::distinct_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(DistinctCount, WithNansAndNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1,   3,  NAN, XXX, 31,  1, 8,   2, 0, XXX, 1,
+                                        XXX, 10, 40,  31,  NAN, 0, NAN, 8, 5, XXX};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
+                                        0, 1, 1, 1, 1, 1, 1, 1, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  auto const expected =
+    static_cast<cudf::size_type>(std::set<T>(input.begin(), input.end()).size());
+  EXPECT_EQ(expected + 1,  // +1 since `NAN` is not in std::set
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+
+  input     = {NAN, NAN, XXX};
+  valid     = {1, 1, 0};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 2;
+  EXPECT_EQ(expected_all_nan,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(DistinctCount, WithNansOnly)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, 70, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 1, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 5;
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+
+  input     = {NAN, NAN, NAN};
+  valid     = {1, 1, 1};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 1;
+  EXPECT_EQ(expected_all_nan,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(DistinctCount, NansAsNullWithNoNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, 70, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 1, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 5;
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+
+  input     = {NAN, NAN, NAN};
+  valid     = {1, 1, 1};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 1;
+  EXPECT_EQ(expected_all_nan,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(DistinctCount, NansAsNullWithNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, XXX, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 4;
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+
+  input     = {NAN, NAN, XXX};
+  valid     = {1, 1, 0};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_null = 1;
+  EXPECT_EQ(expected_all_null,
+            cudf::distinct_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(DistinctCount, NansAsNullWithIgnoreNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, XXX, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 3;
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_NULL));
+
+  input     = {NAN, NAN, NAN};
+  valid     = {1, 1, 1};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 0;
+  EXPECT_EQ(expected_all_nan,
+            cudf::distinct_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(DistinctCount, EmptyColumn)
+{
+  using T = float;
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{};
+
+  constexpr auto expected = 0;
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(DistinctCount, StringColumnWithNull)
+{
+  cudf::test::strings_column_wrapper input_col{
+    {"", "this", "is", "this", "This", "a", "column", "of", "the", "strings"},
+    {1, 1, 1, 1, 1, 1, 1, 1, 0, 1}};
+
+  cudf::size_type const expected =
+    (std::vector<std::string>{"", "this", "is", "This", "a", "column", "of", "strings"}).size();
+  EXPECT_EQ(expected,
+            cudf::distinct_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(DistinctCount, TableWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1{{5, 4, 3, 5, 8, 1, 4, 5, 0, 9, -1},
+                                                       {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{2, 2, 2, -1, 2, 1, 2, 0, 0, 9, -1},
+                                                       {1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0}};
+  cudf::table_view input{{col1, col2}};
+
+  EXPECT_EQ(8, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(10, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(DistinctCount, TableWithSomeNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1{{1, 2, 3, 4, 5, 6}, {1, 0, 1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{1, 1, 1, 1, 1, 1}};
+  cudf::table_view input{{col1, col2}};
+
+  EXPECT_EQ(4, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(6, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(DistinctCount, EmptyColumnedTable)
+{
+  std::vector<cudf::column_view> cols{};
+
+  cudf::table_view input(cols);
+
+  EXPECT_EQ(0, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(0, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(DistinctCount, TableMixedTypes)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1{{5, 4, 3, 5, 8, 1, 4, 5, 0, 9, -1},
+                                                       {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col2{{2, 2, 2, -1, 2, 1, 2, 0, 0, 9, -1},
+                                                      {1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<uint32_t> col3{{2, 2, 2, -1, 2, 1, 2, 0, 0, 9, -1},
+                                                        {1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+
+  EXPECT_EQ(9, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(10, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(DistinctCount, TableWithStringColumnWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1{{0, 9, 8, 9, 6, 5, 4, 3, 2, 1, 0},
+                                                       {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0}};
+  cudf::test::strings_column_wrapper col2{
+    {"", "this", "is", "this", "this", "a", "column", "of", "the", "strings", ""},
+    {1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0}};
+
+  cudf::table_view input{{col1, col2}};
+  EXPECT_EQ(9, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(10, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(DistinctCount, NullableLists)
+{
+  auto const keys = lists_col{
+    {{}, {1, 1}, {1}, {} /*NULL*/, {1}, {} /*NULL*/, {2}, {2, 1}, {2}, {2, 2}, {}, {2, 2}},
+    nulls_at({3, 5})};
+  auto const input = cudf::table_view{{keys}};
+
+  EXPECT_EQ(7, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(8, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(DistinctCount, NullableStructOfStructs)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { Null,   4}  |
+  // 2 |  { {1, 1}, 5}  |  // Same as 0
+  // 3 |  { {1, 2}, 4}  |
+  // 4 |  { Null,   6}  |
+  // 5 |  { Null,   4}  |  // Same as 4
+  // 6 |  Null          |  // Same as 6
+  // 7 |  { {2, 1}, 5}  |
+  // 8 |  Null          |
+
+  auto const keys = [&] {
+    auto a  = cudf::test::fixed_width_column_wrapper<int32_t>{1, XXX, 1, 1, XXX, XXX, 0, 2, 0};
+    auto b  = cudf::test::fixed_width_column_wrapper<int32_t>{1, XXX, 1, 2, XXX, XXX, 0, 1, 0};
+    auto s2 = structs_col{{a, b}, nulls_at({1, 4, 5})};
+
+    auto c = cudf::test::fixed_width_column_wrapper<int32_t>{5, 4, 5, 4, 6, 4, 0, 5, 0};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({6, 8});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const input = cudf::table_view{{keys}};
+
+  EXPECT_EQ(6, cudf::distinct_count(input, null_equality::EQUAL));
+  EXPECT_EQ(8, cudf::distinct_count(input, null_equality::UNEQUAL));
+}
diff --git a/cpp/tests/stream_compaction/distinct_tests.cpp b/cpp/tests/stream_compaction/distinct_tests.cpp
new file mode 100644
index 0000000..586792b
--- /dev/null
+++ b/cpp/tests/stream_compaction/distinct_tests.cpp
@@ -0,0 +1,1414 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cmath>
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr NaN          = std::numeric_limits<double>::quiet_NaN();
+auto constexpr KEEP_ANY     = cudf::duplicate_keep_option::KEEP_ANY;
+auto constexpr KEEP_FIRST   = cudf::duplicate_keep_option::KEEP_FIRST;
+auto constexpr KEEP_LAST    = cudf::duplicate_keep_option::KEEP_LAST;
+auto constexpr KEEP_NONE    = cudf::duplicate_keep_option::KEEP_NONE;
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_col  = cudf::test::fixed_width_column_wrapper<float>;
+using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+using strings_col = cudf::test::strings_column_wrapper;
+using structs_col = cudf::test::structs_column_wrapper;
+
+using cudf::nan_policy;
+using cudf::null_equality;
+using cudf::null_policy;
+using cudf::test::iterators::no_nulls;
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+struct DistinctKeepAny : public cudf::test::BaseFixture {};
+
+struct DistinctKeepFirstLastNone : public cudf::test::BaseFixture {};
+
+TEST_F(DistinctKeepAny, StringKeyColumn)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const col = int32s_col{{5, null, null, 5, 5, 8, 1}, nulls_at({1, 2})};
+  auto const keys =
+    strings_col{{"all", "new", "new", "all", "" /*NULL*/, "the", "strings"}, null_at(4)};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_col_sort  = int32s_col{{5, 5, null, 1, 8}, null_at(2)};
+  auto const exp_keys_sort = strings_col{{"" /*NULL*/, "all", "new", "strings", "the"}, null_at(0)};
+  auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
+
+TEST_F(DistinctKeepFirstLastNone, StringKeyColumn)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col = int32s_col{{0, null, 2, 3, 4, 5, 6}, null_at(1)};
+  auto const keys =
+    strings_col{{"all", "new", "new", "all", "" /*NULL*/, "the", "strings"}, null_at(4)};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col_sort = int32s_col{{4, 0, null, 6, 5}, null_at(2)};
+    auto const exp_keys_sort =
+      strings_col{{"" /*NULL*/, "all", "new", "strings", "the"}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col_sort = int32s_col{{4, 3, 2, 6, 5}, no_nulls()};
+    auto const exp_keys_sort =
+      strings_col{{"" /*NULL*/, "all", "new", "strings", "the"}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col_sort  = int32s_col{{4, 6, 5}, no_nulls()};
+    auto const exp_keys_sort = strings_col{{"" /*NULL*/, "strings", "the"}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, EmptyInputTable)
+{
+  int32s_col col(std::initializer_list<int32_t>{});
+  cudf::table_view input{{col}};
+  std::vector<cudf::size_type> key_idx{0};
+
+  auto got = cudf::distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(DistinctKeepAny, NoColumnInputTable)
+{
+  cudf::table_view input{std::vector<cudf::column_view>()};
+  std::vector<cudf::size_type> key_idx{1, 2};
+
+  auto got = cudf::distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(DistinctKeepAny, EmptyKeys)
+{
+  int32s_col col{{5, 4, 3, 5, 8, 1}, {1, 0, 1, 1, 1, 1}};
+  int32s_col empty_col{};
+  cudf::table_view input{{col}};
+  std::vector<cudf::size_type> key_idx{};
+
+  auto got = cudf::distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view{{empty_col}}, got->view());
+}
+
+TEST_F(DistinctKeepAny, NoNullsTable)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const col1  = int32s_col{6, 6, 6, 3, 5, 8, 5};
+  auto const col2  = floats_col{6, 6, 6, 3, 4, 9, 4};
+  auto const keys1 = int32s_col{20, 20, 20, 20, 19, 21, 9};
+  auto const keys2 = int32s_col{19, 19, 19, 20, 20, 9, 21};
+
+  auto const input   = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const key_idx = std::vector<cudf::size_type>{2, 3};
+
+  auto const exp_col1_sort  = int32s_col{5, 5, 6, 3, 8};
+  auto const exp_col2_sort  = floats_col{4, 4, 6, 3, 9};
+  auto const exp_keys1_sort = int32s_col{9, 19, 20, 20, 21};
+  auto const exp_keys2_sort = int32s_col{21, 20, 19, 20, 9};
+  auto const expected_sort =
+    cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
+
+TEST_F(DistinctKeepAny, NoNullsTableWithNaNs)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys.
+  auto const col1  = int32s_col{6, 6, 6, 1, 1, 1, 3, 5, 8, 5};
+  auto const col2  = floats_col{6, 6, 6, 1, 1, 1, 3, 4, 9, 4};
+  auto const keys1 = int32s_col{20, 20, 20, 15, 15, 15, 20, 19, 21, 9};
+  auto const keys2 = floats_col{19., 19., 19., NaN, NaN, NaN, 20., 20., 9., 21.};
+
+  auto const input   = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const key_idx = std::vector<cudf::size_type>{2, 3};
+
+  // NaNs are unequal.
+  {
+    auto const exp_col1_sort  = int32s_col{5, 1, 1, 1, 5, 6, 3, 8};
+    auto const exp_col2_sort  = floats_col{4, 1, 1, 1, 4, 6, 3, 9};
+    auto const exp_keys1_sort = int32s_col{9, 15, 15, 15, 19, 20, 20, 21};
+    auto const exp_keys2_sort = floats_col{21., NaN, NaN, NaN, 20., 19., 20., 9.};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // NaNs are equal.
+  {
+    auto const exp_col1_sort  = int32s_col{5, 1, 5, 6, 3, 8};
+    auto const exp_col2_sort  = floats_col{4, 1, 4, 6, 3, 9};
+    auto const exp_keys1_sort = int32s_col{9, 15, 19, 20, 20, 21};
+    auto const exp_keys2_sort = floats_col{21., NaN, 20., 19., 20., 9.};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, NoNullsTable)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col1  = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const col2  = floats_col{10, 11, 12, 13, 14, 15, 16};
+  auto const keys1 = int32s_col{20, 20, 20, 20, 19, 21, 9};
+  auto const keys2 = int32s_col{19, 19, 19, 20, 20, 9, 21};
+
+  auto const input   = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const key_idx = std::vector<cudf::size_type>{2, 3};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col1_sort  = int32s_col{6, 4, 0, 3, 5};
+    auto const exp_col2_sort  = floats_col{16, 14, 10, 13, 15};
+    auto const exp_keys1_sort = int32s_col{9, 19, 20, 20, 21};
+    auto const exp_keys2_sort = int32s_col{21, 20, 19, 20, 9};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col1_sort  = int32s_col{6, 4, 2, 3, 5};
+    auto const exp_col2_sort  = floats_col{16, 14, 12, 13, 15};
+    auto const exp_keys1_sort = int32s_col{9, 19, 20, 20, 21};
+    auto const exp_keys2_sort = int32s_col{21, 20, 19, 20, 9};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col1_sort  = int32s_col{6, 4, 3, 5};
+    auto const exp_col2_sort  = floats_col{16, 14, 13, 15};
+    auto const exp_keys1_sort = int32s_col{9, 19, 20, 21};
+    auto const exp_keys2_sort = int32s_col{21, 20, 20, 9};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, SlicedNoNullsTable)
+{
+  auto constexpr dont_care = int32_t{0};
+
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const col1  = int32s_col{dont_care, dont_care, 6, 6, 6, 3, 5, 8, 5, dont_care};
+  auto const col2  = floats_col{dont_care, dont_care, 6, 6, 6, 3, 4, 9, 4, dont_care};
+  auto const keys1 = int32s_col{dont_care, dont_care, 20, 20, 20, 20, 19, 21, 9, dont_care};
+  auto const keys2 = int32s_col{dont_care, dont_care, 19, 19, 19, 20, 20, 9, 21, dont_care};
+
+  auto const input_original = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const input          = cudf::slice(input_original, {2, 9})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{2, 3};
+
+  auto const exp_col1_sort  = int32s_col{5, 5, 6, 3, 8};
+  auto const exp_col2_sort  = floats_col{4, 4, 6, 3, 9};
+  auto const exp_keys1_sort = int32s_col{9, 19, 20, 20, 21};
+  auto const exp_keys2_sort = int32s_col{21, 20, 19, 20, 9};
+  auto const expected_sort =
+    cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
+
+TEST_F(DistinctKeepFirstLastNone, SlicedNoNullsTable)
+{
+  auto constexpr dont_care = int32_t{0};
+
+  // Column(s) used to test needs to have different rows for the same keys.
+  // clang-format off
+  auto const col1  = int32s_col{0, 1, 2, // <- don't care
+                                3, 4, 5, 6, 7, 8, 9, dont_care};
+  auto const col2  = floats_col{10, 11, 12, // <- don't care
+                                13, 14, 15, 16, 17, 18, 19, dont_care};
+  auto const keys1 = int32s_col{20, 20, 20, // <- don't care
+                                20, 20, 20, 20, 19, 21, 9, dont_care};
+  auto const keys2 = int32s_col{19, 19, 19, // <- don't care
+                                19, 19, 19, 20, 20, 9, 21, dont_care};
+  // clang-format on
+  auto const input_original = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const input          = cudf::slice(input_original, {3, 10})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{2, 3};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col1_sort  = int32s_col{9, 7, 3, 6, 8};
+    auto const exp_col2_sort  = floats_col{19, 17, 13, 16, 18};
+    auto const exp_keys1_sort = int32s_col{9, 19, 20, 20, 21};
+    auto const exp_keys2_sort = int32s_col{21, 20, 19, 20, 9};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col1_sort  = int32s_col{9, 7, 5, 6, 8};
+    auto const exp_col2_sort  = floats_col{19, 17, 15, 16, 18};
+    auto const exp_keys1_sort = int32s_col{9, 19, 20, 20, 21};
+    auto const exp_keys2_sort = int32s_col{21, 20, 19, 20, 9};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col1_sort  = int32s_col{9, 7, 6, 8};
+    auto const exp_col2_sort  = floats_col{19, 17, 16, 18};
+    auto const exp_keys1_sort = int32s_col{9, 19, 20, 21};
+    auto const exp_keys2_sort = int32s_col{21, 20, 20, 9};
+    auto const expected_sort =
+      cudf::table_view{{exp_col1_sort, exp_col2_sort, exp_keys1_sort, exp_keys2_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, InputWithNulls)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const col     = int32s_col{5, 4, 4, 1, 8, 1};
+  auto const keys    = int32s_col{{20, null, null, 19, 21, 19}, nulls_at({1, 2})};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const exp_col_sort  = int32s_col{4, 1, 5, 8};
+    auto const exp_keys_sort = int32s_col{{null, 19, 20, 21}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const exp_col_sort  = int32s_col{4, 4, 1, 5, 8};
+    auto const exp_keys_sort = int32s_col{{null, null, 19, 20, 21}, nulls_at({0, 1})};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, InputWithNullsAndNaNs)
+{
+  auto constexpr null{0.0};  // shadow the global `null` variable of type int
+
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const col   = int32s_col{5, 4, 1, 1, 1, 4, 1, 8, 1};
+  auto const keys  = floats_col{{20., null, NaN, NaN, NaN, null, 19., 21., 19.}, nulls_at({1, 5})};
+  auto const input = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal, NaNs are unequal.
+  {
+    auto const exp_col_sort  = int32s_col{4, 1, 5, 8, 1, 1, 1};
+    auto const exp_keys_sort = floats_col{{null, 19., 20., 21., NaN, NaN, NaN}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // Nulls are equal, NaNs are equal.
+  {
+    auto const exp_col_sort  = int32s_col{4, 1, 5, 8, 1};
+    auto const exp_keys_sort = floats_col{{null, 19., 20., 21., NaN}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // Nulls are unequal, NaNs are unequal.
+  {
+    auto const exp_col_sort = int32s_col{4, 4, 1, 5, 8, 1, 1, 1};
+    auto const exp_keys_sort =
+      floats_col{{null, null, 19., 20., 21., NaN, NaN, NaN}, nulls_at({0, 1})};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL, NAN_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // Nulls are unequal, NaNs are equal.
+  {
+    auto const exp_col_sort  = int32s_col{4, 4, 1, 5, 8, 1};
+    auto const exp_keys_sort = floats_col{{null, null, 19., 20., 21., NaN}, nulls_at({0, 1})};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL, NAN_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, InputWithNullsEqual)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const keys    = int32s_col{{20, null, null, 19, 21, 19, 22}, nulls_at({1, 2})};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col_sort  = int32s_col{1, 3, 0, 4, 6};
+    auto const exp_keys_sort = int32s_col{{null, 19, 20, 21, 22}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST, NULL_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col_sort  = int32s_col{2, 5, 0, 4, 6};
+    auto const exp_keys_sort = int32s_col{{null, 19, 20, 21, 22}, null_at(0)};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST, NULL_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col_sort  = int32s_col{0, 4, 6};
+    auto const exp_keys_sort = int32s_col{{20, 21, 22}, no_nulls()};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE, NULL_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select(key_idx));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, InputWithNullsUnequal)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7};
+  auto const keys    = int32s_col{{20, null, null, 19, 21, 19, 22, 20}, nulls_at({1, 2})};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col_sort  = int32s_col{0, 1, 2, 3, 4, 6};
+    auto const exp_keys_sort = int32s_col{{20, null, null, 19, 21, 22}, nulls_at({1, 2})};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col_sort  = int32s_col{1, 2, 4, 5, 6, 7};
+    auto const exp_keys_sort = int32s_col{{null, null, 21, 19, 22, 20}, nulls_at({0, 1})};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col_sort  = int32s_col{1, 2, 4, 6};
+    auto const exp_keys_sort = int32s_col{{null, null, 21, 22}, nulls_at({0, 1})};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, InputWithNaNsEqual)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const keys    = floats_col{20., NaN, NaN, 19., 21., 19., 22.};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col_sort  = int32s_col{0, 1, 3, 4, 6};
+    auto const exp_keys_sort = floats_col{20., NaN, 19., 21., 22.};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST, NULL_EQUAL, NAN_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col_sort  = int32s_col{0, 2, 4, 5, 6};
+    auto const exp_keys_sort = floats_col{20., NaN, 21., 19., 22.};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST, NULL_EQUAL, NAN_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col_sort  = int32s_col{0, 4, 6};
+    auto const exp_keys_sort = floats_col{20., 21., 22.};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE, NULL_EQUAL, NAN_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, InputWithNaNsUnequal)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7};
+  auto const keys    = floats_col{20., NaN, NaN, 19., 21., 19., 22., 20.};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col_sort  = int32s_col{0, 1, 2, 3, 4, 6};
+    auto const exp_keys_sort = floats_col{20., NaN, NaN, 19., 21., 22.};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST, NULL_UNEQUAL, NAN_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col_sort  = int32s_col{1, 2, 4, 5, 6, 7};
+    auto const exp_keys_sort = floats_col{NaN, NaN, 21., 19., 22., 20.};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST, NULL_UNEQUAL, NAN_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col_sort  = int32s_col{1, 2, 4, 6};
+    auto const exp_keys_sort = floats_col{NaN, NaN, 21., 22.};
+    auto const expected_sort = cudf::table_view{{exp_col_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE, NULL_UNEQUAL, NAN_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, BasicLists)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  // clang-format off
+  auto const idx = int32s_col{ 0,  0,   1,      2,   1,      3,      4,  5,   5,      6,      4,       4};
+  auto const keys = lists_col{{}, {}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx_sort  = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const exp_keys_sort = lists_col{{}, {1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+  auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
+
+TEST_F(DistinctKeepFirstLastNone, BasicLists)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  // clang-format off
+  auto const idx = int32s_col{ 0,  1,  2,      3,   4,      5,      6,   7,   8,       9,     10,     11};
+  auto const keys = lists_col{{}, {}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_idx_sort  = int32s_col{0, 2, 3, 5, 6, 7, 9};
+    auto const exp_keys_sort = lists_col{{}, {1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_idx_sort  = int32s_col{1, 3, 4, 5, 8, 9, 11};
+    auto const exp_keys_sort = lists_col{{}, {1, 1}, {1}, {1, 2}, {2}, {2, 1}, {2, 2}};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_idx_sort  = int32s_col{3, 5, 9};
+    auto const exp_keys_sort = lists_col{{1, 1}, {1, 2}, {2, 1}};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, SlicedBasicLists)
+{
+  auto constexpr dont_care = int32_t{0};
+
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const idx  = int32s_col{dont_care, dont_care, 1, 2, 1, 3, 4, 5, 5, 6, 4, 4, dont_care};
+  auto const keys = lists_col{
+    {0, 0}, {0, 0}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}, {5, 5}};
+  auto const input_original = cudf::table_view{{idx, keys}};
+  auto const input          = cudf::slice(input_original, {2, 12})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx_sort  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_val_sort  = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+  auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_val_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
+
+TEST_F(DistinctKeepAny, NullableLists)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  auto const idx = int32s_col{0, 0, 1, 1, 4, 5, 5, 6, 4, 4, 6};
+  auto const keys =
+    lists_col{{{}, {}, {1}, {1}, {2, 2}, {2}, {2}, {} /*NULL*/, {2, 2}, {2, 2}, {} /*NULL*/},
+              nulls_at({7, 10})};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const exp_idx_sort  = int32s_col{0, 1, 4, 5, 6};
+    auto const exp_keys_sort = lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/}, null_at(4)};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const exp_idx_sort = int32s_col{0, 1, 4, 5, 6, 6};
+    auto const exp_keys_sort =
+      lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/, {} /*NULL*/}, nulls_at({4, 5})};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, ListsWithNullsEqual)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  // clang-format off
+  auto const idx = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  auto const keys =
+    lists_col{{{}, {}, {1}, {1}, {2, 2}, {2}, {2}, {} /*NULL*/, {2, 2}, {2, 2}, {} /*NULL*/},
+              nulls_at({7, 10})};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_idx_sort  = int32s_col{0, 2, 4, 5, 7};
+    auto const exp_keys_sort = lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/}, null_at(4)};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST, NULL_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_idx_sort  = int32s_col{1, 3, 6, 9, 10};
+    auto const exp_keys_sort = lists_col{{{}, {1}, {2}, {2, 2}, {} /*NULL*/}, null_at(4)};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST, NULL_EQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_idx  = int32s_col{};
+    auto const exp_keys = lists_col{};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::distinct(input, key_idx, KEEP_NONE, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, ListsWithNullsUnequal)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  // clang-format off
+  auto const idx = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  auto const keys =
+    lists_col{{{}, {}, {1}, {1}, {2, 2}, {2}, {2}, {} /*NULL*/, {2, 2}, {2, 2}, {} /*NULL*/},
+              nulls_at({7, 10})};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_idx_sort = int32s_col{0, 2, 4, 5, 7, 10};
+    auto const exp_keys_sort =
+      lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/, {} /*NULL*/}, nulls_at({4, 5})};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_idx_sort = int32s_col{1, 3, 6, 7, 9, 10};
+    auto const exp_keys_sort =
+      lists_col{{{}, {1}, {2}, {} /*NULL*/, {2, 2}, {} /*NULL*/}, nulls_at({3, 5})};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_idx_sort = int32s_col{7, 10};
+    auto const exp_keys_sort =
+      lists_col{{lists_col{} /*NULL*/, lists_col{} /*NULL*/}, nulls_at({0, 1})};
+    auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, ListsOfStructs)
+{
+  // Constructing a list of structs of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==
+  // 16.  [{Null, 'b'}]
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx     = int32s_col{1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9, 9, 10, 10};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 5, 8, 9, 10, 11, 13, 15};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepFirstLastNone, ListsOfStructs)
+{
+  // Constructing a list of structs of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==
+  // 16.  [{Null, 'b'}]
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 5, 8, 9, 10, 11, 13, 15};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const expect_map   = int32s_col{1, 3, 4, 7, 8, 9, 10, 12, 14, 16};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const expect_map   = int32s_col{4, 8, 9, 10};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, SlicedListsOfStructs)
+{
+  // Constructing a list of struct of two elements
+  // 0.   []                  ==                <- Don't care
+  // 1.   []                  !=                <- Don't care
+  // 2.   Null                ==                <- Don't care
+  // 3.   Null                !=                <- Don't care
+  // 4.   [Null, Null]        !=                <- Don't care
+  // 5.   [Null]              ==                <- Don't care
+  // 6.   [Null]              ==                <- Don't care
+  // 7.   [Null]              !=                <- Don't care
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==                <- Don't care
+  // 16.  [{Null, 'b'}]                         <- Don't care
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx            = int32s_col{1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9, 9, 10, 10};
+  auto const input_original = cudf::table_view{{idx, keys}};
+  auto const input          = cudf::slice(input_original, {8, 15})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{8, 9, 10, 11, 13};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*expect_table, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{8, 9, 10, 11, 13, 14};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, ListsOfEmptyStructs)
+{
+  // 0.  []             ==
+  // 1.  []             !=
+  // 2.  Null           ==
+  // 3.  Null           !=
+  // 4.  [Null, Null]   ==
+  // 5.  [Null, Null]   ==
+  // 6.  [Null, Null]   !=
+  // 7.  [Null]         ==
+  // 8.  [Null]         !=
+  // 9.  [{}]           ==
+  // 10. [{}]           !=
+  // 11. [{}, {}]       ==
+  // 12. [{}, {}]
+
+  auto const structs_null_it = nulls_at({0, 1, 2, 3, 4, 5, 6, 7});
+  auto [structs_null_mask, structs_null_count] =
+    cudf::test::detail::make_null_mask(structs_null_it, structs_null_it + 14);
+  auto const structs =
+    cudf::column_view(cudf::data_type(cudf::type_id::STRUCT),
+                      14,
+                      nullptr,
+                      static_cast<cudf::bitmask_type const*>(structs_null_mask.data()),
+                      structs_null_count);
+
+  auto const offsets       = int32s_col{0, 0, 0, 0, 0, 2, 4, 6, 7, 8, 9, 10, 12, 14};
+  auto const lists_null_it = nulls_at({2, 3});
+  auto [lists_null_mask, lists_null_count] =
+    cudf::test::detail::make_null_mask(lists_null_it, lists_null_it + 13);
+  auto const keys =
+    cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                      13,
+                      nullptr,
+                      static_cast<cudf::bitmask_type const*>(lists_null_mask.data()),
+                      lists_null_count,
+                      0,
+                      {offsets, structs});
+
+  auto const idx     = int32s_col{1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 7, 9, 11};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8, 9, 11};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, EmptyDeepList)
+{
+  // List<List<int>>, where all lists are empty:
+  //
+  // 0. []
+  // 1. []
+  // 2. Null
+  // 3. Null
+
+  auto const keys =
+    lists_col{{lists_col{}, lists_col{}, lists_col{}, lists_col{}}, nulls_at({2, 3})};
+
+  auto const idx     = int32s_col{1, 1, 2, 2};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, StructsOfStructs)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 2}, 4}  |
+  // 2 |  { Null,   6}  |
+  // 3 |  { Null,   4}  |
+  // 4 |  Null          |
+  // 5 |  Null          |  // Same as 4
+  // 6 |  { {1, 1}, 5}  |  // Same as 0
+  // 7 |  { Null,   4}  |  // Same as 3
+  // 8 |  { {2, 1}, 5}  |
+
+  auto s1 = [&] {
+    auto a  = int32s_col{1, 1, XXX, XXX, XXX, XXX, 1, XXX, 2};
+    auto b  = int32s_col{1, 2, XXX, XXX, XXX, XXX, 1, XXX, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({2, 3, 7})};
+
+    auto c = int32s_col{5, 4, 6, 4, XXX, XXX, 5, 4, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({4, 5});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const idx     = int32s_col{0, 1, 2, 3, 4, 4, 0, 3, 8};
+  auto const input   = cudf::table_view{{idx, s1}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 1, 2, 3, 4, 8};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 1, 2, 3, 7, 4, 5, 8};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, SlicedStructsOfStructs)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 2}, 4}  |
+  // 2 |  { Null,   6}  |
+  // 3 |  { Null,   4}  |
+  // 4 |  Null          |
+  // 5 |  Null          |  // Same as 4
+  // 6 |  { {1, 1}, 5}  |  // Same as 0
+  // 7 |  { Null,   4}  |  // Same as 3
+  // 8 |  { {2, 1}, 5}  |
+
+  auto s1 = [&] {
+    auto a  = int32s_col{1, 1, XXX, XXX, XXX, XXX, 1, XXX, 2};
+    auto b  = int32s_col{1, 2, XXX, XXX, XXX, XXX, 1, XXX, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({2, 3, 7})};
+
+    auto c = int32s_col{5, 4, 6, 4, XXX, XXX, 5, 4, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({4, 5});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const idx            = int32s_col{0, 1, 2, 3, 4, 4, 0, 3, 8};
+  auto const input_original = cudf::table_view{{idx, s1}};
+  auto const input          = cudf::slice(input_original, {1, 7})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{6, 1, 2, 3, 4};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{6, 1, 2, 3, 4, 5};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, StructsOfLists)
+{
+  auto const idx  = int32s_col{1, 2, 1, 3, 4, 5, 5, 6, 4, 4};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    auto child2 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    auto child3 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx_sort  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_keys_sort = [] {
+    auto child1 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
+
+TEST_F(DistinctKeepFirstLastNone, StructsOfLists)
+{
+  auto const idx  = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    auto child2 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    auto child3 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const expect_map   = int32s_col{0, 1, 3, 4, 5, 7};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_FIRST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // KEEP_LAST
+  {
+    auto const expect_map   = int32s_col{1, 2, 3, 6, 7, 9};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_LAST);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+
+  // KEEP_NONE
+  {
+    auto const expect_map   = int32s_col{1, 3, 7};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result      = cudf::distinct(input, key_idx, KEEP_NONE);
+    auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result_sort);
+  }
+}
+
+TEST_F(DistinctKeepAny, SlicedStructsOfLists)
+{
+  auto const idx  = int32s_col{0, 0, 1, 2, 1, 3, 4, 5, 5, 6, 4, 4, 70};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{
+      {0, 0}, {0, 0}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}, {5, 5}};
+    auto child2 = lists_col{
+      {0, 0}, {0, 0}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}, {5, 5}};
+    auto child3 = lists_col{
+      {0, 0}, {0, 0}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}, {5, 5}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input_original = cudf::table_view{{idx, keys}};
+  auto const input          = cudf::slice(input_original, {2, 12})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx_sort  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_keys_sort = [] {
+    auto child1 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const expected_sort = cudf::table_view{{exp_idx_sort, exp_keys_sort}};
+
+  auto const result      = cudf::distinct(input, key_idx, KEEP_ANY);
+  auto const result_sort = cudf::sort_by_key(*result, result->select({0}));
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_sort, *result_sort);
+}
diff --git a/cpp/tests/stream_compaction/drop_nans_tests.cpp b/cpp/tests/stream_compaction/drop_nans_tests.cpp
new file mode 100644
index 0000000..bce8b19
--- /dev/null
+++ b/cpp/tests/stream_compaction/drop_nans_tests.cpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cmath>
+#include <cudf/copying.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+struct DropNANsTest : public cudf::test::BaseFixture {};
+
+TEST_F(DropNANsTest, MixedNANsAndNull)
+{
+  using F = float;
+  using D = double;
+  cudf::test::fixed_width_column_wrapper<float> col1{
+    {F(1.0), F(2.0), F(NAN), F(NAN), F(5.0), F(6.0)}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{D(NAN), 40.0, 70.0, 5.0, 2.0, 10.0},
+                                                      {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 2};
+  cudf::test::fixed_width_column_wrapper<float> col1_expected{{2.0, 3.0, 5.0, 6.0}, {1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{40, 70, 2, 10}, {1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{40.0, 70.0, 2.0, 10.0},
+                                                               {1, 0, 1, 0}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nans(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNANsTest, NoNANs)
+{
+  cudf::test::fixed_width_column_wrapper<float> col1{{1.0, 2.0, 3.0, 4.0, 5.0, 6.0},
+                                                     {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 1, 1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 2};
+
+  auto got = cudf::drop_nans(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(DropNANsTest, MixedWithThreshold)
+{
+  using F = float;
+  using D = double;
+  cudf::test::fixed_width_column_wrapper<float> col1{
+    {F(1.0), F(2.0), F(NAN), F(NAN), F(5.0), F(6.0)}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{D(NAN), 40.0, 70.0, D(NAN), 2.0, 10.0},
+                                                      {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 2};
+  cudf::test::fixed_width_column_wrapper<float> col1_expected{{1.0, 2.0, 3.0, 5.0, 6.0},
+                                                              {1, 1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{10, 40, 70, 2, 10},
+                                                                {1, 1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{D(NAN), 40.0, 70.0, 2.0, 10.0},
+                                                               {1, 1, 0, 1, 0}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nans(input, keys, 1);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNANsTest, EmptyTable)
+{
+  cudf::table_view input{std::vector<cudf::column_view>()};
+  cudf::table_view expected{std::vector<cudf::column_view>()};
+  std::vector<cudf::size_type> keys{};
+
+  auto got = cudf::drop_nans(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNANsTest, EmptyColumns)
+{
+  cudf::test::fixed_width_column_wrapper<float> col1{};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{};
+  cudf::test::fixed_width_column_wrapper<double> col3{};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 2};
+  cudf::test::fixed_width_column_wrapper<float> col1_expected{};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nans(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNANsTest, EmptyKeys)
+{
+  using F = float;
+  cudf::test::fixed_width_column_wrapper<float> col1{
+    {F(1.0), F(2.0), F(NAN), F(NAN), F(5.0), F(6.0)}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1}};
+  std::vector<cudf::size_type> keys{};
+
+  auto got = cudf::drop_nans(input, keys);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(DropNANsTest, NonFloatingKey)
+{
+  cudf::test::fixed_width_column_wrapper<float> col1{{1.0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{2};
+  cudf::test::fixed_width_column_wrapper<double> col3{{3.0}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 1};
+  EXPECT_THROW(cudf::drop_nans(input, keys), cudf::logic_error);
+}
diff --git a/cpp/tests/stream_compaction/drop_nulls_tests.cpp b/cpp/tests/stream_compaction/drop_nulls_tests.cpp
new file mode 100644
index 0000000..bff56eb
--- /dev/null
+++ b/cpp/tests/stream_compaction/drop_nulls_tests.cpp
@@ -0,0 +1,228 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <numeric>
+
+struct DropNullsTest : public cudf::test::BaseFixture {};
+
+TEST_F(DropNullsTest, WholeRowIsNull)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 1, 2};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{{true, false, false, true},
+                                                                {1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{10, 40, 5, 2}, {1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{10, 40, 5, 2}, {1, 1, 1, 1}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNullsTest, NoNull)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 1, 1, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 1, 2};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(DropNullsTest, MixedSetOfRows)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 1, 2};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{{true, false, false, true},
+                                                                {1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{10, 40, 5, 2}, {1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{10, 40, 5, 2}, {1, 1, 1, 1}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNullsTest, LargeColumn)
+{
+  // This test is a C++ repro of the failing Python in this issue:
+  // https://github.com/rapidsai/cudf/issues/5456
+  // Specifically, there are two large columns, one nullable, one non-nullable
+  using T       = int32_t;
+  using index_T = int64_t;
+  constexpr cudf::size_type column_size{270000};
+  std::vector<index_T> index(column_size);
+  std::vector<T> data(column_size);
+  std::vector<bool> mask_data(column_size);
+
+  std::iota(index.begin(), index.end(), 0);
+  std::generate_n(data.begin(), column_size, [x = 1]() mutable { return x++ % 3; });
+  std::transform(data.begin(), data.end(), mask_data.begin(), [](auto const& x) { return x != 0; });
+
+  std::vector<T> expected_data(column_size);
+  // zeros are the null elements, remove them
+  auto end           = std::remove_copy(data.begin(), data.end(), expected_data.begin(), 0);
+  auto expected_size = std::distance(expected_data.begin(), end);
+  expected_data.resize(expected_size);
+
+  std::vector<index_T> expected_index(expected_size);
+  std::copy_if(index.begin(), index.end(), expected_index.begin(), [](auto const& x) {
+    return (x - 2) % 3 != 0;
+  });
+
+  // output null mask is all true
+  std::vector<bool> expected_mask(expected_size, true);
+
+  cudf::test::fixed_width_column_wrapper<T> col1(data.begin(), data.end(), mask_data.begin());
+  cudf::test::fixed_width_column_wrapper<index_T> index1(index.begin(), index.end());
+  cudf::table_view input{{index1, col1}};
+  std::vector<cudf::size_type> keys{1};
+
+  cudf::test::fixed_width_column_wrapper<T> exp1(
+    expected_data.begin(), expected_data.end(), expected_mask.begin());
+  cudf::test::fixed_width_column_wrapper<index_T> exp_index1(expected_index.begin(),
+                                                             expected_index.end());
+  cudf::table_view expected{{exp_index1, exp1}};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNullsTest, MixedSetOfRowsWithThreshold)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{{10, 40, 70, 5, 2, 10}, {1, 1, 0, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3{{10, 40, 70, 5, 2, 10}, {1, 1, 1, 1, 1, 1}};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 1, 2};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{{true, false, false, true, false},
+                                                                {1, 1, 1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{{10, 40, 5, 2, 10},
+                                                                {1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{{10, 40, 5, 2, 10}, {1, 1, 1, 1, 1}};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nulls(input, keys, keys.size() - 1);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNullsTest, EmptyTable)
+{
+  cudf::table_view input{std::vector<cudf::column_view>()};
+  cudf::table_view expected{std::vector<cudf::column_view>()};
+  std::vector<cudf::size_type> keys{};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNullsTest, EmptyColumns)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2{};
+  cudf::test::fixed_width_column_wrapper<double> col3{};
+  cudf::table_view input{{col1, col2, col3}};
+  std::vector<cudf::size_type> keys{0, 1, 2};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_expected{};
+  cudf::test::fixed_width_column_wrapper<double> col3_expected{};
+  cudf::table_view expected{{col1_expected, col2_expected, col3_expected}};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(DropNullsTest, EmptyKeys)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{true, false, true, false, true, false},
+                                                       {1, 1, 0, 1, 1, 0}};
+  cudf::table_view input{{col1}};
+  std::vector<cudf::size_type> keys{};
+
+  auto got = cudf::drop_nulls(input, keys);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(DropNullsTest, StringColWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{11, 12, 11, 13, 12, 15},
+                                                       {1, 1, 0, 1, 0, 1}};
+  cudf::test::strings_column_wrapper col2{{"Hi", "Hello", "Hi", "No", "Hello", "Naive"},
+                                          {1, 1, 0, 1, 0, 1}};
+  cudf::table_view input{{col1, col2}};
+  std::vector<cudf::size_type> keys{0, 1};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1_expected{{11, 12, 13, 15}, {1, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col2_expected{{"Hi", "Hello", "No", "Naive"}, {1, 1, 1, 1}};
+  cudf::table_view expected{{col1_expected, col2_expected}};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+template <typename T>
+struct DropNullsTestAll : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(DropNullsTestAll, cudf::test::NumericTypes);
+
+TYPED_TEST(DropNullsTestAll, AllNull)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T> key_col{{true, false, true, false, true, false},
+                                                    {0, 0, 0, 0, 0, 0}};
+  cudf::test::fixed_width_column_wrapper<T> col{{true, false, true, false, true, false},
+                                                {1, 1, 1, 1, 1, 1}};
+  cudf::table_view input{{key_col, col}};
+  std::vector<cudf::size_type> keys{0};
+  cudf::test::fixed_width_column_wrapper<T> expected_col{};
+  cudf::column_view view = expected_col;
+  cudf::table_view expected{{expected_col, expected_col}};
+
+  auto got = cudf::drop_nulls(input, keys);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
diff --git a/cpp/tests/stream_compaction/stable_distinct_tests.cpp b/cpp/tests/stream_compaction/stable_distinct_tests.cpp
new file mode 100644
index 0000000..e28b96f
--- /dev/null
+++ b/cpp/tests/stream_compaction/stable_distinct_tests.cpp
@@ -0,0 +1,1354 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cmath>
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr NaN          = std::numeric_limits<double>::quiet_NaN();
+auto constexpr KEEP_ANY     = cudf::duplicate_keep_option::KEEP_ANY;
+auto constexpr KEEP_FIRST   = cudf::duplicate_keep_option::KEEP_FIRST;
+auto constexpr KEEP_LAST    = cudf::duplicate_keep_option::KEEP_LAST;
+auto constexpr KEEP_NONE    = cudf::duplicate_keep_option::KEEP_NONE;
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+auto constexpr NAN_EQUAL    = cudf::nan_equality::ALL_EQUAL;
+auto constexpr NAN_UNEQUAL  = cudf::nan_equality::UNEQUAL;
+
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_col  = cudf::test::fixed_width_column_wrapper<float>;
+using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+using strings_col = cudf::test::strings_column_wrapper;
+using structs_col = cudf::test::structs_column_wrapper;
+
+using cudf::nan_policy;
+using cudf::null_equality;
+using cudf::null_policy;
+using cudf::test::iterators::no_nulls;
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+struct StableDistinctKeepAny : public cudf::test::BaseFixture {};
+
+struct StableDistinctKeepFirstLastNone : public cudf::test::BaseFixture {};
+
+TEST_F(StableDistinctKeepAny, StringKeyColumn)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const col = int32s_col{{5, 5, null, null, 5, 8, 1}, nulls_at({2, 3})};
+  auto const keys =
+    strings_col{{"all", "all", "new", "new", "" /*NULL*/, "the", "strings"}, null_at(4)};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_col  = int32s_col{{5, null, 5, 8, 1}, null_at(1)};
+  auto const exp_keys = strings_col{{"all", "new", "" /*NULL*/, "the", "strings"}, null_at(2)};
+  auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, StringKeyColumn)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col = int32s_col{{0, null, 2, 3, 4, 5, 6}, null_at(1)};
+  auto const keys =
+    strings_col{{"all", "new", "new", "all", "" /*NULL*/, "the", "strings"}, null_at(4)};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col  = int32s_col{{0, null, 4, 5, 6}, null_at(1)};
+    auto const exp_keys = strings_col{{"all", "new", "" /*NULL*/, "the", "strings"}, null_at(2)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col  = int32s_col{{2, 3, 4, 5, 6}, no_nulls()};
+    auto const exp_keys = strings_col{{"new", "all", "" /*NULL*/, "the", "strings"}, null_at(2)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col  = int32s_col{{4, 5, 6}, no_nulls()};
+    auto const exp_keys = strings_col{{"" /*NULL*/, "the", "strings"}, null_at(0)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, EmptyInputTable)
+{
+  int32s_col col(std::initializer_list<int32_t>{});
+  cudf::table_view input{{col}};
+  std::vector<cudf::size_type> key_idx{0};
+
+  auto got = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(StableDistinctKeepAny, NoColumnInputTable)
+{
+  cudf::table_view input{std::vector<cudf::column_view>()};
+  std::vector<cudf::size_type> key_idx{1, 2};
+
+  auto got = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(StableDistinctKeepAny, EmptyKeys)
+{
+  int32s_col col{{5, 4, 3, 5, 8, 1}, {1, 0, 1, 1, 1, 1}};
+  int32s_col empty_col{};
+  cudf::table_view input{{col}};
+  std::vector<cudf::size_type> key_idx{};
+
+  auto got = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view{{empty_col}}, got->view());
+}
+
+TEST_F(StableDistinctKeepAny, NoNullsTable)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const col1  = int32s_col{6, 6, 6, 3, 5, 8, 5};
+  auto const col2  = floats_col{6, 6, 6, 3, 4, 9, 4};
+  auto const keys1 = int32s_col{20, 20, 20, 20, 19, 21, 9};
+  auto const keys2 = int32s_col{19, 19, 19, 20, 20, 9, 21};
+
+  auto const input   = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const key_idx = std::vector<cudf::size_type>{2, 3};
+
+  auto const exp_col1  = int32s_col{6, 3, 5, 8, 5};
+  auto const exp_col2  = floats_col{6, 3, 4, 9, 4};
+  auto const exp_keys1 = int32s_col{20, 20, 19, 21, 9};
+  auto const exp_keys2 = int32s_col{19, 20, 20, 9, 21};
+  auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(StableDistinctKeepAny, NoNullsTableWithNaNs)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const col1  = int32s_col{6, 6, 6, 1, 1, 1, 3, 5, 8, 5};
+  auto const col2  = floats_col{6, 6, 6, 1, 1, 1, 3, 4, 9, 4};
+  auto const keys1 = int32s_col{20, 20, 20, 15, 15, 15, 20, 19, 21, 9};
+  auto const keys2 = floats_col{19., 19., 19., NaN, NaN, NaN, 20., 20., 9., 21.};
+
+  auto const input   = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const key_idx = std::vector<cudf::size_type>{2, 3};
+
+  // NaNs are unequal.
+  {
+    auto const exp_col1  = int32s_col{6, 1, 1, 1, 3, 5, 8, 5};
+    auto const exp_col2  = floats_col{6, 1, 1, 1, 3, 4, 9, 4};
+    auto const exp_keys1 = int32s_col{20, 15, 15, 15, 20, 19, 21, 9};
+    auto const exp_keys2 = floats_col{19., NaN, NaN, NaN, 20., 20., 9., 21.};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // NaNs are equal.
+  {
+    auto const exp_col1  = int32s_col{6, 1, 3, 5, 8, 5};
+    auto const exp_col2  = floats_col{6, 1, 3, 4, 9, 4};
+    auto const exp_keys1 = int32s_col{20, 15, 20, 19, 21, 9};
+    auto const exp_keys2 = floats_col{19., NaN, 20., 20., 9., 21.};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, NoNullsTable)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col1  = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const col2  = floats_col{10, 11, 12, 13, 14, 15, 16};
+  auto const keys1 = int32s_col{20, 20, 20, 20, 19, 21, 9};
+  auto const keys2 = int32s_col{19, 19, 19, 20, 20, 9, 21};
+
+  auto const input   = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const key_idx = std::vector<cudf::size_type>{2, 3};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col1  = int32s_col{0, 3, 4, 5, 6};
+    auto const exp_col2  = floats_col{10, 13, 14, 15, 16};
+    auto const exp_keys1 = int32s_col{20, 20, 19, 21, 9};
+    auto const exp_keys2 = int32s_col{19, 20, 20, 9, 21};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col1  = int32s_col{2, 3, 4, 5, 6};
+    auto const exp_col2  = floats_col{12, 13, 14, 15, 16};
+    auto const exp_keys1 = int32s_col{20, 20, 19, 21, 9};
+    auto const exp_keys2 = int32s_col{19, 20, 20, 9, 21};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col1  = int32s_col{3, 4, 5, 6};
+    auto const exp_col2  = floats_col{13, 14, 15, 16};
+    auto const exp_keys1 = int32s_col{20, 19, 21, 9};
+    auto const exp_keys2 = int32s_col{20, 20, 9, 21};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, SlicedNoNullsTable)
+{
+  auto constexpr dont_care = int32_t{0};
+
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const col1  = int32s_col{dont_care, dont_care, 6, 6, 6, 3, 5, 8, 5, dont_care};
+  auto const col2  = floats_col{dont_care, dont_care, 6, 6, 6, 3, 4, 9, 4, dont_care};
+  auto const keys1 = int32s_col{dont_care, dont_care, 20, 20, 20, 20, 19, 21, 9, dont_care};
+  auto const keys2 = int32s_col{dont_care, dont_care, 19, 19, 19, 20, 20, 9, 21, dont_care};
+
+  auto const input_original = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const input          = cudf::slice(input_original, {2, 9})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{2, 3};
+
+  auto const exp_col1  = int32s_col{6, 3, 5, 8, 5};
+  auto const exp_col2  = floats_col{6, 3, 4, 9, 4};
+  auto const exp_keys1 = int32s_col{20, 20, 19, 21, 9};
+  auto const exp_keys2 = int32s_col{19, 20, 20, 9, 21};
+  auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, SlicedNoNullsTable)
+{
+  auto constexpr dont_care = int32_t{0};
+
+  // Column(s) used to test needs to have different rows for the same keys.
+  // clang-format off
+  auto const col1  = int32s_col{0, 1, 2, // <- don't care
+                                3, 4, 5, 6, 7, 8, 9, dont_care};
+  auto const col2  = floats_col{10, 11, 12, // <- don't care
+                                13, 14, 15, 16, 17, 18, 19, dont_care};
+  auto const keys1 = int32s_col{20, 20, 20, // <- don't care
+                                20, 20, 20, 20, 19, 21, 9, dont_care};
+  auto const keys2 = int32s_col{19, 19, 19, // <- don't care
+                                19, 19, 19, 20, 20, 9, 21, dont_care};
+  // clang-format on
+  auto const input_original = cudf::table_view{{col1, col2, keys1, keys2}};
+  auto const input          = cudf::slice(input_original, {3, 10})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{2, 3};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col1  = int32s_col{3, 6, 7, 8, 9};
+    auto const exp_col2  = floats_col{13, 16, 17, 18, 19};
+    auto const exp_keys1 = int32s_col{20, 20, 19, 21, 9};
+    auto const exp_keys2 = int32s_col{19, 20, 20, 9, 21};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col1  = int32s_col{5, 6, 7, 8, 9};
+    auto const exp_col2  = floats_col{15, 16, 17, 18, 19};
+    auto const exp_keys1 = int32s_col{20, 20, 19, 21, 9};
+    auto const exp_keys2 = int32s_col{19, 20, 20, 9, 21};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col1  = int32s_col{6, 7, 8, 9};
+    auto const exp_col2  = floats_col{16, 17, 18, 19};
+    auto const exp_keys1 = int32s_col{20, 19, 21, 9};
+    auto const exp_keys2 = int32s_col{20, 20, 9, 21};
+    auto const expected  = cudf::table_view{{exp_col1, exp_col2, exp_keys1, exp_keys2}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, InputWithNulls)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const col     = int32s_col{5, 4, 4, 1, 1, 8};
+  auto const keys    = int32s_col{{20, null, null, 19, 19, 21}, nulls_at({1, 2})};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const exp_col  = int32s_col{5, 4, 1, 8};
+    auto const exp_keys = int32s_col{{20, null, 19, 21}, null_at(1)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const exp_col  = int32s_col{5, 4, 4, 1, 8};
+    auto const exp_keys = int32s_col{{20, null, null, 19, 21}, nulls_at({1, 2})};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, InputWithNullsAndNaNs)
+{
+  auto constexpr null{0.0};  // shadow the global `null` variable of type int
+
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const col   = int32s_col{5, 4, 4, 1, 1, 1, 8, 8, 1};
+  auto const keys  = floats_col{{20., null, null, NaN, NaN, NaN, 19., 19., 21.}, nulls_at({1, 2})};
+  auto const input = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal, NaNs are unequal.
+  {
+    auto const exp_col  = int32s_col{5, 4, 1, 1, 1, 8, 1};
+    auto const exp_keys = floats_col{{20., null, NaN, NaN, NaN, 19., 21.}, null_at(1)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are equal, NaNs are equal.
+  {
+    auto const exp_col  = int32s_col{5, 4, 1, 8, 1};
+    auto const exp_keys = floats_col{{20., null, NaN, 19., 21.}, null_at(1)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are unequal, NaNs are unequal.
+  {
+    auto const exp_col  = int32s_col{5, 4, 4, 1, 1, 1, 8, 1};
+    auto const exp_keys = floats_col{{20., null, null, NaN, NaN, NaN, 19., 21.}, nulls_at({1, 2})};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are unequal, NaNs are equal.
+  {
+    auto const exp_col  = int32s_col{5, 4, 4, 1, 8, 1};
+    auto const exp_keys = floats_col{{20., null, null, NaN, 19., 21.}, nulls_at({1, 2})};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, InputWithNullsEqual)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const keys    = int32s_col{{20, null, null, 19, 21, 19, 22}, nulls_at({1, 2})};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col  = int32s_col{0, 1, 3, 4, 6};
+    auto const exp_keys = int32s_col{{20, null, 19, 21, 22}, null_at(1)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col  = int32s_col{0, 2, 4, 5, 6};
+    auto const exp_keys = int32s_col{{20, null, 21, 19, 22}, null_at(1)};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col  = int32s_col{0, 4, 6};
+    auto const exp_keys = int32s_col{{20, 21, 22}, no_nulls()};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, InputWithNullsUnequal)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7};
+  auto const keys    = int32s_col{{20, null, null, 19, 21, 19, 22, 20}, nulls_at({1, 2})};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col  = int32s_col{0, 1, 2, 3, 4, 6};
+    auto const exp_keys = int32s_col{{20, null, null, 19, 21, 22}, nulls_at({1, 2})};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col  = int32s_col{1, 2, 4, 5, 6, 7};
+    auto const exp_keys = int32s_col{{null, null, 21, 19, 22, 20}, nulls_at({0, 1})};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col  = int32s_col{1, 2, 4, 6};
+    auto const exp_keys = int32s_col{{null, null, 21, 22}, nulls_at({0, 1})};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, InputWithNaNsEqual)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const keys    = floats_col{20., NaN, NaN, 19., 21., 19., 22.};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col  = int32s_col{0, 1, 3, 4, 6};
+    auto const exp_keys = floats_col{20., NaN, 19., 21., 22.};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col  = int32s_col{0, 2, 4, 5, 6};
+    auto const exp_keys = floats_col{20., NaN, 21., 19., 22.};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col  = int32s_col{0, 4, 6};
+    auto const exp_keys = floats_col{20., 21., 22.};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE, NULL_EQUAL, NAN_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, InputWithNaNsUnequal)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const col     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7};
+  auto const keys    = floats_col{20., NaN, NaN, 19., 21., 19., 22., 20.};
+  auto const input   = cudf::table_view{{col, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_col  = int32s_col{0, 1, 2, 3, 4, 6};
+    auto const exp_keys = floats_col{20., NaN, NaN, 19., 21., 22.};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result =
+      cudf::stable_distinct(input, key_idx, KEEP_FIRST, NULL_UNEQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_col  = int32s_col{1, 2, 4, 5, 6, 7};
+    auto const exp_keys = floats_col{NaN, NaN, 21., 19., 22., 20.};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST, NULL_UNEQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_col  = int32s_col{1, 2, 4, 6};
+    auto const exp_keys = floats_col{NaN, NaN, 21., 22.};
+    auto const expected = cudf::table_view{{exp_col, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE, NULL_UNEQUAL, NAN_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, BasicLists)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  // clang-format off
+  auto const idx = int32s_col{ 0,  0,   1,   1,      2,      3,      4,      4,      4,   5,   5,      6};
+  auto const keys = lists_col{{}, {}, {1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx  = int32s_col{0, 1, 2, 3, 4, 5, 6};
+  auto const exp_keys = lists_col{{}, {1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, BasicLists)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  // clang-format off
+  auto const idx = int32s_col{ 0,  1,  2,      3,   4,      5,      6,   7,   8,       9,     10,     11};
+  auto const keys = lists_col{{}, {}, {1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_idx  = int32s_col{0, 2, 3, 5, 6, 7, 9};
+    auto const exp_keys = lists_col{{}, {1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_idx  = int32s_col{1, 3, 4, 5, 8, 9, 11};
+    auto const exp_keys = lists_col{{}, {1, 1}, {1}, {1, 2}, {2}, {2, 1}, {2, 2}};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_idx  = int32s_col{3, 5, 9};
+    auto const exp_keys = lists_col{{1, 1}, {1, 2}, {2, 1}};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, SlicedBasicLists)
+{
+  auto constexpr dont_care = int32_t{0};
+
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const idx  = int32s_col{dont_care, dont_care, 1, 1, 2, 3, 4, 4, 4, 5, 5, 6, dont_care};
+  auto const keys = lists_col{
+    {0, 0}, {0, 0}, {1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}, {5, 5}};
+  auto const input_original = cudf::table_view{{idx, keys}};
+  auto const input          = cudf::slice(input_original, {2, 12})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_val  = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+  auto const expected = cudf::table_view{{exp_idx, exp_val}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(StableDistinctKeepAny, NullableLists)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+  auto const idx = int32s_col{0, 0, 1, 1, 2, 2, 2, 3, 3, 4, 4};
+  auto const keys =
+    lists_col{{{}, {}, {1}, {1}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {} /*NULL*/, {} /*NULL*/},
+              nulls_at({9, 10})};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const exp_idx  = int32s_col{0, 1, 2, 3, 4};
+    auto const exp_keys = lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/}, null_at(4)};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const exp_idx = int32s_col{0, 1, 2, 3, 4, 4};
+    auto const exp_keys =
+      lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/, {} /*NULL*/}, nulls_at({4, 5})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, ListsWithNullsEqual)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const idx = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  auto const keys =
+    lists_col{{{}, {}, {1}, {1}, {2, 2}, {2}, {2}, {} /*NULL*/, {2, 2}, {2, 2}, {} /*NULL*/},
+              nulls_at({7, 10})};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_idx  = int32s_col{0, 2, 4, 5, 7};
+    auto const exp_keys = lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/}, null_at(4)};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_idx  = int32s_col{1, 3, 6, 9, 10};
+    auto const exp_keys = lists_col{{{}, {1}, {2}, {2, 2}, {} /*NULL*/}, null_at(4)};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_idx  = int32s_col{};
+    auto const exp_keys = lists_col{};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE, NULL_EQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, ListsWithNullsUnequal)
+{
+  // Column(s) used to test needs to have different rows for the same keys.
+  auto const idx = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  auto const keys =
+    lists_col{{{}, {}, {1}, {1}, {2, 2}, {2}, {2}, {} /*NULL*/, {2, 2}, {2, 2}, {} /*NULL*/},
+              nulls_at({7, 10})};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const exp_idx = int32s_col{0, 2, 4, 5, 7, 10};
+    auto const exp_keys =
+      lists_col{{{}, {1}, {2, 2}, {2}, {} /*NULL*/, {} /*NULL*/}, nulls_at({4, 5})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const exp_idx = int32s_col{1, 3, 6, 7, 9, 10};
+    auto const exp_keys =
+      lists_col{{{}, {1}, {2}, {} /*NULL*/, {2, 2}, {} /*NULL*/}, nulls_at({3, 5})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const exp_idx  = int32s_col{7, 10};
+    auto const exp_keys = lists_col{{lists_col{} /*NULL*/, lists_col{} /*NULL*/}, nulls_at({0, 1})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, ListsOfStructs)
+{
+  // Constructing a list of structs of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==
+  // 16.  [{Null, 'b'}]
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx     = int32s_col{1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9, 9, 10, 10};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 5, 8, 9, 10, 11, 13, 15};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, ListsOfStructs)
+{
+  // Constructing a list of structs of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==
+  // 16.  [{Null, 'b'}]
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 5, 8, 9, 10, 11, 13, 15};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const expect_map   = int32s_col{1, 3, 4, 7, 8, 9, 10, 12, 14, 16};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const expect_map   = int32s_col{4, 8, 9, 10};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, SlicedListsOfStructs)
+{
+  // Constructing a list of struct of two elements
+  // 0.   []                  ==                <- Don't care
+  // 1.   []                  !=                <- Don't care
+  // 2.   Null                ==                <- Don't care
+  // 3.   Null                !=                <- Don't care
+  // 4.   [Null, Null]        !=                <- Don't care
+  // 5.   [Null]              ==                <- Don't care
+  // 6.   [Null]              ==                <- Don't care
+  // 7.   [Null]              !=                <- Don't care
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==                <- Don't care
+  // 16.  [{Null, 'b'}]                         <- Don't care
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx            = int32s_col{1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9, 9, 10, 10};
+  auto const input_original = cudf::table_view{{idx, keys}};
+  auto const input          = cudf::slice(input_original, {8, 15})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{8, 9, 10, 11, 13};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{8, 9, 10, 11, 13, 14};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, ListsOfEmptyStructs)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+
+  // 0.  []             ==
+  // 1.  []             !=
+  // 2.  Null           ==
+  // 3.  Null           !=
+  // 4.  [Null, Null]   ==
+  // 5.  [Null, Null]   ==
+  // 6.  [Null, Null]   !=
+  // 7.  [Null]         ==
+  // 8.  [Null]         !=
+  // 9.  [{}]           ==
+  // 10. [{}]           !=
+  // 11. [{}, {}]       ==
+  // 12. [{}, {}]
+
+  auto const structs_null_it = nulls_at({0, 1, 2, 3, 4, 5, 6, 7});
+  auto [structs_null_mask, structs_null_count] =
+    cudf::test::detail::make_null_mask(structs_null_it, structs_null_it + 14);
+  auto const structs =
+    cudf::column_view(cudf::data_type(cudf::type_id::STRUCT),
+                      14,
+                      nullptr,
+                      static_cast<cudf::bitmask_type const*>(structs_null_mask.data()),
+                      structs_null_count);
+
+  auto const offsets       = int32s_col{0, 0, 0, 0, 0, 2, 4, 6, 7, 8, 9, 10, 12, 14};
+  auto const lists_null_it = nulls_at({2, 3});
+  auto [lists_null_mask, lists_null_count] =
+    cudf::test::detail::make_null_mask(lists_null_it, lists_null_it + 13);
+  auto const keys =
+    cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                      13,
+                      nullptr,
+                      static_cast<cudf::bitmask_type const*>(lists_null_mask.data()),
+                      lists_null_count,
+                      0,
+                      {offsets, structs});
+
+  auto const idx     = int32s_col{1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 7, 9, 11};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8, 9, 11};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, EmptyDeepList)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+
+  // List<List<int>>, where all lists are empty:
+  //
+  // 0. []
+  // 1. []
+  // 2. Null
+  // 3. Null
+
+  auto const keys =
+    lists_col{{lists_col{}, lists_col{}, lists_col{}, lists_col{}}, nulls_at({2, 3})};
+
+  auto const idx     = int32s_col{1, 1, 2, 2};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, StructsOfStructs)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 1}, 5}  |  // Same as 0
+  // 2 |  { {1, 2}, 4}  |
+  // 3 |  { Null,   6}  |
+  // 4 |  { Null,   4}  |
+  // 5 |  { Null,   4}  |  // Same as 4
+  // 6 |  Null          |
+  // 7 |  Null          |  // Same as 6
+  // 8 |  { {2, 1}, 5}  |
+
+  auto s1 = [&] {
+    auto a  = int32s_col{1, 1, 1, XXX, XXX, XXX, XXX, XXX, 2};
+    auto b  = int32s_col{1, 1, 2, XXX, XXX, XXX, XXX, XXX, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({3, 4, 5})};
+
+    auto c = int32s_col{5, 5, 4, 6, 4, 4, XXX, XXX, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({6, 7});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const idx     = int32s_col{0, 0, 2, 3, 4, 4, 6, 6, 8};
+  auto const input   = cudf::table_view{{idx, s1}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 6, 8};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 4, 6, 6, 8};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, SlicedStructsOfStructs)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 1}, 5}  |  // Same as 0
+  // 2 |  { {1, 2}, 4}  |
+  // 3 |  { Null,   6}  |
+  // 4 |  { Null,   4}  |
+  // 5 |  { Null,   4}  |  // Same as 4
+  // 6 |  Null          |
+  // 7 |  Null          |  // Same as 6
+  // 8 |  { {2, 1}, 5}  |
+
+  auto s1 = [&] {
+    auto a  = int32s_col{1, 1, XXX, XXX, XXX, XXX, 1, XXX, 2};
+    auto b  = int32s_col{1, 2, XXX, XXX, XXX, XXX, 1, XXX, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({3, 4, 5})};
+
+    auto c = int32s_col{5, 4, 6, 4, XXX, XXX, 5, 4, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({6, 7});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const idx            = int32s_col{0, 0, 2, 3, 4, 4, 6, 6, 8};
+  auto const input_original = cudf::table_view{{idx, s1}};
+  auto const input          = cudf::slice(input_original, {1, 7})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{1, 2, 3, 4, 6};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{1, 2, 3, 4, 4, 6};
+    auto const expect_table = cudf::gather(input_original, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, StructsOfLists)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+
+  auto const idx  = int32s_col{1, 1, 2, 3, 4, 4, 4, 5, 5, 6};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_keys = [] {
+    auto child1 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(StableDistinctKeepFirstLastNone, StructsOfLists)
+{
+  auto const idx  = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    auto child2 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    auto child3 = lists_col{{1}, {1, 1}, {1}, {1, 2}, {2, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const expect_map   = int32s_col{0, 1, 3, 4, 5, 7};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const expect_map   = int32s_col{1, 2, 3, 6, 7, 9};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const expect_map   = int32s_col{1, 3, 7};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::stable_distinct(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(StableDistinctKeepAny, SlicedStructsOfLists)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows in contiguous
+  // groups for equivalent keys because KEEP_ANY is nondeterministic.
+
+  auto constexpr dont_care = int32_t{0};
+
+  auto const idx  = int32s_col{dont_care, dont_care, 1, 1, 2, 3, 4, 4, 4, 5, 5, 6, dont_care};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{
+      {0, 0}, {0, 0}, {1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}, {5, 5}};
+    auto child2 = lists_col{
+      {0, 0}, {0, 0}, {1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}, {5, 5}};
+    auto child3 = lists_col{
+      {0, 0}, {0, 0}, {1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}, {5, 5}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input_original = cudf::table_view{{idx, keys}};
+  auto const input          = cudf::slice(input_original, {2, 12})[0];
+  auto const key_idx        = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_keys = [] {
+    auto child1 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::stable_distinct(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
diff --git a/cpp/tests/stream_compaction/unique_count_tests.cpp b/cpp/tests/stream_compaction/unique_count_tests.cpp
new file mode 100644
index 0000000..af0b45b
--- /dev/null
+++ b/cpp/tests/stream_compaction/unique_count_tests.cpp
@@ -0,0 +1,288 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <cmath>
+
+using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+
+using cudf::test::iterators::nulls_at;
+
+using cudf::nan_policy;
+using cudf::null_equality;
+using cudf::null_policy;
+
+constexpr int32_t XXX{70};  // Mark for null elements
+constexpr int32_t YYY{3};   // Mark for null elements
+
+template <typename T>
+struct TypedUniqueCount : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(TypedUniqueCount, cudf::test::NumericTypes);
+
+TYPED_TEST(TypedUniqueCount, NoNull)
+{
+  using T = TypeParam;
+
+  auto const input = cudf::test::make_type_param_vector<T>(
+    {1, 3, 3, 4, 31, 1, 8, 2, 0, 4, 1, 4, 10, 40, 31, 42, 0, 42, 8, 5, 4});
+
+  cudf::test::fixed_width_column_wrapper<T> input_col(input.begin(), input.end());
+
+  // explicit instantiation to one particular type (`double`) to reduce build time
+  std::vector<double> input_data(input.begin(), input.end());
+  auto const new_end = std::unique(input_data.begin(), input_data.end());
+  auto const gold    = std::distance(input_data.begin(), new_end);
+  EXPECT_EQ(gold, cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TYPED_TEST(TypedUniqueCount, TableNoNull)
+{
+  using T = TypeParam;
+
+  auto const input1 = cudf::test::make_type_param_vector<T>(
+    {1, 3, 3, 3, 4, 31, 1, 8, 2, 0, 4, 1, 4, 10, 40, 31, 42, 0, 42, 8, 5, 4});
+  auto const input2 = cudf::test::make_type_param_vector<T>(
+    {3, 3, 3, 4, 31, 1, 8, 5, 0, 4, 1, 4, 10, 40, 31, 42, 0, 42, 8, 5, 4, 1});
+
+  std::vector<std::pair<T, T>> pair_input;
+  std::transform(
+    input1.begin(), input1.end(), input2.begin(), std::back_inserter(pair_input), [](T a, T b) {
+      return std::pair(a, b);
+    });
+
+  cudf::test::fixed_width_column_wrapper<T> input_col1(input1.begin(), input1.end());
+  cudf::test::fixed_width_column_wrapper<T> input_col2(input2.begin(), input2.end());
+  cudf::table_view input_table({input_col1, input_col2});
+
+  auto const new_end = std::unique(pair_input.begin(), pair_input.end());
+  auto const gold    = std::distance(pair_input.begin(), new_end);
+  EXPECT_EQ(gold, cudf::unique_count(input_table, null_equality::EQUAL));
+}
+
+struct UniqueCount : public cudf::test::BaseFixture {};
+
+TEST_F(UniqueCount, WithNull)
+{
+  using T = int32_t;
+
+  std::vector<T> input               = {1,   3,  3,  XXX, 31, 1, 8,  2, 0, XXX, XXX,
+                                        XXX, 10, 40, 31,  42, 0, 42, 8, 5, XXX};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,
+                                        0, 1, 1, 1, 1, 1, 1, 1, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col(input.begin(), input.end(), valid.begin());
+
+  auto const new_end = std::unique(input.begin(), input.end());
+  auto const gold    = std::distance(input.begin(), new_end) - 3;
+  EXPECT_EQ(gold, cudf::unique_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(UniqueCount, IgnoringNull)
+{
+  using T = int32_t;
+
+  std::vector<T> input               = {1,   YYY, YYY, XXX, 31, 1, 8,  2, 0, XXX, 1,
+                                        XXX, 10,  40,  31,  42, 0, 42, 8, 5, XXX};
+  std::vector<cudf::size_type> valid = {1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,
+                                        0, 1, 1, 1, 1, 1, 1, 1, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col(input.begin(), input.end(), valid.begin());
+
+  auto const new_end = std::unique(input.begin(), input.end());
+  // -1 since `YYY, YYY, XXX` is in the same group of equivalent rows
+  auto const gold = std::distance(input.begin(), new_end) - 1;
+  EXPECT_EQ(gold, cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(UniqueCount, WithNansAndNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1,   3,  NAN, XXX, 31,  1, 8,   2, 0, XXX, 1,
+                                        XXX, 10, 40,  31,  NAN, 0, NAN, 8, 5, XXX};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
+                                        0, 1, 1, 1, 1, 1, 1, 1, 1, 0};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  auto const new_end = std::unique(input.begin(), input.end());
+  auto const gold    = std::distance(input.begin(), new_end);
+  EXPECT_EQ(gold, cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+
+  input     = {NAN, NAN, XXX};
+  valid     = {1, 1, 0};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 2;
+  EXPECT_EQ(expected_all_nan,
+            cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(UniqueCount, WithNansOnly)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, 70, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 1, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 5;
+  EXPECT_EQ(expected,
+            cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+
+  input     = {NAN, NAN, NAN};
+  valid     = {1, 1, 1};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 1;
+  EXPECT_EQ(expected_all_nan,
+            cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_VALID));
+}
+
+TEST_F(UniqueCount, NansAsNullWithNoNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, 70, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 1, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 5;
+  EXPECT_EQ(expected, cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+
+  input     = {NAN, NAN, NAN};
+  valid     = {1, 1, 1};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 1;
+  EXPECT_EQ(expected_all_nan,
+            cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(UniqueCount, NansAsNullWithNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, XXX, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 4;
+  EXPECT_EQ(expected, cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+
+  input     = {NAN, NAN, XXX};
+  valid     = {1, 1, 0};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_null = 1;
+  EXPECT_EQ(expected_all_null,
+            cudf::unique_count(input_col, null_policy::INCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(UniqueCount, NansAsNullWithIgnoreNull)
+{
+  using T = float;
+
+  std::vector<T> input               = {1, 3, NAN, XXX, 31};
+  std::vector<cudf::size_type> valid = {1, 1, 1, 0, 1};
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected = 3;
+  EXPECT_EQ(expected, cudf::unique_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_NULL));
+
+  input     = {NAN, NAN, NAN};
+  valid     = {1, 1, 1};
+  input_col = cudf::test::fixed_width_column_wrapper<T>{input.begin(), input.end(), valid.begin()};
+
+  constexpr auto expected_all_nan = 0;
+  EXPECT_EQ(expected_all_nan,
+            cudf::unique_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(UniqueCount, EmptyColumn)
+{
+  using T = float;
+
+  cudf::test::fixed_width_column_wrapper<T> input_col{};
+
+  constexpr auto expected = 0;
+  EXPECT_EQ(expected, cudf::unique_count(input_col, null_policy::EXCLUDE, nan_policy::NAN_IS_NULL));
+}
+
+TEST_F(UniqueCount, NullableLists)
+{
+  auto const keys = lists_col{
+    {{}, {}, {1, 1}, {1}, {1}, {} /*NULL*/, {} /*NULL*/, {2}, {2}, {2, 1}, {2, 2}, {2, 2}},
+    nulls_at({5, 6})};
+  auto const input = cudf::table_view{{keys}};
+
+  EXPECT_EQ(7, cudf::unique_count(input, null_equality::EQUAL));
+  EXPECT_EQ(8, cudf::unique_count(input, null_equality::UNEQUAL));
+}
+
+TEST_F(UniqueCount, NullableStructOfStructs)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 1}, 5}  |  // Same as 0
+  // 2 |  { {1, 2}, 4}  |
+  // 3 |  { Null,   6}  |
+  // 4 |  { Null,   4}  |
+  // 5 |  { Null,   4}  |  // Same as 4
+  // 6 |  Null          |
+  // 7 |  Null          |  // Same as 6
+  // 8 |  { {2, 1}, 5}  |
+
+  auto const keys = [&] {
+    auto a  = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 1, XXX, XXX, XXX, 2, 1, 2};
+    auto b  = cudf::test::fixed_width_column_wrapper<int32_t>{1, 1, 2, XXX, XXX, XXX, 2, 1, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({3, 4, 5})};
+
+    auto c = cudf::test::fixed_width_column_wrapper<int32_t>{5, 5, 4, 6, 4, 4, 3, 3, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({6, 7});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const input = cudf::table_view{{keys}};
+
+  EXPECT_EQ(6, cudf::unique_count(input, null_equality::EQUAL));
+  EXPECT_EQ(8, cudf::unique_count(input, null_equality::UNEQUAL));
+}
diff --git a/cpp/tests/stream_compaction/unique_tests.cpp b/cpp/tests/stream_compaction/unique_tests.cpp
new file mode 100644
index 0000000..d64c6f5
--- /dev/null
+++ b/cpp/tests/stream_compaction/unique_tests.cpp
@@ -0,0 +1,811 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <cmath>
+
+using cudf::nan_policy;
+using cudf::null_equality;
+using cudf::null_policy;
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+auto constexpr null{0};  // null at current level
+auto constexpr XXX{0};   // null pushed down from parent level
+auto constexpr KEEP_ANY     = cudf::duplicate_keep_option::KEEP_ANY;
+auto constexpr KEEP_FIRST   = cudf::duplicate_keep_option::KEEP_FIRST;
+auto constexpr KEEP_LAST    = cudf::duplicate_keep_option::KEEP_LAST;
+auto constexpr KEEP_NONE    = cudf::duplicate_keep_option::KEEP_NONE;
+auto constexpr NULL_EQUAL   = cudf::null_equality::EQUAL;
+auto constexpr NULL_UNEQUAL = cudf::null_equality::UNEQUAL;
+
+using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+using floats_col  = cudf::test::fixed_width_column_wrapper<float>;
+using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+using strings_col = cudf::test::strings_column_wrapper;
+using structs_col = cudf::test::structs_column_wrapper;
+
+struct Unique : public cudf::test::BaseFixture {};
+
+TEST_F(Unique, StringKeyColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col{{5, 4, 4, 5, 5, 8, 1}, {1, 0, 0, 1, 1, 1, 1}};
+  cudf::test::strings_column_wrapper key_col{{"all", "new", "new", "all", "new", "the", "strings"},
+                                             {1, 1, 1, 1, 0, 1, 1}};
+  cudf::table_view input{{col, key_col}};
+  std::vector<cudf::size_type> keys{1};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col{{5, 4, 5, 5, 8, 1}, {1, 0, 1, 1, 1, 1}};
+  cudf::test::strings_column_wrapper exp_key_col{{"all", "new", "all", "new", "the", "strings"},
+                                                 {1, 1, 1, 0, 1, 1}};
+  cudf::table_view expected{{exp_col, exp_key_col}};
+
+  auto got = unique(input, keys, cudf::duplicate_keep_option::KEEP_LAST);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got->view());
+}
+
+TEST_F(Unique, EmptyInputTable)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col(std::initializer_list<int32_t>{});
+  cudf::table_view input{{col}};
+  std::vector<cudf::size_type> keys{1, 2};
+
+  auto got = unique(input, keys, cudf::duplicate_keep_option::KEEP_FIRST, null_equality::EQUAL);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(Unique, NoColumnInputTable)
+{
+  cudf::table_view input{std::vector<cudf::column_view>()};
+  std::vector<cudf::size_type> keys{1, 2};
+
+  auto got = unique(input, keys, cudf::duplicate_keep_option::KEEP_FIRST, null_equality::EQUAL);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(input, got->view());
+}
+
+TEST_F(Unique, EmptyKeys)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col{{5, 4, 3, 5, 8, 1}, {1, 0, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> empty_col{};
+  cudf::table_view input{{col}};
+  std::vector<cudf::size_type> keys{};
+
+  auto got = unique(input, keys, cudf::duplicate_keep_option::KEEP_FIRST, null_equality::EQUAL);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(cudf::table_view{{empty_col}}, got->view());
+}
+
+TEST_F(Unique, NonNullTable)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col1{{5, 4, 3, 5, 8, 5}};
+  cudf::test::fixed_width_column_wrapper<float> col2{{4, 5, 3, 4, 9, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col1_key{{20, 20, 20, 19, 21, 9}};
+  cudf::test::fixed_width_column_wrapper<int32_t> col2_key{{19, 19, 20, 20, 9, 21}};
+
+  cudf::table_view input{{col1, col2, col1_key, col2_key}};
+  std::vector<cudf::size_type> keys{2, 3};
+
+  // Keep the first duplicate row
+  // The expected table would be sorted in ascending order with respect to keys
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col1_first{{5, 3, 5, 8, 5}};
+  cudf::test::fixed_width_column_wrapper<float> exp_col2_first{{4, 3, 4, 9, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col1_key_first{{20, 20, 19, 21, 9}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col2_key_first{{19, 20, 20, 9, 21}};
+  cudf::table_view expected_first{
+    {exp_col1_first, exp_col2_first, exp_col1_key_first, exp_col2_key_first}};
+
+  auto got_first = unique(input, keys, cudf::duplicate_keep_option::KEEP_FIRST);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_first, got_first->view());
+
+  // Keep the last duplicate row
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col1_last{{4, 3, 5, 8, 5}};
+  cudf::test::fixed_width_column_wrapper<float> exp_col2_last{{5, 3, 4, 9, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col1_key_last{{20, 20, 19, 21, 9}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col2_key_last{{19, 20, 20, 9, 21}};
+  cudf::table_view expected_last{
+    {exp_col1_last, exp_col2_last, exp_col1_key_last, exp_col2_key_last}};
+
+  auto got_last = unique(input, keys, cudf::duplicate_keep_option::KEEP_LAST);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_last, got_last->view());
+
+  // Keep no duplicate rows
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col1_unique{{3, 5, 8, 5}};
+  cudf::test::fixed_width_column_wrapper<float> exp_col2_unique{{3, 4, 9, 4}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col1_key_unique{{20, 19, 21, 9}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col2_key_unique{{20, 20, 9, 21}};
+  cudf::table_view expected_unique{
+    {exp_col1_unique, exp_col2_unique, exp_col1_key_unique, exp_col2_key_unique}};
+
+  auto got_unique = unique(input, keys, cudf::duplicate_keep_option::KEEP_NONE);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_unique, got_unique->view());
+}
+
+TEST_F(Unique, KeepFirstWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col{{5, 4, 3, 2, 5, 8, 1}, {1, 0, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> key{{20, 20, 20, 20, 19, 21, 19},
+                                                      {1, 1, 0, 0, 1, 1, 1}};
+  cudf::table_view input{{col, key}};
+  std::vector<cudf::size_type> keys{1};
+
+  // nulls are equal
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col_first_equal{{5, 3, 5, 8, 1},
+                                                                      {1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_key_col_first_equal{{20, 20, 19, 21, 19},
+                                                                          {1, 0, 1, 1, 1}};
+  cudf::table_view expected_first_equal{{exp_col_first_equal, exp_key_col_first_equal}};
+  auto got_first_equal =
+    unique(input, keys, cudf::duplicate_keep_option::KEEP_FIRST, null_equality::EQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_first_equal, got_first_equal->view());
+
+  // nulls are unequal
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col_first_unequal{{5, 3, 2, 5, 8, 1},
+                                                                        {1, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_key_col_first_unequal{
+    {20, 20, 20, 19, 21, 19}, {1, 0, 0, 1, 1, 1}};
+  cudf::table_view expected_first_unequal{{exp_col_first_unequal, exp_key_col_first_unequal}};
+  auto got_first_unequal =
+    unique(input, keys, cudf::duplicate_keep_option::KEEP_FIRST, null_equality::UNEQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_first_unequal, got_first_unequal->view());
+}
+
+TEST_F(Unique, KeepLastWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col{{5, 4, 3, 2, 5, 8, 1}, {1, 0, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> key{{20, 20, 20, 20, 19, 21, 19},
+                                                      {1, 1, 0, 0, 1, 1, 1}};
+  cudf::table_view input{{col, key}};
+  std::vector<cudf::size_type> keys{1};
+
+  // nulls are equal
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col_last_equal{{4, 2, 5, 8, 1},
+                                                                     {0, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_key_col_last_equal{{20, 20, 19, 21, 19},
+                                                                         {1, 0, 1, 1, 1}};
+  cudf::table_view expected_last_equal{{exp_col_last_equal, exp_key_col_last_equal}};
+  auto got_last_equal =
+    unique(input, keys, cudf::duplicate_keep_option::KEEP_LAST, null_equality::EQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_last_equal, got_last_equal->view());
+
+  // nulls are unequal
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col_last_unequal{{4, 3, 2, 5, 8, 1},
+                                                                       {0, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_key_col_last_unequal{{20, 20, 20, 19, 21, 19},
+                                                                           {1, 0, 0, 1, 1, 1}};
+  cudf::table_view expected_last_unequal{{exp_col_last_unequal, exp_key_col_last_unequal}};
+  auto got_last_unequal =
+    unique(input, keys, cudf::duplicate_keep_option::KEEP_LAST, null_equality::UNEQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_last_unequal, got_last_unequal->view());
+}
+
+TEST_F(Unique, KeepNoneWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col{{5, 4, 3, 2, 5, 8, 1}, {1, 0, 1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> key{{20, 20, 20, 20, 19, 21, 19},
+                                                      {1, 1, 0, 0, 1, 1, 1}};
+  cudf::table_view input{{col, key}};
+  std::vector<cudf::size_type> keys{1};
+
+  // nulls are equal
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col_unique_equal{{5, 8, 1}, {1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_key_col_unique_equal{{19, 21, 19}, {1, 1, 1}};
+  cudf::table_view expected_unique_equal{{exp_col_unique_equal, exp_key_col_unique_equal}};
+  auto got_unique_equal =
+    unique(input, keys, cudf::duplicate_keep_option::KEEP_NONE, null_equality::EQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_unique_equal, got_unique_equal->view());
+
+  // nulls are unequal
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_col_unique_unequal{{3, 2, 5, 8, 1},
+                                                                         {1, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> exp_key_col_unique_unequal{{20, 20, 19, 21, 19},
+                                                                             {0, 0, 1, 1, 1}};
+  cudf::table_view expected_unique_unequal{{exp_col_unique_unequal, exp_key_col_unique_unequal}};
+  auto got_unique_unequal =
+    unique(input, keys, cudf::duplicate_keep_option::KEEP_NONE, null_equality::UNEQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected_unique_unequal, got_unique_unequal->view());
+}
+
+TEST_F(Unique, ListsKeepAny)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  // clang-format off
+  auto const idx = int32s_col{0,  0,      2,   1,   1,      3,   5,   5,      6,      4,      4,      4};
+  auto const keys = lists_col{{}, {}, {1, 1}, {1}, {1}, {1, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}, {2, 2}};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx  = int32s_col{0, 2, 1, 3, 5, 6, 4};
+  auto const exp_keys = lists_col{{}, {1, 1}, {1}, {1, 2}, {2}, {2, 1}, {2, 2}};
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(Unique, ListsKeepFirstLastNone)
+{
+  // clang-format off
+  auto const idx = int32s_col{0,  1,      2,   1,   2,      3,   5,   6,      6,      4,      5,      6};
+  auto const keys = lists_col{{}, {}, {1, 1}, {1}, {1}, {1, 2}, {2}, {2}, {2, 1}, {2, 2}, {2, 2}, {2, 2}};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP FIRST
+  {
+    auto const exp_idx  = int32s_col{0, 2, 1, 3, 5, 6, 4};
+    auto const exp_keys = lists_col{{}, {1, 1}, {1}, {1, 2}, {2}, {2, 1}, {2, 2}};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP LAST
+  {
+    auto const exp_idx  = int32s_col{1, 2, 2, 3, 6, 6, 6};
+    auto const exp_keys = lists_col{{}, {1, 1}, {1}, {1, 2}, {2}, {2, 1}, {2, 2}};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // KEEP NONE
+  {
+    auto const exp_idx  = int32s_col{2, 3, 6};
+    auto const exp_keys = lists_col{{1, 1}, {1, 2}, {2, 1}};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(Unique, NullableListsKeepAny)
+{
+  // Column(s) used to test KEEP_ANY needs to have same rows for same keys because KEEP_ANY is
+  // nondeterministic.
+  // clang-format off
+  auto const idx = int32s_col{0,   0,      2,    1,   1,      3,               3,           5,   5,      6,      4,      4};
+  auto const keys = lists_col{{{}, {}, {1, 1}, {1}, {1},     {} /*NULL*/,     {} /*NULL*/, {2}, {2}, {2, 1}, {2, 2}, {2, 2}}, nulls_at({5, 6})};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const exp_idx = int32s_col{0, 2, 1, 3, 5, 6, 4};
+    auto const exp_keys =
+      lists_col{{{}, {1, 1}, {1}, {} /*NULL*/, {2}, {2, 1}, {2, 2}}, null_at(3)};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const exp_idx = int32s_col{0, 2, 1, 3, 3, 5, 6, 4};
+    auto const exp_keys =
+      lists_col{{{}, {1, 1}, {1}, {} /*NULL*/, {} /*NULL*/, {2}, {2, 1}, {2, 2}}, nulls_at({3, 4})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+
+TEST_F(Unique, NullableListsKeepFirstLastNone)
+{
+  // clang-format off
+  auto const idx = int32s_col{0,   1,      2,    1,   2,      3,               4,           5,   6,      6,      4,      5};
+  auto const keys = lists_col{{{}, {}, {1, 1}, {1}, {1},     {} /*NULL*/,     {} /*NULL*/, {2}, {2}, {2, 1}, {2, 2}, {2, 2}}, nulls_at({5, 6})};
+  // clang-format on
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP FIRST
+  {// Nulls are equal.
+   {auto const exp_idx = int32s_col{0, 2, 1, 3, 5, 6, 4};
+  auto const exp_keys = lists_col{{{}, {1, 1}, {1}, {} /*NULL*/, {2}, {2, 1}, {2, 2}}, null_at(3)};
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::unique(input, key_idx, KEEP_FIRST);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+// Nulls are unequal.
+{
+  auto const exp_idx = int32s_col{0, 2, 1, 3, 4, 5, 6, 4};
+  auto const exp_keys =
+    lists_col{{{}, {1, 1}, {1}, {} /*NULL*/, {} /*NULL*/, {2}, {2, 1}, {2, 2}}, nulls_at({3, 4})};
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::unique(input, key_idx, KEEP_FIRST, NULL_UNEQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+}
+
+// KEEP LAST
+{// Nulls are equal.
+ {auto const exp_idx = int32s_col{1, 2, 2, 4, 6, 6, 5};
+auto const exp_keys = lists_col{{{}, {1, 1}, {1}, {} /*NULL*/, {2}, {2, 1}, {2, 2}}, null_at(3)};
+auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+auto const result = cudf::unique(input, key_idx, KEEP_LAST);
+
+CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+// Nulls are unequal.
+{
+  auto const exp_idx = int32s_col{1, 2, 2, 3, 4, 6, 6, 5};
+  auto const exp_keys =
+    lists_col{{{}, {1, 1}, {1}, {} /*NULL*/, {} /*NULL*/, {2}, {2, 1}, {2, 2}}, nulls_at({3, 4})};
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::unique(input, key_idx, KEEP_LAST, NULL_UNEQUAL);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+}
+
+// KEEP NONE
+{
+  // Nulls are equal.
+  {
+    auto const exp_idx  = int32s_col{2, 6};
+    auto const exp_keys = lists_col{{{1, 1}, {2, 1}}, nulls_at({})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_NONE);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const exp_idx  = int32s_col{2, 3, 4, 6};
+    auto const exp_keys = lists_col{{{1, 1}, {} /*NULL*/, {} /*NULL*/, {2, 1}}, nulls_at({1, 2})};
+    auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+    auto const result = cudf::unique(input, key_idx, KEEP_NONE, NULL_UNEQUAL);
+
+    CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+  }
+}
+}
+
+TEST_F(Unique, ListsOfStructsKeepAny)
+{
+  // Constructing a list of structs of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==
+  // 16.  [{Null, 'b'}]
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx     = int32s_col{1, 1, 2, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9, 9, 10, 10};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 5, 8, 9, 10, 11, 13, 15};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(Unique, ListsOfStructsKeepFirstLastNone)
+{
+  // Constructing a list of structs of two elements
+  // 0.   []                  ==
+  // 1.   []                  !=
+  // 2.   Null                ==
+  // 3.   Null                !=
+  // 4.   [Null, Null]        !=
+  // 5.   [Null]              ==
+  // 6.   [Null]              ==
+  // 7.   [Null]              !=
+  // 8.   [{Null, Null}]      !=
+  // 9.   [{1,'a'}, {2,'b'}]  !=
+  // 10.  [{0,'a'}, {2,'b'}]  !=
+  // 11.  [{0,'a'}, {2,'c'}]  ==
+  // 12.  [{0,'a'}, {2,'c'}]  !=
+  // 13.  [{0,Null}]          ==
+  // 14.  [{0,Null}]          !=
+  // 15.  [{Null, 'b'}]       ==
+  // 16.  [{Null, 'b'}]
+
+  auto const structs = [] {
+    auto child1 =
+      int32s_col{{XXX, XXX, XXX, XXX, XXX, null, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, null, null},
+                 nulls_at({5, 16, 17})};
+    auto child2 = strings_col{{"" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*XXX*/,
+                               "" /*null*/,
+                               "a",
+                               "b",
+                               "a",
+                               "b",
+                               "a",
+                               "c",
+                               "a",
+                               "c",
+                               "" /*null*/,
+                               "" /*null*/,
+                               "b",
+                               "b"},
+                              nulls_at({5, 14, 15})};
+
+    return structs_col{{child1, child2}, nulls_at({0, 1, 2, 3, 4})};
+  }();
+
+  auto const offsets = int32s_col{0, 0, 0, 0, 0, 2, 3, 4, 5, 6, 8, 10, 12, 14, 15, 16, 17, 18};
+  auto const null_it = nulls_at({2, 3});
+  auto [null_mask, null_count] = cudf::test::detail::make_null_mask(null_it, null_it + 17);
+
+  auto const keys = cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                                      17,
+                                      nullptr,
+                                      static_cast<cudf::bitmask_type const*>(null_mask.data()),
+                                      null_count,
+                                      0,
+                                      {offsets, structs});
+
+  auto const idx     = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 5, 8, 9, 10, 11, 13, 15};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const expect_map   = int32s_col{1, 3, 4, 7, 8, 9, 10, 12, 14, 16};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const expect_map   = int32s_col{4, 8, 9, 10};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(Unique, ListsOfEmptyStructsKeepAny)
+{
+  // 0.  []             ==
+  // 1.  []             !=
+  // 2.  Null           ==
+  // 3.  Null           !=
+  // 4.  [Null, Null]   ==
+  // 5.  [Null, Null]   ==
+  // 6.  [Null, Null]   !=
+  // 7.  [Null]         ==
+  // 8.  [Null]         !=
+  // 9.  [{}]           ==
+  // 10. [{}]           !=
+  // 11. [{}, {}]       ==
+  // 12. [{}, {}]
+
+  auto const structs_null_it = nulls_at({0, 1, 2, 3, 4, 5, 6, 7});
+  auto [structs_null_mask, structs_null_count] =
+    cudf::test::detail::make_null_mask(structs_null_it, structs_null_it + 14);
+  auto const structs =
+    cudf::column_view(cudf::data_type(cudf::type_id::STRUCT),
+                      14,
+                      nullptr,
+                      static_cast<cudf::bitmask_type const*>(structs_null_mask.data()),
+                      structs_null_count);
+
+  auto const offsets       = int32s_col{0, 0, 0, 0, 0, 2, 4, 6, 7, 8, 9, 10, 12, 14};
+  auto const lists_null_it = nulls_at({2, 3});
+  auto [lists_null_mask, lists_null_count] =
+    cudf::test::detail::make_null_mask(lists_null_it, lists_null_it + 13);
+  auto const keys =
+    cudf::column_view(cudf::data_type(cudf::type_id::LIST),
+                      13,
+                      nullptr,
+                      static_cast<cudf::bitmask_type const*>(lists_null_mask.data()),
+                      lists_null_count,
+                      0,
+                      {offsets, structs});
+
+  auto const idx     = int32s_col{1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 4, 7, 9, 11};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8, 9, 11};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(Unique, EmptyDeepListKeepAny)
+{
+  // List<List<int>>, where all lists are empty:
+  //
+  // 0. []
+  // 1. []
+  // 2. Null
+  // 3. Null
+
+  auto const keys =
+    lists_col{{lists_col{}, lists_col{}, lists_col{}, lists_col{}}, nulls_at({2, 3})};
+
+  auto const idx     = int32s_col{1, 1, 2, 2};
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(Unique, StructsOfStructsKeepAny)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  { {1, 1}, 5}  |
+  // 1 |  { {1, 1}, 5}  |  // Same as 0
+  // 2 |  { {1, 2}, 4}  |
+  // 3 |  { Null,   6}  |
+  // 4 |  { Null,   4}  |
+  // 5 |  { Null,   4}  |  // Same as 4
+  // 6 |  Null          |
+  // 7 |  Null          |  // Same as 6
+  // 8 |  { {2, 1}, 5}  |
+
+  auto s1 = [&] {
+    auto a  = int32s_col{1, 1, 1, XXX, XXX, XXX, 2, 1, 2};
+    auto b  = int32s_col{1, 1, 2, XXX, XXX, XXX, 2, 1, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({3, 4, 5})};
+
+    auto c = int32s_col{5, 5, 4, 6, 4, 4, 3, 3, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({6, 7});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 9});
+  }();
+
+  auto const idx     = int32s_col{0, 0, 1, 2, 3, 3, 4, 4, 5};
+  auto const input   = cudf::table_view{{idx, s1}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // Nulls are equal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 6, 8};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // Nulls are unequal.
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 5, 6, 7, 8};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_ANY, NULL_UNEQUAL);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
+
+TEST_F(Unique, StructsOfListsKeepAny)
+{
+  auto const idx  = int32s_col{1, 1, 2, 3, 4, 4, 4, 5, 5, 6};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  auto const exp_idx  = int32s_col{1, 2, 3, 4, 5, 6};
+  auto const exp_keys = [] {
+    auto child1 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1, 1}, {1, 2}, {2, 2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+  auto const expected = cudf::table_view{{exp_idx, exp_keys}};
+
+  auto const result = cudf::unique(input, key_idx, KEEP_ANY);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, *result);
+}
+
+TEST_F(Unique, StructsOfListsKeepFirstLastNone)
+{
+  auto const idx  = int32s_col{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+  auto const keys = [] {
+    // All child columns are identical.
+    auto child1 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    auto child2 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    auto child3 = lists_col{{1}, {1}, {1, 1}, {1, 2}, {2, 2}, {2, 2}, {2, 2}, {2}, {2}, {2, 1}};
+    return structs_col{{child1, child2, child3}};
+  }();
+
+  auto const input   = cudf::table_view{{idx, keys}};
+  auto const key_idx = std::vector<cudf::size_type>{1};
+
+  // KEEP_FIRST
+  {
+    auto const expect_map   = int32s_col{0, 2, 3, 4, 7, 9};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_FIRST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_LAST
+  {
+    auto const expect_map   = int32s_col{1, 2, 3, 6, 8, 9};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_LAST);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+
+  // KEEP_NONE
+  {
+    auto const expect_map   = int32s_col{2, 3, 9};
+    auto const expect_table = cudf::gather(input, expect_map);
+
+    auto const result = cudf::unique(input, key_idx, KEEP_NONE);
+    CUDF_TEST_EXPECT_TABLES_EQUAL(*expect_table, *result);
+  }
+}
diff --git a/cpp/tests/streams/concatenate_test.cpp b/cpp/tests/streams/concatenate_test.cpp
new file mode 100644
index 0000000..6e6ff58
--- /dev/null
+++ b/cpp/tests/streams/concatenate_test.cpp
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/concatenate.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class ConcatenateTest : public cudf::test::BaseFixture {};
+
+TEST_F(ConcatenateTest, Column)
+{
+  cudf::test::fixed_width_column_wrapper<int> const input1({0, 0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<int> const input2({1, 1, 1, 1, 1});
+  std::vector<cudf::column_view> views{input1, input2};
+  auto result = cudf::concatenate(views, cudf::test::get_default_stream());
+}
+
+TEST_F(ConcatenateTest, Table)
+{
+  cudf::test::fixed_width_column_wrapper<int> const input1({0, 0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<int> const input2({1, 1, 1, 1, 1});
+  cudf::table_view tbl1({input1, input2});
+  cudf::table_view tbl2({input2, input1});
+  std::vector<cudf::table_view> views{tbl1, tbl2};
+  auto result = cudf::concatenate(views, cudf::test::get_default_stream());
+}
+
+TEST_F(ConcatenateTest, Masks)
+{
+  cudf::test::fixed_width_column_wrapper<int> const input1(
+    {{0, 0, 0, 0, 0}, {false, false, false, false, false}});
+  cudf::test::fixed_width_column_wrapper<int> const input2(
+    {{0, 0, 0, 0, 0}, {true, true, true, true, true}});
+  std::vector<cudf::column_view> views{input1, input2};
+  auto result = cudf::concatenate_masks(views, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/copying_test.cpp b/cpp/tests/streams/copying_test.cpp
new file mode 100644
index 0000000..cb09331
--- /dev/null
+++ b/cpp/tests/streams/copying_test.cpp
@@ -0,0 +1,339 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/null_mask.hpp>
+
+#include <limits>
+
+class CopyingTest : public cudf::test::BaseFixture {};
+
+TEST_F(CopyingTest, Gather)
+{
+  constexpr cudf::size_type source_size{1000};
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<int32_t> source_column(data, data + source_size);
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map(data, data + source_size);
+
+  cudf::table_view source_table({source_column});
+
+  cudf::gather(source_table,
+               gather_map,
+               cudf::out_of_bounds_policy::DONT_CHECK,
+               cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, ReverseTable)
+{
+  constexpr cudf::size_type num_values{10};
+
+  auto input = cudf::test::fixed_width_column_wrapper<int32_t, int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + num_values);
+
+  auto input_table = cudf::table_view{{input}};
+  cudf::reverse(input_table, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, ReverseColumn)
+{
+  constexpr cudf::size_type num_values{10};
+
+  auto input = cudf::test::fixed_width_column_wrapper<int32_t, int32_t>(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + num_values);
+
+  cudf::reverse(input, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, ScatterTable)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> source({1, 2, 3, 4, 5, 6});
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const source_table = cudf::table_view({source, source});
+  auto const target_table = cudf::table_view({target, target});
+
+  cudf::scatter(source_table, scatter_map, target_table, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, ScatterScalars)
+{
+  auto const source = cudf::scalar_type_t<int32_t>(100, true, cudf::test::get_default_stream());
+  std::reference_wrapper<const cudf::scalar> slr_ref{source};
+  std::vector<std::reference_wrapper<const cudf::scalar>> source_vector{slr_ref};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> target({10, 20, 30, 40, 50, 60, 70, 80});
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({-3, 3, 1, -1});
+
+  auto const target_table = cudf::table_view({target});
+
+  cudf::scatter(source_vector, scatter_map, target_table, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, AllocateLike)
+{
+  // For same size as input
+  cudf::size_type size = 10;
+
+  auto input = cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<int32_t>()},
+                                         size,
+                                         cudf::mask_state::UNALLOCATED,
+                                         cudf::test::get_default_stream());
+  cudf::allocate_like(
+    input->view(), cudf::mask_allocation_policy::RETAIN, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, AllocateLikeSize)
+{
+  // For same size as input
+  cudf::size_type size     = 10;
+  cudf::size_type new_size = 10;
+
+  auto input = cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<int32_t>()},
+                                         size,
+                                         cudf::mask_state::UNALLOCATED,
+                                         cudf::test::get_default_stream());
+  cudf::allocate_like(input->view(),
+                      new_size,
+                      cudf::mask_allocation_policy::RETAIN,
+                      cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, CopyRangeInPlace)
+{
+  constexpr cudf::size_type size{1000};
+
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> target(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto source_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  cudf::test::fixed_width_column_wrapper<int32_t, typename decltype(source_elements)::value_type>
+    source(source_elements, source_elements + size);
+
+  cudf::mutable_column_view target_view{target};
+
+  constexpr cudf::size_type source_begin{9};
+  constexpr cudf::size_type source_end{size - 50};
+  constexpr cudf::size_type target_begin{30};
+  cudf::copy_range_in_place(
+    source, target_view, source_begin, source_end, target_begin, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, CopyRange)
+{
+  constexpr cudf::size_type size{1000};
+
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> target(
+    thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + size);
+
+  auto source_elements =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 2; });
+  cudf::test::fixed_width_column_wrapper<int32_t, typename decltype(source_elements)::value_type>
+    source(source_elements, source_elements + size);
+
+  cudf::mutable_column_view target_view{target};
+  const cudf::column_view immutable_view{target_view};
+
+  cudf::size_type source_begin{9};
+  cudf::size_type source_end{size - 50};
+  cudf::size_type target_begin{30};
+  cudf::copy_range(source,
+                   immutable_view,
+                   source_begin,
+                   source_end,
+                   target_begin,
+                   cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, Shift)
+{
+  auto input =
+    cudf::test::fixed_width_column_wrapper<int32_t>{std::numeric_limits<int32_t>::min(),
+                                                    cudf::test::make_type_param_scalar<int32_t>(1),
+                                                    cudf::test::make_type_param_scalar<int32_t>(2),
+                                                    cudf::test::make_type_param_scalar<int32_t>(3),
+                                                    cudf::test::make_type_param_scalar<int32_t>(4),
+                                                    cudf::test::make_type_param_scalar<int32_t>(5),
+                                                    std::numeric_limits<int32_t>::max()};
+  auto fill = cudf::scalar_type_t<int32_t>(
+    cudf::test::make_type_param_scalar<int32_t>(7), true, cudf::test::get_default_stream());
+  cudf::shift(input, 2, fill, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, SliceColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2, 3, 4, 5};
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 2, 2, 5};
+  cudf::slice(col, indices, cudf::test::get_default_stream());
+  cudf::slice(col, {1, 3, 2, 2, 2, 5}, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, SliceTable)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2, 3, 4, 5};
+
+  std::vector<cudf::size_type> indices{1, 3, 2, 2, 2, 5};
+  cudf::table_view tbl({col});
+  cudf::slice(tbl, indices, cudf::test::get_default_stream());
+  cudf::slice(tbl, {1, 3, 2, 2, 2, 5}, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, SplitColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2, 3, 4, 5};
+
+  std::vector<cudf::size_type> indices{1, 3, 5};
+  cudf::split(col, indices, cudf::test::get_default_stream());
+  cudf::split(col, {1, 3, 5}, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, SplitTable)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{0, 1, 2, 3, 4, 5};
+
+  std::vector<cudf::size_type> indices{1, 3, 5};
+  cudf::table_view tbl({col});
+  cudf::split(tbl, indices, cudf::test::get_default_stream());
+  cudf::split(tbl, {1, 3, 5}, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, CopyIfElseColumnColumn)
+{
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 0, 0};
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> lhs_w{5, 5, 5, 5};
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> rhs_w{6, 6, 6, 6};
+  cudf::copy_if_else(lhs_w, rhs_w, mask_w, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, CopyIfElseScalarColumn)
+{
+  auto scalar = cudf::scalar_type_t<int32_t>(
+    cudf::test::make_type_param_scalar<int32_t>(7), true, cudf::test::get_default_stream());
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> column{5, 5, 5, 5};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 0, 0};
+  cudf::copy_if_else(scalar, column, mask_w, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, CopyIfElseColumnScalar)
+{
+  auto scalar = cudf::scalar_type_t<int32_t>(
+    cudf::test::make_type_param_scalar<int32_t>(7), true, cudf::test::get_default_stream());
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> column{5, 5, 5, 5};
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 0, 0};
+  cudf::copy_if_else(column, scalar, mask_w, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, CopyIfElseScalarScalar)
+{
+  auto lhs = cudf::scalar_type_t<int32_t>(
+    cudf::test::make_type_param_scalar<int32_t>(7), true, cudf::test::get_default_stream());
+  auto rhs = cudf::scalar_type_t<int32_t>(
+    cudf::test::make_type_param_scalar<int32_t>(6), true, cudf::test::get_default_stream());
+  cudf::test::fixed_width_column_wrapper<bool> mask_w{1, 0, 0, 0};
+  cudf::copy_if_else(lhs, rhs, mask_w, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, BooleanMaskScatter)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> source({1, 5, 6, 8, 9});
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> target(
+    {2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+
+  auto source_table = cudf::table_view({source});
+  auto target_table = cudf::table_view({target});
+
+  cudf::boolean_mask_scatter(source_table, target_table, mask, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, BooleanMaskScatterScalars)
+{
+  std::vector<std::reference_wrapper<const cudf::scalar>> scalars;
+  auto s = cudf::scalar_type_t<int32_t>(1, true, cudf::test::get_default_stream());
+  scalars.emplace_back(s);
+  cudf::test::fixed_width_column_wrapper<int32_t, int32_t> target(
+    {2, 2, 3, 4, 11, 12, 7, 7, 10, 10});
+  cudf::test::fixed_width_column_wrapper<bool> mask(
+    {true, false, false, false, true, true, false, true, true, false});
+
+  auto target_table = cudf::table_view({target});
+
+  cudf::boolean_mask_scatter(scalars, target_table, mask, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, GetElement)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> _col{1, 2};
+  cudf::get_element(_col, 0, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, Sample)
+{
+  cudf::size_type const table_size = 1024;
+  auto const n_samples             = 10;
+  auto const multi_smpl            = cudf::sample_with_replacement::FALSE;
+
+  auto data = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<int16_t> col1(data, data + table_size);
+
+  cudf::table_view input({col1});
+  cudf::sample(input, n_samples, multi_smpl, 0, cudf::test::get_default_stream());
+}
+
+template <typename T>
+using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+TEST_F(CopyingTest, HasNonemptyNulls)
+{
+  auto const input =
+    LCW<int32_t>{{{{1, 2, 3, 4}, cudf::test::iterators::null_at(2)}, {5}, {}, {8, 9, 10}},
+                 cudf::test::iterators::no_nulls()}
+      .release();
+  cudf::has_nonempty_nulls(*input, cudf::test::get_default_stream());
+}
+
+TEST_F(CopyingTest, PurgeNonEmptyNulls)
+{
+  auto const input = LCW<int32_t>{{{{1, 2, 3, 4}, cudf::test::iterators::null_at(2)},
+                                   {5},
+                                   {6, 7},  // <--- Will be set to NULL. Unsanitized row.
+                                   {8, 9, 10}},
+                                  cudf::test::iterators::no_nulls()}
+                       .release();
+
+  // Set nullmask, post construction.
+  // TODO: Once set_null_mask's public API exposes a stream parameter, use that
+  // instead of the detail API.
+  cudf::detail::set_null_mask(
+    input->mutable_view().null_mask(), 2, 3, false, cudf::test::get_default_stream());
+  input->set_null_count(1);
+
+  cudf::purge_nonempty_nulls(*input, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/dictionary_test.cpp b/cpp/tests/streams/dictionary_test.cpp
new file mode 100644
index 0000000..f48e64c
--- /dev/null
+++ b/cpp/tests/streams/dictionary_test.cpp
@@ -0,0 +1,105 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/dictionary/dictionary_factories.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/dictionary/search.hpp>
+#include <cudf/dictionary/update_keys.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class DictionaryTest : public cudf::test::BaseFixture {};
+
+TEST_F(DictionaryTest, Encode)
+{
+  cudf::test::fixed_width_column_wrapper<int> col({1, 2, 3, 4, 5});
+  cudf::data_type int32_type(cudf::type_id::UINT32);
+  cudf::column_view col_view = col;
+  cudf::dictionary::encode(col_view, int32_type, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, Decode)
+{
+  // keys = {0, 2, 6}, indices = {0, 1, 1, 2, 2}
+  std::vector<int32_t> elements{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col(elements.begin(), elements.end());
+  cudf::dictionary_column_view dict_col_view = dict_col;
+  cudf::dictionary::decode(dict_col_view, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, GetIndex)
+{
+  std::vector<int32_t> elements{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col(elements.begin(), elements.end());
+  cudf::dictionary_column_view dict_col_view = dict_col;
+  cudf::numeric_scalar<int32_t> key_scalar(2, true, cudf::test::get_default_stream());
+  cudf::dictionary::get_index(dict_col_view, key_scalar, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, AddKeys)
+{
+  std::vector<int32_t> elements{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col(elements.begin(), elements.end());
+  cudf::dictionary_column_view dict_col_view = dict_col;
+  cudf::test::fixed_width_column_wrapper<int> new_keys_col({8, 9});
+  cudf::dictionary::add_keys(dict_col_view, new_keys_col, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, RemoveKeys)
+{
+  std::vector<int32_t> elements{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col(elements.begin(), elements.end());
+  cudf::dictionary_column_view dict_col_view = dict_col;
+  cudf::test::fixed_width_column_wrapper<int> keys_to_remove_col({2});
+  cudf::dictionary::remove_keys(
+    dict_col_view, keys_to_remove_col, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, RemoveUnsedKeys)
+{
+  std::vector<int32_t> elements{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col(elements.begin(), elements.end());
+  cudf::dictionary_column_view dict_col_view = dict_col;
+  cudf::dictionary::remove_unused_keys(dict_col_view, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, SetKeys)
+{
+  std::vector<int32_t> elements{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col(elements.begin(), elements.end());
+  cudf::dictionary_column_view dict_col_view = dict_col;
+  cudf::test::fixed_width_column_wrapper<int> keys_col({2, 6});
+  cudf::dictionary::set_keys(dict_col_view, keys_col, cudf::test::get_default_stream());
+}
+
+TEST_F(DictionaryTest, MatchDictionaries)
+{
+  std::vector<int32_t> elements_a{0, 2, 2, 6, 6};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col_a(elements_a.begin(), elements_a.end());
+  cudf::dictionary_column_view dict_col_view_a = dict_col_a;
+
+  std::vector<int32_t> elements_b{1, 3, 4, 5, 5};
+  cudf::test::dictionary_column_wrapper<int32_t> dict_col_b(elements_b.begin(), elements_b.end());
+  cudf::dictionary_column_view dict_col_view_b = dict_col_b;
+
+  std::vector<cudf::dictionary_column_view> dicts = {dict_col_view_a, dict_col_view_b};
+
+  cudf::test::fixed_width_column_wrapper<int> keys_col({2, 6});
+  cudf::dictionary::match_dictionaries(dicts, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/filling_test.cpp b/cpp/tests/streams/filling_test.cpp
new file mode 100644
index 0000000..b822743
--- /dev/null
+++ b/cpp/tests/streams/filling_test.cpp
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class FillingTest : public cudf::test::BaseFixture {};
+
+TEST_F(FillingTest, FillInPlace)
+{
+  cudf::test::fixed_width_column_wrapper<int> col({0, 0, 0, 0, 0});
+  auto scalar = cudf::numeric_scalar<int>(5, true, cudf::test::get_default_stream());
+  cudf::mutable_column_view mut_view = col;
+  cudf::fill_in_place(mut_view, 0, 4, scalar, cudf::test::get_default_stream());
+}
+
+TEST_F(FillingTest, Fill)
+{
+  cudf::test::fixed_width_column_wrapper<int> const col({0, 0, 0, 0, 0});
+  auto scalar = cudf::numeric_scalar<int>(5, true, cudf::test::get_default_stream());
+  cudf::fill(col, 0, 4, scalar, cudf::test::get_default_stream());
+}
+
+TEST_F(FillingTest, RepeatVariable)
+{
+  cudf::test::fixed_width_column_wrapper<int> const col({0, 0, 0, 0, 0});
+  cudf::table_view const table({col});
+  cudf::test::fixed_width_column_wrapper<int> const counts({1, 2, 3, 4, 5});
+  cudf::repeat(table, counts, cudf::test::get_default_stream());
+}
+
+TEST_F(FillingTest, RepeatConst)
+{
+  cudf::test::fixed_width_column_wrapper<int> const col({0, 0, 0, 0, 0});
+  cudf::table_view const table({col});
+  cudf::repeat(table, 5, cudf::test::get_default_stream());
+}
+
+TEST_F(FillingTest, SequenceStep)
+{
+  auto init = cudf::numeric_scalar<int>(5, true, cudf::test::get_default_stream());
+  auto step = cudf::numeric_scalar<int>(2, true, cudf::test::get_default_stream());
+  cudf::sequence(10, init, step, cudf::test::get_default_stream());
+}
+
+TEST_F(FillingTest, Sequence)
+{
+  auto init = cudf::numeric_scalar<int>(5, true, cudf::test::get_default_stream());
+  cudf::sequence(10, init, cudf::test::get_default_stream());
+}
+
+TEST_F(FillingTest, CalendricalMonthSequence)
+{
+  cudf::timestamp_scalar<cudf::timestamp_s> init(
+    1629852896L, true, cudf::test::get_default_stream());  // 2021-08-25 00:54:56 GMT
+
+  cudf::calendrical_month_sequence(10, init, 2, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/groupby_test.cpp b/cpp/tests/streams/groupby_test.cpp
new file mode 100644
index 0000000..03cabbc
--- /dev/null
+++ b/cpp/tests/streams/groupby_test.cpp
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <tests/groupby/groupby_test_util.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/groupby.hpp>
+
+using K = int32_t;  // Key type.
+
+template <typename V>
+struct groupby_stream_test : public cudf::test::BaseFixture {
+  cudf::test::fixed_width_column_wrapper<K> keys{1, 2, 3, 1, 2, 2, 1, 3, 3, 2};
+  cudf::test::fixed_width_column_wrapper<V> vals{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
+
+  void test_groupby(std::unique_ptr<cudf::groupby_aggregation>&& agg,
+                    force_use_sort_impl use_sort        = force_use_sort_impl::NO,
+                    cudf::null_policy include_null_keys = cudf::null_policy::INCLUDE,
+                    cudf::sorted keys_are_sorted        = cudf::sorted::NO)
+  {
+    auto requests = [&] {
+      auto requests = std::vector<cudf::groupby::aggregation_request>{};
+      requests.push_back(cudf::groupby::aggregation_request{});
+      requests.front().values = vals;
+      if (use_sort == force_use_sort_impl::YES) {
+        requests.front().aggregations.push_back(
+          cudf::make_nth_element_aggregation<cudf::groupby_aggregation>(0));
+      }
+      requests.front().aggregations.push_back(std::move(agg));
+      return requests;
+    }();
+
+    auto gby =
+      cudf::groupby::groupby{cudf::table_view{{keys}}, include_null_keys, keys_are_sorted, {}, {}};
+    gby.aggregate(requests, cudf::test::get_default_stream());
+    // No need to verify results, for stream test.
+  }
+};
+
+TYPED_TEST_SUITE(groupby_stream_test, cudf::test::AllTypes);
+
+TYPED_TEST(groupby_stream_test, test_count)
+{
+  auto const make_count_agg = [&](cudf::null_policy include_nulls = cudf::null_policy::EXCLUDE) {
+    return cudf::make_count_aggregation<cudf::groupby_aggregation>(include_nulls);
+  };
+
+  this->test_groupby(make_count_agg());
+  this->test_groupby(make_count_agg(), force_use_sort_impl::YES);
+  this->test_groupby(make_count_agg(cudf::null_policy::INCLUDE));
+}
diff --git a/cpp/tests/streams/hash_test.cpp b/cpp/tests/streams/hash_test.cpp
new file mode 100644
index 0000000..0f60c50
--- /dev/null
+++ b/cpp/tests/streams/hash_test.cpp
@@ -0,0 +1,54 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/hashing.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class HashTest : public cudf::test::BaseFixture {};
+
+TEST_F(HashTest, MultiValue)
+{
+  cudf::test::strings_column_wrapper const strings_col(
+    {"",
+     "The quick brown fox",
+     "jumps over the lazy dog.",
+     "All work and no play makes Jack a dull boy",
+     R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"});
+
+  using limits = std::numeric_limits<int32_t>;
+  cudf::test::fixed_width_column_wrapper<int32_t> const ints_col(
+    {0, 100, -100, limits::min(), limits::max()});
+
+  // Different truth values should be equal
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col1({0, 1, 1, 1, 0});
+  cudf::test::fixed_width_column_wrapper<bool> const bools_col2({0, 1, 2, 255, 0});
+
+  using ts = cudf::timestamp_s;
+  cudf::test::fixed_width_column_wrapper<ts, ts::duration> const secs_col(
+    {ts::duration::zero(),
+     static_cast<ts::duration>(100),
+     static_cast<ts::duration>(-100),
+     ts::duration::min(),
+     ts::duration::max()});
+
+  auto const input1 = cudf::table_view({strings_col, ints_col, bools_col1, secs_col});
+
+  auto const output1 = cudf::hash(
+    input1, cudf::hash_id::HASH_MURMUR3, cudf::DEFAULT_HASH_SEED, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/interop_test.cpp b/cpp/tests/streams/interop_test.cpp
new file mode 100644
index 0000000..7eac9e0
--- /dev/null
+++ b/cpp/tests/streams/interop_test.cpp
@@ -0,0 +1,68 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/interop.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+struct ArrowTest : public cudf::test::BaseFixture {};
+
+TEST_F(ArrowTest, ToArrow)
+{
+  int32_t const value{42};
+  auto col = cudf::test::fixed_width_column_wrapper<int32_t>{{value}};
+  cudf::table_view tbl{{col}};
+
+  std::vector<cudf::column_metadata> metadata{{""}};
+  cudf::to_arrow(tbl, metadata, cudf::test::get_default_stream());
+}
+
+TEST_F(ArrowTest, FromArrow)
+{
+  std::vector<int64_t> host_values = {1, 2, 3, 5, 6, 7, 8};
+  std::vector<bool> host_validity  = {true, true, true, false, true, true, true};
+
+  arrow::Int64Builder builder;
+  auto status      = builder.AppendValues(host_values, host_validity);
+  auto maybe_array = builder.Finish();
+  auto array       = *maybe_array;
+
+  auto field  = arrow::field("", arrow::int32());
+  auto schema = arrow::schema({field});
+  auto table  = arrow::Table::Make(schema, {array});
+  cudf::from_arrow(*table, cudf::test::get_default_stream());
+}
+
+TEST_F(ArrowTest, ToArrowScalar)
+{
+  int32_t const value{42};
+  auto cudf_scalar =
+    cudf::make_fixed_width_scalar<int32_t>(value, cudf::test::get_default_stream());
+
+  cudf::column_metadata metadata{""};
+  cudf::to_arrow(*cudf_scalar, metadata, cudf::test::get_default_stream());
+}
+
+TEST_F(ArrowTest, FromArrowScalar)
+{
+  int32_t const value{42};
+  auto arrow_scalar = arrow::MakeScalar(value);
+  cudf::from_arrow(*arrow_scalar, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/replace_test.cpp b/cpp/tests/streams/replace_test.cpp
new file mode 100644
index 0000000..c794f99
--- /dev/null
+++ b/cpp/tests/streams/replace_test.cpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+#include <cudf_test/type_lists.hpp>
+
+class ReplaceTest : public cudf::test::BaseFixture {};
+
+TEST_F(ReplaceTest, ReplaceNullsColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int> input({{0, 0, 0, 0, 0}, {0, 0, 1, 1, 1}});
+  cudf::test::fixed_width_column_wrapper<int> replacement({1, 1, 1, 1, 1});
+  cudf::replace_nulls(input, replacement, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, ReplaceNullsScalar)
+{
+  cudf::test::fixed_width_column_wrapper<int> input({{0, 0, 0, 0, 0}, {0, 0, 1, 1, 1}});
+  auto replacement = cudf::numeric_scalar<int>(1, true, cudf::test::get_default_stream());
+  cudf::replace_nulls(input, replacement, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, ReplaceNullsPolicy)
+{
+  cudf::test::fixed_width_column_wrapper<int> input({{0, 0, 0, 0, 0}, {0, 0, 1, 1, 1}});
+  cudf::replace_nulls(input, cudf::replace_policy::FOLLOWING, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, ReplaceNansColumn)
+{
+  auto nan          = std::numeric_limits<double>::quiet_NaN();
+  auto input_column = cudf::test::make_type_param_vector<double>({0.0, 0.0, nan, nan, nan});
+  cudf::test::fixed_width_column_wrapper<double> input(input_column.begin(), input_column.end());
+  cudf::test::fixed_width_column_wrapper<double> replacement({0, 1, 2, 3, 4});
+  cudf::replace_nans(input, replacement, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, ReplaceNansScalar)
+{
+  auto nan          = std::numeric_limits<double>::quiet_NaN();
+  auto input_column = cudf::test::make_type_param_vector<double>({0.0, 0.0, nan, nan, nan});
+  cudf::test::fixed_width_column_wrapper<double> input(input_column.begin(), input_column.end());
+  auto replacement = cudf::numeric_scalar<double>(4, true, cudf::test::get_default_stream());
+  cudf::replace_nans(input, replacement, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, FindAndReplaceAll)
+{
+  cudf::test::fixed_width_column_wrapper<int> input({0, 0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<int> values_to_replace({0, 0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<int> replacement_values({1, 1, 1, 1, 1});
+  cudf::find_and_replace_all(
+    input, values_to_replace, replacement_values, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, ClampWithReplace)
+{
+  cudf::test::fixed_width_column_wrapper<int> input({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+  auto low          = cudf::numeric_scalar<int>(3, true, cudf::test::get_default_stream());
+  auto low_replace  = cudf::numeric_scalar<int>(5, true, cudf::test::get_default_stream());
+  auto high         = cudf::numeric_scalar<int>(7, true, cudf::test::get_default_stream());
+  auto high_replace = cudf::numeric_scalar<int>(6, true, cudf::test::get_default_stream());
+  cudf::clamp(input, low, low_replace, high, high_replace, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, Clamp)
+{
+  cudf::test::fixed_width_column_wrapper<int> input({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+  auto low  = cudf::numeric_scalar<int>(3, true, cudf::test::get_default_stream());
+  auto high = cudf::numeric_scalar<int>(7, true, cudf::test::get_default_stream());
+  cudf::clamp(input, low, high, cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, NormalizeNansAndZeros)
+{
+  auto nan          = std::numeric_limits<double>::quiet_NaN();
+  auto input_column = cudf::test::make_type_param_vector<double>({-0.0, 0.0, -nan, nan, nan});
+  cudf::test::fixed_width_column_wrapper<double> input(input_column.begin(), input_column.end());
+  cudf::normalize_nans_and_zeros(static_cast<cudf::column_view>(input),
+                                 cudf::test::get_default_stream());
+}
+
+TEST_F(ReplaceTest, NormalizeNansAndZerosMutable)
+{
+  auto nan          = std::numeric_limits<double>::quiet_NaN();
+  auto input_column = cudf::test::make_type_param_vector<double>({-0.0, 0.0, -nan, nan, nan});
+  cudf::test::fixed_width_column_wrapper<double> input(input_column.begin(), input_column.end());
+  cudf::normalize_nans_and_zeros(static_cast<cudf::mutable_column_view>(input),
+                                 cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/search_test.cpp b/cpp/tests/streams/search_test.cpp
new file mode 100644
index 0000000..fbe17fb
--- /dev/null
+++ b/cpp/tests/streams/search_test.cpp
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/search.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class SearchTest : public cudf::test::BaseFixture {};
+
+TEST_F(SearchTest, LowerBound)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> column{10, 20, 30, 40, 50};
+  cudf::test::fixed_width_column_wrapper<int32_t> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{0, 0, 0, 1, 2, 3, 3, 4, 4, 5};
+
+  cudf::lower_bound({cudf::table_view{{column}}},
+                    {cudf::table_view{{values}}},
+                    {cudf::order::ASCENDING},
+                    {cudf::null_order::BEFORE},
+                    cudf::test::get_default_stream());
+}
+
+TEST_F(SearchTest, UpperBound)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> column{10, 20, 30, 40, 50};
+  cudf::test::fixed_width_column_wrapper<int32_t> values{0, 7, 10, 11, 30, 32, 40, 47, 50, 90};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expect{0, 0, 0, 1, 2, 3, 3, 4, 4, 5};
+
+  cudf::upper_bound({cudf::table_view{{column}}},
+                    {cudf::table_view{{values}}},
+                    {cudf::order::ASCENDING},
+                    {cudf::null_order::BEFORE},
+                    cudf::test::get_default_stream());
+}
+
+TEST_F(SearchTest, ContainsScalar)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> column{0, 1, 17, 19, 23, 29, 71};
+  cudf::numeric_scalar<int32_t> scalar{23, true, cudf::test::get_default_stream()};
+
+  cudf::contains(column, scalar, cudf::test::get_default_stream());
+}
+
+TEST_F(SearchTest, ContainsColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> haystack{0, 1, 17, 19, 23, 29, 71};
+  cudf::test::fixed_width_column_wrapper<int32_t> needles{17, 19, 45, 72};
+
+  cudf::test::fixed_width_column_wrapper<bool> expect{1, 1, 0, 0};
+
+  cudf::contains(haystack, needles, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/sorting_test.cpp b/cpp/tests/streams/sorting_test.cpp
new file mode 100644
index 0000000..e481f95
--- /dev/null
+++ b/cpp/tests/streams/sorting_test.cpp
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/sorting.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class SortingTest : public cudf::test::BaseFixture {};
+
+TEST_F(SortingTest, SortedOrder)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const column{10, 20, 30, 40, 50};
+  cudf::table_view const tbl{{column}};
+
+  cudf::sorted_order(tbl, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, StableSortedOrder)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const column{10, 20, 30, 40, 50};
+  cudf::table_view const tbl{{column}};
+
+  cudf::stable_sorted_order(tbl, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, IsSorted)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const column{10, 20, 30, 40, 50};
+  cudf::table_view const tbl{{column}};
+
+  cudf::is_sorted(tbl, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, Sort)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const column{10, 20, 30, 40, 50};
+  cudf::table_view const tbl{{column}};
+
+  cudf::sort(tbl, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, SortByKey)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const values_col{10, 20, 30, 40, 50};
+  cudf::table_view const values{{values_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const keys_col{10, 20, 30, 40, 50};
+  cudf::table_view const keys{{keys_col}};
+
+  cudf::sort_by_key(values, keys, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, StableSortByKey)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const values_col{10, 20, 30, 40, 50};
+  cudf::table_view const values{{values_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const keys_col{10, 20, 30, 40, 50};
+  cudf::table_view const keys{{keys_col}};
+
+  cudf::stable_sort_by_key(values, keys, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, Rank)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const column{10, 20, 30, 40, 50};
+
+  cudf::rank(column,
+             cudf::rank_method::AVERAGE,
+             cudf::order::ASCENDING,
+             cudf::null_policy::EXCLUDE,
+             cudf::null_order::AFTER,
+             false,
+             cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, SegmentedSortedOrder)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const keys_col{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+  cudf::table_view const keys{{keys_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const segment_offsets{3, 7};
+
+  cudf::segmented_sorted_order(keys, segment_offsets, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, StableSegmentedSortedOrder)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const keys_col{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+  cudf::table_view const keys{{keys_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const segment_offsets{3, 7};
+
+  cudf::stable_segmented_sorted_order(
+    keys, segment_offsets, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, SegmentedSortByKey)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const keys_col{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+  cudf::table_view const keys{{keys_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const values_col{7, 6, 9, 3, 4, 5, 1, 2, 0, 4};
+  cudf::table_view const values{{values_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const segment_offsets{0, 3, 7, 10};
+
+  cudf::segmented_sort_by_key(
+    values, keys, segment_offsets, {}, {}, cudf::test::get_default_stream());
+}
+
+TEST_F(SortingTest, StableSegmentedSortByKey)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> const keys_col{9, 8, 7, 6, 5, 4, 3, 2, 1, 0};
+  cudf::table_view const keys{{keys_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const values_col{7, 6, 9, 3, 4, 5, 1, 2, 0, 4};
+  cudf::table_view const values{{values_col}};
+  cudf::test::fixed_width_column_wrapper<int32_t> const segment_offsets{0, 3, 7, 10};
+
+  cudf::stable_segmented_sort_by_key(
+    values, keys, segment_offsets, {}, {}, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/strings/case_test.cpp b/cpp/tests/streams/strings/case_test.cpp
new file mode 100644
index 0000000..df3eabd
--- /dev/null
+++ b/cpp/tests/streams/strings/case_test.cpp
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/strings/capitalize.hpp>
+#include <cudf/strings/case.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class StringsCaseTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsCaseTest, LowerUpper)
+{
+  auto const input =
+    cudf::test::strings_column_wrapper({"",
+                                        "The quick brown fox",
+                                        "jumps over the lazy dog.",
+                                        "all work and no play makes Jack a dull boy",
+                                        R"(!"#$%&'()*+,-./0123456789:;<=>?@[\]^_`{|}~)"});
+  auto view = cudf::strings_column_view(input);
+
+  cudf::strings::to_lower(view, cudf::test::get_default_stream());
+  cudf::strings::to_upper(view, cudf::test::get_default_stream());
+  cudf::strings::swapcase(view, cudf::test::get_default_stream());
+}
+
+TEST_F(StringsCaseTest, Capitalize)
+{
+  auto const input =
+    cudf::test::strings_column_wrapper({"",
+                                        "The Quick Brown Fox",
+                                        "jumps over the lazy dog",
+                                        "all work and no play makes Jack a dull boy"});
+  auto view = cudf::strings_column_view(input);
+
+  auto const delimiter = cudf::string_scalar(" ", true, cudf::test::get_default_stream());
+  cudf::strings::capitalize(view, delimiter, cudf::test::get_default_stream());
+  cudf::strings::is_title(view, cudf::test::get_default_stream());
+  cudf::strings::title(
+    view, cudf::strings::string_character_types::ALPHA, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/strings/find_test.cpp b/cpp/tests/streams/strings/find_test.cpp
new file mode 100644
index 0000000..b734a17
--- /dev/null
+++ b/cpp/tests/streams/strings/find_test.cpp
@@ -0,0 +1,49 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/strings/find.hpp>
+#include <cudf/strings/find_multiple.hpp>
+#include <cudf/strings/findall.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+#include <string>
+
+class StringsFindTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFindTest, Find)
+{
+  auto input = cudf::test::strings_column_wrapper({"Héllo", "thesé", "tést strings", ""});
+  auto view  = cudf::strings_column_view(input);
+
+  auto const target = cudf::string_scalar("é", true, cudf::test::get_default_stream());
+  cudf::strings::find(view, target, 0, -1, cudf::test::get_default_stream());
+  cudf::strings::rfind(view, target, 0, -1, cudf::test::get_default_stream());
+  cudf::strings::find(view, view, 0, cudf::test::get_default_stream());
+  cudf::strings::find_multiple(view, view, cudf::test::get_default_stream());
+  cudf::strings::contains(view, target, cudf::test::get_default_stream());
+  cudf::strings::starts_with(view, target, cudf::test::get_default_stream());
+  cudf::strings::starts_with(view, view, cudf::test::get_default_stream());
+  cudf::strings::ends_with(view, target, cudf::test::get_default_stream());
+  cudf::strings::ends_with(view, view, cudf::test::get_default_stream());
+
+  auto const pattern = std::string("[a-z]");
+  auto const prog    = cudf::strings::regex_program::create(pattern);
+  cudf::strings::findall(view, *prog, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/streams/text/ngrams_test.cpp b/cpp/tests/streams/text/ngrams_test.cpp
new file mode 100644
index 0000000..bce0d2b
--- /dev/null
+++ b/cpp/tests/streams/text/ngrams_test.cpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/generate_ngrams.hpp>
+#include <nvtext/ngrams_tokenize.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/default_stream.hpp>
+
+class TextNGramsTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextNGramsTest, GenerateNgrams)
+{
+  auto const input =
+    cudf::test::strings_column_wrapper({"the", "fox", "jumped", "over", "thé", "dog"});
+  auto const separator = cudf::string_scalar{"_", true, cudf::test::get_default_stream()};
+  nvtext::generate_ngrams(
+    cudf::strings_column_view(input), 3, separator, cudf::test::get_default_stream());
+}
+
+TEST_F(TextNGramsTest, GenerateCharacterNgrams)
+{
+  auto const input =
+    cudf::test::strings_column_wrapper({"the", "fox", "jumped", "over", "thé", "dog"});
+  nvtext::generate_character_ngrams(
+    cudf::strings_column_view(input), 3, cudf::test::get_default_stream());
+}
+
+TEST_F(TextNGramsTest, HashCharacterNgrams)
+{
+  auto input =
+    cudf::test::strings_column_wrapper({"the quick brown fox", "jumped over the lazy dog."});
+  nvtext::hash_character_ngrams(
+    cudf::strings_column_view(input), 5, cudf::test::get_default_stream());
+}
+
+TEST_F(TextNGramsTest, NgramsTokenize)
+{
+  auto input =
+    cudf::test::strings_column_wrapper({"the quick brown fox", "jumped over the lazy dog."});
+  auto const delimiter = cudf::string_scalar{" ", true, cudf::test::get_default_stream()};
+  auto const separator = cudf::string_scalar{"_", true, cudf::test::get_default_stream()};
+  nvtext::ngrams_tokenize(
+    cudf::strings_column_view(input), 2, delimiter, separator, cudf::test::get_default_stream());
+}
diff --git a/cpp/tests/strings/array_tests.cpp b/cpp/tests/strings/array_tests.cpp
new file mode 100644
index 0000000..c7ceb89
--- /dev/null
+++ b/cpp/tests/strings/array_tests.cpp
@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/copying.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsColumnTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsColumnTest, Sort)
+{
+  // cannot initialize std::string with a nullptr so use "<null>" as a place-holder
+  cudf::test::strings_column_wrapper h_strings({"eee", "bb", "<null>", "", "aa", "bbb", "ééé"},
+                                               {1, 1, 0, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper h_expected({"<null>", "", "aa", "bb", "bbb", "eee", "ééé"},
+                                                {0, 1, 1, 1, 1, 1, 1});
+
+  auto results =
+    cudf::sort(cudf::table_view({h_strings}), {cudf::order::ASCENDING}, {cudf::null_order::BEFORE});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), h_expected);
+}
+
+TEST_F(StringsColumnTest, SortZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto results = cudf::sort(cudf::table_view({zero_size_strings_column}));
+  cudf::test::expect_column_empty(results->view().column(0));
+}
+
+class SliceParmsTest : public StringsColumnTest,
+                       public testing::WithParamInterface<cudf::size_type> {};
+
+TEST_P(SliceParmsTest, Slice)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper input(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+
+  cudf::size_type start = 3;
+  cudf::size_type end   = GetParam();
+
+  auto scol    = cudf::slice(input, {start, end});
+  auto results = std::make_unique<cudf::column>(scol.front());
+
+  cudf::test::strings_column_wrapper expected(
+    h_strings.begin() + start,
+    h_strings.begin() + end,
+    thrust::make_transform_iterator(h_strings.begin() + start,
+                                    [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_P(SliceParmsTest, SliceAllNulls)
+{
+  std::vector<char const*> h_strings{nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr};
+  cudf::test::strings_column_wrapper input(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+
+  cudf::size_type start = 3;
+  cudf::size_type end   = GetParam();
+
+  auto scol    = cudf::slice(input, {start, end});
+  auto results = std::make_unique<cudf::column>(scol.front());
+
+  cudf::test::strings_column_wrapper expected(
+    h_strings.begin() + start,
+    h_strings.begin() + end,
+    thrust::make_transform_iterator(h_strings.begin() + start,
+                                    [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_P(SliceParmsTest, SliceAllEmpty)
+{
+  std::vector<char const*> h_strings{"", "", "", "", "", "", ""};
+  cudf::test::strings_column_wrapper input(h_strings.begin(), h_strings.end());
+
+  cudf::size_type start = 3;
+  cudf::size_type end   = GetParam();
+
+  auto scol    = cudf::slice(input, {start, end});
+  auto results = std::make_unique<cudf::column>(scol.front());
+
+  cudf::test::strings_column_wrapper expected(h_strings.begin() + start, h_strings.begin() + end);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+INSTANTIATE_TEST_CASE_P(StringsColumnTest,
+                        SliceParmsTest,
+                        testing::ValuesIn(std::array<cudf::size_type, 3>{5, 6, 7}));
+
+TEST_F(StringsColumnTest, SliceZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto scol    = cudf::slice(zero_size_strings_column, {0, 0});
+  auto results = std::make_unique<cudf::column>(scol.front());
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsColumnTest, Gather)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+
+  cudf::test::fixed_width_column_wrapper<int32_t> gather_map{{4, 1}};
+  auto results = cudf::gather(cudf::table_view{{strings}}, gather_map)->release();
+
+  std::vector<char const*> h_expected{"aa", "bb"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results.front()->view(), expected);
+}
+
+TEST_F(StringsColumnTest, GatherZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  cudf::column_view map_view(cudf::data_type{cudf::type_id::INT32}, 0, nullptr, nullptr, 0);
+  auto results = cudf::gather(cudf::table_view{{zero_size_strings_column}}, map_view)->release();
+  cudf::test::expect_column_empty(results.front()->view());
+}
+
+TEST_F(StringsColumnTest, GatherTooBig)
+{
+  std::vector<int8_t> h_chars(3000000);
+  cudf::test::fixed_width_column_wrapper<int8_t> chars(h_chars.begin(), h_chars.end());
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> offsets({0, 3000000});
+  auto input = cudf::column_view(
+    cudf::data_type{cudf::type_id::STRING}, 1, nullptr, nullptr, 0, 0, {offsets, chars});
+  auto map = thrust::constant_iterator<int8_t>(0);
+  cudf::test::fixed_width_column_wrapper<int8_t> gather_map(map, map + 1000);
+  EXPECT_THROW(cudf::gather(cudf::table_view{{input}}, gather_map), std::overflow_error);
+}
+
+TEST_F(StringsColumnTest, Scatter)
+{
+  cudf::test::strings_column_wrapper target({"eee", "bb", "", "", "aa", "bbb", "ééé"},
+                                            {1, 1, 0, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper source({"1", "22"});
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({4, 1});
+
+  auto results = cudf::scatter(cudf::table_view({source}), scatter_map, cudf::table_view({target}));
+
+  cudf::test::strings_column_wrapper expected({"eee", "22", "", "", "1", "bbb", "ééé"},
+                                              {1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TEST_F(StringsColumnTest, ScatterScalar)
+{
+  cudf::test::strings_column_wrapper target({"eee", "bb", "", "", "aa", "bbb", "ééé"},
+                                            {1, 1, 0, 1, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<int32_t> scatter_map({0, 5});
+
+  cudf::string_scalar scalar("__");
+  auto source  = std::vector<std::reference_wrapper<const cudf::scalar>>({scalar});
+  auto results = cudf::scatter(source, scatter_map, cudf::table_view({target}));
+
+  cudf::test::strings_column_wrapper expected({"__", "bb", "", "", "aa", "__", "ééé"},
+                                              {1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view().column(0), expected);
+}
+
+TEST_F(StringsColumnTest, ScatterZeroSizeStringsColumn)
+{
+  auto const source      = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto const target      = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto const scatter_map = cudf::make_empty_column(cudf::type_id::INT8)->view();
+
+  auto results = cudf::scatter(cudf::table_view({source}), scatter_map, cudf::table_view({target}));
+  cudf::test::expect_column_empty(results->view().column(0));
+
+  cudf::string_scalar scalar("");
+  auto scalar_source = std::vector<std::reference_wrapper<const cudf::scalar>>({scalar});
+  results            = cudf::scatter(scalar_source, scatter_map, cudf::table_view({target}));
+  cudf::test::expect_column_empty(results->view().column(0));
+}
+
+TEST_F(StringsColumnTest, OffsetsBeginEnd)
+{
+  cudf::test::strings_column_wrapper input({"eee", "bb", "", "", "aa", "bbb", "ééé"},
+                                           {1, 1, 0, 1, 1, 1, 1});
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 5});
+  auto scv = cudf::strings_column_view(input);
+  EXPECT_EQ(std::distance(scv.offsets_begin(), scv.offsets_end()),
+            static_cast<std::ptrdiff_t>(scv.size() + 1));
+
+  scv = cudf::strings_column_view(cudf::slice(input, {1, 5}).front());
+  EXPECT_EQ(std::distance(scv.offsets_begin(), scv.offsets_end()),
+            static_cast<std::ptrdiff_t>(scv.size() + 1));
+  EXPECT_EQ(std::distance(scv.chars_begin(), scv.chars_end()), 16L);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/strings/attrs_tests.cpp b/cpp/tests/strings/attrs_tests.cpp
new file mode 100644
index 0000000..c5f3869
--- /dev/null
+++ b/cpp/tests/strings/attrs_tests.cpp
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/attributes.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsAttributesTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsAttributesTest, CodePoints)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  {
+    auto results = cudf::strings::code_points(strings_view);
+
+    cudf::test::fixed_width_column_wrapper<int32_t> expected{
+      101, 101, 101, 98, 98, 97, 97, 98, 98, 98, 50089, 50089, 50089};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsAttributesTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+  cudf::column_view expected_column(cudf::data_type{cudf::type_id::INT32}, 0, nullptr, nullptr, 0);
+
+  auto results = cudf::strings::count_bytes(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected_column);
+  results = cudf::strings::count_characters(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected_column);
+  results = cudf::strings::code_points(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected_column);
+}
+
+TEST_F(StringsAttributesTest, StringsLengths)
+{
+  std::vector<char const*> h_strings{
+    "eee", "bb", nullptr, "", "aa", "ééé", "something a bit longer than 32 bytes"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  {
+    auto results = cudf::strings::count_characters(strings_view);
+    std::vector<int32_t> h_expected{3, 2, 0, 0, 2, 3, 36};
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::count_bytes(strings_view);
+    std::vector<int32_t> h_expected{3, 2, 0, 0, 2, 6, 36};
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsAttributesTest, StringsLengthsLong)
+{
+  std::vector<std::string> h_strings(
+    40000, "something a bit longer than 32 bytes ééé ééé ééé ééé ééé ééé ééé");
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::count_characters(strings_view);
+  std::vector<int32_t> h_expected(h_strings.size(), 64);
+  cudf::test::fixed_width_column_wrapper<int32_t> expected(h_expected.begin(), h_expected.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
diff --git a/cpp/tests/strings/booleans_tests.cpp b/cpp/tests/strings/booleans_tests.cpp
new file mode 100644
index 0000000..0c7fc99
--- /dev/null
+++ b/cpp/tests/strings/booleans_tests.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/convert/convert_booleans.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsConvertTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConvertTest, ToBooleans)
+{
+  std::vector<char const*> h_strings{"false", nullptr, "", "true", "True", "False"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_booleans(strings_view);
+
+  std::vector<bool> h_expected{false, false, false, true, false, false};
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, FromBooleans)
+{
+  std::vector<char const*> h_strings{"true", nullptr, "false", "true", "true", "false"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<bool> h_column{true, false, false, true, true, false};
+  cudf::test::fixed_width_column_wrapper<bool> column(
+    h_column.begin(),
+    h_column.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = cudf::strings::from_booleans(column);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeStringsColumnBoolean)
+{
+  auto const zero_size_column = cudf::make_empty_column(cudf::type_id::BOOL8)->view();
+  auto results                = cudf::strings::from_booleans(zero_size_column);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsConvertTest, ZeroSizeBooleansColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto results                        = cudf::strings::to_booleans(zero_size_strings_column);
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsConvertTest, BooleanError)
+{
+  auto column = cudf::make_numeric_column(cudf::data_type{cudf::type_id::INT32}, 100);
+  EXPECT_THROW(cudf::strings::from_booleans(column->view()), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/case_tests.cpp b/cpp/tests/strings/case_tests.cpp
new file mode 100644
index 0000000..1d82d78
--- /dev/null
+++ b/cpp/tests/strings/case_tests.cpp
@@ -0,0 +1,291 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/capitalize.hpp>
+#include <cudf/strings/case.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsCaseTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsCaseTest, ToLower)
+{
+  std::vector<char const*> h_strings{
+    "Éxamples aBc", "123 456", nullptr, "ARE THE", "tést strings", ""};
+  std::vector<char const*> h_expected{
+    "éxamples abc", "123 456", nullptr, "are the", "tést strings", ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::to_lower(strings_view);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCaseTest, ToUpper)
+{
+  std::vector<char const*> h_strings{
+    "Éxamples aBc", "123 456", nullptr, "ARE THE", "tést strings", ""};
+  std::vector<char const*> h_expected{
+    "ÉXAMPLES ABC", "123 456", nullptr, "ARE THE", "TÉST STRINGS", ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::to_upper(strings_view);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCaseTest, Swapcase)
+{
+  std::vector<char const*> h_strings{
+    "Éxamples aBc", "123 456", nullptr, "ARE THE", "tést strings", ""};
+  std::vector<char const*> h_expected{
+    "éXAMPLES AbC", "123 456", nullptr, "are the", "TÉST STRINGS", ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::swapcase(strings_view);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCaseTest, Capitalize)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"SȺȺnich xyZ", "Examples aBc", "thesé", "", "ARE\tTHE", "tést\tstrings", ""},
+    {1, 1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(strings);
+
+  {
+    auto results = cudf::strings::capitalize(strings_view);
+    cudf::test::strings_column_wrapper expected(
+      {"Sⱥⱥnich xyz", "Examples abc", "Thesé", "", "Are\tthe", "Tést\tstrings", ""},
+      {1, 1, 1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::capitalize(strings_view, std::string(" "));
+    cudf::test::strings_column_wrapper expected(
+      {"Sⱥⱥnich Xyz", "Examples Abc", "Thesé", "", "Are\tthe", "Tést\tstrings", ""},
+      {1, 1, 1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::capitalize(strings_view, std::string(" \t"));
+    cudf::test::strings_column_wrapper expected(
+      {"Sⱥⱥnich Xyz", "Examples Abc", "Thesé", "", "Are\tThe", "Tést\tStrings", ""},
+      {1, 1, 1, 0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsCaseTest, Title)
+{
+  cudf::test::strings_column_wrapper input(
+    {"SȺȺnich", "Examples aBc", "thesé", "", "ARE THE", "tést strings", "", "n2viDIA corp"},
+    {1, 1, 1, 0, 1, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(input);
+
+  auto results = cudf::strings::title(strings_view);
+
+  cudf::test::strings_column_wrapper expected(
+    {"Sⱥⱥnich", "Examples Abc", "Thesé", "", "Are The", "Tést Strings", "", "N2Vidia Corp"},
+    {1, 1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::title(strings_view, cudf::strings::string_character_types::ALPHANUM);
+
+  cudf::test::strings_column_wrapper expected2(
+    {"Sⱥⱥnich", "Examples Abc", "Thesé", "", "Are The", "Tést Strings", "", "N2vidia Corp"},
+    {1, 1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected2);
+}
+
+TEST_F(StringsCaseTest, IsTitle)
+{
+  cudf::test::strings_column_wrapper input({"Sⱥⱥnich",
+                                            "Examples Abc",
+                                            "Thesé Strings",
+                                            "",
+                                            "Are The",
+                                            "Tést strings",
+                                            "",
+                                            "N2Vidia Corp",
+                                            "SNAKE",
+                                            "!Abc",
+                                            " Eagle",
+                                            "A Test",
+                                            "12345",
+                                            "Alpha Not Upper Or Lower: ƻC",
+                                            "one More"},
+                                           {1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+
+  auto results = cudf::strings::is_title(cudf::strings_column_view(input));
+
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    {1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0}, {1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCaseTest, MultiCharUpper)
+{
+  cudf::test::strings_column_wrapper strings{"\u1f52 \u1f83", "\u1e98 \ufb05", "\u0149"};
+  cudf::test::strings_column_wrapper expected{
+    "\u03a5\u0313\u0300 \u1f0b\u0399", "\u0057\u030a \u0053\u0054", "\u02bc\u004e"};
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::to_upper(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::capitalize(strings_view, std::string(" "));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::title(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCaseTest, MultiCharLower)
+{
+  // there's only one of these
+  cudf::test::strings_column_wrapper strings{"\u0130"};
+  cudf::test::strings_column_wrapper expected{"\u0069\u0307"};
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::to_lower(strings_view);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCaseTest, Ascii)
+{
+  // triggering the ascii code path requires some long-ish strings
+  cudf::test::strings_column_wrapper input{
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=-"};
+  auto view     = cudf::strings_column_view(input);
+  auto expected = cudf::test::strings_column_wrapper{
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=-"};
+  auto results = cudf::strings::to_lower(view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  expected = cudf::test::strings_column_wrapper{
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=- ",
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=- ",
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=- ",
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=-"};
+  results = cudf::strings::to_upper(view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::to_upper(cudf::strings_column_view(cudf::slice(input, {1, 3}).front()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, cudf::slice(expected, {1, 3}).front());
+}
+
+TEST_F(StringsCaseTest, LongStrings)
+{
+  // average string length >= AVG_CHAR_BYTES_THRESHOLD as defined in case.cu
+  cudf::test::strings_column_wrapper input{
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=-"};
+  auto view     = cudf::strings_column_view(input);
+  auto expected = cudf::test::strings_column_wrapper{
+    "abcdéfghijklmnopqrstuvwxyzabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "abcdéfghijklmnopqrstuvwxyzabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "abcdéfghijklmnopqrstuvwxyzabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=- ",
+    "abcdéfghijklmnopqrstuvwxyzabcdéfghijklmnopqrstuvwxyz1234567890!@#$%^&*()_+=-"};
+  auto results = cudf::strings::to_lower(view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  expected = cudf::test::strings_column_wrapper{
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZABCDÉFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=- ",
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZABCDÉFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=- ",
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZABCDÉFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=- ",
+    "ABCDÉFGHIJKLMNOPQRSTUVWXYZABCDÉFGHIJKLMNOPQRSTUVWXYZ1234567890!@#$%^&*()_+=-"};
+  results = cudf::strings::to_upper(view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::to_upper(cudf::strings_column_view(cudf::slice(input, {1, 3}).front()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, cudf::slice(expected, {1, 3}).front());
+}
+
+TEST_F(StringsCaseTest, EmptyStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+
+  auto results = cudf::strings::to_lower(strings_view);
+  cudf::test::expect_column_empty(results->view());
+
+  results = cudf::strings::to_upper(strings_view);
+  cudf::test::expect_column_empty(results->view());
+
+  results = cudf::strings::swapcase(strings_view);
+  cudf::test::expect_column_empty(results->view());
+
+  results = cudf::strings::capitalize(strings_view);
+  cudf::test::expect_column_empty(results->view());
+
+  results = cudf::strings::title(strings_view);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsCaseTest, ErrorTest)
+{
+  cudf::test::strings_column_wrapper input{"the column intentionally left blank"};
+  auto view = cudf::strings_column_view(input);
+
+  EXPECT_THROW(cudf::strings::capitalize(view, cudf::string_scalar("", false)), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/chars_types_tests.cpp b/cpp/tests/strings/chars_types_tests.cpp
new file mode 100644
index 0000000..c595977
--- /dev/null
+++ b/cpp/tests/strings/chars_types_tests.cpp
@@ -0,0 +1,336 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/char_types/char_types.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsCharsTest : public cudf::test::BaseFixture {};
+
+class CharsTypes : public StringsCharsTest,
+                   public testing::WithParamInterface<cudf::strings::string_character_types> {};
+
+TEST_P(CharsTypes, AllTypes)
+{
+  std::vector<char const*> h_strings{"Héllo",
+                                     "thesé",
+                                     nullptr,
+                                     "HERE",
+                                     "tést strings",
+                                     "",
+                                     "1.75",
+                                     "-34",
+                                     "+9.8",
+                                     "17¼",
+                                     "x³",
+                                     "2³",
+                                     " 12⅝",
+                                     "1234567890",
+                                     "de",
+                                     "\t\r\n\f "};
+
+  bool expecteds[] = {false, false, false, false, false, false, false, false,
+                      false, false, false, false, false, true,  false, false,  // decimal
+                      false, false, false, false, false, false, false, false,
+                      false, true,  false, true,  false, true,  false, false,  // numeric
+                      false, false, false, false, false, false, false, false,
+                      false, false, false, true,  false, true,  false, false,  // digit
+                      true,  true,  false, true,  false, false, false, false,
+                      false, false, false, false, false, false, true,  false,  // alpha
+                      false, false, false, false, false, false, false, false,
+                      false, false, false, false, false, false, false, true,  // space
+                      false, false, false, true,  false, false, false, false,
+                      false, false, false, false, false, false, false, false,  // upper
+                      false, true,  false, false, false, false, false, false,
+                      false, false, false, false, false, false, true,  false};  // lower
+
+  auto is_parm = GetParam();
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::all_characters_of_type(strings_view, is_parm);
+
+  int x             = static_cast<int>(is_parm);
+  int index         = 0;
+  int strings_count = static_cast<int>(h_strings.size());
+  while (x >>= 1)
+    ++index;
+  bool* sub_expected = &expecteds[index * strings_count];
+
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    sub_expected,
+    sub_expected + strings_count,
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+INSTANTIATE_TEST_CASE_P(StringsCharsTest,
+                        CharsTypes,
+                        testing::ValuesIn(std::array<cudf::strings::string_character_types, 7>{
+                          cudf::strings::string_character_types::DECIMAL,
+                          cudf::strings::string_character_types::NUMERIC,
+                          cudf::strings::string_character_types::DIGIT,
+                          cudf::strings::string_character_types::ALPHA,
+                          cudf::strings::string_character_types::SPACE,
+                          cudf::strings::string_character_types::UPPER,
+                          cudf::strings::string_character_types::LOWER}));
+
+TEST_F(StringsCharsTest, LowerUpper)
+{
+  cudf::test::strings_column_wrapper strings({"a1", "A1", "a!", "A!", "!1", "aA"});
+  auto strings_view = cudf::strings_column_view(strings);
+  auto verify_types =
+    cudf::strings::string_character_types::LOWER | cudf::strings::string_character_types::UPPER;
+  {
+    auto results = cudf::strings::all_characters_of_type(
+      strings_view, cudf::strings::string_character_types::LOWER, verify_types);
+    cudf::test::fixed_width_column_wrapper<bool> expected({1, 0, 1, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::all_characters_of_type(
+      strings_view, cudf::strings::string_character_types::UPPER, verify_types);
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 1, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsCharsTest, Alphanumeric)
+{
+  std::vector<char const*> h_strings{"Héllo",
+                                     "thesé",
+                                     nullptr,
+                                     "HERE",
+                                     "tést strings",
+                                     "",
+                                     "1.75",
+                                     "-34",
+                                     "+9.8",
+                                     "17¼",
+                                     "x³",
+                                     "2³",
+                                     " 12⅝",
+                                     "1234567890",
+                                     "de",
+                                     "\t\r\n\f "};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::all_characters_of_type(
+    strings_view, cudf::strings::string_character_types::ALPHANUM);
+
+  std::vector<bool> h_expected{1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0};
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCharsTest, AlphaNumericSpace)
+{
+  std::vector<char const*> h_strings{"Héllo",
+                                     "thesé",
+                                     nullptr,
+                                     "HERE",
+                                     "tést strings",
+                                     "",
+                                     "1.75",
+                                     "-34",
+                                     "+9.8",
+                                     "17¼",
+                                     "x³",
+                                     "2³",
+                                     " 12⅝",
+                                     "1234567890",
+                                     "de",
+                                     "\t\r\n\f "};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto types =
+    cudf::strings::string_character_types::ALPHANUM | cudf::strings::string_character_types::SPACE;
+  auto results = cudf::strings::all_characters_of_type(
+    strings_view, (cudf::strings::string_character_types)types);
+
+  std::vector<bool> h_expected{1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCharsTest, Numerics)
+{
+  std::vector<char const*> h_strings{"Héllo",
+                                     "thesé",
+                                     nullptr,
+                                     "HERE",
+                                     "tést strings",
+                                     "",
+                                     "1.75",
+                                     "-34",
+                                     "+9.8",
+                                     "17¼",
+                                     "x³",
+                                     "2³",
+                                     " 12⅝",
+                                     "1234567890",
+                                     "de",
+                                     "\t\r\n\f "};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto types = cudf::strings::string_character_types::DIGIT |
+               cudf::strings::string_character_types::DECIMAL |
+               cudf::strings::string_character_types::NUMERIC;
+  auto results = cudf::strings::all_characters_of_type(
+    strings_view, (cudf::strings::string_character_types)types);
+
+  std::vector<bool> h_expected{0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0};
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCharsTest, EmptyStrings)
+{
+  cudf::test::strings_column_wrapper strings({"", "", ""});
+  auto strings_view = cudf::strings_column_view(strings);
+  cudf::test::fixed_width_column_wrapper<bool> expected({0, 0, 0});
+  auto results = cudf::strings::all_characters_of_type(
+    strings_view, cudf::strings::string_character_types::ALPHANUM);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsCharsTest, FilterCharTypes)
+{
+  // The example strings are based on issue 5520
+  cudf::test::strings_column_wrapper strings(
+    {"abc£def", "01234 56789", "℉℧ is not alphanumeric", "but Αγγλικά is", ""});
+  auto results =
+    cudf::strings::filter_characters_of_type(cudf::strings_column_view(strings),
+                                             cudf::strings::string_character_types::ALL_TYPES,
+                                             cudf::string_scalar(" "),
+                                             cudf::strings::string_character_types::ALPHANUM);
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"abc def", "01234 56789", "   is not alphanumeric", "but Αγγλικά is", ""});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  results = cudf::strings::filter_characters_of_type(
+    cudf::strings_column_view(strings), cudf::strings::string_character_types::ALPHANUM);
+  {
+    cudf::test::strings_column_wrapper expected({"£", " ", "℉℧   ", "  ", ""});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  results = cudf::strings::filter_characters_of_type(cudf::strings_column_view(strings),
+                                                     cudf::strings::string_character_types::SPACE);
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"abc£def", "0123456789", "℉℧isnotalphanumeric", "butΑγγλικάis", ""});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  results =
+    cudf::strings::filter_characters_of_type(cudf::strings_column_view(strings),
+                                             cudf::strings::string_character_types::ALL_TYPES,
+                                             cudf::string_scalar("+"),
+                                             cudf::strings::string_character_types::SPACE);
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"+++++++", "+++++ +++++", "++ ++ +++ ++++++++++++", "+++ +++++++ ++", ""});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  results = cudf::strings::filter_characters_of_type(
+    cudf::strings_column_view(strings), cudf::strings::string_character_types::NUMERIC);
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"abc£def", " ", "℉℧ is not alphanumeric", "but Αγγλικά is", ""});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  results =
+    cudf::strings::filter_characters_of_type(cudf::strings_column_view(strings),
+                                             cudf::strings::string_character_types::ALL_TYPES,
+                                             cudf::string_scalar(""),
+                                             cudf::strings::string_character_types::NUMERIC);
+  {
+    cudf::test::strings_column_wrapper expected({"", "0123456789", "", "", ""});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsCharsTest, FilterCharTypesErrors)
+{
+  cudf::test::strings_column_wrapper strings({"strings left intentionally blank"});
+  EXPECT_THROW(
+    cudf::strings::filter_characters_of_type(cudf::strings_column_view(strings),
+                                             cudf::strings::string_character_types::ALL_TYPES,
+                                             cudf::string_scalar(""),
+                                             cudf::strings::string_character_types::ALL_TYPES),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::filter_characters_of_type(cudf::strings_column_view(strings),
+                                             cudf::strings::string_character_types::ALPHANUM,
+                                             cudf::string_scalar(""),
+                                             cudf::strings::string_character_types::NUMERIC),
+    cudf::logic_error);
+}
+
+TEST_F(StringsCharsTest, EmptyStringsColumn)
+{
+  cudf::test::strings_column_wrapper strings;
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::all_characters_of_type(
+    strings_view, cudf::strings::string_character_types::ALPHANUM);
+  EXPECT_EQ(cudf::type_id::BOOL8, results->view().type().id());
+  EXPECT_EQ(0, results->view().size());
+
+  results = cudf::strings::filter_characters_of_type(
+    strings_view, cudf::strings::string_character_types::NUMERIC);
+  EXPECT_EQ(cudf::type_id::STRING, results->view().type().id());
+  EXPECT_EQ(0, results->view().size());
+}
diff --git a/cpp/tests/strings/combine/concatenate_tests.cpp b/cpp/tests/strings/combine/concatenate_tests.cpp
new file mode 100644
index 0000000..95993e6
--- /dev/null
+++ b/cpp/tests/strings/combine/concatenate_tests.cpp
@@ -0,0 +1,515 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+struct StringsCombineTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsCombineTest, Concatenate)
+{
+  std::vector<char const*> h_strings1{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings1(
+    h_strings1.begin(),
+    h_strings1.end(),
+    thrust::make_transform_iterator(h_strings1.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_strings2{"xyz", "abc", "d", "éa", "", nullptr, "f"};
+  cudf::test::strings_column_wrapper strings2(
+    h_strings2.begin(),
+    h_strings2.end(),
+    thrust::make_transform_iterator(h_strings2.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<cudf::column_view> strings_columns;
+  strings_columns.push_back(strings1);
+  strings_columns.push_back(strings2);
+
+  cudf::table_view table(strings_columns);
+
+  {
+    std::vector<char const*> h_expected{"eeexyz", "bbabc", nullptr, "éa", "aa", nullptr, "éééf"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+    auto results = cudf::strings::concatenate(table);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_expected{
+      "eee:xyz", "bb:abc", nullptr, ":éa", "aa:", nullptr, "ééé:f"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+    auto results = cudf::strings::concatenate(table, cudf::string_scalar(":"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_expected{"eee:xyz", "bb:abc", "_:d", ":éa", "aa:", "bbb:_", "ééé:f"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+    auto results =
+      cudf::strings::concatenate(table, cudf::string_scalar(":"), cudf::string_scalar("_"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_expected{"eeexyz", "bbabc", "d", "éa", "aa", "bbb", "éééf"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+    auto results =
+      cudf::strings::concatenate(table, cudf::string_scalar(""), cudf::string_scalar(""));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsCombineTest, ConcatenateSkipNulls)
+{
+  cudf::test::strings_column_wrapper strings1({"eee", "", "", "", "aa", "bbb", "ééé"},
+                                              {1, 0, 0, 1, 1, 1, 1});
+  cudf::test::strings_column_wrapper strings2({"xyz", "", "d", "éa", "", "", "f"},
+                                              {1, 0, 1, 1, 1, 0, 1});
+  cudf::test::strings_column_wrapper strings3({"q", "", "s", "t", "u", "", "w"},
+                                              {1, 1, 1, 1, 1, 0, 1});
+
+  cudf::table_view table({strings1, strings2, strings3});
+
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"eee+xyz+q", "++", "+d+s", "+éa+t", "aa++u", "bbb++", "ééé+f+w"});
+    auto results = cudf::strings::concatenate(table,
+                                              cudf::string_scalar("+"),
+                                              cudf::string_scalar(""),
+                                              cudf::strings::separator_on_nulls::YES);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"eee+xyz+q", "", "d+s", "+éa+t", "aa++u", "bbb", "ééé+f+w"});
+    auto results = cudf::strings::concatenate(table,
+                                              cudf::string_scalar("+"),
+                                              cudf::string_scalar(""),
+                                              cudf::strings::separator_on_nulls::NO);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected(
+      {"eee+xyz+q", "", "", "+éa+t", "aa++u", "", "ééé+f+w"}, {1, 0, 0, 1, 1, 0, 1});
+    auto results = cudf::strings::concatenate(table,
+                                              cudf::string_scalar("+"),
+                                              cudf::string_scalar("", false),
+                                              cudf::strings::separator_on_nulls::NO);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper sep_col({"+", "-", ".", "@", "*", "^^", "#"});
+    auto results = cudf::strings::concatenate(table,
+                                              cudf::strings_column_view(sep_col),
+                                              cudf::string_scalar(""),
+                                              cudf::string_scalar(""),
+                                              cudf::strings::separator_on_nulls::NO);
+
+    cudf::test::strings_column_wrapper expected(
+      {"eee+xyz+q", "", "d.s", "@éa@t", "aa**u", "bbb", "ééé#f#w"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+}
+
+TEST_F(StringsCombineTest, ConcatZeroSizeStringsColumns)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  std::vector<cudf::column_view> strings_columns;
+  strings_columns.push_back(zero_size_strings_column);
+  strings_columns.push_back(zero_size_strings_column);
+  cudf::table_view table(strings_columns);
+  auto results = cudf::strings::concatenate(table);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsCombineTest, SingleColumnErrorCheck)
+{
+  auto const col0 = cudf::make_empty_column(cudf::type_id::STRING);
+  EXPECT_THROW(cudf::strings::concatenate(cudf::table_view{{col0->view()}}), cudf::logic_error);
+}
+
+struct StringsConcatenateWithColSeparatorTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConcatenateWithColSeparatorTest, ExceptionTests)
+{
+  // Exception tests
+  // 0. 0 columns passed
+  // 1. > 0 columns passed; some using non string data types
+  // 2. separator column of different size to column size
+  {
+    EXPECT_THROW(cudf::strings::concatenate(cudf::table_view{},
+                                            cudf::strings_column_view{cudf::column_view{}}),
+                 cudf::logic_error);
+  }
+
+  {
+    auto const col0 = cudf::make_empty_column(cudf::type_id::STRING)->view();
+    cudf::test::fixed_width_column_wrapper<int64_t> col1{{1}};
+
+    EXPECT_THROW(
+      cudf::strings::concatenate(cudf::table_view{{col0, col1}}, cudf::strings_column_view(col0)),
+      cudf::logic_error);
+  }
+
+  {
+    auto col0    = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+    auto sep_col = cudf::test::strings_column_wrapper({"", ""}, {true, false});
+
+    EXPECT_THROW(
+      cudf::strings::concatenate(cudf::table_view{{col0}}, cudf::strings_column_view(sep_col)),
+      cudf::logic_error);
+  }
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, ZeroSizedColumns)
+{
+  auto const col0 = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto results =
+    cudf::strings::concatenate(cudf::table_view{{col0}}, cudf::strings_column_view(col0));
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnEmptyAndNullStringsNoReplacements)
+{
+  auto col0    = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+  auto sep_col = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, false, true});
+
+  auto exp_results =
+    cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, false, false});
+  auto results =
+    cudf::strings::concatenate(cudf::table_view{{col0}}, cudf::strings_column_view(sep_col));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnEmptyAndNullStringsSeparatorReplacement)
+{
+  auto col0    = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+  auto sep_col = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, false, true});
+  auto sep_rep = cudf::string_scalar("");
+
+  auto exp_results =
+    cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+
+  auto results = cudf::strings::concatenate(
+    cudf::table_view{{col0}}, cudf::strings_column_view(sep_col), sep_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnEmptyAndNullStringsColumnReplacement)
+{
+  auto col0    = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+  auto sep_col = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, false, true});
+  auto col_rep = cudf::string_scalar("");
+
+  auto exp_results =
+    cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, false, true});
+
+  auto results = cudf::strings::concatenate(cudf::table_view{{col0}},
+                                            cudf::strings_column_view(sep_col),
+                                            cudf::string_scalar("", false),
+                                            col_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest,
+       SingleColumnEmptyAndNullStringsSeparatorAndColumnReplacement)
+{
+  auto col0    = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, true, false});
+  auto sep_col = cudf::test::strings_column_wrapper({"", "", "", ""}, {false, true, false, true});
+  auto sep_rep = cudf::string_scalar("");
+  auto col_rep = cudf::string_scalar("");
+
+  auto exp_results = cudf::test::strings_column_wrapper({"", "", "", ""});
+
+  auto results = cudf::strings::concatenate(
+    cudf::table_view{{col0}}, cudf::strings_column_view(sep_col), sep_rep, col_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnStringMixNoReplacements)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "bbabc", "invalid", "d", "éa", "invalid", "bbb", "éééf"},
+    {true, false, true, true, false, true, true, false, true, true});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~", "!", "@", "#", "$", "%", "^", "&", "*"},
+    {false, false, true, true, true, true, true, false, true, true});
+
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"", "", "", "bbabc", "", "d", "éa", "", "bbb", "éééf"},
+    {false, false, true, true, false, true, true, false, true, true});
+
+  auto results =
+    cudf::strings::concatenate(cudf::table_view{{col0}}, cudf::strings_column_view(sep_col));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnStringMixSeparatorReplacement)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "bbabc", "invalid", "d", "éa", "invalid", "bbb", "éééf"},
+    {true, false, true, true, false, true, true, false, true, true});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~", "!", "@", "#", "$", "%", "^", "&", "*"},
+    {false, false, false, true, true, true, true, false, true, true});
+  auto sep_rep = cudf::string_scalar("-");
+
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"eeexyz", "", "", "bbabc", "", "d", "éa", "", "bbb", "éééf"},
+    {true, false, true, true, false, true, true, false, true, true});
+
+  auto results = cudf::strings::concatenate(
+    cudf::table_view{{col0}}, cudf::strings_column_view(sep_col), sep_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnStringMixColumnReplacement)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "bbabc", "invalid", "d", "éa", "invalid", "bbb", "éééf"},
+    {true, false, true, true, false, true, true, false, true, true});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~", "!", "@", "#", "$", "%", "^", "&", "*"},
+    {false, false, false, true, true, true, true, false, true, true});
+  auto col_rep = cudf::string_scalar("goobly");
+
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"", "", "", "bbabc", "goobly", "d", "éa", "", "bbb", "éééf"},
+    {false, false, false, true, true, true, true, false, true, true});
+
+  auto results = cudf::strings::concatenate(cudf::table_view{{col0}},
+                                            cudf::strings_column_view(sep_col),
+                                            cudf::string_scalar("", false),
+                                            col_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, SingleColumnStringMixSeparatorAndColumnReplacement)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "bbabc", "invalid", "d", "éa", "invalid", "bbb", "éééf"},
+    {true, false, true, true, false, true, true, false, true, true});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~", "!", "@", "#", "$", "%", "^", "&", "*"},
+    {false, false, false, true, true, true, true, false, true, true});
+  auto sep_rep = cudf::string_scalar("-");
+  auto col_rep = cudf::string_scalar("goobly");
+
+  // All valid, as every invalid element is replaced - a non nullable column
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"eeexyz", "goobly", "", "bbabc", "goobly", "d", "éa", "goobly", "bbb", "éééf"});
+
+  auto results = cudf::strings::concatenate(
+    cudf::table_view{{col0}}, cudf::strings_column_view(sep_col), sep_rep, col_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, MultiColumnEmptyAndNullStringsNoReplacements)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"", "", "", "", "", "", "", ""}, {false, false, true, true, true, true, false, false});
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"", "", "", "", "", "", "", ""}, {false, false, true, true, false, false, true, true});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "", "", "", "", "", "", ""}, {true, false, true, false, true, false, true, false});
+
+  auto exp_results1 = cudf::test::strings_column_wrapper(
+    {"", "", "", "", "", "", "", ""}, {false, false, true, false, false, false, false, false});
+  auto results =
+    cudf::strings::concatenate(cudf::table_view{{col0, col1}}, cudf::strings_column_view(sep_col));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results1, verbosity);
+
+  auto exp_results2 = cudf::test::strings_column_wrapper(
+    {"", "", "", "", "", "", "", ""}, {true, false, true, false, true, false, true, false});
+  results = cudf::strings::concatenate(cudf::table_view{{col0, col1}},
+                                       cudf::strings_column_view(sep_col),
+                                       cudf::string_scalar("", false),
+                                       cudf::string_scalar(""),
+                                       cudf::strings::separator_on_nulls::NO);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results2, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, MultiColumnStringMixNoReplacements)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "éééf", "éa", "", "", "invalid", "null", "NULL", "-1", ""},
+    {true, true, true, true, true, true, false, false, false, false, false, false});
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"foo", "", "éaff", "", "invalid", "NULL", "éaff", "valid", "doo", "", "<null>", "-1"},
+    {true, true, true, false, false, false, true, true, true, false, false, false});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~~~", "", "@", "", "", "", "^^^^", "", "--", "*****", "######"},
+    {true, true, false, true, false, true, false, true, true, true, true, true});
+
+  auto exp_results1 = cudf::test::strings_column_wrapper(
+    {"eeexyzfoo", "<null>~~~", "", "", "", "", "", "", "", "", "", ""},
+    {true, true, false, false, false, false, false, false, false, false, false, false});
+
+  auto results =
+    cudf::strings::concatenate(cudf::table_view{{col0, col1}}, cudf::strings_column_view(sep_col));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results1, verbosity);
+
+  auto exp_results2 = cudf::test::strings_column_wrapper(
+    {"eeexyzfoo", "<null>~~~", "", "éééf", "", "", "", "valid", "doo", "", "", ""},
+    {true, true, false, true, false, true, false, true, true, true, true, true});
+  results = cudf::strings::concatenate(cudf::table_view{{col0, col1}},
+                                       cudf::strings_column_view(sep_col),
+                                       cudf::string_scalar("", false),
+                                       cudf::string_scalar(""),
+                                       cudf::strings::separator_on_nulls::NO);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results2, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, MultiColumnStringMixSeparatorReplacement)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "éééf", "éa", "", "", "invalid", "null", "NULL", "-1", ""},
+    {true, true, true, true, true, true, false, false, false, false, false, false});
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"foo", "", "éaff", "", "invalid", "NULL", "éaff", "valid", "doo", "", "<null>", "-1"},
+    {true, true, true, false, false, false, true, true, true, false, false, false});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~~~", "", "@", "", "", "", "^^^^", "", "--", "*****", "######"},
+    {true, true, false, true, false, true, false, true, true, true, true, true});
+  auto sep_rep = cudf::string_scalar("!!!!!!!");
+
+  auto exp_results1 = cudf::test::strings_column_wrapper(
+    {"eeexyzfoo", "<null>~~~", "!!!!!!!éaff", "éééf", "éa", "", "éaff", "valid", "doo", "", "", ""},
+    {true, true, true, false, false, false, false, false, false, false, false, false});
+
+  auto results = cudf::strings::concatenate(
+    cudf::table_view{{col0, col1}}, cudf::strings_column_view(sep_col), sep_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results1, verbosity);
+
+  auto exp_results2 = cudf::test::strings_column_wrapper(
+    {"eeexyzfoo", "<null>~~~", "!!!!!!!éaff", "éééf", "éa", "", "éaff", "valid", "doo", "", "", ""},
+    {true, true, true, true, true, true, true, true, true, true, true, true});
+
+  results = cudf::strings::concatenate(cudf::table_view{{col0, col1}},
+                                       cudf::strings_column_view(sep_col),
+                                       sep_rep,
+                                       cudf::string_scalar(""),
+                                       cudf::strings::separator_on_nulls::NO);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results2, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, MultiColumnStringMixColumnReplacement)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "éééf", "éa", "", "", "invalid", "null", "NULL", "-1", ""},
+    {true, true, true, true, true, true, false, false, false, false, false, false});
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"foo", "", "éaff", "", "invalid", "NULL", "éaff", "valid", "doo", "", "<null>", "-1"},
+    {true, true, true, false, false, false, true, true, true, false, false, false});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~~~", "", "@", "", "", "", "^^^^", "", "--", "*****", "######"},
+    {true, true, false, true, false, true, false, true, true, true, true, true});
+  auto col_rep = cudf::string_scalar("_col_replacement_");
+
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"eeexyzfoo",
+     "<null>~~~",
+     "",
+     "éééf@_col_replacement_",
+     "",
+     "_col_replacement_",
+     "",
+     "_col_replacement_^^^^valid",
+     "_col_replacement_doo",
+     "_col_replacement_--_col_replacement_",
+     "_col_replacement_*****_col_replacement_",
+     "_col_replacement_######_col_replacement_"},
+    {true, true, false, true, false, true, false, true, true, true, true, true});
+
+  auto results = cudf::strings::concatenate(cudf::table_view{{col0, col1}},
+                                            cudf::strings_column_view(sep_col),
+                                            cudf::string_scalar("", false),
+                                            col_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, MultiColumnStringMixSeparatorAndColumnReplacement)
+{
+  auto col0 = cudf::test::strings_column_wrapper(
+    {"eeexyz", "<null>", "", "éééf", "éa", "", "", "invalid", "null", "NULL", "-1", ""},
+    {true, true, true, true, true, true, false, false, false, false, false, false});
+  auto col1 = cudf::test::strings_column_wrapper(
+    {"foo", "", "éaff", "", "invalid", "NULL", "éaff", "valid", "doo", "", "<null>", "-1"},
+    {true, true, true, false, false, false, true, true, true, false, false, false});
+  auto sep_col = cudf::test::strings_column_wrapper(
+    {"", "~~~", "", "@", "", "", "", "^^^^", "", "--", "*****", "######"},
+    {true, true, false, true, false, true, false, true, true, true, true, true});
+  auto sep_rep = cudf::string_scalar("!!!!!!!!!!");
+  auto col_rep = cudf::string_scalar("_col_replacement_");
+
+  // Every null item (separator/column) is replaced - a non nullable column
+  auto exp_results =
+    cudf::test::strings_column_wrapper({"eeexyzfoo",
+                                        "<null>~~~",
+                                        "!!!!!!!!!!éaff",
+                                        "éééf@_col_replacement_",
+                                        "éa!!!!!!!!!!_col_replacement_",
+                                        "_col_replacement_",
+                                        "_col_replacement_!!!!!!!!!!éaff",
+                                        "_col_replacement_^^^^valid",
+                                        "_col_replacement_doo",
+                                        "_col_replacement_--_col_replacement_",
+                                        "_col_replacement_*****_col_replacement_",
+                                        "_col_replacement_######_col_replacement_"});
+
+  auto results = cudf::strings::concatenate(
+    cudf::table_view{{col0, col1}}, cudf::strings_column_view(sep_col), sep_rep, col_rep);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, exp_results, verbosity);
+}
+
+TEST_F(StringsConcatenateWithColSeparatorTest, MultiColumnNonNullableStrings)
+{
+  auto col0 =
+    cudf::test::strings_column_wrapper({"eeexyz", "<null>", "éaff", "éééf", "", "", "", ""});
+  auto col1 = cudf::test::strings_column_wrapper({"foo", "nan", "", "", "NULL", "éaff", "", ""});
+  auto sep_col = cudf::test::strings_column_wrapper({"", "~~~", "", "@", "", "+++", "", "^^^^"});
+
+  // Every item (separator/column) is used, as everything is valid producing a non nullable column
+  auto exp_results = cudf::test::strings_column_wrapper(
+    {"eeexyzfoo", "<null>~~~nan", "éaff", "éééf@", "NULL", "+++éaff", "", "^^^^"});
+
+  auto results =
+    cudf::strings::concatenate(cudf::table_view{{col0, col1}}, cudf::strings_column_view(sep_col));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, exp_results, verbosity);
+}
diff --git a/cpp/tests/strings/combine/join_list_elements_tests.cpp b/cpp/tests/strings/combine/join_list_elements_tests.cpp
new file mode 100644
index 0000000..4637113
--- /dev/null
+++ b/cpp/tests/strings/combine/join_list_elements_tests.cpp
@@ -0,0 +1,583 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+using namespace cudf::test::iterators;
+
+struct StringsListsConcatenateTest : public cudf::test::BaseFixture {};
+
+namespace {
+using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+using STR_COL   = cudf::test::strings_column_wrapper;
+using INT_LISTS = cudf::test::lists_column_wrapper<int32_t>;
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+}  // namespace
+
+TEST_F(StringsListsConcatenateTest, InvalidInput)
+{
+  // Invalid list type
+  {
+    auto const string_lists = INT_LISTS{{1, 2, 3}, {4, 5, 6}}.release();
+    auto const string_lv    = cudf::lists_column_view(string_lists->view());
+    EXPECT_THROW(cudf::strings::join_list_elements(string_lv), cudf::logic_error);
+  }
+
+  // Invalid scalar separator
+  {
+    auto const string_lists =
+      STR_LISTS{STR_LISTS{""}, STR_LISTS{"", "", ""}, STR_LISTS{"", ""}}.release();
+    auto const string_lv = cudf::lists_column_view(string_lists->view());
+    EXPECT_THROW(cudf::strings::join_list_elements(string_lv, cudf::string_scalar("", false)),
+                 cudf::logic_error);
+  }
+
+  // Invalid column separators
+  {
+    auto const string_lists =
+      STR_LISTS{STR_LISTS{""}, STR_LISTS{"", "", ""}, STR_LISTS{"", ""}}.release();
+    auto const string_lv  = cudf::lists_column_view(string_lists->view());
+    auto const separators = STR_COL{"+++"}.release();  // size doesn't match with lists column size
+    EXPECT_THROW(cudf::strings::join_list_elements(string_lv, separators->view()),
+                 cudf::logic_error);
+  }
+}
+
+TEST_F(StringsListsConcatenateTest, EmptyInput)
+{
+  auto const string_lists = STR_LISTS{}.release();
+  auto const string_lv    = cudf::lists_column_view(string_lists->view());
+  auto const expected     = STR_COL{};
+  auto results            = cudf::strings::join_list_elements(string_lv);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+
+  auto const separators = STR_COL{}.release();
+  results               = cudf::strings::join_list_elements(string_lv, separators->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+}
+
+TEST_F(StringsListsConcatenateTest, ZeroSizeStringsInput)
+{
+  auto const string_lists =
+    STR_LISTS{STR_LISTS{""}, STR_LISTS{"", "", ""}, STR_LISTS{"", ""}, STR_LISTS{}}.release();
+  auto const string_lv = cudf::lists_column_view(string_lists->view());
+
+  // Empty list results in empty string
+  {
+    auto const expected = STR_COL{"", "", "", ""};
+
+    auto results = cudf::strings::join_list_elements(string_lv);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected, verbosity);
+
+    auto const separators = STR_COL{"", "", "", ""}.release();
+    results               = cudf::strings::join_list_elements(string_lv, separators->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected, verbosity);
+  }
+
+  // Empty list results in null
+  {
+    auto const expected = STR_COL{{"", "", "", "" /*NULL*/}, null_at(3)};
+    auto results =
+      cudf::strings::join_list_elements(string_lv,
+                                        cudf::string_scalar(""),
+                                        cudf::string_scalar(""),
+                                        cudf::strings::separator_on_nulls::NO,
+                                        cudf::strings::output_if_empty_list::NULL_ELEMENT);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+
+    auto const separators = STR_COL{"", "", "", ""}.release();
+    results               = cudf::strings::join_list_elements(string_lv,
+                                                separators->view(),
+                                                cudf::string_scalar(""),
+                                                cudf::string_scalar(""),
+                                                cudf::strings::separator_on_nulls::NO,
+                                                cudf::strings::output_if_empty_list::NULL_ELEMENT);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+}
+
+TEST_F(StringsListsConcatenateTest, ColumnHasEmptyListAndNullListInput)
+{
+  auto const string_lists =
+    STR_LISTS{{STR_LISTS{"abc", "def", ""}, STR_LISTS{} /*NULL*/, STR_LISTS{}, STR_LISTS{"gh"}},
+              null_at(1)}
+      .release();
+  auto const string_lv = cudf::lists_column_view(string_lists->view());
+
+  // Empty list results in empty string
+  {
+    auto const expected = STR_COL{{"abc-def-", "" /*NULL*/, "", "gh"}, null_at(1)};
+
+    auto results = cudf::strings::join_list_elements(string_lv, cudf::string_scalar("-"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+
+    auto const separators = STR_COL{"-", "", "", ""}.release();
+    results               = cudf::strings::join_list_elements(string_lv, separators->view());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Empty list results in null
+  {
+    auto const expected = STR_COL{{"abc-def-", "" /*NULL*/, "" /*NULL*/, "gh"}, nulls_at({1, 2})};
+    auto results =
+      cudf::strings::join_list_elements(string_lv,
+                                        cudf::string_scalar("-"),
+                                        cudf::string_scalar(""),
+                                        cudf::strings::separator_on_nulls::NO,
+                                        cudf::strings::output_if_empty_list::NULL_ELEMENT);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+
+    auto const separators = STR_COL{"-", "", "", ""}.release();
+    results               = cudf::strings::join_list_elements(string_lv,
+                                                separators->view(),
+                                                cudf::string_scalar(""),
+                                                cudf::string_scalar(""),
+                                                cudf::strings::separator_on_nulls::NO,
+                                                cudf::strings::output_if_empty_list::NULL_ELEMENT);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+}
+
+TEST_F(StringsListsConcatenateTest, AllNullsStringsInput)
+{
+  auto const string_lists = STR_LISTS{
+    STR_LISTS{{""}, all_nulls()},
+    STR_LISTS{{"", "", ""}, all_nulls()},
+    STR_LISTS{{"", ""},
+              all_nulls()}}.release();
+  auto const string_lv = cudf::lists_column_view(string_lists->view());
+  auto const expected  = STR_COL{{"", "", ""}, all_nulls()};
+
+  auto results = cudf::strings::join_list_elements(string_lv);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+
+  auto const separators = STR_COL{{"", "", ""}, all_nulls()}.release();
+  results               = cudf::strings::join_list_elements(string_lv, separators->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+}
+
+TEST_F(StringsListsConcatenateTest, ScalarSeparator)
+{
+  auto const string_lists = STR_LISTS{{STR_LISTS{{"a", "bb" /*NULL*/, "ccc"}, null_at(1)},
+                                       STR_LISTS{}, /*NULL*/
+                                       STR_LISTS{{"ddd" /*NULL*/, "efgh", "ijk"}, null_at(0)},
+                                       STR_LISTS{"zzz", "xxxxx"},
+                                       STR_LISTS{{"v", "", "", "w"}, nulls_at({1, 2})}},
+                                      null_at(1)}
+                              .release();
+  auto const string_lv = cudf::lists_column_view(string_lists->view());
+
+  // No null replacement
+  {
+    auto const results = cudf::strings::join_list_elements(string_lv, cudf::string_scalar("+++"));
+    std::vector<char const*> h_expected{nullptr, nullptr, nullptr, "zzz+++xxxxx", nullptr};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // With null replacement
+  {
+    auto const results = cudf::strings::join_list_elements(
+      string_lv, cudf::string_scalar("+++"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{
+      "a+++___+++ccc", nullptr, "___+++efgh+++ijk", "zzz+++xxxxx", "v+++___+++___+++w"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Turn off separator-on-nulls
+  {
+    auto const results = cudf::strings::join_list_elements(string_lv,
+                                                           cudf::string_scalar("+++"),
+                                                           cudf::string_scalar(""),
+                                                           cudf::strings::separator_on_nulls::NO);
+    std::vector<char const*> h_expected{"a+++ccc", nullptr, "efgh+++ijk", "zzz+++xxxxx", "v+++w"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+}
+
+TEST_F(StringsListsConcatenateTest, SlicedListsWithScalarSeparator)
+{
+  auto const string_lists = STR_LISTS{
+    {STR_LISTS{{"a", "bb" /*NULL*/, "ccc"}, null_at(1)},
+     STR_LISTS{}, /*NULL*/
+     STR_LISTS{{"ddd" /*NULL*/, "efgh", "ijk"}, null_at(0)},
+     STR_LISTS{"zzz", "xxxxx"},
+     STR_LISTS{"11111", "11111", "11111", "11111", "11111"}, /*NULL*/
+     STR_LISTS{{"abcdef", "012345", "" /*NULL*/, "xxx000"}, null_at(2)},
+     STR_LISTS{{"xyz" /*NULL*/, "11111", "00000"}, null_at(0)},
+     STR_LISTS{"0a0b0c", "5x5y5z"},
+     STR_LISTS{"xxx"}, /*NULL*/
+     STR_LISTS{"ééé", "12345abcdef"},
+     STR_LISTS{"aaaééébbbéééccc", "12345"}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i != 1 && i != 4 && i != 8;
+    })}.release();
+
+  // Sliced the entire lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, cudf::string_scalar("+++"));
+    std::vector<char const*> h_expected{nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        "zzz+++xxxxx",
+                                        nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        "0a0b0c+++5x5y5z",
+                                        nullptr,
+                                        "ééé+++12345abcdef",
+                                        "aaaééébbbéééccc+++12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the entire lists column, with null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, cudf::string_scalar("+++"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{"a+++___+++ccc",
+                                        nullptr,
+                                        "___+++efgh+++ijk",
+                                        "zzz+++xxxxx",
+                                        nullptr,
+                                        "abcdef+++012345+++___+++xxx000",
+                                        "___+++11111+++00000",
+                                        "0a0b0c+++5x5y5z",
+                                        nullptr,
+                                        "ééé+++12345abcdef",
+                                        "aaaééébbbéééccc+++12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the first half of the lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 4})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, cudf::string_scalar("+++"));
+    std::vector<char const*> h_expected{nullptr, nullptr, nullptr, "zzz+++xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the first half of the lists column, with null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 4})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, cudf::string_scalar("+++"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{
+      "a+++___+++ccc", nullptr, "___+++efgh+++ijk", "zzz+++xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the second half of the lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {5, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, cudf::string_scalar("+++"));
+    std::vector<char const*> h_expected{
+      nullptr, nullptr, "0a0b0c+++5x5y5z", nullptr, "ééé+++12345abcdef", "aaaééébbbéééccc+++12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the second half of the lists column, with null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {5, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, cudf::string_scalar("+++"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{"abcdef+++012345+++___+++xxx000",
+                                        "___+++11111+++00000",
+                                        "0a0b0c+++5x5y5z",
+                                        nullptr,
+                                        "ééé+++12345abcdef",
+                                        "aaaééébbbéééccc+++12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the middle part of the lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {3, 8})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, cudf::string_scalar("+++"));
+    std::vector<char const*> h_expected{
+      "zzz+++xxxxx", nullptr, nullptr, nullptr, "0a0b0c+++5x5y5z"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the middle part of the lists column, with null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {3, 8})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, cudf::string_scalar("+++"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{"zzz+++xxxxx",
+                                        nullptr,
+                                        "abcdef+++012345+++___+++xxx000",
+                                        "___+++11111+++00000",
+                                        "0a0b0c+++5x5y5z"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+}
+
+TEST_F(StringsListsConcatenateTest, ColumnSeparators)
+{
+  auto const string_lists = STR_LISTS{{STR_LISTS{{"a", "bb" /*NULL*/, "ccc"}, null_at(1)},
+                                       STR_LISTS{}, /*NULL*/
+                                       STR_LISTS{"0a0b0c", "xyzééé"},
+                                       STR_LISTS{{"ddd" /*NULL*/, "efgh", "ijk"}, null_at(0)},
+                                       STR_LISTS{{"ééé" /*NULL*/, "ááá", "ííí"}, null_at(0)},
+                                       STR_LISTS{"zzz", "xxxxx"}},
+                                      null_at(1)}
+                              .release();
+  auto const string_lv  = cudf::lists_column_view(string_lists->view());
+  auto const separators = STR_COL{
+    {"+++", "***", "!!!" /*NULL*/, "$$$" /*NULL*/, "%%%", "^^^"},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i != 2 && i != 3;
+    })}.release();
+
+  // No null replacement
+  {
+    auto const results = cudf::strings::join_list_elements(string_lv, separators->view());
+    std::vector<char const*> h_expected{nullptr, nullptr, nullptr, nullptr, nullptr, "zzz^^^xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // With null replacement for separators
+  {
+    auto const results =
+      cudf::strings::join_list_elements(string_lv, separators->view(), cudf::string_scalar("|||"));
+    std::vector<char const*> h_expected{
+      nullptr, nullptr, "0a0b0c|||xyzééé", nullptr, nullptr, "zzz^^^xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // With null replacement for strings
+  {
+    auto const results = cudf::strings::join_list_elements(
+      string_lv, separators->view(), cudf::string_scalar("", false), cudf::string_scalar("XXXXX"));
+    std::vector<char const*> h_expected{
+      "a+++XXXXX+++ccc", nullptr, nullptr, nullptr, "XXXXX%%%ááá%%%ííí", "zzz^^^xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // With null replacement for both separators and strings
+  {
+    auto const results = cudf::strings::join_list_elements(
+      string_lv, separators->view(), cudf::string_scalar("|||"), cudf::string_scalar("XXXXX"));
+    std::vector<char const*> h_expected{"a+++XXXXX+++ccc",
+                                        nullptr,
+                                        "0a0b0c|||xyzééé",
+                                        "XXXXX|||efgh|||ijk",
+                                        "XXXXX%%%ááá%%%ííí",
+                                        "zzz^^^xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Turn off separator-on-nulls
+  {
+    auto const results = cudf::strings::join_list_elements(string_lv,
+                                                           separators->view(),
+                                                           cudf::string_scalar("+++"),
+                                                           cudf::string_scalar(""),
+                                                           cudf::strings::separator_on_nulls::NO);
+    std::vector<char const*> h_expected{
+      "a+++ccc", nullptr, "0a0b0c+++xyzééé", "efgh+++ijk", "ááá%%%ííí", "zzz^^^xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+}
+
+TEST_F(StringsListsConcatenateTest, SlicedListsWithColumnSeparators)
+{
+  auto const string_lists = STR_LISTS{
+    {STR_LISTS{{"a", "bb" /*NULL*/, "ccc"}, null_at(1)},
+     STR_LISTS{}, /*NULL*/
+     STR_LISTS{{"ddd" /*NULL*/, "efgh", "ijk"}, null_at(0)},
+     STR_LISTS{"zzz", "xxxxx"},
+     STR_LISTS{"11111", "11111", "11111", "11111", "11111"}, /*NULL*/
+     STR_LISTS{{"abcdef", "012345", "" /*NULL*/, "xxx000"}, null_at(2)},
+     STR_LISTS{{"xyz" /*NULL*/, "11111", "00000"}, null_at(0)},
+     STR_LISTS{"0a0b0c", "5x5y5z"},
+     STR_LISTS{"xxx"}, /*NULL*/
+     STR_LISTS{"ééé", "12345abcdef"},
+     STR_LISTS{"aaaééébbbéééccc", "12345"}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i != 1 && i != 4 && i != 8;
+    })}.release();
+  auto const separators = STR_COL{
+    {"+++", "***", "!!!" /*NULL*/, "$$$" /*NULL*/, "%%%", "^^^", "~!~", "###", "&&&", "-+-", "=+="},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i != 2 && i != 3;
+    })}.release();
+
+  // Sliced the entire lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 11})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {0, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, sep_col);
+    std::vector<char const*> h_expected{nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        nullptr,
+                                        "0a0b0c###5x5y5z",
+                                        nullptr,
+                                        "ééé-+-12345abcdef",
+                                        "aaaééébbbéééccc=+=12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the entire lists column, with null replacements
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 11})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {0, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, sep_col, cudf::string_scalar("|||"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{"a+++___+++ccc",
+                                        nullptr,
+                                        "___|||efgh|||ijk",
+                                        "zzz|||xxxxx",
+                                        nullptr,
+                                        "abcdef^^^012345^^^___^^^xxx000",
+                                        "___~!~11111~!~00000",
+                                        "0a0b0c###5x5y5z",
+                                        nullptr,
+                                        "ééé-+-12345abcdef",
+                                        "aaaééébbbéééccc=+=12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the first half of the lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 4})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {0, 4})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, sep_col);
+    std::vector<char const*> h_expected{nullptr, nullptr, nullptr, nullptr};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the first half of the lists column, with null replacements
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {0, 4})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {0, 4})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, sep_col, cudf::string_scalar("|||"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{
+      "a+++___+++ccc", nullptr, "___|||efgh|||ijk", "zzz|||xxxxx"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the second half of the lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {5, 11})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {5, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, sep_col);
+    std::vector<char const*> h_expected{
+      nullptr, nullptr, "0a0b0c###5x5y5z", nullptr, "ééé-+-12345abcdef", "aaaééébbbéééccc=+=12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the second half of the lists column, with null replacements
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {5, 11})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {5, 11})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, sep_col, cudf::string_scalar("|||"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{"abcdef^^^012345^^^___^^^xxx000",
+                                        "___~!~11111~!~00000",
+                                        "0a0b0c###5x5y5z",
+                                        nullptr,
+                                        "ééé-+-12345abcdef",
+                                        "aaaééébbbéééccc=+=12345"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the middle part of the lists column, no null replacement
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {3, 8})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {3, 8})[0]);
+    auto const results   = cudf::strings::join_list_elements(string_lv, sep_col);
+    std::vector<char const*> h_expected{nullptr, nullptr, nullptr, nullptr, "0a0b0c###5x5y5z"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+
+  // Sliced the middle part of the lists column, with null replacements
+  {
+    auto const string_lv = cudf::lists_column_view(cudf::slice(string_lists->view(), {3, 8})[0]);
+    auto const sep_col   = cudf::strings_column_view(cudf::slice(separators->view(), {3, 8})[0]);
+    auto const results   = cudf::strings::join_list_elements(
+      string_lv, sep_col, cudf::string_scalar("|||"), cudf::string_scalar("___"));
+    std::vector<char const*> h_expected{"zzz|||xxxxx",
+                                        nullptr,
+                                        "abcdef^^^012345^^^___^^^xxx000",
+                                        "___~!~11111~!~00000",
+                                        "0a0b0c###5x5y5z"};
+    auto const expected =
+      STR_COL{h_expected.begin(), h_expected.end(), nulls_from_nullptrs(h_expected)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected, verbosity);
+  }
+}
diff --git a/cpp/tests/strings/combine/join_strings_tests.cpp b/cpp/tests/strings/combine/join_strings_tests.cpp
new file mode 100644
index 0000000..ecc7432
--- /dev/null
+++ b/cpp/tests/strings/combine/join_strings_tests.cpp
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+struct JoinStringsTest : public cudf::test::BaseFixture {};
+
+TEST_F(JoinStringsTest, Join)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "zzzz", "", "aaa", "ééé"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto view1 = cudf::strings_column_view(strings);
+
+  {
+    auto results = cudf::strings::join_strings(view1);
+
+    cudf::test::strings_column_wrapper expected{"eeebbzzzzaaaééé"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::join_strings(view1, cudf::string_scalar("+"));
+
+    cudf::test::strings_column_wrapper expected{"eee+bb+zzzz++aaa+ééé"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results =
+      cudf::strings::join_strings(view1, cudf::string_scalar("+"), cudf::string_scalar("___"));
+
+    cudf::test::strings_column_wrapper expected{"eee+bb+___+zzzz++aaa+ééé"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(JoinStringsTest, JoinLongStrings)
+{
+  std::string data(200, '0');
+  cudf::test::strings_column_wrapper input({data, data, data, data});
+
+  auto results =
+    cudf::strings::join_strings(cudf::strings_column_view(input), cudf::string_scalar("+"));
+
+  auto expected_data = data + "+" + data + "+" + data + "+" + data;
+  cudf::test::strings_column_wrapper expected({expected_data});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(JoinStringsTest, JoinZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+  auto results      = cudf::strings::join_strings(strings_view);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(JoinStringsTest, JoinAllNullStringsColumn)
+{
+  cudf::test::strings_column_wrapper strings({"", "", ""}, {0, 0, 0});
+
+  auto results = cudf::strings::join_strings(cudf::strings_column_view(strings));
+  cudf::test::strings_column_wrapper expected1({""}, {0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected1);
+
+  results = cudf::strings::join_strings(
+    cudf::strings_column_view(strings), cudf::string_scalar(""), cudf::string_scalar("3"));
+  cudf::test::strings_column_wrapper expected2({"333"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected2);
+
+  results = cudf::strings::join_strings(
+    cudf::strings_column_view(strings), cudf::string_scalar("-"), cudf::string_scalar("*"));
+  cudf::test::strings_column_wrapper expected3({"*-*-*"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected3);
+}
diff --git a/cpp/tests/strings/concatenate_tests.cpp b/cpp/tests/strings/concatenate_tests.cpp
new file mode 100644
index 0000000..5cf4015
--- /dev/null
+++ b/cpp/tests/strings/concatenate_tests.cpp
@@ -0,0 +1,109 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <vector>
+
+struct StringsConcatenateTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConcatenateTest, Concatenate)
+{
+  std::vector<char const*> h_strings{"aaa",
+                                     "bb",
+                                     "",
+                                     "cccc",
+                                     "d",
+                                     "ééé",
+                                     "ff",
+                                     "gggg",
+                                     "",
+                                     "h",
+                                     "iiii",
+                                     "jjj",
+                                     "k",
+                                     "lllllll",
+                                     "mmmmm",
+                                     "n",
+                                     "oo",
+                                     "ppp"};
+  cudf::test::strings_column_wrapper strings1(h_strings.data(), h_strings.data() + 6);
+  cudf::test::strings_column_wrapper strings2(h_strings.data() + 6, h_strings.data() + 10);
+  cudf::test::strings_column_wrapper strings3(h_strings.data() + 10,
+                                              h_strings.data() + h_strings.size());
+
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  std::vector<cudf::column_view> strings_columns;
+  strings_columns.push_back(strings1);
+  strings_columns.push_back(zero_size_strings_column);
+  strings_columns.push_back(strings2);
+  strings_columns.push_back(strings3);
+
+  auto results = cudf::concatenate(strings_columns);
+
+  cudf::test::strings_column_wrapper expected(h_strings.begin(), h_strings.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConcatenateTest, ZeroSizeStringsColumns)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  std::vector<cudf::column_view> strings_columns;
+  strings_columns.push_back(zero_size_strings_column);
+  strings_columns.push_back(zero_size_strings_column);
+  strings_columns.push_back(zero_size_strings_column);
+  auto results = cudf::concatenate(strings_columns);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsConcatenateTest, ZeroSizeStringsPlusNormal)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  std::vector<cudf::column_view> strings_columns;
+  strings_columns.push_back(zero_size_strings_column);
+
+  std::vector<char const*> h_strings{"aaa",
+                                     "bb",
+                                     "",
+                                     "cccc",
+                                     "d",
+                                     "ééé",
+                                     "ff",
+                                     "gggg",
+                                     "",
+                                     "h",
+                                     "iiii",
+                                     "jjj",
+                                     "k",
+                                     "lllllll",
+                                     "mmmmm",
+                                     "n",
+                                     "oo",
+                                     "ppp"};
+  cudf::test::strings_column_wrapper strings1(h_strings.data(),
+                                              h_strings.data() + h_strings.size());
+  strings_columns.push_back(strings1);
+
+  auto results = cudf::concatenate(strings_columns);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings1);
+}
diff --git a/cpp/tests/strings/contains_tests.cpp b/cpp/tests/strings/contains_tests.cpp
new file mode 100644
index 0000000..0cb5023
--- /dev/null
+++ b/cpp/tests/strings/contains_tests.cpp
@@ -0,0 +1,813 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <algorithm>
+#include <vector>
+
+struct StringsContainsTests : public cudf::test::BaseFixture {};
+
+TEST_F(StringsContainsTests, ContainsTest)
+{
+  std::vector<char const*> h_strings{"5",
+                                     "hej",
+                                     "\t \n",
+                                     "12345",
+                                     "\\",
+                                     "d",
+                                     "c:\\Tools",
+                                     "+27",
+                                     "1c2",
+                                     "1C2",
+                                     "0:00:0",
+                                     "0:0:00",
+                                     "00:0:0",
+                                     "00:00:0",
+                                     "00:0:00",
+                                     "0:00:00",
+                                     "00:00:00",
+                                     "Hello world !",
+                                     "Hello world!   ",
+                                     "Hello worldcup  !",
+                                     "0123456789",
+                                     "1C2",
+                                     "Xaa",
+                                     "abcdefghxxx",
+                                     "ABCDEFGH",
+                                     "abcdefgh",
+                                     "abc def",
+                                     "abc\ndef",
+                                     "aa\r\nbb\r\ncc\r\n\r\n",
+                                     "abcabc",
+                                     nullptr,
+                                     ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<std::string> patterns{"\\d",
+                                    "\\w+",
+                                    "\\s",
+                                    "\\S",
+                                    "^.*\\\\.*$",
+                                    "[1-5]+",
+                                    "[a-h]+",
+                                    "[A-H]+",
+                                    "[a-h]*",
+                                    "\n",
+                                    "b.\\s*\n",
+                                    ".*c",
+                                    "\\d\\d:\\d\\d:\\d\\d",
+                                    "\\d\\d?:\\d\\d?:\\d\\d?",
+                                    "[Hh]ello [Ww]orld",
+                                    "\\bworld\\b",
+                                    ".*"};
+
+  std::vector<bool> h_expecteds_std{
+    // strings.size x patterns.size
+    true,  false, false, true,  false, false, false, true,  true,  true,  true,  true,  true,
+    true,  true,  true,  true,  false, false, false, true,  true,  false, false, false, false,
+    false, false, false, false, false, false, true,  true,  false, true,  false, true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  false, false, false,
+    false, true,  false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, true,  true,  true,  false, false, false, false, false, false, true,
+    true,  true,  false, false, false, true,  true,  false, true,  true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  false, false, false, false,
+    false, false, true,  false, true,  false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, false, true,  false, false, true,  false, false, false, true,  true,
+    true,  false, false, false, false, false, false, false, false, false, false, true,  true,
+    false, false, false, false, false, false, false, false, false, false, false, true,  false,
+    false, false, true,  true,  false, true,  false, false, false, false, false, false, false,
+    false, true,  true,  true,  false, false, true,  true,  false, true,  true,  true,  true,
+    true,  false, false, false, false, false, false, false, false, false, false, false, true,
+    false, false, false, false, false, false, false, true,  true,  true,  false, true,  false,
+    false, true,  false, false, false, false, false, false, false, true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,
+    false, true,  false, false, true,  false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, true,  true,  false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, true,  true,  false, false,
+    false, false, false, false, false, false, false, true,  false, true,  false, false, false,
+    false, false, false, false, false, false, false, true,  false, false, false, true,  false,
+    true,  true,  true,  true,  true,  false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, true,  false, false,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, true,  true,  true,
+    true,  true,  true,  true,  false, false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, true,  true,  true,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, false, false, false, false, false, false, false, false, false, false,
+    false, false, false, true,  true,  false, false, false, false, false, false, false, false,
+    false, false, false, false, false, true,  true,  true,  true,  true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,  true,
+    true,  true,  true,  true,  true,  true,  true,  true,  true,  false, true};
+
+  thrust::host_vector<bool> h_expecteds(h_expecteds_std);
+
+  for (int idx = 0; idx < static_cast<int>(patterns.size()); ++idx) {
+    std::string ptn  = patterns[idx];
+    bool* h_expected = h_expecteds.data() + (idx * h_strings.size());
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      h_expected,
+      h_expected + h_strings.size(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    auto prog    = cudf::strings::regex_program::create(ptn);
+    auto results = cudf::strings::contains_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, MatchesTest)
+{
+  std::vector<char const*> h_strings{
+    "The quick brown @fox jumps", "ovér the", "lazy @dog", "1234", "00:0:00", nullptr, ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    auto const pattern = std::string("lazy");
+    bool h_expected[]  = {false, false, true, false, false, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      h_expected,
+      h_expected + h_strings.size(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const pattern = std::string("\\d+");
+    bool h_expected[]  = {false, false, false, true, true, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      h_expected,
+      h_expected + h_strings.size(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const pattern = std::string("@\\w+");
+    bool h_expected[]  = {false, false, false, false, false, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      h_expected,
+      h_expected + h_strings.size(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const pattern = std::string(".*");
+    bool h_expected[]  = {true, true, true, true, true, false, true};
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      h_expected,
+      h_expected + h_strings.size(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, MatchesIPV4Test)
+{
+  cudf::test::strings_column_wrapper strings({"5.79.97.178",
+                                              "1.2.3.4",
+                                              "5",
+                                              "5.79",
+                                              "5.79.97",
+                                              "5.79.97.178.100",
+                                              "224.0.0.0",
+                                              "239.255.255.255",
+                                              "5.79.97.178",
+                                              "127.0.0.1"});
+  auto strings_view = cudf::strings_column_view(strings);
+  {  // is_ip: 58 instructions
+    std::string pattern =
+      "^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)"
+      "$";
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {true, true, false, false, false, false, true, true, true, true});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+  {  // is_loopback: 72 instructions
+    std::string pattern =
+      "^127\\.([0-9]|[1-9][0-9]|1([0-9][0-9])|2([0-4][0-9]|5[0-5]))"
+      "\\.([0-9]|[1-9][0-9]|1([0-9][0-9])|2([0-4][0-9]|5[0-5]))"
+      "\\.([0-9]|[1-9][0-9]|1([0-9][0-9])|2([0-4][0-9]|5[0-5]))$";
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, false, false, false, false, false, false, true});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+  {  // is_multicast: 79 instructions
+    std::string pattern =
+      "^(2(2[4-9]|3[0-9]))\\.([0-9]|[1-9][0-9]|1([0-9][0-9])|2([0-4][0-9]|5[0-5]))"
+      "\\.([0-9]|[1-9][0-9]|1([0-9][0-9])|2([0-4][0-9]|5[0-5]))"
+      "\\.([0-9]|[1-9][0-9]|1([0-9][0-9])|2([0-4][0-9]|5[0-5]))$";
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, false, false, false, true, true, false, false});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+  }
+}
+
+TEST_F(StringsContainsTests, OctalTest)
+{
+  cudf::test::strings_column_wrapper strings({"A3", "B", "CDA3EY", "", "99", "\a\t\r"});
+  auto strings_view = cudf::strings_column_view(strings);
+  auto expected     = cudf::test::fixed_width_column_wrapper<bool>({1, 0, 1, 0, 0, 0});
+
+  auto pattern = std::string("\\101");
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern = std::string("\\1013");
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern = std::string("D*\\101\\063");
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern  = std::string("\\719");
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 0, 1, 0});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern  = std::string("[\\7][\\11][\\15]");
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 0, 0, 1});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsContainsTests, HexTest)
+{
+  std::vector<char> ascii_chars(  // all possible matchable chars
+    {thrust::make_counting_iterator<char>(0), thrust::make_counting_iterator<char>(127)});
+  auto const count = static_cast<cudf::size_type>(ascii_chars.size());
+  std::vector<cudf::size_type> offsets(
+    {thrust::make_counting_iterator<cudf::size_type>(0),
+     thrust::make_counting_iterator<cudf::size_type>(0) + count + 1});
+  auto d_chars = cudf::detail::make_device_uvector_sync(
+    ascii_chars, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto d_offsets = cudf::detail::make_device_uvector_sync(
+    offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto input = cudf::make_strings_column(d_chars, d_offsets, {}, 0);
+
+  auto strings_view = cudf::strings_column_view(input->view());
+  for (auto ch : ascii_chars) {
+    std::stringstream str;
+    str << "\\x" << std::setfill('0') << std::setw(2) << std::hex << static_cast<int32_t>(ch);
+    std::string pattern = str.str();
+
+    // only one element in the input should match ch
+    auto true_dat = cudf::detail::make_counting_transform_iterator(
+      0, [ch](auto idx) { return ch == static_cast<char>(idx); });
+    cudf::test::fixed_width_column_wrapper<bool> expected(true_dat, true_dat + count);
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::contains_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+    // also test hex character appearing in character class brackets
+    pattern = "[" + pattern + "]";
+    prog    = cudf::strings::regex_program::create(pattern);
+    results = cudf::strings::contains_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, EmbeddedNullCharacter)
+{
+  std::vector<std::string> data(10);
+  std::generate(data.begin(), data.end(), [n = 0]() mutable {
+    char first      = static_cast<char>('A' + n++);
+    char raw_data[] = {first, '\0', 'B'};
+    return std::string{raw_data, 3};
+  });
+  cudf::test::strings_column_wrapper input(data.begin(), data.end());
+  auto strings_view = cudf::strings_column_view(input);
+
+  auto pattern  = std::string("A");
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({1, 0, 0, 0, 0, 0, 0, 0, 0, 0});
+  auto prog     = cudf::strings::regex_program::create(pattern);
+  auto results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern  = std::string("B");
+  expected = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern  = std::string("J\\0B");
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 0, 0, 0, 0, 0, 0, 1});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern  = std::string("[G-J][\\0]B");
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 0, 0, 0, 1, 1, 1, 1});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  pattern  = std::string("[A-D][\\x00]B");
+  expected = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1, 0, 0, 0, 0, 0, 0});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::contains_re(strings_view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsContainsTests, Errors)
+{
+  EXPECT_THROW(cudf::strings::regex_program::create("(3?)+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("(?:3?)+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("3?+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("{3}a"), cudf::logic_error);
+
+  EXPECT_THROW(cudf::strings::regex_program::create("aaaa{1234,5678}"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("aaaa{123,5678}"), cudf::logic_error);
+}
+
+TEST_F(StringsContainsTests, CountTest)
+{
+  std::vector<char const*> h_strings{
+    "The quick brown @fox jumps ovér the", "lazy @dog", "1:2:3:4", "00:0:00", nullptr, ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    auto pattern = std::string("[tT]he");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      {2, 0, 0, 0, 0, 0}, cudf::test::iterators::nulls_from_nullptrs(h_strings));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto pattern = std::string("@\\w+");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      {1, 1, 0, 0, 0, 0}, cudf::test::iterators::nulls_from_nullptrs(h_strings));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto pattern = std::string("\\d+:\\d+");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      {0, 0, 2, 1, 0, 0}, cudf::test::iterators::nulls_from_nullptrs(h_strings));
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, FixedQuantifier)
+{
+  auto input = cudf::test::strings_column_wrapper({"a", "aa", "aaa", "aaaa", "aaaaa", "aaaaaa"});
+  auto sv    = cudf::strings_column_view(input);
+
+  {
+    // exact match
+    auto pattern = std::string("a{3}");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 0, 1, 1, 1, 2});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    // range match (greedy quantifier)
+    auto pattern = std::string("a{3,5}");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 0, 1, 1, 1, 1});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    // minimum match (greedy quantifier)
+    auto pattern = std::string("a{2,}");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 1, 1, 1, 1, 1});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    // range match (lazy quantifier)
+    auto pattern = std::string("a{2,4}?");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 1, 1, 2, 2, 3});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    // minimum match (lazy quantifier)
+    auto pattern = std::string("a{1,}?");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({1, 2, 3, 4, 5, 6});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    // zero match
+    auto pattern = std::string("aaaa{0}");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 0, 1, 1, 1, 2});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    // poorly formed
+    auto pattern = std::string("aaaa{n,m}");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 0, 0, 0, 0, 0});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, QuantifierErrors)
+{
+  EXPECT_THROW(cudf::strings::regex_program::create("^+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("$+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("(^)+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("($)+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("\\A+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("\\Z+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("(\\A)+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("(\\Z)+"), cudf::logic_error);
+
+  EXPECT_THROW(cudf::strings::regex_program::create("(^($))+"), cudf::logic_error);
+  EXPECT_NO_THROW(cudf::strings::regex_program::create("(^a($))+"));
+  EXPECT_NO_THROW(cudf::strings::regex_program::create("(^(a$))+"));
+}
+
+TEST_F(StringsContainsTests, OverlappedClasses)
+{
+  auto input = cudf::test::strings_column_wrapper({"abcdefg", "defghí", "", "éééééé", "ghijkl"});
+  auto sv = cudf::strings_column_view(input);
+
+  {
+    auto pattern = std::string("[e-gb-da-c]");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({7, 4, 0, 0, 1});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto pattern = std::string("[á-éê-ú]");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({0, 1, 0, 6, 0});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, NegatedClasses)
+{
+  auto input = cudf::test::strings_column_wrapper({"abcdefg", "def\tghí", "", "éeé\néeé", "ABC"});
+  auto sv = cudf::strings_column_view(input);
+
+  {
+    auto pattern = std::string("[^a-f]");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({1, 4, 0, 5, 3});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto pattern = std::string("[^a-eá-é]");
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({2, 5, 0, 1, 3});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::count_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, IncompleteClassesRange)
+{
+  auto input = cudf::test::strings_column_wrapper({"abc-def", "---", "", "ghijkl", "-wxyz-"});
+  auto sv    = cudf::strings_column_view(input);
+
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({1, 0, 0, 1, 1});
+    auto prog    = cudf::strings::regex_program::create("[a-z]");
+    auto results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+    prog    = cudf::strings::regex_program::create("[a-m-z]");  // same as [a-z]
+    results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({1, 1, 0, 1, 1});
+    auto prog    = cudf::strings::regex_program::create("[g-]");
+    auto results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+    prog    = cudf::strings::regex_program::create("[-k]");
+    results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({1, 1, 0, 0, 1});
+    auto prog    = cudf::strings::regex_program::create("[-]");
+    auto results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    prog    = cudf::strings::regex_program::create("[+--]");
+    results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+    prog    = cudf::strings::regex_program::create("[a-c-]");
+    results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    prog    = cudf::strings::regex_program::create("[-d-f]");
+    results = cudf::strings::contains_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, MultiLine)
+{
+  auto input = cudf::test::strings_column_wrapper(
+    {"abé\nfff\nabé", "fff\nabé\nlll", "abé", "", "abé\n", "abe\nabé\n"});
+  auto view = cudf::strings_column_view(input);
+
+  auto pattern = std::string("^abé$");
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto prog_ml =
+    cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::MULTILINE);
+
+  auto expected_contains = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 0, 1, 1});
+  auto results           = cudf::strings::contains_re(view, *prog_ml);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_contains);
+  expected_contains = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 0, 1, 0});
+  results           = cudf::strings::contains_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_contains);
+
+  auto expected_matches = cudf::test::fixed_width_column_wrapper<bool>({1, 0, 1, 0, 1, 0});
+  results               = cudf::strings::matches_re(view, *prog_ml);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_matches);
+  expected_matches = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 0, 1, 0});
+  results          = cudf::strings::matches_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_matches);
+
+  auto expected_count = cudf::test::fixed_width_column_wrapper<int32_t>({2, 1, 1, 0, 1, 1});
+  results             = cudf::strings::count_re(view, *prog_ml);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+  expected_count = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 1, 0, 1, 0});
+  results        = cudf::strings::count_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+}
+
+TEST_F(StringsContainsTests, EndOfString)
+{
+  auto input = cudf::test::strings_column_wrapper(
+    {"abé\nfff\nabé", "fff\nabé\nlll", "abé", "", "abé\n", "abe\nabé\n"});
+  auto view = cudf::strings_column_view(input);
+
+  auto pattern = std::string("\\Aabé\\Z");
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto prog_ml =
+    cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::MULTILINE);
+
+  auto results  = cudf::strings::contains_re(view, *prog);
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  results = cudf::strings::contains_re(view, *prog_ml);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results = cudf::strings::matches_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  results = cudf::strings::matches_re(view, *prog_ml);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results             = cudf::strings::count_re(view, *prog);
+  auto expected_count = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 1, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+  results = cudf::strings::count_re(view, *prog_ml);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+}
+
+TEST_F(StringsContainsTests, DotAll)
+{
+  auto input = cudf::test::strings_column_wrapper({"abc\nfa\nef", "fff\nabbc\nfff", "abcdef", ""});
+  auto view  = cudf::strings_column_view(input);
+
+  auto pattern = std::string("a.*f");
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto prog_dotall =
+    cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::DOTALL);
+
+  auto expected_contains = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 0});
+  auto results           = cudf::strings::contains_re(view, *prog_dotall);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_contains);
+  expected_contains = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 0});
+  results           = cudf::strings::contains_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_contains);
+
+  auto expected_matches = cudf::test::fixed_width_column_wrapper<bool>({1, 0, 1, 0});
+  results               = cudf::strings::matches_re(view, *prog_dotall);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_matches);
+  expected_matches = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 0});
+  results          = cudf::strings::matches_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_matches);
+
+  pattern     = std::string("a.*?f");
+  prog        = cudf::strings::regex_program::create(pattern);
+  prog_dotall = cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::DOTALL);
+
+  auto expected_count = cudf::test::fixed_width_column_wrapper<int32_t>({2, 1, 1, 0});
+  results             = cudf::strings::count_re(view, *prog_dotall);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+  expected_count = cudf::test::fixed_width_column_wrapper<int32_t>({0, 0, 1, 0});
+  results        = cudf::strings::count_re(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+
+  auto both_flags = static_cast<cudf::strings::regex_flags>(cudf::strings::regex_flags::DOTALL |
+                                                            cudf::strings::regex_flags::MULTILINE);
+  expected_count  = cudf::test::fixed_width_column_wrapper<int32_t>({2, 1, 1, 0});
+  auto prog_both  = cudf::strings::regex_program::create(pattern, both_flags);
+  results         = cudf::strings::count_re(view, *prog_both);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_count);
+}
+
+TEST_F(StringsContainsTests, ASCII)
+{
+  auto input = cudf::test::strings_column_wrapper({"abc \t\f\r 12", "áé 　❽❽", "aZ ❽4", "XYZ　8"});
+  auto view = cudf::strings_column_view(input);
+
+  std::string patterns[] = {"\\w+[\\s]+\\d+",
+                            "[^\\W]+\\s+[^\\D]+",
+                            "[\\w]+[^\\S]+[\\d]+",
+                            "[\\w]+\\s+[\\d]+",
+                            "\\w+\\s+\\d+"};
+
+  for (auto ptn : patterns) {
+    auto expected_contains = cudf::test::fixed_width_column_wrapper<bool>({1, 0, 0, 0});
+    auto prog    = cudf::strings::regex_program::create(ptn, cudf::strings::regex_flags::ASCII);
+    auto results = cudf::strings::contains_re(view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_contains);
+
+    expected_contains = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1});
+    prog              = cudf::strings::regex_program::create(ptn);
+    results           = cudf::strings::contains_re(view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_contains);
+  }
+}
+
+TEST_F(StringsContainsTests, MediumRegex)
+{
+  // This results in 95 regex instructions and falls in the 'medium' range.
+  std::string medium_regex =
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com";
+  auto prog = cudf::strings::regex_program::create(medium_regex);
+
+  std::vector<char const*> h_strings{
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com thats all",
+    "1234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234"
+    "5678901234567890",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnop"
+    "qrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    auto results      = cudf::strings::contains_re(strings_view, *prog);
+    bool h_expected[] = {true, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(h_expected,
+                                                          h_expected + h_strings.size());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results      = cudf::strings::matches_re(strings_view, *prog);
+    bool h_expected[] = {true, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(h_expected,
+                                                          h_expected + h_strings.size());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results         = cudf::strings::count_re(strings_view, *prog);
+    int32_t h_expected[] = {1, 0, 0};
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(h_expected,
+                                                             h_expected + h_strings.size());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, LargeRegex)
+{
+  // This results in 115 regex instructions and falls in the 'large' range.
+  std::string large_regex =
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz";
+  auto prog = cudf::strings::regex_program::create(large_regex);
+
+  std::vector<char const*> h_strings{
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz",
+    "1234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234"
+    "5678901234567890",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnop"
+    "qrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    auto results      = cudf::strings::contains_re(strings_view, *prog);
+    bool h_expected[] = {true, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(h_expected,
+                                                          h_expected + h_strings.size());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results      = cudf::strings::matches_re(strings_view, *prog);
+    bool h_expected[] = {true, false, false};
+    cudf::test::fixed_width_column_wrapper<bool> expected(h_expected,
+                                                          h_expected + h_strings.size());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results         = cudf::strings::count_re(strings_view, *prog);
+    int32_t h_expected[] = {1, 0, 0};
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(h_expected,
+                                                             h_expected + h_strings.size());
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+}
+
+TEST_F(StringsContainsTests, ExtraLargeRegex)
+{
+  // This results in 321 regex instructions which is above the 'large' range.
+  std::string data(320, '0');
+  cudf::test::strings_column_wrapper strings({data, data, data, data, data, "00"});
+  auto prog = cudf::strings::regex_program::create(data);
+
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    auto results = cudf::strings::contains_re(strings_view, *prog);
+    cudf::test::fixed_width_column_wrapper<bool> expected({true, true, true, true, true, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::matches_re(strings_view, *prog);
+    cudf::test::fixed_width_column_wrapper<bool> expected({true, true, true, true, true, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::count_re(strings_view, *prog);
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({1, 1, 1, 1, 1, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
diff --git a/cpp/tests/strings/datetime_tests.cpp b/cpp/tests/strings/datetime_tests.cpp
new file mode 100644
index 0000000..bb5c96a
--- /dev/null
+++ b/cpp/tests/strings/datetime_tests.cpp
@@ -0,0 +1,641 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/convert/convert_datetime.hpp>
+#include <cudf/strings/convert/convert_durations.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsDatetimeTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsDatetimeTest, ToTimestamp)
+{
+  std::vector<char const*> h_strings{"1974-02-28T01:23:45Z",
+                                     "2019-07-17T21:34:37Z",
+                                     nullptr,
+                                     "",
+                                     "2019-03-20T12:34:56Z",
+                                     "2020-02-29T00:00:00Z",
+                                     "1921-01-07T14:32:07Z",
+                                     "1969-12-31T23:59:45Z"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<cudf::timestamp_s::rep> h_expected{
+    131246625, 1563399277, 0, 0, 1553085296, 1582934400, -1545730073, -15};
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS}, "%Y-%m-%dT%H:%M:%SZ");
+
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::is_timestamp(strings_view, "%Y-%m-%dT%H:%M:%SZ");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 0, 0, 1, 1, 1, 1},
+                                                           {1, 1, 0, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampAmPm)
+{
+  cudf::test::strings_column_wrapper strings{"1974-02-28 01:23:45 PM",
+                                             "2019-07-17 02:34:56 AM",
+                                             "2019-03-20 12:34:56 PM",
+                                             "2020-02-29 12:00:00 AM",
+                                             "1925-02-07 02:55:08 PM"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS}, "%Y-%m-%d %I:%M:%S %p");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> expected{
+    131289825, 1563330896, 1553085296, 1582934400, -1416819892};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::is_timestamp(strings_view, "%Y-%m-%d %I:%M:%S %p");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampMicrosecond)
+{
+  cudf::test::strings_column_wrapper strings{"1974-02-28 01:23:45.987000",
+                                             "2019-07-17 02:34:56.001234",
+                                             "2019-03-20 12:34:56.100100",
+                                             "2020-02-29 00:00:00.555777",
+                                             "1969-12-31 00:00:01.000055",
+                                             "1944-07-21 11:15:09.333444"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_MILLISECONDS}, "%Y-%m-%d %H:%M:%S.%6f");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> expected_ms{
+    131246625987, 1563330896001, 1553085296100, 1582934400555, -86399000L, -803047490667L};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ms);
+  results = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_NANOSECONDS}, "%Y-%m-%d %H:%M:%S.%6f");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> expected_ns{
+    131246625987000000,
+    1563330896001234000,
+    1553085296100100000,
+    1582934400555777000,
+    -86398999945000,
+    -803047490666556000};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns);
+
+  results = cudf::strings::is_timestamp(strings_view, "%Y-%m-%d %H:%M:%S.%6f");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampMillisecond)
+{
+  cudf::test::strings_column_wrapper strings{"2018-07-04 12:00:00.123",
+                                             "2020-04-06 13:09:00.555",
+                                             "1969-12-31 00:00:00.000",
+                                             "1956-01-23 17:18:19.000"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_MICROSECONDS}, "%Y-%m-%d %H:%M:%S.%3f");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_us, cudf::timestamp_us::rep> expected_us{
+    1530705600123000, 1586178540555000, -86400000000, -439886501000000};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_us);
+  results = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_NANOSECONDS}, "%Y-%m-%d %H:%M:%S.%3f");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> expected_ns{
+    1530705600123000000, 1586178540555000000, -86400000000000, -439886501000000000};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns);
+
+  results = cudf::strings::is_timestamp(strings_view, "%Y-%m-%d %H:%M:%S.%3f");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampTimezone)
+{
+  cudf::test::strings_column_wrapper strings{"1974-02-28 01:23:45+0100",
+                                             "2019-07-17 02:34:56-0300",
+                                             "2019-03-20 12:34:56+1030",
+                                             "2020-02-29 12:00:00-0500",
+                                             "2022-04-07 09:15:00Z",
+                                             "1938-11-23 10:28:49+0700"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS}, "%Y-%m-%d %H:%M:%S%z");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> expected{
+    131243025, 1563341696, 1553047496, 1582995600, 1649322900, -981664271};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::is_timestamp(strings_view, "%Y-%m-%d %H:%M:%S%z");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampSingleSpecifier)
+{
+  cudf::test::strings_column_wrapper strings{"12", "10", "09", "05"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_DAYS}, "%d");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> expected_days{
+    11, 9, 8, 4};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_days);
+
+  results = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_DAYS}, "%m");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> expected_months{
+    334, 273, 243, 120};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_months);
+
+  results = cudf::strings::is_timestamp(strings_view, "%m");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results,
+                                 cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1});
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampVariableFractions)
+{
+  cudf::test::strings_column_wrapper test1{"01:02:03.000001000",
+                                           "01:02:03.000001",
+                                           "01:02:03.1",
+                                           "01:02:03.01",
+                                           "01:02:03.0098700",
+                                           "01:02:03.0023456"};
+  auto strings_view = cudf::strings_column_view(test1);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_NANOSECONDS}, "%H:%M:%S.%9f");
+  auto durations =
+    cudf::cast(results->view(), cudf::data_type{cudf::type_id::DURATION_NANOSECONDS});
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ns> expected1{
+    cudf::duration_ns{3723000001000},
+    cudf::duration_ns{3723000001000},
+    cudf::duration_ns{3723100000000},
+    cudf::duration_ns{3723010000000},
+    cudf::duration_ns{3723009870000},
+    cudf::duration_ns{3723002345600}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*durations, expected1);
+
+  results = cudf::strings::is_timestamp(strings_view, "%H:%M:%S.%f");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results,
+                                 cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 1, 1});
+
+  cudf::test::strings_column_wrapper test2{"01:02:03.100001Z",
+                                           "01:02:03.001Z",
+                                           "01:02:03.1Z",
+                                           "01:02:03.01Z",
+                                           "01:02:03.0098Z",
+                                           "01:02:03.00234Z"};
+  strings_view = cudf::strings_column_view(test2);
+  results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_MICROSECONDS}, "%H:%M:%S.%6f%Z");
+  durations = cudf::cast(results->view(), cudf::data_type{cudf::type_id::DURATION_MICROSECONDS});
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_us> expected2{
+    cudf::duration_us{3723100001},
+    cudf::duration_us{3723001000},
+    cudf::duration_us{3723100000},
+    cudf::duration_us{3723010000},
+    cudf::duration_us{3723009800},
+    cudf::duration_us{3723002340}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*durations, expected2);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampYear)
+{
+  cudf::test::strings_column_wrapper strings{
+    "28/02/74", "17/07/68", "20/03/19", "29/02/20", "07/02/69"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_DAYS}, "%d/%m/%y");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep> expected{
+    1519, 35992, 17975, 18321, -328};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::is_timestamp(strings_view, "%d/%m/%y");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampWeeks)
+{
+  cudf::test::strings_column_wrapper input{
+    "2012-01/3", "2012-04/4", "2023-01/1", "2012-52/5", "2020-44/2", "1960-20/0", "1986-04/6"};
+
+  auto format  = std::string("%Y-%W/%w");
+  auto results = cudf::strings::to_timestamps(
+    cudf::strings_column_view(input), cudf::data_type{cudf::type_id::TIMESTAMP_DAYS}, format);
+  auto expected = cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+    15343, 15365, 19359, 15702, 18569, -3511, 5875};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results          = cudf::strings::is_timestamp(cudf::strings_column_view(input), format);
+  auto is_expected = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+
+  cudf::test::strings_column_wrapper input_iso{
+    "2012-01/3", "2012-04/4", "2023-01/1", "2012-52/5", "2020-44/2", "1960-20/7", "1986-04/6"};
+
+  format  = std::string("%Y-%U/%u");
+  results = cudf::strings::to_timestamps(
+    cudf::strings_column_view(input_iso), cudf::data_type{cudf::type_id::TIMESTAMP_DAYS}, format);
+  expected = cudf::test::fixed_width_column_wrapper<cudf::timestamp_D, cudf::timestamp_D::rep>{
+    15342, 15364, 19358, 15701, 18568, -3512, 5874};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::is_timestamp(cudf::strings_column_view(input_iso), format);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+
+  is_expected = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1, 1, 0, 1});
+  results     = cudf::strings::is_timestamp(cudf::strings_column_view(input), format);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, ToTimestampSingleDigits)
+{
+  cudf::test::strings_column_wrapper strings{"1974-2-28 01:23:45.987000123",
+                                             "2019-7-17 2:34:56.001234567",
+                                             "2019-3-20 12:34:56.100100100",
+                                             "2020-02-2 00:00:00.555777999",
+                                             "1969-12-1 00:00:01.000055000",
+                                             "1944-07-21 11:15:09.333444000",
+                                             "2021-9-8 12:07:30.000000000"};
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::to_timestamps(
+    strings_view, cudf::data_type{cudf::type_id::TIMESTAMP_NANOSECONDS}, "%Y-%m-%d %H:%M:%S.%9f");
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> expected_ns{
+    131246625987000123,
+    1563330896001234567,
+    1553085296100100100,
+    1580601600555777999,
+    -2678398999945000,
+    -803047490666556000,
+    1631102850000000000};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns);
+
+  results = cudf::strings::is_timestamp(strings_view, "%Y-%m-%d %H:%M:%S.%6f");
+  cudf::test::fixed_width_column_wrapper<bool> is_expected({1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, is_expected);
+}
+
+TEST_F(StringsDatetimeTest, IsTimestamp)
+{
+  cudf::test::strings_column_wrapper strings{"2020-10-07 13:02:03 1PM +0130",
+                                             "2020:10:07 01-02-03 1AM +0130",
+                                             "2020-10-7 11:02:03 11AM -1025",
+                                             "2020-13-07 01:02:03 1AM +0000",
+                                             "2020-10-32 01:32:03 1AM +0000",
+                                             "2020-10-07 25:02:03 1AM +0000",
+                                             "2020-10-07 01:62:03 1AM +0000",
+                                             "2020-10-07 01:02:63 1AM +0000",
+                                             "2020-02-29 01:32:03 1AM +0000",
+                                             "2020-02-30 01:32:03 01AM +0000",
+                                             "2020-00-31 01:32:03 1AM +0000",
+                                             "2020-02-00 02:32:03 2AM +0000",
+                                             "2022-08-24 02:32:60 2AM +0000",
+                                             "2020-2-9 9:12:13 9AM +1111"};
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::is_timestamp(strings_view, "%Y-%m-%d %H:%M:%S %I%p %z");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *results,
+    cudf::test::fixed_width_column_wrapper<bool>{1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1});
+}
+
+TEST_F(StringsDatetimeTest, FromTimestamp)
+{
+  std::vector<cudf::timestamp_s::rep> h_timestamps{
+    131246625, 1563399277, 0, 1553085296, 1582934400, -1545730073, -86399};
+  std::vector<char const*> h_expected{"1974-02-28T01:23:45Z",
+                                      "2019-07-17T21:34:37Z",
+                                      nullptr,
+                                      "2019-03-20T12:34:56Z",
+                                      "2020-02-29T00:00:00Z",
+                                      "1921-01-07T14:32:07Z",
+                                      "1969-12-31T00:00:01Z"};
+
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps(
+    h_timestamps.begin(),
+    h_timestamps.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = cudf::strings::from_timestamps(timestamps);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsDatetimeTest, FromTimestampAmPm)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps{
+    1530705600L, 1582934461L, 1451430122L, 1318302183L, -6105994200L};
+  auto results = cudf::strings::from_timestamps(timestamps, "%Y-%m-%d %I:%M:%S %p");
+  cudf::test::strings_column_wrapper expected{"2018-07-04 12:00:00 PM",
+                                              "2020-02-29 12:01:01 AM",
+                                              "2015-12-29 11:02:02 PM",
+                                              "2011-10-11 03:03:03 AM",
+                                              "1776-07-04 06:30:00 PM"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsDatetimeTest, FromTimestampMillisecond)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ms, cudf::timestamp_ms::rep> timestamps_ms{
+    1530705600123, 1582934461007, 1451430122421, 1318302183999, -6106017600047, 128849018880000};
+  auto results = cudf::strings::from_timestamps(timestamps_ms, "%Y-%m-%d %H:%M:%S.%3f");
+  cudf::test::strings_column_wrapper expected_ms{"2018-07-04 12:00:00.123",
+                                                 "2020-02-29 00:01:01.007",
+                                                 "2015-12-29 23:02:02.421",
+                                                 "2011-10-11 03:03:03.999",
+                                                 "1776-07-04 11:59:59.953",
+                                                 "6053-01-23 02:08:00.000"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ms);
+
+  results = cudf::strings::from_timestamps(timestamps_ms, "%Y-%m-%d %H:%M:%S.%f");
+  cudf::test::strings_column_wrapper expected_ms_6f{"2018-07-04 12:00:00.123000",
+                                                    "2020-02-29 00:01:01.007000",
+                                                    "2015-12-29 23:02:02.421000",
+                                                    "2011-10-11 03:03:03.999000",
+                                                    "1776-07-04 11:59:59.953000",
+                                                    "6053-01-23 02:08:00.000000"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ms_6f);
+
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> timestamps_ns{
+    1530705600123456789,
+    1582934461007008009,
+    1451430122421310209,
+    1318302183999777555,
+    -6106017600047047047};
+  results = cudf::strings::from_timestamps(timestamps_ns, "%Y-%m-%d %H:%M:%S.%9f");
+  cudf::test::strings_column_wrapper expected_ns{"2018-07-04 12:00:00.123456789",
+                                                 "2020-02-29 00:01:01.007008009",
+                                                 "2015-12-29 23:02:02.421310209",
+                                                 "2011-10-11 03:03:03.999777555",
+                                                 "1776-07-04 11:59:59.952952953"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns);
+
+  results = cudf::strings::from_timestamps(timestamps_ns, "%Y-%m-%d %H:%M:%S.%f");
+  cudf::test::strings_column_wrapper expected_ns_6f{"2018-07-04 12:00:00.123456",
+                                                    "2020-02-29 00:01:01.007008",
+                                                    "2015-12-29 23:02:02.421310",
+                                                    "2011-10-11 03:03:03.999777",
+                                                    "1776-07-04 11:59:59.952952"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns_6f);
+}
+
+TEST_F(StringsDatetimeTest, FromTimestampTimezone)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps{
+    1530705600L, 1582934461L, 1451430122L, 1318302183L, -2658802500L};
+  auto results = cudf::strings::from_timestamps(timestamps, "%m/%d/%y %H%M%S%z");
+  cudf::test::strings_column_wrapper expected{"07/04/18 120000+0000",
+                                              "02/29/20 000101+0000",
+                                              "12/29/15 230202+0000",
+                                              "10/11/11 030303+0000",
+                                              "09/29/85 194500+0000"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsDatetimeTest, FromTimestampDayOfYear)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps{
+    118800L,       // 1970-01-02 09:00:00
+    1293901860L,   // 2011-01-01 17:11:00
+    318402000L,    // 1980-02-03 05:00:00
+    604996200L,    // 1989-03-04 06:30:00
+    1270413572L,   // 2010-04-04 20:39:32
+    1588734621L,   // 2020-05-06 03:10:21
+    2550814152L,   // 2050-10-31 07:29:12
+    4102518778L,   // 2100-01-01 20:32:58
+    702696234L,    // 1992-04-08 01:23:54
+    6516816203L,   // 2176-07-05 02:43:23
+    26472091292L,  // 2808-11-12 22:41:32
+    4133857172L,   // 2100-12-30 01:39:32
+    1560948892L,   // 2019-06-19 12:54:52
+    4115217600L,   // 2100-05-28 20:00:00
+    -265880250L,   // 1961-07-29 16:22:30
+  };
+  auto results = cudf::strings::from_timestamps(timestamps, "%d/%m/%Y %j");
+  cudf::test::strings_column_wrapper expected{"02/01/1970 002",
+                                              "01/01/2011 001",
+                                              "03/02/1980 034",
+                                              "04/03/1989 063",
+                                              "04/04/2010 094",
+                                              "06/05/2020 127",
+                                              "31/10/2050 304",
+                                              "01/01/2100 001",
+                                              "08/04/1992 099",
+                                              "05/07/2176 187",
+                                              "12/11/2808 317",
+                                              "30/12/2100 364",
+                                              "19/06/2019 170",
+                                              "28/05/2100 148",
+                                              "29/07/1961 210"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+// Format names used for some specifiers in from_timestamps
+// clang-format off
+cudf::test::strings_column_wrapper format_names() {
+  return cudf::test::strings_column_wrapper({"AM", "PM",
+    "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
+    "Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat",
+    "January", "February", "March", "April", "May", "June", "July",
+    "August", "September", "October", "November", "December",
+    "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"});
+}
+// clang-format on
+
+TEST_F(StringsDatetimeTest, FromTimestampDayOfWeekOfYear)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps{
+    1645059720L,  // 2022-02-17
+    1647167880L,  // 2022-03-13
+    1649276040L,  // 2022-04-06
+    1588734621L,  // 2020-05-06
+    1560948892L,  // 2019-06-19
+    -265880250L,  // 1961-07-29
+    1628194442L,  // 2021-08-05
+    1632410760L,  // 2021-09-23
+    1633464842L,  // 2021-10-05
+    1636100042L,  // 2021-11-05
+    // These are a sequence of dates which are particular to the ISO week and
+    // year numbers which shift through Monday and Thursday and nicely includes
+    // a leap year (1980). https://en.wikipedia.org/wiki/ISO_week_date
+    220924800L,  // 1977-01-01
+    221011200L,  // 1977-01-02
+    252374400L,  // 1977-12-31
+    252460800L,  // 1978-01-01
+    252547200L,  // 1978-01-02
+    283910400L,  // 1978-12-31
+    283996800L,  // 1979-01-01
+    315360000L,  // 1979-12-30
+    315446400L,  // 1979-12-31
+    315532800L,  // 1980-01-01
+    346809600L,  // 1980-12-28
+    346896000L,  // 1980-12-29
+    346982400L,  // 1980-12-30
+    347068800L,  // 1980-12-31
+    347155200L,  // 1981-01-01
+    378604800L,  // 1981-12-31
+    378691200L,  // 1982-01-01
+    378777600L,  // 1982-01-02
+    378864000L   // 1982-01-03
+  };
+
+  cudf::test::strings_column_wrapper expected(
+    {"[Thu 17, Feb 2022  4  07  4  07  2022  07]", "[Sun 13, Mar 2022  0  10  7  11  2022  10]",
+     "[Wed 06, Apr 2022  3  14  3  14  2022  14]", "[Wed 06, May 2020  3  18  3  18  2020  19]",
+     "[Wed 19, Jun 2019  3  24  3  24  2019  25]", "[Sat 29, Jul 1961  6  30  6  30  1961  30]",
+     "[Thu 05, Aug 2021  4  31  4  31  2021  31]", "[Thu 23, Sep 2021  4  38  4  38  2021  38]",
+     "[Tue 05, Oct 2021  2  40  2  40  2021  40]", "[Fri 05, Nov 2021  5  44  5  44  2021  44]",
+     "[Sat 01, Jan 1977  6  00  6  00  1976  53]", "[Sun 02, Jan 1977  0  00  7  01  1976  53]",
+     "[Sat 31, Dec 1977  6  52  6  52  1977  52]", "[Sun 01, Jan 1978  0  00  7  01  1977  52]",
+     "[Mon 02, Jan 1978  1  01  1  01  1978  01]", "[Sun 31, Dec 1978  0  52  7  53  1978  52]",
+     "[Mon 01, Jan 1979  1  01  1  00  1979  01]", "[Sun 30, Dec 1979  0  52  7  52  1979  52]",
+     "[Mon 31, Dec 1979  1  53  1  52  1980  01]", "[Tue 01, Jan 1980  2  00  2  00  1980  01]",
+     "[Sun 28, Dec 1980  0  51  7  52  1980  52]", "[Mon 29, Dec 1980  1  52  1  52  1981  01]",
+     "[Tue 30, Dec 1980  2  52  2  52  1981  01]", "[Wed 31, Dec 1980  3  52  3  52  1981  01]",
+     "[Thu 01, Jan 1981  4  00  4  00  1981  01]", "[Thu 31, Dec 1981  4  52  4  52  1981  53]",
+     "[Fri 01, Jan 1982  5  00  5  00  1981  53]", "[Sat 02, Jan 1982  6  00  6  00  1981  53]",
+     "[Sun 03, Jan 1982  0  00  7  01  1981  53]"});
+
+  auto results = cudf::strings::from_timestamps(timestamps,
+                                                "[%a %d, %b %Y  %w  %W  %u  %U  %G  %V]",
+                                                cudf::strings_column_view(format_names()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsDatetimeTest, FromTimestampWeekdayMonthYear)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps{
+    1642951560L,  // 2022-01-23 15:26:00 Sunday
+    1645059720L,  // 2022-02-17 01:02:00 Thursday
+    1647167880L,  // 2022-03-13 10:38:00 Sunday
+    1649276040L,  // 2022-04-06 20:14:00 Wednesday
+    1588734621L,  // 2020-05-06 03:10:21 Wednesday
+    1560948892L,  // 2019-06-19 12:54:52 Wednesday
+    -265880250L,  // 1961-07-29 16:22:30 Saturday
+    1628194442L,  // 2021-08-05 20:14:02 Thursday
+    1632410760L,  // 2021-09-23 15:26:00 Thursday
+    1633464842L,  // 2021-10-05 20:14:02 Tuesday
+    1636100042L,  // 2021-11-05 08:14:02 Friday
+    1638757202L   // 2021-12-06 02:20:00 Monday
+  };
+
+  cudf::test::strings_column_wrapper expected({"[Sunday January 23, 2022: 03 PM]",
+                                               "[Thursday February 17, 2022: 01 AM]",
+                                               "[Sunday March 13, 2022: 10 AM]",
+                                               "[Wednesday April 06, 2022: 08 PM]",
+                                               "[Wednesday May 06, 2020: 03 AM]",
+                                               "[Wednesday June 19, 2019: 12 PM]",
+                                               "[Saturday July 29, 1961: 04 PM]",
+                                               "[Thursday August 05, 2021: 08 PM]",
+                                               "[Thursday September 23, 2021: 03 PM]",
+                                               "[Tuesday October 05, 2021: 08 PM]",
+                                               "[Friday November 05, 2021: 08 AM]",
+                                               "[Monday December 06, 2021: 02 AM]"});
+
+  auto results = cudf::strings::from_timestamps(
+    timestamps, "[%A %B %d, %Y: %I %p]", cudf::strings_column_view(format_names()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsDatetimeTest, FromTimestampAllSpecifiers)
+{
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_ns, cudf::timestamp_ns::rep> input{
+    1645059720000000001L,
+    1647167880000001000L,
+    1649276040001000000L,
+    1588734621123456789L,
+    1560948892987654321L,
+    -265880250010203040L,
+    1628194442090807060L,
+    1632410760500400300L,
+    1633464842000000000L,
+    1636100042999999999L};
+
+  auto results = cudf::strings::from_timestamps(
+    input,
+    "[%d/%m/%y/%Y %H:%I:%M:%S.%f %z:%Z %j %u %U %W %V %G %p %a %A %b %B]",
+    cudf::strings_column_view(format_names()));
+
+  // clang-format off
+  cudf::test::strings_column_wrapper expected({
+  "[17/02/22/2022 01:01:02:00.000000 +0000:UTC 048 4 07 07 07 2022 AM Thu Thursday Feb February]",
+  "[13/03/22/2022 10:10:38:00.000001 +0000:UTC 072 7 11 10 10 2022 AM Sun Sunday Mar March]",
+  "[06/04/22/2022 20:08:14:00.001000 +0000:UTC 096 3 14 14 14 2022 PM Wed Wednesday Apr April]",
+  "[06/05/20/2020 03:03:10:21.123456 +0000:UTC 127 3 18 18 19 2020 AM Wed Wednesday May May]",
+  "[19/06/19/2019 12:12:54:52.987654 +0000:UTC 170 3 24 24 25 2019 PM Wed Wednesday Jun June]",
+  "[29/07/61/1961 16:04:22:29.989796 +0000:UTC 210 6 30 30 30 1961 PM Sat Saturday Jul July]",
+  "[05/08/21/2021 20:08:14:02.090807 +0000:UTC 217 4 31 31 31 2021 PM Thu Thursday Aug August]",
+  "[23/09/21/2021 15:03:26:00.500400 +0000:UTC 266 4 38 38 38 2021 PM Thu Thursday Sep September]",
+  "[05/10/21/2021 20:08:14:02.000000 +0000:UTC 278 2 40 40 40 2021 PM Tue Tuesday Oct October]",
+  "[05/11/21/2021 08:08:14:02.999999 +0000:UTC 309 5 44 44 44 2021 AM Fri Friday Nov November]"});
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsDatetimeTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_column = cudf::make_empty_column(cudf::type_id::TIMESTAMP_SECONDS)->view();
+  auto results                = cudf::strings::from_timestamps(zero_size_column);
+  cudf::test::expect_column_empty(results->view());
+
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  results = cudf::strings::to_timestamps(cudf::strings_column_view(zero_size_strings_column),
+                                         cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS},
+                                         "%Y");
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsDatetimeTest, Errors)
+{
+  cudf::test::strings_column_wrapper strings{"this column intentionally left blank"};
+  cudf::strings_column_view view(strings);
+  EXPECT_THROW(cudf::strings::to_timestamps(view, cudf::data_type{cudf::type_id::INT64}, "%Y"),
+               cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_timestamps(view, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS}, ""),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_timestamps(view, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS}, "%2Y"),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_timestamps(view, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS}, "%g"),
+    cudf::logic_error);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> invalid_timestamps{1530705600};
+  EXPECT_THROW(cudf::strings::from_timestamps(invalid_timestamps), cudf::logic_error);
+  cudf::test::fixed_width_column_wrapper<cudf::timestamp_s, cudf::timestamp_s::rep> timestamps{
+    1530705600};
+  EXPECT_THROW(cudf::strings::from_timestamps(timestamps, ""), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::from_timestamps(timestamps, "%A %B", view), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/durations_tests.cpp b/cpp/tests/strings/durations_tests.cpp
new file mode 100644
index 0000000..1902f90
--- /dev/null
+++ b/cpp/tests/strings/durations_tests.cpp
@@ -0,0 +1,765 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/convert/convert_durations.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/wrappers/durations.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+#include <vector>
+
+struct StringsDurationsTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsDurationsTest, FromToDurations)
+{
+  using T = cudf::duration_s;
+  std::vector<cudf::duration_s> h_durations{
+    T{131246625}, T{1563399277}, T{0}, T{1553085296}, T{1582934400}, T{-1545730073}, T{-86399}};
+  std::vector<char const*> h_expected{"1519 days 01:23:45",
+                                      "18094 days 21:34:37",
+                                      nullptr,
+                                      "17975 days 12:34:56",
+                                      "18321 days 00:00:00",
+                                      "-17890 days 09:27:53",
+                                      "-0 days 23:59:59"};
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> durations(
+    h_durations.begin(),
+    h_durations.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = cudf::strings::from_durations(durations);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  //
+  auto new_durations = cudf::strings::to_durations(cudf::strings_column_view(expected),
+                                                   cudf::data_type(cudf::type_to_id<T>()),
+                                                   "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+}
+
+// Note: ISO format does not have leading zeros. This test does.
+TEST_F(StringsDurationsTest, ISOFormat)
+{
+  using T = cudf::duration_s;
+  cudf::test::fixed_width_column_wrapper<T> durations{
+    T{1530705600L}, T{1582934461L}, T{1451430122L}, T{1318302183L}, T{-6105994200L}};
+  auto results = cudf::strings::from_durations(durations, "P%DDT%HH%MM%SS");
+  cudf::test::strings_column_wrapper expected{"P17716DT12H00M00S",
+                                              "P18321DT00H01M01S",
+                                              "P16798DT23H02M02S",
+                                              "P15258DT03H03M03S",
+                                              "P-70671DT05H30M00S"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  //
+  cudf::test::strings_column_wrapper string_iso{
+    "P17716DT12H0M0S", "P18321DT0H1M1S", "P16798DT23H2M2S", "P15258DT3H3M3S", "P-70671DT5H30M0S"};
+  auto new_durations = cudf::strings::to_durations(cudf::strings_column_view(string_iso),
+                                                   cudf::data_type(cudf::type_to_id<T>()),
+                                                   "P%DDT%HH%MM%SS");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+  new_durations = cudf::strings::to_durations(
+    cudf::strings_column_view(expected), cudf::data_type(cudf::type_to_id<T>()), "P%DDT%HH%MM%SS");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+}
+
+TEST_F(StringsDurationsTest, ISOFormatDaysOnly)
+{
+  using T = cudf::duration_D;
+  cudf::test::fixed_width_column_wrapper<T> durations{
+    T{17716L}, T{18321L}, T{16798L}, T{15258L}, T{-70672L}};
+  auto results1 = cudf::strings::from_durations(durations, "P%DDT%HH%MM%SS");
+  cudf::test::strings_column_wrapper expected1{"P17716DT00H00M00S",
+                                               "P18321DT00H00M00S",
+                                               "P16798DT00H00M00S",
+                                               "P15258DT00H00M00S",
+                                               "P-70672DT00H00M00S"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results1, expected1);
+
+  auto results2 = cudf::strings::from_durations(durations, "P%DD");
+  cudf::test::strings_column_wrapper expected2{
+    "P17716D", "P18321D", "P16798D", "P15258D", "P-70672D"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results2, expected2);
+
+  //
+  cudf::test::strings_column_wrapper string_iso{
+    "P17716DT0H0M0S", "P18321DT0H0M0S", "P16798DT0H0M0S", "P15258DT0H0M0S", "P-70672DT0H0M0S"};
+  auto new_durations1 = cudf::strings::to_durations(cudf::strings_column_view(string_iso),
+                                                    cudf::data_type(cudf::type_to_id<T>()),
+                                                    "P%DDT%HH%MM%SS");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations1, durations);
+  new_durations1 = cudf::strings::to_durations(
+    cudf::strings_column_view(expected1), cudf::data_type(cudf::type_to_id<T>()), "P%DDT%HH%MM%SS");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations1, durations);
+  auto new_durations2 = cudf::strings::to_durations(
+    cudf::strings_column_view(expected2), cudf::data_type(cudf::type_to_id<T>()), "P%DD");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations2, durations);
+}
+
+TEST_F(StringsDurationsTest, ISOFormatSubseconds)
+{
+  using T = cudf::duration_ns;
+  cudf::test::fixed_width_column_wrapper<T> durations{T{0L},
+                                                      T{7000000000L},
+                                                      T{11L},
+                                                      T{10L},
+                                                      T{17716L},
+                                                      T{18321L},
+                                                      T{16798L},
+                                                      T{15258L},
+                                                      T{15258000L},
+                                                      T{-70672L}};
+  auto results = cudf::strings::from_durations(durations, "P%DDT%HH%MM%SS");
+  cudf::test::strings_column_wrapper expected{"P0DT00H00M00S",
+                                              "P0DT00H00M07S",
+                                              "P0DT00H00M00.000000011S",
+                                              "P0DT00H00M00.000000010S",
+                                              "P0DT00H00M00.000017716S",
+                                              "P0DT00H00M00.000018321S",
+                                              "P0DT00H00M00.000016798S",
+                                              "P0DT00H00M00.000015258S",
+                                              "P0DT00H00M00.015258000S",
+                                              "P-0DT00H00M00.000070672S"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  // fully isoformat compliant.
+  cudf::test::strings_column_wrapper string_iso{"P0DT0H0M0S",
+                                                "P0DT0H0M7S",
+                                                "P0DT0H0M0.000000011S",
+                                                "P0DT0H0M0.00000001S",
+                                                "P0DT0H0M0.000017716S",
+                                                "P0DT0H0M0.000018321S",
+                                                "P0DT0H0M0.000016798S",
+                                                "P0DT0H0M0.000015258S",
+                                                "P0DT0H0M0.015258S",
+                                                "P-0DT0H0M0.000070672S"};
+  auto new_durations = cudf::strings::to_durations(cudf::strings_column_view(string_iso),
+                                                   cudf::data_type(cudf::type_to_id<T>()),
+                                                   "P%DDT%HH%MM%SS");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+  new_durations = cudf::strings::to_durations(
+    cudf::strings_column_view(expected), cudf::data_type(cudf::type_to_id<T>()), "P%DDT%HH%MM%SS");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+}
+
+TEST_F(StringsDurationsTest, DurationSeconds)
+{
+  using T = cudf::duration_s;
+  cudf::test::fixed_width_column_wrapper<T> durations{
+    T{0L},          // 0 days 00:00:00
+    T{1L},          // 0 days 00:00:01
+    T{118800L},     // 1 days 09:00:00
+    T{31568404L},   // 365 days 09:00:04
+    T{-118800L},    // -1 days 09:00:00
+    T{-31568404L},  // -366 days +14:59:56
+  };
+  auto results = cudf::strings::from_durations(durations, "%D days %H:%M:%S");
+  cudf::test::strings_column_wrapper expected{"0 days 00:00:00",
+                                              "0 days 00:00:01",
+                                              "1 days 09:00:00",
+                                              "365 days 09:00:04",
+                                              "-1 days 09:00:00",
+                                              "-365 days 09:00:04"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  auto new_durations = cudf::strings::to_durations(cudf::strings_column_view(expected),
+                                                   cudf::data_type(cudf::type_to_id<T>()),
+                                                   "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+}
+
+TEST_F(StringsDurationsTest, DurationDays)
+{
+  using T = cudf::duration_D;
+  cudf::test::fixed_width_column_wrapper<T> durations{
+    T{0L},            // 0 days 00:00:00
+    T{1L},            // 1 days 00:00:00
+    T{-1L},           // -1 days 00:00:00
+    T{800L},          // 800 days 00:00:00
+    T{-800L},         // -800 days 00:00:00
+    T{2147483647L},   //  2147483647 days 00:00:00
+    T{-2147483648L},  // -2147483648 days 00:00:00
+  };
+  auto results = cudf::strings::from_durations(durations, "%D days %H:%M:%S");
+  cudf::test::strings_column_wrapper expected{"0 days 00:00:00",
+                                              "1 days 00:00:00",
+                                              "-1 days 00:00:00",
+                                              "800 days 00:00:00",
+                                              "-800 days 00:00:00",
+                                              "2147483647 days 00:00:00",
+                                              "-2147483648 days 00:00:00"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  auto new_durations = cudf::strings::to_durations(cudf::strings_column_view(expected),
+                                                   cudf::data_type(cudf::type_to_id<T>()),
+                                                   "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*new_durations, durations);
+}
+
+TEST_F(StringsDurationsTest, DurationMilliseconds)
+{
+  using ms = cudf::duration_ms;
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ms> durations_ms{ms{-60000},
+                                                                         ms{1530705600123},
+                                                                         ms{1582934461007},
+                                                                         ms{1451430122420},
+                                                                         ms{1451430122400},
+                                                                         ms{1451430122000},
+                                                                         ms{1318302183999},
+                                                                         ms{-6106017600047}};
+  cudf::test::strings_column_wrapper expected_ms_3f{"-0 days 00:01:00.000",
+                                                    "17716 days 12:00:00.123",
+                                                    "18321 days 00:01:01.007",
+                                                    "16798 days 23:02:02.420",
+                                                    "16798 days 23:02:02.400",
+                                                    "16798 days 23:02:02.000",
+                                                    "15258 days 03:03:03.999",
+                                                    "-70671 days 12:00:00.047"};
+
+  cudf::test::strings_column_wrapper expected_ms_6f{"-0 days 00:01:00.000000",
+                                                    "17716 days 12:00:00.123000",
+                                                    "18321 days 00:01:01.007000",
+                                                    "16798 days 23:02:02.420000",
+                                                    "16798 days 23:02:02.400000",
+                                                    "16798 days 23:02:02.000000",
+                                                    "15258 days 03:03:03.999000",
+                                                    "-70671 days 12:00:00.047000"};
+
+  cudf::test::strings_column_wrapper expected_ms{"-0 days 00:01:00",
+                                                 "17716 days 12:00:00.123",
+                                                 "18321 days 00:01:01.007",
+                                                 "16798 days 23:02:02.420",
+                                                 "16798 days 23:02:02.400",
+                                                 "16798 days 23:02:02",
+                                                 "15258 days 03:03:03.999",
+                                                 "-70671 days 12:00:00.047"};
+  auto results = cudf::strings::from_durations(durations_ms, "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ms);
+
+  //
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_ms_3f),
+                                        cudf::data_type(cudf::type_to_id<ms>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_ms);
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_ms_6f),
+                                        cudf::data_type(cudf::type_to_id<ms>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_ms);
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_ms),
+                                        cudf::data_type(cudf::type_to_id<ms>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_ms);
+}
+
+TEST_F(StringsDurationsTest, DurationMicroseconds)
+{
+  using us = cudf::duration_us;
+  cudf::test::fixed_width_column_wrapper<cudf::duration_us> durations_us{us{-60000},
+                                                                         us{1530705600123},
+                                                                         us{1582934461007},
+                                                                         us{1451430122420},
+                                                                         us{1451430122400},
+                                                                         us{1451430122000},
+                                                                         us{1318302183999},
+                                                                         us{-6106017600047}};
+  cudf::test::strings_column_wrapper expected_us_3f{"-0 days 00:00:00.060",
+                                                    "17 days 17:11:45.600",
+                                                    "18 days 07:42:14.461",
+                                                    "16 days 19:10:30.122",
+                                                    "16 days 19:10:30.122",
+                                                    "16 days 19:10:30.122",
+                                                    "15 days 06:11:42.183",
+                                                    "-70 days 16:06:57.600"};
+
+  cudf::test::strings_column_wrapper expected_us_6f{"-0 days 00:00:00.060000",
+                                                    "17 days 17:11:45.600123",
+                                                    "18 days 07:42:14.461007",
+                                                    "16 days 19:10:30.122420",
+                                                    "16 days 19:10:30.122400",
+                                                    "16 days 19:10:30.122000",
+                                                    "15 days 06:11:42.183999",
+                                                    "-70 days 16:06:57.600047"};
+
+  cudf::test::strings_column_wrapper expected_us{"-0 days 00:00:00.060000",
+                                                 "17 days 17:11:45.600123",
+                                                 "18 days 07:42:14.461007",
+                                                 "16 days 19:10:30.122420",
+                                                 "16 days 19:10:30.122400",
+                                                 "16 days 19:10:30.122000",
+                                                 "15 days 06:11:42.183999",
+                                                 "-70 days 16:06:57.600047"};
+  auto results = cudf::strings::from_durations(durations_us, "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_us);
+
+  //
+  cudf::test::fixed_width_column_wrapper<cudf::duration_us> durations_us_3f{us{-60000},
+                                                                            us{1530705600000},
+                                                                            us{1582934461000},
+                                                                            us{1451430122000},
+                                                                            us{1451430122000},
+                                                                            us{1451430122000},
+                                                                            us{1318302183000},
+                                                                            us{-6106017600000}};
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_us_3f),
+                                        cudf::data_type(cudf::type_to_id<us>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_us_3f);
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_us_6f),
+                                        cudf::data_type(cudf::type_to_id<us>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_us);
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_us),
+                                        cudf::data_type(cudf::type_to_id<us>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_us);
+}
+
+TEST_F(StringsDurationsTest, DurationNanoseconds)
+{
+  using ns = cudf::duration_ns;
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ns> durations_ns{ns{1530705600123456789},
+                                                                         ns{1582934461007008009},
+                                                                         ns{1451430122421310209},
+                                                                         ns{1318302183999777550},
+                                                                         ns{-6106017600047047047}};
+  auto results = cudf::strings::from_durations(durations_ns, "%D days %H:%M:%S");
+  cudf::test::strings_column_wrapper expected_ns_9f{"17716 days 12:00:00.123456789",
+                                                    "18321 days 00:01:01.007008009",
+                                                    "16798 days 23:02:02.421310209",
+                                                    "15258 days 03:03:03.999777550",
+                                                    "-70671 days 12:00:00.047047047"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns_9f);
+
+  cudf::test::strings_column_wrapper expected_ns_6f{"17716 days 12:00:00.123456",
+                                                    "18321 days 00:01:01.007008",
+                                                    "16798 days 23:02:02.421310",
+                                                    "15258 days 03:03:03.999777",
+                                                    "-70671 days 12:00:00.047047"};
+
+  cudf::test::strings_column_wrapper expected_ns{"17716 days 12:00:00.123456789",
+                                                 "18321 days 00:01:01.007008009",
+                                                 "16798 days 23:02:02.421310209",
+                                                 "15258 days 03:03:03.99977755",
+                                                 "-70671 days 12:00:00.047047047"};
+
+  //
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_ns_9f),
+                                        cudf::data_type(cudf::type_to_id<ns>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_ns);
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ns> durations_ns_6f{
+    ns{1530705600123456000},
+    ns{1582934461007008000},
+    ns{1451430122421310000},
+    ns{1318302183999777000},
+    ns{-6106017600047047000}};
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_ns_6f),
+                                        cudf::data_type(cudf::type_to_id<ns>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_ns_6f);
+  results = cudf::strings::to_durations(cudf::strings_column_view(expected_ns),
+                                        cudf::data_type(cudf::type_to_id<ns>()),
+                                        "%D days %H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, durations_ns);
+}
+
+// Hour, Minute, Seconds 0,+,-
+TEST_F(StringsDurationsTest, ParseSingle)
+{
+  cudf::test::strings_column_wrapper string_src{"00",
+                                                "-00",
+                                                "01",
+                                                "-01",
+                                                "23",
+                                                "-23",
+                                                "59",
+                                                "-59",
+                                                "999",
+                                                "-999",
+                                                "",  // error
+                                                "01",
+                                                ""};  // error
+  auto size = cudf::column_view(string_src).size();
+  int32_t expected_v[]{0, 0, 1, -1, 23, -23, 59, -59, 99, -99, 0, 1, 0};
+  auto it1 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_s{i * 3600}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> expected_s1(it1, it1 + size);
+  auto results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                             cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                             "%H");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  auto it2 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_s{i * 60}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> expected_s2(it2, it2 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                        "%M");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s2);
+
+  auto it3 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_s{i}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> expected_s3(it3, it3 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                        "%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s3);
+
+  auto it4 = thrust::make_transform_iterator(expected_v,
+                                             [](auto i) { return cudf::duration_ms{i * 60000}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ms> expected_ms(it4, it4 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_ms>()),
+                                        "%M");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ms);
+}
+
+// Hour, Minute, Seconds
+TEST_F(StringsDurationsTest, ParseMultiple)
+{
+  cudf::test::strings_column_wrapper string_src{"00:00:00",
+                                                "-00:00:00",
+                                                "-00:00:01",
+                                                "-01:01:01",
+                                                "23:00:01",
+                                                "-23:00:01",
+                                                "59:00:00",
+                                                "-59:00:00",
+                                                "999:00:00",
+                                                "-999:00:00",
+                                                "",  // error
+                                                "01:01:01",
+                                                ""};  // error
+  auto size = cudf::column_view(string_src).size();
+  int32_t expected_v[]{0,
+                       0,
+                       -1,
+                       -(3600 + 60 + 1),
+                       23 * 3600 + 1,
+                       -(23 * 3600 + 1),
+                       59 * 3600,
+                       -59 * 3600,
+                       99 * 3600,
+                       -99 * 3600,
+                       0,
+                       3661,
+                       0};
+  auto it1 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_s{i}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> expected_s1(it1, it1 + size);
+  auto results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                             cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                             "%H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  auto it2 = thrust::make_transform_iterator(
+    expected_v, [](auto i) { return cudf::duration_D{i / (24 * 3600)}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_D> expected_D2(it2, it2 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_D>()),
+                                        "%H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_D2);
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_us> expected_us3(it1, it1 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_us>()),
+                                        "%H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_us3);
+}
+
+// 0,+,- on DHMSs
+// subsecond=0,1,2,3,4,5,6,8,9,digits, also leading zeros. subsecond with/without zero HMS.
+TEST_F(StringsDurationsTest, ParseSubsecond)
+{
+  cudf::test::strings_column_wrapper string_src{"00:00:00.000000000",
+                                                "-00:00:00.123456789",
+                                                "-00:00:01.000666999",  // leading zeros
+                                                "-01:01:01.100000000",
+                                                "23:00:01.00000008",    // trailing zero missing
+                                                "-23:00:01.123000000",  // trailing zeros
+                                                "59:00:00",
+                                                "-59:00:00",
+                                                "999:00:00",
+                                                "-999:00:00",
+                                                "",  // error
+                                                "01:01:01",
+                                                ""};  // error
+  auto size = cudf::column_view(string_src).size();
+  int64_t expected_v[]{0,
+                       -123456789L,
+                       -1000666999L,
+                       -((3600 + 60 + 1) * 1000000000L + 100000000L),
+                       (23 * 3600 + 1) * 1000000000L + 80L,
+                       -((23 * 3600 + 1) * 1000000000L + 123000000L),
+                       (59 * 3600) * 1000000000L,
+                       -(59 * 3600) * 1000000000L,
+                       (99 * 3600) * 1000000000L,
+                       -(99 * 3600) * 1000000000L,
+                       0,
+                       (3661) * 1000000000L,
+                       0};
+  auto it1 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_ns{i}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ns> expected_ns1(it1, it1 + size);
+  auto results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                             cudf::data_type(cudf::type_to_id<cudf::duration_ns>()),
+                                             "%H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ns1);
+
+  auto it2 = thrust::make_transform_iterator(expected_v,
+                                             [](auto i) { return cudf::duration_ms{i / 1000000}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ms> expected_ms2(it2, it2 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_ms>()),
+                                        "%H:%M:%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_ms2);
+}
+
+// AM/PM  0,+,- on DHMSs
+TEST_F(StringsDurationsTest, ParseAMPM)
+{
+  cudf::test::strings_column_wrapper string_src{"00:00:00 AM",
+                                                "00:00:00 PM",
+                                                "-00:00:00 AM",
+                                                "-00:00:00 PM",
+                                                "-00:00:01 AM",
+                                                "-00:00:01 PM",
+                                                "-01:01:01 AM",
+                                                "-01:01:01 PM",
+                                                "11:59:59 AM",
+                                                "11:59:59 PM",
+                                                "-11:59:59 AM",
+                                                "-11:59:59 PM",
+                                                "09:00:00",   // error
+                                                "-09:00:00",  // error
+                                                "",           // error
+                                                "01:01:01",   // error
+                                                ""};          // error
+  auto size = cudf::column_view(string_src).size();
+  int32_t expected_v[]{0,
+                       0 + 12 * 3600,
+                       0,
+                       0 - 12 * 3600,
+                       -1,
+                       -1 - 12 * 3600,
+                       -(3600 + 60 + 1),
+                       -(3600 + 60 + 1) - 12 * 3600,
+                       11 * 3600 + 59 * 60 + 59,
+                       11 * 3600 + 59 * 60 + 59 + 12 * 3600,
+                       -(11 * 3600 + 59 * 60 + 59),
+                       -(11 * 3600 + 59 * 60 + 59 + 12 * 3600),
+                       0,
+                       0,
+                       0,
+                       0,
+                       0};
+  auto it1 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_s{i}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> expected_s1(it1, it1 + size);
+  auto results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                             cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                             "%H:%M:%S %p");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  auto it2 = thrust::make_transform_iterator(
+    expected_v, [](auto i) { return cudf::duration_D{i / (24 * 3600)}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_D> expected_D2(it2, it2 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_D>()),
+                                        "%H:%M:%S %p");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_D2);
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_us> expected_us3(it1, it1 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_us>()),
+                                        "%H:%M:%S %p");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_us3);
+}
+
+// R, T, r
+TEST_F(StringsDurationsTest, ParseCompoundSpecifier)
+{
+  // %r
+  cudf::test::strings_column_wrapper string_src{"00:00:00 AM",
+                                                "00:00:00 PM",
+                                                "00:00:01 AM",
+                                                "00:00:01 PM",
+                                                "01:01:01 AM",
+                                                "01:01:01 PM",
+                                                "11:59:59 AM",
+                                                "11:59:59 PM",
+                                                "09:00:00",  // error
+                                                "",          // error
+                                                "01:01:01",  // error
+                                                ""};         // error
+  auto size = cudf::column_view(string_src).size();
+  int32_t expected_v[]{0,
+                       0 + 12 * 3600,
+                       1,
+                       1 + 12 * 3600,
+                       (3600 + 60 + 1),
+                       (3600 + 60 + 1) + 12 * 3600,
+                       11 * 3600 + 59 * 60 + 59,
+                       11 * 3600 + 59 * 60 + 59 + 12 * 3600,
+                       0,
+                       0,
+                       0,
+                       0};
+  auto it1 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_s{i}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> expected_s1(it1, it1 + size);
+  auto results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                             cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                             "%r");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                        "%OI:%OM:%OS %p");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  auto it2 =
+    thrust::make_transform_iterator(expected_v, [](auto i) { return cudf::duration_ms{i * 1000}; });
+  cudf::test::fixed_width_column_wrapper<cudf::duration_ms> expected_s2(it2, it2 + size);
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_ms>()),
+                                        "%r");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s2);
+
+  // %T, %R
+  cudf::test::strings_column_wrapper string_src2{"00:00:00",
+                                                 "12:00:00",
+                                                 "20:44:01",
+                                                 "-20:44:01",
+                                                 "08:01:01",
+                                                 "-08:01:01",
+                                                 "11:59:59",
+                                                 "-11:59:59 AM",
+                                                 "09:00 AM",  // error
+                                                 "",          // error
+                                                 "01:01:01",
+                                                 ""};  // error
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s, int64_t> expected_s3(
+    {0,
+     12 * 3600,
+     (20 * 3600 + 44 * 60 + 1),
+     -(20 * 3600 + 44 * 60 + 1),
+     (8 * 3600 + 60 + 1),
+     -(8 * 3600 + 60 + 1),
+     (11 * 3600 + 59 * 60 + 59),
+     -(11 * 3600 + 59 * 60 + 59),
+     9 * 3600,
+     0,
+     3661,
+     0});
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src2),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                        "%T");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s3);
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s, int64_t> expected_s4(
+    {0,
+     12 * 3600,
+     (20 * 3600 + 44 * 60),
+     -(20 * 3600 + 44 * 60),
+     (8 * 3600 + 60),
+     -(8 * 3600 + 60),
+     (11 * 3600 + 59 * 60),
+     -(11 * 3600 + 59 * 60),
+     9 * 3600,
+     0,
+     3660,
+     0});
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src2),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                        "%R");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s4);
+}
+
+// Escape characters %% %n %t
+// Mixed (for checking only one negative sign)
+TEST_F(StringsDurationsTest, ParseEscapeCharacters)
+{
+  cudf::test::strings_column_wrapper string_src{
+    "00:00%00", "01:01%01", "11:59%59", "11:-59%59", "09:00%00"};
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s, int64_t> expected_s1(
+    {0, 3661, (11 * 3600 + 59 * 60 + 59), -(11 * 3600 + 59 * 60 + 59), 9 * 3600});
+  auto results = cudf::strings::to_durations(cudf::strings_column_view(string_src),
+                                             cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                             "%OH:%M%%%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  results = cudf::strings::from_durations(expected_s1, "%OH:%M%%%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, string_src);
+
+  cudf::test::strings_column_wrapper string_src2{
+    "00\t00\n00", "01\t01\n01", "11\t59\n59", "11\t-59\n59", "09\t00\n00"};
+  results = cudf::strings::to_durations(cudf::strings_column_view(string_src2),
+                                        cudf::data_type(cudf::type_to_id<cudf::duration_s>()),
+                                        "%OH%t%M%n%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_s1);
+
+  results = cudf::strings::from_durations(expected_s1, "%OH%t%M%n%S");
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, string_src2);
+}
+
+TEST_F(StringsDurationsTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_column = cudf::make_empty_column(cudf::type_id::DURATION_SECONDS)->view();
+  auto results                = cudf::strings::from_durations(zero_size_column);
+  cudf::test::expect_column_empty(results->view());
+
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  results = cudf::strings::to_durations(cudf::strings_column_view(zero_size_strings_column),
+                                        cudf::data_type{cudf::type_id::DURATION_SECONDS},
+                                        "%S");
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsDurationsTest, Errors)
+{
+  cudf::test::strings_column_wrapper strings{"this column intentionally left blank"};
+  cudf::strings_column_view view(strings);
+  EXPECT_THROW(cudf::strings::to_durations(view, cudf::data_type{cudf::type_id::INT64}, "%D"),
+               cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_durations(view, cudf::data_type{cudf::type_id::DURATION_SECONDS}, ""),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_durations(view, cudf::data_type{cudf::type_id::DURATION_SECONDS}, "%2H"),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_durations(view, cudf::data_type{cudf::type_id::DURATION_SECONDS}, "%g"),
+    cudf::logic_error);
+  EXPECT_THROW(
+    cudf::strings::to_durations(view, cudf::data_type{cudf::type_id::DURATION_SECONDS}, "%Op"),
+    cudf::logic_error);
+
+  cudf::test::fixed_width_column_wrapper<int64_t> invalid_durations{1530705600};
+  EXPECT_THROW(cudf::strings::from_durations(invalid_durations), cudf::logic_error);
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s> durations{cudf::duration_s{1530705600}};
+  EXPECT_THROW(cudf::strings::from_durations(durations, ""), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/extract_tests.cpp b/cpp/tests/strings/extract_tests.cpp
new file mode 100644
index 0000000..70112f7
--- /dev/null
+++ b/cpp/tests/strings/extract_tests.cpp
@@ -0,0 +1,322 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/strings/extract.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsExtractTests : public cudf::test::BaseFixture {};
+
+TEST_F(StringsExtractTests, ExtractTest)
+{
+  std::vector<char const*> h_strings{
+    "First Last", "Joe Schmoe", "John Smith", "Jane Smith", "Beyonce", "Sting", nullptr, ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<char const*> h_expecteds{"First",
+                                       "Joe",
+                                       "John",
+                                       "Jane",
+                                       nullptr,
+                                       nullptr,
+                                       nullptr,
+                                       nullptr,
+                                       "Last",
+                                       "Schmoe",
+                                       "Smith",
+                                       "Smith",
+                                       nullptr,
+                                       nullptr,
+                                       nullptr,
+                                       nullptr};
+
+  std::string pattern = "(\\w+) (\\w+)";
+
+  cudf::test::strings_column_wrapper expected1(
+    h_expecteds.data(),
+    h_expecteds.data() + h_strings.size(),
+    thrust::make_transform_iterator(h_expecteds.begin(), [](auto str) { return str != nullptr; }));
+  cudf::test::strings_column_wrapper expected2(
+    h_expecteds.data() + h_strings.size(),
+    h_expecteds.data() + h_expecteds.size(),
+    thrust::make_transform_iterator(h_expecteds.data() + h_strings.size(),
+                                    [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(expected1.release());
+  columns.push_back(expected2.release());
+  cudf::table expected(std::move(columns));
+
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::extract(strings_view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, expected);
+}
+
+TEST_F(StringsExtractTests, ExtractDomainTest)
+{
+  cudf::test::strings_column_wrapper strings({"http://www.google.com",
+                                              "gmail.com",
+                                              "github.com",
+                                              "https://pandas.pydata.org",
+                                              "http://www.worldbank.org.kg/",
+                                              "waiterrant.blogspot.com",
+                                              "http://forums.news.cnn.com.ac/",
+                                              "http://forums.news.cnn.ac/",
+                                              "ftp://b.cnn.com/",
+                                              "a.news.uk",
+                                              "a.news.co.uk",
+                                              "https://a.news.co.uk",
+                                              "107-193-100-2.lightspeed.cicril.sbcglobal.net",
+                                              "a23-44-13-2.deploy.static.akamaitechnologies.com"});
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::string pattern = "([\\w]+[\\.].*[^/]|[\\-\\w]+[\\.].*[^/])";
+
+  cudf::test::strings_column_wrapper expected1({
+    "www.google.com",
+    "gmail.com",
+    "github.com",
+    "pandas.pydata.org",
+    "www.worldbank.org.kg",
+    "waiterrant.blogspot.com",
+    "forums.news.cnn.com.ac",
+    "forums.news.cnn.ac",
+    "b.cnn.com",
+    "a.news.uk",
+    "a.news.co.uk",
+    "a.news.co.uk",
+    "107-193-100-2.lightspeed.cicril.sbcglobal.net",
+    "a23-44-13-2.deploy.static.akamaitechnologies.com",
+  });
+  cudf::table_view expected{{expected1}};
+
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::extract(strings_view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, expected);
+}
+
+TEST_F(StringsExtractTests, ExtractEventTest)
+{
+  std::vector<std::string> patterns({"(^[0-9]+\\.?[0-9]*),",
+                                     "search_name=\"([0-9A-Za-z\\s\\-\\(\\)]+)",
+                                     "message.ip=\"([\\w\\.]+)",
+                                     "message.hostname=\"([\\w\\.]+)",
+                                     "message.user_name=\"([\\w\\.\\@]+)",
+                                     "message\\.description=\"([\\w\\.\\s]+)"});
+
+  cudf::test::strings_column_wrapper strings(
+    {"15162388.26, search_name=\"Test Search Name\", orig_time=\"1516238826\", "
+     "info_max_time=\"1566346500.000000000\", info_min_time=\"1566345300.000000000\", "
+     "info_search_time=\"1566305689.361160000\", message.description=\"Test Message Description\", "
+     "message.hostname=\"msg.test.hostname\", message.ip=\"100.100.100.123\", "
+     "message.user_name=\"user@test.com\", severity=\"info\", urgency=\"medium\"'"});
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<std::string> expecteds({"15162388.26",
+                                      "Test Search Name",
+                                      "100.100.100.123",
+                                      "msg.test.hostname",
+                                      "user@test.com",
+                                      "Test Message Description"});
+
+  for (std::size_t idx = 0; idx < patterns.size(); ++idx) {
+    auto pattern = patterns[idx];
+    cudf::test::strings_column_wrapper expected({expecteds[idx]});
+    auto prog    = cudf::strings::regex_program::create(pattern);
+    auto results = cudf::strings::extract(strings_view, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view().column(0), expected);
+  }
+}
+
+TEST_F(StringsExtractTests, MultiLine)
+{
+  auto input = cudf::test::strings_column_wrapper(
+    {"abc\nfff\nabc", "fff\nabc\nlll", "abc", "", "abc\n", "abé\nabc\n"});
+  auto view = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(^[a-c]+$)");
+  cudf::test::strings_column_wrapper expected_multiline({"abc", "abc", "abc", "", "abc", "abc"},
+                                                        {1, 1, 1, 0, 1, 1});
+  auto expected = cudf::table_view{{expected_multiline}};
+  auto prog = cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::MULTILINE);
+  auto results = cudf::strings::extract(view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, expected);
+
+  pattern = std::string("^([a-c]+)$");
+  cudf::test::strings_column_wrapper expected_default({"", "", "abc", "", "abc", ""},
+                                                      {0, 0, 1, 0, 1, 0});
+  expected = cudf::table_view{{expected_default}};
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::extract(view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, expected);
+}
+
+TEST_F(StringsExtractTests, DotAll)
+{
+  auto input = cudf::test::strings_column_wrapper({"abc\nfa\nef", "fff\nabbc\nfff", "abcdef", ""});
+  auto view  = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(a.*f)");
+  cudf::test::strings_column_wrapper expected_dotall({"abc\nfa\nef", "abbc\nfff", "abcdef", ""},
+                                                     {1, 1, 1, 0});
+  auto expected = cudf::table_view{{expected_dotall}};
+  auto prog     = cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::DOTALL);
+  auto results  = cudf::strings::extract(view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, expected);
+
+  cudf::test::strings_column_wrapper expected_default({"", "", "abcdef", ""}, {0, 0, 1, 0});
+  expected = cudf::table_view{{expected_default}};
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::extract(view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, expected);
+}
+
+TEST_F(StringsExtractTests, EmptyExtractTest)
+{
+  std::vector<char const*> h_strings{nullptr, "AAA", "AAA_A", "AAA_AAA_", "A__", ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto pattern = std::string("([^_]*)\\Z");
+
+  std::vector<char const*> h_expected{nullptr, "AAA", "A", "", "", ""};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.data(),
+    h_expected.data() + h_strings.size(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(expected.release());
+  cudf::table table_expected(std::move(columns));
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::extract(strings_view, *prog);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, table_expected);
+}
+
+TEST_F(StringsExtractTests, ExtractAllTest)
+{
+  std::vector<char const*> h_input(
+    {"123 banana 7 eleven", "41 apple", "6 péar 0 pair", nullptr, "", "bees", "4 paré"});
+  auto validity =
+    thrust::make_transform_iterator(h_input.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper input(h_input.begin(), h_input.end(), validity);
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(\\d+) (\\w+)");
+
+  bool valids[] = {true, true, true, false, false, false, true};
+  using LCW     = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"123", "banana", "7", "eleven"},
+                LCW{"41", "apple"},
+                LCW{"6", "péar", "0", "pair"},
+                LCW{},
+                LCW{},
+                LCW{},
+                LCW{"4", "paré"}},
+               valids);
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::extract_all_record(sv, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsExtractTests, Errors)
+{
+  cudf::test::strings_column_wrapper input({"this column intentionally left blank"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern = std::string("\\w+");
+  auto prog    = cudf::strings::regex_program::create(pattern);
+
+  EXPECT_THROW(cudf::strings::extract(sv, *prog), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::extract_all_record(sv, *prog), cudf::logic_error);
+}
+
+TEST_F(StringsExtractTests, MediumRegex)
+{
+  // This results in 95 regex instructions and falls in the 'medium' range.
+  std::string medium_regex =
+    "hello @abc @def (world) The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com";
+  auto prog = cudf::strings::regex_program::create(medium_regex);
+
+  std::vector<char const*> h_strings{
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com thats all",
+    "1234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234"
+    "5678901234567890",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnop"
+    "qrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::extract(strings_view, *prog);
+  std::vector<char const*> h_expected{"world", nullptr, nullptr};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->get_column(0), expected);
+}
+
+TEST_F(StringsExtractTests, LargeRegex)
+{
+  // This results in 115 regex instructions and falls in the 'large' range.
+  std::string large_regex =
+    "hello @abc @def world The (quick) brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz";
+  auto prog = cudf::strings::regex_program::create(large_regex);
+
+  std::vector<char const*> h_strings{
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz",
+    "1234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234"
+    "5678901234567890",
+    "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnop"
+    "qrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::extract(strings_view, *prog);
+  std::vector<char const*> h_expected{"quick", nullptr, nullptr};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->get_column(0), expected);
+}
diff --git a/cpp/tests/strings/factories_test.cu b/cpp/tests/strings/factories_test.cu
new file mode 100644
index 0000000..a3d392c
--- /dev/null
+++ b/cpp/tests/strings/factories_test.cu
@@ -0,0 +1,229 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/execution_policy.h>
+#include <thrust/host_vector.h>
+#include <thrust/pair.h>
+#include <thrust/transform.h>
+
+#include <cstring>
+#include <vector>
+
+struct StringsFactoriesTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFactoriesTest, CreateColumnFromPair)
+{
+  std::vector<char const*> h_test_strings{"the quick brown fox jumps over the lazy dog",
+                                          "the fat cat lays next to the other accénted cat",
+                                          "a slow moving turtlé cannot catch the bird",
+                                          "which can be composéd together to form a more complete",
+                                          "thé result does not include the value in the sum in",
+                                          "",
+                                          nullptr,
+                                          "absent stop words"};
+
+  cudf::size_type memsize = 0;
+  for (auto itr = h_test_strings.begin(); itr != h_test_strings.end(); ++itr)
+    memsize += *itr ? (cudf::size_type)strlen(*itr) : 0;
+  cudf::size_type count = (cudf::size_type)h_test_strings.size();
+  thrust::host_vector<char> h_buffer(memsize);
+  rmm::device_uvector<char> d_buffer(memsize, cudf::get_default_stream());
+  thrust::host_vector<thrust::pair<char const*, cudf::size_type>> strings(count);
+  thrust::host_vector<cudf::size_type> h_offsets(count + 1);
+  cudf::size_type offset = 0;
+  cudf::size_type nulls  = 0;
+  h_offsets[0]           = 0;
+  for (cudf::size_type idx = 0; idx < count; ++idx) {
+    char const* str = h_test_strings[idx];
+    if (!str) {
+      strings[idx] = thrust::pair<char const*, cudf::size_type>{nullptr, 0};
+      nulls++;
+    } else {
+      auto length = (cudf::size_type)strlen(str);
+      memcpy(h_buffer.data() + offset, str, length);
+      strings[idx] = thrust::pair<char const*, cudf::size_type>{d_buffer.data() + offset, length};
+      offset += length;
+    }
+    h_offsets[idx + 1] = offset;
+  }
+  auto d_strings = cudf::detail::make_device_uvector_sync(
+    strings, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  CUDF_CUDA_TRY(cudaMemcpy(d_buffer.data(), h_buffer.data(), memsize, cudaMemcpyDefault));
+  auto column = cudf::make_strings_column(d_strings);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_id::STRING});
+  EXPECT_EQ(column->null_count(), nulls);
+  if (nulls) {
+    EXPECT_TRUE(column->nullable());
+    EXPECT_TRUE(column->has_nulls());
+  }
+  EXPECT_EQ(2, column->num_children());
+
+  cudf::strings_column_view strings_view(column->view());
+  EXPECT_EQ(strings_view.size(), count);
+  EXPECT_EQ(strings_view.offsets().size(), count + 1);
+  EXPECT_EQ(strings_view.chars().size(), memsize);
+
+  // check string data
+  auto h_chars_data = cudf::detail::make_std_vector_sync(
+    cudf::device_span<char const>(strings_view.chars().data<char>(), strings_view.chars().size()),
+    cudf::get_default_stream());
+  auto h_offsets_data = cudf::detail::make_std_vector_sync(
+    cudf::device_span<cudf::size_type const>(
+      strings_view.offsets().data<cudf::size_type>() + strings_view.offset(),
+      strings_view.size() + 1),
+    cudf::get_default_stream());
+  EXPECT_EQ(memcmp(h_buffer.data(), h_chars_data.data(), h_buffer.size()), 0);
+  EXPECT_EQ(
+    memcmp(h_offsets.data(), h_offsets_data.data(), h_offsets.size() * sizeof(cudf::size_type)), 0);
+}
+
+TEST_F(StringsFactoriesTest, CreateColumnFromOffsets)
+{
+  std::vector<char const*> h_test_strings{"the quick brown fox jumps over the lazy dog",
+                                          "the fat cat lays next to the other accénted cat",
+                                          "a slow moving turtlé cannot catch the bird",
+                                          "which can be composéd together to form a more complete",
+                                          "thé result does not include the value in the sum in",
+                                          "",
+                                          nullptr,
+                                          "absent stop words"};
+
+  cudf::size_type memsize = 0;
+  for (auto itr = h_test_strings.begin(); itr != h_test_strings.end(); ++itr)
+    memsize += *itr ? (cudf::size_type)strlen(*itr) : 0;
+  cudf::size_type count = (cudf::size_type)h_test_strings.size();
+  std::vector<char> h_buffer(memsize);
+  std::vector<cudf::size_type> h_offsets(count + 1);
+  cudf::size_type offset         = 0;
+  h_offsets[0]                   = offset;
+  cudf::bitmask_type h_null_mask = 0;
+  cudf::size_type null_count     = 0;
+  for (cudf::size_type idx = 0; idx < count; ++idx) {
+    h_null_mask     = (h_null_mask << 1);
+    char const* str = h_test_strings[idx];
+    if (str) {
+      auto length = (cudf::size_type)strlen(str);
+      memcpy(h_buffer.data() + offset, str, length);
+      offset += length;
+      h_null_mask |= 1;
+    } else
+      null_count++;
+    h_offsets[idx + 1] = offset;
+  }
+
+  std::vector<cudf::bitmask_type> h_nulls{h_null_mask};
+  auto d_buffer = cudf::detail::make_device_uvector_sync(
+    h_buffer, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto d_offsets = cudf::detail::make_device_uvector_sync(
+    h_offsets, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto d_nulls = cudf::detail::make_device_uvector_sync(
+    h_nulls, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto column = cudf::make_strings_column(d_buffer, d_offsets, d_nulls, null_count);
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_id::STRING});
+  EXPECT_EQ(column->null_count(), null_count);
+  EXPECT_EQ(2, column->num_children());
+
+  cudf::strings_column_view strings_view(column->view());
+  EXPECT_EQ(strings_view.size(), count);
+  EXPECT_EQ(strings_view.offsets().size(), count + 1);
+  EXPECT_EQ(strings_view.chars().size(), memsize);
+
+  // check string data
+  auto h_chars_data = cudf::detail::make_std_vector_sync(
+    cudf::device_span<char const>(strings_view.chars().data<char>(), strings_view.chars().size()),
+    cudf::get_default_stream());
+  auto h_offsets_data = cudf::detail::make_std_vector_sync(
+    cudf::device_span<cudf::size_type const>(
+      strings_view.offsets().data<cudf::size_type>() + strings_view.offset(),
+      strings_view.size() + 1),
+    cudf::get_default_stream());
+  EXPECT_EQ(memcmp(h_buffer.data(), h_chars_data.data(), h_buffer.size()), 0);
+  EXPECT_EQ(
+    memcmp(h_offsets.data(), h_offsets_data.data(), h_offsets.size() * sizeof(cudf::size_type)), 0);
+}
+
+TEST_F(StringsFactoriesTest, CreateScalar)
+{
+  std::string value = "test string";
+  auto s            = cudf::make_string_scalar(value);
+  auto string_s     = static_cast<cudf::string_scalar*>(s.get());
+
+  EXPECT_EQ(string_s->to_string(), value);
+  EXPECT_TRUE(string_s->is_valid());
+  EXPECT_TRUE(s->is_valid());
+}
+
+TEST_F(StringsFactoriesTest, EmptyStringsColumn)
+{
+  rmm::device_uvector<char> d_chars{0, cudf::get_default_stream()};
+  auto d_offsets = cudf::detail::make_zeroed_device_uvector_sync<cudf::size_type>(
+    1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  rmm::device_uvector<cudf::bitmask_type> d_nulls{0, cudf::get_default_stream()};
+
+  auto results = cudf::make_strings_column(d_chars, d_offsets, d_nulls, 0);
+  cudf::test::expect_column_empty(results->view());
+
+  rmm::device_uvector<thrust::pair<char const*, cudf::size_type>> d_strings{
+    0, cudf::get_default_stream()};
+  results = cudf::make_strings_column(d_strings);
+  cudf::test::expect_column_empty(results->view());
+}
+
+namespace {
+using string_pair = thrust::pair<char const*, cudf::size_type>;
+struct string_view_to_pair {
+  __device__ string_pair operator()(thrust::pair<cudf::string_view, bool> const& p)
+  {
+    return (p.second) ? string_pair{p.first.data(), p.first.size_bytes()} : string_pair{nullptr, 0};
+  }
+};
+}  // namespace
+
+TEST_F(StringsFactoriesTest, StringPairWithNullsAndEmpty)
+{
+  cudf::test::strings_column_wrapper data(
+    {"", "this", "is", "", "a", "", "column", "of", "strings", "", ""},
+    {0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1});
+
+  auto d_column = cudf::column_device_view::create(data);
+  rmm::device_uvector<string_pair> pairs(d_column->size(), cudf::get_default_stream());
+  thrust::transform(rmm::exec_policy(cudf::get_default_stream()),
+                    d_column->pair_begin<cudf::string_view, true>(),
+                    d_column->pair_end<cudf::string_view, true>(),
+                    pairs.data(),
+                    string_view_to_pair{});
+
+  auto result = cudf::make_strings_column(pairs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), data);
+}
diff --git a/cpp/tests/strings/fill_tests.cpp b/cpp/tests/strings/fill_tests.cpp
new file mode 100644
index 0000000..aadd684
--- /dev/null
+++ b/cpp/tests/strings/fill_tests.cpp
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/scalar/scalar.hpp>
+
+#include <vector>
+
+struct StringsFillTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFillTest, Fill)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper input(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+
+  {
+    auto results = cudf::fill(input, 1, 5, cudf::string_scalar("zz"));
+
+    std::vector<char const*> h_expected{"eee", "zz", "zz", "zz", "zz", "bbb", "ééé"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results = cudf::fill(input, 2, 4, cudf::string_scalar("", false));
+
+    std::vector<char const*> h_expected{"eee", "bb", nullptr, nullptr, "aa", "bbb", "ééé"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results = cudf::fill(input, 5, 5, cudf::string_scalar("zz"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, input);
+  }
+  {
+    auto results = cudf::fill(input, 0, 7, cudf::string_scalar(""));
+    cudf::test::strings_column_wrapper expected({"", "", "", "", "", "", ""},
+                                                {1, 1, 1, 1, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+  {
+    auto results = cudf::fill(input, 0, 7, cudf::string_scalar("", false));
+    cudf::test::strings_column_wrapper expected({"", "", "", "", "", "", ""},
+                                                {0, 0, 0, 0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+}
+
+TEST_F(StringsFillTest, ZeroSizeStringsColumns)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto results = cudf::fill(zero_size_strings_column, 0, 0, cudf::string_scalar(""));
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsFillTest, FillRangeError)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper input(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+
+  EXPECT_THROW(cudf::fill(input, 5, 1, cudf::string_scalar("")), cudf::logic_error);
+  EXPECT_THROW(cudf::fill(input, 5, 9, cudf::string_scalar("")), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/find_multiple_tests.cpp b/cpp/tests/strings/find_multiple_tests.cpp
new file mode 100644
index 0000000..986f86d
--- /dev/null
+++ b/cpp/tests/strings/find_multiple_tests.cpp
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/find_multiple.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsFindMultipleTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFindMultipleTest, FindMultiple)
+{
+  std::vector<char const*> h_strings{"Héllo", "thesé", nullptr, "lease", "test strings", ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<char const*> h_targets{"é", "a", "e", "i", "o", "u", "es"};
+  cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+  auto targets_view = cudf::strings_column_view(targets);
+
+  auto results = cudf::strings::find_multiple(strings_view, targets_view);
+
+  using LCW = cudf::test::lists_column_wrapper<int32_t>;
+  LCW expected({LCW{1, -1, -1, -1, 4, -1, -1},
+                LCW{4, -1, 2, -1, -1, -1, 2},
+                LCW{-1, -1, -1, -1, -1, -1, -1},
+                LCW{-1, 2, 1, -1, -1, -1, -1},
+                LCW{-1, -1, 1, 8, -1, -1, 1},
+                LCW{-1, -1, -1, -1, -1, -1, -1}});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsFindMultipleTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto strings_view                   = cudf::strings_column_view(zero_size_strings_column);
+  std::vector<char const*> h_targets{""};
+  cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+  auto targets_view = cudf::strings_column_view(targets);
+
+  auto results = cudf::strings::find_multiple(strings_view, targets_view);
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(StringsFindMultipleTest, ErrorTest)
+{
+  cudf::test::strings_column_wrapper strings({"this string intentionally left blank"}, {0});
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto empty_view                     = cudf::strings_column_view(zero_size_strings_column);
+  // targets must have at least one string
+  EXPECT_THROW(cudf::strings::find_multiple(strings_view, empty_view), cudf::logic_error);
+
+  // targets cannot have nulls
+  EXPECT_THROW(cudf::strings::find_multiple(strings_view, strings_view), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/find_tests.cpp b/cpp/tests/strings/find_tests.cpp
new file mode 100644
index 0000000..5c0a5b7
--- /dev/null
+++ b/cpp/tests/strings/find_tests.cpp
@@ -0,0 +1,438 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/attributes.hpp>
+#include <cudf/strings/find.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsFindTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFindTest, Find)
+{
+  cudf::test::strings_column_wrapper strings({"Héllo", "thesé", "", "lest", "tést strings", ""},
+                                             {1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(strings);
+
+  {
+    auto const target = cudf::string_scalar("é");
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({1, 4, -1, -1, 1, -1},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::find(strings_view, target);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    results = cudf::strings::rfind(strings_view, target);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({3, -1, -1, 0, -1, -1},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::rfind(strings_view, cudf::string_scalar("l"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const target = cudf::string_scalar("es");
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({-1, 2, -1, 1, -1, -1},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::find(strings_view, target);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    results = cudf::strings::rfind(strings_view, target);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({0, 0, 0, 0, 0, 0},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::find(strings_view, cudf::string_scalar(""));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({5, 5, 0, 4, 12, 0},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::rfind(strings_view, cudf::string_scalar(""));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const targets = cudf::test::strings_column_wrapper({"l", "t", "", "x", "é", "o"});
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({2, 0, 0, -1, 1, -1},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::find(strings_view, cudf::strings_column_view(targets));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({0, 0, 0, 0, 0, 0},
+                                                                     {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::find(strings_view, strings_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsFindTest, FindWithNullTargets)
+{
+  cudf::test::strings_column_wrapper input({"hello hello", "thesé help", "", "helicopter", "", "x"},
+                                           {1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(input);
+
+  auto const targets = cudf::test::strings_column_wrapper(
+    {"lo he", "", "hhh", "cop", "help", "xyz"}, {1, 0, 1, 1, 1, 1});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({3, -1, -1, 4, -1, -1},
+                                                                   {1, 0, 0, 1, 1, 1});
+  auto results = cudf::strings::find(strings_view, cudf::strings_column_view(targets));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsFindTest, FindLongStrings)
+{
+  cudf::test::strings_column_wrapper input(
+    {"Héllo, there world and goodbye",
+     "quick brown fox jumped over the lazy brown dog; the fat cats jump in place without moving",
+     "the following code snippet demonstrates how to use search for values in an ordered range",
+     "it returns the last position where value could be inserted without violating the ordering",
+     "algorithms execution is parallelized as determined by an execution policy. t",
+     "he this is a continuation of previous row to make sure string boundaries are honored",
+     ""});
+  auto view    = cudf::strings_column_view(input);
+  auto results = cudf::strings::find(view, cudf::string_scalar("the"));
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>({7, 28, 0, 11, -1, -1, -1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  auto targets =
+    cudf::test::strings_column_wrapper({"the", "the", "the", "the", "the", "the", "the"});
+  results = cudf::strings::find(view, cudf::strings_column_view(targets));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results  = cudf::strings::rfind(view, cudf::string_scalar("the"));
+  expected = cudf::test::fixed_width_column_wrapper<cudf::size_type>({7, 48, 0, 77, -1, -1, -1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  targets  = cudf::test::strings_column_wrapper({"there", "cat", "the", "", "the", "are", "dog"});
+  results  = cudf::strings::find(view, cudf::strings_column_view(targets));
+  expected = cudf::test::fixed_width_column_wrapper<cudf::size_type>({7, 56, 0, 0, -1, 73, -1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsFindTest, Contains)
+{
+  cudf::test::strings_column_wrapper strings({"Héllo", "thesé", "", "lease", "tést strings", ""},
+                                             {1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 1, 0, 0}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::contains(strings_view, cudf::string_scalar("e"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper targets({"Hello", "é", "e", "x", "", ""},
+                                               {1, 1, 1, 1, 1, 0});
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 1, 0}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::contains(strings_view, cudf::strings_column_view(targets));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsFindTest, ContainsLongStrings)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"Héllo, there world and goodbye",
+     "quick brown fox jumped over the lazy brown dog; the fat cats jump in place without moving",
+     "the following code snippet demonstrates how to use search for values in an ordered range",
+     "it returns the last position where value could be inserted without violating the ordering",
+     "algorithms execution is parallelized as determined by an execution policy. t",
+     "he this is a continuation of previous row to make sure string boundaries are honored",
+     ""});
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::contains(strings_view, cudf::string_scalar("e"));
+  cudf::test::fixed_width_column_wrapper<bool> expected({1, 1, 1, 1, 1, 1, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results = cudf::strings::contains(strings_view, cudf::string_scalar(" the "));
+  cudf::test::fixed_width_column_wrapper<bool> expected2({0, 1, 0, 1, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected2);
+}
+
+TEST_F(StringsFindTest, StartsWith)
+{
+  cudf::test::strings_column_wrapper strings({"Héllo", "thesé", "", "lease", "tést strings", ""},
+                                             {1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 1, 0}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::starts_with(strings_view, cudf::string_scalar("t"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_targets{"éa", "th", "e", "ll", "tést strings", ""};
+    cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+
+    auto targets_view = cudf::strings_column_view(targets);
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 1, 1}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::starts_with(strings_view, targets_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 0, 0}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::starts_with(strings_view, cudf::string_scalar("thesé"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_targets{"éa", "th", "e", "ll", nullptr, ""};
+    cudf::test::strings_column_wrapper targets(
+      h_targets.begin(),
+      h_targets.end(),
+      thrust::make_transform_iterator(h_targets.begin(), [](auto str) { return str != nullptr; }));
+
+    auto targets_view = cudf::strings_column_view(targets);
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 0, 1}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::starts_with(strings_view, targets_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsFindTest, EndsWith)
+{
+  cudf::test::strings_column_wrapper strings({"Héllo", "thesé", "", "lease", "tést strings", ""},
+                                             {1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 0, 0, 1, 0, 0}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::ends_with(strings_view, cudf::string_scalar("se"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_targets{"éa", "sé", "th", "ll", "tést strings", ""};
+    cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+
+    auto targets_view = cudf::strings_column_view(targets);
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 1, 1}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::ends_with(strings_view, targets_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 0, 0}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::ends_with(strings_view, cudf::string_scalar("thesé"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<char const*> h_targets{"éa", "sé", "th", nullptr, "tést strings", ""};
+    cudf::test::strings_column_wrapper targets(
+      h_targets.begin(),
+      h_targets.end(),
+      thrust::make_transform_iterator(h_targets.begin(), [](auto str) { return str != nullptr; }));
+
+    auto targets_view = cudf::strings_column_view(targets);
+    cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 0, 1, 1}, {1, 1, 0, 1, 1, 1});
+    auto results = cudf::strings::ends_with(strings_view, targets_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsFindTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto strings_view                   = cudf::strings_column_view(zero_size_strings_column);
+  auto results = cudf::strings::find(strings_view, cudf::string_scalar("é"));
+  EXPECT_EQ(results->size(), 0);
+  results = cudf::strings::rfind(strings_view, cudf::string_scalar("é"));
+  EXPECT_EQ(results->size(), 0);
+  results = cudf::strings::contains(strings_view, cudf::string_scalar("é"));
+  EXPECT_EQ(results->size(), 0);
+  results = cudf::strings::starts_with(strings_view, cudf::string_scalar("é"));
+  EXPECT_EQ(results->size(), 0);
+  results = cudf::strings::ends_with(strings_view, cudf::string_scalar("é"));
+  EXPECT_EQ(results->size(), 0);
+  results = cudf::strings::starts_with(strings_view, strings_view);
+  EXPECT_EQ(results->size(), 0);
+  results = cudf::strings::ends_with(strings_view, strings_view);
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(StringsFindTest, EmptyTarget)
+{
+  cudf::test::strings_column_wrapper strings({"Héllo", "thesé", "", "lease", "tést strings", ""},
+                                             {1, 1, 0, 1, 1, 1});
+  auto strings_view = cudf::strings_column_view(strings);
+
+  cudf::test::fixed_width_column_wrapper<bool> expected({1, 1, 1, 1, 1, 1}, {1, 1, 0, 1, 1, 1});
+  auto results = cudf::strings::contains(strings_view, cudf::string_scalar(""));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::starts_with(strings_view, cudf::string_scalar(""));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::ends_with(strings_view, cudf::string_scalar(""));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_find({0, 0, 0, 0, 0, 0},
+                                                                        {1, 1, 0, 1, 1, 1});
+  results = cudf::strings::find(strings_view, cudf::string_scalar(""));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_find);
+  auto expected_rfind = cudf::strings::count_characters(strings_view);
+  results             = cudf::strings::rfind(strings_view, cudf::string_scalar(""));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, *expected_rfind);
+}
+
+TEST_F(StringsFindTest, AllEmpty)
+{
+  std::vector<std::string> h_strings{"", "", "", "", ""};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+
+  std::vector<cudf::size_type> h_expected32(h_strings.size(), -1);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected32(h_expected32.begin(),
+                                                                     h_expected32.end());
+
+  std::vector<bool> h_expected8(h_strings.size(), 0);
+  cudf::test::fixed_width_column_wrapper<bool> expected8(h_expected8.begin(), h_expected8.end());
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::find(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected32);
+  results = cudf::strings::rfind(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected32);
+  results = cudf::strings::contains(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  results = cudf::strings::starts_with(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  results = cudf::strings::ends_with(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  std::vector<std::string> h_targets{"abc", "e", "fdg", "g", "p"};
+  cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+  auto targets_view = cudf::strings_column_view(targets);
+  results           = cudf::strings::starts_with(strings_view, targets_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  results = cudf::strings::ends_with(strings_view, targets_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+}
+
+TEST_F(StringsFindTest, AllNull)
+{
+  std::vector<char const*> h_strings{nullptr, nullptr, nullptr, nullptr};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<cudf::size_type> h_expected32(h_strings.size(), -1);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected32(
+    h_expected32.begin(),
+    h_expected32.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<bool> h_expected8(h_strings.size(), -1);
+  cudf::test::fixed_width_column_wrapper<bool> expected8(
+    h_expected8.begin(),
+    h_expected8.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::find(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected32);
+  results = cudf::strings::rfind(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected32);
+  results = cudf::strings::contains(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  results = cudf::strings::starts_with(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  results = cudf::strings::ends_with(strings_view, cudf::string_scalar("e"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  std::vector<std::string> h_targets{"abc", "e", "fdg", "p"};
+  cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+  auto targets_view = cudf::strings_column_view(targets);
+  results           = cudf::strings::starts_with(strings_view, targets_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+  results = cudf::strings::ends_with(strings_view, targets_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected8);
+}
+
+TEST_F(StringsFindTest, ErrorCheck)
+{
+  cudf::test::strings_column_wrapper strings({"1", "2", "3", "4", "5", "6"});
+  auto strings_view = cudf::strings_column_view(strings);
+  cudf::test::strings_column_wrapper targets({"1", "2", "3", "4", "5"});
+  auto targets_view = cudf::strings_column_view(targets);
+
+  EXPECT_THROW(cudf::strings::contains(strings_view, targets_view), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::starts_with(strings_view, targets_view), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::ends_with(strings_view, targets_view), cudf::logic_error);
+
+  EXPECT_THROW(cudf::strings::find(strings_view, cudf::string_scalar(""), 2, 1), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::rfind(strings_view, cudf::string_scalar(""), 2, 1),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::strings::find(strings_view, targets_view), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::find(strings_view, strings_view, -1), cudf::logic_error);
+}
+
+class FindParmsTest : public StringsFindTest,
+                      public testing::WithParamInterface<cudf::size_type> {};
+
+TEST_P(FindParmsTest, Find)
+{
+  std::vector<std::string> h_strings{"hello", "", "these", "are stl", "safe"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  cudf::size_type position = GetParam();
+
+  auto strings_view = cudf::strings_column_view(strings);
+  {
+    auto results = cudf::strings::find(strings_view, cudf::string_scalar("e"), position);
+    std::vector<cudf::size_type> h_expected;
+    for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr)
+      h_expected.push_back(static_cast<cudf::size_type>((*itr).find("e", position)));
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(h_expected.begin(),
+                                                                     h_expected.end());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::rfind(strings_view, cudf::string_scalar("e"), 0, position + 1);
+    std::vector<cudf::size_type> h_expected;
+    for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr)
+      h_expected.push_back(static_cast<cudf::size_type>((*itr).rfind("e", position)));
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(h_expected.begin(),
+                                                                     h_expected.end());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto begin   = static_cast<cudf::size_type>(position);
+    auto results = cudf::strings::find(strings_view, cudf::string_scalar(""), begin);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(
+      {begin, (begin > 0 ? -1 : 0), begin, begin, begin});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+    auto end = static_cast<cudf::size_type>(position + 1);
+    results  = cudf::strings::rfind(strings_view, cudf::string_scalar(""), 0, end);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> rexpected({end, 0, end, end, end});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, rexpected);
+  }
+  {
+    std::vector<std::string> h_targets({"l", "", "", "l", "s"});
+    std::vector<cudf::size_type> h_expected;
+    for (std::size_t i = 0; i < h_strings.size(); ++i)
+      h_expected.push_back(static_cast<cudf::size_type>(h_strings[i].find(h_targets[i], position)));
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(h_expected.begin(),
+                                                                     h_expected.end());
+    cudf::test::strings_column_wrapper targets(h_targets.begin(), h_targets.end());
+    auto results = cudf::strings::find(strings_view, cudf::strings_column_view(targets), position);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+INSTANTIATE_TEST_CASE_P(StringsFindTest,
+                        FindParmsTest,
+                        testing::ValuesIn(std::array<cudf::size_type, 4>{0, 1, 2, 3}));
diff --git a/cpp/tests/strings/findall_tests.cpp b/cpp/tests/strings/findall_tests.cpp
new file mode 100644
index 0000000..fe27bee
--- /dev/null
+++ b/cpp/tests/strings/findall_tests.cpp
@@ -0,0 +1,122 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/strings/findall.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsFindallTests : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFindallTests, FindallTest)
+{
+  bool valids[] = {1, 1, 1, 1, 1, 0, 1, 1};
+  cudf::test::strings_column_wrapper input(
+    {"3-A", "4-May 5-Day 6-Hay", "12-Dec-2021-Jan", "Feb-March", "4 ABC", "", "", "25-9000-Hal"},
+    valids);
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(\\d+)-(\\w+)");
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"3-A"},
+                LCW{"4-May", "5-Day", "6-Hay"},
+                LCW{"12-Dec", "2021-Jan"},
+                LCW{},
+                LCW{},
+                LCW{},
+                LCW{},
+                LCW{"25-9000"}},
+               valids);
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::findall(sv, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsFindallTests, Multiline)
+{
+  cudf::test::strings_column_wrapper input({"abc\nfff\nabc", "fff\nabc\nlll", "abc", "", "abc\n"});
+  auto view = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(^abc$)");
+  using LCW    = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"abc", "abc"}, LCW{"abc"}, LCW{"abc"}, LCW{}, LCW{"abc"}});
+  auto prog = cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::MULTILINE);
+  auto results = cudf::strings::findall(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsFindallTests, DotAll)
+{
+  cudf::test::strings_column_wrapper input({"abc\nfa\nef", "fff\nabbc\nfff", "abcdéf", ""});
+  auto view = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(b.*f)");
+  using LCW    = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"bc\nfa\nef"}, LCW{"bbc\nfff"}, LCW{"bcdéf"}, LCW{}});
+  auto prog    = cudf::strings::regex_program::create(pattern, cudf::strings::regex_flags::DOTALL);
+  auto results = cudf::strings::findall(view, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsFindallTests, MediumRegex)
+{
+  // This results in 15 regex instructions and falls in the 'medium' range.
+  std::string medium_regex = "(\\w+) (\\w+) (\\d+)";
+  auto prog                = cudf::strings::regex_program::create(medium_regex);
+
+  cudf::test::strings_column_wrapper input({"first words 1234 and just numbers 9876", "neither"});
+  auto strings_view = cudf::strings_column_view(input);
+  auto results      = cudf::strings::findall(strings_view, *prog);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"first words 1234", "just numbers 9876"}, LCW{}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsFindallTests, LargeRegex)
+{
+  // This results in 115 regex instructions and falls in the 'large' range.
+  std::string large_regex =
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz";
+  auto prog = cudf::strings::regex_program::create(large_regex);
+
+  cudf::test::strings_column_wrapper input(
+    {"hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+     "http://www.world.com I'm here @home zzzz",
+     "12345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012"
+     "34"
+     "5678901234567890",
+     "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmn"
+     "op"
+     "qrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz"});
+
+  auto strings_view = cudf::strings_column_view(input);
+  auto results      = cudf::strings::findall(strings_view, *prog);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{large_regex.c_str()}, LCW{}, LCW{}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
diff --git a/cpp/tests/strings/fixed_point_tests.cpp b/cpp/tests/strings/fixed_point_tests.cpp
new file mode 100644
index 0000000..0a1c004
--- /dev/null
+++ b/cpp/tests/strings/fixed_point_tests.cpp
@@ -0,0 +1,345 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/strings/convert/convert_fixed_point.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <limits>
+
+struct StringsConvertTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+class StringsFixedPointConvertTest : public StringsConvertTest {};
+
+TYPED_TEST_SUITE(StringsFixedPointConvertTest, cudf::test::FixedPointTypes);
+
+TYPED_TEST(StringsFixedPointConvertTest, ToFixedPoint)
+{
+  using DecimalType = TypeParam;
+  using RepType     = cudf::device_storage_type_t<DecimalType>;
+  using fp_wrapper  = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  cudf::test::strings_column_wrapper strings(
+    {"1.234E3", "-876", "543.2", "-0.12", ".25", "-2E-3", "-.0027", "", "-0.0"});
+  auto results = cudf::strings::to_fixed_point(
+    cudf::strings_column_view(strings),
+    cudf::data_type{cudf::type_to_id<DecimalType>(), numeric::scale_type{-3}});
+  auto const expected =
+    fp_wrapper{{1234000, -876000, 543200, -120, 250, -2, -2, 0, 0}, numeric::scale_type{-3}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results = cudf::strings::to_fixed_point(
+    cudf::strings_column_view(strings),
+    cudf::data_type{cudf::type_to_id<DecimalType>(), numeric::scale_type{2}});
+  auto const expected_scaled = fp_wrapper{{12, -8, 5, 0, 0, 0, 0, 0, 0}, numeric::scale_type{2}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_scaled);
+
+  cudf::test::strings_column_wrapper strings_nulls(
+    {"1234", "-876", "543", "900000", "25E5", "", ""}, {1, 1, 1, 1, 1, 1, 0});
+  results = cudf::strings::to_fixed_point(cudf::strings_column_view(strings_nulls),
+                                          cudf::data_type{cudf::type_to_id<DecimalType>()});
+  auto const expected_nulls = fp_wrapper{
+    {1234, -876, 543, 900000, 2500000, 0, 0}, {1, 1, 1, 1, 1, 1, 0}, numeric::scale_type{0}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_nulls);
+}
+
+TYPED_TEST(StringsFixedPointConvertTest, ToFixedPointVeryLarge)
+{
+  using DecimalType  = TypeParam;
+  using RepType      = cudf::device_storage_type_t<DecimalType>;
+  using fp_wrapper   = cudf::test::fixed_point_column_wrapper<RepType>;
+  auto const strings = cudf::test::strings_column_wrapper({"1234000000000000000000",
+                                                           "-876000000000000000000",
+                                                           "5432e+17",
+                                                           "-12E016",
+                                                           "250000000000000000",
+                                                           "-2800000000000000",
+                                                           "",
+                                                           "-0.0"});
+  auto const results = cudf::strings::to_fixed_point(
+    cudf::strings_column_view(strings),
+    cudf::data_type{cudf::type_to_id<DecimalType>(), numeric::scale_type{20}});
+  auto const expected = fp_wrapper{{12, -8, 5, 0, 0, 0, 0, 0}, numeric::scale_type{20}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsConvertTest, ToFixedPointDecimal128)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const strings = cudf::test::strings_column_wrapper(
+    {"1234000000000000000000",
+     "-876000000000000000000",
+     "5432e+17",
+     "-12E016",
+     "250000000000000000",
+     "-2800000000000000",
+     "",
+     "-0.0",
+     "170141183460469231731687303715884105727",
+     "17014118346046923173168730371588410572700000000000000000000"});
+
+  auto const scale    = scale_type{20};
+  auto const type     = cudf::data_type{cudf::type_to_id<decimal128>(), scale};
+  auto const results  = cudf::strings::to_fixed_point(cudf::strings_column_view(strings), type);
+  auto const max      = cuda::std::numeric_limits<__int128_t>::max();
+  auto const expected = fp_wrapper{{12, -8, 5, 0, 0, 0, 0, 0, 1701411834604692317, max}, scale};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsConvertTest, ToFixedPointLargeScale)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const strings = cudf::test::strings_column_wrapper({"0.05", "0.06", "0.50", "5.01"});
+
+  auto const scale   = scale_type{-25};
+  auto const type    = cudf::data_type{cudf::type_to_id<decimal128>(), scale};
+  auto const results = cudf::strings::to_fixed_point(cudf::strings_column_view(strings), type);
+
+  auto const expected = fp_wrapper{{5, 6, 50, 501}, scale_type{-2}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsConvertTest, FromFixedPointDecimal128)
+{
+  using namespace numeric;
+  using RepType    = cudf::device_storage_type_t<decimal128>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto constexpr max = cuda::std::numeric_limits<__int128_t>::max();
+
+  {
+    auto const input = fp_wrapper{{110, max}, numeric::scale_type{-2}};
+    auto results     = cudf::strings::from_fixed_point(input);
+    auto const expected =
+      cudf::test::strings_column_wrapper({"1.10", "1701411834604692317316873037158841057.27"});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+
+  {
+    auto const input = fp_wrapper{{max}, numeric::scale_type{-38}};
+    auto results     = cudf::strings::from_fixed_point(input);
+    auto const expected =
+      cudf::test::strings_column_wrapper({"1.70141183460469231731687303715884105727"});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+
+  {
+    auto const input = fp_wrapper({110, max}, numeric::scale_type{2});
+    auto results     = cudf::strings::from_fixed_point(input);
+    auto const expected =
+      cudf::test::strings_column_wrapper({"11000", "17014118346046923173168730371588410572700"});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+
+  {
+    auto const input    = fp_wrapper({-222}, numeric::scale_type{0});
+    auto results        = cudf::strings::from_fixed_point(input);
+    auto const expected = cudf::test::strings_column_wrapper({"-222"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  }
+}
+
+TYPED_TEST(StringsFixedPointConvertTest, ToFixedPointVerySmall)
+{
+  using DecimalType  = TypeParam;
+  using RepType      = cudf::device_storage_type_t<DecimalType>;
+  using fp_wrapper   = cudf::test::fixed_point_column_wrapper<RepType>;
+  auto const strings = cudf::test::strings_column_wrapper({"0.00000000000000001234",
+                                                           "-0.0000000000000000876",
+                                                           "543.2e-20",
+                                                           "-12E-18",
+                                                           "+.000000000000000025",
+                                                           "-.00000000002147483647",
+                                                           "",
+                                                           "+0.0"});
+  auto const results = cudf::strings::to_fixed_point(
+    cudf::strings_column_view(strings),
+    cudf::data_type{cudf::type_to_id<DecimalType>(), numeric::scale_type{-20}});
+  auto const expected =
+    fp_wrapper{{1234, -8760, 543, -1200, 2500, -2147483647, 0, 0}, numeric::scale_type{-20}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TYPED_TEST(StringsFixedPointConvertTest, FromFixedPoint)
+{
+  using DecimalType = TypeParam;
+  using RepType     = cudf::device_storage_type_t<DecimalType>;
+  using fp_wrapper  = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const negative_scale = fp_wrapper{{110, 222, 3330, 4444, -550, -6}, numeric::scale_type{-2}};
+  auto results              = cudf::strings::from_fixed_point(negative_scale);
+  cudf::test::strings_column_wrapper negative_expected(
+    {"1.10", "2.22", "33.30", "44.44", "-5.50", "-0.06"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, negative_expected);
+
+  auto const positive_scale =
+    fp_wrapper({110, -222, 3330, 4, -550, 0}, {1, 1, 1, 1, 1, 0}, numeric::scale_type{2});
+  results = cudf::strings::from_fixed_point(positive_scale);
+  cudf::test::strings_column_wrapper positive_expected(
+    {"11000", "-22200", "333000", "400", "-55000", ""}, {1, 1, 1, 1, 1, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, positive_expected);
+
+  auto const zero_scale =
+    fp_wrapper({0, -222, 3330, 4, -550, 0}, {0, 1, 1, 1, 1, 1}, numeric::scale_type{0});
+  results = cudf::strings::from_fixed_point(zero_scale);
+  cudf::test::strings_column_wrapper zero_expected({"", "-222", "3330", "4", "-550", "0"},
+                                                   {0, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, zero_expected);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeStringsColumnFixedPoint)
+{
+  auto zero_size_column = cudf::make_empty_column(cudf::data_type{cudf::type_id::DECIMAL32});
+
+  auto results = cudf::strings::from_fixed_point(zero_size_column->view());
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsConvertTest, ZeroSizeFixedPointColumn)
+{
+  auto zero_size_column = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+
+  auto results = cudf::strings::to_fixed_point(zero_size_column->view(),
+                                               cudf::data_type{cudf::type_id::DECIMAL32});
+  EXPECT_EQ(0, results->size());
+  results = cudf::strings::is_fixed_point(zero_size_column->view());
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsConvertTest, FromToFixedPointError)
+{
+  auto dtype  = cudf::data_type{cudf::type_id::INT32};
+  auto column = cudf::make_numeric_column(dtype, 100);
+  EXPECT_THROW(cudf::strings::from_fixed_point(column->view()), cudf::logic_error);
+
+  cudf::test::strings_column_wrapper strings{"this string intentionally left blank"};
+  cudf::strings_column_view strings_view(strings);
+  EXPECT_THROW(cudf::strings::to_fixed_point(strings_view, dtype), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::is_fixed_point(strings_view, dtype), cudf::logic_error);
+}
+
+TEST_F(StringsConvertTest, IsFixedPoint)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"1234", "+876", "543.2", "-00.120", "1E34", "1.0.02", "", "-0.0"});
+  auto results        = cudf::strings::is_fixed_point(cudf::strings_column_view(strings));
+  auto const expected = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, true, true, true, false, false, true});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results = cudf::strings::is_fixed_point(
+    cudf::strings_column_view(strings),
+    cudf::data_type{cudf::type_id::DECIMAL32, numeric::scale_type{-1}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results = cudf::strings::is_fixed_point(
+    cudf::strings_column_view(strings),
+    cudf::data_type{cudf::type_id::DECIMAL32, numeric::scale_type{1}});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  cudf::test::strings_column_wrapper big_numbers({"2147483647",
+                                                  "-2147483647",
+                                                  "2147483648",
+                                                  "9223372036854775807",
+                                                  "-9223372036854775807",
+                                                  "9223372036854775808",
+                                                  "9223372036854775808000",
+                                                  "100E2147483648",
+                                                  "170141183460469231731687303715884105727",
+                                                  "170141183460469231731687303715884105728"});
+  results               = cudf::strings::is_fixed_point(cudf::strings_column_view(big_numbers),
+                                          cudf::data_type{cudf::type_id::DECIMAL32});
+  auto const expected32 = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, false, false, false, false, false, false, false, false});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected32);
+
+  results               = cudf::strings::is_fixed_point(cudf::strings_column_view(big_numbers),
+                                          cudf::data_type{cudf::type_id::DECIMAL64});
+  auto const expected64 = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, true, true, true, false, false, false, false, false});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected64);
+
+  results                = cudf::strings::is_fixed_point(cudf::strings_column_view(big_numbers),
+                                          cudf::data_type{cudf::type_id::DECIMAL128});
+  auto const expected128 = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, true, true, true, true, true, false, true, false});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected128);
+
+  results = cudf::strings::is_fixed_point(
+    cudf::strings_column_view(big_numbers),
+    cudf::data_type{cudf::type_id::DECIMAL32, numeric::scale_type{10}});
+  auto const expected32_scaled = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, true, true, true, true, false, false, false, false});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected32_scaled);
+
+  results = cudf::strings::is_fixed_point(
+    cudf::strings_column_view(big_numbers),
+    cudf::data_type{cudf::type_id::DECIMAL64, numeric::scale_type{10}});
+  auto const expected64_scaled_positive = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, true, true, true, true, true, false, false, false});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected64_scaled_positive);
+
+  results = cudf::strings::is_fixed_point(
+    cudf::strings_column_view(big_numbers),
+    cudf::data_type{cudf::type_id::DECIMAL64, numeric::scale_type{-5}});
+  auto const expected64_scaled = cudf::test::fixed_width_column_wrapper<bool>(
+    {true, true, true, false, false, false, false, false, false, false});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected64_scaled);
+}
+
+#ifdef NDEBUG
+TEST_F(StringsConvertTest, FixedPointStringConversionOperator)
+#else
+TEST_F(StringsConvertTest, DISABLED_FixedPointStringConversionOperator)
+#endif
+{
+  auto const max = cuda::std::numeric_limits<__int128_t>::max();
+
+  auto const x = numeric::decimal128{max, numeric::scale_type{-10}};
+  EXPECT_EQ(static_cast<std::string>(x), "17014118346046923173168730371.5884105727");
+
+  auto const y = numeric::decimal128{max, numeric::scale_type{10}};
+  EXPECT_EQ(static_cast<std::string>(y), "170141183460469231731687303710000000000");
+
+  auto const z = numeric::decimal128{numeric::scaled_integer{max, numeric::scale_type{10}}};
+  EXPECT_EQ(static_cast<std::string>(z), "1701411834604692317316873037158841057270000000000");
+
+  auto const a = numeric::decimal128{numeric::scaled_integer{max, numeric::scale_type{40}}};
+  EXPECT_EQ(static_cast<std::string>(a),
+            "1701411834604692317316873037158841057270000000000000000000000000000000000000000");
+
+  auto const b = numeric::decimal128{numeric::scaled_integer{max, numeric::scale_type{-20}}};
+  EXPECT_EQ(static_cast<std::string>(b), "1701411834604692317.31687303715884105727");
+
+  auto const c = numeric::decimal128{numeric::scaled_integer{max, numeric::scale_type{-38}}};
+  EXPECT_EQ(static_cast<std::string>(c), "1.70141183460469231731687303715884105727");
+}
diff --git a/cpp/tests/strings/floats_tests.cpp b/cpp/tests/strings/floats_tests.cpp
new file mode 100644
index 0000000..f668c38
--- /dev/null
+++ b/cpp/tests/strings/floats_tests.cpp
@@ -0,0 +1,209 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/convert/convert_floats.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::ALL_ERRORS};
+
+struct StringsConvertTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConvertTest, IsFloat)
+{
+  cudf::test::strings_column_wrapper strings;
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::is_float(strings_view);
+  EXPECT_EQ(cudf::type_id::BOOL8, results->view().type().id());
+  EXPECT_EQ(0, results->view().size());
+
+  cudf::test::strings_column_wrapper strings1({"+175",
+                                               "-9.8",
+                                               "7+2",
+                                               "+-4",
+                                               "6.7e17",
+                                               "-1.2e-5",
+                                               "e",
+                                               ".e",
+                                               "1.e+-2",
+                                               "00.00",
+                                               "1.0e+1.0",
+                                               "1.2.3",
+                                               "+",
+                                               "--",
+                                               ""});
+  results = cudf::strings::is_float(cudf::strings_column_view(strings1));
+  cudf::test::fixed_width_column_wrapper<bool> expected1(
+    {1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected1);
+
+  cudf::test::strings_column_wrapper strings2(
+    {"-34", "9.8", "1234567890", "-917.2e5", "INF", "NAN", "-Inf", "INFINITY"});
+  results = cudf::strings::is_float(cudf::strings_column_view(strings2));
+  cudf::test::fixed_width_column_wrapper<bool> expected2({1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected2);
+}
+
+TEST_F(StringsConvertTest, ToFloats32)
+{
+  std::vector<char const*> h_strings{
+    "1234",    nullptr,        "-876",     "543.2",
+    "-0.12",   ".25",          "-.002",    "",
+    "-0.0",    "1.2e4",        "NAN",      "abc123",
+    "123abc",  "456e",         "-1.78e+5", "-122.33644782123456789",
+    "12e+309", "3.4028236E38", "INF",      "Infinity"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<float> h_expected;
+  std::for_each(h_strings.begin(), h_strings.end(), [&](char const* str) {
+    h_expected.push_back(str ? std::atof(str) : 0);
+  });
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results = cudf::strings::to_floats(strings_view, cudf::data_type{cudf::type_id::FLOAT32});
+
+  cudf::test::fixed_width_column_wrapper<float> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected, verbosity);
+}
+
+TEST_F(StringsConvertTest, FromFloats32)
+{
+  std::vector<float> h_floats{100,
+                              654321.25,
+                              -12761.125,
+                              0,
+                              5,
+                              -4,
+                              std::numeric_limits<float>::quiet_NaN(),
+                              839542223232.79,
+                              -0.0};
+  std::vector<char const*> h_expected{
+    "100.0", "654321.25", "-12761.125", "0.0", "5.0", "-4.0", "NaN", "8.395422433e+11", "-0.0"};
+
+  cudf::test::fixed_width_column_wrapper<float> floats(
+    h_floats.begin(),
+    h_floats.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = cudf::strings::from_floats(floats);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected, verbosity);
+}
+
+TEST_F(StringsConvertTest, ToFloats64)
+{
+  // clang-format off
+  std::vector<const char*> h_strings{
+    "1234",   nullptr,    "-876",     "543.2",         "-0.12",   ".25",
+    "-.002",  "",         "-0.0",     "1.28e256",      "NaN",     "abc123",
+    "123abc", "456e",     "-1.78e+5", "-122.33644782", "12e+309", "1.7976931348623159E308",
+    "-Inf",   "-INFINITY", "1.0",     "1.7976931348623157e+308",  "1.7976931348623157e-307",
+    // subnormal numbers:           v--- smallest double               v--- result is 0
+    "4e-308", "3.3333333333e-320", "4.940656458412465441765688e-324", "1.e-324" };
+  // clang-format on
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<double> h_expected;
+  std::for_each(h_strings.begin(), h_strings.end(), [&](char const* str) {
+    h_expected.push_back(str ? std::atof(str) : 0);
+  });
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results = cudf::strings::to_floats(strings_view, cudf::data_type{cudf::type_id::FLOAT64});
+
+  cudf::test::fixed_width_column_wrapper<double> expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected, verbosity);
+}
+
+TEST_F(StringsConvertTest, FromFloats64)
+{
+  std::vector<double> h_floats{100,
+                               654321.25,
+                               -12761.125,
+                               0,
+                               5,
+                               -4,
+                               std::numeric_limits<double>::quiet_NaN(),
+                               839542223232.794248339,
+                               -0.0};
+  std::vector<char const*> h_expected{
+    "100.0", "654321.25", "-12761.125", "0.0", "5.0", "-4.0", "NaN", "8.395422232e+11", "-0.0"};
+
+  cudf::test::fixed_width_column_wrapper<double> floats(
+    h_floats.begin(),
+    h_floats.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = cudf::strings::from_floats(floats);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected, verbosity);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeStringsColumnFloat)
+{
+  cudf::column_view zero_size_column(
+    cudf::data_type{cudf::type_id::FLOAT32}, 0, nullptr, nullptr, 0);
+  auto results = cudf::strings::from_floats(zero_size_column);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsConvertTest, ZeroSizeFloatsColumn)
+{
+  cudf::column_view zero_size_column(
+    cudf::data_type{cudf::type_id::STRING}, 0, nullptr, nullptr, 0);
+  auto results =
+    cudf::strings::to_floats(zero_size_column, cudf::data_type{cudf::type_id::FLOAT32});
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsConvertTest, FromToFloatsError)
+{
+  auto dtype  = cudf::data_type{cudf::type_id::INT32};
+  auto column = cudf::make_numeric_column(dtype, 100);
+  EXPECT_THROW(cudf::strings::from_floats(column->view()), cudf::logic_error);
+
+  cudf::test::strings_column_wrapper strings{"this string intentionally left blank"};
+  EXPECT_THROW(cudf::strings::to_floats(column->view(), dtype), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/format_lists_tests.cpp b/cpp/tests/strings/format_lists_tests.cpp
new file mode 100644
index 0000000..95dc972
--- /dev/null
+++ b/cpp/tests/strings/format_lists_tests.cpp
@@ -0,0 +1,164 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/convert/convert_lists.hpp>
+
+struct StringsFormatListsTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsFormatListsTest, EmptyList)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const input = STR_LISTS{};
+  auto const view  = cudf::lists_column_view(input);
+
+  auto results = cudf::strings::format_list_column(view);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsFormatListsTest, EmptyNestedList)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const input = STR_LISTS{STR_LISTS{STR_LISTS{}, STR_LISTS{}}, STR_LISTS{STR_LISTS{}}};
+  auto const view  = cudf::lists_column_view(input);
+
+  auto results  = cudf::strings::format_list_column(view);
+  auto expected = cudf::test::strings_column_wrapper({"[[],[]]", "[[]]"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsFormatListsTest, WithNulls)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const input = STR_LISTS{{STR_LISTS{{"a", "", "ccc"}, cudf::test::iterators::null_at(1)},
+                                STR_LISTS{},
+                                STR_LISTS{{"", "bb", "ddd"}, cudf::test::iterators::null_at(0)},
+                                STR_LISTS{"zzz", "xxxxx"},
+                                STR_LISTS{{"v", "", "", "w"}, cudf::test::iterators::null_at(2)}},
+                               cudf::test::iterators::null_at(1)};
+  auto const view  = cudf::lists_column_view(input);
+
+  auto results  = cudf::strings::format_list_column(view);
+  auto expected = cudf::test::strings_column_wrapper(
+    {"[a,NULL,ccc]", "NULL", "[NULL,bb,ddd]", "[zzz,xxxxx]", "[v,,NULL,w]"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsFormatListsTest, CustomParameters)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const input =
+    STR_LISTS{STR_LISTS{{STR_LISTS{{"a", "", "ccc"}, cudf::test::iterators::null_at(1)},
+                         STR_LISTS{},
+                         STR_LISTS{"ddd", "ee", "f"}},
+                        cudf::test::iterators::null_at(1)},
+              {STR_LISTS{"gg", "hhh"}, STR_LISTS{"i", "", "", "jj"}}};
+  auto const view = cudf::lists_column_view(input);
+  auto separators = cudf::test::strings_column_wrapper({": ", "{", "}"});
+
+  auto results = cudf::strings::format_list_column(
+    view, cudf::string_scalar("null"), cudf::strings_column_view(separators));
+  auto expected = cudf::test::strings_column_wrapper(
+    {"{{a: null: ccc}: null: {ddd: ee: f}}", "{{gg: hhh}: {i: : : jj}}"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsFormatListsTest, NestedList)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const input =
+    STR_LISTS{{STR_LISTS{"a", "bb", "ccc"}, STR_LISTS{}, STR_LISTS{"ddd", "ee", "f"}},
+              {STR_LISTS{"gg", "hhh"}, STR_LISTS{"i", "", "", "jj"}}};
+  auto const view = cudf::lists_column_view(input);
+
+  auto results = cudf::strings::format_list_column(view);
+  auto expected =
+    cudf::test::strings_column_wrapper({"[[a,bb,ccc],[],[ddd,ee,f]]", "[[gg,hhh],[i,,,jj]]"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsFormatListsTest, SlicedLists)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  auto const input =
+    STR_LISTS{{STR_LISTS{{"a", "", "bb"}, cudf::test::iterators::null_at(1)},
+               STR_LISTS{},
+               STR_LISTS{{"", "ccc", "dddd"}, cudf::test::iterators::null_at(0)},
+               STR_LISTS{"zzz", ""},
+               STR_LISTS{},
+               STR_LISTS{{"abcdef", "012345", "", ""}, cudf::test::iterators::null_at(2)},
+               STR_LISTS{{"", "11111", "00000"}, cudf::test::iterators::null_at(0)},
+               STR_LISTS{"hey hey", "way way"},
+               STR_LISTS{},
+               STR_LISTS{"ééé", "12345abcdef"},
+               STR_LISTS{"www", "12345"}},
+              cudf::test::iterators::nulls_at({1, 4, 8})};
+
+  // matching expected strings
+  auto const h_expected = std::vector<std::string>({"[a,NULL,bb]",
+                                                    "NULL",
+                                                    "[NULL,ccc,dddd]",
+                                                    "[zzz,]",
+                                                    "NULL",
+                                                    "[abcdef,012345,NULL,]",
+                                                    "[NULL,11111,00000]",
+                                                    "[hey hey,way way]",
+                                                    "NULL",
+                                                    "[ééé,12345abcdef]",
+                                                    "[www,12345]"});
+
+  // set of slice intervals: covers slicing the front, back, and middle
+  std::vector<std::pair<int32_t, int32_t>> index_pairs({{0, 11}, {0, 4}, {3, 8}, {5, 11}});
+  for (auto indexes : index_pairs) {
+    auto sliced   = cudf::lists_column_view(cudf::slice(input, {indexes.first, indexes.second})[0]);
+    auto results  = cudf::strings::format_list_column(sliced);
+    auto expected = cudf::test::strings_column_wrapper(h_expected.begin() + indexes.first,
+                                                       h_expected.begin() + indexes.second);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsFormatListsTest, Errors)
+{
+  using STR_LISTS = cudf::test::lists_column_wrapper<cudf::string_view>;
+
+  cudf::test::lists_column_wrapper<int32_t> invalid({1, 2, 3});
+  EXPECT_THROW(cudf::strings::format_list_column(cudf::lists_column_view(invalid)),
+               cudf::logic_error);
+
+  auto const input = STR_LISTS{STR_LISTS{}, STR_LISTS{}};
+  auto const view  = cudf::lists_column_view(input);
+  auto separators  = cudf::test::strings_column_wrapper({"{", "}"});
+
+  EXPECT_THROW(cudf::strings::format_list_column(
+                 view, cudf::string_scalar(""), cudf::strings_column_view(separators)),
+               cudf::logic_error);
+
+  EXPECT_THROW(cudf::strings::format_list_column(view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/strings/integers_tests.cpp b/cpp/tests/strings/integers_tests.cpp
new file mode 100644
index 0000000..59805f9
--- /dev/null
+++ b/cpp/tests/strings/integers_tests.cpp
@@ -0,0 +1,458 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/strings/convert/convert_integers.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <string>
+#include <vector>
+
+// Using an alias variable for the null elements
+// This will make the code looks cleaner
+constexpr auto NULL_VAL = 0;
+
+struct StringsConvertTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConvertTest, IsIntegerBasicCheck)
+{
+  cudf::test::strings_column_wrapper strings1(
+    {"+175", "-34", "9.8", "17+2", "+-14", "1234567890", "67de", "", "1e10", "-", "++", ""});
+  auto results = cudf::strings::is_integer(cudf::strings_column_view(strings1));
+  cudf::test::fixed_width_column_wrapper<bool> expected1({1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected1);
+
+  cudf::test::strings_column_wrapper strings2(
+    {"0", "+0", "-0", "1234567890", "-27341132", "+012", "023", "-045"});
+  results = cudf::strings::is_integer(cudf::strings_column_view(strings2));
+  cudf::test::fixed_width_column_wrapper<bool> expected2({1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected2);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeIsIntegerBasicCheck)
+{
+  cudf::test::strings_column_wrapper strings;
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::is_integer(strings_view);
+  EXPECT_EQ(cudf::type_id::BOOL8, results->view().type().id());
+  EXPECT_EQ(0, results->view().size());
+}
+
+TEST_F(StringsConvertTest, IsIntegerBoundCheckNoNull)
+{
+  auto strings = cudf::test::strings_column_wrapper(
+    {"+175", "-34", "9.8", "17+2", "+-14", "1234567890", "67de", "", "1e10", "-", "++", ""});
+  auto results = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                           cudf::data_type{cudf::type_id::INT32});
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<bool>({1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  strings = cudf::test::strings_column_wrapper(
+    {"0", "+0", "-0", "1234567890", "-27341132", "+012", "023", "-045"});
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::INT32});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, IsIntegerBoundCheckWithNulls)
+{
+  std::vector<char const*> const h_strings{
+    "eee", "1234", nullptr, "", "-9832", "93.24", "765é", nullptr};
+  auto const strings = cudf::test::strings_column_wrapper(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto const results = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                                 cudf::data_type{cudf::type_id::INT32});
+  // Input has null elements then the output should have the same null mask
+  auto const expected = cudf::test::fixed_width_column_wrapper<bool>(
+    std::initializer_list<int8_t>{0, 1, NULL_VAL, 0, 1, 0, 0, NULL_VAL},
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeIsIntegerBoundCheck)
+{
+  // Empty input
+  auto strings = cudf::test::strings_column_wrapper{};
+  auto results = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                           cudf::data_type{cudf::type_id::INT32});
+  EXPECT_EQ(cudf::type_id::BOOL8, results->view().type().id());
+  EXPECT_EQ(0, results->view().size());
+}
+
+TEST_F(StringsConvertTest, IsIntegerBoundCheckSmallNumbers)
+{
+  auto strings = cudf::test::strings_column_wrapper(
+    {"-200", "-129", "-128", "-120", "0", "120", "127", "130", "150", "255", "300", "500"});
+  auto results = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                           cudf::data_type{cudf::type_id::INT8});
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::UINT8});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  strings = cudf::test::strings_column_wrapper(
+    {"-40000", "-32769", "-32768", "-32767", "-32766", "32765", "32766", "32767", "32768"});
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::INT16});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 1, 1, 1, 1, 1, 1, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::UINT16});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 0, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::INT32});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({1, 1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, IsIntegerBoundCheckLargeNumbers)
+{
+  auto strings =
+    cudf::test::strings_column_wrapper({"-2147483649",   // std::numeric_limits<int32_t>::min() - 1
+                                        "-2147483648",   // std::numeric_limits<int32_t>::min()
+                                        "-2147483647",   // std::numeric_limits<int32_t>::min() + 1
+                                        "2147483646",    // std::numeric_limits<int32_t>::max() - 1
+                                        "2147483647",    // std::numeric_limits<int32_t>::max()
+                                        "2147483648",    // std::numeric_limits<int32_t>::max() + 1
+                                        "4294967294",    // std::numeric_limits<uint32_t>::max() - 1
+                                        "4294967295",    // std::numeric_limits<uint32_t>::max()
+                                        "4294967296"});  // std::numeric_limits<uint32_t>::max() + 1
+  auto results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                           cudf::data_type{cudf::type_id::INT32});
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({0, 1, 1, 1, 1, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::UINT32});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 1, 1, 1, 1, 1, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  strings = cudf::test::strings_column_wrapper(
+    {"-9223372036854775809",    // std::numeric_limits<int64_t>::min() - 1
+     "-9223372036854775808",    // std::numeric_limits<int64_t>::min()
+     "-9223372036854775807",    // std::numeric_limits<int64_t>::min() + 1
+     "9223372036854775806",     // std::numeric_limits<int64_t>::max() - 1
+     "9223372036854775807",     // std::numeric_limits<int64_t>::max()
+     "9223372036854775808",     // std::numeric_limits<int64_t>::max() + 1
+     "18446744073709551614",    // std::numeric_limits<uint64_t>::max() - 1
+     "18446744073709551615",    // std::numeric_limits<uint64_t>::max()
+     "18446744073709551616"});  // std::numeric_limits<uint64_t>::max() + 1
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::INT64});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 1, 1, 1, 1, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results  = cudf::strings::is_integer(cudf::strings_column_view(strings),
+                                      cudf::data_type{cudf::type_id::UINT64});
+  expected = cudf::test::fixed_width_column_wrapper<bool>({0, 0, 0, 1, 1, 1, 1, 1, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, ToInteger)
+{
+  std::vector<char const*> h_strings{"eee",
+                                     "1234",
+                                     nullptr,
+                                     "",
+                                     "-9832",
+                                     "93.24",
+                                     "765é",
+                                     nullptr,
+                                     "-1.78e+5",
+                                     "2147483647",
+                                     "-2147483648",
+                                     "2147483648"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results            = cudf::strings::to_integers(cudf::strings_column_view(strings),
+                                            cudf::data_type{cudf::type_id::INT16});
+  auto const expected_i16 = cudf::test::fixed_width_column_wrapper<int16_t>(
+    std::initializer_list<int16_t>{0, 1234, NULL_VAL, 0, -9832, 93, 765, NULL_VAL, -1, -1, 0, 0},
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_i16);
+
+  results                 = cudf::strings::to_integers(cudf::strings_column_view(strings),
+                                       cudf::data_type{cudf::type_id::INT32});
+  auto const expected_i32 = cudf::test::fixed_width_column_wrapper<int32_t>(
+    std::initializer_list<int32_t>{
+      0, 1234, NULL_VAL, 0, -9832, 93, 765, NULL_VAL, -1, 2147483647, -2147483648, -2147483648},
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_i32);
+
+  results                 = cudf::strings::to_integers(cudf::strings_column_view(strings),
+                                       cudf::data_type{cudf::type_id::UINT32});
+  auto const expected_u32 = cudf::test::fixed_width_column_wrapper<uint32_t>(
+    std::initializer_list<uint32_t>{0,
+                                    1234,
+                                    NULL_VAL,
+                                    0,
+                                    4294957464,
+                                    93,
+                                    765,
+                                    NULL_VAL,
+                                    4294967295,
+                                    2147483647,
+                                    2147483648,
+                                    2147483648},
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_u32);
+}
+
+TEST_F(StringsConvertTest, FromInteger)
+{
+  int32_t minint = std::numeric_limits<int32_t>::min();
+  int32_t maxint = std::numeric_limits<int32_t>::max();
+  std::vector<int32_t> h_integers{100, 987654321, 0, 0, -12761, 0, 5, -4, maxint, minint};
+  std::vector<char const*> h_expected{
+    "100", "987654321", nullptr, "0", "-12761", "0", "5", "-4", "2147483647", "-2147483648"};
+
+  cudf::test::fixed_width_column_wrapper<int32_t> integers(
+    h_integers.begin(),
+    h_integers.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = cudf::strings::from_integers(integers);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_column = cudf::make_empty_column(cudf::type_id::INT32)->view();
+  auto results                = cudf::strings::from_integers(zero_size_column);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsConvertTest, ZeroSizeIntegersColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto results =
+    cudf::strings::to_integers(zero_size_strings_column, cudf::data_type{cudf::type_id::INT32});
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsConvertTest, EmptyStringsColumn)
+{
+  cudf::test::strings_column_wrapper strings({"", "", ""});
+  auto results = cudf::strings::to_integers(cudf::strings_column_view(strings),
+                                            cudf::data_type{cudf::type_id::INT64});
+  cudf::test::fixed_width_column_wrapper<int64_t> expected{0, 0, 0};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(results->view(), expected);
+}
+
+template <typename T>
+class StringsIntegerConvertTest : public StringsConvertTest {};
+
+TYPED_TEST_SUITE(StringsIntegerConvertTest, cudf::test::IntegralTypesNotBool);
+
+TYPED_TEST(StringsIntegerConvertTest, FromToInteger)
+{
+  thrust::host_vector<TypeParam> h_integers(255);
+  std::iota(h_integers.begin(), h_integers.end(), -(TypeParam)(h_integers.size() / 2));
+  h_integers.push_back(std::numeric_limits<TypeParam>::min());
+  h_integers.push_back(std::numeric_limits<TypeParam>::max());
+  auto d_integers = cudf::detail::make_device_uvector_sync(
+    h_integers, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto integers      = cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                            (cudf::size_type)d_integers.size());
+  auto integers_view = integers->mutable_view();
+  CUDF_CUDA_TRY(cudaMemcpy(integers_view.data<TypeParam>(),
+                           d_integers.data(),
+                           d_integers.size() * sizeof(TypeParam),
+                           cudaMemcpyDefault));
+  integers_view.set_null_count(0);
+
+  // convert to strings
+  auto results_strings = cudf::strings::from_integers(integers->view());
+
+  // copy back to host
+  h_integers = cudf::detail::make_host_vector_sync(d_integers, cudf::get_default_stream());
+  std::vector<std::string> h_strings;
+  for (auto itr = h_integers.begin(); itr != h_integers.end(); ++itr)
+    h_strings.push_back(std::to_string(*itr));
+
+  cudf::test::strings_column_wrapper expected(h_strings.begin(), h_strings.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results_strings, expected);
+
+  // convert back to integers
+  auto strings_view = cudf::strings_column_view(results_strings->view());
+  auto results_integers =
+    cudf::strings::to_integers(strings_view, cudf::data_type(cudf::type_to_id<TypeParam>()));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results_integers, integers->view());
+}
+
+//
+template <typename T>
+class StringsFloatConvertTest : public StringsConvertTest {};
+
+using FloatTypes = cudf::test::Types<float, double>;
+TYPED_TEST_SUITE(StringsFloatConvertTest, FloatTypes);
+
+TYPED_TEST(StringsFloatConvertTest, FromToIntegerError)
+{
+  auto dtype  = cudf::data_type{cudf::type_to_id<TypeParam>()};
+  auto column = cudf::make_numeric_column(dtype, 100);
+  EXPECT_THROW(cudf::strings::from_integers(column->view()), cudf::logic_error);
+
+  cudf::test::strings_column_wrapper strings{"this string intentionally left blank"};
+  EXPECT_THROW(cudf::strings::to_integers(column->view(), dtype), cudf::logic_error);
+}
+
+TEST_F(StringsConvertTest, HexToInteger)
+{
+  std::vector<char const*> h_strings{
+    "1234", nullptr, "98BEEF", "1a5", "CAFE", "2face", "0xAABBCCDD", "112233445566"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  {
+    std::vector<int32_t> h_expected;
+    for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr) {
+      if (*itr == nullptr)
+        h_expected.push_back(0);
+      else
+        h_expected.push_back(static_cast<int>(std::stol(std::string(*itr), 0, 16)));
+    }
+
+    auto results = cudf::strings::hex_to_integers(cudf::strings_column_view(strings),
+                                                  cudf::data_type{cudf::type_id::INT32});
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    std::vector<int64_t> h_expected;
+    for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr) {
+      if (*itr == nullptr)
+        h_expected.push_back(0);
+      else
+        h_expected.push_back(std::stol(std::string(*itr), 0, 16));
+    }
+
+    auto results = cudf::strings::hex_to_integers(cudf::strings_column_view(strings),
+                                                  cudf::data_type{cudf::type_id::INT64});
+    cudf::test::fixed_width_column_wrapper<int64_t> expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsConvertTest, IsHex)
+{
+  std::vector<char const*> h_strings{"",
+                                     "1234",
+                                     nullptr,
+                                     "98BEEF",
+                                     "1a5",
+                                     "2face",
+                                     "0xAABBCCDD",
+                                     "112233445566",
+                                     "XYZ",
+                                     "0",
+                                     "0x",
+                                     "x"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::test::fixed_width_column_wrapper<bool> expected({0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0},
+                                                        {1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  auto results = cudf::strings::is_hex(cudf::strings_column_view(strings));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TYPED_TEST(StringsIntegerConvertTest, IntegerToHex)
+{
+  std::vector<TypeParam> h_integers(255);
+  std::generate(h_integers.begin(), h_integers.end(), []() {
+    static TypeParam data = 0;
+    return data++ << (sizeof(TypeParam) - 1) * 8;
+  });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> integers(h_integers.begin(), h_integers.end());
+
+  std::vector<std::string> h_expected(255);
+  std::transform(h_integers.begin(), h_integers.end(), h_expected.begin(), [](auto v) {
+    if (v == 0) { return std::string("00"); }
+    // special handling for single-byte types
+    if constexpr (std::is_same_v<TypeParam, int8_t> || std::is_same_v<TypeParam, uint8_t>) {
+      char const hex_digits[16] = {
+        '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F'};
+      std::string str;
+      str += hex_digits[(v & 0xF0) >> 4];
+      str += hex_digits[(v & 0x0F)];
+      return str;
+    }
+    // all other types work with this
+    std::stringstream str;
+    str << std::setfill('0') << std::setw(sizeof(TypeParam) * 2) << std::hex << std::uppercase << v;
+    return str.str();
+  });
+
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+
+  auto results = cudf::strings::integers_to_hex(integers);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, IntegerToHexWithNull)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> integers(
+    {123456, -1, 0, 0, 12, 12345, 123456789, -123456789}, {1, 1, 1, 0, 1, 1, 1, 1});
+
+  cudf::test::strings_column_wrapper expected(
+    {"01E240", "FFFFFFFF", "00", "", "0C", "3039", "075BCD15", "F8A432EB"},
+    {1, 1, 1, 0, 1, 1, 1, 1});
+
+  auto results = cudf::strings::integers_to_hex(integers);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
diff --git a/cpp/tests/strings/ipv4_tests.cpp b/cpp/tests/strings/ipv4_tests.cpp
new file mode 100644
index 0000000..2b2d573
--- /dev/null
+++ b/cpp/tests/strings/ipv4_tests.cpp
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/convert/convert_ipv4.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsConvertTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConvertTest, IPv4ToIntegers)
+{
+  std::vector<char const*> h_strings{
+    nullptr, "", "hello", "41.168.0.1", "127.0.0.1", "41.197.0.1", "192.168.0.1"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.begin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::ipv4_to_integers(strings_view);
+
+  std::vector<int64_t> h_expected{0, 0, 0, 698875905, 2130706433, 700776449, 3232235521};
+  cudf::test::fixed_width_column_wrapper<int64_t> expected(
+    h_expected.cbegin(),
+    h_expected.cend(),
+    thrust::make_transform_iterator(h_strings.begin(),
+                                    [](auto const str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, IntegersToIPv4)
+{
+  std::vector<char const*> h_strings{
+    "192.168.0.1", "10.0.0.1", nullptr, "0.0.0.0", "41.186.0.1", "41.197.0.1"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.begin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  std::vector<int64_t> h_column{3232235521, 167772161, 0, 0, 700055553, 700776449};
+  cudf::test::fixed_width_column_wrapper<int64_t> column(
+    h_column.cbegin(),
+    h_column.cend(),
+    thrust::make_transform_iterator(h_strings.begin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  auto results = cudf::strings::integers_to_ipv4(column);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeStringsColumnIPV4)
+{
+  auto const zero_size_column = cudf::make_empty_column(cudf::type_id::INT64)->view();
+
+  auto results = cudf::strings::integers_to_ipv4(zero_size_column);
+  cudf::test::expect_column_empty(results->view());
+  results = cudf::strings::ipv4_to_integers(results->view());
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(StringsConvertTest, IPv4Error)
+{
+  auto column = cudf::make_numeric_column(cudf::data_type{cudf::type_id::INT32}, 100);
+  EXPECT_THROW(cudf::strings::integers_to_ipv4(column->view()), cudf::logic_error);
+}
+
+TEST_F(StringsConvertTest, IsIPv4)
+{
+  std::vector<char const*> h_strings{"",
+                                     "123.456.789.10",
+                                     nullptr,
+                                     "0.0.0.0",
+                                     ".111.211.113",
+                                     "127:0:0:1",
+                                     "255.255.255.255",
+                                     "192.168.0.",
+                                     "1...1",
+                                     "127.0.A.1",
+                                     "9.1.2.3.4",
+                                     "8.9"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::test::fixed_width_column_wrapper<bool> expected({0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0},
+                                                        {1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1});
+  auto results = cudf::strings::is_ipv4(cudf::strings_column_view(strings));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
diff --git a/cpp/tests/strings/json_tests.cpp b/cpp/tests/strings/json_tests.cpp
new file mode 100644
index 0000000..d74bb92
--- /dev/null
+++ b/cpp/tests/strings/json_tests.cpp
@@ -0,0 +1,1024 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/json.hpp>
+#include <cudf/strings/replace.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <stdexcept>
+
+// reference:  https://jsonpath.herokuapp.com/
+
+// clang-format off
+std::string json_string{
+  "{"
+    "\"store\": {""\"book\": ["
+        "{"
+          "\"category\": \"reference\","
+          "\"author\": \"Nigel Rees\","
+          "\"title\": \"Sayings of the Century\","
+          "\"price\": 8.95"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"Evelyn Waugh\","
+          "\"title\": \"Sword of Honour\","
+          "\"price\": 12.99"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"Herman Melville\","
+          "\"title\": \"Moby Dick\","
+          "\"isbn\": \"0-553-21311-3\","
+          "\"price\": 8.99"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"J. R. R. Tolkien\","
+          "\"title\": \"The Lord of the Rings\","
+          "\"isbn\": \"0-395-19395-8\","
+          "\"price\": 22.99"
+        "}"
+      "],"
+      "\"bicycle\": {"
+        "\"color\": \"red\","
+        "\"price\": 19.95"
+      "}"
+    "},"
+    "\"expensive\": 10"
+  "}"
+};
+// clang-format on
+
+std::unique_ptr<cudf::column> drop_whitespace(cudf::column_view const& col)
+{
+  cudf::test::strings_column_wrapper whitespace{"\n", "\r", "\t"};
+  cudf::test::strings_column_wrapper repl{"", "", ""};
+
+  cudf::strings_column_view strings(col);
+  cudf::strings_column_view targets(whitespace);
+  cudf::strings_column_view replacements(repl);
+  return cudf::strings::replace(strings, targets, replacements);
+}
+
+struct JsonPathTests : public cudf::test::BaseFixture {};
+
+TEST_F(JsonPathTests, GetJsonObjectRootOp)
+{
+  // root
+  cudf::test::strings_column_wrapper input{json_string};
+  std::string json_path("$");
+  auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+  auto result     = drop_whitespace(*result_raw);
+
+  auto expected = drop_whitespace(input);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+}
+
+TEST_F(JsonPathTests, GetJsonObjectChildOp)
+{
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "{"
+        "\"book\": ["
+          "{"
+            "\"category\": \"reference\","
+            "\"author\": \"Nigel Rees\","
+            "\"title\": \"Sayings of the Century\","
+            "\"price\": 8.95"
+          "},"
+          "{"
+            "\"category\": \"fiction\","
+            "\"author\": \"Evelyn Waugh\","
+            "\"title\": \"Sword of Honour\","
+            "\"price\": 12.99"
+          "},"
+          "{"
+            "\"category\": \"fiction\","
+            "\"author\": \"Herman Melville\","
+            "\"title\": \"Moby Dick\","
+            "\"isbn\": \"0-553-21311-3\","
+            "\"price\": 8.99"
+          "},"
+          "{"
+            "\"category\": \"fiction\","
+            "\"author\": \"J. R. R. Tolkien\","
+            "\"title\": \"The Lord of the Rings\","
+            "\"isbn\": \"0-395-19395-8\","
+            "\"price\": 22.99"
+          "}"
+        "],"
+        "\"bicycle\": {"
+          "\"color\": \"red\","
+          "\"price\": 19.95"
+        "}"
+      "}"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "["
+        "{"
+          "\"category\": \"reference\","
+          "\"author\": \"Nigel Rees\","
+          "\"title\": \"Sayings of the Century\","
+          "\"price\": 8.95"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"Evelyn Waugh\","
+          "\"title\": \"Sword of Honour\","
+          "\"price\": 12.99"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"Herman Melville\","
+          "\"title\": \"Moby Dick\","
+          "\"isbn\": \"0-553-21311-3\","
+          "\"price\": 8.99"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"J. R. R. Tolkien\","
+          "\"title\": \"The Lord of the Rings\","
+          "\"isbn\": \"0-395-19395-8\","
+          "\"price\": 22.99"
+        "}"
+      "]"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectWildcardOp)
+{
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.*");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "["
+        "["
+          "{"
+            "\"category\": \"reference\","
+            "\"author\": \"Nigel Rees\","
+            "\"title\": \"Sayings of the Century\","
+            "\"price\": 8.95"
+          "},"
+          "{"
+            "\"category\": \"fiction\","
+            "\"author\": \"Evelyn Waugh\","
+            "\"title\": \"Sword of Honour\","
+            "\"price\": 12.99"
+          "},"
+          "{"
+            "\"category\": \"fiction\","
+            "\"author\": \"Herman Melville\","
+            "\"title\": \"Moby Dick\","
+            "\"isbn\": \"0-553-21311-3\","
+            "\"price\": 8.99"
+          "},"
+          "{"
+            "\"category\": \"fiction\","
+            "\"author\": \"J. R. R. Tolkien\","
+            "\"title\": \"The Lord of the Rings\","
+            "\"isbn\": \"0-395-19395-8\","
+            "\"price\": 22.99"
+          "}"
+        "],"
+        "{"
+          "\"color\": \"red\","
+          "\"price\": 19.95"
+        "}"
+      "]"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("*");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "["
+        "{"
+          "\"book\": ["
+            "{"
+              "\"category\": \"reference\","
+              "\"author\": \"Nigel Rees\","
+              "\"title\": \"Sayings of the Century\","
+              "\"price\": 8.95"
+            "},"
+            "{"
+              "\"category\": \"fiction\","
+              "\"author\": \"Evelyn Waugh\","
+              "\"title\": \"Sword of Honour\","
+              "\"price\": 12.99"
+            "},"
+            "{"
+              "\"category\": \"fiction\","
+              "\"author\": \"Herman Melville\","
+              "\"title\": \"Moby Dick\","
+              "\"isbn\": \"0-553-21311-3\","
+              "\"price\": 8.99"
+            "},"
+            "{"
+              "\"category\": \"fiction\","
+              "\"author\": \"J. R. R. Tolkien\","
+              "\"title\": \"The Lord of the Rings\","
+              "\"isbn\": \"0-395-19395-8\","
+              "\"price\": 22.99"
+            "}"
+          "],"
+          "\"bicycle\": {"
+            "\"color\": \"red\","
+            "\"price\": 19.95"
+          "}"
+        "},"
+        "10"
+      "]"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectSubscriptOp)
+{
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[2]");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "{"
+        "\"category\": \"fiction\","
+        "\"author\": \"Herman Melville\","
+        "\"title\": \"Moby Dick\","
+        "\"isbn\": \"0-553-21311-3\","
+        "\"price\": 8.99"
+      "}"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store['bicycle']");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "{"
+        "\"color\": \"red\","
+        "\"price\": 19.95"
+      "}"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[*]");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected_raw{
+      "["
+        "{"
+          "\"category\": \"reference\","
+          "\"author\": \"Nigel Rees\","
+          "\"title\": \"Sayings of the Century\","
+          "\"price\": 8.95"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"Evelyn Waugh\","
+          "\"title\": \"Sword of Honour\","
+          "\"price\": 12.99"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"Herman Melville\","
+          "\"title\": \"Moby Dick\","
+          "\"isbn\": \"0-553-21311-3\","
+          "\"price\": 8.99"
+        "},"
+        "{"
+          "\"category\": \"fiction\","
+          "\"author\": \"J. R. R. Tolkien\","
+          "\"title\": \"The Lord of the Rings\","
+          "\"isbn\": \"0-395-19395-8\","
+          "\"price\": 22.99"
+        "}"
+      "]"
+    };
+    // clang-format on
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectFilter)
+{
+  // queries that result in filtering/collating results (mostly meaning - generates new
+  // json instead of just returning parts of the existing string
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[*]['isbn']");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw{R"(["0-553-21311-3","0-395-19395-8"])"};
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[*].category");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw{
+      R"(["reference","fiction","fiction","fiction"])"};
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[*].title");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw{
+      R"(["Sayings of the Century","Sword of Honour","Moby Dick","The Lord of the Rings"])"};
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book.*.price");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw{"[8.95,12.99,8.99,22.99]"};
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  {
+    // spark behavioral difference.
+    //  standard:     "fiction"
+    //  spark:        fiction
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[2].category");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw{"fiction"};
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectNullInputs)
+{
+  {
+    std::string str("{\"a\" : \"b\"}");
+    cudf::test::strings_column_wrapper input({str, str, str, str}, {1, 0, 1, 0});
+
+    std::string json_path("$.a");
+    auto result_raw = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    auto result     = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw({"b", "", "b", ""}, {1, 0, 1, 0});
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectEmptyQuery)
+{
+  // empty query -> null
+  {
+    cudf::test::strings_column_wrapper input{R"({"a" : "b"})"};
+    std::string json_path("");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {0});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectEmptyInputsAndOutputs)
+{
+  // empty string input -> null
+  {
+    cudf::test::strings_column_wrapper input{""};
+    std::string json_path("$");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {0});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  // slightly different from "empty output". in this case, we're
+  // returning something, but it happens to be empty. so we expect
+  // a valid, but empty row
+  {
+    cudf::test::strings_column_wrapper input{R"({"store": { "bicycle" : "" } })"};
+    std::string json_path("$.store.bicycle");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {1});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, GetJsonObjectEmptyInput)
+{
+  cudf::test::strings_column_wrapper input{};
+  std::string json_path("$");
+  auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, input);
+}
+
+// badly formed JSONpath strings
+TEST_F(JsonPathTests, GetJsonObjectIllegalQuery)
+{
+  // can't have more than one root operator, or a root operator anywhere other
+  // than the beginning
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("$$");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), cudf::logic_error);
+  }
+
+  // invalid index
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("$[auh46h-]");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), cudf::logic_error);
+  }
+
+  // invalid index
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("$[[]]");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), cudf::logic_error);
+  }
+
+  // negative index
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("$[-1]");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), cudf::logic_error);
+  }
+
+  // child operator with no name specified
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path(".");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), std::invalid_argument);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("][");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), std::invalid_argument);
+  }
+
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("6hw6,56i3");
+    auto query = [&]() {
+      auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+    };
+    EXPECT_THROW(query(), std::invalid_argument);
+  }
+}
+
+// queries that are legal, but reference invalid parts of the input
+TEST_F(JsonPathTests, GetJsonObjectInvalidQuery)
+{
+  // non-existent field
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("$[*].c");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {0});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  // non-existent field
+  {
+    cudf::test::strings_column_wrapper input{R"({"a": "b"})"};
+    std::string json_path("$[*].c[2]");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {0});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  // non-existent field
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book.price");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {0});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  // out of bounds index
+  {
+    cudf::test::strings_column_wrapper input{json_string};
+    std::string json_path("$.store.book[4]");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    cudf::test::strings_column_wrapper expected({""}, {0});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, MixedOutput)
+{
+  // various queries on:
+  // clang-format off
+  std::vector<std::string> input_strings {
+    "{\"a\": {\"b\" : \"c\"}}",
+
+    "{"
+      "\"a\": {\"b\" : \"c\"},"
+      "\"d\": [{\"e\":123}, {\"f\":-10}]"
+    "}",
+
+    "{"
+      "\"b\": 123"
+    "}",
+
+    "{"
+      "\"a\": [\"y\",500]"
+    "}",
+
+    "{"
+      "\"a\": \"\""
+    "}",
+
+    "{"
+      "\"a\": {"
+                "\"z\": {\"i\": 10, \"j\": 100},"
+                "\"b\": [\"c\",null,true,-1]"
+              "}"
+    "}"
+  };
+  // clang-format on
+  cudf::test::strings_column_wrapper input(input_strings.begin(), input_strings.end());
+  {
+    std::string json_path("$.a");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+      R"({"b" : "c"})",
+      R"({"b" : "c"})",
+      "",
+      "[\"y\",500]",
+      "",
+      "{"
+         "\"z\": {\"i\": 10, \"j\": 100},"
+         "\"b\": [\"c\",null,true,-1]"
+      "}"
+      },
+      {1, 1, 0, 1, 1, 1});
+    // clang-format on
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  {
+    std::string json_path("$.a[1]");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+        "",
+        "",
+        "",
+        "500",
+        "",
+        "",
+      },
+      {0, 0, 0, 1, 0, 0});
+    // clang-format on
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  {
+    std::string json_path("$.a.b");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+      "c",
+      "c",
+      "",
+      "",
+      "",
+      "[\"c\",null,true,-1]"},
+      {1, 1, 0, 0, 0, 1});
+    // clang-format on
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  {
+    std::string json_path("$.a[*]");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+      "[\"c\"]",
+      "[\"c\"]",
+      "",
+      "[\"y\",500]",
+      "[]",
+      "["
+        "{\"i\": 10, \"j\": 100},"
+        "[\"c\",null,true,-1]"
+      "]" },
+      {1, 1, 0, 1, 1, 1});
+    // clang-format on
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+
+  {
+    std::string json_path("$.a.b[*]");
+    auto result = cudf::strings::get_json_object(cudf::strings_column_view(input), json_path);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+      "[]",
+      "[]",
+      "",
+      "",
+      "",
+      "[\"c\",null,true,-1]"},
+      {1, 1, 0, 0, 0, 1});
+    // clang-format on
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, StripQuotes)
+{
+  // we normally expect our outputs here to be
+  // b     (no quotes)
+  // but with string_quotes_from_single_strings false, we expect
+  // "b"   (with quotes)
+  {
+    std::string str("{\"a\" : \"b\"}");
+    cudf::test::strings_column_wrapper input({str, str});
+
+    cudf::strings::get_json_object_options options;
+    options.set_strip_quotes_from_single_strings(false);
+
+    std::string json_path("$.a");
+    auto result_raw =
+      cudf::strings::get_json_object(cudf::strings_column_view(input), json_path, options);
+    auto result = drop_whitespace(*result_raw);
+
+    cudf::test::strings_column_wrapper expected_raw({"\"b\"", "\"b\""});
+    auto expected = drop_whitespace(expected_raw);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, *expected);
+  }
+
+  // a valid, but empty row
+  {
+    cudf::test::strings_column_wrapper input{R"({"store": { "bicycle" : "" } })"};
+    std::string json_path("$.store.bicycle");
+
+    cudf::strings::get_json_object_options options;
+    options.set_strip_quotes_from_single_strings(true);
+
+    auto result =
+      cudf::strings::get_json_object(cudf::strings_column_view(input), json_path, options);
+
+    cudf::test::strings_column_wrapper expected({""});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, AllowSingleQuotes)
+{
+  // Tests allowing single quotes for strings.
+  // Note:  this flag allows a mix of single and double quotes. it doesn't explicitly require
+  // single-quotes only.
+
+  // various queries on:
+  std::vector<std::string> input_strings{
+    // clang-format off
+    "{\'a\': {\'b\' : \'c\'}}",
+
+    "{"
+      "\'a\': {\'b\' : \"c\"},"
+      "\'d\': [{\"e\":123}, {\'f\':-10}]"
+    "}",
+
+    "{"
+      "\'b\': 123"
+    "}",
+
+    "{"
+      "\"a\": [\'y\',500]"
+    "}",
+
+    "{"
+      "\'a\': \"\""
+    "}",
+
+    "{"
+      "\"a\": {"
+                "\'z\': {\'i\': 10, \'j\': 100},"
+                "\'b\': [\'c\',null,true,-1]"
+              "}"
+    "}",
+
+    "{"
+      "\'a\': \"abc'def\""
+    "}",
+
+    "{"
+      "\'a\': \"'abc'def'\""
+    "}",
+    // clang-format on
+  };
+
+  cudf::test::strings_column_wrapper input(input_strings.begin(), input_strings.end());
+  {
+    std::string json_path("$.a");
+
+    cudf::strings::get_json_object_options options;
+    options.set_allow_single_quotes(true);
+
+    auto result =
+      cudf::strings::get_json_object(cudf::strings_column_view(input), json_path, options);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+      R"({'b' : 'c'})",
+      R"({'b' : "c"})",
+      "",
+      "[\'y\',500]",
+      "",
+      "{"
+         "\'z\': {\'i\': 10, \'j\': 100},"
+         "\'b\': [\'c\',null,true,-1]"
+      "}",
+      "abc'def",
+      "'abc'def'"
+      },
+      {1, 1, 0, 1, 1, 1, 1, 1});
+    // clang-format on
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, StringsWithSpecialChars)
+{
+  // make sure we properly handle strings containing special characters
+  // like { } [ ], etc
+  // various queries on:
+
+  {
+    std::vector<std::string> input_strings{
+      // clang-format off
+      "{\"item\" : [{\"key\" : \"value[\"}]}",
+      // clang-format on
+    };
+
+    cudf::test::strings_column_wrapper input(input_strings.begin(), input_strings.end());
+    {
+      std::string json_path("$.item");
+
+      cudf::strings::get_json_object_options options;
+      options.set_allow_single_quotes(true);
+
+      auto result =
+        cudf::strings::get_json_object(cudf::strings_column_view(input), json_path, options);
+
+      // clang-format off
+      cudf::test::strings_column_wrapper expected({
+        R"([{"key" : "value["}])",
+      });
+      // clang-format on
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+    }
+  }
+
+  {
+    std::vector<std::string> input_strings{
+      // clang-format off
+      "{\"a\" : \"[}{}][][{[\\\"}}[\\\"]\"}",
+      // clang-format on
+    };
+
+    cudf::test::strings_column_wrapper input(input_strings.begin(), input_strings.end());
+    {
+      std::string json_path("$.a");
+
+      cudf::strings::get_json_object_options options;
+      options.set_allow_single_quotes(true);
+
+      auto result =
+        cudf::strings::get_json_object(cudf::strings_column_view(input), json_path, options);
+
+      // clang-format off
+      cudf::test::strings_column_wrapper expected({
+        R"([}{}][][{[\"}}[\"])",
+      });
+      // clang-format on
+      CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+    }
+  }
+}
+
+TEST_F(JsonPathTests, EscapeSequences)
+{
+  // valid escape sequences in JSON include
+  // \" \\ \/ \b \f \n \r \t
+  // \uXXXX  where X is a valid hex digit
+
+  std::vector<std::string> input_strings{
+    // clang-format off
+    "{\"a\" : \"\\\" \\\\ \\/ \\b \\f \\n \\r \\t\"}",
+    "{\"a\" : \"\\u1248 \\uacdf \\uACDF \\u10EF\"}"
+    // clang-format on
+  };
+
+  cudf::test::strings_column_wrapper input(input_strings.begin(), input_strings.end());
+  {
+    std::string json_path("$.a");
+
+    cudf::strings::get_json_object_options options;
+    options.set_allow_single_quotes(true);
+
+    auto result =
+      cudf::strings::get_json_object(cudf::strings_column_view(input), json_path, options);
+
+    // clang-format off
+    cudf::test::strings_column_wrapper expected({
+      R"(\" \\ \/ \b \f \n \r \t)",
+      R"(\u1248 \uacdf \uACDF \u10EF)"
+    });
+    // clang-format on
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*result, expected);
+  }
+}
+
+TEST_F(JsonPathTests, MissingFieldsAsNulls)
+{
+  std::string input_string{
+    // clang-format off
+    "{"
+      "\"tup\":"
+      "["
+          "{\"id\":\"1\",\"array\":[1,2]},"
+          "{\"id\":\"2\"},"
+          "{\"id\":\"3\",\"array\":[3,4]},"
+          "{\"id\":\"4\", \"a\": {\"x\": \"5\", \"y\": \"6\"}}"
+      "]"
+    "}"
+    // clang-format on
+  };
+  auto do_test = [&input_string](auto const& json_path_string,
+                                 auto const& default_output,
+                                 auto const& missing_fields_output,
+                                 bool default_valid = true) {
+    cudf::test::strings_column_wrapper input{input_string};
+    cudf::strings::get_json_object_options options;
+
+    // Test default behavior
+    options.set_missing_fields_as_nulls(false);
+    auto const default_result =
+      cudf::strings::get_json_object(cudf::strings_column_view(input), {json_path_string}, options);
+    cudf::test::strings_column_wrapper default_expected({default_output}, {default_valid});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(default_expected, *default_result);
+
+    // Test with missing fields as null
+    options.set_missing_fields_as_nulls(true);
+    auto const missing_fields_result =
+      cudf::strings::get_json_object(cudf::strings_column_view(input), {json_path_string}, options);
+    cudf::test::strings_column_wrapper missing_fields_expected({missing_fields_output}, {1});
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(missing_fields_expected, *missing_fields_result);
+  };
+
+  do_test("$.tup[1].array", "", "null", false);
+  do_test("$.tup[*].array", "[[1,2],[3,4]]", "[[1,2],null,[3,4],null]");
+  do_test("$.x[*].array", "", "null", false);
+  do_test("$.tup[*].a.x", "[\"5\"]", "[null,null,null,\"5\"]");
+}
diff --git a/cpp/tests/strings/like_tests.cpp b/cpp/tests/strings/like_tests.cpp
new file mode 100644
index 0000000..50d8edf
--- /dev/null
+++ b/cpp/tests/strings/like_tests.cpp
@@ -0,0 +1,208 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+struct StringsLikeTests : public cudf::test::BaseFixture {};
+
+TEST_F(StringsLikeTests, Basic)
+{
+  cudf::test::strings_column_wrapper input({"abc", "a bc", "ABC", "abcd", " abc", "", "", "áéêú"},
+                                           {1, 1, 1, 1, 1, 1, 0, 1});
+  auto const sv      = cudf::strings_column_view(input);
+  auto const pattern = std::string("abc");
+  auto const results = cudf::strings::like(sv, pattern);
+  cudf::test::fixed_width_column_wrapper<bool> expected(
+    {true, false, false, false, false, false, false, false}, {1, 1, 1, 1, 1, 1, 0, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsLikeTests, Leading)
+{
+  cudf::test::strings_column_wrapper input({"a", "aa", "aaa", "b", "bb", "bba", "", "áéêú"});
+  auto const sv = cudf::strings_column_view(input);
+  {
+    auto const results = cudf::strings::like(sv, std::string("a%"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {true, true, true, false, false, false, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("__a%"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, true, false, false, true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("á%"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, false, false, false, false, true});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+}
+
+TEST_F(StringsLikeTests, Trailing)
+{
+  cudf::test::strings_column_wrapper input({"a", "aa", "aaa", "b", "bb", "bba", "", "áéêú"});
+  auto const sv = cudf::strings_column_view(input);
+  {
+    auto results = cudf::strings::like(sv, std::string("%a"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {true, true, true, false, false, true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+    results = cudf::strings::like(sv, std::string("%a%"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("%_a"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, true, true, false, false, true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("%_êú"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, false, false, false, false, true});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+}
+
+TEST_F(StringsLikeTests, Place)
+{
+  cudf::test::strings_column_wrapper input({"a", "aa", "aaa", "bab", "ab", "aba", "", "éaé"});
+  auto const sv = cudf::strings_column_view(input);
+  {
+    auto const results = cudf::strings::like(sv, std::string("a_"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, true, false, false, true, false, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("_a_"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, true, true, false, false, false, true});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("__a"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, true, false, false, true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const results = cudf::strings::like(sv, std::string("é_é"));
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, false, false, false, false, true});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+}
+
+TEST_F(StringsLikeTests, Escape)
+{
+  cudf::test::strings_column_wrapper input(
+    {"10%-20%", "10-20", "10%%-20%", "a_b", "b_a", "___", "", "aéb"});
+  auto const sv = cudf::strings_column_view(input);
+  {
+    auto const pattern = std::string("10\\%-20\\%");
+    auto const escape  = std::string("\\");
+    auto const results = cudf::strings::like(sv, pattern, escape);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {true, false, false, false, false, false, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const pattern = std::string("\\__\\_");
+    auto const escape  = std::string("\\");
+    auto const results = cudf::strings::like(sv, pattern, escape);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, false, false, true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const pattern = std::string("10%%%%-20%%");
+    auto const escape  = std::string("%");
+    auto const results = cudf::strings::like(sv, pattern, escape);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, true, false, false, false, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const pattern = std::string("_%__");
+    auto const escape  = std::string("%");
+    auto const results = cudf::strings::like(sv, pattern, escape);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, true, true, true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+  {
+    auto const pattern = std::string("a__b");
+    auto const escape  = std::string("_");
+    auto const results = cudf::strings::like(sv, pattern, escape);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {false, false, false, true, false, false, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+  }
+}
+
+TEST_F(StringsLikeTests, MultiplePatterns)
+{
+  cudf::test::strings_column_wrapper input({"abc", "a1a2b3b4c", "aaabbb", "bbbc", "", "áéêú"});
+  cudf::test::strings_column_wrapper patterns({"a%b%c", "a%c", "a__b", "b__c", "", "áéêú"});
+
+  auto const sv_input    = cudf::strings_column_view(input);
+  auto const sv_patterns = cudf::strings_column_view(patterns);
+  auto const results     = cudf::strings::like(sv_input, sv_patterns);
+  cudf::test::fixed_width_column_wrapper<bool> expected({true, true, false, true, true, true});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(StringsLikeTests, Empty)
+{
+  cudf::test::strings_column_wrapper input({"ooo", "20%", ""});
+  auto sv       = cudf::strings_column_view(input);
+  auto results  = cudf::strings::like(sv, std::string(""));
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({false, false, true});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+
+  auto empty          = cudf::make_empty_column(cudf::type_id::STRING);
+  sv                  = cudf::strings_column_view(empty->view());
+  results             = cudf::strings::like(sv, std::string("20%"));
+  auto expected_empty = cudf::make_empty_column(cudf::type_id::BOOL8);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected_empty->view());
+
+  results = cudf::strings::like(sv, sv);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected_empty->view());
+}
+
+TEST_F(StringsLikeTests, Errors)
+{
+  auto const input       = cudf::test::strings_column_wrapper({"3", "33"});
+  auto const sv          = cudf::strings_column_view(input);
+  auto const invalid_str = cudf::string_scalar("", false);
+
+  EXPECT_THROW(cudf::strings::like(sv, invalid_str), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::like(sv, std::string("3"), invalid_str), cudf::logic_error);
+
+  auto patterns          = cudf::test::strings_column_wrapper({"3", ""}, {1, 0});
+  auto const sv_patterns = cudf::strings_column_view(patterns);
+  EXPECT_THROW(cudf::strings::like(sv, sv_patterns), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::like(sv, sv, invalid_str), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/pad_tests.cpp b/cpp/tests/strings/pad_tests.cpp
new file mode 100644
index 0000000..81ec87a
--- /dev/null
+++ b/cpp/tests/strings/pad_tests.cpp
@@ -0,0 +1,221 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/padding.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/wrap.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsPadTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsPadTest, Padding)
+{
+  std::vector<char const*> h_strings{"eee ddd", "bb cc", nullptr, "", "aa", "bbb", "ééé", "o"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::size_type width = 6;
+  std::string phil      = "+";
+  auto strings_view     = cudf::strings_column_view(strings);
+
+  {
+    auto results = cudf::strings::pad(strings_view, width, cudf::strings::side_type::RIGHT, phil);
+
+    std::vector<char const*> h_expected{
+      "eee ddd", "bb cc+", nullptr, "++++++", "aa++++", "bbb+++", "ééé+++", "o+++++"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::pad(strings_view, width, cudf::strings::side_type::LEFT, phil);
+
+    std::vector<char const*> h_expected{
+      "eee ddd", "+bb cc", nullptr, "++++++", "++++aa", "+++bbb", "+++ééé", "+++++o"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::pad(strings_view, width, cudf::strings::side_type::BOTH, phil);
+
+    std::vector<char const*> h_expected{
+      "eee ddd", "bb cc+", nullptr, "++++++", "++aa++", "+bbb++", "+ééé++", "++o+++"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsPadTest, PaddingBoth)
+{
+  cudf::test::strings_column_wrapper strings({"koala", "foxx", "fox", "chameleon"});
+  std::string phil  = "+";
+  auto strings_view = cudf::strings_column_view(strings);
+
+  {  // even width left justify
+    auto results = cudf::strings::pad(strings_view, 6, cudf::strings::side_type::BOTH, phil);
+    cudf::test::strings_column_wrapper expected({"koala+", "+foxx+", "+fox++", "chameleon"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {  // odd width right justify
+    auto results = cudf::strings::pad(strings_view, 7, cudf::strings::side_type::BOTH, phil);
+    cudf::test::strings_column_wrapper expected({"+koala+", "++foxx+", "++fox++", "chameleon"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsPadTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+  auto results      = cudf::strings::pad(strings_view, 5);
+  cudf::test::expect_column_empty(results->view());
+}
+
+class PadParameters : public StringsPadTest, public testing::WithParamInterface<cudf::size_type> {};
+
+TEST_P(PadParameters, Padding)
+{
+  std::vector<std::string> h_strings{"eee ddd", "bb cc", "aa", "bbb", "fff", "", "o"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  cudf::size_type width = GetParam();
+  auto strings_view     = cudf::strings_column_view(strings);
+  auto results          = cudf::strings::pad(strings_view, width, cudf::strings::side_type::RIGHT);
+
+  std::vector<std::string> h_expected;
+  for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr) {
+    std::string str      = *itr;
+    cudf::size_type size = str.size();
+    if (size < width) str.insert(size, width - size, ' ');
+    h_expected.push_back(str);
+  }
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+INSTANTIATE_TEST_CASE_P(StringsPadTest,
+                        PadParameters,
+                        testing::ValuesIn(std::array<cudf::size_type, 3>{5, 6, 7}));
+
+TEST_F(StringsPadTest, ZFill)
+{
+  std::vector<char const*> h_strings{
+    "654321", "-12345", nullptr, "", "-5", "0987", "4", "+8.5", "éé", "+abé", "é+a", "100-"};
+  cudf::test::strings_column_wrapper input(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(input);
+
+  auto results = cudf::strings::zfill(strings_view, 6);
+
+  std::vector<char const*> h_expected{"654321",
+                                      "-12345",
+                                      nullptr,
+                                      "000000",
+                                      "-00005",
+                                      "000987",
+                                      "000004",
+                                      "+008.5",
+                                      "0000éé",
+                                      "+00abé",
+                                      "000é+a",
+                                      "00100-"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsPadTest, Wrap1)
+{
+  std::vector<char const*> h_strings{"12345", "thesé", nullptr, "ARE THE", "tést strings", ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::size_type width = 3;
+
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::wrap(strings_view, width);
+
+  std::vector<char const*> h_expected{"12345", "thesé", nullptr, "ARE\nTHE", "tést\nstrings", ""};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsPadTest, Wrap2)
+{
+  std::vector<char const*> h_strings{"the quick brown fox jumped over the lazy brown dog",
+                                     "hello, world"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::size_type width = 12;
+
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::wrap(strings_view, width);
+
+  std::vector<char const*> h_expected{"the quick\nbrown fox\njumped over\nthe lazy\nbrown dog",
+                                      "hello, world"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsPadTest, WrapExpectFailure)
+{
+  std::vector<char const*> h_strings{"12345", "thesé", nullptr, "ARE THE", "tést strings", ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  cudf::size_type width = 0;  // this should trigger failure
+
+  auto strings_view = cudf::strings_column_view(strings);
+
+  EXPECT_THROW(cudf::strings::wrap(strings_view, width), cudf::logic_error);
+}
diff --git a/cpp/tests/strings/repeat_strings_tests.cpp b/cpp/tests/strings/repeat_strings_tests.cpp
new file mode 100644
index 0000000..9d08ac9
--- /dev/null
+++ b/cpp/tests/strings/repeat_strings_tests.cpp
@@ -0,0 +1,587 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+using namespace cudf::test::iterators;
+
+namespace {
+using strs_col   = cudf::test::strings_column_wrapper;
+using int32s_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+constexpr int32_t null{0};  // mark for null elements in a column of int32_t values
+constexpr cudf::test::debug_output_level verbosity{cudf::test::debug_output_level::FIRST_ERROR};
+}  // namespace
+
+struct RepeatStringsTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct RepeatStringsTypedTest : public cudf::test::BaseFixture {};
+
+// Test for signed types only, as we will need to use non-positive values.
+using TypesForTest = cudf::test::Types<int8_t, int16_t, int32_t, int64_t>;
+TYPED_TEST_SUITE(RepeatStringsTypedTest, TypesForTest);
+
+TYPED_TEST(RepeatStringsTypedTest, InvalidStringScalar)
+{
+  auto const str    = cudf::string_scalar("", false);
+  auto const result = cudf::strings::repeat_string(str, 3);
+  EXPECT_EQ(result->is_valid(), false);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, ZeroSizeStringScalar)
+{
+  auto const str    = cudf::string_scalar("");
+  auto const result = cudf::strings::repeat_string(str, 3);
+  EXPECT_EQ(result->is_valid(), true);
+  EXPECT_EQ(result->size(), 0);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, ValidStringScalar)
+{
+  auto const str = cudf::string_scalar("abc123xyz-");
+
+  {
+    auto const result        = cudf::strings::repeat_string(str, 3);
+    auto const expected_strs = cudf::string_scalar("abc123xyz-abc123xyz-abc123xyz-");
+    CUDF_TEST_EXPECT_EQUAL_BUFFERS(expected_strs.data(), result->data(), expected_strs.size());
+  }
+
+  // Repeat once.
+  {
+    auto const result = cudf::strings::repeat_string(str, 1);
+    CUDF_TEST_EXPECT_EQUAL_BUFFERS(str.data(), result->data(), str.size());
+  }
+
+  // Zero repeat times.
+  {
+    auto const result = cudf::strings::repeat_string(str, 0);
+    EXPECT_EQ(result->is_valid(), true);
+    EXPECT_EQ(result->size(), 0);
+  }
+
+  // Negative repeat times.
+  {
+    auto const result = cudf::strings::repeat_string(str, -10);
+    EXPECT_EQ(result->is_valid(), true);
+    EXPECT_EQ(result->size(), 0);
+  }
+
+  // Repeat too many times.
+  {
+    EXPECT_THROW(cudf::strings::repeat_string(str, std::numeric_limits<int32_t>::max() / 2),
+                 std::overflow_error);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, ZeroSizeStringsColumnWithScalarRepeatTimes)
+{
+  auto const strs    = strs_col{};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, ZeroSizeStringsColumnWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs         = strs_col{};
+  auto const repeat_times = ints_col{};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), repeat_times);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, AllEmptyStringsColumnWithScalarRepeatTimes)
+{
+  auto const strs    = strs_col{"", "", "", "", ""};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, AllEmptyStringsColumnWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs         = strs_col{"", "", "", "", ""};
+  auto const repeat_times = ints_col{-2, -1, 0, 1, 2};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), repeat_times);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, AllNullStringsColumnWithScalarRepeatTimes)
+{
+  auto const strs    = strs_col{{"" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, all_nulls()};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, AllNullStringsColumnWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs    = strs_col{{"" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, all_nulls()};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  // The repeat_times column contains all valid numbers.
+  {
+    auto const repeat_times = ints_col{-1, 0, 1};
+    auto const results      = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+
+  // The repeat_times column also contains some nulls and some valid numbers.
+  {
+    auto const repeat_times = ints_col{{null, 1, null}, nulls_at({0, 2})};
+    auto const results      = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+
+  // The repeat_times column also contains all nulls.
+  {
+    auto const repeat_times = ints_col{{null, null, null}, all_nulls()};
+    auto const results      = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, StringsColumnWithAllNullColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs         = strs_col{"ABC", "abc", "xyz"};
+  auto const repeat_times = ints_col{{null, null, null}, all_nulls()};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), repeat_times);
+  auto const expected_strs = strs_col{{"" /*NULL*/, "" /*NULL*/, "" /*NULL*/}, all_nulls()};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, ZeroSizeAndNullStringsColumnWithScalarRepeatTimes)
+{
+  auto const strs =
+    strs_col{{"" /*NULL*/, "", "" /*NULL*/, "", "", "" /*NULL*/}, nulls_at({0, 2, 5})};
+  auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(strs), 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, ZeroSizeAndNullStringsColumnWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs =
+    strs_col{{"" /*NULL*/, "", "" /*NULL*/, "", "", "" /*NULL*/}, nulls_at({0, 2, 5})};
+  auto const repeat_times = ints_col{1, 2, 3, 4, 5, 6};
+  auto const results      = cudf::strings::repeat_strings(cudf::strings_column_view(strs), 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+}
+
+TEST_F(RepeatStringsTest, StringsColumnWithColumnRepeatTimesInvalidInput)
+{
+  auto const strs    = strs_col{"abc", "xyz"};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  // Sizes mismatched between strings column and repeat_times column.
+  {
+    auto const repeat_times = int32s_col{1, 2, 3, 4, 5, 6};
+    EXPECT_THROW(cudf::strings::repeat_strings(strs_cv, repeat_times), cudf::logic_error);
+  }
+
+  // Invalid data type for repeat_times column.
+  {
+    auto const repeat_times = cudf::test::fixed_width_column_wrapper<float>{1, 2, 3, 4, 5, 6};
+    EXPECT_THROW(cudf::strings::repeat_strings(strs_cv, repeat_times), cudf::logic_error);
+  }
+
+  // Invalid data type for repeat_times column.
+  {
+    auto const repeat_times = strs_col{"xxx", "xxx"};
+    EXPECT_THROW(cudf::strings::repeat_strings(strs_cv, repeat_times), cudf::logic_error);
+  }
+}
+
+TEST_F(RepeatStringsTest, StringsColumnWithColumnRepeatTimesOverflowOutput)
+{
+  auto const strs    = strs_col{"1", "12", "123", "1234", "12345", "123456", "1234567"};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  auto const half_max = std::numeric_limits<int32_t>::max() / 2;
+  auto const repeat_times =
+    int32s_col{half_max, half_max, half_max, half_max, half_max, half_max, half_max};
+
+  EXPECT_THROW(cudf::strings::repeat_strings(strs_cv, repeat_times), std::overflow_error);
+}
+
+TYPED_TEST(RepeatStringsTypedTest, StringsColumnNoNullWithScalarRepeatTimes)
+{
+  auto const strs    = strs_col{"0a0b0c", "abcxyz", "xyzééé", "ááá", "íí"};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  {
+    auto const results = cudf::strings::repeat_strings(strs_cv, 2);
+    auto const expected_strs =
+      strs_col{"0a0b0c0a0b0c", "abcxyzabcxyz", "xyzéééxyzééé", "áááááá", "íííí"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Repeat once.
+  {
+    auto const results = cudf::strings::repeat_strings(strs_cv, 1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+
+  // Non-positive repeat times.
+  {
+    auto const expected_strs = strs_col{"", "", "", "", ""};
+
+    auto results = cudf::strings::repeat_strings(strs_cv, 0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+
+    results = cudf::strings::repeat_strings(strs_cv, -100);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, StringsColumnNoNullWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs    = strs_col{"0a0b0c", "abcxyz", "xyzééé", "ááá", "íí"};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  // Repeat once.
+  {
+    auto const repeat_times = ints_col{1, 1, 1, 1, 1};
+    auto const results      = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+
+  // repeat_times column has negative values.
+  {
+    auto const repeat_times = ints_col{1, 2, 3, -1, -2};
+    auto const expected_strs = strs_col{"0a0b0c", "abcxyzabcxyz", "xyzéééxyzéééxyzééé", "", ""};
+
+    auto results = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // repeat_times column has nulls.
+  {
+    auto const repeat_times  = ints_col{{1, null, 3, 2, null}, nulls_at({1, 4})};
+    auto const expected_strs = strs_col{
+      {"0a0b0c", "" /*NULL*/, "xyzéééxyzéééxyzééé", "áááááá", "" /*NULL*/}, nulls_at({1, 4})};
+
+    auto results = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, SlicedStringsColumnNoNullWithScalarRepeatTimes)
+{
+  auto const strs = strs_col{"0a0b0c", "abcxyz", "xyzééé", "ááá", "íí"};
+
+  // Sliced the first half of the column.
+  {
+    auto const sliced_strs = cudf::slice(strs, {0, 3})[0];
+    auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(sliced_strs), 2);
+    auto const expected_strs = strs_col{"0a0b0c0a0b0c", "abcxyzabcxyz", "xyzéééxyzééé"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the middle of the column.
+  {
+    auto const sliced_strs = cudf::slice(strs, {1, 3})[0];
+    auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(sliced_strs), 2);
+    auto const expected_strs = strs_col{"abcxyzabcxyz", "xyzéééxyzééé"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the second half of the column.
+  {
+    auto const sliced_strs = cudf::slice(strs, {2, 5})[0];
+    auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(sliced_strs), 2);
+    auto const expected_strs = strs_col{"xyzéééxyzééé", "áááááá", "íííí"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, SlicedStringsColumnNoNullWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs         = strs_col{"0a0b0c", "abcxyz", "xyzééé", "ááá", "íí"};
+  auto const repeat_times = ints_col{1, 2, 3, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+
+  // Sliced the first half of the column.
+  {
+    auto const sliced_strs    = cudf::slice(strs, {0, 3})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {0, 3})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs  = strs_col{"0a0b0c", "abcxyzabcxyz", "xyzéééxyzéééxyzééé"};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the middle of the column.
+  {
+    auto const sliced_strs    = cudf::slice(strs, {1, 3})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {1, 3})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs  = strs_col{"abcxyzabcxyz", "xyzéééxyzéééxyzééé"};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the second half of the column.
+  {
+    auto const sliced_strs    = cudf::slice(strs, {2, 5})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {2, 5})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs = strs_col{"xyzéééxyzéééxyzééé", "áááááá", "íííííí"};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, StringsColumnWithNullsWithScalarRepeatTimes)
+{
+  auto const strs    = strs_col{{"0a0b0c",
+                                 "" /*NULL*/,
+                                 "abcxyz",
+                                 "" /*NULL*/,
+                                 "xyzééé",
+                                 "" /*NULL*/,
+                                 "ááá",
+                                 "íí",
+                                 "",
+                                 "Hello World"},
+                             nulls_at({1, 3, 5})};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  {
+    auto const results       = cudf::strings::repeat_strings(strs_cv, 2);
+    auto const expected_strs = strs_col{{"0a0b0c0a0b0c",
+                                         "" /*NULL*/,
+                                         "abcxyzabcxyz",
+                                         "" /*NULL*/,
+                                         "xyzéééxyzééé",
+                                         "" /*NULL*/,
+                                         "áááááá",
+                                         "íííí",
+                                         "",
+                                         "Hello WorldHello World"},
+                                        nulls_at({1, 3, 5})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Repeat once.
+  {
+    auto const results = cudf::strings::repeat_strings(strs_cv, 1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+
+  // Non-positive repeat times.
+  {
+    auto const expected_strs = strs_col{
+      {"", "" /*NULL*/, "", "" /*NULL*/, "", "" /*NULL*/, "", "", "", ""}, nulls_at({1, 3, 5})};
+
+    auto results = cudf::strings::repeat_strings(strs_cv, 0);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+
+    results = cudf::strings::repeat_strings(strs_cv, -100);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, StringsColumnWithNullsWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs    = strs_col{{"0a0b0c",
+                                 "" /*NULL*/,
+                                 "abcxyz",
+                                 "" /*NULL*/,
+                                 "xyzééé",
+                                 "" /*NULL*/,
+                                 "ááá",
+                                 "íí",
+                                 "",
+                                 "Hello World"},
+                             nulls_at({1, 3, 5})};
+  auto const strs_cv = cudf::strings_column_view(strs);
+
+  // Repeat once.
+  {
+    auto const repeat_times = ints_col{1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
+    auto const results      = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(strs, *results, verbosity);
+  }
+
+  // repeat_times column has negative values.
+  {
+    auto const repeat_times  = ints_col{1, 2, 3, -1, -2, 1, 2, 3, -5, 0};
+    auto const expected_strs = strs_col{{"0a0b0c",
+                                         "" /*NULL*/,
+                                         "abcxyzabcxyzabcxyz",
+                                         "" /*NULL*/,
+                                         "",
+                                         "" /*NULL*/,
+                                         "áááááá",
+                                         "íííííí",
+                                         "",
+                                         ""},
+                                        nulls_at({1, 3, 5})};
+
+    auto results = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // repeat_times column has nulls.
+  {
+    auto const repeat_times =
+      ints_col{{1, 2, null, -1, null, 1, 2, null, -5, 0}, nulls_at({2, 4, 7})};
+    auto const expected_strs = strs_col{{"0a0b0c",
+                                         "" /*NULL*/,
+                                         "" /*NULL*/,
+                                         "" /*NULL*/,
+                                         "" /*NULL*/,
+                                         "" /*NULL*/,
+                                         "áááááá",
+                                         "" /*NULL*/,
+                                         "",
+                                         ""},
+                                        nulls_at({1, 2, 3, 4, 5, 7})};
+
+    auto results = cudf::strings::repeat_strings(strs_cv, repeat_times);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, SlicedStringsColumnWithNullsWithScalarRepeatTimes)
+{
+  auto const strs = strs_col{{"0a0b0c",
+                              "" /*NULL*/,
+                              "abcxyz",
+                              "" /*NULL*/,
+                              "xyzééé",
+                              "" /*NULL*/,
+                              "ááá",
+                              "íí",
+                              "",
+                              "Hello World"},
+                             nulls_at({1, 3, 5})};
+
+  // Sliced the first half of the column.
+  {
+    auto const sliced_strs = cudf::slice(strs, {0, 3})[0];
+    auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(sliced_strs), 2);
+    auto const expected_strs = strs_col{{"0a0b0c0a0b0c", "" /*NULL*/, "abcxyzabcxyz"}, null_at(1)};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the middle of the column.
+  {
+    auto const sliced_strs = cudf::slice(strs, {2, 7})[0];
+    auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(sliced_strs), 2);
+    auto const expected_strs = strs_col{
+      {"abcxyzabcxyz", "" /*NULL*/, "xyzéééxyzééé", "" /*NULL*/, "áááááá"}, nulls_at({1, 3})};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the second half of the column.
+  {
+    auto const sliced_strs = cudf::slice(strs, {6, 10})[0];
+    auto const results = cudf::strings::repeat_strings(cudf::strings_column_view(sliced_strs), 2);
+    auto const expected_strs = strs_col{"áááááá", "íííí", "", "Hello WorldHello World"};
+
+    // The results strings column may have a bitmask with all valid values.
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_strs, *results, verbosity);
+  }
+}
+
+TYPED_TEST(RepeatStringsTypedTest, SlicedStringsColumnWithNullsWithColumnRepeatTimes)
+{
+  using ints_col = cudf::test::fixed_width_column_wrapper<TypeParam>;
+
+  auto const strs = strs_col{{"0a0b0c",
+                              "" /*NULL*/,
+                              "abcxyz",
+                              "" /*NULL*/,
+                              "xyzééé",
+                              "" /*NULL*/,
+                              "ááá",
+                              "íí",
+                              "",
+                              "Hello World"},
+                             nulls_at({1, 3, 5})};
+
+  auto const repeat_times =
+    ints_col{{1, 2, null, -1, null, 1, 2, null, -5, 0, 6, 7, 8, 9, 10}, nulls_at({2, 4, 7})};
+
+  // Sliced the first half of the column.
+  {
+    auto const sliced_strs    = cudf::slice(strs, {0, 3})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {0, 3})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs  = strs_col{{"0a0b0c", "" /*NULL*/, "" /*NULL*/}, nulls_at({1, 2})};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the middle of the column.
+  {
+    auto const sliced_strs    = cudf::slice(strs, {2, 7})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {2, 7})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs  = strs_col{
+       {"" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "" /*NULL*/, "áááááá"}, nulls_at({0, 1, 2, 3})};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the second half of the column, output has nulls.
+  {
+    auto const sliced_strs    = cudf::slice(strs, {6, 10})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {6, 10})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs  = strs_col{{"áááááá", "" /*NULL*/, "", ""}, null_at(1)};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_strs, *results, verbosity);
+  }
+
+  // Sliced the second half of the column, output does not have null.
+  // Since the input has nulls, the output column is nullable (but doesn't have nulls).
+  {
+    auto const sliced_strs    = cudf::slice(strs, {8, 10})[0];
+    auto const sliced_rtimes  = cudf::slice(repeat_times, {8, 10})[0];
+    auto const sliced_strs_cv = cudf::strings_column_view(sliced_strs);
+    auto const expected_strs  = strs_col{"", ""};
+
+    auto results = cudf::strings::repeat_strings(sliced_strs_cv, sliced_rtimes);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_strs, *results, verbosity);
+  }
+}
diff --git a/cpp/tests/strings/replace_regex_tests.cpp b/cpp/tests/strings/replace_regex_tests.cpp
new file mode 100644
index 0000000..d1c545b
--- /dev/null
+++ b/cpp/tests/strings/replace_regex_tests.cpp
@@ -0,0 +1,442 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/replace_re.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsReplaceRegexTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsReplaceRegexTest, ReplaceRegexTest)
+{
+  std::vector<char const*> h_strings{"the quick brown fox jumps over the lazy dog",
+                                     "the fat cat lays next to the other accénted cat",
+                                     "a slow moving turtlé cannot catch the bird",
+                                     "which can be composéd together to form a more complete",
+                                     "thé result does not include the value in the sum in",
+                                     "",
+                                     nullptr};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<char const*> h_expected{"= quick brown fox jumps over = lazy dog",
+                                      "= fat cat lays next to = other accénted cat",
+                                      "a slow moving turtlé cannot catch = bird",
+                                      "which can be composéd together to form a more complete",
+                                      "thé result does not include = value in = sum in",
+                                      "",
+                                      nullptr};
+
+  auto pattern = std::string("(\\bthe\\b)");
+  auto repl    = cudf::string_scalar("=");
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_re(strings_view, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ReplaceMultiRegexTest)
+{
+  std::vector<char const*> h_strings{"the quick brown fox jumps over the lazy dog",
+                                     "the fat cat lays next to the other accénted cat",
+                                     "a slow moving turtlé cannot catch the bird",
+                                     "which can be composéd together to form a more complete",
+                                     "thé result does not include the value in the sum in",
+                                     "",
+                                     nullptr};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<char const*> h_expected{" quick brown fox jumps over  lazy dog",
+                                      " fat cat lays next to  other accénted cat",
+                                      "** slow moving turtlé cannot catch  bird",
+                                      "which can be composéd together to form ** more complete",
+                                      "thé result does not include  value N  sum N",
+                                      "",
+                                      nullptr};
+
+  std::vector<std::string> patterns{"\\bthe\\b", "\\bin\\b", "\\ba\\b"};
+  std::vector<std::string> h_repls{"", "N", "**"};
+  cudf::test::strings_column_wrapper repls(h_repls.begin(), h_repls.end());
+  auto repls_view = cudf::strings_column_view(repls);
+  auto results    = cudf::strings::replace_re(strings_view, patterns, repls_view);
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, InvalidRegex)
+{
+  // these are quantifiers that do not have a preceding character/class
+  EXPECT_THROW(cudf::strings::regex_program::create("*"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("|"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("+"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("ab(*)"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("\\"), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::regex_program::create("\\p"), cudf::logic_error);
+}
+
+TEST_F(StringsReplaceRegexTest, WithEmptyPattern)
+{
+  std::vector<char const*> h_strings{"asd", "xcv"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto empty_pattern = std::string("");
+  auto repl          = cudf::string_scalar("bbb");
+  std::vector<std::string> patterns({empty_pattern});
+  cudf::test::strings_column_wrapper repls({"bbb"});
+  auto repls_view = cudf::strings_column_view(repls);
+  auto results    = cudf::strings::replace_re(strings_view, patterns, repls_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, strings);
+  auto prog = cudf::strings::regex_program::create(empty_pattern);
+  results   = cudf::strings::replace_re(strings_view, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, strings);
+}
+
+TEST_F(StringsReplaceRegexTest, MultiReplacement)
+{
+  cudf::test::strings_column_wrapper input({"aba bcd aba", "abababa abababa"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern = std::string("aba");
+  auto repl    = cudf::string_scalar("_");
+  cudf::test::strings_column_wrapper expected({"_ bcd _", "_b_ abababa"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_re(sv, *prog, repl, 2);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  results = cudf::strings::replace_re(sv, *prog, repl, 0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, input);
+}
+
+TEST_F(StringsReplaceRegexTest, WordBoundary)
+{
+  cudf::test::strings_column_wrapper input({"aba bcd\naba", "zéz", "A1B2-é3", "e é", "_", "a_b"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern  = std::string("\\b");
+  auto repl     = cudf::string_scalar("X");
+  auto expected = cudf::test::strings_column_wrapper(
+    {"XabaX XbcdX\nXabaX", "XzézX", "XA1B2X-Xé3X", "XeX XéX", "X_X", "Xa_bX"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  pattern  = std::string("\\B");
+  expected = cudf::test::strings_column_wrapper(
+    {"aXbXa bXcXd\naXbXa", "zXéXz", "AX1XBX2-éX3", "e é", "_", "aX_Xb"});
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, Alternation)
+{
+  cudf::test::strings_column_wrapper input(
+    {"16  6  brr  232323  1  hello  90", "123 ABC 00 2022", "abé123  4567  89xyz"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern = std::string("(^|\\s)\\d+(\\s|$)");
+  auto repl    = cudf::string_scalar("_");
+  auto expected =
+    cudf::test::strings_column_wrapper({"__ brr __ hello _", "_ABC_2022", "abé123 _ 89xyz"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  pattern = std::string("(\\s|^)\\d+($|\\s)");
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ZeroLengthMatch)
+{
+  cudf::test::strings_column_wrapper input({"DD", "zéz", "DsDs", ""});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern  = std::string("D*");
+  auto repl     = cudf::string_scalar("_");
+  auto expected = cudf::test::strings_column_wrapper({"__", "_z_é_z_", "__s__s_", "_"});
+  auto prog     = cudf::strings::regex_program::create(pattern);
+  auto results  = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  pattern  = std::string("D?s?");
+  expected = cudf::test::strings_column_wrapper({"___", "_z_é_z_", "___", "_"});
+  prog     = cudf::strings::regex_program::create(pattern);
+  results  = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, Multiline)
+{
+  auto const multiline = cudf::strings::regex_flags::MULTILINE;
+
+  cudf::test::strings_column_wrapper input({"bcd\naba\nefg", "aba\naba abab\naba", "aba"});
+  auto sv = cudf::strings_column_view(input);
+
+  // single-replace
+  auto pattern = std::string("^aba$");
+  auto repl    = cudf::string_scalar("_");
+  cudf::test::strings_column_wrapper expected_ml({"bcd\n_\nefg", "_\naba abab\n_", "_"});
+  auto prog    = cudf::strings::regex_program::create(pattern, multiline);
+  auto results = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected_ml);
+
+  cudf::test::strings_column_wrapper expected({"bcd\naba\nefg", "aba\naba abab\naba", "_"});
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::replace_re(sv, *prog, repl);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  // multi-replace
+  std::vector<std::string> patterns({"aba$", "^aba"});
+  cudf::test::strings_column_wrapper repls({">", "<"});
+  results = cudf::strings::replace_re(sv, patterns, cudf::strings_column_view(repls), multiline);
+  cudf::test::strings_column_wrapper multi_expected_ml({"bcd\n>\nefg", ">\n< abab\n>", ">"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, multi_expected_ml);
+
+  results = cudf::strings::replace_re(sv, patterns, cudf::strings_column_view(repls));
+  cudf::test::strings_column_wrapper multi_expected({"bcd\naba\nefg", "<\naba abab\n>", ">"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, multi_expected);
+
+  // backref-replace
+  auto repl_template = std::string("[\\1]");
+  pattern            = std::string("(^aba)");
+  cudf::test::strings_column_wrapper br_expected_ml(
+    {"bcd\n[aba]\nefg", "[aba]\n[aba] abab\n[aba]", "[aba]"});
+  prog    = cudf::strings::regex_program::create(pattern, multiline);
+  results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, br_expected_ml);
+
+  cudf::test::strings_column_wrapper br_expected(
+    {"bcd\naba\nefg", "[aba]\naba abab\naba", "[aba]"});
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, br_expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ReplaceBackrefsRegexTest)
+{
+  std::vector<char const*> h_strings{"the quick brown fox jumps over the lazy dog",
+                                     "the fat cat lays next to the other accénted cat",
+                                     "a slow moving turtlé cannot catch the bird",
+                                     "which can be composéd together to form a more complete",
+                                     "thé result does not include the value in the sum in",
+                                     "",
+                                     nullptr};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(), h_strings.end(), cudf::test::iterators::nulls_from_nullptrs(h_strings));
+  auto sv = cudf::strings_column_view(strings);
+
+  std::vector<char const*> h_expected{"the-quick-brown-fox-jumps-over-the-lazy-dog",
+                                      "the-fat-cat-lays-next-to-the-other-accénted-cat",
+                                      "a-slow-moving-turtlé-cannot-catch-the-bird",
+                                      "which-can-be-composéd-together-to-form-a more-complete",
+                                      "thé-result-does-not-include-the-value-in-the-sum-in",
+                                      "",
+                                      nullptr};
+
+  auto pattern       = std::string("(\\w) (\\w)");
+  auto repl_template = std::string("\\1-\\2");
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ReplaceBackrefsRegexAltIndexPatternTest)
+{
+  cudf::test::strings_column_wrapper input({"12-3 34-5 67-89", "0-99: 777-888:: 5673-0"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern       = std::string("(\\d+)-(\\d+)");
+  auto repl_template = std::string("${2} X ${1}0");
+
+  cudf::test::strings_column_wrapper expected(
+    {"3 X 120 5 X 340 89 X 670", "99 X 00: 888 X 7770:: 0 X 56730"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ReplaceBackrefsRegexReversedTest)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"A543", "Z756", "", "tést-string", "two-thréé four-fivé", "abcd-éfgh", "tést-string-again"});
+  auto sv = cudf::strings_column_view(strings);
+
+  auto pattern       = std::string("([a-z])-([a-zé])");
+  auto repl_template = std::string("X\\2+\\1Z");
+
+  cudf::test::strings_column_wrapper expected({"A543",
+                                               "Z756",
+                                               "",
+                                               "tésXs+tZtring",
+                                               "twXt+oZhréé fouXf+rZivé",
+                                               "abcXé+dZfgh",
+                                               "tésXs+tZtrinXa+gZgain"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, BackrefWithGreedyQuantifier)
+{
+  cudf::test::strings_column_wrapper input(
+    {"<h1>title</h1><h2>ABC</h2>", "<h1>1234567</h1><h2>XYZ</h2>"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern       = std::string("<h1>(.*)</h1><h2>(.*)</h2>");
+  auto repl_template = std::string("<h2>\\1</h2><p>\\2</p>");
+
+  cudf::test::strings_column_wrapper expected(
+    {"<h2>title</h2><p>ABC</p>", "<h2>1234567</h2><p>XYZ</p>"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  pattern = std::string("<h1>([a-z\\d]+)</h1><h2>([A-Z]+)</h2>");
+  prog    = cudf::strings::regex_program::create(pattern);
+  results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ReplaceBackrefsRegexZeroIndexTest)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"TEST123", "TEST1TEST2", "TEST2-TEST1122", "TEST1-TEST-T", "TES3"});
+  auto sv = cudf::strings_column_view(strings);
+
+  auto pattern       = std::string("(TEST)(\\d+)");
+  auto repl_template = std::string("${0}: ${1}, ${2}; ");
+
+  cudf::test::strings_column_wrapper expected({
+    "TEST123: TEST, 123; ",
+    "TEST1: TEST, 1; TEST2: TEST, 2; ",
+    "TEST2: TEST, 2; -TEST1122: TEST, 1122; ",
+    "TEST1: TEST, 1; -TEST-T",
+    "TES3",
+  });
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+// https://github.com/rapidsai/cudf/issues/13404
+TEST_F(StringsReplaceRegexTest, ReplaceBackrefsWithEmptyCapture)
+{
+  cudf::test::strings_column_wrapper input({"one\ntwo", "three\n\n", "four\r\n"});
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern       = std::string("(\r\n|\r)?$");
+  auto repl_template = std::string("[\\1]");
+
+  cudf::test::strings_column_wrapper expected({"one\ntwo[]", "three\n[]\n[]", "four[\r\n][]"});
+  auto prog    = cudf::strings::regex_program::create(pattern);
+  auto results = cudf::strings::replace_with_backrefs(sv, *prog, repl_template);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, ReplaceBackrefsRegexErrorTest)
+{
+  cudf::test::strings_column_wrapper strings({"this string left intentionally blank"});
+  auto view = cudf::strings_column_view(strings);
+
+  // group index(3) exceeds the group count(2)
+  auto prog = cudf::strings::regex_program::create("(\\w).(\\w)");
+  EXPECT_THROW(cudf::strings::replace_with_backrefs(view, *prog, "\\3"), cudf::logic_error);
+  prog = cudf::strings::regex_program::create("");
+  EXPECT_THROW(cudf::strings::replace_with_backrefs(view, *prog, "\\1"), cudf::logic_error);
+  prog = cudf::strings::regex_program::create("(\\w)");
+  EXPECT_THROW(cudf::strings::replace_with_backrefs(view, *prog, ""), cudf::logic_error);
+}
+
+TEST_F(StringsReplaceRegexTest, MediumReplaceRegex)
+{
+  // This results in 95 regex instructions and falls in the 'medium' range.
+  std::string medium_regex =
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com";
+  auto prog = cudf::strings::regex_program::create(medium_regex);
+
+  std::vector<char const*> h_strings{
+    "hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com thats all",
+    "12345678901234567890",
+    "abcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::replace_re(strings_view, *prog);
+  std::vector<char const*> h_expected{
+    " thats all", "12345678901234567890", "abcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsReplaceRegexTest, LargeReplaceRegex)
+{
+  // This results in 117 regex instructions and falls in the 'large' range.
+  std::string large_regex =
+    "hello @abc @def world The (quick) brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz";
+  auto prog = cudf::strings::regex_program::create(large_regex);
+
+  std::vector<char const*> h_strings{
+    "zzzz hello @abc @def world The quick brown @fox jumps over the lazy @dog hello "
+    "http://www.world.com I'm here @home zzzz",
+    "12345678901234567890",
+    "abcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::replace_re(strings_view, *prog);
+  std::vector<char const*> h_expected{
+    "zzzz ", "12345678901234567890", "abcdefghijklmnopqrstuvwxyz"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
diff --git a/cpp/tests/strings/replace_tests.cpp b/cpp/tests/strings/replace_tests.cpp
new file mode 100644
index 0000000..f143983
--- /dev/null
+++ b/cpp/tests/strings/replace_tests.cpp
@@ -0,0 +1,480 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/detail/replace.hpp>
+#include <cudf/strings/replace.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+using algorithm = cudf::strings::detail::replace_algorithm;
+
+struct StringsReplaceTest : public cudf::test::BaseFixture {
+  cudf::test::strings_column_wrapper build_corpus()
+  {
+    std::vector<char const*> h_strings{"the quick brown fox jumps over the lazy dog",
+                                       "the fat cat lays next to the other accénted cat",
+                                       "a slow moving turtlé cannot catch the bird",
+                                       "which can be composéd together to form a more complete",
+                                       "The result does not include the value in the sum in",
+                                       "",
+                                       nullptr};
+
+    return cudf::test::strings_column_wrapper(
+      h_strings.begin(),
+      h_strings.end(),
+      thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  }
+};
+
+TEST_F(StringsReplaceTest, Replace)
+{
+  auto input        = build_corpus();
+  auto strings_view = cudf::strings_column_view(input);
+  // replace all occurrences of 'the ' with '++++ '
+  std::vector<char const*> h_expected{"++++ quick brown fox jumps over ++++ lazy dog",
+                                      "++++ fat cat lays next to ++++ other accénted cat",
+                                      "a slow moving turtlé cannot catch ++++ bird",
+                                      "which can be composéd together to form a more complete",
+                                      "The result does not include ++++ value in ++++ sum in",
+                                      "",
+                                      nullptr};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+
+  auto stream = cudf::get_default_stream();
+  auto mr     = rmm::mr::get_current_device_resource();
+
+  auto results =
+    cudf::strings::replace(strings_view, cudf::string_scalar("the "), cudf::string_scalar("++++ "));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::detail::replace<algorithm::CHAR_PARALLEL>(
+    strings_view, cudf::string_scalar("the "), cudf::string_scalar("++++ "), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::detail::replace<algorithm::ROW_PARALLEL>(
+    strings_view, cudf::string_scalar("the "), cudf::string_scalar("++++ "), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsReplaceTest, ReplaceReplLimit)
+{
+  auto input        = build_corpus();
+  auto strings_view = cudf::strings_column_view(input);
+  auto stream       = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // only remove the first occurrence of 'the '
+  std::vector<char const*> h_expected{"quick brown fox jumps over the lazy dog",
+                                      "fat cat lays next to the other accénted cat",
+                                      "a slow moving turtlé cannot catch bird",
+                                      "which can be composéd together to form a more complete",
+                                      "The result does not include value in the sum in",
+                                      "",
+                                      nullptr};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+  auto results =
+    cudf::strings::replace(strings_view, cudf::string_scalar("the "), cudf::string_scalar(""), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::detail::replace<algorithm::CHAR_PARALLEL>(
+    strings_view, cudf::string_scalar("the "), cudf::string_scalar(""), 1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::detail::replace<algorithm::ROW_PARALLEL>(
+    strings_view, cudf::string_scalar("the "), cudf::string_scalar(""), 1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsReplaceTest, ReplaceReplLimitInputSliced)
+{
+  auto input = build_corpus();
+  // replace first two occurrences of ' ' with '--'
+  std::vector<char const*> h_expected{"the--quick--brown fox jumps over the lazy dog",
+                                      "the--fat--cat lays next to the other accénted cat",
+                                      "a--slow--moving turtlé cannot catch the bird",
+                                      "which--can--be composéd together to form a more complete",
+                                      "The--result--does not include the value in the sum in",
+                                      "",
+                                      nullptr};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+  auto stream = cudf::get_default_stream();
+  auto mr     = rmm::mr::get_current_device_resource();
+  std::vector<cudf::size_type> slice_indices{0, 2, 2, 3, 3, 7};
+  auto sliced_strings  = cudf::slice(input, slice_indices);
+  auto sliced_expected = cudf::slice(expected, slice_indices);
+  for (size_t i = 0; i < sliced_strings.size(); ++i) {
+    auto strings_view = cudf::strings_column_view(sliced_strings[i]);
+    auto results =
+      cudf::strings::replace(strings_view, cudf::string_scalar(" "), cudf::string_scalar("--"), 2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, sliced_expected[i]);
+    results = cudf::strings::detail::replace<algorithm::CHAR_PARALLEL>(
+      strings_view, cudf::string_scalar(" "), cudf::string_scalar("--"), 2, stream, mr);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, sliced_expected[i]);
+    results = cudf::strings::detail::replace<algorithm::ROW_PARALLEL>(
+      strings_view, cudf::string_scalar(" "), cudf::string_scalar("--"), 2, stream, mr);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, sliced_expected[i]);
+  }
+}
+
+TEST_F(StringsReplaceTest, ReplaceTargetOverlap)
+{
+  auto corpus      = build_corpus();
+  auto corpus_view = cudf::strings_column_view(corpus);
+  // replace all occurrences of 'the ' with '+++++++ '
+  auto input = cudf::strings::replace(
+    corpus_view, cudf::string_scalar("the "), cudf::string_scalar("++++++++ "));
+  auto strings_view = cudf::strings_column_view(*input);
+  // replace all occurrences of '+++' with 'plus '
+  std::vector<char const*> h_expected{
+    "plus plus ++ quick brown fox jumps over plus plus ++ lazy dog",
+    "plus plus ++ fat cat lays next to plus plus ++ other accénted cat",
+    "a slow moving turtlé cannot catch plus plus ++ bird",
+    "which can be composéd together to form a more complete",
+    "The result does not include plus plus ++ value in plus plus ++ sum in",
+    "",
+    nullptr};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+
+  auto stream = cudf::get_default_stream();
+  auto mr     = rmm::mr::get_current_device_resource();
+
+  auto results =
+    cudf::strings::replace(strings_view, cudf::string_scalar("+++"), cudf::string_scalar("plus "));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::detail::replace<algorithm::CHAR_PARALLEL>(
+    strings_view, cudf::string_scalar("+++"), cudf::string_scalar("plus "), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = cudf::strings::detail::replace<algorithm::ROW_PARALLEL>(
+    strings_view, cudf::string_scalar("+++"), cudf::string_scalar("plus "), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsReplaceTest, ReplaceTargetOverlapsStrings)
+{
+  auto input        = build_corpus();
+  auto strings_view = cudf::strings_column_view(input);
+  auto stream       = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // replace all occurrences of 'dogthe' with '+'
+  // should not replace anything unless it incorrectly matches across a string boundary
+  auto results =
+    cudf::strings::replace(strings_view, cudf::string_scalar("dogthe"), cudf::string_scalar("+"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+  results = cudf::strings::detail::replace<algorithm::CHAR_PARALLEL>(
+    strings_view, cudf::string_scalar("dogthe"), cudf::string_scalar("+"), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+  results = cudf::strings::detail::replace<algorithm::ROW_PARALLEL>(
+    strings_view, cudf::string_scalar("dogthe"), cudf::string_scalar("+"), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+}
+
+TEST_F(StringsReplaceTest, ReplaceNullInput)
+{
+  std::vector<char const*> h_null_strings(128);
+  auto input = cudf::test::strings_column_wrapper(
+    h_null_strings.begin(), h_null_strings.end(), thrust::make_constant_iterator(false));
+  auto strings_view = cudf::strings_column_view(input);
+  auto stream       = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+  // replace all occurrences of '+' with ''
+  // should not replace anything as input is all null
+  auto results =
+    cudf::strings::replace(strings_view, cudf::string_scalar("+"), cudf::string_scalar(""));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+  results = cudf::strings::detail::replace<algorithm::CHAR_PARALLEL>(
+    strings_view, cudf::string_scalar("+"), cudf::string_scalar(""), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+  results = cudf::strings::detail::replace<algorithm::ROW_PARALLEL>(
+    strings_view, cudf::string_scalar("+"), cudf::string_scalar(""), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+}
+
+TEST_F(StringsReplaceTest, ReplaceEndOfString)
+{
+  auto input        = build_corpus();
+  auto strings_view = cudf::strings_column_view(input);
+  auto stream       = cudf::get_default_stream();
+  auto mr           = rmm::mr::get_current_device_resource();
+
+  // replace all occurrences of 'in' with  ' '
+  std::vector<char const*> h_expected{"the quick brown fox jumps over the lazy dog",
+                                      "the fat cat lays next to the other accénted cat",
+                                      "a slow mov g turtlé cannot catch the bird",
+                                      "which can be composéd together to form a more complete",
+                                      "The result does not  clude the value   the sum  ",
+                                      "",
+                                      nullptr};
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(), h_expected.end(), cudf::test::iterators::nulls_from_nullptrs(h_expected));
+
+  auto results =
+    cudf::strings::replace(strings_view, cudf::string_scalar("in"), cudf::string_scalar(" "));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::detail::replace<cudf::strings::detail::replace_algorithm::CHAR_PARALLEL>(
+    strings_view, cudf::string_scalar("in"), cudf::string_scalar(" "), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::detail::replace<cudf::strings::detail::replace_algorithm::ROW_PARALLEL>(
+    strings_view, cudf::string_scalar("in"), cudf::string_scalar(" "), -1, stream, mr);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsReplaceTest, ReplaceSlice)
+{
+  std::vector<char const*> h_strings{"Héllo", "thesé", nullptr, "ARE THE", "tést strings", ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  {
+    auto results = cudf::strings::replace_slice(strings_view, cudf::string_scalar("___"), 2, 3);
+    std::vector<char const*> h_expected{
+      "Hé___lo", "th___sé", nullptr, "AR___ THE", "té___t strings", "___"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::replace_slice(strings_view, cudf::string_scalar("||"), 3, 3);
+    std::vector<char const*> h_expected{
+      "Hél||lo", "the||sé", nullptr, "ARE|| THE", "tés||t strings", "||"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::replace_slice(strings_view, cudf::string_scalar("x"), -1, -1);
+    std::vector<char const*> h_expected{
+      "Héllox", "theséx", nullptr, "ARE THEx", "tést stringsx", "x"};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsReplaceTest, ReplaceSliceError)
+{
+  cudf::test::strings_column_wrapper input({"Héllo", "thesé", "are not", "important", ""});
+  EXPECT_THROW(
+    cudf::strings::replace_slice(cudf::strings_column_view(input), cudf::string_scalar(""), 4, 1),
+    cudf::logic_error);
+}
+
+TEST_F(StringsReplaceTest, ReplaceMulti)
+{
+  auto input        = build_corpus();
+  auto strings_view = cudf::strings_column_view(input);
+
+  cudf::test::strings_column_wrapper targets({"the ", "a ", "to "});
+  auto targets_view = cudf::strings_column_view(targets);
+
+  {
+    cudf::test::strings_column_wrapper repls({"_ ", "A ", "2 "});
+    auto repls_view = cudf::strings_column_view(repls);
+
+    auto results = cudf::strings::replace(strings_view, targets_view, repls_view);
+
+    std::vector<char const*> h_expected{"_ quick brown fox jumps over _ lazy dog",
+                                        "_ fat cat lays next 2 _ other accénted cat",
+                                        "A slow moving turtlé cannot catch _ bird",
+                                        "which can be composéd together 2 form A more complete",
+                                        "The result does not include _ value in _ sum in",
+                                        "",
+                                        nullptr};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper repls({"* "});
+    auto repls_view = cudf::strings_column_view(repls);
+
+    auto results = cudf::strings::replace(strings_view, targets_view, repls_view);
+
+    std::vector<char const*> h_expected{"* quick brown fox jumps over * lazy dog",
+                                        "* fat cat lays next * * other accénted cat",
+                                        "* slow moving turtlé cannot catch * bird",
+                                        "which can be composéd together * form * more complete",
+                                        "The result does not include * value in * sum in",
+                                        "",
+                                        nullptr};
+    cudf::test::strings_column_wrapper expected(
+      h_expected.begin(),
+      h_expected.end(),
+      thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsReplaceTest, ReplaceMultiLong)
+{
+  // The length of the strings are to trigger the code path governed by the AVG_CHAR_BYTES_THRESHOLD
+  // setting in the multi.cu.
+  auto input = cudf::test::strings_column_wrapper(
+    {"This string needs to be very long to trigger the long-replace internal functions. "
+     "This string needs to be very long to trigger the long-replace internal functions. "
+     "This string needs to be very long to trigger the long-replace internal functions. "
+     "This string needs to be very long to trigger the long-replace internal functions.",
+     "012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012"
+     "345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345"
+     "678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678"
+     "901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901"
+     "2345678901234567890123456789",
+     "012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012"
+     "345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345"
+     "678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678"
+     "901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901"
+     "2345678901234567890123456789",
+     "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+     "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+     "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+     "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+     "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá",
+     "",
+     ""},
+    {1, 1, 1, 1, 0, 1});
+  auto strings_view = cudf::strings_column_view(input);
+
+  auto targets      = cudf::test::strings_column_wrapper({"78901", "bananá", "ápple", "78"});
+  auto targets_view = cudf::strings_column_view(targets);
+
+  {
+    cudf::test::strings_column_wrapper repls({"x", "PEAR", "avocado", "$$"});
+    auto repls_view = cudf::strings_column_view(repls);
+
+    auto results = cudf::strings::replace(strings_view, targets_view, repls_view);
+
+    cudf::test::strings_column_wrapper expected(
+      {"This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions.",
+       "0123456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456"
+       "x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x"
+       "23456x23456x23456x23456x23456x23456x23456x23456x23456x23456$$9",
+       "0123456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456"
+       "x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x23456x"
+       "23456x23456x23456x23456x23456x23456x23456x23456x23456x23456$$9",
+       "Test string for overlap check: bananaavocado PEAR avocadoPEAR banavocado avocado PEAR "
+       "Test string for overlap check: bananaavocado PEAR avocadoPEAR banavocado avocado PEAR "
+       "Test string for overlap check: bananaavocado PEAR avocadoPEAR banavocado avocado PEAR "
+       "Test string for overlap check: bananaavocado PEAR avocadoPEAR banavocado avocado PEAR "
+       "Test string for overlap check: bananaavocado PEAR avocadoPEAR banavocado avocado PEAR",
+       "",
+       ""},
+      {1, 1, 1, 1, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper repls({"*"});
+    auto repls_view = cudf::strings_column_view(repls);
+
+    auto results = cudf::strings::replace(strings_view, targets_view, repls_view);
+
+    cudf::test::strings_column_wrapper expected(
+      {"This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions.",
+       "0123456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*"
+       "23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*"
+       "23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*9",
+       "0123456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*"
+       "23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*"
+       "23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*23456*9",
+       "Test string for overlap check: banana* * ** ban* * * Test string for overlap check: "
+       "banana* * ** ban* * * Test string for overlap check: banana* * ** ban* * * Test string for "
+       "overlap check: banana* * ** ban* * * Test string for overlap check: banana* * ** ban* * *",
+       "",
+       ""},
+      {1, 1, 1, 1, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  {
+    targets =
+      cudf::test::strings_column_wrapper({"01234567890123456789012345678901234567890123456789012345"
+                                          "6789012345678901234567890123456789012"
+                                          "34567890123456789012345678901234567890123456789012345678"
+                                          "9012345678901234567890123456789012345"
+                                          "67890123456789012345678901234567890123456789012345678901"
+                                          "2345678901234567890123456789012345678"
+                                          "90123456789012345678901234567890123456789012345678901234"
+                                          "5678901234567890123456789012345678901"
+                                          "2345678901234567890123456789",
+                                          "78"});
+    targets_view    = cudf::strings_column_view(targets);
+    auto repls      = cudf::test::strings_column_wrapper({""});
+    auto repls_view = cudf::strings_column_view(repls);
+
+    auto results = cudf::strings::replace(strings_view, targets_view, repls_view);
+
+    cudf::test::strings_column_wrapper expected(
+      {"This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions. "
+       "This string needs to be very long to trigger the long-replace internal functions.",
+       "",
+       "",
+       "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+       "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+       "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+       "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá "
+       "Test string for overlap check: bananaápple bananá ápplebananá banápple ápple bananá",
+       "",
+       ""},
+      {1, 1, 1, 1, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsReplaceTest, EmptyStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+  auto results      = cudf::strings::replace(
+    strings_view, cudf::string_scalar("not"), cudf::string_scalar("pertinent"));
+  auto view = results->view();
+  cudf::test::expect_column_empty(results->view());
+}
diff --git a/cpp/tests/strings/reverse_tests.cpp b/cpp/tests/strings/reverse_tests.cpp
new file mode 100644
index 0000000..3df42b6
--- /dev/null
+++ b/cpp/tests/strings/reverse_tests.cpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/strings/reverse.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <vector>
+
+struct StringsReverseTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsReverseTest, Reverse)
+{
+  auto input = cudf::test::strings_column_wrapper(
+    {"abcdef", "12345", "", "", "aébé", "A é Z", "X", "é"}, {1, 1, 1, 0, 1, 1, 1, 1});
+  auto results  = cudf::strings::reverse(cudf::strings_column_view(input));
+  auto expected = cudf::test::strings_column_wrapper(
+    {"fedcba", "54321", "", "", "ébéa", "Z é A", "X", "é"}, {1, 1, 1, 0, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  auto sliced = cudf::slice(input, {1, 7}).front();
+  results     = cudf::strings::reverse(cudf::strings_column_view(sliced));
+  expected =
+    cudf::test::strings_column_wrapper({"54321", "", "", "ébéa", "Z é A", "X"}, {1, 1, 0, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(StringsReverseTest, EmptyStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto results = cudf::strings::reverse(cudf::strings_column_view(zero_size_strings_column));
+  auto view    = results->view();
+  cudf::test::expect_column_empty(results->view());
+}
diff --git a/cpp/tests/strings/slice_tests.cpp b/cpp/tests/strings/slice_tests.cpp
new file mode 100644
index 0000000..92230d0
--- /dev/null
+++ b/cpp/tests/strings/slice_tests.cpp
@@ -0,0 +1,314 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/slice.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/sequence.h>
+
+#include <string>
+#include <vector>
+
+struct StringsSliceTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsSliceTest, Substring)
+{
+  std::vector<char const*> h_strings{"Héllo", "thesé", nullptr, "ARE THE", "tést strings", ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected({"llo", "esé", nullptr, "E T", "st ", ""});
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto strings_column = static_cast<cudf::strings_column_view>(strings);
+  auto results        = cudf::strings::slice_strings(strings_column, 2, 5);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+class Parameters : public StringsSliceTest, public testing::WithParamInterface<cudf::size_type> {};
+
+TEST_P(Parameters, Substring)
+{
+  std::vector<std::string> h_strings{"basic strings", "that can", "be used", "with STL"};
+  cudf::size_type start = GetParam();
+
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto strings_column = cudf::strings_column_view(strings);
+  auto results        = cudf::strings::slice_strings(strings_column, start);
+
+  std::vector<std::string> h_expected;
+  for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr)
+    h_expected.push_back((*itr).substr(start));
+
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_P(Parameters, Substring_From)
+{
+  std::vector<std::string> h_strings{"basic strings", "that can", "be used", "with STL"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto strings_column = cudf::strings_column_view(strings);
+
+  auto param_index = GetParam();
+  thrust::host_vector<int32_t> starts(h_strings.size());
+  thrust::sequence(starts.begin(), starts.end(), param_index);
+  cudf::test::fixed_width_column_wrapper<int32_t> starts_column(starts.begin(), starts.end());
+  thrust::host_vector<int32_t> stops(h_strings.size());
+  thrust::sequence(stops.begin(), stops.end(), param_index + 2);
+  cudf::test::fixed_width_column_wrapper<int32_t> stops_column(stops.begin(), stops.end());
+
+  auto results = cudf::strings::slice_strings(strings_column, starts_column, stops_column);
+
+  std::vector<std::string> h_expected;
+  for (size_t idx = 0; idx < h_strings.size(); ++idx)
+    h_expected.push_back(h_strings[idx].substr(starts[idx], stops[idx] - starts[idx]));
+
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_P(Parameters, SubstringStopZero)
+{
+  cudf::size_type start = GetParam();
+  cudf::test::strings_column_wrapper input({"abc", "défgh", "", "XYZ"});
+  auto view = cudf::strings_column_view(input);
+
+  auto results = cudf::strings::slice_strings(view, start, 0);
+  cudf::test::strings_column_wrapper expected({"", "", "", ""});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  auto starts =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>({start, start, start, start});
+  auto stops = cudf::test::fixed_width_column_wrapper<cudf::size_type>({0, 0, 0, 0});
+
+  results = cudf::strings::slice_strings(view, starts, stops);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_P(Parameters, AllEmpty)
+{
+  std::vector<std::string> h_strings{"", "", "", ""};
+  cudf::size_type start = GetParam();
+
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto strings_column = cudf::strings_column_view(strings);
+  auto results        = cudf::strings::slice_strings(strings_column, start);
+
+  std::vector<std::string> h_expected(h_strings);
+  cudf::test::strings_column_wrapper expected(h_expected.begin(), h_expected.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  thrust::host_vector<int32_t> starts(h_strings.size(), 1);
+  cudf::test::fixed_width_column_wrapper<int32_t> starts_column(starts.begin(), starts.end());
+  thrust::host_vector<int32_t> stops(h_strings.size(), 2);
+  cudf::test::fixed_width_column_wrapper<int32_t> stops_column(stops.begin(), stops.end());
+
+  results = cudf::strings::slice_strings(strings_column, starts_column, stops_column);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_P(Parameters, AllNulls)
+{
+  std::vector<char const*> h_strings{nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::size_type start = GetParam();
+
+  auto strings_column = cudf::strings_column_view(strings);
+  auto results        = cudf::strings::slice_strings(strings_column, start);
+
+  std::vector<char const*> h_expected(h_strings);
+  cudf::test::strings_column_wrapper expected(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  thrust::host_vector<int32_t> starts(h_strings.size(), 1);
+  cudf::test::fixed_width_column_wrapper<int32_t> starts_column(starts.begin(), starts.end());
+  thrust::host_vector<int32_t> stops(h_strings.size(), 2);
+  cudf::test::fixed_width_column_wrapper<int32_t> stops_column(stops.begin(), stops.end());
+
+  results = cudf::strings::slice_strings(strings_column, starts_column, stops_column);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+INSTANTIATE_TEST_CASE_P(StringsSliceTest,
+                        Parameters,
+                        testing::ValuesIn(std::array<cudf::size_type, 3>{1, 2, 3}));
+
+TEST_F(StringsSliceTest, NegativePositions)
+{
+  cudf::test::strings_column_wrapper strings{
+    "a", "bc", "def", "ghij", "klmno", "pqrstu", "vwxyz", ""};
+  auto strings_column = cudf::strings_column_view(strings);
+  {
+    auto results = cudf::strings::slice_strings(strings_column, -1);
+    cudf::test::strings_column_wrapper expected{"a", "c", "f", "j", "o", "u", "z", ""};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(strings_column, 0, -1);
+    cudf::test::strings_column_wrapper expected{"", "b", "de", "ghi", "klmn", "pqrst", "vwxy", ""};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(strings_column, 7, -2, -1);
+    cudf::test::strings_column_wrapper expected{"a", "c", "f", "j", "o", "u", "z", ""};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(strings_column, 7, -7, -1);
+    cudf::test::strings_column_wrapper expected{
+      "a", "cb", "fed", "jihg", "onmlk", "utsrqp", "zyxwv", ""};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(strings_column, -3, -1);
+    cudf::test::strings_column_wrapper expected{"", "b", "de", "hi", "mn", "st", "xy", ""};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsSliceTest, NullPositions)
+{
+  cudf::test::strings_column_wrapper strings{"a", "bc", "def", "ghij", "klmno", "pqrstu", "vwxyz"};
+  auto strings_column = cudf::strings_column_view(strings);
+  {
+    auto results = cudf::strings::slice_strings(strings_column,
+                                                cudf::numeric_scalar<cudf::size_type>(0, false),
+                                                cudf::numeric_scalar<cudf::size_type>(0, false),
+                                                -1);
+    cudf::test::strings_column_wrapper expected{
+      "a", "cb", "fed", "jihg", "onmlk", "utsrqp", "zyxwv"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(strings_column,
+                                                cudf::numeric_scalar<cudf::size_type>(0, false),
+                                                cudf::numeric_scalar<cudf::size_type>(0, false),
+                                                2);
+    cudf::test::strings_column_wrapper expected{"a", "b", "df", "gi", "kmo", "prt", "vxz"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(
+      strings_column, 0, cudf::numeric_scalar<cudf::size_type>(0, false), -1);
+    cudf::test::strings_column_wrapper expected{"a", "b", "d", "g", "k", "p", "v"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(
+      strings_column, cudf::numeric_scalar<cudf::size_type>(0, false), -2, -1);
+    cudf::test::strings_column_wrapper expected{"a", "c", "f", "j", "o", "u", "z"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::slice_strings(
+      strings_column, cudf::numeric_scalar<cudf::size_type>(0, false), -1, 2);
+    cudf::test::strings_column_wrapper expected{"", "b", "d", "gi", "km", "prt", "vx"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsSliceTest, MaxPositions)
+{
+  cudf::test::strings_column_wrapper strings{"a", "bc", "def", "ghij", "klmno", "pqrstu", "vwxyz"};
+  auto strings_column = cudf::strings_column_view(strings);
+  cudf::test::strings_column_wrapper expected{"", "", "", "", "", "", ""};
+
+  auto results = cudf::strings::slice_strings(strings_column, 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::slice_strings(strings_column, 0, -10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::slice_strings(
+    strings_column, cudf::numeric_scalar<cudf::size_type>(0, false), -10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::slice_strings(strings_column, 10, 19);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::slice_strings(strings_column, 10, 19, 9);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::slice_strings(strings_column, -10, -19);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = cudf::strings::slice_strings(strings_column, -10, -19, -1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsSliceTest, Error)
+{
+  cudf::test::strings_column_wrapper strings{"this string intentionally left blank"};
+  auto strings_view = cudf::strings_column_view(strings);
+  EXPECT_THROW(cudf::strings::slice_strings(strings_view, 0, 0, 0), cudf::logic_error);
+
+  auto indexes = cudf::test::fixed_width_column_wrapper<int32_t>({1, 2});
+  EXPECT_THROW(cudf::strings::slice_strings(strings_view, indexes, indexes), cudf::logic_error);
+
+  auto indexes_null = cudf::test::fixed_width_column_wrapper<int32_t>({1}, {0});
+  EXPECT_THROW(cudf::strings::slice_strings(strings_view, indexes_null, indexes_null),
+               cudf::logic_error);
+
+  auto indexes_bad = cudf::test::fixed_width_column_wrapper<float>({1});
+  EXPECT_THROW(cudf::strings::slice_strings(strings_view, indexes_bad, indexes_bad),
+               cudf::logic_error);
+}
+
+TEST_F(StringsSliceTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+  auto strings_view                   = cudf::strings_column_view(zero_size_strings_column);
+
+  auto results = cudf::strings::slice_strings(strings_view, 1, 2);
+  cudf::test::expect_column_empty(results->view());
+
+  auto const starts_column = cudf::make_empty_column(cudf::type_id::INT32)->view();
+  auto const stops_column  = cudf::make_empty_column(cudf::type_id::INT32)->view();
+
+  results = cudf::strings::slice_strings(strings_view, starts_column, stops_column);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsSliceTest, AllEmpty)
+{
+  auto strings_col  = cudf::test::strings_column_wrapper({"", "", "", "", ""});
+  auto strings_view = cudf::strings_column_view(strings_col);
+  auto exp_results  = cudf::column_view(strings_col);
+
+  auto results = cudf::strings::slice_strings(strings_view, 0, -1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results);
+  results = cudf::strings::slice_strings(strings_view, 0, -1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, exp_results);
+}
diff --git a/cpp/tests/strings/split_tests.cpp b/cpp/tests/strings/split_tests.cpp
new file mode 100644
index 0000000..445e283
--- /dev/null
+++ b/cpp/tests/strings/split_tests.cpp
@@ -0,0 +1,944 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/split/partition.hpp>
+#include <cudf/strings/split/split.hpp>
+#include <cudf/strings/split/split_re.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsSplitTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsSplitTest, Split)
+{
+  std::vector<char const*> h_strings{
+    "Héllo thesé", nullptr, "are some", "tést String", "", "no-delimiter"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+
+  std::vector<char const*> h_expected1{"Héllo", nullptr, "are", "tést", "", "no-delimiter"};
+  cudf::test::strings_column_wrapper expected1(
+    h_expected1.begin(),
+    h_expected1.end(),
+    thrust::make_transform_iterator(h_expected1.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected2{"thesé", nullptr, "some", "String", nullptr, nullptr};
+  cudf::test::strings_column_wrapper expected2(
+    h_expected2.begin(),
+    h_expected2.end(),
+    thrust::make_transform_iterator(h_expected2.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::split(strings_view, cudf::string_scalar(" "));
+  EXPECT_TRUE(results->num_columns() == 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, SplitWithMax)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"Héllo::thesé::world", "are::some", "tést::String:", ":last::one", ":::", "x::::y"});
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::strings_column_wrapper expected1({"Héllo", "are", "tést", ":last", "", "x"});
+  cudf::test::strings_column_wrapper expected2(
+    {"thesé::world", "some", "String:", "one", ":", "::y"});
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::split(strings_view, cudf::string_scalar("::"), 1);
+  EXPECT_TRUE(results->num_columns() == 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, SplitWhitespace)
+{
+  std::vector<char const*> h_strings{
+    "Héllo thesé", nullptr, "are\tsome", "tést\nString", "  ", " a  b ", ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+
+  std::vector<char const*> h_expected1{"Héllo", nullptr, "are", "tést", nullptr, "a", nullptr};
+  cudf::test::strings_column_wrapper expected1(
+    h_expected1.begin(),
+    h_expected1.end(),
+    thrust::make_transform_iterator(h_expected1.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected2{"thesé", nullptr, "some", "String", nullptr, "b", nullptr};
+  cudf::test::strings_column_wrapper expected2(
+    h_expected2.begin(),
+    h_expected2.end(),
+    thrust::make_transform_iterator(h_expected2.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::split(strings_view);
+  EXPECT_TRUE(results->num_columns() == 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, SplitWhitespaceWithMax)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"a bc d", "a  bc  d", " ab cd e", "ab cd e ", " ab cd e "});
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::strings_column_wrapper expected1({"a", "a", "ab", "ab", "ab"});
+  cudf::test::strings_column_wrapper expected2({"bc d", "bc  d", "cd e", "cd e ", "cd e "});
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::split(strings_view, cudf::string_scalar(""), 1);
+  EXPECT_TRUE(results->num_columns() == 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, RSplit)
+{
+  std::vector<char const*> h_strings{
+    "héllo", nullptr, "a_bc_déf", "a__bc", "_ab_cd", "ab_cd_", "", " a b ", " a  bbb   c"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+
+  std::vector<char const*> h_expected1{
+    "héllo", nullptr, "a", "a", "", "ab", "", " a b ", " a  bbb   c"};
+  cudf::test::strings_column_wrapper expected1(
+    h_expected1.begin(),
+    h_expected1.end(),
+    thrust::make_transform_iterator(h_expected1.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected2{
+    nullptr, nullptr, "bc", "", "ab", "cd", nullptr, nullptr, nullptr};
+  cudf::test::strings_column_wrapper expected2(
+    h_expected2.begin(),
+    h_expected2.end(),
+    thrust::make_transform_iterator(h_expected2.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected3{
+    nullptr, nullptr, "déf", "bc", "cd", "", nullptr, nullptr, nullptr};
+  cudf::test::strings_column_wrapper expected3(
+    h_expected3.begin(),
+    h_expected3.end(),
+    thrust::make_transform_iterator(h_expected3.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  expected_columns.push_back(expected3.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::rsplit(strings_view, cudf::string_scalar("_"));
+  EXPECT_TRUE(results->num_columns() == 3);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, RSplitWithMax)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"Héllo::thesé::world", "are::some", "tést::String:", ":last::one", ":::", "x::::y"});
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::strings_column_wrapper expected1(
+    {"Héllo::thesé", "are", "tést", ":last", ":", "x::"});
+  cudf::test::strings_column_wrapper expected2({"world", "some", "String:", "one", "", "y"});
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::rsplit(strings_view, cudf::string_scalar("::"), 1);
+  EXPECT_TRUE(results->num_columns() == 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, RSplitWhitespace)
+{
+  std::vector<char const*> h_strings{"héllo", nullptr, "a_bc_déf", "", " a\tb ", " a\r bbb   c"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  cudf::strings_column_view strings_view(strings);
+  std::vector<char const*> h_expected1{"héllo", nullptr, "a_bc_déf", nullptr, "a", "a"};
+  cudf::test::strings_column_wrapper expected1(
+    h_expected1.begin(),
+    h_expected1.end(),
+    thrust::make_transform_iterator(h_expected1.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected2{nullptr, nullptr, nullptr, nullptr, "b", "bbb"};
+  cudf::test::strings_column_wrapper expected2(
+    h_expected2.begin(),
+    h_expected2.end(),
+    thrust::make_transform_iterator(h_expected2.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<char const*> h_expected3{nullptr, nullptr, nullptr, nullptr, nullptr, "c"};
+  cudf::test::strings_column_wrapper expected3(
+    h_expected3.begin(),
+    h_expected3.end(),
+    thrust::make_transform_iterator(h_expected3.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  expected_columns.push_back(expected3.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::rsplit(strings_view);
+  EXPECT_TRUE(results->num_columns() == 3);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, RSplitWhitespaceWithMax)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"a bc d", "a  bc  d", " ab cd e", "ab cd e ", " ab cd e "});
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::strings_column_wrapper expected1({"a bc", "a  bc", " ab cd", "ab cd", " ab cd"});
+  cudf::test::strings_column_wrapper expected2({"d", "d", "e", "e", "e"});
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  auto results = cudf::strings::rsplit(strings_view, cudf::string_scalar(""), 1);
+  EXPECT_TRUE(results->num_columns() == 2);
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, SplitRecord)
+{
+  std::vector<char const*> h_strings{" Héllo thesé", nullptr, "are some  ", "tést String", ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  auto result =
+    cudf::strings::split_record(cudf::strings_column_view(strings), cudf::string_scalar(" "));
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected(
+    {LCW{"", "Héllo", "thesé"}, LCW{}, LCW{"are", "some", "", ""}, LCW{"tést", "String"}, LCW{""}},
+    validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, SplitRecordWithMaxSplit)
+{
+  std::vector<char const*> h_strings{" Héllo thesé", nullptr, "are some  ", "tést String", ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  auto result =
+    cudf::strings::split_record(cudf::strings_column_view(strings), cudf::string_scalar(" "), 1);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected(
+    {LCW{"", "Héllo thesé"}, LCW{}, LCW{"are", "some  "}, LCW{"tést", "String"}, LCW{""}},
+    validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, SplitRecordWhitespace)
+{
+  std::vector<char const*> h_strings{
+    "   Héllo thesé", nullptr, "are\tsome  ", "tést\nString", "  "};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  auto result = cudf::strings::split_record(cudf::strings_column_view(strings));
+  using LCW   = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"Héllo", "thesé"}, LCW{}, LCW{"are", "some"}, LCW{"tést", "String"}, LCW{}},
+               validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, SplitRecordWhitespaceWithMaxSplit)
+{
+  std::vector<char const*> h_strings{
+    "   Héllo thesé  ", nullptr, "are\tsome  ", "tést\nString", "  "};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  auto result =
+    cudf::strings::split_record(cudf::strings_column_view(strings), cudf::string_scalar(""), 1);
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"Héllo", "thesé  "}, LCW{}, LCW{"are", "some  "}, LCW{"tést", "String"}, LCW{}},
+               validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, MultiByteDelimiters)
+{
+  // Overlapping delimiters
+  auto input =
+    cudf::test::strings_column_wrapper({"u::", "w:::x", "y::::z", "::a", ":::b", ":::c:::"});
+  auto view = cudf::strings_column_view(input);
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  {
+    auto result        = cudf::strings::split_record(view, cudf::string_scalar("::"));
+    auto expected_left = LCW({LCW{"u", ""},
+                              LCW{"w", ":x"},
+                              LCW{"y", "", "z"},
+                              LCW{"", "a"},
+                              LCW{"", ":b"},
+                              LCW{"", ":c", ":"}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected_left);
+    result              = cudf::strings::rsplit_record(view, cudf::string_scalar("::"));
+    auto expected_right = LCW({LCW{"u", ""},
+                               LCW{"w:", "x"},
+                               LCW{"y", "", "z"},
+                               LCW{"", "a"},
+                               LCW{":", "b"},
+                               LCW{":", "c:", ""}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected_right);
+  }
+  {
+    auto result = cudf::strings::split(view, cudf::string_scalar("::"));
+
+    auto c0 = cudf::test::strings_column_wrapper({"u", "w", "y", "", "", ""});
+    auto c1 = cudf::test::strings_column_wrapper({"", ":x", "", "a", ":b", ":c"});
+    auto c2 = cudf::test::strings_column_wrapper({"", "", "z", "", "", ":"}, {0, 0, 1, 0, 0, 1});
+    std::vector<std::unique_ptr<cudf::column>> expected_columns;
+    expected_columns.push_back(c0.release());
+    expected_columns.push_back(c1.release());
+    expected_columns.push_back(c2.release());
+    auto expected_left = std::make_unique<cudf::table>(std::move(expected_columns));
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result, *expected_left);
+
+    result = cudf::strings::rsplit(view, cudf::string_scalar("::"));
+
+    c0 = cudf::test::strings_column_wrapper({"u", "w:", "y", "", ":", ":"});
+    c1 = cudf::test::strings_column_wrapper({"", "x", "", "a", "b", "c:"});
+    c2 = cudf::test::strings_column_wrapper({"", "", "z", "", "", ""}, {0, 0, 1, 0, 0, 1});
+    expected_columns.push_back(c0.release());
+    expected_columns.push_back(c1.release());
+    expected_columns.push_back(c2.release());
+    auto expected_right = std::make_unique<cudf::table>(std::move(expected_columns));
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result, *expected_right);
+  }
+
+  // Delimiters that span across adjacent strings
+  input = cudf::test::strings_column_wrapper({"{a=1}:{b=2}:", "{c=3}", ":{}:{}"});
+  view  = cudf::strings_column_view(input);
+  {
+    auto result   = cudf::strings::split_record(view, cudf::string_scalar("}:{"));
+    auto expected = LCW({LCW{"{a=1", "b=2}:"}, LCW{"{c=3}"}, LCW{":{", "}"}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+    result = cudf::strings::rsplit_record(view, cudf::string_scalar("}:{"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+  }
+  {
+    auto result = cudf::strings::split(view, cudf::string_scalar("}:{"));
+
+    auto c0 = cudf::test::strings_column_wrapper({"{a=1", "{c=3}", ":{"});
+    auto c1 = cudf::test::strings_column_wrapper({"b=2}:", "", "}"}, {1, 0, 1});
+    std::vector<std::unique_ptr<cudf::column>> expected_columns;
+    expected_columns.push_back(c0.release());
+    expected_columns.push_back(c1.release());
+    auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result, *expected);
+
+    result = cudf::strings::rsplit(view, cudf::string_scalar("}:{"));
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(*result, *expected);
+  }
+}
+
+TEST_F(StringsSplitTest, SplitRegex)
+{
+  std::vector<char const*> h_strings{" Héllo thesé", nullptr, "are some  ", "tést String", ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper input(h_strings.begin(), h_strings.end(), validity);
+  auto sv = cudf::strings_column_view(input);
+
+  {
+    auto pattern = std::string("\\s+");
+
+    cudf::test::strings_column_wrapper col0({"", "", "are", "tést", ""}, validity);
+    cudf::test::strings_column_wrapper col1({"Héllo", "", "some", "String", ""}, {1, 0, 1, 1, 0});
+    cudf::test::strings_column_wrapper col2({"thesé", "", "", "", ""}, {1, 0, 1, 0, 0});
+    auto expected = cudf::table_view({col0, col1, col2});
+    auto prog     = cudf::strings::regex_program::create(pattern);
+    auto result   = cudf::strings::split_re(sv, *prog);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+
+    // rsplit == split when using default parameters
+    result = cudf::strings::rsplit_re(sv, *prog);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+  }
+
+  {
+    auto pattern = std::string("[eé]");
+
+    cudf::test::strings_column_wrapper col0({" H", "", "ar", "t", ""}, validity);
+    cudf::test::strings_column_wrapper col1({"llo th", "", " som", "st String", ""},
+                                            {1, 0, 1, 1, 0});
+    cudf::test::strings_column_wrapper col2({"s", "", "  ", "", ""}, {1, 0, 1, 0, 0});
+    cudf::test::strings_column_wrapper col3({"", "", "", "", ""}, {1, 0, 0, 0, 0});
+    auto expected = cudf::table_view({col0, col1, col2, col3});
+    auto prog     = cudf::strings::regex_program::create(pattern);
+    auto result   = cudf::strings::split_re(sv, *prog);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+
+    // rsplit == split when using default parameters
+    result = cudf::strings::rsplit_re(sv, *prog);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+  }
+}
+
+TEST_F(StringsSplitTest, SplitRecordRegex)
+{
+  std::vector<char const*> h_strings{" Héllo thesé", nullptr, "are some  ", "tést String", ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper input(h_strings.begin(), h_strings.end(), validity);
+  auto sv = cudf::strings_column_view(input);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  {
+    auto pattern = std::string("\\s+");
+
+    LCW expected(
+      {LCW{"", "Héllo", "thesé"}, LCW{}, LCW{"are", "some", ""}, LCW{"tést", "String"}, LCW{""}},
+      validity);
+    auto prog   = cudf::strings::regex_program::create(pattern);
+    auto result = cudf::strings::split_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+
+    // rsplit == split when using default parameters
+    result = cudf::strings::rsplit_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+  }
+
+  {
+    auto pattern = std::string("[eé]");
+
+    LCW expected({LCW{" H", "llo th", "s", ""},
+                  LCW{},
+                  LCW{"ar", " som", "  "},
+                  LCW{"t", "st String"},
+                  LCW{""}},
+                 validity);
+    auto prog   = cudf::strings::regex_program::create(pattern);
+    auto result = cudf::strings::split_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+
+    // rsplit == split when using default parameters
+    result = cudf::strings::rsplit_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+  }
+}
+
+TEST_F(StringsSplitTest, SplitRegexWithMaxSplit)
+{
+  std::vector<char const*> h_strings{" Héllo\tthesé", nullptr, "are\nsome  ", "tést\rString", ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper input(h_strings.begin(), h_strings.end(), validity);
+  auto sv = cudf::strings_column_view(input);
+  {
+    auto pattern = std::string("\\s+");
+
+    cudf::test::strings_column_wrapper col0({"", "", "are", "tést", ""}, {1, 0, 1, 1, 1});
+    cudf::test::strings_column_wrapper col1({"Héllo\tthesé", "", "some  ", "String", ""},
+                                            {1, 0, 1, 1, 0});
+    auto expected = cudf::table_view({col0, col1});
+    auto prog     = cudf::strings::regex_program::create(pattern);
+    auto result   = cudf::strings::split_re(sv, *prog, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+
+    // split everything is the same output as maxsplit==2 for the test input column here
+    result         = cudf::strings::split_re(sv, *prog, 2);
+    auto expected2 = cudf::strings::split_re(sv, *prog);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected2->view());
+    result = cudf::strings::split_re(sv, *prog, 3);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected2->view());
+  }
+  {
+    auto pattern = std::string("\\s");
+
+    using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+    LCW expected1(
+      {LCW{"", "Héllo\tthesé"}, LCW{}, LCW{"are", "some  "}, LCW{"tést", "String"}, LCW{""}},
+      validity);
+    auto prog   = cudf::strings::regex_program::create(pattern);
+    auto result = cudf::strings::split_record_re(sv, *prog, 1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected1);
+
+    result = cudf::strings::split_record_re(sv, *prog, 2);
+    LCW expected2(
+      {LCW{"", "Héllo", "thesé"}, LCW{}, LCW{"are", "some", " "}, LCW{"tést", "String"}, LCW{""}},
+      validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected2);
+
+    // split everything is the same output as maxsplit==3 for the test input column here
+    result         = cudf::strings::split_record_re(sv, *prog, 3);
+    auto expected0 = cudf::strings::split_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected0->view());
+    result = cudf::strings::split_record_re(sv, *prog, 3);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected0->view());
+  }
+}
+
+TEST_F(StringsSplitTest, SplitRegexWordBoundary)
+{
+  cudf::test::strings_column_wrapper input({"a", "ab", "-+", "e\né"});
+  auto sv = cudf::strings_column_view(input);
+  {
+    auto pattern = std::string("\\b");
+
+    cudf::test::strings_column_wrapper col0({"", "", "-+", ""});
+    cudf::test::strings_column_wrapper col1({"a", "ab", "", "e"}, {1, 1, 0, 1});
+    cudf::test::strings_column_wrapper col2({"", "", "", "\n"}, {1, 1, 0, 1});
+    cudf::test::strings_column_wrapper col3({"", "", "", "é"}, {0, 0, 0, 1});
+    cudf::test::strings_column_wrapper col4({"", "", "", ""}, {0, 0, 0, 1});
+    auto expected = cudf::table_view({col0, col1, col2, col3, col4});
+    auto prog     = cudf::strings::regex_program::create(pattern);
+    auto result   = cudf::strings::split_re(sv, *prog);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+  }
+  {
+    auto pattern = std::string("\\B");
+
+    using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+    LCW expected({LCW{"a"}, LCW{"a", "b"}, LCW{"", "-", "+", ""}, LCW{"e\né"}});
+    auto prog   = cudf::strings::regex_program::create(pattern);
+    auto result = cudf::strings::split_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+  }
+}
+
+TEST_F(StringsSplitTest, RSplitRecord)
+{
+  std::vector<char const*> h_strings{
+    "héllo", nullptr, "a_bc_déf", "a__bc", "_ab_cd", "ab_cd_", "", " a b ", " a  bbb   c"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"héllo"},
+                LCW{},
+                LCW{"a", "bc", "déf"},
+                LCW{"a", "", "bc"},
+                LCW{"", "ab", "cd"},
+                LCW{"ab", "cd", ""},
+                LCW{""},
+                LCW{" a b "},
+                LCW{" a  bbb   c"}},
+               validity);
+  auto result =
+    cudf::strings::rsplit_record(cudf::strings_column_view(strings), cudf::string_scalar("_"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, RSplitRecordWithMaxSplit)
+{
+  std::vector<char const*> h_strings{"héllo",
+                                     nullptr,
+                                     "a_bc_déf",
+                                     "___a__bc",
+                                     "_ab_cd_",
+                                     "ab_cd_",
+                                     "",
+                                     " a b ___",
+                                     "___ a  bbb   c"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"héllo"},
+                LCW{},
+                LCW{"a", "bc", "déf"},
+                LCW{"___a", "", "bc"},
+                LCW{"_ab", "cd", ""},
+                LCW{"ab", "cd", ""},
+                LCW{""},
+                LCW{" a b _", "", ""},
+                LCW{"_", "", " a  bbb   c"}},
+               validity);
+
+  auto result =
+    cudf::strings::rsplit_record(cudf::strings_column_view(strings), cudf::string_scalar("_"), 2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, RSplitRecordWhitespace)
+{
+  std::vector<char const*> h_strings{"héllo", nullptr, "a_bc_déf", "", " a\tb ", " a\r bbb   c"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{"héllo"}, LCW{}, LCW{"a_bc_déf"}, LCW{}, LCW{"a", "b"}, LCW{"a", "bbb", "c"}},
+               validity);
+
+  auto result = cudf::strings::rsplit_record(cudf::strings_column_view(strings));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, RSplitRecordWhitespaceWithMaxSplit)
+{
+  std::vector<char const*> h_strings{
+    "  héllo Asher ", nullptr, "   a_bc_déf   ", "", " a\tb ", " a\r bbb   c"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected(
+    {LCW{"  héllo", "Asher"}, LCW{}, LCW{"a_bc_déf"}, LCW{}, LCW{" a", "b"}, LCW{" a\r bbb", "c"}},
+    validity);
+
+  auto result =
+    cudf::strings::rsplit_record(cudf::strings_column_view(strings), cudf::string_scalar(""), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, RSplitRegexWithMaxSplit)
+{
+  std::vector<char const*> h_strings{" Héllo\tthesé", nullptr, "are some\n ", "tést\rString", ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper input(h_strings.begin(), h_strings.end(), validity);
+  auto sv = cudf::strings_column_view(input);
+
+  auto pattern = std::string("\\s+");
+  auto prog    = cudf::strings::regex_program::create(pattern);
+
+  {
+    cudf::test::strings_column_wrapper col0({" Héllo", "", "are some", "tést", ""}, validity);
+    cudf::test::strings_column_wrapper col1({"thesé", "", "", "String", ""}, {1, 0, 1, 1, 0});
+    auto expected = cudf::table_view({col0, col1});
+    auto result   = cudf::strings::rsplit_re(sv, *prog, 1);
+    CUDF_TEST_EXPECT_TABLES_EQUIVALENT(result->view(), expected);
+  }
+  {
+    using LCW = cudf::test::lists_column_wrapper<cudf::string_view>;
+    LCW expected(
+      {LCW{" Héllo", "thesé"}, LCW{}, LCW{"are some", ""}, LCW{"tést", "String"}, LCW{""}},
+      validity);
+    auto result = cudf::strings::rsplit_record_re(sv, *prog, 1);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected);
+
+    // split everything is the same output as any maxsplit > 2 for the test input column here
+    result         = cudf::strings::rsplit_record_re(sv, *prog, 3);
+    auto expected0 = cudf::strings::rsplit_record_re(sv, *prog);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected0->view());
+    result = cudf::strings::rsplit_record_re(sv, *prog, 3);
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(result->view(), expected0->view());
+  }
+}
+
+TEST_F(StringsSplitTest, SplitZeroSizeStringsColumns)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto prog    = cudf::strings::regex_program::create("\\s");
+  auto results = cudf::strings::split(zero_size_strings_column);
+  EXPECT_TRUE(results->num_columns() == 1);
+  EXPECT_TRUE(results->num_rows() == 0);
+  results = cudf::strings::rsplit(zero_size_strings_column);
+  EXPECT_TRUE(results->num_columns() == 1);
+  EXPECT_TRUE(results->num_rows() == 0);
+  results = cudf::strings::split_re(zero_size_strings_column, *prog);
+  EXPECT_TRUE(results->num_columns() == 1);
+  EXPECT_TRUE(results->num_rows() == 0);
+  results = cudf::strings::rsplit_re(zero_size_strings_column, *prog);
+  EXPECT_TRUE(results->num_columns() == 1);
+  EXPECT_TRUE(results->num_rows() == 0);
+
+  auto target      = cudf::string_scalar(" ");
+  auto list_result = cudf::strings::split_record(zero_size_strings_column);
+  EXPECT_TRUE(list_result->size() == 0);
+  list_result = cudf::strings::rsplit_record(zero_size_strings_column);
+  EXPECT_TRUE(list_result->size() == 0);
+  list_result = cudf::strings::split_record(zero_size_strings_column, target);
+  EXPECT_TRUE(list_result->size() == 0);
+  list_result = cudf::strings::rsplit_record(zero_size_strings_column, target);
+  EXPECT_TRUE(list_result->size() == 0);
+  list_result = cudf::strings::split_record_re(zero_size_strings_column, *prog);
+  EXPECT_TRUE(list_result->size() == 0);
+  list_result = cudf::strings::rsplit_record_re(zero_size_strings_column, *prog);
+  EXPECT_TRUE(list_result->size() == 0);
+}
+
+// This test specifically for https://github.com/rapidsai/custrings/issues/119
+TEST_F(StringsSplitTest, AllNullsCase)
+{
+  cudf::test::strings_column_wrapper input({"", "", ""}, {0, 0, 0});
+  auto sv   = cudf::strings_column_view(input);
+  auto prog = cudf::strings::regex_program::create("-");
+
+  auto results = cudf::strings::split(sv);
+  EXPECT_TRUE(results->num_columns() == 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->get_column(0).view(), input);
+  results = cudf::strings::split(sv, cudf::string_scalar("-"));
+  EXPECT_TRUE(results->num_columns() == 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->get_column(0).view(), input);
+  results = cudf::strings::rsplit(sv);
+  EXPECT_TRUE(results->num_columns() == 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->get_column(0).view(), input);
+  results = cudf::strings::rsplit(sv, cudf::string_scalar("-"));
+  EXPECT_TRUE(results->num_columns() == 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->get_column(0).view(), input);
+  results = cudf::strings::split_re(sv, *prog);
+  EXPECT_TRUE(results->num_columns() == 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->get_column(0).view(), input);
+  results = cudf::strings::rsplit_re(sv, *prog);
+  EXPECT_TRUE(results->num_columns() == 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->get_column(0).view(), input);
+
+  auto target      = cudf::string_scalar(" ");
+  auto list_result = cudf::strings::split_record(sv);
+  using LCW        = cudf::test::lists_column_wrapper<cudf::string_view>;
+  LCW expected({LCW{}, LCW{}, LCW{}}, cudf::test::iterators::all_nulls());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_result->view(), expected);
+  list_result = cudf::strings::rsplit_record(sv);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_result->view(), expected);
+  list_result = cudf::strings::split_record(sv, target);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_result->view(), expected);
+  list_result = cudf::strings::rsplit_record(sv, target);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_result->view(), expected);
+  list_result = cudf::strings::split_record_re(sv, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_result->view(), expected);
+  list_result = cudf::strings::rsplit_record_re(sv, *prog);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_result->view(), expected);
+}
+
+TEST_F(StringsSplitTest, Partition)
+{
+  std::vector<char const*> h_strings{
+    "héllo", nullptr, "a_bc_déf", "a__bc", "_ab_cd", "ab_cd_", "", " a b "};
+  std::vector<char const*> h_expecteds{
+    "héllo", nullptr, "a", "a", "", "ab",    "",       " a b ", "",      nullptr, "_", "_",
+    "_",     "_",     "",  "",  "", nullptr, "bc_déf", "_bc",   "ab_cd", "cd_",   "",  ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+  auto results = cudf::strings::partition(strings_view, cudf::string_scalar("_"));
+  EXPECT_TRUE(results->num_columns() == 3);
+
+  auto exp_itr = h_expecteds.begin();
+  cudf::test::strings_column_wrapper expected1(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected2(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected3(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  expected_columns.push_back(expected3.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, PartitionWhitespace)
+{
+  std::vector<char const*> h_strings{
+    "héllo", nullptr, "a bc déf", "a  bc", " ab cd", "ab cd ", "", "a_b"};
+  std::vector<char const*> h_expecteds{"héllo", nullptr, "a",      "a",   "",      "ab",  "", "a_b",
+                                       "",      nullptr, " ",      " ",   " ",     " ",   "", "",
+                                       "",      nullptr, "bc déf", " bc", "ab cd", "cd ", "", ""};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+  auto results = cudf::strings::partition(strings_view);
+  EXPECT_TRUE(results->num_columns() == 3);
+
+  auto exp_itr = h_expecteds.begin();
+  cudf::test::strings_column_wrapper expected1(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected2(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected3(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  expected_columns.push_back(expected3.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, RPartition)
+{
+  std::vector<char const*> h_strings{
+    "héllo", nullptr, "a_bc_déf", "a__bc", "_ab_cd", "ab_cd_", "", " a b "};
+  std::vector<char const*> h_expecteds{"",      nullptr, "a_bc", "a_", "_ab", "ab_cd", "", "",
+                                       "",      nullptr, "_",    "_",  "_",   "_",     "", "",
+                                       "héllo", nullptr, "déf",  "bc", "cd",  "",      "", " a b "};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+  auto results = cudf::strings::rpartition(strings_view, cudf::string_scalar("_"));
+  EXPECT_TRUE(results->num_columns() == 3);
+
+  auto exp_itr = h_expecteds.begin();
+  cudf::test::strings_column_wrapper expected1(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected2(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected3(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  expected_columns.push_back(expected3.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, RPartitionWhitespace)
+{
+  std::vector<char const*> h_strings{
+    "héllo", nullptr, "a bc déf", "a  bc", " ab cd", "ab cd ", "", "a_b"};
+  std::vector<char const*> h_expecteds{"",      nullptr, "a bc", "a ", " ab", "ab cd", "", "",
+                                       "",      nullptr, " ",    " ",  " ",   " ",     "", "",
+                                       "héllo", nullptr, "déf",  "bc", "cd",  "",      "", "a_b"};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+  auto results = cudf::strings::rpartition(strings_view);
+  EXPECT_TRUE(results->num_columns() == 3);
+
+  auto exp_itr = h_expecteds.begin();
+  cudf::test::strings_column_wrapper expected1(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected2(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  exp_itr += h_strings.size();
+  cudf::test::strings_column_wrapper expected3(
+    exp_itr,
+    exp_itr + h_strings.size(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  std::vector<std::unique_ptr<cudf::column>> expected_columns;
+  expected_columns.push_back(expected1.release());
+  expected_columns.push_back(expected2.release());
+  expected_columns.push_back(expected3.release());
+  auto expected = std::make_unique<cudf::table>(std::move(expected_columns));
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(*results, *expected);
+}
+
+TEST_F(StringsSplitTest, PartitionZeroSizeStringsColumns)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto results = cudf::strings::partition(zero_size_strings_column);
+  EXPECT_TRUE(results->num_columns() == 0);
+  results = cudf::strings::rpartition(zero_size_strings_column);
+  EXPECT_TRUE(results->num_columns() == 0);
+}
+
+TEST_F(StringsSplitTest, InvalidParameter)
+{
+  cudf::test::strings_column_wrapper input({"string left intentionally blank"});
+  auto strings_view = cudf::strings_column_view(input);
+  auto prog         = cudf::strings::regex_program::create("");
+  EXPECT_THROW(cudf::strings::split(strings_view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::strings::rsplit(strings_view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::strings::split_record(strings_view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::strings::rsplit_record(strings_view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::strings::split_re(strings_view, *prog), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::split_record_re(strings_view, *prog), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::rsplit_re(strings_view, *prog), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::rsplit_record_re(strings_view, *prog), cudf::logic_error);
+  EXPECT_THROW(cudf::strings::partition(strings_view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+  EXPECT_THROW(cudf::strings::rpartition(strings_view, cudf::string_scalar("", false)),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/strings/strip_tests.cpp b/cpp/tests/strings/strip_tests.cpp
new file mode 100644
index 0000000..6317947
--- /dev/null
+++ b/cpp/tests/strings/strip_tests.cpp
@@ -0,0 +1,119 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/strip.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsStripTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsStripTest, StripLeft)
+{
+  std::vector<char const*> h_strings{"  aBc  ", "   ", nullptr, "aaaa ", "b", "\tccc ddd"};
+  std::vector<char const*> h_expected{"aBc  ", "", nullptr, "aaaa ", "b", "ccc ddd"};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results = cudf::strings::strip(strings_view, cudf::strings::side_type::LEFT);
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsStripTest, StripRight)
+{
+  std::vector<char const*> h_strings{"  aBc  ", "   ", nullptr, "aaaa ", "b", "\tccc ddd"};
+  std::vector<char const*> h_expected{"  aBc", "", nullptr, "", "b", "\tccc ddd"};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results =
+    cudf::strings::strip(strings_view, cudf::strings::side_type::RIGHT, cudf::string_scalar(" a"));
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsStripTest, StripBoth)
+{
+  std::vector<char const*> h_strings{"  aBc  ", "   ", nullptr, "ééé ", "b", " ccc dddé"};
+  std::vector<char const*> h_expected{"aBc", "", nullptr, "", "b", "ccc ddd"};
+
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  auto results =
+    cudf::strings::strip(strings_view, cudf::strings::side_type::BOTH, cudf::string_scalar(" é"));
+
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsStripTest, EmptyStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+  auto results      = cudf::strings::strip(strings_view);
+  auto view         = results->view();
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsStripTest, AllEmptyStrings)
+{
+  auto input = cudf::test::strings_column_wrapper({"", "", "", "", "", ""}, {1, 1, 0, 1, 1});
+  auto results =
+    cudf::strings::strip(cudf::strings_column_view(input), cudf::strings::side_type::BOTH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, input);
+}
+
+TEST_F(StringsStripTest, InvalidParameter)
+{
+  std::vector<char const*> h_strings{"string left intentionally blank"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto strings_view = cudf::strings_column_view(strings);
+  EXPECT_THROW(cudf::strings::strip(
+                 strings_view, cudf::strings::side_type::BOTH, cudf::string_scalar("", false)),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/strings/translate_tests.cpp b/cpp/tests/strings/translate_tests.cpp
new file mode 100644
index 0000000..ab39732
--- /dev/null
+++ b/cpp/tests/strings/translate_tests.cpp
@@ -0,0 +1,121 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/strings/translate.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct StringsTranslateTest : public cudf::test::BaseFixture {};
+
+std::pair<cudf::char_utf8, cudf::char_utf8> make_entry(char const* from, char const* to)
+{
+  cudf::char_utf8 in  = 0;
+  cudf::char_utf8 out = 0;
+  cudf::strings::detail::to_char_utf8(from, in);
+  if (to) cudf::strings::detail::to_char_utf8(to, out);
+  return std::pair(in, out);
+}
+
+TEST_F(StringsTranslateTest, Translate)
+{
+  std::vector<char const*> h_strings{"eee ddd", "bb cc", nullptr, "", "aa", "débd"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> translate_table{
+    make_entry("b", 0), make_entry("a", "A"), make_entry("é", "E"), make_entry("e", "_")};
+  auto results = cudf::strings::translate(strings_view, translate_table);
+
+  std::vector<char const*> h_expected{"___ ddd", " cc", nullptr, "", "AA", "dEd"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsTranslateTest, ZeroSizeStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto strings_view = cudf::strings_column_view(zero_size_strings_column);
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> translate_table;
+  auto results = cudf::strings::translate(strings_view, translate_table);
+  cudf::test::expect_column_empty(results->view());
+  results = cudf::strings::filter_characters(strings_view, translate_table);
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(StringsTranslateTest, FilterCharacters)
+{
+  std::vector<char const*> h_strings{"eee ddd", "bb cc", nullptr, "", "12309", "débd"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+  auto strings_view = cudf::strings_column_view(strings);
+
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> filter_table{
+    make_entry("a", "c"), make_entry("é", "ú"), make_entry("0", "9")};
+  {
+    auto results = cudf::strings::filter_characters(strings_view, filter_table);
+    cudf::test::strings_column_wrapper expected({"", "bbcc", "", "", "12309", "éb"}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::filter_characters(
+      strings_view, filter_table, cudf::strings::filter_type::REMOVE);
+    cudf::test::strings_column_wrapper expected({"eee ddd", " ", "", "", "", "dd"}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::filter_characters(
+      strings_view, filter_table, cudf::strings::filter_type::KEEP, cudf::string_scalar("_"));
+    cudf::test::strings_column_wrapper expected({"_______", "bb_cc", "", "", "12309", "_éb_"},
+                                                validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = cudf::strings::filter_characters(
+      strings_view, filter_table, cudf::strings::filter_type::REMOVE, cudf::string_scalar("++"));
+    cudf::test::strings_column_wrapper expected(
+      {"eee ddd", "++++ ++++", "", "", "++++++++++", "d++++d"}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(StringsTranslateTest, ErrorTest)
+{
+  cudf::test::strings_column_wrapper h_strings({"string left intentionally blank"});
+  auto strings_view = cudf::strings_column_view(h_strings);
+  std::vector<std::pair<cudf::char_utf8, cudf::char_utf8>> filter_table;
+  EXPECT_THROW(
+    cudf::strings::filter_characters(
+      strings_view, filter_table, cudf::strings::filter_type::KEEP, cudf::string_scalar("", false)),
+    cudf::logic_error);
+}
diff --git a/cpp/tests/strings/urls_tests.cpp b/cpp/tests/strings/urls_tests.cpp
new file mode 100644
index 0000000..2aec721
--- /dev/null
+++ b/cpp/tests/strings/urls_tests.cpp
@@ -0,0 +1,235 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/strings/convert/convert_urls.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <random>
+#include <vector>
+
+struct StringsConvertTest : public cudf::test::BaseFixture {};
+
+TEST_F(StringsConvertTest, UrlEncode)
+{
+  std::vector<char const*> h_strings{"www.nvidia.com/rapids?p=é",
+                                     "/_file-7.txt",
+                                     "a b+c~d",
+                                     "e\tfgh\\jklmnopqrstuvwxyz",
+                                     "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
+                                     "0123456789",
+                                     " \t\f\n",
+                                     nullptr,
+                                     ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::url_encode(strings_view);
+
+  std::vector<char const*> h_expected{"www.nvidia.com%2Frapids%3Fp%3D%C3%A9",
+                                      "%2F_file-7.txt",
+                                      "a%20b%2Bc~d",
+                                      "e%09fgh%5Cjklmnopqrstuvwxyz",
+                                      "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
+                                      "0123456789",
+                                      "%20%09%0C%0A",
+                                      nullptr,
+                                      ""};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.cbegin(),
+    h_expected.cend(),
+    thrust::make_transform_iterator(h_expected.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, UrlDecode)
+{
+  std::vector<char const*> h_strings{"www.nvidia.com/rapids/%3Fp%3D%C3%A9",
+                                     "/_file-1234567890.txt",
+                                     "a%20b%2Bc~defghijklmnopqrstuvwxyz",
+                                     "%25-accent%c3%a9d",
+                                     "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
+                                     "01234567890",
+                                     nullptr,
+                                     ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::url_decode(strings_view);
+
+  std::vector<char const*> h_expected{"www.nvidia.com/rapids/?p=é",
+                                      "/_file-1234567890.txt",
+                                      "a b+c~defghijklmnopqrstuvwxyz",
+                                      "%-accentéd",
+                                      "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
+                                      "01234567890",
+                                      nullptr,
+                                      ""};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.cbegin(),
+    h_expected.cend(),
+    thrust::make_transform_iterator(h_expected.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, UrlDecodeNop)
+{
+  std::vector<char const*> h_strings{"www.nvidia.com/rapids/abc123",
+                                     "/_file-1234567890.txt",
+                                     "abcdefghijklmnopqrstuvwxyz",
+                                     "ABCDEFGHIJKLMNOPQRSTUVWXYZ%",
+                                     "0123456789%0",
+                                     nullptr,
+                                     ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::url_decode(strings_view);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings);
+}
+
+TEST_F(StringsConvertTest, UrlDecodeSliced)
+{
+  std::vector<char const*> h_strings{"www.nvidia.com/rapids/%3Fp%3D%C3%A9%",
+                                     "01/_file-1234567890.txt",
+                                     "a%20b%2Bc~defghijklmnopqrstuvwxyz",
+                                     "%25-accent%c3%a9d",
+                                     "ABCDEFGHIJKLMNOPQRSTUVWXYZ%0",
+                                     "01234567890",
+                                     nullptr,
+                                     ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  std::vector<char const*> h_expected{"www.nvidia.com/rapids/?p=é%",
+                                      "01/_file-1234567890.txt",
+                                      "a b+c~defghijklmnopqrstuvwxyz",
+                                      "%-accentéd",
+                                      "ABCDEFGHIJKLMNOPQRSTUVWXYZ%0",
+                                      "01234567890",
+                                      nullptr,
+                                      ""};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.cbegin(),
+    h_expected.cend(),
+    thrust::make_transform_iterator(h_expected.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  std::vector<cudf::size_type> slice_indices{0, 3, 3, 6, 6, 8};
+  auto sliced_strings  = cudf::slice(strings, slice_indices);
+  auto sliced_expected = cudf::slice(expected, slice_indices);
+  for (size_t i = 0; i < sliced_strings.size(); ++i) {
+    auto strings_view = cudf::strings_column_view(sliced_strings[i]);
+    auto results      = cudf::strings::url_decode(strings_view);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, sliced_expected[i]);
+  }
+}
+
+TEST_F(StringsConvertTest, UrlDecodeLargeStrings)
+{
+  constexpr int string_len = 35000;
+  std::vector<char> string_encoded;
+  string_encoded.reserve(string_len * 3);
+  std::vector<char> string_plain;
+  string_plain.reserve(string_len + 1);
+
+  std::random_device rd;
+  std::mt19937 random_number_generator(rd());
+  std::uniform_int_distribution<int> distribution(0, 4);
+
+  for (int character_idx = 0; character_idx < string_len; character_idx++) {
+    switch (distribution(random_number_generator)) {
+      case 0:
+        string_encoded.push_back('a');
+        string_plain.push_back('a');
+        break;
+      case 1:
+        string_encoded.push_back('b');
+        string_plain.push_back('b');
+        break;
+      case 2:
+        string_encoded.push_back('c');
+        string_plain.push_back('c');
+        break;
+      case 3:
+        string_encoded.push_back('%');
+        string_encoded.push_back('3');
+        string_encoded.push_back('F');
+        string_plain.push_back('?');
+        break;
+      case 4:
+        string_encoded.push_back('%');
+        string_encoded.push_back('3');
+        string_encoded.push_back('D');
+        string_plain.push_back('=');
+        break;
+    }
+  }
+  string_encoded.push_back('\0');
+  string_plain.push_back('\0');
+
+  std::vector<char const*> h_strings{string_encoded.data()};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.cbegin(),
+    h_strings.cend(),
+    thrust::make_transform_iterator(h_strings.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  auto strings_view = cudf::strings_column_view(strings);
+  auto results      = cudf::strings::url_decode(strings_view);
+
+  std::vector<char const*> h_expected{string_plain.data()};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.cbegin(),
+    h_expected.cend(),
+    thrust::make_transform_iterator(h_expected.cbegin(),
+                                    [](auto const str) { return str != nullptr; }));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(StringsConvertTest, ZeroSizeUrlStringsColumn)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto results = cudf::strings::url_encode(zero_size_strings_column);
+  cudf::test::expect_column_empty(results->view());
+  results = cudf::strings::url_decode(zero_size_strings_column);
+  cudf::test::expect_column_empty(results->view());
+}
diff --git a/cpp/tests/structs/structs_column_tests.cpp b/cpp/tests/structs/structs_column_tests.cpp
new file mode 100644
index 0000000..b7617fc
--- /dev/null
+++ b/cpp/tests/structs/structs_column_tests.cpp
@@ -0,0 +1,642 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/error.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
+
+#include <rmm/device_buffer.hpp>
+
+#include <algorithm>
+#include <functional>
+#include <initializer_list>
+#include <iterator>
+#include <memory>
+
+using vector_of_columns = std::vector<std::unique_ptr<cudf::column>>;
+using cudf::size_type;
+
+struct StructColumnWrapperTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedStructColumnWrapperTest : public cudf::test::BaseFixture {};
+
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::DurationTypes,
+                                                  cudf::test::TimestampTypes>;
+
+TYPED_TEST_SUITE(TypedStructColumnWrapperTest, FixedWidthTypesNotBool);
+
+// Test simple struct construction without nullmask, through column factory.
+// Columns must retain their originally set values.
+TYPED_TEST(TypedStructColumnWrapperTest, TestColumnFactoryConstruction)
+{
+  auto names_col =
+    cudf::test::strings_column_wrapper{
+      "Samuel Vimes", "Carrot Ironfoundersson", "Angua von Überwald"}
+      .release();
+
+  int num_rows{names_col->size()};
+
+  auto ages_col =
+    cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{{48, 27, 25}}.release();
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{{true, true, false}}.release();
+
+  vector_of_columns cols;
+  cols.push_back(std::move(names_col));
+  cols.push_back(std::move(ages_col));
+  cols.push_back(std::move(is_human_col));
+
+  auto struct_col = cudf::make_structs_column(num_rows, std::move(cols), 0, {});
+
+  EXPECT_EQ(num_rows, struct_col->size());
+
+  auto struct_col_view{struct_col->view()};
+  EXPECT_TRUE(std::all_of(struct_col_view.child_begin(),
+                          struct_col_view.child_end(),
+                          [&](auto const& child) { return child.size() == num_rows; }));
+
+  // Check child columns for exactly correct values.
+  vector_of_columns expected_children;
+  expected_children.emplace_back(cudf::test::strings_column_wrapper{
+    "Samuel Vimes", "Carrot Ironfoundersson", "Angua von Überwald"}
+                                   .release());
+  expected_children.emplace_back(
+    cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{48, 27, 25}.release());
+  expected_children.emplace_back(
+    cudf::test::fixed_width_column_wrapper<bool>{true, true, false}.release());
+
+  std::for_each(thrust::make_counting_iterator(0),
+                thrust::make_counting_iterator(0) + expected_children.size(),
+                [&](auto idx) {
+                  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(struct_col_view.child(idx),
+                                                      expected_children[idx]->view());
+                });
+}
+
+// Test simple struct construction with nullmasks, through column wrappers.
+// When the struct row is null, the child column value must be null.
+TYPED_TEST(TypedStructColumnWrapperTest, TestColumnWrapperConstruction)
+{
+  std::initializer_list<std::string> names = {"Samuel Vimes",
+                                              "Carrot Ironfoundersson",
+                                              "Angua von Überwald",
+                                              "Cheery Littlebottom",
+                                              "Detritus",
+                                              "Mr Slant"};
+
+  auto num_rows{std::distance(names.begin(), names.end())};
+
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+
+  auto ages_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_col =
+    cudf::test::structs_column_wrapper{{names_col, ages_col, is_human_col}, {1, 1, 1, 0, 1, 1}}
+      .release();
+
+  EXPECT_EQ(num_rows, struct_col->size());
+
+  auto struct_col_view{struct_col->view()};
+  EXPECT_TRUE(std::all_of(struct_col_view.child_begin(),
+                          struct_col_view.child_end(),
+                          [&](auto const& child) { return child.size() == num_rows; }));
+
+  // Check child columns for exactly correct values.
+  vector_of_columns expected_children;
+  expected_children.emplace_back(
+    cudf::test::strings_column_wrapper{names, {1, 1, 1, 0, 1, 1}}.release());
+  expected_children.emplace_back(cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {48, 27, 25, 31, 351, 351},
+    {1, 1, 1, 0, 1, 0}}.release());
+  expected_children.emplace_back(cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false},
+    {1, 1, 0, 0, 1, 0}}.release());
+
+  std::for_each(thrust::make_counting_iterator(0),
+                thrust::make_counting_iterator(0) + expected_children.size(),
+                [&](auto idx) {
+                  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(struct_col_view.child(idx),
+                                                      expected_children[idx]->view());
+                });
+
+  auto expected_struct_col =
+    cudf::test::structs_column_wrapper{std::move(expected_children), {1, 1, 1, 0, 1, 1}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(struct_col_view, expected_struct_col->view());
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, TestStructsContainingLists)
+{
+  // Test structs with two members:
+  //  1. Name: String
+  //  2. List: List<TypeParam>
+
+  std::initializer_list<std::string> names = {"Samuel Vimes",
+                                              "Carrot Ironfoundersson",
+                                              "Angua von Überwald",
+                                              "Cheery Littlebottom",
+                                              "Detritus",
+                                              "Mr Slant"};
+
+  auto num_rows{std::distance(names.begin(), names.end())};
+
+  // `Name` column has all valid values.
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+
+  // `List` member.
+  auto lists_col =
+    cudf::test::lists_column_wrapper<TypeParam, int32_t>{{1, 2, 3}, {4}, {5, 6}, {}, {7, 8}, {9}};
+
+  // Construct a Struct column of 6 rows, with the last two values set to null.
+  auto struct_col =
+    cudf::test::structs_column_wrapper{{names_col, lists_col}, {1, 1, 1, 1, 0, 0}}.release();
+
+  EXPECT_EQ(struct_col->size(), num_rows);
+  EXPECT_EQ(struct_col->view().child(0).size(), num_rows);
+  EXPECT_EQ(struct_col->view().child(1).size(), num_rows);
+
+  // Check that the last two rows are null for all members.
+
+  // For `Name` member, indices 4 and 5 are null.
+  auto expected_names_col = cudf::test::strings_column_wrapper{
+    names.begin(), names.end(), cudf::detail::make_counting_transform_iterator(0, [](auto i) {
+      return i < 4;
+    })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(struct_col->view().child(0), expected_names_col->view());
+
+  // For the `List` member, indices 4, 5 should be null.
+  auto expected_last_two_lists_col = cudf::test::lists_column_wrapper<TypeParam, int32_t>{
+    {
+      {1, 2, 3},
+      {4},
+      {5, 6},
+      {},
+      {7, 8},  // Null.
+      {9}      // Null.
+    },
+    cudf::detail::make_counting_transform_iterator(
+      0, [](auto i) { return i < 4; })}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(struct_col->view().child(1),
+                                      expected_last_two_lists_col->view());
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, StructOfStructs)
+{
+  // Struct<is_human:bool, Struct<names:string, ages:int>>
+
+  auto names = {"Samuel Vimes",
+                "Carrot Ironfoundersson",
+                "Angua von Überwald",
+                "Cheery Littlebottom",
+                "Detritus",
+                "Mr Slant"};
+
+  auto num_rows{std::distance(names.begin(), names.end())};
+
+  // `Name` column has all valid values.
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+
+  auto ages_col =
+    cudf::test::fixed_width_column_wrapper<int32_t>{{48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto struct_1 = cudf::test::structs_column_wrapper{{names_col, ages_col}, {1, 1, 1, 1, 0, 1}};
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_2 =
+    cudf::test::structs_column_wrapper{{is_human_col, struct_1}, {0, 1, 1, 1, 1, 1}}.release();
+
+  EXPECT_EQ(struct_2->size(), num_rows);
+  EXPECT_EQ(struct_2->view().child(0).size(), num_rows);
+  EXPECT_EQ(struct_2->view().child(1).size(), num_rows);
+
+  // Verify that the child/grandchild columns are as expected.
+  auto expected_names_col =
+    cudf::test::strings_column_wrapper(
+      names.begin(),
+      names.end(),
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0 && i != 4; }))
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_names_col, struct_2->child(1).child(0));
+
+  auto expected_ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {48, 27, 25, 31, 351, 351},
+    {0, 1, 1, 1, 0, 0}}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_ages_col, struct_2->child(1).child(1));
+
+  auto expected_bool_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false},
+    {0, 1, 0, 1, 1, 0}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_bool_col, struct_2->child(0));
+
+  // Verify that recursive struct columns may be compared
+  // using expect_columns_equivalent.
+
+  vector_of_columns expected_cols_1;
+  expected_cols_1.emplace_back(std::move(expected_names_col));
+  expected_cols_1.emplace_back(std::move(expected_ages_col));
+  auto expected_struct_1 =
+    cudf::test::structs_column_wrapper(std::move(expected_cols_1), {1, 1, 1, 1, 0, 1}).release();
+
+  vector_of_columns expected_cols_2;
+  expected_cols_2.emplace_back(std::move(expected_bool_col));
+  expected_cols_2.emplace_back(std::move(expected_struct_1));
+  auto expected_struct_2 =
+    cudf::test::structs_column_wrapper(std::move(expected_cols_2), {0, 1, 1, 1, 1, 1}).release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_struct_2, *struct_2);
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, TestNullMaskPropagationForNonNullStruct)
+{
+  // Struct<is_human:bool, Struct<names:string, ages:int>>
+
+  auto names = {"Samuel Vimes",
+                "Carrot Ironfoundersson",
+                "Angua von Überwald",
+                "Cheery Littlebottom",
+                "Detritus",
+                "Mr Slant"};
+
+  auto num_rows{std::distance(names.begin(), names.end())};
+
+  // `Name` column has all valid values.
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+
+  auto ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 1}  // <-- No nulls in ages_col either.
+  };
+
+  auto struct_1 = cudf::test::structs_column_wrapper{
+    {names_col, ages_col}, {1, 1, 1, 1, 1, 1}  // <-- Non-null, bottom level struct.
+  };
+
+  auto is_human_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false}, {1, 1, 0, 1, 1, 0}};
+
+  auto struct_2 =
+    cudf::test::structs_column_wrapper{
+      {is_human_col, struct_1}, {0, 1, 1, 1, 1, 1}  // <-- First row is null, for top-level struct.
+    }
+      .release();
+
+  EXPECT_EQ(struct_2->size(), num_rows);
+  EXPECT_EQ(struct_2->view().child(0).size(), num_rows);
+  EXPECT_EQ(struct_2->view().child(1).size(), num_rows);
+
+  // Verify that the child/grandchild columns are as expected.
+
+  // Top-struct has 1 null (at index 0).
+  // Bottom-level struct had no nulls, but must now report nulls
+  auto expected_names_col =
+    cudf::test::strings_column_wrapper(
+      names.begin(),
+      names.end(),
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 0; }))
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_names_col, struct_2->child(1).child(0));
+
+  auto expected_ages_col = cudf::test::fixed_width_column_wrapper<int32_t>{
+    {48, 27, 25, 31, 351, 351},
+    {0, 1, 1, 1, 1, 1}}.release();
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_ages_col, struct_2->child(1).child(1));
+
+  auto expected_bool_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {true, true, false, false, false, false},
+    {0, 1, 0, 1, 1, 0}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_bool_col, struct_2->child(0));
+
+  // Verify that recursive struct columns may be compared
+  // using expect_columns_equivalent.
+
+  vector_of_columns expected_cols_1;
+  expected_cols_1.emplace_back(std::move(expected_names_col));
+  expected_cols_1.emplace_back(std::move(expected_ages_col));
+  auto expected_struct_1 =
+    cudf::test::structs_column_wrapper(std::move(expected_cols_1), {1, 1, 1, 1, 1, 1}).release();
+
+  vector_of_columns expected_cols_2;
+  expected_cols_2.emplace_back(std::move(expected_bool_col));
+  expected_cols_2.emplace_back(std::move(expected_struct_1));
+  auto expected_struct_2 =
+    cudf::test::structs_column_wrapper(std::move(expected_cols_2), {0, 1, 1, 1, 1, 1}).release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_struct_2, *struct_2);
+}
+
+TEST_F(StructColumnWrapperTest, StructWithNoMembers)
+{
+  auto struct_col{cudf::test::structs_column_wrapper{}.release()};
+  EXPECT_TRUE(struct_col->num_children() == 0);
+  EXPECT_TRUE(struct_col->null_count() == 0);
+  EXPECT_TRUE(struct_col->size() == 0);
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, StructsWithMembersWithDifferentRowCounts)
+{
+  auto numeric_col_5 = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{{1, 2, 3, 4, 5}};
+  auto bool_col_4    = cudf::test::fixed_width_column_wrapper<bool>{1, 0, 1, 0};
+
+  EXPECT_THROW(cudf::test::structs_column_wrapper({numeric_col_5, bool_col_4}), cudf::logic_error);
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, TestListsOfStructs)
+{
+  // Test list containing structs with two members
+  //  1. Name: String
+  //  2. Age:  TypeParam
+
+  std::initializer_list<std::string> names = {"Samuel Vimes",
+                                              "Carrot Ironfoundersson",
+                                              "Angua von Überwald",
+                                              "Cheery Littlebottom",
+                                              "Detritus",
+                                              "Mr Slant"};
+
+  auto num_struct_rows{std::distance(names.begin(), names.end())};
+
+  // `Name` column has all valid values.
+  auto names_col = cudf::test::strings_column_wrapper{names.begin(), names.end()};
+
+  // Numeric column has some nulls.
+  auto ages_col = cudf::test::fixed_width_column_wrapper<TypeParam, int32_t>{
+    {48, 27, 25, 31, 351, 351}, {1, 1, 1, 1, 1, 0}};
+
+  auto struct_col =
+    cudf::test::structs_column_wrapper({names_col, ages_col}, {1, 1, 1, 0, 0, 1}).release();
+
+  EXPECT_EQ(struct_col->size(), num_struct_rows);
+  EXPECT_EQ(struct_col->view().child(0).size(), num_struct_rows);
+
+  auto expected_unchanged_struct_col = cudf::column(*struct_col);
+
+  auto list_offsets_column =
+    cudf::test::fixed_width_column_wrapper<size_type>{0, 2, 3, 5, 6}.release();
+  auto num_list_rows = list_offsets_column->size() - 1;
+
+  auto list_col = cudf::make_lists_column(
+    num_list_rows, std::move(list_offsets_column), std::move(struct_col), 0, {});
+
+  // List of structs was constructed successfully. No exceptions.
+  // Verify that child columns is as it was set.
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected_unchanged_struct_col,
+                                      cudf::lists_column_view(*list_col).child());
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, ListOfStructOfList)
+{
+  using namespace cudf::test;
+
+  auto list_col = lists_column_wrapper<TypeParam, int32_t>{
+    {{0}, {1}, {}, {3}, {4}, {5, 5}, {6}, {}, {8}, {9}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; })};
+
+  // TODO: Struct<List> cannot be compared with expect_columns_equal(),
+  // if the struct has null values. After lists support "equivalence"
+  // comparisons, the structs column needs to be modified to add nulls.
+  auto struct_of_lists_col = structs_column_wrapper{{list_col}}.release();
+
+  auto list_of_struct_of_list_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3; });
+  auto [null_mask, null_count] =
+    detail::make_null_mask(list_of_struct_of_list_validity, list_of_struct_of_list_validity + 5);
+  auto list_of_struct_of_list = cudf::make_lists_column(
+    5,
+    std::move(fixed_width_column_wrapper<size_type>{0, 2, 4, 6, 8, 10}.release()),
+    std::move(struct_of_lists_col),
+    null_count,
+    std::move(null_mask));
+
+  // Compare with expected values.
+
+  auto expected_level0_list = lists_column_wrapper<TypeParam, int32_t>{
+    {{}, {3}, {}, {5, 5}, {}, {9}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; })};
+
+  auto expected_level2_struct = structs_column_wrapper{{expected_level0_list}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(cudf::lists_column_view(*list_of_struct_of_list).child(),
+                                 *expected_level2_struct);
+
+  std::tie(null_mask, null_count) =
+    detail::make_null_mask(list_of_struct_of_list_validity, list_of_struct_of_list_validity + 5);
+  auto expected_level3_list = cudf::make_lists_column(
+    5,
+    std::move(fixed_width_column_wrapper<size_type>{0, 0, 2, 4, 4, 6}.release()),
+    std::move(expected_level2_struct),
+    null_count,
+    std::move(null_mask));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*list_of_struct_of_list, *expected_level3_list);
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, StructOfListOfStruct)
+{
+  using namespace cudf::test;
+
+  auto ints_col = fixed_width_column_wrapper<TypeParam, int32_t>{
+    {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2; })};
+
+  auto structs_col =
+    structs_column_wrapper{
+      {ints_col},
+      cudf::detail::make_counting_transform_iterator(
+        0, [](auto i) { return i < 6; })  // Last 4 structs are null.
+    }
+      .release();
+
+  auto list_validity =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3; });
+  auto [null_mask, null_count] = detail::make_null_mask(list_validity, list_validity + 5);
+
+  auto lists_col = cudf::make_lists_column(
+    5,
+    std::move(fixed_width_column_wrapper<size_type>{0, 2, 4, 6, 8, 10}.release()),
+    std::move(structs_col),
+    null_count,
+    std::move(null_mask));
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  cols.push_back(std::move(lists_col));
+  auto struct_of_list_of_struct = structs_column_wrapper{std::move(cols)}.release();
+
+  // Check that the struct is constructed as expected.
+
+  auto expected_ints_col = fixed_width_column_wrapper<TypeParam, int32_t>{
+    {0, 1, 0, 3, 0, 5, 0, 0, 0, 0}, {0, 1, 0, 1, 0, 1, 0, 0, 0, 0}};
+
+  auto expected_structs_col =
+    structs_column_wrapper{{expected_ints_col}, {1, 1, 1, 1, 1, 1, 0, 0, 0, 0}}.release();
+
+  std::tie(null_mask, null_count) = detail::make_null_mask(list_validity, list_validity + 5);
+
+  auto expected_lists_col = cudf::make_lists_column(
+    5,
+    std::move(fixed_width_column_wrapper<size_type>{0, 2, 4, 6, 8, 10}.release()),
+    std::move(expected_structs_col),
+    null_count,
+    std::move(null_mask));
+
+  // Test that the lists child column is as expected.
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_lists_col, struct_of_list_of_struct->child(0));
+
+  // Test that the outer struct column is as expected.
+  cols.clear();
+  cols.push_back(std::move(expected_lists_col));
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*(structs_column_wrapper{std::move(cols)}.release()),
+                                      *struct_of_list_of_struct);
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, EmptyColumnsOfStructs)
+{
+  using namespace cudf::test;
+
+  {
+    // Empty struct column.
+    auto empty_struct_column = structs_column_wrapper{}.release();
+    EXPECT_TRUE(empty_struct_column->num_children() == 0);
+    EXPECT_TRUE(empty_struct_column->size() == 0);
+    EXPECT_TRUE(empty_struct_column->null_count() == 0);
+  }
+
+  {
+    // Empty struct<list> column.
+    auto empty_list_column = lists_column_wrapper<TypeParam>{};
+    auto struct_column     = structs_column_wrapper{{empty_list_column}}.release();
+    EXPECT_TRUE(struct_column->num_children() == 1);
+    EXPECT_TRUE(struct_column->size() == 0);
+    EXPECT_TRUE(struct_column->null_count() == 0);
+
+    auto empty_list_of_structs = cudf::make_lists_column(
+      0, fixed_width_column_wrapper<size_type>{0}.release(), std::move(struct_column), 0, {});
+
+    EXPECT_TRUE(empty_list_of_structs->size() == 0);
+    EXPECT_TRUE(empty_list_of_structs->null_count() == 0);
+
+    auto child_struct_column = cudf::lists_column_view(*empty_list_of_structs).child();
+    EXPECT_TRUE(child_struct_column.num_children() == 1);
+    EXPECT_TRUE(child_struct_column.size() == 0);
+    EXPECT_TRUE(child_struct_column.null_count() == 0);
+  }
+
+  // TODO: Uncomment test after adding support to compare empty
+  //   lists whose child columns may not be empty.
+  // {
+  //   auto non_empty_column_of_numbers =
+  //     fixed_width_column_wrapper<TypeParam>{1,2,3,4,5}.release();
+  //
+  //   auto list_offsets =
+  //     fixed_width_column_wrapper<size_type>{0}.release();
+  //
+  //   auto empty_list_column =
+  //     cudf::make_lists_column(
+  //       0, std::move(list_offsets), std::move(non_empty_column_of_numbers), 0, {});
+  //
+  //   CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*lists_column_wrapper<TypeParam>{}.release(),
+  //   *empty_list_column); auto struct_column =
+  //   structs_column_wrapper{{empty_list_column}}.release();
+  //   EXPECT_TRUE(struct_column->num_children() == 1);
+  //   EXPECT_TRUE(struct_column->size() == 0);
+  //   EXPECT_TRUE(struct_column->null_count() == 0);
+  // }
+}
+
+TYPED_TEST(TypedStructColumnWrapperTest, CopyColumnFromView)
+{
+  // Testing deep-copying structs from column-views.
+
+  using namespace cudf::test;
+  using T = TypeParam;
+
+  auto numeric_column =
+    fixed_width_column_wrapper<T, int32_t>{{0, 1, 2, 3, 4, 5}, {1, 1, 1, 1, 1, 0}};
+
+  auto lists_column = lists_column_wrapper<T, int32_t>{
+    {{0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}, {5, 5}},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 4; })};
+
+  auto structs_column = structs_column_wrapper{
+    {numeric_column, lists_column},
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i != 3; })};
+
+  auto clone_structs_column = cudf::column(structs_column);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(clone_structs_column, structs_column);
+
+  auto list_of_structs_column =
+    cudf::make_lists_column(
+      3, fixed_width_column_wrapper<int32_t>{0, 2, 4, 6}.release(), structs_column.release(), 0, {})
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(list_of_structs_column->view(),
+                                      cudf::column(list_of_structs_column->view()));
+}
+
+TEST_F(StructColumnWrapperTest, TestStructsColumnWithEmptyChild)
+{
+  // structs_column_views should not superimpose their null mask onto any EMPTY children,
+  // because EMPTY columns cannot have a null mask. This test ensures that
+  // we can construct a structs column with a parent null mask and an EMPTY
+  // child and then view it.
+  auto empty_col = std::make_unique<cudf::column>(
+    cudf::data_type(cudf::type_id::EMPTY), 3, rmm::device_buffer{}, rmm::device_buffer{}, 0);
+  int num_rows{empty_col->size()};
+  vector_of_columns cols;
+  cols.push_back(std::move(empty_col));
+  auto mask_vec = std::vector<bool>{true, false, false};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(mask_vec.begin(), mask_vec.end());
+  auto structs_col =
+    cudf::make_structs_column(num_rows, std::move(cols), null_count, std::move(null_mask));
+  EXPECT_NO_THROW(structs_col->view());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/structs/utilities_tests.cpp b/cpp/tests/structs/utilities_tests.cpp
new file mode 100644
index 0000000..6ecc03b
--- /dev/null
+++ b/cpp/tests/structs/utilities_tests.cpp
@@ -0,0 +1,661 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cudf_test/default_stream.hpp"
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/structs/utilities.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+template <typename T>
+using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+template <typename T>
+using lists = cudf::test::lists_column_wrapper<T, int32_t>;
+
+struct StructUtilitiesTest : cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedStructUtilitiesTest : StructUtilitiesTest {};
+
+TYPED_TEST_SUITE(TypedStructUtilitiesTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(TypedStructUtilitiesTest, ListsAtTopLevel)
+{
+  using T     = TypeParam;
+  using lists = cudf::test::lists_column_wrapper<T, int32_t>;
+  using nums  = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto lists_col = lists{{0, 1}, {22, 33}, {44, 55, 66}};
+  auto nums_col  = nums{{0, 1, 2}, cudf::test::iterators::null_at(6)};
+
+  auto table = cudf::table_view{{lists_col, nums_col}};
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(table, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, NoStructs)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_col    = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto strings_col = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto nuther_nums_col = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+
+  auto table = cudf::table_view{{nums_col, strings_col, nuther_nums_col}};
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(table, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, SingleLevelStruct)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_member    = nums{{0, 1, 22, 333, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto strings_member = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto structs_col = cudf::test::structs_column_wrapper{{nums_member, strings_member}};
+  auto nums_col    = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+  auto table       = cudf::table_view{{nums_col, structs_col}};
+
+  auto expected_nums_col_1  = cudf::column(nums_col);
+  auto expected_structs_col = cudf::test::fixed_width_column_wrapper<bool>{{1, 1, 1, 1, 1, 1, 1}};
+  auto expected_nums_col_2  = cudf::column(static_cast<cudf::structs_column_view>(structs_col)
+                                            .get_sliced_child(0, cudf::get_default_stream()));
+  auto expected_strings_col = cudf::column(static_cast<cudf::structs_column_view>(structs_col)
+                                             .get_sliced_child(1, cudf::get_default_stream()));
+  auto expected             = cudf::table_view{
+                {expected_nums_col_1, expected_structs_col, expected_nums_col_2, expected_strings_col}};
+
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, SingleLevelStructWithNulls)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_member    = nums{{0, 1, 22, 333, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto strings_member = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto structs_col = cudf::test::structs_column_wrapper{{nums_member, strings_member},
+                                                        cudf::test::iterators::null_at(2)};
+  auto nums_col    = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+  auto table       = cudf::table_view{{nums_col, structs_col}};
+
+  auto expected_nums_col_1  = cudf::column(nums_col);
+  auto expected_structs_col = cudf::test::fixed_width_column_wrapper<bool>{
+    {1, 1, 0, 1, 1, 1, 1}, cudf::test::iterators::null_at(2)};
+  auto expected_nums_col_2  = cudf::column(static_cast<cudf::structs_column_view>(structs_col)
+                                            .get_sliced_child(0, cudf::get_default_stream()));
+  auto expected_strings_col = cudf::column(static_cast<cudf::structs_column_view>(structs_col)
+                                             .get_sliced_child(1, cudf::get_default_stream()));
+  auto expected             = cudf::table_view{
+                {expected_nums_col_1, expected_structs_col, expected_nums_col_2, expected_strings_col}};
+
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, StructOfStruct)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_col = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+
+  auto struct_0_nums_member = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto struct_0_strings_member = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto structs_1_structs_member =
+    cudf::test::structs_column_wrapper{{struct_0_nums_member, struct_0_strings_member}};
+
+  auto struct_1_nums_member = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(3)};
+  auto struct_of_structs_col =
+    cudf::test::structs_column_wrapper{{struct_1_nums_member, structs_1_structs_member}};
+  auto table = cudf::table_view{{nums_col, struct_of_structs_col}};
+
+  auto expected_nums_col_1    = cudf::column(nums_col);
+  auto expected_structs_col_1 = cudf::test::fixed_width_column_wrapper<bool>{{1, 1, 1, 1, 1, 1, 1}};
+  auto expected_nums_col_2 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(0, cudf::get_default_stream()));
+  auto expected_structs_col_2 = cudf::test::fixed_width_column_wrapper<bool>{{1, 1, 1, 1, 1, 1, 1}};
+  auto expected_nums_col_3 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(0));
+  auto expected_strings_col =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(1));
+  auto expected = cudf::table_view{{expected_nums_col_1,
+                                    expected_structs_col_1,
+                                    expected_nums_col_2,
+                                    expected_structs_col_2,
+                                    expected_nums_col_3,
+                                    expected_strings_col}};
+
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, StructOfStructWithNullsAtLeafLevel)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_col = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+
+  auto struct_0_nums_member = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto struct_0_strings_member = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto structs_1_structs_member = cudf::test::structs_column_wrapper{
+    {struct_0_nums_member, struct_0_strings_member}, cudf::test::iterators::null_at(2)};
+
+  auto struct_1_nums_member = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(3)};
+  auto struct_of_structs_col =
+    cudf::test::structs_column_wrapper{{struct_1_nums_member, structs_1_structs_member}};
+  auto table = cudf::table_view{{nums_col, struct_of_structs_col}};
+
+  auto expected_nums_col_1    = cudf::column(nums_col);
+  auto expected_structs_col_1 = cudf::test::fixed_width_column_wrapper<bool>{{1, 1, 1, 1, 1, 1, 1}};
+  auto expected_nums_col_2 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(0, cudf::get_default_stream()));
+  auto expected_structs_col_2 = cudf::test::fixed_width_column_wrapper<bool>{
+    {1, 1, 0, 1, 1, 1, 1}, cudf::test::iterators::null_at(2)};
+  auto expected_nums_col_3 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(0));
+  auto expected_strings_col =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(1));
+  auto expected = cudf::table_view{{expected_nums_col_1,
+                                    expected_structs_col_1,
+                                    expected_nums_col_2,
+                                    expected_structs_col_2,
+                                    expected_nums_col_3,
+                                    expected_strings_col}};
+
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, StructOfStructWithNullsAtTopLevel)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_col = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+
+  auto struct_0_nums_member = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto struct_0_strings_member = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto structs_1_structs_member =
+    cudf::test::structs_column_wrapper{{struct_0_nums_member, struct_0_strings_member}};
+
+  auto struct_1_nums_member  = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(3)};
+  auto struct_of_structs_col = cudf::test::structs_column_wrapper{
+    {struct_1_nums_member, structs_1_structs_member}, cudf::test::iterators::null_at(4)};
+  auto table = cudf::table_view{{nums_col, struct_of_structs_col}};
+
+  auto expected_nums_col_1    = cudf::column(nums_col);
+  auto expected_structs_col_1 = cudf::test::fixed_width_column_wrapper<bool>{
+    {1, 1, 1, 1, 0, 1, 1}, cudf::test::iterators::null_at(4)};
+  auto expected_nums_col_2 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(0, cudf::get_default_stream()));
+  auto expected_structs_col_2 = cudf::test::fixed_width_column_wrapper<bool>{
+    {1, 1, 1, 1, 0, 1, 1}, cudf::test::iterators::null_at(4)};
+  auto expected_nums_col_3 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(0));
+  auto expected_strings_col =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(1));
+  auto expected = cudf::table_view{{expected_nums_col_1,
+                                    expected_structs_col_1,
+                                    expected_nums_col_2,
+                                    expected_structs_col_2,
+                                    expected_nums_col_3,
+                                    expected_strings_col}};
+
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, flattened_table->flattened_columns());
+}
+
+TYPED_TEST(TypedStructUtilitiesTest, StructOfStructWithNullsAtAllLevels)
+{
+  using T    = TypeParam;
+  using nums = cudf::test::fixed_width_column_wrapper<T, int32_t>;
+
+  auto nums_col = nums{{0, 1, 2, 3, 4, 5, 6}, cudf::test::iterators::null_at(6)};
+
+  auto struct_0_nums_member = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(0)};
+  auto struct_0_strings_member = cudf::test::strings_column_wrapper{
+    {"", "1", "22", "333", "4444", "55555", "666666"}, cudf::test::iterators::null_at(1)};
+  auto structs_1_structs_member = cudf::test::structs_column_wrapper{
+    {struct_0_nums_member, struct_0_strings_member}, cudf::test::iterators::null_at(2)};
+
+  auto struct_1_nums_member  = nums{{0, 1, 22, 33, 44, 55, 66}, cudf::test::iterators::null_at(3)};
+  auto struct_of_structs_col = cudf::test::structs_column_wrapper{
+    {struct_1_nums_member, structs_1_structs_member}, cudf::test::iterators::null_at(4)};
+  auto table = cudf::table_view{{nums_col, struct_of_structs_col}};
+
+  auto expected_nums_col_1    = cudf::column(nums_col);
+  auto expected_structs_col_1 = cudf::test::fixed_width_column_wrapper<bool>{
+    {1, 1, 1, 1, 0, 1, 1}, cudf::test::iterators::null_at(4)};
+  auto expected_nums_col_2 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(0, cudf::get_default_stream()));
+  auto expected_structs_col_2 =
+    cudf::test::fixed_width_column_wrapper<bool>{{1, 1, 0, 1, 0, 1, 1}, {1, 1, 0, 1, 0, 1, 1}};
+  auto expected_nums_col_3 =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(0));
+  auto expected_strings_col =
+    cudf::column(static_cast<cudf::structs_column_view>(struct_of_structs_col)
+                   .get_sliced_child(1, cudf::get_default_stream())
+                   .child(1));
+  auto expected = cudf::table_view{{expected_nums_col_1,
+                                    expected_structs_col_1,
+                                    expected_nums_col_2,
+                                    expected_structs_col_2,
+                                    expected_nums_col_3,
+                                    expected_strings_col}};
+
+  auto flattened_table =
+    cudf::structs::detail::flatten_nested_columns(table,
+                                                  {},
+                                                  {},
+                                                  cudf::structs::detail::column_nullability::FORCE,
+                                                  cudf::get_default_stream(),
+                                                  rmm::mr::get_current_device_resource());
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, flattened_table->flattened_columns());
+}
+
+struct SuperimposeTest : StructUtilitiesTest {};
+
+template <typename T>
+struct TypedSuperimposeTest : StructUtilitiesTest {};
+
+TYPED_TEST_SUITE(TypedSuperimposeTest, cudf::test::FixedWidthTypes);
+
+void test_non_struct_columns(cudf::column_view const& input)
+{
+  // push_down_nulls() on non-struct columns should return the input column, unchanged.
+  auto [superimposed, backing_data] = cudf::structs::detail::push_down_nulls(
+    input, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(input, superimposed);
+  EXPECT_TRUE(backing_data.new_null_masks.empty());
+  if (input.type().id() != cudf::type_id::STRING && input.type().id() != cudf::type_id::LIST) {
+    EXPECT_TRUE(backing_data.new_columns.empty());
+  }
+}
+
+TYPED_TEST(TypedSuperimposeTest, NoStructInput)
+{
+  using T = TypeParam;
+
+  test_non_struct_columns(cudf::test::fixed_width_column_wrapper<T>{
+    {6, 5, 4, 3, 2, 1, 0}, cudf::test::iterators::null_at(3)});
+  test_non_struct_columns(cudf::test::lists_column_wrapper<T, int32_t>{
+    {{6, 5}, {4, 3}, {2, 1}, {0}}, cudf::test::iterators::null_at(3)});
+  test_non_struct_columns(cudf::test::strings_column_wrapper{
+    {"All", "The", "Leaves", "Are", "Brown"}, cudf::test::iterators::null_at(3)});
+  test_non_struct_columns(cudf::test::dictionary_column_wrapper<std::string>{
+    {"All", "The", "Leaves", "Are", "Brown"}, cudf::test::iterators::null_at(3)});
+}
+
+/**
+ * @brief Helper to construct a numeric member of a struct column.
+ */
+template <typename T, typename NullIter>
+nums<T> make_nums_member(NullIter null_iter = cudf::test::iterators::no_nulls())
+{
+  return nums<T>{{10, 11, 12, 13, 14, 15, 16}, null_iter};
+}
+
+/**
+ * @brief Helper to construct a lists member of a struct column.
+ */
+template <typename T, typename NullIter>
+lists<T> make_lists_member(NullIter null_iter = cudf::test::iterators::no_nulls())
+{
+  return lists<T>{{{20, 20}, {21, 21}, {22, 22}, {23, 23}, {24, 24}, {25, 25}, {26, 26}},
+                  null_iter};
+}
+
+TYPED_TEST(TypedSuperimposeTest, BasicStruct)
+{
+  using T = TypeParam;
+
+  auto nums_member   = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto lists_member  = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto structs_input = cudf::test::structs_column_wrapper{{nums_member, lists_member},
+                                                          cudf::test::iterators::no_nulls()}
+                         .release();
+
+  // Reset STRUCTs' null-mask. Mark first STRUCT row as null.
+  auto structs_view = structs_input->mutable_view();
+  cudf::detail::set_null_mask(structs_view.null_mask(), 0, 1, false, cudf::get_default_stream());
+
+  // At this point, the STRUCT nulls aren't pushed down to members,
+  // even though the parent null-mask was modified.
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(structs_view.child(0),
+                                 make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6})));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(structs_view.child(1),
+                                 make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5})));
+
+  auto [output, backing_data] = cudf::structs::detail::push_down_nulls(
+    structs_view, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // After push_down_nulls(), the struct nulls (i.e. at index-0) should have been pushed
+  // down to the children. All members should have nulls at row-index 0.
+  auto expected_nums_member    = make_nums_member<T>(cudf::test::iterators::nulls_at({0, 3, 6}));
+  auto expected_lists_member   = make_lists_member<T>(cudf::test::iterators::nulls_at({0, 4, 5}));
+  auto expected_structs_output = cudf::test::structs_column_wrapper{
+    {expected_nums_member, expected_lists_member}, cudf::test::iterators::null_at(0)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected_structs_output);
+}
+
+TYPED_TEST(TypedSuperimposeTest, NonNullableParentStruct)
+{
+  // Test that if the parent struct is not nullable, non-struct members should
+  // remain unchanged.
+
+  using T = TypeParam;
+
+  auto nums_member   = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto lists_member  = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto structs_input = cudf::test::structs_column_wrapper{{nums_member, lists_member},
+                                                          cudf::test::iterators::no_nulls()}
+                         .release();
+
+  auto [output, backing_data] = cudf::structs::detail::push_down_nulls(
+    structs_input->view(), cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // After push_down_nulls(), none of the child structs should have changed,
+  // because the parent had no nulls to begin with.
+  auto expected_nums_member    = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto expected_lists_member   = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto expected_structs_output = cudf::test::structs_column_wrapper{
+    {expected_nums_member, expected_lists_member}, cudf::test::iterators::no_nulls()};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected_structs_output);
+}
+
+TYPED_TEST(TypedSuperimposeTest, NestedStruct_ChildNullable_ParentNonNullable)
+{
+  // Test with Struct<Struct>. If the parent struct is not nullable:
+  //   1. Non-struct members should remain unchanged.
+  //   2. Member-structs should have their respective nulls pushed down into grandchildren.
+
+  using T = TypeParam;
+
+  auto nums_member          = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto lists_member         = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto outer_struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  outer_struct_members.push_back(cudf::test::structs_column_wrapper{
+    {nums_member, lists_member}, cudf::test::iterators::no_nulls()}
+                                   .release());
+
+  // Reset STRUCTs' null-mask. Mark first STRUCT row as null.
+  auto structs_view = outer_struct_members.back()->mutable_view();
+  cudf::detail::set_null_mask(structs_view.null_mask(), 0, 1, false, cudf::get_default_stream());
+
+  auto structs_of_structs =
+    cudf::test::structs_column_wrapper{std::move(outer_struct_members)}.release();
+
+  auto [output, backing_data] = cudf::structs::detail::push_down_nulls(
+    structs_of_structs->view(), cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // After push_down_nulls(), outer-struct column should not have pushed nulls to child
+  // structs. But the child struct column must push its nulls to its own children.
+  auto expected_nums_member  = make_nums_member<T>(cudf::test::iterators::nulls_at({0, 3, 6}));
+  auto expected_lists_member = make_lists_member<T>(cudf::test::iterators::nulls_at({0, 4, 5}));
+  auto expected_structs      = cudf::test::structs_column_wrapper{
+         {expected_nums_member, expected_lists_member}, cudf::test::iterators::null_at(0)};
+  auto expected_structs_of_structs = cudf::test::structs_column_wrapper{{expected_structs}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected_structs_of_structs);
+}
+
+TYPED_TEST(TypedSuperimposeTest, NestedStruct_ChildNullable_ParentNullable)
+{
+  // Test with Struct<Struct>.
+  // If both the parent struct and the child are nullable, the leaf nodes should
+  // have a 3-way ANDed null-mask.
+
+  using T = TypeParam;
+
+  auto nums_member          = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto lists_member         = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto outer_struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  outer_struct_members.push_back(cudf::test::structs_column_wrapper{
+    {nums_member, lists_member}, cudf::test::iterators::no_nulls()}
+                                   .release());
+
+  // Reset STRUCTs' null-mask. Mark first STRUCT row as null.
+  auto structs_view = outer_struct_members.back()->mutable_view();
+  auto num_rows     = structs_view.size();
+  cudf::detail::set_null_mask(structs_view.null_mask(), 0, 1, false, cudf::get_default_stream());
+
+  auto structs_of_structs = cudf::test::structs_column_wrapper{std::move(outer_struct_members),
+                                                               std::vector<bool>(num_rows, true)}
+                              .release();
+
+  // Modify STRUCT-of-STRUCT's null-mask. Mark second STRUCT row as null.
+  auto structs_of_structs_view = structs_of_structs->mutable_view();
+  cudf::detail::set_null_mask(
+    structs_of_structs_view.null_mask(), 1, 2, false, cudf::get_default_stream());
+
+  auto [output, backing_data] = cudf::structs::detail::push_down_nulls(
+    structs_of_structs->view(), cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // After push_down_nulls(), outer-struct column should not have pushed nulls to child
+  // structs. But the child struct column must push its nulls to its own children.
+  auto expected_nums_member  = make_nums_member<T>(cudf::test::iterators::nulls_at({0, 1, 3, 6}));
+  auto expected_lists_member = make_lists_member<T>(cudf::test::iterators::nulls_at({0, 1, 4, 5}));
+  auto expected_structs      = cudf::test::structs_column_wrapper{
+         {expected_nums_member, expected_lists_member}, cudf::test::iterators::nulls_at({0, 1})};
+  auto expected_structs_of_structs =
+    cudf::test::structs_column_wrapper{{expected_structs}, cudf::test::iterators::null_at(1)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected_structs_of_structs);
+}
+
+cudf::column_view slice_off_first_and_last_rows(cudf::column_view const& col)
+{
+  return cudf::slice(col, {1, col.size() - 1})[0];
+}
+
+void mark_row_as_null(cudf::mutable_column_view const& col, cudf::size_type row_index)
+{
+  cudf::detail::set_null_mask(
+    col.null_mask(), row_index, row_index + 1, false, cudf::get_default_stream());
+}
+
+TYPED_TEST(TypedSuperimposeTest, Struct_Sliced)
+{
+  // Test with a sliced STRUCT column.
+  // Ensure that push_down_nulls() produces the right results, even when the input is
+  // sliced.
+
+  using T = TypeParam;
+
+  auto nums_member    = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto lists_member   = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto structs_column = cudf::test::structs_column_wrapper{{nums_member, lists_member},
+                                                           cudf::test::iterators::no_nulls()}
+                          .release();
+
+  // Reset STRUCTs' null-mask. Mark second STRUCT row as null.
+  mark_row_as_null(structs_column->mutable_view(), 1);
+
+  // The null masks should now look as follows, with the STRUCT null mask *not* pushed down:
+  // STRUCT:       1111101
+  // nums_member:  0110111
+  // lists_member: 1001111
+
+  // Slice off the first and last rows.
+  auto sliced_structs = slice_off_first_and_last_rows(structs_column->view());
+
+  // After slice(), the null masks will be:
+  // STRUCT:       11110
+  // nums_member:  11011
+  // lists_member: 00111
+
+  auto [output, backing_data] = cudf::structs::detail::push_down_nulls(
+    sliced_structs, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // After push_down_nulls(), the null masks should be:
+  // STRUCT:       11110
+  // nums_member:  11010
+  // lists_member: 00110
+
+  // Construct expected columns using structs_column_wrapper, which should push the parent nulls
+  // down automatically. Then, slice() off the ends.
+  auto expected_nums             = make_nums_member<T>(cudf::test::iterators::nulls_at({1, 3, 6}));
+  auto expected_lists            = make_lists_member<T>(cudf::test::iterators::nulls_at({1, 4, 5}));
+  auto expected_unsliced_structs = cudf::test::structs_column_wrapper{
+    {expected_nums, expected_lists}, cudf::test::iterators::nulls_at({1})};
+  auto expected_structs = slice_off_first_and_last_rows(expected_unsliced_structs);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected_structs);
+}
+
+TYPED_TEST(TypedSuperimposeTest, NestedStruct_Sliced)
+{
+  // Test with a sliced STRUCT<STRUCT> column.
+  // Ensure that push_down_nulls() produces the right results, even when the input is
+  // sliced.
+
+  using T = TypeParam;
+
+  auto nums_member    = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto lists_member   = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto structs_column = cudf::test::structs_column_wrapper{{nums_member, lists_member},
+                                                           cudf::test::iterators::null_at(1)};
+  auto struct_structs_column =
+    cudf::test::structs_column_wrapper{{structs_column}, cudf::test::iterators::no_nulls()}
+      .release();
+
+  // Reset STRUCT<STRUCT>'s null-mask. Mark third row as null.
+  mark_row_as_null(struct_structs_column->mutable_view(), 2);
+
+  // The null masks should now look as follows, with the STRUCT<STRUCT> null mask *not* pushed down:
+  // STRUCT<STRUCT>: 1111011
+  // STRUCT:         1111101
+  // nums_member:    0110101
+  // lists_member:   1001101
+
+  // Slice off the first and last rows.
+  auto sliced_structs = slice_off_first_and_last_rows(struct_structs_column->view());
+
+  // After slice(), the null masks will be:
+  // STRUCT<STRUCT>: 11101
+  // STRUCT:         11110
+  // nums_member:    11010
+  // lists_member:   00110
+
+  auto [output, backing_data] = cudf::structs::detail::push_down_nulls(
+    sliced_structs, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  // After push_down_nulls(), the null masks will be:
+  // STRUCT<STRUCT>: 11101
+  // STRUCT:         11100
+  // nums_member:    11000
+  // lists_member:   00100
+
+  // Construct expected columns using structs_column_wrapper, which should push the parent nulls
+  // down automatically. Then, slice() off the ends.
+  auto expected_nums    = make_nums_member<T>(cudf::test::iterators::nulls_at({3, 6}));
+  auto expected_lists   = make_lists_member<T>(cudf::test::iterators::nulls_at({4, 5}));
+  auto expected_structs = cudf::test::structs_column_wrapper{{expected_nums, expected_lists},
+                                                             cudf::test::iterators::nulls_at({1})};
+  auto expected_struct_structs =
+    cudf::test::structs_column_wrapper{{expected_structs}, cudf::test::iterators::null_at(2)};
+  auto expected_sliced_structs = slice_off_first_and_last_rows(expected_struct_structs);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(output, expected_sliced_structs);
+}
diff --git a/cpp/tests/table/experimental_row_operator_tests.cu b/cpp/tests/table/experimental_row_operator_tests.cu
new file mode 100644
index 0000000..896cc7a
--- /dev/null
+++ b/cpp/tests/table/experimental_row_operator_tests.cu
@@ -0,0 +1,297 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "row_operator_tests_utilities.hpp"
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+
+template <typename T>
+struct TypedTableViewTest : public cudf::test::BaseFixture {};
+
+using NumericTypesNotBool =
+  cudf::test::Concat<cudf::test::IntegralTypesNotBool, cudf::test::FloatingPointTypes>;
+TYPED_TEST_SUITE(TypedTableViewTest, NumericTypesNotBool);
+
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> self_comparison(cudf::table_view input,
+                                              std::vector<cudf::order> const& column_order,
+                                              PhysicalElementComparator comparator);
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> two_table_comparison(cudf::table_view lhs,
+                                                   cudf::table_view rhs,
+                                                   std::vector<cudf::order> const& column_order,
+                                                   PhysicalElementComparator comparator);
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> two_table_equality(cudf::table_view lhs,
+                                                 cudf::table_view rhs,
+                                                 std::vector<cudf::order> const& column_order,
+                                                 PhysicalElementComparator comparator);
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> sorted_order(
+  std::shared_ptr<cudf::experimental::row::lexicographic::preprocessed_table> preprocessed_input,
+  cudf::size_type num_rows,
+  bool has_nested,
+  PhysicalElementComparator comparator,
+  rmm::cuda_stream_view stream);
+
+TYPED_TEST(TypedTableViewTest, TestLexicographicalComparatorTwoTables)
+{
+  using T = TypeParam;
+
+  auto const col1         = cudf::test::fixed_width_column_wrapper<T>{{1, 2, 3, 4}};
+  auto const col2         = cudf::test::fixed_width_column_wrapper<T>{{0, 1, 4, 3}};
+  auto const column_order = std::vector{cudf::order::DESCENDING};
+  auto const lhs          = cudf::table_view{{col1}};
+  auto const rhs          = cudf::table_view{{col2}};
+
+  auto const expected = cudf::test::fixed_width_column_wrapper<bool>{{1, 1, 0, 1}};
+  auto const got      = two_table_comparison(
+    lhs, rhs, column_order, cudf::experimental::row::lexicographic::physical_element_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  auto const sorting_got = two_table_comparison(
+    lhs,
+    rhs,
+    column_order,
+    cudf::experimental::row::lexicographic::sorting_physical_element_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, sorting_got->view());
+}
+
+TYPED_TEST(TypedTableViewTest, TestLexicographicalComparatorSameTable)
+{
+  using T = TypeParam;
+
+  auto const col1         = cudf::test::fixed_width_column_wrapper<T>{{1, 2, 3, 4}};
+  auto const column_order = std::vector{cudf::order::DESCENDING};
+  auto const input_table  = cudf::table_view{{col1}};
+
+  auto const expected = cudf::test::fixed_width_column_wrapper<bool>{{0, 0, 0, 0}};
+  auto const got =
+    self_comparison(input_table,
+                    column_order,
+                    cudf::experimental::row::lexicographic::physical_element_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  auto const sorting_got =
+    self_comparison(input_table,
+                    column_order,
+                    cudf::experimental::row::lexicographic::sorting_physical_element_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, sorting_got->view());
+}
+
+TYPED_TEST(TypedTableViewTest, TestSortSameTableFromTwoTables)
+{
+  using data_col   = cudf::test::fixed_width_column_wrapper<TypeParam>;
+  using int32s_col = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto const col1      = data_col{5, 2, 7, 1, 3};
+  auto const col2      = data_col{};  // empty
+  auto const lhs       = cudf::table_view{{col1}};
+  auto const empty_rhs = cudf::table_view{{col2}};
+
+  auto const stream    = cudf::get_default_stream();
+  auto const test_sort = [stream](auto const& preprocessed,
+                                  auto const& input,
+                                  auto const& comparator,
+                                  auto const& expected) {
+    auto const order = sorted_order(
+      preprocessed, input.num_rows(), cudf::detail::has_nested_columns(input), comparator, stream);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, order->view());
+  };
+
+  auto const test_sort_two_tables = [&](auto const& preprocessed_lhs,
+                                        auto const& preprocessed_empty_rhs) {
+    auto const expected_lhs = int32s_col{3, 1, 4, 0, 2};
+    test_sort(preprocessed_lhs,
+              lhs,
+              cudf::experimental::row::lexicographic::physical_element_comparator{},
+              expected_lhs);
+    test_sort(preprocessed_lhs,
+              lhs,
+              cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+              expected_lhs);
+
+    auto const expected_empty_rhs = int32s_col{};
+    test_sort(preprocessed_empty_rhs,
+              empty_rhs,
+              cudf::experimental::row::lexicographic::physical_element_comparator{},
+              expected_empty_rhs);
+    test_sort(preprocessed_empty_rhs,
+              empty_rhs,
+              cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+              expected_empty_rhs);
+  };
+
+  // Generate preprocessed data for both lhs and lhs at the same time.
+  // Switching order of lhs and rhs tables then sorting them using their preprocessed data should
+  // produce exactly the same result.
+  {
+    auto const [preprocessed_lhs, preprocessed_empty_rhs] =
+      cudf::experimental::row::lexicographic::preprocessed_table::create(
+        lhs, empty_rhs, std::vector{cudf::order::ASCENDING}, {}, stream);
+    test_sort_two_tables(preprocessed_lhs, preprocessed_empty_rhs);
+  }
+  {
+    auto const [preprocessed_empty_rhs, preprocessed_lhs] =
+      cudf::experimental::row::lexicographic::preprocessed_table::create(
+        empty_rhs, lhs, std::vector{cudf::order::ASCENDING}, {}, stream);
+    test_sort_two_tables(preprocessed_lhs, preprocessed_empty_rhs);
+  }
+}
+
+TYPED_TEST(TypedTableViewTest, TestSortSameTableFromTwoTablesWithListsOfStructs)
+{
+  using data_col    = cudf::test::fixed_width_column_wrapper<TypeParam>;
+  using int32s_col  = cudf::test::fixed_width_column_wrapper<int32_t>;
+  using strings_col = cudf::test::strings_column_wrapper;
+  using structs_col = cudf::test::structs_column_wrapper;
+
+  auto const col1 = [] {
+    auto const get_structs = [] {
+      auto child0 = data_col{0, 3, 0, 2};
+      auto child1 = strings_col{"a", "c", "a", "b"};
+      return structs_col{{child0, child1}};
+    };
+    return cudf::make_lists_column(
+      2, int32s_col{0, 2, 4}.release(), get_structs().release(), 0, {});
+  }();
+  auto const col2 = [] {
+    auto const get_structs = [] {
+      auto child0 = data_col{};
+      auto child1 = strings_col{};
+      return structs_col{{child0, child1}};
+    };
+    return cudf::make_lists_column(0, int32s_col{}.release(), get_structs().release(), 0, {});
+  }();
+
+  auto const column_order = std::vector{cudf::order::ASCENDING};
+  auto const lhs          = cudf::table_view{{*col1}};
+  auto const empty_rhs    = cudf::table_view{{*col2}};
+
+  auto const stream    = cudf::get_default_stream();
+  auto const test_sort = [stream](auto const& preprocessed,
+                                  auto const& input,
+                                  auto const& comparator,
+                                  auto const& expected) {
+    auto const order = sorted_order(
+      preprocessed, input.num_rows(), cudf::detail::has_nested_columns(input), comparator, stream);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, order->view());
+  };
+
+  auto const test_sort_two_tables = [&](auto const& preprocessed_lhs,
+                                        auto const& preprocessed_empty_rhs) {
+    auto const expected_lhs = int32s_col{1, 0};
+    test_sort(preprocessed_lhs,
+              lhs,
+              cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+              expected_lhs);
+
+    auto const expected_empty_rhs = int32s_col{};
+    test_sort(preprocessed_empty_rhs,
+              empty_rhs,
+              cudf::experimental::row::lexicographic::sorting_physical_element_comparator{},
+              expected_empty_rhs);
+
+    EXPECT_THROW(test_sort(preprocessed_lhs,
+                           lhs,
+                           cudf::experimental::row::lexicographic::physical_element_comparator{},
+                           expected_lhs),
+                 cudf::logic_error);
+    EXPECT_THROW(test_sort(preprocessed_empty_rhs,
+                           empty_rhs,
+                           cudf::experimental::row::lexicographic::physical_element_comparator{},
+                           expected_empty_rhs),
+                 cudf::logic_error);
+  };
+
+  // Generate preprocessed data for both lhs and lhs at the same time.
+  // Switching order of lhs and rhs tables then sorting them using their preprocessed data should
+  // produce exactly the same result.
+  {
+    auto const [preprocessed_lhs, preprocessed_empty_rhs] =
+      cudf::experimental::row::lexicographic::preprocessed_table::create(
+        lhs, empty_rhs, std::vector{cudf::order::ASCENDING}, {}, stream);
+    test_sort_two_tables(preprocessed_lhs, preprocessed_empty_rhs);
+  }
+  {
+    auto const [preprocessed_empty_rhs, preprocessed_lhs] =
+      cudf::experimental::row::lexicographic::preprocessed_table::create(
+        empty_rhs, lhs, std::vector{cudf::order::ASCENDING}, {}, stream);
+    test_sort_two_tables(preprocessed_lhs, preprocessed_empty_rhs);
+  }
+}
+
+template <typename T>
+struct NaNTableViewTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(NaNTableViewTest, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(NaNTableViewTest, TestLexicographicalComparatorTwoTableNaNCase)
+{
+  using T = TypeParam;
+
+  auto const col1         = cudf::test::fixed_width_column_wrapper<T>{{T(NAN), T(NAN), T(1), T(1)}};
+  auto const col2         = cudf::test::fixed_width_column_wrapper<T>{{T(NAN), T(1), T(NAN), T(1)}};
+  auto const column_order = std::vector{cudf::order::DESCENDING};
+
+  auto const lhs = cudf::table_view{{col1}};
+  auto const rhs = cudf::table_view{{col2}};
+
+  auto const expected = cudf::test::fixed_width_column_wrapper<bool>{{0, 0, 0, 0}};
+  auto const got      = two_table_comparison(
+    lhs, rhs, column_order, cudf::experimental::row::lexicographic::physical_element_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  auto const sorting_expected = cudf::test::fixed_width_column_wrapper<bool>{{0, 1, 0, 0}};
+  auto const sorting_got      = two_table_comparison(
+    lhs,
+    rhs,
+    column_order,
+    cudf::experimental::row::lexicographic::sorting_physical_element_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(sorting_expected, sorting_got->view());
+}
+
+TYPED_TEST(NaNTableViewTest, TestEqualityComparatorTwoTableNaNCase)
+{
+  using T = TypeParam;
+
+  auto const col1         = cudf::test::fixed_width_column_wrapper<T>{{T(NAN), T(NAN), T(1), T(1)}};
+  auto const col2         = cudf::test::fixed_width_column_wrapper<T>{{T(NAN), T(1), T(NAN), T(1)}};
+  auto const column_order = std::vector{cudf::order::DESCENDING};
+
+  auto const lhs = cudf::table_view{{col1}};
+  auto const rhs = cudf::table_view{{col2}};
+
+  auto const expected = cudf::test::fixed_width_column_wrapper<bool>{{0, 0, 0, 1}};
+  auto const got      = two_table_equality(
+    lhs, rhs, column_order, cudf::experimental::row::equality::physical_equality_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+
+  auto const nan_equal_expected = cudf::test::fixed_width_column_wrapper<bool>{{1, 0, 0, 1}};
+  auto const nan_equal_got =
+    two_table_equality(lhs,
+                       rhs,
+                       column_order,
+                       cudf::experimental::row::equality::nan_equal_physical_equality_comparator{});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(nan_equal_expected, nan_equal_got->view());
+}
diff --git a/cpp/tests/table/row_operator_tests_utilities.cu b/cpp/tests/table/row_operator_tests_utilities.cu
new file mode 100644
index 0000000..d1f918c
--- /dev/null
+++ b/cpp/tests/table/row_operator_tests_utilities.cu
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "row_operator_tests_utilities.hpp"
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+#include <thrust/transform.h>
+
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> self_comparison(cudf::table_view input,
+                                              std::vector<cudf::order> const& column_order,
+                                              PhysicalElementComparator comparator)
+{
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+
+  auto const table_comparator =
+    cudf::experimental::row::lexicographic::self_comparator{input, column_order, {}, stream};
+
+  auto output = cudf::make_numeric_column(
+    cudf::data_type(cudf::type_id::BOOL8), input.num_rows(), cudf::mask_state::UNALLOCATED);
+
+  if (cudf::detail::has_nested_columns(input)) {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(input.num_rows()),
+                      thrust::make_counting_iterator(0),
+                      output->mutable_view().data<bool>(),
+                      table_comparator.less<true>(cudf::nullate::NO{}, comparator));
+  } else {
+    thrust::transform(rmm::exec_policy(stream),
+                      thrust::make_counting_iterator(0),
+                      thrust::make_counting_iterator(input.num_rows()),
+                      thrust::make_counting_iterator(0),
+                      output->mutable_view().data<bool>(),
+                      table_comparator.less<false>(cudf::nullate::NO{}, comparator));
+  }
+  return output;
+}
+
+using physical_comparator_t = cudf::experimental::row::lexicographic::physical_element_comparator;
+using sorting_comparator_t =
+  cudf::experimental::row::lexicographic::sorting_physical_element_comparator;
+
+template std::unique_ptr<cudf::column> self_comparison<physical_comparator_t>(
+  cudf::table_view input,
+  std::vector<cudf::order> const& column_order,
+  physical_comparator_t comparator);
+template std::unique_ptr<cudf::column> self_comparison<sorting_comparator_t>(
+  cudf::table_view input,
+  std::vector<cudf::order> const& column_order,
+  sorting_comparator_t comparator);
+
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> two_table_comparison(cudf::table_view lhs,
+                                                   cudf::table_view rhs,
+                                                   std::vector<cudf::order> const& column_order,
+                                                   PhysicalElementComparator comparator)
+{
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+
+  auto const table_comparator = cudf::experimental::row::lexicographic::two_table_comparator{
+    lhs, rhs, column_order, {}, stream};
+  auto const lhs_it = cudf::experimental::row::lhs_iterator(0);
+  auto const rhs_it = cudf::experimental::row::rhs_iterator(0);
+
+  auto output = cudf::make_numeric_column(
+    cudf::data_type(cudf::type_id::BOOL8), lhs.num_rows(), cudf::mask_state::UNALLOCATED);
+
+  if (cudf::detail::has_nested_columns(lhs) || cudf::detail::has_nested_columns(rhs)) {
+    thrust::transform(rmm::exec_policy(stream),
+                      lhs_it,
+                      lhs_it + lhs.num_rows(),
+                      rhs_it,
+                      output->mutable_view().data<bool>(),
+                      table_comparator.less<true>(cudf::nullate::NO{}, comparator));
+  } else {
+    thrust::transform(rmm::exec_policy(stream),
+                      lhs_it,
+                      lhs_it + lhs.num_rows(),
+                      rhs_it,
+                      output->mutable_view().data<bool>(),
+                      table_comparator.less<false>(cudf::nullate::NO{}, comparator));
+  }
+  return output;
+}
+
+template std::unique_ptr<cudf::column> two_table_comparison<physical_comparator_t>(
+  cudf::table_view lhs,
+  cudf::table_view rhs,
+  std::vector<cudf::order> const& column_order,
+  physical_comparator_t comparator);
+template std::unique_ptr<cudf::column> two_table_comparison<sorting_comparator_t>(
+  cudf::table_view lhs,
+  cudf::table_view rhs,
+  std::vector<cudf::order> const& column_order,
+  sorting_comparator_t comparator);
+
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> sorted_order(
+  std::shared_ptr<cudf::experimental::row::lexicographic::preprocessed_table> preprocessed_input,
+  cudf::size_type num_rows,
+  bool has_nested,
+  PhysicalElementComparator comparator,
+  rmm::cuda_stream_view stream)
+{
+  auto output = cudf::make_numeric_column(cudf::data_type(cudf::type_to_id<cudf::size_type>()),
+                                          num_rows,
+                                          cudf::mask_state::UNALLOCATED,
+                                          stream);
+  auto const out_begin = output->mutable_view().begin<cudf::size_type>();
+  thrust::sequence(rmm::exec_policy(stream), out_begin, out_begin + num_rows, 0);
+
+  auto const table_comparator =
+    cudf::experimental::row::lexicographic::self_comparator{preprocessed_input};
+  if (has_nested) {
+    auto const comp = table_comparator.less<true>(cudf::nullate::NO{}, comparator);
+    thrust::stable_sort(rmm::exec_policy(stream), out_begin, out_begin + num_rows, comp);
+  } else {
+    auto const comp = table_comparator.less<false>(cudf::nullate::NO{}, comparator);
+    thrust::stable_sort(rmm::exec_policy(stream), out_begin, out_begin + num_rows, comp);
+  }
+
+  return output;
+}
+
+template std::unique_ptr<cudf::column> sorted_order<physical_comparator_t>(
+  std::shared_ptr<cudf::experimental::row::lexicographic::preprocessed_table> preprocessed_input,
+  cudf::size_type num_rows,
+  bool has_nested,
+  physical_comparator_t comparator,
+  rmm::cuda_stream_view stream);
+template std::unique_ptr<cudf::column> sorted_order<sorting_comparator_t>(
+  std::shared_ptr<cudf::experimental::row::lexicographic::preprocessed_table> preprocessed_input,
+  cudf::size_type num_rows,
+  bool has_nested,
+  sorting_comparator_t comparator,
+  rmm::cuda_stream_view stream);
+
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> two_table_equality(cudf::table_view lhs,
+                                                 cudf::table_view rhs,
+                                                 std::vector<cudf::order> const& column_order,
+                                                 PhysicalElementComparator comparator)
+{
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+
+  auto const table_comparator =
+    cudf::experimental::row::equality::two_table_comparator{lhs, rhs, stream};
+
+  auto const lhs_it = cudf::experimental::row::lhs_iterator(0);
+  auto const rhs_it = cudf::experimental::row::rhs_iterator(0);
+
+  auto output = cudf::make_numeric_column(
+    cudf::data_type(cudf::type_id::BOOL8), lhs.num_rows(), cudf::mask_state::UNALLOCATED);
+
+  if (cudf::detail::has_nested_columns(lhs) or cudf::detail::has_nested_columns(rhs)) {
+    auto const equal_comparator =
+      table_comparator.equal_to<true>(cudf::nullate::NO{}, cudf::null_equality::EQUAL, comparator);
+
+    thrust::transform(rmm::exec_policy(stream),
+                      lhs_it,
+                      lhs_it + lhs.num_rows(),
+                      rhs_it,
+                      output->mutable_view().data<bool>(),
+                      equal_comparator);
+  } else {
+    auto const equal_comparator =
+      table_comparator.equal_to<false>(cudf::nullate::NO{}, cudf::null_equality::EQUAL, comparator);
+
+    thrust::transform(rmm::exec_policy(stream),
+                      lhs_it,
+                      lhs_it + lhs.num_rows(),
+                      rhs_it,
+                      output->mutable_view().data<bool>(),
+                      equal_comparator);
+  }
+  return output;
+}
+
+using physical_equality_t = cudf::experimental::row::equality::physical_equality_comparator;
+using nan_equality_t = cudf::experimental::row::equality::nan_equal_physical_equality_comparator;
+
+template std::unique_ptr<cudf::column> two_table_equality<physical_equality_t>(
+  cudf::table_view lhs,
+  cudf::table_view rhs,
+  std::vector<cudf::order> const& column_order,
+  physical_equality_t comparator);
+template std::unique_ptr<cudf::column> two_table_equality<nan_equality_t>(
+  cudf::table_view lhs,
+  cudf::table_view rhs,
+  std::vector<cudf::order> const& column_order,
+  nan_equality_t comparator);
diff --git a/cpp/tests/table/row_operator_tests_utilities.hpp b/cpp/tests/table/row_operator_tests_utilities.hpp
new file mode 100644
index 0000000..b34bf65
--- /dev/null
+++ b/cpp/tests/table/row_operator_tests_utilities.hpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_view.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+
+#include <vector>
+
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> self_comparison(cudf::table_view input,
+                                              std::vector<cudf::order> const& column_order,
+                                              PhysicalElementComparator comparator);
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> two_table_comparison(cudf::table_view lhs,
+                                                   cudf::table_view rhs,
+                                                   std::vector<cudf::order> const& column_order,
+                                                   PhysicalElementComparator comparator);
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> two_table_equality(cudf::table_view lhs,
+                                                 cudf::table_view rhs,
+                                                 std::vector<cudf::order> const& column_order,
+                                                 PhysicalElementComparator comparator);
+template <typename PhysicalElementComparator>
+std::unique_ptr<cudf::column> sorted_order(
+  std::shared_ptr<cudf::experimental::row::lexicographic::preprocessed_table> preprocessed_input,
+  cudf::size_type num_rows,
+  bool has_nested,
+  PhysicalElementComparator comparator,
+  rmm::cuda_stream_view stream);
diff --git a/cpp/tests/table/row_operators_tests.cpp b/cpp/tests/table/row_operators_tests.cpp
new file mode 100644
index 0000000..d37c14f
--- /dev/null
+++ b/cpp/tests/table/row_operators_tests.cpp
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <vector>
+
+struct RowOperatorTestForNAN : public cudf::test::BaseFixture {};
+
+TEST_F(RowOperatorTestForNAN, NANEquality)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1{{1., double(NAN), 3., 4.}, {1, 1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<double> col2{{1., double(NAN), 3., 4.}, {1, 1, 0, 1}};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(col1, col2);
+}
+
+TEST_F(RowOperatorTestForNAN, NANSorting)
+{
+  // NULL Before
+  cudf::test::fixed_width_column_wrapper<double> input{
+    {0.,
+     double(NAN),
+     -1.,
+     7.,
+     std::numeric_limits<double>::infinity(),
+     1.,
+     -1 * std::numeric_limits<double>::infinity()},
+    {1, 1, 1, 0, 1, 1, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected1{{3, 6, 2, 0, 5, 4, 1}};
+  std::vector<cudf::order> column_order{cudf::order::ASCENDING};
+  std::vector<cudf::null_order> null_precedence_1{cudf::null_order::BEFORE};
+  cudf::table_view input_table{{input}};
+
+  auto got1 = cudf::sorted_order(input_table, column_order, null_precedence_1);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected1, got1->view());
+
+  // NULL After
+
+  std::vector<cudf::null_order> null_precedence_2{cudf::null_order::AFTER};
+  cudf::test::fixed_width_column_wrapper<int32_t> expected2{{6, 2, 0, 5, 4, 1, 3}};
+
+  auto got2 = cudf::sorted_order(input_table, column_order, null_precedence_2);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected2, got2->view());
+}
+
+TEST_F(RowOperatorTestForNAN, NANSortingNonNull)
+{
+  cudf::test::fixed_width_column_wrapper<double> input{
+    {0.,
+     double(NAN),
+     -1.,
+     7.,
+     std::numeric_limits<double>::infinity(),
+     1.,
+     -1 * std::numeric_limits<double>::infinity()}};
+
+  cudf::table_view input_table{{input}};
+
+  auto result = cudf::sorted_order(input_table, {cudf::order::ASCENDING});
+  cudf::test::fixed_width_column_wrapper<int32_t> expected_asc{{6, 2, 0, 5, 3, 4, 1}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_asc, result->view());
+  auto sorted_result = cudf::sort(input_table, {cudf::order::ASCENDING});
+  auto gather_result = cudf::gather(input_table, result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(sorted_result->view().column(0),
+                                      gather_result->view().column(0));
+
+  result = cudf::sorted_order(input_table, {cudf::order::DESCENDING});
+  cudf::test::fixed_width_column_wrapper<int32_t> expected_desc{{1, 4, 3, 5, 0, 2, 6}};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_desc, result->view());
+  sorted_result = cudf::sort(input_table, {cudf::order::DESCENDING});
+  gather_result = cudf::gather(input_table, result->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(sorted_result->view().column(0),
+                                      gather_result->view().column(0));
+}
diff --git a/cpp/tests/table/table_tests.cpp b/cpp/tests/table/table_tests.cpp
new file mode 100644
index 0000000..f8d94af
--- /dev/null
+++ b/cpp/tests/table/table_tests.cpp
@@ -0,0 +1,153 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <memory>
+#include <random>
+
+template <typename T>
+using column_wrapper = cudf::test::fixed_width_column_wrapper<T>;
+
+using s_col_wrapper = cudf::test::strings_column_wrapper;
+
+using CVector     = std::vector<std::unique_ptr<cudf::column>>;
+using column      = cudf::column;
+using column_view = cudf::column_view;
+using TView       = cudf::table_view;
+using Table       = cudf::table;
+
+struct TableTest : public cudf::test::BaseFixture {};
+
+TEST_F(TableTest, EmptyColumnedTable)
+{
+  std::vector<column_view> cols{};
+
+  TView input(cols);
+  cudf::size_type expected = 0;
+
+  EXPECT_EQ(input.num_columns(), expected);
+}
+
+TEST_F(TableTest, ValidateConstructorTableViewToTable)
+{
+  column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  column_wrapper<int8_t> col2{{1, 2, 3, 4}};
+
+  CVector cols;
+  cols.push_back(col1.release());
+  cols.push_back(col2.release());
+
+  Table input_table(std::move(cols));
+
+  Table out_table(input_table.view());
+
+  EXPECT_EQ(input_table.num_columns(), out_table.num_columns());
+  EXPECT_EQ(input_table.num_rows(), out_table.num_rows());
+}
+
+TEST_F(TableTest, GetTableWithSelectedColumns)
+{
+  column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+  column_wrapper<int32_t> col3{{4, 5, 6, 7}};
+  column_wrapper<int64_t> col4{{4, 5, 6, 7}};
+
+  CVector cols;
+  cols.push_back(col1.release());
+  cols.push_back(col2.release());
+  cols.push_back(col3.release());
+  cols.push_back(col4.release());
+
+  Table t(std::move(cols));
+
+  cudf::table_view selected_tview = t.select(std::vector<cudf::size_type>{2, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(t.view().column(2), selected_tview.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(t.view().column(3), selected_tview.column(1));
+}
+
+TEST_F(TableTest, SelectingOutOfBounds)
+{
+  column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+
+  CVector cols;
+  cols.push_back(col1.release());
+  cols.push_back(col2.release());
+
+  Table t(std::move(cols));
+
+  EXPECT_THROW(cudf::table_view selected_tview = t.select(std::vector<cudf::size_type>{0, 1, 2}),
+               std::out_of_range);
+}
+
+TEST_F(TableTest, SelectingNoColumns)
+{
+  column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+
+  CVector cols;
+  cols.push_back(col1.release());
+  cols.push_back(col2.release());
+  Table t(std::move(cols));
+  TView selected_table = t.select(std::vector<cudf::size_type>{});
+
+  EXPECT_EQ(selected_table.num_columns(), 0);
+}
+
+TEST_F(TableTest, CreateFromViewVector)
+{
+  column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+
+  std::vector<TView> views;
+  views.emplace_back(std::vector<column_view>{col1});
+  views.emplace_back(std::vector<column_view>{col2});
+  TView final_view{views};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(final_view.column(0), views[0].column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(final_view.column(1), views[1].column(0));
+}
+
+TEST_F(TableTest, CreateFromViewVectorRowsMismatch)
+{
+  column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  column_wrapper<int16_t> col2{{1, 2, 3}};
+
+  std::vector<TView> views;
+  views.emplace_back(std::vector<column_view>{col1});
+  views.emplace_back(std::vector<column_view>{col2});
+  EXPECT_THROW(TView{views}, cudf::logic_error);
+}
+
+TEST_F(TableTest, CreateFromViewVectorEmptyTables)
+{
+  std::vector<TView> views;
+  views.emplace_back(std::vector<column_view>{});
+  views.emplace_back(std::vector<column_view>{});
+  TView final_view{views};
+  EXPECT_EQ(final_view.num_columns(), 0);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/table/table_view_tests.cu b/cpp/tests/table/table_view_tests.cu
new file mode 100644
index 0000000..77b3c6c
--- /dev/null
+++ b/cpp/tests/table/table_view_tests.cu
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/table/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+#include <vector>
+
+// Compares two tables row by row, if table1 row is less than table2, then corresponding row value
+// in `output` would be `true`/1 else `false`/0.
+struct TableViewTest : public cudf::test::BaseFixture {};
+void row_comparison(cudf::table_view input1,
+                    cudf::table_view input2,
+                    cudf::mutable_column_view output,
+                    std::vector<cudf::order> const& column_order)
+{
+  rmm::cuda_stream_view stream{cudf::get_default_stream()};
+
+  auto device_table_1 = cudf::table_device_view::create(input1, stream);
+  auto device_table_2 = cudf::table_device_view::create(input2, stream);
+  auto d_column_order = cudf::detail::make_device_uvector_sync(
+    column_order, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto comparator = cudf::row_lexicographic_comparator(
+    cudf::nullate::NO{}, *device_table_1, *device_table_2, d_column_order.data());
+
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(input1.num_rows()),
+                    thrust::make_counting_iterator(0),
+                    output.data<int8_t>(),
+                    comparator);
+}
+
+TEST_F(TableViewTest, EmptyColumnedTable)
+{
+  std::vector<cudf::column_view> cols{};
+
+  cudf::table_view input(cols);
+  cudf::size_type expected = 0;
+
+  EXPECT_EQ(input.num_columns(), expected);
+}
+
+TEST_F(TableViewTest, TestLexicographicalComparatorTwoTableCase)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{1, 2, 3, 4}};
+  cudf::test::fixed_width_column_wrapper<int16_t> col2{{0, 1, 4, 3}};
+  std::vector<cudf::order> column_order{cudf::order::DESCENDING};
+
+  cudf::table_view input_table_1{{col1}};
+  cudf::table_view input_table_2{{col2}};
+
+  auto got = cudf::make_numeric_column(
+    cudf::data_type(cudf::type_id::INT8), input_table_1.num_rows(), cudf::mask_state::UNALLOCATED);
+  cudf::test::fixed_width_column_wrapper<int8_t> expected{{1, 1, 0, 1}};
+  row_comparison(input_table_1, input_table_2, got->mutable_view(), column_order);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TEST_F(TableViewTest, TestLexicographicalComparatorSameTable)
+{
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{1, 2, 3, 4}};
+  std::vector<cudf::order> column_order{cudf::order::DESCENDING};
+
+  cudf::table_view input_table_1{{col1}};
+
+  auto got = cudf::make_numeric_column(
+    cudf::data_type(cudf::type_id::INT8), input_table_1.num_rows(), cudf::mask_state::UNALLOCATED);
+  cudf::test::fixed_width_column_wrapper<int8_t> expected{{0, 0, 0, 0}};
+  row_comparison(input_table_1, input_table_1, got->mutable_view(), column_order);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TEST_F(TableViewTest, Select)
+{
+  using cudf::test::fixed_width_column_wrapper;
+
+  fixed_width_column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  fixed_width_column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+  fixed_width_column_wrapper<int32_t> col3{{4, 5, 6, 7}};
+  fixed_width_column_wrapper<int64_t> col4{{4, 5, 6, 7}};
+  cudf::table_view t{{col1, col2, col3, col4}};
+
+  cudf::table_view selected = t.select({2, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(t.column(2), selected.column(0));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(t.column(3), selected.column(1));
+}
+
+TEST_F(TableViewTest, SelectOutOfBounds)
+{
+  using cudf::test::fixed_width_column_wrapper;
+
+  fixed_width_column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  fixed_width_column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+  fixed_width_column_wrapper<int32_t> col3{{4, 5, 6, 7}};
+  fixed_width_column_wrapper<int64_t> col4{{4, 5, 6, 7}};
+  cudf::table_view t{{col1, col2}};
+
+  EXPECT_THROW((void)t.select({2, 3, 4}), std::out_of_range);
+}
+
+TEST_F(TableViewTest, SelectNoColumns)
+{
+  using cudf::test::fixed_width_column_wrapper;
+
+  fixed_width_column_wrapper<int8_t> col1{{1, 2, 3, 4}};
+  fixed_width_column_wrapper<int16_t> col2{{1, 2, 3, 4}};
+  fixed_width_column_wrapper<int32_t> col3{{4, 5, 6, 7}};
+  fixed_width_column_wrapper<int64_t> col4{{4, 5, 6, 7}};
+  cudf::table_view t{{col1, col2, col3, col4}};
+
+  cudf::table_view selected = t.select({});
+  EXPECT_EQ(selected.num_columns(), 0);
+}
diff --git a/cpp/tests/text/bpe_tests.cpp b/cpp/tests/text/bpe_tests.cpp
new file mode 100644
index 0000000..234d8c4
--- /dev/null
+++ b/cpp/tests/text/bpe_tests.cpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <nvtext/bpe_tokenize.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+struct TextBPETokenize : public cudf::test::BaseFixture {};
+
+TEST_F(TextBPETokenize, BytePairEncoding)
+{
+  // partial table based on values from https://huggingface.co/gpt2/raw/main/merges.txt
+  auto mpt = cudf::test::strings_column_wrapper({
+    "e n",    // 12
+    "i t",    // 14
+    "i s",    // 15
+    "e s",    // 18
+    "en t",   // 42
+    "c e",    // 88
+    "es t",   // 139
+    "en ce",  // 338
+    "T h",    // 561
+    "Th is",  // 956
+    "t est",  // 9032
+    "s ent",  // 33830
+  });
+
+  nvtext::bpe_merge_pairs merge_pairs{cudf::strings_column_view(mpt)};
+
+  auto validity = cudf::test::iterators::null_at(4);
+  cudf::test::strings_column_wrapper input({" This\tis  it\n",
+                                            "This is test-sentence-1",
+                                            "This is test sentence-2",
+                                            "This-is test sentence 3",
+                                            "",
+                                            ""},
+                                           validity);
+  auto sv = cudf::strings_column_view(input);
+
+  auto results = nvtext::byte_pair_encoding(sv, merge_pairs);
+
+  auto expected = cudf::test::strings_column_wrapper({" This is it",
+                                                      "This is test - sent ence - 1",
+                                                      "This is test sent ence - 2",
+                                                      "This - is test sent ence 3",
+                                                      "",
+                                                      ""},
+                                                     validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+
+  auto sliced          = cudf::slice(input, {1, 4}).front();
+  auto sliced_expected = cudf::slice(expected, {1, 4}).front();
+
+  results = nvtext::byte_pair_encoding(cudf::strings_column_view(sliced), merge_pairs);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), sliced_expected);
+}
+
+TEST_F(TextBPETokenize, BytePairEncodingSeparator)
+{
+  auto mpt = cudf::test::strings_column_wrapper(
+    {"e n", "i t", "e s", "en t", "c e", "es t", "en ce", "t est", "s ent"});
+  nvtext::bpe_merge_pairs merge_pairs{cudf::strings_column_view(mpt)};
+
+  cudf::test::strings_column_wrapper input(
+    {"test-sentence-1", "test sentence-2", "test sentence 3", " test sentence 4 "});
+  auto sv = cudf::strings_column_view(input);
+
+  auto results = nvtext::byte_pair_encoding(sv, merge_pairs, std::string(" Ġ"));
+
+  auto expected = cudf::test::strings_column_wrapper(
+    {"test - sent ence - 1", "test Ġsent ence - 2", "test Ġsent ence Ġ3", " Ġtest Ġsent ence Ġ4"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(results->view(), expected);
+}
+
+TEST_F(TextBPETokenize, BPE_Empty)
+{
+  auto mpt = cudf::test::strings_column_wrapper({"i s", "i t"});
+  nvtext::bpe_merge_pairs merge_pairs{mpt.release()};
+  auto empty   = cudf::make_empty_column(cudf::type_id::STRING);
+  auto results = nvtext::byte_pair_encoding(cudf::strings_column_view(empty->view()), merge_pairs);
+  EXPECT_EQ(0, results->size());
+}
+
+TEST_F(TextBPETokenize, BPE_Error)
+{
+  auto empty = cudf::make_empty_column(cudf::type_id::STRING);
+  nvtext::bpe_merge_pairs merge_pairs{std::move(empty)};
+  cudf::test::strings_column_wrapper input({"isit"});
+  EXPECT_THROW(nvtext::byte_pair_encoding(cudf::strings_column_view(input), merge_pairs),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/text/edit_distance_tests.cpp b/cpp/tests/text/edit_distance_tests.cpp
new file mode 100644
index 0000000..837a4eb
--- /dev/null
+++ b/cpp/tests/text/edit_distance_tests.cpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <nvtext/edit_distance.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextEditDistanceTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextEditDistanceTest, EditDistance)
+{
+  std::vector<char const*> h_strings{"dog", nullptr, "cat", "mouse", "pup", "", "puppy", "thé"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::vector<char const*> h_targets{"hog", "not", "cake", "house", "fox", nullptr, "puppy", "the"};
+  cudf::test::strings_column_wrapper targets(
+    h_targets.begin(),
+    h_targets.end(),
+    thrust::make_transform_iterator(h_targets.begin(), [](auto str) { return str != nullptr; }));
+  {
+    auto results =
+      nvtext::edit_distance(cudf::strings_column_view(strings), cudf::strings_column_view(targets));
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({1, 3, 2, 1, 3, 0, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper single({"pup"});
+    auto results =
+      nvtext::edit_distance(cudf::strings_column_view(strings), cudf::strings_column_view(single));
+    cudf::test::fixed_width_column_wrapper<int32_t> expected({3, 3, 3, 4, 0, 3, 2, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextEditDistanceTest, EditDistanceMatrix)
+{
+  std::vector<char const*> h_strings{"dog", nullptr, "hog", "frog", "cat", "", "hat", "clog"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  {
+    auto results = nvtext::edit_distance_matrix(cudf::strings_column_view(strings));
+
+    using LCW = cudf::test::lists_column_wrapper<int32_t>;
+    LCW expected({LCW{0, 3, 1, 2, 3, 3, 3, 2},
+                  LCW{3, 0, 3, 4, 3, 0, 3, 4},
+                  LCW{1, 3, 0, 2, 3, 3, 2, 2},
+                  LCW{2, 4, 2, 0, 4, 4, 4, 2},
+                  LCW{3, 3, 3, 4, 0, 3, 1, 3},
+                  LCW{3, 0, 3, 4, 3, 0, 3, 4},
+                  LCW{3, 3, 2, 4, 1, 3, 0, 4},
+                  LCW{2, 4, 2, 2, 3, 4, 4, 0}});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextEditDistanceTest, EmptyTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view strings_view(strings->view());
+  auto results = nvtext::edit_distance(strings_view, strings_view);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::edit_distance_matrix(strings_view);
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(TextEditDistanceTest, ErrorsTest)
+{
+  cudf::test::strings_column_wrapper strings({"pup"});
+  cudf::test::strings_column_wrapper targets({"pup", ""});
+  EXPECT_THROW(
+    nvtext::edit_distance(cudf::strings_column_view(strings), cudf::strings_column_view(targets)),
+    cudf::logic_error);
+  EXPECT_THROW(nvtext::edit_distance_matrix(cudf::strings_column_view(strings)), cudf::logic_error);
+}
diff --git a/cpp/tests/text/jaccard_tests.cpp b/cpp/tests/text/jaccard_tests.cpp
new file mode 100644
index 0000000..987de31
--- /dev/null
+++ b/cpp/tests/text/jaccard_tests.cpp
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <nvtext/jaccard.hpp>
+
+#include <cudf/strings/strings_column_view.hpp>
+
+struct JaccardTest : public cudf::test::BaseFixture {};
+
+TEST_F(JaccardTest, Basic)
+{
+  auto input1 =
+    cudf::test::strings_column_wrapper({"the quick brown fox", "jumped over the lazy dog."});
+  auto input2 =
+    cudf::test::strings_column_wrapper({"the slowest brown cat", "crawled under the jumping fox"});
+
+  auto view1 = cudf::strings_column_view(input1);
+  auto view2 = cudf::strings_column_view(input2);
+
+  auto results = nvtext::jaccard_index(view1, view2, 5);
+
+  auto expected = cudf::test::fixed_width_column_wrapper<float>({0.103448279f, 0.0697674453f});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  expected = cudf::test::fixed_width_column_wrapper<float>({1.0f, 1.0f});
+  results  = nvtext::jaccard_index(view1, view1, 5);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+  results = nvtext::jaccard_index(view2, view2, 10);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(JaccardTest, WithNulls)
+{
+  auto input1 =
+    cudf::test::strings_column_wrapper({"brown fox", "jumps over dog", "", ""}, {1, 1, 0, 1});
+  auto input2 =
+    cudf::test::strings_column_wrapper({"brown cat", "jumps on fox", "", ""}, {1, 1, 1, 0});
+
+  auto view1 = cudf::strings_column_view(input1);
+  auto view2 = cudf::strings_column_view(input2);
+
+  auto results = nvtext::jaccard_index(view1, view2, 5);
+
+  auto expected =
+    cudf::test::fixed_width_column_wrapper<float>({0.25f, 0.200000003f, 0.f, 0.f}, {1, 1, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+
+  expected = cudf::test::fixed_width_column_wrapper<float>({1.0f, 1.0f, 0.f, 0.f}, {1, 1, 0, 1});
+  results  = nvtext::jaccard_index(view1, view1, 7);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*results, expected);
+}
+
+TEST_F(JaccardTest, Errors)
+{
+  auto input = cudf::test::strings_column_wrapper({"1", "2", "3"});
+  auto view  = cudf::strings_column_view(input);
+  // invalid parameter value
+  EXPECT_THROW(nvtext::jaccard_index(view, view, 1), std::invalid_argument);
+  // invalid size
+  auto input2 = cudf::test::strings_column_wrapper({"1", "2"});
+  auto view2  = cudf::strings_column_view(input2);
+  EXPECT_THROW(nvtext::jaccard_index(view, view2, 5), std::invalid_argument);
+}
diff --git a/cpp/tests/text/minhash_tests.cpp b/cpp/tests/text/minhash_tests.cpp
new file mode 100644
index 0000000..b1c961e
--- /dev/null
+++ b/cpp/tests/text/minhash_tests.cpp
@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <nvtext/minhash.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/utilities/span.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+
+#include <vector>
+
+struct MinHashTest : public cudf::test::BaseFixture {};
+
+TEST_F(MinHashTest, Basic)
+{
+  auto validity = cudf::test::iterators::null_at(1);
+  auto input =
+    cudf::test::strings_column_wrapper({"doc 1",
+                                        "",
+                                        "this is doc 2",
+                                        "",
+                                        "doc 3",
+                                        "d",
+                                        "The quick brown fox jumpéd over the lazy brown dog."},
+                                       validity);
+
+  auto view = cudf::strings_column_view(input);
+
+  auto results = nvtext::minhash(view);
+
+  auto expected = cudf::test::fixed_width_column_wrapper<uint32_t>(
+    {1207251914u, 0u, 21141582u, 0u, 1207251914u, 655955059u, 86520422u}, validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  auto results64  = nvtext::minhash64(view);
+  auto expected64 = cudf::test::fixed_width_column_wrapper<uint64_t>({774489391575805754ul,
+                                                                      0ul,
+                                                                      3232308021562742685ul,
+                                                                      0ul,
+                                                                      13145552576991307582ul,
+                                                                      14660046701545912182ul,
+                                                                      398062025280761388ul},
+                                                                     validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results64, expected64);
+}
+
+TEST_F(MinHashTest, LengthEqualsWidth)
+{
+  auto input   = cudf::test::strings_column_wrapper({"abcdé", "fghjk", "lmnop", "qrstu", "vwxyz"});
+  auto view    = cudf::strings_column_view(input);
+  auto results = nvtext::minhash(view, 0, 5);
+  auto expected = cudf::test::fixed_width_column_wrapper<uint32_t>(
+    {3825281041u, 2728681928u, 1984332911u, 3965004915u, 192452857u});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(MinHashTest, MultiSeed)
+{
+  auto input =
+    cudf::test::strings_column_wrapper({"doc 1",
+                                        "this is doc 2",
+                                        "doc 3",
+                                        "d",
+                                        "The quick brown fox jumpéd over the lazy brown dog."});
+
+  auto view = cudf::strings_column_view(input);
+
+  auto seeds   = cudf::test::fixed_width_column_wrapper<uint32_t>({0, 1, 2});
+  auto results = nvtext::minhash(view, cudf::column_view(seeds));
+
+  using LCW = cudf::test::lists_column_wrapper<uint32_t>;
+  // clang-format off
+  LCW expected({LCW{1207251914u, 1677652962u, 1061355987u},
+                LCW{  21141582u,  580916568u, 1258052021u},
+                LCW{1207251914u,  943567174u, 1109272887u},
+                LCW{ 655955059u,  488346356u, 2394664816u},
+                LCW{  86520422u,  236622901u,  102546228u}});
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  auto seeds64   = cudf::test::fixed_width_column_wrapper<uint64_t>({0, 1, 2});
+  auto results64 = nvtext::minhash64(view, cudf::column_view(seeds64));
+
+  using LCW64 = cudf::test::lists_column_wrapper<uint64_t>;
+  // clang-format off
+  LCW64 expected64({LCW64{  774489391575805754ul, 10435654231793485448ul, 1188598072697676120ul},
+                    LCW64{ 3232308021562742685ul,  4445611509348165860ul, 1188598072697676120ul},
+                    LCW64{13145552576991307582ul,  6846192680998069919ul, 1188598072697676120ul},
+                    LCW64{14660046701545912182ul, 17106501326045553694ul, 17713478494106035784ul},
+                    LCW64{  398062025280761388ul,   377720198157450084ul,  984941365662009329ul}});
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results64, expected64);
+}
+
+TEST_F(MinHashTest, MultiSeedWithNullInputRow)
+{
+  auto validity = cudf::test::iterators::null_at(1);
+  auto input    = cudf::test::strings_column_wrapper({"abcdéfgh", "", "", "stuvwxyz"}, validity);
+  auto view     = cudf::strings_column_view(input);
+
+  auto seeds   = cudf::test::fixed_width_column_wrapper<uint32_t>({1, 2});
+  auto results = nvtext::minhash(view, cudf::column_view(seeds));
+
+  using LCW = cudf::test::lists_column_wrapper<uint32_t>;
+  LCW expected({LCW{484984072u, 1074168784u}, LCW{}, LCW{0u, 0u}, LCW{571652169u, 173528385u}},
+               validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  auto seeds64   = cudf::test::fixed_width_column_wrapper<uint64_t>({11, 22});
+  auto results64 = nvtext::minhash64(view, cudf::column_view(seeds64));
+
+  using LCW64 = cudf::test::lists_column_wrapper<uint64_t>;
+  LCW64 expected64({LCW64{2597399324547032480ul, 4461410998582111052ul},
+                    LCW64{},
+                    LCW64{0ul, 0ul},
+                    LCW64{2717781266371273264ul, 6977325820868387259ul}},
+                   validity);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results64, expected64);
+}
+
+TEST_F(MinHashTest, EmptyTest)
+{
+  auto input   = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  auto view    = cudf::strings_column_view(input->view());
+  auto results = nvtext::minhash(view);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::minhash64(view);
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(MinHashTest, ErrorsTest)
+{
+  auto input = cudf::test::strings_column_wrapper({"this string intentionally left blank"});
+  auto view  = cudf::strings_column_view(input);
+  EXPECT_THROW(nvtext::minhash(view, 0, 0), std::invalid_argument);
+  EXPECT_THROW(nvtext::minhash64(view, 0, 0), std::invalid_argument);
+  auto seeds = cudf::test::fixed_width_column_wrapper<uint32_t>();
+  EXPECT_THROW(nvtext::minhash(view, cudf::column_view(seeds)), std::invalid_argument);
+  auto seeds64 = cudf::test::fixed_width_column_wrapper<uint64_t>();
+  EXPECT_THROW(nvtext::minhash64(view, cudf::column_view(seeds64)), std::invalid_argument);
+
+  std::vector<std::string> h_input(50000, "");
+  input = cudf::test::strings_column_wrapper(h_input.begin(), h_input.end());
+  view  = cudf::strings_column_view(input);
+
+  auto const zeroes = thrust::constant_iterator<uint32_t>(0);
+  seeds             = cudf::test::fixed_width_column_wrapper<uint32_t>(zeroes, zeroes + 50000);
+  EXPECT_THROW(nvtext::minhash(view, cudf::column_view(seeds)), std::overflow_error);
+  seeds64 = cudf::test::fixed_width_column_wrapper<uint64_t>(zeroes, zeroes + 50000);
+  EXPECT_THROW(nvtext::minhash64(view, cudf::column_view(seeds64)), std::overflow_error);
+}
diff --git a/cpp/tests/text/ngrams_tests.cpp b/cpp/tests/text/ngrams_tests.cpp
new file mode 100644
index 0000000..7b17958
--- /dev/null
+++ b/cpp/tests/text/ngrams_tests.cpp
@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <nvtext/generate_ngrams.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextGenerateNgramsTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextGenerateNgramsTest, Ngrams)
+{
+  cudf::test::strings_column_wrapper strings{"the", "fox", "jumped", "over", "thé", "dog"};
+  cudf::strings_column_view strings_view(strings);
+  auto const separator = cudf::string_scalar("_");
+
+  {
+    cudf::test::strings_column_wrapper expected{
+      "the_fox", "fox_jumped", "jumped_over", "over_thé", "thé_dog"};
+    auto const results = nvtext::generate_ngrams(strings_view, 2, separator);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+
+  {
+    cudf::test::strings_column_wrapper expected{
+      "the_fox_jumped", "fox_jumped_over", "jumped_over_thé", "over_thé_dog"};
+    auto const results = nvtext::generate_ngrams(strings_view, 3, separator);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected{"th",
+                                                "he",
+                                                "fo",
+                                                "ox",
+                                                "ju",
+                                                "um",
+                                                "mp",
+                                                "pe",
+                                                "ed",
+                                                "ov",
+                                                "ve",
+                                                "er",
+                                                "th",
+                                                "hé",
+                                                "do",
+                                                "og"};
+    auto const results = nvtext::generate_character_ngrams(strings_view, 2);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected{
+      "the", "fox", "jum", "ump", "mpe", "ped", "ove", "ver", "thé", "dog"};
+    auto const results = nvtext::generate_character_ngrams(strings_view, 3);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextGenerateNgramsTest, NgramsWithNulls)
+{
+  std::vector<char const*> h_strings{"the", "fox", "", "jumped", "over", nullptr, "the", "dog"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto const separator = cudf::string_scalar("_");
+
+  cudf::strings_column_view strings_view(strings);
+  {
+    auto const results = nvtext::generate_ngrams(strings_view, 3, separator);
+    cudf::test::strings_column_wrapper expected{
+      "the_fox_jumped", "fox_jumped_over", "jumped_over_the", "over_the_dog"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected{
+      "the", "fox", "jum", "ump", "mpe", "ped", "ove", "ver", "the", "dog"};
+    auto const results = nvtext::generate_character_ngrams(strings_view, 3);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextGenerateNgramsTest, Empty)
+{
+  auto const zero_size_strings_column = cudf::make_empty_column(cudf::type_id::STRING)->view();
+
+  auto const separator = cudf::string_scalar("_");
+
+  auto results =
+    nvtext::generate_ngrams(cudf::strings_column_view(zero_size_strings_column), 2, separator);
+  cudf::test::expect_column_empty(results->view());
+  results = nvtext::generate_character_ngrams(cudf::strings_column_view(zero_size_strings_column));
+  cudf::test::expect_column_empty(results->view());
+}
+
+TEST_F(TextGenerateNgramsTest, Errors)
+{
+  cudf::test::strings_column_wrapper strings{""};
+  auto const separator = cudf::string_scalar("_");
+  // invalid parameter value
+  EXPECT_THROW(nvtext::generate_ngrams(cudf::strings_column_view(strings), 1, separator),
+               cudf::logic_error);
+  EXPECT_THROW(nvtext::generate_character_ngrams(cudf::strings_column_view(strings), 1),
+               cudf::logic_error);
+  // not enough strings to generate ngrams
+  EXPECT_THROW(nvtext::generate_ngrams(cudf::strings_column_view(strings), 3, separator),
+               cudf::logic_error);
+  EXPECT_THROW(nvtext::generate_character_ngrams(cudf::strings_column_view(strings), 3),
+               cudf::logic_error);
+
+  cudf::test::strings_column_wrapper strings_no_tokens({"", "", "", ""}, {1, 0, 1, 0});
+  EXPECT_THROW(nvtext::generate_ngrams(cudf::strings_column_view(strings_no_tokens), 2, separator),
+               cudf::logic_error);
+  EXPECT_THROW(nvtext::generate_character_ngrams(cudf::strings_column_view(strings_no_tokens)),
+               cudf::logic_error);
+}
+
+TEST_F(TextGenerateNgramsTest, NgramsHash)
+{
+  auto input =
+    cudf::test::strings_column_wrapper({"the quick brown fox", "jumped over the lazy dog."});
+
+  auto view    = cudf::strings_column_view(input);
+  auto results = nvtext::hash_character_ngrams(view);
+
+  using LCW = cudf::test::lists_column_wrapper<uint32_t>;
+  // clang-format off
+  LCW expected({LCW{2169381797u, 3924065905u, 1634753325u, 3766025829u,  771291085u,
+                    2286480985u, 2815102125u, 2383213292u, 1587939873u, 3417728802u,
+                     741580288u, 1721912990u, 3322339040u, 2530504717u, 1448945146u},
+                LCW{3542029734u, 2351937583u, 2373822151u, 2610417165u, 1303810911u,
+                    2541942822u, 1736466351u, 3466558519u,  408633648u, 1698719372u,
+                     620653030u,   16851044u,  608863326u,  948572753u, 3672211877u,
+                    4097451013u, 1444462157u, 3762829398u,  743082018u, 2953783152u,
+                    2319357747u}});
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextGenerateNgramsTest, NgramsHashErrors)
+{
+  auto input = cudf::test::strings_column_wrapper({"1", "2", "3"});
+  auto view  = cudf::strings_column_view(input);
+
+  // invalid parameter value
+  EXPECT_THROW(nvtext::hash_character_ngrams(view, 1), cudf::logic_error);
+  // strings not long enough to generate ngrams
+  EXPECT_THROW(nvtext::hash_character_ngrams(view), cudf::logic_error);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/text/ngrams_tokenize_tests.cpp b/cpp/tests/text/ngrams_tokenize_tests.cpp
new file mode 100644
index 0000000..c6fb886
--- /dev/null
+++ b/cpp/tests/text/ngrams_tokenize_tests.cpp
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <nvtext/ngrams_tokenize.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextNgramsTokenizeTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextNgramsTokenizeTest, Tokenize)
+{
+  std::vector<char const*> h_strings{"the fox jumped over the dog",
+                                     "the dog chased  the cat",
+                                     " the cat chased the mouse ",
+                                     nullptr,
+                                     "",
+                                     "the mousé ate the cheese"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  cudf::strings_column_view strings_view(strings);
+
+  {
+    cudf::test::strings_column_wrapper expected{"the_fox",
+                                                "fox_jumped",
+                                                "jumped_over",
+                                                "over_the",
+                                                "the_dog",
+                                                "the_dog",
+                                                "dog_chased",
+                                                "chased_the",
+                                                "the_cat",
+                                                "the_cat",
+                                                "cat_chased",
+                                                "chased_the",
+                                                "the_mouse",
+                                                "the_mousé",
+                                                "mousé_ate",
+                                                "ate_the",
+                                                "the_cheese"};
+    auto results = nvtext::ngrams_tokenize(strings_view, 2, std::string(), std::string("_"));
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected{"the:fox:jumped",
+                                                "fox:jumped:over",
+                                                "jumped:over:the",
+                                                "over:the:dog",
+                                                "the:dog:chased",
+                                                "dog:chased:the",
+                                                "chased:the:cat",
+                                                "the:cat:chased",
+                                                "cat:chased:the",
+                                                "chased:the:mouse",
+                                                "the:mousé:ate",
+                                                "mousé:ate:the",
+                                                "ate:the:cheese"};
+    auto results = nvtext::ngrams_tokenize(strings_view, 3, std::string{" "}, std::string{":"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::strings_column_wrapper expected{"the--fox--jumped--over",
+                                                "fox--jumped--over--the",
+                                                "jumped--over--the--dog",
+                                                "the--dog--chased--the",
+                                                "dog--chased--the--cat",
+                                                "the--cat--chased--the",
+                                                "cat--chased--the--mouse",
+                                                "the--mousé--ate--the",
+                                                "mousé--ate--the--cheese"};
+    auto results = nvtext::ngrams_tokenize(strings_view, 4, std::string{" "}, std::string{"--"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextNgramsTokenizeTest, TokenizeOneGram)
+{
+  cudf::test::strings_column_wrapper strings{"aaa bbb", "  ccc  ddd  ", "eee"};
+  cudf::strings_column_view strings_view(strings);
+  auto const empty = cudf::string_scalar("");
+
+  cudf::test::strings_column_wrapper expected{"aaa", "bbb", "ccc", "ddd", "eee"};
+  auto results = nvtext::ngrams_tokenize(strings_view, 1, empty, empty);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextNgramsTokenizeTest, TokenizeEmptyTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view strings_view(strings->view());
+  auto const empty = cudf::string_scalar("");
+  auto results     = nvtext::ngrams_tokenize(strings_view, 2, empty, empty);
+  EXPECT_EQ(results->size(), 0);
+  EXPECT_EQ(results->has_nulls(), false);
+}
+
+TEST_F(TextNgramsTokenizeTest, TokenizeErrorTest)
+{
+  cudf::test::strings_column_wrapper strings{"this column intentionally left blank"};
+  cudf::strings_column_view strings_view(strings);
+  auto const empty = cudf::string_scalar("");
+  EXPECT_THROW(nvtext::ngrams_tokenize(strings_view, 0, empty, empty), cudf::logic_error);
+}
diff --git a/cpp/tests/text/normalize_tests.cpp b/cpp/tests/text/normalize_tests.cpp
new file mode 100644
index 0000000..5fa3bb2
--- /dev/null
+++ b/cpp/tests/text/normalize_tests.cpp
@@ -0,0 +1,161 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <nvtext/normalize.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextNormalizeTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextNormalizeTest, NormalizeSpaces)
+{
+  std::vector<char const*> h_strings{"the\t fox  jumped over the      dog",
+                                     "the dog\f chased  the cat\r",
+                                     " the cat  chaséd  the mouse\n",
+                                     nullptr,
+                                     "",
+                                     " \r\t\n",
+                                     "no change",
+                                     "the mousé ate the cheese"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  cudf::strings_column_view strings_view(strings);
+
+  std::vector<char const*> h_expected{"the fox jumped over the dog",
+                                      "the dog chased the cat",
+                                      "the cat chaséd the mouse",
+                                      nullptr,
+                                      "",
+                                      "",
+                                      "no change",
+                                      "the mousé ate the cheese"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto const results = nvtext::normalize_spaces(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextNormalizeTest, NormalizeEmptyTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view strings_view(strings->view());
+  auto results = nvtext::normalize_spaces(strings_view);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::normalize_characters(strings_view, true);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::normalize_characters(strings_view, false);
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(TextNormalizeTest, AllNullStrings)
+{
+  cudf::test::strings_column_wrapper strings({"", "", ""}, {0, 0, 0});
+  cudf::strings_column_view strings_view(strings);
+  auto results = nvtext::normalize_spaces(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings);
+  results = nvtext::normalize_characters(strings_view, false);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings);
+}
+
+TEST_F(TextNormalizeTest, SomeNullStrings)
+{
+  cudf::test::strings_column_wrapper strings({"", ".", "a"}, {0, 1, 1});
+  cudf::strings_column_view strings_view(strings);
+  auto results = nvtext::normalize_characters(strings_view, false);
+  cudf::test::strings_column_wrapper expected({"", " . ", "a"}, {0, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextNormalizeTest, NormalizeCharacters)
+{
+  // These include punctuation, accents, whitespace, and CJK characters
+  std::vector<char const*> h_strings{"abc£def",
+                                     nullptr,
+                                     "éè â îô\taeio",
+                                     "\tĂĆĖÑ  Ü",
+                                     "ACEN U",
+                                     "P^NP",
+                                     "$41.07",
+                                     "[a,b]",
+                                     "丏丟",
+                                     ""};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+  cudf::strings_column_view strings_view(strings);
+  {
+    auto results = nvtext::normalize_characters(strings_view, true);
+    cudf::test::strings_column_wrapper expected({"abc£def",
+                                                 "",
+                                                 "ee a io aeio",
+                                                 " acen  u",
+                                                 "acen u",
+                                                 "p ^ np",
+                                                 " $ 41 . 07",
+                                                 " [ a , b ] ",
+                                                 " 丏  丟 ",
+                                                 ""},
+                                                validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = nvtext::normalize_characters(strings_view, false);
+    cudf::test::strings_column_wrapper expected({"abc£def",
+                                                 "",
+                                                 "éè â îô aeio",
+                                                 " ĂĆĖÑ  Ü",
+                                                 "ACEN U",
+                                                 "P ^ NP",
+                                                 " $ 41 . 07",
+                                                 " [ a , b ] ",
+                                                 " 丏  丟 ",
+                                                 ""},
+                                                validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextNormalizeTest, NormalizeSlicedColumn)
+{
+  cudf::test::strings_column_wrapper strings(
+    {"abc£def", "éè â îô\taeio", "ACEN U", "P^NP", "$41.07", "[a,b]", "丏丟"});
+
+  std::vector<cudf::column_view> sliced = cudf::split(strings, {4});
+  auto results = nvtext::normalize_characters(cudf::strings_column_view(sliced.front()), true);
+  cudf::test::strings_column_wrapper expected({"abc£def", "ee a io aeio", "acen u", "p ^ np"});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  results = nvtext::normalize_characters(cudf::strings_column_view(sliced[1]), false);
+  cudf::test::strings_column_wrapper expected2({" $ 41 . 07", " [ a , b ] ", " 丏  丟 "});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected2);
+}
diff --git a/cpp/tests/text/replace_tests.cpp b/cpp/tests/text/replace_tests.cpp
new file mode 100644
index 0000000..f798d59
--- /dev/null
+++ b/cpp/tests/text/replace_tests.cpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <nvtext/replace.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextReplaceTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextReplaceTest, ReplaceTokens)
+{
+  std::vector<char const*> h_strings{"the fox jumped over the dog",
+                                     "is theme of the thesis",
+                                     nullptr,
+                                     "",
+                                     "no change",
+                                     "thé is the cheese is"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::test::strings_column_wrapper targets({"is", "the"});
+  cudf::test::strings_column_wrapper repls({"___", ""});
+  std::vector<char const*> h_expected{" fox jumped over  dog",
+                                      "___ theme of  thesis",
+                                      nullptr,
+                                      "",
+                                      "no change",
+                                      "thé ___  cheese ___"};
+  cudf::test::strings_column_wrapper expected(
+    h_expected.begin(),
+    h_expected.end(),
+    thrust::make_transform_iterator(h_expected.begin(), [](auto str) { return str != nullptr; }));
+
+  auto results = nvtext::replace_tokens(cudf::strings_column_view(strings),
+                                        cudf::strings_column_view(targets),
+                                        cudf::strings_column_view(repls));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = nvtext::replace_tokens(cudf::strings_column_view(strings),
+                                   cudf::strings_column_view(targets),
+                                   cudf::strings_column_view(repls),
+                                   cudf::string_scalar("o "));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextReplaceTest, ReplaceTokensSingleRepl)
+{
+  cudf::test::strings_column_wrapper strings({"this\t is that", "is then \tis", "us them is us"});
+  cudf::test::strings_column_wrapper targets({"is", "us"});
+  cudf::test::strings_column_wrapper repls({"_"});
+  cudf::test::strings_column_wrapper expected({"this\t _ that", "_ then \t_", "_ them _ _"});
+
+  auto results = nvtext::replace_tokens(cudf::strings_column_view(strings),
+                                        cudf::strings_column_view(targets),
+                                        cudf::strings_column_view(repls));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextReplaceTest, ReplaceTokensEmptyTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view strings_view(strings->view());
+  auto const results = nvtext::replace_tokens(strings_view, strings_view, strings_view);
+  EXPECT_EQ(results->size(), 0);
+  EXPECT_EQ(results->has_nulls(), false);
+}
+
+TEST_F(TextReplaceTest, ReplaceTokensErrorTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view strings_view(strings->view());
+  cudf::test::strings_column_wrapper notnulls({"", "", ""});
+  cudf::strings_column_view notnulls_view(notnulls);
+  cudf::test::strings_column_wrapper nulls({"", ""}, {0, 0});
+  cudf::strings_column_view nulls_view(nulls);
+
+  EXPECT_THROW(nvtext::replace_tokens(strings_view, nulls_view, notnulls_view), cudf::logic_error);
+  EXPECT_THROW(nvtext::replace_tokens(strings_view, notnulls_view, nulls_view), cudf::logic_error);
+  EXPECT_THROW(nvtext::replace_tokens(notnulls_view, notnulls_view, strings_view),
+               cudf::logic_error);
+  EXPECT_THROW(
+    nvtext::replace_tokens(notnulls_view, nulls_view, strings_view, cudf::string_scalar("", false)),
+    cudf::logic_error);
+}
+
+TEST_F(TextReplaceTest, FilterTokens)
+{
+  cudf::test::strings_column_wrapper strings({" one two three ", "four  fivé  six", "sevén eight"});
+
+  auto results = nvtext::filter_tokens(cudf::strings_column_view(strings), 1);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, strings);  // no change
+
+  {
+    auto results = nvtext::filter_tokens(cudf::strings_column_view(strings), 4);
+    cudf::test::strings_column_wrapper expected({"   three ", "four  fivé  ", "sevén eight"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results = nvtext::filter_tokens(cudf::strings_column_view(strings), 5);
+    cudf::test::strings_column_wrapper expected({"   three ", "    ", "sevén eight"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto results =
+      nvtext::filter_tokens(cudf::strings_column_view(strings), 4, cudf::string_scalar("--"));
+    cudf::test::strings_column_wrapper expected({" -- -- three ", "four  fivé  --", "sevén eight"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextReplaceTest, FilterTokensEmptyTest)
+{
+  auto strings       = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  auto const results = nvtext::filter_tokens(cudf::strings_column_view(strings->view()), 7);
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(TextReplaceTest, FilterTokensErrorTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  EXPECT_THROW(nvtext::filter_tokens(
+                 cudf::strings_column_view(strings->view()), 1, cudf::string_scalar("", false)),
+               cudf::logic_error);
+  EXPECT_THROW(nvtext::filter_tokens(cudf::strings_column_view(strings->view()),
+                                     1,
+                                     cudf::string_scalar("-"),
+                                     cudf::string_scalar("", false)),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/text/stemmer_tests.cpp b/cpp/tests/text/stemmer_tests.cpp
new file mode 100644
index 0000000..939d2f1
--- /dev/null
+++ b/cpp/tests/text/stemmer_tests.cpp
@@ -0,0 +1,179 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <nvtext/stemmer.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextStemmerTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextStemmerTest, PorterStemmer)
+{
+  std::vector<char const*> h_strings{"abandon",
+                                     nullptr,
+                                     "abbey",
+                                     "cleans",
+                                     "trouble",
+                                     "",
+                                     "yearly",
+                                     "tree",
+                                     "y",
+                                     "by",
+                                     "oats",
+                                     "ivy",
+                                     "private",
+                                     "orrery"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected(
+    {3, 0, 2, 1, 1, 0, 1, 0, 0, 0, 1, 1, 2, 2}, validity);
+  auto const results = nvtext::porter_stemmer_measure(cudf::strings_column_view(strings));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextStemmerTest, IsLetterIndex)
+{
+  std::vector<char const*> h_strings{"abandon",
+                                     nullptr,
+                                     "abbey",
+                                     "cleans",
+                                     "trouble",
+                                     "",
+                                     "yearly",
+                                     "tree",
+                                     "y",
+                                     "by",
+                                     "oats",
+                                     "ivy",
+                                     "private",
+                                     "orrery"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+
+  cudf::strings_column_view sv(strings);
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::VOWEL, 0);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::CONSONANT, 0);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::VOWEL, 5);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::CONSONANT, 5);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::VOWEL, -2);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::CONSONANT, -2);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextStemmerTest, IsLetterIndices)
+{
+  std::vector<char const*> h_strings{"abandon",
+                                     nullptr,
+                                     "abbey",
+                                     "cleans",
+                                     "trouble",
+                                     "",
+                                     "yearly",
+                                     "tree",
+                                     "y",
+                                     "by",
+                                     "oats",
+                                     "ivy",
+                                     "private",
+                                     "orrery"};
+  auto validity =
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; });
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end(), validity);
+  cudf::test::fixed_width_column_wrapper<int32_t> indices(
+    {0, 1, 2, 3, 4, 5, 4, 3, 2, 1, -1, -2, -3, -4});
+
+  cudf::strings_column_view sv(strings);
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::VOWEL, indices);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    auto const results = nvtext::is_letter(sv, nvtext::letter_type::CONSONANT, indices);
+    cudf::test::fixed_width_column_wrapper<bool> expected(
+      {0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1}, validity);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextStemmerTest, EmptyTest)
+{
+  auto strings = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::strings_column_view sv(strings->view());
+  auto results = nvtext::porter_stemmer_measure(sv);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::is_letter(sv, nvtext::letter_type::CONSONANT, 0);
+  EXPECT_EQ(results->size(), 0);
+  auto indices = cudf::make_empty_column(cudf::data_type{cudf::type_id::INT32});
+  results      = nvtext::is_letter(sv, nvtext::letter_type::VOWEL, indices->view());
+  EXPECT_EQ(results->size(), 0);
+}
+
+TEST_F(TextStemmerTest, ErrorTest)
+{
+  auto empty = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  cudf::test::fixed_width_column_wrapper<int32_t> indices({0}, {0});
+  EXPECT_THROW(nvtext::is_letter(
+                 cudf::strings_column_view(empty->view()), nvtext::letter_type::VOWEL, indices),
+               cudf::logic_error);
+  cudf::test::strings_column_wrapper strings({"abc"});
+  EXPECT_THROW(
+    nvtext::is_letter(cudf::strings_column_view(strings), nvtext::letter_type::VOWEL, indices),
+    cudf::logic_error);
+}
diff --git a/cpp/tests/text/subword_tests.cpp b/cpp/tests/text/subword_tests.cpp
new file mode 100644
index 0000000..4db289a
--- /dev/null
+++ b/cpp/tests/text/subword_tests.cpp
@@ -0,0 +1,442 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <nvtext/subword_tokenize.hpp>
+
+#include <fstream>
+#include <iostream>
+#include <vector>
+
+// Global environment for temporary files
+auto const temp_env = static_cast<cudf::test::TempDirTestEnvironment*>(
+  ::testing::AddGlobalTestEnvironment(new cudf::test::TempDirTestEnvironment));
+
+struct TextSubwordTest : public cudf::test::BaseFixture {};
+
+// Create a fake hashed vocab text file for the tests in this source file.
+// The vocab only includes the following words:
+//  'this', 'is', 'a', 'test', 'tést'
+// The period '.' character also has a token id.
+void create_hashed_vocab(std::string const& hash_file)
+{
+  std::vector<std::pair<int, int>> coefficients(23, {65559, 0});
+  std::ofstream outfile(hash_file, std::ofstream::out);
+  outfile << "1\n0\n" << coefficients.size() << "\n";
+  for (auto c : coefficients)
+    outfile << c.first << " " << c.second << "\n";
+  std::vector<uint64_t> hash_table(23, 0);
+  outfile << hash_table.size() << "\n";
+  hash_table[0]  = 3015668L;              // based on values
+  hash_table[1]  = 6205475701751155871L;  // from the
+  hash_table[5]  = 6358029;               // bert_hash_table.txt
+  hash_table[16] = 451412625363L;         // file for the test
+  hash_table[20] = 6206321707968235495L;  // words above
+  for (auto h : hash_table)
+    outfile << h << "\n";
+  outfile << "100\n101\n102\n\n";
+}
+
+TEST(TextSubwordTest, Tokenize)
+{
+  uint32_t nrows = 100;
+  std::vector<char const*> h_strings(nrows, "This is a test. A test this is.");
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+
+  uint32_t max_sequence_length = 16;
+  uint32_t stride              = 16;
+
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         max_sequence_length,
+                                         stride,
+                                         true,    // do_lower_case
+                                         false);  // do_truncate
+
+  EXPECT_EQ(nrows, result.nrows_tensor);
+
+  {
+    std::vector<uint32_t> base_data(
+      {2023, 2003, 1037, 3231, 1012, 1037, 3231, 2023, 2003, 1012, 0, 0, 0, 0, 0, 0});
+    std::vector<uint32_t> h_expected;
+    for (uint32_t idx = 0; idx < nrows; ++idx)
+      h_expected.insert(h_expected.end(), base_data.begin(), base_data.end());
+    cudf::test::fixed_width_column_wrapper<uint32_t> expected(h_expected.begin(), h_expected.end());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected);
+  }
+
+  {
+    std::vector<uint32_t> base_data({1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0});
+    std::vector<uint32_t> h_expected;
+    for (uint32_t idx = 0; idx < nrows; ++idx)
+      h_expected.insert(h_expected.end(), base_data.begin(), base_data.end());
+    cudf::test::fixed_width_column_wrapper<uint32_t> expected(h_expected.begin(), h_expected.end());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected);
+  }
+
+  {
+    std::vector<uint32_t> h_expected;
+    for (uint32_t idx = 0; idx < nrows; ++idx) {
+      // 0,0,9,1,0,9,2,0,9,3,0,9,4,0,9,5,0,9,6,0,9,7,0,9,8,0,9,9,0,9,...
+      h_expected.push_back(idx);
+      h_expected.push_back(0);
+      h_expected.push_back(9);
+    }
+    cudf::test::fixed_width_column_wrapper<uint32_t> expected(h_expected.begin(), h_expected.end());
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected);
+  }
+}
+
+TEST(TextSubwordTest, TokenizeMultiRow)
+{
+  std::vector<char const*> h_strings{"This is a test.", "This is a test. This is a tést."};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+
+  uint32_t max_sequence_length = 8;
+  uint32_t stride              = 6;
+
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         max_sequence_length,
+                                         stride,
+                                         true,    // do_lower_case
+                                         false);  // do_truncate
+
+  EXPECT_EQ(uint32_t{3}, result.nrows_tensor);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_tokens(
+    {2023, 2003, 1037, 3231, 1012, 0,    0,    0,    2023, 2003, 1037, 3231,
+     1012, 2023, 2003, 1037, 2003, 1037, 3231, 1012, 0,    0,    0,    0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected_tokens);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_attn(
+    {1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected_attn);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_metadata({0, 0, 4, 1, 0, 6, 1, 1, 3});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
+
+TEST(TextSubwordTest, TokenizeWithEmptyRow)
+{
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+
+  cudf::test::strings_column_wrapper strings{
+    "This is a test.", "", "This is a test. This is a tést."};
+  auto input = cudf::strings_column_view{strings};
+
+  uint32_t const max_seq = 8;
+  uint32_t const stride  = 6;
+  bool const lower       = true;
+  bool const truncate    = false;
+
+  auto result = nvtext::subword_tokenize(input, *vocab, max_seq, stride, lower, truncate);
+
+  EXPECT_EQ(uint32_t{4}, result.nrows_tensor);
+
+  // clang-format off
+  auto expected_tokens = cudf::test::fixed_width_column_wrapper<uint32_t>(
+    {2023, 2003, 1037, 3231, 1012,   0,    0,    0,
+        0,    0,    0,    0,    0,   0,    0,    0,
+     2023, 2003, 1037, 3231, 1012, 2023, 2003, 1037,   // this one
+     2003, 1037, 3231, 1012,    0,    0,    0,    0}); // continues here
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected_tokens);
+  // clang-format off
+  auto expected_attn = cudf::test::fixed_width_column_wrapper<uint32_t>(
+     {1, 1, 1, 1, 1, 0, 0, 0,
+      0, 0, 0, 0, 0, 0, 0, 0,
+      1, 1, 1, 1, 1, 1, 1, 1,
+      1, 1, 1, 1, 0, 0, 0, 0});
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected_attn);
+  // clang-format off
+  auto expected_metadata = cudf::test::fixed_width_column_wrapper<uint32_t>(
+    {0,0,4, 1,0,0, 2,0,6, 2,1,3}); // note that the 3rd element has 2 tensors
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
+
+TEST(TextSubwordTest, TokenizeMaxEqualsTokens)
+{
+  cudf::test::strings_column_wrapper strings({"This is a test."});
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+
+  uint32_t max_sequence_length = 5;  // five tokens in strings;
+  uint32_t stride              = 5;  // this should not effect the result
+
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         max_sequence_length,
+                                         stride,
+                                         true,    // do_lower_case
+                                         false);  // do_truncate
+
+  EXPECT_EQ(uint32_t{1}, result.nrows_tensor);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_tokens({2023, 2003, 1037, 3231, 1012});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected_tokens);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_attn({1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected_attn);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_metadata({0, 0, 4});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
+
+TEST(TextSubwordTest, ParameterErrors)
+{
+  std::vector<char const*> h_strings{"This is a test.", "This is a test. This is a tést.", "", ""};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+
+  EXPECT_THROW(nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                        *vocab,
+                                        12,     // max_sequence_length
+                                        13,     // stride <= max_sequence_length
+                                        true,   // do_lower_case
+                                        true),  // do_truncate
+               cudf::logic_error);
+
+  EXPECT_THROW(nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                        *vocab,
+                                        858993459,
+                                        5,
+                                        true,   // do_lower_case
+                                        true),  // do_truncate
+               std::overflow_error);
+}
+
+TEST(TextSubwordTest, EmptyStrings)
+{
+  cudf::test::strings_column_wrapper strings;
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab  = nvtext::load_vocabulary_file(hash_file);
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         16,
+                                         16,
+                                         true,    // do_lower_case
+                                         false);  // do_truncate
+  EXPECT_EQ(uint32_t{0}, result.nrows_tensor);
+  EXPECT_EQ(0, result.tensor_token_ids->size());
+  EXPECT_EQ(0, result.tensor_attention_mask->size());
+  EXPECT_EQ(0, result.tensor_metadata->size());
+}
+
+TEST(TextSubwordTest, AllNullStrings)
+{
+  cudf::test::strings_column_wrapper strings({"", "", ""}, {0, 0, 0});
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab  = nvtext::load_vocabulary_file(hash_file);
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         16,
+                                         16,
+                                         true,    // do_lower_case
+                                         false);  // do_truncate
+  EXPECT_EQ(uint32_t{0}, result.nrows_tensor);
+  EXPECT_EQ(0, result.tensor_token_ids->size());
+  EXPECT_EQ(0, result.tensor_attention_mask->size());
+  EXPECT_EQ(0, result.tensor_metadata->size());
+}
+
+TEST(TextSubwordTest, NoTokens)
+{
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+  auto vocab = nvtext::load_vocabulary_file(hash_file);
+
+  cudf::test::strings_column_wrapper strings({"  ", "\n\r", "\t"});
+  auto input = cudf::strings_column_view{strings};
+
+  uint32_t const max_seq = 16;
+  uint32_t const stride  = 16;
+  bool const lower       = true;
+  bool const truncate    = true;
+
+  auto result = nvtext::subword_tokenize(input, *vocab, max_seq, stride, lower, truncate);
+
+  std::vector<uint32_t> zeros(max_seq * input.size(), 0);
+
+  EXPECT_EQ(static_cast<uint32_t>(input.size()), result.nrows_tensor);
+
+  auto expected = cudf::test::fixed_width_column_wrapper<uint32_t>(zeros.begin(), zeros.end());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected);
+  auto expected_metadata =
+    cudf::test::fixed_width_column_wrapper<uint32_t>({0, 0, 0, 1, 0, 0, 2, 0, 0});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
+
+TEST(TextSubwordTest, TokenizeFromVocabStruct)
+{
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_hashed_vocab(hash_file);
+
+  std::vector<char const*> h_strings{"This is a test.", "This is a test. This is a tést."};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto vocab  = nvtext::load_vocabulary_file(hash_file);
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         8,
+                                         6,
+                                         true,   // do_lower_case
+                                         true);  // do_truncate
+
+  EXPECT_EQ(uint32_t{2}, result.nrows_tensor);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_tokens(
+    {2023, 2003, 1037, 3231, 1012, 0, 0, 0, 2023, 2003, 1037, 3231, 1012, 2023, 2003, 1037});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected_tokens);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_attn(
+    {1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected_attn);
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_metadata({0, 0, 4, 1, 0, 7});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
+
+TEST(TextSubwordTest, LoadVocabFileErrors)
+{
+  std::vector<char const*> h_strings{"This is a test.", "This is a test. This is a tést."};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  std::string hash_file = temp_env->get_temp_filepath("nothing.txt");
+  EXPECT_THROW(nvtext::load_vocabulary_file(hash_file), cudf::logic_error);
+}
+
+// This includes the words above and 7 special tokens:
+//  [BOS] [EOS] [UNK] [SEP] [PAD] [CLS] [MASK]
+// The data here was generated by the utility:
+//   cudf.utils.hash_vocab_utils.hash_vocab()
+void create_special_tokens_hashed_vocab(std::string const& hash_file)
+{
+  std::ofstream outfile(hash_file, std::ofstream::out);
+  outfile << "26899\n27424\n3\n";
+  outfile << "1416131940466419714 0\n";
+  outfile << "313740585393291779 2\n";
+  outfile << "17006415773850330120 5\n";
+  outfile << "13\n";
+  outfile << "5903884228619468800\n";
+  outfile << "6205475701751152650\n";
+  outfile << "16285378285009240068\n";
+  outfile << "5162333542489915397\n";
+  outfile << "6064762127302393859\n";
+  outfile << "6173800107753209857\n";
+  outfile << "5322083323972878342\n";
+  outfile << "6242701866907861003\n";
+  outfile << "451412623368\n";
+  outfile << "3014668\n";
+  outfile << "5214737420442796034\n";
+  outfile << "6206321707968233479\n";
+  outfile << "6357001\n";
+  outfile << "1\n2\n3\n\n";
+}
+
+TEST(TextSubwordTest, TokenizeWithSpecialTokens)
+{
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  create_special_tokens_hashed_vocab(hash_file);
+
+  // clang-format off
+  std::vector<const char*> h_strings{
+    "[BOS]This is a tést.[eos]",
+    "[CLS]A test[SEP]this is.",
+    "[PAD] [A][MASK]",
+    "test this [CL",
+    "S] is a ."};
+  // clang-format on
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto vocab  = nvtext::load_vocabulary_file(hash_file);
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         8,
+                                         6,
+                                         true,   // do_lower_case
+                                         true);  // do_truncate
+
+  EXPECT_EQ(static_cast<uint32_t>(h_strings.size()), result.nrows_tensor);
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_tokens(
+    { 5, 7,  8, 9, 10, 12,  6, 0,
+      2, 9, 10, 3,  7,  8, 12, 0,
+      0, 1,  9, 1,  4,  0,  0, 0,
+     10, 7,  1, 1,  0,  0,  0, 0,
+      1, 1,  8, 9, 12,  0,  0, 0});
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_attn(
+    {1, 1, 1, 1, 1, 1, 1, 0,
+     1, 1, 1, 1, 1, 1, 1, 0,
+     1, 1, 1, 1, 1, 0, 0, 0,
+     1, 1, 1, 1, 0, 0, 0, 0,
+     1, 1, 1, 1, 1, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_metadata(
+    {0, 0, 6,
+     1, 0, 6,
+     2, 0, 4,
+     3, 0, 3,
+     4, 0, 4});
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected_tokens);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected_attn);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
+
+TEST(TextSubwordTest, ZeroHashBinCoefficient)
+{
+  std::string hash_file = temp_env->get_temp_filepath("hashed_vocab.txt");
+  {
+    std::ofstream outfile(hash_file, std::ofstream::out);
+    outfile << "26899\n27424\n2\n";
+    outfile << "6321733446031528966 0\n0 0\n9\n";  // zeroes are here
+    outfile << "6206321707968233475\n3014663\n6205475701751152646\n";
+    outfile << "451412623364\n5214737420442796033\n6173800107753209856\n";
+    outfile << "0\n6356997\n6064762127302393858\n";
+    outfile << "0\n1\n2\n";
+  }
+
+  std::vector<char const*> h_strings{".zzzz"};
+  cudf::test::strings_column_wrapper strings(h_strings.begin(), h_strings.end());
+  auto vocab  = nvtext::load_vocabulary_file(hash_file);
+  auto result = nvtext::subword_tokenize(cudf::strings_column_view{strings},
+                                         *vocab,
+                                         8,
+                                         8,
+                                         true,   // do_lower_case
+                                         true);  // do_truncate
+
+  // clang-format off
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_tokens({7, 0, 0, 0, 0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_attn(  {1, 1, 0, 0, 0, 0, 0, 0});
+  cudf::test::fixed_width_column_wrapper<uint32_t> expected_metadata({0, 0, 1});
+  // clang-format on
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_token_ids->view(), expected_tokens);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_attention_mask->view(), expected_attn);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(result.tensor_metadata->view(), expected_metadata);
+}
diff --git a/cpp/tests/text/tokenize_tests.cpp b/cpp/tests/text/tokenize_tests.cpp
new file mode 100644
index 0000000..d78f2df
--- /dev/null
+++ b/cpp/tests/text/tokenize_tests.cpp
@@ -0,0 +1,250 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+
+#include <nvtext/tokenize.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <thrust/iterator/transform_iterator.h>
+
+#include <vector>
+
+struct TextTokenizeTest : public cudf::test::BaseFixture {};
+
+TEST_F(TextTokenizeTest, Tokenize)
+{
+  std::vector<char const*> h_strings{"the fox jumped over the dog",
+                                     "the dog chased  the cat",
+                                     " the cat chased the mouse ",
+                                     nullptr,
+                                     "",
+                                     "the mousé ate the cheese"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::strings_column_wrapper expected{
+    "the", "fox", "jumped", "over", "the",   "dog", "the",   "dog", "chased", "the",   "cat",
+    "the", "cat", "chased", "the",  "mouse", "the", "mousé", "ate", "the",    "cheese"};
+
+  auto results = nvtext::tokenize(strings_view, cudf::string_scalar(" "));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = nvtext::tokenize(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> expected_counts{6, 5, 5, 0, 0, 5};
+  results = nvtext::count_tokens(strings_view, cudf::string_scalar(": #"));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_counts);
+  results = nvtext::count_tokens(strings_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_counts);
+}
+
+TEST_F(TextTokenizeTest, TokenizeMulti)
+{
+  std::vector<char const*> h_strings{"the fox jumped over the dog",
+                                     "the dog chased  the cat",
+                                     "the cat chased the mouse ",
+                                     nullptr,
+                                     "",
+                                     "the over ",
+                                     "the mousé ate the cheese"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::strings_column_wrapper delimiters{"the ", "over "};
+  cudf::strings_column_view delimiters_view(delimiters);
+
+  auto results = nvtext::tokenize(strings_view, delimiters_view);
+
+  cudf::test::strings_column_wrapper expected{
+    "fox jumped ", "dog", "dog chased  ", "cat", "cat chased ", "mouse ", "mousé ate ", "cheese"};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  cudf::test::fixed_width_column_wrapper<int32_t> expected_counts{2, 2, 2, 0, 0, 0, 2};
+  results = nvtext::count_tokens(strings_view, delimiters_view);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected_counts);
+}
+
+TEST_F(TextTokenizeTest, TokenizeErrorTest)
+{
+  cudf::test::strings_column_wrapper strings{"this column intentionally left blank"};
+  cudf::strings_column_view strings_view(strings);
+
+  {
+    cudf::test::strings_column_wrapper delimiters;  // empty delimiters
+    cudf::strings_column_view delimiters_view(delimiters);
+    EXPECT_THROW(nvtext::tokenize(strings_view, delimiters_view), cudf::logic_error);
+    EXPECT_THROW(nvtext::count_tokens(strings_view, delimiters_view), cudf::logic_error);
+  }
+  {
+    cudf::test::strings_column_wrapper delimiters({"", ""}, {0, 0});  // null delimiters
+    cudf::strings_column_view delimiters_view(delimiters);
+    EXPECT_THROW(nvtext::tokenize(strings_view, delimiters_view), cudf::logic_error);
+    EXPECT_THROW(nvtext::count_tokens(strings_view, delimiters_view), cudf::logic_error);
+  }
+}
+
+TEST_F(TextTokenizeTest, CharacterTokenize)
+{
+  std::vector<char const*> h_strings{"the mousé ate the cheese", nullptr, ""};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  cudf::test::strings_column_wrapper expected{"t", "h", "e", " ", "m", "o", "u", "s",
+                                              "é", " ", "a", "t", "e", " ", "t", "h",
+                                              "e", " ", "c", "h", "e", "e", "s", "e"};
+
+  auto results = nvtext::character_tokenize(cudf::strings_column_view(strings));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextTokenizeTest, TokenizeEmptyTest)
+{
+  auto input = cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING});
+  auto view  = cudf::strings_column_view(input->view());
+  cudf::test::strings_column_wrapper all_empty_wrapper({"", "", ""});
+  auto all_empty = cudf::strings_column_view(all_empty_wrapper);
+  cudf::test::strings_column_wrapper all_null_wrapper({"", "", ""}, {0, 0, 0});
+  auto all_null = cudf::strings_column_view(all_null_wrapper);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected({0, 0, 0});
+
+  auto results = nvtext::tokenize(view);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::tokenize(all_empty);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::tokenize(all_null);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::count_tokens(view);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::count_tokens(all_empty);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = nvtext::count_tokens(cudf::strings_column_view(all_null));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  results = nvtext::character_tokenize(view);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::character_tokenize(all_empty);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::character_tokenize(all_null);
+  EXPECT_EQ(results->size(), 0);
+  auto const delimiter = cudf::string_scalar{""};
+  results              = nvtext::tokenize_with_vocabulary(view, all_empty, delimiter);
+  EXPECT_EQ(results->size(), 0);
+  results = nvtext::tokenize_with_vocabulary(all_null, all_empty, delimiter);
+  EXPECT_EQ(results->size(), results->null_count());
+}
+
+TEST_F(TextTokenizeTest, Detokenize)
+{
+  cudf::test::strings_column_wrapper strings{
+    "the", "fox", "jumped", "over",   "the", "dog",   "the", "dog",   "chased", "the",
+    "cat", "the", "cat",    "chased", "the", "mouse", "the", "mousé", "ate",    "cheese"};
+
+  {
+    cudf::test::fixed_width_column_wrapper<int32_t> rows{0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
+                                                         1, 2, 2, 2, 2, 2, 3, 3, 3, 3};
+    auto results = nvtext::detokenize(cudf::strings_column_view(strings), rows);
+    cudf::test::strings_column_wrapper expected{"the fox jumped over the dog",
+                                                "the dog chased the cat",
+                                                "the cat chased the mouse",
+                                                "the mousé ate cheese"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+  {
+    cudf::test::fixed_width_column_wrapper<int16_t> rows{0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
+                                                         1, 2, 2, 2, 2, 2, 3, 3, 3, 0};
+    auto results =
+      nvtext::detokenize(cudf::strings_column_view(strings), rows, cudf::string_scalar("_"));
+    cudf::test::strings_column_wrapper expected{"the_fox_jumped_over_the_dog_cheese",
+                                                "the_dog_chased_the_cat",
+                                                "the_cat_chased_the_mouse",
+                                                "the_mousé_ate"};
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+  }
+}
+
+TEST_F(TextTokenizeTest, DetokenizeErrors)
+{
+  cudf::test::strings_column_wrapper strings{"this column intentionally left blank"};
+  cudf::strings_column_view strings_view(strings);
+
+  cudf::test::fixed_width_column_wrapper<int32_t> one({0});
+  cudf::test::fixed_width_column_wrapper<int32_t> none;
+
+  EXPECT_THROW(nvtext::detokenize(strings_view, none), cudf::logic_error);
+  EXPECT_THROW(nvtext::detokenize(strings_view, one, cudf::string_scalar("", false)),
+               cudf::logic_error);
+}
+
+TEST_F(TextTokenizeTest, Vocabulary)
+{
+  cudf::test::strings_column_wrapper vocabulary(  // leaving out 'cat' on purpose
+    {"ate", "chased", "cheese", "dog", "fox", "jumped", "mouse", "mousé", "over", "the"});
+  auto vocab = nvtext::load_vocabulary(cudf::strings_column_view(vocabulary));
+
+  auto validity = cudf::test::iterators::null_at(1);
+  cudf::test::strings_column_wrapper input({"the fox jumped over the dog",
+                                            "the dog chased the cat",
+                                            "the cat chased the mouse",
+                                            "the mousé  ate  cheese",
+                                            "",
+                                            ""},
+                                           validity);
+  auto input_view = cudf::strings_column_view(input);
+  auto delimiter  = cudf::string_scalar(" ");
+  auto default_id = -7;  // should be the token for the missing 'cat'
+  auto results    = nvtext::tokenize_with_vocabulary(input_view, *vocab, delimiter, default_id);
+
+  using LCW = cudf::test::lists_column_wrapper<cudf::size_type>;
+  // clang-format off
+  LCW expected({LCW{ 9, 4, 5, 8, 9, 3},
+                LCW{ 9, 3, 1, 9,-7},
+                LCW{ 9,-7, 1, 9, 6},
+                LCW{ 9, 7, 0, 2},
+                LCW{}, LCW{}},
+                validity);
+  // clang-format on
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*results, expected);
+}
+
+TEST_F(TextTokenizeTest, TokenizeErrors)
+{
+  cudf::test::strings_column_wrapper empty{};
+  cudf::strings_column_view view(empty);
+  EXPECT_THROW(nvtext::load_vocabulary(view), cudf::logic_error);
+
+  cudf::test::strings_column_wrapper vocab_nulls({""}, {0});
+  cudf::strings_column_view nulls(vocab_nulls);
+  EXPECT_THROW(nvtext::load_vocabulary(nulls), cudf::logic_error);
+
+  cudf::test::strings_column_wrapper some{"hello"};
+  auto vocab = nvtext::load_vocabulary(cudf::strings_column_view(some));
+  EXPECT_THROW(nvtext::tokenize_with_vocabulary(view, *vocab, cudf::string_scalar("", false)),
+               cudf::logic_error);
+}
diff --git a/cpp/tests/transform/bools_to_mask_test.cpp b/cpp/tests/transform/bools_to_mask_test.cpp
new file mode 100644
index 0000000..b795005
--- /dev/null
+++ b/cpp/tests/transform/bools_to_mask_test.cpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <thrust/host_vector.h>
+
+struct MaskToNullTest : public cudf::test::BaseFixture {
+  void run_test(std::vector<bool> input, std::vector<bool> val)
+  {
+    cudf::test::fixed_width_column_wrapper<bool> input_column(
+      input.begin(), input.end(), val.begin());
+    std::transform(val.begin(), val.end(), input.begin(), input.begin(), std::logical_and<bool>());
+
+    auto sample = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      sample, sample + input.size(), input.begin());
+
+    auto [null_mask, null_count] = cudf::bools_to_mask(input_column);
+    cudf::column got_column(expected);
+    got_column.set_null_mask(std::move(*null_mask), null_count);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got_column.view());
+  }
+
+  void run_test(thrust::host_vector<bool> input)
+  {
+    cudf::test::fixed_width_column_wrapper<bool> input_column(input.begin(), input.end());
+
+    auto sample = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+    cudf::test::fixed_width_column_wrapper<int32_t> expected(
+      sample, sample + input.size(), input.begin());
+
+    auto [null_mask, null_count] = cudf::bools_to_mask(input_column);
+    cudf::column got_column(expected);
+    got_column.set_null_mask(std::move(*null_mask), null_count);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got_column.view());
+  }
+};
+
+TEST_F(MaskToNullTest, WithNoNull)
+{
+  std::vector<bool> input({1, 0, 1, 0, 1, 0, 1, 0});
+
+  run_test(input);
+}
+
+TEST_F(MaskToNullTest, WithNull)
+{
+  std::vector<bool> input({1, 0, 1, 0, 1, 0, 1, 0});
+  std::vector<bool> val({1, 1, 1, 1, 1, 1, 0, 1});
+
+  run_test(input, val);
+}
+
+TEST_F(MaskToNullTest, ZeroSize)
+{
+  std::vector<bool> input({});
+  run_test(input);
+}
+
+TEST_F(MaskToNullTest, NonBoolTypeColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> input_column({1, 2, 3, 4, 5});
+
+  EXPECT_THROW(cudf::bools_to_mask(input_column), cudf::logic_error);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/transform/integration/assert_unary.h b/cpp/tests/transform/integration/assert_unary.h
new file mode 100644
index 0000000..98dc5d1
--- /dev/null
+++ b/cpp/tests/transform/integration/assert_unary.h
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+
+namespace transformation {
+template <typename TypeOut, typename TypeIn, typename TypeOpe>
+void ASSERT_UNARY(cudf::column_view const& out, cudf::column_view const& in, TypeOpe&& ope)
+{
+  auto in_h     = cudf::test::to_host<TypeIn>(in);
+  auto in_data  = in_h.first;
+  auto out_h    = cudf::test::to_host<TypeOut>(out);
+  auto out_data = out_h.first;
+
+  ASSERT_TRUE(out_data.size() == in_data.size());
+
+  auto data_comparator = [ope](TypeIn const& in, TypeOut const& out) {
+    EXPECT_EQ(out, static_cast<TypeOut>(ope(in)));
+    return true;
+  };
+  std::equal(in_data.begin(), in_data.end(), out_data.begin(), data_comparator);
+
+  auto in_valid  = in_h.second;
+  auto out_valid = out_h.second;
+
+  ASSERT_TRUE(out_valid.size() == in_valid.size());
+  auto valid_comparator = [](bool const& in, bool const& out) {
+    EXPECT_EQ(out, in);
+    return true;
+  };
+  std::equal(in_valid.begin(), in_valid.end(), out_valid.begin(), valid_comparator);
+}
+
+}  // namespace transformation
diff --git a/cpp/tests/transform/integration/unary_transform_test.cpp b/cpp/tests/transform/integration/unary_transform_test.cpp
new file mode 100644
index 0000000..eff327e
--- /dev/null
+++ b/cpp/tests/transform/integration/unary_transform_test.cpp
@@ -0,0 +1,222 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Copyright 2018-2019 BlazingDB, Inc.
+ *     Copyright 2018 Christian Noboa Mardini <christian@blazingdb.com>
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "assert_unary.h"
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/transform.hpp>
+
+namespace transformation {
+struct UnaryOperationIntegrationTest : public cudf::test::BaseFixture {};
+
+template <class dtype, class Op, class Data>
+void test_udf(char const udf[], Op op, Data data_init, cudf::size_type size, bool is_ptx)
+{
+  auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  auto data_iter = cudf::detail::make_counting_transform_iterator(0, data_init);
+
+  cudf::test::fixed_width_column_wrapper<dtype, typename decltype(data_iter)::value_type> in(
+    data_iter, data_iter + size, all_valid);
+
+  std::unique_ptr<cudf::column> out =
+    cudf::transform(in, udf, cudf::data_type(cudf::type_to_id<dtype>()), is_ptx);
+
+  ASSERT_UNARY<dtype, dtype>(out->view(), in, op);
+}
+
+TEST_F(UnaryOperationIntegrationTest, Transform_FP32_FP32)
+{
+  // c = a*a*a*a
+  char const* cuda =
+    R"***(
+__device__ inline void    fdsf   (
+       float* C,
+       float a
+)
+{
+  *C = a*a*a*a;
+}
+)***";
+
+  char const* ptx =
+    R"***(
+//
+// Generated by NVIDIA NVVM Compiler
+//
+// Compiler Build ID: CL-24817639
+// Cuda compilation tools, release 10.0, V10.0.130
+// Based on LLVM 3.4svn
+//
+
+.version 6.3
+.target sm_70
+.address_size 64
+
+	// .globl	_ZN8__main__7add$241Ef
+.common .global .align 8 .u64 _ZN08NumbaEnv8__main__7add$241Ef;
+.common .global .align 8 .u64 _ZN08NumbaEnv5numba7targets7numbers14int_power_impl12$3clocals$3e13int_power$242Efx;
+
+.visible .func  (.param .b32 func_retval0) _ZN8__main__7add$241Ef(
+	.param .b64 _ZN8__main__7add$241Ef_param_0,
+	.param .b32 _ZN8__main__7add$241Ef_param_1
+)
+{
+	.reg .f32 	%f<4>;
+	.reg .b32 	%r<2>;
+	.reg .b64 	%rd<2>;
+
+
+	ld.param.u64 	%rd1, [_ZN8__main__7add$241Ef_param_0];
+	ld.param.f32 	%f1, [_ZN8__main__7add$241Ef_param_1];
+	mul.f32 	%f2, %f1, %f1;
+	mul.f32 	%f3, %f2, %f2;
+	st.f32 	[%rd1], %f3;
+	mov.u32 	%r1, 0;
+	st.param.b32	[func_retval0+0], %r1;
+	ret;
+}
+)***";
+
+  using dtype    = float;
+  auto op        = [](dtype a) { return a * a * a * a; };
+  auto data_init = [](cudf::size_type row) { return row % 3; };
+
+  test_udf<dtype>(cuda, op, data_init, 500, false);
+  test_udf<dtype>(ptx, op, data_init, 500, true);
+}
+
+TEST_F(UnaryOperationIntegrationTest, Transform_INT32_INT32)
+{
+  // c = a * a - a
+  char const cuda[] =
+    "__device__ inline void f(int* output,int input){*output = input*input - input;}";
+
+  char const* ptx =
+    R"***(
+.func _Z1fPii(
+        .param .b64 _Z1fPii_param_0,
+        .param .b32 _Z1fPii_param_1
+)
+{
+        .reg .b32       %r<4>;
+        .reg .b64       %rd<3>;
+
+
+        ld.param.u64    %rd1, [_Z1fPii_param_0];
+        ld.param.u32    %r1, [_Z1fPii_param_1];
+        cvta.to.global.u64      %rd2, %rd1;
+        mul.lo.s32      %r2, %r1, %r1;
+        sub.s32         %r3, %r2, %r1;
+        st.global.u32   [%rd2], %r3;
+        ret;
+}
+)***";
+
+  using dtype    = int;
+  auto op        = [](dtype a) { return a * a - a; };
+  auto data_init = [](cudf::size_type row) { return row % 78; };
+
+  test_udf<dtype>(cuda, op, data_init, 500, false);
+  test_udf<dtype>(ptx, op, data_init, 500, true);
+}
+
+TEST_F(UnaryOperationIntegrationTest, Transform_INT8_INT8)
+{
+  // Capitalize all the lower case letters
+  // Assuming ASCII, the PTX code is compiled from the following CUDA code
+
+  char const cuda[] =
+    R"***(
+__device__ inline void f(
+  signed char* output,
+  signed char input
+){
+	if(input > 96 && input < 123){
+  	*output = input - 32;
+  }else{
+  	*output = input;
+  }
+}
+)***";
+
+  char const ptx[] =
+    R"***(
+.func _Z1fPcc(
+        .param .b64 _Z1fPcc_param_0,
+        .param .b32 _Z1fPcc_param_1
+)
+{
+        .reg .pred      %p<2>;
+        .reg .b16       %rs<6>;
+        .reg .b32       %r<3>;
+        .reg .b64       %rd<3>;
+
+
+        ld.param.u64    %rd1, [_Z1fPcc_param_0];
+        cvta.to.global.u64      %rd2, %rd1;
+        ld.param.s8     %rs1, [_Z1fPcc_param_1];
+        add.s16         %rs2, %rs1, -97;
+        and.b16         %rs3, %rs2, 255;
+        setp.lt.u16     %p1, %rs3, 26;
+        cvt.u32.u16     %r1, %rs1;
+        add.s32         %r2, %r1, 224;
+        cvt.u16.u32     %rs4, %r2;
+        selp.b16        %rs5, %rs4, %rs1, %p1;
+        st.global.u8    [%rd2], %rs5;
+        ret;
+}
+)***";
+
+  using dtype    = int8_t;
+  auto op        = [](dtype a) { return std::toupper(a); };
+  auto data_init = [](cudf::size_type row) { return 'a' + (row % 26); };
+
+  test_udf<dtype>(cuda, op, data_init, 500, false);
+  test_udf<dtype>(ptx, op, data_init, 500, true);
+}
+
+TEST_F(UnaryOperationIntegrationTest, Transform_Datetime)
+{
+  // Add one day to timestamp in microseconds
+
+  char const cuda[] =
+    R"***(
+__device__ inline void f(cudf::timestamp_us* output, cudf::timestamp_us input)
+{
+  using dur = cuda::std::chrono::duration<int32_t, cuda::std::ratio<86400>>;
+  *output = static_cast<cudf::timestamp_us>(input + dur{1});
+}
+
+)***";
+
+  using dtype = cudf::timestamp_us;
+  auto op     = [](dtype a) {
+    using dur = cuda::std::chrono::duration<int32_t, cuda::std::ratio<86400>>;
+    return static_cast<cudf::timestamp_us>(a + dur{1});
+  };
+  auto random_eng = cudf::test::UniformRandomGenerator<cudf::timestamp_us::rep>(0, 100000000);
+  auto data_init  = [&random_eng](cudf::size_type row) { return random_eng.generate(); };
+
+  test_udf<dtype>(cuda, op, data_init, 500, false);
+}
+
+}  // namespace transformation
diff --git a/cpp/tests/transform/mask_to_bools_test.cpp b/cpp/tests/transform/mask_to_bools_test.cpp
new file mode 100644
index 0000000..9fb4b3d
--- /dev/null
+++ b/cpp/tests/transform/mask_to_bools_test.cpp
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+
+struct MaskToBools : public cudf::test::BaseFixture {};
+
+TEST_F(MaskToBools, NullDataWithZeroLength)
+{
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({});
+  auto out      = cudf::mask_to_bools(nullptr, 0, 0);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, out->view());
+}
+
+TEST_F(MaskToBools, NullDataWithNonZeroLength)
+{
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({});
+
+  EXPECT_THROW(cudf::mask_to_bools(nullptr, 0, 2), cudf::logic_error);
+}
+
+TEST_F(MaskToBools, ImproperBitRange)
+{
+  auto expected = cudf::test::fixed_width_column_wrapper<bool>({});
+
+  EXPECT_THROW(cudf::mask_to_bools(nullptr, 2, 1), cudf::logic_error);
+}
+
+struct MaskToBoolsTest
+  : public MaskToBools,
+    public ::testing::WithParamInterface<std::tuple<cudf::size_type, cudf::size_type>> {};
+
+TEST_P(MaskToBoolsTest, LargeDataSizeTest)
+{
+  auto data                       = std::vector<bool>(10000);
+  auto const [begin_bit, end_bit] = GetParam();
+  std::transform(
+    data.cbegin(), data.cend(), data.begin(), [](auto val) { return rand() % 2 == 0; });
+
+  auto col      = cudf::test::fixed_width_column_wrapper<bool>(data.begin(), data.end());
+  auto expected = cudf::slice(static_cast<cudf::column_view>(col), {begin_bit, end_bit}).front();
+
+  auto mask = cudf::bools_to_mask(col);
+
+  auto out = cudf::mask_to_bools(
+    static_cast<cudf::bitmask_type const*>(mask.first->data()), begin_bit, end_bit);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, out->view());
+}
+
+INSTANTIATE_TEST_CASE_P(MaskToBools,
+                        MaskToBoolsTest,
+                        ::testing::Values(std::make_tuple(0, 0),
+                                          std::make_tuple(0, 500),
+                                          std::make_tuple(500, 7456),
+                                          std::make_tuple(7456, 10000),
+                                          std::make_tuple(0, 10000)));
diff --git a/cpp/tests/transform/nans_to_null_test.cpp b/cpp/tests/transform/nans_to_null_test.cpp
new file mode 100644
index 0000000..2de0664
--- /dev/null
+++ b/cpp/tests/transform/nans_to_null_test.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+template <typename T>
+struct NaNsToNullTest : public cudf::test::BaseFixture {
+  void run_test(cudf::column_view const& input, cudf::column_view const& expected)
+  {
+    auto [null_mask, null_count] = cudf::nans_to_nulls(input);
+    cudf::column got(input);
+    got.set_null_mask(std::move(*null_mask), null_count);
+
+    EXPECT_EQ(expected.null_count(), null_count);
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got.view());
+  }
+
+  std::unique_ptr<cudf::column> create_expected(std::vector<T> const& input,
+                                                std::vector<bool> const& mask = {})
+  {
+    std::vector<T> expected(input);
+    std::vector<bool> expected_mask;
+
+    if (mask.size() > 0) {
+      std::transform(input.begin(),
+                     input.end(),
+                     mask.begin(),
+                     std::back_inserter(expected_mask),
+                     [](T val, bool validity) { return validity and not std::isnan(val); });
+    } else {
+      std::transform(input.begin(), input.end(), std::back_inserter(expected_mask), [](T val) {
+        return not std::isnan(val);
+      });
+    }
+
+    return cudf::test::fixed_width_column_wrapper<T>(
+             expected.begin(), expected.end(), expected_mask.begin())
+      .release();
+  }
+};
+
+using test_types = ::testing::Types<float, double>;
+
+TYPED_TEST_SUITE(NaNsToNullTest, test_types);
+
+TYPED_TEST(NaNsToNullTest, WithMask)
+{
+  using T = TypeParam;
+
+  std::vector<T> input   = {1, NAN, 3, NAN, 5, NAN};
+  std::vector<bool> mask = {1, 1, 1, 1, 0, 0};
+  auto input_column =
+    cudf::test::fixed_width_column_wrapper<T>(input.begin(), input.end(), mask.begin());
+  auto expected_column = this->create_expected(input, mask);
+  this->run_test(input_column, expected_column->view());
+}
+
+TYPED_TEST(NaNsToNullTest, WithNoMask)
+{
+  using T = TypeParam;
+
+  std::vector<T> input = {1, NAN, 3, NAN, 5, NAN};
+  auto input_column    = cudf::test::fixed_width_column_wrapper<T>(input.begin(), input.end());
+  auto expected_column = this->create_expected(input);
+  this->run_test(input_column, expected_column->view());
+}
+
+TYPED_TEST(NaNsToNullTest, NoNANWithMask)
+{
+  using T = TypeParam;
+
+  std::vector<T> input   = {1, 2, 3, 4, 5, 6};
+  std::vector<bool> mask = {1, 1, 1, 1, 0, 0};
+  auto input_column =
+    cudf::test::fixed_width_column_wrapper<T>(input.begin(), input.end(), mask.begin());
+  auto expected_column = this->create_expected(input, mask);
+  this->run_test(input_column, expected_column->view());
+}
+
+TYPED_TEST(NaNsToNullTest, NoNANNoMask)
+{
+  using T = TypeParam;
+
+  std::vector<T> input = {1, 2, 3, 4, 5, 6};
+  auto input_column    = cudf::test::fixed_width_column_wrapper<T>(input.begin(), input.end());
+  auto expected_column = this->create_expected(input);
+  this->run_test(input_column, expected_column->view());
+}
+
+TYPED_TEST(NaNsToNullTest, EmptyColumn)
+{
+  using T = TypeParam;
+
+  std::vector<T> input = {};
+  auto input_column    = cudf::test::fixed_width_column_wrapper<T>(input.begin(), input.end());
+  auto expected_column = this->create_expected(input);
+  this->run_test(input_column, expected_column->view());
+}
+
+struct NaNsToNullFailTest : public cudf::test::BaseFixture {};
+
+TEST_F(NaNsToNullFailTest, StringType)
+{
+  std::vector<std::string> strings{
+    "", "this", "is", "a", "column", "of", "strings", "with", "in", "valid"};
+  cudf::test::strings_column_wrapper input(strings.begin(), strings.end());
+
+  EXPECT_THROW(cudf::nans_to_nulls(input), cudf::logic_error);
+}
+
+TEST_F(NaNsToNullFailTest, IntegerType)
+{
+  std::vector<int32_t> input = {1, 2, 3, 4, 5, 6};
+  auto input_column = cudf::test::fixed_width_column_wrapper<int32_t>(input.begin(), input.end());
+
+  EXPECT_THROW(cudf::nans_to_nulls(input_column), cudf::logic_error);
+}
diff --git a/cpp/tests/transform/one_hot_encode_tests.cpp b/cpp/tests/transform/one_hot_encode_tests.cpp
new file mode 100644
index 0000000..1015370
--- /dev/null
+++ b/cpp/tests/transform/one_hot_encode_tests.cpp
@@ -0,0 +1,272 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/iterator_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/table/table_view.hpp>
+#include <cudf/transform.hpp>
+
+#include <limits>
+
+using lists_col   = cudf::test::lists_column_wrapper<int32_t>;
+using structs_col = cudf::test::structs_column_wrapper;
+using bool_col    = cudf::test::fixed_width_column_wrapper<bool>;
+
+using cudf::test::iterators::null_at;
+using cudf::test::iterators::nulls_at;
+
+template <typename T>
+struct OneHotEncodingTestTyped : public cudf::test::BaseFixture {};
+
+struct OneHotEncodingTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(OneHotEncodingTestTyped, cudf::test::NumericTypes);
+
+TYPED_TEST(OneHotEncodingTestTyped, Basic)
+{
+  auto input    = cudf::test::fixed_width_column_wrapper<int32_t>{8, 8, 8, 9, 9};
+  auto category = cudf::test::fixed_width_column_wrapper<int32_t>{8, 9};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 0, 0};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 1, 1};
+
+  auto expected = cudf::table_view{{col0, col1}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TYPED_TEST(OneHotEncodingTestTyped, Nulls)
+{
+  auto input    = cudf::test::fixed_width_column_wrapper<int32_t>{{8, 8, 8, 9, 9}, {1, 1, 0, 1, 1}};
+  auto category = cudf::test::fixed_width_column_wrapper<int32_t>({8, 9, -1}, {1, 1, 0});
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 0, 0, 0};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 1, 1};
+  auto col2 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 1, 0, 0};
+
+  auto expected = cudf::table_view{{col0, col1, col2}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, Diagonal)
+{
+  auto input    = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5};
+  auto category = cudf::test::fixed_width_column_wrapper<int32_t>{1, 2, 3, 4, 5};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 0, 0, 0, 0};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{0, 1, 0, 0, 0};
+  auto col2 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 1, 0, 0};
+  auto col3 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 1, 0};
+  auto col4 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 0, 1};
+
+  auto expected = cudf::table_view{{col0, col1, col2, col3, col4}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, ZeroInput)
+{
+  auto input    = cudf::test::strings_column_wrapper{};
+  auto category = cudf::test::strings_column_wrapper{"rapids", "cudf"};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{};
+
+  auto expected = cudf::table_view{{col0, col1}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, ZeroCat)
+{
+  auto input    = cudf::test::strings_column_wrapper{"ji", "ji", "ji"};
+  auto category = cudf::test::strings_column_wrapper{};
+
+  auto expected = cudf::table_view{};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, ZeroInputCat)
+{
+  auto input    = cudf::test::strings_column_wrapper{};
+  auto category = cudf::test::strings_column_wrapper{};
+
+  auto expected = cudf::table_view{};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, OneCat)
+{
+  auto input    = cudf::test::strings_column_wrapper{"ji", "ji", "ji"};
+  auto category = cudf::test::strings_column_wrapper{"ji"};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1};
+
+  auto expected = cudf::table_view{{col0}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, NaNs)
+{
+  auto const nan = std::numeric_limits<float>::signaling_NaN();
+
+  auto input    = cudf::test::fixed_width_column_wrapper<float>{8.f, 8.f, 8.f, 9.f, nan};
+  auto category = cudf::test::fixed_width_column_wrapper<float>{8.f, 9.f, nan};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 0, 0};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 1, 0};
+  auto col2 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 0, 1};
+
+  auto expected = cudf::table_view{{col0, col1, col2}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, Strings)
+{
+  auto input = cudf::test::strings_column_wrapper{
+    {"hello", "rapidsai", "cudf", "hello", "cuspatial", "hello", "world", "!"},
+    {1, 1, 1, 1, 0, 1, 1, 0}};
+  auto category = cudf::test::strings_column_wrapper{{"hello", "world", ""}, {1, 1, 0}};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 0, 0, 1, 0, 1, 0, 0};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 0, 0, 0, 1, 0};
+  auto col2 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 0, 1, 0, 0, 1};
+
+  auto expected = cudf::table_view{{col0, col1, col2}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, Dictionary)
+{
+  auto input =
+    cudf::test::dictionary_column_wrapper<std::string>{"aa", "xx", "aa", "aa", "yy", "ef"};
+  auto category = cudf::test::dictionary_column_wrapper<std::string>{"aa", "ef"};
+
+  auto col0 = cudf::test::fixed_width_column_wrapper<bool>{1, 0, 1, 1, 0, 0};
+  auto col1 = cudf::test::fixed_width_column_wrapper<bool>{0, 0, 0, 0, 0, 1};
+
+  auto expected = cudf::table_view{{col0, col1}};
+
+  [[maybe_unused]] auto [res_ptr, got] = cudf::one_hot_encode(input, category);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, MismatchTypes)
+{
+  auto input    = cudf::test::strings_column_wrapper{"xx", "yy", "xx"};
+  auto category = cudf::test::fixed_width_column_wrapper<int64_t>{1};
+
+  EXPECT_THROW(cudf::one_hot_encode(input, category), cudf::logic_error);
+}
+
+TEST_F(OneHotEncodingTest, List)
+{
+  auto const input =
+    lists_col{{{}, {1}, {2, 2}, {2, 2}, {}, {2} /*NULL*/, {2}, {5} /*NULL*/}, nulls_at({5, 7})};
+  auto const categories = lists_col{{{}, {1}, {2, 2}, {2}, {-1}}, null_at(4)};
+
+  auto const col0 = bool_col{1, 0, 0, 0, 1, 0, 0, 0};
+  auto const col1 = bool_col{0, 1, 0, 0, 0, 0, 0, 0};
+  auto const col2 = bool_col{0, 0, 1, 1, 0, 0, 0, 0};
+  auto const col3 = bool_col{0, 0, 0, 0, 0, 0, 1, 0};
+  auto const col4 = bool_col{0, 0, 0, 0, 0, 1, 0, 1};
+
+  auto const expected = cudf::table_view{{col0, col1, col2, col3, col4}};
+
+  [[maybe_unused]] auto const [res_ptr, got] = cudf::one_hot_encode(input, categories);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
+
+TEST_F(OneHotEncodingTest, StructsOfStructs)
+{
+  //  +-----------------+
+  //  |  s1{s2{a,b}, c} |
+  //  +-----------------+
+  // 0 |  Null          |
+  // 1 |  { {1, 2}, 4}  |
+  // 2 |  { Null,   4}  |
+  // 3 |  Null          |
+  // 4 |  { {2, 1}, 5}  |
+  // 5 |  { Null,   4}  |
+  // 6 |  { {2, 1}, 5}  |
+
+  auto const input = [&] {
+    auto a  = cudf::test::fixed_width_column_wrapper<int32_t>{-1, 1, -1, -1, 2, -1, 2};
+    auto b  = cudf::test::fixed_width_column_wrapper<int32_t>{-1, 2, -1, -1, 1, -1, 1};
+    auto s2 = structs_col{{a, b}, nulls_at({2, 5})};
+
+    auto c = cudf::test::fixed_width_column_wrapper<int32_t>{-1, 4, 4, -1, 5, 4, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = nulls_at({0, 3});
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 7});
+  }();
+
+  auto const categories = [&] {
+    auto a  = cudf::test::fixed_width_column_wrapper<int32_t>{-1, 1, -1, 2};
+    auto b  = cudf::test::fixed_width_column_wrapper<int32_t>{-1, 2, -1, 1};
+    auto s2 = structs_col{{a, b}, null_at(2)};
+
+    auto c = cudf::test::fixed_width_column_wrapper<int32_t>{-1, 4, 4, 5};
+    std::vector<std::unique_ptr<cudf::column>> s1_children;
+    s1_children.emplace_back(s2.release());
+    s1_children.emplace_back(c.release());
+    auto const null_it = null_at(0);
+    return structs_col(std::move(s1_children), std::vector<bool>{null_it, null_it + 4});
+  }();
+
+  auto const col0 = bool_col{1, 0, 0, 1, 0, 0, 0};
+  auto const col1 = bool_col{0, 1, 0, 0, 0, 0, 0};
+  auto const col2 = bool_col{0, 0, 1, 0, 0, 1, 0};
+  auto const col3 = bool_col{0, 0, 0, 0, 1, 0, 1};
+
+  auto const expected = cudf::table_view{{col0, col1, col2, col3}};
+
+  [[maybe_unused]] auto const [res_ptr, got] = cudf::one_hot_encode(input, categories);
+
+  CUDF_TEST_EXPECT_TABLES_EQUAL(expected, got);
+}
diff --git a/cpp/tests/transform/row_bit_count_test.cu b/cpp/tests/transform/row_bit_count_test.cu
new file mode 100644
index 0000000..236407e
--- /dev/null
+++ b/cpp/tests/transform/row_bit_count_test.cu
@@ -0,0 +1,764 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/functional.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/tabulate.h>
+#include <thrust/transform.h>
+
+#include <numeric>
+
+template <typename T>
+struct RowBitCountTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(RowBitCountTyped, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(RowBitCountTyped, SimpleTypes)
+{
+  using T = TypeParam;
+
+  auto col = cudf::make_fixed_width_column(cudf::data_type{cudf::type_to_id<T>()}, 16);
+
+  cudf::table_view t({*col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect size of the type per row
+  auto expected = make_fixed_width_column(cudf::data_type{cudf::type_id::INT32}, 16);
+  cudf::mutable_column_view mcv(*expected);
+  thrust::fill(rmm::exec_policy(cudf::get_default_stream()),
+               mcv.begin<cudf::size_type>(),
+               mcv.end<cudf::size_type>(),
+               sizeof(cudf::device_storage_type_t<T>) * CHAR_BIT);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *result);
+}
+
+TYPED_TEST(RowBitCountTyped, SimpleTypesWithNulls)
+{
+  using T = TypeParam;
+
+  auto iter   = thrust::make_counting_iterator(0);
+  auto valids = cudf::detail::make_counting_transform_iterator(0, [](int i) { return i % 2 == 0; });
+  cudf::test::fixed_width_column_wrapper<T> col(iter, iter + 16, valids);
+
+  cudf::table_view t({col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect size of the type + 1 bit per row
+  auto expected = make_fixed_width_column(cudf::data_type{cudf::type_id::INT32}, 16);
+  cudf::mutable_column_view mcv(*expected);
+  thrust::fill(rmm::exec_policy(cudf::get_default_stream()),
+               mcv.begin<cudf::size_type>(),
+               mcv.end<cudf::size_type>(),
+               (sizeof(cudf::device_storage_type_t<T>) * CHAR_BIT) + 1);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *result);
+}
+
+template <typename T>
+std::pair<std::unique_ptr<cudf::column>, std::unique_ptr<cudf::column>> build_list_column()
+{
+  using LCW                           = cudf::test::lists_column_wrapper<T, int>;
+  constexpr cudf::size_type type_size = sizeof(cudf::device_storage_type_t<T>) * CHAR_BIT;
+
+  // {
+  //  {{1, 2}, {3, 4, 5}},
+  //  {{}},
+  //  {LCW{10}},
+  //  {{6, 7, 8}, {9}},
+  //  {{-1, -2}, {-3, -4}},
+  //  {{-5, -6, -7}, {-8, -9}}
+  // }
+  cudf::test::fixed_width_column_wrapper<T> values{
+    1, 2, 3, 4, 5, 10, 6, 7, 8, 9, -1, -2, -3, -4, -5, -6, -7, -8, -9};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> inner_offsets{
+    0, 2, 5, 6, 9, 10, 12, 14, 17, 19};
+  auto inner_list = cudf::make_lists_column(9, inner_offsets.release(), values.release(), 0, {});
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> outer_offsets{0, 2, 2, 3, 5, 7, 9};
+  auto list = cudf::make_lists_column(6, outer_offsets.release(), std::move(inner_list), 0, {});
+
+  // expected size = (num rows at level 1 + num_rows at level 2) + # values in the leaf
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected{
+    ((4 + 8) * CHAR_BIT) + (type_size * 5),
+    ((4 + 0) * CHAR_BIT) + (type_size * 0),
+    ((4 + 4) * CHAR_BIT) + (type_size * 1),
+    ((4 + 8) * CHAR_BIT) + (type_size * 4),
+    ((4 + 8) * CHAR_BIT) + (type_size * 4),
+    ((4 + 8) * CHAR_BIT) + (type_size * 5)};
+
+  return {std::move(list), expected.release()};
+}
+
+TYPED_TEST(RowBitCountTyped, Lists)
+{
+  using T = TypeParam;
+
+  auto [col, expected_sizes] = build_list_column<T>();
+
+  cudf::table_view t({*col});
+  auto result = cudf::row_bit_count(t);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_sizes, *result);
+}
+
+TYPED_TEST(RowBitCountTyped, ListsWithNulls)
+{
+  using T                             = TypeParam;
+  using LCW                           = cudf::test::lists_column_wrapper<T, int>;
+  constexpr cudf::size_type type_size = sizeof(cudf::device_storage_type_t<T>) * CHAR_BIT;
+
+  // {
+  //  {{1, 2}, {3, null, 5}},
+  //  {{}},
+  //  {LCW{10}},
+  //  {{null, 7, null}, null},
+  // }
+  cudf::test::fixed_width_column_wrapper<T> values{{1, 2, 3, 4, 5, 10, 6, 7, 8},
+                                                   {1, 1, 1, 0, 1, 1, 0, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> inner_offsets{0, 2, 5, 6, 9, 9};
+  std::vector<bool> inner_list_validity{1, 1, 1, 1, 0};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(inner_list_validity.begin(), inner_list_validity.end());
+  auto inner_list = cudf::make_lists_column(
+    5, inner_offsets.release(), values.release(), null_count, std::move(null_mask));
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> outer_offsets{0, 2, 2, 3, 5};
+  auto list = cudf::make_lists_column(4, outer_offsets.release(), std::move(inner_list), 0, {});
+
+  cudf::table_view t({*list});
+  auto result = cudf::row_bit_count(t);
+
+  // expected size = (num rows at level 1 + num_rows at level 2) + # values in the leaf + validity
+  // where applicable
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected{
+    ((4 + 8) * CHAR_BIT) + (type_size * 5) + 7,
+    ((4 + 0) * CHAR_BIT) + (type_size * 0),
+    ((4 + 4) * CHAR_BIT) + (type_size * 1) + 2,
+    ((4 + 8) * CHAR_BIT) + (type_size * 3) + 5};
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+struct RowBitCount : public cudf::test::BaseFixture {};
+
+TEST_F(RowBitCount, Strings)
+{
+  std::vector<std::string> strings{"abc", "ï", "", "z", "bananas", "warp", "", "zing"};
+
+  cudf::test::strings_column_wrapper col(strings.begin(), strings.end());
+
+  cudf::table_view t({col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect 1 offset (4 bytes) + length of string per row
+  auto size_iter = cudf::detail::make_counting_transform_iterator(0, [&strings](int i) {
+    return (static_cast<cudf::size_type>(strings[i].size()) + sizeof(cudf::size_type)) * CHAR_BIT;
+  });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(size_iter,
+                                                                   size_iter + strings.size());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, StringsWithNulls)
+{
+  // clang-format off
+  std::vector<std::string> strings { "daïs", "def", "", "z", "bananas", "warp", "", "zing" };
+  std::vector<bool>        valids  {  1,      0,    0,  1,   0,          1,      1,  1 };
+  // clang-format on
+
+  cudf::test::strings_column_wrapper col(strings.begin(), strings.end(), valids.begin());
+
+  cudf::table_view t({col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect 1 offset (4 bytes) + (length of string, or 0 if null) + 1 validity bit per row
+  auto size_iter = cudf::detail::make_counting_transform_iterator(0, [&strings, &valids](int i) {
+    return ((static_cast<cudf::size_type>(valids[i] ? strings[i].size() : 0) +
+             sizeof(cudf::size_type)) *
+            CHAR_BIT) +
+           1;
+  });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(size_iter,
+                                                                   size_iter + strings.size());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+namespace {
+
+/**
+ * @brief __device__ functor to multiply input by 2, defined out of line because __device__ lambdas
+ * cannot be defined in a TEST_F().
+ */
+struct times_2 {
+  int32_t __device__ operator()(int32_t i) const { return i * 2; }
+};
+
+}  // namespace
+
+TEST_F(RowBitCount, StructsWithLists_RowsExceedingASingleBlock)
+{
+  // Tests that `row_bit_count()` can handle struct<list<int32_t>> with more
+  // than max_block_size (256) rows.
+  // With a large number of rows, computation spills to multiple thread-blocks,
+  // thus exercising the branch-stack computation.
+  // The contents of the input column aren't as pertinent to this test as the
+  // column size. For what it's worth, it looks as follows:
+  //   [ struct({0,1}), struct({2,3}), struct({4,5}), ... ]
+
+  auto constexpr num_rows = 1024 * 2;  // Exceeding a block size.
+
+  // List child column = {0, 1, 2, 3, 4, ..., 2*num_rows};
+  auto ints      = cudf::make_numeric_column(cudf::data_type{cudf::type_id::INT32}, num_rows * 2);
+  auto ints_view = ints->mutable_view();
+  thrust::tabulate(rmm::exec_policy(cudf::get_default_stream()),
+                   ints_view.begin<int32_t>(),
+                   ints_view.end<int32_t>(),
+                   thrust::identity{});
+
+  // List offsets = {0, 2, 4, 6, 8, ..., num_rows*2};
+  auto list_offsets =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_id::INT32}, num_rows + 1);
+  auto list_offsets_view = list_offsets->mutable_view();
+  thrust::tabulate(rmm::exec_policy(cudf::get_default_stream()),
+                   list_offsets_view.begin<cudf::size_type>(),
+                   list_offsets_view.end<cudf::size_type>(),
+                   times_2{});
+
+  // List<int32_t> = {{0,1}, {2,3}, {4,5}, ..., {2*(num_rows-1), 2*num_rows-1}};
+  auto lists_column =
+    cudf::make_lists_column(num_rows, std::move(list_offsets), std::move(ints), 0, {});
+
+  // Struct<List<int32_t>.
+  auto struct_members = std::vector<std::unique_ptr<cudf::column>>{};
+  struct_members.emplace_back(std::move(lists_column));
+  auto structs_column = cudf::make_structs_column(num_rows, std::move(struct_members), 0, {});
+
+  // Compute row_bit_count, and compare.
+  auto row_bit_counts = cudf::row_bit_count(cudf::table_view{{structs_column->view()}});
+  auto expected_row_bit_counts =
+    cudf::make_numeric_column(cudf::data_type{cudf::type_id::INT32}, num_rows);
+  thrust::fill_n(rmm::exec_policy(cudf::get_default_stream()),
+                 expected_row_bit_counts->mutable_view().begin<int32_t>(),
+                 num_rows,
+                 CHAR_BIT * (2 * sizeof(int32_t) + sizeof(cudf::size_type)));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(row_bit_counts->view(), expected_row_bit_counts->view());
+}
+
+std::pair<std::unique_ptr<cudf::column>, std::unique_ptr<cudf::column>> build_struct_column()
+{
+  std::vector<bool> struct_validity{0, 1, 1, 1, 1, 0};
+  std::vector<std::string> strings{"abc", "def", "", "z", "bananas", "daïs"};
+
+  cudf::test::fixed_width_column_wrapper<float> col0{0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{{8, 9, 10, 11, 12, 13}, {1, 0, 1, 1, 1, 1}};
+  cudf::test::strings_column_wrapper col2(strings.begin(), strings.end());
+
+  // creating a struct column will cause all child columns to be promoted to have validity
+  cudf::test::structs_column_wrapper struct_col({col0, col1, col2}, struct_validity);
+
+  // expect (1 offset (4 bytes) + (length of string if row is valid) + 1 validity bit) +
+  //        (1 float + 1 validity bit) +
+  //        (1 int16_t + 1 validity bit) +
+  //        (1 validity bit)
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_sizes{84, 108, 84, 92, 140, 84};
+
+  return {struct_col.release(), expected_sizes.release()};
+}
+
+TEST_F(RowBitCount, StructsNoNulls)
+{
+  std::vector<std::string> strings{"abc", "daïs", "", "z", "bananas", "warp"};
+
+  cudf::test::fixed_width_column_wrapper<float> col0{0, 1, 2, 3, 4, 5};
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{8, 9, 10, 11, 12, 13};
+  cudf::test::strings_column_wrapper col2(strings.begin(), strings.end());
+
+  cudf::test::structs_column_wrapper struct_col({col0, col1, col2});
+
+  cudf::table_view t({struct_col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect 1 offset (4 bytes) + (length of string) + 1 float + 1 int16_t
+  auto size_iter = cudf::detail::make_counting_transform_iterator(0, [&strings](int i) {
+    return ((sizeof(float) + sizeof(int16_t)) * CHAR_BIT) +
+           ((static_cast<cudf::size_type>(strings[i].size()) + sizeof(cudf::size_type)) * CHAR_BIT);
+  });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(size_iter,
+                                                                   size_iter + t.num_rows());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, StructsNulls)
+{
+  auto [struct_col, expected_sizes] = build_struct_column();
+  cudf::table_view t({*struct_col});
+  auto result = cudf::row_bit_count(t);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_sizes, *result);
+}
+
+TEST_F(RowBitCount, StructsNested)
+{
+  // struct<struct<int>, int16>
+  cudf::test::fixed_width_column_wrapper<int> col0{0, 1, 2, 3, 4, 5};
+  cudf::test::structs_column_wrapper inner_struct({col0});
+
+  cudf::test::fixed_width_column_wrapper<int16_t> col1{8, 9, 10, 11, 12, 13};
+  cudf::test::structs_column_wrapper struct_col({inner_struct, col1});
+
+  cudf::table_view t({struct_col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect num_rows * (4 + 2) bytes
+  auto size_iter =
+    cudf::detail::make_counting_transform_iterator(0, [&](int i) { return (4 + 2) * CHAR_BIT; });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(size_iter,
+                                                                   size_iter + t.num_rows());
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+std::unique_ptr<cudf::column> build_nested_column1(std::vector<bool> const& struct_validity)
+{
+  // tests the "branching" case ->  list<struct<list> ...>>>
+
+  // List<Struct<List<int>, float, int16>
+
+  // Inner list column
+  cudf::test::lists_column_wrapper<int> list{{1, 2, 3, 4, 5},
+                                             {6, 7, 8},
+                                             {33, 34, 35, 36, 37, 38, 39},
+                                             {-1, -2},
+                                             {-10, -11, -1, -20},
+                                             {40, 41, 42},
+                                             {100, 200, 300},
+                                             {-100, -200, -300}};
+
+  // floats
+  std::vector<float> ages{5, 10, 15, 20, 4, 75, 16, -16};
+  std::vector<bool> ages_validity = {1, 1, 1, 1, 0, 1, 0, 1};
+  auto ages_column =
+    cudf::test::fixed_width_column_wrapper<float>(ages.begin(), ages.end(), ages_validity.begin());
+
+  // int16 values
+  std::vector<int16_t> vals{-1, -2, -3, 1, 2, 3, 8, 9};
+  auto i16_column = cudf::test::fixed_width_column_wrapper<int16_t>(vals.begin(), vals.end());
+
+  // Assemble struct column
+  auto struct_column =
+    cudf::test::structs_column_wrapper({list, ages_column, i16_column}, struct_validity);
+
+  // wrap in a list
+  std::vector<int> outer_offsets{0, 1, 1, 3, 6, 7, 8};
+  cudf::test::fixed_width_column_wrapper<int> outer_offsets_col(outer_offsets.begin(),
+                                                                outer_offsets.end());
+  auto const size = static_cast<cudf::column_view>(outer_offsets_col).size() - 1;
+
+  // Each struct (list child) has size:
+  //    (1 offset (4 bytes) + (list size if row is valid) + 1 validity bit) +
+  //    (1 float + 1 validity bit) +
+  //    (1 int16_t + 1 validity bit) +
+  //    (1 validity bit)
+  // Each top level list has size:
+  //    1 offset (4 bytes) + (list size if row is valid).
+
+  return cudf::make_lists_column(static_cast<cudf::size_type>(size),
+                                 outer_offsets_col.release(),
+                                 struct_column.release(),
+                                 0,
+                                 rmm::device_buffer{});
+}
+
+std::unique_ptr<cudf::column> build_nested_column2(std::vector<bool> const& struct_validity)
+{
+  // List<Struct<List<List<int>>, Struct<int16>>>
+
+  // Inner list column
+  // clang-format off
+  cudf::test::lists_column_wrapper<int> list{
+     {{1, 2, 3, 4, 5}, {2, 3}},
+     {{6, 7, 8}, {8, 9}},
+     {{1, 2}, {3, 4, 5}, {33, 34, 35, 36, 37, 38, 39}}};
+  // clang-format on
+
+  // Inner struct
+  std::vector<int16_t> vals{-1, -2, -3};
+  auto i16_column   = cudf::test::fixed_width_column_wrapper<int16_t>(vals.begin(), vals.end());
+  auto inner_struct = cudf::test::structs_column_wrapper({i16_column});
+
+  // outer struct
+  auto outer_struct = cudf::test::structs_column_wrapper({list, inner_struct}, struct_validity);
+
+  // wrap in a list
+  std::vector<int> outer_offsets{0, 1, 1, 3};
+  cudf::test::fixed_width_column_wrapper<int> outer_offsets_col(outer_offsets.begin(),
+                                                                outer_offsets.end());
+  auto const size = static_cast<cudf::column_view>(outer_offsets_col).size() - 1;
+  return make_lists_column(static_cast<cudf::size_type>(size),
+                           outer_offsets_col.release(),
+                           outer_struct.release(),
+                           0,
+                           rmm::device_buffer{});
+}
+
+TEST_F(RowBitCount, NestedTypes)
+{
+  // List<Struct<List<int>, float, List<int>, int16>
+  {
+    auto const col_no_nulls = build_nested_column1({1, 1, 1, 1, 1, 1, 1, 1});
+    auto const expected_sizes_no_nulls =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{276, 32, 520, 572, 212, 212}
+        .release();
+    cudf::table_view no_nulls_t({*col_no_nulls});
+    auto no_nulls_result = cudf::row_bit_count(no_nulls_t);
+
+    auto const col_nulls = build_nested_column1({0, 0, 1, 1, 1, 1, 1, 1});
+    auto const expected_sizes_with_nulls =
+      cudf::test::fixed_width_column_wrapper<cudf::size_type>{116, 32, 424, 572, 212, 212}
+        .release();
+    cudf::table_view nulls_t({*col_nulls});
+    auto nulls_result = cudf::row_bit_count(nulls_t);
+
+    // List<Struct<List<int>, float, int16>
+    //
+    // this illustrates the difference between a row_bit_count
+    // returning a pre-gather result, or a post-gather result.
+    //
+    // in a post-gather situation, the nulls in the struct would result in the values
+    // nested in the list below to be dropped, resulting in smaller row sizes.
+    //
+    // however, for performance reasons, row_bit_count simply walks the data that is
+    // currently there. so list rows that are null, but have a real span of
+    // offsets (X, Y) instead of (X, X)  will end up getting the child data for those
+    // rows included.
+    //
+    // if row_bit_count() is changed to return a post-gather result (which may be desirable),
+    // the nulls_result case below will start failing and will need to be changed.
+    //
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_sizes_no_nulls, *no_nulls_result);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_sizes_with_nulls, *nulls_result);
+  }
+
+  // List<Struct<List<List<int>>, Struct<int16>>>
+  {
+    auto col_no_nulls = build_nested_column2({1, 1, 1});
+    cudf::table_view no_nulls_t({*col_no_nulls});
+    auto no_nulls_result = cudf::row_bit_count(no_nulls_t);
+
+    auto col_nulls = build_nested_column2({1, 0, 1});
+    cudf::table_view nulls_t({*col_nulls});
+    auto nulls_result = cudf::row_bit_count(nulls_t);
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_sizes_no_nuls{372, 32, 840};
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_sizes_with_nuls{372, 32, 616};
+
+    // same explanation as above
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_sizes_no_nuls, *no_nulls_result);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_sizes_with_nuls, *nulls_result);
+  }
+
+  // test pushing/popping multiple times within one struct, and branch depth > 1
+  //
+  // Struct<int, List<int>, float, List<List<int16>>, Struct<List<int>, List<Struct<List<int>,
+  // float>>, int8_t>>
+  {
+    cudf::test::lists_column_wrapper<int> l0{{1, 2, 3}, {4, 5}, {6, 7, 8, 9}, {5}};
+    cudf::test::lists_column_wrapper<int16_t> l1{
+      {{-1, -2}, {3, 4}}, {{4, 5}, {6, 7, 8}}, {{-6, -7}, {2}}, {{-11, -11}, {-12, -12}, {3}}};
+    cudf::test::lists_column_wrapper<int> l2{{-1, -2}, {4, 5}, {-6, -7}, {1}};
+    cudf::test::lists_column_wrapper<int> l3{{-1, -2, 0}, {5}, {-1, -6, -7}, {1, 2}};
+
+    cudf::test::fixed_width_column_wrapper<int> c0{1, 2, 3, 4};
+    cudf::test::fixed_width_column_wrapper<float> c1{1, 2, 3, 4};
+    cudf::test::fixed_width_column_wrapper<int8_t> c2{1, 2, 3, 4};
+    cudf::test::fixed_width_column_wrapper<float> c3{11, 12, 13, 14};
+
+    // innermost List<Struct<List<int>>>
+    auto innermost_struct = cudf::test::structs_column_wrapper({l3, c3});
+    std::vector<int> l4_offsets{0, 1, 2, 3, 4};
+    cudf::test::fixed_width_column_wrapper<int> l4_offsets_col(l4_offsets.begin(),
+                                                               l4_offsets.end());
+    auto const l4_size = l4_offsets.size() - 1;
+    auto l4            = cudf::make_lists_column(static_cast<cudf::size_type>(l4_size),
+                                      l4_offsets_col.release(),
+                                      innermost_struct.release(),
+                                      0,
+                                      rmm::device_buffer{});
+
+    // inner struct
+    std::vector<std::unique_ptr<cudf::column>> inner_struct_children;
+    inner_struct_children.push_back(l2.release());
+    inner_struct_children.push_back(std::move(l4));
+    auto inner_struct = cudf::test::structs_column_wrapper(std::move(inner_struct_children));
+
+    // outer struct
+    auto struct_col = cudf::test::structs_column_wrapper({c0, l0, c1, l1, inner_struct, c2});
+
+    cudf::table_view t({struct_col});
+    auto result = cudf::row_bit_count(t);
+
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> expected_sizes{648, 568, 664, 568};
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected_sizes, *result);
+  }
+}
+
+TEST_F(RowBitCount, NullsInStringsList)
+{
+  using offsets_wrapper = cudf::test::fixed_width_column_wrapper<cudf::size_type>;
+
+  // clang-format off
+  auto strings = std::vector<std::string>{ "daïs", "def", "", "z", "bananas", "warp", "", "zing" };
+  auto valids  = std::vector<bool>{            1,     0,   0,  1,         0,      1,   1,     1 };
+  // clang-format on
+
+  cudf::test::strings_column_wrapper col(strings.begin(), strings.end(), valids.begin());
+
+  auto offsets   = cudf::test::fixed_width_column_wrapper<int>{0, 2, 4, 6, 8};
+  auto lists_col = cudf::make_lists_column(
+    4,
+    offsets_wrapper{0, 2, 4, 6, 8}.release(),
+    cudf::test::strings_column_wrapper{strings.begin(), strings.end(), valids.begin()}.release(),
+    0,
+    {});
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    cudf::row_bit_count(cudf::table_view{{lists_col->view()}})->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{138, 106, 130, 130});
+}
+
+TEST_F(RowBitCount, EmptyChildColumnInListOfStrings)
+{
+  // Test with a list<string> column with 4 empty list rows.
+  // Note: Since there are no strings in any of the lists,
+  //       the lists column's child can be empty.
+  auto offsets   = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 0, 0, 0};
+  auto lists_col = cudf::make_lists_column(
+    4, offsets.release(), cudf::make_empty_column(cudf::data_type{cudf::type_id::STRING}), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    cudf::row_bit_count(cudf::table_view{{lists_col->view()}})->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{32, 32, 32, 32});
+}
+
+TEST_F(RowBitCount, EmptyChildColumnInListOfLists)
+{
+  // Test with a list<list> column with 4 empty list rows.
+  // Note: Since there are no elements in any of the lists,
+  //       the lists column's child can be empty.
+  auto empty_child_lists_column = [] {
+    auto exemplar = cudf::test::lists_column_wrapper<int32_t>{{0, 1, 2}, {3, 4, 5}};
+    return cudf::empty_like(exemplar);
+  };
+
+  auto offsets   = cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 0, 0, 0, 0};
+  auto lists_col = cudf::make_lists_column(4, offsets.release(), empty_child_lists_column(), 0, {});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(
+    cudf::row_bit_count(cudf::table_view{{lists_col->view()}})->view(),
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{32, 32, 32, 32});
+}
+
+struct sum_functor {
+  cudf::size_type const* s0;
+  cudf::size_type const* s1;
+  cudf::size_type const* s2;
+
+  cudf::size_type operator() __device__(int i) { return s0[i] + s1[i] + s2[i]; }
+};
+
+TEST_F(RowBitCount, Table)
+{
+  // complex nested column
+  auto col0 = build_nested_column1({1, 1, 1, 1, 1, 1, 1, 1});
+  auto col0_sizes =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{276, 32, 520, 572, 212, 212}.release();
+
+  // struct column
+  auto [col1, col1_sizes] = build_struct_column();
+
+  // list column
+  auto [col2, col2_sizes] = build_list_column<int16_t>();
+
+  cudf::table_view t({*col0, *col1, *col2});
+  auto result = cudf::row_bit_count(t);
+
+  // sum all column sizes
+  cudf::column_view cv0 = static_cast<cudf::column_view>(*col0_sizes);
+  cudf::column_view cv1 = static_cast<cudf::column_view>(*col1_sizes);
+  cudf::column_view cv2 = static_cast<cudf::column_view>(*col2_sizes);
+  auto expected =
+    cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::INT32}, t.num_rows());
+  cudf::mutable_column_view mcv(*expected);
+  thrust::transform(
+    rmm::exec_policy(cudf::get_default_stream()),
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(0) + t.num_rows(),
+    mcv.begin<cudf::size_type>(),
+    sum_functor{
+      cv0.data<cudf::size_type>(), cv1.data<cudf::size_type>(), cv2.data<cudf::size_type>()});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected, *result);
+}
+
+TEST_F(RowBitCount, DepthJump)
+{
+  // jump more than 1 branch depth.
+
+  using T = int;
+
+  // struct<list<struct<list<int>>, int>
+  // the jump occurs from depth 2 (the leafmost int column)
+  // to depth 0 (the topmost int column)
+  cudf::test::fixed_width_column_wrapper<T> ____c0{1, 2, 3, 5, 5, 6, 7, 8};
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> ___offsets{0, 2, 4, 6, 8};
+  auto ___c0 = cudf::make_lists_column(4, ___offsets.release(), ____c0.release(), 0, {});
+  std::vector<std::unique_ptr<cudf::column>> __children;
+  __children.push_back(std::move(___c0));
+  cudf::test::structs_column_wrapper __c0(std::move(__children));
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> _offsets{0, 3, 4};
+  auto _c0 = cudf::make_lists_column(2, _offsets.release(), __c0.release(), 0, {});
+  cudf::test::fixed_width_column_wrapper<int> _c1{3, 4};
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.push_back(std::move(_c0));
+  children.push_back(_c1.release());
+  cudf::test::structs_column_wrapper c0(std::move(children));
+
+  cudf::table_view t({c0});
+  auto result = cudf::row_bit_count(t);
+
+  // expected size = (num rows at level 1 + num_rows at level 2) + (# values the leaf int column) +
+  // 1 (value in topmost int column)
+  constexpr cudf::size_type offset_size = sizeof(cudf::size_type) * CHAR_BIT;
+  constexpr cudf::size_type type_size   = sizeof(T) * CHAR_BIT;
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected{
+    ((1 + 3) * offset_size) + (6 * type_size) + (1 * type_size),
+    ((1 + 1) * offset_size) + (2 * type_size) + (1 * type_size)};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, SlicedColumnsFixedWidth)
+{
+  auto const slice_size = 7;
+  cudf::test::fixed_width_column_wrapper<int16_t> c0_unsliced{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  auto c0 = cudf::slice(c0_unsliced, {2, 2 + slice_size});
+
+  cudf::table_view t({c0});
+  auto result = cudf::row_bit_count(t);
+
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected{16, 16, 16, 16, 16, 16, 16};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, SlicedColumnsStrings)
+{
+  auto const slice_size = 7;
+  std::vector<std::string> strings{
+    "banana", "metric", "imperial", "abc", "daïs", "", "fire", "def", "cudf", "xyzw"};
+  cudf::test::strings_column_wrapper c0_unsliced(strings.begin(), strings.end());
+  auto c0 = cudf::slice(c0_unsliced, {3, 3 + slice_size});
+
+  cudf::table_view t({c0});
+  auto result = cudf::row_bit_count(t);
+
+  // expect 1 offset (4 bytes) + length of string per row
+  auto size_iter = cudf::detail::make_counting_transform_iterator(0, [&strings](int i) {
+    return (static_cast<cudf::size_type>(strings[i].size()) + sizeof(cudf::size_type)) * CHAR_BIT;
+  });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(size_iter + 3,
+                                                                   size_iter + 3 + slice_size);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, SlicedColumnsLists)
+{
+  auto const slice_size = 2;
+  cudf::test::lists_column_wrapper<cudf::string_view> c0_unsliced{
+    {{"banana", "v"}, {"cats"}},
+    {{"dogs", "yay"}, {"xyz", ""}, {"daïs"}},
+    {{"fast", "parrot"}, {"orange"}},
+    {{"blue"}, {"red", "yellow"}, {"ultraviolet", "", "green"}}};
+  auto c0 = cudf::slice(c0_unsliced, {1, 1 + slice_size});
+
+  cudf::table_view t({c0});
+  auto result = cudf::row_bit_count(t);
+
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected{408, 320};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, SlicedColumnsStructs)
+{
+  auto const slice_size = 7;
+
+  cudf::test::fixed_width_column_wrapper<int16_t> c0{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  std::vector<std::string> strings{
+    "banana", "metric", "imperial", "abc", "daïs", "", "fire", "def", "cudf", "xyzw"};
+  cudf::test::strings_column_wrapper c1(strings.begin(), strings.end());
+
+  auto struct_col_unsliced = cudf::test::structs_column_wrapper({c0, c1});
+  auto struct_col          = cudf::slice(struct_col_unsliced, {3, 3 + slice_size});
+
+  cudf::table_view t({struct_col});
+  auto result = cudf::row_bit_count(t);
+
+  // expect 1 offset (4 bytes) + length of string per row + 1 int16_t per row
+  auto size_iter = cudf::detail::make_counting_transform_iterator(0, [&strings](int i) {
+    return (static_cast<cudf::size_type>(strings[i].size()) + sizeof(cudf::size_type) +
+            sizeof(int16_t)) *
+           CHAR_BIT;
+  });
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> expected(size_iter + 3,
+                                                                   size_iter + 3 + slice_size);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, *result);
+}
+
+TEST_F(RowBitCount, EmptyTable)
+{
+  {
+    cudf::table_view empty;
+    auto result = cudf::row_bit_count(empty);
+    EXPECT_TRUE(result != nullptr && result->size() == 0);
+  }
+
+  {
+    auto strings = cudf::make_empty_column(cudf::type_id::STRING);
+    auto ints    = cudf::make_empty_column(cudf::type_id::INT32);
+    cudf::table_view empty({*strings, *ints});
+
+    auto result = cudf::row_bit_count(empty);
+    EXPECT_TRUE(result != nullptr && result->size() == 0);
+  }
+}
diff --git a/cpp/tests/transpose/transpose_test.cpp b/cpp/tests/transpose/transpose_test.cpp
new file mode 100644
index 0000000..cf46dd7
--- /dev/null
+++ b/cpp/tests/transpose/transpose_test.cpp
@@ -0,0 +1,202 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/transpose.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <algorithm>
+#include <limits>
+#include <random>
+#include <string>
+
+namespace {
+
+template <typename T, typename F>
+auto generate_vectors(size_t ncols, size_t nrows, F generator)
+{
+  std::vector<std::vector<T>> values(ncols);
+
+  std::for_each(values.begin(), values.end(), [generator, nrows](std::vector<T>& col) {
+    col.resize(nrows);
+    std::generate(col.begin(), col.end(), generator);
+  });
+
+  return values;
+}
+
+template <typename T>
+auto transpose_vectors(std::vector<std::vector<T>> const& input)
+{
+  if (input.empty()) { return input; }
+  size_t ncols = input.size();
+  size_t nrows = input.front().size();
+
+  std::vector<std::vector<T>> transposed(nrows);
+  std::for_each(
+    transposed.begin(), transposed.end(), [=](std::vector<T>& col) { col.resize(ncols); });
+
+  for (size_t col = 0; col < input.size(); ++col) {
+    for (size_t row = 0; row < nrows; ++row) {
+      transposed[row][col] = input[col][row];
+    }
+  }
+
+  return transposed;
+}
+
+template <typename T, typename ColumnWrapper>
+auto make_columns(std::vector<std::vector<T>> const& values)
+{
+  std::vector<ColumnWrapper> columns;
+  columns.reserve(values.size());
+
+  for (auto const& value_col : values) {
+    columns.emplace_back(value_col.begin(), value_col.end());
+  }
+
+  return columns;
+}
+
+template <typename T, typename ColumnWrapper>
+auto make_columns(std::vector<std::vector<T>> const& values,
+                  std::vector<std::vector<cudf::size_type>> const& valids)
+{
+  std::vector<ColumnWrapper> columns;
+  columns.reserve(values.size());
+
+  for (size_t col = 0; col < values.size(); ++col) {
+    columns.emplace_back(values[col].begin(), values[col].end(), valids[col].begin());
+  }
+
+  return columns;
+}
+
+template <typename ColumnWrapper>
+auto make_table_view(std::vector<ColumnWrapper> const& cols)
+{
+  std::vector<cudf::column_view> views(cols.size());
+
+  std::transform(cols.begin(), cols.end(), views.begin(), [](auto const& col) {
+    return static_cast<cudf::column_view>(col);
+  });
+
+  return cudf::table_view(views);
+}
+
+template <typename T>
+void run_test(size_t ncols, size_t nrows, bool add_nulls)
+{
+  using ColumnWrapper = std::conditional_t<std::is_same_v<T, std::string>,
+                                           cudf::test::strings_column_wrapper,
+                                           cudf::test::fixed_width_column_wrapper<T>>;
+
+  std::mt19937 rng(1);
+
+  // Generate values as vector of vectors
+  auto const values = generate_vectors<T>(
+    ncols, nrows, [&rng]() { return cudf::test::make_type_param_scalar<T>(rng()); });
+  auto const valuesT = transpose_vectors(values);
+
+  std::vector<ColumnWrapper> input_cols;
+  std::vector<ColumnWrapper> expected_cols;
+  std::vector<cudf::size_type> expected_nulls(nrows);
+
+  if (add_nulls) {
+    // Generate null mask as vector of vectors
+    auto const valids = generate_vectors<cudf::size_type>(
+      ncols, nrows, [&rng]() { return static_cast<cudf::size_type>(rng() % 3 > 0 ? 1 : 0); });
+    auto const validsT = transpose_vectors(valids);
+
+    // Compute the null counts over each transposed column
+    std::transform(validsT.begin(),
+                   validsT.end(),
+                   expected_nulls.begin(),
+                   [ncols](std::vector<cudf::size_type> const& vec) {
+                     // num nulls = num elems - num valids
+                     return ncols - std::accumulate(vec.begin(), vec.end(), 0);
+                   });
+
+    // Create column wrappers from vector of vectors
+    input_cols    = make_columns<T, ColumnWrapper>(values, valids);
+    expected_cols = make_columns<T, ColumnWrapper>(valuesT, validsT);
+  } else {
+    input_cols    = make_columns<T, ColumnWrapper>(values);
+    expected_cols = make_columns<T, ColumnWrapper>(valuesT);
+  }
+
+  // Create table views from column wrappers
+  auto input_view    = make_table_view(input_cols);
+  auto expected_view = make_table_view(expected_cols);
+
+  auto result      = transpose(input_view);
+  auto result_view = std::get<1>(result);
+
+  ASSERT_EQ(result_view.num_columns(), expected_view.num_columns());
+  for (cudf::size_type i = 0; i < result_view.num_columns(); ++i) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(result_view.column(i), expected_view.column(i));
+    EXPECT_EQ(result_view.column(i).null_count(), expected_nulls[i]);
+  }
+}
+
+}  // namespace
+
+template <typename T>
+class TransposeTest : public cudf::test::BaseFixture {};
+
+// Using std::string here instead of cudf::test::StringTypes allows us to
+// use std::vector<T> utilities in this file just like the fixed-width types.
+// Should consider changing cudf::test::StringTypes to std::string instead of cudf::string_view.
+using StdStringType  = cudf::test::Types<std::string>;
+using TransposeTypes = cudf::test::Concat<cudf::test::FixedWidthTypes, StdStringType>;
+
+TYPED_TEST_SUITE(TransposeTest, TransposeTypes);  // cudf::test::FixedWidthTypes);
+
+TYPED_TEST(TransposeTest, SingleValue) { run_test<TypeParam>(1, 1, false); }
+
+TYPED_TEST(TransposeTest, SingleColumn) { run_test<TypeParam>(1, 1000, false); }
+
+TYPED_TEST(TransposeTest, SingleColumnNulls) { run_test<TypeParam>(1, 1000, true); }
+
+TYPED_TEST(TransposeTest, Square) { run_test<TypeParam>(100, 100, false); }
+
+TYPED_TEST(TransposeTest, SquareNulls) { run_test<TypeParam>(100, 100, true); }
+
+TYPED_TEST(TransposeTest, Slim) { run_test<TypeParam>(10, 1000, false); }
+
+TYPED_TEST(TransposeTest, SlimNulls) { run_test<TypeParam>(10, 1000, true); }
+
+TYPED_TEST(TransposeTest, Fat) { run_test<TypeParam>(1000, 10, false); }
+
+TYPED_TEST(TransposeTest, FatNulls) { run_test<TypeParam>(1000, 10, true); }
+
+TYPED_TEST(TransposeTest, EmptyTable) { run_test<TypeParam>(0, 0, false); }
+
+TYPED_TEST(TransposeTest, EmptyColumns) { run_test<TypeParam>(10, 0, false); }
+
+class TransposeTestError : public cudf::test::BaseFixture {};
+
+TEST_F(TransposeTestError, MismatchedColumns)
+{
+  cudf::test::fixed_width_column_wrapper<uint32_t, int32_t> col1({1, 2, 3});
+  cudf::test::fixed_width_column_wrapper<int8_t> col2{{4, 5, 6}};
+  cudf::test::fixed_width_column_wrapper<float> col3{{7, 8, 9}};
+  cudf::table_view input{{col1, col2, col3}};
+  EXPECT_THROW(cudf::transpose(input), cudf::logic_error);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/types/traits_test.cpp b/cpp/tests/types/traits_test.cpp
new file mode 100644
index 0000000..32d5562
--- /dev/null
+++ b/cpp/tests/types/traits_test.cpp
@@ -0,0 +1,134 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/traits.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <gtest/gtest.h>
+
+#include <algorithm>
+#include <tuple>
+
+template <typename Tuple, typename F, std::size_t... Indices>
+void tuple_for_each_impl(Tuple&& tuple, F&& f, std::index_sequence<Indices...>)
+{
+  (void)std::initializer_list<int>{
+    ((void)(f(std::get<Indices>(std::forward<Tuple>(tuple)))), int{})...};
+}
+
+template <typename F, typename... Args>
+void tuple_for_each(std::tuple<Args...> const& tuple, F&& f)
+{
+  tuple_for_each_impl(tuple, std::forward<F>(f), std::index_sequence_for<Args...>{});
+}
+
+class TraitsTest : public ::testing::Test {};
+
+template <typename T>
+class TypedTraitsTest : public TraitsTest {};
+
+TYPED_TEST_SUITE(TypedTraitsTest, cudf::test::AllTypes);
+
+TEST_F(TraitsTest, NumericDataTypesAreNumeric)
+{
+  EXPECT_TRUE(
+    std::all_of(cudf::test::numeric_type_ids.begin(),
+                cudf::test::numeric_type_ids.end(),
+                [](cudf::type_id type) { return cudf::is_numeric(cudf::data_type{type}); }));
+}
+
+TEST_F(TraitsTest, TimestampDataTypesAreNotNumeric)
+{
+  EXPECT_TRUE(
+    std::none_of(cudf::test::timestamp_type_ids.begin(),
+                 cudf::test::timestamp_type_ids.end(),
+                 [](cudf::type_id type) { return cudf::is_numeric(cudf::data_type{type}); }));
+}
+
+TEST_F(TraitsTest, NumericDataTypesAreNotTimestamps)
+{
+  EXPECT_TRUE(
+    std::none_of(cudf::test::numeric_type_ids.begin(),
+                 cudf::test::numeric_type_ids.end(),
+                 [](cudf::type_id type) { return cudf::is_timestamp(cudf::data_type{type}); }));
+}
+
+TEST_F(TraitsTest, TimestampDataTypesAreTimestamps)
+{
+  EXPECT_TRUE(
+    std::all_of(cudf::test::timestamp_type_ids.begin(),
+                cudf::test::timestamp_type_ids.end(),
+                [](cudf::type_id type) { return cudf::is_timestamp(cudf::data_type{type}); }));
+}
+
+TYPED_TEST(TypedTraitsTest, RelationallyComparable)
+{
+  // All the test types should be comparable with themselves
+  bool comparable = cudf::is_relationally_comparable<TypeParam, TypeParam>();
+  EXPECT_TRUE(comparable);
+}
+
+TYPED_TEST(TypedTraitsTest, NotRelationallyComparable)
+{
+  // No type should be comparable with an empty dummy type
+  struct foo {};
+  bool comparable = cudf::is_relationally_comparable<foo, TypeParam>();
+  EXPECT_FALSE(comparable);
+
+  comparable = cudf::is_relationally_comparable<TypeParam, foo>();
+  EXPECT_FALSE(comparable);
+}
+
+TYPED_TEST(TypedTraitsTest, NotRelationallyComparableWithList)
+{
+  bool comparable = cudf::is_relationally_comparable<TypeParam, cudf::list_view>();
+  EXPECT_FALSE(comparable);
+
+  comparable = cudf::is_relationally_comparable<cudf::list_view, cudf::list_view>();
+  EXPECT_FALSE(comparable);
+}
+
+TYPED_TEST(TypedTraitsTest, EqualityComparable)
+{
+  // All the test types should be comparable with themselves
+  bool comparable = cudf::is_equality_comparable<TypeParam, TypeParam>();
+  EXPECT_TRUE(comparable);
+}
+
+TYPED_TEST(TypedTraitsTest, NotEqualityComparable)
+{
+  // No type should be comparable with an empty dummy type
+  struct foo {};
+  bool comparable = cudf::is_equality_comparable<foo, TypeParam>();
+  EXPECT_FALSE(comparable);
+
+  comparable = cudf::is_equality_comparable<TypeParam, foo>();
+  EXPECT_FALSE(comparable);
+}
+
+TYPED_TEST(TypedTraitsTest, NotEqualityComparableWithList)
+{
+  bool comparable = cudf::is_equality_comparable<TypeParam, cudf::list_view>();
+  EXPECT_FALSE(comparable);
+
+  cudf::is_equality_comparable<cudf::list_view, cudf::list_view>();
+  EXPECT_FALSE(comparable);
+}
+
+// TODO: Tests for is_compound, is_fixed_width
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/types/type_dispatcher_test.cu b/cpp/tests/types/type_dispatcher_test.cu
new file mode 100644
index 0000000..7b2384d
--- /dev/null
+++ b/cpp/tests/types/type_dispatcher_test.cu
@@ -0,0 +1,177 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <rmm/device_uvector.hpp>
+
+struct DispatcherTest : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct TypedDispatcherTest : public DispatcherTest {};
+
+TYPED_TEST_SUITE(TypedDispatcherTest, cudf::test::AllTypes);
+
+namespace {
+template <typename Expected>
+struct type_tester {
+  template <typename Dispatched>
+  bool operator()()
+  {
+    return std::is_same_v<Expected, Dispatched>;
+  }
+};
+}  // namespace
+
+TYPED_TEST(TypedDispatcherTest, TypeToId)
+{
+  EXPECT_TRUE(cudf::type_dispatcher(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                    type_tester<TypeParam>{}));
+}
+
+namespace {
+struct verify_dispatched_type {
+  template <typename T>
+  __host__ __device__ bool operator()(cudf::type_id id)
+  {
+    return id == cudf::type_to_id<T>();
+  }
+};
+
+__global__ void dispatch_test_kernel(cudf::type_id id, bool* d_result)
+{
+  if (0 == threadIdx.x + blockIdx.x * blockDim.x)
+    *d_result = cudf::type_dispatcher(cudf::data_type{id}, verify_dispatched_type{}, id);
+}
+}  // namespace
+
+TYPED_TEST(TypedDispatcherTest, DeviceDispatch)
+{
+  auto result = cudf::detail::make_zeroed_device_uvector_sync<bool>(
+    1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  dispatch_test_kernel<<<1, 1, 0, cudf::get_default_stream().value()>>>(
+    cudf::type_to_id<TypeParam>(), result.data());
+  CUDF_CUDA_TRY(cudaDeviceSynchronize());
+  EXPECT_EQ(true, result.front_element(cudf::get_default_stream()));
+}
+
+struct IdDispatcherTest : public DispatcherTest,
+                          public testing::WithParamInterface<cudf::type_id> {};
+
+INSTANTIATE_TEST_CASE_P(TestAllIds, IdDispatcherTest, testing::ValuesIn(cudf::test::all_type_ids));
+
+TEST_P(IdDispatcherTest, IdToType)
+{
+  auto t = GetParam();
+  EXPECT_TRUE(cudf::type_dispatcher(cudf::data_type{t}, verify_dispatched_type{}, t));
+}
+
+template <typename T>
+struct TypedDoubleDispatcherTest : public DispatcherTest {};
+
+TYPED_TEST_SUITE(TypedDoubleDispatcherTest, cudf::test::AllTypes);
+
+namespace {
+template <typename Expected1, typename Expected2>
+struct two_type_tester {
+  template <typename Dispatched1, typename Dispatched2>
+  bool operator()()
+  {
+    return std::is_same_v<Expected1, Dispatched1> && std::is_same_v<Expected2, Dispatched2>;
+  }
+};
+}  // namespace
+
+TYPED_TEST(TypedDoubleDispatcherTest, TypeToId)
+{
+  EXPECT_TRUE(cudf::double_type_dispatcher(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                           cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                           two_type_tester<TypeParam, TypeParam>{}));
+}
+
+namespace {
+struct verify_double_dispatched_type {
+  template <typename T1, typename T2>
+  __host__ __device__ bool operator()(cudf::type_id id1, cudf::type_id id2)
+  {
+    return id1 == cudf::type_to_id<T1>() && id2 == cudf::type_to_id<T2>();
+  }
+};
+
+__global__ void double_dispatch_test_kernel(cudf::type_id id1, cudf::type_id id2, bool* d_result)
+{
+  if (0 == threadIdx.x + blockIdx.x * blockDim.x)
+    *d_result = cudf::double_type_dispatcher(
+      cudf::data_type{id1}, cudf::data_type{id2}, verify_double_dispatched_type{}, id1, id2);
+}
+}  // namespace
+
+TYPED_TEST(TypedDoubleDispatcherTest, DeviceDoubleDispatch)
+{
+  auto result = cudf::detail::make_zeroed_device_uvector_sync<bool>(
+    1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  double_dispatch_test_kernel<<<1, 1, 0, cudf::get_default_stream().value()>>>(
+    cudf::type_to_id<TypeParam>(), cudf::type_to_id<TypeParam>(), result.data());
+  CUDF_CUDA_TRY(cudaDeviceSynchronize());
+  EXPECT_EQ(true, result.front_element(cudf::get_default_stream()));
+}
+
+struct IdDoubleDispatcherTest : public DispatcherTest,
+                                public testing::WithParamInterface<cudf::type_id> {};
+
+INSTANTIATE_TEST_CASE_P(TestAllIds,
+                        IdDoubleDispatcherTest,
+                        testing::ValuesIn(cudf::test::all_type_ids));
+
+TEST_P(IdDoubleDispatcherTest, IdToType)
+{
+  // Test double-dispatch of all types using the same type for both dispatches
+  auto t = GetParam();
+  EXPECT_TRUE(cudf::double_type_dispatcher(
+    cudf::data_type{t}, cudf::data_type{t}, verify_double_dispatched_type{}, t, t));
+}
+
+struct IdFixedDoubleDispatcherTest : public DispatcherTest,
+                                     public testing::WithParamInterface<cudf::type_id> {};
+
+INSTANTIATE_TEST_CASE_P(TestAllIds,
+                        IdFixedDoubleDispatcherTest,
+                        testing::ValuesIn(cudf::test::all_type_ids));
+
+TEST_P(IdFixedDoubleDispatcherTest, IdToType)
+{
+  // Test double-dispatch of all types against one fixed type, in each direction
+  auto t = GetParam();
+  EXPECT_TRUE(cudf::double_type_dispatcher(cudf::data_type{t},
+                                           cudf::data_type{cudf::type_to_id<float>()},
+                                           verify_double_dispatched_type{},
+                                           t,
+                                           cudf::type_to_id<float>()));
+  EXPECT_TRUE(cudf::double_type_dispatcher(cudf::data_type{cudf::type_to_id<float>()},
+                                           cudf::data_type{t},
+                                           verify_double_dispatched_type{},
+                                           cudf::type_to_id<float>(),
+                                           t));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/unary/cast_tests.cpp b/cpp/tests/unary/cast_tests.cpp
new file mode 100644
index 0000000..d565359
--- /dev/null
+++ b/cpp/tests/unary/cast_tests.cpp
@@ -0,0 +1,1088 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+
+#include <cuda/std/limits>
+
+#include <type_traits>
+#include <vector>
+
+static auto const test_timestamps_D = std::vector<int32_t>{
+  -1528,  // 1965-10-26 GMT
+  17716,  // 2018-07-04 GMT
+  19382,  // 2023-01-25 GMT
+};
+
+static auto const test_timestamps_s = std::vector<int64_t>{
+  -131968728,  // 1965-10-26 14:01:12 GMT
+  1530705600,  // 2018-07-04 12:00:00 GMT
+  1674631932,  // 2023-01-25 07:32:12 GMT
+};
+
+static auto const test_timestamps_ms = std::vector<int64_t>{
+  -131968727238,  // 1965-10-26 14:01:12.762 GMT
+  1530705600000,  // 2018-07-04 12:00:00.000 GMT
+  1674631932929,  // 2023-01-25 07:32:12.929 GMT
+};
+
+static auto const test_timestamps_us = std::vector<int64_t>{
+  -131968727238000,  // 1965-10-26 14:01:12.762000000 GMT
+  1530705600000000,  // 2018-07-04 12:00:00.000000000 GMT
+  1674631932929000,  // 2023-01-25 07:32:12.929000000 GMT
+};
+
+static auto const test_timestamps_ns = std::vector<int64_t>{
+  -131968727238000000,  // 1965-10-26 14:01:12.762000000 GMT
+  1530705600000000000,  // 2018-07-04 12:00:00.000000000 GMT
+  1674631932929000000,  // 2023-01-25 07:32:12.929000000 GMT
+};
+
+static auto const test_durations_D  = test_timestamps_D;
+static auto const test_durations_s  = test_timestamps_s;
+static auto const test_durations_ms = test_timestamps_ms;
+static auto const test_durations_us = test_timestamps_us;
+static auto const test_durations_ns = test_timestamps_ns;
+
+template <typename T, typename R>
+inline auto make_column(std::vector<R> data)
+{
+  return cudf::test::fixed_width_column_wrapper<T, R>(data.begin(), data.end());
+}
+
+template <typename T, typename R>
+inline auto make_column(std::vector<R> data, std::vector<bool> mask)
+{
+  return cudf::test::fixed_width_column_wrapper<T, R>(data.begin(), data.end(), mask.begin());
+}
+
+inline cudf::column make_exp_chrono_column(cudf::type_id type_id)
+{
+  switch (type_id) {
+    case cudf::type_id::TIMESTAMP_DAYS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_timestamps_D.size(),
+        rmm::device_buffer{test_timestamps_D.data(),
+                           test_timestamps_D.size() * sizeof(test_timestamps_D.front()),
+                           cudf::get_default_stream()},
+
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::TIMESTAMP_SECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_timestamps_s.size(),
+        rmm::device_buffer{test_timestamps_s.data(),
+                           test_timestamps_s.size() * sizeof(test_timestamps_s.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::TIMESTAMP_MILLISECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_timestamps_ms.size(),
+        rmm::device_buffer{test_timestamps_ms.data(),
+                           test_timestamps_ms.size() * sizeof(test_timestamps_ms.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::TIMESTAMP_MICROSECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_timestamps_us.size(),
+        rmm::device_buffer{test_timestamps_us.data(),
+                           test_timestamps_us.size() * sizeof(test_timestamps_us.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::TIMESTAMP_NANOSECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_timestamps_ns.size(),
+        rmm::device_buffer{test_timestamps_ns.data(),
+                           test_timestamps_ns.size() * sizeof(test_timestamps_ns.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::DURATION_DAYS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_durations_D.size(),
+        rmm::device_buffer{test_durations_D.data(),
+                           test_durations_D.size() * sizeof(test_durations_D.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::DURATION_SECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_durations_s.size(),
+        rmm::device_buffer{test_durations_s.data(),
+                           test_durations_s.size() * sizeof(test_durations_s.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::DURATION_MILLISECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_durations_ms.size(),
+        rmm::device_buffer{test_durations_ms.data(),
+                           test_durations_ms.size() * sizeof(test_durations_ms.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::DURATION_MICROSECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_durations_us.size(),
+        rmm::device_buffer{test_durations_us.data(),
+                           test_durations_us.size() * sizeof(test_durations_us.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    case cudf::type_id::DURATION_NANOSECONDS:
+      return cudf::column(
+        cudf::data_type{type_id},
+        test_durations_ns.size(),
+        rmm::device_buffer{test_durations_ns.data(),
+                           test_durations_ns.size() * sizeof(test_durations_ns.front()),
+                           cudf::get_default_stream()},
+        rmm::device_buffer{},
+        0);
+    default: CUDF_FAIL("Unsupported type_id");
+  }
+};
+
+template <typename T, typename R>
+inline auto make_column(thrust::host_vector<R> data)
+{
+  return cudf::test::fixed_width_column_wrapper<T, R>(data.begin(), data.end());
+}
+
+template <typename T, typename R>
+inline auto make_column(thrust::host_vector<R> data, thrust::host_vector<bool> mask)
+{
+  return cudf::test::fixed_width_column_wrapper<T, R>(data.begin(), data.end(), mask.begin());
+}
+
+template <typename T, typename R>
+void validate_cast_result(cudf::column_view expected, cudf::column_view actual)
+{
+  using namespace cudf::test;
+  // round-trip through the host because sizeof(T) may not equal sizeof(R)
+  auto [h_data, null_mask] = to_host<T>(expected);
+  if (null_mask.empty()) {
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(make_column<R, T>(h_data), actual);
+  } else {
+    thrust::host_vector<bool> h_null_mask(expected.size());
+    for (cudf::size_type i = 0; i < expected.size(); ++i) {
+      h_null_mask[i] = cudf::bit_is_set(null_mask.data(), i);
+    }
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(make_column<R, T>(h_data, h_null_mask), actual);
+  }
+}
+
+template <typename T>
+inline auto make_data_type()
+{
+  return cudf::data_type{cudf::type_to_id<T>()};
+}
+
+struct CastTimestampsSimple : public cudf::test::BaseFixture {};
+
+TEST_F(CastTimestampsSimple, IsIdempotent)
+{
+  using namespace cudf::test;
+
+  auto timestamps_D  = make_column<cudf::timestamp_D>(test_timestamps_D);
+  auto timestamps_s  = make_column<cudf::timestamp_s>(test_timestamps_s);
+  auto timestamps_ms = make_column<cudf::timestamp_ms>(test_timestamps_ms);
+  auto timestamps_us = make_column<cudf::timestamp_us>(test_timestamps_us);
+  auto timestamps_ns = make_column<cudf::timestamp_ns>(test_timestamps_ns);
+
+  // Timestamps to duration
+  auto timestamps_D_dur = cudf::cast(timestamps_D, make_data_type<cudf::timestamp_D::duration>());
+  auto timestamps_s_dur = cudf::cast(timestamps_s, make_data_type<cudf::timestamp_s::duration>());
+  auto timestamps_ms_dur =
+    cudf::cast(timestamps_ms, make_data_type<cudf::timestamp_ms::duration>());
+  auto timestamps_us_dur =
+    cudf::cast(timestamps_us, make_data_type<cudf::timestamp_us::duration>());
+  auto timestamps_ns_dur =
+    cudf::cast(timestamps_ns, make_data_type<cudf::timestamp_ns::duration>());
+
+  // Duration back to timestamp
+  auto timestamps_D_got =
+    cudf::cast(*timestamps_D_dur, cudf::data_type{cudf::type_id::TIMESTAMP_DAYS});
+  auto timestamps_s_got =
+    cudf::cast(*timestamps_s_dur, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS});
+  auto timestamps_ms_got =
+    cudf::cast(*timestamps_ms_dur, cudf::data_type{cudf::type_id::TIMESTAMP_MILLISECONDS});
+  auto timestamps_us_got =
+    cudf::cast(*timestamps_us_dur, cudf::data_type{cudf::type_id::TIMESTAMP_MICROSECONDS});
+  auto timestamps_ns_got =
+    cudf::cast(*timestamps_ns_dur, cudf::data_type{cudf::type_id::TIMESTAMP_NANOSECONDS});
+
+  validate_cast_result<cudf::timestamp_D, cudf::timestamp_D>(timestamps_D, *timestamps_D_got);
+  validate_cast_result<cudf::timestamp_s, cudf::timestamp_s>(timestamps_s, *timestamps_s_got);
+  validate_cast_result<cudf::timestamp_ms, cudf::timestamp_ms>(timestamps_ms, *timestamps_ms_got);
+  validate_cast_result<cudf::timestamp_us, cudf::timestamp_us>(timestamps_us, *timestamps_us_got);
+  validate_cast_result<cudf::timestamp_ns, cudf::timestamp_ns>(timestamps_ns, *timestamps_ns_got);
+}
+
+struct CastDurationsSimple : public cudf::test::BaseFixture {};
+
+TEST_F(CastDurationsSimple, IsIdempotent)
+{
+  using namespace cudf::test;
+
+  auto durations_D  = make_column<cudf::duration_D>(test_durations_D);
+  auto durations_s  = make_column<cudf::duration_s>(test_durations_s);
+  auto durations_ms = make_column<cudf::duration_ms>(test_durations_ms);
+  auto durations_us = make_column<cudf::duration_us>(test_durations_us);
+  auto durations_ns = make_column<cudf::duration_ns>(test_durations_ns);
+
+  auto durations_D_rep  = cudf::cast(durations_D, make_data_type<cudf::duration_D::rep>());
+  auto durations_s_rep  = cudf::cast(durations_s, make_data_type<cudf::duration_s::rep>());
+  auto durations_ms_rep = cudf::cast(durations_ms, make_data_type<cudf::duration_ms::rep>());
+  auto durations_us_rep = cudf::cast(durations_us, make_data_type<cudf::duration_us::rep>());
+  auto durations_ns_rep = cudf::cast(durations_ns, make_data_type<cudf::duration_ns::rep>());
+
+  auto durations_D_got =
+    cudf::cast(*durations_D_rep, cudf::data_type{cudf::type_id::DURATION_DAYS});
+  auto durations_s_got =
+    cudf::cast(*durations_s_rep, cudf::data_type{cudf::type_id::DURATION_SECONDS});
+  auto durations_ms_got =
+    cudf::cast(*durations_ms_rep, cudf::data_type{cudf::type_id::DURATION_MILLISECONDS});
+  auto durations_us_got =
+    cudf::cast(*durations_us_rep, cudf::data_type{cudf::type_id::DURATION_MICROSECONDS});
+  auto durations_ns_got =
+    cudf::cast(*durations_ns_rep, cudf::data_type{cudf::type_id::DURATION_NANOSECONDS});
+
+  validate_cast_result<cudf::duration_D, cudf::duration_D>(durations_D, *durations_D_got);
+  validate_cast_result<cudf::duration_s, cudf::duration_s>(durations_s, *durations_s_got);
+  validate_cast_result<cudf::duration_ms, cudf::duration_ms>(durations_ms, *durations_ms_got);
+  validate_cast_result<cudf::duration_us, cudf::duration_us>(durations_us, *durations_us_got);
+  validate_cast_result<cudf::duration_ns, cudf::duration_ns>(durations_ns, *durations_ns_got);
+}
+
+template <typename T>
+struct CastChronosTyped : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(CastChronosTyped, cudf::test::ChronoTypes);
+
+// Return a list of chrono type ids whose precision is greater than or equal
+// to the input type id
+std::vector<cudf::type_id> get_higher_precision_chrono_type_ids(cudf::type_id search)
+{
+  size_t idx = 0;
+  std::vector<cudf::type_id> gte_ids{};
+  // Arranged such that for every pair of types, the types that precede them have a lower precision
+  std::vector<cudf::type_id> timestamp_ids{cudf::type_id::TIMESTAMP_DAYS,
+                                           cudf::type_id::DURATION_DAYS,
+                                           cudf::type_id::TIMESTAMP_SECONDS,
+                                           cudf::type_id::DURATION_SECONDS,
+                                           cudf::type_id::TIMESTAMP_MILLISECONDS,
+                                           cudf::type_id::DURATION_MILLISECONDS,
+                                           cudf::type_id::TIMESTAMP_MICROSECONDS,
+                                           cudf::type_id::DURATION_MICROSECONDS,
+                                           cudf::type_id::TIMESTAMP_NANOSECONDS,
+                                           cudf::type_id::DURATION_NANOSECONDS};
+  for (cudf::type_id type_id : timestamp_ids) {
+    if (type_id == search) break;
+    idx++;
+  }
+
+  for (auto i = idx - idx % 2; i < timestamp_ids.size(); ++i)
+    gte_ids.emplace_back(timestamp_ids[i]);
+  return gte_ids;
+}
+
+// Test that all chrono types whose precision is >= to the TypeParam
+// down-casts appropriately to the lower-precision TypeParam
+TYPED_TEST(CastChronosTyped, DownCastingFloorsValues)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+  auto dtype_exp  = make_data_type<T>();
+  auto chrono_exp = make_exp_chrono_column(dtype_exp.id());
+  // Construct a list of the chrono type_ids whose precision is
+  // greater than or equal to the precision of TypeParam's, e.g:
+  // timestamp_ms -> {timestamp_ms, duration_ms, timestamp_us, duration_us, timestamp_ns,
+  // duration_ns}; duration_us -> {timestamp_us, duration_us, timestamp_ns, duration_ns}; etc.
+  auto higher_precision_type_ids = get_higher_precision_chrono_type_ids(cudf::type_to_id<T>());
+  // For each higher-precision type, down-cast to TypeParam and validate
+  // that the values were floored.
+  for (cudf::type_id higher_precision_type_id : higher_precision_type_ids) {
+    auto chrono_src = make_exp_chrono_column(higher_precision_type_id);
+    auto chrono_got = cudf::cast(chrono_src, dtype_exp);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*chrono_got, chrono_exp);
+  }
+}
+
+// Specific test to ensure down-casting to days happens correctly
+TYPED_TEST(CastChronosTyped, DownCastingToDaysFloorsValues)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+
+  auto dtype_src  = make_data_type<T>();
+  auto chrono_src = make_exp_chrono_column(dtype_src.id());
+
+  // Convert {timestamp|duration}_X => timestamp_D
+  auto timestamp_dtype_out = make_data_type<cudf::timestamp_D>();
+  auto timestamps_got      = cudf::cast(chrono_src, timestamp_dtype_out);
+  auto timestamp_exp       = make_column<cudf::timestamp_D>(test_timestamps_D);
+
+  validate_cast_result<cudf::timestamp_D, cudf::timestamp_D>(timestamp_exp, *timestamps_got);
+
+  // Convert {timestamp|duration}_X => duration_D
+  auto duration_dtype_out = make_data_type<cudf::duration_D>();
+  auto duration_got       = cudf::cast(chrono_src, duration_dtype_out);
+  auto duration_exp       = make_column<cudf::duration_D>(test_durations_D);
+
+  validate_cast_result<cudf::duration_D, cudf::duration_D>(duration_exp, *duration_got);
+}
+
+struct CastToTimestamps : public cudf::test::BaseFixture {};
+
+// Cast duration types to timestamps (as integral types can't be converted)
+TEST_F(CastToTimestamps, AllValid)
+{
+  using namespace cudf::test;
+
+  auto durations_D  = make_column<cudf::duration_D>(test_durations_D);
+  auto durations_s  = make_column<cudf::duration_s>(test_durations_s);
+  auto durations_ms = make_column<cudf::duration_ms>(test_durations_ms);
+  auto durations_us = make_column<cudf::duration_us>(test_durations_us);
+  auto durations_ns = make_column<cudf::duration_ns>(test_durations_ns);
+
+  auto timestamps_D_got = cudf::cast(durations_D, cudf::data_type{cudf::type_id::TIMESTAMP_DAYS});
+  auto timestamps_s_got =
+    cudf::cast(durations_s, cudf::data_type{cudf::type_id::TIMESTAMP_SECONDS});
+  auto timestamps_ms_got =
+    cudf::cast(durations_ms, cudf::data_type{cudf::type_id::TIMESTAMP_MILLISECONDS});
+  auto timestamps_us_got =
+    cudf::cast(durations_us, cudf::data_type{cudf::type_id::TIMESTAMP_MICROSECONDS});
+  auto timestamps_ns_got =
+    cudf::cast(durations_ns, cudf::data_type{cudf::type_id::TIMESTAMP_NANOSECONDS});
+
+  validate_cast_result<cudf::duration_D, cudf::timestamp_D>(durations_D, *timestamps_D_got);
+  validate_cast_result<cudf::duration_s, cudf::timestamp_s>(durations_s, *timestamps_s_got);
+  validate_cast_result<cudf::duration_ms, cudf::timestamp_ms>(durations_ms, *timestamps_ms_got);
+  validate_cast_result<cudf::duration_us, cudf::timestamp_us>(durations_us, *timestamps_us_got);
+  validate_cast_result<cudf::duration_ns, cudf::timestamp_ns>(durations_ns, *timestamps_ns_got);
+}
+
+struct CastFromTimestamps : public cudf::test::BaseFixture {};
+
+// Convert timestamps to duration types
+TEST_F(CastFromTimestamps, AllValid)
+{
+  using namespace cudf::test;
+
+  auto timestamps_D  = make_column<cudf::timestamp_D>(test_timestamps_D);
+  auto timestamps_s  = make_column<cudf::timestamp_s>(test_timestamps_s);
+  auto timestamps_ms = make_column<cudf::timestamp_ms>(test_timestamps_ms);
+  auto timestamps_us = make_column<cudf::timestamp_us>(test_timestamps_us);
+  auto timestamps_ns = make_column<cudf::timestamp_ns>(test_timestamps_ns);
+
+  auto duration_D_exp  = make_column<cudf::duration_D>(test_durations_D);
+  auto duration_s_exp  = make_column<cudf::duration_s>(test_durations_s);
+  auto duration_ms_exp = make_column<cudf::duration_us>(test_durations_ms);
+  auto duration_us_exp = make_column<cudf::duration_ms>(test_durations_us);
+  auto duration_ns_exp = make_column<cudf::duration_ns>(test_durations_ns);
+
+  auto durations_D_got  = cudf::cast(timestamps_D, make_data_type<cudf::duration_D>());
+  auto durations_s_got  = cudf::cast(timestamps_s, make_data_type<cudf::duration_s>());
+  auto durations_ms_got = cudf::cast(timestamps_ms, make_data_type<cudf::duration_ms>());
+  auto durations_us_got = cudf::cast(timestamps_us, make_data_type<cudf::duration_us>());
+  auto durations_ns_got = cudf::cast(timestamps_ns, make_data_type<cudf::duration_ns>());
+
+  validate_cast_result<cudf::duration_D, cudf::duration_D>(duration_D_exp, *durations_D_got);
+  validate_cast_result<cudf::duration_s, cudf::duration_s>(duration_s_exp, *durations_s_got);
+  validate_cast_result<cudf::duration_ms, cudf::duration_ms>(duration_ms_exp, *durations_ms_got);
+  validate_cast_result<cudf::duration_us, cudf::duration_us>(duration_us_exp, *durations_us_got);
+  validate_cast_result<cudf::duration_ns, cudf::duration_ns>(duration_ns_exp, *durations_ns_got);
+}
+
+TEST_F(CastFromTimestamps, WithNulls)
+{
+  using namespace cudf::test;
+
+  auto timestamps_D  = make_column<cudf::timestamp_D>(test_timestamps_D, {true, false, true});
+  auto timestamps_s  = make_column<cudf::timestamp_s>(test_timestamps_s, {true, false, true});
+  auto timestamps_ms = make_column<cudf::timestamp_ms>(test_timestamps_ms, {true, false, true});
+  auto timestamps_us = make_column<cudf::timestamp_us>(test_timestamps_us, {true, false, true});
+  auto timestamps_ns = make_column<cudf::timestamp_ns>(test_timestamps_ns, {true, false, true});
+
+  auto duration_D_exp  = make_column<cudf::duration_D>(test_durations_D, {true, false, true});
+  auto duration_s_exp  = make_column<cudf::duration_s>(test_durations_s, {true, false, true});
+  auto duration_ms_exp = make_column<cudf::duration_us>(test_durations_ms, {true, false, true});
+  auto duration_us_exp = make_column<cudf::duration_ms>(test_durations_us, {true, false, true});
+  auto duration_ns_exp = make_column<cudf::duration_ns>(test_durations_ns, {true, false, true});
+
+  auto durations_D_got  = cudf::cast(timestamps_D, make_data_type<cudf::duration_D>());
+  auto durations_s_got  = cudf::cast(timestamps_s, make_data_type<cudf::duration_s>());
+  auto durations_ms_got = cudf::cast(timestamps_ms, make_data_type<cudf::duration_ms>());
+  auto durations_us_got = cudf::cast(timestamps_us, make_data_type<cudf::duration_us>());
+  auto durations_ns_got = cudf::cast(timestamps_ns, make_data_type<cudf::duration_ns>());
+
+  validate_cast_result<cudf::duration_D, cudf::duration_D>(duration_D_exp, *durations_D_got);
+  validate_cast_result<cudf::duration_s, cudf::duration_s>(duration_s_exp, *durations_s_got);
+  validate_cast_result<cudf::duration_ms, cudf::duration_ms>(duration_ms_exp, *durations_ms_got);
+  validate_cast_result<cudf::duration_us, cudf::duration_us>(duration_us_exp, *durations_us_got);
+  validate_cast_result<cudf::duration_ns, cudf::duration_ns>(duration_ns_exp, *durations_ns_got);
+}
+
+template <typename T>
+struct CastToDurations : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(CastToDurations, cudf::test::IntegralTypes);
+
+TYPED_TEST(CastToDurations, AllValid)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+
+  auto durations_D  = make_column<T>(test_durations_D);
+  auto durations_s  = make_column<T>(test_durations_s);
+  auto durations_ms = make_column<T>(test_durations_ms);
+  auto durations_us = make_column<T>(test_durations_us);
+  auto durations_ns = make_column<T>(test_durations_ns);
+
+  auto durations_D_got = cudf::cast(durations_D, cudf::data_type{cudf::type_id::DURATION_DAYS});
+  auto durations_s_got = cudf::cast(durations_s, cudf::data_type{cudf::type_id::DURATION_SECONDS});
+  auto durations_ms_got =
+    cudf::cast(durations_ms, cudf::data_type{cudf::type_id::DURATION_MILLISECONDS});
+  auto durations_us_got =
+    cudf::cast(durations_us, cudf::data_type{cudf::type_id::DURATION_MICROSECONDS});
+  auto durations_ns_got =
+    cudf::cast(durations_ns, cudf::data_type{cudf::type_id::DURATION_NANOSECONDS});
+
+  validate_cast_result<T, cudf::duration_D>(durations_D, *durations_D_got);
+  validate_cast_result<T, cudf::duration_s>(durations_s, *durations_s_got);
+  validate_cast_result<T, cudf::duration_ms>(durations_ms, *durations_ms_got);
+  validate_cast_result<T, cudf::duration_us>(durations_us, *durations_us_got);
+  validate_cast_result<T, cudf::duration_ns>(durations_ns, *durations_ns_got);
+}
+
+template <typename T>
+struct CastFromDurations : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(CastFromDurations, cudf::test::NumericTypes);
+
+TYPED_TEST(CastFromDurations, AllValid)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+
+  auto durations_D  = make_column<cudf::duration_D>(test_durations_D);
+  auto durations_s  = make_column<cudf::duration_s>(test_durations_s);
+  auto durations_ms = make_column<cudf::duration_ms>(test_durations_ms);
+  auto durations_us = make_column<cudf::duration_us>(test_durations_us);
+  auto durations_ns = make_column<cudf::duration_ns>(test_durations_ns);
+
+  auto durations_D_exp  = make_column<T>(test_durations_D);
+  auto durations_s_exp  = make_column<T>(test_durations_s);
+  auto durations_ms_exp = make_column<T>(test_durations_ms);
+  auto durations_us_exp = make_column<T>(test_durations_us);
+  auto durations_ns_exp = make_column<T>(test_durations_ns);
+
+  auto durations_D_got  = cudf::cast(durations_D, make_data_type<T>());
+  auto durations_s_got  = cudf::cast(durations_s, make_data_type<T>());
+  auto durations_ms_got = cudf::cast(durations_ms, make_data_type<T>());
+  auto durations_us_got = cudf::cast(durations_us, make_data_type<T>());
+  auto durations_ns_got = cudf::cast(durations_ns, make_data_type<T>());
+
+  validate_cast_result<T, T>(durations_D_exp, *durations_D_got);
+  validate_cast_result<T, T>(durations_s_exp, *durations_s_got);
+  validate_cast_result<T, T>(durations_ms_exp, *durations_ms_got);
+  validate_cast_result<T, T>(durations_us_exp, *durations_us_got);
+  validate_cast_result<T, T>(durations_ns_exp, *durations_ns_got);
+}
+
+TYPED_TEST(CastFromDurations, WithNulls)
+{
+  using T = TypeParam;
+  using namespace cudf::test;
+
+  auto durations_D  = make_column<cudf::duration_D>(test_durations_D, {true, false, true});
+  auto durations_s  = make_column<cudf::duration_s>(test_durations_s, {true, false, true});
+  auto durations_ms = make_column<cudf::duration_ms>(test_durations_ms, {true, false, true});
+  auto durations_us = make_column<cudf::duration_us>(test_durations_us, {true, false, true});
+  auto durations_ns = make_column<cudf::duration_ns>(test_durations_ns, {true, false, true});
+
+  auto durations_D_exp  = make_column<T>(test_durations_D, {true, false, true});
+  auto durations_s_exp  = make_column<T>(test_durations_s, {true, false, true});
+  auto durations_ms_exp = make_column<T>(test_durations_ms, {true, false, true});
+  auto durations_us_exp = make_column<T>(test_durations_us, {true, false, true});
+  auto durations_ns_exp = make_column<T>(test_durations_ns, {true, false, true});
+
+  auto durations_D_got  = cudf::cast(durations_D, make_data_type<T>());
+  auto durations_s_got  = cudf::cast(durations_s, make_data_type<T>());
+  auto durations_ms_got = cudf::cast(durations_ms, make_data_type<T>());
+  auto durations_us_got = cudf::cast(durations_us, make_data_type<T>());
+  auto durations_ns_got = cudf::cast(durations_ns, make_data_type<T>());
+
+  validate_cast_result<T, T>(durations_D_exp, *durations_D_got);
+  validate_cast_result<T, T>(durations_s_exp, *durations_s_got);
+  validate_cast_result<T, T>(durations_ms_exp, *durations_ms_got);
+  validate_cast_result<T, T>(durations_us_exp, *durations_us_got);
+  validate_cast_result<T, T>(durations_ns_exp, *durations_ns_got);
+}
+
+template <typename T>
+inline auto make_fixed_point_data_type(int32_t scale)
+{
+  return cudf::data_type{cudf::type_to_id<T>(), scale};
+}
+
+struct FixedPointTestSingleType : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct FixedPointTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointTests, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointTests, CastToDouble)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  auto const input    = fp_wrapper{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fw_wrapper{1.729, 17.29, 172.9, 1729.0};
+  auto const result   = cudf::cast(input, make_data_type<double>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastToDoubleLarge)
+{
+  using namespace numeric;
+  using namespace cudf::test;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  auto begin =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 10 * (i + 0.5); });
+  auto begin2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i + 0.5; });
+  auto const input    = fp_wrapper{begin, begin + 2000, scale_type{-1}};
+  auto const expected = fw_wrapper(begin2, begin2 + 2000);
+  auto const result   = cudf::cast(input, make_data_type<double>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastToInt32)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto const input    = fp_wrapper{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fw_wrapper{1, 17, 172, 1729};
+  auto const result   = cudf::cast(input, make_data_type<int32_t>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(FixedPointTestSingleType, CastDecimal64ToInt32)
+{
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<int64_t>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto const input    = fp_wrapper{{7246212000}, numeric::scale_type{-5}};
+  auto const expected = fw_wrapper{72462};
+  auto const result   = cudf::cast(input, make_data_type<int32_t>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastToIntLarge)
+{
+  using namespace numeric;
+  using namespace cudf::test;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto begin  = thrust::make_counting_iterator(0);
+  auto begin2 = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 10 * i; });
+  auto const input    = fp_wrapper{begin, begin + 2000, scale_type{1}};
+  auto const expected = fw_wrapper(begin2, begin2 + 2000);
+  auto const result   = cudf::cast(input, make_data_type<int32_t>());
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastFromDouble)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  auto const input    = fw_wrapper{1.729, 17.29, 172.9, 1729.0};
+  auto const expected = fp_wrapper{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastFromDoubleLarge)
+{
+  using namespace numeric;
+  using namespace cudf::test;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<double>;
+
+  auto begin = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i + 0.5; });
+  auto begin2 =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 10 * (i + 0.5); });
+  auto const input    = fw_wrapper(begin, begin + 2000);
+  auto const expected = fp_wrapper{begin2, begin2 + 2000, scale_type{-1}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastFromInt)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto const input    = fw_wrapper{1729, 172, 17, 1};
+  auto const expected = fp_wrapper{{17, 1, 0, 0}, scale_type{2}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(FixedPointTestSingleType, CastInt32ToDecimal64)
+{
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<int64_t>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto const input    = fw_wrapper{-48938};
+  auto const expected = fp_wrapper{{-4893800000LL}, numeric::scale_type{-5}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<numeric::decimal64>(-5));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, CastFromIntLarge)
+{
+  using namespace numeric;
+  using namespace cudf::test;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+  using fw_wrapper = cudf::test::fixed_width_column_wrapper<int32_t>;
+
+  auto begin  = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return 1000 * i; });
+  auto begin2 = thrust::make_counting_iterator(0);
+  auto const input    = fw_wrapper(begin, begin + 2000);
+  auto const expected = fp_wrapper{begin2, begin2 + 2000, scale_type{3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, FixedPointToFixedPointSameTypeidUp)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapper{{172, 1729, 17290, 172900}, scale_type{-2}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, FixedPointToFixedPointSameTypeidDown)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapper{{17290, 172900, 1729000, 17290000}, scale_type{-4}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-4));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, FixedPointToFixedPointSameTypeidUpPositive)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{1, 12, 123, 1234, 12345, 123456}, scale_type{1}};
+  auto const expected = fp_wrapper{{0, 1, 12, 123, 1234, 12345}, scale_type{2}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, FixedPointToFixedPointSameTypeidEmpty)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{}, scale_type{1}};
+  auto const expected = fp_wrapper{{}, scale_type{2}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(2));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, FixedPointToFixedPointSameTypeidDownPositive)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{0, 1, 12, 123, 1234}, scale_type{2}};
+  auto const expected = fp_wrapper{{0, 1000, 12000, 123000, 1234000}, scale_type{-1}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-1));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal32ToDecimalXX)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int32_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal64ToDecimalXX)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int64_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal128ToDecimalXX)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = __int128_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal32ToDecimalXXWithSmallerScale)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int32_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{172900, 1729000, 17290000, 172900000}, scale_type{-5}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-5));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal64ToDecimalXXWithSmallerScale)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int64_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{172900, 1729000, 17290000, 172900000}, scale_type{-5}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-5));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal128ToDecimalXXWithSmallerScale)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = __int128_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{172900, 1729000, 17290000, 172900000}, scale_type{-5}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(-5));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal32ToDecimalXXWithLargerScale)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int32_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1, 17, 172, 1729}, scale_type{0}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal64ToDecimalXXWithLargerScale)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int64_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1, 17, 172, 1729}, scale_type{0}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(FixedPointTestSingleType, AvoidOverflowDecimal32ToDecimal64)
+{
+  using namespace numeric;
+  using fp_wrapper32 = cudf::test::fixed_point_column_wrapper<int32_t>;
+  using fp_wrapper64 = cudf::test::fixed_point_column_wrapper<int64_t>;
+
+  auto const input    = fp_wrapper32{{9999999}, scale_type{3}};
+  auto const expected = fp_wrapper64{{9999999}, scale_type{3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimal64>(3));
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(FixedPointTestSingleType, AvoidOverflowDecimal32ToInt64)
+{
+  using namespace numeric;
+  using fp_wrapper32 = cudf::test::fixed_point_column_wrapper<int32_t>;
+  using fw_wrapper64 = cudf::test::fixed_width_column_wrapper<int64_t>;
+
+  auto const input    = fp_wrapper32{{9999999}, scale_type{3}};
+  auto const expected = fw_wrapper64{{9999999000}};
+  auto const result   = cudf::cast(input, cudf::data_type{cudf::type_id::INT64});
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal128ToDecimalXXWithLargerScale)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = __int128_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const input    = fp_wrapperFrom{{1729, 17290, 172900, 1729000}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1, 17, 172, 1729}, scale_type{0}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, ValidateCastRescalePrecision)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  // This test is designed to protect against floating point conversion
+  // introducing errors in fixed-point arithmetic. The rescaling that occurs
+  // during casting to different scales should only use fixed-precision math.
+  // Realistically, we are only able to show precision failures due to floating
+  // conversion in a few very specific circumstances where dividing by specific
+  // powers of 10 works against us.  Some examples: 10^23, 10^25, 10^26, 10^27,
+  // 10^30, 10^32, 10^36. See https://godbolt.org/z/cP1MddP8P for a derivation.
+  // For completeness and to ensure that we are not missing any other cases, we
+  // test casting to/from all scales in the range of each decimal type. Values
+  // that are powers of ten show this error more readily than non-powers of 10
+  // because the rescaling factor is a power of 10, meaning that errors in
+  // division are more visible.
+  constexpr auto min_scale = -cuda::std::numeric_limits<RepType>::digits10;
+  for (int input_scale = 0; input_scale >= min_scale; --input_scale) {
+    for (int result_scale = 0; result_scale >= min_scale; --result_scale) {
+      RepType input_value = 1;
+      for (int k = 0; k > input_scale; --k) {
+        input_value *= 10;
+      }
+      RepType result_value = 1;
+      for (int k = 0; k > result_scale; --k) {
+        result_value *= 10;
+      }
+      auto const input    = fp_wrapper{{input_value}, scale_type{input_scale}};
+      auto const expected = fp_wrapper{{result_value}, scale_type{result_scale}};
+      auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(result_scale));
+      CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+    }
+  }
+}
+
+TYPED_TEST(FixedPointTests, Decimal32ToDecimalXXWithLargerScaleAndNullMask)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int32_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const vec      = std::vector{1729, 17290, 172900, 1729000};
+  auto const input    = fp_wrapperFrom{vec.cbegin(), vec.cend(), {1, 1, 1, 0}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1, 17, 172, 1729000}, {1, 1, 1, 0}, scale_type{0}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal64ToDecimalXXWithLargerScaleAndNullMask)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = int64_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const vec      = std::vector{1729, 17290, 172900, 1729000};
+  auto const input    = fp_wrapperFrom{vec.cbegin(), vec.cend(), {1, 1, 1, 0}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1, 17, 172, 1729000}, {1, 1, 1, 0}, scale_type{0}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, Decimal128ToDecimalXXWithLargerScaleAndNullMask)
+{
+  using namespace numeric;
+  using decimalXX      = TypeParam;
+  using RepTypeFrom    = __int128_t;
+  using RepTypeTo      = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapperFrom = cudf::test::fixed_point_column_wrapper<RepTypeFrom>;
+  using fp_wrapperTo   = cudf::test::fixed_point_column_wrapper<RepTypeTo>;
+
+  auto const vec      = std::vector{1729, 17290, 172900, 1729000};
+  auto const input    = fp_wrapperFrom{vec.cbegin(), vec.cend(), {1, 1, 1, 0}, scale_type{-3}};
+  auto const expected = fp_wrapperTo{{1, 17, 172, 1729000}, {1, 1, 1, 0}, scale_type{0}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(0));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointTests, DecimalRescaleOverflowAndNullMask)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const vec      = std::vector{1729, 17290, 172900, 1729000};
+  auto const scale    = cuda::std::numeric_limits<RepType>::digits10 + 1;
+  auto const input    = fp_wrapper{vec.cbegin(), vec.cend(), {1, 0, 0, 1}, scale_type{0}};
+  auto const expected = fp_wrapper{{0, 0, 0, 0}, {1, 0, 0, 1}, scale_type{scale}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimalXX>(scale));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TEST_F(FixedPointTestSingleType, Int32ToInt64Convert)
+{
+  using namespace numeric;
+  using fp_wrapperA = cudf::test::fixed_point_column_wrapper<int32_t>;
+  using fp_wrapperB = cudf::test::fixed_point_column_wrapper<int64_t>;
+
+  auto const input    = fp_wrapperB{{141230900000L}, scale_type{-10}};
+  auto const expected = fp_wrapperA{{14123}, scale_type{-3}};
+  auto const result   = cudf::cast(input, make_fixed_point_data_type<decimal32>(-3));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
diff --git a/cpp/tests/unary/math_ops_test.cpp b/cpp/tests/unary/math_ops_test.cpp
new file mode 100644
index 0000000..b86d798
--- /dev/null
+++ b/cpp/tests/unary/math_ops_test.cpp
@@ -0,0 +1,438 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/dictionary/encode.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cuda/std/climits>
+#include <vector>
+
+template <typename T>
+struct UnaryLogicalOpsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(UnaryLogicalOpsTest, cudf::test::NumericTypes);
+
+TYPED_TEST(UnaryLogicalOpsTest, LogicalNot)
+{
+  cudf::size_type colSize = 1000;
+  std::vector<TypeParam> h_input_v(colSize, false);
+  std::vector<bool> h_expect_v(colSize);
+
+  std::transform(
+    std::cbegin(h_input_v), std::cend(h_input_v), std::begin(h_expect_v), [](TypeParam e) -> bool {
+      return static_cast<bool>(!e);
+    });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(std::cbegin(h_input_v),
+                                                          std::cend(h_input_v));
+  cudf::test::fixed_width_column_wrapper<bool> expected(std::cbegin(h_expect_v),
+                                                        std::cend(h_expect_v));
+
+  auto output = cudf::unary_operation(input, cudf::unary_operator::NOT);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryLogicalOpsTest, SimpleLogicalNot)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{true, true, true, true}};
+  cudf::test::fixed_width_column_wrapper<bool> expected{{false, false, false, false}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::NOT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+  auto encoded = cudf::dictionary::encode(input);
+  output       = cudf::unary_operation(encoded->view(), cudf::unary_operator::NOT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryLogicalOpsTest, SimpleLogicalNotWithNullMask)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{true, true, true, true}, {1, 0, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<bool> expected{{false, true, false, false}, {1, 0, 1, 1}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::NOT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+  auto encoded = cudf::dictionary::encode(input);
+  output       = cudf::unary_operation(encoded->view(), cudf::unary_operator::NOT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryLogicalOpsTest, EmptyLogicalNot)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{};
+  cudf::test::fixed_width_column_wrapper<bool> expected{};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::NOT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+template <typename T>
+struct UnaryMathOpsTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(UnaryMathOpsTest, cudf::test::NumericTypes);
+
+TYPED_TEST(UnaryMathOpsTest, ABS)
+{
+  using T = TypeParam;
+
+  cudf::size_type const colSize = 100;
+  std::vector<T> h_input_v(colSize);
+  std::vector<T> h_expect_v(colSize);
+
+  std::iota(
+    std::begin(h_input_v), std::end(h_input_v), std::is_unsigned_v<T> ? colSize : -1 * colSize);
+
+  std::transform(std::cbegin(h_input_v), std::cend(h_input_v), std::begin(h_expect_v), [](auto e) {
+    return cudf::util::absolute_value(e);
+  });
+
+  cudf::test::fixed_width_column_wrapper<T> const input(std::cbegin(h_input_v),
+                                                        std::cend(h_input_v));
+  cudf::test::fixed_width_column_wrapper<T> const expected(std::cbegin(h_expect_v),
+                                                           std::cend(h_expect_v));
+
+  auto const output = cudf::unary_operation(input, cudf::unary_operator::ABS);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, SQRT)
+{
+  using T = TypeParam;
+
+  cudf::size_type const colSize = 1000;
+  std::vector<T> h_input_v(colSize);
+  std::vector<T> h_expect_v(colSize);
+
+  std::generate(std::begin(h_input_v), std::end(h_input_v), [i = 0]() mutable {
+    ++i;
+    return i * i;
+  });
+
+  std::transform(std::cbegin(h_input_v), std::cend(h_input_v), std::begin(h_expect_v), [](auto e) {
+    return std::sqrt(static_cast<float>(e));
+  });
+
+  cudf::test::fixed_width_column_wrapper<T> const input(std::cbegin(h_input_v),
+                                                        std::cend(h_input_v));
+  cudf::test::fixed_width_column_wrapper<T> const expected(std::cbegin(h_expect_v),
+                                                           std::cend(h_expect_v));
+
+  auto const output = cudf::unary_operation(input, cudf::unary_operator::SQRT);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, SimpleABS)
+{
+  auto const v = cudf::test::make_type_param_vector<TypeParam>({-2, -1, 1, 2});
+  cudf::test::fixed_width_column_wrapper<TypeParam> input(v.begin(), v.end());
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{2, 1, 1, 2}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::ABS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, SimpleSQRT)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 4, 9, 16}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1, 2, 3, 4}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::SQRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, SimpleCBRT)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 27, 125}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1, 3, 5}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::CBRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, SimpleSQRTWithNullMask)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 4, 9, 16}, {1, 1, 0, 1}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1, 2, 9, 4}, {1, 1, 0, 1}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::SQRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, SimpleCBRTWithNullMask)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 27, 125}, {1, 1, 0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1, 3, 125}, {1, 1, 0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::CBRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, EmptyABS)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::ABS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, EmptySQRT)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::SQRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, DictionaryABS)
+{
+  auto const v = cudf::test::make_type_param_vector<TypeParam>({-2, -1, 1, 2, -1, 2, 0});
+  cudf::test::fixed_width_column_wrapper<TypeParam> input_w(v.begin(), v.end());
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_w{{2, 1, 1, 2, 1, 2, 0}};
+  auto expected = cudf::dictionary::encode(expected_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::ABS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected->view(), output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, DictionarySQRT)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input_w{{1, 4, 0, 16}, {1, 1, 0, 1}};
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_w{{1, 2, 0, 4}, {1, 1, 0, 1}};
+  auto expected = cudf::dictionary::encode(expected_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::SQRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected->view(), output->view());
+}
+
+TYPED_TEST(UnaryMathOpsTest, DictionaryCBRT)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input_w{{1, 27, 0, 125}, {1, 1, 0, 1}};
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_w{{1, 3, 0, 5}, {1, 1, 0, 1}};
+  auto expected = cudf::dictionary::encode(expected_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::CBRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected->view(), output->view());
+}
+
+template <typename T>
+struct UnaryMathFloatOpsTest : public cudf::test::BaseFixture {};
+
+using floating_point_type_list = ::testing::Types<float, double>;
+
+TYPED_TEST_SUITE(UnaryMathFloatOpsTest, floating_point_type_list);
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleSIN)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{0.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::SIN);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleCOS)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::COS);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleSINH)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{0.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::SINH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleCOSH)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::COSH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleTANH)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{0.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::TANH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleiASINH)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{0.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::ARCSINH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleACOSH)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{0.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::ARCCOSH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleATANH)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{0.0}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{0.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::ARCTANH);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleFLOOR)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1.1, 3.3, 5.5, 7.7}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1.0, 3.0, 5.0, 7.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::FLOOR);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleCEIL)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1.1, 3.3, 5.5, 7.7}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{2.0, 4.0, 6.0, 8.0}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::CEIL);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleRINT)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{
+    T(1.5), T(3.5), T(-1.5), T(-3.5), T(0.0), T(NAN)};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{
+    T(2.0), T(4.0), T(-2.0), T(-4.0), T(0.0), T(NAN)};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::RINT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleEXP)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T> input{T(1.5), T(3.5), T(-1.5), T(-3.5), T(0.0), T(NAN)};
+  cudf::test::fixed_width_column_wrapper<T> expected{T(std::exp(1.5)),
+                                                     T(std::exp(3.5)),
+                                                     T(std::exp(-1.5)),
+                                                     T(std::exp(-3.5)),
+                                                     T(std::exp(0.0)),
+                                                     T(NAN)};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::EXP);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleLOG)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T> input{
+    T(1.5), T(3.5), T(1.0), T(INFINITY), T(0.0), T(NAN), T(-1.0)};
+  cudf::test::fixed_width_column_wrapper<T> expected{
+    T(std::log(1.5)), T(std::log(3.5)), T(+0.0), T(INFINITY), T(-INFINITY), T(NAN), T(NAN)};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::LOG);
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected, output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, DictionaryFLOOR)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input_w{{1.1, 3.3, 5.5, 7.7}};
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_w{{1.0, 3.0, 5.0, 7.0}};
+  auto expected = cudf::dictionary::encode(expected_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::FLOOR);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected->view(), output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, DictionaryCEIL)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input_w{{1.1, 3.3, 5.5, 7.7}};
+  auto input = cudf::dictionary::encode(input_w);
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected_w{{2.0, 4.0, 6.0, 8.0}};
+  auto expected = cudf::dictionary::encode(expected_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::CEIL);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected->view(), output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, DictionaryEXP)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T> input_w{
+    T(1.5), T(3.5), T(-1.5), T(-3.5), T(0.0), T(NAN)};
+  auto input    = cudf::dictionary::encode(input_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::EXP);
+  auto expect   = cudf::unary_operation(input_w, cudf::unary_operator::EXP);
+  auto expected = cudf::dictionary::encode(expect->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, DictionaryLOG)
+{
+  using T = TypeParam;
+  cudf::test::fixed_width_column_wrapper<T> input_w{
+    T(1.5), T(3.5), T(1.0), T(INFINITY), T(0.0), T(NAN), T(-1.0)};
+  auto input    = cudf::dictionary::encode(input_w);
+  auto output   = cudf::unary_operation(input->view(), cudf::unary_operator::LOG);
+  auto expect   = cudf::unary_operation(input_w, cudf::unary_operator::LOG);
+  auto expected = cudf::dictionary::encode(expect->view());
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(expected->view(), output->view());
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, RINTNonFloatingFail)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> input{{1, 2, 3, 4, 5}};
+  EXPECT_THROW(cudf::unary_operation(input, cudf::unary_operator::RINT), cudf::logic_error);
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, IntegralTypeFail)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{1.0};
+  EXPECT_THROW(cudf::unary_operation(input, cudf::unary_operator::BIT_INVERT), cudf::logic_error);
+  auto d = cudf::dictionary::encode(input);
+  EXPECT_THROW(cudf::unary_operation(d->view(), cudf::unary_operator::BIT_INVERT),
+               cudf::logic_error);
+}
+
+TYPED_TEST(UnaryMathFloatOpsTest, SimpleCBRT)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> input{{1, 27, 343, 4913}};
+  cudf::test::fixed_width_column_wrapper<TypeParam> expected{{1, 3, 7, 17}};
+  auto output = cudf::unary_operation(input, cudf::unary_operator::CBRT);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, output->view());
+}
+
+struct UnaryMathOpsErrorTest : public cudf::test::BaseFixture {};
+
+TEST_F(UnaryMathOpsErrorTest, ArithmeticTypeFail)
+{
+  cudf::test::strings_column_wrapper input{"c"};
+  EXPECT_THROW(cudf::unary_operation(input, cudf::unary_operator::SQRT), cudf::logic_error);
+  auto d = cudf::dictionary::encode(input);
+  EXPECT_THROW(cudf::unary_operation(d->view(), cudf::unary_operator::SQRT), cudf::logic_error);
+}
+
+TEST_F(UnaryMathOpsErrorTest, LogicalOpTypeFail)
+{
+  cudf::test::strings_column_wrapper input{"h"};
+  EXPECT_THROW(cudf::unary_operation(input, cudf::unary_operator::NOT), cudf::logic_error);
+  auto d = cudf::dictionary::encode(input);
+  EXPECT_THROW(cudf::unary_operation(d->view(), cudf::unary_operator::NOT), cudf::logic_error);
+}
diff --git a/cpp/tests/unary/unary_ops_test.cpp b/cpp/tests/unary/unary_ops_test.cpp
new file mode 100644
index 0000000..76d1f76
--- /dev/null
+++ b/cpp/tests/unary/unary_ops_test.cpp
@@ -0,0 +1,408 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+#include <cudf/unary.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <cuda/std/limits>
+
+template <typename T>
+cudf::test::fixed_width_column_wrapper<T> create_fixed_columns(cudf::size_type start,
+                                                               cudf::size_type size,
+                                                               bool nullable)
+{
+  auto iter = cudf::detail::make_counting_transform_iterator(start, [](auto i) { return T(i); });
+
+  if (not nullable) {
+    return cudf::test::fixed_width_column_wrapper<T>(iter, iter + size);
+  } else {
+    auto valids =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+    return cudf::test::fixed_width_column_wrapper<T>(iter, iter + size, valids);
+  }
+}
+
+template <typename T>
+cudf::test::fixed_width_column_wrapper<T> create_expected_columns(cudf::size_type size,
+                                                                  bool nullable,
+                                                                  bool nulls_to_be)
+{
+  if (not nullable) {
+    auto iter = cudf::detail::make_counting_transform_iterator(
+      0, [nulls_to_be](auto i) { return not nulls_to_be; });
+    return cudf::test::fixed_width_column_wrapper<T>(iter, iter + size);
+  } else {
+    auto iter = cudf::detail::make_counting_transform_iterator(
+      0, [nulls_to_be](auto i) { return i % 2 == 0 ? not nulls_to_be : nulls_to_be; });
+    return cudf::test::fixed_width_column_wrapper<T>(iter, iter + size);
+  }
+}
+
+template <typename T>
+struct IsNull : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(IsNull, cudf::test::NumericTypes);
+
+TYPED_TEST(IsNull, AllValid)
+{
+  using T = TypeParam;
+
+  cudf::size_type start                         = 0;
+  cudf::size_type size                          = 10;
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, false);
+  cudf::test::fixed_width_column_wrapper<bool> expected =
+    create_expected_columns<bool>(size, false, true);
+
+  std::unique_ptr<cudf::column> got = cudf::is_null(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNull, WithInvalids)
+{
+  using T = TypeParam;
+
+  cudf::size_type start                         = 0;
+  cudf::size_type size                          = 10;
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, true);
+  cudf::test::fixed_width_column_wrapper<bool> expected =
+    create_expected_columns<bool>(size, true, true);
+
+  std::unique_ptr<cudf::column> got = cudf::is_null(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNull, EmptyColumns)
+{
+  using T = TypeParam;
+
+  cudf::size_type start                         = 0;
+  cudf::size_type size                          = 0;
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, true);
+  cudf::test::fixed_width_column_wrapper<bool> expected =
+    create_expected_columns<bool>(size, true, true);
+
+  std::unique_ptr<cudf::column> got = cudf::is_null(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+template <typename T>
+struct IsNotNull : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(IsNotNull, cudf::test::NumericTypes);
+
+TYPED_TEST(IsNotNull, AllValid)
+{
+  using T = TypeParam;
+
+  cudf::size_type start                         = 0;
+  cudf::size_type size                          = 10;
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, false);
+  cudf::test::fixed_width_column_wrapper<bool> expected =
+    create_expected_columns<bool>(size, false, false);
+
+  std::unique_ptr<cudf::column> got = cudf::is_valid(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNotNull, WithInvalids)
+{
+  using T = TypeParam;
+
+  cudf::size_type start                         = 0;
+  cudf::size_type size                          = 10;
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, true);
+  cudf::test::fixed_width_column_wrapper<bool> expected =
+    create_expected_columns<bool>(size, true, false);
+
+  std::unique_ptr<cudf::column> got = cudf::is_valid(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNotNull, EmptyColumns)
+{
+  using T = TypeParam;
+
+  cudf::size_type start                         = 0;
+  cudf::size_type size                          = 0;
+  cudf::test::fixed_width_column_wrapper<T> col = create_fixed_columns<T>(start, size, true);
+  cudf::test::fixed_width_column_wrapper<bool> expected =
+    create_expected_columns<bool>(size, true, false);
+
+  std::unique_ptr<cudf::column> got = cudf::is_valid(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+template <typename T>
+struct IsNAN : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(IsNAN, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(IsNAN, AllValid)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col{{T(1), T(2), T(NAN), T(4), T(NAN), T(6), T(7)}};
+  cudf::test::fixed_width_column_wrapper<bool> expected = {
+    false, false, true, false, true, false, false};
+
+  std::unique_ptr<cudf::column> got = cudf::is_nan(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNAN, WithNull)
+{
+  using T = TypeParam;
+
+  // The last NAN is null
+  cudf::test::fixed_width_column_wrapper<T> col{{T(1), T(2), T(NAN), T(4), T(NAN), T(6), T(7)},
+                                                {1, 0, 1, 1, 0, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<bool> expected = {
+    false, false, true, false, false, false, false};
+
+  std::unique_ptr<cudf::column> got = cudf::is_nan(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNAN, EmptyColumn)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col{};
+  cudf::test::fixed_width_column_wrapper<bool> expected = {};
+
+  std::unique_ptr<cudf::column> got = cudf::is_nan(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNAN, NonFloatingColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int32_t> col{{1, 2, 5, 3, 5, 6, 7}, {1, 0, 1, 1, 0, 1, 1}};
+
+  EXPECT_THROW(std::unique_ptr<cudf::column> got = cudf::is_nan(col), cudf::logic_error);
+}
+
+template <typename T>
+struct IsNotNAN : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(IsNotNAN, cudf::test::FloatingPointTypes);
+
+TYPED_TEST(IsNotNAN, AllValid)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col{{T(1), T(2), T(NAN), T(4), T(NAN), T(6), T(7)}};
+  cudf::test::fixed_width_column_wrapper<bool> expected = {
+    true, true, false, true, false, true, true};
+
+  std::unique_ptr<cudf::column> got = cudf::is_not_nan(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNotNAN, WithNull)
+{
+  using T = TypeParam;
+
+  // The last NAN is null
+  cudf::test::fixed_width_column_wrapper<T> col{{T(1), T(2), T(NAN), T(4), T(NAN), T(6), T(7)},
+                                                {1, 0, 1, 1, 0, 1, 1}};
+  cudf::test::fixed_width_column_wrapper<bool> expected = {
+    true, true, false, true, true, true, true};
+
+  std::unique_ptr<cudf::column> got = cudf::is_not_nan(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNotNAN, EmptyColumn)
+{
+  using T = TypeParam;
+
+  cudf::test::fixed_width_column_wrapper<T> col{};
+  cudf::test::fixed_width_column_wrapper<bool> expected = {};
+
+  std::unique_ptr<cudf::column> got = cudf::is_not_nan(col);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, got->view());
+}
+
+TYPED_TEST(IsNotNAN, NonFloatingColumn)
+{
+  cudf::test::fixed_width_column_wrapper<int64_t> col{{1, 2, 5, 3, 5, 6, 7}, {1, 0, 1, 1, 0, 1, 1}};
+
+  EXPECT_THROW(std::unique_ptr<cudf::column> got = cudf::is_not_nan(col), cudf::logic_error);
+}
+
+template <typename T>
+struct FixedPointUnaryTests : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(FixedPointUnaryTests, cudf::test::FixedPointTypes);
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryAbs)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{-1234, -3456, -6789, 1234, 3456, 6789}, scale_type{-3}};
+  auto const expected = fp_wrapper{{1234, 3456, 6789, 1234, 3456, 6789}, scale_type{-3}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::ABS);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryAbsPositiveScale)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{-1234, -3456, -6789, 1234, 3456, 6789}, scale_type{1}};
+  auto const expected = fp_wrapper{{1234, 3456, 6789, 1234, 3456, 6789}, scale_type{1}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::ABS);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryAbsLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto a = thrust::make_counting_iterator(-2000);
+  auto b =
+    cudf::detail::make_counting_transform_iterator(-2000, [](auto e) { return std::abs(e); });
+  auto const input    = fp_wrapper{a, a + 4000, scale_type{-1}};
+  auto const expected = fp_wrapper{b, b + 4000, scale_type{-1}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::ABS);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryCeil)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{-1234, -3456, -6789, 1234, 3456, 7000, 0}, scale_type{-3}};
+  auto const expected = fp_wrapper{{-1000, -3000, -6000, 2000, 4000, 7000, 0}, scale_type{-3}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::CEIL);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryCeilLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto a = thrust::make_counting_iterator(-5000);
+  auto b =
+    cudf::detail::make_counting_transform_iterator(-5000, [](int e) { return (e / 10) * 10; });
+  auto const input    = fp_wrapper{a, a + 4000, scale_type{-1}};
+  auto const expected = fp_wrapper{b, b + 4000, scale_type{-1}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::CEIL);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryFloor)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto const input    = fp_wrapper{{-1234, -3456, -6789, 1234, 3456, 6000, 0}, scale_type{-3}};
+  auto const expected = fp_wrapper{{-2000, -4000, -7000, 1000, 3000, 6000, 0}, scale_type{-3}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::FLOOR);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, FixedPointUnaryFloorLarge)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  auto a = thrust::make_counting_iterator(100);
+  auto b =
+    cudf::detail::make_counting_transform_iterator(100, [](auto e) { return (e / 10) * 10; });
+  auto const input    = fp_wrapper{a, a + 4000, scale_type{-1}};
+  auto const expected = fp_wrapper{b, b + 4000, scale_type{-1}};
+  auto const result   = cudf::unary_operation(input, cudf::unary_operator::FLOOR);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, result->view());
+}
+
+TYPED_TEST(FixedPointUnaryTests, ValidateCeilFloorPrecision)
+{
+  using namespace numeric;
+  using decimalXX  = TypeParam;
+  using RepType    = cudf::device_storage_type_t<decimalXX>;
+  using fp_wrapper = cudf::test::fixed_point_column_wrapper<RepType>;
+
+  // This test is designed to protect against floating point conversion
+  // introducing errors in fixed-point arithmetic. The rounding that occurs
+  // during ceil/floor should only use fixed-precision math. Realistically,
+  // we are only able to show precision failures due to floating conversion in
+  // a few very specific circumstances where dividing by specific powers of 10
+  // works against us.  Some examples: 10^23, 10^25, 10^26, 10^27, 10^30,
+  // 10^32, 10^36. See https://godbolt.org/z/cP1MddP8P for a derivation. For
+  // completeness and to ensure that we are not missing any other cases, we
+  // test all scales representable in the range of each decimal type.
+  constexpr auto min_scale = -cuda::std::numeric_limits<RepType>::digits10;
+  for (int input_scale = 0; input_scale >= min_scale; --input_scale) {
+    RepType input_value = 1;
+    for (int k = 0; k > input_scale; --k) {
+      input_value *= 10;
+    }
+    auto const input       = fp_wrapper{{input_value}, scale_type{input_scale}};
+    auto const expected    = fp_wrapper{{input_value}, scale_type{input_scale}};
+    auto const ceil_result = cudf::unary_operation(input, cudf::unary_operator::CEIL);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, ceil_result->view());
+    auto const floor_result = cudf::unary_operation(input, cudf::unary_operator::FLOOR);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(expected, floor_result->view());
+  }
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/utilities/base_fixture.cpp b/cpp/tests/utilities/base_fixture.cpp
new file mode 100644
index 0000000..4d5035e
--- /dev/null
+++ b/cpp/tests/utilities/base_fixture.cpp
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/types.hpp>
+
+namespace cudf {
+namespace test {
+namespace detail {
+
+/**
+ * @copydoc cudf::test::detail::random_generator_incrementing_seed()
+ */
+uint64_t random_generator_incrementing_seed()
+{
+  static uint64_t seed = 0;
+  return ++seed;
+}
+
+}  // namespace detail
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/tests/utilities/column_utilities.cu b/cpp/tests/utilities/column_utilities.cu
new file mode 100644
index 0000000..620e0bf
--- /dev/null
+++ b/cpp/tests/utilities/column_utilities.cu
@@ -0,0 +1,1337 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/get_value.cuh>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/dictionary/dictionary_column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/strings/convert/convert_datetime.hpp>
+#include <cudf/structs/struct_view.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/table/experimental/row_operators.cuh>
+#include <cudf/table/table_device_view.cuh>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/type_dispatcher.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/default_stream.hpp>
+#include <cudf_test/detail/column_utilities.hpp>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/copy.h>
+#include <thrust/distance.h>
+#include <thrust/equal.h>
+#include <thrust/execution_policy.h>
+#include <thrust/generate.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/logical.h>
+#include <thrust/reduce.h>
+#include <thrust/remove.h>
+#include <thrust/scan.h>
+#include <thrust/scatter.h>
+#include <thrust/sequence.h>
+#include <thrust/transform.h>
+
+#include <numeric>
+#include <sstream>
+
+namespace cudf {
+
+namespace test {
+
+namespace {
+
+std::unique_ptr<column> generate_all_row_indices(size_type num_rows)
+{
+  auto indices = cudf::make_fixed_width_column(
+    data_type{type_id::INT32}, num_rows, mask_state::UNALLOCATED, cudf::test::get_default_stream());
+  thrust::sequence(rmm::exec_policy(cudf::test::get_default_stream()),
+                   indices->mutable_view().begin<size_type>(),
+                   indices->mutable_view().end<size_type>(),
+                   0);
+  return indices;
+}
+
+// generate the rows indices that should be checked for the child column of a list column.
+//
+// - if we are just checking for equivalence, we can skip any rows that are nulls. this allows
+//   things like non-empty rows that have been nullified after creation.  they may actually contain
+//   values, but since the row is null they don't matter for equivalency.
+//
+// - if we are checking for exact equality, we need to check all rows.
+//
+//   This allows us to differentiate between:
+//
+//  List<int32_t>:
+//    Length : 1
+//    Offsets : 0, 4
+//    Null count: 1
+//    0
+//       0, 1, 2, 3
+//
+//  List<int32_t>:
+//    Length : 1
+//    Offsets : 0, 0
+//    Null count: 1
+//    0
+//
+std::unique_ptr<column> generate_child_row_indices(lists_column_view const& c,
+                                                   column_view const& row_indices,
+                                                   bool check_exact_equality)
+{
+  // if we are checking for exact equality, we should be checking for "unsanitized" data that may
+  // be hiding underneath nulls. so check all rows instead of just non-null rows
+  if (check_exact_equality) {
+    return generate_all_row_indices(c.get_sliced_child(cudf::test::get_default_stream()).size());
+  }
+
+  // Example input
+  // List<int32_t>:
+  // Length : 7
+  // Offsets : 0, 3, 6, 8, 11, 14, 16, 19
+  //                 |     |                        <-- non-null input rows
+  // Null count: 5
+  // 0010100
+  //    1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 7
+  //                      |  |           |  |  |    <-- child rows of non-null rows
+  //
+  // Desired output:  [6, 7, 11, 12, 13]
+
+  // compute total # of child row indices we will be emitting.
+  auto row_size_iter = cudf::detail::make_counting_transform_iterator(
+    0,
+    [row_indices = row_indices.begin<size_type>(),
+     validity    = c.null_mask(),
+     offsets     = c.offsets().begin<size_type>(),
+     offset      = c.offset()] __device__(int index) {
+      // both null mask and offsets data are not pre-sliced. so we need to add the column offset to
+      // every incoming index.
+      auto const true_index = row_indices[index] + offset;
+      return !validity || cudf::bit_is_set(validity, true_index)
+               ? (offsets[true_index + 1] - offsets[true_index])
+               : 0;
+    });
+  auto const output_size = thrust::reduce(rmm::exec_policy(cudf::test::get_default_stream()),
+                                          row_size_iter,
+                                          row_size_iter + row_indices.size());
+  // no output. done.
+  auto result =
+    cudf::make_fixed_width_column(data_type{type_id::INT32}, output_size, mask_state::UNALLOCATED);
+  if (output_size == 0) { return result; }
+
+  // for all input rows, what position in the output column they will start at.
+  //
+  // output_row_start = [0, 0, 0, 2, 2, 5, 5]
+  //                           |     |              <-- non-null input rows
+  //
+  auto output_row_start = cudf::make_fixed_width_column(
+    data_type{type_id::INT32}, row_indices.size(), mask_state::UNALLOCATED);
+  thrust::exclusive_scan(rmm::exec_policy(cudf::test::get_default_stream()),
+                         row_size_iter,
+                         row_size_iter + row_indices.size(),
+                         output_row_start->mutable_view().begin<size_type>());
+
+  // fill result column with 1s
+  //
+  // result = [1, 1, 1, 1, 1]
+  //
+  thrust::generate(rmm::exec_policy(cudf::test::get_default_stream()),
+                   result->mutable_view().begin<size_type>(),
+                   result->mutable_view().end<size_type>(),
+                   [] __device__() { return 1; });
+
+  // scatter the output row positions into result buffer
+  //
+  // result = [6, 1, 11, 1, 1]
+  //
+  auto output_row_iter = cudf::detail::make_counting_transform_iterator(
+    0,
+    [row_indices  = row_indices.begin<size_type>(),
+     offsets      = c.offsets().begin<size_type>(),
+     offset       = c.offset(),
+     first_offset = cudf::detail::get_value<size_type>(
+       c.offsets(), c.offset(), cudf::test::get_default_stream())] __device__(int index) {
+      auto const true_index = row_indices[index] + offset;
+      return offsets[true_index] - first_offset;
+    });
+  thrust::scatter_if(rmm::exec_policy(cudf::test::get_default_stream()),
+                     output_row_iter,
+                     output_row_iter + row_indices.size(),
+                     output_row_start->view().begin<size_type>(),
+                     row_size_iter,
+                     result->mutable_view().begin<size_type>(),
+                     [] __device__(auto row_size) { return row_size != 0; });
+
+  // generate keys for each output row
+  //
+  // result = [1, 1, 2, 2, 2]
+  //
+  auto keys =
+    cudf::make_fixed_width_column(data_type{type_id::INT32}, output_size, mask_state::UNALLOCATED);
+  thrust::generate(rmm::exec_policy(cudf::test::get_default_stream()),
+                   keys->mutable_view().begin<size_type>(),
+                   keys->mutable_view().end<size_type>(),
+                   [] __device__() { return 0; });
+  thrust::scatter_if(rmm::exec_policy(cudf::test::get_default_stream()),
+                     row_size_iter,
+                     row_size_iter + row_indices.size(),
+                     output_row_start->view().begin<size_type>(),
+                     row_size_iter,
+                     keys->mutable_view().begin<size_type>(),
+                     [] __device__(auto row_size) { return row_size != 0; });
+  thrust::inclusive_scan(rmm::exec_policy(cudf::test::get_default_stream()),
+                         keys->view().begin<size_type>(),
+                         keys->view().end<size_type>(),
+                         keys->mutable_view().begin<size_type>());
+
+  // scan by key to generate final child row indices.
+  // input
+  //    result = [6, 1, 11, 1, 1]
+  //    keys   = [1, 1, 2,  2, 2]
+  //
+  // output
+  //    result = [6, 7, 11, 12, 13]
+  //
+  thrust::inclusive_scan_by_key(rmm::exec_policy(cudf::test::get_default_stream()),
+                                keys->view().begin<size_type>(),
+                                keys->view().end<size_type>(),
+                                result->view().begin<size_type>(),
+                                result->mutable_view().begin<size_type>());
+  return result;
+}
+
+#define PROP_EXPECT_EQ(a, b)                                \
+  do {                                                      \
+    if (verbosity == debug_output_level::QUIET) {           \
+      if (a != b) { return false; }                         \
+    } else {                                                \
+      EXPECT_EQ(a, b);                                      \
+      if (a != b) {                                         \
+        if (verbosity == debug_output_level::FIRST_ERROR) { \
+          return false;                                     \
+        } else {                                            \
+          result = false;                                   \
+        }                                                   \
+      }                                                     \
+    }                                                       \
+  } while (0)
+
+template <bool check_exact_equality>
+struct column_property_comparator {
+  bool types_equivalent(cudf::data_type const& lhs, cudf::data_type const& rhs)
+  {
+    return is_fixed_point(lhs) ? lhs.id() == rhs.id() : lhs == rhs;
+  }
+
+  size_type count_nulls(cudf::column_view const& c, cudf::column_view const& row_indices)
+  {
+    auto validity_iter = cudf::detail::make_counting_transform_iterator(
+      0,
+      [row_indices = row_indices.begin<size_type>(),
+       validity    = c.null_mask(),
+       offset      = c.offset()] __device__(int index) {
+        // both null mask and offsets data are not pre-sliced. so we need to add the column offset
+        // to every incoming index.
+        auto const true_index = row_indices[index] + offset;
+        return !validity || cudf::bit_is_set(validity, true_index) ? 0 : 1;
+      });
+    return thrust::reduce(rmm::exec_policy(cudf::test::get_default_stream()),
+                          validity_iter,
+                          validity_iter + row_indices.size());
+  }
+
+  bool compare_common(cudf::column_view const& lhs,
+                      cudf::column_view const& rhs,
+                      cudf::column_view const& lhs_row_indices,
+                      cudf::column_view const& rhs_row_indices,
+                      debug_output_level verbosity)
+  {
+    bool result = true;
+
+    if (check_exact_equality) {
+      PROP_EXPECT_EQ(lhs.type(), rhs.type());
+    } else {
+      PROP_EXPECT_EQ(types_equivalent(lhs.type(), rhs.type()), true);
+    }
+
+    auto const lhs_size = check_exact_equality ? lhs.size() : lhs_row_indices.size();
+    auto const rhs_size = check_exact_equality ? rhs.size() : rhs_row_indices.size();
+    PROP_EXPECT_EQ(lhs_size, rhs_size);
+
+    if (lhs_size > 0 && check_exact_equality) { PROP_EXPECT_EQ(lhs.nullable(), rhs.nullable()); }
+
+    // DISCUSSION: does this make sense, semantically?
+    auto const lhs_null_count =
+      check_exact_equality ? lhs.null_count() : count_nulls(lhs, lhs_row_indices);
+    auto const rhs_null_count =
+      check_exact_equality ? rhs.null_count() : count_nulls(rhs, rhs_row_indices);
+    PROP_EXPECT_EQ(lhs_null_count, rhs_null_count);
+
+    // equivalent, but not exactly equal columns can have a different number of children if their
+    // sizes are both 0. Specifically, empty string columns may or may not have children.
+    if (check_exact_equality || (lhs.size() > 0 && lhs.null_count() < lhs.size())) {
+      PROP_EXPECT_EQ(lhs.num_children(), rhs.num_children());
+    }
+
+    return result;
+  }
+
+  template <typename T,
+            std::enable_if_t<!std::is_same_v<T, cudf::list_view> &&
+                             !std::is_same_v<T, cudf::struct_view>>* = nullptr>
+  bool operator()(cudf::column_view const& lhs,
+                  cudf::column_view const& rhs,
+                  cudf::column_view const& lhs_row_indices,
+                  cudf::column_view const& rhs_row_indices,
+                  debug_output_level verbosity)
+  {
+    return compare_common(lhs, rhs, lhs_row_indices, rhs_row_indices, verbosity);
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::list_view>>* = nullptr>
+  bool operator()(cudf::column_view const& lhs,
+                  cudf::column_view const& rhs,
+                  cudf::column_view const& lhs_row_indices,
+                  cudf::column_view const& rhs_row_indices,
+                  debug_output_level verbosity)
+  {
+    if (!compare_common(lhs, rhs, lhs_row_indices, rhs_row_indices, verbosity)) { return false; }
+
+    cudf::lists_column_view lhs_l(lhs);
+    cudf::lists_column_view rhs_l(rhs);
+
+    // recurse
+
+    // note: if a column is all nulls (and we are checking for exact equality) or otherwise empty,
+    // no indices are generated and no recursion happens
+    auto lhs_child_indices =
+      generate_child_row_indices(lhs_l, lhs_row_indices, check_exact_equality);
+    if (lhs_child_indices->size() > 0) {
+      auto lhs_child = lhs_l.get_sliced_child(cudf::test::get_default_stream());
+      auto rhs_child = rhs_l.get_sliced_child(cudf::test::get_default_stream());
+      auto rhs_child_indices =
+        generate_child_row_indices(rhs_l, rhs_row_indices, check_exact_equality);
+      return cudf::type_dispatcher(lhs_child.type(),
+                                   column_property_comparator<check_exact_equality>{},
+                                   lhs_child,
+                                   rhs_child,
+                                   *lhs_child_indices,
+                                   *rhs_child_indices,
+                                   verbosity);
+    }
+    return true;
+  }
+
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::struct_view>>* = nullptr>
+  bool operator()(cudf::column_view const& lhs,
+                  cudf::column_view const& rhs,
+                  cudf::column_view const& lhs_row_indices,
+                  cudf::column_view const& rhs_row_indices,
+                  debug_output_level verbosity)
+  {
+    if (!compare_common(lhs, rhs, lhs_row_indices, rhs_row_indices, verbosity)) { return false; }
+
+    structs_column_view l_scv(lhs);
+    structs_column_view r_scv(rhs);
+
+    for (size_type i = 0; i < lhs.num_children(); i++) {
+      column_view lhs_child = l_scv.get_sliced_child(i, cudf::test::get_default_stream());
+      column_view rhs_child = r_scv.get_sliced_child(i, cudf::test::get_default_stream());
+      if (!cudf::type_dispatcher(lhs_child.type(),
+                                 column_property_comparator<check_exact_equality>{},
+                                 lhs_child,
+                                 rhs_child,
+                                 lhs_row_indices,
+                                 rhs_row_indices,
+                                 verbosity)) {
+        return false;
+      }
+    }
+
+    return true;
+  }
+};
+
+template <typename DeviceComparator>
+class corresponding_rows_unequal {
+ public:
+  corresponding_rows_unequal(column_device_view lhs_row_indices_,
+                             column_device_view rhs_row_indices_,
+                             size_type /*fp_ulps*/,
+                             DeviceComparator comp_,
+                             column_device_view /*lhs*/,
+                             column_device_view /*rhs*/)
+    : lhs_row_indices(lhs_row_indices_), rhs_row_indices(rhs_row_indices_), comp(comp_)
+  {
+  }
+
+  __device__ bool operator()(size_type index)
+  {
+    using cudf::experimental::row::lhs_index_type;
+    using cudf::experimental::row::rhs_index_type;
+
+    return !comp(lhs_index_type{lhs_row_indices.element<size_type>(index)},
+                 rhs_index_type{rhs_row_indices.element<size_type>(index)});
+  }
+
+  column_device_view lhs_row_indices;
+  column_device_view rhs_row_indices;
+  DeviceComparator comp;
+};
+
+template <typename DeviceComparator>
+class corresponding_rows_not_equivalent {
+  column_device_view lhs_row_indices;
+  column_device_view rhs_row_indices;
+  size_type const fp_ulps;
+  DeviceComparator comp;
+  column_device_view lhs;
+  column_device_view rhs;
+
+ public:
+  corresponding_rows_not_equivalent(column_device_view lhs_row_indices_,
+                                    column_device_view rhs_row_indices_,
+                                    size_type fp_ulps_,
+                                    DeviceComparator comp_,
+                                    column_device_view lhs_,
+                                    column_device_view rhs_)
+    : lhs_row_indices(lhs_row_indices_),
+      rhs_row_indices(rhs_row_indices_),
+      fp_ulps(fp_ulps_),
+      comp(comp_),
+      lhs(lhs_),
+      rhs(rhs_)
+  {
+  }
+
+  struct typed_element_not_equivalent {
+    template <typename T>
+    __device__ std::enable_if_t<std::is_floating_point_v<T>, bool> operator()(
+      column_device_view const& lhs,
+      column_device_view const& rhs,
+      size_type lhs_index,
+      size_type rhs_index,
+      size_type fp_ulps)
+    {
+      if (lhs.is_valid(lhs_index) and rhs.is_valid(rhs_index)) {
+        T const x = lhs.element<T>(lhs_index);
+        T const y = rhs.element<T>(rhs_index);
+
+        // Must handle inf and nan separately
+        if (std::isinf(x) || std::isinf(y)) {
+          return x != y;  // comparison of (inf==inf) returns true
+        } else if (std::isnan(x) || std::isnan(y)) {
+          return std::isnan(x) != std::isnan(y);  // comparison of (nan==nan) returns false
+        } else {
+          T const abs_x_minus_y = std::abs(x - y);
+          return abs_x_minus_y >= std::numeric_limits<T>::min() &&
+                 abs_x_minus_y > std::numeric_limits<T>::epsilon() * std::abs(x + y) * fp_ulps;
+        }
+      } else {
+        // if either is null, then the inequality was checked already
+        return true;
+      }
+    }
+
+    template <typename T, typename... Args>
+    __device__ std::enable_if_t<not std::is_floating_point_v<T>, bool> operator()(Args...)
+    {
+      // Non-floating point inequality is checked already
+      return true;
+    }
+  };
+
+  __device__ bool operator()(size_type index)
+  {
+    using cudf::experimental::row::lhs_index_type;
+    using cudf::experimental::row::rhs_index_type;
+
+    auto const lhs_index = lhs_row_indices.element<size_type>(index);
+    auto const rhs_index = rhs_row_indices.element<size_type>(index);
+
+    if (not comp(lhs_index_type{lhs_index}, rhs_index_type{rhs_index})) {
+      return type_dispatcher(
+        lhs.type(), typed_element_not_equivalent{}, lhs, rhs, lhs_index, rhs_index, fp_ulps);
+    }
+    return false;
+  }
+};
+
+// Stringify the inconsistent values resulted from the comparison of two columns element-wise
+std::string stringify_column_differences(cudf::device_span<int const> differences,
+                                         column_view const& lhs,
+                                         column_view const& rhs,
+                                         column_view const& lhs_row_indices,
+                                         column_view const& rhs_row_indices,
+                                         debug_output_level verbosity,
+                                         int depth)
+{
+  CUDF_EXPECTS(not differences.empty(), "Shouldn't enter this function if `differences` is empty");
+  std::string const depth_str = depth > 0 ? "depth " + std::to_string(depth) + '\n' : "";
+  // move the differences to the host.
+  auto h_differences =
+    cudf::detail::make_host_vector_sync(differences, cudf::test::get_default_stream());
+  if (verbosity == debug_output_level::ALL_ERRORS) {
+    std::ostringstream buffer;
+    buffer << depth_str << "differences:" << std::endl;
+
+    auto source_table = cudf::table_view({lhs, rhs});
+    auto diff_column =
+      fixed_width_column_wrapper<int32_t>(h_differences.begin(), h_differences.end());
+    auto diff_table = cudf::gather(source_table, diff_column);
+    //  Need to pull back the differences
+    auto const h_left_strings = to_strings(diff_table->get_column(0));
+
+    auto const h_right_strings = to_strings(diff_table->get_column(1));
+    for (size_t i = 0; i < h_differences.size(); ++i)
+      buffer << depth_str << "lhs[" << h_differences[i] << "] = " << h_left_strings[i] << ", rhs["
+             << h_differences[i] << "] = " << h_right_strings[i] << std::endl;
+    return buffer.str();
+  } else {
+    auto const index = h_differences[0];  // only stringify first difference
+
+    auto const lhs_index =
+      cudf::detail::get_value<size_type>(lhs_row_indices, index, cudf::test::get_default_stream());
+    auto const rhs_index =
+      cudf::detail::get_value<size_type>(rhs_row_indices, index, cudf::test::get_default_stream());
+    auto diff_lhs = cudf::slice(lhs, {lhs_index, lhs_index + 1}).front();
+    auto diff_rhs = cudf::slice(rhs, {rhs_index, rhs_index + 1}).front();
+    return depth_str + "first difference: " + "lhs[" + std::to_string(index) +
+           "] = " + to_string(diff_lhs, "") + ", rhs[" + std::to_string(index) +
+           "] = " + to_string(diff_rhs, "");
+  }
+}
+
+// non-nested column types
+template <typename T, bool check_exact_equality>
+struct column_comparator_impl {
+  bool operator()(column_view const& lhs,
+                  column_view const& rhs,
+                  column_view const& lhs_row_indices,
+                  column_view const& rhs_row_indices,
+                  debug_output_level verbosity,
+                  size_type fp_ulps,
+                  int depth)
+  {
+    auto d_lhs_row_indices =
+      cudf::column_device_view::create(lhs_row_indices, cudf::test::get_default_stream());
+    auto d_rhs_row_indices =
+      cudf::column_device_view::create(rhs_row_indices, cudf::test::get_default_stream());
+
+    auto d_lhs = cudf::column_device_view::create(lhs, cudf::test::get_default_stream());
+    auto d_rhs = cudf::column_device_view::create(rhs, cudf::test::get_default_stream());
+
+    auto lhs_tview = table_view{{lhs}};
+    auto rhs_tview = table_view{{rhs}};
+
+    auto const comparator = cudf::experimental::row::equality::two_table_comparator{
+      lhs_tview, rhs_tview, cudf::test::get_default_stream()};
+    auto const has_nulls = cudf::has_nulls(lhs_tview) or cudf::has_nulls(rhs_tview);
+
+    auto const device_comparator = comparator.equal_to<false>(cudf::nullate::DYNAMIC{has_nulls});
+
+    using ComparatorType =
+      std::conditional_t<check_exact_equality,
+                         corresponding_rows_unequal<decltype(device_comparator)>,
+                         corresponding_rows_not_equivalent<decltype(device_comparator)>>;
+
+    auto differences = rmm::device_uvector<int>(
+      lhs_row_indices.size(),
+      cudf::test::get_default_stream());  // worst case: everything different
+    auto input_iter = thrust::make_counting_iterator(0);
+
+    auto diff_map =
+      rmm::device_uvector<bool>(lhs_row_indices.size(), cudf::test::get_default_stream());
+
+    thrust::transform(
+      rmm::exec_policy(cudf::test::get_default_stream()),
+      input_iter,
+      input_iter + lhs_row_indices.size(),
+      diff_map.begin(),
+      ComparatorType(
+        *d_lhs_row_indices, *d_rhs_row_indices, fp_ulps, device_comparator, *d_lhs, *d_rhs));
+
+    auto diff_iter = thrust::copy_if(rmm::exec_policy(cudf::test::get_default_stream()),
+                                     input_iter,
+                                     input_iter + lhs_row_indices.size(),
+                                     diff_map.begin(),
+                                     differences.begin(),
+                                     thrust::identity<bool>{});
+
+    differences.resize(thrust::distance(differences.begin(), diff_iter),
+                       cudf::test::get_default_stream());  // shrink back down
+
+    if (not differences.is_empty()) {
+      if (verbosity != debug_output_level::QUIET) {
+        // GTEST_FAIL() does a return that conflicts with our return type. so hide it in a lambda.
+        [&]() {
+          GTEST_FAIL() << stringify_column_differences(
+            differences, lhs, rhs, lhs_row_indices, rhs_row_indices, verbosity, depth);
+        }();
+      }
+      return false;
+    }
+    return true;
+  }
+};
+
+// forward declaration for nested-type recursion.
+template <bool check_exact_equality>
+struct column_comparator;
+
+// specialization for list columns
+template <bool check_exact_equality>
+struct column_comparator_impl<list_view, check_exact_equality> {
+  bool operator()(column_view const& lhs,
+                  column_view const& rhs,
+                  column_view const& lhs_row_indices,
+                  column_view const& rhs_row_indices,
+                  debug_output_level verbosity,
+                  size_type fp_ulps,
+                  int depth)
+  {
+    lists_column_view lhs_l(lhs);
+    lists_column_view rhs_l(rhs);
+
+    CUDF_EXPECTS(lhs_row_indices.size() == rhs_row_indices.size(), "List column size mismatch");
+    if (lhs_row_indices.is_empty()) { return true; }
+
+    // worst case - everything is different
+    rmm::device_uvector<int> differences(lhs_row_indices.size(), cudf::test::get_default_stream());
+
+    // compare offsets, taking slicing into account
+
+    // left side
+    size_type lhs_shift = cudf::detail::get_value<size_type>(
+      lhs_l.offsets(), lhs_l.offset(), cudf::test::get_default_stream());
+    auto lhs_offsets = thrust::make_transform_iterator(
+      lhs_l.offsets().begin<size_type>() + lhs_l.offset(),
+      [lhs_shift] __device__(size_type offset) { return offset - lhs_shift; });
+    auto lhs_valids = thrust::make_transform_iterator(
+      thrust::make_counting_iterator(0),
+      [mask = lhs_l.null_mask(), offset = lhs_l.offset()] __device__(size_type index) {
+        return mask == nullptr ? true : cudf::bit_is_set(mask, index + offset);
+      });
+
+    // right side
+    size_type rhs_shift = cudf::detail::get_value<size_type>(
+      rhs_l.offsets(), rhs_l.offset(), cudf::test::get_default_stream());
+    auto rhs_offsets = thrust::make_transform_iterator(
+      rhs_l.offsets().begin<size_type>() + rhs_l.offset(),
+      [rhs_shift] __device__(size_type offset) { return offset - rhs_shift; });
+    auto rhs_valids = thrust::make_transform_iterator(
+      thrust::make_counting_iterator(0),
+      [mask = rhs_l.null_mask(), offset = rhs_l.offset()] __device__(size_type index) {
+        return mask == nullptr ? true : cudf::bit_is_set(mask, index + offset);
+      });
+
+    // when checking for equivalency, we can't compare offset values directly, we can only
+    // compare lengths of the rows, and only if valid.  as a concrete example, you could have two
+    // equivalent columns with the following data:
+    //
+    // column A
+    //    offsets =  [0, 3, 5, 7]
+    //    validity = [0, 1, 1, 1]
+    //
+    // column B
+    //   offsets =   [0, 0, 2, 4]
+    //   validity =  [0, 1, 1, 1]
+    //
+    // Row 0 in column A happens to have a positive length, even though the row is null, but column
+    // B does not.  So the offsets for the remaining valid rows are fundamentally different even
+    // though the row lengths are the same.
+    //
+    auto input_iter = thrust::make_counting_iterator(0);
+    auto diff_iter  = thrust::copy_if(
+      rmm::exec_policy(cudf::test::get_default_stream()),
+      input_iter,
+      input_iter + lhs_row_indices.size(),
+      differences.begin(),
+      [lhs_offsets,
+       rhs_offsets,
+       lhs_valids,
+       rhs_valids,
+       lhs_indices = lhs_row_indices.begin<size_type>(),
+       rhs_indices = rhs_row_indices.begin<size_type>()] __device__(size_type index) {
+        auto const lhs_index = lhs_indices[index];
+        auto const rhs_index = rhs_indices[index];
+
+        // check for validity match
+        if (lhs_valids[lhs_index] != rhs_valids[rhs_index]) { return true; }
+
+        // if the row is valid, check that the length of the list is the same. do this
+        // for both the equivalency and exact equality checks.
+        if (lhs_valids[lhs_index] && ((lhs_offsets[lhs_index + 1] - lhs_offsets[lhs_index]) !=
+                                      (rhs_offsets[rhs_index + 1] - rhs_offsets[rhs_index]))) {
+          return true;
+        }
+
+        // if validity matches -and- is false, we can ignore the actual offset values. this
+        // is technically not checking "equal()", but it's how the non-list code path handles it
+        if (!lhs_valids[lhs_index]) { return false; }
+
+        // if checking exact equality, compare the actual offset values
+        if (check_exact_equality && lhs_offsets[lhs_index] != rhs_offsets[rhs_index]) {
+          return true;
+        }
+
+        return false;
+      });
+
+    differences.resize(thrust::distance(differences.begin(), diff_iter),
+                       cudf::test::get_default_stream());  // shrink back down
+
+    if (not differences.is_empty()) {
+      if (verbosity != debug_output_level::QUIET) {
+        // GTEST_FAIL() does a return that conflicts with our return type. so hide it in a lambda.
+        [&]() {
+          GTEST_FAIL() << stringify_column_differences(
+            differences, lhs, rhs, lhs_row_indices, rhs_row_indices, verbosity, depth);
+        }();
+      }
+      return false;
+    }
+
+    // recurse
+    // note: if a column is all nulls (and we are only checking for equivalence) or otherwise empty,
+    // no indices are generated and no recursion happens
+    auto lhs_child_indices =
+      generate_child_row_indices(lhs_l, lhs_row_indices, check_exact_equality);
+    if (lhs_child_indices->size() > 0) {
+      auto lhs_child = lhs_l.get_sliced_child(cudf::test::get_default_stream());
+      auto rhs_child = rhs_l.get_sliced_child(cudf::test::get_default_stream());
+      auto rhs_child_indices =
+        generate_child_row_indices(rhs_l, rhs_row_indices, check_exact_equality);
+      return cudf::type_dispatcher(lhs_child.type(),
+                                   column_comparator<check_exact_equality>{},
+                                   lhs_child,
+                                   rhs_child,
+                                   *lhs_child_indices,
+                                   *rhs_child_indices,
+                                   verbosity,
+                                   fp_ulps,
+                                   depth + 1);
+    }
+
+    return true;
+  }
+};
+
+template <bool check_exact_equality>
+struct column_comparator_impl<struct_view, check_exact_equality> {
+  bool operator()(column_view const& lhs,
+                  column_view const& rhs,
+                  column_view const& lhs_row_indices,
+                  column_view const& rhs_row_indices,
+                  debug_output_level verbosity,
+                  size_type fp_ulps,
+                  int depth)
+  {
+    structs_column_view l_scv(lhs);
+    structs_column_view r_scv(rhs);
+
+    for (size_type i = 0; i < lhs.num_children(); i++) {
+      column_view lhs_child = l_scv.get_sliced_child(i, cudf::test::get_default_stream());
+      column_view rhs_child = r_scv.get_sliced_child(i, cudf::test::get_default_stream());
+      if (!cudf::type_dispatcher(lhs_child.type(),
+                                 column_comparator<check_exact_equality>{},
+                                 lhs_child,
+                                 rhs_child,
+                                 lhs_row_indices,
+                                 rhs_row_indices,
+                                 verbosity,
+                                 fp_ulps,
+                                 depth + 1)) {
+        return false;
+      }
+    }
+    return true;
+  }
+};
+
+template <bool check_exact_equality>
+struct column_comparator {
+  template <typename T>
+  bool operator()(column_view const& lhs,
+                  column_view const& rhs,
+                  column_view const& lhs_row_indices,
+                  column_view const& rhs_row_indices,
+                  debug_output_level verbosity,
+                  size_type fp_ulps,
+                  int depth = 0)
+  {
+    // compare properties
+    if (!cudf::type_dispatcher(lhs.type(),
+                               column_property_comparator<check_exact_equality>{},
+                               lhs,
+                               rhs,
+                               lhs_row_indices,
+                               rhs_row_indices,
+                               verbosity)) {
+      return false;
+    }
+
+    // compare values
+    column_comparator_impl<T, check_exact_equality> comparator{};
+    return comparator(lhs, rhs, lhs_row_indices, rhs_row_indices, verbosity, fp_ulps, depth);
+  }
+};
+
+}  // namespace
+
+namespace detail {
+/**
+ * @copydoc cudf::test::expect_column_properties_equal
+ */
+bool expect_column_properties_equal(column_view const& lhs,
+                                    column_view const& rhs,
+                                    debug_output_level verbosity)
+{
+  auto lhs_indices = generate_all_row_indices(lhs.size());
+  auto rhs_indices = generate_all_row_indices(rhs.size());
+  return cudf::type_dispatcher(lhs.type(),
+                               column_property_comparator<true>{},
+                               lhs,
+                               rhs,
+                               *lhs_indices,
+                               *rhs_indices,
+                               verbosity);
+}
+
+/**
+ * @copydoc cudf::test::expect_column_properties_equivalent
+ */
+bool expect_column_properties_equivalent(column_view const& lhs,
+                                         column_view const& rhs,
+                                         debug_output_level verbosity)
+{
+  auto lhs_indices = generate_all_row_indices(lhs.size());
+  auto rhs_indices = generate_all_row_indices(rhs.size());
+  return cudf::type_dispatcher(lhs.type(),
+                               column_property_comparator<false>{},
+                               lhs,
+                               rhs,
+                               *lhs_indices,
+                               *rhs_indices,
+                               verbosity);
+}
+
+/**
+ * @copydoc cudf::test::expect_columns_equal
+ */
+bool expect_columns_equal(cudf::column_view const& lhs,
+                          cudf::column_view const& rhs,
+                          debug_output_level verbosity)
+{
+  auto lhs_indices = generate_all_row_indices(lhs.size());
+  auto rhs_indices = generate_all_row_indices(rhs.size());
+  return cudf::type_dispatcher(lhs.type(),
+                               column_comparator<true>{},
+                               lhs,
+                               rhs,
+                               *lhs_indices,
+                               *rhs_indices,
+                               verbosity,
+                               cudf::test::default_ulp);
+}
+
+/**
+ * @copydoc cudf::test::expect_columns_equivalent
+ */
+bool expect_columns_equivalent(cudf::column_view const& lhs,
+                               cudf::column_view const& rhs,
+                               debug_output_level verbosity,
+                               size_type fp_ulps)
+{
+  auto lhs_indices = generate_all_row_indices(lhs.size());
+  auto rhs_indices = generate_all_row_indices(rhs.size());
+  return cudf::type_dispatcher(lhs.type(),
+                               column_comparator<false>{},
+                               lhs,
+                               rhs,
+                               *lhs_indices,
+                               *rhs_indices,
+                               verbosity,
+                               fp_ulps);
+}
+
+/**
+ * @copydoc cudf::test::expect_equal_buffers
+ */
+void expect_equal_buffers(void const* lhs, void const* rhs, std::size_t size_bytes)
+{
+  if (size_bytes > 0) {
+    EXPECT_NE(nullptr, lhs);
+    EXPECT_NE(nullptr, rhs);
+  }
+  auto typed_lhs = static_cast<char const*>(lhs);
+  auto typed_rhs = static_cast<char const*>(rhs);
+  EXPECT_TRUE(thrust::equal(rmm::exec_policy(cudf::test::get_default_stream()),
+                            typed_lhs,
+                            typed_lhs + size_bytes,
+                            typed_rhs));
+}
+}  // namespace detail
+
+/**
+ * @copydoc cudf::test::expect_column_empty
+ */
+void expect_column_empty(cudf::column_view const& col)
+{
+  EXPECT_EQ(0, col.size());
+  EXPECT_EQ(0, col.null_count());
+}
+
+/**
+ * @copydoc cudf::test::bitmask_to_host
+ */
+std::vector<bitmask_type> bitmask_to_host(cudf::column_view const& c)
+{
+  if (c.nullable()) {
+    auto num_bitmasks = num_bitmask_words(c.size());
+    std::vector<bitmask_type> host_bitmask(num_bitmasks);
+    if (c.offset() == 0) {
+      CUDF_CUDA_TRY(cudaMemcpy(host_bitmask.data(),
+                               c.null_mask(),
+                               num_bitmasks * sizeof(bitmask_type),
+                               cudaMemcpyDefault));
+    } else {
+      auto mask = copy_bitmask(c.null_mask(), c.offset(), c.offset() + c.size());
+      CUDF_CUDA_TRY(cudaMemcpy(
+        host_bitmask.data(), mask.data(), num_bitmasks * sizeof(bitmask_type), cudaMemcpyDefault));
+    }
+
+    return host_bitmask;
+  } else {
+    return std::vector<bitmask_type>{};
+  }
+}
+
+namespace {
+
+template <typename T, std::enable_if_t<std::is_integral_v<T>>* = nullptr>
+static auto numeric_to_string_precise(T value)
+{
+  return std::to_string(value);
+}
+
+template <typename T, std::enable_if_t<std::is_floating_point_v<T>>* = nullptr>
+static auto numeric_to_string_precise(T value)
+{
+  std::ostringstream o;
+  o << std::setprecision(std::numeric_limits<T>::max_digits10) << value;
+  return o.str();
+}
+
+static auto duration_suffix(cudf::duration_D) { return " days"; }
+
+static auto duration_suffix(cudf::duration_s) { return " seconds"; }
+
+static auto duration_suffix(cudf::duration_ms) { return " milliseconds"; }
+
+static auto duration_suffix(cudf::duration_us) { return " microseconds"; }
+
+static auto duration_suffix(cudf::duration_ns) { return " nanoseconds"; }
+
+std::string get_nested_type_str(cudf::column_view const& view)
+{
+  if (view.type().id() == cudf::type_id::LIST) {
+    lists_column_view lcv(view);
+    return cudf::type_to_name(view.type()) + "<" + (get_nested_type_str(lcv.child())) + ">";
+  }
+
+  if (view.type().id() == cudf::type_id::STRUCT) {
+    std::ostringstream out;
+
+    out << cudf::type_to_name(view.type()) + "<";
+    std::transform(view.child_begin(),
+                   view.child_end(),
+                   std::ostream_iterator<std::string>(out, ","),
+                   [&out](auto const col) { return get_nested_type_str(col); });
+    out << ">";
+    return out.str();
+  }
+
+  return cudf::type_to_name(view.type());
+}
+
+template <typename NestedColumnView>
+std::string nested_offsets_to_string(NestedColumnView const& c, std::string const& delimiter = ", ")
+{
+  column_view offsets = (c.parent()).child(NestedColumnView::offsets_column_index);
+  CUDF_EXPECTS(offsets.type().id() == type_id::INT32,
+               "Column does not appear to be an offsets column");
+  CUDF_EXPECTS(offsets.offset() == 0, "Offsets column has an internal offset!");
+  size_type output_size = c.size() + 1;
+
+  // the first offset value to normalize everything against
+  size_type first =
+    cudf::detail::get_value<size_type>(offsets, c.offset(), cudf::test::get_default_stream());
+  rmm::device_uvector<size_type> shifted_offsets(output_size, cudf::test::get_default_stream());
+
+  // normalize the offset values for the column offset
+  size_type const* d_offsets = offsets.head<size_type>() + c.offset();
+  thrust::transform(
+    rmm::exec_policy(cudf::test::get_default_stream()),
+    d_offsets,
+    d_offsets + output_size,
+    shifted_offsets.begin(),
+    [first] __device__(int32_t offset) { return static_cast<size_type>(offset - first); });
+
+  auto const h_shifted_offsets =
+    cudf::detail::make_host_vector_sync(shifted_offsets, cudf::test::get_default_stream());
+  std::ostringstream buffer;
+  for (size_t idx = 0; idx < h_shifted_offsets.size(); idx++) {
+    buffer << h_shifted_offsets[idx];
+    if (idx < h_shifted_offsets.size() - 1) { buffer << delimiter; }
+  }
+  return buffer.str();
+}
+
+struct column_view_printer {
+  template <typename Element, std::enable_if_t<is_numeric<Element>()>* = nullptr>
+  void operator()(cudf::column_view const& col, std::vector<std::string>& out, std::string const&)
+  {
+    auto h_data = cudf::test::to_host<Element>(col);
+
+    out.resize(col.size());
+
+    if (col.nullable()) {
+      std::transform(thrust::make_counting_iterator(size_type{0}),
+                     thrust::make_counting_iterator(col.size()),
+                     out.begin(),
+                     [&h_data](auto idx) {
+                       return bit_is_set(h_data.second.data(), idx)
+                                ? numeric_to_string_precise(h_data.first[idx])
+                                : std::string("NULL");
+                     });
+
+    } else {
+      std::transform(h_data.first.begin(), h_data.first.end(), out.begin(), [](Element el) {
+        return numeric_to_string_precise(el);
+      });
+    }
+  }
+
+  template <typename Element, std::enable_if_t<is_timestamp<Element>()>* = nullptr>
+  void operator()(cudf::column_view const& col,
+                  std::vector<std::string>& out,
+                  std::string const& indent)
+  {
+    //  For timestamps, convert timestamp column to column of strings, then
+    //  call string version
+    std::string format = [&]() {
+      if constexpr (std::is_same_v<cudf::timestamp_s, Element>) {
+        return std::string{"%Y-%m-%dT%H:%M:%SZ"};
+      } else if constexpr (std::is_same_v<cudf::timestamp_ms, Element>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%3fZ"};
+      } else if constexpr (std::is_same_v<cudf::timestamp_us, Element>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%6fZ"};
+      } else if constexpr (std::is_same_v<cudf::timestamp_ns, Element>) {
+        return std::string{"%Y-%m-%dT%H:%M:%S.%9fZ"};
+      }
+      return std::string{"%Y-%m-%d"};
+    }();
+
+    auto col_as_strings = cudf::strings::from_timestamps(col, format);
+    if (col_as_strings->size() == 0) { return; }
+
+    this->template operator()<cudf::string_view>(*col_as_strings, out, indent);
+  }
+
+  template <typename Element, std::enable_if_t<cudf::is_fixed_point<Element>()>* = nullptr>
+  void operator()(cudf::column_view const& col, std::vector<std::string>& out, std::string const&)
+  {
+    auto const h_data = cudf::test::to_host<Element>(col);
+    if (col.nullable()) {
+      std::transform(thrust::make_counting_iterator(size_type{0}),
+                     thrust::make_counting_iterator(col.size()),
+                     std::back_inserter(out),
+                     [&h_data](auto idx) {
+                       return h_data.second.empty() || bit_is_set(h_data.second.data(), idx)
+                                ? static_cast<std::string>(h_data.first[idx])
+                                : std::string("NULL");
+                     });
+    } else {
+      std::transform(std::cbegin(h_data.first),
+                     std::cend(h_data.first),
+                     std::back_inserter(out),
+                     [col](auto const& fp) { return static_cast<std::string>(fp); });
+    }
+  }
+
+  template <typename Element,
+            std::enable_if_t<std::is_same_v<Element, cudf::string_view>>* = nullptr>
+  void operator()(cudf::column_view const& col, std::vector<std::string>& out, std::string const&)
+  {
+    //
+    //  Implementation for strings, call special to_host variant
+    //
+    if (col.is_empty()) return;
+    auto h_data = cudf::test::to_host<std::string>(col);
+
+    // explicitly replace some special whitespace characters with their literal equivalents
+    auto cleaned = [](std::string_view in) {
+      std::string out(in);
+      auto replace_char = [](std::string& out, char c, std::string_view repl) {
+        for (std::string::size_type pos{}; out.npos != (pos = out.find(c, pos)); pos++) {
+          out.replace(pos, 1, repl);
+        }
+      };
+      replace_char(out, '\a', "\\a");
+      replace_char(out, '\b', "\\b");
+      replace_char(out, '\f', "\\f");
+      replace_char(out, '\r', "\\r");
+      replace_char(out, '\t', "\\t");
+      replace_char(out, '\n', "\\n");
+      replace_char(out, '\v', "\\v");
+      return out;
+    };
+
+    out.resize(col.size());
+    std::transform(thrust::make_counting_iterator(size_type{0}),
+                   thrust::make_counting_iterator(col.size()),
+                   out.begin(),
+                   [&](auto idx) {
+                     return h_data.second.empty() || bit_is_set(h_data.second.data(), idx)
+                              ? cleaned(h_data.first[idx])
+                              : std::string("NULL");
+                   });
+  }
+
+  template <typename Element,
+            std::enable_if_t<std::is_same_v<Element, cudf::dictionary32>>* = nullptr>
+  void operator()(cudf::column_view const& col, std::vector<std::string>& out, std::string const&)
+  {
+    cudf::dictionary_column_view dictionary(col);
+    if (col.is_empty()) return;
+    std::vector<std::string> keys    = to_strings(dictionary.keys());
+    std::vector<std::string> indices = to_strings({dictionary.indices().type(),
+                                                   dictionary.size(),
+                                                   dictionary.indices().head(),
+                                                   dictionary.null_mask(),
+                                                   dictionary.null_count(),
+                                                   dictionary.offset()});
+    out.insert(out.end(), keys.begin(), keys.end());
+    if (!indices.empty()) {
+      std::string first = "\x08 : " + indices.front();  // use : as delimiter
+      out.push_back(first);                             // between keys and indices
+      out.insert(out.end(), indices.begin() + 1, indices.end());
+    }
+  }
+
+  // Print the tick counts with the units
+  template <typename Element, std::enable_if_t<is_duration<Element>()>* = nullptr>
+  void operator()(cudf::column_view const& col, std::vector<std::string>& out, std::string const&)
+  {
+    auto h_data = cudf::test::to_host<Element>(col);
+
+    out.resize(col.size());
+
+    if (col.nullable()) {
+      std::transform(thrust::make_counting_iterator(size_type{0}),
+                     thrust::make_counting_iterator(col.size()),
+                     out.begin(),
+                     [&h_data](auto idx) {
+                       return bit_is_set(h_data.second.data(), idx)
+                                ? numeric_to_string_precise(h_data.first[idx].count()) +
+                                    duration_suffix(h_data.first[idx])
+                                : std::string("NULL");
+                     });
+
+    } else {
+      std::transform(h_data.first.begin(), h_data.first.end(), out.begin(), [](Element el) {
+        return numeric_to_string_precise(el.count()) + duration_suffix(el);
+      });
+    }
+  }
+
+  template <typename Element, std::enable_if_t<std::is_same_v<Element, cudf::list_view>>* = nullptr>
+  void operator()(cudf::column_view const& col,
+                  std::vector<std::string>& out,
+                  std::string const& indent)
+  {
+    lists_column_view lcv(col);
+
+    // propagate slicing to the child if necessary
+    column_view child    = lcv.get_sliced_child(cudf::test::get_default_stream());
+    bool const is_sliced = lcv.offset() > 0 || child.offset() > 0;
+
+    std::string tmp =
+      get_nested_type_str(col) + (is_sliced ? "(sliced)" : "") + ":\n" + indent +
+      "Length : " + std::to_string(lcv.size()) + "\n" + indent +
+      "Offsets : " + (lcv.size() > 0 ? nested_offsets_to_string(lcv) : "") + "\n" +
+      (lcv.parent().nullable()
+         ? indent + "Null count: " + std::to_string(lcv.null_count()) + "\n" +
+             detail::to_string(bitmask_to_host(col), col.size(), indent) + "\n"
+         : "") +
+      // non-nested types don't typically display their null masks, so do it here for convenience.
+      (!is_nested(child.type()) && child.nullable()
+         ? "   " + detail::to_string(bitmask_to_host(child), child.size(), indent) + "\n"
+         : "") +
+      (detail::to_string(child, ", ", indent + "   ")) + "\n";
+
+    out.push_back(tmp);
+  }
+
+  template <typename Element,
+            std::enable_if_t<std::is_same_v<Element, cudf::struct_view>>* = nullptr>
+  void operator()(cudf::column_view const& col,
+                  std::vector<std::string>& out,
+                  std::string const& indent)
+  {
+    structs_column_view view{col};
+
+    std::ostringstream out_stream;
+
+    out_stream << get_nested_type_str(col) << ":\n"
+               << indent << "Length : " << view.size() << ":\n";
+    if (view.nullable()) {
+      out_stream << indent << "Null count: " << view.null_count() << "\n"
+                 << detail::to_string(bitmask_to_host(col), col.size(), indent) << "\n";
+    }
+
+    auto iter = thrust::make_counting_iterator(0);
+    std::transform(
+      iter,
+      iter + view.num_children(),
+      std::ostream_iterator<std::string>(out_stream, "\n"),
+      [&](size_type index) {
+        auto child = view.get_sliced_child(index, cudf::test::get_default_stream());
+
+        // non-nested types don't typically display their null masks, so do it here for convenience.
+        return (!is_nested(child.type()) && child.nullable()
+                  ? "   " + detail::to_string(bitmask_to_host(child), child.size(), indent) + "\n"
+                  : "") +
+               detail::to_string(child, ", ", indent + "   ");
+      });
+
+    out.push_back(out_stream.str());
+  }
+};
+
+}  // namespace
+
+namespace detail {
+
+/**
+ * @copydoc cudf::test::detail::to_strings
+ */
+std::vector<std::string> to_strings(cudf::column_view const& col, std::string const& indent)
+{
+  std::vector<std::string> reply;
+  cudf::type_dispatcher(col.type(), column_view_printer{}, col, reply, indent);
+  return reply;
+}
+
+/**
+ * @copydoc cudf::test::detail::to_string(cudf::column_view, std::string, std::string)
+ *
+ * @param indent Indentation for all output
+ */
+std::string to_string(cudf::column_view const& col,
+                      std::string const& delimiter,
+                      std::string const& indent)
+{
+  std::ostringstream buffer;
+  std::vector<std::string> h_data = to_strings(col, indent);
+
+  buffer << indent;
+  std::copy(h_data.begin(),
+            h_data.end() - (!h_data.empty()),
+            std::ostream_iterator<std::string>(buffer, delimiter.c_str()));
+  if (!h_data.empty()) buffer << h_data.back();
+
+  return buffer.str();
+}
+
+/**
+ * @copydoc cudf::test::detail::to_string(std::vector<bitmask_type>, size_type, std::string)
+ *
+ * @param indent Indentation for all output.  See comment in `to_strings` for
+ * a detailed description.
+ */
+std::string to_string(std::vector<bitmask_type> const& null_mask,
+                      size_type null_mask_size,
+                      std::string const& indent)
+{
+  std::ostringstream buffer;
+  buffer << indent;
+  for (int idx = null_mask_size - 1; idx >= 0; idx--) {
+    buffer << (cudf::bit_is_set(null_mask.data(), idx) ? "1" : "0");
+  }
+  return buffer.str();
+}
+
+}  // namespace detail
+
+/**
+ * @copydoc cudf::test::to_strings
+ */
+std::vector<std::string> to_strings(cudf::column_view const& col)
+{
+  return detail::to_strings(col);
+}
+
+/**
+ * @copydoc cudf::test::to_string(cudf::column_view, std::string)
+ */
+std::string to_string(cudf::column_view const& col, std::string const& delimiter)
+{
+  return detail::to_string(col, delimiter);
+}
+
+/**
+ * @copydoc cudf::test::to_string(std::vector<bitmask_type>, size_type)
+ */
+std::string to_string(std::vector<bitmask_type> const& null_mask, size_type null_mask_size)
+{
+  return detail::to_string(null_mask, null_mask_size);
+}
+
+/**
+ * @copydoc cudf::test::print
+ */
+void print(cudf::column_view const& col, std::ostream& os, std::string const& delimiter)
+{
+  os << to_string(col, delimiter) << std::endl;
+}
+
+/**
+ * @copydoc cudf::test::validate_host_masks
+ */
+bool validate_host_masks(std::vector<bitmask_type> const& expected_mask,
+                         std::vector<bitmask_type> const& got_mask,
+                         size_type number_of_elements)
+{
+  return std::all_of(thrust::make_counting_iterator(0),
+                     thrust::make_counting_iterator(number_of_elements),
+                     [&expected_mask, &got_mask](auto index) {
+                       return cudf::bit_is_set(expected_mask.data(), index) ==
+                              cudf::bit_is_set(got_mask.data(), index);
+                     });
+}
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/tests/utilities/default_stream.cpp b/cpp/tests/utilities/default_stream.cpp
new file mode 100644
index 0000000..52752f7
--- /dev/null
+++ b/cpp/tests/utilities/default_stream.cpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/default_stream.hpp>
+
+namespace cudf {
+namespace test {
+
+rmm::cuda_stream_view const get_default_stream() { return cudf::get_default_stream(); }
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/tests/utilities/identify_stream_usage.cpp b/cpp/tests/utilities/identify_stream_usage.cpp
new file mode 100644
index 0000000..ab2a85a
--- /dev/null
+++ b/cpp/tests/utilities/identify_stream_usage.cpp
@@ -0,0 +1,295 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/stacktrace.hpp>
+
+#include <rmm/cuda_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+#include <cuda_runtime.h>
+
+#include <cstdlib>
+#include <cstring>
+#include <cxxabi.h>
+#include <dlfcn.h>
+#include <execinfo.h>
+#include <iostream>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+
+// We control whether to override cudf::test::get_default_stream or
+// cudf::get_default_stream with a compile-time flag. Thesee are the two valid
+// options:
+// 1. STREAM_MODE_TESTING=OFF: In this mode, cudf::get_default_stream will
+//    return a custom stream and stream_is_invalid will return true if any CUDA
+//    API is called using any of CUDA's default stream constants
+//    (cudaStreamLegacy, cudaStreamDefault, or cudaStreamPerThread). This check
+//    is sufficient to ensure that cudf is using cudf::get_default_stream
+//    everywhere internally rather than implicitly using stream 0,
+//    cudaStreamDefault, cudaStreamLegacy, thrust execution policies, etc. It
+//    is not sufficient to guarantee a stream-ordered API because it will not
+//    identify places in the code that use cudf::get_default_stream instead of
+//    properly forwarding along a user-provided stream.
+// 2. STREAM_MODE_TESTING=ON: In this mode, cudf::test::get_default_stream
+//    returns a custom stream and stream_is_invalid returns true if any CUDA
+//    API is called using any stream other than cudf::test::get_default_stream.
+//    This is a necessary and sufficient condition to ensure that libcudf is
+//    properly passing streams through all of its (tested) APIs.
+
+namespace cudf {
+
+#ifdef STREAM_MODE_TESTING
+namespace test {
+#endif
+
+rmm::cuda_stream_view const get_default_stream()
+{
+  static rmm::cuda_stream stream{};
+  return {stream};
+}
+
+#ifdef STREAM_MODE_TESTING
+}  // namespace test
+#endif
+
+}  // namespace cudf
+
+bool stream_is_invalid(cudaStream_t stream)
+{
+#ifdef STREAM_MODE_TESTING
+  // In this mode the _only_ valid stream is the one returned by cudf::test::get_default_stream.
+  return (stream != cudf::test::get_default_stream().value());
+#else
+  // We explicitly list the possibilities rather than using
+  // `cudf::get_default_stream().value()` because there is no guarantee that
+  // `thrust::device` and the default value of
+  // `cudf::get_default_stream().value()` are actually the same. At present, the
+  // former is `cudaStreamLegacy` while the latter is 0.
+  return (stream == cudaStreamDefault) || (stream == cudaStreamLegacy) ||
+         (stream == cudaStreamPerThread);
+#endif
+}
+
+/**
+ * @brief Print a backtrace and raise an error if stream is a default stream.
+ */
+void check_stream_and_error(cudaStream_t stream)
+{
+  if (stream_is_invalid(stream)) {
+    // Exclude the current function from stacktrace.
+    std::cout << cudf::detail::get_stacktrace(cudf::detail::capture_last_stackframe::NO)
+              << std::endl;
+
+    char const* env_stream_error_mode{std::getenv("GTEST_CUDF_STREAM_ERROR_MODE")};
+    if (env_stream_error_mode && !strcmp(env_stream_error_mode, "print")) {
+      std::cout << "cudf_identify_stream_usage found unexpected stream!" << std::endl;
+    } else {
+      throw std::runtime_error("cudf_identify_stream_usage found unexpected stream!");
+    }
+  }
+}
+
+/**
+ * @brief Container for CUDA APIs that have been overloaded using DEFINE_OVERLOAD.
+ *
+ * This variable must be initialized before everything else.
+ *
+ * @see find_originals for a description of the priorities
+ */
+__attribute__((init_priority(1001))) std::unordered_map<std::string, void*> originals;
+
+/**
+ * @brief Macro for generating functions to override existing CUDA functions.
+ *
+ * Define a new function with the provided signature that checks the used
+ * stream and raises an exception if it is one of CUDA's default streams. If
+ * not, the new function forwards all arguments to the original function.
+ *
+ * Note that since this only defines the function, we do not need default
+ * parameter values since those will be provided by the original declarations
+ * in CUDA itself.
+ *
+ * @see find_originals for a description of the priorities
+ *
+ * @param function The function to overload.
+ * @param signature The function signature (must include names, not just types).
+ * @parameter arguments The function arguments (names only, no types).
+ */
+#define DEFINE_OVERLOAD(function, signature, arguments)     \
+  using function##_t = cudaError_t (*)(signature);          \
+                                                            \
+  cudaError_t function(signature)                           \
+  {                                                         \
+    check_stream_and_error(stream);                         \
+    return ((function##_t)originals[#function])(arguments); \
+  }                                                         \
+  __attribute__((constructor(1002))) void queue_##function() { originals[#function] = nullptr; }
+
+/**
+ * @brief Helper macro to define macro arguments that contain a comma.
+ */
+#define ARG(...) __VA_ARGS__
+
+// clang-format off
+/*
+   We need to overload all the functions from the runtime API (assuming that we
+   don't use the driver API) that accept streams. The main webpage for APIs is
+   https://docs.nvidia.com/cuda/cuda-runtime-api/modules.html#modules. Here are
+   the modules containing any APIs using streams as of 9/20/2022:
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html#group__CUDART__EVENT - Done
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EXTRES__INTEROP.html#group__CUDART__EXTRES__INTEROP
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EXECUTION.html#group__CUDART__EXECUTION - Done
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html#group__CUDART__MEMORY - Done
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY__POOLS.html#group__CUDART__MEMORY__POOLS - Done
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__OPENGL__DEPRECATED.html#group__CUDART__OPENGL__DEPRECATED
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EGL.html#group__CUDART__EGL
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__INTEROP.html#group__CUDART__INTEROP
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__GRAPH.html#group__CUDART__GRAPH
+   - https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__HIGHLEVEL.html#group__CUDART__HIGHLEVEL
+ */
+// clang-format on
+
+// Event APIS:
+// https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html#group__CUDART__EVENT
+DEFINE_OVERLOAD(cudaEventRecord, ARG(cudaEvent_t event, cudaStream_t stream), ARG(event, stream));
+
+DEFINE_OVERLOAD(cudaEventRecordWithFlags,
+                ARG(cudaEvent_t event, cudaStream_t stream, unsigned int flags),
+                ARG(event, stream, flags));
+
+// Execution APIS:
+// https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EXECUTION.html#group__CUDART__EXECUTION
+DEFINE_OVERLOAD(cudaLaunchKernel,
+                ARG(void const* func,
+                    dim3 gridDim,
+                    dim3 blockDim,
+                    void** args,
+                    size_t sharedMem,
+                    cudaStream_t stream),
+                ARG(func, gridDim, blockDim, args, sharedMem, stream));
+DEFINE_OVERLOAD(cudaLaunchCooperativeKernel,
+                ARG(void const* func,
+                    dim3 gridDim,
+                    dim3 blockDim,
+                    void** args,
+                    size_t sharedMem,
+                    cudaStream_t stream),
+                ARG(func, gridDim, blockDim, args, sharedMem, stream));
+DEFINE_OVERLOAD(cudaLaunchHostFunc,
+                ARG(cudaStream_t stream, cudaHostFn_t fn, void* userData),
+                ARG(stream, fn, userData));
+
+// Memory transfer APIS:
+// https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html#group__CUDART__MEMORY
+DEFINE_OVERLOAD(cudaMemPrefetchAsync,
+                ARG(void const* devPtr, size_t count, int dstDevice, cudaStream_t stream),
+                ARG(devPtr, count, dstDevice, stream));
+DEFINE_OVERLOAD(cudaMemcpy2DAsync,
+                ARG(void* dst,
+                    size_t dpitch,
+                    void const* src,
+                    size_t spitch,
+                    size_t width,
+                    size_t height,
+                    cudaMemcpyKind kind,
+                    cudaStream_t stream),
+                ARG(dst, dpitch, src, spitch, width, height, kind, stream));
+DEFINE_OVERLOAD(cudaMemcpy2DFromArrayAsync,
+                ARG(void* dst,
+                    size_t dpitch,
+                    cudaArray_const_t src,
+                    size_t wOffset,
+                    size_t hOffset,
+                    size_t width,
+                    size_t height,
+                    cudaMemcpyKind kind,
+                    cudaStream_t stream),
+                ARG(dst, dpitch, src, wOffset, hOffset, width, height, kind, stream));
+DEFINE_OVERLOAD(cudaMemcpy2DToArrayAsync,
+                ARG(cudaArray_t dst,
+                    size_t wOffset,
+                    size_t hOffset,
+                    void const* src,
+                    size_t spitch,
+                    size_t width,
+                    size_t height,
+                    cudaMemcpyKind kind,
+                    cudaStream_t stream),
+                ARG(dst, wOffset, hOffset, src, spitch, width, height, kind, stream));
+DEFINE_OVERLOAD(cudaMemcpy3DAsync,
+                ARG(cudaMemcpy3DParms const* p, cudaStream_t stream),
+                ARG(p, stream));
+DEFINE_OVERLOAD(cudaMemcpy3DPeerAsync,
+                ARG(cudaMemcpy3DPeerParms const* p, cudaStream_t stream),
+                ARG(p, stream));
+DEFINE_OVERLOAD(
+  cudaMemcpyAsync,
+  ARG(void* dst, void const* src, size_t count, cudaMemcpyKind kind, cudaStream_t stream),
+  ARG(dst, src, count, kind, stream));
+DEFINE_OVERLOAD(cudaMemcpyFromSymbolAsync,
+                ARG(void* dst,
+                    void const* symbol,
+                    size_t count,
+                    size_t offset,
+                    cudaMemcpyKind kind,
+                    cudaStream_t stream),
+                ARG(dst, symbol, count, offset, kind, stream));
+DEFINE_OVERLOAD(cudaMemcpyToSymbolAsync,
+                ARG(void const* symbol,
+                    void const* src,
+                    size_t count,
+                    size_t offset,
+                    cudaMemcpyKind kind,
+                    cudaStream_t stream),
+                ARG(symbol, src, count, offset, kind, stream));
+DEFINE_OVERLOAD(
+  cudaMemset2DAsync,
+  ARG(void* devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream),
+  ARG(devPtr, pitch, value, width, height, stream));
+DEFINE_OVERLOAD(
+  cudaMemset3DAsync,
+  ARG(cudaPitchedPtr pitchedDevPtr, int value, cudaExtent extent, cudaStream_t stream),
+  ARG(pitchedDevPtr, value, extent, stream));
+DEFINE_OVERLOAD(cudaMemsetAsync,
+                ARG(void* devPtr, int value, size_t count, cudaStream_t stream),
+                ARG(devPtr, value, count, stream));
+
+// Memory allocation APIS:
+// https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY__POOLS.html#group__CUDART__MEMORY__POOLS
+DEFINE_OVERLOAD(cudaFreeAsync, ARG(void* devPtr, cudaStream_t stream), ARG(devPtr, stream));
+DEFINE_OVERLOAD(cudaMallocAsync,
+                ARG(void** devPtr, size_t size, cudaStream_t stream),
+                ARG(devPtr, size, stream));
+DEFINE_OVERLOAD(cudaMallocFromPoolAsync,
+                ARG(void** ptr, size_t size, cudaMemPool_t memPool, cudaStream_t stream),
+                ARG(ptr, size, memPool, stream));
+
+/**
+ * @brief Function to collect all the original CUDA symbols corresponding to overloaded functions.
+ *
+ * Note on priorities:
+ * - `originals` must be initialized first, so it is 1001.
+ * - The function names must be added to originals next in the macro, so those are 1002.
+ * - Finally, this function actually finds the original symbols so it is 1003.
+ */
+__attribute__((constructor(1003))) void find_originals()
+{
+  for (auto it : originals) {
+    originals[it.first] = dlsym(RTLD_NEXT, it.first.data());
+  }
+}
diff --git a/cpp/tests/utilities/table_utilities.cu b/cpp/tests/utilities/table_utilities.cu
new file mode 100644
index 0000000..354c0b1
--- /dev/null
+++ b/cpp/tests/utilities/table_utilities.cu
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/table_utilities.hpp>
+
+#include <gmock/gmock.h>
+
+namespace cudf::test::detail {
+void expect_table_properties_equal(cudf::table_view lhs, cudf::table_view rhs)
+{
+  EXPECT_EQ(lhs.num_rows(), rhs.num_rows());
+  EXPECT_EQ(lhs.num_columns(), rhs.num_columns());
+}
+
+void expect_tables_equal(cudf::table_view lhs, cudf::table_view rhs)
+{
+  expect_table_properties_equal(lhs, rhs);
+  for (auto i = 0; i < lhs.num_columns(); ++i) {
+    cudf::test::detail::expect_columns_equal(lhs.column(i), rhs.column(i));
+  }
+}
+
+/**
+ * @copydoc cudf::test::expect_tables_equivalent
+ */
+void expect_tables_equivalent(cudf::table_view lhs, cudf::table_view rhs)
+{
+  auto num_columns = lhs.num_columns();
+  for (auto i = 0; i < num_columns; ++i) {
+    cudf::test::detail::expect_columns_equivalent(lhs.column(i), rhs.column(i));
+  }
+}
+
+}  // namespace cudf::test::detail
diff --git a/cpp/tests/utilities/tdigest_utilities.cu b/cpp/tests/utilities/tdigest_utilities.cu
new file mode 100644
index 0000000..9294aa0
--- /dev/null
+++ b/cpp/tests/utilities/tdigest_utilities.cu
@@ -0,0 +1,157 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/detail/tdigest/tdigest.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/tdigest_utilities.cuh>
+
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/fill.h>
+#include <thrust/for_each.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/tuple.h>
+
+// for use with groupby and reduction aggregation tests.
+
+namespace cudf {
+namespace test {
+
+void tdigest_sample_compare(cudf::tdigest::tdigest_column_view const& tdv,
+                            std::vector<expected_value> const& h_expected)
+{
+  column_view result_mean   = tdv.means();
+  column_view result_weight = tdv.weights();
+
+  auto expected_mean = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, h_expected.size(), mask_state::UNALLOCATED);
+  auto expected_weight = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, h_expected.size(), mask_state::UNALLOCATED);
+  auto sampled_result_mean = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, h_expected.size(), mask_state::UNALLOCATED);
+  auto sampled_result_weight = cudf::make_fixed_width_column(
+    data_type{type_id::FLOAT64}, h_expected.size(), mask_state::UNALLOCATED);
+
+  auto h_expected_src    = std::vector<size_type>(h_expected.size());
+  auto h_expected_mean   = std::vector<double>(h_expected.size());
+  auto h_expected_weight = std::vector<double>(h_expected.size());
+
+  {
+    auto iter = thrust::make_counting_iterator(0);
+    std::for_each_n(iter, h_expected.size(), [&](size_type const index) {
+      h_expected_src[index]    = thrust::get<0>(h_expected[index]);
+      h_expected_mean[index]   = thrust::get<1>(h_expected[index]);
+      h_expected_weight[index] = thrust::get<2>(h_expected[index]);
+    });
+  }
+
+  auto d_expected_src = cudf::detail::make_device_uvector_async(
+    h_expected_src, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto d_expected_mean = cudf::detail::make_device_uvector_async(
+    h_expected_mean, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto d_expected_weight = cudf::detail::make_device_uvector_async(
+    h_expected_weight, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto iter = thrust::make_counting_iterator(0);
+  thrust::for_each(
+    rmm::exec_policy(cudf::get_default_stream()),
+    iter,
+    iter + h_expected.size(),
+    [expected_src_in     = d_expected_src.data(),
+     expected_mean_in    = d_expected_mean.data(),
+     expected_weight_in  = d_expected_weight.data(),
+     expected_mean       = expected_mean->mutable_view().begin<double>(),
+     expected_weight     = expected_weight->mutable_view().begin<double>(),
+     result_mean         = result_mean.begin<double>(),
+     result_weight       = result_weight.begin<double>(),
+     sampled_result_mean = sampled_result_mean->mutable_view().begin<double>(),
+     sampled_result_weight =
+       sampled_result_weight->mutable_view().begin<double>()] __device__(size_type index) {
+      expected_mean[index]         = expected_mean_in[index];
+      expected_weight[index]       = expected_weight_in[index];
+      auto const src_index         = expected_src_in[index];
+      sampled_result_mean[index]   = result_mean[src_index];
+      sampled_result_weight[index] = result_weight[src_index];
+    });
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*expected_mean, *sampled_result_mean);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_weight, *sampled_result_weight);
+}
+
+std::unique_ptr<column> make_expected_tdigest_column(std::vector<expected_tdigest> const& groups)
+{
+  std::vector<std::unique_ptr<column>> tdigests;
+
+  // make an individual digest
+  auto make_digest = [&](expected_tdigest const& tdigest) {
+    std::vector<std::unique_ptr<column>> inner_children;
+    inner_children.push_back(std::make_unique<cudf::column>(tdigest.mean));
+    inner_children.push_back(std::make_unique<cudf::column>(tdigest.weight));
+    // tdigest struct
+    auto tdigests =
+      cudf::make_structs_column(tdigest.mean.size(), std::move(inner_children), 0, {});
+
+    std::vector<size_type> h_offsets{0, tdigest.mean.size()};
+    auto offsets =
+      cudf::make_fixed_width_column(data_type{type_id::INT32}, 2, mask_state::UNALLOCATED);
+    CUDF_CUDA_TRY(cudaMemcpy(offsets->mutable_view().begin<size_type>(),
+                             h_offsets.data(),
+                             sizeof(size_type) * 2,
+                             cudaMemcpyDefault));
+
+    auto list = cudf::make_lists_column(1, std::move(offsets), std::move(tdigests), 0, {});
+
+    auto min_col =
+      cudf::make_fixed_width_column(data_type{type_id::FLOAT64}, 1, mask_state::UNALLOCATED);
+    thrust::fill(rmm::exec_policy(cudf::get_default_stream()),
+                 min_col->mutable_view().begin<double>(),
+                 min_col->mutable_view().end<double>(),
+                 tdigest.min);
+    auto max_col =
+      cudf::make_fixed_width_column(data_type{type_id::FLOAT64}, 1, mask_state::UNALLOCATED);
+    thrust::fill(rmm::exec_policy(cudf::get_default_stream()),
+                 max_col->mutable_view().begin<double>(),
+                 max_col->mutable_view().end<double>(),
+                 tdigest.max);
+
+    std::vector<std::unique_ptr<column>> children;
+    children.push_back(std::move(list));
+    children.push_back(std::move(min_col));
+    children.push_back(std::move(max_col));
+    return make_structs_column(1, std::move(children), 0, {});
+  };
+
+  // build the individual digests
+  std::transform(groups.begin(), groups.end(), std::back_inserter(tdigests), make_digest);
+
+  // concatenate them
+  std::vector<column_view> views;
+  std::transform(tdigests.begin(),
+                 tdigests.end(),
+                 std::back_inserter(views),
+                 [](std::unique_ptr<column> const& c) { return c->view(); });
+
+  return cudf::concatenate(views);
+}
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/tests/utilities_tests/column_utilities_tests.cpp b/cpp/tests/utilities_tests/column_utilities_tests.cpp
new file mode 100644
index 0000000..90a7270
--- /dev/null
+++ b/cpp/tests/utilities_tests/column_utilities_tests.cpp
@@ -0,0 +1,563 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/strings/strings_column_view.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include <type_traits>
+
+template <typename T>
+struct ColumnUtilitiesTest : public cudf::test::BaseFixture {
+  cudf::test::UniformRandomGenerator<cudf::size_type> random;
+
+  ColumnUtilitiesTest() : random{1000, 5000} {}
+
+  auto size() { return random.generate(); }
+
+  auto data_type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+template <typename T>
+struct ColumnUtilitiesTestIntegral : public cudf::test::BaseFixture {};
+template <typename T>
+struct ColumnUtilitiesTestFloatingPoint : public cudf::test::BaseFixture {};
+
+template <typename T>
+struct ColumnUtilitiesTestFixedPoint : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ColumnUtilitiesTest, cudf::test::FixedWidthTypes);
+TYPED_TEST_SUITE(ColumnUtilitiesTestIntegral, cudf::test::IntegralTypes);
+TYPED_TEST_SUITE(ColumnUtilitiesTestFloatingPoint, cudf::test::FloatingPointTypes);
+TYPED_TEST_SUITE(ColumnUtilitiesTestFixedPoint, cudf::test::FixedPointTypes);
+
+TYPED_TEST(ColumnUtilitiesTest, NonNullableToHost)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return cudf::test::make_type_param_scalar<TypeParam>(i); });
+
+  auto size = this->size();
+
+  std::vector<TypeParam> data(sequence, sequence + size);
+  cudf::test::fixed_width_column_wrapper<TypeParam> col(data.begin(), data.end());
+
+  auto host_data = cudf::test::to_host<TypeParam>(col);
+
+  EXPECT_TRUE(std::equal(data.begin(), data.end(), host_data.first.begin()));
+}
+
+TYPED_TEST(ColumnUtilitiesTest, NonNullableToHostWithOffset)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return cudf::test::make_type_param_scalar<TypeParam>(i); });
+
+  auto const size  = this->size();
+  auto const split = 2;
+
+  auto data          = std::vector<TypeParam>(sequence, sequence + size);
+  auto expected_data = std::vector<TypeParam>(sequence + split, sequence + size);
+  auto col           = cudf::test::fixed_width_column_wrapper<TypeParam>(data.begin(), data.end());
+
+  auto const splits = std::vector<cudf::size_type>{split};
+  auto result       = cudf::split(col, splits);
+
+  auto host_data = cudf::test::to_host<TypeParam>(result.back());
+
+  EXPECT_TRUE(std::equal(expected_data.begin(), expected_data.end(), host_data.first.begin()));
+}
+
+TYPED_TEST(ColumnUtilitiesTest, NullableToHostWithOffset)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return cudf::test::make_type_param_scalar<TypeParam>(i); });
+
+  auto split = 2;
+  auto size  = this->size();
+  auto valid = cudf::detail::make_counting_transform_iterator(
+    0, [&split](auto i) { return i <= 10 and i > split; });
+  std::vector<TypeParam> data(sequence, sequence + size);
+  std::vector<TypeParam> expected_data(sequence + split, sequence + size);
+  cudf::test::fixed_width_column_wrapper<TypeParam> col(data.begin(), data.end(), valid);
+
+  std::vector<cudf::size_type> splits{split};
+  std::vector<cudf::column_view> result = cudf::split(col, splits);
+
+  auto host_data = cudf::test::to_host<TypeParam>(result.back());
+
+  EXPECT_TRUE(std::equal(expected_data.begin(), expected_data.end(), host_data.first.begin()));
+
+  auto masks = std::get<0>(cudf::test::detail::make_null_mask_vector(valid + split, valid + size));
+
+  EXPECT_TRUE(cudf::test::validate_host_masks(masks, host_data.second, expected_data.size()));
+}
+
+TYPED_TEST(ColumnUtilitiesTest, NullableToHostAllValid)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(
+    0, [](auto i) { return cudf::test::make_type_param_scalar<TypeParam>(i); });
+
+  auto all_valid = thrust::make_constant_iterator<bool>(true);
+
+  auto size = this->size();
+
+  std::vector<TypeParam> data(sequence, sequence + size);
+  cudf::test::fixed_width_column_wrapper<TypeParam> col(data.begin(), data.end(), all_valid);
+
+  auto host_data = cudf::test::to_host<TypeParam>(col);
+
+  EXPECT_TRUE(std::equal(data.begin(), data.end(), host_data.first.begin()));
+
+  auto masks = std::get<0>(cudf::test::detail::make_null_mask_vector(all_valid, all_valid + size));
+
+  EXPECT_TRUE(cudf::test::validate_host_masks(masks, host_data.second, size));
+}
+
+struct ColumnUtilitiesEquivalenceTest : public cudf::test::BaseFixture {};
+
+TEST_F(ColumnUtilitiesEquivalenceTest, DoubleTest)
+{
+  cudf::test::fixed_width_column_wrapper<double> col1{10. / 3, 22. / 7};
+  cudf::test::fixed_width_column_wrapper<double> col2{31. / 3 - 21. / 3, 19. / 7 + 3. / 7};
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(col1, col2);
+}
+
+TEST_F(ColumnUtilitiesEquivalenceTest, NullabilityTest)
+{
+  auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+  cudf::test::fixed_width_column_wrapper<double> col1{1, 2, 3};
+  cudf::test::fixed_width_column_wrapper<double> col2({1, 2, 3}, all_valid);
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(col1, col2);
+}
+
+struct ColumnUtilitiesStringsTest : public cudf::test::BaseFixture {};
+
+TEST_F(ColumnUtilitiesStringsTest, StringsToHost)
+{
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto host_data  = cudf::test::to_host<std::string>(strings);
+  auto result_itr = host_data.first.begin();
+  for (auto itr = h_strings.begin(); itr != h_strings.end(); ++itr, ++result_itr) {
+    if (*itr) { EXPECT_TRUE((*result_itr) == (*itr)); }
+  }
+}
+
+TEST_F(ColumnUtilitiesStringsTest, StringsToHostAllNulls)
+{
+  std::vector<char const*> h_strings{nullptr, nullptr, nullptr};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+  auto host_data = cudf::test::to_host<std::string>(strings);
+  auto results   = host_data.first;
+  EXPECT_EQ(std::size_t{3}, host_data.first.size());
+  EXPECT_TRUE(std::all_of(results.begin(), results.end(), [](auto s) { return s.empty(); }));
+}
+
+TEST_F(ColumnUtilitiesStringsTest, PrintColumnDuration)
+{
+  char const* delimiter = ",";
+
+  cudf::test::fixed_width_column_wrapper<cudf::duration_s, int32_t> cudf_col({100, 0, 7, 140000});
+
+  auto expected = "100 seconds,0 seconds,7 seconds,140000 seconds";
+
+  EXPECT_EQ(cudf::test::to_string(cudf_col, delimiter), expected);
+}
+
+TYPED_TEST(ColumnUtilitiesTestIntegral, PrintColumnNumeric)
+{
+  char const* delimiter = ",";
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> cudf_col({1, 2, 3, 4, 5});
+  auto std_col = cudf::test::make_type_param_vector<TypeParam>({1, 2, 3, 4, 5});
+
+  std::stringstream tmp;
+  auto string_iter =
+    thrust::make_transform_iterator(std::begin(std_col), [](auto e) { return std::to_string(e); });
+
+  std::copy(string_iter,
+            string_iter + std_col.size() - 1,
+            std::ostream_iterator<std::string>(tmp, delimiter));
+
+  tmp << std::to_string(std_col.back());
+
+  EXPECT_EQ(cudf::test::to_string(cudf_col, delimiter), tmp.str());
+}
+
+TYPED_TEST(ColumnUtilitiesTestIntegral, PrintColumnWithInvalids)
+{
+  char const* delimiter = ",";
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> cudf_col{{1, 2, 3, 4, 5}, {1, 0, 1, 0, 1}};
+  auto std_col = cudf::test::make_type_param_vector<TypeParam>({1, 2, 3, 4, 5});
+
+  std::ostringstream tmp;
+  tmp << std::to_string(std_col[0]) << delimiter << "NULL" << delimiter
+      << std::to_string(std_col[2]) << delimiter << "NULL" << delimiter
+      << std::to_string(std_col[4]);
+
+  EXPECT_EQ(cudf::test::to_string(cudf_col, delimiter), tmp.str());
+}
+
+TYPED_TEST(ColumnUtilitiesTestFloatingPoint, PrintColumnNumeric)
+{
+  char const* delimiter = ",";
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> cudf_col(
+    {10001523.25, 2.0, 3.75, 0.000000034, 5.3});
+
+  auto expected = std::is_same_v<TypeParam, double>
+                    ? "10001523.25,2,3.75,3.4e-08,5.2999999999999998"
+                    : "10001523,2,3.75,3.39999993e-08,5.30000019";
+
+  EXPECT_EQ(cudf::test::to_string(cudf_col, delimiter), expected);
+}
+
+TYPED_TEST(ColumnUtilitiesTestFloatingPoint, PrintColumnWithInvalids)
+{
+  char const* delimiter = ",";
+
+  cudf::test::fixed_width_column_wrapper<TypeParam> cudf_col(
+    {10001523.25, 2.0, 3.75, 0.000000034, 5.3}, {1, 0, 1, 0, 1});
+
+  auto expected = std::is_same_v<TypeParam, double>
+                    ? "10001523.25,NULL,3.75,NULL,5.2999999999999998"
+                    : "10001523,NULL,3.75,NULL,5.30000019";
+
+  EXPECT_EQ(cudf::test::to_string(cudf_col, delimiter), expected);
+}
+
+TEST_F(ColumnUtilitiesStringsTest, StringsToString)
+{
+  char const* delimiter = ",";
+
+  std::vector<char const*> h_strings{"eee", "bb", nullptr, "", "aa", "bbb", "ééé"};
+  cudf::test::strings_column_wrapper strings(
+    h_strings.begin(),
+    h_strings.end(),
+    thrust::make_transform_iterator(h_strings.begin(), [](auto str) { return str != nullptr; }));
+
+  std::ostringstream tmp;
+  tmp << h_strings[0] << delimiter << h_strings[1] << delimiter << "NULL" << delimiter
+      << h_strings[3] << delimiter << h_strings[4] << delimiter << h_strings[5] << delimiter
+      << h_strings[6];
+
+  EXPECT_EQ(cudf::test::to_string(strings, delimiter), tmp.str());
+}
+
+TEST_F(ColumnUtilitiesStringsTest, PrintEscapeStrings)
+{
+  char const* delimiter = ",";
+  cudf::test::strings_column_wrapper input({"e\te\ne", "é\bé\ré", "e\vé\fé\abell"});
+  std::string expected{"e\\te\\ne,é\\bé\\ré,e\\vé\\fé\\abell"};
+  EXPECT_EQ(cudf::test::to_string(input, delimiter), expected);
+}
+
+TYPED_TEST(ColumnUtilitiesTestFixedPoint, NonNullableToHost)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using rep       = cudf::device_storage_type_t<decimalXX>;
+
+  auto const scale = scale_type{-2};
+  auto to_fp       = [&](auto i) { return decimalXX{i, scale}; };
+  auto to_rep      = [](auto i) { return i * 100; };
+  auto fps         = cudf::detail::make_counting_transform_iterator(0, to_fp);
+  auto reps        = cudf::detail::make_counting_transform_iterator(0, to_rep);
+
+  auto const size      = 1000;
+  auto const expected  = std::vector<decimalXX>(fps, fps + size);
+  auto const col       = cudf::test::fixed_point_column_wrapper<rep>(reps, reps + size, scale);
+  auto const host_data = cudf::test::to_host<decimalXX>(col);
+
+  EXPECT_TRUE(std::equal(expected.begin(), expected.end(), host_data.first.begin()));
+}
+
+TYPED_TEST(ColumnUtilitiesTestFixedPoint, NonNullableToHostWithOffset)
+{
+  using namespace numeric;
+  using decimalXX = TypeParam;
+  using rep       = cudf::device_storage_type_t<decimalXX>;
+
+  auto const scale = scale_type{-2};
+  auto to_fp       = [&](auto i) { return decimalXX{i, scale}; };
+  auto to_rep      = [](auto i) { return i * 100; };
+  auto fps         = cudf::detail::make_counting_transform_iterator(0, to_fp);
+  auto reps        = cudf::detail::make_counting_transform_iterator(0, to_rep);
+
+  auto const size  = 1000;
+  auto const split = cudf::size_type{2};
+
+  auto const expected = std::vector<decimalXX>(fps + split, fps + size);
+  auto const col      = cudf::test::fixed_point_column_wrapper<rep>(reps, reps + size, scale);
+  auto const splits   = std::vector<cudf::size_type>{split};
+  auto result         = cudf::split(col, splits);
+
+  auto host_data = cudf::test::to_host<decimalXX>(result.back());
+
+  EXPECT_TRUE(std::equal(expected.begin(), expected.end(), host_data.first.begin()));
+}
+
+struct ColumnUtilitiesListsTest : public cudf::test::BaseFixture {};
+
+TEST_F(ColumnUtilitiesListsTest, Equivalence)
+{
+  // list<int>, nullable vs. non-nullable
+  {
+    auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+    cudf::test::lists_column_wrapper<int> a{{1, 2, 3}, {5, 6}, {8, 9}, {10}, {14, 15}};
+    cudf::test::lists_column_wrapper<int> b{{{1, 2, 3}, {5, 6}, {8, 9}, {10}, {14, 15}}, all_valid};
+
+    // properties
+    CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT(a, b);
+    EXPECT_FALSE(cudf::test::detail::expect_column_properties_equal(
+      a, b, cudf::test::debug_output_level::QUIET));
+
+    // values
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(a, b);
+    EXPECT_FALSE(
+      cudf::test::detail::expect_columns_equal(a, b, cudf::test::debug_output_level::QUIET));
+  }
+
+  // list<list<int>>, nullable vs. non-nullable
+  {
+    auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+    cudf::test::lists_column_wrapper<int> a{{{1, 2, 3}, {5, 6}}, {{8, 9}, {10}}, {{14, 15}}};
+    cudf::test::lists_column_wrapper<int> b{{{{1, 2, 3}, {5, 6}}, {{8, 9}, {10}}, {{14, 15}}},
+                                            all_valid};
+
+    // properties
+    CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT(a, b);
+    EXPECT_FALSE(cudf::test::detail::expect_column_properties_equal(
+      a, b, cudf::test::debug_output_level::QUIET));
+
+    CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(a, b);
+    EXPECT_FALSE(
+      cudf::test::detail::expect_columns_equal(a, b, cudf::test::debug_output_level::QUIET));
+  }
+}
+
+TEST_F(ColumnUtilitiesListsTest, DifferingRowCounts)
+{
+  cudf::test::fixed_width_column_wrapper<int> a{1, 1, 1, 1};
+  cudf::test::fixed_width_column_wrapper<int> b{1, 1, 1, 1, 1};
+
+  EXPECT_FALSE(
+    cudf::test::detail::expect_columns_equal(a, b, cudf::test::debug_output_level::QUIET));
+  EXPECT_FALSE(cudf::test::detail::expect_column_properties_equal(
+    a, b, cudf::test::debug_output_level::QUIET));
+  EXPECT_FALSE(
+    cudf::test::detail::expect_columns_equivalent(a, b, cudf::test::debug_output_level::QUIET));
+  EXPECT_FALSE(cudf::test::detail::expect_column_properties_equivalent(
+    a, b, cudf::test::debug_output_level::QUIET));
+}
+
+TEST_F(ColumnUtilitiesListsTest, UnsanitaryLists)
+{
+  // unsanitary
+  //
+  // List<int32_t>:
+  //  Length : 1
+  //  Offsets : 0, 3
+  //  Null count: 1
+  //  0
+  //    0, 1, 2
+  std::vector<std::unique_ptr<cudf::column>> children;
+  children.emplace_back(
+    std::move(cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 3}.release()));
+  children.emplace_back(std::move(cudf::test::fixed_width_column_wrapper<int>{0, 1, 2}.release()));
+
+  auto l0 = std::make_unique<cudf::column>(cudf::data_type{cudf::type_id::LIST},
+                                           1,
+                                           rmm::device_buffer{},
+                                           cudf::create_null_mask(1, cudf::mask_state::ALL_NULL),
+                                           1,
+                                           std::move(children));
+
+  // sanitary
+  //
+  // List<int32_t>:
+  //  Length : 1
+  //  Offsets : 0, 0
+  //  Null count: 1
+  //    0
+  auto l1 = cudf::test::lists_column_wrapper<int>::make_one_empty_row_column(false);
+
+  // equivalent, but not equal
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(*l0, l1);
+  EXPECT_FALSE(
+    cudf::test::detail::expect_columns_equal(*l0, l1, cudf::test::debug_output_level::QUIET));
+}
+
+TEST_F(ColumnUtilitiesListsTest, DifferentPhysicalStructureBeforeConstruction)
+{
+  // list<int>
+  {
+    std::vector<bool> valids = {0, 0, 1, 0, 1, 0, 0};
+
+    cudf::test::fixed_width_column_wrapper<int> c0_offsets{0, 3, 6, 8, 11, 14, 16, 19};
+    cudf::test::fixed_width_column_wrapper<int> c0_data{
+      1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 7};
+
+    auto [null_mask, null_count] = cudf::test::detail::make_null_mask(valids.begin(), valids.end());
+
+    auto c0 = make_lists_column(
+      7, c0_offsets.release(), c0_data.release(), null_count, std::move(null_mask));
+
+    cudf::test::fixed_width_column_wrapper<int> c1_offsets{0, 0, 0, 2, 2, 5, 5, 5};
+    cudf::test::fixed_width_column_wrapper<int> c1_data{3, 3, 5, 5, 5};
+    auto c1 = make_lists_column(
+      7,
+      c1_offsets.release(),
+      c1_data.release(),
+      null_count,
+      std::get<0>(cudf::test::detail::make_null_mask(valids.begin(), valids.end())));
+
+    // properties
+    CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(*c0, *c1);
+
+    // values
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*c0, *c1);
+  }
+
+  // list<list<struct<int, float>>>
+  {
+    std::vector<bool> level1_valids = {0, 0, 1, 0, 1, 0, 0};
+
+    cudf::test::fixed_width_column_wrapper<int> c0_l1_offsets{0, 1, 2, 4, 4, 7, 7, 7};
+    cudf::test::fixed_width_column_wrapper<int> c0_l2_offsets{0, 1, 2, 5, 6, 7, 10, 14};
+    cudf::test::fixed_width_column_wrapper<int> c0_l3_ints{
+      1, 1, -1, -2, -3, 1, 1, -4, -5, -6, -7, -8, -9, -10};
+    cudf::test::fixed_width_column_wrapper<float> c0_l3_floats{
+      1, 1, 10, 20, 30, 1, 1, 40, 50, 60, 70, 80, 90, 100};
+    cudf::test::structs_column_wrapper c0_l2_data({c0_l3_ints, c0_l3_floats});
+    std::vector<bool> c0_l2_valids = {1, 1, 1, 0, 0, 1, 1};
+
+    auto [null_mask, null_count] =
+      cudf::test::detail::make_null_mask(c0_l2_valids.begin(), c0_l2_valids.end());
+    auto c0_l2 = make_lists_column(
+      7, c0_l2_offsets.release(), c0_l2_data.release(), null_count, std::move(null_mask));
+
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(level1_valids.begin(), level1_valids.end());
+    auto c0 = make_lists_column(
+      7, c0_l1_offsets.release(), std::move(c0_l2), null_count, std::move(null_mask));
+
+    cudf::test::fixed_width_column_wrapper<int> c1_l1_offsets{0, 0, 0, 2, 2, 5, 5, 5};
+    cudf::test::fixed_width_column_wrapper<int> c1_l2_offsets{0, 3, 3, 3, 6, 10};
+    cudf::test::fixed_width_column_wrapper<int> c1_l3_ints{-1, -2, -3, -4, -5, -6, -7, -8, -9, -10};
+    cudf::test::fixed_width_column_wrapper<float> c1_l3_floats{
+      10, 20, 30, 40, 50, 60, 70, 80, 90, 100};
+    cudf::test::structs_column_wrapper c1_l2_data({c1_l3_ints, c1_l3_floats});
+    std::vector<bool> c1_l2_valids = {1, 0, 0, 1, 1};
+
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(c1_l2_valids.begin(), c1_l2_valids.end());
+    auto c1_l2 = make_lists_column(
+      5, c1_l2_offsets.release(), c1_l2_data.release(), null_count, std::move(null_mask));
+
+    std::tie(null_mask, null_count) =
+      cudf::test::detail::make_null_mask(level1_valids.begin(), level1_valids.end());
+    auto c1 = make_lists_column(
+      7, c1_l1_offsets.release(), std::move(c1_l2), null_count, std::move(null_mask));
+
+    // properties
+    CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(*c0, *c1);
+
+    // values
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(*c0, *c1);
+  }
+}
+
+struct ColumnUtilitiesStructsTest : public cudf::test::BaseFixture {};
+
+TEST_F(ColumnUtilitiesStructsTest, Properties)
+{
+  cudf::test::strings_column_wrapper s0_scol0{"mno", "jkl", "ghi", "def", "abc"};
+  cudf::test::fixed_width_column_wrapper<float> s0_scol1{5, 4, 3, 2, 1};
+  cudf::test::strings_column_wrapper s0_sscol0{"5555", "4444", "333", "22", "1"};
+  cudf::test::fixed_width_column_wrapper<float> s0_sscol1{50, 40, 30, 20, 10};
+  cudf::test::lists_column_wrapper<int> s0_sscol2{{1, 2}, {3, 4}, {5}, {6, 7, 8}, {12, 12}};
+  cudf::test::structs_column_wrapper s0_scol2({s0_sscol0, s0_sscol1, s0_sscol2});
+  cudf::test::structs_column_wrapper s_col0({s0_scol0, s0_scol1, s0_scol2});
+
+  auto all_valid = thrust::make_constant_iterator<bool>(true);
+
+  cudf::test::strings_column_wrapper s1_scol0{"mno", "jkl", "ghi", "def", "abc"};
+  cudf::test::fixed_width_column_wrapper<float> s1_scol1{5, 4, 3, 2, 1};
+  cudf::test::strings_column_wrapper s1_sscol0{"5555", "4444", "333", "22", "1"};
+  cudf::test::fixed_width_column_wrapper<float> s1_sscol1{50, 40, 30, 20, 10};
+  cudf::test::lists_column_wrapper<int> s1_sscol2{{{1, 2}, {3, 4}, {5}, {6, 7, 8}, {12, 12}},
+                                                  all_valid};
+  cudf::test::structs_column_wrapper s1_scol2({s1_sscol0, s1_sscol1, s1_sscol2});
+  cudf::test::structs_column_wrapper s_col1({s1_scol0, s1_scol1, s1_scol2});
+
+  // equivalent, but not equal
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUIVALENT(s_col0, s_col1);
+  EXPECT_FALSE(cudf::test::detail::expect_column_properties_equal(
+    s_col0, s_col1, cudf::test::debug_output_level::QUIET));
+
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(s_col0, s_col0);
+  CUDF_TEST_EXPECT_COLUMN_PROPERTIES_EQUAL(s_col1, s_col1);
+}
+
+TEST_F(ColumnUtilitiesStructsTest, Values)
+{
+  cudf::test::strings_column_wrapper s0_scol0{"mno", "jkl", "ghi", "def", "abc"};
+  cudf::test::fixed_width_column_wrapper<float> s0_scol1{5, 4, 3, 2, 1};
+  cudf::test::strings_column_wrapper s0_sscol0{"5555", "4444", "333", "22", "1"};
+  cudf::test::fixed_width_column_wrapper<float> s0_sscol1{50, 40, 30, 20, 10};
+  cudf::test::lists_column_wrapper<int> s0_sscol2{{1, 2}, {3, 4}, {5}, {6, 7, 8}, {12, 12}};
+  cudf::test::structs_column_wrapper s0_scol2({s0_sscol0, s0_sscol1, s0_sscol2});
+  cudf::test::structs_column_wrapper s_col0({s0_scol0, s0_scol1, s0_scol2});
+
+  auto all_valid = thrust::make_constant_iterator<bool>(true);
+
+  cudf::test::strings_column_wrapper s1_scol0{"mno", "jkl", "ghi", "def", "abc"};
+  cudf::test::fixed_width_column_wrapper<float> s1_scol1{5, 4, 3, 2, 1};
+  cudf::test::strings_column_wrapper s1_sscol0{"5555", "4444", "333", "22", "1"};
+  cudf::test::fixed_width_column_wrapper<float> s1_sscol1{50, 40, 30, 20, 10};
+  cudf::test::lists_column_wrapper<int> s1_sscol2{{{1, 2}, {3, 4}, {5}, {6, 7, 8}, {12, 12}},
+                                                  all_valid};
+  cudf::test::structs_column_wrapper s1_scol2({s1_sscol0, s1_sscol1, s1_sscol2});
+  cudf::test::structs_column_wrapper s_col1({s1_scol0, s1_scol1, s1_scol2});
+
+  // equivalent, but not equal
+  CUDF_TEST_EXPECT_COLUMNS_EQUIVALENT(s_col0, s_col1);
+  EXPECT_FALSE(cudf::test::detail::expect_columns_equal(
+    s_col0, s_col1, cudf::test::debug_output_level::QUIET));
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(s_col0, s_col0);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(s_col1, s_col1);
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/utilities_tests/column_wrapper_tests.cpp b/cpp/tests/utilities_tests/column_wrapper_tests.cpp
new file mode 100644
index 0000000..9d2cc25
--- /dev/null
+++ b/cpp/tests/utilities_tests/column_wrapper_tests.cpp
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/detail/iterator.cuh>
+
+template <typename T>
+struct FixedWidthColumnWrapperTest : public cudf::test::BaseFixture,
+                                     cudf::test::UniformRandomGenerator<cudf::size_type> {
+  FixedWidthColumnWrapperTest() : cudf::test::UniformRandomGenerator<cudf::size_type>{1000, 5000} {}
+
+  auto size() { return this->generate(); }
+
+  auto data_type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+TYPED_TEST_SUITE(FixedWidthColumnWrapperTest, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(FixedWidthColumnWrapperTest, EmptyIterator)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence);
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), 0);
+  EXPECT_EQ(view.head(), nullptr);
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_FALSE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+TYPED_TEST(FixedWidthColumnWrapperTest, EmptyList)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam> col{};
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), 0);
+  EXPECT_EQ(view.head(), nullptr);
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_FALSE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NonNullableIteratorConstructor)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  auto size = this->size();
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + size);
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), size);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_FALSE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NonNullableListConstructor)
+{
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({1, 2, 3, 4, 5});
+
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), 5);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_FALSE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NullableIteratorConstructorAllValid)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  auto size = this->size();
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + size, all_valid);
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), size);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NullableListConstructorAllValid)
+{
+  auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({1, 2, 3, 4, 5}, all_valid);
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), 5);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NullableIteratorConstructorAllNull)
+{
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  auto all_null = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return false; });
+
+  auto size = this->size();
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, typename decltype(sequence)::value_type> col(
+    sequence, sequence + size, all_null);
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), size);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_TRUE(view.has_nulls());
+  EXPECT_EQ(view.null_count(), size);
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NullableListConstructorAllNull)
+{
+  auto all_null = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return false; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({1, 2, 3, 4, 5}, all_null);
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), 5);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_TRUE(view.has_nulls());
+  EXPECT_EQ(view.null_count(), 5);
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NullablePairListConstructorAllNull)
+{
+  using p = std::pair<int32_t, bool>;
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col(
+    {p{1, false}, p{2, false}, p{3, false}, p{4, false}, p{5, false}});
+  cudf::column_view view = col;
+
+  EXPECT_EQ(view.size(), 5);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_TRUE(view.has_nulls());
+  EXPECT_EQ(view.null_count(), 5);
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, NullablePairListConstructorAllNullMatch)
+{
+  auto odd_valid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 != 0; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> match_col({1, 2, 3, 4, 5}, odd_valid);
+  cudf::column_view match_view = match_col;
+
+  using p = std::pair<int32_t, bool>;
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({p{1, odd_valid[0]},
+                                                                  p{2, odd_valid[1]},
+                                                                  p{3, odd_valid[2]},
+                                                                  p{4, odd_valid[3]},
+                                                                  p{5, odd_valid[4]}});
+  cudf::column_view view = col;
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view, match_view);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, ReleaseWrapperAllValid)
+{
+  auto all_valid = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return true; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({1, 2, 3, 4, 5}, all_valid);
+  auto colPtr            = col.release();
+  cudf::column_view view = *colPtr;
+  EXPECT_EQ(view.size(), 5);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(FixedWidthColumnWrapperTest, ReleaseWrapperAllNull)
+{
+  auto all_null = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return false; });
+
+  cudf::test::fixed_width_column_wrapper<TypeParam, int32_t> col({1, 2, 3, 4, 5}, all_null);
+  auto colPtr            = col.release();
+  cudf::column_view view = *colPtr;
+  EXPECT_EQ(view.size(), 5);
+  EXPECT_NE(nullptr, view.head());
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_TRUE(view.nullable());
+  EXPECT_TRUE(view.has_nulls());
+  EXPECT_EQ(view.null_count(), 5);
+  EXPECT_EQ(view.offset(), 0);
+}
+
+template <typename T>
+struct StringsColumnWrapperTest : public cudf::test::BaseFixture,
+                                  cudf::test::UniformRandomGenerator<cudf::size_type> {
+  auto data_type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+TYPED_TEST_SUITE(StringsColumnWrapperTest, cudf::test::StringTypes);
+
+TYPED_TEST(StringsColumnWrapperTest, EmptyList)
+{
+  cudf::test::strings_column_wrapper col;
+  cudf::column_view view = col;
+  EXPECT_EQ(view.size(), 0);
+  EXPECT_EQ(view.head(), nullptr);
+  EXPECT_EQ(view.type(), this->data_type());
+  EXPECT_FALSE(view.nullable());
+  EXPECT_FALSE(view.has_nulls());
+  EXPECT_EQ(view.offset(), 0);
+}
+
+TYPED_TEST(StringsColumnWrapperTest, NullablePairListConstructorAllNull)
+{
+  using p = std::pair<std::string, bool>;
+  cudf::test::strings_column_wrapper col(
+    {p{"a", false}, p{"string", false}, p{"test", false}, p{"for", false}, p{"nulls", false}});
+  cudf::strings_column_view view = cudf::column_view(col);
+
+  constexpr auto count = 5;
+  EXPECT_EQ(view.size(), count);
+  EXPECT_EQ(view.offsets().size(), count + 1);
+  // all null entries results in no data allocated to chars
+  EXPECT_EQ(nullptr, view.chars().head());
+  EXPECT_NE(nullptr, view.offsets().head());
+  EXPECT_TRUE(view.has_nulls());
+  EXPECT_EQ(view.null_count(), 5);
+}
+
+TYPED_TEST(StringsColumnWrapperTest, NullablePairListConstructorAllNullMatch)
+{
+  auto odd_valid =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 != 0; });
+
+  cudf::test::strings_column_wrapper match_col({"a", "string", "", "test", "for", "nulls"},
+                                               odd_valid);
+  cudf::column_view match_view = match_col;
+
+  using p = std::pair<std::string, bool>;
+  cudf::test::strings_column_wrapper col({p{"a", odd_valid[0]},
+                                          p{"string", odd_valid[1]},
+                                          p{"", odd_valid[2]},
+                                          p{"test", odd_valid[3]},
+                                          p{"for", odd_valid[4]},
+                                          p{"nulls", odd_valid[5]}});
+  cudf::column_view view = col;
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(view, match_view);
+}
diff --git a/cpp/tests/utilities_tests/default_stream_tests.cpp b/cpp/tests/utilities_tests/default_stream_tests.cpp
new file mode 100644
index 0000000..f5c5587
--- /dev/null
+++ b/cpp/tests/utilities_tests/default_stream_tests.cpp
@@ -0,0 +1,25 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/default_stream.hpp>
+
+#include <cudf_test/cudf_gtest.hpp>
+
+#ifdef CUDA_API_PER_THREAD_DEFAULT_STREAM
+TEST(DefaultStreamTest, PtdsIsEnabled) { EXPECT_TRUE(cudf::is_ptds_enabled()); }
+#else
+TEST(DefaultStreamTest, PtdsIsNotEnabled) { EXPECT_FALSE(cudf::is_ptds_enabled()); }
+#endif
diff --git a/cpp/tests/utilities_tests/lists_column_wrapper_tests.cpp b/cpp/tests/utilities_tests/lists_column_wrapper_tests.cpp
new file mode 100644
index 0000000..35e8604
--- /dev/null
+++ b/cpp/tests/utilities_tests/lists_column_wrapper_tests.cpp
@@ -0,0 +1,1561 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+struct ListColumnWrapperTest : public cudf::test::BaseFixture {};
+template <typename T>
+struct ListColumnWrapperTestTyped : public cudf::test::BaseFixture {
+  ListColumnWrapperTestTyped() {}
+
+  auto data_type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+using FixedWidthTypesNotBool = cudf::test::Concat<cudf::test::IntegralTypesNotBool,
+                                                  cudf::test::FloatingPointTypes,
+                                                  cudf::test::DurationTypes,
+                                                  cudf::test::TimestampTypes>;
+TYPED_TEST_SUITE(ListColumnWrapperTestTyped, FixedWidthTypesNotBool);
+
+TYPED_TEST(ListColumnWrapperTestTyped, List)
+{
+  using T = TypeParam;
+
+  // List<T>, 1 row
+  //
+  // List<T>:
+  // Length : 1
+  // Offsets : 0, 2
+  // Children :
+  //    2, 3
+  //
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{2, 3};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 2);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_data({2, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+
+  // List<T>, 1 row
+  //
+  // List<T>:
+  // Length : 1
+  // Offsets : 0, 2
+  // Children :
+  //    2, 3
+  //
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{{2, 3}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 2);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_data({2, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListWithValidity)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<T>, 1 row
+  //
+  // List<T>:
+  // Length : 1
+  // Offsets : 0, 2
+  // Children :
+  //    2, NULL
+  //
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{2, 3}, valids}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 2);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_data({2, 3}, valids);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+
+  // List<T>, 3 rows
+  //
+  // List<T>:
+  // Length : 3
+  // Offsets : 0, 2, 4, 7
+  // Children :
+  //    2, NULL, 4, NULL, 6, NULL, 8
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {{2, 3}, valids}, {{4, 5}, valids}, {{6, 7, 8}, valids}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 4, 7});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 7);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_data({2, 3, 4, 5, 6, 7, 8}, valids);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListFromIterator)
+{
+  using T = TypeParam;
+
+  // List<T>, 1 row
+  //
+  // List<T>:
+  // Length : 1
+  // Offsets : 0, 5
+  // Children :
+  //    0, 1, 2, 3, 4
+  //
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  cudf::test::lists_column_wrapper<T, typename decltype(sequence)::value_type> list{sequence,
+                                                                                    sequence + 5};
+
+  cudf::lists_column_view lcv(list);
+  EXPECT_EQ(lcv.size(), 1);
+
+  auto offsets = lcv.offsets();
+  EXPECT_EQ(offsets.size(), 2);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 5});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+  auto data = lcv.child();
+  EXPECT_EQ(data.size(), 5);
+  cudf::test::fixed_width_column_wrapper<T, int32_t> e_data({0, 1, 2, 3, 4});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListFromIteratorWithValidity)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<int>, 1 row
+  //
+  // List<int32_t>:
+  // Length : 1
+  // Offsets : 0, 5
+  // Children :
+  //    0, NULL, 2, NULL, 4
+  //
+  auto sequence = cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i; });
+
+  cudf::test::lists_column_wrapper<T, typename decltype(sequence)::value_type> list{
+    sequence, sequence + 5, valids};
+
+  cudf::lists_column_view lcv(list);
+  EXPECT_EQ(lcv.size(), 1);
+
+  auto offsets = lcv.offsets();
+  EXPECT_EQ(offsets.size(), 2);
+  cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 5});
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+  auto data = lcv.child();
+  EXPECT_EQ(data.size(), 5);
+  cudf::test::fixed_width_column_wrapper<T, int32_t> e_data({0, 0, 2, 0, 4}, valids);
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListOfLists)
+{
+  using T = TypeParam;
+
+  // List<List<T>>, 1 row
+  //
+  // List<List<T>>:
+  // Length : 1
+  // Offsets : 0, 2
+  // Children :
+  //    List<T>:
+  //    Length : 2
+  //    Offsets : 0, 2, 4
+  //    Children :
+  //      2, 3, 4, 5
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{2, 3}, {4, 5}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 4);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data({2, 3, 4, 5});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+
+  // List<List<T>> 3 rows
+  //
+  // List<List<T>>:
+  // Length : 3
+  // Offsets : 0, 2, 5, 6
+  // Children :
+  //    List<T>:
+  //    Length : 6
+  //    Offsets : 0, 2, 4, 7, 8, 9, 11
+  //    Children :
+  //      1, 2, 3, 4, 5, 6, 7, 0, 8, 9, 10
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {{1, 2}, {3, 4}}, {{5, 6, 7}, {0}, {8}}, {{9, 10}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 5, 6});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 6);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 7);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 4, 7, 8, 9, 11});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 11);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data(
+      {1, 2, 3, 4, 5, 6, 7, 0, 8, 9, 10});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListOfListsWithValidity)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<List<T>>, 1 row
+  //
+  // List<List<T>>:
+  // Length : 1
+  // Offsets : 0, 2
+  // Children :
+  //    List<T>:
+  //    Length : 2
+  //    Offsets : 0, 2, 4
+  //    Children :
+  //      2, NULL, 4, NULL
+  {
+    // equivalent to { {2, NULL}, {4, NULL} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{{2, 3}, valids}, {{4, 5}, valids}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 4);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data({2, 3, 4, 5}, valids);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+
+  // List<List<T>> 3 rows
+  //
+  // List<List<T>>:
+  // Length : 3
+  // Offsets : 0, 2, 5, 6
+  // Children :
+  //    List<T>:
+  //    Length : 6
+  //    Offsets : 0, 2, 2, 5, 5, 6, 8
+  //    Null count: 2
+  //    110101
+  //    Children :
+  //      1, 2, 5, 6, 7, 8, 9, 10
+  {
+    // equivalent to  { {{1, 2}, NULL}, {{5, 6, 7}, NULL, {8}}, {{9, 10}} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {{{1, 2}, {3, 4}}, valids}, {{{5, 6, 7}, {0}, {8}}, valids}, {{{9, 10}}, valids}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 5, 6});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 6);
+    EXPECT_EQ(childv.null_count(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 7);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 2, 5, 5, 6, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 8);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data({1, 2, 5, 6, 7, 8, 9, 10});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListOfListOfListsWithValidity)
+{
+  using T = TypeParam;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<List<List<T>>>, 2 rows
+  //
+  // List<List<List<T>>>:
+  // Length : 2
+  // Offsets : 0, 2, 4
+  // Children :
+  //    List<List<T>>:
+  //    Length : 4
+  //    Offsets : 0, 2, 2, 4, 6
+  //    Null count: 1
+  //    1101
+  //    Children :
+  //      List<T>:
+  //      Length : 6
+  //      Offsets : 0, 2, 4, 6, 8, 11, 12
+  //      Children :
+  //        1, 2, 3, 4, 10, 20, 30, 40, 50, 60, 70, 0
+  {
+    // equivalent to  { {{{1, 2}, {3, 4}}, NULL}, {{{10, 20}, {30, 40}}, {{50, 60, 70}, {0}}} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {{{{1, 2}, {3, 4}}, {{5, 6, 7}, {0}}}, valids}, {{{10, 20}, {30, 40}}, {{50, 60, 70}, {0}}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 2);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 4);
+    EXPECT_EQ(childv.null_count(), 1);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 5);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 2, 4, 6});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 6);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 7);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets(
+      {0, 2, 4, 6, 8, 11, 12});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+
+    auto child_child_data = child_childv.child();
+    EXPECT_EQ(child_child_data.size(), 12);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_child_data(
+      {1, 2, 3, 4, 10, 20, 30, 40, 50, 60, 70, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_data, child_child_data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, EmptyLists)
+{
+  using T = TypeParam;
+
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  // List<T>, empty
+  //
+  // List<T>:
+  // Length : 0
+  // Offsets :
+  // Children :
+  {
+    // equivalent to  {}
+    cudf::test::lists_column_wrapper<T> list{};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 0);
+  }
+
+  // List<T>, 1 row
+  //
+  // List<T>:
+  // Length : 1
+  // Offsets : 0, 0
+  // Children :
+  {
+    // equivalent to  {}
+    cudf::test::lists_column_wrapper<T, int32_t> list{LCW{}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+  }
+
+  // List<T>, 2 rows
+  //
+  // List<T>:
+  // Length : 2
+  // Offsets : 0, 0, 0
+  // Children :
+  {
+    // equivalent to  {}
+    cudf::test::lists_column_wrapper<T, int32_t> list{LCW{}, LCW{}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 2);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+  }
+
+  // List<L>, mixed
+  //
+  // List<T>:
+  // Length : 3
+  // Offsets : 0, 2, 2, 4
+  // Children :
+  //   1, 2, 3, 4
+  {
+    // equivalent to  {{1, 2}, {}, {3, 4}}
+
+    cudf::test::lists_column_wrapper<T, int32_t> list{{1, 2}, LCW{}, {3, 4}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 2, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child_data = lcv.child();
+    EXPECT_EQ(child_data.size(), 4);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data({1, 2, 3, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+
+  // List<List<T>>, mixed
+  //
+  // List<List<T>>:
+  // Length : 3
+  // Offsets : 0, 1, 4, 7
+  // Children :
+  //   List<int32_t>:
+  //   Length : 7
+  //   Offsets : 0, 0, 2, 2, 4, 4, 8, 8
+  //   Children :
+  //     1, 2, 3, 4, 5, 6, 7, 8
+  {
+    // equivalent to  { {{}}, {{1, 2}, {}, {3, 4}}, {{}, {5, 6, 7, 8}, {}} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {LCW{}}, {{1, 2}, LCW{}, {3, 4}}, {LCW{}, {5, 6, 7, 8}, LCW{}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 4, 7});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 7);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 8);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets(
+      {0, 0, 2, 2, 4, 4, 8, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 8);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data({1, 2, 3, 4, 5, 6, 7, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, EmptyListsWithValidity)
+{
+  using T = TypeParam;
+
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<T>, 2 rows
+  //
+  // List<T>:
+  // Length : 2
+  // Offsets : 0, 0, 0
+  // Null count: 1
+  // 01
+  // Children :
+  {
+    // equivalent to  {{}, NULL}
+    cudf::test::lists_column_wrapper<T, int32_t> list{{LCW{}, LCW{}}, valids};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 2);
+    EXPECT_EQ(lcv.null_count(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+  }
+
+  // List<T>, 3 rows
+  //
+  // List<T>:
+  // Length : 3
+  // Offsets : 0, 0, 0, 0
+  // Null count: 1
+  // 101
+  // Children :
+  {
+    // equivalent to  {{}, NULL, {}}
+    cudf::test::lists_column_wrapper<T, int32_t> list{{LCW{}, {1, 2, 3}, LCW{}}, valids};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+  }
+
+  // List<T>, 3 rows
+  //
+  // List<T>:
+  // Length : 3
+  // Offsets : 0, 0, 0, 3
+  // Null count: 1
+  // 101
+  // Children :
+  //   1, 2, 3
+  {
+    // equivalent to  {{}, NULL, {1, 2, 3}}
+    cudf::test::lists_column_wrapper<T, int32_t> list{{LCW{}, LCW{}, {1, 2, 3}}, valids};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 0, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+  }
+
+  // List<List<T>>, mixed
+  //
+  // List<List<T>>:
+  // Length : 3
+  // Offsets : 0, 1, 1, 4
+  // Null count: 1
+  // 101
+  // Children :
+  //   List<T>:
+  //   Length : 4
+  //   Offsets : 0, 0, 0, 4, 4
+  //   Children :
+  //     5, 6, 7, 8
+  {
+    // equivalent to  { {{}}, NULL, {{}, {5, 6, 7, 8}, {}} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {{LCW{}}, {{1, 2}, LCW{}, {3, 4}}, {LCW{}, {5, 6, 7, 8}, LCW{}}}, valids};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 1, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 4);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 5);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 0, 0, 4, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 4);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_data({5, 6, 7, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, IncompleteHierarchies)
+{
+  using T = TypeParam;
+
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<T, int32_t>;
+
+  // List<List<List<T>>>:
+  // Length : 3
+  // Offsets : 0, 1, 2, 2
+  // Children :
+  //  List<List<T>>:
+  //  Length : 2
+  //  Offsets : 0, 1, 1
+  //  Children :
+  //      List<T>:
+  //      Length : 1
+  //      Offsets : 0, 0
+  //      Children :
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{LCW{}}}, {LCW{}}, LCW{}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 2, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 1);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets({0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+
+    auto child_child_data = child_childv.child();
+    EXPECT_EQ(child_child_data.size(), 0);
+  }
+
+  // List<List<List<T>>>:
+  // Length : 3
+  // Offsets : 0, 0, 1, 2
+  // Children :
+  //   List<List<T>>:
+  //   Length : 2
+  //   Offsets : 0, 0, 1
+  //   Children :
+  //     List<T>:
+  //       Length : 1
+  //       Offsets : 0, 0
+  //       Children :
+  {
+    cudf::test::lists_column_wrapper<T, int32_t> list{LCW{}, {LCW{}}, {{LCW{}}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 1, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 1);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets({0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+
+    auto child_child_data = child_childv.child();
+    EXPECT_EQ(child_child_data.size(), 0);
+  }
+
+  // List<List<List<T>>>:
+  // Length : 3
+  // Offsets : 0, 0, 1, 2
+  // Children :
+  //   List<List<T>>:
+  //   Length : 2
+  //   Offsets : 0, 1, 1
+  //   Children :
+  //       List<T>:
+  //       Length : 1
+  //       Offsets : 0, 3
+  //       Children :
+  //         1, 2, 3
+  {
+    // { {}, {{{1,2,3}}}, {{}} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{LCW{}, {{{1, 2, 3}}}, {LCW{}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 1, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 1);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets({0, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+
+    auto child_child_data = child_childv.child();
+    EXPECT_EQ(child_child_data.size(), 3);
+    cudf::test::fixed_width_column_wrapper<T, int32_t> e_child_child_data({1, 2, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_data, child_child_data);
+  }
+
+  // List<List<List<T>>>:
+  // Length : 3
+  // Offsets : 0, 1, 2, 2
+  // Null count: 1
+  // 011
+  // Children :
+  // List<List<T>>:
+  // Length : 2
+  // Offsets : 0, 1, 1
+  // Children :
+  //   List<T>:
+  //   Length : 1
+  //   Offsets : 0, 0
+  //   Children :
+  {
+    // { {{{}}}, {{}}, null }
+    std::vector<bool> valids{true, true, false};
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{{LCW{}}}, {LCW{}}, LCW{}}, valids.begin()};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 2, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 1);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets({0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+  }
+
+  // List<List<List<T>>>:
+  // Length : 3
+  // Offsets : 0, 1, 1, 2
+  // Null count: 1
+  // 101
+  // Children :
+  // List<List<T>>:
+  // Length : 1
+  // Offsets : 0, 1
+  // Children :
+  //   List<T>:
+  //   Length : 1
+  //   Offsets : 0, 0
+  //   Children :
+  {
+    // { {{{}}}, null, {} }
+    std::vector<bool> valids{true, false, true};
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{{LCW{}}}, {LCW{}}, LCW{}}, valids.begin()};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 1);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 1);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets({0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+  }
+
+  // List<List<T>>:
+  // Length : 3
+  // Offsets : 0, 0, 1, 1
+  // Null count: 1
+  // 110
+  // Children :
+  //   List<T>:
+  //   Length : 1
+  //   Offsets : 0, 0
+  //   Children :
+  {
+    // { null, {{}}, {} }
+    std::vector<bool> valids{false, true, true};
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{{LCW{}}}, {LCW{}}, LCW{}}, valids.begin()};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 1, 1});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 1);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+  }
+
+  // List<List<>>:
+  // Length : 3
+  // Offsets : 0, 0, 0, 0
+  // Null count: 3
+  // 000
+  // Children :
+  //   List<>:
+  //   Length : 0
+  //   Offsets :
+  //   Children :
+  {
+    // { null, null, null }
+    std::vector<bool> valids{false, false, false};
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{{LCW{}}}, {LCW{}}, LCW{}}, valids.begin()};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 0);
+  }
+
+  // List<List<>>:
+  // Length : 3
+  // Offsets : 0, 0, 0, 0
+  // Null count: 3
+  // 000
+  // Children :
+  //   List<>:
+  //   Length : 0
+  //   Offsets :
+  //   Children :
+  {
+    // { null, null, null }
+    std::vector<bool> valids{false, false, false};
+    cudf::test::lists_column_wrapper<T, int32_t> list{{LCW{}, {{LCW{}}}, {LCW{}}}, valids.begin()};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+    EXPECT_EQ(lcv.null_count(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 0);
+  }
+
+  // List<List<List<>>>:
+  // Length : 3
+  // Offsets : 0, 1, 2, 2
+  // Children :
+  //   List<List<>>:
+  //   Length : 2
+  //   Offsets : 0, 0, 0
+  //   Null count: 1
+  //   10
+  //   Children :
+  //      List<>:
+  //      Length : 0
+  //      Offsets :
+  //      Children :
+  {
+    // { {null}, {{}}, {} }
+    std::vector<bool> valids{false};
+    cudf::test::lists_column_wrapper<T, int32_t> list{{{{LCW{}}}, valids.begin()}, {LCW{}}, LCW{}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 2, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 2);
+    EXPECT_EQ(childv.null_count(), 1);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 0, 0});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 0);
+  }
+
+  // big mix of truncated stuff
+  // List<List<List<T>
+  // Length : 4
+  // Offsets : 0, 1, 3, 3, 5
+  // Children :
+  //  List<List<T>>:
+  //  Length : 5
+  //  Offsets : 0, 2, 2, 3, 3, 3
+  //  Children :
+  //      List<T>:
+  //      Length : 3
+  //      Offsets : 0, 3, 5, 5
+  //      Children :
+  //        1, 2, 3, 4, 5
+
+  {
+    // { {{{1, 2, 3}, {4, 5}}}, {{}, {{}}}, {}, {{}, {}} }
+    cudf::test::lists_column_wrapper<T, int32_t> list{
+      {{{1, 2, 3}, {4, 5}}}, {LCW{}, {LCW{}}}, LCW{}, {LCW{}, LCW{}}};
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 4);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 5);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 1, 3, 3, 5});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 5);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 6);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 2, 3, 3, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child = childv.child();
+    cudf::lists_column_view child_childv(child_child);
+    EXPECT_EQ(child_childv.size(), 3);
+
+    auto child_child_offsets = child_childv.offsets();
+    EXPECT_EQ(child_child_offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_offsets({0, 3, 5, 5});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_offsets, child_child_offsets);
+
+    auto child_child_data = child_childv.child();
+    EXPECT_EQ(child_child_data.size(), 5);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_child_data({1, 2, 3, 4, 5});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_data, e_child_child_data);
+  }
+}
+
+TEST_F(ListColumnWrapperTest, ListOfStrings)
+{
+  // List<string>, 2 rows
+  //
+  // List<cudf::string_view>:
+  // Length : 2
+  // Offsets : 0, 2, 5
+  // Children :
+  //    one, two, three, four, five
+  {
+    cudf::test::lists_column_wrapper<cudf::string_view> list{{"one", "two"},
+                                                             {"three", "four", "five"}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 2);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 5});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 5);
+    cudf::test::strings_column_wrapper e_data({"one", "two", "three", "four", "five"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+}
+
+TEST_F(ListColumnWrapperTest, ListOfListOfStrings)
+{
+  // List<List<string>>, 2 rows
+  //
+  // List<List<cudf::string_view>>:
+  // Length : 2
+  // Offsets : 0, 2, 4
+  // Children :
+  //    List<cudf::string_view>:
+  //    Length : 4
+  //    Offsets : 0, 2, 5, 6, 8
+  //    Children :
+  //      one, two, three, four, five, eight, nine, ten
+  {
+    cudf::test::lists_column_wrapper<cudf::string_view> list{
+      {{"one", "two"}, {"three", "four", "five"}}, {{"eight"}, {"nine", "ten"}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 2);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 3);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 4});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 4);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 5);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 5, 6, 8});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_data = childv.child();
+    EXPECT_EQ(child_data.size(), 8);
+    cudf::test::strings_column_wrapper e_child_data(
+      {"one", "two", "three", "four", "five", "eight", "nine", "ten"});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_data, child_data);
+  }
+}
+
+TEST_F(ListColumnWrapperTest, ListOfBools)
+{
+  // List<bool>, 1 row
+  //
+  // List<bool>:
+  // Length : 1
+  // Offsets : 0, 2
+  // Children :
+  //   1, 0
+  //
+  {
+    cudf::test::lists_column_wrapper<bool> list{true, false};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 2);
+    cudf::test::fixed_width_column_wrapper<bool> e_data({true, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+
+  // List<bool>, 1 row
+  //
+  // List<bool>:
+  // Length : 1
+  // Offsets : 0, 3
+  // Children :
+  //   1, 0, 0
+  //
+  {
+    cudf::test::lists_column_wrapper<bool> list{{true, false, false}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 1);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 2);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 3});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 3);
+    cudf::test::fixed_width_column_wrapper<bool> e_data({true, false, false});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+}
+
+TEST_F(ListColumnWrapperTest, ListOfBoolsWithValidity)
+{
+  auto valids =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 0; });
+
+  // List<bool>, 3 rows
+  //
+  // List<bool>:
+  // Length : 3
+  // Offsets : 0, 2, 4, 7
+  // Children :
+  //   1, NULL, 0, NULL, 0, NULL, 0
+  {
+    cudf::test::lists_column_wrapper<bool> list{
+      {{true, true}, valids}, {{false, true}, valids}, {{false, true, false}, valids}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 4, 7});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto data = lcv.child();
+    EXPECT_EQ(data.size(), 7);
+    cudf::test::fixed_width_column_wrapper<bool> e_data(
+      {true, true, false, true, false, true, false}, valids);
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_data, data);
+  }
+}
+
+TEST_F(ListColumnWrapperTest, ListOfListOfBools)
+{
+  // List<List<bool>> 3 rows
+  //
+  // List<List<bool>>:
+  // Length : 3
+  // Offsets : 0, 2, 5, 6
+  // Children :
+  //    List<bool>:
+  //    Length : 6
+  //    Offsets : 0, 2, 4, 7, 8, 9, 11
+  //    Children :
+  //      0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1
+  {
+    cudf::test::lists_column_wrapper<bool> list{
+      {{false, true}, {true, true}}, {{true, false, true}, {true}, {true}}, {{false, true}}};
+
+    cudf::lists_column_view lcv(list);
+    EXPECT_EQ(lcv.size(), 3);
+
+    auto offsets = lcv.offsets();
+    EXPECT_EQ(offsets.size(), 4);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_offsets({0, 2, 5, 6});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_offsets, offsets);
+
+    auto child = lcv.child();
+    cudf::lists_column_view childv(child);
+    EXPECT_EQ(childv.size(), 6);
+
+    auto child_offsets = childv.offsets();
+    EXPECT_EQ(child_offsets.size(), 7);
+    cudf::test::fixed_width_column_wrapper<cudf::size_type> e_child_offsets({0, 2, 4, 7, 8, 9, 11});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_offsets, child_offsets);
+
+    auto child_child_data = childv.child();
+    EXPECT_EQ(child_child_data.size(), 11);
+    cudf::test::fixed_width_column_wrapper<bool> e_child_child_data(
+      {false, true, true, true, true, false, true, true, true, false, true});
+    CUDF_TEST_EXPECT_COLUMNS_EQUAL(e_child_child_data, child_child_data);
+  }
+}
+
+TEST_F(ListColumnWrapperTest, MismatchedHierarchies)
+{
+  using T = int;
+
+  // to disambiguate between {} == 0 and {} == List{0}
+  // Also, see note about compiler issues when declaring nested
+  // empty lists in lists_column_wrapper documentation
+  using LCW = cudf::test::lists_column_wrapper<T>;
+
+  // trying to build a column out of a List<List<int>> column, and a List<int> column
+  // is not valid if the leaf lists are not empty.
+  {
+    auto expect_failure = []() { LCW list{{{1, 2, 3}}, {4, 5}}; };
+    EXPECT_THROW(expect_failure(), cudf::logic_error);
+  }
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListsOfStructs)
+{
+  using T = TypeParam;
+
+  auto num_struct_rows = 8;
+  auto numeric_column  = cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 4, 5, 6, 7, 8};
+  auto bool_column     = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 0, 0, 0, 0};
+  auto struct_column = cudf::test::structs_column_wrapper{{numeric_column, bool_column}}.release();
+  EXPECT_EQ(struct_column->size(), num_struct_rows);
+  EXPECT_TRUE(!struct_column->nullable());
+
+  auto lists_column_offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 8}.release();
+  auto num_lists = lists_column_offsets->size() - 1;
+  auto lists_column =
+    make_lists_column(num_lists, std::move(lists_column_offsets), std::move(struct_column), 0, {});
+
+  // Check if child column is unchanged.
+
+  auto expected_numeric_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 4, 5, 6, 7, 8};
+  auto expected_bool_column = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 0, 0, 0, 0};
+  auto expected_struct_column =
+    cudf::test::structs_column_wrapper{{expected_numeric_column, expected_bool_column}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_struct_column,
+                                 cudf::lists_column_view(*lists_column).child());
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListsOfStructsWithValidity)
+{
+  using T = TypeParam;
+
+  auto num_struct_rows = 8;
+  auto numeric_column  = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {1, 2, 3, 4, 5, 6, 7, 8}, {1, 1, 1, 1, 0, 0, 0, 0}};
+  auto bool_column   = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 0, 0, 0, 0};
+  auto struct_column = cudf::test::structs_column_wrapper{{numeric_column, bool_column}}.release();
+  EXPECT_EQ(struct_column->size(), num_struct_rows);
+  EXPECT_TRUE(!struct_column->nullable());
+
+  auto lists_column_offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 8}.release();
+  auto list_null_mask = {1, 1, 0};
+  auto num_lists      = lists_column_offsets->size() - 1;
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_null_mask.begin(), list_null_mask.end());
+  auto lists_column = cudf::make_lists_column(num_lists,
+                                              std::move(lists_column_offsets),
+                                              std::move(struct_column),
+                                              null_count,
+                                              std::move(null_mask));
+
+  // Check if child column is unchanged.
+
+  auto expected_numeric_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{{1, 2, 3, 4}, {1, 1, 1, 1}};
+  auto expected_bool_column = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1};
+  auto expected_struct_column =
+    cudf::test::structs_column_wrapper{{expected_numeric_column, expected_bool_column}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_struct_column,
+                                 cudf::lists_column_view(*lists_column).child());
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListsOfListsOfStructs)
+{
+  using T = TypeParam;
+
+  auto num_struct_rows = 8;
+  auto numeric_column  = cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 4, 5, 6, 7, 8};
+  auto bool_column     = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 0, 0, 0, 0};
+  auto struct_column = cudf::test::structs_column_wrapper{{numeric_column, bool_column}}.release();
+  EXPECT_EQ(struct_column->size(), num_struct_rows);
+  EXPECT_TRUE(!struct_column->nullable());
+
+  auto lists_column_offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 8}.release();
+  auto num_lists = lists_column_offsets->size() - 1;
+  auto lists_column =
+    make_lists_column(num_lists, std::move(lists_column_offsets), std::move(struct_column), 0, {});
+
+  auto lists_of_lists_column_offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 3}.release();
+  auto num_lists_of_lists               = lists_of_lists_column_offsets->size() - 1;
+  auto lists_of_lists_of_structs_column = make_lists_column(
+    num_lists_of_lists, std::move(lists_of_lists_column_offsets), std::move(lists_column), 0, {});
+
+  // Check if child column is unchanged.
+
+  auto expected_numeric_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{1, 2, 3, 4, 5, 6, 7, 8};
+  auto expected_bool_column = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 0, 0, 0, 0};
+  auto expected_struct_column =
+    cudf::test::structs_column_wrapper{{expected_numeric_column, expected_bool_column}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *expected_struct_column,
+    cudf::lists_column_view{cudf::lists_column_view{*lists_of_lists_of_structs_column}.child()}
+      .child());
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, ListsOfListsOfStructsWithValidity)
+{
+  using T = TypeParam;
+
+  auto num_struct_rows = 8;
+  auto numeric_column  = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    {1, 2, 3, 4, 5, 6, 7, 8}, {1, 1, 1, 1, 0, 0, 0, 0}};
+  auto bool_column   = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1, 0, 0, 0, 0};
+  auto struct_column = cudf::test::structs_column_wrapper{{numeric_column, bool_column}}.release();
+  EXPECT_EQ(struct_column->size(), num_struct_rows);
+  EXPECT_TRUE(!struct_column->nullable());
+
+  auto lists_column_offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 4, 8}.release();
+  auto num_lists      = lists_column_offsets->size() - 1;
+  auto list_null_mask = {1, 1, 0};
+  auto [null_mask, null_count] =
+    cudf::test::detail::make_null_mask(list_null_mask.begin(), list_null_mask.end());
+  auto lists_column = cudf::make_lists_column(num_lists,
+                                              std::move(lists_column_offsets),
+                                              std::move(struct_column),
+                                              null_count,
+                                              std::move(null_mask));
+
+  auto lists_of_lists_column_offsets =
+    cudf::test::fixed_width_column_wrapper<cudf::size_type>{0, 2, 3}.release();
+  auto num_lists_of_lists      = lists_of_lists_column_offsets->size() - 1;
+  auto list_of_lists_null_mask = {1, 0};
+
+  std::tie(null_mask, null_count) = cudf::test::detail::make_null_mask(
+    list_of_lists_null_mask.begin(), list_of_lists_null_mask.end());
+  auto lists_of_lists_of_structs_column =
+    cudf::make_lists_column(num_lists_of_lists,
+                            std::move(lists_of_lists_column_offsets),
+                            std::move(lists_column),
+                            null_count,
+                            std::move(null_mask));
+
+  // Check if child column is unchanged.
+
+  auto expected_numeric_column =
+    cudf::test::fixed_width_column_wrapper<T, int32_t>{{1, 2, 3, 4}, {1, 1, 1, 1}};
+  auto expected_bool_column = cudf::test::fixed_width_column_wrapper<bool>{1, 1, 1, 1};
+  auto expected_struct_column =
+    cudf::test::structs_column_wrapper{{expected_numeric_column, expected_bool_column}}.release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(
+    *expected_struct_column,
+    cudf::lists_column_view{cudf::lists_column_view{*lists_of_lists_of_structs_column}.child()}
+      .child());
+}
+
+TYPED_TEST(ListColumnWrapperTestTyped, LargeListsOfStructsWithValidity)
+{
+  using T = TypeParam;
+
+  auto num_struct_rows = 10000;
+
+  // Creating Struct<Numeric, Bool>.
+  auto numeric_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(num_struct_rows),
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 1; })};
+
+  auto bool_iterator =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 3 == 0; });
+  auto bool_column =
+    cudf::test::fixed_width_column_wrapper<bool>(bool_iterator, bool_iterator + num_struct_rows);
+
+  auto struct_validity_iterator =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 5 == 0; });
+  auto struct_column =
+    cudf::test::structs_column_wrapper{
+      {numeric_column, bool_column},
+      std::vector<bool>(struct_validity_iterator, struct_validity_iterator + num_struct_rows)}
+      .release();
+
+  EXPECT_EQ(struct_column->size(), num_struct_rows);
+
+  // Now, use struct_column to create a list column.
+  // Each list has 50 elements.
+  auto num_list_rows = num_struct_rows / 50;
+  auto list_offset_iterator =
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i * 50; });
+  auto list_offset_column = cudf::test::fixed_width_column_wrapper<cudf::size_type>(
+                              list_offset_iterator, list_offset_iterator + num_list_rows + 1)
+                              .release();
+  auto lists_column = make_lists_column(
+    num_list_rows, std::move(list_offset_column), std::move(struct_column), 0, {});
+
+  // List construction succeeded.
+  // Verify that the child is unchanged.
+
+  auto expected_numeric_column = cudf::test::fixed_width_column_wrapper<T, int32_t>{
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(num_struct_rows),
+    cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 2 == 1; })};
+
+  auto expected_bool_column =
+    cudf::test::fixed_width_column_wrapper<bool>(bool_iterator, bool_iterator + num_struct_rows);
+
+  auto expected_struct_column =
+    cudf::test::structs_column_wrapper{
+      {expected_numeric_column, expected_bool_column},
+      std::vector<bool>(struct_validity_iterator, struct_validity_iterator + num_struct_rows)}
+      .release();
+
+  CUDF_TEST_EXPECT_COLUMNS_EQUAL(*expected_struct_column,
+                                 cudf::lists_column_view(*lists_column).child());
+}
diff --git a/cpp/tests/utilities_tests/logger_tests.cpp b/cpp/tests/utilities_tests/logger_tests.cpp
new file mode 100644
index 0000000..9d44e9d
--- /dev/null
+++ b/cpp/tests/utilities_tests/logger_tests.cpp
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+
+#include <cudf/detail/utilities/logger.hpp>
+
+#include <spdlog/sinks/ostream_sink.h>
+
+#include <string>
+
+class LoggerTest : public cudf::test::BaseFixture {
+  std::ostringstream oss;
+  spdlog::level::level_enum prev_level;
+  std::vector<spdlog::sink_ptr> prev_sinks;
+
+ public:
+  LoggerTest() : prev_level{cudf::logger().level()}, prev_sinks{cudf::logger().sinks()}
+  {
+    cudf::logger().sinks() = {std::make_shared<spdlog::sinks::ostream_sink_mt>(oss)};
+    cudf::logger().set_formatter(
+      std::unique_ptr<spdlog::formatter>(new spdlog::pattern_formatter("%v")));
+  }
+  ~LoggerTest()
+  {
+    cudf::logger().set_level(prev_level);
+    cudf::logger().sinks() = prev_sinks;
+  }
+
+  void clear_sink() { oss.str(""); }
+  std::string sink_content() { return oss.str(); }
+};
+
+TEST_F(LoggerTest, Basic)
+{
+  cudf::logger().critical("crit msg");
+  ASSERT_EQ(this->sink_content(), "crit msg\n");
+}
+
+TEST_F(LoggerTest, DefaultLevel)
+{
+  cudf::logger().trace("trace");
+  cudf::logger().debug("debug");
+  cudf::logger().info("info");
+  cudf::logger().warn("warn");
+  cudf::logger().error("error");
+  cudf::logger().critical("critical");
+  ASSERT_EQ(this->sink_content(), "warn\nerror\ncritical\n");
+}
+
+TEST_F(LoggerTest, CustomLevel)
+{
+  cudf::logger().set_level(spdlog::level::warn);
+  cudf::logger().info("info");
+  cudf::logger().warn("warn");
+  ASSERT_EQ(this->sink_content(), "warn\n");
+
+  this->clear_sink();
+
+  cudf::logger().set_level(spdlog::level::debug);
+  cudf::logger().trace("trace");
+  cudf::logger().debug("debug");
+  ASSERT_EQ(this->sink_content(), "debug\n");
+}
diff --git a/cpp/tests/utilities_tests/span_tests.cu b/cpp/tests/utilities_tests/span_tests.cu
new file mode 100644
index 0000000..881a237
--- /dev/null
+++ b/cpp/tests/utilities_tests/span_tests.cu
@@ -0,0 +1,456 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/span.hpp>
+#include <io/utilities/hostdevice_vector.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_lists.hpp>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_vector.hpp>
+
+#include <thrust/device_vector.h>
+#include <thrust/host_vector.h>
+
+#include <cstddef>
+#include <cstring>
+#include <string>
+
+using cudf::device_span;
+using cudf::host_span;
+using cudf::detail::device_2dspan;
+using cudf::detail::host_2dspan;
+using cudf::detail::hostdevice_2dvector;
+
+template <typename T>
+void expect_equivalent(host_span<T> a, host_span<T> b)
+{
+  EXPECT_EQ(a.size(), b.size());
+  EXPECT_EQ(a.data(), b.data());
+}
+
+template <typename T>
+void expect_equivalent(cudf::detail::hostdevice_span<T> a, cudf::detail::hostdevice_span<T> b)
+{
+  EXPECT_EQ(a.size(), b.size());
+  EXPECT_EQ(a.host_ptr(), b.host_ptr());
+}
+
+template <typename Iterator1, typename T>
+void expect_match(Iterator1 expected, size_t expected_size, host_span<T> input)
+{
+  EXPECT_EQ(expected_size, input.size());
+  for (size_t i = 0; i < expected_size; i++) {
+    EXPECT_EQ(*(expected + i), *(input.begin() + i));
+  }
+}
+
+template <typename T>
+void expect_match(std::string expected, host_span<T> input)
+{
+  return expect_match(expected.begin(), expected.size(), input);
+}
+
+template <typename T>
+void expect_match(std::string expected, cudf::detail::hostdevice_span<T> input)
+{
+  return expect_match(expected.begin(), expected.size(), host_span<T>(input));
+}
+
+std::string const hello_world_message = "hello world";
+std::vector<char> create_hello_world_message()
+{
+  return std::vector<char>(hello_world_message.begin(), hello_world_message.end());
+}
+
+class SpanTest : public cudf::test::BaseFixture {};
+
+TEST(SpanTest, CanCreateFullSubspan)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  expect_equivalent(message_span, message_span.subspan(0, message_span.size()));
+}
+
+TEST(SpanTest, CanTakeFirst)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  expect_match("hello", message_span.first(5));
+}
+
+TEST(SpanTest, CanTakeLast)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  expect_match("world", message_span.last(5));
+}
+
+TEST(SpanTest, CanTakeSubspanFull)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  expect_match("hello world", message_span.subspan(0, 11));
+}
+
+TEST(SpanTest, CanTakeSubspanPartial)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  expect_match("lo w", message_span.subspan(3, 4));
+}
+
+TEST(SpanTest, CanGetFront)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  EXPECT_EQ('h', message_span.front());
+}
+
+TEST(SpanTest, CanGetBack)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  EXPECT_EQ('d', message_span.back());
+}
+
+TEST(SpanTest, CanGetData)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  EXPECT_EQ(message.data(), message_span.data());
+}
+
+TEST(SpanTest, CanDetermineEmptiness)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+  auto const empty_span   = host_span<char>();
+
+  EXPECT_FALSE(message_span.empty());
+  EXPECT_TRUE(empty_span.empty());
+}
+
+TEST(SpanTest, CanGetSize)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+  auto const empty_span   = host_span<char>();
+
+  EXPECT_EQ(static_cast<size_t>(11), message_span.size());
+  EXPECT_EQ(static_cast<size_t>(0), empty_span.size());
+}
+
+TEST(SpanTest, CanGetSizeBytes)
+{
+  auto doubles            = std::vector<double>({6, 3, 2});
+  auto const doubles_span = host_span<double>(doubles.data(), doubles.size());
+  auto const empty_span   = host_span<double>();
+
+  EXPECT_EQ(static_cast<size_t>(24), doubles_span.size_bytes());
+  EXPECT_EQ(static_cast<size_t>(0), empty_span.size_bytes());
+}
+
+TEST(SpanTest, CanCopySpan)
+{
+  auto message = create_hello_world_message();
+  host_span<char> message_span_copy;
+
+  {
+    auto const message_span = host_span<char>(message.data(), message.size());
+
+    message_span_copy = message_span;
+  }
+
+  EXPECT_EQ(message.data(), message_span_copy.data());
+  EXPECT_EQ(message.size(), message_span_copy.size());
+}
+
+TEST(SpanTest, CanSubscriptRead)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  EXPECT_EQ('o', message_span[4]);
+}
+
+TEST(SpanTest, CanSubscriptWrite)
+{
+  auto message            = create_hello_world_message();
+  auto const message_span = host_span<char>(message.data(), message.size());
+
+  message_span[4] = 'x';
+
+  EXPECT_EQ('x', message_span[4]);
+}
+
+TEST(SpanTest, CanConstructFromHostContainers)
+{
+  auto std_vector = std::vector<int>(1);
+  auto h_vector   = thrust::host_vector<int>(1);
+
+  (void)host_span<int>(std_vector);
+  (void)host_span<int>(h_vector);
+
+  auto const std_vector_c = std_vector;
+  auto const h_vector_c   = h_vector;
+
+  (void)host_span<int const>(std_vector_c);
+  (void)host_span<int const>(h_vector_c);
+}
+
+// This test is the only place in libcudf's test suite where using a
+// thrust::device_vector (and therefore the CUDA default stream) is acceptable
+// since we are explicitly testing conversions from thrust::device_vector.
+TEST(SpanTest, CanConstructFromDeviceContainers)
+{
+  auto d_thrust_vector = thrust::device_vector<int>(1);
+  auto d_vector        = rmm::device_vector<int>(1);
+  auto d_uvector       = rmm::device_uvector<int>(1, cudf::get_default_stream());
+
+  (void)device_span<int>(d_thrust_vector);
+  (void)device_span<int>(d_vector);
+  (void)device_span<int>(d_uvector);
+
+  auto const& d_thrust_vector_c = d_thrust_vector;
+  auto const& d_vector_c        = d_vector;
+  auto const& d_uvector_c       = d_uvector;
+
+  (void)device_span<int const>(d_thrust_vector_c);
+  (void)device_span<int const>(d_vector_c);
+  (void)device_span<int const>(d_uvector_c);
+}
+
+__global__ void simple_device_kernel(device_span<bool> result) { result[0] = true; }
+
+TEST(SpanTest, CanUseDeviceSpan)
+{
+  auto d_message = cudf::detail::make_zeroed_device_uvector_async<bool>(
+    1, cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  auto d_span = device_span<bool>(d_message.data(), d_message.size());
+
+  simple_device_kernel<<<1, 1, 0, cudf::get_default_stream().value()>>>(d_span);
+
+  ASSERT_TRUE(d_message.element(0, cudf::get_default_stream()));
+}
+
+class MdSpanTest : public cudf::test::BaseFixture {};
+
+TEST(MdSpanTest, CanDetermineEmptiness)
+{
+  auto const vector            = hostdevice_2dvector<int>(1, 2, cudf::get_default_stream());
+  auto const no_rows_vector    = hostdevice_2dvector<int>(0, 2, cudf::get_default_stream());
+  auto const no_columns_vector = hostdevice_2dvector<int>(1, 0, cudf::get_default_stream());
+
+  EXPECT_FALSE(host_2dspan<int const>{vector}.is_empty());
+  EXPECT_FALSE(device_2dspan<int const>{vector}.is_empty());
+  EXPECT_TRUE(host_2dspan<int const>{no_rows_vector}.is_empty());
+  EXPECT_TRUE(device_2dspan<int const>{no_rows_vector}.is_empty());
+  EXPECT_TRUE(host_2dspan<int const>{no_columns_vector}.is_empty());
+  EXPECT_TRUE(device_2dspan<int const>{no_columns_vector}.is_empty());
+}
+
+__global__ void readwrite_kernel(device_2dspan<int> result)
+{
+  if (result[5][6] == 5) {
+    result[5][6] *= 6;
+  } else {
+    result[5][6] = 5;
+  }
+}
+
+TEST(MdSpanTest, DeviceReadWrite)
+{
+  auto vector = hostdevice_2dvector<int>(11, 23, cudf::get_default_stream());
+
+  readwrite_kernel<<<1, 1, 0, cudf::get_default_stream().value()>>>(vector);
+  readwrite_kernel<<<1, 1, 0, cudf::get_default_stream().value()>>>(vector);
+  vector.device_to_host_sync(cudf::get_default_stream());
+  EXPECT_EQ(vector[5][6], 30);
+}
+
+TEST(MdSpanTest, HostReadWrite)
+{
+  auto vector = hostdevice_2dvector<int>(11, 23, cudf::get_default_stream());
+  auto span   = host_2dspan<int>{vector};
+  span[5][6]  = 5;
+  if (span[5][6] == 5) { span[5][6] *= 6; }
+
+  EXPECT_EQ(vector[5][6], 30);
+}
+
+TEST(MdSpanTest, CanGetSize)
+{
+  auto const vector = hostdevice_2dvector<int>(1, 2, cudf::get_default_stream());
+
+  EXPECT_EQ(host_2dspan<int const>{vector}.size(), vector.size());
+  EXPECT_EQ(device_2dspan<int const>{vector}.size(), vector.size());
+}
+
+TEST(MdSpanTest, CanGetCount)
+{
+  auto const vector = hostdevice_2dvector<int>(11, 23, cudf::get_default_stream());
+
+  EXPECT_EQ(host_2dspan<int const>{vector}.count(), 11ul * 23);
+  EXPECT_EQ(device_2dspan<int const>{vector}.count(), 11ul * 23);
+}
+
+auto get_test_hostdevice_vector()
+{
+  auto v = cudf::detail::hostdevice_vector<char>(0, 11, cudf::get_default_stream());
+  for (auto c : create_hello_world_message()) {
+    v.push_back(c);
+  }
+
+  return v;
+}
+
+TEST(HostDeviceSpanTest, CanCreateFullSubspan)
+{
+  auto message = get_test_hostdevice_vector();
+  auto const message_span =
+    cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+
+  expect_equivalent(message_span, message.subspan(0, message_span.size()));
+}
+
+TEST(HostDeviceSpanTest, CanCreateHostSpan)
+{
+  auto message            = get_test_hostdevice_vector();
+  auto const message_span = host_span<char>(message.host_ptr(), message.size());
+  auto const hd_span =
+    cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+
+  expect_equivalent(message_span, cudf::host_span<char>(hd_span));
+}
+
+TEST(HostDeviceSpanTest, CanTakeSubspanFull)
+{
+  auto message = get_test_hostdevice_vector();
+  auto const message_span =
+    cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+
+  expect_match("hello world", message.subspan(0, 11));
+  expect_match("hello world", message_span.subspan(0, 11));
+}
+
+TEST(HostDeviceSpanTest, CanTakeSubspanPartial)
+{
+  auto message = get_test_hostdevice_vector();
+  auto const message_span =
+    cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+
+  expect_match("lo w", message.subspan(3, 4));
+  expect_match("lo w", message_span.subspan(3, 4));
+}
+
+TEST(HostDeviceSpanTest, CanGetData)
+{
+  auto message = get_test_hostdevice_vector();
+  auto const message_span =
+    cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+
+  EXPECT_EQ(message.host_ptr(), message_span.host_ptr());
+}
+
+TEST(HostDeviceSpanTest, CanGetSize)
+{
+  auto message = get_test_hostdevice_vector();
+  auto const message_span =
+    cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+  auto const empty_span = cudf::detail::hostdevice_span<char>();
+
+  EXPECT_EQ(static_cast<size_t>(11), message_span.size());
+  EXPECT_EQ(static_cast<size_t>(0), empty_span.size());
+}
+
+TEST(HostDeviceSpanTest, CanGetSizeBytes)
+{
+  auto doubles     = std::vector<double>({6, 3, 2});
+  auto doubles_hdv = cudf::detail::hostdevice_vector<double>(0, 3, cudf::get_default_stream());
+  for (auto d : doubles) {
+    doubles_hdv.push_back(d);
+  }
+  auto const doubles_span = cudf::detail::hostdevice_span<double>(doubles_hdv);
+  auto const empty_span   = cudf::detail::hostdevice_span<double>();
+
+  EXPECT_EQ(static_cast<size_t>(24), doubles_span.size_bytes());
+  EXPECT_EQ(static_cast<size_t>(0), empty_span.size_bytes());
+}
+
+TEST(HostDeviceSpanTest, CanCopySpan)
+{
+  auto message = get_test_hostdevice_vector();
+  cudf::detail::hostdevice_span<char> message_span_copy;
+
+  {
+    auto const message_span =
+      cudf::detail::hostdevice_span<char>(message.host_ptr(), message.device_ptr(), message.size());
+
+    message_span_copy = message_span;
+  }
+
+  EXPECT_EQ(message.host_ptr(), message_span_copy.host_ptr());
+  EXPECT_EQ(message.device_ptr(), message_span_copy.device_ptr());
+  EXPECT_EQ(message.size(), message_span_copy.size());
+}
+
+TEST(HostDeviceSpanTest, CanSendToDevice)
+{
+  auto message = get_test_hostdevice_vector();
+
+  message.host_to_device_sync(cudf::get_default_stream());
+
+  char d_message[12];
+  cudaMemcpy(d_message, message.device_ptr(), 11, cudaMemcpyDefault);
+  d_message[11] = '\0';
+
+  EXPECT_EQ(11, strlen(d_message));
+  EXPECT_EQ(std::string(d_message), hello_world_message);
+}
+
+__global__ void simple_device_char_kernel(device_span<char> result)
+{
+  char const* str = "world hello";
+  for (int offset = 0; offset < result.size(); ++offset) {
+    result.data()[offset] = str[offset];
+  }
+}
+
+TEST(HostDeviceSpanTest, CanGetFromDevice)
+{
+  auto message = get_test_hostdevice_vector();
+  message.host_to_device_sync(cudf::get_default_stream());
+  simple_device_char_kernel<<<1, 1, 0, cudf::get_default_stream()>>>(message);
+
+  message.device_to_host_sync(cudf::get_default_stream());
+  expect_match("world hello", cudf::detail::hostdevice_span<char>(message));
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/cpp/tests/utilities_tests/type_check_tests.cpp b/cpp/tests/utilities_tests/type_check_tests.cpp
new file mode 100644
index 0000000..a8f7eaf
--- /dev/null
+++ b/cpp/tests/utilities_tests/type_check_tests.cpp
@@ -0,0 +1,218 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/utilities/type_checks.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/type_lists.hpp>
+
+namespace cudf {
+namespace test {
+
+template <typename T>
+struct ColumnTypeCheckTestTyped : public cudf::test::BaseFixture {};
+
+struct ColumnTypeCheckTest : public cudf::test::BaseFixture {};
+
+TYPED_TEST_SUITE(ColumnTypeCheckTestTyped, cudf::test::FixedWidthTypes);
+
+TYPED_TEST(ColumnTypeCheckTestTyped, SameFixedWidth)
+{
+  fixed_width_column_wrapper<TypeParam> lhs{1, 1}, rhs{2};
+  EXPECT_TRUE(column_types_equal(lhs, rhs));
+}
+
+TEST_F(ColumnTypeCheckTest, SameString)
+{
+  strings_column_wrapper lhs{{'a', 'a'}}, rhs{{'b'}};
+  EXPECT_TRUE(column_types_equal(lhs, rhs));
+
+  strings_column_wrapper lhs2{}, rhs2{{'b'}};
+  EXPECT_TRUE(column_types_equal(lhs2, rhs2));
+
+  strings_column_wrapper lhs3{}, rhs3{};
+  EXPECT_TRUE(column_types_equal(lhs3, rhs3));
+}
+
+TEST_F(ColumnTypeCheckTest, SameList)
+{
+  using LCW = lists_column_wrapper<int32_t>;
+
+  LCW lhs{}, rhs{};
+  EXPECT_TRUE(column_types_equal(lhs, rhs));
+
+  LCW lhs2{{1, 2, 3}}, rhs2{{4, 5}};
+  EXPECT_TRUE(column_types_equal(lhs2, rhs2));
+
+  LCW lhs3{{LCW{1}, LCW{2, 3}}}, rhs3{{LCW{4, 5}}};
+  EXPECT_TRUE(column_types_equal(lhs3, rhs3));
+
+  LCW lhs4{{LCW{1}, LCW{}, LCW{2, 3}}}, rhs4{{LCW{4, 5}, LCW{}}};
+  EXPECT_TRUE(column_types_equal(lhs4, rhs4));
+}
+
+TYPED_TEST(ColumnTypeCheckTestTyped, SameDictionary)
+{
+  using DCW = dictionary_column_wrapper<TypeParam>;
+  DCW lhs{1, 1, 2, 3}, rhs{5, 5};
+  EXPECT_TRUE(column_types_equal(lhs, rhs));
+
+  DCW lhs2{}, rhs2{};
+  EXPECT_TRUE(column_types_equal(lhs2, rhs2));
+}
+
+TEST_F(ColumnTypeCheckTest, SameStruct)
+{
+  using SCW      = structs_column_wrapper;
+  using FCW      = fixed_width_column_wrapper<int32_t>;
+  using StringCW = strings_column_wrapper;
+  using LCW      = lists_column_wrapper<int32_t>;
+  using DCW      = dictionary_column_wrapper<int32_t>;
+
+  FCW lf1{1, 2, 3}, rf1{0, 1};
+  StringCW lf2{"a", "bb", ""}, rf2{"cc", "d"};
+  LCW lf3{LCW{1, 2}, LCW{}, LCW{4}}, rf3{LCW{1}, LCW{2}};
+  DCW lf4{5, 5, 5}, rf4{9, 9};
+
+  SCW lhs{lf1, lf2, lf3, lf4}, rhs{rf1, rf2, rf3, rf4};
+  EXPECT_TRUE(column_types_equal(lhs, rhs));
+}
+
+TEST_F(ColumnTypeCheckTest, DifferentBasics)
+{
+  fixed_width_column_wrapper<int32_t> lhs1{1, 1};
+  strings_column_wrapper rhs1{"a", "bb"};
+
+  EXPECT_FALSE(column_types_equal(lhs1, rhs1));
+
+  lists_column_wrapper<string_view> lhs2{{"hello"}, {"world", "!"}};
+  strings_column_wrapper rhs2{"", "kk"};
+
+  EXPECT_FALSE(column_types_equal(lhs2, rhs2));
+
+  fixed_width_column_wrapper<int32_t> lhs3{1, 1};
+  dictionary_column_wrapper<int32_t> rhs3{2, 2};
+
+  EXPECT_FALSE(column_types_equal(lhs3, rhs3));
+
+  lists_column_wrapper<int32_t> lhs4{{8, 8, 8}, {10, 10}};
+  structs_column_wrapper rhs4{rhs2, rhs3};
+
+  EXPECT_FALSE(column_types_equal(lhs4, rhs4));
+}
+
+TEST_F(ColumnTypeCheckTest, DifferentFixedWidth)
+{
+  fixed_width_column_wrapper<int32_t> lhs1{1, 1};
+  fixed_width_column_wrapper<int64_t> rhs1{2};
+
+  EXPECT_FALSE(column_types_equal(lhs1, rhs1));
+
+  fixed_width_column_wrapper<float> lhs2{1, 1};
+  fixed_width_column_wrapper<double> rhs2{2};
+
+  EXPECT_FALSE(column_types_equal(lhs2, rhs2));
+
+  fixed_width_column_wrapper<timestamp_ms> lhs3{1, 1};
+  fixed_width_column_wrapper<timestamp_us> rhs3{2};
+
+  EXPECT_FALSE(column_types_equal(lhs3, rhs3));
+
+  fixed_width_column_wrapper<duration_D> lhs4{};
+  fixed_width_column_wrapper<duration_us> rhs4{42};
+
+  EXPECT_FALSE(column_types_equal(lhs4, rhs4));
+
+  // Same rep, different scale
+  fixed_point_column_wrapper<int32_t> lhs5({10000}, numeric::scale_type{-3});
+  fixed_point_column_wrapper<int32_t> rhs5({10000}, numeric::scale_type{0});
+
+  EXPECT_FALSE(column_types_equal(lhs5, rhs5));
+  EXPECT_TRUE(column_types_equivalent(lhs5, rhs5));
+
+  // Different rep, same scale
+  fixed_point_column_wrapper<int32_t> lhs6({10000}, numeric::scale_type{-1});
+  fixed_point_column_wrapper<int64_t> rhs6({4200}, numeric::scale_type{-1});
+
+  EXPECT_FALSE(column_types_equal(lhs6, rhs6));
+}
+
+TEST_F(ColumnTypeCheckTest, DifferentDictionary)
+{
+  dictionary_column_wrapper<int32_t, uint32_t> lhs1{1, 1, 1, 2, 2, 3};
+  dictionary_column_wrapper<int64_t, uint32_t> rhs1{0, 0, 42, 42};
+
+  EXPECT_FALSE(column_types_equal(lhs1, rhs1));
+
+  dictionary_column_wrapper<double, uint32_t> lhs2{3.14, 3.14, 5.00};
+  dictionary_column_wrapper<int64_t, uint32_t> rhs2{0, 0, 42, 42};
+
+  EXPECT_FALSE(column_types_equal(lhs2, rhs2));
+
+  dictionary_column_wrapper<int32_t, uint32_t> lhs3{1, 1, 1, 2, 2, 3};
+  dictionary_column_wrapper<duration_s, uint32_t> rhs3{8, 8};
+
+  EXPECT_FALSE(column_types_equal(lhs3, rhs3));
+
+  dictionary_column_wrapper<int32_t, uint32_t> lhs4{1, 1, 2, 3}, rhs4{};
+  EXPECT_FALSE(column_types_equal(lhs4, rhs4));
+}
+
+TEST_F(ColumnTypeCheckTest, DifferentLists)
+{
+  using LCW_i = lists_column_wrapper<int32_t>;
+  using LCW_f = lists_column_wrapper<float>;
+
+  // Different nested level
+  LCW_i lhs1{LCW_i{1, 1, 2, 3}, LCW_i{}, LCW_i{42, 42}};
+  LCW_i rhs1{LCW_i{LCW_i{8, 8, 8}, LCW_i{9, 9}}, LCW_i{LCW_i{42, 42}}};
+
+  EXPECT_FALSE(column_types_equal(lhs1, rhs1));
+
+  // Different base column type
+  LCW_i lhs2{LCW_i{1, 1, 2, 3}, LCW_i{}, LCW_i{42, 42}};
+  LCW_f rhs2{LCW_f{9.0, 9.1}, LCW_f{3.14}, LCW_f{}};
+
+  EXPECT_FALSE(column_types_equal(lhs2, rhs2));
+}
+
+TEST_F(ColumnTypeCheckTest, DifferentStructs)
+{
+  fixed_width_column_wrapper<int32_t> lf1{1, 1, 1};
+  fixed_width_column_wrapper<int64_t> rf1{2, 2};
+
+  structs_column_wrapper lhs1{lf1};
+  structs_column_wrapper rhs1{rf1};
+
+  EXPECT_FALSE(column_types_equal(lhs1, rhs1));
+
+  fixed_width_column_wrapper<int32_t> lf2{1, 1, 1};
+  fixed_width_column_wrapper<int32_t> rf2{2, 2};
+
+  strings_column_wrapper lf3{"a", "b", "c"};
+
+  structs_column_wrapper lhs2{lf2, lf3};
+  structs_column_wrapper rhs2{rf2};
+
+  EXPECT_FALSE(column_types_equal(lhs2, rhs2));
+}
+
+}  // namespace test
+}  // namespace cudf
diff --git a/cpp/tests/utilities_tests/type_list_tests.cpp b/cpp/tests/utilities_tests/type_list_tests.cpp
new file mode 100644
index 0000000..d0b10b7
--- /dev/null
+++ b/cpp/tests/utilities_tests/type_list_tests.cpp
@@ -0,0 +1,250 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/cudf_gtest.hpp>
+#include <cudf_test/type_list_utilities.hpp>
+
+using namespace cudf::test;  // this will make reading code way easier
+
+namespace {
+// Work around to remove parentheses surrounding a type
+template <typename T>
+struct argument_type;
+template <typename T, typename U>
+struct argument_type<T(U)> {
+  using type = U;
+};
+}  // namespace
+/**
+ * @brief Performs a compile-time check that two types are equivalent.
+ *
+ * @note In order to work around commas in macros, any type containing commas
+ * should be wrapped in parentheses.
+ *
+ * Example:
+ * ```
+ * EXPECT_SAME_TYPE(int, int);
+ *
+ * EXPECT_SAME_TYPE(int, float); // compile error
+ *
+ * // Parentheses around types with commas
+ * EXPECT_SAME_TYPE((std::map<int, float>), (std::map<int, float>));
+ * ```
+ */
+#define EXPECT_SAME_TYPE(expected, actual) \
+  static_assert(                           \
+    std::is_same_v<argument_type<void(expected)>::type, argument_type<void(actual)>::type>, "");
+
+/**
+ * @brief Return a string of the demangled name of a type `T`
+ *
+ * This is useful for debugging Type list utilities.
+ *
+ * @tparam T The type whose name is returned as a string
+ * @return std::string The demangled name of `T`
+ */
+template <typename T>
+std::string type_name()
+{
+  int status;
+  char* realname;
+  realname = abi::__cxa_demangle(typeid(T).name(), 0, 0, &status);
+  std::string name{realname};
+  free(realname);
+  return name;
+}
+
+TEST(TypeList, GetSize)
+{
+  static_assert(GetSize<Types<>> == 0, "");
+  static_assert(GetSize<Types<int>> == 1, "");
+  static_assert(GetSize<Types<int, int>> == 2, "");
+  static_assert(GetSize<Types<int, void>> == 2, "");
+}
+
+TEST(TypeList, GetType)
+{
+  EXPECT_SAME_TYPE((GetType<int, 0>), int);
+  EXPECT_SAME_TYPE((GetType<Types<int, float, double>, 0>), int);
+  EXPECT_SAME_TYPE((GetType<Types<int, float, double>, 1>), float);
+  EXPECT_SAME_TYPE((GetType<Types<int, float, double>, 2>), double);
+}
+
+TEST(TypeList, Concat)
+{
+  EXPECT_SAME_TYPE(Concat<>, Types<>);
+  EXPECT_SAME_TYPE((Concat<Types<long, void*, char*>>), (Types<long, void*, char*>));
+
+  EXPECT_SAME_TYPE((Concat<Types<long, void*, char*>, Types<float, char, double>>),
+                   (Types<long, void*, char*, float, char, double>));
+
+  EXPECT_SAME_TYPE(
+    (Concat<Types<long, void*, char*>, Types<float, char, double>, Types<int*, long*, unsigned>>),
+    (Types<long, void*, char*, float, char, double, int*, long*, unsigned>));
+}
+
+TEST(TypeList, Flatten)
+{
+  EXPECT_SAME_TYPE(Flatten<Types<>>, Types<>);
+  EXPECT_SAME_TYPE(Flatten<Types<int>>, Types<int>);
+  EXPECT_SAME_TYPE((Flatten<Types<int, double>>), (Types<int, double>));
+  EXPECT_SAME_TYPE((Flatten<Types<Types<int, double>, float>>), (Types<int, double, float>));
+  EXPECT_SAME_TYPE((Flatten<Types<Types<int, Types<double>>, float>>), (Types<int, double, float>));
+}
+
+TEST(TypeList, CrossProduct)
+{
+  EXPECT_SAME_TYPE(CrossProduct<>, Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<>, Types<>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<>, Types<int>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<>, Types<int, double>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<>, Types<int, double>, Types<>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<>, Types<>, Types<>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, double>, Types<>, Types<>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<int>, Types<>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, double>, Types<>>), Types<>);
+  EXPECT_SAME_TYPE((CrossProduct<Types<int>, Types<int>>), (Types<Types<int, int>>));
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, double>, Types<int>>),
+                   (Types<Types<int, int>, Types<double, int>>));
+  EXPECT_SAME_TYPE((CrossProduct<Types<int>, Types<double, char>>),
+                   (Types<Types<int, double>, Types<int, char>>));
+  EXPECT_SAME_TYPE(
+    (CrossProduct<Types<int, double>, Types<short, char>>),
+    (Types<Types<int, short>, Types<int, char>, Types<double, short>, Types<double, char>>));
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, double>, Types<int>, Types<float>>),
+                   (Types<Types<int, int, float>, Types<double, int, float>>));
+
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, double>, Types<int>, Types<float, char>>),
+                   (Types<Types<int, int, float>,
+                          Types<int, int, char>,
+                          Types<double, int, float>,
+                          Types<double, int, char>>));
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, double>, Types<int, short>, Types<float, char>>),
+                   (Types<Types<int, int, float>,
+                          Types<int, int, char>,
+                          Types<int, short, float>,
+                          Types<int, short, char>,
+                          Types<double, int, float>,
+                          Types<double, int, char>,
+                          Types<double, short, float>,
+                          Types<double, short, char>>));
+  EXPECT_SAME_TYPE((CrossProduct<Types<int, float>, int>),
+                   (Types<Types<int, int>, Types<float, int>>));
+  EXPECT_SAME_TYPE((CrossProduct<int, Types<int, float>>),
+                   (Types<Types<int, int>, Types<int, float>>));
+}
+
+TEST(TypeList, AllSame)
+{
+  static_assert(AllSame::Call<Types<int, int>>::value, "");
+  static_assert(AllSame::Call<Types<int, int>>::value, "");
+  static_assert(!AllSame::Call<Types<bool, int>>::value, "");
+
+  static_assert(AllSame::Call<int, int>::value, "");
+  static_assert(!AllSame::Call<int, bool>::value, "");
+
+  static_assert(AllSame::Call<int, int, int>::value, "");
+  static_assert(!AllSame::Call<int, float, int>::value, "");
+  static_assert(!AllSame::Call<int, int, float>::value, "");
+}
+
+TEST(TypeList, Exists)
+{
+  static_assert(Exists<int, Types<int, char, float>>, "");
+  static_assert(!Exists<int, Types<double, char, float>>, "");
+  static_assert(!Exists<int, Types<>>, "");
+  static_assert(Exists<int, Types<double, char, float, int>>, "");
+  static_assert(!Exists<int, Types<double>>, "");
+  static_assert(Exists<int, Types<int>>, "");
+}
+
+TEST(TypeList, ContainedIn)
+{
+  static_assert(ContainedIn<Types<Types<int, char>>>::Call<Types<int, char>>::value, "");
+  static_assert(!ContainedIn<Types<Types<int, char>>>::Call<Types<int, float>>::value, "");
+  static_assert(!ContainedIn<Types<>>::Call<Types<int, float>>::value, "");
+  static_assert(
+    ContainedIn<Types<Types<int, float>, Types<char, char>>>::Call<Types<int, float>>::value, "");
+  static_assert(
+    !ContainedIn<Types<Types<int, float>, Types<char, char>>>::Call<Types<int, double>>::value, "");
+  static_assert(ContainedIn<Types<Types<int, float>, Types<>>>::Call<Types<>>::value, "");
+  static_assert(!ContainedIn<Types<Types<int, float>, Types<int>>>::Call<Types<>>::value, "");
+}
+
+TEST(TypeList, RemoveIf)
+{
+  EXPECT_SAME_TYPE((RemoveIf<AllSame, Types<>>), Types<>);
+
+  EXPECT_SAME_TYPE((RemoveIf<AllSame, Types<Types<int, int, int>>>), Types<>);
+
+  EXPECT_SAME_TYPE((RemoveIf<AllSame, Types<Types<int, float, int>>>),
+                   (Types<Types<int, float, int>>));
+
+  EXPECT_SAME_TYPE(
+    (RemoveIf<AllSame,
+              Types<Types<int, float, char>, Types<int, int, int>, Types<int, int, char>>>),
+    (Types<Types<int, float, char>, Types<int, int, char>>));
+
+  EXPECT_SAME_TYPE(
+    (RemoveIf<AllSame,
+              Types<Types<int, float, char>, Types<int, float, char>, Types<int, int, char>>>),
+    (Types<Types<int, float, char>, Types<int, float, char>, Types<int, int, char>>));
+
+  EXPECT_SAME_TYPE((RemoveIf<ContainedIn<Types<Types<int, char>, Types<float, int>>>,
+                             Types<Types<char, char>, Types<float, int>, Types<int, int>>>),
+                   (Types<Types<char, char>, Types<int, int>>));
+}
+
+TEST(TypeList, Transform)
+{
+  EXPECT_SAME_TYPE((Transform<Repeat<2>, Types<int, float>>),
+                   (Types<Types<int, int>, Types<float, float>>));
+
+  EXPECT_SAME_TYPE((Transform<Repeat<1>, Types<int, float>>), (Types<Types<int>, Types<float>>));
+  EXPECT_SAME_TYPE((Transform<Repeat<0>, Types<int, float>>), (Types<Types<>, Types<>>));
+  EXPECT_SAME_TYPE((Transform<Repeat<2>, Types<int>>), (Types<Types<int, int>>));
+  EXPECT_SAME_TYPE((Transform<Repeat<1>, Types<int>>), Types<Types<int>>);
+  EXPECT_SAME_TYPE((Transform<Repeat<0>, Types<>>), Types<>);
+}
+
+TEST(TypeList, Append)
+{
+  EXPECT_SAME_TYPE(Append<Types<>>, Types<>);
+  EXPECT_SAME_TYPE((Append<Types<>, int>), Types<int>);
+  EXPECT_SAME_TYPE(Append<Types<int>>, Types<int>);
+  EXPECT_SAME_TYPE((Append<Types<int>, float>), (Types<int, float>));
+  EXPECT_SAME_TYPE((Append<Types<int>, float, char>), (Types<int, float, char>));
+}
+
+TEST(TypeList, Remove)
+{
+  EXPECT_SAME_TYPE((Remove<Types<int, float, char>, 1>), (Types<int, char>));
+  EXPECT_SAME_TYPE((Remove<Types<int, float, char>, 0, 2>), Types<float>);
+  EXPECT_SAME_TYPE((Remove<Types<int, char>>), (Types<int, char>));
+  EXPECT_SAME_TYPE((Remove<Types<int>, 0>), Types<>);
+  EXPECT_SAME_TYPE((Remove<Types<int, char>, 0, 1>), Types<>);
+  EXPECT_SAME_TYPE(Remove<Types<>>, Types<>);
+}
+
+TEST(TypeList, Unique)
+{
+  EXPECT_SAME_TYPE(Unique<Types<>>, Types<>);
+  EXPECT_SAME_TYPE((Unique<Types<int, char, float>>), (Types<int, char, float>));
+  EXPECT_SAME_TYPE((Unique<Types<int, int, float>>), (Types<int, float>));
+  EXPECT_SAME_TYPE((Unique<Types<int, char, char, float>>), (Types<int, char, float>));
+  EXPECT_SAME_TYPE((Unique<Types<int, char, float, float>>), (Types<int, char, float>));
+  EXPECT_SAME_TYPE((Unique<Types<int, float, int, float>>), (Types<int, float>));
+}
diff --git a/cpp/tests/wrappers/timestamps_test.cu b/cpp/tests/wrappers/timestamps_test.cu
new file mode 100644
index 0000000..f7d3df1
--- /dev/null
+++ b/cpp/tests/wrappers/timestamps_test.cu
@@ -0,0 +1,213 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf_test/base_fixture.hpp>
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+#include <cudf_test/timestamp_utilities.cuh>
+#include <cudf_test/type_lists.hpp>
+
+#include <cudf/binaryop.hpp>
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/wrappers/durations.hpp>
+#include <cudf/wrappers/timestamps.hpp>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/logical.h>
+#include <thrust/sequence.h>
+
+template <typename T>
+struct ChronoColumnTest : public cudf::test::BaseFixture {
+  cudf::size_type size() { return cudf::size_type(100); }
+  cudf::data_type type() { return cudf::data_type{cudf::type_to_id<T>()}; }
+};
+
+template <typename ChronoT>
+struct compare_chrono_elements_to_primitive_representation {
+  cudf::column_device_view primitives;
+  cudf::column_device_view chronos;
+
+  compare_chrono_elements_to_primitive_representation(cudf::column_device_view& _primitives,
+                                                      cudf::column_device_view& _chronos)
+    : primitives(_primitives), chronos(_chronos)
+  {
+  }
+
+  template <typename T = ChronoT, std::enable_if_t<cudf::is_timestamp<T>()>* = nullptr>
+  __host__ __device__ bool operator()(const int32_t element_index)
+  {
+    using Primitive = typename ChronoT::rep;
+    auto primitive  = primitives.element<Primitive>(element_index);
+    auto timestamp  = chronos.element<ChronoT>(element_index);
+    return primitive == timestamp.time_since_epoch().count();
+  }
+
+  template <typename T = ChronoT, std::enable_if_t<cudf::is_duration<T>()>* = nullptr>
+  __host__ __device__ bool operator()(const int32_t element_index)
+  {
+    using Primitive = typename ChronoT::rep;
+    auto primitive  = primitives.element<Primitive>(element_index);
+    auto dur        = chronos.element<ChronoT>(element_index);
+    return primitive == dur.count();
+  }
+};
+
+TYPED_TEST_SUITE(ChronoColumnTest, cudf::test::ChronoTypes);
+
+TYPED_TEST(ChronoColumnTest, ChronoDurationsMatchPrimitiveRepresentation)
+{
+  using T   = TypeParam;
+  using Rep = typename T::rep;
+  using namespace cuda::std::chrono;
+
+  auto start      = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto stop       = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+  auto chrono_col = cudf::test::generate_timestamps<T>(
+    this->size(), cudf::test::time_point_ms(start), cudf::test::time_point_ms(stop));
+
+  // round-trip through the host to copy `chrono_col` values
+  // to a new fixed_width_column_wrapper `primitive_col`
+  auto const [chrono_col_data, chrono_col_mask] = cudf::test::to_host<Rep>(chrono_col);
+
+  auto primitive_col =
+    cudf::test::fixed_width_column_wrapper<Rep>(chrono_col_data.begin(), chrono_col_data.end());
+
+  rmm::device_uvector<int32_t> indices(this->size(), cudf::get_default_stream());
+  thrust::sequence(rmm::exec_policy(cudf::get_default_stream()), indices.begin(), indices.end());
+  EXPECT_TRUE(thrust::all_of(rmm::exec_policy(cudf::get_default_stream()),
+                             indices.begin(),
+                             indices.end(),
+                             compare_chrono_elements_to_primitive_representation<T>{
+                               *cudf::column_device_view::create(primitive_col),
+                               *cudf::column_device_view::create(chrono_col)}));
+}
+
+template <typename ChronoT>
+struct compare_chrono_elements {
+  cudf::binary_operator comp;
+  cudf::column_device_view lhs;
+  cudf::column_device_view rhs;
+
+  compare_chrono_elements(cudf::binary_operator _comp,
+                          cudf::column_device_view& _lhs,
+                          cudf::column_device_view& _rhs)
+    : comp(_comp), lhs(_lhs), rhs(_rhs)
+  {
+  }
+
+  __host__ __device__ bool operator()(const int32_t element_index)
+  {
+    auto lhs_elt = lhs.element<ChronoT>(element_index);
+    auto rhs_elt = rhs.element<ChronoT>(element_index);
+    switch (comp) {
+      case cudf::binary_operator::LESS: return lhs_elt < rhs_elt;
+      case cudf::binary_operator::GREATER: return lhs_elt > rhs_elt;
+      case cudf::binary_operator::LESS_EQUAL: return lhs_elt <= rhs_elt;
+      case cudf::binary_operator::GREATER_EQUAL: return lhs_elt >= rhs_elt;
+      default: return false;
+    }
+  }
+};
+
+TYPED_TEST(ChronoColumnTest, ChronosCanBeComparedInDeviceCode)
+{
+  using T = TypeParam;
+  using namespace cuda::std::chrono;
+
+  auto start_lhs = milliseconds(-2500000000000);  // Sat, 11 Oct 1890 19:33:20 GMT
+  auto start_rhs = milliseconds(-2400000000000);  // Tue, 12 Dec 1893 05:20:00 GMT
+  auto stop_lhs  = milliseconds(2500000000000);   // Mon, 22 Mar 2049 04:26:40 GMT
+  auto stop_rhs  = milliseconds(2600000000000);   // Wed, 22 May 2052 14:13:20 GMT
+
+  auto chrono_lhs_col = cudf::test::generate_timestamps<T>(
+    this->size(), cudf::test::time_point_ms(start_lhs), cudf::test::time_point_ms(stop_lhs));
+
+  auto chrono_rhs_col = cudf::test::generate_timestamps<T>(
+    this->size(), cudf::test::time_point_ms(start_rhs), cudf::test::time_point_ms(stop_rhs));
+
+  rmm::device_uvector<int32_t> indices(this->size(), cudf::get_default_stream());
+  thrust::sequence(rmm::exec_policy(cudf::get_default_stream()), indices.begin(), indices.end());
+
+  EXPECT_TRUE(thrust::all_of(
+    rmm::exec_policy(cudf::get_default_stream()),
+    indices.begin(),
+    indices.end(),
+    compare_chrono_elements<TypeParam>{cudf::binary_operator::LESS,
+                                       *cudf::column_device_view::create(chrono_lhs_col),
+                                       *cudf::column_device_view::create(chrono_rhs_col)}));
+
+  EXPECT_TRUE(thrust::all_of(
+    rmm::exec_policy(cudf::get_default_stream()),
+    indices.begin(),
+    indices.end(),
+    compare_chrono_elements<TypeParam>{cudf::binary_operator::GREATER,
+                                       *cudf::column_device_view::create(chrono_rhs_col),
+                                       *cudf::column_device_view::create(chrono_lhs_col)}));
+
+  EXPECT_TRUE(thrust::all_of(
+    rmm::exec_policy(cudf::get_default_stream()),
+    indices.begin(),
+    indices.end(),
+    compare_chrono_elements<TypeParam>{cudf::binary_operator::LESS_EQUAL,
+                                       *cudf::column_device_view::create(chrono_lhs_col),
+                                       *cudf::column_device_view::create(chrono_lhs_col)}));
+
+  EXPECT_TRUE(thrust::all_of(
+    rmm::exec_policy(cudf::get_default_stream()),
+    indices.begin(),
+    indices.end(),
+    compare_chrono_elements<TypeParam>{cudf::binary_operator::GREATER_EQUAL,
+                                       *cudf::column_device_view::create(chrono_rhs_col),
+                                       *cudf::column_device_view::create(chrono_rhs_col)}));
+}
+
+TYPED_TEST(ChronoColumnTest, ChronoFactoryNullMaskAsParm)
+{
+  rmm::device_buffer null_mask{create_null_mask(this->size(), cudf::mask_state::ALL_NULL)};
+  auto column = make_fixed_width_column(cudf::data_type{cudf::type_to_id<TypeParam>()},
+                                        this->size(),
+                                        std::move(null_mask),
+                                        this->size());
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(this->size(), column->null_count());
+  EXPECT_TRUE(column->nullable());
+  EXPECT_TRUE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+TYPED_TEST(ChronoColumnTest, ChronoFactoryNullMaskAsEmptyParm)
+{
+  rmm::device_buffer null_mask{};
+  auto column = make_fixed_width_column(
+    cudf::data_type{cudf::type_to_id<TypeParam>()}, this->size(), std::move(null_mask), 0);
+
+  EXPECT_EQ(column->type(), cudf::data_type{cudf::type_to_id<TypeParam>()});
+  EXPECT_EQ(column->size(), this->size());
+  EXPECT_EQ(0, column->null_count());
+  EXPECT_FALSE(column->nullable());
+  EXPECT_FALSE(column->has_nulls());
+  EXPECT_EQ(0, column->num_children());
+}
+
+CUDF_TEST_PROGRAM_MAIN()
diff --git a/dependencies.yaml b/dependencies.yaml
new file mode 100644
index 0000000..72aaaa1
--- /dev/null
+++ b/dependencies.yaml
@@ -0,0 +1,676 @@
+# Dependency list for https://github.com/rapidsai/dependency-file-generator
+files:
+  all:
+    output: conda
+    matrix:
+      cuda: ["11.8", "12.0"]
+      arch: [x86_64]
+    includes:
+      - build_all
+      - build_cpp
+      - build_wheels
+      - build_python
+      - build_python_common
+      - cudatoolkit
+      - develop
+      - docs
+      - notebooks
+      - py_version
+      - run_common
+      - run_cudf
+      - run_dask_cudf
+      - run_cudf_kafka
+      - run_custreamz
+      - test_cpp
+      - test_python_common
+      - test_python_cudf
+      - test_python_dask_cudf
+  test_cpp:
+    output: none
+    includes:
+      - cudatoolkit
+      - test_cpp
+      - libarrow_run
+  test_python:
+    output: none
+    includes:
+      - cudatoolkit
+      - py_version
+      - test_python_common
+      - test_python_cudf
+      - test_python_dask_cudf
+      - pyarrow_run
+  test_java:
+    output: none
+    includes:
+      - build_all
+      - libarrow_run
+      - cudatoolkit
+      - test_java
+  test_notebooks:
+    output: none
+    includes:
+      - notebooks
+      - py_version
+  checks:
+    output: none
+    includes:
+      - develop
+      - py_version
+  docs:
+    output: none
+    includes:
+      - cudatoolkit
+      - docs
+      - py_version
+  py_build_cudf:
+    output: pyproject
+    pyproject_dir: python/cudf
+    extras:
+      table: build-system
+    includes:
+      - build_all
+      - build_python
+      - build_python_common
+      - build_wheels
+  py_run_cudf:
+    output: pyproject
+    pyproject_dir: python/cudf
+    extras:
+      table: project
+    includes:
+      - run_common
+      - run_cudf
+      - pyarrow_run
+  py_test_cudf:
+    output: pyproject
+    pyproject_dir: python/cudf
+    extras:
+      table: project.optional-dependencies
+      key: test
+    includes:
+      - test_python_common
+      - test_python_cudf
+  py_test_pandas_cudf:
+    output: pyproject
+    pyproject_dir: python/cudf
+    extras:
+      table: project.optional-dependencies
+      key: pandas_tests
+    includes:
+      - test_python_pandas_cudf
+  py_test_cudf_pandas:
+    output: pyproject
+    pyproject_dir: python/cudf
+    extras:
+      table: project.optional-dependencies
+      key: cudf_pandas_tests
+    includes:
+      - test_python_cudf_pandas
+  py_build_dask_cudf:
+    output: pyproject
+    pyproject_dir: python/dask_cudf
+    extras:
+      table: build-system
+    includes:
+      - build_wheels
+  py_run_dask_cudf:
+    output: pyproject
+    pyproject_dir: python/dask_cudf
+    extras:
+      table: project
+    includes:
+      - run_common
+      - run_dask_cudf
+  py_test_dask_cudf:
+    output: pyproject
+    pyproject_dir: python/dask_cudf
+    extras:
+      table: project.optional-dependencies
+      key: test
+    includes:
+      - test_python_common
+      - test_python_dask_cudf
+  py_build_cudf_kafka:
+    output: pyproject
+    pyproject_dir: python/cudf_kafka
+    extras:
+      table: build-system
+    includes:
+      - build_wheels
+      - build_python_common
+  py_run_cudf_kafka:
+    output: pyproject
+    pyproject_dir: python/cudf_kafka
+    extras:
+      table: project
+    includes:
+      - run_cudf_kafka
+  py_test_cudf_kafka:
+    output: pyproject
+    pyproject_dir: python/cudf_kafka
+    extras:
+      table: project.optional-dependencies
+      key: test
+    includes:
+      - test_python_common
+  py_build_custreamz:
+    output: pyproject
+    pyproject_dir: python/custreamz
+    extras:
+      table: build-system
+    includes:
+      - build_wheels
+  py_run_custreamz:
+    output: pyproject
+    pyproject_dir: python/custreamz
+    extras:
+      table: project
+    includes:
+      - run_custreamz
+  py_test_custreamz:
+    output: pyproject
+    pyproject_dir: python/custreamz
+    extras:
+      table: project.optional-dependencies
+      key: test
+    includes:
+      - test_python_common
+channels:
+  - rapidsai
+  - rapidsai-nightly
+  - dask/label/dev
+  - pytorch
+  - conda-forge
+  - nvidia
+dependencies:
+  build_all:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - &cmake_ver cmake>=3.26.4
+          - ninja
+      - output_types: conda
+        packages:
+          - c-compiler
+          - cxx-compiler
+          - dlpack>=0.5,<0.6.0a0
+          - zlib>=1.2.13
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              arch: x86_64
+            packages:
+              - gcc_linux-64=11.*
+              - sysroot_linux-64==2.17
+          - matrix:
+              arch: aarch64
+            packages:
+              - gcc_linux-aarch64=11.*
+              - sysroot_linux-aarch64==2.17
+      - output_types: conda
+        matrices:
+          - matrix:
+              cuda: "12.0"
+            packages:
+              - cuda-version=12.0
+              - cuda-nvcc
+          - matrix:
+              arch: x86_64
+              cuda: "11.8"
+            packages:
+              - nvcc_linux-64=11.8
+          - matrix:
+              arch: aarch64
+              cuda: "11.8"
+            packages:
+              - nvcc_linux-aarch64=11.8
+  build_cpp:
+    common:
+      - output_types: [conda, requirements]
+        packages:
+          - librmm==23.10.*
+          - libkvikio==23.10.*
+      - output_types: conda
+        packages:
+          - fmt>=9.1.0,<10
+          - &gbench benchmark==1.8.0
+          - &gtest gtest>=1.13.0
+          - &gmock gmock>=1.13.0
+          # Hard pin the patch version used during the build. This must be kept
+          # in sync with the version pinned in get_arrow.cmake.
+          - libarrow==12.0.1.*
+          - librdkafka>=1.9.0,<1.10.0a0
+          - spdlog>=1.11.0,<1.12
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              arch: x86_64
+            packages:
+              # Align nvcomp version with rapids-cmake
+              # TODO: not yet available for aarch64 CUDA 12
+              - &nvcomp nvcomp==2.6.1
+          - matrix:
+              arch: aarch64
+              cuda: "11.8"
+            packages:
+              - *nvcomp
+          # TODO: Fallback matrix for aarch64 CUDA 12. After migrating to nvcomp 3,
+          # all CUDA/arch combinations should be supported by existing packages.
+          - matrix:
+            packages:
+  build_wheels:
+    common:
+      - output_types: pyproject
+        packages:
+          - wheel
+          - setuptools
+  build_python_common:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - cython>=3.0.0
+          # Hard pin the patch version used during the build. This must be kept
+          # in sync with the version pinned in get_arrow.cmake.
+          - pyarrow==12.0.1.*
+          # TODO: Pin to numpy<1.25 until cudf requires pandas 2
+          - &numpy numpy>=1.21,<1.25
+  build_python:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - scikit-build>=0.13.1
+          - rmm==23.10.*
+      - output_types: conda
+        packages:
+          - &protobuf protobuf>=4.21,<5
+      - output_types: pyproject
+        packages:
+          - protoc-wheel
+  libarrow_run:
+    common:
+      - output_types: [conda, requirements]
+        packages:
+          # Allow runtime version to float up to minor version
+          - libarrow==12.*
+  pyarrow_run:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          # Allow runtime version to float up to minor version
+          - pyarrow==12.*
+  cudatoolkit:
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              cuda: "12.0"
+            packages:
+              - cuda-version=12.0
+              - cuda-cudart-dev
+              - cuda-nvrtc-dev
+              - cuda-nvtx-dev
+              - libcurand-dev
+          - matrix:
+              cuda: "11.8"
+            packages:
+              - cuda-version=11.8
+              - cudatoolkit
+              - cuda-nvtx=11.8
+              - libcurand-dev=10.3.0.86
+              - libcurand=10.3.0.86
+          - matrix:
+              cuda: "11.5"
+            packages:
+              - cuda-version=11.5
+              - cudatoolkit
+              - cuda-nvtx=11.5
+                # Can't hard pin the version since 11.x is missing many
+                # packages for specific versions
+              - libcurand-dev>=10.2.6.48,<=10.2.7.107
+              - libcurand>=10.2.6.48,<=10.2.7.107
+          - matrix:
+              cuda: "11.4"
+            packages:
+              - cuda-version=11.4
+              - cudatoolkit
+              - &cudanvtx114 cuda-nvtx=11.4
+              - &libcurand_dev114 libcurand-dev>=10.2.5.43,<=10.2.5.120
+              - &libcurand114 libcurand>=10.2.5.43,<=10.2.5.120
+          - matrix:
+              cuda: "11.2"
+            packages:
+              - cuda-version=11.2
+              - cudatoolkit
+                # The NVIDIA channel doesn't publish pkgs older than 11.4 for
+                # these libs, so 11.2 uses 11.4 packages (the oldest
+                # available).
+              - *cudanvtx114
+              - *libcurand_dev114
+              - *libcurand114
+      - output_types: conda
+        matrices:
+          - matrix:
+              cuda: "12.0"
+              arch: x86_64
+            packages:
+              - libcufile-dev
+          - matrix:
+              cuda: "11.8"
+              arch: x86_64
+            packages:
+              - libcufile=1.4.0.31
+              - libcufile-dev=1.4.0.31
+          - matrix:
+              cuda: "11.5"
+              arch: x86_64
+            packages:
+              - libcufile>=1.1.0.37,<=1.1.1.25
+              - libcufile-dev>=1.1.0.37,<=1.1.1.25
+          - matrix:
+              cuda: "11.4"
+              arch: x86_64
+            packages:
+              - &libcufile_114 libcufile>=1.0.0.82,<=1.0.2.10
+              - &libcufile_dev114 libcufile-dev>=1.0.0.82,<=1.0.2.10
+          - matrix:
+              cuda: "11.2"
+              arch: x86_64
+            packages:
+              # The NVIDIA channel doesn't publish pkgs older than 11.4 for these libs,
+              # so 11.2 uses 11.4 packages (the oldest available).
+              - *libcufile_114
+              - *libcufile_dev114
+          # Fallback matrix for aarch64, which doesn't support libcufile.
+          - matrix:
+            packages:
+  develop:
+    common:
+      - output_types: [conda, requirements]
+        packages:
+          - pre-commit
+          # pre-commit requires identify minimum version 1.0, but clang-format requires textproto support and that was
+          # added in 2.5.20, so we need to call out the minimum version needed for our plugins
+          - identify>=2.5.20
+      - output_types: conda
+        packages:
+          - &doxygen doxygen=1.9.1 # pre-commit hook needs a specific version.
+  docs:
+    common:
+      - output_types: [conda]
+        packages:
+          - dask-cuda==23.10.*
+          - *doxygen
+          - libarrow==12.0.1.*
+          - make
+          - myst-nb
+          - nbsphinx
+          - numpydoc
+          - pandoc
+          - pydata-sphinx-theme
+          - scipy
+          - sphinx
+          - sphinx-autobuild
+          - sphinx-copybutton
+          - sphinx-markdown-tables
+          - sphinxcontrib-websupport
+  notebooks:
+    common:
+      - output_types: [conda, requirements]
+        packages:
+          - ipython
+          - notebook
+          - scipy
+  py_version:
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              py: "3.9"
+            packages:
+              - python=3.9
+          - matrix:
+              py: "3.10"
+            packages:
+              - python=3.10
+          - matrix:
+            packages:
+              - python>=3.9,<3.11
+  run_common:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - fsspec>=0.6.0
+          - *numpy
+          - pandas>=1.3,<1.6.0dev0
+  run_cudf:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - cachetools
+          # TODO: Pin to numba<0.58 until #14160 is resolved
+          - &numba numba>=0.57,<0.58
+          - nvtx>=0.2.1
+          - packaging
+          - rich
+          - rmm==23.10.*
+          - typing_extensions>=4.0.0
+          - *protobuf
+      - output_types: conda
+        packages:
+          - cupy>=12.0.0
+          - pip
+          - pip:
+              - git+https://github.com/python-streamz/streamz.git@master
+      - output_types: requirements
+        packages:
+          # pip recognizes the index as a global option for the requirements.txt file
+          # This index is needed for rmm, cubinlinker, ptxcompiler.
+          - --extra-index-url=https://pypi.ngc.nvidia.com
+          - git+https://github.com/python-streamz/streamz.git@master
+          - &cupy_pip cupy-cuda11x>=12.0.0
+      - output_types: pyproject
+        packages:
+          - cubinlinker
+          - *cupy_pip
+          - ptxcompiler
+    specific:
+      - output_types: [conda, requirements, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.0"
+            packages:
+              - cuda-python>=12.0,<13.0a0
+          - matrix: # All CUDA 11 versions
+            packages:
+              - cuda-python>=11.7.1,<12.0a0
+      - output_types: [conda, pyproject]
+        matrices:
+          - matrix:
+              cuda: "12.0"
+            packages:
+          - matrix: # All CUDA 11 versions
+            packages:
+              - cubinlinker
+              - ptxcompiler
+      - output_types: requirements
+        matrices:
+          - matrix:
+              cuda: "12.0"
+            packages:
+          - matrix: # All CUDA 11 versions
+            packages:
+              - cubinlinker-cu11
+              - ptxcompiler-cu11
+  run_dask_cudf:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - dask==2023.9.2
+          - distributed==2023.9.2
+      - output_types: conda
+        packages:
+          - cupy>=12.0.0
+          - dask-core==2023.9.2  # dask-core in conda is the actual package & dask is the meta package
+      - output_types: pyproject
+        packages:
+          - &cudf cudf==23.10.*
+          - *cupy_pip
+  run_cudf_kafka:
+    common:
+      - output_types: [requirements, pyproject]
+        packages:
+          - *cudf
+  run_custreamz:
+    common:
+      - output_types: conda
+        packages:
+          - python-confluent-kafka>=1.9.0,<1.10.0a0
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - streamz
+      - output_types: [requirements, pyproject]
+        packages:
+          - confluent-kafka>=1.9.0,<1.10.0a0
+          - *cudf
+          - cudf_kafka==23.10.*
+  test_cpp:
+    common:
+      - output_types: conda
+        packages:
+          - *cmake_ver
+          - *gbench
+          - *gtest
+          - *gmock
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              cuda: "12.0"
+            packages:
+              - cuda-version=12.0
+              - cuda-sanitizer-api
+          - matrix:
+              cuda: "11.8"
+            packages:
+              - cuda-sanitizer-api=11.8.86
+          - matrix:
+            packages:
+  test_java:
+    common:
+      - output_types: conda
+        packages:
+          - *cmake_ver
+          - maven
+          - openjdk=8.*
+  test_python_common:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - pytest
+          - pytest-cov
+          - pytest-xdist
+  test_python_cudf:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - cramjam
+          - fastavro>=0.22.9
+          - hypothesis
+          - mimesis>=4.1.0
+          - pyorc
+          - pytest-benchmark
+          - pytest-cases
+          - python-snappy>=0.6.0
+          - scipy
+      - output_types: conda
+        packages:
+          - aiobotocore>=2.2.0
+          - boto3>=1.21.21
+          - botocore>=1.24.21
+          - msgpack-python
+          - moto>=4.0.8
+          - s3fs>=2022.3.0
+      - output_types: pyproject
+        packages:
+          - msgpack
+          - &tokenizers tokenizers==0.13.1
+          - &transformers transformers==4.24.0
+          - tzdata
+    specific:
+      - output_types: conda
+        matrices:
+          - matrix:
+              arch: x86_64
+            packages:
+              # Currently, CUDA builds of pytorch do not exist for aarch64. We require
+              # version <1.12.0 because newer versions use nvidia::cuda-toolkit.
+              - pytorch<1.12.0
+              # We only install these on x86_64 to avoid pulling pytorch as a
+              # dependency of transformers.
+              - *tokenizers
+              - *transformers
+          - matrix:
+            packages:
+  test_python_dask_cudf:
+    common:
+      - output_types: [conda, requirements, pyproject]
+        packages:
+          - dask-cuda==23.10.*
+          - *numba
+  test_python_pandas_cudf:
+    common:
+      - output_types: pyproject
+        packages:
+          # dependencies to run pandas tests
+          # https://github.com/pandas-dev/pandas/blob/main/environment.yml
+          # TODO: When pandas 2.0 is the minimum version, can just specify pandas[all]
+          - beautifulsoup4
+          - blosc
+          - brotlipy
+          - boto3
+          - botocore>=1.24.21
+          - bottleneck
+          - fastparquet
+          - flask
+          - fsspec
+          - html5lib
+          - hypothesis
+          - gcsfs
+          - ipython
+          - jinja2
+          - lxml
+          - matplotlib
+          - moto
+          - numba
+          - numexpr
+          - openpyxl
+          - odfpy
+          - py
+          - psycopg2-binary
+          - pyarrow
+          - pymysql
+          - pyreadstat
+          - pytest-asyncio
+          - pytest-reportlog
+          - python-snappy
+          - pyxlsb
+          - s3fs
+          - scipy
+          - sqlalchemy
+          - tables
+          - pandas-gbq
+          - tabulate
+          - xarray
+          - xlrd
+          - xlsxwriter
+          - xlwt
+          - zstandard
+  test_python_cudf_pandas:
+    common:
+      - output_types: pyproject
+        packages:
+          - ipython
+          - openpyxl
diff --git a/docs/cudf/Makefile b/docs/cudf/Makefile
new file mode 100644
index 0000000..0b3a687
--- /dev/null
+++ b/docs/cudf/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    = -n -v -W --keep-going
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = cudf
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/cudf/README.md b/docs/cudf/README.md
new file mode 100644
index 0000000..004f199
--- /dev/null
+++ b/docs/cudf/README.md
@@ -0,0 +1,6 @@
+# Building Documentation
+
+This directory contains the documentation of cuDF Python.
+For more information on how to write, build, and read the documentation,
+see
+[the developer documentation](https://github.com/rapidsai/cudf/blob/HEAD/docs/cudf/source/developer_guide/documentation.md).
diff --git a/docs/cudf/make.bat b/docs/cudf/make.bat
new file mode 100644
index 0000000..7d537e6
--- /dev/null
+++ b/docs/cudf/make.bat
@@ -0,0 +1,36 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+set SPHINXPROJ=cudf
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+
+:end
+popd
diff --git a/docs/cudf/source/_ext/PandasCompat.py b/docs/cudf/source/_ext/PandasCompat.py
new file mode 100644
index 0000000..af2b160
--- /dev/null
+++ b/docs/cudf/source/_ext/PandasCompat.py
@@ -0,0 +1,157 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION
+
+# This file is adapted from official sphinx tutorial for `todo` extension:
+# https://www.sphinx-doc.org/en/master/development/tutorials/todo.html
+
+from docutils import nodes
+from docutils.parsers.rst import Directive
+from sphinx.locale import get_translation
+from sphinx.util.docutils import SphinxDirective
+
+translator = get_translation("sphinx")
+
+
+class PandasCompat(nodes.Admonition, nodes.Element):
+    pass
+
+
+class PandasCompatList(nodes.General, nodes.Element):
+    pass
+
+
+def visit_PandasCompat_node(self, node):
+    self.visit_admonition(node)
+
+
+def depart_PandasCompat_node(self, node):
+    self.depart_admonition(node)
+
+
+class PandasCompatListDirective(Directive):
+    def run(self):
+        return [PandasCompatList("")]
+
+
+class PandasCompatDirective(SphinxDirective):
+
+    # this enables content in the directive
+    has_content = True
+
+    def run(self):
+        targetid = "PandasCompat-%d" % self.env.new_serialno("PandasCompat")
+        targetnode = nodes.target("", "", ids=[targetid])
+
+        PandasCompat_node = PandasCompat("\n".join(self.content))
+        PandasCompat_node += nodes.title(
+            translator("Pandas Compatibility Note"),
+            translator("Pandas Compatibility Note"),
+        )
+        self.state.nested_parse(
+            self.content, self.content_offset, PandasCompat_node
+        )
+
+        if not hasattr(self.env, "PandasCompat_all_pandas_compat"):
+            self.env.PandasCompat_all_pandas_compat = []
+
+        self.env.PandasCompat_all_pandas_compat.append(
+            {
+                "docname": self.env.docname,
+                "PandasCompat": PandasCompat_node.deepcopy(),
+                "target": targetnode,
+            }
+        )
+
+        return [targetnode, PandasCompat_node]
+
+
+def purge_PandasCompats(app, env, docname):
+    if not hasattr(env, "PandasCompat_all_pandas_compat"):
+        return
+
+    env.PandasCompat_all_pandas_compat = [
+        PandasCompat
+        for PandasCompat in env.PandasCompat_all_pandas_compat
+        if PandasCompat["docname"] != docname
+    ]
+
+
+def merge_PandasCompats(app, env, docnames, other):
+    if not hasattr(env, "PandasCompat_all_pandas_compat"):
+        env.PandasCompat_all_pandas_compat = []
+    if hasattr(other, "PandasCompat_all_pandas_compat"):
+        env.PandasCompat_all_pandas_compat.extend(
+            other.PandasCompat_all_pandas_compat
+        )
+
+
+def process_PandasCompat_nodes(app, doctree, fromdocname):
+    if not app.config.include_pandas_compat:
+        for node in doctree.traverse(PandasCompat):
+            node.parent.remove(node)
+
+    # Replace all PandasCompatList nodes with a list of the collected
+    # PandasCompats. Augment each PandasCompat with a backlink to the
+    # original location.
+    env = app.builder.env
+
+    if not hasattr(env, "PandasCompat_all_pandas_compat"):
+        env.PandasCompat_all_pandas_compat = []
+
+    for node in doctree.traverse(PandasCompatList):
+        if not app.config.include_pandas_compat:
+            node.replace_self([])
+            continue
+
+        content = []
+
+        for PandasCompat_info in env.PandasCompat_all_pandas_compat:
+            para = nodes.paragraph()
+
+            # Create a reference back to the original docstring
+            newnode = nodes.reference("", "")
+            innernode = nodes.emphasis(
+                translator("[source]"), translator("[source]")
+            )
+            newnode["refdocname"] = PandasCompat_info["docname"]
+            newnode["refuri"] = app.builder.get_relative_uri(
+                fromdocname, PandasCompat_info["docname"]
+            )
+            newnode["refuri"] += "#" + PandasCompat_info["target"]["refid"]
+            newnode.append(innernode)
+            para += newnode
+
+            # Insert the reference node into PandasCompat node
+            # Note that this node is a deepcopy from the original copy
+            # in the docstring, so changing this does not affect that in the
+            # doc.
+            PandasCompat_info["PandasCompat"].append(para)
+
+            # Insert the PandasCompand node into the PandasCompatList Node
+            content.append(PandasCompat_info["PandasCompat"])
+
+        node.replace_self(content)
+
+
+def setup(app):
+    app.add_config_value("include_pandas_compat", False, "html")
+
+    app.add_node(PandasCompatList)
+    app.add_node(
+        PandasCompat,
+        html=(visit_PandasCompat_node, depart_PandasCompat_node),
+        latex=(visit_PandasCompat_node, depart_PandasCompat_node),
+        text=(visit_PandasCompat_node, depart_PandasCompat_node),
+    )
+
+    # Sphinx directives are lower-cased
+    app.add_directive("pandas-compat", PandasCompatDirective)
+    app.add_directive("pandas-compat-list", PandasCompatListDirective)
+    app.connect("doctree-resolved", process_PandasCompat_nodes)
+    app.connect("env-purge-doc", purge_PandasCompats)
+    app.connect("env-merge-info", merge_PandasCompats)
+
+    return {
+        "version": "0.1",
+        "parallel_read_safe": True,
+        "parallel_write_safe": True,
+    }
diff --git a/docs/cudf/source/_static/EMPTY b/docs/cudf/source/_static/EMPTY
new file mode 100644
index 0000000..e69de29
diff --git a/docs/cudf/source/_static/RAPIDS-logo-purple.png b/docs/cudf/source/_static/RAPIDS-logo-purple.png
new file mode 100644
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diff --git a/docs/cudf/source/_templates/autosummary/class.rst b/docs/cudf/source/_templates/autosummary/class.rst
new file mode 100644
index 0000000..a16cd0d
--- /dev/null
+++ b/docs/cudf/source/_templates/autosummary/class.rst
@@ -0,0 +1,9 @@
+{{ fullname }}
+{{ underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+..
+   Don't include the methods or attributes sections, numpydoc adds them for us instead.
diff --git a/docs/cudf/source/conf.py b/docs/cudf/source/conf.py
new file mode 100644
index 0000000..03b1bb7
--- /dev/null
+++ b/docs/cudf/source/conf.py
@@ -0,0 +1,263 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# cudf documentation build configuration file, created by
+# sphinx-quickstart on Wed May  3 10:59:22 2017.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+
+from docutils.nodes import Text
+from sphinx.addnodes import pending_xref
+
+# -- Custom Extensions ----------------------------------------------------
+sys.path.append(os.path.abspath("./_ext"))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx_copybutton",
+    "numpydoc",
+    "IPython.sphinxext.ipython_console_highlighting",
+    "IPython.sphinxext.ipython_directive",
+    "PandasCompat",
+    "myst_nb",
+]
+
+nb_execution_excludepatterns = ['performance-comparisons.ipynb']
+
+nb_execution_mode = "force"
+nb_execution_timeout = 300
+
+copybutton_prompt_text = ">>> "
+autosummary_generate = True
+
+# Enable automatic generation of systematic, namespaced labels for sections
+myst_heading_anchors = 2
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = {".rst": "restructuredtext"}
+
+# The master toctree document.
+master_doc = "index"
+
+# General information about the project.
+project = "cudf"
+copyright = "2018-2023, NVIDIA Corporation"
+author = "NVIDIA Corporation"
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '23.10'
+# The full version, including alpha/beta/rc tags.
+release = '23.10.00'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = "en"
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This patterns also effect to html_static_path and html_extra_path
+exclude_patterns = ['venv', "**/includes/**",]
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "sphinx"
+
+html_theme_options = {
+    "external_links": [],
+    # https://github.com/pydata/pydata-sphinx-theme/issues/1220
+    "icon_links": [],
+    "github_url": "https://github.com/rapidsai/cudf",
+    "twitter_url": "https://twitter.com/rapidsai",
+    "show_toc_level": 1,
+    "navbar_align": "right",
+}
+include_pandas_compat = True
+
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+
+html_theme = "pydata_sphinx_theme"
+html_logo = "_static/RAPIDS-logo-purple.png"
+
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+
+# -- Options for HTMLHelp output ------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "cudfdoc"
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (
+        master_doc,
+        "cudf.tex",
+        "cudf Documentation",
+        "NVIDIA Corporation",
+        "manual",
+    )
+]
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [(master_doc, "cudf", "cudf Documentation", [author], 1)]
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (
+        master_doc,
+        "cudf",
+        "cudf Documentation",
+        author,
+        "cudf",
+        "One line description of project.",
+        "Miscellaneous",
+    )
+]
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "cupy": ("https://docs.cupy.dev/en/stable/", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "pyarrow": ("https://arrow.apache.org/docs/", None),
+    "pandas": ("https://pandas.pydata.org/docs/", None),
+    "typing_extensions": ("https://typing-extensions.readthedocs.io/en/stable/", None),
+}
+
+# Config numpydoc
+numpydoc_show_inherited_class_members = {
+    # option_context inherits undocumented members from the parent class
+    "cudf.option_context": False,
+}
+
+# Rely on toctrees generated from autosummary on each of the pages we define
+# rather than the autosummaries on the numpydoc auto-generated class pages.
+numpydoc_class_members_toctree = False
+numpydoc_attributes_as_param_list = False
+
+autoclass_content = "class"
+
+# Replace API shorthands with fullname
+_reftarget_aliases = {
+    "cudf.Series": ("cudf.core.series.Series", "cudf.Series"),
+    "cudf.Index": ("cudf.core.index.Index", "cudf.Index"),
+    "cupy.core.core.ndarray": ("cupy.ndarray", "cupy.ndarray"),
+}
+
+
+def resolve_aliases(app, doctree):
+    pending_xrefs = doctree.traverse(condition=pending_xref)
+    for node in pending_xrefs:
+        alias = node.get("reftarget", None)
+        if alias is not None and alias in _reftarget_aliases:
+            real_ref, text_to_render = _reftarget_aliases[alias]
+            node["reftarget"] = real_ref
+
+            text_node = next(
+                iter(node.traverse(lambda n: n.tagname == "#text"))
+            )
+            text_node.parent.replace(text_node, Text(text_to_render, ""))
+
+
+def ignore_internal_references(app, env, node, contnode):
+    name = node.get("reftarget", None)
+    if name == "cudf.core.index.GenericIndex":
+        # We don't exposed docs for `cudf.core.index.GenericIndex`
+        # hence we would want the docstring & mypy references to
+        # use `cudf.Index`
+        node["reftarget"] = "cudf.Index"
+        return contnode
+    return None
+
+
+nitpick_ignore = [
+    ("py:class", "SeriesOrIndex"),
+    ("py:class", "Dtype"),
+    # TODO: Remove this when we figure out why typing_extensions doesn't seem
+    # to map types correctly for intersphinx
+    ("py:class", "typing_extensions.Self"),
+]
+
+def setup(app):
+    app.add_css_file("https://docs.rapids.ai/assets/css/custom.css")
+    app.add_js_file("https://docs.rapids.ai/assets/js/custom.js", loading_method="defer")
+    app.connect("doctree-read", resolve_aliases)
+    app.connect("missing-reference", ignore_internal_references)
diff --git a/docs/cudf/source/cudf_pandas/benchmarks.md b/docs/cudf/source/cudf_pandas/benchmarks.md
new file mode 100644
index 0000000..1a916db
--- /dev/null
+++ b/docs/cudf/source/cudf_pandas/benchmarks.md
@@ -0,0 +1,111 @@
+# Benchmarks
+
+## Database-like ops benchmarks
+
+We reproduced the [Database-like ops benchmark](https://duckdblabs.github.io/db-benchmark/)
+including a solution using `cudf.pandas`. Here are the results:
+
+<figure>
+
+![duckdb-benchmark-groupby-join](../_static/duckdb-benchmark-groupby-join.png)
+
+<figcaption style="text-align: center;">Results of the <a
+href="https://duckdblabs.github.io/db-benchmark/">Database-like ops
+benchmark</a> including <span
+class="title-ref">cudf.pandas</span>.</figcaption>
+</figure>
+
+**Note:** A missing bar in the results for a particular solution
+indicates we ran into an error when executing one or more queries for
+that solution.
+
+You can see the per-query results [here](https://data.rapids.ai/duckdb-benchmark/index.html).
+
+### Steps to reproduce
+
+Below are the steps to reproduce the `cudf.pandas` results.  The steps
+to reproduce the results for other solutions are documented in
+<https://github.com/duckdblabs/db-benchmark#reproduce>.
+
+1. Clone the latest <https://github.com/duckdblabs/db-benchmark>
+2. Build environments for pandas:
+
+```bash
+virtualenv pandas/py-pandas
+```
+
+3. Activate pandas virtualenv:
+
+```bash
+source pandas/py-pandas/bin/activate
+```
+
+4. Install cudf:
+
+```bash
+pip install --extra-index-url=https://pypi.nvidia.com cudf-cu12  # or cudf-cu11
+```
+
+5. Modify pandas join/group code to use `cudf.pandas` and be compatible with pandas 1.5 APIs:
+
+```bash
+diff --git a/pandas/groupby-pandas.py b/pandas/groupby-pandas.py
+index 58eeb26..2ddb209 100755
+--- a/pandas/groupby-pandas.py
++++ b/pandas/groupby-pandas.py
+@@ -1,4 +1,4 @@
+-#!/usr/bin/env python3
++#!/usr/bin/env -S python3 -m cudf.pandas
+
+ print("# groupby-pandas.py", flush=True)
+
+diff --git a/pandas/join-pandas.py b/pandas/join-pandas.py
+index f39beb0..a9ad651 100755
+--- a/pandas/join-pandas.py
++++ b/pandas/join-pandas.py
+@@ -1,4 +1,4 @@
+-#!/usr/bin/env python3
++#!/usr/bin/env -S python3 -m cudf.pandas
+
+ print("# join-pandas.py", flush=True)
+
+@@ -26,7 +26,7 @@ if len(src_jn_y) != 3:
+
+ print("loading datasets " + data_name + ", " + y_data_name[0] + ", " + y_data_name[1] + ", " + y_data_name[2], flush=True)
+
+-x = pd.read_csv(src_jn_x, engine='pyarrow', dtype_backend='pyarrow')
++x = pd.read_csv(src_jn_x, engine='pyarrow')
+
+ # x['id1'] = x['id1'].astype('Int32')
+ # x['id2'] = x['id2'].astype('Int32')
+@@ -35,17 +35,17 @@ x['id4'] = x['id4'].astype('category') # remove after datatable#1691
+ x['id5'] = x['id5'].astype('category')
+ x['id6'] = x['id6'].astype('category')
+
+-small = pd.read_csv(src_jn_y[0], engine='pyarrow', dtype_backend='pyarrow')
++small = pd.read_csv(src_jn_y[0], engine='pyarrow')
+ # small['id1'] = small['id1'].astype('Int32')
+ small['id4'] = small['id4'].astype('category')
+ # small['v2'] = small['v2'].astype('float64')
+-medium = pd.read_csv(src_jn_y[1], engine='pyarrow', dtype_backend='pyarrow')
++medium = pd.read_csv(src_jn_y[1], engine='pyarrow')
+ # medium['id1'] = medium['id1'].astype('Int32')
+ # medium['id2'] = medium['id2'].astype('Int32')
+ medium['id4'] = medium['id4'].astype('category')
+ medium['id5'] = medium['id5'].astype('category')
+ # medium['v2'] = medium['v2'].astype('float64')
+-big = pd.read_csv(src_jn_y[2], engine='pyarrow', dtype_backend='pyarrow')
++big = pd.read_csv(src_jn_y[2], engine='pyarrow')
+ # big['id1'] = big['id1'].astype('Int32')
+ # big['id2'] = big['id2'].astype('Int32')
+ # big['id3'] = big['id3'].astype('Int32')
+```
+
+6. Run Modified pandas benchmarks:
+
+```bash
+./_launcher/solution.R --solution=pandas --task=groupby --nrow=1e7
+./_launcher/solution.R --solution=pandas --task=groupby --nrow=1e8
+./_launcher/solution.R --solution=pandas --task=join --nrow=1e7
+./_launcher/solution.R --solution=pandas --task=join --nrow=1e8
+```
diff --git a/docs/cudf/source/cudf_pandas/faq.md b/docs/cudf/source/cudf_pandas/faq.md
new file mode 100644
index 0000000..bf9c2b9
--- /dev/null
+++ b/docs/cudf/source/cudf_pandas/faq.md
@@ -0,0 +1,161 @@
+# FAQ and Known Issues
+
+## When should I use `cudf.pandas` vs using the cuDF library directly?
+
+`cudf.pandas` is the quickest and easiest way to get pandas code
+running on the GPU. However, there are some situations in which using
+the cuDF library directly should be considered.
+
+- cuDF implements a subset of the pandas API, while `cudf.pandas` will
+  fall back automatically to pandas as needed. If you can write your
+  code to use just the operations supported by cuDF, you will benefit
+  from increased performance by using cuDF directly.
+
+- cuDF does offer some functions and methods that pandas does not. For
+  example, cuDF has a [`.list`
+  accessor](https://docs.rapids.ai/api/cudf/stable/api_docs/list_handling/)
+  for working with list-like data. If you need access to the
+  additional functionality in cuDF, you will need to use the cuDF
+  package directly.
+
+## How closely does this match pandas?
+
+You can use 100% of the pandas API and most things will work
+identically to pandas.
+
+`cudf.pandas` is tested against the entire pandas unit test suite.
+Currently, we're passing **93%** of the 187,000+ unit tests, with the
+goal of passing 100%. Test failures are typically for edge cases and
+due to the small number of behavioral differences between cuDF and
+pandas. You can learn more about these edge cases in
+[Known Limitations](#are-there-any-known-limitations)
+
+We also run nightly tests that track interactions between
+`cudf.pandas` and other third party libraries. See
+[Third-Party Library Compatibility](#does-it-work-with-third-party-libraries).
+
+## How can I tell if `cudf.pandas` is active?
+
+You shouldn't have to write any code differently depending on whether
+`cudf.pandas` is in use or not. You should use `pandas` and things
+should just work.
+
+In a few circumstances during testing and development however, you may
+want to explicitly verify that `cudf.pandas` is active. To do that,
+print the pandas module in your code and review the output; it should
+look something like this:
+
+```python
+%load_ext cudf.pandas
+import pandas as pd
+
+print(pd)
+<module 'pandas' (ModuleAccelerator(fast=cudf, slow=pandas))>
+```
+
+## Does it work with third-party libraries?
+
+`cudf.pandas` is tested with numerous popular third-party libraries.
+`cudf.pandas` will not only work but will accelerate pandas operations
+within these libraries. As part of our CI/CD system, we currently test
+common interactions with the following Python libraries:
+
+| Library          | Status |
+|------------------|--------|
+| cuGraph          | ✅      |
+| cuML             | ✅      |
+| Hvplot           | ✅      |
+| Holoview         | ✅      |
+| Ibis             | ✅      |
+| Joblib           | ❌      |
+| NumPy            | ✅      |
+| Matplotlib       | ✅      |
+| Plotly           | ✅      |
+| PyTorch          | ✅      |
+| Seaborn          | ✅      |
+| Scikit-Learn     | ✅      |
+| SciPy            | ✅      |
+| Tensorflow       | ✅      |
+| XGBoost          | ✅      |
+
+Please review the section on [Known Limitations](#are-there-any-known-limitations)
+for details about what is expected not to work (and why).
+
+## Can I use this with Dask or PySpark?
+
+`cudf.pandas` is not designed for distributed or out-of-core computing
+(OOC) workflows today. If you are looking for accelerated OOC and
+distributed solutions for data processing we recommend Dask and Apache
+Spark.
+
+Both Dask and Apache Spark support accelerated computing through configuration
+based interfaces. Dask allows you to [configure the dataframe
+backend](https://docs.dask.org/en/latest/how-to/selecting-the-collection-backend.html) to use
+cuDF (learn more in [this
+blog](https://medium.com/rapids-ai/easy-cpu-gpu-arrays-and-dataframes-run-your-dask-code-where-youd-like-e349d92351d)) and the [RAPIDS Accelerator for Apache Spark](https://nvidia.github.io/spark-rapids/)
+provides a similar configuration-based plugin for Spark.
+
+## Are there any known limitations?
+
+There are a few known limitations that you should be aware of:
+
+- Because fallback involves copying data from GPU to CPU and back,
+  [value mutability](https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html#mutability-and-copying-of-data)
+  of Pandas objects is not always guaranteed. You should follow the
+  pandas recommendation to favor immutable operations.
+- `cudf.pandas` can't currently interface smoothly with functions that
+  interact with objects using a C API (such as the Python or NumPy C
+  API)
+  - For example, you can write `torch.tensor(df.values)` but not
+    `torch.from_numpy(df.values)`, as the latter uses the NumPy C API
+- For performance reasons, joins and join-based operations are not
+  currently implemented to maintain the same row ordering as standard
+  pandas
+- `cudf.pandas` isn't compatible with directly using `import cudf`
+   and is intended to be used with pandas-based workflows.
+- Global variables can be accessed but can't be modified during CPU-fallback
+
+  ```python
+   %load_ext cudf.pandas
+   import pandas as pd
+
+   lst = [10]
+
+   def udf(x):
+       lst.append(x)
+       return x + lst[0]
+
+   s = pd.Series(range(2)).apply(udf)
+   print(s) # we can access the value in lst
+   0    10
+   1    11
+   dtype: int64
+   print(lst) # lst is unchanged, as this specific UDF could not run on the GPU
+   [10]
+   ```
+- `cudf.pandas` (and cuDF in general) is currently only compatible with pandas 1.5.x.
+
+## Can I force running on the CPU?
+
+To run your code on CPU, just run without activating `cudf.pandas`,
+and "regular pandas" will be used.
+
+If needed, GPU acceleration may be disabled when using `cudf.pandas`
+for testing or benchmarking purposes. To do so, set the
+`CUDF_PANDAS_FALLBACK_MODE` environment variable, e.g.
+
+```bash
+CUDF_PANDAS_FALLBACK_MODE=1 python -m cudf.pandas some_script.py
+```
+
+## Slow tab completion in IPython?
+
+You may experience slow tab completion when inspecting the
+methods/attributes of large dataframes. We expect this issue to be
+resolved in an upcoming release. In the mean time, you may execute the
+following command in IPython before loading `cudf.pandas` to work
+around the issue:
+
+```
+%config IPCompleter.jedi_compute_type_timeout=0
+```
diff --git a/docs/cudf/source/cudf_pandas/how-it-works.md b/docs/cudf/source/cudf_pandas/how-it-works.md
new file mode 100644
index 0000000..ee856c8
--- /dev/null
+++ b/docs/cudf/source/cudf_pandas/how-it-works.md
@@ -0,0 +1,39 @@
+# How it Works
+
+When `cudf.pandas` is activated, `import pandas` (or any of its
+submodules) imports a proxy module, rather than "regular" pandas. This
+proxy module contains proxy types and proxy functions:
+
+```python
+In [1]: %load_ext cudf.pandas
+
+In [2]: import pandas as pd
+
+In [3]: pd
+Out[3]: <module 'pandas' (ModuleAccelerator(fast=cudf, slow=pandas))>
+```
+
+Operations on proxy types/functions execute on the GPU where
+possible and on the CPU otherwise, synchronizing under the hood as
+needed. This applies to pandas operations both in your code and
+in third-party libraries you may be using.
+
+![cudf-pandas-execution-flow](../_static/cudf-pandas-execution-flow.png)
+
+All `cudf.pandas` objects are a proxy to either a GPU (cuDF) or CPU
+(pandas) object at any given time. Attribute lookups and method calls
+are first attempted on the GPU (copying from CPU if necessary).  If
+that fails, the operation is attempted on the CPU (copying from GPU if
+necessary).
+
+Additionally, `cudf.pandas` special cases chained method calls (for
+example `.groupby().rolling().apply()`) that can fail at any level of
+the chain and rewinds and replays the chain minimally to deliver the
+correct result. Data is automatically transferred from host to device
+(and vice versa) only when necessary, avoiding unnecessary device-host
+transfers.
+
+When using `cudf.pandas`, cuDF's [pandas compatibility
+mode](https://docs.rapids.ai/api/cudf/stable/api_docs/options/#available-options)
+is automatically enabled, ensuring consistency with pandas-specific
+semantics like default sort ordering.
diff --git a/docs/cudf/source/cudf_pandas/index.rst b/docs/cudf/source/cudf_pandas/index.rst
new file mode 100644
index 0000000..628194c
--- /dev/null
+++ b/docs/cudf/source/cudf_pandas/index.rst
@@ -0,0 +1,49 @@
+cudf.pandas
+-----------
+
+cuDF pandas accelerator mode (``cudf.pandas``) is built on cuDF and
+**accelerates pandas code** on the GPU.  It supports **100% of the
+Pandas API**, using the GPU for supported operations, and
+automatically **falling back to pandas** for other operations.
+
+.. code-block:: python
+
+   %load_ext cudf.pandas
+   # pandas API is now GPU accelerated
+
+   import pandas as pd
+
+   df = pd.read_csv("filepath")  # uses the GPU!
+   df.groupby("col").mean()  # uses the GPU!
+   df.rolling(window=3).sum()  # uses the GPU!
+   df.apply(set, axis=1)  # uses the CPU (fallback)
+
+.. figure:: ../_static/colab.png
+    :width: 200px
+    :target: https://nvda.ws/rapids-cudf
+
+    Try it on Google Colab!
+
++---------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------+
+| **Zero Code Change Acceleration**                                                           | **Third-Party Library Compatible**                                                                                  |
+|                                                                                             |                                                                                                                     |
+| Just ``%load_ext cudf.pandas`` in Jupyter, or pass ``-m cudf.pandas`` on the command line.  | ``cudf.pandas`` is compatible with most third-party libraries that use pandas.                                      |
++---------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------+
+| **Run the same code on CPU or GPU**                                                         | **100% of the Pandas API**                                                                                          |
+|                                                                                             |                                                                                                                     |
+| Nothing changes, not even your `import` statements, when going from CPU to GPU.             | Combines the full flexibility of Pandas with blazing fast performance of cuDF                                       |
++---------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------+
+
+Starting with the version 23.10.01 release ``cudf.pandas`` is
+available in Open Beta, as part of the ``cudf`` package .  See `RAPIDS
+Quick Start <https://rapids.ai/#quick-start>`_ to get up-and-running
+with ``cudf``.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   usage
+   benchmarks
+   how-it-works
+   faq
diff --git a/docs/cudf/source/cudf_pandas/usage.md b/docs/cudf/source/cudf_pandas/usage.md
new file mode 100644
index 0000000..b174c60
--- /dev/null
+++ b/docs/cudf/source/cudf_pandas/usage.md
@@ -0,0 +1,64 @@
+# Usage
+
+## Jupyter Notebooks and IPython
+
+Load the `cudf.pandas` extension at the beginning of your
+notebook. After that, just `import pandas` and operations will use the
+GPU:
+
+```python
+%load_ext cudf.pandas
+
+import pandas as pd
+
+URL = "https://github.com/plotly/datasets/raw/master/tips.csv"
+df = pd.read_csv(URL)                 # uses the GPU
+df["size"].value_counts()             # uses the GPU
+df.groupby("size").total_bill.mean()  # uses the GPU
+df.apply(list, axis=1)                # uses the CPU (fallback)
+```
+
+## Command-line usage
+
+From the command line, run your Python scripts with `-m cudf.pandas`:
+
+```bash
+python -m cudf.pandas script.py
+```
+
+## Understanding performance - the `cudf.pandas` profiler
+
+`cudf.pandas` will attempt to use the GPU whenever possible and fall
+back to CPU for certain operations. Running your code with the
+`cudf.pandas.profile` magic generates a report showing which
+operations used the GPU and which used the CPU. This can help you
+identify parts of your code that could be rewritten to be more
+GPU-friendly:
+
+```python
+%load_ext cudf.pandas
+import pandas as pd
+```
+
+```python
+%%cudf.pandas.profile
+df = pd.DataFrame({'a': [0, 1, 2], 'b': [3,4,3]})
+
+df.min(axis=1)
+out = df.groupby('a').filter(
+    lambda group: len(group) > 1
+)
+```
+
+![cudf-pandas-profile](../_static/cudf-pandas-profile.png)
+
+When an operation falls back to using the CPU, it's typically because
+that operation isn't implemented by cuDF. The profiler generates a
+handy link to report the missing functionality to the cuDF team.
+
+To profile a script being run from the command-line, pass the
+`--profile` argument:
+
+```bash
+python -m cudf.pandas --profile script.py
+```
diff --git a/docs/cudf/source/developer_guide/benchmarking.md b/docs/cudf/source/developer_guide/benchmarking.md
new file mode 100644
index 0000000..061f896
--- /dev/null
+++ b/docs/cudf/source/developer_guide/benchmarking.md
@@ -0,0 +1,242 @@
+# Benchmarking cuDF
+
+The goal of the benchmarks in this repository is to measure the performance of various cuDF APIs.
+Benchmarks in cuDF are written using the
+[`pytest-benchmark`](https://pytest-benchmark.readthedocs.io/en/latest/index.html) plugin to the
+[`pytest`](https://docs.pytest.org/en/latest/) Python testing framework.
+Using `pytest-benchmark` provides a seamless experience for developers familiar with `pytest`.
+We include benchmarks of both public APIs and internal functions.
+The former give us a macro view of our performance, especially vis-à-vis pandas.
+The latter help us quantify and minimize the overhead of our Python bindings.
+
+```{note}
+Our current benchmarks focus entirely on measuring run time.
+However, minimizing memory footprint can be just as important for some cases.
+In the future, we may update our benchmarks to also include memory usage measurements.
+```
+
+## Benchmark organization
+
+At the top level benchmarks are divided into `internal` and `API` directories.
+API benchmarks are for public features that we expect users to consume.
+Internal benchmarks capture the performance of cuDF internals that have no stability guarantees.
+
+Within each directory, benchmarks are organized based on the type of function.
+Functions in cuDF generally fall into two groups:
+
+1. Methods of classes like `DataFrame` or `Series`.
+2. Free functions operating on the above classes like `cudf.merge`.
+
+The former should be organized into files named `bench_class.py`.
+For example, benchmarks of `DataFrame.eval` belong in `API/bench_dataframe.py`.
+Benchmarks should be written at the highest level of generality possible with respect to the class hierarchy.
+For instance, all classes support the `take` method, so those benchmarks belong in `API/bench_frame_or_index.py`.
+If a method has a slightly different API for different classes, benchmarks should use a minimal common API,
+_unless_ developers expect certain arguments to trigger code paths with very different performance characteristics.
+One example, is `DataFrame.where`, which supports a wide range of inputs (like other `DataFrame`s) that other classes don't support.
+Therefore, we have separate benchmarks for `DataFrame`, in addition to the general benchmarks for all `Frame` and `Index` classes.
+
+```{note}
+`pytest` does not support having two benchmark files with the same name, even if they are in separate directories.
+Therefore, benchmarks of internal methods of _public_ classes go in files suffixed with `_internal`.
+Benchmarks of `DataFrame._apply_boolean_mask`, for instance, belong in `internal/bench_dataframe_internal.py`.
+```
+
+Free functions have more flexibility.
+Broadly speaking, they should be grouped into benchmark files containing similar functionality.
+For example, I/O benchmarks can all live in `bench_io.py`.
+For now those groupings are left to the discretion of developers.
+
+## Running benchmarks
+
+By default, pytest discovers test files and functions prefixed with `test_`.
+For benchmarks, we configure `pytest` to instead search using the `bench_` prefix.
+After installing `pytest-benchmark`, running benchmarks is as simple as just running `pytest`.
+
+When benchmarks are run, the default behavior is to output the results in a table to the terminal.
+A common requirement is to then compare the performance of benchmarks before and after a change.
+We can generate these comparisons by saving the output using the `--benchmark-autosave` option to pytest.
+When using this option, after the benchmarks are run the output will contain a line:
+```
+Saved benchmark data in: /path/to/XXXX_*.json
+```
+
+The `XXXX` is a four-digit number identifying the benchmark.
+If preferred, a user may also use the `--benchmark-save=NAME` option,
+which allows more control over the resulting filename.
+Given two benchmark runs `XXXX` and `YYYY`, benchmarks can then be compared using
+```
+pytest-benchmark compare XXXX YYYY
+```
+Note that the comparison uses the `pytest-benchmark` command rather than the `pytest` command.
+`pytest-benchmark` has a number of additional options that can be used to customize the output.
+The next line contains one useful example, but developers should experiment to find a useful output
+```
+pytest-benchmark compare XXXX YYYY --sort="name" --columns=Mean --name=short --group-by=param
+```
+
+For more details, see the [`pytest-benchmark` documentation](https://pytest-benchmark.readthedocs.io/en/latest/comparing.html).
+
+## Benchmark contents
+
+### Benchmark configuration
+
+Benchmarks must support [comparing to pandas](#comparing-to-pandas) and [being run as tests](#testing-benchmarks).
+To satisfy these requirements, one must follow these rules when writing benchmarks:
+1. Import `cudf` and `cupy` from the config module:
+   ```python
+       from ..common.config import cudf, cupy # Do this
+       import cudf, cupy # Not this
+   ```
+   This enables swapping out for `pandas` and `numpy` respectively.
+2. Avoid hard-coding benchmark dataset sizes, and instead use the sizes advertised by `config.py`.
+   This enables running the benchmarks in "test" mode on small datasets, which will be much faster.
+
+
+### Writing benchmarks
+
+Just as benchmarks should be written in terms of the highest level classes in the hierarchy,
+they should also assume as little as possible about the nature of the data.
+For instance, unless there are meaningful functional differences,
+benchmarks should not care about the dtype or nullability of the data.
+Objects that differ in these ways should be interchangeable for most benchmarks.
+The goal of writing benchmarks in this way is to then automatically benchmark objects with different properties.
+We support this use case with the `benchmark_with_object` decorator.
+
+The use of this decorator is best demonstrated by example:
+
+```python
+@benchmark_with_object(cls="dataframe", dtype="int", cols=6)
+def bench_foo(benchmark, dataframe):
+    benchmark(dataframe.foo)
+```
+
+In the example above `bench_foo` will be run for DataFrames containing six columns of integer data.
+The decorator allows automatically parametrizing the following object properties:
+
+- `cls`: Objects of a specific class, e.g. `DataFrame`.
+- `dtype`: Objects of a specific dtype.
+- `nulls`: Objects with and without null entries.
+- `cols`: Objects with a certain number of columns.
+- `rows`: Objects with a certain number of rows.
+
+In the example, since we did not specify the number of rows or nullability,
+it will be run once for each valid number of rows and for both nullable and non-nullable data.
+The valid set of all parameters (e.g. the numbers of rows) is stored in the `common/config.py` file.
+This decorator allows a developer to write a generic benchmark that works for many types of objects,
+then have that benchmark automatically run for all objects of interest.
+
+### Parametrizing tests
+
+The `benchmark_with_object` decorator covers most use cases and automatically guarantees a baseline of benchmark coverage.
+However, many benchmarks will require more customized objects.
+In some cases those will be the primary targets whose methods are called.
+For instance, a benchmark may require a `Series` with a specific data distribution.
+In others, those objects will be arguments passed to other functions.
+An example of this is `DataFrame.where`, which accepts many types of objects to filter with.
+
+In the first case, fixtures should follow certain rules.
+When writing fixtures, developers should make the data sizes dependent on the benchmarks configuration.
+The `benchmarks/common/config.py` file defines standard data sizes to be used in benchmarks.
+These data sizes can be tweaked for debugging purposes (see [](#testing-benchmarks) below).
+Fixture sizes should be relative to the `NUM_ROWS` and/or `NUM_COLS` variables defined in the config module.
+These rules ensure consistency between these fixtures and those provided by `benchmark_with_object`.
+
+
+## Comparing to pandas
+
+An important aspect of benchmarking cuDF is comparing it to pandas.
+We often want to generate quantitative comparisons, so we need to make that as easy as possible.
+Our benchmarks support this by setting the environment variable `CUDF_BENCHMARKS_USE_PANDAS`.
+When this variable is detected, all benchmarks will automatically be run using pandas instead of cuDF.
+Therefore, comparisons can easily be generated by simply running the benchmarks twice,
+once with the variable set and once without.
+Note that this variable only affects API benchmarks, not internal benchmarks,
+since the latter are not even guaranteed to be valid pandas code.
+
+```{note}
+`CUDF_BENCHMARKS_USE_PANDAS` effectively remaps `cudf` to `pandas` and `cupy` to `numpy`.
+It does so by aliasing these modules in `common.config.py`.
+This aliasing is why it is critical for developers to import these packages from `config.py`.
+```
+
+## Testing benchmarks
+
+Benchmarks need to be kept up to date with API changes in cuDF.
+However, we cannot simply run benchmarks in CI.
+Doing so would consume too many resources, and it would significantly slow down the development cycle
+
+To balance these issues, our benchmarks also support running in "testing" mode.
+To do so, developers can set the `CUDF_BENCHMARKS_DEBUG_ONLY` environment variable.
+When benchmarks are run with this variable, all data sizes are set to a minimum and the number of sizes are reduced.
+Our CI testing takes advantage of this to ensure that benchmarks remain valid code.
+
+```{note}
+The objects provided by `benchmark_with_object` respect the `NUM_ROWS` and `NUM_COLS` defined in `common/config.py`.
+`CUDF_BENCHMARKS_DEBUG_ONLY` works by conditionally redefining these values.
+This is why it is crucial for developers to use these variables when defining custom fixtures or cases.
+```
+
+## Profiling
+
+Although not strictly part of our benchmarking suite, profiling is a common need so we provide some guidelines here.
+Here are two easy ways (there may be others) to profile benchmarks:
+1. The [`pytest-profiling`](https://github.com/man-group/pytest-plugins/tree/master/pytest-profiling) plugin.
+2. The [`py-spy`](https://github.com/benfred/py-spy) package.
+
+Using the former is as simple as adding the `--profile` (or `--profile-svg`) arguments to the `pytest` invocation.
+The latter requires instead invoking pytest from py-spy, like so:
+```
+py-spy record -- pytest bench_foo.py
+```
+Each tool has different strengths and provides somewhat different information.
+Developers should try both and see what works for a particular workflow.
+Developers are also encouraged to share useful alternatives that they discover.
+
+## Advanced Topics
+
+This section discusses some underlying details of how cuDF benchmarks work.
+They are not usually necessary for typical developers or benchmark writers.
+This information is primarily for developers looking to extend the types of objects that can be easily benchmarked.
+
+### Understanding `benchmark_with_object`
+
+Under the hood, `benchmark_with_object` is made up of two critical pieces, fixture unions and some decorator magic.
+
+#### Fixture unions
+
+Fixture unions are a feature of `pytest_cases`.
+A fixture union is a fixture that, when used as a test function parameter,
+will trigger the test to run once for each fixture contained in the union.
+Since most cuDF benchmarks can be run with the same relatively small set of objects,
+our benchmarks generate the Cartesian product of possible fixtures and then create all possible unions.
+
+This feature is critical to the design of our benchmarks.
+For each of the relevant parameter combinations (size, nullability, etc) we programmatically generate a new fixture.
+The resulting fixtures are unambiguously named according to the following scheme:
+`{classname}_dtype_{dtype}[_nulls_{true|false}][[_cols_{num_cols}]_rows_{num_rows}]`.
+If a fixture name does not contain a particular component, it represents a union of all values of that component.
+As an example, consider the fixture `dataframe_dtype_int_rows_100`.
+This fixture is a union of both nullable and non-nullable `DataFrame`s with different numbers of columns.
+
+#### The `benchmark_with_object` decorator
+
+The long names of the above unions are cumbersome when writing tests.
+Moreover, having this information embedded in the name means that in order to change the parameters used,
+the entire benchmark needs to have the fixture name replaced.
+The `benchmark_with_object` decorator is the solution to this problem.
+When used on a test function, it essentially replaces the function parameter name with the true fixture.
+In our original example from above
+
+```python
+@benchmark_with_object(cls="dataframe", dtype="int", cols=6)
+def bench_foo(benchmark, dataframe):
+    benchmark(dataframe.foo)
+```
+
+is functionally equivalent to
+
+```python
+def bench_foo(benchmark, dataframe_dtype_int_cols_6):
+    benchmark(dataframe_dtype_int_cols_6.foo)
+```
diff --git a/docs/cudf/source/developer_guide/contributing_guide.md b/docs/cudf/source/developer_guide/contributing_guide.md
new file mode 100644
index 0000000..65b0e4e
--- /dev/null
+++ b/docs/cudf/source/developer_guide/contributing_guide.md
@@ -0,0 +1,160 @@
+# Contributing Guide
+
+This document focuses on a high-level overview of best practices in cuDF.
+
+## Directory structure and file naming
+
+cuDF generally presents the same importable modules and subpackages as pandas.
+All Cython code is contained in `python/cudf/cudf/_lib`.
+
+## Code style
+
+cuDF employs a number of linters to ensure consistent style across the code base.
+We manage our linters using [`pre-commit`](https://pre-commit.com/).
+Developers are strongly recommended to set up `pre-commit` prior to any development.
+The `.pre-commit-config.yaml` file at the root of the repo is the primary source of truth linting.
+Specifically, cuDF uses the following tools:
+
+- [`ruff`](https://beta.ruff.rs/) checks for general code formatting compliance.
+- [`black`](https://github.com/psf/black) is an automatic code formatter.
+- [`isort`](https://pycqa.github.io/isort/) ensures imports are sorted consistently.
+- [`mypy`](http://mypy-lang.org/) performs static type checking.
+  In conjunction with [type hints](https://docs.python.org/3/library/typing.html),
+  `mypy` can help catch various bugs that are otherwise difficult to find.
+- [`pydocstyle`](https://github.com/PyCQA/pydocstyle/) lints docstring style.
+- [`codespell`](https://github.com/codespell-project/codespell) finds spelling errors.
+
+Linter config data is stored in a number of files.
+We generally use `pyproject.toml` over `setup.cfg` and avoid project-specific files (e.g. `pyproject.toml` > `python/cudf/pyproject.toml`).
+However, differences between tools and the different packages in the repo result in the following caveats:
+
+- `isort` must be configured per project to set which project is the "first party" project.
+
+As a result, we currently maintain both root and project-level `pyproject.toml` files.
+
+For more information on how to use pre-commit hooks, see the code formatting section of the
+[overall contributing guide](https://github.com/rapidsai/cudf/blob/main/CONTRIBUTING.md#python--pre-commit-hooks).
+
+## Deprecating and removing code
+
+cuDF follows the policy of deprecating code for one release prior to removal.
+For example, if we decide to remove an API during the 22.08 release cycle,
+it will be marked as deprecated in the 22.08 release and removed in the 22.10 release.
+Note that if it is explicitly mentioned in a comment (like `# Do not remove until..`),
+do not enforce the deprecation by removing the affected code until the condition in the comment is met.
+All internal usage of deprecated APIs in cuDF should be removed when the API is deprecated.
+This prevents users from encountering unexpected deprecation warnings when using other (non-deprecated) APIs.
+The documentation for the API should also be updated to reflect its deprecation.
+When the time comes to remove a deprecated API, make sure to remove all tests and documentation.
+
+Deprecation messages should:
+- emit a FutureWarning;
+- consist of a single line with no newline characters;
+- indicate replacement APIs, if any exist
+  (deprecation messages are an opportunity to show users better ways to do things);
+- not specify a version when removal will occur (this gives us more flexibility).
+
+For example:
+```python
+warnings.warn(
+    "`Series.foo` is deprecated and will be removed in a future version of cudf. "
+    "Use `Series.new_foo` instead.",
+    FutureWarning
+)
+```
+
+```{warning}
+Deprecations should be signaled using a `FutureWarning` **not a `DeprecationWarning`**!
+`DeprecationWarning` is hidden by default except in code run in the `__main__` module.
+```
+
+Deprecations should also be specified in the respective public API docstring using a
+`deprecated` admonition:
+
+```
+.. deprecated:: 23.08
+    `foo` is deprecated and will be removed in a future version of cudf.
+```
+
+## `pandas` compatibility
+
+Maintaining compatibility with the [pandas API](https://pandas.pydata.org/docs/reference/index.html) is a primary goal of cuDF.
+Developers should always look at pandas APIs when adding a new feature to cuDF.
+When introducing a new cuDF API with a pandas analog, we should match pandas as much as possible.
+Since we try to maintain compatibility even with various edge cases (such as null handling),
+new pandas releases sometimes require changes that break compatibility with old versions.
+As a result, our compatibility target is the latest pandas version.
+
+However, there are occasionally good reasons to deviate from pandas behavior.
+The most common reasons center around performance.
+Some APIs cannot match pandas behavior exactly without incurring exorbitant runtime costs.
+Others may require using additional memory, which is always at a premium in GPU workflows.
+If you are developing a feature and believe that perfect pandas compatibility is infeasible or undesirable,
+you should consult with other members of the team to assess how to proceed.
+
+When such a deviation from pandas behavior is necessary, it should be documented.
+For more information on how to do that, see [our documentation on pandas comparison](./documentation.md#comparing-to-pandas).
+
+## Python vs Cython
+
+cuDF makes substantial use of [Cython](https://cython.org/).
+Cython is a powerful tool, but it is less user-friendly than pure Python.
+It is also more difficult to debug or profile.
+Therefore, developers should generally prefer Python code over Cython where possible.
+
+The primary use-case for Cython in cuDF is to expose libcudf C++ APIs to Python.
+This Cython usage is generally composed of two parts:
+1. A `pxd` file declaring C++ APIs so that they may be used in Cython, and
+2. A `pyx` file containing Cython functions that wrap those C++ APIs so that they can be called from Python.
+
+The latter wrappers should generally be kept as thin as possible to minimize Cython usage.
+For more information see [our Cython layer design documentation](./library_design.md#the-cython-layer).
+
+In some rare cases we may actually benefit from writing pure Cython code to speed up particular code paths.
+Given that most numerical computations in cuDF actually happen in libcudf, however,
+such use cases are quite rare.
+Any attempt to write pure Cython code for this purpose should be justified with benchmarks.
+
+## Exception handling
+
+In alignment with [maintaining compatibility with pandas](#pandas-compatibility),
+any API that cuDF shares with pandas should throw all the same exceptions as the
+corresponding pandas API given the same inputs.
+However, it is not required to match the corresponding pandas API's exception message.
+
+When writing error messages,
+sufficient information should be included to help users locate the source of the error,
+such as including the expected argument type versus the actual argument provided.
+
+For parameters that are not yet supported,
+raise `NotImplementedError`.
+There is no need to mention when the argument will be supported in the future.
+
+### Handling libcudf Exceptions
+
+Standard C++ natively supports various [exception types](https://en.cppreference.com/w/cpp/error/exception),
+which Cython maps to [these Python exception types](https://docs.cython.org/en/latest/src/userguide/wrapping_CPlusPlus.html#exceptions).
+In addition to built-in exceptions, libcudf also raises a few additional types of exceptions.
+cuDF extends Cython's default mapping to account for these exception types.
+When a new libcudf exception type is added, a suitable except clause should be added to cuDF's
+[exception handler](https://github.com/rapidsai/cudf/blob/main/python/cudf/cudf/_lib/cpp/exception_handler.hpp).
+If no built-in Python exception seems like a good match, a new Python exception should be created.
+
+### Raising warnings
+
+Where appropriate, cuDF should throw the same warnings as pandas.
+For instance, API deprecations in cuDF should track pandas as closely as possible.
+However, not all pandas warnings are appropriate for cuDF.
+Common exceptional cases include
+[implementation-dependent performance warnings](https://pandas.pydata.org/docs/reference/api/pandas.errors.PerformanceWarning.html)
+that do not apply to cuDF's internals.
+The decision of whether or not to match pandas must be made on a case-by-case
+basis and is left to the best judgment of developers and PR reviewers.
+
+### Catching warnings from dependencies
+
+cuDF developers should avoid using deprecated APIs from package dependencies.
+However, there may be cases where such uses cannot be avoided, at least in the short term.
+If such cases arise, developers should
+[catch the warnings](https://docs.python.org/3/library/warnings.html#warnings.catch_warnings)
+within cuDF so that they are not visible to the user.
diff --git a/docs/cudf/source/developer_guide/documentation.md b/docs/cudf/source/developer_guide/documentation.md
new file mode 100644
index 0000000..26557de
--- /dev/null
+++ b/docs/cudf/source/developer_guide/documentation.md
@@ -0,0 +1,232 @@
+# Writing documentation
+
+cuDF documentation is split into multiple pieces.
+All core functionality is documented using inline docstrings.
+Additional pages like user or developer guides are written independently.
+While docstrings are written using [reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) (reST),
+the latter are written using [MyST](https://myst-parser.readthedocs.io/en/latest/)
+The inline docstrings are organized using a small set of additional reST pages.
+The results are all then compiled together using [Sphinx](https://www.sphinx-doc.org/en/master/).
+This document discusses each of these components and how to contribute to them.
+
+## Docstrings
+
+cuDF docstrings use the [numpy](https://numpydoc.readthedocs.io/en/latest/format.html) style.
+In lieu of a complete explanation,
+we include here an example of the format and the commonly used sections:
+
+```
+class A:
+    """Brief description of A.
+
+    Longer description of A.
+
+    Parameters
+    ----------
+    x : int
+        Description of x, the first constructor parameter.
+    """
+    def __init__(self, x: int):
+        pass
+
+    def foo(self, bar: str):
+        """Short description of foo.
+
+        Longer description of foo.
+
+        Parameters
+        ----------
+        bar : str
+            Description of bar.
+
+        Returns
+        -------
+        float
+            Description of the return value of foo.
+
+        Raises
+        ------
+        ValueError
+            Explanation of when a ValueError is raised.
+            In this case, a ValueError is raised if bar is "fail".
+
+        Examples
+        --------
+        The examples section is _strongly_ encouraged.
+        Where appropriate, it may mimic the examples for the corresponding pandas API.
+        >>> a = A()
+        >>> a.foo('baz')
+        0.0
+        >>> a.foo('fail')
+        ...
+        ValueError: Failed!
+        """
+        if bar == "fail":
+            raise ValueError("Failed!")
+        return 0.0
+```
+
+`numpydoc` supports a number of other sections of docstrings.
+Developers should familiarize themselves with them, since many are useful in different scenarios.
+Our guidelines include one addition to the standard the `numpydoc` guide.
+Class properties, which are not explicitly covered, should be documented in the getter function.
+That choice makes `help` more useful as well as enabling docstring inheritance in subclasses.
+
+All of our docstrings are validated using [`pydocstyle`](http://www.pydocstyle.org/en/stable/).
+This ensures that docstring style is consistent and conformant across the codebase.
+
+## Published documentation
+
+Documentation is compiled using Sphinx, which pulls docstrings from the code.
+Rather than simply listing all APIs, however, we aim to mimic the pandas documentation.
+To do so, we organize API docs into specific pages and sections.
+These pages are stored in `docs/cudf/source/api_docs`.
+For example, all `DataFrame` documentation is contained in `docs/cudf/source/api_docs/dataframe.rst`.
+That page contains sections like "Computations / descriptive stats" to make APIs more easily discoverable.
+
+Within each section, documentation is created using [`autosummary`](https://www.sphinx-doc.org/en/master/usage/extensions/autosummary.html)
+This plugin makes it easy to generate pages for each documented API.
+To do so, each section of the docs looks like the following:
+
+```
+Section name
+~~~~~~~~~~~~
+.. autosummary::
+   API1
+   API2
+   ...
+```
+
+Each listed will automatically have its docstring rendered into a separate page.
+This layout comes from the [Sphinx theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html) that we use.
+
+````{note}
+Under the hood, autosummary generates stub pages that look like this (using `cudf.concat` as an example):
+
+```
+cudf.concat
+===========
+
+.. currentmodule:: cudf
+
+.. autofunction:: concat
+```
+
+Commands like `autofunction` come from [`autodoc`](https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html).
+This directive will import cudf and pull the docstring from `cudf.concat`.
+This approach allows us to do the minimal amount of manual work in organizing our docs,
+while still matching the pandas layout as closely as possible.
+````
+
+When adding a new API, developers simply have to add the API to the appropriate page.
+Adding the name of the function to the appropriate autosummary list is sufficient for it to be documented.
+
+### Documenting classes
+
+Python classes and the Sphinx plugins used in RAPIDS interact in nontrivial ways.
+`autosummary`'s default page generated for a class uses [`autodoc`](https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html) to automatically detect and document all methods of a class.
+That means that in addition to the manually created `autosummary` pages where class methods are grouped into sections of related features, there is another page for each class where all the methods of that class are automatically summarized in a table for quick access.
+However, we also use the [`numpydoc`](https://numpydoc.readthedocs.io/) extension, which offers the same feature.
+We use both in order to match the contents and style of the pandas documentation as closely as possible.
+
+pandas is also particular about what information is included in a class's documentation.
+While the documentation pages for the major user-facing classes like `DataFrame`, `Series`, and `Index` contain all APIs, less visible classes or subclasses (such as subclasses of `Index`) only include the methods that are specific to those subclasses.
+For example, {py:class}`cudf.CategoricalIndex` only includes `codes` and `categories` on its page, not the entire set of `Index` functionality.
+
+To accommodate these requirements, we take the following approach:
+1. The default `autosummary` template for classes is overridden with a [simpler template that does not generate method or attribute documentation](https://github.com/rapidsai/cudf/blob/main/docs/cudf/source/_templates/autosummary/class.rst). In other words, we disable `autosummary`'s generation of Methods and Attributes lists.
+2. We rely on `numpydoc` entirely for the classes that need their entire APIs listed (`DataFrame`/`Series`/etc). `numpydoc` will automatically populate Methods and Attributes section if (and only if) they are not already defined in the class's docstring.
+3. For classes that should only include a subset of APIs, we include those explicitly in the class's documentation. When those lists exist, `numpydoc` will not override them. If either the Methods or Attributes section should be empty, that section must still be included but should simply contain "None". For example, the class documentation for `CategoricalIndex` could include something like the following:
+
+```
+    Attributes
+    ----------
+    codes
+    categories
+
+    Methods
+    -------
+    None
+
+```
+
+## Comparing to pandas
+
+cuDF aims to provide a pandas-like experience.
+However, for various reasons cuDF APIs may exhibit differences from pandas.
+Where such differences exist, they should be documented.
+We facilitate such documentation with the `pandas-compat` directive.
+The directive should be used inside docstrings like so:
+
+```
+"""Brief
+
+Docstring body
+
+.. pandas-compat::
+    **$API_NAME**
+
+    Explanation of differences
+```
+
+All such API compatibility notes are collected and displayed in the rendered documentation.
+
+## Writing documentation pages
+
+In addition to docstrings, our docs also contain a number of more dedicated user guides.
+These pages are stored in `docs/cudf/source/user_guide`.
+These pages are all written using MyST, a superset of Markdown.
+MyST allows developers to write using familiar Markdown syntax,
+while also providing the full power of reST where needed.
+These pages do not conform to any specific style or set of use cases.
+However, if you develop any sufficiently complex new features,
+consider whether users would benefit from a more complete demonstration of them.
+
+```{note}
+We encourage using links between pages.
+We enable [Myst auto-generated anchors](https://myst-parser.readthedocs.io/en/latest/syntax/optional.html#auto-generated-header-anchors),
+so links should make use of the appropriately namespaced anchors for links rather than adding manual links.
+
+```
+
+## Building documentation
+
+### Requirements
+
+The following are required to build the documentation:
+- A RAPIDS-compatible GPU. This is necessary because the documentation execute code.
+- A working copy of cudf in the same build environment.
+  We recommend following the [build instructions](https://github.com/rapidsai/cudf/blob/main/CONTRIBUTING.md#setting-up-your-build-environment).
+- Sphinx, numpydoc, and MyST-NB.
+  Assuming you follow the build instructions, these should automatically be installed into your environment.
+
+### Building and viewing docs
+
+Once you have a working copy of cudf, building the docs is straightforward:
+1. Navigate to `/path/to/cudf/docs/cudf/`.
+2. Execute `make html`
+
+This will run Sphinx in your shell and generate outputs at `build/html/index.html`.
+To view the results.
+1. Navigate to `build/html`
+2. Execute `python -m http.server`
+
+Then, open a web browser and go to `https://localhost:8000`.
+If something else is currently running on port 8000,
+`python -m http.server` will automatically find the next available port.
+Alternatively, you may specify a port with `python -m http.server $PORT`.
+
+You may build docs on a remote machine but want to view them locally.
+Assuming the other machine's IP address is visible on your local network,
+you can view the docs by replacing `localhost` with the IP address of the host machine.
+Alternatively, you may also forward the port using e.g.
+`ssh -N -f -L localhost:$LOCAL_PORT:localhost:$REMOTE_PORT $REMOTE_IP`.
+That will make `$REMOTE_IP:$REMOTE_PORT` visible at `localhost:$LOCAL_PORT`.
+
+## Documenting cuDF internals
+
+Unlike public APIs, the documentation of internal code (functions, classes, etc) is not linted.
+Documenting internals is strongly encouraged, but not enforced in any particular way.
+Regarding style, either full numpy-style docstrings or regular `#` comments are acceptable.
+The former can be useful for complex or widely used functionality,
+while the latter is fine for small one-off functions.
diff --git a/docs/cudf/source/developer_guide/frame_class_diagram.png b/docs/cudf/source/developer_guide/frame_class_diagram.png
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diff --git a/docs/cudf/source/developer_guide/index.md b/docs/cudf/source/developer_guide/index.md
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+# Developer Guide
+
+```{note}
+At present, this guide only covers the main cuDF library.
+In the future, it may be expanded to also cover dask_cudf, cudf_kafka, and custreamz.
+```
+
+cuDF is a GPU-accelerated, [Pandas-like](https://pandas.pydata.org/) DataFrame library.
+Under the hood, all of cuDF's functionality relies on the CUDA-accelerated `libcudf` C++ library.
+Thus, cuDF's internals are designed to efficiently and robustly map pandas APIs to `libcudf` functions.
+For more information about the `libcudf` library, a good starting point is the
+[developer guide](https://github.com/rapidsai/cudf/blob/main/cpp/docs/DEVELOPER_GUIDE.md).
+
+This document assumes familiarity with the
+[overall contributing guide](https://github.com/rapidsai/cudf/blob/main/CONTRIBUTING.md).
+The goal of this document is to provide more specific guidance for Python developers.
+It covers the structure of the Python code and discusses best practices.
+Additionally, it includes longer sections on more specific topics like testing and benchmarking.
+
+```{toctree}
+:maxdepth: 2
+
+library_design
+contributing_guide
+documentation
+testing
+benchmarking
+options
+pylibcudf
+```
diff --git a/docs/cudf/source/developer_guide/library_design.md b/docs/cudf/source/developer_guide/library_design.md
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+# Library Design
+
+At a high level, cuDF is structured in three layers, each of which serves a distinct purpose:
+
+1. The Frame layer: The user-facing implementation of pandas-like data structures like `DataFrame` and `Series`.
+2. The Column layer: The core internal data structures used to bridge the gap to our lower-level implementations.
+3. The Cython layer: The wrappers around the fast C++ `libcudf` library.
+
+In this document we will review each of these layers, their roles, and the requisite tradeoffs.
+Finally we tie these pieces together to provide a more holistic view of the project.
+
+
+## The Frame layer
+
+% The class diagram below was generated using PlantUML (https://plantuml.com/).
+% PlantUML is a simple textual format for encoding UML documents.
+% We could also use it to generate ASCII art or another format.
+%
+% @startuml
+%
+% class Frame
+% class IndexedFrame
+% class SingleColumnFrame
+% class BaseIndex
+% class GenericIndex
+% class MultiIndex
+% class RangeIndex
+% class DataFrame
+% class Series
+%
+% Frame <|-- IndexedFrame
+%
+% Frame <|-- SingleColumnFrame
+%
+% SingleColumnFrame <|-- Series
+% IndexedFrame <|-- Series
+%
+% IndexedFrame <|-- DataFrame
+%
+% BaseIndex <|-- RangeIndex
+%
+% BaseIndex <|-- MultiIndex
+% Frame <|-- MultiIndex
+%
+% BaseIndex <|-- GenericIndex
+% SingleColumnFrame <|-- GenericIndex
+%
+% @enduml
+
+
+```{image} frame_class_diagram.png
+```
+
+This class diagram shows the relationship between the principal components of the Frame layer:
+All classes in the Frame layer inherit from one or both of the two base classes in this layer: `Frame` and `BaseIndex`.
+The eponymous `Frame` class is, at its core, a simple tabular data structure composed of columnar data.
+Some types of `Frame` contain indexes; in particular, any `DataFrame` or `Series` has an index.
+However, as a general container of columnar data, `Frame` is also the parent class for most types of index.
+
+`BaseIndex`, meanwhile, is essentially an abstract base class encoding the `pandas.Index` API.
+Various subclasses of `BaseIndex` implement this API in specific ways depending on their underlying data.
+For example, `RangeIndex` avoids actually materializing a column, while a `MultiIndex` contains _multiple_ columns.
+Most other index classes consist of a single column of a given type, e.g. strings or datetimes.
+As a result, using a single abstract parent provides the flexibility we need to support these different types.
+
+With those preliminaries out of the way, let's dive in a little bit deeper.
+
+### Frames
+
+`Frame` exposes numerous methods common to all pandas data structures.
+Any methods that have the same API across `Series`, `DataFrame`, and `Index` should be defined here.
+Additionally any (internal) methods that could be used to share code between those classes may also be defined here.
+
+The primary internal subclass of `Frame` is `IndexedFrame`, a `Frame` with an index.
+An `IndexedFrame` represents the first type of object mentioned above: indexed tables.
+In particular, `IndexedFrame` is a parent class for `DataFrame` and `Series`.
+Any pandas methods that are defined for those two classes should be defined here.
+
+The second internal subclass of `Frame` is `SingleColumnFrame`.
+As you may surmise, it is a `Frame` with a single column of data.
+This class is a parent for most types of indexes as well as `Series` (note the diamond inheritance pattern here).
+While `IndexedFrame` provides a large amount of functionality, this class is much simpler.
+It adds some simple APIs provided by all 1D pandas objects, and it flattens outputs where needed.
+
+### Indexes
+
+While we've highlighted some exceptional cases of Indexes before, let's start with the base cases here first.
+`BaseIndex` is intended to be a pure abstract class, i.e. all of its methods should simply raise `NotImplementedError`.
+In practice, `BaseIndex` does have concrete implementations of a small set of methods.
+However, currently many of these implementations are not applicable to all subclasses and will be eventually be removed.
+
+Almost all indexes are subclasses of `GenericIndex`, a single-columned index with the class hierarchy:
+```python
+class GenericIndex(SingleColumnFrame, BaseIndex)
+```
+Integer, float, or string indexes are all composed of a single column of data.
+Most `GenericIndex` methods are inherited from `Frame`, saving us the trouble of rewriting them.
+
+We now consider the three main exceptions to this model:
+
+- A `RangeIndex` is not backed by a column of data, so it inherits directly from `BaseIndex` alone.
+  Wherever possible, its methods have special implementations designed to avoid materializing columns.
+  Where such an implementation is infeasible, we fall back to converting it to an `Int64Index` first instead.
+- A `MultiIndex` is backed by _multiple_ columns of data.
+  Therefore, its inheritance hierarchy looks like `class MultiIndex(Frame, BaseIndex)`.
+  Some of its more `Frame`-like methods may be inherited,
+  but many others must be reimplemented since in many cases a `MultiIndex` is not expected to behave like a `Frame`.
+- Just like in pandas, `Index` itself can never be instantiated.
+  `pandas.Index` is the parent class for indexes,
+  but its constructor returns an appropriate subclass depending on the input data type and shape.
+  Unfortunately, mimicking this behavior requires overriding `__new__`,
+  which in turn makes shared initialization across inheritance trees much more cumbersome to manage.
+  To enable sharing constructor logic across different index classes,
+  we instead define `BaseIndex` as the parent class of all indexes.
+  `Index` inherits from `BaseIndex`, but it masquerades as a `BaseIndex` to match pandas.
+  This class should contain no implementations since it is simply a factory for other indexes.
+
+
+## The Column layer
+
+The next layer in the cuDF stack is the Column layer.
+This layer forms the glue between pandas-like APIs and our underlying data layouts.
+The principal objects in the Column layer are the `ColumnAccessor` and the various `Column` classes.
+The `Column` is cuDF's core data structure that represents a single column of data of a specific data type.
+A `ColumnAccessor` is a dictionary-like interface to a sequence of `Column`s.
+A `Frame` owns a `ColumnAccessor`.
+
+### ColumnAccessor
+
+The primary purpose of the `ColumnAccessor` is to encapsulate pandas column selection semantics.
+Columns may be selected or inserted by index or by label, and label-based selections are as flexible as pandas is.
+For instance, Columns may be selected hierarchically (using tuples) or via wildcards.
+`ColumnAccessor`s also support the `MultiIndex` columns that can result from operations like groupbys.
+
+### Columns
+
+Under the hood, cuDF is built around the [Apache Arrow Format](https://arrow.apache.org).
+This data format is both conducive to high-performance algorithms and suitable for data interchange between libraries.
+The `Column` class encapsulates our implementation of this data format.
+A `Column` is composed of the following:
+
+- A **data type**, specifying the type of each element.
+- A **data buffer** that may store the data for the column elements.
+  Some column types do not have a data buffer, instead storing data in the children columns.
+- A **mask buffer** whose bits represent the validity (null or not null) of each element.
+  Nullability is a core concept in the Arrow data model.
+  Columns whose elements are all valid may not have a mask buffer.
+  Mask buffers are padded to 64 bytes.
+- Its **children**, a tuple of columns used to represent complex types such as structs or lists.
+- A **size** indicating the number of elements in the column.
+- An integer **offset** use to represent the first element of column that is the "slice" of another column.
+  The size of the column then gives the extent of the slice rather than the size of the underlying buffer.
+  A column that is not a slice has an offset of 0.
+
+More information about these fields can be found in the documentation of the
+[Apache Arrow Columnar Format](https://arrow.apache.org/docs/format/Columnar.html),
+which is what the cuDF `Column` is based on.
+
+The `Column` class is implemented in Cython to facilitate interoperability with `libcudf`'s C++ data structures.
+Most higher-level functionality is implemented in the `ColumnBase` subclass.
+These functions rely `Column` APIs to call `libcudf` APIs and translate their results to Python.
+This separation allows `ColumnBase` to be implemented in pure Python, which simplifies development and debugging.
+
+`ColumnBase` provides some standard methods, while other methods only make sense for data of a specific type.
+As a result, we have various subclasses of `ColumnBase` like `NumericalColumn`, `StringColumn`, and `DatetimeColumn`.
+Most dtype-specific decisions should be handled at the level of a specific `Column` subclass.
+Each type of `Column` only implements methods supported by that data type.
+
+Different types of `ColumnBase` are also stored differently in memory according to the Arrow format.
+As one example, a `NumericalColumn` with 1000 `int32` elements and containing nulls is composed of:
+
+1. A data buffer of size 4000 bytes (sizeof(int32) * 1000)
+2. A mask buffer of size 128 bytes (1000/8 padded to a multiple of 64
+   bytes)
+3. No children columns
+
+As another example, a `StringColumn` backing the Series `['do', 'you', 'have', 'any', 'cheese?']` is composed of:
+
+1. No data buffer
+2. No mask buffer as there are no nulls in the Series
+3. Two children columns:
+
+   - A column of UTF-8 characters
+     `['d', 'o', 'y', 'o', 'u', 'h', ..., '?']`
+   - A column of "offsets" to the characters column (in this case,
+     `[0, 2, 5, 9, 12, 19]`)
+
+
+### Data types
+
+cuDF uses [dtypes](https://numpy.org/doc/stable/reference/arrays.dtypes.html) to represent different types of data.
+Since efficient GPU algorithms require preexisting knowledge of data layouts,
+cuDF does not support the arbitrary `object` dtype, but instead defines a few custom types for common use-cases:
+- `ListDtype`: Lists where each element in every list in a Column is of the same type
+- `StructDtype`: Dicts where a given key always maps to values of the same type
+- `CategoricalDtype`: Analogous to the pandas categorical dtype except that the categories are stored in device memory
+- `DecimalDtype`: Fixed-point numbers
+- `IntervalDtype`: Intervals
+
+Note that there is a many-to-one mapping between data types and `Column` classes.
+For instance, all numerical types (floats and ints of different widths) are all managed using `NumericalColumn`.
+
+
+### Buffer
+
+`Column`s are in turn composed of one or more `Buffer`s.
+A `Buffer` represents a single, contiguous, device memory allocation owned by another object.
+A `Buffer` constructed from a preexisting device memory allocation (such as a CuPy array) will view that memory.
+Conversely, when constructed from a host object,
+`Buffer` uses [`rmm.DeviceBuffer`](https://github.com/rapidsai/rmm#devicebuffers) to allocate new memory.
+The data is then copied from the host object into the newly allocated device memory.
+You can read more about [device memory allocation with RMM here](https://github.com/rapidsai/rmm).
+
+
+### Spilling to host memory
+
+Setting the environment variable `CUDF_SPILL=on` enables automatic spilling (and "unspilling") of buffers from
+device to host to enable out-of-memory computation, i.e., computing on objects that occupy more memory than is
+available on the GPU.
+
+Spilling can be enabled in two ways (it is disabled by default):
+  - setting the environment variable `CUDF_SPILL=on`, or
+  - setting the `spill` option in `cudf` by doing `cudf.set_option("spill", True)`.
+
+Additionally, parameters are:
+  - `CUDF_SPILL_ON_DEMAND=ON` / `cudf.set_option("spill_on_demand", True)`, which registers an RMM out-of-memory
+    error handler that spills buffers in order to free up memory. If spilling is enabled, spill on demand is **enabled by default**.
+  - `CUDF_SPILL_DEVICE_LIMIT=<X>` / `cudf.set_option("spill_device_limit", <X>)`, which sets a device memory limit
+    of `<X>` in bytes. This introduces a modest overhead and is **disabled by default**. Furthermore, this is a
+    *soft* limit. The memory usage might exceed the limit if too many buffers are unspillable.
+
+(Buffer-design)=
+#### Design
+
+Spilling consists of two components:
+  - A new buffer sub-class, `SpillableBuffer`, that implements moving of its data from host to device memory in-place.
+  - A spill manager that tracks all instances of `SpillableBuffer` and spills them on demand.
+A global spill manager is used throughout cudf when spilling is enabled, which makes `as_buffer()` return `SpillableBuffer` instead of the default `Buffer` instances.
+
+Accessing `Buffer.get_ptr(...)`, we get the device memory pointer of the buffer. This is unproblematic in the case of `Buffer` but what happens when accessing `SpillableBuffer.get_ptr(...)`, which might have spilled its device memory. In this case, `SpillableBuffer` needs to unspill the memory before returning its device memory pointer. Furthermore, while this device memory pointer is being used (or could be used), `SpillableBuffer` cannot spill its memory back to host memory because doing so would invalidate the device pointer.
+
+To address this, we mark the `SpillableBuffer` as unspillable, we say that the buffer has been _exposed_. This can either be permanent if the device pointer is exposed to external projects or temporary while `libcudf` accesses the device memory.
+
+The `SpillableBuffer.get_ptr(...)` returns the device pointer of the buffer memory but if called within an `acquire_spill_lock` decorator/context, the buffer is only marked unspillable while running within the decorator/context.
+
+#### Statistics
+cuDF supports spilling statistics, which can be very useful for performance profiling and to identify code that renders buffers unspillable.
+
+Three levels of information gathering exist:
+
+  0. disabled (no overhead). 
+  1. gather statistics of duration and number of bytes spilled (very low overhead). 
+  2. gather statistics of each time a spillable buffer is exposed permanently (potential high overhead).
+
+Statistics can be enabled in two ways (it is disabled by default):
+  - setting the environment variable `CUDF_SPILL_STATS=<statistics-level>`, or
+  - setting the `spill_stats` option in `cudf` by doing `cudf.set_option("spill_stats", <statistics-level>)`.
+
+
+It is possible to access the statistics through the spill manager like:
+```python
+>>> import cudf
+>>> from cudf.core.buffer.spill_manager import get_global_manager
+>>> stats = get_global_manager().statistics
+>>> print(stats)
+    Spill Statistics (level=1):
+     Spilling (level >= 1):
+      gpu => cpu: 24B in 0.0033
+```
+
+To have each worker in dask print spill statistics, do something like:
+```python
+    def spill_info():
+        from cudf.core.buffer.spill_manager import get_global_manager
+        print(get_global_manager().statistics)
+    client.submit(spill_info)
+```
+
+## The Cython layer
+
+The lowest level of cuDF is its interaction with `libcudf` via Cython.
+The Cython layer is composed of two components: C++ bindings and Cython wrappers.
+The first component consists of [`.pxd` files](https://cython.readthedocs.io/en/latest/src/tutorial/pxd_files.html),
+Cython declaration files that expose the contents of C++ header files to other Cython files.
+The second component consists of Cython wrappers for this functionality.
+These wrappers are necessary to expose this functionality to pure Python code.
+They also handle translating cuDF objects into their `libcudf` equivalents and invoking `libcudf` functions.
+
+Working with this layer of cuDF requires some familiarity with `libcudf`'s APIs.
+`libcudf` is built around two principal objects whose names are largely self-explanatory: `column` and `table`.
+`libcudf` also defines corresponding non-owning "view" types `column_view` and `table_view`.
+`libcudf` APIs typically accept views and return owning types.
+
+Most cuDF Cython wrappers involve converting `cudf.Column` objects into `column_view` or `table_view` objects,
+calling a `libcudf` API with these arguments, then constructing new `cudf.Column`s from the result.
+By the time code reaches this layer, all questions of pandas compatibility should already have been addressed.
+These functions should be as close to trivial wrappers around `libcudf` APIs as possible.
+
+
+## Putting It All Together
+
+To this point, our discussion has assumed that all cuDF functions follow a strictly linear descent through these layers.
+However, it should be clear that in many cases this approach is not appropriate.
+Many common `Frame` operations do not operate on individual columns but on the `Frame` as a whole.
+Therefore, we in fact have two distinct common patterns for implementations in cuDF.
+
+1. The first pattern is for operations that act on columns of a `Frame` individually.
+   This group includes tasks like reductions and scans (`sum`/`cumsum`).
+   These operations are typically implemented by looping over the columns stored in a `Frame`'s `ColumnAccessor`.
+2. The second pattern is for operations that involve acting on multiple columns at once.
+   This group includes many core operations like grouping or merging.
+   These operations bypass the Column layer altogether, instead going straight from Frame to Cython.
+
+The pandas API also includes a number of helper objects, such as `GroupBy`, `Rolling`, and `Resampler`.
+cuDF implements corresponding objects with the same APIs.
+Internally, these objects typically interact with cuDF objects at the Frame layer via composition.
+However, for performance reasons they frequently access internal attributes and methods of `Frame` and its subclasses.
+
+
+(copy-on-write-dev-doc)=
+
+## Copy-on-write
+
+This section describes the internal implementation details of the copy-on-write feature.
+It is recommended that developers familiarize themselves with [the user-facing documentation](copy-on-write-user-doc) of this functionality before reading through the internals
+below.
+
+The core copy-on-write implementation relies on the factory function `as_exposure_tracked_buffer` and the two classes `ExposureTrackedBuffer` and `BufferSlice`.
+
+An `ExposureTrackedBuffer` is a subclass of the regular `Buffer` that tracks internal and external references to its underlying memory. Internal references are tracked by maintaining [weak references](https://docs.python.org/3/library/weakref.html) to every `BufferSlice` of the underlying memory. External references are tracked through "exposure" status of the underlying memory. A buffer is considered exposed if the device pointer (integer or void*) has been handed out to a library outside of cudf. In this case, we have no way of knowing if the data are being modified by a third party.
+
+`BufferSlice` is a subclass of `ExposureTrackedBuffer` that represents a _slice_ of the memory underlying a exposure tracked buffer.
+
+When the cudf option `"copy_on_write"` is `True`, `as_buffer` calls `as_exposure_tracked_buffer`, which always returns a `BufferSlice`. It is then the slices that determine whether or not to make a copy when a write operation is performed on a `Column` (see below). If multiple slices point to the same underlying memory, then a copy must be made whenever a modification is attempted.
+
+
+### Eager copies when exposing to third-party libraries
+
+If a `Column`/`BufferSlice` is exposed to a third-party library via `__cuda_array_interface__`, we are no longer able to track whether or not modification of the buffer has occurred. Hence whenever
+someone accesses data through the `__cuda_array_interface__`, we eagerly trigger the copy by calling
+`.make_single_owner_inplace` which ensures a true copy of underlying data is made and that the slice is the sole owner. Any future copy requests must also trigger a true physical copy (since we cannot track the lifetime of the third-party object). To handle this we also mark the `Column`/`BufferSlice` as exposed thus indicating that any future shallow-copy requests will trigger a true physical copy rather than a copy-on-write shallow copy.
+
+### Obtaining a read-only object
+
+A read-only object can be quite useful for operations that will not
+mutate the data. This can be achieved by calling `.get_ptr(mode="read")`, and using `cuda_array_interface_wrapper` to wrap a `__cuda_array_interface__` object around it.
+This will not trigger a deep copy even if multiple `BufferSlice` points to the same `ExposureTrackedBuffer`. This API should only be used when the lifetime of the proxy object is restricted to cudf's internal code execution. Handing this out to external libraries or user-facing APIs will lead to untracked references and undefined copy-on-write behavior. We currently use this API for device to host
+copies like in `ColumnBase.data_array_view(mode="read")` which is used for `Column.values_host`.
+
+
+### Internal access to raw data pointers
+
+Since it is unsafe to access the raw pointer associated with a buffer when
+copy-on-write is enabled, in addition to the readonly proxy object described above,
+access to the pointer is gated through `Buffer.get_ptr`. This method accepts a mode
+argument through which the caller indicates how they will access the data associated
+with the buffer. If only read-only access is required (`mode="read"`), this indicates
+that the caller has no intention of modifying the buffer through this pointer.
+In this case, any shallow copies are not unlinked. In contrast, if modification is
+required one may pass `mode="write"`, provoking unlinking of any shallow copies.
+
+
+### Variable width data types
+Weak references are implemented only for fixed-width data types as these are only column
+types that can be mutated in place.
+Requests for deep copies of variable width data types always return shallow copies of the Columns, because these
+types don't support real in-place mutation of the data.
+Internally, we mimic in-place mutations using `_mimic_inplace`, but the resulting data is always a deep copy of the underlying data.
+
+
+### Examples
+
+When copy-on-write is enabled, taking a shallow copy of a `Series` or a `DataFrame` does not
+eagerly create a copy of the data. Instead, it produces a view that will be lazily
+copied when a write operation is performed on any of its copies.
+
+Let's create a series:
+
+```python
+>>> import cudf
+>>> cudf.set_option("copy_on_write", True)
+>>> s1 = cudf.Series([1, 2, 3, 4])
+```
+
+Make a copy of `s1`:
+```python
+>>> s2 = s1.copy(deep=False)
+```
+
+Make another copy, but of `s2`:
+```python
+>>> s3 = s2.copy(deep=False)
+```
+
+Viewing the data and memory addresses show that they all point to the same device memory:
+```python
+>>> s1
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+>>> s2
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+>>> s3
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+
+>>> s1.data._ptr
+139796315897856
+>>> s2.data._ptr
+139796315897856
+>>> s3.data._ptr
+139796315897856
+```
+
+Now, when we perform a write operation on one of them, say on `s2`, a new copy is created
+for `s2` on device and then modified:
+
+```python
+>>> s2[0:2] = 10
+>>> s2
+0    10
+1    10
+2     3
+3     4
+dtype: int64
+>>> s1
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+>>> s3
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+```
+
+If we inspect the memory address of the data, `s1` and `s3` still share the same address but `s2` has a new one:
+
+```python
+>>> s1.data._ptr
+139796315897856
+>>> s3.data._ptr
+139796315897856
+>>> s2.data._ptr
+139796315899392
+```
+
+Now, performing write operation on `s1` will trigger a new copy on device memory as there
+is a weak reference being shared in `s3`:
+
+```python
+>>> s1[0:2] = 11
+>>> s1
+0    11
+1    11
+2     3
+3     4
+dtype: int64
+>>> s2
+0    10
+1    10
+2     3
+3     4
+dtype: int64
+>>> s3
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+```
+
+If we inspect the memory address of the data, the addresses of `s2` and `s3` remain unchanged, but `s1`'s memory address has changed because of a copy operation performed during the writing:
+
+```python
+>>> s2.data._ptr
+139796315899392
+>>> s3.data._ptr
+139796315897856
+>>> s1.data._ptr
+139796315879723
+```
+
+cuDF's copy-on-write implementation is motivated by the pandas proposals documented here:
+1. [Google doc](https://docs.google.com/document/d/1ZCQ9mx3LBMy-nhwRl33_jgcvWo9IWdEfxDNQ2thyTb0/edit#heading=h.iexejdstiz8u)
+2. [Github issue](https://github.com/pandas-dev/pandas/issues/36195)
diff --git a/docs/cudf/source/developer_guide/options.md b/docs/cudf/source/developer_guide/options.md
new file mode 100644
index 0000000..b977c0f
--- /dev/null
+++ b/docs/cudf/source/developer_guide/options.md
@@ -0,0 +1,22 @@
+# Options
+
+The usage of options is explained in the [user guide](options_user_guide).
+This document provides more explanations on how developers work with options internally.
+
+Options are stored as a dictionary in the `cudf.options` module.
+Each option name is its key in the dictionary.
+The value of the option is an instance of an `Options` object.
+
+An `Options` object has the following attributes:
+- `value`: the current value of the option
+- `description`: a text description of the option
+- `validator`: a boolean function that returns `True` if `value` is valid,
+`False` otherwise.
+
+Developers can use `cudf.options._register_option` to add options to the dictionary.
+{py:func}`cudf.get_option` is provided to get option values from the dictionary.
+
+When testing the behavior of a certain option,
+it is advised to use [`yield` fixture](https://docs.pytest.org/en/7.1.x/how-to/fixtures.html#yield-fixtures-recommended) to set up and clean up the option.
+
+See the [API reference](api.options) for more details.
diff --git a/docs/cudf/source/developer_guide/pylibcudf.md b/docs/cudf/source/developer_guide/pylibcudf.md
new file mode 100644
index 0000000..1b321db
--- /dev/null
+++ b/docs/cudf/source/developer_guide/pylibcudf.md
@@ -0,0 +1,155 @@
+# pylibcudf
+
+pylibcudf is a lightweight Cython wrapper around libcudf.
+It aims to provide a near-zero overhead interface to accessing libcudf in Python.
+It should be possible to achieve near-native C++ performance using Cythonized code calling pylibcudf, while also allowing fairly performant usage from Python.
+In addition to these requirements, pylibcudf must also integrate naturally with other Python libraries.
+In other words, it should interoperate fairly transparently with standard Python containers, community protocols like `__cuda_array_interface__`, and common vocabulary types like CuPy arrays.
+
+
+## General Design Principles
+
+To satisfy the goals of pylibcudf, we impose the following set of design principles:
+- Every public function or method should be `cpdef`ed. This allows it to be used in both Cython and Python code. This incurs some slight overhead over `cdef` functions, but we assume that this is acceptable because 1) the vast majority of users will be using pure Python rather than Cython, and 2) the overhead of a `cpdef` function over a `cdef` function is on the order of a nanosecond, while CUDA kernel launch overhead is on the order of a microsecond, so these function overheads should be washed out by typical usage of pylibcudf.
+- Every variable used should be strongly typed and either be a primitive type (int, float, etc) or a cdef class. Any enums in C++ should be mirrored using `cpdef enum`, which will create both a C-style enum in Cython and a PEP 435-style Python enum that will automatically be used in Python.
+- All typing in code should be written using Cython syntax, not PEP 484 Python typing syntax. Not only does this ensure compatibility with Cython < 3, but even with Cython 3 PEP 484 support remains incomplete as of this writing.
+- All cudf code should interact only with pylibcudf, never with libcudf directly.
+- All imports should be relative so that pylibcudf can be easily extracted from cudf later
+  - Exception: All imports of libcudf API bindings in `cudf._lib.cpp` should use absolute imports of `cudf._lib.cpp as libcudf`. We should convert the `cpp` directory into a proper package so that it can be imported as `libcudf` in that fashion. When moving pylibcudf into a separate package, it will be renamed to `libcudf` and only the imports will need to change.
+- Ideally, pylibcudf should depend on nothing other than rmm and pyarrow. This will allow it to be extracted into a a largely standalone library and used in environments where the larger dependency tree of cudf may be cumbersome.
+
+
+## Relationship to libcudf
+
+In general, the relationship between pylibcudf and libcudf can be understood in terms of two components, data structures and algorithms.
+
+(data-structures)=
+
+### Data Structures
+
+Typically, every type in libcudf should have a mirror Cython `cdef` class with an attribute `self.c_obj: unique_ptr[${underlying_type}]` that owns an instance of the underlying libcudf type.
+Each type should also implement a corresponding method `cdef ${cython_type} from_libcudf(${underlying_type} dt)` to enable constructing the Cython object from an underlying libcudf instance.
+Depending on the nature of the type, the function may need to accept a `unique_ptr` and take ownership e.g. `cdef ${cython_type} from_libcudf(unique_ptr[${underlying_type}] obj)`.
+This will typically be the case for types that own GPU data, may want to codify further.
+
+For example, `libcudf::data_type` maps to `pylibcudf.DataType`, which looks like this (implementation omitted):
+```cython
+
+cdef class DataType:
+    cdef data_type c_obj
+
+    cpdef TypeId id(self)
+    cpdef int32_t scale(self)
+
+    @staticmethod
+    cdef DataType from_libcudf(data_type dt)
+```
+
+This allows pylibcudf functions to accept a typed `DataType` parameter and then trivially call underlying libcudf algorithms by accessing the argument's `c_obj`.
+
+#### pylibcudf Tables and Columns
+
+The primary exception to the above set of rules are libcudf's core data owning types, `cudf::table` and `cudf::column`.
+libcudf uses modern C++ idioms based on smart pointers to avoid resource leaks and make code exception-safe.
+To avoid passing around raw pointers, and to ensure that ownership semantics are clear, libcudf has separate `view` types corresponding to data owning types.
+For example, `cudf::column` owns data, while `cudf::column_view` represents an view on a column of data and `cudf::mutable_column_view` represents a mutable view.
+A `column_view` need not actually reference data owned by a `cudf::column`; any memory buffer will do.
+This separation allows libcudf algorithms to clearly communicate ownership expectations and allows multiple views into the same data to coexist.
+
+While libcudf algorithms accept views as inputs, any algorithms that allocate data must return `cudf::column` and `cudf::table` objects.
+libcudf's ownership model is problematic for pylibcudf, which must be able to seamlessly interoperate with data provided by other Python libraries like PyTorch or Numba.
+Therefore, pylibcudf employs the following strategy:
+- pylibcudf defines the `gpumemoryview` type, which (analogous to the [Python `memoryview` type](https://docs.python.org/3/library/stdtypes.html#memoryview)) represents a view into memory owned by another object that it keeps alive using Python's standard reference counting machinery. A `gpumemoryview` is constructible from any object implementing the [CUDA Array Interface protocol](https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html).
+  - This type will eventually be generalized for reuse outside of pylibcudf.
+- pylibcudf defines its own Table and Column classes.
+  - A Table maintains Python references to the Columns it contains, so multiple Tables may share the same Column.
+  - A Column consists of `gpumemoryview`s of its data buffers (which may include children for nested types) and its null mask.
+- `pylibcudf.Table` and `pylibcudf.Column` provide easy access to `cudf::table_view` and `cudf::column_view` objects viewing the same columns/memory. These can be then be used when implementing any pylibcudf algorithm in terms of the underlying libcudf algorithm. Specifically, each of these classes owns an instance of the libcudf view type and provides a method `view` that may be used to access a pointer to that object to be passed to libcudf.
+
+
+### Algorithms
+
+pylibcudf algorithms should look almost exactly like libcudf algorithms.
+Any libcudf function should be mirrored in pylibcudf with an identical signature and libcudf types mapped to corresponding pylibcudf types.
+All calls to libcudf algorithms should perform any requisite Python preprocessing early, then release the GIL prior to calling libcudf.
+For example, here is the implementation of `gather`:
+```cython
+
+cpdef Table gather(
+    Table source_table,
+    Column gather_map,
+    OutOfBoundsPolicy bounds_policy
+):
+    cdef unique_ptr[table] c_result
+    with nogil:
+        c_result = move(
+            cpp_copying.gather(
+                source_table.view(),
+                gather_map.view(),
+                bounds_policy
+            )
+        )
+    return Table.from_libcudf(move(c_result))
+```
+
+There are a couple of notable points from the snippet above:
+- The object returned from libcudf is immediately converted to a pylibcudf type.
+- `cudf::gather` accepts a `cudf::out_of_bounds_policy` enum parameter. `OutOfBoundsPolicy` is an alias for this type in pylibcudf that matches our Python naming conventions (CapsCase instead of snake\_case).
+
+## Miscellaneous Notes
+
+### Cython Scoped Enums
+Cython 3 introduced support for scoped enumerations.
+However, this support has some bugs as well as some easy pitfalls.
+Our usage of enums is intended to minimize the complexity of our code while also working around Cython's limitations.
+
+```{warning}
+The guidance in this section may change often as Cython is updated and our understanding of best practices evolves.
+```
+
+- All pxd files that declare a C++ enum should use `cpdef enum class` declarations.
+  -  Reason: This declaration makes the C++ enum available in Cython code while also transparently creating a Python enum.
+- Any pxd file containing only C++ declarations must still have a corresponding pyx file if any of the declarations are scoped enums.
+  - Reason: The creation of the Python enum requires that Cython actually generate the necessary Python C API code, which will not happen if only a pxd file is present.
+-  If a C++ enum will be part of a pylibcudf module's public API, then it should be imported (not cimported) directly into the pyx file and aliased with a name that matches our Python class naming conventions (CapsCase) instead of our C++ naming convention (snake\_case).
+  - Reason: We want to expose the enum to both Python and Cython consumers of the module. As a side effect, this aliasing avoids [this Cython bug](https://github.com/cython/cython/issues/5609).
+  - Note: Once the above Cython bug is resolved, the enum should also be aliased into the pylibcudf pxd file when it is cimported so that Python and Cython usage will match.
+
+Here is an example of appropriate enum usage.
+
+
+```cython
+# cpp/copying.pxd
+cdef extern from "cudf/copying.hpp" namespace "cudf" nogil:
+    # cpdef here so that we export both a cdef enum class and a Python enum.Enum.
+    cpdef enum class out_of_bounds_policy(bool):
+        NULLIFY
+        DONT_CHECK
+
+
+# cpp/copying.pyx
+# This file is empty, but is required to compile the Python enum in cpp/copying.pxd
+
+
+# pylibcudf/copying.pxd
+
+# cimport the enum using the exact name
+# Once https://github.com/cython/cython/issues/5609 is resolved,
+# this import should instead be
+# from cudf._lib.cpp.copying cimport out_of_bounds_policy as OutOfBoundsPolicy
+from cudf._lib.cpp.copying cimport out_of_bounds_policy
+
+
+# pylibcudf/copying.pyx
+# Access cpp.copying members that aren't part of this module's public API via
+# this module alias
+from cudf._lib.cpp cimport copying as cpp_copying
+from cudf._lib.cpp.copying cimport out_of_bounds_policy
+
+# This import exposes the enum in the public API of this module.
+# It requires a no-cython-lint tag because it will be unused: all typing of
+# parameters etc will need to use the Cython name `out_of_bounds_policy` until
+# the Cython bug is resolved.
+from cudf._lib.cpp.copying import \
+    out_of_bounds_policy as OutOfBoundsPolicy  # no-cython-lint
+```
diff --git a/docs/cudf/source/developer_guide/testing.md b/docs/cudf/source/developer_guide/testing.md
new file mode 100644
index 0000000..e393972
--- /dev/null
+++ b/docs/cudf/source/developer_guide/testing.md
@@ -0,0 +1,251 @@
+# Testing cuDF
+
+## Tooling
+Tests in cuDF are written using [`pytest`](https://docs.pytest.org/en/latest/).
+Test coverage is measured using [`coverage.py`](https://coverage.readthedocs.io/en/latest/),
+specifically the [`pytest-cov`](https://github.com/pytest-dev/pytest-cov) plugin.
+Code coverage reports are uploaded to [Codecov](https://app.codecov.io/gh/rapidsai/cudf).
+Each PR also indicates whether it increases or decreases test coverage.
+
+## Test organization
+
+How tests are organized depends on which of the following two groups they fall into:
+
+1. Free functions such as `cudf.merge` that operate on classes like `DataFrame` or `Series`.
+2. Methods of the above classes.
+
+Tests of free functions should be grouped into files based on the
+[API sections in the documentation](https://docs.rapids.ai/api/cudf/latest/api_docs/index.html).
+This places tests of similar functionality in the same module.
+Tests of class methods should be organized in the same way, except that this organization should be within a subdirectory corresponding to the class.
+For instance, tests of `DataFrame` indexing should be placed into `dataframe/test_indexing.py`.
+In cases where tests may be shared by multiple classes sharing a common parent (e.g. `DataFrame` and `Series` both require `IndexedFrame` tests),
+the tests may be placed in a directory corresponding to the parent class.
+
+## Test contents
+
+### Writing tests
+
+In general, functionality must be tested for both standard and exceptional cases.
+Standard use cases may be covered using parametrization (using `pytest.mark.parametrize`).
+Tests of standard use cases should typically include some coverage of:
+- Different dtypes, including nested dtypes (especially strings)
+- Mixed objects, e.g. binary operations between `DataFrame` and `Series`
+- Operations on scalars
+- Verifying all combinations of parameters for complex APIs like `cudf.merge`.
+
+Here are some of the most common exceptional cases to test:
+1. `Series`/`DataFrame`/`Index` with zero rows
+2. `DataFrame` with zero columns
+3. All null data
+4. For string or list APIs, empty strings/lists
+5. For list APIs, lists containing all null elements or empty strings
+6. For numeric data:
+  1. All 0s.
+  2. All 1s.
+  3. Containing/all inf
+  4. Containing/all nan
+  5. `INT${PRECISION}_MAX` for a given precision (e.g. `2**32` for `int32`).
+
+Most specific APIs will also include a range of other cases.
+
+In general, it is preferable to write separate tests for different exceptional cases.
+Excessive parametrization and branching increases complexity and obfuscates the purpose of a test.
+Typically, exception cases require specific assertions or other special logic, so they are best kept separate.
+The main exception to this rule is tests based on comparison to pandas.
+Such tests may test exceptional cases alongside more typical cases since the logic is generally identical.
+
+### Parametrization: custom fixtures and `pytest.mark.parametrize`
+
+When it comes to parametrizing tests written with `pytest`,
+the two main options are [fixtures](https://docs.pytest.org/en/latest/explanation/fixtures.html)
+and [`mark.parametrize`](https://docs.pytest.org/en/latest/how-to/parametrize.html#pytest-mark-parametrize).
+By virtue of being functions, fixtures are both more verbose and more self-documenting.
+Fixtures also have the significant benefit of being constructed lazily,
+whereas parametrizations are constructed at test collection time.
+
+In general, these approaches are applicable to parametrizations of different complexity.
+For the purpose of this discussion,
+we define a parametrization as "simple" if it is composed of a list (possibly nested) of primitive objects.
+Examples include a list of integers or a list of list of strings.
+This _does not_ include e.g. cuDF or pandas objects.
+In particular, developers should avoid performing GPU memory allocations during test collection.
+
+With that in mind, here are some ground rules for how to parametrize.
+
+Use `pytest.mark.parametrize` when:
+- One test must be run on many inputs and those inputs are simple to construct.
+
+Use fixtures when:
+- One or more tests must be run on the same set of inputs,
+  and all of those inputs can be constructed with simple parametrizations.
+  In practice, that means that it is acceptable to use a fixture like this:
+  ```python
+      @pytest.fixture(params=["a", "b"])
+      def foo(request):
+          if request.param == "a":
+              # Some complex initialization
+          elif request.param == "b":
+              # Some other complex initialization
+  ```
+  In other words, the construction of the fixture may be complex,
+  as long as the parametrization of that construction is simple.
+- One or more tests must be run on the same set of inputs,
+  and at least one of those inputs requires complex parametrizations.
+  In this case, the parametrization of a fixture should be decomposed
+  by using fixtures that depend on other fixtures.
+  ```python
+      @pytest.fixture(params=["a", "b"])
+      def foo(request):
+          if request.param == "a":
+              # Some complex initialization
+          elif request.param == "b":
+              # Some other complex initialization
+
+      @pytest.fixture
+      def bar(foo):
+         # do something with foo like initialize a cudf object.
+
+      def test_some_property(bar):
+          # will be run for each value of bar that results from each value of foo.
+          assert some_property_of(bar)
+  ```
+
+#### Complex parametrizations
+
+The lists above document common use cases.
+However, more complex cases may arise.
+One of the most common alternatives is where, given a set of test cases,
+different tests need to run on different subsets with a nonempty intersection.
+Fixtures and parametrization are only capable of handling the Cartesian product of parameters,
+i.e. "run this test for all values of `a` and all values of `b`".
+
+There are multiple potential solutions to this problem.
+One possibility is to encapsulate common test logic in a helper function,
+then call it from multiple `test_*` functions that construct the necessary inputs.
+Another possibility is to use functions rather than fixtures to construct inputs, allowing for more flexible input construction:
+```python
+def get_values(predicate):
+    values = range(10)
+    yield from filter(predicate, values)
+
+def test_evens():
+    for v in get_values(lambda x: x % 2 == 0):
+        # Execute test
+
+def test_odds():
+    for v in get_values(lambda x: x % 2 == 1):
+        # Execute test
+```
+
+Other approaches are also possible, and the best solution should be discussed on a case-by-case basis during PR review.
+
+### Tests with expected failures (`xfail`s)
+
+In some circumstances it makes sense to mark a test as _expected_ to
+fail, perhaps because the functionality is not yet implemented in
+cuDF. To do so use the
+[`pytest.mark.xfail`](https://docs.pytest.org/en/stable/reference/reference.html#pytest.mark.xfail)
+fixture on the test.
+
+If the test is parametrized and only a single parameter is expected to
+fail, rather than marking the entire test as xfailing, mark the single
+parameter by creating a
+[`pytest.param`](https://docs.pytest.org/en/stable/how-to/skipping.html#skip-xfail-with-parametrize)
+with appropriate marks.
+
+```python
+@pytest.mark.parametrize(
+    "value",
+    [
+        1,
+        2,
+        pytest.param(
+            3, marks=pytest.mark.xfail(reason="code doesn't work for 3")
+        ),
+    ],
+)
+def test_value(value):
+    assert value < 3
+```
+
+When marking an `xfail`ing test, provide a descriptive reason. This
+_should_ include a link to an issue describing the problem so that
+progress towards fixing the problem can be tracked. If no such issue
+exists already, create one!
+
+#### Conditional `xfail`s
+
+Sometimes, a parametrized test is only expected to fail for some
+combination of its parameters. Say, for example, division by zero but
+only if the datatype is `bool`. If all combinations with a given
+parameter are expected to fail, one can mark the parameter with
+`pytest.mark.xfail`, indicating a reason for the expected failure. If
+only _some_ of the combinations are expected to fail, it can be
+tempting to mark the parameter as `xfail`, but this should be avoided.
+A test marked as `xfail` that passes is an "unexpected pass" or
+`XPASS` which is considered a failure by the test suite since we use
+the pytest option
+[`xfail_strict=true`](https://docs.pytest.org/en/latest/how-to/skipping.html#strict-parameter).
+Another option is to use the programmatic `pytest.xfail` function to
+fail in the test body to `xfail` the relevant combination of
+parameters. **DO NOT USE THIS OPTION**. Unlike the mark-based
+approach, `pytest.xfail` *does not* run the rest of the test body, so
+we will never know if the test starts to pass because the bug is
+fixed. Use of `pytest.xfail` is checked for, and forbidden, via
+a pre-commit hook.
+
+Instead, to handle this (hopefully rare) case, we can programmatically
+mark a test as expected to fail under a combination of conditions by
+applying the `pytest.mark.xfail` mark to the current test `request`.
+To achieve this, the test function should take an extra parameter
+named `request`, on which we call `applymarker`:
+
+```python
+@pytest.mark.parametrize("v1", [1, 2, 3])
+@pytest.mark.parametrize("v2", [1, 2, 3])
+def test_sum_lt_6(request, v1, v2):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(v1 == 3 and v2 == 3),
+            reason="Add comment linking to relevant issue",
+        )
+    )
+    assert v1 + v2 < 6
+```
+
+This way, when the bug is fixed, the test suite will fail at this
+point (and we will remember to update the test).
+
+
+### Testing code that throws warnings
+
+Some code may be expected to throw warnings.
+A common example is when a cudf API is deprecated for future removal, but many other possibilities exist as well.
+The cudf testing suite [surfaces all warnings as errors](https://docs.pytest.org/en/latest/how-to/capture-warnings.html#controlling-warnings).
+This includes warnings raised from non-cudf code, such as calls to pandas or pyarrow.
+This setting forces developers to proactively deal with deprecations from other libraries,
+as well as preventing the internal use of deprecated cudf APIs in other parts of the library.
+Just as importantly, it can help catch real errors like integer overflow or division by zero.
+
+When testing code that is expected to throw a warnings, developers should use the
+[`pytest.warns`](https://docs.pytest.org/en/latest/how-to/capture-warnings.html#assertwarnings) context to catch the warning.
+For parametrized tests that raise warnings under specific conditions, use the `testing._utils.expect_warning_if` decorator instead of `pytest.warns`.
+
+```{warning}
+[`warnings.catch_warnings`](https://docs.python.org/3/library/warnings.html#warnings.catch_warnings)
+is a tempting alternative to `pytest.warns`.
+**Do not use this context manager in tests.**
+Unlike `pytest.warns`, which _requires_ that the expected warning be raised,
+`warnings.catch_warnings` simply catches warnings that appear without requiring them.
+The cudf testing suite should avoid such ambiguities.
+```
+
+### Testing utility functions
+
+The `cudf.testing` subpackage provides a handful of utilities for testing the equality of objects.
+The internal `cudf.testing._utils` module provides additional helper functions for use in tests.
+In particular:
+- `testing._utils.assert_eq` is the biggest hammer to reach for. It can be used to compare any pair of objects.
+- For comparing specific objects, use `testing.testing.assert_[frame|series|index]_equal`.
+- For verifying that the expected assertions are raised, use `testing._utils.assert_exceptions_equal`.
diff --git a/docs/cudf/source/index.rst b/docs/cudf/source/index.rst
new file mode 100644
index 0000000..21badd6
--- /dev/null
+++ b/docs/cudf/source/index.rst
@@ -0,0 +1,32 @@
+Welcome to the cuDF documentation!
+==================================
+
+.. figure:: _static/RAPIDS-logo-purple.png
+    :width: 300px
+    :align: center
+
+**cuDF** is a Python GPU DataFrame library (built on the `Apache Arrow
+<https://arrow.apache.org/>`_ columnar memory format) for loading, joining,
+aggregating, filtering, and otherwise manipulating data. cuDF also provides a
+pandas-like API that will be familiar to data engineers & data scientists, so
+they can use it to easily accelerate their workflows without going into
+the details of CUDA programming.
+
+``cudf.pandas`` is built on cuDF and accelerates pandas code on the
+GPU.  It supports 100% of the pandas API, using the GPU for
+supported operations, and automatically falling back to pandas for
+other operations.
+
+.. figure:: _static/duckdb-benchmark-groupby-join.png
+    :width: 750px
+    :align: center
+
+    Results of the `Database-like ops benchmark <https://duckdblabs.github.io/db-benchmark/>`_ including `cudf.pandas`. See details `here <cudf_pandas/benchmarks.html>`_.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   user_guide/index
+   cudf_pandas/index
+   developer_guide/index
diff --git a/docs/cudf/source/user_guide/10min.ipynb b/docs/cudf/source/user_guide/10min.ipynb
new file mode 100644
index 0000000..c3da255
--- /dev/null
+++ b/docs/cudf/source/user_guide/10min.ipynb
@@ -0,0 +1,6162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4c6c548b",
+   "metadata": {},
+   "source": [
+    "# 10 Minutes to cuDF and Dask-cuDF\n",
+    "\n",
+    "Modelled after 10 Minutes to Pandas, this is a short introduction to cuDF and Dask-cuDF, geared mainly towards new users.\n",
+    "\n",
+    "## What are these Libraries?\n",
+    "\n",
+    "[cuDF](https://github.com/rapidsai/cudf) is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating tabular data using a DataFrame style API in the style of [pandas](https://pandas.pydata.org).\n",
+    "\n",
+    "[Dask](https://dask.org/) is a flexible library for parallel computing in Python that makes scaling out your workflow smooth and simple. On the CPU, Dask uses Pandas to execute operations in parallel on DataFrame partitions.\n",
+    "\n",
+    "[Dask-cuDF](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) extends Dask where necessary to allow its DataFrame partitions to be processed using cuDF GPU DataFrames instead of Pandas DataFrames. For instance, when you call `dask_cudf.read_csv(...)`, your cluster's GPUs do the work of parsing the CSV file(s) by calling [`cudf.read_csv()`](https://docs.rapids.ai/api/cudf/stable/api_docs/api/cudf.read_csv.html).\n",
+    "\n",
+    "\n",
+    "## When to use cuDF and Dask-cuDF\n",
+    "\n",
+    "If your workflow is fast enough on a single GPU or your data comfortably fits in memory on a single GPU, you would want to use cuDF. If you want to distribute your workflow across multiple GPUs, have more data than you can fit in memory on a single GPU, or want to analyze data spread across many files at once, you would want to use Dask-cuDF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "36307e42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import cupy as cp\n",
+    "import pandas as pd\n",
+    "\n",
+    "import cudf\n",
+    "import dask_cudf\n",
+    "\n",
+    "cp.random.seed(12)\n",
+    "\n",
+    "#### Portions of this were borrowed and adapted from the\n",
+    "#### cuDF cheatsheet, existing cuDF documentation,\n",
+    "#### and 10 Minutes to Pandas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eff5fc19",
+   "metadata": {},
+   "source": [
+    "## Object Creation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a747886",
+   "metadata": {},
+   "source": [
+    "Creating a `cudf.Series` and `dask_cudf.Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f5e303df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1       2\n",
+       "2       3\n",
+       "3    <NA>\n",
+       "4       4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = cudf.Series([1, 2, 3, None, 4])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9a893956",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = dask_cudf.from_cudf(s, npartitions=2)\n",
+    "# Note the call to head here to show the first few entries, unlike\n",
+    "# cuDF objects, dask-cuDF objects do not have a printing\n",
+    "# representation that shows values since they may not be in local\n",
+    "# memory.\n",
+    "ds.head(n=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d934af4e",
+   "metadata": {},
+   "source": [
+    "Creating a `cudf.DataFrame` and a `dask_cudf.DataFrame` by specifying values for each column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3f53fb8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "    <tr>\n",
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+       "    <tr>\n",
+       "      <th>7</th>\n",
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+       "      <td>7</td>\n",
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+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a   b   c\n",
+       "0    0  19   0\n",
+       "1    1  18   1\n",
+       "2    2  17   2\n",
+       "3    3  16   3\n",
+       "4    4  15   4\n",
+       "5    5  14   5\n",
+       "6    6  13   6\n",
+       "7    7  12   7\n",
+       "8    8  11   8\n",
+       "9    9  10   9\n",
+       "10  10   9  10\n",
+       "11  11   8  11\n",
+       "12  12   7  12\n",
+       "13  13   6  13\n",
+       "14  14   5  14\n",
+       "15  15   4  15\n",
+       "16  16   3  16\n",
+       "17  17   2  17\n",
+       "18  18   1  18\n",
+       "19  19   0  19"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = cudf.DataFrame(\n",
+    "    {\n",
+    "        \"a\": list(range(20)),\n",
+    "        \"b\": list(reversed(range(20))),\n",
+    "        \"c\": list(range(20)),\n",
+    "    }\n",
+    ")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b17db919",
+   "metadata": {},
+   "source": [
+    "Now we will convert our cuDF dataframe into a dask-cuDF equivalent. Here we call out a key difference: to inspect the data we must call a method (here `.head()` to look at the first few values). In the general case (see the end of this notebook), the data in `ddf` will be distributed across multiple GPUs.\n",
+    "\n",
+    "In this small case, we could call `ddf.compute()` to obtain a cuDF object from the dask-cuDF object. In general, we should avoid calling `.compute()` on large dataframes, and restrict ourselves to using it when we have some (relatively) small postprocessed result that we wish to inspect. Hence, throughout this notebook we will generally call `.head()` to inspect the first few values of a dask-cuDF dataframe, occasionally calling out places where we use `.compute()` and why.\n",
+    "\n",
+    "*To understand more of the differences between how cuDF and dask-cuDF behave here, visit the [10 Minutes to Dask](https://docs.dask.org/en/stable/10-minutes-to-dask.html) tutorial after this one.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8904b5ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "0  0  19  0\n",
+       "1  1  18  1\n",
+       "2  2  17  2\n",
+       "3  3  16  3\n",
+       "4  4  15  4"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf = dask_cudf.from_cudf(df, npartitions=2)\n",
+    "ddf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0573b7bb",
+   "metadata": {},
+   "source": [
+    "Creating a `cudf.DataFrame` from a pandas `Dataframe` and a `dask_cudf.Dataframe` from a `cudf.Dataframe`.\n",
+    "\n",
+    "*Note that best practice for using dask-cuDF is to read data directly into a `dask_cudf.DataFrame` with `read_csv` or other builtin I/O routines (discussed below).*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "06a42f3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a     b\n",
+       "0  0   0.1\n",
+       "1  1   0.2\n",
+       "2  2  <NA>\n",
+       "3  3   0.3"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf = pd.DataFrame({\"a\": [0, 1, 2, 3], \"b\": [0.1, 0.2, None, 0.3]})\n",
+    "gdf = cudf.DataFrame.from_pandas(pdf)\n",
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c67de344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a    b\n",
+       "0  0  0.1\n",
+       "1  1  0.2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dask_gdf = dask_cudf.from_cudf(gdf, npartitions=2)\n",
+    "dask_gdf.head(n=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5820795f",
+   "metadata": {},
+   "source": [
+    "## Viewing Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3008757",
+   "metadata": {},
+   "source": [
+    "Viewing the top rows of a GPU dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0c329914",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "0  0  19  0\n",
+       "1  1  18  1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b989e208",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "0  0  19  0\n",
+       "1  1  18  1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16798a32",
+   "metadata": {},
+   "source": [
+    "Sorting by values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2190856d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a   b   c\n",
+       "19  19   0  19\n",
+       "18  18   1  18\n",
+       "17  17   2  17\n",
+       "16  16   3  16\n",
+       "15  15   4  15\n",
+       "14  14   5  14\n",
+       "13  13   6  13\n",
+       "12  12   7  12\n",
+       "11  11   8  11\n",
+       "10  10   9  10\n",
+       "9    9  10   9\n",
+       "8    8  11   8\n",
+       "7    7  12   7\n",
+       "6    6  13   6\n",
+       "5    5  14   5\n",
+       "4    4  15   4\n",
+       "3    3  16   3\n",
+       "2    2  17   2\n",
+       "1    1  18   1\n",
+       "0    0  19   0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(by=\"b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6594bd6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b   c\n",
+       "19  19  0  19\n",
+       "18  18  1  18\n",
+       "17  17  2  17\n",
+       "16  16  3  16\n",
+       "15  15  4  15"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.sort_values(by=\"b\").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3302a647",
+   "metadata": {},
+   "source": [
+    "## Selecting a Column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aebc72ab",
+   "metadata": {},
+   "source": [
+    "Selecting a single column, which initially yields a `cudf.Series` or `dask_cudf.Series`. Calling `compute` results in a `cudf.Series` (equivalent to `df.a`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4dafb4ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      0\n",
+       "1      1\n",
+       "2      2\n",
+       "3      3\n",
+       "4      4\n",
+       "5      5\n",
+       "6      6\n",
+       "7      7\n",
+       "8      8\n",
+       "9      9\n",
+       "10    10\n",
+       "11    11\n",
+       "12    12\n",
+       "13    13\n",
+       "14    14\n",
+       "15    15\n",
+       "16    16\n",
+       "17    17\n",
+       "18    18\n",
+       "19    19\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"a\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b38f05fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    2\n",
+       "3    3\n",
+       "4    4\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf[\"a\"].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5160dd1",
+   "metadata": {},
+   "source": [
+    "## Selecting Rows by Label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ff2093",
+   "metadata": {},
+   "source": [
+    "Selecting rows from index 2 to index 5 from columns 'a' and 'b'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e8870657",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b\n",
+       "2  2  17\n",
+       "3  3  16\n",
+       "4  4  15\n",
+       "5  5  14"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[2:5, [\"a\", \"b\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f041e661",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b\n",
+       "2  2  17\n",
+       "3  3  16\n",
+       "4  4  15\n",
+       "5  5  14"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.loc[2:5, [\"a\", \"b\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e07162",
+   "metadata": {},
+   "source": [
+    "## Selecting Rows by Position"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "435eb76a",
+   "metadata": {},
+   "source": [
+    "Selecting via integers and integer slices, like numpy/pandas. Note that this functionality is not available for Dask-cuDF DataFrames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bc337d5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a     0\n",
+       "b    19\n",
+       "c     0\n",
+       "Name: 0, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "03671456",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b\n",
+       "0  0  19\n",
+       "1  1  18\n",
+       "2  2  17"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[0:3, 0:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa935d5b",
+   "metadata": {},
+   "source": [
+    "You can also select elements of a `DataFrame` or `Series` with direct index access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "79883c37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "3  3  16  3\n",
+       "4  4  15  4"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[3:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2f761695",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    <NA>\n",
+       "4       4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[3:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bf9c0b0",
+   "metadata": {},
+   "source": [
+    "## Boolean Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b08b2b9",
+   "metadata": {},
+   "source": [
+    "Selecting rows in a `DataFrame` or `Series` by direct Boolean indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1eb08f0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "0  0  19  0\n",
+       "1  1  18  1\n",
+       "2  2  17  2\n",
+       "3  3  16  3"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.b > 15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "324dd036",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "0  0  19  0\n",
+       "1  1  18  1\n",
+       "2  2  17  2"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf[ddf.b > 15].head(n=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f95c9ab7",
+   "metadata": {},
+   "source": [
+    "Selecting values from a `DataFrame` where a Boolean condition is met, via the `query` API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fa643410",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b   c\n",
+       "16  16  3  16"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.query(\"b == 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7aa0089f",
+   "metadata": {},
+   "source": [
+    "Note here we call `compute()` rather than `head()` on the dask-cuDF dataframe since we are happy that the number of matching rows will be small (and hence it is reasonable to bring the entire result back)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e2706a02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b   c\n",
+       "16  16  3  16"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.query(\"b == 3\").compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "694dcbad",
+   "metadata": {},
+   "source": [
+    "You can also pass local variables to Dask-cuDF queries, via the `local_dict` keyword. With standard cuDF, you may either use the `local_dict` keyword or directly pass the variable via the `@` keyword. Supported logical operators include `>`, `<`, `>=`, `<=`, `==`, and `!=`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "353b0250",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b   c\n",
+       "16  16  3  16"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf_comparator = 3\n",
+    "df.query(\"b == @cudf_comparator\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a35c8a5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a  b   c\n",
+       "16  16  3  16"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dask_cudf_comparator = 3\n",
+    "ddf.query(\"b == @val\", local_dict={\"val\": dask_cudf_comparator}).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e004749",
+   "metadata": {},
+   "source": [
+    "Using the `isin` method for filtering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "20936418",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c\n",
+       "0  0  19  0\n",
+       "5  5  14  5"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.a.isin([0, 5])]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e456f03",
+   "metadata": {},
+   "source": [
+    "## MultiIndex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e494bd0b",
+   "metadata": {},
+   "source": [
+    "cuDF supports hierarchical indexing of DataFrames using MultiIndex. Grouping hierarchically (see `Grouping` below) automatically produces a DataFrame with a MultiIndex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4ae70724",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex([('a', 1),\n",
+       "            ('a', 2),\n",
+       "            ('b', 3),\n",
+       "            ('b', 4)],\n",
+       "           )"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arrays = [[\"a\", \"a\", \"b\", \"b\"], [1, 2, 3, 4]]\n",
+    "tuples = list(zip(*arrays))\n",
+    "idx = cudf.MultiIndex.from_tuples(tuples)\n",
+    "idx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab232727",
+   "metadata": {},
+   "source": [
+    "This index can back either axis of a DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "cb1d1633",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>first</th>\n",
+       "      <th>second</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.082654</td>\n",
+       "      <td>0.967955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.399417</td>\n",
+       "      <td>0.441425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
+       "      <th>3</th>\n",
+       "      <td>0.784297</td>\n",
+       "      <td>0.793582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.070303</td>\n",
+       "      <td>0.271711</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        first    second\n",
+       "a 1  0.082654  0.967955\n",
+       "  2  0.399417  0.441425\n",
+       "b 3  0.784297  0.793582\n",
+       "  4  0.070303  0.271711"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf1 = cudf.DataFrame(\n",
+    "    {\"first\": cp.random.rand(4), \"second\": cp.random.rand(4)}\n",
+    ")\n",
+    "gdf1.index = idx\n",
+    "gdf1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "73ba31af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">a</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">b</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>first</th>\n",
+       "      <td>0.343382</td>\n",
+       "      <td>0.003700</td>\n",
+       "      <td>0.20043</td>\n",
+       "      <td>0.581614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>second</th>\n",
+       "      <td>0.907812</td>\n",
+       "      <td>0.101512</td>\n",
+       "      <td>0.24179</td>\n",
+       "      <td>0.224180</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               a                  b          \n",
+       "               1         2        3         4\n",
+       "first   0.343382  0.003700  0.20043  0.581614\n",
+       "second  0.907812  0.101512  0.24179  0.224180"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf2 = cudf.DataFrame(\n",
+    "    {\"first\": cp.random.rand(4), \"second\": cp.random.rand(4)}\n",
+    ").T\n",
+    "gdf2.columns = idx\n",
+    "gdf2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f33160",
+   "metadata": {},
+   "source": [
+    "Accessing values of a DataFrame with a MultiIndex, both with `.loc`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1048b7cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "first     0.784297\n",
+       "second    0.793582\n",
+       "Name: ('b', 3), dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf1.loc[(\"b\", 3)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5123f759",
+   "metadata": {},
+   "source": [
+    "And `.iloc`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "369d164d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>first</th>\n",
+       "      <th>second</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.082654</td>\n",
+       "      <td>0.967955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.399417</td>\n",
+       "      <td>0.441425</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        first    second\n",
+       "a 1  0.082654  0.967955\n",
+       "  2  0.399417  0.441425"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf1.iloc[0:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b3b96e9",
+   "metadata": {},
+   "source": [
+    "Missing Data\n",
+    "------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d12141eb",
+   "metadata": {},
+   "source": [
+    "Missing data can be replaced by using the `fillna` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "913a7b5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      1\n",
+       "1      2\n",
+       "2      3\n",
+       "3    999\n",
+       "4      4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.fillna(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "14479a42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.fillna(999).head(n=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d97605e6",
+   "metadata": {},
+   "source": [
+    "## Stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f29a5de0",
+   "metadata": {},
+   "source": [
+    "Calculating descriptive statistics for a `Series`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b1a1666e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.5, 1.666666666666666)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.mean(), s.var()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4879742",
+   "metadata": {},
+   "source": [
+    "This serves as a prototypical example of when we might want to call `.compute()`. The result of computing the mean and variance is a single number in each case, so it is definitely reasonable to look at the entire result!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0cb7a207",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.5, 1.6666666666666667)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.mean().compute(), ds.var().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af792a18",
+   "metadata": {},
+   "source": [
+    "## Applymap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6094cbe",
+   "metadata": {},
+   "source": [
+    "Applying functions to a `Series`. Note that applying user defined functions directly with Dask-cuDF is not yet implemented. For now, you can use [map_partitions](http://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.map_partitions.html) to apply a function to each partition of the distributed dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5b154619",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     10\n",
+       "1     11\n",
+       "2     12\n",
+       "3     13\n",
+       "4     14\n",
+       "5     15\n",
+       "6     16\n",
+       "7     17\n",
+       "8     18\n",
+       "9     19\n",
+       "10    20\n",
+       "11    21\n",
+       "12    22\n",
+       "13    23\n",
+       "14    24\n",
+       "15    25\n",
+       "16    26\n",
+       "17    27\n",
+       "18    28\n",
+       "19    29\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def add_ten(num):\n",
+    "    return num + 10\n",
+    "\n",
+    "\n",
+    "df[\"a\"].apply(add_ten)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8da5c3cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10\n",
+       "1    11\n",
+       "2    12\n",
+       "3    13\n",
+       "4    14\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf[\"a\"].map_partitions(add_ten).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d4fdde1",
+   "metadata": {},
+   "source": [
+    "## Histogramming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b98a7daf",
+   "metadata": {},
+   "source": [
+    "Counting the number of occurrences of each unique value of variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "c7b8ea5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15    1\n",
+       "6     1\n",
+       "1     1\n",
+       "14    1\n",
+       "2     1\n",
+       "5     1\n",
+       "11    1\n",
+       "7     1\n",
+       "17    1\n",
+       "13    1\n",
+       "8     1\n",
+       "16    1\n",
+       "0     1\n",
+       "10    1\n",
+       "4     1\n",
+       "9     1\n",
+       "19    1\n",
+       "18    1\n",
+       "3     1\n",
+       "12    1\n",
+       "Name: a, dtype: int32"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.a.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "cc9d34f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15    1\n",
+       "6     1\n",
+       "1     1\n",
+       "14    1\n",
+       "2     1\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.a.value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "437172ba",
+   "metadata": {},
+   "source": [
+    "## String Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd3fc4f3",
+   "metadata": {},
+   "source": [
+    "Like pandas, cuDF provides string processing methods in the `str` attribute of `Series`. Full documentation of string methods is a work in progress. Please see the [cuDF API documentation](https://docs.rapids.ai/api/cudf/stable/api_docs/series.html#string-handling) for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "86974041",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       a\n",
+       "1       b\n",
+       "2       c\n",
+       "3    aaba\n",
+       "4    baca\n",
+       "5    <NA>\n",
+       "6    caba\n",
+       "7     dog\n",
+       "8     cat\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = cudf.Series([\"A\", \"B\", \"C\", \"Aaba\", \"Baca\", None, \"CABA\", \"dog\", \"cat\"])\n",
+    "s.str.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c6a61a08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       a\n",
+       "1       b\n",
+       "2       c\n",
+       "3    aaba\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = dask_cudf.from_cudf(s, npartitions=2)\n",
+    "ds.str.lower().head(n=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44fe1243",
+   "metadata": {},
+   "source": [
+    "As well as simple manipulation, We can also match strings using [regular expressions](https://docs.rapids.ai/api/cudf/stable/api_docs/api/cudf.core.column.string.StringMethods.match.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "51158a24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "3     True\n",
+       "4    False\n",
+       "5     <NA>\n",
+       "6    False\n",
+       "7    False\n",
+       "8     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.str.match(\"^[aAc].+\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "4f3e36f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "3     True\n",
+       "4    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.str.match(\"^[aAc].+\").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5528afa8",
+   "metadata": {},
+   "source": [
+    "## Concat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e05b1078",
+   "metadata": {},
+   "source": [
+    "Concatenating `Series` and `DataFrames` row-wise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "6c6d10bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1       2\n",
+       "2       3\n",
+       "3    <NA>\n",
+       "4       5\n",
+       "0       1\n",
+       "1       2\n",
+       "2       3\n",
+       "3    <NA>\n",
+       "4       5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = cudf.Series([1, 2, 3, None, 5])\n",
+    "cudf.concat([s, s])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "d3e5cf87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds2 = dask_cudf.from_cudf(s, npartitions=2)\n",
+    "dask_cudf.concat([ds2, ds2]).head(n=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df087d2f",
+   "metadata": {},
+   "source": [
+    "## Join"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89cf5022",
+   "metadata": {},
+   "source": [
+    "Performing SQL style merges. Note that the dataframe order is **not maintained**, but may be restored post-merge by sorting by the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "075c97a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>vals_a</th>\n",
+       "      <th>vals_b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>c</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>e</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>102.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>b</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>d</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key  vals_a vals_b\n",
+       "0   a    10.0  100.0\n",
+       "1   c    12.0  101.0\n",
+       "2   e    14.0  102.0\n",
+       "3   b    11.0   <NA>\n",
+       "4   d    13.0   <NA>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_a = cudf.DataFrame()\n",
+    "df_a[\"key\"] = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n",
+    "df_a[\"vals_a\"] = [float(i + 10) for i in range(5)]\n",
+    "\n",
+    "df_b = cudf.DataFrame()\n",
+    "df_b[\"key\"] = [\"a\", \"c\", \"e\"]\n",
+    "df_b[\"vals_b\"] = [float(i + 100) for i in range(3)]\n",
+    "\n",
+    "merged = df_a.merge(df_b, on=[\"key\"], how=\"left\")\n",
+    "merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "b28fc57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key</th>\n",
+       "      <th>vals_a</th>\n",
+       "      <th>vals_b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>c</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>e</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>102.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>b</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>d</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  key  vals_a vals_b\n",
+       "0   c    12.0  101.0\n",
+       "1   e    14.0  102.0\n",
+       "2   b    11.0   <NA>\n",
+       "3   d    13.0   <NA>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf_a = dask_cudf.from_cudf(df_a, npartitions=2)\n",
+    "ddf_b = dask_cudf.from_cudf(df_b, npartitions=2)\n",
+    "\n",
+    "merged = ddf_a.merge(ddf_b, on=[\"key\"], how=\"left\").head(n=4)\n",
+    "merged"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4695c6e",
+   "metadata": {},
+   "source": [
+    "## Grouping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecb27b06",
+   "metadata": {},
+   "source": [
+    "Like [pandas](https://pandas.pydata.org/docs/user_guide/groupby.html), cuDF and Dask-cuDF support the [Split-Apply-Combine groupby paradigm](https://doi.org/10.18637/jss.v040.i01)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "d8db18d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"agg_col1\"] = [1 if x % 2 == 0 else 0 for x in range(len(df))]\n",
+    "df[\"agg_col2\"] = [1 if x % 3 == 0 else 0 for x in range(len(df))]\n",
+    "\n",
+    "ddf = dask_cudf.from_cudf(df, npartitions=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e2f0961",
+   "metadata": {},
+   "source": [
+    "Grouping and then applying the `sum` function to the grouped data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e8a7f1f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            a    b    c  agg_col2\n",
+       "agg_col1                         \n",
+       "1          90  100   90         4\n",
+       "0         100   90  100         3"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"agg_col1\").sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "4dd090a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>90</td>\n",
+       "      <td>100</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            a    b    c  agg_col2\n",
+       "agg_col1                         \n",
+       "1          90  100   90         4\n",
+       "0         100   90  100         3"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.groupby(\"agg_col1\").sum().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ff1e8bf",
+   "metadata": {},
+   "source": [
+    "Grouping hierarchically then applying the `sum` function to grouped data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4738f0ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>54</td>\n",
+       "      <td>60</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <td>73</td>\n",
+       "      <td>60</td>\n",
+       "      <td>73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>36</td>\n",
+       "      <td>40</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>27</td>\n",
+       "      <td>30</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    a   b   c\n",
+       "agg_col1 agg_col2            \n",
+       "1        0         54  60  54\n",
+       "0        0         73  60  73\n",
+       "1        1         36  40  36\n",
+       "0        1         27  30  27"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby([\"agg_col1\", \"agg_col2\"]).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "9b07feb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>36</td>\n",
+       "      <td>40</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <td>73</td>\n",
+       "      <td>60</td>\n",
+       "      <td>73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>54</td>\n",
+       "      <td>60</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>27</td>\n",
+       "      <td>30</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    a   b   c\n",
+       "agg_col1 agg_col2            \n",
+       "1        1         36  40  36\n",
+       "0        0         73  60  73\n",
+       "1        0         54  60  54\n",
+       "0        1         27  30  27"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.groupby([\"agg_col1\", \"agg_col2\"]).sum().compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "443e179b",
+   "metadata": {},
+   "source": [
+    "Grouping and applying statistical functions to specific columns, using `agg`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "f196ad8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           a     b    c\n",
+       "agg_col1               \n",
+       "1         18  10.0   90\n",
+       "0         19   9.0  100"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"agg_col1\").agg({\"a\": \"max\", \"b\": \"mean\", \"c\": \"sum\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "3853483f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           a     b    c\n",
+       "agg_col1               \n",
+       "1         18  10.0   90\n",
+       "0         19   9.0  100"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.groupby(\"agg_col1\").agg({\"a\": \"max\", \"b\": \"mean\", \"c\": \"sum\"}).compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5bf30e1",
+   "metadata": {},
+   "source": [
+    "## Transpose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ac3b004",
+   "metadata": {},
+   "source": [
+    "Transposing a dataframe, using either the `transpose` method or `T` property. Currently, all columns must have the same type. Transposing is not currently implemented in Dask-cuDF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c5fbdb50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  1  4\n",
+       "1  2  5\n",
+       "2  3  6"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample = cudf.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n",
+    "sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "733ed90c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1  2\n",
+       "a  1  2  3\n",
+       "b  4  5  6"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0915c46",
+   "metadata": {},
+   "source": [
+    "## Time Series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec7f0b81",
+   "metadata": {},
+   "source": [
+    "`DataFrames` supports `datetime` typed columns, which allow users to interact with and filter data based on specific timestamps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a6d45607",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-11-20</td>\n",
+       "      <td>0.986051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018-11-21</td>\n",
+       "      <td>0.232034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018-11-22</td>\n",
+       "      <td>0.397617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2018-11-23</td>\n",
+       "      <td>0.103839</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date     value\n",
+       "0 2018-11-20  0.986051\n",
+       "1 2018-11-21  0.232034\n",
+       "2 2018-11-22  0.397617\n",
+       "3 2018-11-23  0.103839"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import datetime as dt\n",
+    "\n",
+    "date_df = cudf.DataFrame()\n",
+    "date_df[\"date\"] = pd.date_range(\"11/20/2018\", periods=72, freq=\"D\")\n",
+    "date_df[\"value\"] = cp.random.sample(len(date_df))\n",
+    "\n",
+    "search_date = dt.datetime.strptime(\"2018-11-23\", \"%Y-%m-%d\")\n",
+    "date_df.query(\"date <= @search_date\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "fbacaae1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-11-20</td>\n",
+       "      <td>0.986051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018-11-21</td>\n",
+       "      <td>0.232034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018-11-22</td>\n",
+       "      <td>0.397617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2018-11-23</td>\n",
+       "      <td>0.103839</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date     value\n",
+       "0 2018-11-20  0.986051\n",
+       "1 2018-11-21  0.232034\n",
+       "2 2018-11-22  0.397617\n",
+       "3 2018-11-23  0.103839"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "date_ddf = dask_cudf.from_cudf(date_df, npartitions=2)\n",
+    "date_ddf.query(\n",
+    "    \"date <= @search_date\", local_dict={\"search_date\": search_date}\n",
+    ").compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45f9408b",
+   "metadata": {},
+   "source": [
+    "## Categoricals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5eb96f98",
+   "metadata": {},
+   "source": [
+    "`DataFrames` support categorical columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "d735b5cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>grade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>e</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id grade\n",
+       "0   1     a\n",
+       "1   2     b\n",
+       "2   3     b\n",
+       "3   4     a\n",
+       "4   5     a\n",
+       "5   6     e"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf = cudf.DataFrame(\n",
+    "    {\"id\": [1, 2, 3, 4, 5, 6], \"grade\": [\"a\", \"b\", \"b\", \"a\", \"a\", \"e\"]}\n",
+    ")\n",
+    "gdf[\"grade\"] = gdf[\"grade\"].astype(\"category\")\n",
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "9d1ff798",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>grade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id grade\n",
+       "0   1     a\n",
+       "1   2     b\n",
+       "2   3     b"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dgdf = dask_cudf.from_cudf(gdf, npartitions=2)\n",
+    "dgdf.head(n=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9c2bcac",
+   "metadata": {},
+   "source": [
+    "Accessing the categories of a column. Note that this is currently not supported in Dask-cuDF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "a7135eda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StringIndex(['a' 'b' 'e'], dtype='object')"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.grade.cat.categories"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "466e1ed2",
+   "metadata": {},
+   "source": [
+    "Accessing the underlying code values of each categorical observation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f00c615a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    1\n",
+       "3    0\n",
+       "4    0\n",
+       "5    2\n",
+       "dtype: uint8"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.grade.cat.codes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "d209512f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    1\n",
+       "3    0\n",
+       "4    0\n",
+       "5    2\n",
+       "dtype: uint8"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dgdf.grade.cat.codes.compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b391a0d",
+   "metadata": {},
+   "source": [
+    "## Converting to Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfdd172b",
+   "metadata": {},
+   "source": [
+    "Converting a cuDF and Dask-cuDF `DataFrame` to a pandas `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1fcd9c7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c  agg_col1  agg_col2\n",
+       "0  0  19  0         1         1\n",
+       "1  1  18  1         0         0\n",
+       "2  2  17  2         1         0\n",
+       "3  3  16  3         0         1\n",
+       "4  4  15  4         1         0"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head().to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa8a445b",
+   "metadata": {},
+   "source": [
+    "To convert the first few entries to pandas, we similarly call `.head()` on the dask-cuDF dataframe to obtain a local cuDF dataframe, which we can then convert."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "786d39d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c  agg_col1  agg_col2\n",
+       "0  0  19  0         1         1\n",
+       "1  1  18  1         0         0\n",
+       "2  2  17  2         1         0\n",
+       "3  3  16  3         0         1\n",
+       "4  4  15  4         1         0"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.head().to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "584c4594",
+   "metadata": {},
+   "source": [
+    "In contrast, if we want to convert the entire frame, we need to call `.compute()` on `ddf` to get a local cuDF dataframe, and then call `to_pandas()`, followed by subsequent processing. This workflow is less recommended, since it both puts high memory pressure on a single GPU (the `.compute()` call) and does not take advantage of GPU acceleration for processing (the computation happens on in pandas)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "93f06cdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c  agg_col1  agg_col2\n",
+       "0  0  19  0         1         1\n",
+       "1  1  18  1         0         0\n",
+       "2  2  17  2         1         0\n",
+       "3  3  16  3         0         1\n",
+       "4  4  15  4         1         0"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.compute().to_pandas().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a104294a",
+   "metadata": {},
+   "source": [
+    "## Converting to Numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5d3e508",
+   "metadata": {},
+   "source": [
+    "Converting a cuDF or Dask-cuDF `DataFrame` to a numpy `ndarray`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "2948b577",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0, 19,  0,  1,  1],\n",
+       "       [ 1, 18,  1,  0,  0],\n",
+       "       [ 2, 17,  2,  1,  0],\n",
+       "       [ 3, 16,  3,  0,  1],\n",
+       "       [ 4, 15,  4,  1,  0],\n",
+       "       [ 5, 14,  5,  0,  0],\n",
+       "       [ 6, 13,  6,  1,  1],\n",
+       "       [ 7, 12,  7,  0,  0],\n",
+       "       [ 8, 11,  8,  1,  0],\n",
+       "       [ 9, 10,  9,  0,  1],\n",
+       "       [10,  9, 10,  1,  0],\n",
+       "       [11,  8, 11,  0,  0],\n",
+       "       [12,  7, 12,  1,  1],\n",
+       "       [13,  6, 13,  0,  0],\n",
+       "       [14,  5, 14,  1,  0],\n",
+       "       [15,  4, 15,  0,  1],\n",
+       "       [16,  3, 16,  1,  0],\n",
+       "       [17,  2, 17,  0,  0],\n",
+       "       [18,  1, 18,  1,  1],\n",
+       "       [19,  0, 19,  0,  0]])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "1cff6352",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0, 19,  0,  1,  1],\n",
+       "       [ 1, 18,  1,  0,  0],\n",
+       "       [ 2, 17,  2,  1,  0],\n",
+       "       [ 3, 16,  3,  0,  1],\n",
+       "       [ 4, 15,  4,  1,  0],\n",
+       "       [ 5, 14,  5,  0,  0],\n",
+       "       [ 6, 13,  6,  1,  1],\n",
+       "       [ 7, 12,  7,  0,  0],\n",
+       "       [ 8, 11,  8,  1,  0],\n",
+       "       [ 9, 10,  9,  0,  1],\n",
+       "       [10,  9, 10,  1,  0],\n",
+       "       [11,  8, 11,  0,  0],\n",
+       "       [12,  7, 12,  1,  1],\n",
+       "       [13,  6, 13,  0,  0],\n",
+       "       [14,  5, 14,  1,  0],\n",
+       "       [15,  4, 15,  0,  1],\n",
+       "       [16,  3, 16,  1,  0],\n",
+       "       [17,  2, 17,  0,  0],\n",
+       "       [18,  1, 18,  1,  1],\n",
+       "       [19,  0, 19,  0,  0]])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.compute().to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1f09303",
+   "metadata": {},
+   "source": [
+    "Converting a cuDF or Dask-cuDF `Series` to a numpy `ndarray`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "997c89ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"a\"].to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "243df512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf[\"a\"].compute().to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b520acf7",
+   "metadata": {},
+   "source": [
+    "## Converting to Arrow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050e67e5",
+   "metadata": {},
+   "source": [
+    "Converting a cuDF or Dask-cuDF `DataFrame` to a PyArrow `Table`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0ac9e740",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyarrow.Table\n",
+       "a: int64\n",
+       "b: int64\n",
+       "c: int64\n",
+       "agg_col1: int64\n",
+       "agg_col2: int64\n",
+       "----\n",
+       "a: [[0,1,2,3,4,...,15,16,17,18,19]]\n",
+       "b: [[19,18,17,16,15,...,4,3,2,1,0]]\n",
+       "c: [[0,1,2,3,4,...,15,16,17,18,19]]\n",
+       "agg_col1: [[1,0,1,0,1,...,0,1,0,1,0]]\n",
+       "agg_col2: [[1,0,0,1,0,...,1,0,0,1,0]]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.to_arrow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "f3170fc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyarrow.Table\n",
+       "a: int64\n",
+       "b: int64\n",
+       "c: int64\n",
+       "agg_col1: int64\n",
+       "agg_col2: int64\n",
+       "----\n",
+       "a: [[0,1,2,3,4]]\n",
+       "b: [[19,18,17,16,15]]\n",
+       "c: [[0,1,2,3,4]]\n",
+       "agg_col1: [[1,0,1,0,1]]\n",
+       "agg_col2: [[1,0,0,1,0]]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf.head().to_arrow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f0251c6",
+   "metadata": {},
+   "source": [
+    "## Reading/Writing CSV Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d1935c6",
+   "metadata": {},
+   "source": [
+    "Writing to a CSV file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "36f5039f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(\"example_output\"):\n",
+    "    os.mkdir(\"example_output\")\n",
+    "\n",
+    "df.to_csv(\"example_output/foo.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "22c1eb6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf.compute().to_csv(\"example_output/foo_dask.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "320c3968",
+   "metadata": {},
+   "source": [
+    "Reading from a csv file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "c110a80f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a   b   c  agg_col1  agg_col2\n",
+       "0    0  19   0         1         1\n",
+       "1    1  18   1         0         0\n",
+       "2    2  17   2         1         0\n",
+       "3    3  16   3         0         1\n",
+       "4    4  15   4         1         0\n",
+       "5    5  14   5         0         0\n",
+       "6    6  13   6         1         1\n",
+       "7    7  12   7         0         0\n",
+       "8    8  11   8         1         0\n",
+       "9    9  10   9         0         1\n",
+       "10  10   9  10         1         0\n",
+       "11  11   8  11         0         0\n",
+       "12  12   7  12         1         1\n",
+       "13  13   6  13         0         0\n",
+       "14  14   5  14         1         0\n",
+       "15  15   4  15         0         1\n",
+       "16  16   3  16         1         0\n",
+       "17  17   2  17         0         0\n",
+       "18  18   1  18         1         1\n",
+       "19  19   0  19         0         0"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = cudf.read_csv(\"example_output/foo.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "787eae14",
+   "metadata": {},
+   "source": [
+    "Note that for the dask-cuDF case, we use `dask_cudf.read_csv` in preference to `dask_cudf.from_cudf(cudf.read_csv)` since the former can parallelize across multiple GPUs and handle larger CSV files that would fit in memory on a single GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "a699dfef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c  agg_col1  agg_col2\n",
+       "0  0  19  0         1         1\n",
+       "1  1  18  1         0         0\n",
+       "2  2  17  2         1         0\n",
+       "3  3  16  3         0         1\n",
+       "4  4  15  4         1         0"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf = dask_cudf.read_csv(\"example_output/foo_dask.csv\")\n",
+    "ddf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72857b2c",
+   "metadata": {},
+   "source": [
+    "Reading all CSV files in a directory into a single `dask_cudf.DataFrame`, using the star wildcard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "825a0c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b  c  agg_col1  agg_col2\n",
+       "0  0  19  0         1         1\n",
+       "1  1  18  1         0         0\n",
+       "2  2  17  2         1         0\n",
+       "3  3  16  3         0         1\n",
+       "4  4  15  4         1         0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf = dask_cudf.read_csv(\"example_output/*.csv\")\n",
+    "ddf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "763c555b",
+   "metadata": {},
+   "source": [
+    "## Reading/Writing Parquet Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8766d4ac",
+   "metadata": {},
+   "source": [
+    "Writing to parquet files with cuDF's GPU-accelerated parquet writer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "5038b284",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_parquet(\"example_output/temp_parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b49824",
+   "metadata": {},
+   "source": [
+    "Reading parquet files with cuDF's GPU-accelerated parquet reader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "bb657a69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a   b   c  agg_col1  agg_col2\n",
+       "0    0  19   0         1         1\n",
+       "1    1  18   1         0         0\n",
+       "2    2  17   2         1         0\n",
+       "3    3  16   3         0         1\n",
+       "4    4  15   4         1         0\n",
+       "5    5  14   5         0         0\n",
+       "6    6  13   6         1         1\n",
+       "7    7  12   7         0         0\n",
+       "8    8  11   8         1         0\n",
+       "9    9  10   9         0         1\n",
+       "10  10   9  10         1         0\n",
+       "11  11   8  11         0         0\n",
+       "12  12   7  12         1         1\n",
+       "13  13   6  13         0         0\n",
+       "14  14   5  14         1         0\n",
+       "15  15   4  15         0         1\n",
+       "16  16   3  16         1         0\n",
+       "17  17   2  17         0         0\n",
+       "18  18   1  18         1         1\n",
+       "19  19   0  19         0         0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = cudf.read_parquet(\"example_output/temp_parquet\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9a29874",
+   "metadata": {},
+   "source": [
+    "Writing to parquet files from a `dask_cudf.DataFrame` using cuDF's parquet writer under the hood."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "0c3db7b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf.to_parquet(\"example_output/ddf_parquet_files\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90a49967",
+   "metadata": {},
+   "source": [
+    "## Reading/Writing ORC Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de9d03fa",
+   "metadata": {},
+   "source": [
+    "Writing ORC files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "c387f8f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_orc(\"example_output/temp_orc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "242c32a2",
+   "metadata": {},
+   "source": [
+    "And reading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "d4bab6da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>agg_col1</th>\n",
+       "      <th>agg_col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a   b   c  agg_col1  agg_col2\n",
+       "0    0  19   0         1         1\n",
+       "1    1  18   1         0         0\n",
+       "2    2  17   2         1         0\n",
+       "3    3  16   3         0         1\n",
+       "4    4  15   4         1         0\n",
+       "5    5  14   5         0         0\n",
+       "6    6  13   6         1         1\n",
+       "7    7  12   7         0         0\n",
+       "8    8  11   8         1         0\n",
+       "9    9  10   9         0         1\n",
+       "10  10   9  10         1         0\n",
+       "11  11   8  11         0         0\n",
+       "12  12   7  12         1         1\n",
+       "13  13   6  13         0         0\n",
+       "14  14   5  14         1         0\n",
+       "15  15   4  15         0         1\n",
+       "16  16   3  16         1         0\n",
+       "17  17   2  17         0         0\n",
+       "18  18   1  18         1         1\n",
+       "19  19   0  19         0         0"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = cudf.read_orc(\"example_output/temp_orc\")\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c988553d",
+   "metadata": {},
+   "source": [
+    "## Dask Performance Tips\n",
+    "\n",
+    "Like Apache Spark, Dask operations are [lazy](https://en.wikipedia.org/wiki/Lazy_evaluation). Instead of being executed immediately, most operations are added to a task graph and the actual evaluation is delayed until the result is needed.\n",
+    "\n",
+    "Sometimes, though, we want to force the execution of operations. Calling `persist` on a Dask collection fully computes it (or actively computes it in the background), persisting the result into memory. When we're using distributed systems, we may want to wait until `persist` is finished before beginning any downstream operations. We can enforce this contract by using `wait`. Wrapping an operation with `wait` will ensure it doesn't begin executing until all necessary upstream operations have finished.\n",
+    "\n",
+    "The snippets below provide basic examples, using `LocalCUDACluster` to create one dask-worker per GPU on the local machine. For more detailed information about `persist` and `wait`, please see the Dask documentation for [persist](https://docs.dask.org/en/latest/api.html#dask.persist) and [wait](https://docs.dask.org/en/latest/futures.html#distributed.wait). Wait relies on the concept of Futures, which is beyond the scope of this tutorial. For more information on Futures, see the Dask [Futures](https://docs.dask.org/en/latest/futures.html) documentation. For more information about multi-GPU clusters, please see the [dask-cuda](https://github.com/rapidsai/dask-cuda) library (documentation is in progress)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "976a8dca",
+   "metadata": {},
+   "source": [
+    "First, we set up a GPU cluster. With our `client` set up, Dask-cuDF computation will be distributed across the GPUs in the cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "39c82511",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "from dask.distributed import Client, wait\n",
+    "from dask_cuda import LocalCUDACluster\n",
+    "\n",
+    "cluster = LocalCUDACluster()\n",
+    "client = Client(cluster)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "819c2d92",
+   "metadata": {},
+   "source": [
+    "### Persisting Data\n",
+    "\n",
+    "Next, we create our Dask-cuDF DataFrame and apply a transformation, storing the result as a new column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "f5c0ca87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>npartitions=16</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>int64</td>\n",
+       "      <td>int64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625000</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9375000</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999999</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "<div>Dask Name: assign, 4 graph layers</div>"
+      ],
+      "text/plain": [
+       "<dask_cudf.DataFrame | 64 tasks | 16 npartitions>"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nrows = 10000000\n",
+    "\n",
+    "df2 = cudf.DataFrame({\"a\": cp.arange(nrows), \"b\": cp.arange(nrows)})\n",
+    "ddf2 = dask_cudf.from_cudf(df2, npartitions=16)\n",
+    "ddf2[\"c\"] = ddf2[\"a\"] + 5\n",
+    "ddf2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "eec23c4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon Nov 14 03:05:08 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.73.08    Driver Version: 510.73.08    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  Tesla V100-SXM2...  On   | 00000000:06:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    55W / 300W |   4538MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  Tesla V100-SXM2...  On   | 00000000:07:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    56W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   2  Tesla V100-SXM2...  On   | 00000000:0A:00.0 Off |                    0 |\n",
+      "| N/A   33C    P0    55W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   3  Tesla V100-SXM2...  On   | 00000000:0B:00.0 Off |                    0 |\n",
+      "| N/A   31C    P0    55W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   4  Tesla V100-SXM2...  On   | 00000000:85:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    54W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   5  Tesla V100-SXM2...  On   | 00000000:86:00.0 Off |                    0 |\n",
+      "| N/A   33C    P0    56W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   6  Tesla V100-SXM2...  On   | 00000000:89:00.0 Off |                    0 |\n",
+      "| N/A   35C    P0    55W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   7  Tesla V100-SXM2...  On   | 00000000:8A:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    54W / 300W |    336MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A     57132      C   .../python                        333MiB |\n",
+      "|    1   N/A  N/A     57131      C   .../python                        333MiB |\n",
+      "|    2   N/A  N/A     57143      C   .../python                        333MiB |\n",
+      "|    3   N/A  N/A     57124      C   .../python                        333MiB |\n",
+      "|    4   N/A  N/A     57135      C   .../python                        333MiB |\n",
+      "|    5   N/A  N/A     57144      C   .../python                        333MiB |\n",
+      "|    6   N/A  N/A     57126      C   .../python                        333MiB |\n",
+      "|    7   N/A  N/A     57139      C   .../python                        333MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "578a1698",
+   "metadata": {},
+   "source": [
+    "Because Dask is lazy, the computation has not yet occurred. We can see that there are sixty-four tasks in the task graph and we're using about 330 MB of device memory on each GPU. We can force computation by using `persist`. By forcing execution, the result is now explicitly in memory and our task graph only contains one task per partition (the baseline)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "3de4c0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>npartitions=16</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>int64</td>\n",
+       "      <td>int64</td>\n",
+       "      <td>int64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625000</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9375000</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999999</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "<div>Dask Name: assign, 1 graph layer</div>"
+      ],
+      "text/plain": [
+       "<dask_cudf.DataFrame | 16 tasks | 16 npartitions>"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ddf2 = ddf2.persist()\n",
+    "ddf2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "64c9f96c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon Nov 14 03:05:15 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.73.08    Driver Version: 510.73.08    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  Tesla V100-SXM2...  On   | 00000000:06:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    55W / 300W |   4900MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  Tesla V100-SXM2...  On   | 00000000:07:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    56W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   2  Tesla V100-SXM2...  On   | 00000000:0A:00.0 Off |                    0 |\n",
+      "| N/A   33C    P0    55W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   3  Tesla V100-SXM2...  On   | 00000000:0B:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    55W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   4  Tesla V100-SXM2...  On   | 00000000:85:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    55W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   5  Tesla V100-SXM2...  On   | 00000000:86:00.0 Off |                    0 |\n",
+      "| N/A   33C    P0    56W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   6  Tesla V100-SXM2...  On   | 00000000:89:00.0 Off |                    0 |\n",
+      "| N/A   35C    P0    55W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   7  Tesla V100-SXM2...  On   | 00000000:8A:00.0 Off |                    0 |\n",
+      "| N/A   32C    P0    54W / 300W |    698MiB / 32768MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A     57132      C   .../python                        695MiB |\n",
+      "|    1   N/A  N/A     57131      C   .../python                        695MiB |\n",
+      "|    2   N/A  N/A     57143      C   .../python                        695MiB |\n",
+      "|    3   N/A  N/A     57124      C   .../python                        695MiB |\n",
+      "|    4   N/A  N/A     57135      C   .../python                        695MiB |\n",
+      "|    5   N/A  N/A     57144      C   .../python                        695MiB |\n",
+      "|    6   N/A  N/A     57126      C   .../python                        695MiB |\n",
+      "|    7   N/A  N/A     57139      C   .../python                        695MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sleep to ensure the persist finishes and shows in the memory usage\n",
+    "!sleep 5; nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "154d699f",
+   "metadata": {},
+   "source": [
+    "Because we forced computation, we now have a larger object in distributed GPU memory. Note that actual numbers will differ between systems (for example depending on how many devices are available)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f45064d7",
+   "metadata": {},
+   "source": [
+    "### Wait\n",
+    "Depending on our workflow or distributed computing setup, we may want to `wait` until all upstream tasks have finished before proceeding with a specific function. This section shows an example of this behavior, adapted from the Dask documentation.\n",
+    "\n",
+    "First, we create a new Dask DataFrame and define a function that we'll map to every partition in the dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "a021a726",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "nrows = 10000000\n",
+    "\n",
+    "df1 = cudf.DataFrame({\"a\": cp.arange(nrows), \"b\": cp.arange(nrows)})\n",
+    "ddf1 = dask_cudf.from_cudf(df1, npartitions=100)\n",
+    "\n",
+    "\n",
+    "def func(df):\n",
+    "    time.sleep(random.randint(1, 10))\n",
+    "    return (df + 5) * 3 - 11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a3ee73",
+   "metadata": {},
+   "source": [
+    "This function will do a basic transformation of every column in the dataframe, but the time spent in the function will vary due to the `time.sleep` statement randomly adding 1-10 seconds of time. We'll run this on every partition of our dataframe using `map_partitions`, which adds the task to our task-graph, and store the result. We can then call `persist` to force execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "8f091ada",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_ddf = ddf2.map_partitions(func)\n",
+    "results_ddf = results_ddf.persist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c22a1e8",
+   "metadata": {},
+   "source": [
+    "However, some partitions will be done **much** sooner than others. If we had downstream processes that should wait for all partitions to be completed, we can enforce that behavior using `wait`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "fea52d0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DoneAndNotDoneFutures(done={<Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 13)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 1)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 3)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 8)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 10)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 7)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 5)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 6)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 14)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 12)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 9)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 11)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 2)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 4)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 15)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-4a955f5e5fda88923d28b45196632826', 0)>}, not_done=set())"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wait(results_ddf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db619bec",
+   "metadata": {},
+   "source": [
+    "With `wait` completed, we can safely proceed on in our workflow."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8056d08c5310318d9ca4fe60778daf853f02695d9fa19fd0f51ce5f8b089487a"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/cudf/source/user_guide/PandasCompat.md b/docs/cudf/source/user_guide/PandasCompat.md
new file mode 100644
index 0000000..a33a354
--- /dev/null
+++ b/docs/cudf/source/user_guide/PandasCompat.md
@@ -0,0 +1,5 @@
+# Pandas Compatibility Notes
+
+```{eval-rst}
+.. pandas-compat-list::
+```
diff --git a/docs/cudf/source/user_guide/api_docs/dataframe.rst b/docs/cudf/source/user_guide/api_docs/dataframe.rst
new file mode 100644
index 0000000..a8f3edf
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/dataframe.rst
@@ -0,0 +1,280 @@
+=========
+DataFrame
+=========
+.. currentmodule:: cudf
+
+Constructor
+~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame
+
+Attributes and underlying data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+**Axes**
+
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.axes
+   DataFrame.index
+   DataFrame.columns
+
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.dtypes
+   DataFrame.info
+   DataFrame.select_dtypes
+   DataFrame.values
+   DataFrame.ndim
+   DataFrame.size
+   DataFrame.shape
+   DataFrame.memory_usage
+   DataFrame.empty
+
+Conversion
+~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.astype
+   DataFrame.convert_dtypes
+   DataFrame.copy
+
+Indexing, iteration
+~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.head
+   DataFrame.at
+   DataFrame.iat
+   DataFrame.loc
+   DataFrame.iloc
+   DataFrame.insert
+   DataFrame.__iter__
+   DataFrame.items
+   DataFrame.keys
+   DataFrame.iterrows
+   DataFrame.itertuples
+   DataFrame.pop
+   DataFrame.tail
+   DataFrame.isin
+   DataFrame.where
+   DataFrame.mask
+   DataFrame.query
+
+Binary operator functions
+~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.add
+   DataFrame.sub
+   DataFrame.subtract
+   DataFrame.mul
+   DataFrame.multiply
+   DataFrame.truediv
+   DataFrame.div
+   DataFrame.divide
+   DataFrame.floordiv
+   DataFrame.mod
+   DataFrame.pow
+   DataFrame.dot
+   DataFrame.radd
+   DataFrame.rsub
+   DataFrame.rmul
+   DataFrame.rdiv
+   DataFrame.rtruediv
+   DataFrame.rfloordiv
+   DataFrame.rmod
+   DataFrame.rpow
+   DataFrame.round
+   DataFrame.lt
+   DataFrame.gt
+   DataFrame.le
+   DataFrame.ge
+   DataFrame.ne
+   DataFrame.eq
+   DataFrame.product
+
+Function application, GroupBy & window
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.apply
+   DataFrame.applymap
+   DataFrame.apply_chunks
+   DataFrame.apply_rows
+   DataFrame.pipe
+   DataFrame.agg
+   DataFrame.groupby
+   DataFrame.rolling
+
+.. _api.dataframe.stats:
+
+Computations / descriptive stats
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.abs
+   DataFrame.all
+   DataFrame.any
+   DataFrame.clip
+   DataFrame.corr
+   DataFrame.count
+   DataFrame.cov
+   DataFrame.cummax
+   DataFrame.cummin
+   DataFrame.cumprod
+   DataFrame.cumsum
+   DataFrame.describe
+   DataFrame.diff
+   DataFrame.eval
+   DataFrame.kurt
+   DataFrame.kurtosis
+   DataFrame.max
+   DataFrame.mean
+   DataFrame.median
+   DataFrame.min
+   DataFrame.mode
+   DataFrame.pct_change
+   DataFrame.prod
+   DataFrame.product
+   DataFrame.quantile
+   DataFrame.rank
+   DataFrame.round
+   DataFrame.scale
+   DataFrame.skew
+   DataFrame.sum
+   DataFrame.std
+   DataFrame.var
+   DataFrame.nunique
+   DataFrame.value_counts
+
+Reindexing / selection / label manipulation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.add_prefix
+   DataFrame.add_suffix
+   DataFrame.drop
+   DataFrame.drop_duplicates
+   DataFrame.duplicated
+   DataFrame.equals
+   DataFrame.first
+   DataFrame.head
+   DataFrame.last
+   DataFrame.reindex
+   DataFrame.rename
+   DataFrame.reset_index
+   DataFrame.sample
+   DataFrame.searchsorted
+   DataFrame.set_index
+   DataFrame.repeat
+   DataFrame.tail
+   DataFrame.take
+   DataFrame.tile
+   DataFrame.truncate
+
+.. _api.dataframe.missing:
+
+Missing data handling
+~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.backfill
+   DataFrame.bfill
+   DataFrame.dropna
+   DataFrame.ffill
+   DataFrame.fillna
+   DataFrame.interpolate
+   DataFrame.isna
+   DataFrame.isnull
+   DataFrame.nans_to_nulls
+   DataFrame.notna
+   DataFrame.notnull
+   DataFrame.pad
+   DataFrame.replace
+
+Reshaping, sorting, transposing
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.argsort
+   DataFrame.interleave_columns
+   DataFrame.partition_by_hash
+   DataFrame.pivot
+   DataFrame.pivot_table
+   DataFrame.scatter_by_map
+   DataFrame.sort_values
+   DataFrame.sort_index
+   DataFrame.nlargest
+   DataFrame.nsmallest
+   DataFrame.swaplevel
+   DataFrame.stack
+   DataFrame.unstack
+   DataFrame.melt
+   DataFrame.explode
+   DataFrame.to_struct
+   DataFrame.T
+   DataFrame.transpose
+
+Combining / comparing / joining / merging
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.append
+   DataFrame.assign
+   DataFrame.join
+   DataFrame.merge
+   DataFrame.update
+
+Time Series-related
+~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.shift
+   DataFrame.resample
+
+Serialization / IO / conversion
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DataFrame.deserialize
+   DataFrame.device_deserialize
+   DataFrame.device_serialize
+   DataFrame.from_arrow
+   DataFrame.from_dict
+   DataFrame.from_pandas
+   DataFrame.from_records
+   DataFrame.hash_values
+   DataFrame.host_deserialize
+   DataFrame.host_serialize
+   DataFrame.serialize
+   DataFrame.to_arrow
+   DataFrame.to_dict
+   DataFrame.to_dlpack
+   DataFrame.to_parquet
+   DataFrame.to_csv
+   DataFrame.to_cupy
+   DataFrame.to_hdf
+   DataFrame.to_dict
+   DataFrame.to_json
+   DataFrame.to_numpy
+   DataFrame.to_pandas
+   DataFrame.to_feather
+   DataFrame.to_records
+   DataFrame.to_string
+   DataFrame.values
+   DataFrame.values_host
diff --git a/docs/cudf/source/user_guide/api_docs/extension_dtypes.rst b/docs/cudf/source/user_guide/api_docs/extension_dtypes.rst
new file mode 100644
index 0000000..daccb01
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/extension_dtypes.rst
@@ -0,0 +1,170 @@
+================
+Extension Dtypes
+================
+.. currentmodule:: cudf.core.dtypes
+
+cuDF supports a number of extension dtypes that build on top of the types that pandas supports. These dtypes are not directly available in pandas, which instead relies on object dtype arrays that run at Python rather than native speeds. The following dtypes are supported:
+
+
+cudf.CategoricalDtype
+=====================
+.. autosummary::
+   :toctree: api/
+
+   CategoricalDtype
+
+
+Properties and Methods
+----------------------
+.. autosummary::
+   :toctree: api/
+
+    CategoricalDtype.categories
+    CategoricalDtype.construct_from_string
+    CategoricalDtype.deserialize
+    CategoricalDtype.device_deserialize
+    CategoricalDtype.device_serialize
+    CategoricalDtype.from_pandas
+    CategoricalDtype.host_deserialize
+    CategoricalDtype.host_serialize
+    CategoricalDtype.is_dtype
+    CategoricalDtype.name
+    CategoricalDtype.ordered
+    CategoricalDtype.serialize
+    CategoricalDtype.str
+    CategoricalDtype.to_pandas
+    CategoricalDtype.type
+
+
+cudf.Decimal32Dtype
+===================
+.. autosummary::
+   :toctree: api/
+
+   Decimal32Dtype
+
+Properties and Methods
+----------------------
+.. autosummary::
+   :toctree: api/
+
+   Decimal32Dtype.ITEMSIZE
+   Decimal32Dtype.MAX_PRECISION
+   Decimal32Dtype.deserialize
+   Decimal32Dtype.device_deserialize
+   Decimal32Dtype.device_serialize
+   Decimal32Dtype.from_arrow
+   Decimal32Dtype.host_deserialize
+   Decimal32Dtype.host_serialize
+   Decimal32Dtype.is_dtype
+   Decimal32Dtype.itemsize
+   Decimal32Dtype.precision
+   Decimal32Dtype.scale
+   Decimal32Dtype.serialize
+   Decimal32Dtype.str
+   Decimal32Dtype.to_arrow
+
+cudf.Decimal64Dtype
+===================
+.. autosummary::
+   :toctree: api/
+
+   Decimal64Dtype
+
+Properties and Methods
+----------------------
+.. autosummary::
+   :toctree: api/
+
+   Decimal64Dtype.ITEMSIZE
+   Decimal64Dtype.MAX_PRECISION
+   Decimal64Dtype.deserialize
+   Decimal64Dtype.device_deserialize
+   Decimal64Dtype.device_serialize
+   Decimal64Dtype.from_arrow
+   Decimal64Dtype.host_deserialize
+   Decimal64Dtype.host_serialize
+   Decimal64Dtype.is_dtype
+   Decimal64Dtype.itemsize
+   Decimal64Dtype.precision
+   Decimal64Dtype.scale
+   Decimal64Dtype.serialize
+   Decimal64Dtype.str
+   Decimal64Dtype.to_arrow
+
+cudf.Decimal128Dtype
+====================
+.. autosummary::
+   :toctree: api/
+
+   Decimal128Dtype
+
+Properties and Methods
+----------------------
+.. autosummary::
+   :toctree: api/
+
+   Decimal128Dtype.ITEMSIZE
+   Decimal128Dtype.MAX_PRECISION
+   Decimal128Dtype.deserialize
+   Decimal128Dtype.device_deserialize
+   Decimal128Dtype.device_serialize
+   Decimal128Dtype.from_arrow
+   Decimal128Dtype.host_deserialize
+   Decimal128Dtype.host_serialize
+   Decimal128Dtype.is_dtype
+   Decimal128Dtype.itemsize
+   Decimal128Dtype.precision
+   Decimal128Dtype.scale
+   Decimal128Dtype.serialize
+   Decimal128Dtype.str
+   Decimal128Dtype.to_arrow
+
+cudf.ListDtype
+==============
+.. autosummary::
+   :toctree: api/
+
+   ListDtype
+
+Properties and Methods
+----------------------
+.. autosummary::
+   :toctree: api/
+
+   ListDtype.deserialize
+   ListDtype.device_deserialize
+   ListDtype.device_serialize
+   ListDtype.element_type
+   ListDtype.from_arrow
+   ListDtype.host_deserialize
+   ListDtype.host_serialize
+   ListDtype.is_dtype
+   ListDtype.leaf_type
+   ListDtype.serialize
+   ListDtype.to_arrow
+   ListDtype.type
+
+cudf.StructDtype
+================
+.. autosummary::
+   :toctree: api/
+
+   StructDtype
+
+Properties and Methods
+----------------------
+.. autosummary::
+   :toctree: api/
+
+   StructDtype.deserialize
+   StructDtype.device_deserialize
+   StructDtype.device_serialize
+   StructDtype.fields
+   StructDtype.from_arrow
+   StructDtype.host_deserialize
+   StructDtype.host_serialize
+   StructDtype.is_dtype
+   StructDtype.serialize
+   StructDtype.to_arrow
+   StructDtype.type
diff --git a/docs/cudf/source/user_guide/api_docs/general_functions.rst b/docs/cudf/source/user_guide/api_docs/general_functions.rst
new file mode 100644
index 0000000..38e070b
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/general_functions.rst
@@ -0,0 +1,48 @@
+=================
+General Functions
+=================
+.. currentmodule:: cudf
+
+Data manipulations
+------------------
+
+.. autosummary::
+   :toctree: api/
+
+   cudf.concat
+   cudf.crosstab
+   cudf.cut
+   cudf.factorize
+   cudf.get_dummies
+   cudf.melt
+   cudf.merge
+   cudf.pivot
+   cudf.pivot_table
+   cudf.unstack
+
+Top-level conversions
+---------------------
+.. autosummary::
+   :toctree: api/
+
+    cudf.to_numeric
+    cudf.from_dataframe
+    cudf.from_dlpack
+    cudf.from_pandas
+
+Top-level dealing with datetimelike data
+----------------------------------------
+
+.. autosummary::
+   :toctree: api/
+
+    cudf.to_datetime
+    cudf.date_range
+
+Top-level dealing with Interval data
+------------------------------------
+
+.. autosummary::
+   :toctree: api/
+
+    cudf.interval_range
diff --git a/docs/cudf/source/user_guide/api_docs/general_utilities.rst b/docs/cudf/source/user_guide/api_docs/general_utilities.rst
new file mode 100644
index 0000000..d9c53c3
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/general_utilities.rst
@@ -0,0 +1,13 @@
+=================
+General Utilities
+=================
+
+Testing functions
+-----------------
+.. autosummary::
+   :toctree: api/
+
+   cudf.testing.testing.assert_column_equal
+   cudf.testing.testing.assert_frame_equal
+   cudf.testing.testing.assert_index_equal
+   cudf.testing.testing.assert_series_equal
diff --git a/docs/cudf/source/user_guide/api_docs/groupby.rst b/docs/cudf/source/user_guide/api_docs/groupby.rst
new file mode 100644
index 0000000..550a0ef
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/groupby.rst
@@ -0,0 +1,110 @@
+.. _api.groupby:
+
+=======
+GroupBy
+=======
+.. currentmodule:: cudf.core.groupby
+
+GroupBy objects are returned by groupby calls: :func:`cudf.DataFrame.groupby`, :func:`cudf.Series.groupby`, etc.
+
+Indexing, iteration
+-------------------
+.. autosummary::
+   :toctree: api/
+
+   GroupBy.__iter__
+   GroupBy.groups
+
+.. currentmodule:: cudf
+
+.. autosummary::
+   :toctree: api/
+
+   Grouper
+
+.. currentmodule:: cudf.core.groupby.groupby
+
+Function application
+--------------------
+.. autosummary::
+   :toctree: api/
+
+   GroupBy.apply
+   GroupBy.agg
+   SeriesGroupBy.aggregate
+   DataFrameGroupBy.aggregate
+   GroupBy.pipe
+   GroupBy.transform
+
+Computations / descriptive stats
+--------------------------------
+.. autosummary::
+   :toctree: api/
+
+   GroupBy.bfill
+   GroupBy.backfill
+   GroupBy.count
+   GroupBy.cumcount
+   GroupBy.cummax
+   GroupBy.cummin
+   GroupBy.cumsum
+   GroupBy.diff
+   GroupBy.ffill
+   GroupBy.first
+   GroupBy.get_group
+   GroupBy.groups
+   GroupBy.idxmax
+   GroupBy.idxmin
+   GroupBy.last
+   GroupBy.max
+   GroupBy.mean
+   GroupBy.median
+   GroupBy.min
+   GroupBy.ngroup
+   GroupBy.nth
+   GroupBy.nunique
+   GroupBy.pad
+   GroupBy.prod
+   GroupBy.shift
+   GroupBy.size
+   GroupBy.std
+   GroupBy.sum
+   GroupBy.var
+   GroupBy.corr
+   GroupBy.cov
+
+The following methods are available in both ``SeriesGroupBy`` and
+``DataFrameGroupBy`` objects, but may differ slightly, usually in that
+the ``DataFrameGroupBy`` version usually permits the specification of an
+axis argument, and often an argument indicating whether to restrict
+application to columns of a specific data type.
+
+.. autosummary::
+   :toctree: api/
+
+   DataFrameGroupBy.backfill
+   DataFrameGroupBy.bfill
+   DataFrameGroupBy.count
+   DataFrameGroupBy.cumcount
+   DataFrameGroupBy.cummax
+   DataFrameGroupBy.cummin
+   DataFrameGroupBy.cumsum
+   DataFrameGroupBy.describe
+   DataFrameGroupBy.diff
+   DataFrameGroupBy.ffill
+   DataFrameGroupBy.fillna
+   DataFrameGroupBy.idxmax
+   DataFrameGroupBy.idxmin
+   DataFrameGroupBy.nunique
+   DataFrameGroupBy.pad
+   DataFrameGroupBy.quantile
+   DataFrameGroupBy.shift
+   DataFrameGroupBy.size
+
+The following methods are available only for ``SeriesGroupBy`` objects.
+
+.. autosummary::
+   :toctree: api/
+
+   SeriesGroupBy.nunique
+   SeriesGroupBy.unique
diff --git a/docs/cudf/source/user_guide/api_docs/index.rst b/docs/cudf/source/user_guide/api_docs/index.rst
new file mode 100644
index 0000000..01047a3
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/index.rst
@@ -0,0 +1,25 @@
+=============
+API reference
+=============
+
+This page provides a list of all publicly accessible modules, methods and classes through
+``cudf.*`` namespace.
+
+.. toctree::
+    :maxdepth: 2
+    :caption: API Documentation
+
+    series
+    dataframe
+    index_objects
+    groupby
+    general_functions
+    general_utilities
+    window
+    io
+    subword_tokenize
+    string_handling
+    list_handling
+    struct_handling
+    options
+    extension_dtypes
diff --git a/docs/cudf/source/user_guide/api_docs/index_objects.rst b/docs/cudf/source/user_guide/api_docs/index_objects.rst
new file mode 100644
index 0000000..013eaf2
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/index_objects.rst
@@ -0,0 +1,338 @@
+=============
+Index objects
+=============
+
+Index
+-----
+.. currentmodule:: cudf
+
+**Many of these methods or variants thereof are available on the objects
+that contain an index (Series/DataFrame) and those should most likely be
+used before calling these methods directly.**
+
+.. autosummary::
+   :toctree: api/
+
+   Index
+
+Properties
+~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.dtype
+   Index.duplicated
+   Index.empty
+   Index.has_duplicates
+   Index.hasnans
+   Index.is_monotonic
+   Index.is_monotonic_increasing
+   Index.is_monotonic_decreasing
+   Index.is_unique
+   Index.name
+   Index.names
+   Index.ndim
+   Index.nlevels
+   Index.shape
+   Index.size
+   Index.values
+
+Modifying and computations
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.any
+   Index.copy
+   Index.drop_duplicates
+   Index.equals
+   Index.factorize
+   Index.is_boolean
+   Index.is_categorical
+   Index.is_floating
+   Index.is_integer
+   Index.is_interval
+   Index.is_numeric
+   Index.is_object
+   Index.min
+   Index.max
+   Index.rename
+   Index.repeat
+   Index.where
+   Index.take
+   Index.unique
+
+Compatibility with MultiIndex
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.set_names
+
+Missing values
+~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.fillna
+   Index.dropna
+   Index.isna
+   Index.notna
+
+Memory usage
+~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.memory_usage
+
+Conversion
+~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.astype
+   Index.deserialize
+   Index.device_deserialize
+   Index.device_serialize
+   Index.host_deserialize
+   Index.host_serialize
+   Index.serialize
+   Index.tolist
+   Index.to_arrow
+   Index.to_cupy
+   Index.to_list
+   Index.to_numpy
+   Index.to_series
+   Index.to_frame
+   Index.to_pandas
+   Index.to_dlpack
+   Index.from_pandas
+   Index.from_arrow
+
+Sorting
+~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.argsort
+   Index.find_label_range
+   Index.searchsorted
+   Index.sort_values
+
+Time-specific operations
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.shift
+
+Combining / joining / set operations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.append
+   Index.union
+   Index.intersection
+   Index.join
+   Index.difference
+
+Selecting
+~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.get_level_values
+   Index.get_loc
+   Index.get_slice_bound
+   Index.isin
+
+String Operations
+~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   Index.str
+
+.. _api.numericindex:
+
+Numeric Index
+-------------
+.. autosummary::
+   :toctree: api/
+
+   RangeIndex
+   RangeIndex.start
+   RangeIndex.stop
+   RangeIndex.step
+   RangeIndex.to_numpy
+   RangeIndex.to_arrow
+   Int64Index
+   UInt64Index
+   Float64Index
+
+.. _api.categoricalindex:
+
+CategoricalIndex
+----------------
+.. autosummary::
+   :toctree: api/
+
+   CategoricalIndex
+
+Categorical components
+~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   CategoricalIndex.codes
+   CategoricalIndex.categories
+
+Modifying and computations
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   CategoricalIndex.equals
+
+.. _api.intervalindex:
+
+IntervalIndex
+-------------
+.. autosummary::
+   :toctree: api/
+
+   IntervalIndex
+
+IntervalIndex components
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   IntervalIndex.from_breaks
+   IntervalIndex.values
+   IntervalIndex.get_loc
+
+.. _api.multiindex:
+
+MultiIndex
+----------
+.. autosummary::
+   :toctree: api/
+
+   MultiIndex
+
+MultiIndex constructors
+~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   MultiIndex.from_tuples
+   MultiIndex.from_product
+   MultiIndex.from_frame
+   MultiIndex.from_arrow
+
+MultiIndex properties
+~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   MultiIndex.names
+   MultiIndex.levels
+   MultiIndex.codes
+   MultiIndex.nlevels
+
+MultiIndex components
+~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   MultiIndex.to_frame
+   MultiIndex.droplevel
+   MultiIndex.swaplevel
+
+MultiIndex selecting
+~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   MultiIndex.get_loc
+   MultiIndex.get_level_values
+
+.. _api.datetimeindex:
+
+DatetimeIndex
+-------------
+.. autosummary::
+   :toctree: api/
+
+   DatetimeIndex
+
+Time/date components
+~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DatetimeIndex.year
+   DatetimeIndex.month
+   DatetimeIndex.day
+   DatetimeIndex.hour
+   DatetimeIndex.minute
+   DatetimeIndex.second
+   DatetimeIndex.microsecond
+   DatetimeIndex.nanosecond
+   DatetimeIndex.day_of_year
+   DatetimeIndex.dayofyear
+   DatetimeIndex.dayofweek
+   DatetimeIndex.weekday
+   DatetimeIndex.quarter
+   DatetimeIndex.is_leap_year
+
+   DatetimeIndex.isocalendar
+
+Time-specific operations
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DatetimeIndex.round
+   DatetimeIndex.ceil
+   DatetimeIndex.floor
+   DatetimeIndex.tz_convert
+   DatetimeIndex.tz_localize
+
+Conversion
+~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   DatetimeIndex.to_series
+   DatetimeIndex.to_frame
+
+TimedeltaIndex
+--------------
+.. autosummary::
+   :toctree: api/
+
+   TimedeltaIndex
+
+Components
+~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   TimedeltaIndex.days
+   TimedeltaIndex.seconds
+   TimedeltaIndex.microseconds
+   TimedeltaIndex.nanoseconds
+   TimedeltaIndex.components
+   TimedeltaIndex.inferred_freq
+
+Conversion
+~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   TimedeltaIndex.to_series
+   TimedeltaIndex.to_frame
diff --git a/docs/cudf/source/user_guide/api_docs/io.rst b/docs/cudf/source/user_guide/api_docs/io.rst
new file mode 100644
index 0000000..4179707
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/io.rst
@@ -0,0 +1,83 @@
+.. _api.io:
+
+============
+Input/output
+============
+.. currentmodule:: cudf
+
+CSV
+~~~
+.. autosummary::
+   :toctree: api/
+
+   read_csv
+   DataFrame.to_csv
+
+Text
+~~~~
+.. autosummary::
+   :toctree: api/
+
+   read_text
+
+JSON
+~~~~
+.. autosummary::
+   :toctree: api/
+
+   read_json
+   DataFrame.to_json
+
+Parquet
+~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   read_parquet
+   DataFrame.to_parquet
+   cudf.io.parquet.read_parquet_metadata
+   cudf.io.parquet.ParquetDatasetWriter
+   cudf.io.parquet.ParquetDatasetWriter.close
+   cudf.io.parquet.ParquetDatasetWriter.write_table
+
+
+ORC
+~~~
+.. autosummary::
+   :toctree: api/
+
+   read_orc
+   DataFrame.to_orc
+
+HDFStore: PyTables (HDF5)
+~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   read_hdf
+   DataFrame.to_hdf
+
+.. warning::
+
+   HDF reader and writers are not GPU accelerated. These currently use CPU via Pandas.
+   This may be GPU accelerated in the future.
+
+Feather
+~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   read_feather
+   DataFrame.to_feather
+
+.. warning::
+
+   Feather reader and writers are not GPU accelerated. These currently use CPU via Pandas.
+   This may be GPU accelerated in the future.
+
+Avro
+~~~~
+.. autosummary::
+   :toctree: api/
+
+   read_avro
diff --git a/docs/cudf/source/user_guide/api_docs/list_handling.rst b/docs/cudf/source/user_guide/api_docs/list_handling.rst
new file mode 100644
index 0000000..78980e5
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/list_handling.rst
@@ -0,0 +1,27 @@
+List handling
+~~~~~~~~~~~~~
+
+``Series.list`` can be used to access the values of the series as
+lists and apply list methods to it. These can be accessed like
+``Series.list.<function/property>``.
+
+.. currentmodule:: cudf
+.. autosummary::
+   :toctree: api/
+
+   Series.list
+
+.. currentmodule:: cudf.core.column.lists.ListMethods
+.. autosummary::
+   :toctree: api/
+
+   astype
+   concat
+   contains
+   index
+   get
+   leaves
+   len
+   sort_values
+   take
+   unique
diff --git a/docs/cudf/source/user_guide/api_docs/options.rst b/docs/cudf/source/user_guide/api_docs/options.rst
new file mode 100644
index 0000000..b3a4004
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/options.rst
@@ -0,0 +1,25 @@
+.. _api.options:
+
+====================
+Options and settings
+====================
+
+.. autosummary::
+   :toctree: api/
+
+   cudf.get_option
+   cudf.set_option
+   cudf.describe_option
+   cudf.option_context
+
+
+Available options
+-----------------
+
+You can get a list of available options and their descriptions with :func:`~cudf.describe_option`. When called
+with no argument :func:`~cudf.describe_option` will print out the descriptions for all available options.
+
+.. ipython:: python
+
+   import cudf
+   cudf.describe_option()
diff --git a/docs/cudf/source/user_guide/api_docs/series.rst b/docs/cudf/source/user_guide/api_docs/series.rst
new file mode 100644
index 0000000..ebfc1e3
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/series.rst
@@ -0,0 +1,405 @@
+======
+Series
+======
+.. currentmodule:: cudf
+
+Constructor
+-----------
+.. autosummary::
+   :toctree: api/
+
+   Series
+
+Attributes
+----------
+**Axes**
+
+.. autosummary::
+   :toctree: api/
+
+   Series.axes
+   Series.index
+   Series.values
+   Series.data
+   Series.dtype
+   Series.shape
+   Series.ndim
+   Series.nullable
+   Series.nullmask
+   Series.null_count
+   Series.size
+   Series.T
+   Series.memory_usage
+   Series.hasnans
+   Series.has_nulls
+   Series.empty
+   Series.name
+   Series.valid_count
+   Series.values_host
+
+Conversion
+----------
+.. autosummary::
+   :toctree: api/
+
+   Series.astype
+   Series.convert_dtypes
+   Series.copy
+   Series.deserialize
+   Series.device_deserialize
+   Series.device_serialize
+   Series.host_deserialize
+   Series.host_serialize
+   Series.serialize
+   Series.to_list
+   Series.tolist
+   Series.__array__
+   Series.scale
+
+
+Indexing, iteration
+-------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.loc
+   Series.iloc
+   Series.__iter__
+   Series.items
+   Series.iteritems
+   Series.keys
+
+Binary operator functions
+-------------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.add
+   Series.sub
+   Series.subtract
+   Series.mul
+   Series.multiply
+   Series.truediv
+   Series.div
+   Series.divide
+   Series.floordiv
+   Series.mod
+   Series.pow
+   Series.radd
+   Series.rsub
+   Series.rmul
+   Series.rdiv
+   Series.rtruediv
+   Series.rfloordiv
+   Series.rmod
+   Series.rpow
+   Series.round
+   Series.lt
+   Series.gt
+   Series.le
+   Series.ge
+   Series.ne
+   Series.eq
+   Series.product
+   Series.dot
+
+Function application, GroupBy & window
+--------------------------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.apply
+   Series.map
+   Series.groupby
+   Series.rolling
+   Series.pipe
+
+.. _api.series.stats:
+
+Computations / descriptive stats
+--------------------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.abs
+   Series.all
+   Series.any
+   Series.autocorr
+   Series.between
+   Series.clip
+   Series.corr
+   Series.count
+   Series.cov
+   Series.cummax
+   Series.cummin
+   Series.cumprod
+   Series.cumsum
+   Series.describe
+   Series.diff
+   Series.digitize
+   Series.factorize
+   Series.kurt
+   Series.max
+   Series.mean
+   Series.median
+   Series.min
+   Series.mode
+   Series.nlargest
+   Series.nsmallest
+   Series.pct_change
+   Series.prod
+   Series.quantile
+   Series.rank
+   Series.skew
+   Series.std
+   Series.sum
+   Series.var
+   Series.kurtosis
+   Series.unique
+   Series.nunique
+   Series.is_unique
+   Series.is_monotonic
+   Series.is_monotonic_increasing
+   Series.is_monotonic_decreasing
+   Series.value_counts
+
+Reindexing / selection / label manipulation
+-------------------------------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.add_prefix
+   Series.add_suffix
+   Series.drop
+   Series.drop_duplicates
+   Series.duplicated
+   Series.equals
+   Series.first
+   Series.head
+   Series.isin
+   Series.last
+   Series.reindex
+   Series.rename
+   Series.reset_index
+   Series.sample
+   Series.take
+   Series.tail
+   Series.tile
+   Series.truncate
+   Series.where
+   Series.mask
+
+Missing data handling
+---------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.backfill
+   Series.bfill
+   Series.dropna
+   Series.ffill
+   Series.fillna
+   Series.interpolate
+   Series.isna
+   Series.isnull
+   Series.nans_to_nulls
+   Series.notna
+   Series.notnull
+   Series.pad
+   Series.replace
+
+Reshaping, sorting
+------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.argsort
+   Series.sort_values
+   Series.sort_index
+   Series.explode
+   Series.searchsorted
+   Series.repeat
+   Series.transpose
+
+Combining / comparing / joining / merging
+-----------------------------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.append
+   Series.update
+
+Time Series-related
+-------------------
+.. autosummary::
+   :toctree: api/
+
+   Series.shift
+   Series.resample
+
+Accessors
+---------
+
+pandas provides dtype-specific methods under various accessors.
+These are separate namespaces within :class:`Series` that only apply
+to specific data types.
+
+=========================== =================================
+Data Type                   Accessor
+=========================== =================================
+Datetime, Timedelta         :ref:`dt <api.series.dt>`
+String                      :ref:`str <api.series.str>`
+Categorical                 :ref:`cat <api.series.cat>`
+List                        :ref:`list <api.series.list>`
+Struct                      :ref:`struct <api.series.struct>`
+=========================== =================================
+
+.. _api.series.dt:
+
+Datetimelike properties
+~~~~~~~~~~~~~~~~~~~~~~~
+
+``Series.dt`` can be used to access the values of the series as
+datetimelike and return several properties.
+These can be accessed like ``Series.dt.<property>``.
+
+.. currentmodule:: cudf
+.. autosummary::
+   :toctree: api/
+
+   Series.dt
+
+Datetime properties
+^^^^^^^^^^^^^^^^^^^
+.. currentmodule:: cudf.core.series.DatetimeProperties
+
+.. autosummary::
+   :toctree: api/
+
+   year
+   month
+   day
+   hour
+   minute
+   second
+   microsecond
+   nanosecond
+   dayofweek
+   weekday
+   dayofyear
+   day_of_year
+   quarter
+   is_month_start
+   is_month_end
+   is_quarter_start
+   is_quarter_end
+   is_year_start
+   is_year_end
+   is_leap_year
+   days_in_month
+
+Datetime methods
+^^^^^^^^^^^^^^^^
+
+.. autosummary::
+   :toctree: api/
+
+   isocalendar
+   strftime
+   round
+   floor
+   ceil
+   tz_localize
+
+
+Timedelta properties
+^^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: cudf.core.series.TimedeltaProperties
+.. autosummary::
+   :toctree: api/
+
+   days
+   seconds
+   microseconds
+   nanoseconds
+   components
+
+.. _api.series.str:
+.. include:: string_handling.rst
+
+
+.. _api.series.cat:
+
+Categorical accessor
+~~~~~~~~~~~~~~~~~~~~
+
+Categorical-dtype specific methods and attributes are available under
+the ``Series.cat`` accessor.
+
+.. currentmodule:: cudf
+.. autosummary::
+   :toctree: api/
+
+   Series.cat
+
+.. currentmodule:: cudf.core.column.categorical.CategoricalAccessor
+.. autosummary::
+   :toctree: api/
+
+   categories
+   ordered
+   codes
+   reorder_categories
+   add_categories
+   remove_categories
+   set_categories
+   as_ordered
+   as_unordered
+
+
+.. _api.series.list:
+.. include:: list_handling.rst
+
+
+.. _api.series.struct:
+.. include:: struct_handling.rst
+
+
+..
+    The following is needed to ensure the generated pages are created with the
+    correct template (otherwise they would be created in the Series/Index class page)
+
+..
+    .. currentmodule:: cudf
+    .. autosummary::
+       :toctree: api/
+       :template: autosummary/accessor.rst
+
+       Series.str
+       Series.cat
+       Series.dt
+       Index.str
+
+
+Serialization / IO / conversion
+-------------------------------
+.. currentmodule:: cudf
+.. autosummary::
+   :toctree: api/
+
+   Series.to_arrow
+   Series.to_cupy
+   Series.to_dict
+   Series.to_dlpack
+   Series.to_frame
+   Series.to_hdf
+   Series.to_json
+   Series.to_numpy
+   Series.to_pandas
+   Series.to_string
+   Series.from_arrow
+   Series.from_categorical
+   Series.from_masked_array
+   Series.from_pandas
+   Series.hash_values
diff --git a/docs/cudf/source/user_guide/api_docs/string_handling.rst b/docs/cudf/source/user_guide/api_docs/string_handling.rst
new file mode 100644
index 0000000..ab0f085
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/string_handling.rst
@@ -0,0 +1,105 @@
+String handling
+~~~~~~~~~~~~~~~
+
+``Series.str`` can be used to access the values of the series as
+strings and apply several methods to it. These can be accessed like
+``Series.str.<function/property>``.
+
+.. currentmodule:: cudf
+.. autosummary::
+   :toctree: api/
+
+   Series.str
+
+.. currentmodule:: cudf.core.column.string.StringMethods
+.. autosummary::
+   :toctree: api/
+
+   byte_count
+   capitalize
+   cat
+   center
+   character_ngrams
+   character_tokenize
+   code_points
+   contains
+   count
+   detokenize
+   edit_distance
+   edit_distance_matrix
+   endswith
+   extract
+   filter_alphanum
+   filter_characters
+   filter_tokens
+   find
+   findall
+   find_multiple
+   get
+   get_json_object
+   hex_to_int
+   htoi
+   index
+   insert
+   ip2int
+   ip_to_int
+   is_consonant
+   is_vowel
+   isalnum
+   isalpha
+   isdecimal
+   isdigit
+   isempty
+   isfloat
+   ishex
+   isinteger
+   isipv4
+   isspace
+   islower
+   isnumeric
+   isupper
+   istimestamp
+   istitle
+   join
+   len
+   like
+   ljust
+   lower
+   lstrip
+   match
+   ngrams
+   ngrams_tokenize
+   normalize_characters
+   normalize_spaces
+   pad
+   partition
+   porter_stemmer_measure
+   repeat
+   removeprefix
+   removesuffix
+   replace
+   replace_tokens
+   replace_with_backrefs
+   rfind
+   rindex
+   rjust
+   rpartition
+   rsplit
+   rstrip
+   slice
+   slice_from
+   slice_replace
+   split
+   rsplit
+   startswith
+   strip
+   swapcase
+   title
+   token_count
+   tokenize
+   translate
+   upper
+   url_decode
+   url_encode
+   wrap
+   zfill
diff --git a/docs/cudf/source/user_guide/api_docs/struct_handling.rst b/docs/cudf/source/user_guide/api_docs/struct_handling.rst
new file mode 100644
index 0000000..336aa73
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/struct_handling.rst
@@ -0,0 +1,19 @@
+Struct handling
+~~~~~~~~~~~~~~~
+
+``Series.struct`` can be used to access the values of the series as
+Structs and apply struct methods to it. These can be accessed like
+``Series.struct.<function/property>``.
+
+.. currentmodule:: cudf
+.. autosummary::
+   :toctree: api/
+
+   Series.struct
+
+.. currentmodule:: cudf.core.column.struct.StructMethods
+.. autosummary::
+   :toctree: api/
+
+   field
+   explode
diff --git a/docs/cudf/source/user_guide/api_docs/subword_tokenize.rst b/docs/cudf/source/user_guide/api_docs/subword_tokenize.rst
new file mode 100644
index 0000000..cd240fe
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/subword_tokenize.rst
@@ -0,0 +1,12 @@
+================
+SubwordTokenizer
+================
+.. currentmodule:: cudf.core.subword_tokenizer
+
+Constructor
+~~~~~~~~~~~
+.. autosummary::
+   :toctree: api/
+
+   SubwordTokenizer
+   SubwordTokenizer.__call__
diff --git a/docs/cudf/source/user_guide/api_docs/window.rst b/docs/cudf/source/user_guide/api_docs/window.rst
new file mode 100644
index 0000000..6d96cc3
--- /dev/null
+++ b/docs/cudf/source/user_guide/api_docs/window.rst
@@ -0,0 +1,25 @@
+.. _api.window:
+
+======
+Window
+======
+
+Rolling objects are returned by ``.rolling`` calls: :func:`cudf.DataFrame.rolling`, :func:`cudf.Series.rolling`, etc.
+
+.. _api.functions_rolling:
+
+Rolling window functions
+------------------------
+.. currentmodule:: cudf.core.window.rolling
+
+.. autosummary::
+   :toctree: api/
+
+   Rolling.count
+   Rolling.sum
+   Rolling.mean
+   Rolling.var
+   Rolling.std
+   Rolling.min
+   Rolling.max
+   Rolling.apply
diff --git a/docs/cudf/source/user_guide/copy-on-write.md b/docs/cudf/source/user_guide/copy-on-write.md
new file mode 100644
index 0000000..fff8caf
--- /dev/null
+++ b/docs/cudf/source/user_guide/copy-on-write.md
@@ -0,0 +1,179 @@
+(copy-on-write-user-doc)=
+
+# Copy-on-write
+
+Copy-on-write is a memory management strategy that allows multiple cuDF objects containing the same data to refer to the same memory address as long as neither of them modify the underlying data.
+With this approach, any operation that generates an unmodified view of an object (such as copies, slices, or methods like `DataFrame.head`) returns a new object that points to the same memory as the original.
+However, when either the existing or new object is _modified_, a copy of the data is made prior to the modification, ensuring that the changes do not propagate between the two objects.
+This behavior is best understood by looking at the examples below.
+
+The default behaviour in cuDF is for copy-on-write to be disabled, so to use it, one must explicitly
+opt in by setting a cuDF option. It is recommended to set the copy-on-write at the beginning of the
+script execution, because when this setting is changed in middle of a script execution there will
+be un-intended behavior where the objects created when copy-on-write is enabled will still have the
+copy-on-write behavior whereas the objects created when copy-on-write is disabled will have
+different behavior.
+
+## Enabling copy-on-write
+
+1. Use `cudf.set_option`:
+
+    ```python
+    >>> import cudf
+    >>> cudf.set_option("copy_on_write", True)
+    ```
+
+2. Set the environment variable ``CUDF_COPY_ON_WRITE`` to ``1`` prior to the
+launch of the Python interpreter:
+
+    ```bash
+    export CUDF_COPY_ON_WRITE="1" python -c "import cudf"
+    ```
+
+## Disabling copy-on-write
+
+
+Copy-on-write can be disabled by setting the ``copy_on_write`` option to ``False``:
+
+```python
+>>> cudf.set_option("copy_on_write", False)
+```
+
+## Making copies
+
+There are no additional changes required in the code to make use of copy-on-write.
+
+```python
+>>> series = cudf.Series([1, 2, 3, 4])
+```
+
+Performing a shallow copy will create a new Series object pointing to the
+same underlying device memory:
+
+```python
+>>> copied_series = series.copy(deep=False)
+>>> series
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+>>> copied_series
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+```
+
+When a write operation is performed on either ``series`` or
+``copied_series``, a true physical copy of the data is created:
+
+```python
+>>> series[0:2] = 10
+>>> series
+0    10
+1    10
+2     3
+3     4
+dtype: int64
+>>> copied_series
+0    1
+1    2
+2    3
+3    4
+dtype: int64
+```
+
+
+## Notes
+
+When copy-on-write is enabled, there is no longer a concept of views when
+slicing or indexing. In this sense, indexing behaves as one would expect for
+built-in Python containers like `lists`, rather than indexing `numpy arrays`.
+Modifying a "view" created by cuDF will always trigger a copy and will not
+modify the original object.
+
+Copy-on-write produces much more consistent copy semantics. Since every object is a copy of the original, users no longer have to think about when modifications may unexpectedly happen in place. This will bring consistency across operations and bring cudf and pandas behavior into alignment when copy-on-write is enabled for both. Here is one example where pandas and cudf are currently inconsistent without copy-on-write enabled:
+
+```python
+
+>>> import pandas as pd
+>>> s = pd.Series([1, 2, 3, 4, 5])
+>>> s1 = s[0:2]
+>>> s1[0] = 10
+>>> s1
+0    10
+1     2
+dtype: int64
+>>> s
+0    10
+1     2
+2     3
+3     4
+4     5
+dtype: int64
+
+>>> import cudf
+>>> s = cudf.Series([1, 2, 3, 4, 5])
+>>> s1 = s[0:2]
+>>> s1[0] = 10
+>>> s1
+0    10
+1     2
+>>> s
+0    1
+1    2
+2    3
+3    4
+4    5
+dtype: int64
+```
+
+The above inconsistency is solved when copy-on-write is enabled:
+
+```python
+>>> import pandas as pd
+>>> pd.set_option("mode.copy_on_write", True)
+>>> s = pd.Series([1, 2, 3, 4, 5])
+>>> s1 = s[0:2]
+>>> s1[0] = 10
+>>> s1
+0    10
+1     2
+dtype: int64
+>>> s
+0    1
+1    2
+2    3
+3    4
+4    5
+dtype: int64
+
+
+>>> import cudf
+>>> cudf.set_option("copy_on_write", True)
+>>> s = cudf.Series([1, 2, 3, 4, 5])
+>>> s1 = s[0:2]
+>>> s1[0] = 10
+>>> s1
+0    10
+1     2
+dtype: int64
+>>> s
+0    1
+1    2
+2    3
+3    4
+4    5
+dtype: int64
+```
+
+
+### Explicit deep and shallow copies comparison
+
+
+|                     | Copy-on-Write enabled                                                                                                                                                                                          | Copy-on-Write disabled (default)                                                                               |
+|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
+| `.copy(deep=True)`  | A true copy is made and changes don't propagate to the original object.                                                                                                                            | A true copy is made and changes don't propagate to the original object.                  |
+| `.copy(deep=False)` | Memory is shared between the two objects and but any write operation on one object will trigger a true physical copy before the write is performed. Hence changes will not propagate to the original object. | Memory is shared between the two objects and changes performed on one will propagate to the other object. |
diff --git a/docs/cudf/source/user_guide/cupy-interop.ipynb b/docs/cudf/source/user_guide/cupy-interop.ipynb
new file mode 100644
index 0000000..c5b1210
--- /dev/null
+++ b/docs/cudf/source/user_guide/cupy-interop.ipynb
@@ -0,0 +1,1429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8e5e6878",
+   "metadata": {},
+   "source": [
+    "# Interoperability between cuDF and CuPy\n",
+    "\n",
+    "This notebook provides introductory examples of how you can use cuDF and CuPy together to take advantage of CuPy array functionality (such as advanced linear algebra operations)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8b2d45c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import timeit\n",
+    "\n",
+    "import cupy as cp\n",
+    "from packaging import version\n",
+    "\n",
+    "import cudf\n",
+    "\n",
+    "if version.parse(cp.__version__) >= version.parse(\"10.0.0\"):\n",
+    "    cupy_from_dlpack = cp.from_dlpack\n",
+    "else:\n",
+    "    cupy_from_dlpack = cp.fromDlpack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7e64b1a",
+   "metadata": {},
+   "source": [
+    "## Converting a cuDF DataFrame to a CuPy Array\n",
+    "\n",
+    "If we want to convert a cuDF DataFrame to a CuPy ndarray, There are multiple ways to do it:\n",
+    "\n",
+    "1. We can use the [dlpack](https://github.com/dmlc/dlpack) interface.\n",
+    "\n",
+    "2. We can also use `DataFrame.values`.\n",
+    "\n",
+    "3. We can also convert via the [CUDA array interface](https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html) by using cuDF's `to_cupy` functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "45c482ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "118 µs ± 77.2 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
+      "360 µs ± 6.04 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "355 µs ± 722 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "nelem = 10000\n",
+    "df = cudf.DataFrame(\n",
+    "    {\n",
+    "        \"a\": range(nelem),\n",
+    "        \"b\": range(500, nelem + 500),\n",
+    "        \"c\": range(1000, nelem + 1000),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "%timeit arr_cupy = cupy_from_dlpack(df.to_dlpack())\n",
+    "%timeit arr_cupy = df.values\n",
+    "%timeit arr_cupy = df.to_cupy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a565effc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[    0,   500,  1000],\n",
+       "       [    1,   501,  1001],\n",
+       "       [    2,   502,  1002],\n",
+       "       ...,\n",
+       "       [ 9997, 10497, 10997],\n",
+       "       [ 9998, 10498, 10998],\n",
+       "       [ 9999, 10499, 10999]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr_cupy = cupy_from_dlpack(df.to_dlpack())\n",
+    "arr_cupy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0759ab29",
+   "metadata": {},
+   "source": [
+    "## Converting a cuDF Series to a CuPy Array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f35ffbd",
+   "metadata": {},
+   "source": [
+    "There are also multiple ways to convert a cuDF Series to a CuPy array:\n",
+    "\n",
+    "1. We can pass the Series to `cupy.asarray` as cuDF Series exposes [`__cuda_array_interface__`](https://docs-cupy.chainer.org/en/stable/reference/interoperability.html).\n",
+    "2. We can leverage the dlpack interface `to_dlpack()`. \n",
+    "3. We can also use `Series.values`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8f97f304",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "54.4 µs ± 66 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
+      "125 µs ± 1.21 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
+      "119 µs ± 805 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "col = \"a\"\n",
+    "\n",
+    "%timeit cola_cupy = cp.asarray(df[col])\n",
+    "%timeit cola_cupy = cupy_from_dlpack(df[col].to_dlpack())\n",
+    "%timeit cola_cupy = df[col].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f96d5676",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   0,    1,    2, ..., 9997, 9998, 9999])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cola_cupy = cp.asarray(df[col])\n",
+    "cola_cupy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c36e5b88",
+   "metadata": {},
+   "source": [
+    "From here, we can proceed with normal CuPy workflows, such as reshaping the array, getting the diagonal, or calculating the norm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2a7ae43f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    1,    2, ...,  197,  198,  199],\n",
+       "       [ 200,  201,  202, ...,  397,  398,  399],\n",
+       "       [ 400,  401,  402, ...,  597,  598,  599],\n",
+       "       ...,\n",
+       "       [9400, 9401, 9402, ..., 9597, 9598, 9599],\n",
+       "       [9600, 9601, 9602, ..., 9797, 9798, 9799],\n",
+       "       [9800, 9801, 9802, ..., 9997, 9998, 9999]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reshaped_arr = cola_cupy.reshape(50, 200)\n",
+    "reshaped_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b442a30c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   0,  201,  402,  603,  804, 1005, 1206, 1407, 1608, 1809, 2010,\n",
+       "       2211, 2412, 2613, 2814, 3015, 3216, 3417, 3618, 3819, 4020, 4221,\n",
+       "       4422, 4623, 4824, 5025, 5226, 5427, 5628, 5829, 6030, 6231, 6432,\n",
+       "       6633, 6834, 7035, 7236, 7437, 7638, 7839, 8040, 8241, 8442, 8643,\n",
+       "       8844, 9045, 9246, 9447, 9648, 9849])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reshaped_arr.diagonal()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "be7f4d32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(577306.967739)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cp.linalg.norm(reshaped_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b353bded",
+   "metadata": {},
+   "source": [
+    "## Converting a CuPy Array to a cuDF DataFrame\n",
+    "\n",
+    "We can also convert a CuPy ndarray to a cuDF DataFrame. Like before, there are multiple ways to do it:\n",
+    "\n",
+    "1. **Easiest;** We can directly use the `DataFrame` constructor.\n",
+    "\n",
+    "2. We can use CUDA array interface with the `DataFrame` constructor.\n",
+    "\n",
+    "3. We can also use the [dlpack](https://github.com/dmlc/dlpack) interface.\n",
+    "\n",
+    "For the latter two cases, we'll need to make sure that our CuPy array is Fortran contiguous in memory (if it's not already). We can either transpose the array or simply coerce it to be Fortran contiguous beforehand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8887b253",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.3 ms ± 33.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit reshaped_df = cudf.DataFrame(reshaped_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "08ec4ffa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>0</th>\n",
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+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
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+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>190</th>\n",
+       "      <th>191</th>\n",
+       "      <th>192</th>\n",
+       "      <th>193</th>\n",
+       "      <th>194</th>\n",
+       "      <th>195</th>\n",
+       "      <th>196</th>\n",
+       "      <th>197</th>\n",
+       "      <th>198</th>\n",
+       "      <th>199</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>190</td>\n",
+       "      <td>191</td>\n",
+       "      <td>192</td>\n",
+       "      <td>193</td>\n",
+       "      <td>194</td>\n",
+       "      <td>195</td>\n",
+       "      <td>196</td>\n",
+       "      <td>197</td>\n",
+       "      <td>198</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>200</td>\n",
+       "      <td>201</td>\n",
+       "      <td>202</td>\n",
+       "      <td>203</td>\n",
+       "      <td>204</td>\n",
+       "      <td>205</td>\n",
+       "      <td>206</td>\n",
+       "      <td>207</td>\n",
+       "      <td>208</td>\n",
+       "      <td>209</td>\n",
+       "      <td>...</td>\n",
+       "      <td>390</td>\n",
+       "      <td>391</td>\n",
+       "      <td>392</td>\n",
+       "      <td>393</td>\n",
+       "      <td>394</td>\n",
+       "      <td>395</td>\n",
+       "      <td>396</td>\n",
+       "      <td>397</td>\n",
+       "      <td>398</td>\n",
+       "      <td>399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>400</td>\n",
+       "      <td>401</td>\n",
+       "      <td>402</td>\n",
+       "      <td>403</td>\n",
+       "      <td>404</td>\n",
+       "      <td>405</td>\n",
+       "      <td>406</td>\n",
+       "      <td>407</td>\n",
+       "      <td>408</td>\n",
+       "      <td>409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>590</td>\n",
+       "      <td>591</td>\n",
+       "      <td>592</td>\n",
+       "      <td>593</td>\n",
+       "      <td>594</td>\n",
+       "      <td>595</td>\n",
+       "      <td>596</td>\n",
+       "      <td>597</td>\n",
+       "      <td>598</td>\n",
+       "      <td>599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>600</td>\n",
+       "      <td>601</td>\n",
+       "      <td>602</td>\n",
+       "      <td>603</td>\n",
+       "      <td>604</td>\n",
+       "      <td>605</td>\n",
+       "      <td>606</td>\n",
+       "      <td>607</td>\n",
+       "      <td>608</td>\n",
+       "      <td>609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>790</td>\n",
+       "      <td>791</td>\n",
+       "      <td>792</td>\n",
+       "      <td>793</td>\n",
+       "      <td>794</td>\n",
+       "      <td>795</td>\n",
+       "      <td>796</td>\n",
+       "      <td>797</td>\n",
+       "      <td>798</td>\n",
+       "      <td>799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>800</td>\n",
+       "      <td>801</td>\n",
+       "      <td>802</td>\n",
+       "      <td>803</td>\n",
+       "      <td>804</td>\n",
+       "      <td>805</td>\n",
+       "      <td>806</td>\n",
+       "      <td>807</td>\n",
+       "      <td>808</td>\n",
+       "      <td>809</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990</td>\n",
+       "      <td>991</td>\n",
+       "      <td>992</td>\n",
+       "      <td>993</td>\n",
+       "      <td>994</td>\n",
+       "      <td>995</td>\n",
+       "      <td>996</td>\n",
+       "      <td>997</td>\n",
+       "      <td>998</td>\n",
+       "      <td>999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 200 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0    1    2    3    4    5    6    7    8    9    ...  190  191  192  193  \\\n",
+       "0    0    1    2    3    4    5    6    7    8    9  ...  190  191  192  193   \n",
+       "1  200  201  202  203  204  205  206  207  208  209  ...  390  391  392  393   \n",
+       "2  400  401  402  403  404  405  406  407  408  409  ...  590  591  592  593   \n",
+       "3  600  601  602  603  604  605  606  607  608  609  ...  790  791  792  793   \n",
+       "4  800  801  802  803  804  805  806  807  808  809  ...  990  991  992  993   \n",
+       "\n",
+       "   194  195  196  197  198  199  \n",
+       "0  194  195  196  197  198  199  \n",
+       "1  394  395  396  397  398  399  \n",
+       "2  594  595  596  597  598  599  \n",
+       "3  794  795  796  797  798  799  \n",
+       "4  994  995  996  997  998  999  \n",
+       "\n",
+       "[5 rows x 200 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reshaped_df = cudf.DataFrame(reshaped_arr)\n",
+    "reshaped_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6804d291",
+   "metadata": {},
+   "source": [
+    "We can check whether our array is Fortran contiguous by using cupy.isfortran or looking at the [flags](https://docs-cupy.chainer.org/en/stable/reference/generated/cupy.ndarray.html#cupy.ndarray.flags) of the array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "65b8bd0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cp.isfortran(reshaped_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "151982ad",
+   "metadata": {},
+   "source": [
+    "In this case, we'll need to convert it before going to a cuDF DataFrame. In the next two cells, we create the DataFrame by leveraging dlpack and the CUDA array interface, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "27b2f563",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.57 ms ± 9.08 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "fortran_arr = cp.asfortranarray(reshaped_arr)\n",
+    "reshaped_df = cudf.DataFrame(fortran_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0a0cc290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.48 ms ± 7.89 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "fortran_arr = cp.asfortranarray(reshaped_arr)\n",
+    "reshaped_df = cudf.from_dlpack(fortran_arr.toDlpack())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0d2c5beb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
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+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>190</th>\n",
+       "      <th>191</th>\n",
+       "      <th>192</th>\n",
+       "      <th>193</th>\n",
+       "      <th>194</th>\n",
+       "      <th>195</th>\n",
+       "      <th>196</th>\n",
+       "      <th>197</th>\n",
+       "      <th>198</th>\n",
+       "      <th>199</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
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+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>190</td>\n",
+       "      <td>191</td>\n",
+       "      <td>192</td>\n",
+       "      <td>193</td>\n",
+       "      <td>194</td>\n",
+       "      <td>195</td>\n",
+       "      <td>196</td>\n",
+       "      <td>197</td>\n",
+       "      <td>198</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>200</td>\n",
+       "      <td>201</td>\n",
+       "      <td>202</td>\n",
+       "      <td>203</td>\n",
+       "      <td>204</td>\n",
+       "      <td>205</td>\n",
+       "      <td>206</td>\n",
+       "      <td>207</td>\n",
+       "      <td>208</td>\n",
+       "      <td>209</td>\n",
+       "      <td>...</td>\n",
+       "      <td>390</td>\n",
+       "      <td>391</td>\n",
+       "      <td>392</td>\n",
+       "      <td>393</td>\n",
+       "      <td>394</td>\n",
+       "      <td>395</td>\n",
+       "      <td>396</td>\n",
+       "      <td>397</td>\n",
+       "      <td>398</td>\n",
+       "      <td>399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>400</td>\n",
+       "      <td>401</td>\n",
+       "      <td>402</td>\n",
+       "      <td>403</td>\n",
+       "      <td>404</td>\n",
+       "      <td>405</td>\n",
+       "      <td>406</td>\n",
+       "      <td>407</td>\n",
+       "      <td>408</td>\n",
+       "      <td>409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>590</td>\n",
+       "      <td>591</td>\n",
+       "      <td>592</td>\n",
+       "      <td>593</td>\n",
+       "      <td>594</td>\n",
+       "      <td>595</td>\n",
+       "      <td>596</td>\n",
+       "      <td>597</td>\n",
+       "      <td>598</td>\n",
+       "      <td>599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>600</td>\n",
+       "      <td>601</td>\n",
+       "      <td>602</td>\n",
+       "      <td>603</td>\n",
+       "      <td>604</td>\n",
+       "      <td>605</td>\n",
+       "      <td>606</td>\n",
+       "      <td>607</td>\n",
+       "      <td>608</td>\n",
+       "      <td>609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>790</td>\n",
+       "      <td>791</td>\n",
+       "      <td>792</td>\n",
+       "      <td>793</td>\n",
+       "      <td>794</td>\n",
+       "      <td>795</td>\n",
+       "      <td>796</td>\n",
+       "      <td>797</td>\n",
+       "      <td>798</td>\n",
+       "      <td>799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>800</td>\n",
+       "      <td>801</td>\n",
+       "      <td>802</td>\n",
+       "      <td>803</td>\n",
+       "      <td>804</td>\n",
+       "      <td>805</td>\n",
+       "      <td>806</td>\n",
+       "      <td>807</td>\n",
+       "      <td>808</td>\n",
+       "      <td>809</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990</td>\n",
+       "      <td>991</td>\n",
+       "      <td>992</td>\n",
+       "      <td>993</td>\n",
+       "      <td>994</td>\n",
+       "      <td>995</td>\n",
+       "      <td>996</td>\n",
+       "      <td>997</td>\n",
+       "      <td>998</td>\n",
+       "      <td>999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 200 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0    1    2    3    4    5    6    7    8    9    ...  190  191  192  193  \\\n",
+       "0    0    1    2    3    4    5    6    7    8    9  ...  190  191  192  193   \n",
+       "1  200  201  202  203  204  205  206  207  208  209  ...  390  391  392  393   \n",
+       "2  400  401  402  403  404  405  406  407  408  409  ...  590  591  592  593   \n",
+       "3  600  601  602  603  604  605  606  607  608  609  ...  790  791  792  793   \n",
+       "4  800  801  802  803  804  805  806  807  808  809  ...  990  991  992  993   \n",
+       "\n",
+       "   194  195  196  197  198  199  \n",
+       "0  194  195  196  197  198  199  \n",
+       "1  394  395  396  397  398  399  \n",
+       "2  594  595  596  597  598  599  \n",
+       "3  794  795  796  797  798  799  \n",
+       "4  994  995  996  997  998  999  \n",
+       "\n",
+       "[5 rows x 200 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fortran_arr = cp.asfortranarray(reshaped_arr)\n",
+    "reshaped_df = cudf.DataFrame(fortran_arr)\n",
+    "reshaped_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "395e2bba",
+   "metadata": {},
+   "source": [
+    "## Converting a CuPy Array to a cuDF Series\n",
+    "\n",
+    "To convert an array to a Series, we can directly pass the array to the `Series` constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d8518208",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      0\n",
+       "1    201\n",
+       "2    402\n",
+       "3    603\n",
+       "4    804\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series(reshaped_arr.diagonal()).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e159619",
+   "metadata": {},
+   "source": [
+    "## Interweaving CuDF and CuPy for Smooth PyData Workflows\n",
+    "\n",
+    "RAPIDS libraries and the entire GPU PyData ecosystem are developing quickly, but sometimes a one library may not have the functionality you need. One example of this might be taking the row-wise sum (or mean) of a Pandas DataFrame. cuDF's support for row-wise operations isn't mature, so you'd need to either transpose the DataFrame or write a UDF and explicitly calculate the sum across each row. Transposing could lead to hundreds of thousands of columns (which cuDF wouldn't perform well with) depending on your data's shape, and writing a UDF can be time intensive.\n",
+    "\n",
+    "By leveraging the interoperability of the GPU PyData ecosystem, this operation becomes very easy. Let's take the row-wise sum of our previously reshaped cuDF DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2bb8ed81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>190</th>\n",
+       "      <th>191</th>\n",
+       "      <th>192</th>\n",
+       "      <th>193</th>\n",
+       "      <th>194</th>\n",
+       "      <th>195</th>\n",
+       "      <th>196</th>\n",
+       "      <th>197</th>\n",
+       "      <th>198</th>\n",
+       "      <th>199</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>190</td>\n",
+       "      <td>191</td>\n",
+       "      <td>192</td>\n",
+       "      <td>193</td>\n",
+       "      <td>194</td>\n",
+       "      <td>195</td>\n",
+       "      <td>196</td>\n",
+       "      <td>197</td>\n",
+       "      <td>198</td>\n",
+       "      <td>199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>200</td>\n",
+       "      <td>201</td>\n",
+       "      <td>202</td>\n",
+       "      <td>203</td>\n",
+       "      <td>204</td>\n",
+       "      <td>205</td>\n",
+       "      <td>206</td>\n",
+       "      <td>207</td>\n",
+       "      <td>208</td>\n",
+       "      <td>209</td>\n",
+       "      <td>...</td>\n",
+       "      <td>390</td>\n",
+       "      <td>391</td>\n",
+       "      <td>392</td>\n",
+       "      <td>393</td>\n",
+       "      <td>394</td>\n",
+       "      <td>395</td>\n",
+       "      <td>396</td>\n",
+       "      <td>397</td>\n",
+       "      <td>398</td>\n",
+       "      <td>399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>400</td>\n",
+       "      <td>401</td>\n",
+       "      <td>402</td>\n",
+       "      <td>403</td>\n",
+       "      <td>404</td>\n",
+       "      <td>405</td>\n",
+       "      <td>406</td>\n",
+       "      <td>407</td>\n",
+       "      <td>408</td>\n",
+       "      <td>409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>590</td>\n",
+       "      <td>591</td>\n",
+       "      <td>592</td>\n",
+       "      <td>593</td>\n",
+       "      <td>594</td>\n",
+       "      <td>595</td>\n",
+       "      <td>596</td>\n",
+       "      <td>597</td>\n",
+       "      <td>598</td>\n",
+       "      <td>599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>600</td>\n",
+       "      <td>601</td>\n",
+       "      <td>602</td>\n",
+       "      <td>603</td>\n",
+       "      <td>604</td>\n",
+       "      <td>605</td>\n",
+       "      <td>606</td>\n",
+       "      <td>607</td>\n",
+       "      <td>608</td>\n",
+       "      <td>609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>790</td>\n",
+       "      <td>791</td>\n",
+       "      <td>792</td>\n",
+       "      <td>793</td>\n",
+       "      <td>794</td>\n",
+       "      <td>795</td>\n",
+       "      <td>796</td>\n",
+       "      <td>797</td>\n",
+       "      <td>798</td>\n",
+       "      <td>799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>800</td>\n",
+       "      <td>801</td>\n",
+       "      <td>802</td>\n",
+       "      <td>803</td>\n",
+       "      <td>804</td>\n",
+       "      <td>805</td>\n",
+       "      <td>806</td>\n",
+       "      <td>807</td>\n",
+       "      <td>808</td>\n",
+       "      <td>809</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990</td>\n",
+       "      <td>991</td>\n",
+       "      <td>992</td>\n",
+       "      <td>993</td>\n",
+       "      <td>994</td>\n",
+       "      <td>995</td>\n",
+       "      <td>996</td>\n",
+       "      <td>997</td>\n",
+       "      <td>998</td>\n",
+       "      <td>999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 200 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0    1    2    3    4    5    6    7    8    9    ...  190  191  192  193  \\\n",
+       "0    0    1    2    3    4    5    6    7    8    9  ...  190  191  192  193   \n",
+       "1  200  201  202  203  204  205  206  207  208  209  ...  390  391  392  393   \n",
+       "2  400  401  402  403  404  405  406  407  408  409  ...  590  591  592  593   \n",
+       "3  600  601  602  603  604  605  606  607  608  609  ...  790  791  792  793   \n",
+       "4  800  801  802  803  804  805  806  807  808  809  ...  990  991  992  993   \n",
+       "\n",
+       "   194  195  196  197  198  199  \n",
+       "0  194  195  196  197  198  199  \n",
+       "1  394  395  396  397  398  399  \n",
+       "2  594  595  596  597  598  599  \n",
+       "3  794  795  796  797  798  799  \n",
+       "4  994  995  996  997  998  999  \n",
+       "\n",
+       "[5 rows x 200 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reshaped_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f3d4e78",
+   "metadata": {},
+   "source": [
+    "We can just transform it into a CuPy array and use the `axis` argument of `sum`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2dde030d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  19900,   59900,   99900,  139900,  179900,  219900,  259900,\n",
+       "        299900,  339900,  379900,  419900,  459900,  499900,  539900,\n",
+       "        579900,  619900,  659900,  699900,  739900,  779900,  819900,\n",
+       "        859900,  899900,  939900,  979900, 1019900, 1059900, 1099900,\n",
+       "       1139900, 1179900, 1219900, 1259900, 1299900, 1339900, 1379900,\n",
+       "       1419900, 1459900, 1499900, 1539900, 1579900, 1619900, 1659900,\n",
+       "       1699900, 1739900, 1779900, 1819900, 1859900, 1899900, 1939900,\n",
+       "       1979900])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_arr = cupy_from_dlpack(reshaped_df.to_dlpack())\n",
+    "new_arr.sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4450dcc3",
+   "metadata": {},
+   "source": [
+    "With just that single line, we're able to seamlessly move between data structures in this ecosystem, giving us enormous flexibility without sacrificing speed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61bfb868",
+   "metadata": {},
+   "source": [
+    "## Converting a cuDF DataFrame to a CuPy Sparse Matrix\n",
+    "\n",
+    "We can also convert a DataFrame or Series to a CuPy sparse matrix. We might want to do this if downstream processes expect CuPy sparse matrices as an input.\n",
+    "\n",
+    "The sparse matrix data structure is defined by three dense arrays. We'll define a small helper function for cleanliness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e531fd15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cudf_to_cupy_sparse_matrix(data, sparseformat=\"column\"):\n",
+    "    \"\"\"Converts a cuDF object to a CuPy Sparse Column matrix.\"\"\"\n",
+    "    if sparseformat not in (\n",
+    "        \"row\",\n",
+    "        \"column\",\n",
+    "    ):\n",
+    "        raise ValueError(\"Let's focus on column and row formats for now.\")\n",
+    "\n",
+    "    _sparse_constructor = cp.sparse.csc_matrix\n",
+    "    if sparseformat == \"row\":\n",
+    "        _sparse_constructor = cp.sparse.csr_matrix\n",
+    "\n",
+    "    return _sparse_constructor(cupy_from_dlpack(data.to_dlpack()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f5e6ade",
+   "metadata": {},
+   "source": [
+    "We can define a sparsely populated DataFrame to illustrate this conversion to either sparse matrix format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "58c7e074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = cudf.DataFrame()\n",
+    "nelem = 10000\n",
+    "nonzero = 1000\n",
+    "for i in range(20):\n",
+    "    arr = cp.random.normal(5, 5, nelem)\n",
+    "    arr[cp.random.choice(arr.shape[0], nelem - nonzero, replace=False)] = 0\n",
+    "    df[\"a\" + str(i)] = arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9265228d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
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+       "      <th>a18</th>\n",
+       "      <th>a19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "    </tr>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>10.869279</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.526274</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a0   a1   a2   a3   a4   a5        a6   a7   a8        a9  a10  a11  a12  \\\n",
+       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.000000  0.0  0.0  0.000000  0.0  0.0  0.0   \n",
+       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.000000  0.0  0.0 -5.241297  0.0  0.0  0.0   \n",
+       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.000000  0.0  0.0  0.000000  0.0  0.0  0.0   \n",
+       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.000000  0.0  0.0  0.000000  0.0  0.0  0.0   \n",
+       "4  0.0  0.0  0.0  0.0  0.0  0.0  2.526274  0.0  0.0  0.000000  0.0  0.0  0.0   \n",
+       "\n",
+       "        a13        a14  a15  a16  a17  a18        a19  \n",
+       "0   0.00000   0.000000  0.0  0.0  0.0  0.0  11.308953  \n",
+       "1  17.58476   0.000000  0.0  0.0  0.0  0.0   0.000000  \n",
+       "2   0.00000   0.000000  0.0  0.0  0.0  0.0   0.000000  \n",
+       "3   0.00000  10.869279  0.0  0.0  0.0  0.0   0.000000  \n",
+       "4   0.00000   0.000000  0.0  0.0  0.0  0.0   0.000000  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5ba1a551",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (770, 0)\t-1.373354548007899\n",
+      "  (771, 0)\t11.641890592020793\n",
+      "  (644, 0)\t-1.4820515981598015\n",
+      "  (773, 0)\t4.374245789758399\n",
+      "  (646, 0)\t4.58071340724814\n",
+      "  (776, 0)\t5.115792716318899\n",
+      "  (649, 0)\t8.676941295251092\n",
+      "  (522, 0)\t-0.11573951593420229\n",
+      "  (396, 0)\t8.124303607236273\n",
+      "  (652, 0)\t9.359339954077681\n",
+      "  (141, 0)\t8.50710863345112\n",
+      "  (272, 0)\t7.440244879175392\n",
+      "  (1042, 0)\t4.286859524587998\n",
+      "  (275, 0)\t-0.6091666840632348\n",
+      "  (787, 0)\t10.124449357828695\n",
+      "  (915, 0)\t11.391560911074649\n",
+      "  (1043, 0)\t11.478396096078907\n",
+      "  (408, 0)\t11.204049991287349\n",
+      "  (536, 0)\t13.239689100708974\n",
+      "  (26, 0)\t4.951917355877771\n",
+      "  (794, 0)\t2.736556006961319\n",
+      "  (539, 0)\t12.553519350929216\n",
+      "  (412, 0)\t2.8682583361020786\n",
+      "  (540, 0)\t-1.2121388231076713\n",
+      "  (796, 0)\t6.986443354019786\n",
+      "  :\t:\n",
+      "  (9087, 19)\t-2.9543770156500395\n",
+      "  (9440, 19)\t3.903613949374532\n",
+      "  (9186, 19)\t0.3141028170017329\n",
+      "  (9571, 19)\t1.7347840594688502\n",
+      "  (9188, 19)\t14.68745562157488\n",
+      "  (9316, 19)\t13.808308442016436\n",
+      "  (9957, 19)\t9.705810918221086\n",
+      "  (9318, 19)\t9.984168186940485\n",
+      "  (9446, 19)\t5.173000114288142\n",
+      "  (9830, 19)\t3.2442816093793607\n",
+      "  (9835, 19)\t5.713078257113576\n",
+      "  (9580, 19)\t5.373437384911853\n",
+      "  (9326, 19)\t10.736403419943093\n",
+      "  (9711, 19)\t-4.003216472911014\n",
+      "  (9200, 19)\t5.560182026578174\n",
+      "  (9844, 19)\t6.17251145210342\n",
+      "  (9333, 19)\t7.085353006324948\n",
+      "  (9208, 19)\t6.789030498520347\n",
+      "  (9464, 19)\t4.314887636528589\n",
+      "  (9720, 19)\t12.446300974563027\n",
+      "  (9594, 19)\t4.317523130615451\n",
+      "  (9722, 19)\t-2.3257161477576336\n",
+      "  (9723, 19)\t1.9288133227037407\n",
+      "  (9469, 19)\t0.268312217498608\n",
+      "  (9599, 19)\t4.100996763787237\n"
+     ]
+    }
+   ],
+   "source": [
+    "sparse_data = cudf_to_cupy_sparse_matrix(df)\n",
+    "print(sparse_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8e58cd5",
+   "metadata": {},
+   "source": [
+    "From here, we could continue our workflow with a CuPy sparse matrix.\n",
+    "\n",
+    "For a full list of the functionality built into these libraries, we encourage you to check out the API docs for [cuDF](https://docs.rapids.ai/api/cudf/nightly/) and [CuPy](https://docs-cupy.chainer.org/en/stable/index.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/cudf/source/user_guide/data-types.md b/docs/cudf/source/user_guide/data-types.md
new file mode 100644
index 0000000..1f4cfbc
--- /dev/null
+++ b/docs/cudf/source/user_guide/data-types.md
@@ -0,0 +1,155 @@
+# Supported Data Types
+
+cuDF supports many data types supported by NumPy and Pandas, including
+numeric, datetime, timedelta, categorical and string data types. We
+also provide special data types for working with decimals, list-like,
+and dictionary-like data.
+
+All data types in cuDF are [nullable](missing-data).
+
+<div class="special-table">
+
+| Kind of data         | Data type(s)                                                                                                                             |
+|----------------------|------------------------------------------------------------------------------------------------------------------------------------------|
+| Signed integer       | `'int8'`, `'int16'`, `'int32'`, `'int64'`                                                                                                |
+| Unsigned integer     | `'uint32'`, `'uint64'`                                                                                                                   |
+| Floating-point       | `'float32'`, `'float64'`                                                                                                                 |
+| Datetime             | `'datetime64[s]'`, `'datetime64[ms]'`, `'datetime64['us']`, `'datetime64[ns]'`                                                           |
+| Timedelta (duration) | `'timedelta[s]'`, `'timedelta[ms]'`, `'timedelta['us']`, `'timedelta[ns]'`                                                               |
+| Category             | {py:class}`~cudf.core.dtypes.CategoricalDtype`                                                                                           |
+| String               | `'object'` or `'string'`                                                                                                                 |
+| Decimal              | {py:class}`~cudf.core.dtypes.Decimal32Dtype`, {py:class}`~cudf.core.dtypes.Decimal64Dtype`, {py:class}`~cudf.core.dtypes.Decimal128Dtype`|
+| List                 | {py:class}`~cudf.core.dtypes.ListDtype`                                                                                                  |
+| Struct               | {py:class}`~cudf.core.dtypes.StructDtype`                                                                                                |
+
+</div>
+
+## NumPy data types
+
+We use NumPy data types for integer, floating, datetime, timedelta,
+and string data types.  Thus, just like in NumPy,
+`np.dtype("float32")`, `np.float32`, and `"float32"` are all acceptable
+ways to specify the `float32` data type:
+
+```python
+>>> import cudf
+>>> s = cudf.Series([1, 2, 3], dtype="float32")
+>>> s
+0    1.0
+1    2.0
+2    3.0
+dtype: float32
+```
+
+## A note on `object`
+
+The data type associated with string data in cuDF is `"np.object"`.
+
+```python
+>>> import cudf
+>>> s = cudf.Series(["abc", "def", "ghi"])
+>>> s.dtype
+dtype("object")
+```
+
+This is for compatibility with Pandas, but it can be misleading. In
+both NumPy and Pandas, `"object"` is the data type associated data
+composed of arbitrary Python objects (not just strings).  However,
+cuDF does not support storing arbitrary Python objects.
+
+## Decimal data types
+
+We provide special data types for working with decimal data, namely
+{py:class}`~cudf.core.dtypes.Decimal32Dtype`,
+{py:class}`~cudf.core.dtypes.Decimal64Dtype`, and
+{py:class}`~cudf.core.dtypes.Decimal128Dtype`.  Use these data types when you
+need to store values with greater precision than allowed by floating-point
+representation.
+
+Decimal data types in cuDF are based on fixed-point representation.  A
+decimal data type is composed of a _precision_ and a _scale_.  The
+precision represents the total number of digits in each value of this
+dtype. For example, the precision associated with the decimal value
+`1.023` is `4`. The scale is the total number of digits to the right
+of the decimal point. The scale associated with the value `1.023` is
+3.
+
+Each decimal data type is associated with a maximum precision:
+
+```python
+>>> cudf.Decimal32Dtype.MAX_PRECISION
+9.0
+>>> cudf.Decimal64Dtype.MAX_PRECISION
+18.0
+>>> cudf.Decimal128Dtype.MAX_PRECISION
+38
+```
+
+One way to create a decimal Series is from values of type [decimal.Decimal][python-decimal].
+
+```python
+>>> from decimal import Decimal
+>>> s = cudf.Series([Decimal("1.01"), Decimal("4.23"), Decimal("0.5")])
+>>> s
+0    1.01
+1    4.23
+2    0.50
+dtype: decimal128
+>>> s.dtype
+Decimal128Dtype(precision=3, scale=2)
+```
+
+Notice the data type of the result: `1.01`, `4.23`, `0.50` can all be
+represented with a precision of at least 3 and a scale of at least 2.
+
+However, the value `1.234` needs a precision of at least 4, and a
+scale of at least 3, and cannot be fully represented using this data
+type:
+
+```python
+>>> s[1] = Decimal("1.234")  # raises an error
+```
+
+## Nested data types (`List` and `Struct`)
+
+{py:class}`~cudf.core.dtypes.ListDtype` and
+{py:class}`~cudf.core.dtypes.StructDtype` are special data types in cuDF for
+working with list-like and dictionary-like data. These are referred to as
+"nested" data types, because they enable you to store a list of lists, or a
+struct of lists, or a struct of list of lists, etc.,
+
+You can create lists and struct Series from existing Pandas Series of
+lists and dictionaries respectively:
+
+```python
+>>> psr = pd.Series([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
+>>> psr
+0 {'a': 1, 'b': 2}
+1 {'a': 3, 'b': 4}
+dtype: object
+>>> gsr = cudf.from_pandas(psr)
+>>> gsr
+0 {'a': 1, 'b': 2}
+1 {'a': 3, 'b': 4}
+dtype: struct
+>>> gsr.dtype
+StructDtype({'a': dtype('int64'), 'b': dtype('int64')})
+```
+
+Or by reading them from disk, using a [file format that supports nested data](io).
+
+```python
+>>> pdf = pd.DataFrame({"a": [[1, 2], [3, 4, 5], [6, 7, 8]]})
+>>> pdf.to_parquet("lists.pq")
+>>> gdf = cudf.read_parquet("lists.pq")
+>>> gdf
+           a
+0     [1, 2]
+1  [3, 4, 5]
+2  [6, 7, 8]
+>>> gdf["a"].dtype
+ListDtype(int64)
+```
+
+[numpy-dtype]: https://numpy.org/doc/stable/reference/arrays.dtypes.html#arrays-dtypes
+[python-decimal]: https://docs.python.org/3/library/decimal.html#decimal.Decimal
diff --git a/docs/cudf/source/user_guide/groupby.md b/docs/cudf/source/user_guide/groupby.md
new file mode 100644
index 0000000..53ff971
--- /dev/null
+++ b/docs/cudf/source/user_guide/groupby.md
@@ -0,0 +1,274 @@
+---
+myst:
+  substitutions:
+    describe: '`describe`'
+---
+
+(basics-groupby)=
+
+# GroupBy
+
+cuDF supports a small (but important) subset of Pandas' [groupby
+API](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html).
+
+## Summary of supported operations
+
+1. Grouping by one or more columns
+2. Basic aggregations such as "sum", "mean", etc.
+3. Quantile aggregation
+4. A "collect" or `list` aggregation for collecting values in a group
+   into lists
+5. Automatic exclusion of columns with unsupported dtypes ("nuisance"
+   columns) when aggregating
+6. Iterating over the groups of a GroupBy object
+7. `GroupBy.groups` API that returns a mapping of group keys to row
+   labels
+8. `GroupBy.apply` API for performing arbitrary operations on each
+   group. Note that this has very limited functionality compared to the
+   equivalent Pandas function. See the section on
+   [apply](#groupby-apply) for more details.
+9. `GroupBy.pipe` similar to
+   [Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#piping-function-calls).
+
+## Grouping
+
+A GroupBy object is created by grouping the values of a `Series` or
+`DataFrame` by one or more columns:
+
+```python
+>>> import cudf
+>>> df = cudf.DataFrame({'a': [1, 1, 1, 2, 2], 'b': [1, 1, 2, 2, 3], 'c': [1, 2, 3, 4, 5]})
+>>> df
+   a  b  c
+0  1  1  1
+1  1  1  2
+2  1  2  3
+3  2  2  4
+4  2  3  5
+>>> gb1 = df.groupby('a')  # grouping by a single column
+>>> gb2 = df.groupby(['a', 'b'])  # grouping by multiple columns
+>>> gb3 = df.groupby(cudf.Series(['a', 'a', 'b', 'b', 'b']))  # grouping by an external column
+```
+
+````{warning}
+Unlike Pandas, cuDF uses `sort=False` by default to achieve better
+performance, which does not guarantee any particular group order in
+the result.
+
+For example:
+
+```python
+>>> df = cudf.DataFrame({'a' : [2, 2, 1], 'b' : [42, 21, 11]})
+>>> df.groupby('a').sum()
+   b
+a
+2  63
+1  11
+>>> df.to_pandas().groupby('a').sum()
+   b
+a
+1  11
+2  63
+```
+
+Setting `sort=True` will produce Pandas-like output, but with some performance penalty:
+
+```python
+>>> df.groupby('a', sort=True).sum()
+   b
+a
+1  11
+2  63
+```
+````
+
+### Grouping by index levels
+
+You can also group by one or more levels of a MultiIndex:
+
+```python
+>>> df = cudf.DataFrame(
+...     {'a': [1, 1, 1, 2, 2], 'b': [1, 1, 2, 2, 3], 'c': [1, 2, 3, 4, 5]}
+... ).set_index(['a', 'b'])
+...
+>>> df.groupby(level='a')
+```
+
+### The `Grouper` object
+
+A `Grouper` can be used to disambiguate between columns and levels
+when they have the same name:
+
+```python
+>>> df
+   b  c
+b
+1  1  1
+1  1  2
+1  2  3
+2  2  4
+2  3  5
+>>> df.groupby('b', level='b')  # ValueError: Cannot specify both by and level
+>>> df.groupby([cudf.Grouper(key='b'), cudf.Grouper(level='b')])  # OK
+```
+
+## Aggregation
+
+Aggregations on groups are supported via the `agg` method:
+
+```python
+>>> df
+   a  b  c
+0  1  1  1
+1  1  1  2
+2  1  2  3
+3  2  2  4
+4  2  3  5
+>>> df.groupby('a').agg('sum')
+   b  c
+a
+1  4  6
+2  5  9
+>>> df.groupby('a').agg({'b': ['sum', 'min'], 'c': 'mean'})
+    b        c
+  sum min mean
+a
+1   4   1  2.0
+2   5   2  4.5
+>>> df.groupby("a").corr(method="pearson")
+          b          c
+a
+1 b  1.000000  0.866025
+  c  0.866025  1.000000
+2 b  1.000000  1.000000
+  c  1.000000  1.000000
+```
+
+The following table summarizes the available aggregations and the types
+that support them:
+
+```{eval-rst}
+.. table::
+    :class: special-table
+
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | Aggregations / dtypes              | Numeric   | Datetime   | String   | Categorical   | List   | Struct   | Interval   | Decimal   |
+    +====================================+===========+============+==========+===============+========+==========+============+===========+
+    | count                              | ✅        | ✅         | ✅       | ✅            |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | size                               | ✅        | ✅         | ✅       | ✅            |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | sum                                | ✅        | ✅         |          |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | idxmin                             | ✅        | ✅         |          |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | idxmax                             | ✅        | ✅         |          |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | min                                | ✅        | ✅         | ✅       |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | max                                | ✅        | ✅         | ✅       |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | mean                               | ✅        | ✅         |          |               |        |          |            |           |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | var                                | ✅        | ✅         |          |               |        |          |            |           |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | std                                | ✅        | ✅         |          |               |        |          |            |           |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | quantile                           | ✅        | ✅         |          |               |        |          |            |           |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | median                             | ✅        | ✅         |          |               |        |          |            |           |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | nunique                            | ✅        | ✅         | ✅       | ✅            |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | nth                                | ✅        | ✅         | ✅       |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | collect                            | ✅        | ✅         | ✅       |               | ✅     |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | unique                             | ✅        | ✅         | ✅       | ✅            |        |          |            |           |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | corr                               | ✅        |            |          |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+    | cov                                | ✅        |            |          |               |        |          |            | ✅        |
+    +------------------------------------+-----------+------------+----------+---------------+--------+----------+------------+-----------+
+```
+
+## GroupBy apply
+
+To apply function on each group, use the `GroupBy.apply()` method:
+
+```python
+>>> df
+   a  b  c
+0  1  1  1
+1  1  1  2
+2  1  2  3
+3  2  2  4
+4  2  3  5
+>>> df.groupby('a').apply(lambda x: x.max() - x.min())
+   a  b  c
+a
+0  0  1  2
+1  0  1  1
+```
+
+### Limitations
+
+- `apply` works by applying the provided function to each group
+  sequentially, and concatenating the results together. **This can be
+  very slow**, especially for a large number of small groups. For a
+  small number of large groups, it can give acceptable performance.
+- The results may not always match Pandas exactly. For example, cuDF
+  may return a `DataFrame` containing a single column where Pandas
+  returns a `Series`. Some post-processing may be required to match
+  Pandas behavior.
+- cuDF does not support some of the exceptional cases that Pandas
+  supports with `apply`, such as calling [describe] inside the
+  callable.
+
+## Transform
+
+The `.transform()` method aggregates per group, and broadcasts the
+result to the group size, resulting in a Series/DataFrame that is of
+the same size as the input Series/DataFrame.
+
+```python
+>>> import cudf
+>>> df = cudf.DataFrame({'a': [2, 1, 1, 2, 2], 'b': [1, 2, 3, 4, 5]})
+>>> df.groupby('a').transform('max')
+   b
+0  5
+1  3
+2  3
+3  5
+4  5
+```
+
+## Rolling window calculations
+
+Use the `GroupBy.rolling()` method to perform rolling window
+calculations on each group:
+
+```python
+>>> df
+   a  b  c
+0  1  1  1
+1  1  1  2
+2  1  2  3
+3  2  2  4
+4  2  3  5
+```
+
+Rolling window sum on each group with a window size of 2:
+
+```python
+>>> df.groupby('a').rolling(2).sum()
+        a     b     c
+a
+1 0  <NA>  <NA>  <NA>
+  1     2     2     3
+  2     2     3     5
+2 3  <NA>  <NA>  <NA>
+  4     4     5     9
+```
+
+[describe]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#flexible-apply
diff --git a/docs/cudf/source/user_guide/guide-to-udfs.ipynb b/docs/cudf/source/user_guide/guide-to-udfs.ipynb
new file mode 100644
index 0000000..1e6d4b3
--- /dev/null
+++ b/docs/cudf/source/user_guide/guide-to-udfs.ipynb
@@ -0,0 +1,2658 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "77149e57",
+   "metadata": {},
+   "source": [
+    "# Overview of User Defined Functions with cuDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0c6b65ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import cudf\n",
+    "from cudf.datasets import randomdata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8826af13",
+   "metadata": {},
+   "source": [
+    "Like many tabular data processing APIs, cuDF provides a range of composable, DataFrame style operators. While out of the box functions are flexible and useful, it is sometimes necessary to write custom code, or user-defined functions (UDFs), that can be applied to rows, columns, and other groupings of the cells making up the DataFrame.\n",
+    "\n",
+    "In conjunction with the broader GPU PyData ecosystem, cuDF provides interfaces to run UDFs on a variety of data structures. Currently, we can only execute UDFs on numeric, boolean, datetime, and timedelta typed data with partial support for strings in some APIs. This guide covers writing and executing UDFs on the following data structures:\n",
+    "\n",
+    "- Series\n",
+    "- DataFrame\n",
+    "- Rolling Windows Series\n",
+    "- Groupby DataFrames\n",
+    "- CuPy NDArrays\n",
+    "- Numba DeviceNDArrays\n",
+    "\n",
+    "It also demonstrates cuDF's default null handling behavior, and how to write UDFs that can interact with null values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32a8f4fb",
+   "metadata": {},
+   "source": [
+    "## Series UDFs\n",
+    "\n",
+    "You can execute UDFs on Series in two ways:\n",
+    "\n",
+    "- Writing a standard python function and using `cudf.Series.apply`\n",
+    "- Writing a Numba kernel and using Numba's `forall` syntax\n",
+    "\n",
+    "Using `apply` or is simpler, but writing a Numba kernel offers the flexibility to build more complex functions (we'll be writing only simple kernels in this guide)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49399a84",
+   "metadata": {},
+   "source": [
+    "### `cudf.Series.apply`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a209ea2",
+   "metadata": {},
+   "source": [
+    "cuDF provides a similar API to `pandas.Series.apply` for applying scalar UDFs to series objects. Here is a very basic example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e28d5b82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a cuDF series\n",
+    "sr = cudf.Series([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48a9fa5e",
+   "metadata": {},
+   "source": [
+    "UDFs destined for `cudf.Series.apply` might look something like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96aeb19f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a scalar function\n",
+    "def f(x):\n",
+    "    return x + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e61d0169",
+   "metadata": {},
+   "source": [
+    "`cudf.Series.apply` is called like `pd.Series.apply` and returns a new `Series` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8ca08834",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2\n",
+       "1    3\n",
+       "2    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sr.apply(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c98dab03",
+   "metadata": {},
+   "source": [
+    "### Functions with Additional Scalar Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aa3df6f",
+   "metadata": {},
+   "source": [
+    "In addition, `cudf.Series.apply` supports `args=` just like pandas, allowing you to write UDFs that accept an arbitrary number of scalar arguments. Here is an example of such a function and it's API call in both pandas and cuDF:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8d156d01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def g(x, const):\n",
+    "    return x + const"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1dee82d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    43\n",
+       "1    44\n",
+       "2    45\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cuDF apply\n",
+    "sr.apply(g, args=(42,))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22739e28",
+   "metadata": {},
+   "source": [
+    "As a final note, `**kwargs` is not yet supported."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afbf33dc",
+   "metadata": {},
+   "source": [
+    "### Nullable Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc06e8c",
+   "metadata": {},
+   "source": [
+    "The null value `NA` an propagates through unary and binary operations. Thus, `NA + 1`, `abs(NA)`, and `NA == NA` all return `NA`. To make this concrete, let's look at the same example from above, this time using nullable data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bda261dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1    <NA>\n",
+       "2       3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a cuDF series with nulls\n",
+    "sr = cudf.Series([1, cudf.NA, 3])\n",
+    "sr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0123ae07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# redefine the same function from above\n",
+    "def f(x):\n",
+    "    return x + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e95868dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       2\n",
+       "1    <NA>\n",
+       "2       4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cuDF result\n",
+    "sr.apply(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97372e15",
+   "metadata": {},
+   "source": [
+    "Often however you want explicit null handling behavior inside the function. cuDF exposes this capability the same way as pandas, by interacting directly with the `NA` singleton object. Here's an example of a function with explicit null handling:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6c65241b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f_null_sensitive(x):\n",
+    "    # do something if the input is null\n",
+    "    if x is cudf.NA:\n",
+    "        return 42\n",
+    "    else:\n",
+    "        return x + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ab0f4dbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     2\n",
+       "1    42\n",
+       "2     4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cuDF result\n",
+    "sr.apply(f_null_sensitive)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdddc4e8",
+   "metadata": {},
+   "source": [
+    "In addition, `cudf.NA` can be returned from a function directly or conditionally. This capability should allow you to implement custom null handling in a wide variety of cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc7c7e67",
+   "metadata": {},
+   "source": [
+    "### String data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81762aea",
+   "metadata": {},
+   "source": [
+    "Experimental support for a subset of string functionality is available for `apply`. The following string operations are currently supported:\n",
+    "\n",
+    "- `str.count`\n",
+    "- `str.startswith`\n",
+    "- `str.endswith`\n",
+    "- `str.find`\n",
+    "- `str.rfind`\n",
+    "- `str.isalnum`\n",
+    "- `str.isdecimal`\n",
+    "- `str.isdigit`\n",
+    "- `str.islower`\n",
+    "- `str.isupper`\n",
+    "- `str.isalpha`\n",
+    "- `str.istitle`\n",
+    "- `str.isspace`\n",
+    "- `==`, `!=`, `>=`, `<=`, `>`, `<` (between two strings)\n",
+    "- `len` (e.g. `len(some_string))`\n",
+    "- `in` (e.g, `'abc' in some_string`)\n",
+    "- `strip`\n",
+    "- `lstrip`\n",
+    "- `rstrip`\n",
+    "- `upper`\n",
+    "- `lower`\n",
+    "- `+` (string concatenation)\n",
+    "- `replace`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d7d1abd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sr = cudf.Series([\"\", \"abc\", \"some_example\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e8538ba0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(st):\n",
+    "    if len(st) > 0:\n",
+    "        if st.startswith(\"a\"):\n",
+    "            return 1\n",
+    "        elif \"example\" in st:\n",
+    "            return 2\n",
+    "        else:\n",
+    "            return -1\n",
+    "    else:\n",
+    "        return 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23524fd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    42\n",
+      "1     1\n",
+      "2     2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = sr.apply(f)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71c4cb83",
+   "metadata": {},
+   "source": [
+    "### String UDF Memory Considerations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e41a82a1",
+   "metadata": {},
+   "source": [
+    "UDFs that create intermediate strings as part of the computation may require memory tuning. An API is provided for convenience to accomplish this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b26ec6dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cudf.core.udf.utils import set_malloc_heap_size\n",
+    "\n",
+    "set_malloc_heap_size(int(2e9))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54cafbc0",
+   "metadata": {},
+   "source": [
+    "### Lower level control with custom `numba` kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00914f2a",
+   "metadata": {},
+   "source": [
+    "In addition to the Series.apply() method for performing custom operations, you can also pass Series objects directly into [CUDA kernels written with Numba](https://numba.readthedocs.io/en/stable/cuda/kernels.html).\n",
+    "Note that this section requires basic CUDA knowledge. Refer to [numba's CUDA documentation](https://numba.readthedocs.io/en/stable/cuda/index.html) for details.\n",
+    "\n",
+    "The easiest way to write a Numba kernel is to use `cuda.grid(1)` to manage thread indices, and then leverage Numba's `forall` method to configure the kernel for us. Below, define a basic multiplication kernel as an example and use `@cuda.jit` to compile it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "732434f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = randomdata(nrows=5, dtypes={\"a\": int, \"b\": int, \"c\": int}, seed=12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4f5997e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import cuda\n",
+    "\n",
+    "\n",
+    "@cuda.jit\n",
+    "def multiply(in_col, out_col, multiplier):\n",
+    "    i = cuda.grid(1)\n",
+    "    if i < in_col.size:  # boundary guard\n",
+    "        out_col[i] = in_col[i] * multiplier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9667a55",
+   "metadata": {},
+   "source": [
+    "This kernel will take an input array, multiply it by a configurable value (supplied at runtime), and store the result in an output array. Notice that we wrapped our logic in an `if` statement. Because we can launch more threads than the size of our array, we need to make sure that we don't use threads with an index that would be out of bounds. Leaving this out can result in undefined behavior.\n",
+    "\n",
+    "To execute our kernel, must pre-allocate an output array and leverage the `forall` method mentioned above. First, we create a Series of all `0.0` in our DataFrame, since we want `float64` output. Next, we run the kernel with `forall`. `forall` requires us to specify our desired number of tasks, so we'll supply in the length of our Series (which we store in `size`). The [__cuda_array_interface__](https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html) is what allows us to directly call our Numba kernel on our Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ea6008a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = len(df[\"a\"])\n",
+    "df[\"e\"] = 0.0\n",
+    "multiply.forall(size)(df[\"a\"], df[\"e\"], 10.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb69909",
+   "metadata": {},
+   "source": [
+    "After calling our kernel, our DataFrame is now populated with the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "183a82ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>963</td>\n",
+       "      <td>1005</td>\n",
+       "      <td>997</td>\n",
+       "      <td>9630.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>977</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>980</td>\n",
+       "      <td>9770.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1048</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>1019</td>\n",
+       "      <td>10480.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1078</td>\n",
+       "      <td>960</td>\n",
+       "      <td>985</td>\n",
+       "      <td>10780.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>979</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1011</td>\n",
+       "      <td>9790.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c        e\n",
+       "0   963  1005   997   9630.0\n",
+       "1   977  1026   980   9770.0\n",
+       "2  1048  1026  1019  10480.0\n",
+       "3  1078   960   985  10780.0\n",
+       "4   979   982  1011   9790.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab9c305e",
+   "metadata": {},
+   "source": [
+    "This API allows a you to theoretically write arbitrary kernel logic, potentially accessing and using elements of the series at arbitrary indices and use them on cuDF data structures. Advanced developers with some CUDA experience can often use this capability to implement iterative transformations, or spot treat problem areas of a data pipeline with a custom kernel that does the same job faster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0acc6ef2",
+   "metadata": {},
+   "source": [
+    "## DataFrame UDFs\n",
+    "\n",
+    "Like `cudf.Series`, there are multiple ways of using UDFs on dataframes, which essentially amount to UDFs that expect multiple columns as input:\n",
+    "\n",
+    "- `cudf.DataFrame.apply`, which functions like `pd.DataFrame.apply` and expects a row udf\n",
+    "- `cudf.DataFrame.apply_rows`, which is a thin wrapper around numba and expects a numba kernel\n",
+    "- `cudf.DataFrame.apply_chunks`, which is similar to `cudf.DataFrame.apply_rows` but offers lower level control."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2102c3ed",
+   "metadata": {},
+   "source": [
+    "### `cudf.DataFrame.apply`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "238bec41",
+   "metadata": {},
+   "source": [
+    "`cudf.DataFrame.apply` is the main entrypoint for UDFs that expect multiple columns as input and produce a single output column. Functions intended to be consumed by this API are written in terms of a \"row\" argument. The \"row\" is considered to be like a dictionary and contains all of the column values at a certain `iloc` in a `DataFrame`. The function can access these values by key within the function, the keys being the column names corresponding to the desired value. Below is an example function that would be used to add column `A` and column `B` together inside a UDF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "73653918",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(row):\n",
+    "    return row[\"A\"] + row[\"B\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5eb32dd",
+   "metadata": {},
+   "source": [
+    "Let's create some very basic toy data containing at least one null."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "077feb75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   A     B\n",
+       "0  1     4\n",
+       "1  2  <NA>\n",
+       "2  3     6"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = cudf.DataFrame({\"A\": [1, 2, 3], \"B\": [4, cudf.NA, 6]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "609a3da5",
+   "metadata": {},
+   "source": [
+    "Finally call the function as you would in pandas - by using a lambda function to map the UDF onto \"rows\" of the DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "091e39e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       5\n",
+       "1    <NA>\n",
+       "2       9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44e54c31",
+   "metadata": {},
+   "source": [
+    "The same function should produce the same result as pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bd345fab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       5\n",
+       "1    <NA>\n",
+       "2       9\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.to_pandas(nullable=True).apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "004fbbba",
+   "metadata": {},
+   "source": [
+    "Notice that Pandas returns `object` dtype - see notes on this in the caveats section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b11c172",
+   "metadata": {},
+   "source": [
+    "Like `cudf.Series.apply`, these functions support generalized null handling. Here's a function that conditionally returns a different value if a certain input is null:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b70f4b3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a\n",
+       "0     1\n",
+       "1  <NA>\n",
+       "2     3"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def f(row):\n",
+    "    x = row[\"a\"]\n",
+    "    if x is cudf.NA:\n",
+    "        return 0\n",
+    "    else:\n",
+    "        return x + 1\n",
+    "\n",
+    "\n",
+    "df = cudf.DataFrame({\"a\": [1, cudf.NA, 3]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0313c8df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2\n",
+       "1    0\n",
+       "2    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "313c77f3",
+   "metadata": {},
+   "source": [
+    "`cudf.NA` can also be directly returned from a function resulting in data that has the correct nulls in the end, just as if it were run in Pandas. For the following data, the last row fulfills the condition that `1 + 3 > 3` and returns `NA` for that row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "96a7952a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  1  2\n",
+       "1  2  1\n",
+       "2  3  1"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def f(row):\n",
+    "    x = row[\"a\"]\n",
+    "    y = row[\"b\"]\n",
+    "    if x + y > 3:\n",
+    "        return cudf.NA\n",
+    "    else:\n",
+    "        return x + y\n",
+    "\n",
+    "\n",
+    "df = cudf.DataFrame({\"a\": [1, 2, 3], \"b\": [2, 1, 1]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e0815f60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       3\n",
+       "1       3\n",
+       "2    <NA>\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9c674f4",
+   "metadata": {},
+   "source": [
+    "Mixed types are allowed, but will return the common type, rather than object as in Pandas. Here's a null aware op between an int and a float column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "495efd14",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3.14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a     b\n",
+       "0  1   0.5\n",
+       "1  2  <NA>\n",
+       "2  3  3.14"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def f(row):\n",
+    "    return row[\"a\"] + row[\"b\"]\n",
+    "\n",
+    "\n",
+    "df = cudf.DataFrame({\"a\": [1, 2, 3], \"b\": [0.5, cudf.NA, 3.14]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "678b0b5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     1.5\n",
+       "1    <NA>\n",
+       "2    6.14\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce0897c0",
+   "metadata": {},
+   "source": [
+    "Functions may also return scalar values, however the result will be promoted to a safe type regardless of the data. This means even if you have a function like:\n",
+    "\n",
+    "```python\n",
+    "def f(x):\n",
+    "    if x > 1000:\n",
+    "        return 1.5\n",
+    "    else:\n",
+    "        return 2\n",
+    "```\n",
+    "And your data is:\n",
+    "```python\n",
+    "[1,2,3,4,5]\n",
+    "```\n",
+    "You will get floats in the final data even though a float is never returned. This is because Numba ultimately needs to produce one function that can handle any data, which means if there's any possibility a float could result, you must always assume it will happen. Here's an example of a function that returns a scalar in some cases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "acf48d56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a\n",
+       "0  1\n",
+       "1  3\n",
+       "2  5"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def f(row):\n",
+    "    x = row[\"a\"]\n",
+    "    if x > 3:\n",
+    "        return x\n",
+    "    else:\n",
+    "        return 1.5\n",
+    "\n",
+    "\n",
+    "df = cudf.DataFrame({\"a\": [1, 3, 5]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "78a98172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1.5\n",
+       "1    1.5\n",
+       "2    5.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ceaece4",
+   "metadata": {},
+   "source": [
+    "Any number of columns and many arithmetic operators are supported, allowing for complex UDFs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "142c30a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b     c  d  e\n",
+       "0  1  4  <NA>  8  7\n",
+       "1  2  5     4  7  1\n",
+       "2  3  6     4  8  6"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def f(row):\n",
+    "    return row[\"a\"] + (row[\"b\"] - (row[\"c\"] / row[\"d\"])) % row[\"e\"]\n",
+    "\n",
+    "\n",
+    "df = cudf.DataFrame(\n",
+    "    {\n",
+    "        \"a\": [1, 2, 3],\n",
+    "        \"b\": [4, 5, 6],\n",
+    "        \"c\": [cudf.NA, 4, 4],\n",
+    "        \"d\": [8, 7, 8],\n",
+    "        \"e\": [7, 1, 6],\n",
+    "    }\n",
+    ")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "fee9198a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0           <NA>\n",
+       "1    2.428571429\n",
+       "2            8.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.apply(f, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71e30d33",
+   "metadata": {},
+   "source": [
+    "### String Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a3694ea",
+   "metadata": {},
+   "source": [
+    "String data may be used inside `DataFrame.apply` UDFs, subject to the same constraints as those for `Series.apply`. See the section on string handling for `Series` UDFs above for details. Below is a simple example extending the row UDF logic from above in the case of a string column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "cccd59f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>str_col</th>\n",
+       "      <th>scale</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>abc</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ABC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Example</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   str_col  scale\n",
+       "0      abc      1\n",
+       "1      ABC      2\n",
+       "2  Example      3"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str_df = cudf.DataFrame(\n",
+    "    {\"str_col\": [\"abc\", \"ABC\", \"Example\"], \"scale\": [1, 2, 3]}\n",
+    ")\n",
+    "str_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "35737fd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(row):\n",
+    "    st = row[\"str_col\"]\n",
+    "    scale = row[\"scale\"]\n",
+    "\n",
+    "    if len(st) > 5:\n",
+    "        return len(st) + scale\n",
+    "    else:\n",
+    "        return len(st)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4ede4d5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     3\n",
+      "1     3\n",
+      "2    10\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = str_df.apply(f, axis=1)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c587bd2",
+   "metadata": {},
+   "source": [
+    "### Numba kernels for DataFrames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adc6a459",
+   "metadata": {},
+   "source": [
+    "We could apply a UDF on a DataFrame like we did above with `forall`. We'd need to write a kernel that expects multiple inputs, and pass multiple Series as arguments when we execute our kernel. Because this is fairly common and can be difficult to manage, cuDF provides two APIs to streamline this: `apply_rows` and `apply_chunks`. Below, we walk through an example of using `apply_rows`. `apply_chunks` works in a similar way, but also offers more control over low-level kernel behavior.\n",
+    "\n",
+    "Now that we have two numeric columns in our DataFrame, let's write a kernel that uses both of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "90cbcd85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def conditional_add(x, y, out):\n",
+    "    for i, (a, e) in enumerate(zip(x, y)):\n",
+    "        if a > 0:\n",
+    "            out[i] = a + e\n",
+    "        else:\n",
+    "            out[i] = a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bce045f2",
+   "metadata": {},
+   "source": [
+    "Notice that we need to `enumerate` through our `zipped` function arguments (which either match or are mapped to our input column names). We can pass this kernel to `apply_rows`. We'll need to specify a few arguments:\n",
+    "- incols\n",
+    "    - A list of names of input columns that match the function arguments. Or, a dictionary mapping input column names to their corresponding function arguments such as `{'col1': 'arg1'}`.\n",
+    "- outcols\n",
+    "    - A dictionary defining our output column names and their data types. These names must match our function arguments.\n",
+    "- kwargs (optional)\n",
+    "    - We can optionally pass keyword arguments as a dictionary. Since we don't need any, we pass an empty one.\n",
+    "    \n",
+    "While it looks like our function is looping sequentially through our columns, it actually executes in parallel in multiple threads on the GPU. This parallelism is the heart of GPU-accelerated computing. With that background, we're ready to use our UDF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e782daff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "      <th>e</th>\n",
+       "      <th>out</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b     c  d  e  out\n",
+       "0  1  4  <NA>  8  7  8.0\n",
+       "1  2  5     4  7  1  3.0\n",
+       "2  3  6     4  8  6  9.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.apply_rows(\n",
+    "    conditional_add,\n",
+    "    incols={\"a\": \"x\", \"e\": \"y\"},\n",
+    "    outcols={\"out\": np.float64},\n",
+    "    kwargs={},\n",
+    ")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b838b89",
+   "metadata": {},
+   "source": [
+    "As expected, we see our conditional addition worked. At this point, we've successfully executed UDFs on the core data structures of cuDF."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fca97003",
+   "metadata": {},
+   "source": [
+    "### Null Handling in `apply_rows` and `apply_chunks`\n",
+    "\n",
+    "By default, DataFrame methods for applying UDFs like `apply_rows` will handle nulls pessimistically (all rows with a null value will be removed from the output if they are used in the kernel). Exploring how not handling not pessimistically can lead to undefined behavior is outside the scope of this guide. Suffice it to say, pessimistic null handling is the safe and consistent approach. You can see an example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "befd8333",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>963</td>\n",
+       "      <td>1005</td>\n",
+       "      <td>997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>977</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>1019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1078</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>979</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1011</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "0   963  1005   997\n",
+       "1   977  1026  <NA>\n",
+       "2  <NA>  1026  1019\n",
+       "3  1078  <NA>   985\n",
+       "4   979   982  1011"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def gpu_add(a, b, out):\n",
+    "    for i, (x, y) in enumerate(zip(a, b)):\n",
+    "        out[i] = x + y\n",
+    "\n",
+    "\n",
+    "df = randomdata(nrows=5, dtypes={\"a\": int, \"b\": int, \"c\": int}, seed=12)\n",
+    "df.loc[2, \"a\"] = None\n",
+    "df.loc[3, \"b\"] = None\n",
+    "df.loc[1, \"c\"] = None\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c710ce86",
+   "metadata": {},
+   "source": [
+    "In the dataframe above, there are three null values. Each column has a null in a different row. When we use our UDF with `apply_rows`, our output should have two nulls due to pessimistic null handling (because we're not using column `c`, the null value there does not matter to us)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d1f3dcaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>out</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>963</td>\n",
+       "      <td>1005</td>\n",
+       "      <td>997</td>\n",
+       "      <td>1968.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>977</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2003.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>1019</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1078</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>985</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>979</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1011</td>\n",
+       "      <td>1961.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c     out\n",
+       "0   963  1005   997  1968.0\n",
+       "1   977  1026  <NA>  2003.0\n",
+       "2  <NA>  1026  1019    <NA>\n",
+       "3  1078  <NA>   985    <NA>\n",
+       "4   979   982  1011  1961.0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.apply_rows(\n",
+    "    gpu_add, incols=[\"a\", \"b\"], outcols={\"out\": np.float64}, kwargs={}\n",
+    ")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53b9a2f8",
+   "metadata": {},
+   "source": [
+    "As expected, we end up with two nulls in our output. The null values from the columns we used propagated to our output, but the null from the column we ignored did not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bbefa67",
+   "metadata": {},
+   "source": [
+    "## Rolling Window UDFs\n",
+    "\n",
+    "For time-series data, we may need to operate on a small \\\"window\\\" of our column at a time, processing each portion independently. We could slide (\\\"roll\\\") this window over the entire column to answer questions like \\\"What is the 3-day moving average of a stock price over the past year?\"\n",
+    "\n",
+    "We can apply more complex functions to rolling windows to `rolling` Series and DataFrames using `apply`. This example is adapted from cuDF's [API documentation](https://docs.rapids.ai/api/cudf/stable/api_docs/api/cudf.DataFrame.rolling.html). First, we'll create an example Series and then create a `rolling` object from the Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6bc6aea3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    16.0\n",
+       "1    25.0\n",
+       "2    36.0\n",
+       "3    49.0\n",
+       "4    64.0\n",
+       "5    81.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser = cudf.Series([16, 25, 36, 49, 64, 81], dtype=\"float64\")\n",
+    "ser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a4c31df1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Rolling [window=3,min_periods=3,center=False]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rolling = ser.rolling(window=3, min_periods=3, center=False)\n",
+    "rolling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff40d863",
+   "metadata": {},
+   "source": [
+    "Next, we'll define a function to use on our rolling windows. We created this one to highlight how you can include things like loops, mathematical functions, and conditionals. Rolling window UDFs do not yet support null values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "eb5a081b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "\n",
+    "def example_func(window):\n",
+    "    b = 0\n",
+    "    for a in window:\n",
+    "        b = max(b, math.sqrt(a))\n",
+    "    if b == 8:\n",
+    "        return 100\n",
+    "    return b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df8ba31d",
+   "metadata": {},
+   "source": [
+    "We can execute the function by passing it to `apply`. With `window=3`, `min_periods=3`, and `center=False`, our first two values are `null`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ddec3263",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     <NA>\n",
+       "1     <NA>\n",
+       "2      6.0\n",
+       "3      7.0\n",
+       "4    100.0\n",
+       "5      9.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rolling.apply(example_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "187478db",
+   "metadata": {},
+   "source": [
+    "We can apply this function to every column in a DataFrame, too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "8b61094a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>55.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56.0</td>\n",
+       "      <td>56.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>58.0</td>\n",
+       "      <td>58.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>59.0</td>\n",
+       "      <td>59.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b\n",
+       "0  55.0  55.0\n",
+       "1  56.0  56.0\n",
+       "2  57.0  57.0\n",
+       "3  58.0  58.0\n",
+       "4  59.0  59.0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = cudf.DataFrame()\n",
+    "df2[\"a\"] = np.arange(55, 65, dtype=\"float64\")\n",
+    "df2[\"b\"] = np.arange(55, 65, dtype=\"float64\")\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "bb8c3019",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.549834435</td>\n",
+       "      <td>7.549834435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.615773106</td>\n",
+       "      <td>7.615773106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.681145748</td>\n",
+       "      <td>7.681145748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.745966692</td>\n",
+       "      <td>7.745966692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.810249676</td>\n",
+       "      <td>7.810249676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.874007874</td>\n",
+       "      <td>7.874007874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.937253933</td>\n",
+       "      <td>7.937253933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             a            b\n",
+       "0         <NA>         <NA>\n",
+       "1         <NA>         <NA>\n",
+       "2  7.549834435  7.549834435\n",
+       "3  7.615773106  7.615773106\n",
+       "4  7.681145748  7.681145748\n",
+       "5  7.745966692  7.745966692\n",
+       "6  7.810249676  7.810249676\n",
+       "7  7.874007874  7.874007874\n",
+       "8  7.937253933  7.937253933\n",
+       "9        100.0        100.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rolling = df2.rolling(window=3, min_periods=3, center=False)\n",
+    "rolling.apply(example_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4785060",
+   "metadata": {},
+   "source": [
+    "## GroupBy DataFrame UDFs\n",
+    "\n",
+    "We can also apply UDFs to grouped DataFrames using `apply_grouped`.\n",
+    "\n",
+    "First, we'll group our DataFrame based on column `b`, which is either True or False."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "3dc272ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.691674</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Dan</td>\n",
+       "      <td>-0.958380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.480099</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Bob</td>\n",
+       "      <td>-0.729580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.473370</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Xavier</td>\n",
+       "      <td>-0.767454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.067479</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Alice</td>\n",
+       "      <td>-0.380205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.970850</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>0.342905</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a      b       c         e\n",
+       "0 -0.691674   True     Dan -0.958380\n",
+       "1  0.480099  False     Bob -0.729580\n",
+       "2 -0.473370   True  Xavier -0.767454\n",
+       "3  0.067479   True   Alice -0.380205\n",
+       "4 -0.970850  False   Sarah  0.342905"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = randomdata(\n",
+    "    nrows=10, dtypes={\"a\": float, \"b\": bool, \"c\": str, \"e\": float}, seed=12\n",
+    ")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "c0578e0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped = df.groupby([\"b\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4808726f",
+   "metadata": {},
+   "source": [
+    "Next we'll define a function to apply to each group independently. In this case, we'll take the rolling average of column `e`, and call that new column `rolling_avg_e`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "19f0f7fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rolling_avg(e, rolling_avg_e):\n",
+    "    win_size = 3\n",
+    "    for i in range(cuda.threadIdx.x, len(e), cuda.blockDim.x):\n",
+    "        if i < win_size - 1:\n",
+    "            # If there is not enough data to fill the window,\n",
+    "            # take the average to be NaN\n",
+    "            rolling_avg_e[i] = np.nan\n",
+    "        else:\n",
+    "            total = 0\n",
+    "            for j in range(i - win_size + 1, i + 1):\n",
+    "                total += e[j]\n",
+    "            rolling_avg_e[i] = total / win_size"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7566f359",
+   "metadata": {},
+   "source": [
+    "We can execute this with a very similar API to `apply_rows`. This time, though, it's going to execute independently for each group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c43426c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>e</th>\n",
+       "      <th>rolling_avg_e</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.480099</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Bob</td>\n",
+       "      <td>-0.729580</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.970850</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>0.342905</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.801430</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>0.632337</td>\n",
+       "      <td>0.081887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.933157</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Quinn</td>\n",
+       "      <td>-0.420826</td>\n",
+       "      <td>0.184805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.691674</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Dan</td>\n",
+       "      <td>-0.958380</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.473370</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Xavier</td>\n",
+       "      <td>-0.767454</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.067479</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Alice</td>\n",
+       "      <td>-0.380205</td>\n",
+       "      <td>-0.702013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.837494</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Wendy</td>\n",
+       "      <td>-0.057540</td>\n",
+       "      <td>-0.401733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.913899</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Ursula</td>\n",
+       "      <td>0.466252</td>\n",
+       "      <td>0.009502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-0.725581</td>\n",
+       "      <td>True</td>\n",
+       "      <td>George</td>\n",
+       "      <td>0.405245</td>\n",
+       "      <td>0.271319</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          a      b       c         e  rolling_avg_e\n",
+       "1  0.480099  False     Bob -0.729580            NaN\n",
+       "4 -0.970850  False   Sarah  0.342905            NaN\n",
+       "6  0.801430  False   Sarah  0.632337       0.081887\n",
+       "7 -0.933157  False   Quinn -0.420826       0.184805\n",
+       "0 -0.691674   True     Dan -0.958380            NaN\n",
+       "2 -0.473370   True  Xavier -0.767454            NaN\n",
+       "3  0.067479   True   Alice -0.380205      -0.702013\n",
+       "5  0.837494   True   Wendy -0.057540      -0.401733\n",
+       "8  0.913899   True  Ursula  0.466252       0.009502\n",
+       "9 -0.725581   True  George  0.405245       0.271319"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = grouped.apply_grouped(\n",
+    "    rolling_avg, incols=[\"e\"], outcols=dict(rolling_avg_e=np.float64)\n",
+    ")\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8511306",
+   "metadata": {},
+   "source": [
+    "Notice how, with a window size of three in the kernel, the first two values in each group for our output column are null."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0060678c",
+   "metadata": {},
+   "source": [
+    "## Numba Kernels on CuPy Arrays\n",
+    "\n",
+    "We can also execute Numba kernels on CuPy NDArrays, again thanks to the `__cuda_array_interface__`. We can even run the same UDF on the Series and the CuPy array. First, we define a Series and then create a CuPy array from that Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "aa6a8509",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3.,  4., 10.])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import cupy as cp\n",
+    "\n",
+    "s = cudf.Series([1.0, 2, 3, 4, 10])\n",
+    "arr = cp.asarray(s)\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fed556f",
+   "metadata": {},
+   "source": [
+    "Next, we define a UDF and execute it on our Series. We need to allocate a Series of the same size for our output, which we'll call `out`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "0bb8bf93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     5\n",
+       "1    10\n",
+       "2    15\n",
+       "3    20\n",
+       "4    50\n",
+       "dtype: int32"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "@cuda.jit\n",
+    "def multiply_by_5(x, out):\n",
+    "    i = cuda.grid(1)\n",
+    "    if i < x.size:\n",
+    "        out[i] = x[i] * 5\n",
+    "\n",
+    "\n",
+    "out = cudf.Series(cp.zeros(len(s), dtype=\"int32\"))\n",
+    "multiply_by_5.forall(s.shape[0])(s, out)\n",
+    "out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a857b169",
+   "metadata": {},
+   "source": [
+    "Finally, we execute the same function on our array. We allocate an empty array `out` to store our results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "ce60b639",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 5., 10., 15., 20., 50.])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out = cp.empty_like(arr)\n",
+    "multiply_by_5.forall(arr.size)(arr, out)\n",
+    "out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b899d51c",
+   "metadata": {},
+   "source": [
+    "## Caveats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7eb68b",
+   "metadata": {},
+   "source": [
+    "- UDFs are currently only supported for numeric nondecimal scalar types (full support) and strings in `Series.apply` and `DataFrame.apply` (partial support, subject to the caveats outlined above). Attempting to use this API with unsupported types will raise a `TypeError`.\n",
+    "- We do not yet fully support all arithmetic operators. Certain ops like bitwise operations are not currently implemented, but planned in future releases. If an operator is needed, a github issue should be raised so that it can be properly prioritized and implemented."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c690563b",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This guide has covered a lot of content. At this point, you should hopefully feel comfortable writing UDFs (with or without null values) that operate on\n",
+    "\n",
+    "- Series\n",
+    "- DataFrame\n",
+    "- Rolling Windows\n",
+    "- GroupBy DataFrames\n",
+    "- CuPy NDArrays\n",
+    "- Numba DeviceNDArrays\n",
+    "- Generalized NA UDFs\n",
+    "- String UDFs\n",
+    "\n",
+    "\n",
+    "For more information please see the [cuDF](https://docs.rapids.ai/api/cudf/nightly/), [Numba.cuda](https://numba.readthedocs.io/en/stable/cuda/index.html), and [CuPy](https://docs-cupy.chainer.org/en/stable/) documentation."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/cudf/source/user_guide/index.md b/docs/cudf/source/user_guide/index.md
new file mode 100644
index 0000000..1270976
--- /dev/null
+++ b/docs/cudf/source/user_guide/index.md
@@ -0,0 +1,19 @@
+# cuDF User Guide
+
+```{toctree}
+:maxdepth: 2
+
+api_docs/index
+10min
+pandas-comparison
+data-types
+io/index
+missing-data
+groupby
+guide-to-udfs
+cupy-interop
+options
+performance-comparisons/index
+PandasCompat
+copy-on-write
+```
diff --git a/docs/cudf/source/user_guide/io/index.md b/docs/cudf/source/user_guide/io/index.md
new file mode 100644
index 0000000..111224e
--- /dev/null
+++ b/docs/cudf/source/user_guide/io/index.md
@@ -0,0 +1,9 @@
+# Input / Output
+
+```{toctree}
+:maxdepth: 2
+
+
+io
+read-json
+```
diff --git a/docs/cudf/source/user_guide/io/io.md b/docs/cudf/source/user_guide/io/io.md
new file mode 100644
index 0000000..adcdaa5
--- /dev/null
+++ b/docs/cudf/source/user_guide/io/io.md
@@ -0,0 +1,180 @@
+# Input / Output
+
+This page contains Input / Output related APIs in cuDF.
+
+## I/O Supported dtypes
+
+The following table lists are compatible cudf types for each supported
+IO format.
+
+<div class="special-table-wrapper" style="overflow:auto">
+
+```{eval-rst}
+.. table::
+    :class: io-supported-types-table special-table
+    :widths: 15 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
+
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+-------------------+--------+--------+---------+---------+
+    |                       |       CSV       |      Parquet    |       JSON       |       ORC       |  AVRO  |        HDF        |       DLPack    |      Feather      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | Data Type             | Writer | Reader | Writer | Reader | Writer¹ | Reader | Writer | Reader | Reader | Writer² | Reader² | Writer | Reader | Writer² | Reader² |
+    +=======================+========+========+========+========+=========+========+========+========+========+=========+=========+========+========+=========+=========+
+    | int8                  | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | int16                 | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | int32                 | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | int64                 | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | uint8                 | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ✅     | ❌     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | uint16                | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ✅     | ❌     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | uint32                | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ✅     | ❌     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | uint64                | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ❌     | ❌     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | float32               | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | float64               | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | bool                  | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ✅     | ✅     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | str                   | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | category              | ✅     | ❌     | ❌     | ❌     | ❌      | ❌     | ❌     | ❌     | ❌     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | list                  | ❌     | ❌     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ❌     | ❌      | ❌      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | timedelta64[s]        | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ❌     | ❌     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | timedelta64[ms]       | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ❌     | ❌     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | timedelta64[us]       | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ❌     | ❌     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | timedelta64[ns]       | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ❌     | ❌     | ❌     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | datetime64[s]         | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | datetime64[ms]        | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | datetime64[us]        | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | datetime64[ns]        | ✅     | ✅     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ✅     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | struct                | ❌     | ❌     | ✅     | ✅     | ✅      | ✅     | ✅     | ✅     | ❌     | ✅      | ✅      | ❌     | ❌     | ✅      | ✅      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | decimal32             | ✅     | ✅     | ✅     | ✅     | ✅      | ❌     | ✅     | ✅     | ❌     | ❌      | ❌      | ❌     | ❌     | ❌      | ❌      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | decimal64             | ✅     | ✅     | ✅     | ✅     | ✅      | ❌     | ✅     | ✅     | ❌     | ❌      | ❌      | ❌     | ❌     | ❌      | ❌      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+    | decimal128            | ✅     | ✅     | ✅     | ✅     | ✅      | ❌     | ✅     | ✅     | ❌     | ❌      | ❌      | ❌     | ❌     | ❌      | ❌      |
+    +-----------------------+--------+--------+--------+--------+---------+--------+--------+--------+--------+---------+---------+--------+--------+---------+---------+
+```
+
+</div>
+
+
+**Notes:**
+
+- \[¹\] - Not all orientations are GPU-accelerated.
+- \[²\] - Not GPU-accelerated.
+
+## Magnum IO GPUDirect Storage Integration
+
+Many IO APIs can use Magnum IO GPUDirect Storage (GDS) library to optimize
+IO operations.  GDS enables a direct data path for direct memory access
+(DMA) transfers between GPU memory and storage, which avoids a bounce
+buffer through the CPU.  GDS also has a compatibility mode that allows
+the library to fall back to copying through a CPU bounce buffer. The
+SDK is available for download
+[here](https://developer.nvidia.com/gpudirect-storage). GDS is also
+included in CUDA Toolkit 11.4 and higher.
+
+Use of GPUDirect Storage in cuDF is enabled by default, but can be
+disabled through the environment variable `LIBCUDF_CUFILE_POLICY`.
+This variable also controls the GDS compatibility mode.
+
+There are four valid values for the environment variable:
+
+- "GDS": Enable GDS use; GDS compatibility mode is *off*.
+- "ALWAYS": Enable GDS use; GDS compatibility mode is *on*.
+- "KVIKIO": Enable GDS through [KvikIO](https://github.com/rapidsai/kvikio).
+- "OFF": Completely disable GDS use.
+
+If no value is set, behavior will be the same as the "KVIKIO" option.
+
+This environment variable also affects how cuDF treats GDS errors.
+
+- When `LIBCUDF_CUFILE_POLICY` is set to "GDS" and a GDS API call
+  fails for any reason, cuDF falls back to the internal implementation
+  with bounce buffers.
+- When `LIBCUDF_CUFILE_POLICY` is set to "ALWAYS" and a GDS API call
+fails for any reason (unlikely, given that the compatibility mode is
+on), cuDF throws an exception to propagate the error to the user.
+- When `LIBCUDF_CUFILE_POLICY` is set to "KVIKIO" and a KvikIO API
+  call fails for any reason (unlikely, given that KvikIO implements
+  its own compatibility mode) cuDF throws an exception to propagate
+  the error to the user.
+
+For more information about error handling, compatibility mode, and
+tuning parameters in KvikIO see: <https://github.com/rapidsai/kvikio>
+
+Operations that support the use of GPUDirect Storage:
+
+- {py:func}`cudf.read_avro`
+- {py:func}`cudf.read_json`
+- {py:func}`cudf.read_parquet`
+- {py:func}`cudf.read_orc`
+- {py:meth}`cudf.DataFrame.to_csv`
+- {py:func}`cudf.DataFrame.to_json`
+- {py:meth}`cudf.DataFrame.to_parquet`
+- {py:meth}`cudf.DataFrame.to_orc`
+
+Several parameters that can be used to tune the performance of
+GDS-enabled I/O are exposed through environment variables:
+
+- `LIBCUDF_CUFILE_THREAD_COUNT`: Integral value, maximum number of
+  parallel reads/writes per file (default 16);
+- `LIBCUDF_CUFILE_SLICE_SIZE`: Integral value, maximum size of each
+  GDS read/write, in bytes (default 4MB).  Larger I/O operations are
+  split into multiple calls.
+
+## nvCOMP Integration
+
+Some types of compression/decompression can be performed using either
+the [nvCOMP library](https://github.com/NVIDIA/nvcomp) or the internal
+implementation.
+
+Which implementation is used by default depends on the data format and
+the compression type.  Behavior can be influenced through environment
+variable `LIBCUDF_NVCOMP_POLICY`.
+
+There are three valid values for the environment variable:
+
+- "STABLE": Only enable the nvCOMP in places where it has been deemed
+  stable for production use.
+- "ALWAYS": Enable all available uses of nvCOMP, including new,
+  experimental combinations.
+- "OFF": Disable nvCOMP use whenever possible and use the internal
+  implementations instead.
+
+If no value is set, behavior will be the same as the "STABLE" option.
+
+```{eval-rst}
+.. table:: Current policy for nvCOMP use for different types
+    :widths: 20 20 20 20 20 20 20 20 20 20
+
+    +-----------------------+--------+--------+--------------+--------------+---------+--------+--------------+--------------+--------+
+    |                       |       CSV       |            Parquet          |       JSON       |             ORC             |  AVRO  |
+    +-----------------------+--------+--------+--------------+--------------+---------+--------+--------------+--------------+--------+
+    | Compression Type      | Writer | Reader |    Writer    |    Reader    | Writer¹ | Reader |    Writer    |    Reader    | Reader |
+    +=======================+========+========+==============+==============+=========+========+==============+==============+========+
+    | Snappy                | ❌     | ❌     | Stable       | Stable       | ❌      | ❌     | Stable       | Stable       | ❌     |
+    +-----------------------+--------+--------+--------------+--------------+---------+--------+--------------+--------------+--------+
+    | ZSTD                  | ❌     | ❌     | Stable       | Stable       | ❌      | ❌     | Stable       | Stable       | ❌     |
+    +-----------------------+--------+--------+--------------+--------------+---------+--------+--------------+--------------+--------+
+    | DEFLATE               | ❌     | ❌     | ❌           | ❌           | ❌      | ❌     | Experimental | Experimental | ❌     |
+    +-----------------------+--------+--------+--------------+--------------+---------+--------+--------------+--------------+--------+
+```
diff --git a/docs/cudf/source/user_guide/io/read-json.md b/docs/cudf/source/user_guide/io/read-json.md
new file mode 100644
index 0000000..7049c75
--- /dev/null
+++ b/docs/cudf/source/user_guide/io/read-json.md
@@ -0,0 +1,231 @@
+# Working with JSON data
+
+This page contains a tutorial about reading and manipulating JSON data in cuDF.
+
+## Reading JSON data
+
+By default, the cuDF JSON reader expects input data using the
+"records" orientation. Records-oriented JSON data comprises
+an array of objects at the root level, and each object in the
+array corresponds to a row. Records-oriented JSON data begins
+with `[`, ends with `]` and ignores unquoted whitespace.
+Another common variant for JSON data is "JSON Lines", where
+JSON objects are separated by new line characters (`\n`), and
+each object corresponds to a row.
+
+```python
+>>> j = '''[
+    {"a": "v1", "b": 12},
+    {"a": "v2", "b": 7},
+    {"a": "v3", "b": 5}
+]'''
+>>> df_records = cudf.read_json(j, engine='cudf')
+
+>>> j = '\n'.join([
+...     '{"a": "v1", "b": 12}',
+...     '{"a": "v2", "b": 7}',
+...     '{"a": "v3", "b": 5}'
+... ])
+>>> df_lines = cudf.read_json(j, lines=True)
+
+>>> df_lines
+    a   b
+0  v1  12
+1  v2   7
+2  v3   5
+>>> df_records.equals(df_lines)
+True
+```
+
+The cuDF JSON reader also supports arbitrarily-nested combinations
+of JSON objects and arrays, which map to struct and list data types.
+The following examples demonstrate the inputs and outputs for
+reading nested JSON data.
+
+```python
+# Example with columns types:
+# list<int> and struct<k:string>
+>>> j = '''[
+    {"list": [0,1,2], "struct": {"k":"v1"}},
+    {"list": [3,4,5], "struct": {"k":"v2"}}
+]'''
+>>> df = cudf.read_json(j, engine='cudf')
+>>> df
+        list       struct
+0  [0, 1, 2]  {'k': 'v1'}
+1  [3, 4, 5]  {'k': 'v2'}
+
+# Example with columns types:
+# list<struct<k:int>> and struct<k:list<int>, m:int>
+>>> j = '\n'.join([
+...     '{"a": [{"k": 0}], "b": {"k": [0, 1], "m": 5}}',
+...     '{"a": [{"k": 1}, {"k": 2}], "b": {"k": [2, 3], "m": 6}}',
+... ])
+>>> df = cudf.read_json(j, lines=True)
+>>> df
+                      a                      b
+0            [{'k': 0}]  {'k': [0, 1], 'm': 5}
+1  [{'k': 1}, {'k': 2}]  {'k': [2, 3], 'm': 6}
+```
+
+## Handling large and small JSON Lines files
+
+For workloads based on JSON Lines data, cuDF includes reader options
+to assist with data processing: byte range support for large files,
+and multi-source support for small files.
+
+Some workflows require processing large JSON Lines files that may
+exceed GPU memory capacity. The JSON reader in cuDF supports a byte
+range argument that specifies a starting byte offset and size in bytes.
+The reader parses each record that begins within the byte range,
+and for this reason byte ranges do not need to align with record
+boundaries. To avoid skipping rows or reading duplicate rows, byte ranges
+should be adjacent, as shown in the following example.
+
+```python
+>>> num_rows = 10
+>>> j = '\n'.join([
+...     '{"id":%s, "distance": %s, "unit": "m/s"}' % x \
+...     for x in zip(range(num_rows), cupy.random.rand(num_rows))
+... ])
+
+>>> chunk_count = 4
+>>> chunk_size = len(j) // chunk_count + 1
+>>> data = []
+>>> for x in range(chunk_count):
+...    d = cudf.read_json(
+...         j,
+...         lines=True,
+...         byte_range=(chunk_size * x, chunk_size),
+...     )
+...     data.append(d)
+>>> df = cudf.concat(data)
+```
+
+By contrast, some workflows require processing many small JSON
+Lines files. Rather than looping through the sources and
+concatenating the resulting dataframes, the JSON reader in
+cuDF accepts an iterable of data sources. Then the raw inputs
+are concatenated and processed as a single source. Please
+note that the JSON reader in cuDF accepts sources as file paths,
+raw strings, or file-like objects, as well as iterables of these sources.
+
+```python
+>>> j1 = '{"id":0}\n{"id":1}\n'
+>>> j2 = '{"id":2}\n{"id":3}\n'
+
+>>> df = cudf.read_json([j1, j2], lines=True)
+```
+
+## Unpacking list and struct data
+
+After reading JSON data into a cuDF dataframe with list/struct
+column types, the next step in many workflows extracts or
+flattens the data into simple types. For struct columns, one
+solution is extracting the data with the `struct.explode`
+accessor and joining the result to the parent dataframe. The
+following example demonstrates how to extract data from a struct column.
+
+```python
+>>> j = '\n'.join([
+...    '{"x": "Tokyo", "y": {"country": "Japan", "iso2": "JP"}}',
+...    '{"x": "Jakarta", "y": {"country": "Indonesia", "iso2": "ID"}}',
+...    '{"x": "Shanghai", "y": {"country": "China", "iso2": "CN"}}'
+... ])
+>>> df = cudf.read_json(j, lines=True)
+>>> df = df.drop(columns='y').join(df['y'].struct.explode())
+>>> df
+          x    country iso2
+0     Tokyo      Japan   JP
+1   Jakarta  Indonesia   ID
+2  Shanghai      China   CN
+```
+
+For list columns where the order of the elements is meaningful,
+the `list.get` accessor extracts the elements from specific
+positions. The resulting `cudf.Series` object can then be assigned
+to a new column in the dataframe. The following example
+demonstrates how to extract the first and second elements from a
+list column.
+
+```python
+>>> j = '\n'.join([
+...    '{"name": "Peabody, MA", "coord": [42.53, -70.98]}',
+...    '{"name": "Northampton, MA", "coord": [42.32, -72.66]}',
+...    '{"name": "New Bedford, MA", "coord": [41.63, -70.93]}'
+... ])
+
+>>> df = cudf.read_json(j, lines=True)
+>>> df['latitude'] = df['coord'].list.get(0)
+>>> df['longitude'] = df['coord'].list.get(1)
+>>> df = df.drop(columns='coord')
+>>> df
+              name  latitude  longitude
+0      Peabody, MA     42.53     -70.98
+1  Northampton, MA     42.32     -72.66
+2  New Bedford, MA     41.63     -70.93
+```
+
+Finally, for list columns with variable length, the `explode`
+method creates a new dataframe with each element as a row.
+Joining the exploded dataframe on the parent dataframe yields
+an output with all simple types. The following example flattens
+a list column and joins it to the index and additional data from
+the parent dataframe.
+
+```python
+>>> j = '\n'.join([
+...    '{"product": "socks", "ratings": [2, 3, 4]}',
+...    '{"product": "shoes", "ratings": [5, 4, 5, 3]}',
+...    '{"product": "shirts", "ratings": [3, 4]}'
+... ])
+
+>>> df = cudf.read_json(j, lines=True)
+>>> df = df.drop(columns='ratings').join(df['ratings'].explode())
+>>> df
+  product  ratings
+0   socks        2
+0   socks        4
+0   socks        3
+1   shoes        5
+1   shoes        5
+1   shoes        4
+1   shoes        3
+2  shirts        3
+2  shirts        4
+```
+
+## Building JSON data solutions
+
+Sometimes a workflow needs to process JSON data with an object
+root and cuDF provides tools to build solutions for this kind
+of data. If you need to process JSON data with an object root,
+we recommend reading the data as a single JSON Line and then
+unpacking the resulting dataframe. The following example
+reads a JSON object as a single line and then extracts the
+"results" field into a new dataframe.
+
+```python
+>>> j = '''{
+    "metadata" : {"vehicle":"car"},
+    "results": [
+        {"id": 0, "distance": 1.2},
+        {"id": 1, "distance": 2.4},
+        {"id": 2, "distance": 1.7}
+    ]
+}'''
+
+# first read the JSON object with line=True
+>>> df = cudf.read_json(j, lines=True)
+>>> df
+             metadata                                            records
+0  {'vehicle': 'car'}  [{'id': 0, 'distance': 1.2}, {'id': 1, 'distan...
+
+# then explode the 'records' column
+>>> df = df['records'].explode().struct.explode()
+>>> df
+   id  distance
+0   0       1.2
+1   1       2.4
+2   2       1.7
+```
diff --git a/docs/cudf/source/user_guide/missing-data.ipynb b/docs/cudf/source/user_guide/missing-data.ipynb
new file mode 100644
index 0000000..f1404ce
--- /dev/null
+++ b/docs/cudf/source/user_guide/missing-data.ipynb
@@ -0,0 +1,3578 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f8ffbea7",
+   "metadata": {},
+   "source": [
+    "# Working with missing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e3ab093",
+   "metadata": {},
+   "source": [
+    "In this section, we will discuss missing (also referred to as `NA`) values in cudf. cudf supports having missing values in all dtypes. These missing values are represented by `<NA>`. These values are also referenced as \"null values\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d657a82",
+   "metadata": {},
+   "source": [
+    "## How to Detect missing values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ea9f672",
+   "metadata": {},
+   "source": [
+    "To detect missing values, you can use `isna()` and `notna()` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "58050adb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import cudf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "416d73da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = cudf.DataFrame({\"a\": [1, 2, None, 4], \"b\": [0.1, None, 2.3, 17.17]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5dfc6bc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>17.17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a      b\n",
+       "0     1    0.1\n",
+       "1     2   <NA>\n",
+       "2  <NA>    2.3\n",
+       "3     4  17.17"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d7f7a6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a      b\n",
+       "0  False  False\n",
+       "1  False   True\n",
+       "2   True  False\n",
+       "3  False  False"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "40edca67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     True\n",
+       "1     True\n",
+       "2    False\n",
+       "3     True\n",
+       "Name: a, dtype: bool"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"a\"].notna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acdf29d7",
+   "metadata": {},
+   "source": [
+    "One has to be mindful that in Python (and NumPy), the nan's don't compare equal, but None's do. Note that cudf/NumPy uses the fact that `np.nan != np.nan`, and treats `None` like `np.nan`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c269c1f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "None == None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "99fb083a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.nan == np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fdb8bc7",
+   "metadata": {},
+   "source": [
+    "So as compared to above, a scalar equality comparison versus a None/np.nan doesn't provide useful information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "630ef6bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1     <NA>\n",
+       "2    False\n",
+       "3    False\n",
+       "Name: b, dtype: bool"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"b\"] == np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8162e383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = cudf.Series([None, 1, 2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "199775b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    <NA>\n",
+       "1       1\n",
+       "2       2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cd09d80c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    <NA>\n",
+       "1    <NA>\n",
+       "2    <NA>\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s == None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6b23bb0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = cudf.Series([1, 2, np.nan], nan_as_null=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cafb79ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1.0\n",
+       "1    2.0\n",
+       "2    NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "13363897",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s == np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "208a3776",
+   "metadata": {},
+   "source": [
+    "## Float dtypes and missing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c174b88",
+   "metadata": {},
+   "source": [
+    "Because ``NaN`` is a float, a column of integers with even one missing values is cast to floating-point dtype. However this doesn't happen by default.\n",
+    "\n",
+    "By default if a ``NaN`` value is passed to `Series` constructor, it is treated as `<NA>` value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c59c3c54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1       2\n",
+       "2    <NA>\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([1, 2, np.nan])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9eb2d9c",
+   "metadata": {},
+   "source": [
+    "Hence to consider a ``NaN`` as ``NaN`` you will have to pass `nan_as_null=False` parameter into `Series` constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ecc5ae92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1.0\n",
+       "1    2.0\n",
+       "2    NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([1, 2, np.nan], nan_as_null=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1db7b08",
+   "metadata": {},
+   "source": [
+    "## Datetimes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "548d3734",
+   "metadata": {},
+   "source": [
+    "For `datetime64` types, cudf doesn't support having `NaT` values. Instead these values which are specific to numpy and pandas are considered as null values(`<NA>`) in cudf. The actual underlying value of `NaT` is `min(int64)` and cudf retains the underlying value when converting a cudf object to pandas object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "de70f244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2012-01-01 00:00:00.000000\n",
+       "1                          <NA>\n",
+       "2    2012-01-01 00:00:00.000000\n",
+       "dtype: datetime64[us]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "datetime_series = cudf.Series(\n",
+    "    [pd.Timestamp(\"20120101\"), pd.NaT, pd.Timestamp(\"20120101\")]\n",
+    ")\n",
+    "datetime_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8411a914",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   2012-01-01\n",
+       "1          NaT\n",
+       "2   2012-01-01\n",
+       "dtype: datetime64[ns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datetime_series.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df664145",
+   "metadata": {},
+   "source": [
+    "any operations on rows having `<NA>` values in `datetime` column will result in `<NA>` value at the same location in resulting column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "829c32d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0 days 00:00:00\n",
+       "1               <NA>\n",
+       "2    0 days 00:00:00\n",
+       "dtype: timedelta64[us]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datetime_series - datetime_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa8031ef",
+   "metadata": {},
+   "source": [
+    "## Calculations with missing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c587fae2",
+   "metadata": {},
+   "source": [
+    "Null values propagate naturally through arithmetic operations between pandas objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f8f2aec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = cudf.DataFrame(\n",
+    "    {\n",
+    "        \"a\": [1, None, 2, 3, None],\n",
+    "        \"b\": cudf.Series([np.nan, 2, 3.2, 0.1, 1], nan_as_null=False),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0c8a3011",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = cudf.DataFrame(\n",
+    "    {\"a\": [1, 11, 2, 34, 10], \"b\": cudf.Series([0.23, 22, 3.2, None, 1])}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "052f6c2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a    b\n",
+       "0     1  NaN\n",
+       "1  <NA>  2.0\n",
+       "2     2  3.2\n",
+       "3     3  0.1\n",
+       "4  <NA>  1.0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0fb0a083",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>34</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a     b\n",
+       "0   1  0.23\n",
+       "1  11  22.0\n",
+       "2   2   3.2\n",
+       "3  34  <NA>\n",
+       "4  10   1.0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6f8152c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>37</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b\n",
+       "0     2   NaN\n",
+       "1  <NA>  24.0\n",
+       "2     4   6.4\n",
+       "3    37  <NA>\n",
+       "4  <NA>   2.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 + df2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11170d49",
+   "metadata": {},
+   "source": [
+    "While summing the data along a series, `NA` values will be treated as `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "45081790",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1    <NA>\n",
+       "2       2\n",
+       "3       3\n",
+       "4    <NA>\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "39922658",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e99afe0",
+   "metadata": {},
+   "source": [
+    "Since `NA` values are treated as `0`, the mean would result to 2 in this case `(1 + 0 + 2 + 3 + 0)/5 = 2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b2f16ddb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07f2ec5a",
+   "metadata": {},
+   "source": [
+    "To preserve `NA` values in the above calculations, `sum` & `mean` support `skipna` parameter.\n",
+    "By default it's value is\n",
+    "set to `True`, we can change it to `False` to preserve `NA` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d4a463a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].sum(skipna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a944c42e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].mean(skipna=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb8c8f18",
+   "metadata": {},
+   "source": [
+    "Cumulative methods like `cumsum` and `cumprod` ignore `NA` values by default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4f2a7306",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1    <NA>\n",
+       "2       3\n",
+       "3       6\n",
+       "4    <NA>\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8f6054b",
+   "metadata": {},
+   "source": [
+    "To preserve `NA` values in cumulative methods, provide `skipna=False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d4c46776",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1    <NA>\n",
+       "2    <NA>\n",
+       "3    <NA>\n",
+       "4    <NA>\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].cumsum(skipna=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67077d65",
+   "metadata": {},
+   "source": [
+    "## Sum/product of Null/nans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffbb9ca1",
+   "metadata": {},
+   "source": [
+    "The sum of an empty or all-NA Series of a DataFrame is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f430c9ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([np.nan], nan_as_null=False).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "7fde514b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([np.nan], nan_as_null=False).sum(skipna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "56cedd17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([], dtype=\"float64\").sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb188adb",
+   "metadata": {},
+   "source": [
+    "The product of an empty or all-NA Series of a DataFrame is 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d20bbbef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([np.nan], nan_as_null=False).prod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "75abbcfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([np.nan], nan_as_null=False).prod(skipna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "becce0cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cudf.Series([], dtype=\"float64\").prod()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e899e03",
+   "metadata": {},
+   "source": [
+    "## NA values in GroupBy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb20874",
+   "metadata": {},
+   "source": [
+    "`NA` groups in GroupBy are automatically excluded. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1379037c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a    b\n",
+       "0     1  NaN\n",
+       "1  <NA>  2.0\n",
+       "2     2  3.2\n",
+       "3     3  0.1\n",
+       "4  <NA>  1.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d6b91e6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     b\n",
+       "a     \n",
+       "2  3.2\n",
+       "1  NaN\n",
+       "3  0.1"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"a\").mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb83fb11",
+   "metadata": {},
+   "source": [
+    "It is also possible to include `NA` in groups by passing `dropna=False`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "768c3e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>&lt;NA&gt;</th>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        b\n",
+       "a        \n",
+       "2     3.2\n",
+       "1     NaN\n",
+       "3     0.1\n",
+       "<NA>  1.5"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.groupby(\"a\", dropna=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "133816b4",
+   "metadata": {},
+   "source": [
+    "## Inserting missing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "306082ad",
+   "metadata": {},
+   "source": [
+    "All dtypes support insertion of missing value by assignment. Any specific location in series can made null by assigning it to `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "7ddde1fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "series = cudf.Series([1, 2, 3, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "16e54597",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "3    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f628f94d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "series[2] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b30590b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       1\n",
+       "1       2\n",
+       "2    <NA>\n",
+       "3       4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1b123d0",
+   "metadata": {},
+   "source": [
+    "## Filling missing values: fillna"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "114aa23a",
+   "metadata": {},
+   "source": [
+    "`fillna()` can fill in `NA` & `NaN` values with non-NA data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "59e22668",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a    b\n",
+       "0     1  NaN\n",
+       "1  <NA>  2.0\n",
+       "2     2  3.2\n",
+       "3     3  0.1\n",
+       "4  <NA>  1.0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "05c221ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10.0\n",
+       "1     2.0\n",
+       "2     3.2\n",
+       "3     0.1\n",
+       "4     1.0\n",
+       "Name: b, dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"b\"].fillna(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401f91b2",
+   "metadata": {},
+   "source": [
+    "## Filling with cudf Object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79346d6",
+   "metadata": {},
+   "source": [
+    "You can also fillna using a dict or Series that is alignable. The labels of the dict or index of the Series must match the columns of the frame you wish to fill. The use case of this is to fill a DataFrame with the mean of that column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f52c5d8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cupy as cp\n",
+    "\n",
+    "dff = cudf.DataFrame(cp.random.randn(10, 3), columns=list(\"ABC\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6affebe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dff.iloc[3:5, 0] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "1ce1b96f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dff.iloc[4:6, 1] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "90829195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dff.iloc[5:8, 2] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "c0feac14",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.408268</td>\n",
+       "      <td>-0.676643</td>\n",
+       "      <td>-1.274743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.029322</td>\n",
+       "      <td>-0.873593</td>\n",
+       "      <td>-1.214105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.866371</td>\n",
+       "      <td>1.081735</td>\n",
+       "      <td>-0.226840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.812278</td>\n",
+       "      <td>1.074973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.366725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-1.016239</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.675123</td>\n",
+       "      <td>1.067536</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.221568</td>\n",
+       "      <td>2.025961</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.317241</td>\n",
+       "      <td>1.011275</td>\n",
+       "      <td>0.674891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-0.877041</td>\n",
+       "      <td>-1.919394</td>\n",
+       "      <td>-1.029201</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C\n",
+       "0 -0.408268 -0.676643 -1.274743\n",
+       "1 -0.029322 -0.873593 -1.214105\n",
+       "2 -0.866371  1.081735 -0.226840\n",
+       "3       NaN  0.812278  1.074973\n",
+       "4       NaN       NaN -0.366725\n",
+       "5 -1.016239       NaN       NaN\n",
+       "6  0.675123  1.067536       NaN\n",
+       "7  0.221568  2.025961       NaN\n",
+       "8 -0.317241  1.011275  0.674891\n",
+       "9 -0.877041 -1.919394 -1.029201"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "a07c1260",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.408268</td>\n",
+       "      <td>-0.676643</td>\n",
+       "      <td>-1.274743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.029322</td>\n",
+       "      <td>-0.873593</td>\n",
+       "      <td>-1.214105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.866371</td>\n",
+       "      <td>1.081735</td>\n",
+       "      <td>-0.226840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.327224</td>\n",
+       "      <td>0.812278</td>\n",
+       "      <td>1.074973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.327224</td>\n",
+       "      <td>0.316145</td>\n",
+       "      <td>-0.366725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-1.016239</td>\n",
+       "      <td>0.316145</td>\n",
+       "      <td>-0.337393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.675123</td>\n",
+       "      <td>1.067536</td>\n",
+       "      <td>-0.337393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.221568</td>\n",
+       "      <td>2.025961</td>\n",
+       "      <td>-0.337393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.317241</td>\n",
+       "      <td>1.011275</td>\n",
+       "      <td>0.674891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-0.877041</td>\n",
+       "      <td>-1.919394</td>\n",
+       "      <td>-1.029201</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C\n",
+       "0 -0.408268 -0.676643 -1.274743\n",
+       "1 -0.029322 -0.873593 -1.214105\n",
+       "2 -0.866371  1.081735 -0.226840\n",
+       "3 -0.327224  0.812278  1.074973\n",
+       "4 -0.327224  0.316145 -0.366725\n",
+       "5 -1.016239  0.316145 -0.337393\n",
+       "6  0.675123  1.067536 -0.337393\n",
+       "7  0.221568  2.025961 -0.337393\n",
+       "8 -0.317241  1.011275  0.674891\n",
+       "9 -0.877041 -1.919394 -1.029201"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dff.fillna(dff.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "9e70d61a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.408268</td>\n",
+       "      <td>-0.676643</td>\n",
+       "      <td>-1.274743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.029322</td>\n",
+       "      <td>-0.873593</td>\n",
+       "      <td>-1.214105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.866371</td>\n",
+       "      <td>1.081735</td>\n",
+       "      <td>-0.226840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.812278</td>\n",
+       "      <td>1.074973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.316145</td>\n",
+       "      <td>-0.366725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-1.016239</td>\n",
+       "      <td>0.316145</td>\n",
+       "      <td>-0.337393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.675123</td>\n",
+       "      <td>1.067536</td>\n",
+       "      <td>-0.337393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.221568</td>\n",
+       "      <td>2.025961</td>\n",
+       "      <td>-0.337393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.317241</td>\n",
+       "      <td>1.011275</td>\n",
+       "      <td>0.674891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-0.877041</td>\n",
+       "      <td>-1.919394</td>\n",
+       "      <td>-1.029201</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          A         B         C\n",
+       "0 -0.408268 -0.676643 -1.274743\n",
+       "1 -0.029322 -0.873593 -1.214105\n",
+       "2 -0.866371  1.081735 -0.226840\n",
+       "3       NaN  0.812278  1.074973\n",
+       "4       NaN  0.316145 -0.366725\n",
+       "5 -1.016239  0.316145 -0.337393\n",
+       "6  0.675123  1.067536 -0.337393\n",
+       "7  0.221568  2.025961 -0.337393\n",
+       "8 -0.317241  1.011275  0.674891\n",
+       "9 -0.877041 -1.919394 -1.029201"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dff.fillna(dff.mean()[1:3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ace728d",
+   "metadata": {},
+   "source": [
+    "## Dropping axis labels with missing data: dropna"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ccd7115",
+   "metadata": {},
+   "source": [
+    "Missing data can be excluded using `dropna()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "98c57be7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a    b\n",
+       "0     1  NaN\n",
+       "1  <NA>  2.0\n",
+       "2     2  3.2\n",
+       "3     3  0.1\n",
+       "4  <NA>  1.0"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "bc3f273a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a    b\n",
+       "2  2  3.2\n",
+       "3  3  0.1"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.dropna(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a48d4de0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [0, 1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b1954f9",
+   "metadata": {},
+   "source": [
+    "An equivalent `dropna()` is available for Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "2dd8f660",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "2    2\n",
+       "3    3\n",
+       "Name: a, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1[\"a\"].dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "121eb6d7",
+   "metadata": {},
+   "source": [
+    "## Replacing generic values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc4c5f1",
+   "metadata": {},
+   "source": [
+    "Often times we want to replace arbitrary values with other values.\n",
+    "\n",
+    "`replace()` in Series and `replace()` in DataFrame provides an efficient yet flexible way to perform such replacements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e6c14e8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "series = cudf.Series([0.0, 1.0, 2.0, 3.0, 4.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a852f0cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.0\n",
+       "1    1.0\n",
+       "2    2.0\n",
+       "3    3.0\n",
+       "4    4.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f6ac12eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    5.0\n",
+       "1    1.0\n",
+       "2    2.0\n",
+       "3    3.0\n",
+       "4    4.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series.replace(0, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e1b6d7",
+   "metadata": {},
+   "source": [
+    "We can also replace any value with a `<NA>` value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "f0156bff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    <NA>\n",
+       "1     1.0\n",
+       "2     2.0\n",
+       "3     3.0\n",
+       "4     4.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series.replace(0, None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6673eefb",
+   "metadata": {},
+   "source": [
+    "You can replace a list of values by a list of other values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f3110f5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    4.0\n",
+       "1    3.0\n",
+       "2    2.0\n",
+       "3    1.0\n",
+       "4    0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61521e8b",
+   "metadata": {},
+   "source": [
+    "You can also specify a mapping dict:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "45862d05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     10.0\n",
+       "1    100.0\n",
+       "2      2.0\n",
+       "3      3.0\n",
+       "4      4.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "series.replace({0: 10, 1: 100})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04a34549",
+   "metadata": {},
+   "source": [
+    "For a DataFrame, you can specify individual values by column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "348caa64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = cudf.DataFrame({\"a\": [0, 1, 2, 3, 4], \"b\": [5, 6, 7, 8, 9]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "cca41ec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "0  0  5\n",
+       "1  1  6\n",
+       "2  2  7\n",
+       "3  3  8\n",
+       "4  4  9"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "64334693",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     a    b\n",
+       "0  100  100\n",
+       "1    1    6\n",
+       "2    2    7\n",
+       "3    3    8\n",
+       "4    4    9"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace({\"a\": 0, \"b\": 5}, 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f0ceec7",
+   "metadata": {},
+   "source": [
+    "## String/regular expression replacement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6f44740",
+   "metadata": {},
+   "source": [
+    "cudf supports replacing string values using `replace` API:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "031d3533",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = {\"a\": list(range(4)), \"b\": list(\"ab..\"), \"c\": [\"a\", \"b\", None, \"d\"]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "12b41efb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = cudf.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "d450df49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>.</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>.</td>\n",
+       "      <td>d</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b     c\n",
+       "0  0  a     a\n",
+       "1  1  b     b\n",
+       "2  2  .  <NA>\n",
+       "3  3  .     d"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "f823bc46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A Dot</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A Dot</td>\n",
+       "      <td>d</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a      b     c\n",
+       "0  0      a     a\n",
+       "1  1      b     b\n",
+       "2  2  A Dot  <NA>\n",
+       "3  3  A Dot     d"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace(\".\", \"A Dot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bc52f6e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A Dot</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A Dot</td>\n",
+       "      <td>d</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a      b     c\n",
+       "0  0      a     a\n",
+       "1  1   <NA>  <NA>\n",
+       "2  2  A Dot  <NA>\n",
+       "3  3  A Dot     d"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace([\".\", \"b\"], [\"A Dot\", None])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c1087be",
+   "metadata": {},
+   "source": [
+    "Replace a few different values (list -> list):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "7e23eba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>b</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>--</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>--</td>\n",
+       "      <td>d</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a   b     c\n",
+       "0  0   b     b\n",
+       "1  1   b     b\n",
+       "2  2  --  <NA>\n",
+       "3  3  --     d"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace([\"a\", \".\"], [\"b\", \"--\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42845a9c",
+   "metadata": {},
+   "source": [
+    "Only search in column 'b' (dict -> dict):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "d2e79805",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>a</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>replacement value</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>replacement value</td>\n",
+       "      <td>d</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a                  b     c\n",
+       "0  0                  a     a\n",
+       "1  1                  b     b\n",
+       "2  2  replacement value  <NA>\n",
+       "3  3  replacement value     d"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace({\"b\": \".\"}, {\"b\": \"replacement value\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "774b42a6",
+   "metadata": {},
+   "source": [
+    "## Numeric replacement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c1926ac",
+   "metadata": {},
+   "source": [
+    "`replace()` can also be used similar to `fillna()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "355a2f0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = cudf.DataFrame(cp.random.randn(10, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d9eed372",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[np.random.rand(df.shape[0]) > 0.5] = 1.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "ae944244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.089358787</td>\n",
+       "      <td>-0.728419386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-2.141612003</td>\n",
+       "      <td>-0.574415182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.774643462</td>\n",
+       "      <td>2.07287721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.93799853</td>\n",
+       "      <td>-1.054129436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.435293012</td>\n",
+       "      <td>1.163009584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.346623287</td>\n",
+       "      <td>0.31961371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              0             1\n",
+       "0  -0.089358787  -0.728419386\n",
+       "1  -2.141612003  -0.574415182\n",
+       "2          <NA>          <NA>\n",
+       "3   0.774643462    2.07287721\n",
+       "4    0.93799853  -1.054129436\n",
+       "5          <NA>          <NA>\n",
+       "6  -0.435293012   1.163009584\n",
+       "7   1.346623287    0.31961371\n",
+       "8          <NA>          <NA>\n",
+       "9          <NA>          <NA>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace(1.5, None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f32607c",
+   "metadata": {},
+   "source": [
+    "Replacing more than one value is possible by passing a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "59b81c60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df00 = df.iloc[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "01a71d4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>-0.728419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-2.141612</td>\n",
+       "      <td>-0.574415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.774643</td>\n",
+       "      <td>2.072877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.937999</td>\n",
+       "      <td>-1.054129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.435293</td>\n",
+       "      <td>1.163010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.346623</td>\n",
+       "      <td>0.319614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0         1\n",
+       "0  10.000000 -0.728419\n",
+       "1  -2.141612 -0.574415\n",
+       "2   5.000000  5.000000\n",
+       "3   0.774643  2.072877\n",
+       "4   0.937999 -1.054129\n",
+       "5   5.000000  5.000000\n",
+       "6  -0.435293  1.163010\n",
+       "7   1.346623  0.319614\n",
+       "8   5.000000  5.000000\n",
+       "9   5.000000  5.000000"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace([1.5, df00], [5, 10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1080e97b",
+   "metadata": {},
+   "source": [
+    "You can also operate on the DataFrame in place:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "5f0859d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.replace(1.5, None, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "5cf28369",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.089358787</td>\n",
+       "      <td>-0.728419386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-2.141612003</td>\n",
+       "      <td>-0.574415182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.774643462</td>\n",
+       "      <td>2.07287721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.93799853</td>\n",
+       "      <td>-1.054129436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.435293012</td>\n",
+       "      <td>1.163009584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.346623287</td>\n",
+       "      <td>0.31961371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              0             1\n",
+       "0  -0.089358787  -0.728419386\n",
+       "1  -2.141612003  -0.574415182\n",
+       "2          <NA>          <NA>\n",
+       "3   0.774643462    2.07287721\n",
+       "4    0.93799853  -1.054129436\n",
+       "5          <NA>          <NA>\n",
+       "6  -0.435293012   1.163009584\n",
+       "7   1.346623287    0.31961371\n",
+       "8          <NA>          <NA>\n",
+       "9          <NA>          <NA>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/cudf/source/user_guide/options.md b/docs/cudf/source/user_guide/options.md
new file mode 100644
index 0000000..245d3fd
--- /dev/null
+++ b/docs/cudf/source/user_guide/options.md
@@ -0,0 +1,14 @@
+(options_user_guide)=
+
+# Options
+
+cuDF has an options API to configure and customize global behavior.
+This API complements the [pandas.options](https://pandas.pydata.org/docs/user_guide/options.html) API with features specific to cuDF.
+
+{py:func}`cudf.describe_option` will print the option's description,
+the current value, and the default value.
+When no argument is provided,
+all options are printed.
+To set value to a option, use {py:func}`cudf.set_option`.
+
+See the [API reference](api.options) for more details.
diff --git a/docs/cudf/source/user_guide/pandas-comparison.md b/docs/cudf/source/user_guide/pandas-comparison.md
new file mode 100644
index 0000000..22218d4
--- /dev/null
+++ b/docs/cudf/source/user_guide/pandas-comparison.md
@@ -0,0 +1,188 @@
+# Comparison of cuDF and Pandas
+
+cuDF is a DataFrame library that closely matches the Pandas API, but
+when used directly is *not* a full drop-in replacement for Pandas.  There are some
+differences between cuDF and Pandas, both in terms of API and
+behaviour.  This page documents the similarities and differences
+between cuDF and Pandas.
+
+Starting with the v23.10.01 release, cuDF also provides a pandas
+accelerator mode (`cudf.pandas`) that supports 100% of the pandas API
+and accelerates pandas code on the GPU without requiring any code
+change.  See the [`cudf.pandas` documentation](../cudf_pandas/index).
+
+## Supported operations
+
+cuDF supports many of the same data structures and operations as
+Pandas.  This includes `Series`, `DataFrame`, `Index` and
+operations on them such as unary and binary operations, indexing,
+filtering, concatenating, joining, groupby and window operations -
+among many others.
+
+The best way to check if we support a particular Pandas API is to search
+our [API docs](/user_guide/api_docs/index).
+
+## Data types
+
+cuDF supports many of the commonly-used data types in Pandas,
+including numeric, datetime, timestamp, string, and categorical data
+types.  In addition, we support special data types for decimal, list,
+and "struct" values.  See the section on [Data Types](data-types) for
+details.
+
+Note that we do not support custom data types like Pandas'
+`ExtensionDtype`.
+
+## Null (or "missing") values
+
+Unlike Pandas, *all* data types in cuDF are nullable,
+meaning they can contain missing values (represented by `cudf.NA`).
+
+```{code} python
+>>> s = cudf.Series([1, 2, cudf.NA])
+>>> s
+0       1
+1       2
+2    <NA>
+dtype: int64
+```
+
+Nulls are not coerced to `NaN` in any situation;
+compare the behavior of cuDF with Pandas below:
+
+```{code} python
+>>> s = cudf.Series([1, 2, cudf.NA], dtype="category")
+>>> s
+0       1
+1       2
+2    <NA>
+dtype: category
+Categories (2, int64): [1, 2]
+
+>>> s = pd.Series([1, 2, pd.NA], dtype="category")
+>>> s
+0      1
+1      2
+2    NaN
+dtype: category
+Categories (2, int64): [1, 2]
+```
+
+See the docs on [missing data](missing-data) for details.
+
+(pandas-comparison/iteration)=
+
+## Iteration
+
+Iterating over a cuDF `Series`, `DataFrame` or `Index` is not
+supported. This is because iterating over data that resides on the GPU
+will yield *extremely* poor performance, as GPUs are optimized for
+highly parallel operations rather than sequential operations.
+
+In the vast majority of cases, it is possible to avoid iteration and
+use an existing function or method to accomplish the same task. If you
+absolutely must iterate, copy the data from GPU to CPU by using
+`.to_arrow()` or `.to_pandas()`, then copy the result back to GPU
+using `.from_arrow()` or `.from_pandas()`.
+
+## Result ordering
+
+By default, `join` (or `merge`) and `groupby` operations in cuDF
+do *not* guarantee output ordering.
+Compare the results obtained from Pandas and cuDF below:
+
+```{code} python
+>>> import cupy as cp
+>>> cp.random.seed(0)
+>>> import cudf
+>>> df = cudf.DataFrame({'a': cp.random.randint(0, 1000, 1000), 'b': range(1000)})
+>>> df.groupby("a").mean().head()
+         b
+a
+29   193.0
+803  915.0
+5    138.0
+583  300.0
+418  613.0
+>>> df.to_pandas().groupby("a").mean().head()
+            b
+a
+0   70.000000
+1  356.333333
+2  770.000000
+3  838.000000
+4  342.000000
+```
+
+To match Pandas behavior, you must explicitly pass `sort=True`
+or enable the `mode.pandas_compatible` option when trying to
+match Pandas behavior with `sort=False`:
+
+```{code} python
+>>> df.to_pandas().groupby("a", sort=True).mean().head()
+            b
+a
+0   70.000000
+1  356.333333
+2  770.000000
+3  838.000000
+4  342.000000
+
+>>> cudf.set_option("mode.pandas_compatible", True)
+>>> df.groupby("a").mean().head()
+            b
+a
+0   70.000000
+1  356.333333
+2  770.000000
+3  838.000000
+4  342.000000
+```
+
+## Floating-point computation
+
+cuDF leverages GPUs to execute operations in parallel.  This means the
+order of operations is not always deterministic.  This impacts the
+determinism of floating-point operations because floating-point
+arithmetic is non-associative, that is, `a + b` is not equal to `b + a`.
+
+For example, `s.sum()` is not guaranteed to produce identical results
+to Pandas nor produce identical results from run to run, when `s` is a
+Series of floats.  If you need to compare floating point results, you
+should typically do so using the functions provided in the
+[`cudf.testing`](/user_guide/api_docs/general_utilities)
+module, which allow you to compare values up to a desired precision.
+
+## Column names
+
+Unlike Pandas, cuDF does not support duplicate column names.
+It is best to use unique strings for column names.
+
+## No true `"object"` data type
+
+In Pandas and NumPy, the `"object"` data type is used for
+collections of arbitrary Python objects.  For example, in Pandas you
+can do the following:
+
+```{code} python
+>>> import pandas as pd
+>>> s = pd.Series(["a", 1, [1, 2, 3]])
+0            a
+1            1
+2    [1, 2, 3]
+dtype: object
+```
+
+For compatibility with Pandas, cuDF reports the data type for strings
+as `"object"`, but we do *not* support storing or operating on
+collections of arbitrary Python objects.
+
+## `.apply()` function limitations
+
+The `.apply()` function in Pandas accepts a user-defined function
+(UDF) that can include arbitrary operations that are applied to each
+value of a `Series`, `DataFrame`, or in the case of a groupby,
+each group.  cuDF also supports `.apply()`, but it relies on Numba to
+JIT compile the UDF and execute it on the GPU. This can be extremely
+fast, but imposes a few limitations on what operations are allowed in
+the UDF. See the docs on [UDFs](guide-to-udfs) for details.
diff --git a/docs/cudf/source/user_guide/performance-comparisons/index.md b/docs/cudf/source/user_guide/performance-comparisons/index.md
new file mode 100644
index 0000000..9390fa6
--- /dev/null
+++ b/docs/cudf/source/user_guide/performance-comparisons/index.md
@@ -0,0 +1,8 @@
+# Performance comparisons
+
+```{toctree}
+:maxdepth: 2
+
+
+performance-comparisons
+```
diff --git a/docs/cudf/source/user_guide/performance-comparisons/performance-comparisons.ipynb b/docs/cudf/source/user_guide/performance-comparisons/performance-comparisons.ipynb
new file mode 100644
index 0000000..d06c720
--- /dev/null
+++ b/docs/cudf/source/user_guide/performance-comparisons/performance-comparisons.ipynb
@@ -0,0 +1,1651 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Performance comparison"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook compares the performance of `cuDF` and `pandas`. The comparisons performed are on identical data sizes. This notebook primarily showcases the factor\n",
+    "of speedups users can have when the similar `pandas` APIs are run on GPUs using `cudf`.\n",
+    "\n",
+    "The hardware details used to run these performance comparisons are at the end of this page.\n",
+    "\n",
+    "**Note**: This notebook is written to measure performance on NVIDIA GPUs with large memory. If running on hardware with lower memory, please consider lowering the `num_rows` values. Performance results may vary by data size, as well as the CPU and GPU used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import timeit\n",
+    "from io import BytesIO\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import cudf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Concat, count & joins performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>numbers</th>\n",
+       "      <th>business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-316</td>\n",
+       "      <td>Costco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-441</td>\n",
+       "      <td>Costco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>653</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>216</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-165</td>\n",
+       "      <td>Walmart</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999995</th>\n",
+       "      <td>-395</td>\n",
+       "      <td>Walmart</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999996</th>\n",
+       "      <td>-653</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999997</th>\n",
+       "      <td>364</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999998</th>\n",
+       "      <td>159</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999999</th>\n",
+       "      <td>-501</td>\n",
+       "      <td>Walmart</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>300000000 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           numbers business\n",
+       "0             -316   Costco\n",
+       "1             -441   Costco\n",
+       "2              653  Buckees\n",
+       "3              216  Buckees\n",
+       "4             -165  Walmart\n",
+       "...            ...      ...\n",
+       "299999995     -395  Walmart\n",
+       "299999996     -653  Buckees\n",
+       "299999997      364  Buckees\n",
+       "299999998      159  Buckees\n",
+       "299999999     -501  Walmart\n",
+       "\n",
+       "[300000000 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_rows = 300_000_000\n",
+    "pdf = pd.DataFrame(\n",
+    "    {\n",
+    "        \"numbers\": np.random.randint(-1000, 1000, num_rows, dtype=\"int64\"),\n",
+    "        \"business\": np.random.choice(\n",
+    "            [\"McD\", \"Buckees\", \"Walmart\", \"Costco\"], size=num_rows\n",
+    "        ),\n",
+    "    }\n",
+    ")\n",
+    "pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>numbers</th>\n",
+       "      <th>business</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-316</td>\n",
+       "      <td>Costco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-441</td>\n",
+       "      <td>Costco</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>653</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>216</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-165</td>\n",
+       "      <td>Walmart</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999995</th>\n",
+       "      <td>-395</td>\n",
+       "      <td>Walmart</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999996</th>\n",
+       "      <td>-653</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999997</th>\n",
+       "      <td>364</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999998</th>\n",
+       "      <td>159</td>\n",
+       "      <td>Buckees</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299999999</th>\n",
+       "      <td>-501</td>\n",
+       "      <td>Walmart</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>300000000 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           numbers business\n",
+       "0             -316   Costco\n",
+       "1             -441   Costco\n",
+       "2              653  Buckees\n",
+       "3              216  Buckees\n",
+       "4             -165  Walmart\n",
+       "...            ...      ...\n",
+       "299999995     -395  Walmart\n",
+       "299999996     -653  Buckees\n",
+       "299999997      364  Buckees\n",
+       "299999998      159  Buckees\n",
+       "299999999     -501  Walmart\n",
+       "\n",
+       "[300000000 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf = cudf.from_pandas(pdf)\n",
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def timeit_pandas_cudf(pd_obj, gd_obj, func, **kwargs):\n",
+    "    \"\"\"\n",
+    "    A utility function to measure execution time of an\n",
+    "    API(`func`) in pandas & cudf.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    pd_obj : Pandas object\n",
+    "    gd_obj : cuDF object\n",
+    "    func : callable\n",
+    "    \"\"\"\n",
+    "    pandas_time = timeit.timeit(lambda: func(pd_obj), **kwargs)\n",
+    "    cudf_time = timeit.timeit(lambda: func(gd_obj), **kwargs)\n",
+    "    return pandas_time, cudf_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_value_counts, cudf_value_counts = timeit_pandas_cudf(\n",
+    "    pdf, gdf, lambda df: df.value_counts(), number=30\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pdf = pdf.head(100_000_000)\n",
+    "gdf = gdf.head(100_000_000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_concat = timeit.timeit(lambda: pd.concat([pdf, pdf, pdf]), number=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cudf_concat = timeit.timeit(lambda: cudf.concat([gdf, gdf, gdf]), number=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_groupby, cudf_groupby = timeit_pandas_cudf(\n",
+    "    pdf,\n",
+    "    gdf,\n",
+    "    lambda df: df.groupby(\"business\").agg([\"min\", \"max\", \"mean\"]),\n",
+    "    number=30,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "num_rows = 1_000_000\n",
+    "pdf = pd.DataFrame(\n",
+    "    {\n",
+    "        \"numbers\": np.random.randint(-1000, 1000, num_rows, dtype=\"int64\"),\n",
+    "        \"business\": np.random.choice(\n",
+    "            [\"McD\", \"Buckees\", \"Walmart\", \"Costco\"], size=num_rows\n",
+    "        ),\n",
+    "    }\n",
+    ")\n",
+    "gdf = cudf.from_pandas(pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_merge, cudf_merge = timeit_pandas_cudf(\n",
+    "    pdf, gdf, lambda df: df.merge(df), number=30\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "performance_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"cudf speedup vs. pandas\": [\n",
+    "            pandas_value_counts / cudf_value_counts,\n",
+    "            pandas_concat / cudf_concat,\n",
+    "            pandas_groupby / cudf_groupby,\n",
+    "            pandas_merge / cudf_merge,\n",
+    "        ],\n",
+    "    },\n",
+    "    index=[\"value_counts\", \"concat\", \"groupby\", \"merge\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cudf speedup vs. pandas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>value_counts</th>\n",
+       "      <td>282.901300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>concat</th>\n",
+       "      <td>203.624680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>groupby</th>\n",
+       "      <td>138.495762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>merge</th>\n",
+       "      <td>136.519031</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              cudf speedup vs. pandas\n",
+       "value_counts               282.901300\n",
+       "concat                     203.624680\n",
+       "groupby                    138.495762\n",
+       "merge                      136.519031"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = performance_df.plot.bar(\n",
+    "    color=\"#7400ff\",\n",
+    "    ylim=(1, 400),\n",
+    "    rot=0,\n",
+    "    xlabel=\"Operation\",\n",
+    "    ylabel=\"Speedup factor\",\n",
+    ")\n",
+    "ax.bar_label(ax.containers[0], fmt=\"%.0f\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Cleaning up used memory for later benchmarks\n",
+    "del pdf\n",
+    "del gdf\n",
+    "import gc\n",
+    "\n",
+    "_ = gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strings Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "num_rows = 300_000_000\n",
+    "pd_series = pd.Series(\n",
+    "    np.random.choice(\n",
+    "        [\"123\", \"56.234\", \"Walmart\", \"Costco\", \"rapids ai\"], size=num_rows\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "gd_series = cudf.from_pandas(pd_series)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_upper, cudf_upper = timeit_pandas_cudf(\n",
+    "    pd_series, gd_series, lambda s: s.str.upper(), number=20\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_contains, cudf_contains = timeit_pandas_cudf(\n",
+    "    pd_series, gd_series, lambda s: s.str.contains(r\"[0-9][a-z]\"), number=20\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas_isalpha, cudf_isalpha = timeit_pandas_cudf(\n",
+    "    pd_series, gd_series, lambda s: s.str.isalpha(), number=20\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "performance_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"cudf speedup vs. pandas\": [\n",
+    "            pandas_upper / cudf_upper,\n",
+    "            pandas_contains / cudf_contains,\n",
+    "            pandas_isalpha / cudf_isalpha,\n",
+    "        ],\n",
+    "    },\n",
+    "    index=[\"upper\", \"contains\", \"isalpha\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cudf speedup vs. pandas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>upper</th>\n",
+       "      <td>1832.120875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>contains</th>\n",
+       "      <td>1311.758332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>is_alpha</th>\n",
+       "      <td>5752.301339</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          cudf speedup vs. pandas\n",
+       "upper                 1832.120875\n",
+       "contains              1311.758332\n",
+       "is_alpha              5752.301339"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = performance_df.plot.bar(\n",
+    "    color=\"#7400ff\",\n",
+    "    ylim=(1, 7000),\n",
+    "    rot=0,\n",
+    "    xlabel=\"String method\",\n",
+    "    ylabel=\"Speedup factor\",\n",
+    ")\n",
+    "ax.bar_label(ax.containers[0], fmt=\"%.0f\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## User-defined function (UDF) performance (with JIT overhead)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first UDF runs include JIT compilation overhead, due to which the performance of first run and average of next few runs are compared separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999995</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999996</th>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999997</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999998</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999999</th>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         age\n",
+       "0         87\n",
+       "1         71\n",
+       "2         63\n",
+       "3         40\n",
+       "4         92\n",
+       "...      ...\n",
+       "9999995    4\n",
+       "9999996   28\n",
+       "9999997   31\n",
+       "9999998    4\n",
+       "9999999   47\n",
+       "\n",
+       "[10000000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_rows = 10_000_000\n",
+    "pdf_age = pd.DataFrame(\n",
+    "    {\n",
+    "        \"age\": np.random.randint(0, 100, num_rows),\n",
+    "    }\n",
+    ")\n",
+    "pdf_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999995</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999996</th>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999997</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999998</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9999999</th>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10000000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         age\n",
+       "0         87\n",
+       "1         71\n",
+       "2         63\n",
+       "3         40\n",
+       "4         92\n",
+       "...      ...\n",
+       "9999995    4\n",
+       "9999996   28\n",
+       "9999997   31\n",
+       "9999998    4\n",
+       "9999999   47\n",
+       "\n",
+       "[10000000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_age = cudf.from_pandas(pdf_age)\n",
+    "gdf_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def age_udf(row):\n",
+    "    if row[\"age\"] < 18:\n",
+    "        return 0\n",
+    "    elif 18 <= row[\"age\"] < 20:\n",
+    "        return 1\n",
+    "    elif 20 <= row[\"age\"] < 30:\n",
+    "        return 2\n",
+    "    elif 30 <= row[\"age\"] < 40:\n",
+    "        return 3\n",
+    "    elif 40 <= row[\"age\"] < 50:\n",
+    "        return 4\n",
+    "    elif 50 <= row[\"age\"] < 60:\n",
+    "        return 5\n",
+    "    elif 60 <= row[\"age\"] < 70:\n",
+    "        return 6\n",
+    "    else:\n",
+    "        return 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas_int_udf, cudf_int_udf = timeit_pandas_cudf(\n",
+    "    pdf_age, gdf_age, lambda df: df.apply(age_udf, axis=1), number=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def str_isupper_udf(row):\n",
+    "    if row.isupper():\n",
+    "        return 0\n",
+    "    else:\n",
+    "        return 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                    AI\n",
+       "1                   ABC\n",
+       "2           hello world\n",
+       "3                   abc\n",
+       "4           hello world\n",
+       "               ...     \n",
+       "99999995             AI\n",
+       "99999996             AI\n",
+       "99999997            abc\n",
+       "99999998            abc\n",
+       "99999999    hello world\n",
+       "Name: strings, Length: 100000000, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd_series = pd.Series(\n",
+    "    np.random.choice([\"ABC\", \"abc\", \"hello world\", \"AI\"], size=100_000_000),\n",
+    "    name=\"strings\",\n",
+    ")\n",
+    "pd_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                    AI\n",
+       "1                   ABC\n",
+       "2           hello world\n",
+       "3                   abc\n",
+       "4           hello world\n",
+       "               ...     \n",
+       "99999995             AI\n",
+       "99999996             AI\n",
+       "99999997            abc\n",
+       "99999998            abc\n",
+       "99999999    hello world\n",
+       "Name: strings, Length: 100000000, dtype: object"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gd_series = cudf.from_pandas(pd_series)\n",
+    "gd_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas_str_udf, cudf_str_udf = timeit_pandas_cudf(\n",
+    "    pd_series, gd_series, lambda s: s.apply(str_isupper_udf), number=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cudf speedup vs. pandas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Numeric</th>\n",
+       "      <td>362.091673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>String</th>\n",
+       "      <td>204.865789</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         cudf speedup vs. pandas\n",
+       "Numeric               362.091673\n",
+       "String                204.865789"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"cudf speedup vs. pandas\": [\n",
+    "            pandas_int_udf / cudf_int_udf,\n",
+    "            pandas_str_udf / cudf_str_udf,\n",
+    "        ]\n",
+    "    },\n",
+    "    index=[\"Numeric\", \"String\"],\n",
+    ")\n",
+    "performance_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below is the plot showing performance speedup in case of Numeric UDFs & String UDFs on their first runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = performance_df.plot.bar(\n",
+    "    color=\"#7400ff\",\n",
+    "    ylim=(1, 550),\n",
+    "    rot=0,\n",
+    "    xlabel=\"UDF Kind\",\n",
+    "    ylabel=\"Speedup factor\",\n",
+    ")\n",
+    "ax.bar_label(ax.containers[0], fmt=\"%.0f\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## User-defined function (UDF) performance (without JIT overhead)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_int_udf, cudf_int_udf = timeit_pandas_cudf(\n",
+    "    pdf_age, gdf_age, lambda df: df.apply(age_udf, axis=1), number=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas_str_udf, cudf_str_udf = timeit_pandas_cudf(\n",
+    "    pd_series, gd_series, lambda s: s.apply(str_isupper_udf), number=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cudf speedup vs. pandas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Numeric</th>\n",
+       "      <td>95448.144630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>String</th>\n",
+       "      <td>2587.570338</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         cudf speedup vs. pandas\n",
+       "Numeric             95448.144630\n",
+       "String               2587.570338"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"cudf speedup vs. pandas\": [\n",
+    "            pandas_int_udf / cudf_int_udf,\n",
+    "            pandas_str_udf / cudf_str_udf,\n",
+    "        ]\n",
+    "    },\n",
+    "    index=[\"Numeric\", \"String\"],\n",
+    ")\n",
+    "performance_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below is the plot showing performance speedup in case of Numeric UDFs & String UDFs on their consequent runs. In this case the speedup is massive because of no JIT overhead present."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = performance_df.plot.bar(\n",
+    "    color=\"#7400ff\",\n",
+    "    ylim=(1, 100000),\n",
+    "    rot=0,\n",
+    "    xlabel=\"UDF Kind\",\n",
+    "    ylabel=\"Speedup factor\",\n",
+    ")\n",
+    "ax.bar_label(ax.containers[0], fmt=\"%.0f\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## UDF Performance in GroupBy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "num_rows = 100_000_000\n",
+    "pdf = pd.DataFrame()\n",
+    "pdf[\"key\"] = np.random.randint(0, 2, num_rows)\n",
+    "pdf[\"val\"] = np.random.randint(0, 7, num_rows)\n",
+    "\n",
+    "\n",
+    "def custom_formula_udf(df):\n",
+    "    df[\"out\"] = df[\"key\"] * df[\"val\"] - 10\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "gdf = cudf.from_pandas(pdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pandas_udf_groupby, cudf_udf_groupby = timeit_pandas_cudf(\n",
+    "    pdf,\n",
+    "    gdf,\n",
+    "    lambda df: df.groupby([\"key\"], group_keys=False).apply(custom_formula_udf),\n",
+    "    number=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cudf speedup vs. pandas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Grouped UDF</th>\n",
+       "      <td>423.83606</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             cudf speedup vs. pandas\n",
+       "Grouped UDF                423.83606"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_df = pd.DataFrame(\n",
+    "    {\"cudf speedup vs. pandas\": [pandas_udf_groupby / cudf_udf_groupby]},\n",
+    "    index=[\"Grouped UDF\"],\n",
+    ")\n",
+    "performance_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = performance_df.plot.bar(\n",
+    "    color=\"#7400ff\", ylim=(1, 500), rot=0, ylabel=\"Speedup factor\"\n",
+    ")\n",
+    "ax.bar_label(ax.containers[0], fmt=\"%.0f\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# System Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CPU Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Architecture:                    x86_64\n",
+      "CPU op-mode(s):                  32-bit, 64-bit\n",
+      "Byte Order:                      Little Endian\n",
+      "Address sizes:                   46 bits physical, 48 bits virtual\n",
+      "CPU(s):                          80\n",
+      "On-line CPU(s) list:             0-79\n",
+      "Thread(s) per core:              2\n",
+      "Core(s) per socket:              20\n",
+      "Socket(s):                       2\n",
+      "NUMA node(s):                    2\n",
+      "Vendor ID:                       GenuineIntel\n",
+      "CPU family:                      6\n",
+      "Model:                           85\n",
+      "Model name:                      Intel(R) Xeon(R) Gold 6230 CPU @ 2.10GHz\n",
+      "Stepping:                        7\n",
+      "CPU MHz:                         800.049\n",
+      "CPU max MHz:                     3900.0000\n",
+      "CPU min MHz:                     800.0000\n",
+      "BogoMIPS:                        4200.00\n",
+      "Virtualization:                  VT-x\n",
+      "L1d cache:                       1.3 MiB\n",
+      "L1i cache:                       1.3 MiB\n",
+      "L2 cache:                        40 MiB\n",
+      "L3 cache:                        55 MiB\n",
+      "NUMA node0 CPU(s):               0-19,40-59\n",
+      "NUMA node1 CPU(s):               20-39,60-79\n",
+      "Vulnerability Itlb multihit:     KVM: Mitigation: Split huge pages\n",
+      "Vulnerability L1tf:              Not affected\n",
+      "Vulnerability Mds:               Not affected\n",
+      "Vulnerability Meltdown:          Not affected\n",
+      "Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled v\n",
+      "                                 ia prctl and seccomp\n",
+      "Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user\n",
+      "                                  pointer sanitization\n",
+      "Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RS\n",
+      "                                 B filling\n",
+      "Vulnerability Srbds:             Not affected\n",
+      "Vulnerability Tsx async abort:   Mitigation; TSX disabled\n",
+      "Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtr\n",
+      "                                 r pge mca cmov pat pse36 clflush dts acpi mmx f\n",
+      "                                 xsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rd\n",
+      "                                 tscp lm constant_tsc art arch_perfmon pebs bts \n",
+      "                                 rep_good nopl xtopology nonstop_tsc cpuid aperf\n",
+      "                                 mperf pni pclmulqdq dtes64 monitor ds_cpl vmx s\n",
+      "                                 mx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid d\n",
+      "                                 ca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadli\n",
+      "                                 ne_timer aes xsave avx f16c rdrand lahf_lm abm \n",
+      "                                 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 inv\n",
+      "                                 pcid_single intel_ppin ssbd mba ibrs ibpb stibp\n",
+      "                                  ibrs_enhanced tpr_shadow vnmi flexpriority ept\n",
+      "                                  vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep\n",
+      "                                  bmi2 erms invpcid cqm mpx rdt_a avx512f avx512\n",
+      "                                 dq rdseed adx smap clflushopt clwb intel_pt avx\n",
+      "                                 512cd avx512bw avx512vl xsaveopt xsavec xgetbv1\n",
+      "                                  xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm\n",
+      "                                 _mbm_local dtherm ida arat pln pts hwp hwp_act_\n",
+      "                                 window hwp_epp hwp_pkg_req pku ospke avx512_vnn\n",
+      "                                 i md_clear flush_l1d arch_capabilities\n"
+     ]
+    }
+   ],
+   "source": [
+    "!lscpu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GPU Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon Feb  6 17:43:52 2023       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 525.60.04    Driver Version: 525.60.04    CUDA Version: 12.0     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  H100 80GB HBM2e     On   | 00000000:1E:00.0 Off |                    0 |\n",
+      "| N/A   30C    P0    60W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
+      "|                               |                      |             Disabled |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  H100 80GB HBM2e     On   | 00000000:22:00.0 Off |                    0 |\n",
+      "| N/A   30C    P0    60W / 700W |      0MiB / 81559MiB |      0%      Default |\n",
+      "|                               |                      |             Disabled |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b4f3463dcc83b00b9c65791e378b11fabec52613a2a7831cd4af76c548ff6047"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/dask_cudf/Makefile b/docs/dask_cudf/Makefile
new file mode 100644
index 0000000..3772111
--- /dev/null
+++ b/docs/dask_cudf/Makefile
@@ -0,0 +1,21 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?= -n -v
+SPHINXBUILD   ?= sphinx-build
+SPHINXPROJ    = dask_cudf
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/dask_cudf/make.bat b/docs/dask_cudf/make.bat
new file mode 100644
index 0000000..747ffb7
--- /dev/null
+++ b/docs/dask_cudf/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/dask_cudf/source/_static/RAPIDS-logo-purple.png b/docs/dask_cudf/source/_static/RAPIDS-logo-purple.png
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diff --git a/docs/dask_cudf/source/api.rst b/docs/dask_cudf/source/api.rst
new file mode 100644
index 0000000..893f5dd
--- /dev/null
+++ b/docs/dask_cudf/source/api.rst
@@ -0,0 +1,79 @@
+===============
+ API reference
+===============
+
+This page provides a list of all publicly accessible modules, methods,
+and classes in the ``dask_cudf`` namespace.
+
+
+Creating and storing DataFrames
+===============================
+
+:doc:`Like Dask <dask:dataframe-create>`, Dask-cuDF supports creation
+of DataFrames from a variety of storage formats. For on-disk data that
+are not supported directly in Dask-cuDF, we recommend using Dask's
+data reading facilities, followed by calling
+:func:`.from_dask_dataframe` to obtain a Dask-cuDF object.
+
+.. automodule:: dask_cudf
+   :members:
+      from_cudf,
+      from_dask_dataframe,
+      read_csv,
+      read_json,
+      read_orc,
+      to_orc,
+      read_text,
+      read_parquet
+
+.. warning::
+
+   FIXME: where should the following live?
+
+   .. autofunction:: dask_cudf.concat
+
+   .. autofunction:: dask_cudf.from_delayed
+
+Grouping
+========
+
+As discussed in the :doc:`Dask documentation for groupby
+<dask:dataframe-groupby>`, ``groupby``, ``join``, and ``merge``, and
+similar operations that require matching up rows of a DataFrame become
+significantly more challenging in a parallel setting than they are in
+serial. Dask-cuDF has the same challenges, however for certain groupby
+operations, we can take advantage of functionality in cuDF that allows
+us to compute multiple aggregations at once. There are therefore two
+interfaces to grouping in Dask-cuDF, the general
+:meth:`DataFrame.groupby` which returns a
+:class:`.CudfDataFrameGroupBy` object, and a specialized
+:func:`.groupby_agg`. Generally speaking, you should not need to call
+:func:`.groupby_agg` directly, since Dask-cuDF will arrange to call it
+if possible.
+
+.. autoclass:: dask_cudf.groupby.CudfDataFrameGroupBy
+   :members:
+   :inherited-members:
+   :show-inheritance:
+
+.. autofunction:: dask_cudf.groupby_agg
+
+
+DataFrames and Series
+=====================
+
+The core distributed objects provided by Dask-cuDF are the
+:class:`.DataFrame` and :class:`.Series`. These inherit respectively
+from :class:`dask.dataframe.DataFrame` and
+:class:`dask.dataframe.Series`, and so the API is essentially
+identical. The full API is provided below.
+
+.. autoclass:: dask_cudf.DataFrame
+   :members:
+   :inherited-members:
+   :show-inheritance:
+
+.. autoclass:: dask_cudf.Series
+   :members:
+   :inherited-members:
+   :show-inheritance:
diff --git a/docs/dask_cudf/source/conf.py b/docs/dask_cudf/source/conf.py
new file mode 100644
index 0000000..ad629b5
--- /dev/null
+++ b/docs/dask_cudf/source/conf.py
@@ -0,0 +1,82 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "dask-cudf"
+copyright = "2018-2023, NVIDIA Corporation"
+author = "NVIDIA Corporation"
+version = '23.10'
+release = '23.10.00'
+
+language = "en"
+
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.autodoc",
+    "sphinx_copybutton",
+    "numpydoc",
+    "IPython.sphinxext.ipython_console_highlighting",
+    "IPython.sphinxext.ipython_directive",
+    "myst_nb",
+]
+
+templates_path = ["_templates"]
+exclude_patterns = []
+
+copybutton_prompt_text = ">>> "
+
+# Enable automatic generation of systematic, namespaced labels for sections
+myst_heading_anchors = 2
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = "pydata_sphinx_theme"
+html_logo = "_static/RAPIDS-logo-purple.png"
+htmlhelp_basename = "dask-cudfdoc"
+html_use_modindex = True
+
+html_static_path = ["_static"]
+
+pygments_style = "sphinx"
+
+html_theme_options = {
+    "external_links": [],
+    "github_url": "https://github.com/rapidsai/cudf",
+    "twitter_url": "https://twitter.com/rapidsai",
+    "show_toc_level": 1,
+    "navbar_align": "right",
+}
+include_pandas_compat = True
+
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "cupy": ("https://docs.cupy.dev/en/stable/", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "pyarrow": ("https://arrow.apache.org/docs/", None),
+    "cudf": ("https://docs.rapids.ai/api/cudf/stable/", None),
+    "dask": ("https://docs.dask.org/en/stable/", None),
+    "pandas": ("https://pandas.pydata.org/docs/", None),
+}
+
+numpydoc_show_inherited_class_members = True
+numpydoc_class_members_toctree = False
+numpydoc_attributes_as_param_list = False
+
+
+def setup(app):
+    app.add_css_file("https://docs.rapids.ai/assets/css/custom.css")
+    app.add_js_file(
+        "https://docs.rapids.ai/assets/js/custom.js", loading_method="defer"
+    )
diff --git a/docs/dask_cudf/source/index.rst b/docs/dask_cudf/source/index.rst
new file mode 100644
index 0000000..0442ab0
--- /dev/null
+++ b/docs/dask_cudf/source/index.rst
@@ -0,0 +1,112 @@
+.. dask-cudf documentation coordinating file, created by
+   sphinx-quickstart on Mon Feb  6 18:48:11 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to dask-cudf's documentation!
+=====================================
+
+Dask-cuDF is an extension library for the `Dask <https://dask.org>`__
+parallel computing framework that provides a `cuDF
+<https://docs.rapids.ai/api/cudf/stable/>`__-backed distributed
+dataframe with the same API as `Dask dataframes
+<https://docs.dask.org/en/stable/dataframe.html>`__.
+
+If you are familiar with Dask and `pandas <pandas.pydata.org>`__ or
+`cuDF <https://docs.rapids.ai/api/cudf/stable/>`__, then Dask-cuDF
+should feel familiar to you. If not, we recommend starting with `10
+minutes to Dask
+<https://docs.dask.org/en/stable/10-minutes-to-dask.html>`__ followed
+by `10 minutes to cuDF and Dask-cuDF
+<https://docs.rapids.ai/api/cudf/stable/user_guide/10min.html>`__.
+
+When running on multi-GPU systems, `Dask-CUDA
+<https://docs.rapids.ai/api/dask-cuda/stable/>`__ is recommended to
+simplify the setup of the cluster, taking advantage of all features of
+the GPU and networking hardware.
+
+Using Dask-cuDF
+---------------
+
+When installed, Dask-cuDF registers itself as a dataframe backend for
+Dask. This means that in many cases, using cuDF-backed dataframes requires
+only small changes to an existing workflow. The minimal change is to
+select cuDF as the dataframe backend in :doc:`Dask's
+configuration <dask:configuration>`. To do so, we must set the option
+``dataframe.backend`` to ``cudf``. From Python, this can be achieved
+like so::
+
+  import dask
+
+  dask.config.set({"dataframe.backend": "cudf"})
+
+Alternatively, you can set ``DASK_DATAFRAME__BACKEND=cudf`` in the
+environment before running your code.
+
+Dataframe creation from on-disk formats
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If your workflow creates Dask dataframes from on-disk formats
+(for example using :func:`dask.dataframe.read_parquet`), then setting
+the backend may well be enough to migrate your workflow.
+
+For example, consider reading a dataframe from parquet::
+
+   import dask.dataframe as dd
+
+   # By default, we obtain a pandas-backed dataframe
+   df = dd.read_parquet("data.parquet", ...)
+
+
+To obtain a cuDF-backed dataframe, we must set the
+``dataframe.backend`` configuration option::
+
+  import dask
+  import dask.dataframe as dd
+
+  dask.config.set({"dataframe.backend": "cudf"})
+  # This gives us a cuDF-backed dataframe
+  df = dd.read_parquet("data.parquet", ...)
+
+This code will use cuDF's GPU-accelerated :func:`parquet reader
+<cudf.read_parquet>` to read partitions of the data.
+
+Dataframe creation from in-memory formats
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you already have a dataframe in memory and want to convert it to a
+cuDF-backend one, there are two options depending on whether the
+dataframe is already a Dask one or not. If you have a Dask dataframe,
+then you can call :func:`dask.dataframe.to_backend` passing ``"cudf"``
+as the backend; if you have a pandas dataframe then you can either
+call :func:`dask.dataframe.from_pandas` followed by
+:func:`~dask.dataframe.to_backend` or first convert the dataframe with
+:func:`cudf.from_pandas` and then parallelise this with
+:func:`dask_cudf.from_cudf`.
+
+API Reference
+-------------
+
+Generally speaking, Dask-cuDF tries to offer exactly the same API as
+Dask itself. There are, however, some minor differences mostly because
+cuDF does not :doc:`perfectly mirror <cudf:user_guide/PandasCompat>`
+the pandas API, or because cuDF provides additional configuration
+flags (these mostly occur in data reading and writing interfaces).
+
+As a result, straightforward workflows can be migrated without too
+much trouble, but more complex ones that utilise more features may
+need a bit of tweaking. The API documentation describes details of the
+differences and all functionality that Dask-cuDF supports.
+
+.. toctree::
+   :maxdepth: 2
+
+   api
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/fetch_rapids.cmake b/fetch_rapids.cmake
new file mode 100644
index 0000000..4a68c7d
--- /dev/null
+++ b/fetch_rapids.cmake
@@ -0,0 +1,19 @@
+# =============================================================================
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUDF_RAPIDS.cmake)
+  file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-23.10/RAPIDS.cmake
+       ${CMAKE_CURRENT_BINARY_DIR}/CUDF_RAPIDS.cmake
+  )
+endif()
+include(${CMAKE_CURRENT_BINARY_DIR}/CUDF_RAPIDS.cmake)
diff --git a/img/GDF_community.png b/img/GDF_community.png
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diff --git a/java/README.md b/java/README.md
new file mode 100644
index 0000000..2d8e219
--- /dev/null
+++ b/java/README.md
@@ -0,0 +1,126 @@
+# Java API for cudf
+
+This project provides java bindings for cudf, to be able to process large amounts of data on
+a GPU. This is still a work in progress so some APIs may change until the 1.0 release.
+
+## Behavior on Systems with Multiple GPUs
+
+The cudf project currently works with a single GPU per process. The CUDA runtime
+assigns the default GPU to all new operating system threads when they start to
+interact with CUDA. This means that if you use a multi-threaded environment,
+like Java processes tend to be, and try to use a non-default GPU for cudf you
+can run into hard to debug issues including resource leaks, invalid states,
+application crashes, and poor performance.
+
+To prevent this the Java cudf API will remember the device used to initialize
+the Rapids Memory Manager (RMM), and automatically set the thread's active
+device to it, if needed. It will not set the device back when the cudf call
+completes. This is different from most CUDA libraries and can result in
+unexpected behavior if you try to mix these libraries using the same thread.
+
+## Dependency
+
+This is a fat jar with the binary dependencies packaged in the jar.  This means the jar will only
+run on platforms the jar was compiled for.  When this is in an official Maven repository we will
+list the platforms that it is compiled and tested for.  In the mean time you will need to build it
+yourself. In official releases there should be no classifier on the jar and it should run against
+most modern cuda drivers.
+
+```xml
+<dependency>
+    <groupId>ai.rapids</groupId>
+    <artifactId>cudf</artifactId>
+    <version>${cudf.version}</version>
+</dependency>
+```
+
+In some cases there may be a classifier to indicate the version of cuda required. See the
+[Build From Source](#build-from-source) section below for more information about when this
+can happen. No official release of the jar will have a classifier on it.
+
+CUDA 11.0:
+```xml
+<dependency>
+    <groupId>ai.rapids</groupId>
+    <artifactId>cudf</artifactId>
+    <classifier>cuda11</classifier>
+    <version>${cudf.version}</version>
+</dependency>
+```
+
+## Build From Source
+
+Build [libcudf](../cpp) first, and make sure the JDK is installed and available. Specify
+the cmake option `-DCUDF_USE_ARROW_STATIC=ON -DCUDF_ENABLE_ARROW_S3=OFF` when building so
+that Apache Arrow is linked statically to libcudf, as this will help create a jar that
+does not require Arrow and its dependencies to be available in the runtime environment.
+
+After building libcudf, the Java bindings can be built via Maven, e.g.:
+```
+mvn clean install
+```
+
+If you have a compatible GPU on your build system the tests will use it.  If not you will see a
+lot of skipped tests.
+
+### Using the Java CI Docker Image
+
+If you are interested in building a Java cudf jar that is similar to the official releases
+that can run on all modern Linux systems, see the [Java CI README](ci/README.md) for
+instructions on how to build within a Docker environment using devtoolset. Note that
+building the jar without the Docker setup and script will likely produce a jar that can
+only run in environments similar to that of the build machine.
+
+If you decide to build without Docker and the build script, examining the cmake and Maven
+settings in the [Java CI build script](ci/build-in-docker.sh) can be helpful if you are
+encountering difficulties during the build.
+
+## Statically Linking the CUDA Runtime
+
+If you use the default cmake options libcudart will be dynamically linked to libcudf and libcudfjni.
+To build with a static CUDA runtime, build libcudf with the `-DCUDA_STATIC_RUNTIME=ON` as a cmake
+parameter, and similarly build with `-DCUDA_STATIC_RUNTIME=ON` when building the Java bindings
+with Maven.
+
+### Building with a libcudf Archive
+
+When statically linking the CUDA runtime, it is recommended to build cuDF as an archive rather than
+a shared library, as this allows the Java bindings to only have a single shared library that uses
+the CUDA runtime. To build libcudf as an archive, specify `-DBUILD_SHARED_LIBS=OFF` as a cmake
+parameter when building libcudf, then specify `-DCUDF_JNI_LIBCUDF_STATIC=ON` when building the Java
+bindings with Maven.
+
+## Per-thread Default Stream
+
+The JNI code can be built with *per-thread default stream* (PTDS), which gives each host thread its
+own default CUDA stream, and can potentially increase the overlap of data copying and compute
+between different threads (see
+[blog post](https://devblogs.nvidia.com/gpu-pro-tip-cuda-7-streams-simplify-concurrency/)).
+
+Since the PTDS option is for each compilation unit, it should be done at the same time across the
+whole codebase. To enable PTDS, first build cuDF:
+```shell script
+cd src/cudf/cpp/build
+cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=ON
+make -j`nproc`
+make install
+```
+
+then build the jar:
+```shell script
+cd src/cudf/java
+mvn clean install -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=ON
+```
+
+## GPUDirect Storage (GDS)
+
+The JNI code can be built with *GPUDirect Storage* (GDS) support, which enables direct copying
+between GPU device buffers and supported filesystems (see
+https://docs.nvidia.com/gpudirect-storage/).
+
+To enable GDS support, first make sure GDS is installed (see
+https://docs.nvidia.com/gpudirect-storage/troubleshooting-guide/index.html), then run:
+```shell script
+cd src/cudf/java
+mvn clean install -DUSE_GDS=ON
+```
diff --git a/java/buildscripts/build-info b/java/buildscripts/build-info
new file mode 100755
index 0000000..db42cdf
--- /dev/null
+++ b/java/buildscripts/build-info
@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This script generates the build info.
+# Arguments:
+#   version - The current version of cudf java code
+
+echo_build_properties() {
+  echo version=$1
+  echo user=$USER
+  echo revision=$(git rev-parse HEAD)
+  echo branch=$(git rev-parse --abbrev-ref HEAD)
+  echo date=$(date -u +%Y-%m-%dT%H:%M:%SZ)
+}
+
+echo_build_properties $1
diff --git a/java/ci/Dockerfile.centos7 b/java/ci/Dockerfile.centos7
new file mode 100644
index 0000000..b2c6208
--- /dev/null
+++ b/java/ci/Dockerfile.centos7
@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+###
+# Build the image for cudf development environment.
+#
+# Arguments: CUDA_VERSION=11.X.Y
+#
+###
+ARG CUDA_VERSION=11.8.0
+FROM nvidia/cuda:$CUDA_VERSION-devel-centos7
+
+### Install basic requirements
+ARG DEVTOOLSET_VERSION=11
+RUN yum install -y centos-release-scl
+RUN yum install -y devtoolset-${DEVTOOLSET_VERSION} epel-release
+RUN yum install -y git zlib-devel maven tar wget patch ninja-build
+
+## pre-create the CMAKE_INSTALL_PREFIX folder, set writable by any user for Jenkins
+RUN mkdir /usr/local/rapids && mkdir /rapids && chmod 777 /usr/local/rapids && chmod 777 /rapids
+
+ARG CMAKE_VERSION=3.26.4
+RUN cd /usr/local/ && wget --quiet https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-x86_64.tar.gz && \
+   tar zxf cmake-${CMAKE_VERSION}-linux-x86_64.tar.gz && \
+   rm cmake-${CMAKE_VERSION}-linux-x86_64.tar.gz
+
+ENV PATH /usr/local/cmake-${CMAKE_VERSION}-linux-x86_64/bin:$PATH
+
+ARG CCACHE_VERSION=4.6
+RUN cd /tmp && wget --quiet https://github.com/ccache/ccache/releases/download/v${CCACHE_VERSION}/ccache-${CCACHE_VERSION}.tar.gz && \
+   tar zxf ccache-${CCACHE_VERSION}.tar.gz && \
+   rm ccache-${CCACHE_VERSION}.tar.gz && \
+   cd ccache-${CCACHE_VERSION} && \
+   mkdir build && \
+   cd build && \
+   scl enable devtoolset-${DEVTOOLSET_VERSION} \
+      "cmake .. \
+         -DCMAKE_BUILD_TYPE=Release \
+         -DZSTD_FROM_INTERNET=ON \
+         -DREDIS_STORAGE_BACKEND=OFF && \
+      cmake --build . --parallel ${PARALLEL_LEVEL} --target install" && \
+   cd ../.. && \
+   rm -rf ccache-${CCACHE_VERSION}
diff --git a/java/ci/README.md b/java/ci/README.md
new file mode 100644
index 0000000..e9599b3
--- /dev/null
+++ b/java/ci/README.md
@@ -0,0 +1,50 @@
+# Build Jar artifact of cuDF
+
+## Build the docker image
+
+### Prerequisite
+
+1. Docker should be installed.
+2. [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) should be installed.
+
+### Build the docker image
+
+In the root path of cuDF repo, run below command to build the docker image.
+```bash
+docker build -f java/ci/Dockerfile.centos7 --build-arg CUDA_VERSION=11.8.0 -t cudf-build:11.8.0-devel-centos7 .
+```
+
+The following CUDA versions are supported w/ CUDA Enhanced Compatibility:
+* CUDA 11.0+
+
+Change the --build-arg CUDA_VERSION to what you need.
+You can replace the tag "cudf-build:11.8.0-devel-centos7" with another name you like.
+
+## Start the docker then build
+
+### Start the docker
+
+Run below command to start a docker container with GPU.
+```bash
+nvidia-docker run -it cudf-build:11.8.0-devel-centos7 bash
+```
+
+### Download the cuDF source code
+
+You can download the cuDF repo in the docker container or you can mount it into the container.
+Here I choose to download again in the container.
+```bash
+git clone --recursive https://github.com/rapidsai/cudf.git -b branch-23.10
+```
+
+### Build cuDF jar with devtoolset
+
+```bash
+cd cudf
+export WORKSPACE=`pwd`
+scl enable devtoolset-11 "java/ci/build-in-docker.sh"
+```
+
+### The output
+
+You can find the cuDF jar in java/target/ like cudf-23.10.0-SNAPSHOT-cuda11.jar.
diff --git a/java/ci/build-in-docker.sh b/java/ci/build-in-docker.sh
new file mode 100755
index 0000000..72b1742
--- /dev/null
+++ b/java/ci/build-in-docker.sh
@@ -0,0 +1,99 @@
+#!/bin/bash
+
+#
+# Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+set -ex
+gcc --version
+
+SKIP_JAVA_TESTS=${SKIP_JAVA_TESTS:-true}
+BUILD_CPP_TESTS=${BUILD_CPP_TESTS:-OFF}
+ENABLE_CUDA_STATIC_RUNTIME=${ENABLE_CUDA_STATIC_RUNTIME:-ON}
+ENABLE_PTDS=${ENABLE_PTDS:-ON}
+RMM_LOGGING_LEVEL=${RMM_LOGGING_LEVEL:-OFF}
+ENABLE_NVTX=${ENABLE_NVTX:-ON}
+ENABLE_GDS=${ENABLE_GDS:-OFF}
+OUT=${OUT:-out}
+CMAKE_GENERATOR=${CMAKE_GENERATOR:-Ninja}
+
+SIGN_FILE=$1
+#Set absolute path for OUT_PATH
+OUT_PATH="$WORKSPACE/$OUT"
+
+# set on Jenkins parameter
+echo "SIGN_FILE: $SIGN_FILE,\
+ SKIP_JAVA_TESTS: $SKIP_JAVA_TESTS,\
+ BUILD_CPP_TESTS: $BUILD_CPP_TESTS,\
+ ENABLE_CUDA_STATIC_RUNTIME: $ENABLE_CUDA_STATIC_RUNTIME,\
+ ENABLED_PTDS: $ENABLE_PTDS,\
+ ENABLE_NVTX: $ENABLE_NVTX,\
+ ENABLE_GDS: $ENABLE_GDS,\
+ RMM_LOGGING_LEVEL: $RMM_LOGGING_LEVEL,\
+ OUT_PATH: $OUT_PATH"
+
+INSTALL_PREFIX=/usr/local/rapids
+export GIT_COMMITTER_NAME="ci"
+export GIT_COMMITTER_EMAIL="ci@nvidia.com"
+export CUDACXX=/usr/local/cuda/bin/nvcc
+export LIBCUDF_KERNEL_CACHE_PATH=/rapids
+
+###### Build libcudf ######
+rm -rf "$WORKSPACE/cpp/build"
+mkdir -p "$WORKSPACE/cpp/build"
+cd "$WORKSPACE/cpp/build"
+cmake .. -G"${CMAKE_GENERATOR}" \
+         -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX \
+         -DCUDA_STATIC_RUNTIME=$ENABLE_CUDA_STATIC_RUNTIME \
+         -DUSE_NVTX=$ENABLE_NVTX \
+         -DCUDF_USE_ARROW_STATIC=ON \
+         -DCUDF_ENABLE_ARROW_S3=OFF \
+         -DBUILD_TESTS=$BUILD_CPP_TESTS \
+         -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=$ENABLE_PTDS \
+         -DRMM_LOGGING_LEVEL=$RMM_LOGGING_LEVEL \
+         -DBUILD_SHARED_LIBS=OFF
+
+if [[ -z "${PARALLEL_LEVEL}" ]]; then
+    cmake --build .
+else
+    cmake --build . --parallel $PARALLEL_LEVEL
+fi
+cmake --install .
+
+###### Build cudf jar ######
+BUILD_ARG="-Dmaven.repo.local=\"$WORKSPACE/.m2\"\
+ -DskipTests=$SKIP_JAVA_TESTS\
+ -DCUDF_USE_PER_THREAD_DEFAULT_STREAM=$ENABLE_PTDS\
+ -DCUDA_STATIC_RUNTIME=$ENABLE_CUDA_STATIC_RUNTIME\
+ -DCUDF_JNI_LIBCUDF_STATIC=ON\
+ -DUSE_GDS=$ENABLE_GDS -Dtest=*,!CuFileTest,!CudaFatalTest,!ColumnViewNonEmptyNullsTest"
+
+if [ "$SIGN_FILE" == true ]; then
+    # Build javadoc and sources only when SIGN_FILE is true
+    BUILD_ARG="$BUILD_ARG -Prelease"
+fi
+
+if [ -f "$WORKSPACE/java/ci/settings.xml" ]; then
+    # Build with an internal settings.xml
+    BUILD_ARG="$BUILD_ARG -s \"$WORKSPACE/java/ci/settings.xml\""
+fi
+
+cd "$WORKSPACE/java"
+mvn -B clean package $BUILD_ARG
+
+###### Stash Jar files ######
+rm -rf $OUT_PATH
+mkdir -p $OUT_PATH
+cp -f target/*.jar $OUT_PATH
diff --git a/java/dev/cudf_java_styles.xml b/java/dev/cudf_java_styles.xml
new file mode 100644
index 0000000..547b47a
--- /dev/null
+++ b/java/dev/cudf_java_styles.xml
@@ -0,0 +1,22 @@
+<code_scheme name="cudf_java" version="173">
+  <JavaCodeStyleSettings>
+    <option name="JD_ADD_BLANK_AFTER_DESCRIPTION" value="false" />
+  </JavaCodeStyleSettings>
+  <codeStyleSettings language="JAVA">
+    <option name="RIGHT_MARGIN" value="100" />
+    <option name="KEEP_SIMPLE_BLOCKS_IN_ONE_LINE" value="true" />
+    <option name="KEEP_SIMPLE_METHODS_IN_ONE_LINE" value="true" />
+    <option name="KEEP_SIMPLE_LAMBDAS_IN_ONE_LINE" value="true" />
+    <option name="KEEP_SIMPLE_CLASSES_IN_ONE_LINE" value="true" />
+    <option name="KEEP_MULTIPLE_EXPRESSIONS_IN_ONE_LINE" value="true" />
+    <option name="WRAP_LONG_LINES" value="true" />
+    <indentOptions>
+      <option name="INDENT_SIZE" value="2" />
+      <option name="CONTINUATION_INDENT_SIZE" value="4" />
+      <option name="TAB_SIZE" value="2" />
+    </indentOptions>
+    <arrangement>
+      <groups />
+    </arrangement>
+  </codeStyleSettings>
+</code_scheme>
diff --git a/java/pom.xml b/java/pom.xml
new file mode 100644
index 0000000..afcc0e1
--- /dev/null
+++ b/java/pom.xml
@@ -0,0 +1,611 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>ai.rapids</groupId>
+    <artifactId>cudf</artifactId>
+    <version>23.10.0-SNAPSHOT</version>
+
+    <name>cudfjni</name>
+    <description>
+        This project provides java bindings for cudf, to be able to process large amounts of data on a GPU.
+        This is still a work in progress so some APIs may change until the 1.0 release.
+    </description>
+    <url>http://ai.rapids</url>
+
+    <licenses>
+        <license>
+            <name>Apache License, Version 2.0</name>
+            <url>https://www.apache.org/licenses/LICENSE-2.0.txt</url>
+            <distribution>repo</distribution>
+            <comments>A business-friendly OSS license</comments>
+        </license>
+    </licenses>
+    <scm>
+        <connection>scm:git:https://github.com/rapidsai/cudf.git</connection>
+        <developerConnection>scm:git:git@github.com:rapidsai/cudf.git</developerConnection>
+        <tag>HEAD</tag>
+        <url>https://github.com/rapidsai/cudf</url>
+    </scm>
+    <developers>
+        <developer>
+            <id>revans2</id>
+            <name>Robert Evans</name>
+            <email>bobby@apache.org</email>
+            <roles>
+                <role>Committer</role>
+            </roles>
+            <timezone>-6</timezone>
+        </developer>
+        <developer>
+            <id>jlowe</id>
+            <name>Jason Lowe</name>
+            <email>jlowe@nvidia.com</email>
+            <roles>
+                <role>Committer</role>
+            </roles>
+            <timezone>-6</timezone>
+        </developer>
+        <developer>
+            <id>abellina</id>
+            <name>Alessandro Bellina</name>
+            <email>abellina@nvidia.com</email>
+            <roles>
+                <role>Committer</role>
+            </roles>
+            <timezone>-6</timezone>
+        </developer>
+        <developer>
+            <id>tgraves</id>
+            <name>Thomas Graves</name>
+            <email>tgraves@nvidia.com</email>
+            <roles>
+                <role>Committer</role>
+            </roles>
+            <timezone>-6</timezone>
+        </developer>
+        <developer>
+            <id>rjafri</id>
+            <name>Raza Jafri</name>
+            <email>rjafri@nvidia.com</email>
+            <roles>
+                <role>Committer</role>
+            </roles>
+            <timezone>-8</timezone>
+        </developer>
+        <developer>
+            <id>nartal</id>
+            <name>Niranjan Artal</name>
+            <email>nartal@nvidia.com</email>
+            <roles>
+                <role>Committer</role>
+            </roles>
+            <timezone>-8</timezone>
+        </developer>
+    </developers>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>${slf4j.version}</version>
+            <scope>compile</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-api</artifactId>
+            <version>${junit.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-params</artifactId>
+            <version>${junit.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-simple</artifactId>
+            <version>${slf4j.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.mockito</groupId>
+            <artifactId>mockito-core</artifactId>
+            <version>2.25.0</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.arrow</groupId>
+            <artifactId>arrow-vector</artifactId>
+            <version>${arrow.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.parquet</groupId>
+            <artifactId>parquet-avro</artifactId>
+            <version>1.10.0</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-common</artifactId>
+            <version>3.2.4</version>
+            <scope>test</scope>
+        </dependency>
+    </dependencies>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <maven.compiler.source>1.8</maven.compiler.source>
+        <maven.compiler.target>1.8</maven.compiler.target>
+        <junit.version>5.4.2</junit.version>
+        <ai.rapids.refcount.debug>false</ai.rapids.refcount.debug>
+        <ai.rapids.cudf.nvtx.enabled>false</ai.rapids.cudf.nvtx.enabled>
+        <native.build.path>${basedir}/target/cmake-build</native.build.path>
+        <skipNativeCopy>false</skipNativeCopy>
+        <cxx.flags/>
+        <CMAKE_EXPORT_COMPILE_COMMANDS>OFF</CMAKE_EXPORT_COMPILE_COMMANDS>
+        <CUDA_STATIC_RUNTIME>OFF</CUDA_STATIC_RUNTIME>
+        <CUDF_USE_PER_THREAD_DEFAULT_STREAM>OFF</CUDF_USE_PER_THREAD_DEFAULT_STREAM>
+        <USE_GDS>OFF</USE_GDS>
+        <GPU_ARCHS>RAPIDS</GPU_ARCHS>
+        <CUDF_JNI_LIBCUDF_STATIC>OFF</CUDF_JNI_LIBCUDF_STATIC>
+        <native.build.path>${project.build.directory}/cmake-build</native.build.path>
+        <slf4j.version>1.7.30</slf4j.version>
+        <arrow.version>0.15.1</arrow.version>
+        <parallel.level>4</parallel.level>
+        <CUDF_CPP_BUILD_DIR/>
+        <cmake.ccache.opts/>
+    </properties>
+
+    <profiles>
+        <profile>
+            <id>no-cxx-deprecation-warnings</id>
+            <properties>
+                <cxx.flags>-Wno-deprecated-declarations</cxx.flags>
+            </properties>
+        </profile>
+        <profile>
+            <id>default-tests</id>
+            <build>
+                <plugins>
+                    <plugin>
+                        <artifactId>maven-surefire-plugin</artifactId>
+                        <configuration>
+                            <excludes>
+                                <exclude>**/CudaFatalTest.java</exclude>
+                                <exclude>**/ColumnViewNonEmptyNullsTest.java</exclude>
+                            </excludes>
+                        </configuration>
+                        <executions>
+                            <execution>
+                                <id>main-tests</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                            </execution>
+                            <execution>
+                                <id>non-empty-null-test</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                                <configuration>
+                                    <argLine>-da:ai.rapids.cudf.AssertEmptyNulls</argLine>
+                                    <test>*/ColumnViewNonEmptyNullsTest.java</test>
+                                </configuration>
+                            </execution>
+                            <execution>
+                                <id>fatal-cuda-test</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                                <configuration>
+                                    <reuseForks>false</reuseForks>
+                                    <test>*/CudaFatalTest.java</test>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
+        <profile>
+            <id>no-cufile-tests</id>
+            <activation>
+                <property>
+                    <name>USE_GDS</name>
+                    <value>!ON</value>
+                </property>
+            </activation>
+            <build>
+                <plugins>
+                    <plugin>
+                        <artifactId>maven-surefire-plugin</artifactId>
+                        <configuration>
+                            <excludes>
+                                <exclude>**/ColumnViewNonEmptyNullsTest.java</exclude>
+                                <exclude>**/CuFileTest.java</exclude>
+                                <exclude>**/CudaFatalTest.java</exclude>
+                            </excludes>
+                        </configuration>
+                        <executions>
+                            <execution>
+                                <id>main-tests</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                            </execution>
+                            <execution>
+                                <id>fatal-cuda-test</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                                <configuration>
+                                    <reuseForks>false</reuseForks>
+                                    <test>*/CudaFatalTest.java</test>
+                                </configuration>
+                            </execution>
+                            <execution>
+                                <id>non-empty-null-test</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                                <configuration>
+                                    <argLine>-da:ai.rapids.cudf.AssertEmptyNulls</argLine>
+                                    <test>*/ColumnViewNonEmptyNullsTest.java</test>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
+        <profile>
+            <id>release</id>
+            <distributionManagement>
+                <snapshotRepository>
+                    <id>ossrh</id>
+                    <url>https://oss.sonatype.org/content/repositories/snapshots</url>
+                </snapshotRepository>
+            </distributionManagement>
+            <properties>
+                <gpg.passphrase>${GPG_PASSPHRASE}</gpg.passphrase>
+            </properties>
+            <build>
+                <plugins>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-source-plugin</artifactId>
+                        <version>2.2.1</version>
+                        <executions>
+                            <execution>
+                                <id>attach-sources</id>
+                                <goals>
+                                    <goal>jar-no-fork</goal>
+                                </goals>
+                            </execution>
+                            <execution>
+                                <id>test-jars</id>
+                                <goals>
+                                    <goal>test-jar</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                        <configuration>
+                            <excludeResources>true</excludeResources>
+                        </configuration>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-javadoc-plugin</artifactId>
+                        <version>2.9.1</version>
+                        <executions>
+                            <execution>
+                                <id>attach-javadocs</id>
+                                <goals>
+                                    <goal>jar</goal>
+                                </goals>
+                                <configuration>
+                                    <additionalparam>-Xdoclint:none</additionalparam>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.apache.maven.plugins</groupId>
+                        <artifactId>maven-gpg-plugin</artifactId>
+                        <version>1.5</version>
+                        <executions>
+                            <execution>
+                                <id>sign-artifacts</id>
+                                <phase>verify</phase>
+                                <goals>
+                                    <goal>sign</goal>
+                                </goals>
+                            </execution>
+                        </executions>
+                    </plugin>
+                    <plugin>
+                        <groupId>org.sonatype.plugins</groupId>
+                        <artifactId>nexus-staging-maven-plugin</artifactId>
+                        <version>1.6.7</version>
+                        <extensions>true</extensions>
+                        <configuration>
+                            <serverId>ossrh</serverId>
+                            <nexusUrl>https://oss.sonatype.org/</nexusUrl>
+                            <autoReleaseAfterClose>false</autoReleaseAfterClose>
+                        </configuration>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
+    </profiles>
+
+    <build>
+        <resources>
+            <resource>
+                <!-- Include the properties file to provide the build information. -->
+                <directory>${project.build.directory}/extra-resources</directory>
+                <filtering>true</filtering>
+            </resource>
+            <resource>
+                <directory>${basedir}/..</directory>
+                <targetPath>META-INF</targetPath>
+                <includes>
+                    <include>LICENSE</include>
+                </includes>
+            </resource>
+        </resources>
+        <pluginManagement>
+            <plugins>
+                <plugin>
+                    <groupId>org.codehaus.gmaven</groupId>
+                    <artifactId>gmaven-plugin</artifactId>
+                    <version>1.5</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-exec-plugin</artifactId>
+                    <version>1.6.0</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-clean-plugin</artifactId>
+                    <version>3.1.0</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-resources-plugin</artifactId>
+                    <!-- downgrade version so symlinks are followed -->
+                    <version>2.6</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-compiler-plugin</artifactId>
+                    <version>3.8.0</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-surefire-plugin</artifactId>
+                    <version>2.22.0</version>
+                    <dependencies>
+                        <dependency>
+                            <groupId>org.junit.platform</groupId>
+                            <artifactId>junit-platform-surefire-provider</artifactId>
+                            <version>1.2.0</version>
+                        </dependency>
+                        <dependency>
+                            <groupId>org.junit.jupiter</groupId>
+                            <artifactId>junit-jupiter-engine</artifactId>
+                            <version>5.4.2</version>
+                        </dependency>
+                        <dependency>
+                            <!-- to get around bug https://github.com/junit-team/junit5/issues/1367 -->
+                            <groupId>org.apache.maven.surefire</groupId>
+                            <artifactId>surefire-logger-api</artifactId>
+                            <version>2.21.0</version>
+                        </dependency>
+                    </dependencies>
+                </plugin>
+                <plugin>
+                    <groupId>org.apache.maven.plugins</groupId>
+                    <artifactId>maven-failsafe-plugin</artifactId>
+                    <version>2.22.0</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-jar-plugin</artifactId>
+                    <version>3.0.2</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-install-plugin</artifactId>
+                    <version>2.5.2</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-deploy-plugin</artifactId>
+                    <version>2.8.2</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-site-plugin</artifactId>
+                    <version>3.7.1</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-project-info-reports-plugin</artifactId>
+                    <version>3.0.0</version>
+                </plugin>
+            </plugins>
+        </pluginManagement>
+
+        <plugins>
+            <plugin>
+                <artifactId>maven-antrun-plugin</artifactId>
+                <version>1.8</version>
+                <executions>
+                    <execution>
+                        <id>cmake</id>
+                        <phase>validate</phase>
+                        <configuration>
+                            <tasks>
+                                <mkdir dir="${native.build.path}"/>
+                                <exec dir="${native.build.path}"
+                                      failonerror="true"
+                                      executable="cmake">
+                                    <arg value="${basedir}/src/main/native"/>
+                                    <arg line="${cmake.ccache.opts}"/>
+                                    <arg value="-DCUDA_STATIC_RUNTIME=${CUDA_STATIC_RUNTIME}" />
+                                    <arg value="-DCUDF_USE_PER_THREAD_DEFAULT_STREAM=${CUDF_USE_PER_THREAD_DEFAULT_STREAM}" />
+                                    <arg value="-DUSE_GDS=${USE_GDS}" />
+                                    <arg value="-DCMAKE_CXX_FLAGS=${cxx.flags}"/>
+                                    <arg value="-DCMAKE_EXPORT_COMPILE_COMMANDS=${CMAKE_EXPORT_COMPILE_COMMANDS}"/>
+                                    <arg value="-DCUDF_CPP_BUILD_DIR=${CUDF_CPP_BUILD_DIR}"/>
+                                    <arg value="-DGPU_ARCHS=${GPU_ARCHS}"/>
+                                    <arg value="-DCUDF_JNI_LIBCUDF_STATIC=${CUDF_JNI_LIBCUDF_STATIC}"/>
+                                    <arg value="-DCUDF_JNI_ENABLE_PROFILING=${CUDF_JNI_ENABLE_PROFILING}"/>
+                                    <arg value="-DBUILD_SHARED_LIBS=ON"/>
+                                </exec>
+                                <exec dir="${native.build.path}"
+                                      failonerror="true"
+                                      executable="cmake">
+                                    <arg value="--build"/>
+                                    <arg value="."/>
+                                    <arg value="--parallel"/>
+                                    <arg value="${parallel.level}"/>
+                                </exec>
+                                <mkdir dir="${project.build.directory}/extra-resources"/>
+                                <exec executable="bash"
+                                      output="${project.build.directory}/extra-resources/cudf-java-version-info.properties">
+                                    <arg value="${project.basedir}/buildscripts/build-info"/>
+                                    <arg value="${project.version}"/>
+                                </exec>
+                            </tasks>
+                        </configuration>
+                        <goals>
+                            <goal>run</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.codehaus.gmaven</groupId>
+                <artifactId>gmaven-plugin</artifactId>
+                <executions>
+                    <execution>
+                        <id>setproperty</id>
+                        <phase>validate</phase>
+                        <goals>
+                            <goal>execute</goal>
+                        </goals>
+                        <configuration>
+                            <source>
+                                def sout = new StringBuffer(), serr = new StringBuffer()
+                                //This only works on linux
+                                def proc = 'ldd ${native.build.path}/libcudfjni.so'.execute()
+                                proc.consumeProcessOutput(sout, serr)
+                                proc.waitForOrKill(10000)
+                                def libcudf = ~/libcudf.*\\.so\\s+=>\\s+(.*)libcudf.*\\.so\\s+.*/
+                                def cudfm = libcudf.matcher(sout)
+                                if (cudfm.find()) {
+                                    pom.properties['native.cudf.path'] = cudfm.group(1)
+                                } else {
+                                    fail("Could not find cudf as a dependency of libcudfjni out> $sout err> $serr")
+                                }
+
+                                def nvccout = new StringBuffer(), nvccerr = new StringBuffer()
+                                def nvccproc = 'nvcc --version'.execute()
+                                nvccproc.consumeProcessOutput(nvccout, nvccerr)
+                                nvccproc.waitForOrKill(10000)
+                                def cudaPattern = ~/Cuda compilation tools, release ([0-9]+)/
+                                def cm = cudaPattern.matcher(nvccout)
+                                if (cm.find()) {
+                                    def classifier = 'cuda' + cm.group(1)
+                                    pom.properties['cuda.classifier'] = classifier
+                                } else {
+                                    fail('could not find CUDA version')
+                                }
+                            </source>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-jar-plugin</artifactId>
+                <configuration>
+                    <!--Set by groovy script-->
+                    <classifier>${cuda.classifier}</classifier>
+                </configuration>
+                <executions>
+                    <execution>
+                        <goals>
+                            <goal>test-jar</goal>
+                        </goals>
+                        <configuration>
+                            <classifier>tests</classifier>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-surefire-plugin</artifactId>
+                <configuration>
+                    <!-- you can turn this off, by passing -DtrimStackTrace=true when running tests -->
+                    <trimStackTrace>false</trimStackTrace>
+                    <redirectTestOutputToFile>true</redirectTestOutputToFile>
+                    <systemPropertyVariables>
+                        <ai.rapids.refcount.debug>${ai.rapids.refcount.debug}</ai.rapids.refcount.debug>
+                        <ai.rapids.cudf.nvtx.enabled>${ai.rapids.cudf.nvtx.enabled}</ai.rapids.cudf.nvtx.enabled>
+                    </systemPropertyVariables>
+                </configuration>
+            </plugin>
+            <plugin>
+                <artifactId>maven-resources-plugin</artifactId>
+                <executions>
+                    <execution>
+                        <id>copy-native-libs</id>
+                        <phase>generate-resources</phase>
+                        <goals>
+                            <goal>copy-resources</goal>
+                        </goals>
+                        <configuration>
+                            <overwrite>true</overwrite>
+                            <skip>${skipNativeCopy}</skip>
+                            <outputDirectory>${project.build.outputDirectory}/${os.arch}/${os.name}</outputDirectory>
+                            <resources>
+                                <resource>
+                                    <directory>${native.build.path}</directory>
+                                    <includes>
+                                        <include>libcudfjni.so</include>
+                                        <include>libcufilejni.so</include>
+                                        <include>libnvcomp.so</include>
+                                        <include>libnvcomp_gdeflate.so</include>
+                                        <include>libnvcomp_bitcomp.so</include>
+                                    </includes>
+                                </resource>
+                                <resource>
+                                    <!--Set by groovy script-->
+                                    <directory>${native.cudf.path}</directory>
+                                    <includes>
+                                        <include>libcudf.so</include>
+                                    </includes>
+                                </resource>
+                            </resources>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+        </plugins>
+    </build>
+</project>
diff --git a/java/src/main/java/ai/rapids/cudf/Aggregation.java b/java/src/main/java/ai/rapids/cudf/Aggregation.java
new file mode 100644
index 0000000..379750b
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Aggregation.java
@@ -0,0 +1,993 @@
+/*
+ *
+ *  Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.Arrays;
+
+/**
+ * Represents an aggregation operation.  Please note that not all aggregations work, or even make
+ * sense in all types of aggregation operations.
+ */
+abstract class Aggregation {
+    static {
+        NativeDepsLoader.loadNativeDeps();
+    }
+
+    /*
+     * This should be kept in sync with AggregationJni.cpp.  Note that the nativeId here is not the
+     * same as the C++ cudf::aggregation::Kind.  They are very closely related, but both are
+     * implementation details and generally should be hidden from the end user.
+     * Visible for testing.
+     */
+    enum Kind {
+        SUM(0),
+        PRODUCT(1),
+        MIN(2),
+        MAX(3),
+        COUNT(4),
+        ANY(5),
+        ALL(6),
+        SUM_OF_SQUARES(7),
+        MEAN(8),
+        VARIANCE(9), // This can take a delta degrees of freedom
+        STD(10), // This can take a delta degrees of freedom
+        MEDIAN(11),
+        QUANTILE(12),
+        ARGMAX(13),
+        ARGMIN(14),
+        NUNIQUE(15),
+        NTH_ELEMENT(16),
+        ROW_NUMBER(17),
+        COLLECT_LIST(18),
+        COLLECT_SET(19),
+        MERGE_LISTS(20),
+        MERGE_SETS(21),
+        LEAD(22),
+        LAG(23),
+        PTX(24),
+        CUDA(25),
+        M2(26),
+        MERGE_M2(27),
+        RANK(28),
+        DENSE_RANK(29),
+        PERCENT_RANK(30),
+        TDIGEST(31), // This can take a delta argument for accuracy level
+        MERGE_TDIGEST(32), // This can take a delta argument for accuracy level
+        HISTOGRAM(33),
+        MERGE_HISTOGRAM(34);
+
+        final int nativeId;
+
+        Kind(int nativeId) {this.nativeId = nativeId;}
+    }
+
+    /**
+     * An Aggregation that only needs a kind and nothing else.
+     */
+    private static class NoParamAggregation extends Aggregation {
+        public NoParamAggregation(Kind kind) {
+            super(kind);
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createNoParamAgg(kind.nativeId);
+        }
+
+        @Override
+        public int hashCode() {
+            return kind.hashCode();
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof NoParamAggregation) {
+                NoParamAggregation o = (NoParamAggregation) other;
+                return o.kind.equals(this.kind);
+            }
+            return false;
+        }
+    }
+
+    static final class NthAggregation extends Aggregation {
+        private final int offset;
+        private final NullPolicy nullPolicy;
+
+        private NthAggregation(int offset, NullPolicy nullPolicy) {
+            super(Kind.NTH_ELEMENT);
+            this.offset = offset;
+            this.nullPolicy = nullPolicy;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createNthAgg(offset, nullPolicy.includeNulls);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * offset + nullPolicy.hashCode();
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof NthAggregation) {
+                NthAggregation o = (NthAggregation) other;
+                return o.offset == this.offset && o.nullPolicy == this.nullPolicy;
+            }
+            return false;
+        }
+    }
+
+    private static class DdofAggregation extends Aggregation {
+        private final int ddof;
+
+        public DdofAggregation(Kind kind, int ddof) {
+            super(kind);
+            this.ddof = ddof;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createDdofAgg(kind.nativeId, ddof);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * kind.hashCode() + ddof;
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof DdofAggregation) {
+                DdofAggregation o = (DdofAggregation) other;
+                return o.ddof == this.ddof;
+            }
+            return false;
+        }
+    }
+
+    private static class CountLikeAggregation extends Aggregation {
+        private final NullPolicy nullPolicy;
+
+        public CountLikeAggregation(Kind kind, NullPolicy nullPolicy) {
+            super(kind);
+            this.nullPolicy = nullPolicy;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createCountLikeAgg(kind.nativeId, nullPolicy.includeNulls);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * kind.hashCode() + nullPolicy.hashCode();
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof CountLikeAggregation) {
+                CountLikeAggregation o = (CountLikeAggregation) other;
+                return o.nullPolicy == this.nullPolicy;
+            }
+            return false;
+        }
+    }
+
+    private static final class QuantileAggregation extends Aggregation {
+        private final QuantileMethod method;
+        private final double[] quantiles;
+
+        public QuantileAggregation(QuantileMethod method, double[] quantiles) {
+            super(Kind.QUANTILE);
+            this.method = method;
+            this.quantiles = quantiles;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createQuantAgg(method.nativeId, quantiles);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * (31 * kind.hashCode() + method.hashCode()) + Arrays.hashCode(quantiles);
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof QuantileAggregation) {
+                QuantileAggregation o = (QuantileAggregation) other;
+                return this.method == o.method && Arrays.equals(this.quantiles, o.quantiles);
+            }
+            return false;
+        }
+    }
+
+    private static class LeadLagAggregation extends Aggregation {
+        private final int offset;
+        private final ColumnVector defaultOutput;
+
+        LeadLagAggregation(Kind kind, int offset, ColumnVector defaultOutput) {
+            super(kind);
+            this.offset = offset;
+            this.defaultOutput = defaultOutput;
+        }
+
+        @Override
+        long createNativeInstance() {
+            // Default output comes from a different path
+            return Aggregation.createLeadLagAgg(kind.nativeId, offset);
+        }
+
+        @Override
+        public int hashCode() {
+            int ret = 31 * kind.hashCode() + offset;
+            if (defaultOutput != null) {
+                ret = 31 * ret + defaultOutput.hashCode();
+            }
+            return ret;
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof LeadLagAggregation) {
+                LeadLagAggregation o = (LeadLagAggregation) other;
+                boolean ret = o.kind == this.kind && this.offset == o.offset;
+                if (defaultOutput != null) {
+                    ret = ret && defaultOutput.equals(o.defaultOutput);
+                } else if (o.defaultOutput != null) {
+                    // defaultOutput == null and o.defaultOutput != null so they are not equal
+                    ret = false;
+                } // else they are both null which is the same and a noop.
+                return ret;
+            }
+            return false;
+        }
+
+        @Override
+        long getDefaultOutput() {
+            return defaultOutput == null ? 0 : defaultOutput.getNativeView();
+        }
+    }
+
+    static final class CollectListAggregation extends Aggregation {
+        private final NullPolicy nullPolicy;
+
+        private CollectListAggregation(NullPolicy nullPolicy) {
+            super(Kind.COLLECT_LIST);
+            this.nullPolicy = nullPolicy;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createCollectListAgg(nullPolicy.includeNulls);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * kind.hashCode() + nullPolicy.hashCode();
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof CollectListAggregation) {
+                CollectListAggregation o = (CollectListAggregation) other;
+                return o.nullPolicy == this.nullPolicy;
+            }
+            return false;
+        }
+    }
+
+    static final class CollectSetAggregation extends Aggregation {
+        private final NullPolicy nullPolicy;
+        private final NullEquality nullEquality;
+        private final NaNEquality nanEquality;
+
+        private CollectSetAggregation(NullPolicy nullPolicy, NullEquality nullEquality, NaNEquality nanEquality) {
+            super(Kind.COLLECT_SET);
+            this.nullPolicy = nullPolicy;
+            this.nullEquality = nullEquality;
+            this.nanEquality = nanEquality;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createCollectSetAgg(nullPolicy.includeNulls,
+                nullEquality.nullsEqual,
+                nanEquality.nansEqual);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * kind.hashCode()
+                + Boolean.hashCode(nullPolicy.includeNulls)
+                + Boolean.hashCode(nullEquality.nullsEqual)
+                + Boolean.hashCode(nanEquality.nansEqual);
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof CollectSetAggregation) {
+                CollectSetAggregation o = (CollectSetAggregation) other;
+                return o.nullPolicy == this.nullPolicy &&
+                    o.nullEquality == this.nullEquality &&
+                    o.nanEquality == this.nanEquality;
+            }
+            return false;
+        }
+    }
+
+    static final class MergeSetsAggregation extends Aggregation {
+        private final NullEquality nullEquality;
+        private final NaNEquality nanEquality;
+
+        private MergeSetsAggregation(NullEquality nullEquality, NaNEquality nanEquality) {
+            super(Kind.MERGE_SETS);
+            this.nullEquality = nullEquality;
+            this.nanEquality = nanEquality;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createMergeSetsAgg(nullEquality.nullsEqual, nanEquality.nansEqual);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * kind.hashCode()
+                + Boolean.hashCode(nullEquality.nullsEqual)
+                + Boolean.hashCode(nanEquality.nansEqual);
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof MergeSetsAggregation) {
+                MergeSetsAggregation o = (MergeSetsAggregation) other;
+                return o.nullEquality == this.nullEquality && o.nanEquality == this.nanEquality;
+            }
+            return false;
+        }
+    }
+
+    protected final Kind kind;
+
+    protected Aggregation(Kind kind) {
+        this.kind = kind;
+    }
+
+    /**
+     * Get the native view of a ColumnVector that provides default values to be used for some window
+     * aggregations when there is not enough data to do the computation.  This really only happens
+     * for a very few number of window aggregations. Also note that the ownership and life cycle of
+     * the column is controlled outside of this, so don't try to close it.
+     * @return the native view of the column vector or 0.
+     */
+    long getDefaultOutput() {
+        return 0;
+    }
+
+    /**
+     * returns a <code>cudf::aggregation *</code> cast to a long. We don't want to force users to
+     * close an Aggregation. Because of this Aggregation objects are created in pure java, but when
+     * it is time to use them this method is called to return a pointer to the c++ aggregation
+     * instance. All values returned by this can be used multiple times, and should be closed by
+     * calling the static close method. Yes, this creates a lot more JNI calls, but it keeps the
+     * user API clean.
+     */
+    abstract long createNativeInstance();
+
+    @Override
+    public abstract int hashCode();
+
+    @Override
+    public abstract boolean equals(Object other);
+
+    static void close(long[] ptrs) {
+        for (long ptr: ptrs) {
+            if (ptr != 0) {
+                close(ptr);
+            }
+        }
+    }
+
+    static native void close(long ptr);
+
+    static final class SumAggregation extends NoParamAggregation {
+        private SumAggregation() {
+            super(Kind.SUM);
+        }
+    }
+
+    /**
+     * Sum reduction.
+     */
+    static SumAggregation sum() {
+        return new SumAggregation();
+    }
+
+    static final class ProductAggregation extends NoParamAggregation {
+        private ProductAggregation() {
+            super(Kind.PRODUCT);
+        }
+    }
+
+    /**
+     * Product reduction.
+     */
+    static ProductAggregation product() {
+        return new ProductAggregation();
+    }
+
+    static final class MinAggregation extends NoParamAggregation {
+        private MinAggregation() {
+            super(Kind.MIN);
+        }
+    }
+
+    /**
+     * Min reduction.
+     */
+    static MinAggregation min() {
+        return new MinAggregation();
+    }
+
+    static final class MaxAggregation extends NoParamAggregation {
+        private MaxAggregation() {
+            super(Kind.MAX);
+        }
+    }
+
+    /**
+     * Max reduction.
+     */
+    static MaxAggregation max() {
+        return new MaxAggregation();
+    }
+
+    static final class CountAggregation extends CountLikeAggregation {
+        private CountAggregation(NullPolicy nullPolicy) {
+            super(Kind.COUNT, nullPolicy);
+        }
+    }
+
+    /**
+     * Count number of valid, a.k.a. non-null, elements.
+     */
+    static CountAggregation count() {
+        return count(NullPolicy.EXCLUDE);
+    }
+
+    /**
+     * Count number of elements.
+     * @param nullPolicy INCLUDE if nulls should be counted. EXCLUDE if only non-null values
+     *                   should be counted.
+     */
+    static CountAggregation count(NullPolicy nullPolicy) {
+        return new CountAggregation(nullPolicy);
+    }
+
+    static final class AnyAggregation extends NoParamAggregation {
+        private AnyAggregation() {
+            super(Kind.ANY);
+        }
+    }
+
+    /**
+     * Any reduction. Produces a true or 1, depending on the output type,
+     * if any of the elements in the range are true or non-zero, otherwise produces a false or 0.
+     * Null values are skipped.
+     */
+    static AnyAggregation any() {
+        return new AnyAggregation();
+    }
+
+    static final class AllAggregation extends NoParamAggregation {
+        private AllAggregation() {
+            super(Kind.ALL);
+        }
+    }
+
+    /**
+     * All reduction. Produces true or 1, depending on the output type, if all of the elements in
+     * the range are true or non-zero, otherwise produces a false or 0.
+     * Null values are skipped.
+     */
+    static AllAggregation all() {
+        return new AllAggregation();
+    }
+
+    static final class SumOfSquaresAggregation extends NoParamAggregation {
+        private SumOfSquaresAggregation() {
+            super(Kind.SUM_OF_SQUARES);
+        }
+    }
+
+    /**
+     * Sum of squares reduction.
+     */
+    static SumOfSquaresAggregation sumOfSquares() {
+        return new SumOfSquaresAggregation();
+    }
+
+    static final class MeanAggregation extends NoParamAggregation {
+        private MeanAggregation() {
+            super(Kind.MEAN);
+        }
+    }
+
+    /**
+     * Arithmetic mean reduction.
+     */
+    static MeanAggregation mean() {
+        return new MeanAggregation();
+    }
+
+    static final class M2Aggregation extends NoParamAggregation {
+        private M2Aggregation() {
+            super(Kind.M2);
+        }
+    }
+
+    /**
+     * Sum of square of differences from mean.
+     */
+    static M2Aggregation M2() {
+        return new M2Aggregation();
+    }
+
+    static final class VarianceAggregation extends DdofAggregation {
+        private VarianceAggregation(int ddof) {
+            super(Kind.VARIANCE, ddof);
+        }
+    }
+
+    /**
+     * Variance aggregation with 1 as the delta degrees of freedom.
+     */
+    static VarianceAggregation variance() {
+        return variance(1);
+    }
+
+    /**
+     * Variance aggregation.
+     * @param ddof delta degrees of freedom. The divisor used in calculation of variance is
+     *             <code>N - ddof</code>, where N is the population size.
+     */
+    static VarianceAggregation variance(int ddof) {
+        return new VarianceAggregation(ddof);
+    }
+
+
+    static final class StandardDeviationAggregation extends DdofAggregation {
+        private StandardDeviationAggregation(int ddof) {
+            super(Kind.STD, ddof);
+        }
+    }
+
+    /**
+     * Standard deviation aggregation with 1 as the delta degrees of freedom.
+     */
+    static StandardDeviationAggregation standardDeviation() {
+        return standardDeviation(1);
+    }
+
+    /**
+     * Standard deviation aggregation.
+     * @param ddof delta degrees of freedom. The divisor used in calculation of std is
+     *             <code>N - ddof</code>, where N is the population size.
+     */
+    static StandardDeviationAggregation standardDeviation(int ddof) {
+        return new StandardDeviationAggregation(ddof);
+    }
+
+    static final class MedianAggregation extends NoParamAggregation {
+        private MedianAggregation() {
+            super(Kind.MEDIAN);
+        }
+    }
+
+    /**
+     * Median reduction.
+     */
+    static MedianAggregation median() {
+        return new MedianAggregation();
+    }
+
+    /**
+     * Aggregate to compute the specified quantiles. Uses linear interpolation by default.
+     */
+    static QuantileAggregation quantile(double ... quantiles) {
+        return quantile(QuantileMethod.LINEAR, quantiles);
+    }
+
+    /**
+     * Aggregate to compute various quantiles.
+     */
+    static QuantileAggregation quantile(QuantileMethod method, double ... quantiles) {
+        return new QuantileAggregation(method, quantiles);
+    }
+
+    static final class ArgMaxAggregation extends NoParamAggregation {
+        private ArgMaxAggregation() {
+            super(Kind.ARGMAX);
+        }
+    }
+
+    /**
+     * Index of max element. Please note that when using this aggregation with a group by if the
+     * data is not already sorted by the grouping keys it may be automatically sorted
+     * prior to doing the aggregation. This would result in an index into the sorted data being
+     * returned.
+     */
+    static ArgMaxAggregation argMax() {
+        return new ArgMaxAggregation();
+    }
+
+    static final class ArgMinAggregation extends NoParamAggregation {
+        private ArgMinAggregation() {
+            super(Kind.ARGMIN);
+        }
+    }
+
+    /**
+     * Index of min element. Please note that when using this aggregation with a group by if the
+     * data is not already sorted by the grouping keys it may be automatically sorted
+     * prior to doing the aggregation. This would result in an index into the sorted data being
+     * returned.
+     */
+    static ArgMinAggregation argMin() {
+        return new ArgMinAggregation();
+    }
+
+    static final class NuniqueAggregation extends CountLikeAggregation {
+        private NuniqueAggregation(NullPolicy nullPolicy) {
+            super(Kind.NUNIQUE, nullPolicy);
+        }
+    }
+
+    /**
+     * Number of unique, non-null, elements.
+     */
+    static NuniqueAggregation nunique() {
+        return nunique(NullPolicy.EXCLUDE);
+    }
+
+    /**
+     * Number of unique elements.
+     * @param nullPolicy INCLUDE if nulls should be counted else EXCLUDE. If nulls are counted they
+     *                   compare as equal so multiple null values in a range would all only
+     *                   increase the count by 1.
+     */
+    static NuniqueAggregation nunique(NullPolicy nullPolicy) {
+        return new NuniqueAggregation(nullPolicy);
+    }
+
+    /**
+     * Get the nth, non-null, element in a group.
+     * @param offset the offset to look at. Negative numbers go from the end of the group. Any
+     *               value outside of the group range results in a null.
+     */
+    static NthAggregation nth(int offset) {
+        return nth(offset, NullPolicy.INCLUDE);
+    }
+
+    /**
+     * Get the nth element in a group.
+     * @param offset the offset to look at. Negative numbers go from the end of the group. Any
+     *               value outside of the group range results in a null.
+     * @param nullPolicy INCLUDE if nulls should be included in the aggregation or EXCLUDE if they
+     *                   should be skipped.
+     */
+    static NthAggregation nth(int offset, NullPolicy nullPolicy) {
+        return new NthAggregation(offset, nullPolicy);
+    }
+
+    static final class RowNumberAggregation extends NoParamAggregation {
+        private RowNumberAggregation() {
+            super(Kind.ROW_NUMBER);
+        }
+    }
+
+    /**
+     * Get the row number, only makes sense for a window operations.
+     */
+    static RowNumberAggregation rowNumber() {
+        return new RowNumberAggregation();
+    }
+
+    static final class RankAggregation extends NoParamAggregation {
+        private RankAggregation() {
+            super(Kind.RANK);
+        }
+    }
+
+    /**
+     * Get the row's ranking.
+     */
+    static RankAggregation rank() {
+        return new RankAggregation();
+    }
+
+    static final class DenseRankAggregation extends NoParamAggregation {
+        private DenseRankAggregation() {
+            super(Kind.DENSE_RANK);
+        }
+    }
+
+    /**
+     * Get the row's dense ranking.
+     */
+    static DenseRankAggregation denseRank() {
+        return new DenseRankAggregation();
+    }
+
+    static final class PercentRankAggregation extends NoParamAggregation {
+        private PercentRankAggregation() {
+            super(Kind.PERCENT_RANK);
+        }
+    }
+
+    /**
+     * Get the row's percent ranking.
+     */
+    static PercentRankAggregation percentRank() {
+        return new PercentRankAggregation();
+    }
+
+    /**
+     * Collect the values into a list. Nulls will be skipped.
+     */
+    static CollectListAggregation collectList() {
+        return collectList(NullPolicy.EXCLUDE);
+    }
+
+    /**
+     * Collect the values into a list.
+     *
+     * @param nullPolicy Indicates whether to include/exclude nulls during collection.
+     */
+    static CollectListAggregation collectList(NullPolicy nullPolicy) {
+        return new CollectListAggregation(nullPolicy);
+    }
+
+    /**
+     * Collect the values into a set. All null values will be excluded, and all nan values are regarded as
+     * unique instances.
+     */
+    static CollectSetAggregation collectSet() {
+        return collectSet(NullPolicy.EXCLUDE, NullEquality.UNEQUAL, NaNEquality.UNEQUAL);
+    }
+
+    /**
+     * Collect the values into a set.
+     *
+     * @param nullPolicy   Indicates whether to include/exclude nulls during collection.
+     * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+     * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+     */
+    static CollectSetAggregation collectSet(NullPolicy nullPolicy, NullEquality nullEquality, NaNEquality nanEquality) {
+        return new CollectSetAggregation(nullPolicy, nullEquality, nanEquality);
+    }
+
+    static final class MergeListsAggregation extends NoParamAggregation {
+        private MergeListsAggregation() {
+            super(Kind.MERGE_LISTS);
+        }
+    }
+
+    /**
+     * Merge the partial lists produced by multiple CollectListAggregations.
+     * NOTICE: The partial lists to be merged should NOT include any null list element (but can include null list entries).
+     */
+    static MergeListsAggregation mergeLists() {
+        return new MergeListsAggregation();
+    }
+
+    /**
+     * Merge the partial sets produced by multiple CollectSetAggregations. Each null/nan value will be regarded as
+     * a unique instance.
+     */
+    static MergeSetsAggregation mergeSets() {
+        return mergeSets(NullEquality.UNEQUAL, NaNEquality.UNEQUAL);
+    }
+
+    /**
+     * Merge the partial sets produced by multiple CollectSetAggregations.
+     *
+     * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+     * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+     */
+    static MergeSetsAggregation mergeSets(NullEquality nullEquality, NaNEquality nanEquality) {
+        return new MergeSetsAggregation(nullEquality, nanEquality);
+    }
+
+    static final class LeadAggregation extends LeadLagAggregation {
+        private LeadAggregation(int offset, ColumnVector defaultOutput) {
+            super(Kind.LEAD, offset, defaultOutput);
+        }
+    }
+
+    /**
+     * In a rolling window return the value offset entries ahead or the corresponding value from
+     * defaultOutput if it is outside of the window. Note that this does not take any ownership of
+     * defaultOutput and the caller mush ensure that defaultOutput remains valid during the life
+     * time of this aggregation operation.
+     */
+    static LeadAggregation lead(int offset, ColumnVector defaultOutput) {
+        return new LeadAggregation(offset, defaultOutput);
+    }
+
+    static final class LagAggregation extends LeadLagAggregation {
+        private LagAggregation(int offset, ColumnVector defaultOutput) {
+            super(Kind.LAG, offset, defaultOutput);
+        }
+    }
+
+    /**
+     * In a rolling window return the value offset entries behind or the corresponding value from
+     * defaultOutput if it is outside of the window. Note that this does not take any ownership of
+     * defaultOutput and the caller mush ensure that defaultOutput remains valid during the life
+     * time of this aggregation operation.
+     */
+    static LagAggregation lag(int offset, ColumnVector defaultOutput) {
+        return new LagAggregation(offset, defaultOutput);
+    }
+
+    public static final class MergeM2Aggregation extends NoParamAggregation {
+        private MergeM2Aggregation() {
+            super(Kind.MERGE_M2);
+        }
+    }
+
+    /**
+     * Merge the partial M2 values produced by multiple instances of M2Aggregation.
+     */
+    static MergeM2Aggregation mergeM2() {
+        return new MergeM2Aggregation();
+    }
+
+    static class TDigestAggregation extends Aggregation {
+        private final int delta;
+
+        public TDigestAggregation(Kind kind, int delta) {
+            super(kind);
+            this.delta = delta;
+        }
+
+        @Override
+        long createNativeInstance() {
+            return Aggregation.createTDigestAgg(kind.nativeId, delta);
+        }
+
+        @Override
+        public int hashCode() {
+            return 31 * kind.hashCode() + delta;
+        }
+
+        @Override
+        public boolean equals(Object other) {
+            if (this == other) {
+                return true;
+            } else if (other instanceof TDigestAggregation) {
+                TDigestAggregation o = (TDigestAggregation) other;
+                return o.delta == this.delta;
+            }
+            return false;
+        }
+    }
+
+    static TDigestAggregation createTDigest(int delta) {
+        return new TDigestAggregation(Kind.TDIGEST, delta);
+    }
+
+    static TDigestAggregation mergeTDigest(int delta) {
+        return new TDigestAggregation(Kind.MERGE_TDIGEST, delta);
+    }
+
+    static final class HistogramAggregation extends NoParamAggregation {
+        private HistogramAggregation() {
+            super(Kind.HISTOGRAM);
+        }
+    }
+
+    static final class MergeHistogramAggregation extends NoParamAggregation {
+        private MergeHistogramAggregation() {
+            super(Kind.MERGE_HISTOGRAM);
+        }
+    }
+
+    static HistogramAggregation histogram() {
+        return new HistogramAggregation();
+    }
+
+    static MergeHistogramAggregation mergeHistogram() {
+        return new MergeHistogramAggregation();
+    }
+
+    /**
+     * Create one of the aggregations that only needs a kind, no other parameters. This does not
+     * work for all types and for code safety reasons each kind is added separately.
+     */
+    private static native long createNoParamAgg(int kind);
+
+    /**
+     * Create an nth aggregation.
+     */
+    private static native long createNthAgg(int offset, boolean includeNulls);
+
+    /**
+     * Create an aggregation that uses a ddof
+     */
+    private static native long createDdofAgg(int kind, int ddof);
+
+    /**
+     * Create an aggregation that is like count including nulls or not.
+     */
+    private static native long createCountLikeAgg(int kind, boolean includeNulls);
+
+    /**
+     * Create quantile aggregation.
+     */
+    private static native long createQuantAgg(int method, double[] quantiles);
+
+    /**
+     * Create a lead or lag aggregation.
+     */
+    private static native long createLeadLagAgg(int kind, int offset);
+
+    /**
+     * Create a collect list aggregation including nulls or not.
+     */
+    private static native long createCollectListAgg(boolean includeNulls);
+
+    /**
+     * Create a collect set aggregation.
+     */
+    private static native long createCollectSetAgg(boolean includeNulls, boolean nullsEqual, boolean nansEqual);
+
+    /**
+     * Create a merge sets aggregation.
+     */
+    private static native long createMergeSetsAgg(boolean nullsEqual, boolean nansEqual);
+
+    /**
+     * Create a TDigest aggregation.
+     */
+    private static native long createTDigestAgg(int kind, int delta);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Aggregation128Utils.java b/java/src/main/java/ai/rapids/cudf/Aggregation128Utils.java
new file mode 100644
index 0000000..9a0ac70
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Aggregation128Utils.java
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Utility methods for breaking apart and reassembling 128-bit values during aggregations
+ * to enable hash-based aggregations and detect overflows.
+ */
+public class Aggregation128Utils {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  /**
+   * Extract a 32-bit chunk from a 128-bit value.
+   * @param col column of 128-bit values (e.g.: DECIMAL128)
+   * @param outType integer type to use for the output column (e.g.: UINT32 or INT32)
+   * @param chunkIdx index of the 32-bit chunk to extract where 0 is the least significant chunk
+   *                 and 3 is the most significant chunk
+   * @return column containing the specified 32-bit chunk of the input column values. A null input
+   *                row will result in a corresponding null output row.
+   */
+  public static ColumnVector extractInt32Chunk(ColumnView col, DType outType, int chunkIdx) {
+    return new ColumnVector(extractInt32Chunk(col.getNativeView(),
+        outType.getTypeId().getNativeId(), chunkIdx));
+  }
+
+  /**
+   * Reassemble a column of 128-bit values from a table of four 64-bit integer columns and check
+   * for overflow. The 128-bit value is reconstructed by overlapping the 64-bit values by 32-bits.
+   * The least significant 32-bits of the least significant 64-bit value are used directly as the
+   * least significant 32-bits of the final 128-bit value, and the remaining 32-bits are added to
+   * the next most significant 64-bit value. The lower 32-bits of that sum become the next most
+   * significant 32-bits in the final 128-bit value, and the remaining 32-bits are added to the
+   * next most significant 64-bit input value, and so on.
+   *
+   * @param chunks table of four 64-bit integer columns with the columns ordered from least
+   *               significant to most significant. The last column must be of type INT64.
+   * @param type the type to use for the resulting 128-bit value column
+   * @return table containing a boolean column and a 128-bit value column of the requested type.
+   *         The boolean value will be true if an overflow was detected for that row's value when
+   *         it was reassembled. A null input row will result in a corresponding null output row.
+   */
+  public static Table combineInt64SumChunks(Table chunks, DType type) {
+    return new Table(combineInt64SumChunks(chunks.getNativeView(),
+        type.getTypeId().getNativeId(),
+        type.getScale()));
+  }
+
+  private static native long extractInt32Chunk(long columnView, int outTypeId, int chunkIdx);
+
+  private static native long[] combineInt64SumChunks(long chunksTableView, int dtype, int scale);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/AggregationOverWindow.java b/java/src/main/java/ai/rapids/cudf/AggregationOverWindow.java
new file mode 100644
index 0000000..d5544e0
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/AggregationOverWindow.java
@@ -0,0 +1,73 @@
+/*
+ *
+ *  Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An Aggregation instance that also holds a column number and window metadata so the aggregation
+ * can be done over a specific window.
+ */
+public final class AggregationOverWindow {
+    private final RollingAggregationOnColumn wrapped;
+    protected final WindowOptions windowOptions;
+
+    AggregationOverWindow(RollingAggregationOnColumn wrapped, WindowOptions windowOptions) {
+        this.wrapped = wrapped;
+        this.windowOptions = windowOptions;
+
+        if (windowOptions == null) {
+            throw new IllegalArgumentException("WindowOptions cannot be null!");
+        }
+
+        if (windowOptions.getPrecedingCol() != null || windowOptions.getFollowingCol() != null) {
+            throw new UnsupportedOperationException("Dynamic windows (via columns) are currently unsupported!");
+        }
+    }
+
+    public WindowOptions getWindowOptions() {
+        return windowOptions;
+    }
+
+    @Override
+    public int hashCode() {
+        return 31 * super.hashCode() + windowOptions.hashCode();
+    }
+
+    @Override
+    public boolean equals(Object other) {
+        if (other == this) {
+            return true;
+        } else if (other instanceof AggregationOverWindow) {
+            AggregationOverWindow o = (AggregationOverWindow) other;
+            return wrapped.equals(o.wrapped) && windowOptions.equals(o.windowOptions);
+        }
+        return false;
+    }
+
+    int getColumnIndex() {
+        return wrapped.getColumnIndex();
+    }
+
+    long createNativeInstance() {
+        return wrapped.createNativeInstance();
+    }
+
+    long getDefaultOutput() {
+        return wrapped.getDefaultOutput();
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ArrowColumnBuilder.java b/java/src/main/java/ai/rapids/cudf/ArrowColumnBuilder.java
new file mode 100644
index 0000000..b3c9793
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ArrowColumnBuilder.java
@@ -0,0 +1,113 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+
+/**
+ * Column builder from Arrow data. This builder takes in byte buffers referencing
+ * Arrow data and allows efficient building of CUDF ColumnVectors from that Arrow data.
+ * The caller can add multiple batches where each batch corresponds to Arrow data
+ * and those batches get concatenated together after being converted to CUDF
+ * ColumnVectors.
+ * This currently only supports primitive types and Strings, Decimals and nested types
+ * such as list and struct are not supported.
+ */
+public final class ArrowColumnBuilder implements AutoCloseable {
+    private DType type;
+    private final ArrayList<ByteBuffer> data = new ArrayList<>();
+    private final ArrayList<ByteBuffer> validity = new ArrayList<>();
+    private final ArrayList<ByteBuffer> offsets = new ArrayList<>();
+    private final ArrayList<Long> nullCount = new ArrayList<>();
+    private final ArrayList<Long> rows = new ArrayList<>();
+
+    public ArrowColumnBuilder(HostColumnVector.DataType type) {
+      this.type = type.getType();
+    }
+
+    /**
+     * Add an Arrow buffer. This API allows you to add multiple if you want them
+     * combined into a single ColumnVector.
+     * Note, this takes all data, validity, and offsets buffers, but they may not all
+     * be needed based on the data type. The buffer should be null if its not used
+     * for that type.
+     * This API only supports primitive types and Strings, Decimals and nested types
+     * such as list and struct are not supported.
+     * @param rows - number of rows in this Arrow buffer
+     * @param nullCount - number of null values in this Arrow buffer
+     * @param data - ByteBuffer of the Arrow data buffer
+     * @param validity - ByteBuffer of the Arrow validity buffer
+     * @param offsets - ByteBuffer of the Arrow offsets buffer
+     */
+    public void addBatch(long rows, long nullCount, ByteBuffer data, ByteBuffer validity,
+                         ByteBuffer offsets) {
+      this.rows.add(rows);
+      this.nullCount.add(nullCount);
+      this.data.add(data);
+      this.validity.add(validity);
+      this.offsets.add(offsets);
+    }
+
+    /**
+     * Create the immutable ColumnVector, copied to the device based on the Arrow data.
+     * @return - new ColumnVector
+     */
+    public final ColumnVector buildAndPutOnDevice() {
+      int numBatches = rows.size();
+      ArrayList<ColumnVector> allVecs = new ArrayList<>(numBatches);
+      ColumnVector vecRet;
+      try {
+        for (int i = 0; i < numBatches; i++) {
+          allVecs.add(ColumnVector.fromArrow(type, rows.get(i), nullCount.get(i),
+            data.get(i), validity.get(i), offsets.get(i)));
+        }
+        if (numBatches == 1) {
+          vecRet = allVecs.get(0);
+        } else if (numBatches > 1) {
+          vecRet = ColumnVector.concatenate(allVecs.toArray(new ColumnVector[0]));
+        } else {
+          throw new IllegalStateException("Can't build a ColumnVector when no Arrow batches specified");
+        }
+      } finally {
+        // close the vectors that were concatenated
+        if (numBatches > 1) {
+          allVecs.forEach(cv -> cv.close());
+        }
+      }
+      return vecRet;
+    }
+
+    @Override
+    public void close() {
+      // memory buffers owned outside of this
+    }
+
+    @Override
+    public String toString() {
+      return "ArrowColumnBuilder{" +
+        "type=" + type +
+        ", data=" + data +
+        ", validity=" + validity +
+        ", offsets=" + offsets +
+        ", nullCount=" + nullCount +
+        ", rows=" + rows +
+        '}';
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ArrowIPCOptions.java b/java/src/main/java/ai/rapids/cudf/ArrowIPCOptions.java
new file mode 100644
index 0000000..a9f3975
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ArrowIPCOptions.java
@@ -0,0 +1,65 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Options for reading data in Arrow IPC format
+ */
+public class ArrowIPCOptions {
+
+  public interface NeedGpu {
+    /**
+     * A callback to indicate that we are about to start putting data on the GPU.
+     */
+    void needTheGpu();
+  }
+
+  public static ArrowIPCOptions DEFAULT = new ArrowIPCOptions(new Builder());
+
+  private final NeedGpu callback;
+
+  private ArrowIPCOptions(Builder builder) {
+    this.callback = builder.callback;
+  }
+
+  public NeedGpu getCallback() {
+    return callback;
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder {
+    private NeedGpu callback = () -> {};
+
+    public Builder withCallback(NeedGpu callback) {
+      if (callback == null) {
+        this.callback = () -> {};
+      } else {
+        this.callback = callback;
+      }
+      return this;
+    }
+
+    public ArrowIPCOptions build() {
+      return new ArrowIPCOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ArrowIPCWriterOptions.java b/java/src/main/java/ai/rapids/cudf/ArrowIPCWriterOptions.java
new file mode 100644
index 0000000..ee5ae09
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ArrowIPCWriterOptions.java
@@ -0,0 +1,138 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Settings for writing Arrow IPC data.
+ */
+public class ArrowIPCWriterOptions extends WriterOptions {
+
+  public interface DoneOnGpu {
+    /**
+     * A callback to indicate that the table is off of the GPU
+     * and may be closed, even if all of the data is not yet written.
+     * @param table the table that can be closed.
+     */
+    void doneWithTheGpu(Table table);
+  }
+
+  private final long size;
+  private final DoneOnGpu callback;
+
+  private ArrowIPCWriterOptions(Builder builder) {
+    super(builder);
+    this.size = builder.size;
+    this.callback = builder.callback;
+  }
+
+  public long getMaxChunkSize() {
+    return size;
+  }
+
+  public DoneOnGpu getCallback() {
+    return callback;
+  }
+
+  public static class Builder extends WriterBuilder<Builder> {
+    private long size = -1;
+    private DoneOnGpu callback = (ignored) -> {};
+
+    public Builder withMaxChunkSize(long size) {
+      this.size = size;
+      return this;
+    }
+
+    public Builder withCallback(DoneOnGpu callback) {
+      if (callback == null) {
+        this.callback = (ignored) -> {};
+      } else {
+        this.callback = callback;
+      }
+      return this;
+    }
+
+    /**
+     * Add the name(s) for nullable column(s).
+     *
+     * Please note the column names of the nested struct columns should be flattened in sequence.
+     * For examples,
+     * <pre>
+     *   A table with an int column and a struct column:
+     *                   ["int_col", "struct_col":{"field_1", "field_2"}]
+     *   output:
+     *                   ["int_col", "struct_col", "field_1", "field_2"]
+     *
+     *   A table with an int column and a list of non-nested type column:
+     *                   ["int_col", "list_col":[]]
+     *   output:
+     *                   ["int_col", "list_col"]
+     *
+     *   A table with an int column and a list of struct column:
+     *                   ["int_col", "list_struct_col":[{"field_1", "field_2"}]]
+     *   output:
+     *                   ["int_col", "list_struct_col", "field_1", "field_2"]
+     * </pre>
+     *
+     * @param columnNames The column names corresponding to the written table(s).
+     */
+    @Override
+    public Builder withColumnNames(String... columnNames) {
+      return super.withColumnNames(columnNames);
+    }
+
+    /**
+     * Add the name(s) for non-nullable column(s).
+     *
+     * Please note the column names of the nested struct columns should be flattened in sequence.
+     * For examples,
+     * <pre>
+     *   A table with an int column and a struct column:
+     *                   ["int_col", "struct_col":{"field_1", "field_2"}]
+     *   output:
+     *                   ["int_col", "struct_col", "field_1", "field_2"]
+     *
+     *   A table with an int column and a list of non-nested type column:
+     *                   ["int_col", "list_col":[]]
+     *   output:
+     *                   ["int_col", "list_col"]
+     *
+     *   A table with an int column and a list of struct column:
+     *                   ["int_col", "list_struct_col":[{"field_1", "field_2"}]]
+     *   output:
+     *                   ["int_col", "list_struct_col", "field_1", "field_2"]
+     * </pre>
+     *
+     * @param columnNames The column names corresponding to the written table(s).
+     */
+    @Override
+    public Builder withNotNullableColumnNames(String... columnNames) {
+      return super.withNotNullableColumnNames(columnNames);
+    }
+
+    public ArrowIPCWriterOptions build() {
+      return new ArrowIPCWriterOptions(this);
+    }
+  }
+
+  public static final ArrowIPCWriterOptions DEFAULT = new ArrowIPCWriterOptions(new Builder());
+
+  public static Builder builder() {
+    return new Builder();
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/AssertEmptyNulls.java b/java/src/main/java/ai/rapids/cudf/AssertEmptyNulls.java
new file mode 100644
index 0000000..cc1bb67
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/AssertEmptyNulls.java
@@ -0,0 +1,36 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ *  This class is a Helper class to assert there are no non-empty nulls in a ColumnView
+ *
+ *  The reason for the existence of this class is so that we can turn the asserts on/off when needed
+ *  by passing "-da:ai.rapids.cudf.AssertEmptyNulls". We need that behavior because we have tests
+ *  that explicitly test with ColumnViews that contain non-empty nulls but more importantly, there
+ *  could be cases where an external system may not have a requirement of nulls being empty, so for
+ *  us to work with those systems, we can turn off this assert in the field.
+ */
+public class AssertEmptyNulls {
+  public static void assertNullsAreEmpty(ColumnView cv) {
+    if (cv.type.isNestedType() || cv.type.hasOffsets()) {
+      assert !cv.hasNonEmptyNulls() : "Column has non-empty nulls";
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/AvroOptions.java b/java/src/main/java/ai/rapids/cudf/AvroOptions.java
new file mode 100644
index 0000000..973f729
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/AvroOptions.java
@@ -0,0 +1,41 @@
+/*
+ *
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Options for reading an Avro file
+ */
+public class AvroOptions extends ColumnFilterOptions {
+
+    public static AvroOptions DEFAULT = new AvroOptions(new Builder());
+
+    private AvroOptions(Builder builder) {
+        super(builder);
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    public static class Builder extends ColumnFilterOptions.Builder<Builder> {
+        public AvroOptions build() {
+            return new AvroOptions(this);
+        }
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/BaseDeviceMemoryBuffer.java b/java/src/main/java/ai/rapids/cudf/BaseDeviceMemoryBuffer.java
new file mode 100644
index 0000000..e0cd5fa
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/BaseDeviceMemoryBuffer.java
@@ -0,0 +1,158 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Base class for all MemoryBuffers that are in device memory.
+ */
+public abstract class BaseDeviceMemoryBuffer extends MemoryBuffer {
+  protected BaseDeviceMemoryBuffer(long address, long length, MemoryBuffer parent) {
+    super(address, length, parent);
+  }
+
+  protected BaseDeviceMemoryBuffer(long address, long length, MemoryBufferCleaner cleaner) {
+    super(address, length, cleaner);
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at destOffset.
+   * @param destOffset the offset in this to start copying from.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   */
+  public final void copyFromHostBuffer(long destOffset, HostMemoryBuffer src, long srcOffset, long length) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy range dest");
+    src.addressOutOfBoundsCheck(src.address + srcOffset, length, "copy range src");
+    Cuda.memcpy(address + destOffset, src.address + srcOffset, length, CudaMemcpyKind.HOST_TO_DEVICE);
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at destOffset using the specified CUDA stream.
+   * The copy has completed when this returns, but the memory copy could overlap with
+   * operations occurring on other streams.
+   * @param destOffset the offset in this to start copying from.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromHostBuffer(long destOffset, HostMemoryBuffer src,
+      long srcOffset, long length, Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy range dest");
+    src.addressOutOfBoundsCheck(src.address + srcOffset, length, "copy range src");
+    Cuda.memcpy(address + destOffset, src.address + srcOffset, length,
+        CudaMemcpyKind.HOST_TO_DEVICE, stream);
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at destOffset using the specified CUDA stream.
+   * The copy is async and may not have completed when this returns.
+   * @param destOffset the offset in this to start copying from.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromHostBufferAsync(long destOffset, HostMemoryBuffer src,
+                                            long srcOffset, long length, Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy range dest");
+    src.addressOutOfBoundsCheck(src.address + srcOffset, length, "copy range src");
+    Cuda.asyncMemcpy(address + destOffset, src.address + srcOffset, length,
+        CudaMemcpyKind.HOST_TO_DEVICE, stream);
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at destOffset using the specified CUDA stream.
+   * The copy is async and may not have completed when this returns.
+   * @param destOffset the offset in this to start copying from.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromDeviceBufferAsync(long destOffset, BaseDeviceMemoryBuffer src,
+                                              long srcOffset, long length, Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy range dest");
+    src.addressOutOfBoundsCheck(src.address + srcOffset, length, "copy range src");
+    Cuda.asyncMemcpy(address + destOffset, src.address + srcOffset, length,
+        CudaMemcpyKind.DEVICE_TO_DEVICE, stream);
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at the beginning of this.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   */
+  public final void copyFromHostBuffer(HostMemoryBuffer src, long srcOffset, long length) {
+    copyFromHostBuffer(0, src, srcOffset, length);
+  }
+
+  /**
+   * Copy everything from src to this buffer starting at the beginning of this buffer.
+   * @param src - Buffer to copy data from
+   */
+  public final void copyFromHostBuffer(HostMemoryBuffer src) {
+    copyFromHostBuffer(0, src, 0, src.length);
+  }
+
+  /**
+   * Copy entire host buffer starting at the beginning of this buffer using a CUDA stream.
+   * The copy has completed when this returns, but the memory copy could overlap with
+   * operations occurring on other streams.
+   * @param src host buffer to copy from
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromHostBuffer(HostMemoryBuffer src, Cuda.Stream stream) {
+    copyFromHostBuffer(0, src, 0, src.length, stream);
+  }
+
+  /**
+   * Copy entire host buffer starting at the beginning of this buffer using a CUDA stream.
+   * The copy is async and may not have completed when this returns.
+   * @param src host buffer to copy from
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromHostBufferAsync(HostMemoryBuffer src, Cuda.Stream stream) {
+    copyFromHostBufferAsync(0, src, 0, src.length, stream);
+  }
+
+  /**
+   * Slice off a part of the device buffer, copying it instead of reference counting it.
+   * @param offset where to start the slice at.
+   * @param len how many bytes to slice
+   * @return a device buffer that will need to be closed independently from this buffer.
+   */
+  public final DeviceMemoryBuffer sliceWithCopy(long offset, long len) {
+    addressOutOfBoundsCheck(address + offset, len, "slice");
+    DeviceMemoryBuffer ret = null;
+    boolean success = false;
+    try {
+      ret = DeviceMemoryBuffer.allocate(len);
+      Cuda.memcpy(ret.getAddress(), getAddress() + offset, len, CudaMemcpyKind.DEVICE_TO_DEVICE);
+      success = true;
+      return ret;
+    } finally {
+      if (!success && ret != null) {
+        ret.close();
+      }
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/BinaryOp.java b/java/src/main/java/ai/rapids/cudf/BinaryOp.java
new file mode 100644
index 0000000..fe55918
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/BinaryOp.java
@@ -0,0 +1,79 @@
+/*
+ * Copyright (c) 2019-2020,2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import java.util.EnumSet;
+
+/**
+ * Mathematical binary operations.
+ */
+public enum BinaryOp {
+  ADD(0),
+  SUB(1),
+  MUL(2),
+  DIV(3), // divide using common type of lhs and rhs
+  TRUE_DIV(4), // divide after promoting to FLOAT64 point
+  FLOOR_DIV(5), // divide after promoting to FLOAT64 and flooring the result
+  MOD(6),
+  PMOD(7), // pmod
+  PYMOD(8), // mod operator % follow by python's sign rules for negatives
+  POW(9),
+  INT_POW(10), // int ^ int, used to avoid floating point precision loss
+  LOG_BASE(11), // logarithm to the base
+  ATAN2(12), // atan2
+  SHIFT_LEFT(13), // bitwise shift left (<<)
+  SHIFT_RIGHT(14), // bitwise shift right (>>)
+  SHIFT_RIGHT_UNSIGNED(15), // bitwise shift right (>>>)
+  BITWISE_AND(16),
+  BITWISE_OR(17),
+  BITWISE_XOR(18),
+  LOGICAL_AND(19),
+  LOGICAL_OR(20),
+  EQUAL(21),
+  NOT_EQUAL(22),
+  LESS(23),
+  GREATER(24),
+  LESS_EQUAL(25), // <=
+  GREATER_EQUAL(26), // >=
+  NULL_EQUALS(27), // like EQUAL but NULL == NULL is TRUE and NULL == not NULL is FALSE
+  NULL_MAX(28), // MAX but NULL < not NULL
+  NULL_MIN(29), // MIN but NULL > not NULL
+  //NOT IMPLEMENTED YET GENERIC_BINARY(30);
+  NULL_LOGICAL_AND(31),
+  NULL_LOGICAL_OR(32);
+
+
+  static final EnumSet<BinaryOp> COMPARISON = EnumSet.of(
+      EQUAL, NOT_EQUAL, LESS, GREATER, LESS_EQUAL, GREATER_EQUAL);
+  static final EnumSet<BinaryOp> INEQUALITY_COMPARISON = EnumSet.of(
+      LESS, GREATER, LESS_EQUAL, GREATER_EQUAL);
+
+  private static final BinaryOp[] OPS = BinaryOp.values();
+  final int nativeId;
+
+  BinaryOp(int nativeId) {
+    this.nativeId = nativeId;
+  }
+
+  static BinaryOp fromNative(int nativeId) {
+    for (BinaryOp type : OPS) {
+      if (type.nativeId == nativeId) {
+        return type;
+      }
+    }
+    throw new IllegalArgumentException("Could not translate " + nativeId + " into a BinaryOp");
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/BinaryOperable.java b/java/src/main/java/ai/rapids/cudf/BinaryOperable.java
new file mode 100644
index 0000000..48a7861
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/BinaryOperable.java
@@ -0,0 +1,577 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+public interface BinaryOperable {
+  /**
+   * Finds the proper DType for an implicit output.  This follows the typical rules of
+   * C++, Java, and most SQL implementations.
+   * FLOAT64/double >
+   * FLOAT32/float >
+   * INT64/long >
+   * INT32/int >
+   * INT16/short >
+   * INT8/byte/char
+   * <p>
+   * Currently most TIMESTAMPs are treated the same as INT64. TIMESTAMP_DAYS is treated the same
+   * as INT32. All time information is stripped from them.  This may change in the future.
+   * <p>
+   * BOOL8 is treated like an INT8.  Math on boolean operations makes little sense.  If
+   * you want to stay as a BOOL8 you will need to explicitly specify the output type.
+   * For decimal types, DECIMAL32 and DECIMAL64 takes in another parameter `scale`. DType is created
+   * with scale=0 as scale is required. Dtype is discarded for binary operations for decimal
+   * types in cudf as a new DType is created for output type with the new scale.
+   */
+  static DType implicitConversion(BinaryOp op, BinaryOperable lhs, BinaryOperable rhs) {
+    DType a = lhs.getType();
+    DType b = rhs.getType();
+    if (a.equals(DType.FLOAT64) || b.equals(DType.FLOAT64)) {
+      return DType.FLOAT64;
+    }
+    if (a.equals(DType.FLOAT32) || b.equals(DType.FLOAT32)) {
+      return DType.FLOAT32;
+    }
+    if (a.equals(DType.UINT64) || b.equals(DType.UINT64)) {
+      return DType.UINT64;
+    }
+    if (a.equals(DType.INT64) || b.equals(DType.INT64) ||
+        a.equals(DType.TIMESTAMP_MILLISECONDS) || b.equals(DType.TIMESTAMP_MILLISECONDS) ||
+        a.equals(DType.TIMESTAMP_MICROSECONDS) || b.equals(DType.TIMESTAMP_MICROSECONDS) ||
+        a.equals(DType.TIMESTAMP_SECONDS) || b.equals(DType.TIMESTAMP_SECONDS) ||
+        a.equals(DType.TIMESTAMP_NANOSECONDS) || b.equals(DType.TIMESTAMP_NANOSECONDS)) {
+      return DType.INT64;
+    }
+    if (a.equals(DType.UINT32) || b.equals(DType.UINT32)) {
+      return DType.UINT32;
+    }
+    if (a.equals(DType.INT32) || b.equals(DType.INT32) ||
+        a.equals(DType.TIMESTAMP_DAYS) || b.equals(DType.TIMESTAMP_DAYS)) {
+      return DType.INT32;
+    }
+    if (a.equals(DType.UINT16) || b.equals(DType.UINT16)) {
+      return DType.UINT16;
+    }
+    if (a.equals(DType.INT16) || b.equals(DType.INT16)) {
+      return DType.INT16;
+    }
+    if (a.equals(DType.UINT8) || b.equals(DType.UINT8)) {
+      return DType.UINT8;
+    }
+    if (a.equals(DType.INT8) || b.equals(DType.INT8)) {
+      return DType.INT8;
+    }
+    if (a.equals(DType.BOOL8) || b.equals(DType.BOOL8)) {
+      return DType.BOOL8;
+    }
+    if (a.isDecimalType() && b.isDecimalType()) {
+      if (a.typeId != b.typeId) {
+        throw new IllegalArgumentException("Both columns must be of the same fixed_point type");
+      }
+      final int scale = ColumnView.getFixedPointOutputScale(op, lhs.getType(), rhs.getType());
+      // The output precision/size should be at least as large as the input.
+      // It may be larger if room is needed for it based off of the output scale.
+      final DType.DTypeEnum outputEnum;
+      if (scale <= DType.DECIMAL32_MAX_PRECISION && a.typeId == DType.DTypeEnum.DECIMAL32) {
+        outputEnum = DType.DTypeEnum.DECIMAL32;
+      } else if (scale <= DType.DECIMAL64_MAX_PRECISION &&
+          (a.typeId == DType.DTypeEnum.DECIMAL32 || a.typeId == DType.DTypeEnum.DECIMAL64)) {
+        outputEnum = DType.DTypeEnum.DECIMAL64;
+      } else {
+        outputEnum = DType.DTypeEnum.DECIMAL128;
+      }
+      return DType.create(outputEnum, scale);
+    }
+    throw new IllegalArgumentException("Unsupported types " + a + " and " + b);
+  }
+
+  /**
+   * Get the type of this data.
+   */
+  DType getType();
+
+  /**
+   * Multiple different binary operations.
+   * @param op      the operation to perform
+   * @param rhs     the rhs of the operation
+   * @param outType the type of output you want.
+   * @return the result
+   */
+  ColumnVector binaryOp(BinaryOp op, BinaryOperable rhs, DType outType);
+
+  /**
+   * Add one vector to another with the given output type. this + rhs
+   * Output type is ignored for the operations between decimal types and
+   * it is always decimal type.
+   */
+  default ColumnVector add(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.ADD, rhs, outType);
+  }
+
+  /**
+   * Add + operator. this + rhs
+   */
+  default ColumnVector add(BinaryOperable rhs) {
+    return add(rhs, implicitConversion(BinaryOp.ADD, this, rhs));
+  }
+
+  /**
+   * Subtract one vector from another with the given output type. this - rhs
+   * Output type is ignored for the operations between decimal types and
+   * it is always decimal type.
+   */
+  default ColumnVector sub(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.SUB, rhs, outType);
+  }
+
+  /**
+   * Subtract one vector from another. this - rhs
+   */
+  default ColumnVector sub(BinaryOperable rhs) {
+    return sub(rhs, implicitConversion(BinaryOp.SUB, this, rhs));
+  }
+
+  /**
+   * Multiply two vectors together with the given output type. this * rhs
+   * Output type is ignored for the operations between decimal types and
+   * it is always decimal type.
+   */
+  default ColumnVector mul(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.MUL, rhs, outType);
+  }
+
+  /**
+   * Multiply two vectors together. this * rhs
+   */
+  default ColumnVector mul(BinaryOperable rhs) {
+    return mul(rhs, implicitConversion(BinaryOp.MUL, this, rhs));
+  }
+
+  /**
+   * Divide one vector by another with the given output type. this / rhs
+   * Output type is ignored for the operations between decimal types and
+   * it is always decimal type.
+   */
+  default ColumnVector div(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.DIV, rhs, outType);
+  }
+
+  /**
+   * Divide one vector by another. this / rhs
+   */
+  default ColumnVector div(BinaryOperable rhs) {
+    return div(rhs, implicitConversion(BinaryOp.DIV, this, rhs));
+  }
+
+  /**
+   * Divide one vector by another converting to FLOAT64 in between with the given output type.
+   * (double)this / (double)rhs
+   */
+  default ColumnVector trueDiv(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.TRUE_DIV, rhs, outType);
+  }
+
+  /**
+   * Divide one vector by another converting to FLOAT64 in between.
+   * (double)this / (double)rhs
+   */
+  default ColumnVector trueDiv(BinaryOperable rhs) {
+    return trueDiv(rhs, implicitConversion(BinaryOp.TRUE_DIV, this, rhs));
+  }
+
+  /**
+   * Divide one vector by another and calculate the floor of the result with the given output type.
+   * Math.floor(this/rhs)
+   */
+  default ColumnVector floorDiv(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.FLOOR_DIV, rhs, outType);
+  }
+
+  /**
+   * Divide one vector by another and calculate the floor of the result.
+   * Math.floor(this/rhs)
+   */
+  default ColumnVector floorDiv(BinaryOperable rhs) {
+    return floorDiv(rhs, implicitConversion(BinaryOp.FLOOR_DIV, this, rhs));
+  }
+
+  /**
+   * Compute the modulus with the given output type.
+   * this % rhs
+   */
+  default ColumnVector mod(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.MOD, rhs, outType);
+  }
+
+  /**
+   * Compute the modulus.
+   * this % rhs
+   */
+  default ColumnVector mod(BinaryOperable rhs) {
+    return mod(rhs, implicitConversion(BinaryOp.MOD, this, rhs));
+  }
+
+  /**
+   * Compute the power with the given output type.
+   * Math.pow(this, rhs)
+   */
+  default ColumnVector pow(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.POW, rhs, outType);
+  }
+
+  /**
+   * Compute the power.
+   * Math.pow(this, rhs)
+   */
+  default ColumnVector pow(BinaryOperable rhs) {
+    return pow(rhs, implicitConversion(BinaryOp.POW, this, rhs));
+  }
+
+  /**
+   * this == rhs 1 is true 0 is false with the output cast to the given type.
+   */
+  default ColumnVector equalTo(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.EQUAL, rhs, outType);
+  }
+
+  /**
+   * this == rhs 1 is true 0 is false.  The output type is BOOL8.
+   */
+  default ColumnVector equalTo(BinaryOperable rhs) {
+    return equalTo(rhs, DType.BOOL8);
+  }
+
+  /**
+   * this != rhs 1 is true 0 is false with the output cast to the given type.
+   */
+  default ColumnVector notEqualTo(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.NOT_EQUAL, rhs, outType);
+  }
+
+  /**
+   * this != rhs 1 is true 0 is false. The output type is BOOL8.
+   */
+  default ColumnVector notEqualTo(BinaryOperable rhs) {
+    return notEqualTo(rhs, DType.BOOL8);
+  }
+
+  /**
+   * this < rhs 1 is true 0 is false with the output cast to the given type.
+   */
+  default ColumnVector lessThan(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.LESS, rhs, outType);
+  }
+
+  /**
+   * this < rhs 1 is true 0 is false.  The output type is BOOL8.
+   */
+  default ColumnVector lessThan(BinaryOperable rhs) {
+    return lessThan(rhs, DType.BOOL8);
+  }
+
+  /**
+   * this > rhs 1 is true 0 is false with the output cast to the given type.
+   */
+  default ColumnVector greaterThan(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.GREATER, rhs, outType);
+  }
+
+  /**
+   * this > rhs 1 is true 0 is false.  The output type is BOOL8.
+   */
+  default ColumnVector greaterThan(BinaryOperable rhs) {
+    return greaterThan(rhs, DType.BOOL8);
+  }
+
+  /**
+   * this <= rhs 1 is true 0 is false with the output cast to the given type.
+   */
+  default ColumnVector lessOrEqualTo(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.LESS_EQUAL, rhs, outType);
+  }
+
+  /**
+   * this <= rhs 1 is true 0 is false.  The output type is BOOL8.
+   */
+  default ColumnVector lessOrEqualTo(BinaryOperable rhs) {
+    return lessOrEqualTo(rhs, DType.BOOL8);
+  }
+
+  /**
+   * this >= rhs 1 is true 0 is false with the output cast to the given type.
+   */
+  default ColumnVector greaterOrEqualTo(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.GREATER_EQUAL, rhs, outType);
+  }
+
+  /**
+   * this >= rhs 1 is true 0 is false.  The output type is BOOL8.
+   */
+  default ColumnVector greaterOrEqualTo(BinaryOperable rhs) {
+    return greaterOrEqualTo(rhs, DType.BOOL8);
+  }
+
+  /**
+   * Bit wise and (&) with the given output type. this & rhs
+   */
+  default ColumnVector bitAnd(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.BITWISE_AND, rhs, outType);
+  }
+
+  /**
+   * Bit wise and (&). this & rhs
+   */
+  default ColumnVector bitAnd(BinaryOperable rhs) {
+    return bitAnd(rhs, implicitConversion(BinaryOp.BITWISE_AND, this, rhs));
+  }
+
+  /**
+   * Bit wise or (|) with the given output type. this | rhs
+   */
+  default ColumnVector bitOr(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.BITWISE_OR, rhs, outType);
+  }
+
+  /**
+   * Bit wise or (|). this | rhs
+   */
+  default ColumnVector bitOr(BinaryOperable rhs) {
+    return bitOr(rhs, implicitConversion(BinaryOp.BITWISE_OR, this, rhs));
+  }
+
+  /**
+   * Bit wise xor (^) with the given output type. this ^ rhs
+   */
+  default ColumnVector bitXor(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.BITWISE_XOR, rhs, outType);
+  }
+
+  /**
+   * Bit wise xor (^). this ^ rhs
+   */
+  default ColumnVector bitXor(BinaryOperable rhs) {
+    return bitXor(rhs, implicitConversion(BinaryOp.BITWISE_XOR, this, rhs));
+  }
+
+  /**
+   * Logical and (&&) with the given output type. this && rhs
+   */
+  default ColumnVector and(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.LOGICAL_AND, rhs, outType);
+  }
+
+  /**
+   * Logical and (&&). this && rhs
+   */
+  default ColumnVector and(BinaryOperable rhs) {
+    return and(rhs, implicitConversion(BinaryOp.LOGICAL_AND, this, rhs));
+  }
+
+  /**
+   * Logical or (||) with the given output type. this || rhs
+   */
+  default ColumnVector or(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.LOGICAL_OR, rhs, outType);
+  }
+
+  /**
+   * Logical or (||). this || rhs
+   */
+  default ColumnVector or(BinaryOperable rhs) {
+    return or(rhs, implicitConversion(BinaryOp.LOGICAL_OR, this, rhs));
+  }
+
+  /**
+   * Bitwise left shifts the values of this vector by shiftBy.
+   *
+   * If "this" and shiftBy are both vectors then, this[i] << shiftBy[i]
+   * If "this" is a scalar and shiftBy is a vector then returns a vector of size shiftBy.rows
+   *    with the scalar << shiftBy[i]
+   * If "this" is a vector and shiftBy is a scalar then returns a vector of size this.rows
+   *    with this[i] << shiftBy
+   *
+   */
+  default ColumnVector shiftLeft(BinaryOperable shiftBy, DType outType) {
+    return binaryOp(BinaryOp.SHIFT_LEFT, shiftBy, outType);
+  }
+
+  /**
+   * Bitwise left shift the values of this vector by the shiftBy.
+   *
+   * If "this" and shiftBy are both vectors then, this[i] << shiftBy[i]
+   * If "this" is a scalar and shiftBy is a vector then returns a vector of size shiftBy.rows
+   *    with the scalar << shiftBy[i]
+   * If "this" is a vector and shiftBy is a scalar then returns a vector of size this.rows
+   *    with this[i] << shiftBy
+   */
+  default ColumnVector shiftLeft(BinaryOperable shiftBy) {
+    return shiftLeft(shiftBy, implicitConversion(BinaryOp.SHIFT_LEFT, this, shiftBy));
+  }
+
+  /**
+   * Bitwise right shift this vector by the shiftBy.
+   *
+   * If "this" and shiftBy are both vectors then, this[i] >> shiftBy[i]
+   * If "this" is a scalar and shiftBy is a vector then returns a vector of size shiftBy.rows
+   *    with the scalar >> shiftBy[i]
+   * If "this" is a vector and shiftBy is a scalar then returns a vector of size this.rows
+   *    with this[i] >> shiftBy
+   */
+  default ColumnVector shiftRight(BinaryOperable shiftBy, DType outType) {
+    return binaryOp(BinaryOp.SHIFT_RIGHT, shiftBy, outType);
+  }
+
+  /**
+   * Bitwise right shift this vector by the shiftBy.
+   *
+   * If "this" and shiftBy are both vectors then, this[i] >> shiftBy[i]
+   * If "this" is a scalar and shiftBy is a vector then returns a vector of size shiftBy.rows
+   *    with the scalar >> shiftBy[i]
+   * If "this" is a vector and shiftBy is a scalar then returns a vector of size this.rows
+   *    with this[i] >> shiftBy
+   */
+  default ColumnVector shiftRight(BinaryOperable shiftBy) {
+    return shiftRight(shiftBy, implicitConversion(BinaryOp.SHIFT_RIGHT, this, shiftBy));
+  }
+
+  /**
+   * This method bitwise right shifts the values of this vector by the shiftBy.
+   * This method always fills 0 irrespective of the sign of the number.
+   *
+   * If "this" and shiftBy are both vectors then, this[i] >>> shiftBy[i]
+   * If "this" is a scalar and shiftBy is a vector then returns a vector of size shiftBy.rows
+   *    with the scalar >>> shiftBy[i]
+   * If "this" is a vector and shiftBy is a scalar then returns a vector of size this.rows
+   *    with this[i] >>> shiftBy
+   */
+  default ColumnVector shiftRightUnsigned(BinaryOperable shiftBy, DType outType) {
+    return binaryOp(BinaryOp.SHIFT_RIGHT_UNSIGNED, shiftBy, outType);
+  }
+
+  /**
+   * This method bitwise right shifts the values of this vector by the shiftBy.
+   * This method always fills 0 irrespective of the sign of the number.
+   *
+   * If "this" and shiftBy are both vectors then, this[i] >>> shiftBy[i]
+   * If "this" is a scalar and shiftBy is a vector then returns a vector of size shiftBy.rows
+   *    with the scalar >>> shiftBy[i]
+   * If "this" is a vector and shiftBy is a scalar then returns a vector of size this.rows
+   *    with this[i] >>> shiftBy
+   */
+  default ColumnVector shiftRightUnsigned(BinaryOperable shiftBy) {
+    return shiftRightUnsigned(shiftBy, implicitConversion(BinaryOp.SHIFT_RIGHT_UNSIGNED, this,
+        shiftBy));
+  }
+
+  /**
+   * Calculate the log with the specified base
+   */
+  default ColumnVector log(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.LOG_BASE, rhs, outType);
+  }
+
+  /**
+   * Calculate the log with the specified base, output is the same as this.
+   */
+  default ColumnVector log(BinaryOperable rhs) {
+    return log(rhs, getType());
+  }
+
+  /**
+   * The function arctan2(y,x) or atan2(y,x) is defined as the angle in the Euclidean plane, given
+   * in radians, between the positive x axis and the ray to the point (x, y) ≠ (0, 0).
+   */
+  default ColumnVector arctan2(BinaryOperable xCoordinate, DType outType) {
+    return binaryOp(BinaryOp.ATAN2, xCoordinate, outType);
+  }
+
+  /**
+   * The function arctan2(y,x) or atan2(y,x) is defined as the angle in the Euclidean plane, given
+   * in radians, between the positive x axis and the ray to the point (x, y) ≠ (0, 0).
+   */
+  default ColumnVector arctan2(BinaryOperable xCoordinate) {
+    return arctan2(xCoordinate, implicitConversion(BinaryOp.ATAN2, this, xCoordinate));
+  }
+
+  /**
+   * Returns the positive value of lhs mod rhs.
+   *
+   * r = lhs % rhs
+   * if r < 0 then (r + rhs) % rhs
+   * else r
+   *
+   */
+  default ColumnVector pmod(BinaryOperable rhs, DType outputType) {
+    return binaryOp(BinaryOp.PMOD, rhs, outputType);
+  }
+
+  /**
+   * Returns the positive value of lhs mod rhs.
+   *
+   * r = lhs % rhs
+   * if r < 0 then (r + rhs) % rhs
+   * else r
+   *
+   */
+  default ColumnVector pmod(BinaryOperable rhs) {
+    return pmod(rhs, implicitConversion(BinaryOp.PMOD, this, rhs));
+  }
+
+  /**
+   * like equalTo but NULL == NULL is TRUE and NULL == not NULL is FALSE
+   */
+  default ColumnVector equalToNullAware(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.NULL_EQUALS, rhs, outType);
+  }
+
+  /**
+   * like equalTo but NULL == NULL is TRUE and NULL == not NULL is FALSE
+   */
+  default ColumnVector equalToNullAware(BinaryOperable rhs) {
+    return equalToNullAware(rhs, DType.BOOL8);
+  }
+
+  /**
+   * Returns the max non null value.
+   */
+  default ColumnVector maxNullAware(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.NULL_MAX, rhs, outType);
+  }
+
+  /**
+   * Returns the max non null value.
+   */
+  default ColumnVector maxNullAware(BinaryOperable rhs) {
+    return maxNullAware(rhs, implicitConversion(BinaryOp.NULL_MAX, this, rhs));
+  }
+
+  /**
+   * Returns the min non null value.
+   */
+  default ColumnVector minNullAware(BinaryOperable rhs, DType outType) {
+    return binaryOp(BinaryOp.NULL_MIN, rhs, outType);
+  }
+
+  /**
+   * Returns the min non null value.
+   */
+  default ColumnVector minNullAware(BinaryOperable rhs) {
+    return minNullAware(rhs, implicitConversion(BinaryOp.NULL_MIN, this, rhs));
+  }
+
+}
diff --git a/java/src/main/java/ai/rapids/cudf/BitVectorHelper.java b/java/src/main/java/ai/rapids/cudf/BitVectorHelper.java
new file mode 100644
index 0000000..fa96e83
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/BitVectorHelper.java
@@ -0,0 +1,113 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * This class does bit manipulation using byte arithmetic
+ */
+final class BitVectorHelper {
+
+  /**
+   * Shifts that to the left by the required bits then appends to this
+   */
+  static void append(HostMemoryBuffer src, HostMemoryBuffer dst, long dstOffset, long rows) {
+    assert dst.length * 8 - dstOffset >= rows : "validity vector bigger then available space on " +
+        "dst: " + (dst.length * 8 - dstOffset) + " copying space needed: " + rows;
+    long dstByteIndex = dstOffset / 8;
+    int shiftBits = (int) (dstOffset % 8);
+    if (shiftBits > 0) {
+      shiftSrcLeftAndWriteToDst(src, dst, dstByteIndex, shiftBits, rows);
+    } else {
+      dst.copyFromHostBuffer(dstByteIndex, src, 0, getValidityLengthInBytes(rows));
+    }
+  }
+
+  /**
+   * Shifts the src to the left by the given bits and writes 'length' bytes to the destination
+   */
+  private static void shiftSrcLeftAndWriteToDst(HostMemoryBuffer src, HostMemoryBuffer dst,
+                                                long dstOffset, int shiftByBits, long length) {
+    assert shiftByBits > 0 && shiftByBits < 8 : "shiftByBits out of range";
+    int dstMask = 0xFF >> (8 - shiftByBits);
+    // the mask to save the left side of the bits before we shift
+    int srcLeftMask = dstMask << (8 - shiftByBits);
+    int valueFromTheLeftOfTheLastByte = dst.getByte(dstOffset) & dstMask;
+    long i;
+    long byteLength = getValidityLengthInBytes(length);
+    for (i = 0; i < byteLength; i++) {
+      int b = src.getByte(i);
+      int fallingBitsOnTheLeft = b & srcLeftMask;
+      b <<= shiftByBits;
+      b |= valueFromTheLeftOfTheLastByte;
+      dst.setByte(dstOffset + i, (byte) b);
+      valueFromTheLeftOfTheLastByte = fallingBitsOnTheLeft >>> (8 - shiftByBits);
+    }
+    if (((length % 8) + shiftByBits > 8) || length % 8 == 0) {
+      /*
+          Only if the last byte has data that has been shifted to spill over to the next
+          byte execute the
+          following statement.
+       */
+      dst.setByte(dstOffset + i, (byte) (valueFromTheLeftOfTheLastByte | ~dstMask));
+    }
+  }
+
+  /**
+   * This method returns the length in bytes needed to represent X number of rows
+   * e.g. getValidityLengthInBytes(5) => 1 byte
+   * getValidityLengthInBytes(7) => 1 byte
+   * getValidityLengthInBytes(14) => 2 bytes
+   */
+  static long getValidityLengthInBytes(long rows) {
+    return (rows + 7) / 8;
+  }
+
+  /**
+   * This method returns the allocation size of the validity vector which is 64-byte aligned
+   * e.g. getValidityAllocationSizeInBytes(5) => 64 bytes
+   * getValidityAllocationSizeInBytes(14) => 64 bytes
+   * getValidityAllocationSizeInBytes(65) => 128 bytes
+   */
+  static long getValidityAllocationSizeInBytes(long rows) {
+    long numBytes = getValidityLengthInBytes(rows);
+    return ((numBytes + 63) / 64) * 64;
+  }
+
+  /**
+   * Set the validity bit to null for the given index.
+   * @param valid the buffer to set it in.
+   * @param index the index to set it at.
+   * @return 1 if validity changed else 0 if it already was null.
+   */
+  static int setNullAt(HostMemoryBuffer valid, long index) {
+    long bucket = index / 8;
+    byte currentByte = valid.getByte(bucket);
+    int bitmask = ~(1 << (index % 8));
+    int ret = (currentByte >> index) & 0x1;
+    currentByte &= bitmask;
+    valid.setByte(bucket, currentByte);
+    return ret;
+  }
+
+  static boolean isNull(HostMemoryBuffer valid, long index) {
+    int b = valid.getByte(index / 8);
+    int i = b & (1 << (index % 8));
+    return i == 0;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/BufferType.java b/java/src/main/java/ai/rapids/cudf/BufferType.java
new file mode 100644
index 0000000..28e7f53
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/BufferType.java
@@ -0,0 +1,28 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Types of buffers supported by ColumnVectors and HostColumnVectors
+ */
+public enum BufferType {
+  VALIDITY,
+  OFFSET,
+  DATA
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CSVOptions.java b/java/src/main/java/ai/rapids/cudf/CSVOptions.java
new file mode 100644
index 0000000..33c6215
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CSVOptions.java
@@ -0,0 +1,205 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.HashSet;
+import java.util.Set;
+
+/**
+ * Options for reading a CSV file
+ */
+public class CSVOptions extends ColumnFilterOptions {
+
+  public static CSVOptions DEFAULT = new CSVOptions(new Builder());
+
+  private final int headerRow;
+  private final byte delim;
+  private final byte quote;
+  private final byte comment;
+  private final String[] nullValues;
+  private final String[] trueValues;
+  private final String[] falseValues;
+  private final QuoteStyle quoteStyle;
+
+  private CSVOptions(Builder builder) {
+    super(builder);
+    headerRow = builder.headerRow;
+    delim = builder.delim;
+    quote = builder.quote;
+    comment = builder.comment;
+    nullValues = builder.nullValues.toArray(
+        new String[builder.nullValues.size()]);
+    trueValues = builder.trueValues.toArray(
+        new String[builder.trueValues.size()]);
+    falseValues = builder.falseValues.toArray(
+        new String[builder.falseValues.size()]);
+    quoteStyle = builder.quoteStyle;
+  }
+
+  String[] getNullValues() {
+    return nullValues;
+  }
+
+  String[] getTrueValues() {
+    return trueValues;
+  }
+
+  String[] getFalseValues() {
+    return falseValues;
+  }
+
+  int getHeaderRow() {
+    return headerRow;
+  }
+
+  byte getDelim() {
+    return delim;
+  }
+
+  byte getQuote() {
+    return quote;
+  }
+
+  byte getComment() {
+    return comment;
+  }
+
+  QuoteStyle getQuoteStyle() {
+    return quoteStyle;
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder extends ColumnFilterOptions.Builder<Builder> {
+    private static final int NO_HEADER_ROW = -1;
+    private final Set<String> nullValues = new HashSet<>();
+    private final Set<String> trueValues = new HashSet<>();
+    private final Set<String> falseValues = new HashSet<>();
+    private byte comment = 0;
+    private int headerRow = NO_HEADER_ROW;
+    private byte delim = ',';
+    private byte quote = '"';
+    private QuoteStyle quoteStyle = QuoteStyle.MINIMAL;
+
+    /**
+     * Row of the header data (0 based counting).  Negative is no header.
+     */
+    public Builder withHeaderAtRow(int index) {
+      headerRow = index;
+      return this;
+    }
+
+    /**
+     * Set the row of the header to 0, the first line, if hasHeader is true else disables the
+     * header.
+     */
+    public Builder hasHeader(boolean hasHeader) {
+      return withHeaderAtRow(hasHeader ? 0 : NO_HEADER_ROW);
+    }
+
+    /**
+     * Set the row of the header to 0, the first line.
+     */
+    public Builder hasHeader() {
+      return withHeaderAtRow(0);
+    }
+
+    /**
+     * Set the entry deliminator.  Only ASCII chars are currently supported.
+     */
+    public Builder withDelim(char delim) {
+      if (Character.getNumericValue(delim) > 127) {
+        throw new IllegalArgumentException("Only ASCII characters are currently supported");
+      }
+      this.delim = (byte) delim;
+      return this;
+    }
+
+    /**
+     * Set the quote character.  Only ASCII chars are currently supported.
+     */
+    public Builder withQuote(char quote) {
+      if (Character.getNumericValue(quote) > 127) {
+        throw new IllegalArgumentException("Only ASCII characters are currently supported");
+      }
+      this.quote = (byte) quote;
+      return this;
+    }
+
+    /**
+     * Quote style to expect in the input CSV data.
+     *
+     * Note: Only the following quoting styles are supported:
+     *   1. MINIMAL: String columns containing special characters like row-delimiters/
+     *               field-delimiter/quotes will be quoted.
+     *   2. NONE: No quoting is done for any columns.
+     */
+    public Builder withQuoteStyle(QuoteStyle quoteStyle) {
+      if (quoteStyle != QuoteStyle.MINIMAL && quoteStyle != QuoteStyle.NONE) {
+        throw new IllegalArgumentException("Only MINIMAL and NONE quoting styles are supported");
+      }
+      this.quoteStyle = quoteStyle;
+      return this;
+    }
+
+    /**
+     * Set the character that starts the beginning of a comment line. setting to
+     * 0 or '\0' will disable comments. The default is to have no comments.
+     */
+    public Builder withComment(char comment) {
+      if (Character.getNumericValue(quote) > 127) {
+        throw new IllegalArgumentException("Only ASCII characters are currently supported");
+      }
+      this.comment = (byte) comment;
+      return this;
+    }
+
+    public Builder withoutComments() {
+      this.comment = 0;
+      return this;
+    }
+
+    public Builder withNullValue(String... nvs) {
+      for (String nv : nvs) {
+        nullValues.add(nv);
+      }
+      return this;
+    }
+
+    public Builder withTrueValue(String... tvs) {
+      for (String tv : tvs) {
+        trueValues.add(tv);
+      }
+      return this;
+    }
+
+    public Builder withFalseValue(String... fvs) {
+      for (String fv : fvs) {
+        falseValues.add(fv);
+      }
+      return this;
+    }
+
+    public CSVOptions build() {
+      return new CSVOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CSVWriterOptions.java b/java/src/main/java/ai/rapids/cudf/CSVWriterOptions.java
new file mode 100644
index 0000000..a2c9f36
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CSVWriterOptions.java
@@ -0,0 +1,162 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+/**
+ * Options for writing a CSV file
+ */
+public class CSVWriterOptions {
+
+  private String[] columnNames;
+  private Boolean includeHeader = false;
+  private String rowDelimiter = "\n";
+  private byte fieldDelimiter = ',';
+  private String nullValue = "";
+  private String falseValue = "false";
+  private String trueValue = "true";
+  // Quote style used for CSV data.
+  // Currently supports only `MINIMAL` and `NONE`.
+  private QuoteStyle quoteStyle = QuoteStyle.MINIMAL;
+
+  private CSVWriterOptions(Builder builder) {
+    this.columnNames = builder.columnNames.toArray(new String[builder.columnNames.size()]);
+    this.nullValue = builder.nullValue;
+    this.includeHeader = builder.includeHeader;
+    this.fieldDelimiter = builder.fieldDelimiter;
+    this.rowDelimiter = builder.rowDelimiter;
+    this.falseValue = builder.falseValue;
+    this.trueValue = builder.trueValue;
+    this.quoteStyle = builder.quoteStyle;
+  }
+
+  public String[] getColumnNames() {
+    return columnNames;
+  }
+
+  public Boolean getIncludeHeader() {
+    return includeHeader;
+  }
+
+  public String getRowDelimiter() {
+    return rowDelimiter;
+  }
+
+  public byte getFieldDelimiter() {
+    return fieldDelimiter;
+  }
+
+  public String getNullValue() {
+    return nullValue;
+  }
+
+  public String getTrueValue() {
+    return trueValue;
+  }
+
+  public String getFalseValue() {
+    return falseValue;
+  }
+
+  /**
+   * Returns the quoting style used for writing CSV.
+   */
+  public QuoteStyle getQuoteStyle() {
+    return quoteStyle;
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder {
+
+    private List<String> columnNames = Collections.emptyList();
+    private Boolean includeHeader = false;
+    private String rowDelimiter = "\n";
+    private byte fieldDelimiter = ',';
+    private String nullValue = "";
+    private String falseValue = "false";
+    private String trueValue = "true";
+    private QuoteStyle quoteStyle = QuoteStyle.MINIMAL;
+
+    public CSVWriterOptions build() {
+      return new CSVWriterOptions(this);
+    }
+
+    public Builder withColumnNames(List<String> columnNames) {
+      this.columnNames = columnNames;
+      return this;
+    }
+
+    public Builder withColumnNames(String... columnNames) {
+      List<String> columnNamesList = new ArrayList<>();
+      for (String columnName : columnNames) {
+        columnNamesList.add(columnName);
+      }
+      return withColumnNames(columnNamesList);
+    }
+
+    public Builder withIncludeHeader(Boolean includeHeader) {
+      this.includeHeader = includeHeader;
+      return this;
+    }
+
+    public Builder withRowDelimiter(String rowDelimiter) {
+      this.rowDelimiter = rowDelimiter;
+      return this;
+    }
+
+    public Builder withFieldDelimiter(byte fieldDelimiter) {
+      this.fieldDelimiter = fieldDelimiter;
+      return this;
+    }
+
+    public Builder withNullValue(String nullValue) {
+      this.nullValue = nullValue;
+      return this;
+    }
+
+    public Builder withTrueValue(String trueValue) {
+      this.trueValue = trueValue;
+      return this;
+    }
+
+    public Builder withFalseValue(String falseValue) {
+      this.falseValue = falseValue;
+      return this;
+    }
+
+    /**
+     * Sets the quote style used when writing CSV.
+     *
+     * Note: Only the following quoting styles are supported:
+     *   1. MINIMAL: String columns containing special characters like row-delimiters/
+     *               field-delimiter/quotes will be quoted.
+     *   2. NONE: No quoting is done for any columns.
+     */
+    public Builder withQuoteStyle(QuoteStyle quoteStyle) {
+      this.quoteStyle = quoteStyle;
+      return this;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CaptureGroups.java b/java/src/main/java/ai/rapids/cudf/CaptureGroups.java
new file mode 100644
index 0000000..2ab778d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CaptureGroups.java
@@ -0,0 +1,36 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Capture groups setting, closely following cudf::strings::capture_groups.
+ *
+ * For processing a regex pattern containing capture groups. These can be used
+ * to optimize the generated regex instructions where the capture groups do not
+ * require extracting the groups.
+ */
+public enum CaptureGroups {
+  EXTRACT(0),     // capture groups processed normally for extract
+  NON_CAPTURE(1); // convert all capture groups to non-capture groups
+
+  final int nativeId; // Native id, for use with libcudf.
+  private CaptureGroups(int nativeId) { // Only constant values should be used
+    this.nativeId = nativeId;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ChunkedPack.java b/java/src/main/java/ai/rapids/cudf/ChunkedPack.java
new file mode 100644
index 0000000..d44c132
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ChunkedPack.java
@@ -0,0 +1,103 @@
+/*
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * JNI interface to cudf::chunked_pack.
+ *
+ * ChunkedPack has an Iterator-like API with the familiar `hasNext` and `next`
+ * methods. `next` should be used in a loop until `hasNext` returns false.
+ *
+ * However, `ChunkedPack.next` is special because it takes a `DeviceMemoryBuffer` as a
+ * parameter, which means that the caller can call `next` giving any bounce buffer it
+ * may have previously allocated. No requirement exists that the bounce buffer be the
+ * same each time, the only requirement is that their sizes are all the same, and match
+ * the size that was passed to `Table.makeChunkedPack` (which instantiates this class).
+ *
+ * The user of `ChunkedPack` must close `.close()` when done using it to clear up both
+ * host and device resources.
+ */
+public class ChunkedPack implements AutoCloseable {
+  long nativePtr;
+
+  /**
+   * This constructor is invoked by `Table.makeChunkedPack` after creating a native
+   * `cudf::chunked_pack`.
+   * @param nativePtr pointer to a `cudf::chunked_pack`
+   */
+  public ChunkedPack(long nativePtr) {
+    this.nativePtr = nativePtr;
+  }
+
+  /**
+   * Get the final contiguous size of the table we are packing. This is
+   * the size that the final buffer should be, just like if the user called
+   * `cudf::pack` instead.
+   * @return the total number of bytes for the table in contiguous layout
+   */
+  public long getTotalContiguousSize() {
+    return chunkedPackGetTotalContiguousSize(nativePtr);
+  }
+
+  /**
+   * Method to be called to ensure that `ChunkedPack` has work left.
+   * This method should be invoked followed by a call to `next`, until
+   * `hasNext` returns false.
+   * @return true if there is work left to be done (`next` should be called),
+   *         false otherwise.
+   */
+  public boolean hasNext() {
+    return chunkedPackHasNext(nativePtr);
+  }
+
+  /**
+   * Place the next contiguous chunk of our table into `userPtr`.
+   *
+   * This method throws if `hasNext` is false.
+   * @param userPtr the bounce buffer to use for this iteration
+   * @return the number of bytes that we were able to place in `userPtr`. This is
+   *         at most `userPtr.getLength()`.
+   */
+  public long next(DeviceMemoryBuffer userPtr) {
+    return chunkedPackNext(nativePtr, userPtr.getAddress(), userPtr.getLength());
+  }
+
+  /**
+   * Generates opaque table metadata that can be unpacked via `cudf::unpack`
+   * at a later time.
+   * @return a `PackedColumnMetadata` instance referencing cuDF packed table metadata
+   */
+  public PackedColumnMetadata buildMetadata() {
+    return new PackedColumnMetadata(chunkedPackBuildMetadata(nativePtr));
+  }
+
+  @Override
+  public void close() {
+    try {
+      chunkedPackDelete(nativePtr);
+    } finally {
+      nativePtr = 0;
+    }
+  }
+
+  private static native long chunkedPackGetTotalContiguousSize(long nativePtr);
+  private static native boolean chunkedPackHasNext(long nativePtr);
+  private static native long chunkedPackNext(long nativePtr, long userPtr, long userPtrSize);
+  private static native long chunkedPackBuildMetadata(long nativePtr);
+  private static native void chunkedPackDelete(long nativePtr);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CloseableArray.java b/java/src/main/java/ai/rapids/cudf/CloseableArray.java
new file mode 100644
index 0000000..5c75f23
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CloseableArray.java
@@ -0,0 +1,106 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/** Utility class that wraps an array of closeable instances and can be closed */
+public class CloseableArray<T extends AutoCloseable> implements AutoCloseable {
+  private T[] array;
+
+  public static <T extends AutoCloseable> CloseableArray<T> wrap(T[] array) {
+    return new CloseableArray<T>(array);
+  }
+
+  CloseableArray(T[] array) {
+    this.array = array;
+  }
+
+  public int size() {
+    return array.length;
+  }
+
+  public T get(int i) {
+    return array[i];
+  }
+
+  public T set(int i, T obj) {
+    array[i] = obj;
+    return obj;
+  }
+
+  public T[] getArray() {
+    return array;
+  }
+
+  public T[] release() {
+    T[] result = array;
+    array = null;
+    return result;
+  }
+
+  public void closeAt(int i) {
+    try {
+      T toClose = array[i];
+      array[i] = null;
+      toClose.close();
+    } catch (RuntimeException e) {
+      throw e;
+    } catch (Exception e) {
+      throw new RuntimeException(e);
+    }
+  }
+
+  @Override
+  public void close() {
+    close(null);
+  }
+
+  public void close(Exception pendingError) {
+    if (array == null) {
+      return;
+    }
+    T[] toClose = array;
+    array = null;
+    RuntimeException error = null;
+    if (pendingError instanceof RuntimeException) {
+      error = (RuntimeException) pendingError;
+    } else if (pendingError != null) {
+      error = new RuntimeException(pendingError);
+    }
+    for (T obj: toClose) {
+      if (obj != null) {
+        try {
+          obj.close();
+        } catch (RuntimeException e) {
+          if (error != null) {
+            error.addSuppressed(e);
+          } else {
+            error = e;
+          }
+        } catch (Exception e) {
+          if (error != null) {
+            error.addSuppressed(e);
+          } else {
+            error = new RuntimeException(e);
+          }
+        }
+      }
+    }
+    if (error != null) {
+      throw error;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ColumnFilterOptions.java b/java/src/main/java/ai/rapids/cudf/ColumnFilterOptions.java
new file mode 100644
index 0000000..371fe9d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ColumnFilterOptions.java
@@ -0,0 +1,65 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.List;
+
+/**
+ * Base options class for input formats that can filter columns.
+ */
+public abstract class ColumnFilterOptions {
+  // Names of the columns to be returned (other columns are skipped)
+  // If empty all columns are returned.
+  private final String[] includeColumnNames;
+
+  protected ColumnFilterOptions(Builder<?> builder) {
+    includeColumnNames = builder.includeColumnNames.toArray(
+        new String[builder.includeColumnNames.size()]);
+  }
+
+  String[] getIncludeColumnNames() {
+    return includeColumnNames;
+  }
+
+  public static class Builder<T extends Builder> {
+    final List<String> includeColumnNames = new ArrayList<>();
+
+    /**
+     * Include one or more specific columns.  Any column not included will not be read.
+     * @param names the name of the column, or more than one if you want.
+     */
+    public T includeColumn(String... names) {
+      for (String name : names) {
+        includeColumnNames.add(name);
+      }
+      return (T) this;
+    }
+
+    /**
+     * Include one or more specific columns.  Any column not included will not be read.
+     * @param names the name of the column, or more than one if you want.
+     */
+    public T includeColumn(Collection<String> names) {
+      includeColumnNames.addAll(names);
+      return (T) this;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ColumnVector.java b/java/src/main/java/ai/rapids/cudf/ColumnVector.java
new file mode 100644
index 0000000..30e92d2
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ColumnVector.java
@@ -0,0 +1,1767 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+import java.util.function.Consumer;
+
+/**
+ * This class represents the immutable vector of data.  This class holds
+ * references to device(GPU) memory and is reference counted to know when to release it.  Call
+ * close to decrement the reference count when you are done with the column, and call incRefCount
+ * to increment the reference count.
+ */
+public final class ColumnVector extends ColumnView {
+  /**
+   * Interface to handle events for this ColumnVector. Only invoked during
+   * close, hence `onClosed` is the only event.
+   */
+  public interface EventHandler {
+    /**
+     * `onClosed` is invoked with the updated `refCount` during `close`.
+     * The last invocation of `onClosed` will be with `refCount=0`.
+     *
+     * @note the callback is invoked with this `ColumnVector`'s lock held.
+     *
+     * @param cv reference to the ColumnVector we are closing
+     * @param refCount the updated ref count for this ColumnVector at the time
+     *                 of invocation
+     */
+    void onClosed(ColumnVector cv, int refCount);
+  }
+
+  private static final Logger log = LoggerFactory.getLogger(ColumnVector.class);
+
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  private Optional<Long> nullCount = Optional.empty();
+  private int refCount;
+  private EventHandler eventHandler;
+
+  /**
+   * Wrap an existing on device cudf::column with the corresponding ColumnVector. The new
+   * ColumnVector takes ownership of the pointer and will free it when the ref count reaches zero.
+   * @param nativePointer host address of the cudf::column object which will be
+   *                      owned by this instance.
+   */
+  public ColumnVector(long nativePointer) {
+    super(new OffHeapState(nativePointer));
+    assert nativePointer != 0;
+    MemoryCleaner.register(this, offHeap);
+    this.refCount = 0;
+    incRefCountInternal(true);
+  }
+
+  private static OffHeapState makeOffHeap(DType type, long rows, Optional<Long> nullCount,
+      DeviceMemoryBuffer dataBuffer, DeviceMemoryBuffer validityBuffer,
+      DeviceMemoryBuffer offsetBuffer) {
+    long viewHandle = initViewHandle(
+        type, (int)rows, nullCount.orElse(UNKNOWN_NULL_COUNT).intValue(),
+        dataBuffer, validityBuffer, offsetBuffer, null);
+    return new OffHeapState(dataBuffer, validityBuffer, offsetBuffer, null, viewHandle);
+  }
+
+  /**
+   * Create a new column vector based off of data already on the device.
+   * @param type the type of the vector
+   * @param rows the number of rows in this vector.
+   * @param nullCount the number of nulls in the dataset.
+   * @param dataBuffer the data stored on the device.  The column vector takes ownership of the
+   *                   buffer.  Do not use the buffer after calling this.
+   * @param validityBuffer an optional validity buffer. Must be provided if nullCount != 0. The
+   *                      column vector takes ownership of the buffer. Do not use the buffer
+   *                      after calling this.
+   * @param offsetBuffer a host buffer required for strings and string categories. The column
+   *                    vector takes ownership of the buffer. Do not use the buffer after calling
+   *                    this.
+   */
+  public ColumnVector(DType type, long rows, Optional<Long> nullCount,
+      DeviceMemoryBuffer dataBuffer, DeviceMemoryBuffer validityBuffer,
+      DeviceMemoryBuffer offsetBuffer) {
+    super(makeOffHeap(type, rows, nullCount, dataBuffer, validityBuffer, offsetBuffer));
+    assert !type.equals(DType.LIST) : "This constructor should not be used for list type";
+    if (!type.equals(DType.STRING)) {
+      assert offsetBuffer == null : "offsets are only supported for STRING";
+    }
+    assert (nullCount.isPresent() && nullCount.get() <= Integer.MAX_VALUE)
+        || !nullCount.isPresent();
+    MemoryCleaner.register(this, offHeap);
+    this.nullCount = nullCount;
+    this.refCount = 0;
+    incRefCountInternal(true);
+  }
+
+  /**
+   * This method is internal and exposed purely for testing purposes
+   */
+  static OffHeapState makeOffHeap(DType type, long rows, Optional<Long> nullCount,
+      DeviceMemoryBuffer dataBuffer, DeviceMemoryBuffer validityBuffer,
+      DeviceMemoryBuffer offsetBuffer, List<DeviceMemoryBuffer> toClose, long[] childHandles) {
+    long viewHandle = initViewHandle(type, (int)rows, nullCount.orElse(UNKNOWN_NULL_COUNT).intValue(),
+        dataBuffer, validityBuffer,
+        offsetBuffer, childHandles);
+    return new OffHeapState(dataBuffer, validityBuffer, offsetBuffer, toClose, viewHandle);
+  }
+
+  /**
+   * Create a new column vector based off of data already on the device with child columns.
+   * @param type the type of the vector, typically a nested type
+   * @param rows the number of rows in this vector.
+   * @param nullCount the number of nulls in the dataset.
+   * @param dataBuffer the data stored on the device.  The column vector takes ownership of the
+   *                   buffer.  Do not use the buffer after calling this.
+   * @param validityBuffer an optional validity buffer. Must be provided if nullCount != 0. The
+   *                      column vector takes ownership of the buffer. Do not use the buffer
+   *                      after calling this.
+   * @param offsetBuffer a host buffer required for strings and string categories. The column
+   *                    vector takes ownership of the buffer. Do not use the buffer after calling
+   *                    this.
+   * @param toClose  List of buffers to track and close once done, usually in case of children
+   * @param childHandles array of longs for child column view handles.
+   */
+  public ColumnVector(DType type, long rows, Optional<Long> nullCount,
+                      DeviceMemoryBuffer dataBuffer, DeviceMemoryBuffer validityBuffer,
+                      DeviceMemoryBuffer offsetBuffer, List<DeviceMemoryBuffer> toClose, long[] childHandles) {
+    super(makeOffHeap(type, rows, nullCount, dataBuffer, validityBuffer, offsetBuffer, toClose, childHandles));
+    if (!type.equals(DType.STRING) && !type.equals(DType.LIST)) {
+      assert offsetBuffer == null : "offsets are only supported for STRING, LISTS";
+    }
+    assert (nullCount.isPresent() && nullCount.get() <= Integer.MAX_VALUE)
+        || !nullCount.isPresent();
+    MemoryCleaner.register(this, offHeap);
+
+    this.refCount = 0;
+    incRefCountInternal(true);
+  }
+
+  /**
+   * This is a very special constructor that should only ever be called by
+   * fromViewWithContiguousAllocation.  It takes a cudf::column_view * instead of a cudf::column *.
+   * But to maintain memory ownership properly we need to slice the memory in the view off from
+   * a separate buffer that actually owns the memory allocation.
+   * @param viewAddress the address of the cudf::column_view
+   * @param contiguousBuffer the buffer that this is based off of.
+   */
+  private ColumnVector(long viewAddress, DeviceMemoryBuffer contiguousBuffer) {
+    super(new OffHeapState(viewAddress, contiguousBuffer));
+    MemoryCleaner.register(this, offHeap);
+    // TODO we may want to ask for the null count anyways...
+    this.nullCount = Optional.empty();
+
+    this.refCount = 0;
+    incRefCountInternal(true);
+  }
+
+
+  /**
+   * For a ColumnVector this is really just incrementing the reference count.
+   * @return this
+   */
+  @Override
+  public ColumnVector copyToColumnVector() {
+    return incRefCount();
+  }
+
+  /**
+   * Retrieves the column_view for a cudf::column and if it fails to do so, the column is deleted
+   * and the exception is thrown to the caller.
+   * @param nativePointer the cudf::column handle
+   * @return the column_view handle
+   */
+  private static long getColumnViewFromColumn(long nativePointer) {
+    try {
+      return ColumnVector.getNativeColumnView(nativePointer);
+    } catch (CudfException ce) {
+      deleteCudfColumn(nativePointer);
+      throw ce;
+    }
+  }
+
+  static long initViewHandle(DType type, int numRows, int nullCount,
+                             BaseDeviceMemoryBuffer dataBuffer,
+                             BaseDeviceMemoryBuffer validityBuffer,
+                             BaseDeviceMemoryBuffer offsetBuffer, long[] childHandles) {
+    long cd = dataBuffer == null ? 0 : dataBuffer.address;
+    long cdSize = dataBuffer == null ? 0 : dataBuffer.length;
+    long od = offsetBuffer == null ? 0 : offsetBuffer.address;
+    long vd = validityBuffer == null ? 0 : validityBuffer.address;
+    return makeCudfColumnView(type.typeId.getNativeId(), type.getScale(), cd, cdSize,
+        od, vd, nullCount, numRows, childHandles);
+  }
+
+  static ColumnVector fromViewWithContiguousAllocation(long columnViewAddress, DeviceMemoryBuffer buffer) {
+    return new ColumnVector(columnViewAddress, buffer);
+  }
+
+  /**
+   * Set an event handler for this vector. This method can be invoked with null
+   * to unset the handler.
+   *
+   * @param newHandler - the EventHandler to use from this point forward
+   * @return the prior event handler, or null if not set.
+   */
+  public synchronized EventHandler setEventHandler(EventHandler newHandler) {
+    EventHandler prev = this.eventHandler;
+    this.eventHandler = newHandler;
+    return prev;
+  }
+
+  /**
+   * Returns the current event handler for this ColumnVector or null if no handler
+   * is associated.
+   */
+  public synchronized EventHandler getEventHandler() {
+    return this.eventHandler;
+  }
+
+  /**
+   * This is a really ugly API, but it is possible that the lifecycle of a column of
+   * data may not have a clear lifecycle thanks to java and GC. This API informs the leak
+   * tracking code that this is expected for this column, and big scary warnings should
+   * not be printed when this happens.
+   */
+  public void noWarnLeakExpected() {
+    offHeap.noWarnLeakExpected();
+  }
+
+  /**
+   * Close this Vector and free memory allocated for HostMemoryBuffer and DeviceMemoryBuffer
+   */
+  @Override
+  public synchronized void close() {
+    refCount--;
+    offHeap.delRef();
+    if (eventHandler != null) {
+      eventHandler.onClosed(this, refCount);
+    }
+    if (refCount == 0) {
+      super.close();
+      offHeap.clean(false);
+    } else if (refCount < 0) {
+      offHeap.logRefCountDebug("double free " + this);
+      throw new IllegalStateException("Close called too many times " + this);
+    }
+  }
+
+  @Override
+  public String toString() {
+    return "ColumnVector{" +
+        "rows=" + rows +
+        ", type=" + type +
+        ", nullCount=" + nullCount +
+        ", offHeap=" + offHeap +
+        '}';
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // METADATA ACCESS
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Increment the reference count for this column.  You need to call close on this
+   * to decrement the reference count again.
+   */
+  public ColumnVector incRefCount() {
+    return incRefCountInternal(false);
+  }
+
+  private synchronized ColumnVector incRefCountInternal(boolean isFirstTime) {
+    offHeap.addRef();
+    if (refCount <= 0 && !isFirstTime) {
+      offHeap.logRefCountDebug("INC AFTER CLOSE " + this);
+      throw new IllegalStateException("Column is already closed");
+    }
+    refCount++;
+    return this;
+  }
+
+  /**
+   * Returns the number of nulls in the data. Note that this might end up
+   * being a very expensive operation because if the null count is not
+   * known it will be calculated.
+   */
+  public long getNullCount() {
+    if (!nullCount.isPresent()) {
+      nullCount = Optional.of(offHeap.getNativeNullCount());
+    }
+    return nullCount.get();
+  }
+
+  /**
+   * Returns this column's current refcount
+   */
+  public synchronized int getRefCount() {
+    return refCount;
+  }
+
+  /**
+   * Returns if the vector has a validity vector allocated or not.
+   */
+  public boolean hasValidityVector() {
+    return (offHeap.getValid() != null);
+  }
+
+  /**
+   * Returns if the vector has nulls.  Note that this might end up
+   * being a very expensive operation because if the null count is not
+   * known it will be calculated.
+   */
+  public boolean hasNulls() {
+    return getNullCount() > 0;
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // RAW DATA ACCESS
+  /////////////////////////////////////////////////////////////////////////////
+
+
+  /**
+   * Get access to the raw device buffer for this column.  This is intended to be used with a lot
+   * of caution.  The lifetime of the buffer is tied to the lifetime of the column (Do not close
+   * the buffer, as the column will take care of it).  Do not modify the contents of the buffer or
+   * it might negatively impact what happens on the column.  The data must be on the device for
+   * this to work. Strings and string categories do not currently work because their underlying
+   * device layout is currently hidden.
+   * @param type the type of buffer to get access to.
+   * @return the underlying buffer or null if no buffer is associated with it for this column.
+   * Please note that if the column is empty there may be no buffers at all associated with the
+   * column.
+   */
+  public BaseDeviceMemoryBuffer getDeviceBufferFor(BufferType type) {
+    BaseDeviceMemoryBuffer srcBuffer;
+    switch(type) {
+      case VALIDITY:
+        srcBuffer = offHeap.getValid();
+        break;
+      case DATA:
+        srcBuffer = offHeap.getData();
+        break;
+      case OFFSET:
+        srcBuffer = offHeap.getOffsets();
+        break;
+      default:
+        throw new IllegalArgumentException(type + " is not a supported buffer type.");
+
+    }
+    return srcBuffer;
+  }
+
+  /**
+   * Ensures the ByteBuffer passed in is a direct byte buffer.
+   * If it is not then it creates one and copies the data in
+   * the byte buffer passed in to the direct byte buffer
+   * it created and returns it.
+   */
+  private static ByteBuffer bufferAsDirect(ByteBuffer buf) {
+    ByteBuffer bufferOut = buf;
+    if (bufferOut != null && !bufferOut.isDirect()) {
+      bufferOut = ByteBuffer.allocateDirect(buf.remaining());
+      bufferOut.put(buf);
+      bufferOut.flip();
+    }
+    return bufferOut;
+  }
+
+  /**
+   * Create a ColumnVector from the Apache Arrow byte buffers passed in.
+   * Any of the buffers not used for that datatype should be set to null.
+   * The buffers are expected to be off heap buffers, but if they are not,
+   * it will handle copying them to direct byte buffers.
+   * This only supports primitive types. Strings, Decimals and nested types
+   * such as list and struct are not supported.
+   * @param type - type of the column
+   * @param numRows - Number of rows in the arrow column
+   * @param nullCount - Null count
+   * @param data - ByteBuffer of the Arrow data buffer
+   * @param validity - ByteBuffer of the Arrow validity buffer
+   * @param offsets - ByteBuffer of the Arrow offsets buffer
+   * @return - new ColumnVector
+   */
+  public static ColumnVector fromArrow(
+      DType type,
+      long numRows,
+      long nullCount,
+      ByteBuffer data,
+      ByteBuffer validity,
+      ByteBuffer offsets) {
+    long columnHandle = fromArrow(type.typeId.getNativeId(), numRows, nullCount,
+      bufferAsDirect(data), bufferAsDirect(validity), bufferAsDirect(offsets));
+    ColumnVector vec = new ColumnVector(columnHandle);
+    return vec;
+  }
+
+  /**
+   * Create a new vector of length rows, where each row is filled with the Scalar's
+   * value
+   * @param scalar - Scalar to use to fill rows
+   * @param rows - Number of rows in the new ColumnVector
+   * @return - new ColumnVector
+   */
+  public static ColumnVector fromScalar(Scalar scalar, int rows) {
+    long columnHandle = fromScalar(scalar.getScalarHandle(), rows);
+    return new ColumnVector(columnHandle);
+  }
+
+  /**
+   * Create a new struct vector made up of existing columns. Note that this will copy
+   * the contents of the input columns to make a new vector. If you only want to
+   * do a quick temporary computation you can use ColumnView.makeStructView.
+   * @param columns the columns to make the struct from.
+   * @return the new ColumnVector
+   */
+  public static ColumnVector makeStruct(ColumnView... columns) {
+    try (ColumnView cv = ColumnView.makeStructView(columns)) {
+      return cv.copyToColumnVector();
+    }
+  }
+
+  /**
+   * Create a new struct vector made up of existing columns. Note that this will copy
+   * the contents of the input columns to make a new vector. If you only want to
+   * do a quick temporary computation you can use ColumnView.makeStructView.
+   * @param rows the number of rows in the struct. Used for structs with no children.
+   * @param columns the columns to make the struct from.
+   * @return the new ColumnVector
+   */
+  public static ColumnVector makeStruct(long rows, ColumnView... columns) {
+    try (ColumnView cv = ColumnView.makeStructView(rows, columns)) {
+      return cv.copyToColumnVector();
+    }
+  }
+
+  /**
+   * Create a LIST column from the given columns. Each list in the returned column will have the
+   * same number of entries in it as columns passed into this method. Be careful about the
+   * number of rows passed in as there are limits on the maximum output size supported for
+   * column lists.
+   * @param columns the columns to make up the list column, in the order they will appear in the
+   *                resulting lists.
+   * @return the new LIST ColumnVector
+   */
+  public static ColumnVector makeList(ColumnView... columns) {
+    if (columns.length <= 0) {
+      throw new IllegalArgumentException("At least one column is needed to get the row count");
+    }
+    return makeList(columns[0].getRowCount(), columns[0].getType(), columns);
+  }
+
+  /**
+   * Create a LIST column from the given columns. Each list in the returned column will have the
+   * same number of entries in it as columns passed into this method. Be careful about the
+   * number of rows passed in as there are limits on the maximum output size supported for
+   * column lists.
+   * @param rows the number of rows to create, for the special case of an empty list.
+   * @param type the type of the child column, for the special case of an empty list.
+   * @param columns the columns to make up the list column, in the order they will appear in the
+   *                resulting lists.
+   * @return the new LIST ColumnVector
+   */
+  public static ColumnVector makeList(long rows, DType type, ColumnView... columns) {
+    long[] handles = new long[columns.length];
+    for (int i = 0; i < columns.length; i++) {
+      ColumnView cv = columns[i];
+      if (rows != cv.getRowCount()) {
+        throw new IllegalArgumentException("All columns must have the same number of rows");
+      }
+      if (!type.equals(cv.getType())) {
+        throw new IllegalArgumentException("All columns must have the same type");
+      }
+
+      handles[i] = cv.getNativeView();
+    }
+    if (columns.length == 0 && type.isNestedType()) {
+      throw new IllegalArgumentException(
+          "Creating an empty list column of nested types is not currently supported");
+    }
+    return new ColumnVector(makeList(handles, type.typeId.nativeId, type.getScale(), rows));
+  }
+
+  /**
+   * Create a LIST column from the current column and a given offsets column. The output column will
+   * contain lists having elements that are copied from the current column and their sizes are
+   * determined by the given offsets.
+   *
+   * Note that the caller is responsible to make sure the given offsets column is of type INT32 and
+   * it contains valid indices to create a LIST column. There will not be any validity check for
+   * these offsets during calling to this function. If the given offsets are invalid, we may have
+   * bad memory accesses and/or data corruption.
+   *
+   * @param rows the number of rows to create.
+   * @param offsets the offsets pointing to row indices of the current column to create an output
+   *                LIST column.
+   */
+  public ColumnVector makeListFromOffsets(long rows, ColumnView offsets) {
+    return new ColumnVector(makeListFromOffsets(getNativeView(), offsets.getNativeView(), rows));
+  }
+
+  /**
+   * Create a new vector of length rows, starting at the initialValue and going by step each time.
+   * Only numeric types are supported.
+   * @param initialValue the initial value to start at.
+   * @param step the step to add to each subsequent row.
+   * @param rows the total number of rows
+   * @return the new ColumnVector.
+   */
+  public static ColumnVector sequence(Scalar initialValue, Scalar step, int rows) {
+    if (!initialValue.isValid() || !step.isValid()) {
+      throw new IllegalArgumentException("nulls are not supported in sequence");
+    }
+    return new ColumnVector(sequence(initialValue.getScalarHandle(), step.getScalarHandle(), rows));
+  }
+
+  /**
+   * Create a new vector of length rows, starting at the initialValue and going by 1 each time.
+   * Only numeric types are supported.
+   * @param initialValue the initial value to start at.
+   * @param rows the total number of rows
+   * @return the new ColumnVector.
+   */
+  public static ColumnVector sequence(Scalar initialValue, int rows) {
+    if (!initialValue.isValid()) {
+      throw new IllegalArgumentException("nulls are not supported in sequence");
+    }
+    return new ColumnVector(sequence(initialValue.getScalarHandle(), 0, rows));
+  }
+
+  /**
+   * Create a list column in which each row is a sequence of values starting from a `start` value,
+   * incrementing by one, and its cardinality is specified by a `size` value. The `start` and `size`
+   * values used to generate each list is taken from the corresponding row of the input start and
+   * size columns.
+   * @param start first values in the result sequences
+   * @param size numbers of values in the result sequences
+   * @return the new ColumnVector.
+   */
+  public static ColumnVector sequence(ColumnView start, ColumnView size) {
+    assert start.getNullCount() == 0 || size.getNullCount() == 0 : "starts and sizes input " +
+        "columns must not have nulls.";
+    return new ColumnVector(sequences(start.getNativeView(), size.getNativeView(), 0));
+  }
+
+  /**
+   * Create a list column in which each row is a sequence of values starting from a `start` value,
+   * incrementing by a `step` value, and its cardinality is specified by a `size` value.
+   * The values `start`, `step`, and `size` used to generate each list is taken from the
+   * corresponding row of the input starts, steps, and sizes columns.
+   * @param start first values in the result sequences
+   * @param size numbers of values in the result sequences
+   * @param step increment values for the result sequences.
+   * @return the new ColumnVector.
+   */
+  public static ColumnVector sequence(ColumnView start, ColumnView size, ColumnView step) {
+    assert start.getNullCount() == 0 || size.getNullCount() == 0 || step.getNullCount() == 0:
+        "start, size and step must not have nulls.";
+    assert step.getType() == start.getType() : "start and step input columns must" +
+        " have the same type.";
+
+    return new ColumnVector(sequences(start.getNativeView(), size.getNativeView(),
+        step.getNativeView()));
+  }
+
+  /**
+   * Create a new vector by concatenating multiple columns together.
+   * Note that all columns must have the same type.
+   */
+  public static ColumnVector concatenate(ColumnView... columns) {
+    if (columns.length < 2) {
+      throw new IllegalArgumentException("Concatenate requires 2 or more columns");
+    }
+    long[] columnHandles = new long[columns.length];
+    for (int i = 0; i < columns.length; ++i) {
+      columnHandles[i] = columns[i].getNativeView();
+    }
+    return new ColumnVector(concatenate(columnHandles));
+  }
+
+  /**
+   * Concatenate columns of strings together, combining a corresponding row from each column
+   * into a single string row of a new column with no separator string inserted between each
+   * combined string and maintaining null values in combined rows.
+   * @param columns array of columns containing strings, must be non-empty
+   * @return A new java column vector containing the concatenated strings.
+   */
+  public static ColumnVector stringConcatenate(ColumnView[] columns) {
+    try (Scalar emptyString = Scalar.fromString("");
+         Scalar nullString = Scalar.fromString(null)) {
+      return stringConcatenate(emptyString, nullString, columns);
+    }
+  }
+
+  /**
+   * Concatenate columns of strings together, combining a corresponding row from each column into
+   * a single string row of a new column. This version includes the separator for null rows
+   * if 'narep' is valid.
+   * @param separator string scalar inserted between each string being merged.
+   * @param narep string scalar indicating null behavior. If set to null and any string in the row
+   *              is null the resulting string will be null. If not null, null values in any column
+   *              will be replaced by the specified string.
+   * @param columns array of columns containing strings, must be non-empty
+   * @return A new java column vector containing the concatenated strings.
+   */
+  public static ColumnVector stringConcatenate(Scalar separator, Scalar narep, ColumnView[] columns) {
+    return stringConcatenate(separator, narep, columns, true);
+  }
+
+  /**
+   * Concatenate columns of strings together, combining a corresponding row from each column into
+   * a single string row of a new column.
+   * @param separator string scalar inserted between each string being merged.
+   * @param narep string scalar indicating null behavior. If set to null and any string in the row
+   *              is null the resulting string will be null. If not null, null values in any column
+   *              will be replaced by the specified string.
+   * @param columns array of columns containing strings, must be non-empty
+   * @param separateNulls if true, then the separator is included for null rows if
+   *                       `narep` is valid.
+   * @return A new java column vector containing the concatenated strings.
+   */
+  public static ColumnVector stringConcatenate(Scalar separator, Scalar narep, ColumnView[] columns,
+      boolean separateNulls) {
+    assert columns != null : "input columns should not be null";
+    assert columns.length > 0 : "input columns should not be empty";
+    assert separator != null : "separator scalar provided may not be null";
+    assert separator.getType().equals(DType.STRING) : "separator scalar must be a string scalar";
+    assert narep != null : "narep scalar provided may not be null";
+    assert narep.getType().equals(DType.STRING) : "narep scalar must be a string scalar";
+
+    long[] columnViews = new long[columns.length];
+    for(int i = 0; i < columns.length; i++) {
+      assert columns[i] != null : "Column vectors passed may not be null";
+      columnViews[i] = columns[i].getNativeView();
+    }
+
+    return new ColumnVector(stringConcatenation(columnViews, separator.getScalarHandle(),
+        narep.getScalarHandle(), separateNulls));
+  }
+
+  /**
+   * Concatenate columns of strings together using a separator specified for each row
+   * and returns the result as a string column. If the row separator for a given row is null,
+   * output column for that row is null. Null column values for a given row are skipped.
+   * @param columns array of columns containing strings
+   * @param sepCol strings column that provides the separator for a given row
+   * @return A new java column vector containing the concatenated strings with separator between.
+   */
+  public static ColumnVector stringConcatenate(ColumnView[] columns, ColumnView sepCol) {
+    try (Scalar nullString = Scalar.fromString(null);
+         Scalar emptyString = Scalar.fromString("")) {
+      return stringConcatenate(columns, sepCol, nullString, emptyString, false);
+    }
+  }
+
+  /**
+   * Concatenate columns of strings together using a separator specified for each row
+   * and returns the result as a string column. If the row separator for a given row is null,
+   * output column for that row is null unless separatorNarep is provided.
+   * The separator is applied between two output row values if the separateNulls
+   * is `YES` or only between valid rows if separateNulls is `NO`.
+   * @param columns array of columns containing strings
+   * @param sepCol strings column that provides the separator for a given row
+   * @param separatorNarep string scalar indicating null behavior when a separator is null.
+   *                        If set to null and the separator is null the resulting string will
+   *                        be null. If not null, this string will be used in place of a null
+   *                        separator.
+   * @param colNarep string that should be used in place of any null strings
+   *                  found in any column.
+   * @param separateNulls if true, then the separator is included for null rows if
+   *                       `colNarep` is valid.
+   * @return A new java column vector containing the concatenated strings with separator between.
+   */
+  public static ColumnVector stringConcatenate(ColumnView[] columns,
+      ColumnView sepCol, Scalar separatorNarep, Scalar colNarep, boolean separateNulls) {
+    assert columns.length >= 1 : ".stringConcatenate() operation requires at least 1 column";
+    assert separatorNarep != null : "separator narep scalar provided may not be null";
+    assert colNarep != null : "column narep scalar provided may not be null";
+    assert separatorNarep.getType().equals(DType.STRING) : "separator naprep scalar must be a string scalar";
+    assert colNarep.getType().equals(DType.STRING) : "column narep scalar must be a string scalar";
+
+    long[] columnViews = new long[columns.length];
+    for(int i = 0; i < columns.length; i++) {
+      assert columns[i] != null : "Column vectors passed may not be null";
+      columnViews[i] = columns[i].getNativeView();
+    }
+
+    return new ColumnVector(stringConcatenationSepCol(columnViews, sepCol.getNativeView(),
+      separatorNarep.getScalarHandle(), colNarep.getScalarHandle(), separateNulls));
+  }
+
+  /**
+   * Concatenate columns of lists horizontally (row by row), combining a corresponding row
+   * from each column into a single list row of a new column.
+   * NOTICE: Any concatenation involving a null list element will result in a null list.
+   *
+   * @param columns array of columns containing lists, must be non-empty
+   * @return A new java column vector containing the concatenated lists.
+   */
+  public static ColumnVector listConcatenateByRow(ColumnView... columns) {
+    return listConcatenateByRow(false, columns);
+  }
+
+  /**
+   * Concatenate columns of lists horizontally (row by row), combining a corresponding row
+   * from each column into a single list row of a new column.
+   *
+   * @param ignoreNull whether to ignore null list element of input columns: If true, null list
+   *                   will be ignored from concatenation; Otherwise, any concatenation involving
+   *                   a null list element will result in a null list
+   * @param columns    array of columns containing lists, must be non-empty
+   * @return A new java column vector containing the concatenated lists.
+   */
+  public static ColumnVector listConcatenateByRow(boolean ignoreNull, ColumnView... columns) {
+    assert columns != null : "input columns should not be null";
+    assert columns.length > 0 : "input columns should not be empty";
+
+    long[] columnViews = new long[columns.length];
+    for(int i = 0; i < columns.length; i++) {
+      columnViews[i] = columns[i].getNativeView();
+    }
+
+    return new ColumnVector(concatListByRow(columnViews, ignoreNull));
+  }
+
+  /**
+   * Create a new vector containing the MD5 hash of each row in the table.
+   *
+   * @param columns array of columns to hash, must have identical number of rows.
+   * @return the new ColumnVector of 32 character hex strings representing each row's hash value.
+   */
+  public static ColumnVector md5Hash(ColumnView... columns) {
+    if (columns.length < 1) {
+      throw new IllegalArgumentException("MD5 hashing requires at least 1 column of input");
+    }
+    long[] columnViews = new long[columns.length];
+    long size = columns[0].getRowCount();
+
+    for(int i = 0; i < columns.length; i++) {
+      assert columns[i] != null : "Column vectors passed may not be null";
+      assert columns[i].getRowCount() == size : "Row count mismatch, all columns must be the same size";
+      assert !columns[i].getType().isDurationType() : "Unsupported column type Duration";
+      assert !columns[i].getType().isTimestampType() : "Unsupported column type Timestamp";
+      assert !columns[i].getType().isNestedType() || columns[i].getType().equals(DType.LIST) :
+          "Unsupported nested type column";
+      columnViews[i] = columns[i].getNativeView();
+    }
+    return new ColumnVector(hash(columnViews, HashType.HASH_MD5.getNativeId(), 0));
+  }
+
+  /**
+   * Create a new vector containing spark's 32-bit murmur3 hash of each row in the table.
+   * Spark's murmur3 hash uses a different tail processing algorithm.
+   *
+   * @param seed integer seed for the murmur3 hash function
+   * @param columns array of columns to hash, must have identical number of rows.
+   * @return the new ColumnVector of 32-bit values representing each row's hash value.
+   */
+  public static ColumnVector spark32BitMurmurHash3(int seed, ColumnView columns[]) {
+    if (columns.length < 1) {
+      throw new IllegalArgumentException("Murmur3 hashing requires at least 1 column of input");
+    }
+    long[] columnViews = new long[columns.length];
+    long size = columns[0].getRowCount();
+
+    for(int i = 0; i < columns.length; i++) {
+      assert columns[i] != null : "Column vectors passed may not be null";
+      assert columns[i].getRowCount() == size : "Row count mismatch, all columns must be the same size";
+      assert !columns[i].getType().isDurationType() : "Unsupported column type Duration";
+      columnViews[i] = columns[i].getNativeView();
+    }
+    return new ColumnVector(hash(columnViews, HashType.HASH_SPARK_MURMUR3.getNativeId(), seed));
+  }
+
+  /**
+   * Create a new vector containing spark's 32-bit murmur3 hash of each row in the table with the
+   * seed set to 0. Spark's murmur3 hash uses a different tail processing algorithm.
+   *
+   * @param columns array of columns to hash, must have identical number of rows.
+   * @return the new ColumnVector of 32-bit values representing each row's hash value.
+   */
+  public static ColumnVector spark32BitMurmurHash3(ColumnView columns[]) {
+    return spark32BitMurmurHash3(0, columns);
+  }
+
+  /**
+   * Generic method to cast ColumnVector
+   * When casting from a Date, Timestamp, or Boolean to a numerical type the underlying numerical
+   * representation of the data will be used for the cast.
+   *
+   * For Strings:
+   * Casting strings from/to timestamp isn't supported atm.
+   * Please look at {@link ColumnVector#asTimestamp(DType, String)}
+   * and {@link ColumnVector#asStrings(String)} for casting string to timestamp when the format
+   * is known
+   *
+   * Float values when converted to String could be different from the expected default behavior in
+   * Java
+   * e.g.
+   * 12.3 => "12.30000019" instead of "12.3"
+   * Double.POSITIVE_INFINITY => "Inf" instead of "INFINITY"
+   * Double.NEGATIVE_INFINITY => "-Inf" instead of "-INFINITY"
+   *
+   * @param type type of the resulting ColumnVector
+   * @return A new vector allocated on the GPU
+   */
+  @Override
+  public ColumnVector castTo(DType type) {
+    if (this.type.equals(type)) {
+      // Optimization
+      return incRefCount();
+    }
+    return super.castTo(type);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // NATIVE METHODS
+  /////////////////////////////////////////////////////////////////////////////
+
+  private static native long sequence(long initialValue, long step, int rows);
+
+  private static native long sequences(long startHandle, long sizeHandle, long stepHandle)
+      throws CudfException;
+
+  private static native long fromArrow(int type, long col_length,
+      long null_count, ByteBuffer data, ByteBuffer validity,
+      ByteBuffer offsets) throws CudfException;
+
+  private static native long fromScalar(long scalarHandle, int rowCount) throws CudfException;
+
+  private static native long makeList(long[] handles, long typeHandle, int scale, long rows)
+      throws CudfException;
+
+  private static native long makeListFromOffsets(long childHandle, long offsetsHandle, long rows)
+      throws CudfException;
+
+  private static native long concatenate(long[] viewHandles) throws CudfException;
+
+  /**
+   * Native method to concatenate columns of lists horizontally (row by row), combining a row
+   * from each column into a single list.
+   *
+   * @param columnViews array of longs holding the native handles of the column_views to combine.
+   * @return native handle of the resulting cudf column, used to construct the Java column
+   * by the listConcatenateByRow method.
+   */
+  private static native long concatListByRow(long[] columnViews, boolean ignoreNull);
+
+  /**
+   * Native method to concatenate columns of strings together, combining a row from
+   * each column into a single string.
+   *
+   * @param columnViews array of longs holding the native handles of the column_views to combine.
+   * @param separator   string scalar inserted between each string being merged, may not be null.
+   * @param narep       string scalar indicating null behavior. If set to null and any string in
+   *                    the row is null the resulting string will be null. If not null, null
+   *                    values in any column will be replaced by the specified string. The
+   *                    underlying value in the string scalar may be null, but the object passed
+   *                    in may not.
+   * @param separate_nulls boolean if true, then the separator is included for null rows if
+   *                       `narep` is valid.
+   * @return native handle of the resulting cudf column, used to construct the Java column
+   * by the stringConcatenate method.
+   */
+  private static native long stringConcatenation(long[] columnViews, long separator, long narep,
+                                                 boolean separate_nulls);
+
+  /**
+   * Native method to concatenate columns of strings together using a separator specified for each row
+   * and returns the result as a string column.
+   * @param columnViews array of longs holding the native handles of the column_views to combine.
+   * @param sep_column long holding the native handle of the strings_column_view used as separators.
+   * @param separator_narep string scalar indicating null behavior when a separator is null.
+   *                        If set to null and the separator is null the resulting string will
+   *                        be null. If not null, this string will be used in place of a null
+   *                        separator.
+   * @param col_narep string String scalar that should be used in place of any null strings
+   *                         found in any column.
+   * @param separate_nulls boolean if true, then the separator is included for null rows if
+   *                       `col_narep` is valid.
+   * @return native handle of the resulting cudf column, used to construct the Java column.
+   */
+  private static native long stringConcatenationSepCol(long[] columnViews,
+                                                       long sep_column,
+                                                       long separator_narep,
+                                                       long col_narep,
+                                                       boolean separate_nulls);
+
+  /**
+   * Native method to hash each row of the given table. Hashing function dispatched on the
+   * native side using the hashId.
+   *
+   * @param viewHandles array of native handles to the cudf::column_view columns being operated on.
+   * @param hashId integer native ID of the hashing function identifier HashType.
+   * @param seed integer seed for the hash. Only used by serial murmur3 hash.
+   * @return native handle of the resulting cudf column containing the hex-string hashing results.
+   */
+  private static native long hash(long[] viewHandles, int hashId, int seed) throws CudfException;
+
+  /////////////////////////////////////////////////////////////////////////////
+  // INTERNAL/NATIVE ACCESS
+  /////////////////////////////////////////////////////////////////////////////
+
+  ////////
+  // Native methods specific to cudf::column. These either take or create a cudf::column
+  // instead of a cudf::column_view so they need to be used with caution. These should
+  // only be called from the OffHeap inner class.
+  ////////
+
+  /**
+   * Delete the column. This is not private because there are a few cases where Table
+   * may get back an array of columns pointers and we want to have best effort in cleaning them up
+   * on any failure.
+   */
+  static native void deleteCudfColumn(long cudfColumnHandle) throws CudfException;
+
+  private static native int getNativeNullCountColumn(long cudfColumnHandle) throws CudfException;
+
+  private static native void setNativeNullCountColumn(long cudfColumnHandle, int nullCount) throws CudfException;
+
+  /**
+   * Create a cudf::column_view from a cudf::column.
+   * @param cudfColumnHandle the pointer to the cudf::column
+   * @return a pointer to a cudf::column_view
+   * @throws CudfException on any error
+   */
+  static native long getNativeColumnView(long cudfColumnHandle) throws CudfException;
+
+  static native long makeEmptyCudfColumn(int type, int scale);
+
+  /////////////////////////////////////////////////////////////////////////////
+  // HELPER CLASSES
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Holds the off heap state of the column vector so we can clean it up, even if it is leaked.
+   */
+  protected static final class OffHeapState extends MemoryCleaner.Cleaner {
+    // This must be kept in sync with the native code
+    public static final long UNKNOWN_NULL_COUNT = -1;
+    private long columnHandle;
+    private long viewHandle = 0;
+    private List<MemoryBuffer> toClose = new ArrayList<>();
+
+
+    /**
+     * Make a column form an existing cudf::column *.
+     */
+    public OffHeapState(long columnHandle) {
+      this.columnHandle = columnHandle;
+      this.toClose.add(getData());
+      this.toClose.add(getValid());
+      this.toClose.add(getOffsets());
+    }
+
+    /**
+     * Create from existing cudf::column_view and buffers.
+     */
+    public OffHeapState(DeviceMemoryBuffer data, DeviceMemoryBuffer valid, DeviceMemoryBuffer offsets,
+                        List<DeviceMemoryBuffer> buffers,
+                        long viewHandle) {
+      assert(viewHandle != 0);
+      if (data != null) {
+        this.toClose.add(data);
+      }
+      if (valid != null) {
+        this.toClose.add(valid);
+      }
+      if (offsets != null) {
+        this.toClose.add(offsets);
+      }
+      if (buffers != null) {
+        toClose.addAll(buffers);
+      }
+      this.viewHandle = viewHandle;
+    }
+
+    /**
+     * Create from existing cudf::column_view and contiguous buffer.
+     */
+    public OffHeapState(long viewHandle, DeviceMemoryBuffer contiguousBuffer) {
+      assert viewHandle != 0;
+      this.viewHandle = viewHandle;
+      BaseDeviceMemoryBuffer valid = getValid();
+      BaseDeviceMemoryBuffer data = getData();
+      BaseDeviceMemoryBuffer offsets = getOffsets();
+      toClose.add(data);
+      toClose.add(valid);
+      toClose.add(offsets);
+      contiguousBuffer.incRefCount();
+      toClose.add(contiguousBuffer);
+    }
+
+    public long getViewHandle() {
+      if (viewHandle == 0) {
+        viewHandle = ColumnVector.getNativeColumnView(columnHandle);
+      }
+      return viewHandle;
+    }
+
+    public long getNativeNullCount() {
+      if (viewHandle != 0) {
+        return ColumnView.getNativeNullCount(getViewHandle());
+      }
+      return getNativeNullCountColumn(columnHandle);
+    }
+
+    private void setNativeNullCount(int nullCount) throws CudfException {
+      assert viewHandle == 0 : "Cannot set the null count if a view has already been created";
+      assert columnHandle != 0;
+      setNativeNullCountColumn(columnHandle, nullCount);
+    }
+
+    public BaseDeviceMemoryBuffer getData() {
+      return getDataBuffer(getViewHandle());
+    }
+
+    public BaseDeviceMemoryBuffer getValid() {
+      return getValidityBuffer(getViewHandle());
+    }
+
+    public BaseDeviceMemoryBuffer getOffsets() {
+      return getOffsetsBuffer(getViewHandle());
+    }
+
+    @Override
+    public void noWarnLeakExpected() {
+      super.noWarnLeakExpected();
+
+      BaseDeviceMemoryBuffer valid = getValid();
+      BaseDeviceMemoryBuffer data = getData();
+      BaseDeviceMemoryBuffer offsets = getOffsets();
+      if (valid != null) {
+        valid.noWarnLeakExpected();
+      }
+      if (data != null) {
+        data.noWarnLeakExpected();
+      }
+      if(offsets != null) {
+        offsets.noWarnLeakExpected();
+      }
+    }
+
+    @Override
+    public String toString() {
+      return "(ID: " + id + " " + Long.toHexString(columnHandle == 0 ? viewHandle : columnHandle) + ")";
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long address = 0;
+
+      // Always mark the resource as freed even if an exception is thrown.
+      // We cannot know how far it progressed before the exception, and
+      // therefore it is unsafe to retry.
+      Throwable toThrow = null;
+      if (viewHandle != 0) {
+        address = viewHandle;
+        try {
+          ColumnView.deleteColumnView(viewHandle);
+        } catch (Throwable t) {
+          toThrow = t;
+        } finally {
+          viewHandle = 0;
+        }
+        neededCleanup = true;
+      }
+      if (columnHandle != 0) {
+        if (address != 0) {
+          address = columnHandle;
+        }
+        try {
+          ColumnVector.deleteCudfColumn(columnHandle);
+        } catch (Throwable t) {
+          if (toThrow != null) {
+            toThrow.addSuppressed(t);
+          } else {
+            toThrow = t;
+          }
+        } finally {
+          columnHandle = 0;
+        }
+        neededCleanup = true;
+      }
+      if (!toClose.isEmpty()) {
+        try {
+          for (MemoryBuffer toCloseBuff : toClose) {
+            if (toCloseBuff != null) {
+              try {
+                toCloseBuff.close();
+              } catch (Throwable t) {
+                if (toThrow != null) {
+                  toThrow.addSuppressed(t);
+                } else {
+                  toThrow = t;
+                }
+              }
+            }
+          }
+        } finally {
+          toClose.clear();
+        }
+        neededCleanup = true;
+      }
+      if (toThrow != null) {
+        throw new RuntimeException(toThrow);
+      }
+      if (neededCleanup) {
+        if (logErrorIfNotClean) {
+          log.error("A DEVICE COLUMN VECTOR WAS LEAKED (ID: " + id + " " + Long.toHexString(address)+ ")");
+          logRefCountDebug("Leaked vector");
+        }
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return viewHandle == 0 && columnHandle == 0 && toClose.isEmpty();
+    }
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // BUILDER
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Create a new vector.
+   * @param type       the type of vector to build.
+   * @param rows       maximum number of rows that the vector can hold.
+   * @param init       what will initialize the vector.
+   * @return the created vector.
+   */
+  public static ColumnVector build(DType type, int rows, Consumer<Builder> init) {
+    try (Builder builder = HostColumnVector.builder(type, rows)) {
+      init.accept(builder);
+      return builder.buildAndPutOnDevice();
+    }
+  }
+
+  public static ColumnVector build(int rows, long stringBufferSize, Consumer<Builder> init) {
+    try (Builder builder = HostColumnVector.builder(rows, stringBufferSize)) {
+      init.accept(builder);
+      return builder.buildAndPutOnDevice();
+    }
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector boolFromBytes(byte... values) {
+    return build(DType.BOOL8, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * This method is evolving, unstable and currently test only.
+   * Please use with caution and expect it to change in the future.
+   */
+  public static<T> ColumnVector fromLists(HostColumnVector.DataType dataType, List<T>... lists) {
+    try (HostColumnVector host = HostColumnVector.fromLists(dataType, lists)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * This method is evolving, unstable and currently test only.
+   * Please use with caution and expect it to change in the future.
+   */
+  public static ColumnVector fromStructs(HostColumnVector.DataType dataType,
+                                         List<HostColumnVector.StructData> lists) {
+    try (HostColumnVector host = HostColumnVector.fromStructs(dataType, lists)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * This method is evolving, unstable and currently test only.
+   * Please use with caution and expect it to change in the future.
+   */
+  public static ColumnVector fromStructs(HostColumnVector.DataType dataType,
+                                         HostColumnVector.StructData... lists) {
+    try (HostColumnVector host = HostColumnVector.fromStructs(dataType, lists)) {
+      return host.copyToDevice();
+    }
+  }
+  /**
+   * This method is evolving, unstable and currently test only.
+   * Please use with caution and expect it to change in the future.
+   */
+  public static ColumnVector emptyStructs(HostColumnVector.DataType dataType, long numRows) {
+    try (HostColumnVector host = HostColumnVector.emptyStructs(dataType, numRows)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromBooleans(boolean... values) {
+    byte[] bytes = new byte[values.length];
+    for (int i = 0; i < values.length; i++) {
+      bytes[i] = values[i] ? (byte) 1 : (byte) 0;
+    }
+    return build(DType.BOOL8, values.length, (b) -> b.appendArray(bytes));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromBytes(byte... values) {
+    return build(DType.INT8, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned byte type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromUnsignedBytes(byte... values) {
+    return build(DType.UINT8, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromShorts(short... values) {
+    return build(DType.INT16, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned short type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromUnsignedShorts(short... values) {
+    return build(DType.UINT16, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromInts(int... values) {
+    return build(DType.INT32, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned int type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromUnsignedInts(int... values) {
+    return build(DType.UINT32, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromLongs(long... values) {
+    return build(DType.INT64, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned long type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromUnsignedLongs(long... values) {
+    return build(DType.UINT64, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromFloats(float... values) {
+    return build(DType.FLOAT32, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector fromDoubles(double... values) {
+    return build(DType.FLOAT64, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector daysFromInts(int... values) {
+    return build(DType.TIMESTAMP_DAYS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector durationSecondsFromLongs(long... values) {
+    return build(DType.DURATION_SECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector timestampSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_SECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector durationDaysFromInts(int... values) {
+    return build(DType.DURATION_DAYS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector durationMilliSecondsFromLongs(long... values) {
+    return build(DType.DURATION_MILLISECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector timestampMilliSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_MILLISECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector durationMicroSecondsFromLongs(long... values) {
+    return build(DType.DURATION_MICROSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector timestampMicroSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_MICROSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector durationNanoSecondsFromLongs(long... values) {
+    return build(DType.DURATION_NANOSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static ColumnVector timestampNanoSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_NANOSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new decimal vector from unscaled values (int array) and scale.
+   * The created vector is of type DType.DECIMAL32, whose max precision is 9.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static ColumnVector decimalFromInts(int scale, int... values) {
+    try (HostColumnVector host = HostColumnVector.decimalFromInts(scale, values)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new decimal vector from boxed unscaled values (Integer array) and scale.
+   * The created vector is of type DType.DECIMAL32, whose max precision is 9.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static ColumnVector decimalFromBoxedInts(int scale, Integer... values) {
+    try (HostColumnVector host = HostColumnVector.decimalFromBoxedInts(scale, values)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new decimal vector from unscaled values (long array) and scale.
+   * The created vector is of type DType.DECIMAL64, whose max precision is 18.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static ColumnVector decimalFromLongs(int scale, long... values) {
+    try (HostColumnVector host = HostColumnVector.decimalFromLongs(scale, values)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new decimal vector from boxed unscaled values (Long array) and scale.
+   * The created vector is of type DType.DECIMAL64, whose max precision is 18.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static ColumnVector decimalFromBoxedLongs(int scale, Long... values) {
+    try (HostColumnVector host = HostColumnVector.decimalFromBoxedLongs(scale, values)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new decimal vector from double floats with specific DecimalType and RoundingMode.
+   * All doubles will be rescaled if necessary, according to scale of input DecimalType and RoundingMode.
+   * If any overflow occurs in extracting integral part, an IllegalArgumentException will be thrown.
+   * This API is inefficient because of slow double -> decimal conversion, so it is mainly for testing.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static ColumnVector decimalFromDoubles(DType type, RoundingMode mode, double... values) {
+    try (HostColumnVector host = HostColumnVector.decimalFromDoubles(type, mode, values)) {
+      return host.copyToDevice();
+    }
+  }
+
+
+  /**
+   * Create a new decimal vector from BigIntegers
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static ColumnVector decimalFromBigInt(int scale, BigInteger... values) {
+    try (HostColumnVector host = HostColumnVector.decimalFromBigIntegers(scale, values)) {
+      ColumnVector columnVector = host.copyToDevice();
+      return columnVector;
+    }
+  }
+
+  /**
+   * Create a new string vector from the given values.  This API
+   * supports inline nulls. This is really intended to be used only for testing as
+   * it is slow and memory intensive to translate between java strings and UTF8 strings.
+   */
+  public static ColumnVector fromStrings(String... values) {
+    try (HostColumnVector host = HostColumnVector.fromStrings(values)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new string vector from the given values.  This API
+   * supports inline nulls.
+   */
+  public static ColumnVector fromUTF8Strings(byte[]... values) {
+    try (HostColumnVector host = HostColumnVector.fromUTF8Strings(values)) {
+      return host.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than building from primitive array of unscaledValues.
+   * Notice:
+   *  1. All input BigDecimals should share same scale.
+   *  2. The scale will be zero if all input values are null.
+   */
+  public static ColumnVector fromDecimals(BigDecimal... values) {
+    try (HostColumnVector hcv = HostColumnVector.fromDecimals(values)) {
+      return hcv.copyToDevice();
+    }
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedBooleans(Boolean... values) {
+    return build(DType.BOOL8, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedBytes(Byte... values) {
+    return build(DType.INT8, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned byte type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromBoxedUnsignedBytes(Byte... values) {
+    return build(DType.UINT8, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedShorts(Short... values) {
+    return build(DType.INT16, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned short type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromBoxedUnsignedShorts(Short... values) {
+    return build(DType.UINT16, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedInts(Integer... values) {
+    return build(DType.INT32, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned int type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromBoxedUnsignedInts(Integer... values) {
+    return build(DType.UINT32, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedLongs(Long... values) {
+    return build(DType.INT64, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned long type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static ColumnVector fromBoxedUnsignedLongs(Long... values) {
+    return build(DType.UINT64, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedFloats(Float... values) {
+    return build(DType.FLOAT32, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector fromBoxedDoubles(Double... values) {
+    return build(DType.FLOAT64, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector timestampDaysFromBoxedInts(Integer... values) {
+    return build(DType.TIMESTAMP_DAYS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector durationDaysFromBoxedInts(Integer... values) {
+    return build(DType.DURATION_DAYS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector durationSecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_SECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector timestampSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_SECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector durationMilliSecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_MILLISECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector timestampMilliSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_MILLISECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector durationMicroSecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_MICROSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector timestampMicroSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_MICROSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector durationNanoSecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_NANOSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static ColumnVector timestampNanoSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_NANOSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Creates an empty column according to the data type.
+   *
+   * It will create all the nested columns by iterating all the children in the input
+   * type object 'colType'.
+   *
+   * The performance is not good, so use it carefully. We may want to move this implementation
+   * to the native once figuring out a way to pass the nested data type to the native.
+   *
+   * @param colType the data type of the empty column
+   * @return an empty ColumnVector with its children. Each children contains zero elements.
+   * Users should close the ColumnVector to avoid memory leak.
+   */
+  public static ColumnVector empty(HostColumnVector.DataType colType) {
+    if (colType == null || colType.getType() == null) {
+      throw new IllegalArgumentException("The data type and its 'DType' should NOT be null.");
+    }
+    if (colType instanceof HostColumnVector.BasicType) {
+      // Non nested type
+      DType dt = colType.getType();
+      return new ColumnVector(makeEmptyCudfColumn(dt.typeId.getNativeId(), dt.getScale()));
+    } else if (colType instanceof HostColumnVector.ListType) {
+      // List type
+      assert colType.getNumChildren() == 1 : "List type requires one child type";
+      try (ColumnVector child = empty(colType.getChild(0))) {
+        return makeList(child);
+      }
+    } else if (colType instanceof HostColumnVector.StructType) {
+      // Struct type
+      ColumnVector[] children = new ColumnVector[colType.getNumChildren()];
+      try {
+        for (int i = 0; i < children.length; i++) {
+          children[i] = empty(colType.getChild(i));
+        }
+        return makeStruct(children);
+      } finally {
+        for (ColumnVector cv : children) {
+          if (cv != null) cv.close();
+        }
+      }
+    } else {
+      throw new IllegalArgumentException("Unsupported data type: " + colType);
+    }
+  }
+
+  static ColumnVector[] getColumnVectorsFromPointers(long[] nativeHandles) {
+    ColumnVector[] columns = new ColumnVector[nativeHandles.length];
+    try {
+      for (int i = 0; i < nativeHandles.length; i++) {
+        long nativeHandle = nativeHandles[i];
+        // setting address to zero, so we don't clean it in case of an exception as it
+        // will be cleaned up by the constructor
+        nativeHandles[i] = 0;
+        columns[i] = new ColumnVector(nativeHandle);
+      }
+      return columns;
+    } catch (Throwable t) {
+      for (ColumnVector columnVector : columns) {
+        if (columnVector != null) {
+          try {
+            columnVector.close();
+          } catch (Throwable s) {
+            t.addSuppressed(s);
+          }
+        }
+      }
+      for (long nativeHandle : nativeHandles) {
+        if (nativeHandle != 0) {
+          try {
+            deleteCudfColumn(nativeHandle);
+          } catch (Throwable s) {
+            t.addSuppressed(s);
+          }
+        }
+      }
+
+      throw t;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ColumnView.java b/java/src/main/java/ai/rapids/cudf/ColumnView.java
new file mode 100644
index 0000000..0b66701
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ColumnView.java
@@ -0,0 +1,5261 @@
+/*
+ *
+ *  Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.*;
+import java.util.stream.IntStream;
+
+import static ai.rapids.cudf.HostColumnVector.OFFSET_SIZE;
+
+/**
+ * This class represents the column_view of a column analogous to its cudf cpp counterpart.
+ * It holds view information like the native handle and other metadata for a column_view. It also
+ * exposes APIs that would allow operations on a view.
+ */
+public class ColumnView implements AutoCloseable, BinaryOperable {
+
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  public static final long UNKNOWN_NULL_COUNT = -1;
+
+  protected long viewHandle;
+  protected final DType type;
+  protected final long rows;
+  protected final long nullCount;
+  protected final ColumnVector.OffHeapState offHeap;
+
+  /**
+   * Constructs a Column View given a native view address. This asserts that if the ColumnView is
+   * of nested-type it doesn't contain non-empty nulls
+   * @param address the view handle
+   * @throws AssertionError if the address points to a nested-type view with non-empty nulls
+   */
+  ColumnView(long address) {
+    this.viewHandle = address;
+    try {
+      this.type = DType.fromNative(ColumnView.getNativeTypeId(viewHandle), ColumnView.getNativeTypeScale(viewHandle));
+      this.rows = ColumnView.getNativeRowCount(viewHandle);
+      this.nullCount = ColumnView.getNativeNullCount(viewHandle);
+      this.offHeap = null;
+      AssertEmptyNulls.assertNullsAreEmpty(this);
+    } catch (Throwable t) {
+      // offHeap state is null, so there is nothing to clean in offHeap
+      // delete ColumnView to avoid memory leak
+      deleteColumnView(viewHandle);
+      viewHandle = 0;
+      throw t;
+    }
+  }
+
+
+  /**
+   * Intended to be called from ColumnVector when it is being constructed. Because state creates a
+   * cudf::column_view instance and will close it in all cases, we don't want to have to double
+   * close it. This asserts that if the offHeapState is of nested-type it doesn't contain non-empty nulls
+   * @param state the state this view is based off of.
+   * @throws AssertionError if offHeapState points to a nested-type view with non-empty nulls
+   */
+  protected ColumnView(ColumnVector.OffHeapState state) {
+    offHeap = state;
+    try {
+      viewHandle = state.getViewHandle();
+      type = DType.fromNative(ColumnView.getNativeTypeId(viewHandle), ColumnView.getNativeTypeScale(viewHandle));
+      rows = ColumnView.getNativeRowCount(viewHandle);
+      nullCount = ColumnView.getNativeNullCount(viewHandle);
+      AssertEmptyNulls.assertNullsAreEmpty(this);
+    } catch (Throwable t) {
+      // cleanup offHeap
+      offHeap.clean(false);
+      viewHandle = 0;
+      throw t;
+    }
+  }
+
+  /**
+   * Create a new column view based off of data already on the device. Ref count on the buffers
+   * is not incremented and none of the underlying buffers are owned by this view. The returned
+   * ColumnView is only valid as long as the underlying buffers remain valid. If the buffers are
+   * closed before this ColumnView is closed, it will result in undefined behavior.
+   *
+   * If ownership is needed, call {@link ColumnView#copyToColumnVector}
+   *
+   * @param type           the type of the vector
+   * @param rows           the number of rows in this vector.
+   * @param nullCount      the number of nulls in the dataset.
+   * @param validityBuffer an optional validity buffer. Must be provided if nullCount != 0.
+   *                       The ownership doesn't change on this buffer
+   * @param offsetBuffer   a host buffer required for nested types including strings and string
+   *                       categories. The ownership doesn't change on this buffer
+   * @param children       an array of ColumnView children
+   */
+  public ColumnView(DType type, long rows, Optional<Long> nullCount,
+                     BaseDeviceMemoryBuffer validityBuffer,
+                     BaseDeviceMemoryBuffer offsetBuffer, ColumnView[] children) {
+    this(type, (int) rows, nullCount.orElse(UNKNOWN_NULL_COUNT).intValue(),
+        null, validityBuffer, offsetBuffer, children);
+    assert(type.isNestedType());
+    assert (nullCount.isPresent() && nullCount.get() <= Integer.MAX_VALUE)
+        || !nullCount.isPresent();
+  }
+
+  /**
+   * Create a new column view based off of data already on the device. Ref count on the buffers
+   * is not incremented and none of the underlying buffers are owned by this view. The returned
+   * ColumnView is only valid as long as the underlying buffers remain valid. If the buffers are
+   * closed before this ColumnView is closed, it will result in undefined behavior.
+   *
+   * If ownership is needed, call {@link ColumnView#copyToColumnVector}
+   *
+   * @param type           the type of the vector
+   * @param rows           the number of rows in this vector.
+   * @param nullCount      the number of nulls in the dataset.
+   * @param dataBuffer     a host buffer required for nested types including strings and string
+   *                       categories. The ownership doesn't change on this buffer
+   * @param validityBuffer an optional validity buffer. Must be provided if nullCount != 0.
+   *                       The ownership doesn't change on this buffer
+   */
+  public ColumnView(DType type, long rows, Optional<Long> nullCount,
+                    BaseDeviceMemoryBuffer dataBuffer,
+                    BaseDeviceMemoryBuffer validityBuffer) {
+    this(type, (int) rows, nullCount.orElse(UNKNOWN_NULL_COUNT).intValue(),
+        dataBuffer, validityBuffer, null, null);
+    assert (!type.isNestedType());
+    assert (nullCount.isPresent() && nullCount.get() <= Integer.MAX_VALUE)
+        || !nullCount.isPresent();
+  }
+
+  /**
+   * Create a new column view based off of data already on the device. Ref count on the buffers
+   * is not incremented and none of the underlying buffers are owned by this view. The returned
+   * ColumnView is only valid as long as the underlying buffers remain valid. If the buffers are
+   * closed before this ColumnView is closed, it will result in undefined behavior.
+   *
+   * If ownership is needed, call {@link ColumnView#copyToColumnVector}
+   *
+   * @param type           the type of the vector
+   * @param rows           the number of rows in this vector.
+   * @param nullCount      the number of nulls in the dataset.
+   * @param dataBuffer     a host buffer required for nested types including strings and string
+   *                       categories. The ownership doesn't change on this buffer
+   * @param validityBuffer an optional validity buffer. Must be provided if nullCount != 0.
+   *                       The ownership doesn't change on this buffer
+   * @param offsetBuffer   The offsetbuffer for columns that need an offset buffer
+   */
+  public ColumnView(DType type, long rows, Optional<Long> nullCount,
+                    BaseDeviceMemoryBuffer dataBuffer,
+                    BaseDeviceMemoryBuffer validityBuffer, BaseDeviceMemoryBuffer offsetBuffer) {
+    this(type, (int) rows, nullCount.orElse(UNKNOWN_NULL_COUNT).intValue(),
+        dataBuffer, validityBuffer, offsetBuffer, null);
+    assert (!type.isNestedType());
+    assert (nullCount.isPresent() && nullCount.get() <= Integer.MAX_VALUE)
+        || !nullCount.isPresent();
+  }
+
+  private ColumnView(DType type, long rows, int nullCount,
+                     BaseDeviceMemoryBuffer dataBuffer, BaseDeviceMemoryBuffer validityBuffer,
+                     BaseDeviceMemoryBuffer offsetBuffer, ColumnView[] children) {
+    this(ColumnVector.initViewHandle(type, (int) rows, nullCount, dataBuffer, validityBuffer,
+        offsetBuffer, children == null ? new long[]{} :
+            Arrays.stream(children).mapToLong(c -> c.getNativeView()).toArray()));
+  }
+
+  /** Creates a ColumnVector from a column view handle
+   * @return a new ColumnVector
+   */
+  public ColumnVector copyToColumnVector() {
+    return new ColumnVector(ColumnView.copyColumnViewToCV(getNativeView()));
+  }
+
+  /**
+   * USE WITH CAUTION: This method exposes the address of the native cudf::column_view.  This allows
+   * writing custom kernels or other cuda operations on the data.  DO NOT close this column
+   * vector until you are completely done using the native column_view.  DO NOT modify the column in
+   * any way.  This should be treated as a read only data structure. This API is unstable as
+   * the underlying C/C++ API is still not stabilized.  If the underlying data structure
+   * is renamed this API may be replaced.  The underlying data structure can change from release
+   * to release (it is not stable yet) so be sure that your native code is complied against the
+   * exact same version of libcudf as this is released for.
+   */
+  public final long getNativeView() {
+    return viewHandle;
+  }
+
+  static int getFixedPointOutputScale(BinaryOp op, DType lhsType, DType rhsType) {
+    assert (lhsType.isDecimalType() && rhsType.isDecimalType());
+    return fixedPointOutputScale(op.nativeId, lhsType.getScale(), rhsType.getScale());
+  }
+
+  private static native int fixedPointOutputScale(int op, int lhsScale, int rhsScale);
+
+  public final DType getType() {
+    return type;
+  }
+
+  /**
+   * Returns the child column views for this view
+   * Please note that it is the responsibility of the caller to close these views.
+   * @return an array of child column views
+   */
+  public final ColumnView[] getChildColumnViews() {
+    int numChildren = getNumChildren();
+    if (!getType().isNestedType()) {
+      return null;
+    }
+    ColumnView[] views = new ColumnView[numChildren];
+    try {
+      for (int i = 0; i < numChildren; i++) {
+        views[i] = getChildColumnView(i);
+      }
+      return views;
+    } catch(Throwable t) {
+      for (ColumnView v: views) {
+        if (v != null) {
+          v.close();
+        }
+      }
+      throw t;
+    }
+  }
+
+  /**
+   * Returns the child column view at a given index.
+   * Please note that it is the responsibility of the caller to close this view.
+   * @param childIndex the index of the child
+   * @return a column view
+   */
+  public final ColumnView getChildColumnView(int childIndex) {
+    int numChildren = getNumChildren();
+    assert childIndex < numChildren : "children index should be less than " + numChildren;
+    if (!getType().isNestedType()) {
+      return null;
+    }
+    long childColumnView = ColumnView.getChildCvPointer(viewHandle, childIndex);
+    return new ColumnView(childColumnView);
+  }
+
+  /**
+   * Get a ColumnView that is the offsets for this list.
+   * Please note that it is the responsibility of the caller to close this view, and the parent
+   * column must out live this view.
+   */
+  public ColumnView getListOffsetsView() {
+    assert(getType().equals(DType.LIST));
+    return new ColumnView(getListOffsetCvPointer(viewHandle));
+  }
+
+  /**
+   * Gets the data buffer for the current column view (viewHandle).
+   * If the type is LIST, STRUCT it returns null.
+   * @return    If the type is LIST, STRUCT or data buffer is empty it returns null,
+   *            else return the data device buffer
+   */
+  public final BaseDeviceMemoryBuffer getData() {
+    return getDataBuffer(viewHandle);
+  }
+
+  public final BaseDeviceMemoryBuffer getOffsets() {
+    return getOffsetsBuffer(viewHandle);
+  }
+
+  public final BaseDeviceMemoryBuffer getValid() {
+    return getValidityBuffer(viewHandle);
+  }
+
+  /**
+   * Returns the number of nulls in the data. Note that this might end up
+   * being a very expensive operation because if the null count is not
+   * known it will be calculated.
+   */
+  public long getNullCount() {
+    return nullCount;
+  }
+
+  /**
+   * Returns the number of rows in this vector.
+   */
+  public final long getRowCount() {
+    return rows;
+  }
+
+  public final int getNumChildren() {
+    if (!getType().isNestedType()) {
+      return 0;
+    }
+    return ColumnView.getNativeNumChildren(viewHandle);
+  }
+
+
+  /**
+   * Returns the amount of device memory used.
+   */
+  public long getDeviceMemorySize() {
+    return getDeviceMemorySize(getNativeView(), false);
+  }
+
+  @Override
+  public void close() {
+    // close the view handle so long as offHeap is not going to do it for us.
+    if (offHeap == null) {
+      ColumnView.deleteColumnView(viewHandle);
+    }
+    viewHandle = 0;
+  }
+
+  @Override
+  public String toString() {
+    return "ColumnView{" +
+           "rows=" + rows +
+           ", type=" + type +
+           ", nullCount=" + nullCount +
+           '}';
+  }
+
+  /**
+   * Used for string strip function.
+   * Indicates characters to be stripped from the beginning, end, or both of each string.
+   */
+  private enum StripType {
+    LEFT(0),   // strip characters from the beginning of the string
+    RIGHT(1),  // strip characters from the end of the string
+    BOTH(2);   // strip characters from the beginning and end of the string
+    final int nativeId;
+
+    StripType(int nativeId) { this.nativeId = nativeId; }
+  }
+
+  /**
+   * Returns a new ColumnVector with NaNs converted to nulls, preserving the existing null values.
+   */
+  public final ColumnVector nansToNulls() {
+    assert type.equals(DType.FLOAT32) || type.equals(DType.FLOAT64);
+    return new ColumnVector(nansToNulls(this.getNativeView()));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // DEVICE METADATA
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Retrieve the number of characters in each string. Null strings will have value of null.
+   *
+   * @return ColumnVector holding length of string at index 'i' in the original vector
+   */
+  public final ColumnVector getCharLengths() {
+    assert DType.STRING.equals(type) : "char length only available for String type";
+    return new ColumnVector(charLengths(getNativeView()));
+  }
+
+  /**
+   * Retrieve the number of bytes for each string. Null strings will have value of null.
+   *
+   * @return ColumnVector, where each element at i = byte count of string at index 'i' in the original vector
+   */
+  public final ColumnVector getByteCount() {
+    assert type.equals(DType.STRING) : "type has to be a String";
+    return new ColumnVector(byteCount(getNativeView()));
+  }
+
+  /**
+   * Get the number of elements for each list. Null lists will have a value of null.
+   * @return the number of elements in each list as an INT32 value.
+   */
+  public final ColumnVector countElements() {
+    assert DType.LIST.equals(type) : "Only lists are supported";
+    return new ColumnVector(countElements(getNativeView()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is not null, and FALSE for any null entry (as per the validity mask)
+   *
+   * @return - Boolean vector
+   */
+  public final ColumnVector isNotNull() {
+    return new ColumnVector(isNotNullNative(getNativeView()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * FALSE for any entry that is not null, and TRUE for any null entry (as per the validity mask)
+   *
+   * @return - Boolean vector
+   */
+  public final ColumnVector isNull() {
+    return new ColumnVector(isNullNative(getNativeView()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is a fixed-point, and FALSE if its not a fixed-point.
+   * A null will be returned for null entries.
+   *
+   * The sign and the exponent is optional. The decimal point may only appear once.
+   * The integer component must fit within the size limits of the underlying fixed-point
+   * storage type. The value of the integer component is based on the scale of the target
+   * decimalType.
+   *
+   * Example:
+   * vec = ["A", "nan", "Inf", "-Inf", "Infinity", "infinity", "2.1474", "112.383", "-2.14748",
+   *        "NULL", "null", null, "1.2", "1.2e-4", "0.00012"]
+   * vec.isFixedPoint() = [false, false, false, false, false, false, true, true, true, false, false,
+   *                       null, true, true, true]
+   *
+   * @param decimalType the data type that should be used for bounds checking. Note that only
+   *                Decimal types (fixed-point) are allowed.
+   * @return Boolean vector
+   */
+  public final ColumnVector isFixedPoint(DType decimalType) {
+    assert type.equals(DType.STRING);
+    assert decimalType.isDecimalType();
+    return new ColumnVector(isFixedPoint(getNativeView(),
+        decimalType.getTypeId().getNativeId(), decimalType.getScale()));
+  }
+
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is an integer, and FALSE if its not an integer. A null will be returned
+   * for null entries.
+   *
+   * NOTE: Integer doesn't mean a 32-bit integer. It means a number that is not a fraction.
+   * i.e. If this method returns true for a value it could still result in an overflow or underflow
+   * if you convert it to a Java integral type
+   *
+   * @return Boolean vector
+   */
+  public final ColumnVector isInteger() {
+    assert type.equals(DType.STRING);
+    return new ColumnVector(isInteger(getNativeView()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is an integer, and FALSE if its not an integer. A null will be returned
+   * for null entries.
+   *
+   * @param intType the data type that should be used for bounds checking. Note that only
+   *                cudf integer types are allowed including signed/unsigned int8 through int64
+   * @return Boolean vector
+   */
+  public final ColumnVector isInteger(DType intType) {
+    assert type.equals(DType.STRING);
+    assert intType.isBackedByInt() || intType.isBackedByLong() || intType.isBackedByByte()
+        || intType.isBackedByShort();
+    return new ColumnVector(isIntegerWithType(getNativeView(),
+        intType.getTypeId().getNativeId(), intType.getScale()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is a float, and FALSE if its not a float. A null will be returned
+   * for null entries
+   *
+   * NOTE: Float doesn't mean a 32-bit float. It means a number that is a fraction or can be written
+   * as a fraction. i.e. This method will return true for integers as well as floats. Also note if
+   * this method returns true for a value it could still result in an overflow or underflow if you
+   * convert it to a Java float or double
+   *
+   * @return - Boolean vector
+   */
+  public final ColumnVector isFloat() {
+    assert type.equals(DType.STRING);
+    return new ColumnVector(isFloat(getNativeView()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is NaN, and FALSE if null or a valid floating point value
+   * @return - Boolean vector
+   */
+  public final ColumnVector isNan() {
+    return new ColumnVector(isNanNative(getNativeView()));
+  }
+
+  /**
+   * Returns a Boolean vector with the same number of rows as this instance, that has
+   * TRUE for any entry that is null or a valid floating point value, FALSE otherwise
+   * @return - Boolean vector
+   */
+  public final ColumnVector isNotNan() {
+    return new ColumnVector(isNotNanNative(getNativeView()));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Replacement
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Returns a vector with all values "oldValues[i]" replaced with "newValues[i]".
+   * Warning:
+   *    Currently this function doesn't work for Strings or StringCategories.
+   *    NaNs can't be replaced in the original vector but regular values can be replaced with NaNs
+   *    Nulls can't be replaced in the original vector but regular values can be replaced with Nulls
+   *    Mixing of types isn't allowed, the resulting vector will be the same type as the original.
+   *      e.g. You can't replace an integer vector with values from a long vector
+   *
+   * Usage:
+   *    this = {1, 4, 5, 1, 5}
+   *    oldValues = {1, 5, 7}
+   *    newValues = {2, 6, 9}
+   *
+   *    result = this.findAndReplaceAll(oldValues, newValues);
+   *    result = {2, 4, 6, 2, 6}  (1 and 5 replaced with 2 and 6 but 7 wasn't found so no change)
+   *
+   * @param oldValues - A vector containing values that should be replaced
+   * @param newValues - A vector containing new values
+   * @return - A new vector containing the old values replaced with new values
+   */
+  public final ColumnVector findAndReplaceAll(ColumnView oldValues, ColumnView newValues) {
+    return new ColumnVector(findAndReplaceAll(oldValues.getNativeView(), newValues.getNativeView(), this.getNativeView()));
+  }
+
+  /**
+   * Returns a ColumnVector with any null values replaced with a scalar.
+   * The types of the input ColumnVector and Scalar must match, else an error is thrown.
+   *
+   * @param scalar - Scalar value to use as replacement
+   * @return - ColumnVector with nulls replaced by scalar
+   */
+  public final ColumnVector replaceNulls(Scalar scalar) {
+    return new ColumnVector(replaceNullsScalar(getNativeView(), scalar.getScalarHandle()));
+  }
+
+  /**
+   * Returns a ColumnVector with any null values replaced with the corresponding row in the
+   * specified replacement column.
+   * This column and the replacement column must have the same type and number of rows.
+   *
+   * @param replacements column of replacement values
+   * @return column with nulls replaced by corresponding row of replacements column
+   */
+  public final ColumnVector replaceNulls(ColumnView replacements) {
+    return new ColumnVector(replaceNullsColumn(getNativeView(), replacements.getNativeView()));
+  }
+
+  public final ColumnVector replaceNulls(ReplacePolicy policy) {
+    return new ColumnVector(replaceNullsPolicy(getNativeView(), policy.isPreceding));
+  }
+
+  /**
+   * For a BOOL8 vector, computes a vector whose rows are selected from two other vectors
+   * based on the boolean value of this vector in the corresponding row.
+   * If the boolean value in a row is true, the corresponding row is selected from trueValues
+   * otherwise the corresponding row from falseValues is selected.
+   * Note that trueValues and falseValues vectors must be the same length as this vector,
+   * and trueValues and falseValues must have the same data type.
+   * @param trueValues the values to select if a row in this column is true
+   * @param falseValues the values to select if a row in this column is not true
+   * @return the computed vector
+   */
+  public final ColumnVector ifElse(ColumnView trueValues, ColumnView falseValues) {
+    if (!type.equals(DType.BOOL8)) {
+      throw new IllegalArgumentException("Cannot select with a predicate vector of type " + type);
+    }
+    long result = ifElseVV(getNativeView(), trueValues.getNativeView(), falseValues.getNativeView());
+    return new ColumnVector(result);
+  }
+
+  /**
+   * For a BOOL8 vector, computes a vector whose rows are selected from two other inputs
+   * based on the boolean value of this vector in the corresponding row.
+   * If the boolean value in a row is true, the corresponding row is selected from trueValues
+   * otherwise the value from falseValue is selected.
+   * Note that trueValues must be the same length as this vector,
+   * and trueValues and falseValue must have the same data type.
+   * Note that the trueValues vector and falseValue scalar must have the same data type.
+   * @param trueValues the values to select if a row in this column is true
+   * @param falseValue the value to select if a row in this column is not true
+   * @return the computed vector
+   */
+  public final ColumnVector ifElse(ColumnView trueValues, Scalar falseValue) {
+    if (!type.equals(DType.BOOL8)) {
+      throw new IllegalArgumentException("Cannot select with a predicate vector of type " + type);
+    }
+    long result = ifElseVS(getNativeView(), trueValues.getNativeView(), falseValue.getScalarHandle());
+    return new ColumnVector(result);
+  }
+
+  /**
+   * For a BOOL8 vector, computes a vector whose rows are selected from two other inputs
+   * based on the boolean value of this vector in the corresponding row.
+   * If the boolean value in a row is true, the value from trueValue is selected
+   * otherwise the corresponding row from falseValues is selected.
+   * Note that falseValues must be the same length as this vector,
+   * and trueValue and falseValues must have the same data type.
+   * Note that the trueValue scalar and falseValues vector must have the same data type.
+   * @param trueValue the value to select if a row in this column is true
+   * @param falseValues the values to select if a row in this column is not true
+   * @return the computed vector
+   */
+  public final ColumnVector ifElse(Scalar trueValue, ColumnView falseValues) {
+    if (!type.equals(DType.BOOL8)) {
+      throw new IllegalArgumentException("Cannot select with a predicate vector of type " + type);
+    }
+    long result = ifElseSV(getNativeView(), trueValue.getScalarHandle(), falseValues.getNativeView());
+    return new ColumnVector(result);
+  }
+
+  /**
+   * For a BOOL8 vector, computes a vector whose rows are selected from two other inputs
+   * based on the boolean value of this vector in the corresponding row.
+   * If the boolean value in a row is true, the value from trueValue is selected
+   * otherwise the value from falseValue is selected.
+   * Note that the trueValue and falseValue scalars must have the same data type.
+   * @param trueValue the value to select if a row in this column is true
+   * @param falseValue the value to select if a row in this column is not true
+   * @return the computed vector
+   */
+  public final ColumnVector ifElse(Scalar trueValue, Scalar falseValue) {
+    if (!type.equals(DType.BOOL8)) {
+      throw new IllegalArgumentException("Cannot select with a predicate vector of type " + type);
+    }
+    long result = ifElseSS(getNativeView(), trueValue.getScalarHandle(), falseValue.getScalarHandle());
+    return new ColumnVector(result);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Slice/Split and Concatenate
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Slices a column (including null values) into a set of columns
+   * according to a set of indices. The caller owns the ColumnVectors and is responsible
+   * closing them
+   *
+   * The "slice" function divides part of the input column into multiple intervals
+   * of rows using the indices values and it stores the intervals into the output
+   * columns. Regarding the interval of indices, a pair of values are taken from
+   * the indices array in a consecutive manner. The pair of indices are left-closed
+   * and right-open.
+   *
+   * The pairs of indices in the array are required to comply with the following
+   * conditions:
+   * a, b belongs to Range[0, input column size]
+   * a <= b, where the position of a is less or equal to the position of b.
+   *
+   * Exceptional cases for the indices array are:
+   * When the values in the pair are equal, the function returns an empty column.
+   * When the values in the pair are 'strictly decreasing', the outcome is
+   * undefined.
+   * When any of the values in the pair don't belong to the range[0, input column
+   * size), the outcome is undefined.
+   * When the indices array is empty, an empty vector of columns is returned.
+   *
+   * The caller owns the output ColumnVectors and is responsible for closing them.
+   *
+   * @param indices
+   * @return A new ColumnVector array with slices from the original ColumnVector
+   */
+  public final ColumnVector[] slice(int... indices) {
+    long[] nativeHandles = slice(this.getNativeView(), indices);
+    ColumnVector[] columnVectors = new ColumnVector[nativeHandles.length];
+    try {
+      for (int i = 0; i < nativeHandles.length; i++) {
+        long nativeHandle = nativeHandles[i];
+        // setting address to zero, so we don't clean it in case of an exception as it
+        // will be cleaned up by the constructor
+        nativeHandles[i] = 0;
+        columnVectors[i] = new ColumnVector(nativeHandle);
+      }
+    } catch (Throwable t) {
+      try {
+        cleanupColumnViews(nativeHandles, columnVectors, t);
+      } catch (Throwable s) {
+        t.addSuppressed(s);
+      } finally {
+        throw t;
+      }
+    }
+    return columnVectors;
+  }
+
+  /**
+   * Return a subVector from start inclusive to the end of the vector.
+   * @param start the index to start at.
+   */
+  public final ColumnVector subVector(int start) {
+    return subVector(start, (int)rows);
+  }
+
+  /**
+   * Return a subVector.
+   * @param start the index to start at (inclusive).
+   * @param end the index to end at (exclusive).
+   */
+  public final ColumnVector subVector(int start, int end) {
+    ColumnVector [] tmp = slice(start, end);
+    assert tmp.length == 1;
+    return tmp[0];
+  }
+
+  /**
+   * Splits a column (including null values) into a set of columns
+   * according to a set of indices. The caller owns the ColumnVectors and is responsible
+   * closing them.
+   *
+   * The "split" function divides the input column into multiple intervals
+   * of rows using the splits indices values and it stores the intervals into the
+   * output columns. Regarding the interval of indices, a pair of values are taken
+   * from the indices array in a consecutive manner. The pair of indices are
+   * left-closed and right-open.
+   *
+   * The indices array ('splits') is require to be a monotonic non-decreasing set.
+   * The indices in the array are required to comply with the following conditions:
+   * a, b belongs to Range[0, input column size]
+   * a <= b, where the position of a is less or equal to the position of b.
+   *
+   * The split function will take a pair of indices from the indices array
+   * ('splits') in a consecutive manner. For the first pair, the function will
+   * take the value 0 and the first element of the indices array. For the last pair,
+   * the function will take the last element of the indices array and the size of
+   * the input column.
+   *
+   * Exceptional cases for the indices array are:
+   * When the values in the pair are equal, the function return an empty column.
+   * When the values in the pair are 'strictly decreasing', the outcome is
+   * undefined.
+   * When any of the values in the pair don't belong to the range[0, input column
+   * size), the outcome is undefined.
+   * When the indices array is empty, an empty vector of columns is returned.
+   *
+   * The input columns may have different sizes. The number of
+   * columns must be equal to the number of indices in the array plus one.
+   *
+   * Example:
+   * input:   {10, 12, 14, 16, 18, 20, 22, 24, 26, 28}
+   * splits: {2, 5, 9}
+   * output:  {{10, 12}, {14, 16, 18}, {20, 22, 24, 26}, {28}}
+   *
+   * Note that this is very similar to the output from a PartitionedTable.
+   *
+   * @param indices the indexes to split with
+   * @return A new ColumnVector array with slices from the original ColumnVector
+   */
+  public final ColumnVector[] split(int... indices) {
+    ColumnView[] views = splitAsViews(indices);
+    ColumnVector[] columnVectors = new ColumnVector[views.length];
+    try {
+      for (int i = 0; i < views.length; i++) {
+        columnVectors[i] = views[i].copyToColumnVector();
+      }
+      return columnVectors;
+    } catch (Throwable t) {
+      for (ColumnVector cv : columnVectors) {
+        if (cv != null) {
+          cv.close();
+        }
+      }
+      throw t;
+    } finally {
+      for (ColumnView view : views) {
+        view.close();
+      }
+    }
+  }
+
+  /**
+   * Splits a ColumnView (including null values) into a set of ColumnViews
+   * according to a set of indices. No data is moved or copied.
+   *
+   * IMPORTANT NOTE: Nothing is copied out from the vector and the slices will only be relevant for
+   * the lifecycle of the underlying ColumnVector.
+   *
+   * The "split" function divides the input column into multiple intervals
+   * of rows using the splits indices values and it stores the intervals into the
+   * output columns. Regarding the interval of indices, a pair of values are taken
+   * from the indices array in a consecutive manner. The pair of indices are
+   * left-closed and right-open.
+   *
+   * The indices array ('splits') is required to be a monotonic non-decreasing set.
+   * The indices in the array are required to comply with the following conditions:
+   * a, b belongs to Range[0, input column size]
+   * a <= b, where the position of 'a' is less or equal to the position of 'b'.
+   *
+   * The split function will take a pair of indices from the indices array
+   * ('splits') in a consecutive manner. For the first pair, the function will
+   * take the value 0 and the first element of the indices array. For the last pair,
+   * the function will take the last element of the indices array and the size of
+   * the input column.
+   *
+   * Exceptional cases for the indices array are:
+   * When the values in the pair are equal, the function return an empty column.
+   * When the values in the pair are 'strictly decreasing', the outcome is
+   * undefined.
+   * When any of the values in the pair don't belong to the range[0, input column
+   * size), the outcome is undefined.
+   * When the indices array is empty, an empty array of ColumnViews is returned.
+   *
+   * The output columns may have different sizes. The number of
+   * columns must be equal to the number of indices in the array plus one.
+   *
+   * Example:
+   * input:   {10, 12, 14, 16, 18, 20, 22, 24, 26, 28}
+   * splits: {2, 5, 9}
+   * output:  {{10, 12}, {14, 16, 18}, {20, 22, 24, 26}, {28}}
+   *
+   * Note that this is very similar to the output from a PartitionedTable.
+   *
+   *
+   * @param indices the indices to split with
+   * @return A new ColumnView array with slices from the original ColumnView
+   */
+  public ColumnView[] splitAsViews(int... indices) {
+    long[] nativeHandles = split(this.getNativeView(), indices);
+    ColumnView[] columnViews = new ColumnView[nativeHandles.length];
+    try {
+      for (int i = 0; i < nativeHandles.length; i++) {
+        long nativeHandle = nativeHandles[i];
+        // setting address to zero, so we don't clean it in case of an exception as it
+        // will be cleaned up by the constructor
+        nativeHandles[i] = 0;
+        columnViews[i] = new ColumnView(nativeHandle);
+      }
+    } catch (Throwable t) {
+      try {
+        cleanupColumnViews(nativeHandles, columnViews, t);
+      } catch (Throwable s) {
+        t.addSuppressed(s);
+      } finally {
+        throw t;
+      }
+    }
+    return columnViews;
+  }
+
+  static void cleanupColumnViews(long[] nativeHandles, ColumnView[] columnViews, Throwable throwable) {
+    for (ColumnView columnView : columnViews) {
+      if (columnView != null) {
+        try {
+          columnView.close();
+        } catch (Throwable s) {
+          throwable.addSuppressed(s);
+        }
+      }
+    }
+    for (long nativeHandle : nativeHandles) {
+      if (nativeHandle != 0) {
+        try {
+          deleteColumnView(nativeHandle);
+        } catch (Throwable s) {
+          throwable.addSuppressed(s);
+        }
+      }
+    }
+  }
+
+  /**
+   * Create a new vector of "normalized" values, where:
+   *  1. All representations of NaN (and -NaN) are replaced with the normalized NaN value
+   *  2. All elements equivalent to 0.0 (including +0.0 and -0.0) are replaced with +0.0.
+   *  3. All elements that are not equivalent to NaN or 0.0 remain unchanged.
+   *
+   * The documentation for {@link Double#longBitsToDouble(long)}
+   * describes how equivalent values of NaN/-NaN might have different bitwise representations.
+   *
+   * This method may be used to compare different bitwise values of 0.0 or NaN as logically
+   * equivalent. For instance, if these values appear in a groupby key column, without normalization
+   * 0.0 and -0.0 would be erroneously treated as distinct groups, as will each representation of NaN.
+   *
+   * @return A new ColumnVector with all elements equivalent to NaN/0.0 replaced with a normalized equivalent.
+   */
+  public final ColumnVector normalizeNANsAndZeros() {
+    return new ColumnVector(normalizeNANsAndZeros(getNativeView()));
+  }
+
+  /**
+   * Create a deep copy of the column while replacing the null mask. The resultant null mask is the
+   * bitwise merge of null masks in the columns given as arguments.
+   * The result will be sanitized to not contain any non-empty nulls in case of nested types
+   *
+   * @param mergeOp binary operator (BITWISE_AND and BITWISE_OR only)
+   * @param columns array of columns whose null masks are merged, must have identical number of rows.
+   * @return the new ColumnVector with merged null mask.
+   */
+  public final ColumnVector mergeAndSetValidity(BinaryOp mergeOp, ColumnView... columns) {
+    assert mergeOp == BinaryOp.BITWISE_AND || mergeOp == BinaryOp.BITWISE_OR : "Only BITWISE_AND and BITWISE_OR supported right now";
+    long[] columnViews = new long[columns.length];
+    long size = getRowCount();
+
+    for(int i = 0; i < columns.length; i++) {
+      assert columns[i] != null : "Column vectors passed may not be null";
+      assert columns[i].getRowCount() == size : "Row count mismatch, all columns must be the same size";
+      columnViews[i] = columns[i].getNativeView();
+    }
+
+    return new ColumnVector(bitwiseMergeAndSetValidity(getNativeView(), columnViews, mergeOp.nativeId));
+  }
+
+  /**
+   * Creates a deep copy of a column while replacing the validity mask. The validity mask is the
+   * device_vector equivalent of the boolean column given as argument.
+   *
+   * The boolColumn must have the same number of rows as the current column.
+   * The result column will have the same number of rows as the current column.
+   * For all indices `i` where the boolColumn is `true`, the result column will have a valid value at index i.
+   * For all other values (i.e. `false` or `null`), the result column will have nulls.
+   *
+   * If the current column has a null at a given index `i`, and the new validity mask is `true` at index `i`,
+   * then the row value is undefined.
+   *
+   * @param boolColumn bool column whose value is to be used as the validity mask.
+   * @return Deep copy of the column with replaced validity mask.
+   */
+  public final ColumnVector copyWithBooleanColumnAsValidity(ColumnView boolColumn) {
+    return new ColumnVector(copyWithBooleanColumnAsValidity(getNativeView(), boolColumn.getNativeView()));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // DATE/TIME
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Get year from a timestamp.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return - A new INT16 vector allocated on the GPU.
+   */
+  public final ColumnVector year() {
+    assert type.isTimestampType();
+    return new ColumnVector(year(getNativeView()));
+  }
+
+  /**
+   * Get month from a timestamp.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return - A new INT16 vector allocated on the GPU.
+   */
+  public final ColumnVector month() {
+    assert type.isTimestampType();
+    return new ColumnVector(month(getNativeView()));
+  }
+
+  /**
+   * Get day from a timestamp.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return - A new INT16 vector allocated on the GPU.
+   */
+  public final ColumnVector day() {
+    assert type.isTimestampType();
+    return new ColumnVector(day(getNativeView()));
+  }
+
+  /**
+   * Get hour from a timestamp with time resolution.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return - A new INT16 vector allocated on the GPU.
+   */
+  public final ColumnVector hour() {
+    assert type.hasTimeResolution();
+    return new ColumnVector(hour(getNativeView()));
+  }
+
+  /**
+   * Get minute from a timestamp with time resolution.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return - A new INT16 vector allocated on the GPU.
+   */
+  public final ColumnVector minute() {
+    assert type.hasTimeResolution();
+    return new ColumnVector(minute(getNativeView()));
+  }
+
+  /**
+   * Get second from a timestamp with time resolution.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return A new INT16 vector allocated on the GPU.
+   */
+  public final ColumnVector second() {
+    assert type.hasTimeResolution();
+    return new ColumnVector(second(getNativeView()));
+  }
+
+  /**
+   * Get the day of the week from a timestamp.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return A new INT16 vector allocated on the GPU. Monday=1, ..., Sunday=7
+   */
+  public final ColumnVector weekDay() {
+    assert type.isTimestampType();
+    return new ColumnVector(weekDay(getNativeView()));
+  }
+
+  /**
+   * Get the date that is the last day of the month for this timestamp.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return A new TIMESTAMP_DAYS vector allocated on the GPU.
+   */
+  public final ColumnVector lastDayOfMonth() {
+    assert type.isTimestampType();
+    return new ColumnVector(lastDayOfMonth(getNativeView()));
+  }
+
+  /**
+   * Get the day of the year from a timestamp.
+   * <p>
+   * Postconditions - A new vector is allocated with the result. The caller owns the vector and
+   * is responsible for its lifecycle.
+   * @return A new INT16 vector allocated on the GPU. The value is between [1, {365-366}]
+   */
+  public final ColumnVector dayOfYear() {
+    assert type.isTimestampType();
+    return new ColumnVector(dayOfYear(getNativeView()));
+  }
+
+  /**
+   * Get the quarter of the year from a timestamp.
+   * @return A new INT16 vector allocated on the GPU. It will be a value from {1, 2, 3, 4}
+   * corresponding to the quarter of the year.
+   */
+  public final ColumnVector quarterOfYear() {
+    assert type.isTimestampType();
+    return new ColumnVector(quarterOfYear(getNativeView()));
+  }
+
+  /**
+   * Add the specified number of months to the timestamp.
+   * @param months must be a INT16 column indicating the number of months to add. A negative number
+   *               of months works too.
+   * @return the updated timestamp
+   */
+  public final ColumnVector addCalendricalMonths(ColumnView months) {
+    return new ColumnVector(addCalendricalMonths(getNativeView(), months.getNativeView()));
+  }
+
+  /**
+   * Check to see if the year for this timestamp is a leap year or not.
+   * @return BOOL8 vector of results
+   */
+  public final ColumnVector isLeapYear() {
+    return new ColumnVector(isLeapYear(getNativeView()));
+  }
+
+  /**
+   * Rounds all the values in a column to the specified number of decimal places.
+   *
+   * @param decimalPlaces Number of decimal places to round to. If negative, this
+   *                      specifies the number of positions to the left of the decimal point.
+   * @param mode          Rounding method(either HALF_UP or HALF_EVEN)
+   * @return a new ColumnVector with rounded values.
+   */
+  public ColumnVector round(int decimalPlaces, RoundMode mode) {
+    return new ColumnVector(round(this.getNativeView(), decimalPlaces, mode.nativeId));
+  }
+
+  /**
+   * Rounds all the values in a column with decimal places = 0. Default number of decimal places
+   * to round to is 0.
+   *
+   * @param round Rounding method(either HALF_UP or HALF_EVEN)
+   * @return a new ColumnVector with rounded values.
+   */
+  public ColumnVector round(RoundMode round) {
+    return round(0, round);
+  }
+
+  /**
+   * Rounds all the values in a column to the specified number of decimal places with HALF_UP
+   * (default) as Rounding method.
+   *
+   * @param decimalPlaces Number of decimal places to round to. If negative, this
+   *                      specifies the number of positions to the left of the decimal point.
+   * @return a new ColumnVector with rounded values.
+   */
+  public ColumnVector round(int decimalPlaces) {
+    return round(decimalPlaces, RoundMode.HALF_UP);
+  }
+
+  /**
+   * Rounds all the values in a column with these default values:
+   * decimalPlaces = 0
+   * Rounding method = RoundMode.HALF_UP
+   *
+   * @return a new ColumnVector with rounded values.
+   */
+  public ColumnVector round() {
+    return round(0, RoundMode.HALF_UP);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // ARITHMETIC
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Transform a vector using a custom function.  Be careful this is not
+   * simple to do.  You need to be positive you know what type of data you are
+   * processing and how the data is laid out.  This also only works on fixed
+   * length types.
+   * @param udf This function will be applied to every element in the vector
+   * @param isPtx is the code of the function ptx? true or C/C++ false.
+   */
+  public final ColumnVector transform(String udf, boolean isPtx) {
+    return new ColumnVector(transform(getNativeView(), udf, isPtx));
+  }
+
+  /**
+   * Multiple different unary operations. The output is the same type as input.
+   * @param op      the operation to perform
+   * @return the result
+   */
+  public final ColumnVector unaryOp(UnaryOp op) {
+    return new ColumnVector(unaryOperation(getNativeView(), op.nativeId));
+  }
+
+  /**
+   * Calculate the sin, output is the same type as input.
+   */
+  public final ColumnVector sin() {
+    return unaryOp(UnaryOp.SIN);
+  }
+
+  /**
+   * Calculate the cos, output is the same type as input.
+   */
+  public final ColumnVector cos() {
+    return unaryOp(UnaryOp.COS);
+  }
+
+  /**
+   * Calculate the tan, output is the same type as input.
+   */
+  public final ColumnVector tan() {
+    return unaryOp(UnaryOp.TAN);
+  }
+
+  /**
+   * Calculate the arcsin, output is the same type as input.
+   */
+  public final ColumnVector arcsin() {
+    return unaryOp(UnaryOp.ARCSIN);
+  }
+
+  /**
+   * Calculate the arccos, output is the same type as input.
+   */
+  public final ColumnVector arccos() {
+    return unaryOp(UnaryOp.ARCCOS);
+  }
+
+  /**
+   * Calculate the arctan, output is the same type as input.
+   */
+  public final ColumnVector arctan() {
+    return unaryOp(UnaryOp.ARCTAN);
+  }
+
+  /**
+   * Calculate the hyperbolic sin, output is the same type as input.
+   */
+  public final ColumnVector sinh() {
+    return unaryOp(UnaryOp.SINH);
+  }
+
+  /**
+   * Calculate the hyperbolic cos, output is the same type as input.
+   */
+  public final ColumnVector cosh() {
+    return unaryOp(UnaryOp.COSH);
+  }
+
+  /**
+   * Calculate the hyperbolic tan, output is the same type as input.
+   */
+  public final ColumnVector tanh() {
+    return unaryOp(UnaryOp.TANH);
+  }
+
+  /**
+   * Calculate the hyperbolic arcsin, output is the same type as input.
+   */
+  public final ColumnVector arcsinh() {
+    return unaryOp(UnaryOp.ARCSINH);
+  }
+
+  /**
+   * Calculate the hyperbolic arccos, output is the same type as input.
+   */
+  public final ColumnVector arccosh() {
+    return unaryOp(UnaryOp.ARCCOSH);
+  }
+
+  /**
+   * Calculate the hyperbolic arctan, output is the same type as input.
+   */
+  public final ColumnVector arctanh() {
+    return unaryOp(UnaryOp.ARCTANH);
+  }
+
+  /**
+   * Calculate the exp, output is the same type as input.
+   */
+  public final ColumnVector exp() {
+    return unaryOp(UnaryOp.EXP);
+  }
+
+  /**
+   * Calculate the log, output is the same type as input.
+   */
+  public final ColumnVector log() {
+    return unaryOp(UnaryOp.LOG);
+  }
+
+  /**
+   * Calculate the log with base 2, output is the same type as input.
+   */
+  public final ColumnVector log2() {
+    try (Scalar base = Scalar.fromInt(2)) {
+      return binaryOp(BinaryOp.LOG_BASE, base, getType());
+    }
+  }
+
+  /**
+   * Calculate the log with base 10, output is the same type as input.
+   */
+  public final ColumnVector log10() {
+    try (Scalar base = Scalar.fromInt(10)) {
+      return binaryOp(BinaryOp.LOG_BASE, base, getType());
+    }
+  }
+
+  /**
+   * Calculate the sqrt, output is the same type as input.
+   */
+  public final ColumnVector sqrt() {
+    return unaryOp(UnaryOp.SQRT);
+  }
+
+  /**
+   * Calculate the cube root, output is the same type as input.
+   */
+  public final ColumnVector cbrt() {
+    return unaryOp(UnaryOp.CBRT);
+  }
+
+  /**
+   * Calculate the ceil, output is the same type as input.
+   */
+  public final ColumnVector ceil() {
+    return unaryOp(UnaryOp.CEIL);
+  }
+
+  /**
+   * Calculate the floor, output is the same type as input.
+   */
+  public final ColumnVector floor() {
+    return unaryOp(UnaryOp.FLOOR);
+  }
+
+  /**
+   * Calculate the abs, output is the same type as input.
+   */
+  public final ColumnVector abs() {
+    return unaryOp(UnaryOp.ABS);
+  }
+
+  /**
+   * Rounds a floating-point argument to the closest integer value, but returns it as a float.
+   */
+  public final ColumnVector rint() {
+    return unaryOp(UnaryOp.RINT);
+  }
+
+  /**
+   * invert the bits, output is the same type as input.
+   */
+  public final ColumnVector bitInvert() {
+    return unaryOp(UnaryOp.BIT_INVERT);
+  }
+
+  /**
+   * Multiple different binary operations.
+   * @param op      the operation to perform
+   * @param rhs     the rhs of the operation
+   * @param outType the type of output you want.
+   * @return the result
+   */
+  @Override
+  public final ColumnVector binaryOp(BinaryOp op, BinaryOperable rhs, DType outType) {
+    if (rhs instanceof ColumnView) {
+      assert rows == ((ColumnView) rhs).getRowCount();
+      return new ColumnVector(binaryOp(this, (ColumnView) rhs, op, outType));
+    } else {
+      return new ColumnVector(binaryOp(this, (Scalar) rhs, op, outType));
+    }
+  }
+
+  static long binaryOp(ColumnView lhs, ColumnView rhs, BinaryOp op, DType outputType) {
+    return binaryOpVV(lhs.getNativeView(), rhs.getNativeView(),
+        op.nativeId, outputType.typeId.getNativeId(), outputType.getScale());
+  }
+
+  static long binaryOp(ColumnView lhs, Scalar rhs, BinaryOp op, DType outputType) {
+    return binaryOpVS(lhs.getNativeView(), rhs.getScalarHandle(),
+        op.nativeId, outputType.typeId.getNativeId(), outputType.getScale());
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // AGGREGATION
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Computes the sum of all values in the column, returning a scalar
+   * of the same type as this column.
+   */
+  public Scalar sum() {
+    return sum(type);
+  }
+
+  /**
+   * Computes the sum of all values in the column, returning a scalar
+   * of the specified type.
+   */
+  public Scalar sum(DType outType) {
+    return reduce(ReductionAggregation.sum(), outType);
+  }
+
+  /**
+   * Returns the minimum of all values in the column, returning a scalar
+   * of the same type as this column.
+   */
+  public Scalar min() {
+    return reduce(ReductionAggregation.min(), type);
+  }
+
+  /**
+   * Returns the minimum of all values in the column, returning a scalar
+   * of the specified type.
+   * @deprecated the min reduction no longer internally allows for setting the output type, as a
+   * work around this API will cast the input type to the output type for you, but this may not
+   * work in all cases.
+   */
+  @Deprecated
+  public Scalar min(DType outType) {
+    if (!outType.equals(type)) {
+      try (ColumnVector tmp = this.castTo(outType)) {
+        return tmp.min(outType);
+      }
+    }
+    return reduce(ReductionAggregation.min(), outType);
+  }
+
+  /**
+   * Returns the maximum of all values in the column, returning a scalar
+   * of the same type as this column.
+   */
+  public Scalar max() {
+    return reduce(ReductionAggregation.max(), type);
+  }
+
+  /**
+   * Returns the maximum of all values in the column, returning a scalar
+   * of the specified type.
+   * @deprecated the max reduction no longer internally allows for setting the output type, as a
+   * work around this API will cast the input type to the output type for you, but this may not
+   * work in all cases.
+   */
+  @Deprecated
+  public Scalar max(DType outType) {
+    if (!outType.equals(type)) {
+      try (ColumnVector tmp = this.castTo(outType)) {
+        return tmp.max(outType);
+      }
+    }
+    return reduce(ReductionAggregation.max(), outType);
+  }
+
+  /**
+   * Returns the product of all values in the column, returning a scalar
+   * of the same type as this column.
+   */
+  public Scalar product() {
+    return product(type);
+  }
+
+  /**
+   * Returns the product of all values in the column, returning a scalar
+   * of the specified type.
+   */
+  public Scalar product(DType outType) {
+    return reduce(ReductionAggregation.product(), outType);
+  }
+
+  /**
+   * Returns the sum of squares of all values in the column, returning a
+   * scalar of the same type as this column.
+   */
+  public Scalar sumOfSquares() {
+    return sumOfSquares(type);
+  }
+
+  /**
+   * Returns the sum of squares of all values in the column, returning a
+   * scalar of the specified type.
+   */
+  public Scalar sumOfSquares(DType outType) {
+    return reduce(ReductionAggregation.sumOfSquares(), outType);
+  }
+
+  /**
+   * Returns the arithmetic mean of all values in the column, returning a
+   * FLOAT64 scalar unless the column type is FLOAT32 then a FLOAT32 scalar is returned.
+   * Null values are skipped.
+   */
+  public Scalar mean() {
+    DType outType = DType.FLOAT64;
+    if (type.equals(DType.FLOAT32)) {
+      outType = type;
+    }
+    return mean(outType);
+  }
+
+  /**
+   * Returns the arithmetic mean of all values in the column, returning a
+   * scalar of the specified type.
+   * Null values are skipped.
+   * @param outType the output type to return.  Note that only floating point
+   *                types are currently supported.
+   */
+  public Scalar mean(DType outType) {
+    return reduce(ReductionAggregation.mean(), outType);
+  }
+
+  /**
+   * Returns the variance of all values in the column, returning a
+   * FLOAT64 scalar unless the column type is FLOAT32 then a FLOAT32 scalar is returned.
+   * Null values are skipped.
+   */
+  public Scalar variance() {
+    DType outType = DType.FLOAT64;
+    if (type.equals(DType.FLOAT32)) {
+      outType = type;
+    }
+    return variance(outType);
+  }
+
+  /**
+   * Returns the variance of all values in the column, returning a
+   * scalar of the specified type.
+   * Null values are skipped.
+   * @param outType the output type to return.  Note that only floating point
+   *                types are currently supported.
+   */
+  public Scalar variance(DType outType) {
+    return reduce(ReductionAggregation.variance(), outType);
+  }
+
+  /**
+   * Returns the sample standard deviation of all values in the column,
+   * returning a FLOAT64 scalar unless the column type is FLOAT32 then
+   * a FLOAT32 scalar is returned. Nulls are not counted as an element
+   * of the column when calculating the standard deviation.
+   */
+  public Scalar standardDeviation() {
+    DType outType = DType.FLOAT64;
+    if (type.equals(DType.FLOAT32)) {
+      outType = type;
+    }
+    return standardDeviation(outType);
+  }
+
+  /**
+   * Returns the sample standard deviation of all values in the column,
+   * returning a scalar of the specified type. Null's are not counted as
+   * an element of the column when calculating the standard deviation.
+   * @param outType the output type to return.  Note that only floating point
+   *                types are currently supported.
+   */
+  public Scalar standardDeviation(DType outType) {
+    return reduce(ReductionAggregation.standardDeviation(), outType);
+  }
+
+  /**
+   * Returns a boolean scalar that is true if any of the elements in
+   * the column are true or non-zero otherwise false.
+   * Null values are skipped.
+   */
+  public Scalar any() {
+    return any(DType.BOOL8);
+  }
+
+  /**
+   * Returns a scalar is true or 1, depending on the specified type,
+   * if any of the elements in the column are true or non-zero
+   * otherwise false or 0.
+   * Null values are skipped.
+   */
+  public Scalar any(DType outType) {
+    return reduce(ReductionAggregation.any(), outType);
+  }
+
+  /**
+   * Returns a boolean scalar that is true if all of the elements in
+   * the column are true or non-zero otherwise false.
+   * Null values are skipped.
+   */
+  public Scalar all() {
+    return all(DType.BOOL8);
+  }
+
+  /**
+   * Returns a scalar is true or 1, depending on the specified type,
+   * if all of the elements in the column are true or non-zero
+   * otherwise false or 0.
+   * Null values are skipped.
+   * @deprecated the only output type supported is BOOL8.
+   */
+  @Deprecated
+  public Scalar all(DType outType) {
+    return reduce(ReductionAggregation.all(), outType);
+  }
+
+  /**
+   * Computes the reduction of the values in all rows of a column.
+   * Overflows in reductions are not detected. Specifying a higher precision
+   * output type may prevent overflow. Only the MIN and MAX ops are
+   * The null values are skipped for the operation.
+   * @param aggregation The reduction aggregation to perform
+   * @return The scalar result of the reduction operation. If the column is
+   * empty or the reduction operation fails then the
+   * {@link Scalar#isValid()} method of the result will return false.
+   */
+  public Scalar reduce(ReductionAggregation aggregation) {
+    return reduce(aggregation, type);
+  }
+
+  /**
+   * Computes the reduction of the values in all rows of a column.
+   * Overflows in reductions are not detected. Specifying a higher precision
+   * output type may prevent overflow. Only the MIN and MAX ops are
+   * supported for reduction of non-arithmetic types (TIMESTAMP...)
+   * The null values are skipped for the operation.
+   * @param aggregation The reduction aggregation to perform
+   * @param outType The type of scalar value to return. Not all output types are supported
+   *                by all aggregation operations.
+   * @return The scalar result of the reduction operation. If the column is
+   * empty or the reduction operation fails then the
+   * {@link Scalar#isValid()} method of the result will return false.
+   */
+  public Scalar reduce(ReductionAggregation aggregation, DType outType) {
+    long nativeId = aggregation.createNativeInstance();
+    try {
+      return new Scalar(outType, reduce(getNativeView(), nativeId, outType.typeId.getNativeId(), outType.getScale()));
+    } finally {
+      Aggregation.close(nativeId);
+    }
+  }
+
+  /**
+   * Do a segmented reduce where the offsets column indicates which groups in this to combine. The
+   * output type is the same as the input type.
+   * @param offsets an INT32 column with no nulls.
+   * @param aggregation the aggregation to do
+   * @return the result.
+   */
+  public ColumnVector segmentedReduce(ColumnView offsets, SegmentedReductionAggregation aggregation) {
+    return segmentedReduce(offsets, aggregation, NullPolicy.EXCLUDE, type);
+  }
+
+  /**
+   * Do a segmented reduce where the offsets column indicates which groups in this to combine.
+   * @param offsets an INT32 column with no nulls.
+   * @param aggregation the aggregation to do
+   * @param outType the output data type.
+   * @return the result.
+   */
+  public ColumnVector segmentedReduce(ColumnView offsets, SegmentedReductionAggregation aggregation,
+      DType outType) {
+    return segmentedReduce(offsets, aggregation, NullPolicy.EXCLUDE, outType);
+  }
+
+  /**
+   * Do a segmented reduce where the offsets column indicates which groups in this to combine.
+   * @param offsets an INT32 column with no nulls.
+   * @param aggregation the aggregation to do
+   * @param nullPolicy the null policy.
+   * @param outType the output data type.
+   * @return the result.
+   */
+  public ColumnVector segmentedReduce(ColumnView offsets, SegmentedReductionAggregation aggregation,
+      NullPolicy nullPolicy, DType outType) {
+    long nativeId = aggregation.createNativeInstance();
+    try {
+      return new ColumnVector(segmentedReduce(getNativeView(), offsets.getNativeView(), nativeId,
+          nullPolicy.includeNulls, outType.typeId.getNativeId(), outType.getScale()));
+    } finally {
+      Aggregation.close(nativeId);
+    }
+  }
+
+  /**
+   * Segmented gather of the elements within a list element in each row of a list column.
+   * For each list, assuming the size is N, valid indices of gather map ranges in [-N, N).
+   * Out of bound indices refer to null.
+   * @param gatherMap ListColumnView carrying lists of integral indices which maps the
+   * element in list of each row in the source columns to rows of lists in the result columns.
+   * @return the result.
+   */
+  public ColumnVector segmentedGather(ColumnView gatherMap) {
+    return segmentedGather(gatherMap, OutOfBoundsPolicy.NULLIFY);
+  }
+
+  /**
+   * Segmented gather of the elements within a list element in each row of a list column.
+   * @param gatherMap ListColumnView carrying lists of integral indices which maps the
+   * element in list of each row in the source columns to rows of lists in the result columns.
+   * @param policy OutOfBoundsPolicy, `DONT_CHECK` leads to undefined behaviour; `NULLIFY`
+   * replaces out of bounds with null.
+   * @return the result.
+   */
+  public ColumnVector segmentedGather(ColumnView gatherMap, OutOfBoundsPolicy policy) {
+    return new ColumnVector(segmentedGather(getNativeView(), gatherMap.getNativeView(),
+        policy.equals(OutOfBoundsPolicy.NULLIFY)));
+  }
+
+  /**
+   * Do a reduction on the values in a list. The output type will be the type of the data column
+   * of this list.
+   * @param aggregation the aggregation to perform
+   */
+  public ColumnVector listReduce(SegmentedReductionAggregation aggregation) {
+    if (!getType().equals(DType.LIST)) {
+      throw new IllegalArgumentException("listReduce only works on list types");
+    }
+    try (ColumnView offsets = getListOffsetsView();
+         ColumnView data = getChildColumnView(0)) {
+      return data.segmentedReduce(offsets, aggregation);
+    }
+  }
+
+  /**
+   * Do a reduction on the values in a list.
+   * @param aggregation the aggregation to perform
+   * @param outType the type of the output. Typically, this should match with the child type
+   *                of the list.
+   */
+  public ColumnVector listReduce(SegmentedReductionAggregation aggregation, DType outType) {
+    return listReduce(aggregation, NullPolicy.EXCLUDE, outType);
+  }
+
+  /**
+   * Do a reduction on the values in a list.
+   * @param aggregation the aggregation to perform
+   * @param nullPolicy should nulls be included or excluded from the aggregation.
+   * @param outType the type of the output. Typically, this should match with the child type
+   *                of the list.
+   */
+  public ColumnVector listReduce(SegmentedReductionAggregation aggregation, NullPolicy nullPolicy,
+      DType outType) {
+    if (!getType().equals(DType.LIST)) {
+      throw new IllegalArgumentException("listReduce only works on list types");
+    }
+    try (ColumnView offsets = getListOffsetsView();
+         ColumnView data = getChildColumnView(0)) {
+      return data.segmentedReduce(offsets, aggregation, nullPolicy, outType);
+    }
+  }
+
+  /**
+   * Calculate various percentiles of this ColumnVector, which must contain centroids produced by
+   * a t-digest aggregation.
+   *
+   * @param percentiles Required percentiles [0,1]
+   * @return Column containing the approximate percentile values as a list of doubles, in
+   *         the same order as the input percentiles
+   */
+  public final ColumnVector approxPercentile(double[] percentiles) {
+    try (ColumnVector cv = ColumnVector.fromDoubles(percentiles)) {
+      return approxPercentile(cv);
+    }
+  }
+
+  /**
+   * Calculate various percentiles of this ColumnVector, which must contain centroids produced by
+   * a t-digest aggregation.
+   *
+   * @param percentiles Column containing percentiles [0,1]
+   * @return Column containing the approximate percentile values as a list of doubles, in
+   *         the same order as the input percentiles
+   */
+  public final ColumnVector approxPercentile(ColumnVector percentiles) {
+    return new ColumnVector(approxPercentile(getNativeView(), percentiles.getNativeView()));
+  }
+
+  /**
+   * Calculate various quantiles of this ColumnVector.  It is assumed that this is already sorted
+   * in the desired order.
+   * @param method   the method used to calculate the quantiles
+   * @param quantiles the quantile values [0,1]
+   * @return Column containing the approximate percentile values as a list of doubles, in
+   *         the same order as the input percentiles
+   */
+  public final ColumnVector quantile(QuantileMethod method, double[] quantiles) {
+    return new ColumnVector(quantile(getNativeView(), method.nativeId, quantiles));
+  }
+
+  /**
+   * This function aggregates values in a window around each element i of the input
+   * column. Please refer to WindowsOptions for various options that can be passed.
+   * Note: Only rows-based windows are supported.
+   * @param op the operation to perform.
+   * @param options various window function arguments.
+   * @return Column containing aggregate function result.
+   * @throws IllegalArgumentException if unsupported window specification * (i.e. other than {@link WindowOptions.FrameType#ROWS} is used.
+   */
+  public final ColumnVector rollingWindow(RollingAggregation op, WindowOptions options) {
+    // Check that only row-based windows are used.
+    if (!options.getFrameType().equals(WindowOptions.FrameType.ROWS)) {
+      throw new IllegalArgumentException("Expected ROWS-based window specification. Unexpected window type: "
+          + options.getFrameType());
+    }
+
+    long nativePtr = op.createNativeInstance();
+    try {
+      Scalar p = options.getPrecedingScalar();
+      Scalar f = options.getFollowingScalar();
+      return new ColumnVector(
+          rollingWindow(this.getNativeView(),
+              op.getDefaultOutput(),
+              options.getMinPeriods(),
+              nativePtr,
+              p == null || !p.isValid() ? 0 : p.getInt(),
+              f == null || !f.isValid() ? 0 : f.getInt(),
+              options.getPrecedingCol() == null ? 0 : options.getPrecedingCol().getNativeView(),
+              options.getFollowingCol() == null ? 0 : options.getFollowingCol().getNativeView()));
+    } finally {
+      Aggregation.close(nativePtr);
+    }
+  }
+
+  /**
+   * Compute the prefix sum (aka cumulative sum) of the values in this column.
+   * This is just a convenience method for an inclusive scan with a SUM aggregation.
+   */
+  public final ColumnVector prefixSum() {
+    return scan(ScanAggregation.sum());
+  }
+
+  /**
+   * Computes a scan for a column. This is very similar to a running window on the column.
+   * @param aggregation the aggregation to perform
+   * @param scanType should the scan be inclusive, include the current row, or exclusive.
+   * @param nullPolicy how should nulls be treated. Note that some aggregations also include a
+   *                   null policy too. Currently none of those aggregations are supported so
+   *                   it is undefined how they would interact with each other.
+   */
+  public final ColumnVector scan(ScanAggregation aggregation, ScanType scanType, NullPolicy nullPolicy) {
+    long nativeId = aggregation.createNativeInstance();
+    try {
+      return new ColumnVector(scan(getNativeView(), nativeId,
+          scanType.isInclusive, nullPolicy.includeNulls));
+    } finally {
+      Aggregation.close(nativeId);
+    }
+  }
+
+  /**
+   * Computes a scan for a column that excludes nulls.
+   * @param aggregation the aggregation to perform
+   * @param scanType should the scan be inclusive, include the current row, or exclusive.
+   */
+  public final ColumnVector scan(ScanAggregation aggregation, ScanType scanType) {
+    return scan(aggregation, scanType, NullPolicy.EXCLUDE);
+  }
+
+  /**
+   * Computes an inclusive scan for a column that excludes nulls.
+   * @param aggregation the aggregation to perform
+   */
+  public final ColumnVector scan(ScanAggregation aggregation) {
+    return scan(aggregation, ScanType.INCLUSIVE, NullPolicy.EXCLUDE);
+  }
+
+
+
+  /////////////////////////////////////////////////////////////////////////////
+  // LOGICAL
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Returns a vector of the logical `not` of each value in the input
+   * column (this)
+   */
+  public final ColumnVector not() {
+    return unaryOp(UnaryOp.NOT);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // SEARCH
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Find if the `needle` is present in this col
+   *
+   * example:
+   *
+   *  Single Column:
+   *      idx      0   1   2   3   4
+   *      col = { 10, 20, 20, 30, 50 }
+   *  Scalar:
+   *   value = { 20 }
+   *   result = true
+   *
+   * @param needle
+   * @return true if needle is present else false
+   */
+  public boolean contains(Scalar needle) {
+    return containsScalar(getNativeView(), needle.getScalarHandle());
+  }
+
+  /**
+   * Returns a new column of {@link DType#BOOL8} elements having the same size as this column,
+   * each row value is true if the corresponding entry in this column is contained in the
+   * given searchSpace column and false if it is not.
+   * The caller will be responsible for the lifecycle of the new vector.
+   *
+   * example:
+   *
+   *   col         = { 10, 20, 30, 40, 50 }
+   *   searchSpace = { 20, 40, 60, 80 }
+   *
+   *   result = { false, true, false, true, false }
+   *
+   * @param searchSpace
+   * @return A new ColumnVector of type {@link DType#BOOL8}
+   */
+  public final ColumnVector contains(ColumnView searchSpace) {
+    return new ColumnVector(containsVector(getNativeView(), searchSpace.getNativeView()));
+  }
+
+  /**
+   * Returns a column of strings where, for each string row in the input,
+   * the first character after spaces is modified to upper-case,
+   * while all the remaining characters in a word are modified to lower-case.
+   *
+   * Any null string entries return corresponding null output column entries
+   */
+  public final ColumnVector toTitle() {
+    assert type.equals(DType.STRING);
+    return new ColumnVector(title(getNativeView()));
+  }
+
+  /**
+   * Returns a column of capitalized strings.
+   *
+   * If the `delimiters` is an empty string, then only the first character of each
+   * row is capitalized. Otherwise, a non-delimiter character is capitalized after
+   * any delimiter character is found.
+   *
+   * Example:
+   *     input = ["tesT1", "a Test", "Another Test", "a\tb"];
+   *     delimiters = ""
+   *     output is ["Test1", "A test", "Another test", "A\tb"]
+   *     delimiters = " "
+   *     output is ["Test1", "A Test", "Another Test", "A\tb"]
+   *
+   * Any null string entries return corresponding null output column entries.
+   *
+   * @param delimiters Used if identifying words to capitalize. Should not be null.
+   * @return a column of capitalized strings. Users should close the returned column.
+   */
+  public final ColumnVector capitalize(Scalar delimiters) {
+    if (DType.STRING.equals(type) && DType.STRING.equals(delimiters.getType())) {
+      return new ColumnVector(capitalize(getNativeView(), delimiters.getScalarHandle()));
+    }
+    throw new IllegalArgumentException("Both input column and delimiters scalar should be" +
+        " string type. But got column: " + type + ", scalar: " + delimiters.getType());
+  }
+
+  /**
+   * Concatenates all strings in the column into one new string delimited
+   * by an optional separator string.
+   *
+   * This returns a column with one string. Any null entries are ignored unless
+   * the narep parameter specifies a replacement string (not a null value).
+   *
+   * @param separator what to insert to separate each row.
+   * @param narep what to replace nulls with
+   * @return a ColumnVector with a single string in it.
+   */
+  public final ColumnVector joinStrings(Scalar separator, Scalar narep) {
+    if (DType.STRING.equals(type) &&
+        DType.STRING.equals(separator.getType()) &&
+        DType.STRING.equals(narep.getType())) {
+      return new ColumnVector(joinStrings(getNativeView(), separator.getScalarHandle(),
+          narep.getScalarHandle()));
+    }
+    throw new IllegalArgumentException("The column, separator, and narep all need to be STRINGs");
+  }
+  /////////////////////////////////////////////////////////////////////////////
+  // TYPE CAST
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Generic method to cast ColumnVector
+   * When casting from a Date, Timestamp, or Boolean to a numerical type the underlying numerical
+   * representation of the data will be used for the cast.
+   *
+   * For Strings:
+   * Casting strings from/to timestamp isn't supported atm.
+   * Please look at {@link ColumnVector#asTimestamp(DType, String)}
+   * and {@link ColumnVector#asStrings(String)} for casting string to timestamp when the format
+   * is known
+   *
+   * Float values when converted to String could be different from the expected default behavior in
+   * Java
+   * e.g.
+   * 12.3 => "12.30000019" instead of "12.3"
+   * Double.POSITIVE_INFINITY => "Inf" instead of "INFINITY"
+   * Double.NEGATIVE_INFINITY => "-Inf" instead of "-INFINITY"
+   *
+   * @param type type of the resulting ColumnVector
+   * @return A new vector allocated on the GPU
+   */
+  public ColumnVector castTo(DType type) {
+    return new ColumnVector(castTo(getNativeView(), type.typeId.getNativeId(), type.getScale()));
+  }
+
+  /**
+   * This method takes in a nested type and replaces its children with the given views
+   * Note: Make sure the numbers of rows in the leaf node are the same as the child replacing it
+   * otherwise the list can point to elements outside of the column values.
+   *
+   * Note: this method returns a ColumnView that won't live past the ColumnVector that it's
+   * pointing to.
+   *
+   * Ex: List<Int> list = col{{1,3}, {9,3,5}}
+   *
+   * validNewChild = col{8, 3, 9, 2, 0}
+   *
+   * list.replaceChildrenWithViews(1, validNewChild) => col{{8, 3}, {9, 2, 0}}
+   *
+   * invalidNewChild = col{3, 2}
+   * list.replaceChildrenWithViews(1, invalidNewChild) => col{{3, 2}, {invalid, invalid, invalid}}
+   *
+   * invalidNewChild = col{8, 3, 9, 2, 0, 0, 7}
+   * list.replaceChildrenWithViews(1, invalidNewChild) => col{{8, 3}, {9, 2, 0}} // undefined result
+   */
+  public ColumnView replaceChildrenWithViews(int[] indices,
+                                             ColumnView[] views) {
+    assert (type.isNestedType());
+    assert (indices.length == views.length);
+    if (type == DType.LIST) {
+      assert (indices.length == 1);
+    }
+    if (indices.length != views.length) {
+      throw new IllegalArgumentException("The indices size and children size should match");
+    }
+    Map<Integer, ColumnView> map = new HashMap<>();
+    IntStream.range(0, indices.length).forEach(index -> {
+      if (map.containsKey(indices[index])) {
+        throw new IllegalArgumentException("Duplicate mapping found for replacing child index");
+      }
+      map.put(indices[index], views[index]);
+    });
+    List<ColumnView> newChildren = new ArrayList<>(getNumChildren());
+    List<ColumnView> toClose = new ArrayList<>(getNumChildren());
+    try {
+      IntStream.range(0, getNumChildren()).forEach(i -> {
+        ColumnView view = map.remove(i);
+        ColumnView child = getChildColumnView(i);
+        toClose.add(child);
+        if (view == null) {
+          newChildren.add(child);
+        } else {
+          if (child.getRowCount() != view.getRowCount()) {
+            throw new IllegalArgumentException("Child row count doesn't match the old child");
+          }
+          newChildren.add(view);
+        }
+      });
+      if (!map.isEmpty()) {
+        throw new IllegalArgumentException("One or more invalid child indices passed to be " +
+            "replaced");
+      }
+      return new ColumnView(type, getRowCount(), Optional.of(getNullCount()), getValid(),
+          getOffsets(), newChildren.stream().toArray(n -> new ColumnView[n]));
+    } finally {
+      for (ColumnView columnView: toClose) {
+        columnView.close();
+      }
+    }
+  }
+
+  /**
+   * This method takes in a list and returns a new list with the leaf node replaced with the given
+   * view. Make sure the numbers of rows in the leaf node are the same as the child replacing it
+   * otherwise the list can point to elements outside of the column values.
+   *
+   * Note: this method returns a ColumnView that won't live past the ColumnVector that it's
+   * pointing to.
+   *
+   * Ex: List<Int> list = col{{1,3}, {9,3,5}}
+   *
+   * validNewChild = col{8, 3, 9, 2, 0}
+   *
+   * list.replaceChildrenWithViews(1, validNewChild) => col{{8, 3}, {9, 2, 0}}
+   *
+   * invalidNewChild = col{3, 2}
+   * list.replaceChildrenWithViews(1, invalidNewChild) =>
+   *        col{{3, 2}, {invalid, invalid, invalid}} throws an exception
+   *
+   * invalidNewChild = col{8, 3, 9, 2, 0, 0, 7}
+   * list.replaceChildrenWithViews(1, invalidNewChild) =>
+   *       col{{8, 3}, {9, 2, 0}} throws an exception
+   */
+  public ColumnView replaceListChild(ColumnView child) {
+    assert(type == DType.LIST);
+    return replaceChildrenWithViews(new int[]{0}, new ColumnView[]{child});
+  }
+
+  /**
+   * Zero-copy cast between types with the same underlying representation.
+   *
+   * Similar to reinterpret_cast or bit_cast in C++. This will essentially take the underlying data
+   * and update the metadata to reflect a new type. Not all types are supported the width of the
+   * types must match.
+   * @param type the type you want to go to.
+   * @return a ColumnView that cannot outlive the Column that owns the actual data it points to.
+   * @deprecated this has changed to bit_cast in C++ so use that name instead
+   */
+  @Deprecated
+  public ColumnView logicalCastTo(DType type) {
+    return bitCastTo(type);
+  }
+
+  /**
+   * Zero-copy cast between types with the same underlying length.
+   *
+   * Similar to bit_cast in C++. This will take the underlying data and create new metadata
+   * so it is interpreted as a new type. Not all types are supported the width of the
+   * types must match.
+   * @param type the type you want to go to.
+   * @return a ColumnView that cannot outlive the Column that owns the actual data it points to.
+   */
+  public ColumnView bitCastTo(DType type) {
+    return new ColumnView(bitCastTo(getNativeView(),
+        type.typeId.getNativeId(), type.getScale()));
+  }
+
+  /**
+   * Cast to Byte - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to byte
+   * When casting from a Date, Timestamp, or Boolean to a byte type the underlying numerical
+   * representation of the data will be used for the cast.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asBytes() {
+    return castTo(DType.INT8);
+  }
+
+  /**
+   * Cast to list of bytes
+   * This method converts the rows provided by the ColumnVector and casts each row to a list of
+   * bytes with endinanness reversed. Numeric and string types supported, but not timestamps.
+   *
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asByteList() {
+    return new ColumnVector(byteListCast(getNativeView(), true));
+  }
+
+  /**
+   * Cast to list of bytes
+   * This method converts the rows provided by the ColumnVector and casts each row to a list
+   * of bytes. Numeric and string types supported, but not timestamps.
+   *
+   * @param config Flips the byte order (endianness) if true, retains byte order otherwise
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asByteList(boolean config) {
+    return new ColumnVector(byteListCast(getNativeView(), config));
+  }
+
+  /**
+   * Cast to unsigned Byte - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to byte
+   * When casting from a Date, Timestamp, or Boolean to a byte type the underlying numerical
+   * representation of the data will be used for the cast.
+   * <p>
+   * Java does not have an unsigned byte type, so properly decoding these values
+   * will require extra steps on the part of the application.  See
+   * {@link Byte#toUnsignedInt(byte)}.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asUnsignedBytes() {
+    return castTo(DType.UINT8);
+  }
+
+  /**
+   * Cast to Short - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to short
+   * When casting from a Date, Timestamp, or Boolean to a short type the underlying numerical
+   * representation of the data will be used for the cast.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asShorts() {
+    return castTo(DType.INT16);
+  }
+
+  /**
+   * Cast to unsigned Short - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to short
+   * When casting from a Date, Timestamp, or Boolean to a short type the underlying numerical
+   * representation of the data will be used for the cast.
+   * <p>
+   * Java does not have an unsigned short type, so properly decoding these values
+   * will require extra steps on the part of the application.  See
+   * {@link Short#toUnsignedInt(short)}.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asUnsignedShorts() {
+    return castTo(DType.UINT16);
+  }
+
+  /**
+   * Cast to Int - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to int
+   * When casting from a Date, Timestamp, or Boolean to a int type the underlying numerical
+   * representation of the data will be used for the cast.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asInts() {
+    return castTo(DType.INT32);
+  }
+
+  /**
+   * Cast to unsigned Int - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to int
+   * When casting from a Date, Timestamp, or Boolean to a int type the underlying numerical
+   * representation of the data will be used for the cast.
+   * <p>
+   * Java does not have an unsigned int type, so properly decoding these values
+   * will require extra steps on the part of the application.  See
+   * {@link Integer#toUnsignedLong(int)}.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asUnsignedInts() {
+    return castTo(DType.UINT32);
+  }
+
+  /**
+   * Cast to Long - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to long
+   * When casting from a Date, Timestamp, or Boolean to a long type the underlying numerical
+   * representation of the data will be used for the cast.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asLongs() {
+    return castTo(DType.INT64);
+  }
+
+  /**
+   * Cast to unsigned Long - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to long
+   * When casting from a Date, Timestamp, or Boolean to a long type the underlying numerical
+   * representation of the data will be used for the cast.
+   * <p>
+   * Java does not have an unsigned long type, so properly decoding these values
+   * will require extra steps on the part of the application.  See
+   * {@link Long#toUnsignedString(long)}.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asUnsignedLongs() {
+    return castTo(DType.UINT64);
+  }
+
+  /**
+   * Cast to Float - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to float
+   * When casting from a Date, Timestamp, or Boolean to a float type the underlying numerical
+   * representatio of the data will be used for the cast.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asFloats() {
+    return castTo(DType.FLOAT32);
+  }
+
+  /**
+   * Cast to Double - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to double
+   * When casting from a Date, Timestamp, or Boolean to a double type the underlying numerical
+   * representation of the data will be used for the cast.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asDoubles() {
+    return castTo(DType.FLOAT64);
+  }
+
+  /**
+   * Cast to TIMESTAMP_DAYS - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to TIMESTAMP_DAYS
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampDays() {
+    if (type.equals(DType.STRING)) {
+      return asTimestamp(DType.TIMESTAMP_DAYS, "%Y-%m-%dT%H:%M:%SZ%f");
+    }
+    return castTo(DType.TIMESTAMP_DAYS);
+  }
+
+  /**
+   * Cast to TIMESTAMP_DAYS - ColumnVector
+   * This method takes the string value provided by the ColumnVector and casts to TIMESTAMP_DAYS
+   * @param format timestamp string format specifier, ignored if the column type is not string
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampDays(String format) {
+    assert type.equals(DType.STRING) : "A column of type string is required when using a format string";
+    return asTimestamp(DType.TIMESTAMP_DAYS, format);
+  }
+
+  /**
+   * Cast to TIMESTAMP_SECONDS - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to TIMESTAMP_SECONDS
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampSeconds() {
+    if (type.equals(DType.STRING)) {
+      return asTimestamp(DType.TIMESTAMP_SECONDS, "%Y-%m-%dT%H:%M:%SZ%f");
+    }
+    return castTo(DType.TIMESTAMP_SECONDS);
+  }
+
+  /**
+   * Cast to TIMESTAMP_SECONDS - ColumnVector
+   * This method takes the string value provided by the ColumnVector and casts to TIMESTAMP_SECONDS
+   * @param format timestamp string format specifier, ignored if the column type is not string
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampSeconds(String format) {
+    assert type.equals(DType.STRING) : "A column of type string is required when using a format string";
+    return asTimestamp(DType.TIMESTAMP_SECONDS, format);
+  }
+
+  /**
+   * Cast to TIMESTAMP_MICROSECONDS - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to TIMESTAMP_MICROSECONDS
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampMicroseconds() {
+    if (type.equals(DType.STRING)) {
+      return asTimestamp(DType.TIMESTAMP_MICROSECONDS, "%Y-%m-%dT%H:%M:%SZ%f");
+    }
+    return castTo(DType.TIMESTAMP_MICROSECONDS);
+  }
+
+  /**
+   * Cast to TIMESTAMP_MICROSECONDS - ColumnVector
+   * This method takes the string value provided by the ColumnVector and casts to TIMESTAMP_MICROSECONDS
+   * @param format timestamp string format specifier, ignored if the column type is not string
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampMicroseconds(String format) {
+    assert type.equals(DType.STRING) : "A column of type string is required when using a format string";
+    return asTimestamp(DType.TIMESTAMP_MICROSECONDS, format);
+  }
+
+  /**
+   * Cast to TIMESTAMP_MILLISECONDS - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to TIMESTAMP_MILLISECONDS.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampMilliseconds() {
+    if (type.equals(DType.STRING)) {
+      return asTimestamp(DType.TIMESTAMP_MILLISECONDS, "%Y-%m-%dT%H:%M:%SZ%f");
+    }
+    return castTo(DType.TIMESTAMP_MILLISECONDS);
+  }
+
+  /**
+   * Cast to TIMESTAMP_MILLISECONDS - ColumnVector
+   * This method takes the string value provided by the ColumnVector and casts to TIMESTAMP_MILLISECONDS.
+   * @param format timestamp string format specifier, ignored if the column type is not string
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampMilliseconds(String format) {
+    assert type.equals(DType.STRING) : "A column of type string is required when using a format string";
+    return asTimestamp(DType.TIMESTAMP_MILLISECONDS, format);
+  }
+
+  /**
+   * Cast to TIMESTAMP_NANOSECONDS - ColumnVector
+   * This method takes the value provided by the ColumnVector and casts to TIMESTAMP_NANOSECONDS.
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampNanoseconds() {
+    if (type.equals(DType.STRING)) {
+      return asTimestamp(DType.TIMESTAMP_NANOSECONDS, "%Y-%m-%dT%H:%M:%SZ%9f");
+    }
+    return castTo(DType.TIMESTAMP_NANOSECONDS);
+  }
+
+  /**
+   * Cast to TIMESTAMP_NANOSECONDS - ColumnVector
+   * This method takes the string value provided by the ColumnVector and casts to TIMESTAMP_NANOSECONDS.
+   * @param format timestamp string format specifier, ignored if the column type is not string
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asTimestampNanoseconds(String format) {
+    assert type.equals(DType.STRING) : "A column of type string is required when using a format string";
+    return asTimestamp(DType.TIMESTAMP_NANOSECONDS, format);
+  }
+
+  /**
+   * Parse a string to a timestamp. Strings that fail to parse will default to 0, corresponding
+   * to 1970-01-01 00:00:00.000.
+   * @param timestampType timestamp DType that includes the time unit to parse the timestamp into.
+   * @param format strptime format specifier string of the timestamp. Used to parse and convert
+   *               the timestamp with. Supports %Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z format specifiers.
+   *               See https://github.com/rapidsai/custrings/blob/branch-0.10/docs/source/datetime.md
+   *               for full parsing format specification and documentation.
+   * @return A new ColumnVector containing the long representations of the timestamps in the
+   *         original column vector.
+   */
+  public final ColumnVector asTimestamp(DType timestampType, String format) {
+    assert type.equals(DType.STRING) : "A column of type string " +
+        "is required for .to_timestamp() operation";
+    assert format != null : "Format string may not be NULL";
+    assert timestampType.isTimestampType() : "unsupported conversion to non-timestamp DType";
+    // Only nativeID is passed in the below function as timestamp type does not have `scale`.
+    return new ColumnVector(stringTimestampToTimestamp(getNativeView(),
+        timestampType.typeId.getNativeId(), format));
+  }
+
+  /**
+   * Cast to Strings.
+   * Negative timestamp values are not currently supported and will yield undesired results. See
+   * github issue https://github.com/rapidsai/cudf/issues/3116 for details
+   * In case of timestamps it follows the following formats
+   *    {@link DType#TIMESTAMP_DAYS} - "%Y-%m-%d"
+   *    {@link DType#TIMESTAMP_SECONDS} - "%Y-%m-%d %H:%M:%S"
+   *    {@link DType#TIMESTAMP_MICROSECONDS} - "%Y-%m-%d %H:%M:%S.%f"
+   *    {@link DType#TIMESTAMP_MILLISECONDS} - "%Y-%m-%d %H:%M:%S.%f"
+   *    {@link DType#TIMESTAMP_NANOSECONDS} - "%Y-%m-%d %H:%M:%S.%f"
+   *
+   * @return A new vector allocated on the GPU.
+   */
+  public final ColumnVector asStrings() {
+    switch(type.typeId) {
+      case TIMESTAMP_SECONDS:
+        return asStrings("%Y-%m-%d %H:%M:%S");
+      case TIMESTAMP_DAYS:
+        return asStrings("%Y-%m-%d");
+      case TIMESTAMP_MICROSECONDS:
+      case TIMESTAMP_MILLISECONDS:
+      case TIMESTAMP_NANOSECONDS:
+        return asStrings("%Y-%m-%d %H:%M:%S.%f");
+      default:
+        return castTo(DType.STRING);
+    }
+  }
+
+  /**
+   * Method to parse and convert a timestamp column vector to string column vector. A unix
+   * timestamp is a long value representing how many units since 1970-01-01 00:00:00:000 in either
+   * positive or negative direction.
+
+   * No checking is done for invalid formats or invalid timestamp units.
+   * Negative timestamp values are not currently supported and will yield undesired results. See
+   * github issue https://github.com/rapidsai/cudf/issues/3116 for details
+   *
+   * @param format - strftime format specifier string of the timestamp. Its used to parse and convert
+   *               the timestamp with. Supports %m,%j,%d,%H,%M,%S,%y,%Y,%f format specifiers.
+   *               %d 	Day of the month: 01-31
+   *               %m 	Month of the year: 01-12
+   *               %y 	Year without century: 00-99c
+   *               %Y 	Year with century: 0001-9999
+   *               %H 	24-hour of the day: 00-23
+   *               %M 	Minute of the hour: 00-59
+   *               %S 	Second of the minute: 00-59
+   *               %f 	6-digit microsecond: 000000-999999
+   *               See https://github.com/rapidsai/custrings/blob/branch-0.10/docs/source/datetime.md
+   *
+   * Reported bugs
+   * https://github.com/rapidsai/cudf/issues/4160 after the bug is fixed this method should
+   * also support
+   *               %I 	12-hour of the day: 01-12
+   *               %p 	Only 'AM', 'PM'
+   *               %j   day of the year
+   *
+   * @return A new vector allocated on the GPU
+   */
+  public final ColumnVector asStrings(String format) {
+    assert type.isTimestampType() : "unsupported conversion from non-timestamp DType";
+    assert format != null || format.isEmpty(): "Format string may not be NULL or empty";
+
+    return new ColumnVector(timestampToStringTimestamp(this.getNativeView(), format));
+  }
+
+  /**
+   * Verifies that a string column can be parsed to timestamps using the provided format
+   * pattern.
+   *
+   * The format pattern can include the following specifiers: "%Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z"
+   *
+   * | Specifier | Description |
+   * | :-------: | ----------- |
+   * | \%d | Day of the month: 01-31 |
+   * | \%m | Month of the year: 01-12 |
+   * | \%y | Year without century: 00-99 |
+   * | \%Y | Year with century: 0001-9999 |
+   * | \%H | 24-hour of the day: 00-23 |
+   * | \%I | 12-hour of the day: 01-12 |
+   * | \%M | Minute of the hour: 00-59|
+   * | \%S | Second of the minute: 00-59 |
+   * | \%f | 6-digit microsecond: 000000-999999 |
+   * | \%z | UTC offset with format ±HHMM Example +0500 |
+   * | \%j | Day of the year: 001-366 |
+   * | \%p | Only 'AM', 'PM' or 'am', 'pm' are recognized |
+   *
+   * Other specifiers are not currently supported.
+   * The "%f" supports a precision value to read the numeric digits. Specify the
+   * precision with a single integer value (1-9) as follows:
+   * use "%3f" for milliseconds, "%6f" for microseconds and "%9f" for nanoseconds.
+   *
+   * Any null string entry will result in a corresponding null row in the output column.
+   *
+   * This will return a column of type boolean where a `true` row indicates the corresponding
+   * input string can be parsed correctly with the given format.
+   *
+   * @param format String specifying the timestamp format in strings.
+   * @return New boolean ColumnVector.
+   */
+  public final ColumnVector isTimestamp(String format) {
+    return new ColumnVector(isTimestamp(getNativeView(), format));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // LISTS
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * For each list in this column pull out the entry at the given index. If the entry would
+   * go off the end of the list a NULL is returned instead.
+   * @param index 0 based offset into the list. Negative values go backwards from the end of the
+   *              list.
+   * @return a new column of the values at those indexes.
+   */
+  public final ColumnVector extractListElement(int index) {
+    assert type.equals(DType.LIST) : "A column of type LIST is required for .extractListElement()";
+    return new ColumnVector(extractListElement(getNativeView(), index));
+  }
+
+  /**
+   * For each list in this column pull out the entry at the corresponding index specified in
+   * the index column. If the entry goes off the end of the list a NULL is returned instead.
+   *
+   * The index column should have the same row count with the list column.
+   *
+   * @param indices a column of 0 based offsets into the list. Negative values go backwards from
+   *                the end of the list.
+   * @return a new column of the values at those indexes.
+   */
+  public final ColumnVector extractListElement(ColumnView indices) {
+    assert type.equals(DType.LIST) : "A column of type LIST is required for .extractListElement()";
+    assert indices != null && DType.INT32.equals(indices.type)
+        : "indices should be non-null and integer type";
+    assert indices.getRowCount() == rows
+        : "indices must have the same row count with list column";
+    return new ColumnVector(extractListElementV(getNativeView(), indices.getNativeView()));
+  }
+
+  /**
+   * Create a new LIST column by copying elements from the current LIST column ignoring duplicate,
+   * producing a LIST column in which each list contain only unique elements.
+   *
+   * Order of the output elements within each list are not guaranteed to be preserved as in the
+   * input.
+   *
+   * @return A new LIST column having unique list elements.
+   */
+  public final ColumnVector dropListDuplicates() {
+    return new ColumnVector(dropListDuplicates(getNativeView()));
+  }
+
+  /**
+   * Given a LIST column in which each element is a struct containing a <key, value> pair. An output
+   * LIST column is generated by copying elements of the current column in a way such that if a list
+   * contains multiple elements having the same key then only the last element will be copied.
+   *
+   * @return A new LIST column having list elements with unique keys.
+   */
+  public final ColumnVector dropListDuplicatesWithKeysValues() {
+    return new ColumnVector(dropListDuplicatesWithKeysValues(getNativeView()));
+  }
+
+  /**
+   * Flatten each list of lists into a single list.
+   *
+   * The column must have rows that are lists of lists.
+   * Any row containing null list elements will result in a null output row.
+   *
+   * @return A new column vector containing the flattened result
+   */
+  public ColumnVector flattenLists() {
+    return flattenLists(false);
+  }
+
+  /**
+   * Flatten each list of lists into a single list.
+   *
+   * The column must have rows that are lists of lists.
+   *
+   * @param ignoreNull Whether to ignore null list elements in the input column from the operation,
+   *                   or any row containing null list elements will result in a null output row
+   * @return A new column vector containing the flattened result
+   */
+  public ColumnVector flattenLists(boolean ignoreNull) {
+    return new ColumnVector(flattenLists(getNativeView(), ignoreNull));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // STRINGS
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Copy the current column to a new column, each string or list of the output column will have
+   * reverse order of characters or elements.
+   *
+   * @return A new column with lists or strings having reverse order.
+   */
+  public final ColumnVector reverseStringsOrLists() {
+    assert type.equals(DType.STRING) || type.equals(DType.LIST) :
+        "A column of type string or list is required, actual: " + type;
+    return new ColumnVector(reverseStringsOrLists(getNativeView()));
+  }
+
+  /**
+   * Convert a string to upper case.
+   */
+  public final ColumnVector upper() {
+    assert type.equals(DType.STRING) : "A column of type string is required for .upper() operation";
+    return new ColumnVector(upperStrings(getNativeView()));
+  }
+
+  /**
+   * Convert a string to lower case.
+   */
+  public final ColumnVector lower() {
+    assert type.equals(DType.STRING) : "A column of type string is required for .lower() operation";
+    return new ColumnVector(lowerStrings(getNativeView()));
+  }
+
+  /**
+   * Locates the starting index of the first instance of the given string in each row of a column.
+   * 0 indexing, returns -1 if the substring is not found. Overloading stringLocate to support
+   * default values for start (0) and end index.
+   * @param substring scalar containing the string to locate within each row.
+   */
+  public final ColumnVector stringLocate(Scalar substring) {
+    return stringLocate(substring, 0);
+  }
+
+  /**
+   * Locates the starting index of the first instance of the given string in each row of a column.
+   * 0 indexing, returns -1 if the substring is not found. Overloading stringLocate to support
+   * default value for end index (-1, the end of each string).
+   * @param substring scalar containing the string to locate within each row.
+   * @param start character index to start the search from (inclusive).
+   */
+  public final ColumnVector stringLocate(Scalar substring, int start) {
+    return stringLocate(substring, start, -1);
+  }
+
+  /**
+   * Locates the starting index of the first instance of the given string in each row of a column.
+   * 0 indexing, returns -1 if the substring is not found. Can be be configured to start or end
+   * the search mid string.
+   * @param substring scalar containing the string scalar to locate within each row.
+   * @param start character index to start the search from (inclusive).
+   * @param end character index to end the search on (exclusive).
+   */
+  public final ColumnVector stringLocate(Scalar substring, int start, int end) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert substring != null : "target string may not be null";
+    assert substring.getType().equals(DType.STRING) : "substring scalar must be a string scalar";
+    assert start >= 0 : "start index must be a positive value";
+    assert end >= start || end == -1 : "end index must be -1 or >= the start index";
+
+    return new ColumnVector(substringLocate(getNativeView(), substring.getScalarHandle(),
+        start, end));
+  }
+
+  /**
+   * Returns a list of columns by splitting each string using the specified pattern. The number of
+   * rows in the output columns will be the same as the input column. Null entries are added for a
+   * row where split results have been exhausted. Null input entries result in all nulls in the
+   * corresponding rows of the output columns.
+   *
+   * @param pattern UTF-8 encoded string identifying the split pattern for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   * @param splitByRegex a boolean flag indicating whether the input strings will be split by a
+   *                     regular expression pattern or just by a string literal delimiter.
+   * @return list of strings columns as a table.
+   */
+  @Deprecated
+  public final Table stringSplit(String pattern, int limit, boolean splitByRegex) {
+    if (splitByRegex) {
+      return stringSplit(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE), limit);
+    } else {
+      return stringSplit(pattern, limit);
+    }
+  }
+
+  /**
+   * Returns a list of columns by splitting each string using the specified regex program pattern.
+   * The number of rows in the output columns will be the same as the input column. Null entries
+   * are added for the rows where split results have been exhausted. Null input entries result in
+   * all nulls in the corresponding rows of the output columns.
+   *
+   * @param regexProg the regex program with UTF-8 encoded string identifying the split pattern
+   *                  for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   * @return list of strings columns as a table.
+   */
+  public final Table stringSplit(RegexProgram regexProg, int limit) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert regexProg != null : "regex program is null";
+    assert limit != 0 && limit != 1 : "split limit == 0 and limit == 1 are not supported";
+    return new Table(stringSplitRe(this.getNativeView(), regexProg.pattern(), regexProg.combinedFlags(),
+                                   regexProg.capture().nativeId, limit));
+  }
+
+  /**
+   * Returns a list of columns by splitting each string using the specified pattern. The number of
+   * rows in the output columns will be the same as the input column. Null entries are added for a
+   * row where split results have been exhausted. Null input entries result in all nulls in the
+   * corresponding rows of the output columns.
+   *
+   * @param pattern UTF-8 encoded string identifying the split pattern for each input string.
+   * @param splitByRegex a boolean flag indicating whether the input strings will be split by a
+   *                     regular expression pattern or just by a string literal delimiter.
+   * @return list of strings columns as a table.
+   */
+  @Deprecated
+  public final Table stringSplit(String pattern, boolean splitByRegex) {
+    return stringSplit(pattern, -1, splitByRegex);
+  }
+
+  /**
+   * Returns a list of columns by splitting each string using the specified string literal
+   * delimiter. The number of rows in the output columns will be the same as the input column.
+   * Null entries are added for a row where split results have been exhausted. Null input entries
+   * result in all nulls in the corresponding rows of the output columns.
+   *
+   * @param delimiter UTF-8 encoded string identifying the split delimiter for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   * @return list of strings columns as a table.
+   */
+  public final Table stringSplit(String delimiter, int limit) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert delimiter != null : "delimiter is null";
+    assert limit != 0 && limit != 1 : "split limit == 0 and limit == 1 are not supported";
+    return new Table(stringSplit(this.getNativeView(), delimiter, limit));
+  }
+
+  /**
+   * Returns a list of columns by splitting each string using the specified string literal
+   * delimiter. The number of rows in the output columns will be the same as the input column.
+   * Null entries are added for a row where split results have been exhausted. Null input entries
+   * result in all nulls in the corresponding rows of the output columns.
+   *
+   * @param delimiter UTF-8 encoded string identifying the split delimiter for each input string.
+   * @return list of strings columns as a table.
+   */
+  public final Table stringSplit(String delimiter) {
+    return stringSplit(delimiter, -1);
+  }
+
+  /**
+   * Returns a list of columns by splitting each string using the specified regex program pattern.
+   * The number of rows in the output columns will be the same as the input column. Null entries
+   * are added for the rows where split results have been exhausted. Null input entries result in
+   * all nulls in the corresponding rows of the output columns.
+   *
+   * @param regexProg the regex program with UTF-8 encoded string identifying the split pattern
+   *                  for each input string.
+   * @return list of strings columns as a table.
+   */
+  public final Table stringSplit(RegexProgram regexProg) {
+    return stringSplit(regexProg, -1);
+  }
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified pattern.
+   *
+   * @param pattern UTF-8 encoded string identifying the split pattern for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   * @param splitByRegex a boolean flag indicating whether the input strings will be split by a
+   *                     regular expression pattern or just by a string literal delimiter.
+   * @return a LIST column of string elements.
+   */
+  @Deprecated
+  public final ColumnVector stringSplitRecord(String pattern, int limit, boolean splitByRegex) {
+    if (splitByRegex) {
+      return stringSplitRecord(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE), limit);
+    } else {
+      return stringSplitRecord(pattern, limit);
+    }
+  }
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified regex program pattern.
+   *
+   * @param regexProg the regex program with UTF-8 encoded string identifying the split pattern
+   *                  for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   * @return a LIST column of string elements.
+   */
+  public final ColumnVector stringSplitRecord(RegexProgram regexProg, int limit) {
+    assert type.equals(DType.STRING) : "column type must be String";
+    assert regexProg != null : "regex program is null";
+    assert limit != 0 && limit != 1 : "split limit == 0 and limit == 1 are not supported";
+    return new ColumnVector(
+        stringSplitRecordRe(this.getNativeView(), regexProg.pattern(), regexProg.combinedFlags(),
+                            regexProg.capture().nativeId, limit));
+  }
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified pattern.
+   *
+   * @param pattern UTF-8 encoded string identifying the split pattern for each input string.
+   * @param splitByRegex a boolean flag indicating whether the input strings will be split by a
+   *                     regular expression pattern or just by a string literal delimiter.
+   * @return a LIST column of string elements.
+   */
+  @Deprecated
+  public final ColumnVector stringSplitRecord(String pattern, boolean splitByRegex) {
+    return stringSplitRecord(pattern, -1, splitByRegex);
+  }
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified string literal delimiter.
+   *
+   * @param delimiter UTF-8 encoded string identifying the split delimiter for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   * @return a LIST column of string elements.
+   */
+  public final ColumnVector stringSplitRecord(String delimiter, int limit) {
+    assert type.equals(DType.STRING) : "column type must be String";
+    assert delimiter != null : "delimiter is null";
+    assert limit != 0 && limit != 1 : "split limit == 0 and limit == 1 are not supported";
+    return new ColumnVector(stringSplitRecord(this.getNativeView(), delimiter, limit));
+  }
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified string literal delimiter.
+   *
+   * @param delimiter UTF-8 encoded string identifying the split delimiter for each input string.
+   * @return a LIST column of string elements.
+   */
+  public final ColumnVector stringSplitRecord(String delimiter) {
+    return stringSplitRecord(delimiter, -1);
+  }
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified regex program pattern.
+   *
+   * @param regexProg the regex program with UTF-8 encoded string identifying the split pattern
+   *                  for each input string.
+   * @return a LIST column of string elements.
+   */
+  public final ColumnVector stringSplitRecord(RegexProgram regexProg) {
+    return stringSplitRecord(regexProg, -1);
+  }
+
+  /**
+   * Returns a new strings column that contains substrings of the strings in the provided column.
+   * The character positions to retrieve in each string are `[start, <the string end>)`..
+   *
+   * @param start first character index to begin the substring(inclusive).
+   */
+  public final ColumnVector substring(int start) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    return new ColumnVector(substringS(getNativeView(), start));
+  }
+
+  /**
+   * Returns a new strings column that contains substrings of the strings in the provided column.
+   * 0-based indexing, If the stop position is past end of a string's length, then end of string is
+   * used as stop position for that string.
+   * @param start first character index to begin the substring(inclusive).
+   * @param end   last character index to stop the substring(exclusive)
+   * @return A new java column vector containing the substrings.
+   */
+  public final ColumnVector substring(int start, int end) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    return new ColumnVector(substring(getNativeView(), start, end));
+  }
+
+  /**
+   * Returns a new strings column that contains substrings of the strings in the provided column
+   * which uses unique ranges for each string
+   * @param start Vector containing start indices of each string
+   * @param end   Vector containing end indices of each string. -1 indicated to read until end of string.
+   * @return A new java column vector containing the substrings/
+   */
+  public final ColumnVector substring(ColumnView start, ColumnView end) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert (rows == start.getRowCount() && rows == end.getRowCount()) : "Number of rows must be equal";
+    assert (start.getType().equals(DType.INT32) && end.getType().equals(DType.INT32)) : "start and end " +
+        "vectors must be of integer type";
+    return new ColumnVector(substringColumn(getNativeView(), start.getNativeView(), end.getNativeView()));
+  }
+
+  /**
+   * Given a lists column of strings (each row is a list of strings), concatenates the strings
+   * within each row and returns a single strings column result. Each new string is created by
+   * concatenating the strings from the same row (same list element) delimited by the separator
+   * provided. This version of the function relaces nulls with empty string and returns null
+   * for empty list.
+   * @param sepCol strings column that provides separators for concatenation.
+   * @return A new java column vector containing the concatenated strings with separator between.
+   */
+  public final ColumnVector stringConcatenateListElements(ColumnView sepCol) {
+    try (Scalar nullString = Scalar.fromString(null);
+         Scalar emptyString = Scalar.fromString("")) {
+      return stringConcatenateListElements(sepCol, nullString, emptyString,
+          false, false);
+    }
+  }
+
+  /**
+   * Given a lists column of strings (each row is a list of strings), concatenates the strings
+   * within each row and returns a single strings column result.
+   * Each new string is created by concatenating the strings from the same row (same list element)
+   * delimited by the row separator provided in the sepCol strings column.
+   * @param sepCol strings column that provides separators for concatenation.
+   * @param separatorNarep string scalar indicating null behavior when a separator is null.
+   *                        If set to null and the separator is null the resulting string will
+   *                        be null. If not null, this string will be used in place of a null
+   *                        separator.
+   * @param stringNarep string that should be used to replace null strings in any non-null list
+   *                     row. If set to null and the string is null the resulting string will
+   *                     be null. If not null, this string will be used in place of a null value.
+   * @param separateNulls if true, then the separator is included for null rows if
+   *                       `stringNarep` is valid.
+   * @param emptyStringOutputIfEmptyList if set to true, any input row that is an empty list
+   *                          will result in an empty string. Otherwise, it will result in a null.
+   * @return A new java column vector containing the concatenated strings with separator between.
+   */
+  public final ColumnVector stringConcatenateListElements(ColumnView sepCol,
+      Scalar separatorNarep, Scalar stringNarep, boolean separateNulls,
+      boolean emptyStringOutputIfEmptyList) {
+    assert type.equals(DType.LIST) : "column type must be a list";
+    assert separatorNarep != null : "separator narep scalar provided may not be null";
+    assert stringNarep != null : "string narep scalar provided may not be null";
+    assert separatorNarep.getType().equals(DType.STRING) : "separator naprep scalar must be a string scalar";
+    assert stringNarep.getType().equals(DType.STRING) : "string narep scalar must be a string scalar";
+
+    return new ColumnVector(stringConcatenationListElementsSepCol(getNativeView(),
+      sepCol.getNativeView(), separatorNarep.getScalarHandle(), stringNarep.getScalarHandle(),
+      separateNulls, emptyStringOutputIfEmptyList));
+  }
+
+  /**
+   * Given a lists column of strings (each row is a list of strings), concatenates the strings
+   * within each row and returns a single strings column result. Each new string is created by
+   * concatenating the strings from the same row (same list element) delimited by the
+   * separator provided.
+   * @param separator string scalar inserted between each string being merged.
+   * @param narep string scalar indicating null behavior. If set to null and any string in the row
+   *              is null the resulting string will be null. If not null, null values in any
+   *              column will be replaced by the specified string. The underlying value in the
+   *              string scalar may be null, but the object passed in may not.
+   * @param separateNulls if true, then the separator is included for null rows if
+   *                       `narep` is valid.
+   * @param emptyStringOutputIfEmptyList if set to true, any input row that is an empty list
+   *                          will result in an empty string. Otherwise, it will result in a null.
+   * @return A new java column vector containing the concatenated strings with separator between.
+   */
+  public final ColumnVector stringConcatenateListElements(Scalar separator,
+      Scalar narep, boolean separateNulls, boolean emptyStringOutputIfEmptyList) {
+    assert type.equals(DType.LIST) : "column type must be a list";
+    assert separator != null : "separator scalar provided may not be null";
+    assert narep != null : "column narep scalar provided may not be null";
+    assert narep.getType().equals(DType.STRING) : "narep scalar must be a string scalar";
+
+    return new ColumnVector(stringConcatenationListElements(getNativeView(),
+        separator.getScalarHandle(), narep.getScalarHandle(), separateNulls,
+        emptyStringOutputIfEmptyList));
+  }
+
+  /**
+   * Given a strings column, each string in it is repeated a number of times specified by the
+   * <code>repeatTimes</code> parameter.
+   *
+   * In special cases:
+   *  - If <code>repeatTimes</code> is not a positive number, a non-null input string will always
+   *    result in an empty output string.
+   *  - A null input string will always result in a null output string regardless of the value of
+   *    the <code>repeatTimes</code> parameter.
+   *
+   * @param repeatTimes The number of times each input string is repeated.
+   * @return A new java column vector containing repeated strings.
+   */
+  public final ColumnVector repeatStrings(int repeatTimes) {
+    assert type.equals(DType.STRING) : "column type must be String";
+    return new ColumnVector(repeatStrings(getNativeView(), repeatTimes));
+  }
+
+  /**
+   * Given a strings column, an output strings column is generated by repeating each of the input
+   * string by a number of times given by the corresponding row in a <code>repeatTimes</code>
+   * numeric column.
+   *
+   * In special cases:
+   *  - Any null row (from either the input strings column or the <code>repeatTimes</code> column)
+   *    will always result in a null output string.
+   *  - If any value in the <code>repeatTimes</code> column is not a positive number and its
+   *    corresponding input string is not null, the output string will be an empty string.
+   *
+   * @param repeatTimes The column containing numbers of times each input string is repeated.
+   * @return A new java column vector containing repeated strings.
+   */
+  public final ColumnVector repeatStrings(ColumnView repeatTimes) {
+    assert type.equals(DType.STRING) : "column type must be String";
+    return new ColumnVector(repeatStringsWithColumnRepeatTimes(getNativeView(),
+            repeatTimes.getNativeView()));
+  }
+
+   /**
+   * Apply a JSONPath string to all rows in an input strings column.
+   *
+   * Applies a JSONPath string to an incoming strings column where each row in the column
+   * is a valid json string.  The output is returned by row as a strings column.
+   *
+   * For reference, https://tools.ietf.org/id/draft-goessner-dispatch-jsonpath-00.html
+   * Note: Only implements the operators: $ . [] *
+   *
+   * @param path The JSONPath string to be applied to each row
+   * @return new strings ColumnVector containing the retrieved json object strings
+   */
+  public final ColumnVector getJSONObject(Scalar path) {
+    assert(type.equals(DType.STRING)) : "column type must be a String";
+    return new ColumnVector(getJSONObject(getNativeView(), path.getScalarHandle()));
+  }
+
+  /**
+   * Returns a new strings column where target string within each string is replaced with the specified
+   * replacement string.
+   * The replacement proceeds from the beginning of the string to the end, for example,
+   * replacing "aa" with "b" in the string "aaa" will result in "ba" rather than "ab".
+   * Specifying an empty string for replace will essentially remove the target string if found in each string.
+   * Null string entries will return null output string entries.
+   * target Scalar should be string and should not be empty or null.
+   *
+   * @param target String to search for within each string.
+   * @param replace Replacement string if target is found.
+   * @return A new java column vector containing replaced strings
+   */
+  public final ColumnVector stringReplace(Scalar target, Scalar replace) {
+
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert target != null : "target string may not be null";
+    assert target.getType().equals(DType.STRING) : "target string must be a string scalar";
+    assert target.getJavaString().isEmpty() == false : "target scalar may not be empty";
+
+    return new ColumnVector(stringReplace(getNativeView(), target.getScalarHandle(),
+        replace.getScalarHandle()));
+  }
+
+  /**
+   * Returns a new strings column where target strings with each string are replaced with
+   * corresponding replacement strings. For each string in the column, the list of targets
+   * is searched within that string. If a target string is found, it is replaced by the
+   * corresponding entry in the repls column. All occurrences found in each string are replaced.
+   * The repls argument can optionally contain a single string. In this case, all matching
+   * target substrings will be replaced by that single string.
+   *
+   * Example:
+   * cv = ["hello", "goodbye"]
+   * targets = ["e","o"]
+   * repls = ["EE","OO"]
+   * r1 = cv.stringReplace(targets, repls)
+   * r1 is now ["hEEllO", "gOOOOdbyEE"]
+   *
+   * targets = ["e", "o"]
+   * repls = ["_"]
+   * r2 = cv.stringReplace(targets, repls)
+   * r2 is now ["h_ll_", "g__dby_"]
+   *
+   * @param targets Strings to search for in each string.
+   * @param repls Corresponding replacement strings for target strings.
+   * @return A new java column vector containing the replaced strings.
+   */
+  public final ColumnVector stringReplace(ColumnView targets, ColumnView repls) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert targets != null : "target list may not be null";
+    assert targets.getType().equals(DType.STRING) : "target list must be a string column";
+    assert repls != null : "replacement list may not be null";
+    assert repls.getType().equals(DType.STRING) : "replacement list must be a string column";
+
+    return new ColumnVector(stringReplaceMulti(getNativeView(), targets.getNativeView(),
+        repls.getNativeView()));
+  }
+
+  /**
+   * For each string, replaces any character sequence matching the given pattern using the
+   * replacement string scalar.
+   *
+   * @param pattern The regular expression pattern to search within each string.
+   * @param repl The string scalar to replace for each pattern match.
+   * @return A new column vector containing the string results.
+   */
+  @Deprecated
+  public final ColumnVector replaceRegex(String pattern, Scalar repl) {
+    return replaceRegex(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE), repl);
+  }
+
+  /**
+   * For each string, replaces any character sequence matching the given regex program pattern
+   * using the replacement string scalar.
+   *
+   * @param regexProg The regex program with pattern to search within each string.
+   * @param repl The string scalar to replace for each pattern match.
+   * @return A new column vector containing the string results.
+   */
+  public final ColumnVector replaceRegex(RegexProgram regexProg, Scalar repl) {
+    return replaceRegex(regexProg, repl, -1);
+  }
+
+  /**
+   * For each string, replaces any character sequence matching the given pattern using the
+   * replacement string scalar.
+   *
+   * @param pattern The regular expression pattern to search within each string.
+   * @param repl The string scalar to replace for each pattern match.
+   * @param maxRepl The maximum number of times a replacement should occur within each string.
+   * @return A new column vector containing the string results.
+   */
+  @Deprecated
+  public final ColumnVector replaceRegex(String pattern, Scalar repl, int maxRepl) {
+    return replaceRegex(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE), repl, maxRepl);
+  }
+
+  /**
+   * For each string, replaces any character sequence matching the given regex program pattern
+   * using the replacement string scalar.
+   *
+   * @param regexProg The regex program with pattern to search within each string.
+   * @param repl The string scalar to replace for each pattern match.
+   * @param maxRepl The maximum number of times a replacement should occur within each string.
+   * @return A new column vector containing the string results.
+   */
+  public final ColumnVector replaceRegex(RegexProgram regexProg, Scalar repl, int maxRepl) {
+    if (!repl.getType().equals(DType.STRING)) {
+      throw new IllegalArgumentException("Replacement must be a string scalar");
+    }
+    assert regexProg != null : "regex program may not be null";
+    return new ColumnVector(replaceRegex(getNativeView(), regexProg.pattern(), regexProg.combinedFlags(),
+                                         regexProg.capture().nativeId, repl.getScalarHandle(), maxRepl));
+  }
+
+  /**
+   * For each string, replaces any character sequence matching any of the regular expression
+   * patterns with the corresponding replacement strings.
+   *
+   * @param patterns The regular expression patterns to search within each string.
+   * @param repls The string scalars to replace for each corresponding pattern match.
+   * @return A new column vector containing the string results.
+   */
+  public final ColumnVector replaceMultiRegex(String[] patterns, ColumnView repls) {
+    return new ColumnVector(replaceMultiRegex(getNativeView(), patterns,
+        repls.getNativeView()));
+  }
+
+  /**
+   * For each string, replaces any character sequence matching the given pattern
+   * using the replace template for back-references.
+   *
+   * Any null string entries return corresponding null output column entries.
+   *
+   * @param pattern The regular expression patterns to search within each string.
+   * @param replace The replacement template for creating the output string.
+   * @return A new java column vector containing the string results.
+   */
+  @Deprecated
+  public final ColumnVector stringReplaceWithBackrefs(String pattern, String replace) {
+    return stringReplaceWithBackrefs(new RegexProgram(pattern), replace);
+  }
+
+  /**
+   * For each string, replaces any character sequence matching the given regex program
+   * pattern using the replace template for back-references.
+   *
+   * Any null string entries return corresponding null output column entries.
+   *
+   * @param regexProg The regex program with pattern to search within each string.
+   * @param replace The replacement template for creating the output string.
+   * @return A new java column vector containing the string results.
+   */
+  public final ColumnVector stringReplaceWithBackrefs(RegexProgram regexProg, String replace) {
+    assert regexProg != null : "regex program may not be null";
+    return new ColumnVector(
+        stringReplaceWithBackrefs(getNativeView(), regexProg.pattern(), regexProg.combinedFlags(),
+                                  regexProg.capture().nativeId, replace));
+  }
+
+  /**
+   * Add '0' as padding to the left of each string.
+   *
+   * If the string is already width or more characters, no padding is performed.
+   * No strings are truncated.
+   *
+   * Null string entries result in null entries in the output column.
+   *
+   * @param width The minimum number of characters for each string.
+   * @return New column of strings.
+   */
+  public final ColumnVector zfill(int width) {
+    return new ColumnVector(zfill(getNativeView(), width));
+  }
+
+  /**
+   * Pad the Strings column until it reaches the desired length with spaces " " on the right.
+   *
+   * If the string is already width or more characters, no padding is performed.
+   * No strings are truncated.
+   *
+   * Null string entries result in null entries in the output column.
+   *
+   * @param width the minimum number of characters for each string.
+   * @return the new strings column.
+   */
+  public final ColumnVector pad(int width) {
+    return pad(width, PadSide.RIGHT, " ");
+  }
+
+  /**
+   * Pad the Strings column until it reaches the desired length with spaces " ".
+   *
+   * If the string is already width or more characters, no padding is performed.
+   * No strings are truncated.
+   *
+   * Null string entries result in null entries in the output column.
+   *
+   * @param width the minimum number of characters for each string.
+   * @param side where to add new characters.
+   * @return the new strings column.
+   */
+  public final ColumnVector pad(int width, PadSide side) {
+    return pad(width, side, " ");
+  }
+
+  /**
+   * Pad the Strings column until it reaches the desired length.
+   *
+   * If the string is already width or more characters, no padding is performed.
+   * No strings are truncated.
+   *
+   * Null string entries result in null entries in the output column.
+   *
+   * @param width the minimum number of characters for each string.
+   * @param side where to add new characters.
+   * @param fillChar a single character string that holds what should be added.
+   * @return the new strings column.
+   */
+  public final ColumnVector pad(int width, PadSide side, String fillChar) {
+    assert fillChar != null;
+    assert fillChar.length() == 1;
+    return new ColumnVector(pad(getNativeView(), width, side.getNativeId(), fillChar));
+  }
+
+  /**
+   * Checks if each string in a column starts with a specified comparison string, resulting in a
+   * parallel column of the boolean results.
+   * @param pattern scalar containing the string being searched for at the beginning of the column's strings.
+   * @return A new java column vector containing the boolean results.
+   */
+  public final ColumnVector startsWith(Scalar pattern) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert pattern != null : "pattern scalar may not be null";
+    assert pattern.getType().equals(DType.STRING) : "pattern scalar must be a string scalar";
+    return new ColumnVector(stringStartWith(getNativeView(), pattern.getScalarHandle()));
+  }
+
+  /**
+   * Checks if each string in a column ends with a specified comparison string, resulting in a
+   * parallel column of the boolean results.
+   * @param pattern scalar containing the string being searched for at the end of the column's strings.
+   * @return A new java column vector containing the boolean results.
+   */
+  public final ColumnVector endsWith(Scalar pattern) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert pattern != null : "pattern scalar may not be null";
+    assert pattern.getType().equals(DType.STRING) : "pattern scalar must be a string scalar";
+    return new ColumnVector(stringEndWith(getNativeView(), pattern.getScalarHandle()));
+  }
+
+  /**
+   * Removes whitespace from the beginning and end of a string.
+   * @return A new java column vector containing the stripped strings.
+   */
+  public final ColumnVector strip() {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    try (Scalar emptyString = Scalar.fromString("")) {
+      return new ColumnVector(stringStrip(getNativeView(), StripType.BOTH.nativeId,
+          emptyString.getScalarHandle()));
+    }
+  }
+
+  /**
+   * Removes the specified characters from the beginning and end of each string.
+   * @param toStrip UTF-8 encoded characters to strip from each string.
+   * @return A new java column vector containing the stripped strings.
+   */
+  public final ColumnVector strip(Scalar toStrip) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert toStrip != null : "toStrip scalar may not be null";
+    assert toStrip.getType().equals(DType.STRING) : "toStrip must be a string scalar";
+    return new ColumnVector(stringStrip(getNativeView(), StripType.BOTH.nativeId, toStrip.getScalarHandle()));
+  }
+
+  /**
+   * Removes whitespace from the beginning of a string.
+   * @return A new java column vector containing the stripped strings.
+   */
+  public final ColumnVector lstrip() {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    try (Scalar emptyString = Scalar.fromString("")) {
+      return new ColumnVector(stringStrip(getNativeView(), StripType.LEFT.nativeId,
+          emptyString.getScalarHandle()));
+    }
+  }
+
+  /**
+   * Removes the specified characters from the beginning of each string.
+   * @param toStrip UTF-8 encoded characters to strip from each string.
+   * @return A new java column vector containing the stripped strings.
+   */
+  public final ColumnVector lstrip(Scalar toStrip) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert toStrip != null : "toStrip  Scalar may not be null";
+    assert toStrip.getType().equals(DType.STRING) : "toStrip must be a string scalar";
+    return new ColumnVector(stringStrip(getNativeView(), StripType.LEFT.nativeId, toStrip.getScalarHandle()));
+  }
+
+  /**
+   * Removes whitespace from the end of a string.
+   * @return A new java column vector containing the stripped strings.
+   */
+  public final ColumnVector rstrip() {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    try (Scalar emptyString = Scalar.fromString("")) {
+      return new ColumnVector(stringStrip(getNativeView(), StripType.RIGHT.nativeId,
+          emptyString.getScalarHandle()));
+    }
+  }
+
+  /**
+   * Removes the specified characters from the end of each string.
+   * @param toStrip UTF-8 encoded characters to strip from each string.
+   * @return A new java column vector containing the stripped strings.
+   */
+  public final ColumnVector rstrip(Scalar toStrip) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert toStrip != null : "toStrip  Scalar may not be null";
+    assert toStrip.getType().equals(DType.STRING) : "toStrip must be a string scalar";
+    return new ColumnVector(stringStrip(getNativeView(), StripType.RIGHT.nativeId, toStrip.getScalarHandle()));
+  }
+
+  /**
+   * Checks if each string in a column contains a specified comparison string, resulting in a
+   * parallel column of the boolean results.
+   * @param compString scalar containing the string being searched for.
+   * @return A new java column vector containing the boolean results.
+   */
+
+  public final ColumnVector stringContains(Scalar compString) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert compString != null : "compString scalar may not be null";
+    assert compString.getType().equals(DType.STRING) : "compString scalar must be a string scalar";
+    return new ColumnVector(stringContains(getNativeView(), compString.getScalarHandle()));
+  }
+
+  /**
+   * Replaces values less than `lo` in `input` with `lo`,
+   * and values greater than `hi` with `hi`.
+   *
+   * if `lo` is invalid, then lo will not be considered while
+   * evaluating the input (Essentially considered minimum value of that type).
+   * if `hi` is invalid, then hi will not be considered while
+   * evaluating the input (Essentially considered maximum value of that type).
+   *
+   * ```
+   * Example:
+   * input: {1, 2, 3, NULL, 5, 6, 7}
+   *
+   * valid lo and hi
+   * lo: 3, hi: 5, lo_replace : 0, hi_replace : 16
+   * output:{0, 0, 3, NULL, 5, 16, 16}
+   *
+   * invalid lo
+   * lo: NULL, hi: 5, lo_replace : 0, hi_replace : 16
+   * output:{1, 2, 3, NULL, 5, 16, 16}
+   *
+   * invalid hi
+   * lo: 3, hi: NULL, lo_replace : 0, hi_replace : 16
+   * output:{0, 0, 3, NULL, 5, 6, 7}
+   * ```
+   * @param lo - Minimum clamp value. All elements less than `lo` will be replaced by `lo`.
+   *           Ignored if null.
+   * @param hi - Maximum clamp value. All elements greater than `hi` will be replaced by `hi`.
+   *           Ignored if null.
+   * @return Returns a new clamped column as per `lo` and `hi` boundaries
+   */
+  public final ColumnVector clamp(Scalar lo, Scalar hi) {
+    return new ColumnVector(clamper(this.getNativeView(), lo.getScalarHandle(),
+        lo.getScalarHandle(), hi.getScalarHandle(), hi.getScalarHandle()));
+  }
+
+  /**
+   * Replaces values less than `lo` in `input` with `lo_replace`,
+   * and values greater than `hi` with `hi_replace`.
+   *
+   * if `lo` is invalid, then lo will not be considered while
+   * evaluating the input (Essentially considered minimum value of that type).
+   * if `hi` is invalid, then hi will not be considered while
+   * evaluating the input (Essentially considered maximum value of that type).
+   *
+   * @note: If `lo` is valid then `lo_replace` should be valid
+   *        If `hi` is valid then `hi_replace` should be valid
+   *
+   * ```
+   * Example:
+   *    input: {1, 2, 3, NULL, 5, 6, 7}
+   *
+   *    valid lo and hi
+   *    lo: 3, hi: 5, lo_replace : 0, hi_replace : 16
+   *    output:{0, 0, 3, NULL, 5, 16, 16}
+   *
+   *    invalid lo
+   *    lo: NULL, hi: 5, lo_replace : 0, hi_replace : 16
+   *    output:{1, 2, 3, NULL, 5, 16, 16}
+   *
+   *    invalid hi
+   *    lo: 3, hi: NULL, lo_replace : 0, hi_replace : 16
+   *    output:{0, 0, 3, NULL, 5, 6, 7}
+   * ```
+   *
+   * @param lo - Minimum clamp value. All elements less than `lo` will be replaced by `loReplace`. Ignored if null.
+   * @param loReplace - All elements less than `lo` will be replaced by `loReplace`.
+   * @param hi - Maximum clamp value. All elements greater than `hi` will be replaced by `hiReplace`. Ignored if null.
+   * @param hiReplace - All elements greater than `hi` will be replaced by `hiReplace`.
+   * @return - a new clamped column as per `lo` and `hi` boundaries
+   */
+  public final ColumnVector clamp(Scalar lo, Scalar loReplace, Scalar hi, Scalar hiReplace) {
+    return new ColumnVector(clamper(this.getNativeView(), lo.getScalarHandle(),
+        loReplace.getScalarHandle(), hi.getScalarHandle(), hiReplace.getScalarHandle()));
+  }
+
+  /**
+   * Returns a boolean ColumnVector identifying rows which
+   * match the given regex pattern but only at the beginning of the string.
+   *
+   * ```
+   * cv = ["abc", "123", "def456"]
+   * result = cv.matchesRe("\\d+")
+   * r is now [false, true, false]
+   * ```
+   * Any null string entries return corresponding null output column entries.
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   *
+   * @param pattern Regex pattern to match to each string.
+   * @return New ColumnVector of boolean results for each string.
+   */
+  @Deprecated
+  public final ColumnVector matchesRe(String pattern) {
+    return matchesRe(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE));
+  }
+
+  /**
+   * Returns a boolean ColumnVector identifying rows which
+   * match the given regex program pattern but only at the beginning of the string.
+   *
+   * ```
+   * cv = ["abc", "123", "def456"]
+   * p = new RegexProgram("\\d+", CaptureGroups.NON_CAPTURE)
+   * r = cv.matchesRe(p)
+   * r is now [false, true, false]
+   * ```
+   * Any null string entries return corresponding null output column entries.
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   *
+   * @param regexProg Regex program to match to each string.
+   * @return New ColumnVector of boolean results for each string.
+   */
+  public final ColumnVector matchesRe(RegexProgram regexProg) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert regexProg != null : "regex program may not be null";
+    assert !regexProg.pattern().isEmpty() : "pattern string may not be empty";
+    return new ColumnVector(matchesRe(getNativeView(), regexProg.pattern(),
+                                      regexProg.combinedFlags(), regexProg.capture().nativeId));
+  }
+
+  /**
+   * Returns a boolean ColumnVector identifying rows which
+   * match the given regex pattern starting at any location.
+   *
+   * ```
+   * cv = ["abc", "123", "def456"]
+   * r = cv.containsRe("\\d+")
+   * r is now [false, true, true]
+   * ```
+   * Any null string entries return corresponding null output column entries.
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   *
+   * @param pattern Regex pattern to match to each string.
+   * @return New ColumnVector of boolean results for each string.
+   */
+  @Deprecated
+  public final ColumnVector containsRe(String pattern) {
+    return containsRe(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE));
+  }
+
+  /**
+   * Returns a boolean ColumnVector identifying rows which
+   * match the given RegexProgram pattern starting at any location.
+   *
+   * ```
+   * cv = ["abc", "123", "def456"]
+   * p = new RegexProgram("\\d+", CaptureGroups.NON_CAPTURE)
+   * r = cv.containsRe(p)
+   * r is now [false, true, true]
+   * ```
+   * Any null string entries return corresponding null output column entries.
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   *
+   * @param regexProg Regex program to match to each string.
+   * @return New ColumnVector of boolean results for each string.
+   */
+  public final ColumnVector containsRe(RegexProgram regexProg) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert regexProg != null : "regex program may not be null";
+    assert !regexProg.pattern().isEmpty() : "pattern string may not be empty";
+    return new ColumnVector(containsRe(getNativeView(), regexProg.pattern(),
+                                       regexProg.combinedFlags(), regexProg.capture().nativeId));
+  }
+
+  /**
+   * For each captured group specified in the given regular expression
+   * return a column in the table. Null entries are added if the string
+   * does not match. Any null inputs also result in null output entries.
+   *
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   * @param pattern the pattern to use
+   * @return the table of extracted matches
+   * @throws CudfException if any error happens including if the RE does
+   * not contain any capture groups.
+   */
+  @Deprecated
+  public final Table extractRe(String pattern) throws CudfException {
+    return extractRe(new RegexProgram(pattern));
+  }
+
+  /**
+   * For each captured group specified in the given regex program
+   * return a column in the table. Null entries are added if the string
+   * does not match. Any null inputs also result in null output entries.
+   *
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   * @param regexProg the regex program to use
+   * @return the table of extracted matches
+   * @throws CudfException if any error happens including if the regex
+   * program does not contain any capture groups.
+   */
+  public final Table extractRe(RegexProgram regexProg) throws CudfException {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert regexProg != null : "regex program may not be null";
+    return new Table(extractRe(this.getNativeView(), regexProg.pattern(),
+                               regexProg.combinedFlags(), regexProg.capture().nativeId));
+  }
+
+  /**
+   * Extracts all strings that match the given regular expression and corresponds to the
+   * regular expression group index. Any null inputs also result in null output entries.
+   *
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   * @param pattern The regex pattern
+   * @param idx The regex group index
+   * @return A new column vector of extracted matches
+   */
+  @Deprecated
+  public final ColumnVector extractAllRecord(String pattern, int idx) {
+    if (idx == 0) {
+      return extractAllRecord(new RegexProgram(pattern, CaptureGroups.NON_CAPTURE), idx);
+    }
+    return extractAllRecord(new RegexProgram(pattern), idx);
+  }
+
+  /**
+   * Extracts all strings that match the given regex program pattern and corresponds to the
+   * regular expression group index. Any null inputs also result in null output entries.
+   *
+   * For supported regex patterns refer to:
+   * @link https://docs.rapids.ai/api/libcudf/nightly/md_regex.html
+   * @param regexProg The regex program
+   * @param idx The regex group index
+   * @return A new column vector of extracted matches
+   */
+  public final ColumnVector extractAllRecord(RegexProgram regexProg, int idx) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert idx >= 0 : "group index must be at least 0";
+    assert regexProg != null : "regex program may not be null";
+    return new ColumnVector(
+        extractAllRecord(this.getNativeView(), regexProg.pattern(), regexProg.combinedFlags(),
+                         regexProg.capture().nativeId, idx));
+  }
+
+  /**
+   * Returns a boolean ColumnVector identifying rows which
+   * match the given like pattern.
+   *
+   * The like pattern expects only 2 wildcard special characters
+   * - `%` any number of any character (including no characters)
+   * - `_` any single character
+   *
+   * ```
+   * cv = ["azaa", "ababaabba", "aaxa"]
+   * r = cv.like("%a_aa%", "\\")
+   * r is now [true, true, false]
+   * r = cv.like("a__a", "\\")
+   * r is now [true, false, true]
+   * ```
+   *
+   * The escape character is specified to include either `%` or `_` in the search,
+   * which is expected to be either 0 or 1 character.
+   * If more than one character is specified, only the first character is used.
+   *
+   * ```
+   * cv = ["abc_def", "abc1def", "abc_"]
+   * r = cv.like("abc/_d%", "/")
+   * r is now [true, false, false]
+   * ```
+   * Any null string entries return corresponding null output column entries.
+   *
+   * @param pattern Like pattern to match to each string.
+   * @param escapeChar Character specifies the escape prefix; default is "\\".
+   * @return New ColumnVector of boolean results for each string.
+   */
+  public final ColumnVector like(Scalar pattern, Scalar escapeChar) {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    assert pattern != null : "pattern scalar must not be null";
+    assert pattern.getType().equals(DType.STRING) : "pattern scalar must be a string scalar";
+    assert escapeChar != null : "escapeChar scalar must not be null";
+    assert escapeChar.getType().equals(DType.STRING) : "escapeChar scalar must be a string scalar";
+    return new ColumnVector(like(getNativeView(), pattern.getScalarHandle(), escapeChar.getScalarHandle()));
+  }
+
+
+  /**
+   * Converts all character sequences starting with '%' into character code-points
+   * interpreting the 2 following characters as hex values to create the code-point.
+   * For example, the sequence '%20' is converted into byte (0x20) which is a single
+   * space character. Another example converts '%C3%A9' into 2 sequential bytes
+   * (0xc3 and 0xa9 respectively) which is the é character. Overall, 3 characters
+   * are converted into one char byte whenever a '%%' (single percent) character
+   * is encountered in the string.
+   * <p>
+   * Any null entries will result in corresponding null entries in the output column.
+   *
+   * @return a new column instance containing the decoded strings
+   */
+  public final ColumnVector urlDecode() throws CudfException {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    return new ColumnVector(urlDecode(getNativeView()));
+  }
+
+  /**
+   * Converts mostly non-ascii characters and control characters into UTF-8 hex code-points
+   * prefixed with '%'. For example, the space character must be converted to characters '%20' where
+   * the '20' indicates the hex value for space in UTF-8. Likewise, multi-byte characters are
+   * converted to multiple hex characters. For example, the é character is converted to characters
+   * '%C3%A9' where 'C3A9' is the UTF-8 bytes 0xC3A9 for this character.
+   * <p>
+   * Any null entries will result in corresponding null entries in the output column.
+   *
+   * @return a new column instance containing the encoded strings
+   */
+  public final ColumnVector urlEncode() throws CudfException {
+    assert type.equals(DType.STRING) : "column type must be a String";
+    return new ColumnVector(urlEncode(getNativeView()));
+  }
+
+  private static void assertIsSupportedMapKeyType(DType keyType) {
+    boolean isSupportedKeyType =
+      !keyType.equals(DType.EMPTY) && !keyType.equals(DType.LIST) && !keyType.equals(DType.STRUCT);
+    assert isSupportedKeyType : "Map lookup by STRUCT and LIST keys is not supported.";
+  }
+
+  /**
+   * Given a column of type List<Struct<X, Y>> and a key column of type X, return a column of type Y,
+   * where each row in the output column is the Y value corresponding to the X key.
+   * If the key is not found, the corresponding output value is null.
+   * @param keys the column view with keys to lookup in the column
+   * @return a column of values or nulls based on the lookup result
+   */
+  public final ColumnVector getMapValue(ColumnView keys) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    assert keys != null : "Lookup key may not be null";
+    return new ColumnVector(mapLookupForKeys(getNativeView(), keys.getNativeView()));
+  }
+
+  /**
+   * Given a column of type List<Struct<X, Y>> and a key of type X, return a column of type Y,
+   * where each row in the output column is the Y value corresponding to the X key.
+   * If the key is not found, the corresponding output value is null.
+   * @param key the scalar key to lookup in the column
+   * @return a column of values or nulls based on the lookup result
+   */
+  public final ColumnVector getMapValue(Scalar key) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    assert key != null : "Lookup key may not be null";
+    assertIsSupportedMapKeyType(key.getType());
+    return new ColumnVector(mapLookup(getNativeView(), key.getScalarHandle()));
+  }
+
+  /** For a column of type List<Struct<String, String>> and a passed in String key, return a boolean
+   * column for all keys in the structs, It is true if the key exists in the corresponding map for
+   * that row, false otherwise. It will never return null for a row.
+   * @param key the String scalar to lookup in the column
+   * @return a boolean column based on the lookup result
+   */
+  public final ColumnVector getMapKeyExistence(Scalar key) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    assert key != null : "Lookup key may not be null";
+    assertIsSupportedMapKeyType(key.getType());
+    return new ColumnVector(mapContains(getNativeView(), key.getScalarHandle()));
+  }
+
+  /** For a column of type List<Struct<_, _>> and a passed in key column, return a boolean
+   * column for all keys in the map. Each output row is true if the key exists in the corresponding map for
+   * that row, false otherwise. It will never return null for a row.
+   * @param keys the keys to lookup in the column
+   * @return a boolean column based on the lookup result
+   */
+  public final ColumnVector getMapKeyExistence(ColumnView keys) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    assert keys != null : "Lookup key may not be null";
+    assertIsSupportedMapKeyType(keys.getType());
+    return new ColumnVector(mapContainsKeys(getNativeView(), keys.getNativeView()));
+  }
+
+  /**
+   * Create a new struct column view of existing column views. Note that this will NOT copy
+   * the contents of the input columns to make a new vector, but makes a view that must not
+   * outlive the child views that it references. The resulting column cannot be null.
+   * @param rows the number of rows in the struct column. This is needed if no columns
+   *             are provided.
+   * @param columns the columns to add to the struct in the order they should be added
+   * @return the new column view. It is the responsibility of the caller to close this.
+   */
+  public static ColumnView makeStructView(long rows, ColumnView... columns) {
+    long[] handles = new long[columns.length];
+    for (int i = 0; i < columns.length; i++) {
+      ColumnView cv = columns[i];
+      if (rows != cv.getRowCount()) {
+        throw new IllegalArgumentException("All columns must have the same number of rows");
+      }
+      handles[i] = cv.getNativeView();
+    }
+    return new ColumnView(makeStructView(handles, rows));
+  }
+
+  /**
+   * Create a new struct column view of existing column views. Note that this will NOT copy
+   * the contents of the input columns to make a new vector, but makes a view that must not
+   * outlive the child views that it references. The resulting column cannot be null.
+   * @param columns the columns to add to the struct in the order they should be added
+   * @return the new column view. It is the responsibility of the caller to close this.
+   */
+  public static ColumnView makeStructView(ColumnView... columns) {
+    if (columns.length <= 0) {
+      throw new IllegalArgumentException("At least one column is needed to get the row count");
+    }
+    return makeStructView(columns[0].rows, columns);
+  }
+
+  /**
+   * Create a new column view from a raw device buffer. Note that this will NOT copy
+   * the contents of the buffer but only creates a view. The view MUST NOT outlive
+   * the underlying device buffer. The column view will be created without a validity
+   * vector, so it is not possible to create a view containing null elements. Additionally
+   * only fixed-width primitive types are supported.
+   *
+   * @param buffer device memory that will back the column view
+   * @param startOffset byte offset into the device buffer where the column data starts
+   * @param type type of data in the column view
+   * @param rows number of data elements in the column view
+   * @return new column view instance that must not outlive the backing device buffer
+   */
+  public static ColumnView fromDeviceBuffer(BaseDeviceMemoryBuffer buffer,
+                                            long startOffset,
+                                            DType type,
+                                            int rows) {
+    if (buffer == null) {
+      throw new NullPointerException("buffer is null");
+    }
+    int typeSize = type.getSizeInBytes();
+    if (typeSize <= 0) {
+      throw new IllegalArgumentException("Unsupported type: " + type);
+    }
+    if (startOffset < 0) {
+      throw new IllegalArgumentException("Invalid start offset: " + startOffset);
+    }
+    if (rows < 0) {
+      throw new IllegalArgumentException("Invalid row count: " + rows);
+    }
+    long dataSize = typeSize * rows;
+    if (startOffset + dataSize > buffer.length) {
+      throw new IllegalArgumentException("View extends beyond buffer range");
+    }
+    long dataAddress = buffer.getAddress() + startOffset;
+    if (dataAddress % typeSize != 0) {
+      throw new IllegalArgumentException("Data address " + Long.toHexString(dataAddress) +
+          " is misaligned relative to type size of " + typeSize + " bytes");
+    }
+    return new ColumnView(makeCudfColumnView(type.typeId.getNativeId(), type.getScale(),
+        dataAddress, dataSize, 0, 0, 0, rows, null));
+  }
+
+  /**
+   * Create a column of bool values indicating whether the specified scalar
+   * is an element of each row of a list column.
+   * Output `column[i]` is set to null if one or more of the following are true:
+   * 1. The key is null
+   * 2. The column vector list value is null
+   * @param key the scalar to look up
+   * @return a Boolean ColumnVector with the result of the lookup
+   */
+  public final ColumnVector listContains(Scalar key) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    return new ColumnVector(listContains(getNativeView(), key.getScalarHandle()));
+  }
+
+  /**
+   * Create a column of bool values indicating whether the list rows of the first
+   * column contain the corresponding values in the second column.
+   * Output `column[i]` is set to null if one or more of the following are true:
+   * 1. The key value is null
+   * 2. The column vector list value is null
+   * @param key the ColumnVector with look up values
+   * @return a Boolean ColumnVector with the result of the lookup
+   */
+  public final ColumnVector listContainsColumn(ColumnView key) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    return new ColumnVector(listContainsColumn(getNativeView(), key.getNativeView()));
+  }
+
+  /**
+   * Create a column of bool values indicating whether the list rows of the specified
+   * column contain null elements.
+   * Output `column[i]` is set to null iff the input list row is null.
+   * @return a Boolean ColumnVector with the result of the lookup
+   */
+  public final ColumnVector listContainsNulls() {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    return new ColumnVector(listContainsNulls(getNativeView()));
+  }
+
+  /**
+   * Enum to choose behaviour of listIndexOf functions:
+   *   1. FIND_FIRST finds the first occurrence of a search key.
+   *   2. FIND_LAST finds the last occurrence of a search key.
+   */
+  public enum FindOptions {FIND_FIRST, FIND_LAST};
+
+  /**
+   * Create a column of int32 indices, indicating the position of the scalar search key
+   * in each list row.
+   * All indices are 0-based. If a search key is not found, the index is set to -1.
+   * The index is set to null if one of the following is true:
+   * 1. The search key is null.
+   * 2. The list row is null.
+   * @param key The scalar search key
+   * @param findOption Whether to find the first index of the key, or the last.
+   * @return The resultant column of int32 indices
+   */
+  public final ColumnVector listIndexOf(Scalar key, FindOptions findOption) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    boolean isFindFirst = findOption == FindOptions.FIND_FIRST;
+    return new ColumnVector(listIndexOfScalar(getNativeView(), key.getScalarHandle(), isFindFirst));
+  }
+
+  /**
+   * Create a column of int32 indices, indicating the position of each row in the
+   * search key column in the corresponding row of the lists column.
+   * All indices are 0-based. If a search key is not found, the index is set to -1.
+   * The index is set to null if one of the following is true:
+   * 1. The search key row is null.
+   * 2. The list row is null.
+   * @param keys ColumnView of search keys.
+   * @param findOption Whether to find the first index of the key, or the last.
+   * @return The resultant column of int32 indices
+   */
+  public final ColumnVector listIndexOf(ColumnView keys, FindOptions findOption) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    boolean isFindFirst = findOption == FindOptions.FIND_FIRST;
+    return new ColumnVector(listIndexOfColumn(getNativeView(), keys.getNativeView(), isFindFirst));
+  }
+
+  /**
+   * Segmented sort of the elements within a list in each row of a list column.
+   * NOTICE: list columns with nested child are NOT supported yet.
+   *
+   * @param isDescending   whether sorting each row with descending order (or ascending order)
+   * @param isNullSmallest whether to regard the null value as the min value (or the max value)
+   * @return a List ColumnVector with elements in each list sorted
+   */
+  public final ColumnVector listSortRows(boolean isDescending, boolean isNullSmallest) {
+    assert type.equals(DType.LIST) : "column type must be a LIST";
+    return new ColumnVector(listSortRows(getNativeView(), isDescending, isNullSmallest));
+  }
+
+  /**
+   * For each pair of lists from the input lists columns, check if they have any common non-null
+   * elements.
+   *
+   * A null input row in any of the input columns will result in a null output row. During checking
+   * for common elements, nulls within each list are considered as different values while
+   * floating-point NaN values are considered as equal.
+   *
+   * The input lists columns must have the same size and same data type.
+   *
+   * @param lhs The input lists column for one side
+   * @param rhs The input lists column for the other side
+   * @return A column of type BOOL8 containing the check result
+   */
+  public static ColumnVector listsHaveOverlap(ColumnView lhs, ColumnView rhs) {
+    assert lhs.getType().equals(DType.LIST) && rhs.getType().equals(DType.LIST) :
+        "Input columns type must be of type LIST";
+    assert lhs.getRowCount() == rhs.getRowCount() : "Input columns must have the same size";
+    return new ColumnVector(listsHaveOverlap(lhs.getNativeView(), rhs.getNativeView()));
+  }
+
+  /**
+   * Find the intersection without duplicate between lists at each row of the given lists columns.
+   *
+   * A null input row in any of the input lists columns will result in a null output row. During
+   * finding list intersection, nulls and floating-point NaN values within each list are
+   * considered as equal values.
+   *
+   * The input lists columns must have the same size and same data type.
+   *
+   * @param lhs The input lists column for one side
+   * @param rhs The input lists column for the other side
+   * @return A lists column containing the intersection result
+   */
+  public static ColumnVector listsIntersectDistinct(ColumnView lhs, ColumnView rhs) {
+    assert lhs.getType().equals(DType.LIST) && rhs.getType().equals(DType.LIST) :
+        "Input columns type must be of type LIST";
+    assert lhs.getRowCount() == rhs.getRowCount() : "Input columns must have the same size";
+    return new ColumnVector(listsIntersectDistinct(lhs.getNativeView(), rhs.getNativeView()));
+  }
+
+  /**
+   * Find the union without duplicate between lists at each row of the given lists columns.
+   *
+   * A null input row in any of the input lists columns will result in a null output row. During
+   * finding list union, nulls and floating-point NaN values within each list are considered as
+   * equal values.
+   *
+   * The input lists columns must have the same size and same data type.
+   *
+   * @param lhs The input lists column for one side
+   * @param rhs The input lists column for the other side
+   * @return A lists column containing the union result
+   */
+  public static ColumnVector listsUnionDistinct(ColumnView lhs, ColumnView rhs) {
+    assert lhs.getType().equals(DType.LIST) && rhs.getType().equals(DType.LIST) :
+        "Input columns type must be of type LIST";
+    assert lhs.getRowCount() == rhs.getRowCount() : "Input columns must have the same size";
+    return new ColumnVector(listsUnionDistinct(lhs.getNativeView(), rhs.getNativeView()));
+  }
+
+  /**
+   * Find the difference of lists of the left column against lists of the right column.
+   * Specifically, find the elements (without duplicates) from each list of the left column that
+   * do not exist in the corresponding list of the right column.
+   *
+   * A null input row in any of the input lists columns will result in a null output row. During
+   * finding, nulls and floating-point NaN values within each list are considered as equal values.
+   *
+   * The input lists columns must have the same size and same data type.
+   *
+   * @param lhs The input lists column for one side
+   * @param rhs The input lists column for the other side
+   * @return A lists column containing the difference result
+   */
+  public static ColumnVector listsDifferenceDistinct(ColumnView lhs, ColumnView rhs) {
+    assert lhs.getType().equals(DType.LIST) && rhs.getType().equals(DType.LIST) :
+        "Input columns type must be of type LIST";
+    assert lhs.getRowCount() == rhs.getRowCount() : "Input columns must have the same size";
+    return new ColumnVector(listsDifferenceDistinct(lhs.getNativeView(), rhs.getNativeView()));
+  }
+
+  /**
+   * Generate list offsets from sizes of each list.
+   * NOTICE: This API only works for INT32. Otherwise, the behavior is undefined. And no null and negative value is allowed.
+   *
+   * @return a column of list offsets whose size is N + 1
+   */
+  public final ColumnVector generateListOffsets() {
+    return new ColumnVector(generateListOffsets(getNativeView()));
+  }
+
+  /**
+   * Get a single item from the column at the specified index as a Scalar.
+   *
+   * Be careful. This is expensive and may involve running a kernel to copy the data out.
+   *
+   * @param index the index to look at
+   * @return the value at that index as a scalar.
+   * @throws CudfException if the index is out of bounds.
+   */
+  public final Scalar getScalarElement(int index) {
+    return new Scalar(getType(), getElement(getNativeView(), index));
+  }
+
+  /**
+   * Filters elements in each row of this LIST column using `booleanMaskView`
+   * LIST of booleans as a mask.
+   * <p>
+   * Given a list-of-bools column, the function produces
+   * a new `LIST` column of the same type as this column, where each element is copied
+   * from the row *only* if the corresponding `boolean_mask` is non-null and `true`.
+   * <p>
+   * E.g.
+   * column       = { {0,1,2}, {3,4}, {5,6,7}, {8,9} };
+   * boolean_mask = { {0,1,1}, {1,0}, {1,1,1}, {0,0} };
+   * results      = { {1,2},   {3},   {5,6,7}, {} };
+   * <p>
+   * This column and `boolean_mask` must have the same number of rows.
+   * The output column has the same number of rows as this column.
+   * An element is copied to an output row *only*
+   * if the corresponding boolean_mask element is `true`.
+   * An output row is invalid only if the row is invalid.
+   *
+   * @param booleanMaskView A nullable list of bools column used to filter elements in this column
+   * @return List column of the same type as this column, containing filtered list rows
+   * @throws CudfException if `boolean_mask` is not a "lists of bools" column
+   * @throws CudfException if this column and `boolean_mask` have different number of rows
+   */
+  public final ColumnVector applyBooleanMask(ColumnView booleanMaskView) {
+    assert (getType().equals(DType.LIST));
+    assert (booleanMaskView.getType().equals(DType.LIST));
+    assert (getRowCount() == booleanMaskView.getRowCount());
+    return new ColumnVector(applyBooleanMask(getNativeView(), booleanMaskView.getNativeView()));
+  }
+
+  /**
+   * Get the number of bytes needed to allocate a validity buffer for the given number of rows.
+   * According to cudf::bitmask_allocation_size_bytes, the padding boundary for null mask is 64 bytes.
+   */
+  static long getValidityBufferSize(int numRows) {
+    // number of bytes required = Math.ceil(number of bits / 8)
+    long actualBytes = ((long) numRows + 7) >> 3;
+    // padding to the multiplies of the padding boundary(64 bytes)
+    return ((actualBytes + 63) >> 6) << 6;
+  }
+
+  /**
+   * Count how many rows in the column are distinct from one another.
+   * @param nullPolicy if nulls should be included or not.
+   */
+  public int distinctCount(NullPolicy nullPolicy) {
+    return distinctCount(getNativeView(), nullPolicy.includeNulls);
+  }
+
+  /**
+   * Count how many rows in the column are distinct from one another.
+   * Nulls are included.
+   */
+  public int distinctCount() {
+    return distinctCount(getNativeView(), true);
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // INTERNAL/NATIVE ACCESS
+  /////////////////////////////////////////////////////////////////////////////
+
+  static DeviceMemoryBufferView getDataBuffer(long viewHandle) {
+    long address = getNativeDataAddress(viewHandle);
+    if (address == 0) {
+      return null;
+    }
+    long length = getNativeDataLength(viewHandle);
+    return new DeviceMemoryBufferView(address, length);
+  }
+
+  static DeviceMemoryBufferView getValidityBuffer(long viewHandle) {
+    long address = getNativeValidityAddress(viewHandle);
+    if (address == 0) {
+      return null;
+    }
+    long length = getNativeValidityLength(viewHandle);
+    return new DeviceMemoryBufferView(address, length);
+  }
+
+  static DeviceMemoryBufferView getOffsetsBuffer(long viewHandle) {
+    long address = getNativeOffsetsAddress(viewHandle);
+    if (address == 0) {
+      return null;
+    }
+    long length = getNativeOffsetsLength(viewHandle);
+    return new DeviceMemoryBufferView(address, length);
+  }
+
+  // Native Methods
+  private static native int distinctCount(long handle, boolean nullsIncluded);
+
+  /**
+   * Native method to parse and convert a string column vector to unix timestamp. A unix
+   * timestamp is a long value representing how many units since 1970-01-01 00:00:00.000 in either
+   * positive or negative direction. This mirrors the functionality spark sql's to_unix_timestamp.
+   * Strings that fail to parse will default to 0. Supported time units are second, millisecond,
+   * microsecond, and nanosecond. Larger time units for column vectors are not supported yet in cudf.
+   * No checking is done for invalid formats or invalid timestamp units.
+   * Negative timestamp values are not currently supported and will yield undesired results. See
+   * github issue https://github.com/rapidsai/cudf/issues/3116 for details
+   *
+   * @param unit integer native ID of the time unit to parse the timestamp into.
+   * @param format strptime format specifier string of the timestamp. Used to parse and convert
+   *               the timestamp with. Supports %Y,%y,%m,%d,%H,%I,%p,%M,%S,%f,%z format specifiers.
+   *               See https://github.com/rapidsai/custrings/blob/branch-0.10/docs/source/datetime.md
+   *               for full parsing format specification and documentation.
+   * @return native handle of the resulting cudf column, used to construct the Java column vector
+   *         by the timestampToLong method.
+   */
+  private static native long stringTimestampToTimestamp(long viewHandle, int unit, String format);
+
+
+  private static native long isFixedPoint(long viewHandle, int nativeTypeId, int scale);
+
+  private static native long toHex(long viewHandle);
+
+  /**
+   * Native method to concatenate a list column of strings (each row is a list of strings),
+   * concatenates the strings within each row and returns a single strings column result.
+   * Each new string is created by concatenating the strings from the same row (same list element)
+   * delimited by the row separator provided in the `separators` strings column.
+   * @param listColumnHandle long holding the native handle of the column containing lists of strings
+   *                         to concatenate.
+   * @param sepColumn long holding the native handle of the strings column that provides separators
+   *                  for concatenation.
+   * @param separatorNarep string scalar indicating null behavior when a separator is null.
+   *                       If set to null and the separator is null the resulting string will
+   *                       be null. If not null, this string will be used in place of a null
+   *                       separator.
+   * @param colNarep string String scalar that should be used in place of any null strings
+   *                 found in any column.
+   * @param separateNulls boolean if true, then the separator is included for null rows if
+   *                     `colNarep` is valid.
+   * @param emptyStringOutputIfEmptyList boolean if true, any input row that is an empty list
+   *                                     will result in an empty string. Otherwise, it will
+   *                                     result in a null.
+   * @return native handle of the resulting cudf column, used to construct the Java column.
+   */
+  private static native long stringConcatenationListElementsSepCol(long listColumnHandle,
+                                                                   long sepColumn,
+                                                                   long separatorNarep,
+                                                                   long colNarep,
+                                                                   boolean separateNulls,
+                                                                   boolean emptyStringOutputIfEmptyList);
+
+  /**
+   * Native method to concatenate a list column of strings (each row is a list of strings),
+   * concatenates the strings within each row and returns a single strings column result.
+   * Each new string is created by concatenating the strings from the same row (same list element)
+   * delimited by the separator provided.
+   * @param listColumnHandle long holding the native handle of the column containing lists of strings
+   *                     to concatenate.
+   * @param separator string scalar inserted between each string being merged, may not be null.
+   * @param narep string scalar indicating null behavior. If set to null and any string in the row
+   *              is null the resulting string will be null. If not null, null values in any
+   *              column will be replaced by the specified string. The underlying value in the
+   *              string scalar may be null, but the object passed in may not.
+   * @param separateNulls boolean if true, then the separator is included for null rows if
+   *                      `narep` is valid.
+   * @param emptyStringOutputIfEmptyList boolean if true, any input row that is an empty list
+   *                                     will result in an empty string. Otherwise, it will
+   *                                     result in a null.
+   * @return native handle of the resulting cudf column, used to construct the Java column.
+   */
+  private static native long stringConcatenationListElements(long listColumnHandle,
+                                                             long separator,
+                                                             long narep,
+                                                             boolean separateNulls,
+                                                             boolean emptyStringOutputIfEmptyList);
+
+  /**
+   * Native method to repeat each string in the given input strings column a number of times
+   * specified by the <code>repeatTimes</code> parameter.
+   *
+   * In special cases:
+   *  - If <code>repeatTimes</code> is not a positive number, a non-null input string will always
+   *    result in an empty output string.
+   *  - A null input string will always result in a null output string regardless of the value of
+   *    the <code>repeatTimes</code> parameter.
+   *
+   * @param viewHandle long holding the native handle of the column containing strings to repeat.
+   * @param repeatTimes The number of times each input string is repeated.
+   * @return native handle of the resulting cudf strings column containing repeated strings.
+   */
+  private static native long repeatStrings(long viewHandle, int repeatTimes);
+
+  /**
+   * Native method to repeat strings in the given input strings column, each string is repeated
+   * by a different number of times given by the corresponding row in a <code>repeatTimes</code>
+   * numeric column.
+   *
+   * In special cases:
+   *  - Any null row (from either the input strings column or the <code>repeatTimes</code> column)
+   *    will always result in a null output string.
+   *  - If any value in the <code>repeatTimes</code> column is not a positive number and its
+   *    corresponding input string is not null, the output string will be an empty string.
+   *
+   * If the input <code>repeatTimesHandle</code> column does not have a numeric type, or it has a
+   * size that is different from size of the input strings column, an exception will be thrown.
+   *
+   * @param stringsHandle long holding the native handle of the column containing strings to repeat.
+   * @param repeatTimesHandle long holding the native handle of the column containing the numbers
+   *        of times each input string is repeated.
+   * @return native handle of the resulting cudf strings column containing repeated strings.
+   */
+  private static native long repeatStringsWithColumnRepeatTimes(long stringsHandle,
+    long repeatTimesHandle);
+
+
+  private static native long getJSONObject(long viewHandle, long scalarHandle) throws CudfException;
+
+  /**
+   * Native method to parse and convert a timestamp column vector to string column vector. A unix
+   * timestamp is a long value representing how many units since 1970-01-01 00:00:00:000 in either
+   * positive or negative direction. This mirrors the functionality spark sql's from_unixtime.
+   * No checking is done for invalid formats or invalid timestamp units.
+   * Negative timestamp values are not currently supported and will yield undesired results. See
+   * github issue https://github.com/rapidsai/cudf/issues/3116 for details
+   *
+   * @param format - strftime format specifier string of the timestamp. Its used to parse and convert
+   *               the timestamp with. Supports %Y,%y,%m,%d,%H,%M,%S,%f format specifiers.
+   *               %d 	Day of the month: 01-31
+   *               %m 	Month of the year: 01-12
+   *               %y 	Year without century: 00-99c
+   *               %Y 	Year with century: 0001-9999
+   *               %H 	24-hour of the day: 00-23
+   *               %M 	Minute of the hour: 00-59
+   *               %S 	Second of the minute: 00-59
+   *               %f 	6-digit microsecond: 000000-999999
+   *               See http://man7.org/linux/man-pages/man3/strftime.3.html for details
+   *
+   * Reported bugs
+   * https://github.com/rapidsai/cudf/issues/4160 after the bug is fixed this method should
+   * also support
+   *               %I 	12-hour of the day: 01-12
+   *               %p 	Only 'AM', 'PM'
+   *               %j   day of the year
+   *
+   * @return - native handle of the resulting cudf column used to construct the Java column vector
+   */
+  private static native long timestampToStringTimestamp(long viewHandle, String format);
+
+  /**
+   * Native method for locating the starting index of the first instance of a given substring
+   * in each string in the column. 0 indexing, returns -1 if the substring is not found. Can be
+   * be configured to start or end the search mid string.
+   * @param columnView native handle of the cudf::column_view containing strings being operated on.
+   * @param substringScalar string scalar handle containing the string to locate within each row.
+   * @param start character index to start the search from (inclusive).
+   * @param end character index to end the search on (exclusive).
+   */
+  private static native long substringLocate(long columnView, long substringScalar, int start, int end);
+
+  /**
+   * Returns a list of columns by splitting each string using the specified string literal
+   * delimiter. The number of rows in the output columns will be the same as the input column.
+   * Null entries are added for the rows where split results have been exhausted. Null input entries
+   * result in all nulls in the corresponding rows of the output columns.
+   *
+   * @param nativeHandle native handle of the input strings column that being operated on.
+   * @param delimiter UTF-8 encoded string identifying the split delimiter for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   */
+  private static native long[] stringSplit(long nativeHandle, String delimiter, int limit);
+
+  /**
+   * Returns a list of columns by splitting each string using the specified regular expression
+   * pattern. The number of rows in the output columns will be the same as the input column.
+   * Null entries are added for the rows where split results have been exhausted. Null input entries
+   * result in all nulls in the corresponding rows of the output columns.
+   *
+   * @param nativeHandle native handle of the input strings column that being operated on.
+   * @param pattern UTF-8 encoded string identifying the split regular expression pattern for
+   *                each input string.
+   * @param flags regex flags setting.
+   * @param capture capture groups setting.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   */
+  private static native long[] stringSplitRe(long nativeHandle, String pattern, int flags,
+                                             int capture, int limit);
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified string literal delimiter.
+   *
+   * @param nativeHandle native handle of the input strings column that being operated on.
+   * @param delimiter UTF-8 encoded string identifying the split delimiter for each input string.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   */
+  private static native long stringSplitRecord(long nativeHandle, String delimiter, int limit);
+
+  /**
+   * Returns a column that are lists of strings in which each list is made by splitting the
+   * corresponding input string using the specified regular expression pattern.
+   *
+   * @param nativeHandle native handle of the input strings column that being operated on.
+   * @param pattern UTF-8 encoded string identifying the split regular expression pattern for
+   *                each input string.
+   * @param flags regex flags setting.
+   * @param capture capture groups setting.
+   * @param limit the maximum size of the list resulting from splitting each input string,
+   *              or -1 for all possible splits. Note that limit = 0 (all possible splits without
+   *              trailing empty strings) and limit = 1 (no split at all) are not supported.
+   */
+  private static native long stringSplitRecordRe(long nativeHandle, String pattern, int flags,
+                                                 int capture, int limit);
+
+  /**
+   * Native method to calculate substring from a given string column. 0 indexing.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param start      first character index to begin the substring(inclusive).
+   * @param end        last character index to stop the substring(exclusive).
+   */
+  private static native long substring(long columnView, int start, int end) throws CudfException;
+
+  /**
+   * Native method to extract substrings from a given strings column.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param start      first character index to begin the substrings (inclusive).
+   */
+  private static native long substringS(long columnView, int start) throws CudfException;
+
+  /**
+   * Native method to calculate substring from a given string column.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param startColumn handle of cudf::column_view which has start indices of each string.
+   * @param endColumn handle of cudf::column_view which has end indices of each string.
+   */
+  private static native long substringColumn(long columnView, long startColumn, long endColumn)
+      throws CudfException;
+
+  /**
+   * Native method to replace target string by repl string.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param target handle of scalar containing the string being searched.
+   * @param repl handle of scalar containing the string to replace.
+   */
+  private static native long stringReplace(long columnView, long target, long repl) throws CudfException;
+
+  /**
+   * Native method to replace target strings by corresponding repl strings.
+   * @param inputCV native handle of the cudf::column_view being operated on.
+   * @param targetsCV handle of column containing the strings being searched.
+   * @param replsCV handle of column containing the strings to replace (can optionally contain a single string).
+   */
+  private static native long stringReplaceMulti(long inputCV, long targetsCV, long replsCV) throws CudfException;
+
+  /**
+   * Native method for replacing each regular expression pattern match with the specified
+   * replacement string.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param pattern regular expression pattern to search within each string.
+   * @param flags regex flags setting.
+   * @param capture capture groups setting.
+   * @param repl native handle of the cudf::scalar containing the replacement string.
+   * @param maxRepl maximum number of times to replace the pattern within a string
+   * @return native handle of the resulting cudf column containing the string results.
+   */
+  private static native long replaceRegex(long columnView, String pattern, int flags, int capture,
+                                          long repl, long maxRepl) throws CudfException;
+
+  /**
+   * Native method for multiple instance regular expression replacement.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param patterns native handle of the cudf::column_view containing the regex patterns.
+   * @param repls The replacement template for creating the output string.
+   * @return native handle of the resulting cudf column containing the string results.
+   */
+  private static native long replaceMultiRegex(long columnView, String[] patterns,
+                                               long repls) throws CudfException;
+
+  /**
+   * Native method for replacing any character sequence matching the given regex program
+   * pattern using the replace template for back-references.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   * @param pattern The regular expression patterns to search within each string.
+   * @param flags Regex flags setting.
+   * @param capture Capture groups setting.
+   * @param replace The replacement template for creating the output string.
+   * @return native handle of the resulting cudf column containing the string results.
+   */
+  private static native long stringReplaceWithBackrefs(long columnView, String pattern, int flags,
+                                                       int capture, String replace) throws CudfException;
+
+  /**
+   * Native method for checking if strings in a column starts with a specified comparison string.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @param compString handle of scalar containing the string being searched for at the beginning
+  of each string in the column.
+   * @return native handle of the resulting cudf column containing the boolean results.
+   */
+  private static native long stringStartWith(long cudfViewHandle, long compString) throws CudfException;
+
+  /**
+   * Native method for checking if strings in a column ends with a specified comparison string.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @param compString handle of scalar containing the string being searched for at the end
+  of each string in the column.
+   * @return native handle of the resulting cudf column containing the boolean results.
+   */
+  private static native long stringEndWith(long cudfViewHandle, long compString) throws CudfException;
+
+  /**
+   * Native method to strip whitespace from the start and end of a string.
+   * @param columnView native handle of the cudf::column_view being operated on.
+   */
+  private static native long stringStrip(long columnView, int type, long toStrip) throws CudfException;
+
+  /**
+   * Native method for checking if strings match the passed in regex program pattern from the
+   * beginning of the string.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @param pattern string regex pattern.
+   * @param flags regex flags setting.
+   * @param capture capture groups setting.
+   * @return native handle of the resulting cudf column containing the boolean results.
+   */
+  private static native long matchesRe(long cudfViewHandle, String pattern, int flags, int capture) throws CudfException;
+
+  /**
+   * Native method for checking if strings match the passed in regex program pattern starting at any location.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @param pattern string regex pattern.
+   * @param flags regex flags setting.
+   * @param capture capture groups setting.
+   * @return native handle of the resulting cudf column containing the boolean results.
+   */
+  private static native long containsRe(long cudfViewHandle, String pattern, int flags, int capture) throws CudfException;
+
+  /**
+   * Native method for checking if strings match the passed in like pattern
+   * and escape character.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @param patternHandle handle of scalar containing the string like pattern.
+   * @param escapeCharHandle handle of scalar containing the string escape character.
+   * @return native handle of the resulting cudf column containing the boolean results.
+   */
+  private static native long like(long cudfViewHandle, long patternHandle, long escapeCharHandle) throws CudfException;
+
+  /**
+   * Native method for checking if strings in a column contains a specified comparison string.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @param compString handle of scalar containing the string being searched for.
+   * @return native handle of the resulting cudf column containing the boolean results.
+   */
+  private static native long stringContains(long cudfViewHandle, long compString) throws CudfException;
+
+  /**
+   * Native method for extracting results from a regex program pattern. Returns a table handle.
+   *
+   * @param cudfViewHandle Native handle of the cudf::column_view being operated on.
+   * @param pattern String regex pattern.
+   * @param flags Regex flags setting.
+   * @param capture Capture groups setting.
+   */
+  private static native long[] extractRe(long cudfViewHandle, String pattern, int flags, int capture) throws CudfException;
+
+  /**
+   * Native method for extracting all results corresponding to group idx from a regex program pattern.
+   *
+   * @param nativeHandle Native handle of the cudf::column_view being operated on.
+   * @param pattern String regex pattern.
+   * @param flags Regex flags setting.
+   * @param capture Capture groups setting.
+   * @param idx Regex group index. A 0 value means matching the entire regex.
+   * @return Native handle of a string column of the result.
+   */
+  private static native long extractAllRecord(long nativeHandle, String pattern, int flags, int capture, int idx);
+
+  private static native long urlDecode(long cudfViewHandle);
+
+  private static native long urlEncode(long cudfViewHandle);
+
+  /**
+   * Native method for map lookup over a column of List<Struct<String,String>>
+   * @param columnView the column view handle of the map
+   * @param key the string scalar that is the key for lookup
+   * @return a string column handle of the resultant
+   * @throws CudfException
+   */
+  private static native long mapLookup(long columnView, long key) throws CudfException;
+
+  /**
+   * Native method for map lookup over a column of List<Struct<String,String>>
+   * The lookup column must have as many rows as the map column,
+   * and must match the key-type of the map.
+   * A column of values is returned, with the same number of rows as the map column.
+   * If a key is repeated in a map row, the value corresponding to the last matching
+   * key is returned.
+   * If a lookup key is null or not found, the corresponding value is null.
+   * @param columnView the column view handle of the map
+   * @param keys       the column view holding the keys
+   * @return a column of values corresponding the value of the lookup key.
+   * @throws CudfException
+   */
+  private static native long mapLookupForKeys(long columnView, long keys) throws CudfException;
+
+  /**
+   * Native method for check the existence of a key over a column of List<Struct<_, _>>
+   * @param columnView the column view handle of the map
+   * @param key the column view holding the keys
+   * @return boolean column handle of the result
+   * @throws CudfException
+   */
+  private static native long mapContainsKeys(long columnView, long key) throws CudfException;
+
+  /**
+   * Native method for check the existence of a key over a column of List<Struct<String,String>>
+   * @param columnView the column view handle of the map
+   * @param key the string scalar that is the key for lookup
+   * @return boolean column handle of the result
+   * @throws CudfException
+   */
+  private static native long mapContains(long columnView, long key) throws CudfException;
+  /**
+   * Native method to add zeros as padding to the left of each string.
+   */
+  private static native long zfill(long nativeHandle, int width);
+
+  private static native long pad(long nativeHandle, int width, int side, String fillChar);
+
+  private static native long binaryOpVS(long lhs, long rhs, int op, int dtype, int scale);
+
+  private static native long binaryOpVV(long lhs, long rhs, int op, int dtype, int scale);
+
+  private static native long countElements(long viewHandle);
+
+  private static native long byteCount(long viewHandle) throws CudfException;
+
+  private static native long extractListElement(long nativeView, int index);
+
+  private static native long extractListElementV(long nativeView, long indicesView);
+
+  private static native long dropListDuplicates(long nativeView);
+
+  private static native long dropListDuplicatesWithKeysValues(long nativeHandle);
+
+  private static native long flattenLists(long inputHandle, boolean ignoreNull);
+
+  /**
+   * Native method for list lookup
+   * @param nativeView the column view handle of the list
+   * @param key the scalar key handle
+   * @return column handle of the resultant
+   */
+  private static native long listContains(long nativeView, long key);
+
+  /**
+   * Native method for list lookup
+   * @param nativeView the column view handle of the list
+   * @param keyColumn the column handle of look up keys
+   * @return column handle of the resultant
+   */
+  private static native long listContainsColumn(long nativeView, long keyColumn);
+
+  /**
+   * Native method to search list rows for null elements.
+   * @param nativeView the column view handle of the list
+   * @return column handle of the resultant boolean column
+   */
+  private static native long listContainsNulls(long nativeView);
+
+  /**
+   * Native method to find the first (or last) index of a specified scalar key,
+   * in each row of a list column.
+   * @param nativeView the column view handle of the list
+   * @param scalarKeyHandle handle to the scalar search key
+   * @param isFindFirst Whether to find the first index of the key, or the last.
+   * @return column handle of the resultant column of int32 indices
+   */
+  private static native long listIndexOfScalar(long nativeView, long scalarKeyHandle, boolean isFindFirst);
+
+  /**
+   * Native method to find the first (or last) index of each search key in the specified column,
+   * in each row of a list column.
+   * @param nativeView the column view handle of the list
+   * @param keyColumnHandle handle to the search key column
+   * @param isFindFirst Whether to find the first index of the key, or the last.
+   * @return column handle of the resultant column of int32 indices
+   */
+  private static native long listIndexOfColumn(long nativeView, long keyColumnHandle, boolean isFindFirst);
+
+  private static native long listSortRows(long nativeView, boolean isDescending, boolean isNullSmallest);
+
+  private static native long listsHaveOverlap(long lhsViewHandle, long rhsViewHandle);
+
+  private static native long listsIntersectDistinct(long lhsViewHandle, long rhsViewHandle);
+
+  private static native long listsUnionDistinct(long lhsViewHandle, long rhsViewHandle);
+
+  private static native long listsDifferenceDistinct(long lhsViewHandle, long rhsViewHandle);
+
+  private static native long getElement(long nativeView, int index);
+
+  private static native long castTo(long nativeHandle, int type, int scale);
+
+  private static native long bitCastTo(long nativeHandle, int type, int scale);
+
+  private static native long byteListCast(long nativeHandle, boolean config);
+
+  private static native long[] slice(long nativeHandle, int[] indices) throws CudfException;
+
+  private static native long[] split(long nativeHandle, int[] indices) throws CudfException;
+
+  private static native long findAndReplaceAll(long valuesHandle, long replaceHandle, long myself) throws CudfException;
+
+  private static native long round(long nativeHandle, int decimalPlaces, int roundingMethod) throws CudfException;
+
+  private static native long reverseStringsOrLists(long inputHandle);
+
+  /**
+   * Native method to switch all characters in a column of strings to lowercase characters.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @return native handle of the resulting cudf column, used to construct the Java column
+   *         by the lower method.
+   */
+  private static native long lowerStrings(long cudfViewHandle);
+
+  /**
+   * Native method to switch all characters in a column of strings to uppercase characters.
+   * @param cudfViewHandle native handle of the cudf::column_view being operated on.
+   * @return native handle of the resulting cudf column, used to construct the Java column
+   *         by the upper method.
+   */
+  private static native long upperStrings(long cudfViewHandle);
+
+  /**
+   * Native method to compute approx percentiles.
+   * @param cudfColumnHandle T-Digest column
+   * @param percentilesHandle Percentiles
+   * @return native handle of the resulting cudf column, used to construct the Java column
+   *         by the approxPercentile method.
+   */
+  private static native long approxPercentile(long cudfColumnHandle, long percentilesHandle) throws CudfException;
+
+  private static native long quantile(long cudfColumnHandle, int quantileMethod, double[] quantiles) throws CudfException;
+
+  private static native long rollingWindow(
+      long viewHandle,
+      long defaultOutputHandle,
+      int min_periods,
+      long aggPtr,
+      int preceding,
+      int following,
+      long preceding_col,
+      long following_col);
+
+  private static native long scan(long viewHandle, long aggregation,
+      boolean isInclusive, boolean includeNulls) throws CudfException;
+
+  private static native long nansToNulls(long viewHandle) throws CudfException;
+
+  private static native long charLengths(long viewHandle) throws CudfException;
+
+  private static native long replaceNullsScalar(long viewHandle, long scalarHandle) throws CudfException;
+
+  private static native long replaceNullsColumn(long viewHandle, long replaceViewHandle) throws CudfException;
+
+  private static native long replaceNullsPolicy(long nativeView, boolean isPreceding) throws CudfException;
+
+  private static native long ifElseVV(long predVec, long trueVec, long falseVec) throws CudfException;
+
+  private static native long ifElseVS(long predVec, long trueVec, long falseScalar) throws CudfException;
+
+  private static native long ifElseSV(long predVec, long trueScalar, long falseVec) throws CudfException;
+
+  private static native long ifElseSS(long predVec, long trueScalar, long falseScalar) throws CudfException;
+
+  private static native long reduce(long viewHandle, long aggregation, int dtype, int scale) throws CudfException;
+
+  private static native long segmentedReduce(long dataViewHandle, long offsetsViewHandle,
+      long aggregation, boolean includeNulls, int dtype, int scale) throws CudfException;
+
+  private static native long segmentedGather(long sourceColumnHandle, long gatherMapListHandle,
+      boolean isNullifyOutBounds) throws CudfException;
+
+  private static native long isNullNative(long viewHandle);
+
+  private static native long isNanNative(long viewHandle);
+
+  private static native long isFloat(long viewHandle);
+
+  private static native long isInteger(long viewHandle);
+
+  private static native long isIntegerWithType(long viewHandle, int typeId, int typeScale);
+
+  private static native long isNotNanNative(long viewHandle);
+
+  private static native long isNotNullNative(long viewHandle);
+
+  private static native long unaryOperation(long viewHandle, int op);
+
+  private static native long year(long viewHandle) throws CudfException;
+
+  private static native long month(long viewHandle) throws CudfException;
+
+  private static native long day(long viewHandle) throws CudfException;
+
+  private static native long hour(long viewHandle) throws CudfException;
+
+  private static native long minute(long viewHandle) throws CudfException;
+
+  private static native long second(long viewHandle) throws CudfException;
+
+  private static native long weekDay(long viewHandle) throws CudfException;
+
+  private static native long lastDayOfMonth(long viewHandle) throws CudfException;
+
+  private static native long dayOfYear(long viewHandle) throws CudfException;
+
+  private static native long quarterOfYear(long viewHandle) throws CudfException;
+
+  private static native long addCalendricalMonths(long tsViewHandle, long monthsViewHandle);
+
+  private static native long isLeapYear(long viewHandle) throws CudfException;
+
+  private static native boolean containsScalar(long columnViewHaystack, long scalarHandle) throws CudfException;
+
+  private static native long containsVector(long valuesHandle, long searchSpaceHandle) throws CudfException;
+
+  private static native long transform(long viewHandle, String udf, boolean isPtx);
+
+  private static native long clamper(long nativeView, long loScalarHandle, long loScalarReplaceHandle,
+                                     long hiScalarHandle, long hiScalarReplaceHandle);
+
+  protected static native long title(long handle);
+
+  private static native long capitalize(long strsColHandle, long delimitersHandle);
+
+  private static native long joinStrings(long strsHandle, long sepHandle, long narepHandle);
+
+  private static native long makeStructView(long[] handles, long rowCount);
+
+  private static native long isTimestamp(long nativeView, String format);
+  /**
+   * Native method to normalize the various bitwise representations of NAN and zero.
+   *
+   * All occurrences of -NaN are converted to NaN. Likewise, all -0.0 are converted to 0.0.
+   *
+   * @param viewHandle `long` representation of pointer to input column_view.
+   * @return Pointer to a new `column` of normalized values.
+   * @throws CudfException On failure to normalize.
+   */
+  private static native long normalizeNANsAndZeros(long viewHandle) throws CudfException;
+
+  /**
+   * Native method to deep copy a column while replacing the null mask. The null mask is the
+   * bitwise merge of the null masks in the columns given as arguments.
+   *
+   * @param baseHandle column view of the column that is deep copied.
+   * @param viewHandles array of views whose null masks are merged, must have identical row counts.
+   * @return native handle of the copied cudf column with replaced null mask.
+   */
+  private static native long bitwiseMergeAndSetValidity(long baseHandle, long[] viewHandles,
+                                                        int nullConfig) throws CudfException;
+
+  /**
+   * Native method to deep copy a column while replacing the null mask. The null mask is the
+   * device_vector equivalent of the boolean column given as argument.
+   *
+   * The boolColumn must have the same number of rows as the exemplar column.
+   * The result column will have the same number of rows as the exemplar.
+   * For all indices `i` where the boolean column is `true`, the result column will have a valid value at index i.
+   * For all other values (i.e. `false` or `null`), the result column will have nulls.
+   *
+   * If the exemplar column has a null at a given index `i`, and the new validity mask is `true` at index `i`,
+   * then the resultant row value is undefined.
+   *
+   * @param exemplarViewHandle column view of the column that is deep copied.
+   * @param boolColumnViewHandle bool column whose value is to be used as the null mask.
+   * @return Deep copy of the column with replaced null mask.
+   */
+  private static native long copyWithBooleanColumnAsValidity(long exemplarViewHandle,
+                                                             long boolColumnViewHandle) throws CudfException;
+
+  ////////
+  // Native cudf::column_view life cycle and metadata access methods. Life cycle methods
+  // should typically only be called from the OffHeap inner class.
+  ////////
+
+  static native int getNativeTypeId(long viewHandle) throws CudfException;
+
+  static native int getNativeTypeScale(long viewHandle) throws CudfException;
+
+  static native int getNativeRowCount(long viewHandle) throws CudfException;
+
+  static native int getNativeNullCount(long viewHandle) throws CudfException;
+
+  static native void deleteColumnView(long viewHandle) throws CudfException;
+
+  private static native long getNativeDataAddress(long viewHandle) throws CudfException;
+  private static native long getNativeDataLength(long viewHandle) throws CudfException;
+
+  private static native long getNativeOffsetsAddress(long viewHandle) throws CudfException;
+  private static native long getNativeOffsetsLength(long viewHandle) throws CudfException;
+
+  private static native long getNativeValidityAddress(long viewHandle) throws CudfException;
+  private static native long getNativeValidityLength(long viewHandle) throws CudfException;
+
+  static native long makeCudfColumnView(int type, int scale, long data, long dataSize, long offsets,
+                                                long valid, int nullCount, int size, long[] childHandle);
+
+
+  static native long getChildCvPointer(long viewHandle, int childIndex) throws CudfException;
+
+  private static native long getListOffsetCvPointer(long viewHandle) throws CudfException;
+
+  static native int getNativeNumChildren(long viewHandle) throws CudfException;
+
+  // calculate the amount of device memory used by this column including any child columns
+  static native long getDeviceMemorySize(long viewHandle, boolean shouldPadForCpu) throws CudfException;
+
+  static native long copyColumnViewToCV(long viewHandle) throws CudfException;
+
+  static native long generateListOffsets(long handle) throws CudfException;
+
+  static native long applyBooleanMask(long arrayColumnView, long booleanMaskHandle) throws CudfException;
+
+  static native boolean hasNonEmptyNulls(long handle) throws CudfException;
+
+  static native long purgeNonEmptyNulls(long handle) throws CudfException;
+
+  /**
+   * A utility class to create column vector like objects without refcounts and other APIs when
+   * creating the device side vector from host side nested vectors. Eventually this can go away or
+   * be refactored to hold less state like just the handles and the buffers to close.
+   */
+  static class NestedColumnVector {
+
+    private final DeviceMemoryBuffer data;
+    private final DeviceMemoryBuffer valid;
+    private final DeviceMemoryBuffer offsets;
+    private final DType dataType;
+    private final long rows;
+    private final Optional<Long> nullCount;
+    List<NestedColumnVector> children;
+
+    private NestedColumnVector(DType type, long rows, Optional<Long> nullCount,
+        DeviceMemoryBuffer data, DeviceMemoryBuffer valid,
+        DeviceMemoryBuffer offsets, List<NestedColumnVector> children) {
+      this.dataType = type;
+      this.rows = rows;
+      this.nullCount = nullCount;
+      this.data = data;
+      this.valid = valid;
+      this.offsets = offsets;
+      this.children = children;
+    }
+
+    /**
+     * Returns a LIST ColumnVector, for now, after constructing the NestedColumnVector from the host side
+     * nested Column Vector - children. This is used for host side to device side copying internally.
+     * @param type top level dtype, which is LIST currently
+     * @param rows top level number of rows in the LIST column
+     * @param valid validity buffer
+     * @param offsets offsets buffer
+     * @param nullCount nullCount for the LIST column
+     * @param child the host side nested column vector list
+     * @return new ColumnVector of type LIST at the moment
+     */
+    static ColumnVector createColumnVector(DType type, int rows, HostMemoryBuffer data,
+        HostMemoryBuffer valid, HostMemoryBuffer offsets, Optional<Long> nullCount, List<HostColumnVectorCore> child) {
+      List<NestedColumnVector> devChildren = new ArrayList<>();
+      for (HostColumnVectorCore c : child) {
+        devChildren.add(createNewNestedColumnVector(c));
+      }
+      int mainColRows = rows;
+      DType mainColType = type;
+      HostMemoryBuffer mainColValid = valid;
+      HostMemoryBuffer mainColOffsets = offsets;
+      DeviceMemoryBuffer mainDataDevBuff = null;
+      DeviceMemoryBuffer mainValidDevBuff = null;
+      DeviceMemoryBuffer mainOffsetsDevBuff = null;
+      if (mainColValid != null) {
+        long validLen = getValidityBufferSize(mainColRows);
+        mainValidDevBuff = DeviceMemoryBuffer.allocate(validLen);
+        mainValidDevBuff.copyFromHostBuffer(mainColValid, 0, validLen);
+      }
+      if (data != null) {
+        long dataLen = data.length;
+        mainDataDevBuff = DeviceMemoryBuffer.allocate(dataLen);
+        mainDataDevBuff.copyFromHostBuffer(data, 0, dataLen);
+      }
+      if (mainColOffsets != null) {
+        // The offset buffer has (no. of rows + 1) entries, where each entry is INT32.sizeInBytes
+        long offsetsLen = OFFSET_SIZE * (((long)mainColRows) + 1);
+        mainOffsetsDevBuff = DeviceMemoryBuffer.allocate(offsetsLen);
+        mainOffsetsDevBuff.copyFromHostBuffer(mainColOffsets, 0, offsetsLen);
+      }
+      List<DeviceMemoryBuffer> toClose = new ArrayList<>();
+      long[] childHandles = new long[devChildren.size()];
+      try {
+        for (ColumnView.NestedColumnVector ncv : devChildren) {
+          toClose.addAll(ncv.getBuffersToClose());
+        }
+        for (int i = 0; i < devChildren.size(); i++) {
+          childHandles[i] = devChildren.get(i).createViewHandle();
+        }
+        return new ColumnVector(mainColType, mainColRows, nullCount, mainDataDevBuff,
+            mainValidDevBuff, mainOffsetsDevBuff, toClose, childHandles);
+      } finally {
+        for (int i = 0; i < childHandles.length; i++) {
+          if (childHandles[i] != 0) {
+            ColumnView.deleteColumnView(childHandles[i]);
+            childHandles[i] = 0;
+          }
+        }
+      }
+    }
+
+    private static NestedColumnVector createNewNestedColumnVector(
+        HostColumnVectorCore nestedChildren) {
+      if (nestedChildren == null) {
+        return null;
+      }
+      DType colType = nestedChildren.getType();
+      Optional<Long> nullCount = Optional.of(nestedChildren.getNullCount());
+      long colRows = nestedChildren.getRowCount();
+      HostMemoryBuffer colData = nestedChildren.getNestedChildren().isEmpty() ? nestedChildren.getData() : null;
+      HostMemoryBuffer colValid = nestedChildren.getValidity();
+      HostMemoryBuffer colOffsets = nestedChildren.getOffsets();
+
+      List<NestedColumnVector> children = new ArrayList<>();
+      for (HostColumnVectorCore nhcv : nestedChildren.getNestedChildren()) {
+        children.add(createNewNestedColumnVector(nhcv));
+      }
+      return createNestedColumnVector(colType, colRows, nullCount, colData, colValid, colOffsets,
+        children);
+    }
+
+    private long createViewHandle() {
+      long[] childrenColViews = null;
+      try {
+        if (children != null) {
+          childrenColViews = new long[children.size()];
+          for (int i = 0; i < children.size(); i++) {
+            childrenColViews[i] = children.get(i).createViewHandle();
+          }
+        }
+        long dataAddr = data == null ? 0 : data.address;
+        long dataLen = data == null ? 0 : data.length;
+        long offsetAddr = offsets == null ? 0 : offsets.address;
+        long validAddr = valid == null ? 0 : valid.address;
+        int nc = nullCount.orElse(ColumnVector.OffHeapState.UNKNOWN_NULL_COUNT).intValue();
+        return makeCudfColumnView(dataType.typeId.getNativeId(), dataType.getScale(), dataAddr, dataLen,
+            offsetAddr, validAddr, nc, (int) rows, childrenColViews);
+      } finally {
+        if (childrenColViews != null) {
+          for (int i = 0; i < childrenColViews.length; i++) {
+            if (childrenColViews[i] != 0) {
+              deleteColumnView(childrenColViews[i]);
+              childrenColViews[i] = 0;
+            }
+          }
+        }
+      }
+    }
+
+    List<DeviceMemoryBuffer> getBuffersToClose() {
+      List<DeviceMemoryBuffer> buffers = new ArrayList<>();
+      if (children != null) {
+        for (NestedColumnVector ncv : children) {
+          buffers.addAll(ncv.getBuffersToClose());
+        }
+      }
+      if (data != null) {
+        buffers.add(data);
+      }
+      if (valid != null) {
+        buffers.add(valid);
+      }
+      if (offsets != null) {
+        buffers.add(offsets);
+      }
+      return buffers;
+    }
+
+    private static long getEndStringOffset(long totalRows, long index, HostMemoryBuffer offsets) {
+      assert index < totalRows;
+      return offsets.getInt((index + 1) * 4);
+    }
+
+    private static NestedColumnVector createNestedColumnVector(DType type, long rows, Optional<Long> nullCount,
+        HostMemoryBuffer dataBuffer, HostMemoryBuffer validityBuffer,
+        HostMemoryBuffer offsetBuffer, List<NestedColumnVector> child) {
+      DeviceMemoryBuffer data = null;
+      DeviceMemoryBuffer valid = null;
+      DeviceMemoryBuffer offsets = null;
+      if (dataBuffer != null) {
+        long dataLen = rows * type.getSizeInBytes();
+        if (type.equals(DType.STRING)) {
+          // This needs a different type
+          dataLen = getEndStringOffset(rows, rows - 1, offsetBuffer);
+          if (dataLen == 0 && nullCount.get() == 0) {
+            // This is a work around to an issue where a column of all empty strings must have at
+            // least one byte or it will not be interpreted correctly.
+            dataLen = 1;
+          }
+        }
+        data = DeviceMemoryBuffer.allocate(dataLen);
+        data.copyFromHostBuffer(dataBuffer, 0, dataLen);
+      }
+      if (validityBuffer != null) {
+        long validLen = getValidityBufferSize((int)rows);
+        valid = DeviceMemoryBuffer.allocate(validLen);
+        valid.copyFromHostBuffer(validityBuffer, 0, validLen);
+      }
+      if (offsetBuffer != null) {
+        long offsetsLen = OFFSET_SIZE * (rows + 1);
+        offsets = DeviceMemoryBuffer.allocate(offsetsLen);
+        offsets.copyFromHostBuffer(offsetBuffer, 0, offsetsLen);
+      }
+      NestedColumnVector ret = new NestedColumnVector(type, rows, nullCount, data, valid, offsets,
+        child);
+      return ret;
+    }
+  }
+
+
+  /////////////////////////////////////////////////////////////////////////////
+  // DATA MOVEMENT
+  /////////////////////////////////////////////////////////////////////////////
+
+  private static HostColumnVectorCore copyToHostNestedHelper(
+      ColumnView deviceCvPointer, HostMemoryAllocator hostMemoryAllocator) {
+    if (deviceCvPointer == null) {
+      return null;
+    }
+    HostMemoryBuffer hostOffsets = null;
+    HostMemoryBuffer hostValid = null;
+    HostMemoryBuffer hostData = null;
+    List<HostColumnVectorCore> children = new ArrayList<>();
+    BaseDeviceMemoryBuffer currData = null;
+    BaseDeviceMemoryBuffer currOffsets = null;
+    BaseDeviceMemoryBuffer currValidity = null;
+    long currNullCount = 0l;
+    boolean needsCleanup = true;
+    try {
+      long currRows = deviceCvPointer.getRowCount();
+      DType currType = deviceCvPointer.getType();
+      currData = deviceCvPointer.getData();
+      currOffsets = deviceCvPointer.getOffsets();
+      currValidity = deviceCvPointer.getValid();
+      if (currData != null) {
+        hostData = hostMemoryAllocator.allocate(currData.length);
+        hostData.copyFromDeviceBuffer(currData);
+      }
+      if (currValidity != null) {
+        hostValid = hostMemoryAllocator.allocate(currValidity.length);
+        hostValid.copyFromDeviceBuffer(currValidity);
+      }
+      if (currOffsets != null) {
+        hostOffsets = hostMemoryAllocator.allocate(currOffsets.length);
+        hostOffsets.copyFromDeviceBuffer(currOffsets);
+      }
+      int numChildren = deviceCvPointer.getNumChildren();
+      for (int i = 0; i < numChildren; i++) {
+        try(ColumnView childDevPtr = deviceCvPointer.getChildColumnView(i)) {
+          children.add(copyToHostNestedHelper(childDevPtr, hostMemoryAllocator));
+        }
+      }
+      currNullCount = deviceCvPointer.getNullCount();
+      Optional<Long> nullCount = Optional.of(currNullCount);
+      HostColumnVectorCore ret =
+          new HostColumnVectorCore(currType, currRows, nullCount, hostData,
+              hostValid, hostOffsets, children);
+      needsCleanup = false;
+      return ret;
+    } finally {
+      if (currData != null) {
+        currData.close();
+      }
+      if (currOffsets != null) {
+        currOffsets.close();
+      }
+      if (currValidity != null) {
+        currValidity.close();
+      }
+      if (needsCleanup) {
+        if (hostData != null) {
+          hostData.close();
+        }
+        if (hostOffsets != null) {
+          hostOffsets.close();
+        }
+        if (hostValid != null) {
+          hostValid.close();
+        }
+      }
+    }
+  }
+
+  /**
+   * Copy the data to the host.
+   */
+  public HostColumnVector copyToHost(HostMemoryAllocator hostMemoryAllocator) {
+    try (NvtxRange toHost = new NvtxRange("ensureOnHost", NvtxColor.BLUE)) {
+      HostMemoryBuffer hostDataBuffer = null;
+      HostMemoryBuffer hostValidityBuffer = null;
+      HostMemoryBuffer hostOffsetsBuffer = null;
+      BaseDeviceMemoryBuffer valid = getValid();
+      BaseDeviceMemoryBuffer offsets = getOffsets();
+      BaseDeviceMemoryBuffer data = null;
+      DType type = this.type;
+      long rows = this.rows;
+      if (!type.isNestedType()) {
+        data = getData();
+      }
+      boolean needsCleanup = true;
+      try {
+        // We don't have a good way to tell if it is cached on the device or recalculate it on
+        // the host for now, so take the hit here.
+        getNullCount();
+        if (!type.isNestedType()) {
+          if (valid != null) {
+            hostValidityBuffer = hostMemoryAllocator.allocate(valid.getLength());
+            hostValidityBuffer.copyFromDeviceBuffer(valid);
+          }
+          if (offsets != null) {
+            hostOffsetsBuffer = hostMemoryAllocator.allocate(offsets.length);
+            hostOffsetsBuffer.copyFromDeviceBuffer(offsets);
+          }
+          // If a strings column is all null values there is no data buffer allocated
+          if (data != null) {
+            hostDataBuffer = hostMemoryAllocator.allocate(data.length);
+            hostDataBuffer.copyFromDeviceBuffer(data);
+          }
+          HostColumnVector ret = new HostColumnVector(type, rows, Optional.of(nullCount),
+              hostDataBuffer, hostValidityBuffer, hostOffsetsBuffer);
+          needsCleanup = false;
+          return ret;
+        } else {
+          if (data != null) {
+            hostDataBuffer = hostMemoryAllocator.allocate(data.length);
+            hostDataBuffer.copyFromDeviceBuffer(data);
+          }
+
+          if (valid != null) {
+            hostValidityBuffer = hostMemoryAllocator.allocate(valid.getLength());
+            hostValidityBuffer.copyFromDeviceBuffer(valid);
+          }
+          if (offsets != null) {
+            hostOffsetsBuffer = hostMemoryAllocator.allocate(offsets.getLength());
+            hostOffsetsBuffer.copyFromDeviceBuffer(offsets);
+          }
+          List<HostColumnVectorCore> children = new ArrayList<>();
+          for (int i = 0; i < getNumChildren(); i++) {
+            try (ColumnView childDevPtr = getChildColumnView(i)) {
+              children.add(copyToHostNestedHelper(childDevPtr, hostMemoryAllocator));
+            }
+          }
+          HostColumnVector ret = new HostColumnVector(type, rows, Optional.of(nullCount),
+              hostDataBuffer, hostValidityBuffer, hostOffsetsBuffer, children);
+          needsCleanup = false;
+          return ret;
+        }
+      } finally {
+        if (data != null) {
+          data.close();
+        }
+        if (offsets != null) {
+          offsets.close();
+        }
+        if (valid != null) {
+          valid.close();
+        }
+        if (needsCleanup) {
+          if (hostOffsetsBuffer != null) {
+            hostOffsetsBuffer.close();
+          }
+          if (hostDataBuffer != null) {
+            hostDataBuffer.close();
+          }
+          if (hostValidityBuffer != null) {
+            hostValidityBuffer.close();
+          }
+        }
+      }
+    }
+  }
+
+  public HostColumnVector copyToHost() {
+    return copyToHost(DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Calculate the total space required to copy the data to the host. This should be padded to
+   * the alignment that the CPU requires.
+   */
+  public long getHostBytesRequired() {
+    return getDeviceMemorySize(getNativeView(), true);
+  }
+
+  /**
+   * Get the size that the host will align memory allocations to in bytes.
+   */
+  public static native long hostPaddingSizeInBytes();
+
+  /**
+   * Exact check if a column or its descendants have non-empty null rows
+   *
+   * @return Whether the column or its descendants have non-empty null rows
+   */
+  public boolean hasNonEmptyNulls() {
+    return hasNonEmptyNulls(viewHandle);
+  }
+
+  /**
+   * Copies this column into output while purging any non-empty null rows in the column or its
+   * descendants.
+   *
+   * If this column is not of compound type (LIST/STRING/STRUCT/DICTIONARY), the output will be
+   * the same as input.
+   *
+   * The purge operation only applies directly to LIST and STRING columns, but it applies indirectly
+   * to STRUCT/DICTIONARY columns as well, since these columns may have child columns that
+   * are LIST or STRING.
+   *
+   * Examples:
+   * lists = data: [{{0,1}, {2,3}, {4,5}} validity: {true, false, true}]
+   * lists[1] is null, but the list's child column still stores `{2,3}`.
+   *
+   * After purging the contents of the list's null rows, the column's contents will be:
+   * lists = [data: {{0,1}, {4,5}} validity: {true, false, true}]
+   *
+   * @return A new column with equivalent contents to `input`, but with null rows purged
+   */
+  public ColumnVector purgeNonEmptyNulls() {
+    return new ColumnVector(purgeNonEmptyNulls(viewHandle));
+  }
+
+  static ColumnView[] getColumnViewsFromPointers(long[] nativeHandles) {
+    ColumnView[] columns = new ColumnView[nativeHandles.length];
+    try {
+      for (int i = 0; i < nativeHandles.length; i++) {
+        long nativeHandle = nativeHandles[i];
+        // setting address to zero, so we don't clean it in case of an exception as it
+        // will be cleaned up by the constructor
+        nativeHandles[i] = 0;
+        columns[i] = new ColumnView(nativeHandle);
+      }
+      return columns;
+    } catch (Throwable t) {
+      try {
+        cleanupColumnViews(nativeHandles, columns, t);
+      } catch (Throwable s) {
+        t.addSuppressed(s);
+      } finally {
+        throw t;
+      }
+    }
+  }
+
+  /**
+   * Convert this integer column to hexadecimal column and return a new strings column
+   *
+   * Any null entries will result in corresponding null entries in the output column.
+   *
+   * The output character set is '0'-'9' and 'A'-'F'. The output string width will
+   * be a multiple of 2 depending on the size of the integer type. A single leading
+   * zero is applied to the first non-zero output byte if it is less than 0x10.
+   *
+   * Example:
+   * input = [123, -1, 0, 27, 342718233]
+   * s = input.toHex()
+   * s is [ '04D2', 'FFFFFFFF', '00', '1B', '146D7719']
+   *
+   * The example above shows an `INT32` type column where each integer is 4 bytes.
+   * Leading zeros are suppressed unless filling out a complete byte as in
+   * `123 -> '04D2'` instead of `000004D2` or `4D2`.
+   *
+   * @return new string ColumnVector
+   */
+  public ColumnVector toHex() {
+    assert getType().isIntegral() : "Only integers are supported";
+    return new ColumnVector(toHex(this.getNativeView()));
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ColumnWriterOptions.java b/java/src/main/java/ai/rapids/cudf/ColumnWriterOptions.java
new file mode 100644
index 0000000..a95c5f5
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ColumnWriterOptions.java
@@ -0,0 +1,675 @@
+/*
+ *
+ *  Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Per column settings for writing Parquet/ORC files.
+ *
+ * The native also uses the same "column_in_metadata" for both Parquet and ORC.
+ */
+public class ColumnWriterOptions {
+  // `isTimestampTypeInt96` is ignored in ORC
+  private boolean isTimestampTypeInt96;
+  private int precision;
+  private boolean isNullable;
+  private boolean isMap = false;
+  private boolean isBinary = false;
+  private String columnName;
+  // only for Parquet
+  private boolean hasParquetFieldId;
+  private int parquetFieldId;
+
+  private ColumnWriterOptions(AbstractStructBuilder builder) {
+    this.columnName = builder.name;
+    this.isNullable = builder.isNullable;
+    this.hasParquetFieldId = builder.hasParquetFieldId;
+    this.parquetFieldId = builder.parquetFieldId;
+    this.childColumnOptions =
+        (ColumnWriterOptions[]) builder.children.toArray(new ColumnWriterOptions[0]);
+  }
+
+  // The sentinel value of unknown precision (default value)
+  public static int UNKNOWN_PRECISION = -1;
+
+  /**
+   * Constructor used for list
+   */
+  private ColumnWriterOptions(ListBuilder builder) {
+    assert(builder.children.size() == 1) : "Lists can only have one child";
+    this.columnName = builder.name;
+    this.isNullable = builder.isNullable;
+    // we are adding the child twice even though lists have one child only because the way the cudf
+    // has implemented this it requires two children to be set for the list, but it drops the
+    // first one. This is something that is a lower priority and might be fixed in future
+    this.childColumnOptions =
+        new ColumnWriterOptions[]{DUMMY_CHILD, builder.children.get(0)};
+  }
+
+  protected ColumnWriterOptions[] childColumnOptions = {};
+  protected abstract static class AbstractStructBuilder<T extends AbstractStructBuilder,
+      V extends ColumnWriterOptions> extends NestedBuilder<T, V> {
+    /**
+     * Builder specific to build a Struct meta
+     */
+    public AbstractStructBuilder(String name, boolean isNullable) {
+      super(name, isNullable);
+    }
+
+    public AbstractStructBuilder(String name, boolean isNullable, int parquetFieldId) {
+      super(name, isNullable, parquetFieldId);
+    }
+
+    protected AbstractStructBuilder() {
+      super();
+    }
+  }
+
+  // This child is needed as the first child of a List column meta due to how cudf has been
+  // implemented. Cudf drops the first child from the meta if a column is a LIST. This is done
+  // this way due to some complications in the parquet reader. There was change to fix this here:
+  // https://github.com/rapidsai/cudf/pull/7461/commits/5ce33b40abb87cc7b76b5efeb0a3a0215f9ef6fb
+  // but it was reverted later on here:
+  // https://github.com/rapidsai/cudf/pull/7461/commits/f248eb7265de995a95f998d46d897fb0ae47f53e
+  static ColumnWriterOptions DUMMY_CHILD = new ColumnWriterOptions("DUMMY");
+
+  public static abstract class NestedBuilder<T extends NestedBuilder, V extends ColumnWriterOptions> {
+    protected List<ColumnWriterOptions> children = new ArrayList<>();
+    protected boolean isNullable = true;
+    protected String name = "";
+    // Parquet structure needs
+    protected boolean hasParquetFieldId;
+    protected int parquetFieldId;
+
+    /**
+     * Builder specific to build a Struct meta
+     */
+    protected NestedBuilder(String name, boolean isNullable) {
+      this.name = name;
+      this.isNullable = isNullable;
+    }
+
+    protected NestedBuilder(String name, boolean isNullable, int parquetFieldId) {
+      this.name = name;
+      this.isNullable = isNullable;
+      this.hasParquetFieldId = true;
+      this.parquetFieldId = parquetFieldId;
+    }
+
+    protected NestedBuilder() {}
+
+    protected ColumnWriterOptions withColumn(String name, boolean isNullable) {
+      return new ColumnWriterOptions(name, isNullable);
+    }
+
+    protected ColumnWriterOptions withColumn(String name, boolean isNullable, int parquetFieldId) {
+      return new ColumnWriterOptions(name, isNullable, parquetFieldId);
+    }
+
+    protected ColumnWriterOptions withDecimal(String name, int precision,
+                                              boolean isNullable) {
+      return new ColumnWriterOptions(name, false, precision, isNullable);
+    }
+
+    protected ColumnWriterOptions withDecimal(String name, int precision,
+                                              boolean isNullable, int parquetFieldId) {
+      return new ColumnWriterOptions(name, false, precision, isNullable, parquetFieldId);
+    }
+
+    protected ColumnWriterOptions withTimestamp(String name, boolean isInt96,
+                                                boolean isNullable) {
+      return new ColumnWriterOptions(name, isInt96, UNKNOWN_PRECISION, isNullable);
+    }
+
+    protected ColumnWriterOptions withTimestamp(String name, boolean isInt96,
+                                                boolean isNullable, int parquetFieldId) {
+      return new ColumnWriterOptions(name, isInt96, UNKNOWN_PRECISION, isNullable, parquetFieldId);
+    }
+
+    protected ColumnWriterOptions withBinary(String name, boolean isNullable) {
+      ColumnWriterOptions opt = listBuilder(name, isNullable)
+          // The name here does not matter. It will not be included in the final file
+          // This is just to get the metadata to line up properly for the C++ APIs
+          .withColumns(false, "BINARY_DATA")
+          .build();
+      opt.isBinary = true;
+      return opt;
+    }
+
+    protected ColumnWriterOptions withBinary(String name, boolean isNullable, int parquetFieldId) {
+      ColumnWriterOptions opt = listBuilder(name, isNullable)
+          // The name here does not matter. It will not be included in the final file
+          // This is just to get the metadata to line up properly for the C++ APIs
+          .withColumn(false, "BINARY_DATA", parquetFieldId)
+          .build();
+      opt.isBinary = true;
+      return opt;
+    }
+
+    /**
+     * Set the list column meta.
+     * Lists should have only one child in ColumnVector, but the metadata expects a
+     * LIST column to have two children and the first child to be the
+     * {@link ColumnWriterOptions#DUMMY_CHILD}.
+     * This is the current behavior in cudf and will change in future
+     * @return this for chaining.
+     */
+    public T withListColumn(ListColumnWriterOptions child) {
+      assert (child.getChildColumnOptions().length == 2) : "Lists can only have two children";
+      if (child.getChildColumnOptions()[0] != DUMMY_CHILD) {
+        throw new IllegalArgumentException("First child in the list has to be DUMMY_CHILD");
+      }
+      if (child.getChildColumnOptions()[1].getColumnName().isEmpty()) {
+        throw new IllegalArgumentException("Column name can't be empty");
+      }
+      children.add(child);
+      return (T) this;
+    }
+
+    /**
+     * Set the map column meta.
+     * @return this for chaining.
+     */
+    public T withMapColumn(ColumnWriterOptions child) {
+      children.add(child);
+      return (T) this;
+    }
+
+    /**
+     * Set a child struct meta data
+     * @return this for chaining.
+     */
+    public T withStructColumn(StructColumnWriterOptions child) {
+      for (ColumnWriterOptions opt: child.getChildColumnOptions()) {
+        if (opt.getColumnName().isEmpty()) {
+          throw new IllegalArgumentException("Column name can't be empty");
+        }
+      }
+      children.add(child);
+      return (T) this;
+    }
+
+    /**
+     * Set column name
+     */
+    public T withNonNullableColumns(String... names) {
+      withColumns(false, names);
+      return (T) this;
+    }
+
+    /**
+     * Set nullable column meta data
+     */
+    public T withNullableColumns(String... names) {
+      withColumns(true, names);
+      return (T) this;
+    }
+
+    /**
+     * Set a simple child meta data
+     * @return this for chaining.
+     */
+    public T withColumns(boolean nullable, String... names) {
+      for (String n : names) {
+        children.add(withColumn(n, nullable));
+      }
+      return (T) this;
+    }
+
+    /**
+     * Set a simple child meta data
+     * @return this for chaining.
+     */
+    public T withColumn(boolean nullable, String name, int parquetFieldId) {
+      children.add(withColumn(name, nullable, parquetFieldId));
+      return (T) this;
+    }
+
+    /**
+     * Set a Decimal child meta data
+     * @return this for chaining.
+     */
+    public T withDecimalColumn(String name, int precision, boolean nullable) {
+      children.add(withDecimal(name, precision, nullable));
+      return (T) this;
+    }
+
+    /**
+     * Set a Decimal child meta data
+     * @return this for chaining.
+     */
+    public T withDecimalColumn(String name, int precision, boolean nullable, int parquetFieldId) {
+      children.add(withDecimal(name, precision, nullable, parquetFieldId));
+      return (T) this;
+    }
+
+    /**
+     * Set a Decimal child meta data
+     * @return this for chaining.
+     */
+    public T withNullableDecimalColumn(String name, int precision) {
+      withDecimalColumn(name, precision, true);
+      return (T) this;
+    }
+
+    /**
+     * Set a Decimal child meta data
+     * @return this for chaining.
+     */
+    public T withDecimalColumn(String name, int precision) {
+      withDecimalColumn(name, precision, false);
+      return (T) this;
+    }
+
+    /**
+     * Set a binary child meta data
+     * @return this for chaining.
+     */
+    public T withBinaryColumn(String name, boolean nullable, int parquetFieldId) {
+      children.add(withBinary(name, nullable, parquetFieldId));
+      return (T) this;
+    }
+
+    /**
+     * Set a binary child meta data
+     * @return this for chaining.
+     */
+    public T withBinaryColumn(String name, boolean nullable) {
+      children.add(withBinary(name, nullable));
+      return (T) this;
+    }
+
+    /**
+     * Set a timestamp child meta data
+     * @return this for chaining.
+     */
+    public T withTimestampColumn(String name, boolean isInt96, boolean nullable, int parquetFieldId) {
+      children.add(withTimestamp(name, isInt96, nullable, parquetFieldId));
+      return (T) this;
+    }
+
+    /**
+     * Set a timestamp child meta data
+     * @return this for chaining.
+     */
+    public T withTimestampColumn(String name, boolean isInt96, boolean nullable) {
+      children.add(withTimestamp(name, isInt96, nullable));
+      return (T) this;
+    }
+
+    /**
+     * Set a timestamp child meta data
+     * @return this for chaining.
+     */
+    public T withTimestampColumn(String name, boolean isInt96) {
+      withTimestampColumn(name, isInt96, false);
+      return (T) this;
+    }
+
+    /**
+     * Set a timestamp child meta data
+     * @return this for chaining.
+     */
+    public T withNullableTimestampColumn(String name, boolean isInt96) {
+      withTimestampColumn(name, isInt96, true);
+      return (T) this;
+    }
+
+    public abstract V build();
+  }
+
+  public ColumnWriterOptions(String columnName, boolean isTimestampTypeInt96,
+                             int precision, boolean isNullable) {
+    this.isTimestampTypeInt96 = isTimestampTypeInt96;
+    this.precision = precision;
+    this.isNullable = isNullable;
+    this.columnName = columnName;
+  }
+
+  public ColumnWriterOptions(String columnName, boolean isTimestampTypeInt96,
+                             int precision, boolean isNullable, int parquetFieldId) {
+    this(columnName, isTimestampTypeInt96, precision, isNullable);
+    this.hasParquetFieldId = true;
+    this.parquetFieldId = parquetFieldId;
+  }
+
+  public ColumnWriterOptions(String columnName, boolean isNullable) {
+    this.isTimestampTypeInt96 = false;
+    this.precision = UNKNOWN_PRECISION;
+    this.isNullable = isNullable;
+    this.columnName = columnName;
+  }
+
+  public ColumnWriterOptions(String columnName, boolean isNullable, int parquetFieldId) {
+    this(columnName, isNullable);
+    this.hasParquetFieldId = true;
+    this.parquetFieldId = parquetFieldId;
+  }
+
+  public ColumnWriterOptions(String columnName) {
+    this(columnName, true);
+  }
+
+  @FunctionalInterface
+  protected interface ByteArrayProducer {
+    boolean[] apply(ColumnWriterOptions opt);
+  }
+
+  @FunctionalInterface
+  protected interface IntArrayProducer {
+    int[] apply(ColumnWriterOptions opt);
+  }
+
+  boolean[] getFlatIsTimeTypeInt96() {
+    boolean[] ret = {isTimestampTypeInt96};
+    if (childColumnOptions.length > 0) {
+      return getFlatBooleans(ret, (opt) -> opt.getFlatIsTimeTypeInt96());
+    } else {
+      return ret;
+    }
+  }
+
+  protected boolean[] getFlatBooleans(boolean[] ret, ByteArrayProducer producer) {
+    boolean[][] childResults = new boolean[childColumnOptions.length][];
+    int totalChildrenFlatLength = ret.length;
+    for (int i = 0 ; i < childColumnOptions.length ; i++) {
+      ColumnWriterOptions opt = childColumnOptions[i];
+      childResults[i] = producer.apply(opt);
+      totalChildrenFlatLength += childResults[i].length;
+    }
+
+    boolean[] result = new boolean[totalChildrenFlatLength];
+    System.arraycopy(ret, 0, result, 0, ret.length);
+    int copiedSoFar = ret.length;
+    for (int i = 0 ; i < childColumnOptions.length ; i++) {
+      System.arraycopy(childResults[i], 0, result, copiedSoFar, childResults[i].length);
+      copiedSoFar += childResults[i].length;
+    }
+    return result;
+  }
+
+  int[] getFlatPrecision() {
+    int[] ret = {precision};
+    if (childColumnOptions.length > 0) {
+      return getFlatInts(ret, (opt) -> opt.getFlatPrecision());
+    } else {
+      return ret;
+    }
+  }
+
+  boolean[] getFlatHasParquetFieldId() {
+    boolean[] ret = {hasParquetFieldId};
+    if (childColumnOptions.length > 0) {
+      return getFlatBooleans(ret, (opt) -> opt.getFlatHasParquetFieldId());
+    } else {
+      return ret;
+    }
+  }
+
+  int[] getFlatParquetFieldId() {
+    int[] ret = {parquetFieldId};
+    if (childColumnOptions.length > 0) {
+      return getFlatInts(ret, (opt) -> opt.getFlatParquetFieldId());
+    } else {
+      return ret;
+    }
+  }
+
+  boolean[] getFlatIsNullable() {
+    boolean[] ret = {isNullable};
+    if (childColumnOptions.length > 0) {
+      return getFlatBooleans(ret, (opt) -> opt.getFlatIsNullable());
+    } else {
+      return ret;
+    }
+  }
+
+  boolean[] getFlatIsMap() {
+    boolean[] ret = {isMap};
+    if (childColumnOptions.length > 0) {
+      return getFlatBooleans(ret, (opt) -> opt.getFlatIsMap());
+    } else {
+      return ret;
+    }
+  }
+
+  boolean[] getFlatIsBinary() {
+    boolean[] ret = {isBinary};
+    if (childColumnOptions.length > 0) {
+      return getFlatBooleans(ret, (opt) -> opt.getFlatIsBinary());
+    } else {
+      return ret;
+    }
+  }
+
+  int[] getFlatNumChildren() {
+    int[] ret = {childColumnOptions.length};
+    if (childColumnOptions.length > 0) {
+      return getFlatInts(ret, (opt) -> opt.getFlatNumChildren());
+    } else {
+      return ret;
+    }
+  }
+
+  protected int[] getFlatInts(int[] ret, IntArrayProducer producer) {
+    int[][] childResults = new int[childColumnOptions.length][];
+    int totalChildrenFlatLength = ret.length;
+    for (int i = 0 ; i < childColumnOptions.length ; i++) {
+      ColumnWriterOptions opt = childColumnOptions[i];
+      childResults[i] = producer.apply(opt);
+      totalChildrenFlatLength += childResults[i].length;
+    }
+
+    int[] result = new int[totalChildrenFlatLength];
+    System.arraycopy(ret, 0, result, 0, ret.length);
+    int copiedSoFar = ret.length;
+    for (int i = 0 ; i < childColumnOptions.length ; i++) {
+      System.arraycopy(childResults[i], 0, result, copiedSoFar, childResults[i].length);
+      copiedSoFar += childResults[i].length;
+    }
+    return result;
+  }
+
+  String[] getFlatColumnNames() {
+    String[] ret = {columnName};
+    if (childColumnOptions.length > 0) {
+      return getFlatColumnNames(ret);
+    } else {
+      return ret;
+    }
+  }
+
+  protected String[] getFlatColumnNames(String[] ret) {
+    String[][] childResults = new String[childColumnOptions.length][];
+    int totalChildrenFlatLength = ret.length;
+    for (int i = 0 ; i < childColumnOptions.length ; i++) {
+      ColumnWriterOptions opt = childColumnOptions[i];
+      childResults[i] = opt.getFlatColumnNames();
+      totalChildrenFlatLength += childResults[i].length;
+    }
+
+    String[] result = new String[totalChildrenFlatLength];
+    System.arraycopy(ret, 0, result, 0, ret.length);
+    int copiedSoFar = ret.length;
+    for (int i = 0 ; i < childColumnOptions.length ; i++) {
+      System.arraycopy(childResults[i], 0, result, copiedSoFar, childResults[i].length);
+      copiedSoFar += childResults[i].length;
+    }
+    return result;
+  }
+
+  /**
+   * Add a Map Column to the schema.
+   * <p>
+   * Maps are List columns with a Struct named 'key_value' with a child named 'key' and a child
+   * named 'value'. The caller of this method doesn't need to worry about this as this method will
+   * take care of this without the knowledge of the caller.
+   *
+   * Note: This method always returns a nullabe column, cannot return non-nullable column.
+   * Do not use this, use the next function with the parameter `isNullable`.
+   */
+  @Deprecated
+  public static ColumnWriterOptions mapColumn(String name, ColumnWriterOptions key,
+                                              ColumnWriterOptions value) {
+    StructColumnWriterOptions struct = structBuilder("key_value").build();
+    if (key.isNullable) {
+      throw new IllegalArgumentException("key column can not be nullable");
+    }
+    struct.childColumnOptions = new ColumnWriterOptions[]{key, value};
+    ColumnWriterOptions opt = listBuilder(name)
+        .withStructColumn(struct)
+        .build();
+    opt.isMap = true;
+    return opt;
+  }
+
+  /**
+   * Add a Map Column to the schema.
+   * <p>
+   * Maps are List columns with a Struct named 'key_value' with a child named 'key' and a child
+   * named 'value'. The caller of this method doesn't need to worry about this as this method will
+   * take care of this without the knowledge of the caller.
+   *
+   * Note: If this map column is a key of another map, should pass isNullable = false.
+   * e.g.: map1(map2(int, int), int) the map2 should be non-nullable.
+   *
+   * @param isNullable is the returned map nullable.
+   */
+  public static ColumnWriterOptions mapColumn(String name, ColumnWriterOptions key,
+                                              ColumnWriterOptions value, Boolean isNullable) {
+    if (key.isNullable) {
+      throw new IllegalArgumentException("key column can not be nullable");
+    }
+    StructColumnWriterOptions struct = structBuilder("key_value").build();
+    struct.childColumnOptions = new ColumnWriterOptions[]{key, value};
+    ColumnWriterOptions opt = listBuilder(name, isNullable)
+        .withStructColumn(struct)
+        .build();
+    opt.isMap = true;
+    return opt;
+  }
+
+  /**
+   * Creates a ListBuilder for column called 'name'
+   */
+  public static ListBuilder listBuilder(String name) {
+    return new ListBuilder(name, true);
+  }
+
+  /**
+   * Creates a ListBuilder for column called 'name'
+   */
+  public static ListBuilder listBuilder(String name, boolean isNullable) {
+    return new ListBuilder(name, isNullable);
+  }
+
+  /**
+   * Creates a StructBuilder for column called 'name'
+   */
+  public static StructBuilder structBuilder(String name, boolean isNullable) {
+    return new StructBuilder(name, isNullable);
+  }
+
+  /**
+   * Creates a StructBuilder for column called 'name'
+   */
+  public static StructBuilder structBuilder(String name, boolean isNullable, int parquetFieldId) {
+    return new StructBuilder(name, isNullable, parquetFieldId);
+  }
+
+  /**
+   * Creates a StructBuilder for column called 'name'
+   */
+  public static StructBuilder structBuilder(String name) {
+    return new StructBuilder(name, true);
+  }
+
+  /**
+   * Return if the column can have null values
+   */
+  public String getColumnName() {
+    return columnName;
+  }
+
+  /**
+   * Return if the column can have null values
+   */
+  public boolean isNullable() {
+    return isNullable;
+  }
+
+  /**
+   * Return the precision for this column
+   */
+  public int getPrecision() {
+    return precision;
+  }
+
+  /**
+   * Returns true if the writer is expected to write timestamps in INT96
+   */
+  public boolean isTimestampTypeInt96() {
+    return isTimestampTypeInt96;
+  }
+
+  /**
+   * Return the child columnOptions for this column
+   */
+  public ColumnWriterOptions[] getChildColumnOptions() {
+    return childColumnOptions;
+  }
+
+  public static class StructColumnWriterOptions extends ColumnWriterOptions {
+    protected StructColumnWriterOptions(AbstractStructBuilder builder) {
+      super(builder);
+    }
+  }
+
+  public static class ListColumnWriterOptions extends ColumnWriterOptions {
+    protected ListColumnWriterOptions(ListBuilder builder) {
+      super(builder);
+    }
+  }
+
+  public static class StructBuilder extends AbstractStructBuilder<StructBuilder, StructColumnWriterOptions> {
+    public StructBuilder(String name, boolean isNullable) {
+      super(name, isNullable);
+    }
+
+    public StructBuilder(String name, boolean isNullable, int parquetFieldId) {
+      super(name, isNullable, parquetFieldId);
+    }
+
+    public StructColumnWriterOptions build() {
+      return new StructColumnWriterOptions(this);
+    }
+  }
+
+  public static class ListBuilder extends NestedBuilder<ListBuilder, ListColumnWriterOptions> {
+    public ListBuilder(String name, boolean isNullable) {
+      super(name, isNullable);
+    }
+
+    public ListColumnWriterOptions build() {
+      return new ListColumnWriterOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CompressedMetadataWriterOptions.java b/java/src/main/java/ai/rapids/cudf/CompressedMetadataWriterOptions.java
new file mode 100644
index 0000000..fae44a0
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CompressedMetadataWriterOptions.java
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import java.util.Collections;
+import java.util.LinkedHashMap;
+import java.util.Map;
+
+class CompressedMetadataWriterOptions extends WriterOptions {
+
+  private final CompressionType compressionType;
+  private final Map<String, String> metadata;
+
+  <T extends CMWriterBuilder> CompressedMetadataWriterOptions(T builder) {
+    super(builder);
+    compressionType = builder.compressionType;
+    metadata = Collections.unmodifiableMap(builder.metadata);
+  }
+
+  public CompressionType getCompressionType() {
+    return compressionType;
+  }
+
+  public Map<String, String> getMetadata() {
+    return metadata;
+  }
+
+  String[] getMetadataKeys() {
+    return metadata.keySet().toArray(new String[metadata.size()]);
+  }
+
+  String[] getMetadataValues() {
+    return metadata.values().toArray(new String[metadata.size()]);
+  }
+
+  protected static class CMWriterBuilder<T extends CMWriterBuilder> extends WriterBuilder<T> {
+    final Map<String, String> metadata = new LinkedHashMap<>();
+    CompressionType compressionType = CompressionType.AUTO;
+
+    /**
+     * Add a metadata key and a value
+     * @param key
+     * @param value
+     */
+    public T withMetadata(String key, String value) {
+      this.metadata.put(key, value);
+      return (T) this;
+    }
+
+    /**
+     * Add a map of metadata keys and values
+     * @param metadata
+     */
+    public T withMetadata(Map<String, String> metadata) {
+      this.metadata.putAll(metadata);
+      return (T) this;
+    }
+
+    /**
+     * Set the compression type to use for writing
+     * @param compression
+     */
+    public T withCompressionType(CompressionType compression) {
+      this.compressionType = compression;
+      return (T) this;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CompressionMetadataWriterOptions.java b/java/src/main/java/ai/rapids/cudf/CompressionMetadataWriterOptions.java
new file mode 100644
index 0000000..27eb1be
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CompressionMetadataWriterOptions.java
@@ -0,0 +1,128 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.LinkedHashMap;
+import java.util.Map;
+
+public class CompressionMetadataWriterOptions extends ColumnWriterOptions.StructColumnWriterOptions {
+  private final CompressionType compressionType;
+  private final Map<String, String> metadata;
+
+  protected CompressionMetadataWriterOptions(Builder builder) {
+    super(builder);
+    this.compressionType = builder.compressionType;
+    this.metadata = builder.metadata;
+  }
+
+  @Override
+  boolean[] getFlatIsTimeTypeInt96() {
+    return super.getFlatBooleans(new boolean[]{}, (opt) -> opt.getFlatIsTimeTypeInt96());
+  }
+
+  @Override
+  int[] getFlatPrecision() {
+    return super.getFlatInts(new int[]{}, (opt) -> opt.getFlatPrecision());
+  }
+
+  @Override
+  boolean[] getFlatHasParquetFieldId() {
+    return super.getFlatBooleans(new boolean[]{}, (opt) -> opt.getFlatHasParquetFieldId());
+  }
+
+  @Override
+  int[] getFlatParquetFieldId() {
+    return super.getFlatInts(new int[]{}, (opt) -> opt.getFlatParquetFieldId());
+  }
+
+  @Override
+  int[] getFlatNumChildren() {
+    return super.getFlatInts(new int[]{}, (opt) -> opt.getFlatNumChildren());
+  }
+
+  @Override
+  boolean[] getFlatIsNullable() {
+    return super.getFlatBooleans(new boolean[]{}, (opt) -> opt.getFlatIsNullable());
+  }
+
+  @Override
+  boolean[] getFlatIsMap() {
+    return super.getFlatBooleans(new boolean[]{}, (opt) -> opt.getFlatIsMap());
+  }
+
+  @Override
+  boolean[] getFlatIsBinary() {
+    return super.getFlatBooleans(new boolean[]{}, (opt) -> opt.getFlatIsBinary());
+  }
+
+  @Override
+  String[] getFlatColumnNames() {
+    return super.getFlatColumnNames(new String[]{});
+  }
+
+  String[] getMetadataKeys() {
+    return metadata.keySet().toArray(new String[metadata.size()]);
+  }
+
+  String[] getMetadataValues() {
+    return metadata.values().toArray(new String[metadata.size()]);
+  }
+
+  public CompressionType getCompressionType() {
+    return compressionType;
+  }
+
+  public Map<String, String> getMetadata() {
+    return metadata;
+  }
+
+  public int getTopLevelChildren() {
+    return childColumnOptions.length;
+  }
+
+  public abstract static class Builder<T extends Builder,
+        V extends CompressionMetadataWriterOptions> extends AbstractStructBuilder<T, V> {
+    final Map<String, String> metadata = new LinkedHashMap<>();
+    CompressionType compressionType = CompressionType.AUTO;
+
+    /**
+     * Add a metadata key and a value
+     */
+    public T withMetadata(String key, String value) {
+      this.metadata.put(key, value);
+      return (T) this;
+    }
+
+    /**
+     * Add a map of metadata keys and values
+     */
+    public T withMetadata(Map<String, String> metadata) {
+      this.metadata.putAll(metadata);
+      return (T) this;
+    }
+
+    /**
+     * Set the compression type to use for writing
+     */
+    public T withCompressionType(CompressionType compression) {
+      this.compressionType = compression;
+      return (T) this;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CompressionType.java b/java/src/main/java/ai/rapids/cudf/CompressionType.java
new file mode 100644
index 0000000..96edf1a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CompressionType.java
@@ -0,0 +1,64 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Enumeration of compression formats.
+ */
+public enum CompressionType {
+  /** No compression */
+  NONE(0),
+
+  /** Automatically detect or select the compression codec */
+  AUTO(1),
+
+  /** Snappy format using byte-oriented LZ77 */
+  SNAPPY(2),
+
+  /** GZIP format using the DEFLATE algorithm */
+  GZIP(3),
+
+  /** BZIP2 format using Burrows-Wheeler transform */
+  BZIP2(4),
+
+  /** BROTLI format using LZ77 + Huffman + 2nd order context modeling */
+  BROTLI(5),
+
+  /** ZIP format using DEFLATE algorithm */
+  ZIP(6),
+
+  /** XZ format using LZMA(2) algorithm */
+  XZ(7),
+
+  /** ZLIB format, using DEFLATE algorithm */
+  ZLIB(8),
+
+  /** LZ4 format, using LZ77 */
+  LZ4(9),
+
+  /** Lempel–Ziv–Oberhumer format */
+  LZO(10),
+
+  /** Zstandard format */
+  ZSTD(11);
+
+  final int nativeId;
+
+  CompressionType(int nativeId) { this.nativeId = nativeId; }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ContigSplitGroupByResult.java b/java/src/main/java/ai/rapids/cudf/ContigSplitGroupByResult.java
new file mode 100644
index 0000000..99ad940
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ContigSplitGroupByResult.java
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * Used to save groups and uniq key table for `Table.contiguousSplitGroupsAndGenUniqKeys`
+ * Each row in uniq key table is corresponding to a group
+ * Resource management note:
+ * This class is the owner of `groups` and
+ * `uniqKeysTable`(or uniqKeyColumns if table is not constructed)
+ * 1: Use `closeGroups` and `closeUniqKeyTable` to close the resources separately
+ * if you want to close eagerly.
+ * 2: Or auto close them by `AutoCloseable`
+ * Use `releaseGroups` to release the ownership of the `groups` to the caller,
+ * then the caller is responsible to close the `groups`
+ */
+public class ContigSplitGroupByResult implements AutoCloseable {
+  // set by JNI cpp code
+  private ContiguousTable[] groups;
+
+  // set by JNI cpp code, used to construct an uniq key Table
+  private long[] uniqKeyColumns;
+
+  // An additional table is introduced to store the group keys,
+  // and each key is corresponding to a group.
+  private Table uniqKeysTable;
+
+  /**
+   * Get the key table, each row in the key table is corresponding to a group.
+   * Note: Close the key table by `closeUniqKeyTable`
+   *
+   * @return the key table, it could be null if invoking native method `Table.contiguousSplitGroups`
+   * with `genUniqKeys` as false
+   */
+  public Table getUniqKeyTable() {
+    if (uniqKeysTable == null && uniqKeyColumns != null && uniqKeyColumns.length > 0) {
+      // new `Table` asserts uniqKeyColumns.length > 0
+      uniqKeysTable = new Table(uniqKeyColumns);
+      uniqKeyColumns = null;
+    }
+    return uniqKeysTable;
+  }
+
+  /**
+   * Close the key table or key columns
+   */
+  public void closeUniqKeyTable() {
+    if (uniqKeysTable != null) {
+      uniqKeysTable.close();
+      uniqKeysTable = null;
+    } else if (uniqKeyColumns != null) {
+      for (long handle : uniqKeyColumns) {
+        ColumnVector.deleteCudfColumn(handle);
+      }
+      uniqKeyColumns = null;
+    }
+  }
+
+  /**
+   * Get the split group tables.
+   * Note: Close the group tables by `closeGroups`
+   *
+   * @return the split group tables
+   */
+  public ContiguousTable[] getGroups() {
+    return groups;
+  }
+
+  /**
+   * Release the ownership of the `groups`
+   * The caller is responsible to close the returned groups.
+   *
+   * @return split group tables
+   */
+  ContiguousTable[] releaseGroups() {
+    ContiguousTable[] copy = groups;
+    groups = null;
+    return copy;
+  }
+
+  /**
+   * Close the split group tables
+   */
+  public void closeGroups() {
+    if (groups != null) {
+      for (ContiguousTable contig : groups) {
+        contig.close();
+      }
+      groups = null;
+    }
+  }
+
+  @Override
+  public void close() {
+    try {
+      closeUniqKeyTable();
+    } finally {
+      closeGroups();
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ContiguousTable.java b/java/src/main/java/ai/rapids/cudf/ContiguousTable.java
new file mode 100644
index 0000000..8193e4f
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ContiguousTable.java
@@ -0,0 +1,121 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.nio.ByteBuffer;
+
+/**
+ * A table that is backed by a single contiguous device buffer. This makes transfers of the data
+ * much simpler.
+ */
+public final class ContiguousTable implements AutoCloseable {
+  private Table table = null;
+  private DeviceMemoryBuffer buffer;
+  private final long rowCount;
+  private PackedColumnMetadata meta;
+  private ByteBuffer metadataBuffer;
+
+  // This method is invoked by JNI
+  static ContiguousTable fromPackedTable(long metadataHandle,
+                                         long dataAddress,
+                                         long dataLength,
+                                         long rmmBufferAddress,
+                                         long rowCount) {
+    DeviceMemoryBuffer buffer = DeviceMemoryBuffer.fromRmm(dataAddress, dataLength, rmmBufferAddress);
+    return new ContiguousTable(metadataHandle, buffer, rowCount);
+  }
+
+  /** Construct a contiguous table instance given a table and the device buffer backing it. */
+  ContiguousTable(Table table, DeviceMemoryBuffer buffer) {
+    this.meta = new PackedColumnMetadata(createPackedMetadata(table.getNativeView(),
+            buffer.getAddress(), buffer.getLength()));
+    this.table = table;
+    this.buffer = buffer;
+    this.rowCount = table.getRowCount();
+  }
+
+  /**
+   * Construct a contiguous table
+   * @param metadataHandle address of the cudf packed_table host-based metadata instance
+   * @param buffer buffer containing the packed table data
+   * @param rowCount number of rows in the table
+   */
+  ContiguousTable(long metadataHandle, DeviceMemoryBuffer buffer, long rowCount) {
+    this.meta = new PackedColumnMetadata(metadataHandle);
+    this.buffer = buffer;
+    this.rowCount = rowCount;
+  }
+
+  /**
+   * Returns the number of rows in the table. This accessor avoids manifesting
+   * the Table instance if only the row count is needed.
+   */
+  public long getRowCount() {
+    return rowCount;
+  }
+
+  /** Get the table instance, reconstructing it from the metadata if necessary. */
+  public synchronized Table getTable() {
+    if (table == null) {
+      table = Table.fromPackedTable(getMetadataDirectBuffer(), buffer);
+    }
+    return table;
+  }
+
+  /** Get the device buffer backing the contiguous table data. */
+  public DeviceMemoryBuffer getBuffer() {
+    return buffer;
+  }
+
+  /**
+   * Get the byte buffer containing the host metadata describing the schema and layout of the
+   * contiguous table.
+   * <p>
+   * NOTE: This is a direct byte buffer that is backed by the underlying native metadata instance
+   *       and therefore is only valid to be used while this contiguous table instance is valid.
+   *       Attempts to cache and access the resulting buffer after this instance has been destroyed
+   *       will result in undefined behavior including the possibility of segmentation faults
+   *       or data corruption.
+   */
+  public ByteBuffer getMetadataDirectBuffer() {
+    return meta.getMetadataDirectBuffer();
+  }
+
+  /** Close the contiguous table instance and its underlying resources. */
+  @Override
+  public void close() {
+    if (meta != null) {
+      meta.close();
+    }
+
+    if (table != null) {
+      table.close();
+      table = null;
+    }
+
+    if (buffer != null) {
+      buffer.close();
+      buffer = null;
+    }
+  }
+
+  // create packed metadata for a table backed by a single data buffer
+  private static native long createPackedMetadata(long tableView, long dataAddress, long dataSize);
+
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFile.java b/java/src/main/java/ai/rapids/cudf/CuFile.java
new file mode 100644
index 0000000..4baad83
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFile.java
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.File;
+
+/**
+ * JNI wrapper for accessing the cuFile API.
+ * <p>
+ * Using this wrapper requires GPUDirect Storage (GDS)/cuFile to be installed in the target
+ * environment, and the jar to be built with `USE_GDS=ON`. Otherwise it will throw an exception when
+ * loading.
+ * <p>
+ * The Java APIs are experimental and subject to change.
+ *
+ * @see <a href="https://docs.nvidia.com/gpudirect-storage/">GDS documentation</a>
+ */
+public class CuFile {
+  private static final Logger log = LoggerFactory.getLogger(CuFile.class);
+  private static boolean initialized = false;
+  private static CuFileDriver driver;
+
+  static {
+    initialize();
+  }
+
+  /**
+   * Load the native libraries needed for libcufilejni, if not loaded already; open the cuFile
+   * driver, and add a shutdown hook to close it.
+   */
+  static synchronized void initialize() {
+    if (!initialized) {
+      try {
+        NativeDepsLoader.loadNativeDeps(new String[]{"cufilejni"});
+        driver = new CuFileDriver();
+        Runtime.getRuntime().addShutdownHook(new Thread(() -> {
+          driver.close();
+        }));
+        initialized = true;
+      } catch (Throwable t) {
+        // Cannot throw an exception here as the CI/CD machine may not have GDS installed.
+        log.error("Could not load cuFile jni library...", t);
+      }
+    }
+  }
+
+  /**
+   * Check if the libcufilejni library is loaded.
+   *
+   * @return true if the libcufilejni library has been successfully loaded.
+   */
+  public static boolean libraryLoaded() {
+    return initialized;
+  }
+
+  /**
+   * Write a device buffer to a given file path synchronously.
+   * <p>
+   * This method is NOT thread safe if the path points to the same file on disk.
+   *
+   * @param path        The file path to copy to.
+   * @param file_offset The file offset from which to write the buffer.
+   * @param buffer      The device buffer to copy from.
+   */
+  public static void writeDeviceBufferToFile(File path, long file_offset,
+                                             BaseDeviceMemoryBuffer buffer) {
+    writeDeviceMemoryToFile(path, file_offset, buffer.getAddress(), buffer.getLength());
+  }
+
+  /**
+   * Write device memory to a given file path synchronously.
+   * <p>
+   * This method is NOT thread safe if the path points to the same file on disk.
+   *
+   * @param path        The file path to copy to.
+   * @param file_offset The file offset from which to write the buffer.
+   * @param address     The device memory address to copy from.
+   * @param length      The length to copy.
+   */
+  public static void writeDeviceMemoryToFile(File path, long file_offset, long address,
+                                             long length) {
+    writeToFile(path.getAbsolutePath(), file_offset, address, length);
+  }
+
+  /**
+   * Append a device buffer to a given file path synchronously.
+   * <p>
+   * This method is NOT thread safe if the path points to the same file on disk.
+   *
+   * @param path   The file path to copy to.
+   * @param buffer The device buffer to copy from.
+   * @return The file offset from which the buffer was appended.
+   */
+  public static long appendDeviceBufferToFile(File path, BaseDeviceMemoryBuffer buffer) {
+    return appendDeviceMemoryToFile(path, buffer.getAddress(), buffer.getLength());
+  }
+
+  /**
+   * Append device memory to a given file path synchronously.
+   * <p>
+   * This method is NOT thread safe if the path points to the same file on disk.
+   *
+   * @param path    The file path to copy to.
+   * @param address The device memory address to copy from.
+   * @param length  The length to copy.
+   * @return The file offset from which the buffer was appended.
+   */
+  public static long appendDeviceMemoryToFile(File path, long address, long length) {
+    return appendToFile(path.getAbsolutePath(), address, length);
+  }
+
+  /**
+   * Read a file into a device buffer synchronously.
+   * <p>
+   * This method is NOT thread safe if the path points to the same file on disk.
+   *
+   * @param buffer     The device buffer to copy into.
+   * @param path       The file path to copy from.
+   * @param fileOffset The file offset from which to copy the content.
+   */
+  public static void readFileToDeviceBuffer(BaseDeviceMemoryBuffer buffer, File path,
+                                            long fileOffset) {
+    readFileToDeviceMemory(buffer.getAddress(), buffer.getLength(), path, fileOffset);
+  }
+
+  /**
+   * Read a file into device memory synchronously.
+   * <p>
+   * This method is NOT thread safe if the path points to the same file on disk.
+   *
+   * @param address The device memory address to read into.
+   * @param length  The length to read.
+   * @param path       The file path to copy from.
+   * @param fileOffset The file offset from which to copy the content.
+   */
+  public static void readFileToDeviceMemory(long address, long length, File path, long fileOffset) {
+    readFromFile(address, length, path.getAbsolutePath(), fileOffset);
+  }
+
+  private static native void writeToFile(String path, long file_offset, long address, long length);
+
+  private static native long appendToFile(String path, long address, long length);
+
+  private static native void readFromFile(long address, long length, String path, long fileOffset);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileBuffer.java b/java/src/main/java/ai/rapids/cudf/CuFileBuffer.java
new file mode 100644
index 0000000..082a6c6
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileBuffer.java
@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Represents a cuFile buffer.
+ */
+public final class CuFileBuffer extends BaseDeviceMemoryBuffer {
+  private static final int ALIGNMENT = 4096;
+  private final DeviceMemoryBuffer deviceMemoryBuffer;
+  private final CuFileResourceCleaner cleaner;
+
+  static {
+    CuFile.initialize();
+  }
+
+  /**
+   * Construct a new cuFile buffer.
+   *
+   * @param buffer         The device memory buffer used for the cuFile buffer. This buffer is owned
+   *                       by the cuFile buffer, and will be closed when the cuFile buffer is closed.
+   * @param registerBuffer If true, register the cuFile buffer.
+   */
+  private CuFileBuffer(DeviceMemoryBuffer buffer, boolean registerBuffer) {
+    super(buffer.address, buffer.length, (MemoryBufferCleaner) null);
+    if (registerBuffer && !isAligned(buffer)) {
+      buffer.close();
+      throw new IllegalArgumentException(
+          "To register a cuFile buffer, its length must be a multiple of " + ALIGNMENT);
+    }
+    deviceMemoryBuffer = buffer;
+    cleaner = new CuFileResourceCleaner(create(buffer.address, buffer.length, registerBuffer), CuFileBuffer::destroy);
+    MemoryCleaner.register(this, cleaner);
+  }
+
+  /**
+   * Allocate memory for use with cuFile on the GPU. You must close it when done.
+   *
+   * @param bytes          size in bytes to allocate
+   * @param registerBuffer If true, register the cuFile buffer.
+   * @return the buffer
+   */
+  public static CuFileBuffer allocate(long bytes, boolean registerBuffer) {
+    DeviceMemoryBuffer buffer = DeviceMemoryBuffer.allocate(bytes);
+    return new CuFileBuffer(buffer, registerBuffer);
+  }
+
+  @Override
+  public MemoryBuffer slice(long offset, long len) {
+    throw new UnsupportedOperationException("Slice on cuFile buffer is not supported");
+  }
+
+  @Override
+  public void close() {
+    cleaner.close(this);
+    deviceMemoryBuffer.close();
+  }
+
+  long getPointer() {
+    return cleaner.getPointer();
+  }
+
+  private boolean isAligned(BaseDeviceMemoryBuffer buffer) {
+    return buffer.length % ALIGNMENT == 0;
+  }
+
+  private static native long create(long address, long length, boolean registerBuffer);
+
+  private static native void destroy(long pointer);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileDriver.java b/java/src/main/java/ai/rapids/cudf/CuFileDriver.java
new file mode 100644
index 0000000..1934abf
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileDriver.java
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Represents a cuFile driver.
+ */
+final class CuFileDriver implements AutoCloseable {
+  private final CuFileResourceCleaner cleaner;
+
+  CuFileDriver() {
+    cleaner = new CuFileResourceCleaner(create(), CuFileDriver::destroy);
+    MemoryCleaner.register(this, cleaner);
+  }
+
+  @Override
+  public void close() {
+    cleaner.close(this);
+  }
+
+  private static native long create();
+
+  private static native void destroy(long pointer);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileHandle.java b/java/src/main/java/ai/rapids/cudf/CuFileHandle.java
new file mode 100644
index 0000000..2648e0a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileHandle.java
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Represents a cuFile file handle.
+ */
+abstract class CuFileHandle implements AutoCloseable {
+  private final CuFileResourceCleaner cleaner;
+
+  static {
+    CuFile.initialize();
+  }
+
+  protected CuFileHandle(long pointer) {
+    cleaner = new CuFileResourceCleaner(pointer, CuFileHandle::destroy);
+    MemoryCleaner.register(this, cleaner);
+  }
+
+  @Override
+  public void close() {
+    cleaner.close(this);
+  }
+
+  protected long getPointer() {
+    return cleaner.getPointer();
+  }
+
+  private static native void destroy(long pointer);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileReadHandle.java b/java/src/main/java/ai/rapids/cudf/CuFileReadHandle.java
new file mode 100644
index 0000000..e684ca6
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileReadHandle.java
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Represents a cuFile file handle for reading.
+ */
+public final class CuFileReadHandle extends CuFileHandle {
+
+  /**
+   * Construct a reader using the specified file path.
+   *
+   * @param path The file path for reading.
+   */
+  public CuFileReadHandle(String path) {
+    super(create(path));
+  }
+
+  /**
+   * Read the file content into the specified cuFile buffer.
+   *
+   * @param buffer The cuFile buffer to store the content.
+   * @param fileOffset The file offset from which to read.
+   */
+  public void read(CuFileBuffer buffer, long fileOffset) {
+    readIntoBuffer(getPointer(), fileOffset, buffer.getPointer());
+  }
+
+  private static native long create(String path);
+
+  private static native void readIntoBuffer(long file, long fileOffset, long buffer);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileResourceCleaner.java b/java/src/main/java/ai/rapids/cudf/CuFileResourceCleaner.java
new file mode 100644
index 0000000..b33ea1b
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileResourceCleaner.java
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * Keeps track and cleans a cuFile native resource.
+ */
+final class CuFileResourceCleaner extends MemoryCleaner.Cleaner {
+  private static final Logger log = LoggerFactory.getLogger(CuFileResourceCleaner.class);
+
+  private long pointer;
+  private final CuFileResourceDestroyer destroyer;
+  private boolean closed = false;
+
+  CuFileResourceCleaner(long pointer, CuFileResourceDestroyer destroyer) {
+    this.pointer = pointer;
+    this.destroyer = destroyer;
+    addRef();
+  }
+
+  long getPointer() {
+    return pointer;
+  }
+
+  synchronized void close(Object resource) {
+    delRef();
+    if (closed) {
+      logRefCountDebug("double free " + resource);
+      throw new IllegalStateException("Close called too many times " + resource);
+    }
+    clean(false);
+    closed = true;
+  }
+
+  @Override
+  protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+    boolean neededCleanup = false;
+    long origAddress = pointer;
+    if (pointer != 0) {
+      try {
+        destroyer.destroy(pointer);
+      } finally {
+        // Always mark the resource as freed even if an exception is thrown.
+        // We cannot know how far it progressed before the exception, and
+        // therefore it is unsafe to retry.
+        pointer = 0;
+      }
+      neededCleanup = true;
+    }
+    if (neededCleanup && logErrorIfNotClean) {
+      log.error("A CUFile RESOURCE WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+      logRefCountDebug("Leaked cuFile resource");
+    }
+    return neededCleanup;
+  }
+
+  @Override
+  public boolean isClean() {
+    return pointer == 0;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileResourceDestroyer.java b/java/src/main/java/ai/rapids/cudf/CuFileResourceDestroyer.java
new file mode 100644
index 0000000..df7a393
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileResourceDestroyer.java
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Destroys a cuFile native resource.
+ */
+interface CuFileResourceDestroyer {
+  void destroy(long pointer);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CuFileWriteHandle.java b/java/src/main/java/ai/rapids/cudf/CuFileWriteHandle.java
new file mode 100644
index 0000000..e58201f
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CuFileWriteHandle.java
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Represents a cuFile file handle for reading.
+ */
+public final class CuFileWriteHandle extends CuFileHandle {
+
+  /**
+   * Construct a writer using the specified file path.
+   *
+   * @param path The file path for writing.
+   */
+  public CuFileWriteHandle(String path) {
+    super(create(path));
+  }
+
+  /**
+   * Write the specified cuFile buffer into the file.
+   *
+   * @param buffer The cuFile buffer to write from.
+   * @param length The number of bytes to write.
+   * @param fileOffset The starting file offset from which to write.
+   */
+  public void write(CuFileBuffer buffer, long length, long fileOffset) {
+    writeFromBuffer(getPointer(), fileOffset, buffer.getPointer(), length);
+  }
+
+  /**
+   * Append the specified cuFile buffer to the file.
+   *
+   * @param buffer The cuFile buffer to append from.
+   * @param length The number of bytes to append.
+   * @return The file offset from which the buffer was appended.
+   */
+  public long append(CuFileBuffer buffer, long length) {
+    return appendFromBuffer(getPointer(), buffer.getPointer(), length);
+  }
+
+  private static native long create(String path);
+
+  private static native void writeFromBuffer(long file, long fileOffset, long buffer, long length);
+
+  private static native long appendFromBuffer(long file, long buffer, long length);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Cuda.java b/java/src/main/java/ai/rapids/cudf/Cuda.java
new file mode 100755
index 0000000..e1298e2
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Cuda.java
@@ -0,0 +1,605 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.NvtxColor;
+import ai.rapids.cudf.NvtxRange;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class Cuda {
+  // This needs to happen first before calling any native methods.
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  // Defined in driver_types.h in cuda library.
+  static final int CPU_DEVICE_ID = -1;
+  static final long CUDA_STREAM_DEFAULT = 0;
+  static final long CUDA_STREAM_LEGACY = 1;
+  static final long CUDA_STREAM_PER_THREAD = 2;
+  private final static long DEFAULT_STREAM_ID = isPtdsEnabled() ? CUDA_STREAM_PER_THREAD : CUDA_STREAM_LEGACY;
+  private static final Logger log = LoggerFactory.getLogger(Cuda.class);
+  private static Boolean isCompat = null;
+
+  private static class StreamCleaner extends MemoryCleaner.Cleaner {
+    private long stream;
+
+    StreamCleaner(long stream) {
+      this.stream = stream;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long origAddress = stream;
+      if (stream != CUDA_STREAM_DEFAULT &&
+          stream != CUDA_STREAM_LEGACY &&
+          stream != CUDA_STREAM_PER_THREAD) {
+        destroyStream(stream);
+        stream = 0;
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A CUDA STREAM WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+        logRefCountDebug("Leaked stream");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return stream == 0;
+    }
+  }
+
+  /** A class representing a CUDA stream */
+  public static final class Stream implements AutoCloseable {
+    private final StreamCleaner cleaner;
+    boolean closed = false;
+    private final long id;
+
+    /**
+     * Create a new CUDA stream
+     * @param isNonBlocking whether stream should be non-blocking with respect to the default stream
+     */
+    public Stream(boolean isNonBlocking) {
+      this.cleaner = new StreamCleaner(createStream(isNonBlocking));
+      this.id = cleaner.id;
+      MemoryCleaner.register(this, cleaner);
+      cleaner.addRef();
+    }
+
+    private Stream() {
+      // No cleaner for the default stream...
+      this.cleaner = null;
+      this.id = -1;
+    }
+
+    /**
+     * Have this stream not execute new work until the work recorded in event completes.
+     * @param event the event to wait on.
+     */
+    public void waitOn(Event event) {
+      streamWaitEvent(getStream(), event.getEvent());
+    }
+
+    public long getStream() {
+      return cleaner == null ? DEFAULT_STREAM_ID : cleaner.stream;
+    }
+
+    /**
+     * Block the thread to wait until all pending work on this stream completes.  Note that this
+     * does not follow any of the java threading standards.  Interrupt will not work to wake up
+     * the thread.
+     */
+    public void sync() {
+      streamSynchronize(getStream());
+    }
+
+    @Override
+    public String toString() {
+      return "CUDA STREAM (ID: " + id + " " + Long.toHexString(getStream()) + ")";
+    }
+
+    @Override
+    public synchronized void close() {
+      if (cleaner != null) {
+        cleaner.delRef();
+      }
+      if (closed) {
+        cleaner.logRefCountDebug("double free " + this);
+        throw new IllegalStateException("Close called too many times " + this);
+      }
+      if (cleaner != null) {
+        cleaner.clean(false);
+        closed = true;
+      }
+    }
+  }
+
+  public static final Stream DEFAULT_STREAM = new Stream();
+
+  private static class EventCleaner extends MemoryCleaner.Cleaner {
+    private long event;
+
+    EventCleaner(long event) {
+      this.event = event;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long origAddress = event;
+      if (event != 0) {
+        try {
+          destroyEvent(event);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          event = 0;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A CUDA EVENT WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+        logRefCountDebug("Leaked event");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return event == 0;
+    }
+  }
+
+  public static final class Event implements AutoCloseable {
+    private final EventCleaner cleaner;
+    boolean closed = false;
+
+    /**
+     * Create an event that is as fast as possible, timing is disabled and no blockingSync.
+     */
+    public Event() {
+      this(false, false);
+    }
+
+    /**
+     * Create an event to be used for CUDA synchronization.
+     * @param enableTiming true if the event should record timing information.
+     * @param blockingSync true if event should use blocking synchronization.
+     *                     A host thread that calls sync() to wait on an event created with this
+     *                     flag will block until the event actually completes.
+     */
+    public Event(boolean enableTiming, boolean blockingSync) {
+      this.cleaner = new EventCleaner(createEvent(enableTiming, blockingSync));
+      MemoryCleaner.register(this, cleaner);
+      cleaner.addRef();
+    }
+
+    long getEvent() {
+      return cleaner.event;
+    }
+
+    /**
+     * Check to see if the event has completed or not. This is the equivalent of cudaEventQuery.
+     * @return true it has completed else false.
+     */
+    public boolean hasCompleted() {
+      return eventQuery(getEvent());
+    }
+
+    /**
+     * Captures the contents of stream at the time of this call. This event and stream must be on
+     * the same device. Calls such as hasCompleted() or Stream.waitEvent() will then examine or wait for
+     * completion of the work that was captured. Uses of stream after this call do not modify event.
+     * @param stream the stream to record the state of.
+     */
+    public void record(Stream stream) {
+      eventRecord(getEvent(), stream.getStream());
+    }
+
+    /**
+     * Captures the contents of the default stream at the time of this call.
+     */
+    public void record() {
+      record(DEFAULT_STREAM);
+    }
+
+    /**
+     * Block the thread to wait for the event to complete.  Note that this does not follow any of
+     * the java threading standards.  Interrupt will not work to wake up the thread.
+     */
+    public void sync() {
+      eventSynchronize(getEvent());
+    }
+
+    @Override
+    public String toString() {
+      return "CUDA EVENT (ID: " + cleaner.id + " " + Long.toHexString(getEvent()) + ")";
+    }
+
+    @Override
+    public synchronized void close() {
+      cleaner.delRef();
+      if (closed) {
+        cleaner.logRefCountDebug("double free " + this);
+        throw new IllegalStateException("Close called too many times " + this);
+      }
+      cleaner.clean(false);
+      closed = true;
+    }
+  }
+
+  /**
+   * Gets the CUDA compute mode of the current device.
+   *
+   * @return the enum value of CudaComputeMode
+   */
+  public static CudaComputeMode getComputeMode() {
+    return CudaComputeMode.fromNative(Cuda.getNativeComputeMode());
+  }
+
+  /**
+   * Mapping: cudaMemGetInfo(size_t *free, size_t *total)
+   */
+  public static native CudaMemInfo memGetInfo() throws CudaException;
+
+  /**
+   * Allocate pinned memory on the host.  This call takes a long time, but can really speed up
+   * memory transfers.
+   * @param size how much memory, in bytes, to allocate.
+   * @return the address to the allocated memory.
+   * @throws CudaException on any error.
+   */
+  static native long hostAllocPinned(long size) throws CudaException;
+
+  /**
+   * Free memory allocated with hostAllocPinned.
+   * @param ptr the pointer returned by hostAllocPinned.
+   * @throws CudaException on any error.
+   */
+  static native void freePinned(long ptr) throws CudaException;
+
+  /**
+   * Copies bytes between buffers using the default CUDA stream.
+   * The copy has completed when this returns, but the memory copy could overlap with
+   * operations occurring on other streams.
+   * Specifying pointers that do not match the copy direction results in undefined behavior.
+   * @param dst   - Destination memory address
+   * @param src   - Source memory address
+   * @param count - Size in bytes to copy
+   * @param kind  - Type of transfer. {@link CudaMemcpyKind}
+   */
+  static void memcpy(long dst, long src, long count, CudaMemcpyKind kind) {
+    memcpy(dst, src, count, kind, DEFAULT_STREAM);
+  }
+
+  /**
+   * Copies bytes between buffers using the default CUDA stream.
+   * The copy has not necessarily completed when this returns, but the memory copy could
+   * overlap with operations occurring on other streams.
+   * Specifying pointers that do not match the copy direction results in undefined behavior.
+   * @param dst   - Destination memory address
+   * @param src   - Source memory address
+   * @param count - Size in bytes to copy
+   * @param kind  - Type of transfer. {@link CudaMemcpyKind}
+   */
+  static void asyncMemcpy(long dst, long src, long count, CudaMemcpyKind kind) {
+    asyncMemcpy(dst, src, count, kind, DEFAULT_STREAM);
+  }
+
+  /**
+   * Sets count bytes starting at the memory area pointed to by dst, with value.
+   * The operation has completed when this returns, but it could overlap with operations occurring
+   * on other streams.
+   * @param dst   - Destination memory address
+   * @param value - Byte value to set dst with
+   * @param count - Size in bytes to set
+   */
+  public static native void memset(long dst, byte value, long count) throws CudaException;
+
+  /**
+   * Sets count bytes starting at the memory area pointed to by dst, with value.
+   * The operation has not necessarily completed when this returns, but it could overlap with
+   * operations occurring on other streams.
+   * @param dst   - Destination memory address
+   * @param value - Byte value to set dst with
+   * @param count - Size in bytes to set
+   */
+  public static native void asyncMemset(long dst, byte value, long count) throws CudaException;
+
+  /**
+   * Get the id of the current device.
+   * @return the id of the current device
+   * @throws CudaException on any error
+   */
+  public static native int getDevice() throws CudaException;
+
+  /**
+   * Get the device count.
+   * @return returns the number of compute-capable devices
+   * @throws CudaException on any error
+   */
+  public static native int getDeviceCount() throws CudaException;
+
+  /**
+   * Set the id of the current device.
+   * <p>Note this is relative to CUDA_SET_VISIBLE_DEVICES, e.g. if
+   * CUDA_SET_VISIBLE_DEVICES=1,0, and you call setDevice(0), you will get device 1.
+   * <p>Note if RMM has been initialized and the requested device ID does not
+   * match the device used to initialize RMM then this will throw an error.
+   * @throws CudaException on any error
+   */
+  public static native void setDevice(int device) throws CudaException, CudfException;
+
+  /**
+   * Set the device for this thread to the appropriate one. Java loves threads, but cuda requires
+   * each thread to have the device set explicitly or it falls back to CUDA_VISIBLE_DEVICES. Most
+   * JNI calls through the cudf API will do this for you, but if you are writing your own JNI
+   * calls that extend cudf you might want to call this before calling into your JNI APIs to
+   * ensure that the device is set correctly.
+   * @throws CudaException on any error
+   */
+  public static native void autoSetDevice() throws CudaException;
+
+  /**
+   * Get the CUDA Driver version, which is the latest version of CUDA supported by the driver.
+   * The version is returned as (1000 major + 10 minor). For example, CUDA 9.2 would be
+   * represented by 9020. If no driver is installed,then 0 is returned as the driver version.
+   *
+   * @return the CUDA driver version
+   * @throws CudaException on any error
+   */
+  public static native int getDriverVersion() throws CudaException;
+
+  /**
+   * Get the CUDA Runtime version of the current CUDA Runtime instance. The version is returned
+   * as (1000 major + 10 minor). For example, CUDA 9.2 would be represented by 9020.
+   *
+   * @return the CUDA Runtime version
+   * @throws CudaException on any error
+   */
+  public static native int getRuntimeVersion() throws CudaException;
+
+  /**
+   * Gets the CUDA device compute mode of the current device.
+   *
+   * @return the value of cudaComputeMode
+   * @throws CudaException on any error
+   */
+  static native int getNativeComputeMode() throws CudaException;
+
+  /**
+   * Gets the major CUDA compute capability of the current device.
+   *
+   * For reference: https://developer.nvidia.com/cuda-gpus
+   * Hardware Generation	Compute Capability
+   *     Ampere	                8.x
+   *     Turing	                7.5
+   *     Volta	                7.0, 7.2
+   *     Pascal	                6.x
+   *     Maxwell                5.x
+   *     Kepler	                3.x
+   *     Fermi	                2.x
+   *
+   * @return The Major compute capability version number of the current CUDA device
+   * @throws CudaException on any error
+   */
+  public static native int getComputeCapabilityMajor() throws CudaException;
+
+  /**
+   * Gets the minor CUDA compute capability of the current device.
+   *
+   * For reference: https://developer.nvidia.com/cuda-gpus
+   * Hardware Generation	Compute Capability
+   *     Ampere	                8.x
+   *     Turing	                7.5
+   *     Volta	                7.0, 7.2
+   *     Pascal	                6.x
+   *     Maxwell                5.x
+   *     Kepler	                3.x
+   *     Fermi	                2.x
+   *
+   * @return The Minor compute capability version number of the current CUDA device
+   * @throws CudaException on any error
+   */
+  public static native int getComputeCapabilityMinor() throws CudaException;
+
+  /**
+   * Calls cudaFree(0). This can be used to initialize the GPU after a setDevice()
+   * @throws CudaException on any error
+   */
+  public static native void freeZero() throws CudaException;
+
+  /**
+   * Create a CUDA stream
+   * @param isNonBlocking whether stream should be non-blocking with respect to the default stream
+   * @return handle to a CUDA stream
+   * @throws CudaException on any error
+   */
+  static native long createStream(boolean isNonBlocking) throws CudaException;
+
+  /**
+   * Destroy a CUDA stream
+   * @param stream handle to the CUDA stream to destroy
+   * @throws CudaException on any error
+   */
+  static native void destroyStream(long stream) throws CudaException;
+
+  /**
+   * Have this stream not execute new work until the work recorded in event completes.
+   * @param stream the stream handle.
+   * @param event the event handle.
+   */
+  static native void streamWaitEvent(long stream, long event) throws CudaException;
+
+  /**
+   * Block the thread until the pending execution on the stream completes
+   * @param stream the stream handle
+   * @throws CudaException on any error.
+   */
+  static native void streamSynchronize(long stream) throws CudaException;
+
+  /**
+   * Create a CUDA event
+   * @param enableTiming true if timing should be enabled.
+   * @param blockingSync true if blocking sync should be enabled.
+   * @return handle to a CUDA event
+   * @throws CudaException on any error
+   */
+  static native long createEvent(boolean enableTiming, boolean blockingSync) throws CudaException;
+
+  /**
+   * Destroy a CUDA event
+   * @param event handle to the CUDA event to destroy
+   * @throws CudaException on any error
+   */
+  static native void destroyEvent(long event) throws CudaException;
+
+  /**
+   * Check to see if the event happened or not.
+   * @param event the event handle
+   * @return true the event finished else false.
+   * @throws CudaException on any error.
+   */
+  static native boolean eventQuery(long event) throws CudaException;
+
+  /**
+   * Reset the state of this event to be what is on the stream right now.
+   * @param event the event handle
+   * @param stream the stream handle
+   * @throws CudaException on any error.
+   */
+  static native void eventRecord(long event, long stream) throws CudaException;
+
+  /**
+   * Block the thread until the execution recorded in the event is complete.
+   * @param event the event handle
+   * @throws CudaException on any error.
+   */
+  static native void eventSynchronize(long event) throws CudaException;
+
+  /**
+   * Copies bytes between buffers using the specified CUDA stream.
+   * The copy has completed when this returns, but the memory copy could overlap with
+   * operations occurring on other streams.
+   * Specifying pointers that do not match the copy direction results in undefined behavior.
+   * @param dst destination memory address
+   * @param src source memory address
+   * @param count size in bytes to copy
+   * @param kind direction of transfer. {@link CudaMemcpyKind}
+   * @param stream CUDA stream to use for the copy
+   */
+  static void memcpy(long dst, long src, long count, CudaMemcpyKind kind, Stream stream) {
+    memcpyOnStream(dst, src, count, kind.getValue(), stream.getStream());
+  }
+
+  private static native void memcpyOnStream(long dst, long src, long count, int kind,
+      long stream) throws CudaException;
+
+  /**
+   * Copies bytes between buffers using the specified CUDA stream.
+   * The copy has not necessarily completed when this returns, but the memory copy could
+   * overlap with operations occurring on other streams.
+   * Specifying pointers that do not match the copy direction results in undefined behavior.
+   * @param dst destination memory address
+   * @param src source memory address
+   * @param count size in bytes to copy
+   * @param kind direction of transfer. {@link CudaMemcpyKind}
+   * @param stream CUDA stream to use for the copy
+   */
+  static void asyncMemcpy(long dst, long src, long count, CudaMemcpyKind kind, Stream stream) {
+    asyncMemcpyOnStream(dst, src, count, kind.getValue(), stream.getStream());
+  }
+
+  private static native void asyncMemcpyOnStream(long dst, long src, long count, int kind,
+                                                 long stream) throws CudaException;
+
+  /**
+   * This should only be used for tests, to enable or disable tests if the current environment
+   * is not compatible with this version of the library.  Currently it only does some very
+   * basic checks, but these may be expanded in the future depending on needs.
+   * @return true if it is compatible else false.
+   */
+  public static synchronized boolean isEnvCompatibleForTesting() {
+    if (isCompat == null) {
+      if (NativeDepsLoader.libraryLoaded()) {
+        try {
+          int device = getDevice();
+          if (device >= 0) {
+            isCompat = true;
+            return isCompat;
+          }
+        } catch (Throwable e) {
+          log.error("Error trying to detect device", e);
+        }
+      }
+      isCompat = false;
+    }
+    return isCompat;
+  }
+
+  /**
+   * Whether per-thread default stream is enabled.
+   */
+  public static native boolean isPtdsEnabled();
+
+  /**
+   * Copy data from multiple device buffer sources to multiple device buffer destinations.
+   * For each buffer to copy there is a corresponding entry in the destination address, source
+   * address, and copy size vectors.
+   * @param destAddrs vector of device destination addresses
+   * @param srcAddrs vector of device source addresses
+   * @param copySizes vector of copy sizes
+   * @param stream CUDA stream to use for the copy
+   */
+  public static void multiBufferCopyAsync(long [] destAddrs,
+                                          long [] srcAddrs,
+                                          long [] copySizes,
+                                          Stream stream) {
+    // Temporary sub-par stand-in for a multi-buffer copy CUDA kernel
+    assert(destAddrs.length == srcAddrs.length);
+    assert(copySizes.length == destAddrs.length);
+    try (NvtxRange copyRange = new NvtxRange("multiBufferCopyAsync", NvtxColor.CYAN)){
+      for (int i = 0; i < destAddrs.length; i++) {
+        asyncMemcpy(destAddrs[i], srcAddrs[i], copySizes[i], CudaMemcpyKind.DEVICE_TO_DEVICE, stream);
+      }
+    }
+  }
+  /**
+   * Begins an Nsight profiling session, if a profiler is currently attached.
+   * @note if a profiler session has a already started, `profilerStart` has
+   * no effect.
+   */
+  public static native void profilerStart();
+
+  /**
+   * Stops an active Nsight profiling session.
+   * @note if a profiler session isn't active, `profilerStop` has
+   * no effect.
+   */
+  public static native void profilerStop();
+
+  /**
+   * Synchronizes the whole device using cudaDeviceSynchronize.
+   * @note this is very expensive and should almost never be used
+   */
+  public static native void deviceSynchronize();
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudaComputeMode.java b/java/src/main/java/ai/rapids/cudf/CudaComputeMode.java
new file mode 100644
index 0000000..2da246d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudaComputeMode.java
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * This is the Java mapping of CUDA device compute modes.
+ */
+public enum CudaComputeMode {
+  /**
+   * Default compute mode
+   * Multiple threads can use cudaSetDevice() with this device.
+   */
+  DEFAULT(0),
+  /**
+   * Compute-exclusive-thread mode
+   * Only one thread in one process will be able to use cudaSetDevice() with this device.
+   *
+   * WARNING: This mode was deprecated! Using EXCLUSIVE_PROCESS instead.
+   */
+  EXCLUSIVE(1),
+  /**
+   * Compute-prohibited mode
+   * No threads can use cudaSetDevice() with this device.
+   */
+  PROHIBITED(2),
+  /**
+   * Compute-exclusive-process mode
+   * Many threads in one process will be able to use cudaSetDevice() with this device.
+   */
+  EXCLUSIVE_PROCESS(3);
+
+  private CudaComputeMode(int nativeId) {
+    this.nativeId = nativeId;
+  }
+
+  static CudaComputeMode fromNative(int nativeId) {
+    for (CudaComputeMode mode : COMPUTE_MODES) {
+      if (mode.nativeId == nativeId) return mode;
+    }
+    throw new IllegalArgumentException("Could not translate " + nativeId + " into a CudaComputeMode");
+  }
+
+  // mapping to the value of native mode
+  final int nativeId;
+
+  private static final CudaComputeMode[] COMPUTE_MODES = CudaComputeMode.values();
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudaException.java b/java/src/main/java/ai/rapids/cudf/CudaException.java
new file mode 100755
index 0000000..f73a624
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudaException.java
@@ -0,0 +1,329 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * Exception from the cuda language/library. Be aware that because of how cuda does asynchronous
+ * processing exceptions from cuda can be thrown by method calls that did not cause the exception
+ * to take place. These will take place on the same thread that caused the error.
+ * <p>
+ * Please See
+ * <a href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__ERROR.html">the cuda docs</a>
+ * for more details on how this works.
+ * <p>
+ * In general you can recover from cuda errors even in async calls if you make sure that you
+ * don't switch between threads for different parts of processing that can be retried as a chunk.
+ */
+public class CudaException extends RuntimeException {
+  CudaException(String message, int errorCode) {
+    this(message, "No native stacktrace is available.", errorCode);
+  }
+
+  CudaException(String message, String nativeStacktrace, int errorCode) {
+    super(message);
+    this.nativeStacktrace = nativeStacktrace;
+    cudaError = CudaError.parseErrorCode(errorCode);
+  }
+
+  CudaException(String message, String nativeStacktrace, int errorCode, Throwable cause) {
+    super(message, cause);
+    this.nativeStacktrace = nativeStacktrace;
+    cudaError = CudaError.parseErrorCode(errorCode);
+  }
+
+  public String getNativeStacktrace() {
+    return nativeStacktrace;
+  }
+
+  public CudaError getCudaError() {
+    return cudaError;
+  }
+
+  private final String nativeStacktrace;
+
+  private final CudaError cudaError;
+
+  /**
+   * The Java mirror of cudaError, which facilities the tracking of CUDA errors in JVM.
+   */
+  public enum CudaError {
+    UnknownNativeError(-1), // native CUDA error type which Java doesn't have a representation
+    cudaErrorInvalidValue(1),
+    cudaErrorMemoryAllocation(2),
+    cudaErrorInitializationError(3),
+    cudaErrorCudartUnloading(4),
+    cudaErrorProfilerDisabled(5),
+    cudaErrorProfilerNotInitialized(6),
+    cudaErrorProfilerAlreadyStarted(7),
+    cudaErrorProfilerAlreadyStopped(8),
+    cudaErrorInvalidConfiguration(9),
+    cudaErrorInvalidPitchValue(12),
+    cudaErrorInvalidSymbol(13),
+    cudaErrorInvalidHostPointer(16),
+    cudaErrorInvalidDevicePointer(17),
+    cudaErrorInvalidTexture(18),
+    cudaErrorInvalidTextureBinding(19),
+    cudaErrorInvalidChannelDescriptor(20),
+    cudaErrorInvalidMemcpyDirection(21),
+    cudaErrorAddressOfConstant(22),
+    cudaErrorTextureFetchFailed(23),
+    cudaErrorTextureNotBound(24),
+    cudaErrorSynchronizationError(25),
+    cudaErrorInvalidFilterSetting(26),
+    cudaErrorInvalidNormSetting(27),
+    cudaErrorMixedDeviceExecution(28),
+    cudaErrorNotYetImplemented(31),
+    cudaErrorMemoryValueTooLarge(32),
+    cudaErrorStubLibrary(34),
+    cudaErrorInsufficientDriver(35),
+    cudaErrorCallRequiresNewerDriver(36),
+    cudaErrorInvalidSurface(37),
+    cudaErrorDuplicateVariableName(43),
+    cudaErrorDuplicateTextureName(44),
+    cudaErrorDuplicateSurfaceName(45),
+    cudaErrorDevicesUnavailable(46),
+    cudaErrorIncompatibleDriverContext(49),
+    cudaErrorMissingConfiguration(52),
+    cudaErrorPriorLaunchFailure(53),
+    cudaErrorLaunchMaxDepthExceeded(65),
+    cudaErrorLaunchFileScopedTex(66),
+    cudaErrorLaunchFileScopedSurf(67),
+    cudaErrorSyncDepthExceeded(68),
+    cudaErrorLaunchPendingCountExceeded(69),
+    cudaErrorInvalidDeviceFunction(98),
+    cudaErrorNoDevice(100),
+    cudaErrorInvalidDevice(101),
+    cudaErrorDeviceNotLicensed(102),
+    cudaErrorSoftwareValidityNotEstablished(103),
+    cudaErrorStartupFailure(127),
+    cudaErrorInvalidKernelImage(200),
+    cudaErrorDeviceUninitialized(201),
+    cudaErrorMapBufferObjectFailed(205),
+    cudaErrorUnmapBufferObjectFailed(206),
+    cudaErrorArrayIsMapped(207),
+    cudaErrorAlreadyMapped(208),
+    cudaErrorNoKernelImageForDevice(209),
+    cudaErrorAlreadyAcquired(210),
+    cudaErrorNotMapped(211),
+    cudaErrorNotMappedAsArray(212),
+    cudaErrorNotMappedAsPointer(213),
+    cudaErrorECCUncorrectable(214),
+    cudaErrorUnsupportedLimit(215),
+    cudaErrorDeviceAlreadyInUse(216),
+    cudaErrorPeerAccessUnsupported(217),
+    cudaErrorInvalidPtx(218),
+    cudaErrorInvalidGraphicsContext(219),
+    cudaErrorNvlinkUncorrectable(220),
+    cudaErrorJitCompilerNotFound(221),
+    cudaErrorUnsupportedPtxVersion(222),
+    cudaErrorJitCompilationDisabled(223),
+    cudaErrorUnsupportedExecAffinity(224),
+    cudaErrorInvalidSource(300),
+    cudaErrorFileNotFound(301),
+    cudaErrorSharedObjectSymbolNotFound(302),
+    cudaErrorSharedObjectInitFailed(303),
+    cudaErrorOperatingSystem(304),
+    cudaErrorInvalidResourceHandle(400),
+    cudaErrorIllegalState(401),
+    cudaErrorSymbolNotFound(500),
+    cudaErrorNotReady(600),
+    cudaErrorIllegalAddress(700),
+    cudaErrorLaunchOutOfResources(701),
+    cudaErrorLaunchTimeout(702),
+    cudaErrorLaunchIncompatibleTexturing(703),
+    cudaErrorPeerAccessAlreadyEnabled(704),
+    cudaErrorPeerAccessNotEnabled(705),
+    cudaErrorSetOnActiveProcess(708),
+    cudaErrorContextIsDestroyed(709),
+    cudaErrorAssert(710),
+    cudaErrorTooManyPeers(711),
+    cudaErrorHostMemoryAlreadyRegistered(712),
+    cudaErrorHostMemoryNotRegistered(713),
+    cudaErrorHardwareStackError(714),
+    cudaErrorIllegalInstruction(715),
+    cudaErrorMisalignedAddress(716),
+    cudaErrorInvalidAddressSpace(717),
+    cudaErrorInvalidPc(718),
+    cudaErrorLaunchFailure(719),
+    cudaErrorCooperativeLaunchTooLarge(720),
+    cudaErrorNotPermitted(800),
+    cudaErrorNotSupported(801),
+    cudaErrorSystemNotReady(802),
+    cudaErrorSystemDriverMismatch(803),
+    cudaErrorCompatNotSupportedOnDevice(804),
+    cudaErrorMpsConnectionFailed(805),
+    cudaErrorMpsRpcFailure(806),
+    cudaErrorMpsServerNotReady(807),
+    cudaErrorMpsMaxClientsReached(808),
+    cudaErrorMpsMaxConnectionsReached(809),
+    cudaErrorStreamCaptureUnsupported(900),
+    cudaErrorStreamCaptureInvalidated(901),
+    cudaErrorStreamCaptureMerge(902),
+    cudaErrorStreamCaptureUnmatched(903),
+    cudaErrorStreamCaptureUnjoined(904),
+    cudaErrorStreamCaptureIsolation(905),
+    cudaErrorStreamCaptureImplicit(906),
+    cudaErrorCapturedEvent(907),
+    cudaErrorStreamCaptureWrongThread(908),
+    cudaErrorTimeout(909),
+    cudaErrorGraphExecUpdateFailure(910),
+    cudaErrorExternalDevice(911),
+    cudaErrorUnknown(999),
+    cudaErrorApiFailureBase(10000);
+
+    final int code;
+
+    private static Map<Integer, CudaError> codeToError = new HashMap<Integer, CudaError>(){{
+      put(cudaErrorInvalidValue.code, cudaErrorInvalidValue);
+      put(cudaErrorMemoryAllocation.code, cudaErrorMemoryAllocation);
+      put(cudaErrorInitializationError.code, cudaErrorInitializationError);
+      put(cudaErrorCudartUnloading.code, cudaErrorCudartUnloading);
+      put(cudaErrorProfilerDisabled.code, cudaErrorProfilerDisabled);
+      put(cudaErrorProfilerNotInitialized.code, cudaErrorProfilerNotInitialized);
+      put(cudaErrorProfilerAlreadyStarted.code, cudaErrorProfilerAlreadyStarted);
+      put(cudaErrorProfilerAlreadyStopped.code, cudaErrorProfilerAlreadyStopped);
+      put(cudaErrorInvalidConfiguration.code, cudaErrorInvalidConfiguration);
+      put(cudaErrorInvalidPitchValue.code, cudaErrorInvalidPitchValue);
+      put(cudaErrorInvalidSymbol.code, cudaErrorInvalidSymbol);
+      put(cudaErrorInvalidHostPointer.code, cudaErrorInvalidHostPointer);
+      put(cudaErrorInvalidDevicePointer.code, cudaErrorInvalidDevicePointer);
+      put(cudaErrorInvalidTexture.code, cudaErrorInvalidTexture);
+      put(cudaErrorInvalidTextureBinding.code, cudaErrorInvalidTextureBinding);
+      put(cudaErrorInvalidChannelDescriptor.code, cudaErrorInvalidChannelDescriptor);
+      put(cudaErrorInvalidMemcpyDirection.code, cudaErrorInvalidMemcpyDirection);
+      put(cudaErrorAddressOfConstant.code, cudaErrorAddressOfConstant);
+      put(cudaErrorTextureFetchFailed.code, cudaErrorTextureFetchFailed);
+      put(cudaErrorTextureNotBound.code, cudaErrorTextureNotBound);
+      put(cudaErrorSynchronizationError.code, cudaErrorSynchronizationError);
+      put(cudaErrorInvalidFilterSetting.code, cudaErrorInvalidFilterSetting);
+      put(cudaErrorInvalidNormSetting.code, cudaErrorInvalidNormSetting);
+      put(cudaErrorMixedDeviceExecution.code, cudaErrorMixedDeviceExecution);
+      put(cudaErrorNotYetImplemented.code, cudaErrorNotYetImplemented);
+      put(cudaErrorMemoryValueTooLarge.code, cudaErrorMemoryValueTooLarge);
+      put(cudaErrorStubLibrary.code, cudaErrorStubLibrary);
+      put(cudaErrorInsufficientDriver.code, cudaErrorInsufficientDriver);
+      put(cudaErrorCallRequiresNewerDriver.code, cudaErrorCallRequiresNewerDriver);
+      put(cudaErrorInvalidSurface.code, cudaErrorInvalidSurface);
+      put(cudaErrorDuplicateVariableName.code, cudaErrorDuplicateVariableName);
+      put(cudaErrorDuplicateTextureName.code, cudaErrorDuplicateTextureName);
+      put(cudaErrorDuplicateSurfaceName.code, cudaErrorDuplicateSurfaceName);
+      put(cudaErrorDevicesUnavailable.code, cudaErrorDevicesUnavailable);
+      put(cudaErrorIncompatibleDriverContext.code, cudaErrorIncompatibleDriverContext);
+      put(cudaErrorMissingConfiguration.code, cudaErrorMissingConfiguration);
+      put(cudaErrorPriorLaunchFailure.code, cudaErrorPriorLaunchFailure);
+      put(cudaErrorLaunchMaxDepthExceeded.code, cudaErrorLaunchMaxDepthExceeded);
+      put(cudaErrorLaunchFileScopedTex.code, cudaErrorLaunchFileScopedTex);
+      put(cudaErrorLaunchFileScopedSurf.code, cudaErrorLaunchFileScopedSurf);
+      put(cudaErrorSyncDepthExceeded.code, cudaErrorSyncDepthExceeded);
+      put(cudaErrorLaunchPendingCountExceeded.code, cudaErrorLaunchPendingCountExceeded);
+      put(cudaErrorInvalidDeviceFunction.code, cudaErrorInvalidDeviceFunction);
+      put(cudaErrorNoDevice.code, cudaErrorNoDevice);
+      put(cudaErrorInvalidDevice.code, cudaErrorInvalidDevice);
+      put(cudaErrorDeviceNotLicensed.code, cudaErrorDeviceNotLicensed);
+      put(cudaErrorSoftwareValidityNotEstablished.code, cudaErrorSoftwareValidityNotEstablished);
+      put(cudaErrorStartupFailure.code, cudaErrorStartupFailure);
+      put(cudaErrorInvalidKernelImage.code, cudaErrorInvalidKernelImage);
+      put(cudaErrorDeviceUninitialized.code, cudaErrorDeviceUninitialized);
+      put(cudaErrorMapBufferObjectFailed.code, cudaErrorMapBufferObjectFailed);
+      put(cudaErrorUnmapBufferObjectFailed.code, cudaErrorUnmapBufferObjectFailed);
+      put(cudaErrorArrayIsMapped.code, cudaErrorArrayIsMapped);
+      put(cudaErrorAlreadyMapped.code, cudaErrorAlreadyMapped);
+      put(cudaErrorNoKernelImageForDevice.code, cudaErrorNoKernelImageForDevice);
+      put(cudaErrorAlreadyAcquired.code, cudaErrorAlreadyAcquired);
+      put(cudaErrorNotMapped.code, cudaErrorNotMapped);
+      put(cudaErrorNotMappedAsArray.code, cudaErrorNotMappedAsArray);
+      put(cudaErrorNotMappedAsPointer.code, cudaErrorNotMappedAsPointer);
+      put(cudaErrorECCUncorrectable.code, cudaErrorECCUncorrectable);
+      put(cudaErrorUnsupportedLimit.code, cudaErrorUnsupportedLimit);
+      put(cudaErrorDeviceAlreadyInUse.code, cudaErrorDeviceAlreadyInUse);
+      put(cudaErrorPeerAccessUnsupported.code, cudaErrorPeerAccessUnsupported);
+      put(cudaErrorInvalidPtx.code, cudaErrorInvalidPtx);
+      put(cudaErrorInvalidGraphicsContext.code, cudaErrorInvalidGraphicsContext);
+      put(cudaErrorNvlinkUncorrectable.code, cudaErrorNvlinkUncorrectable);
+      put(cudaErrorJitCompilerNotFound.code, cudaErrorJitCompilerNotFound);
+      put(cudaErrorUnsupportedPtxVersion.code, cudaErrorUnsupportedPtxVersion);
+      put(cudaErrorJitCompilationDisabled.code, cudaErrorJitCompilationDisabled);
+      put(cudaErrorUnsupportedExecAffinity.code, cudaErrorUnsupportedExecAffinity);
+      put(cudaErrorInvalidSource.code, cudaErrorInvalidSource);
+      put(cudaErrorFileNotFound.code, cudaErrorFileNotFound);
+      put(cudaErrorSharedObjectSymbolNotFound.code, cudaErrorSharedObjectSymbolNotFound);
+      put(cudaErrorSharedObjectInitFailed.code, cudaErrorSharedObjectInitFailed);
+      put(cudaErrorOperatingSystem.code, cudaErrorOperatingSystem);
+      put(cudaErrorInvalidResourceHandle.code, cudaErrorInvalidResourceHandle);
+      put(cudaErrorIllegalState.code, cudaErrorIllegalState);
+      put(cudaErrorSymbolNotFound.code, cudaErrorSymbolNotFound);
+      put(cudaErrorNotReady.code, cudaErrorNotReady);
+      put(cudaErrorIllegalAddress.code, cudaErrorIllegalAddress);
+      put(cudaErrorLaunchOutOfResources.code, cudaErrorLaunchOutOfResources);
+      put(cudaErrorLaunchTimeout.code, cudaErrorLaunchTimeout);
+      put(cudaErrorLaunchIncompatibleTexturing.code, cudaErrorLaunchIncompatibleTexturing);
+      put(cudaErrorPeerAccessAlreadyEnabled.code, cudaErrorPeerAccessAlreadyEnabled);
+      put(cudaErrorPeerAccessNotEnabled.code, cudaErrorPeerAccessNotEnabled);
+      put(cudaErrorSetOnActiveProcess.code, cudaErrorSetOnActiveProcess);
+      put(cudaErrorContextIsDestroyed.code, cudaErrorContextIsDestroyed);
+      put(cudaErrorAssert.code, cudaErrorAssert);
+      put(cudaErrorTooManyPeers.code, cudaErrorTooManyPeers);
+      put(cudaErrorHostMemoryAlreadyRegistered.code, cudaErrorHostMemoryAlreadyRegistered);
+      put(cudaErrorHostMemoryNotRegistered.code, cudaErrorHostMemoryNotRegistered);
+      put(cudaErrorHardwareStackError.code, cudaErrorHardwareStackError);
+      put(cudaErrorIllegalInstruction.code, cudaErrorIllegalInstruction);
+      put(cudaErrorMisalignedAddress.code, cudaErrorMisalignedAddress);
+      put(cudaErrorInvalidAddressSpace.code, cudaErrorInvalidAddressSpace);
+      put(cudaErrorInvalidPc.code, cudaErrorInvalidPc);
+      put(cudaErrorLaunchFailure.code, cudaErrorLaunchFailure);
+      put(cudaErrorCooperativeLaunchTooLarge.code, cudaErrorCooperativeLaunchTooLarge);
+      put(cudaErrorNotPermitted.code, cudaErrorNotPermitted);
+      put(cudaErrorNotSupported.code, cudaErrorNotSupported);
+      put(cudaErrorSystemNotReady.code, cudaErrorSystemNotReady);
+      put(cudaErrorSystemDriverMismatch.code, cudaErrorSystemDriverMismatch);
+      put(cudaErrorCompatNotSupportedOnDevice.code, cudaErrorCompatNotSupportedOnDevice);
+      put(cudaErrorMpsConnectionFailed.code, cudaErrorMpsConnectionFailed);
+      put(cudaErrorMpsRpcFailure.code, cudaErrorMpsRpcFailure);
+      put(cudaErrorMpsServerNotReady.code, cudaErrorMpsServerNotReady);
+      put(cudaErrorMpsMaxClientsReached.code, cudaErrorMpsMaxClientsReached);
+      put(cudaErrorMpsMaxConnectionsReached.code, cudaErrorMpsMaxConnectionsReached);
+      put(cudaErrorStreamCaptureUnsupported.code, cudaErrorStreamCaptureUnsupported);
+      put(cudaErrorStreamCaptureInvalidated.code, cudaErrorStreamCaptureInvalidated);
+      put(cudaErrorStreamCaptureMerge.code, cudaErrorStreamCaptureMerge);
+      put(cudaErrorStreamCaptureUnmatched.code, cudaErrorStreamCaptureUnmatched);
+      put(cudaErrorStreamCaptureUnjoined.code, cudaErrorStreamCaptureUnjoined);
+      put(cudaErrorStreamCaptureIsolation.code, cudaErrorStreamCaptureIsolation);
+      put(cudaErrorStreamCaptureImplicit.code, cudaErrorStreamCaptureImplicit);
+      put(cudaErrorCapturedEvent.code, cudaErrorCapturedEvent);
+      put(cudaErrorStreamCaptureWrongThread.code, cudaErrorStreamCaptureWrongThread);
+      put(cudaErrorTimeout.code, cudaErrorTimeout);
+      put(cudaErrorGraphExecUpdateFailure.code, cudaErrorGraphExecUpdateFailure);
+      put(cudaErrorExternalDevice.code, cudaErrorExternalDevice);
+      put(cudaErrorUnknown.code, cudaErrorUnknown);
+      put(cudaErrorApiFailureBase.code, cudaErrorApiFailureBase);
+    }};
+
+    CudaError(int errorCode) {
+      this.code = errorCode;
+    }
+
+    public static CudaError parseErrorCode(int errorCode) {
+      if (!codeToError.containsKey(errorCode)) {
+        return UnknownNativeError;
+      }
+      return codeToError.get(errorCode);
+    }
+
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudaFatalException.java b/java/src/main/java/ai/rapids/cudf/CudaFatalException.java
new file mode 100644
index 0000000..067e2c2
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudaFatalException.java
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * CudaFatalException is a kind of CudaException which leaves the process in an inconsistent state
+ * and any further CUDA work will return the same error.
+ * To continue using CUDA, the process must be terminated and relaunched.
+ */
+public class CudaFatalException extends CudaException {
+  CudaFatalException(String message, int errorCode) {
+    this(message, "No native stacktrace is available.", errorCode);
+  }
+
+  CudaFatalException(String message, String nativeStacktrace, int errorCode) {
+    super(message, nativeStacktrace, errorCode);
+  }
+
+  CudaFatalException(String message, String nativeStacktrace, int errorCode, Throwable cause) {
+    super(message, nativeStacktrace, errorCode, cause);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudaMemInfo.java b/java/src/main/java/ai/rapids/cudf/CudaMemInfo.java
new file mode 100755
index 0000000..f4b8213
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudaMemInfo.java
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * Represent free and total device memory.
+ */
+public class CudaMemInfo {
+  /**
+   * free memory in bytes
+   */
+  public final long free;
+  /**
+   * total memory in bytes
+   */
+  public final long total;
+
+  CudaMemInfo(long free, long total) {
+    this.free = free;
+    this.total = total;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudaMemcpyKind.java b/java/src/main/java/ai/rapids/cudf/CudaMemcpyKind.java
new file mode 100755
index 0000000..460a0fa
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudaMemcpyKind.java
@@ -0,0 +1,35 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+enum CudaMemcpyKind {
+  HOST_TO_HOST(0),     /*< Host   -> Host */
+  HOST_TO_DEVICE(1),   /*< Host   -> Device */
+  DEVICE_TO_HOST(2),   /*< Device -> Host */
+  DEVICE_TO_DEVICE(3), /*< Device -> Device */
+  DEFAULT(4);   /*< Direction of the transfer is inferred from the pointer values. Requires
+                          unified virtual addressing */
+
+  private final int value;
+
+  CudaMemcpyKind(int value) {
+    this.value = value;
+  }
+
+  int getValue() {
+    return value;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudaMemoryBuffer.java b/java/src/main/java/ai/rapids/cudf/CudaMemoryBuffer.java
new file mode 100644
index 0000000..c62ae8a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudaMemoryBuffer.java
@@ -0,0 +1,124 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * This class represents data allocated using `cudaMalloc` directly instead of the default RMM
+ * memory resource. Closing this object will effectively release the memory held by the buffer.
+ * Note that because of reference counting if a buffer is sliced it may not actually result in the
+ * memory being released.
+ */
+public class CudaMemoryBuffer extends BaseDeviceMemoryBuffer {
+  private static final Logger log = LoggerFactory.getLogger(CudaMemoryBuffer.class);
+
+  private static final class CudaBufferCleaner extends MemoryBufferCleaner {
+    private long address;
+    private long lengthInBytes;
+    private Cuda.Stream stream;
+
+    CudaBufferCleaner(long address, long lengthInBytes, Cuda.Stream stream) {
+      this.address = address;
+      this.lengthInBytes = lengthInBytes;
+      this.stream = stream;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long origAddress = address;
+      if (address != 0) {
+        long s = stream == null ? 0 : stream.getStream();
+        try {
+          Rmm.freeCuda(address, lengthInBytes, s);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          address = 0;
+          lengthInBytes = 0;
+          stream = null;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A CUDA BUFFER WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+        logRefCountDebug("Leaked device buffer");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return address == 0;
+    }
+  }
+
+  /**
+   * Wrap an existing CUDA allocation in a device memory buffer. The CUDA allocation will be freed
+   * when the resulting device memory buffer instance frees its memory resource (i.e.: when its
+   * reference count goes to zero).
+   * @param address device address of the CUDA memory allocation
+   * @param lengthInBytes length of the CUDA allocation in bytes
+   * @param stream CUDA stream to use for synchronization when freeing the allocation
+   */
+  public CudaMemoryBuffer(long address, long lengthInBytes, Cuda.Stream stream) {
+    super(address, lengthInBytes, new CudaBufferCleaner(address, lengthInBytes, stream));
+  }
+
+  private CudaMemoryBuffer(long address, long lengthInBytes, CudaMemoryBuffer parent) {
+    super(address, lengthInBytes, parent);
+  }
+
+  /**
+   * Allocate memory for use on the GPU. You must close it when done.
+   * @param bytes size in bytes to allocate
+   * @return the buffer
+   */
+  public static CudaMemoryBuffer allocate(long bytes) {
+    return allocate(bytes, Cuda.DEFAULT_STREAM);
+  }
+
+  /**
+   * Allocate memory for use on the GPU. You must close it when done.
+   * @param bytes size in bytes to allocate
+   * @param stream The stream in which to synchronize this command
+   * @return the buffer
+   */
+  public static CudaMemoryBuffer allocate(long bytes, Cuda.Stream stream) {
+    return Rmm.allocCuda(bytes, stream);
+  }
+
+  /**
+   * Slice off a part of the device buffer. Note that this is a zero copy operation and all
+   * slices must be closed along with the original buffer before the memory is released to RMM.
+   * So use this with some caution.
+   * @param offset where to start the slice at.
+   * @param len how many bytes to slice
+   * @return a device buffer that will need to be closed independently from this buffer.
+   */
+  @Override
+  public synchronized final CudaMemoryBuffer slice(long offset, long len) {
+    addressOutOfBoundsCheck(address + offset, len, "slice");
+    incRefCount();
+    return new CudaMemoryBuffer(getAddress() + offset, len, this);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudfColumnSizeOverflowException.java b/java/src/main/java/ai/rapids/cudf/CudfColumnSizeOverflowException.java
new file mode 100755
index 0000000..9e72490
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudfColumnSizeOverflowException.java
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * Exception thrown when CUDF operation results in a column size
+ * exceeding CUDF column size limits
+ */
+public class CudfColumnSizeOverflowException extends CudfException {
+  CudfColumnSizeOverflowException(String message) {
+    super(message);
+  }
+
+  CudfColumnSizeOverflowException(String message, String nativeStacktrace) {
+    super(message, nativeStacktrace);
+  }
+
+  CudfColumnSizeOverflowException(String message, String nativeStacktrace, Throwable cause) {
+    super(message, nativeStacktrace, cause);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/CudfException.java b/java/src/main/java/ai/rapids/cudf/CudfException.java
new file mode 100755
index 0000000..4038c56
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/CudfException.java
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * Exception thrown by cudf itself.
+ */
+public class CudfException extends RuntimeException {
+  CudfException(String message) {
+    this(message, "No native stacktrace is available.");
+  }
+
+  CudfException(String message, String nativeStacktrace) {
+    super(message);
+    this.nativeStacktrace = nativeStacktrace;
+  }
+
+  CudfException(String message, String nativeStacktrace, Throwable cause) {
+    super(message, cause);
+    this.nativeStacktrace = nativeStacktrace;
+  }
+
+  public final String getNativeStacktrace() {
+    return nativeStacktrace;
+  }
+
+  private final String nativeStacktrace;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/DType.java b/java/src/main/java/ai/rapids/cudf/DType.java
new file mode 100644
index 0000000..07bc4fe
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/DType.java
@@ -0,0 +1,528 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import java.math.BigDecimal;
+import java.util.EnumSet;
+import java.util.Objects;
+
+public final class DType {
+
+  public static final int DECIMAL32_MAX_PRECISION = 9;
+  public static final int DECIMAL64_MAX_PRECISION = 18;
+  public static final int DECIMAL128_MAX_PRECISION = 38;
+
+  /* enum representing various types. Whenever a new non-decimal type is added please make sure
+  below sections are updated as well:
+  1. Create a singleton object of the new type.
+  2. Update SINGLETON_DTYPE_LOOKUP to reflect new type. The order should be maintained between
+  DTypeEnum and SINGLETON_DTYPE_LOOKUP */
+  public enum DTypeEnum {
+    EMPTY(0, 0),
+    INT8(1, 1),
+    INT16(2, 2),
+    INT32(4, 3),
+    INT64(8, 4),
+    UINT8(1, 5),
+    UINT16(2, 6),
+    UINT32(4, 7),
+    UINT64(8, 8),
+    FLOAT32(4, 9),
+    FLOAT64(8, 10),
+    /**
+     * Byte wise true non-0/false 0.  In general true will be 1.
+     */
+    BOOL8(1, 11),
+    /**
+     * Days since the UNIX epoch
+     */
+    TIMESTAMP_DAYS(4, 12),
+    /**
+     * s since the UNIX epoch
+     */
+    TIMESTAMP_SECONDS(8, 13),
+    /**
+     * ms since the UNIX epoch
+     */
+    TIMESTAMP_MILLISECONDS(8, 14),
+    /**
+     * microseconds since the UNIX epoch
+     */
+    TIMESTAMP_MICROSECONDS(8, 15),
+    /**
+     * ns since the UNIX epoch
+     */
+    TIMESTAMP_NANOSECONDS(8, 16),
+
+    DURATION_DAYS(4, 17),
+    DURATION_SECONDS(8, 18),
+    DURATION_MILLISECONDS(8, 19),
+    DURATION_MICROSECONDS(8, 20),
+    DURATION_NANOSECONDS(8, 21),
+    //DICTIONARY32(4, 22),
+
+    STRING(0, 23),
+    LIST(0, 24),
+    DECIMAL32(4, 25),
+    DECIMAL64(8, 26),
+    DECIMAL128(16, 27),
+    STRUCT(0, 28);
+
+    final int sizeInBytes;
+    final int nativeId;
+
+    DTypeEnum(int sizeInBytes, int nativeId) {
+      this.sizeInBytes = sizeInBytes;
+      this.nativeId = nativeId;
+    }
+
+    public int getNativeId() { return nativeId; }
+
+    public boolean isDecimalType() { return DType.DECIMALS.contains(this); }
+  }
+
+  final DTypeEnum typeId;
+  private final int scale;
+
+  private DType(DTypeEnum id) {
+    typeId = id;
+    scale = 0;
+  }
+
+  /**
+   * Constructor for Decimal Type
+   * @param id Enum representing data type.
+   * @param decimalScale Scale of fixed point decimal type
+   */
+  private DType(DTypeEnum id, int decimalScale) {
+    typeId = id;
+    scale = decimalScale;
+  }
+
+  public static final DType EMPTY = new DType(DTypeEnum.EMPTY);
+  public static final DType INT8 = new DType(DTypeEnum.INT8);
+  public static final DType INT16 = new DType(DTypeEnum.INT16);
+  public static final DType INT32 = new DType(DTypeEnum.INT32);
+  public static final DType INT64 = new DType(DTypeEnum.INT64);
+  public static final DType UINT8 = new DType(DTypeEnum.UINT8);
+  public static final DType UINT16 = new DType(DTypeEnum.UINT16);
+  public static final DType UINT32 = new DType(DTypeEnum.UINT32);
+  public static final DType UINT64 = new DType(DTypeEnum.UINT64);
+  public static final DType FLOAT32 = new DType(DTypeEnum.FLOAT32);
+  public static final DType FLOAT64 = new DType(DTypeEnum.FLOAT64);
+  public static final DType BOOL8 = new DType(DTypeEnum.BOOL8);
+  public static final DType TIMESTAMP_DAYS = new DType(DTypeEnum.TIMESTAMP_DAYS);
+  public static final DType TIMESTAMP_SECONDS = new DType(DTypeEnum.TIMESTAMP_SECONDS);
+  public static final DType TIMESTAMP_MILLISECONDS = new DType(DTypeEnum.TIMESTAMP_MILLISECONDS);
+  public static final DType TIMESTAMP_MICROSECONDS = new DType(DTypeEnum.TIMESTAMP_MICROSECONDS);
+  public static final DType TIMESTAMP_NANOSECONDS = new DType(DTypeEnum.TIMESTAMP_NANOSECONDS);
+  public static final DType DURATION_DAYS = new DType(DTypeEnum.DURATION_DAYS);
+  public static final DType DURATION_SECONDS = new DType(DTypeEnum.DURATION_SECONDS);
+  public static final DType DURATION_MILLISECONDS = new DType(DTypeEnum.DURATION_MILLISECONDS);
+  public static final DType DURATION_MICROSECONDS = new DType(DTypeEnum.DURATION_MICROSECONDS);
+  public static final DType DURATION_NANOSECONDS = new DType(DTypeEnum.DURATION_NANOSECONDS);
+  public static final DType STRING = new DType(DTypeEnum.STRING);
+  public static final DType LIST = new DType(DTypeEnum.LIST);
+  public static final DType STRUCT = new DType(DTypeEnum.STRUCT);
+
+  /* This is used in fromNative method to return singleton object for non-decimal types.
+  Please make sure the order here is same as that of DTypeEnum. Whenever a new non-decimal
+  type is added in DTypeEnum, this array needs to be updated as well.*/
+  private static final DType[] SINGLETON_DTYPE_LOOKUP = new DType[]{
+      EMPTY,
+      INT8,
+      INT16,
+      INT32,
+      INT64,
+      UINT8,
+      UINT16,
+      UINT32,
+      UINT64,
+      FLOAT32,
+      FLOAT64,
+      BOOL8,
+      TIMESTAMP_DAYS,
+      TIMESTAMP_SECONDS,
+      TIMESTAMP_MILLISECONDS,
+      TIMESTAMP_MICROSECONDS,
+      TIMESTAMP_NANOSECONDS,
+      DURATION_DAYS,
+      DURATION_SECONDS,
+      DURATION_MILLISECONDS,
+      DURATION_MICROSECONDS,
+      DURATION_NANOSECONDS,
+      null, // DICTIONARY32
+      STRING,
+      LIST,
+      null, // DECIMAL32
+      null, // DECIMAL64
+      null, // DECIMAL128
+      STRUCT
+  };
+
+  /**
+   * Returns max precision for Decimal Type.
+   * @return max precision this Decimal Type can hold
+   */
+  public int getDecimalMaxPrecision() {
+    if (!isDecimalType()) {
+      throw new IllegalArgumentException("not a decimal type: " + this);
+    }
+    if (typeId == DTypeEnum.DECIMAL32) return DECIMAL32_MAX_PRECISION;
+    if (typeId == DTypeEnum.DECIMAL64) return DECIMAL64_MAX_PRECISION;
+    return DType.DECIMAL128_MAX_PRECISION;
+  }
+
+  /**
+   * Get the number of decimal places needed to hold the Integral Type.
+   * NOTE: this method is NOT for Decimal Type but for Integral Type.
+   * @return the minimum decimal precision (places) for Integral Type
+   */
+  public int getPrecisionForInt() {
+    // -128 to 127
+    if (typeId == DTypeEnum.INT8) return 3;
+    // -32768 to 32767
+    if (typeId == DTypeEnum.INT16) return 5;
+    // -2147483648 to 2147483647
+    if (typeId == DTypeEnum.INT32) return 10;
+    // -9223372036854775808 to 9223372036854775807
+    if (typeId == DTypeEnum.INT64) return 19;
+
+    throw new IllegalArgumentException("not an integral type: " + this);
+  }
+
+  /**
+   * This only works for fixed width types. Variable width types like strings the value is
+   * undefined and should be ignored.
+   *
+   * @return size of type in bytes.
+   */
+  public int getSizeInBytes() { return typeId.sizeInBytes; }
+
+  /**
+   * Returns scale for Decimal Type.
+   * @return scale base-10 exponent to multiply the unscaled value to produce the decimal value.
+   * Example: Consider unscaled value = 123456
+   *         if scale = -2, decimal value = 123456 * 10^-2 = 1234.56
+   *         if scale = 2, decimal value = 123456 * 10^2 = 12345600
+   */
+  public int getScale() { return scale; }
+
+  /**
+   * Return enum for this DType
+   * @return DTypeEnum
+   */
+  public DTypeEnum getTypeId() {
+    return typeId;
+  }
+
+  @Override
+  public boolean equals(Object o) {
+    if (this == o) return true;
+    if (o == null || getClass() != o.getClass()) return false;
+    DType type = (DType) o;
+    return scale == type.scale && typeId == type.typeId;
+  }
+
+  @Override
+  public int hashCode() {
+    return Objects.hash(typeId, scale);
+  }
+
+  @Override
+  public String toString() {
+    if (isDecimalType()) {
+      return typeId + " scale:" + scale;
+    } else {
+      return String.valueOf(typeId);
+    }
+  }
+
+  /**
+   * Factory method for non-decimal DType instances.
+   * @param dt  enum corresponding to datatype.
+   * @return DType
+   */
+  public static DType create(DTypeEnum dt) {
+    if (DType.DECIMALS.contains(dt)) {
+      throw new IllegalArgumentException("Could not create a Decimal DType without scale");
+    }
+    return DType.fromNative(dt.nativeId, 0);
+  }
+
+  /**
+   * Factory method specialized for decimal DType instances.
+   * @param dt  enum corresponding to datatype.
+   * @param scale base-10 exponent to multiply the unscaled value to produce the decimal value.
+   * Example: Consider unscaled value = 123456
+   *         if scale = -2, decimal value = 123456 * 10^-2 = 1234.56
+   *         if scale = 2, decimal value = 123456 * 10^2 = 12345600
+   * @return DType
+   */
+  public static DType create(DTypeEnum dt, int scale) {
+    if (!DType.DECIMALS.contains(dt)) {
+      throw new IllegalArgumentException("Could not create a non-Decimal DType with scale");
+    }
+    return DType.fromNative(dt.nativeId, scale);
+  }
+
+  /**
+   * Factory method for DType instances
+   * @param nativeId nativeId of DataTypeEnun
+   * @param scale base-10 exponent to multiply the unscaled value to produce the decimal value
+   * Example: Consider unscaled value = 123456
+   *         if scale = -2, decimal value = 123456 * 10^-2 = 1234.56
+   *         if scale = 2, decimal value = 123456 * 10^2 = 12345600
+   * @return DType
+   */
+  public static DType fromNative(int nativeId, int scale) {
+    if (nativeId >=0 && nativeId < SINGLETON_DTYPE_LOOKUP.length) {
+      DType ret = SINGLETON_DTYPE_LOOKUP[nativeId];
+      if (ret != null) {
+        assert ret.typeId.nativeId == nativeId : "Something went wrong and it looks like " +
+          "SINGLETON_DTYPE_LOOKUP is out of sync";
+        return ret;
+      }
+      if (nativeId == DTypeEnum.DECIMAL32.nativeId) {
+        if (-scale > DECIMAL32_MAX_PRECISION) {
+          throw new IllegalArgumentException(
+              "Scale " + (-scale) + " exceeds DECIMAL32_MAX_PRECISION " + DECIMAL32_MAX_PRECISION);
+        }
+        return new DType(DTypeEnum.DECIMAL32, scale);
+      }
+      if (nativeId == DTypeEnum.DECIMAL64.nativeId) {
+        if (-scale > DECIMAL64_MAX_PRECISION) {
+          throw new IllegalArgumentException(
+              "Scale " + (-scale) + " exceeds DECIMAL64_MAX_PRECISION " + DECIMAL64_MAX_PRECISION);
+        }
+        return new DType(DTypeEnum.DECIMAL64, scale);
+      }
+      if (nativeId == DTypeEnum.DECIMAL128.nativeId) {
+        if (-scale > DECIMAL128_MAX_PRECISION) {
+          throw new IllegalArgumentException(
+              "Scale " + (-scale) + " exceeds DECIMAL128_MAX_PRECISION " + DECIMAL128_MAX_PRECISION);
+        }
+        return new DType(DTypeEnum.DECIMAL128, scale);
+      }
+    }
+    throw new IllegalArgumentException("Could not translate " + nativeId + " into a DType");
+  }
+
+  /**
+   * Create decimal-like DType using precision and scale of Java BigDecimal.
+   *
+   * @param dec BigDecimal
+   * @return DType
+   */
+  public static DType fromJavaBigDecimal(BigDecimal dec) {
+    // Notice: Compared to scale of Java BigDecimal, scale of libcudf works in opposite.
+    // So, we negate the scale value before passing it into constructor.
+    if (dec.precision() <= DECIMAL32_MAX_PRECISION) {
+      return new DType(DTypeEnum.DECIMAL32, -dec.scale());
+    } else if (dec.precision() <= DECIMAL64_MAX_PRECISION) {
+      return new DType(DTypeEnum.DECIMAL64, -dec.scale());
+    } else if (dec.precision() <= DECIMAL128_MAX_PRECISION) {
+      return new DType(DTypeEnum.DECIMAL128, -dec.scale());
+    }
+    throw new IllegalArgumentException("Precision " + dec.precision() +
+        " exceeds max precision cuDF can support " + DECIMAL128_MAX_PRECISION);
+  }
+
+  /**
+   * Returns true for timestamps with time level resolution, as opposed to day level resolution
+   */
+  public boolean hasTimeResolution() {
+    return TIME_RESOLUTION.contains(this.typeId);
+  }
+
+  /**
+   * Returns true if this type is backed by int type
+   * Namely this method will return true for the following types
+   *       DType.INT32,
+   *       DType.UINT32,
+   *       DType.DURATION_DAYS,
+   *       DType.TIMESTAMP_DAYS,
+   *       DType.DECIMAL32
+   */
+  public boolean isBackedByInt() {
+    return INTS.contains(this.typeId);
+  }
+
+  /**
+   * Returns true if this type is backed by long type
+   * Namely this method will return true for the following types
+   *       DType.INT64,
+   *       DType.UINT64,
+   *       DType.DURATION_SECONDS,
+   *       DType.DURATION_MILLISECONDS,
+   *       DType.DURATION_MICROSECONDS,
+   *       DType.DURATION_NANOSECONDS,
+   *       DType.TIMESTAMP_SECONDS,
+   *       DType.TIMESTAMP_MILLISECONDS,
+   *       DType.TIMESTAMP_MICROSECONDS,
+   *       DType.TIMESTAMP_NANOSECONDS,
+   *       DType.DECIMAL64
+   */
+  public boolean isBackedByLong() {
+    return LONGS.contains(this.typeId);
+  }
+
+  /**
+   * Returns true if this type is backed by short type
+   * Namely this method will return true for the following types
+   *       DType.INT16,
+   *       DType.UINT16
+   */
+  public boolean isBackedByShort() { return SHORTS.contains(this.typeId); }
+
+  /**
+   * Returns true if this type is backed by byte type
+   * Namely this method will return true for the following types
+   *       DType.INT8,
+   *       DType.UINT8,
+   *       DType.BOOL8
+   */
+  public boolean isBackedByByte() { return BYTES.contains(this.typeId); }
+
+  /**
+   * Returns true if this type is of decimal type
+   * Namely this method will return true for the following types
+   *       DType.DECIMAL32,
+   *       DType.DECIMAL64
+   */
+  public boolean isDecimalType() { return this.typeId.isDecimalType(); }
+
+  /**
+   * Returns true for duration types
+   */
+  public boolean isDurationType() {
+    return DURATION_TYPE.contains(this.typeId);
+  }
+
+  /**
+   * Returns true for strictly Integer types not a type backed by
+   * ints
+   */
+  public boolean isIntegral() {
+    return INTEGRALS.contains(this.typeId);
+  }
+
+ /**
+   * Returns true for nested types
+   */
+  public boolean isNestedType() {
+    return NESTED_TYPE.contains(this.typeId);
+  }
+
+  @Deprecated
+  public boolean isTimestamp() {
+    return TIMESTAMPS.contains(this.typeId);
+  }
+
+  public boolean isTimestampType() {
+    return TIMESTAMPS.contains(this.typeId);
+  }
+
+  /**
+   * Returns true if the type uses a vector of offsets
+   */
+  public boolean hasOffsets() {
+    return OFFSETS_TYPE.contains(this.typeId);
+  }
+
+  private static final EnumSet<DTypeEnum> TIMESTAMPS = EnumSet.of(
+      DTypeEnum.TIMESTAMP_DAYS,
+      DTypeEnum.TIMESTAMP_SECONDS,
+      DTypeEnum.TIMESTAMP_MILLISECONDS,
+      DTypeEnum.TIMESTAMP_MICROSECONDS,
+      DTypeEnum.TIMESTAMP_NANOSECONDS);
+
+  private static final EnumSet<DTypeEnum> TIME_RESOLUTION = EnumSet.of(
+      DTypeEnum.TIMESTAMP_SECONDS,
+      DTypeEnum.TIMESTAMP_MILLISECONDS,
+      DTypeEnum.TIMESTAMP_MICROSECONDS,
+      DTypeEnum.TIMESTAMP_NANOSECONDS);
+
+  private static final EnumSet<DTypeEnum> DURATION_TYPE = EnumSet.of(
+      DTypeEnum.DURATION_DAYS,
+      DTypeEnum.DURATION_MICROSECONDS,
+      DTypeEnum.DURATION_MILLISECONDS,
+      DTypeEnum.DURATION_NANOSECONDS,
+      DTypeEnum.DURATION_SECONDS
+  );
+
+  private static final EnumSet<DTypeEnum> LONGS = EnumSet.of(
+      DTypeEnum.INT64,
+      DTypeEnum.UINT64,
+      DTypeEnum.DURATION_SECONDS,
+      DTypeEnum.DURATION_MILLISECONDS,
+      DTypeEnum.DURATION_MICROSECONDS,
+      DTypeEnum.DURATION_NANOSECONDS,
+      DTypeEnum.TIMESTAMP_SECONDS,
+      DTypeEnum.TIMESTAMP_MILLISECONDS,
+      DTypeEnum.TIMESTAMP_MICROSECONDS,
+      DTypeEnum.TIMESTAMP_NANOSECONDS,
+      // The unscaledValue of DECIMAL64 is of type INT64, which means it can be fetched by getLong.
+      DTypeEnum.DECIMAL64
+  );
+
+  private static final EnumSet<DTypeEnum> INTS = EnumSet.of(
+      DTypeEnum.INT32,
+      DTypeEnum.UINT32,
+      DTypeEnum.DURATION_DAYS,
+      DTypeEnum.TIMESTAMP_DAYS,
+      // The unscaledValue of DECIMAL32 is of type INT32, which means it can be fetched by getInt.
+      DTypeEnum.DECIMAL32
+  );
+
+  private static final EnumSet<DTypeEnum> SHORTS = EnumSet.of(
+      DTypeEnum.INT16,
+      DTypeEnum.UINT16
+  );
+
+  private static final EnumSet<DTypeEnum> BYTES = EnumSet.of(
+      DTypeEnum.INT8,
+      DTypeEnum.UINT8,
+      DTypeEnum.BOOL8
+  );
+
+  private static final EnumSet<DTypeEnum> DECIMALS = EnumSet.of(
+      DTypeEnum.DECIMAL32,
+      DTypeEnum.DECIMAL64,
+      DTypeEnum.DECIMAL128
+  );
+
+  private static final EnumSet<DTypeEnum> NESTED_TYPE = EnumSet.of(
+      DTypeEnum.LIST,
+      DTypeEnum.STRUCT
+  );
+
+  private static final EnumSet<DTypeEnum> OFFSETS_TYPE = EnumSet.of(
+      DTypeEnum.STRING,
+      DTypeEnum.LIST
+  );
+
+  private static final EnumSet<DTypeEnum> INTEGRALS = EnumSet.of(
+    DTypeEnum.INT8,
+    DTypeEnum.INT16,
+    DTypeEnum.INT32,
+    DTypeEnum.INT64,
+    DTypeEnum.UINT8,
+    DTypeEnum.UINT16,
+    DTypeEnum.UINT32,
+    DTypeEnum.UINT64
+  );
+}
diff --git a/java/src/main/java/ai/rapids/cudf/DecimalUtils.java b/java/src/main/java/ai/rapids/cudf/DecimalUtils.java
new file mode 100644
index 0000000..1979bd1
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/DecimalUtils.java
@@ -0,0 +1,164 @@
+/*
+ *
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.math.BigDecimal;
+import java.util.AbstractMap;
+import java.util.Map;
+
+public class DecimalUtils {
+
+  /**
+   * Creates a cuDF decimal type with precision and scale
+   */
+  public static DType createDecimalType(int precision, int scale) {
+    if (precision <= DType.DECIMAL32_MAX_PRECISION) {
+      return DType.create(DType.DTypeEnum.DECIMAL32, -scale);
+    } else if (precision <= DType.DECIMAL64_MAX_PRECISION) {
+      return DType.create(DType.DTypeEnum.DECIMAL64, -scale);
+    } else if (precision <= DType.DECIMAL128_MAX_PRECISION) {
+      return DType.create(DType.DTypeEnum.DECIMAL128, -scale);
+    }
+    throw new IllegalArgumentException("precision overflow: " + precision);
+  }
+
+  /**
+   * Given decimal precision and scale, returns the lower and upper bound of current decimal type.
+   *
+   * Be very careful when comparing these CUDF decimal comparisons really only work
+   * when both types are already the same precision and scale, and when you change the scale
+   * you end up losing information.
+   * @param precision the max precision of decimal type
+   * @param scale the scale of decimal type
+   * @return a Map Entry of BigDecimal, lower bound as the key, upper bound as the value
+   */
+  public static Map.Entry<BigDecimal, BigDecimal> bounds(int precision, int scale) {
+    StringBuilder sb = new StringBuilder();
+    for (int i = 0; i < precision; i++) sb.append("9");
+    sb.append("e");
+    sb.append(-scale);
+    String boundStr = sb.toString();
+    BigDecimal upperBound = new BigDecimal(boundStr);
+    BigDecimal lowerBound = new BigDecimal("-" + boundStr);
+    return new AbstractMap.SimpleImmutableEntry<>(lowerBound, upperBound);
+  }
+
+  /**
+   * With precision and scale, checks each value of input decimal column for out of bound.
+   * @return the boolean column represents whether specific values are out of bound or not
+   */
+  public static ColumnVector outOfBounds(ColumnView input, int precision, int scale) {
+    Map.Entry<BigDecimal, BigDecimal> boundPair = bounds(precision, scale);
+    BigDecimal lowerBound = boundPair.getKey();
+    BigDecimal upperBound = boundPair.getValue();
+    try (ColumnVector over = greaterThan(input, upperBound);
+         ColumnVector under = lessThan(input, lowerBound)) {
+      return over.or(under);
+    }
+  }
+
+  /**
+   * Because the native lessThan operator has issues with comparing decimal values that have different
+   * precision and scale accurately. This method takes some special steps to get rid of these issues.
+   */
+  public static ColumnVector lessThan(ColumnView lhs, BigDecimal rhs) {
+    assert (lhs.getType().isDecimalType());
+    int leftScale = lhs.getType().getScale();
+    int leftPrecision = lhs.getType().getDecimalMaxPrecision();
+
+    // First we have to round the scalar (rhs) to the same scale as lhs.  Because this is a
+    // less than and it is rhs that we are rounding, we will round away from 0 (UP)
+    // to make sure we always return the correct value.
+    // For example:
+    //      100.1 < 100.19
+    // If we rounded down the rhs 100.19 would become 100.1, and now 100.1 is not < 100.1
+    BigDecimal roundedRhs = rhs.setScale(-leftScale, BigDecimal.ROUND_UP);
+
+    if (roundedRhs.precision() > leftPrecision) {
+      // converting rhs to the same precision as lhs would result in an overflow/error, but
+      // the scale is the same so we can still figure this out. For example if LHS precision is
+      // 4 and RHS precision is 5 we get the following...
+      //  9999 <  99999 => true
+      // -9999 <  99999 => true
+      //  9999 < -99999 => false
+      // -9999 < -99999 => false
+      // so the result should be the same as RHS > 0
+      try (Scalar isPositive = Scalar.fromBool(roundedRhs.compareTo(BigDecimal.ZERO) > 0)) {
+        return ColumnVector.fromScalar(isPositive, (int) lhs.getRowCount());
+      }
+    }
+    try (Scalar scalarRhs = Scalar.fromDecimal(roundedRhs.unscaledValue(), lhs.getType())) {
+      return lhs.lessThan(scalarRhs);
+    }
+  }
+
+  /**
+   * Because the native lessThan operator has issues with comparing decimal values that have different
+   * precision and scale accurately. This method takes some special steps to get rid of these issues.
+   */
+  public static ColumnVector lessThan(BinaryOperable lhs, BigDecimal rhs, int numRows) {
+    if (lhs instanceof ColumnView) {
+      return lessThan((ColumnView) lhs, rhs);
+    }
+    Scalar scalarLhs = (Scalar) lhs;
+    if (scalarLhs.isValid()) {
+      try (Scalar isLess = Scalar.fromBool(scalarLhs.getBigDecimal().compareTo(rhs) < 0)) {
+        return ColumnVector.fromScalar(isLess, numRows);
+      }
+    }
+    try (Scalar nullScalar = Scalar.fromNull(DType.BOOL8)) {
+      return ColumnVector.fromScalar(nullScalar, numRows);
+    }
+  }
+
+  /**
+   * Because the native greaterThan operator has issues with comparing decimal values that have different
+   * precision and scale accurately. This method takes some special steps to get rid of these issues.
+   */
+  public static ColumnVector greaterThan(ColumnView lhs, BigDecimal rhs) {
+    assert (lhs.getType().isDecimalType());
+    int cvScale = lhs.getType().getScale();
+    int maxPrecision = lhs.getType().getDecimalMaxPrecision();
+
+    // First we have to round the scalar (rhs) to the same scale as lhs.  Because this is a
+    // greater than and it is rhs that we are rounding, we will round towards 0 (DOWN)
+    // to make sure we always return the correct value.
+    // For example:
+    //      100.2 > 100.19
+    // If we rounded up the rhs 100.19 would become 100.2, and now 100.2 is not > 100.2
+    BigDecimal roundedRhs = rhs.setScale(-cvScale, BigDecimal.ROUND_DOWN);
+
+    if (roundedRhs.precision() > maxPrecision) {
+      // converting rhs to the same precision as lhs would result in an overflow/error, but
+      // the scale is the same so we can still figure this out. For example if LHS precision is
+      // 4 and RHS precision is 5 we get the following...
+      //  9999 >  99999 => false
+      // -9999 >  99999 => false
+      //  9999 > -99999 => true
+      // -9999 > -99999 => true
+      // so the result should be the same as RHS < 0
+      try (Scalar isNegative = Scalar.fromBool(roundedRhs.compareTo(BigDecimal.ZERO) < 0)) {
+        return ColumnVector.fromScalar(isNegative, (int) lhs.getRowCount());
+      }
+    }
+    try (Scalar scalarRhs = Scalar.fromDecimal(roundedRhs.unscaledValue(), lhs.getType())) {
+      return lhs.greaterThan(scalarRhs);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/DefaultHostMemoryAllocator.java b/java/src/main/java/ai/rapids/cudf/DefaultHostMemoryAllocator.java
new file mode 100644
index 0000000..98a5b00
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/DefaultHostMemoryAllocator.java
@@ -0,0 +1,36 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+public class DefaultHostMemoryAllocator implements HostMemoryAllocator {
+  private static final HostMemoryAllocator INSTANCE = new DefaultHostMemoryAllocator();
+  public static HostMemoryAllocator get() {
+    return INSTANCE;
+  }
+
+  @Override
+  public HostMemoryBuffer allocate(long bytes, boolean preferPinned) {
+    return HostMemoryBuffer.allocate(bytes, preferPinned);
+  }
+
+  @Override
+  public HostMemoryBuffer allocate(long bytes) {
+    return HostMemoryBuffer.allocate(bytes);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/DeviceMemoryBuffer.java b/java/src/main/java/ai/rapids/cudf/DeviceMemoryBuffer.java
new file mode 100644
index 0000000..c4d9bdb
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/DeviceMemoryBuffer.java
@@ -0,0 +1,174 @@
+/*
+ *
+ *  Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * This class represents data in some form on the GPU. Closing this object will effectively release
+ * the memory held by the buffer.  Note that because of pooling in RMM or reference counting if a
+ * buffer is sliced it may not actually result in the memory being released.
+ */
+public class DeviceMemoryBuffer extends BaseDeviceMemoryBuffer {
+  private static final Logger log = LoggerFactory.getLogger(DeviceMemoryBuffer.class);
+
+  private static final class DeviceBufferCleaner extends MemoryBufferCleaner {
+    private long address;
+    private long lengthInBytes;
+    private Cuda.Stream stream;
+
+    DeviceBufferCleaner(long address, long lengthInBytes, Cuda.Stream stream) {
+      this.address = address;
+      this.lengthInBytes = lengthInBytes;
+      this.stream = stream;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long origAddress = address;
+      if (address != 0) {
+        long s = stream == null ? 0 : stream.getStream();
+        try {
+          Rmm.free(address, lengthInBytes, s);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          address = 0;
+          lengthInBytes = 0;
+          stream = null;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A DEVICE BUFFER WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+        logRefCountDebug("Leaked device buffer");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return address == 0;
+    }
+  }
+
+  private static final class RmmDeviceBufferCleaner extends MemoryBufferCleaner {
+    private long rmmBufferAddress;
+
+    RmmDeviceBufferCleaner(long rmmBufferAddress) {
+      this.rmmBufferAddress = rmmBufferAddress;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      if (rmmBufferAddress != 0) {
+        Rmm.freeDeviceBuffer(rmmBufferAddress);
+        rmmBufferAddress = 0;
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("WE LEAKED A DEVICE BUFFER!!!!");
+        logRefCountDebug("Leaked device buffer");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return rmmBufferAddress == 0;
+    }
+  }
+
+  /**
+   * Wrap an existing RMM allocation in a device memory buffer. The RMM allocation will be freed
+   * when the resulting device memory buffer instance frees its memory resource (i.e.: when its
+   * reference count goes to zero).
+   * @param address device address of the RMM allocation
+   * @param lengthInBytes length of the RMM allocation in bytes
+   * @param rmmBufferAddress host address of the rmm::device_buffer that owns the device memory
+   * @return new device memory buffer instance that wraps the existing RMM allocation
+   */
+  public static DeviceMemoryBuffer fromRmm(long address, long lengthInBytes, long rmmBufferAddress) {
+    return new DeviceMemoryBuffer(address, lengthInBytes, rmmBufferAddress);
+  }
+
+  DeviceMemoryBuffer(long address, long lengthInBytes, long rmmBufferAddress) {
+    super(address, lengthInBytes, new RmmDeviceBufferCleaner(rmmBufferAddress));
+  }
+
+  DeviceMemoryBuffer(long address, long lengthInBytes, Cuda.Stream stream) {
+    super(address, lengthInBytes, new DeviceBufferCleaner(address, lengthInBytes, stream));
+  }
+
+  private DeviceMemoryBuffer(long address, long lengthInBytes, DeviceMemoryBuffer parent) {
+    super(address, lengthInBytes, parent);
+  }
+
+  /**
+   * Allocate memory for use on the GPU. You must close it when done.
+   * @param bytes size in bytes to allocate
+   * @return the buffer
+   */
+  public static DeviceMemoryBuffer allocate(long bytes) {
+    return allocate(bytes, Cuda.DEFAULT_STREAM);
+  }
+
+  /**
+   * Allocate memory for use on the GPU. You must close it when done.
+   * @param bytes size in bytes to allocate
+   * @param stream The stream in which to synchronize this command
+   * @return the buffer
+   */
+  public static DeviceMemoryBuffer allocate(long bytes, Cuda.Stream stream) {
+    return Rmm.alloc(bytes, stream);
+  }
+
+  /**
+   * Slice off a part of the device buffer. Note that this is a zero copy operation and all
+   * slices must be closed along with the original buffer before the memory is released to RMM.
+   * So use this with some caution.
+   * @param offset where to start the slice at.
+   * @param len how many bytes to slice
+   * @return a device buffer that will need to be closed independently from this buffer.
+   */
+  @Override
+  public synchronized final DeviceMemoryBuffer slice(long offset, long len) {
+    addressOutOfBoundsCheck(address + offset, len, "slice");
+    incRefCount();
+    return new DeviceMemoryBuffer(getAddress() + offset, len, this);
+  }
+
+  /**
+   * Convert a view that is a subset of this Buffer by slicing this.
+   * @param view the view to use as a reference.
+   * @return the sliced buffer.
+   */
+  synchronized final BaseDeviceMemoryBuffer sliceFrom(DeviceMemoryBufferView view) {
+    if (view == null) {
+      return null;
+    }
+    addressOutOfBoundsCheck(view.address, view.length, "sliceFrom");
+    incRefCount();
+    return new DeviceMemoryBuffer(view.address, view.length, this);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/DeviceMemoryBufferView.java b/java/src/main/java/ai/rapids/cudf/DeviceMemoryBufferView.java
new file mode 100644
index 0000000..e48b1cf
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/DeviceMemoryBufferView.java
@@ -0,0 +1,39 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * This class represents data in some form on the GPU. The memory pointed at by this buffer is
+ * not owned by this buffer.  So you have to be sure that this buffer does not outlive the buffer
+ * that is backing it.
+ */
+public class DeviceMemoryBufferView extends BaseDeviceMemoryBuffer {
+  DeviceMemoryBufferView(long address, long lengthInBytes) {
+    // Set the cleaner to null so we don't end up releasing anything
+    super(address, lengthInBytes, (MemoryBufferCleaner) null);
+  }
+
+  /**
+   * At the moment we don't have use for slicing a view.
+   */
+  @Override
+  public synchronized final DeviceMemoryBufferView slice(long offset, long len) {
+    throw new UnsupportedOperationException("Slice on view is not supported");
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/GatherMap.java b/java/src/main/java/ai/rapids/cudf/GatherMap.java
new file mode 100644
index 0000000..12ff741
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/GatherMap.java
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * This class tracks the data associated with a gather map, a buffer of INT32 elements that index
+ * a source table and can be passed to a table gather operation.
+ */
+public class GatherMap implements AutoCloseable {
+  private DeviceMemoryBuffer buffer;
+
+  /**
+   * Construct a gather map instance from a device buffer. The buffer length must be a multiple of
+   * the {@link DType#INT32} size, as each row of the gather map is an INT32.
+   * @param buffer device buffer backing the gather map data
+   */
+  public GatherMap(DeviceMemoryBuffer buffer) {
+    if (buffer.getLength() % DType.INT32.getSizeInBytes() != 0) {
+      throw new IllegalArgumentException("buffer length not a multiple of 4");
+    }
+    this.buffer = buffer;
+  }
+
+  /** Return the number of rows in the gather map */
+  public long getRowCount() {
+    ensureOpen();
+    return buffer.getLength() / 4;
+  }
+
+  /**
+   * Create a column view that can be used to perform a gather operation. Note that the resulting
+   * column view MUST NOT outlive the underlying device buffer within this instance!
+   * @param startRow row offset where the resulting gather map will start
+   * @param numRows number of rows in the resulting gather map
+   * @return column view of gather map data
+   */
+  public ColumnView toColumnView(long startRow, int numRows) {
+    ensureOpen();
+    return ColumnView.fromDeviceBuffer(buffer, startRow * 4, DType.INT32, numRows);
+  }
+
+  /**
+   * Release the underlying device buffer instance. After this is called, closing this instance
+   * will not close the underlying device buffer. It is the responsibility of the caller to close
+   * the returned device buffer.
+   * @return device buffer backing gather map data or null if the buffer has already been released
+   */
+  public DeviceMemoryBuffer releaseBuffer() {
+    DeviceMemoryBuffer result = buffer;
+    buffer = null;
+    return result;
+  }
+
+  /** Close the device buffer backing the gather map data. */
+  @Override
+  public void close() {
+    if (buffer != null) {
+      buffer.close();
+      buffer = null;
+    }
+  }
+
+  private void ensureOpen() {
+    if (buffer == null) {
+      throw new IllegalStateException("instance is closed");
+    }
+    if (buffer.closed) {
+      throw new IllegalStateException("buffer is closed");
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/GroupByAggregation.java b/java/src/main/java/ai/rapids/cudf/GroupByAggregation.java
new file mode 100644
index 0000000..0fae339
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/GroupByAggregation.java
@@ -0,0 +1,340 @@
+/*
+ *
+ *  Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An aggregation that can be used for a reduce.
+ */
+public final class GroupByAggregation {
+  private final Aggregation wrapped;
+
+  private GroupByAggregation(Aggregation wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  Aggregation getWrapped() {
+    return wrapped;
+  }
+
+
+  /**
+   * Add a column to the Aggregation so it can be used on a specific column of data.
+   * @param columnIndex the index of the column to operate on.
+   */
+  public GroupByAggregationOnColumn onColumn(int columnIndex) {
+    return new GroupByAggregationOnColumn(this, columnIndex);
+  }
+
+  @Override
+  public int hashCode() {
+    return wrapped.hashCode();
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other == this) {
+      return true;
+    } else if (other instanceof GroupByAggregation) {
+      GroupByAggregation o = (GroupByAggregation) other;
+      return wrapped.equals(o.wrapped);
+    }
+    return false;
+  }
+
+  /**
+   * Count number of valid, a.k.a. non-null, elements.
+   */
+  public static GroupByAggregation count() {
+    return new GroupByAggregation(Aggregation.count());
+  }
+
+  /**
+   * Count number of elements.
+   * @param nullPolicy INCLUDE if nulls should be counted. EXCLUDE if only non-null values
+   *                   should be counted.
+   */
+  public static GroupByAggregation count(NullPolicy nullPolicy) {
+    return new GroupByAggregation(Aggregation.count(nullPolicy));
+  }
+
+  /**
+   * Sum Aggregation
+   */
+  public static GroupByAggregation sum() {
+    return new GroupByAggregation(Aggregation.sum());
+  }
+
+  /**
+   * Product Aggregation.
+   */
+  public static GroupByAggregation product() {
+    return new GroupByAggregation(Aggregation.product());
+  }
+
+
+  /**
+   * Index of max element. Please note that when using this aggregation if the
+   * data is not already sorted by the grouping keys it may be automatically sorted
+   * prior to doing the aggregation. This would result in an index into the sorted data being
+   * returned.
+   */
+  public static GroupByAggregation argMax() {
+    return new GroupByAggregation(Aggregation.argMax());
+  }
+
+  /**
+   * Index of min element. Please note that when using this aggregation if the
+   * data is not already sorted by the grouping keys it may be automatically sorted
+   * prior to doing the aggregation. This would result in an index into the sorted data being
+   * returned.
+   */
+  public static GroupByAggregation argMin() {
+    return new GroupByAggregation(Aggregation.argMin());
+  }
+
+  /**
+   * Min Aggregation
+   */
+  public static GroupByAggregation min() {
+    return new GroupByAggregation(Aggregation.min());
+  }
+
+  /**
+   * Max Aggregation
+   */
+  public static GroupByAggregation max() {
+    return new GroupByAggregation(Aggregation.max());
+  }
+
+  /**
+   * Arithmetic mean reduction.
+   */
+  public static GroupByAggregation mean() {
+    return new GroupByAggregation(Aggregation.mean());
+  }
+
+  /**
+   * Sum of square of differences from mean.
+   */
+  public static GroupByAggregation M2() {
+    return new GroupByAggregation(Aggregation.M2());
+  }
+
+  /**
+   * Variance aggregation with 1 as the delta degrees of freedom.
+   */
+  public static GroupByAggregation variance() {
+    return new GroupByAggregation(Aggregation.variance());
+  }
+
+  /**
+   * Variance aggregation.
+   * @param ddof delta degrees of freedom. The divisor used in calculation of variance is
+   *             <code>N - ddof</code>, where N is the population size.
+   */
+  public static GroupByAggregation variance(int ddof) {
+    return new GroupByAggregation(Aggregation.variance(ddof));
+  }
+
+  /**
+   * Standard deviation aggregation with 1 as the delta degrees of freedom.
+   */
+  public static GroupByAggregation standardDeviation() {
+    return new GroupByAggregation(Aggregation.standardDeviation());
+  }
+
+  /**
+   * Standard deviation aggregation.
+   * @param ddof delta degrees of freedom. The divisor used in calculation of std is
+   *             <code>N - ddof</code>, where N is the population size.
+   */
+  public static GroupByAggregation standardDeviation(int ddof) {
+    return new GroupByAggregation(Aggregation.standardDeviation(ddof));
+  }
+
+  /**
+   * Aggregate to compute the specified quantiles. Uses linear interpolation by default.
+   */
+  public static GroupByAggregation quantile(double ... quantiles) {
+    return new GroupByAggregation(Aggregation.quantile(quantiles));
+  }
+
+  /**
+   * Aggregate to compute various quantiles.
+   */
+  public static GroupByAggregation quantile(QuantileMethod method, double ... quantiles) {
+    return new GroupByAggregation(Aggregation.quantile(method, quantiles));
+  }
+
+  /**
+   * Median reduction.
+   */
+  public static GroupByAggregation median() {
+    return new GroupByAggregation(Aggregation.median());
+  }
+
+  /**
+   * Number of unique, non-null, elements.
+   */
+  public static GroupByAggregation nunique() {
+    return new GroupByAggregation(Aggregation.nunique());
+  }
+
+  /**
+   * Number of unique elements.
+   * @param nullPolicy INCLUDE if nulls should be counted else EXCLUDE. If nulls are counted they
+   *                   compare as equal so multiple null values in a range would all only
+   *                   increase the count by 1.
+   */
+  public static GroupByAggregation nunique(NullPolicy nullPolicy) {
+    return new GroupByAggregation(Aggregation.nunique(nullPolicy));
+  }
+
+  /**
+   * Get the nth, non-null, element in a group.
+   * @param offset the offset to look at. Negative numbers go from the end of the group. Any
+   *               value outside of the group range results in a null.
+   */
+  public static GroupByAggregation nth(int offset) {
+    return new GroupByAggregation(Aggregation.nth(offset));
+  }
+
+  /**
+   * Get the nth element in a group.
+   * @param offset the offset to look at. Negative numbers go from the end of the group. Any
+   *               value outside of the group range results in a null.
+   * @param nullPolicy INCLUDE if nulls should be included in the aggregation or EXCLUDE if they
+   *                   should be skipped.
+   */
+  public static GroupByAggregation nth(int offset, NullPolicy nullPolicy) {
+    return new GroupByAggregation(Aggregation.nth(offset, nullPolicy));
+  }
+
+  /**
+   * Collect the values into a list. Nulls will be skipped.
+   */
+  public static GroupByAggregation collectList() {
+    return new GroupByAggregation(Aggregation.collectList());
+  }
+
+  /**
+   * Collect the values into a list.
+   *
+   * @param nullPolicy Indicates whether to include/exclude nulls during collection.
+   */
+  public static GroupByAggregation collectList(NullPolicy nullPolicy) {
+    return new GroupByAggregation(Aggregation.collectList(nullPolicy));
+  }
+
+  /**
+   * Collect the values into a set. All null values will be excluded, and all NaN values are regarded as
+   * unique instances.
+   */
+  public static GroupByAggregation collectSet() {
+    return new GroupByAggregation(Aggregation.collectSet());
+  }
+
+  /**
+   * Collect the values into a set.
+   *
+   * @param nullPolicy   Indicates whether to include/exclude nulls during collection.
+   * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+   * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+   */
+  public static GroupByAggregation collectSet(NullPolicy nullPolicy, NullEquality nullEquality, NaNEquality nanEquality) {
+    return new GroupByAggregation(Aggregation.collectSet(nullPolicy, nullEquality, nanEquality));
+  }
+
+  /**
+   * Merge the partial lists produced by multiple CollectListAggregations.
+   * NOTICE: The partial lists to be merged should NOT include any null list element (but can include null list entries).
+   */
+  public static GroupByAggregation mergeLists() {
+    return new GroupByAggregation(Aggregation.mergeLists());
+  }
+
+  /**
+   * Merge the partial sets produced by multiple CollectSetAggregations. Each null/NaN value will be regarded as
+   * a unique instance.
+   */
+  public static GroupByAggregation mergeSets() {
+    return new GroupByAggregation(Aggregation.mergeSets());
+  }
+
+  /**
+   * Merge the partial sets produced by multiple CollectSetAggregations.
+   *
+   * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+   * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+   */
+  public static GroupByAggregation mergeSets(NullEquality nullEquality, NaNEquality nanEquality) {
+    return new GroupByAggregation(Aggregation.mergeSets(nullEquality, nanEquality));
+  }
+
+  /**
+   * Merge the partial M2 values produced by multiple instances of M2Aggregation.
+   */
+  public static GroupByAggregation mergeM2() {
+    return new GroupByAggregation(Aggregation.mergeM2());
+  }
+
+  /**
+   * Compute a t-digest from on a fixed-width numeric input column.
+   *
+   * @param delta Required accuracy (number of buckets).
+   * @return A list of centroids per grouping, where each centroid has a mean value and a
+   *         weight. The number of centroids will be <= delta.
+   */
+  public static GroupByAggregation createTDigest(int delta) {
+    return new GroupByAggregation(Aggregation.createTDigest(delta));
+  }
+
+  /**
+   * Merge t-digests.
+   *
+   * @param delta Required accuracy (number of buckets).
+   * @return A list of centroids per grouping, where each centroid has a mean value and a
+   *         weight. The number of centroids will be <= delta.
+   */
+  public static GroupByAggregation mergeTDigest(int delta) {
+    return new GroupByAggregation(Aggregation.mergeTDigest(delta));
+  }
+
+  /**
+   * Histogram aggregation, computing the frequencies for each unique row.
+   *
+   * A histogram is given as a lists column, in which the first child stores unique rows from
+   * the input values and the second child stores their corresponding frequencies.
+   *
+   * @return A lists of structs column in which each list contains a histogram corresponding to
+   *         an input key.
+   */
+  public static GroupByAggregation histogram() {
+    return new GroupByAggregation(Aggregation.histogram());
+  }
+
+  /**
+   * MergeHistogram aggregation, to merge multiple histograms.
+   *
+   * @return A new histogram in which the frequencies of the unique rows are sum up.
+   */
+  public static GroupByAggregation mergeHistogram() {
+    return new GroupByAggregation(Aggregation.mergeHistogram());
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/GroupByAggregationOnColumn.java b/java/src/main/java/ai/rapids/cudf/GroupByAggregationOnColumn.java
new file mode 100644
index 0000000..c50cf37
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/GroupByAggregationOnColumn.java
@@ -0,0 +1,56 @@
+/*
+ *
+ *  Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * A GroupByAggregation for a specific column in a table.
+ */
+public final class GroupByAggregationOnColumn {
+    protected final GroupByAggregation wrapped;
+    protected final int columnIndex;
+
+    GroupByAggregationOnColumn(GroupByAggregation wrapped, int columnIndex) {
+        this.wrapped = wrapped;
+        this.columnIndex = columnIndex;
+    }
+
+    public int getColumnIndex() {
+        return columnIndex;
+    }
+
+    GroupByAggregation getWrapped() {
+        return wrapped;
+    }
+
+    @Override
+    public int hashCode() {
+        return 31 * wrapped.hashCode() + columnIndex;
+    }
+
+    @Override
+    public boolean equals(Object other) {
+        if (other == this) {
+            return true;
+        } else if (other instanceof GroupByAggregationOnColumn) {
+            GroupByAggregationOnColumn o = (GroupByAggregationOnColumn) other;
+            return wrapped.equals(o.wrapped) && columnIndex == o.columnIndex;
+        }
+        return false;
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/GroupByOptions.java b/java/src/main/java/ai/rapids/cudf/GroupByOptions.java
new file mode 100644
index 0000000..eb769cc
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/GroupByOptions.java
@@ -0,0 +1,124 @@
+/*
+ *
+ *  Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Options for groupby (see cudf::groupby::groupby's constructor)
+ */
+public class GroupByOptions {
+
+  public static GroupByOptions DEFAULT = new GroupByOptions(new Builder());
+
+  private final boolean ignoreNullKeys;
+  private final boolean keysSorted;
+  private final boolean[] keysDescending;
+  private final boolean[] keysNullSmallest;
+
+  private GroupByOptions(Builder builder) {
+    ignoreNullKeys = builder.ignoreNullKeys;
+    keysSorted = builder.keysSorted;
+    keysDescending = builder.keysDescending;
+    keysNullSmallest = builder.keysNullSmallest;
+  }
+
+  boolean getIgnoreNullKeys() {
+    return ignoreNullKeys;
+  }
+
+  boolean getKeySorted() {
+    return keysSorted;
+  }
+
+  boolean[] getKeysDescending() {
+    return keysDescending;
+  }
+
+  boolean[] getKeysNullSmallest() {
+    return keysNullSmallest;
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder {
+    private boolean ignoreNullKeys = false;
+    private boolean keysSorted = false;
+    private boolean[] keysDescending = new boolean[0];
+    private boolean[] keysNullSmallest = new boolean[0];
+
+    /**
+     * If true, the cudf groupby will ignore grouping keys that are null.
+     * The default value is false, so a null in the grouping column will produce a
+     * group.
+     */
+    public Builder withIgnoreNullKeys(boolean ignoreNullKeys) {
+      this.ignoreNullKeys = ignoreNullKeys;
+      return this;
+    }
+
+    /**
+     * Indicates whether rows in `keys` are already sorted.
+     * The default value is false.
+     *
+     * If the `keys` are already sorted, better performance may be achieved by
+     * passing `keysSorted == true` and indicating the ascending/descending
+     * order of each column and null order by calling `withKeysDescending` and
+     * `withKeysNullSmallest`, respectively.
+     */
+    public Builder withKeysSorted(boolean keysSorted) {
+      this.keysSorted = keysSorted;
+      return this;
+    }
+
+    /**
+     * If `keysSorted == true`, indicates whether each
+     * column is ascending/descending. If empty or null, assumes all columns are
+     * ascending. Ignored if `keysSorted == false`.
+     */
+    public Builder withKeysDescending(boolean... keysDescending) {
+      if (keysDescending == null) {
+        // Use empty array instead of null
+        this.keysDescending = new boolean[0];
+      } else {
+        this.keysDescending = keysDescending;
+      }
+      return this;
+    }
+
+    /**
+     * If `keysSorted == true`, indicates the ordering
+     * of null values in each column. If empty or null, assumes all columns
+     * use 'null smallest'. Ignored if `keysSorted == false`.
+     */
+    public Builder withKeysNullSmallest(boolean... keysNullSmallest) {
+      if (keysNullSmallest == null) {
+        // Use empty array instead of null
+        this.keysNullSmallest = new boolean[0];
+      } else {
+        this.keysNullSmallest = keysNullSmallest;
+      }
+      return this;
+    }
+
+    public GroupByOptions build() {
+      return new GroupByOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/GroupByScanAggregation.java b/java/src/main/java/ai/rapids/cudf/GroupByScanAggregation.java
new file mode 100644
index 0000000..006941d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/GroupByScanAggregation.java
@@ -0,0 +1,125 @@
+/*
+ *
+ *  Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An aggregation that can be used for a grouped scan.
+ */
+public final class GroupByScanAggregation {
+  private final Aggregation wrapped;
+
+  private GroupByScanAggregation(Aggregation wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  long createNativeInstance() {
+    return wrapped.createNativeInstance();
+  }
+
+  long getDefaultOutput() {
+    return wrapped.getDefaultOutput();
+  }
+
+  Aggregation getWrapped() {
+    return wrapped;
+  }
+
+  /**
+   * Add a column to the Aggregation so it can be used on a specific column of data.
+   * @param columnIndex the index of the column to operate on.
+   */
+  public GroupByScanAggregationOnColumn onColumn(int columnIndex) {
+    return new GroupByScanAggregationOnColumn(this, columnIndex);
+  }
+
+  @Override
+  public int hashCode() {
+    return wrapped.hashCode();
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other == this) {
+      return true;
+    } else if (other instanceof GroupByScanAggregation) {
+      GroupByScanAggregation o = (GroupByScanAggregation) other;
+      return wrapped.equals(o.wrapped);
+    }
+    return false;
+  }
+
+  /**
+   * Sum Aggregation
+   */
+  public static GroupByScanAggregation sum() {
+    return new GroupByScanAggregation(Aggregation.sum());
+  }
+
+
+  /**
+   * Product Aggregation.
+   */
+  public static GroupByScanAggregation product() {
+    return new GroupByScanAggregation(Aggregation.product());
+  }
+
+  /**
+   * Min Aggregation
+   */
+  public static GroupByScanAggregation min() {
+    return new GroupByScanAggregation(Aggregation.min());
+  }
+
+  /**
+   * Max Aggregation
+   */
+  public static GroupByScanAggregation max() {
+    return new GroupByScanAggregation(Aggregation.max());
+  }
+
+  /**
+   * Count number of elements.
+   * @param nullPolicy INCLUDE if nulls should be counted. EXCLUDE if only non-null values
+   *                   should be counted.
+   */
+  public static GroupByScanAggregation count(NullPolicy nullPolicy) {
+    return new GroupByScanAggregation(Aggregation.count(nullPolicy));
+  }
+
+  /**
+   * Get the row's ranking.
+   */
+  public static GroupByScanAggregation rank() {
+    return new GroupByScanAggregation(Aggregation.rank());
+  }
+
+  /**
+   * Get the row's dense ranking.
+   */
+  public static GroupByScanAggregation denseRank() {
+    return new GroupByScanAggregation(Aggregation.denseRank());
+  }
+
+  /**
+   * Get the row's percent ranking.
+   */
+  public static GroupByScanAggregation percentRank() {
+    return new GroupByScanAggregation(Aggregation.percentRank());
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/GroupByScanAggregationOnColumn.java b/java/src/main/java/ai/rapids/cudf/GroupByScanAggregationOnColumn.java
new file mode 100644
index 0000000..75e4936
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/GroupByScanAggregationOnColumn.java
@@ -0,0 +1,64 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * A GroupByScanAggregation for a specific column in a table.
+ */
+public final class GroupByScanAggregationOnColumn {
+    protected final GroupByScanAggregation wrapped;
+    protected final int columnIndex;
+
+    GroupByScanAggregationOnColumn(GroupByScanAggregation wrapped, int columnIndex) {
+        this.wrapped = wrapped;
+        this.columnIndex = columnIndex;
+    }
+
+    public int getColumnIndex() {
+        return columnIndex;
+    }
+
+    @Override
+    public int hashCode() {
+        return 31 * wrapped.hashCode() + columnIndex;
+    }
+
+    @Override
+    public boolean equals(Object other) {
+        if (other == this) {
+            return true;
+        } else if (other instanceof GroupByScanAggregationOnColumn) {
+            GroupByScanAggregationOnColumn o = (GroupByScanAggregationOnColumn) other;
+            return wrapped.equals(o.wrapped) && columnIndex == o.columnIndex;
+        }
+        return false;
+    }
+
+    long createNativeInstance() {
+        return wrapped.createNativeInstance();
+    }
+
+    long getDefaultOutput() {
+        return wrapped.getDefaultOutput();
+    }
+
+    GroupByScanAggregation getWrapped() {
+        return wrapped;
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HashJoin.java b/java/src/main/java/ai/rapids/cudf/HashJoin.java
new file mode 100644
index 0000000..620a7ce
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HashJoin.java
@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * This class represents a hash table built from the join keys of the right-side table for a
+ * join operation. This hash table can then be reused across a series of left probe tables
+ * to compute gather maps for joins more efficiently when the right-side table is not changing.
+ * It can also be used to query the output row count of a join and then pass that result to the
+ * operation that generates the join gather maps to avoid redundant computation when the output
+ * row count must be checked before manifesting the join gather maps.
+ */
+public class HashJoin implements AutoCloseable {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  private static final Logger log = LoggerFactory.getLogger(HashJoin.class);
+
+  private static class HashJoinCleaner extends MemoryCleaner.Cleaner {
+    private Table buildKeys;
+    private long nativeHandle;
+
+    HashJoinCleaner(Table buildKeys, long nativeHandle) {
+      this.buildKeys = buildKeys;
+      this.nativeHandle = nativeHandle;
+      addRef();
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      long origAddress = nativeHandle;
+      boolean neededCleanup = nativeHandle != 0;
+      if (neededCleanup) {
+        try {
+          destroy(nativeHandle);
+          buildKeys.close();
+          buildKeys = null;
+        } finally {
+          nativeHandle = 0;
+        }
+        if (logErrorIfNotClean) {
+          log.error("A HASH TABLE WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress));
+        }
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return nativeHandle == 0;
+    }
+  }
+
+  private final HashJoinCleaner cleaner;
+  private final boolean compareNulls;
+  private boolean isClosed = false;
+
+  /**
+   * Construct a hash table for a join from a table representing the join key columns from the
+   * right-side table in the join. The resulting instance must be closed to release the
+   * GPU resources associated with the instance.
+   * @param buildKeys table view containing the join keys for the right-side join table
+   * @param compareNulls true if null key values should match otherwise false
+   */
+  public HashJoin(Table buildKeys, boolean compareNulls) {
+    this.compareNulls = compareNulls;
+    Table buildTable = new Table(buildKeys.getColumns());
+    try {
+      long handle = create(buildTable.getNativeView(), compareNulls);
+      this.cleaner = new HashJoinCleaner(buildTable, handle);
+      MemoryCleaner.register(this, cleaner);
+    } catch (Throwable t) {
+      try {
+        buildTable.close();
+      } catch (Throwable t2) {
+        t.addSuppressed(t2);
+      }
+      throw t;
+    }
+  }
+
+  @Override
+  public synchronized void close() {
+    cleaner.delRef();
+    if (isClosed) {
+      cleaner.logRefCountDebug("double free " + this);
+      throw new IllegalStateException("Close called too many times " + this);
+    }
+    cleaner.clean(false);
+    isClosed = true;
+  }
+
+  long getNativeView() {
+    return cleaner.nativeHandle;
+  }
+
+  /** Get the number of join key columns for the table that was used to generate the has table. */
+  public long getNumberOfColumns() {
+    return cleaner.buildKeys.getNumberOfColumns();
+  }
+
+  /** Returns true if the hash table was built to match on nulls otherwise false. */
+  public boolean getCompareNulls() {
+    return compareNulls;
+  }
+
+  private static native long create(long tableView, boolean nullEqual);
+  private static native void destroy(long handle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HashType.java b/java/src/main/java/ai/rapids/cudf/HashType.java
new file mode 100644
index 0000000..081e8aa
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HashType.java
@@ -0,0 +1,49 @@
+/*
+ *
+ *  Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Hash algorithm identifiers, mirroring native enum cudf::hash_id
+ */
+public enum HashType {
+  IDENTITY(0),
+  MURMUR3(1),
+  HASH_SPARK_MURMUR3(2),
+  HASH_MD5(3);
+
+  private static final HashType[] HASH_TYPES = HashType.values();
+  final int nativeId;
+
+  HashType(int nativeId) {
+    this.nativeId = nativeId;
+  }
+
+  public int getNativeId() {
+    return nativeId;
+  }
+
+  public static HashType fromNative(int nativeId) {
+    for (HashType type : HASH_TYPES) {
+      if (type.nativeId == nativeId) {
+        return type;
+      }
+    }
+    throw new IllegalArgumentException("Could not translate " + nativeId + " into a HashType");
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostBufferConsumer.java b/java/src/main/java/ai/rapids/cudf/HostBufferConsumer.java
new file mode 100644
index 0000000..4f8d1d7
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostBufferConsumer.java
@@ -0,0 +1,38 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Provides a set of APIs for consuming host buffers.  This is typically used
+ * when writing out Tables in various file formats.
+ */
+public interface HostBufferConsumer {
+  /**
+   * Consume a buffer.
+   * @param buffer the buffer.  Be sure to close this buffer when you are done
+   *               with it or it will leak.
+   * @param len the length of the buffer that is valid.  The valid data will be 0 until len.
+   */
+  void handleBuffer(HostMemoryBuffer buffer, long len);
+
+  /**
+   * Indicates that no more buffers will be supplied.
+   */
+  default void done() {}
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostBufferProvider.java b/java/src/main/java/ai/rapids/cudf/HostBufferProvider.java
new file mode 100644
index 0000000..251f383
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostBufferProvider.java
@@ -0,0 +1,38 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Provides a set of APIs for providing host buffers to be read.
+ */
+public interface HostBufferProvider extends AutoCloseable {
+  /**
+   * Place data into the given buffer.
+   * @param buffer the buffer to put data into.
+   * @param len the maximum amount of data to put into buffer.  Less is okay if at EOF.
+   * @return the actual amount of data put into the buffer.
+   */
+  long readInto(HostMemoryBuffer buffer, long len);
+
+  /**
+   * Indicates that no more buffers will be supplied.
+   */
+  @Override
+  default void close() {}
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostColumnVector.java b/java/src/main/java/ai/rapids/cudf/HostColumnVector.java
new file mode 100644
index 0000000..3e4baf9
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostColumnVector.java
@@ -0,0 +1,2202 @@
+/*
+ *
+ *  Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Objects;
+import java.util.Optional;
+import java.util.StringJoiner;
+import java.util.function.BiConsumer;
+import java.util.function.Consumer;
+
+/**
+ * Similar to a ColumnVector, but the data is stored in host memory and accessible directly from
+ * the JVM. This class holds references to off heap memory and is reference counted to know when
+ * to release it.  Call close to decrement the reference count when you are done with the column,
+ * and call incRefCount to increment the reference count.
+ */
+public final class HostColumnVector extends HostColumnVectorCore {
+  /**
+   * Interface to handle events for this HostColumnVector. Only invoked during
+   * close, hence `onClosed` is the only event.
+   */
+  public interface EventHandler {
+    /**
+     * `onClosed` is invoked with the updated `refCount` during `close`.
+     * The last invocation of `onClosed` will be with `refCount=0`.
+     *
+     * @note the callback is invoked with this `HostColumnVector`'s lock held.
+     *
+     * @param cv reference to the HostColumnVector we are closing
+     * @param refCount the updated ref count for this HostColumnVector at
+     *                 the time of invocation
+     */
+    void onClosed(HostColumnVector cv, int refCount);
+  }
+
+  /**
+   * The size in bytes of an offset entry
+   */
+  static final int OFFSET_SIZE = DType.INT32.getSizeInBytes();
+
+  private int refCount;
+  private EventHandler eventHandler;
+
+  /**
+   * Create a new column vector with data populated on the host.
+   */
+  HostColumnVector(DType type, long rows, Optional<Long> nullCount,
+                   HostMemoryBuffer hostDataBuffer, HostMemoryBuffer hostValidityBuffer) {
+    this(type, rows, nullCount, hostDataBuffer, hostValidityBuffer, null);
+  }
+
+  /**
+   * Create a new column vector with data populated on the host.
+   * @param type               the type of the vector
+   * @param rows               the number of rows in the vector.
+   * @param nullCount          the number of nulls in the vector.
+   * @param hostDataBuffer     The host side data for the vector. In the case of STRING
+   *                           this is the string data stored as bytes.
+   * @param hostValidityBuffer Arrow-like validity buffer 1 bit per row, with padding for
+   *                           64-bit alignment.
+   * @param offsetBuffer       only valid for STRING this is the offsets into
+   *                           the hostDataBuffer indicating the start and end of a string
+   *                           entry. It should be (rows + 1) ints.
+   * @param nestedHcv          list of child HostColumnVectorCore(s) for complex types
+   */
+
+  //Constructor for lists and struct
+  public HostColumnVector(DType type, long rows, Optional<Long> nullCount,
+                   HostMemoryBuffer hostDataBuffer, HostMemoryBuffer hostValidityBuffer,
+                   HostMemoryBuffer offsetBuffer, List<HostColumnVectorCore> nestedHcv) {
+    super(type, rows, nullCount, hostDataBuffer, hostValidityBuffer, offsetBuffer, nestedHcv);
+    refCount = 0;
+    incRefCountInternal(true);
+  }
+
+  HostColumnVector(DType type, long rows, Optional<Long> nullCount,
+                   HostMemoryBuffer hostDataBuffer, HostMemoryBuffer hostValidityBuffer,
+                   HostMemoryBuffer offsetBuffer) {
+    super(type, rows, nullCount, hostDataBuffer, hostValidityBuffer, offsetBuffer, new ArrayList<>());
+    assert !type.equals(DType.LIST) : "This constructor should not be used for list type";
+    if (nullCount.isPresent() && nullCount.get() > 0 && hostValidityBuffer == null) {
+      throw new IllegalStateException("Buffer cannot have a nullCount without a validity buffer");
+    }
+    if (!type.equals(DType.STRING) && !type.equals(DType.LIST)) {
+      assert offsetBuffer == null : "offsets are only supported for STRING and LIST";
+    }
+    refCount = 0;
+    incRefCountInternal(true);
+  }
+
+  /**
+   * Set an event handler for this host vector. This method can be invoked with
+   * null to unset the handler.
+   *
+   * @param newHandler - the EventHandler to use from this point forward
+   * @return the prior event handler, or null if not set.
+   */
+  public synchronized EventHandler setEventHandler(EventHandler newHandler) {
+    EventHandler prev = this.eventHandler;
+    this.eventHandler = newHandler;
+    return prev;
+  }
+
+  /**
+   * Returns the current event handler for this HostColumnVector or null if no
+   * handler is associated.
+   */
+  public synchronized EventHandler getEventHandler() {
+    return this.eventHandler;
+  }
+
+  /**
+   * This is a really ugly API, but it is possible that the lifecycle of a column of
+   * data may not have a clear lifecycle thanks to java and GC. This API informs the leak
+   * tracking code that this is expected for this column, and big scary warnings should
+   * not be printed when this happens.
+   */
+  public void noWarnLeakExpected() {
+    offHeap.noWarnLeakExpected();
+  }
+
+  /**
+   * Close this Vector and free memory allocated for HostMemoryBuffer and DeviceMemoryBuffer
+   */
+  @Override
+  public synchronized void close() {
+    refCount--;
+    offHeap.delRef();
+    if (eventHandler != null) {
+      eventHandler.onClosed(this, refCount);
+    }
+    if (refCount == 0) {
+      offHeap.clean(false);
+      for( HostColumnVectorCore child : children) {
+        child.close();
+      }
+    } else if (refCount < 0) {
+      offHeap.logRefCountDebug("double free " + this);
+      throw new IllegalStateException("Close called too many times " + this);
+    }
+  }
+
+  @Override
+  public String toString() {
+    return "HostColumnVector{" +
+        "rows=" + rows +
+        ", type=" + type +
+        ", nullCount=" + nullCount +
+        ", offHeap=" + offHeap +
+        '}';
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // METADATA ACCESS
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Increment the reference count for this column.  You need to call close on this
+   * to decrement the reference count again.
+   */
+  public HostColumnVector incRefCount() {
+    return incRefCountInternal(false);
+  }
+
+  private synchronized HostColumnVector incRefCountInternal(boolean isFirstTime) {
+    offHeap.addRef();
+    if (refCount <= 0 && !isFirstTime) {
+      offHeap.logRefCountDebug("INC AFTER CLOSE " + this);
+      throw new IllegalStateException("Column is already closed");
+    }
+    refCount++;
+    return this;
+  }
+
+  /**
+   * Returns this column's current refcount
+   */
+  public synchronized int getRefCount() {
+    return refCount;
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // DATA MOVEMENT
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Copy the data to the device.
+   */
+  public ColumnVector copyToDevice() {
+    if (rows == 0) {
+      if (type.isNestedType()) {
+        return ColumnView.NestedColumnVector.createColumnVector(type, 0,
+                null, null, null, Optional.of(0L), children);
+      } else {
+        return new ColumnVector(type, 0, Optional.of(0L), null, null, null);
+      }
+    }
+    // The simplest way is just to copy the buffers and pass them down.
+    DeviceMemoryBuffer data = null;
+    DeviceMemoryBuffer valid = null;
+    DeviceMemoryBuffer offsets = null;
+    try {
+      if (!type.isNestedType()) {
+        HostMemoryBuffer hdata = this.offHeap.data;
+        if (hdata != null) {
+          long dataLen = rows * type.getSizeInBytes();
+          if (type.equals(DType.STRING)) {
+            // This needs a different type
+            dataLen = getEndStringOffset(rows - 1);
+            if (dataLen == 0 && getNullCount() == 0) {
+              // This is a work around to an issue where a column of all empty strings must have at
+              // least one byte or it will not be interpreted correctly.
+              dataLen = 1;
+            }
+          }
+          data = DeviceMemoryBuffer.allocate(dataLen);
+          data.copyFromHostBuffer(hdata, 0, dataLen);
+        }
+        HostMemoryBuffer hvalid = this.offHeap.valid;
+        if (hvalid != null) {
+          long validLen = ColumnView.getValidityBufferSize((int) rows);
+          valid = DeviceMemoryBuffer.allocate(validLen);
+          valid.copyFromHostBuffer(hvalid, 0, validLen);
+        }
+
+        HostMemoryBuffer hoff = this.offHeap.offsets;
+        if (hoff != null) {
+          long offsetsLen = OFFSET_SIZE * (rows + 1);
+          offsets = DeviceMemoryBuffer.allocate(offsetsLen);
+          offsets.copyFromHostBuffer(hoff, 0, offsetsLen);
+        }
+
+        ColumnVector ret = new ColumnVector(type, rows, nullCount, data, valid, offsets);
+        data = null;
+        valid = null;
+        offsets = null;
+        return ret;
+      } else {
+        return ColumnView.NestedColumnVector.createColumnVector(
+            type, (int) rows, offHeap.data, offHeap.valid, offHeap.offsets, nullCount, children);
+      }
+    } finally {
+      if (data != null) {
+        data.close();
+      }
+      if (valid != null) {
+        valid.close();
+      }
+      if (offsets != null) {
+        offsets.close();
+      }
+    }
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // BUILDER
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Create a new Builder to hold the specified number of rows.  Be sure to close the builder when
+   * done with it. Please try to use {@see #build(int, Consumer)} instead to avoid needing to
+   * close the builder.
+   * @param type the type of vector to build.
+   * @param rows the number of rows this builder can hold
+   * @return the builder to use.
+   */
+  public static Builder builder(DType type, int rows) {
+    return new Builder(type, rows, 0);
+  }
+
+  /**
+   * Create a new Builder to hold the specified number of rows and with enough space to hold the
+   * given amount of string data. Be sure to close the builder when done with it. Please try to
+   * use {@see #build(int, int, Consumer)} instead to avoid needing to close the builder.
+   * @param rows the number of rows this builder can hold
+   * @param stringBufferSize the size of the string buffer to allocate.
+   * @return the builder to use.
+   */
+  public static Builder builder(int rows, long stringBufferSize) {
+    return new HostColumnVector.Builder(DType.STRING, rows, stringBufferSize);
+  }
+
+  /**
+   * Create a new vector.
+   * @param type       the type of vector to build.
+   * @param rows       maximum number of rows that the vector can hold.
+   * @param init       what will initialize the vector.
+   * @return the created vector.
+   */
+  public static HostColumnVector build(DType type, int rows, Consumer<Builder> init) {
+    try (HostColumnVector.Builder builder = builder(type, rows)) {
+      init.accept(builder);
+      return builder.build();
+    }
+  }
+
+  public static HostColumnVector build(int rows, long stringBufferSize, Consumer<Builder> init) {
+    try (HostColumnVector.Builder builder = builder(rows, stringBufferSize)) {
+      init.accept(builder);
+      return builder.build();
+    }
+  }
+
+  public static<T> HostColumnVector fromLists(DataType dataType, List<T>... values) {
+    try (ColumnBuilder cb = new ColumnBuilder(dataType, values.length)) {
+      cb.appendLists(values);
+      return cb.build();
+    }
+  }
+
+  public static HostColumnVector fromStructs(DataType dataType,
+                                             List<StructData> values) {
+    try (ColumnBuilder cb = new ColumnBuilder(dataType, values.size())) {
+      cb.appendStructValues(values);
+      return cb.build();
+    }
+  }
+
+  public static HostColumnVector fromStructs(DataType dataType, StructData... values) {
+    try (ColumnBuilder cb = new ColumnBuilder(dataType, values.length)) {
+      cb.appendStructValues(values);
+      return cb.build();
+    }
+  }
+
+  public static HostColumnVector emptyStructs(DataType dataType, long rows) {
+    StructData sd = new StructData();
+    try (ColumnBuilder cb = new ColumnBuilder(dataType, rows)) {
+      for (long i = 0; i < rows; i++) {
+        cb.append(sd);
+      }
+      return cb.build();
+    }
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector boolFromBytes(byte... values) {
+    return build(DType.BOOL8, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector fromBytes(byte... values) {
+    return build(DType.INT8, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned byte type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromUnsignedBytes(byte... values) {
+    return build(DType.UINT8, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector fromShorts(short... values) {
+    return build(DType.INT16, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned short type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromUnsignedShorts(short... values) {
+    return build(DType.UINT16, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector durationNanosecondsFromLongs(long... values) {
+    return build(DType.DURATION_NANOSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector durationMicrosecondsFromLongs(long... values) {
+    return build(DType.DURATION_MICROSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector durationMillisecondsFromLongs(long... values) {
+    return build(DType.DURATION_MILLISECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector durationSecondsFromLongs(long... values) {
+    return build(DType.DURATION_SECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector durationDaysFromInts(int... values) {
+    return build(DType.DURATION_DAYS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector fromInts(int... values) {
+    return build(DType.INT32, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned int type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromUnsignedInts(int... values) {
+    return build(DType.UINT32, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector fromLongs(long... values) {
+    return build(DType.INT64, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   * <p>
+   * Java does not have an unsigned long type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromUnsignedLongs(long... values) {
+    return build(DType.UINT64, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector fromFloats(float... values) {
+    return build(DType.FLOAT32, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector fromDoubles(double... values) {
+    return build(DType.FLOAT64, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector daysFromInts(int... values) {
+    return build(DType.TIMESTAMP_DAYS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector timestampSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_SECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector timestampMilliSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_MILLISECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector timestampMicroSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_MICROSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new vector from the given values.
+   */
+  public static HostColumnVector timestampNanoSecondsFromLongs(long... values) {
+    return build(DType.TIMESTAMP_NANOSECONDS, values.length, (b) -> b.appendArray(values));
+  }
+
+  /**
+   * Create a new decimal vector from unscaled values (int array) and scale.
+   * The created vector is of type DType.DECIMAL32, whose max precision is 9.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static HostColumnVector decimalFromInts(int scale, int... values) {
+    return build(DType.create(DType.DTypeEnum.DECIMAL32, scale), values.length, (b) -> b.appendUnscaledDecimalArray(values));
+  }
+
+  /**
+   * Create a new decimal vector from boxed unscaled values (Integer array) and scale.
+   * The created vector is of type DType.DECIMAL32, whose max precision is 9.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static HostColumnVector decimalFromBoxedInts(int scale, Integer... values) {
+    return build(DType.create(DType.DTypeEnum.DECIMAL32, scale), values.length, (b) -> {
+      for (Integer v : values) {
+        if (v == null) {
+          b.appendNull();
+        } else {
+          b.appendUnscaledDecimal(v);
+        }
+      }
+    });
+  }
+
+  /**
+   * Create a new decimal vector from unscaled values (long array) and scale.
+   * The created vector is of type DType.DECIMAL64, whose max precision is 18.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static HostColumnVector decimalFromLongs(int scale, long... values) {
+    return build(DType.create(DType.DTypeEnum.DECIMAL64, scale), values.length, (b) -> b.appendUnscaledDecimalArray(values));
+  }
+
+  /**
+   * Create a new decimal vector from boxed unscaled values (Long array) and scale.
+   * The created vector is of type DType.DECIMAL64, whose max precision is 18.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static HostColumnVector decimalFromBoxedLongs(int scale, Long... values) {
+    return build(DType.create(DType.DTypeEnum.DECIMAL64, scale), values.length, (b) -> {
+      for (Long v : values) {
+        if (v == null) {
+          b.appendNull();
+        } else {
+          b.appendUnscaledDecimal(v);
+        }
+      }
+    });
+  }
+
+  /**
+   * Create a new decimal vector from unscaled values (BigInteger array) and scale.
+   * The created vector is of type DType.DECIMAL128.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static HostColumnVector decimalFromBigIntegers(int scale, BigInteger... values) {
+    return build(DType.create(DType.DTypeEnum.DECIMAL128, scale), values.length, (b) -> {
+      for (BigInteger v : values) {
+        if (v == null) {
+          b.appendNull();
+        } else {
+          b.appendUnscaledDecimal(v);
+        }
+      }
+    });
+  }
+
+  /**
+   * Create a new decimal vector from double floats with specific DecimalType and RoundingMode.
+   * All doubles will be rescaled if necessary, according to scale of input DecimalType and RoundingMode.
+   * If any overflow occurs in extracting integral part, an IllegalArgumentException will be thrown.
+   * This API is inefficient because of slow double -> decimal conversion, so it is mainly for testing.
+   * Compared with scale of [[java.math.BigDecimal]], the scale here represents the opposite meaning.
+   */
+  public static HostColumnVector decimalFromDoubles(DType type, RoundingMode mode, double... values) {
+    assert type.isDecimalType();
+    if (type.typeId == DType.DTypeEnum.DECIMAL64) {
+      long[] data = new long[values.length];
+      for (int i = 0; i < values.length; i++) {
+        BigDecimal dec = BigDecimal.valueOf(values[i]).setScale(-type.getScale(), mode);
+        data[i] = dec.unscaledValue().longValueExact();
+      }
+      return build(type, values.length, (b) -> b.appendUnscaledDecimalArray(data));
+    } else {
+      int[] data = new int[values.length];
+      for (int i = 0; i < values.length; i++) {
+        BigDecimal dec = BigDecimal.valueOf(values[i]).setScale(-type.getScale(), mode);
+        data[i] = dec.unscaledValue().intValueExact();
+      }
+      return build(type, values.length, (b) -> b.appendUnscaledDecimalArray(data));
+    }
+  }
+
+  /**
+   * Create a new string vector from the given values.  This API
+   * supports inline nulls. This is really intended to be used only for testing as
+   * it is slow and memory intensive to translate between java strings and UTF8 strings.
+   */
+  public static HostColumnVector fromStrings(String... values) {
+    int rows = values.length;
+    long nullCount = 0;
+    // How many bytes do we need to hold the data.  Sorry this is really expensive
+    long bufferSize = 0;
+    for (String s: values) {
+      if (s == null) {
+        nullCount++;
+      } else {
+        bufferSize += s.getBytes(StandardCharsets.UTF_8).length;
+      }
+    }
+    if (nullCount > 0) {
+      return build(rows, bufferSize, (b) -> b.appendBoxed(values));
+    }
+    return build(rows, bufferSize, (b) -> {
+      for (String s: values) {
+        b.append(s);
+      }
+    });
+  }
+
+  /**
+   * Create a new string vector from the given values.  This API
+   * supports inline nulls.
+   */
+  public static HostColumnVector fromUTF8Strings(byte[]... values) {
+    int rows = values.length;
+    long nullCount = 0;
+    long bufferSize = 0;
+    // How many bytes do we need to hold the data.
+    for (byte[] s: values) {
+      if (s == null) {
+        nullCount++;
+      } else {
+        bufferSize += s.length;
+      }
+    }
+
+    BiConsumer<Builder, byte[]> appendUTF8 = nullCount == 0 ?
+      (b, s) -> b.appendUTF8String(s) :
+      (b, s) -> {
+        if (s == null) {
+          b.appendNull();
+        } else {
+          b.appendUTF8String(s);
+        }
+      };
+
+    return build(rows, bufferSize, (b) -> {
+      for (byte[] s: values) {
+        appendUTF8.accept(b, s);
+      }
+    });
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than building from primitive array of unscaledValues.
+   * Notice:
+   *  1. Input values will be rescaled with min scale (max scale in terms of java.math.BigDecimal),
+   *  which avoids potential precision loss due to rounding. But there exists risk of precision overflow.
+   *  2. The scale will be zero if all input values are null.
+   */
+  public static HostColumnVector fromDecimals(BigDecimal... values) {
+    // 1. Fetch the element with max precision (maxDec). Fill with ZERO if inputs is empty.
+    // 2. Fetch the max scale. Fill with ZERO if inputs is empty.
+    // 3. Rescale the maxDec with the max scale, so to come out the max precision capacity we need.
+    BigDecimal maxDec = Arrays.stream(values).filter(Objects::nonNull)
+        .max(Comparator.comparingInt(BigDecimal::precision))
+        .orElse(BigDecimal.ZERO);
+    int maxScale = Arrays.stream(values).filter(Objects::nonNull)
+        .map(decimal -> decimal.scale())
+        .max(Comparator.naturalOrder())
+        .orElse(0);
+    maxDec = maxDec.setScale(maxScale, RoundingMode.UNNECESSARY);
+
+    return build(DType.fromJavaBigDecimal(maxDec), values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedBooleans(Boolean... values) {
+    return build(DType.BOOL8, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedBytes(Byte... values) {
+    return build(DType.INT8, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned byte type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromBoxedUnsignedBytes(Byte... values) {
+    return build(DType.UINT8, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedShorts(Short... values) {
+    return build(DType.INT16, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned short type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromBoxedUnsignedShorts(Short... values) {
+    return build(DType.UINT16, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector durationNanosecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_NANOSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector durationMicrosecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_MICROSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector durationMillisecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_MILLISECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector durationSecondsFromBoxedLongs(Long... values) {
+    return build(DType.DURATION_SECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector durationDaysFromBoxedInts(Integer... values) {
+    return build(DType.DURATION_DAYS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedInts(Integer... values) {
+    return build(DType.INT32, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned int type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromBoxedUnsignedInts(Integer... values) {
+    return build(DType.UINT32, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedLongs(Long... values) {
+    return build(DType.INT64, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   * <p>
+   * Java does not have an unsigned long type, so the values will be
+   * treated as if the bits represent an unsigned value.
+   */
+  public static HostColumnVector fromBoxedUnsignedLongs(Long... values) {
+    return build(DType.UINT64, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedFloats(Float... values) {
+    return build(DType.FLOAT32, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector fromBoxedDoubles(Double... values) {
+    return build(DType.FLOAT64, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector timestampDaysFromBoxedInts(Integer... values) {
+    return build(DType.TIMESTAMP_DAYS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector timestampSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_SECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector timestampMilliSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_MILLISECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector timestampMicroSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_MICROSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Create a new vector from the given values.  This API supports inline nulls,
+   * but is much slower than using a regular array and should really only be used
+   * for tests.
+   */
+  public static HostColumnVector timestampNanoSecondsFromBoxedLongs(Long... values) {
+    return build(DType.TIMESTAMP_NANOSECONDS, values.length, (b) -> b.appendBoxed(values));
+  }
+
+  /**
+   * Build
+   */
+
+  public static final class ColumnBuilder implements AutoCloseable {
+
+    private DType type;
+    private HostMemoryBuffer data;
+    private HostMemoryBuffer valid;
+    private HostMemoryBuffer offsets;
+    private long nullCount = 0l;
+    //TODO nullable currently not used
+    private boolean nullable;
+    private long rows;
+    private long estimatedRows;
+    private long rowCapacity = 0L;
+    private long validCapacity = 0L;
+    private boolean built = false;
+    private List<ColumnBuilder> childBuilders = new ArrayList<>();
+    private Runnable nullHandler;
+
+    // The value of currentIndex can't exceed Int32.Max. Storing currentIndex as a long is to
+    // adapt HostMemoryBuffer.setXXX, which requires a long offset.
+    private long currentIndex = 0;
+    // Only for Strings: pointer of the byte (data) buffer
+    private int currentStringByteIndex = 0;
+    // Use bit shift instead of multiply to transform row offset to byte offset
+    private int bitShiftBySize = 0;
+    private static final int bitShiftByOffset = (int)(Math.log(OFFSET_SIZE) / Math.log(2));
+
+    public ColumnBuilder(HostColumnVector.DataType type, long estimatedRows) {
+      this.type = type.getType();
+      this.nullable = type.isNullable();
+      this.rows = 0;
+      this.estimatedRows = Math.max(estimatedRows, 1L);
+      this.bitShiftBySize = (int)(Math.log(this.type.getSizeInBytes()) / Math.log(2));
+
+      // initialize the null handler according to the data type
+      this.setupNullHandler();
+
+      for (int i = 0; i < type.getNumChildren(); i++) {
+        childBuilders.add(new ColumnBuilder(type.getChild(i), estimatedRows));
+      }
+    }
+
+    private void setupNullHandler() {
+      if (this.type == DType.LIST) {
+        this.nullHandler = () -> {
+          this.growListBuffersAndRows();
+          this.growValidBuffer();
+          setNullAt(currentIndex++);
+          offsets.setInt(currentIndex << bitShiftByOffset, childBuilders.get(0).getCurrentIndex());
+        };
+      } else if (this.type == DType.STRING) {
+        this.nullHandler = () -> {
+          this.growStringBuffersAndRows(0);
+          this.growValidBuffer();
+          setNullAt(currentIndex++);
+          offsets.setInt(currentIndex << bitShiftByOffset, currentStringByteIndex);
+        };
+      } else if (this.type == DType.STRUCT) {
+        this.nullHandler = () -> {
+          this.growStructBuffersAndRows();
+          this.growValidBuffer();
+          setNullAt(currentIndex++);
+          for (ColumnBuilder childBuilder : childBuilders) {
+            childBuilder.appendNull();
+          }
+        };
+      } else {
+        this.nullHandler = () -> {
+          this.growFixedWidthBuffersAndRows();
+          this.growValidBuffer();
+          setNullAt(currentIndex++);
+        };
+      }
+    }
+
+    public HostColumnVector build() {
+      List<HostColumnVectorCore> hostColumnVectorCoreList = new ArrayList<>();
+      for (ColumnBuilder childBuilder : childBuilders) {
+        hostColumnVectorCoreList.add(childBuilder.buildNestedInternal());
+      }
+      // Aligns the valid buffer size with other buffers in terms of row size, because it grows lazily.
+      if (valid != null) {
+        growValidBuffer();
+      }
+      HostColumnVector hostColumnVector = new HostColumnVector(type, rows, Optional.of(nullCount), data, valid, offsets,
+          hostColumnVectorCoreList);
+      built = true;
+      return hostColumnVector;
+    }
+
+    private HostColumnVectorCore buildNestedInternal() {
+      List<HostColumnVectorCore> hostColumnVectorCoreList = new ArrayList<>();
+      for (ColumnBuilder childBuilder : childBuilders) {
+        hostColumnVectorCoreList.add(childBuilder.buildNestedInternal());
+      }
+      // Aligns the valid buffer size with other buffers in terms of row size, because it grows lazily.
+      if (valid != null) {
+        growValidBuffer();
+      }
+      return new HostColumnVectorCore(type, rows, Optional.of(nullCount), data, valid, offsets, hostColumnVectorCoreList);
+    }
+
+    public ColumnBuilder appendLists(List... inputLists) {
+      for (List inputList : inputLists) {
+        // one row
+        append(inputList);
+      }
+      return this;
+    }
+
+    public ColumnBuilder appendStructValues(List<StructData> inputList) {
+      for (StructData structInput : inputList) {
+        // one row
+        append(structInput);
+      }
+      return this;
+    }
+
+    public ColumnBuilder appendStructValues(StructData... inputList) {
+      for (StructData structInput : inputList) {
+        append(structInput);
+      }
+      return this;
+    }
+
+    /**
+     * Grows valid buffer lazily. The valid buffer won't be materialized until the first null
+     * value appended. This method reuses the rowCapacity to track the sizes of column.
+     * Therefore, please call specific growBuffer method to update rowCapacity before calling
+     * this method.
+     */
+    private void growValidBuffer() {
+      if (valid == null) {
+        long maskBytes = ColumnView.getValidityBufferSize((int) rowCapacity);
+        valid = HostMemoryBuffer.allocate(maskBytes);
+        valid.setMemory(0, valid.length, (byte) 0xFF);
+        validCapacity = rowCapacity;
+        return;
+      }
+      if (validCapacity < rowCapacity) {
+        long maskBytes = ColumnView.getValidityBufferSize((int) rowCapacity);
+        HostMemoryBuffer newValid = HostMemoryBuffer.allocate(maskBytes);
+        newValid.setMemory(0, newValid.length, (byte) 0xFF);
+        valid = copyBuffer(newValid, valid);
+        validCapacity = rowCapacity;
+      }
+    }
+
+    /**
+     * A method automatically grows data buffer for fixed-width columns as needed along with
+     * incrementing the row counts. Please call this method before appending any value or null.
+     */
+    private void growFixedWidthBuffersAndRows() {
+      growFixedWidthBuffersAndRows(1);
+    }
+
+    /**
+     * A method automatically grows data buffer for fixed-width columns for a given size as needed
+     * along with incrementing the row counts. Please call this method before appending
+     * multiple values or nulls.
+     */
+    private void growFixedWidthBuffersAndRows(int numRows) {
+      assert rows + numRows <= Integer.MAX_VALUE : "Row count cannot go over Integer.MAX_VALUE";
+      rows += numRows;
+
+      if (data == null) {
+        long neededSize = Math.max(rows, estimatedRows);
+        data = HostMemoryBuffer.allocate(neededSize << bitShiftBySize);
+        rowCapacity = neededSize;
+      } else if (rows > rowCapacity) {
+        long neededSize = Math.max(rows, rowCapacity * 2);
+        long newCap = Math.min(neededSize, Integer.MAX_VALUE - 1);
+        data = copyBuffer(HostMemoryBuffer.allocate(newCap << bitShiftBySize), data);
+        rowCapacity = newCap;
+      }
+    }
+
+    /**
+     * A method automatically grows offsets buffer for list columns as needed along with
+     * incrementing the row counts. Please call this method before appending any value or null.
+     */
+    private void growListBuffersAndRows() {
+      assert rows + 2 <= Integer.MAX_VALUE : "Row count cannot go over Integer.MAX_VALUE";
+      rows++;
+
+      if (offsets == null) {
+        offsets = HostMemoryBuffer.allocate((estimatedRows + 1) << bitShiftByOffset);
+        offsets.setInt(0, 0);
+        rowCapacity = estimatedRows;
+      } else if (rows > rowCapacity) {
+        long newCap = Math.min(rowCapacity * 2, Integer.MAX_VALUE - 2);
+        offsets = copyBuffer(HostMemoryBuffer.allocate((newCap + 1) << bitShiftByOffset), offsets);
+        rowCapacity = newCap;
+      }
+    }
+
+    /**
+     * A method automatically grows offsets and data buffer for string columns as needed along with
+     * incrementing the row counts. Please call this method before appending any value or null.
+     *
+     * @param stringLength number of bytes required by the next row
+     */
+    private void growStringBuffersAndRows(int stringLength) {
+      assert rows + 2 <= Integer.MAX_VALUE : "Row count cannot go over Integer.MAX_VALUE";
+      rows++;
+
+      if (offsets == null) {
+        // Initialize data buffer with at least 1 byte in case the first appended value is null.
+        data = HostMemoryBuffer.allocate(Math.max(1, stringLength));
+        offsets = HostMemoryBuffer.allocate((estimatedRows + 1) << bitShiftByOffset);
+        offsets.setInt(0, 0);
+        rowCapacity = estimatedRows;
+        return;
+      }
+
+      if (rows > rowCapacity) {
+        long newCap = Math.min(rowCapacity * 2, Integer.MAX_VALUE - 2);
+        offsets = copyBuffer(HostMemoryBuffer.allocate((newCap + 1) << bitShiftByOffset), offsets);
+        rowCapacity = newCap;
+      }
+
+      long currentLength = currentStringByteIndex + stringLength;
+      if (currentLength > data.length) {
+        long requiredLength = data.length;
+        do {
+          requiredLength = requiredLength * 2;
+        } while (currentLength > requiredLength);
+        data = copyBuffer(HostMemoryBuffer.allocate(requiredLength), data);
+      }
+    }
+
+    /**
+     * For struct columns, we only need to update rows and rowCapacity (for the growth of
+     * valid buffer), because struct columns hold no buffer itself.
+     * Please call this method before appending any value or null.
+     */
+    private void growStructBuffersAndRows() {
+      assert rows + 1 <= Integer.MAX_VALUE : "Row count cannot go over Integer.MAX_VALUE";
+      rows++;
+
+      if (rowCapacity == 0) {
+        rowCapacity = estimatedRows;
+      } else if (rows > rowCapacity) {
+        rowCapacity = Math.min(rowCapacity * 2, Integer.MAX_VALUE - 1);
+      }
+    }
+
+    private HostMemoryBuffer copyBuffer(HostMemoryBuffer targetBuffer, HostMemoryBuffer buffer) {
+      try {
+        targetBuffer.copyFromHostBuffer(0, buffer, 0, buffer.length);
+        buffer.close();
+        buffer = targetBuffer;
+        targetBuffer = null;
+      } finally {
+        if (targetBuffer != null) {
+          targetBuffer.close();
+        }
+      }
+      return buffer;
+    }
+
+    /**
+     * Method that sets the null bit in the validity vector
+     * @param index the row index at which the null is marked
+     */
+    private void setNullAt(long index) {
+      assert index < rows : "Index for null value should fit the column with " + rows + " rows";
+      nullCount += BitVectorHelper.setNullAt(valid, index);
+    }
+
+    public final ColumnBuilder appendNull() {
+      nullHandler.run();
+      return this;
+    }
+
+    //For structs
+    private ColumnBuilder append(StructData structData) {
+      assert type.isNestedType();
+      if (type.equals(DType.STRUCT)) {
+        if (structData == null || structData.dataRecord == null) {
+          return appendNull();
+        } else {
+          for (int i = 0; i < structData.getNumFields(); i++) {
+            ColumnBuilder childBuilder = childBuilders.get(i);
+            appendChildOrNull(childBuilder, structData.dataRecord.get(i));
+          }
+          endStruct();
+        }
+      }
+      return this;
+    }
+
+    private boolean allChildrenHaveSameIndex() {
+      if (childBuilders.size() > 0) {
+        int expected = childBuilders.get(0).getCurrentIndex();
+        for (ColumnBuilder child: childBuilders) {
+          if (child.getCurrentIndex() != expected) {
+            return false;
+          }
+        }
+      }
+      return true;
+    }
+
+    /**
+     * If you want to build up a struct column you can get each child `builder.getChild(N)` and
+     * append to all of them, then when you are done call `endStruct` to update this builder.
+     * Do not start to append to the child and then append a null to this without ending the struct
+     * first or you might not get the results that you expected.
+     * @return this for chaining.
+     */
+    public ColumnBuilder endStruct() {
+      assert type.equals(DType.STRUCT) : "This only works for structs";
+      assert allChildrenHaveSameIndex() : "Appending structs data appears to be off " +
+          childBuilders + " should all have the same currentIndex " + type;
+      growStructBuffersAndRows();
+      currentIndex++;
+      return this;
+    }
+
+    /**
+     * If you want to build up a list column you can get `builder.getChild(0)` and append to than,
+     * then when you are done call `endList` and everything that was appended to that builder
+     * will now be in the next list. Do not start to append to the child and then append a null
+     * to this without ending the list first or you might not get the results that you expected.
+     * @return this for chaining.
+     */
+    public ColumnBuilder endList() {
+      assert type.equals(DType.LIST);
+      growListBuffersAndRows();
+      offsets.setInt(++currentIndex << bitShiftByOffset, childBuilders.get(0).getCurrentIndex());
+      return this;
+    }
+
+    // For lists
+    private <T> ColumnBuilder append(List<T> inputList) {
+      if (inputList == null) {
+        appendNull();
+      } else {
+        ColumnBuilder childBuilder = childBuilders.get(0);
+        for (Object listElement : inputList) {
+          appendChildOrNull(childBuilder, listElement);
+        }
+        endList();
+      }
+      return this;
+    }
+
+    private void appendChildOrNull(ColumnBuilder childBuilder, Object listElement) {
+      if (listElement == null) {
+        childBuilder.appendNull();
+      } else if (listElement instanceof Integer) {
+        childBuilder.append((Integer) listElement);
+      } else if (listElement instanceof String) {
+        childBuilder.append((String) listElement);
+      }  else if (listElement instanceof Double) {
+        childBuilder.append((Double) listElement);
+      } else if (listElement instanceof Float) {
+        childBuilder.append((Float) listElement);
+      } else if (listElement instanceof Boolean) {
+        childBuilder.append((Boolean) listElement);
+      } else if (listElement instanceof Long) {
+        childBuilder.append((Long) listElement);
+      } else if (listElement instanceof Byte) {
+        childBuilder.append((Byte) listElement);
+      } else if (listElement instanceof Short) {
+        childBuilder.append((Short) listElement);
+      } else if (listElement instanceof BigDecimal) {
+        childBuilder.append((BigDecimal) listElement);
+      } else if (listElement instanceof BigInteger) {
+        childBuilder.append((BigInteger) listElement);
+      } else if (listElement instanceof List) {
+        childBuilder.append((List<?>) listElement);
+      } else if (listElement instanceof StructData) {
+        childBuilder.append((StructData) listElement);
+      } else if (listElement instanceof byte[]) {
+        childBuilder.appendUTF8String((byte[]) listElement);
+      } else {
+        throw new IllegalStateException("Unexpected element type: " + listElement.getClass());
+      }
+    }
+
+    @Deprecated
+    public void incrCurrentIndex() {
+      currentIndex =  currentIndex + 1;
+    }
+
+    public int getCurrentIndex() {
+      return (int) currentIndex;
+    }
+
+    @Deprecated
+    public int getCurrentByteIndex() {
+      return currentStringByteIndex;
+    }
+
+    public final ColumnBuilder append(byte value) {
+      growFixedWidthBuffersAndRows();
+      assert type.isBackedByByte();
+      assert currentIndex < rows;
+      data.setByte(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public final ColumnBuilder append(short value) {
+      growFixedWidthBuffersAndRows();
+      assert type.isBackedByShort();
+      assert currentIndex < rows;
+      data.setShort(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public final ColumnBuilder append(int value) {
+      growFixedWidthBuffersAndRows();
+      assert type.isBackedByInt();
+      assert currentIndex < rows;
+      data.setInt(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public final ColumnBuilder append(long value) {
+      growFixedWidthBuffersAndRows();
+      assert type.isBackedByLong();
+      assert currentIndex < rows;
+      data.setLong(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public final ColumnBuilder append(float value) {
+      growFixedWidthBuffersAndRows();
+      assert type.equals(DType.FLOAT32);
+      assert currentIndex < rows;
+      data.setFloat(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public final ColumnBuilder append(double value) {
+      growFixedWidthBuffersAndRows();
+      assert type.equals(DType.FLOAT64);
+      assert currentIndex < rows;
+      data.setDouble(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public final ColumnBuilder append(boolean value) {
+      growFixedWidthBuffersAndRows();
+      assert type.equals(DType.BOOL8);
+      assert currentIndex < rows;
+      data.setBoolean(currentIndex++ << bitShiftBySize, value);
+      return this;
+    }
+
+    public ColumnBuilder append(BigDecimal value) {
+      return append(value.setScale(-type.getScale(), RoundingMode.UNNECESSARY).unscaledValue());
+    }
+
+    public ColumnBuilder append(BigInteger unscaledVal) {
+      growFixedWidthBuffersAndRows();
+      assert currentIndex < rows;
+      if (type.typeId == DType.DTypeEnum.DECIMAL32) {
+        data.setInt(currentIndex++ << bitShiftBySize, unscaledVal.intValueExact());
+      } else if (type.typeId == DType.DTypeEnum.DECIMAL64) {
+        data.setLong(currentIndex++ << bitShiftBySize, unscaledVal.longValueExact());
+      } else if (type.typeId == DType.DTypeEnum.DECIMAL128) {
+        byte[] unscaledValueBytes = unscaledVal.toByteArray();
+        byte[] result = convertDecimal128FromJavaToCudf(unscaledValueBytes);
+        data.setBytes(currentIndex++ << bitShiftBySize, result, 0, result.length);
+      } else {
+        throw new IllegalStateException(type + " is not a supported decimal type.");
+      }
+      return this;
+    }
+
+    public ColumnBuilder append(String value) {
+      assert value != null : "appendNull must be used to append null strings";
+      return appendUTF8String(value.getBytes(StandardCharsets.UTF_8));
+    }
+
+    public ColumnBuilder appendUTF8String(byte[] value) {
+      return appendUTF8String(value, 0, value.length);
+    }
+
+    public ColumnBuilder appendUTF8String(byte[] value, int srcOffset, int length) {
+      assert value != null : "appendNull must be used to append null strings";
+      assert srcOffset >= 0;
+      assert length >= 0;
+      assert value.length + srcOffset <= length;
+      assert type.equals(DType.STRING) : " type " + type + " is not String";
+      growStringBuffersAndRows(length);
+      assert currentIndex < rows;
+      if (length > 0) {
+        data.setBytes(currentStringByteIndex, value, srcOffset, length);
+      }
+      currentStringByteIndex += length;
+      offsets.setInt(++currentIndex << bitShiftByOffset, currentStringByteIndex);
+      return this;
+    }
+
+    /**
+     * Append multiple non-null byte values.
+     */
+    public ColumnBuilder append(byte[] value, int srcOffset, int length) {
+      assert type.isBackedByByte();
+      assert srcOffset >= 0;
+      assert length >= 0;
+      assert length + srcOffset <= value.length;
+
+      if (length > 0) {
+        growFixedWidthBuffersAndRows(length);
+        assert currentIndex < rows;
+        data.setBytes(currentIndex, value, srcOffset, length);
+      }
+      currentIndex += length;
+      return this;
+    }
+
+    /**
+     * Appends byte to a LIST of INT8/UINT8
+     */
+    public ColumnBuilder appendByteList(byte[] value) {
+      return appendByteList(value, 0, value.length);
+    }
+
+    /**
+     * Appends bytes to a LIST of INT8/UINT8
+     */
+    public ColumnBuilder appendByteList(byte[] value, int srcOffset, int length) {
+      assert value != null : "appendNull must be used to append null bytes";
+      assert type.equals(DType.LIST) : " type " + type + " is not LIST";
+      getChild(0).append(value, srcOffset, length);
+      return endList();
+    }
+
+    /**
+     * Accepts a byte array containing the two's-complement representation of the unscaled value, which
+     * is in big-endian byte-order. Then, transforms it into the representation of cuDF Decimal128 for
+     * appending.
+     * This method is more efficient than `append(BigInteger unscaledVal)` if we can directly access the
+     * two's-complement representation of a BigDecimal without encoding via the method `toByteArray`.
+     */
+    public ColumnBuilder appendDecimal128(byte[] binary) {
+      growFixedWidthBuffersAndRows();
+      assert type.getTypeId().equals(DType.DTypeEnum.DECIMAL128);
+      assert currentIndex < rows;
+      assert binary.length <= type.getSizeInBytes();
+      byte[] cuBinary = convertDecimal128FromJavaToCudf(binary);
+      data.setBytes(currentIndex++ << bitShiftBySize, cuBinary, 0, cuBinary.length);
+      return this;
+    }
+
+    public ColumnBuilder getChild(int index) {
+      return childBuilders.get(index);
+    }
+
+    /**
+     * Finish and create the immutable ColumnVector, copied to the device.
+     */
+    public final ColumnVector buildAndPutOnDevice() {
+      try (HostColumnVector tmp = build()) {
+        return tmp.copyToDevice();
+      }
+    }
+
+    @Override
+    public void close() {
+      if (!built) {
+        if (data != null) {
+          data.close();
+          data = null;
+        }
+        if (valid != null) {
+          valid.close();
+          valid = null;
+        }
+        if (offsets != null) {
+          offsets.close();
+          offsets = null;
+        }
+        for (ColumnBuilder childBuilder : childBuilders) {
+          childBuilder.close();
+        }
+        built = true;
+      }
+    }
+
+    @Override
+    public String toString() {
+      StringJoiner sj = new StringJoiner(",");
+      for (ColumnBuilder cb : childBuilders) {
+        sj.add(cb.toString());
+      }
+      return "ColumnBuilder{" +
+          "type=" + type +
+          ", children=" + sj +
+          ", data=" + data +
+          ", valid=" + valid +
+          ", currentIndex=" + currentIndex +
+          ", nullCount=" + nullCount +
+          ", estimatedRows=" + estimatedRows +
+          ", populatedRows=" + rows +
+          ", built=" + built +
+          '}';
+    }
+  }
+
+  public static final class Builder implements AutoCloseable {
+    private final long rows;
+    private final DType type;
+    private HostMemoryBuffer data;
+    private HostMemoryBuffer valid;
+    private HostMemoryBuffer offsets;
+    private long currentIndex = 0;
+    private long nullCount;
+    private int currentStringByteIndex = 0;
+    private boolean built;
+
+    /**
+     * Create a builder with a buffer of size rows
+     * @param type       datatype
+     * @param rows       number of rows to allocate.
+     * @param stringBufferSize the size of the string data buffer if we are
+     *                         working with Strings.  It is ignored otherwise.
+     */
+    Builder(DType type, long rows, long stringBufferSize) {
+      this.type = type;
+      this.rows = rows;
+      if (type.equals(DType.STRING)) {
+        if (stringBufferSize <= 0) {
+          // We need at least one byte or we will get NULL back for data
+          stringBufferSize = 1;
+        }
+        this.data = HostMemoryBuffer.allocate(stringBufferSize);
+        // The offsets are ints and there is 1 more than the number of rows.
+        this.offsets = HostMemoryBuffer.allocate((rows + 1) * OFFSET_SIZE);
+        // The first offset is always 0
+        this.offsets.setInt(0, 0);
+      } else {
+        this.data = HostMemoryBuffer.allocate(rows * type.getSizeInBytes());
+      }
+    }
+
+    /**
+     * Create a builder with a buffer of size rows (for testing ONLY).
+     * @param type       datatype
+     * @param rows       number of rows to allocate.
+     * @param testData   a buffer to hold the data (should be large enough to hold rows entries).
+     * @param testValid  a buffer to hold the validity vector (should be large enough to hold
+     *                   rows entries or is null).
+     * @param testOffsets a buffer to hold the offsets for strings and string categories.
+     */
+    Builder(DType type, long rows, HostMemoryBuffer testData,
+            HostMemoryBuffer testValid, HostMemoryBuffer testOffsets) {
+      this.type = type;
+      this.rows = rows;
+      this.data = testData;
+      this.valid = testValid;
+    }
+
+    public final Builder append(boolean value) {
+      assert type.equals(DType.BOOL8);
+      assert currentIndex < rows;
+      data.setByte(currentIndex * type.getSizeInBytes(), value ? (byte)1 : (byte)0);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder append(byte value) {
+      assert type.isBackedByByte();
+      assert currentIndex < rows;
+      data.setByte(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder append(byte value, long count) {
+      assert (count + currentIndex) <= rows;
+      assert type.isBackedByByte();
+      data.setMemory(currentIndex * type.getSizeInBytes(), count, value);
+      currentIndex += count;
+      return this;
+    }
+
+    public final Builder append(short value) {
+      assert type.isBackedByShort();
+      assert currentIndex < rows;
+      data.setShort(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder append(int value) {
+      assert type.isBackedByInt();
+      assert currentIndex < rows;
+      data.setInt(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder append(long value) {
+      assert type.isBackedByLong();
+      assert currentIndex < rows;
+      data.setLong(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder append(float value) {
+      assert type.equals(DType.FLOAT32);
+      assert currentIndex < rows;
+      data.setFloat(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder append(double value) {
+      assert type.equals(DType.FLOAT64);
+      assert currentIndex < rows;
+      data.setDouble(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    /**
+     * Append java.math.BigDecimal into HostColumnVector with UNNECESSARY RoundingMode.
+     * Input decimal should have a larger scale than column vector.Otherwise, an ArithmeticException will be thrown while rescaling.
+     * If unscaledValue after rescaling exceeds the max precision of rapids type,
+     * an ArithmeticException will be thrown while extracting integral.
+     *
+     * @param value BigDecimal value to be appended
+     */
+    public final Builder append(BigDecimal value) {
+      return append(value, RoundingMode.UNNECESSARY);
+    }
+
+    /**
+     * Append java.math.BigDecimal into HostColumnVector with user-defined RoundingMode.
+     * Input decimal will be rescaled according to scale of column type and RoundingMode before appended.
+     * If unscaledValue after rescaling exceeds the max precision of rapids type, an ArithmeticException will be thrown.
+     *
+     * @param value        BigDecimal value to be appended
+     * @param roundingMode rounding mode determines rescaling behavior
+     */
+    public final Builder append(BigDecimal value, RoundingMode roundingMode) {
+      assert type.isDecimalType();
+      assert currentIndex < rows: "appended too many values " + currentIndex + " out of total rows " + rows;
+      BigInteger unscaledValue = value.setScale(-type.getScale(), roundingMode).unscaledValue();
+      if (type.typeId == DType.DTypeEnum.DECIMAL32) {
+        assert value.precision() <= DType.DECIMAL32_MAX_PRECISION : "value exceeds maximum precision for DECIMAL32";
+        data.setInt(currentIndex * type.getSizeInBytes(), unscaledValue.intValueExact());
+      } else if (type.typeId == DType.DTypeEnum.DECIMAL64) {
+        assert value.precision() <= DType.DECIMAL64_MAX_PRECISION : "value exceeds maximum precision for DECIMAL64 ";
+        data.setLong(currentIndex * type.getSizeInBytes(), unscaledValue.longValueExact());
+      } else if (type.typeId == DType.DTypeEnum.DECIMAL128) {
+        assert value.precision() <= DType.DECIMAL128_MAX_PRECISION : "value exceeds maximum precision for DECIMAL128 ";
+        appendUnscaledDecimal(value.unscaledValue());
+        return this;
+      } else {
+        throw new IllegalStateException(type + " is not a supported decimal type.");
+      }
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder appendUnscaledDecimal(int value) {
+      assert type.typeId == DType.DTypeEnum.DECIMAL32;
+      assert currentIndex < rows;
+      data.setInt(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder appendUnscaledDecimal(long value) {
+      assert type.typeId == DType.DTypeEnum.DECIMAL64;
+      assert currentIndex < rows;
+      data.setLong(currentIndex * type.getSizeInBytes(), value);
+      currentIndex++;
+      return this;
+    }
+
+    public final Builder appendUnscaledDecimal(BigInteger value) {
+      assert type.typeId == DType.DTypeEnum.DECIMAL128;
+      assert currentIndex < rows;
+      byte[] unscaledValueBytes = value.toByteArray();
+      byte[] result = convertDecimal128FromJavaToCudf(unscaledValueBytes);
+      data.setBytes(currentIndex*DType.DTypeEnum.DECIMAL128.sizeInBytes, result, 0, result.length);
+      currentIndex++;
+      return this;
+    }
+
+    public Builder append(String value) {
+      assert value != null : "appendNull must be used to append null strings";
+      return appendUTF8String(value.getBytes(StandardCharsets.UTF_8));
+    }
+
+    public Builder appendUTF8String(byte[] value) {
+      return appendUTF8String(value, 0, value.length);
+    }
+
+    public Builder appendUTF8String(byte[] value, int offset, int length) {
+      assert value != null : "appendNull must be used to append null strings";
+      assert offset >= 0;
+      assert length >= 0;
+      assert length + offset <= value.length;
+      assert type.equals(DType.STRING);
+      assert currentIndex < rows;
+      // just for strings we want to throw a real exception if we would overrun the buffer
+      long oldLen = data.getLength();
+      long newLen = oldLen;
+      while (currentStringByteIndex + length > newLen) {
+        newLen *= 2;
+      }
+      if (newLen > Integer.MAX_VALUE) {
+        throw new IllegalStateException("A string buffer is not supported over 2GB in size");
+      }
+      if (newLen != oldLen) {
+        // need to grow the size of the buffer.
+        HostMemoryBuffer newData = HostMemoryBuffer.allocate(newLen);
+        try {
+          newData.copyFromHostBuffer(0, data, 0, currentStringByteIndex);
+          data.close();
+          data = newData;
+          newData = null;
+        } finally {
+          if (newData != null) {
+            newData.close();
+          }
+        }
+      }
+      if (length > 0) {
+        data.setBytes(currentStringByteIndex, value, offset, length);
+      }
+      currentStringByteIndex += length;
+      currentIndex++;
+      offsets.setInt(currentIndex * OFFSET_SIZE, currentStringByteIndex);
+      return this;
+    }
+
+    public Builder appendArray(byte... values) {
+      assert (values.length + currentIndex) <= rows;
+      assert type.isBackedByByte();
+      data.setBytes(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendArray(short... values) {
+      assert type.isBackedByShort();
+      assert (values.length + currentIndex) <= rows;
+      data.setShorts(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendArray(int... values) {
+      assert type.isBackedByInt();
+      assert (values.length + currentIndex) <= rows;
+      data.setInts(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendArray(long... values) {
+      assert type.isBackedByLong();
+      assert (values.length + currentIndex) <= rows;
+      data.setLongs(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendArray(float... values) {
+      assert type.equals(DType.FLOAT32);
+      assert (values.length + currentIndex) <= rows;
+      data.setFloats(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendArray(double... values) {
+      assert type.equals(DType.FLOAT64);
+      assert (values.length + currentIndex) <= rows;
+      data.setDoubles(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendUnscaledDecimalArray(int... values) {
+      assert type.typeId == DType.DTypeEnum.DECIMAL32;
+      assert (values.length + currentIndex) <= rows;
+      data.setInts(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    public Builder appendUnscaledDecimalArray(long... values) {
+      assert type.typeId == DType.DTypeEnum.DECIMAL64;
+      assert (values.length + currentIndex) <= rows;
+      data.setLongs(currentIndex * type.getSizeInBytes(), values, 0, values.length);
+      currentIndex += values.length;
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public Builder appendBoxed(BigDecimal... values) throws IndexOutOfBoundsException {
+      assert type.isDecimalType();
+      for (BigDecimal v : values) {
+        if (v == null) {
+          appendNull();
+        } else {
+          append(v);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Byte... values) throws IndexOutOfBoundsException {
+      for (Byte b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Boolean... values) throws IndexOutOfBoundsException {
+      for (Boolean b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b ? (byte) 1 : (byte) 0);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Short... values) throws IndexOutOfBoundsException {
+      for (Short b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Integer... values) throws IndexOutOfBoundsException {
+      for (Integer b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Long... values) throws IndexOutOfBoundsException {
+      for (Long b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Float... values) throws IndexOutOfBoundsException {
+      for (Float b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(Double... values) throws IndexOutOfBoundsException {
+      for (Double b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    /**
+     * Append multiple values.  This is very slow and should really only be used for tests.
+     * @param values the values to append, including nulls.
+     * @return this for chaining.
+     * @throws {@link IndexOutOfBoundsException}
+     */
+    public final Builder appendBoxed(String... values) throws IndexOutOfBoundsException {
+      for (String b : values) {
+        if (b == null) {
+          appendNull();
+        } else {
+          append(b);
+        }
+      }
+      return this;
+    }
+
+    // TODO see if we can remove this...
+    /**
+     * Append this vector to the end of this vector
+     * @param columnVector - Vector to be added
+     * @return - The CudfColumn based on this builder values
+     */
+    public final Builder append(HostColumnVector columnVector) {
+      assert columnVector.rows <= (rows - currentIndex);
+      assert columnVector.type.equals(type);
+
+      if (type.equals(DType.STRING)) {
+        throw new UnsupportedOperationException(
+            "Appending a string column vector client side is not currently supported");
+      } else {
+        data.copyFromHostBuffer(currentIndex * type.getSizeInBytes(), columnVector.offHeap.data,
+            0L,
+            columnVector.getRowCount() * type.getSizeInBytes());
+      }
+
+      //As this is doing the append on the host assume that a null count is available
+      long otherNc = columnVector.getNullCount();
+      if (otherNc != 0) {
+        if (valid == null) {
+          allocateBitmaskAndSetDefaultValues();
+        }
+        //copy values from intCudfColumn to this
+        BitVectorHelper.append(columnVector.offHeap.valid, valid, currentIndex,
+            columnVector.rows);
+        nullCount += otherNc;
+      }
+      currentIndex += columnVector.rows;
+      return this;
+    }
+
+    private void allocateBitmaskAndSetDefaultValues() {
+      long bitmaskSize = ColumnView.getValidityBufferSize((int) rows);
+      valid = HostMemoryBuffer.allocate(bitmaskSize);
+      valid.setMemory(0, bitmaskSize, (byte) 0xFF);
+    }
+
+    /**
+     * Append null value.
+     */
+    public final Builder appendNull() {
+      setNullAt(currentIndex);
+      currentIndex++;
+      if (type.equals(DType.STRING)) {
+        offsets.setInt(currentIndex * OFFSET_SIZE, currentStringByteIndex);
+      }
+      return this;
+    }
+
+    /**
+     * Set a specific index to null.
+     * @param index
+     */
+    public final Builder setNullAt(long index) {
+      assert index < rows;
+
+      // add null
+      if (this.valid == null) {
+        allocateBitmaskAndSetDefaultValues();
+      }
+      nullCount += BitVectorHelper.setNullAt(valid, index);
+      return this;
+    }
+
+    /**
+     * Finish and create the immutable CudfColumn.
+     */
+    public final HostColumnVector build() {
+      HostColumnVector cv = new HostColumnVector(type,
+          currentIndex, Optional.of(nullCount), data, valid, offsets);
+      built = true;
+      return cv;
+    }
+
+    /**
+     * Finish and create the immutable ColumnVector, copied to the device.
+     */
+    public final ColumnVector buildAndPutOnDevice() {
+      try (HostColumnVector tmp = build()) {
+        return tmp.copyToDevice();
+      }
+    }
+
+    /**
+     * Close this builder and free memory if the CudfColumn wasn't generated. Verifies that
+     * the data was released even in the case of an error.
+     */
+    @Override
+    public final void close() {
+      if (!built) {
+        data.close();
+        data = null;
+        if (valid != null) {
+          valid.close();
+          valid = null;
+        }
+        if (offsets != null) {
+          offsets.close();
+          offsets = null;
+        }
+        built = true;
+      }
+    }
+
+    @Override
+    public String toString() {
+      return "Builder{" +
+          "data=" + data +
+          "type=" + type +
+          ", valid=" + valid +
+          ", currentIndex=" + currentIndex +
+          ", nullCount=" + nullCount +
+          ", rows=" + rows +
+          ", built=" + built +
+          '}';
+    }
+  }
+
+  public static abstract class DataType {
+    abstract DType getType();
+    abstract boolean isNullable();
+    abstract DataType getChild(int index);
+    abstract int getNumChildren();
+  }
+
+  public static class ListType extends HostColumnVector.DataType {
+    private boolean isNullable;
+    private HostColumnVector.DataType child;
+
+    public ListType(boolean isNullable, DataType child) {
+      this.isNullable = isNullable;
+      this.child = child;
+    }
+
+    @Override
+    DType getType() {
+      return DType.LIST;
+    }
+
+    @Override
+    boolean isNullable() {
+      return isNullable;
+    }
+
+    @Override
+    HostColumnVector.DataType getChild(int index) {
+      if (index > 0) {
+        return null;
+      }
+      return child;
+    }
+
+    @Override
+    int getNumChildren() {
+      return 1;
+    }
+  }
+
+  public static class StructData {
+    List<Object> dataRecord;
+
+    public StructData(List<Object> dataRecord) {
+      this.dataRecord = dataRecord;
+    }
+
+    public StructData(Object... data) {
+      this(Arrays.asList(data));
+    }
+
+    public int getNumFields() {
+      if (dataRecord != null) {
+        return dataRecord.size();
+      } else {
+        return 0;
+      }
+    }
+  }
+
+  public static class StructType extends HostColumnVector.DataType {
+    private boolean isNullable;
+    private List<HostColumnVector.DataType> children;
+
+    public StructType(boolean isNullable, List<HostColumnVector.DataType> children) {
+      this.isNullable = isNullable;
+      this.children = children;
+    }
+
+    public StructType(boolean isNullable, DataType... children) {
+      this(isNullable, Arrays.asList(children));
+    }
+
+    @Override
+    DType getType() {
+      return DType.STRUCT;
+    }
+
+    @Override
+    boolean isNullable() {
+      return isNullable;
+    }
+
+    @Override
+    HostColumnVector.DataType getChild(int index) {
+      return children.get(index);
+    }
+
+    @Override
+    int getNumChildren() {
+      return children.size();
+    }
+  }
+
+  public static class BasicType extends HostColumnVector.DataType {
+    private DType type;
+    private boolean isNullable;
+
+    public BasicType(boolean isNullable, DType type) {
+      this.isNullable = isNullable;
+      this.type = type;
+    }
+
+    @Override
+    DType getType() {
+      return type;
+    }
+
+    @Override
+    boolean isNullable() {
+      return isNullable;
+    }
+
+    @Override
+    HostColumnVector.DataType getChild(int index) {
+      return null;
+    }
+
+    @Override
+    int getNumChildren() {
+      return 0;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostColumnVectorCore.java b/java/src/main/java/ai/rapids/cudf/HostColumnVectorCore.java
new file mode 100644
index 0000000..95d209c
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostColumnVectorCore.java
@@ -0,0 +1,663 @@
+/*
+ *
+ *  Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.nio.ByteOrder;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+
+/**
+ * A class that holds Host side Column Vector APIs and the OffHeapState.
+ * Any children of a HostColumnVector will be instantiated via this class.
+ */
+public class HostColumnVectorCore implements AutoCloseable {
+
+  private static final Logger log = LoggerFactory.getLogger(HostColumnVector.class);
+
+  protected final OffHeapState offHeap;
+  protected final DType type;
+  protected long rows;
+  protected Optional<Long> nullCount;
+  protected List<HostColumnVectorCore> children;
+
+
+  public HostColumnVectorCore(DType type, long rows,
+                              Optional<Long> nullCount, HostMemoryBuffer data, HostMemoryBuffer validity,
+                              HostMemoryBuffer offsets, List<HostColumnVectorCore> nestedChildren) {
+    this.offHeap = new OffHeapState(data, validity,  offsets);
+    MemoryCleaner.register(this, offHeap);
+    this.type = type;
+    this.rows = rows;
+    this.nullCount = nullCount;
+    this.children = nestedChildren;
+  }
+
+  /**
+   * Returns the type of this vector.
+   */
+  public DType getType() {
+    return type;
+  }
+
+  /**
+   * Returns the data buffer for a given host side column vector
+   */
+  public HostMemoryBuffer getData() {
+    return offHeap.data;
+  }
+
+  /**
+   * Returns the validity buffer for a given host side column vector
+   */
+  public HostMemoryBuffer getValidity() {
+    return offHeap.valid;
+  }
+
+  /**
+   * Returns the offset buffer
+   */
+  public HostMemoryBuffer getOffsets() {
+    return offHeap.offsets;
+  }
+
+  public HostColumnVectorCore getChildColumnView(int childIndex) {
+    return getNestedChildren().get(childIndex);
+  }
+
+  /**
+   * Returns the number of nulls in the data. Note that this might end up
+   * being a very expensive operation because if the null count is not
+   * known it will be calculated.
+   */
+  public long getNullCount() {
+    if (!nullCount.isPresent()) {
+      throw new IllegalStateException("Calculating an unknown null count on the host is not currently supported");
+    }
+    return nullCount.get();
+  }
+
+  /**
+   * Returns the list of child host column vectors for a given host side column
+   */
+  List<HostColumnVectorCore> getNestedChildren() {
+    return children;
+  }
+
+  /**
+   * Returns the number of rows for a given host side column vector
+   */
+  public long getRowCount() {
+    return rows;
+  }
+
+  /**
+   * Returns the number of children for this column
+   */
+  public int getNumChildren() {
+    return children.size();
+  }
+
+  /**
+   * Return the element at a given row for a give data type
+   * @param rowIndex the row number
+   * @return an object that would need to be casted to appropriate type based on this vector's data type
+   */
+  Object getElement(int rowIndex) {
+    if (type.equals(DType.LIST)) {
+      return getList(rowIndex);
+    } else if (type.equals(DType.STRUCT)) {
+      return getStruct(rowIndex);
+    } else {
+      if (isNull(rowIndex)) {
+        return null;
+      }
+      return readValue(rowIndex);
+    }
+  }
+
+  private Object getString(int rowIndex) {
+    if (isNull(rowIndex)) {
+      return null;
+    }
+    int start = (int)getStartListOffset(rowIndex);
+    int end = (int)getEndListOffset(rowIndex);
+    int size = end - start;
+    byte[] rawData = new byte[size];
+    if (size > 0) {
+      offHeap.data.getBytes(rawData, 0, start, size);
+      return new String(rawData);
+    } else {
+      return new String();
+    }
+  }
+
+
+  /////////////////////////////////////////////////////////////////////////////
+  // DATA ACCESS
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * For testing only.  Allows null checks to go past the number of rows, but not past the end
+   * of the buffer.  NOTE: If the validity vector was allocated by cudf itself it is not
+   * guaranteed to have the same padding, but for all practical purposes it does.  This is
+   * just to verify that the buffer was allocated and initialized properly.
+   */
+  boolean isNullExtendedRange(long index) {
+    long maxNullRow = BitVectorHelper.getValidityAllocationSizeInBytes(rows) * 8;
+    assert (index >= 0 && index < maxNullRow) : "TEST: index is out of range 0 <= " + index + " <" +
+        " " + maxNullRow;
+    if (hasValidityVector()) {
+      if (nullCount.isPresent() && !hasNulls()) {
+        return false;
+      }
+      return BitVectorHelper.isNull(offHeap.valid, index);
+    }
+    return false;
+  }
+
+  /**
+   * Get access to the raw host buffer for this column.  This is intended to be used with a lot
+   * of caution.  The lifetime of the buffer is tied to the lifetime of the column (Do not close
+   * the buffer, as the column will take care of it).  Do not modify the contents of the buffer or
+   * it might negatively impact what happens on the column.  The data must be on the host for this
+   * to work.
+   * @param type the type of buffer to get access to.
+   * @return the underlying buffer or null if no buffer is associated with it for this column.
+   * Please note that if the column is empty there may be no buffers at all associated with the
+   * column.
+   */
+  public HostMemoryBuffer getHostBufferFor(BufferType type) {
+    HostMemoryBuffer srcBuffer = null;
+    switch(type) {
+      case VALIDITY:
+        srcBuffer = offHeap.valid;
+        break;
+      case OFFSET:
+        srcBuffer = offHeap.offsets;
+        break;
+      case DATA:
+        srcBuffer = offHeap.data;
+        break;
+      default:
+        throw new IllegalArgumentException(type + " is not a supported buffer type.");
+    }
+    return srcBuffer;
+  }
+
+  void copyHostBufferBytes(byte[] dst, int dstOffset, BufferType src, long srcOffset,
+                           int length) {
+    assert dstOffset >= 0;
+    assert srcOffset >= 0;
+    assert length >= 0;
+    assert dstOffset + length <= dst.length;
+
+    HostMemoryBuffer srcBuffer = getHostBufferFor(src);
+
+    assert srcOffset + length <= srcBuffer.length : "would copy off end of buffer "
+        + srcOffset + " + " + length + " > " + srcBuffer.length;
+    UnsafeMemoryAccessor.getBytes(dst, dstOffset,
+        srcBuffer.getAddress() + srcOffset, length);
+  }
+  /**
+   * Generic type independent asserts when getting a value from a single index.
+   * @param index where to get the data from.
+   */
+  private void assertsForGet(long index) {
+    assert (index >= 0 && index < rows) : "index is out of range 0 <= " + index + " < " + rows;
+    assert !isNull(index) : " value at " + index + " is null";
+  }
+
+  /**
+   * Get the value at index.
+   */
+  public byte getByte(long index) {
+    assert type.isBackedByByte() : type + " is not stored as a byte.";
+    assertsForGet(index);
+    return offHeap.data.getByte(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the value at index.
+   */
+  public final short getShort(long index) {
+    assert type.isBackedByShort() : type + " is not stored as a short.";
+    assertsForGet(index);
+    return offHeap.data.getShort(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the value at index.
+   */
+  public final int getInt(long index) {
+    assert type.isBackedByInt() : type + " is not stored as a int.";
+    assertsForGet(index);
+    return offHeap.data.getInt(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the starting byte offset for the string at index
+   * Wraps getStartListOffset for backwards compatibility
+   */
+  long getStartStringOffset(long index) {
+    return getStartListOffset(index);
+  }
+
+  /**
+   * Get the starting element offset for the list or string at index
+   */
+  public long getStartListOffset(long index) {
+    assert type.equals(DType.STRING) || type.equals(DType.LIST): type +
+      " is not a supported string or list type.";
+    assert (index >= 0 && index < rows) : "index is out of range 0 <= " + index + " < " + rows;
+    return offHeap.offsets.getInt(index * 4);
+  }
+
+  /**
+   * Get the ending byte offset for the string at index.
+   * Wraps getEndListOffset for backwards compatibility
+   */
+  long getEndStringOffset(long index) {
+    return getEndListOffset(index);
+  }
+
+  /**
+   * Get the ending element offset for the list or string at index.
+   */
+  public long getEndListOffset(long index) {
+    assert type.equals(DType.STRING) || type.equals(DType.LIST): type +
+      " is not a supported string or list type.";
+    assert (index >= 0 && index < rows) : "index is out of range 0 <= " + index + " < " + rows;
+    // The offsets has one more entry than there are rows.
+    return offHeap.offsets.getInt((index + 1) * 4);
+  }
+
+  /**
+   * Get the value at index.
+   */
+  public final long getLong(long index) {
+    // Timestamps with time values are stored as longs
+    assert type.isBackedByLong(): type + " is not stored as a long.";
+    assertsForGet(index);
+    return offHeap.data.getLong(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the value at index.
+   */
+  public final float getFloat(long index) {
+    assert type.equals(DType.FLOAT32) : type + " is not a supported float type.";
+    assertsForGet(index);
+    return offHeap.data.getFloat(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the value at index.
+   */
+  public final double getDouble(long index) {
+    assert type.equals(DType.FLOAT64) : type + " is not a supported double type.";
+    assertsForGet(index);
+    return offHeap.data.getDouble(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the boolean value at index
+   */
+  public final boolean getBoolean(long index) {
+    assert type.equals(DType.BOOL8) : type + " is not a supported boolean type.";
+    assertsForGet(index);
+    return offHeap.data.getBoolean(index * type.getSizeInBytes());
+  }
+
+  /**
+   * Get the BigDecimal value at index.
+   */
+  public final BigDecimal getBigDecimal(long index) {
+    assert type.isDecimalType() : type + " is not a supported decimal type.";
+    assertsForGet(index);
+    if (type.typeId == DType.DTypeEnum.DECIMAL32) {
+      int unscaledValue = offHeap.data.getInt(index * type.getSizeInBytes());
+      return BigDecimal.valueOf(unscaledValue, -type.getScale());
+    } else if (type.typeId == DType.DTypeEnum.DECIMAL64) {
+      long unscaledValue = offHeap.data.getLong(index * type.getSizeInBytes());
+      return BigDecimal.valueOf(unscaledValue, -type.getScale());
+    } else if (type.typeId == DType.DTypeEnum.DECIMAL128) {
+      int sizeInBytes = DType.DTypeEnum.DECIMAL128.sizeInBytes;
+      byte[] dst = new byte[sizeInBytes];
+      // We need to switch the endianness for decimal128 byte arrays between java and native code.
+      offHeap.data.getBytes(dst, 0, (index * sizeInBytes), sizeInBytes);
+      convertInPlaceToBigEndian(dst);
+      return new BigDecimal(new BigInteger(dst), -type.getScale());
+    } else {
+      throw new IllegalStateException(type + " is not a supported decimal type.");
+    }
+  }
+
+  /**
+   * Get the raw UTF8 bytes at index.  This API is faster than getJavaString, but still not
+   * ideal because it is copying the data onto the heap.
+   */
+  public byte[] getUTF8(long index) {
+    assert type.equals(DType.STRING) : type + " is not a supported string type.";
+    assertsForGet(index);
+    int start = (int)getStartListOffset(index);
+    int size = (int)getEndListOffset(index) - start;
+    byte[] rawData = new byte[size];
+    if (size > 0) {
+      offHeap.data.getBytes(rawData, 0, start, size);
+    }
+    return rawData;
+  }
+
+  /**
+   * Get the value at index.  This API is slow as it has to translate the
+   * string representation.  Please use it with caution.
+   */
+  public String getJavaString(long index) {
+    byte[] rawData = getUTF8(index);
+    return new String(rawData, StandardCharsets.UTF_8);
+  }
+
+  /**
+   * WARNING: Special case for lists of int8 or uint8, does not support null list values or lists
+   *
+   * Get array of bytes at index from a list column of int8 or uint8. The column may not be a list
+   * of lists and may not have nulls.
+   */
+  public byte[] getBytesFromList(long rowIndex) {
+    assert type.equals(DType.LIST) : type + " is not a supported list of bytes type.";
+    HostColumnVectorCore listData = children.get(0);
+    assert listData.type.equals(DType.INT8) || listData.type.equals(DType.UINT8)  : type +
+      " is not a supported list of bytes type.";
+    assert !listData.hasNulls() : "byte list column with nulls are not supported";
+    assertsForGet(rowIndex);
+
+    int start = (int)getStartListOffset(rowIndex);
+    int end = (int)getEndListOffset(rowIndex);
+    int size = end - start;
+
+    byte[] result = new byte[size];
+    if (size > 0) {
+      listData.offHeap.data.getBytes(result, 0, start, size);
+    }
+    return result;
+  }
+
+  /**
+   * WARNING: Strictly for test only. This call is not efficient for production.
+   */
+  public List getList(long rowIndex) {
+    assert rowIndex < rows;
+    assert type.equals(DType.LIST);
+    List retList = new ArrayList();
+    int start = (int)getStartListOffset(rowIndex);
+    int end = (int)getEndListOffset(rowIndex);
+    // check if null or empty
+    if (isNull(rowIndex)) {
+      return null;
+    }
+    for(int j = start; j < end; j++) {
+      for (HostColumnVectorCore childHcv : children) {
+        // lists have only 1 child
+        retList.add(childHcv.getElement(j));
+      }
+    }
+    return retList;
+  }
+
+  /**
+   * WARNING: Strictly for test only. This call is not efficient for production.
+   */
+  public HostColumnVector.StructData getStruct(int rowIndex) {
+    assert rowIndex < rows;
+    assert type.equals(DType.STRUCT);
+    List<Object> retList = new ArrayList<>();
+    // check if null or empty
+    if (isNull(rowIndex)) {
+      return null;
+    }
+    for (int k = 0; k < this.getNumChildren(); k++) {
+      retList.add(children.get(k).getElement(rowIndex));
+    }
+    return new HostColumnVector.StructData(retList);
+  }
+
+  /**
+   * Method that returns a boolean to indicate if the element at a given row index is null
+   * @param rowIndex the row index
+   * @return true if null else false
+   */
+  public boolean isNull(long rowIndex) {
+    return rowIndex < 0 || rowIndex >= rows // unknown, hence NULL
+           || hasValidityVector() && BitVectorHelper.isNull(offHeap.valid, rowIndex);
+  }
+
+  /**
+   * Returns if the vector has a validity vector allocated or not.
+   */
+  public boolean hasValidityVector() {
+    return (offHeap.valid != null);
+  }
+
+  /**
+   * Returns if the vector has nulls.  Note that this might end up
+   * being a very expensive operation because if the null count is not
+   * known it will be calculated.
+   */
+  public boolean hasNulls() {
+    return getNullCount() > 0;
+  }
+
+  /**
+   * Helper method that reads in a value at a given row index
+   * @param rowIndex the row index
+   * @return an object that would need to be casted to appropriate type based on this vector's data type
+   */
+  private Object readValue(int rowIndex) {
+    assert rowIndex < rows;
+    int rowOffset = rowIndex * type.getSizeInBytes();
+    switch (type.typeId) {
+      case INT32: // fall through
+      case UINT32: // fall through
+      case TIMESTAMP_DAYS:
+      case DURATION_DAYS: return offHeap.data.getInt(rowOffset);
+      case INT64: // fall through
+      case UINT64: // fall through
+      case DURATION_MICROSECONDS: // fall through
+      case DURATION_MILLISECONDS: // fall through
+      case DURATION_NANOSECONDS: // fall through
+      case DURATION_SECONDS: // fall through
+      case TIMESTAMP_MICROSECONDS: // fall through
+      case TIMESTAMP_MILLISECONDS: // fall through
+      case TIMESTAMP_NANOSECONDS: // fall through
+      case TIMESTAMP_SECONDS: return offHeap.data.getLong(rowOffset);
+      case FLOAT32: return offHeap.data.getFloat(rowOffset);
+      case FLOAT64: return offHeap.data.getDouble(rowOffset);
+      case UINT8: // fall through
+      case INT8: return offHeap.data.getByte(rowOffset);
+      case UINT16: // fall through
+      case INT16: return offHeap.data.getShort(rowOffset);
+      case BOOL8: return offHeap.data.getBoolean(rowOffset);
+      case STRING: return getString(rowIndex);
+      case DECIMAL32: return BigDecimal.valueOf(offHeap.data.getInt(rowOffset), -type.getScale());
+      case DECIMAL64: return BigDecimal.valueOf(offHeap.data.getLong(rowOffset), -type.getScale());
+      default: throw new UnsupportedOperationException("Do not support " + type);
+    }
+  }
+
+  /**
+   * Returns the amount of host memory used to store column/validity data (not metadata).
+   */
+  public long getHostMemorySize() {
+    long totalSize = offHeap.getHostMemorySize();
+    for (HostColumnVectorCore nhcv : children) {
+      totalSize += nhcv.getHostMemorySize();
+    }
+    return totalSize;
+  }
+
+  /**
+   * Close method for the column
+   */
+  @Override
+  public synchronized void close() {
+    for (HostColumnVectorCore child : children) {
+      if (child != null) {
+        child.close();
+      }
+    }
+    offHeap.delRef();
+    offHeap.cleanImpl(false);
+  }
+
+  @Override
+  public String toString() {
+    return "HostColumnVectorCore{" +
+        "rows=" + rows +
+        ", type=" + type +
+        ", nullCount=" + nullCount +
+        ", offHeap=" + offHeap +
+        '}';
+  }
+
+  protected static byte[] convertDecimal128FromJavaToCudf(byte[] bytes) {
+    byte[] finalBytes = new byte[DType.DTypeEnum.DECIMAL128.sizeInBytes];
+    byte lastByte = bytes[0];
+    //Convert to 2's complement representation and make sure the sign bit is extended correctly
+    byte setByte = (lastByte & 0x80) > 0 ? (byte)0xff : (byte)0x00;
+    for(int i = bytes.length; i < finalBytes.length; i++) {
+      finalBytes[i] = setByte;
+    }
+    // After setting the sign bits, reverse the rest of the bytes for endianness
+    for(int k = 0; k < bytes.length; k++) {
+      finalBytes[k] = bytes[bytes.length - k - 1];
+    }
+    return finalBytes;
+  }
+
+  private void convertInPlaceToBigEndian(byte[] dst) {
+    assert ByteOrder.nativeOrder().equals(ByteOrder.LITTLE_ENDIAN);
+    int i =0;
+    int j = dst.length -1;
+    while (j > i) {
+      byte tmp;
+      tmp = dst[j];
+      dst[j] = dst[i];
+      dst[i] = tmp;
+      j--;
+      i++;
+    }
+  }
+  /////////////////////////////////////////////////////////////////////////////
+  // HELPER CLASSES
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Holds the off heap state of the column vector so we can clean it up, even if it is leaked.
+   */
+  protected static final class OffHeapState extends MemoryCleaner.Cleaner {
+    public HostMemoryBuffer data;
+    public HostMemoryBuffer valid = null;
+    public HostMemoryBuffer offsets = null;
+
+    OffHeapState(HostMemoryBuffer data, HostMemoryBuffer valid, HostMemoryBuffer offsets) {
+      this.data = data;
+      this.valid = valid;
+      this.offsets = offsets;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      if (data != null || valid != null || offsets != null) {
+        try {
+          if (data != null) {
+            data.close();
+          }
+          if (offsets != null) {
+            offsets.close();
+          }
+          if (valid != null) {
+            valid.close();
+          }
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          data = null;
+          valid = null;
+          offsets = null;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A HOST COLUMN VECTOR WAS LEAKED (ID: " + id + ")");
+        logRefCountDebug("Leaked vector");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public void noWarnLeakExpected() {
+      super.noWarnLeakExpected();
+      if (data != null) {
+        data.noWarnLeakExpected();
+      }
+      if (valid != null) {
+        valid.noWarnLeakExpected();
+      }
+      if (offsets != null) {
+        offsets.noWarnLeakExpected();
+      }
+    }
+
+    @Override
+    public boolean isClean() {
+      return data == null && valid == null && offsets == null;
+    }
+
+    /**
+     * This returns total memory allocated on the host for the ColumnVector.
+     */
+    public long getHostMemorySize() {
+      long total = 0;
+      if (valid != null) {
+        total += valid.length;
+      }
+      if (data != null) {
+        total += data.length;
+      }
+      if (offsets != null) {
+        total += offsets.length;
+      }
+      return total;
+    }
+
+    @Override
+    public String toString() {
+      return "(ID: " + id + ")";
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostMemoryAllocator.java b/java/src/main/java/ai/rapids/cudf/HostMemoryAllocator.java
new file mode 100644
index 0000000..9834eb8
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostMemoryAllocator.java
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+public interface HostMemoryAllocator {
+
+  /**
+   * Allocate memory, but be sure to close the returned buffer to avoid memory leaks.
+   * @param bytes size in bytes to allocate
+   * @param preferPinned If set to true, the pinned memory pool will be used if possible with a
+   *                    fallback to off-heap memory.  If set to false, the allocation will always
+   *                    be from off-heap memory.
+   * @return the newly created buffer
+   */
+  HostMemoryBuffer allocate(long bytes, boolean preferPinned);
+
+  /**
+   * Allocate memory, but be sure to close the returned buffer to avoid memory leaks. Pinned memory
+   * for allocations preference is up to the implementor
+   *
+   * @param bytes size in bytes to allocate
+   * @return the newly created buffer
+   */
+  HostMemoryBuffer allocate(long bytes);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostMemoryBuffer.java b/java/src/main/java/ai/rapids/cudf/HostMemoryBuffer.java
new file mode 100644
index 0000000..e410657
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostMemoryBuffer.java
@@ -0,0 +1,668 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.EOFException;
+import java.io.File;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.channels.FileChannel.MapMode;
+
+
+/**
+ * This class holds an off-heap buffer in the host/CPU memory.
+ * Please note that instances must be explicitly closed or native memory will be leaked!
+ *
+ * Internally this class will try to use PinnedMemoryPool to allocate and free the memory
+ * it uses by default. To avoid using the pinned memory pool for allocations by default
+ * set the Java system property ai.rapids.cudf.prefer-pinned to false.
+ *
+ * Be aware that the off heap memory limits set by Java do not apply to these buffers.
+ */
+public class HostMemoryBuffer extends MemoryBuffer {
+  private static final boolean defaultPreferPinned;
+  private static final Logger log = LoggerFactory.getLogger(HostMemoryBuffer.class);
+
+  static {
+    boolean preferPinned = true;
+    String propString = System.getProperty("ai.rapids.cudf.prefer-pinned");
+    if (propString != null) {
+      preferPinned = Boolean.parseBoolean(propString);
+    }
+    defaultPreferPinned = preferPinned;
+  }
+
+  private static final class HostBufferCleaner extends MemoryBufferCleaner {
+    private long address;
+    private final long length;
+
+    HostBufferCleaner(long address, long length) {
+      this.address = address;
+      this.length = length;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long origAddress = address;
+      if (address != 0) {
+        try {
+          UnsafeMemoryAccessor.free(address);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          address = 0;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A HOST BUFFER WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+        logRefCountDebug("Leaked host buffer");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return address == 0;
+    }
+  }
+
+  private static final class MmapCleaner extends MemoryBufferCleaner {
+    private long address;
+    private final long length;
+
+    MmapCleaner(long address, long length) {
+      this.address = address;
+      this.length = length;
+    }
+
+    @Override
+    protected boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      if (address != 0) {
+        try {
+          HostMemoryBufferNativeUtils.munmap(address, length);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          address = 0;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A MEMORY MAPPED BUFFER WAS LEAKED!!!!");
+        logRefCountDebug("Leaked mmap buffer");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return address == 0;
+    }
+  }
+
+  /**
+   * Allocate memory, but be sure to close the returned buffer to avoid memory leaks.
+   * @param bytes size in bytes to allocate
+   * @param preferPinned If set to true, the pinned memory pool will be used if possible with a
+   *                    fallback to off-heap memory.  If set to false, the allocation will always
+   *                    be from off-heap memory.
+   * @return the newly created buffer
+   */
+  public static HostMemoryBuffer allocate(long bytes, boolean preferPinned) {
+    if (preferPinned) {
+      HostMemoryBuffer pinnedBuffer = PinnedMemoryPool.tryAllocate(bytes);
+      if (pinnedBuffer != null) {
+        return pinnedBuffer;
+      }
+    }
+    return new HostMemoryBuffer(UnsafeMemoryAccessor.allocate(bytes), bytes);
+  }
+
+  /**
+   * Allocate memory, but be sure to close the returned buffer to avoid memory leaks. Pinned memory
+   * will be preferred for allocations if the java system property ai.rapids.cudf.prefer-pinned is
+   * set to true.
+   * @param bytes size in bytes to allocate
+   * @return the newly created buffer
+   */
+  public static HostMemoryBuffer allocate(long bytes) {
+    return allocate(bytes, defaultPreferPinned);
+  }
+
+  /**
+   * Create a host buffer that is memory-mapped to a file.
+   * @param path path to the file to map into host memory
+   * @param mode mapping type
+   * @param offset file offset where the map will start
+   * @param length the number of bytes to map
+   * @return file-mapped buffer
+   */
+  public static HostMemoryBuffer mapFile(File path, MapMode mode,
+      long offset, long length) throws IOException {
+    // mapping offset must be a multiple of the system page size
+    long offsetDelta = offset & (UnsafeMemoryAccessor.pageSize() - 1);
+    long address;
+    try {
+      address = HostMemoryBufferNativeUtils.mmap(path.getPath(),
+          modeAsInt(mode), offset - offsetDelta, length + offsetDelta);
+    } catch (IOException e) {
+      throw new IOException("Error creating memory map for " + path, e);
+    }
+    return new HostMemoryBuffer(address + offsetDelta, length,
+        new MmapCleaner(address, length + offsetDelta));
+  }
+
+  private static int modeAsInt(MapMode mode) {
+    if (MapMode.READ_ONLY.equals(mode)) {
+      return 0;
+    } else if (MapMode.READ_WRITE.equals(mode)) {
+      return 1;
+    } else {
+      throw new UnsupportedOperationException("Unsupported mapping mode: " + mode);
+    }
+  }
+
+  HostMemoryBuffer(long address, long length) {
+    this(address, length, new HostBufferCleaner(address, length));
+  }
+
+  HostMemoryBuffer(long address, long length, MemoryBufferCleaner cleaner) {
+    super(address, length, cleaner);
+  }
+
+  private HostMemoryBuffer(long address, long lengthInBytes, HostMemoryBuffer parent) {
+    super(address, lengthInBytes, parent);
+    // This is a slice so we are not going to mark it as allocated
+  }
+
+  /**
+   * Return a ByteBuffer that provides access to the underlying memory.  Please note: if the buffer
+   * is larger than a ByteBuffer can handle (2GB) an exception will be thrown.  Also
+   * be aware that the ByteBuffer will be in native endian order, which is different from regular
+   * ByteBuffers that are big endian by default.
+   */
+  public final ByteBuffer asByteBuffer() {
+    assert length <= Integer.MAX_VALUE : "2GB limit on ByteBuffers";
+    return asByteBuffer(0, (int) length);
+  }
+
+  /**
+   * Return a ByteBuffer that provides access to the underlying memory.  Be aware that the
+   * ByteBuffer will be in native endian order, which is different from regular
+   * ByteBuffers that are big endian by default.
+   * @param offset the offset to start at
+   * @param length how many bytes to include.
+   */
+  public final ByteBuffer asByteBuffer(long offset, int length) {
+    addressOutOfBoundsCheck(address + offset, length, "asByteBuffer");
+    return HostMemoryBufferNativeUtils.wrapRangeInBuffer(address + offset, length)
+        .order(ByteOrder.nativeOrder());
+  }
+
+  /**
+   * Copy the contents of the given buffer to this buffer
+   * @param destOffset offset in bytes in this buffer to start copying to
+   * @param srcData    Buffer to be copied from
+   * @param srcOffset  offset in bytes to start copying from in srcData
+   * @param length     number of bytes to copy
+   */
+  public final void copyFromHostBuffer(long destOffset, HostMemoryBuffer srcData, long srcOffset,
+                                       long length) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy from dest");
+    srcData.addressOutOfBoundsCheck(srcData.address + srcOffset, length, "copy from source");
+    UnsafeMemoryAccessor.copyMemory(null, srcData.address + srcOffset, null,
+        address + destOffset, length);
+  }
+
+  /**
+   * Copy len bytes from in to this buffer.
+   * @param destOffset  offset in bytes in this buffer to start copying to
+   * @param in input stream to copy bytes from
+   * @param byteLength number of bytes to copy
+   */
+  final void copyFromStream(long destOffset, InputStream in, long byteLength) throws IOException {
+    addressOutOfBoundsCheck(address + destOffset, byteLength, "copy from stream");
+    byte[] arrayBuffer = new byte[(int) Math.min(1024 * 128, byteLength)];
+    long left = byteLength;
+    while (left > 0) {
+      int amountToCopy = (int) Math.min(arrayBuffer.length, left);
+      int amountRead = in.read(arrayBuffer, 0, amountToCopy);
+      if (amountRead < 0) {
+        throw new EOFException();
+      }
+      setBytes(destOffset, arrayBuffer, 0, amountRead);
+      destOffset += amountRead;
+      left -= amountRead;
+    }
+  }
+
+  /**
+   * Returns the byte value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final byte getByte(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 1, "getByte");
+    return UnsafeMemoryAccessor.getByte(requestedAddress);
+  }
+
+  /**
+   * Sets the byte value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setByte(long offset, byte value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 1, "setByte");
+    UnsafeMemoryAccessor.setByte(requestedAddress, value);
+  }
+
+  /**
+   * Copy a set of bytes to an array from the buffer starting at offset.
+   * @param dst       destination byte array
+   * @param dstOffset starting offset within the destination array
+   * @param srcOffset starting offset within this buffer
+   * @param len       number of bytes to copy
+   */
+  public final void getBytes(byte[] dst, long dstOffset, long srcOffset, long len) {
+    assert len >= 0;
+    assert len <= dst.length - dstOffset;
+    assert srcOffset >= 0;
+    long requestedAddress = this.address + srcOffset;
+    addressOutOfBoundsCheck(requestedAddress, len, "getBytes");
+    UnsafeMemoryAccessor.getBytes(dst, dstOffset, requestedAddress, len);
+  }
+
+  /**
+   * Copy a set of bytes from an array into the buffer at offset.
+   * @param offset the offset from the address to start copying to
+   * @param data   the data to be copied.
+   */
+  public final void setBytes(long offset, byte[] data, long srcOffset, long len) {
+    assert len >= 0 : "length is not allowed " + len;
+    assert len <= data.length - srcOffset;
+    assert srcOffset >= 0;
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, len, "setBytes");
+    UnsafeMemoryAccessor.setBytes(requestedAddress, data, srcOffset, len);
+  }
+
+  /**
+   * Returns the Short value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final short getShort(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 2, "getShort");
+    return UnsafeMemoryAccessor.getShort(requestedAddress);
+  }
+
+  /**
+   * Sets the Short value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setShort(long offset, short value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 2, "setShort");
+    UnsafeMemoryAccessor.setShort(requestedAddress, value);
+  }
+
+  /**
+   * Copy a set of shorts from an array into the buffer at offset.
+   * @param offset    the offset from the address to start copying to
+   * @param data      the data to be copied.
+   * @param srcOffset index in data to start at.
+   */
+  public final void setShorts(long offset, short[] data, long srcOffset, long len) {
+    assert len >= 0 : "length is not allowed " + len;
+    assert len <= data.length - srcOffset;
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, len * 2, "setShorts");
+    UnsafeMemoryAccessor.setShorts(requestedAddress, data, srcOffset, len);
+  }
+
+  /**
+   * Returns the Integer value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final int getInt(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 4, "getInt");
+    return UnsafeMemoryAccessor.getInt(requestedAddress);
+  }
+
+  /**
+   * Sets the Integer value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setInt(long offset, int value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 4, "setInt");
+    UnsafeMemoryAccessor.setInt(requestedAddress, value);
+  }
+
+  /**
+   * Copy a set of ints from an array into the buffer at offset.
+   * @param offset    the offset from the address to start copying to
+   * @param data      the data to be copied.
+   * @param srcOffset index into data to start at
+   */
+  public final void setInts(long offset, int[] data, long srcOffset, long len) {
+    assert len >= 0 : "length is not allowed " + len;
+    assert len <= data.length - srcOffset;
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, len * 4, "setInts");
+    UnsafeMemoryAccessor.setInts(requestedAddress, data, srcOffset, len);
+  }
+
+  /**
+   * Returns the Long value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final long getLong(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 8, "getLong");
+    return UnsafeMemoryAccessor.getLong(requestedAddress);
+  }
+
+  /**
+   * Sets the Long value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setLong(long offset, long value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 8, "setLong");
+    UnsafeMemoryAccessor.setLong(requestedAddress, value);
+  }
+
+  /**
+   * Copy a set of longs to an array from the buffer starting at offset.
+   * @param dst       destination long array
+   * @param dstIndex  starting index within the destination array
+   * @param srcOffset starting offset within this buffer
+   * @param count     number of longs to copy
+   */
+  public final void getLongs(long[] dst, long dstIndex, long srcOffset, int count) {
+    assert count >= 0;
+    assert count <= dst.length - dstIndex;
+    assert srcOffset >= 0;
+    long requestedAddress = this.address + srcOffset;
+    addressOutOfBoundsCheck(requestedAddress, count * 8L, "getLongs");
+    UnsafeMemoryAccessor.getLongs(dst, dstIndex, requestedAddress, count);
+  }
+
+  /**
+   * Copy a set of longs from an array into the buffer at offset.
+   * @param offset    the offset from the address to start copying to
+   * @param data      the data to be copied.
+   * @param srcOffset index into data to start at.
+   */
+  public final void setLongs(long offset, long[] data, long srcOffset, long len) {
+    assert len >= 0 : "length is not allowed " + len;
+    assert len <= data.length - srcOffset;
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, len * 8, "setLongs");
+    UnsafeMemoryAccessor.setLongs(requestedAddress, data, srcOffset, len);
+  }
+
+  /**
+   * Returns the Float value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final float getFloat(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 4, "getFloat");
+    return UnsafeMemoryAccessor.getFloat(requestedAddress);
+  }
+
+  /**
+   * Sets the Float value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setFloat(long offset, float value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 4, "setFloat");
+    UnsafeMemoryAccessor.setFloat(requestedAddress, value);
+  }
+
+  /**
+   * Copy a set of floats from an array into the buffer at offset.
+   * @param offset    the offset from the address to start copying to
+   * @param data      the data to be copied.
+   * @param srcOffset index into data to start at
+   */
+  public final void setFloats(long offset, float[] data, long srcOffset, long len) {
+    assert len >= 0 : "length is not allowed " + len;
+    assert len <= data.length - srcOffset;
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, len * 4, "setFloats");
+    UnsafeMemoryAccessor.setFloats(requestedAddress, data, srcOffset, len);
+  }
+
+  /**
+   * Returns the Double value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final double getDouble(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 8, "getDouble");
+    return UnsafeMemoryAccessor.getDouble(requestedAddress);
+  }
+
+  /**
+   * Sets the Double value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setDouble(long offset, double value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 8, "setDouble");
+    UnsafeMemoryAccessor.setDouble(requestedAddress, value);
+  }
+
+  /**
+   * Copy a set of doubles from an array into the buffer at offset.
+   * @param offset    the offset from the address to start copying to
+   * @param data      the data to be copied.
+   * @param srcOffset index into data to start at
+   */
+  public final void setDoubles(long offset, double[] data, long srcOffset, long len) {
+    assert len >= 0 : "length is not allowed " + len;
+    assert len <= data.length - srcOffset;
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, len * 8, "setDoubles");
+    UnsafeMemoryAccessor.setDoubles(requestedAddress, data, srcOffset, len);
+  }
+
+  /**
+   * Returns the Boolean value at that offset
+   * @param offset - offset from the address
+   * @return - value
+   */
+  public final boolean getBoolean(long offset) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 1, "getBoolean");
+    return UnsafeMemoryAccessor.getBoolean(requestedAddress);
+  }
+
+  /**
+   * Sets the Boolean value at that offset
+   * @param offset - offset from the address
+   * @param value  - value to be set
+   */
+  public final void setBoolean(long offset, boolean value) {
+    long requestedAddress = this.address + offset;
+    addressOutOfBoundsCheck(requestedAddress, 1, "setBoolean");
+    UnsafeMemoryAccessor.setBoolean(requestedAddress, value);
+  }
+
+  /**
+   * Sets the values in this buffer repeatedly
+   * @param offset - offset from the address
+   * @param length - number of bytes to set
+   * @param value  - value to be set
+   */
+  public final void setMemory(long offset, long length, byte value) {
+    addressOutOfBoundsCheck(address + offset, length, "set memory");
+    UnsafeMemoryAccessor.setMemory(address + offset, length, value);
+  }
+
+  final void copyFromMemory(long fromAddress, long len) {
+    addressOutOfBoundsCheck(address, len, "copy from memory");
+    UnsafeMemoryAccessor.copyMemory(null, fromAddress, null, address, len);
+  }
+
+  /**
+   * Copy data from this buffer to the given address.
+   * @param toAddress where the data should go
+   * @param len how much data to copy
+   */
+  final void copyToMemory(long toAddress, long len) {
+    addressOutOfBoundsCheck(address, len, "copy to memory");
+    UnsafeMemoryAccessor.copyMemory(null, address, null, toAddress, len);
+  }
+
+  /**
+   * Synchronously copy from a DeviceMemoryBuffer to a HostMemoryBuffer
+   * @param deviceMemoryBuffer buffer to copy data from
+   */
+  public final void copyFromDeviceBuffer(BaseDeviceMemoryBuffer deviceMemoryBuffer) {
+    addressOutOfBoundsCheck(address, deviceMemoryBuffer.length, "copy range dest");
+    assert !deviceMemoryBuffer.closed;
+    Cuda.memcpy(address, deviceMemoryBuffer.address, deviceMemoryBuffer.length,
+        CudaMemcpyKind.DEVICE_TO_HOST);
+  }
+
+  /**
+   * Copy from a DeviceMemoryBuffer to a HostMemoryBuffer using the specified stream.
+   * The copy has completed when this returns, but the memory copy could overlap with
+   * operations occurring on other streams.
+   * @param deviceMemoryBuffer buffer to copy data from
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromDeviceBuffer(BaseDeviceMemoryBuffer deviceMemoryBuffer,
+                                         Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address, deviceMemoryBuffer.length, "copy range dest");
+    assert !deviceMemoryBuffer.closed;
+    Cuda.memcpy(address, deviceMemoryBuffer.address, deviceMemoryBuffer.length,
+        CudaMemcpyKind.DEVICE_TO_HOST, stream);
+  }
+
+  /**
+   * Copy from a DeviceMemoryBuffer to a HostMemoryBuffer using the specified stream.
+   * The copy is async and may not have completed when this returns.
+   * @param deviceMemoryBuffer buffer to copy data from
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromDeviceBufferAsync(BaseDeviceMemoryBuffer deviceMemoryBuffer,
+                                              Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address, deviceMemoryBuffer.length, "copy range dest");
+    assert !deviceMemoryBuffer.closed;
+    Cuda.asyncMemcpy(address, deviceMemoryBuffer.address, deviceMemoryBuffer.length,
+        CudaMemcpyKind.DEVICE_TO_HOST, stream);
+  }
+
+  /**
+   * Slice off a part of the host buffer.
+   * @param offset where to start the slice at.
+   * @param len how many bytes to slice
+   * @return a host buffer that will need to be closed independently from this buffer.
+   */
+  @Override
+  public final synchronized HostMemoryBuffer slice(long offset, long len) {
+    addressOutOfBoundsCheck(address + offset, len, "slice");
+    refCount++;
+    cleaner.addRef();
+    return new HostMemoryBuffer(address + offset, len, this);
+  }
+
+  /**
+   * Slice off a part of the host buffer, actually making a copy of the data.
+   * @param offset where to start the slice at.
+   * @param len how many bytes to slice
+   * @return a host buffer that will need to be closed independently from this buffer.
+   */
+  public final HostMemoryBuffer sliceWithCopy(long offset, long len) {
+    addressOutOfBoundsCheck(address + offset, len, "slice");
+
+    HostMemoryBuffer ret = null;
+    boolean success = false;
+    try {
+      ret = allocate(len);
+      UnsafeMemoryAccessor.copyMemory(null, address + offset, null, ret.getAddress(), len);
+      success = true;
+      return ret;
+    } finally {
+      if (!success && ret != null) {
+        ret.close();
+      }
+    }
+  }
+
+  /**
+   * WARNING: Debug only method to print buffer. Does not work for buffers over 2GB.
+   */
+  public void printBuffer() {
+    printBuffer(5);
+  }
+
+  /**
+   * WARNING: Debug only method to print buffer.  Does not work for buffers over 2GB.
+   * @param wordsPerRow the number of 32 bit works to print per row.
+   */
+  public void printBuffer(int wordsPerRow) {
+    final int bytesPerWord = 4;
+    final int bytesPerRow = bytesPerWord * wordsPerRow;
+    assert (length == (int)length) : "The buffer is too large to be printed";
+    byte[] data = new byte[(int)length];
+    System.out.println("BUFFER length = " + data.length);
+    getBytes(data, 0, 0, length);
+    for (int i = 0; i < data.length; i++) {
+      if (i % bytesPerWord == 0) {
+        if (i % bytesPerRow == 0) {
+          System.out.println();
+        } else {
+          System.out.print(" ");
+        }
+      }
+      System.out.print(String.format("%02x",((long)data[i]) & 0xFF));
+    }
+    System.out.println();
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostMemoryBufferNativeUtils.java b/java/src/main/java/ai/rapids/cudf/HostMemoryBufferNativeUtils.java
new file mode 100644
index 0000000..616bf27
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostMemoryBufferNativeUtils.java
@@ -0,0 +1,57 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.io.IOException;
+import java.nio.ByteBuffer;
+
+/**
+ * Wrapper for {@link HostMemoryBuffer} native callbacks so that class avoids
+ * loading the native libraries unless one if its methods requires it.
+ */
+class HostMemoryBufferNativeUtils {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  /**
+   * This will turn an address into a ByteBuffer.  The buffer will NOT own the memory
+   * so closing it has no impact on the underlying memory. It should never
+   * be used if the corresponding HostMemoryBuffer is closed.
+   */
+  static native ByteBuffer wrapRangeInBuffer(long address, long len);
+
+  /**
+   * Memory map a portion of a local file
+   * @param file path to the local file to be mapped
+   * @param mode 0=read, 1=read+write
+   * @param offset file offset where map starts. Must be a system page boundary.
+   * @param len number of bytes to map
+   * @return address of the memory-mapped region
+   * @throws IOException I/O error during mapping
+   */
+  static native long mmap(String file, int mode, long offset, long len) throws IOException;
+
+  /**
+   * Unmap a memory region that was memory-mapped.
+   * @param address address of the memory-mapped region
+   * @param length size of the mapped region in bytes
+   */
+  static native void munmap(long address, long length);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/HostMemoryReservation.java b/java/src/main/java/ai/rapids/cudf/HostMemoryReservation.java
new file mode 100644
index 0000000..72c2e65
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/HostMemoryReservation.java
@@ -0,0 +1,32 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Represents some amount of host memory that has been reserved. A reservation guarantees that one
+ * or more allocations up to the reserved amount, minus padding for alignment will succeed. A
+ * reservation typically guarantees the amount can be allocated one, meaning when a buffer
+ * allocated from a reservation is freed it is not returned to the reservation, but to the pool of
+ * memory the reservation originally came from. If more memory is allocated from the reservation
+ * an OutOfMemoryError may be thrown, but it is not guaranteed to happen.
+ *
+ * When the reservation is closed any unused reservation will be returned to the pool of memory
+ * the reservation came from.
+ */
+public interface HostMemoryReservation extends HostMemoryAllocator, AutoCloseable {}
diff --git a/java/src/main/java/ai/rapids/cudf/JCudfSerialization.java b/java/src/main/java/ai/rapids/cudf/JCudfSerialization.java
new file mode 100644
index 0000000..7deb5ba
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/JCudfSerialization.java
@@ -0,0 +1,2007 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.io.BufferedOutputStream;
+import java.io.Closeable;
+import java.io.DataInputStream;
+import java.io.DataOutputStream;
+import java.io.EOFException;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.nio.ByteBuffer;
+import java.util.ArrayDeque;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+
+/**
+ * Serialize and deserialize CUDF tables and columns using a custom format.  The goal of this is
+ * to provide a way to efficiently serialize and deserialize cudf data for distributed
+ * processing within a single application. Typically after a partition like operation has happened.
+ * It is not intended for inter-application communication or for long term storage of data, there
+ * are much better standards based formats for all of that.
+ * <p>
+ * The goal is to transfer data from a local GPU to a remote GPU as quickly and efficiently as
+ * possible using build in java communication channels.  There is no guarantee of compatibility
+ * between different releases of CUDF.  This is to allow us to adapt if internal memory layouts
+ * and formats change.
+ * <p>
+ * This version optimizes for reduced memory transfers, and as such will try to do the fewest number
+ * of transfers possible when putting the data back onto the GPU.  This means that it will slice
+ * a single large memory buffer into smaller buffers used by the resulting ColumnVectors.  The
+ * downside of this is that generally none of the memory can be released until all of the
+ * ColumnVectors are closed.  It is assumed that this will not be a problem because for processing
+ * efficiency after the data is transferred it will likely be combined with other similar batches
+ * from other processes into a single larger buffer.
+ */
+public class JCudfSerialization {
+  /**
+   * Magic number "CUDF" in ASCII, which is 1178883395 if read in LE from big endian, which is
+   * too large for any reasonable metadata for arrow, so we should probably be okay detecting
+   * this, and switching back/forth at a later time.
+   */
+  private static final int SER_FORMAT_MAGIC_NUMBER = 0x43554446;
+  private static final short VERSION_NUMBER = 0x0000;
+
+  private static final class ColumnOffsets {
+    private final long validity;
+    private final long offsets;
+    private final long data;
+    private final long dataLen;
+
+    public ColumnOffsets(long validity, long offsets, long data, long dataLen) {
+      this.validity = validity;
+      this.offsets = offsets;
+      this.data = data;
+      this.dataLen = dataLen;
+    }
+  }
+
+  /**
+   * Holds the metadata about a serialized table. If this is being read from a stream
+   * isInitialized will return true if the metadata was read correctly from the stream.
+   * It will return false if an EOF was encountered at the beginning indicating that
+   * there was no data to be read.
+   */
+  public static final class SerializedTableHeader {
+    private SerializedColumnHeader[] columns;
+    private int numRows;
+    private long dataLen;
+
+    private boolean initialized = false;
+    private boolean dataRead = false;
+
+    public SerializedTableHeader(DataInputStream din) throws IOException {
+      readFrom(din);
+    }
+
+    SerializedTableHeader(SerializedColumnHeader[] columns, int numRows, long dataLen) {
+      this.columns = columns;
+      this.numRows = numRows;
+      this.dataLen = dataLen;
+      initialized = true;
+      dataRead = true;
+    }
+
+    /** Constructor for a row-count only table (no columns) */
+    public SerializedTableHeader(int numRows) {
+      this(new SerializedColumnHeader[0], numRows, 0);
+    }
+
+    /** Get the column header for the corresponding column index */
+    public SerializedColumnHeader getColumnHeader(int columnIndex) {
+      return columns[columnIndex];
+    }
+
+    /**
+     * Set to true once data is successfully read from a stream by readTableIntoBuffer.
+     * @return true if data was read, else false.
+     */
+    public boolean wasDataRead() {
+      return dataRead;
+    }
+
+    /**
+     * Returns the size of a buffer needed to read data into the stream.
+     */
+    public long getDataLen() {
+      return dataLen;
+    }
+
+    /**
+     * Returns the number of rows stored in this table.
+     */
+    public int getNumRows() {
+      return numRows;
+    }
+
+    /**
+     * Returns the number of columns stored in this table
+     */
+    public int getNumColumns() {
+      return columns != null ? columns.length : 0;
+    }
+
+    /**
+     * Returns true if the metadata for this table was read, else false indicating an EOF was
+     * encountered.
+     */
+    public boolean wasInitialized() {
+      return initialized;
+    }
+
+    /**
+     * Returns the number of bytes needed to serialize this table header.
+     * Note that this is only the metadata for the table (i.e.: column types, row counts, etc.)
+     * and does not include the bytes needed to serialize the table data.
+     */
+    public long getSerializedHeaderSizeInBytes() {
+      // table header always has:
+      // - 4-byte magic number
+      // - 2-byte version number
+      // - 4-byte column count
+      // - 4-byte row count
+      // - 8-byte data buffer length
+      long total = 4 + 2 + 4 + 4 + 8;
+      for (SerializedColumnHeader column : columns) {
+        total += column.getSerializedHeaderSizeInBytes();
+      }
+      return total;
+    }
+
+    /** Returns the number of bytes needed to serialize this table header and the table data. */
+    public long getTotalSerializedSizeInBytes() {
+      return getSerializedHeaderSizeInBytes() + dataLen;
+    }
+
+    private void readFrom(DataInputStream din) throws IOException {
+      try {
+        int num = din.readInt();
+        if (num != SER_FORMAT_MAGIC_NUMBER) {
+          throw new IllegalStateException("THIS DOES NOT LOOK LIKE CUDF SERIALIZED DATA. " +
+              "Expected magic number " + SER_FORMAT_MAGIC_NUMBER + " Found " + num);
+        }
+      } catch (EOFException e) {
+        // If we get an EOF at the very beginning don't treat it as an error because we may
+        // have finished reading everything...
+        return;
+      }
+      short version = din.readShort();
+      if (version != VERSION_NUMBER) {
+        throw new IllegalStateException("READING THE WRONG SERIALIZATION FORMAT VERSION FOUND "
+            + version + " EXPECTED " + VERSION_NUMBER);
+      }
+      int numColumns = din.readInt();
+      numRows = din.readInt();
+
+      columns = new SerializedColumnHeader[numColumns];
+      for (int i = 0; i < numColumns; i++) {
+        columns[i] = SerializedColumnHeader.readFrom(din, numRows);
+      }
+
+      dataLen = din.readLong();
+      initialized = true;
+    }
+
+    public void writeTo(DataWriter dout) throws IOException {
+      // Now write out the data
+      dout.writeInt(SER_FORMAT_MAGIC_NUMBER);
+      dout.writeShort(VERSION_NUMBER);
+      dout.writeInt(columns.length);
+      dout.writeInt(numRows);
+
+      // Header for each column...
+      for (SerializedColumnHeader column : columns) {
+        column.writeTo(dout);
+      }
+      dout.writeLong(dataLen);
+    }
+  }
+
+  /** Holds the metadata about a serialized column. */
+  public static final class SerializedColumnHeader {
+    public final DType dtype;
+    public final long nullCount;
+    public final long rowCount;
+    public final SerializedColumnHeader[] children;
+
+    SerializedColumnHeader(DType dtype, long rowCount, long nullCount,
+                           SerializedColumnHeader[] children) {
+      this.dtype = dtype;
+      this.rowCount = rowCount;
+      this.nullCount = nullCount;
+      this.children = children;
+    }
+
+    SerializedColumnHeader(ColumnBufferProvider column, long rowOffset, long numRows) {
+      this.dtype = column.getType();
+      this.rowCount = numRows;
+      long columnNullCount = column.getNullCount();
+      // For a subset of the original column we do not know the null count unless
+      // the original column is either all nulls or no nulls.
+      if (column.getRowCount() == numRows
+          || columnNullCount == 0 || columnNullCount == column.getRowCount()) {
+        this.nullCount = Math.min(columnNullCount, numRows);
+      } else {
+        this.nullCount = ColumnView.UNKNOWN_NULL_COUNT;
+      }
+      ColumnBufferProvider[] childProviders = column.getChildProviders();
+      if (childProviders != null) {
+        children = new SerializedColumnHeader[childProviders.length];
+        long childRowOffset = rowOffset;
+        long childNumRows = numRows;
+        if (dtype.equals(DType.LIST)) {
+          if (numRows > 0) {
+            childRowOffset = column.getOffset(rowOffset);
+            childNumRows = column.getOffset(rowOffset + numRows) - childRowOffset;
+          }
+        }
+        for (int i = 0; i < children.length; i++) {
+          children[i] = new SerializedColumnHeader(childProviders[i], childRowOffset, childNumRows);
+        }
+      } else {
+        children = null;
+      }
+    }
+
+    /** Get the data type of the column */
+    public DType getType() {
+      return dtype;
+    }
+
+    /** Get the row count of the column */
+    public long getRowCount() {
+      return rowCount;
+    }
+
+    /** Get the null count of the column */
+    public long getNullCount() {
+      return nullCount;
+    }
+
+    /** Get the metadata for any child columns or null if there are no children */
+    public SerializedColumnHeader[] getChildren() {
+      return children;
+    }
+
+    /** Get the number of child columns */
+    public int getNumChildren() {
+      return children != null ? children.length : 0;
+    }
+
+    /** Return the number of bytes needed to store this column header in serialized form. */
+    public long getSerializedHeaderSizeInBytes() {
+      // column header always has:
+      // - 4-byte type ID
+      // - 4-byte type scale
+      // - 4-byte null count
+      long total = 4 + 4 + 4;
+
+      if (dtype.isNestedType()) {
+        assert children != null;
+        if (dtype.equals(DType.LIST)) {
+          total += 4;  // 4-byte child row count
+        } else if (dtype.equals(DType.STRUCT)) {
+          total += 4;  // 4-byte child count
+        } else {
+          throw new IllegalStateException("Unexpected nested type: " + dtype);
+        }
+        for (SerializedColumnHeader child : children) {
+          total += child.getSerializedHeaderSizeInBytes();
+        }
+      }
+
+      return total;
+    }
+
+    /** Write this column header to the specified writer */
+    public void writeTo(DataWriter dout) throws IOException {
+      dout.writeInt(dtype.typeId.getNativeId());
+      dout.writeInt(dtype.getScale());
+      dout.writeInt((int) nullCount);
+      if (dtype.isNestedType()) {
+        assert children != null;
+        if (dtype.equals(DType.LIST)) {
+          dout.writeInt((int) children[0].getRowCount());
+        } else if (dtype.equals(DType.STRUCT)) {
+          dout.writeInt(getNumChildren());
+        } else {
+          throw new IllegalStateException("Unexpected nested type: " + dtype);
+        }
+        for (SerializedColumnHeader child : children) {
+          child.writeTo(dout);
+        }
+      }
+    }
+
+    static SerializedColumnHeader readFrom(DataInputStream din, long rowCount) throws IOException {
+      DType dtype = DType.fromNative(din.readInt(), din.readInt());
+      long nullCount = din.readInt();
+      SerializedColumnHeader[] children = null;
+      if (dtype.isNestedType()) {
+        int numChildren;
+        long childRowCount;
+        if (dtype.equals(DType.LIST)) {
+          numChildren = 1;
+          childRowCount = din.readInt();
+        } else if (dtype.equals(DType.STRUCT)) {
+          numChildren = din.readInt();
+          childRowCount = rowCount;
+        } else {
+          throw new IllegalStateException("Unexpected nested type: " + dtype);
+        }
+        children = new SerializedColumnHeader[numChildren];
+        for (int i = 0; i < numChildren; i++) {
+          children[i] = readFrom(din, childRowCount);
+        }
+      }
+      return new SerializedColumnHeader(dtype, rowCount, nullCount, children);
+    }
+  }
+
+  /** Class to hold the header and buffer pair result from host-side concatenation */
+  public static final class HostConcatResult implements AutoCloseable {
+    private final SerializedTableHeader tableHeader;
+    private final HostMemoryBuffer hostBuffer;
+
+    public HostConcatResult(SerializedTableHeader tableHeader, HostMemoryBuffer tableBuffer) {
+      this.tableHeader = tableHeader;
+      this.hostBuffer = tableBuffer;
+    }
+
+    public SerializedTableHeader getTableHeader() {
+      return tableHeader;
+    }
+
+    public HostMemoryBuffer getHostBuffer() {
+      return hostBuffer;
+    }
+
+    /** Build a contiguous table in device memory from this host-concatenated result */
+    public ContiguousTable toContiguousTable() {
+      DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(hostBuffer.length);
+      try {
+        if (hostBuffer.length > 0) {
+          devBuffer.copyFromHostBuffer(hostBuffer);
+        }
+        Table table = sliceUpColumnVectors(tableHeader, devBuffer, hostBuffer);
+        try {
+          return new ContiguousTable(table, devBuffer);
+        } catch (Exception e) {
+          table.close();
+          throw e;
+        }
+      } catch (Exception e) {
+        devBuffer.close();
+        throw e;
+      }
+    }
+
+    @Override
+    public void close() {
+      hostBuffer.close();
+    }
+  }
+
+  /**
+   * Visible for testing
+   */
+  static abstract class ColumnBufferProvider implements AutoCloseable {
+
+    public abstract DType getType();
+
+    public abstract long getNullCount();
+
+    public abstract long getOffset(long index);
+
+    public abstract long getRowCount();
+
+    public abstract HostMemoryBuffer getHostBufferFor(BufferType buffType);
+
+    public abstract long getBufferStartOffset(BufferType buffType);
+
+    public abstract ColumnBufferProvider[] getChildProviders();
+
+    @Override
+    public abstract void close();
+  }
+
+  /**
+   * Visible for testing
+   */
+  static class ColumnProvider extends ColumnBufferProvider {
+    private final HostColumnVectorCore column;
+    private final boolean closeAtEnd;
+    private final ColumnBufferProvider[] childProviders;
+
+    ColumnProvider(HostColumnVectorCore column, boolean closeAtEnd) {
+      this.column = column;
+      this.closeAtEnd = closeAtEnd;
+      if (getType().isNestedType()) {
+        int numChildren = column.getNumChildren();
+        childProviders = new ColumnBufferProvider[numChildren];
+        for (int i = 0; i < numChildren; i++) {
+          childProviders[i] = new ColumnProvider(column.getChildColumnView(i), false);
+        }
+      } else {
+        childProviders = null;
+      }
+    }
+
+    @Override
+    public DType getType() {
+      return column.getType();
+    }
+
+    @Override
+    public long getNullCount() {
+      return column.getNullCount();
+    }
+
+    @Override
+    public long getOffset(long index) {
+      return column.getOffsets().getInt(index * Integer.BYTES);
+    }
+
+    @Override
+    public long getRowCount() {
+      return column.getRowCount();
+    }
+
+    @Override
+    public HostMemoryBuffer getHostBufferFor(BufferType buffType) {
+      switch (buffType) {
+        case VALIDITY: return column.getValidity();
+        case OFFSET: return column.getOffsets();
+        case DATA: return column.getData();
+        default: throw new IllegalStateException("Unexpected buffer type: " + buffType);
+      }
+    }
+
+    @Override
+    public long getBufferStartOffset(BufferType buffType) {
+      // All of the buffers start at 0 for this.
+      return 0;
+    }
+
+    @Override
+    public ColumnBufferProvider[] getChildProviders() {
+      return childProviders;
+    }
+
+    @Override
+    public void close() {
+      if (closeAtEnd) {
+        column.close();
+      }
+    }
+  }
+
+  private static class BufferOffsetProvider extends ColumnBufferProvider {
+    private final SerializedColumnHeader header;
+    private final ColumnOffsets offsets;
+    private final HostMemoryBuffer buffer;
+    private final ColumnBufferProvider[] childProviders;
+
+    private BufferOffsetProvider(SerializedColumnHeader header,
+                                 ColumnOffsets offsets,
+                                 HostMemoryBuffer buffer,
+                                 ColumnBufferProvider[] childProviders) {
+      this.header = header;
+      this.offsets = offsets;
+      this.buffer = buffer;
+      this.childProviders = childProviders;
+    }
+
+    @Override
+    public DType getType() {
+      return header.getType();
+    }
+
+    @Override
+    public long getNullCount() {
+      return header.getNullCount();
+    }
+
+    @Override
+    public long getRowCount() {
+      return header.getRowCount();
+    }
+
+    @Override
+    public HostMemoryBuffer getHostBufferFor(BufferType buffType) {
+      return buffer;
+    }
+
+    @Override
+    public long getBufferStartOffset(BufferType buffType) {
+      switch (buffType) {
+        case DATA:
+          return offsets.data;
+        case OFFSET:
+          return offsets.offsets;
+        case VALIDITY:
+          return offsets.validity;
+        default:
+          throw new IllegalArgumentException("Buffer type " + buffType + " is not supported");
+      }
+    }
+
+    @Override
+    public long getOffset(long index) {
+      assert getType().hasOffsets();
+      assert (index >= 0 && index <= getRowCount()) : "index is out of range 0 <= " + index + " <= " + getRowCount();
+      return buffer.getInt(offsets.offsets + (index * Integer.BYTES));
+    }
+
+    @Override
+    public ColumnBufferProvider[] getChildProviders() {
+      return childProviders;
+    }
+
+    @Override
+    public void close() {
+      // NOOP
+    }
+  }
+
+  /**
+   * Visible for testing
+   */
+  static abstract class DataWriter {
+
+    public abstract void writeByte(byte b) throws IOException;
+
+    public abstract void writeShort(short s) throws IOException;
+
+    public abstract void writeInt(int i) throws IOException;
+
+    public abstract void writeIntNativeOrder(int i) throws IOException;
+
+    public abstract void writeLong(long val) throws IOException;
+
+    /**
+     * Copy data from src starting at srcOffset and going for len bytes.
+     * @param src where to copy from.
+     * @param srcOffset offset to start at.
+     * @param len amount to copy.
+     */
+    public abstract void copyDataFrom(HostMemoryBuffer src, long srcOffset, long len)
+        throws IOException;
+
+    public void copyDataFrom(ColumnBufferProvider column, BufferType buffType,
+                             long offset, long length) throws IOException {
+      HostMemoryBuffer buff = column.getHostBufferFor(buffType);
+      long startOffset = column.getBufferStartOffset(buffType);
+      copyDataFrom(buff, startOffset + offset, length);
+    }
+
+    public void flush() throws IOException {
+      // NOOP by default
+    }
+
+    public abstract void write(byte[] arr, int offset, int length) throws IOException;
+  }
+
+  /**
+   * Visible for testing
+   */
+  static final class DataOutputStreamWriter extends DataWriter {
+    private final byte[] arrayBuffer = new byte[1024 * 128];
+    private final DataOutputStream dout;
+
+    public DataOutputStreamWriter(DataOutputStream dout) {
+      this.dout = dout;
+    }
+
+    @Override
+    public void writeByte(byte b) throws IOException {
+      dout.writeByte(b);
+    }
+
+    @Override
+    public void writeShort(short s) throws IOException {
+      dout.writeShort(s);
+    }
+
+    @Override
+    public void writeInt(int i) throws IOException {
+      dout.writeInt(i);
+    }
+
+    @Override
+    public void writeIntNativeOrder(int i) throws IOException {
+      // TODO this only works on Little Endian Architectures, x86.  If we need
+      // to support others we need to detect the endianness and switch on the right implementation.
+      writeInt(Integer.reverseBytes(i));
+    }
+
+    @Override
+    public void writeLong(long val) throws IOException {
+      dout.writeLong(val);
+    }
+
+    @Override
+    public void copyDataFrom(HostMemoryBuffer src, long srcOffset, long len) throws IOException {
+      long dataLeft = len;
+      while (dataLeft > 0) {
+        int amountToCopy = (int)Math.min(arrayBuffer.length, dataLeft);
+        src.getBytes(arrayBuffer, 0, srcOffset, amountToCopy);
+        dout.write(arrayBuffer, 0, amountToCopy);
+        srcOffset += amountToCopy;
+        dataLeft -= amountToCopy;
+      }
+    }
+
+    @Override
+    public void flush() throws IOException {
+      dout.flush();
+    }
+
+    @Override
+    public void write(byte[] arr, int offset, int length) throws IOException {
+      dout.write(arr, offset, length);
+    }
+  }
+
+  private static final class HostDataWriter extends DataWriter {
+    private final HostMemoryBuffer buffer;
+    private long offset = 0;
+
+    public HostDataWriter(HostMemoryBuffer buffer) {
+      this.buffer = buffer;
+    }
+
+    @Override
+    public void writeByte(byte b) {
+      buffer.setByte(offset, b);
+      offset += 1;
+    }
+
+    @Override
+    public void writeShort(short s) {
+      buffer.setShort(offset, s);
+      offset += 2;
+    }
+
+    @Override
+    public void writeInt(int i) {
+      buffer.setInt(offset, i);
+      offset += 4;
+    }
+
+    @Override
+    public void writeIntNativeOrder(int i) {
+      // This is already in the native order...
+      writeInt(i);
+    }
+
+    @Override
+    public void writeLong(long val) {
+      buffer.setLong(offset, val);
+      offset += 8;
+    }
+
+    @Override
+    public void copyDataFrom(HostMemoryBuffer src, long srcOffset, long len) {
+      buffer.copyFromHostBuffer(offset, src, srcOffset, len);
+      offset += len;
+    }
+
+    @Override
+    public void write(byte[] arr, int srcOffset, int length) {
+      buffer.setBytes(offset, arr, srcOffset, length);
+      offset += length;
+    }
+  }
+
+  /////////////////////////////////////////////
+  // METHODS
+  /////////////////////////////////////////////
+
+
+  /////////////////////////////////////////////
+  // PADDING FOR ALIGNMENT
+  /////////////////////////////////////////////
+  private static long padFor64byteAlignment(long orig) {
+    return ((orig + 63) / 64) * 64;
+  }
+
+  private static long padFor64byteAlignment(DataWriter out, long bytes) throws IOException {
+    final long paddedBytes = padFor64byteAlignment(bytes);
+    while (paddedBytes > bytes) {
+      out.writeByte((byte)0);
+      bytes++;
+    }
+    return paddedBytes;
+  }
+
+  /////////////////////////////////////////////
+  // SERIALIZED SIZE
+  /////////////////////////////////////////////
+
+  private static long getRawStringDataLength(ColumnBufferProvider column, long rowOffset, long numRows) {
+    if (numRows <= 0) {
+      return 0;
+    }
+    long start = column.getOffset(rowOffset);
+    long end = column.getOffset(rowOffset + numRows);
+    return end - start;
+  }
+
+  private static long getSlicedSerializedDataSizeInBytes(ColumnBufferProvider[] columns, long rowOffset, long numRows) {
+    long totalDataSize = 0;
+    for (ColumnBufferProvider column: columns) {
+      totalDataSize += getSlicedSerializedDataSizeInBytes(column, rowOffset, numRows);
+    }
+    return totalDataSize;
+  }
+
+  private static long getSlicedSerializedDataSizeInBytes(ColumnBufferProvider column, long rowOffset, long numRows) {
+    long totalDataSize = 0;
+    DType type = column.getType();
+    if (needsValidityBuffer(column.getNullCount())) {
+      totalDataSize += padFor64byteAlignment(BitVectorHelper.getValidityLengthInBytes(numRows));
+    }
+
+    if (type.hasOffsets()) {
+      if (numRows > 0) {
+        // Add in size of offsets vector
+        totalDataSize += padFor64byteAlignment((numRows + 1) * Integer.BYTES);
+        if (type.equals(DType.STRING)) {
+          totalDataSize += padFor64byteAlignment(getRawStringDataLength(column, rowOffset, numRows));
+        }
+      }
+    } else if (type.getSizeInBytes() > 0) {
+      totalDataSize += padFor64byteAlignment(column.getType().getSizeInBytes() * numRows);
+    }
+
+    if (numRows > 0 && type.isNestedType()) {
+      if (type.equals(DType.LIST)) {
+        ColumnBufferProvider child = column.getChildProviders()[0];
+        long childStartRow = column.getOffset(rowOffset);
+        long childNumRows = column.getOffset(rowOffset + numRows) - childStartRow;
+        totalDataSize += getSlicedSerializedDataSizeInBytes(child, childStartRow, childNumRows);
+      } else if (type.equals(DType.STRUCT)) {
+        for (ColumnBufferProvider childProvider : column.getChildProviders()) {
+          totalDataSize += getSlicedSerializedDataSizeInBytes(childProvider, rowOffset, numRows);
+        }
+      } else {
+        throw new IllegalStateException("Unexpected nested type: " + type);
+      }
+    }
+    return totalDataSize;
+  }
+
+  /**
+   * Get the size in bytes needed to serialize the given data.  The columns should be in host memory
+   * before calling this.
+   * @param columns columns to be serialized.
+   * @param rowOffset the first row to serialize.
+   * @param numRows the number of rows to serialize.
+   * @return the size in bytes needed to serialize the data including the header.
+   */
+  public static long getSerializedSizeInBytes(HostColumnVector[] columns, long rowOffset, long numRows) {
+    ColumnBufferProvider[] providers = providersFrom(columns, false);
+    try {
+      SerializedColumnHeader[] columnHeaders = new SerializedColumnHeader[providers.length];
+      for (int i = 0; i < columnHeaders.length; i++) {
+        columnHeaders[i] = new SerializedColumnHeader(providers[i], rowOffset, numRows);
+      }
+      long dataLen = getSlicedSerializedDataSizeInBytes(providers, rowOffset, numRows);
+      SerializedTableHeader tableHeader = new SerializedTableHeader(columnHeaders,
+          (int) numRows, dataLen);
+      return tableHeader.getTotalSerializedSizeInBytes();
+    } finally {
+      closeAll(providers);
+    }
+  }
+
+  /////////////////////////////////////////////
+  // HELPER METHODS buildIndex
+  /////////////////////////////////////////////
+
+  /** Build a list of column offset descriptors using a pre-order traversal of the columns */
+  static ArrayDeque<ColumnOffsets> buildIndex(SerializedTableHeader header,
+                                              HostMemoryBuffer buffer) {
+    int numTopColumns = header.getNumColumns();
+    ArrayDeque<ColumnOffsets> offsetsList = new ArrayDeque<>();
+    long bufferOffset = 0;
+    for (int i = 0; i < numTopColumns; i++) {
+      SerializedColumnHeader column = header.getColumnHeader(i);
+      bufferOffset = buildIndex(column, buffer, offsetsList, bufferOffset);
+    }
+    return offsetsList;
+  }
+
+  /**
+   * Append a list of column offset descriptors using a pre-order traversal of the column
+   * @param column column offset descriptors will be built for this column and its child columns
+   * @param buffer host buffer backing the column data
+   * @param offsetsList list where column offset descriptors will be appended during traversal
+   * @param bufferOffset offset in the host buffer where the column data begins
+   * @return buffer offset at the end of this column's data including all child columns
+   */
+  private static long buildIndex(SerializedColumnHeader column, HostMemoryBuffer buffer,
+                                 ArrayDeque<ColumnOffsets> offsetsList, long bufferOffset) {
+    long validity = 0;
+    long offsets = 0;
+    long data = 0;
+    long dataLen = 0;
+    long rowCount = column.getRowCount();
+    if (needsValidityBuffer(column.getNullCount())) {
+      long validityLen = padFor64byteAlignment(BitVectorHelper.getValidityLengthInBytes(rowCount));
+      validity = bufferOffset;
+      bufferOffset += validityLen;
+    }
+
+    DType dtype = column.getType();
+    if (dtype.hasOffsets()) {
+      if (rowCount > 0) {
+        long offsetsLen = (rowCount + 1) * Integer.BYTES;
+        offsets = bufferOffset;
+        int startOffset = buffer.getInt(bufferOffset);
+        int endOffset = buffer.getInt(bufferOffset + (rowCount * Integer.BYTES));
+        bufferOffset += padFor64byteAlignment(offsetsLen);
+        if (dtype.equals(DType.STRING)) {
+          dataLen = endOffset - startOffset;
+          data = bufferOffset;
+          bufferOffset += padFor64byteAlignment(dataLen);
+        }
+      }
+    } else if (dtype.getSizeInBytes() > 0) {
+      dataLen = dtype.getSizeInBytes() * rowCount;
+      data = bufferOffset;
+      bufferOffset += padFor64byteAlignment(dataLen);
+    }
+    offsetsList.add(new ColumnOffsets(validity, offsets, data, dataLen));
+
+    SerializedColumnHeader[] children = column.getChildren();
+    if (children != null) {
+      for (SerializedColumnHeader child : children) {
+        bufferOffset = buildIndex(child, buffer, offsetsList, bufferOffset);
+      }
+    }
+
+    return bufferOffset;
+  }
+
+  /////////////////////////////////////////////
+  // HELPER METHODS FOR PROVIDERS
+  /////////////////////////////////////////////
+
+  private static void closeAll(ColumnBufferProvider[] providers) {
+    for (int i = 0; i < providers.length; i++) {
+      providers[i].close();
+    }
+  }
+
+  private static void closeAll(ColumnBufferProvider[][] providers) {
+    for (int i = 0; i < providers.length; i++) {
+      if (providers[i] != null) {
+        closeAll(providers[i]);
+      }
+    }
+  }
+
+  private static ColumnBufferProvider[] providersFrom(ColumnVector[] columns) {
+    HostColumnVector[] onHost = new HostColumnVector[columns.length];
+    boolean success = false;
+    try {
+      for (int i = 0; i < columns.length; i++) {
+        onHost[i] = columns[i].copyToHost();
+      }
+      ColumnBufferProvider[] ret = providersFrom(onHost, true);
+      success = true;
+      return ret;
+    } finally {
+      if (!success) {
+        for (int i = 0; i < onHost.length; i++) {
+          if (onHost[i] != null) {
+            onHost[i].close();
+            onHost[i] = null;
+          }
+        }
+      }
+    }
+  }
+
+  private static ColumnBufferProvider[] providersFrom(HostColumnVector[] columns, boolean closeAtEnd) {
+    ColumnBufferProvider[] providers = new ColumnBufferProvider[columns.length];
+    for (int i = 0; i < columns.length; i++) {
+      providers[i] = new ColumnProvider(columns[i], closeAtEnd);
+    }
+    return providers;
+  }
+
+  /**
+   * For a batch of tables described by a header and corresponding buffer, return a mapping of
+   * top column index to the corresponding column providers for that column across all tables.
+   */
+  private static ColumnBufferProvider[][] providersFrom(SerializedTableHeader[] headers,
+                                                        HostMemoryBuffer[] dataBuffers) {
+    int numColumns = 0;
+    int numTables = headers.length;
+    int numNonEmptyTables = 0;
+    ArrayList<ArrayList<ColumnBufferProvider>> providersPerColumn = null;
+    for (int tableIdx = 0; tableIdx < numTables; tableIdx++) {
+      SerializedTableHeader header = headers[tableIdx];
+      if (tableIdx == 0) {
+        numColumns = header.getNumColumns();
+        providersPerColumn = new ArrayList<>(numColumns);
+        for (int i = 0; i < numColumns; i++) {
+          providersPerColumn.add(new ArrayList<>(numTables));
+        }
+      } else {
+        checkCompatibleTypes(headers[0], header, tableIdx);
+      }
+      // filter out empty tables but keep at least one if all were empty
+      if (headers[tableIdx].getNumRows() > 0 ||
+          (numNonEmptyTables == 0 && tableIdx == numTables - 1)) {
+        numNonEmptyTables++;
+        HostMemoryBuffer dataBuffer = dataBuffers[tableIdx];
+        ArrayDeque<ColumnOffsets> offsets = buildIndex(header, dataBuffer);
+        for (int columnIdx = 0; columnIdx < numColumns; columnIdx++) {
+          ColumnBufferProvider provider = buildBufferOffsetProvider(
+              header.getColumnHeader(columnIdx), offsets, dataBuffer);
+          providersPerColumn.get(columnIdx).add(provider);
+        }
+        assert offsets.isEmpty();
+      } else {
+        assert headers[tableIdx].dataLen == 0;
+      }
+    }
+
+    ColumnBufferProvider[][] result = new ColumnBufferProvider[numColumns][];
+    for (int i = 0; i < numColumns; i++) {
+      result[i] = providersPerColumn.get(i).toArray(new ColumnBufferProvider[0]);
+    }
+    return result;
+  }
+
+  private static void checkCompatibleTypes(SerializedTableHeader expected,
+                                           SerializedTableHeader other,
+                                           int tableIdx) {
+    int numColumns = expected.getNumColumns();
+    if (other.getNumColumns() != numColumns) {
+      throw new IllegalArgumentException("The number of columns did not match " + tableIdx
+          + " " + other.getNumColumns() + " != " + numColumns);
+    }
+    for (int i = 0; i < numColumns; i++) {
+      checkCompatibleTypes(expected.getColumnHeader(i), other.getColumnHeader(i), tableIdx, i);
+    }
+  }
+
+  private static void checkCompatibleTypes(SerializedColumnHeader expected,
+                                           SerializedColumnHeader other,
+                                           int tableIdx, int columnIdx) {
+    DType dtype = expected.getType();
+    if (!dtype.equals(other.getType())) {
+      throw new IllegalArgumentException("Type mismatch at table " + tableIdx +
+          "column " + columnIdx + " expected " + dtype + " but found " + other.getType());
+    }
+    if (dtype.isNestedType()) {
+      SerializedColumnHeader[] expectedChildren = expected.getChildren();
+      SerializedColumnHeader[] otherChildren = other.getChildren();
+      if (expectedChildren.length != otherChildren.length) {
+        throw new IllegalArgumentException("Child count mismatch at table " + tableIdx +
+            "column " + columnIdx + " expected " + expectedChildren.length + " but found " +
+            otherChildren.length);
+      }
+      for (int i = 0; i < expectedChildren.length; i++) {
+        checkCompatibleTypes(expectedChildren[i], otherChildren[i], tableIdx, columnIdx);
+      }
+    }
+  }
+
+  private static BufferOffsetProvider buildBufferOffsetProvider(SerializedColumnHeader header,
+                                                                ArrayDeque<ColumnOffsets> offsets,
+                                                                HostMemoryBuffer dataBuffer) {
+    ColumnOffsets columnOffsets = offsets.remove();
+    ColumnBufferProvider[] childProviders = null;
+    SerializedColumnHeader[] children = header.getChildren();
+    if (children != null) {
+      childProviders = new ColumnBufferProvider[children.length];
+      for (int i = 0; i < children.length; i++) {
+        childProviders[i] = buildBufferOffsetProvider(children[i], offsets, dataBuffer);
+      }
+    }
+    return new BufferOffsetProvider(header, columnOffsets, dataBuffer, childProviders);
+  }
+
+  /////////////////////////////////////////////
+  // HELPER METHODS FOR SerializedTableHeader
+  /////////////////////////////////////////////
+
+  private static SerializedTableHeader calcHeader(ColumnBufferProvider[] columns,
+                                                  long rowOffset,
+                                                  int numRows) {
+    SerializedColumnHeader[] headers = new SerializedColumnHeader[columns.length];
+    for (int i = 0; i < headers.length; i++) {
+      headers[i] = new SerializedColumnHeader(columns[i], rowOffset, numRows);
+    }
+    long dataLength = getSlicedSerializedDataSizeInBytes(columns, rowOffset, numRows);
+    return new SerializedTableHeader(headers, numRows, dataLength);
+  }
+
+  /**
+   * Calculate the new header for a concatenated set of columns.
+   * @param providersPerColumn first index is the column, second index is the table.
+   * @return the new header.
+   */
+  private static SerializedTableHeader calcConcatHeader(ColumnBufferProvider[][] providersPerColumn) {
+    int numColumns = providersPerColumn.length;
+    long rowCount = 0;
+    long totalDataSize = 0;
+    ArrayList<SerializedColumnHeader> headers = new ArrayList<>(numColumns);
+    for (int columnIdx = 0; columnIdx < numColumns; columnIdx++) {
+      totalDataSize += calcConcatColumnHeaderAndSize(headers, providersPerColumn[columnIdx]);
+      if (columnIdx == 0) {
+        rowCount = headers.get(0).getRowCount();
+      } else {
+        assert rowCount == headers.get(columnIdx).getRowCount();
+      }
+    }
+    SerializedColumnHeader[] columnHeaders = headers.toArray(new SerializedColumnHeader[0]);
+    return new SerializedTableHeader(columnHeaders, (int)rowCount, totalDataSize);
+  }
+
+  /**
+   * Calculate a column header describing all of the columns concatenated together
+   * @param outHeaders list that will be appended with the new column header
+   * @param providers columns to be concatenated
+   * @return total bytes needed to store the data for the result column and its children
+   */
+  private static long calcConcatColumnHeaderAndSize(ArrayList<SerializedColumnHeader> outHeaders,
+                                                    ColumnBufferProvider[] providers) {
+    long totalSize = 0;
+    int numTables = providers.length;
+    long rowCount = 0;
+    long nullCount = 0;
+    for (ColumnBufferProvider provider : providers) {
+      rowCount += provider.getRowCount();
+      if (nullCount != ColumnView.UNKNOWN_NULL_COUNT) {
+        long providerNullCount = provider.getNullCount();
+        if (providerNullCount == ColumnView.UNKNOWN_NULL_COUNT) {
+          nullCount = ColumnView.UNKNOWN_NULL_COUNT;
+        } else {
+          nullCount += providerNullCount;
+        }
+      }
+    }
+
+    if (rowCount > Integer.MAX_VALUE) {
+      throw new IllegalArgumentException("Cannot build a batch larger than " + Integer.MAX_VALUE + " rows");
+    }
+
+    if (needsValidityBuffer(nullCount)) {
+      totalSize += padFor64byteAlignment(BitVectorHelper.getValidityLengthInBytes(rowCount));
+    }
+
+    ColumnBufferProvider firstProvider = providers[0];
+    DType dtype = firstProvider.getType();
+    if (dtype.hasOffsets()) {
+      if (rowCount > 0) {
+        totalSize += padFor64byteAlignment((rowCount + 1) * Integer.BYTES);
+        if (dtype.equals(DType.STRING)) {
+          long stringDataLen = 0;
+          for (ColumnBufferProvider provider : providers) {
+            stringDataLen += getRawStringDataLength(provider, 0, provider.getRowCount());
+          }
+          totalSize += padFor64byteAlignment(stringDataLen);
+        }
+      }
+    } else if (dtype.getSizeInBytes() > 0) {
+      totalSize += padFor64byteAlignment(dtype.getSizeInBytes() * rowCount);
+    }
+
+    SerializedColumnHeader[] children = null;
+    if (dtype.isNestedType()) {
+      int numChildren = firstProvider.getChildProviders().length;
+      ArrayList<SerializedColumnHeader> childHeaders = new ArrayList<>(numChildren);
+      ColumnBufferProvider[] childColumnProviders = new ColumnBufferProvider[numTables];
+      for (int childIdx = 0; childIdx < numChildren; childIdx++) {
+        // collect all the providers for the current child and build the child's header
+        for (int tableIdx = 0; tableIdx < numTables; tableIdx++) {
+          childColumnProviders[tableIdx] = providers[tableIdx].getChildProviders()[childIdx];
+        }
+        totalSize += calcConcatColumnHeaderAndSize(childHeaders, childColumnProviders);
+      }
+      children = childHeaders.toArray(new SerializedColumnHeader[0]);
+    }
+
+    outHeaders.add(new SerializedColumnHeader(dtype, rowCount, nullCount, children));
+    return totalSize;
+  }
+
+  /////////////////////////////////////////////
+  // HELPER METHODS FOR DataWriters
+  /////////////////////////////////////////////
+
+  private static DataWriter writerFrom(OutputStream out) {
+    if (!(out instanceof DataOutputStream)) {
+      out = new DataOutputStream(new BufferedOutputStream(out));
+    }
+    return new DataOutputStreamWriter((DataOutputStream) out);
+  }
+
+  private static DataWriter writerFrom(HostMemoryBuffer buffer) {
+    return new HostDataWriter(buffer);
+  }
+
+  /////////////////////////////////////////////
+  // Serialize Data Methods
+  /////////////////////////////////////////////
+
+  private static long copySlicedAndPad(DataWriter out,
+                                       ColumnBufferProvider column,
+                                       BufferType buffer,
+                                       long offset,
+                                       long length) throws IOException {
+    out.copyDataFrom(column, buffer, offset, length);
+    return padFor64byteAlignment(out, length);
+  }
+
+  /////////////////////////////////////////////
+  // VALIDITY
+  /////////////////////////////////////////////
+
+  private static boolean needsValidityBuffer(long nullCount) {
+    return nullCount > 0 || nullCount == ColumnView.UNKNOWN_NULL_COUNT;
+  }
+
+  private static int copyPartialValidity(byte[] dest,
+                                         int destBitOffset,
+                                         ColumnBufferProvider provider,
+                                         int srcBitOffset,
+                                         int lengthBits) {
+    HostMemoryBuffer src = provider.getHostBufferFor(BufferType.VALIDITY);
+    long baseSrcByteOffset = provider.getBufferStartOffset(BufferType.VALIDITY);
+
+    int destStartBytes = destBitOffset / 8;
+    int destStartBitOffset = destBitOffset % 8;
+    long srcStartBytes = baseSrcByteOffset + (srcBitOffset / 8);
+    int srcStartBitOffset = srcBitOffset % 8;
+    int availableDestBits = (dest.length * 8) - destBitOffset;
+    int bitsToCopy = Math.min(lengthBits, availableDestBits);
+
+    int lastIndex = (bitsToCopy + destStartBitOffset + 7) / 8;
+
+    byte allBitsSet = ~0;
+    byte firstSrcMask = (byte)(allBitsSet << destStartBitOffset);
+
+    int srcShift = destStartBitOffset - srcStartBitOffset;
+    if (srcShift > 0) {
+      // Shift left. If we are going to shift this is the path typically taken.
+
+      byte current = src.getByte(srcStartBytes);
+      byte result = (byte)(current << srcShift);
+      // The first time we need to include any data already in dest.
+      result |= dest[destStartBytes] & ~firstSrcMask;
+      dest[destStartBytes] = result;
+
+      // Keep the previous bytes around so we don't have to keep reading from src, which is not free
+      byte previous = current;
+
+      for (int index = 1; index < lastIndex; index++) {
+        current = src.getByte(index + srcStartBytes);
+        result = (byte)(current << srcShift);
+        result |= (previous & 0xFF) >>> (8 - srcShift);
+        dest[index + destStartBytes] = result;
+        previous = current;
+      }
+      return bitsToCopy;
+    } else if (srcShift < 0) {
+      srcShift = -srcShift;
+
+      // shifting right only happens when the buffer runs out of space.
+
+      byte result = src.getByte(srcStartBytes);
+      result = (byte)((result & 0xFF) >>> srcShift);
+      byte next = 0;
+      if (srcStartBytes + 1 < src.length) {
+        next = src.getByte(srcStartBytes + 1);
+      }
+      result |= (byte)(next << 8 - srcShift);
+      result &= firstSrcMask;
+
+      // The first time through we need to include the data already in dest.
+      result |= dest[destStartBytes] & ~firstSrcMask;
+      dest[destStartBytes] = result;
+
+      for (int index = 1; index < lastIndex - 1; index++) {
+        result = next;
+        result = (byte)((result & 0xFF) >>> srcShift);
+        next = src.getByte(srcStartBytes + index + 1);
+        result |= (byte)(next << 8 - srcShift);
+        dest[index + destStartBytes] = result;
+      }
+
+      int idx = lastIndex - 1;
+      if (idx > 0) {
+        result = next;
+        result = (byte) ((result & 0xFF) >>> srcShift);
+        next = 0;
+        if (srcStartBytes + idx + 1 < src.length) {
+          next = src.getByte(srcStartBytes + idx + 1);
+        }
+        result |= (byte) (next << 8 - srcShift);
+        dest[idx + destStartBytes] = result;
+      }
+      return bitsToCopy;
+    } else {
+      src.getBytes(dest, destStartBytes, srcStartBytes, (bitsToCopy + 7) / 8);
+      return bitsToCopy;
+    }
+  }
+
+  // package-private for testing
+  static long copySlicedValidity(DataWriter out,
+                                 ColumnBufferProvider column,
+                                 long rowOffset,
+                                 long numRows) throws IOException {
+    long validityLen = BitVectorHelper.getValidityLengthInBytes(numRows);
+    long byteOffset = (rowOffset / 8);
+    long bytesLeft = validityLen;
+
+    int lshift = (int) rowOffset % 8;
+    if (lshift == 0) {
+      out.copyDataFrom(column, BufferType.VALIDITY, byteOffset, bytesLeft);
+    } else {
+      byte[] arrayBuffer = new byte[128 * 1024];
+      int rowsStoredInArray = 0;
+      int rowsLeftInBatch = (int) numRows;
+      int validityBitOffset = (int) rowOffset;
+      while(rowsLeftInBatch > 0) {
+        int rowsStoredJustNow = copyPartialValidity(arrayBuffer, rowsStoredInArray, column, validityBitOffset, rowsLeftInBatch);
+        assert rowsStoredJustNow > 0;
+        rowsLeftInBatch -= rowsStoredJustNow;
+        rowsStoredInArray += rowsStoredJustNow;
+        validityBitOffset += rowsStoredJustNow;
+        if (rowsStoredInArray == arrayBuffer.length * 8) {
+          out.write(arrayBuffer, 0, arrayBuffer.length);
+          rowsStoredInArray = 0;
+        }
+      }
+      if (rowsStoredInArray > 0) {
+        out.write(arrayBuffer, 0, (rowsStoredInArray + 7) / 8);
+      }
+    }
+    return padFor64byteAlignment(out, validityLen);
+  }
+
+  // Package private for testing
+  static int fillValidity(byte[] dest, int destBitOffset, int lengthBits) {
+    int destStartBytes = destBitOffset / 8;
+    int destStartBits = destBitOffset % 8;
+
+    long lengthBytes = BitVectorHelper.getValidityLengthInBytes(lengthBits);
+    int rshift = destStartBits;
+    int totalCopied = 0;
+    if (rshift != 0) {
+      // Fill in what we need to make it copyable
+      dest[destStartBytes] |= (0xFF << destStartBits);
+      destStartBytes += 1;
+      totalCopied = (8 - destStartBits);
+      // Not used again, but just to be safe
+      destStartBits = 0;
+    }
+    int amountToCopyBytes = (int) Math.min(lengthBytes, dest.length - destStartBytes);
+    for (int i = 0; i < amountToCopyBytes; i++) {
+      dest[i + destStartBytes] = (byte) 0xFF;
+    }
+    totalCopied += amountToCopyBytes * 8;
+    return Math.min(totalCopied, lengthBits);
+  }
+
+  private static long concatValidity(DataWriter out, long numRows,
+                                     ColumnBufferProvider[] providers) throws IOException {
+    long validityLen = BitVectorHelper.getValidityLengthInBytes(numRows);
+    byte[] arrayBuffer = new byte[128 * 1024];
+    int rowsStoredInArray = 0;
+    for (ColumnBufferProvider provider : providers) {
+      int rowsLeftInBatch = (int) provider.getRowCount();
+      int validityBitOffset = 0;
+      while(rowsLeftInBatch > 0) {
+        int rowsStoredJustNow;
+        if (needsValidityBuffer(provider.getNullCount())) {
+          rowsStoredJustNow = copyPartialValidity(arrayBuffer, rowsStoredInArray, provider, validityBitOffset, rowsLeftInBatch);
+        } else {
+          rowsStoredJustNow = fillValidity(arrayBuffer, rowsStoredInArray, rowsLeftInBatch);
+        }
+        assert rowsStoredJustNow > 0;
+        assert rowsStoredJustNow <= rowsLeftInBatch;
+        rowsLeftInBatch -= rowsStoredJustNow;
+        rowsStoredInArray += rowsStoredJustNow;
+        validityBitOffset += rowsStoredJustNow;
+        if (rowsStoredInArray == arrayBuffer.length * 8) {
+          out.write(arrayBuffer, 0, arrayBuffer.length);
+          rowsStoredInArray = 0;
+        }
+      }
+    }
+
+    if (rowsStoredInArray > 0) {
+      int len = (rowsStoredInArray + 7) / 8;
+      out.write(arrayBuffer, 0, len);
+    }
+    return padFor64byteAlignment(out, validityLen);
+  }
+
+  /////////////////////////////////////////////
+  // STRING
+  /////////////////////////////////////////////
+
+  private static long copySlicedStringData(DataWriter out, ColumnBufferProvider column, long rowOffset,
+                                           long numRows) throws IOException {
+    if (numRows > 0) {
+      long startByteOffset = column.getOffset(rowOffset);
+      long endByteOffset = column.getOffset(rowOffset + numRows);
+      long bytesToCopy = endByteOffset - startByteOffset;
+      long srcOffset = startByteOffset;
+      return copySlicedAndPad(out, column, BufferType.DATA, srcOffset, bytesToCopy);
+    }
+    return 0;
+  }
+
+  private static void copyConcatStringData(DataWriter out,
+                                           ColumnBufferProvider[] providers) throws IOException {
+    long totalCopied = 0;
+
+    for (ColumnBufferProvider provider : providers) {
+      long rowCount = provider.getRowCount();
+      if (rowCount > 0) {
+        HostMemoryBuffer dataBuffer = provider.getHostBufferFor(BufferType.DATA);
+        long currentOffset = provider.getBufferStartOffset(BufferType.DATA);
+        long dataLeft = provider.getOffset(rowCount);
+        out.copyDataFrom(dataBuffer, currentOffset, dataLeft);
+        totalCopied += dataLeft;
+      }
+    }
+    padFor64byteAlignment(out, totalCopied);
+  }
+
+  private static long copySlicedOffsets(DataWriter out, ColumnBufferProvider column, long rowOffset,
+                                        long numRows) throws IOException {
+    if (numRows <= 0) {
+      // Don't copy anything, there are no rows
+      return 0;
+    }
+    long bytesToCopy = (numRows + 1) * Integer.BYTES;
+    long srcOffset = rowOffset * Integer.BYTES;
+    if (rowOffset == 0) {
+      return copySlicedAndPad(out, column, BufferType.OFFSET, srcOffset, bytesToCopy);
+    }
+    HostMemoryBuffer buff = column.getHostBufferFor(BufferType.OFFSET);
+    long startOffset = column.getBufferStartOffset(BufferType.OFFSET) + srcOffset;
+    if (bytesToCopy >= Integer.MAX_VALUE) {
+      throw new IllegalStateException("Copy is too large, need to do chunked copy");
+    }
+    ByteBuffer bb = buff.asByteBuffer(startOffset, (int)bytesToCopy);
+    int start = bb.getInt();
+    out.writeIntNativeOrder(0);
+    long total = Integer.BYTES;
+    for (int i = 1; i < (numRows + 1); i++) {
+      int offset = bb.getInt();
+      out.writeIntNativeOrder(offset - start);
+      total += Integer.BYTES;
+    }
+    assert total == bytesToCopy;
+    long ret = padFor64byteAlignment(out, total);
+    return ret;
+  }
+
+  private static void copyConcatOffsets(DataWriter out,
+                                        ColumnBufferProvider[] providers) throws IOException {
+    long totalCopied = 0;
+    int offsetToAdd = 0;
+    for (ColumnBufferProvider provider : providers) {
+      long rowCount = provider.getRowCount();
+      if (rowCount > 0) {
+        HostMemoryBuffer offsetsBuffer = provider.getHostBufferFor(BufferType.OFFSET);
+        long currentOffset = provider.getBufferStartOffset(BufferType.OFFSET);
+        if (totalCopied == 0) {
+          // first chunk of offsets can be copied verbatim
+          totalCopied = (rowCount + 1) * Integer.BYTES;
+          out.copyDataFrom(offsetsBuffer, currentOffset, totalCopied);
+          offsetToAdd = offsetsBuffer.getInt(currentOffset + (rowCount * Integer.BYTES));
+        } else {
+          int localOffset = 0;
+          // first row's offset has already been written when processing the previous table
+          for (int row = 1; row < rowCount + 1; row++) {
+            localOffset = offsetsBuffer.getInt(currentOffset + (row * Integer.BYTES));
+            out.writeIntNativeOrder(localOffset + offsetToAdd);
+          }
+          // last local offset of this chunk is the length of data referenced by offsets
+          offsetToAdd += localOffset;
+          totalCopied += rowCount * Integer.BYTES;
+        }
+      }
+    }
+    padFor64byteAlignment(out, totalCopied);
+  }
+
+  /////////////////////////////////////////////
+  // BASIC DATA
+  /////////////////////////////////////////////
+
+  private static long sliceBasicData(DataWriter out,
+                                     ColumnBufferProvider column,
+                                     long rowOffset,
+                                     long numRows) throws IOException {
+    DType type = column.getType();
+    long bytesToCopy = numRows * type.getSizeInBytes();
+    long srcOffset = rowOffset * type.getSizeInBytes();
+    return copySlicedAndPad(out, column, BufferType.DATA, srcOffset, bytesToCopy);
+  }
+
+  private static void concatBasicData(DataWriter out,
+                                      DType type,
+                                      ColumnBufferProvider[] providers) throws IOException {
+    long totalCopied = 0;
+    for (ColumnBufferProvider provider : providers) {
+      long rowCount = provider.getRowCount();
+      if (rowCount > 0) {
+        HostMemoryBuffer dataBuffer = provider.getHostBufferFor(BufferType.DATA);
+        long currentOffset = provider.getBufferStartOffset(BufferType.DATA);
+        long dataLeft = rowCount * type.getSizeInBytes();
+        out.copyDataFrom(dataBuffer, currentOffset, dataLeft);
+        totalCopied += dataLeft;
+      }
+    }
+    padFor64byteAlignment(out, totalCopied);
+  }
+
+  /////////////////////////////////////////////
+  // COLUMN AND TABLE WRITE
+  /////////////////////////////////////////////
+
+  private static void writeConcat(DataWriter out, SerializedColumnHeader header,
+                                  ColumnBufferProvider[] providers) throws IOException {
+    if (needsValidityBuffer(header.getNullCount())) {
+      concatValidity(out, header.getRowCount(), providers);
+    }
+
+    DType dtype = header.getType();
+    if (dtype.hasOffsets()) {
+      if (header.getRowCount() > 0) {
+        copyConcatOffsets(out, providers);
+        if (dtype.equals(DType.STRING)) {
+          copyConcatStringData(out, providers);
+        }
+      }
+    } else if (dtype.getSizeInBytes() > 0) {
+      concatBasicData(out, dtype, providers);
+    }
+
+    if (dtype.isNestedType()) {
+      int numTables = providers.length;
+      SerializedColumnHeader[] childHeaders = header.getChildren();
+      ColumnBufferProvider[] childColumnProviders = new ColumnBufferProvider[numTables];
+      for (int childIdx = 0; childIdx < childHeaders.length; childIdx++) {
+        // collect all the providers for the current child column
+        for (int tableIdx = 0; tableIdx < numTables; tableIdx++) {
+          childColumnProviders[tableIdx] = providers[tableIdx].getChildProviders()[childIdx];
+        }
+        writeConcat(out, childHeaders[childIdx], childColumnProviders);
+      }
+    }
+  }
+
+  private static void writeSliced(DataWriter out,
+                                  ColumnBufferProvider column,
+                                  long rowOffset,
+                                  long numRows) throws IOException {
+    if (needsValidityBuffer(column.getNullCount())) {
+      try (NvtxRange range = new NvtxRange("Write Validity", NvtxColor.DARK_GREEN)) {
+        copySlicedValidity(out, column, rowOffset, numRows);
+      }
+    }
+
+    DType type = column.getType();
+    if (type.hasOffsets()) {
+      if (numRows > 0) {
+        try (NvtxRange offsetRange = new NvtxRange("Write Offset Data", NvtxColor.ORANGE)) {
+          copySlicedOffsets(out, column, rowOffset, numRows);
+          if (type.equals(DType.STRING)) {
+            try (NvtxRange dataRange = new NvtxRange("Write String Data", NvtxColor.RED)) {
+              copySlicedStringData(out, column, rowOffset, numRows);
+            }
+          }
+        }
+      }
+    } else if (type.getSizeInBytes() > 0){
+      try (NvtxRange range = new NvtxRange("Write Data", NvtxColor.BLUE)) {
+        sliceBasicData(out, column, rowOffset, numRows);
+      }
+    }
+
+    if (numRows > 0 && type.isNestedType()) {
+      if (type.equals(DType.LIST)) {
+        try (NvtxRange range = new NvtxRange("Write List Child", NvtxColor.PURPLE)) {
+          ColumnBufferProvider child = column.getChildProviders()[0];
+          long childStartRow = column.getOffset(rowOffset);
+          long childNumRows = column.getOffset(rowOffset + numRows) - childStartRow;
+          writeSliced(out, child, childStartRow, childNumRows);
+        }
+      } else if (type.equals(DType.STRUCT)) {
+        try (NvtxRange range = new NvtxRange("Write Struct Children", NvtxColor.PURPLE)) {
+          for (ColumnBufferProvider child : column.getChildProviders()) {
+            writeSliced(out, child, rowOffset, numRows);
+          }
+        }
+      } else {
+        throw new IllegalStateException("Unexpected nested type: " + type);
+      }
+    }
+  }
+
+  private static void writeSliced(ColumnBufferProvider[] columns,
+                                  DataWriter out,
+                                  long rowOffset,
+                                  long numRows) throws IOException {
+    assert rowOffset >= 0;
+    assert numRows >= 0;
+    for (int i = 0; i < columns.length; i++) {
+      long rows = columns[i].getRowCount();
+      assert rowOffset + numRows <= rows;
+      assert rows == (int) rows : "can only support an int for indexes";
+    }
+
+    SerializedTableHeader header = calcHeader(columns, rowOffset, (int) numRows);
+    header.writeTo(out);
+
+    try (NvtxRange range = new NvtxRange("Write Sliced", NvtxColor.GREEN)) {
+      for (int i = 0; i < columns.length; i++) {
+        writeSliced(out, columns[i], rowOffset, numRows);
+      }
+    }
+    out.flush();
+  }
+
+  /**
+   * Write all or part of a table out in an internal format.
+   * @param t the table to be written.
+   * @param out the stream to write the serialized table out to.
+   * @param rowOffset the first row to write out.
+   * @param numRows the number of rows to write out.
+   */
+  public static void writeToStream(Table t, OutputStream out, long rowOffset, long numRows)
+      throws IOException {
+    writeToStream(t.getColumns(), out, rowOffset, numRows);
+  }
+
+  /**
+   * Write all or part of a set of columns out in an internal format.
+   * @param columns the columns to be written.
+   * @param out the stream to write the serialized table out to.
+   * @param rowOffset the first row to write out.
+   * @param numRows the number of rows to write out.
+   */
+  public static void writeToStream(ColumnVector[] columns, OutputStream out, long rowOffset,
+                                   long numRows) throws IOException {
+
+    ColumnBufferProvider[] providers = providersFrom(columns);
+    try {
+      DataWriter writer = writerFrom(out);
+      writeSliced(providers, writer, rowOffset, numRows);
+    } finally {
+      closeAll(providers);
+    }
+  }
+
+  /**
+   * Write all or part of a set of columns out in an internal format.
+   * @param columns the columns to be written.
+   * @param out the stream to write the serialized table out to.
+   * @param rowOffset the first row to write out.
+   * @param numRows the number of rows to write out.
+   */
+  public static void writeToStream(HostColumnVector[] columns, OutputStream out, long rowOffset,
+                                   long numRows) throws IOException {
+
+    ColumnBufferProvider[] providers = providersFrom(columns, false);
+    try {
+      DataWriter writer = writerFrom(out);
+      writeSliced(providers, writer, rowOffset, numRows);
+    } finally {
+      closeAll(providers);
+    }
+  }
+
+  /**
+   * Write a rowcount only header to the output stream in a case
+   * where a columnar batch with no columns but a non zero row count is received
+   * @param out the stream to write the serialized table out to.
+   * @param numRows the number of rows to write out.
+   */
+  public static void writeRowsToStream(OutputStream out, long numRows) throws IOException {
+    DataWriter writer = writerFrom(out);
+    SerializedTableHeader header = new SerializedTableHeader((int) numRows);
+    header.writeTo(writer);
+    writer.flush();
+  }
+
+  /**
+   * Take the data from multiple batches stored in the parsed headers and the dataBuffer and write
+   * it out to out as if it were a single buffer.
+   * @param headers the headers parsed from multiple streams.
+   * @param dataBuffers an array of buffers that hold the data, one per header.
+   * @param out what to write the data out to.
+   * @throws IOException on any error.
+   */
+  public static void writeConcatedStream(SerializedTableHeader[] headers,
+                                         HostMemoryBuffer[] dataBuffers,
+                                         OutputStream out) throws IOException {
+    ColumnBufferProvider[][] providersPerColumn = providersFrom(headers, dataBuffers);
+    try {
+      SerializedTableHeader combined = calcConcatHeader(providersPerColumn);
+      DataWriter writer = writerFrom(out);
+      combined.writeTo(writer);
+      try (NvtxRange range = new NvtxRange("Concat Host Side", NvtxColor.GREEN)) {
+        int numColumns = combined.getNumColumns();
+        for (int columnIdx = 0; columnIdx < numColumns; columnIdx++) {
+          ColumnBufferProvider[] providers = providersPerColumn[columnIdx];
+          writeConcat(writer, combined.getColumnHeader(columnIdx), providersPerColumn[columnIdx]);
+        }
+      }
+      writer.flush();
+    } finally {
+      closeAll(providersPerColumn);
+    }
+  }
+
+  /////////////////////////////////////////////
+  // COLUMN AND TABLE READ
+  /////////////////////////////////////////////
+
+  private static HostColumnVectorCore buildHostColumn(SerializedColumnHeader column,
+                                                      ArrayDeque<ColumnOffsets> columnOffsets,
+                                                      HostMemoryBuffer buffer,
+                                                      boolean isRootColumn) {
+    ColumnOffsets offsetsInfo = columnOffsets.remove();
+    SerializedColumnHeader[] children = column.getChildren();
+    int numChildren = children != null ? children.length : 0;
+    List<HostColumnVectorCore> childColumns = new ArrayList<>(numChildren);
+    try {
+      if (children != null) {
+        for (SerializedColumnHeader child : children) {
+          childColumns.add(buildHostColumn(child, columnOffsets, buffer, false));
+        }
+      }
+      DType dtype = column.getType();
+      long rowCount = column.getRowCount();
+      long nullCount = column.getNullCount();
+      HostMemoryBuffer dataBuffer = null;
+      HostMemoryBuffer validityBuffer = null;
+      HostMemoryBuffer offsetsBuffer = null;
+      if (!dtype.isNestedType()) {
+        dataBuffer = buffer.slice(offsetsInfo.data, offsetsInfo.dataLen);
+      }
+      if (needsValidityBuffer(nullCount)) {
+        long validitySize = BitVectorHelper.getValidityLengthInBytes(rowCount);
+        validityBuffer = buffer.slice(offsetsInfo.validity, validitySize);
+      }
+      if (dtype.hasOffsets()) {
+        // one 32-bit integer offset per row plus one additional offset at the end
+        long offsetsSize = rowCount > 0 ? (rowCount + 1) * Integer.BYTES : 0;
+        offsetsBuffer = buffer.slice(offsetsInfo.offsets, offsetsSize);
+      }
+      HostColumnVectorCore result;
+      // Only creates HostColumnVector for root columns, since child columns are managed by their parents.
+      if (isRootColumn) {
+        result = new HostColumnVector(dtype, rowCount,
+            Optional.of(nullCount), dataBuffer, validityBuffer, offsetsBuffer,
+            childColumns);
+      } else {
+        result = new HostColumnVectorCore(dtype, rowCount,
+            Optional.of(nullCount), dataBuffer, validityBuffer, offsetsBuffer,
+            childColumns);
+      }
+      childColumns = null;
+      return result;
+    } finally {
+      if (childColumns != null) {
+        for (HostColumnVectorCore c : childColumns) {
+          c.close();
+        }
+      }
+    }
+  }
+
+  private static long buildColumnView(SerializedColumnHeader column,
+                                      ArrayDeque<ColumnOffsets> columnOffsets,
+                                      DeviceMemoryBuffer combinedBuffer) {
+    ColumnOffsets offsetsInfo = columnOffsets.remove();
+    long[] childViews = null;
+    try {
+      SerializedColumnHeader[] children = column.getChildren();
+      if (children != null) {
+        childViews = new long[children.length];
+        for (int i = 0; i < childViews.length; i++) {
+          childViews[i] = buildColumnView(children[i], columnOffsets, combinedBuffer);
+        }
+      }
+      DType dtype = column.getType();
+      long bufferAddress = combinedBuffer.getAddress();
+      long dataAddress = dtype.isNestedType() ? 0 : bufferAddress + offsetsInfo.data;
+      long validityAddress = needsValidityBuffer(column.getNullCount())
+          ? bufferAddress + offsetsInfo.validity : 0;
+      long offsetsAddress = dtype.hasOffsets() ? bufferAddress + offsetsInfo.offsets : 0;
+      return ColumnView.makeCudfColumnView(
+          dtype.typeId.getNativeId(), dtype.getScale(),
+          dataAddress, offsetsInfo.dataLen,
+          offsetsAddress, validityAddress,
+          (int) column.getNullCount(), (int) column.getRowCount(),
+          childViews);
+    } finally {
+      if (childViews != null) {
+        for (long childView : childViews) {
+          ColumnView.deleteColumnView(childView);
+        }
+      }
+    }
+  }
+
+  private static Table sliceUpColumnVectors(SerializedTableHeader header,
+                                            DeviceMemoryBuffer combinedBuffer,
+                                            HostMemoryBuffer combinedBufferOnHost) {
+    try (NvtxRange range = new NvtxRange("bufferToTable", NvtxColor.PURPLE)) {
+      ArrayDeque<ColumnOffsets> columnOffsets = buildIndex(header, combinedBufferOnHost);
+      int numColumns = header.getNumColumns();
+      ColumnVector[] vectors = new ColumnVector[numColumns];
+      try {
+        for (int i = 0; i < numColumns; i++) {
+          SerializedColumnHeader column = header.getColumnHeader(i);
+          long columnView = buildColumnView(column, columnOffsets, combinedBuffer);
+          vectors[i] = ColumnVector.fromViewWithContiguousAllocation(columnView, combinedBuffer);
+        }
+        assert columnOffsets.isEmpty();
+        return new Table(vectors);
+      } finally {
+        for (ColumnVector cv: vectors) {
+          if (cv != null) {
+            cv.close();
+          }
+        }
+      }
+    }
+  }
+
+  public static Table readAndConcat(SerializedTableHeader[] headers,
+                                    HostMemoryBuffer[] dataBuffers) throws IOException {
+    ContiguousTable ct = concatToContiguousTable(headers, dataBuffers);
+    ct.getBuffer().close();
+    return ct.getTable();
+  }
+
+  /**
+   * Concatenate multiple tables in host memory into a contiguous table in device memory.
+   * @param headers table headers corresponding to the host table buffers
+   * @param dataBuffers host table buffer for each input table to be concatenated
+   * @return contiguous table in device memory
+   */
+  public static ContiguousTable concatToContiguousTable(SerializedTableHeader[] headers,
+                                                        HostMemoryBuffer[] dataBuffers) throws IOException {
+    try (HostConcatResult concatResult = concatToHostBuffer(headers, dataBuffers)) {
+      return concatResult.toContiguousTable();
+    }
+  }
+
+  /**
+   * Concatenate multiple tables in host memory into a single host table buffer.
+   * @param headers table headers corresponding to the host table buffers
+   * @param dataBuffers host table buffer for each input table to be concatenated
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return host table header and buffer
+   */
+  public static HostConcatResult concatToHostBuffer(SerializedTableHeader[] headers,
+                                                    HostMemoryBuffer[] dataBuffers,
+                                                    HostMemoryAllocator hostMemoryAllocator
+                                                    ) throws IOException {
+    ColumnBufferProvider[][] providersPerColumn = providersFrom(headers, dataBuffers);
+    try {
+      SerializedTableHeader combined = calcConcatHeader(providersPerColumn);
+      HostMemoryBuffer hostBuffer = hostMemoryAllocator.allocate(combined.dataLen);
+      try {
+        try (NvtxRange range = new NvtxRange("Concat Host Side", NvtxColor.GREEN)) {
+          DataWriter writer = writerFrom(hostBuffer);
+          int numColumns = combined.getNumColumns();
+          for (int columnIdx = 0; columnIdx < numColumns; columnIdx++) {
+            writeConcat(writer, combined.getColumnHeader(columnIdx), providersPerColumn[columnIdx]);
+          }
+        }
+      } catch (Exception e) {
+        hostBuffer.close();
+        throw e;
+      }
+
+      return new HostConcatResult(combined, hostBuffer);
+    } finally {
+      closeAll(providersPerColumn);
+    }
+  }
+
+    public static HostConcatResult concatToHostBuffer(SerializedTableHeader[] headers,
+                                                      HostMemoryBuffer[] dataBuffers
+                                                      ) throws IOException {
+      return concatToHostBuffer(headers, dataBuffers, DefaultHostMemoryAllocator.get());
+    }
+
+  /**
+   * Deserialize a serialized contiguous table into an array of host columns.
+   *
+   * @param header     serialized table header
+   * @param hostBuffer buffer containing the data for all columns in the serialized table
+   * @return array of host columns representing the data from the serialized table
+   */
+  public static HostColumnVector[] unpackHostColumnVectors(SerializedTableHeader header,
+                                                           HostMemoryBuffer hostBuffer) {
+    ArrayDeque<ColumnOffsets> columnOffsets = buildIndex(header, hostBuffer);
+    int numColumns = header.getNumColumns();
+    HostColumnVector[] columns = new HostColumnVector[numColumns];
+    boolean succeeded = false;
+    try {
+      for (int i = 0; i < numColumns; i++) {
+        SerializedColumnHeader column = header.getColumnHeader(i);
+        columns[i] = (HostColumnVector) buildHostColumn(column, columnOffsets, hostBuffer, true);
+      }
+      assert columnOffsets.isEmpty();
+      succeeded = true;
+    } finally {
+      if (!succeeded) {
+        for (HostColumnVector c : columns) {
+          if (c != null) {
+            c.close();
+          }
+        }
+      }
+    }
+    return columns;
+  }
+
+  /**
+   * After reading a header for a table read the data portion into a host side buffer.
+   * @param in the stream to read the data from.
+   * @param header the header that finished just moments ago.
+   * @param buffer the buffer to write the data into.  If there is not enough room to store
+   *               the data in buffer it will not be read and header will still have dataRead
+   *               set to false.
+   * @throws IOException
+   */
+  public static void readTableIntoBuffer(InputStream in,
+                                         SerializedTableHeader header,
+                                         HostMemoryBuffer buffer) throws IOException {
+    if (header.initialized &&
+        (buffer.length >= header.dataLen)) {
+      try (NvtxRange range = new NvtxRange("Read Data", NvtxColor.RED)) {
+        buffer.copyFromStream(0, in, header.dataLen);
+      }
+      header.dataRead = true;
+    }
+  }
+
+  public static TableAndRowCountPair readTableFrom(SerializedTableHeader header,
+                                                   HostMemoryBuffer hostBuffer) {
+    ContiguousTable contigTable = null;
+    DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(hostBuffer.length);
+    try {
+      if (hostBuffer.length > 0) {
+        try (NvtxRange range = new NvtxRange("Copy Data To Device", NvtxColor.WHITE)) {
+          devBuffer.copyFromHostBuffer(hostBuffer);
+        }
+      }
+      if (header.getNumColumns() > 0) {
+        Table table = sliceUpColumnVectors(header, devBuffer, hostBuffer);
+        contigTable = new ContiguousTable(table, devBuffer);
+      }
+    } finally {
+      if (contigTable == null) {
+        devBuffer.close();
+      }
+    }
+
+    return new TableAndRowCountPair(header.numRows, contigTable);
+  }
+
+  /**
+   * Read a serialize table from the given InputStream.
+   * @param in the stream to read the table data from.
+   * @param hostMemoryAllocator a host memory allocator for an intermediate host memory buffer
+   * @return the deserialized table in device memory, or null if the stream has no table to read
+   * from, an end of the stream at the very beginning.
+   * @throws IOException on any error.
+   * @throws EOFException if the data stream ended unexpectedly in the middle of processing.
+   */
+  public static TableAndRowCountPair readTableFrom(InputStream in,
+      HostMemoryAllocator hostMemoryAllocator) throws IOException {
+    DataInputStream din;
+    if (in instanceof DataInputStream) {
+      din = (DataInputStream) in;
+    } else {
+      din = new DataInputStream(in);
+    }
+
+    SerializedTableHeader header = new SerializedTableHeader(din);
+    if (!header.initialized) {
+      return new TableAndRowCountPair(0, null);
+    }
+
+    try (HostMemoryBuffer hostBuffer = hostMemoryAllocator.allocate(header.dataLen)) {
+      if (header.dataLen > 0) {
+        readTableIntoBuffer(din, header, hostBuffer);
+      }
+      return readTableFrom(header, hostBuffer);
+    }
+  }
+
+  public static TableAndRowCountPair readTableFrom(InputStream in) throws IOException {
+    return readTableFrom(in, DefaultHostMemoryAllocator.get());
+  }
+
+  /** Holds the result of deserializing a table. */
+  public static final class TableAndRowCountPair implements Closeable {
+    private final int numRows;
+    private final ContiguousTable contigTable;
+
+    public TableAndRowCountPair(int numRows, ContiguousTable table) {
+      this.numRows = numRows;
+      this.contigTable = table;
+    }
+
+    @Override
+    public void close() {
+      if (contigTable != null) {
+        contigTable.close();
+      }
+    }
+
+    /** Get the number of rows that were deserialized. */
+    public int getNumRows() {
+          return numRows;
+      }
+
+    /**
+     * Get the Table that was deserialized or null if there was no data
+     * (e.g.: rows without columns).
+     * <p>NOTE: Ownership of the table is not transferred by this method.
+     * The table is still owned by this instance and will be closed when this
+     * instance is closed.
+     */
+    public Table getTable() {
+      if (contigTable != null) {
+        return contigTable.getTable();
+      }
+      return null;
+    }
+
+    /**
+     * Get the ContiguousTable that was deserialized or null if there was no
+     * data (e.g.: rows without columns).
+     * <p>NOTE: Ownership of the contiguous table is not transferred by this
+     * method. The contiguous table is still owned by this instance and will
+     * be closed when this instance is closed.
+     */
+    public ContiguousTable getContiguousTable() {
+      return contigTable;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/JSONOptions.java b/java/src/main/java/ai/rapids/cudf/JSONOptions.java
new file mode 100644
index 0000000..f98687d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/JSONOptions.java
@@ -0,0 +1,118 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.Collection;
+
+/**
+ * Options for reading in JSON encoded data.
+ */
+public final class JSONOptions extends ColumnFilterOptions {
+
+  public static JSONOptions DEFAULT = new JSONOptions(builder());
+
+  private final boolean dayFirst;
+  private final boolean lines;
+  private final boolean recoverWithNull;
+
+  private JSONOptions(Builder builder) {
+    super(builder);
+    dayFirst = builder.dayFirst;
+    lines = builder.lines;
+    recoverWithNull = builder.recoverWithNull;
+  }
+
+  public boolean isDayFirst() {
+    return dayFirst;
+  }
+
+  public boolean isLines() {
+    return lines;
+  }
+
+  /** Return the value of the recoverWithNull option */
+  public boolean isRecoverWithNull() {
+    return recoverWithNull;
+  }
+
+  @Override
+  String[] getIncludeColumnNames() {
+    throw new UnsupportedOperationException("JSON reader didn't support column prune");
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static final class Builder  extends ColumnFilterOptions.Builder<JSONOptions.Builder> {
+    private boolean dayFirst = false;
+    private boolean lines = true;
+
+    private boolean recoverWithNull = false;
+
+    /**
+     * Whether to parse dates as DD/MM versus MM/DD
+     * @param dayFirst true: DD/MM, false, MM/DD
+     * @return
+     */
+    public Builder withDayFirst(boolean dayFirst) {
+      this.dayFirst = dayFirst;
+      return this;
+    }
+
+    /**
+     * Whether to read the file as a json object per line
+     * @param perLine true: per line, false: multi-line
+     * @return builder for chaining
+     */
+    public Builder withLines(boolean perLine) {
+      assert perLine == true : "Cudf does not support multi-line";
+      this.lines = perLine;
+      return this;
+    }
+
+    /**
+     * Specify how to handle invalid lines when parsing json. Setting
+     * recoverWithNull to true will cause null values to be returned
+     * for invalid lines. Setting recoverWithNull to false will cause
+     * the parsing to fail with an exception.
+     *
+     * @param recoverWithNull true: return nulls, false: throw exception
+     * @return builder for chaining
+     */
+    public Builder withRecoverWithNull(boolean recoverWithNull) {
+      this.recoverWithNull = recoverWithNull;
+      return this;
+    }
+
+    @Override
+    public Builder includeColumn(String... names) {
+      throw new UnsupportedOperationException("JSON reader didn't support column prune");
+    }
+
+    @Override
+    public Builder includeColumn(Collection<String> names) {
+      throw new UnsupportedOperationException("JSON reader didn't support column prune");
+    }
+
+    public JSONOptions build() {
+      return new JSONOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/MaskState.java b/java/src/main/java/ai/rapids/cudf/MaskState.java
new file mode 100644
index 0000000..9507061
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/MaskState.java
@@ -0,0 +1,41 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+package ai.rapids.cudf;
+
+enum MaskState {
+  UNALLOCATED(0),
+  UNINITIALIZED(1),
+  ALL_VALID(2),
+  ALL_NULL(3);
+
+  private static final MaskState[] MASK_STATES = MaskState.values();
+  final int nativeId;
+
+  MaskState(int nativeId) {
+    this.nativeId = nativeId;
+  }
+
+  static MaskState fromNative(int nativeId) {
+    for (MaskState type : MASK_STATES) {
+      if (type.nativeId == nativeId) {
+        return type;
+      }
+    }
+    throw new IllegalArgumentException("Could not translate " + nativeId + " into a MaskState");
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/MemoryBuffer.java b/java/src/main/java/ai/rapids/cudf/MemoryBuffer.java
new file mode 100644
index 0000000..e6b3994
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/MemoryBuffer.java
@@ -0,0 +1,284 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/**
+ * Abstract class for representing the Memory Buffer
+ *
+ * NOTE: MemoryBuffer is public to make it easier to work with the class hierarchy,
+ * subclassing beyond what is included in CUDF is not recommended and not supported.
+ */
+abstract public class MemoryBuffer implements AutoCloseable {
+  /**
+   * Interface to handle events for this MemoryBuffer. Only invoked during
+   * close, hence `onClosed` is the only event.
+   */
+  public interface EventHandler {
+    /**
+     * `onClosed` is invoked with the updated `refCount` during `close`.
+     * The last invocation of `onClosed` will be with `refCount=0`.
+     *
+     * @note the callback is invoked with this `MemoryBuffer`'s lock held.
+     *
+     * @param refCount - the updated ref count for this MemoryBuffer at the time
+     *                 of invocation
+     */
+    void onClosed(int refCount);
+  }
+
+  private static final Logger log = LoggerFactory.getLogger(MemoryBuffer.class);
+  protected final long address;
+  protected final long length;
+  protected boolean closed = false;
+  protected int refCount = 0;
+  protected final MemoryBufferCleaner cleaner;
+  protected final long id;
+
+  private EventHandler eventHandler;
+
+  public static abstract class MemoryBufferCleaner extends MemoryCleaner.Cleaner{}
+
+  private static final class SlicedBufferCleaner extends MemoryBufferCleaner {
+    private MemoryBuffer parent;
+
+    SlicedBufferCleaner(MemoryBuffer parent) {
+      this.parent = parent;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      if (parent != null) {
+        if (logErrorIfNotClean) {
+          log.error("A SLICED BUFFER WAS LEAKED(ID: " + id + " parent: " + parent + ")");
+          logRefCountDebug("Leaked sliced buffer");
+        }
+        try {
+          parent.close();
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          parent = null;
+        }
+        return true;
+      }
+      return false;
+    }
+
+    @Override
+    public boolean isClean() {
+      return parent == null;
+    }
+  }
+
+  /**
+   * This is a really ugly API, but it is possible that the lifecycle of a column of
+   * data may not have a clear lifecycle thanks to java and GC. This API informs the leak
+   * tracking code that this is expected for this column, and big scary warnings should
+   * not be printed when this happens.
+   */
+  public void noWarnLeakExpected() {
+    if (cleaner != null) {
+      cleaner.noWarnLeakExpected();
+    }
+  }
+
+  /**
+   * Constructor
+   * @param address location in memory
+   * @param length  size of this buffer
+   * @param cleaner used to clean up the memory. May be null if no cleanup is needed.
+   */
+  protected MemoryBuffer(long address, long length, MemoryBufferCleaner cleaner) {
+    this.address = address;
+    this.length = length;
+    this.cleaner = cleaner;
+    if (cleaner != null) {
+      this.id = cleaner.id;
+      incRefCount();
+      MemoryCleaner.register(this, cleaner);
+    } else {
+      this.id = -1;
+    }
+  }
+
+  /**
+   * Constructor
+   * @param address location in memory
+   * @param length  size of this buffer
+   */
+  protected MemoryBuffer(long address, long length) {
+    this(address, length, (MemoryBufferCleaner)null);
+  }
+
+  /**
+   * Internal constructor used when creating a slice.
+   * @param address location in memory
+   * @param length size of this buffer
+   * @param parent the buffer that should be closed instead of closing this one.
+   */
+  protected MemoryBuffer(long address, long length, MemoryBuffer parent) {
+    this(address, length, new SlicedBufferCleaner(parent));
+  }
+
+  /**
+   * Returns the size of this buffer
+   * @return - size
+   */
+  public final long getLength() {
+    return length;
+  }
+
+  protected final void addressOutOfBoundsCheck(long address, long size, String type) {
+    assert !closed : "Buffer is already closed " + Long.toHexString(this.address);
+    assert size >= 0 : "A positive size is required";
+    assert address >= this.address : "Start address is too low for " + type +
+        " 0x" + Long.toHexString(address) + " < 0x" + Long.toHexString(this.address);
+    assert (address + size) <= (this.address + length) : "End address is too high for " + type +
+        " 0x" + Long.toHexString(address + size) + " < 0x" + Long.toHexString(this.address + length);
+  }
+
+  /**
+   * Returns the location of the data pointed to by this buffer
+   * @return - data address
+   */
+  public final long getAddress() {
+    return address;
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at destOffset using the specified CUDA stream.
+   * The copy has completed when this returns, but the memory copy could overlap with
+   * operations occurring on other streams.
+   * @param destOffset the offset in this to start copying from.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromMemoryBuffer(
+          long destOffset, MemoryBuffer src, long srcOffset, long length, Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy range dest");
+    src.addressOutOfBoundsCheck(src.address + srcOffset, length, "copy range src");
+    Cuda.memcpy(address + destOffset, src.address + srcOffset, length, CudaMemcpyKind.DEFAULT, stream);
+  }
+
+  /**
+   * Copy a subset of src to this buffer starting at destOffset using the specified CUDA stream.
+   * The copy is async and may not have completed when this returns.
+   * @param destOffset the offset in this to start copying from.
+   * @param src what to copy from
+   * @param srcOffset offset into src to start out
+   * @param length how many bytes to copy
+   * @param stream CUDA stream to use
+   */
+  public final void copyFromMemoryBufferAsync(
+          long destOffset, MemoryBuffer src, long srcOffset, long length, Cuda.Stream stream) {
+    addressOutOfBoundsCheck(address + destOffset, length, "copy range dest");
+    src.addressOutOfBoundsCheck(src.address + srcOffset, length, "copy range src");
+    Cuda.asyncMemcpy(address + destOffset, src.address + srcOffset, length, CudaMemcpyKind.DEFAULT, stream);
+  }
+
+  /**
+   * Slice off a part of the buffer. Note that this is a zero copy operation and all
+   * slices must be closed along with the original buffer before the memory is released.
+   * So use this with some caution.
+   *
+   * Note that [[DeviceMemoryBuffer]] and [[HostMemoryBuffer]] support slicing, and override this
+   * function.
+   *
+   * @param offset where to start the slice at.
+   * @param len how many bytes to slice
+   * @return a slice of the original buffer that will need to be closed independently
+   */
+  public abstract MemoryBuffer slice(long offset, long len);
+
+  /**
+   * Set an event handler for this buffer. This method can be invoked with null
+   * to unset the handler.
+   *
+   * @param newHandler - the EventHandler to use from this point forward
+   * @return the prior event handler, or null if not set.
+   */
+  public synchronized EventHandler setEventHandler(EventHandler newHandler) {
+    EventHandler prev = this.eventHandler;
+    this.eventHandler = newHandler;
+    return prev;
+  }
+
+  /**
+   * Returns the current event handler for this buffer or null if no handler
+   * is associated or this buffer is closed.
+   */
+  public synchronized EventHandler getEventHandler() {
+    return this.eventHandler;
+  }
+
+  /**
+   * Close this buffer and free memory
+   */
+  public synchronized void close() {
+    if (cleaner != null) {
+      refCount--;
+      cleaner.delRef();
+      if (eventHandler != null) {
+        eventHandler.onClosed(refCount);
+      }
+      if (refCount == 0) {
+        cleaner.clean(false);
+        closed = true;
+      } else if (refCount < 0) {
+        cleaner.logRefCountDebug("double free " + this);
+        throw new IllegalStateException("Close called too many times " + this);
+      }
+    }
+  }
+
+  @Override
+  public String toString() {
+    long id = -1;
+    if (cleaner != null) {
+      id = cleaner.id;
+    }
+    String name = this.getClass().getSimpleName();
+    return name + "{" +
+        "address=0x" + Long.toHexString(address) +
+        ", length=" + length +
+        ", id=" + id + "}";
+  }
+
+  /**
+   * Increment the reference count for this column.  You need to call close on this
+   * to decrement the reference count again.
+   */
+  public synchronized void incRefCount() {
+    refCount++;
+    cleaner.addRef();
+  }
+
+  /**
+   * Get the current reference count for this buffer.
+   */
+  public synchronized int getRefCount() {
+    return refCount;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/MemoryCleaner.java b/java/src/main/java/ai/rapids/cudf/MemoryCleaner.java
new file mode 100644
index 0000000..032b075
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/MemoryCleaner.java
@@ -0,0 +1,350 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.ast.CompiledExpression;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.lang.ref.ReferenceQueue;
+import java.lang.ref.WeakReference;
+import java.text.SimpleDateFormat;
+import java.util.Arrays;
+import java.util.Date;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.atomic.AtomicLong;
+import java.util.stream.Collectors;
+import java.util.stream.StreamSupport;
+
+/**
+ * ColumnVectors may store data off heap, and because of complicated processing the life time of
+ * an individual vector can vary a lot.  Typically a java finalizer could be used for this but
+ * they can cause a number of performance issues related to gc, and in some cases may effectively
+ * leak resources if the heap is large and GC's end up being delayed.
+ * <p>
+ * To address these issues the primary way to releasing the resources of a ColumnVector that is
+ * stored off of the java heap should be through reference counting. Because memory leaks are
+ * really bad for long lived daemons this is intended to be a backup.
+ * <p>
+ * When a ColumnVector first allocates off heap resources it should register itself with this
+ * along with a Cleaner instance. The Cleaner instance should have no direct links to the
+ * ColumnVector that would prevent the ColumnVector from being garbage collected. This will
+ * use WeakReferences internally to know when the resources have been leaked.
+ * A ColumnVector may keep a reference to the Cleaner instance and either update it as new
+ * resources are allocated or use it to release the resources it is holding.  Once the
+ * ColumnVector's reference count reaches 0 and the resources are released. At some point
+ * later the Cleaner itself will be released.
+ */
+public final class MemoryCleaner {
+  private static final boolean REF_COUNT_DEBUG = Boolean.getBoolean("ai.rapids.refcount.debug");
+  private static final Logger log = LoggerFactory.getLogger(MemoryCleaner.class);
+  private static final AtomicLong idGen = new AtomicLong(0);
+
+  /**
+   * Check if configured the shutdown hook which checks leaks at shutdown time.
+   *
+   * @return true if configured, false otherwise.
+   */
+  public static boolean configuredDefaultShutdownHook() {
+    return REF_COUNT_DEBUG;
+  }
+
+  /**
+   * API that can be used to clean up the resources for a vector, even if there was a leak
+   */
+  public static abstract class Cleaner {
+    private final List<RefCountDebugItem> refCountDebug;
+    public final long id = idGen.incrementAndGet();
+    private boolean leakExpected = false;
+
+    public Cleaner() {
+      if (REF_COUNT_DEBUG) {
+        refCountDebug = new LinkedList<>();
+      } else {
+        refCountDebug = null;
+      }
+    }
+
+    public final void addRef() {
+      if (REF_COUNT_DEBUG && refCountDebug != null) {
+        synchronized(this)  {
+          refCountDebug.add(new MemoryCleaner.RefCountDebugItem("INC"));
+        }
+      }
+    }
+
+    public final void delRef() {
+      if (REF_COUNT_DEBUG && refCountDebug != null) {
+        synchronized(this) {
+          refCountDebug.add(new MemoryCleaner.RefCountDebugItem("DEC"));
+        }
+      }
+    }
+
+    public final void logRefCountDebug(String message) {
+      if (REF_COUNT_DEBUG && refCountDebug != null) {
+        synchronized(this) {
+          log.error("{} (ID: {}): {}", message, id, MemoryCleaner.stringJoin("\n", refCountDebug));
+        }
+      }
+    }
+
+    /**
+     * Clean up any resources not previously released.
+     * @param logErrorIfNotClean if true we should log a leak unless it is expected.
+     * @return true if resources were cleaned up else false.
+     */
+    public final boolean clean(boolean logErrorIfNotClean) {
+      boolean cleaned = cleanImpl(logErrorIfNotClean && !leakExpected);
+      if (cleaned) {
+        all.remove(id);
+      }
+      return cleaned;
+    }
+
+    /**
+     * Return true if a leak is expected for this object else false.
+     */
+    public final boolean isLeakExpected() {
+      return leakExpected;
+    }
+
+    /**
+     * Clean up any resources not previously released.
+     * @param logErrorIfNotClean if true and there are resources to clean up a leak has happened
+     *                           so log it.
+     * @return true if resources were cleaned up else false.
+     */
+    protected abstract boolean cleanImpl(boolean logErrorIfNotClean);
+
+    public void noWarnLeakExpected() {
+      leakExpected = true;
+    }
+
+    /**
+     * Check if the underlying memory has been cleaned up or not.
+     * @return true this is clean else false.
+     */
+    public abstract boolean isClean();
+  }
+
+  static final AtomicLong leakCount = new AtomicLong();
+  private static final Map<Long, CleanerWeakReference> all =
+      new ConcurrentHashMap(); // We want to be thread safe
+  private static final ReferenceQueue<?> collected = new ReferenceQueue<>();
+
+  private static class CleanerWeakReference<T> extends WeakReference<T> {
+
+    private final Cleaner cleaner;
+    final boolean isRmmBlocker;
+
+    public CleanerWeakReference(T orig, Cleaner cleaner, ReferenceQueue collected, boolean isRmmBlocker) {
+      super(orig, collected);
+      this.cleaner = cleaner;
+      this.isRmmBlocker = isRmmBlocker;
+    }
+
+    public void clean() {
+      if (cleaner.clean(true)) {
+        leakCount.incrementAndGet();
+      }
+    }
+  }
+
+  /**
+   * The default GPU as set by user threads.
+   */
+  private static volatile int defaultGpu = -1;
+
+  /**
+   * This should be called from RMM when it is initialized.
+   */
+  static void setDefaultGpu(int defaultGpuId) {
+    defaultGpu = defaultGpuId;
+  }
+
+  private static final Thread t = new Thread(() -> {
+    try {
+      int currentGpuId = -1;
+      while (true) {
+        CleanerWeakReference next = (CleanerWeakReference)collected.remove(100);
+        if (next != null) {
+          try {
+            if (currentGpuId != defaultGpu) {
+              Cuda.setDevice(defaultGpu);
+              currentGpuId = defaultGpu;
+            }
+          } catch (Throwable t) {
+            log.error("ERROR TRYING TO SET GPU ID TO " + defaultGpu, t);
+          }
+          try {
+            next.clean();
+          } catch (Throwable t) {
+            log.error("CAUGHT EXCEPTION WHILE TRYING TO CLEAN " + next, t);
+          }
+          all.remove(next.cleaner.id);
+        }
+      }
+    } catch (InterruptedException e) {
+      // Ignored just exit
+    }
+  }, "Cleaner Thread");
+
+  /**
+   * Default shutdown runnable used to be added to Java default shutdown hook.
+   * It checks the leaks at shutdown time.
+   */
+  private static final Runnable DEFAULT_SHUTDOWN_RUNNABLE = () -> {
+    // If we are debugging things do a best effort to check for leaks at the end
+
+    System.gc();
+    // Avoid issues on shutdown with the cleaner thread.
+    t.interrupt();
+    try {
+      t.join(1000);
+    } catch (InterruptedException e) {
+      // Ignored
+    }
+    if (defaultGpu >= 0) {
+      Cuda.setDevice(defaultGpu);
+    }
+
+    for (CleanerWeakReference cwr : all.values()) {
+      cwr.clean();
+    }
+  };
+
+  private static final Thread DEFAULT_SHUTDOWN_THREAD = new Thread(DEFAULT_SHUTDOWN_RUNNABLE);
+
+  static {
+    t.setDaemon(true);
+    t.start();
+    if (REF_COUNT_DEBUG) {
+      Runtime.getRuntime().addShutdownHook(DEFAULT_SHUTDOWN_THREAD);
+    }
+  }
+
+  /**
+   * De-register the default shutdown hook from Java default Runtime, then return the corresponding
+   * shutdown runnable.
+   * If you want to register the default shutdown runnable in a custom shutdown hook manager
+   * instead of Java default Runtime, should first remove it using this method and then add it
+   *
+   * @return the default shutdown runnable
+   */
+  public static Runnable removeDefaultShutdownHook() {
+    Runtime.getRuntime().removeShutdownHook(DEFAULT_SHUTDOWN_THREAD);
+    return DEFAULT_SHUTDOWN_RUNNABLE;
+  }
+
+  static void register(ColumnVector vec, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(vec, cleaner, collected, true));
+  }
+
+  static void register(HostColumnVectorCore vec, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(vec, cleaner, collected, false));
+  }
+
+  static void register(MemoryBuffer buf, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(buf, cleaner, collected, buf instanceof BaseDeviceMemoryBuffer));
+  }
+
+  static void register(Cuda.Stream stream, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(stream, cleaner, collected, false));
+  }
+
+  static void register(Cuda.Event event, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(event, cleaner, collected, false));
+  }
+
+  static void register(CuFileDriver driver, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(driver, cleaner, collected, false));
+  }
+
+  static void register(CuFileBuffer buffer, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(buffer, cleaner, collected, false));
+  }
+
+  static void register(CuFileHandle handle, Cleaner cleaner) {
+    // It is now registered...
+    all.put(cleaner.id, new CleanerWeakReference(handle, cleaner, collected, false));
+  }
+
+  public static void register(CompiledExpression expr, Cleaner cleaner) {
+    all.put(cleaner.id, new CleanerWeakReference(expr, cleaner, collected, false));
+  }
+
+  static void register(HashJoin hashJoin, Cleaner cleaner) {
+    all.put(cleaner.id, new CleanerWeakReference(hashJoin, cleaner, collected, true));
+  }
+
+  /**
+   * This is not 100% perfect and we can still run into situations where RMM buffers were not
+   * collected and this returns false because of thread race conditions. This is just a best effort.
+   * @return true if there are rmm blockers else false.
+   */
+  static boolean bestEffortHasRmmBlockers() {
+    return all.values().stream().anyMatch(cwr -> cwr.isRmmBlocker && !cwr.cleaner.isClean());
+  }
+
+  /**
+   * Convert elements in it to a String and join them together. Only use for debug messages
+   * where the code execution itself can be disabled as this is not fast.
+   */
+  private static <T> String stringJoin(String delim, Iterable<T> it) {
+    return String.join(delim,
+        StreamSupport.stream(it.spliterator(), false)
+            .map((i) -> i.toString())
+            .collect(Collectors.toList()));
+  }
+
+  /**
+   * When debug is enabled holds information about inc and dec of ref count.
+   */
+  private static final class RefCountDebugItem {
+    final StackTraceElement[] stackTrace;
+    final long timeMs;
+    final String op;
+
+    public RefCountDebugItem(String op) {
+      this.stackTrace = Thread.currentThread().getStackTrace();
+      this.timeMs = System.currentTimeMillis();
+      this.op = op;
+    }
+
+    public String toString() {
+      Date date = new Date(timeMs);
+      // Simple Date Format is horribly expensive only do this when debug is turned on!
+      SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSSS z");
+      return dateFormat.format(date) + ": " + op + "\n"
+          + stringJoin("\n", Arrays.asList(stackTrace))
+          + "\n";
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/MixedJoinSize.java b/java/src/main/java/ai/rapids/cudf/MixedJoinSize.java
new file mode 100644
index 0000000..811f0b9
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/MixedJoinSize.java
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/** This class tracks size information associated with a mixed table join. */
+public final class MixedJoinSize implements AutoCloseable {
+  private final long outputRowCount;
+  // This is in flux, avoid exposing publicly until the dust settles.
+  private ColumnVector matches;
+
+  MixedJoinSize(long outputRowCount, ColumnVector matches) {
+    this.outputRowCount = outputRowCount;
+    this.matches = matches;
+  }
+
+  /** Return the number of output rows that would be generated from the mixed join */
+  public long getOutputRowCount() {
+    return outputRowCount;
+  }
+
+  ColumnVector getMatches() {
+    return matches;
+  }
+
+  @Override
+  public synchronized void close() {
+    matches.close();
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NaNEquality.java b/java/src/main/java/ai/rapids/cudf/NaNEquality.java
new file mode 100644
index 0000000..11f34cd
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NaNEquality.java
@@ -0,0 +1,41 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * How should NaNs be compared in an operation. In floating point there are multiple
+ * different binary representations for NaN.
+ */
+public enum NaNEquality {
+  /**
+   * No NaN representation is considered equal to any NaN representation, even for the
+   * exact same representation.
+   */
+  UNEQUAL(false),
+  /**
+   * All representations of NaN are considered to be equal.
+   */
+  ALL_EQUAL(true);
+
+  NaNEquality(boolean nansEqual) {
+    this.nansEqual = nansEqual;
+  }
+
+  final boolean nansEqual;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NativeDepsLoader.java b/java/src/main/java/ai/rapids/cudf/NativeDepsLoader.java
new file mode 100755
index 0000000..27322cc
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NativeDepsLoader.java
@@ -0,0 +1,232 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+
+/**
+ * This class will load the native dependencies.
+ */
+public class NativeDepsLoader {
+  private static final Logger log = LoggerFactory.getLogger(NativeDepsLoader.class);
+
+  /**
+   * Set this system property to true to prevent unpacked dependency files from
+   * being deleted immediately after they are loaded. The files will still be
+   * scheduled for deletion upon exit.
+   */
+  private static final Boolean preserveDepsAfterLoad = Boolean.getBoolean(
+      "ai.rapids.cudf.preserve-dependencies");
+
+  /**
+   * Defines the loading order for the dependencies. Dependencies are loaded in
+   * stages where all the dependencies in a stage are not interdependent and
+   * therefore can be loaded in parallel. All dependencies within an earlier
+   * stage are guaranteed to have finished loading before any dependencies in
+   * subsequent stages are loaded.
+   */
+  private static final String[][] loadOrder = new String[][]{
+      new String[]{
+          "nvcomp_bitcomp", "nvcomp_gdeflate"
+      },
+      new String[]{
+          "nvcomp"
+      },
+      new String[]{
+          "cudf"
+      },
+      new String[]{
+          "cudfjni"
+      }
+  };
+  private static final ClassLoader loader = NativeDepsLoader.class.getClassLoader();
+
+  private static boolean loaded = false;
+
+  /**
+   * Load the native libraries needed for libcudf, if not loaded already.
+   */
+  public static synchronized void loadNativeDeps() {
+    if (!loaded) {
+      try {
+        loadNativeDeps(loadOrder);
+        loaded = true;
+      } catch (Throwable t) {
+        log.error("Could not load cudf jni library...", t);
+      }
+    }
+  }
+
+  /**
+   * Allows other libraries to reuse the same native deps loading logic. Libraries will be searched
+   * for under ${os.arch}/${os.name}/ in the class path using the class loader for this class.
+   * <br/>
+   * Because this just loads the libraries and loading the libraries themselves needs to be a
+   * singleton operation it is recommended that any library using this provide their own wrapper
+   * function similar to
+   * <pre>
+   *     private static boolean loaded = false;
+   *     static synchronized void loadNativeDeps() {
+   *         if (!loaded) {
+   *             try {
+   *                 // If you also depend on the cudf liobrary being loaded, be sure it is loaded
+   *                 // first
+   *                 ai.rapids.cudf.NativeDepsLoader.loadNativeDeps();
+   *                 ai.rapids.cudf.NativeDepsLoader.loadNativeDeps(new String[]{...});
+   *                 loaded = true;
+   *             } catch (Throwable t) {
+   *                 log.error("Could not load ...", t);
+   *             }
+   *         }
+   *     }
+   * </pre>
+   * This function should be called from the static initialization block of any class that uses
+   * JNI. For example
+   * <pre>
+   *     public class UsesJNI {
+   *         static {
+   *             MyNativeDepsLoader.loadNativeDeps();
+   *         }
+   *     }
+   * </pre>
+   * @param loadOrder the base name of the libraries. For example libfoo.so would be passed in as
+   *                  "foo".  The libraries are loaded in the order provided.
+   * @throws IOException on any error trying to load the libraries.
+   */
+  public static void loadNativeDeps(String[] loadOrder) throws IOException {
+    String os = System.getProperty("os.name");
+    String arch = System.getProperty("os.arch");
+
+    for (String toLoad : loadOrder) {
+      loadDep(os, arch, toLoad);
+    }
+  }
+
+  /**
+   * Load native dependencies in stages, where the dependency libraries in each stage
+   * are loaded only after all libraries in earlier stages have completed loading.
+   * @param loadOrder array of stages with an array of dependency library names in each stage
+   * @throws IOException on any error trying to load the libraries
+   */
+  private static void loadNativeDeps(String[][] loadOrder) throws IOException {
+    String os = System.getProperty("os.name");
+    String arch = System.getProperty("os.arch");
+
+    ExecutorService executor = Executors.newCachedThreadPool();
+    List<List<Future<File>>> allFileFutures = new ArrayList<>();
+
+    // Start unpacking and creating the temporary files for each dependency.
+    // Unpacking a dependency does not depend on stage order.
+    for (String[] stageDependencies : loadOrder) {
+      List<Future<File>> stageFileFutures = new ArrayList<>();
+      allFileFutures.add(stageFileFutures);
+      for (String name : stageDependencies) {
+        stageFileFutures.add(executor.submit(() -> createFile(os, arch, name)));
+      }
+    }
+
+    List<Future<?>> loadCompletionFutures = new ArrayList<>();
+
+    // Proceed stage-by-stage waiting for the dependency file to have been
+    // produced then submit them to the thread pool to be loaded.
+    for (List<Future<File>> stageFileFutures : allFileFutures) {
+      // Submit all dependencies in the stage to be loaded in parallel
+      loadCompletionFutures.clear();
+      for (Future<File> fileFuture : stageFileFutures) {
+        loadCompletionFutures.add(executor.submit(() -> loadDep(fileFuture)));
+      }
+
+      // Wait for all dependencies in this stage to have been loaded
+      for (Future<?> loadCompletionFuture : loadCompletionFutures) {
+        try {
+          loadCompletionFuture.get();
+        } catch (ExecutionException | InterruptedException e) {
+          throw new IOException("Error loading dependencies", e);
+        }
+      }
+    }
+
+    executor.shutdownNow();
+  }
+
+  private static void loadDep(String os, String arch, String baseName) throws IOException {
+    File path = createFile(os, arch, baseName);
+    loadDep(path);
+  }
+
+  /** Load a library at the specified path */
+  private static void loadDep(File path) {
+    System.load(path.getAbsolutePath());
+    if (!preserveDepsAfterLoad) {
+      path.delete();
+    }
+  }
+
+  /** Load a library, waiting for the specified future to produce the path before loading */
+  private static void loadDep(Future<File> fileFuture) {
+    File path;
+    try {
+      path = fileFuture.get();
+    } catch (ExecutionException | InterruptedException e) {
+      throw new RuntimeException("Error loading dependencies", e);
+    }
+    loadDep(path);
+  }
+
+  /** Extract the contents of a library resource into a temporary file */
+  private static File createFile(String os, String arch, String baseName) throws IOException {
+    String path = arch + "/" + os + "/" + System.mapLibraryName(baseName);
+    File loc;
+    URL resource = loader.getResource(path);
+    if (resource == null) {
+      throw new FileNotFoundException("Could not locate native dependency " + path);
+    }
+    try (InputStream in = resource.openStream()) {
+      loc = File.createTempFile(baseName, ".so");
+      loc.deleteOnExit();
+      try (OutputStream out = new FileOutputStream(loc)) {
+        byte[] buffer = new byte[1024 * 16];
+        int read = 0;
+        while ((read = in.read(buffer)) >= 0) {
+          out.write(buffer, 0, read);
+        }
+      }
+    }
+    return loc;
+  }
+
+  public static boolean libraryLoaded() {
+    if (!loaded) {
+      loadNativeDeps();
+    }
+    return loaded;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NullEquality.java b/java/src/main/java/ai/rapids/cudf/NullEquality.java
new file mode 100644
index 0000000..657600d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NullEquality.java
@@ -0,0 +1,33 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * How should nulls be compared in an operation.
+ */
+public enum NullEquality {
+  UNEQUAL(false),
+  EQUAL(true);
+
+  NullEquality(boolean nullsEqual) {
+    this.nullsEqual = nullsEqual;
+  }
+
+  final boolean nullsEqual;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NullPolicy.java b/java/src/main/java/ai/rapids/cudf/NullPolicy.java
new file mode 100644
index 0000000..225eb4a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NullPolicy.java
@@ -0,0 +1,33 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Specify whether to include nulls or exclude nulls in an operation.
+ */
+public enum NullPolicy {
+  EXCLUDE(false),
+  INCLUDE(true);
+
+  NullPolicy(boolean includeNulls) {
+    this.includeNulls = includeNulls;
+  }
+
+  final boolean includeNulls;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NvtxColor.java b/java/src/main/java/ai/rapids/cudf/NvtxColor.java
new file mode 100644
index 0000000..8a2dedb
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NvtxColor.java
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+public enum NvtxColor {
+  GREEN(0xff00ff00),
+  BLUE(0xff0000ff),
+  YELLOW(0xffffff00),
+  PURPLE(0xffff00ff),
+  CYAN(0xff00ffff),
+  RED(0xffff0000),
+  WHITE(0xffffffff),
+  DARK_GREEN(0xff006600),
+  ORANGE(0xffffa500);
+
+  final int colorBits;
+
+  NvtxColor(int colorBits) {
+    this.colorBits = colorBits;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NvtxRange.java b/java/src/main/java/ai/rapids/cudf/NvtxRange.java
new file mode 100644
index 0000000..c683139
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NvtxRange.java
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * This class supports push/pop NVTX profiling ranges, or "scoped" ranges.
+ *
+ * The constructor pushes an NVTX range and the close method pops off the most recent range that
+ * was pushed. Therefore instances of this class should always be used in a try-with-resources
+ * block to guarantee that ranges are always closed in the proper order. For example:
+ * <pre>
+ *   try (NvtxRange a = new NvtxRange("a", NvtxColor.RED)) {
+ *     ...
+ *     try (NvtxRange b = new NvtxRange("b", NvtxColor.BLUE)) {
+ *       ...
+ *     }
+ *     ...
+ *   }
+ * </pre>
+ *
+ * Instances should be associated with a single thread to avoid pushing an NVTX range in
+ * one thread and then trying to pop the range in a different thread.
+ *
+ * Push/pop ranges show a stacking behavior in tools such as Nsight, where newly pushed
+ * ranges are correlated and enclosed by the prior pushed range (in the example above,
+ * "b" is enclosed by "a").
+ */
+public class NvtxRange implements AutoCloseable {
+  private static final boolean isEnabled = Boolean.getBoolean("ai.rapids.cudf.nvtx.enabled");
+
+  static {
+    if (isEnabled) {
+      NativeDepsLoader.loadNativeDeps();
+    }
+  }
+
+  public NvtxRange(String name, NvtxColor color) {
+    this(name, color.colorBits);
+  }
+
+  public NvtxRange(String name, int colorBits) {
+    if (isEnabled) {
+      push(name, colorBits);
+    }
+  }
+
+  @Override
+  public void close() {
+    if (isEnabled) {
+      pop();
+    }
+  }
+
+  private native void push(String name, int colorBits);
+  private native void pop();
+}
diff --git a/java/src/main/java/ai/rapids/cudf/NvtxUniqueRange.java b/java/src/main/java/ai/rapids/cudf/NvtxUniqueRange.java
new file mode 100644
index 0000000..0232290
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/NvtxUniqueRange.java
@@ -0,0 +1,82 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * This class supports start/end NVTX profiling ranges.
+ *
+ * Start/end:
+ *
+ * The constructor instantiates a new NVTX range and keeps a unique handle that comes back
+ * from the NVTX api (nvtxRangeId). The handle is used to later close such a range. This type
+ * of range does not have the same order-of-operation requirements that the push/pop ranges have:
+ * the `NvtxUniqueRange` instance can be passed to other scopes, and even to other threads
+ * for the eventual call to close.
+ *
+ * It can be used in the same try-with-resources way as push/pop, or interleaved with other
+ * ranges, like so:
+ *
+ * <pre>
+ *   NvtxUniqueRange a = new NvtxUniqueRange("a", NvtxColor.RED);
+ *   NvtxUniqueRange b = new NvtxUniqueRange("b", NvtxColor.BLUE);
+ *   a.close();
+ *   b.close();
+ * </pre>
+ */
+public class NvtxUniqueRange implements AutoCloseable {
+  private static final boolean isEnabled = Boolean.getBoolean("ai.rapids.cudf.nvtx.enabled");
+
+  // this is a nvtxRangeId_t in the C++ api side
+  private final long nvtxRangeId;
+
+  // true if this range is already closed
+  private boolean closed;
+
+  static {
+    if (isEnabled) {
+      NativeDepsLoader.loadNativeDeps();
+    }
+  }
+
+  public NvtxUniqueRange(String name, NvtxColor color) {
+    this(name, color.colorBits);
+  }
+
+  public NvtxUniqueRange(String name, int colorBits) {
+    if (isEnabled) {
+      nvtxRangeId = start(name, colorBits);
+    } else {
+      // following the implementation in nvtx3, the default value of 0
+      // is given when NVTX is disabled
+      nvtxRangeId = 0;
+    }
+  }
+
+  @Override
+  public synchronized void close() {
+    if (closed) {
+      throw new IllegalStateException(
+          "Cannot call close on an already closed NvtxUniqueRange!");
+    }
+    closed = true;
+    if (isEnabled) {
+      end(this.nvtxRangeId);
+    }
+  }
+
+  private native long start(String name, int colorBits);
+  private native void end(long nvtxRangeId);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ORCOptions.java b/java/src/main/java/ai/rapids/cudf/ORCOptions.java
new file mode 100644
index 0000000..2ff2530
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ORCOptions.java
@@ -0,0 +1,107 @@
+/*
+ *
+ *  Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * Options for reading a ORC file
+ */
+public class ORCOptions extends ColumnFilterOptions {
+
+  public static ORCOptions DEFAULT = new ORCOptions(new Builder());
+
+  private final boolean useNumPyTypes;
+  private final DType unit;
+  private final String[] decimal128Columns;
+
+  private ORCOptions(Builder builder) {
+    super(builder);
+    decimal128Columns = builder.decimal128Columns.toArray(new String[0]);
+    useNumPyTypes = builder.useNumPyTypes;
+    unit = builder.unit;
+  }
+
+  boolean usingNumPyTypes() {
+    return useNumPyTypes;
+  }
+
+  DType timeUnit() {
+    return unit;
+  }
+
+  String[] getDecimal128Columns() {
+    return decimal128Columns;
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder extends ColumnFilterOptions.Builder<Builder> {
+    private boolean useNumPyTypes = true;
+    private DType unit = DType.EMPTY;
+
+    final List<String> decimal128Columns = new ArrayList<>();
+
+    /**
+     * Specify whether the parser should implicitly promote TIMESTAMP_DAYS
+     * columns to TIMESTAMP_MILLISECONDS for compatibility with NumPy.
+     *
+     * @param useNumPyTypes true to request this conversion, false to avoid.
+     * @return builder for chaining
+     */
+    public Builder withNumPyTypes(boolean useNumPyTypes) {
+      this.useNumPyTypes = useNumPyTypes;
+      return this;
+    }
+
+    /**
+     * Specify the time unit to use when returning timestamps.
+     * @param unit default unit of time specified by the user
+     * @return builder for chaining
+     */
+    public ORCOptions.Builder withTimeUnit(DType unit) {
+      assert unit.isTimestampType();
+      this.unit = unit;
+      return this;
+    }
+
+    /**
+     * Specify decimal columns which shall be read as DECIMAL128. Otherwise, decimal columns
+     * will be read as DECIMAL64 by default in ORC.
+     *
+     * In terms of child columns of nested types, their parents need to be prepended as prefix
+     * of the column name, in case of ambiguity. For struct columns, the names of child columns
+     * are formatted as `{struct_col_name}.{child_col_name}`. For list columns, the data(child)
+     * columns are named as `{list_col_name}.1`.
+     *
+     * @param names names of columns which read as DECIMAL128
+     * @return builder for chaining
+     */
+    public Builder decimal128Column(String... names) {
+      decimal128Columns.addAll(Arrays.asList(names));
+      return this;
+    }
+
+    public ORCOptions build() { return new ORCOptions(this); }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ORCWriterOptions.java b/java/src/main/java/ai/rapids/cudf/ORCWriterOptions.java
new file mode 100644
index 0000000..372f919
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ORCWriterOptions.java
@@ -0,0 +1,42 @@
+/*
+ *
+ *  Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * This class represents settings for writing ORC files. It includes meta data information
+ * that will be used by the ORC writer to write the file.
+ */
+public class ORCWriterOptions extends CompressionMetadataWriterOptions {
+
+  private ORCWriterOptions(Builder builder) {
+    super(builder);
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder extends CompressionMetadataWriterOptions.Builder
+          <Builder, ORCWriterOptions> {
+
+    public ORCWriterOptions build() {
+      return new ORCWriterOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/OrderByArg.java b/java/src/main/java/ai/rapids/cudf/OrderByArg.java
new file mode 100644
index 0000000..fbdd703
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/OrderByArg.java
@@ -0,0 +1,59 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.io.Serializable;
+
+/**
+ * Provides the ordering for specific columns.
+ */
+public final class OrderByArg implements Serializable {
+  final int index;
+  final boolean isDescending;
+  final boolean isNullSmallest;
+
+  OrderByArg(int index, boolean isDescending, boolean isNullSmallest) {
+    this.index = index;
+    this.isDescending = isDescending;
+    this.isNullSmallest = isNullSmallest;
+  }
+
+  public static OrderByArg asc(final int index) {
+    return new OrderByArg(index, false, false);
+  }
+
+  public static OrderByArg desc(final int index) {
+    return new OrderByArg(index, true, false);
+  }
+
+  public static OrderByArg asc(final int index, final boolean isNullSmallest) {
+    return new OrderByArg(index, false, isNullSmallest);
+  }
+
+  public static OrderByArg desc(final int index, final boolean isNullSmallest) {
+    return new OrderByArg(index, true, isNullSmallest);
+  }
+
+  @Override
+  public String toString() {
+    return "ORDER BY " + index +
+        (isDescending ? " DESC " : " ASC ") +
+        (isNullSmallest ? "NULL SMALLEST" : "NULL LARGEST");
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/OutOfBoundsPolicy.java b/java/src/main/java/ai/rapids/cudf/OutOfBoundsPolicy.java
new file mode 100644
index 0000000..262d7f1
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/OutOfBoundsPolicy.java
@@ -0,0 +1,39 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Policy to account for possible out-of-bounds indices
+ *
+ * `NULLIFY` means to nullify output values corresponding to out-of-bounds gather map values.
+ *
+ * `DONT_CHECK` means do not check whether the indices are out-of-bounds, for better
+ *   performance. Use `DONT_CHECK` carefully, as it can result in a CUDA exception if
+ *   the gather map values are actually out of range.
+ *
+ * @note This enum doesn't have a nativeId because the C++ out_of_bounds_policy is a
+ *        a boolean enum. It is just added for clarity in the Java API.
+ */
+public enum OutOfBoundsPolicy {
+  /* Output values corresponding to out-of-bounds indices are null */
+  NULLIFY,
+
+  /* No bounds checking is performed, better performance */
+  DONT_CHECK
+}
diff --git a/java/src/main/java/ai/rapids/cudf/PackedColumnMetadata.java b/java/src/main/java/ai/rapids/cudf/PackedColumnMetadata.java
new file mode 100644
index 0000000..5ee4ddc
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/PackedColumnMetadata.java
@@ -0,0 +1,74 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.nio.ByteBuffer;
+
+/**
+ * Metadata for a table that is backed by a single contiguous device buffer.
+ */
+public final class PackedColumnMetadata implements AutoCloseable {
+  private long metadataHandle = 0;
+  private ByteBuffer metadataBuffer = null;
+
+  // This method is invoked by JNI
+  static PackedColumnMetadata fromPackedColumnMeta(long metadataHandle) {
+    return new PackedColumnMetadata(metadataHandle);
+  }
+
+  /**
+   * Construct the PackedColumnMetadata instance given a metadata handle.
+   * @param metadataHandle address of the cudf packed_table host-based metadata instance
+   */
+  PackedColumnMetadata(long metadataHandle) {
+    this.metadataHandle = metadataHandle;
+  }
+
+  /**
+   * Get the byte buffer containing the host metadata describing the schema and layout of the
+   * contiguous table.
+   * <p>
+   * NOTE: This is a direct byte buffer that is backed by the underlying native metadata instance
+   *       and therefore is only valid to be used while this PackedColumnMetadata instance is valid.
+   *       Attempts to cache and access the resulting buffer after this instance has been destroyed
+   *       will result in undefined behavior including the possibility of segmentation faults
+   *       or data corruption.
+   */
+  public ByteBuffer getMetadataDirectBuffer() {
+    if (metadataBuffer == null) {
+      metadataBuffer = createMetadataDirectBuffer(metadataHandle);
+    }
+    return metadataBuffer.asReadOnlyBuffer();
+  }
+
+  /** Close the PackedColumnMetadata instance and its underlying resources. */
+  @Override
+  public void close() {
+    if (metadataHandle != 0) {
+      closeMetadata(metadataHandle);
+      metadataHandle = 0;
+    }
+  }
+
+  // create a DirectByteBuffer for the packed metadata
+  private static native ByteBuffer createMetadataDirectBuffer(long metadataHandle);
+
+  // release the native metadata resources for a packed table
+  private static native void closeMetadata(long metadataHandle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/PadSide.java b/java/src/main/java/ai/rapids/cudf/PadSide.java
new file mode 100644
index 0000000..518bc22
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/PadSide.java
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+public enum PadSide {
+    LEFT(0),
+    RIGHT(1),
+    BOTH(2);
+
+    private static final PadSide[] PAD_SIDES = PadSide.values();
+    final int nativeId;
+
+    PadSide(int nativeId) {
+        this.nativeId = nativeId;
+    }
+
+    public int getNativeId() {
+        return nativeId;
+    }
+
+    public static PadSide fromNative(int nativeId) {
+        for (PadSide type : PAD_SIDES) {
+            if (type.nativeId == nativeId) {
+                return type;
+            }
+        }
+        throw new IllegalArgumentException("Could not translate " + nativeId + " into a PadSide");
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ParquetChunkedReader.java b/java/src/main/java/ai/rapids/cudf/ParquetChunkedReader.java
new file mode 100644
index 0000000..c34336a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ParquetChunkedReader.java
@@ -0,0 +1,155 @@
+/*
+ *
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.io.File;
+
+/**
+ * Provide an interface for reading a Parquet file in an iterative manner.
+ */
+public class ParquetChunkedReader implements AutoCloseable {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  /**
+   * Construct the reader instance from a read limit and a file path.
+   *
+   * @param chunkSizeByteLimit Limit on total number of bytes to be returned per read,
+   *                           or 0 if there is no limit.
+   * @param filePath Full path of the input Parquet file to read.
+   */
+  public ParquetChunkedReader(long chunkSizeByteLimit, File filePath) {
+    this(chunkSizeByteLimit, ParquetOptions.DEFAULT, filePath);
+  }
+
+  /**
+   * Construct the reader instance from a read limit, a ParquetOptions object, and a file path.
+   *
+   * @param chunkSizeByteLimit Limit on total number of bytes to be returned per read,
+   *                           or 0 if there is no limit.
+   * @param opts The options for Parquet reading.
+   * @param filePath Full path of the input Parquet file to read.
+   */
+  public ParquetChunkedReader(long chunkSizeByteLimit, ParquetOptions opts, File filePath) {
+    handle = create(chunkSizeByteLimit, opts.getIncludeColumnNames(), opts.getReadBinaryAsString(),
+        filePath.getAbsolutePath(), 0, 0, opts.timeUnit().typeId.getNativeId());
+
+    if(handle == 0) {
+      throw new IllegalStateException("Cannot create native chunked Parquet reader object.");
+    }
+  }
+
+  /**
+   * Construct the reader instance from a read limit and a file already read in a memory buffer.
+   *
+   * @param chunkSizeByteLimit Limit on total number of bytes to be returned per read,
+   *                           or 0 if there is no limit.
+   * @param opts The options for Parquet reading.
+   * @param buffer Raw Parquet file content.
+   * @param offset The starting offset into buffer.
+   * @param len The number of bytes to parse the given buffer.
+   */
+  public ParquetChunkedReader(long chunkSizeByteLimit, ParquetOptions opts, HostMemoryBuffer buffer,
+      long offset, long len) {
+    handle = create(chunkSizeByteLimit, opts.getIncludeColumnNames(), opts.getReadBinaryAsString(), null,
+        buffer.getAddress() + offset, len, opts.timeUnit().typeId.getNativeId());
+
+    if(handle == 0) {
+      throw new IllegalStateException("Cannot create native chunked Parquet reader object.");
+    }
+  }
+
+  /**
+   * Check if the given file has anything left to read.
+   *
+   * @return A boolean value indicating if there is more data to read from file.
+   */
+  public boolean hasNext() {
+    if(handle == 0) {
+      throw new IllegalStateException("Native chunked Parquet reader object may have been closed.");
+    }
+
+    if (firstCall) {
+      // This function needs to return true at least once, so an empty table
+      // (but having empty columns instead of no column) can be returned by readChunk()
+      // if the input file has no row.
+      firstCall = false;
+      return true;
+    }
+    return hasNext(handle);
+  }
+
+  /**
+   * Read a chunk of rows in the given Parquet file such that the returning data has total size
+   * does not exceed the given read limit. If the given file has no data, or all data has been read
+   * before by previous calls to this function, a null Table will be returned.
+   *
+   * @return A table of new rows reading from the given file.
+   */
+  public Table readChunk() {
+    if(handle == 0) {
+      throw new IllegalStateException("Native chunked Parquet reader object may have been closed.");
+    }
+
+    long[] columnPtrs = readChunk(handle);
+    return columnPtrs != null ? new Table(columnPtrs) : null;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      close(handle);
+      handle = 0;
+    }
+  }
+
+
+  /**
+   * Auxiliary variable to help {@link #hasNext()} returning true at least once.
+   */
+  private boolean firstCall = true;
+
+  /**
+   * Handle for memory address of the native Parquet chunked reader class.
+   */
+  private long handle;
+
+
+  /**
+   * Create a native chunked Parquet reader object on heap and return its memory address.
+   *
+   * @param chunkSizeByteLimit Limit on total number of bytes to be returned per read,
+   *                           or 0 if there is no limit.
+   * @param filterColumnNames Name of the columns to read, or an empty array if we want to read all.
+   * @param binaryToString Whether to convert the corresponding column to String if it is binary.
+   * @param filePath Full path of the file to read, or given as null if reading from a buffer.
+   * @param bufferAddrs The address of a buffer to read from, or 0 if we are not using that buffer.
+   * @param length The length of the buffer to read from.
+   * @param timeUnit Return type of time unit for timestamps.
+   */
+  private static native long create(long chunkSizeByteLimit, String[] filterColumnNames,
+      boolean[] binaryToString, String filePath, long bufferAddrs, long length, int timeUnit);
+
+  private static native boolean hasNext(long handle);
+
+  private static native long[] readChunk(long handle);
+
+  private static native void close(long handle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ParquetOptions.java b/java/src/main/java/ai/rapids/cudf/ParquetOptions.java
new file mode 100644
index 0000000..1ae1b91
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ParquetOptions.java
@@ -0,0 +1,112 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.List;
+
+/**
+ * Options for reading a parquet file
+ */
+public class ParquetOptions extends ColumnFilterOptions {
+
+  public static ParquetOptions DEFAULT = new ParquetOptions(new Builder());
+
+  private final DType unit;
+  private final boolean[] readBinaryAsString;
+
+  private ParquetOptions(Builder builder) {
+    super(builder);
+    unit = builder.unit;
+    readBinaryAsString = new boolean[builder.binaryAsStringColumns.size()];
+    for (int i = 0 ; i < builder.binaryAsStringColumns.size() ; i++) {
+      readBinaryAsString[i] = builder.binaryAsStringColumns.get(i);
+    }
+  }
+
+  DType timeUnit() {
+    return unit;
+  }
+
+  boolean[] getReadBinaryAsString() {
+    return readBinaryAsString;
+  }
+
+  public static ParquetOptions.Builder builder() {
+    return new Builder();
+  }
+
+  public static class Builder extends ColumnFilterOptions.Builder<Builder> {
+    private DType unit = DType.EMPTY;
+    final List<Boolean> binaryAsStringColumns = new ArrayList<>();
+
+    /**
+     * Specify the time unit to use when returning timestamps.
+     * @param unit default unit of time specified by the user
+     * @return builder for chaining
+     */
+    public Builder withTimeUnit(DType unit) {
+      assert unit.isTimestampType();
+      this.unit = unit;
+      return this;
+    }
+
+    /**
+     * Include one or more specific columns.  Any column not included will not be read.
+     * @param names the name of the column, or more than one if you want.
+     */
+    @Override
+    public Builder includeColumn(String... names) {
+      super.includeColumn(names);
+      for (int i = 0 ; i < names.length ; i++) {
+        binaryAsStringColumns.add(true);
+      }
+      return this;
+    }
+
+    /**
+     * Include this column.
+     * @param name the name of the column
+     * @param isBinary whether this column is to be read in as binary
+     */
+    public Builder includeColumn(String name, boolean isBinary) {
+      includeColumnNames.add(name);
+      binaryAsStringColumns.add(!isBinary);
+      return this;
+    }
+
+    /**
+     * Include one or more specific columns.  Any column not included will not be read.
+     * @param names the name of the column, or more than one if you want.
+     */
+    @Override
+    public Builder includeColumn(Collection<String> names) {
+      super.includeColumn(names);
+      for (int i = 0 ; i < names.size() ; i++) {
+        binaryAsStringColumns.add(true);
+      }
+      return this;
+    }
+
+    public ParquetOptions build() {
+      return new ParquetOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ParquetWriterOptions.java b/java/src/main/java/ai/rapids/cudf/ParquetWriterOptions.java
new file mode 100644
index 0000000..7b58817
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ParquetWriterOptions.java
@@ -0,0 +1,75 @@
+/*
+ *
+ *  Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * This class represents settings for writing Parquet files. It includes meta data information
+ * that will be used by the Parquet writer to write the file
+ */
+public final class ParquetWriterOptions extends CompressionMetadataWriterOptions {
+  private final StatisticsFrequency statsGranularity;
+
+  private ParquetWriterOptions(Builder builder) {
+    super(builder);
+    this.statsGranularity = builder.statsGranularity;
+  }
+
+  public enum StatisticsFrequency {
+    /** Do not generate statistics */
+    NONE(0),
+
+    /** Generate column statistics for each rowgroup */
+    ROWGROUP(1),
+
+    /** Generate column statistics for each page */
+    PAGE(2);
+
+    final int nativeId;
+
+    StatisticsFrequency(int nativeId) {
+      this.nativeId = nativeId;
+    }
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public StatisticsFrequency getStatisticsFrequency() {
+    return statsGranularity;
+  }
+
+  public static class Builder extends CompressionMetadataWriterOptions.Builder
+        <Builder, ParquetWriterOptions> {
+    private StatisticsFrequency statsGranularity = StatisticsFrequency.ROWGROUP;
+
+    public Builder() {
+      super();
+    }
+
+    public Builder withStatisticsFrequency(StatisticsFrequency statsGranularity) {
+      this.statsGranularity = statsGranularity;
+      return this;
+    }
+
+    public ParquetWriterOptions build() {
+      return new ParquetWriterOptions(this);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/PartitionedTable.java b/java/src/main/java/ai/rapids/cudf/PartitionedTable.java
new file mode 100644
index 0000000..27ec89b
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/PartitionedTable.java
@@ -0,0 +1,76 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Class to provide a PartitionedTable
+ */
+public final class PartitionedTable implements AutoCloseable {
+  private final Table table;
+  private final int[] partitionsOffsets;
+
+  /**
+   * The package-private constructor is only called by the partition method in Table
+   * .TableOperation.partition
+   * @param table            - {@link Table} which contains the partitioned data
+   * @param partitionOffsets - This param is used to populate the offsets into the returned table
+   *                         where partitionOffsets[i] indicates the starting position of
+   *                         partition 'i'
+   */
+  PartitionedTable(Table table, int[] partitionOffsets) {
+    this.table = table;
+    this.partitionsOffsets = partitionOffsets;
+  }
+
+  public Table getTable() {
+    return table;
+  }
+
+  public ColumnVector getColumn(int index) {
+    return table.getColumn(index);
+  }
+
+  public long getNumberOfColumns() {
+    return table.getNumberOfColumns();
+  }
+
+  public long getRowCount() {
+    return table.getRowCount();
+  }
+
+  @Override
+  public void close() {
+    table.close();
+  }
+
+  /**
+   * This method returns the partitions on this table. partitionOffsets[i] indicates the
+   * starting position of partition 'i' in the partitioned table. Size of the partitions can
+   * be calculated by the next offset
+   * Ex:
+   * partitionOffsets[0, 12, 12, 49] indicates 4 partitions with the following sizes
+   * partition[0] - 12
+   * partition[1] - 0 (is empty)
+   * partition[2] - 37
+   * partition[3] has the remaining values of the table (N-49)
+   */
+  public int[] getPartitions() {
+    return partitionsOffsets;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/PinnedMemoryPool.java b/java/src/main/java/ai/rapids/cudf/PinnedMemoryPool.java
new file mode 100644
index 0000000..9ce72ba
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/PinnedMemoryPool.java
@@ -0,0 +1,462 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.Comparator;
+import java.util.Iterator;
+import java.util.Objects;
+import java.util.Optional;
+import java.util.SortedSet;
+import java.util.TreeSet;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+
+/**
+ * This provides a pool of pinned memory similar to what RMM does for device memory.
+ */
+public final class PinnedMemoryPool implements AutoCloseable {
+  private static final Logger log = LoggerFactory.getLogger(PinnedMemoryPool.class);
+  private static final long ALIGNMENT = ColumnView.hostPaddingSizeInBytes();
+
+  // These static fields should only ever be accessed when class-synchronized.
+  // Do NOT use singleton_ directly!  Use the getSingleton accessor instead.
+  private static volatile PinnedMemoryPool singleton_ = null;
+  private static Future<PinnedMemoryPool> initFuture = null;
+
+  private final long totalPoolSize;
+  private final long pinnedPoolBase;
+  private final SortedSet<MemorySection> freeHeap = new TreeSet<>(new SortedByAddress());
+  private int numAllocatedSections = 0;
+  private long availableBytes;
+
+  private static class SortedBySize implements Comparator<MemorySection> {
+    @Override
+    public int compare(MemorySection s0, MemorySection s1) {
+      return Long.compare(s0.size, s1.size);
+    }
+  }
+
+  private static class SortedByAddress implements Comparator<MemorySection> {
+    @Override
+    public int compare(MemorySection s0, MemorySection s1) {
+      return Long.compare(s0.baseAddress, s1.baseAddress);
+    }
+  }
+
+  private static class MemorySection {
+    private long baseAddress;
+    private long size;
+
+    MemorySection(long baseAddress, long size) {
+      this.baseAddress = baseAddress;
+      this.size = size;
+    }
+
+    boolean canCombine(MemorySection other) {
+      boolean ret = (other.baseAddress + other.size) == baseAddress ||
+          (baseAddress + size) == other.baseAddress;
+      log.trace("CAN {} COMBINE WITH {} ? {}", this, other, ret);
+      return ret;
+    }
+
+    void combineWith(MemorySection other) {
+      assert canCombine(other);
+      log.trace("COMBINING {} AND {}", this, other);
+      this.baseAddress = Math.min(baseAddress, other.baseAddress);
+      this.size = other.size + this.size;
+      log.trace("COMBINED TO {}\n", this);
+    }
+
+    MemorySection splitOff(long newSize) {
+      assert this.size > newSize;
+      MemorySection ret = new MemorySection(baseAddress, newSize);
+      this.baseAddress += newSize;
+      this.size -= newSize;
+      return ret;
+    }
+
+    @Override
+    public String toString() {
+      return "PINNED: " + size + " bytes (0x" + Long.toHexString(baseAddress)
+          + " to 0x" + Long.toHexString(baseAddress + size) + ")";
+    }
+  }
+
+  private static final class PinnedHostBufferCleaner extends MemoryBuffer.MemoryBufferCleaner {
+    private MemorySection section;
+    private final long origLength;
+
+    PinnedHostBufferCleaner(MemorySection section, long length) {
+      this.section = section;
+      origLength = length;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      boolean neededCleanup = false;
+      long origAddress = 0;
+      if (section != null) {
+        origAddress = section.baseAddress;
+        try {
+          PinnedMemoryPool.freeInternal(section);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          section = null;
+        }
+        neededCleanup = true;
+      }
+      if (neededCleanup && logErrorIfNotClean) {
+        log.error("A PINNED HOST BUFFER WAS LEAKED (ID: " + id + " " + Long.toHexString(origAddress) + ")");
+        logRefCountDebug("Leaked pinned host buffer");
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return section == null;
+    }
+  }
+
+  private static PinnedMemoryPool getSingleton() {
+    if (singleton_ == null) {
+      if (initFuture == null) {
+        return null;
+      }
+
+      synchronized (PinnedMemoryPool.class) {
+        if (singleton_ == null) {
+          try {
+            singleton_ = initFuture.get();
+          } catch (Exception e) {
+            throw new RuntimeException("Error initializing pinned memory pool", e);
+          }
+          initFuture = null;
+        }
+      }
+    }
+    return singleton_;
+  }
+
+  private static void freeInternal(MemorySection section) {
+    Objects.requireNonNull(getSingleton()).free(section);
+  }
+
+  /**
+   * Used to indicate that memory was allocated from a reservation. This primarily is for
+   * keeping track of outstanding allocations.
+   */
+  private static void reserveAllocInternal(MemorySection section) {
+    Objects.requireNonNull(getSingleton()).reserveAllocHappened(section);
+  }
+
+  /**
+   * Initialize the pool.
+   *
+   * @param poolSize size of the pool to initialize.
+   */
+  public static synchronized void initialize(long poolSize) {
+    initialize(poolSize, -1);
+  }
+
+  /**
+   * Initialize the pool.
+   *
+   * @param poolSize size of the pool to initialize.
+   * @param gpuId    gpu id to set to get memory pool from, -1 means to use default
+   */
+  public static synchronized void initialize(long poolSize, int gpuId) {
+    if (isInitialized()) {
+      throw new IllegalStateException("Can only initialize the pool once.");
+    }
+    ExecutorService initService = Executors.newSingleThreadExecutor(runnable -> {
+      Thread t = new Thread(runnable, "pinned pool init");
+      t.setDaemon(true);
+      return t;
+    });
+    initFuture = initService.submit(() -> new PinnedMemoryPool(poolSize, gpuId));
+    initService.shutdown();
+  }
+
+  /**
+   * Check if the pool has been initialized or not.
+   */
+  public static boolean isInitialized() {
+    return getSingleton() != null;
+  }
+
+  /**
+   * Shut down the pool of memory. If there are outstanding allocations this may fail.
+   */
+  public static synchronized void shutdown() {
+    PinnedMemoryPool pool = getSingleton();
+    if (pool != null) {
+      pool.close();
+    }
+    initFuture = null;
+    singleton_ = null;
+  }
+
+  /**
+   * Factory method to create a pinned host memory buffer.
+   *
+   * @param bytes size in bytes to allocate
+   * @return newly created buffer or null if insufficient pinned memory
+   */
+  public static HostMemoryBuffer tryAllocate(long bytes) {
+    HostMemoryBuffer result = null;
+    PinnedMemoryPool pool = getSingleton();
+    if (pool != null) {
+      result = pool.tryAllocateInternal(bytes);
+    }
+    return result;
+  }
+
+  /**
+   * Factory method to create a pinned host memory reservation.
+   *
+   * @param bytes size in bytes to reserve
+   * @return newly created reservation or null if insufficient pinned memory to cover it.
+   */
+  public static HostMemoryReservation tryReserve(long bytes) {
+    HostMemoryReservation result = null;
+    PinnedMemoryPool pool = getSingleton();
+    if (pool != null) {
+      result = pool.tryReserveInternal(bytes);
+    }
+    return result;
+  }
+
+  /**
+   * Factory method to create a host buffer but preferably pointing to pinned memory.
+   * It is not guaranteed that the returned buffer will be pointer to pinned memory.
+   *
+   * @param bytes size in bytes to allocate
+   * @return newly created buffer
+   */
+  public static HostMemoryBuffer allocate(long bytes, HostMemoryAllocator hostMemoryAllocator) {
+    HostMemoryBuffer result = tryAllocate(bytes);
+    if (result == null) {
+      result = hostMemoryAllocator.allocate(bytes, false);
+    }
+    return result;
+  }
+
+  /**
+   * Factory method to create a host buffer but preferably pointing to pinned memory.
+   * It is not guaranteed that the returned buffer will be pointer to pinned memory.
+   *
+   * @param bytes size in bytes to allocate
+   * @return newly created buffer
+   */
+  public static HostMemoryBuffer allocate(long bytes) {
+    return allocate(bytes, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Get the number of bytes free in the pinned memory pool.
+   *
+   * @return amount of free memory in bytes or 0 if the pool is not initialized
+   */
+  public static long getAvailableBytes() {
+    PinnedMemoryPool pool = getSingleton();
+    if (pool != null) {
+      return pool.getAvailableBytesInternal();
+    }
+    return 0;
+  }
+
+  /**
+   * Get the number of bytes that the pinned memory pool was allocated with.
+   */
+  public static long getTotalPoolSizeBytes() {
+    PinnedMemoryPool pool = getSingleton();
+    if (pool != null) {
+      return pool.getTotalPoolSizeInternal();
+    }
+    return 0;
+  }
+
+  private PinnedMemoryPool(long poolSize, int gpuId) {
+    if (gpuId > -1) {
+      // set the gpu device to use
+      Cuda.setDevice(gpuId);
+      Cuda.freeZero();
+    }
+    this.totalPoolSize = poolSize;
+    this.pinnedPoolBase = Cuda.hostAllocPinned(poolSize);
+    freeHeap.add(new MemorySection(pinnedPoolBase, poolSize));
+    this.availableBytes = poolSize;
+  }
+
+  @Override
+  public void close() {
+    assert numAllocatedSections == 0 : "Leaked " + numAllocatedSections + " pinned allocations";
+    Cuda.freePinned(pinnedPoolBase);
+  }
+
+  /**
+   * Pads a length of bytes to the alignment the CPU wants in the worst case. This helps to
+   * calculate the size needed for a reservation if there are multiple buffers.
+   * @param bytes the size in bytes
+   * @return the new padded size in bytes.
+   */
+  public static long padToCpuAlignment(long bytes) {
+    return  ((bytes + ALIGNMENT - 1) / ALIGNMENT) * ALIGNMENT;
+  }
+
+  private synchronized MemorySection tryGetInternal(long bytes, String what) {
+    if (freeHeap.isEmpty()) {
+      log.debug("No free pinned memory left");
+      return null;
+    }
+    // Align the allocation
+    long alignedBytes = padToCpuAlignment(bytes);
+    Optional<MemorySection> firstFit = freeHeap.stream()
+            .filter(section -> section.size >= alignedBytes)
+            .findFirst();
+    if (!firstFit.isPresent()) {
+      if (log.isDebugEnabled()) {
+        MemorySection largest = freeHeap.stream()
+                .max(new SortedBySize())
+                .orElse(new MemorySection(0, 0));
+        log.debug("Insufficient pinned memory. {} needed, {} found", alignedBytes, largest.size);
+      }
+      return null;
+    }
+    MemorySection first = firstFit.get();
+    log.debug("{} {}/{} bytes pinned from {} FREE COUNT {} OUTSTANDING COUNT {}",
+            what, bytes, alignedBytes, first, freeHeap.size(), numAllocatedSections);
+    freeHeap.remove(first);
+    MemorySection allocated;
+    if (first.size == alignedBytes) {
+      allocated = first;
+    } else {
+      allocated = first.splitOff(alignedBytes);
+      freeHeap.add(first);
+    }
+    numAllocatedSections++;
+    availableBytes -= allocated.size;
+    log.debug("{} {} free {} outstanding {}", what, allocated, freeHeap, numAllocatedSections);
+    return allocated;
+  }
+
+  private synchronized HostMemoryBuffer tryAllocateInternal(long bytes) {
+    MemorySection allocated = tryGetInternal(bytes, "allocate");
+    if (allocated == null) {
+      return null;
+    } else {
+      return new HostMemoryBuffer(allocated.baseAddress, bytes,
+              new PinnedHostBufferCleaner(allocated, bytes));
+    }
+  }
+
+  private class PinnedReservation implements HostMemoryReservation {
+    private MemorySection section = null;
+
+    public PinnedReservation(MemorySection section) {
+      this.section = section;
+    }
+
+    @Override
+    public synchronized HostMemoryBuffer allocate(long bytes, boolean preferPinned) {
+      return this.allocate(bytes);
+    }
+
+    @Override
+    public synchronized HostMemoryBuffer allocate(long bytes) {
+      if (section == null || section.size < bytes) {
+        throw new OutOfMemoryError("Reservation didn't have enough space " + bytes + " / " +
+                (section == null ? 0 : section.size));
+      }
+      long alignedSize = padToCpuAlignment(bytes);
+      MemorySection allocated;
+      if (section.size >= bytes && section.size <= alignedSize) {
+        allocated = section;
+        section = null;
+        // No need for reserveAllocInternal because the original section is already tracked
+      } else {
+        allocated = section.splitOff(alignedSize);
+        PinnedMemoryPool.reserveAllocInternal(allocated);
+      }
+      return new HostMemoryBuffer(allocated.baseAddress, bytes,
+              new PinnedHostBufferCleaner(allocated, bytes));
+    }
+
+    @Override
+    public synchronized void close() throws Exception {
+      if (section != null) {
+        try {
+          PinnedMemoryPool.freeInternal(section);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          section = null;
+        }
+      }
+    }
+  }
+
+  private HostMemoryReservation tryReserveInternal(long bytes) {
+    MemorySection allocated = tryGetInternal(bytes, "allocate");
+    if (allocated == null) {
+      return null;
+    } else {
+      return new PinnedReservation(allocated);
+    }
+  }
+
+  private synchronized void free(MemorySection section) {
+    log.debug("Freeing {} with {} outstanding {}", section, freeHeap, numAllocatedSections);
+    availableBytes += section.size;
+    Iterator<MemorySection> it = freeHeap.iterator();
+    while(it.hasNext()) {
+      MemorySection current = it.next();
+      if (section.canCombine(current)) {
+        it.remove();
+        section.combineWith(current);
+      }
+    }
+    freeHeap.add(section);
+    numAllocatedSections--;
+    log.debug("After freeing {} outstanding {}", freeHeap, numAllocatedSections);
+  }
+
+  private synchronized void reserveAllocHappened(MemorySection section) {
+    if (section != null && section.size > 0) {
+      numAllocatedSections++;
+    }
+  }
+
+  private synchronized long getAvailableBytesInternal() {
+    return this.availableBytes;
+  }
+
+  private long getTotalPoolSizeInternal() {
+    return this.totalPoolSize;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/QuantileMethod.java b/java/src/main/java/ai/rapids/cudf/QuantileMethod.java
new file mode 100644
index 0000000..4173c14
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/QuantileMethod.java
@@ -0,0 +1,53 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Interpolation method to use when the desired quantile lies between
+ * two data points i and j.
+ */
+public enum QuantileMethod {
+
+  /**
+   * Linear interpolation between i and j
+   */
+  LINEAR(0),
+  /**
+   * Lower data point (i)
+   */
+  LOWER(1),
+  /**
+   * Higher data point (j)
+   */
+  HIGHER(2),
+  /**
+   * (i + j)/2
+   */
+  MIDPOINT(3),
+  /**
+   * i or j, whichever is nearest
+   */
+  NEAREST(4);
+
+  final int nativeId;
+
+  QuantileMethod(int nativeId) {
+    this.nativeId = nativeId;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/QuoteStyle.java b/java/src/main/java/ai/rapids/cudf/QuoteStyle.java
new file mode 100644
index 0000000..b83a92c
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/QuoteStyle.java
@@ -0,0 +1,33 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+package ai.rapids.cudf;
+
+/**
+ * Quote style for CSV records, closely following cudf::io::quote_style.
+ */
+public enum QuoteStyle {
+    MINIMAL(0),    // Quote only fields which contain special characters
+    ALL(1),        // Quote all fields
+    NONNUMERIC(2), // Quote all non-numeric fields
+    NONE(3);       // Never quote fields; disable quotation parsing
+
+    final int nativeId; // Native id, for use with libcudf.
+    QuoteStyle(int nativeId) {
+        this.nativeId = nativeId;
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Range.java b/java/src/main/java/ai/rapids/cudf/Range.java
new file mode 100644
index 0000000..de4b9bd
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Range.java
@@ -0,0 +1,247 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+
+import java.util.function.Consumer;
+
+/**
+ * Helper utility for creating ranges.
+ */
+public final class Range {
+  /**
+   * Append a range to the builder. 0 inclusive to end exclusive.
+   * @param end last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendBytes(byte end) {
+    return appendBytes((byte) 0, end, (byte) 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendBytes(byte start, byte end) {
+    return appendBytes(start, end, (byte) 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @param step  how must to step by.
+   * @return the builder for chaining.
+   */
+  public static final Consumer<Builder> appendBytes(byte start, byte end, byte step) {
+    assert step > 0;
+    assert start <= end;
+    return (b) -> {
+      for (byte i = start; i < end; i += step) {
+        b.append(i);
+      }
+    };
+  }
+
+  /**
+   * Append a range to the builder. 0 inclusive to end exclusive.
+   * @param end last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendShorts(short end) {
+    return appendShorts((short) 0, end, (short) 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendShorts(short start, short end) {
+    return appendShorts(start, end, (short) 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @param step  how must to step by.
+   * @return the builder for chaining.
+   */
+  public static final Consumer<Builder> appendShorts(short start, short end,
+                                                                  short step) {
+    assert step > 0;
+    assert start <= end;
+    return (b) -> {
+      for (short i = start; i < end; i += step) {
+        b.append(i);
+      }
+    };
+  }
+
+  /**
+   * Append a range to the builder. 0 inclusive to end exclusive.
+   * @param end last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendInts(int end) {
+    return appendInts(0, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendInts(int start, int end) {
+    return appendInts(start, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @param step  how must to step by.
+   * @return the builder for chaining.
+   */
+  public static final Consumer<Builder> appendInts(int start, int end, int step) {
+    assert step > 0;
+    assert start <= end;
+    return (b) -> {
+      for (int i = start; i < end; i += step) {
+        b.append(i);
+      }
+    };
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @param step  how must to step by.
+   * @return the builder for chaining.
+   */
+  public static final Consumer<Builder> appendLongs(long start, long end, long step) {
+    assert step > 0;
+    assert start <= end;
+    return (b) -> {
+      for (long i = start; i < end; i += step) {
+        b.append(i);
+      }
+    };
+  }
+
+  /**
+   * Append a range to the builder. 0 inclusive to end exclusive.
+   * @param end last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendLongs(long end) {
+    return appendLongs(0, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendLongs(long start, long end) {
+    return appendLongs(start, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @param step  how must to step by.
+   * @return the builder for chaining.
+   */
+  public static final Consumer<Builder> appendFloats(float start, float end,
+                                                                  float step) {
+    assert step > 0;
+    assert start <= end;
+    return (b) -> {
+      for (float i = start; i < end; i += step) {
+        b.append(i);
+      }
+    };
+  }
+
+  /**
+   * Append a range to the builder. 0 inclusive to end exclusive.
+   * @param end last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendFloats(float end) {
+    return appendFloats(0, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendFloats(float start, float end) {
+    return appendFloats(start, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @param step  how must to step by.
+   * @return the builder for chaining.
+   */
+  public static final Consumer<Builder> appendDoubles(double start, double end,
+                                                                   double step) {
+    assert step > 0;
+    assert start <= end;
+    return (b) -> {
+      for (double i = start; i < end; i += step) {
+        b.append(i);
+      }
+    };
+  }
+
+  /**
+   * Append a range to the builder. 0 inclusive to end exclusive.
+   * @param end last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendDoubles(double end) {
+    return appendDoubles(0, end, 1);
+  }
+
+  /**
+   * Append a range to the builder. start inclusive to end exclusive.
+   * @param start first entry.
+   * @param end   last entry exclusive.
+   * @return the consumer.
+   */
+  public static final Consumer<Builder> appendDoubles(double start, double end) {
+    return appendDoubles(start, end, 1);
+  }
+
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ReductionAggregation.java b/java/src/main/java/ai/rapids/cudf/ReductionAggregation.java
new file mode 100644
index 0000000..ba8ae37
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ReductionAggregation.java
@@ -0,0 +1,307 @@
+/*
+ *
+ *  Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An aggregation that can be used for a reduce.
+ */
+public final class ReductionAggregation {
+  private final Aggregation wrapped;
+
+  private ReductionAggregation(Aggregation wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  long createNativeInstance() {
+    return wrapped.createNativeInstance();
+  }
+
+  long getDefaultOutput() {
+    return wrapped.getDefaultOutput();
+  }
+
+  Aggregation getWrapped() {
+    return wrapped;
+  }
+
+  @Override
+  public int hashCode() {
+    return wrapped.hashCode();
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other == this) {
+      return true;
+    } else if (other instanceof ReductionAggregation) {
+      ReductionAggregation o = (ReductionAggregation) other;
+      return wrapped.equals(o.wrapped);
+    }
+    return false;
+  }
+
+  /**
+   * Sum Aggregation
+   */
+  public static ReductionAggregation sum() {
+    return new ReductionAggregation(Aggregation.sum());
+  }
+
+  /**
+   * Product Aggregation.
+   */
+  public static ReductionAggregation product() {
+    return new ReductionAggregation(Aggregation.product());
+  }
+
+  /**
+   * Min Aggregation
+   */
+  public static ReductionAggregation min() {
+    return new ReductionAggregation(Aggregation.min());
+  }
+
+  /**
+   * Max Aggregation
+   */
+  public static ReductionAggregation max() {
+    return new ReductionAggregation(Aggregation.max());
+  }
+
+  /**
+   * Any reduction. Produces a true or 1, depending on the output type,
+   * if any of the elements in the range are true or non-zero, otherwise produces a false or 0.
+   * Null values are skipped.
+   */
+  public static ReductionAggregation any() {
+    return new ReductionAggregation(Aggregation.any());
+  }
+
+  /**
+   * All reduction. Produces true or 1, depending on the output type, if all of the elements in
+   * the range are true or non-zero, otherwise produces a false or 0.
+   * Null values are skipped.
+   */
+  public static ReductionAggregation all() {
+    return new ReductionAggregation(Aggregation.all());
+  }
+
+
+  /**
+   * Sum of squares reduction.
+   */
+  public static ReductionAggregation sumOfSquares() {
+    return new ReductionAggregation(Aggregation.sumOfSquares());
+  }
+
+  /**
+   * Arithmetic mean reduction.
+   */
+  public static ReductionAggregation mean() {
+    return new ReductionAggregation(Aggregation.mean());
+  }
+
+
+  /**
+   * Variance aggregation with 1 as the delta degrees of freedom.
+   */
+  public static ReductionAggregation variance() {
+    return new ReductionAggregation(Aggregation.variance());
+  }
+
+  /**
+   * Variance aggregation.
+   * @param ddof delta degrees of freedom. The divisor used in calculation of variance is
+   *             <code>N - ddof</code>, where N is the population size.
+   */
+  public static ReductionAggregation variance(int ddof) {
+    return new ReductionAggregation(Aggregation.variance(ddof));
+  }
+
+  /**
+   * Standard deviation aggregation with 1 as the delta degrees of freedom.
+   */
+  public static ReductionAggregation standardDeviation() {
+    return new ReductionAggregation(Aggregation.standardDeviation());
+  }
+
+  /**
+   * Standard deviation aggregation.
+   * @param ddof delta degrees of freedom. The divisor used in calculation of std is
+   *             <code>N - ddof</code>, where N is the population size.
+   */
+  public static ReductionAggregation standardDeviation(int ddof) {
+    return new ReductionAggregation(Aggregation.standardDeviation(ddof));
+  }
+
+
+  /**
+   * Median reduction.
+   */
+  public static ReductionAggregation median() {
+    return new ReductionAggregation(Aggregation.median());
+  }
+
+  /**
+   * Aggregate to compute the specified quantiles. Uses linear interpolation by default.
+   */
+  public static ReductionAggregation quantile(double ... quantiles) {
+    return new ReductionAggregation(Aggregation.quantile(quantiles));
+  }
+
+  /**
+   * Aggregate to compute various quantiles.
+   */
+  public static ReductionAggregation quantile(QuantileMethod method, double ... quantiles) {
+    return new ReductionAggregation(Aggregation.quantile(method, quantiles));
+  }
+
+
+  /**
+   * Number of unique, non-null, elements.
+   */
+  public static ReductionAggregation nunique() {
+    return new ReductionAggregation(Aggregation.nunique());
+  }
+
+  /**
+   * Number of unique elements.
+   * @param nullPolicy INCLUDE if nulls should be counted else EXCLUDE. If nulls are counted they
+   *                   compare as equal so multiple null values in a range would all only
+   *                   increase the count by 1.
+   */
+  public static ReductionAggregation nunique(NullPolicy nullPolicy) {
+    return new ReductionAggregation(Aggregation.nunique(nullPolicy));
+  }
+
+  /**
+   * Get the nth, non-null, element in a group.
+   * @param offset the offset to look at. Negative numbers go from the end of the group. Any
+   *               value outside of the group range results in a null.
+   */
+  public static ReductionAggregation nth(int offset) {
+    return new ReductionAggregation(Aggregation.nth(offset));
+  }
+
+  /**
+   * Get the nth element in a group.
+   * @param offset the offset to look at. Negative numbers go from the end of the group. Any
+   *               value outside of the group range results in a null.
+   * @param nullPolicy INCLUDE if nulls should be included in the aggregation or EXCLUDE if they
+   *                   should be skipped.
+   */
+  public static ReductionAggregation nth(int offset, NullPolicy nullPolicy) {
+    return new ReductionAggregation(Aggregation.nth(offset, nullPolicy));
+  }
+
+  /**
+   * tDigest reduction.
+   */
+  public static ReductionAggregation createTDigest(int delta) {
+    return new ReductionAggregation(Aggregation.createTDigest(delta));
+  }
+
+  /**
+   * tDigest merge reduction.
+   */
+  public static ReductionAggregation mergeTDigest(int delta) {
+    return new ReductionAggregation(Aggregation.mergeTDigest(delta));
+  }
+
+  /*
+   * Collect the values into a list. Nulls will be skipped.
+   */
+  public static ReductionAggregation collectList() {
+    return new ReductionAggregation(Aggregation.collectList());
+  }
+
+  /**
+   * Collect the values into a list.
+   *
+   * @param nullPolicy Indicates whether to include/exclude nulls during collection.
+   */
+  public static ReductionAggregation collectList(NullPolicy nullPolicy) {
+    return new ReductionAggregation(Aggregation.collectList(nullPolicy));
+  }
+
+  /**
+   * Collect the values into a set. All null values will be excluded, and all NaN values are regarded as
+   * unique instances.
+   */
+  public static ReductionAggregation collectSet() {
+    return new ReductionAggregation(Aggregation.collectSet());
+  }
+
+  /**
+   * Collect the values into a set.
+   *
+   * @param nullPolicy   Indicates whether to include/exclude nulls during collection.
+   * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+   * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+   */
+  public static ReductionAggregation collectSet(NullPolicy nullPolicy,
+      NullEquality nullEquality, NaNEquality nanEquality) {
+    return new ReductionAggregation(Aggregation.collectSet(nullPolicy, nullEquality, nanEquality));
+  }
+
+  /**
+   * Merge the partial lists produced by multiple CollectListAggregations.
+   * NOTICE: The partial lists to be merged should NOT include any null list element (but can include null list entries).
+   */
+  public static ReductionAggregation mergeLists() {
+    return new ReductionAggregation(Aggregation.mergeLists());
+  }
+
+  /**
+   * Merge the partial sets produced by multiple CollectSetAggregations. Each null/NaN value will be regarded as
+   * a unique instance.
+   */
+  public static ReductionAggregation mergeSets() {
+    return new ReductionAggregation(Aggregation.mergeSets());
+  }
+
+  /**
+   * Merge the partial sets produced by multiple CollectSetAggregations.
+   *
+   * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+   * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+   */
+  public static ReductionAggregation mergeSets(NullEquality nullEquality, NaNEquality nanEquality) {
+    return new ReductionAggregation(Aggregation.mergeSets(nullEquality, nanEquality));
+  }
+
+  /**
+   * Create HistogramAggregation, computing the frequencies for each unique row.
+   *
+   * @return A structs column in which the first child stores unique rows from the input and the
+   *         second child stores their corresponding frequencies.
+   */
+  public static ReductionAggregation histogram() {
+    return new ReductionAggregation(Aggregation.histogram());
+  }
+
+  /**
+   * Create MergeHistogramAggregation, to merge multiple histograms.
+   *
+   * @return A new histogram in which the frequencies of the unique rows are sum up.
+   */
+  public static ReductionAggregation mergeHistogram() {
+    return new ReductionAggregation(Aggregation.mergeHistogram());
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RegexFlag.java b/java/src/main/java/ai/rapids/cudf/RegexFlag.java
new file mode 100644
index 0000000..7ed8e03
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RegexFlag.java
@@ -0,0 +1,37 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Regex flags setting, closely following cudf::strings::regex_flags.
+ *
+ * These types can be or'd to combine them. The values are chosen to
+ * leave room for future flags and to match the Python flag values.
+ */
+public enum RegexFlag {
+  DEFAULT(0),   // default
+  MULTILINE(8), // the '^' and '$' honor new-line characters
+  DOTALL(16),   // the '.' matching includes new-line characters
+  ASCII(256);   // use only ASCII when matching built-in character classes
+
+  final int nativeId; // Native id, for use with libcudf.
+  private RegexFlag(int nativeId) { // Only constant values should be used
+    this.nativeId = nativeId;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RegexProgram.java b/java/src/main/java/ai/rapids/cudf/RegexProgram.java
new file mode 100644
index 0000000..191a0b9
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RegexProgram.java
@@ -0,0 +1,134 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.EnumSet;
+
+/**
+ * Regex program class, closely following cudf::strings::regex_program.
+ */
+public class RegexProgram {
+  private String pattern; // regex pattern
+  // regex flags for interpreting special characters in the pattern
+  private EnumSet<RegexFlag> flags;
+  // controls how capture groups in the pattern are used
+  // default is to extract a capture group
+  private CaptureGroups capture;
+
+  /**
+   * Constructor for RegexProgram
+   *
+   * @param pattern Regex pattern
+   */
+  public RegexProgram(String pattern) {
+    this(pattern, EnumSet.of(RegexFlag.DEFAULT), CaptureGroups.EXTRACT);
+  }
+
+  /**
+   * Constructor for RegexProgram
+   *
+   * @param pattern Regex pattern
+   * @param flags Regex flags setting
+   */
+  public RegexProgram(String pattern, EnumSet<RegexFlag> flags) {
+    this(pattern, flags, CaptureGroups.EXTRACT);
+  }
+
+  /**
+   * Constructor for RegexProgram
+   *
+   * @param pattern Regex pattern setting
+   * @param capture Capture groups setting
+   */
+  public RegexProgram(String pattern, CaptureGroups capture) {
+    this(pattern, EnumSet.of(RegexFlag.DEFAULT), capture);
+  }
+
+  /**
+   * Constructor for RegexProgram
+   *
+   * @param pattern Regex pattern
+   * @param flags Regex flags setting
+   * @param capture Capture groups setting
+   */
+  public RegexProgram(String pattern, EnumSet<RegexFlag> flags, CaptureGroups capture) {
+    assert pattern != null : "pattern may not be null";
+    this.pattern = pattern;
+    this.flags = flags;
+    this.capture = capture;
+  }
+
+  /**
+   * Get the pattern used to create this instance
+   *
+   * @param return A regex pattern as a string
+   */
+  public String pattern() {
+    return pattern;
+  }
+
+  /**
+   * Get the regex flags setting used to create this instance
+   *
+   * @param return Regex flags setting
+   */
+  public EnumSet<RegexFlag> flags() {
+    return flags;
+  }
+
+  /**
+   * Reset the regex flags setting for this instance
+   *
+   * @param flags Regex flags setting
+   */
+  public void setFlags(EnumSet<RegexFlag> flags) {
+    this.flags = flags;
+  }
+
+  /**
+   * Get the capture groups setting used to create this instance
+   *
+   * @param return Capture groups setting
+   */
+  public CaptureGroups capture() {
+    return capture;
+  }
+
+  /**
+   * Reset the capture groups setting for this instance
+   *
+   * @param capture Capture groups setting
+   */
+  public void setCapture(CaptureGroups capture) {
+    this.capture = capture;
+  }
+
+  /**
+   * Combine the regex flags using 'or'
+   *
+   * @param return An integer representing the value of combined (or'ed) flags
+   */
+  public int combinedFlags() {
+    int allFlags = 0;
+    for (RegexFlag flag : flags) {
+      allFlags |= flag.nativeId;
+    }
+    return allFlags;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ReplacePolicy.java b/java/src/main/java/ai/rapids/cudf/ReplacePolicy.java
new file mode 100644
index 0000000..2f9cba8
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ReplacePolicy.java
@@ -0,0 +1,46 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Policy to specify the position of replacement values relative to null rows.
+ */
+public enum ReplacePolicy {
+  /**
+   * The replacement value is the first non-null value preceding the null row.
+   */
+  PRECEDING(true),
+  /**
+   * The replacement value is the first non-null value following the null row.
+   */
+  FOLLOWING(false);
+
+  ReplacePolicy(boolean isPreceding) {
+    this.isPreceding = isPreceding;
+  }
+
+  final boolean isPreceding;
+
+  /**
+   * Indicate which column the replacement should happen on.
+   */
+  public ReplacePolicyWithColumn onColumn(int columnNumber) {
+    return new ReplacePolicyWithColumn(columnNumber, this);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ReplacePolicyWithColumn.java b/java/src/main/java/ai/rapids/cudf/ReplacePolicyWithColumn.java
new file mode 100644
index 0000000..d0a072a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ReplacePolicyWithColumn.java
@@ -0,0 +1,46 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * A replacement policy for a specific column
+ */
+public class ReplacePolicyWithColumn {
+  final int column;
+  final ReplacePolicy policy;
+
+  ReplacePolicyWithColumn(int column, ReplacePolicy policy) {
+    this.column = column;
+    this.policy = policy;
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (!(other instanceof ReplacePolicyWithColumn)) {
+      return false;
+    }
+    ReplacePolicyWithColumn ro = (ReplacePolicyWithColumn)other;
+    return this.column == ro.column && this.policy.equals(ro.policy);
+  }
+
+  @Override
+  public int hashCode() {
+    return 31 * column + policy.hashCode();
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Rmm.java b/java/src/main/java/ai/rapids/cudf/Rmm.java
new file mode 100755
index 0000000..66c053f
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Rmm.java
@@ -0,0 +1,584 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import java.io.File;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * This is the binding class for rmm lib.
+ */
+public class Rmm {
+  private static volatile RmmTrackingResourceAdaptor<RmmDeviceMemoryResource> tracker = null;
+  private static volatile RmmDeviceMemoryResource deviceResource = null;
+  private static volatile boolean initialized = false;
+  private static volatile long poolSize = -1;
+  private static volatile boolean poolingEnabled = false;
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  enum LogLoc {
+    NONE(0),
+    FILE(1),
+    STDOUT(2),
+    STDERR(3);
+
+    final int internalId;
+
+    LogLoc(int internalId) {
+      this.internalId = internalId;
+    }
+  }
+
+  /**
+   * What to send RMM alloc and free logs to.
+   */
+  public static class LogConf {
+    final File file;
+    final LogLoc loc;
+
+    private LogConf(File file, LogLoc loc) {
+      this.file = file;
+      this.loc = loc;
+    }
+  }
+
+  /**
+   * Create a config that will write alloc/free logs to a file.
+   */
+  public static LogConf logTo(File location) {
+    return new LogConf(location, LogLoc.FILE);
+  }
+
+  /**
+   * Create a config that will write alloc/free logs to stdout.
+   */
+  public static LogConf logToStdout() {
+    return new LogConf(null, LogLoc.STDOUT);
+  }
+
+  /**
+   * Create a config that will write alloc/free logs to stderr.
+   */
+  public static LogConf logToStderr() {
+    return new LogConf(null, LogLoc.STDERR);
+  }
+
+
+  /**
+   * Get the RmmDeviceMemoryResource that was last set through the java APIs. This will
+   * not return the correct value if the resource was not set using the java APIs. It will
+   * return a null if the resource was never set through the java APIs.
+   */
+  public static synchronized RmmDeviceMemoryResource getCurrentDeviceResource() {
+    return deviceResource;
+  }
+
+  /**
+   * Get the currently set RmmTrackingResourceAdaptor that is set. This might return null if
+   * RMM has nto been initialized.
+   */
+  public static synchronized RmmTrackingResourceAdaptor<RmmDeviceMemoryResource> getTracker() {
+    return tracker;
+  }
+
+  /**
+   * Set the current device resource that RMM should use for all allocations and de-allocations.
+   * This should only be done if you feel comfortable that the current device resource has no
+   * pending allocations. Note that the caller of this is responsible for closing the current
+   * RmmDeviceMemoryResource that is returned by this. Assuming that it was not used to create
+   * the newResource. Please use the `shutdown` API to clear the resource as it does best
+   * effort clean up before shutting it down. If `newResource` is not null this will initialize
+   * the CUDA context for the calling thread if it is not already set. The caller is responsible
+   * for setting the desired CUDA device prior to this call if a specific device is already set.
+   * <p>NOTE: All cudf methods will set the chosen CUDA device in the CUDA context of the calling
+   * thread after this returns and `newResource` was not null.
+   * <p>If `newResource` is null this will unset the default CUDA device and mark RMM as not
+   * initialized.
+   * <p>Be aware that for many of these APIs to work the RmmDeviceMemoryResource will need an
+   * `RmmTrackingResourceAdaptor`. If one is not found and `newResource` is not null it will
+   * be added to `newResource`.
+   * <p>Also be very careful with how you set this up. It is possible to set up an
+   * RmmDeviceMemoryResource that is just bad, like multiple pools or pools on top of an
+   * RmmAsyncMemoryResource, that does pooling already. Unless you know what you are doing it is
+   * best to just use the `initialize` API instead.
+   *
+   * @param newResource the new resource to set. If it is null an RmmCudaMemoryResource will be
+   *                    used, and RMM will be set as not initialized.
+   * @param expectedResource the resource that we expect to be set. This is to let us avoid race
+   *                         conditions with multiple things trying to set this at once. It should
+   *                         never happen, but just to be careful.
+   * @param forceChange if true then the expectedResource check is not done.
+   */
+  public static synchronized RmmDeviceMemoryResource setCurrentDeviceResource(
+      RmmDeviceMemoryResource newResource,
+      RmmDeviceMemoryResource expectedResource,
+      boolean forceChange) {
+    boolean shouldInit = false;
+    boolean shouldDeinit = false;
+    RmmDeviceMemoryResource newResourceToSet = newResource;
+    if (newResourceToSet == null) {
+      // We always want it to be set to something or else it can cause problems...
+      newResourceToSet = new RmmCudaMemoryResource();
+      if (initialized) {
+        shouldDeinit = true;
+      }
+    } else if (!initialized) {
+      shouldInit = true;
+    }
+
+    RmmDeviceMemoryResource oldResource = deviceResource;
+    if (!forceChange && expectedResource != null && deviceResource != null) {
+      long expectedOldHandle = expectedResource.getHandle();
+      long oldHandle = deviceResource.getHandle();
+      if (oldHandle != expectedOldHandle) {
+        throw new RmmException("The expected device resource is not correct " +
+            Long.toHexString(oldHandle) + " != " + Long.toHexString(expectedOldHandle));
+      }
+    }
+
+    poolSize = -1;
+    poolingEnabled = false;
+    setGlobalValsFromResource(newResourceToSet);
+    if (newResource != null && tracker == null) {
+      // No tracker was set, but we need one
+      tracker = new RmmTrackingResourceAdaptor<>(newResourceToSet, 256);
+      newResourceToSet = tracker;
+    }
+    long newHandle = newResourceToSet.getHandle();
+    setCurrentDeviceResourceInternal(newHandle);
+    deviceResource = newResource;
+    if (shouldInit) {
+      initDefaultCudaDevice();
+      MemoryCleaner.setDefaultGpu(Cuda.getDevice());
+      initialized = true;
+    }
+
+    if (shouldDeinit) {
+      cleanupDefaultCudaDevice();
+      initialized = false;
+    }
+    return oldResource;
+  }
+
+  private static void setGlobalValsFromResource(RmmDeviceMemoryResource resource) {
+    if (resource instanceof RmmTrackingResourceAdaptor) {
+      Rmm.tracker = (RmmTrackingResourceAdaptor<RmmDeviceMemoryResource>) resource;
+    } else if (resource instanceof RmmPoolMemoryResource) {
+      Rmm.poolSize = Math.max(((RmmPoolMemoryResource)resource).getMaxSize(), Rmm.poolSize);
+      Rmm.poolingEnabled = true;
+    } else if (resource instanceof RmmArenaMemoryResource) {
+      Rmm.poolSize = Math.max(((RmmArenaMemoryResource)resource).getSize(), Rmm.poolSize);
+      Rmm.poolingEnabled = true;
+    } else if (resource instanceof RmmCudaAsyncMemoryResource) {
+      Rmm.poolSize = Math.max(((RmmCudaAsyncMemoryResource)resource).getSize(), Rmm.poolSize);
+      Rmm.poolingEnabled = true;
+    }
+
+    // Recurse as needed
+    if (resource instanceof RmmWrappingDeviceMemoryResource) {
+      setGlobalValsFromResource(((RmmWrappingDeviceMemoryResource<RmmDeviceMemoryResource>)resource).getWrapped());
+    }
+  }
+
+  /**
+   * Initialize memory manager state and storage. This will always initialize
+   * the CUDA context for the calling thread if it is not already set. The
+   * caller is responsible for setting the desired CUDA device prior to this
+   * call if a specific device is already set.
+   * <p>NOTE: All cudf methods will set the chosen CUDA device in the CUDA
+   * context of the calling thread after this returns.
+   * @param allocationMode Allocation strategy to use. Bit set using
+   *                       {@link RmmAllocationMode#CUDA_DEFAULT},
+   *                       {@link RmmAllocationMode#POOL},
+   *                       {@link RmmAllocationMode#ARENA},
+   *                       {@link RmmAllocationMode#CUDA_ASYNC} and
+   *                       {@link RmmAllocationMode#CUDA_MANAGED_MEMORY}
+   * @param logConf        How to do logging or null if you don't want to
+   * @param poolSize       The initial pool size in bytes
+   * @throws IllegalStateException if RMM has already been initialized
+   */
+  public static synchronized void initialize(int allocationMode, LogConf logConf, long poolSize)
+      throws RmmException {
+    if (initialized) {
+      throw new IllegalStateException("RMM is already initialized");
+    }
+
+    boolean isPool = (allocationMode & RmmAllocationMode.POOL) != 0;
+    boolean isArena = (allocationMode & RmmAllocationMode.ARENA) != 0;
+    boolean isAsync = (allocationMode & RmmAllocationMode.CUDA_ASYNC) != 0;
+    boolean isManaged = (allocationMode & RmmAllocationMode.CUDA_MANAGED_MEMORY) != 0;
+
+    if (isAsync && isManaged) {
+      throw new IllegalArgumentException(
+          "CUDA Unified Memory is not supported in CUDA_ASYNC allocation mode");
+    }
+
+    RmmDeviceMemoryResource resource = null;
+    boolean succeeded = false;
+    try {
+      if (isPool) {
+        if (isManaged) {
+          resource = new RmmPoolMemoryResource<>(new RmmManagedMemoryResource(), poolSize, poolSize);
+        } else {
+          resource = new RmmPoolMemoryResource<>(new RmmCudaMemoryResource(), poolSize, poolSize);
+        }
+      } else if (isArena) {
+        if (isManaged) {
+          resource = new RmmArenaMemoryResource<>(new RmmManagedMemoryResource(), poolSize, false);
+        } else {
+          resource = new RmmArenaMemoryResource<>(new RmmCudaMemoryResource(), poolSize, false);
+        }
+      } else if (isAsync) {
+        resource = new RmmLimitingResourceAdaptor<>(
+            new RmmCudaAsyncMemoryResource(poolSize, poolSize), poolSize, 512);
+      } else if (isManaged) {
+        resource = new RmmManagedMemoryResource();
+      } else {
+        resource = new RmmCudaMemoryResource();
+      }
+
+      if (logConf != null && logConf.loc != LogLoc.NONE) {
+        resource = new RmmLoggingResourceAdaptor<>(resource, logConf, true);
+      }
+
+      resource = new RmmTrackingResourceAdaptor<>(resource, 256);
+      setCurrentDeviceResource(resource, null, false);
+      succeeded = true;
+    } finally {
+      if (!succeeded && resource != null) {
+        resource.close();
+      }
+    }
+  }
+
+  /**
+   * Get the most recently set pool size or -1 if RMM has not been initialized or pooling is
+   * not enabled.
+   */
+  public static synchronized long getPoolSize() {
+    return poolSize;
+  }
+
+  /**
+   * Return true if rmm is initialized and pooling has been enabled, else false.
+   */
+  public static synchronized boolean isPoolingEnabled() {
+    return poolingEnabled;
+  }
+
+  /**
+   * Check if RMM has been initialized already or not.
+   */
+  public static boolean isInitialized() throws RmmException {
+    return initialized;
+  }
+
+  /**
+   * Return the amount of RMM memory allocated in bytes. Note that the result
+   * may be less than the actual amount of allocated memory if underlying RMM
+   * allocator decides to return more memory than what was requested. However,
+   * the result will always be a lower bound on the amount allocated.
+   */
+  public static synchronized long getTotalBytesAllocated() {
+    if (tracker == null) {
+      return 0;
+    } else {
+      return tracker.getTotalBytesAllocated();
+    }
+  }
+
+  /**
+   * Returns the maximum amount of RMM memory (Bytes) outstanding during the
+   * lifetime of the process.
+   */
+  public static synchronized long getMaximumTotalBytesAllocated() {
+    if (tracker == null) {
+      return 0;
+    } else {
+      return tracker.getMaxTotalBytesAllocated();
+    }
+  }
+
+  /**
+   * Resets a scoped maximum counter of RMM memory used to keep track of usage between
+   * code sections while debugging.
+   *
+   * @param initialValue an initial value (in Bytes) to use for this scoped counter
+   */
+  public static synchronized void resetScopedMaximumBytesAllocated(long initialValue) {
+    if (tracker != null) {
+      tracker.resetScopedMaxTotalBytesAllocated(initialValue);
+    }
+  }
+
+  /**
+   * Resets a scoped maximum counter of RMM memory used to keep track of usage between
+   * code sections while debugging.
+   *
+   * This resets the counter to 0 Bytes.
+   */
+  public static synchronized void resetScopedMaximumBytesAllocated() {
+    if (tracker != null) {
+      tracker.resetScopedMaxTotalBytesAllocated(0L);
+    }
+  }
+
+  /**
+   * Returns the maximum amount of RMM memory (Bytes) outstanding since the last
+   * `resetScopedMaximumOutstanding` call was issued (it is "scoped" because it's the
+   * maximum amount seen since the last reset).
+   * <p>
+   * If the memory used is net negative (for example if only frees happened since
+   * reset, and we reset to 0), then result will be 0.
+   * <p>
+   * If `resetScopedMaximumBytesAllocated` is never called, the scope is the whole
+   * program and is equivalent to `getMaximumTotalBytesAllocated`.
+   *
+   * @return the scoped maximum bytes allocated
+   */
+  public static synchronized long getScopedMaximumBytesAllocated() {
+    if (tracker == null) {
+      return 0L;
+    } else {
+      return tracker.getScopedMaxTotalBytesAllocated();
+    }
+  }
+
+  /**
+   * Sets the event handler to be called on RMM events (e.g.: allocation failure).
+   * @param handler event handler to invoke on RMM events or null to clear an existing handler
+   * @throws RmmException if an active handler is already set
+   */
+  public static void setEventHandler(RmmEventHandler handler) throws RmmException {
+    setEventHandler(handler, false);
+  }
+
+  /**
+   * Sets the event handler to be called on RMM events (e.g.: allocation failure) and
+   * optionally enable debug mode (callbacks on every allocate and deallocate)
+   * <p>
+   * NOTE: Only enable debug mode when necessary, as code will run much slower!
+   *
+   * @param handler event handler to invoke on RMM events or null to clear an existing handler
+   * @param enableDebug if true enable debug callbacks in RmmEventHandler
+   *                    (onAllocated, onDeallocated)
+   * @throws RmmException if an active handler is already set
+   */
+  public static synchronized void setEventHandler(RmmEventHandler handler,
+                                     boolean enableDebug) throws RmmException {
+    if (!initialized) {
+      throw new RmmException("RMM has not been initialized");
+    }
+    if (deviceResource instanceof RmmEventHandlerResourceAdaptor) {
+      throw new RmmException("Another event handler is already set");
+    }
+    if (tracker == null) {
+      // This is just to be safe it should always be true if this is initialized.
+      throw new RmmException("A tracker must be set for the event handler to work");
+    }
+    RmmEventHandlerResourceAdaptor<RmmDeviceMemoryResource> newResource =
+        new RmmEventHandlerResourceAdaptor<>(deviceResource, tracker, handler, enableDebug);
+    boolean success = false;
+    try {
+      setCurrentDeviceResource(newResource, deviceResource, false);
+      success = true;
+    } finally {
+      if (!success) {
+        newResource.releaseWrapped();
+      }
+    }
+  }
+
+  /** Clears the active RMM event handler if one is set. */
+  public static synchronized void clearEventHandler() throws RmmException {
+    if (deviceResource != null && deviceResource instanceof RmmEventHandlerResourceAdaptor) {
+      RmmEventHandlerResourceAdaptor<RmmDeviceMemoryResource> orig =
+          (RmmEventHandlerResourceAdaptor<RmmDeviceMemoryResource>)deviceResource;
+      boolean success = false;
+      try {
+        setCurrentDeviceResource(orig.wrapped, orig, false);
+        success = true;
+      } finally {
+        if (success) {
+          orig.releaseWrapped();
+        }
+      }
+    }
+  }
+
+  public static native void initDefaultCudaDevice();
+
+  public static native void cleanupDefaultCudaDevice();
+
+  /**
+   * Shut down any initialized RMM instance.  This should be used very rarely.  It does not need to
+   * be used when shutting down your process because CUDA will handle releasing all of the
+   * resources when your process exits.  This really should only be used if you want to turn off the
+   * memory pool for some reasons.  As such we make an effort to be sure no resources have been
+   * leaked before shutting down.  This may involve forcing a JVM GC to collect any leaked java
+   * objects that still point to CUDA memory.  By default this will do a gc every 2 seconds and
+   * wait for up to 4 seconds before throwing an RmmException if not all of the resources are freed.
+   * @throws RmmException on any error. This includes if there are outstanding allocations that
+   * could not be collected.
+   */
+  public static void shutdown() throws RmmException {
+    shutdown(2, 4, TimeUnit.SECONDS);
+  }
+
+  /**
+   * Shut down any initialized RMM instance.  This should be used very rarely.  It does not need to
+   * be used when shutting down your process because CUDA will handle releasing all of the
+   * resources when your process exits.  This really should only be used if you want to turn off the
+   * memory pool for some reasons.  As such we make an effort to be sure no resources have been
+   * leaked before shutting down.  This may involve forcing a JVM GC to collect any leaked java
+   * objects that still point to CUDA memory.
+   *
+   * @param forceGCInterval how frequently should we force a JVM GC. This is just a recommendation
+   *                        to the JVM to do a gc.
+   * @param maxWaitTime the maximum amount of time to wait for all objects to be collected before
+   *                    throwing an exception.
+   * @param units the units for forceGcInterval and maxWaitTime.
+   * @throws RmmException on any error. This includes if there are outstanding allocations that
+   * could not be collected before maxWaitTime.
+   */
+  public static synchronized void shutdown(long forceGCInterval, long maxWaitTime, TimeUnit units)
+      throws RmmException {
+    long now = System.currentTimeMillis();
+    final long endTime = now + units.toMillis(maxWaitTime);
+    long nextGcTime = now;
+    try {
+      if (MemoryCleaner.bestEffortHasRmmBlockers()) {
+        do {
+          if (nextGcTime <= now) {
+            System.gc();
+            nextGcTime = nextGcTime + units.toMillis(forceGCInterval);
+          }
+          // Check if everything is ready about every 10 ms
+          Thread.sleep(10);
+          now = System.currentTimeMillis();
+        } while (endTime > now && MemoryCleaner.bestEffortHasRmmBlockers());
+      }
+    } catch (InterruptedException e) {
+      // Ignored
+    }
+    if (MemoryCleaner.bestEffortHasRmmBlockers()) {
+      throw new RmmException("Could not shut down RMM there appear to be outstanding allocations");
+    }
+    if (initialized) {
+      if (deviceResource != null) {
+        setCurrentDeviceResource(null, deviceResource, true).close();
+      }
+    }
+  }
+
+  /**
+   * Allocate device memory and return a pointer to device memory, using stream 0.
+   * @param size   The size in bytes of the allocated memory region
+   * @return Returned pointer to the allocated memory
+   */
+  public static DeviceMemoryBuffer alloc(long size) {
+    return alloc(size, null);
+  }
+
+  /**
+   * Allocate device memory and return a pointer to device memory.
+   * @param size   The size in bytes of the allocated memory region
+   * @param stream The stream in which to synchronize this command.
+   * @return Returned pointer to the allocated memory
+   */
+  public static DeviceMemoryBuffer alloc(long size, Cuda.Stream stream) {
+    long s = stream == null ? 0 : stream.getStream();
+    return new DeviceMemoryBuffer(allocInternal(size, s), size, stream);
+  }
+
+  private static native long allocInternal(long size, long stream) throws RmmException;
+
+
+  static native void free(long ptr, long length, long stream) throws RmmException;
+
+  /**
+   * Delete an rmm::device_buffer.
+   */
+  static native void freeDeviceBuffer(long rmmBufferAddress) throws RmmException;
+
+  /**
+   * Allocate device memory using `cudaMalloc` and return a pointer to device memory.
+   * @param size   The size in bytes of the allocated memory region
+   * @param stream The stream in which to synchronize this command.
+   * @return Returned pointer to the allocated memory
+   */
+  public static CudaMemoryBuffer allocCuda(long size, Cuda.Stream stream) {
+    long s = stream == null ? 0 : stream.getStream();
+    return new CudaMemoryBuffer(allocCudaInternal(size, s), size, stream);
+  }
+
+  private static native long allocCudaInternal(long size, long stream) throws RmmException;
+
+  static native void freeCuda(long ptr, long length, long stream) throws RmmException;
+
+  static native long newCudaMemoryResource() throws RmmException;
+
+  static native void releaseCudaMemoryResource(long handle);
+
+  static native long newManagedMemoryResource() throws RmmException;
+
+  static native void releaseManagedMemoryResource(long handle);
+
+  static native long newPoolMemoryResource(long childHandle,
+      long initSize, long maxSize) throws RmmException;
+
+  static native void releasePoolMemoryResource(long handle);
+
+  static native long newArenaMemoryResource(long childHandle,
+      long size, boolean dumpOnOOM) throws RmmException;
+
+  static native void releaseArenaMemoryResource(long handle);
+
+  static native long newCudaAsyncMemoryResource(long size, long release) throws RmmException;
+
+  static native void releaseCudaAsyncMemoryResource(long handle);
+
+  static native long newLimitingResourceAdaptor(long handle, long limit, long align) throws RmmException;
+
+  static native void releaseLimitingResourceAdaptor(long handle);
+
+  static native long newLoggingResourceAdaptor(long handle, int type, String path,
+      boolean autoFlush) throws RmmException;
+
+  static native void releaseLoggingResourceAdaptor(long handle);
+
+
+  static native long newTrackingResourceAdaptor(long handle, long alignment) throws RmmException;
+
+  static native void releaseTrackingResourceAdaptor(long handle);
+
+  static native long nativeGetTotalBytesAllocated(long handle);
+
+  static native long nativeGetMaxTotalBytesAllocated(long handle);
+
+  static native void nativeResetScopedMaxTotalBytesAllocated(long handle, long initValue);
+
+  static native long nativeGetScopedMaxTotalBytesAllocated(long handle);
+
+  static native long newEventHandlerResourceAdaptor(long handle, long trackerHandle,
+      RmmEventHandler handler, long[] allocThresholds, long[] deallocThresholds, boolean debug);
+
+  static native long releaseEventHandlerResourceAdaptor(long handle, boolean debug);
+
+  private static native void setCurrentDeviceResourceInternal(long newHandle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmAllocationMode.java b/java/src/main/java/ai/rapids/cudf/RmmAllocationMode.java
new file mode 100644
index 0000000..966c21b
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmAllocationMode.java
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+public class RmmAllocationMode {
+  /**
+   * Use cudaMalloc for allocation
+   */
+  public static final int CUDA_DEFAULT = 0x00000000;
+  /**
+   * Use pool suballocation strategy
+   */
+  public static final int POOL = 0x00000001;
+  /**
+   * Use cudaMallocManaged rather than cudaMalloc
+   */
+  public static final int CUDA_MANAGED_MEMORY = 0x00000002;
+  /**
+   * Use arena suballocation strategy
+   */
+  public static final int ARENA = 0x00000004;
+  /**
+   * Use CUDA async suballocation strategy
+   */
+  public static final int CUDA_ASYNC = 0x00000008;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmArenaMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmArenaMemoryResource.java
new file mode 100644
index 0000000..4638f52
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmArenaMemoryResource.java
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that will pre-allocate a pool of resources and sub-allocate from this
+ * pool to improve memory performance. This uses an algorithm to try and reduce fragmentation
+ * much more than the RmmPoolMemoryResource does.
+ */
+public class RmmArenaMemoryResource<C extends RmmDeviceMemoryResource>
+    extends RmmWrappingDeviceMemoryResource<C> {
+  private final long size;
+  private final boolean dumpLogOnFailure;
+  private long handle = 0;
+
+
+  /**
+   * Create a new arena memory resource taking ownership of the RmmDeviceMemoryResource that it is
+   * wrapping.
+   * @param wrapped the memory resource to use for the pool. This should not be reused.
+   * @param size the size of the pool
+   * @param dumpLogOnFailure if true, dump memory log when running out of memory.
+   */
+  public RmmArenaMemoryResource(C wrapped, long size, boolean dumpLogOnFailure) {
+    super(wrapped);
+    this.size = size;
+    this.dumpLogOnFailure = dumpLogOnFailure;
+    handle = Rmm.newArenaMemoryResource(wrapped.getHandle(), size, dumpLogOnFailure);
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  public long getSize() {
+    return size;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseArenaMemoryResource(handle);
+      handle = 0;
+    }
+    super.close();
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/ARENA(" + wrapped +
+        ", " + size + ", " + dumpLogOnFailure + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmCudaAsyncMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmCudaAsyncMemoryResource.java
new file mode 100644
index 0000000..fa1f13c
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmCudaAsyncMemoryResource.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that uses `cudaMallocAsync` and `cudaFreeAsync` for allocation and
+ * deallocation.
+ */
+public class RmmCudaAsyncMemoryResource implements RmmDeviceMemoryResource {
+  private final long releaseThreshold;
+  private final long size;
+  private long handle = 0;
+
+  /**
+   * Create a new async memory resource
+   * @param size the initial size of the pool
+   * @param releaseThreshold size in bytes for when memory is released back to cuda
+   */
+  public RmmCudaAsyncMemoryResource(long size, long releaseThreshold) {
+    this.size = size;
+    this.releaseThreshold = releaseThreshold;
+    handle = Rmm.newCudaAsyncMemoryResource(size, releaseThreshold);
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  public long getSize() {
+    return size;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseCudaAsyncMemoryResource(handle);
+      handle = 0;
+    }
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/ASYNC(" + size + ", " + releaseThreshold + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmCudaMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmCudaMemoryResource.java
new file mode 100644
index 0000000..f31e920
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmCudaMemoryResource.java
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that uses `cudaMalloc` and `cudaFree` for allocation and deallocation.
+ */
+public class RmmCudaMemoryResource implements RmmDeviceMemoryResource {
+  private long handle = 0;
+
+  public RmmCudaMemoryResource() {
+    handle = Rmm.newCudaMemoryResource();
+  }
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseCudaMemoryResource(handle);
+      handle = 0;
+    }
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/CUDA()";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmDeviceMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmDeviceMemoryResource.java
new file mode 100644
index 0000000..f446313
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmDeviceMemoryResource.java
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A resource that allocates/deallocates device memory. This is not intended to be something that
+ * a user will just subclass. This is intended to be a wrapper around a C++ class that RMM will
+ * use directly.
+ */
+public interface RmmDeviceMemoryResource extends AutoCloseable {
+  /**
+   * Returns a pointer to the underlying C++ class that implements rmm::mr::device_memory_resource
+   */
+  long getHandle();
+
+  // Remove the exception...
+  void close();
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmEventHandler.java b/java/src/main/java/ai/rapids/cudf/RmmEventHandler.java
new file mode 100644
index 0000000..347ef47
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmEventHandler.java
@@ -0,0 +1,98 @@
+/*
+ *
+ *  Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+public interface RmmEventHandler {
+  /**
+   * Invoked on a memory allocation failure.
+   * @param sizeRequested number of bytes that failed to allocate
+   * @deprecated deprecated in favor of onAllocFailure(long, boolean)
+   * @return true if the memory allocation should be retried or false if it should fail
+   */
+  default boolean onAllocFailure(long sizeRequested) {
+    // this should not be called since it was the previous interface,
+    // and it was abstract before, throwing by default for good measure.
+    throw new UnsupportedOperationException(
+        "Unexpected invocation of deprecated onAllocFailure without retry count.");
+  }
+
+  /**
+   * Invoked after every memory allocation when debug mode is enabled.
+   * @param size number of bytes allocated
+   */
+  default void onAllocated(long size) {}
+
+  /**
+   * Invoked after every memory deallocation when debug mode is enabled.
+   * @param size number of bytes deallocated
+   */
+  default void onDeallocated(long size) {}
+
+  /**
+   * Invoked on a memory allocation failure.
+   * @param sizeRequested number of bytes that failed to allocate
+   * @param retryCount number of times this allocation has been retried after failure
+   * @return true if the memory allocation should be retried or false if it should fail
+   */
+  default boolean onAllocFailure(long sizeRequested, int retryCount) {
+    // newer code should override this implementation of `onAllocFailure` to handle
+    // `retryCount`. Otherwise, we call the prior implementation to not
+    // break existing code.
+    return onAllocFailure(sizeRequested);
+  }
+
+  /**
+   * Get the memory thresholds that will trigger {@link #onAllocThreshold(long)}
+   * to be called when one or more of the thresholds is crossed during a memory allocation.
+   * A threshold is crossed when the total memory allocated before the RMM allocate operation
+   * is less than a threshold value and the threshold value is less than or equal to the
+   * total memory allocated after the RMM memory allocate operation.
+   * @return allocate memory thresholds or null for no thresholds.
+   */
+  long[] getAllocThresholds();
+
+  /**
+   * Get the memory thresholds that will trigger {@link #onDeallocThreshold(long)}
+   * to be called when one or more of the thresholds is crossed during a memory deallocation.
+   * A threshold is crossed when the total memory allocated before the RMM deallocate operation
+   * is greater than or equal to a threshold value and the threshold value is greater than the
+   * total memory allocated after the RMM memory deallocate operation.
+   * @return deallocate memory thresholds or null for no thresholds.
+   */
+  long[] getDeallocThresholds();
+
+  /**
+   * Invoked after an RMM memory allocate operation when an allocate threshold is crossed.
+   * See {@link #getAllocThresholds()} for details on allocate threshold crossing.
+   * <p>NOTE: Any exception thrown by this method will cause the corresponding allocation
+   * that triggered the threshold callback to be released before the exception is
+   * propagated to the application.
+   * @param totalAllocSize total amount of memory allocated after the crossing
+   */
+  void onAllocThreshold(long totalAllocSize);
+
+  /**
+   * Invoked after an RMM memory deallocation operation when a deallocate threshold is crossed.
+   * See {@link #getDeallocThresholds()} for details on deallocate threshold crossing.
+   * <p>NOTE: Any exception thrown by this method will be propagated to the application
+   * after the resource that triggered the threshold was released.
+   * @param totalAllocSize total amount of memory allocated after the crossing
+   */
+  void onDeallocThreshold(long totalAllocSize);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmEventHandlerResourceAdaptor.java b/java/src/main/java/ai/rapids/cudf/RmmEventHandlerResourceAdaptor.java
new file mode 100644
index 0000000..30d1d8a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmEventHandlerResourceAdaptor.java
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import java.util.Arrays;
+
+/**
+ * A device memory resource that will give callbacks in specific situations.
+ */
+public class RmmEventHandlerResourceAdaptor<C extends RmmDeviceMemoryResource>
+    extends RmmWrappingDeviceMemoryResource<C> {
+  private long handle = 0;
+  private final long [] allocThresholds;
+  private final long [] deallocThresholds;
+  private final boolean debug;
+
+  /**
+   * Create a new logging resource adaptor.
+   * @param wrapped the memory resource to get callbacks for. This should not be reused.
+   * @param handler the handler that will get the callbacks
+   * @param tracker the tracking event handler
+   * @param debug true if you want all the callbacks, else false
+   */
+  public RmmEventHandlerResourceAdaptor(C wrapped, RmmTrackingResourceAdaptor<?> tracker,
+      RmmEventHandler handler, boolean debug) {
+    super(wrapped);
+    this.debug = debug;
+    allocThresholds = sortThresholds(handler.getAllocThresholds());
+    deallocThresholds = sortThresholds(handler.getDeallocThresholds());
+    handle = Rmm.newEventHandlerResourceAdaptor(wrapped.getHandle(), tracker.getHandle(), handler,
+        allocThresholds, deallocThresholds, debug);
+  }
+
+  private static long[] sortThresholds(long[] thresholds) {
+    if (thresholds == null) {
+      return null;
+    }
+    long[] result = Arrays.copyOf(thresholds, thresholds.length);
+    Arrays.sort(result);
+    return result;
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseEventHandlerResourceAdaptor(handle, debug);
+      handle = 0;
+    }
+    super.close();
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/EVENT(" + wrapped +
+        ", " + debug + ", " + Arrays.toString(allocThresholds) + ", " +
+        Arrays.toString(deallocThresholds) + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmException.java b/java/src/main/java/ai/rapids/cudf/RmmException.java
new file mode 100755
index 0000000..8e50a2c
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmException.java
@@ -0,0 +1,30 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+
+/**
+ * Exception from RMM allocator.
+ */
+public class RmmException extends RuntimeException {
+  RmmException(String message) {
+    super(message);
+  }
+
+  RmmException(String message, Throwable cause) {
+    super(message, cause);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmLimitingResourceAdaptor.java b/java/src/main/java/ai/rapids/cudf/RmmLimitingResourceAdaptor.java
new file mode 100644
index 0000000..0b0aa6d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmLimitingResourceAdaptor.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that will limit the maximum amount allocated.
+ */
+public class RmmLimitingResourceAdaptor<C extends RmmDeviceMemoryResource>
+    extends RmmWrappingDeviceMemoryResource<C> {
+  private final long limit;
+  private final long alignment;
+  private long handle = 0;
+
+  /**
+   * Create a new limiting resource adaptor.
+   * @param wrapped the memory resource to limit. This should not be reused.
+   * @param limit the allocation limit in bytes
+   * @param alignment the alignment
+   */
+  public RmmLimitingResourceAdaptor(C wrapped, long limit, long alignment) {
+    super(wrapped);
+    this.limit = limit;
+    this.alignment = alignment;
+    handle = Rmm.newLimitingResourceAdaptor(wrapped.getHandle(), limit, alignment);
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseLimitingResourceAdaptor(handle);
+      handle = 0;
+    }
+    super.close();
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/LIMIT(" + wrapped +
+        ", " + limit + ", " + alignment + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmLoggingResourceAdaptor.java b/java/src/main/java/ai/rapids/cudf/RmmLoggingResourceAdaptor.java
new file mode 100644
index 0000000..fe5d7e4
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmLoggingResourceAdaptor.java
@@ -0,0 +1,58 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that will log interactions.
+ */
+public class RmmLoggingResourceAdaptor<C extends RmmDeviceMemoryResource>
+    extends RmmWrappingDeviceMemoryResource<C> {
+  private long handle = 0;
+
+  /**
+   * Create a new logging resource adaptor.
+   * @param wrapped the memory resource to log interactions with. This should not be reused.
+   * @param conf the config of where this should be logged to
+   * @param autoFlush should the results be flushed after each entry or not.
+   */
+  public RmmLoggingResourceAdaptor(C wrapped, Rmm.LogConf conf, boolean autoFlush) {
+    super(wrapped);
+    if (conf.loc == Rmm.LogLoc.NONE) {
+      throw new RmmException("Cannot initialize RmmLoggingResourceAdaptor with no logging");
+    }
+    handle = Rmm.newLoggingResourceAdaptor(wrapped.getHandle(), conf.loc.internalId,
+        conf.file == null ? null : conf.file.getAbsolutePath(), autoFlush);
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseLoggingResourceAdaptor(handle);
+      handle = 0;
+    }
+    super.close();
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/LOG(" + wrapped + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmManagedMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmManagedMemoryResource.java
new file mode 100644
index 0000000..7a2f41c
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmManagedMemoryResource.java
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that uses `cudaMallocManaged` and `cudaFreeManaged` for allocation and
+ * deallocation.
+ */
+public class RmmManagedMemoryResource implements RmmDeviceMemoryResource {
+  private long handle = 0;
+
+  public RmmManagedMemoryResource() {
+    handle = Rmm.newManagedMemoryResource();
+  }
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseManagedMemoryResource(handle);
+      handle = 0;
+    }
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/CUDA_MANAGED()";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmPoolMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmPoolMemoryResource.java
new file mode 100644
index 0000000..7febd68
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmPoolMemoryResource.java
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that will pre-allocate a pool of resources and sub-allocate from this
+ * pool to improve memory performance.
+ */
+public class RmmPoolMemoryResource<C extends RmmDeviceMemoryResource>
+    extends RmmWrappingDeviceMemoryResource<C> {
+  private long handle = 0;
+  private final long initSize;
+  private final long maxSize;
+
+  /**
+   * Create a new pooled memory resource taking ownership of the RmmDeviceMemoryResource that it is
+   * wrapping.
+   * @param wrapped the memory resource to use for the pool. This should not be reused.
+   * @param initSize the size of the initial pool
+   * @param maxSize the size of the maximum pool
+   */
+  public RmmPoolMemoryResource(C wrapped, long initSize, long maxSize) {
+    super(wrapped);
+    this.initSize = initSize;
+    this.maxSize = maxSize;
+    handle = Rmm.newPoolMemoryResource(wrapped.getHandle(), initSize, maxSize);
+  }
+
+  public long getMaxSize() {
+    return maxSize;
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releasePoolMemoryResource(handle);
+      handle = 0;
+    }
+    super.close();
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/POOL(" + wrapped + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmTrackingResourceAdaptor.java b/java/src/main/java/ai/rapids/cudf/RmmTrackingResourceAdaptor.java
new file mode 100644
index 0000000..e9f1b08
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmTrackingResourceAdaptor.java
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A device memory resource that will track some basic statistics about the memory usage.
+ */
+public class RmmTrackingResourceAdaptor<C extends RmmDeviceMemoryResource>
+    extends RmmWrappingDeviceMemoryResource<C> {
+  private long handle = 0;
+
+  /**
+   * Create a new tracking resource adaptor.
+   * @param wrapped the memory resource to track allocations. This should not be reused.
+   * @param alignment the alignment to apply.
+   */
+  public RmmTrackingResourceAdaptor(C wrapped, long alignment) {
+    super(wrapped);
+    handle = Rmm.newTrackingResourceAdaptor(wrapped.getHandle(), alignment);
+  }
+
+  @Override
+  public long getHandle() {
+    return handle;
+  }
+
+  public long getTotalBytesAllocated() {
+    return Rmm.nativeGetTotalBytesAllocated(getHandle());
+  }
+
+  public long getMaxTotalBytesAllocated() {
+    return Rmm.nativeGetMaxTotalBytesAllocated(getHandle());
+  }
+
+  public void resetScopedMaxTotalBytesAllocated(long initValue) {
+    Rmm.nativeResetScopedMaxTotalBytesAllocated(getHandle(), initValue);
+  }
+
+  public long getScopedMaxTotalBytesAllocated() {
+    return Rmm.nativeGetScopedMaxTotalBytesAllocated(getHandle());
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      Rmm.releaseTrackingResourceAdaptor(handle);
+      handle = 0;
+    }
+    super.close();
+  }
+
+  @Override
+  public String toString() {
+    return Long.toHexString(getHandle()) + "/TRACK(" + wrapped + ")";
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RmmWrappingDeviceMemoryResource.java b/java/src/main/java/ai/rapids/cudf/RmmWrappingDeviceMemoryResource.java
new file mode 100644
index 0000000..b764798
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RmmWrappingDeviceMemoryResource.java
@@ -0,0 +1,56 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * A resource that wraps another RmmDeviceMemoryResource
+ */
+public abstract class RmmWrappingDeviceMemoryResource<C extends RmmDeviceMemoryResource>
+    implements RmmDeviceMemoryResource {
+  protected C wrapped = null;
+
+  public RmmWrappingDeviceMemoryResource(C wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  /**
+   * Get the resource that this is wrapping.  Be very careful when using this as the returned value
+   * should not be added to another resource until it has been released.
+   * @return the resource that this is wrapping.
+   */
+  public C getWrapped() {
+    return this.wrapped;
+  }
+
+  /**
+   * Release the wrapped device memory resource and close this.
+   * @return the wrapped DeviceMemoryResource.
+   */
+  public C releaseWrapped() {
+    C ret = this.wrapped;
+    this.wrapped = null;
+    close();
+    return ret;
+  }
+
+  @Override
+  public void close() {
+    if (wrapped != null) {
+      wrapped.close();
+      wrapped = null;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RollingAggregation.java b/java/src/main/java/ai/rapids/cudf/RollingAggregation.java
new file mode 100644
index 0000000..a460bd4
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RollingAggregation.java
@@ -0,0 +1,230 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An aggregation that can be used on rolling windows.
+ */
+public final class RollingAggregation {
+  private final Aggregation wrapped;
+
+  private RollingAggregation(Aggregation wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  long createNativeInstance() {
+    return wrapped.createNativeInstance();
+  }
+
+  long getDefaultOutput() {
+    return wrapped.getDefaultOutput();
+  }
+
+  /**
+   * Add a column to the Aggregation so it can be used on a specific column of data.
+   * @param columnIndex the index of the column to operate on.
+   */
+  public RollingAggregationOnColumn onColumn(int columnIndex) {
+    return new RollingAggregationOnColumn(this, columnIndex);
+  }
+
+  @Override
+  public int hashCode() {
+    return wrapped.hashCode();
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other == this) {
+      return true;
+    } else if (other instanceof RollingAggregation) {
+      RollingAggregation o = (RollingAggregation) other;
+      return wrapped.equals(o.wrapped);
+    }
+    return false;
+  }
+
+  /**
+   * Rolling Window Sum
+   */
+  public static RollingAggregation sum() {
+    return new RollingAggregation(Aggregation.sum());
+  }
+
+
+  /**
+   * Rolling Window Min
+   */
+  public static RollingAggregation min() {
+    return new RollingAggregation(Aggregation.min());
+  }
+
+  /**
+   * Rolling Window Max
+   */
+  public static RollingAggregation max() {
+    return new RollingAggregation(Aggregation.max());
+  }
+
+  /**
+   * Rolling Window Standard Deviation with 1 as delta degrees of freedom(DDOF).
+   */
+  public static RollingAggregation standardDeviation() {
+    return new RollingAggregation(Aggregation.standardDeviation());
+  }
+
+  /**
+   * Rolling Window Standard Deviation with configurable delta degrees of freedom(DDOF).
+   */
+  public static RollingAggregation standardDeviation(int ddof) {
+    return new RollingAggregation(Aggregation.standardDeviation(ddof));
+  }
+
+  /**
+   * Count number of valid, a.k.a. non-null, elements.
+   */
+  public static RollingAggregation count() {
+    return new RollingAggregation(Aggregation.count());
+  }
+
+  /**
+   * Count number of elements.
+   * @param nullPolicy INCLUDE if nulls should be counted. EXCLUDE if only non-null values
+   *                   should be counted.
+   */
+  public static RollingAggregation count(NullPolicy nullPolicy) {
+    return new RollingAggregation(Aggregation.count(nullPolicy));
+  }
+
+  /**
+   * Arithmetic Mean
+   */
+  public static RollingAggregation mean() {
+    return new RollingAggregation(Aggregation.mean());
+  }
+
+
+  /**
+   * Index of max element.
+   */
+  public static RollingAggregation argMax() {
+    return new RollingAggregation(Aggregation.argMax());
+  }
+
+  /**
+   * Index of min element.
+   */
+  public static RollingAggregation argMin() {
+    return new RollingAggregation(Aggregation.argMin());
+  }
+
+
+  /**
+   * Get the row number.
+   */
+  public static RollingAggregation rowNumber() {
+    return new RollingAggregation(Aggregation.rowNumber());
+  }
+
+
+  /**
+   * In a rolling window return the value offset entries ahead or null if it is outside of the
+   * window.
+   */
+  public static RollingAggregation lead(int offset) {
+    return lead(offset, null);
+  }
+
+  /**
+   * In a rolling window return the value offset entries ahead or the corresponding value from
+   * defaultOutput if it is outside of the window. Note that this does not take any ownership of
+   * defaultOutput and the caller mush ensure that defaultOutput remains valid during the life
+   * time of this aggregation operation.
+   */
+  public static RollingAggregation lead(int offset, ColumnVector defaultOutput) {
+    return new RollingAggregation(Aggregation.lead(offset, defaultOutput));
+  }
+
+
+
+  /**
+   * In a rolling window return the value offset entries behind or null if it is outside of the
+   * window.
+   */
+  public static RollingAggregation lag(int offset) {
+    return lag(offset, null);
+  }
+
+  /**
+   * In a rolling window return the value offset entries behind or the corresponding value from
+   * defaultOutput if it is outside of the window. Note that this does not take any ownership of
+   * defaultOutput and the caller mush ensure that defaultOutput remains valid during the life
+   * time of this aggregation operation.
+   */
+  public static RollingAggregation lag(int offset, ColumnVector defaultOutput) {
+    return new RollingAggregation(Aggregation.lag(offset, defaultOutput));
+  }
+
+
+  /**
+   * Collect the values into a list. Nulls will be skipped.
+   */
+  public static RollingAggregation collectList() {
+    return new RollingAggregation(Aggregation.collectList());
+  }
+
+  /**
+   * Collect the values into a list.
+   *
+   * @param nullPolicy Indicates whether to include/exclude nulls during collection.
+   */
+  public static RollingAggregation collectList(NullPolicy nullPolicy) {
+    return new RollingAggregation(Aggregation.collectList(nullPolicy));
+  }
+
+
+  /**
+   * Collect the values into a set. All null values will be excluded, and all nan values are regarded as
+   * unique instances.
+   */
+  public static RollingAggregation collectSet() {
+    return new RollingAggregation(Aggregation.collectSet());
+  }
+
+  /**
+   * Collect the values into a set.
+   *
+   * @param nullPolicy   Indicates whether to include/exclude nulls during collection.
+   * @param nullEquality Flag to specify whether null entries within each list should be considered equal.
+   * @param nanEquality  Flag to specify whether NaN values in floating point column should be considered equal.
+   */
+  public static RollingAggregation collectSet(NullPolicy nullPolicy, NullEquality nullEquality, NaNEquality nanEquality) {
+    return new RollingAggregation(Aggregation.collectSet(nullPolicy, nullEquality, nanEquality));
+  }
+
+  /**
+   * Select the nth element from a specified window.
+   *
+   * @param n          Indicates the index of the element to be selected from the window
+   * @param nullPolicy Indicates whether null elements are to be skipped, or not
+   */
+  public static RollingAggregation nth(int n, NullPolicy nullPolicy) {
+    return new RollingAggregation(Aggregation.nth(n, nullPolicy));
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RollingAggregationOnColumn.java b/java/src/main/java/ai/rapids/cudf/RollingAggregationOnColumn.java
new file mode 100644
index 0000000..a6b1484
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RollingAggregationOnColumn.java
@@ -0,0 +1,65 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * A RollingAggregation for a specific column in a table.
+ */
+public final class RollingAggregationOnColumn {
+    protected final RollingAggregation wrapped;
+    protected final int columnIndex;
+
+    RollingAggregationOnColumn(RollingAggregation wrapped, int columnIndex) {
+        this.wrapped = wrapped;
+        this.columnIndex = columnIndex;
+    }
+
+    public int getColumnIndex() {
+        return columnIndex;
+    }
+
+
+    public AggregationOverWindow overWindow(WindowOptions windowOptions) {
+        return new AggregationOverWindow(this, windowOptions);
+    }
+
+    @Override
+    public int hashCode() {
+        return 31 * wrapped.hashCode() + columnIndex;
+    }
+
+    @Override
+    public boolean equals(Object other) {
+        if (other == this) {
+            return true;
+        } else if (other instanceof RollingAggregationOnColumn) {
+            RollingAggregationOnColumn o = (RollingAggregationOnColumn) other;
+            return wrapped.equals(o.wrapped) && columnIndex == o.columnIndex;
+        }
+        return false;
+    }
+
+    long createNativeInstance() {
+        return wrapped.createNativeInstance();
+    }
+
+    long getDefaultOutput() {
+        return wrapped.getDefaultOutput();
+    }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/RoundMode.java b/java/src/main/java/ai/rapids/cudf/RoundMode.java
new file mode 100644
index 0000000..72b02d2
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/RoundMode.java
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * Rounding modes supported in round method.
+ * HALF_UP : Rounding mode to round towards "nearest neighbor". If both neighbors are
+ * equidistant, then round up.
+ * HALF_EVEN : Rounding mode to round towards the "nearest neighbor". If both neighbors are
+ * equidistant, round towards the even neighbor.
+ */
+public enum RoundMode {
+  HALF_UP(0),
+  HALF_EVEN(1);
+  final int nativeId;
+
+  RoundMode(int nativeId) { this.nativeId = nativeId; }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Scalar.java b/java/src/main/java/ai/rapids/cudf/Scalar.java
new file mode 100644
index 0000000..70538ab
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Scalar.java
@@ -0,0 +1,969 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.nio.ByteOrder;
+import java.nio.charset.StandardCharsets;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Objects;
+
+/**
+ * A single scalar value.
+ */
+public final class Scalar implements AutoCloseable, BinaryOperable {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  private static final Logger LOG = LoggerFactory.getLogger(Scalar.class);
+
+  private final DType type;
+  private int refCount;
+  private final OffHeapState offHeap;
+
+  public static Scalar fromNull(DType type) {
+    switch (type.typeId) {
+    case EMPTY:
+    case BOOL8:
+      return new Scalar(type, makeBool8Scalar(false, false));
+    case INT8:
+      return new Scalar(type, makeInt8Scalar((byte)0, false));
+    case UINT8:
+      return new Scalar(type, makeUint8Scalar((byte)0, false));
+    case INT16:
+      return new Scalar(type, makeInt16Scalar((short)0, false));
+    case UINT16:
+      return new Scalar(type, makeUint16Scalar((short)0, false));
+    case INT32:
+      return new Scalar(type, makeInt32Scalar(0, false));
+    case UINT32:
+      return new Scalar(type, makeUint32Scalar(0, false));
+    case TIMESTAMP_DAYS:
+      return new Scalar(type, makeTimestampDaysScalar(0, false));
+    case FLOAT32:
+      return new Scalar(type, makeFloat32Scalar(0, false));
+    case FLOAT64:
+      return new Scalar(type, makeFloat64Scalar(0, false));
+    case INT64:
+      return new Scalar(type, makeInt64Scalar(0, false));
+    case UINT64:
+      return new Scalar(type, makeUint64Scalar(0, false));
+    case TIMESTAMP_SECONDS:
+    case TIMESTAMP_MILLISECONDS:
+    case TIMESTAMP_MICROSECONDS:
+    case TIMESTAMP_NANOSECONDS:
+      return new Scalar(type, makeTimestampTimeScalar(type.typeId.getNativeId(), 0, false));
+    case STRING:
+      return new Scalar(type, makeStringScalar(null, false));
+    case DURATION_DAYS:
+      return new Scalar(type, makeDurationDaysScalar(0, false));
+    case DURATION_MICROSECONDS:
+    case DURATION_MILLISECONDS:
+    case DURATION_NANOSECONDS:
+    case DURATION_SECONDS:
+      return new Scalar(type, makeDurationTimeScalar(type.typeId.getNativeId(), 0, false));
+    case DECIMAL32:
+      return new Scalar(type, makeDecimal32Scalar(0, type.getScale(), false));
+    case DECIMAL64:
+      return new Scalar(type, makeDecimal64Scalar(0L, type.getScale(), false));
+    case DECIMAL128:
+      return new Scalar(type, makeDecimal128Scalar(BigInteger.ZERO.toByteArray(), type.getScale(), false));
+    case LIST:
+      throw new IllegalArgumentException("Please call 'listFromNull' to create a null list scalar.");
+    default:
+      throw new IllegalArgumentException("Unexpected type: " + type);
+    }
+  }
+
+  public static Scalar fromBool(boolean value) {
+    return new Scalar(DType.BOOL8, makeBool8Scalar(value, true));
+  }
+
+  public static Scalar fromBool(Boolean value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.BOOL8);
+    }
+    return Scalar.fromBool(value.booleanValue());
+  }
+
+  public static Scalar fromByte(byte value) {
+    return new Scalar(DType.INT8, makeInt8Scalar(value, true));
+  }
+
+  public static Scalar fromByte(Byte value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.INT8);
+    }
+    return Scalar.fromByte(value.byteValue());
+  }
+
+  public static Scalar fromUnsignedByte(byte value) {
+    return new Scalar(DType.UINT8, makeUint8Scalar(value, true));
+  }
+
+  public static Scalar fromUnsignedByte(Byte value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.UINT8);
+    }
+    return Scalar.fromUnsignedByte(value.byteValue());
+  }
+
+  public static Scalar fromShort(short value) {
+    return new Scalar(DType.INT16, makeInt16Scalar(value, true));
+  }
+
+  public static Scalar fromShort(Short value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.INT16);
+    }
+    return Scalar.fromShort(value.shortValue());
+  }
+
+  public static Scalar fromUnsignedShort(short value) {
+    return new Scalar(DType.UINT16, makeUint16Scalar(value, true));
+  }
+
+  public static Scalar fromUnsignedShort(Short value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.UINT16);
+    }
+    return Scalar.fromUnsignedShort(value.shortValue());
+  }
+
+  /**
+   * Returns a DURATION_DAYS scalar
+   * @param value - days
+   * @return - Scalar value
+   */
+  public static Scalar durationDaysFromInt(int value) {
+    return new Scalar(DType.DURATION_DAYS, makeDurationDaysScalar(value, true));
+  }
+
+  /**
+   * Returns a DURATION_DAYS scalar
+   * @param value - days
+   * @return - Scalar value
+   */
+  public static Scalar durationDaysFromInt(Integer value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.DURATION_DAYS);
+    }
+    return Scalar.durationDaysFromInt(value.intValue());
+  }
+
+  public static Scalar fromInt(int value) {
+    return new Scalar(DType.INT32, makeInt32Scalar(value, true));
+  }
+
+  public static Scalar fromInt(Integer value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.INT32);
+    }
+    return Scalar.fromInt(value.intValue());
+  }
+
+  public static Scalar fromUnsignedInt(int value) {
+    return new Scalar(DType.UINT32, makeUint32Scalar(value, true));
+  }
+
+  public static Scalar fromUnsignedInt(Integer value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.UINT32);
+    }
+    return Scalar.fromUnsignedInt(value.intValue());
+  }
+
+  public static Scalar fromLong(long value) {
+    return new Scalar(DType.INT64, makeInt64Scalar(value, true));
+  }
+
+  public static Scalar fromLong(Long value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.INT64);
+    }
+    return Scalar.fromLong(value.longValue());
+  }
+
+  public static Scalar fromUnsignedLong(long value) {
+    return new Scalar(DType.UINT64, makeUint64Scalar(value, true));
+  }
+
+  public static Scalar fromUnsignedLong(Long value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.UINT64);
+    }
+    return Scalar.fromUnsignedLong(value.longValue());
+  }
+
+  public static Scalar fromFloat(float value) {
+    return new Scalar(DType.FLOAT32, makeFloat32Scalar(value, true));
+  }
+
+  public static Scalar fromDecimal(int scale, int unscaledValue) {
+    long handle = makeDecimal32Scalar(unscaledValue, scale, true);
+    return new Scalar(DType.create(DType.DTypeEnum.DECIMAL32, scale), handle);
+  }
+
+  public static Scalar fromDecimal(int scale, long unscaledValue) {
+    long handle = makeDecimal64Scalar(unscaledValue, scale, true);
+    return new Scalar(DType.create(DType.DTypeEnum.DECIMAL64, scale), handle);
+  }
+
+  public static Scalar fromDecimal(int scale, BigInteger unscaledValue) {
+    byte[] unscaledValueBytes = unscaledValue.toByteArray();
+    byte[] finalBytes =  convertDecimal128FromJavaToCudf(unscaledValueBytes);
+    long handle = makeDecimal128Scalar(finalBytes, scale, true);
+    return new Scalar(DType.create(DType.DTypeEnum.DECIMAL128, scale), handle);
+  }
+
+  public static Scalar fromFloat(Float value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.FLOAT32);
+    }
+    return Scalar.fromFloat(value.floatValue());
+  }
+
+  public static Scalar fromDouble(double value) {
+    return new Scalar(DType.FLOAT64, makeFloat64Scalar(value, true));
+  }
+
+  public static Scalar fromDouble(Double value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.FLOAT64);
+    }
+    return Scalar.fromDouble(value.doubleValue());
+  }
+
+  public static Scalar fromDecimal(BigDecimal value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.create(DType.DTypeEnum.DECIMAL64, 0));
+    }
+    DType dt = DType.fromJavaBigDecimal(value);
+    return fromDecimal(value.unscaledValue(), dt);
+  }
+
+  public static Scalar fromDecimal(BigInteger unscaledValue, DType dt) {
+    if (unscaledValue == null) {
+      return Scalar.fromNull(dt);
+    }
+    long handle;
+    if (dt.typeId == DType.DTypeEnum.DECIMAL32) {
+      handle = makeDecimal32Scalar(unscaledValue.intValueExact(), dt.getScale(), true);
+    } else if (dt.typeId == DType.DTypeEnum.DECIMAL64) {
+      handle = makeDecimal64Scalar(unscaledValue.longValueExact(), dt.getScale(), true);
+    } else {
+      byte[] unscaledValueBytes = unscaledValue.toByteArray();
+      byte[] finalBytes =  convertDecimal128FromJavaToCudf(unscaledValueBytes);
+      handle = makeDecimal128Scalar(finalBytes, dt.getScale(), true);
+    }
+    return new Scalar(dt, handle);
+  }
+
+  public static Scalar timestampDaysFromInt(int value) {
+    return new Scalar(DType.TIMESTAMP_DAYS, makeTimestampDaysScalar(value, true));
+  }
+
+  public static Scalar timestampDaysFromInt(Integer value) {
+    if (value == null) {
+      return Scalar.fromNull(DType.TIMESTAMP_DAYS);
+    }
+    return Scalar.timestampDaysFromInt(value.intValue());
+  }
+
+  /**
+   * Returns a duration scalar based on the type parameter.
+   * @param type - dtype of scalar to be returned
+   * @param value - corresponding value for the scalar
+   * @return - Scalar of the respective type
+   */
+  public static Scalar durationFromLong(DType type, long value) {
+    if (type.isDurationType()) {
+      if (type.equals(DType.DURATION_DAYS)) {
+        int intValue = (int)value;
+        if (value != intValue) {
+          throw new IllegalArgumentException("value too large for type " + type + ": " + value);
+        }
+        return durationDaysFromInt(intValue);
+      } else {
+        return new Scalar(type, makeDurationTimeScalar(type.typeId.getNativeId(), value, true));
+      }
+    } else {
+      throw new IllegalArgumentException("type is not a timestamp: " + type);
+    }
+  }
+
+  /**
+   * Returns a duration scalar based on the type parameter.
+   * @param type - dtype of scalar to be returned
+   * @param value - corresponding value for the scalar
+   * @return - Scalar of the respective type
+   */
+  public static Scalar durationFromLong(DType type, Long value) {
+    if (value == null) {
+      return Scalar.fromNull(type);
+    }
+    return Scalar.durationFromLong(type, value.longValue());
+  }
+
+  public static Scalar timestampFromLong(DType type, long value) {
+    if (type.isTimestampType()) {
+      if (type.equals(DType.TIMESTAMP_DAYS)) {
+        int intValue = (int)value;
+        if (value != intValue) {
+          throw new IllegalArgumentException("value too large for type " + type + ": " + value);
+        }
+        return timestampDaysFromInt(intValue);
+      } else {
+        return new Scalar(type, makeTimestampTimeScalar(type.typeId.getNativeId(), value, true));
+      }
+    } else {
+      throw new IllegalArgumentException("type is not a timestamp: " + type);
+    }
+  }
+
+  public static Scalar timestampFromLong(DType type, Long value) {
+    if (value == null) {
+      return Scalar.fromNull(type);
+    }
+    return Scalar.timestampFromLong(type, value.longValue());
+  }
+
+  public static Scalar fromString(String value) {
+    return fromUTF8String(value == null ? null : value.getBytes(StandardCharsets.UTF_8));
+  }
+
+  /**
+   * Creates a String scalar from an array of UTF8 bytes.
+   * @param value the array of UTF8 bytes
+   * @return a String scalar
+   */
+  public static Scalar fromUTF8String(byte[] value) {
+    if (value == null) {
+      return fromNull(DType.STRING);
+    }
+    return new Scalar(DType.STRING, makeStringScalar(value, true));
+  }
+
+  /**
+   * Creates a null scalar of list type.
+   *
+   * Having this special API because the element type is required to build an empty
+   * nested column as the underlying column of the list scalar.
+   *
+   * @param elementType the data type of the element in the list.
+   * @return a null scalar of list type
+   */
+  public static Scalar listFromNull(HostColumnVector.DataType elementType) {
+    try (ColumnVector col = ColumnVector.empty(elementType)) {
+      return new Scalar(DType.LIST, makeListScalar(col.getNativeView(), false));
+    }
+  }
+
+  /**
+   * Creates a scalar of list from a ColumnView.
+   *
+   * All the rows in the ColumnView will be copied into the Scalar. So the ColumnView
+   * can be closed after this call completes.
+   */
+  public static Scalar listFromColumnView(ColumnView list) {
+    if (list == null) {
+      throw new IllegalArgumentException("'list' should NOT be null." +
+          " Please call 'listFromNull' to create a null list scalar.");
+    }
+    return new Scalar(DType.LIST, makeListScalar(list.getNativeView(), true));
+  }
+
+  /**
+   * Creates a null scalar of struct type.
+   *
+   * @param elementTypes data types of children in the struct
+   * @return a null scalar of struct type
+   */
+  public static Scalar structFromNull(HostColumnVector.DataType... elementTypes) {
+    ColumnVector[] children = new ColumnVector[elementTypes.length];
+    long[] childHandles = new long[elementTypes.length];
+    RuntimeException error = null;
+    try {
+      for (int i = 0; i < elementTypes.length; i++) {
+        // Build column vector having single null value rather than empty column vector,
+        // because struct scalar requires row count of children columns == 1.
+        children[i] = buildNullColumnVector(elementTypes[i]);
+        childHandles[i] = children[i].getNativeView();
+      }
+      return new Scalar(DType.STRUCT, makeStructScalar(childHandles, false));
+    } catch (RuntimeException ex) {
+      error = ex;
+      throw ex;
+    } catch (Exception ex) {
+      error = new RuntimeException(ex);
+      throw ex;
+    } finally {
+      // close all empty children
+      for (ColumnVector child : children) {
+        // We closed all created ColumnViews when we hit null. Therefore we exit the loop.
+        if (child == null) break;
+        // suppress exception during the close process to ensure that all elements are closed
+        try {
+          child.close();
+        } catch (Exception ex) {
+          if (error == null) {
+            error = new RuntimeException(ex);
+            continue;
+          }
+          error.addSuppressed(ex);
+        }
+      }
+      if (error != null) throw error;
+    }
+  }
+
+  /**
+   * Creates a scalar of struct from a ColumnView.
+   *
+   * @param columns children columns of struct
+   * @return a Struct scalar
+   */
+  public static Scalar structFromColumnViews(ColumnView... columns) {
+    if (columns == null) {
+      throw new IllegalArgumentException("input columns should NOT be null");
+    }
+    long[] columnHandles = new long[columns.length];
+    for (int i = 0; i < columns.length; i++) {
+      columnHandles[i] = columns[i].getNativeView();
+    }
+    return new Scalar(DType.STRUCT, makeStructScalar(columnHandles, true));
+  }
+
+  /**
+   * Build column vector of single row who holds a null value
+   *
+   * @param hostType host data type of null column vector
+   * @return the null vector
+   */
+  private static ColumnVector buildNullColumnVector(HostColumnVector.DataType hostType) {
+    DType dt = hostType.getType();
+    if (!dt.isNestedType()) {
+      try (HostColumnVector.Builder builder = HostColumnVector.builder(dt, 1)) {
+        builder.appendNull();
+        try (HostColumnVector hcv = builder.build()) {
+          return hcv.copyToDevice();
+        }
+      }
+    } else if (dt.typeId == DType.DTypeEnum.LIST) {
+      // type of List doesn't matter here because of type erasure in Java
+      try (HostColumnVector hcv = HostColumnVector.fromLists(hostType, (List<Integer>) null)) {
+        return hcv.copyToDevice();
+      }
+    } else if (dt.typeId == DType.DTypeEnum.STRUCT) {
+      try (HostColumnVector hcv = HostColumnVector.fromStructs(
+          hostType, (HostColumnVector.StructData) null)) {
+        return hcv.copyToDevice();
+      }
+    } else {
+      throw new IllegalArgumentException("Unsupported data type: " + hostType);
+    }
+  }
+
+  private static native void closeScalar(long scalarHandle);
+  private static native boolean isScalarValid(long scalarHandle);
+  private static native byte getByte(long scalarHandle);
+  private static native short getShort(long scalarHandle);
+  private static native int getInt(long scalarHandle);
+  private static native long getLong(long scalarHandle);
+  private static native byte[] getBigIntegerBytes(long scalarHandle);
+  private static native float getFloat(long scalarHandle);
+  private static native double getDouble(long scalarHandle);
+  private static native byte[] getUTF8(long scalarHandle);
+  private static native long getListAsColumnView(long scalarHandle);
+  private static native long[] getChildrenFromStructScalar(long scalarHandle);
+  private static native long makeBool8Scalar(boolean isValid, boolean value);
+  private static native long makeInt8Scalar(byte value, boolean isValid);
+  private static native long makeUint8Scalar(byte value, boolean isValid);
+  private static native long makeInt16Scalar(short value, boolean isValid);
+  private static native long makeUint16Scalar(short value, boolean isValid);
+  private static native long makeInt32Scalar(int value, boolean isValid);
+  private static native long makeUint32Scalar(int value, boolean isValid);
+  private static native long makeInt64Scalar(long value, boolean isValid);
+  private static native long makeUint64Scalar(long value, boolean isValid);
+  private static native long makeFloat32Scalar(float value, boolean isValid);
+  private static native long makeFloat64Scalar(double value, boolean isValid);
+  private static native long makeStringScalar(byte[] value, boolean isValid);
+  private static native long makeDurationDaysScalar(int value, boolean isValid);
+  private static native long makeDurationTimeScalar(int dtype, long value, boolean isValid);
+  private static native long makeTimestampDaysScalar(int value, boolean isValid);
+  private static native long makeTimestampTimeScalar(int dtypeNativeId, long value, boolean isValid);
+  private static native long makeDecimal32Scalar(int value, int scale, boolean isValid);
+  private static native long makeDecimal64Scalar(long value, int scale, boolean isValid);
+  private static native long makeDecimal128Scalar(byte[] value, int scale, boolean isValid);
+  private static native long makeListScalar(long viewHandle, boolean isValid);
+  private static native long makeStructScalar(long[] viewHandles, boolean isValid);
+  private static native long repeatString(long scalarHandle, int repeatTimes);
+
+  Scalar(DType type, long scalarHandle) {
+    this.type = type;
+    this.offHeap = new OffHeapState(scalarHandle);
+    incRefCount();
+  }
+
+  /**
+   * Increment the reference count for this scalar.  You need to call close on this
+   * to decrement the reference count again.
+   */
+  public synchronized Scalar incRefCount() {
+    if (offHeap.scalarHandle == 0) {
+      offHeap.logRefCountDebug("INC AFTER CLOSE " + this);
+      throw new IllegalStateException("Scalar is already closed");
+    }
+    ++refCount;
+    return this;
+  }
+
+  long getScalarHandle() {
+    return offHeap.scalarHandle;
+  }
+
+  /**
+   * Free the memory associated with a scalar.
+   */
+  @Override
+  public synchronized void close() {
+    refCount--;
+    offHeap.delRef();
+    if (refCount == 0) {
+      offHeap.clean(false);
+    } else if (refCount < 0) {
+      offHeap.logRefCountDebug("double free " + this);
+      throw new IllegalStateException("Close called too many times " + this);
+    }
+  }
+
+  @Override
+  public DType getType() {
+    return type;
+  }
+
+  public boolean isValid() {
+    return isScalarValid(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as a boolean.
+   */
+  public boolean getBoolean() {
+    return getByte(getScalarHandle()) != 0;
+  }
+
+  /**
+   * Returns the scalar value as a byte.
+   */
+  public byte getByte() {
+    return getByte(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as a short.
+   */
+  public short getShort() {
+    return getShort(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as an int.
+   */
+  public int getInt() {
+    return getInt(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as a long.
+   */
+  public long getLong() {
+    return getLong(getScalarHandle());
+  }
+
+  /**
+   * Returns the BigDecimal unscaled scalar value as a byte array.
+   */
+  public byte[] getBigInteger() {
+    byte[] res = getBigIntegerBytes(getScalarHandle());
+    convertInPlaceToBigEndian(res);
+    return res;
+  }
+
+  /**
+   * Returns the scalar value as a float.
+   */
+  public float getFloat() {
+    return getFloat(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as a double.
+   */
+  public double getDouble() {
+    return getDouble(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as a BigDecimal.
+   */
+  public BigDecimal getBigDecimal() {
+    if (this.type.typeId == DType.DTypeEnum.DECIMAL32) {
+      return BigDecimal.valueOf(getInt(), -type.getScale());
+    } else if (this.type.typeId == DType.DTypeEnum.DECIMAL64) {
+      return BigDecimal.valueOf(getLong(), -type.getScale());
+    } else if (this.type.typeId == DType.DTypeEnum.DECIMAL128) {
+      return new BigDecimal(new BigInteger(getBigInteger()), -type.getScale());
+    }
+    throw new IllegalArgumentException("Couldn't getBigDecimal from nonDecimal scalar");
+  }
+
+  /**
+   * Returns the scalar value as a Java string.
+   */
+  public String getJavaString() {
+    return new String(getUTF8(getScalarHandle()), StandardCharsets.UTF_8);
+  }
+
+  /**
+   * Returns the scalar value as UTF-8 data.
+   */
+  public byte[] getUTF8() {
+    return getUTF8(getScalarHandle());
+  }
+
+  /**
+   * Returns the scalar value as a ColumnView. Callers should close the returned ColumnView to
+   * avoid memory leak.
+   *
+   * The returned ColumnView is only valid as long as the Scalar remains valid. If the Scalar
+   * is closed before this ColumnView is closed, using this ColumnView will result in undefined
+   * behavior.
+   */
+  public ColumnView getListAsColumnView() {
+    assert DType.LIST.equals(type) : "Cannot get list for the vector of type " + type;
+    return new ColumnView(getListAsColumnView(getScalarHandle()));
+  }
+
+  /**
+   * Fetches views of children columns from struct scalar.
+   * The returned ColumnViews should be closed appropriately. Otherwise, a native memory leak will occur.
+   *
+   * @return array of column views refer to children of struct scalar
+   */
+  public ColumnView[] getChildrenFromStructScalar() {
+    assert DType.STRUCT.equals(type) : "Cannot get table for the vector of type " + type;
+
+    long[] childHandles = getChildrenFromStructScalar(getScalarHandle());
+    return ColumnView.getColumnViewsFromPointers(childHandles);
+  }
+
+  @Override
+  public ColumnVector binaryOp(BinaryOp op, BinaryOperable rhs, DType outType) {
+    if (rhs instanceof ColumnView) {
+      ColumnView cvRhs = (ColumnView) rhs;
+      return new ColumnVector(binaryOp(this, cvRhs, op, outType));
+    } else {
+      throw new IllegalArgumentException(rhs.getClass() + " is not supported as a binary op with " +
+          "Scalar");
+    }
+  }
+
+  static long binaryOp(Scalar lhs, ColumnView rhs, BinaryOp op, DType outputType) {
+    return binaryOpSV(lhs.getScalarHandle(), rhs.getNativeView(),
+        op.nativeId, outputType.typeId.getNativeId(), outputType.getScale());
+  }
+
+  private static native long binaryOpSV(long lhs, long rhs, int op, int dtype, int scale);
+
+  @Override
+  public boolean equals(Object o) {
+    if (this == o) return true;
+    if (o == null || getClass() != o.getClass()) return false;
+    Scalar other = (Scalar) o;
+    if (!type.equals(other.type)) return false;
+    boolean valid = isValid();
+    if (valid != other.isValid()) return false;
+    if (!valid) return true;
+    switch (type.typeId) {
+    case EMPTY:
+      return true;
+    case BOOL8:
+      return getBoolean() == other.getBoolean();
+    case INT8:
+    case UINT8:
+      return getByte() == other.getByte();
+    case INT16:
+    case UINT16:
+      return getShort() == other.getShort();
+    case INT32:
+    case UINT32:
+    case TIMESTAMP_DAYS:
+    case DECIMAL32:
+      return getInt() == other.getInt();
+    case FLOAT32:
+      return getFloat() == other.getFloat();
+    case FLOAT64:
+      return getDouble() == other.getDouble();
+    case INT64:
+    case UINT64:
+    case TIMESTAMP_SECONDS:
+    case TIMESTAMP_MILLISECONDS:
+    case TIMESTAMP_MICROSECONDS:
+    case TIMESTAMP_NANOSECONDS:
+    case DECIMAL64:
+      return getLong() == other.getLong();
+    case DECIMAL128:
+      return getBigDecimal().equals(other.getBigDecimal());
+    case STRING:
+      return Arrays.equals(getUTF8(), other.getUTF8());
+    case LIST:
+      try (ColumnView viewMe = getListAsColumnView();
+           ColumnView viewO = other.getListAsColumnView()) {
+        return viewMe.equals(viewO);
+      }
+    default:
+      throw new IllegalStateException("Unexpected type: " + type);
+    }
+  }
+
+  @Override
+  public int hashCode() {
+    int valueHash = 0;
+    if (isValid()) {
+      switch (type.typeId) {
+      case EMPTY:
+        valueHash = 0;
+        break;
+      case BOOL8:
+        valueHash = getBoolean() ? 1 : 0;
+        break;
+      case INT8:
+      case UINT8:
+        valueHash = getByte();
+        break;
+      case INT16:
+      case UINT16:
+        valueHash = getShort();
+        break;
+      case INT32:
+      case UINT32:
+      case TIMESTAMP_DAYS:
+      case DECIMAL32:
+      case DURATION_DAYS:
+        valueHash = getInt();
+        break;
+      case INT64:
+      case UINT64:
+      case TIMESTAMP_SECONDS:
+      case TIMESTAMP_MILLISECONDS:
+      case TIMESTAMP_MICROSECONDS:
+      case TIMESTAMP_NANOSECONDS:
+      case DECIMAL64:
+      case DURATION_MICROSECONDS:
+      case DURATION_SECONDS:
+      case DURATION_MILLISECONDS:
+      case DURATION_NANOSECONDS:
+        valueHash = Long.hashCode(getLong());
+        break;
+      case FLOAT32:
+        valueHash = Float.hashCode(getFloat());
+        break;
+      case FLOAT64:
+        valueHash = Double.hashCode(getDouble());
+        break;
+      case STRING:
+        valueHash = Arrays.hashCode(getUTF8());
+        break;
+      case LIST:
+        try (ColumnView v = getListAsColumnView()) {
+          valueHash = v.hashCode();
+        }
+        break;
+      case DECIMAL128:
+        valueHash = getBigDecimal().hashCode();
+        break;
+      default:
+        throw new IllegalStateException("Unknown scalar type: " + type);
+      }
+    }
+    return Objects.hash(type, valueHash);
+  }
+
+  @Override
+  public String toString() {
+    StringBuilder sb = new StringBuilder("Scalar{type=");
+    sb.append(type);
+    if (getScalarHandle() != 0) {
+      sb.append(" value=");
+      switch (type.typeId) {
+      case BOOL8:
+        sb.append(getBoolean());
+        break;
+      case INT8:
+        sb.append(getByte());
+        break;
+      case UINT8:
+        sb.append(Byte.toUnsignedInt(getByte()));
+        break;
+      case INT16:
+        sb.append(getShort());
+        break;
+      case UINT16:
+        sb.append(Short.toUnsignedInt(getShort()));
+        break;
+      case INT32:
+      case TIMESTAMP_DAYS:
+        sb.append(getInt());
+        break;
+      case UINT32:
+        sb.append(Integer.toUnsignedLong(getInt()));
+        break;
+      case INT64:
+      case TIMESTAMP_SECONDS:
+      case TIMESTAMP_MILLISECONDS:
+      case TIMESTAMP_MICROSECONDS:
+      case TIMESTAMP_NANOSECONDS:
+        sb.append(getLong());
+        break;
+      case UINT64:
+        sb.append(Long.toUnsignedString(getLong()));
+        break;
+      case FLOAT32:
+        sb.append(getFloat());
+        break;
+      case FLOAT64:
+        sb.append(getDouble());
+        break;
+      case STRING:
+        sb.append('"');
+        sb.append(getJavaString());
+        sb.append('"');
+        break;
+      case DECIMAL32:
+        // FALL THROUGH
+      case DECIMAL64:
+        // FALL THROUGH
+      case DECIMAL128:
+        sb.append(getBigDecimal());
+        break;
+      case LIST:
+        try (ColumnView v = getListAsColumnView()) {
+          // It's not easy to pull out the elements so just a simple string of some metadata.
+          sb.append(v.toString());
+        }
+        break;
+      default:
+        throw new IllegalArgumentException("Unknown scalar type: " + type);
+      }
+    }
+
+    sb.append("} (ID: ");
+    sb.append(offHeap.id);
+    sb.append(" ");
+    sb.append(Long.toHexString(offHeap.scalarHandle));
+    sb.append(")");
+    return sb.toString();
+  }
+
+
+  /**
+   * Repeat the given string scalar a number of times specified by the <code>repeatTimes</code>
+   * parameter. If that parameter has a non-positive value, an empty (valid) string scalar will be
+   * returned. An invalid input scalar will always result in an invalid output scalar regardless
+   * of the value of <code>repeatTimes</code>.
+   *
+   * @param repeatTimes The number of times the input string is copied to the output.
+   * @return The resulting scalar containing repeated result of the current string.
+   */
+  public Scalar repeatString(int repeatTimes) {
+    return new Scalar(DType.STRING, repeatString(getScalarHandle(), repeatTimes));
+  }
+
+  private static byte[] convertDecimal128FromJavaToCudf(byte[] bytes) {
+    byte[] finalBytes = new byte[DType.DTypeEnum.DECIMAL128.sizeInBytes];
+    byte lastByte = bytes[0];
+    //Convert to 2's complement representation and make sure the sign bit is extended correctly
+    byte setByte = (lastByte & 0x80) > 0 ? (byte)0xff : (byte)0x00;
+    for(int i = bytes.length; i < finalBytes.length; i++) {
+      finalBytes[i] = setByte;
+    }
+    // After setting the sign bits, reverse the rest of the bytes for endianness
+    for(int k = 0; k < bytes.length; k++) {
+      finalBytes[k] = bytes[bytes.length - k - 1];
+    }
+    return finalBytes;
+  }
+
+  private void convertInPlaceToBigEndian(byte[] res) {
+    assert ByteOrder.nativeOrder().equals(ByteOrder.LITTLE_ENDIAN);
+    int i =0;
+    int j = res.length -1;
+    while (j > i) {
+      byte tmp;
+      tmp = res[j];
+      res[j] = res[i];
+      res[i] = tmp;
+      j--;
+      i++;
+    }
+  }
+
+  /**
+   * Holds the off-heap state of the scalar so it can be cleaned up, even if it is leaked.
+   */
+  private static class OffHeapState extends MemoryCleaner.Cleaner {
+    private long scalarHandle;
+
+    OffHeapState(long scalarHandle) {
+      this.scalarHandle = scalarHandle;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      if (scalarHandle != 0) {
+        if (logErrorIfNotClean) {
+          LOG.error("A SCALAR WAS LEAKED(ID: " + id + " " + Long.toHexString(scalarHandle) + ")");
+          logRefCountDebug("Leaked scalar");
+        }
+        try {
+          closeScalar(scalarHandle);
+        } finally {
+          // Always mark the resource as freed even if an exception is thrown.
+          // We cannot know how far it progressed before the exception, and
+          // therefore it is unsafe to retry.
+          scalarHandle = 0;
+        }
+        return true;
+      }
+      return false;
+    }
+
+    @Override
+    public boolean isClean() {
+      return scalarHandle == 0;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ScanAggregation.java b/java/src/main/java/ai/rapids/cudf/ScanAggregation.java
new file mode 100644
index 0000000..752fd57
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ScanAggregation.java
@@ -0,0 +1,107 @@
+/*
+ *
+ *  Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An aggregation that can be used for a scan.
+ */
+public final class ScanAggregation {
+  private final Aggregation wrapped;
+
+  private ScanAggregation(Aggregation wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  long createNativeInstance() {
+    return wrapped.createNativeInstance();
+  }
+
+  long getDefaultOutput() {
+    return wrapped.getDefaultOutput();
+  }
+
+  Aggregation getWrapped() {
+    return wrapped;
+  }
+
+  @Override
+  public int hashCode() {
+    return wrapped.hashCode();
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other == this) {
+      return true;
+    } else if (other instanceof ScanAggregation) {
+      ScanAggregation o = (ScanAggregation) other;
+      return wrapped.equals(o.wrapped);
+    }
+    return false;
+  }
+
+  /**
+   * Sum Aggregation
+   */
+  public static ScanAggregation sum() {
+    return new ScanAggregation(Aggregation.sum());
+  }
+
+  /**
+   * Product Aggregation.
+   */
+  public static ScanAggregation product() {
+    return new ScanAggregation(Aggregation.product());
+  }
+
+  /**
+   * Min Aggregation
+   */
+  public static ScanAggregation min() {
+    return new ScanAggregation(Aggregation.min());
+  }
+
+  /**
+   * Max Aggregation
+   */
+  public static ScanAggregation max() {
+    return new ScanAggregation(Aggregation.max());
+  }
+
+  /**
+   * Get the row's ranking.
+   */
+  public static ScanAggregation rank() {
+    return new ScanAggregation(Aggregation.rank());
+  }
+
+  /**
+   * Get the row's dense ranking.
+   */
+  public static ScanAggregation denseRank() {
+    return new ScanAggregation(Aggregation.denseRank());
+  }
+
+  /**
+   * Get the row's percent rank.
+   */
+  public static ScanAggregation percentRank() {
+    return new ScanAggregation(Aggregation.percentRank());
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ScanType.java b/java/src/main/java/ai/rapids/cudf/ScanType.java
new file mode 100644
index 0000000..1fb3ff7
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ScanType.java
@@ -0,0 +1,39 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Scan operation type.
+ */
+public enum ScanType {
+  /**
+   * Include the current row in the scan.
+   */
+  INCLUSIVE(true),
+  /**
+   * Exclude the current row from the scan.
+   */
+  EXCLUSIVE(false);
+
+  ScanType(boolean isInclusive) {
+    this.isInclusive = isInclusive;
+  }
+
+  final boolean isInclusive;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Schema.java b/java/src/main/java/ai/rapids/cudf/Schema.java
new file mode 100644
index 0000000..79e66cb
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Schema.java
@@ -0,0 +1,103 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The schema of data to be read in.
+ */
+public class Schema {
+  public static final Schema INFERRED = new Schema();
+  private final List<String> names;
+  private final List<DType> types;
+
+  private Schema(List<String> names, List<DType> types) {
+    this.names = new ArrayList<>(names);
+    this.types = new ArrayList<>(types);
+  }
+
+  /**
+   * Inferred schema.
+   */
+  private Schema() {
+    names = null;
+    types = null;
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  public String[] getColumnNames() {
+    if (names == null) {
+      return null;
+    }
+    return names.toArray(new String[names.size()]);
+  }
+
+  int[] getTypeIds() {
+    if (types == null) {
+      return null;
+    }
+    int[] ret = new int[types.size()];
+    for (int i = 0; i < types.size(); i++) {
+      ret[i] = types.get(i).getTypeId().nativeId;
+    }
+    return ret;
+  }
+
+  int[] getTypeScales() {
+    if (types == null) {
+      return null;
+    }
+    int[] ret = new int[types.size()];
+    for (int i = 0; i < types.size(); i++) {
+      ret[i] = types.get(i).getScale();
+    }
+    return ret;
+  }
+
+  DType[] getTypes() {
+    if (types == null) {
+      return null;
+    }
+    DType[] ret = new DType[types.size()];
+    for (int i = 0; i < types.size(); i++) {
+      ret[i] = types.get(i);
+    }
+    return ret;
+  }
+
+  public static class Builder {
+    private final List<String> names = new ArrayList<>();
+    private final List<DType> types = new ArrayList<>();
+
+    public Builder column(DType type, String name) {
+      types.add(type);
+      names.add(name);
+      return this;
+    }
+
+    public Schema build() {
+      return new Schema(names, types);
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/SegmentedReductionAggregation.java b/java/src/main/java/ai/rapids/cudf/SegmentedReductionAggregation.java
new file mode 100644
index 0000000..7ed150a
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/SegmentedReductionAggregation.java
@@ -0,0 +1,104 @@
+/*
+ *
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * An aggregation that can be used for a reduce.
+ */
+public final class SegmentedReductionAggregation {
+  private final Aggregation wrapped;
+
+  private SegmentedReductionAggregation(Aggregation wrapped) {
+    this.wrapped = wrapped;
+  }
+
+  long createNativeInstance() {
+    return wrapped.createNativeInstance();
+  }
+
+  long getDefaultOutput() {
+    return wrapped.getDefaultOutput();
+  }
+
+  Aggregation getWrapped() {
+    return wrapped;
+  }
+
+  @Override
+  public int hashCode() {
+    return wrapped.hashCode();
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (other == this) {
+      return true;
+    } else if (other instanceof SegmentedReductionAggregation) {
+      SegmentedReductionAggregation o = (SegmentedReductionAggregation) other;
+      return wrapped.equals(o.wrapped);
+    }
+    return false;
+  }
+
+  /**
+   * Sum Aggregation
+   */
+  public static SegmentedReductionAggregation sum() {
+    return new SegmentedReductionAggregation(Aggregation.sum());
+  }
+
+  /**
+   * Product Aggregation.
+   */
+  public static SegmentedReductionAggregation product() {
+    return new SegmentedReductionAggregation(Aggregation.product());
+  }
+
+  /**
+   * Min Aggregation
+   */
+  public static SegmentedReductionAggregation min() {
+    return new SegmentedReductionAggregation(Aggregation.min());
+  }
+
+  /**
+   * Max Aggregation
+   */
+  public static SegmentedReductionAggregation max() {
+    return new SegmentedReductionAggregation(Aggregation.max());
+  }
+
+  /**
+   * Any reduction. Produces a true or 1, depending on the output type,
+   * if any of the elements in the range are true or non-zero, otherwise produces a false or 0.
+   * Null values are skipped.
+   */
+  public static SegmentedReductionAggregation any() {
+    return new SegmentedReductionAggregation(Aggregation.any());
+  }
+
+  /**
+   * All reduction. Produces true or 1, depending on the output type, if all of the elements in
+   * the range are true or non-zero, otherwise produces a false or 0.
+   * Null values are skipped.
+   */
+  public static SegmentedReductionAggregation all() {
+    return new SegmentedReductionAggregation(Aggregation.all());
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/StreamedTableReader.java b/java/src/main/java/ai/rapids/cudf/StreamedTableReader.java
new file mode 100644
index 0000000..d845edc
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/StreamedTableReader.java
@@ -0,0 +1,42 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Provides an interface for reading multiple tables from a single input source.
+ */
+public interface StreamedTableReader extends AutoCloseable {
+    /**
+     * Get the next table if available.
+     * @return the next Table or null if done reading tables.
+     * @throws CudfException on any error.
+     */
+    Table getNextIfAvailable() throws CudfException;
+
+    /**
+     * Get the next table if available.
+     * @param rowTarget the target number of rows to read (this is really just best effort).
+     * @return the next Table or null if done reading tables.
+     * @throws CudfException on any error.
+     */
+    Table getNextIfAvailable(int rowTarget) throws CudfException;
+
+    @Override
+    void close() throws CudfException;
+}
diff --git a/java/src/main/java/ai/rapids/cudf/Table.java b/java/src/main/java/ai/rapids/cudf/Table.java
new file mode 100644
index 0000000..51a33eb
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/Table.java
@@ -0,0 +1,4782 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.BasicType;
+import ai.rapids.cudf.HostColumnVector.DataType;
+import ai.rapids.cudf.HostColumnVector.ListType;
+import ai.rapids.cudf.HostColumnVector.StructData;
+import ai.rapids.cudf.HostColumnVector.StructType;
+import ai.rapids.cudf.ast.CompiledExpression;
+
+import java.io.File;
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.MathContext;
+import java.math.RoundingMode;
+import java.nio.ByteBuffer;
+import java.util.*;
+
+/**
+ * Class to represent a collection of ColumnVectors and operations that can be performed on them
+ * collectively.
+ * The refcount on the columns will be increased once they are passed in
+ */
+public final class Table implements AutoCloseable {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  private final long rows;
+  private long nativeHandle;
+  private ColumnVector[] columns;
+
+  /**
+   * Table class makes a copy of the array of {@link ColumnVector}s passed to it. The class
+   * will decrease the refcount
+   * on itself and all its contents when closed and free resources if refcount is zero
+   * @param columns - Array of ColumnVectors
+   */
+  public Table(ColumnVector... columns) {
+    assert columns != null && columns.length > 0 : "ColumnVectors can't be null or empty";
+    rows = columns[0].getRowCount();
+
+    for (ColumnVector columnVector : columns) {
+      assert (null != columnVector) : "ColumnVectors can't be null";
+      assert (rows == columnVector.getRowCount()) : "All columns should have the same number of " +
+          "rows " + columnVector.getType();
+    }
+
+    // Since Arrays are mutable objects make a copy
+    this.columns = new ColumnVector[columns.length];
+    long[] viewPointers = new long[columns.length];
+    for (int i = 0; i < columns.length; i++) {
+      this.columns[i] = columns[i];
+      columns[i].incRefCount();
+      viewPointers[i] = columns[i].getNativeView();
+    }
+
+    nativeHandle = createCudfTableView(viewPointers);
+  }
+
+  /**
+   * Create a Table from an array of existing on device cudf::column pointers. Ownership of the
+   * columns is transferred to the ColumnVectors held by the new Table. In the case of an exception
+   * the columns will be deleted.
+   * @param cudfColumns - Array of nativeHandles
+   */
+  public Table(long[] cudfColumns) {
+    assert cudfColumns != null && cudfColumns.length > 0 : "CudfColumns can't be null or empty";
+    this.columns = ColumnVector.getColumnVectorsFromPointers(cudfColumns);
+    try {
+      long[] views = new long[columns.length];
+      for (int i = 0; i < columns.length; i++) {
+        views[i] = columns[i].getNativeView();
+      }
+      nativeHandle = createCudfTableView(views);
+      this.rows = columns[0].getRowCount();
+    } catch (Throwable t) {
+      for (ColumnVector column : columns) {
+        try {
+          column.close();
+        } catch (Throwable s) {
+          t.addSuppressed(s);
+        }
+      }
+      throw t;
+    }
+  }
+
+  /**
+   * Provides a faster way to get access to the columns. Only to be used internally, and it should
+   * never be modified in anyway.
+   */
+  ColumnVector[] getColumns() {
+    return columns;
+  }
+
+  /** Return the native table view handle for this table */
+  public long getNativeView() {
+    return nativeHandle;
+  }
+
+  /**
+   * Return the {@link ColumnVector} at the specified index. If you want to keep a reference to
+   * the column around past the life time of the table, you will need to increment the reference
+   * count on the column yourself.
+   */
+  public ColumnVector getColumn(int index) {
+    assert index < columns.length;
+    return columns[index];
+  }
+
+  public final long getRowCount() {
+    return rows;
+  }
+
+  public final int getNumberOfColumns() {
+    return columns.length;
+  }
+
+  @Override
+  public void close() {
+    if (nativeHandle != 0) {
+      deleteCudfTable(nativeHandle);
+      nativeHandle = 0;
+    }
+    if (columns != null) {
+      for (int i = 0; i < columns.length; i++) {
+        columns[i].close();
+        columns[i] = null;
+      }
+      columns = null;
+    }
+  }
+
+  @Override
+  public String toString() {
+    return "Table{" +
+        "columns=" + Arrays.toString(columns) +
+        ", cudfTable=" + nativeHandle +
+        ", rows=" + rows +
+        '}';
+  }
+
+  /**
+   * Returns the Device memory buffer size.
+   */
+  public long getDeviceMemorySize() {
+    long total = 0;
+    for (ColumnVector cv: columns) {
+      total += cv.getDeviceMemorySize();
+    }
+    return total;
+  }
+
+  /**
+   * This method is internal and exposed purely for testing purpopses
+   */
+  static Table removeNullMasksIfNeeded(Table table) {
+    return new Table(removeNullMasksIfNeeded(table.nativeHandle));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // NATIVE APIs
+  /////////////////////////////////////////////////////////////////////////////
+
+  private static native long[] removeNullMasksIfNeeded(long tableView) throws CudfException;
+
+  private static native ContiguousTable[] contiguousSplit(long inputTable, int[] indices);
+
+  private static native long makeChunkedPack(long inputTable, long bounceBufferSize, long tempMemoryResource);
+
+  private static native long[] partition(long inputTable, long partitionView,
+      int numberOfPartitions, int[] outputOffsets);
+
+  private static native long[] hashPartition(long inputTable,
+                                             int[] columnsToHash,
+                                             int hashTypeId,
+                                             int numberOfPartitions,
+                                             int seed,
+                                             int[] outputOffsets) throws CudfException;
+
+  private static native long[] roundRobinPartition(long inputTable,
+                                                   int numberOfPartitions,
+                                                   int startPartition,
+                                                   int[] outputOffsets) throws CudfException;
+
+  private static native void deleteCudfTable(long handle) throws CudfException;
+
+  private static native long bound(long inputTable, long valueTable,
+                                   boolean[] descFlags, boolean[] areNullsSmallest, boolean isUpperBound) throws CudfException;
+
+  /**
+   * Ugly long function to read CSV.  This is a long function to avoid the overhead of reaching
+   * into a java
+   * object to try and pull out all of the options.  If this becomes unwieldy we can change it.
+   * @param columnNames       names of all of the columns, even the ones filtered out
+   * @param dTypeIds          native types IDs of all of the columns.
+   * @param dTypeScales       scale of the type for all of the columns.
+   * @param filterColumnNames name of the columns to read, or an empty array if we want to read
+   *                          all of them
+   * @param filePath          the path of the file to read, or null if no path should be read.
+   * @param address           the address of the buffer to read from or 0 if we should not.
+   * @param length            the length of the buffer to read from.
+   * @param headerRow         the 0 based index row of the header can be -1
+   * @param delim             character deliminator (must be ASCII).
+   * @param quoteStyle        quote style expected to be used in the input (represented as int)
+   * @param quote             character quote (must be ASCII).
+   * @param comment           character that starts a comment line (must be ASCII) use '\0'
+   * @param nullValues        values that should be treated as nulls
+   * @param trueValues        values that should be treated as boolean true
+   * @param falseValues       values that should be treated as boolean false
+   */
+  private static native long[] readCSV(String[] columnNames,
+                                       int[] dTypeIds, int[] dTypeScales,
+                                       String[] filterColumnNames,
+                                       String filePath, long address, long length,
+                                       int headerRow, byte delim, int quoteStyle, byte quote,
+                                       byte comment, String[] nullValues,
+                                       String[] trueValues, String[] falseValues) throws CudfException;
+
+  /**
+   * read JSON data and return a pointer to a TableWithMeta object.
+   */
+  private static native long readJSON(String[] columnNames,
+                                        int[] dTypeIds, int[] dTypeScales,
+                                        String filePath, long address, long length,
+                                        boolean dayFirst, boolean lines,
+                                        boolean recoverWithNulls) throws CudfException;
+
+  private static native long readAndInferJSON(long address, long length,
+      boolean dayFirst, boolean lines, boolean recoverWithNulls) throws CudfException;
+
+  /**
+   * Read in Parquet formatted data.
+   * @param filterColumnNames  name of the columns to read, or an empty array if we want to read
+   *                           all of them
+   * @param binaryToString     whether to convert this column to String if binary
+   * @param filePath           the path of the file to read, or null if no path should be read.
+   * @param address            the address of the buffer to read from or 0 if we should not.
+   * @param length             the length of the buffer to read from.
+   * @param timeUnit           return type of TimeStamp in units
+   */
+  private static native long[] readParquet(String[] filterColumnNames, boolean[] binaryToString, String filePath,
+                                           long address, long length, int timeUnit) throws CudfException;
+
+  /**
+   * Read in Avro formatted data.
+   * @param filterColumnNames  name of the columns to read, or an empty array if we want to read
+   *                           all of them
+   * @param filePath           the path of the file to read, or null if no path should be read.
+   * @param address            the address of the buffer to read from or 0 if we should not.
+   * @param length             the length of the buffer to read from.
+   */
+  private static native long[] readAvro(String[] filterColumnNames, String filePath,
+                                        long address, long length) throws CudfException;
+
+  /**
+   * Setup everything to write parquet formatted data to a file.
+   * @param columnNames     names that correspond to the table columns
+   * @param numChildren     Children of the top level
+   * @param flatNumChildren flattened list of children per column
+   * @param nullable        true if the column can have nulls else false
+   * @param metadataKeys    Metadata key names to place in the Parquet file
+   * @param metadataValues  Metadata values corresponding to metadataKeys
+   * @param compression     native compression codec ID
+   * @param statsFreq       native statistics frequency ID
+   * @param isInt96         true if timestamp type is int96
+   * @param precisions      precision list containing all the precisions of the decimal types in
+   *                        the columns
+   * @param isMapValues     true if a column is a map
+   * @param isBinaryValues  true if a column is a binary
+   * @param filename        local output path
+   * @return a handle that is used in later calls to writeParquetChunk and writeParquetEnd.
+   */
+  private static native long writeParquetFileBegin(String[] columnNames,
+                                                   int numChildren,
+                                                   int[] flatNumChildren,
+                                                   boolean[] nullable,
+                                                   String[] metadataKeys,
+                                                   String[] metadataValues,
+                                                   int compression,
+                                                   int statsFreq,
+                                                   boolean[] isInt96,
+                                                   int[] precisions,
+                                                   boolean[] isMapValues,
+                                                   boolean[] isBinaryValues,
+                                                   boolean[] hasParquetFieldIds,
+                                                   int[] parquetFieldIds,
+                                                   String filename) throws CudfException;
+
+  /**
+   * Setup everything to write parquet formatted data to a buffer.
+   * @param columnNames     names that correspond to the table columns
+   * @param numChildren     Children of the top level
+   * @param flatNumChildren flattened list of children per column
+   * @param nullable        true if the column can have nulls else false
+   * @param metadataKeys    Metadata key names to place in the Parquet file
+   * @param metadataValues  Metadata values corresponding to metadataKeys
+   * @param compression     native compression codec ID
+   * @param statsFreq       native statistics frequency ID
+   * @param isInt96         true if timestamp type is int96
+   * @param precisions      precision list containing all the precisions of the decimal types in
+   *                        the columns
+   * @param isMapValues     true if a column is a map
+   * @param isBinaryValues  true if a column is a binary
+   * @param consumer        consumer of host buffers produced.
+   * @return a handle that is used in later calls to writeParquetChunk and writeParquetEnd.
+   */
+  private static native long writeParquetBufferBegin(String[] columnNames,
+                                                     int numChildren,
+                                                     int[] flatNumChildren,
+                                                     boolean[] nullable,
+                                                     String[] metadataKeys,
+                                                     String[] metadataValues,
+                                                     int compression,
+                                                     int statsFreq,
+                                                     boolean[] isInt96,
+                                                     int[] precisions,
+                                                     boolean[] isMapValues,
+                                                     boolean[] isBinaryValues,
+                                                     boolean[] hasParquetFieldIds,
+                                                     int[] parquetFieldIds,
+                                                     HostBufferConsumer consumer,
+                                                     HostMemoryAllocator hostMemoryAllocator
+                                                     ) throws CudfException;
+
+  /**
+   * Write out a table to an open handle.
+   * @param handle the handle to the writer.
+   * @param table the table to write out.
+   * @param tableMemSize the size of the table in bytes to help with memory allocation.
+   */
+  private static native void writeParquetChunk(long handle, long table, long tableMemSize);
+
+  /**
+   * Finish writing out parquet.
+   * @param handle the handle.  Do not use again once this returns.
+   */
+  private static native void writeParquetEnd(long handle);
+
+  /**
+   * Read in ORC formatted data.
+   * @param filterColumnNames name of the columns to read, or an empty array if we want to read
+   *                          all of them
+   * @param filePath          the path of the file to read, or null if no path should be read.
+   * @param address           the address of the buffer to read from or 0 for no buffer.
+   * @param length            the length of the buffer to read from.
+   * @param usingNumPyTypes   whether the parser should implicitly promote TIMESTAMP
+   *                          columns to TIMESTAMP_MILLISECONDS for compatibility with NumPy.
+   * @param timeUnit          return type of TimeStamp in units
+   * @param decimal128Columns name of the columns which are read as Decimal128 rather than Decimal64
+   */
+  private static native long[] readORC(String[] filterColumnNames,
+                                       String filePath, long address, long length,
+                                       boolean usingNumPyTypes, int timeUnit,
+                                       String[] decimal128Columns) throws CudfException;
+
+  /**
+   * Setup everything to write ORC formatted data to a file.
+   * @param columnNames     names that correspond to the table columns
+   * @param numChildren     Children of the top level
+   * @param flatNumChildren flattened list of children per column
+   * @param nullable        true if the column can have nulls else false
+   * @param metadataKeys    Metadata key names to place in the Parquet file
+   * @param metadataValues  Metadata values corresponding to metadataKeys
+   * @param compression     native compression codec ID
+   * @param precisions      precision list containing all the precisions of the decimal types in
+   *                        the columns
+   * @param isMapValues     true if a column is a map
+   * @param filename        local output path
+   * @return a handle that is used in later calls to writeORCChunk and writeORCEnd.
+   */
+  private static native long writeORCFileBegin(String[] columnNames,
+                                               int numChildren,
+                                               int[] flatNumChildren,
+                                               boolean[] nullable,
+                                               String[] metadataKeys,
+                                               String[] metadataValues,
+                                               int compression,
+                                               int[] precisions,
+                                               boolean[] isMapValues,
+                                               String filename) throws CudfException;
+
+  /**
+   * Setup everything to write ORC formatted data to a buffer.
+   * @param columnNames     names that correspond to the table columns
+   * @param numChildren     Children of the top level
+   * @param flatNumChildren flattened list of children per column
+   * @param nullable        true if the column can have nulls else false
+   * @param metadataKeys    Metadata key names to place in the Parquet file
+   * @param metadataValues  Metadata values corresponding to metadataKeys
+   * @param compression     native compression codec ID
+   * @param precisions      precision list containing all the precisions of the decimal types in
+   *                        the columns
+   * @param isMapValues     true if a column is a map
+   * @param consumer        consumer of host buffers produced.
+   * @return a handle that is used in later calls to writeORCChunk and writeORCEnd.
+   */
+  private static native long writeORCBufferBegin(String[] columnNames,
+                                                 int numChildren,
+                                                 int[] flatNumChildren,
+                                                 boolean[] nullable,
+                                                 String[] metadataKeys,
+                                                 String[] metadataValues,
+                                                 int compression,
+                                                 int[] precisions,
+                                                 boolean[] isMapValues,
+                                                 HostBufferConsumer consumer,
+                                                 HostMemoryAllocator hostMemoryAllocator
+                                                 ) throws CudfException;
+
+  /**
+   * Write out a table to an open handle.
+   * @param handle the handle to the writer.
+   * @param table the table to write out.
+   * @param tableMemSize the size of the table in bytes to help with memory allocation.
+   */
+  private static native void writeORCChunk(long handle, long table, long tableMemSize);
+
+  /**
+   * Finish writing out ORC.
+   * @param handle the handle.  Do not use again once this returns.
+   */
+  private static native void writeORCEnd(long handle);
+
+  /**
+   * Setup everything to write Arrow IPC formatted data to a file.
+   * @param columnNames names that correspond to the table columns
+   * @param filename local output path
+   * @return a handle that is used in later calls to writeArrowIPCChunk and writeArrowIPCEnd.
+   */
+  private static native long writeArrowIPCFileBegin(String[] columnNames, String filename);
+
+  /**
+   * Setup everything to write Arrow IPC formatted data to a buffer.
+   * @param columnNames names that correspond to the table columns
+   * @param consumer consumer of host buffers produced.
+   * @param hostMemoryAllocator allocator for host memory buffers.
+   * @return a handle that is used in later calls to writeArrowIPCChunk and writeArrowIPCEnd.
+   */
+  private static native long writeArrowIPCBufferBegin(String[] columnNames,
+                                                      HostBufferConsumer consumer,
+                                                      HostMemoryAllocator hostMemoryAllocator);
+
+  /**
+   * Convert a cudf table to an arrow table handle.
+   * @param handle the handle to the writer.
+   * @param tableHandle the table to convert
+   */
+  private static native long convertCudfToArrowTable(long handle,
+                                                     long tableHandle);
+
+  /**
+   * Write out a table to an open handle.
+   * @param handle the handle to the writer.
+   * @param arrowHandle the arrow table to write out.
+   * @param maxChunkSize the maximum number of rows that could
+   *                     be written out in a single chunk.  Generally this setting will be
+   *                     followed unless for some reason the arrow table is not a single group.
+   *                     This can happen when reading arrow data, but not when converting from
+   *                     cudf.
+   */
+  private static native void writeArrowIPCArrowChunk(long handle,
+                                                     long arrowHandle,
+                                                     long maxChunkSize);
+
+  /**
+   * Finish writing out Arrow IPC.
+   * @param handle the handle.  Do not use again once this returns.
+   */
+  private static native void writeArrowIPCEnd(long handle);
+
+  /**
+   * Setup everything to read an Arrow IPC formatted data file.
+   * @param path local input path
+   * @return a handle that is used in later calls to readArrowIPCChunk and readArrowIPCEnd.
+   */
+  private static native long readArrowIPCFileBegin(String path);
+
+  /**
+   * Setup everything to read Arrow IPC formatted data from a provider.
+   * @param provider the class that will provide the data.
+   * @return a handle that is used in later calls to readArrowIPCChunk and readArrowIPCEnd.
+   */
+  private static native long readArrowIPCBufferBegin(ArrowReaderWrapper provider);
+
+  /**
+   * Read the next chunk/table of data.
+   * @param handle the handle that is holding the data.
+   * @param rowTarget the number of rows to read.
+   * @return a pointer to an arrow table handle.
+   */
+  private static native long readArrowIPCChunkToArrowTable(long handle, int rowTarget);
+
+  /**
+   * Close the arrow table handle returned by readArrowIPCChunkToArrowTable or
+   * convertCudfToArrowTable
+   */
+  private static native void closeArrowTable(long arrowHandle);
+
+  /**
+   * Convert an arrow table handle as returned by readArrowIPCChunkToArrowTable to
+   * cudf table handles.
+   */
+  private static native long[] convertArrowTableToCudf(long arrowHandle);
+
+  /**
+   * Finish reading the data.  We are done.
+   * @param handle the handle to clean up.
+   */
+  private static native void readArrowIPCEnd(long handle);
+
+  private static native long[] groupByAggregate(long inputTable, int[] keyIndices, int[] aggColumnsIndices,
+                                                long[] aggInstances, boolean ignoreNullKeys,
+                                                boolean keySorted, boolean[] keysDescending,
+                                                boolean[] keysNullSmallest) throws CudfException;
+
+  private static native long[] groupByScan(long inputTable, int[] keyIndices, int[] aggColumnsIndices,
+      long[] aggInstances, boolean ignoreNullKeys,
+      boolean keySorted, boolean[] keysDescending,
+      boolean[] keysNullSmallest) throws CudfException;
+
+  private static native long[] groupByReplaceNulls(long inputTable, int[] keyIndices,
+      int[] replaceColumnsIndices,
+      boolean[] isPreceding, boolean ignoreNullKeys,
+      boolean keySorted, boolean[] keysDescending,
+      boolean[] keysNullSmallest) throws CudfException;
+
+  private static native long[] rollingWindowAggregate(
+      long inputTable,
+      int[] keyIndices,
+      long[] defaultOutputs,
+      int[] aggColumnsIndices,
+      long[] aggInstances,
+      int[] minPeriods,
+      int[] preceding,
+      int[] following,
+      boolean[] unboundedPreceding,
+      boolean[] unboundedFollowing,
+      boolean ignoreNullKeys) throws CudfException;
+
+  private static native long[] rangeRollingWindowAggregate(long inputTable, int[] keyIndices, int[] orderByIndices, boolean[] isOrderByAscending,
+                                                           int[] aggColumnsIndices, long[] aggInstances, int[] minPeriods,
+                                                           long[] preceding, long[] following, int[] precedingRangeExtent, int[] followingRangeExtent,
+                                                           boolean ignoreNullKeys) throws CudfException;
+
+  private static native long sortOrder(long inputTable, long[] sortKeys, boolean[] isDescending,
+      boolean[] areNullsSmallest) throws CudfException;
+
+  private static native long[] orderBy(long inputTable, long[] sortKeys, boolean[] isDescending,
+                                       boolean[] areNullsSmallest) throws CudfException;
+
+  private static native long[] merge(long[] tableHandles, int[] sortKeyIndexes,
+                                     boolean[] isDescending, boolean[] areNullsSmallest) throws CudfException;
+
+  private static native long[] leftJoinGatherMaps(long leftKeys, long rightKeys,
+                                                  boolean compareNullsEqual) throws CudfException;
+
+  private static native long leftJoinRowCount(long leftTable, long rightHashJoin) throws CudfException;
+
+  private static native long[] leftHashJoinGatherMaps(long leftTable, long rightHashJoin) throws CudfException;
+
+  private static native long[] leftHashJoinGatherMapsWithCount(long leftTable, long rightHashJoin,
+                                                               long outputRowCount) throws CudfException;
+
+  private static native long[] innerJoinGatherMaps(long leftKeys, long rightKeys,
+                                                   boolean compareNullsEqual) throws CudfException;
+
+  private static native long innerJoinRowCount(long table, long hashJoin) throws CudfException;
+
+  private static native long[] innerHashJoinGatherMaps(long table, long hashJoin) throws CudfException;
+
+  private static native long[] innerHashJoinGatherMapsWithCount(long table, long hashJoin,
+                                                                long outputRowCount) throws CudfException;
+
+  private static native long[] fullJoinGatherMaps(long leftKeys, long rightKeys,
+                                                  boolean compareNullsEqual) throws CudfException;
+
+  private static native long fullJoinRowCount(long leftTable, long rightHashJoin) throws CudfException;
+
+  private static native long[] fullHashJoinGatherMaps(long leftTable, long rightHashJoin) throws CudfException;
+
+  private static native long[] fullHashJoinGatherMapsWithCount(long leftTable, long rightHashJoin,
+                                                               long outputRowCount) throws CudfException;
+
+  private static native long[] leftSemiJoinGatherMap(long leftKeys, long rightKeys,
+                                                     boolean compareNullsEqual) throws CudfException;
+
+  private static native long[] leftAntiJoinGatherMap(long leftKeys, long rightKeys,
+                                                     boolean compareNullsEqual) throws CudfException;
+
+  private static native long conditionalLeftJoinRowCount(long leftTable, long rightTable,
+                                                         long condition) throws CudfException;
+
+  private static native long[] conditionalLeftJoinGatherMaps(long leftTable, long rightTable,
+                                                             long condition) throws CudfException;
+
+  private static native long[] conditionalLeftJoinGatherMapsWithCount(long leftTable, long rightTable,
+                                                                      long condition,
+                                                                      long rowCount) throws CudfException;
+
+  private static native long conditionalInnerJoinRowCount(long leftTable, long rightTable,
+                                                          long condition) throws CudfException;
+
+  private static native long[] conditionalInnerJoinGatherMaps(long leftTable, long rightTable,
+                                                              long condition) throws CudfException;
+
+  private static native long[] conditionalInnerJoinGatherMapsWithCount(long leftTable, long rightTable,
+                                                                       long condition,
+                                                                       long rowCount) throws CudfException;
+
+  private static native long[] conditionalFullJoinGatherMaps(long leftTable, long rightTable,
+                                                             long condition) throws CudfException;
+
+  private static native long conditionalLeftSemiJoinRowCount(long leftTable, long rightTable,
+                                                             long condition) throws CudfException;
+
+  private static native long[] conditionalLeftSemiJoinGatherMap(long leftTable, long rightTable,
+                                                                long condition) throws CudfException;
+
+  private static native long[] conditionalLeftSemiJoinGatherMapWithCount(long leftTable, long rightTable,
+                                                                         long condition,
+                                                                         long rowCount) throws CudfException;
+
+  private static native long conditionalLeftAntiJoinRowCount(long leftTable, long rightTable,
+                                                             long condition) throws CudfException;
+
+  private static native long[] conditionalLeftAntiJoinGatherMap(long leftTable, long rightTable,
+                                                                long condition) throws CudfException;
+
+  private static native long[] conditionalLeftAntiJoinGatherMapWithCount(long leftTable, long rightTable,
+                                                                         long condition,
+                                                                         long rowCount) throws CudfException;
+
+  private static native long[] mixedLeftJoinSize(long leftKeysTable, long rightKeysTable,
+                                                 long leftConditionTable, long rightConditionTable,
+                                                 long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftJoinGatherMaps(long leftKeysTable, long rightKeysTable,
+                                                       long leftConditionTable, long rightConditionTable,
+                                                       long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftJoinGatherMapsWithSize(long leftKeysTable, long rightKeysTable,
+                                                               long leftConditionTable, long rightConditionTable,
+                                                               long condition, boolean compareNullsEqual,
+                                                               long outputRowCount, long matchesColumnView);
+
+  private static native long[] mixedInnerJoinSize(long leftKeysTable, long rightKeysTable,
+                                                  long leftConditionTable, long rightConditionTable,
+                                                  long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedInnerJoinGatherMaps(long leftKeysTable, long rightKeysTable,
+                                                        long leftConditionTable, long rightConditionTable,
+                                                        long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedInnerJoinGatherMapsWithSize(long leftKeysTable, long rightKeysTable,
+                                                                long leftConditionTable, long rightConditionTable,
+                                                                long condition, boolean compareNullsEqual,
+                                                                long outputRowCount, long matchesColumnView);
+
+  private static native long[] mixedFullJoinGatherMaps(long leftKeysTable, long rightKeysTable,
+                                                       long leftConditionTable, long rightConditionTable,
+                                                       long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftSemiJoinSize(long leftKeysTable, long rightKeysTable,
+                                                     long leftConditionTable, long rightConditionTable,
+                                                     long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftSemiJoinGatherMap(long leftKeysTable, long rightKeysTable,
+                                                          long leftConditionTable, long rightConditionTable,
+                                                          long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftSemiJoinGatherMapWithSize(long leftKeysTable, long rightKeysTable,
+                                                                  long leftConditionTable, long rightConditionTable,
+                                                                  long condition, boolean compareNullsEqual,
+                                                                  long outputRowCount, long matchesColumnView);
+
+  private static native long[] mixedLeftAntiJoinSize(long leftKeysTable, long rightKeysTable,
+                                                     long leftConditionTable, long rightConditionTable,
+                                                     long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftAntiJoinGatherMap(long leftKeysTable, long rightKeysTable,
+                                                          long leftConditionTable, long rightConditionTable,
+                                                          long condition, boolean compareNullsEqual);
+
+  private static native long[] mixedLeftAntiJoinGatherMapWithSize(long leftKeysTable, long rightKeysTable,
+                                                                  long leftConditionTable, long rightConditionTable,
+                                                                  long condition, boolean compareNullsEqual,
+                                                                  long outputRowCount, long matchesColumnView);
+
+  private static native long[] crossJoin(long leftTable, long rightTable) throws CudfException;
+
+  private static native long[] concatenate(long[] cudfTablePointers) throws CudfException;
+
+  private static native long interleaveColumns(long input);
+
+  private static native long[] filter(long input, long mask);
+
+  private static native long[] dropDuplicates(long nativeHandle, int[] keyColumns,
+                                              int keepValue, boolean nullsEqual) throws CudfException;
+
+  private static native long[] gather(long tableHandle, long gatherView, boolean checkBounds);
+
+  private static native long[] scatterTable(long srcTableHandle, long scatterView,
+                                            long targetTableHandle)
+                                            throws CudfException;
+  private static native long[] scatterScalars(long[] srcScalarHandles, long scatterView,
+                                             long targetTableHandle)
+                                             throws CudfException;
+
+  private static native long[] convertToRows(long nativeHandle);
+
+  private static native long[] convertToRowsFixedWidthOptimized(long nativeHandle);
+
+  private static native long[] convertFromRows(long nativeColumnView, int[] types, int[] scale);
+
+  private static native long[] convertFromRowsFixedWidthOptimized(long nativeColumnView, int[] types, int[] scale);
+
+  private static native long[] repeatStaticCount(long tableHandle, int count);
+
+  private static native long[] repeatColumnCount(long tableHandle,
+                                                 long columnHandle);
+
+  private static native long rowBitCount(long tableHandle) throws CudfException;
+
+  private static native long[] explode(long tableHandle, int index);
+
+  private static native long[] explodePosition(long tableHandle, int index);
+
+  private static native long[] explodeOuter(long tableHandle, int index);
+
+  private static native long[] explodeOuterPosition(long tableHandle, int index);
+
+  private static native long createCudfTableView(long[] nativeColumnViewHandles);
+
+  private static native long[] columnViewsFromPacked(ByteBuffer metadata, long dataAddress);
+
+  private static native ContigSplitGroupByResult contiguousSplitGroups(long inputTable,
+                                                                int[] keyIndices,
+                                                                boolean ignoreNullKeys,
+                                                                boolean keySorted,
+                                                                boolean[] keysDescending,
+                                                                boolean[] keysNullSmallest,
+                                                                boolean genUniqKeys);
+
+  private static native long[] sample(long tableHandle, long n, boolean replacement, long seed);
+
+  private static native int distinctCount(long handle, boolean nullsEqual);
+
+  /////////////////////////////////////////////////////////////////////////////
+  // TABLE CREATION APIs
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Read a CSV file using the default CSVOptions.
+   * @param schema the schema of the file.  You may use Schema.INFERRED to infer the schema.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readCSV(Schema schema, File path) {
+    return readCSV(schema, CSVOptions.DEFAULT, path);
+  }
+
+  /**
+   * Read a CSV file.
+   * @param schema the schema of the file.  You may use Schema.INFERRED to infer the schema.
+   * @param opts various CSV parsing options.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readCSV(Schema schema, CSVOptions opts, File path) {
+    return new Table(
+        readCSV(schema.getColumnNames(), schema.getTypeIds(), schema.getTypeScales(),
+            opts.getIncludeColumnNames(), path.getAbsolutePath(),
+            0, 0,
+            opts.getHeaderRow(),
+            opts.getDelim(),
+            opts.getQuoteStyle().nativeId,
+            opts.getQuote(),
+            opts.getComment(),
+            opts.getNullValues(),
+            opts.getTrueValues(),
+            opts.getFalseValues()));
+  }
+
+  /**
+   * Read CSV formatted data using the default CSVOptions.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param buffer raw UTF8 formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readCSV(Schema schema, byte[] buffer) {
+    return readCSV(schema, CSVOptions.DEFAULT, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read CSV formatted data.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param opts various CSV parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readCSV(Schema schema, CSVOptions opts, byte[] buffer) {
+    return readCSV(schema, opts, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read CSV formatted data.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param opts various CSV parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readCSV(Schema schema, CSVOptions opts, byte[] buffer, long offset,
+                              long len, HostMemoryAllocator hostMemoryAllocator) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.length - offset;
+    assert offset >= 0 && offset < buffer.length;
+    try (HostMemoryBuffer newBuf = hostMemoryAllocator.allocate(len)) {
+      newBuf.setBytes(0, buffer, offset, len);
+      return readCSV(schema, opts, newBuf, 0, len);
+    }
+  }
+
+
+  public static Table readCSV(Schema schema, CSVOptions opts, byte[] buffer, long offset,
+                              long len) {
+    return readCSV(schema, opts, buffer, offset, len, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Read CSV formatted data.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param opts various CSV parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readCSV(Schema schema, CSVOptions opts, HostMemoryBuffer buffer,
+                              long offset, long len) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.getLength() - offset;
+    assert offset >= 0 && offset < buffer.length;
+    return new Table(readCSV(schema.getColumnNames(), schema.getTypeIds(), schema.getTypeScales(),
+        opts.getIncludeColumnNames(), null,
+        buffer.getAddress() + offset, len,
+        opts.getHeaderRow(),
+        opts.getDelim(),
+        opts.getQuoteStyle().nativeId,
+        opts.getQuote(),
+        opts.getComment(),
+        opts.getNullValues(),
+        opts.getTrueValues(),
+        opts.getFalseValues()));
+  }
+
+  private static native void writeCSVToFile(long table,
+                                            String[] columnNames,
+                                            boolean includeHeader,
+                                            String rowDelimiter,
+                                            byte fieldDelimiter,
+                                            String nullValue,
+                                            String trueValue,
+                                            String falseValue,
+                                            int quoteStyle,
+                                            String outputPath) throws CudfException;
+
+  public void writeCSVToFile(CSVWriterOptions options, String outputPath) {
+    writeCSVToFile(nativeHandle,
+                   options.getColumnNames(),
+                   options.getIncludeHeader(),
+                   options.getRowDelimiter(),
+                   options.getFieldDelimiter(),
+                   options.getNullValue(),
+                   options.getTrueValue(),
+                   options.getFalseValue(),
+                   options.getQuoteStyle().nativeId,
+                   outputPath);
+  }
+
+  private static native long startWriteCSVToBuffer(String[] columnNames,
+                                                   boolean includeHeader,
+                                                   String rowDelimiter,
+                                                   byte fieldDelimiter,
+                                                   String nullValue,
+                                                   String trueValue,
+                                                   String falseValue,
+                                                   int quoteStyle,
+                                                   HostBufferConsumer buffer,
+                                                   HostMemoryAllocator hostMemoryAllocator
+                                                   ) throws CudfException;
+
+  private static native void writeCSVChunkToBuffer(long writerHandle, long tableHandle);
+
+  private static native void endWriteCSVToBuffer(long writerHandle);
+
+  private static class CSVTableWriter extends TableWriter {
+    private HostBufferConsumer consumer;
+
+    private CSVTableWriter(CSVWriterOptions options, HostBufferConsumer consumer,
+        HostMemoryAllocator hostMemoryAllocator) {
+      super(startWriteCSVToBuffer(options.getColumnNames(),
+          options.getIncludeHeader(),
+          options.getRowDelimiter(),
+          options.getFieldDelimiter(),
+          options.getNullValue(),
+          options.getTrueValue(),
+          options.getFalseValue(),
+          options.getQuoteStyle().nativeId,
+          consumer, hostMemoryAllocator));
+      this.consumer = consumer;
+    }
+
+    @Override
+    public void write(Table table) {
+      if (writerHandle == 0) {
+        throw new IllegalStateException("Writer was already closed");
+      }
+      writeCSVChunkToBuffer(writerHandle, table.nativeHandle);
+    }
+
+    @Override
+    public void close() throws CudfException {
+      if (writerHandle != 0) {
+        endWriteCSVToBuffer(writerHandle);
+        writerHandle = 0;
+      }
+      if (consumer != null) {
+        consumer.done();
+        consumer = null;
+      }
+    }
+  }
+
+  public static TableWriter getCSVBufferWriter(CSVWriterOptions options,
+      HostBufferConsumer bufferConsumer, HostMemoryAllocator hostMemoryAllocator) {
+    return new CSVTableWriter(options, bufferConsumer, hostMemoryAllocator);
+  }
+
+   public static TableWriter getCSVBufferWriter(CSVWriterOptions options,
+      HostBufferConsumer bufferConsumer) {
+    return getCSVBufferWriter(options, bufferConsumer, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Read a JSON file using the default JSONOptions.
+   * @param schema the schema of the file.  You may use Schema.INFERRED to infer the schema.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readJSON(Schema schema, File path) {
+    return readJSON(schema, JSONOptions.DEFAULT, path);
+  }
+
+  /**
+   * Read JSON formatted data using the default JSONOptions.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param buffer raw UTF8 formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readJSON(Schema schema, byte[] buffer) {
+    return readJSON(schema, JSONOptions.DEFAULT, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read JSON formatted data.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param opts various JSON parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readJSON(Schema schema, JSONOptions opts, byte[] buffer) {
+    return readJSON(schema, opts, buffer, 0, buffer.length);
+  }
+
+  private static Table gatherJSONColumns(Schema schema, TableWithMeta twm) {
+    String[] neededColumns = schema.getColumnNames();
+    if (neededColumns == null || neededColumns.length == 0) {
+      return twm.releaseTable();
+    } else {
+      String[] foundNames = twm.getColumnNames();
+      HashMap<String, Integer> indices = new HashMap<>();
+      for (int i = 0; i < foundNames.length; i++) {
+        indices.put(foundNames[i], i);
+      }
+      // We might need to rearrange the columns to match what we want.
+      DType[] types = schema.getTypes();
+      ColumnVector[] columns = new ColumnVector[neededColumns.length];
+      try (Table tbl = twm.releaseTable()) {
+        for (int i = 0; i < columns.length; i++) {
+          String neededColumnName = neededColumns[i];
+          Integer index = indices.get(neededColumnName);
+          if (index != null) {
+            columns[i] = tbl.getColumn(index).incRefCount();
+          } else {
+            try (Scalar s = Scalar.fromNull(types[i])) {
+              columns[i] = ColumnVector.fromScalar(s, (int)tbl.getRowCount());
+            }
+          }
+        }
+        return new Table(columns);
+      } finally {
+        for (ColumnVector c: columns) {
+          if (c != null) {
+            c.close();
+          }
+        }
+      }
+    }
+  }
+
+  /**
+   * Read a JSON file.
+   * @param schema the schema of the file.  You may use Schema.INFERRED to infer the schema.
+   * @param opts various JSON parsing options.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readJSON(Schema schema, JSONOptions opts, File path) {
+    try (TableWithMeta twm = new TableWithMeta(
+            readJSON(schema.getColumnNames(), schema.getTypeIds(), schema.getTypeScales(),
+                    path.getAbsolutePath(),
+                    0, 0,
+                    opts.isDayFirst(), opts.isLines(), opts.isRecoverWithNull()))) {
+
+      return gatherJSONColumns(schema, twm);
+    }
+  }
+
+  /**
+   * Read JSON formatted data.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param opts various JSON parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readJSON(Schema schema, JSONOptions opts, byte[] buffer, long offset,
+                               long len, HostMemoryAllocator hostMemoryAllocator) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.length - offset;
+    assert offset >= 0 && offset < buffer.length;
+    try (HostMemoryBuffer newBuf = hostMemoryAllocator.allocate(len)) {
+      newBuf.setBytes(0, buffer, offset, len);
+      return readJSON(schema, opts, newBuf, 0, len);
+    }
+  }
+
+  public static Table readJSON(Schema schema, JSONOptions opts, byte[] buffer, long offset,
+                               long len) {
+    return readJSON(schema, opts, buffer, offset, len, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Read JSON formatted data and infer the column names and schema.
+   * @param opts various JSON parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @return the data parsed as a table on the GPU and the metadata for the table returned.
+   */
+  public static TableWithMeta readJSON(JSONOptions opts, HostMemoryBuffer buffer,
+      long offset, long len) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.length - offset;
+    assert offset >= 0 && offset < buffer.length;
+    return new TableWithMeta(readAndInferJSON(buffer.getAddress() + offset, len,
+        opts.isDayFirst(), opts.isLines(), opts.isRecoverWithNull()));
+  }
+
+  /**
+   * Read JSON formatted data.
+   * @param schema the schema of the data. You may use Schema.INFERRED to infer the schema.
+   * @param opts various JSON parsing options.
+   * @param buffer raw UTF8 formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readJSON(Schema schema, JSONOptions opts, HostMemoryBuffer buffer,
+                              long offset, long len) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.length - offset;
+    assert offset >= 0 && offset < buffer.length;
+    try (TableWithMeta twm = new TableWithMeta(readJSON(schema.getColumnNames(),
+            schema.getTypeIds(), schema.getTypeScales(), null,
+            buffer.getAddress() + offset, len, opts.isDayFirst(), opts.isLines(),
+            opts.isRecoverWithNull()))) {
+      return gatherJSONColumns(schema, twm);
+    }
+  }
+
+  /**
+   * Read a Parquet file using the default ParquetOptions.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readParquet(File path) {
+    return readParquet(ParquetOptions.DEFAULT, path);
+  }
+
+  /**
+   * Read a Parquet file.
+   * @param opts various parquet parsing options.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readParquet(ParquetOptions opts, File path) {
+    return new Table(readParquet(opts.getIncludeColumnNames(), opts.getReadBinaryAsString(),
+        path.getAbsolutePath(), 0, 0, opts.timeUnit().typeId.getNativeId()));
+  }
+
+  /**
+   * Read parquet formatted data.
+   * @param buffer raw parquet formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readParquet(byte[] buffer) {
+    return readParquet(ParquetOptions.DEFAULT, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read parquet formatted data.
+   * @param opts various parquet parsing options.
+   * @param buffer raw parquet formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readParquet(ParquetOptions opts, byte[] buffer) {
+    return readParquet(opts, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read parquet formatted data.
+   * @param opts various parquet parsing options.
+   * @param buffer raw parquet formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readParquet(ParquetOptions opts, byte[] buffer, long offset, long len,
+      HostMemoryAllocator hostMemoryAllocator) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.length - offset;
+    assert offset >= 0 && offset < buffer.length;
+    try (HostMemoryBuffer newBuf = hostMemoryAllocator.allocate(len)) {
+      newBuf.setBytes(0, buffer, offset, len);
+      return readParquet(opts, newBuf, 0, len);
+    }
+  }
+
+  public static Table readParquet(ParquetOptions opts, byte[] buffer, long offset, long len) {
+    return readParquet(opts, buffer, offset, len, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Read parquet formatted data.
+   * @param opts various parquet parsing options.
+   * @param buffer raw parquet formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readParquet(ParquetOptions opts, HostMemoryBuffer buffer,
+                                  long offset, long len) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.getLength() - offset;
+    assert offset >= 0 && offset < buffer.length;
+    return new Table(readParquet(opts.getIncludeColumnNames(), opts.getReadBinaryAsString(),
+        null, buffer.getAddress() + offset, len, opts.timeUnit().typeId.getNativeId()));
+  }
+
+  /**
+   * Read an Avro file using the default AvroOptions.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readAvro(File path) {
+    return readAvro(AvroOptions.DEFAULT, path);
+  }
+
+  /**
+   * Read an Avro file.
+   * @param opts various Avro parsing options.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readAvro(AvroOptions opts, File path) {
+    return new Table(readAvro(opts.getIncludeColumnNames(),
+        path.getAbsolutePath(), 0, 0));
+  }
+
+  /**
+   * Read Avro formatted data.
+   * @param buffer raw Avro formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readAvro(byte[] buffer) {
+    return readAvro(AvroOptions.DEFAULT, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read Avro formatted data.
+   * @param opts various Avro parsing options.
+   * @param buffer raw Avro formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readAvro(AvroOptions opts, byte[] buffer) {
+    return readAvro(opts, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read Avro formatted data.
+   * @param opts various Avro parsing options.
+   * @param buffer raw Avro formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readAvro(AvroOptions opts, byte[] buffer, long offset, long len,
+      HostMemoryAllocator hostMemoryAllocator) {
+    assert offset >= 0 && offset < buffer.length;
+    assert len <= buffer.length - offset;
+    len = len > 0 ? len : buffer.length - offset;
+
+    try (HostMemoryBuffer newBuf = hostMemoryAllocator.allocate(len)) {
+      newBuf.setBytes(0, buffer, offset, len);
+      return readAvro(opts, newBuf, 0, len);
+    }
+  }
+
+  public static Table readAvro(AvroOptions opts, byte[] buffer, long offset, long len) {
+    return readAvro(opts, buffer, offset, len, DefaultHostMemoryAllocator.get());
+  }
+
+
+  /**
+   * Read Avro formatted data.
+   * @param opts various Avro parsing options.
+   * @param buffer raw Avro formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readAvro(AvroOptions opts, HostMemoryBuffer buffer,
+                               long offset, long len) {
+    assert offset >= 0 && offset < buffer.length;
+    assert len <= buffer.length - offset;
+    len = len > 0 ? len : buffer.length - offset;
+
+    return new Table(readAvro(opts.getIncludeColumnNames(),
+        null, buffer.getAddress() + offset, len));
+  }
+
+  /**
+   * Read a ORC file using the default ORCOptions.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readORC(File path) {
+    return readORC(ORCOptions.DEFAULT, path);
+  }
+
+  /**
+   * Read a ORC file.
+   * @param opts ORC parsing options.
+   * @param path the local file to read.
+   * @return the file parsed as a table on the GPU.
+   */
+  public static Table readORC(ORCOptions opts, File path) {
+    return new Table(readORC(opts.getIncludeColumnNames(),
+        path.getAbsolutePath(), 0, 0,
+        opts.usingNumPyTypes(), opts.timeUnit().typeId.getNativeId(),
+        opts.getDecimal128Columns()));
+  }
+
+  /**
+   * Read ORC formatted data.
+   * @param buffer raw ORC formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readORC(byte[] buffer) {
+    return readORC(ORCOptions.DEFAULT, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read ORC formatted data.
+   * @param opts various ORC parsing options.
+   * @param buffer raw ORC formatted bytes.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readORC(ORCOptions opts, byte[] buffer) {
+    return readORC(opts, buffer, 0, buffer.length);
+  }
+
+  /**
+   * Read ORC formatted data.
+   * @param opts various ORC parsing options.
+   * @param buffer raw ORC formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readORC(ORCOptions opts, byte[] buffer, long offset, long len,
+      HostMemoryAllocator hostMemoryAllocator) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.length - offset;
+    assert offset >= 0 && offset < buffer.length;
+    try (HostMemoryBuffer newBuf = hostMemoryAllocator.allocate(len)) {
+      newBuf.setBytes(0, buffer, offset, len);
+      return readORC(opts, newBuf, 0, len);
+    }
+  }
+
+  public static Table readORC(ORCOptions opts, byte[] buffer, long offset, long len) {
+    return readORC(opts, buffer, offset, len, DefaultHostMemoryAllocator.get());
+  }
+
+
+  /**
+   * Read ORC formatted data.
+   * @param opts various ORC parsing options.
+   * @param buffer raw ORC formatted bytes.
+   * @param offset the starting offset into buffer.
+   * @param len the number of bytes to parse.
+   * @return the data parsed as a table on the GPU.
+   */
+  public static Table readORC(ORCOptions opts, HostMemoryBuffer buffer,
+                              long offset, long len) {
+    if (len <= 0) {
+      len = buffer.length - offset;
+    }
+    assert len > 0;
+    assert len <= buffer.getLength() - offset;
+    assert offset >= 0 && offset < buffer.length;
+    return new Table(readORC(opts.getIncludeColumnNames(),
+        null, buffer.getAddress() + offset, len,
+        opts.usingNumPyTypes(), opts.timeUnit().typeId.getNativeId(),
+        opts.getDecimal128Columns()));
+  }
+
+  private static class ParquetTableWriter extends TableWriter {
+    HostBufferConsumer consumer;
+
+    private ParquetTableWriter(ParquetWriterOptions options, File outputFile) {
+      super(writeParquetFileBegin(options.getFlatColumnNames(),
+          options.getTopLevelChildren(),
+          options.getFlatNumChildren(),
+          options.getFlatIsNullable(),
+          options.getMetadataKeys(),
+          options.getMetadataValues(),
+          options.getCompressionType().nativeId,
+          options.getStatisticsFrequency().nativeId,
+          options.getFlatIsTimeTypeInt96(),
+          options.getFlatPrecision(),
+          options.getFlatIsMap(),
+          options.getFlatIsBinary(),
+          options.getFlatHasParquetFieldId(),
+          options.getFlatParquetFieldId(),
+          outputFile.getAbsolutePath()));
+      this.consumer = null;
+    }
+
+    private ParquetTableWriter(ParquetWriterOptions options, HostBufferConsumer consumer,
+        HostMemoryAllocator hostMemoryAllocator) {
+      super(writeParquetBufferBegin(options.getFlatColumnNames(),
+          options.getTopLevelChildren(),
+          options.getFlatNumChildren(),
+          options.getFlatIsNullable(),
+          options.getMetadataKeys(),
+          options.getMetadataValues(),
+          options.getCompressionType().nativeId,
+          options.getStatisticsFrequency().nativeId,
+          options.getFlatIsTimeTypeInt96(),
+          options.getFlatPrecision(),
+          options.getFlatIsMap(),
+          options.getFlatIsBinary(),
+          options.getFlatHasParquetFieldId(),
+          options.getFlatParquetFieldId(),
+          consumer, hostMemoryAllocator));
+      this.consumer = consumer;
+    }
+
+    @Override
+    public void write(Table table) {
+      if (writerHandle == 0) {
+        throw new IllegalStateException("Writer was already closed");
+      }
+      writeParquetChunk(writerHandle, table.nativeHandle, table.getDeviceMemorySize());
+    }
+
+    @Override
+    public void close() throws CudfException {
+      if (writerHandle != 0) {
+        writeParquetEnd(writerHandle);
+      }
+      writerHandle = 0;
+      if (consumer != null) {
+        consumer.done();
+        consumer = null;
+      }
+    }
+  }
+
+  /**
+   * Get a table writer to write parquet data to a file.
+   * @param options the parquet writer options.
+   * @param outputFile where to write the file.
+   * @return a table writer to use for writing out multiple tables.
+   */
+  public static TableWriter writeParquetChunked(ParquetWriterOptions options, File outputFile) {
+    return new ParquetTableWriter(options, outputFile);
+  }
+
+  /**
+   * Get a table writer to write parquet data and handle each chunk with a callback.
+   * @param options the parquet writer options.
+   * @param consumer a class that will be called when host buffers are ready with parquet
+   *                 formatted data in them.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return a table writer to use for writing out multiple tables.
+   */
+  public static TableWriter writeParquetChunked(ParquetWriterOptions options,
+                                                HostBufferConsumer consumer,
+                                                HostMemoryAllocator hostMemoryAllocator) {
+    return new ParquetTableWriter(options, consumer, hostMemoryAllocator);
+  }
+
+  public static TableWriter writeParquetChunked(ParquetWriterOptions options,
+                                                HostBufferConsumer consumer) {
+    return writeParquetChunked(options, consumer, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * This is an evolving API and most likely be removed in future releases. Please use with the
+   * caveat that this will not exist in the near future.
+   * @param options the Parquet writer options.
+   * @param consumer a class that will be called when host buffers are ready with Parquet
+   *                 formatted data in them.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @param columnViews ColumnViews to write to Parquet
+   */
+  public static void writeColumnViewsToParquet(ParquetWriterOptions options,
+                                               HostBufferConsumer consumer,
+                                               HostMemoryAllocator hostMemoryAllocator,
+                                               ColumnView... columnViews) {
+    assert columnViews != null && columnViews.length > 0 : "ColumnViews can't be null or empty";
+    long rows = columnViews[0].getRowCount();
+
+    for (ColumnView columnView : columnViews) {
+      assert (null != columnView) : "ColumnViews can't be null";
+      assert (rows == columnView.getRowCount()) : "All columns should have the same number of " +
+          "rows " + columnView.getType();
+    }
+
+    // Since Arrays are mutable objects make a copy
+    long[] viewPointers = new long[columnViews.length];
+    for (int i = 0; i < columnViews.length; i++) {
+      viewPointers[i] = columnViews[i].getNativeView();
+    }
+
+    long nativeHandle = createCudfTableView(viewPointers);
+    try {
+      try (
+        ParquetTableWriter writer = new ParquetTableWriter(options, consumer, hostMemoryAllocator)
+      ) {
+        long total = 0;
+        for (ColumnView cv : columnViews) {
+          total += cv.getDeviceMemorySize();
+        }
+        writeParquetChunk(writer.writerHandle, nativeHandle, total);
+      }
+    } finally {
+      deleteCudfTable(nativeHandle);
+    }
+  }
+
+  public static void writeColumnViewsToParquet(ParquetWriterOptions options,
+                                               HostBufferConsumer consumer,
+                                               ColumnView... columnViews) {
+    writeColumnViewsToParquet(options, consumer, DefaultHostMemoryAllocator.get(), columnViews);
+  }
+
+  private static class ORCTableWriter extends TableWriter {
+    HostBufferConsumer consumer;
+
+    private ORCTableWriter(ORCWriterOptions options, File outputFile) {
+      super(writeORCFileBegin(options.getFlatColumnNames(),
+          options.getTopLevelChildren(),
+          options.getFlatNumChildren(),
+          options.getFlatIsNullable(),
+          options.getMetadataKeys(),
+          options.getMetadataValues(),
+          options.getCompressionType().nativeId,
+          options.getFlatPrecision(),
+          options.getFlatIsMap(),
+          outputFile.getAbsolutePath()));
+      this.consumer = null;
+    }
+
+    private ORCTableWriter(ORCWriterOptions options, HostBufferConsumer consumer,
+        HostMemoryAllocator hostMemoryAllocator) {
+      super(writeORCBufferBegin(options.getFlatColumnNames(),
+          options.getTopLevelChildren(),
+          options.getFlatNumChildren(),
+          options.getFlatIsNullable(),
+          options.getMetadataKeys(),
+          options.getMetadataValues(),
+          options.getCompressionType().nativeId,
+          options.getFlatPrecision(),
+          options.getFlatIsMap(),
+          consumer, hostMemoryAllocator));
+      this.consumer = consumer;
+    }
+
+    @Override
+    public void write(Table table) {
+      if (writerHandle == 0) {
+        throw new IllegalStateException("Writer was already closed");
+      }
+      writeORCChunk(writerHandle, table.nativeHandle, table.getDeviceMemorySize());
+    }
+
+    @Override
+    public void close() throws CudfException {
+      if (writerHandle != 0) {
+        writeORCEnd(writerHandle);
+      }
+      writerHandle = 0;
+      if (consumer != null) {
+        consumer.done();
+        consumer = null;
+      }
+    }
+  }
+
+  /**
+   * Get a table writer to write ORC data to a file.
+   * @param options the ORC writer options.
+   * @param outputFile where to write the file.
+   * @return a table writer to use for writing out multiple tables.
+   */
+  public static TableWriter writeORCChunked(ORCWriterOptions options, File outputFile) {
+    return new ORCTableWriter(options, outputFile);
+  }
+
+  /**
+   * Get a table writer to write ORC data and handle each chunk with a callback.
+   * @param options the ORC writer options.
+   * @param consumer a class that will be called when host buffers are ready with ORC
+   *                 formatted data in them.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return a table writer to use for writing out multiple tables.
+   */
+  public static TableWriter writeORCChunked(ORCWriterOptions options, HostBufferConsumer consumer,
+      HostMemoryAllocator hostMemoryAllocator) {
+    return new ORCTableWriter(options, consumer, hostMemoryAllocator);
+  }
+
+  public static TableWriter writeORCChunked(ORCWriterOptions options, HostBufferConsumer consumer) {
+    return writeORCChunked(options, consumer, DefaultHostMemoryAllocator.get());
+  }
+
+  private static class ArrowIPCTableWriter extends TableWriter {
+    private final ArrowIPCWriterOptions.DoneOnGpu callback;
+    private HostBufferConsumer consumer;
+    private long maxChunkSize;
+
+    private ArrowIPCTableWriter(ArrowIPCWriterOptions options, File outputFile) {
+      super(writeArrowIPCFileBegin(options.getColumnNames(), outputFile.getAbsolutePath()));
+      this.callback = options.getCallback();
+      this.consumer = null;
+      this.maxChunkSize = options.getMaxChunkSize();
+    }
+
+    private ArrowIPCTableWriter(ArrowIPCWriterOptions options, HostBufferConsumer consumer,
+        HostMemoryAllocator hostMemoryAllocator) {
+      super(writeArrowIPCBufferBegin(options.getColumnNames(), consumer, hostMemoryAllocator));
+      this.callback = options.getCallback();
+      this.consumer = consumer;
+      this.maxChunkSize = options.getMaxChunkSize();
+    }
+
+    @Override
+    public void write(Table table) {
+      if (writerHandle == 0) {
+        throw new IllegalStateException("Writer was already closed");
+      }
+      long arrowHandle = convertCudfToArrowTable(writerHandle, table.nativeHandle);
+      try {
+        callback.doneWithTheGpu(table);
+        writeArrowIPCArrowChunk(writerHandle, arrowHandle, maxChunkSize);
+      } finally {
+        closeArrowTable(arrowHandle);
+      }
+    }
+
+    @Override
+    public void close() throws CudfException {
+      if (writerHandle != 0) {
+        writeArrowIPCEnd(writerHandle);
+      }
+      writerHandle = 0;
+      if (consumer != null) {
+        consumer.done();
+        consumer = null;
+      }
+    }
+  }
+
+  /**
+   * Get a table writer to write arrow IPC data to a file.
+   * @param options the arrow IPC writer options.
+   * @param outputFile where to write the file.
+   * @return a table writer to use for writing out multiple tables.
+   */
+  public static TableWriter writeArrowIPCChunked(ArrowIPCWriterOptions options, File outputFile) {
+    return new ArrowIPCTableWriter(options, outputFile);
+  }
+
+  /**
+   * Get a table writer to write arrow IPC data and handle each chunk with a callback.
+   * @param options the arrow IPC writer options.
+   * @param consumer a class that will be called when host buffers are ready with arrow IPC
+   *                 formatted data in them.
+   * @param hostMemoryAllocator allocator for host memory buffers
+   * @return a table writer to use for writing out multiple tables.
+   */
+  public static TableWriter writeArrowIPCChunked(ArrowIPCWriterOptions options,
+                                                 HostBufferConsumer consumer,
+                                                 HostMemoryAllocator hostMemoryAllocator) {
+    return new ArrowIPCTableWriter(options, consumer, hostMemoryAllocator);
+  }
+
+  public static TableWriter writeArrowIPCChunked(ArrowIPCWriterOptions options,
+                                                 HostBufferConsumer consumer) {
+    return writeArrowIPCChunked(options, consumer, DefaultHostMemoryAllocator.get());
+  }
+
+  private static class ArrowReaderWrapper implements AutoCloseable {
+    private HostBufferProvider provider;
+    private HostMemoryBuffer buffer;
+    private final HostMemoryAllocator hostMemoryAllocator;
+
+    private ArrowReaderWrapper(HostBufferProvider provider,
+        HostMemoryAllocator hostMemoryAllocator) {
+      this.provider = provider;
+      this.hostMemoryAllocator = hostMemoryAllocator;
+      buffer = this.hostMemoryAllocator.allocate(10 * 1024 * 1024, false);
+    }
+
+    // Called From JNI
+    public long readInto(long dstAddress, long amount) {
+      long totalRead = 0;
+      long amountLeft = amount;
+      while (amountLeft > 0) {
+        long amountToCopy = Math.min(amountLeft, buffer.length);
+        long amountRead = provider.readInto(buffer, amountToCopy);
+        buffer.copyToMemory(totalRead + dstAddress, amountRead);
+        amountLeft -= amountRead;
+        totalRead += amountRead;
+        if (amountRead < amountToCopy) {
+          // EOF
+          amountLeft = 0;
+        }
+      }
+      return totalRead;
+    }
+
+    @Override
+    public void close()  {
+      if (provider != null) {
+        provider.close();
+        provider = null;
+      }
+
+      if (buffer != null) {
+        buffer.close();
+        buffer = null;
+      }
+    }
+  }
+
+  private static class ArrowIPCStreamedTableReader implements StreamedTableReader {
+    private final ArrowIPCOptions.NeedGpu callback;
+    private long handle;
+    private ArrowReaderWrapper provider;
+
+    private ArrowIPCStreamedTableReader(ArrowIPCOptions options, File inputFile) {
+      this.provider = null;
+      this.handle = readArrowIPCFileBegin(
+              inputFile.getAbsolutePath());
+      this.callback = options.getCallback();
+    }
+
+    private ArrowIPCStreamedTableReader(ArrowIPCOptions options, HostBufferProvider provider,
+      HostMemoryAllocator hostMemoryAllocator) {
+      this.provider = new ArrowReaderWrapper(provider, hostMemoryAllocator);
+      this.handle = readArrowIPCBufferBegin(this.provider);
+      this.callback = options.getCallback();
+    }
+
+    @Override
+    public Table getNextIfAvailable() throws CudfException {
+      // In this case rowTarget is the minimum number of rows to read.
+      return getNextIfAvailable(1);
+    }
+
+    @Override
+    public Table getNextIfAvailable(int rowTarget) throws CudfException {
+      long arrowTableHandle = readArrowIPCChunkToArrowTable(handle, rowTarget);
+      try {
+        if (arrowTableHandle == 0) {
+          return null;
+        }
+        callback.needTheGpu();
+        return new Table(convertArrowTableToCudf(arrowTableHandle));
+      } finally {
+        closeArrowTable(arrowTableHandle);
+      }
+    }
+
+    @Override
+    public void close() throws CudfException {
+      if (handle != 0) {
+        readArrowIPCEnd(handle);
+      }
+      handle = 0;
+      if (provider != null) {
+        provider.close();
+        provider = null;
+      }
+    }
+  }
+
+  /**
+   * Get a reader that will return tables.
+   * @param options options for reading.
+   * @param inputFile the file to read the Arrow IPC formatted data from
+   * @return a reader.
+   */
+  public static StreamedTableReader readArrowIPCChunked(ArrowIPCOptions options, File inputFile) {
+    return new ArrowIPCStreamedTableReader(options, inputFile);
+  }
+
+  /**
+   * Get a reader that will return tables.
+   * @param inputFile the file to read the Arrow IPC formatted data from
+   * @return a reader.
+   */
+  public static StreamedTableReader readArrowIPCChunked(File inputFile) {
+    return readArrowIPCChunked(ArrowIPCOptions.DEFAULT, inputFile);
+  }
+
+  /**
+   * Get a reader that will return tables.
+   * @param options options for reading.
+   * @param provider what will provide the data being read.
+   * @return a reader.
+   */
+
+  public static StreamedTableReader readArrowIPCChunked(ArrowIPCOptions options,
+                                                        HostBufferProvider provider,
+                                                        HostMemoryAllocator hostMemoryAllocator) {
+    return new ArrowIPCStreamedTableReader(options, provider, hostMemoryAllocator);
+  }
+
+  public static StreamedTableReader readArrowIPCChunked(ArrowIPCOptions options,
+                                                        HostBufferProvider provider) {
+    return new ArrowIPCStreamedTableReader(options, provider, DefaultHostMemoryAllocator.get());
+  }
+
+  /**
+   * Get a reader that will return tables.
+   * @param provider what will provide the data being read.
+   * @return a reader.
+   */
+  public static StreamedTableReader readArrowIPCChunked(HostBufferProvider provider) {
+    return readArrowIPCChunked(ArrowIPCOptions.DEFAULT, provider);
+  }
+
+  /**
+   * Concatenate multiple tables together to form a single table.
+   * The schema of each table (i.e.: number of columns and types of each column) must be equal
+   * across all tables and will determine the schema of the resulting table.
+   */
+  public static Table concatenate(Table... tables) {
+    if (tables.length < 2) {
+      throw new IllegalArgumentException("concatenate requires 2 or more tables");
+    }
+    int numColumns = tables[0].getNumberOfColumns();
+    long[] tableHandles = new long[tables.length];
+    for (int i = 0; i < tables.length; ++i) {
+      tableHandles[i] = tables[i].nativeHandle;
+      assert tables[i].getNumberOfColumns() == numColumns : "all tables must have the same schema";
+    }
+    return new Table(concatenate(tableHandles));
+  }
+
+  /**
+   * Interleave all columns into a single column. Columns must all have the same data type and length.
+   *
+   * Example:
+   * ```
+   * input  = [[A1, A2, A3], [B1, B2, B3]]
+   * return = [A1, B1, A2, B2, A3, B3]
+   * ```
+   *
+   * @return The interleaved columns as a single column
+   */
+  public ColumnVector interleaveColumns() {
+    assert this.getNumberOfColumns() >= 2 : ".interleaveColumns() operation requires at least 2 columns";
+    return new ColumnVector(interleaveColumns(this.nativeHandle));
+  }
+
+  /**
+   * Repeat each row of this table count times.
+   * @param count the number of times to repeat each row.
+   * @return the new Table.
+   */
+  public Table repeat(int count) {
+    return new Table(repeatStaticCount(this.nativeHandle, count));
+  }
+
+  /**
+   * Create a new table by repeating each row of this table. The number of
+   * repetitions of each row is defined by the corresponding value in counts.
+   * @param counts the number of times to repeat each row. Cannot have nulls, must be an
+   *               Integer type, and must have one entry for each row in the table.
+   * @return the new Table.
+   * @throws CudfException on any error.
+   */
+  public Table repeat(ColumnView counts) {
+    return new Table(repeatColumnCount(this.nativeHandle, counts.getNativeView()));
+  }
+
+  /**
+   * Partition this table using the mapping in partitionMap. partitionMap must be an integer
+   * column. The number of rows in partitionMap must be the same as this table.  Each row
+   * in the map will indicate which partition the rows in the table belong to.
+   * @param partitionMap the partitions for each row.
+   * @param numberOfPartitions number of partitions
+   * @return {@link PartitionedTable} Table that exposes a limited functionality of the
+   * {@link Table} class
+   */
+  public PartitionedTable partition(ColumnView partitionMap, int numberOfPartitions) {
+    int[] partitionOffsets = new int[numberOfPartitions];
+    return new PartitionedTable(new Table(partition(
+        getNativeView(),
+        partitionMap.getNativeView(),
+        partitionOffsets.length,
+        partitionOffsets)), partitionOffsets);
+  }
+
+  /**
+   * Find smallest indices in a sorted table where values should be inserted to maintain order.
+   * <pre>
+   * Example:
+   *
+   *  Single column:
+   *      idx            0   1   2   3   4
+   *   inputTable  =   { 10, 20, 20, 30, 50 }
+   *   valuesTable =   { 20 }
+   *   result      =   { 1 }
+   *
+   *  Multi Column:
+   *      idx                0    1    2    3    4
+   *   inputTable      = {{  10,  20,  20,  20,  20 },
+   *                      { 5.0,  .5,  .5,  .7,  .7 },
+   *                      {  90,  77,  78,  61,  61 }}
+   *   valuesTable     = {{ 20 },
+   *                      { .7 },
+   *                      { 61 }}
+   *   result          = {  3 }
+   * </pre>
+   * The input table and the values table need to be non-empty (row count > 0)
+   * @param areNullsSmallest per column, true if nulls are assumed smallest
+   * @param valueTable the table of values to find insertion locations for
+   * @param descFlags per column indicates the ordering, true if descending.
+   * @return ColumnVector with lower bound indices for all rows in valueTable
+   */
+  public ColumnVector lowerBound(boolean[] areNullsSmallest,
+      Table valueTable, boolean[] descFlags) {
+    assertForBounds(valueTable);
+    return new ColumnVector(bound(this.nativeHandle, valueTable.nativeHandle,
+      descFlags, areNullsSmallest, false));
+  }
+
+  /**
+   * Find smallest indices in a sorted table where values should be inserted to maintain order.
+   * This is a convenience method. It pulls out the columns indicated by the args and sets up the
+   * ordering properly to call `lowerBound`.
+   * @param valueTable the table of values to find insertion locations for
+   * @param args the sort order used to sort this table.
+   * @return ColumnVector with lower bound indices for all rows in valueTable
+   */
+  public ColumnVector lowerBound(Table valueTable, OrderByArg... args) {
+    boolean[] areNullsSmallest = new boolean[args.length];
+    boolean[] descFlags = new boolean[args.length];
+    ColumnVector[] inputColumns = new ColumnVector[args.length];
+    ColumnVector[] searchColumns = new ColumnVector[args.length];
+    for (int i = 0; i < args.length; i++) {
+      areNullsSmallest[i] = args[i].isNullSmallest;
+      descFlags[i] = args[i].isDescending;
+      inputColumns[i] = columns[args[i].index];
+      searchColumns[i] = valueTable.columns[args[i].index];
+    }
+    try (Table input = new Table(inputColumns);
+         Table search = new Table(searchColumns)) {
+      return input.lowerBound(areNullsSmallest, search, descFlags);
+    }
+  }
+
+  /**
+   * Find largest indices in a sorted table where values should be inserted to maintain order.
+   * Given a sorted table return the upper bound.
+   * <pre>
+   * Example:
+   *
+   *  Single column:
+   *      idx            0   1   2   3   4
+   *   inputTable  =   { 10, 20, 20, 30, 50 }
+   *   valuesTable =   { 20 }
+   *   result      =   { 3 }
+   *
+   *  Multi Column:
+   *      idx                0    1    2    3    4
+   *   inputTable      = {{  10,  20,  20,  20,  20 },
+   *                      { 5.0,  .5,  .5,  .7,  .7 },
+   *                      {  90,  77,  78,  61,  61 }}
+   *   valuesTable     = {{ 20 },
+   *                      { .7 },
+   *                      { 61 }}
+   *   result          = {  5 }
+   * </pre>
+   * The input table and the values table need to be non-empty (row count > 0)
+   * @param areNullsSmallest per column, true if nulls are assumed smallest
+   * @param valueTable the table of values to find insertion locations for
+   * @param descFlags per column indicates the ordering, true if descending.
+   * @return ColumnVector with upper bound indices for all rows in valueTable
+   */
+  public ColumnVector upperBound(boolean[] areNullsSmallest,
+      Table valueTable, boolean[] descFlags) {
+    assertForBounds(valueTable);
+    return new ColumnVector(bound(this.nativeHandle, valueTable.nativeHandle,
+      descFlags, areNullsSmallest, true));
+  }
+
+  /**
+   * Find largest indices in a sorted table where values should be inserted to maintain order.
+   * This is a convenience method. It pulls out the columns indicated by the args and sets up the
+   * ordering properly to call `upperBound`.
+   * @param valueTable the table of values to find insertion locations for
+   * @param args the sort order used to sort this table.
+   * @return ColumnVector with upper bound indices for all rows in valueTable
+   */
+  public ColumnVector upperBound(Table valueTable, OrderByArg... args) {
+    boolean[] areNullsSmallest = new boolean[args.length];
+    boolean[] descFlags = new boolean[args.length];
+    ColumnVector[] inputColumns = new ColumnVector[args.length];
+    ColumnVector[] searchColumns = new ColumnVector[args.length];
+    for (int i = 0; i < args.length; i++) {
+      areNullsSmallest[i] = args[i].isNullSmallest;
+      descFlags[i] = args[i].isDescending;
+      inputColumns[i] = columns[args[i].index];
+      searchColumns[i] = valueTable.columns[args[i].index];
+    }
+    try (Table input = new Table(inputColumns);
+         Table search = new Table(searchColumns)) {
+      return input.upperBound(areNullsSmallest, search, descFlags);
+    }
+  }
+
+  private void assertForBounds(Table valueTable) {
+    assert this.getRowCount() != 0 : "Input table cannot be empty";
+    assert valueTable.getRowCount() != 0 : "Value table cannot be empty";
+    for (int i = 0; i < Math.min(columns.length, valueTable.columns.length); i++) {
+      assert valueTable.columns[i].getType().equals(this.getColumn(i).getType()) :
+          "Input and values tables' data types do not match";
+    }
+  }
+
+  /**
+   * Joins two tables all of the left against all of the right. Be careful as this
+   * gets very big and you can easily use up all of the GPUs memory.
+   * @param right the right table
+   * @return the joined table.  The order of the columns returned will be left columns,
+   * right columns.
+   */
+  public Table crossJoin(Table right) {
+    return new Table(Table.crossJoin(this.nativeHandle, right.nativeHandle));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // TABLE MANIPULATION APIs
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Get back a gather map that can be used to sort the data. This allows you to sort by data
+   * that does not appear in the final result and not pay the cost of gathering the data that
+   * is only needed for sorting.
+   * @param args what order to sort the data by
+   * @return a gather map
+   */
+  public ColumnVector sortOrder(OrderByArg... args) {
+    long[] sortKeys = new long[args.length];
+    boolean[] isDescending = new boolean[args.length];
+    boolean[] areNullsSmallest = new boolean[args.length];
+    for (int i = 0; i < args.length; i++) {
+      int index = args[i].index;
+      assert (index >= 0 && index < columns.length) :
+          "index is out of range 0 <= " + index + " < " + columns.length;
+      isDescending[i] = args[i].isDescending;
+      areNullsSmallest[i] = args[i].isNullSmallest;
+      sortKeys[i] = columns[index].getNativeView();
+    }
+
+    return new ColumnVector(sortOrder(nativeHandle, sortKeys, isDescending, areNullsSmallest));
+  }
+
+  /**
+   * Orders the table using the sortkeys returning a new allocated table. The caller is
+   * responsible for cleaning up
+   * the {@link ColumnVector} returned as part of the output {@link Table}
+   * <p>
+   * Example usage: orderBy(true, OrderByArg.asc(0), OrderByArg.desc(3)...);
+   * @param args Suppliers to initialize sortKeys.
+   * @return Sorted Table
+   */
+  public Table orderBy(OrderByArg... args) {
+    long[] sortKeys = new long[args.length];
+    boolean[] isDescending = new boolean[args.length];
+    boolean[] areNullsSmallest = new boolean[args.length];
+    for (int i = 0; i < args.length; i++) {
+      int index = args[i].index;
+      assert (index >= 0 && index < columns.length) :
+          "index is out of range 0 <= " + index + " < " + columns.length;
+      isDescending[i] = args[i].isDescending;
+      areNullsSmallest[i] = args[i].isNullSmallest;
+      sortKeys[i] = columns[index].getNativeView();
+    }
+
+    return new Table(orderBy(nativeHandle, sortKeys, isDescending, areNullsSmallest));
+  }
+
+  /**
+   * Merge multiple already sorted tables keeping the sort order the same.
+   * This is a more efficient version of concatenate followed by orderBy, but requires that
+   * the input already be sorted.
+   * @param tables the tables that should be merged.
+   * @param args the ordering of the tables.  Should match how they were sorted
+   *             initially.
+   * @return a combined sorted table.
+   */
+  public static Table merge(Table[] tables, OrderByArg... args) {
+    assert tables.length > 0;
+    long[] tableHandles = new long[tables.length];
+    Table first = tables[0];
+    assert args.length <= first.columns.length;
+    for (int i = 0; i < tables.length; i++) {
+      Table t = tables[i];
+      assert t != null;
+      assert t.columns.length == first.columns.length;
+      tableHandles[i] = t.nativeHandle;
+    }
+    int[] sortKeyIndexes = new int[args.length];
+    boolean[] isDescending = new boolean[args.length];
+    boolean[] areNullsSmallest = new boolean[args.length];
+    for (int i = 0; i < args.length; i++) {
+      int index = args[i].index;
+      assert (index >= 0 && index < first.columns.length) :
+          "index is out of range 0 <= " + index + " < " + first.columns.length;
+      isDescending[i] = args[i].isDescending;
+      areNullsSmallest[i] = args[i].isNullSmallest;
+      sortKeyIndexes[i] = index;
+    }
+
+    return new Table(merge(tableHandles, sortKeyIndexes, isDescending, areNullsSmallest));
+  }
+
+  /**
+   * Merge multiple already sorted tables keeping the sort order the same.
+   * This is a more efficient version of concatenate followed by orderBy, but requires that
+   * the input already be sorted.
+   * @param tables the tables that should be merged.
+   * @param args the ordering of the tables.  Should match how they were sorted
+   *             initially.
+   * @return a combined sorted table.
+   */
+  public static Table merge(List<Table> tables, OrderByArg... args) {
+    return merge(tables.toArray(new Table[tables.size()]), args);
+  }
+
+  /**
+   * Returns aggregate operations grouped by columns provided in indices
+   * @param groupByOptions Options provided in the builder
+   * @param indices columns to be considered for groupBy
+   */
+  public GroupByOperation groupBy(GroupByOptions groupByOptions, int... indices) {
+    return groupByInternal(groupByOptions, indices);
+  }
+
+  /**
+   * Returns aggregate operations grouped by columns provided in indices
+   * with default options as below:
+   *  - null is considered as key while grouping.
+   *  - keys are not presorted.
+   *  - empty key order array.
+   *  - empty null order array.
+   * @param indices columns to be considered for groupBy
+   */
+  public GroupByOperation groupBy(int... indices) {
+    return groupByInternal(GroupByOptions.builder().withIgnoreNullKeys(false).build(),
+        indices);
+  }
+
+  private GroupByOperation groupByInternal(GroupByOptions groupByOptions, int[] indices) {
+    int[] operationIndicesArray = copyAndValidate(indices);
+    return new GroupByOperation(this, groupByOptions, operationIndicesArray);
+  }
+
+  /**
+   * Round-robin partition a table into the specified number of partitions. The first row is placed
+   * in the specified starting partition, the next row is placed in the next partition, and so on.
+   * When the last partition is reached then next partition is partition 0 and the algorithm
+   * continues until all rows have been placed in partitions, evenly distributing the rows
+   * among the partitions.
+   * @param numberOfPartitions - number of partitions to use
+   * @param startPartition - starting partition index (i.e.: where first row is placed).
+   * @return - {@link PartitionedTable} - Table that exposes a limited functionality of the
+   * {@link Table} class
+   */
+  public PartitionedTable roundRobinPartition(int numberOfPartitions, int startPartition) {
+    int[] partitionOffsets = new int[numberOfPartitions];
+    return new PartitionedTable(new Table(Table.roundRobinPartition(nativeHandle,
+        numberOfPartitions, startPartition,
+        partitionOffsets)), partitionOffsets);
+  }
+
+  public TableOperation onColumns(int... indices) {
+    int[] operationIndicesArray = copyAndValidate(indices);
+    return new TableOperation(this, operationIndicesArray);
+  }
+
+  private int[] copyAndValidate(int[] indices) {
+    int[] operationIndicesArray = new int[indices.length];
+    for (int i = 0; i < indices.length; i++) {
+      operationIndicesArray[i] = indices[i];
+      assert operationIndicesArray[i] >= 0 && operationIndicesArray[i] < columns.length :
+          "operation index is out of range 0 <= " + operationIndicesArray[i] + " < " + columns.length;
+    }
+    return operationIndicesArray;
+  }
+
+  /**
+   * Filters this table using a column of boolean values as a mask, returning a new one.
+   * <p>
+   * Given a mask column, each element `i` from the input columns
+   * is copied to the output columns if the corresponding element `i` in the mask is
+   * non-null and `true`. This operation is stable: the input order is preserved.
+   * <p>
+   * This table and mask columns must have the same number of rows.
+   * <p>
+   * The output table has size equal to the number of elements in boolean_mask
+   * that are both non-null and `true`.
+   * <p>
+   * If the original table row count is zero, there is no error, and an empty table is returned.
+   * @param mask column of type {@link DType#BOOL8} used as a mask to filter
+   *             the input column
+   * @return table containing copy of all elements of this table passing
+   * the filter defined by the boolean mask
+   */
+  public Table filter(ColumnView mask) {
+    assert mask.getType().equals(DType.BOOL8) : "Mask column must be of type BOOL8";
+    assert getRowCount() == 0 || getRowCount() == mask.getRowCount() : "Mask column has incorrect size";
+    return new Table(filter(nativeHandle, mask.getNativeView()));
+  }
+
+  /**
+   * Enum to specify which of duplicate rows/elements will be copied to the output.
+   */
+  public enum DuplicateKeepOption {
+    KEEP_ANY(0),
+    KEEP_FIRST(1),
+    KEEP_LAST(2),
+    KEEP_NONE(3);
+
+    final int keepValue;
+
+    DuplicateKeepOption(int keepValue) {
+      this.keepValue = keepValue;
+    }
+  }
+
+  /**
+   * Copy rows of the current table to an output table such that duplicate rows in the key columns
+   * are ignored (i.e., only one row from the duplicate ones will be copied). These keys columns are
+   * a subset of the current table columns and their indices are specified by an input array.
+   *
+   * The order of rows in the output table is not specified.
+   *
+   * @param keyColumns Array of indices representing key columns from the current table.
+   * @param keep Option specifying to keep any, first, last, or none of the found duplicates.
+   * @param nullsEqual Flag to denote whether nulls are treated as equal when comparing rows of the
+   *                   key columns to check for uniqueness.
+   *
+   * @return Table with unique keys.
+   */
+  public Table dropDuplicates(int[] keyColumns, DuplicateKeepOption keep, boolean nullsEqual) {
+    assert keyColumns.length >= 1 : "Input keyColumns must contain indices of at least one column";
+    return new Table(dropDuplicates(nativeHandle, keyColumns, keep.keepValue, nullsEqual));
+  }
+
+  /**
+   * Count how many rows in the table are distinct from one another.
+   * @param nullEqual if nulls should be considered equal to each other or not.
+   */
+  public int distinctCount(NullEquality nullsEqual) {
+    return distinctCount(nativeHandle, nullsEqual.nullsEqual);
+  }
+
+  /**
+   * Count how many rows in the table are distinct from one another.
+   * Nulls are considered to be equal to one another.
+   */
+  public int distinctCount() {
+    return distinctCount(nativeHandle, true);
+  }
+
+  /**
+   * Split a table at given boundaries, but the result of each split has memory that is laid out
+   * in a contiguous range of memory.  This allows for us to optimize copying the data in a single
+   * operation.
+   *
+   * <code>
+   * Example:
+   * input:   [{10, 12, 14, 16, 18, 20, 22, 24, 26, 28},
+   *           {50, 52, 54, 56, 58, 60, 62, 64, 66, 68}]
+   * splits:  {2, 5, 9}
+   * output:  [{{10, 12}, {14, 16, 18}, {20, 22, 24, 26}, {28}},
+   *           {{50, 52}, {54, 56, 58}, {60, 62, 64, 66}, {68}}]
+   * </code>
+   * @param indices A vector of indices where to make the split
+   * @return The tables split at those points. NOTE: It is the responsibility of the caller to
+   * close the result. Each table and column holds a reference to the original buffer. But both
+   * the buffer and the table must be closed for the memory to be released.
+   */
+  public ContiguousTable[] contiguousSplit(int... indices) {
+    return contiguousSplit(nativeHandle, indices);
+  }
+
+  /**
+   * Create an instance of `ChunkedPack` which can be used to pack this table
+   * contiguously in memory utilizing a bounce buffer of size `bounceBufferSize`.
+   *
+   * This version of `makeChunkedPack` takes a `RmmDviceMemoryResource`, which can be used
+   * to pre-allocate all scratch and temporary space required for the state of `cudf::chunked_pack`.
+   *
+   * The caller is responsible for calling close on the returned `ChunkedPack` object.
+   *
+   * @param bounceBufferSize The size of bounce buffer that will be utilized to pack into
+   * @param tempMemoryResource A memory resource that is used to satisfy allocations for
+   *                           temporary and thrust scratch space.
+   * @return An instance of `ChunkedPack` that the caller must use to finish the operation.
+   */
+  public ChunkedPack makeChunkedPack(
+      long bounceBufferSize, RmmDeviceMemoryResource tempMemoryResource) {
+    long tempMemoryResourceHandle = tempMemoryResource.getHandle();
+    return new ChunkedPack(
+      makeChunkedPack(nativeHandle, bounceBufferSize, tempMemoryResourceHandle));
+  }
+
+  /**
+   * Create an instance of `ChunkedPack` which can be used to pack this table
+   * contiguously in memory utilizing a bounce buffer of size `bounceBufferSize`.
+   *
+   * This version of `makeChunkedPack` makes use of the default per-device memory resource,
+   * for scratch and temporary space required for the state of `cudf::chunked_pack`.
+   *
+   * The caller is responsible for calling close on the returned `ChunkedPack` object.
+   *
+   * @param bounceBufferSize The size of bounce buffer that will be utilized to pack into
+   * @return An instance of `ChunkedPack` that the caller must use to finish the operation.
+   */
+  public ChunkedPack makeChunkedPack(long bounceBufferSize) {
+    return new ChunkedPack(
+      makeChunkedPack(nativeHandle, bounceBufferSize, 0));
+  }
+
+  /**
+   * Explodes a list column's elements.
+   *
+   * Any list is exploded, which means the elements of the list in each row are expanded
+   * into new rows in the output. The corresponding rows for other columns in the input
+   * are duplicated.
+   *
+   * <code>
+   * Example:
+   * input:  [[5,10,15], 100],
+   *         [[20,25],   200],
+   *         [[30],      300]
+   * index: 0
+   * output: [5,         100],
+   *         [10,        100],
+   *         [15,        100],
+   *         [20,        200],
+   *         [25,        200],
+   *         [30,        300]
+   * </code>
+   *
+   * Nulls propagate in different ways depending on what is null.
+   * <code>
+   * input:  [[5,null,15], 100],
+   *         [null,        200]
+   * index: 0
+   * output: [5,           100],
+   *         [null,        100],
+   *         [15,          100]
+   * </code>
+   * Note that null lists are completely removed from the output
+   * and nulls inside lists are pulled out and remain.
+   *
+   * @param index Column index to explode inside the table.
+   * @return A new table with explode_col exploded.
+   */
+  public Table explode(int index) {
+    assert 0 <= index && index < columns.length : "Column index is out of range";
+    assert columns[index].getType().equals(DType.LIST) : "Column to explode must be of type LIST";
+    return new Table(explode(nativeHandle, index));
+  }
+
+  /**
+   * Explodes a list column's elements and includes a position column.
+   *
+   * Any list is exploded, which means the elements of the list in each row are expanded into new rows
+   * in the output. The corresponding rows for other columns in the input are duplicated. A position
+   * column is added that has the index inside the original list for each row. Example:
+   * <code>
+   * input:  [[5,10,15], 100],
+   *         [[20,25],   200],
+   *         [[30],      300]
+   * index: 0
+   * output: [0,   5,    100],
+   *         [1,   10,   100],
+   *         [2,   15,   100],
+   *         [0,   20,   200],
+   *         [1,   25,   200],
+   *         [0,   30,   300]
+   * </code>
+   *
+   * Nulls and empty lists propagate in different ways depending on what is null or empty.
+   * <code>
+   * input:  [[5,null,15], 100],
+   *         [null,        200]
+   * index: 0
+   * output: [5,           100],
+   *         [null,        100],
+   *         [15,          100]
+   * </code>
+   *
+   * Note that null lists are not included in the resulting table, but nulls inside
+   * lists and empty lists will be represented with a null entry for that column in that row.
+   *
+   * @param index Column index to explode inside the table.
+   * @return A new table with exploded value and position. The column order of return table is
+   *         [cols before explode_input, explode_position, explode_value, cols after explode_input].
+   */
+  public Table explodePosition(int index) {
+    assert 0 <= index && index < columns.length : "Column index is out of range";
+    assert columns[index].getType().equals(DType.LIST) : "Column to explode must be of type LIST";
+    return new Table(explodePosition(nativeHandle, index));
+  }
+
+  /**
+   * Explodes a list column's elements.
+   *
+   * Any list is exploded, which means the elements of the list in each row are expanded
+   * into new rows in the output. The corresponding rows for other columns in the input
+   * are duplicated.
+   *
+   * <code>
+   * Example:
+   * input:  [[5,10,15], 100],
+   *         [[20,25],   200],
+   *         [[30],      300],
+   * index: 0
+   * output: [5,         100],
+   *         [10,        100],
+   *         [15,        100],
+   *         [20,        200],
+   *         [25,        200],
+   *         [30,        300]
+   * </code>
+   *
+   * Nulls propagate in different ways depending on what is null.
+   * <code>
+   *  input:  [[5,null,15], 100],
+   *          [null,        200]
+   * index: 0
+   * output:  [5,           100],
+   *          [null,        100],
+   *          [15,          100],
+   *          [null,        200]
+   * </code>
+   * Note that null lists are completely removed from the output
+   * and nulls inside lists are pulled out and remain.
+   *
+   * @param index Column index to explode inside the table.
+   * @return A new table with explode_col exploded.
+   */
+  public Table explodeOuter(int index) {
+    assert 0 <= index && index < columns.length : "Column index is out of range";
+    assert columns[index].getType().equals(DType.LIST) : "Column to explode must be of type LIST";
+    return new Table(explodeOuter(nativeHandle, index));
+  }
+
+  /**
+   * Explodes a list column's elements retaining any null entries or empty lists and includes a
+   * position column.
+   *
+   * Any list is exploded, which means the elements of the list in each row are expanded into new rows
+   * in the output. The corresponding rows for other columns in the input are duplicated. A position
+   * column is added that has the index inside the original list for each row. Example:
+   *
+   * <code>
+   * Example:
+   * input:  [[5,10,15], 100],
+   *         [[20,25],   200],
+   *         [[30],      300],
+   * index: 0
+   * output: [0,   5,    100],
+   *         [1,   10,   100],
+   *         [2,   15,   100],
+   *         [0,   20,   200],
+   *         [1,   25,   200],
+   *         [0,   30,   300]
+   * </code>
+   *
+   * Nulls and empty lists propagate as null entries in the result.
+   * <code>
+   * input:  [[5,null,15], 100],
+   *         [null,        200],
+   *         [[],          300]
+   * index: 0
+   * output: [0,     5,    100],
+   *         [1,  null,    100],
+   *         [2,    15,    100],
+   *         [0,  null,    200],
+   *         [0,  null,    300]
+   * </code>
+   *
+   *    returns
+   *
+   * @param index Column index to explode inside the table.
+   * @return A new table with exploded value and position. The column order of return table is
+   *         [cols before explode_input, explode_position, explode_value, cols after explode_input].
+   */
+  public Table explodeOuterPosition(int index) {
+    assert 0 <= index && index < columns.length : "Column index is out of range";
+    assert columns[index].getType().equals(DType.LIST) : "Column to explode must be of type LIST";
+    return new Table(explodeOuterPosition(nativeHandle, index));
+  }
+
+  /**
+   * Returns an approximate cumulative size in bits of all columns in the `table_view` for each row.
+   * This function counts bits instead of bytes to account for the null mask which only has one
+   * bit per row. Each row in the returned column is the sum of the per-row bit size for each column
+   * in the table.
+   *
+   * In some cases, this is an inexact approximation. Specifically, columns of lists and strings
+   * require N+1 offsets to represent N rows. It is up to the caller to calculate the small
+   * additional overhead of the terminating offset for any group of rows being considered.
+   *
+   * This function returns the per-row bit sizes as the columns are currently formed. This can
+   * end up being larger than the number you would get by gathering the rows. Specifically,
+   * the push-down of struct column validity masks can nullify rows that contain data for
+   * string or list columns. In these cases, the size returned is conservative such that:
+   * row_bit_count(column(x)) >= row_bit_count(gather(column(x)))
+   *
+   * @return INT32 column of bit size per row of the table
+   */
+  public ColumnVector rowBitCount() {
+    return new ColumnVector(rowBitCount(getNativeView()));
+  }
+
+  /**
+   * Gathers the rows of this table according to `gatherMap` such that row "i"
+   * in the resulting table's columns will contain row "gatherMap[i]" from this table.
+   * The number of rows in the result table will be equal to the number of elements in
+   * `gatherMap`.
+   *
+   * A negative value `i` in the `gatherMap` is interpreted as `i+n`, where
+   * `n` is the number of rows in this table.
+
+   * @param gatherMap the map of indexes.  Must be non-nullable and integral type.
+   * @return the resulting Table.
+   */
+  public Table gather(ColumnView gatherMap) {
+    return gather(gatherMap, OutOfBoundsPolicy.NULLIFY);
+  }
+
+  /**
+   * Gathers the rows of this table according to `gatherMap` such that row "i"
+   * in the resulting table's columns will contain row "gatherMap[i]" from this table.
+   * The number of rows in the result table will be equal to the number of elements in
+   * `gatherMap`.
+   *
+   * A negative value `i` in the `gatherMap` is interpreted as `i+n`, where
+   * `n` is the number of rows in this table.
+   *
+   * @param gatherMap the map of indexes.  Must be non-nullable and integral type.
+   * @param outOfBoundsPolicy policy to use when an out-of-range value is in `gatherMap`.
+   * @return the resulting Table.
+   */
+  public Table gather(ColumnView gatherMap, OutOfBoundsPolicy outOfBoundsPolicy) {
+    boolean checkBounds = outOfBoundsPolicy == OutOfBoundsPolicy.NULLIFY;
+    return new Table(gather(nativeHandle, gatherMap.getNativeView(), checkBounds));
+  }
+
+  /**
+   * Scatters values from the source table into the target table out-of-place, returning a new
+   * result table. The scatter is performed according to a scatter map such that row `scatterMap[i]`
+   * of the destination table gets row `i` of the source table. All other rows of the destination
+   * table equal corresponding rows of the target table.
+   *
+   * The number of columns in source must match the number of columns in target and their
+   * corresponding data types must be the same.
+   *
+   * If the same index appears more than once in the scatter map, the result is undefined.
+   *
+   * A negative value `i` in the `scatterMap` is interpreted as `i + n`, where `n` is the number of
+   * rows in the `target` table.
+   *
+   * @param scatterMap The map of indexes. Must be non-nullable and integral type.
+   * @param target The table into which rows from the current table are to be scattered out-of-place.
+   * @return A new table which is the result of out-of-place scattering the source table into the
+   *         target table.
+   */
+  public Table scatter(ColumnView scatterMap, Table target) {
+    return new Table(scatterTable(nativeHandle, scatterMap.getNativeView(), target.getNativeView()));
+  }
+
+  /**
+   * Scatters values from the source rows into the target table out-of-place, returning a new result
+   * table. The scatter is performed according to a scatter map such that row `scatterMap[i]` of the
+   * destination table is replaced by the source row `i`. All other rows of the destination table
+   * equal corresponding rows of the target table.
+   *
+   * The number of elements in source must match the number of columns in target and their
+   * corresponding data types must be the same.
+   *
+   * If the same index appears more than once in the scatter map, the result is undefined.
+   *
+   * A negative value `i` in the `scatterMap` is interpreted as `i + n`, where `n` is the number of
+   * rows in the `target` table.
+   *
+   * @param source The input scalars containing values to be scattered into the target table.
+   * @param scatterMap The map of indexes. Must be non-nullable and integral type.
+   * @param target The table into which the values from source are to be scattered out-of-place.
+   * @return A new table which is the result of out-of-place scattering the source values into the
+   *         target table.
+   */
+  public static Table scatter(Scalar[] source, ColumnView scatterMap, Table target) {
+    long[] srcScalarHandles = new long[source.length];
+    for(int i = 0; i < source.length; ++i) {
+      assert source[i] != null : "Scalar vectors passed in should not contain null";
+      srcScalarHandles[i] = source[i].getScalarHandle();
+    }
+    return new Table(scatterScalars(srcScalarHandles, scatterMap.getNativeView(),
+        target.getNativeView()));
+  }
+
+  private static GatherMap[] buildJoinGatherMaps(long[] gatherMapData) {
+    long bufferSize = gatherMapData[0];
+    long leftAddr = gatherMapData[1];
+    long leftHandle = gatherMapData[2];
+    long rightAddr = gatherMapData[3];
+    long rightHandle = gatherMapData[4];
+    GatherMap[] maps = new GatherMap[2];
+    maps[0] = new GatherMap(DeviceMemoryBuffer.fromRmm(leftAddr, bufferSize, leftHandle));
+    maps[1] = new GatherMap(DeviceMemoryBuffer.fromRmm(rightAddr, bufferSize, rightHandle));
+    return maps;
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the table argument represents the key columns from the right table. Two {@link GatherMap}
+   * instances will be returned that can be used to gather the left and right tables,
+   * respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightKeys join key columns from the right table
+   * @param compareNullsEqual true if null key values should match otherwise false
+   * @return left and right table gather maps
+   */
+  public GatherMap[] leftJoinGatherMaps(Table rightKeys, boolean compareNullsEqual) {
+    if (getNumberOfColumns() != rightKeys.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightKeys.getNumberOfColumns());
+    }
+    long[] gatherMapData =
+        leftJoinGatherMaps(getNativeView(), rightKeys.getNativeView(), compareNullsEqual);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows resulting from a left equi-join between two tables.
+   * It is assumed this table instance holds the key columns from the left table, and the
+   * {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * @param rightHash hash table built from join key columns from the right table
+   * @return row count of the join result
+   */
+  public long leftJoinRowCount(HashJoin rightHash) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    return leftJoinRowCount(getNativeView(), rightHash.getNativeView());
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Two {@link GatherMap} instances will be returned that can be used to gather the left and right
+   * tables, respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightHash hash table built from join key columns from the right table
+   * @return left and right table gather maps
+   */
+  public GatherMap[] leftJoinGatherMaps(HashJoin rightHash) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    long[] gatherMapData = leftHashJoinGatherMaps(getNativeView(), rightHash.getNativeView());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Two {@link GatherMap} instances will be returned that can be used to gather the left and right
+   * tables, respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #leftJoinRowCount(HashJoin)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightHash hash table built from join key columns from the right table
+   * @param outputRowCount number of output rows in the join result
+   * @return left and right table gather maps
+   */
+  public GatherMap[] leftJoinGatherMaps(HashJoin rightHash, long outputRowCount) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    long[] gatherMapData = leftHashJoinGatherMapsWithCount(getNativeView(),
+        rightHash.getNativeView(), outputRowCount);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows from the result of a left join between two tables when a
+   * conditional expression is true. It is assumed this table instance holds the columns from
+   * the left table, and the table argument represents the columns from the right table.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @return row count for the join result
+   */
+  public long conditionalLeftJoinRowCount(Table rightTable, CompiledExpression condition) {
+    return conditionalLeftJoinRowCount(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle());
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @return left and right table gather maps
+   */
+  public GatherMap[] conditionalLeftJoinGatherMaps(Table rightTable,
+                                                   CompiledExpression condition) {
+    long[] gatherMapData =
+        conditionalLeftJoinGatherMaps(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #conditionalLeftJoinRowCount(Table, CompiledExpression)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @param outputRowCount number of output rows in the join result
+   * @return left and right table gather maps
+   */
+  public GatherMap[] conditionalLeftJoinGatherMaps(Table rightTable,
+                                                   CompiledExpression condition,
+                                                   long outputRowCount) {
+    long[] gatherMapData =
+        conditionalLeftJoinGatherMapsWithCount(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle(), outputRowCount);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes output size information for a left join between two tables using a mix of equality
+   * and inequality conditions. The entire join condition is assumed to be a logical AND of the
+   * equality condition and inequality condition.
+   * NOTE: It is the responsibility of the caller to close the resulting size information object
+   * or native resources can be leaked!
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return size information for the join
+   */
+  public static MixedJoinSize mixedLeftJoinSize(Table leftKeys, Table rightKeys,
+                                                Table leftConditional, Table rightConditional,
+                                                CompiledExpression condition,
+                                                NullEquality nullEquality) {
+    long[] mixedSizeInfo = mixedLeftJoinSize(
+            leftKeys.getNativeView(), rightKeys.getNativeView(),
+            leftConditional.getNativeView(), rightConditional.getNativeView(),
+            condition.getNativeHandle(), nullEquality == NullEquality.EQUAL);
+    assert mixedSizeInfo.length == 2;
+    long outputRowCount = mixedSizeInfo[0];
+    long matchesColumnHandle = mixedSizeInfo[1];
+    return new MixedJoinSize(outputRowCount, new ColumnVector(matchesColumnHandle));
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return left and right table gather maps
+   */
+  public static GatherMap[] mixedLeftJoinGatherMaps(Table leftKeys, Table rightKeys,
+                                                    Table leftConditional, Table rightConditional,
+                                                    CompiledExpression condition,
+                                                    NullEquality nullEquality) {
+    long[] gatherMapData = mixedLeftJoinGatherMaps(
+            leftKeys.getNativeView(), rightKeys.getNativeView(),
+            leftConditional.getNativeView(), rightConditional.getNativeView(),
+            condition.getNativeHandle(),
+            nullEquality == NullEquality.EQUAL);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a left join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the left join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing the size result from
+   * {@link #mixedLeftJoinSize(Table, Table, Table, Table, CompiledExpression, NullEquality)}
+   * when the output size was computed previously.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @param joinSize mixed join size result
+   * @return left and right table gather maps
+   */
+  public static GatherMap[] mixedLeftJoinGatherMaps(Table leftKeys, Table rightKeys,
+                                                    Table leftConditional, Table rightConditional,
+                                                    CompiledExpression condition,
+                                                    NullEquality nullEquality,
+                                                    MixedJoinSize joinSize) {
+    long[] gatherMapData = mixedLeftJoinGatherMapsWithSize(
+            leftKeys.getNativeView(), rightKeys.getNativeView(),
+            leftConditional.getNativeView(), rightConditional.getNativeView(),
+            condition.getNativeHandle(),
+            nullEquality == NullEquality.EQUAL,
+            joinSize.getOutputRowCount(), joinSize.getMatches().getNativeView());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the table argument represents the key columns from the right table. Two {@link GatherMap}
+   * instances will be returned that can be used to gather the left and right tables,
+   * respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightKeys join key columns from the right table
+   * @param compareNullsEqual true if null key values should match otherwise false
+   * @return left and right table gather maps
+   */
+  public GatherMap[] innerJoinGatherMaps(Table rightKeys, boolean compareNullsEqual) {
+    if (getNumberOfColumns() != rightKeys.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightKeys.getNumberOfColumns());
+    }
+    long[] gatherMapData =
+        innerJoinGatherMaps(getNativeView(), rightKeys.getNativeView(), compareNullsEqual);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows resulting from an inner equi-join between two tables.
+   * @param otherHash hash table built from join key columns from the other table
+   * @return row count of the join result
+   */
+  public long innerJoinRowCount(HashJoin otherHash) {
+    if (getNumberOfColumns() != otherHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "otherKeys: " + otherHash.getNumberOfColumns());
+    }
+    return innerJoinRowCount(getNativeView(), otherHash.getNativeView());
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Two {@link GatherMap} instances will be returned that can be used to gather the left and right
+   * tables, respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightHash hash table built from join key columns from the right table
+   * @return left and right table gather maps
+   */
+  public GatherMap[] innerJoinGatherMaps(HashJoin rightHash) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    long[] gatherMapData = innerHashJoinGatherMaps(getNativeView(), rightHash.getNativeView());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Two {@link GatherMap} instances will be returned that can be used to gather the left and right
+   * tables, respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #innerJoinRowCount(HashJoin)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightHash hash table built from join key columns from the right table
+   * @param outputRowCount number of output rows in the join result
+   * @return left and right table gather maps
+   */
+  public GatherMap[] innerJoinGatherMaps(HashJoin rightHash, long outputRowCount) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    long[] gatherMapData = innerHashJoinGatherMapsWithCount(getNativeView(),
+        rightHash.getNativeView(), outputRowCount);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows from the result of an inner join between two tables when a
+   * conditional expression is true. It is assumed this table instance holds the columns from
+   * the left table, and the table argument represents the columns from the right table.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @return row count for the join result
+   */
+  public long conditionalInnerJoinRowCount(Table rightTable,
+                                           CompiledExpression condition) {
+    return conditionalInnerJoinRowCount(getNativeView(), rightTable.getNativeView(),
+        condition.getNativeHandle());
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightTable the right side table of the join
+   * @param condition conditional expression to evaluate during the join
+   * @return left and right table gather maps
+   */
+  public GatherMap[] conditionalInnerJoinGatherMaps(Table rightTable,
+                                                    CompiledExpression condition) {
+    long[] gatherMapData =
+        conditionalInnerJoinGatherMaps(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #conditionalInnerJoinRowCount(Table, CompiledExpression)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @param outputRowCount number of output rows in the join result
+   * @return left and right table gather maps
+   */
+  public GatherMap[] conditionalInnerJoinGatherMaps(Table rightTable,
+                                                    CompiledExpression condition,
+                                                    long outputRowCount) {
+    long[] gatherMapData =
+        conditionalInnerJoinGatherMapsWithCount(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle(), outputRowCount);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes output size information for an inner join between two tables using a mix of equality
+   * and inequality conditions. The entire join condition is assumed to be a logical AND of the
+   * equality condition and inequality condition.
+   * NOTE: It is the responsibility of the caller to close the resulting size information object
+   * or native resources can be leaked!
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return size information for the join
+   */
+  public static MixedJoinSize mixedInnerJoinSize(Table leftKeys, Table rightKeys,
+                                                 Table leftConditional, Table rightConditional,
+                                                 CompiledExpression condition,
+                                                 NullEquality nullEquality) {
+    long[] mixedSizeInfo = mixedInnerJoinSize(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(), nullEquality == NullEquality.EQUAL);
+    assert mixedSizeInfo.length == 2;
+    long outputRowCount = mixedSizeInfo[0];
+    long matchesColumnHandle = mixedSizeInfo[1];
+    return new MixedJoinSize(outputRowCount, new ColumnVector(matchesColumnHandle));
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return left and right table gather maps
+   */
+  public static GatherMap[] mixedInnerJoinGatherMaps(Table leftKeys, Table rightKeys,
+                                                     Table leftConditional, Table rightConditional,
+                                                     CompiledExpression condition,
+                                                     NullEquality nullEquality) {
+    long[] gatherMapData = mixedInnerJoinGatherMaps(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(),
+        nullEquality == NullEquality.EQUAL);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an inner join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the inner join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing the size result from
+   * {@link #mixedInnerJoinSize(Table, Table, Table, Table, CompiledExpression, NullEquality)}
+   * when the output size was computed previously.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @param joinSize mixed join size result
+   * @return left and right table gather maps
+   */
+  public static GatherMap[] mixedInnerJoinGatherMaps(Table leftKeys, Table rightKeys,
+                                                     Table leftConditional, Table rightConditional,
+                                                     CompiledExpression condition,
+                                                     NullEquality nullEquality,
+                                                     MixedJoinSize joinSize) {
+    long[] gatherMapData = mixedInnerJoinGatherMapsWithSize(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(),
+        nullEquality == NullEquality.EQUAL,
+        joinSize.getOutputRowCount(), joinSize.getMatches().getNativeView());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of an full equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the table argument represents the key columns from the right table. Two {@link GatherMap}
+   * instances will be returned that can be used to gather the left and right tables,
+   * respectively, to produce the result of the full join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightKeys join key columns from the right table
+   * @param compareNullsEqual true if null key values should match otherwise false
+   * @return left and right table gather maps
+   */
+  public GatherMap[] fullJoinGatherMaps(Table rightKeys, boolean compareNullsEqual) {
+    if (getNumberOfColumns() != rightKeys.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightKeys.getNumberOfColumns());
+    }
+    long[] gatherMapData =
+        fullJoinGatherMaps(getNativeView(), rightKeys.getNativeView(), compareNullsEqual);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows resulting from a full equi-join between two tables.
+   * It is assumed this table instance holds the key columns from the left table, and the
+   * {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Note that unlike {@link #leftJoinRowCount(HashJoin)} and {@link #innerJoinRowCount(HashJoin),
+   * this will perform some redundant calculations compared to
+   * {@link #fullJoinGatherMaps(HashJoin, long)}.
+   * @param rightHash hash table built from join key columns from the right table
+   * @return row count of the join result
+   */
+  public long fullJoinRowCount(HashJoin rightHash) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    return fullJoinRowCount(getNativeView(), rightHash.getNativeView());
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a full equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Two {@link GatherMap} instances will be returned that can be used to gather the left and right
+   * tables, respectively, to produce the result of the full join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightHash hash table built from join key columns from the right table
+   * @return left and right table gather maps
+   */
+  public GatherMap[] fullJoinGatherMaps(HashJoin rightHash) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    long[] gatherMapData = fullHashJoinGatherMaps(getNativeView(), rightHash.getNativeView());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a full equi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the {@link HashJoin} argument has been constructed from the key columns from the right table.
+   * Two {@link GatherMap} instances will be returned that can be used to gather the left and right
+   * tables, respectively, to produce the result of the full join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #fullJoinRowCount(HashJoin)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightHash hash table built from join key columns from the right table
+   * @param outputRowCount number of output rows in the join result
+   * @return left and right table gather maps
+   */
+  public GatherMap[] fullJoinGatherMaps(HashJoin rightHash, long outputRowCount) {
+    if (getNumberOfColumns() != rightHash.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightHash.getNumberOfColumns());
+    }
+    long[] gatherMapData = fullHashJoinGatherMapsWithCount(getNativeView(),
+        rightHash.getNativeView(), outputRowCount);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a full join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the full join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param rightTable the right side table of the join
+   * @param condition conditional expression to evaluate during the join
+   * @return left and right table gather maps
+   */
+  public GatherMap[] conditionalFullJoinGatherMaps(Table rightTable,
+                                                   CompiledExpression condition) {
+    long[] gatherMapData =
+        conditionalFullJoinGatherMaps(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle());
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  /**
+   * Computes the gather maps that can be used to manifest the result of a full join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * Two {@link GatherMap} instances will be returned that can be used to gather
+   * the left and right tables, respectively, to produce the result of the full join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return left and right table gather maps
+   */
+  public static GatherMap[] mixedFullJoinGatherMaps(Table leftKeys, Table rightKeys,
+                                                    Table leftConditional, Table rightConditional,
+                                                    CompiledExpression condition,
+                                                    NullEquality nullEquality) {
+    long[] gatherMapData = mixedFullJoinGatherMaps(
+            leftKeys.getNativeView(), rightKeys.getNativeView(),
+            leftConditional.getNativeView(), rightConditional.getNativeView(),
+            condition.getNativeHandle(),
+            nullEquality == NullEquality.EQUAL);
+    return buildJoinGatherMaps(gatherMapData);
+  }
+
+  private static GatherMap buildSemiJoinGatherMap(long[] gatherMapData) {
+    long bufferSize = gatherMapData[0];
+    long leftAddr = gatherMapData[1];
+    long leftHandle = gatherMapData[2];
+    return new GatherMap(DeviceMemoryBuffer.fromRmm(leftAddr, bufferSize, leftHandle));
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left semi-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the table argument represents the key columns from the right table. The {@link GatherMap}
+   * instance returned can be used to gather the left table to produce the result of the
+   * left semi-join.
+   * It is the responsibility of the caller to close the resulting gather map instance.
+   * @param rightKeys join key columns from the right table
+   * @param compareNullsEqual true if null key values should match otherwise false
+   * @return left table gather map
+   */
+  public GatherMap leftSemiJoinGatherMap(Table rightKeys, boolean compareNullsEqual) {
+    if (getNumberOfColumns() != rightKeys.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightKeys.getNumberOfColumns());
+    }
+    long[] gatherMapData =
+        leftSemiJoinGatherMap(getNativeView(), rightKeys.getNativeView(), compareNullsEqual);
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows from the result of a left semi join between two tables when a
+   * conditional expression is true. It is assumed this table instance holds the columns from
+   * the left table, and the table argument represents the columns from the right table.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @return row count for the join result
+   */
+  public long conditionalLeftSemiJoinRowCount(Table rightTable,
+                                              CompiledExpression condition) {
+    return conditionalLeftSemiJoinRowCount(getNativeView(), rightTable.getNativeView(),
+        condition.getNativeHandle());
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left semi join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. The {@link GatherMap} instance returned can be used to gather the left table
+   * to produce the result of the left semi join.
+   * It is the responsibility of the caller to close the resulting gather map instance.
+   * @param rightTable the right side table of the join
+   * @param condition conditional expression to evaluate during the join
+   * @return left table gather map
+   */
+  public GatherMap conditionalLeftSemiJoinGatherMap(Table rightTable,
+                                                    CompiledExpression condition) {
+    long[] gatherMapData =
+        conditionalLeftSemiJoinGatherMap(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle());
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left semi join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. The {@link GatherMap} instance returned can be used to gather the left table
+   * to produce the result of the left semi join.
+   * It is the responsibility of the caller to close the resulting gather map instance.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #conditionalLeftSemiJoinRowCount(Table, CompiledExpression)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightTable the right side table of the join
+   * @param condition conditional expression to evaluate during the join
+   * @param outputRowCount number of output rows in the join result
+   * @return left table gather map
+   */
+  public GatherMap conditionalLeftSemiJoinGatherMap(Table rightTable,
+                                                    CompiledExpression condition,
+                                                    long outputRowCount) {
+    long[] gatherMapData =
+        conditionalLeftSemiJoinGatherMapWithCount(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle(), outputRowCount);
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes output size information for a left semi join between two tables using a mix of
+   * equality and inequality conditions. The entire join condition is assumed to be a logical AND
+   * of the equality condition and inequality condition.
+   * NOTE: It is the responsibility of the caller to close the resulting size information object
+   * or native resources can be leaked!
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return size information for the join
+   */
+  public static MixedJoinSize mixedLeftSemiJoinSize(Table leftKeys, Table rightKeys,
+                                                    Table leftConditional, Table rightConditional,
+                                                    CompiledExpression condition,
+                                                    NullEquality nullEquality) {
+    long[] mixedSizeInfo = mixedLeftSemiJoinSize(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(), nullEquality == NullEquality.EQUAL);
+    assert mixedSizeInfo.length == 2;
+    long outputRowCount = mixedSizeInfo[0];
+    long matchesColumnHandle = mixedSizeInfo[1];
+    return new MixedJoinSize(outputRowCount, new ColumnVector(matchesColumnHandle));
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left semi join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * A {@link GatherMap} instance will be returned that can be used to gather
+   * the left table to produce the result of the left semi join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return left and right table gather maps
+   */
+  public static GatherMap mixedLeftSemiJoinGatherMap(Table leftKeys, Table rightKeys,
+                                                     Table leftConditional, Table rightConditional,
+                                                     CompiledExpression condition,
+                                                     NullEquality nullEquality) {
+    long[] gatherMapData = mixedLeftSemiJoinGatherMap(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(),
+        nullEquality == NullEquality.EQUAL);
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left semi join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * A {@link GatherMap} instance will be returned that can be used to gather
+   * the left table to produce the result of the left semi join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing the size result from
+   * {@link #mixedLeftSemiJoinSize(Table, Table, Table, Table, CompiledExpression, NullEquality)}
+   * when the output size was computed previously.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @param joinSize mixed join size result
+   * @return left and right table gather maps
+   */
+  public static GatherMap mixedLeftSemiJoinGatherMap(Table leftKeys, Table rightKeys,
+                                                     Table leftConditional, Table rightConditional,
+                                                     CompiledExpression condition,
+                                                     NullEquality nullEquality,
+                                                     MixedJoinSize joinSize) {
+    long[] gatherMapData = mixedLeftSemiJoinGatherMapWithSize(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(),
+        nullEquality == NullEquality.EQUAL,
+        joinSize.getOutputRowCount(), joinSize.getMatches().getNativeView());
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left anti-join between
+   * two tables. It is assumed this table instance holds the key columns from the left table, and
+   * the table argument represents the key columns from the right table. The {@link GatherMap}
+   * instance returned can be used to gather the left table to produce the result of the
+   * left anti-join.
+   * It is the responsibility of the caller to close the resulting gather map instance.
+   * @param rightKeys join key columns from the right table
+   * @param compareNullsEqual true if null key values should match otherwise false
+   * @return left table gather map
+   */
+  public GatherMap leftAntiJoinGatherMap(Table rightKeys, boolean compareNullsEqual) {
+    if (getNumberOfColumns() != rightKeys.getNumberOfColumns()) {
+      throw new IllegalArgumentException("column count mismatch, this: " + getNumberOfColumns() +
+          "rightKeys: " + rightKeys.getNumberOfColumns());
+    }
+    long[] gatherMapData =
+        leftAntiJoinGatherMap(getNativeView(), rightKeys.getNativeView(), compareNullsEqual);
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the number of rows from the result of a left anti join between two tables when a
+   * conditional expression is true. It is assumed this table instance holds the columns from
+   * the left table, and the table argument represents the columns from the right table.
+   * @param rightTable the right side table of the join in the join
+   * @param condition conditional expression to evaluate during the join
+   * @return row count for the join result
+   */
+  public long conditionalLeftAntiJoinRowCount(Table rightTable,
+                                              CompiledExpression condition) {
+    return conditionalLeftAntiJoinRowCount(getNativeView(), rightTable.getNativeView(),
+        condition.getNativeHandle());
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left anti join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. The {@link GatherMap} instance returned can be used to gather the left table
+   * to produce the result of the left anti join.
+   * It is the responsibility of the caller to close the resulting gather map instance.
+   * @param rightTable the right side table of the join
+   * @param condition conditional expression to evaluate during the join
+   * @return left table gather map
+   */
+  public GatherMap conditionalLeftAntiJoinGatherMap(Table rightTable,
+                                                    CompiledExpression condition) {
+    long[] gatherMapData =
+        conditionalLeftAntiJoinGatherMap(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle());
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left anti join between
+   * two tables when a conditional expression is true. It is assumed this table instance holds
+   * the columns from the left table, and the table argument represents the columns from the
+   * right table. The {@link GatherMap} instance returned can be used to gather the left table
+   * to produce the result of the left anti join.
+   * It is the responsibility of the caller to close the resulting gather map instance.
+   * This interface allows passing an output row count that was previously computed from
+   * {@link #conditionalLeftAntiJoinRowCount(Table, CompiledExpression)}.
+   * WARNING: Passing a row count that is smaller than the actual row count will result
+   * in undefined behavior.
+   * @param rightTable the right side table of the join
+   * @param condition conditional expression to evaluate during the join
+   * @param outputRowCount number of output rows in the join result
+   * @return left table gather map
+   */
+  public GatherMap conditionalLeftAntiJoinGatherMap(Table rightTable,
+                                                    CompiledExpression condition,
+                                                    long outputRowCount) {
+    long[] gatherMapData =
+        conditionalLeftAntiJoinGatherMapWithCount(getNativeView(), rightTable.getNativeView(),
+            condition.getNativeHandle(), outputRowCount);
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes output size information for a left anti join between two tables using a mix of
+   * equality and inequality conditions. The entire join condition is assumed to be a logical AND
+   * of the equality condition and inequality condition.
+   * NOTE: It is the responsibility of the caller to close the resulting size information object
+   * or native resources can be leaked!
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return size information for the join
+   */
+  public static MixedJoinSize mixedLeftAntiJoinSize(Table leftKeys, Table rightKeys,
+                                                    Table leftConditional, Table rightConditional,
+                                                    CompiledExpression condition,
+                                                    NullEquality nullEquality) {
+    long[] mixedSizeInfo = mixedLeftAntiJoinSize(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(), nullEquality == NullEquality.EQUAL);
+    assert mixedSizeInfo.length == 2;
+    long outputRowCount = mixedSizeInfo[0];
+    long matchesColumnHandle = mixedSizeInfo[1];
+    return new MixedJoinSize(outputRowCount, new ColumnVector(matchesColumnHandle));
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left anti join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * A {@link GatherMap} instance will be returned that can be used to gather
+   * the left table to produce the result of the left anti join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @return left and right table gather maps
+   */
+  public static GatherMap mixedLeftAntiJoinGatherMap(Table leftKeys, Table rightKeys,
+                                                     Table leftConditional, Table rightConditional,
+                                                     CompiledExpression condition,
+                                                     NullEquality nullEquality) {
+    long[] gatherMapData = mixedLeftAntiJoinGatherMap(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(),
+        nullEquality == NullEquality.EQUAL);
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * Computes the gather map that can be used to manifest the result of a left anti join between
+   * two tables using a mix of equality and inequality conditions. The entire join condition is
+   * assumed to be a logical AND of the equality condition and inequality condition.
+   * A {@link GatherMap} instance will be returned that can be used to gather
+   * the left table to produce the result of the left anti join.
+   * It is the responsibility of the caller to close the resulting gather map instances.
+   * This interface allows passing the size result from
+   * {@link #mixedLeftAntiJoinSize(Table, Table, Table, Table, CompiledExpression, NullEquality)}
+   * when the output size was computed previously.
+   * @param leftKeys the left table's key columns for the equality condition
+   * @param rightKeys the right table's key columns for the equality condition
+   * @param leftConditional the left table's columns needed to evaluate the inequality condition
+   * @param rightConditional the right table's columns needed to evaluate the inequality condition
+   * @param condition the inequality condition of the join
+   * @param nullEquality whether nulls should compare as equal
+   * @param joinSize mixed join size result
+   * @return left and right table gather maps
+   */
+  public static GatherMap mixedLeftAntiJoinGatherMap(Table leftKeys, Table rightKeys,
+                                                     Table leftConditional, Table rightConditional,
+                                                     CompiledExpression condition,
+                                                     NullEquality nullEquality,
+                                                     MixedJoinSize joinSize) {
+    long[] gatherMapData = mixedLeftAntiJoinGatherMapWithSize(
+        leftKeys.getNativeView(), rightKeys.getNativeView(),
+        leftConditional.getNativeView(), rightConditional.getNativeView(),
+        condition.getNativeHandle(),
+        nullEquality == NullEquality.EQUAL,
+        joinSize.getOutputRowCount(), joinSize.getMatches().getNativeView());
+    return buildSemiJoinGatherMap(gatherMapData);
+  }
+
+  /**
+   * For details about how this method functions refer to
+   * {@link #convertToRowsFixedWidthOptimized()}.
+   *
+   * The only thing different between this method and {@link #convertToRowsFixedWidthOptimized()}
+   * is that this can handle roughly 250M columns while {@link #convertToRowsFixedWidthOptimized()}
+   * can only handle columns less than 100
+   */
+  public ColumnVector[] convertToRows() {
+    long[] ptrs = convertToRows(nativeHandle);
+    return ColumnVector.getColumnVectorsFromPointers(ptrs);
+  }
+
+  /**
+   * Convert this table of columns into a row major format that is useful for interacting with other
+   * systems that do row major processing of the data. Currently only fixed-width column types are
+   * supported.
+   * <p/>
+   * The output is one or more ColumnVectors that are lists of bytes. A ColumnVector that is a
+   * list of bytes can have at most 2GB of data stored in it. Multiple ColumnVectors are returned
+   * if not all of the data can fit in a single one.
+   * <p/>
+   * Each row in the returned ColumnVector array corresponds to a row in the input table. The rows
+   * will be in the same order as the input Table. The first ColumnVector in the array will hold
+   * the first N rows followed by the second ColumnVector and so on.  The following illustrates
+   * this and also shows some of the internal structure that will be explained later.
+   * <p/><pre>
+   * result[0]:
+   *  | row 0 | validity for row 0 | padding |
+   *  ...
+   *  | row N | validity for row N | padding |
+   *  result[1]:
+   *  |row N+1 | validity for row N+1 | padding |
+   *  ...
+   * </pre>
+   *
+   * The format of each row is similar in layout to a C struct where each column will have padding
+   * in front of it to align it properly. Each row has padding inserted at the end so the next row
+   * is aligned to a 64-bit boundary. This is so that the first column will always start at the
+   * beginning (first byte) of the list of bytes and each row has a consistent layout for fixed
+   * width types.
+   * <p/>
+   * Validity bytes are added to the end of the row. There will be one byte for each 8 columns in a
+   * row. Because the validity is byte aligned there is no padding between it and the last column
+   * in the row.
+   * <p/>
+   * For example a table consisting of the following columns A, B, C with the corresponding types
+   * <p/><pre>
+   *   | A - BOOL8 (8-bit) | B - INT16 (16-bit) | C - DURATION_DAYS (32-bit) |
+   * </pre>
+   * <p/>
+   *  Will have a layout that looks like
+   *  <p/><pre>
+   *  | A_0 | P | B_0 | B_1 | C_0 | C_1 | C_2 | C_3 | V0 | P | P | P | P | P | P | P |
+   * </pre>
+   * <p/>
+   * In this P corresponds to a byte of padding, [LETTER]_[NUMBER] represents the NUMBER
+   * byte of the corresponding LETTER column, and V[NUMBER] is a validity byte for the `NUMBER * 8`
+   * to `(NUMBER + 1) * 8` columns.
+   * <p/>
+   * The order of the columns will not be changed, but to reduce the total amount of padding it is
+   * recommended to order the columns in the following way.
+   * <p/>
+   * <ol>
+   *  <li>64-bit columns</li>
+   *  <li>32-bit columns</li>
+   *  <li>16-bit columns</li>
+   *  <li>8-bit columns</li>
+   * </ol>
+   * <p/>
+   * This way padding is only inserted at the end of a row to make the next column 64-bit aligned.
+   * So for the example above if the columns were ordered C, B, A the layout would be.
+   * <pre>
+   * | C_0 | C_1 | C_2 | C_3 | B_0 | B_1 | A_0 | V0 |
+   * </pre>
+   * This would have reduced the overall size of the data transferred by half.
+   * <p/>
+   * One of the main motivations for doing a row conversion on the GPU is to avoid cache problems
+   * when walking through columnar data on the CPU in a row wise manner. If you are not transferring
+   * very many columns it is likely to be more efficient to just pull back the columns and walk
+   * through them. This is especially true of a single column of fixed width data. The extra
+   * padding will slow down the transfer and looking at only a handful of buffers is not likely to
+   * cause cache issues.
+   * <p/>
+   * There are some limits on the size of a single row.  If the row is larger than 1KB this will
+   * throw an exception.
+   */
+  public ColumnVector[] convertToRowsFixedWidthOptimized() {
+    long[] ptrs = convertToRowsFixedWidthOptimized(nativeHandle);
+    return ColumnVector.getColumnVectorsFromPointers(ptrs);
+  }
+
+  /**
+   * Convert a column of list of bytes that is formatted like the output from `convertToRows`
+   * and convert it back to a table.
+   *
+   * NOTE: This method doesn't support nested types
+   *
+   * @param vec the row data to process.
+   * @param schema the types of each column.
+   * @return the parsed table.
+   */
+  public static Table convertFromRows(ColumnView vec, DType ... schema) {
+    int[] types = new int[schema.length];
+    int[] scale = new int[schema.length];
+    for (int i = 0; i < schema.length; i++) {
+      types[i] = schema[i].typeId.nativeId;
+      scale[i] = schema[i].getScale();
+
+    }
+    return new Table(convertFromRows(vec.getNativeView(), types, scale));
+  }
+
+  /**
+   * Convert a column of list of bytes that is formatted like the output from `convertToRows`
+   * and convert it back to a table.
+   *
+   * NOTE: This method doesn't support nested types
+   *
+   * @param vec the row data to process.
+   * @param schema the types of each column.
+   * @return the parsed table.
+   */
+  public static Table convertFromRowsFixedWidthOptimized(ColumnView vec, DType ... schema) {
+    int[] types = new int[schema.length];
+    int[] scale = new int[schema.length];
+    for (int i = 0; i < schema.length; i++) {
+      types[i] = schema[i].typeId.nativeId;
+      scale[i] = schema[i].getScale();
+
+    }
+    return new Table(convertFromRowsFixedWidthOptimized(vec.getNativeView(), types, scale));
+  }
+
+  /**
+   * Construct a table from a packed representation.
+   * @param metadata host-based metadata for the table
+   * @param data GPU data buffer for the table
+   * @return table which is zero-copy reconstructed from the packed-form
+   */
+  public static Table fromPackedTable(ByteBuffer metadata, DeviceMemoryBuffer data) {
+    // Ensure the metadata buffer is direct so it can be passed to JNI
+    ByteBuffer directBuffer = metadata;
+    if (!directBuffer.isDirect()) {
+      directBuffer = ByteBuffer.allocateDirect(metadata.remaining());
+      directBuffer.put(metadata);
+      directBuffer.flip();
+    }
+
+    long[] columnViewAddresses = columnViewsFromPacked(directBuffer, data.getAddress());
+    ColumnVector[] columns = new ColumnVector[columnViewAddresses.length];
+    Table result = null;
+    try {
+      for (int i = 0; i < columns.length; i++) {
+        long columnViewAddress = columnViewAddresses[i];
+        // setting address to zero, so we don't clean it in case of an exception as it
+        // will be cleaned up by the ColumnView  constructor
+        columnViewAddresses[i] = 0;
+        columns[i] = ColumnVector.fromViewWithContiguousAllocation(columnViewAddress, data);
+      }
+      result = new Table(columns);
+    } catch (Throwable t) {
+      try {
+        ColumnView.cleanupColumnViews(columnViewAddresses, columns, t);
+      } catch (Throwable s){
+        t.addSuppressed(s);
+      } finally {
+        throw t;
+      }
+    }
+
+    // close columns to leave the resulting table responsible for freeing underlying columns
+    for (ColumnVector column : columns) {
+      column.close();
+    }
+
+    return result;
+  }
+
+
+  /**
+   * Gather `n` samples from table randomly
+   * Note: does not preserve the ordering
+   * Example:
+   * input: {col1: {1, 2, 3, 4, 5}, col2: {6, 7, 8, 9, 10}}
+   * n: 3
+   * replacement: false
+   *
+   * output:       {col1: {3, 1, 4}, col2: {8, 6, 9}}
+   *
+   * replacement: true
+   *
+   * output:       {col1: {3, 1, 1}, col2: {8, 6, 6}}
+   *
+   * throws "logic_error" if `n` > table rows and `replacement` == FALSE.
+   * throws "logic_error" if `n` < 0.
+   *
+   * @param n non-negative number of samples expected from table
+   * @param replacement Allow or disallow sampling of the same row more than once.
+   * @param seed Seed value to initiate random number generator.
+   *
+   * @return Table containing samples
+   */
+  public Table sample(long n, boolean replacement, long seed) {
+    return new Table(sample(nativeHandle, n, replacement, seed));
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // HELPER CLASSES
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * class to encapsulate indices and table
+   */
+  private final static class Operation {
+    final int[] indices;
+    final Table table;
+
+    Operation(Table table, int... indices) {
+      this.indices = indices;
+      this.table = table;
+    }
+  }
+
+  /**
+   * Internal class used to keep track of operations on a given column.
+   */
+  private static final class ColumnOps {
+    private final HashMap<Aggregation, List<Integer>> ops = new HashMap<>();
+
+    /**
+     * Add an operation on a given column
+     * @param op the operation
+     * @param index the column index the operation is on.
+     * @return 1 if it was not a duplicate or 0 if it was a duplicate.  This is mostly for
+     * bookkeeping so we can easily allocate the correct data size later on.
+     */
+    public int add(Aggregation op, int index) {
+      int ret = 0;
+      List<Integer> indexes = ops.get(op);
+      if (indexes == null) {
+        ret++;
+        indexes = new ArrayList<>();
+        ops.put(op, indexes);
+      }
+      indexes.add(index);
+      return ret;
+    }
+
+    public Set<Aggregation> operations() {
+      return ops.keySet();
+    }
+
+    public Collection<List<Integer>> outputIndices() {
+      return ops.values();
+    }
+  }
+
+  /**
+   * Internal class used to keep track of operations on a given column.
+   */
+  private static final class ColumnWindowOps {
+    // Map AggOp -> Output column index.
+    private final HashMap<AggregationOverWindow, List<Integer>> ops = new HashMap<>();
+
+    public int add(AggregationOverWindow op, int index) {
+      int ret = 0;
+      List<Integer> indexes = ops.get(op);
+      if (indexes == null) {
+        ret++;
+        indexes = new ArrayList<>();
+        ops.put(op, indexes);
+      }
+      indexes.add(index);
+      return ret;
+    }
+
+    public Set<AggregationOverWindow> operations() {
+      return ops.keySet();
+    }
+
+    public Collection<List<Integer>> outputIndices() {
+      return ops.values();
+    }
+  }
+
+  /**
+   * Class representing groupby operations
+   */
+  public static final class GroupByOperation {
+
+    private final Operation operation;
+    private final GroupByOptions groupByOptions;
+
+    GroupByOperation(final Table table, GroupByOptions groupByOptions, final int... indices) {
+      operation = new Operation(table, indices);
+      this.groupByOptions = groupByOptions;
+    }
+
+    /**
+     * Aggregates the group of columns represented by indices
+     * Usage:
+     *      aggregate(count(), max(2),...);
+     *      example:
+     *        input : 1, 1, 1
+     *                1, 2, 1
+     *                2, 4, 5
+     *
+     *        table.groupBy(0, 2).count()
+     *
+     *                col0, col1
+     *        output:   1,   1
+     *                  1,   2
+     *                  2,   1 ==> aggregated count
+     */
+    public Table aggregate(GroupByAggregationOnColumn... aggregates) {
+      assert aggregates != null;
+
+      // To improve performance and memory we want to remove duplicate operations
+      // and also group the operations by column so hopefully cudf can do multiple aggregations
+      // in a single pass.
+
+      // Use a tree map to make debugging simpler (columns are all in the same order)
+      TreeMap<Integer, ColumnOps> groupedOps = new TreeMap<>();
+      // Total number of operations that will need to be done.
+      int keysLength = operation.indices.length;
+      int totalOps = 0;
+      for (int outputIndex = 0; outputIndex < aggregates.length; outputIndex++) {
+        GroupByAggregationOnColumn agg = aggregates[outputIndex];
+        ColumnOps ops = groupedOps.computeIfAbsent(agg.getColumnIndex(), (idx) -> new ColumnOps());
+        totalOps += ops.add(agg.getWrapped().getWrapped(), outputIndex + keysLength);
+      }
+      int[] aggColumnIndexes = new int[totalOps];
+      long[] aggOperationInstances = new long[totalOps];
+      try {
+        int opIndex = 0;
+        for (Map.Entry<Integer, ColumnOps> entry: groupedOps.entrySet()) {
+          int columnIndex = entry.getKey();
+          for (Aggregation operation: entry.getValue().operations()) {
+            aggColumnIndexes[opIndex] = columnIndex;
+            aggOperationInstances[opIndex] = operation.createNativeInstance();
+            opIndex++;
+          }
+        }
+        assert opIndex == totalOps : opIndex + " == " + totalOps;
+
+        try (Table aggregate = new Table(groupByAggregate(
+            operation.table.nativeHandle,
+            operation.indices,
+            aggColumnIndexes,
+            aggOperationInstances,
+            groupByOptions.getIgnoreNullKeys(),
+            groupByOptions.getKeySorted(),
+            groupByOptions.getKeysDescending(),
+            groupByOptions.getKeysNullSmallest()))) {
+          // prepare the final table
+          ColumnVector[] finalCols = new ColumnVector[keysLength + aggregates.length];
+
+          // get the key columns
+          for (int aggIndex = 0; aggIndex < keysLength; aggIndex++) {
+            finalCols[aggIndex] = aggregate.getColumn(aggIndex);
+          }
+
+          int inputColumn = keysLength;
+          // Now get the aggregation columns
+          for (ColumnOps ops: groupedOps.values()) {
+            for (List<Integer> indices: ops.outputIndices()) {
+              for (int outIndex: indices) {
+                finalCols[outIndex] = aggregate.getColumn(inputColumn);
+              }
+              inputColumn++;
+            }
+          }
+          return new Table(finalCols);
+        }
+      } finally {
+        Aggregation.close(aggOperationInstances);
+      }
+    }
+
+    /**
+     * Computes row-based window aggregation functions on the Table/projection,
+     * based on windows specified in the argument.
+     *
+     * This method enables queries such as the following SQL:
+     *
+     *  SELECT user_id,
+     *         MAX(sales_amt) OVER(PARTITION BY user_id ORDER BY date
+     *                             ROWS BETWEEN 1 PRECEDING and 1 FOLLOWING)
+     *  FROM my_sales_table WHERE ...
+     *
+     * Each window-aggregation is represented by a different {@link AggregationOverWindow} argument,
+     * indicating:
+     *  1. the {@link Aggregation.Kind},
+     *  2. the number of rows preceding and following the current row, within a window,
+     *  3. the minimum number of observations within the defined window
+     *
+     * This method returns a {@link Table} instance, with one result column for each specified
+     * window aggregation.
+     *
+     * In this example, for the following input:
+     *
+     *  [ // user_id,  sales_amt
+     *    { "user1",     10      },
+     *    { "user2",     20      },
+     *    { "user1",     20      },
+     *    { "user1",     10      },
+     *    { "user2",     30      },
+     *    { "user2",     80      },
+     *    { "user1",     50      },
+     *    { "user1",     60      },
+     *    { "user2",     40      }
+     *  ]
+     *
+     * Partitioning (grouping) by `user_id` yields the following `sales_amt` vector
+     * (with 2 groups, one for each distinct `user_id`):
+     *
+     *    [ 10,  20,  10,  50,  60,  20,  30,  80,  40 ]
+     *      <-------user1-------->|<------user2------->
+     *
+     * The SUM aggregation is applied with 1 preceding and 1 following
+     * row, with a minimum of 1 period. The aggregation window is thus 3 rows wide,
+     * yielding the following column:
+     *
+     *    [ 30, 40,  80, 120, 110,  50, 130, 150, 120 ]
+     *
+     * @param windowAggregates the window-aggregations to be performed
+     * @return Table instance, with each column containing the result of each aggregation.
+     * @throws IllegalArgumentException if the window arguments are not of type
+     * {@link WindowOptions.FrameType#ROWS},
+     * i.e. a timestamp column is specified for a window-aggregation.
+     */
+    public Table aggregateWindows(AggregationOverWindow... windowAggregates) {
+      // To improve performance and memory we want to remove duplicate operations
+      // and also group the operations by column so hopefully cudf can do multiple aggregations
+      // in a single pass.
+
+      // Use a tree map to make debugging simpler (columns are all in the same order)
+      TreeMap<Integer, ColumnWindowOps> groupedOps = new TreeMap<>(); // Map agg-col-id -> Agg ColOp.
+      // Total number of operations that will need to be done.
+      int totalOps = 0;
+      for (int outputIndex = 0; outputIndex < windowAggregates.length; outputIndex++) {
+        AggregationOverWindow agg = windowAggregates[outputIndex];
+        if (agg.getWindowOptions().getFrameType() != WindowOptions.FrameType.ROWS) {
+          throw new IllegalArgumentException("Expected ROWS-based window specification. Unexpected window type: "
+                  + agg.getWindowOptions().getFrameType());
+        }
+        ColumnWindowOps ops = groupedOps.computeIfAbsent(agg.getColumnIndex(), (idx) -> new ColumnWindowOps());
+        totalOps += ops.add(agg, outputIndex);
+      }
+
+      int[] aggColumnIndexes = new int[totalOps];
+      long[] aggInstances = new long[totalOps];
+      try {
+        int[] aggPrecedingWindows = new int[totalOps];
+        int[] aggFollowingWindows = new int[totalOps];
+        boolean[] unboundedPreceding = new boolean[totalOps];
+        boolean[] unboundedFollowing = new boolean[totalOps];
+        int[] aggMinPeriods = new int[totalOps];
+        long[] defaultOutputs = new long[totalOps];
+        int opIndex = 0;
+        for (Map.Entry<Integer, ColumnWindowOps> entry: groupedOps.entrySet()) {
+          int columnIndex = entry.getKey();
+          for (AggregationOverWindow operation: entry.getValue().operations()) {
+            aggColumnIndexes[opIndex] = columnIndex;
+            aggInstances[opIndex] = operation.createNativeInstance();
+            Scalar p = operation.getWindowOptions().getPrecedingScalar();
+            aggPrecedingWindows[opIndex] = p == null || !p.isValid() ? 0 : p.getInt();
+            Scalar f = operation.getWindowOptions().getFollowingScalar();
+            aggFollowingWindows[opIndex] = f == null || ! f.isValid() ? 1 : f.getInt();
+            unboundedPreceding[opIndex] = operation.getWindowOptions().isUnboundedPreceding();
+            unboundedFollowing[opIndex] = operation.getWindowOptions().isUnboundedFollowing();
+            aggMinPeriods[opIndex] = operation.getWindowOptions().getMinPeriods();
+            defaultOutputs[opIndex] = operation.getDefaultOutput();
+            opIndex++;
+          }
+        }
+        assert opIndex == totalOps : opIndex + " == " + totalOps;
+
+        try (Table aggregate = new Table(rollingWindowAggregate(
+            operation.table.nativeHandle,
+            operation.indices,
+            defaultOutputs,
+            aggColumnIndexes,
+            aggInstances, aggMinPeriods, aggPrecedingWindows, aggFollowingWindows,
+            unboundedPreceding, unboundedFollowing,
+            groupByOptions.getIgnoreNullKeys()))) {
+          // prepare the final table
+          ColumnVector[] finalCols = new ColumnVector[windowAggregates.length];
+
+          int inputColumn = 0;
+          // Now get the aggregation columns
+          for (ColumnWindowOps ops: groupedOps.values()) {
+            for (List<Integer> indices: ops.outputIndices()) {
+              for (int outIndex: indices) {
+                finalCols[outIndex] = aggregate.getColumn(inputColumn);
+              }
+              inputColumn++;
+            }
+          }
+          return new Table(finalCols);
+        }
+      } finally {
+        Aggregation.close(aggInstances);
+      }
+    }
+
+    /**
+     * Computes range-based window aggregation functions on the Table/projection,
+     * based on windows specified in the argument.
+     *
+     * This method enables queries such as the following SQL:
+     *
+     *  SELECT user_id,
+     *         MAX(sales_amt) OVER(PARTITION BY user_id ORDER BY date
+     *                             RANGE BETWEEN INTERVAL 1 DAY PRECEDING and CURRENT ROW)
+     *  FROM my_sales_table WHERE ...
+     *
+     * Each window-aggregation is represented by a different {@link AggregationOverWindow} argument,
+     * indicating:
+     *  1. the {@link Aggregation.Kind},
+     *  2. the index for the timestamp column to base the window definitions on
+     *  2. the number of DAYS preceding and following the current row's date, to consider in the window
+     *  3. the minimum number of observations within the defined window
+     *
+     * This method returns a {@link Table} instance, with one result column for each specified
+     * window aggregation.
+     *
+     * In this example, for the following input:
+     *
+     *  [ // user,  sales_amt,  YYYYMMDD (date)
+     *    { "user1",   10,      20200101    },
+     *    { "user2",   20,      20200101    },
+     *    { "user1",   20,      20200102    },
+     *    { "user1",   10,      20200103    },
+     *    { "user2",   30,      20200101    },
+     *    { "user2",   80,      20200102    },
+     *    { "user1",   50,      20200107    },
+     *    { "user1",   60,      20200107    },
+     *    { "user2",   40,      20200104    }
+     *  ]
+     *
+     * Partitioning (grouping) by `user_id`, and ordering by `date` yields the following `sales_amt` vector
+     * (with 2 groups, one for each distinct `user_id`):
+     *
+     * Date :(202001-)  [ 01,  02,  03,  07,  07,    01,   01,   02,  04 ]
+     * Input:           [ 10,  20,  10,  50,  60,    20,   30,   80,  40 ]
+     *                    <-------user1-------->|<---------user2--------->
+     *
+     * The SUM aggregation is applied, with 1 day preceding, and 1 day following, with a minimum of 1 period.
+     * The aggregation window is thus 3 *days* wide, yielding the following output column:
+     *
+     *  Results:        [ 30,  40,  30,  110, 110,  130,  130,  130,  40 ]
+     *
+     * @param windowAggregates the window-aggregations to be performed
+     * @return Table instance, with each column containing the result of each aggregation.
+     * @throws IllegalArgumentException if the window arguments are not of type
+     * {@link WindowOptions.FrameType#RANGE} or the orderBys are not of (Boolean-exclusive) integral type
+     * i.e. the timestamp-column was not specified for the aggregation.
+     */
+    public Table aggregateWindowsOverRanges(AggregationOverWindow... windowAggregates) {
+      // To improve performance and memory we want to remove duplicate operations
+      // and also group the operations by column so hopefully cudf can do multiple aggregations
+      // in a single pass.
+
+      // Use a tree map to make debugging simpler (columns are all in the same order)
+      TreeMap<Integer, ColumnWindowOps> groupedOps = new TreeMap<>(); // Map agg-col-id -> Agg ColOp.
+      // Total number of operations that will need to be done.
+      int totalOps = 0;
+      for (int outputIndex = 0; outputIndex < windowAggregates.length; outputIndex++) {
+        AggregationOverWindow agg = windowAggregates[outputIndex];
+        if (agg.getWindowOptions().getFrameType() != WindowOptions.FrameType.RANGE) {
+          throw new IllegalArgumentException("Expected range-based window specification. Unexpected window type: "
+              + agg.getWindowOptions().getFrameType());
+        }
+
+        DType orderByType = operation.table.getColumn(agg.getWindowOptions().getOrderByColumnIndex()).getType();
+        switch (orderByType.getTypeId()) {
+          case INT8:
+          case INT16:
+          case INT32:
+          case INT64:
+          case UINT8:
+          case UINT16:
+          case UINT32:
+          case UINT64:
+          case FLOAT32:
+          case FLOAT64:
+          case TIMESTAMP_MILLISECONDS:
+          case TIMESTAMP_SECONDS:
+          case TIMESTAMP_DAYS:
+          case TIMESTAMP_NANOSECONDS:
+          case TIMESTAMP_MICROSECONDS:
+          case DECIMAL32:
+          case DECIMAL64:
+          case DECIMAL128:
+          case STRING:
+            break;
+          default:
+            throw new IllegalArgumentException("Expected range-based window orderBy's " +
+                "type: integral (Boolean-exclusive), decimal, timestamp, and string");
+        }
+
+        ColumnWindowOps ops = groupedOps.computeIfAbsent(agg.getColumnIndex(), (idx) -> new ColumnWindowOps());
+        totalOps += ops.add(agg, outputIndex);
+      }
+
+      int[] aggColumnIndexes = new int[totalOps];
+      int[] orderByColumnIndexes = new int[totalOps];
+      boolean[] isOrderByOrderAscending = new boolean[totalOps];
+      long[] aggInstances = new long[totalOps];
+      long[] aggPrecedingWindows = new long[totalOps];
+      long[] aggFollowingWindows = new long[totalOps];
+      try {
+        int[] aggPrecedingWindowsExtent = new int[totalOps];
+        int[] aggFollowingWindowsExtent = new int[totalOps];
+        int[] aggMinPeriods = new int[totalOps];
+        int opIndex = 0;
+        for (Map.Entry<Integer, ColumnWindowOps> entry: groupedOps.entrySet()) {
+          int columnIndex = entry.getKey();
+          for (AggregationOverWindow op: entry.getValue().operations()) {
+            aggColumnIndexes[opIndex] = columnIndex;
+            aggInstances[opIndex] = op.createNativeInstance();
+            WindowOptions windowOptions = op.getWindowOptions();
+            Scalar p = windowOptions.getPrecedingScalar();
+            Scalar f = windowOptions.getFollowingScalar();
+            if ((p == null || !p.isValid()) && !(windowOptions.isUnboundedPreceding() || windowOptions.isCurrentRowPreceding())) {
+              throw new IllegalArgumentException("Some kind of preceding must be set and a preceding column is not currently supported");
+            }
+            if ((f == null || !f.isValid()) && !(windowOptions.isUnboundedFollowing() || windowOptions.isCurrentRowFollowing())) {
+              throw new IllegalArgumentException("some kind of following must be set and a follow column is not currently supported");
+            }
+            aggPrecedingWindows[opIndex] = p == null ? 0 : p.getScalarHandle();
+            aggFollowingWindows[opIndex] = f == null ? 0 : f.getScalarHandle();
+            aggPrecedingWindowsExtent[opIndex] = windowOptions.getPrecedingBoundsExtent().nominalValue;
+            aggFollowingWindowsExtent[opIndex] = windowOptions.getFollowingBoundsExtent().nominalValue;
+            aggMinPeriods[opIndex] = op.getWindowOptions().getMinPeriods();
+            assert (op.getWindowOptions().getFrameType() == WindowOptions.FrameType.RANGE);
+            orderByColumnIndexes[opIndex] = op.getWindowOptions().getOrderByColumnIndex();
+            isOrderByOrderAscending[opIndex] = op.getWindowOptions().isOrderByOrderAscending();
+            if (op.getDefaultOutput() != 0) {
+              throw new IllegalArgumentException("Operations with a default output are not " +
+                  "supported on time based rolling windows");
+            }
+
+            opIndex++;
+          }
+        }
+        assert opIndex == totalOps : opIndex + " == " + totalOps;
+
+        try (Table aggregate = new Table(rangeRollingWindowAggregate(
+            operation.table.nativeHandle,
+            operation.indices,
+            orderByColumnIndexes,
+            isOrderByOrderAscending,
+            aggColumnIndexes,
+            aggInstances, aggMinPeriods, aggPrecedingWindows, aggFollowingWindows,
+            aggPrecedingWindowsExtent, aggFollowingWindowsExtent,
+            groupByOptions.getIgnoreNullKeys()))) {
+          // prepare the final table
+          ColumnVector[] finalCols = new ColumnVector[windowAggregates.length];
+
+          int inputColumn = 0;
+          // Now get the aggregation columns
+          for (ColumnWindowOps ops: groupedOps.values()) {
+            for (List<Integer> indices: ops.outputIndices()) {
+              for (int outIndex: indices) {
+                finalCols[outIndex] = aggregate.getColumn(inputColumn);
+              }
+              inputColumn++;
+            }
+          }
+          return new Table(finalCols);
+        }
+      } finally {
+        Aggregation.close(aggInstances);
+      }
+    }
+
+    public Table scan(GroupByScanAggregationOnColumn... aggregates) {
+      assert aggregates != null;
+
+      // To improve performance and memory we want to remove duplicate operations
+      // and also group the operations by column so hopefully cudf can do multiple aggregations
+      // in a single pass.
+
+      // Use a tree map to make debugging simpler (columns are all in the same order)
+      TreeMap<Integer, ColumnOps> groupedOps = new TreeMap<>();
+      // Total number of operations that will need to be done.
+      int keysLength = operation.indices.length;
+      int totalOps = 0;
+      for (int outputIndex = 0; outputIndex < aggregates.length; outputIndex++) {
+        GroupByScanAggregationOnColumn agg = aggregates[outputIndex];
+        ColumnOps ops = groupedOps.computeIfAbsent(agg.getColumnIndex(), (idx) -> new ColumnOps());
+        totalOps += ops.add(agg.getWrapped().getWrapped(), outputIndex + keysLength);
+      }
+      int[] aggColumnIndexes = new int[totalOps];
+      long[] aggOperationInstances = new long[totalOps];
+      try {
+        int opIndex = 0;
+        for (Map.Entry<Integer, ColumnOps> entry: groupedOps.entrySet()) {
+          int columnIndex = entry.getKey();
+          for (Aggregation operation: entry.getValue().operations()) {
+            aggColumnIndexes[opIndex] = columnIndex;
+            aggOperationInstances[opIndex] = operation.createNativeInstance();
+            opIndex++;
+          }
+        }
+        assert opIndex == totalOps : opIndex + " == " + totalOps;
+
+        try (Table aggregate = new Table(groupByScan(
+            operation.table.nativeHandle,
+            operation.indices,
+            aggColumnIndexes,
+            aggOperationInstances,
+            groupByOptions.getIgnoreNullKeys(),
+            groupByOptions.getKeySorted(),
+            groupByOptions.getKeysDescending(),
+            groupByOptions.getKeysNullSmallest()))) {
+          // prepare the final table
+          ColumnVector[] finalCols = new ColumnVector[keysLength + aggregates.length];
+
+          // get the key columns
+          for (int aggIndex = 0; aggIndex < keysLength; aggIndex++) {
+            finalCols[aggIndex] = aggregate.getColumn(aggIndex);
+          }
+
+          int inputColumn = keysLength;
+          // Now get the aggregation columns
+          for (ColumnOps ops: groupedOps.values()) {
+            for (List<Integer> indices: ops.outputIndices()) {
+              for (int outIndex: indices) {
+                finalCols[outIndex] = aggregate.getColumn(inputColumn);
+              }
+              inputColumn++;
+            }
+          }
+          return new Table(finalCols);
+        }
+      } finally {
+        Aggregation.close(aggOperationInstances);
+      }
+    }
+
+    public Table replaceNulls(ReplacePolicyWithColumn... replacements) {
+      assert replacements != null;
+
+      // TODO in the future perhaps to improve performance and memory we want to
+      //  remove duplicate operations.
+
+      boolean[] isPreceding = new boolean[replacements.length];
+      int [] columnIndexes = new int[replacements.length];
+
+      for (int index = 0; index < replacements.length; index++) {
+        isPreceding[index] = replacements[index].policy.isPreceding;
+        columnIndexes[index] = replacements[index].column;
+      }
+
+      return new Table(groupByReplaceNulls(
+          operation.table.nativeHandle,
+          operation.indices,
+          columnIndexes,
+          isPreceding,
+          groupByOptions.getIgnoreNullKeys(),
+          groupByOptions.getKeySorted(),
+          groupByOptions.getKeysDescending(),
+          groupByOptions.getKeysNullSmallest()));
+    }
+
+    /**
+     * Splits the groups in a single table into separate tables according to the grouping keys.
+     * Each split table represents a single group.
+     *
+     * This API will be used by some grouping related operators to process the data
+     * group by group.
+     *
+     * Example:
+     *   Grouping column index: 0
+     *   Input: A table of 3 rows (two groups)
+     *             a    1
+     *             b    2
+     *             b    3
+     *
+     * Result:
+     *   Two tables, one group one table.
+     *   Result[0]:
+     *              a    1
+     *
+     *   Result[1]:
+     *              b    2
+     *              b    3
+     *
+     * Note, the order of the groups returned is NOT always the same with that in the input table.
+     * The split is done in native to avoid copying the offset array to JVM.
+     *
+     * @return The tables split according to the groups in the table. NOTE: It is the
+     * responsibility of the caller to close the result. Each table and column holds a
+     * reference to the original buffer. But both the buffer and the table must be closed
+     * for the memory to be released.
+     */
+    public ContiguousTable[] contiguousSplitGroups() {
+      try (ContigSplitGroupByResult ret = Table.contiguousSplitGroups(
+          operation.table.nativeHandle,
+          operation.indices,
+          groupByOptions.getIgnoreNullKeys(),
+          groupByOptions.getKeySorted(),
+          groupByOptions.getKeysDescending(),
+          groupByOptions.getKeysNullSmallest(),
+          false) // not generate uniq key table
+      ) {
+        // take the ownership of the `groups` in ContigSplitGroupByResult
+        return ret.releaseGroups();
+      }
+    }
+
+    /**
+     * Similar to {@link #contiguousSplitGroups}, return an extra uniq key table in which
+     * each row is corresponding to a group split.
+     *
+     * Splits the groups in a single table into separate tables according to the grouping keys.
+     * Each split table represents a single group.
+     *
+     * Example, see the example in {@link #contiguousSplitGroups}
+     * The `uniqKeysTable` in ContigSplitGroupByResult is:
+     *    a
+     *    b
+     *  Note: only 2 rows because of only has 2 split groups
+     *
+     * @return The split groups and uniq key table.
+     */
+    public ContigSplitGroupByResult contiguousSplitGroupsAndGenUniqKeys() {
+      return Table.contiguousSplitGroups(
+              operation.table.nativeHandle,
+              operation.indices,
+              groupByOptions.getIgnoreNullKeys(),
+              groupByOptions.getKeySorted(),
+              groupByOptions.getKeysDescending(),
+              groupByOptions.getKeysNullSmallest(),
+              true); // generate uniq key table
+    }
+  }
+
+  public static final class TableOperation {
+
+    private final Operation operation;
+
+    TableOperation(final Table table, final int... indices) {
+      operation = new Operation(table, indices);
+    }
+
+    /**
+     * Hash partition a table into the specified number of partitions. Uses the default MURMUR3
+     * hashing.
+     * @param numberOfPartitions - number of partitions to use
+     * @return - {@link PartitionedTable} - Table that exposes a limited functionality of the
+     * {@link Table} class
+     */
+    public PartitionedTable hashPartition(int numberOfPartitions) {
+      return hashPartition(HashType.MURMUR3, numberOfPartitions);
+    }
+
+    /**
+     * Hash partition a table into the specified number of partitions.
+     * @param type the type of hash to use. Depending on the type of hash different restrictions
+     *             on the hash column(s) may exist. Not all hash functions are guaranteed to work
+     *             besides IDENTITY and MURMUR3.
+     * @param numberOfPartitions - number of partitions to use
+     * @return {@link PartitionedTable} - Table that exposes a limited functionality of the
+     * {@link Table} class
+     */
+    public PartitionedTable hashPartition(HashType type, int numberOfPartitions) {
+      final int DEFAULT_HASH_SEED = 0;
+      return hashPartition(type, numberOfPartitions, DEFAULT_HASH_SEED);
+    }
+
+    /**
+     * Hash partition a table into the specified number of partitions.
+     * @param type the type of hash to use. Depending on the type of hash different restrictions
+     *             on the hash column(s) may exist. Not all hash functions are guaranteed to work
+     *             besides IDENTITY and MURMUR3.
+     * @param numberOfPartitions number of partitions to use
+     * @param seed the seed value for hashing
+     * @return Table that exposes a limited functionality of the {@link Table} class
+     */
+    public PartitionedTable hashPartition(HashType type, int numberOfPartitions, int seed) {
+      int[] partitionOffsets = new int[numberOfPartitions];
+      return new PartitionedTable(new Table(Table.hashPartition(
+          operation.table.nativeHandle,
+          operation.indices,
+          type.nativeId,
+          partitionOffsets.length,
+          seed,
+          partitionOffsets)), partitionOffsets);
+    }
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // BUILDER
+  /////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Create a table on the GPU with data from the CPU.  This is not fast and intended mostly for
+   * tests.
+   */
+  public static final class TestBuilder {
+    private final List<DataType> types = new ArrayList<>();
+    private final List<Object> typeErasedData = new ArrayList<>();
+
+    public TestBuilder column(String... values) {
+      types.add(new BasicType(true, DType.STRING));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Boolean... values) {
+      types.add(new BasicType(true, DType.BOOL8));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Byte... values) {
+      types.add(new BasicType(true, DType.INT8));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Short... values) {
+      types.add(new BasicType(true, DType.INT16));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Integer... values) {
+      types.add(new BasicType(true, DType.INT32));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Long... values) {
+      types.add(new BasicType(true, DType.INT64));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Float... values) {
+      types.add(new BasicType(true, DType.FLOAT32));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Double... values) {
+      types.add(new BasicType(true, DType.FLOAT64));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(ListType dataType, List<?>... values) {
+      types.add(dataType);
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(String[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.STRING)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Boolean[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.BOOL8)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Byte[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.INT8)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Short[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.INT16)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Integer[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.INT32)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Long[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.INT64)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Float[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.FLOAT32)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(Double[]... values) {
+      types.add(new ListType(true, new BasicType(true, DType.FLOAT64)));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(StructType dataType, StructData... values) {
+      types.add(dataType);
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder column(StructType dataType, StructData[]... values) {
+      types.add(new ListType(true, dataType));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder timestampDayColumn(Integer... values) {
+      types.add(new BasicType(true, DType.TIMESTAMP_DAYS));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder timestampNanosecondsColumn(Long... values) {
+      types.add(new BasicType(true, DType.TIMESTAMP_NANOSECONDS));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder timestampMillisecondsColumn(Long... values) {
+      types.add(new BasicType(true, DType.TIMESTAMP_MILLISECONDS));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder timestampMicrosecondsColumn(Long... values) {
+      types.add(new BasicType(true, DType.TIMESTAMP_MICROSECONDS));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder timestampSecondsColumn(Long... values) {
+      types.add(new BasicType(true, DType.TIMESTAMP_SECONDS));
+      typeErasedData.add(values);
+      return this;
+    }
+
+    public TestBuilder decimal32Column(int scale, Integer... unscaledValues) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, scale)));
+      typeErasedData.add(unscaledValues);
+      return this;
+    }
+
+    public TestBuilder decimal32Column(int scale, RoundingMode mode, Double... values) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, scale)));
+      BigDecimal[] data = Arrays.stream(values).map((x) -> {
+        if (x == null) return null;
+        return BigDecimal.valueOf(x).setScale(-scale, mode);
+      }).toArray(BigDecimal[]::new);
+      typeErasedData.add(data);
+      return this;
+    }
+
+    public TestBuilder decimal32Column(int scale, RoundingMode mode, String... values) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, scale)));
+      BigDecimal[] data = Arrays.stream(values).map((x) -> {
+        if (x == null) return null;
+        return new BigDecimal(x).setScale(-scale, mode);
+      }).toArray(BigDecimal[]::new);
+      typeErasedData.add(data);
+      return this;
+    }
+
+    public TestBuilder decimal64Column(int scale, Long... unscaledValues) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL64, scale)));
+      typeErasedData.add(unscaledValues);
+      return this;
+    }
+
+    public TestBuilder decimal64Column(int scale, RoundingMode mode, Double... values) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL64, scale)));
+      BigDecimal[] data = Arrays.stream(values).map((x) -> {
+        if (x == null) return null;
+        return BigDecimal.valueOf(x).setScale(-scale, mode);
+      }).toArray(BigDecimal[]::new);
+      typeErasedData.add(data);
+      return this;
+    }
+
+    public TestBuilder decimal64Column(int scale, RoundingMode mode, String... values) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL64, scale)));
+      BigDecimal[] data = Arrays.stream(values).map((x) -> {
+        if (x == null) return null;
+        return new BigDecimal(x).setScale(-scale, mode);
+      }).toArray(BigDecimal[]::new);
+      typeErasedData.add(data);
+      return this;
+    }
+
+    public TestBuilder decimal128Column(int scale, RoundingMode mode, BigInteger... values) {
+      types.add(new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL128, scale)));
+      BigDecimal[] data = Arrays.stream(values).map((x) -> {
+        if (x == null) return null;
+        return new BigDecimal(x, scale, new MathContext(38, mode));
+      }).toArray(BigDecimal[]::new);
+      typeErasedData.add(data);
+      return this;
+    }
+
+    private static ColumnVector from(DType type, Object dataArray) {
+      ColumnVector ret = null;
+      switch (type.typeId) {
+        case STRING:
+          ret = ColumnVector.fromStrings((String[]) dataArray);
+          break;
+        case BOOL8:
+          ret = ColumnVector.fromBoxedBooleans((Boolean[]) dataArray);
+          break;
+        case INT8:
+          ret = ColumnVector.fromBoxedBytes((Byte[]) dataArray);
+          break;
+        case INT16:
+          ret = ColumnVector.fromBoxedShorts((Short[]) dataArray);
+          break;
+        case INT32:
+          ret = ColumnVector.fromBoxedInts((Integer[]) dataArray);
+          break;
+        case INT64:
+          ret = ColumnVector.fromBoxedLongs((Long[]) dataArray);
+          break;
+        case TIMESTAMP_DAYS:
+          ret = ColumnVector.timestampDaysFromBoxedInts((Integer[]) dataArray);
+          break;
+        case TIMESTAMP_SECONDS:
+          ret = ColumnVector.timestampSecondsFromBoxedLongs((Long[]) dataArray);
+          break;
+        case TIMESTAMP_MILLISECONDS:
+          ret = ColumnVector.timestampMilliSecondsFromBoxedLongs((Long[]) dataArray);
+          break;
+        case TIMESTAMP_MICROSECONDS:
+          ret = ColumnVector.timestampMicroSecondsFromBoxedLongs((Long[]) dataArray);
+          break;
+        case TIMESTAMP_NANOSECONDS:
+          ret = ColumnVector.timestampNanoSecondsFromBoxedLongs((Long[]) dataArray);
+          break;
+        case FLOAT32:
+          ret = ColumnVector.fromBoxedFloats((Float[]) dataArray);
+          break;
+        case FLOAT64:
+          ret = ColumnVector.fromBoxedDoubles((Double[]) dataArray);
+          break;
+        case DECIMAL32:
+        case DECIMAL64:
+        case DECIMAL128:
+          int scale = type.getScale();
+          if (dataArray instanceof Integer[]) {
+            BigDecimal[] data = Arrays.stream(((Integer[]) dataArray))
+                .map((i) -> i == null ? null : BigDecimal.valueOf(i, -scale))
+                .toArray(BigDecimal[]::new);
+            ret = ColumnVector.build(type, data.length, (b) -> b.appendBoxed(data));
+          } else if (dataArray instanceof Long[]) {
+            BigDecimal[] data = Arrays.stream(((Long[]) dataArray))
+                .map((i) -> i == null ? null : BigDecimal.valueOf(i, -scale))
+                .toArray(BigDecimal[]::new);
+            ret = ColumnVector.build(type, data.length, (b) -> b.appendBoxed(data));
+          } else if (dataArray instanceof BigDecimal[]) {
+            BigDecimal[] data = (BigDecimal[]) dataArray;
+            ret = ColumnVector.build(type, data.length, (b) -> b.appendBoxed(data));
+          } else {
+            throw new IllegalArgumentException(
+                "Data array of invalid type(" + dataArray.getClass() + ") to build decimal column");
+          }
+          break;
+        default:
+          throw new IllegalArgumentException(type + " is not supported yet");
+      }
+      return ret;
+    }
+
+    @SuppressWarnings("unchecked")
+    private static <T> ColumnVector fromLists(DataType dataType, Object[] dataArray) {
+      List[] dataLists = new List[dataArray.length];
+      for (int i = 0; i < dataLists.length; ++i) {
+        // The element in dataArray can be an array or list, because the below overloaded
+        // version accepts a List of Array as rows.
+        //  `public TestBuilder column(ListType dataType, List<?>... values)`
+        Object dataList = dataArray[i];
+        dataLists[i] = dataList == null ? null :
+            (dataList instanceof List ? (List)dataList : Arrays.asList((Object[])dataList));
+      }
+      return ColumnVector.fromLists(dataType, dataLists);
+    }
+
+    private static ColumnVector fromStructs(DataType dataType, StructData[] dataArray) {
+      return ColumnVector.fromStructs(dataType, dataArray);
+    }
+
+    public Table build() {
+      List<ColumnVector> columns = new ArrayList<>(types.size());
+      try {
+        for (int i = 0; i < types.size(); i++) {
+          DataType dataType = types.get(i);
+          DType dtype = dataType.getType();
+          Object dataArray = typeErasedData.get(i);
+          if (dtype.isNestedType()) {
+            if (dtype.equals(DType.LIST)) {
+              columns.add(fromLists(dataType, (Object[]) dataArray));
+            } else if (dtype.equals(DType.STRUCT)) {
+              columns.add(fromStructs(dataType, (StructData[]) dataArray));
+            } else {
+              throw new IllegalStateException("Unexpected nested type: " + dtype);
+            }
+          } else {
+            columns.add(from(dtype, dataArray));
+          }
+        }
+        return new Table(columns.toArray(new ColumnVector[columns.size()]));
+      } finally {
+        for (ColumnVector cv : columns) {
+          cv.close();
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/TableDebug.java b/java/src/main/java/ai/rapids/cudf/TableDebug.java
new file mode 100644
index 0000000..18ec4b0
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/TableDebug.java
@@ -0,0 +1,280 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.Locale;
+import java.util.function.Consumer;
+
+public class TableDebug {
+
+  /**
+   * Specify one of
+   * -Dai.rapids.cudf.debug.output=stderr       to print directly to standard error (default)
+   * -Dai.rapids.cudf.debug.output=stdout       to print directly to standard output
+   * -Dai.rapids.cudf.debug.output=log[_level]  to redirect to a logging subsystem that can
+   * further be
+   * configured.
+   * Supported log levels:
+   * debug (default)
+   * info
+   * warn
+   * error
+   */
+  public static final String OUTPUT_STREAM = "ai.rapids.cudf.debug.output";
+  private static final Logger log = LoggerFactory.getLogger(TableDebug.class);
+
+  public enum Output {
+    STDOUT(System.out::println),
+    STDERR(System.err::println),
+    LOG(log::debug),
+    LOG_DEBUG(log::debug),
+    LOG_INFO(log::info),
+    LOG_WARN(log::warn),
+    LOG_ERROR(log::error);
+
+    private final Consumer<String> printFunc;
+
+    Output(Consumer<String> pf) {
+      this.printFunc = pf;
+    }
+
+    final void println(String s) {
+      printFunc.accept(s);
+    }
+  }
+
+
+  public static class Builder {
+    private Output outputMode = Output.STDERR;
+
+    public Builder() {
+      try {
+        outputMode = Output.valueOf(
+            System.getProperty(OUTPUT_STREAM, Output.STDERR.name())
+                .toUpperCase(Locale.US));
+      } catch (Throwable e) {
+        log.warn("Failed to parse the output mode", e);
+      }
+    }
+
+    public Builder withOutput(Output outputMode) {
+      this.outputMode = outputMode;
+      return this;
+    }
+
+    public final TableDebug build() {
+      return new TableDebug(outputMode);
+    }
+  }
+
+  public static Builder builder() {
+    return new Builder();
+  }
+
+  private static final TableDebug DEFAULT_DEBUG = builder().build();
+
+  public static TableDebug get() {
+    return DEFAULT_DEBUG;
+  }
+
+  private final Output output;
+
+  private TableDebug(Output output) {
+    this.output = output;
+  }
+
+  /**
+   * Print the contents of a table. Note that this should never be
+   * called from production code, as it is very slow.  Also note that this is not production
+   * code.  You might need/want to update how the data shows up or add in support for more
+   * types as this really is just for debugging.
+   * @param name  the name of the table to print out.
+   * @param table the table to print out.
+   */
+  public synchronized void debug(String name, Table table) {
+    output.println("DEBUG " + name + " " + table);
+    for (int col = 0; col < table.getNumberOfColumns(); col++) {
+      debug(String.valueOf(col), table.getColumn(col));
+    }
+  }
+
+  /**
+   * Print the contents of a column. Note that this should never be
+   * called from production code, as it is very slow.  Also note that this is not production
+   * code.  You might need/want to update how the data shows up or add in support for more
+   * types as this really is just for debugging.
+   * @param name the name of the column to print out.
+   * @param col  the column to print out.
+   */
+  public synchronized void debug(String name, ColumnView col) {
+    debugGPUAddrs(name, col);
+    try (HostColumnVector hostCol = col.copyToHost()) {
+      debug(name, hostCol);
+    }
+  }
+
+  private synchronized void debugGPUAddrs(String name, ColumnView col) {
+    try (BaseDeviceMemoryBuffer data = col.getData();
+         BaseDeviceMemoryBuffer validity = col.getValid()) {
+      output.println("GPU COLUMN " + name + " - NC: " + col.getNullCount()
+          + " DATA: " + data + " VAL: " + validity);
+    }
+    if (col.getType() == DType.STRUCT) {
+      for (int i = 0; i < col.getNumChildren(); i++) {
+        try (ColumnView child = col.getChildColumnView(i)) {
+          debugGPUAddrs(name + ":CHILD_" + i, child);
+        }
+      }
+    } else if (col.getType() == DType.LIST) {
+      try (ColumnView child = col.getChildColumnView(0)) {
+        debugGPUAddrs(name + ":DATA", child);
+      }
+    }
+  }
+
+
+  /**
+   * Print the contents of a column. Note that this should never be
+   * called from production code, as it is very slow.  Also note that this is not production
+   * code.  You might need/want to update how the data shows up or add in support for more
+   * types as this really is just for debugging.
+   * @param name    the name of the column to print out.
+   * @param hostCol the column to print out.
+   */
+  public synchronized void debug(String name, HostColumnVectorCore hostCol) {
+    DType type = hostCol.getType();
+    output.println("COLUMN " + name + " - " + type);
+    if (type.isDecimalType()) {
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } else {
+          output.println(i + " " + hostCol.getBigDecimal(i));
+        }
+      }
+    } else if (DType.STRING.equals(type)) {
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } else {
+          output.println(i + " \"" + hostCol.getJavaString(i) + "\" " +
+              hexString(hostCol.getUTF8(i)));
+        }
+      }
+    } else if (DType.INT32.equals(type)
+        || DType.INT8.equals(type)
+        || DType.INT16.equals(type)
+        || DType.INT64.equals(type)
+        || DType.TIMESTAMP_DAYS.equals(type)
+        || DType.TIMESTAMP_SECONDS.equals(type)
+        || DType.TIMESTAMP_MICROSECONDS.equals(type)
+        || DType.TIMESTAMP_MILLISECONDS.equals(type)
+        || DType.TIMESTAMP_NANOSECONDS.equals(type)
+        || DType.UINT8.equals(type)
+        || DType.UINT16.equals(type)
+        || DType.UINT32.equals(type)
+        || DType.UINT64.equals(type)) {
+      debugInteger(hostCol, type);
+    } else if (DType.BOOL8.equals(type)) {
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } else {
+          output.println(i + " " + hostCol.getBoolean(i));
+        }
+      }
+    } else if (DType.FLOAT64.equals(type)) {
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } else {
+          output.println(i + " " + hostCol.getDouble(i));
+        }
+      }
+    } else if (DType.FLOAT32.equals(type)) {
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } else {
+          output.println(i + " " + hostCol.getFloat(i));
+        }
+      }
+    } else if (DType.STRUCT.equals(type)) {
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } // The struct child columns are printed out later on.
+      }
+      for (int i = 0; i < hostCol.getNumChildren(); i++) {
+        debug(name + ":CHILD_" + i, hostCol.getChildColumnView(i));
+      }
+    } else if (DType.LIST.equals(type)) {
+      output.println("OFFSETS");
+      for (int i = 0; i < hostCol.getRowCount(); i++) {
+        if (hostCol.isNull(i)) {
+          output.println(i + " NULL");
+        } else {
+          output.println(i + " [" + hostCol.getStartListOffset(i) + " - " +
+              hostCol.getEndListOffset(i) + ")");
+        }
+      }
+      debug(name + ":DATA", hostCol.getChildColumnView(0));
+    } else {
+      output.println("TYPE " + type + " NOT SUPPORTED FOR DEBUG PRINT");
+    }
+  }
+
+
+  private void debugInteger(HostColumnVectorCore hostCol, DType intType) {
+    for (int i = 0; i < hostCol.getRowCount(); i++) {
+      if (hostCol.isNull(i)) {
+        output.println(i + " NULL");
+      } else {
+        final int sizeInBytes = intType.getSizeInBytes();
+        final Object value;
+        switch (sizeInBytes) {
+          case Byte.BYTES:
+            value = hostCol.getByte(i);
+            break;
+          case Short.BYTES:
+            value = hostCol.getShort(i);
+            break;
+          case Integer.BYTES:
+            value = hostCol.getInt(i);
+            break;
+          case Long.BYTES:
+            value = hostCol.getLong(i);
+            break;
+          default:
+            throw new IllegalArgumentException("INFEASIBLE: Unsupported integer-like type " + intType);
+        }
+        output.println(i + " " + value);
+      }
+    }
+  }
+
+
+  private static String hexString(byte[] bytes) {
+    StringBuilder str = new StringBuilder();
+    for (byte b : bytes) {
+      str.append(String.format("%02x", b & 0xff));
+    }
+    return str.toString();
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/TableWithMeta.java b/java/src/main/java/ai/rapids/cudf/TableWithMeta.java
new file mode 100644
index 0000000..b6b8ad6
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/TableWithMeta.java
@@ -0,0 +1,67 @@
+/*
+ *
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+
+package ai.rapids.cudf;
+
+/**
+ * A table along with some metadata about the table. This is typically returned when
+ * reading data from an input file where the metadata can be important.
+ */
+public class TableWithMeta implements AutoCloseable {
+  private long handle;
+
+  TableWithMeta(long handle) {
+    this.handle = handle;
+  }
+
+  /**
+   * Get the table out of this metadata. Note that this can only be called once. Later calls
+   * will return a null.
+   */
+  public Table releaseTable() {
+    long[] ptr = releaseTable(handle);
+    if (ptr == null) {
+      return null;
+    } else {
+      return new Table(ptr);
+    }
+  }
+
+  /**
+   * Get the names of the top level columns. In the future new APIs can be added to get
+   * names of child columns.
+   */
+  public String[] getColumnNames() {
+    return getColumnNames(handle);
+  }
+
+  @Override
+  public void close() {
+    if (handle != 0) {
+      close(handle);
+      handle = 0;
+    }
+  }
+
+  private static native void close(long handle);
+
+  private static native long[] releaseTable(long handle);
+
+  private static native String[] getColumnNames(long handle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/TableWriter.java b/java/src/main/java/ai/rapids/cudf/TableWriter.java
new file mode 100644
index 0000000..c5bf735
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/TableWriter.java
@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+/**
+ * Provides an interface for writing out Table information in multiple steps.
+ * A TableWriter will be returned from one of various factory functions in the Table class that
+ * let you set the format of the data and its destination. After that write can be called one or
+ * more times. When you are done writing call close to finish.
+ */
+public abstract class TableWriter implements AutoCloseable {
+  protected long writerHandle;
+
+  TableWriter(long writerHandle) { this.writerHandle = writerHandle; }
+
+  /**
+   * Write out a table. Note that all columns must be in the same order each time this is called
+   * and the format of each table cannot change.
+   * @param table what to write out.
+   */
+  abstract public void write(Table table) throws CudfException;
+
+  @Override
+  abstract public void close() throws CudfException;
+
+  public static class WriteStatistics {
+    public final long numCompressedBytes; // The number of bytes that were successfully compressed
+    public final long numFailedBytes;     // The number of bytes that failed to compress
+    public final long numSkippedBytes;    // The number of bytes that were skipped during compression
+    public final double compressionRatio; // The compression ratio for the successfully compressed data
+
+    public WriteStatistics(long numCompressedBytes, long numFailedBytes, long numSkippedBytes,
+        double compressionRatio) {
+      this.numCompressedBytes = numCompressedBytes;
+      this.numFailedBytes = numFailedBytes;
+      this.numSkippedBytes = numSkippedBytes;
+      this.compressionRatio = compressionRatio;
+    }
+  }
+
+  /**
+   * Get the write statistics for the writer up to the last write call.
+   * Currently, only ORC and Parquet writers support write statistics.
+   * Calling this method on other writers will return null.
+   * @return The write statistics.
+   */
+  public WriteStatistics getWriteStatistics() {
+    double[] statsData = getWriteStatistics(writerHandle);
+    assert statsData.length == 4 : "Unexpected write statistics data length";
+    return new WriteStatistics((long) statsData[0], (long) statsData[1], (long) statsData[2],
+        statsData[3]);
+  }
+
+  /**
+   * Get the write statistics for the writer up to the last write call.
+   * The data returned from native method is encoded as an array of doubles.
+   * @param writerHandle The handle to the writer.
+   * @return The write statistics.
+   */
+  private static native double[] getWriteStatistics(long writerHandle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/UnaryOp.java b/java/src/main/java/ai/rapids/cudf/UnaryOp.java
new file mode 100644
index 0000000..eac054d
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/UnaryOp.java
@@ -0,0 +1,60 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package ai.rapids.cudf;
+
+/**
+ * Mathematical unary operations.
+ */
+public enum UnaryOp {
+  SIN(0),
+  COS(1),
+  TAN(2),
+  ARCSIN(3),
+  ARCCOS(4),
+  ARCTAN(5),
+  SINH(6),
+  COSH(7),
+  TANH(8),
+  ARCSINH(9),
+  ARCCOSH(10),
+  ARCTANH(11),
+  EXP(12),
+  LOG(13),
+  SQRT(14),
+  CBRT(15),
+  CEIL(16),
+  FLOOR(17),
+  ABS(18),
+  RINT(19),
+  BIT_INVERT(20),
+  NOT(21);
+
+  private static final UnaryOp[] OPS = UnaryOp.values();
+  final int nativeId;
+
+  UnaryOp(int nativeId) {
+    this.nativeId = nativeId;
+  }
+
+  static UnaryOp fromNative(int nativeId) {
+    for (UnaryOp type : OPS) {
+      if (type.nativeId == nativeId) {
+        return type;
+      }
+    }
+    throw new IllegalArgumentException("Could not translate " + nativeId + " into a UnaryOp");
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/UnsafeMemoryAccessor.java b/java/src/main/java/ai/rapids/cudf/UnsafeMemoryAccessor.java
new file mode 100644
index 0000000..ebc3f43
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/UnsafeMemoryAccessor.java
@@ -0,0 +1,374 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.lang.reflect.Field;
+
+/**
+ * UnsafeMemory Accessor for accessing memory on host
+ */
+class UnsafeMemoryAccessor {
+
+  public static final long BYTE_ARRAY_OFFSET;
+  public static final long SHORT_ARRAY_OFFSET;
+  public static final long INT_ARRAY_OFFSET;
+  public static final long LONG_ARRAY_OFFSET;
+  public static final long FLOAT_ARRAY_OFFSET;
+  public static final long DOUBLE_ARRAY_OFFSET;
+  private static final sun.misc.Unsafe UNSAFE;
+  /**
+   * Limits the number of bytes to copy per {@link sun.misc.Unsafe#copyMemory(long, long, long)} to
+   * allow safepoint polling during a large copy.
+   */
+  private static final long UNSAFE_COPY_THRESHOLD = 1024L * 1024L;
+  private static Logger log = LoggerFactory.getLogger(UnsafeMemoryAccessor.class);
+
+  static {
+    sun.misc.Unsafe unsafe = null;
+    try {
+      Field unsafeField = sun.misc.Unsafe.class.getDeclaredField("theUnsafe");
+      unsafeField.setAccessible(true);
+      unsafe = (sun.misc.Unsafe) unsafeField.get(null);
+      BYTE_ARRAY_OFFSET = unsafe.arrayBaseOffset(byte[].class);
+      SHORT_ARRAY_OFFSET = unsafe.arrayBaseOffset(short[].class);
+      INT_ARRAY_OFFSET = unsafe.arrayBaseOffset(int[].class);
+      LONG_ARRAY_OFFSET = unsafe.arrayBaseOffset(long[].class);
+      FLOAT_ARRAY_OFFSET = unsafe.arrayBaseOffset(float[].class);
+      DOUBLE_ARRAY_OFFSET = unsafe.arrayBaseOffset(double[].class);
+    } catch (Throwable t) {
+      log.error("Failed to get unsafe object, got this error: ", t);
+      UNSAFE = null;
+      throw new NullPointerException("Failed to get unsafe object, got this error: " + t.getMessage());
+    }
+    UNSAFE = unsafe;
+  }
+
+  /**
+   * Get the system memory page size.
+   * @return system memory page size in bytes
+   */
+  public static int pageSize() {
+    return UNSAFE.pageSize();
+  }
+
+  /**
+   * Allocate bytes on host
+   * @param bytes - number of bytes to allocate
+   * @return - allocated address
+   */
+  public static long allocate(long bytes) {
+    return UNSAFE.allocateMemory(bytes);
+  }
+
+  /**
+   * Free memory at that location
+   * @param address - memory location
+   */
+  public static void free(long address) {
+    UNSAFE.freeMemory(address);
+  }
+
+  /**
+   * Sets the values at this address repeatedly
+   * @param address - memory location
+   * @param size    - number of bytes to set
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setMemory(long address, long size, byte value) {
+    UNSAFE.setMemory(address, size, value);
+  }
+
+  /**
+   * Sets the Byte value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setByte(long address, byte value) {
+    UNSAFE.putByte(address, value);
+  }
+
+  /**
+   * Sets an array of bytes.
+   * @param address - memory address
+   * @param values  to be set
+   * @param offset  index into values to start at.
+   * @param len     the number of bytes to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setBytes(long address, byte[] values, long offset, long len) {
+    copyMemory(values, UnsafeMemoryAccessor.BYTE_ARRAY_OFFSET + offset,
+        null, address, len);
+  }
+
+  /**
+   * Returns the Byte value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static byte getByte(long address) {
+    return UNSAFE.getByte(address);
+  }
+
+  /**
+   * Copy out an array of bytes.
+   * @param dst       where to write the data
+   * @param dstOffset index into values to start writing at.
+   * @param address   src memory address
+   * @param len       the number of bytes to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void getBytes(byte[] dst, long dstOffset, long address, long len) {
+    copyMemory(null, address,
+        dst, UnsafeMemoryAccessor.BYTE_ARRAY_OFFSET + dstOffset, len);
+  }
+
+  /**
+   * Returns the Integer value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static int getInt(long address) {
+    return UNSAFE.getInt(address);
+  }
+
+  /**
+   * Sets the Integer value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setInt(long address, int value) {
+    UNSAFE.putInt(address, value);
+  }
+
+  /**
+   * Sets an array of ints.
+   * @param address memory address
+   * @param values  to be set
+   * @param offset  index into values to start at.
+   * @param len     the number of ints to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setInts(long address, int[] values, long offset, long len) {
+    copyMemory(values, UnsafeMemoryAccessor.INT_ARRAY_OFFSET + (offset * 4),
+        null, address, len * 4);
+  }
+
+  /**
+   * Sets the Long value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setLong(long address, long value) {
+    UNSAFE.putLong(address, value);
+  }
+
+  /**
+   * Sets an array of longs.
+   * @param address memory address
+   * @param values  to be set
+   * @param offset  index into values to start at
+   * @param len     the number of longs to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setLongs(long address, long[] values, long offset, long len) {
+    copyMemory(values, UnsafeMemoryAccessor.LONG_ARRAY_OFFSET + (offset * 8),
+        null, address, len * 8);
+  }
+
+  /**
+   * Returns the Long value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static long getLong(long address) {
+    return UNSAFE.getLong(address);
+  }
+
+  /**
+   * Copy out an array of longs.
+   * @param dst       where to write the data
+   * @param dstIndex  index into values to start writing at.
+   * @param address   src memory address
+   * @param count     the number of longs to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void getLongs(long[] dst, long dstIndex, long address, int count) {
+    copyMemory(null, address,
+        dst, UnsafeMemoryAccessor.LONG_ARRAY_OFFSET + (dstIndex * 8), count * 8);
+  }
+
+  /**
+   * Returns the Short value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static short getShort(long address) {
+    return UNSAFE.getShort(address);
+  }
+
+  /**
+   * Sets the Short value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setShort(long address, short value) {
+    UNSAFE.putShort(address, value);
+  }
+
+  /**
+   * Sets an array of shorts.
+   * @param address memory address
+   * @param values  to be set
+   * @param offset  index into values to start at
+   * @param len     the number of shorts to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setShorts(long address, short[] values, long offset, long len) {
+    copyMemory(values, UnsafeMemoryAccessor.SHORT_ARRAY_OFFSET + (offset * 2),
+        null, address, len * 2);
+  }
+
+  /**
+   * Sets the Double value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setDouble(long address, double value) {
+    UNSAFE.putDouble(address, value);
+  }
+
+  /**
+   * Sets an array of doubles.
+   * @param address memory address
+   * @param values  to be set
+   * @param offset  index into values to start at
+   * @param len     the number of doubles to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setDoubles(long address, double[] values, long offset, long len) {
+    copyMemory(values, UnsafeMemoryAccessor.DOUBLE_ARRAY_OFFSET + (offset * 8),
+        null, address, len * 8);
+  }
+
+  /**
+   * Returns the Double value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static double getDouble(long address) {
+    return UNSAFE.getDouble(address);
+  }
+
+  /**
+   * Returns the Float value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static float getFloat(long address) {
+    return UNSAFE.getFloat(address);
+  }
+
+  /**
+   * Sets the Float value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setFloat(long address, float value) {
+    UNSAFE.putFloat(address, value);
+  }
+
+  /**
+   * Sets an array of floats.
+   * @param address memory address
+   * @param values  to be set
+   * @param offset  the index in values to start at
+   * @param len     the number of floats to copy
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setFloats(long address, float[] values, long offset, long len) {
+    copyMemory(values, UnsafeMemoryAccessor.FLOAT_ARRAY_OFFSET + (offset * 4),
+        null, address, len * 4);
+  }
+
+  /**
+   * Returns the Boolean value at this address
+   * @param address - memory address
+   * @return - value
+   * @throws IndexOutOfBoundsException
+   */
+  public static boolean getBoolean(long address) {
+    return getByte(address) != 0 ? true : false;
+  }
+
+  /**
+   * Sets the Boolean value at that address
+   * @param address - memory address
+   * @param value   - value to be set
+   * @throws IndexOutOfBoundsException
+   */
+  public static void setBoolean(long address, boolean value) {
+    setByte(address, (byte) (value ? 1 : 0));
+  }
+
+
+  /**
+   * Copy memory from one address to the other.
+   */
+  public static void copyMemory(Object src, long srcOffset, Object dst, long dstOffset,
+                                long length) {
+    // Check if dstOffset is before or after srcOffset to determine if we should copy
+    // forward or backwards. This is necessary in case src and dst overlap.
+    if (dstOffset < srcOffset) {
+      while (length > 0) {
+        long size = Math.min(length, UNSAFE_COPY_THRESHOLD);
+        UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size);
+        length -= size;
+        srcOffset += size;
+        dstOffset += size;
+      }
+    } else {
+      srcOffset += length;
+      dstOffset += length;
+      while (length > 0) {
+        long size = Math.min(length, UNSAFE_COPY_THRESHOLD);
+        srcOffset -= size;
+        dstOffset -= size;
+        UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size);
+        length -= size;
+      }
+
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/WindowOptions.java b/java/src/main/java/ai/rapids/cudf/WindowOptions.java
new file mode 100644
index 0000000..ef6b3ce
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/WindowOptions.java
@@ -0,0 +1,370 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+/**
+  * Options for rolling windows.
+ */
+public class WindowOptions implements AutoCloseable {
+
+  enum FrameType {ROWS, RANGE}
+
+  /**
+   * Extent of (range) window bounds.
+   * Analogous to cudf::range_window_bounds::extent_type.
+   */
+  enum RangeExtentType {
+    CURRENT_ROW(0), // Bounds defined as the first/last row that matches the current row.
+    BOUNDED(1),     // Bounds defined as the first/last row that falls within
+                    // a specified range from the current row.
+    UNBOUNDED(2);   // Bounds stretching to the first/last row in the entire group.
+
+    public final int nominalValue;
+
+    RangeExtentType(int n) {
+      this.nominalValue = n;
+    }
+  }
+
+  private final int minPeriods;
+  private final Scalar precedingScalar;
+  private final Scalar followingScalar;
+  private final ColumnVector precedingCol;
+  private final ColumnVector followingCol;
+  private final int orderByColumnIndex;
+  private final boolean orderByOrderAscending;
+  private final FrameType frameType;
+  private final RangeExtentType precedingBoundsExtent;
+  private final RangeExtentType followingBoundsExtent;
+
+  private WindowOptions(Builder builder) {
+    this.minPeriods = builder.minPeriods;
+    this.precedingScalar = builder.precedingScalar;
+    if (precedingScalar != null) {
+      precedingScalar.incRefCount();
+    }
+    this.followingScalar = builder.followingScalar;
+    if (followingScalar != null) {
+      followingScalar.incRefCount();
+    }
+    this.precedingCol = builder.precedingCol;
+    if (precedingCol != null) {
+      precedingCol.incRefCount();
+    }
+    this.followingCol = builder.followingCol;
+    if (followingCol != null) {
+      followingCol.incRefCount();
+    }
+    this.orderByColumnIndex = builder.orderByColumnIndex;
+    this.orderByOrderAscending = builder.orderByOrderAscending;
+    this.frameType = orderByColumnIndex == -1? FrameType.ROWS : FrameType.RANGE;
+    this.precedingBoundsExtent = builder.precedingBoundsExtent;
+    this.followingBoundsExtent = builder.followingBoundsExtent;
+  }
+
+  @Override
+  public boolean equals(Object other) {
+    if (this == other) {
+      return true;
+    } else if (other instanceof WindowOptions) {
+      WindowOptions o = (WindowOptions) other;
+      boolean ret = this.minPeriods == o.minPeriods &&
+              this.orderByColumnIndex == o.orderByColumnIndex &&
+              this.orderByOrderAscending == o.orderByOrderAscending &&
+              this.frameType == o.frameType &&
+              this.precedingBoundsExtent == o.precedingBoundsExtent &&
+              this.followingBoundsExtent == o.followingBoundsExtent;
+      if (precedingCol != null) {
+        ret = ret && precedingCol.equals(o.precedingCol);
+      }
+      if (followingCol != null) {
+        ret = ret && followingCol.equals(o.followingCol);
+      }
+      if (precedingScalar != null) {
+        ret = ret && precedingScalar.equals(o.precedingScalar);
+      }
+      if (followingScalar != null) {
+        ret = ret && followingScalar.equals(o.followingScalar);
+      }
+      return ret;
+    }
+    return false;
+  }
+
+  @Override
+  public int hashCode() {
+    int ret = 7;
+    ret = 31 * ret + minPeriods;
+    ret = 31 * ret + orderByColumnIndex;
+    ret = 31 * ret + Boolean.hashCode(orderByOrderAscending);
+    ret = 31 * ret + frameType.hashCode();
+    if (precedingCol != null) {
+      ret = 31 * ret + precedingCol.hashCode();
+    }
+    if (followingCol != null) {
+      ret = 31 * ret + followingCol.hashCode();
+    }
+    if (precedingScalar != null) {
+      ret = 31 * ret + precedingScalar.hashCode();
+    }
+    if (followingScalar != null) {
+      ret = 31 * ret + followingScalar.hashCode();
+    }
+    ret = 31 * ret + precedingBoundsExtent.hashCode();
+    ret = 31 * ret + followingBoundsExtent.hashCode();
+    return ret;
+  }
+
+  public static Builder builder(){
+    return new Builder();
+  }
+
+  int getMinPeriods() { return  this.minPeriods; }
+
+  Scalar getPrecedingScalar() { return this.precedingScalar; }
+
+  Scalar getFollowingScalar() { return this.followingScalar; }
+
+  ColumnVector getPrecedingCol() { return precedingCol; }
+
+  ColumnVector getFollowingCol() { return this.followingCol; }
+
+  @Deprecated
+  int getTimestampColumnIndex() { return getOrderByColumnIndex(); }
+
+  int getOrderByColumnIndex() { return this.orderByColumnIndex; }
+
+  @Deprecated
+  boolean isTimestampOrderAscending() { return isOrderByOrderAscending(); }
+
+  boolean isOrderByOrderAscending() { return this.orderByOrderAscending; }
+
+  boolean isUnboundedPreceding() { return this.precedingBoundsExtent == RangeExtentType.UNBOUNDED; }
+
+  boolean isUnboundedFollowing() { return this.followingBoundsExtent == RangeExtentType.UNBOUNDED; }
+
+  boolean isCurrentRowPreceding() { return this.precedingBoundsExtent == RangeExtentType.CURRENT_ROW; }
+
+  boolean isCurrentRowFollowing() { return this.followingBoundsExtent == RangeExtentType.CURRENT_ROW; }
+
+  RangeExtentType getPrecedingBoundsExtent() { return this.precedingBoundsExtent; }
+  RangeExtentType getFollowingBoundsExtent() { return this.followingBoundsExtent; }
+
+  FrameType getFrameType() { return frameType; }
+
+  public static class Builder {
+    private int minPeriods = 1;
+    // for range window
+    private Scalar precedingScalar = null;
+    private Scalar followingScalar = null;
+    private ColumnVector precedingCol = null;
+    private ColumnVector followingCol = null;
+    private int orderByColumnIndex = -1;
+    private boolean orderByOrderAscending = true;
+    private RangeExtentType precedingBoundsExtent = RangeExtentType.BOUNDED;
+    private RangeExtentType followingBoundsExtent = RangeExtentType.BOUNDED;
+
+    /**
+     * Set the minimum number of observation required to evaluate an element.  If there are not
+     * enough elements for a given window a null is placed in the result instead.
+     */
+    public Builder minPeriods(int minPeriods) {
+      if (minPeriods < 0 ) {
+        throw  new IllegalArgumentException("Minimum observations must be non negative");
+      }
+      this.minPeriods = minPeriods;
+      return this;
+    }
+
+    /**
+     * Set the size of the window, one entry per row. This does not take ownership of the
+     * columns passed in so you have to be sure that the lifetime of the column outlives
+     * this operation.
+     * @param precedingCol the number of rows preceding the current row and
+     *                     precedingCol will be live outside of WindowOptions.
+     * @param followingCol the number of rows following the current row and
+     *                     following will be live outside of WindowOptions.
+     */
+    public Builder window(ColumnVector precedingCol, ColumnVector followingCol) {
+      if (precedingCol == null || precedingCol.hasNulls()) {
+        throw new IllegalArgumentException("preceding cannot be null or have nulls");
+      }
+      if (followingCol == null || followingCol.hasNulls()) {
+        throw new IllegalArgumentException("following cannot be null or have nulls");
+      }
+      if (precedingBoundsExtent != RangeExtentType.BOUNDED || precedingScalar != null) {
+        throw new IllegalStateException("preceding has already been set a different way");
+      }
+      if (followingBoundsExtent != RangeExtentType.BOUNDED || followingScalar != null) {
+        throw new IllegalStateException("following has already been set a different way");
+      }
+      this.precedingCol = precedingCol;
+      this.followingCol = followingCol;
+      return this;
+    }
+
+    /**
+     * Set the size of the range window.
+     * @param precedingScalar the relative number preceding the current row and
+     *                        the precedingScalar will be live outside of WindowOptions.
+     * @param followingScalar the relative number following the current row and
+     *                        the followingScalar will be live outside of WindowOptions
+     */
+    public Builder window(Scalar precedingScalar, Scalar followingScalar) {
+      return preceding(precedingScalar).following(followingScalar);
+    }
+
+    /**
+     * @deprecated Use orderByColumnIndex(int index)
+     */
+    @Deprecated
+    public Builder timestampColumnIndex(int index) {
+      return orderByColumnIndex(index);
+    }
+
+    public Builder orderByColumnIndex(int index) {
+      this.orderByColumnIndex = index;
+      return this;
+    }
+
+    /**
+     * @deprecated Use orderByAscending()
+     */
+    @Deprecated
+    public Builder timestampAscending() {
+      return orderByAscending();
+    }
+
+    public Builder orderByAscending() {
+      this.orderByOrderAscending = true;
+      return this;
+    }
+
+    public Builder orderByDescending() {
+      this.orderByOrderAscending = false;
+      return this;
+    }
+
+    /**
+     * @deprecated Use orderByDescending()
+     */
+    @Deprecated
+    public Builder timestampDescending() {
+      return orderByDescending();
+    }
+
+    public Builder currentRowPreceding() {
+      if (precedingCol != null || precedingScalar != null) {
+        throw new IllegalStateException("preceding has already been set a different way");
+      }
+      this.precedingBoundsExtent = RangeExtentType.CURRENT_ROW;
+      return this;
+    }
+
+    public Builder currentRowFollowing() {
+      if (followingCol != null || followingScalar != null) {
+        throw new IllegalStateException("following has already been set a different way");
+      }
+      this.followingBoundsExtent = RangeExtentType.CURRENT_ROW;
+      return this;
+    }
+
+    public Builder unboundedPreceding() {
+      if (precedingCol != null || precedingScalar != null) {
+        throw new IllegalStateException("preceding has already been set a different way");
+      }
+      this.precedingBoundsExtent = RangeExtentType.UNBOUNDED;
+      return this;
+    }
+
+    public Builder unboundedFollowing() {
+      if (followingCol != null || followingScalar != null) {
+        throw new IllegalStateException("following has already been set a different way");
+      }
+      this.followingBoundsExtent = RangeExtentType.UNBOUNDED;
+      return this;
+    }
+
+    /**
+     * Set the relative number preceding the current row for range window
+     * @return this for chaining
+     */
+    public Builder preceding(Scalar preceding) {
+      if (preceding == null || !preceding.isValid()) {
+        throw new IllegalArgumentException("preceding cannot be null");
+      }
+      if (precedingBoundsExtent != RangeExtentType.BOUNDED || precedingCol != null) {
+        throw new IllegalStateException("preceding has already been set a different way");
+      }
+      this.precedingScalar = preceding;
+      return this;
+    }
+
+    /**
+     * Set the relative number following the current row for range window
+     * @return this for chaining
+     */
+    public Builder following(Scalar following) {
+      if (following == null || !following.isValid()) {
+        throw new IllegalArgumentException("following cannot be null");
+      }
+      if (followingBoundsExtent != RangeExtentType.BOUNDED || followingCol != null) {
+        throw new IllegalStateException("following has already been set a different way");
+      }
+      this.followingScalar = following;
+      return this;
+    }
+
+    public WindowOptions build() {
+      return new WindowOptions(this);
+    }
+  }
+
+  public synchronized WindowOptions incRefCount() {
+    if (precedingScalar != null) {
+      precedingScalar.incRefCount();
+    }
+    if (followingScalar != null) {
+      followingScalar.incRefCount();
+    }
+    if (precedingCol != null) {
+      precedingCol.incRefCount();
+    }
+    if (followingCol != null) {
+      followingCol.incRefCount();
+    }
+    return this;
+  }
+
+  @Override
+  public void close() {
+    if (precedingScalar != null) {
+      precedingScalar.close();
+    }
+    if (followingScalar != null) {
+      followingScalar.close();
+    }
+    if (precedingCol != null) {
+      precedingCol.close();
+    }
+    if (followingCol != null) {
+      followingCol.close();
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/WriterOptions.java b/java/src/main/java/ai/rapids/cudf/WriterOptions.java
new file mode 100644
index 0000000..5d5af30
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/WriterOptions.java
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+class WriterOptions {
+
+  private final String[] columnNames;
+  private final boolean[] columnNullability;
+
+  <T extends WriterBuilder> WriterOptions(T builder) {
+    columnNames = (String[]) builder.columnNames.toArray(new String[builder.columnNames.size()]);
+    columnNullability = new boolean[builder.columnNullability.size()];
+    for (int i = 0; i < builder.columnNullability.size(); i++) {
+      columnNullability[i] = (boolean)builder.columnNullability.get(i);
+    }
+  }
+
+  public String[] getColumnNames() {
+    return columnNames;
+  }
+
+  public boolean[] getColumnNullability() {
+    return columnNullability;
+  }
+
+  protected static class WriterBuilder<T extends WriterBuilder> {
+    final List<String> columnNames = new ArrayList<>();
+    final List<Boolean> columnNullability = new ArrayList<>();
+
+    /**
+     * Add column name(s). For Parquet column names are not optional.
+     * @param columnNames
+     */
+    public T withColumnNames(String... columnNames) {
+      this.columnNames.addAll(Arrays.asList(columnNames));
+      for (int i = 0; i < columnNames.length; i++) {
+        this.columnNullability.add(true);
+      }
+      return (T) this;
+    }
+
+    /**
+     * Add column name that is not nullable
+     * @param columnNames
+     */
+    public T withNotNullableColumnNames(String... columnNames) {
+      this.columnNames.addAll(Arrays.asList(columnNames));
+      for (int i = 0; i < columnNames.length; i++) {
+        this.columnNullability.add(false);
+      }
+      return (T) this;
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/AstExpression.java b/java/src/main/java/ai/rapids/cudf/ast/AstExpression.java
new file mode 100644
index 0000000..5ac15f7
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/AstExpression.java
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+
+/** Base class of every node in an AST */
+public abstract class AstExpression {
+  /**
+   * Enumeration for the types of AST nodes that can appear in a serialized AST.
+   * NOTE: This must be kept in sync with the `jni_serialized_node_type` in CompiledExpression.cpp!
+   */
+  protected enum ExpressionType {
+    VALID_LITERAL(0),
+    NULL_LITERAL(1),
+    COLUMN_REFERENCE(2),
+    UNARY_EXPRESSION(3),
+    BINARY_EXPRESSION(4);
+
+    private final byte nativeId;
+
+    ExpressionType(int nativeId) {
+      this.nativeId = (byte) nativeId;
+      assert this.nativeId == nativeId;
+    }
+
+    /** Get the size in bytes to serialize this node type */
+    int getSerializedSize() {
+      return Byte.BYTES;
+    }
+
+    /** Serialize this node type to the specified buffer */
+    void serialize(ByteBuffer bb) {
+      bb.put(nativeId);
+    }
+  }
+
+  public CompiledExpression compile() {
+    int size = getSerializedSize();
+    ByteBuffer bb = ByteBuffer.allocate(size);
+    bb.order(ByteOrder.nativeOrder());
+    serialize(bb);
+    return new CompiledExpression(bb.array());
+  }
+
+  /** Get the size in bytes of the serialized form of this node and all child nodes */
+  abstract int getSerializedSize();
+
+  /**
+   * Serialize this node and all child nodes.
+   * @param bb buffer to receive the serialized data
+   */
+  abstract void serialize(ByteBuffer bb);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/BinaryOperation.java b/java/src/main/java/ai/rapids/cudf/ast/BinaryOperation.java
new file mode 100644
index 0000000..c39c1c3
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/BinaryOperation.java
@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import java.nio.ByteBuffer;
+
+/** A binary operation consisting of an operator and two operands. */
+public class BinaryOperation extends AstExpression {
+  private final BinaryOperator op;
+  private final AstExpression leftInput;
+  private final AstExpression rightInput;
+
+  public BinaryOperation(BinaryOperator op, AstExpression leftInput, AstExpression rightInput) {
+    this.op = op;
+    this.leftInput = leftInput;
+    this.rightInput = rightInput;
+  }
+
+  @Override
+  int getSerializedSize() {
+    return ExpressionType.BINARY_EXPRESSION.getSerializedSize() +
+        op.getSerializedSize() +
+        leftInput.getSerializedSize() +
+        rightInput.getSerializedSize();
+  }
+
+  @Override
+  void serialize(ByteBuffer bb) {
+    ExpressionType.BINARY_EXPRESSION.serialize(bb);
+    op.serialize(bb);
+    leftInput.serialize(bb);
+    rightInput.serialize(bb);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/BinaryOperator.java b/java/src/main/java/ai/rapids/cudf/ast/BinaryOperator.java
new file mode 100644
index 0000000..be90265
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/BinaryOperator.java
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import java.nio.ByteBuffer;
+
+/**
+ * Enumeration of AST operators that can appear in a binary operation.
+ * NOTE: This must be kept in sync with `jni_to_binary_operator` in CompiledExpression.cpp!
+ */
+public enum BinaryOperator {
+  ADD(0),                 // operator +
+  SUB(1),                 // operator -
+  MUL(2),                 // operator *
+  DIV(3),                 // operator / using common type of lhs and rhs
+  TRUE_DIV(4),            // operator / after promoting type to floating point
+  FLOOR_DIV(5),           // operator / after promoting to 64 bit floating point and then flooring the result
+  MOD(6),                 // operator %
+  PYMOD(7),               // operator % using Python's sign rules for negatives
+  POW(8),                 // lhs ^ rhs
+  EQUAL(9),               // operator ==
+  NULL_EQUAL(10),         // operator == using Spark rules for null inputs
+  NOT_EQUAL(11),          // operator !=
+  LESS(12),               // operator <
+  GREATER(13),            // operator >
+  LESS_EQUAL(14),         // operator <=
+  GREATER_EQUAL(15),      // operator >=
+  BITWISE_AND(16),        // operator &
+  BITWISE_OR(17),         // operator |
+  BITWISE_XOR(18),        // operator ^
+  LOGICAL_AND(19),        // operator &&
+  NULL_LOGICAL_AND(20),   // operator && using Spark rules for null inputs
+  LOGICAL_OR(21),         // operator ||
+  NULL_LOGICAL_OR(22);    // operator || using Spark rules for null inputs
+
+  private final byte nativeId;
+
+  BinaryOperator(int nativeId) {
+    this.nativeId = (byte) nativeId;
+    assert this.nativeId == nativeId;
+  }
+
+  /** Get the size in bytes to serialize this operator */
+  int getSerializedSize() {
+    return Byte.BYTES;
+  }
+
+  /** Serialize this operator to the specified buffer */
+  void serialize(ByteBuffer bb) {
+    bb.put(nativeId);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/ColumnReference.java b/java/src/main/java/ai/rapids/cudf/ast/ColumnReference.java
new file mode 100644
index 0000000..4860a08
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/ColumnReference.java
@@ -0,0 +1,51 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import java.nio.ByteBuffer;
+
+/** A reference to a column in an input table. */
+public final class ColumnReference extends AstExpression {
+  private final int columnIndex;
+  private final TableReference tableSource;
+
+  /** Construct a column reference to either the only or leftmost input table */
+  public ColumnReference(int columnIndex) {
+    this(columnIndex, TableReference.LEFT);
+  }
+
+  /** Construct a column reference to the specified column index in the specified table */
+  public ColumnReference(int columnIndex, TableReference tableSource) {
+    this.columnIndex = columnIndex;
+    this.tableSource = tableSource;
+  }
+
+  @Override
+  int getSerializedSize() {
+    // node type + table ref + column index
+    return ExpressionType.COLUMN_REFERENCE.getSerializedSize() +
+        tableSource.getSerializedSize() +
+        Integer.BYTES;
+  }
+
+  @Override
+  void serialize(ByteBuffer bb) {
+    ExpressionType.COLUMN_REFERENCE.serialize(bb);
+    tableSource.serialize(bb);
+    bb.putInt(columnIndex);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/CompiledExpression.java b/java/src/main/java/ai/rapids/cudf/ast/CompiledExpression.java
new file mode 100644
index 0000000..ea5dc00
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/CompiledExpression.java
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import ai.rapids.cudf.ColumnVector;
+import ai.rapids.cudf.MemoryCleaner;
+import ai.rapids.cudf.NativeDepsLoader;
+import ai.rapids.cudf.Table;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+/** This class wraps a native compiled AST and must be closed to avoid native memory leaks. */
+public class CompiledExpression implements AutoCloseable {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  private static final Logger log = LoggerFactory.getLogger(CompiledExpression.class);
+
+  private static class CompiledExpressionCleaner extends MemoryCleaner.Cleaner {
+    private long nativeHandle;
+
+    CompiledExpressionCleaner(long nativeHandle) {
+      this.nativeHandle = nativeHandle;
+    }
+
+    @Override
+    protected synchronized boolean cleanImpl(boolean logErrorIfNotClean) {
+      long origAddress = nativeHandle;
+      boolean neededCleanup = nativeHandle != 0;
+      if (neededCleanup) {
+        try {
+          destroy(nativeHandle);
+        } finally {
+          nativeHandle = 0;
+        }
+        if (logErrorIfNotClean) {
+          log.error("AN AST COMPILED EXPRESSION WAS LEAKED (ID: " +
+              id + " " + Long.toHexString(origAddress));
+        }
+      }
+      return neededCleanup;
+    }
+
+    @Override
+    public boolean isClean() {
+      return nativeHandle == 0;
+    }
+  }
+
+  private final CompiledExpressionCleaner cleaner;
+  private boolean isClosed = false;
+
+  /** Construct a compiled expression from a serialized AST */
+  CompiledExpression(byte[] serializedExpression) {
+    this(compile(serializedExpression));
+  }
+
+  /** Construct a compiled expression from a native compiled AST pointer */
+  CompiledExpression(long nativeHandle) {
+    this.cleaner = new CompiledExpressionCleaner(nativeHandle);
+    MemoryCleaner.register(this, cleaner);
+    cleaner.addRef();
+  }
+
+  /**
+   * Compute a new column by applying this AST expression to the specified table. All
+   * {@link ColumnReference} instances within the expression will use the sole input table,
+   * even if they try to specify a non-existent table, e.g.: {@link TableReference#RIGHT}.
+   * @param table input table for this expression
+   * @return new column computed from this expression applied to the input table
+   */
+  public ColumnVector computeColumn(Table table) {
+    return new ColumnVector(computeColumn(cleaner.nativeHandle, table.getNativeView()));
+  }
+
+  @Override
+  public synchronized void close() {
+    cleaner.delRef();
+    if (isClosed) {
+      cleaner.logRefCountDebug("double free " + this);
+      throw new IllegalStateException("Close called too many times " + this);
+    }
+    cleaner.clean(false);
+    isClosed = true;
+  }
+
+  /** Returns the native address of a compiled expression. Intended for internal cudf use only. */
+  public long getNativeHandle() {
+    return cleaner.nativeHandle;
+  }
+
+  private static native long compile(byte[] serializedExpression);
+  private static native long computeColumn(long astHandle, long tableHandle);
+  private static native void destroy(long handle);
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/Literal.java b/java/src/main/java/ai/rapids/cudf/ast/Literal.java
new file mode 100644
index 0000000..427dd28
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/Literal.java
@@ -0,0 +1,276 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import ai.rapids.cudf.DType;
+
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+
+/** A literal value in an AST expression. */
+public final class Literal extends AstExpression {
+  private final DType type;
+  private final byte[] serializedValue;
+
+  /** Construct a null literal of the specified type. */
+  public static Literal ofNull(DType type) {
+    return new Literal(type, null);
+  }
+
+  /** Construct a boolean literal with the specified value. */
+  public static Literal ofBoolean(boolean value) {
+    return new Literal(DType.BOOL8, new byte[] { value ? (byte) 1 : (byte) 0 });
+  }
+
+  /** Construct a boolean literal with the specified value or null. */
+  public static Literal ofBoolean(Boolean value) {
+    if (value == null) {
+      return ofNull(DType.BOOL8);
+    }
+    return ofBoolean(value.booleanValue());
+  }
+
+  /** Construct a byte literal with the specified value. */
+  public static Literal ofByte(byte value) {
+    return new Literal(DType.INT8, new byte[] { value });
+  }
+
+  /** Construct a byte literal with the specified value or null. */
+  public static Literal ofByte(Byte value) {
+    if (value == null) {
+      return ofNull(DType.INT8);
+    }
+    return ofByte(value.byteValue());
+  }
+
+  /** Construct a short literal with the specified value. */
+  public static Literal ofShort(short value) {
+    byte[] serializedValue = new byte[Short.BYTES];
+    ByteBuffer.wrap(serializedValue).order(ByteOrder.nativeOrder()).putShort(value);
+    return new Literal(DType.INT16, serializedValue);
+  }
+
+  /** Construct a short literal with the specified value or null. */
+  public static Literal ofShort(Short value) {
+    if (value == null) {
+      return ofNull(DType.INT16);
+    }
+    return ofShort(value.shortValue());
+  }
+
+  /** Construct an integer literal with the specified value. */
+  public static Literal ofInt(int value) {
+    return ofIntBasedType(DType.INT32, value);
+  }
+
+  /** Construct an integer literal with the specified value or null. */
+  public static Literal ofInt(Integer value) {
+    if (value == null) {
+      return ofNull(DType.INT32);
+    }
+    return ofInt(value.intValue());
+  }
+
+  /** Construct a long literal with the specified value. */
+  public static Literal ofLong(long value) {
+    return ofLongBasedType(DType.INT64, value);
+  }
+
+  /** Construct a long literal with the specified value or null. */
+  public static Literal ofLong(Long value) {
+    if (value == null) {
+      return ofNull(DType.INT64);
+    }
+    return ofLong(value.longValue());
+  }
+
+  /** Construct a float literal with the specified value. */
+  public static Literal ofFloat(float value) {
+    byte[] serializedValue = new byte[Float.BYTES];
+    ByteBuffer.wrap(serializedValue).order(ByteOrder.nativeOrder()).putFloat(value);
+    return new Literal(DType.FLOAT32, serializedValue);
+  }
+
+  /** Construct a float literal with the specified value or null. */
+  public static Literal ofFloat(Float value) {
+    if (value == null) {
+      return ofNull(DType.FLOAT32);
+    }
+    return ofFloat(value.floatValue());
+  }
+
+  /** Construct a double literal with the specified value. */
+  public static Literal ofDouble(double value) {
+    byte[] serializedValue = new byte[Double.BYTES];
+    ByteBuffer.wrap(serializedValue).order(ByteOrder.nativeOrder()).putDouble(value);
+    return new Literal(DType.FLOAT64, serializedValue);
+  }
+
+  /** Construct a double literal with the specified value or null. */
+  public static Literal ofDouble(Double value) {
+    if (value == null) {
+      return ofNull(DType.FLOAT64);
+    }
+    return ofDouble(value.doubleValue());
+  }
+
+  /** Construct a timestamp days literal with the specified value. */
+  public static Literal ofTimestampDaysFromInt(int value) {
+    return ofIntBasedType(DType.TIMESTAMP_DAYS, value);
+  }
+
+  /** Construct a timestamp days literal with the specified value or null. */
+  public static Literal ofTimestampDaysFromInt(Integer value) {
+    if (value == null) {
+      return ofNull(DType.TIMESTAMP_DAYS);
+    }
+    return ofTimestampDaysFromInt(value.intValue());
+  }
+
+  /** Construct a long-based timestamp literal with the specified value. */
+  public static Literal ofTimestampFromLong(DType type, long value) {
+    if (!type.isTimestampType()) {
+      throw new IllegalArgumentException("type is not a timestamp: " + type);
+    }
+    if (type.equals(DType.TIMESTAMP_DAYS)) {
+      int intValue = (int)value;
+      if (value != intValue) {
+        throw new IllegalArgumentException("value too large for type " + type + ": " + value);
+      }
+      return ofTimestampDaysFromInt(intValue);
+    }
+    return ofLongBasedType(type, value);
+  }
+
+  /** Construct a long-based timestamp literal with the specified value or null. */
+  public static Literal ofTimestampFromLong(DType type, Long value) {
+    if (value == null) {
+      return ofNull(type);
+    }
+    return ofTimestampFromLong(type, value.longValue());
+  }
+
+  /** Construct a duration days literal with the specified value. */
+  public static Literal ofDurationDaysFromInt(int value) {
+    return ofIntBasedType(DType.DURATION_DAYS, value);
+  }
+
+  /** Construct a duration days literal with the specified value or null. */
+  public static Literal ofDurationDaysFromInt(Integer value) {
+    if (value == null) {
+      return ofNull(DType.DURATION_DAYS);
+    }
+    return ofDurationDaysFromInt(value.intValue());
+  }
+
+  /** Construct a long-based duration literal with the specified value. */
+  public static Literal ofDurationFromLong(DType type, long value) {
+    if (!type.isDurationType()) {
+      throw new IllegalArgumentException("type is not a timestamp: " + type);
+    }
+    if (type.equals(DType.DURATION_DAYS)) {
+      int intValue = (int)value;
+      if (value != intValue) {
+        throw new IllegalArgumentException("value too large for type " + type + ": " + value);
+      }
+      return ofDurationDaysFromInt(intValue);
+    }
+    return ofLongBasedType(type, value);
+  }
+
+  /** Construct a long-based duration literal with the specified value or null. */
+  public static Literal ofDurationFromLong(DType type, Long value) {
+    if (value == null) {
+      return ofNull(type);
+    }
+    return ofDurationFromLong(type, value.longValue());
+  }
+
+  /** Construct a string literal with the specified value or null. */
+  public static Literal ofString(String value) {
+    if (value == null) {
+      return ofNull(DType.STRING);
+    }
+    byte[] stringBytes = value.getBytes();
+    byte[] serializedValue = new byte[stringBytes.length + Integer.BYTES];
+    ByteBuffer.wrap(serializedValue).order(ByteOrder.nativeOrder()).putInt(stringBytes.length);
+    System.arraycopy(stringBytes, 0, serializedValue, Integer.BYTES, stringBytes.length);
+    return new Literal(DType.STRING, serializedValue);
+  }
+
+  Literal(DType type, byte[] serializedValue) {
+    this.type = type;
+    this.serializedValue = serializedValue;
+  }
+
+  @Override
+  int getSerializedSize() {
+    ExpressionType nodeType = serializedValue != null
+        ? ExpressionType.VALID_LITERAL : ExpressionType.NULL_LITERAL;
+    int size = nodeType.getSerializedSize() + getDataTypeSerializedSize();
+    if (serializedValue != null) {
+      size += serializedValue.length;
+    }
+    return size;
+  }
+
+  @Override
+  void serialize(ByteBuffer bb) {
+    ExpressionType nodeType = serializedValue != null
+        ? ExpressionType.VALID_LITERAL : ExpressionType.NULL_LITERAL;
+    nodeType.serialize(bb);
+    serializeDataType(bb);
+    if (serializedValue != null) {
+      bb.put(serializedValue);
+    }
+  }
+
+  private int getDataTypeSerializedSize() {
+    int nativeTypeId = type.getTypeId().getNativeId();
+    assert nativeTypeId == (byte) nativeTypeId : "Type ID does not fit in a byte";
+    if (type.isDecimalType()) {
+      assert type.getScale() == (byte) type.getScale() : "Decimal scale does not fit in a byte";
+      return 2;
+    }
+    return 1;
+  }
+
+  private void serializeDataType(ByteBuffer bb) {
+    byte nativeTypeId = (byte) type.getTypeId().getNativeId();
+    assert nativeTypeId == type.getTypeId().getNativeId() : "DType ID does not fit in a byte";
+    bb.put(nativeTypeId);
+    if (type.isDecimalType()) {
+      byte scale = (byte) type.getScale();
+      assert scale == (byte) type.getScale() : "Decimal scale does not fit in a byte";
+      bb.put(scale);
+    }
+  }
+
+  private static Literal ofIntBasedType(DType type, int value) {
+    assert type.getSizeInBytes() == Integer.BYTES;
+    byte[] serializedValue = new byte[Integer.BYTES];
+    ByteBuffer.wrap(serializedValue).order(ByteOrder.nativeOrder()).putInt(value);
+    return new Literal(type, serializedValue);
+  }
+
+  private static Literal ofLongBasedType(DType type, long value) {
+    assert type.getSizeInBytes() == Long.BYTES;
+    byte[] serializedValue = new byte[Long.BYTES];
+    ByteBuffer.wrap(serializedValue).order(ByteOrder.nativeOrder()).putLong(value);
+    return new Literal(type, serializedValue);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/TableReference.java b/java/src/main/java/ai/rapids/cudf/ast/TableReference.java
new file mode 100644
index 0000000..1225577
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/TableReference.java
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+
+import java.nio.ByteBuffer;
+
+/**
+ * Enumeration of tables that can be referenced in an AST.
+ * NOTE: This must be kept in sync with `jni_to_table_reference` code in CompiledExpression.cpp!
+ */
+public enum TableReference {
+  LEFT(0),
+  RIGHT(1);
+  // OUTPUT is an AST implementation detail and should not appear in user-built expressions.
+
+  private final byte nativeId;
+
+  TableReference(int nativeId) {
+    this.nativeId = (byte) nativeId;
+    assert this.nativeId == nativeId;
+  }
+
+  /** Get the size in bytes to serialize this table reference */
+  int getSerializedSize() {
+    return Byte.BYTES;
+  }
+
+  /** Serialize this table reference to the specified buffer */
+  void serialize(ByteBuffer bb) {
+    bb.put(nativeId);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/UnaryOperation.java b/java/src/main/java/ai/rapids/cudf/ast/UnaryOperation.java
new file mode 100644
index 0000000..03c4c45
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/UnaryOperation.java
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import java.nio.ByteBuffer;
+
+/** A unary operation consisting of an operator and an operand. */
+public final class UnaryOperation extends AstExpression {
+  private final UnaryOperator op;
+  private final AstExpression input;
+
+  public UnaryOperation(UnaryOperator op, AstExpression input) {
+    this.op = op;
+    this.input = input;
+  }
+
+  @Override
+  int getSerializedSize() {
+    return ExpressionType.UNARY_EXPRESSION.getSerializedSize() +
+        op.getSerializedSize() +
+        input.getSerializedSize();
+  }
+
+  @Override
+  void serialize(ByteBuffer bb) {
+    ExpressionType.UNARY_EXPRESSION.serialize(bb);
+    op.serialize(bb);
+    input.serialize(bb);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/ast/UnaryOperator.java b/java/src/main/java/ai/rapids/cudf/ast/UnaryOperator.java
new file mode 100644
index 0000000..530a2ee
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/ast/UnaryOperator.java
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import java.nio.ByteBuffer;
+
+/**
+ * Enumeration of AST operators that can appear in a unary operation.
+ * NOTE: This must be kept in sync with `jni_to_unary_operator` in CompiledExpression.cpp!
+ */
+public enum UnaryOperator {
+  IDENTITY(0),          // Identity function
+  IS_NULL(1),           // Check if operand is null
+  SIN(2),               // Trigonometric sine
+  COS(3),               // Trigonometric cosine
+  TAN(4),               // Trigonometric tangent
+  ARCSIN(5),            // Trigonometric sine inverse
+  ARCCOS(6),            // Trigonometric cosine inverse
+  ARCTAN(7),            // Trigonometric tangent inverse
+  SINH(8),              // Hyperbolic sine
+  COSH(9),              // Hyperbolic cosine
+  TANH(10),              // Hyperbolic tangent
+  ARCSINH(11),          // Hyperbolic sine inverse
+  ARCCOSH(12),          // Hyperbolic cosine inverse
+  ARCTANH(13),          // Hyperbolic tangent inverse
+  EXP(14),              // Exponential (base e, Euler number)
+  LOG(15),              // Natural Logarithm (base e)
+  SQRT(16),             // Square-root (x^0.5)
+  CBRT(17),             // Cube-root (x^(1.0/3))
+  CEIL(18),             // Smallest integer value not less than arg
+  FLOOR(19),            // largest integer value not greater than arg
+  ABS(20),              // Absolute value
+  RINT(21),             // Rounds the floating-point argument arg to an integer value
+  BIT_INVERT(22),       // Bitwise Not (~)
+  NOT(23),              // Logical Not (!)
+  CAST_TO_INT64(24),    // Cast value to int64_t
+  CAST_TO_UINT64(25),   // Cast value to uint64_t
+  CAST_TO_FLOAT64(26);  // Cast value to double
+
+  private final byte nativeId;
+
+  UnaryOperator(int nativeId) {
+    this.nativeId = (byte) nativeId;
+    assert this.nativeId == nativeId;
+  }
+  /** Get the size in bytes to serialize this operator */
+  int getSerializedSize() {
+    return Byte.BYTES;
+  }
+
+  /** Serialize this operator to the specified buffer */
+  void serialize(ByteBuffer bb) {
+    bb.put(nativeId);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/nvcomp/BatchedLZ4Compressor.java b/java/src/main/java/ai/rapids/cudf/nvcomp/BatchedLZ4Compressor.java
new file mode 100644
index 0000000..1aa7e5e
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/nvcomp/BatchedLZ4Compressor.java
@@ -0,0 +1,320 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+import ai.rapids.cudf.BaseDeviceMemoryBuffer;
+import ai.rapids.cudf.CloseableArray;
+import ai.rapids.cudf.Cuda;
+import ai.rapids.cudf.DefaultHostMemoryAllocator;
+import ai.rapids.cudf.DeviceMemoryBuffer;
+import ai.rapids.cudf.HostMemoryAllocator;
+import ai.rapids.cudf.HostMemoryBuffer;
+import ai.rapids.cudf.MemoryBuffer;
+import ai.rapids.cudf.NvtxColor;
+import ai.rapids.cudf.NvtxRange;
+
+/** Multi-buffer LZ4 compressor */
+public class BatchedLZ4Compressor {
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  static final long MAX_CHUNK_SIZE = 16777216;  // in bytes
+  // each chunk has a 64-bit integer value as metadata containing the compressed size
+  static final long METADATA_BYTES_PER_CHUNK = 8;
+
+  private final long chunkSize;
+  private final long targetIntermediateBufferSize;
+  private final long maxOutputChunkSize;
+
+  /**
+   * Construct a batched LZ4 compressor instance
+   * @param chunkSize maximum amount of uncompressed data to compress as a single chunk. Inputs
+   *                  larger than this will be compressed in multiple chunks.
+   * @param targetIntermediateBufferSize desired maximum size of intermediate device buffers
+   *                                     used during compression.
+   */
+  public BatchedLZ4Compressor(long chunkSize, long targetIntermediateBufferSize) {
+    validateChunkSize(chunkSize);
+    this.chunkSize = chunkSize;
+    this.maxOutputChunkSize = NvcompJni.batchedLZ4CompressGetMaxOutputChunkSize(chunkSize);
+    assert maxOutputChunkSize < Integer.MAX_VALUE;
+    this.targetIntermediateBufferSize = Math.max(targetIntermediateBufferSize, maxOutputChunkSize);
+  }
+
+  /**
+   * Compress a batch of buffers with LZ4. The input buffers will be closed.
+   * @param origInputs buffers to compress
+   * @param stream CUDA stream to use
+   * @return compressed buffers corresponding to the input buffers
+   */
+  public DeviceMemoryBuffer[] compress(BaseDeviceMemoryBuffer[] origInputs, Cuda.Stream stream) {
+    try (CloseableArray<BaseDeviceMemoryBuffer> inputs = CloseableArray.wrap(origInputs)) {
+      if (chunkSize <= 0) {
+        throw new IllegalArgumentException("Illegal chunk size: " + chunkSize);
+      }
+      final int numInputs = inputs.size();
+      if (numInputs == 0) {
+        return new DeviceMemoryBuffer[0];
+      }
+
+      // Each buffer is broken up into chunkSize chunks for compression.  Calculate how many
+      // chunks are needed for each input buffer.
+      int[] chunksPerInput = new int[numInputs];
+      int numChunks = 0;
+      for (int i = 0; i < numInputs; i++) {
+        BaseDeviceMemoryBuffer buffer = inputs.get(i);
+        int numBufferChunks = getNumChunksInBuffer(buffer);
+        chunksPerInput[i] = numBufferChunks;
+        numChunks += numBufferChunks;
+      }
+
+      // Allocate buffers for each chunk and generate parallel lists of chunk source addresses,
+      // chunk destination addresses, and sizes.
+      try (CloseableArray<DeviceMemoryBuffer> compressedBuffers =
+               allocCompressedBuffers(numChunks, stream);
+           DeviceMemoryBuffer compressedChunkSizes =
+               DeviceMemoryBuffer.allocate(numChunks * 8L, stream)) {
+        long[] inputChunkAddrs = new long[numChunks];
+        long[] inputChunkSizes = new long[numChunks];
+        long[] outputChunkAddrs = new long[numChunks];
+        buildAddrsAndSizes(inputs, inputChunkAddrs, inputChunkSizes,
+            compressedBuffers, outputChunkAddrs);
+
+        long[] outputChunkSizes;
+        final long tempBufferSize = NvcompJni.batchedLZ4CompressGetTempSize(numChunks, chunkSize);
+        try (DeviceMemoryBuffer addrsAndSizes =
+                 putAddrsAndSizesOnDevice(inputChunkAddrs, inputChunkSizes, outputChunkAddrs, stream);
+             DeviceMemoryBuffer tempBuffer = DeviceMemoryBuffer.allocate(tempBufferSize, stream)) {
+          final long devOutputAddrsPtr = addrsAndSizes.getAddress() + numChunks * 8L;
+          final long devInputSizesPtr = devOutputAddrsPtr + numChunks * 8L;
+          NvcompJni.batchedLZ4CompressAsync(
+              addrsAndSizes.getAddress(),
+              devInputSizesPtr,
+              chunkSize,
+              numChunks,
+              tempBuffer.getAddress(),
+              tempBufferSize,
+              devOutputAddrsPtr,
+              compressedChunkSizes.getAddress(),
+              stream.getStream());
+        }
+
+        // Synchronously copy the resulting compressed sizes per chunk.
+        outputChunkSizes = getOutputChunkSizes(compressedChunkSizes, stream);
+
+        // inputs are no longer needed at this point, so free them early
+        inputs.close();
+
+        // Combine compressed chunks into output buffers corresponding to each original input
+        return stitchOutput(chunksPerInput, compressedChunkSizes, outputChunkAddrs,
+            outputChunkSizes, stream);
+      }
+    }
+  }
+
+  static void validateChunkSize(long chunkSize) {
+    if (chunkSize <= 0  || chunkSize > MAX_CHUNK_SIZE) {
+      throw new IllegalArgumentException("Invalid chunk size: " + chunkSize + " Max chunk size is: "
+          + MAX_CHUNK_SIZE + " bytes");
+    }
+  }
+
+  private static long ceilingDivide(long x, long y) {
+    return (x + y - 1) / y;
+  }
+
+  private int getNumChunksInBuffer(MemoryBuffer buffer) {
+    return (int) ceilingDivide(buffer.getLength(), chunkSize);
+  }
+
+  private CloseableArray<DeviceMemoryBuffer> allocCompressedBuffers(long numChunks,
+                                                                    Cuda.Stream stream) {
+    final long chunksPerBuffer = targetIntermediateBufferSize / maxOutputChunkSize;
+    final long numBuffers = ceilingDivide(numChunks, chunksPerBuffer);
+    if (numBuffers > Integer.MAX_VALUE) {
+      throw new IllegalStateException("Too many chunks");
+    }
+    try (NvtxRange range = new NvtxRange("allocCompressedBuffers", NvtxColor.YELLOW)) {
+      CloseableArray<DeviceMemoryBuffer> buffers = CloseableArray.wrap(
+          new DeviceMemoryBuffer[(int) numBuffers]);
+      try {
+        // allocate all of the max-chunks intermediate compressed buffers
+        for (int i = 0; i < buffers.size() - 1; ++i) {
+          buffers.set(i, DeviceMemoryBuffer.allocate(chunksPerBuffer * maxOutputChunkSize, stream));
+        }
+        // allocate the tail intermediate compressed buffer that may be smaller than the others
+        buffers.set(buffers.size() - 1, DeviceMemoryBuffer.allocate(
+            (numChunks - chunksPerBuffer * (buffers.size() - 1)) * maxOutputChunkSize, stream));
+        return buffers;
+      } catch (Exception e) {
+        buffers.close(e);
+        throw e;
+      }
+    }
+  }
+
+  // Fill in the inputChunkAddrs, inputChunkSizes, and outputChunkAddrs arrays to point
+  // into the chunks in the input and output buffers.
+  private void buildAddrsAndSizes(CloseableArray<BaseDeviceMemoryBuffer> inputs,
+                                  long[] inputChunkAddrs,
+                                  long[] inputChunkSizes,
+                                  CloseableArray<DeviceMemoryBuffer> compressedBuffers,
+                                  long[] outputChunkAddrs) {
+    // setup the input addresses and sizes
+    int chunkIdx = 0;
+    for (BaseDeviceMemoryBuffer input : inputs.getArray()) {
+      final int numChunksInBuffer = getNumChunksInBuffer(input);
+      for (int i = 0; i < numChunksInBuffer; i++) {
+        inputChunkAddrs[chunkIdx] = input.getAddress() + i * chunkSize;
+        inputChunkSizes[chunkIdx] = (i != numChunksInBuffer - 1) ? chunkSize
+            : (input.getLength() - (long) i * chunkSize);
+        ++chunkIdx;
+      }
+    }
+    assert chunkIdx == inputChunkAddrs.length;
+    assert chunkIdx == inputChunkSizes.length;
+
+    // setup output addresses
+    chunkIdx = 0;
+    for (DeviceMemoryBuffer buffer : compressedBuffers.getArray()) {
+      assert buffer.getLength() % maxOutputChunkSize == 0;
+      long numChunksInBuffer = buffer.getLength() / maxOutputChunkSize;
+      long baseAddr = buffer.getAddress();
+      for (int i = 0; i < numChunksInBuffer; i++) {
+        outputChunkAddrs[chunkIdx++] = baseAddr + i * maxOutputChunkSize;
+      }
+    }
+    assert chunkIdx == outputChunkAddrs.length;
+  }
+
+  // Write input addresses, output addresses and sizes contiguously into a DeviceMemoryBuffer.
+  private DeviceMemoryBuffer putAddrsAndSizesOnDevice(long[] inputAddrs,
+                                                      long[] inputSizes,
+                                                      long[] outputAddrs,
+                                                      Cuda.Stream stream) {
+    final long totalSize = inputAddrs.length * 8L * 3; // space for input, output, and size arrays
+    final long outputAddrsOffset = inputAddrs.length * 8L;
+    final long sizesOffset = outputAddrsOffset + inputAddrs.length * 8L;
+    try (NvtxRange range = new NvtxRange("putAddrsAndSizesOnDevice", NvtxColor.YELLOW)) {
+      try (HostMemoryBuffer hostbuf = hostMemoryAllocator.allocate(totalSize);
+           DeviceMemoryBuffer result = DeviceMemoryBuffer.allocate(totalSize)) {
+        hostbuf.setLongs(0, inputAddrs, 0, inputAddrs.length);
+        hostbuf.setLongs(outputAddrsOffset, outputAddrs, 0, outputAddrs.length);
+        for (int i = 0; i < inputSizes.length; i++) {
+          hostbuf.setLong(sizesOffset + i * 8L, inputSizes[i]);
+        }
+        result.copyFromHostBuffer(hostbuf, stream);
+        result.incRefCount();
+        return result;
+      }
+    }
+  }
+
+  // Synchronously copy the resulting compressed sizes from device memory to host memory.
+  private long[] getOutputChunkSizes(BaseDeviceMemoryBuffer devChunkSizes, Cuda.Stream stream) {
+    try (NvtxRange range = new NvtxRange("getOutputChunkSizes", NvtxColor.YELLOW)) {
+      try (HostMemoryBuffer hostbuf = hostMemoryAllocator.allocate(devChunkSizes.getLength())) {
+        hostbuf.copyFromDeviceBuffer(devChunkSizes, stream);
+        int numChunks = (int) (devChunkSizes.getLength() / 8);
+        long[] result = new long[numChunks];
+        for (int i = 0; i < numChunks; i++) {
+          long size = hostbuf.getLong(i * 8L);
+          assert size < Integer.MAX_VALUE : "output size is too big";
+          result[i] = size;
+        }
+        return result;
+      }
+    }
+  }
+
+  // Stitch together the individual chunks into the result buffers.
+  // Each result buffer has metadata at the beginning, followed by compressed chunks.
+  // This is done by building up parallel lists of source addr, dest addr and size and
+  // then calling multiBufferCopyAsync()
+  private DeviceMemoryBuffer[] stitchOutput(int[] chunksPerInput,
+                                            DeviceMemoryBuffer compressedChunkSizes,
+                                            long[] outputChunkAddrs,
+                                            long[] outputChunkSizes,
+                                            Cuda.Stream stream) {
+    try (NvtxRange range = new NvtxRange("stitchOutput", NvtxColor.YELLOW)) {
+      final int numOutputs = chunksPerInput.length;
+      final long chunkSizesAddr = compressedChunkSizes.getAddress();
+      long[] outputBufferSizes = calcOutputBufferSizes(chunksPerInput, outputChunkSizes);
+      try (CloseableArray<DeviceMemoryBuffer> outputs =
+               CloseableArray.wrap(new DeviceMemoryBuffer[numOutputs])) {
+        // Each chunk needs to be copied, and each output needs a copy of the
+        // compressed chunk size vector representing the metadata.
+        final int totalBuffersToCopy = numOutputs + outputChunkAddrs.length;
+        long[] destAddrs = new long[totalBuffersToCopy];
+        long[] srcAddrs = new long[totalBuffersToCopy];
+        long[] sizes = new long[totalBuffersToCopy];
+        int copyBufferIdx = 0;
+        int chunkIdx = 0;
+        for (int outputIdx = 0; outputIdx < numOutputs; outputIdx++) {
+          DeviceMemoryBuffer outputBuffer = DeviceMemoryBuffer.allocate(outputBufferSizes[outputIdx]);
+          final long outputBufferAddr = outputBuffer.getAddress();
+          outputs.set(outputIdx, outputBuffer);
+          final long numChunks = chunksPerInput[outputIdx];
+          final long metadataSize = numChunks * METADATA_BYTES_PER_CHUNK;
+
+          // setup a copy of the metadata at the front of the output buffer
+          srcAddrs[copyBufferIdx] = chunkSizesAddr + chunkIdx * 8;
+          destAddrs[copyBufferIdx] = outputBufferAddr;
+          sizes[copyBufferIdx] = metadataSize;
+          ++copyBufferIdx;
+
+          // setup copies of the compressed chunks for this output buffer
+          long nextChunkAddr = outputBufferAddr + metadataSize;
+          for (int i = 0; i < numChunks; ++i) {
+            srcAddrs[copyBufferIdx] = outputChunkAddrs[chunkIdx];
+            destAddrs[copyBufferIdx] = nextChunkAddr;
+            final long chunkSize = outputChunkSizes[chunkIdx];
+            sizes[copyBufferIdx] = chunkSize;
+            copyBufferIdx++;
+            chunkIdx++;
+            nextChunkAddr += chunkSize;
+          }
+        }
+        assert copyBufferIdx == totalBuffersToCopy;
+        assert chunkIdx == outputChunkAddrs.length;
+        assert chunkIdx == outputChunkSizes.length;
+
+        Cuda.multiBufferCopyAsync(destAddrs, srcAddrs, sizes, stream);
+        return outputs.release();
+      }
+    }
+  }
+
+  // Calculate the list of sizes for each output buffer (metadata plus size of compressed chunks)
+  private long[] calcOutputBufferSizes(int[] chunksPerInput,
+                                       long[] outputChunkSizes) {
+    long[] sizes = new long[chunksPerInput.length];
+    int chunkIdx = 0;
+    for (int i = 0; i < sizes.length; i++) {
+      final int chunksInBuffer = chunksPerInput[i];
+      final int chunkEndIdx = chunkIdx + chunksInBuffer;
+      // metadata stored in front of compressed data
+      long bufferSize = METADATA_BYTES_PER_CHUNK * chunksInBuffer;
+      // add in the compressed chunk sizes to get the total size
+      while (chunkIdx < chunkEndIdx) {
+        bufferSize += outputChunkSizes[chunkIdx++];
+      }
+      sizes[i] = bufferSize;
+    }
+    assert chunkIdx == outputChunkSizes.length;
+    return sizes;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/nvcomp/BatchedLZ4Decompressor.java b/java/src/main/java/ai/rapids/cudf/nvcomp/BatchedLZ4Decompressor.java
new file mode 100644
index 0000000..40ad4d5
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/nvcomp/BatchedLZ4Decompressor.java
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+import ai.rapids.cudf.CloseableArray;
+import ai.rapids.cudf.Cuda;
+import ai.rapids.cudf.BaseDeviceMemoryBuffer;
+import ai.rapids.cudf.DeviceMemoryBuffer;
+import ai.rapids.cudf.HostMemoryBuffer;
+import ai.rapids.cudf.NvtxColor;
+import ai.rapids.cudf.NvtxRange;
+
+import java.util.Arrays;
+
+/** LZ4 decompressor that operates on multiple input buffers in a batch */
+public class BatchedLZ4Decompressor {
+  /**
+   * Asynchronously decompress a batch of buffers
+   * @param chunkSize maximum uncompressed block size, must match value used during compression
+   * @param origInputs buffers to decompress, will be closed by this operation
+   * @param outputs output buffers that will contain the compressed results, each must be sized
+   *                to the exact decompressed size of the corresponding input
+   * @param stream CUDA stream to use
+   */
+  public static void decompressAsync(long chunkSize,
+                                     BaseDeviceMemoryBuffer[] origInputs,
+                                     BaseDeviceMemoryBuffer[] outputs,
+                                     Cuda.Stream stream) {
+    try (CloseableArray<BaseDeviceMemoryBuffer> inputs =
+             CloseableArray.wrap(Arrays.copyOf(origInputs, origInputs.length))) {
+      BatchedLZ4Compressor.validateChunkSize(chunkSize);
+      if (origInputs.length != outputs.length) {
+        throw new IllegalArgumentException("number of inputs must match number of outputs");
+      }
+      final int numInputs = inputs.size();
+      if (numInputs == 0) {
+        return;
+      }
+
+      int[] chunksPerInput = new int[numInputs];
+      long totalChunks = 0;
+      for (int i = 0; i < numInputs; i++) {
+        // use output size to determine number of chunks in the input, as the output buffer
+        // must be exactly sized to the uncompressed data
+        BaseDeviceMemoryBuffer buffer = outputs[i];
+        int numBufferChunks = getNumChunksInBuffer(chunkSize, buffer);
+        chunksPerInput[i] = numBufferChunks;
+        totalChunks += numBufferChunks;
+      }
+
+      final long tempBufferSize = NvcompJni.batchedLZ4DecompressGetTempSize(totalChunks, chunkSize);
+      try (DeviceMemoryBuffer devAddrsSizes =
+               buildAddrsSizesBuffer(chunkSize, totalChunks, inputs.getArray(), chunksPerInput,
+                   outputs, stream);
+           DeviceMemoryBuffer devTemp = DeviceMemoryBuffer.allocate(tempBufferSize)) {
+        // buffer containing addresses and sizes contains four vectors of longs in this order:
+        // - compressed chunk input addresses
+        // - chunk output buffer addresses
+        // - compressed chunk sizes
+        // - uncompressed chunk sizes
+        final long inputAddrsPtr = devAddrsSizes.getAddress();
+        final long outputAddrsPtr = inputAddrsPtr + totalChunks * 8;
+        final long inputSizesPtr = outputAddrsPtr + totalChunks * 8;
+        final long outputSizesPtr = inputSizesPtr + totalChunks * 8;
+        NvcompJni.batchedLZ4DecompressAsync(
+            inputAddrsPtr,
+            inputSizesPtr,
+            outputSizesPtr,
+            totalChunks,
+            devTemp.getAddress(),
+            devTemp.getLength(),
+            outputAddrsPtr,
+            stream.getStream());
+      }
+    }
+  }
+
+  private static int getNumChunksInBuffer(long chunkSize, BaseDeviceMemoryBuffer buffer) {
+    return (int) ((buffer.getLength() + chunkSize - 1) / chunkSize);
+  }
+
+  /**
+   * Build a device memory buffer containing four vectors of longs in the following order:
+   * <ul>
+   *   <li>compressed chunk input addresses</li>
+   *   <li>uncompressed chunk output addresses</li>
+   *   <li>compressed chunk sizes</li>
+   *   <li>uncompressed chunk sizes</li>
+   * </ul>
+   * Each vector contains as many longs as the number of chunks being decompressed
+   * @param chunkSize maximum uncompressed size of a chunk
+   * @param totalChunks total number of chunks to be decompressed
+   * @param inputs device buffers containing the compressed data
+   * @param chunksPerInput number of compressed chunks per input buffer
+   * @param outputs device buffers that will hold the uncompressed output
+   * @param stream CUDA stream to use
+   * @return device buffer containing address and size vectors
+   */
+  private static DeviceMemoryBuffer buildAddrsSizesBuffer(long chunkSize,
+                                                          long totalChunks,
+                                                          BaseDeviceMemoryBuffer[] inputs,
+                                                          int[] chunksPerInput,
+                                                          BaseDeviceMemoryBuffer[] outputs,
+                                                          Cuda.Stream stream) {
+    final long totalBufferSize = totalChunks * 8L * 4L;
+    try (NvtxRange range = new NvtxRange("buildAddrSizesBuffer", NvtxColor.YELLOW)) {
+      try (HostMemoryBuffer metadata = fetchMetadata(totalChunks, inputs, chunksPerInput, stream);
+           HostMemoryBuffer hostAddrsSizes = HostMemoryBuffer.allocate(totalBufferSize);
+           DeviceMemoryBuffer devAddrsSizes = DeviceMemoryBuffer.allocate(totalBufferSize)) {
+        // Build four long vectors in the AddrsSizes buffer:
+        // - compressed input address (one per chunk)
+        // - uncompressed output address (one per chunk)
+        // - compressed input size (one per chunk)
+        // - uncompressed input size (one per chunk)
+        final long srcAddrsOffset = 0;
+        final long destAddrsOffset = srcAddrsOffset + totalChunks * 8L;
+        final long srcSizesOffset = destAddrsOffset + totalChunks * 8L;
+        final long destSizesOffset = srcSizesOffset + totalChunks * 8L;
+        long chunkIdx = 0;
+        for (int inputIdx = 0; inputIdx < inputs.length; inputIdx++) {
+          final BaseDeviceMemoryBuffer input = inputs[inputIdx];
+          final BaseDeviceMemoryBuffer output = outputs[inputIdx];
+          final int numChunksInInput = chunksPerInput[inputIdx];
+          long srcAddr = input.getAddress() +
+              BatchedLZ4Compressor.METADATA_BYTES_PER_CHUNK * numChunksInInput;
+          long destAddr = output.getAddress();
+          final long chunkIdxEnd = chunkIdx + numChunksInInput;
+          while (chunkIdx < chunkIdxEnd) {
+            final long srcChunkSize = metadata.getLong(chunkIdx * 8);
+            final long destChunkSize = (chunkIdx < chunkIdxEnd - 1) ? chunkSize
+                : output.getAddress() + output.getLength() - destAddr;
+            hostAddrsSizes.setLong(srcAddrsOffset + chunkIdx * 8, srcAddr);
+            hostAddrsSizes.setLong(destAddrsOffset + chunkIdx * 8, destAddr);
+            hostAddrsSizes.setLong(srcSizesOffset + chunkIdx * 8, srcChunkSize);
+            hostAddrsSizes.setLong(destSizesOffset + chunkIdx * 8, destChunkSize);
+            srcAddr += srcChunkSize;
+            destAddr += destChunkSize;
+            ++chunkIdx;
+          }
+        }
+        devAddrsSizes.copyFromHostBuffer(hostAddrsSizes, stream);
+        devAddrsSizes.incRefCount();
+        return devAddrsSizes;
+      }
+    }
+  }
+
+  /**
+   * Fetch the metadata at the front of each input in a single, contiguous host buffer.
+   * @param totalChunks total number of compressed chunks
+   * @param inputs buffers containing the compressed data
+   * @param chunksPerInput number of compressed chunks for the corresponding input
+   * @param stream CUDA stream to use
+   * @return host buffer containing all of the metadata
+   */
+  private static HostMemoryBuffer fetchMetadata(long totalChunks,
+                                                BaseDeviceMemoryBuffer[] inputs,
+                                                int[] chunksPerInput,
+                                                Cuda.Stream stream) {
+    try (NvtxRange range = new NvtxRange("fetchMetadata", NvtxColor.PURPLE)) {
+      // one long per chunk containing the compressed size
+      final long totalMetadataSize = totalChunks * BatchedLZ4Compressor.METADATA_BYTES_PER_CHUNK;
+      // Build corresponding vectors of destination addresses, source addresses and sizes.
+      long[] destAddrs = new long[inputs.length];
+      long[] srcAddrs = new long[inputs.length];
+      long[] sizes = new long[inputs.length];
+      try (HostMemoryBuffer hostMetadata = HostMemoryBuffer.allocate(totalMetadataSize);
+           DeviceMemoryBuffer devMetadata = DeviceMemoryBuffer.allocate(totalMetadataSize)) {
+        long destCopyAddr = devMetadata.getAddress();
+        for (int inputIdx = 0; inputIdx < inputs.length; inputIdx++) {
+          final BaseDeviceMemoryBuffer input = inputs[inputIdx];
+          final long copySize = chunksPerInput[inputIdx] * BatchedLZ4Compressor.METADATA_BYTES_PER_CHUNK;
+          destAddrs[inputIdx] = destCopyAddr;
+          srcAddrs[inputIdx] = input.getAddress();
+          sizes[inputIdx] = copySize;
+          destCopyAddr += copySize;
+        }
+        Cuda.multiBufferCopyAsync(destAddrs, srcAddrs, sizes, stream);
+        hostMetadata.copyFromDeviceBuffer(devMetadata, stream);
+        hostMetadata.incRefCount();
+        return hostMetadata;
+      }
+    }
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/nvcomp/CompressionType.java b/java/src/main/java/ai/rapids/cudf/nvcomp/CompressionType.java
new file mode 100644
index 0000000..70f0a02
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/nvcomp/CompressionType.java
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+/** Enumeration of data types that can be compressed. */
+public enum CompressionType {
+  CHAR(0),
+  UCHAR(1),
+  SHORT(2),
+  USHORT(3),
+  INT(4),
+  UINT(5),
+  LONGLONG(6),
+  ULONGLONG(7),
+  BITS(0xff);
+
+  private static final CompressionType[] types = CompressionType.values();
+
+  final int nativeId;
+
+  CompressionType(int nativeId) {
+    this.nativeId = nativeId;
+  }
+
+  /** Lookup the CompressionType that corresponds to the specified native identifier */
+  public static CompressionType fromNativeId(int id) {
+    for (CompressionType type : types) {
+      if (type.nativeId == id) {
+        return type;
+      }
+    }
+    throw new IllegalArgumentException("Unknown compression type ID: " + id);
+  }
+
+  /** Get the native code identifier for the type */
+  public final int toNativeId() {
+    return nativeId;
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompCudaException.java b/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompCudaException.java
new file mode 100644
index 0000000..8a1a7e6
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompCudaException.java
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+/** Exception thrown from nvcomp indicating a CUDA error occurred. */
+public class NvcompCudaException extends NvcompException {
+  NvcompCudaException(String message) {
+    super(message);
+  }
+
+  NvcompCudaException(String message, Throwable cause) {
+    super(message, cause);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompException.java b/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompException.java
new file mode 100644
index 0000000..48ade45
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompException.java
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+/** Base class for all nvcomp-specific exceptions */
+public class NvcompException extends RuntimeException {
+  NvcompException(String message) {
+    super(message);
+  }
+
+  NvcompException(String message, Throwable cause) {
+    super(message, cause);
+  }
+}
diff --git a/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompJni.java b/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompJni.java
new file mode 100644
index 0000000..5709400
--- /dev/null
+++ b/java/src/main/java/ai/rapids/cudf/nvcomp/NvcompJni.java
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+import ai.rapids.cudf.NativeDepsLoader;
+
+/** Raw JNI interface to the nvcomp library. */
+class NvcompJni {
+  static {
+    NativeDepsLoader.loadNativeDeps();
+  }
+
+  /**
+   * Get the temporary workspace size required to perform compression of entire LZ4 batch.
+   * @param batchSize number of chunks in the batch
+   * @param maxChunkSize maximum size of an uncompressed chunk in bytes
+   * @return The size of required temporary workspace in bytes to compress the batch.
+   */
+  static native long batchedLZ4CompressGetTempSize(long batchSize, long maxChunkSize);
+
+  /**
+   * Get the maximum size any chunk could compress to in a LZ4 batch. This is the minimum amount of
+   * output memory to allocate per chunk when batch compressing.
+   * @param maxChunkSize maximum size of an uncompressed chunk size in bytes
+   * @return maximum compressed output size of a chunk
+   */
+  static native long batchedLZ4CompressGetMaxOutputChunkSize(long maxChunkSize);
+
+  /**
+   * Asynchronously compress a batch of buffers with LZ4. Note that
+   * compressedSizesOutPtr must point to pinned memory for this operation
+   * to be asynchronous.
+   * @param devInPtrs device address of uncompressed buffer addresses vector
+   * @param devInSizes device address of uncompressed buffer sizes vector
+   * @param chunkSize maximum size of an uncompressed chunk in bytes
+   * @param batchSize number of chunks in the batch
+   * @param tempPtr device address of the temporary workspace buffer
+   * @param tempSize size of the temporary workspace buffer in bytes
+   * @param devOutPtrs device address of output buffer addresses vector
+   * @param compressedSizesOutPtr device address where to write the sizes of the
+   *                              compressed data written to the corresponding
+   *                              output buffers. Must point to a buffer with
+   *                              at least 8 bytes of memory per output buffer
+   *                              in the batch.
+   * @param stream CUDA stream to use
+   */
+  static native void batchedLZ4CompressAsync(
+      long devInPtrs,
+      long devInSizes,
+      long chunkSize,
+      long batchSize,
+      long tempPtr,
+      long tempSize,
+      long devOutPtrs,
+      long compressedSizesOutPtr,
+      long stream);
+
+  /**
+   * Computes the temporary storage size in bytes needed to decompress a LZ4-compressed batch.
+   * @param numChunks number of chunks in the batch
+   * @param maxUncompressedChunkBytes maximum uncompressed size of any chunk in bytes
+   * @return number of temporary storage bytes needed to decompress the batch
+   */
+  static native long batchedLZ4DecompressGetTempSize(
+      long numChunks,
+      long maxUncompressedChunkBytes);
+
+  /**
+   * Asynchronously decompress a batch of LZ4-compressed data buffers.
+   * @param devInPtrs device address of compressed input buffer addresses vector
+   * @param devInSizes device address of compressed input buffer sizes vector
+   * @param devOutSizes device address of uncompressed buffer sizes vector
+   * @param batchSize number of buffers in the batch
+   * @param tempPtr device address of the temporary decompression space
+   * @param tempSize size of the temporary decompression space in bytes
+   * @param devOutPtrs device address of uncompressed output buffer addresses vector
+   * @param stream CUDA stream to use
+   */
+  static native void batchedLZ4DecompressAsync(
+      long devInPtrs,
+      long devInSizes,
+      long devOutSizes,
+      long batchSize,
+      long tempPtr,
+      long tempSize,
+      long devOutPtrs,
+      long stream);
+
+  /**
+   * Asynchronously calculates the decompressed size needed for each chunk.
+   * @param devInPtrs device address of compressed input buffer addresses vector
+   * @param devInSizes device address of compressed input buffer sizes vector
+   * @param devOutSizes device address of calculated decompress sizes vector
+   * @param batchSize number of buffers in the batch
+   * @param stream CUDA stream to use
+   */
+  static native void batchedLZ4GetDecompressSizeAsync(
+      long devInPtrs,
+      long devInSizes,
+      long devOutSizes,
+      long batchSize,
+      long stream);
+}
diff --git a/java/src/main/native/.clang-format b/java/src/main/native/.clang-format
new file mode 100644
index 0000000..e086653
--- /dev/null
+++ b/java/src/main/native/.clang-format
@@ -0,0 +1,204 @@
+---
+# Reference: https://clang.llvm.org/docs/ClangFormatStyleOptions.html
+Language:        Cpp
+# BasedOnStyle:  LLVM
+# no indentation (-2 from indent, which is 2)
+AccessModifierOffset: -2
+AlignAfterOpenBracket: Align
+# int aaaa = 12;
+# int b    = 23;
+# int ccc  = 23;
+# leaving OFF
+AlignConsecutiveAssignments: false
+# int         aaaa = 12;
+# float       b = 23;
+# std::string ccc = 23;
+# leaving OFF
+AlignConsecutiveDeclarations: false
+##define A                                                                      \
+#  int aaaa;                                                                    \
+#  int b;                                                                       \
+#  int dddddddddd;
+# leaving ON
+AlignEscapedNewlines: Right
+# int aaa = bbbbbbbbbbbbbbb +
+#           ccccccccccccccc;
+# leaving ON
+AlignOperands:   true
+# true:                                   false:
+# int a;     // My comment a      vs.     int a; // My comment a
+# int b = 2; // comment  b                int b = 2; // comment about b
+# leaving ON
+AlignTrailingComments: true
+# squeezes a long declaration's arguments to the next line:
+#true:
+#void myFunction(
+#	int a, int b, int c, int d, int e);
+#
+#false:
+#void myFunction(int a,
+#				int b,
+#				int c,
+#				int d,
+#				int e);
+# leaving ON
+AllowAllParametersOfDeclarationOnNextLine: true
+# changed to ON, as we use short blocks on same lines
+AllowShortBlocksOnASingleLine: true
+# set this to ON, we use this in a few places
+AllowShortCaseLabelsOnASingleLine: true
+# set this to ON
+AllowShortFunctionsOnASingleLine: Inline
+AllowShortIfStatementsOnASingleLine: false
+AllowShortLoopsOnASingleLine: false
+# Deprecated option.
+# PenaltyReturnTypeOnItsOwnLine applies, as we set this to None,
+# where it tries to break after the return type automatically
+AlwaysBreakAfterDefinitionReturnType: None
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: false
+AlwaysBreakTemplateDeclarations: MultiLine
+
+# if all the arguments for a function don't fit in a single line,
+# with a value of "false", it'll split each argument into different lines
+BinPackArguments: true
+BinPackParameters: true
+
+# if this is set to Custom, the BraceWrapping flags apply
+BreakBeforeBraces: Custom
+BraceWrapping:
+  AfterClass:      false
+  AfterControlStatement: false
+  AfterEnum:       false
+  AfterFunction:   false
+  AfterNamespace:  false
+  AfterObjCDeclaration: false
+  AfterStruct:     false
+  AfterUnion:      false
+  AfterExternBlock: false
+  BeforeCatch:     false
+  BeforeElse:      false
+  IndentBraces:    false
+  SplitEmptyFunction: false
+  SplitEmptyRecord: false
+  SplitEmptyNamespace: false
+
+# will break after operators when a line is too long
+BreakBeforeBinaryOperators: None
+# not in docs.. so that's nice
+BreakBeforeInheritanceComma: false
+# This will break inheritance list and align on colon,
+# it also places each inherited class in a different line.
+# Leaving ON
+BreakInheritanceList: BeforeColon
+
+#
+#true:
+#veryVeryVeryVeryVeryVeryVeryVeryVeryVeryVeryLongDescription
+#	? firstValue
+#	: SecondValueVeryVeryVeryVeryLong;
+#
+#false:
+#veryVeryVeryVeryVeryVeryVeryVeryVeryVeryVeryLongDescription ?
+#	firstValue :
+#	SecondValueVeryVeryVeryVeryLong;
+BreakBeforeTernaryOperators: false
+
+BreakConstructorInitializersBeforeComma: false
+BreakConstructorInitializers: BeforeColon
+BreakAfterJavaFieldAnnotations: true
+BreakStringLiterals: true
+# So the line lengths in cudf are not following a limit, at the moment.
+# Usually it's a long comment that makes the line length inconsistent.
+# Command I used to find max line lengths (from cpp directory):
+#   find include src tests|grep "\." |xargs -I{} bash -c "awk '{print length}' {} | sort -rn | head -1"|sort -n
+# I picked 100, as it seemed somewhere around median
+ColumnLimit:     100
+# TODO: not aware of any of these at this time
+CommentPragmas:  '^ IWYU pragma:'
+# So it doesn't put subsequent namespaces in the same line
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: false
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+# TODO: adds spaces around the element list
+# in initializer: vector<T> x{ {}, ..., {} }
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+DisableFormat:   false
+ExperimentalAutoDetectBinPacking: false
+# } // namespace a => useful
+FixNamespaceComments: true
+ForEachMacros:
+  - foreach
+  - Q_FOREACH
+  - BOOST_FOREACH
+IncludeBlocks:   Regroup
+IncludeCategories:
+  - Regex:           '<[[:alnum:]]+>'
+    Priority:        0
+  - Regex:           '<[[:alnum:].]+>'
+    Priority:        1
+  - Regex:           '<.*>'
+    Priority:        2
+  - Regex:           '.*/.*'
+    Priority:        3
+  - Regex:           '.*'
+    Priority:        4
+# if a header matches this in an include group, it will be moved up to the
+# top of the group.
+IncludeIsMainRegex: '(Test)?$'
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+JavaScriptQuotes: Leave
+JavaScriptWrapImports: true
+KeepEmptyLinesAtTheStartOfBlocks: true
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBinPackProtocolList: Auto
+ObjCBlockIndentWidth: 2
+ObjCSpaceAfterProperty: false
+ObjCSpaceBeforeProtocolList: true
+
+# Penalties: leaving unchanged for now
+# https://stackoverflow.com/questions/26635370/in-clang-format-what-do-the-penalties-do
+PenaltyBreakAssignment: 2
+PenaltyBreakBeforeFirstCallParameter: 19
+PenaltyBreakComment: 300
+PenaltyBreakFirstLessLess: 120
+PenaltyBreakString: 1000
+PenaltyBreakTemplateDeclaration: 10
+PenaltyExcessCharacter: 1000000
+# As currently set, we don't see return types being
+# left on their own line, leaving at 60
+PenaltyReturnTypeOnItsOwnLine: 60
+
+# char* foo vs char *foo, picking Right aligned
+PointerAlignment: Right
+ReflowComments:  true
+# leaving ON, but this could be something to turn OFF
+SortIncludes:    true
+SortUsingDeclarations: true
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: true
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCpp11BracedList: false
+SpaceBeforeCtorInitializerColon: true
+SpaceBeforeInheritanceColon: true
+SpaceBeforeParens: ControlStatements
+SpaceBeforeRangeBasedForLoopColon: true
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles:  false
+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        8
+UseTab:          Never
+...
diff --git a/java/src/main/native/CMakeLists.txt b/java/src/main/native/CMakeLists.txt
new file mode 100644
index 0000000..128989f
--- /dev/null
+++ b/java/src/main/native/CMakeLists.txt
@@ -0,0 +1,266 @@
+# =============================================================================
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+include(../../../../fetch_rapids.cmake)
+include(rapids-cmake)
+include(rapids-cuda)
+include(rapids-find)
+include(rapids-cpm)
+rapids_cpm_init()
+
+# Use GPU_ARCHS if it is defined
+if(DEFINED GPU_ARCHS)
+  set(CMAKE_CUDA_ARCHITECTURES "${GPU_ARCHS}")
+endif()
+rapids_cuda_init_architectures(CUDF_JNI)
+
+project(
+  CUDF_JNI
+  VERSION 23.10.00
+  LANGUAGES C CXX CUDA
+)
+
+# ##################################################################################################
+# * build options ---------------------------------------------------------------------------------
+
+option(USE_NVTX "Build with NVTX support" ON)
+option(BUILD_SHARED_LIBS "Build cuDF JNI shared libraries" ON)
+option(BUILD_TESTS "Configure CMake to build tests" ON)
+option(CUDF_USE_PER_THREAD_DEFAULT_STREAM "Build with per-thread default stream" OFF)
+option(CUDA_STATIC_RUNTIME "Statically link the CUDA runtime" OFF)
+option(USE_GDS "Build with GPUDirect Storage (GDS)/cuFile support" OFF)
+option(CUDF_JNI_LIBCUDF_STATIC "Link with libcudf.a" OFF)
+option(CUDF_JNI_ENABLE_PROFILING "Build with profiling support" ON)
+
+message(VERBOSE "CUDF_JNI: Build with NVTX support: ${USE_NVTX}")
+message(VERBOSE "CUDF_JNI: Build cuDF JNI shared libraries: ${BUILD_SHARED_LIBS}")
+message(VERBOSE "CUDF_JNI: Configure CMake to build tests: ${BUILD_TESTS}")
+message(VERBOSE
+        "CUDF_JNI: Build with per-thread default stream: ${CUDF_USE_PER_THREAD_DEFAULT_STREAM}"
+)
+message(VERBOSE "CUDF_JNI: Statically link the CUDA runtime: ${CUDA_STATIC_RUNTIME}")
+message(VERBOSE "CUDF_JNI: Build with GPUDirect Storage support: ${USE_GDS}")
+message(VERBOSE "CUDF_JNI: Link with libcudf statically: ${CUDF_JNI_LIBCUDF_STATIC}")
+
+set(CUDF_SOURCE_DIR "${PROJECT_SOURCE_DIR}/../../../../cpp")
+if(NOT DEFINED CUDF_CPP_BUILD_DIR OR CUDF_CPP_BUILD_DIR STREQUAL "")
+  if(DEFINED ENV{CUDF_CPP_BUILD_DIR})
+    set(CUDF_CPP_BUILD_DIR "$ENV{CUDF_CPP_BUILD_DIR}")
+  else()
+    set(CUDF_CPP_BUILD_DIR "${CUDF_SOURCE_DIR}/build")
+  endif()
+endif()
+
+set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/Modules/"
+                      "${CUDF_SOURCE_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}
+)
+
+# ##################################################################################################
+# * compiler options ------------------------------------------------------------------------------
+
+set(CUDF_CXX_FLAGS "")
+set(CUDF_CUDA_FLAGS "")
+set(CUDF_CXX_DEFINITIONS "")
+set(CUDF_CUDA_DEFINITIONS "")
+
+rapids_find_package(CUDAToolkit REQUIRED)
+include(ConfigureCUDA) # set other CUDA compilation flags
+
+if(CUDF_USE_PER_THREAD_DEFAULT_STREAM)
+  message(STATUS "Using per-thread default stream")
+  add_compile_definitions(CUDA_API_PER_THREAD_DEFAULT_STREAM CUDF_USE_PER_THREAD_DEFAULT_STREAM)
+endif()
+
+# ##################################################################################################
+# * build type ------------------------------------------------------------------------------------
+#   Set a default build type if none was specified
+rapids_cmake_build_type("Release")
+
+# ##################################################################################################
+# * CUDF ------------------------------------------------------------------------------------------
+
+set(cudf_ROOT "${CUDF_CPP_BUILD_DIR}")
+rapids_find_package(cudf REQUIRED)
+
+# ##################################################################################################
+# * nvcomp------------------------------------------------------------------------------------------
+
+if(NOT DEFINED nvcomp_DIR)
+  set(nvcomp_DIR "${CUDF_CPP_BUILD_DIR}/_deps/nvcomp-build")
+endif()
+rapids_find_package(nvcomp REQUIRED)
+
+# ##################################################################################################
+# * find JNI -------------------------------------------------------------------------------------
+find_package(JNI REQUIRED)
+if(JNI_FOUND)
+  message(STATUS "JDK with JNI in ${JNI_INCLUDE_DIRS}")
+else()
+  message(FATAL_ERROR "JDK with JNI not found, please check your settings.")
+endif()
+
+# ##################################################################################################
+# * GDS/cufile ------------------------------------------------------------------------------------
+
+if(USE_GDS)
+  message(STATUS "Building with GPUDirect Storage (GDS)/cuFile support")
+  find_package(cuFile REQUIRED)
+endif()
+
+# ##################################################################################################
+# * library targets -------------------------------------------------------------------------------
+
+add_library(
+  cudfjni
+  src/Aggregation128UtilsJni.cpp
+  src/AggregationJni.cpp
+  src/ChunkedPackJni.cpp
+  src/ChunkedReaderJni.cpp
+  src/CudfJni.cpp
+  src/CudaJni.cpp
+  src/ColumnVectorJni.cpp
+  src/ColumnViewJni.cpp
+  src/ColumnViewJni.cu
+  src/CompiledExpression.cpp
+  src/ContiguousTableJni.cpp
+  src/HashJoinJni.cpp
+  src/HostMemoryBufferNativeUtilsJni.cpp
+  src/NvcompJni.cpp
+  src/NvtxRangeJni.cpp
+  src/NvtxUniqueRangeJni.cpp
+  src/PackedColumnMetadataJni.cpp
+  src/RmmJni.cpp
+  src/ScalarJni.cpp
+  src/TableJni.cpp
+  src/aggregation128_utils.cu
+  src/maps_column_view.cu
+  src/row_conversion.cu
+  src/check_nvcomp_output_sizes.cu
+)
+
+# Disable NVTX if necessary
+if(NOT USE_NVTX)
+  target_compile_definitions(cudfjni PUBLIC NVTX_DISABLE)
+endif()
+
+if(CUDF_JNI_ENABLE_PROFILING)
+  target_compile_definitions(cudfjni PRIVATE CUDF_JNI_ENABLE_PROFILING)
+endif()
+
+if(CUDF_JNI_LIBCUDF_STATIC AND BUILD_SHARED_LIBS)
+  # When linking against libcudf.a, the JNI library will include the old libcudf.so. For
+  # backwards-compatibility for software that expects to find libcudf.so in the JVM environment
+  # after cudf has loaded, the JNI code and libcudf.a will be combined into libcudf.so. A stub
+  # library will be created for libcudfjni.so that will simply require libcudf.so for backwards
+  # compatibility with software that expects to find libcudfjni.so at runtime.
+  set_target_properties(cudfjni PROPERTIES OUTPUT_NAME "cudf")
+  add_library(cudfjnistub SHARED src/emptyfile.cpp)
+  set_target_properties(cudfjnistub PROPERTIES OUTPUT_NAME "cudfjni")
+  target_link_libraries(cudfjnistub -Wl,--no-as-needed cudfjni -Wl,--as-needed)
+endif()
+
+# ##################################################################################################
+# * include paths ---------------------------------------------------------------------------------
+
+target_include_directories(
+  cudfjni PUBLIC "${CMAKE_BINARY_DIR}/include" "${CMAKE_SOURCE_DIR}/include"
+                 "${CMAKE_SOURCE_DIR}/src" "${JNI_INCLUDE_DIRS}"
+)
+
+# ##################################################################################################
+# * compile options
+# ---------------------------------------------------------------------------------
+
+# Override RPATH for cudfjni
+set_target_properties(
+  cudfjni
+  PROPERTIES BUILD_RPATH "\$ORIGIN"
+             INSTALL_RPATH "\$ORIGIN"
+             # set target compile options
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             CUDA_STANDARD 17
+             CUDA_STANDARD_REQUIRED ON
+             POSITION_INDEPENDENT_CODE ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+)
+
+target_compile_options(
+  cudfjni PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_FLAGS}>"
+                  "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_FLAGS}>"
+)
+
+target_compile_definitions(
+  cudfjni PUBLIC "$<$<COMPILE_LANGUAGE:CXX>:${CUDF_CXX_DEFINITIONS}>"
+                 "$<$<COMPILE_LANGUAGE:CUDA>:${CUDF_CUDA_DEFINITIONS}>"
+)
+
+if(USE_GDS)
+  add_library(cufilejni src/CuFileJni.cpp)
+  set_target_properties(
+    cufilejni
+    PROPERTIES BUILD_RPATH "\$ORIGIN"
+               INSTALL_RPATH "\$ORIGIN"
+               # set target compile options
+               CXX_STANDARD 17
+               CXX_STANDARD_REQUIRED ON
+               POSITION_INDEPENDENT_CODE ON
+               INTERFACE_POSITION_INDEPENDENT_CODE ON
+  )
+  target_include_directories(cufilejni PRIVATE "${cuFile_INCLUDE_DIRS}")
+  target_link_libraries(cufilejni PRIVATE cudfjni "${cuFile_LIBRARIES}")
+endif()
+
+# ##################################################################################################
+# * link libraries --------------------------------------------------------------------------------
+
+set(CUDF_LINK PUBLIC cudf::cudf)
+if(CUDF_JNI_LIBCUDF_STATIC)
+  # Whole-link libcudf.a into the shared library but not its dependencies
+  set(CUDF_LINK PRIVATE -Wl,--whole-archive cudf::cudf -Wl,--no-whole-archive PUBLIC cudf::cudf)
+endif()
+
+# When nvcomp is installed we need to use nvcomp::nvcomp but from the cudf build directory it will
+# just be nvcomp.
+target_link_libraries(
+  cudfjni ${CUDF_LINK} PRIVATE $<TARGET_NAME_IF_EXISTS:nvcomp>
+                               $<TARGET_NAME_IF_EXISTS:nvcomp::nvcomp>
+)
+
+# ##################################################################################################
+# * cudart options --------------------------------------------------------------------------------
+#   cudart can be statically linked or dynamically linked. The python ecosystem wants dynamic
+#   linking
+
+if(CUDA_STATIC_RUNTIME)
+  # Tell CMake what CUDA language runtime to use
+  set_target_properties(cudfjni PROPERTIES CUDA_RUNTIME_LIBRARY Static)
+else()
+  # Tell CMake what CUDA language runtime to use
+  set_target_properties(cudfjni PROPERTIES CUDA_RUNTIME_LIBRARY Shared)
+endif()
+
+# ##################################################################################################
+# * install shared libraries ----------------------------------------------------------------------
+if(TARGET nvcomp::nvcomp)
+  add_custom_command(
+    TARGET cudfjni
+    PRE_LINK
+    COMMAND
+      ${CMAKE_COMMAND} -E copy $<TARGET_FILE:nvcomp::nvcomp> $<TARGET_FILE:nvcomp::nvcomp_gdeflate>
+      $<TARGET_FILE:nvcomp::nvcomp_bitcomp> "${PROJECT_BINARY_DIR}"
+    COMMENT "Copying nvcomp libraries to ${PROJECT_BINARY_DIR}"
+  )
+endif()
diff --git a/java/src/main/native/clang-format.README b/java/src/main/native/clang-format.README
new file mode 100644
index 0000000..6c13289
--- /dev/null
+++ b/java/src/main/native/clang-format.README
@@ -0,0 +1,13 @@
+README
+======
+
+To apply code formatting to a file you are working on, currently you can do this manually using
+clang-format-7:
+
+This will edit the file, and print to stdout:
+
+  clang-format [file]
+
+This will edit the file in place, do this if you are sure of what you are doing:
+
+  clang-format -i [file]
diff --git a/java/src/main/native/include/jni_utils.hpp b/java/src/main/native/include/jni_utils.hpp
new file mode 100644
index 0000000..f342fca
--- /dev/null
+++ b/java/src/main/native/include/jni_utils.hpp
@@ -0,0 +1,942 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <algorithm>
+#include <memory>
+#include <vector>
+
+#include <jni.h>
+
+#include <cudf/utilities/error.hpp>
+#include <rmm/detail/error.hpp>
+
+namespace cudf {
+namespace jni {
+
+constexpr jint MINIMUM_JNI_VERSION = JNI_VERSION_1_6;
+
+constexpr char const *CUDA_ERROR_CLASS = "ai/rapids/cudf/CudaException";
+constexpr char const *CUDA_FATAL_ERROR_CLASS = "ai/rapids/cudf/CudaFatalException";
+constexpr char const *CUDF_ERROR_CLASS = "ai/rapids/cudf/CudfException";
+constexpr char const *CUDF_OVERFLOW_ERROR_CLASS = "ai/rapids/cudf/CudfColumnSizeOverflowException";
+constexpr char const *CUDF_DTYPE_ERROR_CLASS = "ai/rapids/cudf/CudfException";
+constexpr char const *INDEX_OOB_CLASS = "java/lang/ArrayIndexOutOfBoundsException";
+constexpr char const *ILLEGAL_ARG_CLASS = "java/lang/IllegalArgumentException";
+constexpr char const *NPE_CLASS = "java/lang/NullPointerException";
+constexpr char const *OOM_CLASS = "java/lang/OutOfMemoryError";
+
+/**
+ * @brief indicates that a JNI error of some kind was thrown and the main
+ * function should return.
+ */
+class jni_exception : public std::runtime_error {
+public:
+  jni_exception(char const *const message) : std::runtime_error(message) {}
+  jni_exception(std::string const &message) : std::runtime_error(message) {}
+};
+
+/**
+ * @brief throw a java exception and a C++ one for flow control.
+ */
+inline void throw_java_exception(JNIEnv *const env, const char *class_name, const char *message) {
+  jclass ex_class = env->FindClass(class_name);
+  if (ex_class != NULL) {
+    env->ThrowNew(ex_class, message);
+  }
+  throw jni_exception(message);
+}
+
+/**
+ * @brief check if an java exceptions have been thrown and if so throw a C++
+ * exception so the flow control stop processing.
+ */
+inline void check_java_exception(JNIEnv *const env) {
+  if (env->ExceptionCheck()) {
+    // Not going to try to get the message out of the Exception, too complex and
+    // might fail.
+    throw jni_exception("JNI Exception...");
+  }
+}
+
+/**
+ * @brief Helper to convert a pointer to a jlong.
+ *
+ * This is useful when, for instance, converting a cudf::column pointer
+ * to a jlong, for use in JNI.
+ */
+template <typename T> jlong ptr_as_jlong(T *ptr) {
+  return reinterpret_cast<jlong>(ptr);
+}
+
+/**
+ * @brief Helper to release the data held by a unique_ptr, and return
+ * the pointer as a jlong.
+ */
+template <typename T> jlong release_as_jlong(std::unique_ptr<T> &&ptr) {
+  return ptr_as_jlong(ptr.release());
+}
+
+/**
+ * @brief Helper to release the data held by a unique_ptr, and return
+ * the pointer as a jlong.
+ */
+template <typename T> jlong release_as_jlong(std::unique_ptr<T> &ptr) {
+  return release_as_jlong(std::move(ptr));
+}
+
+class native_jdoubleArray_accessor {
+public:
+  jdouble *getArrayElements(JNIEnv *const env, jdoubleArray arr) const {
+    return env->GetDoubleArrayElements(arr, NULL);
+  }
+
+  jdoubleArray newArray(JNIEnv *const env, int len) const { return env->NewDoubleArray(len); }
+
+  void setArrayRegion(JNIEnv *const env, jdoubleArray jarr, int start, int len,
+                      jdouble const *arr) const {
+    env->SetDoubleArrayRegion(jarr, start, len, arr);
+  }
+
+  void releaseArrayElements(JNIEnv *const env, jdoubleArray jarr, jdouble *arr, jint mode) const {
+    env->ReleaseDoubleArrayElements(jarr, arr, mode);
+  }
+};
+
+class native_jlongArray_accessor {
+public:
+  jlong *getArrayElements(JNIEnv *const env, jlongArray arr) const {
+    return env->GetLongArrayElements(arr, NULL);
+  }
+
+  jlongArray newArray(JNIEnv *const env, int len) const { return env->NewLongArray(len); }
+
+  void setArrayRegion(JNIEnv *const env, jlongArray jarr, int start, int len,
+                      jlong const *arr) const {
+    env->SetLongArrayRegion(jarr, start, len, arr);
+  }
+
+  void releaseArrayElements(JNIEnv *const env, jlongArray jarr, jlong *arr, jint mode) const {
+    env->ReleaseLongArrayElements(jarr, arr, mode);
+  }
+};
+
+class native_jintArray_accessor {
+public:
+  jint *getArrayElements(JNIEnv *const env, jintArray arr) const {
+    return env->GetIntArrayElements(arr, NULL);
+  }
+
+  jintArray newArray(JNIEnv *const env, int len) const { return env->NewIntArray(len); }
+
+  void setArrayRegion(JNIEnv *const env, jintArray jarr, int start, int len,
+                      jint const *arr) const {
+    env->SetIntArrayRegion(jarr, start, len, arr);
+  }
+
+  void releaseArrayElements(JNIEnv *const env, jintArray jarr, jint *arr, jint mode) const {
+    env->ReleaseIntArrayElements(jarr, arr, mode);
+  }
+};
+
+class native_jbyteArray_accessor {
+public:
+  jbyte *getArrayElements(JNIEnv *const env, jbyteArray arr) const {
+    return env->GetByteArrayElements(arr, NULL);
+  }
+
+  jbyteArray newArray(JNIEnv *const env, int len) const { return env->NewByteArray(len); }
+
+  void setArrayRegion(JNIEnv *const env, jbyteArray jarr, int start, int len,
+                      jbyte const *arr) const {
+    env->SetByteArrayRegion(jarr, start, len, arr);
+  }
+
+  void releaseArrayElements(JNIEnv *const env, jbyteArray jarr, jbyte *arr, jint mode) const {
+    env->ReleaseByteArrayElements(jarr, arr, mode);
+  }
+};
+
+class native_jbooleanArray_accessor {
+public:
+  jboolean *getArrayElements(JNIEnv *const env, jbooleanArray arr) const {
+    return env->GetBooleanArrayElements(arr, NULL);
+  }
+
+  jbooleanArray newArray(JNIEnv *const env, int len) const { return env->NewBooleanArray(len); }
+
+  void setArrayRegion(JNIEnv *const env, jbooleanArray jarr, int start, int len,
+                      jboolean const *arr) const {
+    env->SetBooleanArrayRegion(jarr, start, len, arr);
+  }
+
+  void releaseArrayElements(JNIEnv *const env, jbooleanArray jarr, jboolean *arr, jint mode) const {
+    env->ReleaseBooleanArrayElements(jarr, arr, mode);
+  }
+};
+
+/**
+ * @brief RAII for java arrays to be sure it is handled correctly.
+ *
+ * By default any changes to the array will be committed back when
+ * the destructor is called unless cancel is called first.
+ */
+template <typename N_TYPE, typename J_ARRAY_TYPE, typename ACCESSOR> class native_jArray {
+private:
+  ACCESSOR access{};
+  JNIEnv *const env;
+  J_ARRAY_TYPE orig;
+  int len;
+  mutable N_TYPE *data_ptr;
+
+  void init_data_ptr() const {
+    if (orig != nullptr && data_ptr == nullptr) {
+      data_ptr = access.getArrayElements(env, orig);
+      check_java_exception(env);
+    }
+  }
+
+public:
+  native_jArray(native_jArray const &) = delete;
+  native_jArray &operator=(native_jArray const &) = delete;
+
+  native_jArray(JNIEnv *const env, J_ARRAY_TYPE orig)
+      : env(env), orig(orig), len(0), data_ptr(NULL) {
+    if (orig != NULL) {
+      len = env->GetArrayLength(orig);
+      check_java_exception(env);
+    }
+  }
+
+  native_jArray(JNIEnv *const env, int len)
+      : env(env), orig(access.newArray(env, len)), len(len), data_ptr(NULL) {
+    check_java_exception(env);
+  }
+
+  native_jArray(JNIEnv *const env, N_TYPE const *arr, int len)
+      : env(env), orig(access.newArray(env, len)), len(len), data_ptr(NULL) {
+    check_java_exception(env);
+    access.setArrayRegion(env, orig, 0, len, arr);
+    check_java_exception(env);
+  }
+
+  native_jArray(JNIEnv *const env, const std::vector<N_TYPE> &arr)
+      : env(env), orig(access.newArray(env, arr.size())), len(arr.size()), data_ptr(NULL) {
+    check_java_exception(env);
+    access.setArrayRegion(env, orig, 0, len, arr.data());
+    check_java_exception(env);
+  }
+
+  bool is_null() const noexcept { return orig == NULL; }
+
+  int size() const noexcept { return len; }
+
+  N_TYPE operator[](int index) const {
+    if (orig == NULL) {
+      throw_java_exception(env, NPE_CLASS, "pointer is NULL");
+    }
+    if (index < 0 || index >= len) {
+      throw_java_exception(env, INDEX_OOB_CLASS, "NOT IN BOUNDS");
+    }
+    return data()[index];
+  }
+
+  N_TYPE &operator[](int index) {
+    if (orig == NULL) {
+      throw_java_exception(env, NPE_CLASS, "pointer is NULL");
+    }
+    if (index < 0 || index >= len) {
+      throw_java_exception(env, INDEX_OOB_CLASS, "NOT IN BOUNDS");
+    }
+    return data()[index];
+  }
+
+  const N_TYPE *const data() const {
+    init_data_ptr();
+    return data_ptr;
+  }
+
+  N_TYPE *data() {
+    init_data_ptr();
+    return data_ptr;
+  }
+
+  const N_TYPE *const begin() const { return data(); }
+
+  N_TYPE *begin() { return data(); }
+
+  const N_TYPE *const end() const { return data() + size(); }
+
+  N_TYPE *end() { return data() + size(); }
+
+  const J_ARRAY_TYPE get_jArray() const { return orig; }
+
+  J_ARRAY_TYPE get_jArray() { return orig; }
+
+  /**
+   * @brief Conversion to std::vector
+   *
+   * @tparam target_t Target data type
+   * @return std::vector<target_t> Vector with the copied contents
+   */
+  template <typename target_t = N_TYPE> std::vector<target_t> to_vector() const {
+    std::vector<target_t> ret;
+    ret.reserve(size());
+    std::copy(begin(), end(), std::back_inserter(ret));
+    return ret;
+  }
+
+  /**
+   * @brief if data has been written back into this array, don't commit
+   * it.
+   */
+  void cancel() {
+    if (data_ptr != NULL && orig != NULL) {
+      access.releaseArrayElements(env, orig, data_ptr, JNI_ABORT);
+      data_ptr = NULL;
+    }
+  }
+
+  void commit() {
+    if (data_ptr != NULL && orig != NULL) {
+      access.releaseArrayElements(env, orig, data_ptr, 0);
+      data_ptr = NULL;
+    }
+  }
+
+  ~native_jArray() { commit(); }
+};
+
+using native_jdoubleArray = native_jArray<jdouble, jdoubleArray, native_jdoubleArray_accessor>;
+using native_jlongArray = native_jArray<jlong, jlongArray, native_jlongArray_accessor>;
+using native_jintArray = native_jArray<jint, jintArray, native_jintArray_accessor>;
+using native_jbyteArray = native_jArray<jbyte, jbyteArray, native_jbyteArray_accessor>;
+
+/**
+ * @brief Specialization of native_jArray for jboolean
+ *
+ * This class adds special support for conversion to std::vector<X>, where the element
+ * value is chosen depending on the jboolean value.
+ */
+struct native_jbooleanArray
+    : native_jArray<jboolean, jbooleanArray, native_jbooleanArray_accessor> {
+  native_jbooleanArray(JNIEnv *const env, jbooleanArray orig)
+      : native_jArray<jboolean, jbooleanArray, native_jbooleanArray_accessor>(env, orig) {}
+
+  native_jbooleanArray(native_jbooleanArray const &) = delete;
+  native_jbooleanArray &operator=(native_jbooleanArray const &) = delete;
+
+  template <typename target_t>
+  std::vector<target_t> transform_if_else(target_t const &if_true, target_t const &if_false) const {
+    std::vector<target_t> ret;
+    ret.reserve(size());
+    std::transform(begin(), end(), std::back_inserter(ret),
+                   [&](jboolean const &b) { return b ? if_true : if_false; });
+    return ret;
+  }
+};
+
+/**
+ * @brief wrapper around native_jlongArray to make it take pointers instead.
+ *
+ * By default any changes to the array will be committed back when
+ * the destructor is called unless cancel is called first.
+ */
+template <typename T> class native_jpointerArray {
+private:
+  native_jlongArray wrapped;
+  JNIEnv *const env;
+
+public:
+  native_jpointerArray(native_jpointerArray const &) = delete;
+  native_jpointerArray &operator=(native_jpointerArray const &) = delete;
+
+  native_jpointerArray(JNIEnv *const env, jlongArray orig) : wrapped(env, orig), env(env) {}
+
+  native_jpointerArray(JNIEnv *const env, int len) : wrapped(env, len), env(env) {}
+
+  native_jpointerArray(JNIEnv *const env, T *arr, int len) : wrapped(env, arr, len), env(env) {}
+
+  bool is_null() const noexcept { return wrapped.is_null(); }
+
+  int size() const noexcept { return wrapped.size(); }
+
+  T *operator[](int index) const {
+    if (data() == NULL) {
+      throw_java_exception(env, NPE_CLASS, "pointer is NULL");
+    }
+    if (index < 0 || index >= wrapped.size()) {
+      throw_java_exception(env, INDEX_OOB_CLASS, "NOT IN BOUNDS");
+    }
+    return data()[index];
+  }
+
+  T *&operator[](int index) {
+    if (data() == NULL) {
+      throw_java_exception(env, NPE_CLASS, "pointer is NULL");
+    }
+    if (index < 0 || index >= wrapped.size()) {
+      throw_java_exception(env, INDEX_OOB_CLASS, "NOT IN BOUNDS");
+    }
+    return data()[index];
+  }
+
+  T *const *data() const { return reinterpret_cast<T *const *>(wrapped.data()); }
+
+  T **data() { return reinterpret_cast<T **>(wrapped.data()); }
+
+  T *const *begin() const { return data(); }
+  T *const *end() const { return data() + size(); }
+
+  const jlongArray get_jArray() const { return wrapped.get_jArray(); }
+
+  jlongArray get_jArray() { return wrapped.get_jArray(); }
+
+  void assert_no_nulls() const {
+    if (std::any_of(data(), data() + size(), [](T *const ptr) { return ptr == nullptr; })) {
+      throw_java_exception(env, NPE_CLASS, "pointer is NULL");
+    }
+  }
+
+  /**
+   * @brief Convert from `T*[]` to `vector<T>`.
+   */
+  std::vector<T> get_dereferenced() const {
+    assert_no_nulls();
+    auto ret = std::vector<T>{};
+    ret.reserve(size());
+    std::transform(data(), data() + size(), std::back_inserter(ret),
+                   [](T *const &p) { return *p; });
+    return ret;
+  }
+
+  /**
+   * @brief if data has been written back into this array, don't commit
+   * it.
+   */
+  void cancel() { wrapped.cancel(); }
+
+  void commit() { wrapped.commit(); }
+};
+
+/**
+ * @brief wrapper around native_jlongArray to hold pointers that are deleted
+ * if not released, like std::unique_ptr.
+ *
+ * By default any changes to the array will be committed back when
+ * released unless cancel is called first.
+ */
+template <typename T, typename D = std::default_delete<T>> class unique_jpointerArray {
+private:
+  std::unique_ptr<native_jpointerArray<T>> wrapped;
+  D del;
+
+public:
+  unique_jpointerArray(unique_jpointerArray const &) = delete;
+  unique_jpointerArray &operator=(unique_jpointerArray const &) = delete;
+
+  unique_jpointerArray(JNIEnv *const env, jlongArray orig)
+      : wrapped(new native_jpointerArray<T>(env, orig)) {}
+
+  unique_jpointerArray(JNIEnv *const env, jlongArray orig, const D &del)
+      : wrapped(new native_jpointerArray<T>(env, orig)), del(del) {}
+
+  unique_jpointerArray(JNIEnv *const env, int len)
+      : wrapped(new native_jpointerArray<T>(env, len)) {}
+
+  unique_jpointerArray(JNIEnv *const env, int len, const D &del)
+      : wrapped(new native_jpointerArray<T>(env, len)), del(del) {}
+
+  unique_jpointerArray(JNIEnv *const env, T *arr, int len)
+      : wrapped(new native_jpointerArray<T>(env, arr, len)) {}
+
+  unique_jpointerArray(JNIEnv *const env, T *arr, int len, const D &del)
+      : wrapped(new native_jpointerArray<T>(env, arr, len)), del(del) {}
+
+  bool is_null() const noexcept { return wrapped == NULL || wrapped->is_null(); }
+
+  int size() const noexcept { return wrapped == NULL ? 0 : wrapped->size(); }
+
+  void reset(int index, T *new_ptr = NULL) {
+    if (wrapped == NULL) {
+      throw std::logic_error("using unique_jpointerArray after release");
+    }
+    T *old = (*wrapped)[index];
+    if (old != new_ptr) {
+      (*wrapped)[index] = new_ptr;
+      del(old);
+    }
+  }
+
+  T *get(int index) {
+    if (wrapped == NULL) {
+      throw std::logic_error("using unique_jpointerArray after release");
+    }
+    return (*wrapped)[index];
+  }
+
+  T *const *get() {
+    if (wrapped == NULL) {
+      throw std::logic_error("using unique_jpointerArray after release");
+    }
+    return wrapped->data();
+  }
+
+  jlongArray release() {
+    if (wrapped == NULL) {
+      return NULL;
+    }
+    wrapped->commit();
+    jlongArray ret = wrapped->get_jArray();
+    wrapped.reset(NULL);
+    return ret;
+  }
+
+  ~unique_jpointerArray() {
+    if (wrapped != NULL) {
+      for (int i = 0; i < wrapped->size(); i++) {
+        reset(i, NULL);
+      }
+    }
+  }
+};
+
+/**
+ * @brief RAII for jstring to be sure it is handled correctly.
+ */
+class native_jstring {
+private:
+  JNIEnv *env;
+  jstring orig;
+  mutable const char *cstr;
+  mutable size_t cstr_length;
+
+  void init_cstr() const {
+    if (orig != NULL && cstr == NULL) {
+      cstr_length = env->GetStringUTFLength(orig);
+      cstr = env->GetStringUTFChars(orig, 0);
+      check_java_exception(env);
+    }
+  }
+
+public:
+  native_jstring(native_jstring const &) = delete;
+  native_jstring &operator=(native_jstring const &) = delete;
+
+  native_jstring(native_jstring &&other) noexcept
+      : env(other.env), orig(other.orig), cstr(other.cstr), cstr_length(other.cstr_length) {
+    other.cstr = NULL;
+  }
+
+  native_jstring(JNIEnv *const env, jstring orig)
+      : env(env), orig(orig), cstr(NULL), cstr_length(0) {}
+
+  native_jstring &operator=(native_jstring const &&other) {
+    if (orig != NULL && cstr != NULL) {
+      env->ReleaseStringUTFChars(orig, cstr);
+    }
+    this->env = other.env;
+    this->orig = other.orig;
+    this->cstr = other.cstr;
+    this->cstr_length = other.cstr_length;
+    other.cstr = NULL;
+    return *this;
+  }
+
+  bool is_null() const noexcept { return orig == NULL; }
+
+  const char *get() const {
+    init_cstr();
+    return cstr;
+  }
+
+  size_t size_bytes() const {
+    init_cstr();
+    return cstr_length;
+  }
+
+  bool is_empty() const {
+    if (cstr != NULL) {
+      return cstr_length <= 0;
+    } else if (orig != NULL) {
+      jsize len = env->GetStringLength(orig);
+      check_java_exception(env);
+      return len <= 0;
+    }
+    return true;
+  }
+
+  const jstring get_jstring() const { return orig; }
+
+  ~native_jstring() {
+    if (orig != NULL && cstr != NULL) {
+      env->ReleaseStringUTFChars(orig, cstr);
+    }
+  }
+};
+
+/**
+ * @brief jobjectArray wrapper to make accessing it more convenient.
+ */
+template <typename T> class native_jobjectArray {
+private:
+  JNIEnv *const env;
+  jobjectArray orig;
+  int len;
+
+public:
+  native_jobjectArray(JNIEnv *const env, jobjectArray orig) : env(env), orig(orig), len(0) {
+    if (orig != NULL) {
+      len = env->GetArrayLength(orig);
+      check_java_exception(env);
+    }
+  }
+
+  bool is_null() const noexcept { return orig == NULL; }
+
+  int size() const noexcept { return len; }
+
+  T operator[](int index) const { return get(index); }
+
+  T get(int index) const {
+    if (orig == NULL) {
+      throw_java_exception(env, NPE_CLASS, "jobjectArray pointer is NULL");
+    }
+    T ret = static_cast<T>(env->GetObjectArrayElement(orig, index));
+    check_java_exception(env);
+    return ret;
+  }
+
+  void set(int index, const T &val) {
+    if (orig == NULL) {
+      throw_java_exception(env, NPE_CLASS, "jobjectArray pointer is NULL");
+    }
+    env->SetObjectArrayElement(orig, index, val);
+    check_java_exception(env);
+  }
+
+  jobjectArray wrapped() { return orig; }
+};
+
+/**
+ * @brief jobjectArray wrapper to make accessing strings safe through RAII
+ * and convenient.
+ */
+class native_jstringArray {
+private:
+  JNIEnv *const env;
+  native_jobjectArray<jstring> arr;
+  mutable std::vector<native_jstring> cache;
+  mutable std::vector<std::string> cpp_cache;
+  mutable std::vector<const char *> c_cache;
+
+  void init_cache() const {
+    if (!arr.is_null() && cache.empty()) {
+      int size = this->size();
+      cache.reserve(size);
+      for (int i = 0; i < size; i++) {
+        cache.push_back(native_jstring(env, arr.get(i)));
+      }
+    }
+  }
+
+  void init_c_cache() const {
+    if (!arr.is_null() && c_cache.empty()) {
+      init_cache();
+      int size = this->size();
+      c_cache.reserve(size);
+      for (int i = 0; i < size; i++) {
+        c_cache.push_back(cache[i].get());
+      }
+    }
+  }
+
+  void init_cpp_cache() const {
+    if (!arr.is_null() && cpp_cache.empty()) {
+      init_cache();
+      int size = this->size();
+      cpp_cache.reserve(size);
+      for (int i = 0; i < size; i++) {
+        cpp_cache.push_back(cache[i].get());
+      }
+    }
+  }
+
+  void update_caches(int index, jstring val) {
+    if (!cache.empty()) {
+      cache[index] = native_jstring(env, val);
+      if (!c_cache.empty()) {
+        c_cache[index] = cache[index].get();
+      }
+
+      if (!cpp_cache.empty()) {
+        cpp_cache[index] = cache[index].get();
+      }
+    } else if (!c_cache.empty() || !cpp_cache.empty()) {
+      // Illegal state
+      throw std::logic_error("CACHING IS MESSED UP");
+    }
+  }
+
+public:
+  native_jstringArray(JNIEnv *const env, jobjectArray orig) : env(env), arr(env, orig) {}
+
+  bool is_null() const noexcept { return arr.is_null(); }
+
+  int size() const noexcept { return arr.size(); }
+
+  native_jstring &operator[](int index) const { return get(index); }
+
+  native_jstring &get(int index) const {
+    if (arr.is_null()) {
+      throw_java_exception(env, cudf::jni::NPE_CLASS, "jstringArray pointer is NULL");
+    }
+    init_cache();
+    return cache[index];
+  }
+
+  const char **const as_c_array() const {
+    init_c_cache();
+    return c_cache.data();
+  }
+
+  const std::vector<std::string> as_cpp_vector() const {
+    init_cpp_cache();
+    return cpp_cache;
+  }
+
+  void set(int index, jstring val) {
+    arr.set(index, val);
+    update_caches(index, val);
+  }
+
+  void set(int index, const native_jstring &val) {
+    arr.set(index, val.get_jstring());
+    update_caches(index, val.get_jstring());
+  }
+
+  void set(int index, const char *val) {
+    jstring str = env->NewStringUTF(val);
+    check_java_exception(env);
+    arr.set(index, str);
+    update_caches(index, str);
+  }
+};
+
+/**
+ * @brief create a cuda exception from a given cudaError_t
+ */
+inline jthrowable cuda_exception(JNIEnv *const env, cudaError_t status, jthrowable cause = NULL) {
+  const char *ex_class_name;
+
+  // Calls cudaGetLastError twice. It is nearly certain that a fatal error occurred if the second
+  // call doesn't return with cudaSuccess.
+  cudaGetLastError();
+  auto const last = cudaGetLastError();
+  // Call cudaDeviceSynchronize to ensure `last` did not result from an asynchronous error.
+  // between two calls.
+  if (status == last && last == cudaDeviceSynchronize()) {
+    ex_class_name = cudf::jni::CUDA_FATAL_ERROR_CLASS;
+  } else {
+    ex_class_name = cudf::jni::CUDA_ERROR_CLASS;
+  }
+
+  jclass ex_class = env->FindClass(ex_class_name);
+  if (ex_class == NULL) {
+    return NULL;
+  }
+  jmethodID ctor_id =
+      env->GetMethodID(ex_class, "<init>", "(Ljava/lang/String;ILjava/lang/Throwable;)V");
+  if (ctor_id == NULL) {
+    return NULL;
+  }
+
+  jstring msg = env->NewStringUTF(cudaGetErrorString(status));
+  if (msg == NULL) {
+    return NULL;
+  }
+
+  jint err_code = static_cast<jint>(status);
+
+  jobject ret = env->NewObject(ex_class, ctor_id, msg, err_code, cause);
+  return (jthrowable)ret;
+}
+
+inline void jni_cuda_check(JNIEnv *const env, cudaError_t cuda_status) {
+  if (cudaSuccess != cuda_status) {
+    jthrowable jt = cuda_exception(env, cuda_status);
+    if (jt != NULL) {
+      env->Throw(jt);
+    }
+    throw jni_exception(std::string("CUDA ERROR: code ") +
+                        std::to_string(static_cast<int>(cuda_status)));
+  }
+}
+
+inline auto add_global_ref(JNIEnv *env, jobject jobj) {
+  auto new_global_ref = env->NewGlobalRef(jobj);
+  if (new_global_ref == nullptr) {
+    throw cudf::jni::jni_exception("global ref");
+  }
+  return new_global_ref;
+}
+
+inline nullptr_t del_global_ref(JNIEnv *env, jobject jobj) {
+  if (jobj != nullptr) {
+    env->DeleteGlobalRef(jobj);
+  }
+  return nullptr;
+}
+
+} // namespace jni
+} // namespace cudf
+
+#define JNI_EXCEPTION_OCCURRED_CHECK(env, ret_val)                                                 \
+  {                                                                                                \
+    if (env->ExceptionOccurred()) {                                                                \
+      return ret_val;                                                                              \
+    }                                                                                              \
+  }
+
+#define JNI_THROW_NEW(env, class_name, message, ret_val)                                           \
+  {                                                                                                \
+    jclass ex_class = env->FindClass(class_name);                                                  \
+    if (ex_class == NULL) {                                                                        \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    env->ThrowNew(ex_class, message);                                                              \
+    return ret_val;                                                                                \
+  }
+
+// Throw a new exception only if one is not pending then always return with the specified value
+#define JNI_CHECK_THROW_CUDF_EXCEPTION(env, class_name, message, stacktrace, ret_val)              \
+  {                                                                                                \
+    JNI_EXCEPTION_OCCURRED_CHECK(env, ret_val);                                                    \
+    auto const ex_class = env->FindClass(class_name);                                              \
+    if (ex_class == nullptr) {                                                                     \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const ctor_id =                                                                           \
+        env->GetMethodID(ex_class, "<init>", "(Ljava/lang/String;Ljava/lang/String;)V");           \
+    if (ctor_id == nullptr) {                                                                      \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const empty_str = std::string{""};                                                        \
+    auto const jmessage = env->NewStringUTF(message == nullptr ? empty_str.c_str() : message);     \
+    if (jmessage == nullptr) {                                                                     \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const jstacktrace =                                                                       \
+        env->NewStringUTF(stacktrace == nullptr ? empty_str.c_str() : stacktrace);                 \
+    if (jstacktrace == nullptr) {                                                                  \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const jobj = env->NewObject(ex_class, ctor_id, jmessage, jstacktrace);                    \
+    if (jobj == nullptr) {                                                                         \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    env->Throw(reinterpret_cast<jthrowable>(jobj));                                                \
+    return ret_val;                                                                                \
+  }
+
+// Throw a new exception only if one is not pending then always return with the specified value
+#define JNI_CHECK_THROW_CUDA_EXCEPTION(env, class_name, message, stacktrace, error_code, ret_val)  \
+  {                                                                                                \
+    JNI_EXCEPTION_OCCURRED_CHECK(env, ret_val);                                                    \
+    auto const ex_class = env->FindClass(class_name);                                              \
+    if (ex_class == nullptr) {                                                                     \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const ctor_id =                                                                           \
+        env->GetMethodID(ex_class, "<init>", "(Ljava/lang/String;Ljava/lang/String;I)V");          \
+    if (ctor_id == nullptr) {                                                                      \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const empty_str = std::string{""};                                                        \
+    auto const jmessage = env->NewStringUTF(message == nullptr ? empty_str.c_str() : message);     \
+    if (jmessage == nullptr) {                                                                     \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const jstacktrace =                                                                       \
+        env->NewStringUTF(stacktrace == nullptr ? empty_str.c_str() : stacktrace);                 \
+    if (jstacktrace == nullptr) {                                                                  \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    auto const jerror_code = static_cast<jint>(error_code);                                        \
+    auto const jobj = env->NewObject(ex_class, ctor_id, jmessage, jstacktrace, jerror_code);       \
+    if (jobj == nullptr) {                                                                         \
+      return ret_val;                                                                              \
+    }                                                                                              \
+    env->Throw(reinterpret_cast<jthrowable>(jobj));                                                \
+    return ret_val;                                                                                \
+  }
+
+#define JNI_NULL_CHECK(env, obj, error_msg, ret_val)                                               \
+  {                                                                                                \
+    if ((obj) == 0) {                                                                              \
+      JNI_THROW_NEW(env, cudf::jni::NPE_CLASS, error_msg, ret_val);                                \
+    }                                                                                              \
+  }
+
+#define JNI_ARG_CHECK(env, obj, error_msg, ret_val)                                                \
+  {                                                                                                \
+    if (!(obj)) {                                                                                  \
+      JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS, error_msg, ret_val);                        \
+    }                                                                                              \
+  }
+
+#define CATCH_STD_CLASS(env, class_name, ret_val)                                                  \
+  catch (const rmm::out_of_memory &e) {                                                            \
+    JNI_EXCEPTION_OCCURRED_CHECK(env, ret_val);                                                    \
+    auto const what =                                                                              \
+        std::string("Could not allocate native memory: ") + (e.what() == nullptr ? "" : e.what()); \
+    JNI_THROW_NEW(env, cudf::jni::OOM_CLASS, what.c_str(), ret_val);                               \
+  }                                                                                                \
+  catch (const cudf::fatal_cuda_error &e) {                                                        \
+    JNI_CHECK_THROW_CUDA_EXCEPTION(env, cudf::jni::CUDA_FATAL_ERROR_CLASS, e.what(),               \
+                                   e.stacktrace(), e.error_code(), ret_val);                       \
+  }                                                                                                \
+  catch (const cudf::cuda_error &e) {                                                              \
+    JNI_CHECK_THROW_CUDA_EXCEPTION(env, cudf::jni::CUDA_ERROR_CLASS, e.what(), e.stacktrace(),     \
+                                   e.error_code(), ret_val);                                       \
+  }                                                                                                \
+  catch (const cudf::data_type_error &e) {                                                         \
+    JNI_CHECK_THROW_CUDF_EXCEPTION(env, cudf::jni::CUDF_DTYPE_ERROR_CLASS, e.what(),               \
+                                   e.stacktrace(), ret_val);                                       \
+  }                                                                                                \
+  catch (std::overflow_error const &e) {                                                           \
+    JNI_CHECK_THROW_CUDF_EXCEPTION(env, cudf::jni::CUDF_OVERFLOW_ERROR_CLASS, e.what(),            \
+                                   "No native stacktrace is available.", ret_val);                 \
+  }                                                                                                \
+  catch (const std::exception &e) {                                                                \
+    char const *stacktrace = "No native stacktrace is available.";                                 \
+    if (auto const cudf_ex = dynamic_cast<cudf::logic_error const *>(&e); cudf_ex != nullptr) {    \
+      stacktrace = cudf_ex->stacktrace();                                                          \
+    }                                                                                              \
+    /* Double check whether the thrown exception is unrecoverable CUDA error or not. */            \
+    /* Like cudf::detail::throw_cuda_error, it is nearly certain that a fatal error  */            \
+    /* occurred if the second call doesn't return with cudaSuccess. */                             \
+    cudaGetLastError();                                                                            \
+    auto const last = cudaFree(0);                                                                 \
+    if (cudaSuccess != last && last == cudaDeviceSynchronize()) {                                  \
+      /* Throw CudaFatalException since the thrown exception is unrecoverable CUDA error */        \
+      JNI_CHECK_THROW_CUDA_EXCEPTION(env, cudf::jni::CUDA_FATAL_ERROR_CLASS, e.what(), stacktrace, \
+                                     last, ret_val);                                               \
+    }                                                                                              \
+    JNI_CHECK_THROW_CUDF_EXCEPTION(env, class_name, e.what(), stacktrace, ret_val);                \
+  }
+
+#define CATCH_STD(env, ret_val) CATCH_STD_CLASS(env, cudf::jni::CUDF_ERROR_CLASS, ret_val)
diff --git a/java/src/main/native/include/maps_column_view.hpp b/java/src/main/native/include/maps_column_view.hpp
new file mode 100644
index 0000000..5ac8d5c
--- /dev/null
+++ b/java/src/main/native/include/maps_column_view.hpp
@@ -0,0 +1,152 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+
+class scalar;
+
+namespace jni {
+
+/**
+ * @brief Given a column-view of LIST<STRUCT<K,V>>, an instance of this class
+ * provides an abstraction of a column of maps.
+ *
+ * Each list row is treated as a map of key->value, with possibly repeated keys.
+ * The list may be looked up by a scalar key, or by a column of keys, to
+ * retrieve the corresponding value.
+ */
+class maps_column_view {
+public:
+  maps_column_view(lists_column_view const &lists_of_structs,
+                   rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+  // Rule of 5.
+  maps_column_view(maps_column_view const &maps_view) = default;
+  maps_column_view(maps_column_view &&maps_view) = default;
+  maps_column_view &operator=(maps_column_view const &) = default;
+  maps_column_view &operator=(maps_column_view &&) = default;
+  ~maps_column_view() = default;
+
+  /**
+   * @brief Returns number of map rows in the column.
+   */
+  size_type size() const { return keys_.size(); }
+
+  /**
+   * @brief Getter for keys as a list column.
+   *
+   * Note: Keys are not deduped. Repeated keys are returned in order.
+   */
+  lists_column_view const &keys() const { return keys_; }
+
+  /**
+   * @brief Getter for values as a list column.
+   *
+   * Note: Values for repeated keys are not dropped.
+   */
+  lists_column_view const &values() const { return values_; }
+
+  /**
+   * @brief Map lookup by a column of keys.
+   *
+   * The lookup column must have as many rows as the map column,
+   * and must match the key-type of the map.
+   * A column of values is returned, with the same number of rows as the map column.
+   * If a key is repeated in a map row, the value corresponding to the last matching
+   * key is returned.
+   * If a lookup key is null or not found, the corresponding value is null.
+   *
+   * @param keys Column of keys to be looked up in each corresponding map row.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory.
+   * @return std::unique_ptr<column> Column of values corresponding the value of the lookup key.
+   */
+  std::unique_ptr<column> get_values_for(
+      column_view const &keys, rmm::cuda_stream_view stream = cudf::get_default_stream(),
+      rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource()) const;
+
+  /**
+   * @brief Map lookup by a scalar key.
+   *
+   * The type of the lookup scalar must match the key-type of the map.
+   * A column of values is returned, with the same number of rows as the map column.
+   * If a key is repeated in a map row, the value corresponding to the last matching
+   * key is returned.
+   * If the lookup key is null or not found, the corresponding value is null.
+   *
+   * @param keys Column of keys to be looked up in each corresponding map row.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory.
+   * @return std::unique_ptr<column>
+   */
+  std::unique_ptr<column> get_values_for(
+      scalar const &key, rmm::cuda_stream_view stream = cudf::get_default_stream(),
+      rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource()) const;
+
+  /**
+   * @brief Check if each map row contains a specified scalar key.
+   *
+   * The type of the lookup scalar must match the key-type of the map.
+   * A column of values is returned, with the same number of rows as the map column.
+   *
+   * Each row in the returned column contains a bool indicating whether the row contains
+   * the specified key (`true`) or not (`false`).
+   * The returned column contains no nulls. i.e. If the search key is null, or if the
+   * map row is null, the result row is `false`.
+   *
+   * @param key Scalar key to be looked up in each corresponding map row.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory.
+   * @return std::unique_ptr<column>
+   */
+  std::unique_ptr<column>
+  contains(scalar const &key, rmm::cuda_stream_view stream = cudf::get_default_stream(),
+           rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource()) const;
+
+  /**
+   * @brief Check if each map row contains keys specified by a column
+   *
+   * The type of the lookup column must match the key-type of the map.
+   * A column of values is returned, with the same number of rows as the map column.
+   *
+   * Each row in the returned column contains a bool indicating whether the row contains
+   * the specified key (`true`) or not (`false`).
+   * The returned column contains no nulls. i.e. If the search key is null, or if the
+   * map row is null, the result row is `false`.
+   *
+   * @param keys Column of keys to be looked up in each corresponding map row.
+   * @param stream CUDA stream used for device memory operations and kernel launches.
+   * @param mr Device memory resource used to allocate the returned column's device memory.
+   * @return std::unique_ptr<column>
+   */
+
+  std::unique_ptr<column>
+  contains(column_view const &key, rmm::cuda_stream_view stream = cudf::get_default_stream(),
+           rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource()) const;
+
+private:
+  lists_column_view keys_, values_;
+};
+
+} // namespace jni
+} // namespace cudf
diff --git a/java/src/main/native/src/Aggregation128UtilsJni.cpp b/java/src/main/native/src/Aggregation128UtilsJni.cpp
new file mode 100644
index 0000000..71c36cb
--- /dev/null
+++ b/java/src/main/native/src/Aggregation128UtilsJni.cpp
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "aggregation128_utils.hpp"
+#include "cudf_jni_apis.hpp"
+#include "dtype_utils.hpp"
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation128Utils_extractInt32Chunk(
+    JNIEnv *env, jclass, jlong j_column_view, jint j_out_dtype, jint j_chunk_idx) {
+  JNI_NULL_CHECK(env, j_column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cview = reinterpret_cast<cudf::column_view const *>(j_column_view);
+    auto dtype = cudf::jni::make_data_type(j_out_dtype, 0);
+    return cudf::jni::release_as_jlong(cudf::jni::extract_chunk32(*cview, dtype, j_chunk_idx));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Aggregation128Utils_combineInt64SumChunks(
+    JNIEnv *env, jclass, jlong j_table_view, jint j_dtype, jint j_scale) {
+  JNI_NULL_CHECK(env, j_table_view, "table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto tview = reinterpret_cast<cudf::table_view const *>(j_table_view);
+    std::unique_ptr<cudf::table> result =
+        cudf::jni::assemble128_from_sum(*tview, cudf::jni::make_data_type(j_dtype, j_scale));
+    return cudf::jni::convert_table_for_return(env, result);
+  }
+  CATCH_STD(env, 0);
+}
+}
diff --git a/java/src/main/native/src/AggregationJni.cpp b/java/src/main/native/src/AggregationJni.cpp
new file mode 100644
index 0000000..bc62e95
--- /dev/null
+++ b/java/src/main/native/src/AggregationJni.cpp
@@ -0,0 +1,280 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/aggregation.hpp>
+
+#include "cudf_jni_apis.hpp"
+
+extern "C" {
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Aggregation_close(JNIEnv *env, jclass class_object,
+                                                             jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto to_del = reinterpret_cast<cudf::aggregation *>(ptr);
+    delete to_del;
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createNoParamAgg(JNIEnv *env,
+                                                                         jclass class_object,
+                                                                         jint kind) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ret = [&] {
+      // These numbers come from Aggregation.java and must stay in sync
+      switch (kind) {
+        case 0: // SUM
+          return cudf::make_sum_aggregation();
+        case 1: // PRODUCT
+          return cudf::make_product_aggregation();
+        case 2: // MIN
+          return cudf::make_min_aggregation();
+        case 3: // MAX
+          return cudf::make_max_aggregation();
+        // case 4 COUNT
+        case 5: // ANY
+          return cudf::make_any_aggregation();
+        case 6: // ALL
+          return cudf::make_all_aggregation();
+        case 7: // SUM_OF_SQUARES
+          return cudf::make_sum_of_squares_aggregation();
+        case 8: // MEAN
+          return cudf::make_mean_aggregation();
+        // case 9: VARIANCE
+        // case 10: STD
+        case 11: // MEDIAN
+          return cudf::make_median_aggregation();
+        // case 12: QUANTILE
+        case 13: // ARGMAX
+          return cudf::make_argmax_aggregation();
+        case 14: // ARGMIN
+          return cudf::make_argmin_aggregation();
+        // case 15: NUNIQUE
+        // case 16: NTH_ELEMENT
+        case 17: // ROW_NUMBER
+          return cudf::make_row_number_aggregation();
+        // case 18: COLLECT_LIST
+        // case 19: COLLECT_SET
+        case 20: // MERGE_LISTS
+          return cudf::make_merge_lists_aggregation();
+        // case 21: MERGE_SETS
+        // case 22: LEAD
+        // case 23: LAG
+        // case 24: PTX
+        // case 25: CUDA
+        case 26: // M2
+          return cudf::make_m2_aggregation();
+        case 27: // MERGE_M2
+          return cudf::make_merge_m2_aggregation();
+        case 28: // RANK
+          return cudf::make_rank_aggregation(cudf::rank_method::MIN, {},
+                                             cudf::null_policy::INCLUDE);
+        case 29: // DENSE_RANK
+          return cudf::make_rank_aggregation(cudf::rank_method::DENSE, {},
+                                             cudf::null_policy::INCLUDE);
+        case 30: // ANSI SQL PERCENT_RANK
+          return cudf::make_rank_aggregation(cudf::rank_method::MIN, {}, cudf::null_policy::INCLUDE,
+                                             {}, cudf::rank_percentage::ONE_NORMALIZED);
+        case 33: // HISTOGRAM
+          return cudf::make_histogram_aggregation();
+        case 34: // MERGE_HISTOGRAM
+          return cudf::make_merge_histogram_aggregation();
+
+        default: throw std::logic_error("Unsupported No Parameter Aggregation Operation");
+      }
+    }();
+
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createNthAgg(JNIEnv *env,
+                                                                     jclass class_object,
+                                                                     jint offset,
+                                                                     jboolean include_nulls) {
+  try {
+    cudf::jni::auto_set_device(env);
+
+    std::unique_ptr<cudf::aggregation> ret = cudf::make_nth_element_aggregation(
+        offset, include_nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE);
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createDdofAgg(JNIEnv *env,
+                                                                      jclass class_object,
+                                                                      jint kind, jint ddof) {
+  try {
+    cudf::jni::auto_set_device(env);
+
+    std::unique_ptr<cudf::aggregation> ret;
+    // These numbers come from Aggregation.java and must stay in sync
+    switch (kind) {
+      case 9: // VARIANCE
+        ret = cudf::make_variance_aggregation(ddof);
+        break;
+      case 10: // STD
+        ret = cudf::make_std_aggregation(ddof);
+        break;
+      default: throw std::logic_error("Unsupported DDOF Aggregation Operation");
+    }
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createTDigestAgg(JNIEnv *env,
+                                                                         jclass class_object,
+                                                                         jint kind, jint delta) {
+  try {
+    cudf::jni::auto_set_device(env);
+
+    std::unique_ptr<cudf::aggregation> ret;
+    // These numbers come from Aggregation.java and must stay in sync
+    switch (kind) {
+      case 31: // TDIGEST
+        ret = cudf::make_tdigest_aggregation<cudf::groupby_aggregation>(delta);
+        break;
+      case 32: // MERGE_TDIGEST
+        ret = cudf::make_merge_tdigest_aggregation<cudf::groupby_aggregation>(delta);
+        break;
+      default: throw std::logic_error("Unsupported TDigest Aggregation Operation");
+    }
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createCountLikeAgg(JNIEnv *env,
+                                                                           jclass class_object,
+                                                                           jint kind,
+                                                                           jboolean include_nulls) {
+  try {
+    cudf::jni::auto_set_device(env);
+
+    cudf::null_policy policy =
+        include_nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
+    std::unique_ptr<cudf::aggregation> ret;
+    // These numbers come from Aggregation.java and must stay in sync
+    switch (kind) {
+      case 4: // COUNT
+        ret = cudf::make_count_aggregation(policy);
+        break;
+      case 15: // NUNIQUE
+        ret = cudf::make_nunique_aggregation(policy);
+        break;
+      default: throw std::logic_error("Unsupported Count Like Aggregation Operation");
+    }
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createQuantAgg(JNIEnv *env,
+                                                                       jclass class_object,
+                                                                       jint j_method,
+                                                                       jdoubleArray j_quantiles) {
+  JNI_NULL_CHECK(env, j_quantiles, "quantiles are null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+
+    const cudf::jni::native_jdoubleArray quantiles(env, j_quantiles);
+
+    std::vector<double> quants(quantiles.data(), quantiles.data() + quantiles.size());
+    cudf::interpolation interp = static_cast<cudf::interpolation>(j_method);
+
+    std::unique_ptr<cudf::aggregation> ret = cudf::make_quantile_aggregation(quants, interp);
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createLeadLagAgg(JNIEnv *env,
+                                                                         jclass class_object,
+                                                                         jint kind, jint offset) {
+  try {
+    cudf::jni::auto_set_device(env);
+
+    std::unique_ptr<cudf::aggregation> ret;
+    // These numbers come from Aggregation.java and must stay in sync
+    switch (kind) {
+      case 22: // LEAD
+        ret = cudf::make_lead_aggregation(offset);
+        break;
+      case 23: // LAG
+        ret = cudf::make_lag_aggregation(offset);
+        break;
+      default: throw std::logic_error("Unsupported Lead/Lag Aggregation Operation");
+    }
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createCollectListAgg(
+    JNIEnv *env, jclass class_object, jboolean include_nulls) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::null_policy policy =
+        include_nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
+    std::unique_ptr<cudf::aggregation> ret = cudf::make_collect_list_aggregation(policy);
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createCollectSetAgg(JNIEnv *env,
+                                                                            jclass class_object,
+                                                                            jboolean include_nulls,
+                                                                            jboolean nulls_equal,
+                                                                            jboolean nans_equal) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::null_policy null_policy =
+        include_nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
+    cudf::null_equality null_equality =
+        nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    cudf::nan_equality nan_equality =
+        nans_equal ? cudf::nan_equality::ALL_EQUAL : cudf::nan_equality::UNEQUAL;
+    std::unique_ptr<cudf::aggregation> ret =
+        cudf::make_collect_set_aggregation(null_policy, null_equality, nan_equality);
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Aggregation_createMergeSetsAgg(JNIEnv *env,
+                                                                           jclass class_object,
+                                                                           jboolean nulls_equal,
+                                                                           jboolean nans_equal) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::null_equality null_equality =
+        nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    cudf::nan_equality nan_equality =
+        nans_equal ? cudf::nan_equality::ALL_EQUAL : cudf::nan_equality::UNEQUAL;
+    std::unique_ptr<cudf::aggregation> ret =
+        cudf::make_merge_sets_aggregation(null_equality, nan_equality);
+    return reinterpret_cast<jlong>(ret.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/ChunkedPackJni.cpp b/java/src/main/native/src/ChunkedPackJni.cpp
new file mode 100644
index 0000000..746a67e
--- /dev/null
+++ b/java/src/main/native/src/ChunkedPackJni.cpp
@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cudf_jni_apis.hpp"
+
+extern "C" {
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_ChunkedPack_chunkedPackDelete(JNIEnv *env, jclass,
+                                                                         jlong chunked_pack) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cs = reinterpret_cast<cudf::chunked_pack *>(chunked_pack);
+    delete cs;
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ChunkedPack_chunkedPackGetTotalContiguousSize(
+    JNIEnv *env, jclass, jlong chunked_pack) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cs = reinterpret_cast<cudf::chunked_pack *>(chunked_pack);
+    return cs->get_total_contiguous_size();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jboolean JNICALL Java_ai_rapids_cudf_ChunkedPack_chunkedPackHasNext(JNIEnv *env, jclass,
+                                                                              jlong chunked_pack) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cs = reinterpret_cast<cudf::chunked_pack *>(chunked_pack);
+    return cs->has_next();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ChunkedPack_chunkedPackNext(JNIEnv *env, jclass,
+                                                                        jlong chunked_pack,
+                                                                        jlong user_ptr,
+                                                                        jlong user_ptr_size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cs = reinterpret_cast<cudf::chunked_pack *>(chunked_pack);
+    auto user_buffer_span = cudf::device_span<uint8_t>(reinterpret_cast<uint8_t *>(user_ptr),
+                                                       static_cast<std::size_t>(user_ptr_size));
+    return cs->next(user_buffer_span);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL
+Java_ai_rapids_cudf_ChunkedPack_chunkedPackBuildMetadata(JNIEnv *env, jclass, jlong chunked_pack) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cs = reinterpret_cast<cudf::chunked_pack *>(chunked_pack);
+    std::unique_ptr<std::vector<uint8_t>> result = cs->build_metadata();
+    return reinterpret_cast<jlong>(result.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/ChunkedReaderJni.cpp b/java/src/main/native/src/ChunkedReaderJni.cpp
new file mode 100644
index 0000000..8d0a8bd
--- /dev/null
+++ b/java/src/main/native/src/ChunkedReaderJni.cpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <memory>
+#include <vector>
+
+#include <cudf/column/column.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/table/table.hpp>
+
+#include "cudf_jni_apis.hpp"
+#include "jni_utils.hpp"
+
+// This function is defined in `TableJni.cpp`.
+jlongArray
+cudf::jni::convert_table_for_return(JNIEnv *env, std::unique_ptr<cudf::table> &&table_result,
+                                    std::vector<std::unique_ptr<cudf::column>> &&extra_columns);
+
+// This file is for the code related to chunked reader (Parquet, ORC, etc.).
+
+extern "C" {
+
+// This function should take all the parameters that `Table.readParquet` takes,
+// plus one more parameter `long chunkSizeByteLimit`.
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ParquetChunkedReader_create(
+    JNIEnv *env, jclass, jlong chunk_read_limit, jobjectArray filter_col_names,
+    jbooleanArray j_col_binary_read, jstring inp_file_path, jlong buffer, jlong buffer_length,
+    jint unit) {
+  JNI_NULL_CHECK(env, j_col_binary_read, "Null col_binary_read", 0);
+  bool read_buffer = true;
+  if (buffer == 0) {
+    JNI_NULL_CHECK(env, inp_file_path, "Input file or buffer must be supplied", 0);
+    read_buffer = false;
+  } else if (inp_file_path != nullptr) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "Cannot pass in both a buffer and an inp_file_path", 0);
+  } else if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring filename(env, inp_file_path);
+    if (!read_buffer && filename.is_empty()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "inp_file_path cannot be empty", 0);
+    }
+
+    cudf::jni::native_jstringArray n_filter_col_names(env, filter_col_names);
+
+    // TODO: This variable is unused now, but we still don't know what to do with it yet.
+    // As such, it needs to stay here for a little more time before we decide to use it again,
+    // or remove it completely.
+    cudf::jni::native_jbooleanArray n_col_binary_read(env, j_col_binary_read);
+    (void)n_col_binary_read;
+
+    auto const source = read_buffer ?
+                            cudf::io::source_info(reinterpret_cast<char *>(buffer),
+                                                  static_cast<std::size_t>(buffer_length)) :
+                            cudf::io::source_info(filename.get());
+
+    auto opts_builder = cudf::io::parquet_reader_options::builder(source);
+    if (n_filter_col_names.size() > 0) {
+      opts_builder = opts_builder.columns(n_filter_col_names.as_cpp_vector());
+    }
+    auto const read_opts = opts_builder.convert_strings_to_categories(false)
+                               .timestamp_type(cudf::data_type(static_cast<cudf::type_id>(unit)))
+                               .build();
+
+    return reinterpret_cast<jlong>(new cudf::io::chunked_parquet_reader(
+        static_cast<std::size_t>(chunk_read_limit), read_opts));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jboolean JNICALL Java_ai_rapids_cudf_ParquetChunkedReader_hasNext(JNIEnv *env, jclass,
+                                                                            jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const reader_ptr = reinterpret_cast<cudf::io::chunked_parquet_reader *const>(handle);
+    return reader_ptr->has_next();
+  }
+  CATCH_STD(env, false);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ParquetChunkedReader_readChunk(JNIEnv *env, jclass,
+                                                                                jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const reader_ptr = reinterpret_cast<cudf::io::chunked_parquet_reader *const>(handle);
+    auto chunk = reader_ptr->read_chunk();
+    return chunk.tbl ? cudf::jni::convert_table_for_return(env, chunk.tbl) : nullptr;
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_ParquetChunkedReader_close(JNIEnv *env, jclass,
+                                                                      jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", );
+
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cudf::io::chunked_parquet_reader *>(handle);
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/ColumnVectorJni.cpp b/java/src/main/native/src/ColumnVectorJni.cpp
new file mode 100644
index 0000000..8fb7df7
--- /dev/null
+++ b/java/src/main/native/src/ColumnVectorJni.cpp
@@ -0,0 +1,387 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <algorithm>
+
+#include <arrow/api.h>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/interop.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/interop.hpp>
+#include <cudf/lists/combine.hpp>
+#include <cudf/lists/detail/concatenate.hpp>
+#include <cudf/lists/filling.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/reshape.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include "cudf_jni_apis.hpp"
+#include "dtype_utils.hpp"
+#include "jni_utils.hpp"
+
+using cudf::jni::ptr_as_jlong;
+using cudf::jni::release_as_jlong;
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_sequence(JNIEnv *env, jclass,
+                                                                  jlong j_initial_val, jlong j_step,
+                                                                  jint row_count) {
+  JNI_NULL_CHECK(env, j_initial_val, "scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto initial_val = reinterpret_cast<cudf::scalar const *>(j_initial_val);
+    auto step = reinterpret_cast<cudf::scalar const *>(j_step);
+    return release_as_jlong(step ? cudf::sequence(row_count, *initial_val, *step) :
+                                   cudf::sequence(row_count, *initial_val));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_sequences(JNIEnv *env, jclass,
+                                                                   jlong j_start_handle,
+                                                                   jlong j_size_handle,
+                                                                   jlong j_step_handle) {
+  JNI_NULL_CHECK(env, j_start_handle, "start is null", 0);
+  JNI_NULL_CHECK(env, j_size_handle, "size is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto start = reinterpret_cast<cudf::column_view const *>(j_start_handle);
+    auto size = reinterpret_cast<cudf::column_view const *>(j_size_handle);
+    auto step = reinterpret_cast<cudf::column_view const *>(j_step_handle);
+    auto ret =
+        step ? cudf::lists::sequences(*start, *step, *size) : cudf::lists::sequences(*start, *size);
+    return release_as_jlong(ret);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_fromArrow(
+    JNIEnv *env, jclass, jint j_type, jlong j_col_length, jlong j_null_count, jobject j_data_obj,
+    jobject j_validity_obj, jobject j_offsets_obj) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::type_id n_type = static_cast<cudf::type_id>(j_type);
+    // not all the buffers are used for all types
+    void const *data_address = 0;
+    int data_length = 0;
+    if (j_data_obj != 0) {
+      data_address = env->GetDirectBufferAddress(j_data_obj);
+      data_length = env->GetDirectBufferCapacity(j_data_obj);
+    }
+    void const *validity_address = 0;
+    int validity_length = 0;
+    if (j_validity_obj != 0) {
+      validity_address = env->GetDirectBufferAddress(j_validity_obj);
+      validity_length = env->GetDirectBufferCapacity(j_validity_obj);
+    }
+    void const *offsets_address = 0;
+    int offsets_length = 0;
+    if (j_offsets_obj != 0) {
+      offsets_address = env->GetDirectBufferAddress(j_offsets_obj);
+      offsets_length = env->GetDirectBufferCapacity(j_offsets_obj);
+    }
+    auto data_buffer =
+        arrow::Buffer::Wrap(static_cast<const char *>(data_address), static_cast<int>(data_length));
+    auto null_buffer = arrow::Buffer::Wrap(static_cast<const char *>(validity_address),
+                                           static_cast<int>(validity_length));
+    auto offsets_buffer = arrow::Buffer::Wrap(static_cast<const char *>(offsets_address),
+                                              static_cast<int>(offsets_length));
+
+    std::shared_ptr<arrow::Array> arrow_array;
+    switch (n_type) {
+      case cudf::type_id::DECIMAL32:
+        JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS, "Don't support converting DECIMAL32 yet",
+                      0);
+        break;
+      case cudf::type_id::DECIMAL64:
+        JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS, "Don't support converting DECIMAL64 yet",
+                      0);
+        break;
+      case cudf::type_id::STRUCT:
+        JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS, "Don't support converting STRUCT yet", 0);
+        break;
+      case cudf::type_id::LIST:
+        JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS, "Don't support converting LIST yet", 0);
+        break;
+      case cudf::type_id::DICTIONARY32:
+        JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS,
+                      "Don't support converting DICTIONARY32 yet", 0);
+        break;
+      case cudf::type_id::STRING:
+        arrow_array = std::make_shared<arrow::StringArray>(j_col_length, offsets_buffer,
+                                                           data_buffer, null_buffer, j_null_count);
+        break;
+      default:
+        // this handles the primitive types
+        arrow_array = cudf::detail::to_arrow_array(n_type, j_col_length, data_buffer, null_buffer,
+                                                   j_null_count);
+    }
+    auto name_and_type = arrow::field("col", arrow_array->type());
+    std::vector<std::shared_ptr<arrow::Field>> fields = {name_and_type};
+    std::shared_ptr<arrow::Schema> schema = std::make_shared<arrow::Schema>(fields);
+    auto arrow_table =
+        arrow::Table::Make(schema, std::vector<std::shared_ptr<arrow::Array>>{arrow_array});
+    auto retCols = cudf::from_arrow(*(arrow_table))->release();
+    if (retCols.size() != 1) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "Must result in one column", 0);
+    }
+    return release_as_jlong(retCols[0]);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_stringConcatenation(
+    JNIEnv *env, jclass, jlongArray column_handles, jlong separator, jlong narep,
+    jboolean separate_nulls) {
+  JNI_NULL_CHECK(env, column_handles, "array of column handles is null", 0);
+  JNI_NULL_CHECK(env, separator, "separator string scalar object is null", 0);
+  JNI_NULL_CHECK(env, narep, "narep string scalar object is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const auto &separator_scalar = *reinterpret_cast<cudf::string_scalar *>(separator);
+    const auto &narep_scalar = *reinterpret_cast<cudf::string_scalar *>(narep);
+    auto null_policy = separate_nulls ? cudf::strings::separator_on_nulls::YES :
+                                        cudf::strings::separator_on_nulls::NO;
+
+    cudf::jni::native_jpointerArray<cudf::column_view> n_cudf_columns(env, column_handles);
+    auto column_views = n_cudf_columns.get_dereferenced();
+    return release_as_jlong(cudf::strings::concatenate(
+        cudf::table_view(column_views), separator_scalar, narep_scalar, null_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_stringConcatenationSepCol(
+    JNIEnv *env, jclass, jlongArray column_handles, jlong sep_handle, jlong separator_narep,
+    jlong col_narep, jboolean separate_nulls) {
+  JNI_NULL_CHECK(env, column_handles, "array of column handles is null", 0);
+  JNI_NULL_CHECK(env, sep_handle, "separator column handle is null", 0);
+  JNI_NULL_CHECK(env, separator_narep, "separator narep string scalar object is null", 0);
+  JNI_NULL_CHECK(env, col_narep, "column narep string scalar object is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const auto &separator_narep_scalar = *reinterpret_cast<cudf::string_scalar *>(separator_narep);
+    const auto &col_narep_scalar = *reinterpret_cast<cudf::string_scalar *>(col_narep);
+    auto null_policy = separate_nulls ? cudf::strings::separator_on_nulls::YES :
+                                        cudf::strings::separator_on_nulls::NO;
+
+    cudf::jni::native_jpointerArray<cudf::column_view> n_cudf_columns(env, column_handles);
+    auto column_views = n_cudf_columns.get_dereferenced();
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(sep_handle);
+    cudf::strings_column_view strings_column(*column);
+    return release_as_jlong(cudf::strings::concatenate(cudf::table_view(column_views),
+                                                       strings_column, separator_narep_scalar,
+                                                       col_narep_scalar, null_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_concatListByRow(JNIEnv *env, jclass,
+                                                                         jlongArray column_handles,
+                                                                         jboolean ignore_null) {
+  JNI_NULL_CHECK(env, column_handles, "array of column handles is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto null_policy = ignore_null ? cudf::lists::concatenate_null_policy::IGNORE :
+                                     cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW;
+
+    cudf::jni::native_jpointerArray<cudf::column_view> n_cudf_columns(env, column_handles);
+    auto column_views = n_cudf_columns.get_dereferenced();
+    return release_as_jlong(
+        cudf::lists::concatenate_rows(cudf::table_view(column_views), null_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_makeList(JNIEnv *env, jobject j_object,
+                                                                  jlongArray handles, jlong j_type,
+                                                                  jint scale, jlong row_count) {
+  using ScalarType = cudf::scalar_type_t<cudf::size_type>;
+  JNI_NULL_CHECK(env, handles, "native view handles are null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto children = cudf::jni::native_jpointerArray<cudf::column_view>(env, handles);
+    auto children_vector = children.get_dereferenced();
+    auto zero = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+    zero->set_valid_async(true);
+    static_cast<ScalarType *>(zero.get())->set_value(0);
+
+    if (children.size() == 0) {
+      // special case because cudf::interleave_columns does not support no columns
+      auto offsets = cudf::make_column_from_scalar(*zero, row_count + 1);
+      cudf::data_type n_data_type = cudf::jni::make_data_type(j_type, scale);
+      auto empty_col = cudf::make_empty_column(n_data_type);
+      return release_as_jlong(cudf::make_lists_column(
+          row_count, std::move(offsets), std::move(empty_col), 0, rmm::device_buffer()));
+    } else {
+      auto count = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+      count->set_valid_async(true);
+      static_cast<ScalarType *>(count.get())->set_value(children.size());
+
+      std::unique_ptr<cudf::column> offsets = cudf::sequence(row_count + 1, *zero, *count);
+      auto data_col = cudf::interleave_columns(cudf::table_view(children_vector));
+      return release_as_jlong(cudf::make_lists_column(
+          row_count, std::move(offsets), std::move(data_col), 0, rmm::device_buffer()));
+    }
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_makeListFromOffsets(
+    JNIEnv *env, jobject j_object, jlong child_handle, jlong offsets_handle, jlong row_count) {
+  JNI_NULL_CHECK(env, child_handle, "child_handle is null", 0)
+  JNI_NULL_CHECK(env, offsets_handle, "offsets_handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const child_cv = reinterpret_cast<cudf::column_view const *>(child_handle);
+    auto const offsets_cv = reinterpret_cast<cudf::column_view const *>(offsets_handle);
+    CUDF_EXPECTS(offsets_cv->type().id() == cudf::type_id::INT32,
+                 "Input offsets does not have type INT32.");
+
+    return release_as_jlong(cudf::make_lists_column(
+        static_cast<cudf::size_type>(row_count), std::make_unique<cudf::column>(*offsets_cv),
+        std::make_unique<cudf::column>(*child_cv), 0, {}));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_fromScalar(JNIEnv *env, jclass,
+                                                                    jlong j_scalar,
+                                                                    jint row_count) {
+  JNI_NULL_CHECK(env, j_scalar, "scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto scalar_val = reinterpret_cast<cudf::scalar const *>(j_scalar);
+    if (scalar_val->type().id() == cudf::type_id::STRUCT && row_count == 0) {
+      // Specialize the creation of empty struct column, since libcudf doesn't support it.
+      auto struct_scalar = reinterpret_cast<cudf::struct_scalar const *>(j_scalar);
+      auto children = cudf::empty_like(struct_scalar->view())->release();
+      auto mask_buffer = cudf::create_null_mask(0, cudf::mask_state::UNALLOCATED);
+      return release_as_jlong(
+          cudf::make_structs_column(0, std::move(children), 0, std::move(mask_buffer)));
+    } else {
+      return release_as_jlong(cudf::make_column_from_scalar(*scalar_val, row_count));
+    }
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_concatenate(JNIEnv *env, jclass clazz,
+                                                                     jlongArray column_handles) {
+  JNI_NULL_CHECK(env, column_handles, "input columns are null", 0);
+  using cudf::column;
+  using cudf::column_view;
+  try {
+    cudf::jni::auto_set_device(env);
+    auto columns =
+        cudf::jni::native_jpointerArray<column_view>{env, column_handles}.get_dereferenced();
+    auto const is_lists_column = columns[0].type().id() == cudf::type_id::LIST;
+    return release_as_jlong(
+        is_lists_column ? cudf::lists::detail::concatenate(columns, cudf::get_default_stream(),
+                                                           rmm::mr::get_current_device_resource()) :
+                          cudf::concatenate(columns));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_hash(JNIEnv *env, jobject j_object,
+                                                              jlongArray column_handles,
+                                                              jint hash_function_id, jint seed) {
+  JNI_NULL_CHECK(env, column_handles, "array of column handles is null", 0);
+
+  try {
+    auto column_views =
+        cudf::jni::native_jpointerArray<cudf::column_view>{env, column_handles}.get_dereferenced();
+    return release_as_jlong(cudf::hash(cudf::table_view{column_views},
+                                       static_cast<cudf::hash_id>(hash_function_id), seed));
+  }
+  CATCH_STD(env, 0);
+}
+
+////////
+// Native methods specific to cudf::column. These either take or return a cudf::column
+// instead of a cudf::column_view so they need to be used with caution. These should
+// only be called from the CudfColumn child class.
+////////
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_ColumnVector_deleteCudfColumn(JNIEnv *env,
+                                                                         jobject j_object,
+                                                                         jlong handle) {
+  JNI_NULL_CHECK(env, handle, "column handle is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cudf::column *>(handle);
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_ColumnVector_setNativeNullCountColumn(JNIEnv *env,
+                                                                                 jobject j_object,
+                                                                                 jlong handle,
+                                                                                 jint null_count) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column *column = reinterpret_cast<cudf::column *>(handle);
+    column->set_null_count(null_count);
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_getNativeColumnView(JNIEnv *env,
+                                                                             jobject j_object,
+                                                                             jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column *column = reinterpret_cast<cudf::column *>(handle);
+    return ptr_as_jlong(new cudf::column_view{*column});
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_makeEmptyCudfColumn(JNIEnv *env, jclass,
+                                                                             jint j_type,
+                                                                             jint scale) {
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(j_type, scale);
+    return release_as_jlong(cudf::make_empty_column(n_data_type));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnVector_getNativeNullCountColumn(JNIEnv *env,
+                                                                                 jobject j_object,
+                                                                                 jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column *column = reinterpret_cast<cudf::column *>(handle);
+    return static_cast<jint>(column->null_count());
+  }
+  CATCH_STD(env, 0);
+}
+} // extern "C"
diff --git a/java/src/main/native/src/ColumnViewJni.cpp b/java/src/main/native/src/ColumnViewJni.cpp
new file mode 100644
index 0000000..0ddaa2c
--- /dev/null
+++ b/java/src/main/native/src/ColumnViewJni.cpp
@@ -0,0 +1,2575 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ColumnViewJni.hpp"
+#include <numeric>
+
+#include <jni.h>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/binaryop.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/datetime.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/lists/combine.hpp>
+#include <cudf/lists/contains.hpp>
+#include <cudf/lists/count_elements.hpp>
+#include <cudf/lists/detail/concatenate.hpp>
+#include <cudf/lists/extract.hpp>
+#include <cudf/lists/gather.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/lists/reverse.hpp>
+#include <cudf/lists/set_operations.hpp>
+#include <cudf/lists/sorting.hpp>
+#include <cudf/lists/stream_compaction.hpp>
+#include <cudf/null_mask.hpp>
+#include <cudf/quantiles.hpp>
+#include <cudf/reduction.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/reshape.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/round.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/search.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/strings/attributes.hpp>
+#include <cudf/strings/capitalize.hpp>
+#include <cudf/strings/case.hpp>
+#include <cudf/strings/combine.hpp>
+#include <cudf/strings/contains.hpp>
+#include <cudf/strings/convert/convert_booleans.hpp>
+#include <cudf/strings/convert/convert_datetime.hpp>
+#include <cudf/strings/convert/convert_fixed_point.hpp>
+#include <cudf/strings/convert/convert_floats.hpp>
+#include <cudf/strings/convert/convert_integers.hpp>
+#include <cudf/strings/convert/convert_urls.hpp>
+#include <cudf/strings/extract.hpp>
+#include <cudf/strings/find.hpp>
+#include <cudf/strings/findall.hpp>
+#include <cudf/strings/json.hpp>
+#include <cudf/strings/padding.hpp>
+#include <cudf/strings/regex/regex_program.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/strings/replace.hpp>
+#include <cudf/strings/replace_re.hpp>
+#include <cudf/strings/reverse.hpp>
+#include <cudf/strings/slice.hpp>
+#include <cudf/strings/split/split.hpp>
+#include <cudf/strings/split/split_re.hpp>
+#include <cudf/strings/strip.hpp>
+#include <cudf/structs/structs_column_view.hpp>
+#include <cudf/tdigest/tdigest_column_view.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+#include <cudf/unary.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include "cudf_jni_apis.hpp"
+#include "dtype_utils.hpp"
+#include "jni_utils.hpp"
+#include "maps_column_view.hpp"
+
+using cudf::jni::ptr_as_jlong;
+using cudf::jni::release_as_jlong;
+
+namespace {
+
+std::size_t pad_size(std::size_t size, bool const should_pad_for_cpu) {
+  if (should_pad_for_cpu) {
+    constexpr std::size_t ALIGN = sizeof(std::max_align_t);
+    return (size + (ALIGN - 1)) & ~(ALIGN - 1);
+  } else {
+    return size;
+  }
+}
+
+std::size_t calc_device_memory_size(cudf::column_view const &view, bool const pad_for_cpu) {
+  std::size_t total = 0;
+  auto row_count = view.size();
+
+  if (view.nullable()) {
+    total += pad_size(cudf::bitmask_allocation_size_bytes(row_count), pad_for_cpu);
+  }
+
+  auto dtype = view.type();
+  if (cudf::is_fixed_width(dtype)) {
+    total += pad_size(cudf::size_of(dtype) * view.size(), pad_for_cpu);
+  }
+
+  return std::accumulate(view.child_begin(), view.child_end(), total,
+                         [pad_for_cpu](std::size_t t, cudf::column_view const &v) {
+                           return t + calc_device_memory_size(v, pad_for_cpu);
+                         });
+}
+
+} // anonymous namespace
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_upperStrings(JNIEnv *env, jobject j_object,
+                                                                    jlong handle) {
+  JNI_NULL_CHECK(env, handle, "column is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::strings_column_view strings_column(*column);
+    return release_as_jlong(cudf::strings::to_upper(strings_column));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_lowerStrings(JNIEnv *env, jobject j_object,
+                                                                    jlong handle) {
+  JNI_NULL_CHECK(env, handle, "column is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::strings_column_view strings_column(*column);
+    return release_as_jlong(cudf::strings::to_lower(strings_column));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_replaceNullsScalar(JNIEnv *env, jclass,
+                                                                          jlong j_col,
+                                                                          jlong j_scalar) {
+  JNI_NULL_CHECK(env, j_col, "column is null", 0);
+  JNI_NULL_CHECK(env, j_scalar, "scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view col = *reinterpret_cast<cudf::column_view *>(j_col);
+    auto val = reinterpret_cast<cudf::scalar *>(j_scalar);
+    return release_as_jlong(cudf::replace_nulls(col, *val));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_replaceNullsColumn(JNIEnv *env, jclass,
+                                                                          jlong j_col,
+                                                                          jlong j_replace_col) {
+  JNI_NULL_CHECK(env, j_col, "column is null", 0);
+  JNI_NULL_CHECK(env, j_replace_col, "replacement column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto col = reinterpret_cast<cudf::column_view *>(j_col);
+    auto replacements = reinterpret_cast<cudf::column_view *>(j_replace_col);
+    return release_as_jlong(cudf::replace_nulls(*col, *replacements));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_replaceNullsPolicy(JNIEnv *env, jclass,
+                                                                          jlong j_col,
+                                                                          jboolean is_preceding) {
+  JNI_NULL_CHECK(env, j_col, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view col = *reinterpret_cast<cudf::column_view *>(j_col);
+    return release_as_jlong(cudf::replace_nulls(
+        col, is_preceding ? cudf::replace_policy::PRECEDING : cudf::replace_policy::FOLLOWING));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_distinctCount(JNIEnv *env, jclass,
+                                                                    jlong j_col,
+                                                                    jboolean nulls_included) {
+  JNI_NULL_CHECK(env, j_col, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view col = *reinterpret_cast<cudf::column_view *>(j_col);
+
+    return cudf::distinct_count(
+        col, nulls_included ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE,
+        cudf::nan_policy::NAN_IS_VALID);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_ifElseVV(JNIEnv *env, jclass,
+                                                                jlong j_pred_vec, jlong j_true_vec,
+                                                                jlong j_false_vec) {
+  JNI_NULL_CHECK(env, j_pred_vec, "predicate column is null", 0);
+  JNI_NULL_CHECK(env, j_true_vec, "true column is null", 0);
+  JNI_NULL_CHECK(env, j_false_vec, "false column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto pred_vec = reinterpret_cast<cudf::column_view *>(j_pred_vec);
+    auto true_vec = reinterpret_cast<cudf::column_view *>(j_true_vec);
+    auto false_vec = reinterpret_cast<cudf::column_view *>(j_false_vec);
+    return release_as_jlong(cudf::copy_if_else(*true_vec, *false_vec, *pred_vec));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_ifElseVS(JNIEnv *env, jclass,
+                                                                jlong j_pred_vec, jlong j_true_vec,
+                                                                jlong j_false_scalar) {
+  JNI_NULL_CHECK(env, j_pred_vec, "predicate column is null", 0);
+  JNI_NULL_CHECK(env, j_true_vec, "true column is null", 0);
+  JNI_NULL_CHECK(env, j_false_scalar, "false scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto pred_vec = reinterpret_cast<cudf::column_view *>(j_pred_vec);
+    auto true_vec = reinterpret_cast<cudf::column_view *>(j_true_vec);
+    auto false_scalar = reinterpret_cast<cudf::scalar *>(j_false_scalar);
+    return release_as_jlong(cudf::copy_if_else(*true_vec, *false_scalar, *pred_vec));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_ifElseSV(JNIEnv *env, jclass,
+                                                                jlong j_pred_vec,
+                                                                jlong j_true_scalar,
+                                                                jlong j_false_vec) {
+  JNI_NULL_CHECK(env, j_pred_vec, "predicate column is null", 0);
+  JNI_NULL_CHECK(env, j_true_scalar, "true scalar is null", 0);
+  JNI_NULL_CHECK(env, j_false_vec, "false column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto pred_vec = reinterpret_cast<cudf::column_view *>(j_pred_vec);
+    auto true_scalar = reinterpret_cast<cudf::scalar *>(j_true_scalar);
+    auto false_vec = reinterpret_cast<cudf::column_view *>(j_false_vec);
+    return release_as_jlong(cudf::copy_if_else(*true_scalar, *false_vec, *pred_vec));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_ifElseSS(JNIEnv *env, jclass,
+                                                                jlong j_pred_vec,
+                                                                jlong j_true_scalar,
+                                                                jlong j_false_scalar) {
+  JNI_NULL_CHECK(env, j_pred_vec, "predicate column is null", 0);
+  JNI_NULL_CHECK(env, j_true_scalar, "true scalar is null", 0);
+  JNI_NULL_CHECK(env, j_false_scalar, "false scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto pred_vec = reinterpret_cast<cudf::column_view *>(j_pred_vec);
+    auto true_scalar = reinterpret_cast<cudf::scalar *>(j_true_scalar);
+    auto false_scalar = reinterpret_cast<cudf::scalar *>(j_false_scalar);
+    return release_as_jlong(cudf::copy_if_else(*true_scalar, *false_scalar, *pred_vec));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getElement(JNIEnv *env, jclass, jlong from,
+                                                                  jint index) {
+  JNI_NULL_CHECK(env, from, "from column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto from_vec = reinterpret_cast<cudf::column_view *>(from);
+    return release_as_jlong(cudf::get_element(*from_vec, index));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_reduce(JNIEnv *env, jclass, jlong j_col_view,
+                                                              jlong j_agg, jint j_dtype,
+                                                              jint scale) {
+  JNI_NULL_CHECK(env, j_col_view, "column view is null", 0);
+  JNI_NULL_CHECK(env, j_agg, "aggregation is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto col = reinterpret_cast<cudf::column_view *>(j_col_view);
+    auto agg = reinterpret_cast<cudf::aggregation *>(j_agg);
+    cudf::data_type out_dtype = cudf::jni::make_data_type(j_dtype, scale);
+    return release_as_jlong(
+        cudf::reduce(*col, *dynamic_cast<cudf::reduce_aggregation *>(agg), out_dtype));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_segmentedReduce(
+    JNIEnv *env, jclass, jlong j_data_view, jlong j_offsets_view, jlong j_agg,
+    jboolean include_nulls, jint j_dtype, jint scale) {
+  JNI_NULL_CHECK(env, j_data_view, "data column view is null", 0);
+  JNI_NULL_CHECK(env, j_offsets_view, "offsets column view is null", 0);
+  JNI_NULL_CHECK(env, j_agg, "aggregation is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto data = reinterpret_cast<cudf::column_view *>(j_data_view);
+    auto offsets = reinterpret_cast<cudf::column_view *>(j_offsets_view);
+    auto agg = reinterpret_cast<cudf::aggregation *>(j_agg);
+    auto s_agg = dynamic_cast<cudf::segmented_reduce_aggregation *>(agg);
+    JNI_ARG_CHECK(env, s_agg != nullptr, "agg is not a cudf::segmented_reduce_aggregation", 0)
+    auto null_policy = include_nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
+    cudf::data_type out_dtype = cudf::jni::make_data_type(j_dtype, scale);
+    return release_as_jlong(
+        cudf::segmented_reduce(*data, *offsets, *s_agg, out_dtype, null_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_segmentedGather(
+    JNIEnv *env, jclass, jlong source_column, jlong gather_map_list, jboolean nullify_out_bounds) {
+  JNI_NULL_CHECK(env, source_column, "source column view is null", 0);
+  JNI_NULL_CHECK(env, gather_map_list, "gather map is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const &src_col =
+        cudf::lists_column_view(*reinterpret_cast<cudf::column_view *>(source_column));
+    auto const &gather_map =
+        cudf::lists_column_view(*reinterpret_cast<cudf::column_view *>(gather_map_list));
+    auto out_bounds_policy = nullify_out_bounds ? cudf::out_of_bounds_policy::NULLIFY :
+                                                  cudf::out_of_bounds_policy::DONT_CHECK;
+    return release_as_jlong(cudf::lists::segmented_gather(src_col, gather_map, out_bounds_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_scan(JNIEnv *env, jclass, jlong j_col_view,
+                                                            jlong j_agg, jboolean is_inclusive,
+                                                            jboolean include_nulls) {
+  JNI_NULL_CHECK(env, j_col_view, "column view is null", 0);
+  JNI_NULL_CHECK(env, j_agg, "aggregation is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto col = reinterpret_cast<cudf::column_view *>(j_col_view);
+    auto agg = reinterpret_cast<cudf::aggregation *>(j_agg);
+    auto scan_type = is_inclusive ? cudf::scan_type::INCLUSIVE : cudf::scan_type::EXCLUSIVE;
+    auto null_policy = include_nulls ? cudf::null_policy::INCLUDE : cudf::null_policy::EXCLUDE;
+    return release_as_jlong(
+        cudf::scan(*col, *dynamic_cast<cudf::scan_aggregation *>(agg), scan_type, null_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_approxPercentile(JNIEnv *env, jclass clazz,
+                                                                        jlong input_column,
+                                                                        jlong percentiles_column) {
+  JNI_NULL_CHECK(env, input_column, "input_column native handle is null", 0);
+  JNI_NULL_CHECK(env, percentiles_column, "percentiles_column native handle is null", 0);
+  try {
+    using namespace cudf;
+    using tdigest_column_view = cudf::tdigest::tdigest_column_view;
+    jni::auto_set_device(env);
+    auto const tdigest_view =
+        tdigest_column_view{structs_column_view{*reinterpret_cast<column_view *>(input_column)}};
+    auto const p_percentiles = reinterpret_cast<column_view *>(percentiles_column);
+    return release_as_jlong(percentile_approx(tdigest_view, *p_percentiles));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_quantile(JNIEnv *env, jclass clazz,
+                                                                jlong input_column,
+                                                                jint quantile_method,
+                                                                jdoubleArray jquantiles) {
+  JNI_NULL_CHECK(env, input_column, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jdoubleArray native_quantiles(env, jquantiles);
+    std::vector<double> quantiles(native_quantiles.data(),
+                                  native_quantiles.data() + native_quantiles.size());
+    cudf::column_view *n_input_column = reinterpret_cast<cudf::column_view *>(input_column);
+    cudf::interpolation n_quantile_method = static_cast<cudf::interpolation>(quantile_method);
+    return release_as_jlong(cudf::quantile(*n_input_column, quantiles, n_quantile_method));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_rollingWindow(
+    JNIEnv *env, jclass clazz, jlong input_col, jlong default_output_col, jint min_periods,
+    jlong agg_ptr, jint preceding, jint following, jlong preceding_col, jlong following_col) {
+
+  JNI_NULL_CHECK(env, input_col, "native handle is null", 0);
+  JNI_NULL_CHECK(env, agg_ptr, "aggregation handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_input_col = reinterpret_cast<cudf::column_view *>(input_col);
+    cudf::column_view *n_default_output_col =
+        reinterpret_cast<cudf::column_view *>(default_output_col);
+    cudf::column_view *n_preceding_col = reinterpret_cast<cudf::column_view *>(preceding_col);
+    cudf::column_view *n_following_col = reinterpret_cast<cudf::column_view *>(following_col);
+    cudf::rolling_aggregation *agg =
+        dynamic_cast<cudf::rolling_aggregation *>(reinterpret_cast<cudf::aggregation *>(agg_ptr));
+    JNI_ARG_CHECK(env, agg != nullptr, "aggregation is not an instance of rolling_aggregation", 0);
+
+    std::unique_ptr<cudf::column> ret;
+    if (n_default_output_col != nullptr) {
+      if (n_preceding_col != nullptr && n_following_col != nullptr) {
+        CUDF_FAIL("A default output column is not currently supported with variable length "
+                  "preceding and following");
+        // ret = cudf::rolling_window(*n_input_col, *n_default_output_col,
+        //        *n_preceding_col, *n_following_col, min_periods, agg);
+      } else {
+        ret = cudf::rolling_window(*n_input_col, *n_default_output_col, preceding, following,
+                                   min_periods, *agg);
+      }
+
+    } else {
+      if (n_preceding_col != nullptr && n_following_col != nullptr) {
+        ret = cudf::rolling_window(*n_input_col, *n_preceding_col, *n_following_col, min_periods,
+                                   *agg);
+      } else {
+        ret = cudf::rolling_window(*n_input_col, preceding, following, min_periods, *agg);
+      }
+    }
+    return release_as_jlong(ret);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ColumnView_slice(JNIEnv *env, jclass clazz,
+                                                                  jlong input_column,
+                                                                  jintArray slice_indices) {
+  JNI_NULL_CHECK(env, input_column, "native handle is null", 0);
+  JNI_NULL_CHECK(env, slice_indices, "slice indices are null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_column = reinterpret_cast<cudf::column_view *>(input_column);
+    cudf::jni::native_jintArray n_slice_indices(env, slice_indices);
+    std::vector<cudf::size_type> indices(n_slice_indices.begin(), n_slice_indices.end());
+
+    std::vector<cudf::column_view> result = cudf::slice(*n_column, indices);
+    cudf::jni::native_jlongArray n_result(env, result.size());
+
+    std::transform(result.begin(), result.end(), n_result.begin(),
+                   [](cudf::column_view const &result_col) {
+                     return ptr_as_jlong(new cudf::column{result_col});
+                   });
+
+    return n_result.get_jArray();
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_extractListElement(JNIEnv *env, jclass,
+                                                                          jlong column_view,
+                                                                          jint index) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::lists_column_view lcv(*cv);
+    return release_as_jlong(cudf::lists::extract_list_element(lcv, index));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_extractListElementV(JNIEnv *env, jclass,
+                                                                           jlong column_view,
+                                                                           jlong indices_view) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, indices_view, "indices is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *indices = reinterpret_cast<cudf::column_view *>(indices_view);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::lists_column_view lcv(*cv);
+    return release_as_jlong(cudf::lists::extract_list_element(lcv, *indices));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dropListDuplicates(JNIEnv *env, jclass,
+                                                                          jlong column_view) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_cv = reinterpret_cast<cudf::column_view const *>(column_view);
+    return release_as_jlong(cudf::lists::distinct(cudf::lists_column_view{*input_cv}));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dropListDuplicatesWithKeysValues(
+    JNIEnv *env, jclass, jlong keys_vals_handle) {
+  JNI_NULL_CHECK(env, keys_vals_handle, "keys_vals_handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_cv = reinterpret_cast<cudf::column_view const *>(keys_vals_handle);
+    JNI_ARG_CHECK(env, input_cv->type().id() == cudf::type_id::LIST,
+                  "Input column is not a lists column.", 0);
+
+    auto const lists_keys_vals = cudf::lists_column_view(*input_cv);
+    auto const keys_vals = lists_keys_vals.child();
+    JNI_ARG_CHECK(env, keys_vals.type().id() == cudf::type_id::STRUCT,
+                  "Input column has child that is not a structs column.", 0);
+    JNI_ARG_CHECK(env, keys_vals.num_children() == 2,
+                  "Input column has child that does not have 2 children.", 0);
+
+    return release_as_jlong(
+        cudf::jni::lists_distinct_by_key(lists_keys_vals, cudf::get_default_stream()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_flattenLists(JNIEnv *env, jclass,
+                                                                    jlong input_handle,
+                                                                    jboolean ignore_null) {
+  JNI_NULL_CHECK(env, input_handle, "input_handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const null_policy = ignore_null ? cudf::lists::concatenate_null_policy::IGNORE :
+                                           cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW;
+    auto const input_cv = reinterpret_cast<cudf::column_view const *>(input_handle);
+    return release_as_jlong(cudf::lists::concatenate_list_elements(*input_cv, null_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listContains(JNIEnv *env, jclass,
+                                                                    jlong column_view,
+                                                                    jlong lookup_key) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_key, "lookup scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::lists_column_view lcv(*cv);
+    cudf::scalar *lookup_scalar = reinterpret_cast<cudf::scalar *>(lookup_key);
+    return release_as_jlong(cudf::lists::contains(lcv, *lookup_scalar));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listContainsNulls(JNIEnv *env, jclass,
+                                                                         jlong column_view) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cv = reinterpret_cast<cudf::column_view *>(column_view);
+    auto lcv = cudf::lists_column_view{*cv};
+    return release_as_jlong(cudf::lists::contains_nulls(lcv));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listContainsColumn(JNIEnv *env, jclass,
+                                                                          jlong column_view,
+                                                                          jlong lookup_key_cv) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_key_cv, "lookup column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::lists_column_view lcv(*cv);
+    cudf::column_view *lookup_cv = reinterpret_cast<cudf::column_view *>(lookup_key_cv);
+    return release_as_jlong(cudf::lists::contains(lcv, *lookup_cv));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listIndexOfScalar(JNIEnv *env, jclass,
+                                                                         jlong column_view,
+                                                                         jlong lookup_key,
+                                                                         jboolean is_find_first) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_key, "lookup scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = reinterpret_cast<cudf::column_view const *>(column_view);
+    auto const lcv = cudf::lists_column_view{*cv};
+    auto const lookup_key_scalar = reinterpret_cast<cudf::scalar const *>(lookup_key);
+    auto const find_option = is_find_first ? cudf::lists::duplicate_find_option::FIND_FIRST :
+                                             cudf::lists::duplicate_find_option::FIND_LAST;
+    return release_as_jlong(cudf::lists::index_of(lcv, *lookup_key_scalar, find_option));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listIndexOfColumn(JNIEnv *env, jclass,
+                                                                         jlong column_view,
+                                                                         jlong lookup_keys,
+                                                                         jboolean is_find_first) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_keys, "lookup key column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = reinterpret_cast<cudf::column_view const *>(column_view);
+    auto const lcv = cudf::lists_column_view{*cv};
+    auto const lookup_key_column = reinterpret_cast<cudf::column_view const *>(lookup_keys);
+    auto const find_option = is_find_first ? cudf::lists::duplicate_find_option::FIND_FIRST :
+                                             cudf::lists::duplicate_find_option::FIND_LAST;
+    return release_as_jlong(cudf::lists::index_of(lcv, *lookup_key_column, find_option));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listSortRows(JNIEnv *env, jclass,
+                                                                    jlong column_view,
+                                                                    jboolean is_descending,
+                                                                    jboolean is_null_smallest) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto sort_order = is_descending ? cudf::order::DESCENDING : cudf::order::ASCENDING;
+    auto null_order = is_null_smallest ? cudf::null_order::BEFORE : cudf::null_order::AFTER;
+    auto *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    return release_as_jlong(
+        cudf::lists::sort_lists(cudf::lists_column_view(*cv), sort_order, null_order));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_generateListOffsets(JNIEnv *env, jclass,
+                                                                           jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = reinterpret_cast<cudf::column_view const *>(handle);
+    return release_as_jlong(cudf::jni::generate_list_offsets(*cv));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listsHaveOverlap(JNIEnv *env, jclass,
+                                                                        jlong lhs_handle,
+                                                                        jlong rhs_handle) {
+  JNI_NULL_CHECK(env, lhs_handle, "lhs_handle is null", 0)
+  JNI_NULL_CHECK(env, rhs_handle, "rhs_handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const lhs = reinterpret_cast<cudf::column_view const *>(lhs_handle);
+    auto const rhs = reinterpret_cast<cudf::column_view const *>(rhs_handle);
+    auto overlap_result =
+        cudf::lists::have_overlap(cudf::lists_column_view{*lhs}, cudf::lists_column_view{*rhs},
+                                  cudf::null_equality::UNEQUAL, cudf::nan_equality::ALL_EQUAL);
+    cudf::jni::post_process_list_overlap(*lhs, *rhs, overlap_result);
+    return release_as_jlong(overlap_result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listsIntersectDistinct(JNIEnv *env, jclass,
+                                                                              jlong lhs_handle,
+                                                                              jlong rhs_handle) {
+  JNI_NULL_CHECK(env, lhs_handle, "lhs_handle is null", 0)
+  JNI_NULL_CHECK(env, rhs_handle, "rhs_handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const lhs = reinterpret_cast<cudf::column_view const *>(lhs_handle);
+    auto const rhs = reinterpret_cast<cudf::column_view const *>(rhs_handle);
+    return release_as_jlong(cudf::lists::intersect_distinct(
+        cudf::lists_column_view{*lhs}, cudf::lists_column_view{*rhs}, cudf::null_equality::EQUAL,
+        cudf::nan_equality::ALL_EQUAL));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listsUnionDistinct(JNIEnv *env, jclass,
+                                                                          jlong lhs_handle,
+                                                                          jlong rhs_handle) {
+  JNI_NULL_CHECK(env, lhs_handle, "lhs_handle is null", 0)
+  JNI_NULL_CHECK(env, rhs_handle, "rhs_handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const lhs = reinterpret_cast<cudf::column_view const *>(lhs_handle);
+    auto const rhs = reinterpret_cast<cudf::column_view const *>(rhs_handle);
+    return release_as_jlong(
+        cudf::lists::union_distinct(cudf::lists_column_view{*lhs}, cudf::lists_column_view{*rhs},
+                                    cudf::null_equality::EQUAL, cudf::nan_equality::ALL_EQUAL));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_listsDifferenceDistinct(JNIEnv *env, jclass,
+                                                                               jlong lhs_handle,
+                                                                               jlong rhs_handle) {
+  JNI_NULL_CHECK(env, lhs_handle, "lhs_handle is null", 0)
+  JNI_NULL_CHECK(env, rhs_handle, "rhs_handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const lhs = reinterpret_cast<cudf::column_view const *>(lhs_handle);
+    auto const rhs = reinterpret_cast<cudf::column_view const *>(rhs_handle);
+    return release_as_jlong(cudf::lists::difference_distinct(
+        cudf::lists_column_view{*lhs}, cudf::lists_column_view{*rhs}, cudf::null_equality::EQUAL,
+        cudf::nan_equality::ALL_EQUAL));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_reverseStringsOrLists(JNIEnv *env, jclass,
+                                                                             jlong input_handle) {
+  JNI_NULL_CHECK(env, input_handle, "input_handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+
+    auto const input = reinterpret_cast<cudf::column_view const *>(input_handle);
+    switch (input->type().id()) {
+      case cudf::type_id::STRING:
+        return release_as_jlong(cudf::strings::reverse(cudf::strings_column_view{*input}));
+      case cudf::type_id::LIST:
+        return release_as_jlong(cudf::lists::reverse(cudf::lists_column_view{*input}));
+      default:
+        JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                      "A column of type string or list is required for reverse()", 0);
+    }
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ColumnView_stringSplit(JNIEnv *env, jclass,
+                                                                        jlong input_handle,
+                                                                        jstring delimiter_obj,
+                                                                        jint limit) {
+  JNI_NULL_CHECK(env, input_handle, "input_handle is null", 0);
+
+  if (limit == 0 || limit == 1) {
+    // Cannot achieve the results of splitting with limit == 0 or limit == 1.
+    // This is because cudf operates on a different parameter (`max_split`) which is converted from
+    // limit. When limit == 0 or limit == 1, max_split will be non-positive and will result in an
+    // unlimited split.
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "limit == 0 and limit == 1 are not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::column_view const *>(input_handle);
+    auto const strings_column = cudf::strings_column_view{*input};
+    auto const delimiter_jstr = cudf::jni::native_jstring(env, delimiter_obj);
+    auto const delimiter = std::string(delimiter_jstr.get(), delimiter_jstr.size_bytes());
+    auto const max_split = limit > 1 ? limit - 1 : limit;
+    auto result = cudf::strings::split(strings_column, cudf::string_scalar{delimiter}, max_split);
+    return cudf::jni::convert_table_for_return(env, std::move(result));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ColumnView_stringSplitRe(
+    JNIEnv *env, jclass, jlong input_handle, jstring pattern_obj, jint regex_flags,
+    jint capture_groups, jint limit) {
+  JNI_NULL_CHECK(env, input_handle, "input_handle is null", 0);
+
+  if (limit == 0 || limit == 1) {
+    // Cannot achieve the results of splitting with limit == 0 or limit == 1.
+    // This is because cudf operates on a different parameter (`max_split`) which is converted from
+    // limit. When limit == 0 or limit == 1, max_split will be non-positive and will result in an
+    // unlimited split.
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "limit == 0 and limit == 1 are not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::column_view const *>(input_handle);
+    auto const strings_column = cudf::strings_column_view{*input};
+    auto const pattern_jstr = cudf::jni::native_jstring(env, pattern_obj);
+    auto const pattern = std::string(pattern_jstr.get(), pattern_jstr.size_bytes());
+    auto const max_split = limit > 1 ? limit - 1 : limit;
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern, flags, groups);
+    auto result = cudf::strings::split_re(strings_column, *regex_prog, max_split);
+    return cudf::jni::convert_table_for_return(env, std::move(result));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringSplitRecord(JNIEnv *env, jclass,
+                                                                         jlong input_handle,
+                                                                         jstring delimiter_obj,
+                                                                         jint limit) {
+  JNI_NULL_CHECK(env, input_handle, "input_handle is null", 0);
+
+  if (limit == 0 || limit == 1) {
+    // Cannot achieve the results of splitting with limit == 0 or limit == 1.
+    // This is because cudf operates on a different parameter (`max_split`) which is converted from
+    // limit. When limit == 0 or limit == 1, max_split will be non-positive and will result in an
+    // unlimited split.
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "limit == 0 and limit == 1 are not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::column_view const *>(input_handle);
+    auto const strings_column = cudf::strings_column_view{*input};
+    auto const delimiter_jstr = cudf::jni::native_jstring(env, delimiter_obj);
+    auto const delimiter = std::string(delimiter_jstr.get(), delimiter_jstr.size_bytes());
+    auto const max_split = limit > 1 ? limit - 1 : limit;
+    auto result =
+        cudf::strings::split_record(strings_column, cudf::string_scalar{delimiter}, max_split);
+    return release_as_jlong(result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringSplitRecordRe(
+    JNIEnv *env, jclass, jlong input_handle, jstring pattern_obj, jint regex_flags,
+    jint capture_groups, jint limit) {
+  JNI_NULL_CHECK(env, input_handle, "input_handle is null", 0);
+
+  if (limit == 0 || limit == 1) {
+    // Cannot achieve the results of splitting with limit == 0 or limit == 1.
+    // This is because cudf operates on a different parameter (`max_split`) which is converted from
+    // limit. When limit == 0 or limit == 1, max_split will be non-positive and will result in an
+    // unlimited split.
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "limit == 0 and limit == 1 are not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::column_view const *>(input_handle);
+    auto const strings_column = cudf::strings_column_view{*input};
+    auto const pattern_jstr = cudf::jni::native_jstring(env, pattern_obj);
+    auto const pattern = std::string(pattern_jstr.get(), pattern_jstr.size_bytes());
+    auto const max_split = limit > 1 ? limit - 1 : limit;
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern, flags, groups);
+    auto result = cudf::strings::split_record_re(strings_column, *regex_prog, max_split);
+    return release_as_jlong(result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ColumnView_split(JNIEnv *env, jclass clazz,
+                                                                  jlong input_column,
+                                                                  jintArray split_indices) {
+  JNI_NULL_CHECK(env, input_column, "native handle is null", 0);
+  JNI_NULL_CHECK(env, split_indices, "split indices are null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_column = reinterpret_cast<cudf::column_view *>(input_column);
+    cudf::jni::native_jintArray n_split_indices(env, split_indices);
+    std::vector<cudf::size_type> indices(n_split_indices.begin(), n_split_indices.end());
+
+    std::vector<cudf::column_view> result = cudf::split(*n_column, indices);
+    cudf::jni::native_jlongArray n_result(env, result.size());
+
+    std::transform(result.begin(), result.end(), n_result.begin(),
+                   [](cudf::column_view const &result_col) {
+                     return ptr_as_jlong(new cudf::column_view{result_col});
+                   });
+
+    return n_result.get_jArray();
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_countElements(JNIEnv *env, jclass clazz,
+                                                                     jlong view_handle) {
+  JNI_NULL_CHECK(env, view_handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_column = reinterpret_cast<cudf::column_view *>(view_handle);
+    return release_as_jlong(cudf::lists::count_elements(cudf::lists_column_view(*n_column)));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_charLengths(JNIEnv *env, jclass clazz,
+                                                                   jlong view_handle) {
+  JNI_NULL_CHECK(env, view_handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_column = reinterpret_cast<cudf::column_view *>(view_handle);
+    return release_as_jlong(cudf::strings::count_characters(cudf::strings_column_view(*n_column)));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_byteCount(JNIEnv *env, jclass clazz,
+                                                                 jlong view_handle) {
+  JNI_NULL_CHECK(env, view_handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_column = reinterpret_cast<cudf::column_view *>(view_handle);
+    return release_as_jlong(cudf::strings::count_bytes(cudf::strings_column_view(*n_column)));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_findAndReplaceAll(JNIEnv *env, jclass clazz,
+                                                                         jlong old_values_handle,
+                                                                         jlong new_values_handle,
+                                                                         jlong input_handle) {
+  JNI_NULL_CHECK(env, old_values_handle, "values column is null", 0);
+  JNI_NULL_CHECK(env, new_values_handle, "replace column is null", 0);
+  JNI_NULL_CHECK(env, input_handle, "input column is null", 0);
+
+  using cudf::column;
+  using cudf::column_view;
+
+  try {
+    cudf::jni::auto_set_device(env);
+    column_view *input_column = reinterpret_cast<column_view *>(input_handle);
+    column_view *old_values_column = reinterpret_cast<column_view *>(old_values_handle);
+    column_view *new_values_column = reinterpret_cast<column_view *>(new_values_handle);
+    return release_as_jlong(
+        cudf::find_and_replace_all(*input_column, *old_values_column, *new_values_column));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isNullNative(JNIEnv *env, jclass,
+                                                                    jlong handle) {
+  JNI_NULL_CHECK(env, handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::is_null(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isNotNullNative(JNIEnv *env, jclass,
+                                                                       jlong handle) {
+  JNI_NULL_CHECK(env, handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::is_valid(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isNanNative(JNIEnv *env, jclass,
+                                                                   jlong handle) {
+  JNI_NULL_CHECK(env, handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::is_nan(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isNotNanNative(JNIEnv *env, jclass,
+                                                                      jlong handle) {
+  JNI_NULL_CHECK(env, handle, "input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::is_not_nan(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_unaryOperation(JNIEnv *env, jclass,
+                                                                      jlong input_ptr,
+                                                                      jint int_op) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    cudf::unary_operator op = static_cast<cudf::unary_operator>(int_op);
+    return release_as_jlong(cudf::unary_operation(*input, op));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_round(JNIEnv *env, jclass, jlong input_ptr,
+                                                             jint decimal_places,
+                                                             jint rounding_method) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    cudf::rounding_method method = static_cast<cudf::rounding_method>(rounding_method);
+    return release_as_jlong(cudf::round(*input, decimal_places, method));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_year(JNIEnv *env, jclass, jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_year(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_month(JNIEnv *env, jclass, jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_month(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_day(JNIEnv *env, jclass, jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_day(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_hour(JNIEnv *env, jclass, jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_hour(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_minute(JNIEnv *env, jclass,
+                                                              jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_minute(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_second(JNIEnv *env, jclass,
+                                                              jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_second(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_weekDay(JNIEnv *env, jclass,
+                                                               jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_weekday(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_lastDayOfMonth(JNIEnv *env, jclass,
+                                                                      jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::last_day_of_month(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_dayOfYear(JNIEnv *env, jclass,
+                                                                 jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::day_of_year(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_quarterOfYear(JNIEnv *env, jclass,
+                                                                     jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::extract_quarter(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_addCalendricalMonths(JNIEnv *env, jclass,
+                                                                            jlong ts_ptr,
+                                                                            jlong months_ptr) {
+  JNI_NULL_CHECK(env, ts_ptr, "ts is null", 0);
+  JNI_NULL_CHECK(env, months_ptr, "months is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *ts = reinterpret_cast<cudf::column_view *>(ts_ptr);
+    const cudf::column_view *months = reinterpret_cast<cudf::column_view *>(months_ptr);
+    return release_as_jlong(cudf::datetime::add_calendrical_months(*ts, *months));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isLeapYear(JNIEnv *env, jclass,
+                                                                  jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::datetime::is_leap_year(*input));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_castTo(JNIEnv *env, jclass, jlong handle,
+                                                              jint type, jint scale) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(type, scale);
+    if (n_data_type == column->type()) {
+      return ptr_as_jlong(new cudf::column(*column));
+    }
+    if (n_data_type.id() == cudf::type_id::STRING) {
+      switch (column->type().id()) {
+        case cudf::type_id::BOOL8: return release_as_jlong(cudf::strings::from_booleans(*column));
+        case cudf::type_id::FLOAT32:
+        case cudf::type_id::FLOAT64: return release_as_jlong(cudf::strings::from_floats(*column));
+        case cudf::type_id::INT8:
+        case cudf::type_id::UINT8:
+        case cudf::type_id::INT16:
+        case cudf::type_id::UINT16:
+        case cudf::type_id::INT32:
+        case cudf::type_id::UINT32:
+        case cudf::type_id::INT64:
+        case cudf::type_id::UINT64: return release_as_jlong(cudf::strings::from_integers(*column));
+        case cudf::type_id::DECIMAL32:
+        case cudf::type_id::DECIMAL64:
+        case cudf::type_id::DECIMAL128:
+          return release_as_jlong(cudf::strings::from_fixed_point(*column));
+        default: JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "Invalid data type", 0);
+      }
+    } else if (column->type().id() == cudf::type_id::STRING) {
+      switch (n_data_type.id()) {
+        case cudf::type_id::BOOL8: return release_as_jlong(cudf::strings::to_booleans(*column));
+        case cudf::type_id::FLOAT32:
+        case cudf::type_id::FLOAT64:
+          return release_as_jlong(cudf::strings::to_floats(*column, n_data_type));
+        case cudf::type_id::INT8:
+        case cudf::type_id::UINT8:
+        case cudf::type_id::INT16:
+        case cudf::type_id::UINT16:
+        case cudf::type_id::INT32:
+        case cudf::type_id::UINT32:
+        case cudf::type_id::INT64:
+        case cudf::type_id::UINT64:
+          return release_as_jlong(cudf::strings::to_integers(*column, n_data_type));
+        case cudf::type_id::DECIMAL32:
+        case cudf::type_id::DECIMAL64:
+        case cudf::type_id::DECIMAL128:
+          return release_as_jlong(cudf::strings::to_fixed_point(*column, n_data_type));
+        default: JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "Invalid data type", 0);
+      }
+    } else if (cudf::is_timestamp(n_data_type) && cudf::is_numeric(column->type())) {
+      // This is a temporary workaround to allow Java to cast from integral types into a timestamp
+      // without forcing an intermediate duration column to be manifested.  Ultimately this style of
+      // "reinterpret" casting will be supported via https://github.com/rapidsai/cudf/pull/5358
+      if (n_data_type.id() == cudf::type_id::TIMESTAMP_DAYS) {
+        if (column->type().id() != cudf::type_id::INT32) {
+          JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                        "Numeric cast to TIMESTAMP_DAYS requires INT32", 0);
+        }
+      } else {
+        if (column->type().id() != cudf::type_id::INT64) {
+          JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                        "Numeric cast to non-day timestamp requires INT64", 0);
+        }
+      }
+      cudf::data_type duration_type = cudf::jni::timestamp_to_duration(n_data_type);
+      cudf::column_view duration_view = cudf::column_view(
+          duration_type, column->size(), column->head(), column->null_mask(), column->null_count());
+      return release_as_jlong(cudf::cast(duration_view, n_data_type));
+    } else if (cudf::is_timestamp(column->type()) && cudf::is_numeric(n_data_type)) {
+      // This is a temporary workaround to allow Java to cast from timestamp types to integral types
+      // without forcing an intermediate duration column to be manifested.  Ultimately this style of
+      // "reinterpret" casting will be supported via https://github.com/rapidsai/cudf/pull/5358
+      cudf::data_type duration_type = cudf::jni::timestamp_to_duration(column->type());
+      cudf::column_view duration_view = cudf::column_view(
+          duration_type, column->size(), column->head(), column->null_mask(), column->null_count());
+      return release_as_jlong(cudf::cast(duration_view, n_data_type));
+    } else {
+      return release_as_jlong(cudf::cast(*column, n_data_type));
+    }
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_bitCastTo(JNIEnv *env, jclass, jlong handle,
+                                                                 jint type, jint scale) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(type, scale);
+    return ptr_as_jlong(new cudf::column_view{cudf::bit_cast(*column, n_data_type)});
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_byteListCast(JNIEnv *env, jobject j_object,
+                                                                    jlong handle,
+                                                                    jboolean endianness_config) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::flip_endianness config(static_cast<cudf::flip_endianness>(endianness_config));
+    return release_as_jlong(byte_cast(*column, config));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringTimestampToTimestamp(
+    JNIEnv *env, jobject j_object, jlong handle, jint time_unit, jstring formatObj) {
+  JNI_NULL_CHECK(env, handle, "column is null", 0);
+  JNI_NULL_CHECK(env, formatObj, "format is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring format(env, formatObj);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::strings_column_view strings_column(*column);
+
+    return release_as_jlong(cudf::strings::to_timestamps(
+        strings_column, cudf::data_type(static_cast<cudf::type_id>(time_unit)), format.get()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isTimestamp(JNIEnv *env, jclass,
+                                                                   jlong handle,
+                                                                   jstring formatObj) {
+  JNI_NULL_CHECK(env, handle, "column is null", 0);
+  JNI_NULL_CHECK(env, formatObj, "format is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring format(env, formatObj);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::strings_column_view strings_column(*column);
+    return release_as_jlong(cudf::strings::is_timestamp(strings_column, format.get()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_timestampToStringTimestamp(
+    JNIEnv *env, jobject j_object, jlong handle, jstring j_format) {
+  JNI_NULL_CHECK(env, handle, "column is null", 0);
+  JNI_NULL_CHECK(env, j_format, "format is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring format(env, j_format);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::strings::from_timestamps(*column, format.get()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jboolean JNICALL Java_ai_rapids_cudf_ColumnView_containsScalar(JNIEnv *env,
+                                                                         jobject j_object,
+                                                                         jlong j_view_handle,
+                                                                         jlong j_scalar_handle) {
+  JNI_NULL_CHECK(env, j_view_handle, "haystack vector is null", false);
+  JNI_NULL_CHECK(env, j_scalar_handle, "scalar needle is null", false);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column_view = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::scalar *scalar = reinterpret_cast<cudf::scalar *>(j_scalar_handle);
+
+    return cudf::contains(*column_view, *scalar);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_containsVector(JNIEnv *env, jobject j_object,
+                                                                      jlong j_values_handle,
+                                                                      jlong j_search_space_handle) {
+  JNI_NULL_CHECK(env, j_values_handle, "values vector is null", false);
+  JNI_NULL_CHECK(env, j_search_space_handle, "search_space vector is null", false);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const search_space_ptr =
+        reinterpret_cast<cudf::column_view const *>(j_search_space_handle);
+    auto const values_ptr = reinterpret_cast<cudf::column_view const *>(j_values_handle);
+
+    // The C++ API `cudf::contains` requires that the search space is the first parameter.
+    return release_as_jlong(cudf::contains(*search_space_ptr, *values_ptr));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_transform(JNIEnv *env, jobject j_object,
+                                                                 jlong handle, jstring j_udf,
+                                                                 jboolean j_is_ptx) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::jni::native_jstring n_j_udf(env, j_udf);
+    std::string n_udf(n_j_udf.get());
+    return release_as_jlong(
+        cudf::transform(*column, n_udf, cudf::data_type(cudf::type_id::INT32), j_is_ptx));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringStartWith(JNIEnv *env,
+                                                                       jobject j_object,
+                                                                       jlong j_view_handle,
+                                                                       jlong comp_string) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", false);
+  JNI_NULL_CHECK(env, comp_string, "comparison string scalar is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column_view = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::strings_column_view strings_column(*column_view);
+    cudf::string_scalar *comp_scalar = reinterpret_cast<cudf::string_scalar *>(comp_string);
+    return release_as_jlong(cudf::strings::starts_with(strings_column, *comp_scalar));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringEndWith(JNIEnv *env, jobject j_object,
+                                                                     jlong j_view_handle,
+                                                                     jlong comp_string) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", false);
+  JNI_NULL_CHECK(env, comp_string, "comparison string scalar is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column_view = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::strings_column_view strings_column(*column_view);
+    cudf::string_scalar *comp_scalar = reinterpret_cast<cudf::string_scalar *>(comp_string);
+    return release_as_jlong(cudf::strings::ends_with(strings_column, *comp_scalar));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringContains(JNIEnv *env, jobject j_object,
+                                                                      jlong j_view_handle,
+                                                                      jlong comp_string) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", false);
+  JNI_NULL_CHECK(env, comp_string, "comparison string scalar is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column_view = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::strings_column_view strings_column(*column_view);
+    cudf::string_scalar *comp_scalar = reinterpret_cast<cudf::string_scalar *>(comp_string);
+    return release_as_jlong(cudf::strings::contains(strings_column, *comp_scalar));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_matchesRe(JNIEnv *env, jobject j_object,
+                                                                 jlong j_view_handle,
+                                                                 jstring pattern_obj,
+                                                                 jint regex_flags,
+                                                                 jint capture_groups) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", false);
+  JNI_NULL_CHECK(env, pattern_obj, "pattern is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const column_view = reinterpret_cast<cudf::column_view const *>(j_view_handle);
+    auto const strings_column = cudf::strings_column_view{*column_view};
+    auto const pattern = cudf::jni::native_jstring(env, pattern_obj);
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern.get(), flags, groups);
+    return release_as_jlong(cudf::strings::matches_re(strings_column, *regex_prog));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_containsRe(JNIEnv *env, jobject j_object,
+                                                                  jlong j_view_handle,
+                                                                  jstring pattern_obj,
+                                                                  jint regex_flags,
+                                                                  jint capture_groups) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", false);
+  JNI_NULL_CHECK(env, pattern_obj, "pattern is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const column_view = reinterpret_cast<cudf::column_view const *>(j_view_handle);
+    auto const strings_column = cudf::strings_column_view{*column_view};
+    auto const pattern = cudf::jni::native_jstring(env, pattern_obj);
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const capture = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern.get(), flags, capture);
+    return release_as_jlong(cudf::strings::contains_re(strings_column, *regex_prog));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_like(JNIEnv *env, jobject j_object,
+                                                            jlong j_view_handle, jlong pattern,
+                                                            jlong escapeChar) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", false);
+  JNI_NULL_CHECK(env, pattern, "pattern is null", false);
+  JNI_NULL_CHECK(env, escapeChar, "escape character is null", false);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const column_view = reinterpret_cast<cudf::column_view const *>(j_view_handle);
+    auto const strings_column = cudf::strings_column_view{*column_view};
+    auto const pattern_scalar = reinterpret_cast<cudf::string_scalar const *>(pattern);
+    auto const escape_scalar = reinterpret_cast<cudf::string_scalar const *>(escapeChar);
+    return release_as_jlong(cudf::strings::like(strings_column, *pattern_scalar, *escape_scalar));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_binaryOpVV(JNIEnv *env, jclass,
+                                                                  jlong lhs_view, jlong rhs_view,
+                                                                  jint int_op, jint out_dtype,
+                                                                  jint scale) {
+  JNI_NULL_CHECK(env, lhs_view, "lhs is null", 0);
+  JNI_NULL_CHECK(env, rhs_view, "rhs is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto lhs = reinterpret_cast<cudf::column_view *>(lhs_view);
+    auto rhs = reinterpret_cast<cudf::column_view *>(rhs_view);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(out_dtype, scale);
+    cudf::binary_operator op = static_cast<cudf::binary_operator>(int_op);
+
+    if (lhs->type().id() == cudf::type_id::STRUCT) {
+      auto out = make_fixed_width_column(n_data_type, lhs->size(), cudf::mask_state::UNALLOCATED);
+
+      if (op == cudf::binary_operator::NULL_EQUALS) {
+        out->set_null_mask(rmm::device_buffer{}, 0);
+      } else {
+        auto [new_mask, null_count] = cudf::bitmask_and(cudf::table_view{{*lhs, *rhs}});
+        out->set_null_mask(std::move(new_mask), null_count);
+      }
+
+      auto out_view = out->mutable_view();
+      cudf::binops::compiled::detail::apply_sorting_struct_binary_op(
+          out_view, *lhs, *rhs, false, false, op, cudf::get_default_stream());
+      return release_as_jlong(out);
+    }
+
+    return release_as_jlong(cudf::binary_operation(*lhs, *rhs, op, n_data_type));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_fixedPointOutputScale(JNIEnv *env, jclass,
+                                                                            jint int_op,
+                                                                            jint lhs_scale,
+                                                                            jint rhs_scale) {
+  try {
+    // we just return the scale as the types will be the same as the lhs input
+    return cudf::binary_operation_fixed_point_scale(static_cast<cudf::binary_operator>(int_op),
+                                                    lhs_scale, rhs_scale);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_binaryOpVS(JNIEnv *env, jclass,
+                                                                  jlong lhs_view, jlong rhs_ptr,
+                                                                  jint int_op, jint out_dtype,
+                                                                  jint scale) {
+  JNI_NULL_CHECK(env, lhs_view, "lhs is null", 0);
+  JNI_NULL_CHECK(env, rhs_ptr, "rhs is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto lhs = reinterpret_cast<cudf::column_view *>(lhs_view);
+    cudf::scalar *rhs = reinterpret_cast<cudf::scalar *>(rhs_ptr);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(out_dtype, scale);
+    cudf::binary_operator op = static_cast<cudf::binary_operator>(int_op);
+
+    if (lhs->type().id() == cudf::type_id::STRUCT) {
+      auto out = make_fixed_width_column(n_data_type, lhs->size(), cudf::mask_state::UNALLOCATED);
+
+      if (op == cudf::binary_operator::NULL_EQUALS) {
+        out->set_null_mask(rmm::device_buffer{}, 0);
+      } else {
+        auto [new_mask, new_null_count] = cudf::binops::scalar_col_valid_mask_and(*lhs, *rhs);
+        out->set_null_mask(std::move(new_mask), new_null_count);
+      }
+
+      auto rhsv = cudf::make_column_from_scalar(*rhs, 1);
+      auto out_view = out->mutable_view();
+      cudf::binops::compiled::detail::apply_sorting_struct_binary_op(
+          out_view, *lhs, rhsv->view(), false, true, op, cudf::get_default_stream());
+      return release_as_jlong(out);
+    }
+
+    return release_as_jlong(cudf::binary_operation(*lhs, *rhs, op, n_data_type));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_substringS(JNIEnv *env, jclass,
+                                                                  jlong cv_handle, jint start) {
+  JNI_NULL_CHECK(env, cv_handle, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = reinterpret_cast<cudf::column_view const *>(cv_handle);
+    auto const scv = cudf::strings_column_view{*cv};
+    return release_as_jlong(cudf::strings::slice_strings(scv, start));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_substring(JNIEnv *env, jclass,
+                                                                 jlong column_view, jint start,
+                                                                 jint end) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    return release_as_jlong(cudf::strings::slice_strings(scv, start, end));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_substringColumn(JNIEnv *env, jclass,
+                                                                       jlong column_view,
+                                                                       jlong start_column,
+                                                                       jlong end_column) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, start_column, "column is null", 0);
+  JNI_NULL_CHECK(env, end_column, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::column_view *sc = reinterpret_cast<cudf::column_view *>(start_column);
+    cudf::column_view *ec = reinterpret_cast<cudf::column_view *>(end_column);
+    return release_as_jlong(cudf::strings::slice_strings(scv, *sc, *ec));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_substringLocate(JNIEnv *env, jclass,
+                                                                       jlong column_view,
+                                                                       jlong substring, jint start,
+                                                                       jint end) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, substring, "target string scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::string_scalar *ss_scalar = reinterpret_cast<cudf::string_scalar *>(substring);
+    return release_as_jlong(cudf::strings::find(scv, *ss_scalar, start, end));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringReplace(JNIEnv *env, jclass,
+                                                                     jlong column_view,
+                                                                     jlong target, jlong replace) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, target, "target string scalar is null", 0);
+  JNI_NULL_CHECK(env, replace, "replace string scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::string_scalar *ss_target = reinterpret_cast<cudf::string_scalar *>(target);
+    cudf::string_scalar *ss_replace = reinterpret_cast<cudf::string_scalar *>(replace);
+    return release_as_jlong(cudf::strings::replace(scv, *ss_target, *ss_replace));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringReplaceMulti(JNIEnv *env, jclass,
+                                                                          jlong inputs_cv,
+                                                                          jlong targets_cv,
+                                                                          jlong repls_cv) {
+  JNI_NULL_CHECK(env, inputs_cv, "column is null", 0);
+  JNI_NULL_CHECK(env, targets_cv, "targets string column view is null", 0);
+  JNI_NULL_CHECK(env, repls_cv, "repls string column view is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(inputs_cv);
+    cudf::strings_column_view scv(*cv);
+    cudf::column_view *cvtargets = reinterpret_cast<cudf::column_view *>(targets_cv);
+    cudf::strings_column_view scvtargets(*cvtargets);
+    cudf::column_view *cvrepls = reinterpret_cast<cudf::column_view *>(repls_cv);
+    cudf::strings_column_view scvrepls(*cvrepls);
+    return release_as_jlong(cudf::strings::replace(scv, scvtargets, scvrepls));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_mapLookupForKeys(JNIEnv *env, jclass,
+                                                                        jlong map_column_view,
+                                                                        jlong lookup_keys) {
+  JNI_NULL_CHECK(env, map_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_keys, "lookup key is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const *cv = reinterpret_cast<cudf::column_view *>(map_column_view);
+    auto const *column_keys = reinterpret_cast<cudf::column_view *>(lookup_keys);
+    auto const maps_view = cudf::jni::maps_column_view{*cv};
+    return release_as_jlong(maps_view.get_values_for(*column_keys));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_mapLookup(JNIEnv *env, jclass,
+                                                                 jlong map_column_view,
+                                                                 jlong lookup_key) {
+  JNI_NULL_CHECK(env, map_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_key, "lookup key is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const *cv = reinterpret_cast<cudf::column_view *>(map_column_view);
+    auto const *scalar_key = reinterpret_cast<cudf::scalar *>(lookup_key);
+    auto const maps_view = cudf::jni::maps_column_view{*cv};
+    return release_as_jlong(maps_view.get_values_for(*scalar_key));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_mapContainsKeys(JNIEnv *env, jclass,
+                                                                       jlong map_column_view,
+                                                                       jlong lookup_keys) {
+  JNI_NULL_CHECK(env, map_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_keys, "lookup key is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const *cv = reinterpret_cast<cudf::column_view *>(map_column_view);
+    auto const *column_key = reinterpret_cast<cudf::column_view *>(lookup_keys);
+    auto const maps_view = cudf::jni::maps_column_view{*cv};
+    return release_as_jlong(maps_view.contains(*column_key));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_mapContains(JNIEnv *env, jclass,
+                                                                   jlong map_column_view,
+                                                                   jlong lookup_key) {
+  JNI_NULL_CHECK(env, map_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, lookup_key, "lookup key is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const *cv = reinterpret_cast<cudf::column_view *>(map_column_view);
+    auto const *scalar_key = reinterpret_cast<cudf::scalar *>(lookup_key);
+    auto const maps_view = cudf::jni::maps_column_view{*cv};
+    return release_as_jlong(maps_view.contains(*scalar_key));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_replaceRegex(
+    JNIEnv *env, jclass, jlong j_column_view, jstring j_pattern, jint regex_flags,
+    jint capture_groups, jlong j_repl, jlong j_maxrepl) {
+
+  JNI_NULL_CHECK(env, j_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, j_pattern, "pattern string is null", 0);
+  JNI_NULL_CHECK(env, j_repl, "replace scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = reinterpret_cast<cudf::column_view const *>(j_column_view);
+    auto const strings_column = cudf::strings_column_view{*cv};
+    auto const pattern = cudf::jni::native_jstring(env, j_pattern);
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern.get(), flags, groups);
+    auto const repl = reinterpret_cast<cudf::string_scalar const *>(j_repl);
+    return release_as_jlong(
+        cudf::strings::replace_re(strings_column, *regex_prog, *repl, j_maxrepl));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_replaceMultiRegex(JNIEnv *env, jclass,
+                                                                         jlong j_column_view,
+                                                                         jobjectArray j_patterns,
+                                                                         jlong j_repls) {
+
+  JNI_NULL_CHECK(env, j_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, j_patterns, "patterns is null", 0);
+  JNI_NULL_CHECK(env, j_repls, "repls is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto cv = reinterpret_cast<cudf::column_view const *>(j_column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::jni::native_jstringArray patterns(env, j_patterns);
+    auto repl_cv = reinterpret_cast<cudf::column_view const *>(j_repls);
+    cudf::strings_column_view repl_scv(*repl_cv);
+    return release_as_jlong(cudf::strings::replace_re(scv, patterns.as_cpp_vector(), repl_scv));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringReplaceWithBackrefs(
+    JNIEnv *env, jclass, jlong j_column_view, jstring pattern_obj, jint regex_flags,
+    jint capture_groups, jstring replace_obj) {
+
+  JNI_NULL_CHECK(env, j_column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, pattern_obj, "pattern string is null", 0);
+  JNI_NULL_CHECK(env, replace_obj, "replace string is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = reinterpret_cast<cudf::column_view const *>(j_column_view);
+    auto const strings_column = cudf::strings_column_view{*cv};
+    auto const pattern = cudf::jni::native_jstring(env, pattern_obj);
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern.get(), flags, groups);
+    cudf::jni::native_jstring ss_replace(env, replace_obj);
+    return release_as_jlong(
+        cudf::strings::replace_with_backrefs(strings_column, *regex_prog, ss_replace.get()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_zfill(JNIEnv *env, jclass, jlong column_view,
+                                                             jint j_width) {
+
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::size_type width = reinterpret_cast<cudf::size_type>(j_width);
+    return release_as_jlong(cudf::strings::zfill(scv, width));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_pad(JNIEnv *env, jclass, jlong column_view,
+                                                           jint j_width, jint j_side,
+                                                           jstring fill_char) {
+
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, fill_char, "fill_char is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::size_type width = reinterpret_cast<cudf::size_type>(j_width);
+    cudf::strings::side_type side = static_cast<cudf::strings::side_type>(j_side);
+    cudf::jni::native_jstring ss_fill(env, fill_char);
+    return release_as_jlong(cudf::strings::pad(scv, width, side, ss_fill.get()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringStrip(JNIEnv *env, jclass,
+                                                                   jlong column_view,
+                                                                   jint strip_type,
+                                                                   jlong to_strip) {
+  JNI_NULL_CHECK(env, column_view, "column is null", 0);
+  JNI_NULL_CHECK(env, to_strip, "to_strip scalar is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_view);
+    cudf::strings_column_view scv(*cv);
+    cudf::strings::side_type s_striptype = static_cast<cudf::strings::side_type>(strip_type);
+    cudf::string_scalar *ss_tostrip = reinterpret_cast<cudf::string_scalar *>(to_strip);
+    return release_as_jlong(cudf::strings::strip(scv, s_striptype, *ss_tostrip));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_ColumnView_extractRe(JNIEnv *env, jclass,
+                                                                      jlong j_view_handle,
+                                                                      jstring pattern_obj,
+                                                                      jint regex_flags,
+                                                                      jint capture_groups) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", nullptr);
+  JNI_NULL_CHECK(env, pattern_obj, "pattern is null", nullptr);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const column_view = reinterpret_cast<cudf::column_view const *>(j_view_handle);
+    auto const strings_column = cudf::strings_column_view{*column_view};
+    auto const pattern = cudf::jni::native_jstring(env, pattern_obj);
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern.get(), flags, groups);
+    return cudf::jni::convert_table_for_return(env,
+                                               cudf::strings::extract(strings_column, *regex_prog));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_extractAllRecord(
+    JNIEnv *env, jclass, jlong j_view_handle, jstring pattern_obj, jint regex_flags,
+    jint capture_groups, jint idx) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", 0);
+  JNI_NULL_CHECK(env, pattern_obj, "pattern is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const column_view = reinterpret_cast<cudf::column_view const *>(j_view_handle);
+    auto const strings_column = cudf::strings_column_view{*column_view};
+    auto const pattern = cudf::jni::native_jstring(env, pattern_obj);
+    auto const flags = static_cast<cudf::strings::regex_flags>(regex_flags);
+    auto const groups = static_cast<cudf::strings::capture_groups>(capture_groups);
+    auto const regex_prog = cudf::strings::regex_program::create(pattern.get(), flags, groups);
+    auto result = (idx == 0) ? cudf::strings::findall(strings_column, *regex_prog) :
+                               cudf::strings::extract_all_record(strings_column, *regex_prog);
+    return release_as_jlong(result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_urlDecode(JNIEnv *env, jclass,
+                                                                 jlong j_view_handle) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto view_ptr = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::strings_column_view strings_view(*view_ptr);
+    return release_as_jlong(cudf::strings::url_decode(strings_view));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_urlEncode(JNIEnv *env, jclass,
+                                                                 jlong j_view_handle) {
+  JNI_NULL_CHECK(env, j_view_handle, "column is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto view_ptr = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::strings_column_view strings_view(*view_ptr);
+    return release_as_jlong(cudf::strings::url_encode(strings_view));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_normalizeNANsAndZeros(JNIEnv *env,
+                                                                             jclass clazz,
+                                                                             jlong input_column) {
+  using cudf::column_view;
+
+  JNI_NULL_CHECK(env, input_column, "Input column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    return release_as_jlong(
+        cudf::normalize_nans_and_zeros(*reinterpret_cast<column_view *>(input_column)));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_bitwiseMergeAndSetValidity(
+    JNIEnv *env, jobject j_object, jlong base_column, jlongArray column_handles, jint bin_op) {
+  JNI_NULL_CHECK(env, base_column, "base column native handle is null", 0);
+  JNI_NULL_CHECK(env, column_handles, "array of column handles is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *original_column = reinterpret_cast<cudf::column_view *>(base_column);
+    std::unique_ptr<cudf::column> copy(new cudf::column(*original_column));
+    cudf::jni::native_jpointerArray<cudf::column_view> n_cudf_columns(env, column_handles);
+
+    if (n_cudf_columns.size() == 0) {
+      copy->set_null_mask({}, 0);
+      return release_as_jlong(copy);
+    }
+
+    cudf::binary_operator op = static_cast<cudf::binary_operator>(bin_op);
+    switch (op) {
+      case cudf::binary_operator::BITWISE_AND: {
+        auto cols = n_cudf_columns.get_dereferenced();
+        cols.push_back(copy->view());
+        auto table_view = cudf::table_view{cols};
+        auto [new_bitmask, null_count] = cudf::bitmask_and(table_view);
+        copy->set_null_mask(std::move(new_bitmask), null_count);
+        break;
+      }
+      case cudf::binary_operator::BITWISE_OR: {
+        auto input_table = cudf::table_view{n_cudf_columns.get_dereferenced()};
+        auto [tmp_new_bitmask, tmp_null_count] = cudf::bitmask_or(input_table);
+        copy->set_null_mask(std::move(tmp_new_bitmask), tmp_null_count);
+        // and the bitmask with the original column
+        cudf::table_view table_view{std::vector<cudf::column_view>{copy->view(), *original_column}};
+        auto [new_bitmask, null_count] = cudf::bitmask_and(table_view);
+        copy->set_null_mask(std::move(new_bitmask), null_count);
+        break;
+      }
+      default: JNI_THROW_NEW(env, cudf::jni::ILLEGAL_ARG_CLASS, "Unsupported merge operation", 0);
+    }
+    auto const copy_cv = copy->view();
+    if (cudf::has_nonempty_nulls(copy_cv)) {
+      copy = cudf::purge_nonempty_nulls(copy_cv);
+    }
+
+    return release_as_jlong(copy);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_copyWithBooleanColumnAsValidity(
+    JNIEnv *env, jobject j_object, jlong exemplar_handle, jlong validity_column_handle) {
+  JNI_NULL_CHECK(env, exemplar_handle, "ColumnView handle is null", 0);
+  JNI_NULL_CHECK(env, validity_column_handle, "Validity column handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const exemplar = *reinterpret_cast<cudf::column_view *>(exemplar_handle);
+    auto const validity = *reinterpret_cast<cudf::column_view *>(validity_column_handle);
+    return release_as_jlong(
+        cudf::jni::new_column_with_boolean_column_as_validity(exemplar, validity));
+  }
+  CATCH_STD(env, 0);
+}
+
+////////
+// Native cudf::column_view life cycle and metadata access methods. Life cycle methods
+// should typically only be called from the CudfColumn inner class.
+////////
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_makeCudfColumnView(
+    JNIEnv *env, jclass, jint j_type, jint scale, jlong j_data, jlong j_data_size, jlong j_offset,
+    jlong j_valid, jint j_null_count, jint size, jlongArray j_children) {
+
+  try {
+    using cudf::column_view;
+    cudf::jni::auto_set_device(env);
+    cudf::type_id n_type = static_cast<cudf::type_id>(j_type);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(j_type, scale);
+
+    void *data = reinterpret_cast<void *>(j_data);
+    cudf::bitmask_type *valid = reinterpret_cast<cudf::bitmask_type *>(j_valid);
+    if (valid == nullptr) {
+      j_null_count = 0;
+    }
+
+    if (j_null_count < 0) { // Check for unknown null count.
+      // Calculate concrete null count.
+      j_null_count = cudf::null_count(valid, 0, size);
+    }
+
+    if (n_type == cudf::type_id::STRING) {
+      if (size == 0) {
+        return ptr_as_jlong(
+            new cudf::column_view(cudf::data_type{cudf::type_id::STRING}, 0, nullptr, nullptr, 0));
+      } else {
+        JNI_NULL_CHECK(env, j_offset, "offset is null", 0);
+        // This must be kept in sync with how string columns are created
+        // offsets are always the first child
+        // data is the second child
+
+        cudf::size_type *offsets = reinterpret_cast<cudf::size_type *>(j_offset);
+        cudf::column_view offsets_column(cudf::data_type{cudf::type_id::INT32}, size + 1, offsets,
+                                         nullptr, 0);
+        cudf::column_view data_column(cudf::data_type{cudf::type_id::INT8}, j_data_size, data,
+                                      nullptr, 0);
+        return ptr_as_jlong(new cudf::column_view(cudf::data_type{cudf::type_id::STRING}, size,
+                                                  nullptr, valid, j_null_count, 0,
+                                                  {offsets_column, data_column}));
+      }
+    } else if (n_type == cudf::type_id::LIST) {
+      JNI_NULL_CHECK(env, j_children, "children of a list are null", 0);
+      cudf::jni::native_jpointerArray<cudf::column_view> children(env, j_children);
+      JNI_ARG_CHECK(env, (children.size() == 1), "LIST children size is not 1", 0);
+      cudf::size_type offsets_size = 0;
+      cudf::size_type *offsets = nullptr;
+      if (size != 0) {
+        JNI_NULL_CHECK(env, j_offset, "offset is null", 0);
+        offsets_size = size + 1;
+        offsets = reinterpret_cast<cudf::size_type *>(j_offset);
+      }
+      cudf::column_view offsets_column(cudf::data_type{cudf::type_id::INT32}, offsets_size, offsets,
+                                       nullptr, 0);
+      return ptr_as_jlong(new cudf::column_view(cudf::data_type{cudf::type_id::LIST}, size, nullptr,
+                                                valid, j_null_count, 0,
+                                                {offsets_column, *children[0]}));
+    } else if (n_type == cudf::type_id::STRUCT) {
+      JNI_NULL_CHECK(env, j_children, "children of a struct are null", 0);
+      cudf::jni::native_jpointerArray<cudf::column_view> children(env, j_children);
+      std::vector<column_view> children_vector = children.get_dereferenced();
+      return ptr_as_jlong(new cudf::column_view(cudf::data_type{cudf::type_id::STRUCT}, size,
+                                                nullptr, valid, j_null_count, 0, children_vector));
+    } else {
+      return ptr_as_jlong(new cudf::column_view(n_data_type, size, data, valid, j_null_count));
+    }
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_getNativeTypeId(JNIEnv *env, jobject j_object,
+                                                                      jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    return static_cast<jint>(column->type().id());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_getNativeTypeScale(JNIEnv *env, jclass,
+                                                                         jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    return column->type().scale();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_getNativeRowCount(JNIEnv *env, jclass,
+                                                                        jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    return static_cast<jint>(column->size());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_getNativeNullCount(JNIEnv *env,
+                                                                         jobject j_object,
+                                                                         jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    return static_cast<jint>(column->null_count());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_ColumnView_deleteColumnView(JNIEnv *env,
+                                                                       jobject j_object,
+                                                                       jlong handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    delete view;
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getNativeDataAddress(JNIEnv *env, jclass,
+                                                                            jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    jlong result = 0;
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    if (column->type().id() == cudf::type_id::STRING) {
+      if (column->size() > 0) {
+        cudf::strings_column_view view = cudf::strings_column_view(*column);
+        cudf::column_view data_view = view.chars();
+        result = reinterpret_cast<jlong>(data_view.data<char>());
+      }
+    } else if (column->type().id() != cudf::type_id::LIST &&
+               column->type().id() != cudf::type_id::STRUCT) {
+      result = reinterpret_cast<jlong>(column->data<char>());
+    }
+    return result;
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getNativeDataLength(JNIEnv *env, jclass,
+                                                                           jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    jlong result = 0;
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    if (column->type().id() == cudf::type_id::STRING) {
+      if (column->size() > 0) {
+        cudf::strings_column_view view = cudf::strings_column_view(*column);
+        cudf::column_view data_view = view.chars();
+        result = data_view.size();
+      }
+    } else if (column->type().id() != cudf::type_id::LIST &&
+               column->type().id() != cudf::type_id::STRUCT) {
+      result = cudf::size_of(column->type()) * column->size();
+    }
+    return result;
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_ColumnView_getNativeNumChildren(JNIEnv *env,
+                                                                           jobject j_object,
+                                                                           jlong handle) {
+
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    // Strings has children(offsets and chars) but not a nested child() we care about here.
+    if (column->type().id() == cudf::type_id::STRING) {
+      return 0;
+    } else if (column->type().id() == cudf::type_id::LIST) {
+      // first child is always offsets in lists which we do not want to count here
+      return static_cast<jint>(column->num_children() - 1);
+    } else if (column->type().id() == cudf::type_id::STRUCT) {
+      return static_cast<jint>(column->num_children());
+    } else {
+      return 0;
+    }
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getChildCvPointer(JNIEnv *env,
+                                                                         jobject j_object,
+                                                                         jlong handle,
+                                                                         jint child_index) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    auto const is_list = column->type().id() == cudf::type_id::LIST;
+    auto const child = column->child(child_index + (is_list ? 1 : 0));
+    return ptr_as_jlong(new cudf::column_view(child));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getListOffsetCvPointer(JNIEnv *env,
+                                                                              jobject j_object,
+                                                                              jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::lists_column_view view = cudf::lists_column_view(*column);
+    cudf::column_view offsets_view = view.offsets();
+    return ptr_as_jlong(new cudf::column_view(offsets_view));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getNativeOffsetsAddress(JNIEnv *env, jclass,
+                                                                               jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    jlong result = 0;
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    if (column->type().id() == cudf::type_id::STRING) {
+      if (column->size() > 0) {
+        cudf::strings_column_view view = cudf::strings_column_view(*column);
+        cudf::column_view offsets_view = view.offsets();
+        result = ptr_as_jlong(offsets_view.data<char>());
+      }
+    } else if (column->type().id() == cudf::type_id::LIST) {
+      if (column->size() > 0) {
+        cudf::lists_column_view view = cudf::lists_column_view(*column);
+        cudf::column_view offsets_view = view.offsets();
+        result = ptr_as_jlong(offsets_view.data<char>());
+      }
+    }
+    return result;
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getNativeOffsetsLength(JNIEnv *env, jclass,
+                                                                              jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    jlong result = 0;
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    if (column->type().id() == cudf::type_id::STRING) {
+      if (column->size() > 0) {
+        cudf::strings_column_view view = cudf::strings_column_view(*column);
+        cudf::column_view offsets_view = view.offsets();
+        result = sizeof(int) * offsets_view.size();
+      }
+    } else if (column->type().id() == cudf::type_id::LIST) {
+      if (column->size() > 0) {
+        cudf::lists_column_view view = cudf::lists_column_view(*column);
+        cudf::column_view offsets_view = view.offsets();
+        result = sizeof(int) * offsets_view.size();
+      }
+    }
+    return result;
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getNativeValidityAddress(JNIEnv *env, jclass,
+                                                                                jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    return ptr_as_jlong(column->null_mask());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getNativeValidityLength(JNIEnv *env, jclass,
+                                                                               jlong handle) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(handle);
+    jlong result = 0;
+    if (column->null_mask() != nullptr) {
+      result = cudf::bitmask_allocation_size_bytes(column->size());
+    }
+    return result;
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getDeviceMemorySize(JNIEnv *env, jclass,
+                                                                           jlong handle,
+                                                                           jboolean pad_for_cpu) {
+  JNI_NULL_CHECK(env, handle, "native handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto view = reinterpret_cast<cudf::column_view const *>(handle);
+    return calc_device_memory_size(*view, pad_for_cpu);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_hostPaddingSizeInBytes(JNIEnv *env, jclass) {
+  return sizeof(std::max_align_t);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_clamper(JNIEnv *env, jobject j_object,
+                                                               jlong handle, jlong j_lo_scalar,
+                                                               jlong j_lo_replace_scalar,
+                                                               jlong j_hi_scalar,
+                                                               jlong j_hi_replace_scalar) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+  JNI_NULL_CHECK(env, j_lo_scalar, "lo scalar is null", 0)
+  JNI_NULL_CHECK(env, j_lo_replace_scalar, "lo scalar replace value is null", 0)
+  JNI_NULL_CHECK(env, j_hi_scalar, "lo scalar is null", 0)
+  JNI_NULL_CHECK(env, j_hi_replace_scalar, "lo scalar replace value is null", 0)
+  using cudf::clamp;
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *column_view = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::scalar *lo_scalar = reinterpret_cast<cudf::scalar *>(j_lo_scalar);
+    cudf::scalar *lo_replace_scalar = reinterpret_cast<cudf::scalar *>(j_lo_replace_scalar);
+    cudf::scalar *hi_scalar = reinterpret_cast<cudf::scalar *>(j_hi_scalar);
+    cudf::scalar *hi_replace_scalar = reinterpret_cast<cudf::scalar *>(j_hi_replace_scalar);
+
+    return release_as_jlong(
+        clamp(*column_view, *lo_scalar, *lo_replace_scalar, *hi_scalar, *hi_replace_scalar));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_title(JNIEnv *env, jobject j_object,
+                                                             jlong handle) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::strings::title(*view));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_capitalize(JNIEnv *env, jobject j_object,
+                                                                  jlong strs_handle,
+                                                                  jlong delimiters_handle) {
+
+  JNI_NULL_CHECK(env, strs_handle, "native view handle is null", 0)
+  JNI_NULL_CHECK(env, delimiters_handle, "delimiters scalar handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(strs_handle);
+    cudf::string_scalar *deli = reinterpret_cast<cudf::string_scalar *>(delimiters_handle);
+    return release_as_jlong(cudf::strings::capitalize(*view, *deli));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_joinStrings(JNIEnv *env, jobject j_object,
+                                                                   jlong strs_handle,
+                                                                   jlong separator_handle,
+                                                                   jlong narep_handle) {
+
+  JNI_NULL_CHECK(env, strs_handle, "native view handle is null", 0)
+  JNI_NULL_CHECK(env, separator_handle, "separator scalar handle is null", 0)
+  JNI_NULL_CHECK(env, narep_handle, "narep scalar handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(strs_handle);
+    cudf::string_scalar *sep = reinterpret_cast<cudf::string_scalar *>(separator_handle);
+    cudf::string_scalar *narep = reinterpret_cast<cudf::string_scalar *>(narep_handle);
+    return release_as_jlong(cudf::strings::join_strings(*view, *sep, *narep));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_makeStructView(JNIEnv *env, jobject j_object,
+                                                                      jlongArray handles,
+                                                                      jlong row_count) {
+
+  JNI_NULL_CHECK(env, handles, "native view handles are null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto children = cudf::jni::native_jpointerArray<cudf::column_view>{env, handles};
+    auto children_vector = children.get_dereferenced();
+    return ptr_as_jlong(new cudf::column_view(cudf::data_type{cudf::type_id::STRUCT}, row_count,
+                                              nullptr, nullptr, 0, 0, children_vector));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_nansToNulls(JNIEnv *env, jobject j_object,
+                                                                   jlong handle) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = *reinterpret_cast<cudf::column_view *>(handle);
+    // get a new null mask by setting all the nans to null
+    auto [new_nullmask, new_null_count] = cudf::nans_to_nulls(input);
+    // create a column_view which is a no-copy wrapper around the original column without the null
+    // mask
+    auto const input_without_nullmask = cudf::column_view(
+        input.type(), input.size(), input.head<void>(), nullptr, 0, input.offset(),
+        std::vector<cudf::column_view>{input.child_begin(), input.child_end()});
+    // create a column by deep copying `input_without_nullmask`.
+    auto deep_copy = std::make_unique<cudf::column>(input_without_nullmask);
+    deep_copy->set_null_mask(std::move(*new_nullmask), new_null_count);
+    return release_as_jlong(deep_copy);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isFloat(JNIEnv *env, jobject j_object,
+                                                               jlong handle) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::strings::is_float(*view));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isInteger(JNIEnv *env, jobject j_object,
+                                                                 jlong handle) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    return release_as_jlong(cudf::strings::is_integer(*view));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isFixedPoint(JNIEnv *env, jobject,
+                                                                    jlong handle, jint j_dtype,
+                                                                    jint scale) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::data_type fp_dtype = cudf::jni::make_data_type(j_dtype, scale);
+    return release_as_jlong(cudf::strings::is_fixed_point(*view, fp_dtype));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_isIntegerWithType(JNIEnv *env, jobject,
+                                                                         jlong handle, jint j_dtype,
+                                                                         jint scale) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    cudf::data_type int_dtype = cudf::jni::make_data_type(j_dtype, scale);
+    return release_as_jlong(cudf::strings::is_integer(*view, int_dtype));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_copyColumnViewToCV(JNIEnv *env,
+                                                                          jobject j_object,
+                                                                          jlong handle) {
+
+  JNI_NULL_CHECK(env, handle, "native view handle is null", 0)
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *view = reinterpret_cast<cudf::column_view *>(handle);
+    return ptr_as_jlong(new cudf::column(*view));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_getJSONObject(JNIEnv *env, jclass,
+                                                                     jlong j_view_handle,
+                                                                     jlong j_scalar_handle) {
+
+  JNI_NULL_CHECK(env, j_view_handle, "view cannot be null", 0);
+  JNI_NULL_CHECK(env, j_scalar_handle, "path cannot be null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::column_view *n_column_view = reinterpret_cast<cudf::column_view *>(j_view_handle);
+    cudf::strings_column_view n_strings_col_view(*n_column_view);
+    cudf::string_scalar *n_scalar_path = reinterpret_cast<cudf::string_scalar *>(j_scalar_handle);
+    return release_as_jlong(cudf::strings::get_json_object(n_strings_col_view, *n_scalar_path));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringConcatenationListElementsSepCol(
+    JNIEnv *env, jclass, jlong column_handle, jlong sep_handle, jlong separator_narep,
+    jlong col_narep, jboolean separate_nulls, jboolean empty_string_output_if_empty_list) {
+  JNI_NULL_CHECK(env, column_handle, "column handle is null", 0);
+  JNI_NULL_CHECK(env, sep_handle, "separator column handle is null", 0);
+  JNI_NULL_CHECK(env, separator_narep, "separator narep string scalar object is null", 0);
+  JNI_NULL_CHECK(env, col_narep, "column narep string scalar object is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const auto &separator_narep_scalar = *reinterpret_cast<cudf::string_scalar *>(separator_narep);
+    const auto &col_narep_scalar = *reinterpret_cast<cudf::string_scalar *>(col_narep);
+    auto null_policy = separate_nulls ? cudf::strings::separator_on_nulls::YES :
+                                        cudf::strings::separator_on_nulls::NO;
+    auto empty_list_output = empty_string_output_if_empty_list ?
+                                 cudf::strings::output_if_empty_list::EMPTY_STRING :
+                                 cudf::strings::output_if_empty_list::NULL_ELEMENT;
+
+    cudf::column_view *column = reinterpret_cast<cudf::column_view *>(sep_handle);
+    cudf::strings_column_view strings_column(*column);
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_handle);
+    cudf::lists_column_view lcv(*cv);
+    return release_as_jlong(
+        cudf::strings::join_list_elements(lcv, strings_column, separator_narep_scalar,
+                                          col_narep_scalar, null_policy, empty_list_output));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_stringConcatenationListElements(
+    JNIEnv *env, jclass, jlong column_handle, jlong separator, jlong narep, jboolean separate_nulls,
+    jboolean empty_string_output_if_empty_list) {
+  JNI_NULL_CHECK(env, column_handle, "column handle is null", 0);
+  JNI_NULL_CHECK(env, separator, "separator string scalar object is null", 0);
+  JNI_NULL_CHECK(env, narep, "separator narep string scalar object is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const auto &separator_scalar = *reinterpret_cast<cudf::string_scalar *>(separator);
+    const auto &narep_scalar = *reinterpret_cast<cudf::string_scalar *>(narep);
+    auto null_policy = separate_nulls ? cudf::strings::separator_on_nulls::YES :
+                                        cudf::strings::separator_on_nulls::NO;
+    auto empty_list_output = empty_string_output_if_empty_list ?
+                                 cudf::strings::output_if_empty_list::EMPTY_STRING :
+                                 cudf::strings::output_if_empty_list::NULL_ELEMENT;
+
+    cudf::column_view *cv = reinterpret_cast<cudf::column_view *>(column_handle);
+    cudf::lists_column_view lcv(*cv);
+    return release_as_jlong(cudf::strings::join_list_elements(lcv, separator_scalar, narep_scalar,
+                                                              null_policy, empty_list_output));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_repeatStrings(JNIEnv *env, jclass,
+                                                                     jlong strings_handle,
+                                                                     jint repeat_times) {
+  JNI_NULL_CHECK(env, strings_handle, "strings_handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const cv = *reinterpret_cast<cudf::column_view *>(strings_handle);
+    auto const strs_col = cudf::strings_column_view(cv);
+    return release_as_jlong(cudf::strings::repeat_strings(strs_col, repeat_times));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_repeatStringsWithColumnRepeatTimes(
+    JNIEnv *env, jclass, jlong strings_handle, jlong repeat_times_handle) {
+  JNI_NULL_CHECK(env, strings_handle, "strings_handle is null", 0);
+  JNI_NULL_CHECK(env, repeat_times_handle, "repeat_times_handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const strings_cv = *reinterpret_cast<cudf::column_view *>(strings_handle);
+    auto const strs_col = cudf::strings_column_view(strings_cv);
+    auto const repeat_times_cv = *reinterpret_cast<cudf::column_view *>(repeat_times_handle);
+    return release_as_jlong(cudf::strings::repeat_strings(strs_col, repeat_times_cv));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_applyBooleanMask(
+    JNIEnv *env, jclass, jlong list_column_handle, jlong boolean_mask_list_column_handle) {
+  JNI_NULL_CHECK(env, list_column_handle, "list handle is null", 0);
+  JNI_NULL_CHECK(env, boolean_mask_list_column_handle, "boolean mask handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+
+    cudf::column_view const *list_column =
+        reinterpret_cast<cudf::column_view const *>(list_column_handle);
+    cudf::lists_column_view const list_view = cudf::lists_column_view(*list_column);
+
+    cudf::column_view const *boolean_mask_list_column =
+        reinterpret_cast<cudf::column_view const *>(boolean_mask_list_column_handle);
+    cudf::lists_column_view const boolean_mask_list_view =
+        cudf::lists_column_view(*boolean_mask_list_column);
+
+    return release_as_jlong(cudf::lists::apply_boolean_mask(list_view, boolean_mask_list_view));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jboolean JNICALL
+Java_ai_rapids_cudf_ColumnView_hasNonEmptyNulls(JNIEnv *env, jclass, jlong column_view_handle) {
+  JNI_NULL_CHECK(env, column_view_handle, "column_view handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const *cv = reinterpret_cast<cudf::column_view const *>(column_view_handle);
+    return cudf::has_nonempty_nulls(*cv);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL
+Java_ai_rapids_cudf_ColumnView_purgeNonEmptyNulls(JNIEnv *env, jclass, jlong column_view_handle) {
+  JNI_NULL_CHECK(env, column_view_handle, "column_view handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const *cv = reinterpret_cast<cudf::column_view const *>(column_view_handle);
+    return release_as_jlong(cudf::purge_nonempty_nulls(*cv));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnView_toHex(JNIEnv *env, jclass, jlong input_ptr) {
+  JNI_NULL_CHECK(env, input_ptr, "input is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const cudf::column_view *input = reinterpret_cast<cudf::column_view *>(input_ptr);
+    return release_as_jlong(cudf::strings::integers_to_hex(*input));
+  }
+  CATCH_STD(env, 0);
+}
+} // extern "C"
diff --git a/java/src/main/native/src/ColumnViewJni.cu b/java/src/main/native/src/ColumnViewJni.cu
new file mode 100644
index 0000000..56aea0b
--- /dev/null
+++ b/java/src/main/native/src/ColumnViewJni.cu
@@ -0,0 +1,224 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <vector>
+
+#include <cudf/column/column_device_view.cuh>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/labeling/label_segments.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/stream_compaction.hpp>
+#include <cudf/detail/valid_if.cuh>
+#include <cudf/lists/list_device_view.cuh>
+#include <cudf/lists/lists_column_device_view.cuh>
+#include <cudf/table/table.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/span.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+#include <thrust/functional.h>
+#include <thrust/logical.h>
+#include <thrust/scan.h>
+#include <thrust/tabulate.h>
+
+#include "ColumnViewJni.hpp"
+
+namespace cudf::jni {
+
+std::unique_ptr<cudf::column>
+new_column_with_boolean_column_as_validity(cudf::column_view const &exemplar,
+                                           cudf::column_view const &validity_column) {
+  CUDF_EXPECTS(validity_column.type().id() == type_id::BOOL8,
+               "Validity column must be of type bool");
+  CUDF_EXPECTS(validity_column.size() == exemplar.size(),
+               "Exemplar and validity columns must have the same size");
+
+  auto validity_device_view = cudf::column_device_view::create(validity_column);
+  auto validity_begin = cudf::detail::make_optional_iterator<bool>(
+      *validity_device_view, cudf::nullate::DYNAMIC{validity_column.has_nulls()});
+  auto validity_end = validity_begin + validity_device_view->size();
+  auto [null_mask, null_count] = cudf::detail::valid_if(
+      validity_begin, validity_end,
+      [] __device__(auto optional_bool) { return optional_bool.value_or(false); },
+      cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+  auto const exemplar_without_null_mask = cudf::column_view{
+      exemplar.type(),
+      exemplar.size(),
+      exemplar.head<void>(),
+      nullptr,
+      0,
+      exemplar.offset(),
+      std::vector<cudf::column_view>{exemplar.child_begin(), exemplar.child_end()}};
+  auto deep_copy = std::make_unique<cudf::column>(exemplar_without_null_mask);
+  deep_copy->set_null_mask(std::move(null_mask), null_count);
+  return deep_copy;
+}
+
+std::unique_ptr<cudf::column> generate_list_offsets(cudf::column_view const &list_length,
+                                                    rmm::cuda_stream_view stream) {
+  CUDF_EXPECTS(list_length.type().id() == cudf::type_id::INT32,
+               "Input column does not have type INT32.");
+
+  auto const begin_iter = list_length.template begin<cudf::size_type>();
+  auto const end_iter = list_length.template end<cudf::size_type>();
+
+  auto offsets_column = make_numeric_column(data_type{type_id::INT32}, list_length.size() + 1,
+                                            mask_state::UNALLOCATED, stream);
+  auto offsets_view = offsets_column->mutable_view();
+  auto d_offsets = offsets_view.template begin<int32_t>();
+
+  thrust::inclusive_scan(rmm::exec_policy(stream), begin_iter, end_iter, d_offsets + 1);
+  CUDF_CUDA_TRY(cudaMemsetAsync(d_offsets, 0, sizeof(int32_t), stream));
+
+  return offsets_column;
+}
+
+namespace {
+
+/**
+ * @brief Check if the input list has any null elements.
+ *
+ * @param list The input list.
+ * @return The boolean result indicating if the input list has null elements.
+ */
+__device__ bool list_has_nulls(list_device_view list) {
+  return thrust::any_of(thrust::seq, thrust::make_counting_iterator(0),
+                        thrust::make_counting_iterator(list.size()),
+                        [&list](auto const idx) { return list.is_null(idx); });
+}
+
+} // namespace
+
+void post_process_list_overlap(cudf::column_view const &lhs, cudf::column_view const &rhs,
+                               std::unique_ptr<cudf::column> const &overlap_result,
+                               rmm::cuda_stream_view stream) {
+  // If both of the input columns do not have nulls, we don't need to do anything here.
+  if (!lists_column_view{lhs}.child().has_nulls() && !lists_column_view{rhs}.child().has_nulls()) {
+    return;
+  }
+
+  auto const overlap_cv = overlap_result->view();
+  auto const lhs_cdv_ptr = column_device_view::create(lhs, stream);
+  auto const rhs_cdv_ptr = column_device_view::create(rhs, stream);
+  auto const overlap_cdv_ptr = column_device_view::create(overlap_cv, stream);
+
+  // Create a new bitmask to satisfy Spark's arrays_overlap's special behavior.
+  auto validity = rmm::device_uvector<bool>(overlap_cv.size(), stream);
+  thrust::tabulate(rmm::exec_policy(stream), validity.begin(), validity.end(),
+                   [lhs = cudf::detail::lists_column_device_view{*lhs_cdv_ptr},
+                    rhs = cudf::detail::lists_column_device_view{*rhs_cdv_ptr},
+                    overlap_result = *overlap_cdv_ptr] __device__(auto const idx) {
+                     if (overlap_result.is_null(idx) ||
+                         overlap_result.template element<bool>(idx)) {
+                       return true;
+                     }
+
+                     // `lhs_list` and `rhs_list` should not be null, otherwise
+                     // `overlap_result[idx]` is null and that has been handled above.
+                     auto const lhs_list = list_device_view{lhs, idx};
+                     auto const rhs_list = list_device_view{rhs, idx};
+
+                     // Only proceed if both lists are non-empty.
+                     if (lhs_list.size() == 0 || rhs_list.size() == 0) {
+                       return true;
+                     }
+
+                     // Only proceed if at least one list has nulls.
+                     if (!list_has_nulls(lhs_list) && !list_has_nulls(rhs_list)) {
+                       return true;
+                     }
+
+                     // Here, the input lists satisfy all the conditions below so we output a
+                     // null:
+                     //  - Both of the input lists have no non-null common element, and
+                     //  - They are both non-empty, and
+                     //  - Either of them contains null elements.
+                     return false;
+                   });
+
+  // Create a new nullmask from the validity data.
+  auto [new_null_mask, new_null_count] =
+      cudf::detail::valid_if(validity.begin(), validity.end(), thrust::identity{},
+                             cudf::get_default_stream(), rmm::mr::get_current_device_resource());
+
+  if (new_null_count > 0) {
+    // If the `overlap_result` column is nullable, perform `bitmask_and` of its nullmask and the
+    // new nullmask.
+    if (overlap_cv.nullable()) {
+      auto [null_mask, null_count] = cudf::detail::bitmask_and(
+          std::vector<bitmask_type const *>{
+              overlap_cv.null_mask(), static_cast<bitmask_type const *>(new_null_mask.data())},
+          std::vector<cudf::size_type>{0, 0}, overlap_cv.size(), stream,
+          rmm::mr::get_current_device_resource());
+      overlap_result->set_null_mask(std::move(null_mask), null_count);
+    } else {
+      // Just set the output nullmask as the new nullmask.
+      overlap_result->set_null_mask(std::move(new_null_mask), new_null_count);
+    }
+  }
+}
+
+std::unique_ptr<cudf::column> lists_distinct_by_key(cudf::lists_column_view const &input,
+                                                    rmm::cuda_stream_view stream) {
+  if (input.is_empty()) {
+    return empty_like(input.parent());
+  }
+
+  auto const child = input.get_sliced_child(stream);
+
+  // Generate labels for the input list elements.
+  auto labels = rmm::device_uvector<cudf::size_type>(child.size(), stream);
+  cudf::detail::label_segments(input.offsets_begin(), input.offsets_end(), labels.begin(),
+                               labels.end(), stream);
+
+  // Use `cudf::duplicate_keep_option::KEEP_LAST` so this will produce the desired behavior when
+  // being called in `create_map` in spark-rapids.
+  // Other options comparing nulls and NaNs are set as all-equal.
+  auto out_columns =
+      cudf::detail::stable_distinct(
+          table_view{{column_view{cudf::device_span<cudf::size_type const>{labels}}, child.child(0),
+                      child.child(1)}}, // input table
+          std::vector<size_type>{0, 1}, // key columns
+          cudf::duplicate_keep_option::KEEP_LAST, cudf::null_equality::EQUAL,
+          cudf::nan_equality::ALL_EQUAL, stream, rmm::mr::get_current_device_resource())
+          ->release();
+  auto const out_labels = out_columns.front()->view();
+
+  // Assemble a structs column of <out_keys, out_vals>.
+  auto out_structs_members = std::vector<std::unique_ptr<cudf::column>>();
+  out_structs_members.emplace_back(std::move(out_columns[1]));
+  out_structs_members.emplace_back(std::move(out_columns[2]));
+  auto out_structs =
+      cudf::make_structs_column(out_labels.size(), std::move(out_structs_members), 0, {});
+
+  // Assemble a lists column of structs<out_keys, out_vals>.
+  auto out_offsets = make_numeric_column(data_type{type_to_id<size_type>()}, input.size() + 1,
+                                         mask_state::UNALLOCATED, stream);
+  auto const offsets_begin = out_offsets->mutable_view().template begin<size_type>();
+  auto const labels_begin = out_labels.template begin<size_type>();
+  cudf::detail::labels_to_offsets(labels_begin, labels_begin + out_labels.size(), offsets_begin,
+                                  offsets_begin + out_offsets->size(), stream);
+
+  return cudf::make_lists_column(
+      input.size(), std::move(out_offsets), std::move(out_structs), input.null_count(),
+      cudf::detail::copy_bitmask(input.parent(), stream, rmm::mr::get_current_device_resource()),
+      stream);
+}
+
+} // namespace cudf::jni
diff --git a/java/src/main/native/src/ColumnViewJni.hpp b/java/src/main/native/src/ColumnViewJni.hpp
new file mode 100644
index 0000000..1206111
--- /dev/null
+++ b/java/src/main/native/src/ColumnViewJni.hpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::jni {
+
+/**
+ * @brief Creates a deep copy of the exemplar column, with its validity set to the equivalent
+ * of the boolean `validity` column's value.
+ *
+ * The bool_column must have the same number of rows as the exemplar column.
+ * The result column will have the same number of rows as the exemplar.
+ * For all indices `i` where the boolean column is `true`, the result column will have a valid value
+ * at index i. For all other values (i.e. `false` or `null`), the result column will have nulls.
+ *
+ * @param exemplar The column to be deep copied.
+ * @param bool_column bool column whose value is to be used as the validity.
+ * @return Deep copy of the exemplar, with the replaced validity.
+ */
+std::unique_ptr<cudf::column>
+new_column_with_boolean_column_as_validity(cudf::column_view const &exemplar,
+                                           cudf::column_view const &bool_column);
+
+/**
+ * @brief Generates list offsets with lengths of each list.
+ *
+ * For example,
+ * Given a list column: [[1,2,3], [4,5], [6], [], [7,8]]
+ * The list lengths of it: [3, 2, 1, 0, 2]
+ * The list offsets of it: [0, 3, 5, 6, 6, 8]
+ *
+ * @param list_length The column represents list lengths.
+ * @return The column represents list offsets.
+ */
+std::unique_ptr<cudf::column>
+generate_list_offsets(cudf::column_view const &list_length,
+                      rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Perform a special treatment for the results of `cudf::lists::have_overlap` to produce the
+ *        results that match with Spark's `arrays_overlap`.
+ *
+ * The function `arrays_overlap` of Apache Spark has a special behavior that needs to be addressed.
+ * In particular, the result of checking overlap between two lists will be a null element instead of
+ * a `false` value (as output by `cudf::lists::have_overlap`) if:
+ *  - Both of the input lists have no non-null common element, and
+ *  - They are both non-empty, and
+ *  - Either of them contains null elements.
+ *
+ * This function performs post-processing on the results of `cudf::lists::have_overlap`, adding
+ * special treatment to produce an output column that matches with the behavior described above.
+ *
+ * @param lhs The input lists column for one side.
+ * @param rhs The input lists column for the other side.
+ * @param overlap_result The result column generated by checking list overlap in cudf.
+ */
+void post_process_list_overlap(cudf::column_view const &lhs, cudf::column_view const &rhs,
+                               std::unique_ptr<cudf::column> const &overlap_result,
+                               rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Generates lists column by copying elements that are distinct by key from each input list
+ * row to the corresponding output row.
+ *
+ * The input lists column must be given such that each list element is a struct of <key, value>
+ * pair. With such input, a list containing distinct by key elements are defined such that the keys
+ * of all elements in the list are distinct (i.e., any two keys are always compared unequal).
+ *
+ * There will not be any validity check for the input. The caller is responsible to make sure that
+ * the input lists column has the right structure.
+ *
+ * @return A new list columns in which the elements in each list are distinct by key.
+ */
+std::unique_ptr<cudf::column> lists_distinct_by_key(cudf::lists_column_view const &input,
+                                                    rmm::cuda_stream_view stream);
+
+} // namespace cudf::jni
diff --git a/java/src/main/native/src/CompiledExpression.cpp b/java/src/main/native/src/CompiledExpression.cpp
new file mode 100644
index 0000000..56c96b2
--- /dev/null
+++ b/java/src/main/native/src/CompiledExpression.cpp
@@ -0,0 +1,412 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstdint>
+#include <memory>
+#include <stdexcept>
+#include <vector>
+
+#include <cudf/ast/expressions.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/transform.hpp>
+#include <cudf/types.hpp>
+
+#include "cudf_jni_apis.hpp"
+#include "jni_compiled_expr.hpp"
+
+namespace {
+
+/** Utility class to read data from the serialized AST buffer generated from Java */
+class jni_serialized_ast {
+  jbyte const *data_ptr;      // pointer to the current entity to deserialize
+  jbyte const *const end_ptr; // pointer to the byte immediately after the AST serialized data
+
+  /** Throws an error if there is insufficient space left to read the specified number of bytes */
+  void check_for_eof(std::size_t num_bytes_to_read) {
+    if (data_ptr + num_bytes_to_read > end_ptr) {
+      throw std::runtime_error("Unexpected end of serialized data");
+    }
+  }
+
+public:
+  jni_serialized_ast(cudf::jni::native_jbyteArray &jni_data)
+      : data_ptr(jni_data.begin()), end_ptr(jni_data.end()) {}
+
+  /** Returns true if there is no data remaining to be read */
+  bool at_eof() { return data_ptr == end_ptr; }
+
+  /** Read a byte from the serialized AST data buffer */
+  jbyte read_byte() {
+    check_for_eof(sizeof(jbyte));
+    return *data_ptr++;
+  }
+
+  /** Read a multi-byte value from the serialized AST data buffer */
+  template <typename T> T read() {
+    if constexpr (std::is_same_v<T, std::string>) {
+      auto const size = read<cudf::size_type>();
+      check_for_eof(size);
+      auto const result = std::string(reinterpret_cast<char const *>(data_ptr), size);
+      data_ptr += size;
+      return result;
+    } else {
+      check_for_eof(sizeof(T));
+      // use memcpy since data may be misaligned
+      T result;
+      memcpy(reinterpret_cast<jbyte *>(&result), data_ptr, sizeof(T));
+      data_ptr += sizeof(T);
+      return result;
+    }
+  }
+
+  /** Decode a libcudf data type from the serialized AST data buffer */
+  cudf::data_type read_cudf_type() {
+    auto const dtype_id = static_cast<cudf::type_id>(read_byte());
+    switch (dtype_id) {
+      case cudf::type_id::INT8:
+      case cudf::type_id::INT16:
+      case cudf::type_id::INT32:
+      case cudf::type_id::INT64:
+      case cudf::type_id::UINT8:
+      case cudf::type_id::UINT16:
+      case cudf::type_id::UINT32:
+      case cudf::type_id::UINT64:
+      case cudf::type_id::FLOAT32:
+      case cudf::type_id::FLOAT64:
+      case cudf::type_id::BOOL8:
+      case cudf::type_id::TIMESTAMP_DAYS:
+      case cudf::type_id::TIMESTAMP_SECONDS:
+      case cudf::type_id::TIMESTAMP_MILLISECONDS:
+      case cudf::type_id::TIMESTAMP_MICROSECONDS:
+      case cudf::type_id::TIMESTAMP_NANOSECONDS:
+      case cudf::type_id::DURATION_DAYS:
+      case cudf::type_id::DURATION_SECONDS:
+      case cudf::type_id::DURATION_MILLISECONDS:
+      case cudf::type_id::DURATION_MICROSECONDS:
+      case cudf::type_id::DURATION_NANOSECONDS:
+      case cudf::type_id::STRING: {
+        return cudf::data_type(dtype_id);
+      }
+      case cudf::type_id::DECIMAL32:
+      case cudf::type_id::DECIMAL64: {
+        int32_t const scale = read_byte();
+        return cudf::data_type(dtype_id, scale);
+      }
+      default: throw new std::invalid_argument("unrecognized cudf data type");
+    }
+  }
+};
+
+/**
+ * Enumeration of the AST expression types that can appear in the serialized data.
+ * NOTE: This must be kept in sync with the NodeType enumeration in AstNode.java!
+ */
+enum class jni_serialized_expression_type : int8_t {
+  VALID_LITERAL = 0,
+  NULL_LITERAL = 1,
+  COLUMN_REFERENCE = 2,
+  UNARY_OPERATION = 3,
+  BINARY_OPERATION = 4
+};
+
+/**
+ * Convert a Java AST serialized byte representing an AST unary operator into the
+ * corresponding libcudf AST operator.
+ * NOTE: This must be kept in sync with the enumeration in UnaryOperator.java!
+ */
+cudf::ast::ast_operator jni_to_unary_operator(jbyte jni_op_value) {
+  switch (jni_op_value) {
+    case 0: return cudf::ast::ast_operator::IDENTITY;
+    case 1: return cudf::ast::ast_operator::IS_NULL;
+    case 2: return cudf::ast::ast_operator::SIN;
+    case 3: return cudf::ast::ast_operator::COS;
+    case 4: return cudf::ast::ast_operator::TAN;
+    case 5: return cudf::ast::ast_operator::ARCSIN;
+    case 6: return cudf::ast::ast_operator::ARCCOS;
+    case 7: return cudf::ast::ast_operator::ARCTAN;
+    case 8: return cudf::ast::ast_operator::SINH;
+    case 9: return cudf::ast::ast_operator::COSH;
+    case 10: return cudf::ast::ast_operator::TANH;
+    case 11: return cudf::ast::ast_operator::ARCSINH;
+    case 12: return cudf::ast::ast_operator::ARCCOSH;
+    case 13: return cudf::ast::ast_operator::ARCTANH;
+    case 14: return cudf::ast::ast_operator::EXP;
+    case 15: return cudf::ast::ast_operator::LOG;
+    case 16: return cudf::ast::ast_operator::SQRT;
+    case 17: return cudf::ast::ast_operator::CBRT;
+    case 18: return cudf::ast::ast_operator::CEIL;
+    case 19: return cudf::ast::ast_operator::FLOOR;
+    case 20: return cudf::ast::ast_operator::ABS;
+    case 21: return cudf::ast::ast_operator::RINT;
+    case 22: return cudf::ast::ast_operator::BIT_INVERT;
+    case 23: return cudf::ast::ast_operator::NOT;
+    case 24: return cudf::ast::ast_operator::CAST_TO_INT64;
+    case 25: return cudf::ast::ast_operator::CAST_TO_UINT64;
+    case 26: return cudf::ast::ast_operator::CAST_TO_FLOAT64;
+    default: throw std::invalid_argument("unexpected JNI AST unary operator value");
+  }
+}
+
+/**
+ * Convert a Java AST serialized byte representing an AST binary operator into the
+ * corresponding libcudf AST operator.
+ * NOTE: This must be kept in sync with the enumeration in BinaryOperator.java!
+ */
+cudf::ast::ast_operator jni_to_binary_operator(jbyte jni_op_value) {
+  switch (jni_op_value) {
+    case 0: return cudf::ast::ast_operator::ADD;
+    case 1: return cudf::ast::ast_operator::SUB;
+    case 2: return cudf::ast::ast_operator::MUL;
+    case 3: return cudf::ast::ast_operator::DIV;
+    case 4: return cudf::ast::ast_operator::TRUE_DIV;
+    case 5: return cudf::ast::ast_operator::FLOOR_DIV;
+    case 6: return cudf::ast::ast_operator::MOD;
+    case 7: return cudf::ast::ast_operator::PYMOD;
+    case 8: return cudf::ast::ast_operator::POW;
+    case 9: return cudf::ast::ast_operator::EQUAL;
+    case 10: return cudf::ast::ast_operator::NULL_EQUAL;
+    case 11: return cudf::ast::ast_operator::NOT_EQUAL;
+    case 12: return cudf::ast::ast_operator::LESS;
+    case 13: return cudf::ast::ast_operator::GREATER;
+    case 14: return cudf::ast::ast_operator::LESS_EQUAL;
+    case 15: return cudf::ast::ast_operator::GREATER_EQUAL;
+    case 16: return cudf::ast::ast_operator::BITWISE_AND;
+    case 17: return cudf::ast::ast_operator::BITWISE_OR;
+    case 18: return cudf::ast::ast_operator::BITWISE_XOR;
+    case 19: return cudf::ast::ast_operator::LOGICAL_AND;
+    case 20: return cudf::ast::ast_operator::NULL_LOGICAL_AND;
+    case 21: return cudf::ast::ast_operator::LOGICAL_OR;
+    case 22: return cudf::ast::ast_operator::NULL_LOGICAL_OR;
+    default: throw std::invalid_argument("unexpected JNI AST binary operator value");
+  }
+}
+
+/**
+ * Convert a Java AST serialized byte representing an AST table reference into the
+ * corresponding libcudf AST table reference.
+ * NOTE: This must be kept in sync with the enumeration in TableReference.java!
+ */
+cudf::ast::table_reference jni_to_table_reference(jbyte jni_value) {
+  switch (jni_value) {
+    case 0: return cudf::ast::table_reference::LEFT;
+    case 1: return cudf::ast::table_reference::RIGHT;
+    default: throw std::invalid_argument("unexpected JNI table reference value");
+  }
+}
+
+/** Functor for type-dispatching the creation of an AST literal */
+struct make_literal {
+  /** Construct an AST literal from a numeric value */
+  template <typename T, std::enable_if_t<cudf::is_numeric<T>()> * = nullptr>
+  cudf::ast::literal &operator()(cudf::data_type dtype, bool is_valid,
+                                 cudf::jni::ast::compiled_expr &compiled_expr,
+                                 jni_serialized_ast &jni_ast) {
+    std::unique_ptr<cudf::scalar> scalar_ptr = cudf::make_numeric_scalar(dtype);
+    scalar_ptr->set_valid_async(is_valid);
+    if (is_valid) {
+      T val = jni_ast.read<T>();
+      using ScalarType = cudf::scalar_type_t<T>;
+      static_cast<ScalarType *>(scalar_ptr.get())->set_value(val);
+    }
+
+    auto &numeric_scalar = static_cast<cudf::numeric_scalar<T> &>(*scalar_ptr);
+    return compiled_expr.add_literal(std::make_unique<cudf::ast::literal>(numeric_scalar),
+                                     std::move(scalar_ptr));
+  }
+
+  /** Construct an AST literal from a timestamp value */
+  template <typename T, std::enable_if_t<cudf::is_timestamp<T>()> * = nullptr>
+  cudf::ast::literal &operator()(cudf::data_type dtype, bool is_valid,
+                                 cudf::jni::ast::compiled_expr &compiled_expr,
+                                 jni_serialized_ast &jni_ast) {
+    std::unique_ptr<cudf::scalar> scalar_ptr = cudf::make_timestamp_scalar(dtype);
+    scalar_ptr->set_valid_async(is_valid);
+    if (is_valid) {
+      T val = jni_ast.read<T>();
+      using ScalarType = cudf::scalar_type_t<T>;
+      static_cast<ScalarType *>(scalar_ptr.get())->set_value(val);
+    }
+
+    auto &timestamp_scalar = static_cast<cudf::timestamp_scalar<T> &>(*scalar_ptr);
+    return compiled_expr.add_literal(std::make_unique<cudf::ast::literal>(timestamp_scalar),
+                                     std::move(scalar_ptr));
+  }
+
+  /** Construct an AST literal from a duration value */
+  template <typename T, std::enable_if_t<cudf::is_duration<T>()> * = nullptr>
+  cudf::ast::literal &operator()(cudf::data_type dtype, bool is_valid,
+                                 cudf::jni::ast::compiled_expr &compiled_expr,
+                                 jni_serialized_ast &jni_ast) {
+    std::unique_ptr<cudf::scalar> scalar_ptr = cudf::make_duration_scalar(dtype);
+    scalar_ptr->set_valid_async(is_valid);
+    if (is_valid) {
+      T val = jni_ast.read<T>();
+      using ScalarType = cudf::scalar_type_t<T>;
+      static_cast<ScalarType *>(scalar_ptr.get())->set_value(val);
+    }
+
+    auto &duration_scalar = static_cast<cudf::duration_scalar<T> &>(*scalar_ptr);
+    return compiled_expr.add_literal(std::make_unique<cudf::ast::literal>(duration_scalar),
+                                     std::move(scalar_ptr));
+  }
+
+  /** Construct an AST literal from a string value */
+  template <typename T, std::enable_if_t<std::is_same_v<T, cudf::string_view>> * = nullptr>
+  cudf::ast::literal &operator()(cudf::data_type dtype, bool is_valid,
+                                 cudf::jni::ast::compiled_expr &compiled_expr,
+                                 jni_serialized_ast &jni_ast) {
+    std::unique_ptr<cudf::scalar> scalar_ptr = [&]() {
+      if (is_valid) {
+        std::string val = jni_ast.read<std::string>();
+        return std::make_unique<cudf::string_scalar>(val, is_valid);
+      } else {
+        return std::make_unique<cudf::string_scalar>(rmm::device_buffer{}, is_valid);
+      }
+    }();
+
+    auto &str_scalar = static_cast<cudf::string_scalar &>(*scalar_ptr);
+    return compiled_expr.add_literal(std::make_unique<cudf::ast::literal>(str_scalar),
+                                     std::move(scalar_ptr));
+  }
+
+  /** Default functor implementation to catch type dispatch errors */
+  template <typename T, std::enable_if_t<!cudf::is_numeric<T>() && !cudf::is_timestamp<T>() &&
+                                         !cudf::is_duration<T>() &&
+                                         !std::is_same_v<T, cudf::string_view>> * = nullptr>
+  cudf::ast::literal &operator()(cudf::data_type dtype, bool is_valid,
+                                 cudf::jni::ast::compiled_expr &compiled_expr,
+                                 jni_serialized_ast &jni_ast) {
+    throw std::logic_error("Unsupported AST literal type");
+  }
+};
+
+/** Decode a serialized AST literal */
+cudf::ast::literal &compile_literal(bool is_valid, cudf::jni::ast::compiled_expr &compiled_expr,
+                                    jni_serialized_ast &jni_ast) {
+  auto const dtype = jni_ast.read_cudf_type();
+  return cudf::type_dispatcher(dtype, make_literal{}, dtype, is_valid, compiled_expr, jni_ast);
+}
+
+/** Decode a serialized AST column reference */
+cudf::ast::column_reference &compile_column_reference(cudf::jni::ast::compiled_expr &compiled_expr,
+                                                      jni_serialized_ast &jni_ast) {
+  auto const table_ref = jni_to_table_reference(jni_ast.read_byte());
+  cudf::size_type const column_index = jni_ast.read<int>();
+  return compiled_expr.add_column_ref(
+      std::make_unique<cudf::ast::column_reference>(column_index, table_ref));
+}
+
+// forward declaration
+cudf::ast::expression &compile_expression(cudf::jni::ast::compiled_expr &compiled_expr,
+                                          jni_serialized_ast &jni_ast);
+
+/** Decode a serialized AST unary expression */
+cudf::ast::operation &compile_unary_expression(cudf::jni::ast::compiled_expr &compiled_expr,
+                                               jni_serialized_ast &jni_ast) {
+  auto const ast_op = jni_to_unary_operator(jni_ast.read_byte());
+  cudf::ast::expression &child_expression = compile_expression(compiled_expr, jni_ast);
+  return compiled_expr.add_operation(
+      std::make_unique<cudf::ast::operation>(ast_op, child_expression));
+}
+
+/** Decode a serialized AST binary expression */
+cudf::ast::operation &compile_binary_expression(cudf::jni::ast::compiled_expr &compiled_expr,
+                                                jni_serialized_ast &jni_ast) {
+  auto const ast_op = jni_to_binary_operator(jni_ast.read_byte());
+  cudf::ast::expression &left_child = compile_expression(compiled_expr, jni_ast);
+  cudf::ast::expression &right_child = compile_expression(compiled_expr, jni_ast);
+  return compiled_expr.add_operation(
+      std::make_unique<cudf::ast::operation>(ast_op, left_child, right_child));
+}
+
+/** Decode a serialized AST expression by reading the expression type and dispatching */
+cudf::ast::expression &compile_expression(cudf::jni::ast::compiled_expr &compiled_expr,
+                                          jni_serialized_ast &jni_ast) {
+  auto const expression_type = static_cast<jni_serialized_expression_type>(jni_ast.read_byte());
+  switch (expression_type) {
+    case jni_serialized_expression_type::VALID_LITERAL:
+      return compile_literal(true, compiled_expr, jni_ast);
+    case jni_serialized_expression_type::NULL_LITERAL:
+      return compile_literal(false, compiled_expr, jni_ast);
+    case jni_serialized_expression_type::COLUMN_REFERENCE:
+      return compile_column_reference(compiled_expr, jni_ast);
+    case jni_serialized_expression_type::UNARY_OPERATION:
+      return compile_unary_expression(compiled_expr, jni_ast);
+    case jni_serialized_expression_type::BINARY_OPERATION:
+      return compile_binary_expression(compiled_expr, jni_ast);
+    default: throw std::invalid_argument("data is not a serialized AST expression");
+  }
+}
+
+/** Decode a serialized AST into a native libcudf AST and associated resources */
+std::unique_ptr<cudf::jni::ast::compiled_expr> compile_serialized_ast(jni_serialized_ast &jni_ast) {
+  auto jni_expr_ptr = std::make_unique<cudf::jni::ast::compiled_expr>();
+  (void)compile_expression(*jni_expr_ptr, jni_ast);
+
+  if (!jni_ast.at_eof()) {
+    throw std::invalid_argument("Extra bytes at end of serialized AST");
+  }
+
+  return jni_expr_ptr;
+}
+
+} // anonymous namespace
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ast_CompiledExpression_compile(JNIEnv *env, jclass,
+                                                                           jbyteArray jni_data) {
+  JNI_NULL_CHECK(env, jni_data, "Serialized AST data is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jbyteArray jbytes(env, jni_data);
+    jni_serialized_ast jni_ast(jbytes);
+    auto compiled_expr_ptr = compile_serialized_ast(jni_ast);
+    jbytes.cancel();
+    return reinterpret_cast<jlong>(compiled_expr_ptr.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ast_CompiledExpression_computeColumn(JNIEnv *env,
+                                                                                 jclass,
+                                                                                 jlong j_ast,
+                                                                                 jlong j_table) {
+  JNI_NULL_CHECK(env, j_ast, "Compiled AST pointer is null", 0);
+  JNI_NULL_CHECK(env, j_table, "Table view pointer is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto compiled_expr_ptr = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_ast);
+    auto tview_ptr = reinterpret_cast<cudf::table_view const *>(j_table);
+    std::unique_ptr<cudf::column> result =
+        cudf::compute_column(*tview_ptr, compiled_expr_ptr->get_top_expression());
+    return reinterpret_cast<jlong>(result.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_ast_CompiledExpression_destroy(JNIEnv *env, jclass,
+                                                                          jlong jni_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ptr = reinterpret_cast<cudf::jni::ast::compiled_expr *>(jni_handle);
+    delete ptr;
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/ContiguousTableJni.cpp b/java/src/main/native/src/ContiguousTableJni.cpp
new file mode 100644
index 0000000..8c99c77
--- /dev/null
+++ b/java/src/main/native/src/ContiguousTableJni.cpp
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cudf_jni_apis.hpp"
+
+namespace {
+
+#define CONTIGUOUS_TABLE_CLASS "ai/rapids/cudf/ContiguousTable"
+#define CONTIGUOUS_TABLE_FACTORY_SIG(param_sig) "(" param_sig ")L" CONTIGUOUS_TABLE_CLASS ";"
+
+jclass Contiguous_table_jclass;
+jmethodID From_packed_table_method;
+
+#define GROUP_BY_RESULT_CLASS "ai/rapids/cudf/ContigSplitGroupByResult"
+jclass Contig_split_group_by_result_jclass;
+jfieldID Contig_split_group_by_result_groups_field;
+jfieldID Contig_split_group_by_result_uniq_key_columns_field;
+
+} // anonymous namespace
+
+namespace cudf {
+namespace jni {
+
+bool cache_contiguous_table_jni(JNIEnv *env) {
+  jclass cls = env->FindClass(CONTIGUOUS_TABLE_CLASS);
+  if (cls == nullptr) {
+    return false;
+  }
+
+  From_packed_table_method =
+      env->GetStaticMethodID(cls, "fromPackedTable", CONTIGUOUS_TABLE_FACTORY_SIG("JJJJJ"));
+  if (From_packed_table_method == nullptr) {
+    return false;
+  }
+
+  // Convert local reference to global so it cannot be garbage collected.
+  Contiguous_table_jclass = static_cast<jclass>(env->NewGlobalRef(cls));
+  if (Contiguous_table_jclass == nullptr) {
+    return false;
+  }
+  return true;
+}
+
+void release_contiguous_table_jni(JNIEnv *env) {
+  Contiguous_table_jclass = cudf::jni::del_global_ref(env, Contiguous_table_jclass);
+}
+
+bool cache_contig_split_group_by_result_jni(JNIEnv *env) {
+  jclass cls = env->FindClass(GROUP_BY_RESULT_CLASS);
+  if (cls == nullptr) {
+    return false;
+  }
+
+  Contig_split_group_by_result_groups_field =
+      env->GetFieldID(cls, "groups", "[Lai/rapids/cudf/ContiguousTable;");
+  if (Contig_split_group_by_result_groups_field == nullptr) {
+    return false;
+  }
+  Contig_split_group_by_result_uniq_key_columns_field =
+      env->GetFieldID(cls, "uniqKeyColumns", "[J");
+  if (Contig_split_group_by_result_uniq_key_columns_field == nullptr) {
+    return false;
+  }
+
+  // Convert local reference to global so it cannot be garbage collected.
+  Contig_split_group_by_result_jclass = static_cast<jclass>(env->NewGlobalRef(cls));
+  if (Contig_split_group_by_result_jclass == nullptr) {
+    return false;
+  }
+  return true;
+}
+
+void release_contig_split_group_by_result_jni(JNIEnv *env) {
+  Contig_split_group_by_result_jclass = del_global_ref(env, Contig_split_group_by_result_jclass);
+}
+
+jobject contig_split_group_by_result_from(JNIEnv *env, jobjectArray &groups) {
+  jobject gbr = env->AllocObject(Contig_split_group_by_result_jclass);
+  env->SetObjectField(gbr, Contig_split_group_by_result_groups_field, groups);
+  return gbr;
+}
+
+jobject contig_split_group_by_result_from(JNIEnv *env, jobjectArray &groups,
+                                          jlongArray &uniq_key_columns) {
+  jobject gbr = env->AllocObject(Contig_split_group_by_result_jclass);
+  env->SetObjectField(gbr, Contig_split_group_by_result_groups_field, groups);
+  env->SetObjectField(gbr, Contig_split_group_by_result_uniq_key_columns_field, uniq_key_columns);
+  return gbr;
+}
+
+jobject contiguous_table_from(JNIEnv *env, cudf::packed_columns &split, long row_count) {
+  jlong metadata_address = reinterpret_cast<jlong>(split.metadata.get());
+  jlong data_address = reinterpret_cast<jlong>(split.gpu_data->data());
+  jlong data_size = static_cast<jlong>(split.gpu_data->size());
+  jlong rmm_buffer_address = reinterpret_cast<jlong>(split.gpu_data.get());
+
+  jobject contig_table_obj = env->CallStaticObjectMethod(
+      Contiguous_table_jclass, From_packed_table_method, metadata_address, data_address, data_size,
+      rmm_buffer_address, row_count);
+
+  if (contig_table_obj != nullptr) {
+    split.metadata.release();
+    split.gpu_data.release();
+  }
+
+  return contig_table_obj;
+}
+
+native_jobjectArray<jobject> contiguous_table_array(JNIEnv *env, jsize length) {
+  return native_jobjectArray<jobject>(
+      env, env->NewObjectArray(length, Contiguous_table_jclass, nullptr));
+}
+
+} // namespace jni
+} // namespace cudf
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ContiguousTable_createPackedMetadata(
+    JNIEnv *env, jclass, jlong j_table, jlong j_buffer_addr, jlong j_buffer_length) {
+  JNI_NULL_CHECK(env, j_table, "input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto table = reinterpret_cast<cudf::table_view const *>(j_table);
+    auto data_addr = reinterpret_cast<uint8_t const *>(j_buffer_addr);
+    auto data_size = static_cast<size_t>(j_buffer_length);
+    auto metadata_ptr = new std::vector<uint8_t>(cudf::pack_metadata(*table, data_addr, data_size));
+    return reinterpret_cast<jlong>(metadata_ptr);
+  }
+  CATCH_STD(env, 0);
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/CuFileJni.cpp b/java/src/main/native/src/CuFileJni.cpp
new file mode 100644
index 0000000..ef16528
--- /dev/null
+++ b/java/src/main/native/src/CuFileJni.cpp
@@ -0,0 +1,517 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cstring>
+
+#include <cufile.h>
+#include <fcntl.h>
+#include <unistd.h>
+
+#include <cudf/utilities/error.hpp>
+#include <sys/stat.h>
+#include <sys/types.h>
+
+#include "cudf_jni_apis.hpp"
+#include "jni_utils.hpp"
+
+namespace {
+
+/**
+ * @brief Get the error description based on the CUDA driver error code.
+ *
+ * @param cu_result CUDA driver error code.
+ * @return Description for the error.
+ */
+char const *GetCuErrorString(CUresult cu_result) {
+  char const *description;
+  if (cuGetErrorName(cu_result, &description) != CUDA_SUCCESS)
+    description = "unknown cuda error";
+  return description;
+}
+
+/**
+ * @brief Get the error description based on the integer error code.
+ *
+ * cuFile APIs return both cuFile specific error codes as well as POSIX error codes for ease of use.
+ *
+ * @param error_code Integer error code.
+ * @return Description of the error.
+ */
+std::string cuFileGetErrorString(int error_code) {
+  return IS_CUFILE_ERR(error_code) ? std::string(CUFILE_ERRSTR(error_code)) :
+                                     std::string(std::strerror(error_code));
+}
+
+/**
+ * @brief Get the error description based on the cuFile return status.
+ *
+ * @param status cuFile return status.
+ * @return Description of the error.
+ */
+std::string cuFileGetErrorString(CUfileError_t status) {
+  std::string error = cuFileGetErrorString(status.err);
+  if (IS_CUDA_ERR(status)) {
+    error.append(".").append(GetCuErrorString(status.cu_err));
+  }
+  return error;
+}
+
+/**
+ * @brief RAII wrapper for the cuFile driver.
+ */
+class cufile_driver {
+public:
+  /** @brief Construct a new driver instance by opening the cuFile driver. */
+  cufile_driver() {
+    auto const status = cuFileDriverOpen();
+    if (status.err != CU_FILE_SUCCESS) {
+      CUDF_FAIL("Failed to initialize cuFile driver: " + cuFileGetErrorString(status));
+    }
+  }
+
+  // Disable copy (and move) semantics.
+  cufile_driver(cufile_driver const &) = delete;
+  cufile_driver &operator=(cufile_driver const &) = delete;
+
+  /** @brief Destroy the driver instance by closing the cuFile driver. */
+  ~cufile_driver() { cuFileDriverClose(); }
+};
+
+/** @brief RAII wrapper for a device buffer used by cuFile. */
+class cufile_buffer {
+public:
+  /**
+   * @brief Construct a new cuFile buffer.
+   *
+   * @param device_pointer Pointer to the device buffer.
+   * @param size The size of the allocated device buffer.
+   * @param register_buffer Whether to register the buffer with cuFile. This should only be set to
+   * true if this buffer is being reused and is 4KiB aligned.
+   */
+  cufile_buffer(void *device_pointer, std::size_t size, bool register_buffer = false)
+      : device_pointer_{device_pointer}, size_{size}, register_buffer_{register_buffer} {
+    if (register_buffer_) {
+      auto const status = cuFileBufRegister(device_pointer_, size_, 0);
+      if (status.err != CU_FILE_SUCCESS) {
+        CUDF_FAIL("Failed to register cuFile buffer: " + cuFileGetErrorString(status));
+      }
+    }
+  }
+
+  // Disable copy (and move) semantics.
+  cufile_buffer(cufile_buffer const &) = delete;
+  cufile_buffer &operator=(cufile_buffer const &) = delete;
+
+  /** @brief Destroy the buffer by de-registering it if necessary. */
+  ~cufile_buffer() {
+    if (register_buffer_) {
+      cuFileBufDeregister(device_pointer_);
+    }
+  }
+
+  /**
+   * @brief Get the pointer to the underlying device buffer.
+   *
+   * @return Pointer to the device buffer.
+   */
+  void *device_pointer() const { return device_pointer_; }
+
+  /**
+   * @brief Get the size of the underlying device buffer.
+   *
+   * @return The size of the device buffer.
+   */
+  std::size_t size() const { return size_; }
+
+private:
+  /// Pointer to the device buffer.
+  void *device_pointer_;
+  /// Size of the device buffer.
+  std::size_t size_;
+  /// Whether to register the buffer with cuFile.
+  bool register_buffer_;
+};
+
+/** @brief RAII wrapper for a file descriptor and the corresponding cuFile handle. */
+class cufile_file {
+public:
+  /**
+   * @brief Construct a file wrapper.
+   *
+   * Should not be called directly; use the following factory methods instead.
+   *
+   * @param file_descriptor A valid file descriptor.
+   */
+  explicit cufile_file(int file_descriptor) : file_descriptor_{file_descriptor} {
+    CUfileDescr_t cufile_descriptor{CU_FILE_HANDLE_TYPE_OPAQUE_FD, file_descriptor_};
+    auto const status = cuFileHandleRegister(&cufile_handle_, &cufile_descriptor);
+    if (status.err != CU_FILE_SUCCESS) {
+      close(file_descriptor_);
+      CUDF_FAIL("Failed to register cuFile handle: " + cuFileGetErrorString(status));
+    }
+  }
+
+  /**
+   * @brief Factory method to create a file wrapper for reading.
+   *
+   * @param path Absolute path of the file to read from.
+   * @return std::unique_ptr<cufile_file> for reading.
+   */
+  static auto make_reader(char const *path) {
+    auto const file_descriptor = open(path, O_RDONLY | O_DIRECT);
+    if (file_descriptor < 0) {
+      CUDF_FAIL("Failed to open file to read: " + cuFileGetErrorString(errno));
+    }
+    return std::make_unique<cufile_file>(file_descriptor);
+  }
+
+  /**
+   * @brief Factory method to create a file wrapper for writing.
+   *
+   * @param path Absolute path of the file to write to.
+   * @return std::unique_ptr<cufile_file> for writing.
+   */
+  static auto make_writer(char const *path) {
+    auto const file_descriptor = open(path, O_CREAT | O_WRONLY | O_DIRECT, S_IRUSR | S_IWUSR);
+    if (file_descriptor < 0) {
+      CUDF_FAIL("Failed to open file to write: " + cuFileGetErrorString(errno));
+    }
+    return std::make_unique<cufile_file>(file_descriptor);
+  }
+
+  // Disable copy (and move) semantics.
+  cufile_file(cufile_file const &) = delete;
+  cufile_file &operator=(cufile_file const &) = delete;
+
+  /** @brief Destroy the file wrapper by de-registering the cuFile handle and closing the file. */
+  ~cufile_file() {
+    cuFileHandleDeregister(cufile_handle_);
+    close(file_descriptor_);
+  }
+
+  /**
+   * @brief Read the file into a device buffer.
+   *
+   * @param buffer Device buffer to read the file content into.
+   * @param file_offset Starting offset from which to read the file.
+   */
+  void read(cufile_buffer const &buffer, std::size_t file_offset) const {
+    auto const status =
+        cuFileRead(cufile_handle_, buffer.device_pointer(), buffer.size(), file_offset, 0);
+
+    if (status < 0) {
+      if (IS_CUFILE_ERR(status)) {
+        CUDF_FAIL("Failed to read file into buffer: " + cuFileGetErrorString(status));
+      } else {
+        CUDF_FAIL("Failed to read file into buffer: " + cuFileGetErrorString(errno));
+      }
+    }
+
+    CUDF_EXPECTS(static_cast<std::size_t>(status) == buffer.size(),
+                 "Size of bytes read is different from buffer size");
+  }
+
+  /**
+   * @brief Write a device buffer to the file.
+   *
+   * @param buffer The device buffer to write.
+   * @param size The number of bytes to write.
+   * @param file_offset Starting offset from which to write the buffer.
+   */
+  void write(cufile_buffer const &buffer, std::size_t size, std::size_t file_offset) {
+    auto const status = cuFileWrite(cufile_handle_, buffer.device_pointer(), size, file_offset, 0);
+
+    if (status < 0) {
+      if (IS_CUFILE_ERR(status)) {
+        CUDF_FAIL("Failed to write buffer to file: " + cuFileGetErrorString(status));
+      } else {
+        CUDF_FAIL("Failed to write buffer to file: " + cuFileGetErrorString(errno));
+      }
+    }
+
+    CUDF_EXPECTS(static_cast<std::size_t>(status) == size,
+                 "Size of bytes written is different from the specified size");
+  }
+
+  /**
+   * @brief Append a device buffer to the file.
+   *
+   * @param buffer The device buffer to append.
+   * @param size The number of bytes to append.
+   * @return The file offset from which the buffer was appended.
+   */
+  std::size_t append(cufile_buffer const &buffer, std::size_t size) {
+    struct stat stat_buffer;
+    auto const status = fstat(file_descriptor_, &stat_buffer);
+    if (status < 0) {
+      CUDF_FAIL("Failed to get file status for appending: " + cuFileGetErrorString(errno));
+    }
+
+    auto const file_offset = static_cast<std::size_t>(stat_buffer.st_size);
+    write(buffer, size, file_offset);
+    return file_offset;
+  }
+
+private:
+  /// The underlying file descriptor.
+  int file_descriptor_;
+  /// The registered cuFile handle.
+  CUfileHandle_t cufile_handle_{};
+};
+
+} // anonymous namespace
+
+extern "C" {
+
+/**
+ * @brief Create a new cuFile driver wrapper.
+ *
+ * @param env The JNI environment.
+ * @return Pointer address to the new driver wrapper instance.
+ */
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_CuFileDriver_create(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    return reinterpret_cast<jlong>(new cufile_driver());
+  }
+  CATCH_STD(env, 0);
+}
+
+/**
+ * @brief Destroy the given cuFile driver wrapper.
+ *
+ * @param env The JNI environment.
+ * @param pointer Pointer address to the driver wrapper instance.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFileDriver_destroy(JNIEnv *env, jclass,
+                                                                jlong pointer) {
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cufile_driver *>(pointer);
+  }
+  CATCH_STD(env, );
+}
+
+/**
+ * @brief Create a new cuFile buffer wrapper.
+ *
+ * @param env The JNI environment.
+ * @param device_pointer Pointer address to the device buffer.
+ * @param size The size of the device buffer.
+ * @param register_buffer If true, register the cuFile buffer.
+ * @return Pointer address to the new buffer wrapper instance.
+ */
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_CuFileBuffer_create(JNIEnv *env, jclass,
+                                                                jlong device_pointer, jlong size,
+                                                                jboolean register_buffer) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto *buffer =
+        new cufile_buffer(reinterpret_cast<void *>(device_pointer), size, register_buffer);
+    return reinterpret_cast<jlong>(buffer);
+  }
+  CATCH_STD(env, 0);
+}
+
+/**
+ * @brief Destroy the given cuFile buffer wrapper.
+ *
+ * @param env The JNI environment.
+ * @param pointer Pointer address to the buffer wrapper instance.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFileBuffer_destroy(JNIEnv *env, jclass,
+                                                                jlong pointer) {
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cufile_buffer *>(pointer);
+  }
+  CATCH_STD(env, );
+}
+
+/**
+ * @brief Create a new cuFile file handle wrapper for reading.
+ *
+ * @param env The JNI environment.
+ * @param path The file path to read from.
+ * @return Pointer address to the new file handle wrapper instance.
+ */
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_CuFileReadHandle_create(JNIEnv *env, jclass,
+                                                                    jstring path) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto file = cufile_file::make_reader(env->GetStringUTFChars(path, nullptr));
+    return reinterpret_cast<jlong>(file.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+/**
+ * @brief Read the content into the specified buffer.
+ *
+ * @param env The JNI environment.
+ * @param file Pointer to the cuFile file object.
+ * @param file_offset The file offset from which to read.
+ * @param buffer Pointer to the cuFile buffer object.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFileReadHandle_readIntoBuffer(JNIEnv *env, jclass,
+                                                                           jlong file,
+                                                                           jlong file_offset,
+                                                                           jlong buffer) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto *file_ptr = reinterpret_cast<cufile_file *>(file);
+    auto *buffer_ptr = reinterpret_cast<cufile_buffer *>(buffer);
+    file_ptr->read(*buffer_ptr, file_offset);
+  }
+  CATCH_STD(env, );
+}
+
+/**
+ * @brief Create a new cuFile file handle wrapper for writing.
+ *
+ * @param env The JNI environment.
+ * @param path The file path to write to.
+ * @return Pointer address to the new file handle wrapper instance.
+ */
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_CuFileWriteHandle_create(JNIEnv *env, jclass,
+                                                                     jstring path) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto file = cufile_file::make_writer(env->GetStringUTFChars(path, nullptr));
+    return reinterpret_cast<jlong>(file.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+/**
+ * @brief Write the content of the specified buffer into the file.
+ *
+ * @param env The JNI environment.
+ * @param file Pointer to the cuFile file object.
+ * @param file_offset The file offset from which to write.
+ * @param buffer Pointer to the cuFile buffer object.
+ * @param size Number of bytes to write.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFileWriteHandle_writeFromBuffer(
+    JNIEnv *env, jclass, jlong file, jlong file_offset, jlong buffer, jlong size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto *file_ptr = reinterpret_cast<cufile_file *>(file);
+    auto *buffer_ptr = reinterpret_cast<cufile_buffer *>(buffer);
+    file_ptr->write(*buffer_ptr, size, file_offset);
+  }
+  CATCH_STD(env, );
+}
+
+/**
+ * @brief Append the content of the specified buffer into the file.
+ *
+ * @param env The JNI environment.
+ * @param file Pointer to the cuFile file object.
+ * @param buffer Pointer to the cuFile buffer object.
+ * @param size Number of bytes to append
+ * @return The file offset from which the buffer was appended.
+ */
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_CuFileWriteHandle_appendFromBuffer(JNIEnv *env, jclass,
+                                                                              jlong file,
+                                                                              jlong buffer,
+                                                                              jlong size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto *file_ptr = reinterpret_cast<cufile_file *>(file);
+    auto *buffer_ptr = reinterpret_cast<cufile_buffer *>(buffer);
+    return file_ptr->append(*buffer_ptr, size);
+  }
+  CATCH_STD(env, -1);
+}
+
+/**
+ * @brief Destroy the given cuFile file handle wrapper.
+ *
+ * @param env The JNI environment.
+ * @param pointer Pointer address to the file handle wrapper instance.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFileHandle_destroy(JNIEnv *env, jclass,
+                                                                jlong pointer) {
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cufile_file *>(pointer);
+  }
+  CATCH_STD(env, );
+}
+
+/**
+ * @brief Write a device buffer into a given file path.
+ *
+ * @param env The JNI environment.
+ * @param path Absolute path of the file to copy the buffer to.
+ * @param file_offset The file offset from which the buffer was written.
+ * @param device_pointer Pointer address to the device buffer.
+ * @param size Number of bytes to write.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFile_writeToFile(JNIEnv *env, jclass, jstring path,
+                                                              jlong file_offset,
+                                                              jlong device_pointer, jlong size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cufile_buffer buffer{reinterpret_cast<void *>(device_pointer), static_cast<std::size_t>(size)};
+    auto writer = cufile_file::make_writer(env->GetStringUTFChars(path, nullptr));
+    writer->write(buffer, size, file_offset);
+  }
+  CATCH_STD(env, );
+}
+
+/**
+ * @brief Append a device buffer into a given file path.
+ *
+ * @param env The JNI environment.
+ * @param path Absolute path of the file to copy the buffer to.
+ * @param device_pointer Pointer address to the device buffer.
+ * @param size Number of bytes to append.
+ * @return The file offset from which the buffer was appended.
+ */
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_CuFile_appendToFile(JNIEnv *env, jclass, jstring path,
+                                                                jlong device_pointer, jlong size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cufile_buffer buffer{reinterpret_cast<void *>(device_pointer), static_cast<std::size_t>(size)};
+    auto writer = cufile_file::make_writer(env->GetStringUTFChars(path, nullptr));
+    return writer->append(buffer, size);
+  }
+  CATCH_STD(env, -1);
+}
+
+/**
+ * @brief Read from a given file path into a device buffer.
+ *
+ * @param env The JNI environment.
+ * @param device_pointer Pointer address to the device buffer.
+ * @param size The size of the device buffer.
+ * @param path Absolute path of the file to copy from.
+ * @param file_offset The file offset from which to copy content.
+ */
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_CuFile_readFromFile(JNIEnv *env, jclass,
+                                                               jlong device_pointer, jlong size,
+                                                               jstring path, jlong file_offset) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cufile_buffer buffer{reinterpret_cast<void *>(device_pointer), static_cast<std::size_t>(size)};
+    auto const reader = cufile_file::make_reader(env->GetStringUTFChars(path, nullptr));
+    reader->read(buffer, file_offset);
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/CudaJni.cpp b/java/src/main/native/src/CudaJni.cpp
new file mode 100644
index 0000000..2fe550c
--- /dev/null
+++ b/java/src/main/native/src/CudaJni.cpp
@@ -0,0 +1,414 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/utilities/error.hpp>
+#include <rmm/device_buffer.hpp>
+
+#ifdef CUDF_JNI_ENABLE_PROFILING
+#include <cuda_profiler_api.h>
+#endif
+
+#include "jni_utils.hpp"
+
+namespace {
+
+/** The CUDA device that should be used by all threads using cudf */
+int Cudf_device{cudaInvalidDeviceId};
+
+thread_local int Thread_device = cudaInvalidDeviceId;
+
+} // anonymous namespace
+
+namespace cudf {
+namespace jni {
+
+/** Set the device to use for cudf */
+void set_cudf_device(int device) {
+  Cudf_device = device;
+}
+
+/**
+ * If a cudf device has been specified then this ensures the calling thread
+ * is using the same device.
+ */
+void auto_set_device(JNIEnv *env) {
+  if (Cudf_device != cudaInvalidDeviceId) {
+    if (Thread_device != Cudf_device) {
+      cudaError_t cuda_status = cudaSetDevice(Cudf_device);
+      jni_cuda_check(env, cuda_status);
+      Thread_device = Cudf_device;
+    }
+  }
+}
+
+/** Fills all the bytes in the buffer 'buf' with 'value'. */
+void device_memset_async(JNIEnv *env, rmm::device_buffer &buf, char value) {
+  cudaError_t cuda_status = cudaMemsetAsync((void *)buf.data(), value, buf.size());
+  jni_cuda_check(env, cuda_status);
+}
+
+} // namespace jni
+} // namespace cudf
+
+extern "C" {
+
+JNIEXPORT jobject JNICALL Java_ai_rapids_cudf_Cuda_memGetInfo(JNIEnv *env, jclass clazz) {
+  try {
+    cudf::jni::auto_set_device(env);
+
+    size_t free, total;
+    CUDF_CUDA_TRY(cudaMemGetInfo(&free, &total));
+
+    jclass info_class = env->FindClass("Lai/rapids/cudf/CudaMemInfo;");
+    if (info_class == NULL) {
+      return NULL;
+    }
+
+    jmethodID ctor_id = env->GetMethodID(info_class, "<init>", "(JJ)V");
+    if (ctor_id == NULL) {
+      return NULL;
+    }
+
+    jobject info_obj = env->NewObject(info_class, ctor_id, (jlong)free, (jlong)total);
+    // No need to check for exceptions of null return value as we are just handing the object back
+    // to the JVM which will handle throwing any exceptions that happened in the constructor.
+    return info_obj;
+  }
+  CATCH_STD(env, nullptr);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Cuda_hostAllocPinned(JNIEnv *env, jclass, jlong size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    void *ret = nullptr;
+    CUDF_CUDA_TRY(cudaMallocHost(&ret, size));
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_freePinned(JNIEnv *env, jclass, jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    CUDF_CUDA_TRY(cudaFreeHost(reinterpret_cast<void *>(ptr)));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_memset(JNIEnv *env, jclass, jlong dst, jbyte value,
+                                                       jlong count, jint kind) {
+  JNI_NULL_CHECK(env, dst, "dst memory pointer is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    CUDF_CUDA_TRY(cudaMemsetAsync((void *)dst, value, count));
+    CUDF_CUDA_TRY(cudaStreamSynchronize(0));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_asyncMemset(JNIEnv *env, jclass, jlong dst,
+                                                            jbyte value, jlong count, jint kind) {
+  JNI_NULL_CHECK(env, dst, "dst memory pointer is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    CUDF_CUDA_TRY(cudaMemsetAsync((void *)dst, value, count));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getDevice(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    jint dev;
+    CUDF_CUDA_TRY(cudaGetDevice(&dev));
+    return dev;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getDeviceCount(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    jint count;
+    CUDF_CUDA_TRY(cudaGetDeviceCount(&count));
+    return count;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_setDevice(JNIEnv *env, jclass, jint dev) {
+  try {
+    if (Cudf_device != cudaInvalidDeviceId && dev != Cudf_device) {
+      cudf::jni::throw_java_exception(env, cudf::jni::CUDF_ERROR_CLASS,
+                                      "Cannot change device after RMM init");
+    }
+    CUDF_CUDA_TRY(cudaSetDevice(dev));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_autoSetDevice(JNIEnv *env, jclass, jint dev) {
+  try {
+    cudf::jni::auto_set_device(env);
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getDriverVersion(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    jint driver_version;
+    CUDF_CUDA_TRY(cudaDriverGetVersion(&driver_version));
+    return driver_version;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getRuntimeVersion(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    jint runtime_version;
+    CUDF_CUDA_TRY(cudaRuntimeGetVersion(&runtime_version));
+    return runtime_version;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getNativeComputeMode(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    int device;
+    CUDF_CUDA_TRY(cudaGetDevice(&device));
+    cudaDeviceProp device_prop;
+    CUDF_CUDA_TRY(cudaGetDeviceProperties(&device_prop, device));
+    return device_prop.computeMode;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getComputeCapabilityMajor(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    int device;
+    CUDF_CUDA_TRY(::cudaGetDevice(&device));
+    int attribute_value;
+    CUDF_CUDA_TRY(
+        ::cudaDeviceGetAttribute(&attribute_value, ::cudaDevAttrComputeCapabilityMajor, device));
+    return attribute_value;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Cuda_getComputeCapabilityMinor(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    int device;
+    CUDF_CUDA_TRY(::cudaGetDevice(&device));
+    int attribute_value;
+    CUDF_CUDA_TRY(
+        ::cudaDeviceGetAttribute(&attribute_value, ::cudaDevAttrComputeCapabilityMinor, device));
+    return attribute_value;
+  }
+  CATCH_STD(env, -2);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_freeZero(JNIEnv *env, jclass) {
+  try {
+    cudf::jni::auto_set_device(env);
+    CUDF_CUDA_TRY(cudaFree(0));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Cuda_createStream(JNIEnv *env, jclass,
+                                                              jboolean isNonBlocking) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudaStream_t stream = nullptr;
+    auto flags = isNonBlocking ? cudaStreamNonBlocking : cudaStreamDefault;
+    CUDF_CUDA_TRY(cudaStreamCreateWithFlags(&stream, flags));
+    return reinterpret_cast<jlong>(stream);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_destroyStream(JNIEnv *env, jclass, jlong jstream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto stream = reinterpret_cast<cudaStream_t>(jstream);
+    CUDF_CUDA_TRY(cudaStreamDestroy(stream));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_streamWaitEvent(JNIEnv *env, jclass, jlong jstream,
+                                                                jlong jevent) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto stream = reinterpret_cast<cudaStream_t>(jstream);
+    auto event = reinterpret_cast<cudaEvent_t>(jevent);
+    CUDF_CUDA_TRY(cudaStreamWaitEvent(stream, event, 0));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_streamSynchronize(JNIEnv *env, jclass,
+                                                                  jlong jstream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto stream = reinterpret_cast<cudaStream_t>(jstream);
+    CUDF_CUDA_TRY(cudaStreamSynchronize(stream));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Cuda_createEvent(JNIEnv *env, jclass,
+                                                             jboolean enableTiming,
+                                                             jboolean blockingSync) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudaEvent_t event = nullptr;
+    unsigned int flags = 0;
+    if (!enableTiming) {
+      flags = flags | cudaEventDisableTiming;
+    }
+    if (blockingSync) {
+      flags = flags | cudaEventBlockingSync;
+    }
+    CUDF_CUDA_TRY(cudaEventCreateWithFlags(&event, flags));
+    return reinterpret_cast<jlong>(event);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_destroyEvent(JNIEnv *env, jclass, jlong jevent) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto event = reinterpret_cast<cudaEvent_t>(jevent);
+    CUDF_CUDA_TRY(cudaEventDestroy(event));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jboolean JNICALL Java_ai_rapids_cudf_Cuda_eventQuery(JNIEnv *env, jclass, jlong jevent) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto event = reinterpret_cast<cudaEvent_t>(jevent);
+    auto result = cudaEventQuery(event);
+    if (result == cudaSuccess) {
+      return true;
+    } else if (result == cudaErrorNotReady) {
+      return false;
+    } // else
+    CUDF_CUDA_TRY(result);
+  }
+  CATCH_STD(env, false);
+  return false;
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_eventRecord(JNIEnv *env, jclass, jlong jevent,
+                                                            jlong jstream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto event = reinterpret_cast<cudaEvent_t>(jevent);
+    auto stream = reinterpret_cast<cudaStream_t>(jstream);
+    CUDF_CUDA_TRY(cudaEventRecord(event, stream));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_eventSynchronize(JNIEnv *env, jclass,
+                                                                 jlong jevent) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto event = reinterpret_cast<cudaEvent_t>(jevent);
+    CUDF_CUDA_TRY(cudaEventSynchronize(event));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_memcpyOnStream(JNIEnv *env, jclass, jlong jdst,
+                                                               jlong jsrc, jlong count, jint jkind,
+                                                               jlong jstream) {
+  if (count == 0) {
+    return;
+  }
+  JNI_ARG_CHECK(env, jdst != 0, "dst memory pointer is null", );
+  JNI_ARG_CHECK(env, jsrc != 0, "src memory pointer is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    auto dst = reinterpret_cast<void *>(jdst);
+    auto src = reinterpret_cast<void *>(jsrc);
+    auto kind = static_cast<cudaMemcpyKind>(jkind);
+    auto stream = reinterpret_cast<cudaStream_t>(jstream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(dst, src, count, kind, stream));
+    CUDF_CUDA_TRY(cudaStreamSynchronize(stream));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_asyncMemcpyOnStream(JNIEnv *env, jclass, jlong jdst,
+                                                                    jlong jsrc, jlong count,
+                                                                    jint jkind, jlong jstream) {
+  if (count == 0) {
+    return;
+  }
+  JNI_ARG_CHECK(env, jdst != 0, "dst memory pointer is null", );
+  JNI_ARG_CHECK(env, jsrc != 0, "src memory pointer is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    auto dst = reinterpret_cast<void *>(jdst);
+    auto src = reinterpret_cast<void *>(jsrc);
+    auto kind = static_cast<cudaMemcpyKind>(jkind);
+    auto stream = reinterpret_cast<cudaStream_t>(jstream);
+    CUDF_CUDA_TRY(cudaMemcpyAsync(dst, src, count, kind, stream));
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_profilerStart(JNIEnv *env, jclass clazz) {
+#ifdef CUDF_JNI_ENABLE_PROFILING
+  try {
+    cudaProfilerStart();
+  }
+  CATCH_STD(env, );
+#else
+  cudf::jni::throw_java_exception(env, cudf::jni::CUDF_ERROR_CLASS,
+                                  "This library was built without CUDA profiler support.");
+#endif
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_profilerStop(JNIEnv *env, jclass clazz) {
+#ifdef CUDF_JNI_ENABLE_PROFILING
+  try {
+    cudaProfilerStop();
+  }
+  CATCH_STD(env, );
+#else
+  cudf::jni::throw_java_exception(env, cudf::jni::CUDF_ERROR_CLASS,
+                                  "This library was built without CUDA profiler support.");
+#endif
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Cuda_deviceSynchronize(JNIEnv *env, jclass clazz) {
+  try {
+    cudf::jni::auto_set_device(env);
+    CUDF_CUDA_TRY(cudaDeviceSynchronize());
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/CudfJni.cpp b/java/src/main/native/src/CudfJni.cpp
new file mode 100644
index 0000000..0f14308
--- /dev/null
+++ b/java/src/main/native/src/CudfJni.cpp
@@ -0,0 +1,197 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <sstream>
+
+#include <cudf/copying.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include "cudf_jni_apis.hpp"
+
+namespace {
+
+// handles detaching a thread from the JVM when the thread terminates
+class jvm_detach_on_destruct {
+public:
+  explicit jvm_detach_on_destruct(JavaVM *jvm) : jvm{jvm} {}
+
+  ~jvm_detach_on_destruct() { jvm->DetachCurrentThread(); }
+
+private:
+  JavaVM *jvm;
+};
+
+} // anonymous namespace
+
+namespace cudf {
+namespace jni {
+
+#ifdef CUDA_API_PER_THREAD_DEFAULT_STREAM
+constexpr bool is_ptds_enabled{true};
+#else
+constexpr bool is_ptds_enabled{false};
+#endif
+
+static jclass Host_memory_buffer_jclass;
+static jfieldID Host_buffer_address;
+static jfieldID Host_buffer_length;
+
+#define HOST_MEMORY_BUFFER_CLASS "ai/rapids/cudf/HostMemoryBuffer"
+#define HOST_MEMORY_BUFFER_SIG(param_sig) "(" param_sig ")L" HOST_MEMORY_BUFFER_CLASS ";"
+
+static bool cache_host_memory_buffer_jni(JNIEnv *env) {
+  jclass cls = env->FindClass(HOST_MEMORY_BUFFER_CLASS);
+  if (cls == nullptr) {
+    return false;
+  }
+
+  Host_buffer_address = env->GetFieldID(cls, "address", "J");
+  if (Host_buffer_address == nullptr) {
+    return false;
+  }
+
+  Host_buffer_length = env->GetFieldID(cls, "length", "J");
+  if (Host_buffer_length == nullptr) {
+    return false;
+  }
+
+  // Convert local reference to global so it cannot be garbage collected.
+  Host_memory_buffer_jclass = static_cast<jclass>(env->NewGlobalRef(cls));
+  if (Host_memory_buffer_jclass == nullptr) {
+    return false;
+  }
+  return true;
+}
+
+static void release_host_memory_buffer_jni(JNIEnv *env) {
+  Host_memory_buffer_jclass = del_global_ref(env, Host_memory_buffer_jclass);
+}
+
+jobject allocate_host_buffer(JNIEnv *env, jlong amount, jboolean prefer_pinned,
+                             jobject host_memory_allocator) {
+  auto const host_memory_allocator_class = env->GetObjectClass(host_memory_allocator);
+  auto const allocateMethodId =
+      env->GetMethodID(host_memory_allocator_class, "allocate", HOST_MEMORY_BUFFER_SIG("JZ"));
+  jobject ret =
+      env->CallObjectMethod(host_memory_allocator, allocateMethodId, amount, prefer_pinned);
+
+  if (env->ExceptionCheck()) {
+    throw std::runtime_error("allocateHostBuffer threw an exception");
+  }
+  return ret;
+}
+
+jlong get_host_buffer_address(JNIEnv *env, jobject buffer) {
+  return env->GetLongField(buffer, Host_buffer_address);
+}
+
+jlong get_host_buffer_length(JNIEnv *env, jobject buffer) {
+  return env->GetLongField(buffer, Host_buffer_length);
+}
+
+// Get the JNI environment, attaching the current thread to the JVM if necessary. If the thread
+// needs to be attached, the thread will automatically detach when the thread terminates.
+JNIEnv *get_jni_env(JavaVM *jvm) {
+  JNIEnv *env = nullptr;
+  jint rc = jvm->GetEnv(reinterpret_cast<void **>(&env), MINIMUM_JNI_VERSION);
+  if (rc == JNI_OK) {
+    return env;
+  }
+  if (rc == JNI_EDETACHED) {
+    JavaVMAttachArgs attach_args;
+    attach_args.version = MINIMUM_JNI_VERSION;
+    attach_args.name = const_cast<char *>("cudf thread");
+    attach_args.group = NULL;
+
+    if (jvm->AttachCurrentThreadAsDaemon(reinterpret_cast<void **>(&env), &attach_args) == JNI_OK) {
+      // use thread_local object to detach the thread from the JVM when thread terminates.
+      thread_local jvm_detach_on_destruct detacher(jvm);
+    } else {
+      throw std::runtime_error("unable to attach to JVM");
+    }
+
+    return env;
+  }
+
+  throw std::runtime_error("error detecting thread attach state with JVM");
+}
+
+} // namespace jni
+} // namespace cudf
+
+extern "C" {
+
+JNIEXPORT jint JNI_OnLoad(JavaVM *vm, void *) {
+  JNIEnv *env;
+  if (vm->GetEnv(reinterpret_cast<void **>(&env), cudf::jni::MINIMUM_JNI_VERSION) != JNI_OK) {
+    return JNI_ERR;
+  }
+
+  // make sure libcudf and the JNI library are built with the same PTDS mode
+  if (cudf::is_ptds_enabled() != cudf::jni::is_ptds_enabled) {
+    std::ostringstream ss;
+    ss << "Libcudf is_ptds_enabled=" << cudf::is_ptds_enabled()
+       << ", which does not match cudf jni is_ptds_enabled=" << cudf::jni::is_ptds_enabled
+       << ". They need to be built with the same per-thread default stream flag.";
+    env->ThrowNew(env->FindClass("java/lang/RuntimeException"), ss.str().c_str());
+    return JNI_ERR;
+  }
+
+  // cache any class objects and method IDs here
+  if (!cudf::jni::cache_contiguous_table_jni(env)) {
+    if (!env->ExceptionCheck()) {
+      env->ThrowNew(env->FindClass("java/lang/RuntimeException"),
+                    "Unable to locate contiguous table methods needed by JNI");
+    }
+    return JNI_ERR;
+  }
+
+  if (!cudf::jni::cache_contig_split_group_by_result_jni(env)) {
+    if (!env->ExceptionCheck()) {
+      env->ThrowNew(env->FindClass("java/lang/RuntimeException"),
+                    "Unable to locate group by table result methods needed by JNI");
+    }
+    return JNI_ERR;
+  }
+
+  if (!cudf::jni::cache_host_memory_buffer_jni(env)) {
+    if (!env->ExceptionCheck()) {
+      env->ThrowNew(env->FindClass("java/lang/RuntimeException"),
+                    "Unable to locate host memory buffer methods needed by JNI");
+    }
+    return JNI_ERR;
+  }
+
+  return cudf::jni::MINIMUM_JNI_VERSION;
+}
+
+JNIEXPORT void JNI_OnUnload(JavaVM *vm, void *) {
+  JNIEnv *env = nullptr;
+  if (vm->GetEnv(reinterpret_cast<void **>(&env), cudf::jni::MINIMUM_JNI_VERSION) != JNI_OK) {
+    return;
+  }
+
+  // release cached class objects here.
+  cudf::jni::release_contiguous_table_jni(env);
+  cudf::jni::release_contig_split_group_by_result_jni(env);
+  cudf::jni::release_host_memory_buffer_jni(env);
+}
+
+JNIEXPORT jboolean JNICALL Java_ai_rapids_cudf_Cuda_isPtdsEnabled(JNIEnv *env, jclass) {
+  return cudf::jni::is_ptds_enabled;
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/HashJoinJni.cpp b/java/src/main/native/src/HashJoinJni.cpp
new file mode 100644
index 0000000..0f78aef
--- /dev/null
+++ b/java/src/main/native/src/HashJoinJni.cpp
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/join.hpp>
+
+#include "cudf_jni_apis.hpp"
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_HashJoin_create(JNIEnv *env, jclass, jlong j_table,
+                                                            jboolean j_nulls_equal) {
+  JNI_NULL_CHECK(env, j_table, "table handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto tview = reinterpret_cast<cudf::table_view const *>(j_table);
+    auto nulleq = j_nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    auto hash_join_ptr = new cudf::hash_join(*tview, nulleq);
+    return reinterpret_cast<jlong>(hash_join_ptr);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_HashJoin_destroy(JNIEnv *env, jclass, jlong j_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto hash_join_ptr = reinterpret_cast<cudf::hash_join *>(j_handle);
+    delete hash_join_ptr;
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/HostMemoryBufferNativeUtilsJni.cpp b/java/src/main/native/src/HostMemoryBufferNativeUtilsJni.cpp
new file mode 100644
index 0000000..f9e05d2
--- /dev/null
+++ b/java/src/main/native/src/HostMemoryBufferNativeUtilsJni.cpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <errno.h>
+#include <fcntl.h>
+#include <jni.h>
+#include <string.h>
+#include <unistd.h>
+
+#include <sys/mman.h>
+#include <sys/types.h>
+
+#include "jni_utils.hpp"
+
+extern "C" {
+
+JNIEXPORT jobject JNICALL Java_ai_rapids_cudf_HostMemoryBufferNativeUtils_wrapRangeInBuffer(
+    JNIEnv *env, jclass, jlong addr, jlong len) {
+  return env->NewDirectByteBuffer(reinterpret_cast<void *>(addr), len);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_HostMemoryBufferNativeUtils_mmap(
+    JNIEnv *env, jclass, jstring jpath, jint mode, jlong offset, jlong length) {
+  JNI_NULL_CHECK(env, jpath, "path is null", 0);
+  JNI_ARG_CHECK(env, (mode == 0 || mode == 1), "bad mode value", 0);
+  try {
+    cudf::jni::native_jstring path(env, jpath);
+
+    int fd = open(path.get(), (mode == 0) ? O_RDONLY : O_RDWR);
+    if (fd == -1) {
+      cudf::jni::throw_java_exception(env, "java/io/IOException", strerror(errno));
+    }
+
+    void *address = mmap(NULL, length, (mode == 0) ? PROT_READ : PROT_READ | PROT_WRITE, MAP_SHARED,
+                         fd, offset);
+    if (address == MAP_FAILED) {
+      char const *error_msg = strerror(errno);
+      close(fd);
+      cudf::jni::throw_java_exception(env, "java/io/IOException", error_msg);
+    }
+
+    close(fd);
+    return reinterpret_cast<jlong>(address);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_HostMemoryBufferNativeUtils_munmap(JNIEnv *env, jclass,
+                                                                              jlong address,
+                                                                              jlong length) {
+  JNI_NULL_CHECK(env, address, "address is NULL", );
+  try {
+    int rc = munmap(reinterpret_cast<void *>(address), length);
+    if (rc == -1) {
+      cudf::jni::throw_java_exception(env, "java/io/IOException", strerror(errno));
+    }
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/NvcompJni.cpp b/java/src/main/native/src/NvcompJni.cpp
new file mode 100644
index 0000000..e616b7f
--- /dev/null
+++ b/java/src/main/native/src/NvcompJni.cpp
@@ -0,0 +1,171 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <nvcomp.h>
+
+#include <nvcomp/lz4.h>
+#include <rmm/device_uvector.hpp>
+
+#include "check_nvcomp_output_sizes.hpp"
+#include "cudf_jni_apis.hpp"
+
+namespace {
+
+constexpr char const *NVCOMP_ERROR_CLASS = "ai/rapids/cudf/nvcomp/NvcompException";
+constexpr char const *NVCOMP_CUDA_ERROR_CLASS = "ai/rapids/cudf/nvcomp/NvcompCudaException";
+constexpr char const *ILLEGAL_ARG_CLASS = "java/lang/IllegalArgumentException";
+constexpr char const *UNSUPPORTED_CLASS = "java/lang/UnsupportedOperationException";
+
+void check_nvcomp_status(JNIEnv *env, nvcompStatus_t status) {
+  switch (status) {
+    case nvcompSuccess: break;
+    case nvcompErrorInvalidValue:
+      cudf::jni::throw_java_exception(env, ILLEGAL_ARG_CLASS, "nvcomp invalid value");
+      break;
+    case nvcompErrorNotSupported:
+      cudf::jni::throw_java_exception(env, UNSUPPORTED_CLASS, "nvcomp unsupported");
+      break;
+    case nvcompErrorCannotDecompress:
+      cudf::jni::throw_java_exception(env, NVCOMP_ERROR_CLASS, "nvcomp cannot decompress");
+      break;
+    case nvcompErrorCudaError:
+      cudf::jni::throw_java_exception(env, NVCOMP_CUDA_ERROR_CLASS, "nvcomp CUDA error");
+      break;
+    case nvcompErrorInternal:
+      cudf::jni::throw_java_exception(env, NVCOMP_ERROR_CLASS, "nvcomp internal error");
+      break;
+    default:
+      cudf::jni::throw_java_exception(env, NVCOMP_ERROR_CLASS, "nvcomp unknown error");
+      break;
+  }
+}
+
+} // anonymous namespace
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_nvcomp_NvcompJni_batchedLZ4CompressGetTempSize(
+    JNIEnv *env, jclass, jlong j_batch_size, jlong j_max_chunk_size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto batch_size = static_cast<std::size_t>(j_batch_size);
+    auto max_chunk_size = static_cast<std::size_t>(j_max_chunk_size);
+    std::size_t temp_size = 0;
+    auto status = nvcompBatchedLZ4CompressGetTempSize(batch_size, max_chunk_size,
+                                                      nvcompBatchedLZ4DefaultOpts, &temp_size);
+    check_nvcomp_status(env, status);
+    return static_cast<jlong>(temp_size);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL
+Java_ai_rapids_cudf_nvcomp_NvcompJni_batchedLZ4CompressGetMaxOutputChunkSize(
+    JNIEnv *env, jclass, jlong j_max_chunk_size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto max_chunk_size = static_cast<std::size_t>(j_max_chunk_size);
+    std::size_t max_output_size = 0;
+    auto status = nvcompBatchedLZ4CompressGetMaxOutputChunkSize(
+        max_chunk_size, nvcompBatchedLZ4DefaultOpts, &max_output_size);
+    check_nvcomp_status(env, status);
+    return static_cast<jlong>(max_output_size);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_nvcomp_NvcompJni_batchedLZ4CompressAsync(
+    JNIEnv *env, jclass, jlong j_in_ptrs, jlong j_in_sizes, jlong j_chunk_size, jlong j_batch_size,
+    jlong j_temp_ptr, jlong j_temp_size, jlong j_out_ptrs, jlong j_compressed_sizes_out_ptr,
+    jlong j_stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto in_ptrs = reinterpret_cast<void const *const *>(j_in_ptrs);
+    auto in_sizes = reinterpret_cast<std::size_t const *>(j_in_sizes);
+    auto chunk_size = static_cast<std::size_t>(j_chunk_size);
+    auto batch_size = static_cast<std::size_t>(j_batch_size);
+    auto temp_ptr = reinterpret_cast<void *>(j_temp_ptr);
+    auto temp_size = static_cast<std::size_t>(j_temp_size);
+    auto out_ptrs = reinterpret_cast<void *const *>(j_out_ptrs);
+    auto compressed_out_sizes = reinterpret_cast<std::size_t *>(j_compressed_sizes_out_ptr);
+    auto stream = reinterpret_cast<cudaStream_t>(j_stream);
+    auto status = nvcompBatchedLZ4CompressAsync(in_ptrs, in_sizes, chunk_size, batch_size, temp_ptr,
+                                                temp_size, out_ptrs, compressed_out_sizes,
+                                                nvcompBatchedLZ4DefaultOpts, stream);
+    check_nvcomp_status(env, status);
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_nvcomp_NvcompJni_batchedLZ4DecompressGetTempSize(
+    JNIEnv *env, jclass, jlong j_batch_size, jlong j_chunk_size) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto batch_size = static_cast<std::size_t>(j_batch_size);
+    auto chunk_size = static_cast<std::size_t>(j_chunk_size);
+    std::size_t temp_size = 0;
+    auto status = nvcompBatchedLZ4DecompressGetTempSize(batch_size, chunk_size, &temp_size);
+    check_nvcomp_status(env, status);
+    return static_cast<jlong>(temp_size);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_nvcomp_NvcompJni_batchedLZ4DecompressAsync(
+    JNIEnv *env, jclass, jlong j_in_ptrs, jlong j_in_sizes, jlong j_out_sizes, jlong j_batch_size,
+    jlong j_temp_ptr, jlong j_temp_size, jlong j_out_ptrs, jlong j_stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto compressed_ptrs = reinterpret_cast<void const *const *>(j_in_ptrs);
+    auto compressed_sizes = reinterpret_cast<std::size_t const *>(j_in_sizes);
+    auto uncompressed_sizes = reinterpret_cast<std::size_t const *>(j_out_sizes);
+    auto batch_size = static_cast<std::size_t>(j_batch_size);
+    auto temp_ptr = reinterpret_cast<void *>(j_temp_ptr);
+    auto temp_size = static_cast<std::size_t>(j_temp_size);
+    auto uncompressed_ptrs = reinterpret_cast<void *const *>(j_out_ptrs);
+    auto stream = reinterpret_cast<cudaStream_t>(j_stream);
+    auto uncompressed_statuses = rmm::device_uvector<nvcompStatus_t>(batch_size, stream);
+    auto actual_uncompressed_sizes = rmm::device_uvector<std::size_t>(batch_size, stream);
+    auto status = nvcompBatchedLZ4DecompressAsync(
+        compressed_ptrs, compressed_sizes, uncompressed_sizes, actual_uncompressed_sizes.data(),
+        batch_size, temp_ptr, temp_size, uncompressed_ptrs, uncompressed_statuses.data(), stream);
+    check_nvcomp_status(env, status);
+    if (!cudf::java::check_nvcomp_output_sizes(uncompressed_sizes, actual_uncompressed_sizes.data(),
+                                               batch_size, stream)) {
+      cudf::jni::throw_java_exception(env, NVCOMP_ERROR_CLASS,
+                                      "nvcomp decompress output size mismatch");
+    }
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_nvcomp_NvcompJni_batchedLZ4GetDecompressSizeAsync(
+    JNIEnv *env, jclass, jlong j_in_ptrs, jlong j_in_sizes, jlong j_out_sizes, jlong j_batch_size,
+    jlong j_stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto compressed_ptrs = reinterpret_cast<void const *const *>(j_in_ptrs);
+    auto compressed_sizes = reinterpret_cast<std::size_t const *>(j_in_sizes);
+    auto uncompressed_sizes = reinterpret_cast<std::size_t *>(j_out_sizes);
+    auto batch_size = static_cast<std::size_t>(j_batch_size);
+    auto stream = reinterpret_cast<cudaStream_t>(j_stream);
+    auto status = nvcompBatchedLZ4GetDecompressSizeAsync(compressed_ptrs, compressed_sizes,
+                                                         uncompressed_sizes, batch_size, stream);
+    check_nvcomp_status(env, status);
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/NvtxRangeJni.cpp b/java/src/main/native/src/NvtxRangeJni.cpp
new file mode 100644
index 0000000..1f12b2e
--- /dev/null
+++ b/java/src/main/native/src/NvtxRangeJni.cpp
@@ -0,0 +1,42 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/nvtx/nvtx3.hpp>
+
+#include "jni_utils.hpp"
+#include "nvtx_common.hpp"
+
+extern "C" {
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_NvtxRange_push(JNIEnv *env, jclass clazz, jstring name,
+                                                          jint color_bits) {
+  try {
+    cudf::jni::native_jstring range_name(env, name);
+    nvtx3::color range_color(static_cast<nvtx3::color::value_type>(color_bits));
+    nvtx3::event_attributes attr{range_color, range_name.get()};
+    nvtxDomainRangePushEx(nvtx3::domain::get<cudf::jni::java_domain>(), attr.get());
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_NvtxRange_pop(JNIEnv *env, jclass clazz) {
+  try {
+    nvtxDomainRangePop(nvtx3::domain::get<cudf::jni::java_domain>());
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/NvtxUniqueRangeJni.cpp b/java/src/main/native/src/NvtxUniqueRangeJni.cpp
new file mode 100644
index 0000000..d6c321b
--- /dev/null
+++ b/java/src/main/native/src/NvtxUniqueRangeJni.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/nvtx/nvtx3.hpp>
+
+#include "jni_utils.hpp"
+#include "nvtx_common.hpp"
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_NvtxUniqueRange_start(JNIEnv *env, jclass clazz,
+                                                                  jstring name, jint color_bits) {
+  try {
+    cudf::jni::native_jstring range_name(env, name);
+    nvtx3::color range_color(static_cast<nvtx3::color::value_type>(color_bits));
+    nvtx3::event_attributes attr{range_color, range_name.get()};
+    auto nvtxRangeId =
+        nvtxDomainRangeStartEx(nvtx3::domain::get<cudf::jni::java_domain>(), attr.get());
+    return static_cast<jlong>(nvtxRangeId);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_NvtxUniqueRange_end(JNIEnv *env, jclass clazz,
+                                                               jlong nvtxRangeId) {
+  try {
+    nvtxDomainRangeEnd(nvtx3::domain::get<cudf::jni::java_domain>(),
+                       static_cast<nvtxRangeId_t>(nvtxRangeId));
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/PackedColumnMetadataJni.cpp b/java/src/main/native/src/PackedColumnMetadataJni.cpp
new file mode 100644
index 0000000..7ec3e12
--- /dev/null
+++ b/java/src/main/native/src/PackedColumnMetadataJni.cpp
@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cudf_jni_apis.hpp"
+
+extern "C" {
+
+JNIEXPORT jobject JNICALL Java_ai_rapids_cudf_PackedColumnMetadata_createMetadataDirectBuffer(
+    JNIEnv *env, jclass, jlong j_metadata_ptr) {
+  JNI_NULL_CHECK(env, j_metadata_ptr, "metadata is null", nullptr);
+  try {
+    auto metadata = reinterpret_cast<std::vector<uint8_t> *>(j_metadata_ptr);
+    return env->NewDirectByteBuffer(const_cast<uint8_t *>(metadata->data()), metadata->size());
+  }
+  CATCH_STD(env, nullptr);
+}
+
+JNIEXPORT void JNICALL
+Java_ai_rapids_cudf_PackedColumnMetadata_closeMetadata(JNIEnv *env, jclass, jlong j_metadata_ptr) {
+  JNI_NULL_CHECK(env, j_metadata_ptr, "metadata is null", );
+  try {
+    auto metadata = reinterpret_cast<std::vector<uint8_t> *>(j_metadata_ptr);
+    delete metadata;
+  }
+  CATCH_STD(env, );
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/RmmJni.cpp b/java/src/main/native/src/RmmJni.cpp
new file mode 100644
index 0000000..3c49d15
--- /dev/null
+++ b/java/src/main/native/src/RmmJni.cpp
@@ -0,0 +1,765 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <atomic>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <limits>
+#include <mutex>
+
+#include <rmm/mr/device/aligned_resource_adaptor.hpp>
+#include <rmm/mr/device/arena_memory_resource.hpp>
+#include <rmm/mr/device/cuda_async_memory_resource.hpp>
+#include <rmm/mr/device/cuda_memory_resource.hpp>
+#include <rmm/mr/device/limiting_resource_adaptor.hpp>
+#include <rmm/mr/device/logging_resource_adaptor.hpp>
+#include <rmm/mr/device/managed_memory_resource.hpp>
+#include <rmm/mr/device/owning_wrapper.hpp>
+#include <rmm/mr/device/per_device_resource.hpp>
+#include <rmm/mr/device/pool_memory_resource.hpp>
+
+#include "cudf_jni_apis.hpp"
+
+using rmm::mr::device_memory_resource;
+using rmm::mr::logging_resource_adaptor;
+
+namespace {
+
+constexpr char const *RMM_EXCEPTION_CLASS = "ai/rapids/cudf/RmmException";
+
+/**
+ * @brief Base class so we can template tracking_resource_adaptor but
+ * still hold all instances of it without issues.
+ */
+class base_tracking_resource_adaptor : public device_memory_resource {
+public:
+  virtual std::size_t get_total_allocated() = 0;
+
+  virtual std::size_t get_max_total_allocated() = 0;
+
+  virtual void reset_scoped_max_total_allocated(std::size_t initial_value) = 0;
+
+  virtual std::size_t get_scoped_max_total_allocated() = 0;
+};
+
+/**
+ * @brief An RMM device memory resource that delegates to another resource
+ * while tracking the amount of memory allocated.
+ *
+ * @tparam Upstream Type of memory resource that will be wrapped.
+ * @tparam size_align The size to which all allocation requests are
+ * aligned. Must be a value >= 1.
+ */
+template <typename Upstream>
+class tracking_resource_adaptor final : public base_tracking_resource_adaptor {
+public:
+  /**
+   * @brief Constructs a new tracking resource adaptor that delegates to
+   * `mr` for all allocation operations while tracking the amount of memory
+   * allocated.
+   *
+   * @param mr The resource to use for memory allocation operations.
+   * @param size_alignment The alignment to which the `mr` resource will
+   * round up all memory allocation size requests.
+   */
+  tracking_resource_adaptor(Upstream *mr, std::size_t size_alignment)
+      : resource{mr}, size_align{size_alignment} {}
+
+  Upstream *get_wrapped_resource() { return resource; }
+
+  std::size_t get_total_allocated() override { return total_allocated.load(); }
+
+  std::size_t get_max_total_allocated() override { return max_total_allocated; }
+
+  void reset_scoped_max_total_allocated(std::size_t initial_value) override {
+    std::scoped_lock lock(max_total_allocated_mutex);
+    scoped_allocated = initial_value;
+    scoped_max_total_allocated = initial_value;
+  }
+
+  std::size_t get_scoped_max_total_allocated() override {
+    std::scoped_lock lock(max_total_allocated_mutex);
+    return scoped_max_total_allocated;
+  }
+
+  bool supports_get_mem_info() const noexcept override { return resource->supports_get_mem_info(); }
+
+  bool supports_streams() const noexcept override { return resource->supports_streams(); }
+
+private:
+  Upstream *const resource;
+  std::size_t const size_align;
+  // sum of what is currently allocated
+  std::atomic_size_t total_allocated{0};
+
+  // the maximum total allocated for the lifetime of this class
+  std::size_t max_total_allocated{0};
+
+  // the sum of what is currently outstanding from the last
+  // `reset_scoped_max_total_allocated` call. This can be negative.
+  std::atomic_long scoped_allocated{0};
+
+  // the maximum total allocated relative to the last
+  // `reset_scoped_max_total_allocated` call.
+  long scoped_max_total_allocated{0};
+
+  std::mutex max_total_allocated_mutex;
+
+  void *do_allocate(std::size_t num_bytes, rmm::cuda_stream_view stream) override {
+    // adjust size of allocation based on specified size alignment
+    num_bytes = (num_bytes + size_align - 1) / size_align * size_align;
+
+    auto result = resource->allocate(num_bytes, stream);
+    if (result) {
+      total_allocated += num_bytes;
+      scoped_allocated += num_bytes;
+      std::scoped_lock lock(max_total_allocated_mutex);
+      max_total_allocated = std::max(total_allocated.load(), max_total_allocated);
+      scoped_max_total_allocated = std::max(scoped_allocated.load(), scoped_max_total_allocated);
+    }
+    return result;
+  }
+
+  void do_deallocate(void *p, std::size_t size, rmm::cuda_stream_view stream) override {
+    size = (size + size_align - 1) / size_align * size_align;
+
+    resource->deallocate(p, size, stream);
+
+    if (p) {
+      total_allocated -= size;
+      scoped_allocated -= size;
+    }
+  }
+
+  std::pair<size_t, size_t> do_get_mem_info(rmm::cuda_stream_view stream) const override {
+    return resource->get_mem_info(stream);
+  }
+};
+
+template <typename Upstream>
+tracking_resource_adaptor<Upstream> *make_tracking_adaptor(Upstream *upstream,
+                                                           std::size_t size_alignment) {
+  return new tracking_resource_adaptor<Upstream>{upstream, size_alignment};
+}
+
+/**
+ * @brief An RMM device memory resource adaptor that delegates to the wrapped resource
+ * for most operations but will call Java to handle certain situations (e.g.: allocation failure).
+ */
+class java_event_handler_memory_resource : public device_memory_resource {
+public:
+  java_event_handler_memory_resource(JNIEnv *env, jobject jhandler, jlongArray jalloc_thresholds,
+                                     jlongArray jdealloc_thresholds,
+                                     device_memory_resource *resource_to_wrap,
+                                     base_tracking_resource_adaptor *tracker)
+      : resource(resource_to_wrap), tracker(tracker) {
+    if (env->GetJavaVM(&jvm) < 0) {
+      throw std::runtime_error("GetJavaVM failed");
+    }
+
+    jclass cls = env->GetObjectClass(jhandler);
+    if (cls == nullptr) {
+      throw cudf::jni::jni_exception("class not found");
+    }
+    on_alloc_fail_method = env->GetMethodID(cls, "onAllocFailure", "(JI)Z");
+    if (on_alloc_fail_method == nullptr) {
+      use_old_alloc_fail_interface = true;
+      on_alloc_fail_method = env->GetMethodID(cls, "onAllocFailure", "(J)Z");
+      if (on_alloc_fail_method == nullptr) {
+        throw cudf::jni::jni_exception("onAllocFailure method");
+      }
+    } else {
+      use_old_alloc_fail_interface = false;
+    }
+    on_alloc_threshold_method = env->GetMethodID(cls, "onAllocThreshold", "(J)V");
+    if (on_alloc_threshold_method == nullptr) {
+      throw cudf::jni::jni_exception("onAllocThreshold method");
+    }
+    on_dealloc_threshold_method = env->GetMethodID(cls, "onDeallocThreshold", "(J)V");
+    if (on_dealloc_threshold_method == nullptr) {
+      throw cudf::jni::jni_exception("onDeallocThreshold method");
+    }
+
+    update_thresholds(env, alloc_thresholds, jalloc_thresholds);
+    update_thresholds(env, dealloc_thresholds, jdealloc_thresholds);
+
+    handler_obj = cudf::jni::add_global_ref(env, jhandler);
+  }
+
+  virtual ~java_event_handler_memory_resource() {
+    // This should normally be called by a JVM thread. If the JVM environment is missing then this
+    // is likely being triggered by the C++ runtime during shutdown. In that case the JVM may
+    // already be destroyed and this thread should not try to attach to get an environment.
+    JNIEnv *env = nullptr;
+    if (jvm->GetEnv(reinterpret_cast<void **>(&env), cudf::jni::MINIMUM_JNI_VERSION) == JNI_OK) {
+      handler_obj = cudf::jni::del_global_ref(env, handler_obj);
+    }
+    handler_obj = nullptr;
+  }
+
+  device_memory_resource *get_wrapped_resource() { return resource; }
+
+  bool supports_get_mem_info() const noexcept override { return resource->supports_get_mem_info(); }
+
+  bool supports_streams() const noexcept override { return resource->supports_streams(); }
+
+private:
+  device_memory_resource *const resource;
+  base_tracking_resource_adaptor *const tracker;
+  jmethodID on_alloc_fail_method;
+  bool use_old_alloc_fail_interface;
+  jmethodID on_alloc_threshold_method;
+  jmethodID on_dealloc_threshold_method;
+
+  // sorted memory thresholds to trigger callbacks
+  std::vector<std::size_t> alloc_thresholds{};
+  std::vector<std::size_t> dealloc_thresholds{};
+
+  static void update_thresholds(JNIEnv *env, std::vector<std::size_t> &thresholds,
+                                jlongArray from_java) {
+    thresholds.clear();
+    if (from_java != nullptr) {
+      cudf::jni::native_jlongArray jvalues(env, from_java);
+      thresholds.insert(thresholds.end(), jvalues.data(), jvalues.data() + jvalues.size());
+    } else {
+      // use a single, maximum-threshold value so we don't have to always check for the corner case.
+      thresholds.push_back(std::numeric_limits<std::size_t>::max());
+    }
+  }
+
+  bool on_alloc_fail(std::size_t num_bytes, int retry_count) {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    jboolean result = false;
+    if (!use_old_alloc_fail_interface) {
+      result =
+          env->CallBooleanMethod(handler_obj, on_alloc_fail_method, static_cast<jlong>(num_bytes),
+                                 static_cast<jint>(retry_count));
+
+    } else {
+      result =
+          env->CallBooleanMethod(handler_obj, on_alloc_fail_method, static_cast<jlong>(num_bytes));
+    }
+    if (env->ExceptionCheck()) {
+      throw std::runtime_error("onAllocFailure handler threw an exception");
+    }
+    return result;
+  }
+
+  void check_for_threshold_callback(std::size_t low, std::size_t high,
+                                    std::vector<std::size_t> const &thresholds,
+                                    jmethodID callback_method, char const *callback_name,
+                                    std::size_t current_total) {
+    if (high >= thresholds.front() && low < thresholds.back()) {
+      // could use binary search, but assumption is threshold count is very small
+      auto it = std::find_if(thresholds.begin(), thresholds.end(),
+                             [=](std::size_t t) -> bool { return low < t && high >= t; });
+      if (it != thresholds.end()) {
+        JNIEnv *env = cudf::jni::get_jni_env(jvm);
+        env->CallVoidMethod(handler_obj, callback_method, current_total);
+        if (env->ExceptionCheck()) {
+          throw std::runtime_error("onAllocThreshold handler threw an exception");
+        }
+      }
+    }
+  }
+
+  std::pair<size_t, size_t> do_get_mem_info(rmm::cuda_stream_view stream) const override {
+    return resource->get_mem_info(stream);
+  }
+
+protected:
+  JavaVM *jvm;
+  jobject handler_obj;
+
+  void *do_allocate(std::size_t num_bytes, rmm::cuda_stream_view stream) override {
+    std::size_t total_before;
+    void *result;
+    // a non-zero retry_count signifies that the `on_alloc_fail`
+    // callback is being invoked while re-attempting an allocation
+    // that had previously failed.
+    int retry_count = 0;
+    while (true) {
+      try {
+        total_before = tracker->get_total_allocated();
+        result = resource->allocate(num_bytes, stream);
+        break;
+      } catch (rmm::out_of_memory const &e) {
+        if (!on_alloc_fail(num_bytes, retry_count++)) {
+          throw;
+        }
+      }
+    }
+    auto total_after = tracker->get_total_allocated();
+
+    try {
+      check_for_threshold_callback(total_before, total_after, alloc_thresholds,
+                                   on_alloc_threshold_method, "onAllocThreshold", total_after);
+    } catch (std::exception const &e) {
+      // Free the allocation as app will think the exception means the memory was not allocated.
+      resource->deallocate(result, num_bytes, stream);
+      throw;
+    }
+
+    return result;
+  }
+
+  void do_deallocate(void *p, std::size_t size, rmm::cuda_stream_view stream) override {
+    auto total_before = tracker->get_total_allocated();
+    resource->deallocate(p, size, stream);
+    auto total_after = tracker->get_total_allocated();
+    check_for_threshold_callback(total_after, total_before, dealloc_thresholds,
+                                 on_dealloc_threshold_method, "onDeallocThreshold", total_after);
+  }
+};
+
+class java_debug_event_handler_memory_resource final : public java_event_handler_memory_resource {
+public:
+  java_debug_event_handler_memory_resource(JNIEnv *env, jobject jhandler,
+                                           jlongArray jalloc_thresholds,
+                                           jlongArray jdealloc_thresholds,
+                                           device_memory_resource *resource_to_wrap,
+                                           base_tracking_resource_adaptor *tracker)
+      : java_event_handler_memory_resource(env, jhandler, jalloc_thresholds, jdealloc_thresholds,
+                                           resource_to_wrap, tracker) {
+    jclass cls = env->GetObjectClass(jhandler);
+    if (cls == nullptr) {
+      throw cudf::jni::jni_exception("class not found");
+    }
+
+    on_allocated_method = env->GetMethodID(cls, "onAllocated", "(J)V");
+    if (on_allocated_method == nullptr) {
+      throw cudf::jni::jni_exception("onAllocated method");
+    }
+
+    on_deallocated_method = env->GetMethodID(cls, "onDeallocated", "(J)V");
+    if (on_deallocated_method == nullptr) {
+      throw cudf::jni::jni_exception("onDeallocated method");
+    }
+  }
+
+private:
+  jmethodID on_allocated_method;
+  jmethodID on_deallocated_method;
+
+  void on_allocated_callback(std::size_t num_bytes, rmm::cuda_stream_view stream) {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    env->CallVoidMethod(handler_obj, on_allocated_method, num_bytes);
+    if (env->ExceptionCheck()) {
+      throw std::runtime_error("onAllocated handler threw an exception");
+    }
+  }
+
+  void on_deallocated_callback(void *p, std::size_t size, rmm::cuda_stream_view stream) {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    env->CallVoidMethod(handler_obj, on_deallocated_method, size);
+  }
+
+  void *do_allocate(std::size_t num_bytes, rmm::cuda_stream_view stream) override {
+    void *result = java_event_handler_memory_resource::do_allocate(num_bytes, stream);
+    on_allocated_callback(num_bytes, stream);
+    return result;
+  }
+
+  void do_deallocate(void *p, std::size_t size, rmm::cuda_stream_view stream) override {
+    java_event_handler_memory_resource::do_deallocate(p, size, stream);
+    on_deallocated_callback(p, size, stream);
+  }
+};
+
+} // anonymous namespace
+
+extern "C" {
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_initDefaultCudaDevice(JNIEnv *env, jclass clazz) {
+  // make sure the CUDA device is setup in the context
+  cudaError_t cuda_status = cudaFree(0);
+  cudf::jni::jni_cuda_check(env, cuda_status);
+  int device_id;
+  cuda_status = cudaGetDevice(&device_id);
+  cudf::jni::jni_cuda_check(env, cuda_status);
+  // Now that RMM has successfully initialized, setup all threads calling
+  // cudf to use the same device RMM is using.
+  cudf::jni::set_cudf_device(device_id);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_cleanupDefaultCudaDevice(JNIEnv *env, jclass clazz) {
+  cudf::jni::set_cudf_device(cudaInvalidDeviceId);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_allocInternal(JNIEnv *env, jclass clazz, jlong size,
+                                                              jlong stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource();
+    auto c_stream = rmm::cuda_stream_view(reinterpret_cast<cudaStream_t>(stream));
+    void *ret = mr->allocate(size, c_stream);
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_free(JNIEnv *env, jclass clazz, jlong ptr,
+                                                    jlong size, jlong stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource();
+    void *cptr = reinterpret_cast<void *>(ptr);
+    auto c_stream = rmm::cuda_stream_view(reinterpret_cast<cudaStream_t>(stream));
+    mr->deallocate(cptr, size, c_stream);
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_freeDeviceBuffer(JNIEnv *env, jclass clazz,
+                                                                jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    rmm::device_buffer *cptr = reinterpret_cast<rmm::device_buffer *>(ptr);
+    delete cptr;
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_allocCudaInternal(JNIEnv *env, jclass clazz,
+                                                                  jlong size, jlong stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    void *ptr{nullptr};
+    RMM_CUDA_TRY_ALLOC(cudaMalloc(&ptr, size));
+    return reinterpret_cast<jlong>(ptr);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_freeCuda(JNIEnv *env, jclass clazz, jlong ptr,
+                                                        jlong size, jlong stream) {
+  try {
+    cudf::jni::auto_set_device(env);
+    void *cptr = reinterpret_cast<void *>(ptr);
+    RMM_ASSERT_CUDA_SUCCESS(cudaFree(cptr));
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newCudaMemoryResource(JNIEnv *env, jclass clazz) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ret = new rmm::mr::cuda_memory_resource();
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseCudaMemoryResource(JNIEnv *env, jclass clazz,
+                                                                         jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<rmm::mr::cuda_memory_resource *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newManagedMemoryResource(JNIEnv *env,
+                                                                         jclass clazz) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ret = new rmm::mr::managed_memory_resource();
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseManagedMemoryResource(JNIEnv *env,
+                                                                            jclass clazz,
+                                                                            jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<rmm::mr::managed_memory_resource *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newPoolMemoryResource(JNIEnv *env, jclass clazz,
+                                                                      jlong child, jlong init,
+                                                                      jlong max) {
+  JNI_NULL_CHECK(env, child, "child is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto wrapped = reinterpret_cast<rmm::mr::device_memory_resource *>(child);
+    auto ret =
+        new rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource>(wrapped, init, max);
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releasePoolMemoryResource(JNIEnv *env, jclass clazz,
+                                                                         jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr =
+        reinterpret_cast<rmm::mr::pool_memory_resource<rmm::mr::device_memory_resource> *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newArenaMemoryResource(JNIEnv *env, jclass clazz,
+                                                                       jlong child, jlong init,
+                                                                       jboolean dump_on_oom) {
+  JNI_NULL_CHECK(env, child, "child is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto wrapped = reinterpret_cast<rmm::mr::device_memory_resource *>(child);
+    auto ret = new rmm::mr::arena_memory_resource<rmm::mr::device_memory_resource>(wrapped, init,
+                                                                                   dump_on_oom);
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseArenaMemoryResource(JNIEnv *env, jclass clazz,
+                                                                          jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr =
+        reinterpret_cast<rmm::mr::arena_memory_resource<rmm::mr::device_memory_resource> *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newCudaAsyncMemoryResource(JNIEnv *env,
+                                                                           jclass clazz, jlong init,
+                                                                           jlong release) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ret = new rmm::mr::cuda_async_memory_resource(init, release);
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseCudaAsyncMemoryResource(JNIEnv *env,
+                                                                              jclass clazz,
+                                                                              jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<rmm::mr::cuda_async_memory_resource *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newLimitingResourceAdaptor(JNIEnv *env,
+                                                                           jclass clazz,
+                                                                           jlong child, jlong limit,
+                                                                           jlong align) {
+  JNI_NULL_CHECK(env, child, "child is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto wrapped = reinterpret_cast<rmm::mr::device_memory_resource *>(child);
+    auto ret = new rmm::mr::limiting_resource_adaptor<rmm::mr::device_memory_resource>(
+        wrapped, limit, align);
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseLimitingResourceAdaptor(JNIEnv *env,
+                                                                              jclass clazz,
+                                                                              jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr =
+        reinterpret_cast<rmm::mr::limiting_resource_adaptor<rmm::mr::device_memory_resource> *>(
+            ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newLoggingResourceAdaptor(JNIEnv *env, jclass clazz,
+                                                                          jlong child, jint type,
+                                                                          jstring jpath,
+                                                                          jboolean auto_flush) {
+  JNI_NULL_CHECK(env, child, "child is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto wrapped = reinterpret_cast<rmm::mr::device_memory_resource *>(child);
+    switch (type) {
+      case 1: // File
+      {
+        cudf::jni::native_jstring path(env, jpath);
+        auto ret = new logging_resource_adaptor<rmm::mr::device_memory_resource>(
+            wrapped, path.get(), auto_flush);
+        return reinterpret_cast<jlong>(ret);
+      }
+      case 2: // stdout
+      {
+        auto ret = new logging_resource_adaptor<rmm::mr::device_memory_resource>(wrapped, std::cout,
+                                                                                 auto_flush);
+        return reinterpret_cast<jlong>(ret);
+      }
+      case 3: // stderr
+      {
+        auto ret = new logging_resource_adaptor<rmm::mr::device_memory_resource>(wrapped, std::cerr,
+                                                                                 auto_flush);
+        return reinterpret_cast<jlong>(ret);
+      }
+      default: throw std::logic_error("unsupported logging location type");
+    }
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseLoggingResourceAdaptor(JNIEnv *env,
+                                                                             jclass clazz,
+                                                                             jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr =
+        reinterpret_cast<rmm::mr::logging_resource_adaptor<rmm::mr::device_memory_resource> *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newTrackingResourceAdaptor(JNIEnv *env,
+                                                                           jclass clazz,
+                                                                           jlong child,
+                                                                           jlong align) {
+  JNI_NULL_CHECK(env, child, "child is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto wrapped = reinterpret_cast<rmm::mr::device_memory_resource *>(child);
+    auto ret = new tracking_resource_adaptor<rmm::mr::device_memory_resource>(wrapped, align);
+    return reinterpret_cast<jlong>(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseTrackingResourceAdaptor(JNIEnv *env,
+                                                                              jclass clazz,
+                                                                              jlong ptr) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<tracking_resource_adaptor<rmm::mr::device_memory_resource> *>(ptr);
+    delete mr;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_nativeGetTotalBytesAllocated(JNIEnv *env,
+                                                                             jclass clazz,
+                                                                             jlong ptr) {
+  JNI_NULL_CHECK(env, ptr, "adaptor is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<tracking_resource_adaptor<rmm::mr::device_memory_resource> *>(ptr);
+    return mr->get_total_allocated();
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_nativeGetMaxTotalBytesAllocated(JNIEnv *env,
+                                                                                jclass clazz,
+                                                                                jlong ptr) {
+  JNI_NULL_CHECK(env, ptr, "adaptor is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<tracking_resource_adaptor<rmm::mr::device_memory_resource> *>(ptr);
+    return mr->get_max_total_allocated();
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_nativeResetScopedMaxTotalBytesAllocated(JNIEnv *env,
+                                                                                       jclass clazz,
+                                                                                       jlong ptr,
+                                                                                       jlong init) {
+  JNI_NULL_CHECK(env, ptr, "adaptor is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<tracking_resource_adaptor<rmm::mr::device_memory_resource> *>(ptr);
+    mr->reset_scoped_max_total_allocated(init);
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_nativeGetScopedMaxTotalBytesAllocated(JNIEnv *env,
+                                                                                      jclass clazz,
+                                                                                      jlong ptr) {
+  JNI_NULL_CHECK(env, ptr, "adaptor is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<tracking_resource_adaptor<rmm::mr::device_memory_resource> *>(ptr);
+    return mr->get_scoped_max_total_allocated();
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newEventHandlerResourceAdaptor(
+    JNIEnv *env, jclass, jlong child, jlong tracker, jobject handler_obj,
+    jlongArray jalloc_thresholds, jlongArray jdealloc_thresholds, jboolean enable_debug) {
+  JNI_NULL_CHECK(env, child, "child is null", 0);
+  JNI_NULL_CHECK(env, tracker, "tracker is null", 0);
+  try {
+    auto wrapped = reinterpret_cast<rmm::mr::device_memory_resource *>(child);
+    auto t =
+        reinterpret_cast<tracking_resource_adaptor<rmm::mr::device_memory_resource> *>(tracker);
+    if (enable_debug) {
+      auto ret = new java_debug_event_handler_memory_resource(env, handler_obj, jalloc_thresholds,
+                                                              jdealloc_thresholds, wrapped, t);
+      return reinterpret_cast<jlong>(ret);
+    } else {
+      auto ret = new java_event_handler_memory_resource(env, handler_obj, jalloc_thresholds,
+                                                        jdealloc_thresholds, wrapped, t);
+      return reinterpret_cast<jlong>(ret);
+    }
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_releaseEventHandlerResourceAdaptor(
+    JNIEnv *env, jclass clazz, jlong ptr, jboolean enable_debug) {
+  try {
+    cudf::jni::auto_set_device(env);
+    if (enable_debug) {
+      auto mr = reinterpret_cast<java_debug_event_handler_memory_resource *>(ptr);
+      delete mr;
+    } else {
+      auto mr = reinterpret_cast<java_event_handler_memory_resource *>(ptr);
+      delete mr;
+    }
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Rmm_setCurrentDeviceResourceInternal(JNIEnv *env,
+                                                                                jclass clazz,
+                                                                                jlong new_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto mr = reinterpret_cast<rmm::mr::device_memory_resource *>(new_handle);
+    rmm::mr::set_current_device_resource(mr);
+  }
+  CATCH_STD(env, )
+}
+}
diff --git a/java/src/main/native/src/ScalarJni.cpp b/java/src/main/native/src/ScalarJni.cpp
new file mode 100644
index 0000000..e47728f
--- /dev/null
+++ b/java/src/main/native/src/ScalarJni.cpp
@@ -0,0 +1,576 @@
+/*
+ * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/binaryop.hpp>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/fixed_point/fixed_point.hpp>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/repeat_strings.hpp>
+#include <cudf/types.hpp>
+
+#include "cudf_jni_apis.hpp"
+#include "dtype_utils.hpp"
+
+using cudf::jni::release_as_jlong;
+
+extern "C" {
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Scalar_closeScalar(JNIEnv *env, jclass,
+                                                              jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::scalar *s = reinterpret_cast<cudf::scalar *>(scalar_handle);
+    delete s;
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jboolean JNICALL Java_ai_rapids_cudf_Scalar_isScalarValid(JNIEnv *env, jclass,
+                                                                    jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::scalar *s = reinterpret_cast<cudf::scalar *>(scalar_handle);
+    return static_cast<jboolean>(s->is_valid());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jbyte JNICALL Java_ai_rapids_cudf_Scalar_getByte(JNIEnv *env, jclass,
+                                                           jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<int8_t>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    return static_cast<jbyte>(s->value());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jshort JNICALL Java_ai_rapids_cudf_Scalar_getShort(JNIEnv *env, jclass,
+                                                             jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<int16_t>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    return static_cast<jshort>(s->value());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Scalar_getInt(JNIEnv *env, jclass, jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<int32_t>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    return static_cast<jint>(s->value());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_getLong(JNIEnv *env, jclass,
+                                                           jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<int64_t>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    return static_cast<jlong>(s->value());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jfloat JNICALL Java_ai_rapids_cudf_Scalar_getFloat(JNIEnv *env, jclass,
+                                                             jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<float>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    return static_cast<jfloat>(s->value());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jdouble JNICALL Java_ai_rapids_cudf_Scalar_getDouble(JNIEnv *env, jclass,
+                                                               jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<double>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    return static_cast<jdouble>(s->value());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jbyteArray JNICALL Java_ai_rapids_cudf_Scalar_getBigIntegerBytes(JNIEnv *env, jclass,
+                                                                           jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    using ScalarType = cudf::scalar_type_t<__int128_t>;
+    auto s = reinterpret_cast<ScalarType *>(scalar_handle);
+    auto val = s->value();
+    jbyte const *ptr = reinterpret_cast<jbyte const *>(&val);
+    cudf::jni::native_jbyteArray jbytes{env, ptr, sizeof(__int128_t)};
+    return jbytes.get_jArray();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jbyteArray JNICALL Java_ai_rapids_cudf_Scalar_getUTF8(JNIEnv *env, jclass,
+                                                                jlong scalar_handle) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto s = reinterpret_cast<cudf::string_scalar *>(scalar_handle);
+    std::string val{s->to_string()};
+    if (val.size() > 0x7FFFFFFF) {
+      cudf::jni::throw_java_exception(env, "java/lang/IllegalArgumentException",
+                                      "string scalar too large");
+    }
+    cudf::jni::native_jbyteArray jbytes{env, reinterpret_cast<jbyte const *>(val.data()),
+                                        static_cast<int>(val.size())};
+    return jbytes.get_jArray();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_getListAsColumnView(JNIEnv *env, jclass,
+                                                                       jlong scalar_handle) {
+  JNI_NULL_CHECK(env, scalar_handle, "scalar handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto s = reinterpret_cast<cudf::list_scalar *>(scalar_handle);
+    // Creates a column view in heap with the stack one, to let JVM take care of its
+    // life cycle.
+    return reinterpret_cast<jlong>(new cudf::column_view(s->view()));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL
+Java_ai_rapids_cudf_Scalar_getChildrenFromStructScalar(JNIEnv *env, jclass, jlong scalar_handle) {
+  JNI_NULL_CHECK(env, scalar_handle, "scalar handle is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    const auto s = reinterpret_cast<cudf::struct_scalar *>(scalar_handle);
+    const cudf::table_view &table = s->view();
+    cudf::jni::native_jpointerArray<cudf::column_view> column_handles(env, table.num_columns());
+    for (int i = 0; i < table.num_columns(); i++) {
+      column_handles[i] = new cudf::column_view(table.column(i));
+    }
+    return column_handles.get_jArray();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeBool8Scalar(JNIEnv *env, jclass,
+                                                                   jboolean value,
+                                                                   jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::BOOL8));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int8_t>;
+      int8_t val = value ? 1 : 0;
+      static_cast<ScalarType *>(s.get())->set_value(val);
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeInt8Scalar(JNIEnv *env, jclass, jbyte value,
+                                                                  jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT8));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int8_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int8_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeUint8Scalar(JNIEnv *env, jclass, jbyte value,
+                                                                   jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::UINT8));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<uint8_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<uint8_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeInt16Scalar(JNIEnv *env, jclass,
+                                                                   jshort value,
+                                                                   jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT16));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int16_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int16_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeUint16Scalar(JNIEnv *env, jclass,
+                                                                    jshort value,
+                                                                    jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::UINT16));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<uint16_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<uint16_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeDurationDaysScalar(JNIEnv *env, jclass,
+                                                                          jint value,
+                                                                          jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_duration_scalar(cudf::data_type(cudf::type_id::DURATION_DAYS));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int32_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int32_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeInt32Scalar(JNIEnv *env, jclass, jint value,
+                                                                   jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int32_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int32_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeUint32Scalar(JNIEnv *env, jclass, jint value,
+                                                                    jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::UINT32));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<uint32_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<uint32_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeInt64Scalar(JNIEnv *env, jclass, jlong value,
+                                                                   jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT64));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int64_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int64_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeUint64Scalar(JNIEnv *env, jclass,
+                                                                    jlong value,
+                                                                    jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::UINT64));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<uint64_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<uint64_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeFloat32Scalar(JNIEnv *env, jclass,
+                                                                     jfloat value,
+                                                                     jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::FLOAT32));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<float>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<float>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeFloat64Scalar(JNIEnv *env, jclass,
+                                                                     jdouble value,
+                                                                     jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::FLOAT64));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<double>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<double>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeStringScalar(JNIEnv *env, jclass,
+                                                                    jbyteArray value,
+                                                                    jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::string strval;
+    if (is_valid) {
+      cudf::jni::native_jbyteArray jbytes{env, value};
+      strval.assign(reinterpret_cast<char *>(jbytes.data()), jbytes.size());
+    }
+
+    auto s = new cudf::string_scalar{strval, static_cast<bool>(is_valid)};
+    return reinterpret_cast<jlong>(s);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeTimestampDaysScalar(JNIEnv *env, jclass,
+                                                                           jint value,
+                                                                           jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_timestamp_scalar(cudf::data_type(cudf::type_id::TIMESTAMP_DAYS));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int32_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int32_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeDurationTimeScalar(JNIEnv *env, jclass,
+                                                                          jint jdtype_id,
+                                                                          jlong value,
+                                                                          jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto dtype_id = static_cast<cudf::type_id>(jdtype_id);
+    std::unique_ptr<cudf::scalar> s = cudf::make_duration_scalar(cudf::data_type(dtype_id));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int64_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int64_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeTimestampTimeScalar(JNIEnv *env, jclass,
+                                                                           jint jdtype_id,
+                                                                           jlong value,
+                                                                           jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto dtype_id = static_cast<cudf::type_id>(jdtype_id);
+    std::unique_ptr<cudf::scalar> s = cudf::make_timestamp_scalar(cudf::data_type(dtype_id));
+    s->set_valid_async(is_valid);
+    if (is_valid) {
+      using ScalarType = cudf::scalar_type_t<int64_t>;
+      static_cast<ScalarType *>(s.get())->set_value(static_cast<int64_t>(value));
+    }
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeDecimal32Scalar(JNIEnv *env, jclass,
+                                                                       jint value, jint scale,
+                                                                       jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const value_ = static_cast<int32_t>(value);
+    auto const scale_ = numeric::scale_type{static_cast<int32_t>(scale)};
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_fixed_point_scalar<numeric::decimal32>(value_, scale_);
+    s->set_valid_async(is_valid);
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeDecimal64Scalar(JNIEnv *env, jclass,
+                                                                       jlong value, jint scale,
+                                                                       jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const value_ = static_cast<int64_t>(value);
+    auto const scale_ = numeric::scale_type{static_cast<int32_t>(scale)};
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_fixed_point_scalar<numeric::decimal64>(value_, scale_);
+    s->set_valid_async(is_valid);
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeDecimal128Scalar(JNIEnv *env, jclass,
+                                                                        jbyteArray value,
+                                                                        jint scale,
+                                                                        jboolean is_valid) {
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const scale_ = numeric::scale_type{static_cast<int32_t>(scale)};
+    cudf::jni::native_jbyteArray jbytes{env, value};
+    auto const value_ = reinterpret_cast<__int128_t *>(jbytes.data());
+    std::unique_ptr<cudf::scalar> s =
+        cudf::make_fixed_point_scalar<numeric::decimal128>(*value_, scale_);
+    s->set_valid_async(is_valid);
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_binaryOpSV(JNIEnv *env, jclass, jlong lhs_ptr,
+                                                              jlong rhs_view, jint int_op,
+                                                              jint out_dtype, jint scale) {
+  JNI_NULL_CHECK(env, lhs_ptr, "lhs is null", 0);
+  JNI_NULL_CHECK(env, rhs_view, "rhs is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::scalar *lhs = reinterpret_cast<cudf::scalar *>(lhs_ptr);
+    auto rhs = reinterpret_cast<cudf::column_view *>(rhs_view);
+    cudf::data_type n_data_type = cudf::jni::make_data_type(out_dtype, scale);
+    cudf::binary_operator op = static_cast<cudf::binary_operator>(int_op);
+
+    if (lhs->type().id() == cudf::type_id::STRUCT) {
+      auto out = make_fixed_width_column(n_data_type, rhs->size(), cudf::mask_state::UNALLOCATED);
+
+      if (op == cudf::binary_operator::NULL_EQUALS) {
+        out->set_null_mask(rmm::device_buffer{}, 0);
+      } else {
+        auto [new_mask, new_null_count] = cudf::binops::scalar_col_valid_mask_and(*rhs, *lhs);
+        out->set_null_mask(std::move(new_mask), new_null_count);
+      }
+
+      auto lhs_col = cudf::make_column_from_scalar(*lhs, 1);
+      auto out_view = out->mutable_view();
+      cudf::binops::compiled::detail::apply_sorting_struct_binary_op(
+          out_view, lhs_col->view(), *rhs, true, false, op, cudf::get_default_stream());
+      return release_as_jlong(out);
+    }
+
+    return release_as_jlong(cudf::binary_operation(*lhs, *rhs, op, n_data_type));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeListScalar(JNIEnv *env, jclass,
+                                                                  jlong view_handle,
+                                                                  jboolean is_valid) {
+  JNI_NULL_CHECK(env, view_handle, "Column view should NOT be null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto col_view = reinterpret_cast<cudf::column_view *>(view_handle);
+
+    // Instead of calling the `cudf::empty_like` to create an empty column when `is_valid`
+    // is false, always passes the input view to the scalar, to avoid copying the column
+    // twice.
+    // Let the Java layer make sure the view is empty when `is_valid` is false.
+    cudf::scalar *s = new cudf::list_scalar(*col_view);
+    s->set_valid_async(is_valid);
+    return reinterpret_cast<jlong>(s);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_makeStructScalar(JNIEnv *env, jclass,
+                                                                    jlongArray handles,
+                                                                    jboolean is_valid) {
+  JNI_NULL_CHECK(env, handles, "native view handles are null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    std::unique_ptr<cudf::column_view> ret;
+    cudf::jni::native_jpointerArray<cudf::column_view> column_pointers(env, handles);
+    std::vector<cudf::column_view> columns;
+    columns.reserve(column_pointers.size());
+    std::transform(column_pointers.data(), column_pointers.data() + column_pointers.size(),
+                   std::back_inserter(columns), [](auto const &col_ptr) { return *col_ptr; });
+    auto s = std::make_unique<cudf::struct_scalar>(
+        cudf::host_span<cudf::column_view const>{columns}, is_valid);
+    return reinterpret_cast<jlong>(s.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Scalar_repeatString(JNIEnv *env, jclass, jlong handle,
+                                                                jint repeat_times) {
+  JNI_NULL_CHECK(env, handle, "scalar handle is null", 0)
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const str = *reinterpret_cast<cudf::string_scalar *>(handle);
+    return reinterpret_cast<jlong>(cudf::strings::repeat_string(str, repeat_times).release());
+  }
+  CATCH_STD(env, 0);
+}
+
+} // extern "C"
diff --git a/java/src/main/native/src/TableJni.cpp b/java/src/main/native/src/TableJni.cpp
new file mode 100644
index 0000000..b208ef8
--- /dev/null
+++ b/java/src/main/native/src/TableJni.cpp
@@ -0,0 +1,3605 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <algorithm>
+
+#include <arrow/io/api.h>
+#include <arrow/ipc/api.h>
+#include <cudf/aggregation.hpp>
+#include <cudf/column/column.hpp>
+#include <cudf/concatenate.hpp>
+#include <cudf/contiguous_split.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/filling.hpp>
+#include <cudf/groupby.hpp>
+#include <cudf/hashing.hpp>
+#include <cudf/interop.hpp>
+#include <cudf/io/avro.hpp>
+#include <cudf/io/csv.hpp>
+#include <cudf/io/data_sink.hpp>
+#include <cudf/io/json.hpp>
+#include <cudf/io/orc.hpp>
+#include <cudf/io/parquet.hpp>
+#include <cudf/join.hpp>
+#include <cudf/lists/explode.hpp>
+#include <cudf/merge.hpp>
+#include <cudf/partitioning.hpp>
+#include <cudf/replace.hpp>
+#include <cudf/reshape.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/search.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/stream_compaction.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/span.hpp>
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/mr/device/device_memory_resource.hpp>
+#include <thrust/iterator/counting_iterator.h>
+
+#include "csv_chunked_writer.hpp"
+#include "cudf_jni_apis.hpp"
+#include "dtype_utils.hpp"
+#include "jni_compiled_expr.hpp"
+#include "jni_utils.hpp"
+#include "jni_writer_data_sink.hpp"
+#include "row_conversion.hpp"
+
+namespace cudf {
+namespace jni {
+
+/**
+ * @brief The base class for table writer.
+ *
+ * By storing a pointer to this base class instead of pointer to specific writer class, we can
+ * retrieve common data like `sink` and `stats` for any derived writer class without the need of
+ * casting or knowing its type.
+ */
+struct jni_table_writer_handle_base {
+  explicit jni_table_writer_handle_base(
+      std::unique_ptr<jni_writer_data_sink> &&sink_,
+      std::shared_ptr<cudf::io::writer_compression_statistics> &&stats_)
+      : sink{std::move(sink_)}, stats{std::move(stats_)} {}
+
+  std::unique_ptr<jni_writer_data_sink> sink;
+  std::shared_ptr<cudf::io::writer_compression_statistics> stats;
+};
+
+template <typename Writer>
+struct jni_table_writer_handle final : public jni_table_writer_handle_base {
+  explicit jni_table_writer_handle(std::unique_ptr<Writer> &&writer_)
+      : jni_table_writer_handle_base(nullptr, nullptr), writer{std::move(writer_)} {}
+  explicit jni_table_writer_handle(
+      std::unique_ptr<Writer> &&writer_, std::unique_ptr<jni_writer_data_sink> &&sink_,
+      std::shared_ptr<cudf::io::writer_compression_statistics> &&stats_)
+      : jni_table_writer_handle_base(std::move(sink_), std::move(stats_)),
+        writer{std::move(writer_)} {}
+
+  std::unique_ptr<Writer> writer;
+};
+
+typedef jni_table_writer_handle<cudf::io::parquet_chunked_writer> native_parquet_writer_handle;
+typedef jni_table_writer_handle<cudf::io::orc_chunked_writer> native_orc_writer_handle;
+
+class native_arrow_ipc_writer_handle final {
+public:
+  explicit native_arrow_ipc_writer_handle(const std::vector<std::string> &col_names,
+                                          const std::string &file_name)
+      : initialized(false), column_names(col_names), file_name(file_name) {}
+
+  explicit native_arrow_ipc_writer_handle(const std::vector<std::string> &col_names,
+                                          const std::shared_ptr<arrow::io::OutputStream> &sink)
+      : initialized(false), column_names(col_names), file_name(""), sink(sink) {}
+
+private:
+  bool initialized;
+  std::vector<std::string> column_names;
+  std::vector<cudf::column_metadata> columns_meta;
+  std::string file_name;
+  std::shared_ptr<arrow::io::OutputStream> sink;
+  std::shared_ptr<arrow::ipc::RecordBatchWriter> writer;
+
+public:
+  void write(std::shared_ptr<arrow::Table> &arrow_tab, int64_t max_chunk) {
+    if (!initialized) {
+      if (!sink) {
+        auto tmp_sink = arrow::io::FileOutputStream::Open(file_name);
+        if (!tmp_sink.ok()) {
+          throw std::runtime_error(tmp_sink.status().message());
+        }
+        sink = *tmp_sink;
+      }
+
+      // There is an option to have a file writer too, with metadata
+      auto tmp_writer = arrow::ipc::MakeStreamWriter(sink, arrow_tab->schema());
+      if (!tmp_writer.ok()) {
+        throw std::runtime_error(tmp_writer.status().message());
+      }
+      writer = *tmp_writer;
+      initialized = true;
+    }
+    if (arrow_tab->num_rows() == 0) {
+      // Arrow C++ IPC writer will not write an empty batch in the case of an
+      // empty table, so need to write an empty batch explicitly.
+      // For more please see https://issues.apache.org/jira/browse/ARROW-17912.
+      auto empty_batch = arrow::RecordBatch::MakeEmpty(arrow_tab->schema());
+      auto status = writer->WriteRecordBatch(*(*empty_batch));
+      if (!status.ok()) {
+        throw std::runtime_error("writer failed to write batch with the following error: " +
+                                 status.ToString());
+      }
+    } else {
+      auto status = writer->WriteTable(*arrow_tab, max_chunk);
+      if (!status.ok()) {
+        throw std::runtime_error("writer failed to write table with the following error: " +
+                                 status.ToString());
+      };
+    }
+  }
+
+  void close() {
+    if (initialized) {
+      {
+        auto status = writer->Close();
+        if (!status.ok()) {
+          throw std::runtime_error("Closing writer failed with the following error: " +
+                                   status.ToString());
+        }
+      }
+      {
+        auto status = sink->Close();
+        if (!status.ok()) {
+          throw std::runtime_error("Closing sink failed with the following error: " +
+                                   status.ToString());
+        }
+      }
+    }
+    initialized = false;
+  }
+
+  std::vector<cudf::column_metadata> get_column_metadata(const cudf::table_view &tview) {
+    if (!column_names.empty() && columns_meta.empty()) {
+      // Rebuild the structure of column meta according to table schema.
+      // All the tables written by this writer should share the same schema,
+      // so build column metadata only once.
+      columns_meta.reserve(tview.num_columns());
+      size_t idx = 0;
+      for (auto itr = tview.begin(); itr < tview.end(); ++itr) {
+        // It should consume the column names only when a column is
+        //   - type of struct, or
+        //   - not a child.
+        columns_meta.push_back(build_one_column_meta(*itr, idx));
+      }
+      if (idx < column_names.size()) {
+        throw cudf::jni::jni_exception("Too many column names are provided.");
+      }
+    }
+    return columns_meta;
+  }
+
+private:
+  cudf::column_metadata build_one_column_meta(const cudf::column_view &cview, size_t &idx,
+                                              const bool consume_name = true) {
+    auto col_meta = cudf::column_metadata{};
+    if (consume_name) {
+      col_meta.name = get_column_name(idx++);
+    }
+    // Process children
+    if (cview.type().id() == cudf::type_id::LIST) {
+      // list type:
+      //   - requires a stub metadata for offset column(index: 0).
+      //   - does not require a name for the child column(index 1).
+      col_meta.children_meta = {{}, build_one_column_meta(cview.child(1), idx, false)};
+    } else if (cview.type().id() == cudf::type_id::STRUCT) {
+      // struct type always consumes the column names.
+      col_meta.children_meta.reserve(cview.num_children());
+      for (auto itr = cview.child_begin(); itr < cview.child_end(); ++itr) {
+        col_meta.children_meta.push_back(build_one_column_meta(*itr, idx));
+      }
+    } else if (cview.type().id() == cudf::type_id::DICTIONARY32) {
+      // not supported yet in JNI, nested type?
+      throw cudf::jni::jni_exception("Unsupported type 'DICTIONARY32'");
+    }
+    return col_meta;
+  }
+
+  std::string &get_column_name(const size_t idx) {
+    if (idx < 0 || idx >= column_names.size()) {
+      throw cudf::jni::jni_exception("Missing names for columns or nested struct columns");
+    }
+    return column_names[idx];
+  }
+};
+
+class jni_arrow_output_stream final : public arrow::io::OutputStream {
+public:
+  explicit jni_arrow_output_stream(JNIEnv *env, jobject callback, jobject host_memory_allocator) {
+    if (env->GetJavaVM(&jvm) < 0) {
+      throw std::runtime_error("GetJavaVM failed");
+    }
+
+    jclass cls = env->GetObjectClass(callback);
+    if (cls == nullptr) {
+      throw cudf::jni::jni_exception("class not found");
+    }
+
+    handle_buffer_method =
+        env->GetMethodID(cls, "handleBuffer", "(Lai/rapids/cudf/HostMemoryBuffer;J)V");
+    if (handle_buffer_method == nullptr) {
+      throw cudf::jni::jni_exception("handleBuffer method");
+    }
+    this->callback = add_global_ref(env, callback);
+    this->host_memory_allocator = add_global_ref(env, host_memory_allocator);
+  }
+
+  virtual ~jni_arrow_output_stream() {
+    // This should normally be called by a JVM thread. If the JVM environment is missing then this
+    // is likely being triggered by the C++ runtime during shutdown. In that case the JVM may
+    // already be destroyed and this thread should not try to attach to get an environment.
+    JNIEnv *env = nullptr;
+    if (jvm->GetEnv(reinterpret_cast<void **>(&env), cudf::jni::MINIMUM_JNI_VERSION) == JNI_OK) {
+      callback = del_global_ref(env, callback);
+      current_buffer = del_global_ref(env, current_buffer);
+      host_memory_allocator = del_global_ref(env, host_memory_allocator);
+    }
+    callback = nullptr;
+    current_buffer = nullptr;
+    host_memory_allocator = nullptr;
+  }
+
+  arrow::Status Write(const std::shared_ptr<arrow::Buffer> &data) override {
+    return Write(data->data(), data->size());
+  }
+
+  arrow::Status Write(const void *data, int64_t nbytes) override {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    int64_t left_to_copy = nbytes;
+    const char *copy_from = static_cast<const char *>(data);
+    while (left_to_copy > 0) {
+      long buffer_amount_available = current_buffer_len - current_buffer_written;
+      if (buffer_amount_available <= 0) {
+        // should never be < 0, but just to be safe
+        rotate_buffer(env);
+        buffer_amount_available = current_buffer_len - current_buffer_written;
+      }
+      long amount_to_copy =
+          left_to_copy < buffer_amount_available ? left_to_copy : buffer_amount_available;
+      char *copy_to = current_buffer_data + current_buffer_written;
+
+      std::memcpy(copy_to, copy_from, amount_to_copy);
+      copy_from = copy_from + amount_to_copy;
+      current_buffer_written += amount_to_copy;
+      total_written += amount_to_copy;
+      left_to_copy -= amount_to_copy;
+    }
+    return arrow::Status::OK();
+  }
+
+  arrow::Status Flush() override {
+    if (current_buffer_written > 0) {
+      JNIEnv *env = cudf::jni::get_jni_env(jvm);
+      handle_buffer(env, current_buffer, current_buffer_written);
+      current_buffer = del_global_ref(env, current_buffer);
+      current_buffer_len = 0;
+      current_buffer_data = nullptr;
+      current_buffer_written = 0;
+    }
+    return arrow::Status::OK();
+  }
+
+  arrow::Status Close() override {
+    auto ret = Flush();
+    is_closed = true;
+    return ret;
+  }
+
+  arrow::Status Abort() override {
+    is_closed = true;
+    return arrow::Status::OK();
+  }
+
+  arrow::Result<int64_t> Tell() const override { return total_written; }
+
+  bool closed() const override { return is_closed; }
+
+private:
+  void rotate_buffer(JNIEnv *env) {
+    if (current_buffer != nullptr) {
+      handle_buffer(env, current_buffer, current_buffer_written);
+    }
+    current_buffer = del_global_ref(env, current_buffer);
+    jobject tmp_buffer = allocate_host_buffer(env, alloc_size, true, host_memory_allocator);
+    current_buffer = add_global_ref(env, tmp_buffer);
+    current_buffer_len = get_host_buffer_length(env, current_buffer);
+    current_buffer_data = reinterpret_cast<char *>(get_host_buffer_address(env, current_buffer));
+    current_buffer_written = 0;
+  }
+
+  void handle_buffer(JNIEnv *env, jobject buffer, jlong len) {
+    env->CallVoidMethod(callback, handle_buffer_method, buffer, len);
+    if (env->ExceptionCheck()) {
+      throw std::runtime_error("handleBuffer threw an exception");
+    }
+  }
+
+  JavaVM *jvm;
+  jobject callback;
+  jmethodID handle_buffer_method;
+  jobject current_buffer = nullptr;
+  char *current_buffer_data = nullptr;
+  long current_buffer_len = 0;
+  long current_buffer_written = 0;
+  int64_t total_written = 0;
+  long alloc_size = MINIMUM_WRITE_BUFFER_SIZE;
+  bool is_closed = false;
+  jobject host_memory_allocator;
+};
+
+class jni_arrow_input_stream final : public arrow::io::InputStream {
+public:
+  explicit jni_arrow_input_stream(JNIEnv *env, jobject callback)
+      : mm(arrow::default_cpu_memory_manager()) {
+    if (env->GetJavaVM(&jvm) < 0) {
+      throw std::runtime_error("GetJavaVM failed");
+    }
+
+    jclass cls = env->GetObjectClass(callback);
+    if (cls == nullptr) {
+      throw cudf::jni::jni_exception("class not found");
+    }
+
+    read_into_method = env->GetMethodID(cls, "readInto", "(JJ)J");
+    if (read_into_method == nullptr) {
+      throw cudf::jni::jni_exception("readInto method");
+    }
+
+    this->callback = add_global_ref(env, callback);
+  }
+
+  virtual ~jni_arrow_input_stream() {
+    // This should normally be called by a JVM thread. If the JVM environment is missing then this
+    // is likely being triggered by the C++ runtime during shutdown. In that case the JVM may
+    // already be destroyed and this thread should not try to attach to get an environment.
+    JNIEnv *env = nullptr;
+    if (jvm->GetEnv(reinterpret_cast<void **>(&env), cudf::jni::MINIMUM_JNI_VERSION) == JNI_OK) {
+      callback = del_global_ref(env, callback);
+    }
+    callback = nullptr;
+  }
+
+  arrow::Result<int64_t> Read(int64_t nbytes, void *out) override {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    jlong ret = read_into(env, ptr_as_jlong(out), nbytes);
+    total_read += ret;
+    return ret;
+  }
+
+  arrow::Result<std::shared_ptr<arrow::Buffer>> Read(int64_t nbytes) override {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    arrow::Result<std::shared_ptr<arrow::ResizableBuffer>> tmp_buffer =
+        arrow::AllocateResizableBuffer(nbytes);
+    if (!tmp_buffer.ok()) {
+      return tmp_buffer;
+    }
+    jlong amount_read = read_into(env, ptr_as_jlong((*tmp_buffer)->data()), nbytes);
+    arrow::Status stat = (*tmp_buffer)->Resize(amount_read);
+    if (!stat.ok()) {
+      return stat;
+    }
+    return tmp_buffer;
+  }
+
+  arrow::Status Close() override {
+    is_closed = true;
+    return arrow::Status::OK();
+  }
+
+  arrow::Status Abort() override {
+    is_closed = true;
+    return arrow::Status::OK();
+  }
+
+  arrow::Result<int64_t> Tell() const override { return total_read; }
+
+  bool closed() const override { return is_closed; }
+
+private:
+  jlong read_into(JNIEnv *env, jlong addr, jlong len) {
+    jlong ret = env->CallLongMethod(callback, read_into_method, addr, len);
+    if (env->ExceptionCheck()) {
+      throw std::runtime_error("readInto threw an exception");
+    }
+    return ret;
+  }
+
+  JavaVM *jvm;
+  jobject callback;
+  jmethodID read_into_method;
+  int64_t total_read = 0;
+  bool is_closed = false;
+  std::vector<uint8_t> tmp_buffer;
+  std::shared_ptr<arrow::MemoryManager> mm;
+};
+
+class native_arrow_ipc_reader_handle final {
+public:
+  explicit native_arrow_ipc_reader_handle(const std::string &file_name) {
+    auto tmp_source = arrow::io::ReadableFile::Open(file_name);
+    if (!tmp_source.ok()) {
+      throw std::runtime_error(tmp_source.status().message());
+    }
+    source = *tmp_source;
+    auto tmp_reader = arrow::ipc::RecordBatchStreamReader::Open(source);
+    if (!tmp_reader.ok()) {
+      throw std::runtime_error(tmp_reader.status().message());
+    }
+    reader = *tmp_reader;
+  }
+
+  explicit native_arrow_ipc_reader_handle(std::shared_ptr<arrow::io::InputStream> source)
+      : source(source) {
+    auto tmp_reader = arrow::ipc::RecordBatchStreamReader::Open(source);
+    if (!tmp_reader.ok()) {
+      throw std::runtime_error(tmp_reader.status().message());
+    }
+    reader = *tmp_reader;
+  }
+
+  std::shared_ptr<arrow::Table> next(int32_t row_target) {
+    int64_t total_rows = 0;
+    bool done = false;
+    std::vector<std::shared_ptr<arrow::RecordBatch>> batches;
+    while (!done) {
+      arrow::Result<std::shared_ptr<arrow::RecordBatch>> batch = reader->Next();
+      if (!batch.ok()) {
+        throw std::runtime_error(batch.status().message());
+      }
+      if (!*batch) {
+        done = true;
+      } else {
+        batches.push_back(*batch);
+        total_rows += (*batch)->num_rows();
+        done = (total_rows >= row_target);
+      }
+    }
+    if (batches.empty()) {
+      // EOF
+      return std::unique_ptr<arrow::Table>();
+    }
+    arrow::Result<std::shared_ptr<arrow::Table>> tmp =
+        arrow::Table::FromRecordBatches(reader->schema(), batches);
+    if (!tmp.ok()) {
+      throw std::runtime_error(tmp.status().message());
+    }
+    return *tmp;
+  }
+
+  std::shared_ptr<arrow::io::InputStream> source;
+  std::shared_ptr<arrow::ipc::RecordBatchReader> reader;
+
+  void close() {
+    auto status = source->Close();
+    if (!status.ok()) {
+      throw std::runtime_error("Closing source failed with the following error: " +
+                               status.ToString());
+    }
+  }
+};
+
+jlongArray convert_table_for_return(JNIEnv *env, std::unique_ptr<cudf::table> &&table_result,
+                                    std::vector<std::unique_ptr<cudf::column>> &&extra_columns) {
+  std::vector<std::unique_ptr<cudf::column>> ret = table_result->release();
+  int table_cols = ret.size();
+  int num_columns = table_cols + extra_columns.size();
+  cudf::jni::native_jlongArray outcol_handles(env, num_columns);
+  std::transform(ret.begin(), ret.end(), outcol_handles.begin(),
+                 [](auto &col) { return release_as_jlong(col); });
+  std::transform(extra_columns.begin(), extra_columns.end(), outcol_handles.begin() + table_cols,
+                 [](auto &col) { return release_as_jlong(col); });
+  return outcol_handles.get_jArray();
+}
+
+jlongArray convert_table_for_return(JNIEnv *env, std::unique_ptr<cudf::table> &table_result,
+                                    std::vector<std::unique_ptr<cudf::column>> &&extra_columns) {
+  return convert_table_for_return(env, std::move(table_result), std::move(extra_columns));
+}
+
+jlongArray convert_table_for_return(JNIEnv *env, std::unique_ptr<cudf::table> &first_table,
+                                    std::unique_ptr<cudf::table> &second_table) {
+  return convert_table_for_return(env, first_table, second_table->release());
+}
+
+// Convert the JNI boolean array of key column sort order to a vector of cudf::order
+// for groupby.
+std::vector<cudf::order> resolve_column_order(JNIEnv *env, jbooleanArray jkeys_sort_desc,
+                                              int key_size) {
+  cudf::jni::native_jbooleanArray keys_sort_desc(env, jkeys_sort_desc);
+  auto keys_sort_num = keys_sort_desc.size();
+  // The number of column order should be 0 or equal to the number of key.
+  if (keys_sort_num != 0 && keys_sort_num != key_size) {
+    throw cudf::jni::jni_exception("key-column and key-sort-order size mismatch.");
+  }
+
+  std::vector<cudf::order> column_order(keys_sort_num);
+  if (keys_sort_num > 0) {
+    std::transform(keys_sort_desc.data(), keys_sort_desc.data() + keys_sort_num,
+                   column_order.begin(), [](jboolean is_desc) {
+                     return is_desc ? cudf::order::DESCENDING : cudf::order::ASCENDING;
+                   });
+  }
+  return column_order;
+}
+
+// Convert the JNI boolean array of key column null order to a vector of cudf::null_order
+// for groupby.
+std::vector<cudf::null_order> resolve_null_precedence(JNIEnv *env, jbooleanArray jkeys_null_first,
+                                                      int key_size) {
+  cudf::jni::native_jbooleanArray keys_null_first(env, jkeys_null_first);
+  auto null_order_num = keys_null_first.size();
+  // The number of null order should be 0 or equal to the number of key.
+  if (null_order_num != 0 && null_order_num != key_size) {
+    throw cudf::jni::jni_exception("key-column and key-null-order size mismatch.");
+  }
+
+  std::vector<cudf::null_order> null_precedence(null_order_num);
+  if (null_order_num > 0) {
+    std::transform(keys_null_first.data(), keys_null_first.data() + null_order_num,
+                   null_precedence.begin(), [](jboolean null_before) {
+                     return null_before ? cudf::null_order::BEFORE : cudf::null_order::AFTER;
+                   });
+  }
+  return null_precedence;
+}
+
+namespace {
+
+int set_column_metadata(
+    cudf::io::column_in_metadata &column_metadata, std::vector<std::string> &col_names,
+    cudf::jni::native_jbooleanArray &nullability, cudf::jni::native_jbooleanArray &is_int96,
+    cudf::jni::native_jintArray &precisions, cudf::jni::native_jbooleanArray &is_map,
+    cudf::jni::native_jbooleanArray &hasParquetFieldIds,
+    cudf::jni::native_jintArray &parquetFieldIds, cudf::jni::native_jintArray &children,
+    int num_children, int read_index, cudf::jni::native_jbooleanArray &is_binary) {
+  int write_index = 0;
+  for (int i = 0; i < num_children; i++, write_index++) {
+    cudf::io::column_in_metadata child;
+    child.set_name(col_names[read_index]).set_nullability(nullability[read_index]);
+    if (precisions[read_index] > -1) {
+      child.set_decimal_precision(precisions[read_index]);
+    }
+    if (!is_int96.is_null()) {
+      child.set_int96_timestamps(is_int96[read_index]);
+    }
+    if (!is_binary.is_null()) {
+      child.set_output_as_binary(is_binary[read_index]);
+    }
+    if (is_map[read_index]) {
+      child.set_list_column_as_map();
+    }
+    if (!parquetFieldIds.is_null() && hasParquetFieldIds[read_index]) {
+      child.set_parquet_field_id(parquetFieldIds[read_index]);
+    }
+    column_metadata.add_child(child);
+    int childs_children = children[read_index++];
+    if (childs_children > 0) {
+      read_index = set_column_metadata(
+          column_metadata.child(write_index), col_names, nullability, is_int96, precisions, is_map,
+          hasParquetFieldIds, parquetFieldIds, children, childs_children, read_index, is_binary);
+    }
+  }
+  return read_index;
+}
+
+void createTableMetaData(JNIEnv *env, jint num_children, jobjectArray &j_col_names,
+                         jintArray &j_children, jbooleanArray &j_col_nullability,
+                         jbooleanArray &j_is_int96, jintArray &j_precisions,
+                         jbooleanArray &j_is_map, cudf::io::table_input_metadata &metadata,
+                         jbooleanArray &j_hasParquetFieldIds, jintArray &j_parquetFieldIds,
+                         jbooleanArray &j_is_binary) {
+  cudf::jni::auto_set_device(env);
+  cudf::jni::native_jstringArray col_names(env, j_col_names);
+  cudf::jni::native_jbooleanArray col_nullability(env, j_col_nullability);
+  cudf::jni::native_jbooleanArray is_int96(env, j_is_int96);
+  cudf::jni::native_jintArray precisions(env, j_precisions);
+  cudf::jni::native_jbooleanArray hasParquetFieldIds(env, j_hasParquetFieldIds);
+  cudf::jni::native_jintArray parquetFieldIds(env, j_parquetFieldIds);
+  cudf::jni::native_jintArray children(env, j_children);
+  cudf::jni::native_jbooleanArray is_map(env, j_is_map);
+  cudf::jni::native_jbooleanArray is_binary(env, j_is_binary);
+
+  auto cpp_names = col_names.as_cpp_vector();
+
+  int top_level_children = num_children;
+
+  metadata.column_metadata.resize(top_level_children);
+  int read_index = 0; // the read_index, which will be used to read the arrays
+  for (int i = read_index, write_index = 0; i < top_level_children; i++, write_index++) {
+    metadata.column_metadata[write_index]
+        .set_name(cpp_names[read_index])
+        .set_nullability(col_nullability[read_index]);
+    if (precisions[read_index] > -1) {
+      metadata.column_metadata[write_index].set_decimal_precision(precisions[read_index]);
+    }
+    if (!is_int96.is_null()) {
+      metadata.column_metadata[write_index].set_int96_timestamps(is_int96[read_index]);
+    }
+    if (!is_binary.is_null()) {
+      metadata.column_metadata[write_index].set_output_as_binary(is_binary[read_index]);
+    }
+    if (is_map[read_index]) {
+      metadata.column_metadata[write_index].set_list_column_as_map();
+    }
+    if (!parquetFieldIds.is_null() && hasParquetFieldIds[read_index]) {
+      metadata.column_metadata[write_index].set_parquet_field_id(parquetFieldIds[read_index]);
+    }
+    int childs_children = children[read_index++];
+    if (childs_children > 0) {
+      read_index =
+          set_column_metadata(metadata.column_metadata[write_index], cpp_names, col_nullability,
+                              is_int96, precisions, is_map, hasParquetFieldIds, parquetFieldIds,
+                              children, childs_children, read_index, is_binary);
+    }
+  }
+}
+
+// Check that window parameters are valid.
+bool valid_window_parameters(native_jintArray const &values,
+                             native_jpointerArray<cudf::aggregation> const &ops,
+                             native_jintArray const &min_periods, native_jintArray const &preceding,
+                             native_jintArray const &following) {
+  return values.size() == ops.size() && values.size() == min_periods.size() &&
+         values.size() == preceding.size() && values.size() == following.size();
+}
+
+// Check that window parameters are valid.
+bool valid_window_parameters(native_jintArray const &values,
+                             native_jpointerArray<cudf::aggregation> const &ops,
+                             native_jintArray const &min_periods,
+                             native_jpointerArray<cudf::scalar> const &preceding,
+                             native_jpointerArray<cudf::scalar> const &following) {
+  return values.size() == ops.size() && values.size() == min_periods.size() &&
+         values.size() == preceding.size() && values.size() == following.size();
+}
+
+// Convert a cudf gather map pair into the form that Java expects
+// The resulting Java long array contains the following at each index:
+//   0: Size of each gather map in bytes
+//   1: Device address of the gather map for the left table
+//   2: Host address of the rmm::device_buffer instance that owns the left gather map data
+//   3: Device address of the gather map for the right table
+//   4: Host address of the rmm::device_buffer instance that owns the right gather map data
+jlongArray gather_maps_to_java(JNIEnv *env,
+                               std::pair<std::unique_ptr<rmm::device_uvector<cudf::size_type>>,
+                                         std::unique_ptr<rmm::device_uvector<cudf::size_type>>>
+                                   maps) {
+  // release the underlying device buffer to Java
+  auto left_map_buffer = std::make_unique<rmm::device_buffer>(maps.first->release());
+  auto right_map_buffer = std::make_unique<rmm::device_buffer>(maps.second->release());
+  cudf::jni::native_jlongArray result(env, 5);
+  result[0] = static_cast<jlong>(left_map_buffer->size());
+  result[1] = ptr_as_jlong(left_map_buffer->data());
+  result[2] = release_as_jlong(left_map_buffer);
+  result[3] = ptr_as_jlong(right_map_buffer->data());
+  result[4] = release_as_jlong(right_map_buffer);
+  return result.get_jArray();
+}
+
+// Convert a cudf gather map into the form that Java expects
+// The resulting Java long array contains the following at each index:
+//   0: Size of the gather map in bytes
+//   1: Device address of the gather map
+//   2: Host address of the rmm::device_buffer instance that owns the gather map data
+jlongArray gather_map_to_java(JNIEnv *env,
+                              std::unique_ptr<rmm::device_uvector<cudf::size_type>> map) {
+  // release the underlying device buffer to Java
+  auto gather_map_buffer = std::make_unique<rmm::device_buffer>(map->release());
+  cudf::jni::native_jlongArray result(env, 3);
+  result[0] = static_cast<jlong>(gather_map_buffer->size());
+  result[1] = ptr_as_jlong(gather_map_buffer->data());
+  result[2] = release_as_jlong(gather_map_buffer);
+  return result.get_jArray();
+}
+
+// Generate gather maps needed to manifest the result of an equi-join between two tables.
+template <typename T>
+jlongArray join_gather_maps(JNIEnv *env, jlong j_left_keys, jlong j_right_keys,
+                            jboolean compare_nulls_equal, T join_func) {
+  JNI_NULL_CHECK(env, j_left_keys, "left_table is null", NULL);
+  JNI_NULL_CHECK(env, j_right_keys, "right_table is null", NULL);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_keys = reinterpret_cast<cudf::table_view const *>(j_left_keys);
+    auto right_keys = reinterpret_cast<cudf::table_view const *>(j_right_keys);
+    auto nulleq = compare_nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    return gather_maps_to_java(env, join_func(*left_keys, *right_keys, nulleq));
+  }
+  CATCH_STD(env, NULL);
+}
+
+// Generate gather maps needed to manifest the result of an equi-join between a left table and
+// a hash table built from the join's right table.
+template <typename T>
+jlongArray hash_join_gather_maps(JNIEnv *env, jlong j_left_keys, jlong j_right_hash_join,
+                                 T join_func) {
+  JNI_NULL_CHECK(env, j_left_keys, "left table is null", NULL);
+  JNI_NULL_CHECK(env, j_right_hash_join, "hash join is null", NULL);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_keys = reinterpret_cast<cudf::table_view const *>(j_left_keys);
+    auto hash_join = reinterpret_cast<cudf::hash_join const *>(j_right_hash_join);
+    return gather_maps_to_java(env, join_func(*left_keys, *hash_join));
+  }
+  CATCH_STD(env, NULL);
+}
+
+// Generate gather maps needed to manifest the result of a conditional join between two tables.
+template <typename T>
+jlongArray cond_join_gather_maps(JNIEnv *env, jlong j_left_table, jlong j_right_table,
+                                 jlong j_condition, T join_func) {
+  JNI_NULL_CHECK(env, j_left_table, "left_table is null", NULL);
+  JNI_NULL_CHECK(env, j_right_table, "right_table is null", NULL);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", NULL);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto right_table = reinterpret_cast<cudf::table_view const *>(j_right_table);
+    auto condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    return gather_maps_to_java(
+        env, join_func(*left_table, *right_table, condition->get_top_expression()));
+  }
+  CATCH_STD(env, NULL);
+}
+
+// Generate a gather map needed to manifest the result of a semi/anti join between two tables.
+template <typename T>
+jlongArray join_gather_single_map(JNIEnv *env, jlong j_left_keys, jlong j_right_keys,
+                                  jboolean compare_nulls_equal, T join_func) {
+  JNI_NULL_CHECK(env, j_left_keys, "left_table is null", NULL);
+  JNI_NULL_CHECK(env, j_right_keys, "right_table is null", NULL);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_keys = reinterpret_cast<cudf::table_view const *>(j_left_keys);
+    auto right_keys = reinterpret_cast<cudf::table_view const *>(j_right_keys);
+    auto nulleq = compare_nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    return gather_map_to_java(env, join_func(*left_keys, *right_keys, nulleq));
+  }
+  CATCH_STD(env, NULL);
+}
+
+// Generate a gather map needed to manifest the result of a conditional semi/anti join
+// between two tables.
+template <typename T>
+jlongArray cond_join_gather_single_map(JNIEnv *env, jlong j_left_table, jlong j_right_table,
+                                       jlong j_condition, T join_func) {
+  JNI_NULL_CHECK(env, j_left_table, "left_table is null", NULL);
+  JNI_NULL_CHECK(env, j_right_table, "right_table is null", NULL);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", NULL);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto right_table = reinterpret_cast<cudf::table_view const *>(j_right_table);
+    auto condition = reinterpret_cast<cudf::jni::ast::compiled_expr *>(j_condition);
+    return gather_map_to_java(
+        env, join_func(*left_table, *right_table, condition->get_top_expression()));
+  }
+  CATCH_STD(env, NULL);
+}
+
+template <typename T>
+jlongArray mixed_join_size(JNIEnv *env, jlong j_left_keys, jlong j_right_keys,
+                           jlong j_left_condition, jlong j_right_condition, jlong j_condition,
+                           jboolean j_nulls_equal, T join_size_func) {
+  JNI_NULL_CHECK(env, j_left_keys, "left keys table is null", 0);
+  JNI_NULL_CHECK(env, j_right_keys, "right keys table is null", 0);
+  JNI_NULL_CHECK(env, j_left_condition, "left condition table is null", 0);
+  JNI_NULL_CHECK(env, j_right_condition, "right condition table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const left_keys = reinterpret_cast<cudf::table_view const *>(j_left_keys);
+    auto const right_keys = reinterpret_cast<cudf::table_view const *>(j_right_keys);
+    auto const left_condition = reinterpret_cast<cudf::table_view const *>(j_left_condition);
+    auto const right_condition = reinterpret_cast<cudf::table_view const *>(j_right_condition);
+    auto const condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto const nulls_equal =
+        j_nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    auto [join_size, matches_per_row] =
+        join_size_func(*left_keys, *right_keys, *left_condition, *right_condition,
+                       condition->get_top_expression(), nulls_equal);
+    if (matches_per_row->size() > std::numeric_limits<cudf::size_type>::max()) {
+      throw std::runtime_error("Too many values in device buffer to convert into a column");
+    }
+    auto col_size = static_cast<size_type>(matches_per_row->size());
+    auto col_data = matches_per_row->release();
+    cudf::jni::native_jlongArray result(env, 2);
+    result[0] = static_cast<jlong>(join_size);
+    result[1] = ptr_as_jlong(new cudf::column{cudf::data_type{cudf::type_id::INT32}, col_size,
+                                              std::move(col_data), rmm::device_buffer{}, 0});
+    return result.get_jArray();
+  }
+  CATCH_STD(env, NULL);
+}
+
+template <typename T>
+jlongArray mixed_join_gather_maps(JNIEnv *env, jlong j_left_keys, jlong j_right_keys,
+                                  jlong j_left_condition, jlong j_right_condition,
+                                  jlong j_condition, jboolean j_nulls_equal, T join_func) {
+  JNI_NULL_CHECK(env, j_left_keys, "left keys table is null", 0);
+  JNI_NULL_CHECK(env, j_right_keys, "right keys table is null", 0);
+  JNI_NULL_CHECK(env, j_left_condition, "left condition table is null", 0);
+  JNI_NULL_CHECK(env, j_right_condition, "right condition table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const left_keys = reinterpret_cast<cudf::table_view const *>(j_left_keys);
+    auto const right_keys = reinterpret_cast<cudf::table_view const *>(j_right_keys);
+    auto const left_condition = reinterpret_cast<cudf::table_view const *>(j_left_condition);
+    auto const right_condition = reinterpret_cast<cudf::table_view const *>(j_right_condition);
+    auto const condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto const nulls_equal =
+        j_nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    return gather_maps_to_java(env,
+                               join_func(*left_keys, *right_keys, *left_condition, *right_condition,
+                                         condition->get_top_expression(), nulls_equal));
+  }
+  CATCH_STD(env, NULL);
+}
+
+template <typename T>
+jlongArray mixed_join_gather_single_map(JNIEnv *env, jlong j_left_keys, jlong j_right_keys,
+                                        jlong j_left_condition, jlong j_right_condition,
+                                        jlong j_condition, jboolean j_nulls_equal, T join_func) {
+  JNI_NULL_CHECK(env, j_left_keys, "left keys table is null", 0);
+  JNI_NULL_CHECK(env, j_right_keys, "right keys table is null", 0);
+  JNI_NULL_CHECK(env, j_left_condition, "left condition table is null", 0);
+  JNI_NULL_CHECK(env, j_right_condition, "right condition table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const left_keys = reinterpret_cast<cudf::table_view const *>(j_left_keys);
+    auto const right_keys = reinterpret_cast<cudf::table_view const *>(j_right_keys);
+    auto const left_condition = reinterpret_cast<cudf::table_view const *>(j_left_condition);
+    auto const right_condition = reinterpret_cast<cudf::table_view const *>(j_right_condition);
+    auto const condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto const nulls_equal =
+        j_nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL;
+    return gather_map_to_java(env,
+                              join_func(*left_keys, *right_keys, *left_condition, *right_condition,
+                                        condition->get_top_expression(), nulls_equal));
+  }
+  CATCH_STD(env, NULL);
+}
+
+std::pair<std::size_t, cudf::device_span<cudf::size_type const>>
+get_mixed_size_info(JNIEnv *env, jlong j_output_row_count, jlong j_matches_view) {
+  auto const row_count = static_cast<std::size_t>(j_output_row_count);
+  auto const matches = reinterpret_cast<cudf::column_view const *>(j_matches_view);
+  return std::make_pair(row_count, cudf::device_span<cudf::size_type const>(
+                                       matches->template data<cudf::size_type>(), matches->size()));
+}
+
+cudf::column_view remove_validity_from_col(cudf::column_view column_view) {
+  if (!cudf::is_compound(column_view.type())) {
+    if (column_view.nullable() && column_view.null_count() == 0) {
+      // null_mask is allocated but no nulls present therefore we create a new column_view without
+      // the null_mask to avoid things blowing up in reading the parquet file
+      return cudf::column_view(column_view.type(), column_view.size(), column_view.head(), nullptr,
+                               0, column_view.offset());
+    } else {
+      return cudf::column_view(column_view);
+    }
+  } else {
+    std::vector<cudf::column_view> children;
+    children.reserve(column_view.num_children());
+    for (auto it = column_view.child_begin(); it != column_view.child_end(); it++) {
+      children.push_back(remove_validity_from_col(*it));
+    }
+    if (!column_view.nullable() || column_view.null_count() != 0) {
+      return cudf::column_view(column_view.type(), column_view.size(), nullptr,
+                               column_view.null_mask(), column_view.null_count(),
+                               column_view.offset(), children);
+    } else {
+      return cudf::column_view(column_view.type(), column_view.size(), nullptr, nullptr, 0,
+                               column_view.offset(), children);
+    }
+  }
+}
+
+cudf::table_view remove_validity_if_needed(cudf::table_view *input_table_view) {
+  std::vector<cudf::column_view> views;
+  views.reserve(input_table_view->num_columns());
+  for (auto it = input_table_view->begin(); it != input_table_view->end(); it++) {
+    views.push_back(remove_validity_from_col(*it));
+  }
+
+  return cudf::table_view(views);
+}
+
+} // namespace
+
+} // namespace jni
+} // namespace cudf
+
+using cudf::jni::convert_table_for_return;
+using cudf::jni::ptr_as_jlong;
+using cudf::jni::release_as_jlong;
+
+extern "C" {
+
+// This is a method purely added for testing remove_validity_if_needed method
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_removeNullMasksIfNeeded(JNIEnv *env, jclass,
+                                                                               jlong j_table_view) {
+  JNI_NULL_CHECK(env, j_table_view, "table view handle is null", 0);
+  try {
+    cudf::table_view *tview = reinterpret_cast<cudf::table_view *>(j_table_view);
+    cudf::table_view result = cudf::jni::remove_validity_if_needed(tview);
+    cudf::table m_tbl(result);
+    std::vector<std::unique_ptr<cudf::column>> cols = m_tbl.release();
+    auto results = cudf::jni::native_jlongArray(env, cols.size());
+    std::transform(cols.begin(), cols.end(), results.begin(),
+                   [](auto &col) { return release_as_jlong(col); });
+    return results.get_jArray();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_createCudfTableView(JNIEnv *env, jclass,
+                                                                      jlongArray j_cudf_columns) {
+  JNI_NULL_CHECK(env, j_cudf_columns, "columns are null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jpointerArray<cudf::column_view> n_cudf_columns(env, j_cudf_columns);
+
+    std::vector<cudf::column_view> column_views = n_cudf_columns.get_dereferenced();
+    return ptr_as_jlong(new cudf::table_view(column_views));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_deleteCudfTable(JNIEnv *env, jclass,
+                                                                 jlong j_cudf_table_view) {
+  JNI_NULL_CHECK(env, j_cudf_table_view, "table view handle is null", );
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cudf::table_view *>(j_cudf_table_view);
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_columnViewsFromPacked(JNIEnv *env, jclass,
+                                                                             jobject buffer_obj,
+                                                                             jlong j_data_address) {
+  // The GPU data address can be null when the table is empty, so it is not null-checked here.
+  JNI_NULL_CHECK(env, buffer_obj, "metadata is null", nullptr);
+  try {
+    cudf::jni::auto_set_device(env);
+    void const *metadata_address = env->GetDirectBufferAddress(buffer_obj);
+    JNI_NULL_CHECK(env, metadata_address, "metadata buffer address is null", nullptr);
+    cudf::table_view table = cudf::unpack(static_cast<uint8_t const *>(metadata_address),
+                                          reinterpret_cast<uint8_t const *>(j_data_address));
+    cudf::jni::native_jlongArray views(env, table.num_columns());
+    for (int i = 0; i < table.num_columns(); i++) {
+      // TODO Exception handling is not ideal, if no exceptions are thrown ownership of the new cv
+      // is passed to Java. If an exception is thrown we need to free it, but this needs to be
+      // coordinated with the Java side because one column may have changed ownership while
+      // another may not have. We don't want to double free the view so for now we just let it
+      // leak because it should be a small amount of host memory.
+      //
+      // In the ideal case we would keep the view where it is at, and pass in a pointer to it
+      // That pointer would then be copied when Java takes ownership of it, but that adds an
+      // extra JNI call that I would like to avoid for performance reasons.
+      views[i] = ptr_as_jlong(new cudf::column_view(table.column(i)));
+    }
+    views.commit();
+
+    return views.get_jArray();
+  }
+  CATCH_STD(env, nullptr);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_sortOrder(JNIEnv *env, jclass,
+                                                            jlong j_input_table,
+                                                            jlongArray j_sort_keys_columns,
+                                                            jbooleanArray j_is_descending,
+                                                            jbooleanArray j_are_nulls_smallest) {
+
+  // input validations & verifications
+  JNI_NULL_CHECK(env, j_input_table, "input table is null", 0);
+  JNI_NULL_CHECK(env, j_sort_keys_columns, "sort keys columns is null", 0);
+  JNI_NULL_CHECK(env, j_is_descending, "sort order array is null", 0);
+  JNI_NULL_CHECK(env, j_are_nulls_smallest, "null order array is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jpointerArray<cudf::column_view> n_sort_keys_columns(env,
+                                                                           j_sort_keys_columns);
+    jsize num_columns = n_sort_keys_columns.size();
+    const cudf::jni::native_jbooleanArray n_is_descending(env, j_is_descending);
+    jsize num_columns_is_desc = n_is_descending.size();
+
+    JNI_ARG_CHECK(env, num_columns_is_desc == num_columns,
+                  "columns and is_descending lengths don't match", 0);
+
+    const cudf::jni::native_jbooleanArray n_are_nulls_smallest(env, j_are_nulls_smallest);
+    jsize num_columns_null_smallest = n_are_nulls_smallest.size();
+
+    JNI_ARG_CHECK(env, num_columns_null_smallest == num_columns,
+                  "columns and is_descending lengths don't match", 0);
+
+    std::vector<cudf::order> order =
+        n_is_descending.transform_if_else(cudf::order::DESCENDING, cudf::order::ASCENDING);
+    std::vector<cudf::null_order> null_order =
+        n_are_nulls_smallest.transform_if_else(cudf::null_order::BEFORE, cudf::null_order::AFTER);
+
+    std::vector<cudf::column_view> sort_keys = n_sort_keys_columns.get_dereferenced();
+    return release_as_jlong(cudf::sorted_order(cudf::table_view{sort_keys}, order, null_order));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_orderBy(JNIEnv *env, jclass,
+                                                               jlong j_input_table,
+                                                               jlongArray j_sort_keys_columns,
+                                                               jbooleanArray j_is_descending,
+                                                               jbooleanArray j_are_nulls_smallest) {
+
+  // input validations & verifications
+  JNI_NULL_CHECK(env, j_input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, j_sort_keys_columns, "sort keys columns is null", NULL);
+  JNI_NULL_CHECK(env, j_is_descending, "sort order array is null", NULL);
+  JNI_NULL_CHECK(env, j_are_nulls_smallest, "null order array is null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jpointerArray<cudf::column_view> n_sort_keys_columns(env,
+                                                                           j_sort_keys_columns);
+    jsize num_columns = n_sort_keys_columns.size();
+    const cudf::jni::native_jbooleanArray n_is_descending(env, j_is_descending);
+    jsize num_columns_is_desc = n_is_descending.size();
+
+    JNI_ARG_CHECK(env, num_columns_is_desc == num_columns,
+                  "columns and is_descending lengths don't match", 0);
+
+    const cudf::jni::native_jbooleanArray n_are_nulls_smallest(env, j_are_nulls_smallest);
+    jsize num_columns_null_smallest = n_are_nulls_smallest.size();
+
+    JNI_ARG_CHECK(env, num_columns_null_smallest == num_columns,
+                  "columns and areNullsSmallest lengths don't match", 0);
+
+    std::vector<cudf::order> order =
+        n_is_descending.transform_if_else(cudf::order::DESCENDING, cudf::order::ASCENDING);
+
+    std::vector<cudf::null_order> null_order =
+        n_are_nulls_smallest.transform_if_else(cudf::null_order::BEFORE, cudf::null_order::AFTER);
+
+    std::vector<cudf::column_view> sort_keys = n_sort_keys_columns.get_dereferenced();
+    auto sorted_col = cudf::sorted_order(cudf::table_view{sort_keys}, order, null_order);
+
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(j_input_table);
+    return convert_table_for_return(env, cudf::gather(*input_table, sorted_col->view()));
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_merge(JNIEnv *env, jclass,
+                                                             jlongArray j_table_handles,
+                                                             jintArray j_sort_key_indexes,
+                                                             jbooleanArray j_is_descending,
+                                                             jbooleanArray j_are_nulls_smallest) {
+
+  // input validations & verifications
+  JNI_NULL_CHECK(env, j_table_handles, "input tables are null", NULL);
+  JNI_NULL_CHECK(env, j_sort_key_indexes, "key indexes is null", NULL);
+  JNI_NULL_CHECK(env, j_is_descending, "sort order array is null", NULL);
+  JNI_NULL_CHECK(env, j_are_nulls_smallest, "null order array is null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jpointerArray<cudf::table_view> n_table_handles(env, j_table_handles);
+
+    const cudf::jni::native_jintArray n_sort_key_indexes(env, j_sort_key_indexes);
+    jsize num_columns = n_sort_key_indexes.size();
+    const cudf::jni::native_jbooleanArray n_is_descending(env, j_is_descending);
+    jsize num_columns_is_desc = n_is_descending.size();
+
+    JNI_ARG_CHECK(env, num_columns_is_desc == num_columns,
+                  "columns and is_descending lengths don't match", NULL);
+
+    const cudf::jni::native_jbooleanArray n_are_nulls_smallest(env, j_are_nulls_smallest);
+    jsize num_columns_null_smallest = n_are_nulls_smallest.size();
+
+    JNI_ARG_CHECK(env, num_columns_null_smallest == num_columns,
+                  "columns and areNullsSmallest lengths don't match", NULL);
+
+    std::vector<int> indexes = n_sort_key_indexes.to_vector<int>();
+    std::vector<cudf::order> order =
+        n_is_descending.transform_if_else(cudf::order::DESCENDING, cudf::order::ASCENDING);
+    std::vector<cudf::null_order> null_order =
+        n_are_nulls_smallest.transform_if_else(cudf::null_order::BEFORE, cudf::null_order::AFTER);
+    std::vector<cudf::table_view> tables = n_table_handles.get_dereferenced();
+
+    return convert_table_for_return(env, cudf::merge(tables, indexes, order, null_order));
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_readCSV(
+    JNIEnv *env, jclass, jobjectArray col_names, jintArray j_types, jintArray j_scales,
+    jobjectArray filter_col_names, jstring inputfilepath, jlong buffer, jlong buffer_length,
+    jint header_row, jbyte delim, jint j_quote_style, jbyte quote, jbyte comment,
+    jobjectArray null_values, jobjectArray true_values, jobjectArray false_values) {
+  JNI_NULL_CHECK(env, null_values, "null_values must be supplied, even if it is empty", NULL);
+
+  bool read_buffer = true;
+  if (buffer == 0) {
+    JNI_NULL_CHECK(env, inputfilepath, "input file or buffer must be supplied", NULL);
+    read_buffer = false;
+  } else if (inputfilepath != NULL) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "cannot pass in both a buffer and an inputfilepath", NULL);
+  } else if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported",
+                  NULL);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstringArray n_col_names(env, col_names);
+    cudf::jni::native_jintArray n_types(env, j_types);
+    cudf::jni::native_jintArray n_scales(env, j_scales);
+    if (n_types.is_null() != n_scales.is_null()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "types and scales must match null",
+                    NULL);
+    }
+    std::vector<cudf::data_type> data_types;
+    if (!n_types.is_null()) {
+      if (n_types.size() != n_scales.size()) {
+        JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "types and scales must match size",
+                      NULL);
+      }
+      data_types.reserve(n_types.size());
+      std::transform(n_types.begin(), n_types.end(), n_scales.begin(),
+                     std::back_inserter(data_types), [](auto type, auto scale) {
+                       return cudf::data_type{static_cast<cudf::type_id>(type), scale};
+                     });
+    }
+
+    cudf::jni::native_jstring filename(env, inputfilepath);
+    if (!read_buffer && filename.is_empty()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "inputfilepath can't be empty",
+                    NULL);
+    }
+
+    cudf::jni::native_jstringArray n_null_values(env, null_values);
+    cudf::jni::native_jstringArray n_true_values(env, true_values);
+    cudf::jni::native_jstringArray n_false_values(env, false_values);
+    cudf::jni::native_jstringArray n_filter_col_names(env, filter_col_names);
+
+    auto source = read_buffer ? cudf::io::source_info{reinterpret_cast<char *>(buffer),
+                                                      static_cast<std::size_t>(buffer_length)} :
+                                cudf::io::source_info{filename.get()};
+    auto const quote_style = static_cast<cudf::io::quote_style>(j_quote_style);
+
+    cudf::io::csv_reader_options opts = cudf::io::csv_reader_options::builder(source)
+                                            .delimiter(delim)
+                                            .header(header_row)
+                                            .names(n_col_names.as_cpp_vector())
+                                            .dtypes(data_types)
+                                            .use_cols_names(n_filter_col_names.as_cpp_vector())
+                                            .true_values(n_true_values.as_cpp_vector())
+                                            .false_values(n_false_values.as_cpp_vector())
+                                            .na_values(n_null_values.as_cpp_vector())
+                                            .keep_default_na(false)
+                                            .na_filter(n_null_values.size() > 0)
+                                            .quoting(quote_style)
+                                            .quotechar(quote)
+                                            .comment(comment)
+                                            .build();
+
+    return convert_table_for_return(env, cudf::io::read_csv(opts).tbl);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeCSVToFile(
+    JNIEnv *env, jclass, jlong j_table_handle, jobjectArray j_column_names, jboolean include_header,
+    jstring j_row_delimiter, jbyte j_field_delimiter, jstring j_null_value, jstring j_true_value,
+    jstring j_false_value, jint j_quote_style, jstring j_output_path) {
+  JNI_NULL_CHECK(env, j_table_handle, "table handle cannot be null.", );
+  JNI_NULL_CHECK(env, j_column_names, "column name array cannot be null", );
+  JNI_NULL_CHECK(env, j_row_delimiter, "row delimiter cannot be null", );
+  JNI_NULL_CHECK(env, j_field_delimiter, "field delimiter cannot be null", );
+  JNI_NULL_CHECK(env, j_null_value, "null representation string cannot be itself null", );
+  JNI_NULL_CHECK(env, j_true_value, "representation string for `true` cannot be null", );
+  JNI_NULL_CHECK(env, j_false_value, "representation string for `false` cannot be null", );
+  JNI_NULL_CHECK(env, j_output_path, "output path cannot be null", );
+
+  try {
+    cudf::jni::auto_set_device(env);
+
+    auto const native_output_path = cudf::jni::native_jstring{env, j_output_path};
+    auto const output_path = native_output_path.get();
+
+    auto const table = reinterpret_cast<cudf::table_view *>(j_table_handle);
+    auto const n_column_names = cudf::jni::native_jstringArray{env, j_column_names};
+    auto const column_names = n_column_names.as_cpp_vector();
+
+    auto const line_terminator = cudf::jni::native_jstring{env, j_row_delimiter};
+    auto const na_rep = cudf::jni::native_jstring{env, j_null_value};
+    auto const true_value = cudf::jni::native_jstring{env, j_true_value};
+    auto const false_value = cudf::jni::native_jstring{env, j_false_value};
+    auto const quote_style = static_cast<cudf::io::quote_style>(j_quote_style);
+
+    auto options = cudf::io::csv_writer_options::builder(cudf::io::sink_info{output_path}, *table)
+                       .names(column_names)
+                       .include_header(static_cast<bool>(include_header))
+                       .line_terminator(line_terminator.get())
+                       .inter_column_delimiter(j_field_delimiter)
+                       .na_rep(na_rep.get())
+                       .true_value(true_value.get())
+                       .false_value(false_value.get())
+                       .quoting(quote_style);
+
+    cudf::io::write_csv(options.build());
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_startWriteCSVToBuffer(
+    JNIEnv *env, jclass, jobjectArray j_column_names, jboolean include_header,
+    jstring j_row_delimiter, jbyte j_field_delimiter, jstring j_null_value, jstring j_true_value,
+    jstring j_false_value, jint j_quote_style, jobject j_buffer, jobject host_memory_allocator) {
+  JNI_NULL_CHECK(env, j_column_names, "column name array cannot be null", 0);
+  JNI_NULL_CHECK(env, j_row_delimiter, "row delimiter cannot be null", 0);
+  JNI_NULL_CHECK(env, j_field_delimiter, "field delimiter cannot be null", 0);
+  JNI_NULL_CHECK(env, j_null_value, "null representation string cannot be itself null", 0);
+  JNI_NULL_CHECK(env, j_buffer, "output buffer cannot be null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+
+    auto data_sink =
+        std::make_unique<cudf::jni::jni_writer_data_sink>(env, j_buffer, host_memory_allocator);
+
+    auto const n_column_names = cudf::jni::native_jstringArray{env, j_column_names};
+    auto const column_names = n_column_names.as_cpp_vector();
+
+    auto const line_terminator = cudf::jni::native_jstring{env, j_row_delimiter};
+    auto const na_rep = cudf::jni::native_jstring{env, j_null_value};
+    auto const true_value = cudf::jni::native_jstring{env, j_true_value};
+    auto const false_value = cudf::jni::native_jstring{env, j_false_value};
+    auto const quote_style = static_cast<cudf::io::quote_style>(j_quote_style);
+
+    auto options = cudf::io::csv_writer_options::builder(cudf::io::sink_info{data_sink.get()},
+                                                         cudf::table_view{})
+                       .names(column_names)
+                       .include_header(static_cast<bool>(include_header))
+                       .line_terminator(line_terminator.get())
+                       .inter_column_delimiter(j_field_delimiter)
+                       .na_rep(na_rep.get())
+                       .true_value(true_value.get())
+                       .false_value(false_value.get())
+                       .quoting(quote_style)
+                       .build();
+
+    return ptr_as_jlong(new cudf::jni::io::csv_chunked_writer{options, data_sink});
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeCSVChunkToBuffer(JNIEnv *env, jclass,
+                                                                       jlong j_writer_handle,
+                                                                       jlong j_table_handle) {
+  JNI_NULL_CHECK(env, j_writer_handle, "writer handle cannot be null.", );
+  JNI_NULL_CHECK(env, j_table_handle, "table handle cannot be null.", );
+
+  auto const table = reinterpret_cast<cudf::table_view *>(j_table_handle);
+  auto writer = reinterpret_cast<cudf::jni::io::csv_chunked_writer *>(j_writer_handle);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    writer->write(*table);
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_endWriteCSVToBuffer(JNIEnv *env, jclass,
+                                                                     jlong j_writer_handle) {
+  JNI_NULL_CHECK(env, j_writer_handle, "writer handle cannot be null.", );
+
+  using cudf::jni::io::csv_chunked_writer;
+  auto writer =
+      std::unique_ptr<csv_chunked_writer>{reinterpret_cast<csv_chunked_writer *>(j_writer_handle)};
+
+  try {
+    cudf::jni::auto_set_device(env);
+    writer->close();
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_readAndInferJSON(
+    JNIEnv *env, jclass, jlong buffer, jlong buffer_length, jboolean day_first, jboolean lines,
+    jboolean recover_with_null) {
+
+  JNI_NULL_CHECK(env, buffer, "buffer cannot be null", 0);
+  if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+
+    auto source = cudf::io::source_info{reinterpret_cast<char *>(buffer),
+                                        static_cast<std::size_t>(buffer_length)};
+
+    auto const recovery_mode = recover_with_null ?
+                                   cudf::io::json_recovery_mode_t::RECOVER_WITH_NULL :
+                                   cudf::io::json_recovery_mode_t::FAIL;
+    cudf::io::json_reader_options_builder opts = cudf::io::json_reader_options::builder(source)
+                                                     .dayfirst(static_cast<bool>(day_first))
+                                                     .lines(static_cast<bool>(lines))
+                                                     .recovery_mode(recovery_mode);
+
+    auto result =
+        std::make_unique<cudf::io::table_with_metadata>(cudf::io::read_json(opts.build()));
+
+    return reinterpret_cast<jlong>(result.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_TableWithMeta_close(JNIEnv *env, jclass, jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", );
+
+  try {
+    cudf::jni::auto_set_device(env);
+    delete reinterpret_cast<cudf::io::table_with_metadata *>(handle);
+  }
+  CATCH_STD(env, );
+}
+
+JNIEXPORT jobjectArray JNICALL Java_ai_rapids_cudf_TableWithMeta_getColumnNames(JNIEnv *env, jclass,
+                                                                                jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", nullptr);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ptr = reinterpret_cast<cudf::io::table_with_metadata *>(handle);
+    auto length = ptr->metadata.schema_info.size();
+    auto ret = static_cast<jobjectArray>(
+        env->NewObjectArray(length, env->FindClass("java/lang/String"), nullptr));
+    for (size_t i = 0; i < length; i++) {
+      env->SetObjectArrayElement(ret, i,
+                                 env->NewStringUTF(ptr->metadata.schema_info[i].name.c_str()));
+    }
+
+    return ret;
+  }
+  CATCH_STD(env, nullptr);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_TableWithMeta_releaseTable(JNIEnv *env, jclass,
+                                                                            jlong handle) {
+  JNI_NULL_CHECK(env, handle, "handle is null", nullptr);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto ptr = reinterpret_cast<cudf::io::table_with_metadata *>(handle);
+    if (ptr->tbl) {
+      return convert_table_for_return(env, ptr->tbl);
+    } else {
+      return nullptr;
+    }
+  }
+  CATCH_STD(env, nullptr);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_readJSON(
+    JNIEnv *env, jclass, jobjectArray col_names, jintArray j_types, jintArray j_scales,
+    jstring inputfilepath, jlong buffer, jlong buffer_length, jboolean day_first, jboolean lines,
+    jboolean recover_with_null) {
+
+  bool read_buffer = true;
+  if (buffer == 0) {
+    JNI_NULL_CHECK(env, inputfilepath, "input file or buffer must be supplied", 0);
+    read_buffer = false;
+  } else if (inputfilepath != NULL) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "cannot pass in both a buffer and an inputfilepath", 0);
+  } else if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported", 0);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstringArray n_col_names(env, col_names);
+    cudf::jni::native_jintArray n_types(env, j_types);
+    cudf::jni::native_jintArray n_scales(env, j_scales);
+    if (n_types.is_null() != n_scales.is_null()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "types and scales must match null",
+                    0);
+    }
+    std::vector<cudf::data_type> data_types;
+    if (!n_types.is_null()) {
+      if (n_types.size() != n_scales.size()) {
+        JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "types and scales must match size",
+                      0);
+      }
+      data_types.reserve(n_types.size());
+      std::transform(n_types.begin(), n_types.end(), n_scales.begin(),
+                     std::back_inserter(data_types), [](auto const &type, auto const &scale) {
+                       return cudf::data_type{static_cast<cudf::type_id>(type), scale};
+                     });
+    }
+
+    cudf::jni::native_jstring filename(env, inputfilepath);
+    if (!read_buffer && filename.is_empty()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "inputfilepath can't be empty", 0);
+    }
+
+    auto source = read_buffer ? cudf::io::source_info{reinterpret_cast<char *>(buffer),
+                                                      static_cast<std::size_t>(buffer_length)} :
+                                cudf::io::source_info{filename.get()};
+
+    cudf::io::json_recovery_mode_t recovery_mode =
+        recover_with_null ? cudf::io::json_recovery_mode_t::RECOVER_WITH_NULL :
+                            cudf::io::json_recovery_mode_t::FAIL;
+    cudf::io::json_reader_options_builder opts = cudf::io::json_reader_options::builder(source)
+                                                     .dayfirst(static_cast<bool>(day_first))
+                                                     .lines(static_cast<bool>(lines))
+                                                     .recovery_mode(recovery_mode);
+
+    if (!n_col_names.is_null() && data_types.size() > 0) {
+      if (n_col_names.size() != n_types.size()) {
+        JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                      "types and column names must match size", 0);
+      }
+
+      std::map<std::string, cudf::data_type> map;
+
+      auto col_names_vec = n_col_names.as_cpp_vector();
+      std::transform(col_names_vec.begin(), col_names_vec.end(), data_types.begin(),
+                     std::inserter(map, map.end()),
+                     [](std::string a, cudf::data_type b) { return std::make_pair(a, b); });
+      opts.dtypes(map);
+    } else if (data_types.size() > 0) {
+      opts.dtypes(data_types);
+    } else {
+      // should infer the types
+    }
+
+    auto result =
+        std::make_unique<cudf::io::table_with_metadata>(cudf::io::read_json(opts.build()));
+
+    return reinterpret_cast<jlong>(result.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_readParquet(
+    JNIEnv *env, jclass, jobjectArray filter_col_names, jbooleanArray j_col_binary_read,
+    jstring inputfilepath, jlong buffer, jlong buffer_length, jint unit) {
+
+  JNI_NULL_CHECK(env, j_col_binary_read, "null col_binary_read", 0);
+  bool read_buffer = true;
+  if (buffer == 0) {
+    JNI_NULL_CHECK(env, inputfilepath, "input file or buffer must be supplied", NULL);
+    read_buffer = false;
+  } else if (inputfilepath != NULL) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "cannot pass in both a buffer and an inputfilepath", NULL);
+  } else if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported",
+                  NULL);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring filename(env, inputfilepath);
+    if (!read_buffer && filename.is_empty()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "inputfilepath can't be empty",
+                    NULL);
+    }
+
+    cudf::jni::native_jstringArray n_filter_col_names(env, filter_col_names);
+    cudf::jni::native_jbooleanArray n_col_binary_read(env, j_col_binary_read);
+
+    auto source = read_buffer ? cudf::io::source_info(reinterpret_cast<char *>(buffer),
+                                                      static_cast<std::size_t>(buffer_length)) :
+                                cudf::io::source_info(filename.get());
+
+    auto builder = cudf::io::parquet_reader_options::builder(source);
+    if (n_filter_col_names.size() > 0) {
+      builder = builder.columns(n_filter_col_names.as_cpp_vector());
+    }
+
+    cudf::io::parquet_reader_options opts =
+        builder.convert_strings_to_categories(false)
+            .timestamp_type(cudf::data_type(static_cast<cudf::type_id>(unit)))
+            .build();
+    return convert_table_for_return(env, cudf::io::read_parquet(opts).tbl);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_readAvro(JNIEnv *env, jclass,
+                                                                jobjectArray filter_col_names,
+                                                                jstring inputfilepath, jlong buffer,
+                                                                jlong buffer_length, jint unit) {
+
+  const bool read_buffer = (buffer != 0);
+  if (!read_buffer) {
+    JNI_NULL_CHECK(env, inputfilepath, "input file or buffer must be supplied", NULL);
+  } else if (inputfilepath != NULL) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "cannot pass in both a buffer and an inputfilepath", NULL);
+  } else if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported",
+                  NULL);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring filename(env, inputfilepath);
+    if (!read_buffer && filename.is_empty()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "inputfilepath can't be empty",
+                    NULL);
+    }
+
+    cudf::jni::native_jstringArray n_filter_col_names(env, filter_col_names);
+
+    auto source = read_buffer ? cudf::io::source_info(reinterpret_cast<char *>(buffer),
+                                                      static_cast<std::size_t>(buffer_length)) :
+                                cudf::io::source_info(filename.get());
+
+    cudf::io::avro_reader_options opts = cudf::io::avro_reader_options::builder(source)
+                                             .columns(n_filter_col_names.as_cpp_vector())
+                                             .build();
+    return convert_table_for_return(env, cudf::io::read_avro(opts).tbl);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_writeParquetBufferBegin(
+    JNIEnv *env, jclass, jobjectArray j_col_names, jint j_num_children, jintArray j_children,
+    jbooleanArray j_col_nullability, jobjectArray j_metadata_keys, jobjectArray j_metadata_values,
+    jint j_compression, jint j_stats_freq, jbooleanArray j_isInt96, jintArray j_precisions,
+    jbooleanArray j_is_map, jbooleanArray j_is_binary, jbooleanArray j_hasParquetFieldIds,
+    jintArray j_parquetFieldIds, jobject consumer, jobject host_memory_allocator) {
+  JNI_NULL_CHECK(env, j_col_names, "null columns", 0);
+  JNI_NULL_CHECK(env, j_col_nullability, "null nullability", 0);
+  JNI_NULL_CHECK(env, j_metadata_keys, "null metadata keys", 0);
+  JNI_NULL_CHECK(env, j_metadata_values, "null metadata values", 0);
+  JNI_NULL_CHECK(env, consumer, "null consumer", 0);
+  try {
+    std::unique_ptr<cudf::jni::jni_writer_data_sink> data_sink(
+        new cudf::jni::jni_writer_data_sink(env, consumer, host_memory_allocator));
+
+    using namespace cudf::io;
+    using namespace cudf::jni;
+    sink_info sink{data_sink.get()};
+    table_input_metadata metadata;
+    createTableMetaData(env, j_num_children, j_col_names, j_children, j_col_nullability, j_isInt96,
+                        j_precisions, j_is_map, metadata, j_hasParquetFieldIds, j_parquetFieldIds,
+                        j_is_binary);
+
+    auto meta_keys = cudf::jni::native_jstringArray{env, j_metadata_keys}.as_cpp_vector();
+    auto meta_values = cudf::jni::native_jstringArray{env, j_metadata_values}.as_cpp_vector();
+
+    std::map<std::string, std::string> kv_metadata;
+    std::transform(meta_keys.begin(), meta_keys.end(), meta_values.begin(),
+                   std::inserter(kv_metadata, kv_metadata.end()),
+                   [](auto const &key, auto const &value) {
+                     // The metadata value will be ignored if it is empty.
+                     // We modify it into a space character to workaround such issue.
+                     return std::make_pair(key, value.empty() ? std::string(" ") : value);
+                   });
+
+    auto stats = std::make_shared<cudf::io::writer_compression_statistics>();
+    chunked_parquet_writer_options opts =
+        chunked_parquet_writer_options::builder(sink)
+            .metadata(std::move(metadata))
+            .compression(static_cast<compression_type>(j_compression))
+            .stats_level(static_cast<statistics_freq>(j_stats_freq))
+            .key_value_metadata({kv_metadata})
+            .compression_statistics(stats)
+            .build();
+    auto writer_ptr = std::make_unique<cudf::io::parquet_chunked_writer>(opts);
+    cudf::jni::native_parquet_writer_handle *ret = new cudf::jni::native_parquet_writer_handle(
+        std::move(writer_ptr), std::move(data_sink), std::move(stats));
+    return ptr_as_jlong(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_writeParquetFileBegin(
+    JNIEnv *env, jclass, jobjectArray j_col_names, jint j_num_children, jintArray j_children,
+    jbooleanArray j_col_nullability, jobjectArray j_metadata_keys, jobjectArray j_metadata_values,
+    jint j_compression, jint j_stats_freq, jbooleanArray j_isInt96, jintArray j_precisions,
+    jbooleanArray j_is_map, jbooleanArray j_is_binary, jbooleanArray j_hasParquetFieldIds,
+    jintArray j_parquetFieldIds, jstring j_output_path) {
+  JNI_NULL_CHECK(env, j_col_names, "null columns", 0);
+  JNI_NULL_CHECK(env, j_col_nullability, "null nullability", 0);
+  JNI_NULL_CHECK(env, j_metadata_keys, "null metadata keys", 0);
+  JNI_NULL_CHECK(env, j_metadata_values, "null metadata values", 0);
+  JNI_NULL_CHECK(env, j_output_path, "null output path", 0);
+  try {
+    cudf::jni::native_jstring output_path(env, j_output_path);
+
+    using namespace cudf::io;
+    using namespace cudf::jni;
+    table_input_metadata metadata;
+    createTableMetaData(env, j_num_children, j_col_names, j_children, j_col_nullability, j_isInt96,
+                        j_precisions, j_is_map, metadata, j_hasParquetFieldIds, j_parquetFieldIds,
+                        j_is_binary);
+
+    auto meta_keys = cudf::jni::native_jstringArray{env, j_metadata_keys}.as_cpp_vector();
+    auto meta_values = cudf::jni::native_jstringArray{env, j_metadata_values}.as_cpp_vector();
+
+    std::map<std::string, std::string> kv_metadata;
+    std::transform(meta_keys.begin(), meta_keys.end(), meta_values.begin(),
+                   std::inserter(kv_metadata, kv_metadata.end()),
+                   [](auto const &key, auto const &value) {
+                     // The metadata value will be ignored if it is empty.
+                     // We modify it into a space character to workaround such issue.
+                     return std::make_pair(key, value.empty() ? std::string(" ") : value);
+                   });
+
+    sink_info sink{output_path.get()};
+    auto stats = std::make_shared<cudf::io::writer_compression_statistics>();
+    chunked_parquet_writer_options opts =
+        chunked_parquet_writer_options::builder(sink)
+            .metadata(std::move(metadata))
+            .compression(static_cast<compression_type>(j_compression))
+            .stats_level(static_cast<statistics_freq>(j_stats_freq))
+            .key_value_metadata({kv_metadata})
+            .compression_statistics(stats)
+            .build();
+
+    auto writer_ptr = std::make_unique<cudf::io::parquet_chunked_writer>(opts);
+    cudf::jni::native_parquet_writer_handle *ret = new cudf::jni::native_parquet_writer_handle(
+        std::move(writer_ptr), nullptr, std::move(stats));
+    return ptr_as_jlong(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeParquetChunk(JNIEnv *env, jclass,
+                                                                   jlong j_state, jlong j_table,
+                                                                   jlong mem_size) {
+  JNI_NULL_CHECK(env, j_table, "null table", );
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  using namespace cudf::io;
+  cudf::table_view *tview_with_empty_nullmask = reinterpret_cast<cudf::table_view *>(j_table);
+  cudf::table_view tview = cudf::jni::remove_validity_if_needed(tview_with_empty_nullmask);
+  cudf::jni::native_parquet_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_parquet_writer_handle *>(j_state);
+
+  if (state->sink) {
+    long alloc_size = std::max(cudf::jni::MINIMUM_WRITE_BUFFER_SIZE, mem_size / 2);
+    state->sink->set_alloc_size(alloc_size);
+  }
+  try {
+    cudf::jni::auto_set_device(env);
+    state->writer->write(tview);
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeParquetEnd(JNIEnv *env, jclass,
+                                                                 jlong j_state) {
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  using namespace cudf::io;
+  cudf::jni::native_parquet_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_parquet_writer_handle *>(j_state);
+  std::unique_ptr<cudf::jni::native_parquet_writer_handle> make_sure_we_delete(state);
+  try {
+    cudf::jni::auto_set_device(env);
+    state->writer->close();
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_readORC(
+    JNIEnv *env, jclass, jobjectArray filter_col_names, jstring inputfilepath, jlong buffer,
+    jlong buffer_length, jboolean usingNumPyTypes, jint unit, jobjectArray dec128_col_names) {
+  bool read_buffer = true;
+  if (buffer == 0) {
+    JNI_NULL_CHECK(env, inputfilepath, "input file or buffer must be supplied", NULL);
+    read_buffer = false;
+  } else if (inputfilepath != NULL) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                  "cannot pass in both a buffer and an inputfilepath", NULL);
+  } else if (buffer_length <= 0) {
+    JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "An empty buffer is not supported",
+                  NULL);
+  }
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring filename(env, inputfilepath);
+    if (!read_buffer && filename.is_empty()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "inputfilepath can't be empty",
+                    NULL);
+    }
+
+    cudf::jni::native_jstringArray n_filter_col_names(env, filter_col_names);
+
+    cudf::jni::native_jstringArray n_dec128_col_names(env, dec128_col_names);
+
+    auto source = read_buffer ?
+                      cudf::io::source_info(reinterpret_cast<char *>(buffer), buffer_length) :
+                      cudf::io::source_info(filename.get());
+
+    auto builder = cudf::io::orc_reader_options::builder(source);
+    if (n_filter_col_names.size() > 0) {
+      builder = builder.columns(n_filter_col_names.as_cpp_vector());
+    }
+
+    cudf::io::orc_reader_options opts =
+        builder.use_index(false)
+            .use_np_dtypes(static_cast<bool>(usingNumPyTypes))
+            .timestamp_type(cudf::data_type(static_cast<cudf::type_id>(unit)))
+            .decimal128_columns(n_dec128_col_names.as_cpp_vector())
+            .build();
+    return convert_table_for_return(env, cudf::io::read_orc(opts).tbl);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_writeORCBufferBegin(
+    JNIEnv *env, jclass, jobjectArray j_col_names, jint j_num_children, jintArray j_children,
+    jbooleanArray j_col_nullability, jobjectArray j_metadata_keys, jobjectArray j_metadata_values,
+    jint j_compression, jintArray j_precisions, jbooleanArray j_is_map, jobject consumer,
+    jobject host_memory_allocator) {
+  JNI_NULL_CHECK(env, j_col_names, "null columns", 0);
+  JNI_NULL_CHECK(env, j_col_nullability, "null nullability", 0);
+  JNI_NULL_CHECK(env, j_metadata_keys, "null metadata keys", 0);
+  JNI_NULL_CHECK(env, j_metadata_values, "null metadata values", 0);
+  JNI_NULL_CHECK(env, consumer, "null consumer", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    using namespace cudf::io;
+    using namespace cudf::jni;
+    table_input_metadata metadata;
+    // ORC has no `j_is_int96`, but `createTableMetaData` needs a lvalue.
+    jbooleanArray j_is_int96 = NULL;
+    // ORC has no `j_parquetFieldIds`, but `createTableMetaData` needs a lvalue.
+    jbooleanArray j_hasParquetFieldIds = NULL;
+    jintArray j_parquetFieldIds = NULL;
+    // temp stub
+    jbooleanArray j_is_binary = NULL;
+
+    createTableMetaData(env, j_num_children, j_col_names, j_children, j_col_nullability, j_is_int96,
+                        j_precisions, j_is_map, metadata, j_hasParquetFieldIds, j_parquetFieldIds,
+                        j_is_binary);
+
+    auto meta_keys = cudf::jni::native_jstringArray{env, j_metadata_keys}.as_cpp_vector();
+    auto meta_values = cudf::jni::native_jstringArray{env, j_metadata_values}.as_cpp_vector();
+
+    std::map<std::string, std::string> kv_metadata;
+    std::transform(meta_keys.begin(), meta_keys.end(), meta_values.begin(),
+                   std::inserter(kv_metadata, kv_metadata.end()),
+                   [](const std::string &k, const std::string &v) { return std::make_pair(k, v); });
+
+    std::unique_ptr<cudf::jni::jni_writer_data_sink> data_sink(
+        new cudf::jni::jni_writer_data_sink(env, consumer, host_memory_allocator));
+    sink_info sink{data_sink.get()};
+
+    auto stats = std::make_shared<cudf::io::writer_compression_statistics>();
+    chunked_orc_writer_options opts = chunked_orc_writer_options::builder(sink)
+                                          .metadata(std::move(metadata))
+                                          .compression(static_cast<compression_type>(j_compression))
+                                          .enable_statistics(ORC_STATISTICS_ROW_GROUP)
+                                          .key_value_metadata(kv_metadata)
+                                          .compression_statistics(stats)
+                                          .build();
+    auto writer_ptr = std::make_unique<cudf::io::orc_chunked_writer>(opts);
+    cudf::jni::native_orc_writer_handle *ret = new cudf::jni::native_orc_writer_handle(
+        std::move(writer_ptr), std::move(data_sink), std::move(stats));
+    return ptr_as_jlong(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_writeORCFileBegin(
+    JNIEnv *env, jclass, jobjectArray j_col_names, jint j_num_children, jintArray j_children,
+    jbooleanArray j_col_nullability, jobjectArray j_metadata_keys, jobjectArray j_metadata_values,
+    jint j_compression, jintArray j_precisions, jbooleanArray j_is_map, jstring j_output_path) {
+  JNI_NULL_CHECK(env, j_col_names, "null columns", 0);
+  JNI_NULL_CHECK(env, j_col_nullability, "null nullability", 0);
+  JNI_NULL_CHECK(env, j_metadata_keys, "null metadata keys", 0);
+  JNI_NULL_CHECK(env, j_metadata_values, "null metadata values", 0);
+  JNI_NULL_CHECK(env, j_output_path, "null output path", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    using namespace cudf::io;
+    using namespace cudf::jni;
+    cudf::jni::native_jstring output_path(env, j_output_path);
+    table_input_metadata metadata;
+    // ORC has no `j_is_int96`, but `createTableMetaData` needs a lvalue.
+    jbooleanArray j_is_int96 = NULL;
+    // ORC has no `j_parquetFieldIds`, but `createTableMetaData` needs a lvalue.
+    jbooleanArray j_hasParquetFieldIds = NULL;
+    jintArray j_parquetFieldIds = NULL;
+    // temp stub
+    jbooleanArray j_is_binary = NULL;
+    createTableMetaData(env, j_num_children, j_col_names, j_children, j_col_nullability, j_is_int96,
+                        j_precisions, j_is_map, metadata, j_hasParquetFieldIds, j_parquetFieldIds,
+                        j_is_binary);
+
+    auto meta_keys = cudf::jni::native_jstringArray{env, j_metadata_keys}.as_cpp_vector();
+    auto meta_values = cudf::jni::native_jstringArray{env, j_metadata_values}.as_cpp_vector();
+
+    std::map<std::string, std::string> kv_metadata;
+    std::transform(meta_keys.begin(), meta_keys.end(), meta_values.begin(),
+                   std::inserter(kv_metadata, kv_metadata.end()),
+                   [](const std::string &k, const std::string &v) { return std::make_pair(k, v); });
+
+    sink_info sink{output_path.get()};
+    auto stats = std::make_shared<cudf::io::writer_compression_statistics>();
+    chunked_orc_writer_options opts = chunked_orc_writer_options::builder(sink)
+                                          .metadata(std::move(metadata))
+                                          .compression(static_cast<compression_type>(j_compression))
+                                          .enable_statistics(ORC_STATISTICS_ROW_GROUP)
+                                          .key_value_metadata(kv_metadata)
+                                          .compression_statistics(stats)
+                                          .build();
+    auto writer_ptr = std::make_unique<cudf::io::orc_chunked_writer>(opts);
+    cudf::jni::native_orc_writer_handle *ret =
+        new cudf::jni::native_orc_writer_handle(std::move(writer_ptr), nullptr, std::move(stats));
+    return ptr_as_jlong(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeORCChunk(JNIEnv *env, jclass, jlong j_state,
+                                                               jlong j_table, jlong mem_size) {
+  JNI_NULL_CHECK(env, j_table, "null table", );
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  using namespace cudf::io;
+  cudf::table_view *tview_orig = reinterpret_cast<cudf::table_view *>(j_table);
+  cudf::table_view tview = cudf::jni::remove_validity_if_needed(tview_orig);
+  cudf::jni::native_orc_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_orc_writer_handle *>(j_state);
+
+  if (state->sink) {
+    long alloc_size = std::max(cudf::jni::MINIMUM_WRITE_BUFFER_SIZE, mem_size / 2);
+    state->sink->set_alloc_size(alloc_size);
+  }
+  try {
+    cudf::jni::auto_set_device(env);
+    state->writer->write(tview);
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeORCEnd(JNIEnv *env, jclass, jlong j_state) {
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  using namespace cudf::io;
+  cudf::jni::native_orc_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_orc_writer_handle *>(j_state);
+  std::unique_ptr<cudf::jni::native_orc_writer_handle> make_sure_we_delete(state);
+  try {
+    cudf::jni::auto_set_device(env);
+    state->writer->close();
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jdoubleArray JNICALL Java_ai_rapids_cudf_TableWriter_getWriteStatistics(JNIEnv *env,
+                                                                                  jclass,
+                                                                                  jlong j_state) {
+  JNI_NULL_CHECK(env, j_state, "null state", nullptr);
+
+  using namespace cudf::io;
+  auto const state = reinterpret_cast<cudf::jni::jni_table_writer_handle_base const *>(j_state);
+  try {
+    cudf::jni::auto_set_device(env);
+    if (!state->stats) {
+      return nullptr;
+    }
+
+    auto const &stats = *state->stats;
+    auto output = cudf::jni::native_jdoubleArray(env, 4);
+    output[0] = static_cast<jdouble>(stats.num_compressed_bytes());
+    output[1] = static_cast<jdouble>(stats.num_failed_bytes());
+    output[2] = static_cast<jdouble>(stats.num_skipped_bytes());
+    output[3] = static_cast<jdouble>(stats.compression_ratio());
+
+    return output.get_jArray();
+  }
+  CATCH_STD(env, nullptr)
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_writeArrowIPCBufferBegin(
+    JNIEnv *env, jclass, jobjectArray j_col_names, jobject consumer,
+    jobject host_memory_allocator) {
+  JNI_NULL_CHECK(env, j_col_names, "null columns", 0);
+  JNI_NULL_CHECK(env, consumer, "null consumer", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstringArray col_names(env, j_col_names);
+
+    std::shared_ptr<cudf::jni::jni_arrow_output_stream> data_sink(
+        new cudf::jni::jni_arrow_output_stream(env, consumer, host_memory_allocator));
+
+    cudf::jni::native_arrow_ipc_writer_handle *ret =
+        new cudf::jni::native_arrow_ipc_writer_handle(col_names.as_cpp_vector(), data_sink);
+    return ptr_as_jlong(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_writeArrowIPCFileBegin(JNIEnv *env, jclass,
+                                                                        jobjectArray j_col_names,
+                                                                        jstring j_output_path) {
+  JNI_NULL_CHECK(env, j_col_names, "null columns", 0);
+  JNI_NULL_CHECK(env, j_output_path, "null output path", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstringArray col_names(env, j_col_names);
+    cudf::jni::native_jstring output_path(env, j_output_path);
+
+    cudf::jni::native_arrow_ipc_writer_handle *ret =
+        new cudf::jni::native_arrow_ipc_writer_handle(col_names.as_cpp_vector(), output_path.get());
+    return ptr_as_jlong(ret);
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_convertCudfToArrowTable(JNIEnv *env, jclass,
+                                                                          jlong j_state,
+                                                                          jlong j_table) {
+  JNI_NULL_CHECK(env, j_table, "null table", 0);
+  JNI_NULL_CHECK(env, j_state, "null state", 0);
+
+  cudf::table_view *tview = reinterpret_cast<cudf::table_view *>(j_table);
+  cudf::jni::native_arrow_ipc_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_arrow_ipc_writer_handle *>(j_state);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    // The semantics of this function are confusing:
+    // The return value is a pointer to a heap-allocated shared_ptr<arrow::Table>.
+    // i.e. the shared_ptr<> is on the heap.
+    // The pointer to the shared_ptr<> is returned as a jlong.
+    using result_t = std::shared_ptr<arrow::Table>;
+
+    auto result = cudf::to_arrow(*tview, state->get_column_metadata(*tview));
+    return ptr_as_jlong(new result_t{result});
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeArrowIPCArrowChunk(JNIEnv *env, jclass,
+                                                                         jlong j_state,
+                                                                         jlong arrow_table_handle,
+                                                                         jlong max_chunk) {
+  JNI_NULL_CHECK(env, arrow_table_handle, "null arrow table", );
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  std::shared_ptr<arrow::Table> *handle =
+      reinterpret_cast<std::shared_ptr<arrow::Table> *>(arrow_table_handle);
+  cudf::jni::native_arrow_ipc_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_arrow_ipc_writer_handle *>(j_state);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    state->write(*handle, max_chunk);
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_writeArrowIPCEnd(JNIEnv *env, jclass,
+                                                                  jlong j_state) {
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  cudf::jni::native_arrow_ipc_writer_handle *state =
+      reinterpret_cast<cudf::jni::native_arrow_ipc_writer_handle *>(j_state);
+  std::unique_ptr<cudf::jni::native_arrow_ipc_writer_handle> make_sure_we_delete(state);
+  try {
+    cudf::jni::auto_set_device(env);
+    state->close();
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_readArrowIPCFileBegin(JNIEnv *env, jclass,
+                                                                       jstring j_input_path) {
+  JNI_NULL_CHECK(env, j_input_path, "null input path", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jstring input_path(env, j_input_path);
+    return ptr_as_jlong(new cudf::jni::native_arrow_ipc_reader_handle(input_path.get()));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT long JNICALL Java_ai_rapids_cudf_Table_readArrowIPCBufferBegin(JNIEnv *env, jclass,
+                                                                         jobject provider) {
+  JNI_NULL_CHECK(env, provider, "null provider", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    std::shared_ptr<cudf::jni::jni_arrow_input_stream> data_source(
+        new cudf::jni::jni_arrow_input_stream(env, provider));
+    return ptr_as_jlong(new cudf::jni::native_arrow_ipc_reader_handle(data_source));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_readArrowIPCChunkToArrowTable(JNIEnv *env, jclass,
+                                                                                jlong j_state,
+                                                                                jint row_target) {
+  JNI_NULL_CHECK(env, j_state, "null state", 0);
+
+  cudf::jni::native_arrow_ipc_reader_handle *state =
+      reinterpret_cast<cudf::jni::native_arrow_ipc_reader_handle *>(j_state);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    // This is a little odd because we have to return a pointer
+    // and arrow wants to deal with shared pointers for everything.
+    auto result = state->next(row_target);
+    return result ? ptr_as_jlong(new std::shared_ptr<arrow::Table>{result}) : 0;
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_closeArrowTable(JNIEnv *env, jclass,
+                                                                 jlong arrow_table_handle) {
+  std::shared_ptr<arrow::Table> *handle =
+      reinterpret_cast<std::shared_ptr<arrow::Table> *>(arrow_table_handle);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    delete handle;
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlongArray JNICALL
+Java_ai_rapids_cudf_Table_convertArrowTableToCudf(JNIEnv *env, jclass, jlong arrow_table_handle) {
+  JNI_NULL_CHECK(env, arrow_table_handle, "null arrow handle", 0);
+
+  std::shared_ptr<arrow::Table> *handle =
+      reinterpret_cast<std::shared_ptr<arrow::Table> *>(arrow_table_handle);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    return convert_table_for_return(env, cudf::from_arrow(*(handle->get())));
+  }
+  CATCH_STD(env, 0)
+}
+
+JNIEXPORT void JNICALL Java_ai_rapids_cudf_Table_readArrowIPCEnd(JNIEnv *env, jclass,
+                                                                 jlong j_state) {
+  JNI_NULL_CHECK(env, j_state, "null state", );
+
+  cudf::jni::native_arrow_ipc_reader_handle *state =
+      reinterpret_cast<cudf::jni::native_arrow_ipc_reader_handle *>(j_state);
+  std::unique_ptr<cudf::jni::native_arrow_ipc_reader_handle> make_sure_we_delete(state);
+  try {
+    cudf::jni::auto_set_device(env);
+    state->close();
+  }
+  CATCH_STD(env, )
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_leftJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jboolean compare_nulls_equal) {
+  return cudf::jni::join_gather_maps(
+      env, j_left_keys, j_right_keys, compare_nulls_equal,
+      [](cudf::table_view const &left, cudf::table_view const &right, cudf::null_equality nulleq) {
+        return cudf::left_join(left, right, nulleq);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_leftJoinRowCount(JNIEnv *env, jclass,
+                                                                   jlong j_left_table,
+                                                                   jlong j_right_hash_join) {
+  JNI_NULL_CHECK(env, j_left_table, "left table is null", 0);
+  JNI_NULL_CHECK(env, j_right_hash_join, "right hash join is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto hash_join = reinterpret_cast<cudf::hash_join const *>(j_right_hash_join);
+    auto row_count = hash_join->left_join_size(*left_table);
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_leftHashJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_hash_join) {
+  return cudf::jni::hash_join_gather_maps(
+      env, j_left_table, j_right_hash_join,
+      [](cudf::table_view const &left, cudf::hash_join const &hash) {
+        return hash.left_join(left);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_leftHashJoinGatherMapsWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_hash_join, jlong j_output_row_count) {
+  auto output_row_count = static_cast<std::size_t>(j_output_row_count);
+  return cudf::jni::hash_join_gather_maps(
+      env, j_left_table, j_right_hash_join,
+      [output_row_count](cudf::table_view const &left, cudf::hash_join const &hash) {
+        return hash.left_join(left, output_row_count);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_conditionalLeftJoinRowCount(JNIEnv *env, jclass,
+                                                                              jlong j_left_table,
+                                                                              jlong j_right_table,
+                                                                              jlong j_condition) {
+  JNI_NULL_CHECK(env, j_left_table, "left_table is null", 0);
+  JNI_NULL_CHECK(env, j_right_table, "right_table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto right_table = reinterpret_cast<cudf::table_view const *>(j_right_table);
+    auto condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto row_count = cudf::conditional_left_join_size(*left_table, *right_table,
+                                                      condition->get_top_expression());
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalLeftJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  return cudf::jni::cond_join_gather_maps(
+      env, j_left_table, j_right_table, j_condition,
+      [](cudf::table_view const &left, cudf::table_view const &right,
+         cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_left_join(left, right, cond_expr);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalLeftJoinGatherMapsWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition,
+    jlong j_row_count) {
+  auto row_count = static_cast<std::size_t>(j_row_count);
+  return cudf::jni::cond_join_gather_maps(
+      env, j_left_table, j_right_table, j_condition,
+      [row_count](cudf::table_view const &left, cudf::table_view const &right,
+                  cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_left_join(left, right, cond_expr, row_count);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftJoinSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_size(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_join_size(left_keys, right_keys, left_condition, right_condition,
+                                          condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_gather_maps(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_join(left_keys, right_keys, left_condition, right_condition,
+                                     condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftJoinGatherMapsWithSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal, jlong j_output_row_count,
+    jlong j_matches_view) {
+  auto size_info = cudf::jni::get_mixed_size_info(env, j_output_row_count, j_matches_view);
+  return cudf::jni::mixed_join_gather_maps(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [&size_info](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+                   cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+                   cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_join(left_keys, right_keys, left_condition, right_condition,
+                                     condition, nulls_equal, size_info);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_innerJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jboolean compare_nulls_equal) {
+  return cudf::jni::join_gather_maps(
+      env, j_left_keys, j_right_keys, compare_nulls_equal,
+      [](cudf::table_view const &left, cudf::table_view const &right, cudf::null_equality nulleq) {
+        return cudf::inner_join(left, right, nulleq);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_innerJoinRowCount(JNIEnv *env, jclass,
+                                                                    jlong j_left_table,
+                                                                    jlong j_right_hash_join) {
+  JNI_NULL_CHECK(env, j_left_table, "left table is null", 0);
+  JNI_NULL_CHECK(env, j_right_hash_join, "right hash join is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto hash_join = reinterpret_cast<cudf::hash_join const *>(j_right_hash_join);
+    auto row_count = hash_join->inner_join_size(*left_table);
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_innerHashJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_hash_join) {
+  return cudf::jni::hash_join_gather_maps(
+      env, j_left_table, j_right_hash_join,
+      [](cudf::table_view const &left, cudf::hash_join const &hash) {
+        return hash.inner_join(left);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_innerHashJoinGatherMapsWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_hash_join, jlong j_output_row_count) {
+  auto output_row_count = static_cast<std::size_t>(j_output_row_count);
+  return cudf::jni::hash_join_gather_maps(
+      env, j_left_table, j_right_hash_join,
+      [output_row_count](cudf::table_view const &left, cudf::hash_join const &hash) {
+        return hash.inner_join(left, output_row_count);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_conditionalInnerJoinRowCount(JNIEnv *env, jclass,
+                                                                               jlong j_left_table,
+                                                                               jlong j_right_table,
+                                                                               jlong j_condition) {
+  JNI_NULL_CHECK(env, j_left_table, "left_table is null", 0);
+  JNI_NULL_CHECK(env, j_right_table, "right_table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto right_table = reinterpret_cast<cudf::table_view const *>(j_right_table);
+    auto condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto row_count = cudf::conditional_inner_join_size(*left_table, *right_table,
+                                                       condition->get_top_expression());
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalInnerJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  return cudf::jni::cond_join_gather_maps(
+      env, j_left_table, j_right_table, j_condition,
+      [](cudf::table_view const &left, cudf::table_view const &right,
+         cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_inner_join(left, right, cond_expr);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalInnerJoinGatherMapsWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition,
+    jlong j_row_count) {
+  auto row_count = static_cast<std::size_t>(j_row_count);
+  return cudf::jni::cond_join_gather_maps(
+      env, j_left_table, j_right_table, j_condition,
+      [row_count](cudf::table_view const &left, cudf::table_view const &right,
+                  cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_inner_join(left, right, cond_expr, row_count);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedInnerJoinSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_size(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_inner_join_size(left_keys, right_keys, left_condition, right_condition,
+                                           condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedInnerJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_gather_maps(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_inner_join(left_keys, right_keys, left_condition, right_condition,
+                                      condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedInnerJoinGatherMapsWithSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal, jlong j_output_row_count,
+    jlong j_matches_view) {
+  auto size_info = cudf::jni::get_mixed_size_info(env, j_output_row_count, j_matches_view);
+  return cudf::jni::mixed_join_gather_maps(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [&size_info](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+                   cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+                   cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_inner_join(left_keys, right_keys, left_condition, right_condition,
+                                      condition, nulls_equal, size_info);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_fullJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jboolean compare_nulls_equal) {
+  return cudf::jni::join_gather_maps(
+      env, j_left_keys, j_right_keys, compare_nulls_equal,
+      [](cudf::table_view const &left, cudf::table_view const &right, cudf::null_equality nulleq) {
+        return cudf::full_join(left, right, nulleq);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_fullJoinRowCount(JNIEnv *env, jclass,
+                                                                   jlong j_left_table,
+                                                                   jlong j_right_hash_join) {
+  JNI_NULL_CHECK(env, j_left_table, "left table is null", 0);
+  JNI_NULL_CHECK(env, j_right_hash_join, "right hash join is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto hash_join = reinterpret_cast<cudf::hash_join const *>(j_right_hash_join);
+    auto row_count = hash_join->full_join_size(*left_table);
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_fullHashJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_hash_join) {
+  return cudf::jni::hash_join_gather_maps(
+      env, j_left_table, j_right_hash_join,
+      [](cudf::table_view const &left, cudf::hash_join const &hash) {
+        return hash.full_join(left);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_fullHashJoinGatherMapsWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_hash_join, jlong j_output_row_count) {
+  auto output_row_count = static_cast<std::size_t>(j_output_row_count);
+  return cudf::jni::hash_join_gather_maps(
+      env, j_left_table, j_right_hash_join,
+      [output_row_count](cudf::table_view const &left, cudf::hash_join const &hash) {
+        return hash.full_join(left, output_row_count);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalFullJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  return cudf::jni::cond_join_gather_maps(
+      env, j_left_table, j_right_table, j_condition,
+      [](cudf::table_view const &left, cudf::table_view const &right,
+         cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_full_join(left, right, cond_expr);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedFullJoinGatherMaps(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_gather_maps(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_full_join(left_keys, right_keys, left_condition, right_condition,
+                                     condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_leftSemiJoinGatherMap(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jboolean compare_nulls_equal) {
+  return cudf::jni::join_gather_single_map(
+      env, j_left_keys, j_right_keys, compare_nulls_equal,
+      [](cudf::table_view const &left, cudf::table_view const &right, cudf::null_equality nulleq) {
+        return cudf::left_semi_join(left, right, nulleq);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_conditionalLeftSemiJoinRowCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  JNI_NULL_CHECK(env, j_left_table, "left_table is null", 0);
+  JNI_NULL_CHECK(env, j_right_table, "right_table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto right_table = reinterpret_cast<cudf::table_view const *>(j_right_table);
+    auto condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto row_count = cudf::conditional_left_semi_join_size(*left_table, *right_table,
+                                                           condition->get_top_expression());
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalLeftSemiJoinGatherMap(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  return cudf::jni::cond_join_gather_single_map(
+      env, j_left_table, j_right_table, j_condition,
+      [](cudf::table_view const &left, cudf::table_view const &right,
+         cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_left_semi_join(left, right, cond_expr);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalLeftSemiJoinGatherMapWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition,
+    jlong j_row_count) {
+  auto row_count = static_cast<std::size_t>(j_row_count);
+  return cudf::jni::cond_join_gather_single_map(
+      env, j_left_table, j_right_table, j_condition,
+      [row_count](cudf::table_view const &left, cudf::table_view const &right,
+                  cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_left_semi_join(left, right, cond_expr, row_count);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftSemiJoinSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_size(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_semi_join_size(left_keys, right_keys, left_condition,
+                                               right_condition, condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftSemiJoinGatherMap(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_gather_single_map(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_semi_join(left_keys, right_keys, left_condition, right_condition,
+                                          condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftSemiJoinGatherMapWithSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal, jlong j_output_row_count,
+    jlong j_matches_view) {
+  auto size_info = cudf::jni::get_mixed_size_info(env, j_output_row_count, j_matches_view);
+  return cudf::jni::mixed_join_gather_single_map(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [&size_info](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+                   cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+                   cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_semi_join(left_keys, right_keys, left_condition, right_condition,
+                                          condition, nulls_equal, size_info);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_leftAntiJoinGatherMap(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jboolean compare_nulls_equal) {
+  return cudf::jni::join_gather_single_map(
+      env, j_left_keys, j_right_keys, compare_nulls_equal,
+      [](cudf::table_view const &left, cudf::table_view const &right, cudf::null_equality nulleq) {
+        return cudf::left_anti_join(left, right, nulleq);
+      });
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_conditionalLeftAntiJoinRowCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  JNI_NULL_CHECK(env, j_left_table, "left_table is null", 0);
+  JNI_NULL_CHECK(env, j_right_table, "right_table is null", 0);
+  JNI_NULL_CHECK(env, j_condition, "condition is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto left_table = reinterpret_cast<cudf::table_view const *>(j_left_table);
+    auto right_table = reinterpret_cast<cudf::table_view const *>(j_right_table);
+    auto condition = reinterpret_cast<cudf::jni::ast::compiled_expr const *>(j_condition);
+    auto row_count = cudf::conditional_left_anti_join_size(*left_table, *right_table,
+                                                           condition->get_top_expression());
+    return static_cast<jlong>(row_count);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalLeftAntiJoinGatherMap(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition) {
+  return cudf::jni::cond_join_gather_single_map(
+      env, j_left_table, j_right_table, j_condition,
+      [](cudf::table_view const &left, cudf::table_view const &right,
+         cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_left_anti_join(left, right, cond_expr);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_conditionalLeftAntiJoinGatherMapWithCount(
+    JNIEnv *env, jclass, jlong j_left_table, jlong j_right_table, jlong j_condition,
+    jlong j_row_count) {
+  auto row_count = static_cast<std::size_t>(j_row_count);
+  return cudf::jni::cond_join_gather_single_map(
+      env, j_left_table, j_right_table, j_condition,
+      [row_count](cudf::table_view const &left, cudf::table_view const &right,
+                  cudf::ast::expression const &cond_expr) {
+        return cudf::conditional_left_anti_join(left, right, cond_expr, row_count);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftAntiJoinSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_size(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_anti_join_size(left_keys, right_keys, left_condition,
+                                               right_condition, condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftAntiJoinGatherMap(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal) {
+  return cudf::jni::mixed_join_gather_single_map(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+         cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+         cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_anti_join(left_keys, right_keys, left_condition, right_condition,
+                                          condition, nulls_equal);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_mixedLeftAntiJoinGatherMapWithSize(
+    JNIEnv *env, jclass, jlong j_left_keys, jlong j_right_keys, jlong j_left_condition,
+    jlong j_right_condition, jlong j_condition, jboolean j_nulls_equal, jlong j_output_row_count,
+    jlong j_matches_view) {
+  auto size_info = cudf::jni::get_mixed_size_info(env, j_output_row_count, j_matches_view);
+  return cudf::jni::mixed_join_gather_single_map(
+      env, j_left_keys, j_right_keys, j_left_condition, j_right_condition, j_condition,
+      j_nulls_equal,
+      [&size_info](cudf::table_view const &left_keys, cudf::table_view const &right_keys,
+                   cudf::table_view const &left_condition, cudf::table_view const &right_condition,
+                   cudf::ast::expression const &condition, cudf::null_equality nulls_equal) {
+        return cudf::mixed_left_anti_join(left_keys, right_keys, left_condition, right_condition,
+                                          condition, nulls_equal, size_info);
+      });
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_crossJoin(JNIEnv *env, jclass,
+                                                                 jlong left_table,
+                                                                 jlong right_table) {
+  JNI_NULL_CHECK(env, left_table, "left_table is null", NULL);
+  JNI_NULL_CHECK(env, right_table, "right_table is null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const left = reinterpret_cast<cudf::table_view const *>(left_table);
+    auto const right = reinterpret_cast<cudf::table_view const *>(right_table);
+    return convert_table_for_return(env, cudf::cross_join(*left, *right));
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_interleaveColumns(JNIEnv *env, jclass,
+                                                                    jlongArray j_cudf_table_view) {
+
+  JNI_NULL_CHECK(env, j_cudf_table_view, "table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::table_view *table_view = reinterpret_cast<cudf::table_view *>(j_cudf_table_view);
+    return release_as_jlong(cudf::interleave_columns(*table_view));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_concatenate(JNIEnv *env, jclass,
+                                                                   jlongArray table_handles) {
+  JNI_NULL_CHECK(env, table_handles, "input tables are null", NULL);
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jpointerArray<cudf::table_view> tables(env, table_handles);
+    std::vector<cudf::table_view> const to_concat = tables.get_dereferenced();
+    return convert_table_for_return(env, cudf::concatenate(to_concat));
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_partition(JNIEnv *env, jclass,
+                                                                 jlong input_table,
+                                                                 jlong partition_column,
+                                                                 jint number_of_partitions,
+                                                                 jintArray output_offsets) {
+
+  JNI_NULL_CHECK(env, input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, partition_column, "partition_column is null", NULL);
+  JNI_NULL_CHECK(env, output_offsets, "output_offsets is null", NULL);
+  JNI_ARG_CHECK(env, number_of_partitions > 0, "number_of_partitions is zero", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const n_input_table = reinterpret_cast<cudf::table_view const *>(input_table);
+    auto const n_part_column = reinterpret_cast<cudf::column_view const *>(partition_column);
+
+    auto [partitioned_table, partition_offsets] =
+        cudf::partition(*n_input_table, *n_part_column, number_of_partitions);
+
+    // for what ever reason partition returns the length of the result at then
+    // end and hash partition/round robin do not, so skip the last entry for
+    // consistency
+    cudf::jni::native_jintArray n_output_offsets(env, output_offsets);
+    std::copy(partition_offsets.begin(), partition_offsets.end() - 1, n_output_offsets.begin());
+
+    return convert_table_for_return(env, partitioned_table);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_hashPartition(
+    JNIEnv *env, jclass, jlong input_table, jintArray columns_to_hash, jint hash_function,
+    jint number_of_partitions, jint seed, jintArray output_offsets) {
+
+  JNI_NULL_CHECK(env, input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, columns_to_hash, "columns_to_hash is null", NULL);
+  JNI_NULL_CHECK(env, output_offsets, "output_offsets is null", NULL);
+  JNI_ARG_CHECK(env, number_of_partitions > 0, "number_of_partitions is zero", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const hash_func = static_cast<cudf::hash_id>(hash_function);
+    auto const hash_seed = static_cast<uint32_t>(seed);
+    auto const n_input_table = reinterpret_cast<cudf::table_view const *>(input_table);
+    cudf::jni::native_jintArray n_columns_to_hash(env, columns_to_hash);
+    JNI_ARG_CHECK(env, n_columns_to_hash.size() > 0, "columns_to_hash is zero", NULL);
+
+    std::vector<cudf::size_type> columns_to_hash_vec(n_columns_to_hash.begin(),
+                                                     n_columns_to_hash.end());
+
+    auto [partitioned_table, partition_offsets] = cudf::hash_partition(
+        *n_input_table, columns_to_hash_vec, number_of_partitions, hash_func, hash_seed);
+
+    cudf::jni::native_jintArray n_output_offsets(env, output_offsets);
+    std::copy(partition_offsets.begin(), partition_offsets.end(), n_output_offsets.begin());
+
+    return convert_table_for_return(env, partitioned_table);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_roundRobinPartition(
+    JNIEnv *env, jclass, jlong input_table, jint num_partitions, jint start_partition,
+    jintArray output_offsets) {
+  JNI_NULL_CHECK(env, input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, output_offsets, "output_offsets is null", NULL);
+  JNI_ARG_CHECK(env, num_partitions > 0, "num_partitions <= 0", NULL);
+  JNI_ARG_CHECK(env, start_partition >= 0, "start_partition is negative", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto n_input_table = reinterpret_cast<cudf::table_view *>(input_table);
+
+    auto [partitioned_table, partition_offsets] =
+        cudf::round_robin_partition(*n_input_table, num_partitions, start_partition);
+
+    cudf::jni::native_jintArray n_output_offsets(env, output_offsets);
+    std::copy(partition_offsets.begin(), partition_offsets.end(), n_output_offsets.begin());
+
+    return convert_table_for_return(env, partitioned_table);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_groupByAggregate(
+    JNIEnv *env, jclass, jlong input_table, jintArray keys, jintArray aggregate_column_indices,
+    jlongArray agg_instances, jboolean ignore_null_keys, jboolean jkey_sorted,
+    jbooleanArray jkeys_sort_desc, jbooleanArray jkeys_null_first) {
+  JNI_NULL_CHECK(env, input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, keys, "input keys are null", NULL);
+  JNI_NULL_CHECK(env, aggregate_column_indices, "input aggregate_column_indices are null", NULL);
+  JNI_NULL_CHECK(env, agg_instances, "agg_instances are null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::table_view *n_input_table = reinterpret_cast<cudf::table_view *>(input_table);
+    cudf::jni::native_jintArray n_keys(env, keys);
+    cudf::jni::native_jintArray n_values(env, aggregate_column_indices);
+    cudf::jni::native_jpointerArray<cudf::aggregation> n_agg_instances(env, agg_instances);
+    std::vector<cudf::column_view> n_keys_cols;
+    n_keys_cols.reserve(n_keys.size());
+    for (int i = 0; i < n_keys.size(); i++) {
+      n_keys_cols.push_back(n_input_table->column(n_keys[i]));
+    }
+
+    cudf::table_view n_keys_table(n_keys_cols);
+    auto column_order = cudf::jni::resolve_column_order(env, jkeys_sort_desc, n_keys.size());
+    auto null_precedence = cudf::jni::resolve_null_precedence(env, jkeys_null_first, n_keys.size());
+    cudf::groupby::groupby grouper(
+        n_keys_table, ignore_null_keys ? cudf::null_policy::EXCLUDE : cudf::null_policy::INCLUDE,
+        jkey_sorted ? cudf::sorted::YES : cudf::sorted::NO, column_order, null_precedence);
+
+    // Aggregates are passed in already grouped by column, so we just need to fill it in
+    // as we go.
+    std::vector<cudf::groupby::aggregation_request> requests;
+
+    int previous_index = -1;
+    for (int i = 0; i < n_values.size(); i++) {
+      cudf::groupby::aggregation_request req;
+      int col_index = n_values[i];
+
+      cudf::groupby_aggregation *agg =
+          dynamic_cast<cudf::groupby_aggregation *>(n_agg_instances[i]);
+      JNI_ARG_CHECK(env, agg != nullptr, "aggregation is not an instance of groupby_aggregation",
+                    nullptr);
+      std::unique_ptr<cudf::groupby_aggregation> cloned(
+          dynamic_cast<cudf::groupby_aggregation *>(agg->clone().release()));
+
+      if (col_index == previous_index) {
+        requests.back().aggregations.push_back(std::move(cloned));
+      } else {
+        req.values = n_input_table->column(col_index);
+        req.aggregations.push_back(std::move(cloned));
+        requests.push_back(std::move(req));
+      }
+      previous_index = col_index;
+    }
+
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::groupby::aggregation_result>> result =
+        grouper.aggregate(requests);
+
+    std::vector<std::unique_ptr<cudf::column>> result_columns;
+    int agg_result_size = result.second.size();
+    for (int agg_result_index = 0; agg_result_index < agg_result_size; agg_result_index++) {
+      int col_agg_size = result.second[agg_result_index].results.size();
+      for (int col_agg_index = 0; col_agg_index < col_agg_size; col_agg_index++) {
+        result_columns.push_back(std::move(result.second[agg_result_index].results[col_agg_index]));
+      }
+    }
+    return convert_table_for_return(env, result.first, std::move(result_columns));
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_groupByScan(
+    JNIEnv *env, jclass, jlong input_table, jintArray keys, jintArray aggregate_column_indices,
+    jlongArray agg_instances, jboolean ignore_null_keys, jboolean jkey_sorted,
+    jbooleanArray jkeys_sort_desc, jbooleanArray jkeys_null_first) {
+  JNI_NULL_CHECK(env, input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, keys, "input keys are null", NULL);
+  JNI_NULL_CHECK(env, aggregate_column_indices, "input aggregate_column_indices are null", NULL);
+  JNI_NULL_CHECK(env, agg_instances, "agg_instances are null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::table_view *n_input_table = reinterpret_cast<cudf::table_view *>(input_table);
+    cudf::jni::native_jintArray n_keys(env, keys);
+    cudf::jni::native_jintArray n_values(env, aggregate_column_indices);
+    cudf::jni::native_jpointerArray<cudf::aggregation> n_agg_instances(env, agg_instances);
+    std::vector<cudf::column_view> n_keys_cols;
+    n_keys_cols.reserve(n_keys.size());
+    for (int i = 0; i < n_keys.size(); i++) {
+      n_keys_cols.push_back(n_input_table->column(n_keys[i]));
+    }
+
+    cudf::table_view n_keys_table(n_keys_cols);
+    auto column_order = cudf::jni::resolve_column_order(env, jkeys_sort_desc, n_keys.size());
+    auto null_precedence = cudf::jni::resolve_null_precedence(env, jkeys_null_first, n_keys.size());
+    cudf::groupby::groupby grouper(
+        n_keys_table, ignore_null_keys ? cudf::null_policy::EXCLUDE : cudf::null_policy::INCLUDE,
+        jkey_sorted ? cudf::sorted::YES : cudf::sorted::NO, column_order, null_precedence);
+
+    // Aggregates are passed in already grouped by column, so we just need to fill it in
+    // as we go.
+    std::vector<cudf::groupby::scan_request> requests;
+
+    int previous_index = -1;
+    for (int i = 0; i < n_values.size(); i++) {
+      cudf::groupby::scan_request req;
+      int col_index = n_values[i];
+
+      cudf::groupby_scan_aggregation *agg =
+          dynamic_cast<cudf::groupby_scan_aggregation *>(n_agg_instances[i]);
+      JNI_ARG_CHECK(env, agg != nullptr,
+                    "aggregation is not an instance of groupby_scan_aggregation", nullptr);
+      std::unique_ptr<cudf::groupby_scan_aggregation> cloned(
+          dynamic_cast<cudf::groupby_scan_aggregation *>(agg->clone().release()));
+
+      if (col_index == previous_index) {
+        requests.back().aggregations.push_back(std::move(cloned));
+      } else {
+        req.values = n_input_table->column(col_index);
+        req.aggregations.push_back(std::move(cloned));
+        requests.push_back(std::move(req));
+      }
+      previous_index = col_index;
+    }
+
+    std::pair<std::unique_ptr<cudf::table>, std::vector<cudf::groupby::aggregation_result>> result =
+        grouper.scan(requests);
+
+    std::vector<std::unique_ptr<cudf::column>> result_columns;
+    int agg_result_size = result.second.size();
+    for (int agg_result_index = 0; agg_result_index < agg_result_size; agg_result_index++) {
+      int col_agg_size = result.second[agg_result_index].results.size();
+      for (int col_agg_index = 0; col_agg_index < col_agg_size; col_agg_index++) {
+        result_columns.push_back(std::move(result.second[agg_result_index].results[col_agg_index]));
+      }
+    }
+    return convert_table_for_return(env, result.first, std::move(result_columns));
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_groupByReplaceNulls(
+    JNIEnv *env, jclass, jlong input_table, jintArray keys, jintArray replace_column_indices,
+    jbooleanArray is_preceding, jboolean ignore_null_keys, jboolean jkey_sorted,
+    jbooleanArray jkeys_sort_desc, jbooleanArray jkeys_null_first) {
+  JNI_NULL_CHECK(env, input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, keys, "input keys are null", NULL);
+  JNI_NULL_CHECK(env, replace_column_indices, "input replace_column_indices are null", NULL);
+  JNI_NULL_CHECK(env, is_preceding, "is_preceding are null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::table_view *n_input_table = reinterpret_cast<cudf::table_view *>(input_table);
+    cudf::jni::native_jintArray n_keys(env, keys);
+    cudf::jni::native_jintArray n_values(env, replace_column_indices);
+    cudf::jni::native_jbooleanArray n_is_preceding(env, is_preceding);
+    std::vector<cudf::column_view> n_keys_cols;
+    n_keys_cols.reserve(n_keys.size());
+    for (int i = 0; i < n_keys.size(); i++) {
+      n_keys_cols.push_back(n_input_table->column(n_keys[i]));
+    }
+
+    cudf::table_view n_keys_table(n_keys_cols);
+    auto column_order = cudf::jni::resolve_column_order(env, jkeys_sort_desc, n_keys.size());
+    auto null_precedence = cudf::jni::resolve_null_precedence(env, jkeys_null_first, n_keys.size());
+    cudf::groupby::groupby grouper(
+        n_keys_table, ignore_null_keys ? cudf::null_policy::EXCLUDE : cudf::null_policy::INCLUDE,
+        jkey_sorted ? cudf::sorted::YES : cudf::sorted::NO, column_order, null_precedence);
+
+    // Aggregates are passed in already grouped by column, so we just need to fill it in
+    // as we go.
+    std::vector<cudf::column_view> n_replace_cols;
+    n_replace_cols.reserve(n_values.size());
+    for (int i = 0; i < n_values.size(); i++) {
+      n_replace_cols.push_back(n_input_table->column(n_values[i]));
+    }
+    cudf::table_view n_replace_table(n_replace_cols);
+
+    std::vector<cudf::replace_policy> policies = n_is_preceding.transform_if_else(
+        cudf::replace_policy::PRECEDING, cudf::replace_policy::FOLLOWING);
+
+    auto [keys, results] = grouper.replace_nulls(n_replace_table, policies);
+    return convert_table_for_return(env, keys, results);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_filter(JNIEnv *env, jclass,
+                                                              jlong input_jtable, jlong mask_jcol) {
+  JNI_NULL_CHECK(env, input_jtable, "input table is null", 0);
+  JNI_NULL_CHECK(env, mask_jcol, "mask column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    auto const mask = reinterpret_cast<cudf::column_view const *>(mask_jcol);
+    return convert_table_for_return(env, cudf::apply_boolean_mask(*input, *mask));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jint JNICALL Java_ai_rapids_cudf_Table_distinctCount(JNIEnv *env, jclass,
+                                                               jlong input_jtable,
+                                                               jboolean nulls_equal) {
+  JNI_NULL_CHECK(env, input_jtable, "input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(input_jtable);
+
+    return cudf::distinct_count(*input, nulls_equal ? cudf::null_equality::EQUAL :
+                                                      cudf::null_equality::UNEQUAL);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_dropDuplicates(JNIEnv *env, jclass,
+                                                                      jlong input_jtable,
+                                                                      jintArray key_columns,
+                                                                      jint keep,
+                                                                      jboolean nulls_equal) {
+  JNI_NULL_CHECK(env, input_jtable, "input table is null", 0);
+  JNI_NULL_CHECK(env, key_columns, "input key_columns is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(input_jtable);
+
+    static_assert(sizeof(jint) == sizeof(cudf::size_type), "Integer types mismatched.");
+    auto const native_keys_indices = cudf::jni::native_jintArray(env, key_columns);
+    auto const keys_indices =
+        std::vector<cudf::size_type>(native_keys_indices.begin(), native_keys_indices.end());
+    auto const keep_option = [&] {
+      switch (keep) {
+        case 0: return cudf::duplicate_keep_option::KEEP_ANY;
+        case 1: return cudf::duplicate_keep_option::KEEP_FIRST;
+        case 2: return cudf::duplicate_keep_option::KEEP_LAST;
+        case 3: return cudf::duplicate_keep_option::KEEP_NONE;
+        default:
+          JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "Invalid `keep` option",
+                        cudf::duplicate_keep_option::KEEP_ANY);
+      }
+    }();
+
+    auto result =
+        cudf::distinct(*input, keys_indices, keep_option,
+                       nulls_equal ? cudf::null_equality::EQUAL : cudf::null_equality::UNEQUAL,
+                       cudf::nan_equality::ALL_EQUAL, rmm::mr::get_current_device_resource());
+    return convert_table_for_return(env, result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_gather(JNIEnv *env, jclass, jlong j_input,
+                                                              jlong j_map, jboolean check_bounds) {
+  JNI_NULL_CHECK(env, j_input, "input table is null", 0);
+  JNI_NULL_CHECK(env, j_map, "map column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(j_input);
+    auto const map = reinterpret_cast<cudf::column_view const *>(j_map);
+    auto bounds_policy =
+        check_bounds ? cudf::out_of_bounds_policy::NULLIFY : cudf::out_of_bounds_policy::DONT_CHECK;
+    return convert_table_for_return(env, cudf::gather(*input, *map, bounds_policy));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL
+Java_ai_rapids_cudf_Table_convertToRowsFixedWidthOptimized(JNIEnv *env, jclass, jlong input_table) {
+  JNI_NULL_CHECK(env, input_table, "input table is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const n_input_table = reinterpret_cast<cudf::table_view const *>(input_table);
+    std::vector<std::unique_ptr<cudf::column>> cols =
+        cudf::jni::convert_to_rows_fixed_width_optimized(*n_input_table);
+    int num_columns = cols.size();
+    cudf::jni::native_jlongArray outcol_handles(env, num_columns);
+    std::transform(cols.begin(), cols.end(), outcol_handles.begin(),
+                   [](auto &col) { return release_as_jlong(col); });
+    return outcol_handles.get_jArray();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_scatterTable(JNIEnv *env, jclass,
+                                                                    jlong j_input, jlong j_map,
+                                                                    jlong j_target) {
+  JNI_NULL_CHECK(env, j_input, "input table is null", 0);
+  JNI_NULL_CHECK(env, j_map, "map column is null", 0);
+  JNI_NULL_CHECK(env, j_target, "target table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(j_input);
+    auto const map = reinterpret_cast<cudf::column_view const *>(j_map);
+    auto const target = reinterpret_cast<cudf::table_view const *>(j_target);
+    return convert_table_for_return(env, cudf::scatter(*input, *map, *target));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_scatterScalars(JNIEnv *env, jclass,
+                                                                      jlongArray j_input,
+                                                                      jlong j_map, jlong j_target) {
+  JNI_NULL_CHECK(env, j_input, "input scalars array is null", 0);
+  JNI_NULL_CHECK(env, j_map, "map column is null", 0);
+  JNI_NULL_CHECK(env, j_target, "target table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const scalars_array = cudf::jni::native_jpointerArray<cudf::scalar>(env, j_input);
+    std::vector<std::reference_wrapper<cudf::scalar const>> input;
+    std::transform(scalars_array.begin(), scalars_array.end(), std::back_inserter(input),
+                   [](auto &scalar) { return std::ref(*scalar); });
+    auto const map = reinterpret_cast<cudf::column_view const *>(j_map);
+    auto const target = reinterpret_cast<cudf::table_view const *>(j_target);
+    return convert_table_for_return(env, cudf::scatter(input, *map, *target));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_convertToRows(JNIEnv *env, jclass,
+                                                                     jlong input_table) {
+  JNI_NULL_CHECK(env, input_table, "input table is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const n_input_table = reinterpret_cast<cudf::table_view const *>(input_table);
+    std::vector<std::unique_ptr<cudf::column>> cols = cudf::jni::convert_to_rows(*n_input_table);
+    int num_columns = cols.size();
+    cudf::jni::native_jlongArray outcol_handles(env, num_columns);
+    std::transform(cols.begin(), cols.end(), outcol_handles.begin(),
+                   [](auto &col) { return release_as_jlong(col); });
+    return outcol_handles.get_jArray();
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_convertFromRowsFixedWidthOptimized(
+    JNIEnv *env, jclass, jlong input_column, jintArray types, jintArray scale) {
+  JNI_NULL_CHECK(env, input_column, "input column is null", 0);
+  JNI_NULL_CHECK(env, types, "types is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::lists_column_view const list_input{*reinterpret_cast<cudf::column_view *>(input_column)};
+    cudf::jni::native_jintArray n_types(env, types);
+    cudf::jni::native_jintArray n_scale(env, scale);
+    if (n_types.size() != n_scale.size()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "types and scales must match size",
+                    NULL);
+    }
+    std::vector<cudf::data_type> types_vec;
+    std::transform(n_types.begin(), n_types.end(), n_scale.begin(), std::back_inserter(types_vec),
+                   [](jint type, jint scale) { return cudf::jni::make_data_type(type, scale); });
+    std::unique_ptr<cudf::table> result =
+        cudf::jni::convert_from_rows_fixed_width_optimized(list_input, types_vec);
+    return convert_table_for_return(env, result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_convertFromRows(JNIEnv *env, jclass,
+                                                                       jlong input_column,
+                                                                       jintArray types,
+                                                                       jintArray scale) {
+  JNI_NULL_CHECK(env, input_column, "input column is null", 0);
+  JNI_NULL_CHECK(env, types, "types is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::lists_column_view const list_input{*reinterpret_cast<cudf::column_view *>(input_column)};
+    cudf::jni::native_jintArray n_types(env, types);
+    cudf::jni::native_jintArray n_scale(env, scale);
+    if (n_types.size() != n_scale.size()) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "types and scales must match size",
+                    NULL);
+    }
+    std::vector<cudf::data_type> types_vec;
+    std::transform(n_types.begin(), n_types.end(), n_scale.begin(), std::back_inserter(types_vec),
+                   [](jint type, jint scale) { return cudf::jni::make_data_type(type, scale); });
+    std::unique_ptr<cudf::table> result = cudf::jni::convert_from_rows(list_input, types_vec);
+    return convert_table_for_return(env, result);
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_repeatStaticCount(JNIEnv *env, jclass,
+                                                                         jlong input_jtable,
+                                                                         jint count) {
+  JNI_NULL_CHECK(env, input_jtable, "input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    return convert_table_for_return(env, cudf::repeat(*input, count));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_repeatColumnCount(JNIEnv *env, jclass,
+                                                                         jlong input_jtable,
+                                                                         jlong count_jcol) {
+  JNI_NULL_CHECK(env, input_jtable, "input table is null", 0);
+  JNI_NULL_CHECK(env, count_jcol, "count column is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    auto const count = reinterpret_cast<cudf::column_view const *>(count_jcol);
+    return convert_table_for_return(env, cudf::repeat(*input, *count));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_bound(JNIEnv *env, jclass, jlong input_jtable,
+                                                        jlong values_jtable,
+                                                        jbooleanArray desc_flags,
+                                                        jbooleanArray are_nulls_smallest,
+                                                        jboolean is_upper_bound) {
+  JNI_NULL_CHECK(env, input_jtable, "input table is null", 0);
+  JNI_NULL_CHECK(env, values_jtable, "values table is null", 0);
+  using cudf::column;
+  using cudf::table_view;
+  try {
+    cudf::jni::auto_set_device(env);
+    table_view *input = reinterpret_cast<table_view *>(input_jtable);
+    table_view *values = reinterpret_cast<table_view *>(values_jtable);
+    cudf::jni::native_jbooleanArray const n_desc_flags(env, desc_flags);
+    cudf::jni::native_jbooleanArray const n_are_nulls_smallest(env, are_nulls_smallest);
+
+    std::vector<cudf::order> column_desc_flags{
+        n_desc_flags.transform_if_else(cudf::order::DESCENDING, cudf::order::ASCENDING)};
+    std::vector<cudf::null_order> column_null_orders{
+        n_are_nulls_smallest.transform_if_else(cudf::null_order::BEFORE, cudf::null_order::AFTER)};
+
+    JNI_ARG_CHECK(env, (column_desc_flags.size() == column_null_orders.size()),
+                  "null-order and sort-order size mismatch", 0);
+
+    return release_as_jlong(
+        is_upper_bound ? cudf::upper_bound(*input, *values, column_desc_flags, column_null_orders) :
+                         cudf::lower_bound(*input, *values, column_desc_flags, column_null_orders));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jobjectArray JNICALL Java_ai_rapids_cudf_Table_contiguousSplit(JNIEnv *env, jclass,
+                                                                         jlong input_table,
+                                                                         jintArray split_indices) {
+  JNI_NULL_CHECK(env, input_table, "native handle is null", 0);
+  JNI_NULL_CHECK(env, split_indices, "split indices are null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::table_view *n_table = reinterpret_cast<cudf::table_view *>(input_table);
+    cudf::jni::native_jintArray n_split_indices(env, split_indices);
+
+    std::vector<cudf::size_type> indices(n_split_indices.data(),
+                                         n_split_indices.data() + n_split_indices.size());
+
+    std::vector<cudf::packed_table> result = cudf::contiguous_split(*n_table, indices);
+    cudf::jni::native_jobjectArray<jobject> n_result =
+        cudf::jni::contiguous_table_array(env, result.size());
+    for (size_t i = 0; i < result.size(); i++) {
+      n_result.set(
+          i, cudf::jni::contiguous_table_from(env, result[i].data, result[i].table.num_rows()));
+    }
+    return n_result.wrapped();
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_makeChunkedPack(JNIEnv *env, jclass,
+                                                                  jlong input_table,
+                                                                  jlong bounce_buffer_size,
+                                                                  jlong memoryResourceHandle) {
+  JNI_NULL_CHECK(env, input_table, "native handle is null", 0);
+
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::table_view *n_table = reinterpret_cast<cudf::table_view *>(input_table);
+    // `temp_mr` is the memory resource that `cudf::chunked_pack` will use to create temporary
+    // and scratch memory only.
+    auto temp_mr = memoryResourceHandle != 0 ?
+                       reinterpret_cast<rmm::mr::device_memory_resource *>(memoryResourceHandle) :
+                       rmm::mr::get_current_device_resource();
+    auto chunked_pack = cudf::chunked_pack::create(*n_table, bounce_buffer_size, temp_mr);
+    return reinterpret_cast<jlong>(chunked_pack.release());
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_rollingWindowAggregate(
+    JNIEnv *env, jclass, jlong j_input_table, jintArray j_keys, jlongArray j_default_output,
+    jintArray j_aggregate_column_indices, jlongArray j_agg_instances, jintArray j_min_periods,
+    jintArray j_preceding, jintArray j_following, jbooleanArray j_unbounded_preceding,
+    jbooleanArray j_unbounded_following, jboolean ignore_null_keys) {
+
+  JNI_NULL_CHECK(env, j_input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, j_keys, "input keys are null", NULL);
+  JNI_NULL_CHECK(env, j_aggregate_column_indices, "input aggregate_column_indices are null", NULL);
+  JNI_NULL_CHECK(env, j_agg_instances, "agg_instances are null", NULL);
+  JNI_NULL_CHECK(env, j_default_output, "default_outputs are null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+
+    using cudf::jni::valid_window_parameters;
+
+    // Convert from j-types to native.
+    cudf::table_view *input_table{reinterpret_cast<cudf::table_view *>(j_input_table)};
+    cudf::jni::native_jintArray keys{env, j_keys};
+    cudf::jni::native_jintArray values{env, j_aggregate_column_indices};
+    cudf::jni::native_jpointerArray<cudf::aggregation> agg_instances(env, j_agg_instances);
+    cudf::jni::native_jpointerArray<cudf::column_view> default_output(env, j_default_output);
+    cudf::jni::native_jintArray min_periods{env, j_min_periods};
+    cudf::jni::native_jintArray preceding{env, j_preceding};
+    cudf::jni::native_jintArray following{env, j_following};
+    cudf::jni::native_jbooleanArray unbounded_preceding{env, j_unbounded_preceding};
+    cudf::jni::native_jbooleanArray unbounded_following{env, j_unbounded_following};
+
+    if (not valid_window_parameters(values, agg_instances, min_periods, preceding, following)) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                    "Number of aggregation columns must match number of agg ops, and window-specs",
+                    nullptr);
+    }
+
+    // Extract table-view.
+    cudf::table_view groupby_keys{
+        input_table->select(std::vector<cudf::size_type>(keys.data(), keys.data() + keys.size()))};
+
+    std::vector<std::unique_ptr<cudf::column>> result_columns;
+    for (int i(0); i < values.size(); ++i) {
+      cudf::rolling_aggregation *agg = dynamic_cast<cudf::rolling_aggregation *>(agg_instances[i]);
+      JNI_ARG_CHECK(env, agg != nullptr, "aggregation is not an instance of rolling_aggregation",
+                    nullptr);
+
+      int agg_column_index = values[i];
+      auto const preceding_window_bounds = unbounded_preceding[i] ?
+                                               cudf::window_bounds::unbounded() :
+                                               cudf::window_bounds::get(preceding[i]);
+      auto const following_window_bounds = unbounded_following[i] ?
+                                               cudf::window_bounds::unbounded() :
+                                               cudf::window_bounds::get(following[i]);
+
+      if (default_output[i] != nullptr) {
+        result_columns.emplace_back(cudf::grouped_rolling_window(
+            groupby_keys, input_table->column(agg_column_index), *default_output[i],
+            preceding_window_bounds, following_window_bounds, min_periods[i], *agg));
+      } else {
+        result_columns.emplace_back(cudf::grouped_rolling_window(
+            groupby_keys, input_table->column(agg_column_index), preceding_window_bounds,
+            following_window_bounds, min_periods[i], *agg));
+      }
+    }
+
+    auto result_table = std::make_unique<cudf::table>(std::move(result_columns));
+    return convert_table_for_return(env, result_table);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_rangeRollingWindowAggregate(
+    JNIEnv *env, jclass, jlong j_input_table, jintArray j_keys, jintArray j_orderby_column_indices,
+    jbooleanArray j_is_orderby_ascending, jintArray j_aggregate_column_indices,
+    jlongArray j_agg_instances, jintArray j_min_periods, jlongArray j_preceding,
+    jlongArray j_following, jintArray j_preceding_extent, jintArray j_following_extent,
+    jboolean ignore_null_keys) {
+
+  JNI_NULL_CHECK(env, j_input_table, "input table is null", NULL);
+  JNI_NULL_CHECK(env, j_keys, "input keys are null", NULL);
+  JNI_NULL_CHECK(env, j_orderby_column_indices, "input orderby_column_indices are null", NULL);
+  JNI_NULL_CHECK(env, j_is_orderby_ascending, "input orderby_ascending is null", NULL);
+  JNI_NULL_CHECK(env, j_aggregate_column_indices, "input aggregate_column_indices are null", NULL);
+  JNI_NULL_CHECK(env, j_agg_instances, "agg_instances are null", NULL);
+  JNI_NULL_CHECK(env, j_preceding, "preceding are null", NULL);
+  JNI_NULL_CHECK(env, j_following, "following are null", NULL);
+
+  try {
+    cudf::jni::auto_set_device(env);
+
+    using cudf::jni::valid_window_parameters;
+
+    // Convert from j-types to native.
+    cudf::table_view *input_table{reinterpret_cast<cudf::table_view *>(j_input_table)};
+    cudf::jni::native_jintArray keys{env, j_keys};
+    cudf::jni::native_jintArray orderbys{env, j_orderby_column_indices};
+    cudf::jni::native_jbooleanArray orderbys_ascending{env, j_is_orderby_ascending};
+    cudf::jni::native_jintArray values{env, j_aggregate_column_indices};
+    cudf::jni::native_jpointerArray<cudf::aggregation> agg_instances(env, j_agg_instances);
+    cudf::jni::native_jintArray min_periods{env, j_min_periods};
+    cudf::jni::native_jintArray preceding_extent{env, j_preceding_extent};
+    cudf::jni::native_jintArray following_extent{env, j_following_extent};
+    cudf::jni::native_jpointerArray<cudf::scalar> preceding(env, j_preceding);
+    cudf::jni::native_jpointerArray<cudf::scalar> following(env, j_following);
+
+    if (not valid_window_parameters(values, agg_instances, min_periods, preceding, following)) {
+      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException",
+                    "Number of aggregation columns must match number of agg ops, and window-specs",
+                    nullptr);
+    }
+
+    // Extract table-view.
+    cudf::table_view groupby_keys{
+        input_table->select(std::vector<cudf::size_type>(keys.data(), keys.data() + keys.size()))};
+
+    std::vector<std::unique_ptr<cudf::column>> result_columns;
+    for (int i(0); i < values.size(); ++i) {
+      int agg_column_index = values[i];
+      cudf::column_view const &order_by_column = input_table->column(orderbys[i]);
+      cudf::data_type order_by_type = order_by_column.type();
+      cudf::data_type duration_type = order_by_type;
+
+      // Range extents are defined as:
+      // a) 0 == CURRENT ROW
+      // b) 1 == BOUNDED
+      // c) 2 == UNBOUNDED
+      // Must set unbounded_type for only the BOUNDED case.
+      auto constexpr CURRENT_ROW = 0;
+      auto constexpr BOUNDED = 1;
+      auto constexpr UNBOUNDED = 2;
+      if (preceding_extent[i] != BOUNDED || following_extent[i] != BOUNDED) {
+        switch (order_by_type.id()) {
+          case cudf::type_id::TIMESTAMP_DAYS:
+            duration_type = cudf::data_type{cudf::type_id::DURATION_DAYS};
+            break;
+          case cudf::type_id::TIMESTAMP_SECONDS:
+            duration_type = cudf::data_type{cudf::type_id::DURATION_SECONDS};
+            break;
+          case cudf::type_id::TIMESTAMP_MILLISECONDS:
+            duration_type = cudf::data_type{cudf::type_id::DURATION_MILLISECONDS};
+            break;
+          case cudf::type_id::TIMESTAMP_MICROSECONDS:
+            duration_type = cudf::data_type{cudf::type_id::DURATION_MICROSECONDS};
+            break;
+          case cudf::type_id::TIMESTAMP_NANOSECONDS:
+            duration_type = cudf::data_type{cudf::type_id::DURATION_NANOSECONDS};
+            break;
+          default: break;
+        }
+      }
+
+      cudf::rolling_aggregation *agg = dynamic_cast<cudf::rolling_aggregation *>(agg_instances[i]);
+      JNI_ARG_CHECK(env, agg != nullptr, "aggregation is not an instance of rolling_aggregation",
+                    nullptr);
+
+      auto const make_window_bounds = [&](auto const &range_extent, auto const *p_scalar) {
+        if (range_extent == CURRENT_ROW) {
+          return cudf::range_window_bounds::current_row(duration_type);
+        } else if (range_extent == UNBOUNDED) {
+          return cudf::range_window_bounds::unbounded(duration_type);
+        } else {
+          return cudf::range_window_bounds::get(*p_scalar);
+        }
+      };
+
+      result_columns.emplace_back(cudf::grouped_range_rolling_window(
+          groupby_keys, order_by_column,
+          orderbys_ascending[i] ? cudf::order::ASCENDING : cudf::order::DESCENDING,
+          input_table->column(agg_column_index),
+          make_window_bounds(preceding_extent[i], preceding[i]),
+          make_window_bounds(following_extent[i], following[i]), min_periods[i], *agg));
+    }
+
+    auto result_table = std::make_unique<cudf::table>(std::move(result_columns));
+    return convert_table_for_return(env, result_table);
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_explode(JNIEnv *env, jclass,
+                                                               jlong input_jtable,
+                                                               jint column_index) {
+  JNI_NULL_CHECK(env, input_jtable, "explode: input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    auto const col_index = static_cast<cudf::size_type>(column_index);
+    return convert_table_for_return(env, cudf::explode(*input_table, col_index));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_explodePosition(JNIEnv *env, jclass,
+                                                                       jlong input_jtable,
+                                                                       jint column_index) {
+  JNI_NULL_CHECK(env, input_jtable, "explode: input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    auto const col_index = static_cast<cudf::size_type>(column_index);
+    return convert_table_for_return(env, cudf::explode_position(*input_table, col_index));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_explodeOuter(JNIEnv *env, jclass,
+                                                                    jlong input_jtable,
+                                                                    jint column_index) {
+  JNI_NULL_CHECK(env, input_jtable, "explode: input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    auto const col_index = static_cast<cudf::size_type>(column_index);
+    return convert_table_for_return(env, cudf::explode_outer(*input_table, col_index));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_explodeOuterPosition(JNIEnv *env, jclass,
+                                                                            jlong input_jtable,
+                                                                            jint column_index) {
+  JNI_NULL_CHECK(env, input_jtable, "explode: input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(input_jtable);
+    auto const col_index = static_cast<cudf::size_type>(column_index);
+    return convert_table_for_return(env, cudf::explode_outer_position(*input_table, col_index));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Table_rowBitCount(JNIEnv *env, jclass, jlong j_table) {
+  JNI_NULL_CHECK(env, j_table, "table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(j_table);
+    return release_as_jlong(cudf::row_bit_count(*input_table));
+  }
+  CATCH_STD(env, 0);
+}
+
+JNIEXPORT jobject JNICALL Java_ai_rapids_cudf_Table_contiguousSplitGroups(
+    JNIEnv *env, jclass, jlong jinput_table, jintArray jkey_indices, jboolean jignore_null_keys,
+    jboolean jkey_sorted, jbooleanArray jkeys_sort_desc, jbooleanArray jkeys_null_first,
+    jboolean genUniqKeys) {
+  JNI_NULL_CHECK(env, jinput_table, "table native handle is null", 0);
+  JNI_NULL_CHECK(env, jkey_indices, "key indices are null", 0);
+  // Two main steps to split the groups in the input table.
+  //    1) Calls `cudf::groupby::groupby::get_groups` to get the group offsets and
+  //       the grouped table.
+  //    2) Calls `cudf::contiguous_split` to execute the split over the grouped table
+  //       according to the group offsets.
+  try {
+    cudf::jni::auto_set_device(env);
+    cudf::jni::native_jintArray n_key_indices(env, jkey_indices);
+    auto const input_table = reinterpret_cast<cudf::table_view const *>(jinput_table);
+
+    // Prepares arguments for the groupby:
+    //   (keys, null_handling, keys_are_sorted, column_order, null_precedence)
+    std::vector<cudf::size_type> key_indices(n_key_indices.data(),
+                                             n_key_indices.data() + n_key_indices.size());
+    auto keys = input_table->select(key_indices);
+    auto null_handling =
+        jignore_null_keys ? cudf::null_policy::EXCLUDE : cudf::null_policy::INCLUDE;
+    auto keys_are_sorted = jkey_sorted ? cudf::sorted::YES : cudf::sorted::NO;
+    auto column_order = cudf::jni::resolve_column_order(env, jkeys_sort_desc, key_indices.size());
+    auto null_precedence =
+        cudf::jni::resolve_null_precedence(env, jkeys_null_first, key_indices.size());
+
+    // Constructs a groupby
+    cudf::groupby::groupby grouper(keys, null_handling, keys_are_sorted, column_order,
+                                   null_precedence);
+
+    // 1) Gets the groups(keys, offsets, values) from groupby.
+    //
+    // Uses only the non-key columns as the input values instead of the whole table,
+    // to avoid duplicated key columns in output of `get_groups`.
+    // The code looks like a little more complicated, but it can reduce the peak memory.
+    auto num_value_cols = input_table->num_columns() - key_indices.size();
+    std::vector<cudf::size_type> value_indices;
+    value_indices.reserve(num_value_cols);
+    // column indices start with 0.
+    cudf::size_type index = 0;
+    while (value_indices.size() < num_value_cols) {
+      if (std::find(key_indices.begin(), key_indices.end(), index) == key_indices.end()) {
+        // not key column, so adds it as value column.
+        value_indices.emplace_back(index);
+      }
+      index++;
+    }
+    cudf::table_view values_view = input_table->select(value_indices);
+    // execute grouping
+    cudf::groupby::groupby::groups groups = grouper.get_groups(values_view);
+
+    // When builds the table view from keys and values of 'groups', restores the
+    // original order of columns (same order with that in input table).
+    std::vector<cudf::column_view> grouped_cols(key_indices.size() + num_value_cols);
+    // key columns
+    auto key_view = groups.keys->view();
+    auto key_view_it = key_view.begin();
+    for (auto key_id : key_indices) {
+      grouped_cols.at(key_id) = std::move(*key_view_it);
+      key_view_it++;
+    }
+    // value columns
+    auto value_view = groups.values->view();
+    auto value_view_it = value_view.begin();
+    for (auto value_id : value_indices) {
+      grouped_cols.at(value_id) = std::move(*value_view_it);
+      value_view_it++;
+    }
+    cudf::table_view grouped_table(grouped_cols);
+    // When no key columns, uses the input table instead, because the output
+    // of 'get_groups' is empty.
+    auto &grouped_view = key_indices.empty() ? *input_table : grouped_table;
+
+    // Resolves the split indices from offsets vector directly to avoid copying. Since
+    // the offsets vector may be very large if there are too many small groups.
+    std::vector<cudf::size_type> &split_indices = groups.offsets;
+    // Offsets layout is [0, split indices..., num_rows] or [0] for empty keys, so
+    // need to removes the first and last elements. First remove last one.
+    split_indices.pop_back();
+
+    // generate uniq keys by using `gather` method, this means remove the duplicated keys
+    std::unique_ptr<cudf::table> group_by_result_table;
+    if (genUniqKeys) {
+      // generate gather map column from `split_indices`
+      auto begin = std::cbegin(split_indices);
+      auto end = std::cend(split_indices);
+      auto const size = cudf::distance(begin, end);
+      auto const vec = thrust::host_vector<cudf::size_type>(begin, end);
+      auto buf = rmm::device_buffer{vec.data(), size * sizeof(cudf::size_type),
+                                    cudf::get_default_stream()};
+      auto gather_map_col = std::make_unique<cudf::column>(
+          cudf::data_type{cudf::type_id::INT32}, size, std::move(buf), rmm::device_buffer{}, 0);
+
+      // gather the first key in each group to remove duplicated ones.
+      group_by_result_table = cudf::gather(groups.keys->view(), gather_map_col->view());
+    }
+
+    // remove the first 0 if it exists
+    if (!split_indices.empty()) {
+      split_indices.erase(split_indices.begin());
+    }
+
+    // 2) Splits the groups.
+    std::vector<cudf::packed_table> result = cudf::contiguous_split(grouped_view, split_indices);
+    // Release the grouped table right away after split done.
+    groups.keys.reset(nullptr);
+    groups.values.reset(nullptr);
+
+    //  Returns the split result.
+    cudf::jni::native_jobjectArray<jobject> n_result =
+        cudf::jni::contiguous_table_array(env, result.size());
+    for (size_t i = 0; i < result.size(); i++) {
+      n_result.set(
+          i, cudf::jni::contiguous_table_from(env, result[i].data, result[i].table.num_rows()));
+    }
+
+    jobjectArray groups_array = n_result.wrapped();
+
+    if (genUniqKeys) {
+      jlongArray keys_array = convert_table_for_return(env, group_by_result_table);
+      return cudf::jni::contig_split_group_by_result_from(env, groups_array, keys_array);
+    } else {
+      return cudf::jni::contig_split_group_by_result_from(env, groups_array);
+    }
+  }
+  CATCH_STD(env, NULL);
+}
+
+JNIEXPORT jlongArray JNICALL Java_ai_rapids_cudf_Table_sample(JNIEnv *env, jclass, jlong j_input,
+                                                              jlong n, jboolean replacement,
+                                                              jlong seed) {
+  JNI_NULL_CHECK(env, j_input, "input table is null", 0);
+  try {
+    cudf::jni::auto_set_device(env);
+    auto const input = reinterpret_cast<cudf::table_view const *>(j_input);
+    auto sample_with_replacement =
+        replacement ? cudf::sample_with_replacement::TRUE : cudf::sample_with_replacement::FALSE;
+    return convert_table_for_return(env, cudf::sample(*input, n, sample_with_replacement, seed));
+  }
+  CATCH_STD(env, 0);
+}
+} // extern "C"
diff --git a/java/src/main/native/src/aggregation128_utils.cu b/java/src/main/native/src/aggregation128_utils.cu
new file mode 100644
index 0000000..101a2ed
--- /dev/null
+++ b/java/src/main/native/src/aggregation128_utils.cu
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cstddef>
+#include <utility>
+#include <vector>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/utilities/error.hpp>
+#include <rmm/exec_policy.hpp>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+
+#include "aggregation128_utils.hpp"
+
+namespace {
+
+// Functor to reassemble a 128-bit value from four 64-bit chunks with overflow detection.
+class chunk_assembler : public thrust::unary_function<cudf::size_type, __int128_t> {
+public:
+  chunk_assembler(bool *overflows, uint64_t const *chunks0, uint64_t const *chunks1,
+                  uint64_t const *chunks2, int64_t const *chunks3)
+      : overflows(overflows), chunks0(chunks0), chunks1(chunks1), chunks2(chunks2),
+        chunks3(chunks3) {}
+
+  __device__ __int128_t operator()(cudf::size_type i) const {
+    // Starting with the least significant input and moving to the most significant, propagate the
+    // upper 32-bits of the previous column into the next column, i.e.: propagate the "carry" bits
+    // of each 64-bit chunk into the next chunk.
+    uint64_t const c0 = chunks0[i];
+    uint64_t const c1 = chunks1[i] + (c0 >> 32);
+    uint64_t const c2 = chunks2[i] + (c1 >> 32);
+    int64_t const c3 = chunks3[i] + (c2 >> 32);
+    uint64_t const lower64 = (c1 << 32) | static_cast<uint32_t>(c0);
+    int64_t const upper64 = (c3 << 32) | static_cast<uint32_t>(c2);
+
+    // check for overflow by ensuring the sign bit matches the top carry bits
+    int32_t const replicated_sign_bit = static_cast<int32_t>(c3) >> 31;
+    int32_t const top_carry_bits = static_cast<int32_t>(c3 >> 32);
+    overflows[i] = (replicated_sign_bit != top_carry_bits);
+
+    return (static_cast<__int128_t>(upper64) << 64) | lower64;
+  }
+
+private:
+  // output column for overflow detected
+  bool *const overflows;
+
+  // input columns for the four 64-bit values
+  uint64_t const *const chunks0;
+  uint64_t const *const chunks1;
+  uint64_t const *const chunks2;
+  int64_t const *const chunks3;
+};
+
+} // anonymous namespace
+
+namespace cudf::jni {
+
+// Extract a 32-bit chunk from a 128-bit value.
+std::unique_ptr<cudf::column> extract_chunk32(cudf::column_view const &in_col, cudf::data_type type,
+                                              int chunk_idx, rmm::cuda_stream_view stream) {
+  CUDF_EXPECTS(in_col.type().id() == cudf::type_id::DECIMAL128, "not a 128-bit type");
+  CUDF_EXPECTS(chunk_idx >= 0 && chunk_idx < 4, "invalid chunk index");
+  CUDF_EXPECTS(type.id() == cudf::type_id::INT32 || type.id() == cudf::type_id::UINT32,
+               "not a 32-bit integer type");
+  auto const num_rows = in_col.size();
+  auto out_col =
+      cudf::make_fixed_width_column(type, num_rows, copy_bitmask(in_col), in_col.null_count());
+  auto out_view = out_col->mutable_view();
+  auto const in_begin = in_col.begin<int32_t>();
+
+  // Build an iterator for every fourth 32-bit value, i.e.: one "chunk" of a __int128_t value
+  thrust::transform_iterator transform_iter{thrust::counting_iterator{0},
+                                            [] __device__(auto i) { return i * 4; }};
+  thrust::permutation_iterator stride_iter{in_begin + chunk_idx, transform_iter};
+
+  thrust::copy(rmm::exec_policy(stream), stride_iter, stride_iter + num_rows,
+               out_view.data<int32_t>());
+  return out_col;
+}
+
+// Reassemble a column of 128-bit values from four 64-bit integer columns with overflow detection.
+std::unique_ptr<cudf::table> assemble128_from_sum(cudf::table_view const &chunks_table,
+                                                  cudf::data_type output_type,
+                                                  rmm::cuda_stream_view stream) {
+  CUDF_EXPECTS(output_type.id() == cudf::type_id::DECIMAL128, "not a 128-bit type");
+  CUDF_EXPECTS(chunks_table.num_columns() == 4, "must be 4 column table");
+  auto const num_rows = chunks_table.num_rows();
+  auto const chunks0 = chunks_table.column(0);
+  auto const chunks1 = chunks_table.column(1);
+  auto const chunks2 = chunks_table.column(2);
+  auto const chunks3 = chunks_table.column(3);
+  CUDF_EXPECTS(cudf::size_of(chunks0.type()) == 8 && cudf::size_of(chunks1.type()) == 8 &&
+                   cudf::size_of(chunks2.type()) == 8 &&
+                   chunks3.type().id() == cudf::type_id::INT64,
+               "chunks type mismatch");
+  std::vector<std::unique_ptr<cudf::column>> columns;
+  columns.push_back(cudf::make_fixed_width_column(cudf::data_type{cudf::type_id::BOOL8}, num_rows,
+                                                  copy_bitmask(chunks0), chunks0.null_count()));
+  columns.push_back(cudf::make_fixed_width_column(output_type, num_rows, copy_bitmask(chunks0),
+                                                  chunks0.null_count()));
+  auto overflows_view = columns[0]->mutable_view();
+  auto assembled_view = columns[1]->mutable_view();
+  thrust::transform(rmm::exec_policy(stream), thrust::make_counting_iterator<cudf::size_type>(0),
+                    thrust::make_counting_iterator<cudf::size_type>(num_rows),
+                    assembled_view.begin<__int128_t>(),
+                    chunk_assembler(overflows_view.begin<bool>(), chunks0.begin<uint64_t>(),
+                                    chunks1.begin<uint64_t>(), chunks2.begin<uint64_t>(),
+                                    chunks3.begin<int64_t>()));
+  return std::make_unique<cudf::table>(std::move(columns));
+}
+
+} // namespace cudf::jni
diff --git a/java/src/main/native/src/aggregation128_utils.hpp b/java/src/main/native/src/aggregation128_utils.hpp
new file mode 100644
index 0000000..a143760
--- /dev/null
+++ b/java/src/main/native/src/aggregation128_utils.hpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <memory>
+
+#include <cudf/column/column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf::jni {
+
+/**
+ * @brief Extract a 32-bit integer column from a column of 128-bit values.
+ *
+ * Given a 128-bit input column, a 32-bit integer column is returned corresponding to
+ * the index of which 32-bit chunk of the original 128-bit values to extract.
+ * 0 corresponds to the least significant chunk, and 3 corresponds to the most
+ * significant chunk.
+ *
+ * A null input row will result in a corresponding null output row.
+ *
+ * @param col       Column of 128-bit values
+ * @param dtype     Integer type to use for the output column (e.g.: UINT32 or INT32)
+ * @param chunk_idx Index of the 32-bit chunk to extract
+ * @param stream    CUDA stream to use
+ * @return          A column containing the extracted 32-bit integer values
+ */
+std::unique_ptr<cudf::column>
+extract_chunk32(cudf::column_view const &col, cudf::data_type dtype, int chunk_idx,
+                rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+/**
+ * @brief Reassemble a 128-bit column from four 64-bit integer columns with overflow detection.
+ *
+ * The 128-bit value is reconstructed by overlapping the 64-bit values by 32-bits. The least
+ * significant 32-bits of the least significant 64-bit value are used directly as the least
+ * significant 32-bits of the final 128-bit value, and the remaining 32-bits are added to the next
+ * most significant 64-bit value. The lower 32-bits of that sum become the next most significant
+ * 32-bits in the final 128-bit value, and the remaining 32-bits are added to the next most
+ * significant 64-bit input value, and so on.
+ *
+ * A null input row will result in a corresponding null output row.
+ *
+ * @param chunks_table Table of four 64-bit integer columns with the columns ordered from least
+ *                     significant to most significant. The last column must be an INT64 column.
+ * @param output_type  The type to use for the resulting 128-bit value column
+ * @param stream       CUDA stream to use
+ * @return             Table containing a boolean column and a 128-bit value column of the
+ *                     requested type. The boolean value will be true if an overflow was detected
+ *                     for that row's value.
+ */
+std::unique_ptr<cudf::table>
+assemble128_from_sum(cudf::table_view const &chunks_table, cudf::data_type output_type,
+                     rmm::cuda_stream_view stream = cudf::get_default_stream());
+
+} // namespace cudf::jni
diff --git a/java/src/main/native/src/check_nvcomp_output_sizes.cu b/java/src/main/native/src/check_nvcomp_output_sizes.cu
new file mode 100644
index 0000000..9443998
--- /dev/null
+++ b/java/src/main/native/src/check_nvcomp_output_sizes.cu
@@ -0,0 +1,47 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include <cudf/detail/nvtx/nvtx3.hpp>
+#include <cudf/utilities/error.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/equal.h>
+
+#include "check_nvcomp_output_sizes.hpp"
+
+namespace {
+
+struct java_domain {
+  static constexpr char const *name{"Java"};
+};
+
+} // anonymous namespace
+
+namespace cudf {
+namespace java {
+
+/**
+ * Check that the vector of expected uncompressed sizes matches the vector of actual compressed
+ * sizes. Both vectors are assumed to be in device memory and contain num_chunks elements.
+ */
+bool check_nvcomp_output_sizes(std::size_t const *dev_uncompressed_sizes,
+                               std::size_t const *dev_actual_uncompressed_sizes,
+                               std::size_t num_chunks, rmm::cuda_stream_view stream) {
+  NVTX3_FUNC_RANGE_IN(java_domain);
+  return thrust::equal(rmm::exec_policy(stream), dev_uncompressed_sizes,
+                       dev_uncompressed_sizes + num_chunks, dev_actual_uncompressed_sizes);
+}
+
+} // namespace java
+} // namespace cudf
diff --git a/java/src/main/native/src/check_nvcomp_output_sizes.hpp b/java/src/main/native/src/check_nvcomp_output_sizes.hpp
new file mode 100644
index 0000000..00b3647
--- /dev/null
+++ b/java/src/main/native/src/check_nvcomp_output_sizes.hpp
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf {
+namespace java {
+
+/**
+ * Check that the vector of expected uncompressed sizes matches the vector of actual compressed
+ * sizes. Both vectors are assumed to be in device memory and contain num_chunks elements.
+ */
+bool check_nvcomp_output_sizes(std::size_t const *dev_uncompressed_sizes,
+                               std::size_t const *dev_actual_uncompressed_sizes,
+                               std::size_t num_chunks, rmm::cuda_stream_view stream);
+} // namespace java
+} // namespace cudf
diff --git a/java/src/main/native/src/csv_chunked_writer.hpp b/java/src/main/native/src/csv_chunked_writer.hpp
new file mode 100644
index 0000000..1f1e73a
--- /dev/null
+++ b/java/src/main/native/src/csv_chunked_writer.hpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cassert>
+
+#include <cudf/io/csv.hpp>
+
+#include "jni_writer_data_sink.hpp"
+
+namespace cudf::jni::io {
+
+/**
+ * @brief Class to write multiple Tables into the jni_writer_data_sink.
+ */
+class csv_chunked_writer {
+
+  cudf::io::csv_writer_options _options;
+  std::unique_ptr<cudf::jni::jni_writer_data_sink> _sink;
+
+  bool _first_write_completed = false; ///< Decides if header should be written.
+
+public:
+  explicit csv_chunked_writer(cudf::io::csv_writer_options options,
+                              std::unique_ptr<cudf::jni::jni_writer_data_sink> &sink)
+      : _options{options}, _sink{std::move(sink)} {
+    auto const &sink_info = _options.get_sink();
+    // Assert invariants.
+    CUDF_EXPECTS(sink_info.type() != cudf::io::io_type::FILEPATH,
+                 "Currently, chunked CSV writes to files is not supported.");
+
+    // Note: csv_writer_options ties the sink(s) to the options, and exposes
+    // no way to modify the sinks afterwards.
+    // Ideally, the options would have been separate from the tables written,
+    // and the destination sinks.
+    // Here, we retain a modifiable reference to the sink, and confirm the
+    // options point to the same sink.
+    CUDF_EXPECTS(sink_info.num_sinks() == 1, "csv_chunked_writer should have exactly one sink.");
+    CUDF_EXPECTS(sink_info.user_sinks()[0] == _sink.get(), "Sink mismatch.");
+  }
+
+  void write(cudf::table_view const &table) {
+    if (_first_write_completed) {
+      _options.enable_include_header(false); // Don't write header after the first write.
+    }
+
+    _options.set_table(table);
+    _options.set_rows_per_chunk(table.num_rows());
+
+    cudf::io::write_csv(_options);
+    _first_write_completed = true;
+  }
+
+  void close() {
+    // Flush pending writes to sink.
+    _sink->flush();
+  }
+};
+
+} // namespace cudf::jni::io
diff --git a/java/src/main/native/src/cudf_jni_apis.hpp b/java/src/main/native/src/cudf_jni_apis.hpp
new file mode 100644
index 0000000..867df80
--- /dev/null
+++ b/java/src/main/native/src/cudf_jni_apis.hpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/contiguous_split.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+
+#include "jni_utils.hpp"
+
+namespace cudf {
+namespace jni {
+
+/**
+ * @brief Detach all columns from the specified table, and pointers to them as an array.
+ *
+ * This function takes a table (presumably returned by some operation), and turns it into an
+ * array of column* (as jlongs).
+ * The lifetime of the columns is decoupled from that of the table, and is managed by the caller.
+ *
+ * @param env The JNI environment
+ * @param table_result the table to convert for return
+ * @param extra_columns columns not in the table that will be appended to the result.
+ */
+jlongArray
+convert_table_for_return(JNIEnv *env, std::unique_ptr<cudf::table> &table_result,
+                         std::vector<std::unique_ptr<cudf::column>> &&extra_columns = {});
+
+/**
+ * @copydoc convert_table_for_return(JNIEnv*, std::unique_ptr<cudf::table>&,
+ *                                   std::vector<std::unique_ptr<cudf::column>>&&)
+ */
+jlongArray
+convert_table_for_return(JNIEnv *env, std::unique_ptr<cudf::table> &&table_result,
+                         std::vector<std::unique_ptr<cudf::column>> &&extra_columns = {});
+
+//
+// ContiguousTable APIs
+//
+
+bool cache_contiguous_table_jni(JNIEnv *env);
+
+void release_contiguous_table_jni(JNIEnv *env);
+
+jobject contiguous_table_from(JNIEnv *env, cudf::packed_columns &split, long row_count);
+
+native_jobjectArray<jobject> contiguous_table_array(JNIEnv *env, jsize length);
+
+/**
+ * @brief Cache the JNI jclass and JNI jfield of Java `ContigSplitGroupByResult`
+ *
+ * @param env the JNI Env pointer
+ * @return if success
+ */
+bool cache_contig_split_group_by_result_jni(JNIEnv *env);
+
+/**
+ * @brief Release the JNI jclass and JNI jfield of Java `ContigSplitGroupByResult`
+ *
+ * @param env the JNI Env pointer
+ */
+void release_contig_split_group_by_result_jni(JNIEnv *env);
+
+/**
+ * @brief Construct a Java `ContigSplitGroupByResult` from contiguous tables.
+ *
+ * @param env the JNI Env pointer
+ * @param groups the contiguous tables
+ * @return a Java `ContigSplitGroupByResult`
+ */
+jobject contig_split_group_by_result_from(JNIEnv *env, jobjectArray &groups);
+
+/**
+ * @brief Construct a Java `ContigSplitGroupByResult` from contiguous tables.
+ *
+ * @param env the JNI Env pointer
+ * @param groups the contiguous tables
+ * @param groups the contiguous tables
+ * @return a Java `ContigSplitGroupByResult`
+ */
+jobject contig_split_group_by_result_from(JNIEnv *env, jobjectArray &groups,
+                                          jlongArray &uniq_key_columns);
+
+//
+// HostMemoryBuffer APIs
+//
+
+/**
+ * Allocate a HostMemoryBuffer
+ */
+jobject allocate_host_buffer(JNIEnv *env, jlong amount, jboolean prefer_pinned,
+                             jobject host_memory_allocator);
+
+/**
+ * Get the address of a HostMemoryBuffer
+ */
+jlong get_host_buffer_address(JNIEnv *env, jobject buffer);
+
+/**
+ * Get the length of a HostMemoryBuffer
+ */
+jlong get_host_buffer_length(JNIEnv *env, jobject buffer);
+
+// Get the JNI environment, attaching the current thread to the JVM if necessary. If the thread
+// needs to be attached, the thread will automatically detach when the thread terminates.
+JNIEnv *get_jni_env(JavaVM *jvm);
+
+/** Set the device to use for cudf */
+void set_cudf_device(int device);
+
+/**
+ * If the current thread has not set the CUDA device via Cuda.setDevice then this could
+ * set the device, throw an exception, or do nothing depending on how the application has
+ * configured it via Cuda.setAutoSetDeviceMode.
+ */
+void auto_set_device(JNIEnv *env);
+
+/**
+ * Fills all the bytes in the buffer 'buf' with 'value'.
+ * The operation has not necessarily completed when this returns, but it could overlap with
+ * operations occurring on other streams.
+ */
+void device_memset_async(JNIEnv *env, rmm::device_buffer &buf, char value);
+
+} // namespace jni
+} // namespace cudf
diff --git a/java/src/main/native/src/dtype_utils.hpp b/java/src/main/native/src/dtype_utils.hpp
new file mode 100644
index 0000000..4de8a94
--- /dev/null
+++ b/java/src/main/native/src/dtype_utils.hpp
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <jni.h>
+
+#include <cudf/types.hpp>
+
+namespace cudf {
+namespace jni {
+
+// convert a timestamp type to the corresponding duration type
+inline cudf::data_type timestamp_to_duration(cudf::data_type dt) {
+  cudf::type_id duration_type_id;
+  switch (dt.id()) {
+    case cudf::type_id::TIMESTAMP_DAYS: duration_type_id = cudf::type_id::DURATION_DAYS; break;
+    case cudf::type_id::TIMESTAMP_SECONDS:
+      duration_type_id = cudf::type_id::DURATION_SECONDS;
+      break;
+    case cudf::type_id::TIMESTAMP_MILLISECONDS:
+      duration_type_id = cudf::type_id::DURATION_MILLISECONDS;
+      break;
+    case cudf::type_id::TIMESTAMP_MICROSECONDS:
+      duration_type_id = cudf::type_id::DURATION_MICROSECONDS;
+      break;
+    case cudf::type_id::TIMESTAMP_NANOSECONDS:
+      duration_type_id = cudf::type_id::DURATION_NANOSECONDS;
+      break;
+    default: throw std::logic_error("Unexpected type in timestamp_to_duration");
+  }
+  return cudf::data_type(duration_type_id);
+}
+
+inline bool is_decimal_type(cudf::type_id n_type) {
+  return n_type == cudf::type_id::DECIMAL32 || n_type == cudf::type_id::DECIMAL64 ||
+         n_type == cudf::type_id::DECIMAL128;
+}
+
+// create data_type including scale for decimal type
+inline cudf::data_type make_data_type(jint out_dtype, jint scale) {
+  cudf::type_id n_type = static_cast<cudf::type_id>(out_dtype);
+  cudf::data_type n_data_type;
+  if (is_decimal_type(n_type)) {
+    n_data_type = cudf::data_type(n_type, scale);
+  } else {
+    n_data_type = cudf::data_type(n_type);
+  }
+  return n_data_type;
+}
+
+} // namespace jni
+} // namespace cudf
diff --git a/java/src/main/native/src/emptyfile.cpp b/java/src/main/native/src/emptyfile.cpp
new file mode 100644
index 0000000..67fa3ac
--- /dev/null
+++ b/java/src/main/native/src/emptyfile.cpp
@@ -0,0 +1,17 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Intentionally empty
diff --git a/java/src/main/native/src/jni_compiled_expr.hpp b/java/src/main/native/src/jni_compiled_expr.hpp
new file mode 100644
index 0000000..74010f7
--- /dev/null
+++ b/java/src/main/native/src/jni_compiled_expr.hpp
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <vector>
+
+namespace cudf {
+namespace jni {
+namespace ast {
+
+/**
+ * A class to capture all of the resources associated with a compiled AST expression.
+ * AST nodes do not own their child nodes, so every node in the expression tree
+ * must be explicitly tracked in order to free the underlying resources for each node.
+ *
+ * This should be cleaned up a bit after the libcudf AST refactoring in
+ * https://github.com/rapidsai/cudf/pull/8815 when a virtual destructor is added to the
+ * base AST node type. Then we do not have to track every AST node type separately.
+ */
+class compiled_expr {
+  /** All expression nodes within the expression tree */
+  std::vector<std::unique_ptr<cudf::ast::expression>> expressions;
+
+  /** GPU scalar instances that correspond to literal nodes */
+  std::vector<std::unique_ptr<cudf::scalar>> scalars;
+
+public:
+  cudf::ast::literal &add_literal(std::unique_ptr<cudf::ast::literal> literal_ptr,
+                                  std::unique_ptr<cudf::scalar> scalar_ptr) {
+    expressions.push_back(std::move(literal_ptr));
+    scalars.push_back(std::move(scalar_ptr));
+    return static_cast<cudf::ast::literal &>(*expressions.back());
+  }
+
+  cudf::ast::column_reference &
+  add_column_ref(std::unique_ptr<cudf::ast::column_reference> ref_ptr) {
+    expressions.push_back(std::move(ref_ptr));
+    return static_cast<cudf::ast::column_reference &>(*expressions.back());
+  }
+
+  cudf::ast::operation &add_operation(std::unique_ptr<cudf::ast::operation> expr_ptr) {
+    expressions.push_back(std::move(expr_ptr));
+    return static_cast<cudf::ast::operation &>(*expressions.back());
+  }
+
+  /** Return the expression node at the top of the tree */
+  cudf::ast::expression &get_top_expression() const { return *expressions.back(); }
+};
+
+} // namespace ast
+} // namespace jni
+} // namespace cudf
diff --git a/java/src/main/native/src/jni_writer_data_sink.hpp b/java/src/main/native/src/jni_writer_data_sink.hpp
new file mode 100644
index 0000000..efac611
--- /dev/null
+++ b/java/src/main/native/src/jni_writer_data_sink.hpp
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/io/data_sink.hpp>
+
+#include "cudf_jni_apis.hpp"
+#include "jni_utils.hpp"
+
+namespace cudf::jni {
+
+constexpr long MINIMUM_WRITE_BUFFER_SIZE = 10 * 1024 * 1024; // 10 MB
+
+class jni_writer_data_sink final : public cudf::io::data_sink {
+public:
+  explicit jni_writer_data_sink(JNIEnv *env, jobject callback, jobject host_memory_allocator) {
+    if (env->GetJavaVM(&jvm) < 0) {
+      throw std::runtime_error("GetJavaVM failed");
+    }
+
+    jclass cls = env->GetObjectClass(callback);
+    if (cls == nullptr) {
+      throw cudf::jni::jni_exception("class not found");
+    }
+
+    handle_buffer_method =
+        env->GetMethodID(cls, "handleBuffer", "(Lai/rapids/cudf/HostMemoryBuffer;J)V");
+    if (handle_buffer_method == nullptr) {
+      throw cudf::jni::jni_exception("handleBuffer method");
+    }
+
+    this->callback = add_global_ref(env, callback);
+    this->host_memory_allocator = add_global_ref(env, host_memory_allocator);
+  }
+
+  virtual ~jni_writer_data_sink() {
+    // This should normally be called by a JVM thread. If the JVM environment is missing then this
+    // is likely being triggered by the C++ runtime during shutdown. In that case the JVM may
+    // already be destroyed and this thread should not try to attach to get an environment.
+    JNIEnv *env = nullptr;
+    if (jvm->GetEnv(reinterpret_cast<void **>(&env), cudf::jni::MINIMUM_JNI_VERSION) == JNI_OK) {
+      callback = del_global_ref(env, callback);
+      current_buffer = del_global_ref(env, current_buffer);
+      host_memory_allocator = del_global_ref(env, host_memory_allocator);
+    }
+    callback = nullptr;
+    current_buffer = nullptr;
+    host_memory_allocator = nullptr;
+  }
+
+  void host_write(void const *data, size_t size) override {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    long left_to_copy = static_cast<long>(size);
+    const char *copy_from = static_cast<const char *>(data);
+    while (left_to_copy > 0) {
+      long buffer_amount_available = current_buffer_len - current_buffer_written;
+      if (buffer_amount_available <= 0) {
+        // should never be < 0, but just to be safe
+        rotate_buffer(env);
+        buffer_amount_available = current_buffer_len - current_buffer_written;
+      }
+      long amount_to_copy =
+          left_to_copy < buffer_amount_available ? left_to_copy : buffer_amount_available;
+      char *copy_to = current_buffer_data + current_buffer_written;
+
+      std::memcpy(copy_to, copy_from, amount_to_copy);
+      copy_from = copy_from + amount_to_copy;
+      current_buffer_written += amount_to_copy;
+      total_written += amount_to_copy;
+      left_to_copy -= amount_to_copy;
+    }
+  }
+
+  bool supports_device_write() const override { return true; }
+
+  void device_write(void const *gpu_data, size_t size, rmm::cuda_stream_view stream) override {
+    JNIEnv *env = cudf::jni::get_jni_env(jvm);
+    long left_to_copy = static_cast<long>(size);
+    const char *copy_from = static_cast<const char *>(gpu_data);
+    while (left_to_copy > 0) {
+      long buffer_amount_available = current_buffer_len - current_buffer_written;
+      if (buffer_amount_available <= 0) {
+        // should never be < 0, but just to be safe
+        stream.synchronize();
+        rotate_buffer(env);
+        buffer_amount_available = current_buffer_len - current_buffer_written;
+      }
+      long amount_to_copy =
+          left_to_copy < buffer_amount_available ? left_to_copy : buffer_amount_available;
+      char *copy_to = current_buffer_data + current_buffer_written;
+
+      CUDF_CUDA_TRY(cudaMemcpyAsync(copy_to, copy_from, amount_to_copy, cudaMemcpyDeviceToHost,
+                                    stream.value()));
+
+      copy_from = copy_from + amount_to_copy;
+      current_buffer_written += amount_to_copy;
+      total_written += amount_to_copy;
+      left_to_copy -= amount_to_copy;
+    }
+    stream.synchronize();
+  }
+
+  std::future<void> device_write_async(void const *gpu_data, size_t size,
+                                       rmm::cuda_stream_view stream) override {
+    // Call the sync version until figuring out how to write asynchronously.
+    device_write(gpu_data, size, stream);
+    return std::async(std::launch::deferred, [] {});
+  }
+
+  void flush() override {
+    if (current_buffer_written > 0) {
+      JNIEnv *env = cudf::jni::get_jni_env(jvm);
+      handle_buffer(env, current_buffer, current_buffer_written);
+      current_buffer = del_global_ref(env, current_buffer);
+      current_buffer_len = 0;
+      current_buffer_data = nullptr;
+      current_buffer_written = 0;
+    }
+  }
+
+  size_t bytes_written() override { return total_written; }
+
+  void set_alloc_size(long size) { this->alloc_size = size; }
+
+private:
+  void rotate_buffer(JNIEnv *env) {
+    if (current_buffer != nullptr) {
+      handle_buffer(env, current_buffer, current_buffer_written);
+    }
+    current_buffer = del_global_ref(env, current_buffer);
+    jobject tmp_buffer = allocate_host_buffer(env, alloc_size, true, host_memory_allocator);
+    current_buffer = add_global_ref(env, tmp_buffer);
+    current_buffer_len = get_host_buffer_length(env, current_buffer);
+    current_buffer_data = reinterpret_cast<char *>(get_host_buffer_address(env, current_buffer));
+    current_buffer_written = 0;
+  }
+
+  void handle_buffer(JNIEnv *env, jobject buffer, jlong len) {
+    env->CallVoidMethod(callback, handle_buffer_method, buffer, len);
+    if (env->ExceptionCheck()) {
+      throw std::runtime_error("handleBuffer threw an exception");
+    }
+  }
+
+  JavaVM *jvm;
+  jobject callback;
+  jmethodID handle_buffer_method;
+  jobject current_buffer = nullptr;
+  char *current_buffer_data = nullptr;
+  long current_buffer_len = 0;
+  long current_buffer_written = 0;
+  size_t total_written = 0;
+  long alloc_size = MINIMUM_WRITE_BUFFER_SIZE;
+  jobject host_memory_allocator;
+};
+
+} // namespace cudf::jni
diff --git a/java/src/main/native/src/maps_column_view.cu b/java/src/main/native/src/maps_column_view.cu
new file mode 100644
index 0000000..1af7689
--- /dev/null
+++ b/java/src/main/native/src/maps_column_view.cu
@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/replace.hpp>
+#include <cudf/lists/detail/contains.hpp>
+#include <cudf/lists/detail/extract.hpp>
+#include <cudf/scalar/scalar.hpp>
+#include <maps_column_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+namespace cudf::jni {
+
+namespace {
+column_view make_lists(column_view const &lists_child, lists_column_view const &lists_of_structs) {
+  return column_view{data_type{type_id::LIST},
+                     lists_of_structs.size(),
+                     nullptr,
+                     lists_of_structs.null_mask(),
+                     lists_of_structs.null_count(),
+                     lists_of_structs.offset(),
+                     {lists_of_structs.offsets(), lists_child}};
+}
+} // namespace
+
+maps_column_view::maps_column_view(lists_column_view const &lists_of_structs,
+                                   rmm::cuda_stream_view stream)
+    : keys_{make_lists(lists_of_structs.child().child(0), lists_of_structs)},
+      values_{make_lists(lists_of_structs.child().child(1), lists_of_structs)} {
+  auto const structs = lists_of_structs.child();
+  CUDF_EXPECTS(structs.type().id() == type_id::STRUCT,
+               "maps_column_view input must have exactly 1 child (STRUCT) column.");
+  CUDF_EXPECTS(structs.num_children() == 2,
+               "maps_column_view key-value struct must have exactly 2 children.");
+}
+
+template <typename KeyT>
+std::unique_ptr<column> get_values_for_impl(maps_column_view const &maps_view,
+                                            KeyT const &lookup_keys, rmm::cuda_stream_view stream,
+                                            rmm::mr::device_memory_resource *mr) {
+  auto const keys_ = maps_view.keys();
+  auto const values_ = maps_view.values();
+  CUDF_EXPECTS(lookup_keys.type().id() == keys_.child().type().id(),
+               "Lookup keys must have the same type as the keys of the map column.");
+  auto key_indices =
+      lists::detail::index_of(keys_, lookup_keys, lists::duplicate_find_option::FIND_LAST, stream,
+                              rmm::mr::get_current_device_resource());
+  auto constexpr absent_offset = size_type{-1};
+  auto constexpr nullity_offset = std::numeric_limits<size_type>::min();
+  thrust::replace(rmm::exec_policy(stream), key_indices->mutable_view().template begin<size_type>(),
+                  key_indices->mutable_view().template end<size_type>(), absent_offset,
+                  nullity_offset);
+  return lists::detail::extract_list_element(values_, key_indices->view(), stream, mr);
+}
+
+std::unique_ptr<column>
+maps_column_view::get_values_for(column_view const &lookup_keys, rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource *mr) const {
+  CUDF_EXPECTS(lookup_keys.size() == size(),
+               "Lookup keys must have the same size as the map column.");
+
+  return get_values_for_impl(*this, lookup_keys, stream, mr);
+}
+
+std::unique_ptr<column>
+maps_column_view::get_values_for(scalar const &lookup_key, rmm::cuda_stream_view stream,
+                                 rmm::mr::device_memory_resource *mr) const {
+  return get_values_for_impl(*this, lookup_key, stream, mr);
+}
+
+template <typename KeyT>
+std::unique_ptr<column> contains_impl(maps_column_view const &maps_view, KeyT const &lookup_keys,
+                                      rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource *mr) {
+  auto const keys = maps_view.keys();
+  CUDF_EXPECTS(lookup_keys.type().id() == keys.child().type().id(),
+               "Lookup keys must have the same type as the keys of the map column.");
+  auto const contains =
+      lists::detail::contains(keys, lookup_keys, stream, rmm::mr::get_current_device_resource());
+  // Replace nulls with BOOL8{false};
+  auto const scalar_false = numeric_scalar<bool>{false, true, stream};
+  return detail::replace_nulls(contains->view(), scalar_false, stream, mr);
+}
+
+std::unique_ptr<column> maps_column_view::contains(column_view const &lookup_keys,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource *mr) const {
+  CUDF_EXPECTS(lookup_keys.size() == size(),
+               "Lookup keys must have the same size as the map column.");
+
+  return contains_impl(*this, lookup_keys, stream, mr);
+}
+
+std::unique_ptr<column> maps_column_view::contains(scalar const &lookup_key,
+                                                   rmm::cuda_stream_view stream,
+                                                   rmm::mr::device_memory_resource *mr) const {
+  return contains_impl(*this, lookup_key, stream, mr);
+}
+
+} // namespace cudf::jni
diff --git a/java/src/main/native/src/nvtx_common.hpp b/java/src/main/native/src/nvtx_common.hpp
new file mode 100644
index 0000000..8b5b04f
--- /dev/null
+++ b/java/src/main/native/src/nvtx_common.hpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+namespace cudf {
+namespace jni {
+
+struct java_domain {
+  static constexpr char const *name{"Java"};
+};
+
+} // namespace jni
+} // namespace cudf
diff --git a/java/src/main/native/src/row_conversion.cu b/java/src/main/native/src/row_conversion.cu
new file mode 100644
index 0000000..d93d38c
--- /dev/null
+++ b/java/src/main/native/src/row_conversion.cu
@@ -0,0 +1,2341 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cooperative_groups.h>
+#include <cudf/column/column_factories.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/sequence.hpp>
+#include <cudf/detail/utilities/cuda.cuh>
+#include <cudf/detail/utilities/integer_utils.hpp>
+#include <cudf/detail/utilities/vector_factories.hpp>
+#include <cudf/lists/lists_column_device_view.cuh>
+#include <cudf/scalar/scalar_factories.hpp>
+#include <cudf/strings/strings_column_view.hpp>
+#include <cudf/table/table.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/bit.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <cudf/utilities/error.hpp>
+#include <cudf/utilities/traits.hpp>
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+#include <thrust/binary_search.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scan.h>
+#include <type_traits>
+
+#include "row_conversion.hpp"
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+#define ASYNC_MEMCPY_SUPPORTED
+#endif
+
+#if !defined(__CUDA_ARCH__) || defined(ASYNC_MEMCPY_SUPPORTED)
+#include <cuda/barrier>
+#endif // #if !defined(__CUDA_ARCH__) || defined(ASYNC_MEMCPY_SUPPORTED)
+
+#include <algorithm>
+#include <cstdarg>
+#include <cstdint>
+#include <iostream>
+#include <iterator>
+#include <limits>
+#include <optional>
+#include <tuple>
+
+namespace {
+
+constexpr auto JCUDF_ROW_ALIGNMENT = 8;
+
+constexpr auto MAX_BATCH_SIZE = std::numeric_limits<cudf::size_type>::max();
+
+// Number of rows each block processes in the two kernels. Tuned via nsight
+constexpr auto NUM_STRING_ROWS_PER_BLOCK_TO_ROWS = 1024;
+constexpr auto NUM_STRING_ROWS_PER_BLOCK_FROM_ROWS = 64;
+constexpr auto MIN_STRING_BLOCKS = 32;
+constexpr auto MAX_STRING_BLOCKS = MAX_BATCH_SIZE;
+
+constexpr auto NUM_WARPS_IN_BLOCK = 32;
+
+} // anonymous namespace
+
+// needed to suppress warning about cuda::barrier
+#pragma nv_diag_suppress static_var_with_dynamic_init
+
+using namespace cudf;
+using detail::make_device_uvector_async;
+using rmm::device_uvector;
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+using cuda::aligned_size_t;
+#else
+template <std::size_t> using aligned_size_t = size_t; // Local stub for cuda::aligned_size_t.
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+namespace cudf {
+namespace jni {
+namespace detail {
+
+/*
+ * This module converts data from row-major to column-major and from column-major to row-major. It
+ * is a transpose of the data of sorts, but there are a few complicating factors. They are spelled
+ * out below:
+ *
+ * Row Batches:
+ * The row data has to fit inside a cuDF column, which limits it to 2 gigs currently. The calling
+ * code attempts to keep the data size under 2 gigs, but due to padding this isn't always the case,
+ * so being able to break this up into multiple columns is necessary. Internally, this is referred
+ * to as the row batch, which is a group of rows that will fit into this 2 gig space requirement.
+ * There are typically 1 of these batches, but there can be 2.
+ *
+ * Async Memcpy:
+ * The CUDA blocks are using memcpy_async, which allows for the device to schedule memcpy operations
+ * and then wait on them to complete at a later time with a barrier. On Ampere or later hardware
+ * there is dedicated hardware to do this copy and on pre-Ampere it should generate the same code
+ * that a hand-rolled loop would generate, so performance should be the same or better than a
+ * hand-rolled kernel.
+ *
+ * Tile Info:
+ * Each CUDA block will work on a single tile info before exiting. This single tile consumes all
+ * available shared memory. The kernel reads data into shared memory and then back out from shared
+ * memory to device memory via memcpy_async. This kernel is completely memory bound.
+ *
+ * Batch Data:
+ * This structure contains all the row batches and some book-keeping data necessary for the batches
+ * such as row numbers for the batches.
+ *
+ * Tiles:
+ * The tile info describes a tile of data to process. In a GPU with 48KB this equates to about 221
+ * bytes in each direction of a table. The tiles are kept as square as possible to attempt to
+ * coalesce memory operations. The taller a tile is the better coalescing of columns, but row
+ * coalescing suffers. The wider a tile is the better the row coalescing, but columns coalescing
+ * suffers. The code attempts to produce a square tile to balance the coalescing. It starts by
+ * figuring out the optimal byte length and then adding columns to the data until the tile is too
+ * large. Since rows are different width with different alignment requirements, this isn't typically
+ * exact. Once a width is found the tiles are generated vertically with that width and height and
+ * then the process repeats. This means all the tiles will be the same height, but will have
+ * different widths based on what columns they encompass. Tiles in a vertical row will all have the
+ * same dimensions.
+ *
+ *   --------------------------------
+ *   | 4   5.0f || True   8   3   1 |
+ *   | 3   6.0f || False  3   1   1 |
+ *   | 2   7.0f || True   7   4   1 |
+ *   | 1   8.0f || False  2   5   1 |
+ *   --------------------------------
+ *   | 0   9.0f || True   6   7   1 |
+ *   ...
+ */
+
+/**
+ * @brief The CUDA blocks work on one tile_info struct of data.
+ *        This structure defines the workspaces for the blocks.
+ *
+ */
+struct tile_info {
+  int start_col;
+  int start_row;
+  int end_col;
+  int end_row;
+  int batch_number;
+
+  __device__ inline size_type get_shared_row_size(size_type const *const col_offsets,
+                                                  size_type const *const col_sizes) const {
+    // this calculation is invalid if there are holes in the data such as a variable-width column.
+    // It is wrong in a safe way in that it will say this row size is larger than it should be, so
+    // we are not losing data we are just not as efficient as we could be with shared memory. This
+    // may be a problem if the tile is computed without regard to variable width offset/length sizes
+    // in that we overrun shared memory.
+    return util::round_up_unsafe(col_offsets[end_col] + col_sizes[end_col] - col_offsets[start_col],
+                                 JCUDF_ROW_ALIGNMENT);
+  }
+
+  __device__ inline size_type num_cols() const { return end_col - start_col + 1; }
+
+  __device__ inline size_type num_rows() const { return end_row - start_row + 1; }
+};
+
+/**
+ * @brief Returning rows is done in a byte cudf column. This is limited in size by
+ *        `size_type` and so output is broken into batches of rows that fit inside
+ *        this limit.
+ *
+ */
+struct row_batch {
+  size_type num_bytes;                   // number of bytes in this batch
+  size_type row_count;                   // number of rows in the batch
+  device_uvector<size_type> row_offsets; // offsets column of output cudf column
+};
+
+/**
+ * @brief Holds information about the batches of data to be processed
+ *
+ */
+struct batch_data {
+  device_uvector<size_type> batch_row_offsets;      // offsets to each row in incoming data
+  device_uvector<size_type> d_batch_row_boundaries; // row numbers for the start of each batch
+  std::vector<size_type>
+      batch_row_boundaries;           // row numbers for the start of each batch: 0, 1500, 2700
+  std::vector<row_batch> row_batches; // information about each batch such as byte count
+};
+
+/**
+ * @brief builds row size information for tables that contain strings
+ *
+ * @param tbl table from which to compute row size information
+ * @param fixed_width_and_validity_size size of fixed-width and validity data in this table
+ * @param stream cuda stream on which to operate
+ * @return pair of device vector of size_types of the row sizes of the table and a device vector of
+ * offsets into the string column
+ */
+std::pair<rmm::device_uvector<size_type>, rmm::device_uvector<strings_column_view::offset_iterator>>
+build_string_row_offsets(table_view const &tbl, size_type fixed_width_and_validity_size,
+                         rmm::cuda_stream_view stream) {
+  auto const num_rows = tbl.num_rows();
+  rmm::device_uvector<size_type> d_row_sizes(num_rows, stream);
+  thrust::uninitialized_fill(rmm::exec_policy(stream), d_row_sizes.begin(), d_row_sizes.end(), 0);
+
+  auto d_offsets_iterators = [&]() {
+    std::vector<strings_column_view::offset_iterator> offsets_iterators;
+    auto offsets_iter = thrust::make_transform_iterator(
+        tbl.begin(), [](auto const &col) -> strings_column_view::offset_iterator {
+          if (!is_fixed_width(col.type())) {
+            CUDF_EXPECTS(col.type().id() == type_id::STRING, "only string columns are supported!");
+            return strings_column_view(col).offsets_begin();
+          } else {
+            return nullptr;
+          }
+        });
+    std::copy_if(offsets_iter, offsets_iter + tbl.num_columns(),
+                 std::back_inserter(offsets_iterators),
+                 [](auto const &offset_ptr) { return offset_ptr != nullptr; });
+    return make_device_uvector_async(offsets_iterators, stream,
+                                     rmm::mr::get_current_device_resource());
+  }();
+
+  auto const num_columns = static_cast<size_type>(d_offsets_iterators.size());
+
+  thrust::for_each(rmm::exec_policy(stream), thrust::make_counting_iterator(0),
+                   thrust::make_counting_iterator(num_columns * num_rows),
+                   [d_offsets_iterators = d_offsets_iterators.data(), num_columns, num_rows,
+                    d_row_sizes = d_row_sizes.data()] __device__(auto element_idx) {
+                     auto const row = element_idx % num_rows;
+                     auto const col = element_idx / num_rows;
+                     auto const val =
+                         d_offsets_iterators[col][row + 1] - d_offsets_iterators[col][row];
+                     atomicAdd(&d_row_sizes[row], val);
+                   });
+
+  // transform the row sizes to include fixed width size and alignment
+  thrust::transform(rmm::exec_policy(stream), d_row_sizes.begin(), d_row_sizes.end(),
+                    d_row_sizes.begin(), [fixed_width_and_validity_size] __device__(auto row_size) {
+                      return util::round_up_unsafe(fixed_width_and_validity_size + row_size,
+                                                   JCUDF_ROW_ALIGNMENT);
+                    });
+
+  return {std::move(d_row_sizes), std::move(d_offsets_iterators)};
+}
+
+/**
+ * @brief functor to return the offset of a row in a table with string columns
+ *
+ */
+struct string_row_offset_functor {
+  string_row_offset_functor(device_span<size_type const> d_row_offsets)
+      : d_row_offsets(d_row_offsets){};
+
+  __device__ inline size_type operator()(int row_number, int) const {
+    return d_row_offsets[row_number];
+  }
+
+  device_span<size_type const> d_row_offsets;
+};
+
+/**
+ * @brief functor to return the offset of a row in a table with only fixed-width columns
+ *
+ */
+struct fixed_width_row_offset_functor {
+  fixed_width_row_offset_functor(size_type fixed_width_only_row_size)
+      : _fixed_width_only_row_size(fixed_width_only_row_size){};
+
+  __device__ inline size_type operator()(int row_number, int tile_row_start) const {
+    return (row_number - tile_row_start) * _fixed_width_only_row_size;
+  }
+
+  size_type _fixed_width_only_row_size;
+};
+
+/**
+ * @brief Copies data from row-based JCUDF format to column-based cudf format.
+ *
+ * This optimized version of the conversion is faster for fixed-width tables that do not have more
+ * than 100 columns.
+ *
+ * @param num_rows number of rows in the incoming table
+ * @param num_columns number of columns in the incoming table
+ * @param row_size length in bytes of each row
+ * @param input_offset_in_row offset to each row of data
+ * @param num_bytes total number of bytes in the incoming data
+ * @param output_data array of pointers to the output data
+ * @param output_nm array of pointers to the output null masks
+ * @param input_data pointing to the incoming row data
+ */
+__global__ void
+copy_from_rows_fixed_width_optimized(const size_type num_rows, const size_type num_columns,
+                                     const size_type row_size, const size_type *input_offset_in_row,
+                                     const size_type *num_bytes, int8_t **output_data,
+                                     bitmask_type **output_nm, const int8_t *input_data) {
+  // We are going to copy the data in two passes.
+  // The first pass copies a chunk of data into shared memory.
+  // The second pass copies that chunk from shared memory out to the final location.
+
+  // Because shared memory is limited we copy a subset of the rows at a time.
+  // For simplicity we will refer to this as a row_group
+
+  // In practice we have found writing more than 4 columns of data per thread
+  // results in performance loss. As such we are using a 2 dimensional
+  // kernel in terms of threads, but not in terms of blocks. Columns are
+  // controlled by the y dimension (there is no y dimension in blocks). Rows
+  // are controlled by the x dimension (there are multiple blocks in the x
+  // dimension).
+
+  size_type const rows_per_group = blockDim.x;
+  size_type const row_group_start = blockIdx.x;
+  size_type const row_group_stride = gridDim.x;
+  size_type const row_group_end = (num_rows + rows_per_group - 1) / rows_per_group + 1;
+
+  extern __shared__ int8_t shared_data[];
+
+  // Because we are copying fixed width only data and we stride the rows
+  // this thread will always start copying from shared data in the same place
+  int8_t *row_tmp = &shared_data[row_size * threadIdx.x];
+  int8_t *row_vld_tmp = &row_tmp[input_offset_in_row[num_columns - 1] + num_bytes[num_columns - 1]];
+
+  for (auto row_group_index = row_group_start; row_group_index < row_group_end;
+       row_group_index += row_group_stride) {
+    // Step 1: Copy the data into shared memory
+    // We know row_size is always aligned with and a multiple of int64_t;
+    int64_t *long_shared = reinterpret_cast<int64_t *>(shared_data);
+    int64_t const *long_input = reinterpret_cast<int64_t const *>(input_data);
+
+    auto const shared_output_index = threadIdx.x + (threadIdx.y * blockDim.x);
+    auto const shared_output_stride = blockDim.x * blockDim.y;
+    auto const row_index_end = std::min(num_rows, ((row_group_index + 1) * rows_per_group));
+    auto const num_rows_in_group = row_index_end - (row_group_index * rows_per_group);
+    auto const shared_length = row_size * num_rows_in_group;
+
+    size_type const shared_output_end = shared_length / sizeof(int64_t);
+
+    auto const start_input_index = (row_size * row_group_index * rows_per_group) / sizeof(int64_t);
+
+    for (size_type shared_index = shared_output_index; shared_index < shared_output_end;
+         shared_index += shared_output_stride) {
+      long_shared[shared_index] = long_input[start_input_index + shared_index];
+    }
+    // Wait for all of the data to be in shared memory
+    __syncthreads();
+
+    // Step 2 copy the data back out
+
+    // Within the row group there should be 1 thread for each row.  This is a
+    // requirement for launching the kernel
+    auto const row_index = (row_group_index * rows_per_group) + threadIdx.x;
+    // But we might not use all of the threads if the number of rows does not go
+    // evenly into the thread count. We don't want those threads to exit yet
+    // because we may need them to copy data in for the next row group.
+    uint32_t active_mask = __ballot_sync(0xffff'ffffu, row_index < num_rows);
+    if (row_index < num_rows) {
+      auto const col_index_start = threadIdx.y;
+      auto const col_index_stride = blockDim.y;
+      for (auto col_index = col_index_start; col_index < num_columns;
+           col_index += col_index_stride) {
+        auto const col_size = num_bytes[col_index];
+        int8_t const *col_tmp = &(row_tmp[input_offset_in_row[col_index]]);
+        int8_t *col_output = output_data[col_index];
+        switch (col_size) {
+          case 1: {
+            col_output[row_index] = *col_tmp;
+            break;
+          }
+          case 2: {
+            int16_t *short_col_output = reinterpret_cast<int16_t *>(col_output);
+            short_col_output[row_index] = *reinterpret_cast<const int16_t *>(col_tmp);
+            break;
+          }
+          case 4: {
+            int32_t *int_col_output = reinterpret_cast<int32_t *>(col_output);
+            int_col_output[row_index] = *reinterpret_cast<const int32_t *>(col_tmp);
+            break;
+          }
+          case 8: {
+            int64_t *long_col_output = reinterpret_cast<int64_t *>(col_output);
+            long_col_output[row_index] = *reinterpret_cast<const int64_t *>(col_tmp);
+            break;
+          }
+          default: {
+            auto const output_offset = col_size * row_index;
+            // TODO this should just not be supported for fixed width columns, but just in case...
+            for (auto b = 0; b < col_size; b++) {
+              col_output[b + output_offset] = col_tmp[b];
+            }
+            break;
+          }
+        }
+
+        bitmask_type *nm = output_nm[col_index];
+        int8_t *valid_byte = &row_vld_tmp[col_index / 8];
+        size_type byte_bit_offset = col_index % 8;
+        int predicate = *valid_byte & (1 << byte_bit_offset);
+        uint32_t bitmask = __ballot_sync(active_mask, predicate);
+        if (row_index % 32 == 0) {
+          nm[word_index(row_index)] = bitmask;
+        }
+      } // end column loop
+    }   // end row copy
+    // wait for the row_group to be totally copied before starting on the next row group
+    __syncthreads();
+  }
+}
+
+__global__ void copy_to_rows_fixed_width_optimized(
+    const size_type start_row, const size_type num_rows, const size_type num_columns,
+    const size_type row_size, const size_type *output_offset_in_row, const size_type *num_bytes,
+    const int8_t **input_data, const bitmask_type **input_nm, int8_t *output_data) {
+  // We are going to copy the data in two passes.
+  // The first pass copies a chunk of data into shared memory.
+  // The second pass copies that chunk from shared memory out to the final location.
+
+  // Because shared memory is limited we copy a subset of the rows at a time.
+  // We do not support copying a subset of the columns in a row yet, so we don't
+  // currently support a row that is wider than shared memory.
+  // For simplicity we will refer to this as a row_group
+
+  // In practice we have found reading more than 4 columns of data per thread
+  // results in performance loss. As such we are using a 2 dimensional
+  // kernel in terms of threads, but not in terms of blocks. Columns are
+  // controlled by the y dimension (there is no y dimension in blocks). Rows
+  // are controlled by the x dimension (there are multiple blocks in the x
+  // dimension).
+
+  size_type rows_per_group = blockDim.x;
+  size_type row_group_start = blockIdx.x;
+  size_type row_group_stride = gridDim.x;
+  size_type row_group_end = (num_rows + rows_per_group - 1) / rows_per_group + 1;
+
+  extern __shared__ int8_t shared_data[];
+
+  // Because we are copying fixed width only data and we stride the rows
+  // this thread will always start copying to shared data in the same place
+  int8_t *row_tmp = &shared_data[row_size * threadIdx.x];
+  int8_t *row_vld_tmp =
+      &row_tmp[output_offset_in_row[num_columns - 1] + num_bytes[num_columns - 1]];
+
+  for (size_type row_group_index = row_group_start; row_group_index < row_group_end;
+       row_group_index += row_group_stride) {
+    // Within the row group there should be 1 thread for each row.  This is a
+    // requirement for launching the kernel
+    size_type row_index = start_row + (row_group_index * rows_per_group) + threadIdx.x;
+    // But we might not use all of the threads if the number of rows does not go
+    // evenly into the thread count. We don't want those threads to exit yet
+    // because we may need them to copy data back out.
+    if (row_index < (start_row + num_rows)) {
+      size_type col_index_start = threadIdx.y;
+      size_type col_index_stride = blockDim.y;
+      for (size_type col_index = col_index_start; col_index < num_columns;
+           col_index += col_index_stride) {
+        size_type col_size = num_bytes[col_index];
+        int8_t *col_tmp = &(row_tmp[output_offset_in_row[col_index]]);
+        const int8_t *col_input = input_data[col_index];
+        switch (col_size) {
+          case 1: {
+            *col_tmp = col_input[row_index];
+            break;
+          }
+          case 2: {
+            const int16_t *short_col_input = reinterpret_cast<const int16_t *>(col_input);
+            *reinterpret_cast<int16_t *>(col_tmp) = short_col_input[row_index];
+            break;
+          }
+          case 4: {
+            const int32_t *int_col_input = reinterpret_cast<const int32_t *>(col_input);
+            *reinterpret_cast<int32_t *>(col_tmp) = int_col_input[row_index];
+            break;
+          }
+          case 8: {
+            const int64_t *long_col_input = reinterpret_cast<const int64_t *>(col_input);
+            *reinterpret_cast<int64_t *>(col_tmp) = long_col_input[row_index];
+            break;
+          }
+          default: {
+            size_type input_offset = col_size * row_index;
+            // TODO this should just not be supported for fixed width columns, but just in case...
+            for (size_type b = 0; b < col_size; b++) {
+              col_tmp[b] = col_input[b + input_offset];
+            }
+            break;
+          }
+        }
+        // atomicOr only works on 32 bit or 64 bit  aligned values, and not byte aligned
+        // so we have to rewrite the addresses to make sure that it is 4 byte aligned
+        int8_t *valid_byte = &row_vld_tmp[col_index / 8];
+        size_type byte_bit_offset = col_index % 8;
+        uint64_t fixup_bytes = reinterpret_cast<uint64_t>(valid_byte) % 4;
+        int32_t *valid_int = reinterpret_cast<int32_t *>(valid_byte - fixup_bytes);
+        size_type int_bit_offset = byte_bit_offset + (fixup_bytes * 8);
+        // Now copy validity for the column
+        if (input_nm[col_index]) {
+          if (bit_is_set(input_nm[col_index], row_index)) {
+            atomicOr_block(valid_int, 1 << int_bit_offset);
+          } else {
+            atomicAnd_block(valid_int, ~(1 << int_bit_offset));
+          }
+        } else {
+          // It is valid so just set the bit
+          atomicOr_block(valid_int, 1 << int_bit_offset);
+        }
+      } // end column loop
+    }   // end row copy
+    // wait for the row_group to be totally copied into shared memory
+    __syncthreads();
+
+    // Step 2: Copy the data back out
+    // We know row_size is always aligned with and a multiple of int64_t;
+    int64_t *long_shared = reinterpret_cast<int64_t *>(shared_data);
+    int64_t *long_output = reinterpret_cast<int64_t *>(output_data);
+
+    size_type shared_input_index = threadIdx.x + (threadIdx.y * blockDim.x);
+    size_type shared_input_stride = blockDim.x * blockDim.y;
+    size_type row_index_end = ((row_group_index + 1) * rows_per_group);
+    if (row_index_end > num_rows) {
+      row_index_end = num_rows;
+    }
+    size_type num_rows_in_group = row_index_end - (row_group_index * rows_per_group);
+    size_type shared_length = row_size * num_rows_in_group;
+
+    size_type shared_input_end = shared_length / sizeof(int64_t);
+
+    size_type start_output_index = (row_size * row_group_index * rows_per_group) / sizeof(int64_t);
+
+    for (size_type shared_index = shared_input_index; shared_index < shared_input_end;
+         shared_index += shared_input_stride) {
+      long_output[start_output_index + shared_index] = long_shared[shared_index];
+    }
+    __syncthreads();
+    // Go for the next round
+  }
+}
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+#define MEMCPY(dst, src, size, barrier) cuda::memcpy_async(dst, src, size, barrier)
+#else
+#define MEMCPY(dst, src, size, barrier) memcpy(dst, src, size)
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+/**
+ * @brief copy data from cudf columns into JCUDF format, which is row-based
+ *
+ * @tparam RowOffsetFunctor iterator that gives the size of a specific row of the table.
+ * @param num_rows total number of rows in the table
+ * @param num_columns total number of columns in the table
+ * @param shmem_used_per_tile shared memory amount each `tile_info` is using
+ * @param tile_infos span of `tile_info` structs the define the work
+ * @param input_data pointer to raw table data
+ * @param col_sizes array of sizes for each element in a column - one per column
+ * @param col_offsets offset into input data row for each column's start
+ * @param row_offsets offset to a specific row in the output data
+ * @param batch_row_boundaries row numbers for batch starts
+ * @param output_data pointer to output data
+ *
+ */
+template <typename RowOffsetFunctor>
+__global__ void copy_to_rows(const size_type num_rows, const size_type num_columns,
+                             const size_type shmem_used_per_tile,
+                             device_span<const tile_info> tile_infos, const int8_t **input_data,
+                             const size_type *col_sizes, const size_type *col_offsets,
+                             RowOffsetFunctor row_offsets, size_type const *batch_row_boundaries,
+                             int8_t **output_data) {
+  // We are going to copy the data in two passes.
+  // The first pass copies a chunk of data into shared memory.
+  // The second pass copies that chunk from shared memory out to the final location.
+
+  // Because shared memory is limited we copy a subset of the rows at a time.
+  // This has been broken up for us in the tile_info struct, so we don't have
+  // any calculation to do here, but it is important to note.
+
+  auto const group = cooperative_groups::this_thread_block();
+  auto const warp = cooperative_groups::tiled_partition<cudf::detail::warp_size>(group);
+  extern __shared__ int8_t shared_data[];
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  __shared__ cuda::barrier<cuda::thread_scope_block> tile_barrier;
+  if (group.thread_rank() == 0) {
+    init(&tile_barrier, group.size());
+  }
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+  auto const tile = tile_infos[blockIdx.x];
+  auto const num_tile_cols = tile.num_cols();
+  auto const num_tile_rows = tile.num_rows();
+  auto const tile_row_size = tile.get_shared_row_size(col_offsets, col_sizes);
+  auto const starting_column_offset = col_offsets[tile.start_col];
+
+  // to do the copy we need to do n column copies followed by m element copies OR we have to do m
+  // element copies followed by r row copies. When going from column to row it is much easier to
+  // copy by elements first otherwise we would need a running total of the column sizes for our
+  // tile, which isn't readily available. This makes it more appealing to copy element-wise from
+  // input data into shared matching the end layout and do row-based memcopies out.
+
+  // read each column across the tile
+  // each warp takes a column with each thread of a warp taking a row this is done with cooperative
+  // groups where each column is chosen by the tiled partition and each thread in that partition
+  // works on a row
+  for (int relative_col = warp.meta_group_rank(); relative_col < num_tile_cols;
+       relative_col += warp.meta_group_size()) {
+
+    auto const absolute_col = relative_col + tile.start_col;
+    auto const col_size = col_sizes[absolute_col];
+    auto const col_offset = col_offsets[absolute_col];
+    auto const relative_col_offset = col_offset - starting_column_offset;
+    auto const col_ptr = input_data[absolute_col];
+
+    if (col_ptr == nullptr) {
+      // variable-width data column
+      continue;
+    }
+
+    for (int relative_row = warp.thread_rank(); relative_row < num_tile_rows;
+         relative_row += warp.size()) {
+
+      if (relative_row >= num_tile_rows) {
+        // out of bounds
+        continue;
+      }
+      auto const absolute_row = relative_row + tile.start_row;
+
+      auto const shared_offset = relative_row * tile_row_size + relative_col_offset;
+      auto const input_src = col_ptr + col_size * absolute_row;
+
+      // copy the element from global memory
+      switch (col_size) {
+        case 2: {
+          const int16_t *short_col_input = reinterpret_cast<const int16_t *>(input_src);
+          *reinterpret_cast<int16_t *>(&shared_data[shared_offset]) = *short_col_input;
+          break;
+        }
+        case 4: {
+          const int32_t *int_col_input = reinterpret_cast<const int32_t *>(input_src);
+          *reinterpret_cast<int32_t *>(&shared_data[shared_offset]) = *int_col_input;
+          break;
+        }
+        case 8: {
+          const int64_t *long_col_input = reinterpret_cast<const int64_t *>(input_src);
+          *reinterpret_cast<int64_t *>(&shared_data[shared_offset]) = *long_col_input;
+          break;
+        }
+        case 1: shared_data[shared_offset] = *input_src; break;
+        default: {
+          for (int i = 0; i < col_size; ++i) {
+            shared_data[shared_offset] = *input_src;
+          }
+          break;
+        }
+      }
+    }
+  }
+
+  auto const tile_output_buffer = output_data[tile.batch_number];
+  auto const row_batch_start = tile.batch_number == 0 ? 0 : batch_row_boundaries[tile.batch_number];
+
+  // no async copies above waiting on the barrier, so we sync the group here to ensure all copies to
+  // shared memory are completed before copying data out
+  group.sync();
+
+  // each warp takes a row
+  for (int copy_row = warp.meta_group_rank(); copy_row < tile.num_rows();
+       copy_row += warp.meta_group_size()) {
+    auto const src = &shared_data[tile_row_size * copy_row];
+    auto const dst = tile_output_buffer + row_offsets(copy_row + tile.start_row, row_batch_start) +
+                     starting_column_offset;
+#ifdef ASYNC_MEMCPY_SUPPORTED
+    cuda::memcpy_async(warp, dst, src, tile_row_size, tile_barrier);
+#else
+    for (int b = warp.thread_rank(); b < tile_row_size; b += warp.size()) {
+      dst[b] = src[b];
+    }
+#endif
+  }
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // wait on the last copies to complete
+  tile_barrier.arrive_and_wait();
+#else
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+}
+
+/**
+ * @brief copy data from row-based format to cudf columns
+ *
+ * @tparam RowOffsetFunctor iterator that gives the size of a specific row of the table.
+ * @param num_rows total number of rows in the table
+ * @param num_columns total number of columns in the table
+ * @param shmem_used_per_tile amount of shared memory that is used by a tile
+ * @param row_offsets offset to a specific row in the output data
+ * @param batch_row_boundaries row numbers for batch starts
+ * @param output_data pointer to output data, partitioned by data size
+ * @param validity_offsets offset into input data row for validity data
+ * @param tile_infos information about the tiles of work
+ * @param input_nm pointer to input data
+ *
+ */
+template <typename RowOffsetFunctor>
+__global__ void
+copy_validity_to_rows(const size_type num_rows, const size_type num_columns,
+                      const size_type shmem_used_per_tile, RowOffsetFunctor row_offsets,
+                      size_type const *batch_row_boundaries, int8_t **output_data,
+                      const size_type validity_offset, device_span<const tile_info> tile_infos,
+                      const bitmask_type **input_nm) {
+  extern __shared__ int8_t shared_data[];
+
+  // each thread of warp reads a single int32 of validity - so we read 128 bytes then ballot_sync
+  // the bits and write the result to shmem after we fill shared mem memcpy it out in a blob.
+  auto const group = cooperative_groups::this_thread_block();
+  auto const warp = cooperative_groups::tiled_partition<cudf::detail::warp_size>(group);
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // Initialize cuda barriers for each tile.
+  __shared__ cuda::barrier<cuda::thread_scope_block> shared_tile_barrier;
+  if (group.thread_rank() == 0) {
+    init(&shared_tile_barrier, group.size());
+  }
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+  auto tile = tile_infos[blockIdx.x];
+  auto const num_tile_cols = tile.num_cols();
+  auto const num_tile_rows = tile.num_rows();
+
+  auto const threads_per_warp = warp.size();
+  auto const rows_per_read = cudf::detail::size_in_bits<bitmask_type>();
+
+  auto const num_sections_x = util::div_rounding_up_unsafe(num_tile_cols, threads_per_warp);
+  auto const num_sections_y = util::div_rounding_up_unsafe(num_tile_rows, rows_per_read);
+  auto const validity_data_row_length = util::round_up_unsafe(
+      util::div_rounding_up_unsafe(num_tile_cols, CHAR_BIT), JCUDF_ROW_ALIGNMENT);
+  auto const total_sections = num_sections_x * num_sections_y;
+
+  // the tile is divided into sections. A warp operates on a section at a time.
+  for (int my_section_idx = warp.meta_group_rank(); my_section_idx < total_sections;
+       my_section_idx += warp.meta_group_size()) {
+    // convert to rows and cols
+    auto const section_x = my_section_idx % num_sections_x;
+    auto const section_y = my_section_idx / num_sections_x;
+    auto const relative_col = section_x * threads_per_warp + warp.thread_rank();
+    auto const relative_row = section_y * rows_per_read;
+    auto const absolute_col = relative_col + tile.start_col;
+    auto const absolute_row = relative_row + tile.start_row;
+    auto const participating = absolute_col < num_columns && absolute_row < num_rows;
+    auto const participation_mask = __ballot_sync(0xFFFF'FFFFu, participating);
+
+    if (participating) {
+      auto my_data = input_nm[absolute_col] != nullptr ?
+                         input_nm[absolute_col][word_index(absolute_row)] :
+                         std::numeric_limits<uint32_t>::max();
+
+      // every thread that is participating in the warp has 4 bytes, but it's column-based data and
+      // we need it in row-based. So we shuffle the bits around with ballot_sync to make the bytes
+      // we actually write.
+      bitmask_type dw_mask = 0x1;
+      for (int i = 0; i < threads_per_warp && relative_row + i < num_rows; ++i, dw_mask <<= 1) {
+        auto validity_data = __ballot_sync(participation_mask, my_data & dw_mask);
+        // lead thread in each warp writes data
+        auto const validity_write_offset =
+            validity_data_row_length * (relative_row + i) + (relative_col / CHAR_BIT);
+        if (warp.thread_rank() == 0) {
+          *reinterpret_cast<bitmask_type *>(&shared_data[validity_write_offset]) = validity_data;
+        }
+      }
+    }
+  }
+
+  auto const output_data_base =
+      output_data[tile.batch_number] + validity_offset + tile.start_col / CHAR_BIT;
+
+  // each warp copies a row at a time
+  auto const row_bytes = util::div_rounding_up_unsafe(num_tile_cols, CHAR_BIT);
+  auto const row_batch_start = tile.batch_number == 0 ? 0 : batch_row_boundaries[tile.batch_number];
+
+  // make sure entire tile has finished copy
+  // Note that this was copied from above just under the for loop due to nsight complaints about
+  // divergent threads
+  group.sync();
+
+  for (int relative_row = warp.meta_group_rank(); relative_row < num_tile_rows;
+       relative_row += warp.meta_group_size()) {
+    auto const src = &shared_data[validity_data_row_length * relative_row];
+    auto const dst = output_data_base + row_offsets(relative_row + tile.start_row, row_batch_start);
+#ifdef ASYNC_MEMCPY_SUPPORTED
+    cuda::memcpy_async(warp, dst, src, row_bytes, shared_tile_barrier);
+#else
+    for (int b = warp.thread_rank(); b < row_bytes; b += warp.size()) {
+      dst[b] = src[b];
+    }
+#endif
+  }
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // wait for tile of data to arrive
+  shared_tile_barrier.arrive_and_wait();
+#else
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+}
+
+/**
+ * @brief kernel to copy string data to JCUDF row format
+ *
+ * @tparam RowOffsetFunctor iterator for row offsets into the destination data
+ * @param num_rows number of rows in this portion of the table
+ * @param num_variable_columns number of columns of variable-width data
+ * @param variable_input_data variable width data column pointers
+ * @param variable_col_output_offsets output offset information for variable-width columns
+ * @param variable_col_offsets input offset information for variable-width columns
+ * @param fixed_width_row_size offset to variable-width data in a row
+ * @param row_offsets offsets for each row in output data
+ * @param batch_row_offset row start for this batch
+ * @param output_data pointer to output data for this batch
+ *
+ */
+template <typename RowOffsetFunctor>
+__global__ void copy_strings_to_rows(size_type const num_rows, size_type const num_variable_columns,
+                                     int8_t const **variable_input_data,
+                                     size_type const *variable_col_output_offsets,
+                                     size_type const **variable_col_offsets,
+                                     size_type fixed_width_row_size, RowOffsetFunctor row_offsets,
+                                     size_type const batch_row_offset, int8_t *output_data) {
+  // Each block will take a group of rows controlled by NUM_STRING_ROWS_PER_BLOCK_TO_ROWS. Each warp
+  // will copy a row at a time. The base thread will first go through column data and fill out
+  // offset/length information for the column. Then all threads of the warp will participate in the
+  // memcpy of the string data.
+  auto const my_block = cooperative_groups::this_thread_block();
+  auto const warp = cooperative_groups::tiled_partition<cudf::detail::warp_size>(my_block);
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  cuda::barrier<cuda::thread_scope_block> block_barrier;
+#endif
+
+  auto const start_row =
+      blockIdx.x * NUM_STRING_ROWS_PER_BLOCK_TO_ROWS + warp.meta_group_rank() + batch_row_offset;
+  auto const end_row =
+      std::min(num_rows, static_cast<size_type>(start_row + NUM_STRING_ROWS_PER_BLOCK_TO_ROWS));
+
+  for (int row = start_row; row < end_row; row += warp.meta_group_size()) {
+    auto offset = fixed_width_row_size; // initial offset to variable-width data
+    auto const base_row_offset = row_offsets(row, 0);
+    for (int col = 0; col < num_variable_columns; ++col) {
+      auto const string_start_offset = variable_col_offsets[col][row];
+      auto const string_length = variable_col_offsets[col][row + 1] - string_start_offset;
+      if (warp.thread_rank() == 0) {
+        // write the offset/length to column
+        uint32_t *output_dest = reinterpret_cast<uint32_t *>(
+            &output_data[base_row_offset + variable_col_output_offsets[col]]);
+        output_dest[0] = offset;
+        output_dest[1] = string_length;
+      }
+      auto string_output_dest = &output_data[base_row_offset + offset];
+      auto string_output_src = &variable_input_data[col][string_start_offset];
+      warp.sync();
+#ifdef ASYNC_MEMCPY_SUPPORTED
+      cuda::memcpy_async(warp, string_output_dest, string_output_src, string_length, block_barrier);
+#else
+      for (int c = warp.thread_rank(); c < string_length; c += warp.size()) {
+        string_output_dest[c] = string_output_src[c];
+      }
+#endif
+      offset += string_length;
+    }
+  }
+}
+/**
+ * @brief copy data from row-based format to cudf columns
+ *
+ * @tparam RowOffsetFunctor iterator that gives the size of a specific row of the table.
+ * @param num_rows total number of rows in the table
+ * @param num_columns total number of columns in the table
+ * @param shmem_used_per_tile amount of shared memory that is used by a tile
+ * @param row_offsets offset to a specific row in the input data
+ * @param batch_row_boundaries row numbers for batch starts
+ * @param output_data pointers to column data
+ * @param col_sizes array of sizes for each element in a column - one per column
+ * @param col_offsets offset into input data row for each column's start
+ * @param tile_infos information about the tiles of work
+ * @param input_data pointer to input data
+ *
+ */
+template <typename RowOffsetFunctor>
+__global__ void copy_from_rows(const size_type num_rows, const size_type num_columns,
+                               const size_type shmem_used_per_tile, RowOffsetFunctor row_offsets,
+                               size_type const *batch_row_boundaries, int8_t **output_data,
+                               const size_type *col_sizes, const size_type *col_offsets,
+                               device_span<const tile_info> tile_infos, const int8_t *input_data) {
+  // We are going to copy the data in two passes.
+  // The first pass copies a chunk of data into shared memory.
+  // The second pass copies that chunk from shared memory out to the final location.
+
+  // Because shared memory is limited we copy a subset of the rows at a time. This has been broken
+  // up for us in the tile_info struct, so we don't have any calculation to do here, but it is
+  // important to note.
+
+  // To speed up some of the random access memory we do, we copy col_sizes and col_offsets to shared
+  // memory for each of the tiles that we work on
+
+  auto const group = cooperative_groups::this_thread_block();
+  auto const warp = cooperative_groups::tiled_partition<cudf::detail::warp_size>(group);
+  extern __shared__ int8_t shared[];
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // Initialize cuda barriers for each tile.
+  __shared__ cuda::barrier<cuda::thread_scope_block> tile_barrier;
+  if (group.thread_rank() == 0) {
+    init(&tile_barrier, group.size());
+  }
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+  {
+    auto const fetch_tile = tile_infos[blockIdx.x];
+    auto const fetch_tile_start_row = fetch_tile.start_row;
+    auto const starting_col_offset = col_offsets[fetch_tile.start_col];
+    auto const fetch_tile_row_size = fetch_tile.get_shared_row_size(col_offsets, col_sizes);
+    auto const row_batch_start =
+        fetch_tile.batch_number == 0 ? 0 : batch_row_boundaries[fetch_tile.batch_number];
+
+    for (int absolute_row = warp.meta_group_rank() + fetch_tile.start_row;
+         absolute_row <= fetch_tile.end_row; absolute_row += warp.meta_group_size()) {
+      warp.sync();
+      auto shared_offset = (absolute_row - fetch_tile_start_row) * fetch_tile_row_size;
+      auto dst = &shared[shared_offset];
+      auto src = &input_data[row_offsets(absolute_row, row_batch_start) + starting_col_offset];
+      // copy the data
+#ifdef ASYNC_MEMCPY_SUPPORTED
+      cuda::memcpy_async(warp, dst, src, fetch_tile_row_size, tile_barrier);
+#else
+      for (int b = warp.thread_rank(); b < fetch_tile_row_size; b += warp.size()) {
+        dst[b] = src[b];
+      }
+#endif
+    }
+  }
+
+  {
+    auto const tile = tile_infos[blockIdx.x];
+    auto const rows_in_tile = tile.num_rows();
+    auto const cols_in_tile = tile.num_cols();
+    auto const tile_row_size = tile.get_shared_row_size(col_offsets, col_sizes);
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+    // ensure our data is ready
+    tile_barrier.arrive_and_wait();
+#else
+    group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+    // Now we copy from shared memory to final destination. The data is laid out in rows in shared
+    // memory, so the reads for a column will be "vertical". Because of this and the different sizes
+    // for each column, this portion is handled on row/column basis. to prevent each thread working
+    // on a single row and also to ensure that all threads can do work in the case of more threads
+    // than rows, we do a global index instead of a double for loop with col/row.
+    for (int relative_row = warp.thread_rank(); relative_row < rows_in_tile;
+         relative_row += warp.size()) {
+
+      auto const absolute_row = relative_row + tile.start_row;
+      auto const shared_memory_row_offset = tile_row_size * relative_row;
+
+      for (int relative_col = warp.meta_group_rank(); relative_col < cols_in_tile;
+           relative_col += warp.meta_group_size()) {
+        auto const absolute_col = relative_col + tile.start_col;
+
+        auto const shared_memory_offset =
+            col_offsets[absolute_col] - col_offsets[tile.start_col] + shared_memory_row_offset;
+        auto const column_size = col_sizes[absolute_col];
+
+        int8_t *shmem_src = &shared[shared_memory_offset];
+        int8_t *dst = &output_data[absolute_col][absolute_row * column_size];
+
+        MEMCPY(dst, shmem_src, column_size, tile_barrier);
+      }
+    }
+  }
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // wait on the last copies to complete
+  tile_barrier.arrive_and_wait();
+#else
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+}
+
+/**
+ * @brief copy data from row-based format to cudf columns
+ *
+ * @tparam RowOffsetFunctor iterator that gives the size of a specific row of the table.
+ * @param num_rows total number of rows in the table
+ * @param num_columns total number of columns in the table
+ * @param shmem_used_per_tile amount of shared memory that is used by a tile
+ * @param row_offsets offset to the first column a specific row in the input data
+ * @param batch_row_boundaries row numbers for batch starts
+ * @param output_nm pointers to null masks for columns
+ * @param validity_offsets offset into input data row for validity data
+ * @param tile_infos information about the tiles of work
+ * @param input_data pointer to input data
+ *
+ */
+template <typename RowOffsetFunctor>
+__global__ void
+copy_validity_from_rows(const size_type num_rows, const size_type num_columns,
+                        const size_type shmem_used_per_tile, RowOffsetFunctor row_offsets,
+                        size_type const *batch_row_boundaries, bitmask_type **output_nm,
+                        const size_type validity_offset, device_span<const tile_info> tile_infos,
+                        const int8_t *input_data) {
+  extern __shared__ int8_t shared[];
+
+  using cudf::detail::warp_size;
+
+  // each thread of warp reads a single byte of validity - so we read 32 bytes then ballot_sync the
+  // bits and write the result to shmem after we fill shared mem memcpy it out in a blob. Probably
+  // need knobs for number of rows vs columns to balance read/write
+
+  //        C0  C1  C2  C3  C4  C5  C6  C7
+  //  R0    1   0   1   0   0   1   1   0       <-- thread 0 reads byte r0
+  //  R1    1   1   1   1   1   1   1   0       <-- thread 1 reads byte r1
+  //  R2    0   0   1   0   0   1   1   0       <-- thread 2 reads byte r2
+  //  ...
+  //  R31   1   1   1   1   1   1   1   1       <-- thread 31 reads byte r31
+  //        ^
+  //        |  1 bit of each input byte, by column, are swizzled into a single 32 bit word via
+  //        __ballot_sync, representing 32 rows of that column.
+
+  auto const group = cooperative_groups::this_thread_block();
+  auto const warp = cooperative_groups::tiled_partition<cudf::detail::warp_size>(group);
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // Initialize cuda barriers for each tile.
+  __shared__ cuda::barrier<cuda::thread_scope_block> shared_tile_barrier;
+  if (group.thread_rank() == 0) {
+    init(&shared_tile_barrier, group.size());
+  }
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+
+  auto const tile = tile_infos[blockIdx.x];
+  auto const tile_start_col = tile.start_col;
+  auto const tile_start_row = tile.start_row;
+  auto const num_tile_cols = tile.num_cols();
+  auto const num_tile_rows = tile.num_rows();
+
+  auto const threads_per_warp = warp.size();
+  auto const cols_per_read = CHAR_BIT;
+
+  auto const rows_per_read = static_cast<size_type>(threads_per_warp);
+  auto const num_sections_x = util::div_rounding_up_safe(num_tile_cols, cols_per_read);
+  auto const num_sections_y = util::div_rounding_up_safe(num_tile_rows, rows_per_read);
+  auto const validity_data_col_length = num_sections_y * 4; // words to bytes
+  auto const total_sections = num_sections_x * num_sections_y;
+
+  // the tile is divided into sections. A warp operates on a section at a time.
+  for (int my_section_idx = warp.meta_group_rank(); my_section_idx < total_sections;
+       my_section_idx += warp.meta_group_size()) {
+    // convert section to row and col
+    auto const section_x = my_section_idx % num_sections_x;
+    auto const section_y = my_section_idx / num_sections_x;
+    auto const relative_col = section_x * cols_per_read;
+    auto const relative_row = section_y * rows_per_read + warp.thread_rank();
+    auto const absolute_col = relative_col + tile_start_col;
+    auto const absolute_row = relative_row + tile_start_row;
+    auto const row_batch_start =
+        tile.batch_number == 0 ? 0 : batch_row_boundaries[tile.batch_number];
+
+    auto const participation_mask = __ballot_sync(0xFFFF'FFFFu, absolute_row < num_rows);
+
+    if (absolute_row < num_rows) {
+      auto const my_byte = input_data[row_offsets(absolute_row, row_batch_start) + validity_offset +
+                                      (absolute_col / cols_per_read)];
+
+      // so every thread that is participating in the warp has a byte, but it's row-based data and
+      // we need it in column-based. So we shuffle the bits around to make the bytes we actually
+      // write.
+      for (int i = 0, byte_mask = 0x1; (i < cols_per_read) && ((relative_col + i) < num_columns);
+           ++i, byte_mask <<= 1) {
+        auto const validity_data = __ballot_sync(participation_mask, my_byte & byte_mask);
+        // lead thread in each warp writes data
+        if (warp.thread_rank() == 0) {
+          auto const validity_write_offset =
+              validity_data_col_length * (relative_col + i) + relative_row / cols_per_read;
+          *reinterpret_cast<bitmask_type *>(&shared[validity_write_offset]) = validity_data;
+        }
+      }
+    }
+  }
+
+  // now memcpy the shared memory out to the final destination
+  auto const col_words = util::div_rounding_up_unsafe(num_tile_rows, CHAR_BIT * 4);
+
+  // make sure entire tile has finished copy
+  group.sync();
+
+  for (int relative_col = warp.meta_group_rank(); relative_col < num_tile_cols;
+       relative_col += warp.meta_group_size()) {
+    auto const absolute_col = relative_col + tile_start_col;
+    auto dst = output_nm[absolute_col] + word_index(tile_start_row);
+    auto const src =
+        reinterpret_cast<bitmask_type *>(&shared[validity_data_col_length * relative_col]);
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+    cuda::memcpy_async(warp, dst, src, aligned_size_t<4>(validity_data_col_length),
+                       shared_tile_barrier);
+#else
+    for (int b = warp.thread_rank(); b < col_words; b += warp.size()) {
+      dst[b] = src[b];
+    }
+#endif
+  }
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  // wait for tile of data to arrive
+  shared_tile_barrier.arrive_and_wait();
+#else
+  group.sync();
+#endif // ASYNC_MEMCPY_SUPPORTED
+}
+
+/**
+ * @brief copies string data from jcudf row format to cudf columns
+ *
+ * @tparam RowOffsetFunctor iterator for row offsets into the destination data
+ * @param row_offsets offsets for each row in input data
+ * @param string_row_offsets offset data into jcudf row data for each string
+ * @param string_lengths length of each incoming string in each column
+ * @param string_column_offsets offset column data for cudf column
+ * @param string_col_data output cudf string column data
+ * @param row_data jcudf row data
+ * @param num_rows number of rows in data
+ * @param num_string_columns number of string columns in the table
+ */
+template <typename RowOffsetFunctor>
+__global__ void copy_strings_from_rows(RowOffsetFunctor row_offsets, int32_t **string_row_offsets,
+                                       int32_t **string_lengths, size_type **string_column_offsets,
+                                       char **string_col_data, int8_t const *row_data,
+                                       size_type const num_rows,
+                                       size_type const num_string_columns) {
+  // Each warp takes a tile, which is a single column and up to ROWS_PER_BLOCK rows. A tile will not
+  // wrap around the bottom of the table. The warp will copy the strings for each row in the tile.
+  // Traversing in row-major order to coalesce the offsets and size reads.
+  auto my_block = cooperative_groups::this_thread_block();
+  auto warp = cooperative_groups::tiled_partition<cudf::detail::warp_size>(my_block);
+#ifdef ASYNC_MEMCPY_SUPPORTED
+  cuda::barrier<cuda::thread_scope_block> block_barrier;
+#endif
+
+  // workaround for not being able to take a reference to a constexpr host variable
+  auto const ROWS_PER_BLOCK = NUM_STRING_ROWS_PER_BLOCK_FROM_ROWS;
+  auto const tiles_per_col = util::div_rounding_up_unsafe(num_rows, ROWS_PER_BLOCK);
+  auto const starting_tile = blockIdx.x * warp.meta_group_size() + warp.meta_group_rank();
+  auto const num_tiles = tiles_per_col * num_string_columns;
+  auto const tile_stride = warp.meta_group_size() * gridDim.x;
+  // Each warp will copy strings in its tile. This is handled by all the threads of a warp passing
+  // the same parameters to async_memcpy and all threads in the warp participating in the copy.
+  for (auto my_tile = starting_tile; my_tile < num_tiles; my_tile += tile_stride) {
+    auto const starting_row = (my_tile % tiles_per_col) * ROWS_PER_BLOCK;
+    auto const col = my_tile / tiles_per_col;
+    auto const str_len = string_lengths[col];
+    auto const str_row_off = string_row_offsets[col];
+    auto const str_col_off = string_column_offsets[col];
+    auto str_col_data = string_col_data[col];
+    for (int row = starting_row; row < starting_row + ROWS_PER_BLOCK && row < num_rows; ++row) {
+      auto const src = &row_data[row_offsets(row, 0) + str_row_off[row]];
+      auto dst = &str_col_data[str_col_off[row]];
+
+#ifdef ASYNC_MEMCPY_SUPPORTED
+      cuda::memcpy_async(warp, dst, src, str_len[row], block_barrier);
+#else
+      for (int c = warp.thread_rank(); c < str_len[row]; c += warp.size()) {
+        dst[c] = src[c];
+      }
+#endif
+    }
+  }
+}
+
+/**
+ * @brief Calculate the dimensions of the kernel for fixed width only columns.
+ *
+ * @param [in] num_columns the number of columns being copied.
+ * @param [in] num_rows the number of rows being copied.
+ * @param [in] size_per_row the size each row takes up when padded.
+ * @param [out] blocks the size of the blocks for the kernel
+ * @param [out] threads the size of the threads for the kernel
+ * @return the size in bytes of shared memory needed for each block.
+ */
+static int calc_fixed_width_kernel_dims(const size_type num_columns, const size_type num_rows,
+                                        const size_type size_per_row, dim3 &blocks, dim3 &threads) {
+  // We have found speed degrades when a thread handles more than 4 columns.
+  // Each block is 2 dimensional. The y dimension indicates the columns.
+  // We limit this to 32 threads in the y dimension so we can still
+  // have at least 32 threads in the x dimension (1 warp) which should
+  // result in better coalescing of memory operations. We also
+  // want to guarantee that we are processing a multiple of 32 threads
+  // in the x dimension because we use atomic operations at the block
+  // level when writing validity data out to main memory, and that would
+  // need to change if we split a word of validity data between blocks.
+  int const y_block_size = min(util::div_rounding_up_safe(num_columns, 4), 32);
+  int const x_possible_block_size = 1024 / y_block_size;
+  // 48KB is the default setting for shared memory per block according to the cuda tutorials
+  // If someone configures the GPU to only have 16 KB this might not work.
+  int const max_shared_size = 48 * 1024;
+  // If we don't have enough shared memory there is no point in having more threads
+  // per block that will just sit idle
+  auto const max_block_size = std::min(x_possible_block_size, max_shared_size / size_per_row);
+  // Make sure that the x dimension is a multiple of 32 this not only helps
+  // coalesce memory access it also lets us do a ballot sync for validity to write
+  // the data back out the warp level.  If x is a multiple of 32 then each thread in the y
+  // dimension is associated with one or more warps, that should correspond to the validity
+  // words directly.
+  int const block_size = (max_block_size / 32) * 32;
+  CUDF_EXPECTS(block_size != 0, "Row size is too large to fit in shared memory");
+
+  // The maximum number of blocks supported in the x dimension is 2 ^ 31 - 1
+  // but in practice having too many can cause some overhead that I don't totally
+  // understand. Playing around with this having as little as 600 blocks appears
+  // to be able to saturate memory on V100, so this is an order of magnitude higher
+  // to try and future proof this a bit.
+  int const num_blocks = std::clamp((num_rows + block_size - 1) / block_size, 1, 10240);
+
+  blocks.x = num_blocks;
+  blocks.y = 1;
+  blocks.z = 1;
+  threads.x = block_size;
+  threads.y = y_block_size;
+  threads.z = 1;
+  return size_per_row * block_size;
+}
+
+/**
+ * When converting to rows it is possible that the size of the table was too big to fit
+ * in a single column. This creates an output column for a subset of the rows in a table
+ * going from start row and containing the next num_rows.  Most of the parameters passed
+ * into this function are common between runs and should be calculated once.
+ */
+static std::unique_ptr<column> fixed_width_convert_to_rows(
+    const size_type start_row, const size_type num_rows, const size_type num_columns,
+    const size_type size_per_row, rmm::device_uvector<size_type> &column_start,
+    rmm::device_uvector<size_type> &column_size, rmm::device_uvector<const int8_t *> &input_data,
+    rmm::device_uvector<const bitmask_type *> &input_nm, const scalar &zero,
+    const scalar &scalar_size_per_row, rmm::cuda_stream_view stream,
+    rmm::mr::device_memory_resource *mr) {
+  int64_t const total_allocation = size_per_row * num_rows;
+  // We made a mistake in the split somehow
+  CUDF_EXPECTS(total_allocation < std::numeric_limits<size_type>::max(),
+               "Table is too large to fit!");
+
+  // Allocate and set the offsets row for the byte array
+  std::unique_ptr<column> offsets =
+      cudf::detail::sequence(num_rows + 1, zero, scalar_size_per_row, stream, mr);
+
+  std::unique_ptr<column> data =
+      make_numeric_column(data_type(type_id::INT8), static_cast<size_type>(total_allocation),
+                          mask_state::UNALLOCATED, stream, mr);
+
+  dim3 blocks;
+  dim3 threads;
+  int shared_size =
+      detail::calc_fixed_width_kernel_dims(num_columns, num_rows, size_per_row, blocks, threads);
+
+  copy_to_rows_fixed_width_optimized<<<blocks, threads, shared_size, stream.value()>>>(
+      start_row, num_rows, num_columns, size_per_row, column_start.data(), column_size.data(),
+      input_data.data(), input_nm.data(), data->mutable_view().data<int8_t>());
+
+  return make_lists_column(num_rows, std::move(offsets), std::move(data), 0,
+                           rmm::device_buffer{0, stream, mr}, stream, mr);
+}
+
+static inline bool are_all_fixed_width(std::vector<data_type> const &schema) {
+  return std::all_of(schema.begin(), schema.end(),
+                     [](const data_type &t) { return is_fixed_width(t); });
+}
+
+/**
+ * @brief Given a set of fixed width columns, calculate how the data will be laid out in memory.
+ *
+ * @param [in] schema the types of columns that need to be laid out.
+ * @param [out] column_start the byte offset where each column starts in the row.
+ * @param [out] column_size the size in bytes of the data for each columns in the row.
+ * @return the size in bytes each row needs.
+ */
+static inline int32_t compute_fixed_width_layout(std::vector<data_type> const &schema,
+                                                 std::vector<size_type> &column_start,
+                                                 std::vector<size_type> &column_size) {
+  // We guarantee that the start of each column is 64-bit aligned so anything can go
+  // there, but to make the code simple we will still do an alignment for it.
+  int32_t at_offset = 0;
+  for (auto col = schema.begin(); col < schema.end(); col++) {
+    size_type s = size_of(*col);
+    column_size.emplace_back(s);
+    std::size_t allocation_needed = s;
+    std::size_t alignment_needed = allocation_needed; // They are the same for fixed width types
+    at_offset = util::round_up_unsafe(at_offset, static_cast<int32_t>(alignment_needed));
+    column_start.emplace_back(at_offset);
+    at_offset += allocation_needed;
+  }
+
+  // Now we need to add in space for validity
+  // Eventually we can think about nullable vs not nullable, but for now we will just always add
+  // it in
+  int32_t const validity_bytes_needed =
+      util::div_rounding_up_safe<int32_t>(schema.size(), CHAR_BIT);
+  // validity comes at the end and is byte aligned so we can pack more in.
+  at_offset += validity_bytes_needed;
+  // Now we need to pad the end so all rows are 64 bit aligned
+  return util::round_up_unsafe(at_offset, JCUDF_ROW_ALIGNMENT);
+}
+
+/**
+ * @brief column sizes and column start offsets for a table
+ */
+struct column_info_s {
+  size_type size_per_row;
+  std::vector<size_type> column_starts;
+  std::vector<size_type> column_sizes;
+  std::vector<size_type> variable_width_column_starts;
+
+  column_info_s &operator=(column_info_s const &other) = delete;
+  column_info_s &operator=(column_info_s &&other) = delete;
+};
+
+/**
+ * @brief Compute information about a table such as bytes per row and offsets.
+ *
+ * @tparam iterator iterator of column schema data
+ * @param begin starting iterator of column schema
+ * @param end ending iterator of column schema
+ * @param column_starts column start offsets
+ * @param column_sizes size in bytes of each column
+ * @return size of the fixed_width data portion of a row.
+ */
+template <typename iterator>
+column_info_s compute_column_information(iterator begin, iterator end) {
+  size_type size_per_row = 0;
+  std::vector<size_type> column_starts;
+  std::vector<size_type> column_sizes;
+  std::vector<size_type> variable_width_column_starts;
+
+  column_starts.reserve(std::distance(begin, end) + 1);
+  column_sizes.reserve(std::distance(begin, end));
+
+  for (auto col_type = begin; col_type != end; ++col_type) {
+    bool const compound_type = is_compound(*col_type);
+
+    // a list or string column will write a single uint64 of data here for offset/length
+    auto const col_size = compound_type ? sizeof(uint32_t) + sizeof(uint32_t) : size_of(*col_type);
+
+    // align size for this type - They are the same for fixed width types and 4 bytes for variable
+    // width length/offset combos
+    size_type const alignment_needed = compound_type ? __alignof(uint32_t) : col_size;
+    size_per_row = util::round_up_unsafe(size_per_row, alignment_needed);
+    if (compound_type) {
+      variable_width_column_starts.push_back(size_per_row);
+    }
+    column_starts.push_back(size_per_row);
+    column_sizes.push_back(col_size);
+    size_per_row += col_size;
+  }
+
+  // add validity offset to the end of fixed_width offsets
+  auto validity_offset = size_per_row;
+  column_starts.push_back(validity_offset);
+
+  // validity is byte-aligned in the JCUDF format
+  size_per_row +=
+      util::div_rounding_up_safe(static_cast<size_type>(std::distance(begin, end)), CHAR_BIT);
+
+  return {size_per_row, std::move(column_starts), std::move(column_sizes),
+          std::move(variable_width_column_starts)};
+}
+
+/**
+ * @brief Build `tile_info` for the validity data to break up the work.
+ *
+ * @param num_columns number of columns in the table
+ * @param num_rows number of rows in the table
+ * @param shmem_limit_per_tile size of shared memory available to a single gpu tile
+ * @param row_batches batched row information for multiple output locations
+ * @return vector of `tile_info` structs for validity data
+ */
+std::vector<detail::tile_info>
+build_validity_tile_infos(size_type const &num_columns, size_type const &num_rows,
+                          size_type const &shmem_limit_per_tile,
+                          std::vector<row_batch> const &row_batches) {
+  auto const desired_rows_and_columns = static_cast<int>(sqrt(shmem_limit_per_tile));
+  auto const column_stride = util::round_up_unsafe(
+      [&]() {
+        if (desired_rows_and_columns > num_columns) {
+          // not many columns, build a single tile for table width and ship it off
+          return num_columns;
+        } else {
+          return util::round_down_safe(desired_rows_and_columns, CHAR_BIT);
+        }
+      }(),
+      JCUDF_ROW_ALIGNMENT);
+
+  // we fit as much as we can given the column stride note that an element in the table takes just 1
+  // bit, but a row with a single element still takes 8 bytes!
+  auto const bytes_per_row = util::round_up_safe(
+      util::div_rounding_up_unsafe(column_stride, CHAR_BIT), JCUDF_ROW_ALIGNMENT);
+  auto const row_stride =
+      std::min(num_rows, util::round_down_safe(shmem_limit_per_tile / bytes_per_row, 64));
+  std::vector<detail::tile_info> validity_tile_infos;
+  validity_tile_infos.reserve(num_columns / column_stride * num_rows / row_stride);
+  for (int col = 0; col < num_columns; col += column_stride) {
+    int current_tile_row_batch = 0;
+    int rows_left_in_batch = row_batches[current_tile_row_batch].row_count;
+    int row = 0;
+    while (row < num_rows) {
+      if (rows_left_in_batch == 0) {
+        current_tile_row_batch++;
+        rows_left_in_batch = row_batches[current_tile_row_batch].row_count;
+      }
+      int const tile_height = std::min(row_stride, rows_left_in_batch);
+      validity_tile_infos.emplace_back(
+          detail::tile_info{col, row, std::min(col + column_stride - 1, num_columns - 1),
+                            row + tile_height - 1, current_tile_row_batch});
+      row += tile_height;
+      rows_left_in_batch -= tile_height;
+    }
+  }
+
+  return validity_tile_infos;
+}
+
+/**
+ * @brief functor that returns the size of a row or 0 is row is greater than the number of rows in
+ * the table
+ *
+ * @tparam RowSize iterator that returns the size of a specific row
+ */
+template <typename RowSize> struct row_size_functor {
+  row_size_functor(size_type row_end, RowSize row_sizes, size_type last_row_end)
+      : _row_end(row_end), _row_sizes(row_sizes), _last_row_end(last_row_end) {}
+
+  __device__ inline uint64_t operator()(int i) const {
+    return i >= _row_end ? 0 : _row_sizes[i + _last_row_end];
+  }
+
+  size_type _row_end;
+  RowSize _row_sizes;
+  size_type _last_row_end;
+};
+
+/**
+ * @brief Builds batches of rows that will fit in the size limit of a column.
+ *
+ * @tparam RowSize iterator that gives the size of a specific row of the table.
+ * @param num_rows Total number of rows in the table
+ * @param row_sizes iterator that gives the size of a specific row of the table.
+ * @param all_fixed_width bool indicating all data in this table is fixed width
+ * @param stream stream to operate on for this work
+ * @param mr memory resource used to allocate any returned data
+ * @returns vector of size_type's that indicate row numbers for batch boundaries and a
+ * device_uvector of row offsets
+ */
+template <typename RowSize>
+batch_data build_batches(size_type num_rows, RowSize row_sizes, bool all_fixed_width,
+                         rmm::cuda_stream_view stream, rmm::mr::device_memory_resource *mr) {
+  auto const total_size = thrust::reduce(rmm::exec_policy(stream), row_sizes, row_sizes + num_rows);
+  auto const num_batches = static_cast<int32_t>(
+      util::div_rounding_up_safe(total_size, static_cast<uint64_t>(MAX_BATCH_SIZE)));
+  auto const num_offsets = num_batches + 1;
+  std::vector<row_batch> row_batches;
+  std::vector<size_type> batch_row_boundaries;
+  device_uvector<size_type> batch_row_offsets(all_fixed_width ? 0 : num_rows, stream);
+
+  // at most max gpu memory / 2GB iterations.
+  batch_row_boundaries.reserve(num_offsets);
+  batch_row_boundaries.push_back(0);
+  size_type last_row_end = 0;
+  device_uvector<uint64_t> cumulative_row_sizes(num_rows, stream);
+  thrust::inclusive_scan(rmm::exec_policy(stream), row_sizes, row_sizes + num_rows,
+                         cumulative_row_sizes.begin());
+
+  // This needs to be split this into 2 gig batches. Care must be taken to avoid a batch larger than
+  // 2 gigs. Imagine a table with 900 meg rows. The batches should occur every 2 rows, but if a
+  // lower bound is run at 2 gigs, 4 gigs, 6 gigs. the batches will be 2 rows, 2 rows, 3 rows, which
+  // will be invalid. The previous batch size must be taken into account when building a new batch.
+  // One way is to pull the batch size back to the host and add it to MAX_BATCH_SIZE for the lower
+  // bound search. The other method involves keeping everything on device, but subtracting the
+  // previous batch from cumulative_row_sizes based on index. This involves no synchronization
+  // between GPU and CPU, but involves more work on the GPU. These further need to be broken on a
+  // 32-row boundary to match the fixed_width optimized versions.
+
+  while (last_row_end < num_rows) {
+    auto offset_row_sizes = thrust::make_transform_iterator(
+        cumulative_row_sizes.begin(),
+        [last_row_end, cumulative_row_sizes = cumulative_row_sizes.data()] __device__(auto i) {
+          return i - cumulative_row_sizes[last_row_end];
+        });
+    auto search_start = offset_row_sizes + last_row_end;
+    auto search_end = offset_row_sizes + num_rows;
+
+    // find the next MAX_BATCH_SIZE boundary
+    auto const lb =
+        thrust::lower_bound(rmm::exec_policy(stream), search_start, search_end, MAX_BATCH_SIZE);
+    size_type const batch_size = lb - search_start;
+
+    size_type const row_end = lb == search_end ?
+                                  batch_size + last_row_end :
+                                  last_row_end + util::round_down_safe(batch_size, 32);
+
+    // build offset list for each row in this batch
+    auto const num_rows_in_batch = row_end - last_row_end;
+
+    // build offset list for each row in this batch
+    auto const num_entries = row_end - last_row_end + 1;
+    device_uvector<size_type> output_batch_row_offsets(num_entries, stream, mr);
+
+    auto row_size_iter_bounded = cudf::detail::make_counting_transform_iterator(
+        0, row_size_functor(row_end, row_sizes, last_row_end));
+
+    thrust::exclusive_scan(rmm::exec_policy(stream), row_size_iter_bounded,
+                           row_size_iter_bounded + num_entries, output_batch_row_offsets.begin());
+
+    auto const batch_bytes = output_batch_row_offsets.element(num_rows_in_batch, stream);
+
+    // The output_batch_row_offsets vector is used as the offset column of the returned data. This
+    // needs to be individually allocated, but the kernel needs a contiguous array of offsets or
+    // more global lookups are necessary.
+    if (!all_fixed_width) {
+      cudaMemcpy(batch_row_offsets.data() + last_row_end, output_batch_row_offsets.data(),
+                 num_rows_in_batch * sizeof(size_type), cudaMemcpyDefault);
+    }
+
+    batch_row_boundaries.push_back(row_end);
+    row_batches.push_back({batch_bytes, num_rows_in_batch, std::move(output_batch_row_offsets)});
+
+    last_row_end = row_end;
+  }
+
+  return {std::move(batch_row_offsets),
+          make_device_uvector_async(batch_row_boundaries, stream,
+                                    rmm::mr::get_current_device_resource()),
+          std::move(batch_row_boundaries), std::move(row_batches)};
+}
+
+/**
+ * @brief Computes the number of tiles necessary given a tile height and batch offsets
+ *
+ * @param batch_row_boundaries row boundaries for each batch
+ * @param desired_tile_height height of each tile in the table
+ * @param stream stream to use
+ * @return number of tiles necessary
+ */
+int compute_tile_counts(device_span<size_type const> const &batch_row_boundaries,
+                        int desired_tile_height, rmm::cuda_stream_view stream) {
+  size_type const num_batches = batch_row_boundaries.size() - 1;
+  device_uvector<size_type> num_tiles(num_batches, stream);
+  auto iter = thrust::make_counting_iterator(0);
+  thrust::transform(rmm::exec_policy(stream), iter, iter + num_batches, num_tiles.begin(),
+                    [desired_tile_height,
+                     batch_row_boundaries =
+                         batch_row_boundaries.data()] __device__(auto batch_index) -> size_type {
+                      return util::div_rounding_up_unsafe(batch_row_boundaries[batch_index + 1] -
+                                                              batch_row_boundaries[batch_index],
+                                                          desired_tile_height);
+                    });
+  return thrust::reduce(rmm::exec_policy(stream), num_tiles.begin(), num_tiles.end());
+}
+
+/**
+ * @brief Builds the `tile_info` structs for a given table.
+ *
+ * @param tiles span of tiles to populate
+ * @param batch_row_boundaries boundary to row batches
+ * @param column_start starting column of the tile
+ * @param column_end ending column of the tile
+ * @param desired_tile_height height of the tile
+ * @param total_number_of_rows total number of rows in the table
+ * @param stream stream to use
+ * @return number of tiles created
+ */
+size_type
+build_tiles(device_span<tile_info> tiles,
+            device_uvector<size_type> const &batch_row_boundaries, // comes from build_batches
+            int column_start, int column_end, int desired_tile_height, int total_number_of_rows,
+            rmm::cuda_stream_view stream) {
+  size_type const num_batches = batch_row_boundaries.size() - 1;
+  device_uvector<size_type> num_tiles(num_batches, stream);
+  auto iter = thrust::make_counting_iterator(0);
+  thrust::transform(rmm::exec_policy(stream), iter, iter + num_batches, num_tiles.begin(),
+                    [desired_tile_height,
+                     batch_row_boundaries =
+                         batch_row_boundaries.data()] __device__(auto batch_index) -> size_type {
+                      return util::div_rounding_up_unsafe(batch_row_boundaries[batch_index + 1] -
+                                                              batch_row_boundaries[batch_index],
+                                                          desired_tile_height);
+                    });
+
+  size_type const total_tiles =
+      thrust::reduce(rmm::exec_policy(stream), num_tiles.begin(), num_tiles.end());
+
+  device_uvector<size_type> tile_starts(num_batches + 1, stream);
+  auto tile_iter = cudf::detail::make_counting_transform_iterator(
+      0, [num_tiles = num_tiles.data(), num_batches] __device__(auto i) {
+        return (i < num_batches) ? num_tiles[i] : 0;
+      });
+  thrust::exclusive_scan(rmm::exec_policy(stream), tile_iter, tile_iter + num_batches + 1,
+                         tile_starts.begin()); // in tiles
+
+  thrust::transform(
+      rmm::exec_policy(stream), iter, iter + total_tiles, tiles.begin(),
+      [=, tile_starts = tile_starts.data(),
+       batch_row_boundaries = batch_row_boundaries.data()] __device__(size_type tile_index) {
+        // what batch this tile falls in
+        auto const batch_index_iter =
+            thrust::upper_bound(thrust::seq, tile_starts, tile_starts + num_batches, tile_index);
+        auto const batch_index = std::distance(tile_starts, batch_index_iter) - 1;
+        // local index within the tile
+        int const local_tile_index = tile_index - tile_starts[batch_index];
+        // the start row for this batch.
+        int const batch_row_start = batch_row_boundaries[batch_index];
+        // the start row for this tile
+        int const tile_row_start = batch_row_start + (local_tile_index * desired_tile_height);
+        // the end row for this tile
+        int const max_row =
+            std::min(total_number_of_rows - 1,
+                     batch_index + 1 > num_batches ?
+                         std::numeric_limits<size_type>::max() :
+                         static_cast<int>(batch_row_boundaries[batch_index + 1]) - 1);
+        int const tile_row_end =
+            std::min(batch_row_start + ((local_tile_index + 1) * desired_tile_height) - 1, max_row);
+
+        // stuff the tile
+        return tile_info{column_start, tile_row_start, column_end, tile_row_end,
+                         static_cast<int>(batch_index)};
+      });
+
+  return total_tiles;
+}
+
+/**
+ * @brief Determines what data should be operated on by each tile for the incoming table.
+ *
+ * @tparam TileCallback Callback that receives the start and end columns of tiles
+ * @param column_sizes vector of the size of each column
+ * @param column_starts vector of the offset of each column
+ * @param first_row_batch_size size of the first row batch to limit max tile size since a tile
+ * is unable to span batches
+ * @param total_number_of_rows total number of rows in the table
+ * @param shmem_limit_per_tile shared memory allowed per tile
+ * @param f callback function called when building a tile
+ */
+template <typename TileCallback>
+void determine_tiles(std::vector<size_type> const &column_sizes,
+                     std::vector<size_type> const &column_starts,
+                     size_type const first_row_batch_size, size_type const total_number_of_rows,
+                     size_type const &shmem_limit_per_tile, TileCallback f) {
+  // tile infos are organized with the tile going "down" the columns this provides the most
+  // coalescing of memory access
+  int current_tile_width = 0;
+  int current_tile_start_col = 0;
+
+  // the ideal tile height has lots of 8-byte reads and 8-byte writes. The optimal read/write would
+  // be memory cache line sized access, but since other tiles will read/write the edges this may not
+  // turn out to be overly important. For now, we will attempt to build a square tile as far as byte
+  // sizes. x * y = shared_mem_size. Which translates to x^2 = shared_mem_size since we want them
+  // equal, so height and width are sqrt(shared_mem_size). The trick is that it's in bytes, not rows
+  // or columns.
+  auto const square_bias = 32; // bias towards columns for performance reasons
+  auto const optimal_square_len = static_cast<size_type>(sqrt(shmem_limit_per_tile));
+  auto const desired_tile_height = util::round_up_safe<int>(
+      std::min(optimal_square_len / square_bias, total_number_of_rows), cudf::detail::warp_size);
+  auto const tile_height = std::clamp(desired_tile_height, 1, first_row_batch_size);
+
+  int row_size = 0;
+
+  // march each column and build the tiles of appropriate sizes
+  for (uint col = 0; col < column_sizes.size(); ++col) {
+    auto const col_size = column_sizes[col];
+
+    // align size for this type
+    auto const alignment_needed = col_size; // They are the same for fixed width types
+    auto const row_size_aligned = util::round_up_unsafe(row_size, alignment_needed);
+    auto const row_size_with_this_col = row_size_aligned + col_size;
+    auto const row_size_with_end_pad =
+        util::round_up_unsafe(row_size_with_this_col, JCUDF_ROW_ALIGNMENT);
+
+    if (row_size_with_end_pad * tile_height > shmem_limit_per_tile) {
+      // too large, close this tile, generate vertical tiles and restart
+      f(current_tile_start_col, col == 0 ? col : col - 1, tile_height);
+
+      row_size =
+          util::round_up_unsafe((column_starts[col] + column_sizes[col]) & 7, alignment_needed);
+      row_size += col_size; // alignment required for shared memory tile boundary to match alignment
+                            // of output row
+      current_tile_start_col = col;
+      current_tile_width = 0;
+    } else {
+      row_size = row_size_with_this_col;
+      current_tile_width++;
+    }
+  }
+
+  // build last set of tiles
+  if (current_tile_width > 0) {
+    f(current_tile_start_col, static_cast<int>(column_sizes.size()) - 1, tile_height);
+  }
+}
+
+/**
+ * @brief convert cudf table into JCUDF row format
+ *
+ * @tparam offsetFunctor functor type for offset functor
+ * @param tbl table to convert to JCUDF row format
+ * @param batch_info information about the batches of data
+ * @param offset_functor functor that returns the starting offset of each row
+ * @param column_info information about incoming columns
+ * @param variable_width_offsets optional vector of offsets for variable-with columns
+ * @param stream stream used
+ * @param mr selected memory resource for returned data
+ * @return vector of list columns containing byte columns of the JCUDF row data
+ */
+template <typename offsetFunctor>
+std::vector<std::unique_ptr<column>> convert_to_rows(
+    table_view const &tbl, batch_data &batch_info, offsetFunctor offset_functor,
+    column_info_s const &column_info,
+    std::optional<rmm::device_uvector<strings_column_view::offset_iterator>> variable_width_offsets,
+    rmm::cuda_stream_view stream, rmm::mr::device_memory_resource *mr) {
+  int device_id;
+  CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+  int total_shmem_in_bytes;
+  CUDF_CUDA_TRY(
+      cudaDeviceGetAttribute(&total_shmem_in_bytes, cudaDevAttrMaxSharedMemoryPerBlock, device_id));
+
+#ifndef __CUDA_ARCH__ // __host__ code.
+  // Need to reduce total shmem available by the size of barriers in the kernel's shared memory
+  total_shmem_in_bytes -=
+      util::round_up_unsafe(sizeof(cuda::barrier<cuda::thread_scope_block>), 16ul);
+#endif // __CUDA_ARCH__
+
+  auto const shmem_limit_per_tile = total_shmem_in_bytes;
+
+  auto const num_rows = tbl.num_rows();
+  auto const fixed_width_only = !variable_width_offsets.has_value();
+
+  auto select_columns = [](auto const &tbl, auto column_predicate) {
+    std::vector<column_view> cols;
+    std::copy_if(tbl.begin(), tbl.end(), std::back_inserter(cols),
+                 [&](auto c) { return column_predicate(c); });
+    return table_view(cols);
+  };
+
+  auto dev_col_sizes = make_device_uvector_async(column_info.column_sizes, stream,
+                                                 rmm::mr::get_current_device_resource());
+  auto dev_col_starts = make_device_uvector_async(column_info.column_starts, stream,
+                                                  rmm::mr::get_current_device_resource());
+
+  // Get the pointers to the input columnar data ready
+  auto const data_begin = thrust::make_transform_iterator(tbl.begin(), [](auto const &c) {
+    return is_compound(c.type()) ? nullptr : c.template data<int8_t>();
+  });
+  std::vector<int8_t const *> input_data(data_begin, data_begin + tbl.num_columns());
+
+  // validity code handles variable and fixed-width data, so give it everything
+  auto const nm_begin =
+      thrust::make_transform_iterator(tbl.begin(), [](auto const &c) { return c.null_mask(); });
+  std::vector<bitmask_type const *> input_nm(nm_begin, nm_begin + tbl.num_columns());
+
+  auto dev_input_data =
+      make_device_uvector_async(input_data, stream, rmm::mr::get_current_device_resource());
+  auto dev_input_nm =
+      make_device_uvector_async(input_nm, stream, rmm::mr::get_current_device_resource());
+
+  // the first batch always exists unless we were sent an empty table
+  auto const first_batch_size = batch_info.row_batches[0].row_count;
+
+  std::vector<rmm::device_buffer> output_buffers;
+  std::vector<int8_t *> output_data;
+  output_data.reserve(batch_info.row_batches.size());
+  output_buffers.reserve(batch_info.row_batches.size());
+  std::transform(batch_info.row_batches.begin(), batch_info.row_batches.end(),
+                 std::back_inserter(output_buffers), [&](auto const &batch) {
+                   return rmm::device_buffer(batch.num_bytes, stream, mr);
+                 });
+  std::transform(output_buffers.begin(), output_buffers.end(), std::back_inserter(output_data),
+                 [](auto &buf) { return static_cast<int8_t *>(buf.data()); });
+
+  auto dev_output_data = make_device_uvector_async(output_data, stream, mr);
+
+  int info_count = 0;
+  detail::determine_tiles(
+      column_info.column_sizes, column_info.column_starts, first_batch_size, num_rows,
+      shmem_limit_per_tile,
+      [&gpu_batch_row_boundaries = batch_info.d_batch_row_boundaries, &info_count,
+       &stream](int const start_col, int const end_col, int const tile_height) {
+        int i = detail::compute_tile_counts(gpu_batch_row_boundaries, tile_height, stream);
+        info_count += i;
+      });
+
+  // allocate space for tiles
+  device_uvector<detail::tile_info> gpu_tile_infos(info_count, stream);
+  int tile_offset = 0;
+
+  detail::determine_tiles(
+      column_info.column_sizes, column_info.column_starts, first_batch_size, num_rows,
+      shmem_limit_per_tile,
+      [&gpu_batch_row_boundaries = batch_info.d_batch_row_boundaries, &gpu_tile_infos, num_rows,
+       &tile_offset, stream](int const start_col, int const end_col, int const tile_height) {
+        tile_offset += detail::build_tiles(
+            {gpu_tile_infos.data() + tile_offset, gpu_tile_infos.size() - tile_offset},
+            gpu_batch_row_boundaries, start_col, end_col, tile_height, num_rows, stream);
+      });
+
+  // build validity tiles for ALL columns, variable and fixed width.
+  auto validity_tile_infos = detail::build_validity_tile_infos(
+      tbl.num_columns(), num_rows, shmem_limit_per_tile, batch_info.row_batches);
+
+  auto dev_validity_tile_infos = make_device_uvector_async(validity_tile_infos, stream,
+                                                           rmm::mr::get_current_device_resource());
+
+  auto const validity_offset = column_info.column_starts.back();
+
+  // blast through the entire table and convert it
+  detail::copy_to_rows<<<gpu_tile_infos.size(), NUM_WARPS_IN_BLOCK * cudf::detail::warp_size,
+                         total_shmem_in_bytes, stream.value()>>>(
+      num_rows, tbl.num_columns(), shmem_limit_per_tile, gpu_tile_infos, dev_input_data.data(),
+      dev_col_sizes.data(), dev_col_starts.data(), offset_functor,
+      batch_info.d_batch_row_boundaries.data(),
+      reinterpret_cast<int8_t **>(dev_output_data.data()));
+
+  // note that validity gets the entire table and not the fixed-width portion
+  detail::copy_validity_to_rows<<<validity_tile_infos.size(),
+                                  NUM_WARPS_IN_BLOCK * cudf::detail::warp_size,
+                                  total_shmem_in_bytes, stream.value()>>>(
+      num_rows, tbl.num_columns(), shmem_limit_per_tile, offset_functor,
+      batch_info.d_batch_row_boundaries.data(), dev_output_data.data(), validity_offset,
+      dev_validity_tile_infos, dev_input_nm.data());
+
+  if (!fixed_width_only) {
+    // build table view for variable-width data only
+    auto const variable_width_table =
+        select_columns(tbl, [](auto col) { return is_compound(col.type()); });
+
+    CUDF_EXPECTS(!variable_width_table.is_empty(), "No variable-width columns when expected!");
+    CUDF_EXPECTS(variable_width_offsets.has_value(), "No variable width offset data!");
+
+    auto const variable_data_begin =
+        thrust::make_transform_iterator(variable_width_table.begin(), [](auto const &c) {
+          strings_column_view const scv{c};
+          return is_compound(c.type()) ? scv.chars().template data<int8_t>() : nullptr;
+        });
+    std::vector<int8_t const *> variable_width_input_data(
+        variable_data_begin, variable_data_begin + variable_width_table.num_columns());
+
+    auto dev_variable_input_data = make_device_uvector_async(
+        variable_width_input_data, stream, rmm::mr::get_current_device_resource());
+    auto dev_variable_col_output_offsets = make_device_uvector_async(
+        column_info.variable_width_column_starts, stream, rmm::mr::get_current_device_resource());
+
+    for (uint i = 0; i < batch_info.row_batches.size(); i++) {
+      auto const batch_row_offset = batch_info.batch_row_boundaries[i];
+      auto const batch_num_rows = batch_info.row_batches[i].row_count;
+
+      dim3 const string_blocks(std::min(
+          MAX_STRING_BLOCKS,
+          util::div_rounding_up_unsafe(batch_num_rows, NUM_STRING_ROWS_PER_BLOCK_TO_ROWS)));
+
+      detail::copy_strings_to_rows<<<string_blocks, NUM_WARPS_IN_BLOCK * cudf::detail::warp_size, 0,
+                                     stream.value()>>>(
+          batch_num_rows, variable_width_table.num_columns(), dev_variable_input_data.data(),
+          dev_variable_col_output_offsets.data(), variable_width_offsets->data(),
+          column_info.size_per_row, offset_functor, batch_row_offset,
+          reinterpret_cast<int8_t *>(output_data[i]));
+    }
+  }
+
+  // split up the output buffer into multiple buffers based on row batch sizes and create list of
+  // byte columns
+  std::vector<std::unique_ptr<column>> ret;
+  ret.reserve(batch_info.row_batches.size());
+  auto counting_iter = thrust::make_counting_iterator(0);
+  std::transform(counting_iter, counting_iter + batch_info.row_batches.size(),
+                 std::back_inserter(ret), [&](auto batch) {
+                   auto const offset_count = batch_info.row_batches[batch].row_offsets.size();
+                   auto offsets =
+                       std::make_unique<column>(data_type{type_id::INT32}, (size_type)offset_count,
+                                                batch_info.row_batches[batch].row_offsets.release(),
+                                                rmm::device_buffer{}, 0);
+                   auto data = std::make_unique<column>(
+                       data_type{type_id::INT8}, batch_info.row_batches[batch].num_bytes,
+                       std::move(output_buffers[batch]), rmm::device_buffer{}, 0);
+
+                   return make_lists_column(
+                       batch_info.row_batches[batch].row_count, std::move(offsets), std::move(data),
+                       0, rmm::device_buffer{0, cudf::get_default_stream(), mr}, stream, mr);
+                 });
+
+  return ret;
+}
+
+} // namespace detail
+
+/**
+ * @brief convert a cudf table to JCUDF row format
+ *
+ * @param tbl incoming table to convert
+ * @param stream stream to use for operations
+ * @param mr memory resource used for returned data
+ * @return vector of list columns containing byte columns of the JCUDF row data
+ */
+std::vector<std::unique_ptr<column>> convert_to_rows(table_view const &tbl,
+                                                     rmm::cuda_stream_view stream,
+                                                     rmm::mr::device_memory_resource *mr) {
+  auto const num_columns = tbl.num_columns();
+  auto const num_rows = tbl.num_rows();
+
+  auto const fixed_width_only = std::all_of(
+      tbl.begin(), tbl.end(), [](column_view const &c) { return is_fixed_width(c.type()); });
+
+  // Break up the work into tiles, which are a starting and ending row/col #. This tile size is
+  // calculated based on the shared memory size available we want a single tile to fill up the
+  // entire shared memory space available for the transpose-like conversion.
+
+  // There are two different processes going on here. The GPU conversion of the data and the writing
+  // of the data into the list of byte columns that are a maximum of 2 gigs each due to offset
+  // maximum size. The GPU conversion portion has to understand this limitation because the column
+  // must own the data inside and as a result it must be a distinct allocation for that column.
+  // Copying the data into these final buffers would be prohibitively expensive, so care is taken to
+  // ensure the GPU writes to the proper buffer. The tiles are broken at the boundaries of specific
+  // rows based on the row sizes up to that point. These are row batches and they are decided first
+  // before building the tiles so the tiles can be properly cut around them.
+
+  auto schema_column_iter =
+      thrust::make_transform_iterator(tbl.begin(), [](auto const &i) { return i.type(); });
+
+  auto column_info =
+      detail::compute_column_information(schema_column_iter, schema_column_iter + num_columns);
+  auto const size_per_row = column_info.size_per_row;
+  if (fixed_width_only) {
+    // total encoded row size. This includes fixed-width data and validity only. It does not include
+    // variable-width data since it isn't copied with the fixed-width and validity kernel.
+    auto row_size_iter = thrust::make_constant_iterator<uint64_t>(
+        util::round_up_unsafe(size_per_row, JCUDF_ROW_ALIGNMENT));
+
+    auto batch_info = detail::build_batches(num_rows, row_size_iter, fixed_width_only, stream, mr);
+
+    detail::fixed_width_row_offset_functor offset_functor(
+        util::round_up_unsafe(size_per_row, JCUDF_ROW_ALIGNMENT));
+
+    return detail::convert_to_rows(tbl, batch_info, offset_functor, std::move(column_info),
+                                   std::nullopt, stream, mr);
+  } else {
+    auto offset_data = detail::build_string_row_offsets(tbl, size_per_row, stream);
+    auto &row_sizes = std::get<0>(offset_data);
+
+    auto row_size_iter = cudf::detail::make_counting_transform_iterator(
+        0, detail::row_size_functor(num_rows, row_sizes.data(), 0));
+
+    auto batch_info = detail::build_batches(num_rows, row_size_iter, fixed_width_only, stream, mr);
+
+    detail::string_row_offset_functor offset_functor(batch_info.batch_row_offsets);
+
+    return detail::convert_to_rows(tbl, batch_info, offset_functor, std::move(column_info),
+                                   std::make_optional(std::move(std::get<1>(offset_data))), stream,
+                                   mr);
+  }
+}
+
+std::vector<std::unique_ptr<column>>
+convert_to_rows_fixed_width_optimized(table_view const &tbl, rmm::cuda_stream_view stream,
+                                      rmm::mr::device_memory_resource *mr) {
+  auto const num_columns = tbl.num_columns();
+
+  std::vector<data_type> schema;
+  schema.resize(num_columns);
+  std::transform(tbl.begin(), tbl.end(), schema.begin(),
+                 [](auto i) -> data_type { return i.type(); });
+
+  if (detail::are_all_fixed_width(schema)) {
+    std::vector<size_type> column_start;
+    std::vector<size_type> column_size;
+
+    int32_t const size_per_row =
+        detail::compute_fixed_width_layout(schema, column_start, column_size);
+    auto dev_column_start = make_device_uvector_async(column_start, stream, mr);
+    auto dev_column_size = make_device_uvector_async(column_size, stream, mr);
+
+    // Make the number of rows per batch a multiple of 32 so we don't have to worry about splitting
+    // validity at a specific row offset.  This might change in the future.
+    auto const max_rows_per_batch =
+        util::round_down_safe(std::numeric_limits<size_type>::max() / size_per_row, 32);
+
+    auto const num_rows = tbl.num_rows();
+
+    // Get the pointers to the input columnar data ready
+    std::vector<const int8_t *> input_data;
+    std::vector<bitmask_type const *> input_nm;
+    for (size_type column_number = 0; column_number < num_columns; column_number++) {
+      column_view cv = tbl.column(column_number);
+      input_data.emplace_back(cv.data<int8_t>());
+      input_nm.emplace_back(cv.null_mask());
+    }
+    auto dev_input_data = make_device_uvector_async(input_data, stream, mr);
+    auto dev_input_nm = make_device_uvector_async(input_nm, stream, mr);
+
+    using ScalarType = scalar_type_t<size_type>;
+    auto zero = make_numeric_scalar(data_type(type_id::INT32), stream.value());
+    zero->set_valid_async(true, stream);
+    static_cast<ScalarType *>(zero.get())->set_value(0, stream);
+
+    auto step = make_numeric_scalar(data_type(type_id::INT32), stream.value());
+    step->set_valid_async(true, stream);
+    static_cast<ScalarType *>(step.get())->set_value(static_cast<size_type>(size_per_row), stream);
+
+    std::vector<std::unique_ptr<column>> ret;
+    for (size_type row_start = 0; row_start < num_rows; row_start += max_rows_per_batch) {
+      size_type row_count = num_rows - row_start;
+      row_count = row_count > max_rows_per_batch ? max_rows_per_batch : row_count;
+      ret.emplace_back(detail::fixed_width_convert_to_rows(
+          row_start, row_count, num_columns, size_per_row, dev_column_start, dev_column_size,
+          dev_input_data, dev_input_nm, *zero, *step, stream, mr));
+    }
+
+    return ret;
+  } else {
+    CUDF_FAIL("Only fixed width types are currently supported");
+  }
+}
+
+/**
+ * @brief convert from JCUDF row format to cudf columns
+ *
+ * @param input vector of list columns containing byte columns of the JCUDF row data
+ * @param schema incoming schema of the data
+ * @param stream stream to use for compute
+ * @param mr memory resource for returned data
+ * @return cudf table of the data
+ */
+std::unique_ptr<table> convert_from_rows(lists_column_view const &input,
+                                         std::vector<data_type> const &schema,
+                                         rmm::cuda_stream_view stream,
+                                         rmm::mr::device_memory_resource *mr) {
+  // verify that the types are what we expect
+  column_view child = input.child();
+  auto const list_type = child.type().id();
+  CUDF_EXPECTS(list_type == type_id::INT8 || list_type == type_id::UINT8,
+               "Only a list of bytes is supported as input");
+
+  // convert any strings in the schema to two int32 columns
+  // This allows us to leverage the fixed-width copy code to fill in our offset and string length
+  // data.
+  std::vector<data_type> string_schema;
+  string_schema.reserve(schema.size());
+  for (auto i : schema) {
+    if (i.id() == type_id::STRING) {
+      string_schema.push_back(data_type(type_id::INT32));
+      string_schema.push_back(data_type(type_id::INT32));
+    } else {
+      string_schema.push_back(i);
+    }
+  }
+
+  auto const num_columns = string_schema.size();
+  auto const num_rows = input.parent().size();
+
+  int device_id;
+  CUDF_CUDA_TRY(cudaGetDevice(&device_id));
+  int total_shmem_in_bytes;
+  CUDF_CUDA_TRY(
+      cudaDeviceGetAttribute(&total_shmem_in_bytes, cudaDevAttrMaxSharedMemoryPerBlock, device_id));
+
+#ifndef __CUDA_ARCH__ // __host__ code.
+  // Need to reduce total shmem available by the size of barriers in the kernel's shared memory
+  total_shmem_in_bytes -=
+      util::round_up_unsafe(sizeof(cuda::barrier<cuda::thread_scope_block>), 16ul);
+#endif // __CUDA_ARCH__
+
+  auto const shmem_limit_per_tile = total_shmem_in_bytes;
+
+  auto column_info = detail::compute_column_information(string_schema.begin(), string_schema.end());
+  auto const size_per_row = util::round_up_unsafe(column_info.size_per_row, JCUDF_ROW_ALIGNMENT);
+
+  // Ideally we would check that the offsets are all the same, etc. but for now this is probably
+  // fine
+  CUDF_EXPECTS(size_per_row * num_rows <= child.size(), "The layout of the data appears to be off");
+  auto dev_col_starts = make_device_uvector_async(column_info.column_starts, stream,
+                                                  rmm::mr::get_current_device_resource());
+  auto dev_col_sizes = make_device_uvector_async(column_info.column_sizes, stream,
+                                                 rmm::mr::get_current_device_resource());
+
+  // Allocate the columns we are going to write into
+  std::vector<std::unique_ptr<column>> output_columns;
+  std::vector<std::unique_ptr<column>> string_row_offset_columns;
+  std::vector<std::unique_ptr<column>> string_length_columns;
+  std::vector<int8_t *> output_data;
+  std::vector<bitmask_type *> output_nm;
+  std::vector<int32_t *> string_row_offsets;
+  std::vector<int32_t *> string_lengths;
+  for (auto i : schema) {
+    auto make_col = [&output_data, &output_nm](data_type type, size_type num_rows, bool include_nm,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource *mr) {
+      auto column = make_fixed_width_column(
+          type, num_rows, include_nm ? mask_state::UNINITIALIZED : mask_state::UNALLOCATED, stream,
+          mr);
+      auto mut = column->mutable_view();
+      output_data.emplace_back(mut.data<int8_t>());
+      if (include_nm) {
+        output_nm.emplace_back(mut.null_mask());
+      }
+      return column;
+    };
+    if (i.id() == type_id::STRING) {
+      auto const int32type = data_type(type_id::INT32);
+      auto offset_col =
+          make_col(int32type, num_rows, true, stream, rmm::mr::get_current_device_resource());
+      string_row_offsets.push_back(offset_col->mutable_view().data<int32_t>());
+      string_row_offset_columns.emplace_back(std::move(offset_col));
+      auto length_col =
+          make_col(int32type, num_rows, false, stream, rmm::mr::get_current_device_resource());
+      string_lengths.push_back(length_col->mutable_view().data<int32_t>());
+      string_length_columns.emplace_back(std::move(length_col));
+      // placeholder
+      output_columns.emplace_back(make_empty_column(type_id::STRING));
+    } else {
+      output_columns.emplace_back(make_col(i, num_rows, true, stream, mr));
+    }
+  }
+
+  auto dev_string_row_offsets =
+      make_device_uvector_async(string_row_offsets, stream, rmm::mr::get_current_device_resource());
+  auto dev_string_lengths =
+      make_device_uvector_async(string_lengths, stream, rmm::mr::get_current_device_resource());
+
+  // build the row_batches from the passed in list column
+  std::vector<detail::row_batch> row_batches;
+  row_batches.push_back(
+      {detail::row_batch{child.size(), num_rows, device_uvector<size_type>(0, stream)}});
+
+  auto dev_output_data =
+      make_device_uvector_async(output_data, stream, rmm::mr::get_current_device_resource());
+  auto dev_output_nm =
+      make_device_uvector_async(output_nm, stream, rmm::mr::get_current_device_resource());
+
+  // only ever get a single batch when going from rows, so boundaries are 0, num_rows
+  constexpr auto num_batches = 2;
+  device_uvector<size_type> gpu_batch_row_boundaries(num_batches, stream);
+
+  thrust::transform(rmm::exec_policy(stream), thrust::make_counting_iterator(0),
+                    thrust::make_counting_iterator(num_batches), gpu_batch_row_boundaries.begin(),
+                    [num_rows] __device__(auto i) { return i == 0 ? 0 : num_rows; });
+
+  int info_count = 0;
+  detail::determine_tiles(
+      column_info.column_sizes, column_info.column_starts, num_rows, num_rows, shmem_limit_per_tile,
+      [&gpu_batch_row_boundaries, &info_count, &stream](int const start_col, int const end_col,
+                                                        int const tile_height) {
+        info_count += detail::compute_tile_counts(gpu_batch_row_boundaries, tile_height, stream);
+      });
+
+  // allocate space for tiles
+  device_uvector<detail::tile_info> gpu_tile_infos(info_count, stream);
+
+  int tile_offset = 0;
+  detail::determine_tiles(
+      column_info.column_sizes, column_info.column_starts, num_rows, num_rows, shmem_limit_per_tile,
+      [&gpu_batch_row_boundaries, &gpu_tile_infos, num_rows, &tile_offset,
+       stream](int const start_col, int const end_col, int const tile_height) {
+        tile_offset += detail::build_tiles(
+            {gpu_tile_infos.data() + tile_offset, gpu_tile_infos.size() - tile_offset},
+            gpu_batch_row_boundaries, start_col, end_col, tile_height, num_rows, stream);
+      });
+
+  dim3 const blocks(gpu_tile_infos.size());
+
+  // validity needs to be calculated based on the actual number of final table columns
+  auto validity_tile_infos =
+      detail::build_validity_tile_infos(schema.size(), num_rows, shmem_limit_per_tile, row_batches);
+
+  auto dev_validity_tile_infos = make_device_uvector_async(validity_tile_infos, stream,
+                                                           rmm::mr::get_current_device_resource());
+
+  dim3 const validity_blocks(validity_tile_infos.size());
+
+  if (dev_string_row_offsets.size() == 0) {
+    detail::fixed_width_row_offset_functor offset_functor(size_per_row);
+
+    detail::copy_from_rows<<<gpu_tile_infos.size(), NUM_WARPS_IN_BLOCK * cudf::detail::warp_size,
+                             total_shmem_in_bytes, stream.value()>>>(
+        num_rows, num_columns, shmem_limit_per_tile, offset_functor,
+        gpu_batch_row_boundaries.data(), dev_output_data.data(), dev_col_sizes.data(),
+        dev_col_starts.data(), gpu_tile_infos, child.data<int8_t>());
+
+    detail::copy_validity_from_rows<<<validity_tile_infos.size(),
+                                      NUM_WARPS_IN_BLOCK * cudf::detail::warp_size,
+                                      total_shmem_in_bytes, stream.value()>>>(
+        num_rows, num_columns, shmem_limit_per_tile, offset_functor,
+        gpu_batch_row_boundaries.data(), dev_output_nm.data(), column_info.column_starts.back(),
+        dev_validity_tile_infos, child.data<int8_t>());
+
+  } else {
+    detail::string_row_offset_functor offset_functor(device_span<size_type const>{input.offsets()});
+    detail::copy_from_rows<<<gpu_tile_infos.size(), NUM_WARPS_IN_BLOCK * cudf::detail::warp_size,
+                             total_shmem_in_bytes, stream.value()>>>(
+        num_rows, num_columns, shmem_limit_per_tile, offset_functor,
+        gpu_batch_row_boundaries.data(), dev_output_data.data(), dev_col_sizes.data(),
+        dev_col_starts.data(), gpu_tile_infos, child.data<int8_t>());
+
+    detail::copy_validity_from_rows<<<validity_tile_infos.size(),
+                                      NUM_WARPS_IN_BLOCK * cudf::detail::warp_size,
+                                      total_shmem_in_bytes, stream.value()>>>(
+        num_rows, num_columns, shmem_limit_per_tile, offset_functor,
+        gpu_batch_row_boundaries.data(), dev_output_nm.data(), column_info.column_starts.back(),
+        dev_validity_tile_infos, child.data<int8_t>());
+
+    std::vector<device_uvector<size_type>> string_col_offsets;
+    std::vector<rmm::device_uvector<char>> string_data_cols;
+    std::vector<size_type *> string_col_offset_ptrs;
+    std::vector<char *> string_data_col_ptrs;
+    for (auto &col_string_lengths : string_lengths) {
+      device_uvector<size_type> output_string_offsets(num_rows + 1, stream, mr);
+      auto tmp = [num_rows, col_string_lengths] __device__(auto const &i) {
+        return i < num_rows ? col_string_lengths[i] : 0;
+      };
+      auto bounded_iter = cudf::detail::make_counting_transform_iterator(0, tmp);
+      thrust::exclusive_scan(rmm::exec_policy(stream), bounded_iter, bounded_iter + num_rows + 1,
+                             output_string_offsets.begin());
+
+      // allocate destination string column
+      rmm::device_uvector<char> string_data(output_string_offsets.element(num_rows, stream), stream,
+                                            mr);
+
+      string_col_offset_ptrs.push_back(output_string_offsets.data());
+      string_data_col_ptrs.push_back(string_data.data());
+      string_col_offsets.push_back(std::move(output_string_offsets));
+      string_data_cols.push_back(std::move(string_data));
+    }
+    auto dev_string_col_offsets = make_device_uvector_async(string_col_offset_ptrs, stream,
+                                                            rmm::mr::get_current_device_resource());
+    auto dev_string_data_cols = make_device_uvector_async(string_data_col_ptrs, stream,
+                                                          rmm::mr::get_current_device_resource());
+
+    dim3 const string_blocks(
+        std::min(std::max(MIN_STRING_BLOCKS, num_rows / NUM_STRING_ROWS_PER_BLOCK_FROM_ROWS),
+                 MAX_STRING_BLOCKS));
+
+    detail::copy_strings_from_rows<<<string_blocks, NUM_WARPS_IN_BLOCK * cudf::detail::warp_size, 0,
+                                     stream.value()>>>(
+        offset_functor, dev_string_row_offsets.data(), dev_string_lengths.data(),
+        dev_string_col_offsets.data(), dev_string_data_cols.data(), child.data<int8_t>(), num_rows,
+        static_cast<cudf::size_type>(string_col_offsets.size()));
+
+    // merge strings back into output_columns
+    int string_idx = 0;
+    for (int i = 0; i < static_cast<int>(schema.size()); ++i) {
+      if (schema[i].id() == type_id::STRING) {
+        // stuff real string column
+        auto const null_count = string_row_offset_columns[string_idx]->null_count();
+        auto string_data = string_row_offset_columns[string_idx].release()->release();
+        output_columns[i] =
+            make_strings_column(num_rows, std::move(string_col_offsets[string_idx]),
+                                std::move(string_data_cols[string_idx]),
+                                std::move(*string_data.null_mask.release()), null_count);
+        string_idx++;
+      }
+    }
+  }
+
+  for (auto &col : output_columns) {
+    col->set_null_count(cudf::null_count(col->view().null_mask(), 0, col->size()));
+  }
+  return std::make_unique<table>(std::move(output_columns));
+}
+
+std::unique_ptr<table> convert_from_rows_fixed_width_optimized(
+    lists_column_view const &input, std::vector<data_type> const &schema,
+    rmm::cuda_stream_view stream, rmm::mr::device_memory_resource *mr) {
+  // verify that the types are what we expect
+  column_view child = input.child();
+  auto const list_type = child.type().id();
+  CUDF_EXPECTS(list_type == type_id::INT8 || list_type == type_id::UINT8,
+               "Only a list of bytes is supported as input");
+
+  auto const num_columns = schema.size();
+
+  if (detail::are_all_fixed_width(schema)) {
+    std::vector<size_type> column_start;
+    std::vector<size_type> column_size;
+
+    auto const num_rows = input.parent().size();
+    auto const size_per_row = detail::compute_fixed_width_layout(schema, column_start, column_size);
+
+    // Ideally we would check that the offsets are all the same, etc. but for now this is probably
+    // fine
+    CUDF_EXPECTS(size_per_row * num_rows == child.size(),
+                 "The layout of the data appears to be off");
+    auto dev_column_start =
+        make_device_uvector_async(column_start, stream, rmm::mr::get_current_device_resource());
+    auto dev_column_size =
+        make_device_uvector_async(column_size, stream, rmm::mr::get_current_device_resource());
+
+    // Allocate the columns we are going to write into
+    std::vector<std::unique_ptr<column>> output_columns;
+    std::vector<int8_t *> output_data;
+    std::vector<bitmask_type *> output_nm;
+    for (int i = 0; i < static_cast<int>(num_columns); i++) {
+      auto column =
+          make_fixed_width_column(schema[i], num_rows, mask_state::UNINITIALIZED, stream, mr);
+      auto mut = column->mutable_view();
+      output_data.emplace_back(mut.data<int8_t>());
+      output_nm.emplace_back(mut.null_mask());
+      output_columns.emplace_back(std::move(column));
+    }
+
+    auto dev_output_data = make_device_uvector_async(output_data, stream, mr);
+    auto dev_output_nm = make_device_uvector_async(output_nm, stream, mr);
+
+    dim3 blocks;
+    dim3 threads;
+    int shared_size =
+        detail::calc_fixed_width_kernel_dims(num_columns, num_rows, size_per_row, blocks, threads);
+
+    detail::copy_from_rows_fixed_width_optimized<<<blocks, threads, shared_size, stream.value()>>>(
+        num_rows, num_columns, size_per_row, dev_column_start.data(), dev_column_size.data(),
+        dev_output_data.data(), dev_output_nm.data(), child.data<int8_t>());
+
+    for (auto &col : output_columns) {
+      col->set_null_count(cudf::null_count(col->view().null_mask(), 0, col->size()));
+    }
+    return std::make_unique<table>(std::move(output_columns));
+  } else {
+    CUDF_FAIL("Only fixed width types are currently supported");
+  }
+}
+
+} // namespace jni
+
+} // namespace cudf
diff --git a/java/src/main/native/src/row_conversion.hpp b/java/src/main/native/src/row_conversion.hpp
new file mode 100644
index 0000000..e463187
--- /dev/null
+++ b/java/src/main/native/src/row_conversion.hpp
@@ -0,0 +1,52 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <memory>
+
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/table/table_view.hpp>
+#include <cudf/utilities/default_stream.hpp>
+#include <rmm/cuda_stream_view.hpp>
+
+namespace cudf {
+namespace jni {
+
+std::vector<std::unique_ptr<cudf::column>> convert_to_rows_fixed_width_optimized(
+    cudf::table_view const &tbl,
+    // TODO need something for validity
+    rmm::cuda_stream_view stream = cudf::get_default_stream(),
+    rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource());
+
+std::vector<std::unique_ptr<cudf::column>>
+convert_to_rows(cudf::table_view const &tbl,
+                // TODO need something for validity
+                rmm::cuda_stream_view stream = cudf::get_default_stream(),
+                rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource());
+
+std::unique_ptr<cudf::table> convert_from_rows_fixed_width_optimized(
+    cudf::lists_column_view const &input, std::vector<cudf::data_type> const &schema,
+    rmm::cuda_stream_view stream = cudf::get_default_stream(),
+    rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource());
+
+std::unique_ptr<cudf::table>
+convert_from_rows(cudf::lists_column_view const &input, std::vector<cudf::data_type> const &schema,
+                  rmm::cuda_stream_view stream = cudf::get_default_stream(),
+                  rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource());
+
+} // namespace jni
+} // namespace cudf
diff --git a/java/src/test/java/ai/rapids/cudf/Aggregation128UtilsTest.java b/java/src/test/java/ai/rapids/cudf/Aggregation128UtilsTest.java
new file mode 100644
index 0000000..11e2aff
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/Aggregation128UtilsTest.java
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.math.BigInteger;
+
+public class Aggregation128UtilsTest extends CudfTestBase {
+  @Test
+  public void testExtractInt32Chunks() {
+    BigInteger[] intvals = new BigInteger[] {
+        null,
+        new BigInteger("123456789abcdef0f0debc9a78563412", 16),
+        new BigInteger("123456789abcdef0f0debc9a78563412", 16),
+        new BigInteger("123456789abcdef0f0debc9a78563412", 16),
+        null
+    };
+    try (ColumnVector cv = ColumnVector.decimalFromBigInt(-38, intvals);
+         ColumnVector chunk1 = Aggregation128Utils.extractInt32Chunk(cv, DType.UINT32, 0);
+         ColumnVector chunk2 = Aggregation128Utils.extractInt32Chunk(cv, DType.UINT32, 1);
+         ColumnVector chunk3 = Aggregation128Utils.extractInt32Chunk(cv, DType.UINT32, 2);
+         ColumnVector chunk4 = Aggregation128Utils.extractInt32Chunk(cv, DType.INT32, 3);
+         Table actualChunks = new Table(chunk1, chunk2, chunk3, chunk4);
+         ColumnVector expectedChunk1 = ColumnVector.fromBoxedUnsignedInts(
+             null, 0x78563412, 0x78563412, 0x78563412, null);
+         ColumnVector expectedChunk2 = ColumnVector.fromBoxedUnsignedInts(
+             null, -0x0f214366, -0x0f214366, -0x0f214366, null);
+         ColumnVector expectedChunk3 = ColumnVector.fromBoxedUnsignedInts(
+             null, -0x65432110, -0x65432110, -0x65432110, null);
+         ColumnVector expectedChunk4 = ColumnVector.fromBoxedInts(
+             null, 0x12345678, 0x12345678, 0x12345678, null);
+         Table expectedChunks = new Table(expectedChunk1, expectedChunk2, expectedChunk3, expectedChunk4)) {
+      AssertUtils.assertTablesAreEqual(expectedChunks, actualChunks);
+    }
+  }
+
+  @Test
+  public void testCombineInt64SumChunks() {
+    try (ColumnVector chunks0 = ColumnVector.fromBoxedUnsignedLongs(
+             null, 0L, 1L, 0L, 0L, 0x12345678L, 0x123456789L, 0x1234567812345678L, 0xfedcba9876543210L);
+         ColumnVector chunks1 = ColumnVector.fromBoxedUnsignedLongs(
+             null, 0L, 2L, 0L, 0L, 0x9abcdef0L, 0x9abcdef01L, 0x1122334455667788L, 0xaceaceaceaceaceaL);
+         ColumnVector chunks2 = ColumnVector.fromBoxedUnsignedLongs(
+             null, 0L, 3L, 0L, 0L, 0x11223344L, 0x556677889L, 0x99aabbccddeeff00L, 0xbdfbdfbdfbdfbdfbL);
+         ColumnVector chunks3 = ColumnVector.fromBoxedLongs(
+             null, 0L, -1L, 0x100000000L, 0x80000000L, 0x55667788L, 0x01234567L, 0x66554434L, -0x42042043L);
+         Table chunksTable = new Table(chunks0, chunks1, chunks2, chunks3);
+         Table actual = Aggregation128Utils.combineInt64SumChunks(chunksTable, DType.create(DType.DTypeEnum.DECIMAL128, -20));
+         ColumnVector expectedOverflows = ColumnVector.fromBoxedBooleans(
+             null, false, false, true, true, false, false, true, false);
+         ColumnVector expectedValues = ColumnVector.decimalFromBigInt(-20,
+             null,
+             new BigInteger("0", 16),
+             new BigInteger("-fffffffcfffffffdffffffff", 16),
+             new BigInteger("0", 16),
+             new BigInteger("-80000000000000000000000000000000", 16),
+             new BigInteger("55667788112233449abcdef012345678", 16),
+             new BigInteger("123456c56677892abcdef0223456789", 16),
+             new BigInteger("ef113244679ace0012345678", 16),
+             new BigInteger("7bf7bf7ba8ca8ca8e9ab678276543210", 16));
+         Table expected = new Table(expectedOverflows, expectedValues)) {
+      AssertUtils.assertTablesAreEqual(expected, actual);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ArrowColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/ArrowColumnVectorTest.java
new file mode 100644
index 0000000..2a11b24
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ArrowColumnVectorTest.java
@@ -0,0 +1,337 @@
+/*
+ *
+ *  Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+
+import ai.rapids.cudf.HostColumnVector.ListType;
+import ai.rapids.cudf.HostColumnVector.StructType;
+
+import org.apache.arrow.memory.BufferAllocator;
+import org.apache.arrow.memory.RootAllocator;
+import org.apache.arrow.vector.BigIntVector;
+import org.apache.arrow.vector.DateDayVector;
+import org.apache.arrow.vector.DecimalVector;
+import org.apache.arrow.vector.Float4Vector;
+import org.apache.arrow.vector.Float8Vector;
+import org.apache.arrow.vector.IntVector;
+import org.apache.arrow.vector.VarCharVector;
+import org.apache.arrow.vector.complex.ListVector;
+import org.apache.arrow.vector.complex.StructVector;
+import org.apache.arrow.vector.util.Text;
+
+import org.junit.jupiter.api.Test;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class ArrowColumnVectorTest extends CudfTestBase {
+
+  @Test
+  void testArrowIntMultiBatches() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.INT32));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    int numVecs = 4;
+    IntVector[] vectors = new IntVector[numVecs];
+    try {
+      ArrayList<Integer> expectedArr = new ArrayList<Integer>();
+      for (int j = 0; j < numVecs; j++) {
+        int pos = 0;
+        int count = 10000;
+        IntVector vector = new IntVector("intVec", allocator);
+        int start = count * j;
+        int end = count * (j + 1);
+        for (int i = start; i < end; i++) {
+          expectedArr.add(i);
+          ((IntVector) vector).setSafe(pos, i);
+          pos++;
+        }
+        vector.setValueCount(count);
+        vectors[j] = vector;
+        ByteBuffer data = vector.getDataBuffer().nioBuffer();
+        ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+        builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      }
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromBoxedInts(expectedArr.toArray(new Integer[0]))) {
+        assertEquals(cv.getType(), DType.INT32);
+        assertColumnsAreEqual(expected, cv, "ints");
+      }
+    } finally {
+      for (int i = 0; i < numVecs; i++) {
+        vectors[i].close();
+      }
+    }
+  }
+
+  @Test
+  void testArrowLong() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.INT64));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (BigIntVector vector = new BigIntVector("vec", allocator)) {
+      ArrayList<Long> expectedArr = new ArrayList<Long>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        expectedArr.add(new Long(i));
+        ((BigIntVector) vector).setSafe(i, i);
+      }
+      vector.setValueCount(count);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromBoxedLongs(expectedArr.toArray(new Long[0]))) {
+        assertEquals(cv.getType(), DType.INT64);
+        assertColumnsAreEqual(expected, cv, "Longs");
+      }
+    }
+  }
+
+  @Test
+  void testArrowLongOnHeap() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.INT64));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (BigIntVector vector = new BigIntVector("vec", allocator)) {
+      ArrayList<Long> expectedArr = new ArrayList<Long>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        expectedArr.add(new Long(i));
+        ((BigIntVector) vector).setSafe(i, i);
+      }
+      vector.setValueCount(count);
+      // test that we handle convert buffer to direct byte buffer if its on the heap
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer dataOnHeap = ByteBuffer.allocate(data.remaining());
+      dataOnHeap.put(data);
+      dataOnHeap.flip();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      ByteBuffer validOnHeap = ByteBuffer.allocate(valid.remaining());
+      validOnHeap.put(data);
+      validOnHeap.flip();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), dataOnHeap, validOnHeap, null);
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromBoxedLongs(expectedArr.toArray(new Long[0]))) {
+        assertEquals(cv.getType(), DType.INT64);
+        assertColumnsAreEqual(expected, cv, "Longs");
+      }
+    }
+  }
+
+  @Test
+  void testArrowDouble() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.FLOAT64));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (Float8Vector vector = new Float8Vector("vec", allocator)) {
+      ArrayList<Double> expectedArr = new ArrayList<Double>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        expectedArr.add(new Double(i));
+        ((Float8Vector) vector).setSafe(i, i);
+      }
+      vector.setValueCount(count);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      double[] array = expectedArr.stream().mapToDouble(i->i).toArray();
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromDoubles(array)) {
+        assertEquals(cv.getType(), DType.FLOAT64);
+        assertColumnsAreEqual(expected, cv, "doubles");
+      }
+    }
+  }
+
+  @Test
+  void testArrowFloat() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.FLOAT32));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (Float4Vector vector = new Float4Vector("vec", allocator)) {
+      ArrayList<Float> expectedArr = new ArrayList<Float>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        expectedArr.add(new Float(i));
+        ((Float4Vector) vector).setSafe(i, i);
+      }
+      vector.setValueCount(count);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      float[] floatArray = new float[expectedArr.size()];
+      int i = 0;
+      for (Float f : expectedArr) {
+        floatArray[i++] = (f != null ? f : Float.NaN); // Or whatever default you want.
+      }
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromFloats(floatArray)) {
+        assertEquals(cv.getType(), DType.FLOAT32);
+        assertColumnsAreEqual(expected, cv, "floats");
+      }
+    }
+  }
+
+  @Test
+  void testArrowString() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.STRING));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (VarCharVector vector = new VarCharVector("vec", allocator)) {
+      ArrayList<String> expectedArr = new ArrayList<String>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        String toAdd = i + "testString";
+        expectedArr.add(toAdd);
+        ((VarCharVector) vector).setSafe(i, new Text(toAdd));
+      }
+      vector.setValueCount(count);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      ByteBuffer offsets = vector.getOffsetBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, offsets);
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromStrings(expectedArr.toArray(new String[0]))) {
+        assertEquals(cv.getType(), DType.STRING);
+        assertColumnsAreEqual(expected, cv, "Strings");
+      }
+    }
+  }
+
+  @Test
+  void testArrowStringOnHeap() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.STRING));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (VarCharVector vector = new VarCharVector("vec", allocator)) {
+      ArrayList<String> expectedArr = new ArrayList<String>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        String toAdd = i + "testString";
+        expectedArr.add(toAdd);
+        ((VarCharVector) vector).setSafe(i, new Text(toAdd));
+      }
+      vector.setValueCount(count);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      ByteBuffer offsets = vector.getOffsetBuffer().nioBuffer();
+      ByteBuffer dataOnHeap = ByteBuffer.allocate(data.remaining());
+      dataOnHeap.put(data);
+      dataOnHeap.flip();
+      ByteBuffer validOnHeap = ByteBuffer.allocate(valid.remaining());
+      validOnHeap.put(data);
+      validOnHeap.flip();
+      ByteBuffer offsetsOnHeap = ByteBuffer.allocate(offsets.remaining());
+      offsetsOnHeap.put(offsets);
+      offsetsOnHeap.flip();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), dataOnHeap, validOnHeap, offsetsOnHeap);
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.fromStrings(expectedArr.toArray(new String[0]));) {
+        assertEquals(cv.getType(), DType.STRING);
+        assertColumnsAreEqual(expected, cv, "Strings");
+      }
+    }
+  }
+
+  @Test
+  void testArrowDays() {
+    ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.TIMESTAMP_DAYS));
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (DateDayVector vector = new DateDayVector("vec", allocator)) {
+      ArrayList<Integer> expectedArr = new ArrayList<Integer>();
+      int count = 10000;
+      for (int i = 0; i < count; i++) {
+        expectedArr.add(i);
+        ((DateDayVector) vector).setSafe(i, i);
+      }
+      vector.setValueCount(count);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      int[] array = expectedArr.stream().mapToInt(i->i).toArray();
+      try (ColumnVector cv = builder.buildAndPutOnDevice();
+           ColumnVector expected = ColumnVector.daysFromInts(array);) {
+        assertEquals(cv.getType(), DType.TIMESTAMP_DAYS);
+        assertColumnsAreEqual(expected, cv, "timestamp days");
+      }
+    }
+  }
+
+  @Test
+  void testArrowDecimalThrows() {
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (DecimalVector vector = new DecimalVector("vec", allocator, 7, 3)) {
+      ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, 3)));
+      ((DecimalVector) vector).setSafe(0, -3);
+      ((DecimalVector) vector).setSafe(1, 1);
+      ((DecimalVector) vector).setSafe(2, 2);
+      ((DecimalVector) vector).setSafe(3, 3);
+      ((DecimalVector) vector).setSafe(4, 4);
+      ((DecimalVector) vector).setSafe(5, 5);
+      vector.setValueCount(6);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      assertThrows(IllegalArgumentException.class, () -> {
+        builder.buildAndPutOnDevice();
+      });
+    }
+  }
+
+  @Test
+  void testArrowDecimal64Throws() {
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (DecimalVector vector = new DecimalVector("vec", allocator, 18, 0)) {
+      ArrowColumnBuilder builder = new ArrowColumnBuilder(new HostColumnVector.BasicType(true, DType.create(DType.DTypeEnum.DECIMAL64, -11)));
+      ((DecimalVector) vector).setSafe(0, -3);
+      ((DecimalVector) vector).setSafe(1, 1);
+      ((DecimalVector) vector).setSafe(2, 2);
+      vector.setValueCount(3);
+      ByteBuffer data = vector.getDataBuffer().nioBuffer();
+      ByteBuffer valid = vector.getValidityBuffer().nioBuffer();
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), data, valid, null);
+      assertThrows(IllegalArgumentException.class, () -> {
+        builder.buildAndPutOnDevice();
+      });
+    }
+  }
+
+  @Test
+  void testArrowListThrows() {
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (ListVector vector = ListVector.empty("list", allocator)) {
+      ArrowColumnBuilder builder = new ArrowColumnBuilder(new ListType(true, new HostColumnVector.BasicType(true, DType.STRING)));
+      // buffer don't matter as we expect it to throw anyway
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), null, null, null);
+      assertThrows(IllegalArgumentException.class, () -> {
+        builder.buildAndPutOnDevice();
+      });
+    }
+  }
+
+  @Test
+  void testArrowStructThrows() {
+    BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
+    try (StructVector vector = StructVector.empty("struct", allocator)) {
+      ArrowColumnBuilder builder = new ArrowColumnBuilder(new StructType(true, new HostColumnVector.BasicType(true, DType.STRING)));
+      // buffer don't matter as we expect it to throw anyway
+      builder.addBatch(vector.getValueCount(), vector.getNullCount(), null, null, null);
+      assertThrows(IllegalArgumentException.class, () -> {
+        builder.buildAndPutOnDevice();
+      });
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/AssertUtils.java b/java/src/test/java/ai/rapids/cudf/AssertUtils.java
new file mode 100644
index 0000000..184e7dd
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/AssertUtils.java
@@ -0,0 +1,272 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+/** Utility methods for asserting in unit tests */
+public class AssertUtils {
+
+  /**
+   * Checks and asserts that passed in columns match
+   * @param expect The expected result column
+   * @param cv The input column
+   */
+  public static void assertColumnsAreEqual(ColumnView expect, ColumnView cv) {
+    assertColumnsAreEqual(expect, cv, "unnamed");
+  }
+
+  /**
+   * Checks and asserts that passed in columns match
+   * @param expected The expected result column
+   * @param cv The input column
+   * @param colName The name of the column
+   */
+  public static void assertColumnsAreEqual(ColumnView expected, ColumnView cv, String colName) {
+    assertPartialColumnsAreEqual(expected, 0, expected.getRowCount(), cv, colName, true, false);
+  }
+
+  /**
+   * Checks and asserts that passed in host columns match
+   * @param expected The expected result host column
+   * @param cv The input host column
+   * @param colName The name of the host column
+   */
+  public static void assertColumnsAreEqual(HostColumnVector expected, HostColumnVector cv, String colName) {
+    assertPartialColumnsAreEqual(expected, 0, expected.getRowCount(), cv, colName, true, false);
+  }
+
+  /**
+   * Checks and asserts that passed in Struct columns match
+   * @param expected The expected result Struct column
+   * @param cv The input Struct column
+   */
+  public static void assertStructColumnsAreEqual(ColumnView expected, ColumnView cv) {
+    assertPartialStructColumnsAreEqual(expected, 0, expected.getRowCount(), cv, "unnamed", true, false);
+  }
+
+  /**
+   * Checks and asserts that passed in Struct columns match
+   * @param expected The expected result Struct column
+   * @param rowOffset The row number to look from
+   * @param length The number of rows to consider
+   * @param cv The input Struct column
+   * @param colName The name of the column
+   * @param enableNullCountCheck Whether to check for nulls in the Struct column
+   * @param enableNullabilityCheck Whether the table have a validity mask
+   */
+  public static void assertPartialStructColumnsAreEqual(ColumnView expected, long rowOffset, long length,
+      ColumnView cv, String colName, boolean enableNullCountCheck, boolean enableNullabilityCheck) {
+    try (HostColumnVector hostExpected = expected.copyToHost();
+         HostColumnVector hostcv = cv.copyToHost()) {
+      assertPartialColumnsAreEqual(hostExpected, rowOffset, length, hostcv, colName, enableNullCountCheck, enableNullabilityCheck);
+    }
+  }
+
+  /**
+   * Checks and asserts that passed in columns match
+   * @param expected The expected result column
+   * @param cv The input column
+   * @param colName The name of the column
+   * @param enableNullCheck Whether to check for nulls in the column
+   * @param enableNullabilityCheck Whether the table have a validity mask
+   */
+  public static void assertPartialColumnsAreEqual(ColumnView expected, long rowOffset, long length,
+      ColumnView cv, String colName, boolean enableNullCheck, boolean enableNullabilityCheck) {
+    try (HostColumnVector hostExpected = expected.copyToHost();
+         HostColumnVector hostcv = cv.copyToHost()) {
+      assertPartialColumnsAreEqual(hostExpected, rowOffset, length, hostcv, colName, enableNullCheck, enableNullabilityCheck);
+    }
+  }
+
+  /**
+   * Checks and asserts that passed in host columns match
+   * @param expected The expected result host column
+   * @param rowOffset start row index
+   * @param length  number of rows from starting offset
+   * @param cv The input host column
+   * @param colName The name of the host column
+   * @param enableNullCountCheck Whether to check for nulls in the host column
+   */
+  public static void assertPartialColumnsAreEqual(HostColumnVectorCore expected, long rowOffset, long length,
+                                                  HostColumnVectorCore cv, String colName, boolean enableNullCountCheck, boolean enableNullabilityCheck) {
+    assertEquals(expected.getType(), cv.getType(), "Type For Column " + colName);
+    assertEquals(length, cv.getRowCount(), "Row Count For Column " + colName);
+    assertEquals(expected.getNumChildren(), cv.getNumChildren(), "Child Count for Column " + colName);
+    if (enableNullCountCheck) {
+      assertEquals(expected.getNullCount(), cv.getNullCount(), "Null Count For Column " + colName);
+    } else {
+      // TODO add in a proper check when null counts are supported by serializing a partitioned column
+    }
+    if (enableNullabilityCheck) {
+      assertEquals(expected.hasValidityVector(), cv.hasValidityVector(), "Column nullability is different than expected");
+    }
+    DType type = expected.getType();
+    for (long expectedRow = rowOffset; expectedRow < (rowOffset + length); expectedRow++) {
+      long tableRow = expectedRow - rowOffset;
+      assertEquals(expected.isNull(expectedRow), cv.isNull(tableRow),
+          "NULL for Column " + colName + " Row " + tableRow);
+      if (!expected.isNull(expectedRow)) {
+        switch (type.typeId) {
+          case BOOL8: // fall through
+          case INT8: // fall through
+          case UINT8:
+            assertEquals(expected.getByte(expectedRow), cv.getByte(tableRow),
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case INT16: // fall through
+          case UINT16:
+            assertEquals(expected.getShort(expectedRow), cv.getShort(tableRow),
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case INT32: // fall through
+          case UINT32: // fall through
+          case TIMESTAMP_DAYS:
+          case DURATION_DAYS:
+          case DECIMAL32:
+            assertEquals(expected.getInt(expectedRow), cv.getInt(tableRow),
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case INT64: // fall through
+          case UINT64: // fall through
+          case DURATION_MICROSECONDS: // fall through
+          case DURATION_MILLISECONDS: // fall through
+          case DURATION_NANOSECONDS: // fall through
+          case DURATION_SECONDS: // fall through
+          case TIMESTAMP_MICROSECONDS: // fall through
+          case TIMESTAMP_MILLISECONDS: // fall through
+          case TIMESTAMP_NANOSECONDS: // fall through
+          case TIMESTAMP_SECONDS:
+          case DECIMAL64:
+            assertEquals(expected.getLong(expectedRow), cv.getLong(tableRow),
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case DECIMAL128:
+            assertEquals(expected.getBigDecimal(expectedRow), cv.getBigDecimal(tableRow),
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case FLOAT32:
+            CudfTestBase.assertEqualsWithinPercentage(expected.getFloat(expectedRow), cv.getFloat(tableRow), 0.0001,
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case FLOAT64:
+            CudfTestBase.assertEqualsWithinPercentage(expected.getDouble(expectedRow), cv.getDouble(tableRow), 0.0001,
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case STRING:
+            assertArrayEquals(expected.getUTF8(expectedRow), cv.getUTF8(tableRow),
+                "Column " + colName + " Row " + tableRow);
+            break;
+          case LIST:
+            HostMemoryBuffer expectedOffsets = expected.getOffsets();
+            HostMemoryBuffer cvOffsets = cv.getOffsets();
+            int expectedChildRows = expectedOffsets.getInt((expectedRow + 1) * 4) -
+                expectedOffsets.getInt(expectedRow * 4);
+            int cvChildRows = cvOffsets.getInt((tableRow + 1) * 4) -
+                cvOffsets.getInt(tableRow * 4);
+            assertEquals(expectedChildRows, cvChildRows, "Child row count for Column " +
+                colName + " Row " + tableRow);
+            break;
+          case STRUCT:
+            // parent column only has validity which was checked above
+            break;
+          default:
+            throw new IllegalArgumentException(type + " is not supported yet");
+        }
+      }
+    }
+
+    if (type.isNestedType()) {
+      switch (type.typeId) {
+        case LIST:
+          int expectedChildRowOffset = 0;
+          int numChildRows = 0;
+          if (length > 0) {
+            HostMemoryBuffer expectedOffsets = expected.getOffsets();
+            HostMemoryBuffer cvOffsets = cv.getOffsets();
+            expectedChildRowOffset = expectedOffsets.getInt(rowOffset * 4);
+            numChildRows = expectedOffsets.getInt((rowOffset + length) * 4) -
+                expectedChildRowOffset;
+          }
+          assertPartialColumnsAreEqual(expected.getNestedChildren().get(0), expectedChildRowOffset,
+              numChildRows, cv.getNestedChildren().get(0), colName + " list child",
+              enableNullCountCheck, enableNullabilityCheck);
+          break;
+        case STRUCT:
+          List<HostColumnVectorCore> expectedChildren = expected.getNestedChildren();
+          List<HostColumnVectorCore> cvChildren = cv.getNestedChildren();
+          for (int i = 0; i < expectedChildren.size(); i++) {
+            HostColumnVectorCore expectedChild = expectedChildren.get(i);
+            HostColumnVectorCore cvChild = cvChildren.get(i);
+            String childName = colName + " child " + i;
+            assertEquals(length, cvChild.getRowCount(), "Row Count for Column " + colName);
+            assertPartialColumnsAreEqual(expectedChild, rowOffset, length, cvChild,
+                colName, enableNullCountCheck, enableNullabilityCheck);
+          }
+          break;
+        default:
+          throw new IllegalArgumentException(type + " is not supported yet");
+      }
+    }
+  }
+
+  /**
+   * Checks and asserts that the two tables from a given rowindex match based on a provided schema.
+   * @param expected the expected result table
+   * @param rowOffset the row number to start checking from
+   * @param length the number of rows to check
+   * @param table the input table to compare against expected
+   * @param enableNullCheck whether to check for nulls or not
+   * @param enableNullabilityCheck whether the table have a validity mask
+   */
+  public static void assertPartialTablesAreEqual(Table expected, long rowOffset, long length, Table table,
+                                                 boolean enableNullCheck, boolean enableNullabilityCheck) {
+    assertEquals(expected.getNumberOfColumns(), table.getNumberOfColumns());
+    assertEquals(length, table.getRowCount(), "ROW COUNT");
+    for (int col = 0; col < expected.getNumberOfColumns(); col++) {
+      ColumnVector expect = expected.getColumn(col);
+      ColumnVector cv = table.getColumn(col);
+      String name = String.valueOf(col);
+      if (rowOffset != 0 || length != expected.getRowCount()) {
+        name = name + " PART " + rowOffset + "-" + (rowOffset + length - 1);
+      }
+      assertPartialColumnsAreEqual(expect, rowOffset, length, cv, name, enableNullCheck, enableNullabilityCheck);
+    }
+  }
+
+  /**
+   * Checks and asserts that the two tables match
+   * @param expected the expected result table
+   * @param table the input table to compare against expected
+   */
+  public static void assertTablesAreEqual(Table expected, Table table) {
+    assertPartialTablesAreEqual(expected, 0, expected.getRowCount(), table, true, false);
+  }
+
+  public static void assertTableTypes(DType[] expectedTypes, Table t) {
+    int len = t.getNumberOfColumns();
+    assertEquals(expectedTypes.length, len);
+    for (int i = 0; i < len; i++) {
+      ColumnVector vec = t.getColumn(i);
+      DType type = vec.getType();
+      assertEquals(expectedTypes[i], type, "Types don't match at " + i);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/BinaryOpTest.java b/java/src/test/java/ai/rapids/cudf/BinaryOpTest.java
new file mode 100644
index 0000000..28fcfb7
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/BinaryOpTest.java
@@ -0,0 +1,1925 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.BasicType;
+import ai.rapids.cudf.HostColumnVector.Builder;
+import ai.rapids.cudf.HostColumnVector.DataType;
+import ai.rapids.cudf.HostColumnVector.StructData;
+import ai.rapids.cudf.HostColumnVector.StructType;
+
+import org.junit.jupiter.api.Test;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.util.Arrays;
+import java.util.List;
+import java.util.stream.IntStream;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static ai.rapids.cudf.TestUtils.*;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class BinaryOpTest extends CudfTestBase {
+  private static final int dec32Scale_1 = 2;
+  private static final int dec32Scale_2 = -3;
+  private static final int dec64Scale_1 = 6;
+  private static final int dec64Scale_2 = -2;
+
+  private static final Integer[] INTS_1 = new Integer[]{1, 2, 3, 4, 5, null, 100};
+  private static final Integer[] INTS_2 = new Integer[]{10, 20, 30, 40, 50, 60, 100};
+  private static final Integer[] UINTS_1 = new Integer[]{10, -20, 30, -40, 50, -60, 100};
+  private static final Integer[] UINTS_2 = new Integer[]{-10, 20, -30, 40, 50, -60, 100};
+  private static final Byte[] BYTES_1 = new Byte[]{-1, 7, 123, null, 50, 60, 100};
+  private static final Byte[] UBYTES_1 = new Byte[]{-1, 7, 123, null, -50, 60, -100};
+  private static final Float[] FLOATS_1 = new Float[]{1f, 10f, 100f, 5.3f, 50f, 100f, null};
+  private static final Float[] FLOATS_2 = new Float[]{10f, 20f, 30f, 40f, 50f, 60f, 100f};
+  private static final Long[] LONGS_1 = new Long[]{1L, 2L, 3L, 4L, 5L, null, 100L};
+  private static final Long[] LONGS_2 = new Long[]{10L, 20L, 30L, 40L, 50L, 60L, 100L};
+  private static final Double[] DOUBLES_1 = new Double[]{1.0, 10.0, 100.0, 5.3, 50.0, 100.0, null};
+  private static final Double[] DOUBLES_2 = new Double[]{10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 100.0};
+  private static final Boolean[] BOOLEANS_1 = new Boolean[]{true, true, false, false, null};
+  private static final Boolean[] BOOLEANS_2 = new Boolean[]{true, false, true, false, true};
+  private static final int[] SHIFT_BY = new int[]{1, 2, 3, 4, 5, 10, 20};
+  private static final int[] DECIMAL32_1 = new int[]{1000, 2000, 3000, 4000, 5000};
+  private static final int[] DECIMAL32_2 = new int[]{100, 200, 300, 400, 50};
+  private static final long[] DECIMAL64_1 = new long[]{10L, 23L, 12L, 24L, 123456789L};
+  private static final long[] DECIMAL64_2 = new long[]{33041L, 97290L, 36438L, 25379L, 48473L};
+
+  private static final StructData INT_SD_1 = new StructData(1);
+  private static final StructData INT_SD_2 = new StructData(2);
+  private static final StructData INT_SD_3 = new StructData(3);
+  private static final StructData INT_SD_4 = new StructData(4);
+  private static final StructData INT_SD_5 = new StructData(5);
+  private static final StructData INT_SD_NULL = new StructData((List) null);
+  private static final StructData INT_SD_100 = new StructData(100);
+
+  private static final StructData[] int_struct_data_1 =
+      new StructData[]{null, INT_SD_1, null, INT_SD_3, INT_SD_4, INT_SD_5, INT_SD_NULL, INT_SD_100};
+  private static final StructData[] int_struct_data_2 =
+      new StructData[]{null, null, INT_SD_2, INT_SD_3, INT_SD_100, INT_SD_5, INT_SD_NULL, INT_SD_4};
+  private static final DataType structType =
+      new StructType(true, new BasicType(true, DType.INT32));
+
+  private static final BigInteger[] DECIMAL128_1 = new BigInteger[]{new BigInteger("1234567891234567"), new BigInteger("1234567891234567"),
+      new BigInteger("1234567891234567"), new BigInteger("1234567891234567"), new BigInteger("1234567891234567")};
+  private static final BigInteger[] DECIMAL128_2 = new BigInteger[]{new BigInteger("234567891234567"), new BigInteger("234567891234567"),
+      new BigInteger("234567891234567"), new BigInteger("234567891234567"), new BigInteger("234567891234567")};
+
+  private static final BigDecimal[] BIGDECIMAL32_1 = new BigDecimal[]{
+          BigDecimal.valueOf(12, dec32Scale_1),
+          BigDecimal.valueOf(11, dec32Scale_1),
+          BigDecimal.valueOf(20, dec32Scale_1),
+          null,
+          BigDecimal.valueOf(25, dec32Scale_1)
+  };
+
+  private static final BigDecimal[] BIGDECIMAL32_2 = new BigDecimal[]{
+          BigDecimal.valueOf(12, dec32Scale_2),
+          BigDecimal.valueOf(2, dec32Scale_2),
+          null,
+          BigDecimal.valueOf(16, dec32Scale_2),
+          BigDecimal.valueOf(10, dec32Scale_2)
+  };
+
+  interface CpuOpVV {
+    void computeNullSafe(Builder ret, HostColumnVector lhs, HostColumnVector rhs, int index);
+  }
+
+  interface CpuOpVS<S> {
+    void computeNullSafe(Builder ret, HostColumnVector lhs, S rhs, int index);
+  }
+
+  interface CpuOpSV<S> {
+    void computeNullSafe(Builder ret, S lhs, HostColumnVector rhs, int index);
+  }
+
+  public static ColumnVector forEach(DType retType, ColumnVector lhs, ColumnVector rhs, CpuOpVV op) {
+    return forEach(retType, lhs, rhs, op, false);
+  }
+
+  public static ColumnVector forEach(DType retType, ColumnVector lhs, ColumnVector rhs, CpuOpVV op, boolean evalNulls) {
+    int len = (int)lhs.getRowCount();
+    try (HostColumnVector hostLHS  = lhs.copyToHost();
+         HostColumnVector hostRHS = rhs.copyToHost();
+         Builder builder = HostColumnVector.builder(retType, len)) {
+      for (int i = 0; i < len; i++) {
+        if (!evalNulls && (hostLHS.isNull(i) || hostRHS.isNull(i))) {
+          builder.appendNull();
+        } else {
+          op.computeNullSafe(builder, hostLHS, hostRHS, i);
+        }
+      }
+      return builder.buildAndPutOnDevice();
+    }
+  }
+
+  public static <S> ColumnVector forEachS(DType retType, ColumnVector lhs, S rhs, CpuOpVS<S> op) {
+    return forEachS(retType, lhs, rhs, op, false);
+  }
+
+  public static <S> ColumnVector forEachS(DType retType, ColumnVector lhs, S rhs, CpuOpVS<S> op, boolean evalNulls) {
+    int len = (int)lhs.getRowCount();
+    try (HostColumnVector hostLHS = lhs.copyToHost();
+         Builder builder = HostColumnVector.builder(retType, len)) {
+      for (int i = 0; i < len; i++) {
+        if (!evalNulls && (hostLHS.isNull(i) || rhs == null)) {
+          builder.appendNull();
+        } else {
+          op.computeNullSafe(builder, hostLHS, rhs, i);
+        }
+      }
+      return builder.buildAndPutOnDevice();
+    }
+  }
+
+  public static <S> ColumnVector forEachS(DType retType, S lhs, ColumnVector rhs, CpuOpSV<S> op) {
+    return forEachS(retType, lhs, rhs, op, false);
+  }
+
+  public static <S> ColumnVector forEachS(DType retType, S lhs, ColumnVector rhs, CpuOpSV<S> op, boolean evalNulls) {
+    int len = (int)rhs.getRowCount();
+    try (HostColumnVector hostRHS = rhs.copyToHost();
+        Builder builder = HostColumnVector.builder(retType, len)) {
+      for (int i = 0; i < len; i++) {
+        if (!evalNulls && (hostRHS.isNull(i) || lhs == null)) {
+          builder.appendNull();
+        } else {
+          op.computeNullSafe(builder, lhs, hostRHS, i);
+        }
+      }
+      return builder.buildAndPutOnDevice();
+    }
+  }
+
+  private double pmod(double i1, double i2) {
+    double r = i1 % i2;
+    if (r < 0) return (r + i2) % i2;
+    else return r;
+  }
+
+  private long pmod(long i1, long i2) {
+   long r = i1 % i2;
+   if (r < 0) return (r + i2) % i2;
+   else return r;
+  }
+
+  private int pmod(int i1, int i2) {
+    int r = i1 % i2;
+    if (r < 0) return (r + i2) % i2;
+    else return r;
+  }
+
+  @Test
+  public void testPmod() {
+
+    Double[] d1 = TestUtils.getDoubles(23423423424L, 50, ALL ^ NULL);
+    Double[] d2 = TestUtils.getDoubles(56456456454L, 50, NULL);
+
+    Integer[] i1 = TestUtils.getIntegers(76576554564L, 50, NULL);
+    Integer[] i2 = TestUtils.getIntegers(34502395934L, 50, NULL);
+
+    Long[] l1 = TestUtils.getLongs(29843248234L, 50, NULL);
+    Long[] l2 = TestUtils.getLongs(23423049234L, 50, NULL);
+
+    try (ColumnVector icv1 = ColumnVector.fromBoxedInts(i1);
+         ColumnVector icv2 = ColumnVector.fromBoxedInts(i2);
+         ColumnVector lcv1 = ColumnVector.fromBoxedLongs(l1);
+         ColumnVector lcv2 = ColumnVector.fromBoxedLongs(l2);
+         ColumnVector dcv1 = ColumnVector.fromBoxedDoubles(d1);
+         ColumnVector dcv2 = ColumnVector.fromBoxedDoubles(d2)) {
+
+      // Ints
+      try (ColumnVector pmod = icv1.pmod(icv2);
+           ColumnVector expected = forEach(DType.INT32, icv1, icv2,
+               (b, l, r, i) -> b.append(pmod(l.getInt(i), r.getInt(i))))) {
+        assertColumnsAreEqual(expected, pmod, "int32");
+      }
+
+      try (Scalar s = Scalar.fromInt(11);
+           ColumnVector pmod = icv1.pmod(s);
+           ColumnVector expected = forEachS(DType.INT32, icv1, 11,
+               (b, l, r, i) -> b.append(pmod(l.getInt(i) , r)))) {
+        assertColumnsAreEqual(expected, pmod, "int32 + scalar int32");
+      }
+
+      try (Scalar s = Scalar.fromInt(11);
+           ColumnVector pmod = s.pmod(icv2);
+           ColumnVector expected = forEachS(DType.INT32, 11, icv2,
+               (b, l, r, i) -> b.append(pmod(l , r.getInt(i))))) {
+        assertColumnsAreEqual(expected, pmod, "scalar int32 + int32");
+      }
+
+      // Long
+      try (ColumnVector pmod = lcv1.pmod(lcv2);
+           ColumnVector expected = forEach(DType.INT64, lcv1, lcv2,
+               (b, l, r, i) -> b.append(pmod(l.getLong(i), r.getLong(i))))) {
+        assertColumnsAreEqual(expected, pmod, "int64");
+      }
+
+      try (Scalar s = Scalar.fromLong(11L);
+           ColumnVector pmod = lcv1.pmod(s);
+           ColumnVector expected = forEachS(DType.INT64, lcv1, 11L,
+               (b, l, r, i) -> b.append(pmod(l.getLong(i) , r)))) {
+        assertColumnsAreEqual(expected, pmod, "int64 + scalar int64");
+      }
+
+      try (Scalar s = Scalar.fromLong(11L);
+           ColumnVector pmod = s.pmod(lcv2);
+           ColumnVector expected = forEachS(DType.INT64, 11L, lcv2,
+               (b, l, r, i) -> b.append(pmod(l , r.getLong(i))))) {
+        assertColumnsAreEqual(expected, pmod, "scalar int64 + int64");
+      }
+
+      // Double
+      try (ColumnVector pmod = dcv1.pmod(dcv2);
+           ColumnVector expected = forEach(DType.FLOAT64, dcv1, dcv2,
+               (b, l, r, i) -> b.append(pmod(l.getDouble(i), r.getDouble(i))))) {
+        assertColumnsAreEqual(expected, pmod, "float64");
+      }
+
+      try (Scalar s = Scalar.fromDouble(1.1d);
+           ColumnVector pmod = dcv1.pmod(s);
+           ColumnVector expected = forEachS(DType.FLOAT64, dcv1, 1.1d,
+               (b, l, r, i) -> b.append(pmod(l.getDouble(i) , r)))) {
+        assertColumnsAreEqual(expected, pmod, "float64 + scalar float64");
+      }
+
+      try (Scalar s = Scalar.fromDouble(1.1d);
+           ColumnVector pmod = s.pmod(dcv2);
+           ColumnVector expected = forEachS(DType.FLOAT64, 1.1d, dcv2,
+               (b, l, r, i) -> b.append(pmod(l , r.getDouble(i))))) {
+        assertColumnsAreEqual(expected, pmod, "scalar float64 + float64");
+      }
+    }
+  }
+
+  @Test
+  public void testAdd() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedInts(INTS_2);
+         ColumnVector uicv1 = ColumnVector.fromBoxedUnsignedInts(UINTS_1);
+         ColumnVector uicv2 = ColumnVector.fromBoxedUnsignedInts(UINTS_2);
+         ColumnVector bcv1 = ColumnVector.fromBoxedBytes(BYTES_1);
+         ColumnVector ubcv1 = ColumnVector.fromBoxedUnsignedBytes(UBYTES_1);
+         ColumnVector fcv1 = ColumnVector.fromBoxedFloats(FLOATS_1);
+         ColumnVector fcv2 = ColumnVector.fromBoxedFloats(FLOATS_2);
+         ColumnVector lcv1 = ColumnVector.fromBoxedLongs(LONGS_1);
+         ColumnVector lcv2 = ColumnVector.fromBoxedLongs(LONGS_2);
+         ColumnVector ulcv1 = ColumnVector.fromBoxedUnsignedLongs(LONGS_1);
+         ColumnVector dcv1 = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector dcv2 = ColumnVector.fromBoxedDoubles(DOUBLES_2);
+         ColumnVector dec32cv1 = ColumnVector.fromDecimals(BIGDECIMAL32_1);
+         ColumnVector dec32cv2 = ColumnVector.fromDecimals(BIGDECIMAL32_2);
+         ColumnVector dec64cv1 = ColumnVector.decimalFromLongs(-dec64Scale_1, DECIMAL64_1);
+         ColumnVector dec64cv2 = ColumnVector.decimalFromLongs(-dec64Scale_2, DECIMAL64_2);
+         ColumnVector dec128cv1 = ColumnVector.decimalFromBigInt(-dec64Scale_1, DECIMAL128_1);
+         ColumnVector dec128cv2 = ColumnVector.decimalFromBigInt(-dec64Scale_2, DECIMAL128_2)) {
+      try (ColumnVector add = icv1.add(icv2);
+           ColumnVector expected = forEach(DType.INT32, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) + r.getInt(i)))) {
+        assertColumnsAreEqual(expected, add, "int32");
+      }
+
+      try (ColumnVector add = uicv1.add(uicv2);
+           ColumnVector expected = forEach(DType.UINT32, uicv1, uicv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) + r.getInt(i)))) {
+        assertColumnsAreEqual(expected, add, "uint32");
+      }
+
+      try (ColumnVector add = icv1.add(bcv1);
+           ColumnVector expected = forEach(DType.INT32, icv1, bcv1,
+                   (b, l, r, i) -> b.append(l.getInt(i) + r.getByte(i)))) {
+        assertColumnsAreEqual(expected, add, "int32 + byte");
+      }
+
+      try (ColumnVector add = uicv1.add(ubcv1);
+           ColumnVector expected = forEach(DType.UINT32, uicv1, ubcv1,
+                   (b, l, r, i) -> b.append(l.getInt(i) + Byte.toUnsignedInt(r.getByte(i))))) {
+        assertColumnsAreEqual(expected, add, "uint32 + uint8");
+      }
+
+      try (ColumnVector add = fcv1.add(fcv2);
+           ColumnVector expected = forEach(DType.FLOAT32, fcv1, fcv2,
+                   (b, l, r, i) -> b.append(l.getFloat(i) + r.getFloat(i)))) {
+        assertColumnsAreEqual(expected, add, "float32");
+      }
+
+      try (ColumnVector addIntFirst = icv1.add(fcv2, DType.FLOAT32);
+           ColumnVector addFloatFirst = fcv2.add(icv1)) {
+        assertColumnsAreEqual(addIntFirst, addFloatFirst, "int + float vs float + int");
+      }
+
+      try (ColumnVector add = lcv1.add(lcv2);
+           ColumnVector expected = forEach(DType.INT64, lcv1, lcv2,
+                   (b, l, r, i) -> b.append(l.getLong(i) + r.getLong(i)))) {
+        assertColumnsAreEqual(expected, add, "int64");
+      }
+
+      try (ColumnVector add = lcv1.add(bcv1);
+           ColumnVector expected = forEach(DType.INT64, lcv1, bcv1,
+                   (b, l, r, i) -> b.append(l.getLong(i) + r.getByte(i)))) {
+        assertColumnsAreEqual(expected, add, "int64 + byte");
+      }
+
+      try (ColumnVector add = ulcv1.add(ubcv1);
+           ColumnVector expected = forEach(DType.UINT64, ulcv1, ubcv1,
+                   (b, l, r, i) -> b.append(l.getLong(i) + Byte.toUnsignedLong(r.getByte(i))))) {
+        assertColumnsAreEqual(expected, add, "int64 + byte");
+      }
+
+      try (ColumnVector add = dcv1.add(dcv2);
+           ColumnVector expected = forEach(DType.FLOAT64, dcv1, dcv2,
+                   (b, l, r, i) -> b.append(l.getDouble(i) + r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, add, "float64");
+      }
+
+      try (ColumnVector addIntFirst = icv1.add(dcv2, DType.FLOAT64);
+           ColumnVector addDoubleFirst = dcv2.add(icv1)) {
+        assertColumnsAreEqual(addIntFirst, addDoubleFirst, "int + double vs double + int");
+      }
+
+      try (ColumnVector add = dec32cv1.add(dec32cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL32, -2), dec32cv1, dec32cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).add(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, add, "dec32");
+        }
+      }
+
+      try (ColumnVector add = dec64cv1.add(dec64cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL64, -6), dec64cv1, dec64cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).add(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, add, "dec64");
+        }
+      }
+
+      try (ColumnVector add = dec128cv1.add(dec128cv2)) {
+        try (ColumnVector expected = forEach(
+            DType.create(DType.DTypeEnum.DECIMAL128, -6), dec128cv1, dec128cv2,
+            (b, l, r, i) -> b.append(l.getBigDecimal(i).add(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, add, "dec128");
+        }
+      }
+
+      try (Scalar s = Scalar.fromDecimal(2, 100);
+           ColumnVector add = dec32cv1.add(s)) {
+        try (ColumnVector expected = forEachS(
+                DType.create(DType.DTypeEnum.DECIMAL32, -2), dec32cv1, BigDecimal.valueOf(100, -2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).add(r)))) {
+          assertColumnsAreEqual(expected, add, "dec32 + scalar");
+        }
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector add = lcv1.add(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, lcv1, 1.1f,
+                   (b, l, r, i) -> b.append(l.getLong(i) + r))) {
+        assertColumnsAreEqual(expected, add, "int64 + scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector add = s.add(bcv1);
+           ColumnVector expected = forEachS(DType.INT16, (short) 100,  bcv1,
+                   (b, l, r, i) -> b.append((short)(l + r.getByte(i))))) {
+        assertColumnsAreEqual(expected, add, "scalar short + byte");
+      }
+
+      try (Scalar s = Scalar.fromUnsignedShort((short) 0x89ab);
+           ColumnVector add = s.add(ubcv1);
+           ColumnVector expected = forEachS(DType.UINT16, (short) 0x89ab,  ubcv1,
+                   (b, l, r, i) -> b.append((short)(Short.toUnsignedInt(l) + Byte.toUnsignedInt(r.getByte(i)))))) {
+        assertColumnsAreEqual(expected, add, "scalar uint16 + uint8");
+      }
+    }
+  }
+
+  @Test
+  public void testSub() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedInts(INTS_2);
+         ColumnVector uicv1 = ColumnVector.fromBoxedUnsignedInts(UINTS_1);
+         ColumnVector uicv2 = ColumnVector.fromBoxedUnsignedInts(UINTS_2);
+         ColumnVector bcv1 = ColumnVector.fromBoxedBytes(BYTES_1);
+         ColumnVector ubcv1 = ColumnVector.fromBoxedUnsignedBytes(UBYTES_1);
+         ColumnVector fcv1 = ColumnVector.fromBoxedFloats(FLOATS_1);
+         ColumnVector fcv2 = ColumnVector.fromBoxedFloats(FLOATS_2);
+         ColumnVector lcv1 = ColumnVector.fromBoxedLongs(LONGS_1);
+         ColumnVector lcv2 = ColumnVector.fromBoxedLongs(LONGS_2);
+         ColumnVector ulcv1 = ColumnVector.fromBoxedUnsignedLongs(LONGS_1);
+         ColumnVector dcv1 = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector dcv2 = ColumnVector.fromBoxedDoubles(DOUBLES_2);
+         ColumnVector dec32cv1 = ColumnVector.fromDecimals(BIGDECIMAL32_1);
+         ColumnVector dec32cv2 = ColumnVector.fromDecimals(BIGDECIMAL32_2);
+         ColumnVector dec64cv1 = ColumnVector.decimalFromLongs(-dec64Scale_1, DECIMAL64_1);
+         ColumnVector dec64cv2 = ColumnVector.decimalFromLongs(-dec64Scale_2, DECIMAL64_2);
+         ColumnVector dec128cv1 = ColumnVector.decimalFromBigInt(-dec64Scale_1, DECIMAL128_1);
+         ColumnVector dec128cv2 = ColumnVector.decimalFromBigInt(-dec64Scale_2, DECIMAL128_2)) {
+      try (ColumnVector sub = icv1.sub(icv2);
+           ColumnVector expected = forEach(DType.INT32, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) - r.getInt(i)))) {
+        assertColumnsAreEqual(expected, sub, "int32");
+      }
+
+      try (ColumnVector sub = uicv1.sub(uicv2);
+           ColumnVector expected = forEach(DType.UINT32, uicv1, uicv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) - r.getInt(i)))) {
+        assertColumnsAreEqual(expected, sub, "uint32");
+      }
+
+      try (ColumnVector sub = icv1.sub(bcv1);
+           ColumnVector expected = forEach(DType.INT32, icv1, bcv1,
+                   (b, l, r, i) -> b.append(l.getInt(i) - r.getByte(i)))) {
+        assertColumnsAreEqual(expected, sub, "int32 - byte");
+      }
+
+      try (ColumnVector sub = uicv1.sub(ubcv1);
+           ColumnVector expected = forEach(DType.UINT32, uicv1, ubcv1,
+                   (b, l, r, i) -> b.append(l.getInt(i) - Byte.toUnsignedInt(r.getByte(i))))) {
+        assertColumnsAreEqual(expected, sub, "uint32 - uint8");
+      }
+
+      try (ColumnVector sub = fcv1.sub(fcv2);
+           ColumnVector expected = forEach(DType.FLOAT32, fcv1, fcv2,
+                   (b, l, r, i) -> b.append(l.getFloat(i) - r.getFloat(i)))) {
+        assertColumnsAreEqual(expected, sub, "float32");
+      }
+
+      try (ColumnVector sub = icv1.sub(fcv2, DType.FLOAT32);
+           ColumnVector expected = forEach(DType.FLOAT32, icv1, fcv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) - r.getFloat(i)))) {
+        assertColumnsAreEqual(expected, sub, "int - float");
+      }
+
+      try (ColumnVector sub = lcv1.sub(lcv2);
+           ColumnVector expected = forEach(DType.INT64, lcv1, lcv2,
+                   (b, l, r, i) -> b.append(l.getLong(i) - r.getLong(i)))) {
+        assertColumnsAreEqual(expected, sub, "int64");
+      }
+
+      try (ColumnVector sub = lcv1.sub(bcv1);
+           ColumnVector expected = forEach(DType.INT64, lcv1, bcv1,
+                   (b, l, r, i) -> b.append(l.getLong(i) - r.getByte(i)))) {
+        assertColumnsAreEqual(expected, sub, "int64 - byte");
+      }
+
+      try (ColumnVector sub = ulcv1.sub(ubcv1);
+           ColumnVector expected = forEach(DType.UINT64, ulcv1, ubcv1,
+                   (b, l, r, i) -> b.append(l.getLong(i) - Byte.toUnsignedLong(r.getByte(i))))) {
+        assertColumnsAreEqual(expected, sub, "uint64 - uint8");
+      }
+
+      try (ColumnVector sub = dcv1.sub(dcv2);
+           ColumnVector expected = forEach(DType.FLOAT64, dcv1, dcv2,
+                   (b, l, r, i) -> b.append(l.getDouble(i) - r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, sub, "float64");
+      }
+
+      try (ColumnVector sub = dcv2.sub(icv1);
+           ColumnVector expected = forEach(DType.FLOAT64, dcv2, icv1,
+                   (b, l, r, i) -> b.append(l.getDouble(i) - r.getInt(i)))) {
+        assertColumnsAreEqual(expected, sub, "double - int");
+      }
+
+      try (ColumnVector sub = dec32cv1.sub(dec32cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL32, -2), dec32cv1, dec32cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).subtract(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, sub, "dec32");
+        }
+      }
+
+      try (ColumnVector sub = dec64cv1.sub(dec64cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL64, -6), dec64cv1, dec64cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).subtract(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, sub, "dec64");
+        }
+      }
+
+      try (Scalar s = Scalar.fromDecimal(2, 100);
+           ColumnVector sub = dec32cv1.sub(s)) {
+        try (ColumnVector expected = forEachS(
+                DType.create(DType.DTypeEnum.DECIMAL32, -2), dec32cv1, BigDecimal.valueOf(100, -2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).subtract(r)))) {
+          assertColumnsAreEqual(expected, sub, "dec32 - scalar");
+        }
+      }
+
+      try (ColumnVector sub = dec128cv1.sub(dec128cv2)) {
+        try (ColumnVector expected = forEach(
+            DType.create(DType.DTypeEnum.DECIMAL128, -6), dec128cv1, dec128cv2,
+            (b, l, r, i) -> b.append(l.getBigDecimal(i).subtract(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, sub, "dec128");
+        }
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector sub = lcv1.sub(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, lcv1, 1.1f,
+                   (b, l, r, i) -> b.append(l.getLong(i) - r))) {
+        assertColumnsAreEqual(expected, sub, "int64 - scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector sub = s.sub(bcv1);
+           ColumnVector expected = forEachS(DType.INT16, (short) 100,  bcv1,
+                   (b, l, r, i) -> b.append((short)(l - r.getByte(i))))) {
+        assertColumnsAreEqual(expected, sub, "scalar short - byte");
+      }
+
+      try (Scalar s = Scalar.fromUnsignedShort((short) 0x89ab);
+           ColumnVector sub = s.sub(ubcv1);
+           ColumnVector expected = forEachS(DType.UINT16, (short) 0x89ab,  ubcv1,
+                   (b, l, r, i) -> b.append((short)(Short.toUnsignedInt(l) - Byte.toUnsignedInt(r.getByte(i)))))) {
+        assertColumnsAreEqual(expected, sub, "scalar uint16 - uint8");
+      }
+    }
+  }
+
+  // The rest of the tests are very basic to ensure that operations plumbing is in place, not to
+  // exhaustively test
+  // The underlying implementation.
+
+  @Test
+  public void testMul() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector dec32cv1 = ColumnVector.fromDecimals(BIGDECIMAL32_1);
+         ColumnVector dec32cv2 = ColumnVector.fromDecimals(BIGDECIMAL32_2);
+         ColumnVector dec64cv1 = ColumnVector.decimalFromLongs(-dec64Scale_1, DECIMAL64_1);
+         ColumnVector dec64cv2 = ColumnVector.decimalFromLongs(-dec64Scale_2, DECIMAL64_2);
+         ColumnVector dec128cv1 = ColumnVector.decimalFromBigInt(-dec64Scale_1, DECIMAL128_1);
+         ColumnVector dec128cv2 = ColumnVector.decimalFromBigInt(-dec64Scale_2, DECIMAL128_2)) {
+      try (ColumnVector answer = icv.mul(dcv);
+           ColumnVector expected = forEach(DType.FLOAT64, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) * r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 * double");
+      }
+
+      try (ColumnVector mul = dec32cv1.mul(dec32cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL32, 1), dec32cv1, dec32cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).multiply(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, mul, "dec32");
+        }
+      }
+
+      try (ColumnVector mul = dec64cv1.mul(dec64cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL64, -4), dec64cv1, dec64cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).multiply(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, mul, "dec64");
+        }
+      }
+
+      try (Scalar s = Scalar.fromDecimal(2, 100);
+           ColumnVector mul = dec32cv1.mul(s)) {
+        try (ColumnVector expected = forEachS(
+                DType.create(DType.DTypeEnum.DECIMAL32, 0), dec32cv1, BigDecimal.valueOf(100, -2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).multiply(r)))) {
+          assertColumnsAreEqual(expected, mul, "dec32 * scalar");
+        }
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector answer = icv.mul(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, icv, 1.1f,
+                   (b, l, r, i) -> b.append(l.getInt(i) * r))) {
+        assertColumnsAreEqual(expected, answer, "int64 * scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.mul(icv);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l * r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short * int32");
+      }
+
+      try (Scalar s = Scalar.fromUnsignedShort((short) 0x89ab);
+           ColumnVector uicv = ColumnVector.fromBoxedUnsignedInts(UINTS_1);
+           ColumnVector answer = s.mul(uicv);
+           ColumnVector expected = forEachS(DType.UINT32, (short) 0x89ab,  uicv,
+                   (b, l, r, i) -> b.append(Short.toUnsignedInt(l) * r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar uint16 * uint32");
+      }
+
+      try (ColumnVector mul = dec128cv1.mul(dec128cv2)) {
+        try (ColumnVector expected = forEach(
+            DType.create(DType.DTypeEnum.DECIMAL128, dec128cv1.type.getScale() + dec128cv2.type.getScale()), dec128cv1, dec128cv2,
+            (b, l, r, i) -> b.append(l.getBigDecimal(i).multiply(r.getBigDecimal(i))))) {
+          assertColumnsAreEqual(expected, mul, "dec128");
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testDiv() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector dec32cv1 = ColumnVector.fromDecimals(BIGDECIMAL32_1);
+         ColumnVector dec32cv2 = ColumnVector.fromDecimals(BIGDECIMAL32_2);
+         ColumnVector dec64cv1 = ColumnVector.decimalFromLongs(-dec64Scale_1, DECIMAL64_1);
+         ColumnVector dec64cv2 = ColumnVector.decimalFromLongs(-dec64Scale_2, DECIMAL64_2)) {
+      try (ColumnVector answer = icv.div(dcv);
+           ColumnVector expected = forEach(DType.FLOAT64, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) / r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 / double");
+      }
+
+      try (ColumnVector div = dec32cv1.div(dec32cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL32, -5), dec32cv1, dec32cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).divide(
+                        r.getBigDecimal(i), 5, RoundingMode.DOWN), RoundingMode.DOWN))) {
+          assertColumnsAreEqual(expected, div, "dec32");
+        }
+      }
+
+      try (ColumnVector div = dec64cv1.div(dec64cv2)) {
+        try (ColumnVector expected = forEach(
+                DType.create(DType.DTypeEnum.DECIMAL64, -8), dec64cv1, dec64cv2,
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).divide(
+                        r.getBigDecimal(i), 8, RoundingMode.DOWN), RoundingMode.DOWN))) {
+          assertColumnsAreEqual(expected, div, "dec64");
+        }
+      }
+
+      try (Scalar s = Scalar.fromDecimal(2, 100);
+           ColumnVector div = s.div(dec32cv1)) {
+        try (ColumnVector expected = forEachS(
+                DType.create(DType.DTypeEnum.DECIMAL32, 4), BigDecimal.valueOf(100, -2), dec32cv1,
+                (b, l, r, i) -> b.append(l.divide(r.getBigDecimal(i), -4, RoundingMode.DOWN)))) {
+          assertColumnsAreEqual(expected, div, "scalar dec32 / dec32");
+        }
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector answer = icv.div(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, icv, 1.1f,
+                   (b, l, r, i) -> b.append(l.getInt(i) / r))) {
+        assertColumnsAreEqual(expected, answer, "int64 / scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.div(icv);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l / r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short / int32");
+      }
+
+      try (Scalar s = Scalar.fromUnsignedShort((short) 0x89ab);
+           ColumnVector uicv = ColumnVector.fromBoxedUnsignedInts(UINTS_1);
+           ColumnVector answer = s.div(uicv);
+           ColumnVector expected = forEachS(DType.UINT32, (short) 0x89ab,  uicv,
+                   (b, l, r, i) -> b.append((int)(Short.toUnsignedLong(l) / Integer.toUnsignedLong(r.getInt(i)))))) {
+        assertColumnsAreEqual(expected, answer, "scalar uint16 / uint32");
+      }
+    }
+  }
+
+  @Test
+  public void testTrueDiv() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1)) {
+      try (ColumnVector answer = icv.trueDiv(dcv);
+           ColumnVector expected = forEach(DType.FLOAT64, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) / r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 / double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector answer = icv.trueDiv(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, icv, 1.1f,
+                   (b, l, r, i) -> b.append(l.getInt(i) / r))) {
+        assertColumnsAreEqual(expected, answer, "int64 / scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.trueDiv(icv);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l / r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short / int32");
+      }
+    }
+  }
+
+  @Test
+  public void testFloorDiv() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1)) {
+      try (ColumnVector answer = icv.floorDiv(dcv);
+           ColumnVector expected = forEach(DType.FLOAT64, icv, dcv,
+                   (b, l, r, i) -> b.append(Math.floor(l.getInt(i) / r.getDouble(i))))) {
+        assertColumnsAreEqual(expected, answer, "int32 / double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector answer = icv.floorDiv(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, icv, 1.1f,
+                   (b, l, r, i) -> b.append((float)Math.floor(l.getInt(i) / r)))) {
+        assertColumnsAreEqual(expected, answer, "int64 / scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.floorDiv(icv);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l / r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short / int32");
+      }
+
+      try (Scalar s = Scalar.fromUnsignedShort((short) 0x89ab);
+           ColumnVector uicv = ColumnVector.fromBoxedUnsignedInts(UINTS_1);
+           ColumnVector answer = s.floorDiv(uicv);
+           ColumnVector expected = forEachS(DType.UINT32, (short) 0x89ab,  uicv,
+                   (b, l, r, i) -> b.append((int)(Short.toUnsignedLong(l) / Integer.toUnsignedLong(r.getInt(i)))))) {
+        assertColumnsAreEqual(expected, answer, "scalar uint16 / uint32");
+      }
+    }
+  }
+
+  @Test
+  public void testMod() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1)) {
+      try (ColumnVector answer = icv.mod(dcv);
+           ColumnVector expected = forEach(DType.FLOAT64, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) % r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 % double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector answer = icv.mod(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, icv, 1.1f,
+                   (b, l, r, i) -> b.append(l.getInt(i) % r))) {
+        assertColumnsAreEqual(expected, answer, "int64 % scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.mod(icv);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l % r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short % int32");
+      }
+    }
+  }
+
+  @Test
+  public void testPow() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1)) {
+      try (ColumnVector answer = icv.pow(dcv);
+           ColumnVector expected = forEach(DType.FLOAT64, icv, dcv,
+                   (b, l, r, i) -> b.append(Math.pow(l.getInt(i), r.getDouble(i))))) {
+        assertColumnsAreEqual(expected, answer, "int32 pow double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.1f);
+           ColumnVector answer = icv.pow(s);
+           ColumnVector expected = forEachS(DType.FLOAT32, icv, 1.1f,
+                   (b, l, r, i) -> b.append((float)Math.pow(l.getInt(i), r)))) {
+        assertColumnsAreEqual(expected, answer, "int64 pow scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.pow(icv);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv,
+                   (b, l, r, i) -> b.append((int)Math.pow(l, r.getInt(i))))) {
+        assertColumnsAreEqual(expected, answer, "scalar short pow int32");
+      }
+    }
+  }
+
+  @Test
+  public void testEqual() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2);
+         ColumnVector dec32cv_1 = ColumnVector.decimalFromInts(-dec32Scale_1, DECIMAL32_1);
+         ColumnVector dec32cv_2 = ColumnVector.decimalFromInts(-dec32Scale_2, DECIMAL32_2)) {
+      try (ColumnVector answer = icv.equalTo(dcv);
+           ColumnVector expected = forEach(DType.BOOL8, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) == r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 == double");
+      }
+
+      try (ColumnVector answer = dec32cv_1.equalTo(dec32cv_2);
+           ColumnVector expected = forEach(DType.BOOL8, dec32cv_1, dec32cv_2,
+                   (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r.getBigDecimal(i)) == 0))) {
+        assertColumnsAreEqual(expected, answer, "dec32 == dec32 ");
+      }
+
+      try (Scalar s = Scalar.fromDecimal(-2, 200);
+           ColumnVector answer = dec32cv_2.equalTo(s)) {
+        try (ColumnVector expected = forEachS(DType.BOOL8, dec32cv_1, BigDecimal.valueOf(200, 2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r) == 0))) {
+          assertColumnsAreEqual(expected, answer, "dec32 == scalar dec32");
+        }
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.equalTo(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv, 1.0f,
+                   (b, l, r, i) -> b.append(l.getInt(i) == r))) {
+        assertColumnsAreEqual(expected, answer, "int64 == scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.equalTo(icv);
+           ColumnVector expected = forEachS(DType.BOOL8, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l == r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short == int32");
+      }
+
+      Short[] unsignedShorts = new Short[]{(short)0x89ab, (short)0xffff, 0, 1};
+      Integer[] unsignedInts = new Integer[]{0x89ab, 0xffff, 0, 1};
+      try (ColumnVector uscv = ColumnVector.fromBoxedUnsignedShorts(unsignedShorts);
+           ColumnVector uicv = ColumnVector.fromBoxedUnsignedInts(unsignedInts);
+           ColumnVector answer = uscv.equalTo(uicv);
+           ColumnVector expected = forEach(DType.BOOL8, uscv, uicv,
+                   (b, l, r, i) -> b.append(Short.toUnsignedInt(l.getShort(i)) == r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "uint16 == uint32");
+      }
+
+      try (ColumnVector answersv = sscv.equalTo(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? false :
+            l == r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 == struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.equalTo(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+            (b, l, r, i) -> b.append(l.isNull(i) ? false :
+            r == l.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 == scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.equalTo(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+            (b, l, r, i) -> b.append(l.isNull(i) || r.isNull(i) ||
+            l.getStruct(i).dataRecord.get(0) == null || r.getStruct(i).dataRecord.get(0) == null ?
+            false : l.getStruct(i).dataRecord.get(0) == r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 == struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringEqualScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.equalTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.equalTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.equalTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, null, true, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testStringEqualScalarNotPresent() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("boo")) {
+
+      try (ColumnVector answer = a.equalTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.equalTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, null, false, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testNotEqual() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2);
+         ColumnVector dec32cv_1 = ColumnVector.decimalFromInts(-dec32Scale_1, DECIMAL32_1);
+         ColumnVector dec32cv_2 = ColumnVector.decimalFromInts(-dec32Scale_2, DECIMAL32_2)) {
+      try (ColumnVector answer = icv.notEqualTo(dcv);
+           ColumnVector expected = forEach(DType.BOOL8, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) != r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 != double");
+      }
+
+      try (ColumnVector answer = dec32cv_1.notEqualTo(dec32cv_2);
+           ColumnVector expected = forEach(DType.BOOL8, dec32cv_1, dec32cv_2,
+                   (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r.getBigDecimal(i)) != 0))) {
+        assertColumnsAreEqual(expected, answer, "dec32 != dec32 ");
+      }
+
+      try (Scalar s = Scalar.fromDecimal(-2, 200);
+           ColumnVector answer = dec32cv_2.notEqualTo(s)) {
+        try (ColumnVector expected = forEachS(DType.BOOL8, dec32cv_1, BigDecimal.valueOf(200, 2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r) != 0))) {
+          assertColumnsAreEqual(expected, answer, "dec32 != scalar dec32");
+        }
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.notEqualTo(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv, 1.0f,
+                   (b, l, r, i) -> b.append(l.getInt(i) != r))) {
+        assertColumnsAreEqual(expected, answer, "int64 != scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.notEqualTo(icv);
+           ColumnVector expected = forEachS(DType.BOOL8, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l != r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short != int32");
+      }
+
+      try (ColumnVector answersv = sscv.notEqualTo(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? true : l != r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 != struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.notEqualTo(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+            (b, l, r, i) -> b.append(l.isNull(i) ? true : l.getStruct(i).dataRecord.get(0) != r))) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 != scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.notEqualTo(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+            (b, l, r, i) -> b.append(l.isNull(i) ? !r.isNull(i) :
+            r.isNull(i) || l.getStruct(i).dataRecord.get(0) != r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 != struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringNotEqualScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.notEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.notEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.notEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, false, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testStringNotEqualScalarNotPresent() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("abc")) {
+
+      try (ColumnVector answer = a.notEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.notEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, true, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testLessThan() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2);
+         ColumnVector dec32cv_1 = ColumnVector.decimalFromInts(-dec32Scale_1, DECIMAL32_1);
+         ColumnVector dec32cv_2 = ColumnVector.decimalFromInts(-dec32Scale_2, DECIMAL32_2)) {
+      try (ColumnVector answer = icv.lessThan(dcv);
+           ColumnVector expected = forEach(DType.BOOL8, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) < r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 < double");
+      }
+
+      try (ColumnVector answer = dec32cv_1.lessThan(dec32cv_2);
+           ColumnVector expected = forEach(DType.BOOL8, dec32cv_1, dec32cv_2,
+                   (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r.getBigDecimal(i)) < 0))) {
+        assertColumnsAreEqual(expected, answer, "dec32 < dec32 ");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.lessThan(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv, 1.0f,
+                   (b, l, r, i) -> b.append(l.getInt(i) < r))) {
+        assertColumnsAreEqual(expected, answer, "int64 < scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.lessThan(icv);
+           ColumnVector expected = forEachS(DType.BOOL8, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l < r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short < int32");
+      }
+
+      try (ColumnVector answersv = sscv.lessThan(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? false :
+            l < (Integer) r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 < struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.lessThan(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+            (b, l, r, i) -> b.append(l.isNull(i) ? true :
+            (Integer) l.getStruct(i).dataRecord.get(0) < r))) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 < scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.lessThan(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+            (b, l, r, i) -> b.append(l.isNull(i) ? true : r.isNull(i) ||
+            (Integer)l.getStruct(i).dataRecord.get(0) < (Integer)r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 < struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringLessThanScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.lessThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.lessThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.lessThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, false, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+
+  @Test
+  public void testStringLessThanScalarNotPresent() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("abc")) {
+
+      try (ColumnVector answer = a.lessThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.lessThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.lessThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, false, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testGreaterThan() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2);
+         ColumnVector dec32cv1 = ColumnVector.fromDecimals(BIGDECIMAL32_1);
+         ColumnVector dec32cv2 = ColumnVector.fromDecimals(BIGDECIMAL32_2)) {
+      try (ColumnVector answer = icv.greaterThan(dcv);
+           ColumnVector expected = forEach(DType.BOOL8, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) > r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 > double");
+      }
+
+      try (ColumnVector answer = dec32cv2.greaterThan(dec32cv1);
+           ColumnVector expected = forEach(DType.BOOL8, dec32cv2, dec32cv1,
+                   (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r.getBigDecimal(i)) > 0))) {
+        assertColumnsAreEqual(expected, answer, "dec32 > dec32 ");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.greaterThan(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv, 1.0f,
+                   (b, l, r, i) -> b.append(l.getInt(i) > r))) {
+        assertColumnsAreEqual(expected, answer, "int64 > scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.greaterThan(icv);
+           ColumnVector expected = forEachS(DType.BOOL8, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l > r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short > int32");
+      }
+
+      try (ColumnVector answersv = sscv.greaterThan(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? true :
+            l > (Integer) r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 > struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.greaterThan(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+            (b, l, r, i) -> b.append(l.isNull(i) ? false :
+            (Integer) l.getStruct(i).dataRecord.get(0) > r))) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 > scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.greaterThan(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+            (b, l, r, i) -> b.append(l.isNull(i) ? false : r.isNull(i) ||
+            (Integer)l.getStruct(i).dataRecord.get(0) > (Integer)r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 > struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringGreaterThanScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.greaterThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.greaterThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.greaterThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, null, false, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testStringGreaterThanScalarNotPresent() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("boo")) {
+
+      try (ColumnVector answer = a.greaterThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.greaterThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.greaterThan(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, null, false, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testLessOrEqualTo() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2);
+         ColumnVector dec32cv = ColumnVector.decimalFromInts(-dec32Scale_2, DECIMAL32_2)) {
+      try (ColumnVector answer = icv.lessOrEqualTo(dcv);
+           ColumnVector expected = forEach(DType.BOOL8, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) <= r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 <= double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.lessOrEqualTo(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv, 1.0f,
+                   (b, l, r, i) -> b.append(l.getInt(i) <= r))) {
+        assertColumnsAreEqual(expected, answer, "int64 <= scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.lessOrEqualTo(icv);
+           ColumnVector expected = forEachS(DType.BOOL8, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l <= r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short <= int32");
+      }
+
+      try (Scalar s = Scalar.fromDecimal(-2, 200);
+           ColumnVector answer = dec32cv.lessOrEqualTo(s)) {
+        try (ColumnVector expected = forEachS(DType.BOOL8, dec32cv, BigDecimal.valueOf(200, 2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r) <= 0))) {
+          assertColumnsAreEqual(expected, answer, "dec32 <= scalar dec32");
+        }
+      }
+
+      try (ColumnVector answersv = sscv.lessOrEqualTo(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? false :
+            l <= (Integer) r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 <= struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.lessOrEqualTo(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+           (b, l, r, i) -> b.append(l.isNull(i) ? true :
+           (Integer) l.getStruct(i).dataRecord.get(0) <= r))) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 <= scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.lessOrEqualTo(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+           (b, l, r, i) -> b.append(l.isNull(i) ? true : !r.isNull(i) &&
+           (Integer)l.getStruct(i).dataRecord.get(0) <= (Integer)r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 <= struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringLessOrEqualToScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.lessOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.lessOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.lessOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, true, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testStringLessOrEqualToScalarNotPresent() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("boo")) {
+
+      try (ColumnVector answer = a.lessOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.lessOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.lessOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, true, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testGreaterOrEqualTo() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2);
+         ColumnVector dec32cv = ColumnVector.decimalFromInts(-dec32Scale_2, DECIMAL32_2)) {
+      try (ColumnVector answer = icv.greaterOrEqualTo(dcv);
+           ColumnVector expected = forEach(DType.BOOL8, icv, dcv,
+                   (b, l, r, i) -> b.append(l.getInt(i) >= r.getDouble(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 >= double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.greaterOrEqualTo(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv, 1.0f,
+                   (b, l, r, i) -> b.append(l.getInt(i) >= r))) {
+      assertColumnsAreEqual(expected, answer, "int64 >= scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.greaterOrEqualTo(icv);
+           ColumnVector expected = forEachS(DType.BOOL8, (short) 100,  icv,
+                   (b, l, r, i) -> b.append(l >= r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short >= int32");
+      }
+
+      try (Scalar s = Scalar.fromDecimal(-2, 200);
+           ColumnVector answer = dec32cv.greaterOrEqualTo(s)) {
+        try (ColumnVector expected = forEachS(DType.BOOL8, dec32cv, BigDecimal.valueOf(200, 2),
+                (b, l, r, i) -> b.append(l.getBigDecimal(i).compareTo(r) >= 0))) {
+          assertColumnsAreEqual(expected, answer, "dec32 >= scalar dec32");
+        }
+      }
+
+      try (ColumnVector answersv = sscv.greaterOrEqualTo(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? true : l >= (Integer) r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 >= struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.greaterOrEqualTo(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+            (b, l, r, i) -> b.append(l.isNull(i) ? false : (Integer) l.getStruct(i).dataRecord.get(0) >= r))) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 >= scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.greaterOrEqualTo(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+            (b, l, r, i) -> b.append(l.isNull(i) ? false : !r.isNull(i) &&
+            (Integer)l.getStruct(i).dataRecord.get(0) >= (Integer)r.getStruct(i).dataRecord.get(0)))) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 >= struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringGreaterOrEqualToScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.greaterOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.greaterOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.greaterOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, null, true, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testStringGreaterOrEqualToScalarNotPresent() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("abc")) {
+
+      try (ColumnVector answer = a.greaterOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, true)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.greaterOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.greaterOrEqualTo(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, null, true, null)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testBitAnd() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedInts(INTS_2)) {
+      try (ColumnVector answer = icv1.bitAnd(icv2);
+           ColumnVector expected = forEach(DType.INT32, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) & r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 & int32");
+      }
+
+      try (Scalar s = Scalar.fromInt(0x01);
+           ColumnVector answer = icv1.bitAnd(s);
+           ColumnVector expected = forEachS(DType.INT32, icv1, 0x01,
+                   (b, l, r, i) -> b.append(l.getInt(i) & r))) {
+        assertColumnsAreEqual(expected, answer, "int32 & scalar int32");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.bitAnd(icv1);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv1,
+                   (b, l, r, i) -> b.append(l & r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short & int32");
+      }
+    }
+  }
+
+  @Test
+  public void testBitOr() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedInts(INTS_2)) {
+      try (ColumnVector answer = icv1.bitOr(icv2);
+           ColumnVector expected = forEach(DType.INT32, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) | r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 | int32");
+      }
+
+      try (Scalar s = Scalar.fromInt(0x01);
+           ColumnVector answer = icv1.bitOr(s);
+           ColumnVector expected = forEachS(DType.INT32, icv1, 0x01,
+                   (b, l, r, i) -> b.append(l.getInt(i) | r))) {
+        assertColumnsAreEqual(expected, answer, "int32 | scalar int32");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.bitOr(icv1);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv1,
+                   (b, l, r, i) -> b.append(l | r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short | int32");
+      }
+    }
+  }
+
+  @Test
+  public void testBitXor() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedInts(INTS_2)) {
+      try (ColumnVector answer = icv1.bitXor(icv2);
+           ColumnVector expected = forEach(DType.INT32, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getInt(i) ^ r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 ^ int32");
+      }
+
+      try (Scalar s = Scalar.fromInt(0x01);
+           ColumnVector answer = icv1.bitXor(s);
+           ColumnVector expected = forEachS(DType.INT32, icv1, 0x01,
+                   (b, l, r, i) -> b.append(l.getInt(i) ^ r))) {
+        assertColumnsAreEqual(expected, answer, "int32 ^ scalar int32");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.bitXor(icv1);
+           ColumnVector expected = forEachS(DType.INT32, (short) 100,  icv1,
+                   (b, l, r, i) -> b.append(l ^ r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "scalar short ^ int32");
+      }
+    }
+  }
+
+  @Test
+  public void testNullAnd() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedBooleans(
+        true, true, true,
+        false, false, false,
+        null, null, null);
+         ColumnVector icv2 = ColumnVector.fromBoxedBooleans(
+             true, false, null,
+             true, false, null,
+             true, false, null)) {
+      try (ColumnVector answer = icv1.binaryOp(BinaryOp.NULL_LOGICAL_AND, icv2, DType.BOOL8);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(
+               true, false, null,
+               false, false, false,
+               null, false, null)) {
+        assertColumnsAreEqual(expected, answer, "boolean NULL AND boolean");
+      }
+    }
+  }
+
+  @Test
+  public void testNullOr() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedBooleans(
+        true, true, true,
+        false, false, false,
+        null, null, null);
+         ColumnVector icv2 = ColumnVector.fromBoxedBooleans(
+             true, false, null,
+             true, false, null,
+             true, false, null)) {
+      try (ColumnVector answer = icv1.binaryOp(BinaryOp.NULL_LOGICAL_OR, icv2, DType.BOOL8);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(
+               true, true, true,
+               true, false, null,
+               true, null, null)) {
+        assertColumnsAreEqual(expected, answer, "boolean NULL OR boolean");
+      }
+    }
+  }
+
+  @Test
+  public void testAnd() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedBooleans(BOOLEANS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedBooleans(BOOLEANS_2)) {
+      try (ColumnVector answer = icv1.and(icv2);
+           ColumnVector expected = forEach(DType.BOOL8, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getBoolean(i) && r.getBoolean(i)))) {
+        assertColumnsAreEqual(expected, answer, "boolean AND boolean");
+      }
+
+      try (Scalar s = Scalar.fromBool(true);
+           ColumnVector answer = icv1.and(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv1, true,
+               (b, l, r, i) -> b.append(l.getBoolean(i) && r))) {
+        assertColumnsAreEqual(expected, answer, "boolean AND true");
+      }
+
+      try (Scalar s = Scalar.fromBool(false);
+           ColumnVector answer = icv1.and(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv1, false,
+                   (b, l, r, i) -> b.append(l.getBoolean(i) && r))) {
+        assertColumnsAreEqual(expected, answer, "boolean AND false");
+      }
+
+      try (Scalar s = Scalar.fromBool(true);
+           ColumnVector answer = icv1.and(s);
+           ColumnVector expected = forEachS(DType.BOOL8, true, icv1,
+               (b, l, r, i) -> b.append(l && r.getBoolean(i)))) {
+        assertColumnsAreEqual(expected, answer, "true AND boolean");
+      }
+
+      try (Scalar s = Scalar.fromBool(false);
+           ColumnVector answer = icv1.and(s);
+           ColumnVector expected = forEachS(DType.BOOL8, false, icv1,
+                   (b, l, r, i) -> b.append(l && r.getBoolean(i)))) {
+        assertColumnsAreEqual(expected, answer, "false AND boolean");
+      }
+    }
+  }
+
+  @Test
+  public void testOr() {
+    try (ColumnVector icv1 = ColumnVector.fromBoxedBooleans(BOOLEANS_1);
+         ColumnVector icv2 = ColumnVector.fromBoxedBooleans(BOOLEANS_2)) {
+      try (ColumnVector answer = icv1.or(icv2);
+           ColumnVector expected = forEach(DType.BOOL8, icv1, icv2,
+                   (b, l, r, i) -> b.append(l.getBoolean(i) || r.getBoolean(i)))) {
+        assertColumnsAreEqual(expected, answer, "boolean OR boolean");
+      }
+
+      try (Scalar s = Scalar.fromBool(true);
+           ColumnVector answer = icv1.or(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv1, true,
+                   (b, l, r, i) -> b.append(l.getBoolean(i) || r))) {
+        assertColumnsAreEqual(expected, answer, "boolean OR true");
+      }
+
+      try (Scalar s = Scalar.fromBool(false);
+           ColumnVector answer = icv1.or(s);
+           ColumnVector expected = forEachS(DType.BOOL8, icv1, false,
+               (b, l, r, i) -> b.append(l.getBoolean(i) || r))) {
+        assertColumnsAreEqual(expected, answer, "boolean OR false");
+      }
+
+      try (Scalar s = Scalar.fromBool(true);
+           ColumnVector answer = icv1.or(s);
+           ColumnVector expected = forEachS(DType.BOOL8, true, icv1,
+                   (b, l, r, i) -> b.append(l || r.getBoolean(i)))) {
+        assertColumnsAreEqual(expected, answer, "true OR boolean");
+      }
+
+      try (Scalar s = Scalar.fromBool(false);
+           ColumnVector answer = icv1.or(s);
+           ColumnVector expected = forEachS(DType.BOOL8, false, icv1,
+               (b, l, r, i) -> b.append(l || r.getBoolean(i)))) {
+        assertColumnsAreEqual(expected, answer, "false OR boolean");
+      }
+    }
+  }
+
+  @Test
+  public void testShiftLeft() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_2);
+         ColumnVector shiftBy = ColumnVector.fromInts(SHIFT_BY)) {
+      try (ColumnVector answer = icv.shiftLeft(shiftBy);
+           ColumnVector expected = forEach(DType.INT32, icv, shiftBy,
+               (b, l, r, i) -> b.append(l.getInt(i) << r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 shifted left");
+      }
+
+      try (Scalar s = Scalar.fromInt(4);
+           ColumnVector answer = icv.shiftLeft(s, DType.INT64);
+           ColumnVector expected = forEachS(DType.INT64, icv, 4,
+               (b, l, r, i) -> b.append(((long)l.getInt(i) << r)))) {
+        assertColumnsAreEqual(expected, answer, "int32 << scalar = int64");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 0x0000FFFF);
+           ColumnVector answer = s.shiftLeft(shiftBy, DType.INT16);
+           ColumnVector expected = forEachS(DType.INT16, (short) 0x0000FFFF,  shiftBy,
+               (b, l, r, i) -> {
+                 int shifted = l << r.getInt(i);
+                 b.append((short) shifted);
+               })) {
+        assertColumnsAreEqual(expected, answer, "scalar short << int32");
+      }
+    }
+  }
+
+  @Test
+  public void testShiftRight() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_2);
+         ColumnVector shiftBy = ColumnVector.fromInts(SHIFT_BY)) {
+      try (ColumnVector answer = icv.shiftRight(shiftBy);
+           ColumnVector expected = forEach(DType.INT32, icv, shiftBy,
+               (b, l, r, i) -> b.append(l.getInt(i) >> r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 shifted right");
+      }
+
+      try (Scalar s = Scalar.fromInt(4);
+           ColumnVector answer = icv.shiftRight(s, DType.INT64);
+           ColumnVector expected = forEachS(DType.INT64, icv, 4,
+               (b, l, r, i) -> b.append(((long)(l.getInt(i) >> r))))) {
+        assertColumnsAreEqual(expected, answer, "int32 >> scalar = int64");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 0x0000FFFF);
+           ColumnVector answer = s.shiftRight(shiftBy, DType.INT16);
+           ColumnVector expected = forEachS(DType.INT16, (short) 0x0000FFFF,  shiftBy,
+               (b, l, r, i) -> {
+                 int shifted = l >> r.getInt(i);
+                 b.append((short) shifted);
+               })) {
+        assertColumnsAreEqual(expected, answer, "scalar short >> int32 = int16");
+      }
+    }
+  }
+
+  @Test
+  public void testShiftRightUnsigned() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_2);
+         ColumnVector shiftBy = ColumnVector.fromInts(SHIFT_BY)) {
+      try (ColumnVector answer = icv.shiftRightUnsigned(shiftBy);
+           ColumnVector expected = forEach(DType.INT32, icv, shiftBy,
+               (b, l, r, i) -> b.append(l.getInt(i) >>> r.getInt(i)))) {
+        assertColumnsAreEqual(expected, answer, "int32 shifted right unsigned");
+      }
+
+      try (Scalar s = Scalar.fromInt(4);
+           ColumnVector answer = icv.shiftRightUnsigned(s, DType.INT64);
+           ColumnVector expected = forEachS(DType.INT64, icv, 4,
+               (b, l, r, i) -> b.append(((long)(l.getInt(i) >>> r))))) {
+        assertColumnsAreEqual(expected, answer, "int32 >>> scalar = int64");
+      }
+    }
+  }
+
+  @Test
+  public void testLogBase10() {
+    try (ColumnVector dcv1 = ColumnVector.fromBoxedDoubles(DOUBLES_2);
+         Scalar base = Scalar.fromInt(10);
+         ColumnVector answer = dcv1.log(base);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(Arrays.stream(DOUBLES_2)
+            .map(Math::log10)
+            .toArray(Double[]::new))) {
+      assertColumnsAreEqual(expected, answer, "log10");
+    }
+  }
+
+  @Test
+  public void testLogBase2() {
+    try (ColumnVector dcv1 = ColumnVector.fromBoxedDoubles(DOUBLES_2);
+         Scalar base = Scalar.fromInt(2);
+         ColumnVector answer = dcv1.log(base);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(Arrays.stream(DOUBLES_2)
+             .map(n -> Math.log(n) / Math.log(2))
+             .toArray(Double[]::new))) {
+      assertColumnsAreEqual(expected, answer, "log2");
+    }
+  }
+
+  @Test
+  public void testArctan2() {
+    Integer[] xInt = {7, 1, 2, 10};
+    Integer[] yInt = {4, 10, 8, 2};
+
+    Double[] xDouble = TestUtils.getDoubles(98234234523432423L, 50, ALL ^ NULL);
+    Double[] yDouble = TestUtils.getDoubles(23623274238423532L, 50, ALL ^ NULL);
+
+    try (ColumnVector yDoubleCV = ColumnVector.fromBoxedDoubles(yDouble);
+         ColumnVector xDoubleCV = ColumnVector.fromBoxedDoubles(xDouble);
+         ColumnVector yIntCV = ColumnVector.fromBoxedInts(yInt);
+         ColumnVector xIntCV = ColumnVector.fromBoxedInts(xInt);
+         ColumnVector resultDouble = yDoubleCV.arctan2(xDoubleCV);
+         ColumnVector resultInt = yIntCV.arctan2(xIntCV, DType.FLOAT64);
+         ColumnVector expectedInt = ColumnVector.fromDoubles(IntStream.range(0,xInt.length)
+             .mapToDouble(n -> Math.atan2(yInt[n], xInt[n])).toArray());
+         ColumnVector expectedDouble = ColumnVector.fromDoubles(IntStream.range(0,xDouble.length)
+             .mapToDouble(n -> Math.atan2(yDouble[n], xDouble[n])).toArray())) {
+      assertColumnsAreEqual(expectedInt, resultInt);
+      assertColumnsAreEqual(expectedDouble, resultDouble);
+    }
+  }
+
+  @Test
+  public void testEqualNullAware() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector intscalar = ColumnVector.fromInts(4);
+         Scalar sscv = Scalar.structFromColumnViews(intscalar);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector structcv1 = ColumnVector.fromStructs(structType, int_struct_data_1);
+         ColumnVector structcv2 = ColumnVector.fromStructs(structType, int_struct_data_2)) {
+      try (ColumnVector answer = icv.equalToNullAware(dcv);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, false, false,
+                   false, false)) {
+        assertColumnsAreEqual(expected, answer, "int32 <=> double");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.equalToNullAware(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, false, false,
+                   false, false)) {
+        assertColumnsAreEqual(expected, answer, "int32 <=> scalar float");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 100);
+           ColumnVector answer = s.equalToNullAware(icv);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false, false,
+                   false, true)) {
+        assertColumnsAreEqual(expected, answer, "scalar short <=> int32");
+      }
+
+      try (ColumnVector answersv = sscv.equalToNullAware(structcv1);
+           ColumnVector expectedsv = forEachS(DType.BOOL8, 4, structcv1,
+            (b, l, r, i) -> b.append(r.isNull(i) ? false :
+            l == r.getStruct(i).dataRecord.get(0)), true)) {
+        assertColumnsAreEqual(expectedsv, answersv, "scalar struct int32 <=> struct int32");
+      }
+
+      try (ColumnVector answervs = structcv1.equalToNullAware(sscv);
+           ColumnVector expectedvs = forEachS(DType.BOOL8, structcv1, 4,
+            (b, l, r, i) -> b.append(l.isNull(i) ? false :
+            l.getStruct(i).dataRecord.get(0) == r), true)) {
+        assertColumnsAreEqual(expectedvs, answervs, "struct int32 <=> scalar struct int32");
+      }
+
+      try (ColumnVector answervv = structcv1.equalToNullAware(structcv2);
+           ColumnVector expectedvv = forEach(DType.BOOL8, structcv1, structcv2,
+            (b, l, r, i) -> b.append(l.isNull(i) || r.isNull(i) ? l.isNull(i) && r.isNull(i) :
+            l.getStruct(i).dataRecord.get(0) == r.getStruct(i).dataRecord.get(0)), true)) {
+        assertColumnsAreEqual(expectedvv, answervv, "struct int32 <=> struct int32");
+      }
+    }
+  }
+
+  @Test
+  public void testStringEqualNullAwareScalar() {
+    try (ColumnVector a = ColumnVector.fromStrings("a", "b", "c", "d");
+         ColumnVector b = ColumnVector.fromStrings("a", "b", "b", "a");
+         ColumnVector c = ColumnVector.fromStrings("a", null, "b", null);
+         Scalar s = Scalar.fromString("b")) {
+
+      try (ColumnVector answer = a.equalToNullAware(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, false, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = b.equalToNullAware(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+
+      try (ColumnVector answer = c.equalToNullAware(s);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, false)) {
+        assertColumnsAreEqual(expected, answer);
+      }
+    }
+  }
+
+  @Test
+  public void testMaxNullAware() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1)) {
+      try (ColumnVector answer = icv.maxNullAware(dcv);
+           ColumnVector expected = ColumnVector.fromBoxedDoubles(1.0, 10.0,  100.0, 5.3, 50.0,
+                   100.0, 100.0)) {
+        assertColumnsAreEqual(expected, answer, "max(int32, double)");
+      }
+
+      try (Scalar s = Scalar.fromFloat(1.0f);
+           ColumnVector answer = icv.maxNullAware(s);
+           ColumnVector expected = ColumnVector.fromBoxedFloats(1f, 2f, 3f, 4f, 5f, 1f, 100f)) {
+        assertColumnsAreEqual(expected, answer, "max(int32, scalar float)");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 99);
+           ColumnVector answer = s.maxNullAware(icv);
+           ColumnVector expected = ColumnVector.fromBoxedInts(99, 99, 99, 99, 99, 99, 100)) {
+        assertColumnsAreEqual(expected, answer, "max(scalar short, int32)");
+      }
+    }
+  }
+
+  @Test
+  public void testMinNullAware() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1)) {
+      try (ColumnVector answer = icv.minNullAware(dcv);
+           ColumnVector expected = ColumnVector.fromBoxedDoubles(1.0, 2.0, 3.0, 4.0, 5.0, 100.0, 100.0)) {
+        assertColumnsAreEqual(expected, answer, "min(int32, double)");
+      }
+
+      try (Scalar s = Scalar.fromFloat(3.1f);
+           ColumnVector answer = icv.minNullAware(s);
+           ColumnVector expected = ColumnVector.fromBoxedFloats(1f, 2f, 3f, 3.1f, 3.1f, 3.1f, 3.1f)) {
+        assertColumnsAreEqual(expected, answer, "min(int32, scalar float)");
+      }
+
+      try (Scalar s = Scalar.fromShort((short) 99);
+           ColumnVector answer = s.minNullAware(icv);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 99, 99)) {
+        assertColumnsAreEqual(expected, answer, "min(scalar short, int32)");
+      }
+    }
+  }
+
+  @Test
+  public void testDecimalTypeThrowsException() {
+    try (ColumnVector dec64cv1 = ColumnVector.decimalFromLongs(-dec64Scale_1+10, DECIMAL64_1);
+         ColumnVector dec64cv2 = ColumnVector.decimalFromLongs(-dec64Scale_2- 10 , DECIMAL64_2)) {
+      assertThrows(ArithmeticException.class,
+              () -> {
+                try (ColumnVector expected = forEach
+                        (DType.create(DType.DTypeEnum.DECIMAL64, -6), dec64cv1, dec64cv2,
+                                (b, l, r, i) -> b.append(l.getBigDecimal(i).add(r.getBigDecimal(i))))) {
+                }
+              });
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ByteColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/ByteColumnVectorTest.java
new file mode 100644
index 0000000..7b476c3
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ByteColumnVectorTest.java
@@ -0,0 +1,243 @@
+/*
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import java.util.Random;
+import java.util.function.Consumer;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class ByteColumnVectorTest extends CudfTestBase {
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (HostColumnVector shortColumnVector = HostColumnVector.build(DType.INT8, 3,
+        (b) -> b.append((byte) 1))) {
+      assertFalse(shortColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEquals(cv.getByte(0), 2);
+      assertEquals(cv.getByte(1), 3);
+      assertEquals(cv.getByte(2), 5);
+    };
+    try (HostColumnVector bcv = HostColumnVector.fromBytes(new byte[]{2, 3, 5})) {
+      verify.accept(bcv);
+    }
+    try (HostColumnVector bcv = ColumnBuilderHelper.fromBytes(true, new byte[]{2, 3, 5})) {
+      verify.accept(bcv);
+    }
+  }
+
+  @Test
+  public void testUnsignedArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEquals(0xff, Byte.toUnsignedInt(cv.getByte(0)), 0xff);
+      assertEquals(128, Byte.toUnsignedInt(cv.getByte(1)), 128);
+      assertEquals(5, Byte.toUnsignedInt(cv.getByte(2)), 5);
+    };
+    try (HostColumnVector bcv = HostColumnVector.fromUnsignedBytes(new byte[]{(byte)0xff, (byte)128, 5})) {
+      verify.accept(bcv);
+    }
+    try (HostColumnVector bcv = ColumnBuilderHelper.fromBytes(false,
+        new byte[]{(byte)0xff, (byte)128, 5})) {
+      verify.accept(bcv);
+    }
+  }
+
+  @Test
+  public void testAppendRepeatingValues() {
+    try (HostColumnVector byteColumnVector = HostColumnVector.build(DType.INT8, 3,
+        (b) -> b.append((byte) 2, 3L))) {
+      assertFalse(byteColumnVector.hasNulls());
+      assertEquals(byteColumnVector.getByte(0), 2);
+      assertEquals(byteColumnVector.getByte(1), 2);
+      assertEquals(byteColumnVector.getByte(2), 2);
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertThrows(AssertionError.class, () -> cv.getByte(3));
+      assertFalse(cv.hasNulls());
+    };
+    try (HostColumnVector bcv = HostColumnVector.fromBytes(new byte[]{2, 3, 5})) {
+      verify.accept(bcv);
+    }
+    try (HostColumnVector bcv = ColumnBuilderHelper.fromBytes(true, new byte[]{2, 3, 5})) {
+      verify.accept(bcv);
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertThrows(AssertionError.class, () -> cv.getByte(-1));
+    };
+    try (HostColumnVector bcv = HostColumnVector.fromBytes(new byte[]{2, 3, 5})) {
+      verify.accept(bcv);
+    }
+    try (HostColumnVector bcv = ColumnBuilderHelper.fromBytes(true, new byte[]{2, 3, 5})) {
+      verify.accept(bcv);
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector bcv = HostColumnVector.fromBoxedBytes(
+        new Byte[]{2, 3, 4, 5, 6, 7, null, null})) {
+      verify.accept(bcv);
+    }
+    try (HostColumnVector bcv = ColumnBuilderHelper.fromBoxedBytes(true,
+        new Byte[]{2, 3, 4, 5, 6, 7, null, null})) {
+      verify.accept(bcv);
+    }
+  }
+
+  @Test
+  public void testAddingUnsignedNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertEquals(128, Byte.toUnsignedInt(cv.getByte(4)));
+      assertEquals(254, Byte.toUnsignedInt(cv.getByte(5)));
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector bcv = HostColumnVector.fromBoxedUnsignedBytes(
+        new Byte[]{2, 3, 4, 5, (byte)128, (byte)254, null, null})) {
+      verify.accept(bcv);
+    }
+    try (HostColumnVector bcv = ColumnBuilderHelper.fromBoxedBytes(false,
+        new Byte[]{2, 3, 4, 5, (byte)128, (byte)254, null, null})) {
+      verify.accept(bcv);
+    }
+  }
+
+  @Test
+  public void testCastToByte() {
+    final int[] DATES = {17897}; //Jan 01, 2019
+
+    try (ColumnVector doubleColumnVector = ColumnVector.fromDoubles(4.3, 3.8, 8);
+         ColumnVector shortColumnVector = ColumnVector.fromShorts(new short[]{100});
+         ColumnVector dateColumnVector = ColumnVector.daysFromInts(DATES);
+         ColumnVector byteColumnVector1 = doubleColumnVector.asBytes();
+         ColumnVector byteColumnVector2 = shortColumnVector.asBytes();
+         ColumnVector byteColumnVector3 = dateColumnVector.asBytes();
+         ColumnVector expected1 = ColumnVector.fromBytes((byte)4, (byte)3, (byte)8);
+         ColumnVector expected2 = ColumnVector.fromBytes((byte)100);
+         ColumnVector expected3 = ColumnVector.fromBytes((byte)-23)) {
+      AssertUtils.assertColumnsAreEqual(expected1, byteColumnVector1);
+      AssertUtils.assertColumnsAreEqual(expected2, byteColumnVector2);
+      AssertUtils.assertColumnsAreEqual(expected3, byteColumnVector3);
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.INT8, 3)) {
+      assertThrows(AssertionError.class,
+          () -> builder.append((byte) 2).appendNull().append((byte) 5, (byte) 4).build());
+    }
+  }
+
+  @Test
+  void testAppendVector() {
+    Random random = new Random(192312989128L);
+    for (int dstSize = 1; dstSize <= 100; dstSize++) {
+      for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+        final int srcSize = dstSize - dstPrefilledSize;
+        for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+          try (Builder dst = HostColumnVector.builder(DType.INT8, dstSize);
+               HostColumnVector src = HostColumnVector.build(DType.INT8, srcSize, (b) -> {
+                 for (int i = 0; i < srcSize; i++) {
+                   if (random.nextBoolean()) {
+                     b.appendNull();
+                   } else {
+                     b.append((byte) random.nextInt());
+                   }
+                 }
+               });
+               Builder gtBuilder = HostColumnVector.builder(DType.INT8, dstPrefilledSize)) {
+            assertEquals(dstSize, srcSize + dstPrefilledSize);
+            //add the first half of the prefilled list
+            for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+              if (random.nextBoolean()) {
+                dst.appendNull();
+                gtBuilder.appendNull();
+              } else {
+                byte a = (byte) random.nextInt();
+                dst.append(a);
+                gtBuilder.append(a);
+              }
+            }
+            // append the src vector
+            dst.append(src);
+            try (HostColumnVector dstVector = dst.build();
+                 HostColumnVector gt = gtBuilder.build()) {
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                assertEquals(gt.isNull(i), dstVector.isNull(i));
+                if (!gt.isNull(i)) {
+                  assertEquals(gt.getByte(i), dstVector.getByte(i));
+                }
+              }
+              for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                assertEquals(src.isNull(j), dstVector.isNull(i));
+                if (!src.isNull(j)) {
+                  assertEquals(src.getByte(j), dstVector.getByte(i));
+                }
+              }
+              if (dstVector.hasValidityVector()) {
+                long maxIndex =
+                    BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                  assertFalse(dstVector.isNullExtendedRange(i));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ColumnBuilderHelper.java b/java/src/test/java/ai/rapids/cudf/ColumnBuilderHelper.java
new file mode 100644
index 0000000..679386c
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ColumnBuilderHelper.java
@@ -0,0 +1,170 @@
+/*
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.Objects;
+import java.util.function.Consumer;
+
+/**
+ * ColumnBuilderHelper helps to test ColumnBuilder with existed ColumnVector tests.
+ */
+public class ColumnBuilderHelper {
+
+  public static HostColumnVector build(
+      HostColumnVector.DataType type,
+      int rows,
+      Consumer<HostColumnVector.ColumnBuilder> init) {
+    try (HostColumnVector.ColumnBuilder b = new HostColumnVector.ColumnBuilder(type, rows)) {
+      init.accept(b);
+      return b.build();
+    }
+  }
+
+  public static ColumnVector buildOnDevice(
+      HostColumnVector.DataType type,
+      int rows,
+      Consumer<HostColumnVector.ColumnBuilder> init) {
+    try (HostColumnVector.ColumnBuilder b = new HostColumnVector.ColumnBuilder(type, rows)) {
+      init.accept(b);
+      return b.buildAndPutOnDevice();
+    }
+  }
+
+  public static HostColumnVector decimalFromBigInts(int scale, BigInteger... values) {
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(true, DType.create(DType.DTypeEnum.DECIMAL128, -scale)),
+        values.length,
+        (b) -> {
+          for (BigInteger v : values)
+            if (v == null) b.appendNull();
+            else b.appendDecimal128(v.toByteArray());
+        });
+  }
+
+  public static HostColumnVector fromBoxedBytes(boolean signed, Byte... values) {
+    DType dt = signed ? DType.INT8 : DType.UINT8;
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(true, dt),
+        values.length,
+        (b) -> {
+          for (Byte v : values)
+            if (v == null) b.appendNull();
+            else b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromBoxedDoubles(Double... values) {
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(true, DType.FLOAT64),
+        values.length,
+        (b) -> {
+          for (Double v : values)
+            if (v == null) b.appendNull();
+            else b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromBoxedInts(boolean signed, Integer... values) {
+    DType dt = signed ? DType.INT32 : DType.UINT32;
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(true, dt),
+        values.length,
+        (b) -> {
+          for (Integer v : values)
+            if (v == null) b.appendNull();
+            else b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromBoxedLongs(boolean signed, Long... values) {
+    DType dt = signed ? DType.INT64 : DType.UINT64;
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(true, dt),
+        values.length,
+        (b) -> {
+          for (Long v : values)
+            if (v == null) b.appendNull();
+            else b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromBytes(boolean signed, byte... values) {
+    DType dt = signed ? DType.INT8 : DType.UINT8;
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(false, dt),
+        values.length,
+        (b) -> {
+          for (byte v : values) b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromDecimals(BigDecimal... values) {
+    // Simply copy from HostColumnVector.fromDecimals
+    BigDecimal maxDec = Arrays.stream(values).filter(Objects::nonNull)
+        .max(Comparator.comparingInt(BigDecimal::precision))
+        .orElse(BigDecimal.ZERO);
+    int maxScale = Arrays.stream(values).filter(Objects::nonNull)
+        .map(decimal -> decimal.scale())
+        .max(Comparator.naturalOrder())
+        .orElse(0);
+    maxDec = maxDec.setScale(maxScale, RoundingMode.UNNECESSARY);
+
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(true, DType.fromJavaBigDecimal(maxDec)),
+        values.length,
+        (b) -> {
+          for (BigDecimal v : values)
+            if (v == null) b.appendNull();
+            else b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromDoubles(double... values) {
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(false, DType.FLOAT64),
+        values.length,
+        (b) -> {
+          for (double v : values) b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromInts(boolean signed, int... values) {
+    DType dt = signed ? DType.INT32 : DType.UINT32;
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(false, dt),
+        values.length,
+        (b) -> {
+          for (int v : values) b.append(v);
+        });
+  }
+
+  public static HostColumnVector fromLongs(boolean signed, long... values) {
+    DType dt = signed ? DType.INT64 : DType.UINT64;
+    return ColumnBuilderHelper.build(
+        new HostColumnVector.BasicType(false, dt),
+        values.length,
+        (b) -> {
+          for (long v : values) b.append(v);
+        });
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java
new file mode 100644
index 0000000..9a0f8bd
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ColumnVectorTest.java
@@ -0,0 +1,6889 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.ColumnView.FindOptions;
+import ai.rapids.cudf.HostColumnVector.*;
+import com.google.common.collect.Lists;
+import org.junit.jupiter.api.Disabled;
+import org.junit.jupiter.api.Test;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Optional;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.function.Supplier;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static ai.rapids.cudf.AssertUtils.assertStructColumnsAreEqual;
+import static ai.rapids.cudf.AssertUtils.assertTablesAreEqual;
+import static ai.rapids.cudf.QuantileMethod.*;
+import static org.junit.jupiter.api.Assertions.*;
+import static org.junit.jupiter.api.Assumptions.assumeTrue;
+
+public class ColumnVectorTest extends CudfTestBase {
+
+  public static final double PERCENTAGE = 0.0001;
+
+  // IEEE 754 NaN values
+  static final float POSITIVE_FLOAT_NAN_LOWER_RANGE = Float.intBitsToFloat(0x7f800001);
+  static final float POSITIVE_FLOAT_NAN_UPPER_RANGE = Float.intBitsToFloat(0x7fffffff);
+  static final float NEGATIVE_FLOAT_NAN_LOWER_RANGE = Float.intBitsToFloat(0xff800001);
+  static final float NEGATIVE_FLOAT_NAN_UPPER_RANGE = Float.intBitsToFloat(0xffffffff);
+
+  static final double POSITIVE_DOUBLE_NAN_LOWER_RANGE = Double.longBitsToDouble(0x7ff0000000000001L);
+  static final double POSITIVE_DOUBLE_NAN_UPPER_RANGE = Double.longBitsToDouble(0x7fffffffffffffffL);
+  static final double NEGATIVE_DOUBLE_NAN_LOWER_RANGE = Double.longBitsToDouble(0xfff0000000000001L);
+  static final double NEGATIVE_DOUBLE_NAN_UPPER_RANGE = Double.longBitsToDouble(0xffffffffffffffffL);
+
+  // c = a * a - a
+  static String ptx = "***(" +
+      "      .func _Z1fPii(" +
+      "        .param .b64 _Z1fPii_param_0," +
+      "        .param .b32 _Z1fPii_param_1" +
+      "  )" +
+      "  {" +
+      "        .reg .b32       %r<4>;" +
+      "        .reg .b64       %rd<3>;" +
+      "    ld.param.u64    %rd1, [_Z1fPii_param_0];" +
+      "    ld.param.u32    %r1, [_Z1fPii_param_1];" +
+      "    cvta.to.global.u64      %rd2, %rd1;" +
+      "    mul.lo.s32      %r2, %r1, %r1;" +
+      "    sub.s32         %r3, %r2, %r1;" +
+      "    st.global.u32   [%rd2], %r3;" +
+      "    ret;" +
+      "  }" +
+      ")***";
+
+  static String cuda = "__device__ inline void f(" +
+      "int* output," +
+      "int input" +
+      "){" +
+      "*output = input*input - input;" +
+      "}";
+
+  @Test
+  void testTransformVector() {
+    try (ColumnVector cv = ColumnVector.fromBoxedInts(2,3,null,4);
+         ColumnVector cv1 = cv.transform(ptx, true);
+         ColumnVector cv2 = cv.transform(cuda, false);
+         ColumnVector expected = ColumnVector.fromBoxedInts(2*2-2, 3*3-3, null, 4*4-4)) {
+      assertColumnsAreEqual(expected, cv1);
+      assertColumnsAreEqual(expected, cv2);
+    }
+  }
+
+  @Test
+  void testDistinctCount() {
+    try (ColumnVector cv = ColumnVector.fromBoxedLongs(5L, 3L, null, null, 5L)) {
+      assertEquals(3, cv.distinctCount());
+      assertEquals(2, cv.distinctCount(NullPolicy.EXCLUDE));
+    }
+  }
+
+  @Test
+  void testClampDouble() {
+    try (ColumnVector cv = ColumnVector.fromDoubles(2.33d, 32.12d, -121.32d, 0.0d, 0.00001d,
+        Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, Double.NaN);
+         Scalar num = Scalar.fromDouble(0);
+         Scalar loReplace = Scalar.fromDouble(-1);
+         Scalar hiReplace = Scalar.fromDouble(1);
+         ColumnVector result = cv.clamp(num, loReplace, num, hiReplace);
+         ColumnVector expected = ColumnVector.fromDoubles(1.0d, 1.0d, -1.0d, 0.0d, 1.0d, -1.0d,
+             1.0d, Double.NaN)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testClampFloat() {
+    try (ColumnVector cv = ColumnVector.fromBoxedFloats(2.33f, 32.12f, null, -121.32f, 0.0f, 0.00001f, Float.NEGATIVE_INFINITY,
+        Float.POSITIVE_INFINITY, Float.NaN);
+         Scalar num = Scalar.fromFloat(0);
+         Scalar loReplace = Scalar.fromFloat(-1);
+         Scalar hiReplace = Scalar.fromFloat(1);
+         ColumnVector result = cv.clamp(num, loReplace, num, hiReplace);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(1.0f, 1.0f, null, -1.0f, 0.0f, 1.0f, -1.0f, 1.0f, Float.NaN)) {
+     assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testClampLong() {
+    try (ColumnVector cv = ColumnVector.fromBoxedLongs(1l, 3l, 6l, -2l, 23l, -0l, -90l, null);
+         Scalar num = Scalar.fromLong(0);
+         Scalar loReplace = Scalar.fromLong(-1);
+         Scalar hiReplace = Scalar.fromLong(1);
+         ColumnVector result = cv.clamp(num, loReplace, num, hiReplace);
+         ColumnVector expected = ColumnVector.fromBoxedLongs(1l, 1l, 1l, -1l, 1l, 0l, -1l, null)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testClampShort() {
+    try (ColumnVector cv = ColumnVector.fromShorts(new short[]{1, 3, 6, -2, 23, -0, -90});
+         Scalar lo = Scalar.fromShort((short)1);
+         Scalar hi = Scalar.fromShort((short)2);
+         ColumnVector result = cv.clamp(lo, hi);
+         ColumnVector expected = ColumnVector.fromShorts(new short[]{1, 2, 2, 1, 2, 1, 1})) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testClampInt() {
+      try (ColumnVector cv = ColumnVector.fromInts(1, 3, 6, -2, 23, -0, -90);
+           Scalar num = Scalar.fromInt(0);
+           Scalar hiReplace = Scalar.fromInt(1);
+           Scalar loReplace = Scalar.fromInt(-1);
+           ColumnVector result = cv.clamp(num, loReplace, num, hiReplace);
+           ColumnVector expected = ColumnVector.fromInts(1, 1, 1, -1, 1, 0, -1)) {
+          assertColumnsAreEqual(expected, result);
+      }
+  }
+
+  @Test
+  void testGetElementInt() {
+    try (ColumnVector cv = ColumnVector.fromBoxedInts(3, 2, 1, null);
+         Scalar s0 = cv.getScalarElement(0);
+         Scalar s1 = cv.getScalarElement(1);
+         Scalar s2 = cv.getScalarElement(2);
+         Scalar s3 = cv.getScalarElement(3)) {
+      assertEquals(3, s0.getInt());
+      assertEquals(2, s1.getInt());
+      assertEquals(1, s2.getInt());
+      assertFalse(s3.isValid());
+    }
+  }
+
+  @Test
+  void testGetElementByte() {
+    try (ColumnVector cv = ColumnVector.fromBoxedBytes((byte)3, (byte)2, (byte)1, null);
+         Scalar s0 = cv.getScalarElement(0);
+         Scalar s1 = cv.getScalarElement(1);
+         Scalar s2 = cv.getScalarElement(2);
+         Scalar s3 = cv.getScalarElement(3)) {
+      assertEquals(3, s0.getByte());
+      assertEquals(2, s1.getByte());
+      assertEquals(1, s2.getByte());
+      assertFalse(s3.isValid());
+    }
+  }
+
+  @Test
+  void testGetElementFloat() {
+    try (ColumnVector cv = ColumnVector.fromBoxedFloats(3f, 2f, 1f, null);
+         Scalar s0 = cv.getScalarElement(0);
+         Scalar s1 = cv.getScalarElement(1);
+         Scalar s2 = cv.getScalarElement(2);
+         Scalar s3 = cv.getScalarElement(3)) {
+      assertEquals(3f, s0.getFloat());
+      assertEquals(2f, s1.getFloat());
+      assertEquals(1f, s2.getFloat());
+      assertFalse(s3.isValid());
+    }
+  }
+
+  @Test
+  void testGetElementString() {
+    try (ColumnVector cv = ColumnVector.fromStrings("3a", "2b", "1c", null);
+         Scalar s0 = cv.getScalarElement(0);
+         Scalar s1 = cv.getScalarElement(1);
+         Scalar s2 = cv.getScalarElement(2);
+         Scalar s3 = cv.getScalarElement(3)) {
+      assertEquals("3a", s0.getJavaString());
+      assertEquals("2b", s1.getJavaString());
+      assertEquals("1c", s2.getJavaString());
+      assertFalse(s3.isValid());
+    }
+  }
+
+  @Test
+  void testGetElementDecimal() {
+    try (ColumnVector cv = ColumnVector.decimalFromLongs(1,3, 2, 1, -1);
+         Scalar s0 = cv.getScalarElement(0);
+         Scalar s1 = cv.getScalarElement(1);
+         Scalar s2 = cv.getScalarElement(2);
+         Scalar s3 = cv.getScalarElement(3)) {
+      assertEquals(1, s0.getType().getScale());
+      assertEquals(new BigDecimal("3E+1"), s0.getBigDecimal());
+      assertEquals(new BigDecimal("2E+1"), s1.getBigDecimal());
+      assertEquals(new BigDecimal("1E+1"), s2.getBigDecimal());
+      assertEquals(new BigDecimal("-1E+1"), s3.getBigDecimal());
+    }
+  }
+
+  @Test
+  void testGetElementList() {
+    HostColumnVector.DataType dt = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    try (ColumnVector cv = ColumnVector.fromLists(dt, Arrays.asList(3, 2),
+        Arrays.asList(1), Arrays.asList(), null);
+         Scalar s0 = cv.getScalarElement(0);
+         ColumnView s0Cv = s0.getListAsColumnView();
+         ColumnVector expected0 = ColumnVector.fromInts(3, 2);
+         Scalar s1 = cv.getScalarElement(1);
+         ColumnView s1Cv = s1.getListAsColumnView();
+         ColumnVector expected1 = ColumnVector.fromInts(1);
+         Scalar s2 = cv.getScalarElement(2);
+         ColumnView s2Cv = s2.getListAsColumnView();
+         ColumnVector expected2 = ColumnVector.fromInts();
+         Scalar s3 = cv.getScalarElement(3)) {
+      assertColumnsAreEqual(expected0, s0Cv);
+      assertColumnsAreEqual(expected1, s1Cv);
+      assertColumnsAreEqual(expected2, s2Cv);
+      assertFalse(s3.isValid());
+    }
+  }
+
+ @Test
+  void testStringCreation() {
+    try (ColumnVector cv = ColumnVector.fromStrings("d", "sd", "sde", null, "END");
+         HostColumnVector host = cv.copyToHost();
+         ColumnVector backAgain = host.copyToDevice()) {
+      assertColumnsAreEqual(cv, backAgain);
+    }
+  }
+
+  @Test
+  void testUTF8StringCreation() {
+    try (ColumnVector cv = ColumnVector.fromUTF8Strings(
+            "d".getBytes(StandardCharsets.UTF_8),
+            "sd".getBytes(StandardCharsets.UTF_8),
+            "sde".getBytes(StandardCharsets.UTF_8),
+            null,
+            "END".getBytes(StandardCharsets.UTF_8));
+         ColumnVector expected = ColumnVector.fromStrings("d", "sd", "sde", null, "END")) {
+      assertColumnsAreEqual(expected, cv);
+    }
+  }
+
+  @Test
+  void testRefCountLeak() throws InterruptedException {
+    assumeTrue(Boolean.getBoolean("ai.rapids.cudf.flaky-tests-enabled"));
+    long expectedLeakCount = MemoryCleaner.leakCount.get() + 1;
+    ColumnVector.fromInts(1, 2, 3);
+    long maxTime = System.currentTimeMillis() + 10_000;
+    long leakNow;
+    do {
+      System.gc();
+      Thread.sleep(50);
+      leakNow = MemoryCleaner.leakCount.get();
+    } while (leakNow != expectedLeakCount && System.currentTimeMillis() < maxTime);
+    assertEquals(expectedLeakCount, MemoryCleaner.leakCount.get());
+  }
+
+  @Test
+  void testJoinStrings() {
+    try (ColumnVector in = ColumnVector.fromStrings("A", "B", "C", "D", null, "E");
+         ColumnVector expected = ColumnVector.fromStrings("A-B-C-D-null-E");
+         Scalar sep = Scalar.fromString("-");
+         Scalar narep = Scalar.fromString("null");
+         ColumnVector found = in.joinStrings(sep, narep)) {
+      assertColumnsAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testConcatTypeError() {
+    try (ColumnVector v0 = ColumnVector.fromInts(1, 2, 3, 4);
+         ColumnVector v1 = ColumnVector.fromFloats(5.0f, 6.0f)) {
+      assertThrows(CudfException.class, () -> ColumnVector.concatenate(v0, v1));
+    }
+  }
+
+  @Test
+  void testConcatNoNulls() {
+    try (ColumnVector v0 = ColumnVector.fromInts(1, 2, 3, 4);
+         ColumnVector v1 = ColumnVector.fromInts(5, 6, 7);
+         ColumnVector v2 = ColumnVector.fromInts(8, 9);
+         ColumnVector v = ColumnVector.concatenate(v0, v1, v2);
+         ColumnVector expected = ColumnVector.fromInts(1, 2, 3, 4, 5, 6, 7, 8, 9)) {
+      assertColumnsAreEqual(expected, v);
+    }
+  }
+
+  @Test
+  void testConcatWithNulls() {
+    try (ColumnVector v0 = ColumnVector.fromDoubles(1, 2, 3, 4);
+         ColumnVector v1 = ColumnVector.fromDoubles(5, 6, 7);
+         ColumnVector v2 = ColumnVector.fromBoxedDoubles(null, 9.0);
+         ColumnVector v = ColumnVector.concatenate(v0, v1, v2);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(1., 2., 3., 4., 5., 6., 7., null, 9.)) {
+      assertColumnsAreEqual(expected, v);
+    }
+  }
+
+  @Test
+  void testConcatStrings() {
+    try (ColumnVector v0 = ColumnVector.fromStrings("0","1","2",null);
+         ColumnVector v1 = ColumnVector.fromStrings(null, "5", "6","7");
+         ColumnVector expected = ColumnVector.fromStrings(
+           "0","1","2",null,
+           null,"5","6","7");
+         ColumnVector v = ColumnVector.concatenate(v0, v1)) {
+      assertColumnsAreEqual(v, expected);
+    }
+  }
+
+  @Test
+  void testConcatTimestamps() {
+    try (ColumnVector v0 = ColumnVector.timestampMicroSecondsFromBoxedLongs(0L, 1L, 2L, null);
+         ColumnVector v1 = ColumnVector.timestampMicroSecondsFromBoxedLongs(null, 5L, 6L, 7L);
+         ColumnVector expected = ColumnVector.timestampMicroSecondsFromBoxedLongs(
+           0L, 1L, 2L, null,
+           null, 5L, 6L, 7L);
+         ColumnVector v = ColumnVector.concatenate(v0, v1)) {
+      assertColumnsAreEqual(v, expected);
+    }
+  }
+
+  @Test
+  void testNormalizeNANsAndZeros() {
+    // Must check boundaries of NaN representation, as described in javadoc for Double#longBitsToDouble.
+    // @see java.lang.Double#longBitsToDouble
+    // <quote>
+    //      If the argument is any value in the range 0x7ff0000000000001L through 0x7fffffffffffffffL,
+    //      or in the range 0xfff0000000000001L through 0xffffffffffffffffL, the result is a NaN.
+    // </quote>
+    final double MIN_PLUS_NaN  = Double.longBitsToDouble(0x7ff0000000000001L);
+    final double MAX_PLUS_NaN  = Double.longBitsToDouble(0x7fffffffffffffffL);
+    final double MAX_MINUS_NaN = Double.longBitsToDouble(0xfff0000000000001L);
+    final double MIN_MINUS_NaN = Double.longBitsToDouble(0xffffffffffffffffL);
+
+    Double[]  ins = new Double[] {0.0, -0.0, Double.NaN, MIN_PLUS_NaN, MAX_PLUS_NaN, MIN_MINUS_NaN, MAX_MINUS_NaN, null};
+    Double[] outs = new Double[] {0.0,  0.0, Double.NaN,   Double.NaN,   Double.NaN,    Double.NaN,    Double.NaN, null};
+
+    try (ColumnVector input = ColumnVector.fromBoxedDoubles(ins);
+         ColumnVector expectedColumn = ColumnVector.fromBoxedDoubles(outs);
+         ColumnVector normalizedColumn = input.normalizeNANsAndZeros()) {
+      try (HostColumnVector expected = expectedColumn.copyToHost();
+           HostColumnVector normalized = normalizedColumn.copyToHost()) {
+        for (int i = 0; i<input.getRowCount(); ++i) {
+          if (expected.isNull(i)) {
+            assertTrue(normalized.isNull(i));
+          }
+          else {
+            assertEquals(
+                    Double.doubleToRawLongBits(expected.getDouble(i)),
+                    Double.doubleToRawLongBits(normalized.getDouble(i))
+            );
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMD5HashStrings() {
+    try (ColumnVector v0 = ColumnVector.fromStrings(
+          "a", "B\n", "dE\"\u0100\t\u0101 \ud720\ud721",
+          "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+          "in the MD5 hash function. This string needed to be longer.",
+          null, null);
+         ColumnVector v1 = ColumnVector.fromStrings(
+           null, "c", "\\Fg2\'",
+           "A 60 character string to test MD5's message padding algorithm",
+           "hiJ\ud720\ud721\ud720\ud721", null);
+         ColumnVector v2 = ColumnVector.fromStrings(
+           "a", "B\nc",  "dE\"\u0100\t\u0101 \ud720\ud721\\Fg2\'",
+           "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+           "in the MD5 hash function. This string needed to be longer.A 60 character string to " +
+           "test MD5's message padding algorithm",
+           "hiJ\ud720\ud721\ud720\ud721", null);
+         ColumnVector result01 = ColumnVector.md5Hash(v0, v1);
+         ColumnVector result2 = ColumnVector.md5Hash(v2);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "0cc175b9c0f1b6a831c399e269772661", "f5112705c2f6dc7d3fc6bd496df6c2e8",
+          "d49db62680847e0e7107e0937d29668e", "8fa29148f63c1fe9248fdc4644e3a193",
+          "1bc221b25e6c4825929e884092f4044f", "d41d8cd98f00b204e9800998ecf8427e")) {
+      assertColumnsAreEqual(result01, expected);
+      assertColumnsAreEqual(result2, expected);
+    }
+  }
+
+  @Test
+  void testMD5HashInts() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedInts(0, 100, null, null, Integer.MIN_VALUE, null);
+         ColumnVector v1 = ColumnVector.fromBoxedInts(0, null, -100, null, null, Integer.MAX_VALUE);
+         ColumnVector result = ColumnVector.md5Hash(v0, v1);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "7dea362b3fac8e00956a4952a3d4f474", "cdc824bf721df654130ed7447fb878ac",
+          "7fb89558e395330c6a10ab98915fcafb", "d41d8cd98f00b204e9800998ecf8427e",
+          "152afd7bf4dbe26f85032eee0269201a", "ed0622e1179e101cf7edc0b952cc5262")) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testMD5HashDoubles() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(
+          0.0, null, 100.0, -100.0, Double.MIN_NORMAL, Double.MAX_VALUE,
+          POSITIVE_DOUBLE_NAN_UPPER_RANGE, POSITIVE_DOUBLE_NAN_LOWER_RANGE,
+          NEGATIVE_DOUBLE_NAN_UPPER_RANGE, NEGATIVE_DOUBLE_NAN_LOWER_RANGE,
+          Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY);
+         ColumnVector result = ColumnVector.md5Hash(v);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "7dea362b3fac8e00956a4952a3d4f474", "d41d8cd98f00b204e9800998ecf8427e",
+          "6f5b4a57fd3aeb25cd33aa6c56512fd4", "b36ce1b64164e8f12c52ee5f1131ec01",
+          "f7fbcdce3cf1bea8defd4ca29dabeb75", "d466cb643c6da6c31c88f4d482bccfd3",
+          "bf26d90b64827fdbc58da0aa195156fe", "bf26d90b64827fdbc58da0aa195156fe",
+          "bf26d90b64827fdbc58da0aa195156fe", "bf26d90b64827fdbc58da0aa195156fe",
+          "73c82437c94e197f7e35e14f0140497a", "740660a5f71e7a264fca45330c34da31")) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testMD5HashFloats() {
+    try (ColumnVector v = ColumnVector.fromBoxedFloats(
+          0f, 100f, -100f, Float.MIN_NORMAL, Float.MAX_VALUE, null,
+          POSITIVE_FLOAT_NAN_LOWER_RANGE, POSITIVE_FLOAT_NAN_UPPER_RANGE,
+          NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE,
+          Float.POSITIVE_INFINITY, Float.NEGATIVE_INFINITY);
+         ColumnVector result = ColumnVector.md5Hash(v);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "f1d3ff8443297732862df21dc4e57262", "a5d1e9463fae706307f90b05e9e6db9a",
+          "556915a037c2ce1adfbedd7ca24794ea", "59331a73da50b419339c0d67a9ec1a97",
+          "0ac9ada9698891bfc3f74bcee7e3f675", "d41d8cd98f00b204e9800998ecf8427e",
+          "d6fd2bac25776d9a7269ca0e24b21b36", "d6fd2bac25776d9a7269ca0e24b21b36",
+          "d6fd2bac25776d9a7269ca0e24b21b36", "d6fd2bac25776d9a7269ca0e24b21b36",
+          "55e3a4db046ad9065bd7d64243de408f", "33b552ad34a825b275f5f2b59778b5c3")){
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testMD5HashBools() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedBooleans(null, true, false, true, null, false);
+         ColumnVector v1 = ColumnVector.fromBoxedBooleans(null, true, false, null, false, true);
+         ColumnVector result = ColumnVector.md5Hash(v0, v1);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "d41d8cd98f00b204e9800998ecf8427e", "249ba6277758050695e8f5909bacd6d3",
+          "c4103f122d27677c9db144cae1394a66", "55a54008ad1ba589aa210d2629c1df41",
+          "93b885adfe0da089cdf634904fd59f71", "441077cc9e57554dd476bdfb8b8b8102")) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testMD5HashMixed() {
+    try (ColumnVector strings = ColumnVector.fromStrings(
+          "a", "B\n", "dE\"\u0100\t\u0101 \ud720\ud721",
+          "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+          "in the MD5 hash function. This string needed to be longer.",
+          null, null);
+         ColumnVector integers = ColumnVector.fromBoxedInts(0, 100, -100, Integer.MIN_VALUE, Integer.MAX_VALUE, null);
+         ColumnVector doubles = ColumnVector.fromBoxedDoubles(
+          0.0, 100.0, -100.0, POSITIVE_DOUBLE_NAN_LOWER_RANGE, POSITIVE_DOUBLE_NAN_UPPER_RANGE, null);
+         ColumnVector floats = ColumnVector.fromBoxedFloats(
+          0f, 100f, -100f, NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE, null);
+         ColumnVector bools = ColumnVector.fromBoxedBooleans(true, false, null, false, true, null);
+         ColumnVector result = ColumnVector.md5Hash(strings, integers, doubles, floats, bools);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "c12c8638819fdd8377bbf537a4ebf0b4", "abad86357c1ae28eeb89f4b59700946a",
+          "7e376255c6354716cd63418208dc7b90", "2f64d6a1d5b730fd97115924cf9aa486",
+          "9f9d26bb5d25d56453a91f0558370fa4", "d41d8cd98f00b204e9800998ecf8427e")) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testMD5HashLists() {
+    List<String> list1 = Arrays.asList("dE\"\u0100\t\u0101 \u0500\u0501", "\\Fg2\'");
+    List<String> list2 = Arrays.asList("A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+    "in the MD5 hash function. This string needed to be longer.", "", null, "A 60 character string to test MD5's message padding algorithm");
+    List<String> list3 = Arrays.asList("hiJ\ud720\ud721\ud720\ud721");
+    List<String> list4 = null;
+    try (ColumnVector v = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+    new HostColumnVector.BasicType(true, DType.STRING)), list1, list2, list3, list4);
+         ColumnVector result = ColumnVector.md5Hash(v);
+         ColumnVector expected = ColumnVector.fromStrings(
+          "675c30ce6d1b27dcb5009b01be42e9bd", "8fa29148f63c1fe9248fdc4644e3a193",
+          "1bc221b25e6c4825929e884092f4044f", "d41d8cd98f00b204e9800998ecf8427e")) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashStrings() {
+    try (ColumnVector v0 = ColumnVector.fromStrings(
+           "a", "B\nc",  "dE\"\u0100\t\u0101 \ud720\ud721\\Fg2\'",
+           "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+           "in the MD5 hash function. This string needed to be longer.A 60 character string to " +
+           "test MD5's message padding algorithm",
+           "hiJ\ud720\ud721\ud720\ud721", null);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(42, new ColumnVector[]{v0});
+         ColumnVector expected = ColumnVector.fromBoxedInts(1485273170, 1709559900, 1423943036, 176121990, 1199621434, 42)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashInts() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedInts(0, 100, null, null, Integer.MIN_VALUE, null);
+         ColumnVector v1 = ColumnVector.fromBoxedInts(0, null, -100, null, null, Integer.MAX_VALUE);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(42, new ColumnVector[]{v0, v1});
+         ColumnVector expected = ColumnVector.fromBoxedInts(59727262, 751823303, -1080202046, 42, 723455942, 133916647)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashDoubles() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(
+          0.0, null, 100.0, -100.0, Double.MIN_NORMAL, Double.MAX_VALUE,
+          POSITIVE_DOUBLE_NAN_UPPER_RANGE, POSITIVE_DOUBLE_NAN_LOWER_RANGE,
+          NEGATIVE_DOUBLE_NAN_UPPER_RANGE, NEGATIVE_DOUBLE_NAN_LOWER_RANGE,
+          Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(new ColumnVector[]{v});
+         ColumnVector expected = ColumnVector.fromBoxedInts(1669671676, 0, -544903190, -1831674681, 150502665, 474144502, 1428788237, 1428788237, 1428788237, 1428788237, 420913893, 1915664072)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashTimestamps() {
+    // The hash values were derived from Apache Spark in a manner similar to the one documented at
+    // https://github.com/rapidsai/cudf/blob/aa7ca46dcd9e/cpp/tests/hashing/hash_test.cpp#L281-L307
+    try (ColumnVector v = ColumnVector.timestampMicroSecondsFromBoxedLongs(
+        0L, null, 100L, -100L, 0x123456789abcdefL, null, -0x123456789abcdefL);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(42, new ColumnVector[]{v});
+         ColumnVector expected = ColumnVector.fromBoxedInts(-1670924195, 42, 1114849490, 904948192, 657182333, 42, -57193045)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashDecimal64() {
+    // The hash values were derived from Apache Spark in a manner similar to the one documented at
+    // https://github.com/rapidsai/cudf/blob/aa7ca46dcd9e/cpp/tests/hashing/hash_test.cpp#L281-L307
+    try (ColumnVector v = ColumnVector.decimalFromLongs(-7,
+        0L, 100L, -100L, 0x123456789abcdefL, -0x123456789abcdefL);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(42, new ColumnVector[]{v});
+         ColumnVector expected = ColumnVector.fromBoxedInts(-1670924195, 1114849490, 904948192, 657182333, -57193045)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashDecimal32() {
+    // The hash values were derived from Apache Spark in a manner similar to the one documented at
+    // https://github.com/rapidsai/cudf/blob/aa7ca46dcd9e/cpp/tests/hashing/hash_test.cpp#L281-L307
+    try (ColumnVector v = ColumnVector.decimalFromInts(-3,
+        0, 100, -100, 0x12345678, -0x12345678);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(42, new ColumnVector[]{v});
+         ColumnVector expected = ColumnVector.fromBoxedInts(-1670924195, 1114849490, 904948192, -958054811, -1447702630)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashDates() {
+    // The hash values were derived from Apache Spark in a manner similar to the one documented at
+    // https://github.com/rapidsai/cudf/blob/aa7ca46dcd9e/cpp/tests/hashing/hash_test.cpp#L281-L307
+    try (ColumnVector v = ColumnVector.timestampDaysFromBoxedInts(
+        0, null, 100, -100, 0x12345678, null, -0x12345678);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(42, new ColumnVector[]{v});
+         ColumnVector expected = ColumnVector.fromBoxedInts(933211791, 42, 751823303, -1080202046, -1721170160, 42, 1852996993)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashFloats() {
+    try (ColumnVector v = ColumnVector.fromBoxedFloats(
+          0f, 100f, -100f, Float.MIN_NORMAL, Float.MAX_VALUE, null,
+          POSITIVE_FLOAT_NAN_LOWER_RANGE, POSITIVE_FLOAT_NAN_UPPER_RANGE,
+          NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE,
+          Float.POSITIVE_INFINITY, Float.NEGATIVE_INFINITY);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(411, new ColumnVector[]{v});
+         ColumnVector expected = ColumnVector.fromBoxedInts(-235179434, 1812056886, 2028471189, 1775092689, -1531511762, 411, -1053523253, -1053523253, -1053523253, -1053523253, -1526256646, 930080402)){
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashBools() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedBooleans(null, true, false, true, null, false);
+         ColumnVector v1 = ColumnVector.fromBoxedBooleans(null, true, false, null, false, true);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(0, new ColumnVector[]{v0, v1});
+         ColumnVector expected = ColumnVector.fromBoxedInts(0, -1589400010, -239939054, -68075478, 593689054, -1194558265)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashMixed() {
+    try (ColumnVector strings = ColumnVector.fromStrings(
+          "a", "B\n", "dE\"\u0100\t\u0101 \ud720\ud721",
+          "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+          "in the MD5 hash function. This string needed to be longer.",
+          null, null);
+         ColumnVector integers = ColumnVector.fromBoxedInts(0, 100, -100, Integer.MIN_VALUE, Integer.MAX_VALUE, null);
+         ColumnVector doubles = ColumnVector.fromBoxedDoubles(
+          0.0, 100.0, -100.0, POSITIVE_DOUBLE_NAN_LOWER_RANGE, POSITIVE_DOUBLE_NAN_UPPER_RANGE, null);
+         ColumnVector floats = ColumnVector.fromBoxedFloats(
+          0f, 100f, -100f, NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE, null);
+         ColumnVector bools = ColumnVector.fromBoxedBooleans(true, false, null, false, true, null);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(1868, new ColumnVector[]{strings, integers, doubles, floats, bools});
+         ColumnVector expected = ColumnVector.fromBoxedInts(1936985022, 720652989, 339312041, 1400354989, 769988643, 1868)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashStruct() {
+    try (ColumnVector strings = ColumnVector.fromStrings(
+        "a", "B\n", "dE\"\u0100\t\u0101 \ud720\ud721",
+        "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+            "in the MD5 hash function. This string needed to be longer.",
+        null, null);
+         ColumnVector integers = ColumnVector.fromBoxedInts(0, 100, -100, Integer.MIN_VALUE, Integer.MAX_VALUE, null);
+         ColumnVector doubles = ColumnVector.fromBoxedDoubles(
+             0.0, 100.0, -100.0, POSITIVE_DOUBLE_NAN_LOWER_RANGE, POSITIVE_DOUBLE_NAN_UPPER_RANGE, null);
+         ColumnVector floats = ColumnVector.fromBoxedFloats(
+             0f, 100f, -100f, NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE, null);
+         ColumnVector bools = ColumnVector.fromBoxedBooleans(true, false, null, false, true, null);
+         ColumnView structs = ColumnView.makeStructView(strings, integers, doubles, floats, bools);
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(1868, new ColumnView[]{structs});
+         ColumnVector expected = ColumnVector.spark32BitMurmurHash3(1868, new ColumnVector[]{strings, integers, doubles, floats, bools})) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashNestedStruct() {
+    try (ColumnVector strings = ColumnVector.fromStrings(
+        "a", "B\n", "dE\"\u0100\t\u0101 \ud720\ud721",
+        "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+            "in the MD5 hash function. This string needed to be longer.",
+        null, null);
+         ColumnVector integers = ColumnVector.fromBoxedInts(0, 100, -100, Integer.MIN_VALUE, Integer.MAX_VALUE, null);
+         ColumnVector doubles = ColumnVector.fromBoxedDoubles(
+             0.0, 100.0, -100.0, POSITIVE_DOUBLE_NAN_LOWER_RANGE, POSITIVE_DOUBLE_NAN_UPPER_RANGE, null);
+         ColumnVector floats = ColumnVector.fromBoxedFloats(
+             0f, 100f, -100f, NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE, null);
+         ColumnVector bools = ColumnVector.fromBoxedBooleans(true, false, null, false, true, null);
+         ColumnView structs1 = ColumnView.makeStructView(strings, integers);
+         ColumnView structs2 = ColumnView.makeStructView(structs1, doubles);
+         ColumnView structs3 = ColumnView.makeStructView(bools);
+         ColumnView structs = ColumnView.makeStructView(structs2, floats, structs3);
+         ColumnVector expected = ColumnVector.spark32BitMurmurHash3(1868, new ColumnVector[]{strings, integers, doubles, floats, bools});
+         ColumnVector result = ColumnVector.spark32BitMurmurHash3(1868, new ColumnView[]{structs})) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSpark32BitMurmur3HashListsAndNestedLists() {
+    try (ColumnVector stringListCV = ColumnVector.fromLists(
+             new ListType(true, new BasicType(true, DType.STRING)),
+             Arrays.asList(null, "a"),
+             Arrays.asList("B\n", ""),
+             Arrays.asList("dE\"\u0100\t\u0101", " \ud720\ud721"),
+             Collections.singletonList("A very long (greater than 128 bytes/char string) to test a multi" +
+             " hash-step data point in the Murmur3 hash function. This string needed to be longer."),
+             Collections.singletonList(""),
+             null);
+         ColumnVector strings1 = ColumnVector.fromStrings(
+             "a", "B\n", "dE\"\u0100\t\u0101",
+             "A very long (greater than 128 bytes/char string) to test a multi hash-step data point " +
+             "in the Murmur3 hash function. This string needed to be longer.", null, null);
+         ColumnVector strings2 = ColumnVector.fromStrings(
+             null, "", " \ud720\ud721", null, "", null);
+         ColumnView stringStruct = ColumnView.makeStructView(strings1, strings2);
+         ColumnVector stringExpected = ColumnVector.spark32BitMurmurHash3(1868, new ColumnView[]{stringStruct});
+         ColumnVector stringResult = ColumnVector.spark32BitMurmurHash3(1868, new ColumnView[]{stringListCV});
+         ColumnVector intListCV = ColumnVector.fromLists(
+             new ListType(true, new BasicType(true, DType.INT32)),
+             null,
+             Arrays.asList(0, -2, 3),
+             Collections.singletonList(Integer.MAX_VALUE),
+             Arrays.asList(5, -6, null),
+             Collections.singletonList(Integer.MIN_VALUE),
+             null);
+         ColumnVector integers1 = ColumnVector.fromBoxedInts(null, 0, null, 5, Integer.MIN_VALUE, null);
+         ColumnVector integers2 = ColumnVector.fromBoxedInts(null, -2, Integer.MAX_VALUE, null, null, null);
+         ColumnVector integers3 = ColumnVector.fromBoxedInts(null, 3, null, -6, null, null);
+         ColumnVector intExpected =
+             ColumnVector.spark32BitMurmurHash3(1868, new ColumnVector[]{integers1, integers2, integers3});
+         ColumnVector intResult = ColumnVector.spark32BitMurmurHash3(1868, new ColumnVector[]{intListCV});
+         ColumnVector doubles = ColumnVector.fromBoxedDoubles(
+          0.0, 100.0, -100.0, POSITIVE_DOUBLE_NAN_LOWER_RANGE, POSITIVE_DOUBLE_NAN_UPPER_RANGE, null);
+         ColumnVector floats = ColumnVector.fromBoxedFloats(
+          0f, 100f, -100f, NEGATIVE_FLOAT_NAN_LOWER_RANGE, NEGATIVE_FLOAT_NAN_UPPER_RANGE, null);
+         ColumnView structCV = ColumnView.makeStructView(intListCV, stringListCV, doubles, floats);
+         ColumnVector nestedExpected =
+             ColumnVector.spark32BitMurmurHash3(1868, new ColumnView[]{intListCV, strings1, strings2, doubles, floats});
+         ColumnVector nestedResult =
+             ColumnVector.spark32BitMurmurHash3(1868, new ColumnView[]{structCV})) {
+      assertColumnsAreEqual(stringExpected, stringResult);
+      assertColumnsAreEqual(intExpected, intResult);
+      assertColumnsAreEqual(nestedExpected, nestedResult);
+    }
+  }
+
+  @Test
+  void isNotNullTestEmptyColumn() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans();
+         ColumnVector result = v.isNotNull()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void isNotNullTest() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts(1, 2, null, 4, null, 6);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, false, true, false, true);
+         ColumnVector result = v.isNotNull()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void isNotNullTestAllNulls() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts(null, null, null, null, null, null);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false, false, false);
+         ColumnVector result = v.isNotNull()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void isNotNullTestAllNotNulls() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts(1,2,3,4,5,6);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true, true, true);
+         ColumnVector result = v.isNotNull()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void isNullTest() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts(1, 2, null, 4, null, 6);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, false, true, false);
+         ColumnVector result = v.isNull()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void isNullTestEmptyColumn() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans();
+         ColumnVector result = v.isNull()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+   @Test
+  void isNanTestWithNulls() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(null, null, Double.NaN, null, Double.NaN, null);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(null, null, Float.NaN, null, Float.NaN, null);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, false, true, false);
+         ColumnVector result = v.isNan();
+         ColumnVector resultF = vF.isNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNanForTypeMismatch() {
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector v = ColumnVector.fromStrings("foo", "bar", "baz");
+           ColumnVector result = v.isNan()) {}
+    });
+  }
+
+  @Test
+  void isNanTest() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(1.0, 2.0, Double.NaN, 4.0, Double.NaN, 6.0);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(1.1f, 2.2f, Float.NaN, 4.4f, Float.NaN, 6.6f);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, false, true, false);
+         ColumnVector result = v.isNan();
+         ColumnVector resultF = vF.isNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNanTestEmptyColumn() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles();
+         ColumnVector vF = ColumnVector.fromBoxedFloats();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans();
+         ColumnVector result = v.isNan();
+         ColumnVector resultF = vF.isNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNanTestAllNotNans() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(1.0, 2.0, 3.0, 4.0, 5.0, 6.0);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false, false, false);
+         ColumnVector result = v.isNan();
+         ColumnVector resultF = vF.isNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNanTestAllNans() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(Double.NaN, Double.NaN, Double.NaN, Double.NaN, Double.NaN, Double.NaN);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(Float.NaN, Float.NaN, Float.NaN, Float.NaN, Float.NaN, Float.NaN);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true, true, true);
+         ColumnVector result = v.isNan();
+         ColumnVector resultF = vF.isNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNotNanTestWithNulls() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(null, null, Double.NaN, null, Double.NaN, null);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(null, null, Float.NaN, null, Float.NaN, null);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, false, true, false, true);
+         ColumnVector result = v.isNotNan();
+         ColumnVector resultF = vF.isNotNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNotNanForTypeMismatch() {
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector v = ColumnVector.fromStrings("foo", "bar", "baz");
+           ColumnVector result = v.isNotNan()) {}
+    });
+  }
+
+  @Test
+  void isNotNanTest() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(1.0, 2.0, Double.NaN, 4.0, Double.NaN, 6.0);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(1.1f, 2.2f, Float.NaN, 4.4f, Float.NaN, 6.6f);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, false, true, false, true);
+         ColumnVector result = v.isNotNan();
+         ColumnVector resultF = vF.isNotNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNotNanTestEmptyColumn() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles();
+         ColumnVector vF = ColumnVector.fromBoxedFloats();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans();
+         ColumnVector result = v.isNotNan();
+         ColumnVector resultF = vF.isNotNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNotNanTestAllNotNans() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(1.0, 2.0, 3.0, 4.0, 5.0, 6.0);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true, true, true);
+         ColumnVector result = v.isNotNan();
+         ColumnVector resultF = vF.isNotNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void isNotNanTestAllNans() {
+    try (ColumnVector v = ColumnVector.fromBoxedDoubles(Double.NaN, Double.NaN, Double.NaN, Double.NaN, Double.NaN, Double.NaN);
+         ColumnVector vF = ColumnVector.fromBoxedFloats(Float.NaN, Float.NaN, Float.NaN, Float.NaN, Float.NaN, Float.NaN);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false, false, false);
+         ColumnVector result = v.isNotNan();
+         ColumnVector resultF = vF.isNotNan()) {
+      assertColumnsAreEqual(expected, result);
+      assertColumnsAreEqual(expected, resultF);
+    }
+  }
+
+  @Test
+  void roundFloatsHalfUp() {
+    try (ColumnVector v = ColumnVector.fromBoxedFloats(1.234f, 25.66f, null, 154.9f, 2346f);
+         ColumnVector result1 = v.round();
+         ColumnVector result2 = v.round(1, RoundMode.HALF_UP);
+         ColumnVector result3 = v.round(-1, RoundMode.HALF_UP);
+         ColumnVector expected1 = ColumnVector.fromBoxedFloats(1f, 26f, null, 155f, 2346f);
+         ColumnVector expected2 = ColumnVector.fromBoxedFloats(1.2f, 25.7f, null, 154.9f, 2346f);
+         ColumnVector expected3 = ColumnVector.fromBoxedFloats(0f, 30f, null, 150f, 2350f)) {
+      assertColumnsAreEqual(expected1, result1);
+      assertColumnsAreEqual(expected2, result2);
+      assertColumnsAreEqual(expected3, result3);
+    }
+  }
+
+  @Test
+  void roundFloatsHalfEven() {
+    try (ColumnVector v = ColumnVector.fromBoxedFloats(1.5f, 2.5f, 1.35f, null, 1.25f, 15f, 25f);
+         ColumnVector result1 = v.round(RoundMode.HALF_EVEN);
+         ColumnVector result2 = v.round(1, RoundMode.HALF_EVEN);
+         ColumnVector result3 = v.round(-1, RoundMode.HALF_EVEN);
+         ColumnVector expected1 = ColumnVector.fromBoxedFloats(2f, 2f, 1f, null, 1f, 15f, 25f);
+         ColumnVector expected2 = ColumnVector.fromBoxedFloats(1.5f, 2.5f, 1.4f, null, 1.2f, 15f, 25f);
+         ColumnVector expected3 = ColumnVector.fromBoxedFloats(0f, 0f, 0f, null, 0f, 20f, 20f)) {
+      assertColumnsAreEqual(expected1, result1);
+      assertColumnsAreEqual(expected2, result2);
+      assertColumnsAreEqual(expected3, result3);
+    }
+  }
+
+  @Test
+  void roundIntsHalfUp() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts(12, 135, 160, -1454, null, -1500, -140, -150);
+         ColumnVector result1 = v.round(2, RoundMode.HALF_UP);
+         ColumnVector result2 = v.round(-2, RoundMode.HALF_UP);
+         ColumnVector expected1 = ColumnVector.fromBoxedInts(12, 135, 160, -1454, null, -1500, -140, -150);
+         ColumnVector expected2 = ColumnVector.fromBoxedInts(0, 100, 200, -1500, null, -1500, -100, -200)) {
+      assertColumnsAreEqual(expected1, result1);
+      assertColumnsAreEqual(expected2, result2);
+    }
+  }
+
+  @Test
+  void roundIntsHalfEven() {
+    try (ColumnVector v = ColumnVector.fromBoxedInts(12, 24, 135, 160, null, 1450, 1550, -1650);
+         ColumnVector result1 = v.round(2, RoundMode.HALF_EVEN);
+         ColumnVector result2 = v.round(-2, RoundMode.HALF_EVEN);
+         ColumnVector expected1 = ColumnVector.fromBoxedInts(12, 24, 135, 160, null, 1450, 1550, -1650);
+         ColumnVector expected2 = ColumnVector.fromBoxedInts(0, 0, 100, 200, null, 1400, 1600, -1600)) {
+      assertColumnsAreEqual(expected1, result1);
+      assertColumnsAreEqual(expected2, result2);
+    }
+  }
+
+  @Test
+  void roundDecimal() {
+    final int dec32Scale1 = -2;
+    final int resultScale1 = -3;
+
+    final int[] DECIMAL32_1 = new int[]{14, 15, 16, 24, 25, 26} ;
+    final int[] expectedHalfUp = new int[]{1, 2, 2, 2, 3, 3};
+    final int[] expectedHalfEven = new int[]{1, 2, 2, 2, 2, 3};
+    try (ColumnVector v = ColumnVector.decimalFromInts(-dec32Scale1, DECIMAL32_1);
+         ColumnVector roundHalfUp = v.round(-3, RoundMode.HALF_UP);
+         ColumnVector roundHalfEven = v.round(-3, RoundMode.HALF_EVEN);
+         ColumnVector answerHalfUp = ColumnVector.decimalFromInts(-resultScale1, expectedHalfUp);
+         ColumnVector answerHalfEven = ColumnVector.decimalFromInts(-resultScale1, expectedHalfEven)) {
+      assertColumnsAreEqual(answerHalfUp, roundHalfUp);
+      assertColumnsAreEqual(answerHalfEven, roundHalfEven);
+    }
+  }
+
+  @Test
+  void decimal128Cv() {
+    final int dec32Scale1 = -2;
+    BigInteger bigInteger1 = new BigInteger("-831457");
+    BigInteger bigInteger2 = new BigInteger("14");
+    BigInteger bigInteger3 = new BigInteger("152345742357340573405745");
+    final BigInteger[] bigInts = new BigInteger[] {bigInteger1, bigInteger2, bigInteger3};
+    try (ColumnVector v = ColumnVector.decimalFromBigInt(-dec32Scale1, bigInts);
+         HostColumnVector hostColumnVector = v.copyToHost()) {
+      assertEquals(bigInteger1, hostColumnVector.getBigDecimal(0).unscaledValue());
+      assertEquals(bigInteger2, hostColumnVector.getBigDecimal(1).unscaledValue());
+      assertEquals(bigInteger3, hostColumnVector.getBigDecimal(2).unscaledValue());
+    }
+  }
+
+  static final long HOST_ALIGN_BYTES = ColumnView.hostPaddingSizeInBytes();
+
+  static void assertHostAligned(long expectedDeviceSize, ColumnView cv) {
+      long deviceSize = cv.getDeviceMemorySize();
+      assertEquals(expectedDeviceSize, deviceSize);
+      long hostSize = cv.getHostBytesRequired();
+      assert(hostSize >= deviceSize);
+      long roundedHostSize = (hostSize / HOST_ALIGN_BYTES) * HOST_ALIGN_BYTES;
+      assertEquals(hostSize, roundedHostSize, "The host size should be a multiple of " +
+              HOST_ALIGN_BYTES);
+  }
+
+  @Test
+  void testGetDeviceMemorySizeNonStrings() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 6);
+         ColumnVector v1 = ColumnVector.fromBoxedInts(1, 2, 3, null, null, 4, 5, 6)) {
+      assertHostAligned(24, v0); // (6*4B)
+      assertHostAligned(96, v1); // (8*4B) + 64B(for validity vector)
+    }
+  }
+
+  @Test
+  void testGetDeviceMemorySizeStrings() {
+    if (ColumnView.hostPaddingSizeInBytes() != 8) {
+      System.err.println("HOST PADDING SIZE: " + ColumnView.hostPaddingSizeInBytes());
+    }
+    try (ColumnVector v0 = ColumnVector.fromStrings("onetwothree", "four", "five");
+         ColumnVector v1 = ColumnVector.fromStrings("onetwothree", "four", null, "five")) {
+      assertHostAligned(35, v0); //19B data + 4*4B offsets = 35
+      assertHostAligned(103, v1); //19B data + 5*4B + 64B validity vector = 103B
+    }
+  }
+
+  @SuppressWarnings("unchecked")
+  @Test
+  void testGetDeviceMemorySizeLists() {
+    DataType svType = new ListType(true, new BasicType(true, DType.STRING));
+    DataType ivType = new ListType(false, new BasicType(false, DType.INT32));
+    try (ColumnVector sv = ColumnVector.fromLists(svType,
+        Arrays.asList("first", "second", "third"),
+        Arrays.asList("fourth", null),
+        null);
+         ColumnVector iv = ColumnVector.fromLists(ivType,
+             Arrays.asList(1, 2, 3),
+             Collections.singletonList(4),
+             Arrays.asList(5, 6),
+             Collections.singletonList(7))) {
+      // 64 bytes for validity of list column
+      // 16 bytes for offsets of list column
+      // 64 bytes for validity of string column
+      // 24 bytes for offsets of string column
+      // 22 bytes of string character size
+      assertHostAligned(64+16+64+24+22, sv);
+
+      // 20 bytes for offsets of list column
+      // 28 bytes for data of INT32 column
+      assertHostAligned(20+28, iv);
+    }
+  }
+
+  @Test
+  void testGetDeviceMemorySizeStructs() {
+    DataType structType = new StructType(true,
+        new ListType(true, new BasicType(true, DType.STRING)),
+        new BasicType(true, DType.INT64));
+    try (ColumnVector v = ColumnVector.fromStructs(structType,
+        new StructData(
+            Arrays.asList("first", "second", "third"),
+            10L),
+        new StructData(
+            Arrays.asList("fourth", null),
+            20L),
+        new StructData(
+            null,
+            null),
+        null)) {
+      // 64 bytes for validity of the struct column
+      // 64 bytes for validity of list column
+      // 20 bytes for offsets of list column
+      // 64 bytes for validity of string column
+      // 28 bytes for offsets of string column
+      // 22 bytes of string character size
+      // 64 bytes for validity of int64 column
+      // 28 bytes for data of the int64 column
+      assertHostAligned(64+64+20+64+28+22+64+28, v);
+    }
+  }
+
+  @Test
+  void testSequenceInt() {
+    try (Scalar zero = Scalar.fromInt(0);
+         Scalar one = Scalar.fromInt(1);
+         Scalar negOne = Scalar.fromInt(-1);
+         Scalar nulls = Scalar.fromNull(DType.INT32)) {
+
+      try (
+          ColumnVector cv = ColumnVector.sequence(zero, 5);
+          ColumnVector expected = ColumnVector.fromInts(0, 1, 2, 3, 4)) {
+        assertColumnsAreEqual(expected, cv);
+      }
+
+
+      try (ColumnVector cv = ColumnVector.sequence(one, negOne,6);
+           ColumnVector expected = ColumnVector.fromInts(1, 0, -1, -2, -3, -4)) {
+        assertColumnsAreEqual(expected, cv);
+      }
+
+      try (ColumnVector cv = ColumnVector.sequence(zero, 0);
+           ColumnVector expected = ColumnVector.fromInts()) {
+        assertColumnsAreEqual(expected, cv);
+      }
+
+      assertThrows(IllegalArgumentException.class, () -> {
+        try (ColumnVector cv = ColumnVector.sequence(nulls, 5)) { }
+      });
+
+      assertThrows(CudfException.class, () -> {
+        try (ColumnVector cv = ColumnVector.sequence(zero, -3)) { }
+      });
+    }
+  }
+
+  @Test
+  void testSequenceDouble() {
+    try (Scalar zero = Scalar.fromDouble(0.0);
+         Scalar one = Scalar.fromDouble(1.0);
+         Scalar negOneDotOne = Scalar.fromDouble(-1.1)) {
+
+      try (
+          ColumnVector cv = ColumnVector.sequence(zero, 5);
+          ColumnVector expected = ColumnVector.fromDoubles(0, 1, 2, 3, 4)) {
+        assertColumnsAreEqual(expected, cv);
+      }
+
+
+      try (ColumnVector cv = ColumnVector.sequence(one, negOneDotOne,6);
+           ColumnVector expected = ColumnVector.fromDoubles(1, -0.1, -1.2, -2.3, -3.4, -4.5)) {
+        assertColumnsAreEqual(expected, cv);
+      }
+
+      try (ColumnVector cv = ColumnVector.sequence(zero, 0);
+           ColumnVector expected = ColumnVector.fromDoubles()) {
+        assertColumnsAreEqual(expected, cv);
+      }
+    }
+  }
+
+  @Test
+  void testSequenceOtherTypes() {
+    assertThrows(CudfException.class, () -> {
+      try (Scalar s = Scalar.fromString("0");
+      ColumnVector cv = ColumnVector.sequence(s, s, 5)) {}
+    });
+
+    assertThrows(CudfException.class, () -> {
+      try (Scalar s = Scalar.fromBool(false);
+           ColumnVector cv = ColumnVector.sequence(s, s, 5)) {}
+    });
+
+    assertThrows(CudfException.class, () -> {
+      try (Scalar s = Scalar.timestampDaysFromInt(100);
+           ColumnVector cv = ColumnVector.sequence(s, s, 5)) {}
+    });
+  }
+
+  @Test
+  void testSequencesInt() {
+    try (ColumnVector start = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5);
+         ColumnVector size = ColumnVector.fromBoxedInts(2, 3, 2, 0, 1);
+         ColumnVector step = ColumnVector.fromBoxedInts(2, -1, 1, 1, 0);
+         ColumnVector cv = ColumnVector.sequence(start, size, step);
+         ColumnVector cv1 = ColumnVector.sequence(start, size);
+         ColumnVector expectCv = ColumnVector.fromLists(
+             new ListType(true, new BasicType(false, DType.INT32)),
+             Arrays.asList(1, 3),
+             Arrays.asList(2, 1, 0),
+             Arrays.asList(3, 4),
+             Arrays.asList(),
+             Arrays.asList(5));
+         ColumnVector expectCv1 = ColumnVector.fromLists(
+             new ListType(true, new BasicType(false, DType.INT32)),
+             Arrays.asList(1, 2),
+             Arrays.asList(2, 3, 4),
+             Arrays.asList(3, 4),
+             Arrays.asList(),
+             Arrays.asList(5))) {
+      assertColumnsAreEqual(expectCv, cv);
+      assertColumnsAreEqual(expectCv1, cv1);
+    }
+  }
+
+  @Test
+  void testSequencesDouble() {
+    try (ColumnVector start = ColumnVector.fromBoxedDoubles(1.2, 2.2, 3.2, 4.2, 5.2);
+         ColumnVector size = ColumnVector.fromBoxedInts(2, 3, 2, 0, 1);
+         ColumnVector step = ColumnVector.fromBoxedDoubles(2.1, -1.1, 1.1, 1.1, 0.1);
+         ColumnVector cv = ColumnVector.sequence(start, size, step);
+         ColumnVector cv1 = ColumnVector.sequence(start, size);
+         ColumnVector expectCv = ColumnVector.fromLists(
+             new ListType(true, new BasicType(false, DType.FLOAT64)),
+             Arrays.asList(1.2, 3.3),
+             Arrays.asList(2.2, 1.1, 0.0),
+             Arrays.asList(3.2, 4.3),
+             Arrays.asList(),
+             Arrays.asList(5.2));
+         ColumnVector expectCv1 = ColumnVector.fromLists(
+             new ListType(true, new BasicType(false, DType.FLOAT64)),
+             Arrays.asList(1.2, 2.2),
+             Arrays.asList(2.2, 3.2, 4.2),
+             Arrays.asList(3.2, 4.2),
+             Arrays.asList(),
+             Arrays.asList(5.2))) {
+      assertColumnsAreEqual(expectCv, cv);
+      assertColumnsAreEqual(expectCv1, cv1);
+    }
+  }
+
+  @Test
+  void testFromScalarZeroRows() {
+    // magic number to invoke factory method specialized for decimal types
+    int mockScale = -8;
+    for (DType.DTypeEnum type : DType.DTypeEnum.values()) {
+      Scalar s = null;
+      try {
+        switch (type) {
+        case BOOL8:
+          s = Scalar.fromBool(true);
+          break;
+        case INT8:
+          s = Scalar.fromByte((byte) 5);
+          break;
+        case UINT8:
+          s = Scalar.fromUnsignedByte((byte) 254);
+          break;
+        case INT16:
+          s = Scalar.fromShort((short) 12345);
+          break;
+        case UINT16:
+          s = Scalar.fromUnsignedShort((short) 65432);
+          break;
+        case INT32:
+          s = Scalar.fromInt(123456789);
+          break;
+        case UINT32:
+          s = Scalar.fromUnsignedInt(0xfedcba98);
+          break;
+        case INT64:
+          s = Scalar.fromLong(1234567890123456789L);
+          break;
+        case UINT64:
+          s = Scalar.fromUnsignedLong(0xfedcba9876543210L);
+          break;
+        case FLOAT32:
+          s = Scalar.fromFloat(1.2345f);
+          break;
+        case FLOAT64:
+          s = Scalar.fromDouble(1.23456789);
+          break;
+        case DECIMAL32:
+          s = Scalar.fromDecimal(mockScale, 123456789);
+          break;
+        case DECIMAL64:
+          s = Scalar.fromDecimal(mockScale, 1234567890123456789L);
+          break;
+        case DECIMAL128:
+          s = Scalar.fromDecimal(mockScale, new BigInteger("1234567890123456789"));
+          break;
+        case TIMESTAMP_DAYS:
+          s = Scalar.timestampDaysFromInt(12345);
+          break;
+        case TIMESTAMP_SECONDS:
+        case TIMESTAMP_MILLISECONDS:
+        case TIMESTAMP_MICROSECONDS:
+        case TIMESTAMP_NANOSECONDS:
+          s = Scalar.timestampFromLong(DType.create(type), 1234567890123456789L);
+          break;
+        case STRING:
+          s = Scalar.fromString("hello, world!");
+          break;
+        case DURATION_DAYS:
+          s = Scalar.durationDaysFromInt(3);
+          break;
+        case DURATION_SECONDS:
+        case DURATION_MILLISECONDS:
+        case DURATION_MICROSECONDS:
+        case DURATION_NANOSECONDS:
+          s = Scalar.durationFromLong(DType.create(type), 21313);
+          break;
+        case EMPTY:
+          continue;
+        case STRUCT:
+          try (ColumnVector col1 = ColumnVector.fromInts(1);
+               ColumnVector col2 = ColumnVector.fromStrings("A");
+               ColumnVector col3 = ColumnVector.fromDoubles(1.23)) {
+            s = Scalar.structFromColumnViews(col1, col2, col3);
+          }
+          break;
+        case LIST:
+          try (ColumnVector list = ColumnVector.fromInts(1, 2, 3)) {
+            s = Scalar.listFromColumnView(list);
+          }
+          break;
+        default:
+          throw new IllegalArgumentException("Unexpected type: " + type);
+        }
+
+        try (ColumnVector c = ColumnVector.fromScalar(s, 0)) {
+          if (type.isDecimalType()) {
+            assertEquals(DType.create(type, mockScale), c.getType());
+          } else {
+            assertEquals(DType.create(type), c.getType());
+          }
+          assertEquals(0, c.getRowCount());
+          assertEquals(0, c.getNullCount());
+        }
+      } finally {
+        if (s != null) {
+          s.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testGetNativeView() {
+    try (ColumnVector cv = ColumnVector.fromInts(1, 3, 4, 5)) {
+      //not a real test whats being returned is a view but this is the best we can do
+      assertNotEquals(0L, cv.getNativeView());
+    }
+  }
+
+  @Test
+  void testFromScalar() {
+    final int rowCount = 4;
+    for (DType.DTypeEnum type : DType.DTypeEnum.values()) {
+      if(type.isDecimalType()) {
+        continue;
+      }
+      Scalar s = null;
+      ColumnVector expected = null;
+      ColumnVector result = null;
+      try {
+        switch (type) {
+        case BOOL8:
+          s = Scalar.fromBool(true);
+          expected = ColumnVector.fromBoxedBooleans(true, true, true, true);
+          break;
+        case INT8: {
+          byte v = (byte) 5;
+          s = Scalar.fromByte(v);
+          expected = ColumnVector.fromBoxedBytes(v, v, v, v);
+          break;
+        }
+        case UINT8: {
+          byte v = (byte) 254;
+          s = Scalar.fromUnsignedByte(v);
+          expected = ColumnVector.fromBoxedUnsignedBytes(v, v, v, v);
+          break;
+        }
+        case INT16: {
+          short v = (short) 12345;
+          s = Scalar.fromShort(v);
+          expected = ColumnVector.fromBoxedShorts(v, v, v, v);
+          break;
+        }
+        case UINT16: {
+          short v = (short) 0x89ab;
+          s = Scalar.fromUnsignedShort(v);
+          expected = ColumnVector.fromBoxedUnsignedShorts(v, v, v, v);
+          break;
+        }
+        case INT32: {
+          int v = 123456789;
+          s = Scalar.fromInt(v);
+          expected = ColumnVector.fromBoxedInts(v, v, v, v);
+          break;
+        }
+        case UINT32: {
+          int v = 0x89abcdef;
+          s = Scalar.fromUnsignedInt(v);
+          expected = ColumnVector.fromBoxedUnsignedInts(v, v, v, v);
+          break;
+        }
+        case INT64: {
+          long v = 1234567890123456789L;
+          s = Scalar.fromLong(v);
+          expected = ColumnVector.fromBoxedLongs(v, v, v, v);
+          break;
+        }
+        case UINT64: {
+          long v = 0xfedcba9876543210L;
+          s = Scalar.fromUnsignedLong(v);
+          expected = ColumnVector.fromBoxedUnsignedLongs(v, v, v, v);
+          break;
+        }
+        case FLOAT32: {
+          float v = 1.2345f;
+          s = Scalar.fromFloat(v);
+          expected = ColumnVector.fromBoxedFloats(v, v, v, v);
+          break;
+        }
+        case FLOAT64: {
+          double v = 1.23456789;
+          s = Scalar.fromDouble(v);
+          expected = ColumnVector.fromBoxedDoubles(v, v, v, v);
+          break;
+        }
+        case TIMESTAMP_DAYS: {
+          int v = 12345;
+          s = Scalar.timestampDaysFromInt(v);
+          expected = ColumnVector.daysFromInts(v, v, v, v);
+          break;
+        }
+        case TIMESTAMP_SECONDS: {
+          long v = 1234567890123456789L;
+          s = Scalar.timestampFromLong(DType.TIMESTAMP_SECONDS, v);
+          expected = ColumnVector.timestampSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case TIMESTAMP_MILLISECONDS: {
+          long v = 1234567890123456789L;
+          s = Scalar.timestampFromLong(DType.TIMESTAMP_MILLISECONDS, v);
+          expected = ColumnVector.timestampMilliSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case TIMESTAMP_MICROSECONDS: {
+          long v = 1234567890123456789L;
+          s = Scalar.timestampFromLong(DType.TIMESTAMP_MICROSECONDS, v);
+          expected = ColumnVector.timestampMicroSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case TIMESTAMP_NANOSECONDS: {
+          long v = 1234567890123456789L;
+          s = Scalar.timestampFromLong(DType.TIMESTAMP_NANOSECONDS, v);
+          expected = ColumnVector.timestampNanoSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case STRING: {
+          String v = "hello, world!";
+          s = Scalar.fromString(v);
+          expected = ColumnVector.fromStrings(v, v, v, v);
+          break;
+        }
+        case DURATION_DAYS: {
+          int v = 13;
+          s = Scalar.durationDaysFromInt(v);
+          expected = ColumnVector.durationDaysFromInts(v, v, v, v);
+          break;
+        }
+        case DURATION_MICROSECONDS: {
+          long v = 1123123123L;
+          s = Scalar.durationFromLong(DType.DURATION_MICROSECONDS, v);
+          expected = ColumnVector.durationMicroSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case DURATION_MILLISECONDS: {
+          long v = 11212432423L;
+          s = Scalar.durationFromLong(DType.DURATION_MILLISECONDS, v);
+          expected = ColumnVector.durationMilliSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case DURATION_NANOSECONDS: {
+          long v = 12353245343L;
+          s = Scalar.durationFromLong(DType.DURATION_NANOSECONDS, v);
+          expected = ColumnVector.durationNanoSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case DURATION_SECONDS: {
+          long v = 132342321123L;
+          s = Scalar.durationFromLong(DType.DURATION_SECONDS, v);
+          expected = ColumnVector.durationSecondsFromLongs(v, v, v, v);
+          break;
+        }
+        case EMPTY:
+          continue;
+        case STRUCT:
+          try (ColumnVector col0 = ColumnVector.fromInts(1);
+               ColumnVector col1 = ColumnVector.fromBoxedDoubles((Double) null);
+               ColumnVector col2 = ColumnVector.fromStrings("a");
+               ColumnVector col3 = ColumnVector.fromDecimals(BigDecimal.TEN);
+               ColumnVector col4 = ColumnVector.daysFromInts(10)) {
+            s = Scalar.structFromColumnViews(col0, col1, col2, col3, col4);
+            StructData structData = new StructData(1, null, "a", BigDecimal.TEN, 10);
+            expected = ColumnVector.fromStructs(new HostColumnVector.StructType(true,
+                    new HostColumnVector.BasicType(true, DType.INT32),
+                    new HostColumnVector.BasicType(true, DType.FLOAT64),
+                    new HostColumnVector.BasicType(true, DType.STRING),
+                    new HostColumnVector.BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, 0)),
+                    new HostColumnVector.BasicType(true, DType.TIMESTAMP_DAYS)),
+                structData, structData, structData, structData);
+          }
+          break;
+        case LIST:
+          try (ColumnVector list = ColumnVector.fromInts(1, 2, 3)) {
+            s = Scalar.listFromColumnView(list);
+            expected = ColumnVector.fromLists(
+                new HostColumnVector.ListType(true,
+                    new HostColumnVector.BasicType(true, DType.INT32)),
+                Arrays.asList(1, 2, 3),
+                Arrays.asList(1, 2, 3),
+                Arrays.asList(1, 2, 3),
+                Arrays.asList(1, 2, 3));
+          }
+          break;
+        default:
+          throw new IllegalArgumentException("Unexpected type: " + type);
+        }
+
+        result = ColumnVector.fromScalar(s, rowCount);
+        assertColumnsAreEqual(expected, result);
+      } finally {
+        if (s != null) {
+          s.close();
+        }
+        if (expected != null) {
+          expected.close();
+        }
+        if (result != null) {
+          result.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFromScalarNull() {
+    final int rowCount = 4;
+    for (DType.DTypeEnum typeEnum : DType.DTypeEnum.values()) {
+      if (typeEnum == DType.DTypeEnum.EMPTY || typeEnum == DType.DTypeEnum.LIST || typeEnum == DType.DTypeEnum.STRUCT) {
+        continue;
+      }
+      DType dType;
+      if (typeEnum.isDecimalType()) {
+        // magic number to invoke factory method specialized for decimal types
+        dType = DType.create(typeEnum, -8);
+      } else {
+        dType = DType.create(typeEnum);
+      }
+      try (Scalar s = Scalar.fromNull(dType);
+           ColumnVector c = ColumnVector.fromScalar(s, rowCount);
+           HostColumnVector hc = c.copyToHost()) {
+        assertEquals(typeEnum, c.getType().typeId);
+        assertEquals(rowCount, c.getRowCount());
+        assertEquals(rowCount, c.getNullCount());
+        for (int i = 0; i < rowCount; ++i) {
+          assertTrue(hc.isNull(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFromScalarNullByte() {
+    int numNulls = 3000;
+    try (Scalar s = Scalar.fromNull(DType.INT8);
+         ColumnVector tmp = ColumnVector.fromScalar(s, numNulls);
+         HostColumnVector input = tmp.copyToHost()) {
+      assertEquals(numNulls, input.getRowCount());
+      assertEquals(input.getNullCount(), numNulls);
+      for (int i = 0; i < numNulls; i++){
+        assertTrue(input.isNull(i));
+      }
+    }
+  }
+
+  @Test
+  void testFromScalarNullList() {
+    final int rowCount = 4;
+    for (DType.DTypeEnum typeEnum : DType.DTypeEnum.values()) {
+      DType dType = typeEnum.isDecimalType() ? DType.create(typeEnum, -8): DType.create(typeEnum);
+      DataType hDataType;
+      if (DType.EMPTY.equals(dType)) {
+        continue;
+      } else if (DType.LIST.equals(dType)) {
+        // list of list of int32
+        hDataType = new ListType(true, new BasicType(true, DType.INT32));
+      } else if (DType.STRUCT.equals(dType)) {
+        // list of struct of int32
+        hDataType = new StructType(true, new BasicType(true, DType.INT32));
+      } else {
+        // list of non nested type
+        hDataType = new BasicType(true, dType);
+      }
+      try (Scalar s = Scalar.listFromNull(hDataType);
+           ColumnVector c = ColumnVector.fromScalar(s, rowCount);
+           HostColumnVector hc = c.copyToHost()) {
+        assertEquals(DType.LIST, c.getType());
+        assertEquals(rowCount, c.getRowCount());
+        assertEquals(rowCount, c.getNullCount());
+        for (int i = 0; i < rowCount; ++i) {
+          assertTrue(hc.isNull(i));
+        }
+
+        try (ColumnView child = c.getChildColumnView(0)) {
+          assertEquals(dType, child.getType());
+          assertEquals(0L, child.getRowCount());
+          assertEquals(0L, child.getNullCount());
+          if (child.getType().isNestedType()) {
+            try (ColumnView grandson = child.getChildColumnView(0)) {
+              assertEquals(DType.INT32, grandson.getType());
+              assertEquals(0L, grandson.getRowCount());
+              assertEquals(0L, grandson.getNullCount());
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFromScalarListOfList() {
+    HostColumnVector.DataType childType = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    HostColumnVector.DataType resultType = new HostColumnVector.ListType(true, childType);
+    try (ColumnVector list = ColumnVector.fromLists(childType,
+             Arrays.asList(1, 2, 3),
+             Arrays.asList(4, 5, 6));
+         Scalar s = Scalar.listFromColumnView(list)) {
+      try (ColumnVector ret = ColumnVector.fromScalar(s, 2);
+           ColumnVector expected = ColumnVector.fromLists(resultType,
+                   Arrays.asList(Arrays.asList(1, 2, 3),Arrays.asList(4, 5, 6)),
+                   Arrays.asList(Arrays.asList(1, 2, 3),Arrays.asList(4, 5, 6)))) {
+        assertColumnsAreEqual(expected, ret);
+      }
+      // empty row
+      try (ColumnVector ret = ColumnVector.fromScalar(s, 0)) {
+        assertEquals(ret.getRowCount(), 0);
+        assertEquals(ret.getNullCount(), 0);
+      }
+    }
+  }
+
+  @Test
+  void testFromScalarListOfStruct() {
+    HostColumnVector.DataType childType = new HostColumnVector.StructType(true,
+            new HostColumnVector.BasicType(true, DType.INT32),
+            new HostColumnVector.BasicType(true, DType.STRING));
+    HostColumnVector.DataType resultType = new HostColumnVector.ListType(true, childType);
+    try (ColumnVector list = ColumnVector.fromStructs(childType,
+            new HostColumnVector.StructData(1, "s1"),
+            new HostColumnVector.StructData(2, "s2"));
+         Scalar s = Scalar.listFromColumnView(list)) {
+      try (ColumnVector ret = ColumnVector.fromScalar(s, 2);
+           ColumnVector expected = ColumnVector.fromLists(resultType,
+                   Arrays.asList(new HostColumnVector.StructData(1, "s1"),
+                                 new HostColumnVector.StructData(2, "s2")),
+                   Arrays.asList(new HostColumnVector.StructData(1, "s1"),
+                                 new HostColumnVector.StructData(2, "s2")))) {
+        assertColumnsAreEqual(expected, ret);
+      }
+      // empty row
+      try (ColumnVector ret = ColumnVector.fromScalar(s, 0)) {
+        assertEquals(ret.getRowCount(), 0);
+        assertEquals(ret.getNullCount(), 0);
+      }
+    }
+  }
+
+  @Test
+  void testFromScalarNullStruct() {
+    final int rowCount = 4;
+    for (DType.DTypeEnum typeEnum : DType.DTypeEnum.values()) {
+      DType dType = typeEnum.isDecimalType() ? DType.create(typeEnum, -8) : DType.create(typeEnum);
+      DataType hDataType;
+      if (DType.EMPTY.equals(dType)) {
+        continue;
+      } else if (DType.LIST.equals(dType)) {
+        // list of list of int32
+        hDataType = new ListType(true, new BasicType(true, DType.INT32));
+      } else if (DType.STRUCT.equals(dType)) {
+        // list of struct of int32
+        hDataType = new StructType(true, new BasicType(true, DType.INT32));
+      } else {
+        // list of non nested type
+        hDataType = new BasicType(true, dType);
+      }
+      try (Scalar s = Scalar.structFromNull(hDataType, hDataType, hDataType);
+           ColumnVector c = ColumnVector.fromScalar(s, rowCount);
+           HostColumnVector hc = c.copyToHost()) {
+        assertEquals(DType.STRUCT, c.getType());
+        assertEquals(rowCount, c.getRowCount());
+        assertEquals(rowCount, c.getNullCount());
+        for (int i = 0; i < rowCount; ++i) {
+          assertTrue(hc.isNull(i));
+        }
+        assertEquals(3, c.getNumChildren());
+        ColumnView[] children = new ColumnView[]{c.getChildColumnView(0),
+            c.getChildColumnView(1), c.getChildColumnView(2)};
+        try {
+          for (ColumnView child : children) {
+            assertEquals(dType, child.getType());
+            assertEquals(rowCount, child.getRowCount());
+            assertEquals(rowCount, child.getNullCount());
+            if (child.getType() == DType.LIST) {
+              try (ColumnView childOfChild = child.getChildColumnView(0)) {
+                assertEquals(DType.INT32, childOfChild.getType());
+                assertEquals(0L, childOfChild.getRowCount());
+                assertEquals(0L, childOfChild.getNullCount());
+              }
+            } else if (child.getType() == DType.STRUCT) {
+              assertEquals(1, child.getNumChildren());
+              try (ColumnView childOfChild = child.getChildColumnView(0)) {
+                assertEquals(DType.INT32, childOfChild.getType());
+                assertEquals(rowCount, childOfChild.getRowCount());
+                assertEquals(rowCount, childOfChild.getNullCount());
+              }
+            }
+          }
+        } finally {
+          for (ColumnView cv : children) cv.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testReplaceNullsScalarEmptyColumn() {
+    try (ColumnVector input = ColumnVector.fromBoxedBooleans();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans();
+         Scalar s = Scalar.fromBool(false);
+         ColumnVector result = input.replaceNulls(s)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsScalarBoolsWithAllNulls() {
+    try (ColumnVector input = ColumnVector.fromBoxedBooleans(null, null, null, null);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false);
+         Scalar s = Scalar.fromBool(false);
+         ColumnVector result = input.replaceNulls(s)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsScalarSomeNullBools() {
+    try (ColumnVector input = ColumnVector.fromBoxedBooleans(false, null, null, false);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, false);
+         Scalar s = Scalar.fromBool(true);
+         ColumnVector result = input.replaceNulls(s)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsScalarIntegersWithAllNulls() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(null, null, null, null);
+         ColumnVector expected = ColumnVector.fromBoxedInts(0, 0, 0, 0);
+         Scalar s = Scalar.fromInt(0);
+         ColumnVector result = input.replaceNulls(s)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsScalarSomeNullIntegers() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(1, 2, null, 4, null);
+         ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, 999, 4, 999);
+         Scalar s = Scalar.fromInt(999);
+         ColumnVector result = input.replaceNulls(s)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsScalarFailsOnTypeMismatch() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(1, 2, null, 4, null);
+         Scalar s = Scalar.fromBool(true)) {
+      assertThrows(CudfException.class, () -> input.replaceNulls(s).close());
+    }
+  }
+
+  @Test
+  void testReplaceNullsWithNullScalar() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(1, 2, null, 4, null);
+         Scalar s = Scalar.fromNull(input.getType());
+         ColumnVector result = input.replaceNulls(s)) {
+      assertColumnsAreEqual(input, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsPolicy() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(null, 1, 2, null, 4, null);
+         ColumnVector preceding = input.replaceNulls(ReplacePolicy.PRECEDING);
+         ColumnVector expectedPre = ColumnVector.fromBoxedInts(null, 1, 2, 2, 4, 4);
+         ColumnVector following = input.replaceNulls(ReplacePolicy.FOLLOWING);
+         ColumnVector expectedFol = ColumnVector.fromBoxedInts(1, 1, 2, 4, 4, null)) {
+      assertColumnsAreEqual(expectedPre, preceding);
+      assertColumnsAreEqual(expectedFol, following);
+    }
+  }
+
+  @Test
+  void testReplaceNullsColumnEmptyColumn() {
+    try (ColumnVector input = ColumnVector.fromBoxedBooleans();
+         ColumnVector r = ColumnVector.fromBoxedBooleans();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans();
+         ColumnVector result = input.replaceNulls(r)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsColumnBools() {
+    try (ColumnVector input = ColumnVector.fromBoxedBooleans(null, true, null, false);
+         ColumnVector r = ColumnVector.fromBoxedBooleans(false, null, true, true);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, false);
+         ColumnVector result = input.replaceNulls(r)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsColumnIntegers() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(1, 2, null, 4, null);
+         ColumnVector r = ColumnVector.fromBoxedInts(996, 997, 998, 909, null);
+         ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, 998, 4, null);
+         ColumnVector result = input.replaceNulls(r)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReplaceNullsColumnFailsOnTypeMismatch() {
+    try (ColumnVector input = ColumnVector.fromBoxedInts(1, 2, null, 4, null);
+         ColumnVector r = ColumnVector.fromBoxedBooleans(true)) {
+      assertThrows(CudfException.class, () -> input.replaceNulls(r).close());
+    }
+  }
+
+  static QuantileMethod[] methods = {LINEAR, LOWER, HIGHER, MIDPOINT, NEAREST};
+  static double[] quantiles = {0.0, 0.25, 0.33, 0.5, 1.0};
+
+  @Test
+  void testQuantilesOnIntegerInput() {
+    double[][] exactExpected = {
+        {-1.0,   1.0,   1.0,   2.5,   9.0},  // LINEAR
+        {  -1,     1,     1,     2,     9},  // LOWER
+        {  -1,     1,     1,     3,     9},  // HIGHER
+        {-1.0,   1.0,   1.0,   2.5,   9.0},  // MIDPOINT
+        {  -1,     1,     1,     2,     9}}; // NEAREST
+
+    try (ColumnVector cv = ColumnVector.fromBoxedInts(-1, 0, 1, 1, 2, 3, 4, 6, 7, 9)) {
+      for (int i = 0 ; i < methods.length ; i++) {
+        try (ColumnVector result = cv.quantile(methods[i], quantiles);
+             HostColumnVector hostResult = result.copyToHost()) {
+          double[] expected = exactExpected[i];
+          assertEquals(expected.length, hostResult.getRowCount());
+          for (int j = 0; j < expected.length; j++) {
+            assertEqualsWithinPercentage(expected[j], hostResult.getDouble(j), PERCENTAGE, methods[i] + " " + quantiles[j]);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testQuantilesOnDoubleInput() {
+    double[][] exactExpected = {
+        {-1.01, 0.8, 0.9984, 2.13, 6.8},  // LINEAR
+        {-1.01, 0.8,    0.8, 2.13, 6.8},  // LOWER
+        {-1.01, 0.8,   1.11, 2.13, 6.8},  // HIGHER
+        {-1.01, 0.8,  0.955, 2.13, 6.8},  // MIDPOINT
+        {-1.01, 0.8,   1.11, 2.13, 6.8}}; // NEAREST
+
+    try (ColumnVector cv = ColumnVector.fromBoxedDoubles(-1.01, 0.15, 0.8, 1.11, 2.13, 3.4, 4.17, 5.7, 6.8)) {
+      for (int i = 0 ; i < methods.length ; i++) {
+        try (ColumnVector result = cv.quantile(methods[i], quantiles);
+             HostColumnVector hostResult = result.copyToHost()) {
+          double[] expected = exactExpected[i];
+          assertEquals(expected.length, hostResult.getRowCount());
+          for (int j = 0; j < expected.length; j++) {
+            assertEqualsWithinPercentage(expected[j], hostResult.getDouble(j), PERCENTAGE, methods[i] + " " + quantiles[j]);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSubvector() {
+    try (ColumnVector vec = ColumnVector.fromBoxedInts(1, 2, 3, null, 5);
+         ColumnVector expected = ColumnVector.fromBoxedInts(2, 3, null, 5);
+         ColumnVector found = vec.subVector(1, 5)) {
+      assertColumnsAreEqual(expected, found);
+    }
+
+    try (ColumnVector vec = ColumnVector.fromStrings("1", "2", "3", null, "5");
+         ColumnVector expected = ColumnVector.fromStrings("2", "3", null, "5");
+         ColumnVector found = vec.subVector(1, 5)) {
+      assertColumnsAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testSlice() {
+    try(ColumnVector cv = ColumnVector.fromBoxedInts(10, 12, null, null, 18, 20, 22, 24, 26, 28)) {
+      Integer[][] expectedSlice = {
+          {12, null},
+          {20, 22, 24, 26},
+          {null, null},
+          {}};
+
+      ColumnVector[] slices = cv.slice(1, 3, 5, 9, 2, 4, 8, 8);
+
+      try {
+        for (int i = 0; i < slices.length; i++) {
+          final int sliceIndex = i;
+          try (HostColumnVector slice = slices[sliceIndex].copyToHost()) {
+            assertEquals(expectedSlice[sliceIndex].length, slices[sliceIndex].getRowCount());
+            IntStream.range(0, expectedSlice[sliceIndex].length).forEach(rowCount -> {
+              if (expectedSlice[sliceIndex][rowCount] == null) {
+                assertTrue(slice.isNull(rowCount));
+              } else {
+                assertEquals(expectedSlice[sliceIndex][rowCount],
+                    slice.getInt(rowCount));
+              }
+            });
+          }
+        }
+        assertEquals(4, slices.length);
+      } finally {
+        for (int i = 0 ; i < slices.length ; i++) {
+          if (slices[i] != null) {
+            slices[i].close();
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testStringSlice() {
+    try(ColumnVector cv = ColumnVector.fromStrings("foo", "bar", null, null, "baz", "hello", "world", "cuda", "is", "great")) {
+      String[][] expectedSlice = {
+          {"foo", "bar"},
+          {null, null, "baz"},
+          {null, "baz", "hello"}};
+
+      ColumnVector[] slices = cv.slice(0, 2, 2, 5, 3, 6);
+
+      try {
+        for (int i = 0; i < slices.length; i++) {
+          final int sliceIndex = i;
+          try (HostColumnVector slice = slices[sliceIndex].copyToHost()) {
+            assertEquals(expectedSlice[sliceIndex].length, slices[sliceIndex].getRowCount());
+            IntStream.range(0, expectedSlice[sliceIndex].length).forEach(rowCount -> {
+              if (expectedSlice[sliceIndex][rowCount] == null) {
+                assertTrue(slice.isNull(rowCount));
+              } else {
+                assertEquals(expectedSlice[sliceIndex][rowCount],
+                    slice.getJavaString(rowCount));
+              }
+            });
+          }
+        }
+        assertEquals(3, slices.length);
+      } finally {
+        for (int i = 0 ; i < slices.length ; i++) {
+          if (slices[i] != null) {
+            slices[i].close();
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSplitWithArray() {
+    assumeTrue(Cuda.isEnvCompatibleForTesting());
+    try(ColumnVector cv = ColumnVector.fromBoxedInts(10, 12, null, null, 18, 20, 22, 24, 26, 28)) {
+      Integer[][] expectedData = {
+          {10},
+          {12, null},
+          {null, 18},
+          {20, 22, 24, 26},
+          {28}};
+
+      ColumnVector[] splits = cv.split(1, 3, 5, 9);
+      try {
+        assertEquals(expectedData.length, splits.length);
+        for (int splitIndex = 0; splitIndex < splits.length; splitIndex++) {
+          try (HostColumnVector subVec = splits[splitIndex].copyToHost()) {
+            assertEquals(expectedData[splitIndex].length, subVec.getRowCount());
+            for (int subIndex = 0; subIndex < expectedData[splitIndex].length; subIndex++) {
+              Integer expected = expectedData[splitIndex][subIndex];
+              if (expected == null) {
+                assertTrue(subVec.isNull(subIndex));
+              } else {
+                assertEquals(expected, subVec.getInt(subIndex));
+              }
+            }
+          }
+        }
+      } finally {
+        for (int i = 0 ; i < splits.length ; i++) {
+          if (splits[i] != null) {
+            splits[i].close();
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWithOddSlices() {
+    try (ColumnVector cv = ColumnVector.fromBoxedInts(10, 12, null, null, 18, 20, 22, 24, 26, 28)) {
+      assertThrows(CudfException.class, () -> cv.slice(1, 3, 5, 9, 2, 4, 8));
+    }
+  }
+
+  @Test
+  void testTrimStringsWhiteSpace() {
+    try (ColumnVector cv = ColumnVector.fromStrings(" 123", "123 ", null, " 123 ", "\t\t123\n\n");
+         ColumnVector trimmed = cv.strip();
+         ColumnVector expected = ColumnVector.fromStrings("123", "123", null, "123", "123")) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testTrimStrings() {
+    try (ColumnVector cv = ColumnVector.fromStrings("123", "123 ", null, "1231", "\t\t123\n\n");
+         Scalar one = Scalar.fromString(" 1");
+         ColumnVector trimmed = cv.strip(one);
+         ColumnVector expected = ColumnVector.fromStrings("23", "23", null, "23", "\t\t123\n\n")) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testLeftTrimStringsWhiteSpace() {
+    try (ColumnVector cv = ColumnVector.fromStrings(" 123", "123 ", null, " 123 ", "\t\t123\n\n");
+         ColumnVector trimmed = cv.lstrip();
+         ColumnVector expected = ColumnVector.fromStrings("123", "123 ", null, "123 ", "123\n\n")) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testLeftTrimStrings() {
+    try (ColumnVector cv = ColumnVector.fromStrings("123", " 123 ", null, "1231", "\t\t123\n\n");
+         Scalar one = Scalar.fromString(" 1");
+         ColumnVector trimmed = cv.lstrip(one);
+         ColumnVector expected = ColumnVector.fromStrings("23", "23 ", null, "231", "\t\t123\n\n")) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testRightTrimStringsWhiteSpace() {
+    try (ColumnVector cv = ColumnVector.fromStrings(" 123", "123 ", null, " 123 ", "\t\t123\n\n");
+         ColumnVector trimmed = cv.rstrip();
+         ColumnVector expected = ColumnVector.fromStrings(" 123", "123", null, " 123", "\t\t123")) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testRightTrimStrings() {
+    try (ColumnVector cv = ColumnVector.fromStrings("123", "123 ", null, "1231 ", "\t\t123\n\n");
+         Scalar one = Scalar.fromString(" 1");
+         ColumnVector trimmed = cv.rstrip(one);
+         ColumnVector expected = ColumnVector.fromStrings("123", "123", null, "123", "\t\t123\n\n")) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testTrimStringsThrowsException() {
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("123", "123 ", null, "1231", "\t\t123\n\n");
+           Scalar nullStr =  Scalar.fromString(null);
+           ColumnVector trimmed = cv.strip(nullStr)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("123", "123 ", null, "1231", "\t\t123\n\n");
+           Scalar one = Scalar.fromInt(1);
+           ColumnVector trimmed = cv.strip(one)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("123", "123 ", null, "1231", "\t\t123\n\n");
+           ColumnVector result = cv.strip(null)) {}
+    });
+  }
+
+  @Test
+  void testTrimEmptyStringsWithNulls() {
+    try (ColumnVector cv = ColumnVector.fromStrings("", null);
+         ColumnVector trimmed = cv.strip();
+         ColumnVector expected = ColumnVector.fromStrings("", null)) {
+      assertColumnsAreEqual(expected, trimmed);
+    }
+  }
+
+  @Test
+  void testAppendStrings() {
+    try (HostColumnVector cv = HostColumnVector.build(10, 0, (b) -> {
+      b.append("123456789");
+      b.append("1011121314151617181920");
+      b.append("");
+      b.appendNull();
+    })) {
+      assertEquals(4, cv.getRowCount());
+      assertEquals("123456789", cv.getJavaString(0));
+      assertEquals("1011121314151617181920", cv.getJavaString(1));
+      assertEquals("", cv.getJavaString(2));
+      assertTrue(cv.isNull(3));
+    }
+  }
+
+  @Test
+  void testCountElements() {
+    DataType dt = new ListType(true, new BasicType(true, DType.INT32));
+    try (ColumnVector cv = ColumnVector.fromLists(dt, Arrays.asList(1),
+        Arrays.asList(1, 2), null, Arrays.asList(null, null),
+        Arrays.asList(1, 2, 3), Arrays.asList(1, 2, 3, 4));
+         ColumnVector lengths = cv.countElements();
+         ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, null, 2, 3, 4)) {
+      assertColumnsAreEqual(expected, lengths);
+    }
+  }
+
+  @Test
+  void testStringLengths() {
+    try (ColumnVector cv = ColumnVector.fromStrings("1", "12", null, "123", "1234");
+      ColumnVector lengths = cv.getCharLengths();
+      ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, null, 3, 4)) {
+      assertColumnsAreEqual(expected, lengths);
+    }
+  }
+
+  @Test
+  void testGetByteCount() {
+    try (ColumnVector cv = ColumnVector.fromStrings("1", "12", "123", null, "1234");
+         ColumnVector byteLengthVector = cv.getByteCount();
+         ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, 3, null, 4)) {
+      assertColumnsAreEqual(expected, byteLengthVector);
+    }
+  }
+
+  @Test
+  void testEmptyStringColumnOpts() {
+    try (ColumnVector cv = ColumnVector.fromStrings()) {
+      try (ColumnVector len = cv.getCharLengths()) {
+        assertEquals(0, len.getRowCount());
+      }
+
+      try (ColumnVector mask = ColumnVector.fromBoxedBooleans();
+           Table input = new Table(cv);
+           Table filtered = input.filter(mask)) {
+        assertEquals(0, filtered.getColumn(0).getRowCount());
+      }
+
+      try (ColumnVector len = cv.getByteCount()) {
+        assertEquals(0, len.getRowCount());
+      }
+
+      try (ColumnVector lower = cv.lower();
+           ColumnVector upper = cv.upper()) {
+        assertColumnsAreEqual(cv, lower);
+        assertColumnsAreEqual(cv, upper);
+      }
+    }
+  }
+
+  @Test
+  void testStringManipulation() {
+    try (ColumnVector v = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf",
+                                                   "g\nH", "IJ\"\u0100\u0101\u0500\u0501",
+                                                   "kl m", "Nop1", "\\qRs2", "3tuV\'",
+                                                   "wX4Yz", "\ud720\ud721");
+         ColumnVector e_lower = ColumnVector.fromStrings("a", "b", "cd", "\u0481\u0481", "e\tf",
+                                                         "g\nh", "ij\"\u0101\u0101\u0501\u0501",
+                                                         "kl m", "nop1", "\\qrs2", "3tuv\'",
+                                                         "wx4yz", "\ud720\ud721");
+         ColumnVector e_upper = ColumnVector.fromStrings("A", "B", "CD", "\u0480\u0480", "E\tF",
+                                                         "G\nH", "IJ\"\u0100\u0100\u0500\u0500",
+                                                         "KL M", "NOP1", "\\QRS2", "3TUV\'",
+                                                         "WX4YZ", "\ud720\ud721");
+         ColumnVector lower = v.lower();
+         ColumnVector upper = v.upper()) {
+      assertColumnsAreEqual(lower, e_lower);
+      assertColumnsAreEqual(upper, e_upper);
+    }
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromInts(1, 2, 3, 4);
+           ColumnVector lower = cv.lower()) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromInts(1, 2, 3, 4);
+           ColumnVector upper = cv.upper()) {}
+    });
+  }
+
+  @Test
+  void testStringManipulationWithNulls() {
+    // Special characters in order of usage, capital and small cyrillic koppa
+    // Latin A with macron, and cyrillic komi de
+    // \ud720 and \ud721 are UTF-8 characters without corresponding upper and lower characters
+    try (ColumnVector v = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf",
+                                                   "g\nH", "IJ\"\u0100\u0101\u0500\u0501",
+                                                   "kl m", "Nop1", "\\qRs2", null,
+                                                   "3tuV\'", "wX4Yz", "\ud720\ud721");
+         ColumnVector e_lower = ColumnVector.fromStrings("a", "b", "cd", "\u0481\u0481", "e\tf",
+                                                         "g\nh", "ij\"\u0101\u0101\u0501\u0501",
+                                                         "kl m", "nop1", "\\qrs2", null,
+                                                         "3tuv\'", "wx4yz", "\ud720\ud721");
+         ColumnVector e_upper = ColumnVector.fromStrings("A", "B", "CD", "\u0480\u0480", "E\tF",
+                                                         "G\nH", "IJ\"\u0100\u0100\u0500\u0500",
+                                                         "KL M", "NOP1", "\\QRS2", null,
+                                                         "3TUV\'", "WX4YZ", "\ud720\ud721");
+         ColumnVector lower = v.lower();
+         ColumnVector upper = v.upper();) {
+      assertColumnsAreEqual(lower, e_lower);
+      assertColumnsAreEqual(upper, e_upper);
+    }
+  }
+
+  @Test
+  void testStringConcat() {
+    try (ColumnVector v = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf",
+        "g\nH", "IJ\"\u0100\u0101\u0500\u0501",
+        "kl m", "Nop1", "\\qRs2", "3tuV\'",
+        "wX4Yz", "\ud720\ud721");
+         ColumnVector e_concat = ColumnVector.fromStrings("aa", "BB", "cdcd",
+             "\u0480\u0481\u0480\u0481", "E\tfE\tf", "g\nHg\nH",
+             "IJ\"\u0100\u0101\u0500\u0501IJ\"\u0100\u0101\u0500\u0501",
+             "kl mkl m", "Nop1Nop1", "\\qRs2\\qRs2", "3tuV\'3tuV\'",
+             "wX4YzwX4Yz", "\ud720\ud721\ud720\ud721");
+         Scalar emptyString = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(emptyString, emptyString, new ColumnView[]{v, v})) {
+      assertColumnsAreEqual(concat, e_concat);
+    }
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("B", "cd", "\u0480\u0481", "E\tf");
+           ColumnVector cv = ColumnVector.fromInts(1, 2, 3, 4);
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector concat = ColumnVector.stringConcatenate(emptyString, emptyString, new ColumnView[]{sv, cv})) {
+      }
+    });
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector sv1 = ColumnVector.fromStrings("a", "B", "cd");
+           ColumnVector sv2 = ColumnVector.fromStrings("a", "B");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector concat = ColumnVector.stringConcatenate(emptyString, emptyString,
+               new ColumnVector[]{sv1, sv2})) {
+      }
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (Scalar emptyString = Scalar.fromString("");
+           ColumnVector concat = ColumnVector.stringConcatenate(emptyString, emptyString, new ColumnView[]{})) {
+      }
+    });
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString("");
+           Scalar nullString = Scalar.fromString(null);
+           ColumnVector concat = ColumnVector.stringConcatenate(nullString, emptyString, new ColumnView[]{sv, sv})) {
+      }
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector concat = ColumnVector.stringConcatenate(null, emptyString, new ColumnView[]{sv, sv})) {
+      }
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector concat = ColumnVector.stringConcatenate(emptyString, null, new ColumnView[]{sv, sv})) {
+      }
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector concat = ColumnVector.stringConcatenate(emptyString, emptyString, new ColumnView[]{sv, null})) {
+      }
+    });
+  }
+
+  @Test
+  void testStringConcatWithNulls() {
+    try (ColumnVector v = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf",
+        "g\nH", "IJ\"\u0100\u0101\u0500\u0501",
+        "kl m", "Nop1", "\\qRs2", null,
+        "3tuV\'", "wX4Yz", "\ud720\ud721");
+         ColumnVector e_concat = ColumnVector.fromStrings("aa", "BB", "cdcd",
+             "\u0480\u0481\u0480\u0481", "E\tfE\tf", "g\nHg\nH",
+             "IJ\"\u0100\u0101\u0500\u0501IJ\"\u0100\u0101\u0500\u0501",
+             "kl mkl m", "Nop1Nop1", "\\qRs2\\qRs2", "NULLNULL",
+             "3tuV\'3tuV\'", "wX4YzwX4Yz", "\ud720\ud721\ud720\ud721");
+         Scalar emptyString = Scalar.fromString("");
+         Scalar nullSubstitute = Scalar.fromString("NULL");
+         ColumnVector concat = ColumnVector.stringConcatenate(emptyString, nullSubstitute, new ColumnView[]{v, v})) {
+      assertColumnsAreEqual(concat, e_concat);
+    }
+
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector v = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf",
+          "g\nH", "IJ\"\u0100\u0101\u0500\u0501",
+          "kl m", "Nop1", "\\qRs2", null,
+          "3tuV\'", "wX4Yz", "\ud720\ud721");
+           Scalar emptyString = Scalar.fromString("");
+           Scalar nullSubstitute = Scalar.fromString("NULL");
+           ColumnVector concat = ColumnVector.stringConcatenate(emptyString, nullSubstitute, new ColumnView[]{v})) {
+      }
+    });
+  }
+
+  @Test
+  void testStringConcatSeparators() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf", null, null, "\\G\u0100");
+         ColumnVector sv2 = ColumnVector.fromStrings("b", "C", "\u0500\u0501", "x\nYz", null, null, "", null);
+         ColumnVector e_concat = ColumnVector.fromStrings("aA1\t\ud721b", "BA1\t\ud721C", "cdA1\t\ud721\u0500\u0501",
+             "\u0480\u0481A1\t\ud721x\nYz", null, null, null, null);
+         Scalar separatorString = Scalar.fromString("A1\t\ud721");
+         Scalar nullString = Scalar.fromString(null);
+         ColumnVector concat = ColumnVector.stringConcatenate(separatorString, nullString, new ColumnView[]{sv1, sv2})) {
+      assertColumnsAreEqual(concat, e_concat);
+    }
+  }
+
+  @Test
+  void testStringConcatSeparatorsEmptyStringForNull() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "B", "cd", "\u0480\u0481", "E\tf", null, null, "\\G\u0100");
+         ColumnVector sv2 = ColumnVector.fromStrings("b", "C", "\u0500\u0501", "x\nYz", null, null, "", null);
+         ColumnVector e_concat = ColumnVector.fromStrings("aA1\t\ud721b", "BA1\t\ud721C", "cdA1\t\ud721\u0500\u0501",
+             "\u0480\u0481A1\t\ud721x\nYz", "E\tf", "", "", "\\G\u0100");
+         Scalar separatorString = Scalar.fromString("A1\t\ud721");
+         Scalar narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(separatorString, narep, new ColumnView[]{sv1, sv2}, false)) {
+      assertColumnsAreEqual(concat, e_concat);
+    }
+  }
+
+  @Test
+  void testConcatWsTypeError() {
+    try (ColumnVector v0 = ColumnVector.fromInts(1, 2, 3, 4);
+         ColumnVector v1 = ColumnVector.fromFloats(5.0f, 6.0f);
+         ColumnVector sep_col = ColumnVector.fromStrings("-*");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar nullString = Scalar.fromString(null)) {
+      assertThrows(CudfException.class, () -> ColumnVector.stringConcatenate(
+          new ColumnView[]{v0, v1}, sep_col, separatorString, nullString, false));
+    }
+  }
+
+  @Test
+  void testConcatWsNoColumn() {
+    try (ColumnVector sep_col = ColumnVector.fromStrings("-*");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar nullString = Scalar.fromString(null)) {
+      assertThrows(AssertionError.class, () -> ColumnVector.stringConcatenate(
+          new ColumnView[]{}, sep_col, separatorString, nullString, false));
+    }
+  }
+
+  @Test
+  void testStringConcatWsSimple() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a");
+         ColumnVector sv2 = ColumnVector.fromStrings("B");
+         ColumnVector sv3 = ColumnVector.fromStrings("cd");
+         ColumnVector sv4 = ColumnVector.fromStrings("\u0480\u0481");
+         ColumnVector sv5 = ColumnVector.fromStrings("E\tf");
+         ColumnVector sv6 = ColumnVector.fromStrings("M");
+         ColumnVector sv7 = ColumnVector.fromStrings("\\G\u0100");
+         ColumnVector sep_col = ColumnVector.fromStrings("-*");
+         ColumnVector e_concat = ColumnVector.fromStrings("a-*B-*cd-*\u0480\u0481-*E\tf-*M-*\\G\u0100");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(
+             new ColumnView[]{sv1, sv2, sv3, sv4, sv5, sv6, sv7}, sep_col, separatorString,
+             col_narep, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSimpleOtherApi() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a");
+         ColumnVector sv2 = ColumnVector.fromStrings("B");
+         ColumnVector sv3 = ColumnVector.fromStrings("cd");
+         ColumnVector sv4 = ColumnVector.fromStrings("\u0480\u0481");
+         ColumnVector sv5 = ColumnVector.fromStrings("E\tf");
+         ColumnVector sv6 = ColumnVector.fromStrings("M");
+         ColumnVector sv7 = ColumnVector.fromStrings("\\G\u0100");
+         ColumnVector sep_col = ColumnVector.fromStrings("-*");
+         ColumnVector e_concat = ColumnVector.fromStrings("a-*B-*cd-*\u0480\u0481-*E\tf-*M-*\\G\u0100");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(
+             new ColumnView[]{sv1, sv2, sv3, sv4, sv5, sv6, sv7}, sep_col)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsOneCol() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a");
+         ColumnVector sep_col = ColumnVector.fromStrings("-*");
+         ColumnVector e_concat = ColumnVector.fromStrings("a");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(
+             new ColumnView[]{sv1}, sep_col, separatorString,
+             col_narep, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsNullSep() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "c");
+         ColumnVector sv2 = ColumnVector.fromStrings("b", "d");
+         Scalar nullString = Scalar.fromString(null);
+         ColumnVector sep_col = ColumnVector.fromScalar(nullString, 2);
+         ColumnVector e_concat = ColumnVector.fromScalar(nullString, 2);
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1, sv2},
+             sep_col, separatorString, col_narep, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsNullValueInCol() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "c", null);
+         ColumnVector sv2 = ColumnVector.fromStrings("b", "", "e");
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("a-b", "c-", "e");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1, sv2},
+             sep_col, separatorString, col_narep, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsNullValueInColKeepNull() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "c", null);
+         ColumnVector sv2 = ColumnVector.fromStrings("b", "", "e");
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("a-b", "c-", null);
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString(null);
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1, sv2},
+             sep_col, separatorString, col_narep, true)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsNullValueInColSepTrue() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "c", null);
+         ColumnVector sv2 = ColumnVector.fromStrings("b", "", "e");
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "-");
+         // this is failing?
+         ColumnVector e_concat = ColumnVector.fromStrings("a-b", "c-", "-e");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1, sv2},
+             sep_col, separatorString, col_narep, true)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleCol() {
+    try (ColumnVector sv1 = ColumnVector.fromStrings("a", "c", "e");
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("a", "c", "e");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1},
+             sep_col, separatorString, col_narep, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsNullAllCol() {
+    try (Scalar nullString = Scalar.fromString(null);
+         ColumnVector sv1 = ColumnVector.fromScalar(nullString, 3);
+         ColumnVector sv2 = ColumnVector.fromScalar(nullString, 3);
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("", "", "");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1, sv2},
+             sep_col, separatorString, col_narep, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsNullAllColSepTrue() {
+    try (Scalar nullString = Scalar.fromString(null);
+         ColumnVector sv1 = ColumnVector.fromScalar(nullString, 3);
+         ColumnVector sv2 = ColumnVector.fromScalar(nullString, 3);
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("-", "-", "-");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = ColumnVector.stringConcatenate(new ColumnView[]{sv1, sv2},
+             sep_col, separatorString, col_narep, true)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListCol() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList("b", "c", "d"),
+           Arrays.asList("\u0480\u0481", null, "asdfbe", null));
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "*");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", "b-c-d", "\u0480\u0481*asdfbe");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col, separatorString,
+             col_narep, false, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColDefaultApi() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList("b", "c", "d"),
+           Arrays.asList("\u0480\u0481", null, "asdfbe", null));
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-", "*");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", "b-c-d", "\u0480\u0481*asdfbe");
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColScalarSep() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList("b", "c", "d"),
+           Arrays.asList("\u0480\u0481", null, "asdfbe", null));
+         Scalar separatorString = Scalar.fromString("-");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", "b-c-d", "\u0480\u0481-asdfbe");
+         Scalar narep = Scalar.fromString("");
+         ColumnVector concat = cv1.stringConcatenateListElements(separatorString, narep, false,
+             false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColAllNulls() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList(null, null, null));
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", null);
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col, separatorString,
+             col_narep, false, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColAllNullsScalarSep() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList(null, null, null));
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", null);
+         Scalar separatorString = Scalar.fromString("-");
+         Scalar narep = Scalar.fromString("");
+         ColumnVector concat = cv1.stringConcatenateListElements(separatorString, narep,
+             false, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColAllNullsSepTrue() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList(null, null, null));
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", null);
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col, separatorString,
+             col_narep, true, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColAllNullsKeepNulls() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa"), Arrays.asList(null, null, null));
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa", null);
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString(null);
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col, separatorString,
+             col_narep, true, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColEmptyArray() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa", "bbbb"), Arrays.asList());
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa-bbbb", null);
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         // set the parameter to return null on empty array
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col, separatorString,
+             col_narep, false, false)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testStringConcatWsSingleListColEmptyArrayReturnEmpty() {
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+           new HostColumnVector.BasicType(true, DType.STRING)),
+           Arrays.asList("aaa", "bbbb"), Arrays.asList());
+         ColumnVector sep_col = ColumnVector.fromStrings("-", "-");
+         ColumnVector e_concat = ColumnVector.fromStrings("aaa-bbbb", "");
+         Scalar separatorString = Scalar.fromString(null);
+         Scalar col_narep = Scalar.fromString("");
+         // set the parameter to return empty string on empty array
+         ColumnVector concat = cv1.stringConcatenateListElements(sep_col, separatorString,
+             col_narep, false, true)) {
+      assertColumnsAreEqual(e_concat, concat);
+    }
+  }
+
+  @Test
+  void testRepeatStringsWithScalarRepeatTimes() {
+    // Empty strings column.
+    try (ColumnVector input = ColumnVector.fromStrings("", "", "");
+         ColumnVector results = input.repeatStrings(1)) {
+      assertColumnsAreEqual(input, results);
+    }
+
+    // Zero repeatTimes.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "xyz", "123");
+         ColumnVector results = input.repeatStrings(0);
+         ColumnVector expected = ColumnVector.fromStrings("", "", "")) {
+      assertColumnsAreEqual(expected, results);
+    }
+
+    // Negative repeatTimes.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "xyz", "123");
+         ColumnVector results = input.repeatStrings(-1);
+         ColumnVector expected = ColumnVector.fromStrings("", "", "")) {
+      assertColumnsAreEqual(expected, results);
+    }
+
+    // Strings column containing both null and empty, output is copied exactly from input.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "", null, "123", null);
+         ColumnVector results = input.repeatStrings(1)) {
+      assertColumnsAreEqual(input, results);
+    }
+
+    // Strings column containing both null and empty.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "", null, "123", null);
+         ColumnVector results = input.repeatStrings( 2);
+         ColumnVector expected = ColumnVector.fromStrings("abcabc", "", null, "123123", null)) {
+      assertColumnsAreEqual(expected, results);
+    }
+  }
+
+  @Test
+  void testRepeatStringsWithColumnRepeatTimes() {
+    // Empty strings column.
+    try (ColumnVector input = ColumnVector.fromStrings("", "", "");
+         ColumnVector repeatTimes = ColumnVector.fromInts(-1, 0, 1);
+         ColumnVector results = input.repeatStrings(repeatTimes)) {
+      assertColumnsAreEqual(input, results);
+    }
+
+    // Zero and negative repeatTimes.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "xyz", "123", "456", "789", "a1");
+         ColumnVector repeatTimes = ColumnVector.fromInts(-200, -100, 0, 0, 1, 2);
+         ColumnVector results = input.repeatStrings(repeatTimes);
+         ColumnVector expected = ColumnVector.fromStrings("", "", "", "", "789", "a1a1")) {
+      assertColumnsAreEqual(expected, results);
+    }
+
+    // Strings column contains both null and empty, output is copied exactly from input.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "", null, "123", null);
+         ColumnVector repeatTimes = ColumnVector.fromInts(1, 1, 1, 1, 1);
+         ColumnVector results = input.repeatStrings(repeatTimes)) {
+      assertColumnsAreEqual(input, results);
+    }
+
+    // Strings column contains both null and empty.
+    try (ColumnVector input = ColumnVector.fromStrings("abc", "", null, "123", null);
+         ColumnVector repeatTimes = ColumnVector.fromInts(2, 3, 1, 3, 2);
+         ColumnVector results = input.repeatStrings(repeatTimes);
+         ColumnVector expected = ColumnVector.fromStrings("abcabc", "", null, "123123123", null)) {
+      assertColumnsAreEqual(expected, results);
+    }
+  }
+
+  @Test
+  void testListConcatByRow() {
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.INT32)),
+        Arrays.asList(0), Arrays.asList(1, 2, 3), null, Arrays.asList(), Arrays.asList());
+         ColumnVector result = ColumnVector.listConcatenateByRow(cv)) {
+      assertColumnsAreEqual(cv, result);
+    }
+
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.INT32)),
+        Arrays.asList(0), Arrays.asList(1, 2, 3), null, Arrays.asList(), Arrays.asList());
+         ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.INT32)),
+             Arrays.asList(1, 2, 3), Arrays.asList((Integer) null), Arrays.asList(10, 12), Arrays.asList(100, 200, 300),
+             Arrays.asList());
+         ColumnVector result = ColumnVector.listConcatenateByRow(cv1, cv2);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.INT32)),
+             Arrays.asList(0, 1, 2, 3), Arrays.asList(1, 2, 3, null), null, Arrays.asList(100, 200, 300),
+             Arrays.asList())) {
+      assertColumnsAreEqual(expect, result);
+    }
+
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.STRING)),
+        Arrays.asList("AAA", "BBB"), Arrays.asList("aaa"), Arrays.asList("111"), Arrays.asList("X"),
+        Arrays.asList());
+         ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList(), Arrays.asList("bbb", "ccc"), null, Arrays.asList((String) null),
+             Arrays.asList());
+         ColumnVector cv3 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("CCC"), Arrays.asList(), Arrays.asList("222", "333"), Arrays.asList("Z"),
+             Arrays.asList());
+         ColumnVector result = ColumnVector.listConcatenateByRow(cv1, cv2, cv3);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("AAA", "BBB", "CCC"), Arrays.asList("aaa", "bbb", "ccc"), null,
+             Arrays.asList("X", null, "Z"), Arrays.asList())) {
+      assertColumnsAreEqual(expect, result);
+    }
+
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.FLOAT64)),
+        Arrays.asList(1.23, 0.0, Double.NaN), Arrays.asList(), null, Arrays.asList(-1.23e10, null));
+         ColumnVector result = ColumnVector.listConcatenateByRow(cv, cv, cv);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.FLOAT64)),
+             Arrays.asList(1.23, 0.0, Double.NaN, 1.23, 0.0, Double.NaN, 1.23, 0.0, Double.NaN),
+             Arrays.asList(), null, Arrays.asList(-1.23e10, null, -1.23e10, null, -1.23e10, null))) {
+      assertColumnsAreEqual(expect, result);
+    }
+
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.ListType(true,
+              new HostColumnVector.BasicType(true, DType.INT32))), Arrays.asList(Arrays.asList(1)));
+         ColumnVector result = ColumnVector.listConcatenateByRow(cv, cv);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.ListType(true,
+              new HostColumnVector.BasicType(true, DType.INT32))), Arrays.asList(Arrays.asList(1), Arrays.asList(1)))){
+      assertColumnsAreEqual(expect, result);
+    }
+
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.ListType(true,
+              new HostColumnVector.BasicType(true, DType.INT32))), Arrays.asList(Arrays.asList(1, null, 2)));
+         ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.ListType(true,
+              new HostColumnVector.BasicType(true, DType.INT32))), Arrays.asList(Arrays.asList(null, null, 5, 6, null)));
+         ColumnVector result = ColumnVector.listConcatenateByRow(cv1, cv2);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.ListType(true,
+              new HostColumnVector.BasicType(true, DType.INT32))), Arrays.asList(Arrays.asList(1, null, 2), Arrays.asList(null, null, 5, 6, null)))){
+      assertColumnsAreEqual(expect, result);
+    }
+
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromInts(1, 2, 3);
+           ColumnVector result = ColumnVector.listConcatenateByRow(cv, cv)) {
+      }
+    });
+
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.BasicType(true, DType.INT32)), Arrays.asList(1, 2, 3));
+           ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+               new HostColumnVector.BasicType(true, DType.INT32)), Arrays.asList(1, 2), Arrays.asList(3));
+           ColumnVector result = ColumnVector.listConcatenateByRow(cv1, cv2)) {
+      }
+    });
+
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.BasicType(true, DType.INT32)), Arrays.asList(1, 2, 3));
+           ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+               new HostColumnVector.BasicType(true, DType.INT64)), Arrays.asList(1L));
+           ColumnVector result = ColumnVector.listConcatenateByRow(cv1, cv2)) {
+      }
+    });
+  }
+
+  @Test
+  void testListConcatByRowIgnoreNull() {
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.INT32)),
+        Arrays.asList(0), Arrays.asList(1, 2, 3), null, Arrays.asList(), Arrays.asList());
+         ColumnVector result = ColumnVector.listConcatenateByRow(true, cv)) {
+      assertColumnsAreEqual(cv, result);
+    }
+
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.INT32)),
+        Arrays.asList((Integer) null), Arrays.asList(1, 2, 3), null, Arrays.asList(), null);
+         ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.INT32)),
+             Arrays.asList(1, 2, 3), null, Arrays.asList(10, 12), Arrays.asList(100, 200, 300), null);
+         ColumnVector result = ColumnVector.listConcatenateByRow(true, cv1, cv2);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.INT32)),
+             Arrays.asList(null, 1, 2, 3), Arrays.asList(1, 2, 3), Arrays.asList(10, 12),
+             Arrays.asList(100, 200, 300), null)) {
+      assertColumnsAreEqual(expect, result);
+    }
+
+    try (ColumnVector cv1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.STRING)),
+        Arrays.asList("AAA", "BBB"), Arrays.asList("aaa"), Arrays.asList("111"), null, null);
+         ColumnVector cv2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             null, Arrays.asList("bbb", "ccc"), null, Arrays.asList("Y", null), null);
+         ColumnVector cv3 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("CCC"), Arrays.asList(), Arrays.asList("222", "333"), null, null);
+         ColumnVector result = ColumnVector.listConcatenateByRow(true, cv1, cv2, cv3);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("AAA", "BBB", "CCC"), Arrays.asList("aaa", "bbb", "ccc"),
+             Arrays.asList("111", "222", "333"), Arrays.asList("Y", null), null)) {
+      assertColumnsAreEqual(expect, result);
+    }
+
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.FLOAT64)),
+        Arrays.asList(1.23, 0.0, Double.NaN), Arrays.asList(), null, Arrays.asList(-1.23e10, null));
+         ColumnVector result = ColumnVector.listConcatenateByRow(true, cv, cv, cv);
+         ColumnVector expect = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.FLOAT64)),
+             Arrays.asList(1.23, 0.0, Double.NaN, 1.23, 0.0, Double.NaN, 1.23, 0.0, Double.NaN),
+             Arrays.asList(), null, Arrays.asList(-1.23e10, null, -1.23e10, null, -1.23e10, null))) {
+      assertColumnsAreEqual(expect, result);
+    }
+  }
+
+  @Test
+  void testFlattenLists() {
+    HostColumnVector.ListType listType = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    HostColumnVector.ListType listOfListsType = new HostColumnVector.ListType(true, listType);
+
+    // Input does not have nulls.
+    try (ColumnVector input = ColumnVector.fromLists(listOfListsType,
+           Arrays.asList(Arrays.asList(1, 2), Arrays.asList(3), Arrays.asList(4, 5, 6)),
+           Arrays.asList(Arrays.asList(7, 8, 9), Arrays.asList(10, 11, 12, 13, 14, 15)));
+         ColumnVector result = input.flattenLists();
+         ColumnVector expected = ColumnVector.fromLists(listType,
+           Arrays.asList(1, 2, 3, 4, 5, 6),
+           Arrays.asList(7, 8, 9, 10, 11, 12, 13, 14, 15))) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    // Input has nulls.
+    try (ColumnVector input = ColumnVector.fromLists(listOfListsType,
+          Arrays.asList(null, Arrays.asList(3), Arrays.asList(4, 5, 6)),
+          Arrays.asList(Arrays.asList(null, 8, 9), Arrays.asList(10, 11, 12, 13, 14, null)))) {
+      try (ColumnVector result = input.flattenLists(false);
+           ColumnVector expected = ColumnVector.fromLists(listType,
+             null,
+             Arrays.asList(null, 8, 9, 10, 11, 12, 13, 14, null))) {
+        assertColumnsAreEqual(expected, result);
+      }
+      try (ColumnVector result = input.flattenLists(true);
+           ColumnVector expected = ColumnVector.fromLists(listType,
+             Arrays.asList(3, 4, 5, 6),
+             Arrays.asList(null, 8, 9, 10, 11, 12, 13, 14, null))) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testPrefixSum() {
+    try (ColumnVector v1 = ColumnVector.fromLongs(1, 2, 3, 5, 8, 10);
+         ColumnVector summed = v1.prefixSum();
+         ColumnVector expected = ColumnVector.fromLongs(1, 3, 6, 11, 19, 29)) {
+      assertColumnsAreEqual(expected, summed);
+    }
+  }
+
+  @Test
+  void testScanSum() {
+    try (ColumnVector v1 = ColumnVector.fromBoxedInts(1, 2, null, 3, 5, 8, 10)) {
+      try (ColumnVector result = v1.scan(ScanAggregation.sum(), ScanType.INCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 3, null, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.sum(), ScanType.INCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 3, null, 6, 11, 19, 29)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.sum(), ScanType.EXCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(0, 1, 3, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.sum(), ScanType.EXCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(0, 1, null, 3, 6, 11, 19)) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testScanMax() {
+    try (ColumnVector v1 = ColumnVector.fromBoxedInts(1, 2, null, 3, 5, 8, 10)) {
+      try (ColumnVector result = v1.scan(ScanAggregation.max(), ScanType.INCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, null, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.max(), ScanType.INCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, null, 3, 5, 8, 10)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.max(), ScanType.EXCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(Integer.MIN_VALUE, 1, 2, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.max(), ScanType.EXCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(Integer.MIN_VALUE, 1, null, 2, 3, 5, 8)) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testScanMin() {
+    try (ColumnVector v1 = ColumnVector.fromBoxedInts(1, 2, null, 3, 5, 8, 10)) {
+      try (ColumnVector result = v1.scan(ScanAggregation.min(), ScanType.INCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 1, null, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.min(), ScanType.INCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 1, null, 1, 1, 1, 1)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.min(), ScanType.EXCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(Integer.MAX_VALUE, 1, 1, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.min(), ScanType.EXCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(Integer.MAX_VALUE, 1, null, 1, 1, 1, 1)) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testScanProduct() {
+    try (ColumnVector v1 = ColumnVector.fromBoxedInts(1, 2, null, 3, 5, 8, 10)) {
+      try (ColumnVector result = v1.scan(ScanAggregation.product(), ScanType.INCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, null, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.product(), ScanType.INCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 2, null, 6, 30, 240, 2400)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.product(), ScanType.EXCLUSIVE, NullPolicy.INCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 1, 2, null, null, null, null)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      try (ColumnVector result = v1.scan(ScanAggregation.product(), ScanType.EXCLUSIVE, NullPolicy.EXCLUDE);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1, 1, null, 2, 6, 30, 240)) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testScanRank() {
+    try (ColumnVector col1 = ColumnVector.fromBoxedInts(-97, -97, -97, null, -16, 5, null, null, 6, 6, 34, null);
+         ColumnVector col2 = ColumnVector.fromBoxedInts(3, 3, 4, 7, 7, 7, 7, 7, 8, 8, 8, 9);
+         ColumnVector struct_order = ColumnVector.makeStruct(col1, col2);
+         ColumnVector expected = ColumnVector.fromBoxedInts(
+            1, 1, 3, 4, 5, 6, 7, 7, 9, 9, 11, 12)) {
+      try (ColumnVector result = struct_order.scan(ScanAggregation.rank(),
+              ScanType.INCLUSIVE, NullPolicy.INCLUDE)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Exclude should have identical results
+      try (ColumnVector result = struct_order.scan(ScanAggregation.rank(),
+              ScanType.INCLUSIVE, NullPolicy.EXCLUDE)
+              ) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Rank aggregations do not support ScanType.EXCLUSIVE
+    }
+  }
+
+  @Test
+  void testScanDenseRank() {
+    try (ColumnVector col1 = ColumnVector.fromBoxedInts(-97, -97, -97, null, -16, 5, null, null, 6, 6, 34, null);
+         ColumnVector col2 = ColumnVector.fromBoxedInts(3, 3, 4, 7, 7, 7, 7, 7, 8, 8, 8, 9);
+         ColumnVector struct_order = ColumnVector.makeStruct(col1, col2);
+         ColumnVector expected = ColumnVector.fromBoxedInts(
+            1, 1, 2, 3, 4, 5, 6, 6, 7, 7, 8, 9)) {
+      try (ColumnVector result = struct_order.scan(ScanAggregation.denseRank(),
+              ScanType.INCLUSIVE, NullPolicy.INCLUDE)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Exclude should have identical results
+      try (ColumnVector result = struct_order.scan(ScanAggregation.denseRank(),
+              ScanType.INCLUSIVE, NullPolicy.EXCLUDE)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Dense rank aggregations do not support ScanType.EXCLUSIVE
+    }
+  }
+
+  @Test
+  void testScanPercentRank() {
+    try (ColumnVector col1 = ColumnVector.fromBoxedInts(-97, -97, -97, null, -16, 5, null, null, 6, 6, 34, null);
+         ColumnVector col2 = ColumnVector.fromBoxedInts(  3,   3,   4,    7,   7, 7,    7,    7, 8, 8,  8,    9);
+         ColumnVector struct_order = ColumnVector.makeStruct(col1, col2);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(
+            0.0, 0.0, 2.0/11, 3.0/11, 4.0/11, 5.0/11, 6.0/11, 6.0/11, 8.0/11, 8.0/11, 10.0/11, 1.0)) {
+      try (ColumnVector result = struct_order.scan(ScanAggregation.percentRank(),
+              ScanType.INCLUSIVE, NullPolicy.INCLUDE)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Exclude should have identical results
+      try (ColumnVector result = struct_order.scan(ScanAggregation.percentRank(),
+              ScanType.INCLUSIVE, NullPolicy.EXCLUDE)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Percent rank aggregations do not support ScanType.EXCLUSIVE
+    }
+  }
+
+  @Test
+  void testWindowStatic() {
+    try (Scalar one = Scalar.fromInt(1);
+         Scalar two = Scalar.fromInt(2);
+         WindowOptions options = WindowOptions.builder()
+             .window(two, one)
+             .minPeriods(2).build()) {
+      try (ColumnVector v1 = ColumnVector.fromInts(5, 4, 7, 6, 8)) {
+        try (ColumnVector expected = ColumnVector.fromLongs(9, 16, 17, 21, 14);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.sum(), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector expected = ColumnVector.fromInts(4, 4, 4, 6, 6);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.min(), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector expected = ColumnVector.fromInts(5, 7, 7, 8, 8);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.max(), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        // The rolling window produces the same result type as the input
+        try (ColumnVector expected = ColumnVector.fromDoubles(4.5, 16.0 / 3, 17.0 / 3, 7, 7);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.mean(), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector expected = ColumnVector.fromBoxedInts(4, 7, 6, 8, null);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.lead(1), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector expected = ColumnVector.fromBoxedInts(null, 5, 4, 7, 6);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.lag(1), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector defaultOutput = ColumnVector.fromInts(-1, -2, -3, -4, -5);
+             ColumnVector expected = ColumnVector.fromBoxedInts(-1, 5, 4, 7, 6);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.lag(1, defaultOutput), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector expected = ColumnVector.fromBoxedDoubles(0.7071d, 1.5275d, 1.5275d, 1d, 1.4142);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.standardDeviation(), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+
+        try (ColumnVector expected =
+                 ColumnVector.fromBoxedDoubles(Double.POSITIVE_INFINITY, 2.1602d, 2.1602d, 1.4142d, Double.POSITIVE_INFINITY);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.standardDeviation(2), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowStaticCounts() {
+    try (Scalar one = Scalar.fromInt(1);
+         Scalar two = Scalar.fromInt(2);
+         WindowOptions options = WindowOptions.builder()
+             .window(two, one)
+             .minPeriods(2).build()) {
+      try (ColumnVector v1 = ColumnVector.fromBoxedInts(5, 4, null, 6, 8)) {
+        try (ColumnVector expected = ColumnVector.fromInts(2, 2, 2, 2, 2);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.count(NullPolicy.EXCLUDE), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+        try (ColumnVector expected = ColumnVector.fromInts(2, 3, 3, 3, 2);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.count(NullPolicy.INCLUDE), options)) {
+          assertColumnsAreEqual(expected, result);
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowDynamicNegative() {
+    try (ColumnVector precedingCol = ColumnVector.fromInts(3, 3, 3, 4, 4);
+         ColumnVector followingCol = ColumnVector.fromInts(-1, -1, -1, -1, 0)) {
+      try (WindowOptions window = WindowOptions.builder()
+          .minPeriods(2).window(precedingCol, followingCol).build()) {
+        try (ColumnVector v1 = ColumnVector.fromInts(5, 4, 7, 6, 8);
+             ColumnVector expected = ColumnVector.fromBoxedLongs(null, null, 9L, 16L, 25L);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.sum(), window)) {
+          assertColumnsAreEqual(expected, result);
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowLag() {
+    try (Scalar negOne = Scalar.fromInt(-1);
+         Scalar two = Scalar.fromInt(2);
+         WindowOptions window = WindowOptions.builder()
+             .minPeriods(1)
+             .window(two, negOne).build()) {
+      try (ColumnVector v1 = ColumnVector.fromInts(5, 4, 7, 6, 8);
+           ColumnVector expected = ColumnVector.fromBoxedInts(null, 5, 4, 7, 6);
+           ColumnVector result = v1.rollingWindow(RollingAggregation.max(), window)) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testWindowDynamic() {
+    try (ColumnVector precedingCol = ColumnVector.fromInts(1, 2, 3, 1, 2);
+         ColumnVector followingCol = ColumnVector.fromInts(2, 2, 2, 2, 2)) {
+      try (WindowOptions window = WindowOptions.builder().minPeriods(2)
+          .window(precedingCol, followingCol).build()) {
+        try (ColumnVector v1 = ColumnVector.fromInts(5, 4, 7, 6, 8);
+             ColumnVector expected = ColumnVector.fromLongs(16, 22, 30, 14, 14);
+             ColumnVector result = v1.rollingWindow(RollingAggregation.sum(), window)) {
+          assertColumnsAreEqual(expected, result);
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowThrowsException() {
+    try (Scalar one = Scalar.fromInt(1);
+         Scalar two = Scalar.fromInt(2);
+         Scalar three = Scalar.fromInt(3);
+         ColumnVector arraywindowCol = ColumnVector.fromBoxedInts(1, 2, 3 ,1, 1)) {
+      assertThrows(IllegalStateException.class, () -> {
+        try (WindowOptions options = WindowOptions.builder()
+            .window(three, two).minPeriods(3)
+            .window(arraywindowCol, arraywindowCol).build()) {
+        }
+      });
+
+      assertThrows(IllegalArgumentException.class, () -> {
+        try (WindowOptions options = WindowOptions.builder()
+            .window(two, one)
+            .minPeriods(1)
+            .orderByColumnIndex(0)
+            .build()) {
+          arraywindowCol.rollingWindow(RollingAggregation.sum(), options);
+        }
+      });
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAll() {
+    try(ColumnVector vector = ColumnVector.fromInts(1, 4, 1, 5, 3, 3, 1, 2, 9, 8);
+        ColumnVector oldValues = ColumnVector.fromInts(1, 4, 7); // 7 doesn't exist, nothing to replace
+        ColumnVector replacedValues = ColumnVector.fromInts(7, 6, 1);
+        ColumnVector expectedVector = ColumnVector.fromInts(7, 6, 7, 5, 3, 3, 7, 2, 9, 8);
+        ColumnVector newVector = vector.findAndReplaceAll(oldValues, replacedValues)) {
+        assertColumnsAreEqual(expectedVector, newVector);
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllFloat() {
+    try(ColumnVector vector = ColumnVector.fromFloats(1.0f, 4.2f, 1.3f, 5.7f, 3f, 3f, 1.0f, 2.6f, 0.9f, 8.3f);
+        ColumnVector oldValues = ColumnVector.fromFloats(1.0f, 4.2f, 7); // 7 doesn't exist, nothing to replace
+        ColumnVector replacedValues = ColumnVector.fromFloats(7.3f, 6.7f, 1.0f);
+        ColumnVector expectedVector = ColumnVector.fromFloats(7.3f, 6.7f, 1.3f, 5.7f, 3f, 3f, 7.3f, 2.6f, 0.9f, 8.3f);
+        ColumnVector newVector = vector.findAndReplaceAll(oldValues, replacedValues)) {
+      assertColumnsAreEqual(expectedVector, newVector);
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllTimeUnits() {
+    try(ColumnVector vector = ColumnVector.timestampMicroSecondsFromLongs(1l, 1l, 2l, 8l);
+        ColumnVector oldValues = ColumnVector.timestampMicroSecondsFromLongs(1l, 2l, 7l); // 7 dosn't exist, nothing to replace
+        ColumnVector replacedValues = ColumnVector.timestampMicroSecondsFromLongs(9l, 4l, 0l);
+        ColumnVector expectedVector = ColumnVector.timestampMicroSecondsFromLongs(9l, 9l, 4l, 8l);
+        ColumnVector newVector = vector.findAndReplaceAll(oldValues, replacedValues)) {
+      assertColumnsAreEqual(expectedVector, newVector);
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllMixingTypes() {
+    try(ColumnVector vector = ColumnVector.fromInts(1, 4, 1, 5, 3, 3, 1, 2, 9, 8);
+        ColumnVector oldValues = ColumnVector.fromInts(1, 4, 7); // 7 doesn't exist, nothing to replace
+        ColumnVector replacedValues = ColumnVector.fromFloats(7.0f, 6, 1)) {
+      assertThrows(CudfException.class, () -> vector.findAndReplaceAll(oldValues, replacedValues));
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllStrings() {
+    try(ColumnVector vector = ColumnVector.fromStrings("spark", "scala", "spark", "hello", "code");
+        ColumnVector oldValues = ColumnVector.fromStrings("spark","code","hello");
+        ColumnVector replacedValues = ColumnVector.fromStrings("sparked", "codec", "hi");
+        ColumnVector expectedValues = ColumnVector.fromStrings("sparked", "scala", "sparked", "hi", "codec");
+        ColumnVector cv = vector.findAndReplaceAll(oldValues, replacedValues)) {
+      assertColumnsAreEqual(expectedValues, cv);
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllWithNull() {
+    try(ColumnVector vector = ColumnVector.fromBoxedInts(1, 4, 1, 5, 3, 3, 1, null, 9, 8);
+        ColumnVector oldValues = ColumnVector.fromBoxedInts(1, 4, 8);
+        ColumnVector replacedValues = ColumnVector.fromBoxedInts(7, 6, null);
+        ColumnVector expectedVector = ColumnVector.fromBoxedInts(7, 6, 7, 5, 3, 3, 7, null, 9, null);
+        ColumnVector newVector = vector.findAndReplaceAll(oldValues, replacedValues)) {
+      assertColumnsAreEqual(expectedVector, newVector);
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllNulllWithValue() {
+    // null values cannot be replaced using findAndReplaceAll();
+    try(ColumnVector vector = ColumnVector.fromBoxedInts(1, 4, 1, 5, 3, 3, 1, null, 9, 8);
+        ColumnVector oldValues = ColumnVector.fromBoxedInts(1, 4, null);
+        ColumnVector replacedValues = ColumnVector.fromBoxedInts(7, 6, 8)) {
+      assertThrows(CudfException.class, () -> vector.findAndReplaceAll(oldValues, replacedValues));
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllFloatNan() {
+    // Float.NaN != Float.NaN therefore it cannot be replaced
+    try(ColumnVector vector = ColumnVector.fromFloats(1.0f, 4.2f, 1.3f, 5.7f, 3f, 3f, 1.0f, 2.6f, Float.NaN, 8.3f);
+        ColumnVector oldValues = ColumnVector.fromFloats(1.0f, 4.2f, Float.NaN);
+        ColumnVector replacedValues = ColumnVector.fromFloats(7.3f, 6.7f, 0);
+        ColumnVector expectedVector = ColumnVector.fromFloats(7.3f, 6.7f, 1.3f, 5.7f, 3f, 3f, 7.3f, 2.6f, Float.NaN, 8.3f);
+        ColumnVector newVector = vector.findAndReplaceAll(oldValues, replacedValues)) {
+      assertColumnsAreEqual(expectedVector, newVector);
+    }
+  }
+
+  @Test
+  void testFindAndReplaceAllWithFloatNan() {
+    try(ColumnVector vector = ColumnVector.fromFloats(1.0f, 4.2f, 1.3f, 5.7f, 3f, 3f, 1.0f, 2.6f, Float.NaN, 8.3f);
+        ColumnVector oldValues = ColumnVector.fromFloats(1.0f, 4.2f, 8.3f);
+        ColumnVector replacedValues = ColumnVector.fromFloats(7.3f, Float.NaN, 0);
+        ColumnVector expectedVector = ColumnVector.fromFloats(7.3f, Float.NaN, 1.3f, 5.7f, 3f, 3f, 7.3f, 2.6f, Float.NaN, 0);
+        ColumnVector newVector = vector.findAndReplaceAll(oldValues, replacedValues)) {
+      assertColumnsAreEqual(expectedVector, newVector);
+    }
+  }
+
+  @Test
+  void emptyStringColumnFindReplaceAll() {
+    try (ColumnVector cv = ColumnVector.fromStrings(null, "A", "B", "C",   "");
+         ColumnVector expected = ColumnVector.fromStrings(null, "A", "B", "C",   null);
+         ColumnVector from = ColumnVector.fromStrings("");
+         ColumnVector to = ColumnVector.fromStrings((String)null);
+         ColumnVector replaced = cv.findAndReplaceAll(from, to)) {
+      assertColumnsAreEqual(expected, replaced);
+    }
+  }
+
+  @Test
+  void testBitCast() {
+    try (ColumnVector cv = ColumnVector.decimalFromLongs(-2, 1L, 2L, 100L, 552L);
+         ColumnVector expected = ColumnVector.fromLongs(1L, 2L, 100L, 552L);
+         ColumnView casted = cv.bitCastTo(DType.INT64)) {
+      assertColumnsAreEqual(expected, casted);
+    }
+  }
+
+  @Test
+  void testFixedWidthCast() {
+    int[] values = new int[]{1,3,4,5,2};
+    long[] longValues = Arrays.stream(values).asLongStream().toArray();
+    double[] doubleValues = Arrays.stream(values).asDoubleStream().toArray();
+    byte[] byteValues = new byte[values.length];
+    float[] floatValues = new float[values.length];
+    short[] shortValues = new short[values.length];
+    IntStream.range(0, values.length).forEach(i -> {
+      byteValues[i] = (byte)values[i];
+      floatValues[i] = (float)values[i];
+      shortValues[i] = (short)values[i];
+    });
+
+    try (ColumnVector cv = ColumnVector.fromInts(values);
+         ColumnVector expectedUnsignedInts = ColumnVector.fromUnsignedInts(values);
+         ColumnVector unsignedInts = cv.asUnsignedInts();
+         ColumnVector expectedBytes = ColumnVector.fromBytes(byteValues);
+         ColumnVector bytes = cv.asBytes();
+         ColumnVector expectedUnsignedBytes = ColumnVector.fromUnsignedBytes(byteValues);
+         ColumnVector unsignedBytes = cv.asUnsignedBytes();
+         ColumnVector expectedFloats = ColumnVector.fromFloats(floatValues);
+         ColumnVector floats = cv.asFloats();
+         ColumnVector expectedDoubles = ColumnVector.fromDoubles(doubleValues);
+         ColumnVector doubles = cv.asDoubles();
+         ColumnVector expectedLongs = ColumnVector.fromLongs(longValues);
+         ColumnVector longs = cv.asLongs();
+         ColumnVector expectedUnsignedLongs = ColumnVector.fromUnsignedLongs(longValues);
+         ColumnVector unsignedLongs = cv.asUnsignedLongs();
+         ColumnVector expectedShorts = ColumnVector.fromShorts(shortValues);
+         ColumnVector shorts = cv.asShorts();
+         ColumnVector expectedUnsignedShorts = ColumnVector.fromUnsignedShorts(shortValues);
+         ColumnVector unsignedShorts = cv.asUnsignedShorts();
+         ColumnVector expectedDays = ColumnVector.daysFromInts(values);
+         ColumnVector days = cv.asTimestampDays();
+         ColumnVector expectedUs = ColumnVector.timestampMicroSecondsFromLongs(longValues);
+         ColumnVector us = longs.asTimestampMicroseconds();
+         ColumnVector expectedNs = ColumnVector.timestampNanoSecondsFromLongs(longValues);
+         ColumnVector ns = longs.asTimestampNanoseconds();
+         ColumnVector expectedMs = ColumnVector.timestampMilliSecondsFromLongs(longValues);
+         ColumnVector ms = longs.asTimestampMilliseconds();
+         ColumnVector expectedS = ColumnVector.timestampSecondsFromLongs(longValues);
+         ColumnVector s = longs.asTimestampSeconds();) {
+      assertColumnsAreEqual(expectedUnsignedInts, unsignedInts);
+      assertColumnsAreEqual(expectedBytes, bytes);
+      assertColumnsAreEqual(expectedUnsignedBytes, unsignedBytes);
+      assertColumnsAreEqual(expectedShorts, shorts);
+      assertColumnsAreEqual(expectedUnsignedShorts, unsignedShorts);
+      assertColumnsAreEqual(expectedLongs, longs);
+      assertColumnsAreEqual(expectedUnsignedLongs, unsignedLongs);
+      assertColumnsAreEqual(expectedDoubles, doubles);
+      assertColumnsAreEqual(expectedFloats, floats);
+      assertColumnsAreEqual(expectedDays, days);
+      assertColumnsAreEqual(expectedUs, us);
+      assertColumnsAreEqual(expectedMs, ms);
+      assertColumnsAreEqual(expectedNs, ns);
+      assertColumnsAreEqual(expectedS, s);
+    }
+  }
+
+  @Test
+  void testCastBigDecimalToString() {
+    BigDecimal[] bigValues = {new BigDecimal("923121331938210123.321"),
+        new BigDecimal("9223372036854775808.191"),
+        new BigDecimal("-9.223"),
+        new BigDecimal("0.000"),
+        new BigDecimal("9328323982309091029831.002")
+    };
+
+    try (ColumnVector cv = ColumnVector.fromDecimals(bigValues);
+         ColumnVector values = cv.castTo(DType.STRING);
+         ColumnVector expected = ColumnVector.fromStrings("923121331938210123.321",
+             "9223372036854775808.191",
+             "-9.223",
+             "0.000",
+            "9328323982309091029831.002")) {
+      assertColumnsAreEqual(expected, values);
+    }
+
+    BigDecimal[] bigValues0 = {new BigDecimal("992983283728193827182918744829283742232")};
+    try {
+      ColumnVector cv = ColumnVector.fromDecimals(bigValues0);
+      if (cv != null) {
+        cv.close();
+      }
+      fail("Precision check should've thrown an IllegalArgumentException");
+    } catch (IllegalArgumentException iae) {
+    }
+  }
+
+  @Test
+  void testCastStringToBigDecimal() {
+    String[] bigValues = {"923121331938210123.321",
+        "9223372036854775808.191",
+       "9328323982309091029831.002"
+    };
+
+    try (ColumnVector cv = ColumnVector.fromStrings(bigValues);
+        ColumnVector values = cv.castTo(DType.create(DType.DTypeEnum.DECIMAL128, -3));
+        ColumnVector expected = ColumnVector.fromDecimals(new BigDecimal("923121331938210123.321"),
+            new BigDecimal("9223372036854775808.191"),
+            new BigDecimal("9328323982309091029831.002"))) {
+      assertColumnsAreEqual(expected, values);
+    }
+  }
+
+  @Test
+  void testCastByteToString() {
+
+    Byte[] byteValues = {1, 3, 45, -0, null, Byte.MIN_VALUE, Byte.MAX_VALUE};
+    String[] stringByteValues = getStringArray(byteValues);
+
+    testCastFixedWidthToStringsAndBack(DType.INT8, () -> ColumnVector.fromBoxedBytes(byteValues), () -> ColumnVector.fromStrings(stringByteValues));
+  }
+
+  @Test
+  void testCastShortToString() {
+
+    Short[] shortValues = {1, 3, 45, -0, null, Short.MIN_VALUE, Short.MAX_VALUE};
+    String[] stringShortValues = getStringArray(shortValues);
+
+    testCastFixedWidthToStringsAndBack(DType.INT16, () -> ColumnVector.fromBoxedShorts(shortValues), () -> ColumnVector.fromStrings(stringShortValues));
+  }
+
+  @Test
+  void testCastIntToString() {
+    Integer[] integerArray = {1, -2, 3, null, 8, Integer.MIN_VALUE, Integer.MAX_VALUE};
+    String[] stringIntValues = getStringArray(integerArray);
+
+    testCastFixedWidthToStringsAndBack(DType.INT32, () -> ColumnVector.fromBoxedInts(integerArray), () -> ColumnVector.fromStrings(stringIntValues));
+  }
+
+  @Test
+  void testCastLongToString() {
+
+    Long[] longValues = {null, 3l, 2l, -43l, null, Long.MIN_VALUE, Long.MAX_VALUE};
+    String[] stringLongValues = getStringArray(longValues);
+
+    testCastFixedWidthToStringsAndBack(DType.INT64, () -> ColumnVector.fromBoxedLongs(longValues), () -> ColumnVector.fromStrings(stringLongValues));
+  }
+
+  @Test
+  void testCastFloatToString() {
+
+    Float[] floatValues = {Float.NaN, null, 03f, -004f, 12f};
+    String[] stringFloatValues = getStringArray(floatValues);
+
+    testCastFixedWidthToStringsAndBack(DType.FLOAT32, () -> ColumnVector.fromBoxedFloats(floatValues), () -> ColumnVector.fromStrings(stringFloatValues));
+  }
+
+  @Test
+  void testCastDoubleToString() {
+
+    Double[] doubleValues = {Double.NaN, Double.NEGATIVE_INFINITY, 4d, 98d, null, Double.POSITIVE_INFINITY};
+    //Creating the string array manually because of the way cudf converts POSITIVE_INFINITY to "Inf" instead of "INFINITY"
+    String[] stringDoubleValues = {"NaN","-Inf", "4.0", "98.0", null, "Inf"};
+
+    testCastFixedWidthToStringsAndBack(DType.FLOAT64, () -> ColumnVector.fromBoxedDoubles(doubleValues), () -> ColumnVector.fromStrings(stringDoubleValues));
+  }
+
+  @Test
+  void testCastBoolToString() {
+
+    Boolean[] booleans = {true, false, false};
+    String[] stringBools = getStringArray(booleans);
+
+    testCastFixedWidthToStringsAndBack(DType.BOOL8, () -> ColumnVector.fromBoxedBooleans(booleans), () -> ColumnVector.fromStrings(stringBools));
+  }
+
+  @Test
+  void testCastDecimal32ToString() {
+
+    Integer[] unScaledValues = {0, null, 3, 2, -43, null, 5234, -73451, 348093, -234810};
+    String[] strDecimalValues = new String[unScaledValues.length];
+    for (int scale : new int[]{-2, -1, 0, 1, 2}) {
+      for (int i = 0; i < strDecimalValues.length; i++) {
+        Long value = unScaledValues[i] == null ? null : Long.valueOf(unScaledValues[i]);
+        strDecimalValues[i] = dumpDecimal(value, scale);
+      }
+
+      testCastFixedWidthToStringsAndBack(DType.create(DType.DTypeEnum.DECIMAL32, scale),
+          () -> ColumnVector.decimalFromBoxedInts(scale, unScaledValues),
+          () -> ColumnVector.fromStrings(strDecimalValues));
+    }
+  }
+
+  @Test
+  void testCastDecimal64ToString() {
+
+    Long[] unScaledValues = {0l, null, 3l, 2l, -43l, null, 234802l, -94582l, 1234208124l, -2342348023812l};
+    String[] strDecimalValues = new String[unScaledValues.length];
+    for (int scale : new int[]{-5, -2, -1, 0, 1, 2, 5}) {
+      for (int i = 0; i < strDecimalValues.length; i++) {
+        strDecimalValues[i] = dumpDecimal(unScaledValues[i], scale);
+      }
+
+      testCastFixedWidthToStringsAndBack(DType.create(DType.DTypeEnum.DECIMAL64, scale),
+          () -> ColumnVector.decimalFromBoxedLongs(scale, unScaledValues),
+          () -> ColumnVector.fromStrings(strDecimalValues));
+    }
+  }
+
+  /**
+   * Helper function to create decimal strings which can be processed by castStringToDecimal functor.
+   * We can not simply create decimal string via `String.valueOf`, because castStringToDecimal doesn't
+   * support scientific notations so far.
+   *
+   * issue for scientific notation: https://github.com/rapidsai/cudf/issues/7665
+   */
+  private static String dumpDecimal(Long unscaledValue, int scale) {
+    if (unscaledValue == null) return null;
+
+    StringBuilder builder = new StringBuilder();
+    if (unscaledValue < 0) builder.append('-');
+    String absValue = String.valueOf(Math.abs(unscaledValue));
+
+    if (scale >= 0) {
+      builder.append(absValue);
+      for (int i = 0; i < scale; i++) builder.append('0');
+      return builder.toString();
+    }
+
+    if (absValue.length() <= -scale) {
+      builder.append('0').append('.');
+      for (int i = 0; i < -scale - absValue.length(); i++) builder.append('0');
+      builder.append(absValue);
+    } else {
+      int split = absValue.length() + scale;
+      builder.append(absValue.substring(0, split))
+          .append('.')
+          .append(absValue.substring(split));
+    }
+    return builder.toString();
+  }
+
+  private static <T> String[] getStringArray(T[] input) {
+    String[] result = new String[input.length];
+    for (int i = 0 ; i < input.length ; i++) {
+      if (input[i] == null) {
+        result[i] = null;
+      } else {
+        result[i] = String.valueOf(input[i]);
+      }
+    }
+    return result;
+  }
+
+  private static void testCastFixedWidthToStringsAndBack(DType type, Supplier<ColumnVector> fixedWidthSupplier,
+                                                         Supplier<ColumnVector> stringColumnSupplier) {
+    try (ColumnVector fixedWidthColumn = fixedWidthSupplier.get();
+         ColumnVector stringColumn = stringColumnSupplier.get();
+         ColumnVector fixedWidthCastedToString = fixedWidthColumn.castTo(DType.STRING);
+         ColumnVector stringCastedToFixedWidth = stringColumn.castTo(type)) {
+      assertColumnsAreEqual(stringColumn, fixedWidthCastedToString);
+      assertColumnsAreEqual(fixedWidthColumn, stringCastedToFixedWidth);
+    }
+  }
+
+  @Test
+  void testCastIntToDecimal() {
+    testCastNumericToDecimalsAndBack(DType.INT32, true, 0,
+        () -> ColumnVector.fromBoxedInts(1, -21, 345, null, 8008, Integer.MIN_VALUE, Integer.MAX_VALUE),
+        () -> ColumnVector.fromBoxedInts(1, -21, 345, null, 8008, Integer.MIN_VALUE, Integer.MAX_VALUE),
+        new Long[]{1L, -21L, 345L, null, 8008L, (long) Integer.MIN_VALUE, (long) Integer.MAX_VALUE}
+    );
+    testCastNumericToDecimalsAndBack(DType.INT32, false, -2,
+        () -> ColumnVector.fromBoxedInts(1, -21, 345, null, 8008, 0, 123456),
+        () -> ColumnVector.fromBoxedInts(1, -21, 345, null, 8008, 0, 123456),
+        new Long[]{100L, -2100L, 34500L, null, 800800L, 0L, 12345600L}
+    );
+    testCastNumericToDecimalsAndBack(DType.INT32, false, 2,
+        () -> ColumnVector.fromBoxedInts(1, -21, 345, null, 8008, 0, 123456),
+        () -> ColumnVector.fromBoxedInts(0, 0, 300, null, 8000, 0, 123400),
+        new Long[]{0L, 0L, 3L, null, 80L, 0L, 1234L}
+    );
+  }
+
+  @Test
+  void testCastLongToDecimal() {
+    testCastNumericToDecimalsAndBack(DType.INT64, false, 0,
+        () -> ColumnVector.fromBoxedLongs(1L, -21L, 345L, null, 8008L, Long.MIN_VALUE, Long.MAX_VALUE),
+        () -> ColumnVector.fromBoxedLongs(1L, -21L, 345L, null, 8008L, Long.MIN_VALUE, Long.MAX_VALUE),
+        new Long[]{1L, -21L, 345L, null, 8008L, Long.MIN_VALUE, Long.MAX_VALUE}
+    );
+    testCastNumericToDecimalsAndBack(DType.INT64, false, -1,
+        () -> ColumnVector.fromBoxedLongs(1L, -21L, 345L, null, 8008L, 0L, 123456L),
+        () -> ColumnVector.fromBoxedLongs(1L, -21L, 345L, null, 8008L, 0L, 123456L),
+        new Long[]{10L, -210L, 3450L, null, 80080L, 0L, 1234560L}
+    );
+    testCastNumericToDecimalsAndBack(DType.INT64, false, 1,
+        () -> ColumnVector.fromBoxedLongs(1L, -21L, 345L, null, 8018L, 0L, 123456L),
+        () -> ColumnVector.fromBoxedLongs(0L, -20L, 340L, null, 8010L, 0L, 123450L),
+        new Long[]{0L, -2L, 34L, null, 801L, 0L, 12345L}
+    );
+  }
+
+  @Test
+  void testCastDecimal64ToDecimal128() {
+    testCastDecimal128(DType.DTypeEnum.DECIMAL64, DType.DTypeEnum.DECIMAL128, 0,
+        () -> ColumnVector.fromBoxedLongs(1L, -21L, 345L, null, 8008L, Long.MIN_VALUE, Long.MAX_VALUE),
+        () -> ColumnVector.fromDecimals(new BigDecimal(1), new BigDecimal(-21), new BigDecimal(345),
+            null, new BigDecimal(8008), new BigDecimal(Long.MIN_VALUE), new BigDecimal(Long.MAX_VALUE)),
+        new BigInteger[]{new BigInteger("1"), new BigInteger("-21"),
+            new BigInteger("345"), null, new BigInteger("8008"),
+            new BigInteger(String.valueOf(Long.MIN_VALUE)),
+            new BigInteger(String.valueOf(Long.MAX_VALUE))}
+    );
+    testCastDecimal128(DType.DTypeEnum.DECIMAL32, DType.DTypeEnum.DECIMAL128, 0,
+        () -> ColumnVector.fromBoxedInts(1, 21, 345, null, 8008, Integer.MIN_VALUE, Integer.MAX_VALUE),
+        () -> ColumnVector.decimalFromBigInt(0, new BigInteger("1"), new BigInteger("21"),
+            new BigInteger("345"), null, new BigInteger("8008"),
+            new BigInteger(String.valueOf(Integer.MIN_VALUE)),
+            new BigInteger(String.valueOf(Integer.MAX_VALUE))),
+        new BigInteger[]{new BigInteger("1"), new BigInteger("21"),
+            new BigInteger("345"), null, new BigInteger("8008"),
+            new BigInteger(String.valueOf(Integer.MIN_VALUE)),
+            new BigInteger(String.valueOf(Integer.MAX_VALUE))}
+    );
+  }
+
+  @Test
+  void testCastFloatToDecimal() {
+    testCastNumericToDecimalsAndBack(DType.FLOAT32, true, 0,
+        () -> ColumnVector.fromBoxedFloats(1.0f, 2.1f, -3.23f, null, 2.41281f, 1378952.001f),
+        () -> ColumnVector.fromBoxedFloats(1f, 2f, -3f, null, 2f, 1378952f),
+        new Long[]{1L, 2L, -3L, null, 2L, 1378952L}
+    );
+    testCastNumericToDecimalsAndBack(DType.FLOAT32, true, -1,
+        () -> ColumnVector.fromBoxedFloats(1.0f, 2.1f, -3.23f, null, 2.41281f, 1378952.001f),
+        () -> ColumnVector.fromBoxedFloats(1f, 2.1f, -3.2f, null, 2.4f, 1378952f),
+        new Long[]{10L, 21L, -32L, null, 24L, 13789520L}
+    );
+    testCastNumericToDecimalsAndBack(DType.FLOAT32, true, 1,
+        () -> ColumnVector.fromBoxedFloats(1.0f, 21.1f, -300.23f, null, 24128.1f, 1378952.001f),
+        () -> ColumnVector.fromBoxedFloats(0f, 20f, -300f, null, 24120f, 1378950f),
+        new Long[]{0L, 2L, -30L, null, 2412L, 137895L}
+    );
+  }
+
+  @Test
+  void testCastDoubleToDecimal() {
+    testCastNumericToDecimalsAndBack(DType.FLOAT64, false, 0,
+        () -> ColumnVector.fromBoxedDoubles(1.0, 2.1, -3.23, null, 2.41281, (double) Long.MAX_VALUE),
+        () -> ColumnVector.fromBoxedDoubles(1.0, 2.0, -3.0, null, 2.0, (double) Long.MAX_VALUE),
+        new Long[]{1L, 2L, -3L, null, 2L, Long.MAX_VALUE}
+    );
+    testCastNumericToDecimalsAndBack(DType.FLOAT64, false, -2,
+        () -> ColumnVector.fromBoxedDoubles(1.0, 2.1, -3.23, null, 2.41281, -55.01999),
+        () -> ColumnVector.fromBoxedDoubles(1.0, 2.1, -3.23, null, 2.41, -55.01),
+        new Long[]{100L, 210L, -323L, null, 241L, -5501L}
+    );
+    testCastNumericToDecimalsAndBack(DType.FLOAT64, false, 1,
+        () -> ColumnVector.fromBoxedDoubles(1.0, 23.1, -3089.23, null, 200.41281, -199.01999),
+        () -> ColumnVector.fromBoxedDoubles(0.0, 20.0, -3080.0, null, 200.0, -190.0),
+        new Long[]{0L, 2L, -308L, null, 20L, -19L}
+    );
+  }
+
+  @Test
+  void testCastDecimalToDecimal() {
+    // DECIMAL32(scale: 0) -> DECIMAL32(scale: 0)
+    testCastNumericToDecimalsAndBack(DType.create(DType.DTypeEnum.DECIMAL32, 0), true, -0,
+        () -> ColumnVector.decimalFromInts(0, 1, 12, -234, 5678, Integer.MIN_VALUE / 100),
+        () -> ColumnVector.decimalFromInts(0, 1, 12, -234, 5678, Integer.MIN_VALUE / 100),
+        new Long[]{1L, 12L, -234L, 5678L, (long) Integer.MIN_VALUE / 100}
+    );
+    // DECIMAL32(scale: 0) -> DECIMAL64(scale: -2)
+    testCastNumericToDecimalsAndBack(DType.create(DType.DTypeEnum.DECIMAL32, 0), false, -2,
+        () -> ColumnVector.decimalFromInts(0, 1, 12, -234, 5678, Integer.MIN_VALUE / 100),
+        () -> ColumnVector.decimalFromInts(0, 1, 12, -234, 5678, Integer.MIN_VALUE / 100),
+        new Long[]{100L, 1200L, -23400L, 567800L, (long) Integer.MIN_VALUE / 100 * 100}
+    );
+    // DECIMAL64(scale: -3) -> DECIMAL64(scale: -1)
+    DType dt = DType.create(DType.DTypeEnum.DECIMAL64, -3);
+    testCastNumericToDecimalsAndBack(dt, false, -1,
+        () -> ColumnVector.decimalFromDoubles(dt, RoundingMode.UNNECESSARY, -1000.1, 1.222, 0.03, -4.678, 16789431.0),
+        () -> ColumnVector.decimalFromDoubles(dt, RoundingMode.UNNECESSARY, -1000.1, 1.2, 0, -4.6, 16789431.0),
+        new Long[]{-10001L, 12L, 0L, -46L, 167894310L}
+    );
+    // DECIMAL64(scale: -3) -> DECIMAL64(scale: 2)
+    DType dt2 = DType.create(DType.DTypeEnum.DECIMAL64, -3);
+    testCastNumericToDecimalsAndBack(dt2, false, 2,
+        () -> ColumnVector.decimalFromDoubles(dt2, RoundingMode.UNNECESSARY, -1013.1, 14.222, 780.03, -4.678, 16789431.0),
+        () -> ColumnVector.decimalFromDoubles(dt2, RoundingMode.UNNECESSARY, -1000, 0, 700, 0, 16789400),
+        new Long[]{-10L, 0L, 7L, 0L, 167894L}
+    );
+    // DECIMAL64(scale: -3) -> DECIMAL32(scale: -3)
+    testCastNumericToDecimalsAndBack(dt2, true, -3,
+        () -> ColumnVector.decimalFromDoubles(dt2, RoundingMode.UNNECESSARY, -1013.1, 14.222, 780.03, -4.678, 16789.0),
+        () -> ColumnVector.decimalFromDoubles(dt2, RoundingMode.UNNECESSARY, -1013.1, 14.222, 780.03, -4.678, 16789.0),
+        new Long[]{-1013100L, 14222L, 780030L, -4678L, 16789000L}
+    );
+  }
+
+  private static void testCastNumericToDecimalsAndBack(DType sourceType, boolean isDec32, int scale,
+                                                       Supplier<ColumnVector> sourceData,
+                                                       Supplier<ColumnVector> returnData,
+                                                       Long[] unscaledDecimal) {
+    DType decimalType = DType.create(isDec32 ? DType.DTypeEnum.DECIMAL32 : DType.DTypeEnum.DECIMAL64, scale);
+    try (ColumnVector sourceColumn = sourceData.get();
+         ColumnVector expectedColumn = returnData.get();
+         ColumnVector decimalColumn = sourceColumn.castTo(decimalType);
+         HostColumnVector hostDecimalColumn = decimalColumn.copyToHost();
+         ColumnVector returnColumn = decimalColumn.castTo(sourceType)) {
+      for (int i = 0; i < sourceColumn.rows; i++) {
+        Long actual = hostDecimalColumn.isNull(i) ? null :
+            (isDec32 ? hostDecimalColumn.getInt(i) : hostDecimalColumn.getLong(i));
+        assertEquals(unscaledDecimal[i], actual);
+      }
+      assertColumnsAreEqual(expectedColumn, returnColumn);
+    }
+  }
+
+  private static void testCastDecimal128(DType.DTypeEnum sourceType, DType.DTypeEnum targetType, int scale,
+                                         Supplier<ColumnVector> sourceData,
+                                         Supplier<ColumnVector> returnData,
+                                         Object[] unscaledDecimal) {
+    DType decimalType = DType.create(targetType, scale);
+    try (ColumnVector sourceColumn = sourceData.get();
+         ColumnVector expectedColumn = returnData.get();
+         ColumnVector decimalColumn = sourceColumn.castTo(decimalType);
+         HostColumnVector hostDecimalColumn = decimalColumn.copyToHost();
+         ColumnVector returnColumn = decimalColumn.castTo(DType.create(decimalType.typeId, scale))) {
+      for (int i = 0; i < sourceColumn.rows; i++) {
+        Object actual = hostDecimalColumn.isNull(i) ? null :
+            (decimalType.typeId == DType.DTypeEnum.DECIMAL128 ? hostDecimalColumn.getBigDecimal(i).unscaledValue() :
+                ((decimalType.typeId == DType.DTypeEnum.DECIMAL64) ? hostDecimalColumn.getLong(i) : hostDecimalColumn.getInt(i)));
+        assertEquals(unscaledDecimal[i], actual);
+      }
+      assertColumnsAreEqual(expectedColumn, returnColumn);
+    }
+  }
+
+  @Test
+  void testIsTimestamp() {
+      final String[] TIMESTAMP_STRINGS = {
+          "2018-07-04 12:00:00",
+          "",
+          null,
+          "2023-01-25",
+          "2023-01-25 07:32:12",
+          "2018-07-04 12:00:00"
+      };
+
+      try (ColumnVector timestampStrings = ColumnVector.fromStrings(TIMESTAMP_STRINGS);
+           ColumnVector isTimestamp = timestampStrings.isTimestamp("%Y-%m-%d %H:%M:%S");
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(
+               true, false, null, false, true, true)) {
+          assertColumnsAreEqual(expected, isTimestamp);
+      }
+
+      try (ColumnVector timestampStrings = ColumnVector.fromStrings(TIMESTAMP_STRINGS);
+           ColumnVector isTimestamp = timestampStrings.isTimestamp("%Y-%m-%d");
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(
+                   true, false, null, true, true, true)) {
+          assertColumnsAreEqual(expected, isTimestamp);
+      }
+  }
+
+  @Test
+  void testCastTimestampAsString() {
+    final String[] TIMES_S_STRING = {
+        "2018-07-04 12:00:00",
+        "2023-01-25 07:32:12",
+        "2018-07-04 12:00:00"};
+
+    final long[] TIMES_S = {
+        1530705600L,   //'2018-07-04 12:00:00'
+        1674631932L,   //'2023-01-25 07:32:12'
+        1530705600L};  //'2018-07-04 12:00:00'
+
+    final long[] TIMES_NS = {
+        1530705600115254330L,   //'2018-07-04 12:00:00.115254330'
+        1674631932929861604L,   //'2023-01-25 07:32:12.929861604'
+        1530705600115254330L};  //'2018-07-04 12:00:00.115254330'
+
+    final String[] TIMES_NS_STRING = {
+        "2018-07-04 12:00:00.115254330",
+        "2023-01-25 07:32:12.929861604",
+        "2018-07-04 12:00:00.115254330"};
+
+    // all supported formats by cudf
+    final String[] TIMES_NS_STRING_ALL = {
+        "04::07::18::2018::12::00::00::115254330",
+        "25::01::23::2023::07::32::12::929861604",
+        "04::07::18::2018::12::00::00::115254330"};
+
+    // Seconds
+    try (ColumnVector s_string_times = ColumnVector.fromStrings(TIMES_S_STRING);
+         ColumnVector s_timestamps = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector timestampsAsStrings = s_timestamps.asStrings("%Y-%m-%d %H:%M:%S");
+         ColumnVector timestampsAsStringsUsingDefaultFormat = s_timestamps.asStrings()) {
+      assertColumnsAreEqual(s_string_times, timestampsAsStrings);
+      assertColumnsAreEqual(timestampsAsStringsUsingDefaultFormat, timestampsAsStrings);
+    }
+
+    // Nanoseconds
+    try (ColumnVector ns_string_times = ColumnVector.fromStrings(TIMES_NS_STRING);
+         ColumnVector ns_timestamps = ColumnVector.timestampNanoSecondsFromLongs(TIMES_NS);
+         ColumnVector ns_string_times_all = ColumnVector.fromStrings(TIMES_NS_STRING_ALL);
+         ColumnVector allSupportedFormatsTimestampAsStrings = ns_timestamps.asStrings("%d::%m::%y::%Y::%H::%M::%S::%9f");
+         ColumnVector timestampsAsStrings = ns_timestamps.asStrings("%Y-%m-%d %H:%M:%S.%9f")) {
+      assertColumnsAreEqual(ns_string_times, timestampsAsStrings);
+      assertColumnsAreEqual(allSupportedFormatsTimestampAsStrings, ns_string_times_all);
+    }
+  }
+
+  @Test
+  @Disabled("Negative timestamp values are not currently supported. " +
+      "See github issue https://github.com/rapidsai/cudf/issues/3116 for details")
+  void testCastNegativeTimestampAsString() {
+    final String[] NEG_TIME_S_STRING = {"1965-10-26 14:01:12",
+        "1960-02-06 19:22:11"};
+
+    final long[] NEG_TIME_S = {-131968728L,   //'1965-10-26 14:01:12'
+        -312439069L};   //'1960-02-06 19:22:11'
+
+    final long[] NEG_TIME_NS = {-131968727761702469L};   //'1965-10-26 14:01:12.238297531'
+
+    final String[] NEG_TIME_NS_STRING = {"1965-10-26 14:01:12.238297531"};
+
+    // Seconds
+    try (ColumnVector unsupported_s_string_times = ColumnVector.fromStrings(NEG_TIME_S_STRING);
+         ColumnVector unsupported_s_timestamps = ColumnVector.timestampSecondsFromLongs(NEG_TIME_S)) {
+      assertColumnsAreEqual(unsupported_s_string_times, unsupported_s_timestamps);
+    }
+
+    // Nanoseconds
+    try (ColumnVector unsupported_ns_string_times = ColumnVector.fromStrings(NEG_TIME_NS_STRING);
+         ColumnVector unsupported_ns_timestamps = ColumnVector.timestampSecondsFromLongs(NEG_TIME_NS)) {
+      assertColumnsAreEqual(unsupported_ns_string_times, unsupported_ns_timestamps);
+    }
+  }
+
+  @Test
+  void testCastStringToByteList() {
+    List<Byte> list1 = Arrays.asList((byte)0x54, (byte)0x68, (byte)0xc3, (byte)0xa9, (byte)0x73,
+      (byte)0xc3, (byte)0xa9);
+    List<Byte> list2 = null;
+    List<Byte> list3 = Arrays.asList((byte)0x0d, (byte)0xed, (byte)0x9c, (byte)0xa0, (byte)0xc3,
+      (byte)0xa9, (byte)0xed, (byte)0x9c, (byte)0xa1);
+    List<Byte> list4 = Arrays.asList((byte)0x41, (byte)0x52, (byte)0xc3, (byte)0xa9);
+    List<Byte> list5 = Arrays.asList((byte)0x5c, (byte)0x54, (byte)0x48, (byte)0x45, (byte)0x09,
+      (byte)0x38, (byte)0xed, (byte)0x9c, (byte)0xa0);
+    List<Byte> list6 = Arrays.asList((byte)0x74, (byte)0xc3, (byte)0xa9, (byte)0x73, (byte)0x74,
+      (byte)0x20, (byte)0x73, (byte)0x74, (byte)0x72, (byte)0x69, (byte)0x6e, (byte)0x67, (byte)0x73);
+    List<Byte> list7 = Arrays.asList();
+    List<Byte> list8 = Arrays.asList((byte)0xc3, (byte)0xa9, (byte)0xc3, (byte)0xa9);
+
+    try(ColumnVector cv = ColumnVector.fromStrings("Thésé", null, "\r\ud720é\ud721", "ARé",
+    "\\THE\t8\ud720", "tést strings", "", "éé");
+        ColumnVector res = cv.asByteList(true);
+        ColumnVector expected = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.BasicType(true, DType.UINT8)), list1, list2, list3, list4, list5,
+          list6, list7, list8)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testCastIntegerToByteList() {
+    List<Byte> list1 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0x00, (byte)0x00);
+    List<Byte> list2 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0x00, (byte)0x64);
+    List<Byte> list3 = Arrays.asList((byte)0xff, (byte)0xff, (byte)0xff, (byte)0x9c);
+    List<Byte> list4 = Arrays.asList((byte)0x80, (byte)0x00, (byte)0x00, (byte)0x00);
+    List<Byte> list5 = Arrays.asList((byte)0x7f, (byte)0xff, (byte)0xff, (byte)0xff);
+
+    try(ColumnVector cv = ColumnVector.fromBoxedInts(0, 100, -100, Integer.MIN_VALUE, Integer.MAX_VALUE);
+        ColumnVector res = cv.asByteList(true);
+        ColumnVector expected = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.BasicType(true, DType.UINT8)), list1, list2, list3, list4, list5)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testCastFloatToByteList() {
+    List<Byte> list1 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0x00, (byte)0x00);
+    List<Byte> list2 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0xc8, (byte)0x42);
+    List<Byte> list3 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0xc8, (byte)0xc2);
+    List<Byte> list4 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0xc0, (byte)0x7f);
+    List<Byte> list5 = Arrays.asList((byte)0xff, (byte)0xff, (byte)0x7f, (byte)0x7f);
+    List<Byte> list6 = Arrays.asList((byte)0x00, (byte)0x00, (byte)0x80, (byte)0xff);
+
+    try(ColumnVector cv = ColumnVector.fromBoxedFloats((float)0.0, (float)100.0, (float)-100.0,
+          -Float.NaN, Float.MAX_VALUE, Float.NEGATIVE_INFINITY);
+        ColumnVector res = cv.asByteList(false);
+        ColumnVector expected = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+          new HostColumnVector.BasicType(true, DType.UINT8)), list1, list2, list3, list4, list5, list6)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testGetBytesFromList() {
+    List<Byte> list = Arrays.asList((byte)0x41, (byte)0x52, (byte)0xc3, (byte)0xa9);
+    try(ColumnVector cv = ColumnVector.fromStrings("ARé");
+        ColumnVector bytes = cv.asByteList(false);
+        HostColumnVector hostRes = bytes.copyToHost()) {
+      byte[] result = hostRes.getBytesFromList(0);
+      for(int i = 0; i < result.length; i++) {
+        assertEquals(list.get(i).byteValue(), result[i]);
+      }
+    }
+  }
+
+  @Test
+  void testContainsScalar() {
+    try (ColumnVector columnVector = ColumnVector.fromInts(1, 43, 42, 11, 2);
+    Scalar s0 = Scalar.fromInt(3);
+    Scalar s1 = Scalar.fromInt(43)) {
+      assertFalse(columnVector.contains(s0));
+      assertTrue(columnVector.contains(s1));
+    }
+  }
+
+  @Test
+  void testContainsVector() {
+    try (ColumnVector columnVector = ColumnVector.fromBoxedInts(1, null, 43, 42, 11, 2);
+         ColumnVector cv0 = ColumnVector.fromBoxedInts(1, 3, null, 11);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, null, false, false, true, false);
+         ColumnVector result = columnVector.contains(cv0)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    try (ColumnVector columnVector = ColumnVector.fromStrings("1", "43", "42", "11", "2");
+         ColumnVector cv0 = ColumnVector.fromStrings("1", "3", "11");
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, false, true, false);
+         ColumnVector result = columnVector.contains(cv0)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testStringOpsEmpty() {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector found = sv.stringContains(emptyString);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, null, true)) {
+          assertColumnsAreEqual(found, expected);
+      }
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector found = sv.startsWith(emptyString);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, null, true)) {
+          assertColumnsAreEqual(found, expected);
+      }
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector found = sv.endsWith(emptyString);
+           ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, null, true)) {
+          assertColumnsAreEqual(found, expected);
+      }
+      try (ColumnVector sv = ColumnVector.fromStrings("Héllo", "thésé", null, "ARé", "tést strings");
+           Scalar emptyString = Scalar.fromString("");
+           ColumnVector found = sv.stringLocate(emptyString, 0, -1);
+           ColumnVector expected = ColumnVector.fromBoxedInts(0, 0, null, 0, 0)) {
+          assertColumnsAreEqual(found, expected);
+      }
+  }
+
+  @Test
+  void testStringFindOperations() {
+    try (ColumnVector testStrings = ColumnVector.fromStrings("", null, "abCD", "1a\"\u0100B1", "a\"\u0100B1", "1a\"\u0100B",
+                                      "1a\"\u0100B1\n\t\'", "1a\"\u0100B1\u0453\u1322\u5112", "1a\"\u0100B1Fg26",
+                                      "1a\"\u0100B1\\\"\r1a\"\u0100B1", "1a\"\u0100B1\u0498\u1321\u51091a\"\u0100B1",
+                                      "1a\"\u0100B1H2O11a\"\u0100B1", "1a\"\u0100B1\\\"\r1a\"\u0100B1",
+                                      "\n\t\'1a\"\u0100B1", "\u0453\u1322\u51121a\"\u0100B1", "Fg261a\"\u0100B1");
+         ColumnVector emptyStrings = ColumnVector.fromStrings();
+         Scalar patternString = Scalar.fromString("1a\"\u0100B1");
+         ColumnVector startsResult = testStrings.startsWith(patternString);
+         ColumnVector endsResult = testStrings.endsWith(patternString);
+         ColumnVector containsResult = testStrings.stringContains(patternString);
+         ColumnVector expectedStarts = ColumnVector.fromBoxedBooleans(false, null, false, true, false,
+                                                                      false, true, true, true, true, true,
+                                                                      true, true, false, false, false);
+         ColumnVector expectedEnds = ColumnVector.fromBoxedBooleans(false, null, false, true, false,
+                                                                    false, false, false, false, true, true,
+                                                                    true, true, true, true, true);
+         ColumnVector expectedContains = ColumnVector.fromBoxedBooleans(false, null, false, true, false, false,
+                                                                        true, true, true, true, true,
+                                                                        true, true, true, true, true);
+         ColumnVector startsEmpty = emptyStrings.startsWith(patternString);
+         ColumnVector endsEmpty = emptyStrings.endsWith(patternString);
+         ColumnVector containsEmpty = emptyStrings.stringContains(patternString);
+         ColumnVector expectedEmpty = ColumnVector.fromBoxedBooleans()) {
+      assertColumnsAreEqual(startsResult, expectedStarts);
+      assertColumnsAreEqual(endsResult, expectedEnds);
+      assertColumnsAreEqual(expectedContains, containsResult);
+      assertColumnsAreEqual(startsEmpty, expectedEmpty);
+      assertColumnsAreEqual(endsEmpty, expectedEmpty);
+      assertColumnsAreEqual(expectedEmpty, containsEmpty);
+    }
+  }
+
+  @Test
+  void testExtractRe() {
+    try (ColumnVector input = ColumnVector.fromStrings("a1", "b2", "c3", null);
+         Table expected = new Table.TestBuilder()
+             .column("a", "b", null, null)
+             .column("1", "2", null, null)
+             .build()) {
+      try (Table found = input.extractRe("([ab])(\\d)")) {
+        assertTablesAreEqual(expected, found);
+      }
+      try (Table found = input.extractRe(new RegexProgram("([ab])(\\d)"))) {
+        assertTablesAreEqual(expected, found);
+      }
+    }
+  }
+
+  @Test
+  void testExtractAllRecord() {
+    String pattern = "([ab])(\\d)";
+    RegexProgram regexProg = new RegexProgram(pattern);
+    try (ColumnVector v = ColumnVector.fromStrings("a1", "b2", "c3", null, "a1b1c3a2");
+         ColumnVector expectedIdx0 = ColumnVector.fromLists(
+             new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("a1"),
+             Arrays.asList("b2"),
+             Arrays.asList(),
+             null,
+             Arrays.asList("a1", "b1", "a2"));
+         ColumnVector expectedIdx12 = ColumnVector.fromLists(
+             new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("a", "1"),
+             Arrays.asList("b", "2"),
+             null,
+             null,
+             Arrays.asList("a", "1", "b", "1", "a", "2"))) {
+      try (ColumnVector resultIdx0 = v.extractAllRecord(pattern, 0);
+           ColumnVector resultIdx1 = v.extractAllRecord(pattern, 1);
+           ColumnVector resultIdx2 = v.extractAllRecord(pattern, 2)) {
+        assertColumnsAreEqual(expectedIdx0, resultIdx0);
+        assertColumnsAreEqual(expectedIdx12, resultIdx1);
+        assertColumnsAreEqual(expectedIdx12, resultIdx2);
+      }
+      try (ColumnVector resultIdx0 = v.extractAllRecord(regexProg, 0);
+           ColumnVector resultIdx1 = v.extractAllRecord(regexProg, 1);
+           ColumnVector resultIdx2 = v.extractAllRecord(regexProg, 2)) {
+        assertColumnsAreEqual(expectedIdx0, resultIdx0);
+        assertColumnsAreEqual(expectedIdx12, resultIdx1);
+        assertColumnsAreEqual(expectedIdx12, resultIdx2);
+      }
+    }
+  }
+
+  @Test
+  void testMatchesRe() {
+    String patternString1 = "\\d+";
+    String patternString2 = "[A-Za-z]+\\s@[A-Za-z]+";
+    String patternString3 = ".*";
+    String patternString4 = "";
+    RegexProgram regexProg1 = new RegexProgram(patternString1, CaptureGroups.NON_CAPTURE);
+    RegexProgram regexProg2 = new RegexProgram(patternString2, CaptureGroups.NON_CAPTURE);
+    RegexProgram regexProg3 = new RegexProgram(patternString3, CaptureGroups.NON_CAPTURE);
+    RegexProgram regexProg4 = new RegexProgram(patternString4, CaptureGroups.NON_CAPTURE);
+    try (ColumnVector testStrings = ColumnVector.fromStrings("", null, "abCD", "ovér the",
+             "lazy @dog", "1234", "00:0:00");
+         ColumnVector expected1 = ColumnVector.fromBoxedBooleans(false, null, false, false, false,
+             true, true);
+         ColumnVector expected2 = ColumnVector.fromBoxedBooleans(false, null, false, false, true,
+             false, false);
+         ColumnVector expected3 = ColumnVector.fromBoxedBooleans(true, null, true, true, true,
+             true, true)) {
+      try (ColumnVector res1 = testStrings.matchesRe(patternString1);
+           ColumnVector res2 = testStrings.matchesRe(patternString2);
+           ColumnVector res3 = testStrings.matchesRe(patternString3)) {
+        assertColumnsAreEqual(expected1, res1);
+        assertColumnsAreEqual(expected2, res2);
+        assertColumnsAreEqual(expected3, res3);
+      }
+      try (ColumnVector res1 = testStrings.matchesRe(regexProg1);
+           ColumnVector res2 = testStrings.matchesRe(regexProg2);
+           ColumnVector res3 = testStrings.matchesRe(regexProg3)) {
+        assertColumnsAreEqual(expected1, res1);
+        assertColumnsAreEqual(expected2, res2);
+        assertColumnsAreEqual(expected3, res3);
+      }
+      assertThrows(AssertionError.class, () -> {
+        try (ColumnVector res = testStrings.matchesRe(patternString4)) {}
+      });
+      assertThrows(AssertionError.class, () -> {
+        try (ColumnVector res = testStrings.matchesRe(regexProg4)) {}
+      });
+    }
+  }
+
+  @Test
+  void testContainsRe() {
+    String patternString1 = "\\d+";
+    String patternString2 = "[A-Za-z]+\\s@[A-Za-z]+";
+    String patternString3 = ".*";
+    String patternString4 = "";
+    RegexProgram regexProg1 = new RegexProgram(patternString1, CaptureGroups.NON_CAPTURE);
+    RegexProgram regexProg2 = new RegexProgram(patternString2, CaptureGroups.NON_CAPTURE);
+    RegexProgram regexProg3 = new RegexProgram(patternString3, CaptureGroups.NON_CAPTURE);
+    RegexProgram regexProg4 = new RegexProgram(patternString4, CaptureGroups.NON_CAPTURE);
+    try (ColumnVector testStrings = ColumnVector.fromStrings(null, "abCD", "ovér the",
+             "lazy @dog", "1234", "00:0:00", "abc1234abc", "there @are 2 lazy @dogs");
+         ColumnVector expected1 = ColumnVector.fromBoxedBooleans(null, false, false, false,
+             true, true, true, true);
+         ColumnVector expected2 = ColumnVector.fromBoxedBooleans(null, false, false, true,
+             false, false, false, true);
+         ColumnVector expected3 = ColumnVector.fromBoxedBooleans(null, true, true, true,
+             true, true, true, true)) {
+      try (ColumnVector res1 = testStrings.containsRe(patternString1);
+           ColumnVector res2 = testStrings.containsRe(patternString2);
+           ColumnVector res3 = testStrings.containsRe(patternString3)) {
+        assertColumnsAreEqual(expected1, res1);
+        assertColumnsAreEqual(expected2, res2);
+        assertColumnsAreEqual(expected3, res3);
+      }
+      try (ColumnVector res1 = testStrings.containsRe(regexProg1);
+           ColumnVector res2 = testStrings.containsRe(regexProg2);
+           ColumnVector res3 = testStrings.containsRe(regexProg3)) {
+        assertColumnsAreEqual(expected1, res1);
+        assertColumnsAreEqual(expected2, res2);
+        assertColumnsAreEqual(expected3, res3);
+      }
+    }
+    try (ColumnVector testStrings = ColumnVector.fromStrings("", null, "abCD", "ovér the",
+             "lazy @dog", "1234", "00:0:00", "abc1234abc", "there @are 2 lazy @dogs")) {
+      assertThrows(AssertionError.class, () -> {
+        try (ColumnVector res = testStrings.containsRe(patternString4)) {}
+      });
+      assertThrows(AssertionError.class, () -> {
+        try (ColumnVector res = testStrings.containsRe(regexProg4)) {}
+      });
+    }
+  }
+
+  @Test
+  void testContainsReEmptyInput() {
+    String patternString1 = ".*";
+    RegexProgram regexProg1 = new RegexProgram(patternString1, CaptureGroups.NON_CAPTURE);
+    try (ColumnVector testStrings = ColumnVector.fromStrings("");
+         ColumnVector res1 = testStrings.containsRe(patternString1);
+         ColumnVector resReProg1 = testStrings.containsRe(regexProg1);
+         ColumnVector expected1 = ColumnVector.fromBoxedBooleans(true)) {
+      assertColumnsAreEqual(expected1, res1);
+      assertColumnsAreEqual(expected1, resReProg1);
+    }
+  }
+
+  @Test
+  void testLike() {
+    // Default escape character
+    try (ColumnVector testStrings = ColumnVector.fromStrings(
+           "a", "aa", "aaa", "aba", "b", "bb", "bba", "", "áéêú", "a1b2c3");
+         Scalar patternString1 = Scalar.fromString("a1b2c3");
+         Scalar patternString2 = Scalar.fromString("__a%");
+         Scalar defaultEscape = Scalar.fromString("\\");
+         ColumnVector res1 = testStrings.like(patternString1, defaultEscape);
+         ColumnVector res2 = testStrings.like(patternString2, defaultEscape);
+         ColumnVector expected1 = ColumnVector.fromBoxedBooleans(
+           false, false, false, false, false, false, false, false, false, true);
+         ColumnVector expected2 = ColumnVector.fromBoxedBooleans(
+           false, false, true, true, false, false, true, false, false, false)) {
+      assertColumnsAreEqual(expected1, res1);
+      assertColumnsAreEqual(expected2, res2);
+    }
+    // Non-default escape character
+    try (ColumnVector testStrings = ColumnVector.fromStrings(
+           "10%-20%", "10-20", "10%%-20%", "a_b", "b_a", "___", "", "aéb", "_%_", "_%a");
+         Scalar patternString1 = Scalar.fromString("10%%%%-20%%");
+         Scalar patternString2 = Scalar.fromString("___%%");
+         Scalar escapeChar1 = Scalar.fromString("%");
+         Scalar escapeChar2 = Scalar.fromString("_");
+         ColumnVector res1 = testStrings.like(patternString1, escapeChar1);
+         ColumnVector res2 = testStrings.like(patternString2, escapeChar2);
+         ColumnVector expected1 = ColumnVector.fromBoxedBooleans(
+           false, false, true, false, false, false, false, false, false, false);
+         ColumnVector expected2 = ColumnVector.fromBoxedBooleans(
+           false, false, false, false, false, false, false, false, true, true)) {
+      assertColumnsAreEqual(expected1, res1);
+      assertColumnsAreEqual(expected2, res2);
+    }
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector testStrings = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar defaultEscape = Scalar.fromString("\\");
+           ColumnVector res = testStrings.like(null, defaultEscape)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector testStrings = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar patternString = Scalar.fromString("");
+           ColumnVector res = testStrings.like(patternString, null)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector testStrings = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar patternString = Scalar.fromString("");
+           Scalar intScalar = Scalar.fromInt(1);
+           ColumnVector res = testStrings.like(patternString, intScalar)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector testStrings = ColumnVector.fromStrings("a", "B", "cd", null, "");
+           Scalar intScalar = Scalar.fromInt(1);
+           Scalar defaultEscape = Scalar.fromString("\\");
+           ColumnVector res = testStrings.like(intScalar, defaultEscape)) {}
+    });
+  }
+
+  @Test
+  void testUrlDecode() {
+    String[] inputs = new String[] {
+        "foobar.site%2Fq%3Fx%3D%C3%A9%25",
+        "a%2Bb%2Dc%2Ad%2Fe",
+        "1%092%0A3",
+        "abc%401%2523",
+        "abc123",
+        " %09%0D%0A%0C",
+        "",
+        null
+    };
+    String[] expectedOutputs = new String[] {
+        "foobar.site/q?x=é%",
+        "a+b-c*d/e",
+        "1\t2\n3",
+        "abc@1%23",
+        "abc123",
+        " \t\r\n\f",
+        "",
+        null
+    };
+
+    try (ColumnVector v = ColumnVector.fromStrings(inputs);
+         ColumnVector expected = ColumnVector.fromStrings(expectedOutputs);
+         ColumnVector actual = v.urlDecode()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testUrlEncode() {
+    String[] inputs = new String[] {
+        "foobar.site/q?x=é%",
+        "a+b-c*d/e",
+        "1\t2\n3",
+        "abc@1%23",
+        "abc123",
+        " \t\r\n\f",
+        "",
+        null
+    };
+    String[] expectedOutputs = new String[] {
+        "foobar.site%2Fq%3Fx%3D%C3%A9%25",
+        "a%2Bb-c%2Ad%2Fe",
+        "1%092%0A3",
+        "abc%401%2523",
+        "abc123",
+        "%20%09%0D%0A%0C",
+        "",
+        null
+    };
+
+    try (ColumnVector v = ColumnVector.fromStrings(inputs);
+         ColumnVector expected = ColumnVector.fromStrings(expectedOutputs);
+         ColumnVector actual = v.urlEncode()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testStringFindOperationsThrowsException() {
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString(null);
+           ColumnVector concat = sv.startsWith(emptyString)) {}
+    });
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString(null);
+           ColumnVector concat = sv.endsWith(emptyString)) {}
+    });
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar emptyString = Scalar.fromString(null);
+           ColumnVector concat = sv.stringContains(emptyString)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           ColumnVector concat = sv.startsWith(null)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           ColumnVector concat = sv.endsWith(null)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar intScalar = Scalar.fromInt(1);
+           ColumnVector concat = sv.startsWith(intScalar)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar intScalar = Scalar.fromInt(1);
+           ColumnVector concat = sv.endsWith(intScalar)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector sv = ColumnVector.fromStrings("a", "B", "cd");
+           Scalar intScalar = Scalar.fromInt(1);
+           ColumnVector concat = sv.stringContains(intScalar)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector v = ColumnVector.fromInts(1, 43, 42, 11, 2);
+           Scalar patternString = Scalar.fromString("a");
+           ColumnVector concat = v.startsWith(patternString)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector v = ColumnVector.fromInts(1, 43, 42, 11, 2);
+           Scalar patternString = Scalar.fromString("a");
+           ColumnVector concat = v.endsWith(patternString)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector v = ColumnVector.fromInts(1, 43, 42, 11, 2);
+           Scalar patternString = Scalar.fromString("a");
+           ColumnVector concat = v.stringContains(patternString)) {}
+    });
+  }
+
+  @Test
+  void testStringLocate() {
+    try(ColumnVector v = ColumnVector.fromStrings("Héllo", "thésé", null, "\r\ud720é\ud721", "ARé",
+                                                  "\\THE\t8\ud720", "tést strings", "", "éé");
+        ColumnVector e_locate1 = ColumnVector.fromBoxedInts(1, 2, null, 2, 2, -1, 1, -1, 0);
+        ColumnVector e_locate2 = ColumnVector.fromBoxedInts(-1, 2, null, -1, -1, -1, 1, -1, -1);
+        ColumnVector e_locate3 = ColumnVector.fromBoxedInts(-1, -1, null, 1, -1, 6, -1, -1, -1);
+        Scalar pattern1 = Scalar.fromString("é");
+        Scalar pattern2 = Scalar.fromString("és");
+        Scalar pattern3 = Scalar.fromString("\ud720");
+        ColumnVector locate1 = v.stringLocate(pattern1, 0, -1);
+        ColumnVector locate2 = v.stringLocate(pattern2, 0, -1);
+        ColumnVector locate3 = v.stringLocate(pattern3, 0, -1)) {
+      assertColumnsAreEqual(locate1, e_locate1);
+      assertColumnsAreEqual(locate2, e_locate2);
+      assertColumnsAreEqual(locate3, e_locate3);
+    }
+  }
+
+  @Test
+  void testStringLocateOffsets() {
+    try(ColumnVector v = ColumnVector.fromStrings("Héllo", "thésé", null, "\r\ud720é\ud721", "ARé",
+                                                  "\\THE\t8\ud720", "tést strings", "", "éé");
+        Scalar pattern = Scalar.fromString("é");
+        ColumnVector e_empty = ColumnVector.fromBoxedInts(-1, -1, null, -1, -1, -1, -1, -1, -1);
+        ColumnVector e_start = ColumnVector.fromBoxedInts(-1, 2, null, 2, 2, -1, -1, -1, -1);
+        ColumnVector e_end = ColumnVector.fromBoxedInts(1, -1, null, -1, -1, -1, 1, -1, 0);
+        ColumnVector locate_empty = v.stringLocate(pattern, 13, -1);
+        ColumnVector locate_start = v.stringLocate(pattern, 2, -1);
+        ColumnVector locate_end = v.stringLocate(pattern, 0, 2)) {
+      assertColumnsAreEqual(locate_empty, e_empty);
+      assertColumnsAreEqual(locate_start, e_start);
+      assertColumnsAreEqual(locate_end, e_end);
+    }
+  }
+
+  @Test
+  void testStringLocateThrowsException() {
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("Héllo", "thésé", null, "ARé", "tést strings");
+           ColumnVector locate = cv.stringLocate(null, 0, -1)) {}
+    });
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("Héllo", "thésé", null, "ARé", "tést strings");
+           Scalar pattern = Scalar.fromString(null);
+           ColumnVector locate = cv.stringLocate(pattern, 0, -1)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("Héllo", "thésé", null, "ARé", "tést strings");
+           Scalar intScalar = Scalar.fromInt(1);
+           ColumnVector locate = cv.stringLocate(intScalar, 0, -1)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("Héllo", "thésé", null, "ARé", "tést strings");
+           Scalar pattern = Scalar.fromString("é");
+           ColumnVector locate = cv.stringLocate(pattern, -2, -1)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromStrings("Héllo", "thésé", null, "ARé", "tést strings");
+           Scalar pattern = Scalar.fromString("é");
+           ColumnVector locate = cv.stringLocate(pattern, 2, 1)) {}
+    });
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromInts(1, 43, 42, 11, 2);
+           Scalar pattern = Scalar.fromString("é");
+           ColumnVector concat = cv.stringLocate(pattern, 0, -1)) {}
+    });
+  }
+
+  @Test
+  void testsubstring() {
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo", "thésé", null,"", "ARé", "strings");
+         ColumnVector e_allParameters = ColumnVector.fromStrings("llo", "ésé", null, "", "é", "rin");
+         ColumnVector e_withoutStop = ColumnVector.fromStrings("llo", "ésé", null, "", "é", "rings");
+         ColumnVector substring_allParam = v.substring(2, 5);
+         ColumnVector substring_NoEnd = v.substring(2)) {
+      assertColumnsAreEqual(e_allParameters, substring_allParam);
+      assertColumnsAreEqual(e_withoutStop, substring_NoEnd);
+    }
+  }
+
+  @Test
+  void testExtractListElements() {
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo there", "thésé", null, "", "ARé some", "test strings");
+         ColumnVector expected = ColumnVector.fromStrings("Héllo", "thésé", null, "", "ARé", "test");
+         ColumnVector list = v.stringSplitRecord(" ");
+         ColumnVector result = list.extractListElement(0)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testExtractListElementsV() {
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo there", "thésé", null, "", "ARé some", "test strings");
+         ColumnVector indices = ColumnVector.fromInts(0, 2, 0, 0, 1, -1);
+         ColumnVector expected = ColumnVector.fromStrings("Héllo", null, null, "", "some", "strings");
+         ColumnVector list = v.stringSplitRecord(" ");
+         ColumnVector result = list.extractListElement(indices)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testDropListDuplicates() {
+    List<Integer> list1 = Arrays.asList(1, 2);
+    List<Integer> list2 = Arrays.asList(3, 4, 5);
+    List<Integer> list3 = Arrays.asList(null, 0, 6, 6, 0);
+    List<Integer> dedupeList3 = Arrays.asList(0, 6, null);
+    List<Integer> list4 = Arrays.asList(null, 6, 7, null, 7);
+    List<Integer> dedupeList4 = Arrays.asList(6, 7, null);
+    List<Integer> list5 = null;
+
+    HostColumnVector.DataType listType = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType, list1, list2, dedupeList3, dedupeList4, list5);
+         ColumnVector tmp = v.dropListDuplicates();
+         // Note dropping duplicates does not have any ordering guarantee, so sort to make it all
+         // consistent
+         ColumnVector result = tmp.listSortRows(false, false)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testDropListDuplicatesWithKeysValues() {
+    try(ColumnVector inputChildKeys = ColumnVector.fromBoxedInts(
+            1, 2, // list1
+            3, 4, 5, // list2
+            null, 0, 6, 6, 0, // list3
+            null, 6, 7, null, 7 // list 4
+            // list5 (empty)
+        );
+        ColumnVector inputChildVals = ColumnVector.fromBoxedInts(
+            10, 20, // list1
+            30, 40, 50, // list2
+            60, 70, 80, 90, 100, // list3
+            110, 120, 130, 140, 150 // list4
+            // list5 (empty)
+        );
+        ColumnVector inputStructsKeysVals = ColumnVector.makeStruct(inputChildKeys, inputChildVals);
+        ColumnVector inputOffsets = ColumnVector.fromInts(0, 2, 5, 10, 15, 15);
+        ColumnVector inputListsKeysVals = inputStructsKeysVals.makeListFromOffsets(5, inputOffsets)
+    ) {
+      // Test full input:
+      try(ColumnVector expectedChildKeys = ColumnVector.fromBoxedInts(
+              1, 2, // list1
+              3, 4, 5, // list2
+              0, 6, null, // list3
+              6, 7, null // list4
+              // list5 (empty)
+          );
+          ColumnVector expectedChildVals = ColumnVector.fromBoxedInts(
+              10, 20, // list1
+              30, 40, 50, // list2
+              100, 90, 60, // list3
+              120, 150, 140 // list4
+              // list5 (empty)
+          );
+          ColumnVector expectedStructsKeysVals = ColumnVector.makeStruct(expectedChildKeys, expectedChildVals);
+          ColumnVector expectedOffsets = ColumnVector.fromInts(0, 2, 5, 8, 11, 11);
+          ColumnVector expectedListsKeysVals = expectedStructsKeysVals.makeListFromOffsets(5, expectedOffsets);
+
+          ColumnVector output = inputListsKeysVals.dropListDuplicatesWithKeysValues();
+          ColumnVector sortedOutput = output.listSortRows(false, false)
+      ) {
+        assertColumnsAreEqual(expectedListsKeysVals, sortedOutput);
+      }
+
+      // Test sliced input:
+      try(ColumnVector expectedChildKeys = ColumnVector.fromBoxedInts(
+              3, 4, 5, // list1
+              0, 6, null // list2
+          );
+          ColumnVector expectedChildVals = ColumnVector.fromBoxedInts(
+              30, 40, 50, // list1
+              100, 90, 60 // list2
+          );
+          ColumnVector expectedStructsKeysVals = ColumnVector.makeStruct(expectedChildKeys, expectedChildVals);
+          ColumnVector expectedOffsets = ColumnVector.fromInts(0, 3, 6);
+          ColumnVector expectedListsKeysVals = expectedStructsKeysVals.makeListFromOffsets(2, expectedOffsets);
+
+          ColumnVector inputSliced = inputListsKeysVals.subVector(1, 3);
+          ColumnVector output = inputSliced.dropListDuplicatesWithKeysValues();
+          ColumnVector sortedOutput = output.listSortRows(false, false)
+      ) {
+        assertColumnsAreEqual(expectedListsKeysVals, sortedOutput);
+      }
+    }
+  }
+
+  @Test
+  void testDropListDuplicatesWithKeysValuesNullable() {
+    try(ColumnVector inputChildKeys = ColumnVector.fromBoxedInts(
+            1, 2, // list1
+            // list2 (null)
+            3, 4, 5, // list3
+            null, 0, 6, 6, 0, // list4
+            null, 6, 7, null, 7 // list 5
+            // list6 (null)
+        );
+        ColumnVector inputChildVals = ColumnVector.fromBoxedInts(
+            10, 20, // list1
+            // list2 (null)
+            30, 40, 50, // list3
+            60, 70, 80, 90, 100, // list4
+            110, 120, 130, 140, 150 // list5
+            // list6 (null)
+        );
+        ColumnVector inputStructsKeysVals = ColumnVector.makeStruct(inputChildKeys, inputChildVals);
+        ColumnVector inputOffsets = ColumnVector.fromInts(0, 2, 2, 5, 10, 15, 15);
+        ColumnVector tmpInputListsKeysVals = inputStructsKeysVals.makeListFromOffsets(6,inputOffsets);
+        ColumnVector templateBitmask = ColumnVector.fromBoxedInts(1, null, 1, 1, 1, null);
+        ColumnVector inputListsKeysVals = tmpInputListsKeysVals.mergeAndSetValidity(BinaryOp.BITWISE_AND, templateBitmask)
+    ) {
+      // Test full input:
+      try(ColumnVector expectedChildKeys = ColumnVector.fromBoxedInts(
+              1, 2, // list1
+              // list2 (null)
+              3, 4, 5, // list3
+              0, 6, null, // list4
+              6, 7, null // list5
+              // list6 (null)
+          );
+          ColumnVector expectedChildVals = ColumnVector.fromBoxedInts(
+              10, 20, // list1
+              // list2 (null)
+              30, 40, 50, // list3
+              100, 90, 60, // list4
+              120, 150, 140 // list5
+              // list6 (null)
+          );
+          ColumnVector expectedStructsKeysVals = ColumnVector.makeStruct(expectedChildKeys, expectedChildVals);
+          ColumnVector expectedOffsets = ColumnVector.fromInts(0, 2, 2, 5, 8, 11, 11);
+          ColumnVector tmpExpectedListsKeysVals = expectedStructsKeysVals.makeListFromOffsets(6, expectedOffsets);
+          ColumnVector expectedListsKeysVals = tmpExpectedListsKeysVals.mergeAndSetValidity(BinaryOp.BITWISE_AND, templateBitmask);
+
+          ColumnVector output = inputListsKeysVals.dropListDuplicatesWithKeysValues();
+          ColumnVector sortedOutput = output.listSortRows(false, false)
+      ) {
+        assertColumnsAreEqual(expectedListsKeysVals, sortedOutput);
+      }
+
+      // Test sliced input:
+      try(ColumnVector expectedChildKeys = ColumnVector.fromBoxedInts(
+              // list1 (null)
+              3, 4, 5, // list2
+              0, 6, null // list3
+          );
+          ColumnVector expectedChildVals = ColumnVector.fromBoxedInts(
+              // list1 (null)
+              30, 40, 50, // list2
+              100, 90, 60 // list3
+          );
+          ColumnVector expectedStructsKeysVals = ColumnVector.makeStruct(expectedChildKeys, expectedChildVals);
+          ColumnVector expectedOffsets = ColumnVector.fromInts(0, 0, 3, 6);
+          ColumnVector tmpExpectedListsKeysVals = expectedStructsKeysVals.makeListFromOffsets(3, expectedOffsets);
+          ColumnVector slicedTemplateBitmask = ColumnVector.fromBoxedInts(null, 1, 1);
+          ColumnVector expectedListsKeysVals = tmpExpectedListsKeysVals.mergeAndSetValidity(BinaryOp.BITWISE_AND, slicedTemplateBitmask);
+
+          ColumnVector inputSliced = inputListsKeysVals.subVector(1, 4);
+          ColumnVector output = inputSliced.dropListDuplicatesWithKeysValues();
+          ColumnVector sortedOutput = output.listSortRows(false, false)
+      ) {
+        assertColumnsAreEqual(expectedListsKeysVals, sortedOutput);
+      }
+    }
+  }
+
+  @SafeVarargs
+  public static <T> ColumnVector makeListsColumn(DType childDType, List<T>... rows) {
+    HostColumnVector.DataType childType = new HostColumnVector.BasicType(true, childDType);
+    HostColumnVector.DataType listType  = new HostColumnVector.ListType(true, childType);
+    return ColumnVector.fromLists(listType, rows);
+  }
+
+  @Test
+  void testListContainsString() {
+    List<String> list0 = Arrays.asList("Héllo there", "thésé");
+    List<String> list1 = Arrays.asList("", "ARé some", "test strings");
+    List<String> list2 = Arrays.asList(null, "", "ARé some", "test strings", "thésé");
+    List<String> list3 = Arrays.asList(null, "", "ARé some", "test strings");
+    List<String> list4 = null;
+    try (ColumnVector input = makeListsColumn(DType.STRING, list0, list1, list2, list3, list4);
+         Scalar searchKey = Scalar.fromString("thésé");
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, true, false, null);
+         ColumnVector result = input.listContains(searchKey)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testListContainsInt() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, 6);
+    List<Integer> list2 = Arrays.asList(7, 8, 9);
+    List<Integer> list3 = null;
+    try (ColumnVector input =  makeListsColumn(DType.INT32, list0, list1, list2, list3);
+         Scalar searchKey = Scalar.fromInt(7);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, true, null);
+         ColumnVector result = input.listContains(searchKey)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testListContainsStringCol() {
+    List<String> list0 = Arrays.asList("Héllo there", "thésé");
+    List<String> list1 = Arrays.asList("", "ARé some", "test strings");
+    List<String> list2 = Arrays.asList("FOO", "", "ARé some", "test");
+    List<String> list3 = Arrays.asList(null, "FOO", "", "ARé some", "test");
+    List<String> list4 = Arrays.asList(null, "FOO", "", "ARé some", "test");
+    List<String> list5 = null;
+    try (ColumnVector input = makeListsColumn(DType.STRING, list0, list1, list2, list3, list4, list5);
+         ColumnVector searchKeys = ColumnVector.fromStrings("thésé", "", "test", "test", "iotA", null);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, true, true, false, null);
+         ColumnVector result = input.listContainsColumn(searchKeys)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testListContainsIntCol() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, 6);
+    List<Integer> list2 = Arrays.asList(null, 8, 9);
+    List<Integer> list3 = Arrays.asList(null, 8, 9);
+    List<Integer> list4 = null;
+    try (ColumnVector input = makeListsColumn(DType.INT32, list0, list1, list2, list3, list4);
+         ColumnVector searchKeys = ColumnVector.fromBoxedInts(3, 3, 8, 3, null);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, false, true, false, null);
+         ColumnVector result = input.listContainsColumn(searchKeys)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testListContainsNulls() {
+    List<String> list0 = Arrays.asList("Héllo there", "thésé");
+    List<String> list1 = Arrays.asList("", "ARé some", "test strings");
+    List<String> list2 = Arrays.asList("FOO", "", "ARé some", "test");
+    List<String> list3 = Arrays.asList(null, "FOO", "", "ARé some", "test");
+    List<String> list4 = Arrays.asList(null, "FOO", "", "ARé some", "test");
+    List<String> list5 = null;
+    try (ColumnVector input = makeListsColumn(DType.STRING, list0, list1, list2, list3, list4, list5);
+         ColumnVector result = input.listContainsNulls();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, true, true, null)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testListIndexOfString() {
+    List<String> list0 = Arrays.asList("Héllo there", "thésé");
+    List<String> list1 = Arrays.asList("", "ARé some", "test strings");
+    List<String> list2 = Arrays.asList(null, "", "ARé some", "thésé", "test strings", "thésé");
+    List<String> list3 = Arrays.asList(null, "", "ARé some", "test strings");
+    List<String> list4 = null;
+    try (ColumnVector input = makeListsColumn(DType.STRING, list0, list1, list2, list3, list4);
+         Scalar searchKey = Scalar.fromString("thésé");
+         ColumnVector expectedFirst = ColumnVector.fromBoxedInts(1, -1, 3, -1, null);
+         ColumnVector resultFirst = input.listIndexOf(searchKey, FindOptions.FIND_FIRST);
+         ColumnVector expectedLast = ColumnVector.fromBoxedInts(1, -1, 5, -1, null);
+         ColumnVector resultLast = input.listIndexOf(searchKey, FindOptions.FIND_LAST)) {
+      assertColumnsAreEqual(expectedFirst, resultFirst);
+      assertColumnsAreEqual(expectedLast, resultLast);
+    }
+  }
+
+  @Test
+  void testListIndexOfInt() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, 6);
+    List<Integer> list2 = Arrays.asList(7, 8, 9, 7);
+    List<Integer> list3 = null;
+    try (ColumnVector input = makeListsColumn(DType.INT32, list0, list1, list2, list3);
+         Scalar searchKey = Scalar.fromInt(7);
+         ColumnVector expectedFirst = ColumnVector.fromBoxedInts(-1, -1, 0, null);
+         ColumnVector resultFirst = input.listIndexOf(searchKey, FindOptions.FIND_FIRST);
+         ColumnVector expectedLast = ColumnVector.fromBoxedInts(-1, -1, 3, null);
+         ColumnVector resultLast = input.listIndexOf(searchKey, FindOptions.FIND_LAST)) {
+      assertColumnsAreEqual(expectedFirst, resultFirst);
+      assertColumnsAreEqual(expectedLast, resultLast);
+    }
+  }
+
+  @Test
+  void testListIndexOfStringCol() {
+    List<String> list0 = Arrays.asList("Héllo there", "thésé");
+    List<String> list1 = Arrays.asList("", "ARé some", "test strings");
+    List<String> list2 = Arrays.asList("FOO", "", "ARé some", "test");
+    List<String> list3 = Arrays.asList(null, "FOO", "", "test", "ARé some", "test");
+    List<String> list4 = Arrays.asList(null, "FOO", "", "ARé some", "test");
+    List<String> list5 = null;
+    try (ColumnVector input = makeListsColumn(DType.STRING, list0, list1, list2, list3, list4, list5);
+         ColumnVector searchKeys = ColumnVector.fromStrings("thésé", "", "test", "test", "iotA", null);
+         ColumnVector expectedFirst = ColumnVector.fromBoxedInts(1, 0, 3, 3, -1, null);
+         ColumnVector resultFirst = input.listIndexOf(searchKeys, FindOptions.FIND_FIRST);
+         ColumnVector expectedLast = ColumnVector.fromBoxedInts(1, 0, 3, 5, -1, null);
+         ColumnVector resultLast = input.listIndexOf(searchKeys, FindOptions.FIND_LAST)) {
+      assertColumnsAreEqual(expectedFirst, resultFirst);
+      assertColumnsAreEqual(expectedLast, resultLast);
+    }
+  }
+
+  @Test
+  void testListIndexOfIntCol() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, 6);
+    List<Integer> list2 = Arrays.asList(null, 8, 9, 8);
+    List<Integer> list3 = Arrays.asList(null, 8, 9);
+    List<Integer> list4 = null;
+    try (ColumnVector input = makeListsColumn(DType.INT32, list0, list1, list2, list3, list4);
+         ColumnVector searchKeys = ColumnVector.fromBoxedInts(3, 3, 8, 3, null);
+         ColumnVector expectedFirst = ColumnVector.fromBoxedInts(2, -1, 1, -1, null);
+         ColumnVector resultFirst = input.listIndexOf(searchKeys, FindOptions.FIND_FIRST);
+         ColumnVector expectedLast = ColumnVector.fromBoxedInts(2, -1, 3, -1, null);
+         ColumnVector resultLast = input.listIndexOf(searchKeys, FindOptions.FIND_LAST)) {
+      assertColumnsAreEqual(expectedFirst, resultFirst);
+      assertColumnsAreEqual(expectedLast, resultLast);
+    }
+  }
+
+  @Test
+  void testListSortRowsWithIntChild() {
+    List<Integer> list1 = Arrays.asList(1, 3, 0, 2);
+    List<Integer> ascSortedList1 = Arrays.asList(0, 1, 2, 3);
+    List<Integer> decSortedList1 = Arrays.asList(3, 2, 1, 0);
+
+    List<Integer> list2 = Arrays.asList(7, 5, 6, 4);
+    List<Integer> ascSortedList2 = Arrays.asList(4, 5, 6, 7);
+    List<Integer> decSortedList2 = Arrays.asList(7, 6, 5, 4);
+
+    List<Integer> list3 = Arrays.asList(-8, null, -9, -10);
+    List<Integer> ascSortedList3 = Arrays.asList(-10, -9, -8, null);
+    List<Integer> ascSortedNullMinList3 = Arrays.asList(null, -10, -9, -8);
+    List<Integer> decSortedList3 = Arrays.asList(null, -8, -9, -10);
+    List<Integer> decSortedNullMinList3 = Arrays.asList(-8, -9, -10, null);
+
+    List<Integer> list4 = Arrays.asList(null, -12, null, 11);
+    List<Integer> ascSortedList4 = Arrays.asList(-12, 11, null, null);
+    List<Integer> ascSortedNullMinList4 = Arrays.asList(null, null, -12, 11);
+    List<Integer> decSortedList4 = Arrays.asList(null, null, 11, -12);
+    List<Integer> decSortedNullMinList4 = Arrays.asList(11, -12, null, null);
+
+    List<Integer> list5 = null;
+
+    HostColumnVector.ListType listType = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    // Ascending + NullLargest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             ascSortedList1, ascSortedList2, ascSortedList3, ascSortedList4, list5);
+         ColumnVector result = v.listSortRows(false, false)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    // Descending + NullLargest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             decSortedList1, decSortedList2, decSortedList3, decSortedList4, list5);
+         ColumnVector result = v.listSortRows(true, false)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    // Ascending + NullSmallest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             ascSortedList1, ascSortedList2, ascSortedNullMinList3, ascSortedNullMinList4, list5);
+         ColumnVector result = v.listSortRows(false, true)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    // Descending + NullSmallest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             decSortedList1, decSortedList2, decSortedNullMinList3, decSortedNullMinList4, list5);
+         ColumnVector result = v.listSortRows(true, true)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testListSortRowsWithStringChild() {
+    List<String> list1 = Arrays.asList("b", "d", "a", "c");
+    List<String> ascSortedList1 = Arrays.asList("a", "b", "c", "d");
+    List<String> decSortedList1 = Arrays.asList("d", "c", "b", "a");
+
+    List<String> list2 = Arrays.asList("h", "f", "g", "e");
+    List<String> ascSortedList2 = Arrays.asList("e", "f", "g", "h");
+    List<String> decSortedList2 = Arrays.asList("h", "g", "f", "e");
+
+    List<String> list3 = Arrays.asList("C", null, "B", "A");
+    List<String> ascSortedList3 = Arrays.asList("A", "B", "C", null);
+    List<String> ascSortedNullMinList3 = Arrays.asList(null, "A", "B", "C");
+    List<String> decSortedList3 = Arrays.asList(null, "C", "B", "A");
+    List<String> decSortedNullMinList3 = Arrays.asList("C", "B", "A", null);
+
+    List<String> list4 = Arrays.asList(null, "D", null, "d");
+    List<String> ascSortedList4 = Arrays.asList("D", "d", null, null);
+    List<String> ascSortedNullMinList4 = Arrays.asList(null, null, "D", "d");
+    List<String> decSortedList4 = Arrays.asList(null, null, "d", "D");
+    List<String> decSortedNullMinList4 = Arrays.asList("d", "D", null, null);
+
+    List<String> list5 = null;
+
+    HostColumnVector.ListType listType = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.STRING));
+    // Ascending + NullLargest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             ascSortedList1, ascSortedList2, ascSortedList3, ascSortedList4, list5);
+         ColumnVector result = v.listSortRows(false, false)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    // Descending + NullLargest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             decSortedList1, decSortedList2, decSortedList3, decSortedList4, list5);
+         ColumnVector result = v.listSortRows(true, false)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    // Ascending + NullSmallest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             ascSortedList1, ascSortedList2, ascSortedNullMinList3, ascSortedNullMinList4, list5);
+         ColumnVector result = v.listSortRows(false, true)) {
+      assertColumnsAreEqual(expected, result);
+    }
+    // Descending + NullSmallest
+    try (ColumnVector v = ColumnVector.fromLists(listType, list1, list2, list3, list4, list5);
+         ColumnVector expected = ColumnVector.fromLists(listType,
+             decSortedList1, decSortedList2, decSortedNullMinList3, decSortedNullMinList4, list5);
+         ColumnVector result = v.listSortRows(true, true)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testSetOperations() {
+    List<Double> lhsList1 = Arrays.asList(Double.NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 0.0);
+    List<Double> lhsList2 = Arrays.asList(Double.NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0);
+    List<Double> lhsList3 = null;
+    List<Double> lhsList4 = Arrays.asList(Double.NaN, 5.0, 0.0, 0.0, 0.0, 0.0, null, 1.0);
+
+    List<Double> rhsList1 = Arrays.asList(1.0, 0.5, null, 0.0, 0.0, null, Double.NaN);
+    List<Double> rhsList2 = Arrays.asList(2.0, 1.0, null, 0.0, 0.0, null);
+    List<Double> rhsList3 = Arrays.asList(2.0, 1.0, null, 0.0, 0.0, null);
+    List<Double> rhsList4 = null;
+
+    // Set intersection result:
+    List<Double> expectedIntersectionList1 = Arrays.asList(null, 0.0, Double.NaN);
+    List<Double> expectedIntersectionList2 = Arrays.asList(null, 0.0, 1.0);
+
+    // Set union result:
+    List<Double> expectedUnionList1 = Arrays.asList(null, 0.0, 0.5, 1.0, 5.0, Double.NaN);
+    List<Double> expectedUnionList2 = Arrays.asList(null, 0.0, 1.0, 2.0, 5.0, Double.NaN);
+
+    // Set difference result:
+    List<Double> expectedDifferenceList1 = Arrays.asList(5.0);
+    List<Double> expectedDifferenceList2 = Arrays.asList(5.0, Double.NaN);
+
+    try(ColumnVector lhs = makeListsColumn(DType.FLOAT64, lhsList1, lhsList2, lhsList3, lhsList4);
+        ColumnVector rhs = makeListsColumn(DType.FLOAT64, rhsList1, rhsList2, rhsList3, rhsList4)) {
+
+      // Test listsHaveOverlap:
+      try(ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, null, null);
+          ColumnVector result = ColumnVector.listsHaveOverlap(lhs, rhs)) {
+        assertColumnsAreEqual(expected, result);
+      }
+
+      // Test listsIntersectDistinct:
+      try(ColumnVector expected = makeListsColumn(DType.FLOAT64, expectedIntersectionList1,
+              expectedIntersectionList2, null, null);
+          ColumnVector result = ColumnVector.listsIntersectDistinct(lhs, rhs);
+          ColumnVector resultSorted = result.listSortRows(false, true)) {
+        assertColumnsAreEqual(expected, resultSorted);
+      }
+
+      // Test listsUnionDistinct:
+      try(ColumnVector expected = makeListsColumn(DType.FLOAT64, expectedUnionList1,
+          expectedUnionList2, null, null);
+          ColumnVector result = ColumnVector.listsUnionDistinct(lhs, rhs);
+          ColumnVector resultSorted = result.listSortRows(false, true)) {
+        assertColumnsAreEqual(expected, resultSorted);
+      }
+
+      // Test listsDifferenceDistinct:
+      try(ColumnVector expected = makeListsColumn(DType.FLOAT64, expectedDifferenceList1,
+          expectedDifferenceList2, null, null);
+          ColumnVector result = ColumnVector.listsDifferenceDistinct(lhs, rhs);
+          ColumnVector resultSorted = result.listSortRows(false, true)) {
+        assertColumnsAreEqual(expected, resultSorted);
+      }
+    }
+  }
+
+  @Test
+  void testReverseString() {
+    try (ColumnVector input = ColumnVector.fromStrings("abcdef", "12345", "", "", "aébé",
+           "A é Z", "X", "é");
+         ColumnVector expected = ColumnVector.fromStrings("fedcba", "54321", "", "", "ébéa",
+           "Z é A", "X", "é");
+         ColumnVector result = input.reverseStringsOrLists()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testReverseList() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, null);
+    List<Integer> emptyList = Collections.emptyList();
+    List<Integer> reversedList0 = Arrays.asList(3, 2, 1);
+    List<Integer> reversedList1 = Arrays.asList(null, 5, 4);
+
+    try (ColumnVector input = makeListsColumn(DType.INT32,
+           emptyList, list0, emptyList, null, list1);
+         ColumnVector expected = makeListsColumn(DType.INT32,
+           emptyList, reversedList0, emptyList, null, reversedList1);
+         ColumnVector result = input.reverseStringsOrLists()) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testStringSplit() {
+    String pattern = " ";
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo there all", "thésé", null, "",
+             "ARé some things", "test strings here");
+         Table expectedSplitLimit2 = new Table.TestBuilder()
+             .column("Héllo", "thésé", null, "", "ARé", "test")
+             .column("there all", null, null, null, "some things", "strings here")
+             .build();
+         Table expectedSplitAll = new Table.TestBuilder()
+             .column("Héllo", "thésé", null, "", "ARé", "test")
+             .column("there", null, null, null, "some", "strings")
+             .column("all", null, null, null, "things", "here")
+             .build();
+         Table resultSplitLimit2 = v.stringSplit(pattern, 2);
+         Table resultSplitAll = v.stringSplit(pattern)) {
+      assertTablesAreEqual(expectedSplitLimit2, resultSplitLimit2);
+      assertTablesAreEqual(expectedSplitAll, resultSplitAll);
+    }
+  }
+
+  @Test
+  void testStringSplitByRegularExpression() {
+    String pattern = "[_ ]";
+    RegexProgram regexProg = new RegexProgram(pattern, CaptureGroups.NON_CAPTURE);
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo_there all", "thésé", null, "",
+             "ARé some_things", "test_strings_here");
+         Table expectedSplitLimit2 = new Table.TestBuilder()
+             .column("Héllo", "thésé", null, "", "ARé", "test")
+             .column("there all", null, null, null, "some_things", "strings_here")
+             .build();
+         Table expectedSplitAll = new Table.TestBuilder()
+             .column("Héllo", "thésé", null, "", "ARé", "test")
+             .column("there", null, null, null, "some", "strings")
+             .column("all", null, null, null, "things", "here")
+             .build()) {
+      try (Table resultSplitLimit2 = v.stringSplit(pattern, 2, true);
+           Table resultSplitAll = v.stringSplit(pattern, true)) {
+        assertTablesAreEqual(expectedSplitLimit2, resultSplitLimit2);
+        assertTablesAreEqual(expectedSplitAll, resultSplitAll);
+      }
+      try (Table resultSplitLimit2 = v.stringSplit(regexProg, 2);
+           Table resultSplitAll = v.stringSplit(regexProg)) {
+        assertTablesAreEqual(expectedSplitLimit2, resultSplitLimit2);
+        assertTablesAreEqual(expectedSplitAll, resultSplitAll);
+      }
+    }
+  }
+
+  @Test
+  void testStringSplitRecord() {
+    String pattern = " ";
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo there all", "thésé", null, "",
+             "ARé some things", "test strings here");
+         ColumnVector expectedSplitLimit2 = ColumnVector.fromLists(
+             new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("Héllo", "there all"),
+             Arrays.asList("thésé"),
+             null,
+             Arrays.asList(""),
+             Arrays.asList("ARé", "some things"),
+             Arrays.asList("test", "strings here"));
+         ColumnVector expectedSplitAll = ColumnVector.fromLists(
+             new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("Héllo", "there", "all"),
+             Arrays.asList("thésé"),
+             null,
+             Arrays.asList(""),
+             Arrays.asList("ARé", "some", "things"),
+             Arrays.asList("test", "strings", "here"));
+         ColumnVector resultSplitLimit2 = v.stringSplitRecord(pattern, 2);
+         ColumnVector resultSplitAll = v.stringSplitRecord(pattern)) {
+      assertColumnsAreEqual(expectedSplitLimit2, resultSplitLimit2);
+      assertColumnsAreEqual(expectedSplitAll, resultSplitAll);
+    }
+  }
+
+  @Test
+  void testStringSplitRecordByRegularExpression() {
+    String pattern = "[_ ]";
+    RegexProgram regexProg = new RegexProgram(pattern, CaptureGroups.NON_CAPTURE);
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo_there all", "thésé", null, "",
+             "ARé some_things", "test_strings_here");
+         ColumnVector expectedSplitLimit2 = ColumnVector.fromLists(
+             new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("Héllo", "there all"),
+             Arrays.asList("thésé"),
+             null,
+             Arrays.asList(""),
+             Arrays.asList("ARé", "some_things"),
+             Arrays.asList("test", "strings_here"));
+         ColumnVector expectedSplitAll = ColumnVector.fromLists(
+             new HostColumnVector.ListType(true,
+                 new HostColumnVector.BasicType(true, DType.STRING)),
+             Arrays.asList("Héllo", "there", "all"),
+             Arrays.asList("thésé"),
+             null,
+             Arrays.asList(""),
+             Arrays.asList("ARé", "some", "things"),
+             Arrays.asList("test", "strings", "here"))) {
+      try (ColumnVector resultSplitLimit2 = v.stringSplitRecord(pattern, 2, true);
+           ColumnVector resultSplitAll = v.stringSplitRecord(pattern, true)) {
+        assertColumnsAreEqual(expectedSplitLimit2, resultSplitLimit2);
+        assertColumnsAreEqual(expectedSplitAll, resultSplitAll);
+      }
+      try (ColumnVector resultSplitLimit2 = v.stringSplitRecord(regexProg, 2);
+           ColumnVector resultSplitAll = v.stringSplitRecord(regexProg)) {
+        assertColumnsAreEqual(expectedSplitLimit2, resultSplitLimit2);
+        assertColumnsAreEqual(expectedSplitAll, resultSplitAll);
+      }
+    }
+  }
+
+  @Test
+  void testsubstringColumn() {
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo", "thésé", null, "", "ARé", "strings");
+         ColumnVector start = ColumnVector.fromInts(2, 1, 1, 1, 0, 1);
+         ColumnVector end = ColumnVector.fromInts(5, 3, 1, 1, -1, -1);
+         ColumnVector expected = ColumnVector.fromStrings("llo", "hé", null, "", "ARé", "trings");
+         ColumnVector result = v.substring(start, end)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testsubstringThrowsException() {
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector v = ColumnVector.fromStrings("Héllo", "thésé", null, "", "ARé", "strings");
+           ColumnVector start = ColumnVector.fromInts(2, 1, 1, 1, 0, 1);
+           ColumnVector end = ColumnVector.fromInts(5, 3, 1, 1, -1);
+           ColumnVector substring = v.substring(start, end)) {
+      }
+    });
+  }
+
+  @Test
+  void teststringReplace() {
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo", "thésssé", null, "", "ARé", "sssstrings");
+         ColumnVector e_allParameters = ColumnVector.fromStrings("Héllo", "théSsé", null, "", "ARé", "SStrings");
+         Scalar target = Scalar.fromString("ss");
+         Scalar replace = Scalar.fromString("S");
+         ColumnVector replace_allParameters = v.stringReplace(target, replace)) {
+      assertColumnsAreEqual(e_allParameters, replace_allParameters);
+    }
+  }
+
+  @Test
+  void teststringReplaceThrowsException() {
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector testStrings = ColumnVector.fromStrings("Héllo", "thésé", null, "", "ARé", "strings");
+           Scalar target= Scalar.fromString("");
+           Scalar replace=Scalar.fromString("a");
+           ColumnVector result = testStrings.stringReplace(target,replace)){}
+    });
+  }
+
+  @Test
+  void teststringReplaceMulti() {
+    try (ColumnVector v = ColumnVector.fromStrings("Héllo", "thésssé", null, "", "ARé", "sssstrings");
+         ColumnVector e_allParameters = ColumnVector.fromStrings("Hello", "theSse", null, "", "ARe", "SStrings");
+         ColumnVector targets = ColumnVector.fromStrings("ss", "é");
+         ColumnVector repls = ColumnVector.fromStrings("S", "e");
+         ColumnVector replace_allParameters = v.stringReplace(targets, repls)) {
+      assertColumnsAreEqual(e_allParameters, replace_allParameters);
+    }
+  }
+
+  @Test
+  void teststringReplaceMultiThrowsException() {
+    assertThrows(AssertionError.class, () -> {
+      try (ColumnVector testStrings = ColumnVector.fromStrings("Héllo", "thésé", null, "", "ARé", "strings");
+           ColumnVector targets = ColumnVector.fromInts(0, 1);
+           ColumnVector repls = null;
+           ColumnVector result = testStrings.stringReplace(targets,repls)){}
+    });
+  }
+
+  @Test
+  void testReplaceRegex() {
+    try (ColumnVector v = ColumnVector.fromStrings("title and Title with title", "nothing", null, "Title");
+         Scalar repl = Scalar.fromString("Repl")) {
+      String pattern = "[tT]itle";
+      RegexProgram regexProg = new RegexProgram(pattern, CaptureGroups.NON_CAPTURE);
+
+      try (ColumnVector actual = v.replaceRegex(pattern, repl);
+           ColumnVector expected =
+               ColumnVector.fromStrings("Repl and Repl with Repl", "nothing", null, "Repl")) {
+        assertColumnsAreEqual(expected, actual);
+      }
+
+      try (ColumnVector actual = v.replaceRegex(pattern, repl, 0)) {
+        assertColumnsAreEqual(v, actual);
+      }
+
+      try (ColumnVector actual = v.replaceRegex(pattern, repl, 1);
+           ColumnVector expected =
+               ColumnVector.fromStrings("Repl and Title with title", "nothing", null, "Repl")) {
+        assertColumnsAreEqual(expected, actual);
+      }
+
+      try (ColumnVector actual = v.replaceRegex(regexProg, repl);
+           ColumnVector expected =
+               ColumnVector.fromStrings("Repl and Repl with Repl", "nothing", null, "Repl")) {
+        assertColumnsAreEqual(expected, actual);
+      }
+
+      try (ColumnVector actual = v.replaceRegex(regexProg, repl, 0)) {
+        assertColumnsAreEqual(v, actual);
+      }
+
+      try (ColumnVector actual = v.replaceRegex(regexProg, repl, 1);
+           ColumnVector expected =
+               ColumnVector.fromStrings("Repl and Title with title", "nothing", null, "Repl")) {
+        assertColumnsAreEqual(expected, actual);
+      }
+    }
+  }
+
+  @Test
+  void testReplaceMultiRegex() {
+    try (ColumnVector v =
+             ColumnVector.fromStrings("title and Title with title", "nothing", null, "Title");
+         ColumnVector repls = ColumnVector.fromStrings("Repl", "**");
+         ColumnVector actual = v.replaceMultiRegex(new String[] { "[tT]itle", "and|th" }, repls);
+         ColumnVector expected =
+             ColumnVector.fromStrings("Repl ** Repl wi** Repl", "no**ing", null, "Repl")) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testStringReplaceWithBackrefs() {
+
+    try (ColumnVector v = ColumnVector.fromStrings("<h1>title</h1>", "<h1>another title</h1>", null);
+         ColumnVector expected = ColumnVector.fromStrings("<h2>title</h2>",
+             "<h2>another title</h2>", null);
+         ColumnVector actual = v.stringReplaceWithBackrefs("<h1>(.*)</h1>", "<h2>\\1</h2>");
+         ColumnVector actualRe =
+             v.stringReplaceWithBackrefs(new RegexProgram("<h1>(.*)</h1>"), "<h2>\\1</h2>")) {
+      assertColumnsAreEqual(expected, actual);
+      assertColumnsAreEqual(expected, actualRe);
+    }
+
+    try (ColumnVector v = ColumnVector.fromStrings("2020-1-01", "2020-2-02", null);
+         ColumnVector expected = ColumnVector.fromStrings("2020-01-01", "2020-02-02", null);
+         ColumnVector actual = v.stringReplaceWithBackrefs("-([0-9])-", "-0\\1-");
+         ColumnVector actualRe =
+             v.stringReplaceWithBackrefs(new RegexProgram("-([0-9])-"), "-0\\1-")) {
+      assertColumnsAreEqual(expected, actual);
+      assertColumnsAreEqual(expected, actualRe);
+    }
+
+    try (ColumnVector v = ColumnVector.fromStrings("2020-01-1", "2020-02-2", "2020-03-3invalid", null);
+         ColumnVector expected = ColumnVector.fromStrings("2020-01-01", "2020-02-02",
+             "2020-03-3invalid", null);
+         ColumnVector actual = v.stringReplaceWithBackrefs("-([0-9])$", "-0\\1");
+         ColumnVector actualRe =
+             v.stringReplaceWithBackrefs(new RegexProgram("-([0-9])$"), "-0\\1")) {
+      assertColumnsAreEqual(expected, actual);
+      assertColumnsAreEqual(expected, actualRe);
+    }
+
+    try (ColumnVector v = ColumnVector.fromStrings("2020-01-1 random_text", "2020-02-2T12:34:56",
+             "2020-03-3invalid", null);
+         ColumnVector expected = ColumnVector.fromStrings("2020-01-01 random_text",
+             "2020-02-02T12:34:56", "2020-03-3invalid", null);
+         ColumnVector actual = v.stringReplaceWithBackrefs("-([0-9])([ T])", "-0\\1\\2");
+         ColumnVector actualRe =
+             v.stringReplaceWithBackrefs(new RegexProgram("-([0-9])([ T])"), "-0\\1\\2")) {
+      assertColumnsAreEqual(expected, actual);
+      assertColumnsAreEqual(expected, actualRe);
+    }
+
+    // test zero as group index
+    try (ColumnVector v = ColumnVector.fromStrings("aa-11 b2b-345", "aa-11a 1c-2b2 b2-c3", "11-aa", null);
+         ColumnVector expected = ColumnVector.fromStrings("aa-11:aa:11; b2b-345:b:345;",
+             "aa-11:aa:11;a 1c-2:c:2;b2 b2-c3", "11-aa", null);
+         ColumnVector actual = v.stringReplaceWithBackrefs("([a-z]+)-([0-9]+)", "${0}:${1}:${2};");
+         ColumnVector actualRe =
+             v.stringReplaceWithBackrefs(new RegexProgram("([a-z]+)-([0-9]+)"), "${0}:${1}:${2};")) {
+      assertColumnsAreEqual(expected, actual);
+      assertColumnsAreEqual(expected, actualRe);
+    }
+
+    // group index exceeds group count
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector v = ColumnVector.fromStrings("ABC123defgh");
+           ColumnVector r = v.stringReplaceWithBackrefs("([A-Z]+)([0-9]+)([a-z]+)", "\\4")) {
+      }
+    });
+
+    // group index exceeds group count
+    assertThrows(CudfException.class, () -> {
+      try (ColumnVector v = ColumnVector.fromStrings("ABC123defgh");
+           ColumnVector r =
+               v.stringReplaceWithBackrefs(new RegexProgram("([A-Z]+)([0-9]+)([a-z]+)"), "\\4")) {
+      }
+    });
+  }
+
+  @Test
+  void testLPad() {
+      try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+           ColumnVector expected = ColumnVector.fromStrings("A1", "23", "45678", null);
+           ColumnVector actual = v.pad(2, PadSide.LEFT, "A")) {
+          assertColumnsAreEqual(expected, actual);
+      }
+      try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+           ColumnVector expected = ColumnVector.fromStrings("___1", "__23", "45678", null);
+           ColumnVector actual = v.pad(4, PadSide.LEFT, "_")) {
+          assertColumnsAreEqual(expected, actual);
+      }
+  }
+
+  @Test
+  void testRPad() {
+      try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+           ColumnVector expected = ColumnVector.fromStrings("1A", "23", "45678", null);
+           ColumnVector actual = v.pad(2, PadSide.RIGHT, "A")) {
+          assertColumnsAreEqual(expected, actual);
+      }
+      try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+           ColumnVector expected = ColumnVector.fromStrings("1___", "23__", "45678", null);
+           ColumnVector actual = v.pad(4, PadSide.RIGHT, "_")) {
+          assertColumnsAreEqual(expected, actual);
+      }
+  }
+
+  @Test
+  void testPad() {
+      try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+           ColumnVector expected = ColumnVector.fromStrings("1A", "23", "45678", null);
+           ColumnVector actual = v.pad(2, PadSide.BOTH, "A")) {
+          assertColumnsAreEqual(expected, actual);
+      }
+      try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+           ColumnVector expected = ColumnVector.fromStrings("_1__", "_23_", "45678", null);
+           ColumnVector actual = v.pad(4, PadSide.BOTH, "_")) {
+          assertColumnsAreEqual(expected, actual);
+      }
+  }
+
+  @Test
+  void testZfill() {
+    try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+         ColumnVector expected = ColumnVector.fromStrings("01", "23", "45678", null);
+         ColumnVector actual = v.zfill(2)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+    try (ColumnVector v = ColumnVector.fromStrings("1", "23", "45678", null);
+         ColumnVector expected = ColumnVector.fromStrings("0001", "0023", "45678", null);
+         ColumnVector actual = v.zfill(4)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testStringTitlize() {
+    try (ColumnVector cv = ColumnVector.fromStrings("sPark", "sqL", "lowercase", null, "", "UPPERCASE");
+         ColumnVector result = cv.toTitle();
+         ColumnVector expected = ColumnVector.fromStrings("Spark", "Sql", "Lowercase", null, "", "Uppercase")) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testStringCapitalize() {
+    try (ColumnVector cv = ColumnVector.fromStrings("s Park", "S\nqL", "lower \tcase",
+                                                    null, "", "UPPER\rCASE")) {
+      try (Scalar deli = Scalar.fromString("");
+           ColumnVector result = cv.capitalize(deli);
+           ColumnVector expected = ColumnVector.fromStrings("S park", "S\nql", "Lower \tcase",
+                                                            null, "", "Upper\rcase")) {
+        assertColumnsAreEqual(expected, result);
+      }
+      try (Scalar deli = Scalar.fromString(" ");
+           ColumnVector result = cv.capitalize(deli);
+           ColumnVector expected = ColumnVector.fromStrings("S Park", "S\nql", "Lower \tcase",
+                                                            null, "", "Upper\rcase")) {
+        assertColumnsAreEqual(expected, result);
+      }
+      try (Scalar deli = Scalar.fromString(" \t\n");
+           ColumnVector result = cv.capitalize(deli);
+           ColumnVector expected = ColumnVector.fromStrings("S Park", "S\nQl", "Lower \tCase",
+                                                             null, "", "Upper\rcase")) {
+        assertColumnsAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testNansToNulls() {
+    Float[] floats = new Float[]{1.2f, Float.POSITIVE_INFINITY, Float.NEGATIVE_INFINITY, null,
+        Float.NaN, Float.MAX_VALUE, Float.MIN_VALUE, 435243.2323f, POSITIVE_FLOAT_NAN_LOWER_RANGE,
+        POSITIVE_FLOAT_NAN_UPPER_RANGE, NEGATIVE_FLOAT_NAN_LOWER_RANGE,
+        NEGATIVE_FLOAT_NAN_UPPER_RANGE};
+
+    Float[] expectedFloats = new Float[]{1.2f, Float.POSITIVE_INFINITY,
+        Float.NEGATIVE_INFINITY, null, null, Float.MAX_VALUE, Float.MIN_VALUE, 435243.2323f,
+        null, null, null, null};
+
+    Double[] doubles = new Double[]{1.2d, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, null,
+        Double.NaN, Double.MAX_VALUE, Double.MIN_VALUE, 435243.2323d, POSITIVE_DOUBLE_NAN_LOWER_RANGE,
+        POSITIVE_DOUBLE_NAN_UPPER_RANGE, NEGATIVE_DOUBLE_NAN_LOWER_RANGE,
+        NEGATIVE_DOUBLE_NAN_UPPER_RANGE};
+
+   Double[] expectedDoubles = new Double[]{1.2d, Double.POSITIVE_INFINITY,
+        Double.NEGATIVE_INFINITY, null, null, Double.MAX_VALUE, Double.MIN_VALUE,
+        435243.2323d, null, null, null, null};
+
+    try (ColumnVector cvFloat = ColumnVector.fromBoxedFloats(floats);
+         ColumnVector cvDouble = ColumnVector.fromBoxedDoubles(doubles);
+         ColumnVector resultFloat = cvFloat.nansToNulls();
+         ColumnVector resultDouble = cvDouble.nansToNulls();
+         ColumnVector expectedFloat = ColumnVector.fromBoxedFloats(expectedFloats);
+         ColumnVector expectedDouble = ColumnVector.fromBoxedDoubles(expectedDoubles)) {
+      assertColumnsAreEqual(expectedFloat, resultFloat);
+      assertColumnsAreEqual(expectedDouble, resultDouble);
+    }
+  }
+
+  @Test
+  void testIsIntegerWithBounds() {
+    String[] intStrings = {"A", "nan", "Inf", "-Inf", "3.5",
+        String.valueOf(Byte.MIN_VALUE),
+        String.valueOf(Byte.MIN_VALUE + 1L),
+        String.valueOf(Byte.MIN_VALUE - 1L),
+        String.valueOf(Byte.MAX_VALUE),
+        String.valueOf(Byte.MAX_VALUE + 1L),
+        String.valueOf(Byte.MAX_VALUE - 1L),
+        String.valueOf(Short.MIN_VALUE),
+        String.valueOf(Short.MIN_VALUE + 1L),
+        String.valueOf(Short.MIN_VALUE - 1L),
+        String.valueOf(Short.MAX_VALUE),
+        String.valueOf(Short.MAX_VALUE + 1L),
+        String.valueOf(Short.MAX_VALUE - 1L),
+        String.valueOf(Integer.MIN_VALUE),
+        String.valueOf(Integer.MIN_VALUE + 1L),
+        String.valueOf(Integer.MIN_VALUE - 1L),
+        String.valueOf(Integer.MAX_VALUE),
+        String.valueOf(Integer.MAX_VALUE + 1L),
+        String.valueOf(Integer.MAX_VALUE - 1L),
+        String.valueOf(Long.MIN_VALUE),
+        String.valueOf(Long.MIN_VALUE + 1L),
+        "-9223372036854775809",
+        String.valueOf(Long.MAX_VALUE),
+        "9223372036854775808",
+        String.valueOf(Long.MAX_VALUE - 1L)};
+    try (ColumnVector intStringCV = ColumnVector.fromStrings(intStrings);
+         ColumnVector isByte = intStringCV.isInteger(DType.INT8);
+         ColumnVector expectedByte = ColumnVector.fromBoxedBooleans(
+             false, false, false, false, false,
+             true, true, false, true, false, true,
+             false, false, false, false, false, false,
+             false, false, false, false, false, false,
+             false, false, false, false, false, false);
+         ColumnVector isShort = intStringCV.isInteger(DType.INT16);
+         ColumnVector expectedShort = ColumnVector.fromBoxedBooleans(
+             false, false, false, false, false,
+             true, true, true, true, true, true,
+             true, true, false, true, false, true,
+             false, false, false, false, false, false,
+             false, false, false, false, false, false);
+         ColumnVector isInt = intStringCV.isInteger(DType.INT32);
+         ColumnVector expectedInt = ColumnVector.fromBoxedBooleans(
+             false, false, false, false, false,
+             true, true, true, true, true, true,
+             true, true, true, true, true, true,
+             true, true, false, true, false, true,
+             false, false, false, false, false, false);
+         ColumnVector isLong = intStringCV.isInteger(DType.INT64);
+         ColumnVector expectedLong = ColumnVector.fromBoxedBooleans(
+             false, false, false, false, false,
+             true, true, true, true, true, true,
+             true, true, true, true, true, true,
+             true, true, true, true, true, true,
+             true, true, false, true, false, true)) {
+      assertColumnsAreEqual(expectedByte, isByte);
+      assertColumnsAreEqual(expectedShort, isShort);
+      assertColumnsAreEqual(expectedInt, isInt);
+      assertColumnsAreEqual(expectedLong, isLong);
+    }
+  }
+
+  @Test
+  void testIsInteger() {
+    String[] intStrings = {"A", "nan", "Inf", "-Inf", "Infinity", "infinity", "2147483647",
+        "2147483648", "-2147483648", "-2147483649", "NULL", "null", null, "1.2", "1.2e-4", "0.00012"};
+    String[] longStrings = {"A", "nan", "Inf", "-Inf", "Infinity", "infinity",
+        "9223372036854775807", "9223372036854775808", "-9223372036854775808",
+        "-9223372036854775809", "NULL", "null", null, "1.2", "1.2e-4", "0.00012"};
+    try (ColumnVector intStringCV = ColumnVector.fromStrings(intStrings);
+         ColumnVector longStringCV = ColumnVector.fromStrings(longStrings);
+         ColumnVector isInt = intStringCV.isInteger();
+         ColumnVector isLong = longStringCV.isInteger();
+         ColumnVector ints = intStringCV.asInts();
+         ColumnVector longs = longStringCV.asLongs();
+         ColumnVector expectedInts = ColumnVector.fromBoxedInts(0, 0, 0, 0, 0, 0, Integer.MAX_VALUE,
+             Integer.MIN_VALUE, Integer.MIN_VALUE, Integer.MAX_VALUE, 0, 0, null, 1, 1, 0);
+         ColumnVector expectedLongs = ColumnVector.fromBoxedLongs(0l, 0l, 0l, 0l, 0l, 0l, Long.MAX_VALUE,
+             Long.MIN_VALUE, Long.MIN_VALUE, Long.MAX_VALUE, 0l, 0l, null, 1l, 1l, 0l);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false, false,
+             false, true, true, true, true, false, false, null, false, false, false)) {
+      assertColumnsAreEqual(expected, isInt);
+      assertColumnsAreEqual(expected, isLong);
+      assertColumnsAreEqual(expectedInts, ints);
+      assertColumnsAreEqual(expectedLongs, longs);
+    }
+  }
+
+  @Test
+  void testIsFixedPoint() {
+    String[] decimalStrings = {"A", "nan", "Inf", "-Inf", "Infinity", "infinity",
+        "2.1474", "112.383", "-2.14748", "NULL", "null", null, "1.2", "1.2e-4", "0.00012"};
+
+    DType dt = DType.create(DType.DTypeEnum.DECIMAL32, -3);
+    try (ColumnVector decStringCV = ColumnVector.fromStrings(decimalStrings);
+         ColumnVector isFixedPoint = decStringCV.isFixedPoint(dt);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, false, false, false, false
+             , false, true, true, true, false, false, null, true, true, true)) {
+      assertColumnsAreEqual(expected, isFixedPoint);
+    }
+  }
+
+  @Test
+  void testIsFloat() {
+    String[] floatStrings = {"A", "nan", "Inf", "-Inf", "Infinity", "infinity", "-0.0", "0.0",
+        "3.4028235E38", "3.4028236E38", "-3.4028235E38", "-3.4028236E38", "1.2e-24", "NULL", "null",
+        null, "423"};
+    try (ColumnVector floatStringCV = ColumnVector.fromStrings(floatStrings);
+         ColumnVector isFloat = floatStringCV.isFloat();
+         ColumnVector floats = floatStringCV.asFloats();
+         ColumnVector expectedFloats = ColumnVector.fromBoxedFloats(0f, Float.NaN, Float.POSITIVE_INFINITY,
+             Float.NEGATIVE_INFINITY, Float.POSITIVE_INFINITY, Float.POSITIVE_INFINITY, -0f, 0f,
+             Float.MAX_VALUE, Float.POSITIVE_INFINITY, -Float.MAX_VALUE, Float.NEGATIVE_INFINITY,
+             1.2e-24f, 0f, 0f, null, 423f);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, true, true,
+             true, true, true, true, true, true, true, true, false, false, null, true)) {
+      assertColumnsAreEqual(expected, isFloat);
+      assertColumnsAreEqual(expectedFloats, floats);
+    }
+  }
+
+  @Test
+  void testIsDouble() {
+    String[] doubleStrings = {"A", "nan", "Inf", "-Inf", "Infinity", "infinity", "-0.0", "0.0",
+        "1.7976931348623157E308",
+        // Current CUDF Code does not detect overflow for this. "1.7976931348623158E308",
+        // So we make it a little larger for this test
+        "1.7976931348623159E308",
+        "-1.7976931348623157E308",
+        // Current CUDF Code does not detect overflow for this. "-1.7976931348623158E308",
+        // So we make it a little larger for this test
+        "-1.7976931348623159E308",
+        "1.2e-234", "NULL", "null", null, "423"};
+    try (ColumnVector doubleStringCV = ColumnVector.fromStrings(doubleStrings);
+         ColumnVector isDouble = doubleStringCV.isFloat();
+         ColumnVector doubles = doubleStringCV.asDoubles();
+         ColumnVector expectedDoubles = ColumnVector.fromBoxedDoubles(0d, Double.NaN,
+             Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY,
+             -0d, 0d, Double.MAX_VALUE, Double.POSITIVE_INFINITY, -Double.MAX_VALUE, Double.NEGATIVE_INFINITY,
+             1.2e-234d, 0d, 0d, null, 423d);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(false, true, true, true, true,
+             true, true, true, true, true, true, true, true, false, false, null, true)) {
+      assertColumnsAreEqual(expected, isDouble);
+      assertColumnsAreEqual(expectedDoubles, doubles);
+    }
+  }
+
+  @Test
+  void testCreateDurationDays() {
+    Integer[] days = {100, 10, 23, 1, -1, 0, Integer.MAX_VALUE, null, Integer.MIN_VALUE};
+
+    try (ColumnVector durationDays = ColumnVector.durationDaysFromBoxedInts(days);
+         HostColumnVector hc = durationDays.copyToHost()) {
+      assertTrue(hc.hasNulls());
+      assertEquals(DType.DURATION_DAYS, hc.getType());
+      for (int i = 0; i < days.length; i++) {
+        assertEquals(days[i] == null, hc.isNull(i));
+        if (!hc.isNull(i)) {
+          assertEquals(days[i], hc.getInt(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testCreateDurationSeconds() {
+    Long[] secs = {10230L, 10L, 203L, 1L, -1L, 0L, Long.MAX_VALUE, null, Long.MIN_VALUE};
+
+    try (ColumnVector durationSeconds = ColumnVector.durationSecondsFromBoxedLongs(secs);
+         HostColumnVector hc = durationSeconds.copyToHost()) {
+      assertTrue(hc.hasNulls());
+      assertEquals(DType.DURATION_SECONDS, hc.getType());
+      for (int i = 0 ; i < secs.length ; i++) {
+        assertEquals(secs[i] == null, hc.isNull(i));
+        if (!hc.isNull(i)) {
+          assertEquals(secs[i], hc.getLong(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testCreateDurationMilliseconds() {
+    Long[] ms = {12342340230L, 12112340L, 2230233L, 1L, -1L, 0L, Long.MAX_VALUE, null,
+        Long.MIN_VALUE};
+
+    try (ColumnVector durationMs = ColumnVector.durationMilliSecondsFromBoxedLongs(ms);
+         HostColumnVector hc = durationMs.copyToHost()) {
+      assertTrue(hc.hasNulls());
+      assertEquals(DType.DURATION_MILLISECONDS, hc.getType());
+      for (int i = 0 ; i < ms.length ; i++) {
+        assertEquals(ms[i] == null, hc.isNull(i));
+        if (!hc.isNull(i)) {
+          assertEquals(ms[i], hc.getLong(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testCreateDurationMicroseconds() {
+    Long[] us = {1234234230L, 132350L, 289877803L, 1L, -1L, 0L, Long.MAX_VALUE, null,
+        Long.MIN_VALUE};
+
+    try (ColumnVector durationUs = ColumnVector.durationMicroSecondsFromBoxedLongs(us);
+         HostColumnVector hc = durationUs.copyToHost()) {
+      assertTrue(hc.hasNulls());
+      assertEquals(DType.DURATION_MICROSECONDS, hc.getType());
+      for (int i = 0 ; i < us.length ; i++) {
+        assertEquals(us[i] == null, hc.isNull(i));
+        if (!hc.isNull(i)) {
+          assertEquals(us[i], hc.getLong(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testCreateDurationNanoseconds() {
+    Long[] ns = {1234234230L, 198832350L, 289877803L, 1L, -1L, 0L, Long.MAX_VALUE, null,
+        Long.MIN_VALUE};
+
+    try (ColumnVector durationNs = ColumnVector.durationNanoSecondsFromBoxedLongs(ns);
+         HostColumnVector hc = durationNs.copyToHost()) {
+      assertTrue(hc.hasNulls());
+      assertEquals(DType.DURATION_NANOSECONDS, hc.getType());
+      for (int i = 0 ; i < ns.length ; i++) {
+        assertEquals(ns[i] == null, hc.isNull(i));
+        if (!hc.isNull(i)) {
+          assertEquals(ns[i], hc.getLong(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testListCv() {
+    List<Integer> list1 = Arrays.asList(0, 1, 2, 3);
+    List<Integer> list2 = Arrays.asList(6, 2, 4, 5);
+    List<Integer> list3 = Arrays.asList(0, 7, 3, 4, 2);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Integer> ret1 = hcv.getList(0);
+      List<Integer> ret2 = hcv.getList(1);
+      List<Integer> ret3 = hcv.getList(2);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+      assertEquals(list3, ret3, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvEmpty() {
+    List<Integer> list1 = Arrays.asList(0, 1, 2, 3);
+    List<Integer> list2 = Arrays.asList(6, 2, 4, 5);
+    List<Integer> list3 = new ArrayList<>();
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Integer> ret1 = hcv.getList(0);
+      List<Integer> ret2 = hcv.getList(1);
+      List<Integer> ret3 = hcv.getList(2);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+      assertEquals(list3, ret3, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvStrings() {
+    List<String> list1 = Arrays.asList("0", "1", "2", "3");
+    List<String> list2 = Arrays.asList("4", null, "6", null);
+    List<String> list3 = null;
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.STRING)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<String> ret1 = hcv.getList(0);
+      List<String> ret2 = hcv.getList(1);
+      List<String> ret3 = hcv.getList(2);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+      assertEquals(list3, ret3, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvDoubles() {
+    List<Double> list1 = Arrays.asList(0.1, 1.2, 2.3, 3.4);
+    List<Double> list2 = Arrays.asList(6.7, 7.8, 8.9, 5.6);
+    List<Double> list3 = Arrays.asList(0.1, 7.8, 3.4, 4.5, 2.3);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.FLOAT64)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Double> ret1 = hcv.getList(0);
+      List<Double> ret2 = hcv.getList(1);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvBytes() {
+    List<Byte> list1 = Arrays.asList((byte)1, (byte)3, (byte)5, (byte)7);
+    List<Byte> list2 = Arrays.asList((byte)0, (byte)2, (byte)4, (byte)6);
+    List<Byte> list3 = Arrays.asList((byte)1, (byte)4, (byte)9, (byte)0);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT8)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Byte> ret1 = hcv.getList(0);
+      List<Byte> ret2 = hcv.getList(1);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvShorts() {
+    List<Short> list1 = Arrays.asList((short)1, (short)3, (short)5, (short)7);
+    List<Short> list2 = Arrays.asList((short)0, (short)2, (short)4, (short)6);
+    List<Short> list3 = Arrays.asList((short)1, (short)4, (short)9, (short)0);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT16)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Short> ret1 = hcv.getList(0);
+      List<Short> ret2 = hcv.getList(1);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvFloats() {
+    List<Float> list1 = Arrays.asList(0.1F, 1.2F, 2.3F, 3.4F);
+    List<Float> list2 = Arrays.asList(6.7F, 7.8F, 8.9F, 5.6F);
+    List<Float> list3 = Arrays.asList(0.1F, 7.8F, 3.4F, 4.5F, 2.3F);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.FLOAT32)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Double> ret1 = hcv.getList(0);
+      List<Double> ret2 = hcv.getList(1);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvLongs() {
+    List<Long> list1 = Arrays.asList(10L, 20L, 30L, 40L);
+    List<Long> list2 = Arrays.asList(6L, 7L, 8L, 9L);
+    List<Long> list3 = Arrays.asList(1L, 100L, 200L, 300L, 400L);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT64)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Long> ret1 = hcv.getList(0);
+      List<Long> ret2 = hcv.getList(1);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListCvBools() {
+    List<Boolean> list1 = Arrays.asList(true, false, false, true);
+    List<Boolean> list2 = Arrays.asList(false, true, false, false);
+    List<Boolean> list3 = Arrays.asList(true, true, true, true);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.BOOL8)), list1, list2, list3);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<Boolean> ret1 = hcv.getList(0);
+      List<Boolean> ret2 = hcv.getList(1);
+      assertEquals(list1, ret1, "Lists don't match");
+      assertEquals(list2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListOfListsCv() {
+    List<Integer> list1 = Arrays.asList(1, 2, 3);
+    List<Integer> list2 = Arrays.asList(4, 5, 6);
+    List<Integer> list3 = Arrays.asList(10, 20, 30);
+    List<Integer> list4 = Arrays.asList(40, 50, 60);
+    List<List<Integer>> mainList1 = new ArrayList<>();
+    mainList1.add(list1);
+    mainList1.add(list2);
+    List<List<Integer>> mainList2 = new ArrayList<>();
+    mainList2.add(list3);
+    mainList2.add(list4);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32))),
+        mainList1, mainList2);
+    HostColumnVector hcv = res.copyToHost()) {
+      List<List<Integer>> ret1 = hcv.getList(0);
+      List<List<Integer>> ret2 = hcv.getList(1);
+      assertEquals(mainList1, ret1, "Lists don't match");
+      assertEquals(mainList2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListOfListsCvStrings() {
+    List<String> list1 = Arrays.asList("1", "23", "10");
+    List<String> list2 = Arrays.asList("13", "14", "17");
+    List<String> list3 = Arrays.asList("24", "25", "27");
+    List<String> list4 = Arrays.asList("29", "88", "19");
+    List<List<String>> mainList1 = new ArrayList<>();
+    mainList1.add(list1);
+    mainList1.add(list2);
+    List<List<String>> mainList2 = new ArrayList<>();
+    mainList2.add(list3);
+    mainList2.add(list4);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.STRING))), mainList1, mainList2);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<List<String>> ret1 = hcv.getList(0);
+      List<List<String>> ret2 = hcv.getList(1);
+      assertEquals(mainList1, ret1, "Lists don't match");
+      assertEquals(mainList2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListOfListsCvDoubles() {
+    List<Double> list1 = Arrays.asList(1.1, 2.2, 3.3);
+    List<Double> list2 = Arrays.asList(4.4, 5.5, 6.6);
+    List<Double> list3 = Arrays.asList(10.1, 20.2, 30.3);
+    List<List<Double>> mainList1 = new ArrayList<>();
+    mainList1.add(list1);
+    mainList1.add(list2);
+    List<List<Double>> mainList2 = new ArrayList<>();
+    mainList2.add(list3);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.FLOAT64))), mainList1, mainList2);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<List<Double>> ret1 = hcv.getList(0);
+      List<List<Double>> ret2 = hcv.getList(1);
+      assertEquals(mainList1, ret1, "Lists don't match");
+      assertEquals(mainList2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testListOfListsCvDecimals() {
+    List<BigDecimal> list1 = Arrays.asList(BigDecimal.valueOf(1.1), BigDecimal.valueOf(2.2), BigDecimal.valueOf(3.3));
+    List<BigDecimal> list2 = Arrays.asList(BigDecimal.valueOf(4.4), BigDecimal.valueOf(5.5), BigDecimal.valueOf(6.6));
+    List<BigDecimal> list3 = Arrays.asList(BigDecimal.valueOf(10.1), BigDecimal.valueOf(20.2), BigDecimal.valueOf(30.3));
+    List<List<BigDecimal>> mainList1 = new ArrayList<>();
+    mainList1.add(list1);
+    mainList1.add(list2);
+    List<List<BigDecimal>> mainList2 = new ArrayList<>();
+    mainList2.add(list3);
+
+    HostColumnVector.BasicType basicType = new HostColumnVector.BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, -1));
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.ListType(true, basicType)), mainList1, mainList2);
+        HostColumnVector hcv = res.copyToHost()) {
+      List<List<BigDecimal>> ret1 = hcv.getList(0);
+      List<List<BigDecimal>> ret2 = hcv.getList(1);
+      assertEquals(mainList1, ret1, "Lists don't match");
+      assertEquals(mainList2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testConcatLists() {
+    List<Integer> list1 = Arrays.asList(0, 1, 2, 3);
+    List<Integer> list2 = Arrays.asList(6, 2, 4, 5);
+    List<Integer> list3 = Arrays.asList(0, 7, 3, 4, 2);
+    List<Integer> list4 = Arrays.asList(10, 11, 12, 13);
+    List<Integer> list5 = Arrays.asList(16, 12, 14, 15);
+    List<Integer> list6 = Arrays.asList(1, 10, 20, 30, 40);
+
+    try(ColumnVector res1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32)), list1, list2, list3);
+    ColumnVector res2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32)), list4, list5, list6);
+    ColumnVector v = ColumnVector.concatenate(res1, res2);
+    ColumnVector expected =  ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32)), list1, list2, list3, list4, list5, list6)) {
+      assertEquals(expected.getRowCount(), 6L, "Expected column row count is incorrect");
+      assertColumnsAreEqual(expected, v);
+    }
+  }
+
+
+  @Test
+  void testConcatListsStrings() {
+    List<String> list = Arrays.asList("0", "1", "2", "3");
+    List<String> list2 = Arrays.asList("4", null, "6", null);
+    List<String> list3 = null;
+    try (ColumnVector res1 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.STRING)), list, list3);
+         ColumnVector res2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+             new HostColumnVector.BasicType(true, DType.STRING)), list2);
+         ColumnVector v = ColumnVector.concatenate(res1, res2);
+         ColumnVector expected = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+             new HostColumnVector.BasicType(true, DType.STRING)) , list, list3, list2)) {
+      assert res1.getNullCount() == 1: "Null count is incorrect on input column";
+      assert res2.getNullCount() == 0 : "Null count is incorrect on input column";
+      try(ColumnView cView1 = res1.getChildColumnView(0);
+          ColumnView cView2 = res2.getChildColumnView(0)) {
+        assert cView1.getNullCount() == 0 : "Null count is incorrect on input column";
+        assert cView2.getNullCount() == 2 : "Null count is incorrect on input column";
+      }
+      assertColumnsAreEqual(expected, v);
+    }
+  }
+
+  @Test
+  void testNullsInLists() {
+    List<String> val1 = Arrays.asList("Hello", "there");
+    List<String> val2 = Arrays.asList("these");
+    List<String> val3 = null;
+    List<String> val4 = Arrays.asList();
+    List<String> val5 = Arrays.asList("ARe", "some");
+    List<String> val6 = Arrays.asList("test", "strings");
+    try(ColumnVector expected = ColumnVector.fromLists(
+        new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.STRING)),
+        val1, val2, val3, val4, val5, val6);
+        HostColumnVector hostColumnVector = expected.copyToHost()) {
+      List<String> ret1 = hostColumnVector.getList(0);
+      List<String> ret2 = hostColumnVector.getList(1);
+      List<String> ret3 = hostColumnVector.getList(2);
+      List<String> ret4 = hostColumnVector.getList(3);
+      List<String> ret5 = hostColumnVector.getList(4);
+      List<String> ret6 = hostColumnVector.getList(5);
+      assertEquals(val1, ret1, "Lists don't match");
+      assertEquals(val2, ret2, "Lists don't match");
+      assertEquals(val3, ret3, "Lists don't match");
+      //TODO this is not clear semantically to me right now
+      assertEquals(val4, ret4, "Lists should be empty");
+      assertEquals(val5, ret5, "Lists don't match");
+      assertEquals(val6, ret6, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testHcvOfInts() {
+    List<Integer> val1 = Arrays.asList(1, 22);
+    List<Integer> val2 = Arrays.asList(333);
+    List<Integer> val3 = null;
+    List<Integer> val4 = Arrays.asList();
+    List<Integer> val5 = Arrays.asList(4444, 55555);
+    List<Integer> val6 = Arrays.asList(666666, 7777777);
+    try(ColumnVector expected = ColumnVector.fromLists(
+        new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.INT32)),
+        val1, val2, val3, val4, val5, val6);
+        HostColumnVector hostColumnVector = expected.copyToHost()) {
+      List<String> ret1 = hostColumnVector.getList(0);
+      List<String> ret2 = hostColumnVector.getList(1);
+      List<String> ret3 = hostColumnVector.getList(2);
+      List<String> ret4 = hostColumnVector.getList(3);
+      List<String> ret5 = hostColumnVector.getList(4);
+      List<String> ret6 = hostColumnVector.getList(5);
+      assertEquals(val1, ret1, "Lists don't match");
+      assertEquals(val2, ret2, "Lists don't match");
+      assertEquals(val3, ret3, "Lists don't match");
+      assertEquals(val4, ret4, "Lists don't match");
+      assertEquals(val5, ret5, "Lists don't match");
+      assertEquals(val6, ret6, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testHcvOfDecimals() {
+    List<BigDecimal>[] data = new List[6];
+    data[0] = Arrays.asList(BigDecimal.ONE, BigDecimal.TEN);
+    data[1] = Arrays.asList(BigDecimal.ZERO);
+    data[2] = null;
+    data[3] = Arrays.asList();
+    data[4] = Arrays.asList(BigDecimal.valueOf(123), BigDecimal.valueOf(1, -2));
+    data[5] = Arrays.asList(BigDecimal.valueOf(100, -3), BigDecimal.valueOf(2, -4));
+    try(ColumnVector expected = ColumnVector.fromLists(
+        new HostColumnVector.ListType(true,
+            new HostColumnVector.BasicType(true, DType.create(DType.DTypeEnum.DECIMAL32, 0))), data);
+        HostColumnVector hcv = expected.copyToHost()) {
+      for (int i = 0; i < data.length; i++) {
+        if (data[i] == null) {
+          assertNull(hcv.getList(i));
+          continue;
+        }
+        List<BigDecimal> exp = data[i].stream()
+            .map((dec -> (dec == null) ? null : dec.setScale(0, RoundingMode.UNNECESSARY)))
+            .collect(Collectors.toList());
+        assertEquals(exp, hcv.getList(i));
+      }
+    }
+  }
+
+  @Test
+  void testConcatListsOfLists() {
+    List<Integer> list1 = Arrays.asList(1, 2, 3);
+    List<Integer> list2 = Arrays.asList(4, 5, 6);
+    List<Integer> list3 = Arrays.asList(10, 20, 30);
+    List<Integer> list4 = Arrays.asList(40, 50, 60);
+    List<List<Integer>> mainList = new ArrayList<>();
+    mainList.add(list1);
+    mainList.add(list2);
+    List<List<Integer>> mainList2 = new ArrayList<>();
+    mainList2.add(list3);
+    mainList2.add(list4);
+    try (ColumnVector res1 =  ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32))), mainList);
+         ColumnVector res2 = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+             new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32))), mainList2);
+         ColumnVector v = ColumnVector.concatenate(res1, res2);
+         ColumnVector expected = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+             new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32))), mainList, mainList2)) {
+      assertColumnsAreEqual(expected, v);
+    }
+  }
+
+  @Test
+  void testContiguousSplitConstructor() {
+    try (Table tmp = new Table.TestBuilder().column(1, 2).column(3, 4).build();
+         ContiguousTable ct = tmp.contiguousSplit()[0]) {
+      // table should not be referencing the device buffer yet
+      assertEquals(1, ct.getBuffer().getRefCount());
+
+      // get the table to force it to be instantiated
+      Table ignored = ct.getTable();
+
+      // one reference for the device buffer itself, two more for the column using it
+      assertEquals(3, ct.getBuffer().getRefCount());
+    }
+  }
+
+  @Test
+  void testHcvForStruct() {
+    List<HostColumnVector.DataType> children =
+        Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+            new HostColumnVector.BasicType(true, DType.INT64));
+    HostColumnVector.StructType type = new HostColumnVector.StructType(true, children);
+    List data1 = Arrays.asList(10, 20L);
+    List data2 = Arrays.asList(50, 60L);
+    List data3 = Arrays.asList(null, 80L);
+    List data4 = null;
+    HostColumnVector.StructData structData1 = new HostColumnVector.StructData(data1);
+    HostColumnVector.StructData structData2 = new HostColumnVector.StructData(data2);
+    HostColumnVector.StructData structData3 = new HostColumnVector.StructData(data3);
+    HostColumnVector.StructData structData4 = new HostColumnVector.StructData(data4);
+    try (HostColumnVector hcv = HostColumnVector.fromStructs(type, Arrays.asList(structData1, structData2, structData3, structData4));
+         ColumnVector columnVector = hcv.copyToDevice();
+         HostColumnVector hcv1 = columnVector.copyToHost();
+         ColumnVector expected = hcv1.copyToDevice()) {
+      assertEquals(expected.getRowCount(), 4L, "Expected column row count is incorrect");
+      HostColumnVector.StructData retData1 = hcv1.getStruct(0);
+      HostColumnVector.StructData retData2 = hcv1.getStruct(1);
+      HostColumnVector.StructData retData3 = hcv1.getStruct(2);
+      HostColumnVector.StructData retData4 = hcv1.getStruct(3);
+      assertEquals(data1, retData1.dataRecord);
+      assertEquals(data2, retData2.dataRecord);
+      assertEquals(data3, retData3.dataRecord);
+      assertEquals(data4, retData4);
+      assertStructColumnsAreEqual(expected, columnVector);
+    }
+  }
+
+  @Test
+  void testStructChildValidity() {
+    List<HostColumnVector.DataType> children =
+        Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+            new HostColumnVector.BasicType(true, DType.INT64));
+    HostColumnVector.StructType type = new HostColumnVector.StructType(true, children);
+    List data1 = Arrays.asList(1, 2L);
+    List data2 = Arrays.asList(4, 5L);
+    List data3 = null;
+    List data4 = Arrays.asList(8, null);
+    HostColumnVector.StructData structData1 = new HostColumnVector.StructData(data1);
+    HostColumnVector.StructData structData2 = new HostColumnVector.StructData(data2);
+    HostColumnVector.StructData structData3 = new HostColumnVector.StructData(data3);
+    HostColumnVector.StructData structData4 = new HostColumnVector.StructData(data4);
+    try (HostColumnVector hcv = HostColumnVector.fromStructs(type, Arrays.asList(structData1, structData2, structData3, structData4));
+         ColumnVector columnVector = hcv.copyToDevice();
+         HostColumnVector hcv1 = columnVector.copyToHost();
+         ColumnVector expected = hcv1.copyToDevice()) {
+      assertFalse(hcv.isNull(0));
+      assertFalse(hcv.isNull(1));
+      assertTrue(hcv.isNull(2));
+      assertFalse(hcv.isNull(3));
+      HostColumnVectorCore intChildCol = hcv.children.get(0);
+      HostColumnVectorCore longChildCol = hcv.children.get(1);
+      assertFalse(intChildCol.isNull(0));
+      assertFalse(intChildCol.isNull(1));
+      assertTrue(intChildCol.isNull(2));
+      assertFalse(intChildCol.isNull(3));
+      assertFalse(longChildCol.isNull(0));
+      assertFalse(longChildCol.isNull(1));
+      assertTrue(longChildCol.isNull(2));
+      assertTrue(longChildCol.isNull(3));
+
+      intChildCol = hcv1.children.get(0);
+      longChildCol = hcv1.children.get(1);
+
+      assertFalse(intChildCol.isNull(0));
+      assertFalse(intChildCol.isNull(1));
+      assertTrue(intChildCol.isNull(2));
+      assertFalse(intChildCol.isNull(3));
+      assertFalse(longChildCol.isNull(0));
+      assertFalse(longChildCol.isNull(1));
+      assertTrue(longChildCol.isNull(2));
+      assertTrue(longChildCol.isNull(3));
+      assertStructColumnsAreEqual(expected, columnVector);
+    }
+  }
+
+  @Test
+  void testGetMapValueForKeys() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList(1, 2)));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList(2, 3)));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList(5, 4)));
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+        new HostColumnVector.BasicType(true, DType.INT32)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3);
+         ColumnVector lookupKey = ColumnVector.fromInts(1, 6, 5);
+         ColumnVector res = cv.getMapValue(lookupKey);
+         ColumnVector expected = ColumnVector.fromBoxedInts(2, null, 4)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testGetMapValueForInteger() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList(1, 2)));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList(1, 3)));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList(5, 4)));
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+        new HostColumnVector.BasicType(true, DType.INT32)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3);
+         Scalar lookupKey = Scalar.fromInt(1);
+         ColumnVector res = cv.getMapValue(lookupKey);
+         ColumnVector expected = ColumnVector.fromBoxedInts(2, 3, null)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testGetMapValueForStrings() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a", "b")));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a", "c")));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(new HostColumnVector.StructData(Arrays.asList("e", "d")));
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING),
+        new HostColumnVector.BasicType(true, DType.STRING)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3);
+         Scalar lookupKey = Scalar.fromString("a");
+         ColumnVector res = cv.getMapValue(lookupKey);
+         ColumnVector expected = ColumnVector.fromStrings("b", "c", null)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testGetMapValueEmptyInput() {
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING),
+        new HostColumnVector.BasicType(true, DType.STRING)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType));
+         Scalar lookupKey = Scalar.fromString("a");
+         ColumnVector res = cv.getMapValue(lookupKey);
+         ColumnVector expected = ColumnVector.fromStrings()) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testGetMapKeyExistenceForKeys() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(new HostColumnVector.StructData(1, 2));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(new HostColumnVector.StructData(1, 3));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(new HostColumnVector.StructData(5, 4));
+    List<HostColumnVector.StructData> list4 = Arrays.asList(new HostColumnVector.StructData(1, 7));
+    List<HostColumnVector.StructData> list5 = Arrays.asList(new HostColumnVector.StructData(1, null));
+    List<HostColumnVector.StructData> list6 = Arrays.asList(new HostColumnVector.StructData(null, null));
+    List<HostColumnVector.StructData> list7 = Arrays.asList(new HostColumnVector.StructData());
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+        new HostColumnVector.BasicType(true, DType.INT32)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3, list4, list5, list6, list7);
+         ColumnVector lookup = ColumnVector.fromInts(1, 5, 5, 5, 5, 5, 6);
+         ColumnVector resValidKey = cv.getMapKeyExistence(lookup);
+         ColumnVector expectedValid = ColumnVector.fromBooleans(true, false, true, false, false, false, false)) {
+      assertColumnsAreEqual(expectedValid, resValidKey);
+    }
+  }
+
+  @Test
+  void testGetMapKeyExistenceForInteger() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(new HostColumnVector.StructData(1, 2));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(new HostColumnVector.StructData(1, 3));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(new HostColumnVector.StructData(5, 4));
+    List<HostColumnVector.StructData> list4 = Arrays.asList(new HostColumnVector.StructData(1, 7));
+    List<HostColumnVector.StructData> list5 = Arrays.asList(new HostColumnVector.StructData(1, null));
+    List<HostColumnVector.StructData> list6 = Arrays.asList(new HostColumnVector.StructData(null, null));
+    List<HostColumnVector.StructData> list7 = Arrays.asList(new HostColumnVector.StructData());
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+            new HostColumnVector.BasicType(true, DType.INT32)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3, list4, list5, list6, list7);
+         Scalar lookup1 = Scalar.fromInt(1);
+         ColumnVector resValidKey = cv.getMapKeyExistence(lookup1);
+         ColumnVector expectedValid = ColumnVector.fromBoxedBooleans(true, true, false, true, true, false, false);
+         ColumnVector expectedNull = ColumnVector.fromBoxedBooleans(false, false, false, false, false, false, false);
+         Scalar lookupNull = Scalar.fromNull(DType.INT32);
+         ColumnVector resNullKey = cv.getMapKeyExistence(lookupNull)) {
+      assertColumnsAreEqual(expectedValid, resValidKey);
+      assertColumnsAreEqual(expectedNull, resNullKey);
+    }
+
+    AssertionError e = assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3, list4, list5, list6, list7);
+           Scalar key = null;
+           ColumnVector resNullKey = cv.getMapKeyExistence(key)) {
+      }
+    });
+    assertTrue(e.getMessage().contains("Lookup key may not be null"));
+  }
+
+  @Test
+  void testGetMapKeyExistenceForStrings() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(new HostColumnVector.StructData("a", "b"));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(new HostColumnVector.StructData("a", "c"));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(new HostColumnVector.StructData("e", "d"));
+    List<HostColumnVector.StructData> list4 = Arrays.asList(new HostColumnVector.StructData("a", "g"));
+    List<HostColumnVector.StructData> list5 = Arrays.asList(new HostColumnVector.StructData("a", null));
+    List<HostColumnVector.StructData> list6 = Arrays.asList(new HostColumnVector.StructData(null, null));
+    List<HostColumnVector.StructData> list7 = Arrays.asList(new HostColumnVector.StructData());
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING),
+            new HostColumnVector.BasicType(true, DType.STRING)));
+    try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3, list4, list5, list6, list7);
+         Scalar lookupA = Scalar.fromString("a");
+         ColumnVector resValidKey = cv.getMapKeyExistence(lookupA);
+         ColumnVector expectedValid = ColumnVector.fromBoxedBooleans(true, true, false, true, true, false, false);
+         ColumnVector expectedNull = ColumnVector.fromBoxedBooleans(false, false, false, false, false, false, false);
+         Scalar lookupNull = Scalar.fromNull(DType.STRING);
+         ColumnVector resNullKey = cv.getMapKeyExistence(lookupNull)) {
+      assertColumnsAreEqual(expectedValid, resValidKey);
+      assertColumnsAreEqual(expectedNull, resNullKey);
+    }
+
+    AssertionError e = assertThrows(AssertionError.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromLists(new HostColumnVector.ListType(true, structType), list1, list2, list3, list4, list5, list6, list7);
+           Scalar key = null;
+           ColumnVector resNullKey = cv.getMapKeyExistence(key)) {
+      }
+    });
+    assertTrue(e.getMessage().contains("Lookup key may not be null"));
+  }
+  @Test
+  void testListOfStructsOfStructs() {
+    List<HostColumnVector.StructData> list1 = Arrays.asList(
+        new HostColumnVector.StructData(Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a")))));
+    List<HostColumnVector.StructData> list2 = Arrays.asList(
+        new HostColumnVector.StructData(Arrays.asList(new HostColumnVector.StructData(Arrays.asList("b")))));
+    List<HostColumnVector.StructData> list3 = Arrays.asList(
+        new HostColumnVector.StructData(Arrays.asList(new HostColumnVector.StructData(Arrays.asList("c")))));
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true, Arrays.asList(new HostColumnVector.StructType(true,
+        Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING)))));
+    HostColumnVector.ListType schema = new HostColumnVector.ListType(true, structType);
+    try (ColumnVector cv = ColumnVector.fromLists(schema, list1, list2, list3);
+         HostColumnVector hostColumnVector = cv.copyToHost();
+         ColumnVector expected = hostColumnVector.copyToDevice()) {
+      assertColumnsAreEqual(expected, cv);
+    }
+  }
+
+  @Test
+  void testCopyToColumnVector() {
+    List<Integer> list1 = Arrays.asList(10, 11, 12, 13);
+    List<Integer> list2 = Arrays.asList(16, 12, 14, 15);
+    List<Integer> list3 = Arrays.asList(0, 7, 3, 4, 2);
+
+    try(ColumnVector res = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32)), list1, list2, list3);
+        ColumnView childColumnView = res.getChildColumnView(0);
+        ColumnVector copiedChildCv = childColumnView.copyToColumnVector();
+        ColumnVector expected =
+            ColumnVector.fromInts(10, 11, 12, 13, 16, 12, 14, 15, 0, 7, 3, 4, 2)) {
+      assertColumnsAreEqual(expected, copiedChildCv);
+    }
+  }
+
+  @Test
+  void testGetJSONObject() {
+    String jsonString = "{ \"store\": {\n" +
+        "    \"book\": [\n" +
+        "      { \"category\": \"reference\",\n" +
+        "        \"author\": \"Nigel Rees\",\n" +
+        "        \"title\": \"Sayings of the Century\",\n" +
+        "        \"price\": 8.95\n" +
+        "      },\n" +
+        "      { \"category\": \"fiction\",\n" +
+        "        \"author\": \"Evelyn Waugh\",\n" +
+        "        \"title\": \"Sword of Honour\",\n" +
+        "        \"price\": 12.99\n" +
+        "      },\n" +
+        "      { \"category\": \"fiction\",\n" +
+        "        \"author\": \"Herman Melville\",\n" +
+        "        \"title\": \"Moby Dick\",\n" +
+        "        \"isbn\": \"0-553-21311-3\",\n" +
+        "        \"price\": 8.99\n" +
+        "      },\n" +
+        "      { \"category\": \"fiction\",\n" +
+        "        \"author\": \"J. R. R. Tolkien\",\n" +
+        "        \"title\": \"The Lord of the Rings\",\n" +
+        "        \"isbn\": \"0-395-19395-8\",\n" +
+        "        \"price\": 22.99\n" +
+        "      }\n" +
+        "    ],\n" +
+        "    \"bicycle\": {\n" +
+        "      \"color\": \"red\",\n" +
+        "      \"price\": 19.95\n" +
+        "    }\n" +
+        "  }\n" +
+        "}";
+
+    try (ColumnVector json = ColumnVector.fromStrings(jsonString, jsonString);
+         ColumnVector expectedAuthors = ColumnVector.fromStrings("[\"Nigel Rees\",\"Evelyn " +
+             "Waugh\",\"Herman Melville\",\"J. R. R. Tolkien\"]", "[\"Nigel Rees\",\"Evelyn " +
+             "Waugh\",\"Herman Melville\",\"J. R. R. Tolkien\"]");
+         Scalar path = Scalar.fromString("$.store.book[*].author");
+         ColumnVector gotAuthors = json.getJSONObject(path)) {
+      assertColumnsAreEqual(expectedAuthors, gotAuthors);
+    }
+  }
+
+  @Test
+  void testMakeStructEmpty() {
+    final int numRows = 10;
+    try (ColumnVector expected = ColumnVector.emptyStructs(new StructType(false, new ArrayList<>()), numRows);
+         ColumnVector created = ColumnVector.makeStruct(numRows)) {
+      assertColumnsAreEqual(expected, created);
+    }
+  }
+
+  @Test
+  void testMakeStruct() {
+    try (ColumnVector expected = ColumnVector.fromStructs(new StructType(false,
+            Arrays.asList(
+                new BasicType(false, DType.INT32),
+                new BasicType(false, DType.INT32),
+                new BasicType(false, DType.INT32))),
+        new HostColumnVector.StructData(1, 2, 3),
+        new HostColumnVector.StructData(4, 5, 6));
+         ColumnVector child1 = ColumnVector.fromInts(1, 4);
+         ColumnVector child2 = ColumnVector.fromInts(2, 5);
+         ColumnVector child3 = ColumnVector.fromInts(3, 6);
+         ColumnVector created = ColumnVector.makeStruct(child1, child2, child3)) {
+      assertColumnsAreEqual(expected, created);
+    }
+  }
+
+  @Test
+  void testMakeListEmpty() {
+    final int numRows = 4;
+    List<List<String>> emptyListOfList = new ArrayList<>();
+    emptyListOfList.add(Arrays.asList());
+    try (
+        ColumnVector expectedList =
+             ColumnVector.fromLists(
+                 new ListType(false, new BasicType(false, DType.STRING)),
+                 Arrays.asList(),
+                 Arrays.asList(),
+                 Arrays.asList(),
+                 Arrays.asList());
+         ColumnVector expectedListOfList = ColumnVector.fromLists(new HostColumnVector.ListType(false,
+                 new HostColumnVector.ListType(false,
+                     new HostColumnVector.BasicType(false, DType.STRING))),
+             emptyListOfList, emptyListOfList, emptyListOfList, emptyListOfList);
+
+         ColumnVector createdList = ColumnVector.makeList(numRows, DType.STRING);
+         ColumnVector createdListOfList = ColumnVector.makeList(createdList)) {
+      assertColumnsAreEqual(expectedList, createdList);
+      assertColumnsAreEqual(expectedListOfList, createdListOfList);
+    }
+  }
+
+  @Test
+  void testMakeList() {
+    List<Integer> list1 = Arrays.asList(1, 3);
+    List<Integer> list2 = Arrays.asList(2, 4);
+    List<Integer> list3 = Arrays.asList(5, 7, 9);
+    List<Integer> list4 = Arrays.asList(6, 8, 10);
+    List<List<Integer>> mainList1 = new ArrayList<>(Arrays.asList(list1, list3));
+    List<List<Integer>> mainList2 = new ArrayList<>(Arrays.asList(list2, list4));
+    try (ColumnVector expectedList1 =
+             ColumnVector.fromLists(new ListType(false,
+                 new BasicType(false, DType.INT32)), list1, list2);
+         ColumnVector expectedList2 =
+             ColumnVector.fromLists(new ListType(false,
+                 new BasicType(false, DType.INT32)), list3, list4);
+         ColumnVector expectedListOfList = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32))),
+             mainList1, mainList2);
+         ColumnVector child1 = ColumnVector.fromInts(1, 2);
+         ColumnVector child2 = ColumnVector.fromInts(3, 4);
+         ColumnVector child3 = ColumnVector.fromInts(5, 6);
+         ColumnVector child4 = ColumnVector.fromInts(7, 8);
+         ColumnVector child5 = ColumnVector.fromInts(9, 10);
+         ColumnVector createdList1 = ColumnVector.makeList(child1, child2);
+         ColumnVector createdList2 = ColumnVector.makeList(child3, child4, child5);
+         ColumnVector createdListOfList = ColumnVector.makeList(createdList1, createdList2);
+         HostColumnVector hcv = createdListOfList.copyToHost()) {
+
+      assertColumnsAreEqual(expectedList1, createdList1);
+      assertColumnsAreEqual(expectedList2, createdList2);
+      assertColumnsAreEqual(expectedListOfList, createdListOfList);
+
+      List<List<Integer>> ret1 = hcv.getList(0);
+      List<List<Integer>> ret2 = hcv.getList(1);
+      assertEquals(mainList1, ret1, "Lists don't match");
+      assertEquals(mainList2, ret2, "Lists don't match");
+    }
+  }
+
+  @Test
+  void testReplaceLeafNodeInList() {
+    try (
+        ColumnVector c1 = ColumnVector.fromInts(1, 2);
+        ColumnVector c2 = ColumnVector.fromInts(8, 3);
+        ColumnVector c3 = ColumnVector.fromInts(9, 8);
+        ColumnVector c4 = ColumnVector.fromInts(2, 6);
+        ColumnVector expected = ColumnVector.makeList(c1, c2, c3, c4);
+        ColumnVector child1 =
+            ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                RoundingMode.HALF_UP, 770.892, 961.110);
+        ColumnVector child2 =
+            ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                RoundingMode.HALF_UP, 524.982, 479.946);
+        ColumnVector child3 =
+            ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                RoundingMode.HALF_UP, 346.997, 479.946);
+        ColumnVector child4 =
+            ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                RoundingMode.HALF_UP, 87.764, 414.239);
+        ColumnVector created = ColumnVector.makeList(child1, child2, child3, child4);
+        ColumnVector newChild = ColumnVector.fromInts(1, 8, 9, 2, 2, 3, 8, 6);
+        ColumnView replacedView = created.replaceListChild(newChild)) {
+      try (ColumnVector replaced = replacedView.copyToColumnVector()) {
+        assertColumnsAreEqual(expected, replaced);
+      }
+    }
+  }
+
+  @Test
+  void testReplaceLeafNodeInListWithIllegal() {
+    Exception e = assertThrows(IllegalArgumentException.class, () -> {
+      try (ColumnVector child1 =
+               ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                   RoundingMode.HALF_UP, 770.892, 961.110);
+           ColumnVector child2 =
+               ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                   RoundingMode.HALF_UP, 524.982, 479.946);
+           ColumnVector child3 =
+               ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                   RoundingMode.HALF_UP, 346.997, 479.946);
+           ColumnVector child4 =
+               ColumnVector.decimalFromDoubles(DType.create(DType.DTypeEnum.DECIMAL64, 3),
+                   RoundingMode.HALF_UP, 87.764, 414.239);
+           ColumnVector created = ColumnVector.makeList(child1, child2, child3, child4);
+           ColumnVector newChild = ColumnVector.fromInts(0, 1, 8, 9, 2, 2, 3, 8, 6);
+           ColumnView replacedView = created.replaceListChild(newChild)) {
+      }
+    });
+    assertTrue(e.getMessage().contains("Child row count doesn't match the old child"));
+  }
+
+  @Test
+  void testReplaceColumnInStruct() {
+    try (ColumnVector expected = ColumnVector.fromStructs(new StructType(false,
+            Arrays.asList(
+                new BasicType(false, DType.INT32),
+                new BasicType(false, DType.INT32),
+                new BasicType(false, DType.INT32))),
+        new HostColumnVector.StructData(1, 5, 3),
+        new HostColumnVector.StructData(4, 9, 6));
+         ColumnVector child1 = ColumnVector.fromInts(1, 4);
+         ColumnVector child2 = ColumnVector.fromInts(2, 5);
+         ColumnVector child3 = ColumnVector.fromInts(3, 6);
+         ColumnVector created = ColumnVector.makeStruct(child1, child2, child3);
+         ColumnVector replaceWith = ColumnVector.fromInts(5, 9);
+         ColumnView replacedView = created.replaceChildrenWithViews(new int[]{1},
+             new ColumnVector[]{replaceWith})) {
+      try (ColumnVector replaced = replacedView.copyToColumnVector()) {
+        assertColumnsAreEqual(expected, replaced);
+      }
+    }
+  }
+
+  @Test
+  void testReplaceIllegalIndexColumnInStruct() {
+    Exception e = assertThrows(IllegalArgumentException.class, () -> {
+      try (ColumnVector child1 = ColumnVector.fromInts(1, 4);
+           ColumnVector child2 = ColumnVector.fromInts(2, 5);
+           ColumnVector child3 = ColumnVector.fromInts(3, 6);
+           ColumnVector created = ColumnVector.makeStruct(child1, child2, child3);
+           ColumnVector replaceWith = ColumnVector.fromInts(5, 9);
+           ColumnView replacedView = created.replaceChildrenWithViews(new int[]{5},
+               new ColumnVector[]{replaceWith})) {
+      }
+    });
+    assertTrue(e.getMessage().contains("One or more invalid child indices passed to be replaced"));
+  }
+
+  @Test
+  void testReplaceSameIndexColumnInStruct() {
+    Exception e = assertThrows(IllegalArgumentException.class, () -> {
+      try (ColumnVector child1 = ColumnVector.fromInts(1, 4);
+           ColumnVector child2 = ColumnVector.fromInts(2, 5);
+           ColumnVector child3 = ColumnVector.fromInts(3, 6);
+           ColumnVector created = ColumnVector.makeStruct(child1, child2, child3);
+           ColumnVector replaceWith = ColumnVector.fromInts(5, 9);
+           ColumnView replacedView = created.replaceChildrenWithViews(new int[]{1, 1},
+               new ColumnVector[]{replaceWith, replaceWith})) {
+      }
+    });
+    assertTrue(e.getMessage().contains("Duplicate mapping found for replacing child index"));
+  }
+
+  @Test
+  void testCopyWithBooleanColumnAsValidity() {
+    final Boolean T = true;
+    final Boolean F = false;
+    final Integer X = null;
+
+    // Straight-line: Invalidate every other row.
+    try (ColumnVector exemplar = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+         ColumnVector validity = ColumnVector.fromBoxedBooleans(F, T, F, T, F, T, F, T, F, T);
+         ColumnVector expected = ColumnVector.fromBoxedInts(X, 2, X, 4, X, 6, X, 8, X, 10);
+         ColumnVector result = exemplar.copyWithBooleanColumnAsValidity(validity)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    // Straight-line: Invalidate all Rows.
+    try (ColumnVector exemplar = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+         ColumnVector validity = ColumnVector.fromBoxedBooleans(F, F, F, F, F, F, F, F, F, F);
+         ColumnVector expected = ColumnVector.fromBoxedInts(X, X, X, X, X, X, X, X, X, X);
+         ColumnVector result = exemplar.copyWithBooleanColumnAsValidity(validity)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    // Nulls in the validity column are treated as invalid.
+    try (ColumnVector exemplar = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+         ColumnVector validity = ColumnVector.fromBoxedBooleans(F, T, F, T, F, T, F, null, F, null);
+         ColumnVector expected = ColumnVector.fromBoxedInts(X, 2, X, 4, X, 6, X, X, X, X);
+         ColumnVector result = exemplar.copyWithBooleanColumnAsValidity(validity)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    // Negative case: Mismatch in row count.
+    Exception x = assertThrows(CudfException.class, () ->  {
+      try (ColumnVector exemplar = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+         ColumnVector validity = ColumnVector.fromBoxedBooleans(F, T, F, T);
+         ColumnVector result = exemplar.copyWithBooleanColumnAsValidity(validity)) {
+      }
+    });
+    assertTrue(x.getMessage().contains("Exemplar and validity columns must have the same size"));
+  }
+
+  @Test
+  void testSegmentedGather() {
+    HostColumnVector.DataType dt = new ListType(true, new BasicType(true, DType.STRING));
+    try (ColumnVector source = ColumnVector.fromLists(dt,
+        Lists.newArrayList("a", "b", null, "c"),
+        null,
+        Lists.newArrayList(),
+        Lists.newArrayList(null, "A", "B", "C", "D"));
+         ColumnVector gatherMap = ColumnVector.fromLists(
+             new ListType(false, new BasicType(false, DType.INT32)),
+             Lists.newArrayList(-3, 0, 2, 3, 4),
+             Lists.newArrayList(),
+             Lists.newArrayList(1),
+             Lists.newArrayList(1, -4, 5, -1, -6));
+         ColumnVector actual = source.segmentedGather(gatherMap);
+         ColumnVector expected = ColumnVector.fromLists(dt,
+             Lists.newArrayList("b", "a", null, "c", null),
+             null,
+             Lists.newArrayList((String) null),
+             Lists.newArrayList("A", "A", null, "D", null))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testGenerateListOffsets() {
+    try (ColumnVector index = ColumnVector.fromInts(1, 3, 3, 0, 2, 0, 0, 5, 10, 25);
+         ColumnVector actual = index.generateListOffsets();
+         ColumnVector expected = ColumnVector.fromInts(0, 1, 4, 7, 7, 9, 9, 9, 14, 24, 49)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+
+    try (ColumnVector index = ColumnVector.fromInts(0, 0, 1, 0, 0);
+         ColumnVector actual = index.generateListOffsets();
+         ColumnVector expected = ColumnVector.fromInts(0, 0, 0, 1, 1, 1)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testApplyBooleanMaskFromListOfInt() {
+    try (
+        ColumnVector elementCv = ColumnVector.fromBoxedInts(
+            11, 12, // list1
+            21, 22, 23, // list2
+            null, 32, 33, null, 35, // list3
+            null, 42, 43, null, 45 // list 4
+            // list5 (empty)
+        );
+        ColumnVector offsetsCv = ColumnVector.fromInts(0, 2, 5, 10, 15, 15);
+        ColumnVector listOfIntCv = elementCv.makeListFromOffsets(5, offsetsCv);
+
+        ColumnVector boolCv = ColumnVector.fromBoxedBooleans(
+            true, false, // list1
+            true, false, true, // list2
+            true, false, true, false, true, // list3
+            true, false, true, false, true // list 4
+            // list5 (empty)
+        );
+        ColumnVector listOfBoolCv = boolCv.makeListFromOffsets(5, offsetsCv);
+
+        // apply boolean mask
+        ColumnVector actualCv = listOfIntCv.applyBooleanMask(listOfBoolCv);
+
+        ColumnVector expectedElementCv = ColumnVector.fromBoxedInts(
+            11, // list1
+            21, 23, // list2
+            null, 33, 35, // list3
+            null, 43, 45 // list 4
+            // list5 (empty)
+        );
+        ColumnVector expectedOffsetsCv = ColumnVector.fromInts(0, 1, 3, 6, 9, 9);
+        ColumnVector expectedCv = expectedElementCv.makeListFromOffsets(5, expectedOffsetsCv)
+    ) {
+      assertColumnsAreEqual(expectedCv, actualCv);
+    }
+  }
+
+  @Test
+  void testApplyBooleanMaskFromListOfStructure() {
+    try (
+        ColumnVector keyCv = ColumnVector.fromBoxedInts(
+            11, 12, // list1
+            21, 22, 23, // list2
+            null, 32, 33, null, 35, // list3
+            null, 42, 43, null, 45 // list 4
+            // list5 (empty)
+        );
+        ColumnVector valCv = ColumnVector.fromBoxedInts(
+            11, 12, // list1
+            21, 22, 23, // list2
+            31, 32, 33, 34, 35, // list3
+            41, 42, 43, 44, 45 // list4
+            // list5 (empty)
+        );
+        ColumnVector structCv = ColumnVector.makeStruct(keyCv, valCv);
+        ColumnVector offsetsCv = ColumnVector.fromInts(0, 2, 5, 10, 15, 15);
+        ColumnVector listOfStructCv = structCv.makeListFromOffsets(5, offsetsCv);
+
+        ColumnVector boolCv = ColumnVector.fromBoxedBooleans(
+            true, false, // list1
+            true, false, true, // list2
+            true, false, true, false, true, // list3
+            true, false, true, false, true // list 4
+            // list5 (empty)
+        );
+        ColumnVector listOfBoolCv = boolCv.makeListFromOffsets(5, offsetsCv);
+
+        // apply boolean mask
+        ColumnVector actualCv = listOfStructCv.applyBooleanMask(listOfBoolCv);
+
+        ColumnVector expectedKeyCv = ColumnVector.fromBoxedInts(
+            11, // list1
+            21, 23, // list2
+            null, 33, 35, // list3
+            null, 43, 45 // list 4
+            // list5 (empty)
+        );
+        ColumnVector expectedValCv = ColumnVector.fromBoxedInts(
+            11, // list1
+            21, 23, // list2
+            31, 33, 35, // list3
+            41, 43, 45 // list4
+            // list5 (empty)
+        );
+        ColumnVector expectedStructCv = ColumnVector.makeStruct(expectedKeyCv, expectedValCv);
+        ColumnVector expectedOffsetsCv = ColumnVector.fromInts(0, 1, 3, 6, 9, 9);
+        ColumnVector expectedCv = expectedStructCv.makeListFromOffsets(5, expectedOffsetsCv)
+    ) {
+      assertColumnsAreEqual(expectedCv, actualCv);
+    }
+  }
+
+  @Test
+  void testColumnViewWithNonEmptyNullsIsCleared() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, null);
+    List<Integer> list2 = Arrays.asList(7, 8, 9);
+    List<Integer> list3 = null;
+    final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+    try (ColumnVector input = ColumnVectorTest.makeListsColumn(DType.INT32, list0, list1, list2, list3);
+         BaseDeviceMemoryBuffer baseValidityBuffer = input.getDeviceBufferFor(BufferType.VALIDITY);
+         BaseDeviceMemoryBuffer baseOffsetBuffer = input.getDeviceBufferFor(BufferType.OFFSET);
+         HostMemoryBuffer newValidity = hostMemoryAllocator.allocate(BitVectorHelper.getValidityAllocationSizeInBytes(4))) {
+
+      newValidity.copyFromDeviceBuffer(baseValidityBuffer);
+      // we are setting list1 with 3 elements to null. This will result in a non-empty null in the
+      // ColumnView at index 1
+      BitVectorHelper.setNullAt(newValidity, 1);
+      // validityBuffer will be closed by offHeapState later
+      DeviceMemoryBuffer validityBuffer = DeviceMemoryBuffer.allocate(BitVectorHelper.getValidityAllocationSizeInBytes(4));
+      try {
+        // offsetBuffer will be closed by offHeapState later
+        DeviceMemoryBuffer offsetBuffer = DeviceMemoryBuffer.allocate(baseOffsetBuffer.getLength());
+        try {
+          validityBuffer.copyFromHostBuffer(newValidity);
+          offsetBuffer.copyFromMemoryBuffer(0, baseOffsetBuffer, 0,
+              baseOffsetBuffer.length, Cuda.DEFAULT_STREAM);
+
+          // The new offHeapState will have 2 nulls, one null at index 4 from the original ColumnVector
+          // the other at index 1 which is non-empty
+          ColumnVector.OffHeapState offHeapState = ColumnVector.makeOffHeap(input.type, input.rows, Optional.of(2L),
+              null, validityBuffer, offsetBuffer,
+              null, Arrays.stream(input.getChildColumnViews()).mapToLong((c) -> c.viewHandle).toArray());
+          try {
+            new ColumnView(offHeapState);
+          } catch (AssertionError ae) {
+            assert offHeapState.isClean();
+          }
+        } catch (Exception e) {
+          if (!offsetBuffer.closed) {
+            offsetBuffer.close();
+          }
+        }
+      } catch (Exception e) {
+        if (!validityBuffer.closed) {
+          validityBuffer.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testEventHandlerIsCalledForEachClose() {
+    final AtomicInteger onClosedWasCalled = new AtomicInteger(0);
+    try (ColumnVector cv = ColumnVector.fromInts(1,2,3,4)) {
+      cv.setEventHandler((col, refCount) -> {
+        assertEquals(cv, col);
+        onClosedWasCalled.incrementAndGet();
+      });
+    }
+    assertEquals(1, onClosedWasCalled.get());
+  }
+
+  @Test
+  public void testHostEventHandlerIsCalledForEachClose() {
+    final AtomicInteger onClosedWasCalled = new AtomicInteger(0);
+    try (HostColumnVector cv = HostColumnVector.fromInts(1,2,3,4)) {
+      cv.setEventHandler((col, refCount) -> {
+        assertEquals(cv, col);
+        onClosedWasCalled.incrementAndGet();
+      });
+    }
+    assertEquals(1, onClosedWasCalled.get());
+  }
+
+  @Test
+  public void testEventHandlerIsNotCalledIfNotSet() {
+    final AtomicInteger onClosedWasCalled = new AtomicInteger(0);
+    try (ColumnVector cv = ColumnVector.fromInts(1,2,3,4)) {
+      assertNull(cv.getEventHandler());
+    }
+    assertEquals(0, onClosedWasCalled.get());
+
+    try (ColumnVector cv = ColumnVector.fromInts(1,2,3,4)) {
+      cv.setEventHandler((col, refCount) -> {
+        onClosedWasCalled.incrementAndGet();
+      });
+      cv.setEventHandler(null);
+    }
+    assertEquals(0, onClosedWasCalled.get());
+  }
+
+  @Test
+  public void testHostEventHandlerIsNotCalledIfNotSet() {
+    final AtomicInteger onClosedWasCalled = new AtomicInteger(0);
+    try (HostColumnVector cv = HostColumnVector.fromInts(1,2,3,4)) {
+      assertNull(cv.getEventHandler());
+    }
+    assertEquals(0, onClosedWasCalled.get());
+
+    try (HostColumnVector cv = HostColumnVector.fromInts(1,2,3,4)) {
+      cv.setEventHandler((col, refCount) -> {
+        onClosedWasCalled.incrementAndGet();
+      });
+      cv.setEventHandler(null);
+    }
+    assertEquals(0, onClosedWasCalled.get());
+  }
+
+  /**
+   * Test that the ColumnView with unknown null-counts still returns
+   * the correct null-count when queried.
+   */
+  @Test
+  public void testColumnViewNullCount() {
+    try (ColumnVector vector = ColumnVector.fromBoxedInts(1, 2, null, 3, null, 4, null, 5, null, 6);
+         ColumnView view = new ColumnView(DType.INT32,
+                                          vector.getRowCount(),
+                                          Optional.empty(), // Unknown null count.
+                                          vector.getDeviceBufferFor(BufferType.DATA),
+                                          vector.getDeviceBufferFor(BufferType.VALIDITY),
+                                          vector.getDeviceBufferFor(BufferType.OFFSET))) {
+      assertEquals(vector.getNullCount(), view.getNullCount());
+    }
+  }
+
+  @Test
+  public void testUseAfterFree() {
+    ColumnVector vector = ColumnVector.fromBoxedInts(1, 2, 3);
+    vector.close();
+    assertThrows(NullPointerException.class, vector::getDeviceMemorySize);
+  }
+
+  @Test
+  public void testConvertIntegerToHex() {
+    try (
+      ColumnVector input = ColumnVector.fromInts(14, 2621, 50);
+      ColumnVector expected = ColumnVector.fromStrings("0E", "0A3D", "32");
+      ColumnVector actual = input.toHex()) {
+        assertColumnsAreEqual(expected, actual);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ColumnViewNonEmptyNullsTest.java b/java/src/test/java/ai/rapids/cudf/ColumnViewNonEmptyNullsTest.java
new file mode 100644
index 0000000..8d5351d
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ColumnViewNonEmptyNullsTest.java
@@ -0,0 +1,138 @@
+/*
+ *
+ *  Copyright (c) 2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.Optional;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+/**
+ * This class will house only tests that need to explicitly set non-empty nulls
+ */
+public class ColumnViewNonEmptyNullsTest extends CudfTestBase {
+
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  @Test
+  void testAndNullReconfigureNulls() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedInts(0, 100, null, null, Integer.MIN_VALUE, null);
+         ColumnVector v1 = ColumnVector.fromBoxedInts(0, 100, 1, 2, Integer.MIN_VALUE, null);
+         ColumnVector intResult = v1.mergeAndSetValidity(BinaryOp.BITWISE_AND, v0);
+         ColumnVector v2 = ColumnVector.fromStrings("0", "100", "1", "2", "MIN_VALUE", "3");
+         ColumnVector v3 = v0.mergeAndSetValidity(BinaryOp.BITWISE_AND, v1, v2);
+         ColumnVector stringResult = v2.mergeAndSetValidity(BinaryOp.BITWISE_AND, v0, v1);
+         ColumnVector stringExpected = ColumnVector.fromStrings("0", "100", null, null, "MIN_VALUE", null);
+         ColumnVector noMaskResult = v2.mergeAndSetValidity(BinaryOp.BITWISE_AND)) {
+      assertColumnsAreEqual(v0, intResult);
+      assertColumnsAreEqual(v0, v3);
+      assertColumnsAreEqual(stringExpected, stringResult);
+      assertColumnsAreEqual(v2, noMaskResult);
+    }
+  }
+
+  @Test
+  void testOrNullReconfigureNulls() {
+    try (ColumnVector v0 = ColumnVector.fromBoxedInts(0, 100, null, null, Integer.MIN_VALUE, null);
+         ColumnVector v1 = ColumnVector.fromBoxedInts(0, 100, 1, 2, Integer.MIN_VALUE, null);
+         ColumnVector v2 = ColumnVector.fromBoxedInts(0, 100, 1, 2, Integer.MIN_VALUE, Integer.MAX_VALUE);
+         ColumnVector intResultV0 = v1.mergeAndSetValidity(BinaryOp.BITWISE_OR, v0);
+         ColumnVector intResultV0V1 = v1.mergeAndSetValidity(BinaryOp.BITWISE_OR, v0, v1);
+         ColumnVector intResultMulti = v1.mergeAndSetValidity(BinaryOp.BITWISE_OR, v0, v0, v1, v1, v0, v1, v0);
+         ColumnVector intResultv0v1v2 = v2.mergeAndSetValidity(BinaryOp.BITWISE_OR, v0, v1, v2);
+         ColumnVector v3 = ColumnVector.fromStrings("0", "100", "1", "2", "MIN_VALUE", "3");
+         ColumnVector stringResult = v3.mergeAndSetValidity(BinaryOp.BITWISE_OR, v0, v1);
+         ColumnVector stringExpected = ColumnVector.fromStrings("0", "100", "1", "2", "MIN_VALUE", null);
+         ColumnVector noMaskResult = v3.mergeAndSetValidity(BinaryOp.BITWISE_OR)) {
+      assertColumnsAreEqual(v0, intResultV0);
+      assertColumnsAreEqual(v1, intResultV0V1);
+      assertColumnsAreEqual(v1, intResultMulti);
+      assertColumnsAreEqual(v2, intResultv0v1v2);
+      assertColumnsAreEqual(stringExpected, stringResult);
+      assertColumnsAreEqual(v3, noMaskResult);
+    }
+  }
+
+  /**
+   * The caller needs to make sure to close the returned ColumnView
+   */
+  private ColumnView[] getColumnViewWithNonEmptyNulls() {
+    List<Integer> list0 = Arrays.asList(1, 2, 3);
+    List<Integer> list1 = Arrays.asList(4, 5, null);
+    List<Integer> list2 = Arrays.asList(7, 8, 9);
+    List<Integer> list3 = null;
+    ColumnVector input = ColumnVectorTest.makeListsColumn(DType.INT32, list0, list1, list2, list3);
+    // Modify the validity buffer
+    BaseDeviceMemoryBuffer dmb = input.getDeviceBufferFor(BufferType.VALIDITY);
+    try (HostMemoryBuffer newValidity = hostMemoryAllocator.allocate(64)) {
+      newValidity.copyFromDeviceBuffer(dmb);
+      BitVectorHelper.setNullAt(newValidity, 1);
+      dmb.copyFromHostBuffer(newValidity);
+    }
+    try (HostColumnVector hostColumnVector = input.copyToHost()) {
+      assert (hostColumnVector.isNull(1));
+      assert (hostColumnVector.isNull(3));
+    }
+    try (ColumnVector expectedOffsetsBeforePurge = ColumnVector.fromInts(0, 3, 6, 9, 9)) {
+      ColumnView offsetsCvBeforePurge = input.getListOffsetsView();
+      assertColumnsAreEqual(expectedOffsetsBeforePurge, offsetsCvBeforePurge);
+    }
+    ColumnView colWithNonEmptyNulls = new ColumnView(input.type, input.rows, Optional.of(2L), dmb,
+        input.getDeviceBufferFor(BufferType.OFFSET), input.getChildColumnViews());
+    assertEquals(2, colWithNonEmptyNulls.nullCount);
+    return new ColumnView[]{input, colWithNonEmptyNulls};
+  }
+
+  @Test
+  void testPurgeNonEmptyNullsList() {
+    ColumnView[] values = getColumnViewWithNonEmptyNulls();
+    try (ColumnView colWithNonEmptyNulls = values[1];
+         ColumnView input = values[0];
+         // purge non-empty nulls
+         ColumnView colWithEmptyNulls = colWithNonEmptyNulls.purgeNonEmptyNulls();
+         ColumnVector expectedOffsetsAfterPurge = ColumnVector.fromInts(0, 3, 3, 6, 6);
+         ColumnView offsetsCvAfterPurge = colWithEmptyNulls.getListOffsetsView()) {
+      assertTrue(colWithNonEmptyNulls.hasNonEmptyNulls());
+      assertColumnsAreEqual(expectedOffsetsAfterPurge, offsetsCvAfterPurge);
+      assertFalse(colWithEmptyNulls.hasNonEmptyNulls());
+    }
+  }
+
+  @Test
+  void testPurgeNonEmptyNullsStruct() {
+    ColumnView[] values = getColumnViewWithNonEmptyNulls();
+    try (ColumnView listCol = values[1];
+         ColumnView input = values[0];
+         ColumnView stringsCol = ColumnVector.fromStrings("A", "col", "of", "Strings");
+         ColumnView structView = ColumnView.makeStructView(stringsCol, listCol);
+         ColumnView structWithEmptyNulls = structView.purgeNonEmptyNulls();
+         ColumnView newListChild = structWithEmptyNulls.getChildColumnView(1);
+         ColumnVector expectedOffsetsAfterPurge = ColumnVector.fromInts(0, 3, 3, 6, 6);
+         ColumnView offsetsCvAfterPurge = newListChild.getListOffsetsView()) {
+      assertColumnsAreEqual(expectedOffsetsAfterPurge, offsetsCvAfterPurge);
+      assertFalse(newListChild.hasNonEmptyNulls());
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/CuFileTest.java b/java/src/test/java/ai/rapids/cudf/CuFileTest.java
new file mode 100644
index 0000000..8945b66
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/CuFileTest.java
@@ -0,0 +1,157 @@
+/*
+ * Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.AfterEach;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+
+import java.io.File;
+import java.io.IOException;
+
+import static org.junit.jupiter.api.Assertions.*;
+import static org.junit.jupiter.api.Assumptions.assumeTrue;
+
+public class CuFileTest extends CudfTestBase {
+
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  @AfterEach
+  void tearDown() {
+    if (PinnedMemoryPool.isInitialized()) {
+      PinnedMemoryPool.shutdown();
+    }
+  }
+
+  @Test
+  public void testCopyToFile(@TempDir File tempDir) {
+    assumeTrue(CuFile.libraryLoaded());
+    File tempFile = new File(tempDir, "tempFile");
+    assertFalse(tempFile.exists());
+    verifyCopyToFile(tempFile);
+  }
+
+  @Test
+  public void testCopyToExistingFile(@TempDir File tempDir) throws IOException {
+    assumeTrue(CuFile.libraryLoaded());
+    File tempFile = new File(tempDir, "tempFile");
+    assertTrue(tempFile.createNewFile());
+    verifyCopyToFile(tempFile);
+  }
+
+  @Test
+  public void testAppendToFile(@TempDir File tempDir) {
+    assumeTrue(CuFile.libraryLoaded());
+    File tempFile = new File(tempDir, "tempFile");
+    assertFalse(tempFile.exists());
+    verifyAppendToFile(tempFile);
+  }
+
+  @Test
+  public void testAppendToExistingFile(@TempDir File tempDir) throws IOException {
+    assumeTrue(CuFile.libraryLoaded());
+    File tempFile = new File(tempDir, "tempFile");
+    assertTrue(tempFile.createNewFile());
+    verifyAppendToFile(tempFile);
+  }
+
+  private void verifyCopyToFile(File tempFile) {
+    try (HostMemoryBuffer orig = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer from = DeviceMemoryBuffer.allocate(16);
+         DeviceMemoryBuffer to = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer dest = hostMemoryAllocator.allocate(16)) {
+      orig.setLong(0, 123456789);
+      from.copyFromHostBuffer(orig);
+      CuFile.writeDeviceBufferToFile(tempFile, 0, from);
+      CuFile.readFileToDeviceBuffer(to, tempFile, 0);
+      dest.copyFromDeviceBuffer(to);
+      assertEquals(123456789, dest.getLong(0));
+    }
+  }
+
+  private void verifyAppendToFile(File tempFile) {
+    try (HostMemoryBuffer orig = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer from = DeviceMemoryBuffer.allocate(16);
+         DeviceMemoryBuffer to = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer dest = hostMemoryAllocator.allocate(16)) {
+      orig.setLong(0, 123456789);
+      from.copyFromHostBuffer(orig);
+      assertEquals(0, CuFile.appendDeviceBufferToFile(tempFile, from));
+
+      orig.setLong(0, 987654321);
+      from.copyFromHostBuffer(orig);
+      assertEquals(16, CuFile.appendDeviceBufferToFile(tempFile, from));
+
+      CuFile.readFileToDeviceBuffer(to, tempFile, 0);
+      dest.copyFromDeviceBuffer(to);
+      assertEquals(123456789, dest.getLong(0));
+
+      CuFile.readFileToDeviceBuffer(to, tempFile, 16);
+      dest.copyFromDeviceBuffer(to);
+      assertEquals(987654321, dest.getLong(0));
+    }
+  }
+
+  @Test
+  public void testRegisteringUnalignedBufferThrowsException() {
+    assumeTrue(CuFile.libraryLoaded());
+    assertThrows(IllegalArgumentException.class, () -> {
+      //noinspection EmptyTryBlock
+      try (CuFileBuffer ignored = CuFileBuffer.allocate(4095, true)) {
+      }
+    });
+  }
+
+  @Test
+  public void testReadWriteUnregisteredBuffer(@TempDir File tempDir) {
+    assumeTrue(CuFile.libraryLoaded());
+    File tempFile = new File(tempDir, "tempFile");
+    verifyReadWrite(tempFile, 16, false);
+  }
+
+  @Test
+  public void testReadWriteRegisteredBuffer(@TempDir File tempDir) {
+    assumeTrue(CuFile.libraryLoaded());
+    File tempFile = new File(tempDir, "tempFile");
+    verifyReadWrite(tempFile, 4096, true);
+  }
+
+  private void verifyReadWrite(File tempFile, int length, boolean registerBuffer) {
+    try (HostMemoryBuffer orig = hostMemoryAllocator.allocate(length);
+         CuFileBuffer from = CuFileBuffer.allocate(length, registerBuffer);
+         CuFileWriteHandle writer = new CuFileWriteHandle(tempFile.getAbsolutePath())) {
+      orig.setLong(0, 123456789);
+      from.copyFromHostBuffer(orig);
+      writer.write(from, length, 0);
+
+      orig.setLong(0, 987654321);
+      from.copyFromHostBuffer(orig);
+      assertEquals(length, writer.append(from, length));
+    }
+    try (CuFileBuffer to = CuFileBuffer.allocate(length, registerBuffer);
+         CuFileReadHandle reader = new CuFileReadHandle(tempFile.getAbsolutePath());
+         HostMemoryBuffer dest = hostMemoryAllocator.allocate(length)) {
+      reader.read(to, 0);
+      dest.copyFromDeviceBuffer(to);
+      assertEquals(123456789, dest.getLong(0));
+
+      reader.read(to, length);
+      dest.copyFromDeviceBuffer(to);
+      assertEquals(987654321, dest.getLong(0));
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/CudaFatalTest.java b/java/src/test/java/ai/rapids/cudf/CudaFatalTest.java
new file mode 100644
index 0000000..70d0925
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/CudaFatalTest.java
@@ -0,0 +1,67 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class CudaFatalTest {
+
+  @Test
+  public void testCudaFatalException() {
+    try (ColumnVector cv = ColumnVector.fromInts(1, 2, 3, 4, 5)) {
+
+      try (ColumnView badCv = ColumnView.fromDeviceBuffer(new BadDeviceBuffer(), 0, DType.INT8, 256);
+           ColumnView ret = badCv.sub(badCv);
+           HostColumnVector hcv = ret.copyToHost()) {
+      } catch (CudaException ignored) {
+      }
+
+      // CUDA API invoked by libcudf failed because of previous unrecoverable fatal error
+      assertThrows(CudaFatalException.class, () -> {
+        try (ColumnVector cv2 = cv.asLongs()) {
+        } catch (CudaFatalException ex) {
+          assertEquals(CudaException.CudaError.cudaErrorIllegalAddress, ex.getCudaError());
+          throw ex;
+        }
+      });
+    }
+
+    // CUDA API invoked by RMM failed because of previous unrecoverable fatal error
+    assertThrows(CudaFatalException.class, () -> {
+      try (ColumnVector cv = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5)) {
+      } catch (CudaFatalException ex) {
+        assertEquals(CudaException.CudaError.cudaErrorIllegalAddress, ex.getCudaError());
+        throw ex;
+      }
+    });
+  }
+
+  private static class BadDeviceBuffer extends BaseDeviceMemoryBuffer {
+    public BadDeviceBuffer() {
+      super(256L, 256L, (MemoryBufferCleaner) null);
+    }
+
+    @Override
+    public MemoryBuffer slice(long offset, long len) {
+      return null;
+    }
+  }
+
+}
diff --git a/java/src/test/java/ai/rapids/cudf/CudaTest.java b/java/src/test/java/ai/rapids/cudf/CudaTest.java
new file mode 100644
index 0000000..2edd7f3
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/CudaTest.java
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class CudaTest {
+
+  @Test
+  public void testGetCudaRuntimeInfo() {
+    // The driver version is not necessarily larger than runtime version. Drivers of previous
+    // version are also able to support runtime of later version, only if they support same
+    // kinds of computeModes.
+    assert Cuda.getDriverVersion() >= 1000;
+    assert Cuda.getRuntimeVersion() >= 1000;
+    assertEquals(Cuda.getNativeComputeMode(), Cuda.getComputeMode().nativeId);
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testCudaException() {
+    assertThrows(CudaException.class, () -> {
+          try {
+            Cuda.memset(Long.MAX_VALUE, (byte) 0, 1024);
+          } catch (CudaFatalException ignored) {
+          } catch (CudaException ex) {
+            assertEquals(CudaException.CudaError.cudaErrorInvalidValue, ex.getCudaError());
+            throw ex;
+          }
+        }
+    );
+    // non-fatal CUDA error will not fail subsequent CUDA calls
+    try (ColumnVector cv = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5)) {
+    }
+  }
+
+}
diff --git a/java/src/test/java/ai/rapids/cudf/CudfTestBase.java b/java/src/test/java/ai/rapids/cudf/CudfTestBase.java
new file mode 100644
index 0000000..a4450e2
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/CudfTestBase.java
@@ -0,0 +1,96 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.AfterAll;
+import org.junit.jupiter.api.BeforeEach;
+
+import static org.junit.jupiter.api.Assertions.fail;
+import static org.junit.jupiter.api.Assumptions.assumeTrue;
+
+public class CudfTestBase {
+  static final long RMM_POOL_SIZE_DEFAULT = 512 * 1024 * 1024;
+
+  final int rmmAllocationMode;
+  final long rmmPoolSize;
+
+  public CudfTestBase() {
+    this(RmmAllocationMode.POOL, RMM_POOL_SIZE_DEFAULT);
+  }
+
+  public CudfTestBase(int allocationMode, long poolSize) {
+    this.rmmAllocationMode = allocationMode;
+    this.rmmPoolSize = poolSize;
+  }
+
+  @BeforeEach
+  void beforeEach() {
+    assumeTrue(Cuda.isEnvCompatibleForTesting());
+    if (!Rmm.isInitialized()) {
+      Rmm.initialize(rmmAllocationMode, Rmm.logToStderr(), rmmPoolSize);
+    }
+  }
+
+  @AfterAll
+  static void afterAll() {
+    if (Rmm.isInitialized()) {
+      Rmm.shutdown();
+    }
+  }
+
+  private static boolean doublesAreEqualWithinPercentage(double expected, double actual, double percentage) {
+    // doubleToLongBits will take care of returning true when both operands have same long value
+    // including +ve infinity, -ve infinity or NaNs
+    if (Double.doubleToLongBits(expected) != Double.doubleToLongBits(actual)) {
+      if (expected != 0) {
+        return Math.abs((expected - actual) / expected) <= percentage;
+      } else {
+        return Math.abs(expected - actual) <= percentage;
+      }
+    } else {
+      return true;
+    }
+  }
+
+  /**
+   * Fails if the absolute difference between expected and actual values as a percentage of the expected
+   * value is greater than the threshold
+   * i.e. Math.abs((expected - actual) / expected) > percentage, if expected != 0
+   * else Math.abs(expected - actual) > percentage
+   */
+  static void assertEqualsWithinPercentage(double expected, double actual, double percentage) {
+     assertEqualsWithinPercentage(expected, actual, percentage, "");
+  }
+
+  /**
+   * Fails if the absolute difference between expected and actual values as a percentage of the expected
+   * value is greater than the threshold
+   * i.e. Math.abs((expected - actual) / expected) > percentage, if expected != 0
+   * else Math.abs(expected - actual) > percentage
+   */
+  static void assertEqualsWithinPercentage(double expected, double actual, double percentage, String message) {
+    if (!doublesAreEqualWithinPercentage(expected, actual, percentage)) {
+      String msg = message + " Math.abs(expected - actual)";
+      String eq = (expected != 0 ?
+                      " / Math.abs(expected) = " + Math.abs((expected - actual) / expected)
+                    : " = " + Math.abs(expected - actual));
+      fail(msg + eq + " is not <= " + percentage + " expected(" + expected + ") actual(" + actual + ")");
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/Date32ColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/Date32ColumnVectorTest.java
new file mode 100644
index 0000000..024a602
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/Date32ColumnVectorTest.java
@@ -0,0 +1,72 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class Date32ColumnVectorTest extends CudfTestBase {
+
+  private static final int[] DATES = {17897, //Jan 01, 2019
+      17532, //Jan 01, 2018
+      17167, //Jan 01, 2017
+      16802, //Jan 01, 2016
+      16437}; //Jan 01, 2015
+
+  private static final int[] DATES_2 = {17897, //Jan 01, 2019
+      17898, //Jan 02, 2019
+      17899, //Jan 03, 2019
+      17900, //Jan 04, 2019
+      17901}; //Jan 05, 2019
+
+  @Test
+  public void getYear() {
+    try (ColumnVector daysColumnVector = ColumnVector.daysFromInts(DATES);
+         ColumnVector tmp = daysColumnVector.year();
+         HostColumnVector result = tmp.copyToHost()) {
+      int expected = 2019;
+      for (int i = 0; i < DATES.length; i++) {
+        assertEquals(expected - i, result.getShort(i)); //2019 to 2015
+      }
+    }
+  }
+
+  @Test
+  public void getMonth() {
+    try (ColumnVector daysColumnVector = ColumnVector.daysFromInts(DATES);
+         ColumnVector tmp = daysColumnVector.month();
+         HostColumnVector result = tmp.copyToHost()) {
+      for (int i = 0; i < DATES.length; i++) {
+        assertEquals(1, result.getShort(i)); //Jan of every year
+      }
+    }
+  }
+
+  @Test
+  public void getDay() {
+    try (ColumnVector daysColumnVector = ColumnVector.daysFromInts(DATES_2);
+         ColumnVector tmp = daysColumnVector.day();
+         HostColumnVector result = tmp.copyToHost()) {
+      for (int i = 0; i < DATES_2.length; i++) {
+        assertEquals(i + 1, result.getShort(i)); //1 to 5
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/Date64ColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/Date64ColumnVectorTest.java
new file mode 100644
index 0000000..a634034
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/Date64ColumnVectorTest.java
@@ -0,0 +1,95 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class Date64ColumnVectorTest extends CudfTestBase {
+  private static final long[] DATES = {-131968727238L,   //'1965-10-26 14:01:12.762'
+      1530705600000L,   //'2018-07-04 12:00:00.000'
+      1674631932929L};  //'2023-01-25 07:32:12.929'
+
+  @Test
+  public void getYear() {
+    try (ColumnVector date64ColumnVector = ColumnVector.timestampMilliSecondsFromLongs(DATES);
+         ColumnVector tmp = date64ColumnVector.year();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(1965, result.getShort(0));
+      assertEquals(2018, result.getShort(1));
+      assertEquals(2023, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getMonth() {
+    try (ColumnVector date64ColumnVector = ColumnVector.timestampMilliSecondsFromLongs(DATES);
+         ColumnVector tmp = date64ColumnVector.month();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(10, result.getShort(0));
+      assertEquals(7, result.getShort(1));
+      assertEquals(1, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getDay() {
+    try (ColumnVector date64ColumnVector = ColumnVector.timestampMilliSecondsFromLongs(DATES);
+         ColumnVector tmp = date64ColumnVector.day();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(26, result.getShort(0));
+      assertEquals(4, result.getShort(1));
+      assertEquals(25, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getHour() {
+    try (ColumnVector date64ColumnVector = ColumnVector.timestampMilliSecondsFromLongs(DATES);
+         ColumnVector tmp = date64ColumnVector.hour();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(14, result.getShort(0));
+      assertEquals(12, result.getShort(1));
+      assertEquals(7, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getMinute() {
+    try (ColumnVector date64ColumnVector = ColumnVector.timestampMilliSecondsFromLongs(DATES);
+         ColumnVector tmp = date64ColumnVector.minute();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(1, result.getShort(0));
+      assertEquals(0, result.getShort(1));
+      assertEquals(32, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getSecond() {
+    try (ColumnVector date64ColumnVector = ColumnVector.timestampMilliSecondsFromLongs(DATES);
+         ColumnVector tmp = date64ColumnVector.second();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(12, result.getShort(0));
+      assertEquals(0, result.getShort(1));
+      assertEquals(12, result.getShort(2));
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/DecimalColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/DecimalColumnVectorTest.java
new file mode 100644
index 0000000..87d01c6
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/DecimalColumnVectorTest.java
@@ -0,0 +1,425 @@
+/*
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.util.Arrays;
+import java.util.Objects;
+import java.util.Random;
+import java.util.function.Consumer;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class DecimalColumnVectorTest extends CudfTestBase {
+  private static final Random rdSeed = new Random(1234);
+  private static final int dec32Scale = 4;
+  private static final int dec64Scale = 10;
+  private static final int dec128Scale = 30;
+
+  private static final BigDecimal[] decimal32Zoo = new BigDecimal[20];
+  private static final BigDecimal[] decimal64Zoo = new BigDecimal[20];
+  private static final BigDecimal[] decimal128Zoo = new BigDecimal[20];
+  private static final int[] unscaledDec32Zoo = new int[decimal32Zoo.length];
+  private static final long[] unscaledDec64Zoo = new long[decimal64Zoo.length];
+  private static final BigInteger[] unscaledDec128Zoo = new BigInteger[decimal128Zoo.length];
+
+  private final BigDecimal[] boundaryDecimal32 = new BigDecimal[]{
+      new BigDecimal("999999999"), new BigDecimal("-999999999")};
+
+  private final BigDecimal[] boundaryDecimal64 = new BigDecimal[]{
+      new BigDecimal("999999999999999999"), new BigDecimal("-999999999999999999")};
+
+  private final BigDecimal[] boundaryDecimal128 = new BigDecimal[]{
+      new BigDecimal("99999999999999999999999999999999999999"), new BigDecimal("-99999999999999999999999999999999999999")};
+
+  private final BigDecimal[] overflowDecimal32 = new BigDecimal[]{
+      BigDecimal.valueOf(Integer.MAX_VALUE), BigDecimal.valueOf(Integer.MIN_VALUE)};
+
+  private final BigDecimal[] overflowDecimal64 = new BigDecimal[]{
+      BigDecimal.valueOf(Long.MAX_VALUE), BigDecimal.valueOf(Long.MIN_VALUE)};
+
+  private final BigDecimal[] overflowDecimal128 = new BigDecimal[]{
+      new BigDecimal("340282367000000000000000000000000000001"),
+      new BigDecimal("-340282367000000000000000000000000000001")};
+
+  @BeforeAll
+  public static void setup() {
+    for (int i = 0; i < decimal32Zoo.length; i++) {
+      unscaledDec32Zoo[i] = rdSeed.nextInt() / 100;
+      unscaledDec64Zoo[i] = rdSeed.nextLong() / 100;
+      unscaledDec128Zoo[i] = BigInteger.valueOf(rdSeed.nextLong()).multiply(BigInteger.valueOf(rdSeed.nextLong()));
+      if (rdSeed.nextBoolean()) {
+        // Create BigDecimal with slight variance on scale, in order to test building cv from inputs with different scales.
+        decimal32Zoo[i] = BigDecimal.valueOf(rdSeed.nextInt() / 100, dec32Scale - rdSeed.nextInt(2));
+      } else {
+        decimal32Zoo[i] = null;
+      }
+      if (rdSeed.nextBoolean()) {
+        // Create BigDecimal with slight variance on scale, in order to test building cv from inputs with different scales.
+        decimal64Zoo[i] = BigDecimal.valueOf(rdSeed.nextLong() / 100, dec64Scale - rdSeed.nextInt(2));
+      } else {
+        decimal64Zoo[i] = null;
+      }
+      if (rdSeed.nextBoolean()) {
+        BigInteger unscaledVal = BigInteger.valueOf(rdSeed.nextLong()).multiply(BigInteger.valueOf(rdSeed.nextLong()));
+        decimal128Zoo[i] = new BigDecimal(unscaledVal, dec128Scale);
+      } else {
+        decimal128Zoo[i] = null;
+      }
+    }
+  }
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (ColumnVector cv = ColumnVector.build(DType.create(DType.DTypeEnum.DECIMAL32, -5), 3,
+        (b) -> b.append(BigDecimal.valueOf(123456789, 5)))) {
+      assertFalse(cv.hasNulls());
+    }
+    try (ColumnVector cv = ColumnVector.build(DType.create(DType.DTypeEnum.DECIMAL64, -10), 3,
+        (b) -> b.append(BigDecimal.valueOf(1023040506070809L, 10)))) {
+      assertFalse(cv.hasNulls());
+    }
+    // test building ColumnVector from BigDecimal values with varying scales
+    try (ColumnVector cv = ColumnVector.build(DType.create(DType.DTypeEnum.DECIMAL64, -5), 7,
+        (b) -> b.append(BigDecimal.valueOf(123456, 0), RoundingMode.UNNECESSARY)
+            .append(BigDecimal.valueOf(123456, 2), RoundingMode.UNNECESSARY)
+            .append(BigDecimal.valueOf(123456, 5))
+            .append(BigDecimal.valueOf(123456, 7), RoundingMode.HALF_UP)
+            .append(BigDecimal.valueOf(123456, 7), RoundingMode.FLOOR)
+            .append(BigDecimal.valueOf(123456, 9), RoundingMode.HALF_DOWN)
+            .append(BigDecimal.valueOf(123456, 9), RoundingMode.CEILING))) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        assertEquals(12345600000L, hcv.getLong(0));
+        assertEquals(123456000L, hcv.getLong(1));
+        assertEquals(123456L, hcv.getLong(2));
+        assertEquals(1235L, hcv.getLong(3));
+        assertEquals(1234L, hcv.getLong(4));
+        assertEquals(12L, hcv.getLong(5));
+        assertEquals(13L, hcv.getLong(6));
+      }
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    try (HostColumnVector decColumnVector = HostColumnVector.fromDecimals(decimal32Zoo)) {
+      assertThrows(AssertionError.class, () -> decColumnVector.getBigDecimal(decimal32Zoo.length));
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    try (HostColumnVector doubleColumnVector = HostColumnVector.fromDecimals(decimal32Zoo)) {
+      assertThrows(AssertionError.class, () -> doubleColumnVector.getBigDecimal(-1));
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    try (HostColumnVector cv = HostColumnVector.fromDecimals(decimal64Zoo)) {
+      for (int i = 0; i < decimal64Zoo.length; ++i) {
+        assertEquals(decimal64Zoo[i] == null, cv.isNull(i));
+      }
+      assertEquals(Arrays.stream(decimal64Zoo).filter(Objects::isNull).count(), cv.getNullCount());
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.create(DType.DTypeEnum.DECIMAL32, -dec32Scale), 3)) {
+      assertThrows(AssertionError.class, () -> builder.appendBoxed(decimal32Zoo).build());
+    }
+    try (Builder builder = HostColumnVector.builder(DType.create(DType.DTypeEnum.DECIMAL64, -dec64Scale), 3)) {
+      assertThrows(AssertionError.class, () -> builder.appendUnscaledDecimalArray(unscaledDec64Zoo).build());
+    }
+  }
+
+  @Test
+  public void testDecimalValidation() {
+    // precision overflow
+    assertThrows(IllegalArgumentException.class, () -> HostColumnVector.fromDecimals(overflowDecimal128));
+
+    assertThrows(IllegalArgumentException.class, () -> {
+      try (ColumnVector ignored = ColumnVector.decimalFromInts(
+          -(DType.DECIMAL32_MAX_PRECISION + 1), unscaledDec32Zoo)) {
+      }
+    });
+    assertThrows(IllegalArgumentException.class, () -> {
+      try (ColumnVector ignored = ColumnVector.decimalFromLongs(
+          -(DType.DECIMAL64_MAX_PRECISION + 1), unscaledDec64Zoo)) {
+      }
+    });
+    // precision overflow due to rescaling by min scale
+    assertThrows(IllegalArgumentException.class, () -> {
+      try (ColumnVector ignored = ColumnVector.fromDecimals(
+          BigDecimal.valueOf(1.23e30), BigDecimal.valueOf(1.2e-7))) {
+      }
+    });
+    // exactly hit the MAX_PRECISION_DECIMAL128 after rescaling
+    assertDoesNotThrow(() -> {
+      try (ColumnVector ignored = ColumnVector.fromDecimals(
+          BigDecimal.valueOf(1.23e30), BigDecimal.valueOf(1.2e-6))) {
+      }
+    });
+  }
+
+  @Test
+  public void testDecimalGeneral() {
+    // Safe max precision of Decimal32 is 9, so integers have 10 digits will be backed by DECIMAL64.
+    try (ColumnVector cv = ColumnVector.fromDecimals(overflowDecimal32)) {
+      assertEquals(DType.create(DType.DTypeEnum.DECIMAL64, 0), cv.getType());
+    }
+
+    try (ColumnVector cv = ColumnVector.fromDecimals(overflowDecimal64)) {
+      assertEquals(DType.create(DType.DTypeEnum.DECIMAL128, 0), cv.getType());
+    }
+    // Create DECIMAL64 vector with small values
+    try (ColumnVector cv =  ColumnVector.decimalFromLongs(0, 0L)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        assertTrue(hcv.getType().isBackedByLong());
+        assertEquals(0L, hcv.getBigDecimal(0).longValue());
+      }
+    }
+  }
+
+  @Test
+  public void testDecimalFromDecimals() {
+    DecimalColumnVectorTest.testDecimalImpl(DType.DTypeEnum.DECIMAL32, dec32Scale, decimal32Zoo);
+    DecimalColumnVectorTest.testDecimalImpl(DType.DTypeEnum.DECIMAL64, dec64Scale, decimal64Zoo);
+    DecimalColumnVectorTest.testDecimalImpl(DType.DTypeEnum.DECIMAL128, dec128Scale, decimal128Zoo);
+    DecimalColumnVectorTest.testDecimalImpl(DType.DTypeEnum.DECIMAL32, 0, boundaryDecimal32);
+    DecimalColumnVectorTest.testDecimalImpl(DType.DTypeEnum.DECIMAL64, 0, boundaryDecimal64);
+    DecimalColumnVectorTest.testDecimalImpl(DType.DTypeEnum.DECIMAL128, 0, boundaryDecimal128);
+  }
+
+  private static void testDecimalImpl(DType.DTypeEnum decimalType, int scale, BigDecimal[] decimalZoo) {
+    Consumer<HostColumnVector> assertions = (hcv) -> {
+      assertEquals(-scale, hcv.getType().getScale());
+      assertEquals(hcv.getType().typeId, decimalType);
+      assertEquals(decimalZoo.length, hcv.rows);
+      for (int i = 0; i < decimalZoo.length; i++) {
+        assertEquals(decimalZoo[i] == null, hcv.isNull(i));
+        if (decimalZoo[i] != null) {
+          BigDecimal actual;
+          switch (decimalType) {
+          case DECIMAL32:
+            actual = BigDecimal.valueOf(hcv.getInt(i), scale);
+            break;
+          case DECIMAL64:
+            actual = BigDecimal.valueOf(hcv.getLong(i), scale);
+            break;
+          default:
+            actual = hcv.getBigDecimal(i);
+          }
+          assertEquals(decimalZoo[i].subtract(actual).longValueExact(), 0L);
+        }
+      }
+    };
+    try (ColumnVector cv = ColumnVector.fromDecimals(decimalZoo)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        assertions.accept(hcv);
+      }
+    }
+    try (HostColumnVector hcv = ColumnBuilderHelper.fromDecimals(decimalZoo)) {
+      assertions.accept(hcv);
+    }
+  }
+
+  @Test
+  public void testDecimalFromInts() {
+    try (ColumnVector cv = ColumnVector.decimalFromInts(-DecimalColumnVectorTest.dec32Scale, DecimalColumnVectorTest.unscaledDec32Zoo)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        for (int i = 0; i < DecimalColumnVectorTest.unscaledDec32Zoo.length; i++) {
+          assertEquals(DecimalColumnVectorTest.unscaledDec32Zoo[i], hcv.getInt(i));
+          assertEquals(BigDecimal.valueOf(DecimalColumnVectorTest.unscaledDec32Zoo[i], DecimalColumnVectorTest.dec32Scale), hcv.getBigDecimal(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testDecimalFromLongs() {
+    try (ColumnVector cv = ColumnVector.decimalFromLongs(-DecimalColumnVectorTest.dec64Scale, DecimalColumnVectorTest.unscaledDec64Zoo)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        for (int i = 0; i < DecimalColumnVectorTest.unscaledDec64Zoo.length; i++) {
+          assertEquals(DecimalColumnVectorTest.unscaledDec64Zoo[i], hcv.getLong(i));
+          assertEquals(BigDecimal.valueOf(DecimalColumnVectorTest.unscaledDec64Zoo[i], DecimalColumnVectorTest.dec64Scale), hcv.getBigDecimal(i));
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testDecimalFromBigInts() {
+    try (ColumnVector cv = ColumnVector.decimalFromBigInt(-DecimalColumnVectorTest.dec128Scale, DecimalColumnVectorTest.unscaledDec128Zoo)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        for (int i = 0; i < DecimalColumnVectorTest.unscaledDec128Zoo.length; i++) {
+          assertEquals(DecimalColumnVectorTest.unscaledDec128Zoo[i], hcv.getBigDecimal(i).unscaledValue());
+        }
+      }
+    }
+    try (HostColumnVector hcv = ColumnBuilderHelper.decimalFromBigInts(-DecimalColumnVectorTest.dec128Scale, DecimalColumnVectorTest.unscaledDec128Zoo)) {
+      for (int i = 0; i < DecimalColumnVectorTest.unscaledDec128Zoo.length; i++) {
+        assertEquals(DecimalColumnVectorTest.unscaledDec128Zoo[i], hcv.getBigDecimal(i).unscaledValue());
+      }
+    }
+  }
+
+  @Test
+  public void testDecimalFromDoubles() {
+    DType dt = DType.create(DType.DTypeEnum.DECIMAL32, -3);
+    try (ColumnVector cv = ColumnVector.decimalFromDoubles(dt, RoundingMode.DOWN,123456, -2.4567, 3.00001, -1111e-5)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        assertEquals(123456, hcv.getBigDecimal(0).doubleValue());
+        assertEquals(-2.456, hcv.getBigDecimal(1).doubleValue());
+        assertEquals(3, hcv.getBigDecimal(2).doubleValue());
+        assertEquals(-0.011, hcv.getBigDecimal(3).doubleValue());
+      }
+    }
+    dt = DType.create(DType.DTypeEnum.DECIMAL64, -10);
+    try (ColumnVector cv = ColumnVector.decimalFromDoubles(dt, RoundingMode.HALF_UP, 1.2345678, -2.45e-9, 3.000012, -51111e-15)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        assertEquals(1.2345678, hcv.getBigDecimal(0).doubleValue());
+        assertEquals(-2.5e-9, hcv.getBigDecimal(1).doubleValue());
+        assertEquals(3.000012, hcv.getBigDecimal(2).doubleValue());
+        assertEquals(-1e-10, hcv.getBigDecimal(3).doubleValue());
+      }
+    }
+    dt = DType.create(DType.DTypeEnum.DECIMAL64, 10);
+    try (ColumnVector cv = ColumnVector.decimalFromDoubles(dt, RoundingMode.UP, 1.234e20, -12.34e8, 1.1e10)) {
+      try (HostColumnVector hcv = cv.copyToHost()) {
+        assertEquals(1.234e20, hcv.getBigDecimal(0).doubleValue());
+        assertEquals(-1e10, hcv.getBigDecimal(1).doubleValue());
+        assertEquals(2e10, hcv.getBigDecimal(2).doubleValue());
+      }
+    }
+    assertThrows(ArithmeticException.class,
+        () -> {
+          final DType dt1 = DType.create(DType.DTypeEnum.DECIMAL32, -5);
+          try (ColumnVector cv = ColumnVector.decimalFromDoubles(dt1, RoundingMode.UNNECESSARY, 30000)) {
+          }
+        });
+    assertThrows(ArithmeticException.class,
+        () -> {
+          final DType dt1 = DType.create(DType.DTypeEnum.DECIMAL64, 10);
+          try (ColumnVector cv = ColumnVector.decimalFromDoubles(dt1, RoundingMode.FLOOR, 1e100)) {
+          }
+        });
+  }
+
+  @Test
+  public void testAppendVector() {
+    for (DType decType : new DType[]{
+        DType.create(DType.DTypeEnum.DECIMAL32, -6),
+        DType.create(DType.DTypeEnum.DECIMAL64, -10)}) {
+      for (int dstSize = 1; dstSize <= 100; dstSize++) {
+        for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+          final int srcSize = dstSize - dstPrefilledSize;
+          for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+            try (Builder dst = HostColumnVector.builder(decType, dstSize);
+                 HostColumnVector src = HostColumnVector.build(decType, srcSize, (b) -> {
+                   for (int i = 0; i < srcSize; i++) {
+                     if (rdSeed.nextBoolean()) {
+                       b.appendNull();
+                     } else {
+                       b.append(BigDecimal.valueOf(rdSeed.nextInt() / 100, -decType.getScale()));
+                     }
+                   }
+                 });
+                 Builder gtBuilder = HostColumnVector.builder(decType, dstPrefilledSize)) {
+              assertEquals(dstSize, srcSize + dstPrefilledSize);
+              //add the first half of the prefilled list
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                if (rdSeed.nextBoolean()) {
+                  dst.appendNull();
+                  gtBuilder.appendNull();
+                } else {
+                  BigDecimal a = BigDecimal.valueOf(rdSeed.nextInt() / 100, -decType.getScale());
+                  if (decType.typeId == DType.DTypeEnum.DECIMAL32) {
+                    dst.appendUnscaledDecimal(a.unscaledValue().intValueExact());
+                  } else {
+                    dst.appendUnscaledDecimal(a.unscaledValue().longValueExact());
+                  }
+                  gtBuilder.append(a);
+                }
+              }
+              // append the src vector
+              dst.append(src);
+              try (HostColumnVector dstVector = dst.build();
+                   HostColumnVector gt = gtBuilder.build()) {
+                for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                  assertEquals(gt.isNull(i), dstVector.isNull(i));
+                  if (!gt.isNull(i)) {
+                    assertEquals(gt.getBigDecimal(i), dstVector.getBigDecimal(i));
+                  }
+                }
+                for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                  assertEquals(src.isNull(j), dstVector.isNull(i));
+                  if (!src.isNull(j)) {
+                    assertEquals(src.getBigDecimal(j), dstVector.getBigDecimal(i));
+                  }
+                }
+                if (dstVector.hasValidityVector()) {
+                  long maxIndex =
+                      BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                  for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                    assertFalse(dstVector.isNullExtendedRange(i));
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testColumnVectorFromScalar() {
+    try (Scalar s = Scalar.fromDecimal(-3, 1233456)) {
+      try (ColumnVector cv = ColumnVector.fromScalar(s, 10)) {
+        assertEquals(s.getType(), cv.getType());
+        assertEquals(10L, cv.getRowCount());
+        try (HostColumnVector hcv = cv.copyToHost()) {
+          for (int i = 0; i < cv.getRowCount(); i++) {
+            assertEquals(s.getInt(), hcv.getInt(i));
+            assertEquals(s.getBigDecimal(), hcv.getBigDecimal(i));
+          }
+        }
+      }
+    }
+    try (Scalar s = Scalar.fromDecimal(-6, 123456789098L)) {
+      try (ColumnVector cv = ColumnVector.fromScalar(s, 10)) {
+        assertEquals(s.getType(), cv.getType());
+        assertEquals(10L, cv.getRowCount());
+        try (HostColumnVector hcv = cv.copyToHost()) {
+          for (int i = 0; i < cv.getRowCount(); i++) {
+            assertEquals(s.getLong(), hcv.getLong(i));
+            assertEquals(s.getBigDecimal(), hcv.getBigDecimal(i));
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/DoubleColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/DoubleColumnVectorTest.java
new file mode 100644
index 0000000..fa34429
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/DoubleColumnVectorTest.java
@@ -0,0 +1,174 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import java.util.Random;
+import java.util.function.Consumer;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class DoubleColumnVectorTest extends CudfTestBase {
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (ColumnVector doubleColumnVector = ColumnVector.build(DType.FLOAT64, 3,
+        (b) -> b.append(1.0))) {
+      assertFalse(doubleColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEqualsWithinPercentage(cv.getDouble(0), 2.1, 0.01);
+      assertEqualsWithinPercentage(cv.getDouble(1), 3.02, 0.01);
+      assertEqualsWithinPercentage(cv.getDouble(2), 5.003, 0.001);
+    };
+    try (HostColumnVector dcv = HostColumnVector.fromDoubles(2.1, 3.02, 5.003)) {
+      verify.accept(dcv);
+    }
+    try (HostColumnVector dcv = ColumnBuilderHelper.fromDoubles(2.1, 3.02, 5.003)) {
+      verify.accept(dcv);
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertThrows(AssertionError.class, () -> cv.getDouble(3));
+      assertFalse(cv.hasNulls());
+    };
+    try (HostColumnVector dcv = HostColumnVector.fromDoubles(2.1, 3.02, 5.003)) {
+      verify.accept(dcv);
+    }
+    try (HostColumnVector dcv = ColumnBuilderHelper.fromDoubles(2.1, 3.02, 5.003)) {
+      verify.accept(dcv);
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertThrows(AssertionError.class, () -> cv.getDouble(-1));
+    };
+    try (HostColumnVector dcv = HostColumnVector.fromDoubles(2.1, 3.02, 5.003)) {
+      verify.accept(dcv);
+    }
+    try (HostColumnVector dcv = ColumnBuilderHelper.fromDoubles(2.1, 3.02, 5.003)) {
+      verify.accept(dcv);
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector dcv =
+             HostColumnVector.fromBoxedDoubles(2.0, 3.0, 4.0, 5.0, 6.0, 7.0, null, null)) {
+      verify.accept(dcv);
+    }
+    try (HostColumnVector dcv = ColumnBuilderHelper.fromBoxedDoubles(
+        2.0, 3.0, 4.0, 5.0, 6.0, 7.0, null, null)) {
+      verify.accept(dcv);
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.FLOAT64, 3)) {
+      assertThrows(AssertionError.class,
+          () -> builder.append(2.1).appendNull().appendArray(new double[]{5.003, 4.0}).build());
+    }
+  }
+
+  @Test
+  void testAppendVector() {
+    Random random = new Random(192312989128L);
+    for (int dstSize = 1; dstSize <= 100; dstSize++) {
+      for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+        final int srcSize = dstSize - dstPrefilledSize;
+        for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+          try (Builder dst = HostColumnVector.builder(DType.FLOAT64, dstSize);
+               HostColumnVector src = HostColumnVector.build(DType.FLOAT64, srcSize, (b) -> {
+                 for (int i = 0; i < srcSize; i++) {
+                   if (random.nextBoolean()) {
+                     b.appendNull();
+                   } else {
+                     b.append(random.nextDouble());
+                   }
+                 }
+               });
+               Builder gtBuilder = HostColumnVector.builder(DType.FLOAT64, dstPrefilledSize)) {
+            assertEquals(dstSize, srcSize + dstPrefilledSize);
+            //add the first half of the prefilled list
+            for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+              if (random.nextBoolean()) {
+                dst.appendNull();
+                gtBuilder.appendNull();
+              } else {
+                double a = random.nextDouble();
+                dst.append(a);
+                gtBuilder.append(a);
+              }
+            }
+            // append the src vector
+            dst.append(src);
+            try (HostColumnVector dstVector = dst.build();
+                 HostColumnVector gt = gtBuilder.build()) {
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                assertEquals(gt.isNull(i), dstVector.isNull(i));
+                if (!gt.isNull(i)) {
+                  assertEquals(gt.getDouble(i), dstVector.getDouble(i));
+                }
+              }
+              for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                assertEquals(src.isNull(j), dstVector.isNull(i));
+                if (!src.isNull(j)) {
+                  assertEquals(src.getDouble(j), dstVector.getDouble(i));
+                }
+              }
+              if (dstVector.hasValidityVector()) {
+                long maxIndex =
+                    BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                  assertFalse(dstVector.isNullExtendedRange(i));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/FloatColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/FloatColumnVectorTest.java
new file mode 100644
index 0000000..25eca84
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/FloatColumnVectorTest.java
@@ -0,0 +1,164 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import java.util.Random;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class FloatColumnVectorTest extends CudfTestBase {
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (ColumnVector floatColumnVector = ColumnVector.build(DType.FLOAT32, 3,
+        (b) -> b.append(1.0f))) {
+      assertFalse(floatColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testArrayAllocation() {
+    try (HostColumnVector floatColumnVector = HostColumnVector.fromFloats(2.1f, 3.02f, 5.003f)) {
+      assertFalse(floatColumnVector.hasNulls());
+      assertEqualsWithinPercentage(floatColumnVector.getFloat(0), 2.1, 0.01);
+      assertEqualsWithinPercentage(floatColumnVector.getFloat(1), 3.02, 0.01);
+      assertEqualsWithinPercentage(floatColumnVector.getFloat(2), 5.003, 0.001);
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    try (HostColumnVector floatColumnVector = HostColumnVector.fromFloats(2.1f, 3.02f, 5.003f)) {
+      assertThrows(AssertionError.class, () -> floatColumnVector.getFloat(3));
+      assertFalse(floatColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    try (HostColumnVector floatColumnVector = HostColumnVector.fromFloats(2.1f, 3.02f, 5.003f)) {
+      assertFalse(floatColumnVector.hasNulls());
+      assertThrows(AssertionError.class, () -> floatColumnVector.getFloat(-1));
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    try (HostColumnVector cv = HostColumnVector.fromBoxedFloats(
+        new Float[]{2f, 3f, 4f, 5f, 6f, 7f, null, null})) {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.FLOAT32, 3)) {
+      assertThrows(AssertionError.class,
+          () -> builder.append(2.1f).appendNull().appendArray(5.003f, 4.0f).build());
+    }
+  }
+
+  @Test
+  public void testCastToFloat() {
+    try (ColumnVector doubleColumnVector = ColumnVector.fromDoubles(new double[]{4.3, 3.8, 8});
+         ColumnVector shortColumnVector = ColumnVector.fromShorts(new short[]{100});
+         ColumnVector tmp1 = doubleColumnVector.asFloats();
+         HostColumnVector floatColumnVector1 = tmp1.copyToHost();
+         ColumnVector tmp2 = shortColumnVector.asFloats();
+         HostColumnVector floatColumnVector2 = tmp2.copyToHost()) {
+      assertEqualsWithinPercentage(4.3, floatColumnVector1.getFloat(0), 0.001);
+      assertEqualsWithinPercentage(3.8, floatColumnVector1.getFloat(1), 0.001);
+      assertEquals(8, floatColumnVector1.getFloat(2));
+      assertEquals(100, floatColumnVector2.getFloat(0));
+    }
+  }
+
+  @Test
+  void testAppendVector() {
+    Random random = new Random(192312989128L);
+    for (int dstSize = 1; dstSize <= 100; dstSize++) {
+      for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+        final int srcSize = dstSize - dstPrefilledSize;
+        for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+          try (Builder dst = HostColumnVector.builder(DType.FLOAT32, dstSize);
+               HostColumnVector src = HostColumnVector.build(DType.FLOAT32, srcSize, (b) -> {
+                 for (int i = 0; i < srcSize; i++) {
+                   if (random.nextBoolean()) {
+                     b.appendNull();
+                   } else {
+                     b.append(random.nextFloat());
+                   }
+                 }
+               });
+               Builder gtBuilder = HostColumnVector.builder(DType.FLOAT32,
+                   dstPrefilledSize)) {
+            assertEquals(dstSize, srcSize + dstPrefilledSize);
+            //add the first half of the prefilled list
+            for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+              if (random.nextBoolean()) {
+                dst.appendNull();
+                gtBuilder.appendNull();
+              } else {
+                float a = random.nextFloat();
+                dst.append(a);
+                gtBuilder.append(a);
+              }
+            }
+            // append the src vector
+            dst.append(src);
+            try (HostColumnVector dstVector = dst.build();
+                 HostColumnVector gt = gtBuilder.build()) {
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                assertEquals(gt.isNull(i), dstVector.isNull(i));
+                if (!gt.isNull(i)) {
+                  assertEquals(gt.getFloat(i), dstVector.getFloat(i));
+                }
+              }
+              for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                assertEquals(src.isNull(j), dstVector.isNull(i));
+                if (!src.isNull(j)) {
+                  assertEquals(src.getFloat(j), dstVector.getFloat(i));
+                }
+              }
+              if (dstVector.hasValidityVector()) {
+                long maxIndex =
+                    BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                  assertFalse(dstVector.isNullExtendedRange(i));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/GatherMapTest.java b/java/src/test/java/ai/rapids/cudf/GatherMapTest.java
new file mode 100644
index 0000000..8bab049
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/GatherMapTest.java
@@ -0,0 +1,102 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+import org.mockito.Mockito;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertSame;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class GatherMapTest {
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  @Test
+  void testInvalidBuffer() {
+    try (DeviceMemoryBuffer buffer = DeviceMemoryBuffer.allocate(707)) {
+      assertThrows(IllegalArgumentException.class, () -> new GatherMap(buffer));
+    }
+  }
+
+  @Test
+  void testRowCount() {
+    try (GatherMap map = new GatherMap(DeviceMemoryBuffer.allocate(700))) {
+      assertEquals(175, map.getRowCount());
+    }
+  }
+
+  @Test
+  void testClose() {
+    DeviceMemoryBuffer mockBuffer = Mockito.mock(DeviceMemoryBuffer.class);
+    GatherMap map = new GatherMap(mockBuffer);
+    map.close();
+    Mockito.verify(mockBuffer).close();
+  }
+
+  @Test
+  void testReleaseBuffer() {
+    DeviceMemoryBuffer mockBuffer = Mockito.mock(DeviceMemoryBuffer.class);
+    GatherMap map = new GatherMap(mockBuffer);
+    DeviceMemoryBuffer buffer = map.releaseBuffer();
+    assertSame(mockBuffer, buffer);
+    map.close();
+    Mockito.verify(mockBuffer, Mockito.never()).close();
+  }
+
+  @Test
+  void testInvalidColumnView() {
+    try (GatherMap map = new GatherMap(DeviceMemoryBuffer.allocate(1024))) {
+      assertThrows(IllegalArgumentException.class, () -> map.toColumnView(0, 257));
+      assertThrows(IllegalArgumentException.class, () -> map.toColumnView(257, 0));
+      assertThrows(IllegalArgumentException.class, () -> map.toColumnView(-4, 253));
+      assertThrows(IllegalArgumentException.class, () -> map.toColumnView(4, -2));
+    }
+  }
+
+  @Test
+  void testToColumnView() {
+    try (HostMemoryBuffer hostBuffer = hostMemoryAllocator.allocate(8 * 4)) {
+      hostBuffer.setInts(0, new int[]{10, 11, 12, 13, 14, 15, 16, 17}, 0, 8);
+      try (DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(8*4)) {
+        devBuffer.copyFromHostBuffer(hostBuffer);
+        devBuffer.incRefCount();
+        try (GatherMap map = new GatherMap(devBuffer)) {
+          ColumnView view = map.toColumnView(0, 8);
+          assertEquals(DType.INT32, view.getType());
+          assertEquals(0, view.getNullCount());
+          assertEquals(8, view.getRowCount());
+          try (HostMemoryBuffer viewHostBuffer = hostMemoryAllocator.allocate(8 * 4)) {
+            viewHostBuffer.copyFromDeviceBuffer(view.getData());
+            for (int i = 0; i < 8; i++) {
+              assertEquals(i + 10, viewHostBuffer.getInt(4*i));
+            }
+          }
+          view = map.toColumnView(3, 2);
+          assertEquals(DType.INT32, view.getType());
+          assertEquals(0, view.getNullCount());
+          assertEquals(2, view.getRowCount());
+          try (HostMemoryBuffer viewHostBuffer = hostMemoryAllocator.allocate(8)) {
+            viewHostBuffer.copyFromDeviceBuffer(view.getData());
+            assertEquals(13, viewHostBuffer.getInt(0));
+            assertEquals(14, viewHostBuffer.getInt(4));
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/HashJoinTest.java b/java/src/test/java/ai/rapids/cudf/HashJoinTest.java
new file mode 100644
index 0000000..be61253
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/HashJoinTest.java
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class HashJoinTest {
+  @Test
+  void testGetNumberOfColumns() {
+    try (Table t = new Table.TestBuilder().column(1, 2).column(3, 4).column(5, 6).build();
+         HashJoin hashJoin = new HashJoin(t, false)) {
+      assertEquals(3, hashJoin.getNumberOfColumns());
+    }
+  }
+
+  @Test
+  void testGetCompareNulls() {
+    try (Table t = new Table.TestBuilder().column(1, 2, 3, 4).column(5, 6, 7, 8).build()) {
+      try (HashJoin hashJoin = new HashJoin(t, false)) {
+        assertFalse(hashJoin.getCompareNulls());
+      }
+      try (HashJoin hashJoin = new HashJoin(t, true)) {
+        assertTrue(hashJoin.getCompareNulls());
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/HostMemoryBufferTest.java b/java/src/test/java/ai/rapids/cudf/HostMemoryBufferTest.java
new file mode 100644
index 0000000..e848d4a
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/HostMemoryBufferTest.java
@@ -0,0 +1,301 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.channels.FileChannel;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.util.Arrays;
+import java.util.Random;
+
+import org.junit.jupiter.api.AfterEach;
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assumptions.assumeTrue;
+
+public class HostMemoryBufferTest extends CudfTestBase {
+  @AfterEach
+  void teardown() {
+    if (PinnedMemoryPool.isInitialized()) {
+      PinnedMemoryPool.shutdown();
+    }
+  }
+
+  @Test
+  void testRefCountLeak() throws InterruptedException {
+    assumeTrue(Boolean.getBoolean("ai.rapids.cudf.flaky-tests-enabled"));
+    long expectedLeakCount = MemoryCleaner.leakCount.get() + 1;
+    HostMemoryBuffer.allocate(1);
+    long maxTime = System.currentTimeMillis() + 10_000;
+    long leakNow;
+    do {
+      System.gc();
+      Thread.sleep(50);
+      leakNow = MemoryCleaner.leakCount.get();
+    } while (leakNow != expectedLeakCount && System.currentTimeMillis() < maxTime);
+    assertEquals(expectedLeakCount, MemoryCleaner.leakCount.get());
+  }
+
+  @Test
+  void asByteBuffer() {
+    final long size = 1024;
+    try (HostMemoryBuffer buff = HostMemoryBuffer.allocate(size)) {
+      ByteBuffer dbuff = buff.asByteBuffer();
+      assertEquals(size, dbuff.capacity());
+      assertEquals(ByteOrder.nativeOrder(), dbuff.order());
+      dbuff.putInt(101);
+      dbuff.putDouble(101.1);
+      assertEquals(101, buff.getInt(0));
+      assertEquals(101.1, buff.getDouble(4));
+    }
+  }
+
+  @Test
+  void testDoubleFree() {
+    HostMemoryBuffer buffer = HostMemoryBuffer.allocate(1);
+    buffer.close();
+    assertThrows(IllegalStateException.class, () -> buffer.close() );
+  }
+
+  @Test
+  public void testGetInt() {
+    try (HostMemoryBuffer hostMemoryBuffer = HostMemoryBuffer.allocate(16)) {
+      long offset = 1;
+      hostMemoryBuffer.setInt(offset * DType.INT32.getSizeInBytes(), 2);
+      assertEquals(2, hostMemoryBuffer.getInt(offset * DType.INT32.getSizeInBytes()));
+    }
+  }
+
+  @Test
+  public void testGetByte() {
+    try (HostMemoryBuffer hostMemoryBuffer = HostMemoryBuffer.allocate(16)) {
+      long offset = 1;
+      hostMemoryBuffer.setByte(offset * DType.INT8.getSizeInBytes(), (byte) 2);
+      assertEquals((byte) 2, hostMemoryBuffer.getByte(offset * DType.INT8.getSizeInBytes()));
+    }
+  }
+
+  @Test
+  public void testGetLong() {
+    try (HostMemoryBuffer hostMemoryBuffer = HostMemoryBuffer.allocate(16)) {
+      long offset = 1;
+      hostMemoryBuffer.setLong(offset * DType.INT64.getSizeInBytes(), 3);
+      assertEquals(3, hostMemoryBuffer.getLong(offset * DType.INT64.getSizeInBytes()));
+    }
+  }
+
+  @Test
+  public void testGetLongs() {
+    try (HostMemoryBuffer hostMemoryBuffer = HostMemoryBuffer.allocate(16)) {
+      hostMemoryBuffer.setLong(0, 3);
+      hostMemoryBuffer.setLong(DType.INT64.getSizeInBytes(), 10);
+      long[] results = new long[2];
+      hostMemoryBuffer.getLongs(results, 0, 0, 2);
+      assertEquals(3, results[0]);
+      assertEquals(10, results[1]);
+    }
+  }
+
+  @Test
+  public void testGetLength() {
+    try (HostMemoryBuffer hostMemoryBuffer = HostMemoryBuffer.allocate(16)) {
+      long length = hostMemoryBuffer.getLength();
+      assertEquals(16, length);
+    }
+  }
+
+  @Test
+  public void testCopyFromDeviceBuffer() {
+    try (HostMemoryBuffer init = HostMemoryBuffer.allocate(16);
+         DeviceMemoryBuffer tmp = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer to = HostMemoryBuffer.allocate(16)) {
+      init.setLong(0, 123456789);
+      tmp.copyFromHostBuffer(init);
+      to.copyFromDeviceBuffer(tmp);
+      assertEquals(123456789, to.getLong(0));
+    }
+  }
+
+  @Test
+  public void testFilemap() throws Exception {
+    Random random = new Random(12345L);
+    final int pageSize = UnsafeMemoryAccessor.pageSize();
+    final int bufferSize = pageSize * 5;
+    byte[] testbuf = new byte[bufferSize];
+    random.nextBytes(testbuf);
+    Path tempFile = Files.createTempFile("mmaptest", ".data");
+    try {
+      Files.write(tempFile, testbuf);
+
+      // verify we can map the whole file
+      try (HostMemoryBuffer hmb = HostMemoryBuffer.mapFile(tempFile.toFile(),
+          FileChannel.MapMode.READ_ONLY, 0,bufferSize)) {
+        assertEquals(bufferSize, hmb.length);
+        byte[] bytes = new byte[(int) hmb.length];
+        hmb.getBytes(bytes, 0, 0, hmb.length);
+        assertArrayEquals(testbuf, bytes);
+      }
+
+      // verify we can map at offsets that aren't a page boundary
+      int mapOffset = pageSize + 1;
+      int mapLength = pageSize * 2 + 7;
+      try (HostMemoryBuffer hmb = HostMemoryBuffer.mapFile(tempFile.toFile(),
+          FileChannel.MapMode.READ_ONLY, mapOffset, mapLength)) {
+        assertEquals(mapLength, hmb.length);
+        byte[] expected = Arrays.copyOfRange(testbuf, mapOffset, mapOffset + mapLength);
+        byte[] bytes = new byte[(int) hmb.length];
+        hmb.getBytes(bytes, 0, 0, hmb.length);
+        assertArrayEquals(expected, bytes);
+      }
+
+      // verify we can modify the file via a writable mapping
+      mapOffset = pageSize * 3 + 123;
+      mapLength = bufferSize - mapOffset - 456;
+      byte[] newData = new byte[mapLength];
+      random.nextBytes(newData);
+      try (HostMemoryBuffer hmb = HostMemoryBuffer.mapFile(tempFile.toFile(),
+          FileChannel.MapMode.READ_WRITE, mapOffset, mapLength)) {
+        hmb.setBytes(0, newData, 0, newData.length);
+      }
+      byte[] data = Files.readAllBytes(tempFile);
+      System.arraycopy(newData, 0, testbuf, mapOffset, mapLength);
+      assertArrayEquals(testbuf, data);
+    } finally {
+      Files.delete(tempFile);
+    }
+  }
+
+  public static void initPinnedPoolIfNeeded(long size) {
+    long available = PinnedMemoryPool.getAvailableBytes();
+    if (available < size) {
+      if (PinnedMemoryPool.isInitialized()) {
+        PinnedMemoryPool.shutdown();
+      }
+      PinnedMemoryPool.initialize(size + 2048);
+    }
+  }
+
+  public static byte[] rba(int size, long seed) {
+    Random random = new Random(12345L);
+    byte[] data = new byte[size];
+    random.nextBytes(data);
+    return data;
+  }
+
+  public static byte[] rba(int size) {
+    return rba(size, 12345L);
+  }
+
+  @Test
+  public void testCopyWithStream() {
+    long length = 1 * 1024 * 1024;
+    initPinnedPoolIfNeeded(length * 2);
+    byte[] data = rba((int)length);
+    byte[] result = new byte[data.length];
+    try (Cuda.Stream stream1 = new Cuda.Stream(true);
+         Cuda.Stream stream2 = new Cuda.Stream(true);
+         HostMemoryBuffer hostBuffer = PinnedMemoryPool.allocate(data.length);
+         DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(data.length);
+         HostMemoryBuffer hostBuffer2 = PinnedMemoryPool.allocate(data.length)) {
+      hostBuffer.setBytes(0, data, 0, data.length);
+      devBuffer.copyFromHostBuffer(hostBuffer, stream1);
+      hostBuffer2.copyFromDeviceBuffer(devBuffer, stream2);
+      hostBuffer2.getBytes(result, 0, 0, result.length);
+      assertArrayEquals(data, result);
+    }
+  }
+
+  @Test
+  public void simpleEventTest() {
+    long length = 1 * 1024 * 1024;
+    initPinnedPoolIfNeeded(length * 2);
+    byte[] data = rba((int)length);
+    byte[] result = new byte[data.length];
+    try (Cuda.Stream stream1 = new Cuda.Stream(true);
+         Cuda.Stream stream2 = new Cuda.Stream(true);
+         Cuda.Event event1 = new Cuda.Event();
+         Cuda.Event event2 = new Cuda.Event();
+         HostMemoryBuffer hostBuffer = PinnedMemoryPool.allocate(data.length);
+         DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(data.length);
+         HostMemoryBuffer hostBuffer2 = PinnedMemoryPool.allocate(data.length)) {
+      hostBuffer.setBytes(0, data, 0, data.length);
+      devBuffer.copyFromHostBufferAsync(hostBuffer, stream1);
+      event1.record(stream1);
+      stream2.waitOn(event1);
+      hostBuffer2.copyFromDeviceBufferAsync(devBuffer, stream2);
+      event2.record(stream2);
+      event2.sync();
+      hostBuffer2.getBytes(result, 0, 0, result.length);
+      assertArrayEquals(data, result);
+    }
+  }
+
+  @Test
+  public void simpleEventQueryTest() throws InterruptedException {
+    long length = 1 * 1024 * 1024;
+    initPinnedPoolIfNeeded(length * 2);
+    byte[] data = rba((int)length);
+    byte[] result = new byte[data.length];
+    try (Cuda.Stream stream1 = new Cuda.Stream(true);
+         Cuda.Stream stream2 = new Cuda.Stream(true);
+         Cuda.Event event1 = new Cuda.Event();
+         Cuda.Event event2 = new Cuda.Event();
+         HostMemoryBuffer hostBuffer = PinnedMemoryPool.allocate(data.length);
+         DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(data.length);
+         HostMemoryBuffer hostBuffer2 = PinnedMemoryPool.allocate(data.length)) {
+      hostBuffer.setBytes(0, data, 0, data.length);
+      devBuffer.copyFromHostBufferAsync(hostBuffer, stream1);
+      event1.record(stream1);
+      stream2.waitOn(event1);
+      hostBuffer2.copyFromDeviceBufferAsync(devBuffer, stream2);
+      event2.record(stream2);
+      while (!event2.hasCompleted()) {
+        Thread.sleep(100);
+      }
+      hostBuffer2.getBytes(result, 0, 0, result.length);
+      assertArrayEquals(data, result);
+    }
+  }
+
+  @Test
+  public void simpleStreamSynchTest() {
+    long length = 1 * 1024 * 1024;
+    initPinnedPoolIfNeeded(length * 2);
+    byte[] data = rba((int)length);
+    byte[] result = new byte[data.length];
+    try (Cuda.Stream stream1 = new Cuda.Stream(true);
+         Cuda.Stream stream2 = new Cuda.Stream(true);
+         HostMemoryBuffer hostBuffer = PinnedMemoryPool.allocate(data.length);
+         DeviceMemoryBuffer devBuffer = DeviceMemoryBuffer.allocate(data.length);
+         HostMemoryBuffer hostBuffer2 = PinnedMemoryPool.allocate(data.length)) {
+      hostBuffer.setBytes(0, data, 0, data.length);
+      devBuffer.copyFromHostBufferAsync(hostBuffer, stream1);
+      stream1.sync();
+      hostBuffer2.copyFromDeviceBufferAsync(devBuffer, stream2);
+      stream2.sync();
+      hostBuffer2.getBytes(result, 0, 0, result.length);
+      assertArrayEquals(data, result);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/IfElseTest.java b/java/src/test/java/ai/rapids/cudf/IfElseTest.java
new file mode 100644
index 0000000..a078bef
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/IfElseTest.java
@@ -0,0 +1,1181 @@
+/*
+ *
+ *  Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.Arguments;
+import org.junit.jupiter.params.provider.MethodSource;
+
+import java.util.stream.Stream;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class IfElseTest extends CudfTestBase {
+  private static Stream<Arguments> createBooleanVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Boolean[]{false, false, false, true, true},
+            new Boolean[]{true, true, true, false, false},
+            new Boolean[]{true, true, false, false, true}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Boolean[]{null, false, false, true, null},
+            new Boolean[]{true, null, null, false, null},
+            new Boolean[]{true, null, false, false, null})
+    );
+  }
+
+  private static Stream<Arguments> createBooleanVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Boolean[]{false, false, false, true, true},
+            Boolean.FALSE,
+            new Boolean[]{false, false, false, false, true}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Boolean[]{null, false, false, true, null},
+            null,
+            new Boolean[]{null, null, false, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createBooleanSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            Boolean.FALSE,
+            new Boolean[]{false, false, false, true, true},
+            new Boolean[]{false, false, false, true, false}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Boolean[]{null, false, false, true, null},
+            new Boolean[]{null, false, null, true, null})
+    );
+  }
+
+  private static Stream<Arguments> createBooleanSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            Boolean.FALSE,
+            Boolean.TRUE,
+            new Boolean[]{true, true, false, true, false}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            Boolean.FALSE,
+            new Boolean[]{false, false, null, false, null}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            Boolean.FALSE,
+            null,
+            new Boolean[]{null, null, false, null, false})
+    );
+  }
+
+  private static Stream<Arguments> createByteVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Byte[]{(byte) 10, (byte) -128, (byte) 127, (byte) -1, (byte) 0},
+            new Byte[]{(byte) -2, (byte) 1, (byte) 16, (byte) -63, (byte) 42},
+            new Byte[]{(byte) -2, (byte) 1, (byte) 127, (byte) -63, (byte) 0}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Byte[]{null, (byte) -128, (byte) 127, (byte) -1, null},
+            new Byte[]{(byte) -2, null, null, (byte) -63, null},
+            new Byte[]{(byte) -2, null, (byte) 127, (byte) -63, null})
+        );
+  }
+
+  private static Stream<Arguments> createByteVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Byte[]{(byte) 10, (byte) -128, (byte) 127, (byte) -1, (byte) 0},
+            (byte) -2,
+            new Byte[]{(byte) -2, (byte) -2, (byte) 127, (byte) -2, (byte) 0}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Byte[]{null, (byte) -128, (byte) 127, (byte) -1, null},
+            null,
+            new Byte[]{null, null, (byte) 127, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createByteSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            (byte) -128,
+            new Byte[]{(byte) -2, (byte) 1, (byte) 16, (byte) -63, (byte) 42},
+            new Byte[]{(byte) -2, (byte) 1, (byte) -128, (byte) -63, (byte) -128}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Byte[]{null, (byte) 1, (byte) 16, null, (byte) 42},
+            new Byte[]{null, (byte) 1, null, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createByteSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            (byte) -128,
+            (byte) 42,
+            new Byte[]{(byte) 42, (byte) 42, (byte) -128, (byte) 42, (byte) -128}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            (byte) -128,
+            null,
+            new Byte[]{null, null, (byte) -128, null, (byte) -128})
+    );
+  }
+
+  private static Stream<Arguments> createShortVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Short[]{(short) 1024, (short) -128, (short) 127, (short) -1, (short) 0},
+            new Short[]{(short) -2048, (short) 1, (short) 16, (short) -63, (short) 42},
+            new Short[]{(short) -2048, (short) 1, (short) 127, (short) -63, (short) 0}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Short[]{null, (short) -128, (short) 127, (short) -1, null},
+            new Short[]{(short) -2048, null, null, (short) -63, null},
+            new Short[]{(short) -2048, null, (short) 127, (short) -63, null})
+    );
+  }
+
+  private static Stream<Arguments> createShortVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Short[]{(short) 1024, (short) -128, (short) 127, (short) -1, (short) 0},
+            (short) -2048,
+            new Short[]{(short) -2048, (short) -2048, (short) 127, (short) -2048, (short) 0}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Short[]{null, (short) -128, (short) 127, (short) -1, null},
+            null,
+            new Short[]{null, null, (short) 127, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createShortSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            (short) -1287,
+            new Short[]{(short) -2048, (short) 1, (short) 16, (short) -63, (short) 42},
+            new Short[]{(short) -2048, (short) 1, (short) -1287, (short) -63, (short) -1287}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Short[]{null, (short) 1, (short) 16, null, (short) 42},
+            new Short[]{null, (short) 1, null, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createShortSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            (short) -1287,
+            (short) 421,
+            new Short[]{(short) 421, (short) 421, (short) -1287, (short) 421, (short) -1287}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            (short) -1287,
+            null,
+            new Short[]{null, null, (short) -1287, null, (short) -1287})
+    );
+  }
+
+  private static Stream<Arguments> createIntVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Integer[]{10240, -128, 127, -1, 0},
+            new Integer[]{-20480, 1, 16, -63, 42},
+            new Integer[]{-20480, 1, 127, -63, 0}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Integer[]{null, -128, 127, -1, null},
+            new Integer[]{-20480, null, null, -63, null},
+            new Integer[]{-20480, null, 127, -63, null})
+    );
+  }
+
+  private static Stream<Arguments> createIntVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Integer[]{10240, -128, 127, -1, 0},
+            -20480,
+            new Integer[]{-20480, -20480, 127, -20480, 0}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Integer[]{null, -128, 127, -1, null},
+            null,
+            new Integer[]{null, null, 127, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createIntSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -12875,
+            new Integer[]{-2, 1, 16, -63, 42},
+            new Integer[]{-2, 1, -12875, -63, -12875}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Integer[]{null, 1, 16, null, 42},
+            new Integer[]{null, 1, null, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createIntSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -12875,
+            42321,
+            new Integer[]{42321, 42321, -12875, 42321, -12875}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -12875,
+            null,
+            new Integer[]{null, null, -12875, null, -12875})
+    );
+  }
+
+  private static Stream<Arguments> createLongVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Long[]{1024056789L, -128L, 127L, -1L, 0L},
+            new Long[]{-2048012345L, 1L, 16L, -63L, 42L},
+            new Long[]{-2048012345L, 1L, 127L, -63L, 0L}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Long[]{null, -128L, 127L, -1L, null},
+            new Long[]{-2048012345L, null, null, -63L, null},
+            new Long[]{-2048012345L, null, 127L, -63L, null})
+    );
+  }
+
+  private static Stream<Arguments> createLongVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Long[]{10240L, -128L, 127L, -1L, 0L},
+            -2048012345L,
+            new Long[]{-2048012345L, -2048012345L, 127L, -2048012345L, 0L}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Long[]{null, -128L, 127L, -1L, null},
+            null,
+            new Long[]{null, null, 127L, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createLongSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -12875L,
+            new Long[]{-2L, 1L, 16L, -63L, 42L},
+            new Long[]{-2L, 1L, -12875L, -63L, -12875L}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Long[]{null, 1L, 16L, null, 42L},
+            new Long[]{null, 1L, null, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createLongSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -12875L,
+            42321L,
+            new Long[]{42321L, 42321L, -12875L, 42321L, -12875L}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -12875L,
+            null,
+            new Long[]{null, null, -12875L, null, -12875L})
+    );
+  }
+
+  private static Stream<Arguments> createFloatVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Float[]{10240.56789f, -128f, 127f, -1f, 0f},
+            new Float[]{-20480.12345f, 1f, 16f, -6.3f, 42f},
+            new Float[]{-20480.12345f, 1f, 127f, -6.3f, 0f}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Float[]{null, -128f, 127f, -1f, null},
+            new Float[]{-20480.12345f, null, null, -6.3f, null},
+            new Float[]{-20480.12345f, null, 127f, -6.3f, null})
+    );
+  }
+
+  private static Stream<Arguments> createFloatVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Float[]{10240f, -128f, 127f, -1f, 0f},
+            -20480.12345f,
+            new Float[]{-20480.12345f, -20480.12345f, 127f, -20480.12345f, 0f}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Float[]{null, -128f, 127f, -1f, null},
+            null,
+            new Float[]{null, null, 127f, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createFloatSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -128.75f,
+            new Float[]{-2f, 1f, 16f, -6.3f, 42f},
+            new Float[]{-2f, 1f, -128.75f, -6.3f, -128.75f}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Float[]{null, 1f, 16f, null, 42f},
+            new Float[]{null, 1f, null, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createFloatSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -128.75f,
+            4232.1f,
+            new Float[]{4232.1f, 4232.1f, -128.75f, 4232.1f, -128.75f}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -128.75f,
+            null,
+            new Float[]{null, null, -128.75f, null, -128.75f})
+    );
+  }
+
+  private static Stream<Arguments> createDoubleVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Double[]{10240.56789, -128., 127., -1., 0.},
+            new Double[]{-20480.12345, 1., 16., -6.3, 42.},
+            new Double[]{-20480.12345, 1., 127., -6.3, 0.}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Double[]{null, -128., 127., -1., null},
+            new Double[]{-20480.12345, null, null, -6.3, null},
+            new Double[]{-20480.12345, null, 127., -6.3, null})
+    );
+  }
+
+  private static Stream<Arguments> createDoubleVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Double[]{10240., -128., 127., -1., 0.},
+            -20480.12345,
+            new Double[]{-20480.12345, -20480.12345, 127., -20480.12345, 0.}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new Double[]{null, -128., 127., -1., null},
+            null,
+            new Double[]{null, null, 127., null, null})
+    );
+  }
+
+  private static Stream<Arguments> createDoubleSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -128.75,
+            new Double[]{-2., 1., 16., -6.3, 42.},
+            new Double[]{-2., 1., -128.75, -6.3, -128.75}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new Double[]{null, 1., 16., null, 42.},
+            new Double[]{null, 1., null, null, null})
+    );
+  }
+
+  private static Stream<Arguments> createDoubleSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -128.75,
+            4232.1,
+            new Double[]{4232.1, 4232.1, -128.75, 4232.1, -128.75}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            -128.75,
+            null,
+            new Double[]{null, null, -128.75, null, -128.75})
+    );
+  }
+
+  private static Stream<Arguments> createStringVVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new String[]{"hello", "world", "how", "are", "you"},
+            new String[]{"why", "fine", "thanks", "for", "asking"},
+            new String[]{"why", "fine", "how", "for", "you"}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new String[]{null, "world", "how", "are", null},
+            new String[]{"why", null, null, "for", null},
+            new String[]{"why", null, "how", "for", null})
+    );
+  }
+
+  private static Stream<Arguments> createStringVSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new String[]{"hello", "world", "how", "are", "you"},
+            "foo",
+            new String[]{"foo", "foo", "how", "foo", "you"}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            new String[]{null, "world", "how", "are", null},
+            null,
+            new String[]{null, null, "how", null, null})
+    );
+  }
+
+  private static Stream<Arguments> createStringSVParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            "bar",
+            new String[]{"why", "fine", "thanks", "for", "asking"},
+            new String[]{"why", "fine", "bar", "for", "bar"}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            new String[]{null, "world", "how", "are", null},
+            new String[]{null, "world", null, "are", null})
+    );
+  }
+
+  private static Stream<Arguments> createStringSSParams() {
+    return Stream.of(
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            "hello",
+            "world",
+            new String[]{"world", "world", "hello", "world", "hello"}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            null,
+            "world",
+            new String[]{"world", "world", null, "world", null}),
+        Arguments.of(
+            new Boolean[]{false, false, true, false, true},
+            "hello",
+            null,
+            new String[]{null, null, "hello", null, "hello"})
+    );
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBooleanVVParams")
+  void testBooleanVV(Boolean[] predVals, Boolean[] trueVals, Boolean[] falseVals, Boolean[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedBooleans(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedBooleans(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBooleanVSParams")
+  void testBooleanVS(Boolean[] predVals, Boolean[] trueVals, Boolean falseVal, Boolean[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedBooleans(trueVals);
+         Scalar falseScalar = Scalar.fromBool(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBooleanSVParams")
+  void testBooleanSV(Boolean[] predVals, Boolean trueVal, Boolean[] falseVals, Boolean[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromBool(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedBooleans(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBooleanSSParams")
+  void testBooleanSS(Boolean[] predVals, Boolean trueVal, Boolean falseVal, Boolean[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromBool(trueVal);
+         Scalar falseScalar = Scalar.fromBool(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createByteVVParams")
+  void testByteVV(Boolean[] predVals, Byte[] trueVals, Byte[] falseVals, Byte[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedBytes(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedBytes(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedBytes(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createByteVSParams")
+  void testByteVS(Boolean[] predVals, Byte[] trueVals, Byte falseVal, Byte[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedBytes(trueVals);
+         Scalar falseScalar = Scalar.fromByte(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedBytes(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createByteSVParams")
+  void testBytesSV(Boolean[] predVals, Byte trueVal, Byte[] falseVals, Byte[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromByte(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedBytes(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedBytes(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createByteSSParams")
+  void testBytesSS(Boolean[] predVals, Byte trueVal, Byte falseVal, Byte[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromByte(trueVal);
+         Scalar falseScalar = Scalar.fromByte(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedBytes(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createShortVVParams")
+  void testShortVV(Boolean[] predVals, Short[] trueVals, Short[] falseVals, Short[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedShorts(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedShorts(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedShorts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createShortVSParams")
+  void testShortVS(Boolean[] predVals, Short[] trueVals, Short falseVal, Short[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedShorts(trueVals);
+         Scalar falseScalar = Scalar.fromShort(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedShorts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createShortSVParams")
+  void testShortsSV(Boolean[] predVals, Short trueVal, Short[] falseVals, Short[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromShort(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedShorts(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedShorts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createShortSSParams")
+  void testShortsSS(Boolean[] predVals, Short trueVal, Short falseVal, Short[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromShort(trueVal);
+         Scalar falseScalar = Scalar.fromShort(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedShorts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntVVParams")
+  void testIntVV(Boolean[] predVals, Integer[] trueVals, Integer[] falseVals, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedInts(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedInts(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntVSParams")
+  void testIntVS(Boolean[] predVals, Integer[] trueVals, Integer falseVal, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedInts(trueVals);
+         Scalar falseScalar = Scalar.fromInt(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntSVParams")
+  void testIntsSV(Boolean[] predVals, Integer trueVal, Integer[] falseVals, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromInt(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedInts(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntSSParams")
+  void testIntsSS(Boolean[] predVals, Integer trueVal, Integer falseVal, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromInt(trueVal);
+         Scalar falseScalar = Scalar.fromInt(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVVParams")
+  void testLongVV(Boolean[] predVals, Long[] trueVals, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedLongs(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVSParams")
+  void testLongVS(Boolean[] predVals, Long[] trueVals, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedLongs(trueVals);
+         Scalar falseScalar = Scalar.fromLong(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSVParams")
+  void testLongsSV(Boolean[] predVals, Long trueVal, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromLong(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSSParams")
+  void testLongsSS(Boolean[] predVals, Long trueVal, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromLong(trueVal);
+         Scalar falseScalar = Scalar.fromLong(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createFloatVVParams")
+  void testFloatVV(Boolean[] predVals, Float[] trueVals, Float[] falseVals, Float[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedFloats(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedFloats(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createFloatVSParams")
+  void testFloatVS(Boolean[] predVals, Float[] trueVals, Float falseVal, Float[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedFloats(trueVals);
+         Scalar falseScalar = Scalar.fromFloat(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createFloatSVParams")
+  void testFloatsSV(Boolean[] predVals, Float trueVal, Float[] falseVals, Float[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromFloat(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedFloats(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createFloatSSParams")
+  void testFloatsSS(Boolean[] predVals, Float trueVal, Float falseVal, Float[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromFloat(trueVal);
+         Scalar falseScalar = Scalar.fromFloat(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createDoubleVVParams")
+  void testDoubleVV(Boolean[] predVals, Double[] trueVals, Double[] falseVals, Double[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedDoubles(trueVals);
+         ColumnVector falseVec = ColumnVector.fromBoxedDoubles(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createDoubleVSParams")
+  void testDoubleVS(Boolean[] predVals, Double[] trueVals, Double falseVal, Double[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromBoxedDoubles(trueVals);
+         Scalar falseScalar = Scalar.fromDouble(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createDoubleSVParams")
+  void testDoublesSV(Boolean[] predVals, Double trueVal, Double[] falseVals, Double[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromDouble(trueVal);
+         ColumnVector falseVec = ColumnVector.fromBoxedDoubles(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createDoubleSSParams")
+  void testDoublesSS(Boolean[] predVals, Double trueVal, Double falseVal, Double[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromDouble(trueVal);
+         Scalar falseScalar = Scalar.fromDouble(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntVVParams")
+  void testTimestampDaysVV(Boolean[] predVals, Integer[] trueVals, Integer[] falseVals, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampDaysFromBoxedInts(trueVals);
+         ColumnVector falseVec = ColumnVector.timestampDaysFromBoxedInts(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.timestampDaysFromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntVSParams")
+  void testTimestampDaysVS(Boolean[] predVals, Integer[] trueVals, Integer falseVal, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampDaysFromBoxedInts(trueVals);
+         Scalar falseScalar = Scalar.timestampDaysFromInt(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.timestampDaysFromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntSVParams")
+  void testTimestampDaysSV(Boolean[] predVals, Integer trueVal, Integer[] falseVals, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampDaysFromInt(trueVal);
+         ColumnVector falseVec = ColumnVector.timestampDaysFromBoxedInts(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.timestampDaysFromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntSSParams")
+  void testTimestampDaysSS(Boolean[] predVals, Integer trueVal, Integer falseVal, Integer[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampDaysFromInt(trueVal);
+         Scalar falseScalar = Scalar.timestampDaysFromInt(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.timestampDaysFromBoxedInts(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVVParams")
+  void testTimestampSecondsVV(Boolean[] predVals, Long[] trueVals, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampSecondsFromBoxedLongs(trueVals);
+         ColumnVector falseVec = ColumnVector.timestampSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.timestampSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVSParams")
+  void testTimestampSecondsVS(Boolean[] predVals, Long[] trueVals, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampSecondsFromBoxedLongs(trueVals);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_SECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.timestampSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSVParams")
+  void testTimestampSecondsSV(Boolean[] predVals, Long trueVal, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_SECONDS, trueVal);
+         ColumnVector falseVec = ColumnVector.timestampSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.timestampSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSSParams")
+  void testTimestampSecondsSS(Boolean[] predVals, Long trueVal, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_SECONDS, trueVal);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_SECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.timestampSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVVParams")
+  void testTimestampMilliSecondsVV(Boolean[] predVals, Long[] trueVals, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampMilliSecondsFromBoxedLongs(trueVals);
+         ColumnVector falseVec = ColumnVector.timestampMilliSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.timestampMilliSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVSParams")
+  void testTimestampMilliSecondsVS(Boolean[] predVals, Long[] trueVals, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampMilliSecondsFromBoxedLongs(trueVals);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MILLISECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.timestampMilliSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSVParams")
+  void testTimestampMilliSecondsSV(Boolean[] predVals, Long trueVal, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MILLISECONDS, trueVal);
+         ColumnVector falseVec = ColumnVector.timestampMilliSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.timestampMilliSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSSParams")
+  void testTimestampMilliSecondsSS(Boolean[] predVals, Long trueVal, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MILLISECONDS, trueVal);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MILLISECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.timestampMilliSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVVParams")
+  void testTimestampMicroSecondsVV(Boolean[] predVals, Long[] trueVals, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampMicroSecondsFromBoxedLongs(trueVals);
+         ColumnVector falseVec = ColumnVector.timestampMicroSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.timestampMicroSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVSParams")
+  void testTimestampMicroSecondsVS(Boolean[] predVals, Long[] trueVals, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampMicroSecondsFromBoxedLongs(trueVals);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MICROSECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.timestampMicroSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSVParams")
+  void testTimestampMicroSecondsSV(Boolean[] predVals, Long trueVal, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MICROSECONDS, trueVal);
+         ColumnVector falseVec = ColumnVector.timestampMicroSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.timestampMicroSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSSParams")
+  void testTimestampMicroSecondsSS(Boolean[] predVals, Long trueVal, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MICROSECONDS, trueVal);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_MICROSECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.timestampMicroSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVVParams")
+  void testTimestampNanoSecondsVV(Boolean[] predVals, Long[] trueVals, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampNanoSecondsFromBoxedLongs(trueVals);
+         ColumnVector falseVec = ColumnVector.timestampNanoSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.timestampNanoSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongVSParams")
+  void testTimestampNanoSecondsVS(Boolean[] predVals, Long[] trueVals, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.timestampNanoSecondsFromBoxedLongs(trueVals);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_NANOSECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.timestampNanoSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSVParams")
+  void testTimestampNanoSecondsSV(Boolean[] predVals, Long trueVal, Long[] falseVals, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_NANOSECONDS, trueVal);
+         ColumnVector falseVec = ColumnVector.timestampNanoSecondsFromBoxedLongs(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.timestampNanoSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createLongSSParams")
+  void testTimestampNanoSecondsSS(Boolean[] predVals, Long trueVal, Long falseVal, Long[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.timestampFromLong(DType.TIMESTAMP_NANOSECONDS, trueVal);
+         Scalar falseScalar = Scalar.timestampFromLong(DType.TIMESTAMP_NANOSECONDS, falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.timestampNanoSecondsFromBoxedLongs(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createStringVVParams")
+  void testStringVV(Boolean[] predVals, String[] trueVals, String[] falseVals, String[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromStrings(trueVals);
+         ColumnVector falseVec = ColumnVector.fromStrings(falseVals);
+         ColumnVector result = pred.ifElse(trueVec, falseVec);
+         ColumnVector expected = ColumnVector.fromStrings(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createStringVSParams")
+  void testStringVS(Boolean[] predVals, String[] trueVals, String falseVal, String[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         ColumnVector trueVec = ColumnVector.fromStrings(trueVals);
+         Scalar falseScalar = Scalar.fromString(falseVal);
+         ColumnVector result = pred.ifElse(trueVec, falseScalar);
+         ColumnVector expected = ColumnVector.fromStrings(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createStringSVParams")
+  void testStringSV(Boolean[] predVals, String trueVal, String[] falseVals, String[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromString(trueVal);
+         ColumnVector falseVec = ColumnVector.fromStrings(falseVals);
+         ColumnVector result = pred.ifElse(trueScalar, falseVec);
+         ColumnVector expected = ColumnVector.fromStrings(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createStringSSParams")
+  void testStringSS(Boolean[] predVals, String trueVal, String falseVal, String[] expectVals) {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(predVals);
+         Scalar trueScalar = Scalar.fromString(trueVal);
+         Scalar falseScalar = Scalar.fromString(falseVal);
+         ColumnVector result = pred.ifElse(trueScalar, falseScalar);
+         ColumnVector expected = ColumnVector.fromStrings(expectVals)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testMismatchedTypesVV() {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(true, false, false, true);
+         ColumnVector trueVec = ColumnVector.fromBoxedInts(1, 2, 3, 4);
+         ColumnVector falseVec = ColumnVector.fromBoxedLongs(5L, 6L, 7L, 8L)) {
+      assertThrows(CudfException.class, () -> pred.ifElse(trueVec, falseVec));
+    }
+  }
+
+  @Test
+  void testMismatchedTypesVS() {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(true, false, false, true);
+         ColumnVector trueVec = ColumnVector.fromBoxedLongs(1L, 2L, 3L, 4L);
+         Scalar falseScalar = Scalar.fromString("hey")) {
+      assertThrows(CudfException.class, () -> pred.ifElse(trueVec, falseScalar));
+    }
+  }
+
+  @Test
+  void testMismatchedTypesSV() {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(true, false, false, true);
+         Scalar trueScalar = Scalar.fromByte((byte) 1);
+         ColumnVector falseVec = ColumnVector.fromBoxedInts(0, 2, 4, 6)) {
+      assertThrows(CudfException.class, () -> pred.ifElse(trueScalar, falseVec));
+    }
+  }
+
+  @Test
+  void testMismatchedTypesSS() {
+    try (ColumnVector pred = ColumnVector.fromBoxedBooleans(true, false, false, true);
+         Scalar trueScalar = Scalar.fromByte((byte) 1);
+         Scalar falseScalar = Scalar.fromString("hey")) {
+      assertThrows(CudfException.class, () -> pred.ifElse(trueScalar, falseScalar));
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/IntColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/IntColumnVectorTest.java
new file mode 100644
index 0000000..7d6311f
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/IntColumnVectorTest.java
@@ -0,0 +1,227 @@
+/*
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import java.util.Random;
+import java.util.function.Consumer;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class IntColumnVectorTest extends CudfTestBase {
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (ColumnVector intColumnVector = ColumnVector.build(DType.INT32, 3, (b) -> b.append(1))) {
+      assertFalse(intColumnVector.hasNulls());
+    }
+    try (ColumnVector intColumnVector = ColumnBuilderHelper.buildOnDevice(
+        new HostColumnVector.BasicType(true, DType.INT32), 3, (b) -> b.append(1))) {
+      assertFalse(intColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEquals(cv.getInt(0), 2);
+      assertEquals(cv.getInt(1), 3);
+      assertEquals(cv.getInt(2), 5);
+    };
+    try (HostColumnVector cv = HostColumnVector.fromInts(2, 3, 5)) {
+      verify.accept(cv);
+    }
+    try (HostColumnVector cv = ColumnBuilderHelper.fromInts(true, 2, 3, 5)) {
+      verify.accept(cv);
+    }
+  }
+
+  @Test
+  public void testUnsignedArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEquals(0xfedcba98L, Integer.toUnsignedLong(cv.getInt(0)));
+      assertEquals(0x80000000L, Integer.toUnsignedLong(cv.getInt(1)));
+      assertEquals(5, Integer.toUnsignedLong(cv.getInt(2)));
+    };
+    try (HostColumnVector cv = HostColumnVector.fromUnsignedInts(0xfedcba98, 0x80000000, 5)) {
+      verify.accept(cv);
+    }
+    try (HostColumnVector cv = ColumnBuilderHelper.fromInts(false, 0xfedcba98, 0x80000000, 5)) {
+      verify.accept(cv);
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertThrows(AssertionError.class, () -> cv.getInt(3));
+      assertFalse(cv.hasNulls());
+    };
+    try (HostColumnVector icv = HostColumnVector.fromInts(2, 3, 5)) {
+      verify.accept(icv);
+    }
+    try (HostColumnVector icv = ColumnBuilderHelper.fromInts(true, 2, 3, 5)) {
+      verify.accept(icv);
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertThrows(AssertionError.class, () -> cv.getInt(-1));
+    };
+    try (HostColumnVector icv = HostColumnVector.fromInts(2, 3, 5)) {
+      verify.accept(icv);
+    }
+    try (HostColumnVector icv = ColumnBuilderHelper.fromInts(true, 2, 3, 5)) {
+      verify.accept(icv);
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector cv = HostColumnVector.fromBoxedInts(2, 3, 4, 5, 6, 7, null, null)) {
+      verify.accept(cv);
+    }
+    try (HostColumnVector cv = ColumnBuilderHelper.fromBoxedInts(true, 2, 3, 4, 5, 6, 7, null, null)) {
+      verify.accept(cv);
+    }
+  }
+
+  @Test
+  public void testAddingUnsignedNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertEquals(0xfedbca98L, Integer.toUnsignedLong(cv.getInt(4)));
+      assertEquals(0x80000000L, Integer.toUnsignedLong(cv.getInt(5)));
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector cv = HostColumnVector.fromBoxedUnsignedInts(
+            2, 3, 4, 5, 0xfedbca98, 0x80000000, null, null)) {
+      verify.accept(cv);
+    }
+    try (HostColumnVector cv = ColumnBuilderHelper.fromBoxedInts(false,
+        2, 3, 4, 5, 0xfedbca98, 0x80000000, null, null)) {
+      verify.accept(cv);
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.INT32, 3)) {
+      assertThrows(AssertionError.class,
+          () -> builder.append(2).appendNull().appendArray(new int[]{5, 4}).build());
+    }
+  }
+
+  @Test
+  public void testCastToInt() {
+    try (ColumnVector doubleColumnVector = ColumnVector.fromDoubles(new double[]{4.3, 3.8, 8});
+         ColumnVector shortColumnVector = ColumnVector.fromShorts(new short[]{100});
+         ColumnVector intColumnVector1 = doubleColumnVector.asInts();
+         ColumnVector expected1 = ColumnVector.fromInts(4, 3, 8);
+         ColumnVector intColumnVector2 = shortColumnVector.asInts();
+         ColumnVector expected2 = ColumnVector.fromInts(100)) {
+      AssertUtils.assertColumnsAreEqual(expected1, intColumnVector1);
+      AssertUtils.assertColumnsAreEqual(expected2, intColumnVector2);
+    }
+  }
+
+  @Test
+  void testAppendVector() {
+    Random random = new Random(192312989128L);
+    for (int dstSize = 1; dstSize <= 100; dstSize++) {
+      for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+        final int srcSize = dstSize - dstPrefilledSize;
+        for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+          try (Builder dst = HostColumnVector.builder(DType.INT32, dstSize);
+               HostColumnVector src = HostColumnVector.build(DType.INT32, srcSize, (b) -> {
+                 for (int i = 0; i < srcSize; i++) {
+                   if (random.nextBoolean()) {
+                     b.appendNull();
+                   } else {
+                     b.append(random.nextInt());
+                   }
+                 }
+               });
+               Builder gtBuilder = HostColumnVector.builder(DType.INT32,
+                   dstPrefilledSize)) {
+            assertEquals(dstSize, srcSize + dstPrefilledSize);
+            //add the first half of the prefilled list
+            for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+              if (random.nextBoolean()) {
+                dst.appendNull();
+                gtBuilder.appendNull();
+              } else {
+                int a = random.nextInt();
+                dst.append(a);
+                gtBuilder.append(a);
+              }
+            }
+            // append the src vector
+            dst.append(src);
+            try (HostColumnVector dstVector = dst.build();
+                 HostColumnVector gt = gtBuilder.build()) {
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                assertEquals(gt.isNull(i), dstVector.isNull(i));
+                if (!gt.isNull(i)) {
+                  assertEquals(gt.getInt(i), dstVector.getInt(i));
+                }
+              }
+              for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                assertEquals(src.isNull(j), dstVector.isNull(i));
+                if (!src.isNull(j)) {
+                  assertEquals(src.getInt(j), dstVector.getInt(i));
+                }
+              }
+              if (dstVector.hasValidityVector()) {
+                long maxIndex =
+                    BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                  assertFalse(dstVector.isNullExtendedRange(i));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/LargeTableTest.java b/java/src/test/java/ai/rapids/cudf/LargeTableTest.java
new file mode 100644
index 0000000..d5e0942
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/LargeTableTest.java
@@ -0,0 +1,66 @@
+/*
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ */
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * Test for operations on tables with large row counts.
+ */
+public class LargeTableTest extends CudfTestBase {
+
+  static final long RMM_POOL_SIZE_LARGE = 10L * 1024 * 1024 * 1024;
+
+  public LargeTableTest() {
+    // Set large RMM pool size. Ensure that the test does not run out of memory,
+    // for large row counts.
+    super(RmmAllocationMode.POOL, RMM_POOL_SIZE_LARGE);
+  }
+
+  /**
+   * Tests that exploding large array columns will result in CudfColumnOverflowException
+   * if the column size limit is crossed.
+   */
+  @Test
+  public void testExplodeOverflow() {
+    int numRows = 1000_000;
+    int arraySize = 1000;
+    String str = "abc";
+
+    // 1 Million rows, each row being { "abc", [ 0, 0, 0... ] },
+    // with 1000 elements in the array in each row.
+    // When the second column is exploded, it produces 1 Billion rows.
+    // The string row is repeated once for each element in the array,
+    // thus producing a 1 Billion row string column, with 3 Billion chars
+    // in the child column. This should cause an overflow exception.
+    boolean [] arrBools = new boolean[arraySize];
+    for (char i = 0; i < arraySize; ++i) { arrBools[i] = false; }
+    Exception exception = assertThrows(CudfColumnSizeOverflowException.class, ()->{
+        try (Scalar strScalar = Scalar.fromString(str);
+             ColumnVector arrRow = ColumnVector.fromBooleans(arrBools);
+             Scalar arrScalar = Scalar.listFromColumnView(arrRow);
+             ColumnVector strVector = ColumnVector.fromScalar(strScalar, numRows);
+             ColumnVector arrVector = ColumnVector.fromScalar(arrScalar, numRows);
+             Table inputTable = new Table(strVector, arrVector);
+             Table outputTable = inputTable.explode(1)) {
+          assertEquals(outputTable.getColumns()[0].getRowCount(), numRows * arraySize);
+          fail("Exploding this large table should have caused a CudfColumnSizeOverflowException.");
+        }});
+    assertTrue(exception.getMessage().contains("Size of output exceeds the column size limit"));
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/LongColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/LongColumnVectorTest.java
new file mode 100644
index 0000000..193992f
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/LongColumnVectorTest.java
@@ -0,0 +1,217 @@
+/*
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import java.util.Random;
+import java.util.function.Consumer;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class LongColumnVectorTest extends CudfTestBase {
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (ColumnVector longColumnVector = ColumnVector.build(DType.INT64, 3, (b) -> b.append(1L))) {
+      assertFalse(longColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEquals(cv.getLong(0), 2);
+      assertEquals(cv.getLong(1), 3);
+      assertEquals(cv.getLong(2), 5);
+    };
+    try (HostColumnVector lcv = HostColumnVector.fromLongs(2L, 3L, 5L)) {
+      verify.accept(lcv);
+    }
+    try (HostColumnVector lcv = ColumnBuilderHelper.fromLongs(true,2L, 3L, 5L)) {
+      verify.accept(lcv);
+    }
+  }
+
+  @Test
+  public void testUnsignedArrayAllocation() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertEquals(Long.toUnsignedString(0xfedcba9876543210L),
+          Long.toUnsignedString(cv.getLong(0)));
+      assertEquals(Long.toUnsignedString(0x8000000000000000L),
+          Long.toUnsignedString(cv.getLong(1)));
+      assertEquals(5L, cv.getLong(2));
+    };
+    try (HostColumnVector lcv = HostColumnVector.fromUnsignedLongs(
+        0xfedcba9876543210L, 0x8000000000000000L, 5L)) {
+      verify.accept(lcv);
+    }
+    try (HostColumnVector lcv = ColumnBuilderHelper.fromLongs(false,
+        0xfedcba9876543210L, 0x8000000000000000L, 5L)) {
+      verify.accept(lcv);
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertThrows(AssertionError.class, () -> cv.getLong(3));
+      assertFalse(cv.hasNulls());
+    };
+    try (HostColumnVector lcv = HostColumnVector.fromLongs(2L, 3L, 5L)) {
+      verify.accept(lcv);
+    }
+    try (HostColumnVector lcv = ColumnBuilderHelper.fromLongs(true, 2L, 3L, 5L)) {
+      verify.accept(lcv);
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertFalse(cv.hasNulls());
+      assertThrows(AssertionError.class, () -> cv.getLong(-1));
+    };
+    try (HostColumnVector lcv = HostColumnVector.fromLongs(2L, 3L, 5L)) {
+      verify.accept(lcv);
+    }
+    try (HostColumnVector lcv = ColumnBuilderHelper.fromLongs(true, 2L, 3L, 5L)) {
+      verify.accept(lcv);
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector lcv = HostColumnVector.fromBoxedLongs(2L, 3L, 4L, 5L, 6L, 7L, null, null)) {
+      verify.accept(lcv);
+    }
+    try (HostColumnVector lcv = ColumnBuilderHelper.fromBoxedLongs(true,
+        2L, 3L, 4L, 5L, 6L, 7L, null, null)) {
+      verify.accept(lcv);
+    }
+  }
+
+  @Test
+  public void testAddingUnsignedNullValues() {
+    Consumer<HostColumnVector> verify = (cv) -> {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertEquals(Long.toUnsignedString(0xfedcba9876543210L),
+          Long.toUnsignedString(cv.getLong(4)));
+      assertEquals(Long.toUnsignedString(0x8000000000000000L),
+          Long.toUnsignedString(cv.getLong(5)));
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    };
+    try (HostColumnVector lcv = HostColumnVector.fromBoxedUnsignedLongs(
+        2L, 3L, 4L, 5L, 0xfedcba9876543210L, 0x8000000000000000L, null, null)) {
+      verify.accept(lcv);
+    }
+    try (HostColumnVector lcv = ColumnBuilderHelper.fromBoxedLongs(false,
+        2L, 3L, 4L, 5L, 0xfedcba9876543210L, 0x8000000000000000L, null, null)) {
+      verify.accept(lcv);
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.INT64, 3)) {
+      assertThrows(AssertionError.class,
+          () -> builder.append(2L).appendNull().append(5L).append(4L).build());
+    }
+  }
+
+  @Test
+  void testAppendVector() {
+    Random random = new Random(192312989128L);
+    for (int dstSize = 1; dstSize <= 100; dstSize++) {
+      for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+        final int srcSize = dstSize - dstPrefilledSize;
+        for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+          try (Builder dst = HostColumnVector.builder(DType.INT64, dstSize);
+               HostColumnVector src = HostColumnVector.build(DType.INT64, srcSize, (b) -> {
+                 for (int i = 0; i < srcSize; i++) {
+                   if (random.nextBoolean()) {
+                     b.appendNull();
+                   } else {
+                     b.append(random.nextLong());
+                   }
+                 }
+               });
+               Builder gtBuilder = HostColumnVector.builder(DType.INT64,
+                   dstPrefilledSize)) {
+            assertEquals(dstSize, srcSize + dstPrefilledSize);
+            //add the first half of the prefilled list
+            for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+              if (random.nextBoolean()) {
+                dst.appendNull();
+                gtBuilder.appendNull();
+              } else {
+                long a = random.nextLong();
+                dst.append(a);
+                gtBuilder.append(a);
+              }
+            }
+            // append the src vector
+            dst.append(src);
+            try (HostColumnVector dstVector = dst.build();
+                 HostColumnVector gt = gtBuilder.build()) {
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                assertEquals(gt.isNull(i), dstVector.isNull(i));
+                if (!gt.isNull(i)) {
+                  assertEquals(gt.getLong(i), dstVector.getLong(i));
+                }
+              }
+              for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                assertEquals(src.isNull(j), dstVector.isNull(i));
+                if (!src.isNull(j)) {
+                  assertEquals(src.getLong(j), dstVector.getLong(i));
+                }
+              }
+              if (dstVector.hasValidityVector()) {
+                long maxIndex =
+                    BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                  assertFalse(dstVector.isNullExtendedRange(i));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/MemoryBufferTest.java b/java/src/test/java/ai/rapids/cudf/MemoryBufferTest.java
new file mode 100644
index 0000000..ec36b4f
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/MemoryBufferTest.java
@@ -0,0 +1,220 @@
+/*
+ *
+ *  Copyright (c) 2021-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.concurrent.atomic.AtomicInteger;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+public class MemoryBufferTest extends CudfTestBase {
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  private static final byte[] BYTES = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+  private static final byte[] EXPECTED = {0, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
+
+  @Test
+  public void testAddressOutOfBoundsExceptionWhenCopying() {
+    try (HostMemoryBuffer from = hostMemoryAllocator.allocate(16);
+         HostMemoryBuffer to = hostMemoryAllocator.allocate(16)) {
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(-1, from, 0, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(16, from, 0, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(0, from, -1, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(0, from, 16, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(0, from, 0, -1, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(0, from, 0, 17, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(1, from, 0, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBuffer(0, from, 1, 16, Cuda.DEFAULT_STREAM));
+    }
+  }
+
+  @Test
+  public void testAddressOutOfBoundsExceptionWhenCopyingAsync() {
+    try (HostMemoryBuffer from = hostMemoryAllocator.allocate(16);
+         HostMemoryBuffer to = hostMemoryAllocator.allocate(16)) {
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(-1, from, 0, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(16, from, 0, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(0, from, -1, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(0, from, 16, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(0, from, 0, -1, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(0, from, 0, 17, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(1, from, 0, 16, Cuda.DEFAULT_STREAM));
+      assertThrows(AssertionError.class, () -> to.copyFromMemoryBufferAsync(0, from, 1, 16, Cuda.DEFAULT_STREAM));
+    }
+  }
+
+  @Test
+  public void testCopyingFromDeviceToDevice() {
+    try (HostMemoryBuffer in = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer from = DeviceMemoryBuffer.allocate(16);
+         DeviceMemoryBuffer to = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer out = hostMemoryAllocator.allocate(16)) {
+      in.setBytes(0, BYTES, 0, 16);
+      from.copyFromHostBuffer(in);
+      to.copyFromMemoryBuffer(0, from, 0, 16, Cuda.DEFAULT_STREAM);
+      to.copyFromMemoryBuffer(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      out.copyFromDeviceBuffer(to);
+      verifyOutput(out);
+    }
+  }
+
+  @Test
+  public void testCopyingFromDeviceToDeviceAsync() {
+    try (HostMemoryBuffer in = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer from = DeviceMemoryBuffer.allocate(16);
+         DeviceMemoryBuffer to = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer out = hostMemoryAllocator.allocate(16)) {
+      in.setBytes(0, BYTES, 0, 16);
+      from.copyFromHostBuffer(in);
+      to.copyFromMemoryBufferAsync(0, from, 0, 16, Cuda.DEFAULT_STREAM);
+      to.copyFromMemoryBufferAsync(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      out.copyFromDeviceBufferAsync(to, Cuda.DEFAULT_STREAM);
+      Cuda.DEFAULT_STREAM.sync();
+      verifyOutput(out);
+    }
+  }
+
+  @Test
+  public void testCopyingFromHostToHost() {
+    try (HostMemoryBuffer from = hostMemoryAllocator.allocate(16);
+         HostMemoryBuffer to = hostMemoryAllocator.allocate(16)) {
+      from.setBytes(0, BYTES, 0, 16);
+      to.setBytes(0, BYTES, 0, 16);
+      to.copyFromMemoryBuffer(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      verifyOutput(to);
+    }
+  }
+
+  @Test
+  public void testCopyingFromHostToHostAsync() {
+    try (HostMemoryBuffer from = hostMemoryAllocator.allocate(16);
+         HostMemoryBuffer to = hostMemoryAllocator.allocate(16)) {
+      from.setBytes(0, BYTES, 0, 16);
+      to.setBytes(0, BYTES, 0, 16);
+      to.copyFromMemoryBufferAsync(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      verifyOutput(to);
+    }
+  }
+
+  @Test
+  public void testCopyingFromHostToDevice() {
+    try (HostMemoryBuffer from = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer to = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer out = hostMemoryAllocator.allocate(16)) {
+      from.setBytes(0, BYTES, 0, 16);
+      to.copyFromMemoryBuffer(0, from, 0, 16, Cuda.DEFAULT_STREAM);
+      to.copyFromMemoryBufferAsync(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      out.copyFromDeviceBuffer(to);
+      verifyOutput(out);
+    }
+  }
+
+  @Test
+  public void testCopyingFromHostToDeviceAsync() {
+    try (HostMemoryBuffer from = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer to = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer out = hostMemoryAllocator.allocate(16)) {
+      from.setBytes(0, BYTES, 0, 16);
+      to.copyFromMemoryBufferAsync(0, from, 0, 16, Cuda.DEFAULT_STREAM);
+      to.copyFromMemoryBufferAsync(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      out.copyFromDeviceBufferAsync(to, Cuda.DEFAULT_STREAM);
+      Cuda.DEFAULT_STREAM.sync();
+      verifyOutput(out);
+    }
+  }
+
+  @Test
+  public void testCopyingFromDeviceToHost() {
+    try (HostMemoryBuffer in = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer from = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer to = hostMemoryAllocator.allocate(16)) {
+      in.setBytes(0, BYTES, 0, 16);
+      from.copyFromHostBuffer(in);
+      to.setBytes(0, BYTES, 0, 16);
+      to.copyFromMemoryBuffer(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      verifyOutput(to);
+    }
+  }
+
+  @Test
+  public void testCopyingFromDeviceToHostAsync() {
+    try (HostMemoryBuffer in = hostMemoryAllocator.allocate(16);
+         DeviceMemoryBuffer from = DeviceMemoryBuffer.allocate(16);
+         HostMemoryBuffer to = hostMemoryAllocator.allocate(16)) {
+      in.setBytes(0, BYTES, 0, 16);
+      from.copyFromHostBuffer(in);
+      to.setBytes(0, BYTES, 0, 16);
+      to.copyFromMemoryBufferAsync(1, from, 2, 3, Cuda.DEFAULT_STREAM);
+      Cuda.DEFAULT_STREAM.sync();
+      verifyOutput(to);
+    }
+  }
+
+  private void verifyOutput(HostMemoryBuffer out) {
+    byte[] bytes = new byte[16];
+    out.getBytes(bytes, 0, 0, 16);
+    assertArrayEquals(EXPECTED, bytes);
+  }
+
+  @Test
+  public void testEventHandlerIsCalledForEachClose() {
+    final AtomicInteger onClosedWasCalled = new AtomicInteger(0);
+    try (DeviceMemoryBuffer b = DeviceMemoryBuffer.allocate(256)) {
+      b.setEventHandler(refCount -> onClosedWasCalled.incrementAndGet());
+    }
+    assertEquals(1, onClosedWasCalled.get());
+    onClosedWasCalled.set(0);
+
+    try (DeviceMemoryBuffer b = DeviceMemoryBuffer.allocate(256)) {
+      b.setEventHandler(refCount -> onClosedWasCalled.incrementAndGet());
+      DeviceMemoryBuffer sliced = b.slice(0, b.getLength());
+      sliced.close();
+    }
+    assertEquals(2, onClosedWasCalled.get());
+  }
+
+  @Test
+  public void testEventHandlerIsNotCalledIfNotSet() {
+    final AtomicInteger onClosedWasCalled = new AtomicInteger(0);
+    try (DeviceMemoryBuffer b = DeviceMemoryBuffer.allocate(256)) {
+      assertNull(b.getEventHandler());
+    }
+    assertEquals(0, onClosedWasCalled.get());
+    try (DeviceMemoryBuffer b = DeviceMemoryBuffer.allocate(256)) {
+      b.setEventHandler(refCount -> onClosedWasCalled.incrementAndGet());
+      b.setEventHandler(null);
+    }
+    assertEquals(0, onClosedWasCalled.get());
+  }
+
+  @Test
+  public void testEventHandlerReturnsPreviousHandlerOnReset() {
+    try (DeviceMemoryBuffer b = DeviceMemoryBuffer.allocate(256)) {
+      MemoryBuffer.EventHandler handler = refCount -> {};
+      MemoryBuffer.EventHandler handler2 = refCount -> {};
+
+      assertNull(b.setEventHandler(handler));
+      assertEquals(handler, b.setEventHandler(null));
+
+      assertNull(b.setEventHandler(handler2));
+      assertEquals(handler2, b.setEventHandler(handler));
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/NvtxTest.java b/java/src/test/java/ai/rapids/cudf/NvtxTest.java
new file mode 100644
index 0000000..f01b0a1
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/NvtxTest.java
@@ -0,0 +1,72 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+
+public class NvtxTest {
+  @Test
+  public void testNvtxStartEndEnclosed() {
+    NvtxUniqueRange range1 = new NvtxUniqueRange("start/end", NvtxColor.RED);
+    NvtxUniqueRange range2 = new NvtxUniqueRange("enclosed start/end", NvtxColor.BLUE);
+    range2.close();
+    range1.close();
+  }
+
+  @Test
+  public void testNvtxStartEndCloseOutOfOrder() {
+    NvtxUniqueRange range1 = new NvtxUniqueRange("start/end closes first", NvtxColor.RED);
+    NvtxUniqueRange range2 = new NvtxUniqueRange("start/end closes later", NvtxColor.BLUE);
+    range1.close();
+    range2.close();
+  }
+
+  @Test
+  public void testNvtxPushPop() {
+    try(NvtxRange range1 = new NvtxRange("push/pop", NvtxColor.RED)) {
+      try(NvtxRange range2 = new NvtxRange("enclosed push/pop", NvtxColor.BLUE)) {
+      }
+    }
+  }
+
+  @Test
+  public void testNvtxPushPopEnclosingStartEnd() {
+    try(NvtxRange range1 = new NvtxRange("push/pop", NvtxColor.RED)) {
+      NvtxUniqueRange range2 = new NvtxUniqueRange("enclosed start/end", NvtxColor.BLUE);
+      range2.close();
+    }
+  }
+
+  @Test
+  public void testNvtxPushPopAndStartEndCloseOutOfOrder() {
+    NvtxUniqueRange range2;
+    try(NvtxRange range1 = new NvtxRange("push/pop closes first", NvtxColor.RED)) {
+      range2 = new NvtxUniqueRange("start/end closes later", NvtxColor.BLUE);
+    }
+    range2.close();
+  }
+
+  @Test
+  public void testNvtxUniqueRangeCloseMultipleTimes() {
+    NvtxUniqueRange range = new NvtxUniqueRange("range", NvtxColor.RED);
+    range.close();
+    assertThrows(IllegalStateException.class, () -> {
+      range.close();
+    });
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/PinnedMemoryPoolTest.java b/java/src/test/java/ai/rapids/cudf/PinnedMemoryPoolTest.java
new file mode 100644
index 0000000..16628d7
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/PinnedMemoryPoolTest.java
@@ -0,0 +1,134 @@
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.AfterEach;
+import org.junit.jupiter.api.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+class PinnedMemoryPoolTest extends CudfTestBase {
+  private static final Logger log = LoggerFactory.getLogger(PinnedMemoryPoolTest.class);
+
+  @AfterEach
+  void teardown() {
+    if (PinnedMemoryPool.isInitialized()) {
+      PinnedMemoryPool.shutdown();
+    }
+  }
+
+  @Test
+  void init() {
+    assertFalse(PinnedMemoryPool.isInitialized());
+    PinnedMemoryPool.initialize(1024*1024*500L);
+    assertTrue(PinnedMemoryPool.isInitialized());
+    PinnedMemoryPool.shutdown();
+    assertFalse(PinnedMemoryPool.isInitialized());
+  }
+
+  @Test
+  void allocate() {
+    PinnedMemoryPool.initialize(1024*1024*500L);
+    for (int i = 2048000; i < 1024*1024*1024; i = i * 2) {
+      log.warn("STARTING TEST FOR size = " + i);
+      HostMemoryBuffer buff = null;
+      HostMemoryBuffer buff2 = null;
+      HostMemoryBuffer buff3 = null;
+      try {
+        buff = PinnedMemoryPool.allocate(i);
+        assertEquals(i, buff.length);
+        buff2 = PinnedMemoryPool.allocate(i / 2);
+        assertEquals(i/2, buff2.length);
+        buff.close();
+        buff = null;
+        buff3 = PinnedMemoryPool.allocate(i * 2);
+        assertEquals(i * 2, buff3.length);
+      } finally {
+        if (buff != null) {
+          buff.close();
+        }
+        if (buff3 != null) {
+          buff3.close();
+        }
+        if (buff2 != null) {
+          buff2.close();
+        }
+      }
+      log.warn("DONE TEST FOR size = " + i + "\n");
+    }
+  }
+
+  @Test
+  void testFragmentationAndExhaustion() {
+    final long poolSize = 15 * 1024L;
+    PinnedMemoryPool.initialize(poolSize);
+    assertEquals(poolSize, PinnedMemoryPool.getAvailableBytes());
+    HostMemoryBuffer[] buffers = new HostMemoryBuffer[5];
+    try {
+      buffers[0] = PinnedMemoryPool.tryAllocate(1024);
+      assertNotNull(buffers[0]);
+      assertEquals(14*1024L, PinnedMemoryPool.getAvailableBytes());
+      buffers[1] = PinnedMemoryPool.tryAllocate(2048);
+      assertNotNull(buffers[1]);
+      assertEquals(12*1024L, PinnedMemoryPool.getAvailableBytes());
+      buffers[2] = PinnedMemoryPool.tryAllocate(4096);
+      assertNotNull(buffers[2]);
+      assertEquals(8*1024L, PinnedMemoryPool.getAvailableBytes());
+      buffers[1].close();
+      assertEquals(10*1024L, PinnedMemoryPool.getAvailableBytes());
+      buffers[1] = null;
+      buffers[1] = PinnedMemoryPool.tryAllocate(8192);
+      assertNotNull(buffers[1]);
+      assertEquals(2*1024L, PinnedMemoryPool.getAvailableBytes());
+      buffers[3] = PinnedMemoryPool.tryAllocate(2048);
+      assertNotNull(buffers[3]);
+      assertEquals(0L, PinnedMemoryPool.getAvailableBytes());
+      buffers[4] = PinnedMemoryPool.tryAllocate(64);
+      assertNull(buffers[4]);
+      buffers[0].close();
+      assertEquals(1024L, PinnedMemoryPool.getAvailableBytes());
+      buffers[0] = null;
+      buffers[4] = PinnedMemoryPool.tryAllocate(64);
+      assertNotNull(buffers[4]);
+      assertEquals(1024L - 64, PinnedMemoryPool.getAvailableBytes());
+    } finally {
+      for (HostMemoryBuffer buffer : buffers) {
+        if (buffer != null) {
+          buffer.close();
+        }
+      }
+    }
+    assertEquals(poolSize, PinnedMemoryPool.getAvailableBytes());
+  }
+
+  @Test
+  void testZeroSizedAllocation() {
+    final long poolSize = 4 * 1024L;
+    PinnedMemoryPool.initialize(poolSize);
+    assertEquals(poolSize, PinnedMemoryPool.getAvailableBytes());
+    try (HostMemoryBuffer buffer = PinnedMemoryPool.tryAllocate(0)) {
+      assertNotNull(buffer);
+      assertEquals(0, buffer.getLength());
+      assertEquals(poolSize, PinnedMemoryPool.getAvailableBytes());
+    }
+    assertEquals(poolSize, PinnedMemoryPool.getAvailableBytes());
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ReductionTest.java b/java/src/test/java/ai/rapids/cudf/ReductionTest.java
new file mode 100644
index 0000000..8cc7df1
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ReductionTest.java
@@ -0,0 +1,627 @@
+/*
+ *
+ *  Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+package ai.rapids.cudf;
+
+import com.google.common.collect.Lists;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.Arguments;
+import org.junit.jupiter.params.provider.MethodSource;
+
+import java.util.EnumSet;
+import java.util.List;
+import java.util.stream.Stream;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+class ReductionTest extends CudfTestBase {
+  public static final double DELTAD = 0.00001;
+  public static final float DELTAF = 0.001f;
+
+  // reduction operations that produce a floating point value
+  private static final EnumSet<Aggregation.Kind> FLOAT_REDUCTIONS = EnumSet.of(
+      Aggregation.Kind.MEAN,
+      Aggregation.Kind.STD,
+      Aggregation.Kind.VARIANCE,
+      Aggregation.Kind.QUANTILE);
+
+  // reduction operations that produce a floating point value
+  private static final EnumSet<Aggregation.Kind> BOOL_REDUCTIONS = EnumSet.of(
+      Aggregation.Kind.ANY,
+      Aggregation.Kind.ALL);
+
+  private static Scalar buildExpectedScalar(ReductionAggregation op,
+      HostColumnVector.DataType dataType, Object expectedObject) {
+
+    if (expectedObject == null) {
+      return Scalar.fromNull(dataType.getType());
+    }
+    if (FLOAT_REDUCTIONS.contains(op.getWrapped().kind)) {
+      if (dataType.getType().equals(DType.FLOAT32)) {
+        return Scalar.fromFloat((Float) expectedObject);
+      }
+      return Scalar.fromDouble((Double) expectedObject);
+    }
+    if (BOOL_REDUCTIONS.contains(op.getWrapped().kind)) {
+      return Scalar.fromBool((Boolean) expectedObject);
+    }
+    switch (dataType.getType().typeId) {
+    case BOOL8:
+      return Scalar.fromBool((Boolean) expectedObject);
+    case INT8:
+      return Scalar.fromByte((Byte) expectedObject);
+    case INT16:
+      return Scalar.fromShort((Short) expectedObject);
+    case INT32:
+      return Scalar.fromInt((Integer) expectedObject);
+    case INT64:
+      return Scalar.fromLong((Long) expectedObject);
+    case FLOAT32:
+      return Scalar.fromFloat((Float) expectedObject);
+    case FLOAT64:
+      return Scalar.fromDouble((Double) expectedObject);
+    case TIMESTAMP_DAYS:
+      return Scalar.timestampDaysFromInt((Integer) expectedObject);
+    case TIMESTAMP_SECONDS:
+    case TIMESTAMP_MILLISECONDS:
+    case TIMESTAMP_MICROSECONDS:
+    case TIMESTAMP_NANOSECONDS:
+      return Scalar.timestampFromLong(dataType.getType(), (Long) expectedObject);
+    case STRING:
+      return Scalar.fromString((String) expectedObject);
+    case LIST:
+      HostColumnVector.DataType et = dataType.getChild(0);
+      ColumnVector col = null;
+      try {
+        switch (et.getType().typeId) {
+        case BOOL8:
+          col = et.isNullable() ? ColumnVector.fromBoxedBooleans((Boolean[]) expectedObject) :
+              ColumnVector.fromBooleans((boolean[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case INT8:
+          col = et.isNullable() ? ColumnVector.fromBoxedBytes((Byte[]) expectedObject) :
+              ColumnVector.fromBytes((byte[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case INT16:
+          col = et.isNullable() ? ColumnVector.fromBoxedShorts((Short[]) expectedObject) :
+              ColumnVector.fromShorts((short[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case INT32:
+          col = et.isNullable() ? ColumnVector.fromBoxedInts((Integer[]) expectedObject) :
+              ColumnVector.fromInts((int[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case INT64:
+          col = et.isNullable() ? ColumnVector.fromBoxedLongs((Long[]) expectedObject) :
+              ColumnVector.fromLongs((long[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case FLOAT32:
+          col = et.isNullable() ? ColumnVector.fromBoxedFloats((Float[]) expectedObject) :
+              ColumnVector.fromFloats((float[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case FLOAT64:
+          col = et.isNullable() ? ColumnVector.fromBoxedDoubles((Double[]) expectedObject) :
+              ColumnVector.fromDoubles((double[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        case STRING:
+          col = ColumnVector.fromStrings((String[]) expectedObject);
+          return Scalar.listFromColumnView(col);
+        default:
+          throw new IllegalArgumentException("Unexpected element type of List: " + et);
+        }
+      } finally {
+        if (col != null) {
+          col.close();
+        }
+      }
+    default:
+      throw new IllegalArgumentException("Unexpected type: " + dataType);
+    }
+  }
+
+  private static Stream<Arguments> createBooleanParams() {
+    Boolean[] vals = new Boolean[]{true, true, null, false, true, false, null};
+    HostColumnVector.DataType bool = new HostColumnVector.BasicType(true, DType.BOOL8);
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Boolean[0], bool, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), new Boolean[]{null, null, null}, bool, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), vals, bool, true, 0.),
+        Arguments.of(ReductionAggregation.min(), vals, bool, false, 0.),
+        Arguments.of(ReductionAggregation.max(), vals, bool, true, 0.),
+        Arguments.of(ReductionAggregation.product(), vals, bool, false, 0.),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, bool, true, 0.),
+        Arguments.of(ReductionAggregation.mean(), vals, bool, 0.6, DELTAD),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, bool, 0.5477225575051662, DELTAD),
+        Arguments.of(ReductionAggregation.variance(), vals, bool, 0.3, DELTAD),
+        Arguments.of(ReductionAggregation.any(), vals, bool, true, 0.),
+        Arguments.of(ReductionAggregation.all(), vals, bool, false, 0.)
+    );
+  }
+
+  private static Stream<Arguments> createByteParams() {
+    Byte[] vals = new Byte[]{-1, 7, 123, null, 50, 60, 100};
+    HostColumnVector.DataType int8 = new HostColumnVector.BasicType(true, DType.INT8);
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Byte[0], int8, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), new Byte[]{null, null, null}, int8, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), vals, int8, (byte) 83, 0.),
+        Arguments.of(ReductionAggregation.min(), vals, int8, (byte) -1, 0.),
+        Arguments.of(ReductionAggregation.max(), vals, int8, (byte) 123, 0.),
+        Arguments.of(ReductionAggregation.product(), vals, int8, (byte) 160, 0.),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, int8, (byte) 47, 0.),
+        Arguments.of(ReductionAggregation.mean(), vals, int8, 56.5, DELTAD),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, int8, 49.24530434467839, DELTAD),
+        Arguments.of(ReductionAggregation.variance(), vals, int8, 2425.1, DELTAD),
+        Arguments.of(ReductionAggregation.any(), vals, int8, true, 0.),
+        Arguments.of(ReductionAggregation.all(), vals, int8, true, 0.)
+    );
+  }
+
+  private static Stream<Arguments> createShortParams() {
+    Short[] vals = new Short[]{-1, 7, 123, null, 50, 60, 100};
+    HostColumnVector.DataType int16 = new HostColumnVector.BasicType(true, DType.INT16);
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Short[0], int16, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), new Short[]{null, null, null}, int16, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), vals, int16, (short) 339, 0.),
+        Arguments.of(ReductionAggregation.min(), vals, int16, (short) -1, 0.),
+        Arguments.of(ReductionAggregation.max(), vals, int16, (short) 123, 0.),
+        Arguments.of(ReductionAggregation.product(), vals, int16, (short) -22624, 0.),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, int16, (short) 31279, 0.),
+        Arguments.of(ReductionAggregation.mean(), vals, int16, 56.5, DELTAD),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, int16, 49.24530434467839, DELTAD),
+        Arguments.of(ReductionAggregation.variance(), vals, int16, 2425.1, DELTAD),
+        Arguments.of(ReductionAggregation.any(), vals, int16, true, 0.),
+        Arguments.of(ReductionAggregation.all(), vals, int16, true, 0.)
+    );
+  }
+
+  private static Stream<Arguments> createIntParams() {
+    Integer[] vals = new Integer[]{-1, 7, 123, null, 50, 60, 100};
+    HostColumnVector.BasicType int32 = new HostColumnVector.BasicType(true, DType.INT32);
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Integer[0], int32, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), new Integer[]{null, null, null}, int32, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), vals, int32, 339, 0.),
+        Arguments.of(ReductionAggregation.min(), vals, int32, -1, 0.),
+        Arguments.of(ReductionAggregation.max(), vals, int32, 123, 0.),
+        Arguments.of(ReductionAggregation.product(), vals, int32, -258300000, 0.),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, int32, 31279, 0.),
+        Arguments.of(ReductionAggregation.mean(), vals, int32, 56.5, DELTAD),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, int32, 49.24530434467839, DELTAD),
+        Arguments.of(ReductionAggregation.variance(), vals, int32, 2425.1, DELTAD),
+        Arguments.of(ReductionAggregation.any(), vals, int32, true, 0.),
+        Arguments.of(ReductionAggregation.all(), vals, int32, true, 0.)
+    );
+  }
+
+  private static Stream<Arguments> createLongParams() {
+    Long[] vals = new Long[]{-1L, 7L, 123L, null, 50L, 60L, 100L};
+    HostColumnVector.BasicType int64 = new HostColumnVector.BasicType(true, DType.INT64);
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Long[0], int64, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), new Long[]{null, null, null}, int64, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), vals, int64, 339L, 0.),
+        Arguments.of(ReductionAggregation.min(), vals, int64, -1L, 0.),
+        Arguments.of(ReductionAggregation.max(), vals, int64, 123L, 0.),
+        Arguments.of(ReductionAggregation.product(), vals, int64, -258300000L, 0.),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, int64, 31279L, 0.),
+        Arguments.of(ReductionAggregation.mean(), vals, int64, 56.5, DELTAD),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, int64, 49.24530434467839, DELTAD),
+        Arguments.of(ReductionAggregation.variance(), vals, int64, 2425.1, DELTAD),
+        Arguments.of(ReductionAggregation.any(), vals, int64, true, 0.),
+        Arguments.of(ReductionAggregation.all(), vals, int64, true, 0.),
+        Arguments.of(ReductionAggregation.quantile(0.5), vals, int64, 55.0, DELTAD),
+        Arguments.of(ReductionAggregation.quantile(0.9), vals, int64, 111.5, DELTAD)
+    );
+  }
+
+  private static Stream<Arguments> createFloatParams() {
+    Float[] vals = new Float[]{-1f, 7f, 123f, null, 50f, 60f, 100f};
+    Float[] notNulls = new Float[]{-1f, 7f, 123f, 50f, 60f, 100f};
+    Float[] repeats = new Float[]{Float.MIN_VALUE, 7f, 7f, null, null, Float.NaN, Float.NaN, 50f, 50f, 100f};
+    HostColumnVector.BasicType fp32 = new HostColumnVector.BasicType(true, DType.FLOAT32);
+    HostColumnVector.DataType listOfFloat = new HostColumnVector.ListType(
+        true, new HostColumnVector.BasicType(true, DType.FLOAT32));
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Float[0], fp32, null, 0f),
+        Arguments.of(ReductionAggregation.sum(), new Float[]{null, null, null}, fp32, null, 0f),
+        Arguments.of(ReductionAggregation.sum(), vals, fp32, 339f, 0f),
+        Arguments.of(ReductionAggregation.min(), vals, fp32, -1f, 0f),
+        Arguments.of(ReductionAggregation.max(), vals, fp32, 123f, 0f),
+        Arguments.of(ReductionAggregation.product(), vals, fp32, -258300000f, 0f),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, fp32, 31279f, 0f),
+        Arguments.of(ReductionAggregation.mean(), vals, fp32, 56.5f, DELTAF),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, fp32, 49.24530434467839f, DELTAF),
+        Arguments.of(ReductionAggregation.variance(), vals, fp32, 2425.1f, DELTAF),
+        Arguments.of(ReductionAggregation.any(), vals, fp32, true, 0f),
+        Arguments.of(ReductionAggregation.all(), vals, fp32, true, 0f),
+        Arguments.of(ReductionAggregation.collectList(NullPolicy.INCLUDE), vals, listOfFloat, vals, 0f),
+        Arguments.of(ReductionAggregation.collectList(), vals, listOfFloat, notNulls, 0f),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.EXCLUDE, NullEquality.EQUAL, NaNEquality.ALL_EQUAL),
+            repeats, listOfFloat,
+            new Float[]{Float.MIN_VALUE, 7f, 50f, 100f, Float.NaN}, 0f),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.EQUAL, NaNEquality.ALL_EQUAL),
+            repeats, listOfFloat,
+            new Float[]{Float.MIN_VALUE, 7f, 50f, 100f, Float.NaN, null}, 0f),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.UNEQUAL, NaNEquality.ALL_EQUAL),
+            repeats, listOfFloat,
+            new Float[]{Float.MIN_VALUE, 7f, 50f, 100f, Float.NaN, null, null}, 0f),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.EQUAL, NaNEquality.UNEQUAL),
+            repeats, listOfFloat,
+            new Float[]{Float.MIN_VALUE, 7f, 50f, 100f, Float.NaN, Float.NaN, null}, 0f),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.UNEQUAL, NaNEquality.UNEQUAL),
+            repeats, listOfFloat,
+            new Float[]{Float.MIN_VALUE, 7f, 50f, 100f, Float.NaN, Float.NaN, null, null}, 0f),
+        Arguments.of(ReductionAggregation.collectSet(),
+            repeats, listOfFloat,
+            new Float[]{Float.MIN_VALUE, 7f, 50f, 100f, Float.NaN, Float.NaN}, 0f)
+    );
+  }
+
+  private static Stream<Arguments> createDoubleParams() {
+    Double[] vals = new Double[]{-1., 7., 123., null, 50., 60., 100.};
+    Double[] notNulls = new Double[]{-1., 7., 123., 50., 60., 100.};
+    Double[] repeats = new Double[]{Double.MIN_VALUE, 7., 7., null, null, Double.NaN, Double.NaN, 50., 50., 100.};
+    HostColumnVector.BasicType fp64 = new HostColumnVector.BasicType(true, DType.FLOAT64);
+    HostColumnVector.DataType listOfDouble = new HostColumnVector.ListType(
+        true, new HostColumnVector.BasicType(true, DType.FLOAT64));
+    return Stream.of(
+        Arguments.of(ReductionAggregation.sum(), new Double[0], fp64, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), new Double[]{null, null, null}, fp64, null, 0.),
+        Arguments.of(ReductionAggregation.sum(), vals, fp64, 339., 0.),
+        Arguments.of(ReductionAggregation.min(), vals, fp64, -1., 0.),
+        Arguments.of(ReductionAggregation.max(), vals, fp64, 123., 0.),
+        Arguments.of(ReductionAggregation.product(), vals, fp64, -258300000., 0.),
+        Arguments.of(ReductionAggregation.sumOfSquares(), vals, fp64, 31279., 0.),
+        Arguments.of(ReductionAggregation.mean(), vals, fp64, 56.5, DELTAD),
+        Arguments.of(ReductionAggregation.standardDeviation(), vals, fp64, 49.24530434467839, DELTAD),
+        Arguments.of(ReductionAggregation.variance(), vals, fp64, 2425.1, DELTAD),
+        Arguments.of(ReductionAggregation.any(), vals, fp64, true, 0.),
+        Arguments.of(ReductionAggregation.all(), vals, fp64, true, 0.),
+        Arguments.of(ReductionAggregation.quantile(0.5), vals, fp64, 55.0, DELTAD),
+        Arguments.of(ReductionAggregation.quantile(0.9), vals, fp64, 111.5, DELTAD),
+        Arguments.of(ReductionAggregation.collectList(NullPolicy.INCLUDE), vals, listOfDouble, vals, 0.),
+        Arguments.of(ReductionAggregation.collectList(NullPolicy.EXCLUDE), vals, listOfDouble, notNulls, 0.),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.EXCLUDE, NullEquality.EQUAL, NaNEquality.ALL_EQUAL),
+            repeats, listOfDouble,
+            new Double[]{Double.MIN_VALUE, 7., 50., 100., Double.NaN}, 0.),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.EQUAL, NaNEquality.ALL_EQUAL),
+            repeats, listOfDouble,
+            new Double[]{Double.MIN_VALUE, 7., 50., 100., Double.NaN, null}, 0.),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.UNEQUAL, NaNEquality.ALL_EQUAL),
+            repeats, listOfDouble,
+            new Double[]{Double.MIN_VALUE, 7., 50., 100., Double.NaN, null, null}, 0.),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.EQUAL, NaNEquality.UNEQUAL),
+            repeats, listOfDouble,
+            new Double[]{Double.MIN_VALUE, 7., 50., 100., Double.NaN, Double.NaN, null}, 0.),
+        Arguments.of(ReductionAggregation.collectSet(
+                NullPolicy.INCLUDE, NullEquality.UNEQUAL, NaNEquality.UNEQUAL),
+            repeats, listOfDouble,
+            new Double[]{Double.MIN_VALUE, 7., 50., 100., Double.NaN, Double.NaN, null, null}, 0.),
+        Arguments.of(ReductionAggregation.collectSet(),
+            repeats, listOfDouble,
+            new Double[]{Double.MIN_VALUE, 7., 50., 100., Double.NaN, Double.NaN}, 0.)
+    );
+  }
+
+  private static Stream<Arguments> createTimestampDaysParams() {
+    Integer[] vals = new Integer[]{-1, 7, 123, null, 50, 60, 100};
+    HostColumnVector.BasicType tsDay = new HostColumnVector.BasicType(true, DType.TIMESTAMP_DAYS);
+    return Stream.of(
+        Arguments.of(ReductionAggregation.max(), new Integer[0], tsDay, null),
+        Arguments.of(ReductionAggregation.max(), new Integer[]{null, null, null}, tsDay, null),
+        Arguments.of(ReductionAggregation.max(), vals, tsDay, 123),
+        Arguments.of(ReductionAggregation.min(), vals, tsDay, -1)
+    );
+  }
+
+  private static Stream<Arguments> createTimestampResolutionParams(HostColumnVector.BasicType type) {
+    Long[] vals = new Long[]{-1L, 7L, 123L, null, 50L, 60L, 100L};
+    return Stream.of(
+        Arguments.of(ReductionAggregation.max(), new Long[0], type, null),
+        Arguments.of(ReductionAggregation.max(), new Long[]{null, null, null}, type, null),
+        Arguments.of(ReductionAggregation.min(), vals, type, -1L),
+        Arguments.of(ReductionAggregation.max(), vals, type, 123L)
+    );
+  }
+
+  private static Stream<Arguments> createTimestampSecondsParams() {
+    return createTimestampResolutionParams(
+        new HostColumnVector.BasicType(true, DType.TIMESTAMP_SECONDS));
+  }
+
+  private static Stream<Arguments> createTimestampMilliSecondsParams() {
+    return createTimestampResolutionParams(
+        new HostColumnVector.BasicType(true, DType.TIMESTAMP_MILLISECONDS));
+  }
+
+  private static Stream<Arguments> createTimestampMicroSecondsParams() {
+    return createTimestampResolutionParams(
+        new HostColumnVector.BasicType(true, DType.TIMESTAMP_MICROSECONDS));
+  }
+
+  private static Stream<Arguments> createTimestampNanoSecondsParams() {
+    return createTimestampResolutionParams(
+        new HostColumnVector.BasicType(true, DType.TIMESTAMP_NANOSECONDS));
+  }
+
+  private static Stream<Arguments> createFloatArrayParams() {
+    List<Float>[] inputs = new List[]{
+        Lists.newArrayList(-1f, 7f, null),
+        Lists.newArrayList(7f, 50f, 60f, Float.NaN),
+        Lists.newArrayList(),
+        Lists.newArrayList(60f, 100f, Float.NaN, null)
+    };
+    HostColumnVector.DataType fpList = new HostColumnVector.ListType(
+        true, new HostColumnVector.BasicType(true, DType.FLOAT32));
+    return Stream.of(
+        Arguments.of(ReductionAggregation.mergeLists(), inputs, fpList,
+            new Float[]{-1f, 7f, null,
+                7f, 50f, 60f, Float.NaN,
+                60f, 100f, Float.NaN, null}, 0f),
+        Arguments.of(ReductionAggregation.mergeSets(NullEquality.EQUAL, NaNEquality.ALL_EQUAL),
+            inputs, fpList,
+            new Float[]{-1f, 7f, 50f, 60f, 100f, Float.NaN, null}, 0f),
+        Arguments.of(ReductionAggregation.mergeSets(NullEquality.UNEQUAL, NaNEquality.ALL_EQUAL),
+            inputs, fpList,
+            new Float[]{-1f, 7f, 50f, 60f, 100f, Float.NaN, null, null}, 0f),
+        Arguments.of(ReductionAggregation.mergeSets(NullEquality.EQUAL, NaNEquality.UNEQUAL),
+            inputs, fpList,
+            new Float[]{-1f, 7f, 50f, 60f, 100f, Float.NaN, Float.NaN, null}, 0f),
+        Arguments.of(ReductionAggregation.mergeSets(),
+            inputs, fpList,
+            new Float[]{-1f, 7f, 50f, 60f, 100f, Float.NaN, Float.NaN, null, null}, 0f)
+    );
+  }
+
+  private static void assertEqualsDelta(ReductionAggregation op, Scalar expected, Scalar result,
+                                        Double percentage) {
+    if (FLOAT_REDUCTIONS.contains(op.getWrapped().kind)) {
+      assertEqualsWithinPercentage(expected.getDouble(), result.getDouble(), percentage);
+    } else if (expected.getType().typeId == DType.DTypeEnum.LIST) {
+      try (ColumnVector expectedAsList = ColumnVector.fromScalar(expected, 1);
+           ColumnVector resultAsList = ColumnVector.fromScalar(result, 1);
+           ColumnVector expectedSorted = expectedAsList.listSortRows(false, false);
+           ColumnVector resultSorted = resultAsList.listSortRows(false, false)) {
+        AssertUtils.assertColumnsAreEqual(expectedSorted, resultSorted);
+      }
+    } else {
+      assertEquals(expected, result);
+    }
+  }
+
+  private static void assertEqualsDelta(ReductionAggregation op, Scalar expected, Scalar result,
+                                        Float percentage) {
+    if (FLOAT_REDUCTIONS.contains(op.getWrapped().kind)) {
+      assertEqualsWithinPercentage(expected.getFloat(), result.getFloat(), percentage);
+    } else if (expected.getType().typeId == DType.DTypeEnum.LIST) {
+      try (ColumnVector expectedAsList = ColumnVector.fromScalar(expected, 1);
+           ColumnVector resultAsList = ColumnVector.fromScalar(result, 1);
+           ColumnVector expectedSorted = expectedAsList.listSortRows(false, false);
+           ColumnVector resultSorted = resultAsList.listSortRows(false, false)) {
+        AssertUtils.assertColumnsAreEqual(expectedSorted, resultSorted);
+      }
+    } else {
+      assertEquals(expected, result);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createBooleanParams")
+  void testBoolean(ReductionAggregation op, Boolean[] values,
+      HostColumnVector.DataType type, Object expectedObject, Double delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedBooleans(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createByteParams")
+  void testByte(ReductionAggregation op, Byte[] values,
+      HostColumnVector.DataType type, Object expectedObject, Double delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedBytes(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createShortParams")
+  void testShort(ReductionAggregation op, Short[] values,
+      HostColumnVector.DataType type, Object expectedObject, Double delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedShorts(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createIntParams")
+  void testInt(ReductionAggregation op, Integer[] values,
+      HostColumnVector.DataType type, Object expectedObject, Double delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedInts(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createLongParams")
+  void testLong(ReductionAggregation op, Long[] values,
+      HostColumnVector.DataType type, Object expectedObject, Double delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedLongs(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createFloatParams")
+  void testFloat(ReductionAggregation op, Float[] values,
+      HostColumnVector.DataType type, Object expectedObject, Float delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedFloats(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createDoubleParams")
+  void testDouble(ReductionAggregation op, Double[] values,
+      HostColumnVector.DataType type, Object expectedObject, Double delta) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromBoxedDoubles(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createTimestampDaysParams")
+  void testTimestampDays(ReductionAggregation op, Integer[] values,
+      HostColumnVector.DataType type, Object expectedObject) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.timestampDaysFromBoxedInts(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEquals(expected, result);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createTimestampSecondsParams")
+  void testTimestampSeconds(ReductionAggregation op, Long[] values,
+      HostColumnVector.DataType type, Object expectedObject) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.timestampSecondsFromBoxedLongs(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEquals(expected, result);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createTimestampMilliSecondsParams")
+  void testTimestampMilliseconds(ReductionAggregation op, Long[] values,
+      HostColumnVector.DataType type, Object expectedObject) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.timestampMilliSecondsFromBoxedLongs(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEquals(expected, result);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createTimestampMicroSecondsParams")
+  void testTimestampMicroseconds(ReductionAggregation op, Long[] values,
+      HostColumnVector.DataType type, Object expectedObject) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.timestampMicroSecondsFromBoxedLongs(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEquals(expected, result);
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @MethodSource("createTimestampNanoSecondsParams")
+  void testTimestampNanoseconds(ReductionAggregation op, Long[] values,
+      HostColumnVector.DataType type, Object expectedObject) {
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.timestampNanoSecondsFromBoxedLongs(values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEquals(expected, result);
+    }
+  }
+
+  @ParameterizedTest
+  @MethodSource("createFloatArrayParams")
+  void testFloatArray(ReductionAggregation op, List<Float>[] values,
+      HostColumnVector.DataType type, Object expectedObject, Float delta) {
+    HostColumnVector.DataType listType = new HostColumnVector.ListType(
+        true, new HostColumnVector.BasicType(true, DType.FLOAT32));
+    try (Scalar expected = buildExpectedScalar(op, type, expectedObject);
+         ColumnVector v = ColumnVector.fromLists(listType, values);
+         Scalar result = v.reduce(op, expected.getType())) {
+      assertEqualsDelta(op, expected, result, delta);
+    }
+  }
+
+  @Test
+  void testWithSetOutputType() {
+    try (Scalar expected = Scalar.fromLong(1 * 2 * 3 * 4L);
+         ColumnVector cv = ColumnVector.fromBytes(new byte[]{1, 2, 3, 4});
+         Scalar result = cv.product(DType.INT64)) {
+      assertEquals(expected, result);
+    }
+
+    try (Scalar expected = Scalar.fromLong(1 + 2 + 3 + 4L);
+         ColumnVector cv = ColumnVector.fromBytes(new byte[]{1, 2, 3, 4});
+         Scalar result = cv.sum(DType.INT64)) {
+      assertEquals(expected, result);
+    }
+
+    try (Scalar expected = Scalar.fromLong((1 * 1L) + (2 * 2L) + (3 * 3L) + (4 * 4L));
+         ColumnVector cv = ColumnVector.fromBytes(new byte[]{1, 2, 3, 4});
+         Scalar result = cv.sumOfSquares(DType.INT64)) {
+      assertEquals(expected, result);
+    }
+
+    try (Scalar expected = Scalar.fromFloat((1 + 2 + 3 + 4f) / 4);
+         ColumnVector cv = ColumnVector.fromBytes(new byte[]{1, 2, 3, 4});
+         Scalar result = cv.mean(DType.FLOAT32)) {
+      assertEquals(expected, result);
+    }
+
+    try (Scalar expected = Scalar.fromFloat(1.666667f);
+         ColumnVector cv = ColumnVector.fromBytes(new byte[]{1, 2, 3, 4});
+         Scalar result = cv.variance(DType.FLOAT32)) {
+      assertEquals(expected, result);
+    }
+
+    try (Scalar expected = Scalar.fromFloat(1.2909945f);
+         ColumnVector cv = ColumnVector.fromBytes(new byte[]{1, 2, 3, 4});
+         Scalar result = cv.standardDeviation(DType.FLOAT32)) {
+      assertEquals(expected, result);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/RmmMemoryAccessorTest.java b/java/src/test/java/ai/rapids/cudf/RmmMemoryAccessorTest.java
new file mode 100644
index 0000000..a9ee36e
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/RmmMemoryAccessorTest.java
@@ -0,0 +1,99 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.io.File;
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.util.concurrent.TimeUnit;
+import java.util.stream.Stream;
+
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertNotEquals;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+class RmmMemoryAccessorTest extends CudfTestBase {
+  @Test
+  public void log() throws IOException {
+    if (Rmm.isInitialized()) {
+      Rmm.shutdown();
+    }
+    File f = File.createTempFile("ALL_LOG",".csv");
+    f.deleteOnExit();
+    Rmm.initialize(RmmAllocationMode.CUDA_DEFAULT, Rmm.logTo(f), 1024*1024*1024);
+    try (DeviceMemoryBuffer address = Rmm.alloc(10, Cuda.DEFAULT_STREAM)) {
+      assertNotEquals(0, address);
+    }
+    Rmm.shutdown();
+    StringBuilder log = new StringBuilder();
+    try (Stream<String> stream = Files.lines(f.toPath(), StandardCharsets.UTF_8))
+    {
+        stream.forEach(s -> log.append(s).append("\n"));
+    }
+    System.err.println(log);
+    assertNotNull(log.toString());
+    assertTrue(0 < log.length());
+  }
+
+
+  @Test
+  public void init() {
+    if (Rmm.isInitialized()) {
+      Rmm.shutdown();
+    }
+    assertFalse(Rmm.isInitialized());
+    Rmm.initialize(RmmAllocationMode.CUDA_DEFAULT, Rmm.logToStderr(), -1);
+    assertTrue(Rmm.isInitialized());
+    Rmm.shutdown();
+    assertFalse(Rmm.isInitialized());
+  }
+
+  @Test
+  public void shutdown() {
+    if (Rmm.isInitialized()) {
+      Rmm.shutdown();
+    }
+    Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 2048);
+    try (DeviceMemoryBuffer buffer = DeviceMemoryBuffer.allocate(1024)) {
+      assertThrows(RmmException.class, () -> Rmm.shutdown(500, 2000, TimeUnit.MILLISECONDS));
+    }
+    Rmm.shutdown();
+  }
+
+  @Test
+  public void allocate() {
+    try (DeviceMemoryBuffer address = Rmm.alloc(10, Cuda.DEFAULT_STREAM)) {
+      assertNotEquals(0, address.address);
+    }
+  }
+
+  @Test
+  public void doubleInitFails() {
+    if (!Rmm.isInitialized()) {
+      Rmm.initialize(RmmAllocationMode.CUDA_DEFAULT, Rmm.logToStderr(), 0);
+    }
+    assertThrows(IllegalStateException.class,
+        () -> Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 1024 * 1024));
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/RmmTest.java b/java/src/test/java/ai/rapids/cudf/RmmTest.java
new file mode 100644
index 0000000..cd53cf7
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/RmmTest.java
@@ -0,0 +1,614 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.AfterEach;
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.ValueSource;
+
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.concurrent.atomic.AtomicLong;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+import static org.junit.jupiter.api.Assertions.fail;
+import static org.junit.jupiter.api.Assumptions.assumeFalse;
+
+public class RmmTest {
+  private static final long TOO_MUCH_MEMORY = 3L * 1024 * 1024 * 1024 * 1024 * 1024 * 1024;
+
+  @BeforeEach
+  public void setup() {
+    if (Rmm.isInitialized()) {
+      Rmm.shutdown();
+    }
+  }
+
+  @AfterEach
+  public void teardown() {
+    if (Rmm.isInitialized()) {
+      Rmm.shutdown();
+    }
+  }
+
+  @Test
+  public void testCreateAdaptors() {
+    final long poolSize = 32 * 1024 * 1024; // 32 MiB
+    try (RmmCudaMemoryResource r = new RmmCudaMemoryResource()) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmCudaAsyncMemoryResource r = new RmmCudaAsyncMemoryResource(poolSize, poolSize)) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmManagedMemoryResource r = new RmmManagedMemoryResource()) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmArenaMemoryResource<RmmCudaMemoryResource> r =
+             new RmmArenaMemoryResource<>(new RmmCudaMemoryResource(), poolSize, false)) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmPoolMemoryResource<RmmCudaMemoryResource> r =
+             new RmmPoolMemoryResource<>(new RmmCudaMemoryResource(), poolSize, poolSize)) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmLimitingResourceAdaptor<RmmCudaMemoryResource> r =
+             new RmmLimitingResourceAdaptor<>(new RmmCudaMemoryResource(), poolSize, 64)) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmLoggingResourceAdaptor<RmmCudaMemoryResource> r =
+             new RmmLoggingResourceAdaptor<>(new RmmCudaMemoryResource(), Rmm.logToStderr(), true)) {
+      assert(r.getHandle() != 0);
+    }
+    try (RmmTrackingResourceAdaptor<RmmCudaMemoryResource> r =
+             new RmmTrackingResourceAdaptor<>(new RmmCudaMemoryResource(), 64)) {
+      assert(r.getHandle() != 0);
+      assert(r.getTotalBytesAllocated() == 0);
+      assert(r.getMaxTotalBytesAllocated() == 0);
+      assert(r.getScopedMaxTotalBytesAllocated() == 0);
+      r.resetScopedMaxTotalBytesAllocated(1024);
+      assert(r.getScopedMaxTotalBytesAllocated() == 1024);
+    }
+  }
+
+  @ParameterizedTest
+  @ValueSource(ints = {
+      RmmAllocationMode.CUDA_DEFAULT,
+      RmmAllocationMode.POOL,
+      RmmAllocationMode.ARENA})
+  public void testTotalAllocated(int rmmAllocMode) {
+    Rmm.initialize(rmmAllocMode, Rmm.logToStderr(), 512 * 1024 * 1024);
+    assertEquals(0, Rmm.getTotalBytesAllocated());
+    try (DeviceMemoryBuffer ignored = Rmm.alloc(1024)) {
+      assertEquals(1024, Rmm.getTotalBytesAllocated());
+    }
+    assertEquals(0, Rmm.getTotalBytesAllocated());
+  }
+
+  @ParameterizedTest
+  @ValueSource(ints = {
+      RmmAllocationMode.CUDA_DEFAULT,
+      RmmAllocationMode.POOL,
+      RmmAllocationMode.ARENA})
+  public void testMaxOutstanding(int rmmAllocMode) {
+    Rmm.initialize(rmmAllocMode, Rmm.logToStderr(), 512 * 1024 * 1024);
+    assertEquals(0, Rmm.getMaximumTotalBytesAllocated());
+    try (DeviceMemoryBuffer ignored = Rmm.alloc(1024)) {
+      assertEquals(1024, Rmm.getMaximumTotalBytesAllocated());
+    }
+    assertEquals(0, Rmm.getTotalBytesAllocated());
+    assertEquals(1024, Rmm.getMaximumTotalBytesAllocated());
+  }
+
+  @ParameterizedTest
+  @ValueSource(ints = {
+      RmmAllocationMode.CUDA_DEFAULT,
+      RmmAllocationMode.POOL,
+      RmmAllocationMode.ARENA})
+  public void testScopedMaxOutstanding(int rmmAllocMode) {
+    Rmm.initialize(rmmAllocMode, Rmm.logToStderr(), 512 * 1024 * 1024);
+    assertEquals(0, Rmm.getMaximumTotalBytesAllocated());
+    try (DeviceMemoryBuffer ignored = Rmm.alloc(1024);
+         DeviceMemoryBuffer ignored2 = Rmm.alloc(1024)) {
+      assertEquals(2048, Rmm.getScopedMaximumBytesAllocated());
+    }
+    assertEquals(0, Rmm.getTotalBytesAllocated());
+    assertEquals(2048, Rmm.getScopedMaximumBytesAllocated());
+
+    Rmm.resetScopedMaximumBytesAllocated();
+    assertEquals(0, Rmm.getScopedMaximumBytesAllocated());
+    assertEquals(2048, Rmm.getMaximumTotalBytesAllocated());
+
+    DeviceMemoryBuffer ignored = Rmm.alloc(1024);
+    ignored.close();
+    assertEquals(1024, Rmm.getScopedMaximumBytesAllocated());
+    assertEquals(2048, Rmm.getMaximumTotalBytesAllocated());
+    assertEquals(0, Rmm.getTotalBytesAllocated());
+
+    // a non-zero value is the new minimum
+    DeviceMemoryBuffer ignored2 = Rmm.alloc(1024);
+    ignored2.close();
+    Rmm.resetScopedMaximumBytesAllocated(10000);
+    assertEquals(10000, Rmm.getScopedMaximumBytesAllocated());
+    assertEquals(2048, Rmm.getMaximumTotalBytesAllocated());
+
+    try(DeviceMemoryBuffer ignored3 = Rmm.alloc(1024)) {
+      Rmm.resetScopedMaximumBytesAllocated(1024);
+      try (DeviceMemoryBuffer ignored4 = Rmm.alloc(20480)) {
+        assertEquals(21504, Rmm.getScopedMaximumBytesAllocated());
+        assertEquals(21504, Rmm.getMaximumTotalBytesAllocated());
+      }
+    }
+  }
+
+  @ParameterizedTest
+  @ValueSource(ints = {
+      RmmAllocationMode.CUDA_DEFAULT,
+      RmmAllocationMode.POOL,
+      RmmAllocationMode.ARENA})
+  public void testScopedMaxOutstandingNegative(int rmmAllocMode) {
+    Rmm.initialize(rmmAllocMode, Rmm.logToStderr(), 512 * 1024 * 1024);
+    assertEquals(0, Rmm.getMaximumTotalBytesAllocated());
+    try (DeviceMemoryBuffer ignored = Rmm.alloc(1024);
+         DeviceMemoryBuffer ignored2 = Rmm.alloc(1024)) {
+      assertEquals(2048, Rmm.getScopedMaximumBytesAllocated());
+      Rmm.resetScopedMaximumBytesAllocated();
+      assertEquals(0, Rmm.getScopedMaximumBytesAllocated());
+    }
+    // because we allocated a net -2048 Bytes since reset
+    assertEquals(0, Rmm.getScopedMaximumBytesAllocated());
+    DeviceMemoryBuffer ignored = Rmm.alloc(1024);
+    ignored.close();
+    assertEquals(0, Rmm.getScopedMaximumBytesAllocated());
+
+    // if we allocate 2KB and then 256B we start seeing a positive local maximum
+    try (DeviceMemoryBuffer ignored2 = Rmm.alloc(2048);
+         DeviceMemoryBuffer ignored3 = Rmm.alloc(256)) {
+      assertEquals(256, Rmm.getScopedMaximumBytesAllocated());
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @ValueSource(ints = {
+      RmmAllocationMode.CUDA_DEFAULT,
+      RmmAllocationMode.POOL,
+      RmmAllocationMode.ARENA})
+  public void testEventHandler(int rmmAllocMode) {
+    AtomicInteger invokedCount = new AtomicInteger();
+    AtomicLong amountRequested = new AtomicLong();
+    AtomicInteger timesRetried = new AtomicInteger();
+    AtomicLong totalAllocated = new AtomicLong();
+    AtomicLong totalDeallocated = new AtomicLong();
+
+    RmmEventHandler handler = new BaseRmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        int count = invokedCount.incrementAndGet();
+        timesRetried.set(retryCount);
+        amountRequested.set(sizeRequested);
+        return count != 3;
+      }
+
+      @Override
+      public void onAllocated(long sizeAllocated) {
+        totalAllocated.addAndGet(sizeAllocated);
+      }
+
+      @Override
+      public void onDeallocated(long sizeDeallocated) {
+        totalDeallocated.addAndGet(sizeDeallocated);
+      }
+    };
+
+    Rmm.initialize(rmmAllocMode, Rmm.logToStderr(), 512 * 1024 * 1024);
+    Rmm.setEventHandler(handler);
+    DeviceMemoryBuffer addr = Rmm.alloc(1024);
+    addr.close();
+    assertTrue(addr.address != 0);
+    assertEquals(0, invokedCount.get());
+
+    // by default, we don't get callbacks on allocated or deallocated
+    assertEquals(0, totalAllocated.get());
+    assertEquals(0, totalDeallocated.get());
+
+    // Try to allocate too much
+    long requested = TOO_MUCH_MEMORY;
+    try {
+      addr = Rmm.alloc(requested);
+      addr.close();
+      fail("should have failed to allocate");
+    } catch (OutOfMemoryError | RmmException ignored) {
+    }
+
+    assertEquals(3, invokedCount.get());
+    assertEquals(2, timesRetried.get());
+    assertEquals(requested, amountRequested.get());
+
+    // verify after a failure we can still allocate something more reasonable
+    requested = 8192;
+    addr = Rmm.alloc(requested);
+    addr.close();
+
+    // test the debug event handler
+    Rmm.clearEventHandler();
+    Rmm.setEventHandler(handler, /*enableDebug*/ true);
+    addr = Rmm.alloc(1024);
+    addr.close();
+    assertEquals(1024, totalAllocated.get());
+    assertEquals(1024, totalDeallocated.get());
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testSetEventHandlerTwice() {
+    Rmm.initialize(RmmAllocationMode.CUDA_DEFAULT, Rmm.logToStderr(), 0L);
+    // installing an event handler the first time should not be an error
+    Rmm.setEventHandler(new BaseRmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        return false;
+      }
+    });
+
+    // installing a second event handler is an error
+    RmmEventHandler otherHandler = new BaseRmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        return true;
+      }
+    };
+    assertThrows(RmmException.class, () -> Rmm.setEventHandler(otherHandler));
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testClearEventHandler() {
+    Rmm.initialize(RmmAllocationMode.CUDA_DEFAULT, Rmm.logToStderr(), 0L);
+    // clearing the event handler when it isn't set is not an error
+    Rmm.clearEventHandler();
+
+    // create an event handler that will always retry
+    RmmEventHandler retryHandler = new BaseRmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        return true;
+      }
+    };
+
+    Rmm.setEventHandler(retryHandler);
+    Rmm.clearEventHandler();
+
+    // verify handler is no longer installed, alloc should fail
+    try {
+      DeviceMemoryBuffer addr = Rmm.alloc(TOO_MUCH_MEMORY);
+      addr.close();
+      fail("should have failed to allocate");
+    } catch (OutOfMemoryError | RmmException ignored) {
+    }
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testAllocOnlyThresholds() {
+    final AtomicInteger allocInvocations = new AtomicInteger(0);
+    final AtomicInteger deallocInvocations = new AtomicInteger(0);
+    final AtomicLong allocated = new AtomicLong(0);
+
+    Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 1024 * 1024L);
+
+    RmmEventHandler handler = new RmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        return false;
+      }
+
+      @Override
+      public long[] getAllocThresholds() {
+        return new long[] { 32 * 1024, 8 * 1024 };
+      }
+
+      @Override
+      public long[] getDeallocThresholds() {
+        return null;
+      }
+
+      @Override
+      public void onAllocThreshold(long totalAllocSize) {
+        allocInvocations.getAndIncrement();
+        allocated.set(totalAllocSize);
+      }
+
+      @Override
+      public void onDeallocThreshold(long totalAllocSize) {
+        deallocInvocations.getAndIncrement();
+      }
+    };
+
+    Rmm.setEventHandler(handler);
+    DeviceMemoryBuffer[] addrs = new DeviceMemoryBuffer[5];
+    try {
+      addrs[0] = Rmm.alloc(6 * 1024);
+      assertEquals(0, allocInvocations.get());
+      addrs[1] = Rmm.alloc(2 * 1024);
+      assertEquals(1, allocInvocations.get());
+      assertEquals(8 * 1024, allocated.get());
+      addrs[2] = Rmm.alloc(21 * 1024);
+      assertEquals(1, allocInvocations.get());
+      addrs[3] = Rmm.alloc(8 * 1024);
+      assertEquals(2, allocInvocations.get());
+      assertEquals(37 * 1024, allocated.get());
+      addrs[4] = Rmm.alloc(8 * 1024);
+      assertEquals(2, allocInvocations.get());
+    } finally {
+      for (DeviceMemoryBuffer addr : addrs) {
+        if (addr != null) {
+          addr.close();
+        }
+      }
+    }
+
+    assertEquals(2, allocInvocations.get());
+    assertEquals(0, deallocInvocations.get());
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testThresholds() {
+    final AtomicInteger allocInvocations = new AtomicInteger(0);
+    final AtomicInteger deallocInvocations = new AtomicInteger(0);
+    final AtomicLong allocated = new AtomicLong(0);
+
+    Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 1024 * 1024L);
+
+    RmmEventHandler handler = new RmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        return false;
+      }
+
+      @Override
+      public long[] getAllocThresholds() {
+        return new long[] { 8 * 1024 };
+      }
+
+      @Override
+      public long[] getDeallocThresholds() {
+        return new long[] { 6 * 1024 };
+      }
+
+      @Override
+      public void onAllocThreshold(long totalAllocSize) {
+        allocInvocations.getAndIncrement();
+        allocated.set(totalAllocSize);
+      }
+
+      @Override
+      public void onDeallocThreshold(long totalAllocSize) {
+        deallocInvocations.getAndIncrement();
+        allocated.set(totalAllocSize);
+      }
+    };
+
+    Rmm.setEventHandler(handler);
+    DeviceMemoryBuffer[] addrs = new DeviceMemoryBuffer[5];
+    try {
+      addrs[0] = Rmm.alloc(6 * 1024);
+      assertEquals(0, allocInvocations.get());
+      assertEquals(0, deallocInvocations.get());
+      addrs[0].close();
+      addrs[0] = null;
+      assertEquals(0, allocInvocations.get());
+      assertEquals(1, deallocInvocations.get());
+      assertEquals(0, allocated.get());
+      addrs[0] = Rmm.alloc(12 * 1024);
+      assertEquals(1, allocInvocations.get());
+      assertEquals(1, deallocInvocations.get());
+      assertEquals(12 * 1024, allocated.get());
+      addrs[1] = Rmm.alloc(6 * 1024);
+      assertEquals(1, allocInvocations.get());
+      assertEquals(1, deallocInvocations.get());
+      addrs[0].close();
+      addrs[0] = null;
+      assertEquals(1, allocInvocations.get());
+      assertEquals(1, deallocInvocations.get());
+      addrs[0] = Rmm.alloc(4 * 1024);
+      assertEquals(2, allocInvocations.get());
+      assertEquals(1, deallocInvocations.get());
+      assertEquals(10 * 1024, allocated.get());
+      addrs[1].close();
+      addrs[1] = null;
+      assertEquals(2, allocInvocations.get());
+      assertEquals(2, deallocInvocations.get());
+      assertEquals(4 * 1024, allocated.get());
+      addrs[0].close();
+      addrs[0] = null;
+      assertEquals(2, allocInvocations.get());
+      assertEquals(2, deallocInvocations.get());
+    } finally {
+      for (DeviceMemoryBuffer addr : addrs) {
+        if (addr != null) {
+          addr.close();
+        }
+      }
+    }
+
+    assertEquals(2, allocInvocations.get());
+    assertEquals(2, deallocInvocations.get());
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testExceptionHandling() {
+    Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 1024 * 1024L);
+
+    RmmEventHandler handler = new RmmEventHandler() {
+      @Override
+      public boolean onAllocFailure(long sizeRequested, int retryCount) {
+        throw new AllocFailException();
+      }
+
+      @Override
+      public long[] getAllocThresholds() {
+        return new long[] { 8 * 1024 };
+      }
+
+      @Override
+      public long[] getDeallocThresholds() {
+        return new long[] { 6 * 1024 };
+      }
+
+      @Override
+      public void onAllocThreshold(long totalAllocSize) {
+        throw new AllocThresholdException();
+      }
+
+      @Override
+      public void onDeallocThreshold(long totalAllocSize) {
+        throw new DeallocThresholdException();
+      }
+    };
+
+    Rmm.setEventHandler(handler);
+    DeviceMemoryBuffer addr = Rmm.alloc(6 * 1024);
+    assertThrows(DeallocThresholdException.class, addr::close);
+    assertThrows(AllocThresholdException.class, () -> Rmm.alloc(12 * 1024));
+    assertThrows(AllocFailException.class, () -> Rmm.alloc(TOO_MUCH_MEMORY));
+  }
+
+  @Test
+  public void testThreadAutoDeviceSetup() throws Exception {
+    // A smoke-test for automatic CUDA device setup for threads calling
+    // into cudf. Hard to fully test without requiring multiple CUDA devices.
+    Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 1024 * 1024L);
+    DeviceMemoryBuffer buff = Rmm.alloc(1024);
+    try {
+      ExecutorService executor = Executors.newSingleThreadExecutor();
+      Future<Boolean> future = executor.submit(() -> {
+        DeviceMemoryBuffer localBuffer = Rmm.alloc(2048);
+        localBuffer.close();
+        buff.close();
+        return true;
+      });
+      assertTrue(future.get());
+      executor.shutdown();
+    } catch (Exception t) {
+      buff.close();
+      throw t;
+    }
+  }
+
+  @Tag("noSanitizer")
+  @ParameterizedTest
+  @ValueSource(ints = {
+      RmmAllocationMode.CUDA_DEFAULT,
+      RmmAllocationMode.POOL,
+      RmmAllocationMode.ARENA})
+  public void testSetDeviceThrowsAfterRmmInit(int rmmAllocMode) {
+    Rmm.initialize(rmmAllocMode, Rmm.logToStderr(), 1024 * 1024);
+    assertThrows(CudfException.class, () -> Cuda.setDevice(Cuda.getDevice() + 1));
+    // Verify that auto set device does not
+    Cuda.autoSetDevice();
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testPoolSize() {
+    Rmm.initialize(RmmAllocationMode.POOL, Rmm.logToStderr(), 1024);
+    try (DeviceMemoryBuffer ignored1 = Rmm.alloc(1024)) {
+      assertThrows(OutOfMemoryError.class,
+          () -> {
+            DeviceMemoryBuffer ignored2 = Rmm.alloc(1024);
+            ignored2.close();
+      });
+    }
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testCudaAsyncMemoryResourceSize() {
+    try {
+      Rmm.initialize(RmmAllocationMode.CUDA_ASYNC, Rmm.logToStderr(), 1024);
+    } catch (CudfException e) {
+      // CUDA 11.2 introduced cudaMallocAsync, older CUDA Toolkit will skip this test.
+      assumeFalse(e.getMessage().contains("cudaMallocAsync not supported"));
+      throw e;
+    }
+    try (DeviceMemoryBuffer ignored1 = Rmm.alloc(1024)) {
+      assertThrows(OutOfMemoryError.class,
+          () -> {
+            DeviceMemoryBuffer ignored2 = Rmm.alloc(1024);
+            ignored2.close();
+          });
+    }
+  }
+
+  @Tag("noSanitizer")
+  @Test
+  public void testCudaAsyncIsIncompatibleWithManaged() {
+    assertThrows(IllegalArgumentException.class,
+        () -> Rmm.initialize(
+            RmmAllocationMode.CUDA_ASYNC | RmmAllocationMode.CUDA_MANAGED_MEMORY,
+            Rmm.logToStderr(), 1024));
+  }
+
+  @Test
+  public void testCudaMemoryBuffer() {
+    Rmm.initialize(RmmAllocationMode.ARENA, Rmm.logToStderr(), 8 * 1024 * 1024);
+    try (CudaMemoryBuffer one = CudaMemoryBuffer.allocate(512);
+         CudaMemoryBuffer two = CudaMemoryBuffer.allocate(1024)) {
+      assertEquals(512, one.length);
+      assertEquals(1024, two.length);
+      assertEquals(0, Rmm.getTotalBytesAllocated());
+    }
+  }
+
+  private static class AllocFailException extends RuntimeException {
+  }
+
+  private static class AllocThresholdException extends RuntimeException {
+  }
+
+  private static class DeallocThresholdException extends RuntimeException {
+  }
+
+  private static abstract class BaseRmmEventHandler implements RmmEventHandler {
+    @Override
+    public long[] getAllocThresholds() {
+      return null;
+    }
+
+    @Override
+    public long[] getDeallocThresholds() {
+      return null;
+    }
+
+    @Override
+    public void onAllocThreshold(long totalAllocSize) {
+    }
+
+    @Override
+    public void onDeallocThreshold(long totalAllocSize) {
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ScalarTest.java b/java/src/test/java/ai/rapids/cudf/ScalarTest.java
new file mode 100644
index 0000000..f4b652a
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ScalarTest.java
@@ -0,0 +1,461 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.BasicType;
+import ai.rapids.cudf.HostColumnVector.ListType;
+import ai.rapids.cudf.HostColumnVector.StructType;
+
+import org.junit.jupiter.api.Test;
+
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.nio.charset.StandardCharsets;
+import java.util.Arrays;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static org.junit.jupiter.api.Assertions.*;
+
+public class ScalarTest extends CudfTestBase {
+  @Test
+  public void testDoubleClose() {
+    Scalar s = Scalar.fromNull(DType.INT32);
+    s.close();
+    assertThrows(IllegalStateException.class, s::close);
+  }
+
+  @Test
+  public void testIncRefAndDoubleFree() {
+    Scalar s = Scalar.fromNull(DType.INT32);
+    try (Scalar ignored1 = s) {
+      try (Scalar ignored2 = s.incRefCount()) {
+        try (Scalar ignored3 = s.incRefCount()) {
+        }
+      }
+    }
+    assertThrows(IllegalStateException.class, s::close);
+  }
+
+  @Test
+  public void testNull() {
+    for (DType.DTypeEnum dataType : DType.DTypeEnum.values()) {
+      DType type;
+      if (dataType.isDecimalType()) {
+        type = DType.create(dataType, -3);
+      } else {
+        type = DType.create(dataType);
+      }
+      if (!type.isNestedType()) {
+        try (Scalar s = Scalar.fromNull(type)) {
+          assertEquals(type, s.getType());
+          assertFalse(s.isValid(), "null validity for " + type);
+        }
+      }
+
+      // create elementType for nested types
+      HostColumnVector.DataType hDataType;
+      if (DType.EMPTY.equals(type)) {
+        continue;
+      } else if (DType.LIST.equals(type)) {
+        // list of list of int32
+        hDataType = new ListType(true, new BasicType(true, DType.INT32));
+      } else if (DType.STRUCT.equals(type)) {
+        // list of struct of int32
+        hDataType = new StructType(true, new BasicType(true, DType.INT32));
+      } else {
+        // list of non nested type
+        hDataType = new BasicType(true, type);
+      }
+
+      // test list scalar with elementType(`type`)
+      try (Scalar s = Scalar.listFromNull(hDataType); ColumnView listCv = s.getListAsColumnView()) {
+        assertFalse(s.isValid(), "null validity for " + type);
+        assertEquals(DType.LIST, s.getType());
+        assertEquals(type, listCv.getType());
+        assertEquals(0L, listCv.getRowCount());
+        assertEquals(0L, listCv.getNullCount());
+        if (type.isNestedType()) {
+          try (ColumnView child = listCv.getChildColumnView(0)) {
+            assertEquals(DType.INT32, child.getType());
+            assertEquals(0L, child.getRowCount());
+            assertEquals(0L, child.getNullCount());
+          }
+        }
+      }
+
+      // test struct scalar with elementType(`type`)
+      try (Scalar s = Scalar.structFromNull(hDataType, hDataType, hDataType)) {
+        assertFalse(s.isValid(), "null validity for " + type);
+        assertEquals(DType.STRUCT, s.getType());
+
+        ColumnView[] children = s.getChildrenFromStructScalar();
+        try {
+          for (ColumnView child : children) {
+            assertEquals(hDataType.getType(), child.getType());
+            assertEquals(1L, child.getRowCount());
+            assertEquals(1L, child.getNullCount());
+          }
+        } finally {
+          for (ColumnView child : children) child.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testBool() {
+    try (Scalar s = Scalar.fromBool(false)) {
+      assertEquals(DType.BOOL8, s.getType());
+      assertTrue(s.isValid());
+      assertFalse(s.getBoolean());
+    }
+  }
+
+  @Test
+  public void testByte() {
+    try (Scalar s = Scalar.fromByte((byte) 1)) {
+      assertEquals(DType.INT8, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(1, s.getByte());
+    }
+  }
+
+  @Test
+  public void testShort() {
+    try (Scalar s = Scalar.fromShort((short) 2)) {
+      assertEquals(DType.INT16, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(2, s.getShort());
+    }
+  }
+
+  @Test
+  public void testInt() {
+    try (Scalar s = Scalar.fromInt(3)) {
+      assertEquals(DType.INT32, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(3, s.getInt());
+    }
+  }
+
+  @Test
+  public void testLong() {
+    try (Scalar s = Scalar.fromLong(4)) {
+      assertEquals(DType.INT64, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(4L, s.getLong());
+    }
+  }
+
+  @Test
+  public void testFloat() {
+    try (Scalar s = Scalar.fromFloat(5.1f)) {
+      assertEquals(DType.FLOAT32, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(5.1f, s.getFloat());
+    }
+  }
+
+  @Test
+  public void testDouble() {
+    try (Scalar s = Scalar.fromDouble(6.2)) {
+      assertEquals(DType.FLOAT64, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(6.2, s.getDouble());
+    }
+  }
+
+  @Test
+  public void testDecimal() {
+    BigDecimal[] bigDecimals = new BigDecimal[] {
+        BigDecimal.valueOf(1234, 0),
+        BigDecimal.valueOf(12345678, 2),
+        BigDecimal.valueOf(1234567890123L, 6),
+        new BigDecimal(new BigInteger("12312341234123412341234123412341234120"), 4)
+    };
+    for (BigDecimal dec : bigDecimals) {
+      try (Scalar s = Scalar.fromDecimal(dec)) {
+        DType dtype = DType.fromJavaBigDecimal(dec);
+        assertEquals(dtype, s.getType());
+        assertTrue(s.isValid());
+        if (dtype.getTypeId() == DType.DTypeEnum.DECIMAL64) {
+          assertEquals(dec.unscaledValue().longValueExact(), s.getLong());
+        } else if (dtype.getTypeId() == DType.DTypeEnum.DECIMAL32) {
+          assertEquals(dec.unscaledValue().intValueExact(), s.getInt());
+        } else if (dtype.getTypeId() == DType.DTypeEnum.DECIMAL128) {
+          assertEquals(dec.unscaledValue(), s.getBigDecimal().unscaledValue());
+        }
+        assertEquals(dec, s.getBigDecimal());
+      }
+
+      try (Scalar s = Scalar.fromDecimal(-dec.scale(), dec.unscaledValue().intValueExact())) {
+        assertEquals(dec, s.getBigDecimal());
+      } catch (java.lang.ArithmeticException ex) {
+        try (Scalar s = Scalar.fromDecimal(-dec.scale(), dec.unscaledValue().longValueExact())) {
+          assertEquals(dec, s.getBigDecimal());
+          assertTrue(s.getType().isBackedByLong());
+        } catch (java.lang.ArithmeticException e) {
+          try (Scalar s = Scalar.fromDecimal(-dec.scale(), dec.unscaledValue())) {
+            assertEquals(dec, s.getBigDecimal());
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testTimestampDays() {
+    try (Scalar s = Scalar.timestampDaysFromInt(7)) {
+      assertEquals(DType.TIMESTAMP_DAYS, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(7, s.getInt());
+    }
+  }
+
+  @Test
+  public void testTimestampSeconds() {
+    try (Scalar s = Scalar.timestampFromLong(DType.TIMESTAMP_SECONDS, 8)) {
+      assertEquals(DType.TIMESTAMP_SECONDS, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(8L, s.getLong());
+    }
+  }
+
+  @Test
+  public void testTimestampMilliseconds() {
+    try (Scalar s = Scalar.timestampFromLong(DType.TIMESTAMP_MILLISECONDS, 9)) {
+      assertEquals(DType.TIMESTAMP_MILLISECONDS, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(9L, s.getLong());
+    }
+  }
+
+  @Test
+  public void testTimestampMicroseconds() {
+    try (Scalar s = Scalar.timestampFromLong(DType.TIMESTAMP_MICROSECONDS, 10)) {
+      assertEquals(DType.TIMESTAMP_MICROSECONDS, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(10L, s.getLong());
+    }
+  }
+
+  @Test
+  public void testTimestampNanoseconds() {
+    try (Scalar s = Scalar.timestampFromLong(DType.TIMESTAMP_NANOSECONDS, 11)) {
+      assertEquals(DType.TIMESTAMP_NANOSECONDS, s.getType());
+      assertTrue(s.isValid());
+      assertEquals(11L, s.getLong());
+    }
+  }
+
+  @Test
+  public void testString() {
+    try (Scalar s = Scalar.fromString("TEST")) {
+      assertEquals(DType.STRING, s.getType());
+      assertTrue(s.isValid());
+      assertEquals("TEST", s.getJavaString());
+      assertArrayEquals(new byte[] {'T', 'E', 'S', 'T'}, s.getUTF8());
+    }
+  }
+
+  @Test
+  public void testUTF8String() {
+    try (Scalar s = Scalar.fromUTF8String("TEST".getBytes(StandardCharsets.UTF_8))) {
+      assertEquals(DType.STRING, s.getType());
+      assertTrue(s.isValid());
+      assertEquals("TEST", s.getJavaString());
+      assertArrayEquals(new byte[] {'T', 'E', 'S', 'T'}, s.getUTF8());
+    }
+    try (Scalar s = Scalar.fromUTF8String("".getBytes(StandardCharsets.UTF_8))) {
+      assertEquals(DType.STRING, s.getType());
+      assertTrue(s.isValid());
+      assertEquals("", s.getJavaString());
+      assertArrayEquals(new byte[] {}, s.getUTF8());
+    }
+  }
+
+  @Test
+  public void testList() {
+    // list of int
+    try (ColumnVector listInt = ColumnVector.fromInts(1, 2, 3, 4);
+         Scalar s = Scalar.listFromColumnView(listInt)) {
+      assertEquals(DType.LIST, s.getType());
+      assertTrue(s.isValid());
+      try (ColumnView v = s.getListAsColumnView()) {
+        assertColumnsAreEqual(listInt, v);
+      }
+    }
+
+    // list of list
+    HostColumnVector.DataType listDT =
+        new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32));
+    try (ColumnVector listList =
+             ColumnVector.fromLists(listDT, Arrays.asList(1, 2, 3), Arrays.asList(4, 5, 6));
+         Scalar s = Scalar.listFromColumnView(listList)) {
+      assertEquals(DType.LIST, s.getType());
+      assertTrue(s.isValid());
+      try (ColumnView v = s.getListAsColumnView()) {
+        assertColumnsAreEqual(listList, v);
+      }
+    }
+  }
+
+  @Test
+  public void testStruct() {
+    try (ColumnVector col0 = ColumnVector.fromInts(1);
+         ColumnVector col1 = ColumnVector.fromBoxedDoubles(1.2);
+         ColumnVector col2 = ColumnVector.fromStrings("a");
+         ColumnVector col3 = ColumnVector.fromDecimals(BigDecimal.TEN);
+         ColumnVector col4 = ColumnVector.daysFromInts(10);
+         ColumnVector col5 = ColumnVector.durationSecondsFromLongs(12345L);
+         Scalar s = Scalar.structFromColumnViews(col0, col1, col2, col3, col4, col5, col0, col1)) {
+      assertEquals(DType.STRUCT, s.getType());
+      assertTrue(s.isValid());
+      ColumnView[] children = s.getChildrenFromStructScalar();
+      try {
+        assertColumnsAreEqual(col0, children[0]);
+        assertColumnsAreEqual(col1, children[1]);
+        assertColumnsAreEqual(col2, children[2]);
+        assertColumnsAreEqual(col3, children[3]);
+        assertColumnsAreEqual(col4, children[4]);
+        assertColumnsAreEqual(col5, children[5]);
+        assertColumnsAreEqual(col0, children[6]);
+        assertColumnsAreEqual(col1, children[7]);
+      } finally {
+        for (ColumnView child : children) child.close();
+      }
+    }
+
+    // test Struct Scalar with null members
+    try (ColumnVector col0 = ColumnVector.fromInts(1);
+         ColumnVector col1 = ColumnVector.fromBoxedDoubles((Double) null);
+         ColumnVector col2 = ColumnVector.fromStrings((String) null);
+         Scalar s1 = Scalar.structFromColumnViews(col0, col1, col2);
+         Scalar s2 = Scalar.structFromColumnViews(col1, col2)) {
+      ColumnView[] children = s1.getChildrenFromStructScalar();
+      try {
+        assertColumnsAreEqual(col0, children[0]);
+        assertColumnsAreEqual(col1, children[1]);
+        assertColumnsAreEqual(col2, children[2]);
+      } finally {
+        for (ColumnView child : children) child.close();
+      }
+
+      ColumnView[] children2 = s2.getChildrenFromStructScalar();
+      try {
+        assertColumnsAreEqual(col1, children2[0]);
+        assertColumnsAreEqual(col2, children2[1]);
+      } finally {
+        for (ColumnView child : children2) child.close();
+      }
+    }
+
+    // test Struct Scalar with single column
+    try (ColumnVector col0 = ColumnVector.fromInts(1234);
+         Scalar s = Scalar.structFromColumnViews(col0)) {
+      ColumnView[] children = s.getChildrenFromStructScalar();
+      try {
+        assertColumnsAreEqual(col0, children[0]);
+      } finally {
+        children[0].close();
+      }
+    }
+
+    // test Struct Scalar without column
+    try (Scalar s = Scalar.structFromColumnViews()) {
+      assertEquals(DType.STRUCT, s.getType());
+      assertTrue(s.isValid());
+      ColumnView[] children = s.getChildrenFromStructScalar();
+      assertEquals(0, children.length);
+    }
+
+    // test Struct Scalar with nested types
+    HostColumnVector.DataType listType =
+        new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32));
+    HostColumnVector.DataType structType =
+        new HostColumnVector.StructType(true, new HostColumnVector.BasicType(true, DType.INT32),
+            new HostColumnVector.BasicType(true, DType.INT64));
+    HostColumnVector.DataType nestedStructType = new HostColumnVector.StructType(
+        true, new HostColumnVector.BasicType(true, DType.STRING), listType, structType);
+    try (ColumnVector strCol = ColumnVector.fromStrings("AAAAAA");
+         ColumnVector listCol = ColumnVector.fromLists(listType, Arrays.asList(1, 2, 3, 4, 5));
+         ColumnVector structCol =
+             ColumnVector.fromStructs(structType, new HostColumnVector.StructData(1, -1L));
+         ColumnVector nestedStructCol = ColumnVector.fromStructs(nestedStructType,
+             new HostColumnVector.StructData(
+                 null, Arrays.asList(1, 2, null), new HostColumnVector.StructData(null, 10L)));
+         Scalar s = Scalar.structFromColumnViews(strCol, listCol, structCol, nestedStructCol)) {
+      assertEquals(DType.STRUCT, s.getType());
+      assertTrue(s.isValid());
+      ColumnView[] children = s.getChildrenFromStructScalar();
+      try {
+        assertColumnsAreEqual(strCol, children[0]);
+        assertColumnsAreEqual(listCol, children[1]);
+        assertColumnsAreEqual(structCol, children[2]);
+        assertColumnsAreEqual(nestedStructCol, children[3]);
+      } finally {
+        for (ColumnView child : children) child.close();
+      }
+    }
+  }
+
+  @Test
+  public void testRepeatString() {
+    // Invalid scalar.
+    try (Scalar nullString = Scalar.fromString(null)) {
+      Scalar result = nullString.repeatString(5);
+      assertFalse(result.isValid());
+    }
+
+    // Empty string.
+    try (Scalar emptyString = Scalar.fromString("")) {
+      Scalar result = emptyString.repeatString(5);
+      assertTrue(result.isValid());
+      assertEquals("", result.getJavaString());
+    }
+
+    // Negative repeatTimes.
+    try (Scalar s = Scalar.fromString("Hello World");
+         Scalar result = s.repeatString(-100)) {
+      assertTrue(result.isValid());
+      assertEquals("", result.getJavaString());
+    }
+
+    // Zero repeatTimes.
+    try (Scalar s = Scalar.fromString("Hello World");
+         Scalar result = s.repeatString(0)) {
+      assertTrue(result.isValid());
+      assertEquals("", result.getJavaString());
+    }
+
+    // Trivial input, output is copied exactly from input.
+    try (Scalar s = Scalar.fromString("Hello World");
+         Scalar result = s.repeatString(1)) {
+      assertTrue(result.isValid());
+      assertEquals(s.getJavaString(), result.getJavaString());
+    }
+
+    // Trivial input.
+    try (Scalar s = Scalar.fromString("abcxyz-");
+         Scalar result = s.repeatString(3)) {
+      assertTrue(result.isValid());
+      assertEquals("abcxyz-abcxyz-abcxyz-", result.getJavaString());
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/SegmentedReductionTest.java b/java/src/test/java/ai/rapids/cudf/SegmentedReductionTest.java
new file mode 100644
index 0000000..c902ab9
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/SegmentedReductionTest.java
@@ -0,0 +1,115 @@
+/*
+ *
+ *  Copyright (c) 2022, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.Arrays;
+
+class SegmentedReductionTest extends CudfTestBase {
+
+  @Test
+  public void testListSum() {
+    HostColumnVector.DataType dt = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    try (ColumnVector listCv = ColumnVector.fromLists(dt,
+        Arrays.asList(1, 2, 3),
+        Arrays.asList(2, 3, 4),
+        null,
+        Arrays.asList(null, 1, 2));
+         ColumnVector excludeExpected = ColumnVector.fromBoxedInts(6, 9, null, 3);
+         ColumnVector nullExcluded = listCv.listReduce(SegmentedReductionAggregation.sum(), NullPolicy.EXCLUDE, DType.INT32);
+         ColumnVector includeExpected = ColumnVector.fromBoxedInts(6, 9, null, null);
+         ColumnVector nullIncluded = listCv.listReduce(SegmentedReductionAggregation.sum(), NullPolicy.INCLUDE, DType.INT32)) {
+      AssertUtils.assertColumnsAreEqual(excludeExpected, nullExcluded);
+      AssertUtils.assertColumnsAreEqual(includeExpected, nullIncluded);
+    }
+  }
+
+  @Test
+  public void testListMin() {
+    HostColumnVector.DataType dt = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    try (ColumnVector listCv = ColumnVector.fromLists(dt,
+        Arrays.asList(1, 2, 3),
+        Arrays.asList(2, 3, 4),
+        null,
+        Arrays.asList(null, 1, 2));
+         ColumnVector excludeExpected = ColumnVector.fromBoxedInts(1, 2, null, 1);
+         ColumnVector nullExcluded = listCv.listReduce(SegmentedReductionAggregation.min(), NullPolicy.EXCLUDE, DType.INT32);
+         ColumnVector includeExpected = ColumnVector.fromBoxedInts(1, 2, null, null);
+         ColumnVector nullIncluded = listCv.listReduce(SegmentedReductionAggregation.min(), NullPolicy.INCLUDE, DType.INT32)) {
+      AssertUtils.assertColumnsAreEqual(excludeExpected, nullExcluded);
+      AssertUtils.assertColumnsAreEqual(includeExpected, nullIncluded);
+    }
+  }
+
+  @Test
+  public void testListMax() {
+    HostColumnVector.DataType dt = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.INT32));
+    try (ColumnVector listCv = ColumnVector.fromLists(dt,
+        Arrays.asList(1, 2, 3),
+        Arrays.asList(2, 3, 4),
+        null,
+        Arrays.asList(null, 1, 2));
+         ColumnVector excludeExpected = ColumnVector.fromBoxedInts(3, 4, null, 2);
+         ColumnVector nullExcluded = listCv.listReduce(SegmentedReductionAggregation.max(), NullPolicy.EXCLUDE, DType.INT32);
+         ColumnVector includeExpected = ColumnVector.fromBoxedInts(3, 4, null, null);
+         ColumnVector nullIncluded = listCv.listReduce(SegmentedReductionAggregation.max(), NullPolicy.INCLUDE, DType.INT32)) {
+      AssertUtils.assertColumnsAreEqual(excludeExpected, nullExcluded);
+      AssertUtils.assertColumnsAreEqual(includeExpected, nullIncluded);
+    }
+  }
+
+  @Test
+  public void testListAny() {
+    HostColumnVector.DataType dt = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.BOOL8));
+    try (ColumnVector listCv = ColumnVector.fromLists(dt,
+        Arrays.asList(true, false, false),
+        Arrays.asList(false, false, false),
+        null,
+        Arrays.asList(null, true, false));
+         ColumnVector excludeExpected = ColumnVector.fromBoxedBooleans(true, false, null, true);
+         ColumnVector nullExcluded = listCv.listReduce(SegmentedReductionAggregation.any(), NullPolicy.EXCLUDE, DType.BOOL8);
+         ColumnVector includeExpected = ColumnVector.fromBoxedBooleans(true, false, null, null);
+         ColumnVector nullIncluded = listCv.listReduce(SegmentedReductionAggregation.any(), NullPolicy.INCLUDE, DType.BOOL8)) {
+      AssertUtils.assertColumnsAreEqual(excludeExpected, nullExcluded);
+      AssertUtils.assertColumnsAreEqual(includeExpected, nullIncluded);
+    }
+  }
+
+  @Test
+  public void testListAll() {
+    HostColumnVector.DataType dt = new HostColumnVector.ListType(true,
+        new HostColumnVector.BasicType(true, DType.BOOL8));
+    try (ColumnVector listCv = ColumnVector.fromLists(dt,
+        Arrays.asList(true, true, true),
+        Arrays.asList(false, true, false),
+        null,
+        Arrays.asList(null, true, true));
+         ColumnVector excludeExpected = ColumnVector.fromBoxedBooleans(true, false, null, true);
+         ColumnVector nullExcluded = listCv.listReduce(SegmentedReductionAggregation.all(), NullPolicy.EXCLUDE, DType.BOOL8);
+         ColumnVector includeExpected = ColumnVector.fromBoxedBooleans(true, false, null, null);
+         ColumnVector nullIncluded = listCv.listReduce(SegmentedReductionAggregation.all(), NullPolicy.INCLUDE, DType.BOOL8)) {
+      AssertUtils.assertColumnsAreEqual(excludeExpected, nullExcluded);
+      AssertUtils.assertColumnsAreEqual(includeExpected, nullIncluded);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ShortColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/ShortColumnVectorTest.java
new file mode 100644
index 0000000..528c082
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ShortColumnVectorTest.java
@@ -0,0 +1,178 @@
+/*
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import java.util.Random;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertFalse;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class ShortColumnVectorTest extends CudfTestBase {
+
+  @Test
+  public void testCreateColumnVectorBuilder() {
+    try (ColumnVector shortColumnVector = ColumnVector.build(DType.INT16, 3,
+        (b) -> b.append((short) 1))) {
+      assertFalse(shortColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testArrayAllocation() {
+    try (HostColumnVector shortColumnVector =
+             HostColumnVector.fromShorts((short) 2, (short) 3, (short) 5)) {
+      assertFalse(shortColumnVector.hasNulls());
+      assertEquals(shortColumnVector.getShort(0), 2);
+      assertEquals(shortColumnVector.getShort(1), 3);
+      assertEquals(shortColumnVector.getShort(2), 5);
+    }
+  }
+
+  @Test
+  public void testUnsignedArrayAllocation() {
+    try (HostColumnVector v =
+             HostColumnVector.fromUnsignedShorts((short) 0xfedc, (short) 32768, (short) 5)) {
+      assertFalse(v.hasNulls());
+      assertEquals(0xfedc, Short.toUnsignedInt(v.getShort(0)));
+      assertEquals(32768, Short.toUnsignedInt(v.getShort(1)));
+      assertEquals(5, Short.toUnsignedInt(v.getShort(2)));
+    }
+  }
+
+  @Test
+  public void testUpperIndexOutOfBoundsException() {
+    try (HostColumnVector shortColumnVector =
+             HostColumnVector.fromShorts((short) 2, (short) 3, (short) 5)) {
+      assertThrows(AssertionError.class, () -> shortColumnVector.getShort(3));
+      assertFalse(shortColumnVector.hasNulls());
+    }
+  }
+
+  @Test
+  public void testLowerIndexOutOfBoundsException() {
+    try (HostColumnVector shortColumnVector =
+             HostColumnVector.fromShorts((short) 2, (short) 3, (short) 5)) {
+      assertFalse(shortColumnVector.hasNulls());
+      assertThrows(AssertionError.class, () -> shortColumnVector.getShort(-1));
+    }
+  }
+
+  @Test
+  public void testAddingNullValues() {
+    try (HostColumnVector cv =
+             HostColumnVector.fromBoxedShorts(new Short[]{2, 3, 4, 5, 6, 7, null, null})) {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    }
+  }
+
+  @Test
+  public void testAddingUnsignedNullValues() {
+    try (HostColumnVector cv = HostColumnVector.fromBoxedUnsignedShorts(
+             new Short[]{2, 3, 4, 5, (short)32768, (short)0xffff, null, null})) {
+      assertTrue(cv.hasNulls());
+      assertEquals(2, cv.getNullCount());
+      for (int i = 0; i < 6; i++) {
+        assertFalse(cv.isNull(i));
+      }
+      assertEquals(32768, Short.toUnsignedInt(cv.getShort(4)));
+      assertEquals(0xffff, Short.toUnsignedInt(cv.getShort(5)));
+      assertTrue(cv.isNull(6));
+      assertTrue(cv.isNull(7));
+    }
+  }
+
+  @Test
+  public void testOverrunningTheBuffer() {
+    try (Builder builder = HostColumnVector.builder(DType.INT16, 3)) {
+      assertThrows(AssertionError.class,
+          () -> builder.append((short) 2).appendNull().appendArray(new short[]{5, 4}).build());
+    }
+  }
+
+  @Test
+  void testAppendVector() {
+    Random random = new Random(192312989128L);
+    for (int dstSize = 1; dstSize <= 100; dstSize++) {
+      for (int dstPrefilledSize = 0; dstPrefilledSize < dstSize; dstPrefilledSize++) {
+        final int srcSize = dstSize - dstPrefilledSize;
+        for (int sizeOfDataNotToAdd = 0; sizeOfDataNotToAdd <= dstPrefilledSize; sizeOfDataNotToAdd++) {
+          try (Builder dst = HostColumnVector.builder(DType.INT16, dstSize);
+               HostColumnVector src = HostColumnVector.build(DType.INT16, srcSize, (b) -> {
+                 for (int i = 0; i < srcSize; i++) {
+                   if (random.nextBoolean()) {
+                     b.appendNull();
+                   } else {
+                     b.append((short) random.nextInt());
+                   }
+                 }
+               });
+               Builder gtBuilder = HostColumnVector.builder(DType.INT16,
+                   dstPrefilledSize)) {
+            assertEquals(dstSize, srcSize + dstPrefilledSize);
+            //add the first half of the prefilled list
+            for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+              if (random.nextBoolean()) {
+                dst.appendNull();
+                gtBuilder.appendNull();
+              } else {
+                short a = (short) random.nextInt();
+                dst.append(a);
+                gtBuilder.append(a);
+              }
+            }
+            // append the src vector
+            dst.append(src);
+            try (HostColumnVector dstVector = dst.build();
+                 HostColumnVector gt = gtBuilder.build()) {
+              for (int i = 0; i < dstPrefilledSize - sizeOfDataNotToAdd; i++) {
+                assertEquals(gt.isNull(i), dstVector.isNull(i));
+                if (!gt.isNull(i)) {
+                  assertEquals(gt.getShort(i), dstVector.getShort(i));
+                }
+              }
+              for (int i = dstPrefilledSize - sizeOfDataNotToAdd, j = 0; i < dstSize - sizeOfDataNotToAdd && j < srcSize; i++, j++) {
+                assertEquals(src.isNull(j), dstVector.isNull(i));
+                if (!src.isNull(j)) {
+                  assertEquals(src.getShort(j), dstVector.getShort(i));
+                }
+              }
+              if (dstVector.hasValidityVector()) {
+                long maxIndex =
+                    BitVectorHelper.getValidityAllocationSizeInBytes(dstVector.getRowCount()) * 8;
+                for (long i = dstSize - sizeOfDataNotToAdd; i < maxIndex; i++) {
+                  assertFalse(dstVector.isNullExtendedRange(i));
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/TableTest.java b/java/src/test/java/ai/rapids/cudf/TableTest.java
new file mode 100644
index 0000000..faa73ac
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/TableTest.java
@@ -0,0 +1,9209 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.BasicType;
+import ai.rapids.cudf.HostColumnVector.Builder;
+import ai.rapids.cudf.HostColumnVector.DataType;
+import ai.rapids.cudf.HostColumnVector.ListType;
+import ai.rapids.cudf.HostColumnVector.StructData;
+import ai.rapids.cudf.HostColumnVector.StructType;
+
+import ai.rapids.cudf.ast.BinaryOperation;
+import ai.rapids.cudf.ast.BinaryOperator;
+import ai.rapids.cudf.ast.ColumnReference;
+import ai.rapids.cudf.ast.CompiledExpression;
+import ai.rapids.cudf.ast.TableReference;
+import com.google.common.base.Charsets;
+import com.google.common.collect.Lists;
+import com.google.common.collect.Maps;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.parquet.hadoop.ParquetFileReader;
+import org.apache.parquet.hadoop.util.HadoopInputFile;
+import org.apache.parquet.schema.GroupType;
+import org.apache.parquet.schema.MessageType;
+import org.apache.parquet.schema.OriginalType;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+
+import java.io.*;
+import java.math.BigDecimal;
+import java.math.BigInteger;
+import java.math.RoundingMode;
+import java.nio.ByteBuffer;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.util.*;
+import java.util.function.Function;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static ai.rapids.cudf.AssertUtils.assertPartialColumnsAreEqual;
+import static ai.rapids.cudf.AssertUtils.assertPartialTablesAreEqual;
+import static ai.rapids.cudf.AssertUtils.assertTableTypes;
+import static ai.rapids.cudf.AssertUtils.assertTablesAreEqual;
+import static ai.rapids.cudf.ColumnWriterOptions.mapColumn;
+import static ai.rapids.cudf.ParquetWriterOptions.listBuilder;
+import static ai.rapids.cudf.ParquetWriterOptions.structBuilder;
+import static ai.rapids.cudf.Table.TestBuilder;
+import static ai.rapids.cudf.Table.removeNullMasksIfNeeded;
+import static org.junit.jupiter.api.Assertions.assertArrayEquals;
+import static org.junit.jupiter.api.Assertions.assertDoesNotThrow;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotEquals;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+import static org.junit.jupiter.api.Assertions.assertNull;
+import static org.junit.jupiter.api.Assertions.assertThrows;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+public class TableTest extends CudfTestBase {
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  private static final File TEST_PARQUET_FILE = TestUtils.getResourceAsFile("acq.parquet");
+  private static final File TEST_PARQUET_FILE_CHUNKED_READ = TestUtils.getResourceAsFile("splittable.parquet");
+  private static final File TEST_PARQUET_FILE_BINARY = TestUtils.getResourceAsFile("binary.parquet");
+  private static final File TEST_ORC_FILE = TestUtils.getResourceAsFile("TestOrcFile.orc");
+  private static final File TEST_ORC_TIMESTAMP_DATE_FILE = TestUtils.getResourceAsFile("timestamp-date-test.orc");
+  private static final File TEST_DECIMAL_PARQUET_FILE = TestUtils.getResourceAsFile("decimal.parquet");
+  private static final File TEST_ALL_TYPES_PLAIN_AVRO_FILE = TestUtils.getResourceAsFile("alltypes_plain.avro");
+  private static final File TEST_SIMPLE_CSV_FILE = TestUtils.getResourceAsFile("simple.csv");
+  private static final File TEST_SIMPLE_JSON_FILE = TestUtils.getResourceAsFile("people.json");
+  private static final File TEST_JSON_ERROR_FILE = TestUtils.getResourceAsFile("people_with_invalid_lines.json");
+
+  private static final Schema CSV_DATA_BUFFER_SCHEMA = Schema.builder()
+      .column(DType.INT32, "A")
+      .column(DType.FLOAT64, "B")
+      .column(DType.INT64, "C")
+      .build();
+
+  private static final byte[] CSV_DATA_BUFFER = ("A|B|C\n" +
+      "'0'|'110.0'|'120'\n" +
+      "1|111.0|121\n" +
+      "2|112.0|122\n" +
+      "3|113.0|123\n" +
+      "4|114.0|124\n" +
+      "5|115.0|125\n" +
+      "6|116.0|126\n" +
+      "7|NULL|127\n" +
+      "8|118.2|128\n" +
+      "9|119.8|129").getBytes(StandardCharsets.UTF_8);
+
+  void assertTablesHaveSameValues(HashMap<Object, Integer>[] expectedTable, Table table) {
+    assertEquals(expectedTable.length, table.getNumberOfColumns());
+    int numCols = table.getNumberOfColumns();
+    long numRows = table.getRowCount();
+    for (int col = 0; col < numCols; col++) {
+      for (long row = 0; row < numRows; row++) {
+        try (HostColumnVector cv = table.getColumn(col).copyToHost()) {
+          Object key = 0;
+          if (cv.getType().equals(DType.INT32)) {
+            key = cv.getInt(row);
+          } else {
+            key = cv.getDouble(row);
+          }
+          assertTrue(expectedTable[col].containsKey(key));
+          Integer count = expectedTable[col].get(key);
+          if (count == 1) {
+            expectedTable[col].remove(key);
+          } else {
+            expectedTable[col].put(key, count - 1);
+          }
+        }
+      }
+    }
+    for (int i = 0 ; i < expectedTable.length ; i++) {
+      assertTrue(expectedTable[i].isEmpty());
+    }
+  }
+
+  @Test
+  void testDistinctCount() {
+    try (Table table1 = new Table.TestBuilder()
+            .column(5, 3, null, null, 5)
+            .build()) {
+      assertEquals(3, table1.distinctCount());
+      assertEquals(4, table1.distinctCount(NullEquality.UNEQUAL));
+    }
+  }
+
+  @Test
+  void testMergeSimple() {
+    try (Table table1 = new Table.TestBuilder()
+            .column(5, 3, 3, 1, 1)
+            .column(5, 3, null, 1, 2)
+            .column(1, 3, 5, 7, 9)
+            .build();
+         Table table2 = new Table.TestBuilder()
+                 .column(1, 2, 7)
+                 .column(3, 2, 2)
+                 .column(1, 3, 10)
+                 .build();
+         Table expected = new Table.TestBuilder()
+                 .column(1, 1, 1, 2, 3, 3, 5, 7)
+                 .column(3, 2, 1, 2, null, 3, 5, 2)
+                 .column(1, 9, 7, 3, 5, 3, 1, 10)
+                 .build();
+         Table sortedTable1 = table1.orderBy(OrderByArg.asc(0), OrderByArg.desc(1));
+         Table sortedTable2 = table2.orderBy(OrderByArg.asc(0), OrderByArg.desc(1));
+         Table merged = Table.merge(Arrays.asList(sortedTable1, sortedTable2), OrderByArg.asc(0), OrderByArg.desc(1))) {
+      assertTablesAreEqual(expected, merged);
+    }
+  }
+
+  @Test
+  void testOrderByAD() {
+    try (Table table = new Table.TestBuilder()
+        .column(5, 3, 3, 1, 1)
+        .column(5, 3, 4, 1, 2)
+        .column(1, 3, 5, 7, 9)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 1, 3, 3, 5)
+             .column(2, 1, 4, 3, 5)
+             .column(9, 7, 5, 3, 1)
+             .build();
+         Table sortedTable = table.orderBy(OrderByArg.asc(0), OrderByArg.desc(1))) {
+      assertTablesAreEqual(expected, sortedTable);
+    }
+  }
+
+  @Test
+  void testSortOrderSimple() {
+    try (Table table = new Table.TestBuilder()
+        .column(5, 3, 3, 1, 1)
+        .column(5, 3, 4, 1, 2)
+        .column(1, 3, 5, 7, 9)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 1, 3, 3, 5)
+             .column(2, 1, 4, 3, 5)
+             .column(9, 7, 5, 3, 1)
+             .build();
+         ColumnVector gatherMap = table.sortOrder(OrderByArg.asc(0), OrderByArg.desc(1));
+         Table sortedTable = table.gather(gatherMap)) {
+      assertTablesAreEqual(expected, sortedTable);
+    }
+  }
+
+  @Test
+  void testOrderByDD() {
+    try (Table table = new Table.TestBuilder()
+        .column(5, 3, 3, 1, 1)
+        .column(5, 3, 4, 1, 2)
+        .column(1, 3, 5, 7, 9)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(5, 3, 3, 1, 1)
+             .column(5, 4, 3, 2, 1)
+             .column(1, 5, 3, 9, 7)
+             .build();
+         Table sortedTable = table.orderBy(OrderByArg.desc(0), OrderByArg.desc(1))) {
+      assertTablesAreEqual(expected, sortedTable);
+    }
+  }
+
+  @Test
+  void testOrderByWithNulls() {
+    try (Table table = new Table.TestBuilder()
+        .column(5, null, 3, 1, 1)
+        .column(5, 3, 4, null, null)
+        .column("4", "3", "2", "1", "0")
+        .column(1, 3, 5, 7, 9)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 1, 3, 5, null)
+             .column(null, null, 4, 5, 3)
+             .column("1", "0", "2", "4", "3")
+             .column(7, 9, 5, 1, 3)
+             .build();
+         Table sortedTable = table.orderBy(OrderByArg.asc(0), OrderByArg.desc(1))) {
+      assertTablesAreEqual(expected, sortedTable);
+    }
+  }
+
+  @Test
+  void testOrderByWithNullsAndStrings() {
+    try (Table table = new Table.TestBuilder()
+        .column("4", "3", "2", "1", "0")
+        .column(5, null, 3, 1, 1)
+        .column(5, 3, 4, null, null)
+        .column(1, 3, 5, 7, 9)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column("0", "1", "2", "3", "4")
+             .column(1, 1, 3, null, 5)
+             .column(null, null, 4, 3, 5)
+             .column(9, 7, 5, 3, 1)
+             .build();
+         Table sortedTable = table.orderBy(OrderByArg.asc(0))) {
+      assertTablesAreEqual(expected, sortedTable);
+    }
+  }
+
+  @Test
+  void testTableCreationIncreasesRefCountWithDoubleFree() {
+    //tests the Table increases the refcount on column vectors
+    assertThrows(IllegalStateException.class, () -> {
+      try (ColumnVector v1 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5));
+           ColumnVector v2 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5))) {
+        assertDoesNotThrow(() -> {
+          try (Table t = new Table(new ColumnVector[]{v1, v2})) {
+            v1.close();
+            v2.close();
+          }
+        });
+      }
+    });
+  }
+
+  @Test
+  void testGetRows() {
+    try (ColumnVector v1 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5));
+         ColumnVector v2 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5));
+         Table t = new Table(new ColumnVector[]{v1, v2})) {
+      assertEquals(5, t.getRowCount());
+    }
+  }
+
+  @Test
+  void testSettingNullVectors() {
+    ColumnVector[] columnVectors = null;
+    assertThrows(AssertionError.class, () -> new Table(columnVectors));
+  }
+
+  @Test
+  void testAllRowsSize() {
+    try (ColumnVector v1 = ColumnVector.build(DType.INT32, 4, Range.appendInts(4));
+         ColumnVector v2 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5))) {
+      assertThrows(AssertionError.class, () -> {
+        try (Table t = new Table(new ColumnVector[]{v1, v2})) {
+        }
+      });
+    }
+  }
+
+  @Test
+  void testGetNumberOfColumns() {
+    try (ColumnVector v1 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5));
+         ColumnVector v2 = ColumnVector.build(DType.INT32, 5, Range.appendInts(5));
+         Table t = new Table(new ColumnVector[]{v1, v2})) {
+      assertEquals(2, t.getNumberOfColumns());
+    }
+  }
+
+  @Test
+  void testReadJSONFile() {
+    Schema schema = Schema.builder()
+        .column(DType.STRING, "name")
+        .column(DType.INT32, "age")
+        .build();
+    JSONOptions opts = JSONOptions.builder()
+        .withLines(true)
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column("Michael", "Andy", "Justin")
+        .column(null, 30, 19)
+        .build();
+        Table table = Table.readJSON(schema, opts, TEST_SIMPLE_JSON_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadJSONFileWithInvalidLines() {
+    Schema schema = Schema.builder()
+            .column(DType.STRING, "name")
+            .column(DType.INT32, "age")
+            .build();
+
+    // test with recoverWithNulls=true
+    {
+      JSONOptions opts = JSONOptions.builder()
+              .withLines(true)
+              .withRecoverWithNull(true)
+              .build();
+      try (Table expected = new Table.TestBuilder()
+              .column("Michael", "Andy", null, "Justin")
+              .column(null, 30, null, 19)
+              .build();
+           Table table = Table.readJSON(schema, opts, TEST_JSON_ERROR_FILE)) {
+        assertTablesAreEqual(expected, table);
+      }
+    }
+
+    // test with recoverWithNulls=false
+    {
+      JSONOptions opts = JSONOptions.builder()
+              .withLines(true)
+              .withRecoverWithNull(false)
+              .build();
+      assertThrows(CudfException.class, () ->
+        Table.readJSON(schema, opts, TEST_JSON_ERROR_FILE));
+    }
+  }
+
+  @Test
+  void testReadJSONFileWithDifferentColumnOrder() {
+    Schema schema = Schema.builder()
+        .column(DType.INT32, "age")
+        .column(DType.STRING, "name")
+        .build();
+    JSONOptions opts = JSONOptions.builder()
+        .withLines(true)
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column(null, 30, 19)
+        .column("Michael", "Andy", "Justin")
+        .build();
+         Table table = Table.readJSON(schema, opts, TEST_SIMPLE_JSON_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadJSONBufferInferred() {
+    JSONOptions opts = JSONOptions.builder()
+        .withDayFirst(true)
+        .build();
+    byte[] data = ("[false,A,1,2]\n" +
+        "[true,B,2,3]\n" +
+        "[false,C,3,4]\n" +
+        "[true,D,4,5]").getBytes(StandardCharsets.UTF_8);
+    try (Table expected = new Table.TestBuilder()
+        .column(false, true, false, true)
+        .column("A", "B", "C", "D")
+        .column(1L, 2L, 3L, 4L)
+        .column(2L, 3L, 4L, 5L)
+        .build();
+         Table table = Table.readJSON(Schema.INFERRED, opts, data)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadJSONSubColumns() {
+    // JSON file has 2 columns, here only read 1 column
+    Schema schema = Schema.builder()
+        .column(DType.INT32, "age")
+        .build();
+    JSONOptions opts = JSONOptions.builder()
+        .withLines(true)
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column(null, 30, 19)
+        .build();
+         Table table = Table.readJSON(schema, opts, TEST_SIMPLE_JSON_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadJSONBuffer() {
+    // JSON reader will set the column according to the iterator if can't infer the name
+    // So we must set the same name accordingly
+    Schema schema = Schema.builder()
+        .column(DType.STRING, "0")
+        .column(DType.INT32, "1")
+        .column(DType.INT32, "2")
+        .build();
+    JSONOptions opts = JSONOptions.builder()
+        .build();
+    byte[] data = ("[A,1,2]\n" +
+        "[B,2,3]\n" +
+        "[C,3,4]\n" +
+        "[D,4,5]").getBytes(StandardCharsets.UTF_8);
+    try (Table expected = new Table.TestBuilder()
+        .column("A", "B", "C", "D")
+        .column(1, 2, 3, 4)
+        .column(2, 3, 4, 5)
+        .build();
+         Table table = Table.readJSON(schema, opts, data)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadJSONBufferWithOffset() {
+    // JSON reader will set the column according to the iterator if can't infer the name
+    // So we must set the same name accordingly
+    Schema schema = Schema.builder()
+        .column(DType.STRING, "0")
+        .column(DType.INT32, "1")
+        .column(DType.INT32, "2")
+        .build();
+    JSONOptions opts = JSONOptions.builder()
+        .build();
+    int bytesToIgnore = 8;
+    byte[] data = ("[A,1,2]\n" +
+        "[B,2,3]\n" +
+        "[C,3,4]\n" +
+        "[D,4,5]").getBytes(StandardCharsets.UTF_8);
+    try (Table expected = new Table.TestBuilder()
+        .column("B", "C", "D")
+        .column(2, 3, 4)
+        .column(3, 4, 5)
+        .build();
+         Table table = Table.readJSON(schema, opts, data,
+             bytesToIgnore, data.length - bytesToIgnore)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadJSONTableWithMeta() {
+    JSONOptions opts = JSONOptions.builder()
+            .build();
+    byte[] data = ("{ \"A\": 1, \"B\": 2, \"C\": \"X\"}\n" +
+            "{ \"A\": 2, \"B\": 4, \"C\": \"Y\"}\n" +
+            "{ \"A\": 3, \"B\": 6, \"C\": \"Z\"}\n" +
+            "{ \"A\": 4, \"B\": 8, \"C\": \"W\"}\n").getBytes(StandardCharsets.UTF_8);
+    final int numBytes = data.length;
+    try (HostMemoryBuffer hostbuf = hostMemoryAllocator.allocate(numBytes)) {
+      hostbuf.setBytes(0, data, 0, numBytes);
+      try (Table expected = new Table.TestBuilder()
+              .column(1L, 2L, 3L, 4L)
+              .column(2L, 4L, 6L, 8L)
+              .column("X", "Y", "Z", "W")
+              .build();
+         TableWithMeta tablemeta = Table.readJSON(opts, hostbuf, 0, numBytes);
+         Table table = tablemeta.releaseTable()) {
+        assertArrayEquals(new String[] { "A", "B", "C" }, tablemeta.getColumnNames());
+        assertTablesAreEqual(expected, table);
+      }
+    }
+  }
+
+  @Test
+  void testReadCSVPrune() {
+    Schema schema = Schema.builder()
+        .column(DType.INT32, "A")
+        .column(DType.FLOAT64, "B")
+        .column(DType.INT64, "C")
+        .build();
+    CSVOptions opts = CSVOptions.builder()
+        .includeColumn("A")
+        .includeColumn("B")
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+        .column(110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.2, 119.8)
+        .build();
+         Table table = Table.readCSV(schema, opts, TEST_SIMPLE_CSV_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadCSVBufferInferred() {
+    CSVOptions opts = CSVOptions.builder()
+        .includeColumn("A")
+        .includeColumn("B")
+        .hasHeader()
+        .withComment('#')
+        .build();
+    byte[] data = ("A,B,C\n" +
+        "0,110.0,120'\n" +
+        "#0.5,1.0,200\n" +
+        "1,111.0,121\n" +
+        "2,112.0,122\n" +
+        "3,113.0,123\n" +
+        "4,114.0,124\n" +
+        "5,115.0,125\n" +
+        "6,116.0,126\n" +
+        "7,117.0,127\n" +
+        "8,118.2,128\n" +
+        "9,119.8,129").getBytes(StandardCharsets.UTF_8);
+    try (Table expected = new Table.TestBuilder()
+        .column(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L)
+        .column(110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.2, 119.8)
+        .build();
+         Table table = Table.readCSV(Schema.INFERRED, opts, data)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadCSVBuffer() {
+    CSVOptions opts = CSVOptions.builder()
+        .includeColumn("A")
+        .includeColumn("B")
+        .hasHeader()
+        .withDelim('|')
+        .withQuote('\'')
+        .withNullValue("NULL")
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+        .column(110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, null, 118.2, 119.8)
+        .build();
+         Table table = Table.readCSV(TableTest.CSV_DATA_BUFFER_SCHEMA, opts,
+             TableTest.CSV_DATA_BUFFER)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadCSVWithOffset() {
+    CSVOptions opts = CSVOptions.builder()
+        .includeColumn("A")
+        .includeColumn("B")
+        .hasHeader(false)
+        .withDelim('|')
+        .withNullValue("NULL")
+        .build();
+    int bytesToIgnore = 24;
+    try (Table expected = new Table.TestBuilder()
+        .column(1, 2, 3, 4, 5, 6, 7, 8, 9)
+        .column(111.0, 112.0, 113.0, 114.0, 115.0, 116.0, null, 118.2, 119.8)
+        .build();
+         Table table = Table.readCSV(TableTest.CSV_DATA_BUFFER_SCHEMA, opts,
+             TableTest.CSV_DATA_BUFFER, bytesToIgnore, CSV_DATA_BUFFER.length - bytesToIgnore)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadCSVOtherTypes() {
+    final byte[] CSV_DATA_WITH_TYPES = ("A,B,C,D\n" +
+        "0,true,120,\"zero\"\n" +
+        "1,True,121,\"one\"\n" +
+        "2,false,122,\"two\"\n" +
+        "3,false,123,\"three\"\n" +
+        "4,TRUE,124,\"four\"\n" +
+        "5,true,125,\"five\"\n" +
+        "6,true,126,\"six\"\n" +
+        "7,NULL,127,NULL\n" +
+        "8,false,128,\"eight\"\n" +
+        "9,false,129,\"nine\uD80C\uDC3F\"").getBytes(StandardCharsets.UTF_8);
+
+    final Schema CSV_DATA_WITH_TYPES_SCHEMA = Schema.builder()
+        .column(DType.INT32, "A")
+        .column(DType.BOOL8, "B")
+        .column(DType.INT64, "C")
+        .column(DType.STRING, "D")
+        .build();
+
+    CSVOptions opts = CSVOptions.builder()
+        .includeColumn("A", "B", "D")
+        .hasHeader(true)
+        .withNullValue("NULL")
+        .withQuote('"')
+        .withTrueValue("true", "True", "TRUE")
+        .withFalseValue("false")
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+        .column(true, true, false, false, true, true, true, null, false, false)
+        .column("zero", "one", "two", "three", "four", "five", "six", null, "eight", "nine\uD80C\uDC3F")
+        .build();
+         Table table = Table.readCSV(CSV_DATA_WITH_TYPES_SCHEMA, opts, CSV_DATA_WITH_TYPES)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadCSV() {
+    Schema schema = Schema.builder()
+        .column(DType.INT32, "A")
+        .column(DType.FLOAT64, "B")
+        .column(DType.INT64, "C")
+        .column(DType.STRING, "D")
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+        .column(110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.2, 119.8)
+        .column(120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L)
+        .column("one", "two", "three", "four", "five", "six", "seven\ud801\uddb8", "eight\uBF68", "nine\u03E8", "ten")
+        .build();
+         Table table = Table.readCSV(schema, TEST_SIMPLE_CSV_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  private void testWriteCSVToFileImpl(char fieldDelim, boolean includeHeader,
+                                      String trueValue, String falseValue) throws IOException {
+    File outputFile = File.createTempFile("testWriteCSVToFile", ".csv");
+    Schema schema = Schema.builder()
+                          .column(DType.INT32, "i")
+                          .column(DType.FLOAT64, "f")
+                          .column(DType.BOOL8, "b")
+                          .column(DType.STRING, "str")
+                          .build();
+    CSVWriterOptions writeOptions = CSVWriterOptions.builder()
+                                               .withColumnNames(schema.getColumnNames())
+                                               .withIncludeHeader(includeHeader)
+                                               .withFieldDelimiter((byte)fieldDelim)
+                                               .withRowDelimiter("\n")
+                                               .withNullValue("\\N")
+                                               .withTrueValue(trueValue)
+                                               .withFalseValue(falseValue)
+                                               .build();
+    try (Table inputTable
+          = new Table.TestBuilder()
+              .column(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+              .column(0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0)
+              .column(false, true, false, true, false, true, false, true, false, true)
+              .column("All", "the", "leaves", "are", "brown", "and", "the", "sky", "is", "grey")
+              .build()) {
+      inputTable.writeCSVToFile(writeOptions, outputFile.getAbsolutePath());
+
+      // Read back.
+      CSVOptions readOptions = CSVOptions.builder()
+                                         .includeColumn("i")
+                                         .includeColumn("f")
+                                         .includeColumn("b")
+                                         .includeColumn("str")
+                                         .hasHeader(includeHeader)
+                                         .withDelim(fieldDelim)
+                                         .withTrueValue(trueValue)
+                                         .withFalseValue(falseValue)
+                                         .build();
+      try (Table readTable = Table.readCSV(schema, readOptions, outputFile)) {
+        assertTablesAreEqual(inputTable, readTable);
+      }
+    } finally {
+      outputFile.delete();
+    }
+  }
+
+  @Test
+  void testWriteCSVToFile() throws IOException {
+    final boolean INCLUDE_HEADER = true;
+    final boolean NO_HEADER = false;
+    testWriteCSVToFileImpl(',', INCLUDE_HEADER, "true", "false");
+    testWriteCSVToFileImpl(',', NO_HEADER, "TRUE", "FALSE");
+    testWriteCSVToFileImpl('\u0001', INCLUDE_HEADER, "T", "F");
+    testWriteCSVToFileImpl('\u0001', NO_HEADER, "True", "False");
+  }
+
+  private void testWriteUnquotedCSVToFileImpl(char fieldDelim) throws IOException {
+    File outputFile = File.createTempFile("testWriteUnquotedCSVToFile", ".csv");
+    Schema schema = Schema.builder()
+                          .column(DType.STRING, "str")
+                          .build();
+    CSVWriterOptions writeOptions = CSVWriterOptions.builder()
+                                               .withColumnNames(schema.getColumnNames())
+                                               .withIncludeHeader(false)
+                                               .withFieldDelimiter((byte)fieldDelim)
+                                               .withRowDelimiter("\n")
+                                               .withNullValue("\\N")
+                                               .withQuoteStyle(QuoteStyle.NONE)
+                                               .build();
+    try (Table inputTable
+          = new Table.TestBuilder()
+              .column("All" + fieldDelim + "the" + fieldDelim + "leaves",
+                      "are\"brown",
+                      "and\nthe\nsky\nis\ngrey")
+              .build()) {
+      inputTable.writeCSVToFile(writeOptions, outputFile.getAbsolutePath());
+
+      // Read back.
+      CSVOptions readOptions = CSVOptions.builder()
+                                         .includeColumn("str")
+                                         .hasHeader(false)
+                                         .withDelim(fieldDelim)
+                                         .withQuoteStyle(QuoteStyle.NONE)
+                                         .build();
+      try (Table readTable = Table.readCSV(schema, readOptions, outputFile);
+           Table expected = new Table.TestBuilder()
+             .column("All", "are\"brown", "and", "the", "sky", "is", "grey")
+             .build()) {
+        assertTablesAreEqual(expected, readTable);
+      }
+    } finally {
+      outputFile.delete();
+    }
+  }
+
+  @Test
+  void testWriteUnquotedCSVToFile() throws IOException {
+    testWriteUnquotedCSVToFileImpl(',');
+    testWriteUnquotedCSVToFileImpl('\u0001');
+  }
+
+  private void testChunkedCSVWriterUnquotedImpl(char fieldDelim) throws IOException {
+    Schema schema = Schema.builder()
+                          .column(DType.STRING, "str")
+                          .build();
+    CSVWriterOptions writeOptions = CSVWriterOptions.builder()
+                                               .withColumnNames(schema.getColumnNames())
+                                               .withIncludeHeader(false)
+                                               .withFieldDelimiter((byte)fieldDelim)
+                                               .withRowDelimiter("\n")
+                                               .withNullValue("\\N")
+                                               .withQuoteStyle(QuoteStyle.NONE)
+                                               .build();
+    try (Table inputTable
+          = new Table.TestBuilder()
+              .column("All" + fieldDelim + "the" + fieldDelim + "leaves",
+                      "are\"brown",
+                      "and\nthe\nsky\nis\ngrey")
+              .build();
+          MyBufferConsumer consumer = new MyBufferConsumer()) {
+
+      try (TableWriter writer = Table.getCSVBufferWriter(writeOptions, consumer)) {
+        writer.write(inputTable);
+        writer.write(inputTable);
+        writer.write(inputTable);
+      }
+
+      // Read back.
+      CSVOptions readOptions = CSVOptions.builder()
+                                         .includeColumn("str")
+                                         .hasHeader(false)
+                                         .withDelim(fieldDelim)
+                                         .withNullValue("\\N")
+                                         .withQuoteStyle(QuoteStyle.NONE)
+                                         .build();
+      try (Table readTable = Table.readCSV(schema, readOptions, consumer.buffer, 0, consumer.offset);
+           Table section = new Table.TestBuilder()
+             .column("All", "are\"brown", "and", "the", "sky", "is", "grey")
+             .build();
+           Table expected  = Table.concatenate(section, section, section)) {
+        assertTablesAreEqual(expected, readTable);
+      }
+    }
+  }
+
+  @Test
+  void testChunkedCSVWriterUnquoted() throws IOException {
+    testChunkedCSVWriterUnquotedImpl(',');
+    testChunkedCSVWriterUnquotedImpl('\u0001');
+  }
+
+  private void testChunkedCSVWriterImpl(char fieldDelim, boolean includeHeader,
+                                        String trueValue, String falseValue) throws IOException {
+    Schema schema = Schema.builder()
+                          .column(DType.INT32, "i")
+                          .column(DType.FLOAT64, "f")
+                          .column(DType.BOOL8, "b")
+                          .column(DType.STRING, "str")
+                          .build();
+    CSVWriterOptions writeOptions = CSVWriterOptions.builder()
+                                               .withColumnNames(schema.getColumnNames())
+                                               .withIncludeHeader(includeHeader)
+                                               .withFieldDelimiter((byte)fieldDelim)
+                                               .withRowDelimiter("\n")
+                                               .withNullValue("\\N")
+                                               .withTrueValue(trueValue)
+                                               .withFalseValue(falseValue)
+                                               .build();
+    try (Table inputTable
+          = new Table.TestBuilder()
+              .column(0, 1, 2, 3, 4, 5, 6, 7, 8, null)
+              .column(0.0, 1.0, 2.0, 3.0, 4.0, null, 6.0, 7.0, 8.0, 9.0)
+              .column(false, true, null, true, false, true, null, true, false, true)
+              .column("All", "the", "leaves", "are", "brown", "and", "the", "sky", "is", null)
+              .build();
+          MyBufferConsumer consumer = new MyBufferConsumer()) {
+
+      try (TableWriter writer = Table.getCSVBufferWriter(writeOptions, consumer)) {
+        writer.write(inputTable);
+        writer.write(inputTable);
+        writer.write(inputTable);
+      }
+
+      // Read back.
+      CSVOptions readOptions = CSVOptions.builder()
+                                         .includeColumn("i")
+                                         .includeColumn("f")
+                                         .includeColumn("b")
+                                         .includeColumn("str")
+                                         .hasHeader(includeHeader)
+                                         .withDelim(fieldDelim)
+                                         .withNullValue("\\N")
+                                         .withTrueValue(trueValue)
+                                         .withFalseValue(falseValue)
+                                         .build();
+      try (Table readTable = Table.readCSV(schema, readOptions, consumer.buffer, 0, consumer.offset);
+           Table expected  = Table.concatenate(inputTable, inputTable, inputTable)) {
+        assertTablesAreEqual(expected, readTable);
+      }
+    }
+  }
+
+  @Test
+  void testChunkedCSVWriter() throws IOException {
+    final boolean INCLUDE_HEADER = true;
+    final boolean NO_HEADER = false;
+    testChunkedCSVWriterImpl(',', NO_HEADER, "true", "false");
+    testChunkedCSVWriterImpl(',', INCLUDE_HEADER, "TRUE", "FALSE");
+    testChunkedCSVWriterImpl('\u0001', NO_HEADER, "T", "F");
+    testChunkedCSVWriterImpl('\u0001', INCLUDE_HEADER, "True", "False");
+  }
+
+  @Test
+  void testReadParquet() {
+    ParquetOptions opts = ParquetOptions.builder()
+        .includeColumn("loan_id")
+        .includeColumn("zip")
+        .includeColumn("num_units")
+        .build();
+    try (Table table = Table.readParquet(opts, TEST_PARQUET_FILE)) {
+      long rows = table.getRowCount();
+      assertEquals(1000, rows);
+      assertTableTypes(new DType[]{DType.INT64, DType.INT32, DType.INT32}, table);
+    }
+  }
+
+  @Test
+  void testReadParquetBinary() {
+    ParquetOptions opts = ParquetOptions.builder()
+        .includeColumn("value1", true)
+        .includeColumn("value2", false)
+        .build();
+    try (Table table = Table.readParquet(opts, TEST_PARQUET_FILE_BINARY)) {
+      assertTableTypes(new DType[]{DType.STRING, DType.STRING}, table);
+      ColumnView columnView = table.getColumn(0);
+      assertEquals(DType.STRING, columnView.getType());
+    }
+  }
+
+  List<Byte> asList(String str) {
+    byte[] bytes = str.getBytes(Charsets.UTF_8);
+    List<Byte> ret = new ArrayList<>(bytes.length);
+    for(int i = 0; i < bytes.length; i++) {
+      ret.add(bytes[i]);
+    }
+    return ret;
+  }
+
+  @Test
+  void testParquetWriteToBufferChunkedBinary() {
+    // We create a String table and a Binary table with the same data in them to
+    // avoid trying to read the binary data back in the same way. At least until the
+    // API for that is stable
+    String string1 = "ABC";
+    String string2 = "DEF";
+    List<Byte> bin1 = asList(string1);
+    List<Byte> bin2 = asList(string2);
+
+    try (Table binTable = new Table.TestBuilder()
+        .column(new ListType(true, new BasicType(false, DType.UINT8)),
+            bin1, bin2)
+        .build();
+         Table stringTable = new Table.TestBuilder()
+             .column(string1, string2)
+             .build();
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      ParquetWriterOptions options = ParquetWriterOptions.builder()
+          .withBinaryColumn("_c0", true)
+          .build();
+
+      try (TableWriter writer = Table.writeParquetChunked(options, consumer)) {
+        writer.write(binTable);
+        writer.write(binTable);
+        writer.write(binTable);
+      }
+      ParquetOptions opts = ParquetOptions.builder()
+          .includeColumn("_c0")
+          .build();
+      try (Table table1 = Table.readParquet(opts, consumer.buffer, 0, consumer.offset);
+           Table concat = Table.concatenate(stringTable, stringTable, stringTable)) {
+        assertTablesAreEqual(concat, table1);
+      }
+    }
+  }
+
+  @Test
+  void testReadParquetBuffer() throws IOException {
+    ParquetOptions opts = ParquetOptions.builder()
+        .includeColumn("loan_id")
+        .includeColumn("coborrow_credit_score")
+        .includeColumn("borrower_credit_score")
+        .build();
+
+    byte[] buffer = new byte[(int) TEST_PARQUET_FILE.length() + 1024];
+    int bufferLen = 0;
+    try (FileInputStream in = new FileInputStream(TEST_PARQUET_FILE)) {
+      bufferLen = in.read(buffer);
+    }
+    try (Table table = Table.readParquet(opts, buffer, 0, bufferLen)) {
+      long rows = table.getRowCount();
+      assertEquals(1000, rows);
+      assertTableTypes(new DType[]{DType.INT64, DType.FLOAT64, DType.FLOAT64}, table);
+    }
+  }
+
+  @Test
+  void testReadParquetFull() {
+    try (Table table = Table.readParquet(TEST_PARQUET_FILE)) {
+      long rows = table.getRowCount();
+      assertEquals(1000, rows);
+
+      DType[] expectedTypes = new DType[]{
+          DType.INT64, // loan_id
+          DType.INT32, // orig_channel
+          DType.FLOAT64, // orig_interest_rate
+          DType.INT32, // orig_upb
+          DType.INT32, // orig_loan_term
+          DType.TIMESTAMP_DAYS, // orig_date
+          DType.TIMESTAMP_DAYS, // first_pay_date
+          DType.FLOAT64, // orig_ltv
+          DType.FLOAT64, // orig_cltv
+          DType.FLOAT64, // num_borrowers
+          DType.FLOAT64, // dti
+          DType.FLOAT64, // borrower_credit_score
+          DType.INT32, // first_home_buyer
+          DType.INT32, // loan_purpose
+          DType.INT32, // property_type
+          DType.INT32, // num_units
+          DType.INT32, // occupancy_status
+          DType.INT32, // property_state
+          DType.INT32, // zip
+          DType.FLOAT64, // mortgage_insurance_percent
+          DType.INT32, // product_type
+          DType.FLOAT64, // coborrow_credit_score
+          DType.FLOAT64, // mortgage_insurance_type
+          DType.INT32, // relocation_mortgage_indicator
+          DType.INT32, // quarter
+          DType.INT32 // seller_id
+      };
+
+      assertTableTypes(expectedTypes, table);
+    }
+  }
+
+  @Test
+  void testReadParquetContainsDecimalData() {
+    try (Table table = Table.readParquet(TEST_DECIMAL_PARQUET_FILE)) {
+      long rows = table.getRowCount();
+      assertEquals(100, rows);
+      DType[] expectedTypes = new DType[]{
+          DType.create(DType.DTypeEnum.DECIMAL64, 0), // Decimal(18, 0)
+          DType.create(DType.DTypeEnum.DECIMAL32, -3), // Decimal(7, 3)
+          DType.create(DType.DTypeEnum.DECIMAL64, -10),  // Decimal(10, 10)
+          DType.create(DType.DTypeEnum.DECIMAL32, 0),  // Decimal(1, 0)
+          DType.create(DType.DTypeEnum.DECIMAL64, -15),  // Decimal(18, 15)
+          DType.create(DType.DTypeEnum.DECIMAL128, -10),  // Decimal(20, 10)
+          DType.INT64,
+          DType.FLOAT32
+      };
+      assertTableTypes(expectedTypes, table);
+    }
+  }
+
+  @Test
+  void testChunkedReadParquet() {
+    try (ParquetChunkedReader reader = new ParquetChunkedReader(240000,
+        TEST_PARQUET_FILE_CHUNKED_READ)) {
+      int numChunks = 0;
+      long totalRows = 0;
+      while(reader.hasNext()) {
+        ++numChunks;
+        try(Table chunk = reader.readChunk()) {
+          totalRows += chunk.getRowCount();
+        }
+      }
+      assertEquals(2, numChunks);
+      assertEquals(40000, totalRows);
+    }
+  }
+
+  @Test
+  void testReadAvro() {
+    AvroOptions opts = AvroOptions.builder()
+        .includeColumn("bool_col")
+        .includeColumn("int_col")
+        .includeColumn("timestamp_col")
+        .build();
+
+    try (Table expected = new Table.TestBuilder()
+        .column(true, false, true, false, true, false, true, false)
+        .column(0, 1, 0, 1, 0, 1, 0, 1)
+        .column(1235865600000000L, 1235865660000000L, 1238544000000000L, 1238544060000000L,
+            1233446400000000L, 1233446460000000L, 1230768000000000L, 1230768060000000L)
+        .build();
+        Table table = Table.readAvro(opts, TEST_ALL_TYPES_PLAIN_AVRO_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadAvroBuffer() throws IOException{
+    AvroOptions opts = AvroOptions.builder()
+        .includeColumn("bool_col")
+        .includeColumn("timestamp_col")
+        .build();
+
+    byte[] buffer = Files.readAllBytes(TEST_ALL_TYPES_PLAIN_AVRO_FILE.toPath());
+    int bufferLen = buffer.length;
+    try (Table expected = new Table.TestBuilder()
+        .column(true, false, true, false, true, false, true, false)
+        .column(1235865600000000L, 1235865660000000L, 1238544000000000L, 1238544060000000L,
+            1233446400000000L, 1233446460000000L, 1230768000000000L, 1230768060000000L)
+        .build();
+        Table table = Table.readAvro(opts, buffer, 0, bufferLen)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadAvroFull() {
+    try (Table expected = new Table.TestBuilder()
+        .column(4, 5, 6, 7, 2, 3, 0, 1)
+        .column(true, false, true, false, true, false, true, false)
+        .column(0, 1, 0, 1, 0, 1, 0, 1)
+        .column(0, 1, 0, 1, 0, 1, 0, 1)
+        .column(0, 1, 0, 1, 0, 1, 0, 1)
+        .column(0L, 10L, 0L, 10L, 0L, 10L, 0L, 10L)
+        .column(0.0f, 1.100000023841858f, 0.0f, 1.100000023841858f, 0.0f, 1.100000023841858f, 0.0f, 1.100000023841858f)
+        .column(0.0d, 10.1d, 0.0d, 10.1d, 0.0d, 10.1d, 0.0d, 10.1d)
+        .column("03/01/09", "03/01/09", "04/01/09", "04/01/09", "02/01/09", "02/01/09", "01/01/09", "01/01/09")
+        .column("0", "1", "0", "1", "0", "1", "0", "1")
+        .column(1235865600000000L, 1235865660000000L, 1238544000000000L, 1238544060000000L,
+            1233446400000000L, 1233446460000000L, 1230768000000000L, 1230768060000000L)
+        .build();
+        Table table = Table.readAvro(TEST_ALL_TYPES_PLAIN_AVRO_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadORC() {
+    ORCOptions opts = ORCOptions.builder()
+        .includeColumn("string1")
+        .includeColumn("float1")
+        .includeColumn("int1")
+        .build();
+    try (Table expected = new Table.TestBuilder()
+        .column("hi","bye")
+        .column(1.0f,2.0f)
+        .column(65536,65536)
+        .build();
+         Table table = Table.readORC(opts, TEST_ORC_FILE)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadORCBuffer() throws IOException {
+    ORCOptions opts = ORCOptions.builder()
+        .includeColumn("string1")
+        .includeColumn("float1")
+        .includeColumn("int1")
+        .build();
+
+    int bufferLen = 0;
+    byte[] buffer = Files.readAllBytes(TEST_ORC_FILE.toPath());
+    bufferLen = buffer.length;
+    try (Table expected = new Table.TestBuilder()
+        .column("hi","bye")
+        .column(1.0f,2.0f)
+        .column(65536,65536)
+        .build();
+         Table table = Table.readORC(opts, buffer, 0, bufferLen)) {
+      assertTablesAreEqual(expected, table);
+    }
+  }
+
+  @Test
+  void testReadORCFull() {
+    try (Table expected = new Table.TestBuilder()
+        .column(false, true)
+        .column((byte)1, (byte)100)
+        .column((short)1024, (short)2048)
+        .column(65536, 65536)
+        .column(9223372036854775807L,9223372036854775807L)
+        .column(1.0f, 2.0f)
+        .column(-15.0, -5.0)
+        .column("hi", "bye")
+        .build();
+         Table table = Table.readORC(TEST_ORC_FILE)) {
+      assertTablesAreEqual(expected,  table);
+    }
+  }
+
+  @Test
+  void testReadORCNumPyTypes() {
+    // by default ORC will promote TIMESTAMP_DAYS to TIMESTAMP_MILLISECONDS
+    DType found;
+    try (Table table = Table.readORC(TEST_ORC_TIMESTAMP_DATE_FILE)) {
+      assertEquals(2, table.getNumberOfColumns());
+      found = table.getColumn(0).getType();
+      assertTrue(found.isTimestampType());
+      assertEquals(DType.TIMESTAMP_MILLISECONDS, table.getColumn(1).getType());
+    }
+
+    // specifying no NumPy types should load them as TIMESTAMP_DAYS
+    ORCOptions opts = ORCOptions.builder().withNumPyTypes(false).build();
+    try (Table table = Table.readORC(opts, TEST_ORC_TIMESTAMP_DATE_FILE)) {
+      assertEquals(2, table.getNumberOfColumns());
+      assertEquals(found, table.getColumn(0).getType());
+      assertEquals(DType.TIMESTAMP_DAYS, table.getColumn(1).getType());
+    }
+  }
+
+  @Test
+  void testReadORCTimeUnit() {
+    // specifying no NumPy types should load them as TIMESTAMP_DAYS.
+    // specifying a specific type will return the result in that unit
+    ORCOptions opts = ORCOptions.builder()
+        .withNumPyTypes(false)
+        .withTimeUnit(DType.TIMESTAMP_SECONDS)
+        .build();
+    try (Table table = Table.readORC(opts, TEST_ORC_TIMESTAMP_DATE_FILE)) {
+      assertEquals(2, table.getNumberOfColumns());
+      assertEquals(DType.TIMESTAMP_SECONDS, table.getColumn(0).getType());
+      assertEquals(DType.TIMESTAMP_DAYS, table.getColumn(1).getType());
+    }
+  }
+
+  @Test
+  void testCrossJoin() {
+    try (Table leftTable = new Table.TestBuilder()
+            .column(100, 101, 102)
+            .build();
+         Table rightTable = new Table.TestBuilder()
+                 .column(200, null)
+                 .build();
+         Table expected = new Table.TestBuilder()
+                 .column(  100, 100,  101, 101,  102, 102) // left
+                 .column( null, 200, null, 200, null, 200) // right
+                 .build();
+         Table joinedTable = leftTable.crossJoin(rightTable);
+         Table orderedJoinedTable =
+                 joinedTable.orderBy(
+                     OrderByArg.asc(0, true),
+                     OrderByArg.asc(1, true))) {
+      assertTablesAreEqual(expected, orderedJoinedTable);
+    }
+  }
+
+  private void verifyJoinGatherMaps(GatherMap[] maps, Table expected) {
+    assertEquals(2, maps.length);
+    int numRows = (int) expected.getRowCount();
+    assertEquals(numRows, maps[0].getRowCount());
+    assertEquals(numRows, maps[1].getRowCount());
+    try (ColumnVector leftMap = maps[0].toColumnView(0, numRows).copyToColumnVector();
+         ColumnVector rightMap = maps[1].toColumnView(0, numRows).copyToColumnVector();
+         Table result = new Table(leftMap, rightMap);
+         Table orderedResult = result.orderBy(OrderByArg.asc(0, true))) {
+      assertTablesAreEqual(expected, orderedResult);
+    }
+  }
+
+  private void verifySemiJoinGatherMap(GatherMap map, Table expected) {
+    int numRows = (int) expected.getRowCount();
+    assertEquals(numRows, map.getRowCount());
+    try (ColumnVector leftMap = map.toColumnView(0, numRows).copyToColumnVector();
+         Table result = new Table(leftMap);
+         Table orderedResult = result.orderBy(OrderByArg.asc(0, true))) {
+      assertTablesAreEqual(expected, orderedResult);
+    }
+  }
+
+  @Test
+  void testLeftJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 8, 9)
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 3)
+             .build()) {
+      GatherMap[] maps = leftKeys.leftJoinGatherMaps(rightKeys, false);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testLeftJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder()
+            .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+            .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.leftJoinGatherMaps(rightKeys, true);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testLeftHashJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         HashJoin rightHash = new HashJoin(rightKeys, false);
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 8, 9)
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 3)
+             .build()) {
+      GatherMap[] maps = leftKeys.leftJoinGatherMaps(rightHash);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testLeftHashJoinGatherMapsWithCount() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         HashJoin rightHash = new HashJoin(rightKeys, false);
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 8, 9)
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 3)
+             .build()) {
+      long rowCount = leftKeys.leftJoinRowCount(rightHash);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = leftKeys.leftJoinGatherMaps(rightHash, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testLeftHashJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         HashJoin rightHash = new HashJoin(rightKeys, true);
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.leftJoinGatherMaps(rightHash);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testLeftHashJoinGatherMapsNullsWithCount() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         HashJoin rightHash = new HashJoin(rightKeys,true);
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build()) {
+      long rowCount = leftKeys.leftJoinRowCount(rightHash);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = leftKeys.leftJoinGatherMaps(rightHash, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalLeftJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2, 2, 2,   3,   4, 5, 5,   6, 7,   8, 9, 9)
+             .column(inv, inv, 0, 1, 3, inv, inv, 0, 1, inv, 1, inv, 0, 1)
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalLeftJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalLeftJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalLeftJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalLeftJoinGatherMapsWithCount() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2, 2, 2,   3,   4, 5, 5,   6, 7,   8, 9, 9)
+             .column(inv, inv, 0, 1, 3, inv, inv, 0, 1, inv, 1, inv, 0, 1)
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalLeftJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = left.conditionalLeftJoinGatherMaps(right, condition, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalLeftJoinGatherMapsNullsWithCount() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalLeftJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = left.conditionalLeftJoinGatherMaps(right, condition, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+            new ColumnReference(1, TableReference.LEFT),
+            new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3,  4,  5)
+             .column(7, 8, 9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 8,   9)
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, inv)
+             .build()) {
+      GatherMap[] maps = Table.mixedLeftJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.UNEQUAL);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+            new ColumnReference(1, TableReference.LEFT),
+            new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0,   1,   2,   3,   4,   5,   6, 7, 7, 8,   9)
+             .column(0, inv, inv, inv, inv, inv, inv, 0, 2, 1, inv)
+             .build()) {
+      GatherMap[] maps = Table.mixedLeftJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.EQUAL);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftJoinGatherMapsWithSize() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+            new ColumnReference(1, TableReference.LEFT),
+            new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(  0,   1, 2,   3,   4,   5,   6, 7, 8,   9)
+             .column(inv, inv, 2, inv, inv, inv, inv, 0, 1, inv)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedLeftJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.UNEQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      GatherMap[] maps = Table.mixedLeftJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.UNEQUAL, sizeInfo);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftJoinGatherMapsNullsWithSize() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+            new ColumnReference(1, TableReference.LEFT),
+            new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0,   1,   2,   3,   4,   5,   6, 7, 7, 8,   9)
+             .column(0, inv, inv, inv, inv, inv, inv, 0, 2, 1, inv)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedLeftJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.EQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      GatherMap[] maps = Table.mixedLeftJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+              NullEquality.EQUAL, sizeInfo);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testInnerJoinGatherMaps() {
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .column(2, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.innerJoinGatherMaps(rightKeys, false);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testInnerJoinGatherMapsNulls() {
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 7, 8, 8, 9) // left
+             .column(2, 0, 1, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.innerJoinGatherMaps(rightKeys, true);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testInnerHashJoinGatherMaps() {
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         HashJoin rightHash = new HashJoin(rightKeys, false);
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .column(2, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.innerJoinGatherMaps(rightHash);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testInnerHashJoinGatherMapsWithCount() {
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         HashJoin rightHash = new HashJoin(rightKeys, false);
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .column(2, 0, 1, 3) // right
+             .build()) {
+      long rowCount = leftKeys.innerJoinRowCount(rightHash);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = leftKeys.innerJoinGatherMaps(rightHash, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testInnerHashJoinGatherMapsNulls() {
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         HashJoin rightHash = new HashJoin(rightKeys, true);
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 7, 8, 8, 9) // left
+             .column(2, 0, 1, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.innerJoinGatherMaps(rightHash);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testInnerHashJoinGatherMapsNullsWithCount() {
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         HashJoin rightHash = new HashJoin(rightKeys, true);
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 7, 8, 8, 9) // left
+             .column(2, 0, 1, 0, 1, 3) // right
+             .build()) {
+      long rowCount = leftKeys.innerJoinRowCount(rightHash);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = leftKeys.innerJoinGatherMaps(rightHash, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalInnerJoinGatherMaps() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 2, 2, 5, 5, 7, 9, 9)
+             .column(0, 1, 3, 0, 1, 1, 0, 1)
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalInnerJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  // Test non-null-supporting equality at least once.
+  @Test
+  void testConditionalInnerJoinGatherMapsEqual() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 9) // left
+             .column(2, 3) // right
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalInnerJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalInnerJoinGatherMapsNulls() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 7, 8, 8, 9) // left
+             .column(2, 0, 1, 0, 1, 3) // right
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalInnerJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalInnerJoinGatherMapsWithCount() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 2, 2, 5, 5, 7, 9, 9)
+             .column(0, 1, 3, 0, 1, 1, 0, 1)
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalInnerJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = left.conditionalInnerJoinGatherMaps(right, condition, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalInnerJoinGatherMapsNullsWithCount() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 7, 8, 8, 9) // left
+             .column(2, 0, 1, 0, 1, 3) // right
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalInnerJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = left.conditionalInnerJoinGatherMaps(right, condition, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedInnerJoinGatherMaps() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8)
+             .column(2, 0, 1)
+             .build()) {
+      GatherMap[] maps = Table.mixedInnerJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.UNEQUAL);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedInnerJoinGatherMapsNulls() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0, 7, 7, 8)
+             .column(0, 0, 2, 1)
+             .build()) {
+      GatherMap[] maps = Table.mixedInnerJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.EQUAL);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedInnerJoinGatherMapsWithSize() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8)
+             .column(2, 0, 1)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedInnerJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.UNEQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      GatherMap[] maps = Table.mixedInnerJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.UNEQUAL, sizeInfo);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedInnerJoinGatherMapsNullsWithSize() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0, 7, 7, 8)
+             .column(0, 0, 2, 1)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedInnerJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.EQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      GatherMap[] maps = Table.mixedInnerJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.EQUAL, sizeInfo);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFullJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, null, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, null).build();
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.fullJoinGatherMaps(rightKeys, false);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFullJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+             .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.fullJoinGatherMaps(rightKeys, true);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFullHashJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, null, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, null).build();
+         HashJoin rightHash = new HashJoin(rightKeys, false);
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.fullJoinGatherMaps(rightHash);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFullHashJoinGatherMapsWithCount() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, null, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, null).build();
+         HashJoin rightHash = new HashJoin(rightKeys, false);
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 3) // right
+             .build()) {
+      long rowCount = leftKeys.fullJoinRowCount(rightHash);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = leftKeys.fullJoinGatherMaps(rightHash, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFullHashJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         HashJoin rightHash = new HashJoin(rightKeys, true);
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build()) {
+      GatherMap[] maps = leftKeys.fullJoinGatherMaps(rightHash);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testFullHashJoinGatherMapsNullsWithCount() {
+    final int inv = Integer.MIN_VALUE;
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         HashJoin rightHash = new HashJoin(rightKeys, true);
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build()) {
+      long rowCount = leftKeys.fullJoinRowCount(rightHash);
+      assertEquals(expected.getRowCount(), rowCount);
+      GatherMap[] maps = leftKeys.fullJoinGatherMaps(rightHash, rowCount);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalFullJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv, inv,   0,   1, 2, 2, 2,   3,   4, 5, 5,   6, 7,   8, 9, 9)
+             .column(  2,   4,   5, inv, inv, 0, 1, 3, inv, inv, 0, 1, inv, 1, inv, 0, 1)
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalFullJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConditionalFullJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 7, 8, 8, 9) // left
+             .column(  4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, 0, 1, 3) // right
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      GatherMap[] maps = left.conditionalFullJoinGatherMaps(right, condition);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedFullJoinGatherMaps() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+            new ColumnReference(1, TableReference.LEFT),
+            new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv, inv,   0,   1, 2,   3,   4,   5,   6, 7, 8,   9)
+             .column(  3,   4,   5, inv, inv, 2, inv, inv, inv, inv, 0, 1, inv)
+             .build()) {
+      GatherMap[] maps = Table.mixedFullJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.UNEQUAL);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedFullJoinGatherMapsNulls() {
+    final int inv = Integer.MIN_VALUE;
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(inv, inv, inv, 0,   1,   2,   3,   4,   5,   6, 7, 7, 8,   9)
+             .column(  3,   4,   5, 0, inv, inv, inv, inv, inv, inv, 0, 2, 1, inv)
+             .build()) {
+      GatherMap[] maps = Table.mixedFullJoinGatherMaps(leftKeys, rightKeys, left, right, condition,
+          NullEquality.EQUAL);
+      try {
+        verifyJoinGatherMaps(maps, expected);
+      } finally {
+        for (GatherMap map : maps) {
+          map.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftSemiJoinGatherMap() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8)
+             .build();
+         GatherMap map = Table.mixedLeftSemiJoinGatherMap(leftKeys, rightKeys, left, right,
+             condition, NullEquality.UNEQUAL)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testMixedLeftSemiJoinGatherMapNulls() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0, 7, 8)
+             .build();
+         GatherMap map = Table.mixedLeftSemiJoinGatherMap(leftKeys, rightKeys, left, right,
+             condition, NullEquality.EQUAL)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testMixedLeftSemiJoinGatherMapWithSize() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedLeftSemiJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.UNEQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      try (GatherMap map = Table.mixedLeftSemiJoinGatherMap(leftKeys, rightKeys, left, right,
+          condition, NullEquality.UNEQUAL, sizeInfo)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftSemiJoinGatherMapNullsWithSize() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0, 7, 8)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedLeftSemiJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.EQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      try (GatherMap map = Table.mixedLeftSemiJoinGatherMap(leftKeys, rightKeys, left, right,
+          condition, NullEquality.EQUAL, sizeInfo)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftAntiJoinGatherMap() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 5, 6, 9)
+             .build();
+         GatherMap map = Table.mixedLeftAntiJoinGatherMap(leftKeys, rightKeys, left, right,
+             condition, NullEquality.UNEQUAL)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testMixedLeftAntiJoinGatherMapNulls() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4, 5, 6, 9)
+             .build();
+         GatherMap map = Table.mixedLeftAntiJoinGatherMap(leftKeys, rightKeys, left, right,
+             condition, NullEquality.EQUAL)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testMixedLeftAntiJoinGatherMapWithSize() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8)
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5)
+             .column(7, 8, 9, 0, 1, 2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 5, 6, 9)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedLeftAntiJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.UNEQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      try (GatherMap map = Table.mixedLeftAntiJoinGatherMap(leftKeys, rightKeys, left, right,
+          condition, NullEquality.UNEQUAL, sizeInfo)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testMixedLeftAntiJoinGatherMapNullsWithSize() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(1, TableReference.LEFT),
+        new ColumnReference(1, TableReference.RIGHT));
+    try (CompiledExpression condition = expr.compile();
+         Table left = new Table.TestBuilder()
+             .column(null, 3, 9, 0, 1, 7, 4, null, 5, 8)
+             .column(   1, 2, 3, 4, 5, 6, 7,    8, 9, 0)
+             .build();
+         Table leftKeys = new Table(left.getColumn(0));
+         Table right = new Table.TestBuilder()
+             .column(null, 5, null, 8, 10, 32)
+             .column(   0, 1,    2, 3,  4,  5)
+             .column(   7, 8,    9, 0,  1,  2).build();
+         Table rightKeys = new Table(right.getColumn(0));
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4, 5, 6, 9)
+             .build();
+         MixedJoinSize sizeInfo = Table.mixedLeftAntiJoinSize(leftKeys, rightKeys, left, right,
+             condition, NullEquality.EQUAL)) {
+      assertEquals(expected.getRowCount(), sizeInfo.getOutputRowCount());
+      try (GatherMap map = Table.mixedLeftAntiJoinGatherMap(leftKeys, rightKeys, left, right,
+          condition, NullEquality.EQUAL, sizeInfo)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testLeftSemiJoinGatherMap() {
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .build();
+         GatherMap map = leftKeys.leftSemiJoinGatherMap(rightKeys, false)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testLeftSemiJoinGatherMapNulls() {
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .build();
+         GatherMap map = leftKeys.leftSemiJoinGatherMap(rightKeys, true)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testConditionalLeftSemiJoinGatherMap() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 5, 7, 9) // left
+             .build();
+         CompiledExpression condition = expr.compile();
+         GatherMap map = left.conditionalLeftSemiJoinGatherMap(right, condition)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testConditionalLeftSemiJoinGatherMapNulls() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .build();
+         CompiledExpression condition = expr.compile();
+         GatherMap map = left.conditionalLeftSemiJoinGatherMap(right, condition)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testConditionalLeftSemiJoinGatherMapWithCount() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 5, 7, 9) // left
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalLeftSemiJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      try (GatherMap map =
+               left.conditionalLeftSemiJoinGatherMap(right, condition, rowCount)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testConditionalLeftSemiJoinGatherMapNullsWithCount() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(2, 7, 8, 9) // left
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalLeftSemiJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      try (GatherMap map =
+               left.conditionalLeftSemiJoinGatherMap(right, condition, rowCount)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testAntiSemiJoinGatherMap() {
+    try (Table leftKeys = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table rightKeys = new Table.TestBuilder().column(6, 5, 9, 8, 10, 32).build();
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 5, 6) // left
+             .build();
+         GatherMap map = leftKeys.leftAntiJoinGatherMap(rightKeys, false)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testAntiSemiJoinGatherMapNulls() {
+    try (Table leftKeys = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table rightKeys = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 5, 6) // left
+             .build();
+         GatherMap map = leftKeys.leftAntiJoinGatherMap(rightKeys, true)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testConditionalLeftAntiJoinGatherMap() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 6, 8) // left
+             .build();
+         CompiledExpression condition = expr.compile();
+         GatherMap map = left.conditionalLeftAntiJoinGatherMap(right, condition)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testConditionalAntiSemiJoinGatherMapNulls() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 5, 6) // left
+             .build();
+         CompiledExpression condition = expr.compile();
+         GatherMap map = left.conditionalLeftAntiJoinGatherMap(right, condition)) {
+      verifySemiJoinGatherMap(map, expected);
+    }
+  }
+
+  @Test
+  void testConditionalLeftAntiJoinGatherMapWithCount() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.GREATER,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder().column(2, 3, 9, 0, 1, 7, 4, 6, 5, 8).build();
+         Table right = new Table.TestBuilder()
+             .column(6, 5, 9, 8, 10, 32)
+             .column(0, 1, 2, 3, 4, 5).build();
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 6, 8) // left
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalLeftAntiJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      try (GatherMap map =
+               left.conditionalLeftAntiJoinGatherMap(right, condition, rowCount)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testConditionalAntiSemiJoinGatherMapNullsWithCount() {
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.NULL_EQUAL,
+        new ColumnReference(0, TableReference.LEFT),
+        new ColumnReference(0, TableReference.RIGHT));
+    try (Table left = new Table.TestBuilder()
+        .column(2, 3, 9, 0, 1, 7, 4, null, null, 8)
+        .build();
+         Table right = new Table.TestBuilder()
+             .column(null, null, 9, 8, 10, 32)
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(0, 1, 3, 4, 5, 6) // left
+             .build();
+         CompiledExpression condition = expr.compile()) {
+      long rowCount = left.conditionalLeftAntiJoinRowCount(right, condition);
+      assertEquals(expected.getRowCount(), rowCount);
+      try (GatherMap map =
+               left.conditionalLeftAntiJoinGatherMap(right, condition, rowCount)) {
+        verifySemiJoinGatherMap(map, expected);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsNulls() {
+    boolean[] descFlags = new boolean[1];
+    try (Table table = new TestBuilder()
+            .column(null, 20, 20, 20, 30)
+            .build();
+        Table values = new TestBuilder()
+            .column(15)
+            .build();
+         ColumnVector expected = ColumnVector.fromBoxedInts(1)) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsValuesSizeBigger() {
+    boolean[] descFlags = new boolean[2];
+    try(Table table = new TestBuilder()
+            .column(90, 100, 120, 130, 135)
+            .column(.5, .5, .5, .7, .7)
+            .build();
+        Table values = new TestBuilder()
+            .column(120)
+            .column(.3)
+            .column(.7)
+            .build()) {
+      assertThrows(CudfException.class, () -> getBoundsCv(descFlags, true, table, values));
+      assertThrows(CudfException.class, () ->  getBoundsCv(descFlags, false, table, values));
+    }
+  }
+
+  @Test
+  void testBoundsInputSizeBigger() {
+    boolean[] descFlags = new boolean[3];
+    try(Table table = new TestBuilder()
+            .column(90, 100, 120, 130, 135)
+            .column(.5, .5, .5, .7, .7)
+            .column(90, 100, 120, 130, 135)
+            .build();
+        Table values = new TestBuilder()
+            .column(120)
+            .column(.3)
+            .build()) {
+      assertThrows(CudfException.class, () -> getBoundsCv(descFlags, true, table, values));
+      assertThrows(CudfException.class, () -> getBoundsCv(descFlags, false, table, values));
+    }
+  }
+
+  @Test
+  void testBoundsMultiCol() {
+    boolean[] descFlags = new boolean[4];
+    try (Table table = new TestBuilder()
+            .column(10, 20, 20, 20, 20)
+            .column(5.0, .5, .5, .7, .7)
+            .column("1","2","3","4","4")
+            .column(90, 77, 78, 61, 61)
+            .build();
+        Table values = new TestBuilder()
+            .column(20)
+            .column(0.7)
+            .column("4")
+            .column(61)
+            .build()) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(5)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(3)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsFloatsMultiVal() {
+    boolean[] descFlags = new boolean[1];
+    try (Table table = new TestBuilder()
+            .column(10.0, 20.6, 20.7)
+            .build();
+        Table values = new TestBuilder()
+            .column(20.3, 20.8)
+            .build();
+         ColumnVector expected = ColumnVector.fromBoxedInts(1, 3)) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsFloatsSingleCol() {
+    boolean[] descFlags = {false};
+    try(Table table = new TestBuilder()
+            .column(10.0, 20.6, 20.7)
+            .build();
+        Table values = new TestBuilder()
+            .column(20.6)
+            .build()) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(2)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsFloatsSingleColDesc() {
+    boolean[] descFlags = new boolean[] {true};
+    try(Table table = new TestBuilder()
+        .column(20.7, 20.6, 10.0)
+        .build();
+        Table values = new TestBuilder()
+            .column(20.6)
+            .build()) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(2)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsIntsSingleCol() {
+    boolean[] descFlags = new boolean[1];
+    try(Table table = new TestBuilder()
+            .column(10, 20, 20, 20, 20)
+            .build();
+        Table values = new TestBuilder()
+            .column(20)
+            .build()) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(5)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values);
+           ColumnVector expected = ColumnVector.fromBoxedInts(1)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsIntsSingleColDesc() {
+    boolean[] descFlags = new boolean[]{true};
+    try (Table table = new TestBuilder()
+        .column(20, 20, 20, 20, 10)
+        .build();
+         Table values = new TestBuilder()
+             .column(5)
+             .build();
+         ColumnVector expected = ColumnVector.fromBoxedInts(5)) {
+      try (ColumnVector columnVector = getBoundsCv(descFlags, true, table, values)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+      try (ColumnVector columnVector = getBoundsCv(descFlags, false, table, values)) {
+        assertColumnsAreEqual(expected, columnVector);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsString() {
+    boolean[] descFlags = new boolean[1];
+    try (ColumnVector cIn = ColumnVector.build(DType.STRING, 4, (b) -> {
+           for (int i = 0; i < 4; i++) {
+             b.appendUTF8String(String.valueOf(i).getBytes());
+           }
+        });
+        Table table = new Table(cIn);
+        ColumnVector cVal = ColumnVector.fromStrings("0");
+        Table values = new Table(cVal)) {
+      try (ColumnVector cv = getBoundsCv(descFlags, true, table, values);
+           ColumnVector expected = ColumnVector.fromInts(1)) {
+        assertColumnsAreEqual(expected, cv);
+      }
+      try (ColumnVector cv = getBoundsCv(descFlags, false, table, values);
+           ColumnVector expected = ColumnVector.fromInts(0)) {
+        assertColumnsAreEqual(expected, cv);
+      }
+    }
+  }
+
+  @Test
+  void testBoundsEmptyValues() {
+    boolean[] descFlags = new boolean[1];
+    try (ColumnVector cv = ColumnVector.fromBoxedLongs();
+         Table table = new TestBuilder()
+             .column(10, 20, 20, 20, 20)
+             .build();
+         Table values = new Table(cv)) {
+      assertThrows(AssertionError.class,
+          () -> getBoundsCv(descFlags, true, table, values).close());
+      assertThrows(AssertionError.class,
+          () -> getBoundsCv(descFlags, false, table, values).close());
+    }
+  }
+
+  @Test
+  void testBoundsEmptyInput() {
+    boolean[] descFlags = new boolean[1];
+    try (ColumnVector cv =  ColumnVector.fromBoxedLongs();
+         Table table = new Table(cv);
+         Table values = new TestBuilder()
+             .column(20)
+             .build()) {
+      assertThrows(AssertionError.class,
+          () -> getBoundsCv(descFlags, true, table, values).close());
+      assertThrows(AssertionError.class,
+          () -> getBoundsCv(descFlags, false, table, values).close());
+    }
+  }
+
+  private ColumnVector getBoundsCv(boolean[] descFlags, boolean isUpperBound,
+      Table table, Table values) {
+    boolean[] nullsAreSmallest = new boolean[descFlags.length];
+    Arrays.fill(nullsAreSmallest, true);
+    return isUpperBound ?
+        table.upperBound(nullsAreSmallest, values, descFlags) :
+        table.lowerBound(nullsAreSmallest, values, descFlags);
+  }
+
+  @Test
+  void testRepeat() {
+    try (Table t = new Table.TestBuilder()
+            .column(1, 2)
+            .column("a", "b")
+            .decimal32Column(-3, 12, -25)
+            .decimal64Column(2, 11111L, -22222L)
+            .build();
+         Table expected = new Table.TestBuilder()
+                 .column(1, 1, 1, 2, 2, 2)
+                 .column("a", "a", "a", "b", "b", "b")
+                 .decimal32Column(-3, 12, 12, 12, -25, -25, -25)
+                 .decimal64Column(2, 11111L, 11111L, 11111L, -22222L, -22222L, -22222L)
+                 .build();
+         Table repeated = t.repeat(3)) {
+      assertTablesAreEqual(expected, repeated);
+    }
+  }
+
+  @Test
+  void testRepeatColumn() {
+    try (Table t = new Table.TestBuilder()
+            .column(1, 2)
+            .column("a", "b")
+            .decimal32Column(-3, 12, -25)
+            .decimal64Column(2, 11111L, -22222L)
+            .build();
+         ColumnVector repeats = ColumnVector.fromBytes((byte)1, (byte)4);
+         Table expected = new Table.TestBuilder()
+                 .column(1, 2, 2, 2, 2)
+                 .column("a", "b", "b", "b", "b")
+                 .decimal32Column(-3, 12, -25, -25, -25, -25)
+                 .decimal64Column(2, 11111L, -22222L, -22222L, -22222L, -22222L)
+                 .build();
+         Table repeated = t.repeat(repeats)) {
+      assertTablesAreEqual(expected, repeated);
+    }
+  }
+
+  @Test
+  void testInterleaveIntColumns() {
+    try (Table t = new Table.TestBuilder()
+          .column(1,2,3,4,5)
+          .column(6,7,8,9,10)
+          .build();
+         ColumnVector expected = ColumnVector.fromInts(1,6,2,7,3,8,4,9,5,10);
+         ColumnVector actual = t.interleaveColumns()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testInterleaveFloatColumns() {
+    try (Table t = new Table.TestBuilder()
+        .column(1f,2f,3f,4f,5f)
+        .column(6f,7f,8f,9f,10f)
+        .build();
+         ColumnVector expected = ColumnVector.fromFloats(1f,6f,2f,7f,3f,8f,4f,9f,5f,10f);
+         ColumnVector actual = t.interleaveColumns()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testInterleaveDecimalColumns() {
+    try (Table t = new Table.TestBuilder()
+        .decimal32Column(-2, 123, 456, 789)
+        .decimal32Column(-2,-100, -200, -300)
+        .build();
+         ColumnVector expected = ColumnVector.decimalFromInts(-2, 123, -100, 456, -200, 789, -300);
+         ColumnVector actual = t.interleaveColumns()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+    try (Table t = new Table.TestBuilder()
+        .decimal64Column(-5, 123456790L, 987654321L)
+        .decimal64Column(-5,-123456790L, -987654321L)
+        .build();
+         ColumnVector expected = ColumnVector.decimalFromLongs(-5, 123456790L, -123456790L, 987654321L, -987654321L);
+         ColumnVector actual = t.interleaveColumns()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testInterleaveStringColumns() {
+    try (Table t = new Table.TestBuilder()
+        .column("a", "b", "c")
+        .column("d", "e", "f")
+        .build();
+         ColumnVector expected = ColumnVector.fromStrings("a", "d", "b", "e", "c", "f");
+         ColumnVector actual = t.interleaveColumns()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testInterleaveMixedColumns() {
+    try (Table t = new Table.TestBuilder()
+        .column(1f,2f,3f,4f,5f)
+        .column(6,7,8,9,10)
+        .build()) {
+      assertThrows(CudfException.class, () -> t.interleaveColumns(),
+          "All columns must have the same data type in interleave_columns");
+    }
+  }
+
+  @Test
+  void testConcatNoNulls() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(1, 2, 3)
+        .column("1", "2", "3")
+        .timestampMicrosecondsColumn(1L, 2L, 3L)
+        .column(11.0, 12.0, 13.0)
+        .decimal32Column(-3, 1, 2, 3)
+        .decimal64Column(-10, 1L, 2L, 3L)
+        .build();
+         Table t2 = new Table.TestBuilder()
+             .column(4, 5)
+             .column("4", "3")
+             .timestampMicrosecondsColumn(4L, 3L)
+             .column(14.0, 15.0)
+             .decimal32Column(-3, 4, 5)
+             .decimal64Column(-10, 4L, 5L)
+             .build();
+         Table t3 = new Table.TestBuilder()
+             .column(6, 7, 8, 9)
+             .column("4", "1", "2", "2")
+             .timestampMicrosecondsColumn(4L, 1L, 2L, 2L)
+             .column(16.0, 17.0, 18.0, 19.0)
+             .decimal32Column(-3, 6, 7, 8, 9)
+             .decimal64Column(-10, 6L, 7L, 8L, 9L)
+             .build();
+         Table concat = Table.concatenate(t1, t2, t3);
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4, 5, 6, 7, 8, 9)
+             .column("1", "2", "3", "4", "3", "4", "1", "2", "2")
+             .timestampMicrosecondsColumn(1L, 2L, 3L, 4L, 3L, 4L, 1L, 2L, 2L)
+             .column(11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0)
+             .decimal32Column(-3, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+             .decimal64Column(-10, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L)
+             .build()) {
+      assertTablesAreEqual(expected, concat);
+    }
+  }
+
+  @Test
+  void testConcatWithNulls() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(1, null, 3)
+        .column(11.0, 12.0, 13.0)
+        .decimal32Column(-3, 1, null, 3)
+        .decimal64Column(-10, 11L, 12L, 13L)
+        .build();
+         Table t2 = new Table.TestBuilder()
+             .column(4, null)
+             .column(14.0, 15.0)
+             .decimal32Column(-3, 4, null)
+             .decimal64Column(-10, 14L, 15L)
+             .build();
+         Table t3 = new Table.TestBuilder()
+             .column(6, 7, 8, 9)
+             .column(null, null, 18.0, 19.0)
+             .decimal32Column(-3, 6, 7, 8, 9)
+             .decimal64Column(-10, null, null, 18L, 19L)
+             .build();
+         Table concat = Table.concatenate(t1, t2, t3);
+         Table expected = new Table.TestBuilder()
+             .column(1, null, 3, 4, null, 6, 7, 8, 9)
+             .column(11.0, 12.0, 13.0, 14.0, 15.0, null, null, 18.0, 19.0)
+             .decimal32Column(-3, 1, null, 3, 4, null, 6, 7, 8, 9)
+             .decimal64Column(-10, 11L, 12L, 13L, 14L, 15L, null, null, 18L, 19L)
+             .build()) {
+      assertTablesAreEqual(expected, concat);
+    }
+  }
+
+  @Test
+  void testContiguousSplit() {
+    ContiguousTable[] splits = null;
+    try (Table t1 = new Table.TestBuilder()
+        .column(10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .column(50, 52, 54, 56, 58, 60, 62, 64, 66, null)
+        .decimal32Column(-3, 10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .decimal64Column(-8, 50L, 52L, 54L, 56L, 58L, 60L, 62L, 64L, 66L, null)
+        .build()) {
+      splits = t1.contiguousSplit(2, 5, 9);
+      assertEquals(4, splits.length);
+      assertEquals(2, splits[0].getRowCount());
+      assertEquals(2, splits[0].getTable().getRowCount());
+      assertEquals(3, splits[1].getRowCount());
+      assertEquals(3, splits[1].getTable().getRowCount());
+      assertEquals(4, splits[2].getRowCount());
+      assertEquals(4, splits[2].getTable().getRowCount());
+      assertEquals(1, splits[3].getRowCount());
+      assertEquals(1, splits[3].getTable().getRowCount());
+    } finally {
+      if (splits != null) {
+        for (int i = 0; i < splits.length; i++) {
+          splits[i].close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testChunkedPackBasic() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .column(50, 52, 54, 56, 58, 60, 62, 64, 66, null)
+        .decimal32Column(-3, 10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .decimal64Column(-8, 50L, 52L, 54L, 56L, 58L, 60L, 62L, 64L, 66L, null)
+        .build();
+        DeviceMemoryBuffer bounceBuffer = DeviceMemoryBuffer.allocate(10L*1024*1024);
+        ChunkedPack cp = t1.makeChunkedPack(10L*1024*1024);
+        PackedColumnMetadata meta = cp.buildMetadata()) {
+
+      // unpack to bounce buffer
+      assertEquals(true, cp.hasNext());
+      assertEquals(cp.getTotalContiguousSize(), cp.next(bounceBuffer));
+      assertEquals(false, cp.hasNext());
+
+      try (Table unpacked = Table.fromPackedTable(meta.getMetadataDirectBuffer(), bounceBuffer)) {
+        assertTablesAreEqual(t1, unpacked);
+      }
+    }
+  }
+
+  @Test
+  void testChunkedPackTwoPasses() {
+    // this test packes ~2MB worth of long into a 1MB bounce buffer
+    // this is 3 iterations because of the validity buffer
+    Long[] longs = new Long[256*1024];
+    try (Table t1 = new Table.TestBuilder().column(longs).build();
+         DeviceMemoryBuffer bounceBuffer = DeviceMemoryBuffer.allocate(1L*1024*1024);
+         ChunkedPack cp = t1.makeChunkedPack(1L*1024*1024);
+         PackedColumnMetadata meta = cp.buildMetadata();
+         DeviceMemoryBuffer target = DeviceMemoryBuffer.allocate(cp.getTotalContiguousSize())) {
+      long offset = 0;
+
+      // unpack to bounce buffer
+      assertEquals(true, cp.hasNext());
+      while (cp.hasNext()) {
+        long copied = cp.next(bounceBuffer);
+        target.copyFromDeviceBufferAsync(
+          offset, target, 0, copied, Cuda.DEFAULT_STREAM);
+        offset += copied;
+      }
+
+      assertEquals(offset, cp.getTotalContiguousSize());
+
+      try (Table unpacked = Table.fromPackedTable(meta.getMetadataDirectBuffer(), target)) {
+        assertTablesAreEqual(t1, unpacked);
+      }
+    }
+  }
+
+  @Test
+  void testContiguousSplitWithStrings() {
+    ContiguousTable[] splits = null;
+    try (Table t1 = new Table.TestBuilder()
+        .column(10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .column(50, 52, 54, 56, 58, 60, 62, 64, 66, null)
+        .column("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
+        .decimal32Column(-3, 10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .decimal64Column(-8, 50L, 52L, 54L, 56L, 58L, 60L, 62L, 64L, 66L, null)
+        .build()) {
+      splits = t1.contiguousSplit(2, 5, 9);
+      assertEquals(4, splits.length);
+      assertEquals(2, splits[0].getRowCount());
+      assertEquals(2, splits[0].getTable().getRowCount());
+      assertEquals(3, splits[1].getRowCount());
+      assertEquals(3, splits[1].getTable().getRowCount());
+      assertEquals(4, splits[2].getRowCount());
+      assertEquals(4, splits[2].getTable().getRowCount());
+      assertEquals(1, splits[3].getRowCount());
+      assertEquals(1, splits[3].getTable().getRowCount());
+    } finally {
+      if (splits != null) {
+        for (int i = 0; i < splits.length; i++) {
+          splits[i].close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testContiguousSplitWithStringsChunked() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .column(50, 52, 54, 56, 58, 60, 62, 64, 66, null)
+        .column("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
+        .decimal32Column(-3, 10, 12, 14, 16, 18, 20, 22, 24, null, 28)
+        .decimal64Column(-8, 50L, 52L, 54L, 56L, 58L, 60L, 62L, 64L, 66L, null)
+        .build();
+        DeviceMemoryBuffer bounceBuffer = DeviceMemoryBuffer.allocate(2L*1024*1024);
+        ChunkedPack cp = t1.makeChunkedPack(2L*1024*1024);
+        PackedColumnMetadata meta = cp.buildMetadata()) {
+
+      // unpack to bounce buffer
+      assertEquals(true, cp.hasNext());
+      assertEquals(cp.getTotalContiguousSize(), cp.next(bounceBuffer));
+      assertEquals(false, cp.hasNext());
+
+      try (Table unpacked = Table.fromPackedTable(meta.getMetadataDirectBuffer(), bounceBuffer)) {
+        assertTablesAreEqual(t1, unpacked);
+      }
+    }
+  }
+
+  @Test
+  void testPartStability() {
+    final int PARTS = 5;
+    int expectedPart = -1;
+    try (Table start = new Table.TestBuilder().column(0).build();
+         PartitionedTable out = start.onColumns(0).hashPartition(PARTS)) {
+      // Lets figure out what partitions this is a part of.
+      int[] parts = out.getPartitions();
+      for (int i = 0; i < parts.length; i++) {
+        if (parts[i] > 0) {
+          expectedPart = i;
+        }
+      }
+    }
+    final int COUNT = 20;
+    for (int numEntries = 1; numEntries < COUNT; numEntries++) {
+      try (ColumnVector data = ColumnVector.build(DType.INT32, numEntries, Range.appendInts(0, numEntries));
+           Table t = new Table(data);
+           PartitionedTable out = t.onColumns(0).hashPartition(PARTS);
+           HostColumnVector tmp = out.getColumn(0).copyToHost()) {
+        // Now we need to get the range out for the partition we expect
+        int[] parts = out.getPartitions();
+        int start = expectedPart == 0 ? 0 : parts[expectedPart - 1];
+        int end = parts[expectedPart];
+        boolean found = false;
+        for (int i = start; i < end; i++) {
+          if (tmp.getInt(i) == 0) {
+            found = true;
+            break;
+          }
+        }
+        assertTrue(found);
+      }
+    }
+  }
+
+  @Test
+  void testPartition() {
+    try (Table t = new Table.TestBuilder()
+        .column(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
+        .build();
+         ColumnVector parts = ColumnVector
+             .fromInts(1, 2, 1, 2, 1, 2, 1, 2, 1, 2);
+         PartitionedTable pt = t.partition(parts, 3)) {
+      assertArrayEquals(new int[]{0, 0, 5}, pt.getPartitions());
+      // order within partitions is not guaranteed, so sort each partition to compare
+      ColumnVector[] slicedColumns = pt.getTable().getColumn(0).slice(0, 5, 5, 10);
+      try (Table part1 = new Table(slicedColumns[0]);
+           Table part1Sorted = part1.orderBy(OrderByArg.asc(0));
+           Table part1Expected = new Table.TestBuilder().column(1, 3, 5, 7, 9).build();
+           Table part2 = new Table(slicedColumns[1]);
+           Table part2Sorted = part2.orderBy(OrderByArg.asc(0));
+           Table part2Expected = new Table.TestBuilder().column(2, 4, 6, 8, 10).build()) {
+        assertTablesAreEqual(part1Expected, part1Sorted);
+        assertTablesAreEqual(part2Expected, part2Sorted);
+      } finally {
+        for (ColumnVector c : slicedColumns) {
+          c.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testIdentityHashPartition() {
+    final int count = 1024 * 1024;
+    try (ColumnVector aIn = ColumnVector.build(DType.INT64, count, Range.appendLongs(count));
+         ColumnVector bIn = ColumnVector.build(DType.INT32, count, (b) -> {
+           for (int i = 0; i < count; i++) {
+             b.append(i / 2);
+           }
+         });
+         ColumnVector cIn = ColumnVector.build(DType.STRING, count, (b) -> {
+           for (int i = 0; i < count; i++) {
+             b.appendUTF8String(String.valueOf(i).getBytes());
+           }
+         })) {
+
+      HashSet<Long> expected = new HashSet<>();
+      for (long i = 0; i < count; i++) {
+        expected.add(i);
+      }
+      try (Table input = new Table(new ColumnVector[]{aIn, bIn, cIn});
+           PartitionedTable output = input.onColumns(0).hashPartition(HashType.IDENTITY, 5)) {
+        int[] parts = output.getPartitions();
+        assertEquals(5, parts.length);
+        assertEquals(0, parts[0]);
+        int previous = 0;
+        long rows = 0;
+        for (int i = 1; i < parts.length; i++) {
+          assertTrue(parts[i] >= previous);
+          rows += parts[i] - previous;
+          previous = parts[i];
+        }
+        assertTrue(rows <= count);
+        try (HostColumnVector aOut = output.getColumn(0).copyToHost();
+             HostColumnVector bOut = output.getColumn(1).copyToHost();
+             HostColumnVector cOut = output.getColumn(2).copyToHost()) {
+
+          for (int i = 0; i < count; i++) {
+            long fromA = aOut.getLong(i);
+            long fromB = bOut.getInt(i);
+            String fromC = cOut.getJavaString(i);
+            assertTrue(expected.remove(fromA));
+            assertEquals(fromA / 2, fromB);
+            assertEquals(String.valueOf(fromA), fromC, "At Index " + i);
+          }
+          assertTrue(expected.isEmpty());
+        }
+      }
+    }
+  }
+
+  @Test
+  void testHashPartition() {
+    final int count = 1024 * 1024;
+    try (ColumnVector aIn = ColumnVector.build(DType.INT64, count, Range.appendLongs(count));
+         ColumnVector bIn = ColumnVector.build(DType.INT32, count, (b) -> {
+           for (int i = 0; i < count; i++) {
+             b.append(i / 2);
+           }
+         });
+         ColumnVector cIn = ColumnVector.build(DType.STRING, count, (b) -> {
+           for (int i = 0; i < count; i++) {
+             b.appendUTF8String(String.valueOf(i).getBytes());
+           }
+         })) {
+
+      HashSet<Long> expected = new HashSet<>();
+      for (long i = 0; i < count; i++) {
+        expected.add(i);
+      }
+      try (Table input = new Table(new ColumnVector[]{aIn, bIn, cIn});
+           PartitionedTable output = input.onColumns(0).hashPartition(5)) {
+        int[] parts = output.getPartitions();
+        assertEquals(5, parts.length);
+        assertEquals(0, parts[0]);
+        int previous = 0;
+        long rows = 0;
+        for (int i = 1; i < parts.length; i++) {
+          assertTrue(parts[i] >= previous);
+          rows += parts[i] - previous;
+          previous = parts[i];
+        }
+        assertTrue(rows <= count);
+        try (HostColumnVector aOut = output.getColumn(0).copyToHost();
+             HostColumnVector bOut = output.getColumn(1).copyToHost();
+             HostColumnVector cOut = output.getColumn(2).copyToHost()) {
+
+          for (int i = 0; i < count; i++) {
+            long fromA = aOut.getLong(i);
+            long fromB = bOut.getInt(i);
+            String fromC = cOut.getJavaString(i);
+            assertTrue(expected.remove(fromA));
+            assertEquals(fromA / 2, fromB);
+            assertEquals(String.valueOf(fromA), fromC, "At Index " + i);
+          }
+          assertTrue(expected.isEmpty());
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripEmpty() throws IOException {
+    DataType listStringsType = new ListType(true, new BasicType(true, DType.STRING));
+    DataType mapType = new ListType(true,
+        new StructType(true,
+            new BasicType(false, DType.STRING),
+            new BasicType(false, DType.STRING)));
+    DataType structType = new StructType(true,
+        new BasicType(true, DType.INT8),
+        new BasicType(false, DType.FLOAT32));
+    try (ColumnVector emptyInt = ColumnVector.fromInts();
+         ColumnVector emptyDouble = ColumnVector.fromDoubles();
+         ColumnVector emptyString = ColumnVector.fromStrings();
+         ColumnVector emptyListString = ColumnVector.fromLists(listStringsType);
+         ColumnVector emptyMap = ColumnVector.fromLists(mapType);
+         ColumnVector emptyStruct = ColumnVector.fromStructs(structType);
+         Table t = new Table(emptyInt, emptyInt, emptyDouble, emptyString,
+             emptyListString, emptyMap, emptyStruct)) {
+      ByteArrayOutputStream bout = new ByteArrayOutputStream();
+      JCudfSerialization.writeToStream(t, bout, 0, 0);
+      ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+      DataInputStream din = new DataInputStream(bin);
+      try (JCudfSerialization.TableAndRowCountPair result = JCudfSerialization.readTableFrom(din)) {
+        assertTablesAreEqual(t, result.getTable());
+        assertEquals(result.getTable(), result.getContiguousTable().getTable());
+        assertNotNull(result.getContiguousTable().getBuffer());
+      }
+    }
+  }
+
+  @Test
+  void testSerializationZeroColumns() throws IOException {
+    ByteArrayOutputStream bout = new ByteArrayOutputStream();
+    JCudfSerialization.writeRowsToStream(bout, 10);
+    ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+    try (JCudfSerialization.TableAndRowCountPair result = JCudfSerialization.readTableFrom(bin)) {
+      assertNull(result.getTable());
+      assertNull(result.getContiguousTable());
+      assertEquals(10, result.getNumRows());
+    }
+  }
+
+  @Test
+  void testSerializationZeroColsZeroRows() throws IOException {
+    ByteArrayOutputStream bout = new ByteArrayOutputStream();
+    JCudfSerialization.writeRowsToStream(bout, 0);
+    ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+    try (JCudfSerialization.TableAndRowCountPair result = JCudfSerialization.readTableFrom(bin)) {
+      assertNull(result.getTable());
+      assertNull(result.getContiguousTable());
+      assertEquals(0, result.getNumRows());
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripConcatOnHostEmpty() throws IOException {
+    DataType listStringsType = new ListType(true, new BasicType(true, DType.STRING));
+    DataType mapType = new ListType(true,
+        new StructType(true,
+            new BasicType(false, DType.STRING),
+            new BasicType(false, DType.STRING)));
+    DataType structType = new StructType(true,
+        new BasicType(true, DType.INT8),
+        new BasicType(false, DType.FLOAT32));
+    try (ColumnVector emptyInt = ColumnVector.fromInts();
+         ColumnVector emptyDouble = ColumnVector.fromDoubles();
+         ColumnVector emptyString = ColumnVector.fromStrings();
+         ColumnVector emptyListString = ColumnVector.fromLists(listStringsType);
+         ColumnVector emptyMap = ColumnVector.fromLists(mapType);
+         ColumnVector emptyStruct = ColumnVector.fromStructs(structType);
+         Table t = new Table(emptyInt, emptyInt, emptyDouble, emptyString,
+             emptyListString, emptyMap, emptyStruct)) {
+      ByteArrayOutputStream bout = new ByteArrayOutputStream();
+      JCudfSerialization.writeToStream(t, bout, 0, 0);
+      JCudfSerialization.writeToStream(t, bout, 0, 0);
+      ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+      DataInputStream din = new DataInputStream(bin);
+
+      ArrayList<JCudfSerialization.SerializedTableHeader> headers = new ArrayList<>();
+      List<HostMemoryBuffer> buffers = new ArrayList<>();
+      try {
+        JCudfSerialization.SerializedTableHeader head;
+        long numRows = 0;
+        do {
+          head = new JCudfSerialization.SerializedTableHeader(din);
+          if (head.wasInitialized()) {
+            HostMemoryBuffer buff = hostMemoryAllocator.allocate(head.getDataLen());
+            buffers.add(buff);
+            JCudfSerialization.readTableIntoBuffer(din, head, buff);
+            assert head.wasDataRead();
+            numRows += head.getNumRows();
+            assert numRows <= Integer.MAX_VALUE;
+            headers.add(head);
+          }
+        } while (head.wasInitialized());
+        assert numRows == t.getRowCount();
+        try (Table found = JCudfSerialization.readAndConcat(
+            headers.toArray(new JCudfSerialization.SerializedTableHeader[headers.size()]),
+            buffers.toArray(new HostMemoryBuffer[buffers.size()]))) {
+          assertTablesAreEqual(t, found);
+        }
+      } finally {
+        for (HostMemoryBuffer buff: buffers) {
+          buff.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripToHostEmpty() throws IOException {
+    DataType listStringsType = new ListType(true, new BasicType(true, DType.STRING));
+    DataType mapType = new ListType(true,
+            new StructType(true,
+                    new BasicType(false, DType.STRING),
+                    new BasicType(false, DType.STRING)));
+    DataType structType = new StructType(true,
+            new BasicType(true, DType.INT8),
+            new BasicType(false, DType.FLOAT32));
+    try (ColumnVector emptyInt = ColumnVector.fromInts();
+         ColumnVector emptyDouble = ColumnVector.fromDoubles();
+         ColumnVector emptyString = ColumnVector.fromStrings();
+         ColumnVector emptyListString = ColumnVector.fromLists(listStringsType);
+         ColumnVector emptyMap = ColumnVector.fromLists(mapType);
+         ColumnVector emptyStruct = ColumnVector.fromStructs(structType);
+         Table t = new Table(emptyInt, emptyInt, emptyDouble, emptyString,
+                 emptyListString, emptyMap, emptyStruct)) {
+      testSerializationRoundTripToHost(t);
+    }
+  }
+
+  @Test
+  void testRoundRobinPartition() {
+    try (Table t = new Table.TestBuilder()
+        .column(     100,      202,      3003,    40004,        5,      -60,       1,      null,        3,  null,        5,     null,        7, null,        9,      null,       11,      null,        13,      null,       15)
+        .column(    true,     true,     false,    false,     true,     null,     true,     true,     null, false,    false,     null,     true, true,     null,     false,    false,      null,      true,      true,     null)
+        .column( (byte)1,  (byte)2,      null,  (byte)4,  (byte)5,  (byte)6,  (byte)1,  (byte)2,  (byte)3,  null,  (byte)5,  (byte)6,  (byte)7, null,  (byte)9,  (byte)10, (byte)11,      null,  (byte)13,  (byte)14, (byte)15)
+        .column((short)6, (short)5,  (short)4,     null, (short)2, (short)1, (short)1, (short)2, (short)3,  null, (short)5, (short)6, (short)7, null, (short)9, (short)10,     null, (short)12, (short)13, (short)14,     null)
+        .column(      1L,     null,     1001L,      50L,   -2000L,     null,       1L,       2L,       3L,    4L,     null,       6L,       7L,   8L,       9L,      null,      11L,       12L,       13L,       14L,     null)
+        .column(   10.1f,      20f, Float.NaN,  3.1415f,     -60f,     null,       1f,       2f,       3f,    4f,       5f,     null,       7f,   8f,       9f,       10f,      11f,      null,       13f,       14f,      15f)
+        .column(    10.1,     20.0,      33.1,   3.1415,    -60.5,     null,       1.,       2.,       3.,    4.,       5.,       6.,     null,   8.,       9.,       10.,      11.,       12.,      null,       14.,      15.)
+        .timestampDayColumn(99, 100,      101,      102,      103,      104,        1,        2,        3,     4,        5,        6,        7, null,        9,        10,       11,        12,        13,      null,       15)
+        .timestampMillisecondsColumn(9L, 1006L, 101L, 5092L, null,      88L,       1L,       2L,       3L,    4L,       5L,       6L,       7L,   8L,     null,       10L,      11L,       12L,       13L,       14L,      15L)
+        .timestampSecondsColumn(1L, null,  3L,       4L,       5L,       6L,       1L,       2L,       3L,    4L,       5L,       6L,       7L,   8L,       9L,      null,      11L,       12L,       13L,       14L,      15L)
+        .column(     "A",      "B",       "C",      "D",     null, "TESTING",     "1",      "2",      "3",   "4",      "5",      "6",      "7", null,      "9",      "10",     "11",      "12",      "13",      null,     "15")
+        .column(     "A",      "A",       "C",      "C",     null, "TESTING",     "1",      "2",      "3",   "4",      "5",      "6",      "7", null,      "9",      "10",     "11",      "12",      "13",      null,     "15")
+        .decimal32Column(-3, 100,      202,      3003,    40004,        5,      -60,       1,      null,        3,  null,        5,     null,        7, null,        9,      null,       11,      null,        13,      null,       15)
+        .decimal64Column(      -8, 1L,     null,     1001L,      50L,   -2000L,     null,       1L,       2L,       3L,    4L,     null,       6L,       7L,   8L,       9L,      null,      11L,       12L,       13L,       14L,     null)
+        .build()) {
+      try (Table expectedTable = new Table.TestBuilder()
+          .column(     100,   40004,        1,  null,        7,      null,        13,      202,        5,     null,        5, null,       11,      null,      3003,       -60,        3,     null,        9,      null,       15)
+          .column(    true,   false,     true, false,     true,     false,      true,     true,     true,     true,    false, true,    false,      true,     false,      null,     null,     null,     null,      null,     null)
+          .column( (byte)1, (byte)4,  (byte)1,  null,  (byte)7,  (byte)10,  (byte)13,  (byte)2,  (byte)5,  (byte)2,  (byte)5, null, (byte)11,  (byte)14,      null,   (byte)6,  (byte)3,  (byte)6,  (byte)9,      null, (byte)15)
+          .column((short)6,    null, (short)1,  null, (short)7, (short)10, (short)13, (short)5, (short)2, (short)2, (short)5, null,     null, (short)14,  (short)4,  (short)1, (short)3, (short)6, (short)9, (short)12,     null)
+          .column(      1L,     50L,       1L,    4L,       7L,      null,       13L,     null,   -2000L,       2L,     null,   8L,      11L,       14L,     1001L,      null,       3L,       6L,       9L,       12L,     null)
+          .column(   10.1f, 3.1415f,       1f,    4f,       7f,       10f,       13f,      20f,     -60f,       2f,       5f,   8f,      11f,       14f, Float.NaN,      null,       3f,     null,       9f,      null,      15f)
+          .column(    10.1,  3.1415,       1.,    4.,     null,       10.,      null,     20.0,    -60.5,       2.,       5.,   8.,      11.,       14.,      33.1,      null,       3.,       6.,       9.,       12.,      15.)
+          .timestampDayColumn(99, 102,      1,     4,        7,        10,        13,      100,      103,        2,        5, null,       11,      null,       101,       104,        3,        6,        9,        12,       15)
+          .timestampMillisecondsColumn(9L, 5092L, 1L, 4L,   7L,       10L,       13L,    1006L,     null,       2L,       5L,   8L,      11L,       14L,      101L,       88L,       3L,       6L,     null,       12L,      15L)
+          .timestampSecondsColumn(1L, 4L,   1L,   4L,       7L,      null,       13L,     null,       5L,       2L,       5L,   8L,      11L,       14L,        3L,        6L,       3L,       6L,       9L,       12L,      15L)
+          .column(     "A",     "D",       "1",  "4",      "7",      "10",      "13",      "B",     null,      "2",      "5", null,     "11",      null,       "C", "TESTING",      "3",      "6",      "9",      "12",     "15")
+          .column(     "A",     "C",       "1",  "4",      "7",      "10",      "13",      "A",     null,      "2",      "5", null,     "11",      null,       "C", "TESTING",      "3",      "6",      "9",      "12",     "15")
+          .decimal32Column(-3,     100,   40004,        1,  null,        7,      null,        13,      202,        5,     null,        5, null,       11,      null,      3003,       -60,        3,     null,        9,      null,       15)
+          .decimal64Column(-8, 1L,     50L,       1L,    4L,       7L,      null,       13L,     null,   -2000L,       2L,     null,   8L,      11L,       14L,     1001L,      null,       3L,       6L,       9L,       12L,     null)
+          .build();
+           PartitionedTable pt = t.roundRobinPartition(3, 0)) {
+        assertTablesAreEqual(expectedTable, pt.getTable());
+        int[] parts = pt.getPartitions();
+        assertEquals(3, parts.length);
+        assertEquals(0, parts[0]);
+        assertEquals(7, parts[1]);
+        assertEquals(14, parts[2]);
+      }
+
+      try (Table expectedTable = new Table.TestBuilder()
+          .column(      3003,       -60,        3,     null,        9,      null,       15,     100,   40004,        1,  null,        7,      null,        13,      202,        5,     null,        5, null,       11,      null)
+          .column(     false,      null,     null,     null,     null,      null,     null,    true,   false,     true, false,     true,     false,      true,     true,     true,     true,    false, true,    false,      true)
+          .column(      null,   (byte)6,  (byte)3,  (byte)6,  (byte)9,      null, (byte)15, (byte)1, (byte)4,  (byte)1,  null,  (byte)7,  (byte)10,  (byte)13,  (byte)2,  (byte)5,  (byte)2,  (byte)5, null, (byte)11,  (byte)14)
+          .column(  (short)4,  (short)1, (short)3, (short)6, (short)9, (short)12,     null,(short)6,    null, (short)1,  null, (short)7, (short)10, (short)13, (short)5, (short)2, (short)2, (short)5, null,     null, (short)14)
+          .column(     1001L,      null,       3L,       6L,       9L,       12L,     null,      1L,     50L,       1L,    4L,       7L,      null,       13L,     null,   -2000L,       2L,     null,   8L,      11L,       14L)
+          .column( Float.NaN,      null,       3f,     null,       9f,      null,      15f,   10.1f, 3.1415f,       1f,    4f,       7f,       10f,       13f,      20f,     -60f,       2f,       5f,   8f,      11f,       14f)
+          .column(      33.1,      null,       3.,       6.,       9.,       12.,      15.,    10.1,  3.1415,       1.,    4.,     null,       10.,      null,     20.0,    -60.5,       2.,       5.,   8.,      11.,       14.)
+          .timestampDayColumn(101, 104,         3,        6,        9,        12,       15,      99,     102,        1,     4,        7,        10,        13,      100,      103,        2,        5, null,       11,      null)
+          .timestampMillisecondsColumn(101L, 88L, 3L,    6L,     null,       12L,      15L,      9L,   5092L,       1L,    4L,       7L,       10L,       13L,    1006L,     null,       2L,       5L,   8L,      11L,       14L)
+          .timestampSecondsColumn(3L, 6L,      3L,       6L,       9L,       12L,      15L,      1L,      4L,       1L,    4L,       7L,      null,       13L,     null,       5L,       2L,       5L,   8L,      11L,       14L)
+          .column(       "C", "TESTING",      "3",      "6",      "9",      "12",     "15",     "A",     "D",       "1",  "4",      "7",      "10",      "13",      "B",     null,      "2",      "5", null,     "11",      null)
+          .column(       "C", "TESTING",      "3",      "6",      "9",      "12",     "15",     "A",     "C",       "1",  "4",      "7",      "10",      "13",      "A",     null,      "2",      "5", null,     "11",      null)
+          .decimal32Column(-3,      3003,       -60,        3,     null,        9,      null,       15,     100,   40004,        1,  null,        7,      null,        13,      202,        5,     null,        5, null,       11,      null)
+          .decimal64Column(-8,     1001L,      null,       3L,       6L,       9L,       12L,     null,      1L,     50L,       1L,    4L,       7L,      null,       13L,     null,   -2000L,       2L,     null,   8L,      11L,       14L)
+          .build();
+           PartitionedTable pt = t.roundRobinPartition(3, 1)) {
+        assertTablesAreEqual(expectedTable, pt.getTable());
+        int[] parts = pt.getPartitions();
+        assertEquals(3, parts.length);
+        assertEquals(0, parts[0]);
+        assertEquals(7, parts[1]);
+        assertEquals(14, parts[2]);
+      }
+
+      try (Table expectedTable = new Table.TestBuilder()
+          .column(      202,        5,     null,        5, null,       11,      null,      3003,       -60,        3,     null,        9,      null,       15,     100,   40004,        1,  null,        7,      null,        13)
+          .column(     true,     true,     true,    false, true,    false,      true,     false,      null,     null,     null,     null,      null,     null,    true,   false,     true, false,     true,     false,      true)
+          .column(  (byte)2,  (byte)5,  (byte)2,  (byte)5, null, (byte)11,  (byte)14,      null,   (byte)6,  (byte)3,  (byte)6,  (byte)9,      null, (byte)15, (byte)1, (byte)4,  (byte)1,  null,  (byte)7,  (byte)10,  (byte)13)
+          .column( (short)5, (short)2, (short)2, (short)5, null,     null, (short)14,  (short)4,  (short)1, (short)3, (short)6, (short)9, (short)12,     null,(short)6,    null, (short)1,  null, (short)7, (short)10, (short)13)
+          .column(     null,   -2000L,       2L,     null,   8L,      11L,       14L,     1001L,      null,       3L,       6L,       9L,       12L,     null,      1L,     50L,       1L,    4L,       7L,      null,       13L)
+          .column(      20f,     -60f,       2f,       5f,   8f,      11f,       14f, Float.NaN,      null,       3f,     null,       9f,      null,      15f,   10.1f, 3.1415f,       1f,    4f,       7f,       10f,       13f)
+          .column(     20.0,    -60.5,       2.,       5.,   8.,      11.,       14.,      33.1,      null,       3.,       6.,       9.,       12.,      15.,    10.1,  3.1415,       1.,    4.,     null,       10.,      null)
+          .timestampDayColumn(100, 103,       2,        5, null,       11,      null,       101,       104,        3,        6,        9,        12,       15,      99,     102,        1,     4,        7,        10,        13)
+          .timestampMillisecondsColumn(1006L, null, 2L, 5L,  8L,      11L,       14L,      101L,      88L,       3L,       6L,      null,       12L,      15L,      9L,   5092L,       1L,    4L,       7L,       10L,       13L)
+          .timestampSecondsColumn(null, 5L,  2L,       5L,   8L,      11L,       14L,        3L,        6L,       3L,       6L,       9L,       12L,      15L,      1L,      4L,       1L,    4L,       7L,      null,       13L)
+          .column(      "B",     null,      "2",      "5", null,     "11",      null,       "C", "TESTING",      "3",      "6",      "9",      "12",     "15",     "A",     "D",       "1",  "4",      "7",      "10",      "13")
+          .column(      "A",     null,      "2",      "5", null,     "11",      null,       "C", "TESTING",      "3",      "6",      "9",      "12",     "15",     "A",     "C",       "1",  "4",      "7",      "10",      "13")
+          .decimal32Column(-3,      202,        5,     null,        5, null,       11,      null,      3003,       -60,        3,     null,        9,      null,       15,     100,   40004,        1,  null,        7,      null,        13)
+          .decimal64Column(-8,     null,   -2000L,       2L,     null,   8L,      11L,       14L,     1001L,      null,       3L,       6L,       9L,       12L,     null,      1L,     50L,       1L,    4L,       7L,      null,       13L)
+          .build();
+           PartitionedTable pt = t.roundRobinPartition(3, 2)) {
+        assertTablesAreEqual(expectedTable, pt.getTable());
+        int[] parts = pt.getPartitions();
+        assertEquals(3, parts.length);
+        assertEquals(0, parts[0]);
+        assertEquals(7, parts[1]);
+        assertEquals(14, parts[2]);
+      }
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripConcatHostSide() throws IOException {
+    try (Table t = buildTestTable()) {
+      for (int sliceAmount = 1; sliceAmount < t.getRowCount(); sliceAmount ++) {
+        ByteArrayOutputStream bout = new ByteArrayOutputStream();
+        for (int i = 0; i < t.getRowCount(); i += sliceAmount) {
+          int len = (int) Math.min(t.getRowCount() - i, sliceAmount);
+          JCudfSerialization.writeToStream(t, bout, i, len);
+        }
+        ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+        DataInputStream din = new DataInputStream(bin);
+        ArrayList<JCudfSerialization.SerializedTableHeader> headers = new ArrayList<>();
+        List<HostMemoryBuffer> buffers = new ArrayList<>();
+        try {
+          JCudfSerialization.SerializedTableHeader head;
+          long numRows = 0;
+          do {
+            head = new JCudfSerialization.SerializedTableHeader(din);
+            if (head.wasInitialized()) {
+              HostMemoryBuffer buff = hostMemoryAllocator.allocate(100 * 1024);
+              buffers.add(buff);
+              JCudfSerialization.readTableIntoBuffer(din, head, buff);
+              assert head.wasDataRead();
+              numRows += head.getNumRows();
+              assert numRows <= Integer.MAX_VALUE;
+              headers.add(head);
+            }
+          } while (head.wasInitialized());
+          assert numRows == t.getRowCount();
+          try (Table found = JCudfSerialization.readAndConcat(
+              headers.toArray(new JCudfSerialization.SerializedTableHeader[headers.size()]),
+              buffers.toArray(new HostMemoryBuffer[buffers.size()]))) {
+            assertPartialTablesAreEqual(t, 0, t.getRowCount(), found, true, false);
+          }
+        } finally {
+          for (HostMemoryBuffer buff: buffers) {
+            buff.close();
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripToHost() throws IOException {
+    try (Table t = buildTestTable()) {
+      testSerializationRoundTripToHost(t);
+    }
+  }
+
+  private void testSerializationRoundTripToHost(Table t) throws IOException {
+    long rowCount = t.getRowCount();
+    ByteArrayOutputStream bout = new ByteArrayOutputStream();
+    JCudfSerialization.writeToStream(t, bout, 0, rowCount);
+    ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+    DataInputStream din = new DataInputStream(bin);
+
+    JCudfSerialization.SerializedTableHeader header =
+            new JCudfSerialization.SerializedTableHeader(din);
+    assertTrue(header.wasInitialized());
+    try (HostMemoryBuffer buffer = hostMemoryAllocator.allocate(header.getDataLen())) {
+      JCudfSerialization.readTableIntoBuffer(din, header, buffer);
+      assertTrue(header.wasDataRead());
+      HostColumnVector[] hostColumns =
+              JCudfSerialization.unpackHostColumnVectors(header, buffer);
+      try {
+        assertEquals(t.getNumberOfColumns(), hostColumns.length);
+        for (int i = 0; i < hostColumns.length; i++) {
+          HostColumnVector actual = hostColumns[i];
+          assertEquals(rowCount, actual.getRowCount());
+          try (HostColumnVector expected = t.getColumn(i).copyToHost()) {
+            assertPartialColumnsAreEqual(expected, 0, rowCount, actual, "COLUMN " + i, true, false);
+          }
+        }
+      } finally {
+        for (HostColumnVector c: hostColumns) {
+          // close child columns for multiple times should NOT throw exceptions
+          for (int i = 0; i < c.getNumChildren(); i++) {
+            c.getChildColumnView(i).close();
+          }
+          c.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testConcatHost() throws IOException {
+    try (Table t1 = new Table.TestBuilder()
+        .column(
+            1, 2, null, 4, 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+            1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null)
+        .decimal32Column(-3,
+            1, 2, null, 4, 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+            1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(
+                 null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null)
+             .decimal32Column(-3,
+                 null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null,
+                 1, 2, null, 4 , 5, 6, 7, 8, 9, 10, null, 12, 13, 14, null, null)
+             .build();
+         Table t2 = t1.concatenate(t1, t1)) {
+      ByteArrayOutputStream out = new ByteArrayOutputStream();
+      JCudfSerialization.writeToStream(t2, out, 10, t2.getRowCount() - 10);
+      DataInputStream in = new DataInputStream(new ByteArrayInputStream(out.toByteArray()));
+      JCudfSerialization.SerializedTableHeader header = new JCudfSerialization.SerializedTableHeader(in);
+      assert header.wasInitialized();
+      try (HostMemoryBuffer buff = hostMemoryAllocator.allocate(header.getDataLen())) {
+        JCudfSerialization.readTableIntoBuffer(in, header, buff);
+        assert header.wasDataRead();
+        try (Table result = JCudfSerialization.readAndConcat(
+            new JCudfSerialization.SerializedTableHeader[] {header, header},
+            new HostMemoryBuffer[] {buff, buff})) {
+          assertPartialTablesAreEqual(expected, 0, expected.getRowCount(), result, true, false);
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripSlicedHostSide() throws IOException {
+    try (Table t = buildTestTable()) {
+      for (int sliceAmount = 1; sliceAmount < t.getRowCount(); sliceAmount ++) {
+        ByteArrayOutputStream bout = new ByteArrayOutputStream();
+        for (int i = 0; i < t.getRowCount(); i += sliceAmount) {
+          int len = (int) Math.min(t.getRowCount() - i, sliceAmount);
+          JCudfSerialization.writeToStream(t, bout, i, len);
+        }
+        ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+        DataInputStream din = new DataInputStream(bin);
+        ArrayList<JCudfSerialization.SerializedTableHeader> headers = new ArrayList<>();
+        List<HostMemoryBuffer> buffers = new ArrayList<>();
+        try {
+          JCudfSerialization.SerializedTableHeader head;
+          long numRows = 0;
+          do {
+            head = new JCudfSerialization.SerializedTableHeader(din);
+            if (head.wasInitialized()) {
+              HostMemoryBuffer buff = hostMemoryAllocator.allocate(100 * 1024);
+              buffers.add(buff);
+              JCudfSerialization.readTableIntoBuffer(din, head, buff);
+              assert head.wasDataRead();
+              numRows += head.getNumRows();
+              assert numRows <= Integer.MAX_VALUE;
+              headers.add(head);
+            }
+          } while (head.wasInitialized());
+          assert numRows == t.getRowCount();
+          ByteArrayOutputStream bout2 = new ByteArrayOutputStream();
+          JCudfSerialization.writeConcatedStream(
+              headers.toArray(new JCudfSerialization.SerializedTableHeader[headers.size()]),
+              buffers.toArray(new HostMemoryBuffer[buffers.size()]), bout2);
+          ByteArrayInputStream bin2 = new ByteArrayInputStream(bout2.toByteArray());
+          try (JCudfSerialization.TableAndRowCountPair found = JCudfSerialization.readTableFrom(bin2)) {
+            assertPartialTablesAreEqual(t, 0, t.getRowCount(), found.getTable(), true, false);
+            assertEquals(found.getTable(), found.getContiguousTable().getTable());
+            assertNotNull(found.getContiguousTable().getBuffer());
+          }
+          JCudfSerialization.TableAndRowCountPair tp = JCudfSerialization.readTableFrom(bin2);
+          assertNull(tp.getTable());
+          assertNull(tp.getContiguousTable());
+        } finally {
+          for (HostMemoryBuffer buff: buffers) {
+            buff.close();
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSerializationRoundTripSliced() throws IOException {
+    try (Table t = buildTestTable()) {
+      for (int sliceAmount = 1; sliceAmount < t.getRowCount(); sliceAmount ++) {
+        for (int i = 0; i < t.getRowCount(); i += sliceAmount) {
+          ByteArrayOutputStream bout = new ByteArrayOutputStream();
+          int len = (int) Math.min(t.getRowCount() - i, sliceAmount);
+          JCudfSerialization.writeToStream(t, bout, i, len);
+          ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+          try (JCudfSerialization.TableAndRowCountPair found = JCudfSerialization.readTableFrom(bin)) {
+            assertPartialTablesAreEqual(t, i, len, found.getTable(), i == 0 && len == t.getRowCount(), false);
+            assertEquals(found.getTable(), found.getContiguousTable().getTable());
+            assertNotNull(found.getContiguousTable().getBuffer());
+          }
+          JCudfSerialization.TableAndRowCountPair tp = JCudfSerialization.readTableFrom(bin);
+          assertNull(tp.getTable());
+          assertNull(tp.getContiguousTable());
+        }
+      }
+    }
+  }
+
+  @Test
+  void testSerializationReconstructFromMetadata() throws IOException {
+    try (Table t = buildTestTable()) {
+      ByteArrayOutputStream bout = new ByteArrayOutputStream();
+      JCudfSerialization.writeToStream(t, bout, 0, t.getRowCount());
+      ByteArrayInputStream bin = new ByteArrayInputStream(bout.toByteArray());
+      try (JCudfSerialization.TableAndRowCountPair trcp = JCudfSerialization.readTableFrom(bin)) {
+        ContiguousTable contigTable = trcp.getContiguousTable();
+        DeviceMemoryBuffer oldbuf = contigTable.getBuffer();
+        try (DeviceMemoryBuffer newbuf = oldbuf.sliceWithCopy(0, oldbuf.getLength())) {
+          ByteBuffer metadata = contigTable.getMetadataDirectBuffer();
+          try (Table newTable = Table.fromPackedTable(metadata, newbuf)) {
+            assertTablesAreEqual(t, newTable);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testValidityFill() {
+    byte[] buff = new byte[2];
+    buff[0] = 0;
+    int bitsToFill = (buff.length * 8) - 1;
+    assertEquals(bitsToFill, JCudfSerialization.fillValidity(buff, 1, bitsToFill));
+    assertEquals(buff[0], 0xFFFFFFFE);
+    assertEquals(buff[1], 0xFFFFFFFF);
+  }
+
+  @Test
+  void testGroupByScan() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(  "1",  "1",  "1",  "1",  "1",  "1",  "1",  "2",  "2",  "2",  "2") // GBY Key#0
+        .column(   0,    1,    3,    3,    5,    5,    5,    5,    5,    5,    5)  // GBY Key#1
+        .column(12.0, 14.0, 13.0, 17.0, 17.0, 17.0, null, null, 11.0, null, 10.0)
+        .column(  -9, null,   -5,   0,     4,    4,    8,    2,    2,    2, null)
+        .build()) {
+      try (Table result = t1
+          .groupBy(GroupByOptions.builder()
+              .withKeysSorted(true)
+              .withKeysDescending(false, false)
+              .build(), 0, 1)
+          .scan(GroupByScanAggregation.sum().onColumn(2),
+              GroupByScanAggregation.count(NullPolicy.INCLUDE).onColumn(2),
+              GroupByScanAggregation.min().onColumn(2),
+              GroupByScanAggregation.max().onColumn(2),
+              GroupByScanAggregation.rank().onColumn(3),
+              GroupByScanAggregation.denseRank().onColumn(3),
+              GroupByScanAggregation.percentRank().onColumn(3));
+           Table expected = new Table.TestBuilder()
+               .column(  "1",  "1",  "1",  "1",  "1",  "1",  "1",  "2",  "2",  "2",  "2")
+               .column(   0,    1,    3,    3,    5,    5,    5,    5,    5,    5,    5)
+               .column(12.0, 14.0, 13.0, 30.0, 17.0, 34.0, null, null, 11.0, null, 21.0)
+               .column(   0,    0,    0,    1,    0,    1,    2,    0,    1,    2,    3) // odd why is this not 1 based?
+               .column(12.0, 14.0, 13.0, 13.0, 17.0, 17.0, null, null, 11.0, null, 10.0)
+               .column(12.0, 14.0, 13.0, 17.0, 17.0, 17.0, null, null, 11.0, null, 11.0)
+               .column(   1,    1,    1,    2,    1,    1,    3,    1,    1,    1,    4)
+               .column(   1,    1,    1,    2,    1,    1,    2,    1,    1,    1,    2)
+               .column( 0.0,  0.0,  0.0,  1.0,  0.0,  0.0,  1.0,  0.0,  0.0,  0.0,  1.0)
+               .build()) {
+        assertTablesAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByReplaceNulls() {
+    try (Table t1 = new Table.TestBuilder()
+        .column( "1",  "1",  "1",  "1",  "1",  "1",  "1",  "2",  "2",  "2",  "2")
+        .column(   0,    1,    3,    3,    5,    5,    5,    5,    5,    5,    5)
+        .column(null, 14.0, 13.0, 17.0, 17.0, 17.0, null, null, 11.0, null, null)
+        .build()) {
+      try (Table result = t1
+          .groupBy(GroupByOptions.builder()
+              .withKeysSorted(true)
+              .withKeysDescending(false, false)
+              .build(), 0, 1)
+          .replaceNulls(ReplacePolicy.PRECEDING.onColumn(2),
+              ReplacePolicy.FOLLOWING.onColumn(2));
+           Table expected = new Table.TestBuilder()
+               .column( "1",  "1",  "1",  "1",  "1",  "1",  "1",  "2",  "2",  "2",  "2")
+               .column(   0,    1,    3,    3,    5,    5,    5,    5,    5,    5,    5)
+               .column(null, 14.0, 13.0, 17.0, 17.0, 17.0, 17.0, null, 11.0, 11.0, 11.0)
+               .column(null, 14.0, 13.0, 17.0, 17.0, 17.0, null, 11.0, 11.0, null, null)
+               .build()) {
+        assertTablesAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByApproxPercentileReproCase() {
+    double[] percentiles = {0.25, 0.50, 0.75};
+    try (Table t1 = new Table.TestBuilder()
+            .column("a", "a", "b", "c", "d")
+            .column(1084.0, 1719.0, 15948.0, 148029.0, 1269761.0)
+            .build();
+         Table t2 = t1
+            .groupBy(0)
+            .aggregate(GroupByAggregation.createTDigest(100).onColumn(1));
+         Table sorted = t2.orderBy(OrderByArg.asc(0));
+         ColumnVector actual = sorted.getColumn(1).approxPercentile(percentiles);
+         ColumnVector expected = ColumnVector.fromLists(
+             new ListType(false, new BasicType(false, DType.FLOAT64)),
+             Arrays.asList(1084.0, 1084.0, 1719.0),
+             Arrays.asList(15948.0, 15948.0, 15948.0),
+             Arrays.asList(148029.0, 148029.0, 148029.0),
+             Arrays.asList(1269761.0, 1269761.0, 1269761.0)
+         )) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testGroupByApproxPercentile() {
+    double[] percentiles = {0.25, 0.50, 0.75};
+    try (Table t1 = new Table.TestBuilder()
+            .column("a", "a", "a", "b", "b", "b")
+            .column(100, 150, 160, 70, 110, 160)
+            .build();
+      Table t2 = t1
+          .groupBy(0)
+          .aggregate(GroupByAggregation.createTDigest(1000).onColumn(1));
+      Table sorted = t2.orderBy(OrderByArg.asc(0));
+      ColumnVector actual = sorted.getColumn(1).approxPercentile(percentiles);
+      ColumnVector expected = ColumnVector.fromLists(
+        new ListType(false, new BasicType(false, DType.FLOAT64)),
+          Arrays.asList(100d, 150d, 160d),
+          Arrays.asList(70d, 110d, 160d)
+      )) {
+        assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testMergeApproxPercentile() {
+    double[] percentiles = {0.25, 0.50, 0.75};
+    try (Table t1 = new Table.TestBuilder()
+            .column("a", "a", "a", "b", "b", "b")
+            .column(100, 150, 160, 70, 110, 160)
+            .build();
+         Table t2 = t1
+                 .groupBy(0)
+                 .aggregate(GroupByAggregation.createTDigest(1000).onColumn(1));
+         Table t3 = t1
+                 .groupBy(0)
+                 .aggregate(GroupByAggregation.createTDigest(1000).onColumn(1));
+         Table t4 = Table.concatenate(t2, t3);
+         Table t5 = t4
+                 .groupBy(0)
+                 .aggregate(GroupByAggregation.mergeTDigest(1000).onColumn(1));
+         Table sorted = t5.orderBy(OrderByArg.asc(0));
+         ColumnVector actual = sorted.getColumn(1).approxPercentile(percentiles);
+         ColumnVector expected = ColumnVector.fromLists(
+                 new ListType(false, new BasicType(false, DType.FLOAT64)),
+                 Arrays.asList(100d, 150d, 160d),
+                 Arrays.asList(70d, 110d, 160d)
+         )) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testMergeApproxPercentile2() {
+    double[] percentiles = {0.25, 0.50, 0.75};
+    try (Table t1 = new Table.TestBuilder()
+            .column("a", "a", "a", "b", "b", "b")
+            .column(70, 110, 160, 100, 150, 160)
+            .build();
+         Table t2 = t1
+                 .groupBy(0)
+                 .aggregate(GroupByAggregation.createTDigest(1000).onColumn(1));
+         Table t3 = t1
+                 .groupBy(0)
+                 .aggregate(GroupByAggregation.createTDigest(1000).onColumn(1));
+         Table t4 = Table.concatenate(t2, t3);
+         Table t5 = t4
+                 .groupBy(0)
+                 .aggregate(GroupByAggregation.mergeTDigest(1000).onColumn(1));
+         Table sorted = t5.orderBy(OrderByArg.asc(0));
+         ColumnVector actual = sorted.getColumn(1).approxPercentile(percentiles);
+         ColumnVector expected = ColumnVector.fromLists(
+                 new ListType(false, new BasicType(false, DType.FLOAT64)),
+                 Arrays.asList(70d, 110d, 160d),
+                 Arrays.asList(100d, 150d, 160d)
+         )) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testCreateTDigestReduction() {
+    try (Table t1 = new Table.TestBuilder()
+            .column(100, 150, 160, 70, 110, 160)
+            .build();
+         Scalar tdigest = t1.getColumn(0)
+            .reduce(ReductionAggregation.createTDigest(1000), DType.STRUCT)) {
+      assertEquals(DType.STRUCT, tdigest.getType());
+
+      try (CloseableArray columns = CloseableArray.wrap(tdigest.getChildrenFromStructScalar())) {
+        assertEquals(3, columns.size());
+        try (HostColumnVector centroids = ((ColumnView) columns.get(0)).copyToHost();
+           HostColumnVector min = ((ColumnView) columns.get(1)).copyToHost();
+           HostColumnVector max = ((ColumnView) columns.get(2)).copyToHost()) {
+          assertEquals(DType.LIST, centroids.getType());
+          assertEquals(DType.FLOAT64, min.getType());
+          assertEquals(DType.FLOAT64, max.getType());
+          assertEquals(1, min.getRowCount());
+          assertEquals(1, max.getRowCount());
+          assertEquals(70, min.getDouble(0));
+          assertEquals(160, max.getDouble(0));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMergeTDigestReduction() {
+    StructType centroidStruct = new StructType(false,
+            new BasicType(false, DType.FLOAT64), // mean
+            new BasicType(false, DType.FLOAT64)); // weight
+
+    ListType centroidList = new ListType(false, centroidStruct);
+
+    StructType tdigestType = new StructType(false,
+            centroidList,
+            new BasicType(false, DType.FLOAT64), // min
+            new BasicType(false, DType.FLOAT64)); // max
+
+    try (ColumnVector tdigests = ColumnVector.fromStructs(tdigestType,
+            new StructData(Arrays.asList(
+                    new StructData(1.0, 100.0),
+                    new StructData(2.0, 50.0)),
+                    1.0, // min
+                    2.0), // max
+            new StructData(Arrays.asList(
+                    new StructData(3.0, 200.0),
+                    new StructData(4.0, 99.0)),
+                    3.0, // min
+                    4.0)); // max
+      Scalar merged = tdigests.reduce(ReductionAggregation.mergeTDigest(1000), DType.STRUCT)) {
+
+      assertEquals(DType.STRUCT, merged.getType());
+      try (CloseableArray columns = CloseableArray.wrap(merged.getChildrenFromStructScalar())) {
+        assertEquals(3, columns.size());
+        try (HostColumnVector centroids = ((ColumnView) columns.get(0)).copyToHost();
+             HostColumnVector min = ((ColumnView) columns.get(1)).copyToHost();
+             HostColumnVector max = ((ColumnView) columns.get(2)).copyToHost()) {
+          assertEquals(3, columns.size());
+          assertEquals(DType.LIST, centroids.getType());
+          assertEquals(DType.FLOAT64, min.getType());
+          assertEquals(DType.FLOAT64, max.getType());
+          assertEquals(1, min.getRowCount());
+          assertEquals(1, max.getRowCount());
+          assertEquals(1.0, min.getDouble(0));
+          assertEquals(4.0, max.getDouble(0));
+          assertEquals(1, centroids.rows);
+
+          List list = centroids.getList(0);
+          assertEquals(4, list.size());
+
+          StructData data = (StructData) list.get(0);
+          assertEquals(1.0, data.dataRecord.get(0));
+          assertEquals(100.0, data.dataRecord.get(1));
+
+          data = (StructData) list.get(1);
+          assertEquals(2.0, data.dataRecord.get(0));
+          assertEquals(50.0, data.dataRecord.get(1));
+
+          data = (StructData) list.get(2);
+          assertEquals(3.0, data.dataRecord.get(0));
+          assertEquals(200.0, data.dataRecord.get(1));
+
+          data = (StructData) list.get(3);
+          assertEquals(4.0, data.dataRecord.get(0));
+          assertEquals(99.0, data.dataRecord.get(1));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testGroupbyHistogram() {
+    StructType histogramStruct = new StructType(false,
+        new BasicType(false, DType.INT32), // values
+        new BasicType(false, DType.INT64)); // frequencies
+    ListType histogramList = new ListType(false, histogramStruct);
+
+    // key = 0: values = [2, 2, -3, -2, 2]
+    // key = 1: values = [2, 0, 5, 2, 1]
+    // key = 2: values = [-3, 1, 1, 2, 2]
+    try (Table input = new Table.TestBuilder()
+        .column(2, 0, 2, 1, 1, 1, 0, 0, 0, 1, 2, 2, 1, 0, 2)
+        .column(-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1, 2, 1, 2, 2)
+        .build();
+         Table result = input.groupBy(0)
+             .aggregate(GroupByAggregation.histogram().onColumn(1));
+         Table sortedResult = result.orderBy(OrderByArg.asc(0));
+         ColumnVector sortedOutHistograms = sortedResult.getColumn(1).listSortRows(false, false);
+
+         ColumnVector expectedKeys = ColumnVector.fromInts(0, 1, 2);
+         ColumnVector expectedHistograms = ColumnVector.fromLists(histogramList,
+             Arrays.asList(new StructData(-3, 1L), new StructData(-2, 1L), new StructData(2, 3L)),
+             Arrays.asList(new StructData(0, 1L), new StructData(1, 1L), new StructData(2, 2L),
+                 new StructData(5, 1L)),
+             Arrays.asList(new StructData(-3, 1L), new StructData(1, 2L), new StructData(2, 2L)))
+    ) {
+      assertColumnsAreEqual(expectedKeys, sortedResult.getColumn(0));
+      assertColumnsAreEqual(expectedHistograms, sortedOutHistograms);
+    }
+  }
+
+  @Test
+  void testGroupbyMergeHistogram() {
+    StructType histogramStruct = new StructType(false,
+        new BasicType(false, DType.INT32), // values
+        new BasicType(false, DType.INT64)); // frequencies
+    ListType histogramList = new ListType(false, histogramStruct);
+
+    // key = 0: histograms = [[<-3, 1>, <-2, 1>, <2, 3>], [<0, 1>, <1, 1>], [<-3, 3>, <0, 1>, <1, 2>]]
+    // key = 1: histograms = [[<-2, 1>, <1, 3>, <2, 2>], [<0, 2>, <1, 1>, <2, 2>]]
+    try (Table input = new Table.TestBuilder()
+        .column(0, 1, 0, 1, 0)
+        .column(histogramStruct,
+            new StructData[]{new StructData(-3, 1L), new StructData(-2, 1L), new StructData(2, 3L)},
+            new StructData[]{new StructData(-2, 1L), new StructData(1, 3L), new StructData(2, 2L)},
+            new StructData[]{new StructData(0, 1L), new StructData(1, 1L)},
+            new StructData[]{new StructData(0, 2L), new StructData(1, 1L), new StructData(2, 2L)},
+            new StructData[]{new StructData(-3, 3L), new StructData(0, 1L), new StructData(1, 2L)})
+        .build();
+         Table result = input.groupBy(0)
+             .aggregate(GroupByAggregation.mergeHistogram().onColumn(1));
+         Table sortedResult = result.orderBy(OrderByArg.asc(0));
+         ColumnVector sortedOutHistograms = sortedResult.getColumn(1).listSortRows(false, false);
+
+         ColumnVector expectedKeys = ColumnVector.fromInts(0, 1);
+         ColumnVector expectedHistograms = ColumnVector.fromLists(histogramList,
+             Arrays.asList(new StructData(-3, 4L), new StructData(-2, 1L), new StructData(0, 2L),
+                           new StructData(1, 3L), new StructData(2, 3L)),
+             Arrays.asList(new StructData(-2, 1L), new StructData(0, 2L), new StructData(1, 4L),
+                           new StructData(2, 4L)))
+    ) {
+      assertColumnsAreEqual(expectedKeys, sortedResult.getColumn(0));
+      assertColumnsAreEqual(expectedHistograms, sortedOutHistograms);
+    }
+  }
+
+  @Test
+  void testReductionHistogram() {
+    StructType histogramStruct = new StructType(false,
+        new BasicType(false, DType.INT32), // values
+        new BasicType(false, DType.INT64)); // frequencies
+
+    try (ColumnVector input = ColumnVector.fromInts(-3, 2, 1, 2, 0, 5, 2, -3, -2, 2, 1);
+         Scalar result = input.reduce(ReductionAggregation.histogram(), DType.LIST);
+         ColumnVector resultCV = result.getListAsColumnView().copyToColumnVector();
+         Table resultTable = new Table(resultCV);
+         Table sortedResult = resultTable.orderBy(OrderByArg.asc(0));
+
+         ColumnVector expectedHistograms = ColumnVector.fromStructs(histogramStruct,
+             new StructData(-3, 2L), new StructData(-2, 1L), new StructData(0, 1L),
+             new StructData(1, 2L), new StructData(2, 4L), new StructData(5, 1L))
+    ) {
+      assertColumnsAreEqual(expectedHistograms, sortedResult.getColumn(0));
+    }
+  }
+
+  @Test
+  void testReductionMergeHistogram() {
+    StructType histogramStruct = new StructType(false,
+        new BasicType(false, DType.INT32), // values
+        new BasicType(false, DType.INT64)); // frequencies
+
+    try (ColumnVector input = ColumnVector.fromStructs(histogramStruct,
+             new StructData(-3, 2L), new StructData(2, 1L), new StructData(1, 1L),
+             new StructData(2, 2L), new StructData(0, 4L), new StructData(5, 1L),
+             new StructData(2, 2L), new StructData(-3, 3L), new StructData(-2, 5L),
+             new StructData(2, 3L), new StructData(1, 4L));
+         Scalar result = input.reduce(ReductionAggregation.mergeHistogram(), DType.LIST);
+         ColumnVector resultCV = result.getListAsColumnView().copyToColumnVector();
+         Table resultTable = new Table(resultCV);
+         Table sortedResult = resultTable.orderBy(OrderByArg.asc(0));
+
+         ColumnVector expectedHistograms = ColumnVector.fromStructs(histogramStruct,
+             new StructData(-3, 5L), new StructData(-2, 5L), new StructData(0, 4L),
+             new StructData(1, 5L), new StructData(2, 8L), new StructData(5, 1L))
+    ) {
+      assertColumnsAreEqual(expectedHistograms, sortedResult.getColumn(0));
+    }
+  }
+  @Test
+  void testGroupByMinMaxDecimal() {
+    try (Table t1 = new Table.TestBuilder()
+        .column( "1",  "1", "1", "1", "2")
+        .column(0, 1, 3 , 3, 4)
+        .decimal128Column(-4, RoundingMode.HALF_UP,
+            new BigInteger("123456789123456789"),
+            new BigInteger("7979879879879798"),
+            new BigInteger("17979879879879798"),
+            new BigInteger("2234563472398472398"),
+            null)
+        .build()) {
+      try (Table result = t1
+          .groupBy(GroupByOptions.builder()
+              .withKeysSorted(true)
+              .withKeysDescending(false, false)
+              .build(), 0, 1)
+          .scan(GroupByScanAggregation.min().onColumn(2),
+              GroupByScanAggregation.max().onColumn(2));
+           Table expected = new Table.TestBuilder()
+               .column( "1",  "1", "1", "1", "2")
+               .column(0, 1, 3, 3, 4)
+               .decimal128Column(-4, RoundingMode.HALF_UP,
+                   new BigInteger("123456789123456789"),
+                   new BigInteger("7979879879879798"),
+                   new BigInteger("17979879879879798"),
+                   new BigInteger("17979879879879798"),
+                   null)
+               .decimal128Column(-4, RoundingMode.HALF_UP,
+                   new BigInteger("123456789123456789"),
+                   new BigInteger("7979879879879798"),
+                   new BigInteger("17979879879879798"),
+                   new BigInteger("2234563472398472398"),
+                   null)
+               .build()) {
+        assertTablesAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByMinMaxDecimalAgg() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(-341142443, 48424546)
+        .decimal128Column(-2, RoundingMode.HALF_DOWN,
+            new BigInteger("2978603952268112009"),
+            new BigInteger("571526248386900094"))
+        .build()) {
+      try (Table result = t1
+          .groupBy(GroupByOptions.builder()
+              .build(), 0)
+          .aggregate(GroupByAggregation.max().onColumn(1));
+           Table expected = new Table.TestBuilder()
+               .column(-341142443, 48424546)
+               .decimal128Column(-2, RoundingMode.HALF_DOWN,
+                   new BigInteger("2978603952268112009"),
+                   new BigInteger("571526248386900094"))
+               .build()) {
+        assertTablesAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByCountDecimal() {
+    try (Table t1 = new Table.TestBuilder()
+        .column( "1",  "1", "1", "1", "2")
+        .column(0, 1, 3 , 3, 4)
+        .decimal128Column(-4, RoundingMode.HALF_UP,
+            new BigInteger("123456789123456789"),
+            new BigInteger("7979879879879798"),
+            new BigInteger("17979879879879798"),
+            new BigInteger("2234563472398472398"),
+            null)
+        .build()) {
+      try (Table result = t1
+          .groupBy(GroupByOptions.builder()
+              .withKeysSorted(true)
+              .withKeysDescending(false, false)
+              .build(), 0, 1)
+          .aggregate(GroupByAggregation.count().onColumn(2));
+           Table expected = new Table.TestBuilder()
+               .column( "1",  "1", "1", "2")
+               .column(0, 1, 3, 4)
+               .column(1, 1, 2, 0)
+               .build()) {
+        assertTablesAreEqual(expected, result);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByUniqueCount() {
+    try (Table t1 = new Table.TestBuilder()
+            .column( "1",  "1",  "1",  "1",  "1",  "1")
+            .column(   1,    3,    3,    5,    5,    0)
+            .column(12.0, 14.0, 13.0, 17.0, 17.0, 17.0)
+            .build()) {
+      try (Table t3 = t1
+              .groupBy(0, 1)
+              .aggregate(GroupByAggregation.nunique().onColumn(0));
+           Table sorted = t3.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table expected = new Table.TestBuilder()
+                   .column( "1",  "1",  "1",  "1")
+                   .column(   0,    1,    3,    5)
+                   .column(   1,    1,    1,    1)
+                   .build()) {
+        assertTablesAreEqual(expected, sorted);
+      }
+    }
+  }
+
+  @Test
+  void testOrderByDecimal() {
+    try (Table t1 = new Table.TestBuilder()
+        .column( "1",  "1", "1", "1")
+        .column(0, 1, 3 , 3)
+        .decimal64Column(4,
+            123456L,
+            124567L,
+            125678L,
+            126789L)
+        .build()) {
+      try (Table sorted = t1.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table expected = new Table.TestBuilder()
+               .column( "1",  "1", "1", "1")
+               .column(   0,    1, 3, 3)
+               .decimal64Column(4,
+                   123456L,
+                   124567L,
+                   125678L,
+                   126789L)
+               .build()) {
+        assertTablesAreEqual(expected, sorted);
+
+      }
+    }
+  }
+
+  @Test
+  void testGroupByUniqueCountNulls() {
+    try (Table t1 = new Table.TestBuilder()
+            .column( "1",  "1",  "1",  "1",  "1",  "1")
+            .column(   1,    3,    3,    5,    5,    0)
+            .column(null, null, 13.0, null, null, null)
+            .build()) {
+      try (Table t3 = t1
+              .groupBy(0, 1)
+              .aggregate(GroupByAggregation.nunique(NullPolicy.INCLUDE).onColumn(0));
+           Table sorted = t3.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table expected = new Table.TestBuilder()
+                   .column( "1",  "1",  "1",  "1")
+                   .column(   0,    1,    3,    5)
+                   .column(   1,    1,    1,    1)
+                   .build()) {
+        assertTablesAreEqual(expected, sorted);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByCount() {
+    try (Table t1 = new Table.TestBuilder().column( "1",  "1",  "1",  "1",  "1",  "1")
+                                           .column(   1,    3,    3,    5,    5,    0)
+                                           .column(12.0, 14.0, 13.0, 17.0, 17.0, 17.0)
+                                           .build()) {
+      try (Table t3 = t1.groupBy(0, 1)
+          .aggregate(GroupByAggregation.count().onColumn(0));
+           HostColumnVector aggOut1 = t3.getColumn(2).copyToHost()) {
+        // verify t3
+        assertEquals(4, t3.getRowCount());
+        Map<Object, Integer> expectedAggregateResult = new HashMap() {
+          {
+            // value, count
+            put(1, 2);
+            put(2, 2);
+          }
+        };
+        for (int i = 0; i < 4; ++i) {
+          int key = aggOut1.getInt(i);
+          assertTrue(expectedAggregateResult.containsKey(key));
+          Integer count = expectedAggregateResult.get(key);
+          if (count == 1) {
+            expectedAggregateResult.remove(key);
+          } else {
+            expectedAggregateResult.put(key, count - 1);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingCount() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .decimal32Column(-1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // Decimal GBY Key
+        .decimal64Column(1, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L) // Decimal OBY Key
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table decSorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(4), OrderByArg.asc(5));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           Scalar two = Scalar.fromInt(2);
+           Scalar one = Scalar.fromInt(1)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+        ColumnVector decSortedAggColumn = decSorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, decSortedAggColumn);
+
+        try (WindowOptions window = WindowOptions.builder()
+            .minPeriods(1)
+            .window(two, one)
+            .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation.count().onColumn(3).overWindow(window));
+               Table decWindowAggResults = decSorted.groupBy(0, 4)
+                   .aggregateWindows(RollingAggregation.count().onColumn(3).overWindow(window));
+               ColumnVector expect = ColumnVector.fromBoxedInts(2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2)) {
+            assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expect, decWindowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingMin() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .decimal32Column(-1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // Decimal GBY Key
+        .decimal64Column(1, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L) // Decimal OBY Key
+        .decimal64Column(2, 7L, 5L, 1L, 9L, 7L, 9L, 8L, 2L, 8L, 0L, 6L, 6L) // Decimal Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table decSorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(4), OrderByArg.asc(5));
+           ColumnVector expectSortedAggCol = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           ColumnVector expectDecSortedAggCol = ColumnVector.decimalFromLongs(2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           Scalar two = Scalar.fromInt(2);
+           Scalar one = Scalar.fromInt(1)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggCol, sortedAggColumn);
+        ColumnVector decSortedAggColumn = decSorted.getColumn(6);
+        assertColumnsAreEqual(expectDecSortedAggCol, decSortedAggColumn);
+
+        try (WindowOptions window = WindowOptions.builder()
+            .minPeriods(1)
+            .window(two, one)
+            .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation.min().onColumn(3).overWindow(window));
+               Table decWindowAggResults = decSorted.groupBy(0, 4)
+                   .aggregateWindows(RollingAggregation.min().onColumn(6).overWindow(window));
+               ColumnVector expect = ColumnVector.fromBoxedInts(5, 1, 1, 1, 7, 7, 2, 2, 0, 0, 0, 6);
+               ColumnVector decExpect = ColumnVector.decimalFromLongs(2, 5, 1, 1, 1, 7, 7, 2, 2, 0, 0, 0, 6)) {
+            assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(decExpect, decWindowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingMax() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .decimal32Column(-1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // Decimal GBY Key
+        .decimal64Column(1, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L) // Decimal OBY Key
+        .decimal64Column(2, 7L, 5L, 1L, 9L, 7L, 9L, 8L, 2L, 8L, 0L, 6L, 6L) // Decimal Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table decSorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(4), OrderByArg.asc(5));
+           ColumnVector expectSortedAggCol = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           ColumnVector expectDecSortedAggCol = ColumnVector.decimalFromLongs(2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           Scalar two = Scalar.fromInt(2);
+           Scalar one = Scalar.fromInt(1)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggCol, sortedAggColumn);
+        ColumnVector decSortedAggColumn = decSorted.getColumn(6);
+        assertColumnsAreEqual(expectDecSortedAggCol, decSortedAggColumn);
+
+        try (WindowOptions window = WindowOptions.builder()
+            .minPeriods(1)
+            .window(two, one)
+            .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation.max().onColumn(3).overWindow(window));
+               Table decWindowAggResults = decSorted.groupBy(0, 4)
+                   .aggregateWindows(RollingAggregation.max().onColumn(6).overWindow(window));
+               ColumnVector expect = ColumnVector.fromBoxedInts(7, 7, 9, 9, 9, 9, 9, 8, 8, 8, 6, 6);
+               ColumnVector decExpect = ColumnVector.decimalFromLongs(2, 7, 7, 9, 9, 9, 9, 9, 8, 8, 8, 6, 6)) {
+            assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(decExpect, decWindowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingSum() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           Scalar two = Scalar.fromInt(2);
+           Scalar one = Scalar.fromInt(1)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+        try (WindowOptions window = WindowOptions.builder()
+            .minPeriods(1)
+            .window(two, one)
+            .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation.sum().onColumn(3).overWindow(window));
+               ColumnVector expectAggResult = ColumnVector.fromBoxedLongs(12L, 13L, 15L, 10L, 16L, 24L, 19L, 10L, 8L, 14L, 12L, 12L)) {
+            assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingRowNumber() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .decimal32Column(-1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // Decimal GBY Key
+        .decimal64Column(1, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L) // Decimal OBY Key
+        .decimal64Column(2, 7L, 5L, 1L, 9L, 7L, 9L, 8L, 2L, 8L, 0L, 6L, 6L) // Decimal Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table decSorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(4), OrderByArg.asc(5));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           ColumnVector expectDecSortedAggColumn = ColumnVector.decimalFromLongs(2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+        ColumnVector decSortedAggColumn = decSorted.getColumn(6);
+        assertColumnsAreEqual(expectDecSortedAggColumn, decSortedAggColumn);
+
+        WindowOptions.Builder windowBuilder = WindowOptions.builder().minPeriods(1);
+
+        try (Scalar two = Scalar.fromInt(2);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(two, one).build();
+             WindowOptions options1 = windowBuilder.window(two, one).build()) {
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation
+                  .rowNumber()
+                  .onColumn(3)
+                  .overWindow(options));
+               Table decWindowAggResults = decSorted.groupBy(0, 4)
+                   .aggregateWindows(RollingAggregation
+                       .rowNumber()
+                       .onColumn(6)
+                       .overWindow(options1));
+               ColumnVector expectAggResult = ColumnVector.fromBoxedInts(1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2)) {
+            assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expectAggResult, decWindowAggResults.getColumn(0));
+          }
+        }
+
+        try (Scalar three = Scalar.fromInt(3);
+             Scalar two = Scalar.fromInt(2);
+             WindowOptions options = windowBuilder.window(three, two).build();
+             WindowOptions options1 = windowBuilder.window(three, two).build()) {
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation
+                  .rowNumber()
+                  .onColumn(3)
+                  .overWindow(options));
+               Table decWindowAggResults = decSorted.groupBy(0, 4)
+                   .aggregateWindows(RollingAggregation
+                       .rowNumber()
+                       .onColumn(6)
+                       .overWindow(options1));
+               ColumnVector expectAggResult = ColumnVector.fromBoxedInts(1, 2, 3, 3, 1, 2, 3, 3, 1, 2, 3, 3)) {
+            assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expectAggResult, decWindowAggResults.getColumn(0));
+          }
+        }
+
+        try (Scalar four = Scalar.fromInt(4);
+             Scalar three = Scalar.fromInt(3);
+             WindowOptions options = windowBuilder.window(four, three).build();
+             WindowOptions options1 = windowBuilder.window(four, three).build()) {
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation
+                  .rowNumber()
+                  .onColumn(3)
+                  .overWindow(options));
+               Table decWindowAggResults = decSorted.groupBy(0, 4)
+                   .aggregateWindows(RollingAggregation
+                       .rowNumber()
+                       .onColumn(6)
+                       .overWindow(options1));
+               ColumnVector expectAggResult = ColumnVector.fromBoxedInts(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4)) {
+            assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expectAggResult, decWindowAggResults.getColumn(0));
+          }
+        }
+       }
+    }
+  }
+
+  @Test
+  void testWindowingCollectList() {
+    RollingAggregation aggCollectWithNulls = RollingAggregation.collectList(NullPolicy.INCLUDE);
+    RollingAggregation aggCollect = RollingAggregation.collectList();
+    try (Scalar two = Scalar.fromInt(2);
+         Scalar one = Scalar.fromInt(1);
+         WindowOptions winOpts = WindowOptions.builder()
+             .minPeriods(1)
+             .window(two, one)
+             .build()) {
+      StructType nestedType = new StructType(false,
+          new BasicType(false, DType.INT32), new BasicType(false, DType.STRING));
+      try (Table raw = new Table.TestBuilder()
+          .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+          .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+          .column(1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8) // OBY Key
+          .column(7, 5, 1, 9, 7, 9, 8, 2, null, 0, 6, null) // Agg Column of INT32
+          .column(nestedType,                          // Agg Column of Struct
+              new StructData(1, "s1"), new StructData(2, "s2"), new StructData(3, "s3"),
+              new StructData(4, "s4"), new StructData(11, "s11"), new StructData(22, "s22"),
+              new StructData(33, "s33"), new StructData(44, "s44"), new StructData(111, "s111"),
+              new StructData(222, "s222"), new StructData(333, "s333"), new StructData(444, "s444")
+          ).build();
+           ColumnVector expectSortedAggColumn = ColumnVector
+               .fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, null, 0, 6, null)) {
+        try (Table sorted = raw.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2))) {
+          ColumnVector sortedAggColumn = sorted.getColumn(3);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          // Primitive type: INT32
+          //  a) including nulls
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollectWithNulls.onColumn(3).overWindow(winOpts));
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.INT32)),
+                   Arrays.asList(7, 5), Arrays.asList(7, 5, 1), Arrays.asList(5, 1, 9), Arrays.asList(1, 9),
+                   Arrays.asList(7, 9), Arrays.asList(7, 9, 8), Arrays.asList(9, 8, 2), Arrays.asList(8, 2),
+                   Arrays.asList(null, 0), Arrays.asList(null, 0, 6), Arrays.asList(0, 6, null), Arrays.asList(6, null))) {
+            assertColumnsAreEqual(expected, windowAggResults.getColumn(0));
+          }
+          //  b) excluding nulls
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollect.onColumn(3).overWindow(winOpts));
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.INT32)),
+                   Arrays.asList(7, 5), Arrays.asList(7, 5, 1), Arrays.asList(5, 1, 9), Arrays.asList(1, 9),
+                   Arrays.asList(7, 9), Arrays.asList(7, 9, 8), Arrays.asList(9, 8, 2), Arrays.asList(8, 2),
+                   Arrays.asList(0), Arrays.asList(0, 6), Arrays.asList(0, 6), Arrays.asList(6))) {
+            assertColumnsAreEqual(expected, windowAggResults.getColumn(0));
+          }
+
+          // Nested type: Struct
+          List<StructData>[] expectedNestedData = new List[12];
+          expectedNestedData[0] = Arrays.asList(new StructData(1, "s1"), new StructData(2, "s2"));
+          expectedNestedData[1] = Arrays.asList(new StructData(1, "s1"), new StructData(2, "s2"), new StructData(3, "s3"));
+          expectedNestedData[2] = Arrays.asList(new StructData(2, "s2"), new StructData(3, "s3"), new StructData(4, "s4"));
+          expectedNestedData[3] = Arrays.asList(new StructData(3, "s3"), new StructData(4, "s4"));
+          expectedNestedData[4] = Arrays.asList(new StructData(11, "s11"), new StructData(22, "s22"));
+          expectedNestedData[5] = Arrays.asList(new StructData(11, "s11"), new StructData(22, "s22"), new StructData(33, "s33"));
+          expectedNestedData[6] = Arrays.asList(new StructData(22, "s22"), new StructData(33, "s33"), new StructData(44, "s44"));
+          expectedNestedData[7] = Arrays.asList(new StructData(33, "s33"), new StructData(44, "s44"));
+          expectedNestedData[8] = Arrays.asList(new StructData(111, "s111"), new StructData(222, "s222"));
+          expectedNestedData[9] = Arrays.asList(new StructData(111, "s111"), new StructData(222, "s222"), new StructData(333, "s333"));
+          expectedNestedData[10] = Arrays.asList(new StructData(222, "s222"), new StructData(333, "s333"), new StructData(444, "s444"));
+          expectedNestedData[11] = Arrays.asList(new StructData(333, "s333"), new StructData(444, "s444"));
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollect.onColumn(4).overWindow(winOpts));
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, nestedType), expectedNestedData)) {
+            assertColumnsAreEqual(expected, windowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingCollectSet() {
+    RollingAggregation aggCollect = RollingAggregation.collectSet();
+    RollingAggregation aggCollectWithEqNulls = RollingAggregation.collectSet(NullPolicy.INCLUDE,
+        NullEquality.EQUAL, NaNEquality.UNEQUAL);
+    RollingAggregation aggCollectWithUnEqNulls = RollingAggregation.collectSet(NullPolicy.INCLUDE,
+        NullEquality.UNEQUAL, NaNEquality.UNEQUAL);
+    RollingAggregation aggCollectWithEqNaNs = RollingAggregation.collectSet(NullPolicy.INCLUDE,
+        NullEquality.EQUAL, NaNEquality.ALL_EQUAL);
+
+    try (Scalar two = Scalar.fromInt(2);
+         Scalar one = Scalar.fromInt(1);
+         WindowOptions winOpts = WindowOptions.builder()
+             .minPeriods(1)
+             .window(two, one)
+             .build()) {
+      try (Table raw = new Table.TestBuilder()
+          .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+          .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+          .column(1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8) // OBY Key
+          .column(5, 5, 1, 1, 1, 4, 3, 4, null, null, 6, 7) // Agg Column of INT32
+          .column(1.1, 1.1, null, 2.2, -3.0, 1.3e-7, -3.0, Double.NaN, 1e-3, null, Double.NaN, Double.NaN) // Agg Column of FLOAT64
+          .build();
+           ColumnVector expectSortedAggColumn = ColumnVector
+               .fromBoxedInts(5, 5, 1, 1, 1, 4, 3, 4, null, null, 6, 7)) {
+        try (Table sorted = raw.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2))) {
+          ColumnVector sortedAggColumn = sorted.getColumn(3);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          // Primitive type: INT32
+          //  a) excluding NULLs
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollect.onColumn(3).overWindow(winOpts));
+               ColumnVector resultSorted = windowAggResults.getColumn(0).listSortRows(false, false);
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.INT32)),
+                   Arrays.asList(5), Arrays.asList(1, 5), Arrays.asList(1, 5), Arrays.asList(1),
+                   Arrays.asList(1, 4), Arrays.asList(1, 3, 4), Arrays.asList(3, 4), Arrays.asList(3, 4),
+                   Arrays.asList(), Arrays.asList(6), Arrays.asList(6, 7), Arrays.asList(6, 7))) {
+            assertColumnsAreEqual(expected, resultSorted);
+          }
+          //  b) including NULLs AND NULLs are equal
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollectWithEqNulls.onColumn(3).overWindow(winOpts));
+               ColumnVector resultSorted = windowAggResults.getColumn(0).listSortRows(false, false);
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.INT32)),
+                   Arrays.asList(5), Arrays.asList(1, 5), Arrays.asList(1, 5), Arrays.asList(1),
+                   Arrays.asList(1, 4), Arrays.asList(1, 3, 4), Arrays.asList(3, 4), Arrays.asList(3, 4),
+                   Arrays.asList((Integer) null), Arrays.asList(6, null), Arrays.asList(6, 7, null), Arrays.asList(6, 7))) {
+            assertColumnsAreEqual(expected, resultSorted);
+          }
+          //  c) including NULLs AND NULLs are unequal
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollectWithUnEqNulls.onColumn(3).overWindow(winOpts));
+               ColumnVector resultSorted = windowAggResults.getColumn(0).listSortRows(false, false);
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.INT32)),
+                   Arrays.asList(5), Arrays.asList(1, 5), Arrays.asList(1, 5), Arrays.asList(1),
+                   Arrays.asList(1, 4), Arrays.asList(1, 3, 4), Arrays.asList(3, 4), Arrays.asList(3, 4),
+                   Arrays.asList(null, null), Arrays.asList(6, null, null), Arrays.asList(6, 7, null), Arrays.asList(6, 7))) {
+            assertColumnsAreEqual(expected, resultSorted);
+          }
+
+          // Primitive type: FLOAT64
+          //  a) excluding NULLs
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollect.onColumn(4).overWindow(winOpts));
+               ColumnVector resultSorted = windowAggResults.getColumn(0).listSortRows(false, false);
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.FLOAT64)),
+                   Arrays.asList(1.1), Arrays.asList(1.1), Arrays.asList(1.1, 2.2), Arrays.asList(2.2),
+                   Arrays.asList(-3.0, 1.3e-7), Arrays.asList(-3.0, 1.3e-7),
+                   Arrays.asList(-3.0, 1.3e-7, Double.NaN), Arrays.asList(-3.0, Double.NaN),
+                   Arrays.asList(1e-3), Arrays.asList(1e-3, Double.NaN),
+                   Arrays.asList(Double.NaN, Double.NaN), Arrays.asList(Double.NaN, Double.NaN))) {
+            assertColumnsAreEqual(expected, resultSorted);
+          }
+          //  b) including NULLs AND NULLs are equal
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollectWithEqNulls.onColumn(4).overWindow(winOpts));
+               ColumnVector resultSorted = windowAggResults.getColumn(0).listSortRows(false, false);
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.FLOAT64)),
+                   Arrays.asList(1.1), Arrays.asList(1.1, null), Arrays.asList(1.1, 2.2, null), Arrays.asList(2.2, null),
+                   Arrays.asList(-3.0, 1.3e-7), Arrays.asList(-3.0, 1.3e-7),
+                   Arrays.asList(-3.0, 1.3e-7, Double.NaN), Arrays.asList(-3.0, Double.NaN),
+                   Arrays.asList(1e-3, null), Arrays.asList(1e-3, Double.NaN, null),
+                   Arrays.asList(Double.NaN, Double.NaN, null), Arrays.asList(Double.NaN, Double.NaN))) {
+            assertColumnsAreEqual(expected, resultSorted);
+          }
+          //  c) including NULLs AND NULLs are equal AND NaNs are equal
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(aggCollectWithEqNaNs.onColumn(4).overWindow(winOpts));
+               ColumnVector resultSorted = windowAggResults.getColumn(0).listSortRows(false, false);
+               ColumnVector expected = ColumnVector.fromLists(
+                   new ListType(false, new BasicType(false, DType.FLOAT64)),
+                   Arrays.asList(1.1), Arrays.asList(1.1, null), Arrays.asList(1.1, 2.2, null), Arrays.asList(2.2, null),
+                   Arrays.asList(-3.0, 1.3e-7), Arrays.asList(-3.0, 1.3e-7),
+                   Arrays.asList(-3.0, 1.3e-7, Double.NaN), Arrays.asList(-3.0, Double.NaN),
+                   Arrays.asList(1e-3, null), Arrays.asList(1e-3, Double.NaN, null),
+                   Arrays.asList(Double.NaN, null), Arrays.asList(Double.NaN))) {
+            assertColumnsAreEqual(expected, resultSorted);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingLead() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Int Agg Column
+        .decimal32Column(-1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // Decimal GBY Key
+        .decimal64Column(1, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L) // Decimal OBY Key
+        .decimal64Column(-2, 7L, 5L, 1L, 9L, 7L, 9L, 8L, 2L, 8L, 0L, 6L, 6L) // Decimal Agg Column
+        .column(new ListType(false, new BasicType(true, DType.INT32)),
+            Arrays.asList(11, 12, null, 13), Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null),  Arrays.asList(16),
+            Arrays.asList(21, null, null, 22), Arrays.asList(23, 24), Arrays.asList(25, 26, 27),  Arrays.asList(28, 29, null),
+            Arrays.asList(null, 31), Arrays.asList(32, 33, 34), Arrays.asList(35, 36),  Arrays.asList(37, 38, 39)) // List Agg COLUMN
+        .column(new StructType(true,
+                new BasicType(true, DType.INT32),
+                new BasicType(true, DType.STRING)),
+            new StructData(1, "s1"), new StructData(null, "s2"), new StructData(2, null), new StructData(3, "s3"),
+            new StructData(11, "s11"), null, new StructData(13, "s13"), new StructData(14, "s14"),
+            new StructData(111, "s111"), new StructData(null, "s112"), new StructData(2, "s222"), new StructData(3, "s333")) //STRUCT Agg COLUMN
+        .build()) {
+
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table decSorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(4), OrderByArg.asc(5));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           ColumnVector expectDecSortedAggColumn = ColumnVector.decimalFromLongs(-2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+        ColumnVector decSortedAggColumn = decSorted.getColumn(6);
+        assertColumnsAreEqual(expectDecSortedAggColumn, decSortedAggColumn);
+
+        WindowOptions.Builder windowBuilder = WindowOptions.builder().minPeriods(1);
+
+        try (Scalar two = Scalar.fromInt(2);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(two, one).build();
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lead(0)
+                     .onColumn(3) // Int Agg Column
+                     .overWindow(options));
+             Table decWindowAggResults = decSorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lead(0)
+                     .onColumn(6) // Decimal Agg Column
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(0)
+                     .onColumn(7) // List Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(0)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+             ColumnVector decExpectAggResult = ColumnVector.decimalFromLongs(-2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(11, 12, null, 13), Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null), Arrays.asList(16),
+                 Arrays.asList(21, null, null, 22), Arrays.asList(23, 24), Arrays.asList(25, 26, 27), Arrays.asList(28, 29, null),
+                 Arrays.asList(null, 31), Arrays.asList(32, 33, 34), Arrays.asList(35, 36), Arrays.asList(37, 38, 39));
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(1, "s1"), new StructData(null, "s2"), new StructData(2, null), new StructData(3, "s3"),
+                 new StructData(11, "s11"), null, new StructData(13, "s13"), new StructData(14, "s14"),
+                 new StructData(111, "s111"), new StructData(null, "s112"), new StructData(2, "s222"), new StructData(3, "s333"))) {
+
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+
+        try (Scalar zero = Scalar.fromInt(0);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(zero, one).build();
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lead(1)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lead(1)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(1)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(1)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(5, 1, 9, null, 9, 8, 2, null, 0, 6, 6, null);
+             ColumnVector decExpectAggResult = decimalFromBoxedInts(true, -2, 5, 1, 9, null, 9, 8, 2, null, 0, 6, 6, null);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null), Arrays.asList(16), null,
+                 Arrays.asList(23, 24), Arrays.asList(25, 26, 27), Arrays.asList(28, 29, null), null,
+                 Arrays.asList(32, 33, 34), Arrays.asList(35, 36), Arrays.asList(37, 38, 39), null);
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(null, "s2"), new StructData(2, null), new StructData(3, "s3"), null,
+                 null, new StructData(13, "s13"), new StructData(14, "s14"), null,
+                 new StructData(null, "s112"), new StructData(2, "s222"), new StructData(3, "s333"), null)) {
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+
+        try (Scalar zero = Scalar.fromInt(0);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(zero, one).build();
+             ColumnVector defaultOutput = ColumnVector.fromBoxedInts(0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11);
+             ColumnVector decDefaultOutput = ColumnVector.decimalFromLongs(-2, 0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11);
+             ColumnVector listDefaultOutput = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(111), Arrays.asList(222), Arrays.asList(333), Arrays.asList(444, null, 555),
+                 Arrays.asList(-11), Arrays.asList(-22), Arrays.asList(-33), Arrays.asList(-44),
+                 Arrays.asList(6), Arrays.asList(6), Arrays.asList(6), Arrays.asList(6, null, null));
+             ColumnVector structDefaultOutput = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(-1, "s1"), new StructData(null, "s2"), new StructData(-2, null), new StructData(-3, "s3"),
+                 new StructData(-11, "s11"), null, new StructData(-13, "s13"), new StructData(-14, "s14"),
+                 new StructData(-111, "s111"), new StructData(null, "s112"), new StructData(-222, "s222"), new StructData(-333, "s333"));
+
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lead(1, defaultOutput)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lead(1, decDefaultOutput)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(1, listDefaultOutput)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(1, structDefaultOutput)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(5, 1, 9, -3, 9, 8, 2, -7, 0, 6, 6, -11);
+             ColumnVector decExpectAggResult = ColumnVector.decimalFromLongs(-2, 5, 1, 9, -3, 9, 8, 2, -7, 0, 6, 6, -11);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null), Arrays.asList(16), Arrays.asList(444, null, 555),
+                 Arrays.asList(23, 24), Arrays.asList(25, 26, 27), Arrays.asList(28, 29, null), Arrays.asList(-44),
+                 Arrays.asList(32, 33, 34), Arrays.asList(35, 36), Arrays.asList(37, 38, 39), Arrays.asList(6, null, null));
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(null, "s2"), new StructData(2, null), new StructData(3, "s3"), new StructData(-3, "s3"),
+                 null, new StructData(13, "s13"), new StructData(14, "s14"), new StructData(-14, "s14"),
+                 new StructData(null, "s112"), new StructData(2, "s222"), new StructData(3, "s333"), new StructData(-333, "s333"))) {
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+
+        // Outside bounds
+        try (Scalar zero = Scalar.fromInt(0);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(zero, one).build();
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lead(3)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lead(3)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(3)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lead(3)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(null, null, null, null, null, null, null, null, null, null, null, null);
+             ColumnVector decExpectAggResult = decimalFromBoxedInts(true, -2, null, null, null, null, null, null, null, null, null, null, null, null);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 null, null, null, null, null, null, null, null, null, null, null, null);
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 null, null, null, null, null, null, null, null, null, null, null, null)){
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingLag() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .decimal32Column(-1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // Decimal GBY Key
+        .decimal64Column(1, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L) // Decimal OBY Key
+        .decimal64Column(-2, 7L, 5L, 1L, 9L, 7L, 9L, 8L, 2L, 8L, 0L, 6L, 6L) // Decimal Agg Column
+        .column(new ListType(false, new BasicType(true, DType.INT32)),
+            Arrays.asList(11, 12, null, 13), Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null),  Arrays.asList(16),
+            Arrays.asList(21, null, null, 22), Arrays.asList(23, 24), Arrays.asList(25, 26, 27),  Arrays.asList(28, 29, null),
+            Arrays.asList(null, 31), Arrays.asList(32, 33, 34), Arrays.asList(35, 36),  Arrays.asList(37, 38, 39)) // List Agg COLUMN
+        .column(new StructType(true,
+                new BasicType(true, DType.INT32),
+                new BasicType(true, DType.STRING)),
+            new StructData(1, "s1"), new StructData(null, "s2"), new StructData(2, null), new StructData(3, "s3"),
+            new StructData(11, "s11"), null, new StructData(13, "s13"), new StructData(14, "s14"),
+            new StructData(111, "s111"), new StructData(null, "s112"), new StructData(2, "s222"), new StructData(3, "s333")) //STRUCT Agg COLUMN
+        .build()) {
+
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table decSorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(4), OrderByArg.asc(5));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+           ColumnVector decExpectSortedAggColumn = ColumnVector.decimalFromLongs(-2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+        ColumnVector decSortedAggColumn = decSorted.getColumn(6);
+        assertColumnsAreEqual(decExpectSortedAggColumn, decSortedAggColumn);
+
+        WindowOptions.Builder windowBuilder = WindowOptions.builder().minPeriods(1);
+
+        try (Scalar two = Scalar.fromInt(2);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(two, one).build();
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lag(0)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lag(0)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(0)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(0)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+             ColumnVector decExpectAggResult = ColumnVector.decimalFromLongs(-2, 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(11, 12, null, 13), Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null), Arrays.asList(16),
+                 Arrays.asList(21, null, null, 22), Arrays.asList(23, 24), Arrays.asList(25, 26, 27), Arrays.asList(28, 29, null),
+                 Arrays.asList(null, 31), Arrays.asList(32, 33, 34), Arrays.asList(35, 36), Arrays.asList(37, 38, 39));
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(1, "s1"), new StructData(null, "s2"), new StructData(2, null), new StructData(3, "s3"),
+                 new StructData(11, "s11"), null, new StructData(13, "s13"), new StructData(14, "s14"),
+                 new StructData(111, "s111"), new StructData(null, "s112"), new StructData(2, "s222"), new StructData(3, "s333"))) {
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+
+        try (Scalar zero = Scalar.fromInt(0);
+             Scalar two = Scalar.fromInt(2);
+             WindowOptions options = windowBuilder.window(two, zero).build();
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lag(1)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lag(1)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(1)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(1)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(null, 7, 5, 1, null, 7, 9, 8, null, 8, 0, 6);
+             ColumnVector decExpectAggResult = decimalFromBoxedInts(true, -2, null, 7, 5, 1, null, 7, 9, 8, null, 8, 0, 6);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 null, Arrays.asList(11, 12, null, 13), Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null),
+                 null, Arrays.asList(21, null, null, 22), Arrays.asList(23, 24), Arrays.asList(25, 26, 27),
+                 null, Arrays.asList(null, 31), Arrays.asList(32, 33, 34), Arrays.asList(35, 36));
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 null, new StructData(1, "s1"), new StructData(null, "s2"), new StructData(2, null),
+                 null, new StructData(11, "s11"), null, new StructData(13, "s13"),
+                 null, new StructData(111, "s111"), new StructData(null, "s112"), new StructData(2, "s222"))) {
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+
+        try (Scalar zero = Scalar.fromInt(0);
+             Scalar two = Scalar.fromInt(2);
+             WindowOptions options = windowBuilder.window(two, zero).build();
+             ColumnVector defaultOutput = ColumnVector.fromBoxedInts(0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11);
+             ColumnVector decDefaultOutput = ColumnVector.decimalFromLongs(-2, 0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11);
+             ColumnVector listDefaultOutput = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(111), Arrays.asList(222), Arrays.asList(333), Arrays.asList(444, null, 555),
+                 Arrays.asList(-11), Arrays.asList(-22), Arrays.asList(-33), Arrays.asList(-44),
+                 Arrays.asList(6), Arrays.asList(6), Arrays.asList(6), Arrays.asList(6, null, null));
+             ColumnVector structDefaultOutput = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(-1, "s1"), new StructData(null, "s2"), new StructData(-2, null), new StructData(-3, "s3"),
+                 new StructData(-11, "s11"), null, new StructData(-13, "s13"), new StructData(-14, "s14"),
+                 new StructData(-111, "s111"), new StructData(null, "s112"), new StructData(-222, "s222"), new StructData(-333, "s333"));
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lag(1, defaultOutput)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lag(1, decDefaultOutput)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(1, listDefaultOutput)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(1, structDefaultOutput)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(0, 7, 5, 1, -4, 7, 9, 8, -8, 8, 0, 6);
+             ColumnVector decExpectAggResult = ColumnVector.decimalFromLongs(-2, 0, 7, 5, 1, -4, 7, 9, 8, -8, 8, 0, 6);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 Arrays.asList(111), Arrays.asList(11, 12, null, 13), Arrays.asList(14, null, 15, null), Arrays.asList((Integer) null),
+                 Arrays.asList(-11), Arrays.asList(21, null, null, 22), Arrays.asList(23, 24), Arrays.asList(25, 26, 27),
+                 Arrays.asList(6), Arrays.asList(null, 31), Arrays.asList(32, 33, 34), Arrays.asList(35, 36));
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 new StructData(-1, "s1"), new StructData(1, "s1"), new StructData(null, "s2"), new StructData(2, null),
+                 new StructData(-11, "s11"), new StructData(11, "s11"), null, new StructData(13, "s13"),
+                 new StructData(-111, "s111"), new StructData(111, "s111"), new StructData(null, "s112"), new StructData(2, "s222"))) {
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+
+        // Outside bounds
+        try (Scalar zero = Scalar.fromInt(0);
+             Scalar one = Scalar.fromInt(1);
+             WindowOptions options = windowBuilder.window(one, zero).build();
+             Table windowAggResults = sorted.groupBy(0, 1)
+                 .aggregateWindows(RollingAggregation
+                     .lag(3)
+                     .onColumn(3) //Int Agg COLUMN
+                     .overWindow(options));
+             Table decWindowAggResults = sorted.groupBy(0, 4)
+                 .aggregateWindows(RollingAggregation
+                     .lag(3)
+                     .onColumn(6) //Decimal Agg COLUMN
+                     .overWindow(options));
+             Table listWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(3)
+                     .onColumn(7) //LIST Agg COLUMN
+                     .overWindow(options));
+             Table structWindowAggResults = sorted.groupBy(0, 1).aggregateWindows(
+                 RollingAggregation
+                     .lag(3)
+                     .onColumn(8) //STRUCT Agg COLUMN
+                     .overWindow(options));
+             ColumnVector expectAggResult = ColumnVector.fromBoxedInts(null, null, null, null, null, null, null, null, null, null, null, null);
+             ColumnVector decExpectAggResult = decimalFromBoxedInts(true, -2, null, null, null, null, null, null, null, null, null, null, null, null);
+             ColumnVector listExpectAggResult = ColumnVector.fromLists(
+                 new HostColumnVector.ListType(true, new HostColumnVector.BasicType(true, DType.INT32)),
+                 null, null, null, null, null, null, null, null, null, null, null, null);
+             ColumnVector structExpectAggResult = ColumnVector.fromStructs(
+                 new StructType(true,
+                     new BasicType(true, DType.INT32),
+                     new BasicType(true, DType.STRING)),
+                 null, null, null, null, null, null, null, null, null, null, null, null);) {
+          assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          assertColumnsAreEqual(decExpectAggResult, decWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(listExpectAggResult, listWindowAggResults.getColumn(0));
+          assertColumnsAreEqual(structExpectAggResult, structWindowAggResults.getColumn(0));
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingMean() {
+    try (Table unsorted = new Table.TestBuilder().column( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column( 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column( 7, 5, 3, 7, 7, 9, 8, 4, 8, 0, 4, 8) // Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           ColumnVector expectedSortedAggCol = ColumnVector.fromBoxedInts(7, 5, 3, 7, 7, 9, 8, 4, 8, 0, 4, 8)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectedSortedAggCol, sortedAggColumn);
+
+        try (Scalar one = Scalar.fromInt(1);
+             Scalar two = Scalar.fromInt(2);
+             WindowOptions window = WindowOptions.builder()
+                 .minPeriods(1)
+                 .window(two, one)
+                 .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(RollingAggregation.mean().onColumn(3).overWindow(window));
+               ColumnVector expect = ColumnVector.fromBoxedDoubles(6.0d, 5.0d, 5.0d, 5.0d, 8.0d, 8.0d, 7.0d, 6.0d, 4.0d, 4.0d, 4.0d, 6.0d)) {
+            assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingNthElement() {
+    final Integer X = null;
+    try (Table unsorted = new Table.TestBuilder()
+        .column(  1,  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // 0: GBY Key
+        .column(  3,  3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1) // 1: GBY Key
+        .column( 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0) // 2: OBY Key
+        .column(  X,  4, 0, X, 4, X, 9, 7, 7, 3, 5, 7) // 3: Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           ColumnVector expectedSortedAggCol = ColumnVector.fromBoxedInts(7, 5, 3, 7, 7, 9, X, 4, X, 0, 4, X)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectedSortedAggCol, sortedAggColumn);
+
+        try (Scalar one = Scalar.fromInt(1);
+             Scalar two = Scalar.fromInt(2);
+             WindowOptions window = WindowOptions.builder()
+                 .minPeriods(1)
+                 .window(two, one)
+                 .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(
+                RollingAggregation.nth(0, NullPolicy.INCLUDE).onColumn(3).overWindow(window),
+                RollingAggregation.nth(-1, NullPolicy.INCLUDE).onColumn(3).overWindow(window),
+                RollingAggregation.nth(1, NullPolicy.INCLUDE).onColumn(3).overWindow(window),
+                RollingAggregation.nth(0, NullPolicy.EXCLUDE).onColumn(3).overWindow(window),
+                RollingAggregation.nth(-1, NullPolicy.EXCLUDE).onColumn(3).overWindow(window),
+                RollingAggregation.nth(1, NullPolicy.EXCLUDE).onColumn(3).overWindow(window));
+               ColumnVector expect_first = ColumnVector.fromBoxedInts(7, 7, 5, 3, 7, 7, 9, X, X, X, 0, 4);
+               ColumnVector expect_last = ColumnVector.fromBoxedInts(5, 3, 7, 7, 9, X, 4, 4, 0, 4, X, X);
+               ColumnVector expect_1th  = ColumnVector.fromBoxedInts(5, 5, 3, 7, 9, 9, X, 4, 0, 0, 4, X);
+               ColumnVector expect_first_skip_null  =
+                 ColumnVector.fromBoxedInts(7, 7, 5, 3, 7, 7, 9, 4, 0, 0, 0, 4);
+               ColumnVector expect_last_skip_null  =
+                 ColumnVector.fromBoxedInts(5, 3, 7, 7, 9, 9, 4, 4, 0, 4, 4, 4);
+               ColumnVector expect_1th_skip_null  =
+                 ColumnVector.fromBoxedInts(5, 5, 3, 7, 9, 9, 4, X, X, 4, 4, X)) {
+            assertColumnsAreEqual(expect_first, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expect_last, windowAggResults.getColumn(1));
+            assertColumnsAreEqual(expect_1th, windowAggResults.getColumn(2));
+            assertColumnsAreEqual(expect_first_skip_null, windowAggResults.getColumn(3));
+            assertColumnsAreEqual(expect_last_skip_null, windowAggResults.getColumn(4));
+            assertColumnsAreEqual(expect_1th_skip_null, windowAggResults.getColumn(5));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingOnMultipleDifferentColumns() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column( 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .column( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column( 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           ColumnVector expectedSortedAggCol = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectedSortedAggCol, sortedAggColumn);
+
+        try (Scalar one = Scalar.fromInt(1);
+             Scalar two = Scalar.fromInt(2);
+             Scalar three = Scalar.fromInt(3);
+             // Window (1,1), with a minimum of 1 reading.
+             WindowOptions window_1 = WindowOptions.builder()
+                 .minPeriods(1)
+                 .window(two, one)
+                 .build();
+
+             // Window (2,2), with a minimum of 2 readings.
+             WindowOptions window_2 = WindowOptions.builder()
+                 .minPeriods(2)
+                 .window(three, two)
+                 .build();
+
+             // Window (1,1), with a minimum of 3 readings.
+             WindowOptions window_3 = WindowOptions.builder()
+                 .minPeriods(3)
+                 .window(two, one)
+                 .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+              .aggregateWindows(
+                  RollingAggregation.sum().onColumn(3).overWindow(window_1),
+                  RollingAggregation.max().onColumn(3).overWindow(window_1),
+                  RollingAggregation.sum().onColumn(3).overWindow(window_2),
+                  RollingAggregation.min().onColumn(2).overWindow(window_3)
+              );
+               ColumnVector expect_0 = ColumnVector.fromBoxedLongs(12L, 13L, 15L, 10L, 16L, 24L, 19L, 10L, 8L, 14L, 12L, 12L);
+               ColumnVector expect_1 = ColumnVector.fromBoxedInts(7, 7, 9, 9, 9, 9, 9, 8, 8, 8, 6, 6);
+               ColumnVector expect_2 = ColumnVector.fromBoxedLongs(13L, 22L, 22L, 15L, 24L, 26L, 26L, 19L, 14L, 20L, 20L, 12L);
+               ColumnVector expect_3 = ColumnVector.fromBoxedInts(null, 1, 1, null, null, 3, 3, null, null, 5, 5, null)) {
+            assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+            assertColumnsAreEqual(expect_2, windowAggResults.getColumn(2));
+            assertColumnsAreEqual(expect_3, windowAggResults.getColumn(3));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowingWithoutGroupByColumns() {
+    try (Table unsorted = new Table.TestBuilder().column( 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+        .column( 7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+        .build();
+         ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0))) {
+        ColumnVector sortedAggColumn = sorted.getColumn(1);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+        try (Scalar one = Scalar.fromInt(1);
+             Scalar two = Scalar.fromInt(2);
+             WindowOptions window = WindowOptions.builder()
+                 .minPeriods(1)
+                 .window(two, one)
+                 .build()) {
+
+          try (Table windowAggResults = sorted.groupBy().aggregateWindows(
+              RollingAggregation.sum().onColumn(1).overWindow(window));
+               ColumnVector expectAggResult = ColumnVector.fromBoxedLongs(12L, 13L, 15L, 17L, 25L, 24L, 19L, 18L, 10L, 14L, 12L, 12L)
+          ) {
+            assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testWindowWithUnboundedPrecedingUnboundedFollowing() {
+    try (Table unsorted = new Table.TestBuilder()
+            .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+            .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+            .column(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6) // OBY Key
+            .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6) // Agg Column
+            .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(3);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+        try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .unboundedFollowing()
+                .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindows(RollingAggregation.sum().onColumn(3).overWindow(window));
+               ColumnVector expectAggResult = ColumnVector.fromBoxedLongs(22L, 22L, 22L, 22L, 26L, 26L, 26L, 26L, 20L, 20L, 20L, 20L)) {
+            assertColumnsAreEqual(expectAggResult, windowAggResults.getColumn(0));
+          }
+        }
+      }
+    }
+  }
+
+  private Scalar getScalar(DType type, long value) {
+    if (type.equals(DType.INT32)) {
+      return Scalar.fromInt((int) value);
+    } else if (type.equals(DType.INT64)) {
+      return Scalar.fromLong(value);
+    } else if (type.equals(DType.INT16)) {
+      return Scalar.fromShort((short) value);
+    } else if (type.equals(DType.INT8)) {
+      return Scalar.fromByte((byte) value);
+    } else if (type.equals(DType.UINT8)) {
+      return Scalar.fromUnsignedByte((byte) value);
+    } else if (type.equals(DType.UINT16)) {
+      return Scalar.fromUnsignedShort((short) value);
+    } else if (type.equals(DType.UINT32)) {
+      return Scalar.fromUnsignedInt((int) value);
+    } else if (type.equals(DType.UINT64)) {
+      return Scalar.fromUnsignedLong(value);
+    } else if (type.equals(DType.TIMESTAMP_DAYS)) {
+      return Scalar.durationFromLong(DType.DURATION_DAYS, value);
+    } else if (type.equals(DType.TIMESTAMP_SECONDS)) {
+      return Scalar.durationFromLong(DType.DURATION_SECONDS, value);
+    } else if (type.equals(DType.TIMESTAMP_MILLISECONDS)) {
+      return Scalar.durationFromLong(DType.DURATION_MILLISECONDS, value);
+    } else if (type.equals(DType.TIMESTAMP_MICROSECONDS)) {
+      return Scalar.durationFromLong(DType.DURATION_MICROSECONDS, value);
+    } else if (type.equals(DType.TIMESTAMP_NANOSECONDS)) {
+      return Scalar.durationFromLong(DType.DURATION_NANOSECONDS, value);
+    } else {
+      return Scalar.fromNull(type);
+    }
+  }
+
+  @Test
+  void testRangeWindowingCount() {
+    try (
+        Table unsorted = new Table.TestBuilder()
+            .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+            .column(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+            .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+            .column(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L) // orderBy Key
+            .column((short) 1, (short)1, (short)2, (short)3, (short)3, (short)3, (short)4, (short)4, (short)5, (short)5, (short)6, (short)6, (short)7) // orderBy Key
+            .column(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // orderBy Key
+            .column((byte) 1, (byte)1, (byte)2, (byte)3, (byte)3, (byte)3, (byte)4, (byte)4, (byte)5, (byte)5, (byte)6, (byte)6, (byte)7) // orderBy Key
+            .timestampDayColumn(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // timestamp orderBy Key
+            .timestampSecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+            .timestampMicrosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+            .timestampMillisecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+            .timestampNanosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+            .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar preceding = getScalar(type, 1L);
+               Scalar following = getScalar(type, 1L)) {
+            try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .window(preceding, following)
+                .orderByColumnIndex(orderIndex)
+                .build()) {
+              try (Table windowAggResults = sorted.groupBy(0, 1).aggregateWindowsOverRanges(
+                  RollingAggregation.count().onColumn(2).overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(3, 3, 4, 2, 4, 4, 4, 4, 4, 4, 5, 5, 3)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingLead() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L) // orderBy Key
+        .column((short) 1, (short)1, (short)2, (short)3, (short)3, (short)3, (short)4, (short)4, (short)5, (short)5, (short)6, (short)6, (short)7) // orderBy Key
+        .column(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // orderBy Key
+        .column((byte) 1, (byte)1, (byte)2, (byte)3, (byte)3, (byte)3, (byte)4, (byte)4, (byte)5, (byte)5, (byte)6, (byte)6, (byte)7) // orderBy Key
+        .timestampDayColumn(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // Timestamp orderBy Key
+        .timestampSecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMicrosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMillisecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampNanosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar preceding = getScalar(type, 1L);
+               Scalar following = getScalar(type, 1L)) {
+            try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .window(preceding, following)
+                .orderByColumnIndex(orderIndex)
+                .build()) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(RollingAggregation.lead(1)
+                      .onColumn(2)
+                      .overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(5, 1, 9, null, 9, 8, 2, null, 0, 6, 6, 8, null)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingMax() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L) // orderBy Key
+        .column((short) 1, (short)1, (short)2, (short)3, (short)3, (short)3, (short)4, (short)4, (short)5, (short)5, (short)6, (short)6, (short)7) // orderBy Key
+        .column(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // orderBy Key
+        .column((byte) 1, (byte)1, (byte)2, (byte)3, (byte)3, (byte)3, (byte)4, (byte)4, (byte)5, (byte)5, (byte)6, (byte)6, (byte)7) // orderBy Key
+        .timestampDayColumn(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // Timestamp orderBy Key
+        .timestampSecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMicrosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMillisecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampNanosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar preceding = getScalar(type, 1L);
+               Scalar following = getScalar(type, 1L)) {
+            try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .window(preceding, following)
+                .orderByColumnIndex(orderIndex)
+                .build()) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(RollingAggregation.max().onColumn(2).overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(7, 7, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+
+            try (Scalar one = Scalar.fromInt(1);
+                 Scalar two = Scalar.fromInt(2);
+                 WindowOptions window = WindowOptions.builder()
+                     .minPeriods(1)
+                     .window(two, one)
+                     .build()) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindows(RollingAggregation.max().onColumn(2).overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(7, 7, 9, 9, 9, 9, 9, 8, 8, 8, 6, 8, 8)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingRowNumber() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L) // orderBy Key
+        .column((short) 1, (short)1, (short)2, (short)3, (short)3, (short)3, (short)4, (short)4, (short)5, (short)5, (short)6, (short)6, (short)7) // orderBy Key
+        .column(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // orderBy Key
+        .column((byte) 1, (byte)1, (byte)2, (byte)3, (byte)3, (byte)3, (byte)4, (byte)4, (byte)5, (byte)5, (byte)6, (byte)6, (byte)7) // orderBy Key
+        .timestampDayColumn(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // Timestamp orderBy Key
+        .timestampSecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMicrosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMillisecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampNanosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar preceding = getScalar(type, 2L);
+               Scalar following = getScalar(type, 0L)) {
+            try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .window(preceding, following)
+                .orderByColumnIndex(orderIndex)
+                .build()) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(RollingAggregation.rowNumber().onColumn(2).overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 5)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingCountDescendingTimestamps() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1,  1, 1, 1, 1,  1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0,  1, 1, 1, 1,  2, 2, 2, 2, 2) // GBY Key
+        .column(7, 5, 1, 9,  7, 9, 8, 2,  8, 0, 6, 6, 8) // Agg Column
+        .column((short)7, (short)6, (short)6, (short)5, (short)5, (short)4, (short)4, (short)3, (short)3, (short)3, (short)2, (short)1, (short)1)
+        .column(7L, 6L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 2L, 1L, 1L)
+        .column(7, 6, 6, 5,  5, 4, 4, 3,  3, 3, 2, 1, 1)
+        .column((byte)7, (byte)6, (byte)6, (byte)5,  (byte)5, (byte)4, (byte)4, (byte)3,  (byte)3, (byte)3, (byte)2, (byte)1, (byte)1)
+        .timestampDayColumn(7, 6, 6, 5,  5, 4, 4, 3,  3, 3, 2, 1, 1) // Timestamp Key
+        .timestampSecondsColumn(7L, 6L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 2L, 1L, 1L)
+        .timestampMicrosecondsColumn(7L, 6L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 2L, 1L, 1L)
+        .timestampMillisecondsColumn(7L, 6L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 2L, 1L, 1L)
+        .timestampNanosecondsColumn(7L, 6L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 2L, 1L, 1L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.desc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar preceding_0 = getScalar(type, 2L);
+               Scalar following_0 = getScalar(type, 1L);
+               Scalar preceding_1 = getScalar(type, 3L);
+               Scalar following_1 = getScalar(type, 0L)) {
+
+            try (WindowOptions window_0 = WindowOptions.builder()
+                  .minPeriods(1)
+                  .window(preceding_0, following_0)
+                  .orderByColumnIndex(orderIndex)
+                  .orderByDescending()
+                  .build();
+
+                 WindowOptions window_1 = WindowOptions.builder()
+                  .minPeriods(1)
+                  .window(preceding_1, following_1)
+                  .orderByColumnIndex(orderIndex)
+                  .orderByDescending()
+                  .build()) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(
+                      RollingAggregation.count().onColumn(2).overWindow(window_0),
+                      RollingAggregation.sum().onColumn(2).overWindow(window_1));
+                   ColumnVector expect_0 = ColumnVector.fromBoxedInts(3, 4, 4, 4, 3, 4, 4, 4, 3, 3, 5, 5, 5);
+                   ColumnVector expect_1 = ColumnVector.fromBoxedLongs(7L, 13L, 13L, 22L, 7L, 24L, 24L, 26L, 8L, 8L, 14L, 28L, 28L)) {
+                assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+                assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingWithoutGroupByColumns() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(             7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L) // orderBy Key
+        .column((short) 1, (short)1, (short)2, (short)3, (short)3, (short)3, (short)4, (short)4, (short)5, (short)5, (short)6, (short)6, (short)7) // orderBy Key
+        .column(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // orderBy Key
+        .column((byte) 1, (byte)1, (byte)2, (byte)3, (byte)3, (byte)3, (byte)4, (byte)4, (byte)5, (byte)5, (byte)6, (byte)6, (byte)7) // orderBy Key
+        .timestampDayColumn(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // Timestamp orderBy Key
+        .timestampSecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMicrosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMillisecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampNanosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(0);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar preceding = getScalar(type, 1L);
+               Scalar following = getScalar(type, 1L)) {
+            try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .window(preceding, following)
+                .orderByColumnIndex(orderIndex)
+                .build();) {
+
+              try (Table windowAggResults = sorted.groupBy()
+                  .aggregateWindowsOverRanges(RollingAggregation.count().onColumn(1).overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(3, 3, 6, 6, 6, 6, 7, 7, 6, 6, 5, 5, 3)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingOrderByUnsupportedDataTypeExceptions() {
+    try (Table table = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(true, false, true, false, true, false, true, false, false, false, false, false, false) // orderBy Key
+        .build()) {
+
+      try (Scalar one = Scalar.fromInt(1);
+           WindowOptions rangeBasedWindow = WindowOptions.builder()
+               .minPeriods(1)
+               .window(one, one)
+               .orderByColumnIndex(3)
+               .build()) {
+
+        assertThrows(IllegalArgumentException.class,
+            () -> table
+                .groupBy(0, 1)
+                .aggregateWindowsOverRanges(RollingAggregation.max().onColumn(2).overWindow(rangeBasedWindow)));
+      }
+    }
+  }
+
+  @Test
+  void testInvalidWindowTypeExceptions() {
+    try (Scalar one = Scalar.fromInt(1);
+         Table table = new Table.TestBuilder()
+             .column(             1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+             .column(             0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+             .timestampDayColumn( 1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // Timestamp Key
+             .column(             7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+             .build()) {
+
+
+      try (WindowOptions rowBasedWindow = WindowOptions.builder()
+          .minPeriods(1)
+          .window(one, one)
+          .build()) {
+        assertThrows(IllegalArgumentException.class, () -> table.groupBy(0, 1).aggregateWindowsOverRanges(RollingAggregation.max().onColumn(3).overWindow(rowBasedWindow)));
+      }
+
+      try (WindowOptions rangeBasedWindow = WindowOptions.builder()
+          .minPeriods(1)
+          .window(one, one)
+          .orderByColumnIndex(2)
+          .build()) {
+        assertThrows(IllegalArgumentException.class, () -> table.groupBy(0, 1).aggregateWindows(RollingAggregation.max().onColumn(3).overWindow(rangeBasedWindow)));
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingCountUnboundedPreceding() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2) // GBY Key
+        .column(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L) // orderBy Key
+        .column((short) 1, (short)1, (short)2, (short)3, (short)3, (short)3, (short)4, (short)4, (short)5, (short)5, (short)6, (short)6, (short)7) // orderBy Key
+        .column(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // orderBy Key
+        .column((byte) 1, (byte)1, (byte)2, (byte)3, (byte)3, (byte)3, (byte)4, (byte)4, (byte)5, (byte)5, (byte)6, (byte)6, (byte)7) // orderBy Key
+        .timestampDayColumn(1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7) // Timestamp orderBy Key
+        .timestampSecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMicrosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampMillisecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .timestampNanosecondsColumn(1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar following = getScalar(type, 1L)) {
+            try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following)
+                .orderByColumnIndex(orderIndex)
+                .build();) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(RollingAggregation.count().onColumn(2).overWindow(window));
+                   ColumnVector expect = ColumnVector.fromBoxedInts(3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5)) {
+                assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingWithStringOrderByColumn() {
+    final String X = null;
+    final int orderIndex = 3; // Index of order-by column.
+    try (Table unsorted = new Table.TestBuilder()
+            .column(1, 1, 1, 1, 1, 1,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+            .column(0, 0, 0, 0, 0, 0,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+            .column(7, 5, 1, 9, 7, 9,  8, 2, 8, 0, 6, 6, 8) // Agg Column
+            .column("0", "1", "2", "3", "4", "5", X, X, "1", "2", "4", "5", "7") // String orderBy Key
+            .build()) {
+      try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(3, true));
+           ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+        ColumnVector sortedAggColumn = sorted.getColumn(2);
+        assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+        try (WindowOptions unboundedPrecedingAndFollowing = WindowOptions.builder()
+                     .minPeriods(1)
+                     .unboundedPreceding()
+                     .unboundedFollowing()
+                     .orderByColumnIndex(orderIndex)
+                     .build();
+             WindowOptions unboundedPrecedingAndCurrentRow = WindowOptions.builder()
+                     .minPeriods(1)
+                     .unboundedPreceding()
+                     .currentRowFollowing()
+                     .orderByColumnIndex(orderIndex)
+                     .build();
+             WindowOptions currentRowAndUnboundedFollowing = WindowOptions.builder()
+                     .minPeriods(1)
+                     .currentRowPreceding()
+                     .unboundedFollowing()
+                     .orderByColumnIndex(orderIndex)
+                     .build()) {
+
+          try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(
+                          RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndFollowing),
+                          RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndCurrentRow),
+                          RollingAggregation.count().onColumn(2).overWindow(currentRowAndUnboundedFollowing));
+               ColumnVector expect_0 = ColumnVector.fromBoxedInts(6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7);
+               ColumnVector expect_1 = ColumnVector.fromBoxedInts(1, 2, 3, 4, 5, 6, 2, 2, 3, 4, 5, 6, 7);
+               ColumnVector expect_2 = ColumnVector.fromBoxedInts(6, 5, 4, 3, 2, 1, 7, 7, 5, 4, 3, 2, 1)) {
+
+            assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+            assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+            assertColumnsAreEqual(expect_2, windowAggResults.getColumn(2));
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingCountUnboundedASCWithNullsFirst() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 0, 0,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(7, 5, 1, 9, 7, 9,  8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column( null, null, null, 2, 3, 5, null, null, 1, 2, 4, 5, 7) // Timestamp Key
+        .column( null, null, null, 2L, 3L, 5L, null, null, 1L, 2L, 4L, 5L, 7L) // orderBy Key
+        .column( null, null, null, (short)2, (short)3, (short)5, null, null, (short)1, (short)2, (short)4, (short)5, (short)7) // orderBy Key
+        .column( null, null, null, (byte)2, (byte)3, (byte)5, null, null, (byte)1, (byte)2, (byte)4, (byte)5, (byte)7) // orderBy Key
+        .timestampDayColumn( null, null, null, 2, 3, 5, null, null, 1, 2, 4, 5, 7) // Timestamp orderBy Key
+        .timestampSecondsColumn( null, null, null, 2L, 3L, 5L, null, null, 1L, 2L, 4L, 5L, 7L)
+        .timestampMicrosecondsColumn( null, null, null, 2L, 3L, 5L, null, null, 1L, 2L, 4L, 5L, 7L)
+        .timestampMillisecondsColumn( null, null, null, 2L, 3L, 5L, null, null, 1L, 2L, 4L, 5L, 7L)
+        .timestampNanosecondsColumn( null, null, null, 2L, 3L, 5L, null, null, 1L, 2L, 4L, 5L, 7L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex, true));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar following1 = getScalar(type, 1L);
+               Scalar preceding1 = getScalar(type, 1L);
+               Scalar following0 = getScalar(type, 0L);
+               Scalar preceding0 = getScalar(type, 0L);) {
+            try (WindowOptions unboundedPrecedingOneFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following1)
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions onePrecedingUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding1)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions unboundedPrecedingAndFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions unboundedPrecedingAndCurrentRow = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following0)
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions currentRowAndUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding0)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .build();) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingOneFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(onePrecedingUnboundedFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndCurrentRow),
+                      RollingAggregation.count().onColumn(2).overWindow(currentRowAndUnboundedFollowing));
+                   ColumnVector expect_0 = ColumnVector.fromBoxedInts(3, 3, 3, 5, 5, 6, 2, 2, 4, 4, 6, 6, 7);
+                   ColumnVector expect_1 = ColumnVector.fromBoxedInts(6, 6, 6, 3, 3, 1, 7, 7, 5, 5, 3, 3, 1);
+                   ColumnVector expect_2 = ColumnVector.fromBoxedInts(6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7);
+                   ColumnVector expect_3 = ColumnVector.fromBoxedInts(3, 3, 3, 4, 5, 6, 2, 2, 3, 4, 5, 6, 7);
+                   ColumnVector expect_4 = ColumnVector.fromBoxedInts(6, 6, 6, 3, 2, 1, 7, 7, 5, 4, 3, 2, 1)) {
+
+                assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+                assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+                assertColumnsAreEqual(expect_2, windowAggResults.getColumn(2));
+                assertColumnsAreEqual(expect_3, windowAggResults.getColumn(3));
+                assertColumnsAreEqual(expect_4, windowAggResults.getColumn(4));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingCountUnboundedDESCWithNullsFirst() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 0, 0,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(7, 5, 1, 9, 7, 9,  8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(null, null, null, 5, 3, 2,  null, null, 7, 5, 4, 2, 1) // Timestamp Key
+        .column(null, null, null, 5L, 3L, 2L,  null, null, 7L, 5L, 4L, 2L, 1L) // orderby Key
+        .column(null, null, null, (short)5, (short)3, (short)2,  null, null, (short)7, (short)5, (short)4, (short)2, (short)1) // orderby Key
+        .column(null, null, null, (byte)5, (byte)3, (byte)2,  null, null, (byte)7, (byte)5, (byte)4, (byte)2, (byte)1) // orderby Key
+        .timestampDayColumn(null, null, null, 5, 3, 2, null, null, 7, 5, 4, 2, 1) // Timestamp orderby Key
+        .timestampSecondsColumn( null, null, null, 5L, 3L, 2L,  null, null, 7L, 5L, 4L, 2L, 1L)
+        .timestampMicrosecondsColumn( null, null, null, 5L, 3L, 2L,  null, null, 7L, 5L, 4L, 2L, 1L)
+        .timestampMillisecondsColumn( null, null, null, 5L, 3L, 2L,  null, null, 7L, 5L, 4L, 2L, 1L)
+        .timestampNanosecondsColumn( null, null, null, 5L, 3L, 2L,  null, null, 7L, 5L, 4L, 2L, 1L)
+        .build()) {
+
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.desc(orderIndex, false));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar following1 = getScalar(type, 1L);
+               Scalar preceding1 = getScalar(type, 1L);
+               Scalar following0 = getScalar(type, 0L);
+               Scalar preceding0 = getScalar(type, 0L);) {
+
+            try (WindowOptions unboundedPrecedingOneFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following1)
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions onePrecedingUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding1)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions unboundedPrecedingAndFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions unboundedPrecedingAndCurrentRow = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following0)
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions currentRowAndUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding0)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingOneFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(onePrecedingUnboundedFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndCurrentRow),
+                      RollingAggregation.count().onColumn(2).overWindow(currentRowAndUnboundedFollowing));
+                   ColumnVector expect_0 = ColumnVector.fromBoxedInts(3, 3, 3, 4, 6, 6, 2, 2, 3, 5, 5, 7, 7);
+                   ColumnVector expect_1 = ColumnVector.fromBoxedInts(6, 6, 6, 3, 2, 2, 7, 7, 5, 4, 4, 2, 2);
+                   ColumnVector expect_2 = ColumnVector.fromBoxedInts(6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7);
+                   ColumnVector expect_3 = ColumnVector.fromBoxedInts(3, 3, 3, 4, 5, 6, 2, 2, 3, 4, 5, 6, 7);
+                   ColumnVector expect_4 = ColumnVector.fromBoxedInts(6, 6, 6, 3, 2, 1, 7, 7, 5, 4, 3, 2, 1)) {
+
+                assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+                assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+                assertColumnsAreEqual(expect_2, windowAggResults.getColumn(2));
+                assertColumnsAreEqual(expect_3, windowAggResults.getColumn(3));
+                assertColumnsAreEqual(expect_4, windowAggResults.getColumn(4));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingCountUnboundedASCWithNullsLast() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 0, 0,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(7, 5, 1, 9, 7, 9,  8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column(2, 3, 5, null, null, null,  1, 2, 4, 5, 7, null, null) // Timestamp Key
+        .column(2L, 3L, 5L, null, null, null, 1L, 2L, 4L, 5L, 7L, null, null) // order by Key
+        .column((short)2, (short)3, (short)5, null, null, null, (short)1, (short)2, (short)4, (short)5, (short)7, null, null) // order by Key
+        .column((byte)2, (byte)3, (byte)5, null, null, null, (byte)1, (byte)2, (byte)4, (byte)5, (byte)7, null, null) // order by Key
+        .timestampDayColumn( 2, 3, 5, null, null, null,  1, 2, 4, 5, 7, null, null) // Timestamp order by Key
+        .timestampSecondsColumn( 2L, 3L, 5L, null, null, null, 1L, 2L, 4L, 5L, 7L, null, null)
+        .timestampMicrosecondsColumn( 2L, 3L, 5L, null, null, null, 1L, 2L, 4L, 5L, 7L, null, null)
+        .timestampMillisecondsColumn( 2L, 3L, 5L, null, null, null, 1L, 2L, 4L, 5L, 7L, null, null)
+        .timestampNanosecondsColumn( 2L, 3L, 5L, null, null, null, 1L, 2L, 4L, 5L, 7L, null, null)
+        .build()) {
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(orderIndex, false));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar following1 = getScalar(type, 1L);
+               Scalar preceding1 = getScalar(type, 1L);
+               Scalar following0 = getScalar(type, 0L);
+               Scalar preceding0 = getScalar(type, 0L);) {
+            try (WindowOptions unboundedPrecedingOneFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following1)
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions onePrecedingUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding1)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions unboundedPrecedingAndFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions unboundedPrecedingAndCurrentRow = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following0)
+                .orderByColumnIndex(orderIndex)
+                .build();
+
+            WindowOptions currentRowAndUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding0)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .build();) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingOneFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(onePrecedingUnboundedFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndCurrentRow),
+                      RollingAggregation.count().onColumn(2).overWindow(currentRowAndUnboundedFollowing));
+                   ColumnVector expect_0 = ColumnVector.fromBoxedInts(2, 2, 3, 6, 6, 6, 2, 2, 4, 4, 5, 7, 7);
+                   ColumnVector expect_1 = ColumnVector.fromBoxedInts(6, 6, 4, 3, 3, 3, 7, 7, 5, 5, 3, 2, 2);
+                   ColumnVector expect_2 = ColumnVector.fromBoxedInts(6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7);
+                   ColumnVector expect_3 = ColumnVector.fromBoxedInts(1, 2, 3, 6, 6, 6, 1, 2, 3, 4, 5, 7, 7);
+                   ColumnVector expect_4 = ColumnVector.fromBoxedInts(6, 5, 4, 3, 3, 3, 7, 6, 5, 4, 3, 2, 2)) {
+
+                assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+                assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+                assertColumnsAreEqual(expect_2, windowAggResults.getColumn(2));
+                assertColumnsAreEqual(expect_3, windowAggResults.getColumn(3));
+                assertColumnsAreEqual(expect_4, windowAggResults.getColumn(4));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRangeWindowingCountUnboundedDESCWithNullsLast() {
+    Integer X = null;
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(0, 0, 0, 0, 0, 0,  1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(7, 5, 1, 9, 7, 9,  8, 2, 8, 0, 6, 6, 8) // Agg Column
+        .column( 5, 3, 2, null, null, null, 7, 5, 4, 2, 1, null, null) // Timestamp Key
+        .column(5L, 3L, 2L, null, null, null, 7L, 5L, 4L, 2L, 1L, null, null) // Timestamp Key
+        .column((short)5, (short)3, (short)2, null, null, null, (short)7, (short)5, (short)4, (short)2, (short)1, null, null) // Timestamp Key
+        .column((byte)5, (byte)3, (byte)2, null, null, null, (byte)7, (byte)5, (byte)4, (byte)2, (byte)1, null, null) // Timestamp Key
+        .timestampDayColumn( 5, 3, 2, X, X, X,  7, 5, 4, 2, 1, X, X) // Timestamp Key
+        .timestampSecondsColumn( 5L, 3L, 2L, null, null, null, 7L, 5L, 4L, 2L, 1L, null, null)
+        .timestampMicrosecondsColumn( 5L, 3L, 2L, null, null, null, 7L, 5L, 4L, 2L, 1L, null, null)
+        .timestampMillisecondsColumn( 5L, 3L, 2L, null, null, null, 7L, 5L, 4L, 2L, 1L, null, null)
+        .timestampNanosecondsColumn( 5L, 3L, 2L, null, null, null, 7L, 5L, 4L, 2L, 1L, null, null)
+        .build()) {
+      for (int orderIndex = 3; orderIndex < unsorted.getNumberOfColumns(); orderIndex++) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.desc(orderIndex, true));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6, 8)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(2);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          DType type = unsorted.getColumn(orderIndex).getType();
+          try (Scalar following1 = getScalar(type, 1L);
+               Scalar preceding1 = getScalar(type, 1L);
+               Scalar following0 = getScalar(type, 0L);
+               Scalar preceding0 = getScalar(type, 0L);) {
+            try (WindowOptions unboundedPrecedingOneFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following1)
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions onePrecedingUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding1)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions unboundedPrecedingAndFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions unboundedPrecedingAndCurrentRow = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(following0)
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();
+
+            WindowOptions currentRowAndUnboundedFollowing = WindowOptions.builder()
+                .minPeriods(1)
+                .preceding(preceding0)
+                .unboundedFollowing()
+                .orderByColumnIndex(orderIndex)
+                .orderByDescending()
+                .build();) {
+
+              try (Table windowAggResults = sorted.groupBy(0, 1)
+                  .aggregateWindowsOverRanges(
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingOneFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(onePrecedingUnboundedFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndFollowing),
+                      RollingAggregation.count().onColumn(2).overWindow(unboundedPrecedingAndCurrentRow),
+                      RollingAggregation.count().onColumn(2).overWindow(currentRowAndUnboundedFollowing));
+                   ColumnVector expect_0 = ColumnVector.fromBoxedInts(1, 3, 3, 6, 6, 6, 1, 3, 3, 5, 5, 7, 7);
+                   ColumnVector expect_1 = ColumnVector.fromBoxedInts(6, 5, 5, 3, 3, 3, 7, 6, 6, 4, 4, 2, 2);
+                   ColumnVector expect_2 = ColumnVector.fromBoxedInts(6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7);
+                   ColumnVector expect_3 = ColumnVector.fromBoxedInts(1, 2, 3, 6, 6, 6, 1, 2, 3, 4, 5, 7, 7);
+                   ColumnVector expect_4 = ColumnVector.fromBoxedInts(6, 5, 4, 3, 3, 3, 7, 6, 5, 4, 3, 2, 2)) {
+
+                assertColumnsAreEqual(expect_0, windowAggResults.getColumn(0));
+                assertColumnsAreEqual(expect_1, windowAggResults.getColumn(1));
+                assertColumnsAreEqual(expect_2, windowAggResults.getColumn(2));
+                assertColumnsAreEqual(expect_3, windowAggResults.getColumn(3));
+                assertColumnsAreEqual(expect_4, windowAggResults.getColumn(4));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  /**
+   * Helper for constructing BigInteger from int
+   * @param x Integer value
+   * @return BigInteger equivalent of x
+   */
+  private static BigInteger big(int x)
+  {
+    return new BigInteger("" + x);
+  }
+
+  /**
+   * Helper to get scalar for preceding == Decimal(value),
+   * with data width depending upon the order-by
+   * column index:
+   *   orderby_col_idx = 2 -> Decimal32
+   *   orderby_col_idx = 3 -> Decimal64
+   *   orderby_col_idx = 4 -> Decimal128
+   */
+  private static Scalar getDecimalScalarRangeBounds(int scale, int unscaledValue, int orderby_col_idx)
+  {
+    switch(orderby_col_idx)
+    {
+      case 2: return Scalar.fromDecimal(scale, unscaledValue);
+      case 3: return Scalar.fromDecimal(scale, Long.valueOf(unscaledValue));
+      case 4: return Scalar.fromDecimal(scale, big(unscaledValue));
+      default:
+        throw new IllegalStateException("Unexpected order by column index: "
+                                        + orderby_col_idx);
+    }
+  }
+
+  @Test
+  void testRangeWindowsWithDecimalOrderBy() {
+    try (Table unsorted = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+        .decimal32Column(-1, 4000, 3000, 2000, 1000,
+                             4000, 3000, 2000, 1000,
+                             4000, 3000, 2000, 1000) // Decimal OBY Key
+        .decimal64Column(-1, 4000l, 3000l, 2000l, 1000l,
+                             4000l, 3000l, 2000l, 1000l,
+                             4000l, 3000l, 2000l, 1000l) // Decimal OBY Key
+        .decimal128Column(-1, RoundingMode.UNNECESSARY,
+                              big(4000), big(3000), big(2000), big(1000),
+                              big(4000), big(3000), big(2000), big(1000),
+                              big(4000), big(3000), big(2000), big(1000))
+        .column(9, 1, 5, 7, 2, 8, 9, 7, 6, 6, 0, 8) // Agg Column
+        .build()) {
+
+      // Columns 2,3,4 are decimal order-by columns of type DECIMAL32, DECIMAL64,
+      // and DECIMAL128 respectively, with similarly ordered values.
+      // In the following loop, each decimal type is tested as the order-by column,
+      // producing the same results with similar range bounds.
+      for (int decimal_oby_col_idx = 2; decimal_oby_col_idx <= 4; ++decimal_oby_col_idx) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0),
+                                             OrderByArg.asc(1),
+                                             OrderByArg.asc(decimal_oby_col_idx));
+            ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(5);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          // Test Window functionality with range window (200 PRECEDING and 100 FOLLOWING)
+          try (Scalar preceding200 = getDecimalScalarRangeBounds(0, 200, decimal_oby_col_idx);
+               Scalar following100 = getDecimalScalarRangeBounds(2, 1, decimal_oby_col_idx);
+               WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .window(preceding200, following100)
+                .orderByColumnIndex(decimal_oby_col_idx)
+                .build()) {
+
+            try (Table windowAggResults = sorted.groupBy(0, 1)
+                                                .aggregateWindowsOverRanges(RollingAggregation.count()
+                                                                                              .onColumn(5)
+                                                                                              .overWindow(window));
+                ColumnVector expect = ColumnVector.fromBoxedInts(2, 3, 4, 3, 2, 3, 4, 3, 2, 3, 4, 3)) {
+              assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            }
+          }
+
+          // Test Window functionality with range window (UNBOUNDED PRECEDING and CURRENT ROW)
+          try (Scalar current_row = getDecimalScalarRangeBounds(0, 0, decimal_oby_col_idx);
+               WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .following(current_row)
+                .orderByColumnIndex(decimal_oby_col_idx)
+                .build()) {
+
+            try (Table windowAggResults = sorted.groupBy(0, 1)
+                                                .aggregateWindowsOverRanges(RollingAggregation.count()
+                                                                                              .onColumn(5)
+                                                                                              .overWindow(window));
+                ColumnVector expect = ColumnVector.fromBoxedInts(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4)) {
+              assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            }
+          }
+
+          // Test Window functionality with range window (UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING)
+          try (WindowOptions window = WindowOptions.builder()
+                .minPeriods(1)
+                .unboundedPreceding()
+                .unboundedFollowing()
+                .orderByColumnIndex(decimal_oby_col_idx)
+                .build()) {
+
+            try (Table windowAggResults = sorted.groupBy(0, 1)
+                                                .aggregateWindowsOverRanges(RollingAggregation.count()
+                                                                                              .onColumn(5)
+                                                                                              .overWindow(window));
+                ColumnVector expect = ColumnVector.fromBoxedInts(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)) {
+              assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  /**
+   * Helper to get scalar for preceding == Decimal(value),
+   * with data width depending upon the order-by column index:
+   *   orderby_col_idx = 2 -> FLOAT32
+   *   orderby_col_idx = 3 -> FLOAT64
+   */
+  private static Scalar getFloatingPointScalarRangeBounds(float value, int orderby_col_idx)
+  {
+    switch(orderby_col_idx)
+    {
+      case 2: return Scalar.fromFloat(value);
+      case 3: return Scalar.fromDouble(Double.valueOf(value));
+      default:
+        throw new IllegalStateException("Unexpected order by column index: "
+                + orderby_col_idx);
+    }
+  }
+
+  @Test
+  void testRangeWindowsWithFloatOrderBy() {
+    try (Table unsorted = new Table.TestBuilder()
+            .column(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) // GBY Key
+            .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3) // GBY Key
+            .column(400f, 300f, 200f, 100f,
+                    400f, 300f, 200f, 100f,
+                    400f, 300f, 200f, 100f) // Float OBY Key
+            .column(400.0, 300.0, 200.0, 100.0,
+                    400.0, 300.0, 200.0, 100.0,
+                    400.0, 300.0, 200.0, 100.0) // Double OBY Key
+            .column(9, 1, 5, 7, 2, 8, 9, 7, 6, 6, 0, 8) // Agg Column
+            .build()) {
+
+      // Columns 2-3 are order-by columns of type FLOAT32 and FLOAT64 respectively, with similarly ordered values.
+      // In the following loop, each float type is tested as the order-by column,
+      // producing the same results with similar range bounds.
+      for (int float_oby_col_idx = 2; float_oby_col_idx <= 3; ++float_oby_col_idx) {
+        try (Table sorted = unsorted.orderBy(OrderByArg.asc(0),
+                OrderByArg.asc(1),
+                OrderByArg.asc(float_oby_col_idx));
+             ColumnVector expectSortedAggColumn = ColumnVector.fromBoxedInts(7, 5, 1, 9, 7, 9, 8, 2, 8, 0, 6, 6)) {
+          ColumnVector sortedAggColumn = sorted.getColumn(4);
+          assertColumnsAreEqual(expectSortedAggColumn, sortedAggColumn);
+
+          // Test Window functionality with range window (200 PRECEDING and 100 FOLLOWING)
+          try (Scalar preceding200 = getFloatingPointScalarRangeBounds(200, float_oby_col_idx);
+               Scalar following100 = getFloatingPointScalarRangeBounds(100, float_oby_col_idx);
+               WindowOptions window = WindowOptions.builder()
+                       .minPeriods(1)
+                       .window(preceding200, following100)
+                       .orderByColumnIndex(float_oby_col_idx)
+                       .build()) {
+
+            try (Table windowAggResults = sorted.groupBy(0, 1)
+                    .aggregateWindowsOverRanges(RollingAggregation.count()
+                            .onColumn(4)
+                            .overWindow(window));
+                 ColumnVector expect = ColumnVector.fromBoxedInts(2, 3, 4, 3, 2, 3, 4, 3, 2, 3, 4, 3)) {
+              assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            }
+          }
+
+          // Test Window functionality with range window (UNBOUNDED PRECEDING and CURRENT ROW)
+          try (Scalar current_row = getFloatingPointScalarRangeBounds(0, float_oby_col_idx);
+               WindowOptions window = WindowOptions.builder()
+                       .minPeriods(1)
+                       .unboundedPreceding()
+                       .following(current_row)
+                       .orderByColumnIndex(float_oby_col_idx)
+                       .build()) {
+
+            try (Table windowAggResults = sorted.groupBy(0, 1)
+                    .aggregateWindowsOverRanges(RollingAggregation.count()
+                            .onColumn(4)
+                            .overWindow(window));
+                 ColumnVector expect = ColumnVector.fromBoxedInts(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4)) {
+              assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            }
+          }
+
+          // Test Window functionality with range window (UNBOUNDED PRECEDING and UNBOUNDED FOLLOWING)
+          try (WindowOptions window = WindowOptions.builder()
+                  .minPeriods(1)
+                  .unboundedPreceding()
+                  .unboundedFollowing()
+                  .orderByColumnIndex(float_oby_col_idx)
+                  .build()) {
+
+            try (Table windowAggResults = sorted.groupBy(0, 1)
+                    .aggregateWindowsOverRanges(RollingAggregation.count()
+                            .onColumn(4)
+                            .overWindow(window));
+                 ColumnVector expect = ColumnVector.fromBoxedInts(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)) {
+              assertColumnsAreEqual(expect, windowAggResults.getColumn(0));
+            }
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testGroupByCountWithNulls() {
+    try (Table t1 = new Table.TestBuilder().column(null, null,    1,    1,    1,    1)
+                                           .column(   1,    1,    1,    1,    1,    1)
+                                           .column(   1,    1, null, null,    1,    1)
+                                           .column(   1,    1,    1, null,    1,    1)
+                                           .build()) {
+      try (Table tmp = t1.groupBy(0).aggregate(
+          GroupByAggregation.count().onColumn(1),
+          GroupByAggregation.count().onColumn(2),
+          GroupByAggregation.count().onColumn(3));
+           Table t3 = tmp.orderBy(OrderByArg.asc(0, true));
+           HostColumnVector groupCol = t3.getColumn(0).copyToHost();
+           HostColumnVector countCol = t3.getColumn(1).copyToHost();
+           HostColumnVector nullCountCol = t3.getColumn(2).copyToHost();
+           HostColumnVector nullCountCol2 = t3.getColumn(3).copyToHost()) {
+        // verify t3
+        assertEquals(2, t3.getRowCount());
+
+        // compare the grouping columns
+        assertTrue(groupCol.isNull(0));
+        assertEquals(groupCol.getInt(1), 1);
+
+        // compare the agg columns
+        // count(1)
+        assertEquals(countCol.getInt(0), 2);
+        assertEquals(countCol.getInt(1), 4);
+
+        // count(2)
+        assertEquals(nullCountCol.getInt(0), 2);
+        assertEquals(nullCountCol.getInt(1), 2); // counts only the non-nulls
+
+        // count(3)
+        assertEquals(nullCountCol2.getInt(0), 2);
+        assertEquals(nullCountCol2.getInt(1), 3); // counts only the non-nulls
+      }
+    }
+  }
+
+  @Test
+  void testGroupByCountWithNullsIncluded() {
+    try (Table t1 = new Table.TestBuilder()
+            .column(null, null,    1,    1,    1,    1)
+            .column(   1,    1,    1,    1,    1,    1)
+            .column(   1,    1, null, null,    1,    1)
+            .column(   1,    1,    1, null,    1,    1)
+            .build()) {
+      try (Table tmp = t1.groupBy(0).aggregate(
+          GroupByAggregation.count(NullPolicy.INCLUDE).onColumn(1),
+          GroupByAggregation.count(NullPolicy.INCLUDE).onColumn(2),
+          GroupByAggregation.count(NullPolicy.INCLUDE).onColumn(3),
+          GroupByAggregation.count().onColumn(3));
+           Table t3 = tmp.orderBy(OrderByArg.asc(0, true));
+           HostColumnVector groupCol = t3.getColumn(0).copyToHost();
+           HostColumnVector countCol = t3.getColumn(1).copyToHost();
+           HostColumnVector nullCountCol = t3.getColumn(2).copyToHost();
+           HostColumnVector nullCountCol2 = t3.getColumn(3).copyToHost();
+           HostColumnVector nullCountCol3 = t3.getColumn(4).copyToHost()) {
+        // verify t3
+        assertEquals(2, t3.getRowCount());
+
+        // compare the grouping columns
+        assertTrue(groupCol.isNull(0));
+        assertEquals(groupCol.getInt(1), 1);
+
+        // compare the agg columns
+        // count(1, true)
+        assertEquals(countCol.getInt(0), 2);
+        assertEquals(countCol.getInt(1), 4);
+
+        // count(2, true)
+        assertEquals(nullCountCol.getInt(0), 2);
+        assertEquals(nullCountCol.getInt(1), 4); // counts including nulls
+
+        // count(3, true)
+        assertEquals(nullCountCol2.getInt(0), 2);
+        assertEquals(nullCountCol2.getInt(1), 4); // counts including nulls
+
+        // count(3)
+        assertEquals(nullCountCol3.getInt(0), 2);
+        assertEquals(nullCountCol3.getInt(1), 3); // counts only the non-nulls
+      }
+    }
+  }
+
+  @Test
+  void testGroupByCountWithCollapsingNulls() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(null, null,    1,    1,    1,    1)
+        .column(   1,    1,    1,    1,    1,    1)
+        .column(   1,    1, null, null,    1,    1)
+        .column(   1,    1,    1, null,    1,    1)
+        .build()) {
+
+      GroupByOptions options = GroupByOptions.builder()
+          .withIgnoreNullKeys(true)
+          .build();
+
+      try (Table tmp = t1.groupBy(options, 0).aggregate(
+          GroupByAggregation.count().onColumn(1),
+          GroupByAggregation.count().onColumn(2),
+          GroupByAggregation.count().onColumn(3));
+           Table t3 = tmp.orderBy(OrderByArg.asc(0, true));
+           HostColumnVector groupCol = t3.getColumn(0).copyToHost();
+           HostColumnVector countCol = t3.getColumn(1).copyToHost();
+           HostColumnVector nullCountCol = t3.getColumn(2).copyToHost();
+           HostColumnVector nullCountCol2 = t3.getColumn(3).copyToHost()) {
+        // (null, 1) => became (1) because we are ignoring nulls
+        assertEquals(1, t3.getRowCount());
+
+        // compare the grouping columns
+        assertEquals(groupCol.getInt(0), 1);
+
+        // compare the agg columns
+        // count(1)
+        assertEquals(countCol.getInt(0), 4);
+
+        // count(2)
+        assertEquals(nullCountCol.getInt(0), 2); // counts only the non-nulls
+
+        // count(3)
+        assertEquals(nullCountCol2.getInt(0), 3); // counts only the non-nulls
+      }
+    }
+  }
+
+  @Test
+  void testGroupByMax() {
+    try (Table t1 = new Table.TestBuilder().column(   1,    1,    1,    1,    1,    1)
+                                           .column(   1,    3,    3,    5,    5,    0)
+                                           .column(12.0, 14.0, 13.0, 17.0, 17.0, 17.0)
+                                           .build()) {
+      try (Table t3 = t1.groupBy(0, 1).aggregate(GroupByAggregation.max().onColumn(2));
+           HostColumnVector aggOut1 = t3.getColumn(2).copyToHost()) {
+        // verify t3
+        assertEquals(4, t3.getRowCount());
+        Map<Double, Integer> expectedAggregateResult = new HashMap() {
+          {
+            // value, count
+            put(12.0, 1);
+            put(14.0, 1);
+            put(17.0, 2);
+          }
+        };
+        for (int i = 0; i < 4; ++i) {
+          Double key = aggOut1.getDouble(i);
+          assertTrue(expectedAggregateResult.containsKey(key));
+          Integer count = expectedAggregateResult.get(key);
+          if (count == 1) {
+            expectedAggregateResult.remove(key);
+          } else {
+            expectedAggregateResult.put(key, count - 1);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testGroupByArgMax() {
+    // ArgMax is a sort based aggregation.
+    try (Table t1 = new Table.TestBuilder()
+            .column(   1,    1,    1,    1,    1,    1)
+            .column(   0,    1,    2,    2,    3,    3)
+            .column(17.0, 14.0, 14.0, 17.0, 17.1, 17.0)
+            .build()) {
+      try (Table t3 = t1.groupBy(0, 1)
+              .aggregate(GroupByAggregation.argMax().onColumn(2));
+           Table sorted = t3
+              .orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table expected = new Table.TestBuilder()
+                   .column(1, 1, 1, 1)
+                   .column(0, 1, 2, 3)
+                   .column(0, 1, 3, 4)
+                   .build()) {
+        assertTablesAreEqual(expected, sorted);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByArgMin() {
+    // ArgMin is a sort based aggregation
+    try (Table t1 = new Table.TestBuilder()
+            .column(   1,    1,    1,    1,    1,    1)
+            .column(   0,    1,    2,    2,    3,    3)
+            .column(17.0, 14.0, 14.0, 17.0, 17.1, 17.0)
+            .build()) {
+      try (Table t3 = t1.groupBy(0, 1)
+              .aggregate(GroupByAggregation.argMin().onColumn(2));
+           Table sorted = t3
+                   .orderBy(OrderByArg.asc(0), OrderByArg.asc(1), OrderByArg.asc(2));
+           Table expected = new Table.TestBuilder()
+                   .column(1, 1, 1, 1)
+                   .column(0, 1, 2, 3)
+                   .column(0, 1, 2, 5)
+                   .build()) {
+        assertTablesAreEqual(expected, sorted);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByMinBool() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(true, null, false, true, null, null)
+        .column(   1,    1,     2,    2,    3,    3).build();
+         Table other = t1.groupBy(1).aggregate(GroupByAggregation.min().onColumn(0));
+         Table ordered = other.orderBy(OrderByArg.asc(0));
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3)
+             .column (true, false, null)
+             .build()) {
+      assertTablesAreEqual(expected, ordered);
+    }
+  }
+
+  @Test
+  void testGroupByMaxBool() {
+    try (Table t1 = new Table.TestBuilder()
+        .column(false, null, false, true, null, null)
+        .column(   1,    1,     2,    2,    3,    3).build();
+         Table other = t1.groupBy(1).aggregate(GroupByAggregation.max().onColumn(0));
+         Table ordered = other.orderBy(OrderByArg.asc(0));
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3)
+             .column (false, true, null)
+             .build()) {
+      assertTablesAreEqual(expected, ordered);
+    }
+  }
+
+  @Test
+  void testGroupByDuplicateAggregates() {
+    try (Table t1 = new Table.TestBuilder().column(   1,    1,    1,    1,    1,    1)
+                                           .column(   1,    3,    3,    5,    5,    0)
+                                           .column(12.0, 14.0, 13.0, 15.0, 17.0, 18.0)
+                                           .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 1, 1, 1)
+             .column(1, 3, 5, 0)
+             .column(12.0, 14.0, 17.0, 18.0)
+             .column(12.0, 13.0, 15.0, 18.0)
+             .column(12.0, 13.0, 15.0, 18.0)
+             .column(12.0, 14.0, 17.0, 18.0)
+             .column(12.0, 13.0, 15.0, 18.0)
+             .column(   1,    2,    2,    1).build()) {
+      try (Table t3 = t1.groupBy(0, 1)
+          .aggregate(
+              GroupByAggregation.max().onColumn(2),
+              GroupByAggregation.min().onColumn(2),
+              GroupByAggregation.min().onColumn(2),
+              GroupByAggregation.max().onColumn(2),
+              GroupByAggregation.min().onColumn(2),
+              GroupByAggregation.count().onColumn(1));
+          Table t4 = t3.orderBy(OrderByArg.asc(2))) {
+        // verify t4
+        assertEquals(4, t4.getRowCount());
+        assertTablesAreEqual(t4, expected);
+
+        assertEquals(t3.getColumn(0).getRefCount(), 1);
+        assertEquals(t3.getColumn(1).getRefCount(), 1);
+        assertEquals(t3.getColumn(2).getRefCount(), 2);
+        assertEquals(t3.getColumn(3).getRefCount(), 3);
+        assertEquals(t3.getColumn(4).getRefCount(), 3);
+        assertEquals(t3.getColumn(5).getRefCount(), 2);
+        assertEquals(t3.getColumn(6).getRefCount(), 3);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByMin() {
+    try (Table t1 = new Table.TestBuilder().column(   1,    1,    1,    1,    1,    1)
+                                           .column(   1,    3,    3,    5,    5,    0)
+                                           .column(  12,   14,   13,   17,   17,   17)
+                                           .build()) {
+      try (Table t3 = t1.groupBy(0, 1).aggregate(GroupByAggregation.min().onColumn(2));
+           HostColumnVector aggOut0 = t3.getColumn(2).copyToHost()) {
+        // verify t3
+        assertEquals(4, t3.getRowCount());
+        Map<Integer, Integer> expectedAggregateResult = new HashMap() {
+          {
+            // value, count
+            put(12, 1);
+            put(13, 1);
+            put(17, 2);
+          }
+        };
+        // check to see the aggregate column type depends on the source column
+        // in this case the source column is Integer, therefore the result should be Integer type
+        assertEquals(DType.INT32, aggOut0.getType());
+        for (int i = 0; i < 4; ++i) {
+          int key = aggOut0.getInt(i);
+          assertTrue(expectedAggregateResult.containsKey(key));
+          Integer count = expectedAggregateResult.get(key);
+          if (count == 1) {
+            expectedAggregateResult.remove(key);
+          } else {
+            expectedAggregateResult.put(key, count - 1);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testGroupBySum() {
+    try (Table t1 = new Table.TestBuilder().column(   1,    1,    1,    1,    1,    1)
+                                           .column(   1,    3,    3,    5,    5,    0)
+                                           .column(12.0, 14.0, 13.0, 17.0, 17.0, 17.0)
+                                           .build()) {
+      try (Table t3 = t1.groupBy(0, 1).aggregate(GroupByAggregation.sum().onColumn(2));
+           HostColumnVector aggOut1 = t3.getColumn(2).copyToHost()) {
+        // verify t3
+        assertEquals(4, t3.getRowCount());
+        Map<Double, Integer> expectedAggregateResult = new HashMap() {
+          {
+            // value, count
+            put(12.0, 1);
+            put(27.0, 1);
+            put(34.0, 1);
+            put(17.0, 1);
+          }
+        };
+        for (int i = 0; i < 4; ++i) {
+          Double key = aggOut1.getDouble(i);
+          assertTrue(expectedAggregateResult.containsKey(key));
+          Integer count = expectedAggregateResult.get(key);
+          if (count == 1) {
+            expectedAggregateResult.remove(key);
+          } else {
+            expectedAggregateResult.put(key, count - 1);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testGroupByM2() {
+    // A trivial test:
+    try (Table input = new Table.TestBuilder().column(1, 2, 3, 1, 2, 2, 1, 3, 3, 2)
+             .column(0, 1, -2, 3, -4, -5, -6, 7, -8, 9)
+             .build();
+         Table results = input.groupBy(0).aggregate(GroupByAggregation.M2()
+               .onColumn(1));
+         Table expected = new Table.TestBuilder().column(1, 2, 3)
+             .column(42.0, 122.75, 114.0)
+             .build()) {
+      assertTablesAreEqual(expected, results);
+    }
+
+    // Test with values have nulls (the values associated with key=2 has both nulls and non-nulls,
+    // while the values associated with key=5 are all nulls):
+    try (Table input = new Table.TestBuilder().column(1, 2, 5, 3, 4, 5, 2, 3, 2, 5)
+             .column(0, null, null, 2, 3, null, 5, 6, 7, null)
+             .build();
+         Table results = input.groupBy(0).aggregate(GroupByAggregation.M2()
+             .onColumn(1));
+         Table expected = new Table.TestBuilder().column(1, 2, 3, 4, 5)
+             .column(0.0, 2.0, 8.0, 0.0, null)
+             .build()) {
+      assertTablesAreEqual(expected, results);
+    }
+
+    // Test with floating-point values having NaN:
+    try (Table input = new Table.TestBuilder().column(4, 3, 1, 2, 3, 1, 2, 2, 1, null, 3, 2, 4, 4)
+             .column(null, null, 0.0, 1.0, 2.0, 3.0, 4.0, Double.NaN, 6.0, 7.0, 8.0, 9.0, 10.0, Double.NaN)
+             .build();
+         Table results = input.groupBy(0).aggregate(GroupByAggregation.M2()
+             .onColumn(1));
+         Table expected = new Table.TestBuilder().column(1, 2, 3, 4, null)
+             .column(18.0, Double.NaN, 18.0, Double.NaN, 0.0)
+             .build()) {
+      assertTablesAreEqual(expected, results);
+    }
+
+    // Test with floating-point values having NaN and +/- Inf
+    // (The values associated with:
+    //   key=1: have only NaN
+    //   key=2: have only +Inf
+    //   key=3: have only -Inf
+    //   key=4: have NaN and +/- Inf,
+    //   key=5: have normal numbers):
+    try (Table input = new Table.TestBuilder().column(1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4)
+             .column(Double.NaN,
+                     Double.POSITIVE_INFINITY,
+                     Double.NEGATIVE_INFINITY,
+                     Double.POSITIVE_INFINITY,
+                     5.0,
+                     //
+                     Double.NaN,
+                     Double.POSITIVE_INFINITY,
+                     Double.NEGATIVE_INFINITY,
+                     Double.NEGATIVE_INFINITY,
+                     10.0,
+                     //
+                     Double.NaN,
+                     Double.POSITIVE_INFINITY,
+                     Double.NEGATIVE_INFINITY,
+                     Double.POSITIVE_INFINITY)
+             .build();
+         Table results = input.groupBy(0).aggregate(GroupByAggregation.M2()
+             .onColumn(1));
+         Table expected = new Table.TestBuilder().column(1, 2, 3, 4, 5)
+             .column(Double.NaN, Double.NaN, Double.NaN, Double.NaN, 12.5)
+             .build()) {
+      assertTablesAreEqual(expected, results);
+    }
+  }
+
+  @Test
+  void testGroupByMergeM2() {
+    StructType nestedType = new StructType(false,
+        new BasicType(true, DType.INT32),
+        new BasicType(true, DType.FLOAT64),
+        new BasicType(true, DType.FLOAT64));
+
+    try (Table partialResults1 = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(nestedType,
+                 struct(1, 0.0, 0.0),
+                 struct(1, 1.0, 0.0),
+                 struct(0, null, null),
+                 struct(0, null, null))
+             .build();
+         Table partialResults2 = new Table.TestBuilder()
+             .column(1, 2, 3)
+             .column(nestedType,
+                 struct(1, 3.0, 0.0),
+                 struct(1, 4.0, 0.0),
+                 struct(1, 2.0, 0.0))
+             .build();
+         Table partialResults3 = new Table.TestBuilder()
+             .column(1, 2)
+             .column(nestedType,
+                 struct(1, 6.0, 0.0),
+                 struct(1, Double.NaN, Double.NaN))
+             .build();
+         Table partialResults4 = new Table.TestBuilder()
+             .column(2, 3, 4)
+             .column(nestedType,
+                 struct(1, 9.0, 0.0),
+                 struct(1, 8.0, 0.0),
+                 struct(2, Double.NaN, Double.NaN))
+             .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(nestedType,
+                 struct(3, 3.0, 18.0),
+                 struct(4, Double.NaN, Double.NaN),
+                 struct(2, 5.0, 18.0),
+                 struct(2, Double.NaN, Double.NaN))
+             .build()) {
+      try (Table concatenatedResults = Table.concatenate(
+             partialResults1,
+             partialResults2,
+             partialResults3,
+             partialResults4);
+           Table finalResults = concatenatedResults.groupBy(0).aggregate(
+               GroupByAggregation.mergeM2().onColumn(1))
+           ) {
+        assertTablesAreEqual(expected, finalResults);
+      }
+    }
+  }
+
+  @Test
+  void testGroupByFirstExcludeNulls() {
+    try (Table input = new Table.TestBuilder()
+            .column(  1,   1,    1,  1,  2,    2,  2,    2)
+            .column(null, 13, null, 12, 14, null, 15, null)
+            .build();
+         Table expected = new Table.TestBuilder()
+                 .column(1, 2)
+                 .column(13, 14)
+                 .build();
+         Table found = input.groupBy(0).aggregate(
+             GroupByAggregation.nth(0, NullPolicy.EXCLUDE).onColumn(1))) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testGroupByLastExcludeNulls() {
+    try (Table input = new Table.TestBuilder()
+            .column(  1,   1,    1,  1,  2,    2,  2,    2)
+            .column(null, 13, null, 12, 14, null, 15, null)
+            .build();
+         Table expected = new Table.TestBuilder()
+                 .column(1, 2)
+                 .column(12, 15)
+                 .build();
+         Table found = input.groupBy(0).aggregate(
+             GroupByAggregation.nth(-1, NullPolicy.EXCLUDE).onColumn(1))) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testGroupByFirstIncludeNulls() {
+    try (Table input = new Table.TestBuilder()
+            .column(  1,   1,    1,  1,  2,    2,  2,    2)
+            .column(null, 13, null, 12, 14, null, 15, null)
+            .build();
+         Table expected = new Table.TestBuilder()
+                 .column(1, 2)
+                 .column(null, 14)
+                 .build();
+         Table found = input.groupBy(0).aggregate(
+             GroupByAggregation.nth(0, NullPolicy.INCLUDE).onColumn(1))) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testGroupByLastIncludeNulls() {
+    try (Table input = new Table.TestBuilder()
+            .column(  1,   1,    1,  1,  2,    2,  2,    2)
+            .column(null, 13, null, 12, 14, null, 15, null)
+            .build();
+         Table expected = new Table.TestBuilder()
+                 .column(1, 2)
+                 .column(12, null)
+                 .build();
+         Table found = input.groupBy(0).aggregate(
+             GroupByAggregation.nth(-1, NullPolicy.INCLUDE).onColumn(1))) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testGroupByAvg() {
+    try (Table t1 = new Table.TestBuilder().column( 1,  1,  1,  1,  1,  1)
+                                           .column( 1,  3,  3,  5,  5,  0)
+                                           .column(12, 14, 13,  1, 17, 17)
+                                           .build()) {
+      try (Table t3 = t1.groupBy(0, 1).aggregate(GroupByAggregation.mean().onColumn(2));
+           HostColumnVector aggOut1 = t3.getColumn(2).copyToHost()) {
+        // verify t3
+        assertEquals(4, t3.getRowCount());
+        Map<Double, Integer> expectedAggregateResult = new HashMap() {
+          {
+            // value, count
+            put(12.0, 1);
+            put(13.5, 1);
+            put(17.0, 1);
+            put(9.0, 1);
+          }
+        };
+        for (int i = 0; i < 4; ++i) {
+          Double key = aggOut1.getDouble(i);
+          assertTrue(expectedAggregateResult.containsKey(key));
+          Integer count = expectedAggregateResult.get(key);
+          if (count == 1) {
+            expectedAggregateResult.remove(key);
+          } else {
+            expectedAggregateResult.put(key, count - 1);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testMultiAgg() {
+    try (Table t1 = new Table.TestBuilder().column(  1,   1,   1,   1,   1,    1)
+                                           .column(  2,   2,   2,   3,   3,    3)
+                                           .column(5.0, 2.3, 3.4, 2.3, 1.3, 12.2)
+                                           .column(  3,   1,   7,  -1,   9,    0)
+                                           .build()) {
+      try (Table t2 = t1.groupBy(0, 1).aggregate(
+          GroupByAggregation.count().onColumn(0),
+          GroupByAggregation.max().onColumn(3),
+          GroupByAggregation.min().onColumn(2),
+          GroupByAggregation.mean().onColumn(2),
+          GroupByAggregation.sum().onColumn(2));
+           HostColumnVector countOut = t2.getColumn(2).copyToHost();
+           HostColumnVector maxOut = t2.getColumn(3).copyToHost();
+           HostColumnVector minOut = t2.getColumn(4).copyToHost();
+           HostColumnVector avgOut = t2.getColumn(5).copyToHost();
+           HostColumnVector sumOut = t2.getColumn(6).copyToHost()) {
+        assertEquals(2, t2.getRowCount());
+
+        // verify count
+        assertEquals(3, countOut.getInt(0));
+        assertEquals(3, countOut.getInt(1));
+
+        // verify mean
+        List<Double> sortedMean = new ArrayList<>();
+        sortedMean.add(avgOut.getDouble(0));
+        sortedMean.add(avgOut.getDouble(1));
+        sortedMean = sortedMean.stream()
+            .sorted(Comparator.naturalOrder())
+            .collect(Collectors.toList());
+
+        assertEqualsWithinPercentage(3.5666f, sortedMean.get(0), 0.0001);
+        assertEqualsWithinPercentage(5.2666f, sortedMean.get(1), 0.0001);
+
+        // verify sum
+        List<Double> sortedSum = new ArrayList<>();
+        sortedSum.add(sumOut.getDouble(0));
+        sortedSum.add(sumOut.getDouble(1));
+        sortedSum = sortedSum.stream()
+            .sorted(Comparator.naturalOrder())
+            .collect(Collectors.toList());
+
+        assertEqualsWithinPercentage(10.7f, sortedSum.get(0), 0.0001);
+        assertEqualsWithinPercentage(15.8f, sortedSum.get(1), 0.0001);
+
+        // verify min
+        List<Double> sortedMin = new ArrayList<>();
+        sortedMin.add(minOut.getDouble(0));
+        sortedMin.add(minOut.getDouble(1));
+        sortedMin = sortedMin.stream()
+            .sorted(Comparator.naturalOrder())
+            .collect(Collectors.toList());
+
+        assertEqualsWithinPercentage(1.3f, sortedMin.get(0), 0.0001);
+        assertEqualsWithinPercentage(2.3f, sortedMin.get(1), 0.0001);
+
+        // verify max
+        List<Integer> sortedMax = new ArrayList<>();
+        sortedMax.add(maxOut.getInt(0));
+        sortedMax.add(maxOut.getInt(1));
+        sortedMax = sortedMax.stream()
+            .sorted(Comparator.naturalOrder())
+            .collect(Collectors.toList());
+
+        assertEquals(7, sortedMax.get(0));
+        assertEquals(9, sortedMax.get(1));
+      }
+    }
+  }
+
+  @Test
+  void testSumWithStrings() {
+    try (Table t = new Table.TestBuilder()
+        .column("1-URGENT", "3-MEDIUM", "1-URGENT", "3-MEDIUM")
+        .column(5289L, 5203L, 5303L, 5206L)
+        .build();
+         Table result = t.groupBy(0).aggregate(
+             GroupByAggregation.sum().onColumn(1));
+         Table expected = new Table.TestBuilder()
+             .column("1-URGENT", "3-MEDIUM")
+             .column(5289L + 5303L, 5203L + 5206L)
+             .build()) {
+      assertTablesAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testGroupByNoAggs() {
+    try (Table t1 = new Table.TestBuilder().column(   1,    1,    1,    1,    1,    1)
+        .column(   1,    3,    3,    5,    5,    0)
+        .column(  12,   14,   13,   17,   17,   17)
+        .decimal32Column(-3,   12,   14,   13,   111,   222,   333)
+        .decimal64Column(-3,   12L,   14L,   13L,   111L,   222L,   333L)
+        .build()) {
+      try (Table t3 = t1.groupBy(0, 1).aggregate()) {
+        // verify t3
+        assertEquals(4, t3.getRowCount());
+      }
+    }
+  }
+
+  /**
+   * A wrapper for ContiguousTable[] to implement AutoCloseable
+   */
+  static class ContiguousSplitRes implements AutoCloseable {
+    // to be closed
+    private ContiguousTable[] splits;
+
+    public ContiguousSplitRes(ContiguousTable[] splits) {
+      this.splits = splits;
+    }
+
+    public ContiguousTable[] getSplits() {
+      return splits;
+    }
+
+    @Override
+    public void close() throws Exception {
+      if (splits != null) {
+        for (ContiguousTable t : splits) { t.close(); }
+      }
+    }
+  }
+
+  @Test
+  void testGroupByContiguousSplitGroups() throws Exception {
+    try (Table table = new Table.TestBuilder()
+        .column(   1,    1,    1,    1,    1,    1)
+        .column(   1,    3,    3,    5,    5,    5)
+        .column(  12,   14,   13,   17,   16,   18)
+        .column("s1", "s2", "s3", "s4", "s5", "s6")
+        .build()) {
+      // Normal case with primitive types.
+      try (Table expected1 = new Table.TestBuilder()
+              .column(   1)
+              .column(   1)
+              .column(  12)
+              .column("s1").build();
+           Table expected2 = new Table.TestBuilder()
+              .column(   1,    1)
+              .column(   3,    3)
+              .column(  14,   13)
+              .column("s2", "s3").build();
+           Table expected3 = new Table.TestBuilder()
+              .column(   1,    1,    1)
+              .column(   5,    5,    5)
+              .column(  17,   16,   18)
+              .column("s4", "s5", "s6").build();
+           Table expected4 = new Table.TestBuilder()
+              .column(   1,    1,    1)
+              .column(   1,    3,    5).build();
+           ContiguousSplitRes splitsRes = new ContiguousSplitRes(
+               table.groupBy(0, 1).contiguousSplitGroups());
+           ContigSplitGroupByResult r =
+               table.groupBy(0, 1).contiguousSplitGroupsAndGenUniqKeys()) {
+        ContiguousTable[] splits = splitsRes.getSplits();
+        ContiguousTable[] splits2 = r.getGroups();
+        Table uniqKeys = r.getUniqKeyTable();
+
+        for (ContiguousTable[] currSplits : Arrays.asList(splits, splits2)) {
+          assertEquals(3, currSplits.length);
+          for (ContiguousTable ct : currSplits) {
+            if (ct.getRowCount() == 1) {
+              assertTablesAreEqual(expected1, ct.getTable());
+            } else if (ct.getRowCount() == 2) {
+              assertTablesAreEqual(expected2, ct.getTable());
+            } else if (ct.getRowCount() == 3) {
+              assertTablesAreEqual(expected3, ct.getTable());
+            } else {
+              throw new RuntimeException("unexpected behavior: contiguousSplitGroups");
+            }
+          }
+        }
+
+        // verify uniq keys table
+        assertTablesAreEqual(expected4, uniqKeys);
+      }
+
+      // Empty key columns, the whole table is a group.
+      try(ContiguousSplitRes splitsRes = new ContiguousSplitRes(
+          table.groupBy().contiguousSplitGroups());
+          ContigSplitGroupByResult r = table.groupBy().contiguousSplitGroupsAndGenUniqKeys()) {
+        ContiguousTable[] splits = splitsRes.getSplits();
+        ContiguousTable[] splits2 = r.getGroups();
+        Table uniqKeys = r.getUniqKeyTable();
+
+        assertEquals(1, splits.length);
+        assertTablesAreEqual(table, splits[0].getTable());
+
+        assertEquals(1, splits2.length);
+        assertTablesAreEqual(table, splits2[0].getTable());
+
+        // Table should contain 1 or more columns,
+        // If group by empty, keys table should be null;
+        assertNull(uniqKeys);
+      }
+
+      // Row count is 0
+      try(
+          Table emptyTable = new Table.TestBuilder()
+              .column(new Integer[0])
+              .column(new Integer[0])
+              .column(new Integer[0])
+              .column(new String[0]).build();
+          ContiguousSplitRes splitsRes = new ContiguousSplitRes(
+              emptyTable.groupBy(0, 1).contiguousSplitGroups());
+          ContigSplitGroupByResult r =
+              emptyTable.groupBy(0, 1).contiguousSplitGroupsAndGenUniqKeys()) {
+        ContiguousTable[] splits = splitsRes.getSplits();
+        ContiguousTable[] splits2 = r.getGroups();
+        Table uniqKeys = r.getUniqKeyTable();
+
+        // the first of tmpSplits is empty split
+        assertEquals(0, emptyTable.getRowCount());
+
+        assertEquals(1, splits.length);
+        assertEquals(0, splits[0].getTable().getRowCount());
+
+        assertEquals(1, splits2.length);
+        assertEquals(0, splits2[0].getTable().getRowCount());
+        assertEquals(0, uniqKeys.getRowCount());
+      }
+    }
+  }
+
+  @Test
+  void testGroupByCollectListIncludeNulls() {
+    try (Table input = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 4)
+        .column(null, 13, null, 12, 14, null, 15, null, null, 0)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(new ListType(false, new BasicType(true, DType.INT32)),
+                 Arrays.asList(null, 13, null, 12),
+                 Arrays.asList(14, null, 15, null),
+                 Arrays.asList((Integer) null),
+                 Arrays.asList(0))
+             .build();
+         Table found = input.groupBy(0).aggregate(
+             GroupByAggregation.collectList(NullPolicy.INCLUDE).onColumn(1))) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testGroupByMergeLists() {
+    ListType listOfInts = new ListType(false, new BasicType(false, DType.INT32));
+    ListType listOfStructs = new ListType(false, new StructType(false,
+        new BasicType(false, DType.INT32), new BasicType(false, DType.STRING)));
+    try (Table input = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 4)
+        .column(listOfInts,
+            Arrays.asList(1, 2), Arrays.asList(3), Arrays.asList(7, 8), Arrays.asList(4, 5, 6),
+            Arrays.asList(8, 9), Arrays.asList(8, 9, 10), Arrays.asList(10, 11), Arrays.asList(11, 12),
+            Arrays.asList(13, 13), Arrays.asList(14, 15, 15))
+        .column(listOfStructs,
+            Arrays.asList(new StructData(1, "s1"), new StructData(2, "s2")),
+            Arrays.asList(new StructData(2, "s3"), new StructData(3, "s4")),
+            Arrays.asList(new StructData(2, "s2")),
+            Arrays.asList(),
+            Arrays.asList(new StructData(11, "s11")),
+            Arrays.asList(new StructData(22, "s22"), new StructData(33, "s33")),
+            Arrays.asList(),
+            Arrays.asList(new StructData(22, "s22"), new StructData(33, "s33"), new StructData(44, "s44")),
+            Arrays.asList(new StructData(333, "s333"), new StructData(222, "s222"), new StructData(111, "s111")),
+            Arrays.asList(new StructData(222, "s222"), new StructData(444, "s444")))
+        .build();
+         Table expectedListOfInts = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(listOfInts,
+                 Arrays.asList(1, 2, 3, 7 ,8, 4, 5, 6),
+                 Arrays.asList(8, 9, 8, 9, 10, 10, 11, 11, 12),
+                 Arrays.asList(13, 13),
+                 Arrays.asList(14, 15, 15))
+             .build();
+         Table expectedListOfStructs = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(listOfStructs,
+                 Arrays.asList(new StructData(1, "s1"), new StructData(2, "s2"),
+                     new StructData(2, "s3"), new StructData(3, "s4"), new StructData(2, "s2")),
+                 Arrays.asList(new StructData(11, "s11"), new StructData(22, "s22"), new StructData(33, "s33"),
+                     new StructData(22, "s22"), new StructData(33, "s33"), new StructData(44, "s44")),
+                 Arrays.asList(new StructData(333, "s333"), new StructData(222, "s222"), new StructData(111, "s111")),
+                 Arrays.asList(new StructData(222, "s222"), new StructData(444, "s444")))
+             .build();
+         Table retListOfInts = input.groupBy(0).aggregate(GroupByAggregation.mergeLists().onColumn(1));
+         Table retListOfStructs = input.groupBy(0).aggregate(GroupByAggregation.mergeLists().onColumn(2))) {
+      assertTablesAreEqual(expectedListOfInts, retListOfInts);
+      assertTablesAreEqual(expectedListOfStructs, retListOfStructs);
+    }
+  }
+
+  @Test
+  void testGroupByCollectSetIncludeNulls() {
+    // test with null unequal and nan unequal
+    GroupByAggregation collectSet = GroupByAggregation.collectSet(NullPolicy.INCLUDE,
+        NullEquality.UNEQUAL, NaNEquality.UNEQUAL);
+    try (Table input = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4)
+        .column(null, 13, null, 13, 14, null, 15, null, 4, 1, 1, 4, 0, 0, 0, 0)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(new ListType(false, new BasicType(true, DType.INT32)),
+                 Arrays.asList(13, null, null), Arrays.asList(14, 15, null, null),
+                 Arrays.asList(1, 4), Arrays.asList(0))
+             .build();
+         Table found = input.groupBy(0).aggregate(collectSet.onColumn(1));
+         ColumnVector listsSorted = found.getColumn(1).listSortRows(false, false)) {
+      assertColumnsAreEqual(expected.getColumn(0), found.getColumn(0));
+      assertColumnsAreEqual(expected.getColumn(1), listsSorted);
+    }
+    // test with null equal and nan unequal
+    collectSet = GroupByAggregation.collectSet(NullPolicy.INCLUDE,
+        NullEquality.EQUAL, NaNEquality.UNEQUAL);
+    try (Table input = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4)
+        .column(null, 13.0, null, 13.0,
+            14.1, Double.NaN, 13.9, Double.NaN,
+            Double.NaN, null, 1.0, null,
+            null, null, null, null)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(new ListType(false, new BasicType(true, DType.FLOAT64)),
+                 Arrays.asList(13.0, null),
+                 Arrays.asList(13.9, 14.1, Double.NaN, Double.NaN),
+                 Arrays.asList(1.0, Double.NaN, null),
+                 Arrays.asList((Integer) null))
+             .build();
+         Table found = input.groupBy(0).aggregate(collectSet.onColumn(1));
+         ColumnVector listsSorted = found.getColumn(1).listSortRows(false, false)) {
+      assertColumnsAreEqual(expected.getColumn(0), found.getColumn(0));
+      assertColumnsAreEqual(expected.getColumn(1), listsSorted);
+    }
+    // test with null equal and nan equal
+    collectSet = GroupByAggregation.collectSet(NullPolicy.INCLUDE,
+        NullEquality.EQUAL, NaNEquality.ALL_EQUAL);
+    try (Table input = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4)
+        .column(null, 13.0, null, 13.0,
+            14.1, Double.NaN, 13.9, Double.NaN,
+            0.0, 0.0, 0.00, 0.0,
+            Double.NaN, Double.NaN, null, null)
+        .build();
+         Table expected = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(new ListType(false, new BasicType(true, DType.FLOAT64)),
+                 Arrays.asList(13.0, null),
+                 Arrays.asList(13.9, 14.1, Double.NaN),
+                 Arrays.asList(0.0),
+                 Arrays.asList(Double.NaN, (Integer) null))
+             .build();
+         Table found = input.groupBy(0).aggregate(collectSet.onColumn(1));
+         ColumnVector listsSorted = found.getColumn(1).listSortRows(false, false)) {
+      assertColumnsAreEqual(expected.getColumn(0), found.getColumn(0));
+      assertColumnsAreEqual(expected.getColumn(1), listsSorted);
+    }
+  }
+
+  @Test
+  void testGroupByMergeSets() {
+    ListType listOfInts = new ListType(false, new BasicType(false, DType.INT32));
+    ListType listOfDoubles = new ListType(false, new BasicType(false, DType.FLOAT64));
+    try (Table input = new Table.TestBuilder()
+        .column(1, 1, 1, 1, 2, 2, 2, 2, 3, 4)
+        .column(listOfInts,
+            Arrays.asList(1, 2), Arrays.asList(3), Arrays.asList(7, 8), Arrays.asList(4, 5, 6),
+            Arrays.asList(8, 9), Arrays.asList(8, 9, 10), Arrays.asList(10, 11), Arrays.asList(11, 12),
+            Arrays.asList(13, 13), Arrays.asList(14, 15, 15))
+        .column(listOfDoubles,
+            Arrays.asList(Double.NaN, 1.2), Arrays.asList(), Arrays.asList(Double.NaN), Arrays.asList(-3e10),
+            Arrays.asList(1.1, 2.2, 3.3), Arrays.asList(3.3, 2.2), Arrays.asList(), Arrays.asList(),
+            Arrays.asList(1e3, Double.NaN, 1e-3, Double.NaN), Arrays.asList())
+        .build();
+         Table expectedListOfInts = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(listOfInts,
+                 Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8),
+                 Arrays.asList(8, 9, 10, 11, 12),
+                 Arrays.asList(13),
+                 Arrays.asList(14, 15))
+             .build();
+         Table expectedListOfDoubles = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(listOfDoubles,
+                 Arrays.asList(-3e10, 1.2, Double.NaN, Double.NaN),
+                 Arrays.asList(1.1, 2.2, 3.3),
+                 Arrays.asList(1e-3, 1e3, Double.NaN, Double.NaN),
+                 Arrays.asList())
+             .build();
+         Table expectedListOfDoublesNaNEq = new Table.TestBuilder()
+             .column(1, 2, 3, 4)
+             .column(listOfDoubles,
+                 Arrays.asList(-3e10, 1.2, Double.NaN),
+                 Arrays.asList(1.1, 2.2, 3.3),
+                 Arrays.asList(1e-3, 1e3, Double.NaN),
+                 Arrays.asList())
+             .build();
+         Table retListOfInts = input.groupBy(0).aggregate(GroupByAggregation.mergeSets().onColumn(1));
+         Table retListOfDoubles = input.groupBy(0).aggregate(GroupByAggregation.mergeSets().onColumn(2));
+         Table retListOfDoublesNaNEq = input.groupBy(0).aggregate(
+             GroupByAggregation.mergeSets(NullEquality.UNEQUAL, NaNEquality.ALL_EQUAL).onColumn(2));
+         ColumnVector listsIntsSorted = retListOfInts.getColumn(1).listSortRows(false, false);
+         ColumnVector listsDoublesSorted = retListOfDoubles.getColumn(1).listSortRows(false, false);
+         ColumnVector listsDoublesNaNEqSorted = retListOfDoublesNaNEq.getColumn(1).listSortRows(false, false)) {
+      assertColumnsAreEqual(expectedListOfInts.getColumn(0), retListOfInts.getColumn(0));
+      assertColumnsAreEqual(expectedListOfDoubles.getColumn(0), retListOfDoubles.getColumn(0));
+      assertColumnsAreEqual(expectedListOfDoublesNaNEq.getColumn(0), retListOfDoublesNaNEq.getColumn(0));
+
+      assertColumnsAreEqual(expectedListOfInts.getColumn(1), listsIntsSorted);
+      assertColumnsAreEqual(expectedListOfDoubles.getColumn(1), listsDoublesSorted);
+      assertColumnsAreEqual(expectedListOfDoublesNaNEq.getColumn(1), listsDoublesNaNEqSorted);
+
+    }
+  }
+
+  @Test
+  void testRowBitCount() {
+    try (Table t = new Table.TestBuilder()
+        .column(0, 1, null, 3)                 // 33 bits per row (4 bytes + valid bit)
+        .column(0.0, null, 2.0, 3.0)           // 65 bits per row (8 bytes + valid bit)
+        .column("zero", null, "two", "three")  // 33 bits (4 byte offset + valid bit) + char bits
+        .build();
+         ColumnVector expected = ColumnVector.fromInts(163, 131, 155, 171);
+         ColumnVector actual = t.rowBitCount()) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testRowBitCountEmpty() {
+    try (Table t = new Table.TestBuilder()
+            .column(new Integer[0])
+            .column(new Double[0])
+            .column(new String[0])
+            .build();
+         ColumnVector c = t.rowBitCount()) {
+      assertEquals(DType.INT32, c.getType());
+      assertEquals(0, c.getRowCount());
+    }
+  }
+
+  @Test
+  void testSimpleGather() {
+    try (Table testTable = new Table.TestBuilder()
+            .column(1, 2, 3, 4, 5)
+            .column("A", "AA", "AAA", "AAAA", "AAAAA")
+            .decimal32Column(-3, 1, 2, 3, 4, 5)
+            .decimal64Column(-8, 100001L, 200002L, 300003L, 400004L, 500005L)
+            .build();
+         ColumnVector gatherMap = ColumnVector.fromInts(0, 2, 4, -2);
+         Table expected = new Table.TestBuilder()
+                 .column(1, 3, 5, 4)
+                 .column("A", "AAA", "AAAAA", "AAAA")
+                 .decimal32Column(-3, 1, 3, 5, 4)
+                 .decimal64Column(-8, 100001L, 300003L, 500005L, 400004L)
+                 .build();
+         Table found = testTable.gather(gatherMap)) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+  @Test
+  void testBoundsCheckedGather() {
+    try (Table testTable = new Table.TestBuilder()
+            .column(1, 2, 3, 4, 5)
+            .column("A", "AA", "AAA", "AAAA", "AAAAA")
+            .decimal32Column(-3, 1, 2, 3, 4, 5)
+            .decimal64Column(-8, 100001L, 200002L, 300003L, 400004L, 500005L)
+            .build();
+         ColumnVector gatherMap = ColumnVector.fromInts(0, 100, 4, -2);
+         Table expected = new Table.TestBuilder()
+                 .column(1, null, 5, 4)
+                 .column("A", null, "AAAAA", "AAAA")
+                 .decimal32Column(-3, 1, null, 5, 4)
+                 .decimal64Column(-8, 100001L, null, 500005L, 400004L)
+                 .build();
+         Table found = testTable.gather(gatherMap)) {
+      assertTablesAreEqual(expected, found);
+    }
+  }
+
+
+  @Test
+  void testScatterTable() {
+    try (Table srcTable = new Table.TestBuilder()
+            .column(1, 2, 3, 4, 5)
+            .column("A", "AA", "AAA", "AAAA", "AAAAA")
+            .decimal32Column(-3, 1, 2, 3, 4, 5)
+            .decimal64Column(-8, 100001L, 200002L, 300003L, 400004L, 500005L)
+            .build();
+         ColumnVector scatterMap = ColumnVector.fromInts(0, 2, 4, -2);
+         Table targetTable = new Table.TestBuilder()
+            .column(-1, -2, -3, -4, -5)
+            .column("B", "BB", "BBB", "BBBB", "BBBBB")
+            .decimal32Column(-3, -1, -2, -3, -4, -5)
+            .decimal64Column(-8, -100001L, -200002L, -300003L, -400004L, -500005L)
+            .build();
+         Table expected = new Table.TestBuilder()
+            .column(1, -2, 2, 4, 3)
+            .column("A", "BB", "AA", "AAAA", "AAA")
+            .decimal32Column(-3, 1, -2, 2, 4, 3)
+            .decimal64Column(-8, 100001L, -200002L, 200002L, 400004L, 300003L)
+            .build();
+         Table result = srcTable.scatter(scatterMap, targetTable)) {
+      assertTablesAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  void testScatterScalars() {
+    try (Scalar s1 = Scalar.fromInt(0);
+         Scalar s2 = Scalar.fromString("A");
+         ColumnVector scatterMap = ColumnVector.fromInts(0, 2, -1);
+         Table targetTable = new Table.TestBuilder()
+            .column(-1, -2, -3, -4, -5)
+            .column("B", "BB", "BBB", "BBBB", "BBBBB")
+            .build();
+         Table expected = new Table.TestBuilder()
+            .column(0, -2, 0, -4, 0)
+            .column("A", "BB", "A", "BBBB", "A")
+            .build();
+         Table result = Table.scatter(new Scalar[] { s1, s2 }, scatterMap, targetTable)) {
+       assertTablesAreEqual(expected, result);
+     }
+  }
+
+  @Test
+  void testMaskWithoutValidity() {
+    try (ColumnVector mask = ColumnVector.fromBoxedBooleans(true, false, true, false, true);
+         ColumnVector fromInts = ColumnVector.fromInts(1, 2, 3, 4, 5);
+         ColumnVector fromStrings = ColumnVector.fromStrings("1", "2", "3", "4", "5");
+         ColumnVector fromDecimals = ColumnVector.decimalFromLongs(-3, 123L, -234L, 345L, 1000L, -2000L);
+         Table input = new Table(fromInts, fromStrings, fromDecimals);
+         Table filteredTable = input.filter(mask);
+         ColumnVector expectedInts = ColumnVector.fromInts(1, 3, 5);
+         ColumnVector expectedStrings = ColumnVector.fromStrings("1", "3", "5");
+         ColumnVector expectedDecimals = ColumnVector.decimalFromLongs(-3, 123L, 345L, -2000L);
+         Table expected = new Table(expectedInts, expectedStrings, expectedDecimals)) {
+      assertTablesAreEqual(expected, filteredTable);
+    }
+  }
+
+  @Test
+  void testMaskWithValidity() {
+    final int numRows = 5;
+    try (Builder builder = HostColumnVector.builder(DType.BOOL8, numRows)) {
+      for (int i = 0; i < numRows; ++i) {
+        builder.append((byte) 1);
+        if (i % 2 != 0) {
+          builder.setNullAt(i);
+        }
+      }
+      try (ColumnVector mask = builder.buildAndPutOnDevice();
+           ColumnVector fromInts = ColumnVector.fromBoxedInts(1, null, 2, 3, null);
+           Table input = new Table(fromInts);
+           Table filteredTable = input.filter(mask);
+           HostColumnVector filtered = filteredTable.getColumn(0).copyToHost()) {
+        assertEquals(DType.INT32, filtered.getType());
+        assertEquals(3, filtered.getRowCount());
+        assertEquals(1, filtered.getInt(0));
+        assertEquals(2, filtered.getInt(1));
+        assertTrue(filtered.isNull(2));
+      }
+    }
+  }
+
+  @Test
+  void testMaskDataOnly() {
+    byte[] maskVals = new byte[]{0, 1, 0, 1, 1};
+    try (ColumnVector mask = ColumnVector.boolFromBytes(maskVals);
+         ColumnVector fromBytes = ColumnVector.fromBoxedBytes((byte) 1, null, (byte) 2, (byte) 3, null);
+         Table input = new Table(fromBytes);
+         Table filteredTable = input.filter(mask);
+         HostColumnVector filtered = filteredTable.getColumn(0).copyToHost()) {
+      assertEquals(DType.INT8, filtered.getType());
+      assertEquals(3, filtered.getRowCount());
+      assertTrue(filtered.isNull(0));
+      assertEquals(3, filtered.getByte(1));
+      assertTrue(filtered.isNull(2));
+    }
+  }
+
+
+  @Test
+  void testAllFilteredFromData() {
+    Boolean[] maskVals = new Boolean[5];
+    Arrays.fill(maskVals, false);
+    try (ColumnVector mask = ColumnVector.fromBoxedBooleans(maskVals);
+         ColumnVector fromInts = ColumnVector.fromBoxedInts(1, null, 2, 3, null);
+         ColumnVector fromDecimal32s = ColumnVector.decimalFromInts(-3, 1, 2, 3, 4, 5);
+         ColumnVector fromDecimal64s = ColumnVector.decimalFromLongs(-11, 1L, 2L, 3L, 4L, 5L);
+         Table input = new Table(fromInts, fromDecimal32s, fromDecimal64s);
+         Table filteredTable = input.filter(mask)) {
+      ColumnVector filtered = filteredTable.getColumn(0);
+      assertEquals(DType.INT32, filtered.getType());
+      assertEquals(0, filtered.getRowCount());
+      filtered = filteredTable.getColumn(1);
+      assertEquals(DType.create(DType.DTypeEnum.DECIMAL32, -3), filtered.getType());
+      assertEquals(0, filtered.getRowCount());
+      filtered = filteredTable.getColumn(2);
+      assertEquals(DType.create(DType.DTypeEnum.DECIMAL64, -11), filtered.getType());
+      assertEquals(0, filtered.getRowCount());
+    }
+  }
+
+  @Test
+  void testAllFilteredFromValidity() {
+    final int numRows = 5;
+    try (Builder builder = HostColumnVector.builder(DType.BOOL8, numRows)) {
+      for (int i = 0; i < numRows; ++i) {
+        builder.append((byte) 1);
+        builder.setNullAt(i);
+      }
+      try (ColumnVector mask = builder.buildAndPutOnDevice();
+           ColumnVector fromInts = ColumnVector.fromBoxedInts(1, null, 2, 3, null);
+           ColumnVector fromDecimal32s = ColumnVector.decimalFromInts(-3, 1, 2, 3, 4, 5);
+           ColumnVector fromDecimal64s = ColumnVector.decimalFromLongs(-11, 1L, 2L, 3L, 4L, 5L);
+           Table input = new Table(fromInts, fromDecimal32s, fromDecimal64s);
+           Table filteredTable = input.filter(mask)) {
+        ColumnVector filtered = filteredTable.getColumn(0);
+        assertEquals(DType.INT32, filtered.getType());
+        assertEquals(0, filtered.getRowCount());
+        filtered = filteredTable.getColumn(1);
+        assertEquals(DType.create(DType.DTypeEnum.DECIMAL32, -3), filtered.getType());
+        assertEquals(0, filtered.getRowCount());
+        filtered = filteredTable.getColumn(2);
+        assertEquals(DType.create(DType.DTypeEnum.DECIMAL64, -11), filtered.getType());
+        assertEquals(0, filtered.getRowCount());
+      }
+    }
+  }
+
+  ColumnView replaceValidity(ColumnView cv, DeviceMemoryBuffer validity, long nullCount) {
+    assert (validity.length >= BitVectorHelper.getValidityAllocationSizeInBytes(cv.rows));
+    if (cv.type.isNestedType()) {
+      ColumnView[] children = cv.getChildColumnViews();
+      try {
+        return new ColumnView(cv.type,
+            cv.rows,
+            Optional.of(nullCount),
+            validity,
+            cv.getOffsets(),
+            children);
+      } finally {
+        for (ColumnView v : children) {
+          if (v != null) {
+            v.close();
+          }
+        }
+      }
+    } else {
+      return new ColumnView(cv.type, cv.rows, Optional.of(nullCount), cv.getData(), validity, cv.getOffsets());
+    }
+  }
+
+  @Test
+  void testRemoveNullMasksIfNeeded() {
+    ListType nestedType = new ListType(true, new StructType(false,
+        new BasicType(true, DType.INT32),
+        new BasicType(true, DType.INT64)));
+
+    List data1 = Arrays.asList(10, 20L);
+    List data2 = Arrays.asList(50, 60L);
+    HostColumnVector.StructData structData1 = new HostColumnVector.StructData(data1);
+    HostColumnVector.StructData structData2 = new HostColumnVector.StructData(data2);
+
+    //First we create ColumnVectors
+    try (ColumnVector nonNullVector0 = ColumnVector.fromBoxedInts(1, 2, 3);
+         ColumnVector nonNullVector2 = ColumnVector.fromStrings("1", "2", "3");
+         ColumnVector nonNullVector1 = ColumnVector.fromLists(nestedType,
+             Arrays.asList(structData1, structData2),
+             Arrays.asList(structData1, structData2),
+             Arrays.asList(structData1, structData2))) {
+      //Then we take the created ColumnVectors and add validity masks even though the nullCount = 0
+      long allocSize = BitVectorHelper.getValidityAllocationSizeInBytes(nonNullVector0.rows);
+      try (DeviceMemoryBuffer dm0 = DeviceMemoryBuffer.allocate(allocSize);
+           DeviceMemoryBuffer dm1 = DeviceMemoryBuffer.allocate(allocSize);
+           DeviceMemoryBuffer dm2 = DeviceMemoryBuffer.allocate(allocSize);
+           DeviceMemoryBuffer dm3_child =
+               DeviceMemoryBuffer.allocate(BitVectorHelper.getValidityAllocationSizeInBytes(2))) {
+        Cuda.memset(dm0.address, (byte) 0xFF, allocSize);
+        Cuda.memset(dm1.address, (byte) 0xFF, allocSize);
+        Cuda.memset(dm2.address, (byte) 0xFF, allocSize);
+        Cuda.memset(dm3_child.address, (byte) 0xFF,
+            BitVectorHelper.getValidityAllocationSizeInBytes(2));
+
+        try (ColumnView cv0View = replaceValidity(nonNullVector0, dm0, 0);
+             ColumnVector cv0 = cv0View.copyToColumnVector();
+             ColumnView struct = nonNullVector1.getChildColumnView(0);
+             ColumnView structChild0 = struct.getChildColumnView(0);
+             ColumnView newStructChild0 = replaceValidity(structChild0, dm3_child, 0);
+             ColumnView newStruct = struct.replaceChildrenWithViews(new int[]{0}, new ColumnView[]{newStructChild0});
+             ColumnView list = nonNullVector1.replaceChildrenWithViews(new int[]{0}, new ColumnView[]{newStruct});
+             ColumnView cv1View = replaceValidity(list, dm1, 0);
+             ColumnVector cv1 = cv1View.copyToColumnVector();
+             ColumnView cv2View = replaceValidity(nonNullVector2, dm2, 0);
+             ColumnVector cv2 = cv2View.copyToColumnVector()) {
+
+          try (Table t = new Table(new ColumnVector[]{cv0, cv1, cv2});
+               Table tableWithoutNullMask = removeNullMasksIfNeeded(t);
+               ColumnView tableStructChild0 = t.getColumn(1).getChildColumnView(0).getChildColumnView(0);
+               ColumnVector tableStructChild0Cv = tableStructChild0.copyToColumnVector();
+               Table expected = new Table(new ColumnVector[]{nonNullVector0, nonNullVector1,
+                nonNullVector2})) {
+            assertTrue(t.getColumn(0).hasValidityVector());
+            assertTrue(t.getColumn(1).hasValidityVector());
+            assertTrue(t.getColumn(2).hasValidityVector());
+            assertTrue(tableStructChild0Cv.hasValidityVector());
+
+            assertPartialTablesAreEqual(expected,
+                0,
+                expected.getRowCount(),
+                tableWithoutNullMask,
+                true,
+                true);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  void testRemoveNullMasksIfNeededWithNulls() {
+    ListType nestedType = new ListType(true, new StructType(true,
+        new BasicType(true, DType.INT32),
+        new BasicType(true, DType.INT64)));
+
+    List data1 = Arrays.asList(0, 10L);
+    List data2 = Arrays.asList(50, null);
+    HostColumnVector.StructData structData1 = new HostColumnVector.StructData(data1);
+    HostColumnVector.StructData structData2 = new HostColumnVector.StructData(data2);
+
+    //First we create ColumnVectors
+    try (ColumnVector nonNullVector0 = ColumnVector.fromBoxedInts(1, null, 2, 3);
+         ColumnVector nonNullVector1 = ColumnVector.fromStrings("1", "2", null, "3");
+         ColumnVector nonNullVector2 = ColumnVector.fromLists(nestedType,
+             Arrays.asList(structData1, structData2),
+             null,
+             Arrays.asList(structData1, structData2),
+             Arrays.asList(structData1, structData2))) {
+      try (Table expected = new Table(new ColumnVector[]{nonNullVector0, nonNullVector1, nonNullVector2});
+           Table unchangedTable = removeNullMasksIfNeeded(expected)) {
+        assertTablesAreEqual(expected, unchangedTable);
+      }
+    }
+  }
+
+  @Test
+  void testMismatchedSizesForFilter() {
+    Boolean[] maskVals = new Boolean[3];
+    Arrays.fill(maskVals, true);
+    try (ColumnVector mask = ColumnVector.fromBoxedBooleans(maskVals);
+         ColumnVector fromInts = ColumnVector.fromBoxedInts(1, null, 2, 3, null);
+         Table input = new Table(fromInts)) {
+      assertThrows(AssertionError.class, () -> input.filter(mask).close());
+    }
+  }
+
+  @Test
+  void testTableBasedFilter() {
+    byte[] maskVals = new byte[]{0, 1, 0, 1, 1};
+    try (ColumnVector mask = ColumnVector.boolFromBytes(maskVals);
+         ColumnVector fromInts = ColumnVector.fromBoxedInts(1, null, 2, 3, null);
+         ColumnVector fromStrings = ColumnVector.fromStrings("one", "two", "three", null, "five");
+         ColumnVector fromDecimals = ColumnVector.fromDecimals(BigDecimal.ZERO, null, BigDecimal.ONE, null, BigDecimal.TEN);
+         Table input = new Table(fromInts, fromStrings, fromDecimals);
+         Table filtered = input.filter(mask);
+         ColumnVector expectedFromInts = ColumnVector.fromBoxedInts(null, 3, null);
+         ColumnVector expectedFromStrings = ColumnVector.fromStrings("two", null, "five");
+         ColumnVector expectedFromDecimals = ColumnVector.fromDecimals(null, null, BigDecimal.TEN);
+         Table expected = new Table(expectedFromInts, expectedFromStrings, expectedFromDecimals)) {
+      assertTablesAreEqual(expected, filtered);
+    }
+  }
+
+  @Test
+  void testDropDuplicates() {
+    int[] keyColumns = new int[]{ 1 };
+
+    try (ColumnVector col1 = ColumnVector.fromBoxedInts(5, null, 3, 5, 8, 1);
+         ColumnVector col2 = ColumnVector.fromBoxedInts(20, null, null, 19, 21, 19);
+         Table input = new Table(col1, col2)) {
+
+      // Keep the first duplicate element.
+      try (Table result = input.dropDuplicates(keyColumns, Table.DuplicateKeepOption.KEEP_FIRST, true);
+           Table resultSorted = result.orderBy(OrderByArg.asc(1, true));
+           ColumnVector expectedCol1 = ColumnVector.fromBoxedInts(null, 5, 5, 8);
+           ColumnVector expectedCol2 = ColumnVector.fromBoxedInts(null, 19, 20, 21);
+           Table expected = new Table(expectedCol1, expectedCol2)) {
+        assertTablesAreEqual(expected, resultSorted);
+      }
+
+      // Keep the last duplicate element.
+      try (Table result = input.dropDuplicates(keyColumns, Table.DuplicateKeepOption.KEEP_LAST, true);
+           Table resultSorted = result.orderBy(OrderByArg.asc(1, true));
+           ColumnVector expectedCol1 = ColumnVector.fromBoxedInts(3, 1, 5, 8);
+           ColumnVector expectedCol2 = ColumnVector.fromBoxedInts(null, 19, 20, 21);
+           Table expected = new Table(expectedCol1, expectedCol2)) {
+        assertTablesAreEqual(expected, resultSorted);
+      }
+    }
+  }
+
+  private enum Columns {
+    BOOL("BOOL"),
+    INT("INT"),
+    BYTE("BYTE"),
+    LONG("LONG"),
+    STRING("STRING"),
+    FLOAT("FLOAT"),
+    DOUBLE("DOUBLE"),
+    DECIMAL64("DECIMAL64"),
+    DECIMAL128("DECIMAL128"),
+    STRUCT("STRUCT"),
+    STRUCT_DEC128("STRUCT_DEC128"),
+    LIST("LIST"),
+    LIST_STRUCT("LIST_STRUCT"),
+    LIST_DEC128("LIST_DEC128");
+
+    final String name;
+
+    Columns(String columnName) {
+      this.name = columnName;
+    }
+  }
+
+  private static class WriteUtils {
+
+    private static final Map<Columns, Function<TestBuilder, TestBuilder>> addColumnFn = Maps.newHashMap();
+
+    static {
+      addColumnFn.put(Columns.BOOL, (t) -> t.column(true, false, false, true, false));
+      addColumnFn.put(Columns.INT, (t) -> t.column(5, 1, 0, 2, 7));
+      addColumnFn.put(Columns.LONG, (t) -> t.column(3l, 9l, 4l, 2l, 20l));
+      addColumnFn.put(Columns.BYTE, (t) -> t.column(new Byte[]{2, 3, 4, 5, 9}));
+      addColumnFn.put(Columns.STRING, (t) -> t.column("this", "is", "a", "test", "string"));
+      addColumnFn.put(Columns.FLOAT, (t) -> t.column(1.0f, 3.5f, 5.9f, 7.1f, 9.8f));
+      addColumnFn.put(Columns.DOUBLE, (t) -> t.column(5.0d, 9.5d, 0.9d, 7.23d, 2.8d));
+      addColumnFn.put(Columns.DECIMAL64, (t) ->
+          t.decimal64Column(-5, 1L, 323L, 12398423L, -231312412L, 239893414231L));
+      addColumnFn.put(Columns.DECIMAL128, (t) ->
+          t.decimal128Column(-10, RoundingMode.UNNECESSARY, BigInteger.ONE, BigInteger.ZERO,
+              BigInteger.TEN, new BigInteger("100000000000000000000000000000"),
+              new BigInteger("-1234567890123456789012345678")));
+
+      BasicType dec64Type = new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL64, 0));
+      StructType structType = new StructType(true,
+          new BasicType(true, DType.INT32), new BasicType(true, DType.STRING), dec64Type);
+      addColumnFn.put(Columns.STRUCT, (t) -> t.column(structType,
+          struct(1, "k1", BigDecimal.ONE),
+          struct(2, "k2", BigDecimal.ZERO),
+          struct(3, "k3", BigDecimal.TEN),
+          struct(4, "k4", BigDecimal.valueOf(Long.MAX_VALUE)),
+          new HostColumnVector.StructData((List) null)));
+      BasicType dec128Type = new BasicType(true, DType.create(DType.DTypeEnum.DECIMAL128, -5));
+      addColumnFn.put(Columns.STRUCT_DEC128, (t) ->
+          t.column(new StructType(false, dec128Type),
+              struct(BigDecimal.valueOf(Integer.MAX_VALUE, 5)),
+              struct(BigDecimal.valueOf(Long.MAX_VALUE, 5)),
+              struct(new BigDecimal("111111111122222222223333333333").setScale(5)),
+              struct(new BigDecimal("123456789123456789123456789").setScale(5)),
+              struct((BigDecimal) null)));
+
+      addColumnFn.put(Columns.LIST, (t) ->
+          t.column(new ListType(false, new BasicType(false, DType.INT32)),
+              Arrays.asList(1, 2),
+              Arrays.asList(3, 4),
+              Arrays.asList(5),
+              Arrays.asList(6, 7),
+              Arrays.asList(8, 9, 10)));
+      addColumnFn.put(Columns.LIST_STRUCT, (t) ->
+          t.column(new ListType(true, structType),
+              Arrays.asList(struct(1, "k1", BigDecimal.ONE), struct(2, "k2", BigDecimal.ONE),
+                  struct(3, "k3", BigDecimal.ONE)),
+              Arrays.asList(struct(4, "k4", BigDecimal.ONE), struct(5, "k5", BigDecimal.ONE)),
+              Arrays.asList(struct(6, "k6", BigDecimal.ONE)),
+              Arrays.asList(new HostColumnVector.StructData((List) null)),
+              (List) null));
+      addColumnFn.put(Columns.LIST_DEC128, (t) ->
+          t.column(new ListType(true, new StructType(false, dec128Type)),
+              Arrays.asList(struct(BigDecimal.valueOf(Integer.MAX_VALUE, 5)),
+                  struct(BigDecimal.valueOf(Integer.MIN_VALUE, 5))),
+              Arrays.asList(struct(BigDecimal.valueOf(Long.MAX_VALUE, 5)),
+                  struct(BigDecimal.valueOf(0, 5)), struct(BigDecimal.valueOf(-1, 5))),
+              Arrays.asList(struct(new BigDecimal("111111111122222222223333333333").setScale(5))),
+              Arrays.asList(struct(new BigDecimal("123456789123456789123456789").setScale(5))),
+              Arrays.asList(struct((BigDecimal) null))));
+    }
+
+    static TestBuilder addColumn(TestBuilder tb, String colName) {
+      if (!addColumnFn.containsKey(Columns.valueOf(colName))) {
+        throw new IllegalArgumentException("Unknown column name: " + colName);
+      }
+      return addColumnFn.get(Columns.valueOf(colName)).apply(tb);
+    }
+
+    static String[] getAllColumns(boolean withDecimal128) {
+      List<String> columns = Lists.newArrayList(
+          Columns.BOOL.name, Columns.INT.name, Columns.BYTE.name, Columns.LONG.name,
+          Columns.STRING.name, Columns.FLOAT.name, Columns.DOUBLE.name, Columns.DECIMAL64.name,
+          Columns.STRUCT.name, Columns.LIST.name, Columns.LIST_STRUCT.name);
+      if (withDecimal128) {
+        columns.add(Columns.DECIMAL128.name);
+        columns.add(Columns.STRUCT_DEC128.name);
+        columns.add(Columns.LIST_DEC128.name);
+      }
+      String[] ret = new String[columns.size()];
+      columns.toArray(ret);
+      return ret;
+    }
+
+    static String[] getNonNestedColumns(boolean withDecimal128) {
+      List<String> columns = Lists.newArrayList(
+          Columns.BOOL.name, Columns.INT.name, Columns.BYTE.name, Columns.LONG.name,
+          Columns.STRING.name, Columns.FLOAT.name, Columns.DOUBLE.name, Columns.DECIMAL64.name);
+      if (withDecimal128) {
+        columns.add(Columns.DECIMAL128.name);
+      }
+      String[] ret = new String[columns.size()];
+      columns.toArray(ret);
+      return ret;
+    }
+
+    static void buildWriterOptions(ColumnWriterOptions.NestedBuilder builder, List<String> columns) {
+      for (String colName : columns) {
+        buildWriterOptions(builder, colName);
+      }
+    }
+
+    static void buildWriterOptions(ColumnWriterOptions.NestedBuilder builder, String... columns) {
+      for (String colName : columns) {
+        buildWriterOptions(builder, colName);
+      }
+    }
+
+    static void buildWriterOptions(ColumnWriterOptions.NestedBuilder builder, String colName) {
+      switch (Columns.valueOf(colName)) {
+      case BOOL:
+      case INT:
+      case LONG:
+      case FLOAT:
+      case DOUBLE:
+      case BYTE:
+      case STRING:
+        builder.withColumns(false, colName);
+        break;
+      case DECIMAL64:
+        builder.withDecimalColumn(colName, DType.DECIMAL64_MAX_PRECISION);
+        break;
+      case DECIMAL128:
+        builder.withDecimalColumn(colName, DType.DECIMAL128_MAX_PRECISION);
+        break;
+      case STRUCT:
+        builder.withStructColumn(structBuilder(colName)
+            .withNullableColumns("ch_int")
+            .withNullableColumns("ch_str")
+            .withDecimalColumn("ch_dec64", DType.DECIMAL64_MAX_PRECISION, true)
+            .build());
+        break;
+      case LIST:
+        builder.withListColumn(listBuilder(colName, false)
+            .withNonNullableColumns("ch_int")
+            .build());
+        break;
+      case LIST_STRUCT:
+        builder.withListColumn(listBuilder(colName)
+            .withStructColumn(structBuilder(colName)
+                .withNullableColumns("ch_int")
+                .withNullableColumns("ch_str")
+                .withDecimalColumn("ch_dec64", DType.DECIMAL64_MAX_PRECISION, true)
+                .build())
+            .build());
+        break;
+      case STRUCT_DEC128:
+        builder.withStructColumn(structBuilder(colName, false)
+            .withDecimalColumn("ch_dec128", DType.DECIMAL128_MAX_PRECISION, true)
+            .build());
+        break;
+      case LIST_DEC128:
+        builder.withListColumn(listBuilder(colName)
+            .withStructColumn(structBuilder(colName, false)
+                .withDecimalColumn("ch_dec128", DType.DECIMAL128_MAX_PRECISION, true)
+                .build())
+            .build());
+        break;
+      default:
+        throw new IllegalArgumentException("should NOT reach here");
+      }
+    }
+  }
+
+  private Table getExpectedFileTable(String... selectColumns) {
+    return getExpectedFileTable(Lists.newArrayList(selectColumns));
+  }
+
+  private Table getExpectedFileTable(List<String> selectColumns) {
+    TestBuilder tb = new TestBuilder();
+    for (String c : selectColumns) {
+      WriteUtils.addColumn(tb, c);
+    }
+    return tb.build();
+  }
+
+  private Table getExpectedFileTableWithDecimals() {
+    return new TestBuilder()
+        .column(true, false, false, true, false)
+        .column(5, 1, 0, 2, 7)
+        .column(new Byte[]{2, 3, 4, 5, 9})
+        .column(3l, 9l, 4l, 2l, 20l)
+        .column("this", "is", "a", "test", "string")
+        .column(1.0f, 3.5f, 5.9f, 7.1f, 9.8f)
+        .column(5.0d, 9.5d, 0.9d, 7.23d, 2.8d)
+        .decimal32Column(3, 298, 2473, 2119, 1273, 9879)
+        .decimal64Column(4, 398l, 1322l, 983237l, 99872l, 21337l)
+        .build();
+  }
+
+  private final class MyBufferConsumer implements HostBufferConsumer, AutoCloseable {
+    public final HostMemoryBuffer buffer;
+    long offset = 0;
+
+    public MyBufferConsumer() {
+      buffer = hostMemoryAllocator.allocate(10 * 1024 * 1024);
+    }
+
+    @Override
+    public void handleBuffer(HostMemoryBuffer src, long len) {
+      try {
+        this.buffer.copyFromHostBuffer(offset, src, 0, len);
+        offset += len;
+      } finally {
+        src.close();
+      }
+    }
+
+    @Override
+    public void close() {
+      buffer.close();
+    }
+  }
+
+  private final class MyBufferProvider implements HostBufferProvider {
+    private final MyBufferConsumer wrapped;
+    long offset = 0;
+
+    private MyBufferProvider(MyBufferConsumer wrapped) {
+      this.wrapped = wrapped;
+    }
+
+    @Override
+    public long readInto(HostMemoryBuffer buffer, long len) {
+      long amountLeft = wrapped.offset - offset;
+      long amountToCopy = Math.max(0, Math.min(len, amountLeft));
+      if (amountToCopy > 0) {
+        buffer.copyFromHostBuffer(0, wrapped.buffer, offset, amountToCopy);
+        offset += amountToCopy;
+      }
+      return amountToCopy;
+    }
+  }
+
+  @Test
+  void testParquetWriteToBufferChunkedInt96() {
+    try (Table table0 = getExpectedFileTableWithDecimals();
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      ParquetWriterOptions options = ParquetWriterOptions.builder()
+          .withNonNullableColumns("_c0", "_c1", "_c2", "_c3", "_c4", "_c5", "_c6")
+          .withDecimalColumn("_c7", 5)
+          .withDecimalColumn("_c8", 5)
+          .build();
+
+      TableDebug.get().debug("default stderr table0", table0);
+      TableDebug.builder()
+        .withOutput(TableDebug.Output.STDOUT)
+        .build().debug("stdout table0", table0);
+      TableDebug.builder()
+          .withOutput(TableDebug.Output.LOG)
+          .build().debug("slf4j default debug table0", table0);
+      TableDebug.builder()
+          .withOutput(TableDebug.Output.LOG_ERROR)
+          .build().debug("slf4j error table0", table0);
+
+      try (TableWriter writer = Table.writeParquetChunked(options, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+      }
+      try (Table table1 = Table.readParquet(ParquetOptions.DEFAULT, consumer.buffer, 0, consumer.offset);
+           Table concat = Table.concatenate(table0, table0, table0)) {
+        assertTablesAreEqual(concat, table1);
+      }
+    }
+  }
+
+  @Test
+  void testParquetWriteMap() throws IOException {
+    ParquetWriterOptions options = ParquetWriterOptions.builder()
+        .withMapColumn(mapColumn("my_map",
+            new ColumnWriterOptions("key0", false),
+            new ColumnWriterOptions("value0"),
+            true)).build();
+    File f = File.createTempFile("test-map", ".parquet");
+    List<HostColumnVector.StructData> list1 =
+        Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a", "b")));
+    List<HostColumnVector.StructData> list2 =
+        Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a", "c")));
+    List<HostColumnVector.StructData> list3 =
+     Arrays.asList(new HostColumnVector.StructData(Arrays.asList("e", "d")));
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true,
+     Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING),
+        new HostColumnVector.BasicType(true, DType.STRING)));
+    try (ColumnVector listColumn = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            structType), list1, list2, list3);
+         Table t0 = new Table(listColumn)) {
+      try (TableWriter writer = Table.writeParquetChunked(options, f)) {
+        writer.write(t0);
+      }
+      ParquetFileReader reader =
+       ParquetFileReader.open(HadoopInputFile.fromPath(new Path(f.getAbsolutePath()),
+           new Configuration()));
+      MessageType schema = reader.getFooter().getFileMetaData().getSchema();
+      assertEquals(OriginalType.MAP, schema.getType("my_map").getOriginalType());
+    }
+    try (ColumnVector cv = Table.readParquet(f).getColumn(0);
+         ColumnVector res = cv.getMapValue(Scalar.fromString("a"));
+         ColumnVector expected = ColumnVector.fromStrings("b", "c", null)) {
+      assertColumnsAreEqual(expected, res);
+    }
+  }
+
+  @Test
+  void testParquetWriteToBufferChunkedWithNested() {
+    ParquetWriterOptions.Builder optBuilder = ParquetWriterOptions.builder();
+    WriteUtils.buildWriterOptions(optBuilder, WriteUtils.getAllColumns(false));
+    ParquetWriterOptions options = optBuilder.build();
+    try (Table table0 = getExpectedFileTable(WriteUtils.getAllColumns(false));
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      try (TableWriter writer = Table.writeParquetChunked(options, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+      }
+      try (Table table1 = Table.readParquet(ParquetOptions.DEFAULT, consumer.buffer, 0,
+          consumer.offset);
+           Table concat = Table.concatenate(table0, table0, table0)) {
+        assertTablesAreEqual(concat, table1);
+      }
+    }
+  }
+
+  @Test
+  void testParquetWriteToBufferChunked() {
+    ParquetWriterOptions.Builder optBuilder = ParquetWriterOptions.builder();
+    List<String> columns = Lists.newArrayList(WriteUtils.getNonNestedColumns(false));
+    columns.add(Columns.STRUCT.name);
+    WriteUtils.buildWriterOptions(optBuilder, columns);
+    ParquetWriterOptions options = optBuilder.build();
+    ParquetWriterOptions optionsNoCompress = optBuilder.withCompressionType(CompressionType.NONE).build();
+    try (Table table0 = getExpectedFileTable(columns);
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      try (TableWriter writer = Table.writeParquetChunked(options, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+
+        TableWriter.WriteStatistics statistics = writer.getWriteStatistics();
+        assertNotEquals(0, statistics.numCompressedBytes);
+        assertEquals(0, statistics.numFailedBytes);
+        assertEquals(0, statistics.numSkippedBytes);
+        assertNotEquals(Double.NaN, statistics.compressionRatio);
+      }
+      try (Table table1 = Table.readParquet(ParquetOptions.DEFAULT, consumer.buffer, 0, consumer.offset);
+           Table concat = Table.concatenate(table0, table0, table0)) {
+        assertTablesAreEqual(concat, table1);
+      }
+      try (TableWriter writer = Table.writeParquetChunked(optionsNoCompress, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+
+        TableWriter.WriteStatistics statistics = writer.getWriteStatistics();
+        assertEquals(0, statistics.numCompressedBytes);
+        assertEquals(0, statistics.numFailedBytes);
+        assertEquals(0, statistics.numSkippedBytes);
+        assertEquals(Double.NaN, statistics.compressionRatio);
+      }
+    }
+  }
+
+  @Test
+  void testParquetWriteToFileWithNames() throws IOException {
+    File tempFile = File.createTempFile("test-names", ".parquet");
+    try (Table table0 = getExpectedFileTableWithDecimals()) {
+      ParquetWriterOptions options = ParquetWriterOptions.builder()
+          .withNonNullableColumns("first", "second", "third", "fourth", "fifth", "sixth", "seventh")
+          .withDecimalColumn("eighth", 5)
+          .withDecimalColumn("ninth", 6)
+          .withCompressionType(CompressionType.NONE)
+          .withStatisticsFrequency(ParquetWriterOptions.StatisticsFrequency.NONE)
+          .build();
+      try (TableWriter writer = Table.writeParquetChunked(options, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      try (Table table2 = Table.readParquet(tempFile.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table2);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testParquetWriteToFileWithNamesAndMetadata() throws IOException {
+    File tempFile = File.createTempFile("test-names-metadata", ".parquet");
+    try (Table table0 = getExpectedFileTableWithDecimals()) {
+      ParquetWriterOptions options = ParquetWriterOptions.builder()
+          .withNonNullableColumns("first", "second", "third", "fourth", "fifth", "sixth", "seventh")
+          .withDecimalColumn("eighth", 6)
+          .withDecimalColumn("ninth", 8)
+          .withMetadata("somekey", "somevalue")
+          .withCompressionType(CompressionType.NONE)
+          .withStatisticsFrequency(ParquetWriterOptions.StatisticsFrequency.NONE)
+          .build();
+      try (TableWriter writer = Table.writeParquetChunked(options, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      try (Table table2 = Table.readParquet(tempFile.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table2);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testParquetWriteToFileUncompressedNoStats() throws IOException {
+    File tempFile = File.createTempFile("test-uncompressed", ".parquet");
+    try (Table table0 = getExpectedFileTableWithDecimals()) {
+      ParquetWriterOptions options = ParquetWriterOptions.builder()
+          .withNonNullableColumns("_c0", "_c1", "_c2", "_c3", "_c4", "_c5", "_c6")
+          .withDecimalColumn("_c7", 4)
+          .withDecimalColumn("_c8", 6)
+          .withCompressionType(CompressionType.NONE)
+          .withStatisticsFrequency(ParquetWriterOptions.StatisticsFrequency.NONE)
+          .build();
+      try (TableWriter writer = Table.writeParquetChunked(options, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      try (Table table2 = Table.readParquet(tempFile.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table2);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testParquetWriteWithFieldId() throws IOException {
+    // field IDs are:
+    // c1: -1, c2: 2, c3: 3, c31: 31, c32: 32, c4: -4, c5: not specified
+    ColumnWriterOptions.StructBuilder sBuilder =
+        structBuilder("c3", true, 3)
+            .withColumn(true, "c31", 31)
+            .withColumn(true, "c32", 32);
+    ParquetWriterOptions options = ParquetWriterOptions.builder()
+        .withColumn(true, "c1", -1)
+        .withDecimalColumn("c2", 9, true, 2)
+        .withStructColumn(sBuilder.build())
+        .withTimestampColumn("c4", true, true, -4)
+        .withColumns( true, "c5")
+        .build();
+
+    File tempFile = File.createTempFile("test-field-id", ".parquet");
+    try {
+      HostColumnVector.StructType structType = new HostColumnVector.StructType(
+          true,
+          new HostColumnVector.BasicType(true, DType.STRING),
+          new HostColumnVector.BasicType(true, DType.STRING));
+
+      try (Table table0 = new Table.TestBuilder()
+          .column(true, false) // c1
+          .decimal32Column(0, 298, 2473) // c2
+          .column(structType, // c3
+              new HostColumnVector.StructData("a", "b"), new HostColumnVector.StructData("a", "b"))
+          .timestampMicrosecondsColumn(1000L, 2000L) // c4
+          .column("a", "b") // c5
+          .build()) {
+        try (TableWriter writer = Table.writeParquetChunked(options, tempFile.getAbsoluteFile())) {
+          writer.write(table0);
+        }
+      }
+
+      try (ParquetFileReader reader = ParquetFileReader.open(HadoopInputFile.fromPath(
+          new Path(tempFile.getAbsolutePath()),
+          new Configuration()))) {
+        MessageType schema = reader.getFooter().getFileMetaData().getSchema();
+        assert (schema.getFields().get(0).getId().intValue() == -1);
+        assert (schema.getFields().get(1).getId().intValue() == 2);
+        assert (schema.getFields().get(2).getId().intValue() == 3);
+        assert (((GroupType) schema.getFields().get(2)).getFields().get(0).getId().intValue() == 31);
+        assert (((GroupType) schema.getFields().get(2)).getFields().get(1).getId().intValue() == 32);
+        assert (schema.getFields().get(3).getId().intValue() == -4);
+        assert (schema.getFields().get(4).getId() == null);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testParquetWriteWithFieldIdNestNotSpecified() throws IOException {
+    // field IDs are:
+    // c0: no field ID
+    // c1: 1
+    // c2: no field ID
+    //   c21: 21
+    //   c22: no field ID
+    // c3: 3
+    //   c31: 31
+    //   c32: no field ID
+    // c4: 0
+    ColumnWriterOptions.StructBuilder c2Builder =
+        structBuilder("c2", true)
+            .withColumn(true, "c21", 21)
+            .withColumns(true, "c22");
+    ColumnWriterOptions.StructBuilder c3Builder =
+        structBuilder("c3", true, 3)
+            .withColumn(true, "c31", 31)
+            .withColumns(true, "c32");
+    ParquetWriterOptions options = ParquetWriterOptions.builder()
+        .withColumns(true, "c0")
+        .withDecimalColumn("c1", 9, true, 1)
+        .withStructColumn(c2Builder.build())
+        .withStructColumn(c3Builder.build())
+        .withColumn(true, "c4", 0)
+        .build();
+
+    File tempFile = File.createTempFile("test-field-id", ".parquet");
+    try {
+      HostColumnVector.StructType structType = new HostColumnVector.StructType(
+          true,
+          new HostColumnVector.BasicType(true, DType.STRING),
+          new HostColumnVector.BasicType(true, DType.STRING));
+
+      try (Table table0 = new Table.TestBuilder()
+          .column(true, false) // c0
+          .decimal32Column(0, 298, 2473) // c1
+          .column(structType, // c2
+              new HostColumnVector.StructData("a", "b"), new HostColumnVector.StructData("a", "b"))
+          .column(structType, // c3
+              new HostColumnVector.StructData("a", "b"), new HostColumnVector.StructData("a", "b"))
+          .column("a", "b") // c4
+          .build()) {
+        try (TableWriter writer = Table.writeParquetChunked(options, tempFile.getAbsoluteFile())) {
+          writer.write(table0);
+        }
+      }
+
+      try (ParquetFileReader reader = ParquetFileReader.open(HadoopInputFile.fromPath(
+          new Path(tempFile.getAbsolutePath()),
+          new Configuration()))) {
+        MessageType schema = reader.getFooter().getFileMetaData().getSchema();
+        assert (schema.getFields().get(0).getId() == null);
+        assert (schema.getFields().get(1).getId().intValue() == 1);
+        assert (schema.getFields().get(2).getId() == null);
+        assert (((GroupType) schema.getFields().get(2)).getFields().get(0).getId().intValue() == 21);
+        assert (((GroupType) schema.getFields().get(2)).getFields().get(1).getId() == null);
+        assert (((GroupType) schema.getFields().get(3)).getFields().get(0).getId().intValue() == 31);
+        assert (((GroupType) schema.getFields().get(3)).getFields().get(1).getId() == null);
+        assert (schema.getFields().get(4).getId().intValue() == 0);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  /** Return a column where DECIMAL64 has been up-casted to DECIMAL128 */
+  private ColumnVector castDecimal64To128(ColumnView c) {
+    DType dtype = c.getType();
+    switch (dtype.getTypeId()) {
+      case DECIMAL64:
+        return c.castTo(DType.create(DType.DTypeEnum.DECIMAL128, dtype.getScale()));
+      case STRUCT:
+      case LIST:
+      {
+        ColumnView[] oldViews = c.getChildColumnViews();
+        assert oldViews != null;
+        ColumnVector[] newChildren = new ColumnVector[oldViews.length];
+        try {
+          for (int i = 0; i < oldViews.length; i++) {
+            newChildren[i] = castDecimal64To128(oldViews[i]);
+          }
+          try (ColumnView newView = new ColumnView(dtype, c.getRowCount(),
+              Optional.of(c.getNullCount()), c.getValid(), c.getOffsets(), newChildren)) {
+            return newView.copyToColumnVector();
+          }
+        } finally {
+          for (ColumnView v : oldViews) {
+            v.close();
+          }
+          for (ColumnVector v : newChildren) {
+            if (v != null) {
+              v.close();
+            }
+          }
+        }
+      }
+      default:
+        if (c instanceof ColumnVector) {
+          return ((ColumnVector) c).incRefCount();
+        } else {
+          return c.copyToColumnVector();
+        }
+    }
+  }
+
+  /** Return a new Table with any DECIMAL64 columns up-casted to DECIMAL128 */
+  private Table castDecimal64To128(Table t) {
+    final int numCols = t.getNumberOfColumns();
+    ColumnVector[] cols = new ColumnVector[numCols];
+    try {
+      for (int i = 0; i < numCols; i++) {
+        cols[i] = castDecimal64To128(t.getColumn(i));
+      }
+      return new Table(cols);
+    } finally {
+      for (ColumnVector c : cols) {
+        if (c != null) {
+          c.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void testArrowIPCWriteToFileWithNamesAndMetadata() throws IOException {
+    File tempFile = File.createTempFile("test-names-metadata", ".arrow");
+    String[] columnNames = WriteUtils.getNonNestedColumns(false);
+    try (Table table0 = getExpectedFileTable(columnNames)) {
+      ArrowIPCWriterOptions options = ArrowIPCWriterOptions.builder()
+              .withColumnNames(columnNames)
+              .build();
+      try (TableWriter writer = Table.writeArrowIPCChunked(options, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      // Reading from Arrow converts decimals to DECIMAL128
+      try (StreamedTableReader reader = Table.readArrowIPCChunked(tempFile);
+           Table expected = castDecimal64To128(table0)) {
+        boolean done = false;
+        int count = 0;
+        while (!done) {
+          try (Table t = reader.getNextIfAvailable()) {
+            if (t == null) {
+              done = true;
+            } else {
+              assertTablesAreEqual(expected, t);
+              count++;
+            }
+          }
+        }
+        assertEquals(1, count);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testArrowIPCWriteToBufferChunked() {
+    String[] nonNestedCols = WriteUtils.getNonNestedColumns(false);
+    List<String> columns = Lists.newArrayList(nonNestedCols);
+    columns.add(Columns.STRUCT.name);
+    columns.add(Columns.LIST.name);
+    columns.add(Columns.LIST_STRUCT.name);
+    try (Table table0 = getExpectedFileTable(columns);
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      ArrowIPCWriterOptions options = ArrowIPCWriterOptions.builder()
+              .withColumnNames(nonNestedCols)
+              .withColumnNames(Columns.STRUCT.name, "int", "str", "dec64")
+              .withColumnNames(Columns.LIST.name)
+              .withColumnNames(Columns.LIST_STRUCT.name, "int", "str", "dec64")
+              .build();
+      try (TableWriter writer = Table.writeArrowIPCChunked(options, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+      }
+      // Reading from Arrow converts decimals to DECIMAL128
+      try (StreamedTableReader reader = Table.readArrowIPCChunked(new MyBufferProvider(consumer));
+           Table expected = castDecimal64To128(table0)) {
+        boolean done = false;
+        int count = 0;
+        while (!done) {
+          try (Table t = reader.getNextIfAvailable()) {
+            if (t == null) {
+              done = true;
+            } else {
+              assertTablesAreEqual(expected, t);
+              count++;
+            }
+          }
+        }
+        assertEquals(3, count);
+      }
+    }
+  }
+
+  @Test
+  void testArrowIPCWriteEmptyToBufferChunked() {
+    try (Table emptyTable = new Table.TestBuilder().timestampDayColumn().build();
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      ArrowIPCWriterOptions options = ArrowIPCWriterOptions.builder()
+              .withColumnNames("day")
+              .build();
+      try (TableWriter writer = Table.writeArrowIPCChunked(options, consumer)) {
+        writer.write(emptyTable);
+      }
+      try (StreamedTableReader reader = Table.readArrowIPCChunked(new MyBufferProvider(consumer))) {
+        boolean done = false;
+        int count = 0;
+        while (!done) {
+          try (Table t = reader.getNextIfAvailable()) {
+            if (t == null) {
+              done = true;
+            } else {
+              assertTablesAreEqual(emptyTable, t);
+              count++;
+            }
+          }
+        }
+        // Expect one empty batch for the empty table.
+        assertEquals(1, count);
+      }
+    }
+  }
+
+  @Test
+  void testORCWriteToBufferChunked() {
+    String[] selectedColumns = WriteUtils.getAllColumns(false);
+    try (Table table0 = getExpectedFileTable(selectedColumns);
+         MyBufferConsumer consumer = new MyBufferConsumer()) {
+      ORCWriterOptions.Builder builder = ORCWriterOptions.builder();
+      WriteUtils.buildWriterOptions(builder, selectedColumns);
+      ORCWriterOptions opts = builder.build();
+      ORCWriterOptions optsNoCompress = builder.withCompressionType(CompressionType.NONE).build();
+      try (TableWriter writer = Table.writeORCChunked(opts, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+
+        TableWriter.WriteStatistics statistics = writer.getWriteStatistics();
+        assertNotEquals(0, statistics.numCompressedBytes);
+        assertEquals(0, statistics.numFailedBytes);
+        assertEquals(0, statistics.numSkippedBytes);
+        assertNotEquals(Double.NaN, statistics.compressionRatio);
+      }
+      try (Table table1 = Table.readORC(ORCOptions.DEFAULT, consumer.buffer, 0, consumer.offset);
+           Table concat = Table.concatenate(table0, table0, table0)) {
+        assertTablesAreEqual(concat, table1);
+      }
+      try (TableWriter writer = Table.writeORCChunked(optsNoCompress, consumer)) {
+        writer.write(table0);
+        writer.write(table0);
+        writer.write(table0);
+
+        TableWriter.WriteStatistics statistics = writer.getWriteStatistics();
+        assertEquals(0, statistics.numCompressedBytes);
+        assertEquals(0, statistics.numFailedBytes);
+        assertEquals(0, statistics.numSkippedBytes);
+        assertEquals(Double.NaN, statistics.compressionRatio);
+      }
+    }
+  }
+
+  @Test
+  void testORCWriteToFileChunked() throws IOException {
+    File tempFile = File.createTempFile("test", ".orc");
+    String[] selectedColumns = WriteUtils.getAllColumns(false);
+    try (Table table0 = getExpectedFileTable(selectedColumns)) {
+      ORCWriterOptions.Builder builder = ORCWriterOptions.builder();
+      WriteUtils.buildWriterOptions(builder, selectedColumns);
+      ORCWriterOptions opts = builder.build();
+      try (TableWriter writer = Table.writeORCChunked(opts, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      try (Table table1 = Table.readORC(tempFile.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table1);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testORCWriteMapChunked() throws IOException {
+    ORCWriterOptions options = ORCWriterOptions.builder()
+            .withMapColumn(mapColumn("my_map",
+                    new ColumnWriterOptions("key0", false),
+                    new ColumnWriterOptions("value0"),
+                    true)).build();
+    File f = File.createTempFile("test-map", ".parquet");
+    List<HostColumnVector.StructData> list1 =
+            Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a", "b")));
+    List<HostColumnVector.StructData> list2 =
+            Arrays.asList(new HostColumnVector.StructData(Arrays.asList("a", "c")));
+    List<HostColumnVector.StructData> list3 =
+            Arrays.asList(new HostColumnVector.StructData(Arrays.asList("e", "d")));
+    HostColumnVector.StructType structType = new HostColumnVector.StructType(true,
+            Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING),
+                    new HostColumnVector.BasicType(true, DType.STRING)));
+    try (ColumnVector listColumn = ColumnVector.fromLists(new HostColumnVector.ListType(true,
+            structType), list1, list2, list3);
+         Table t0 = new Table(listColumn)) {
+      try (TableWriter writer = Table.writeORCChunked(options, f)) {
+        writer.write(t0);
+      }
+      try (Table res = Table.readORC(f)) {
+        assertTablesAreEqual(t0, res);
+      }
+    }
+  }
+
+  @Test
+  void testORCWriteToFileWithColNames() throws IOException {
+    File tempFile = File.createTempFile("test", ".orc");
+    String[] colNames = WriteUtils.getNonNestedColumns(false);
+    try (Table table0 = getExpectedFileTable(colNames)) {
+      ORCWriterOptions.Builder optBuilder = ORCWriterOptions.builder();
+      WriteUtils.buildWriterOptions(optBuilder, colNames);
+      ORCWriterOptions options = optBuilder.build();
+      try (TableWriter writer = Table.writeORCChunked(options, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      ORCOptions opts = ORCOptions.builder().includeColumn(colNames).build();
+      try (Table table1 = Table.readORC(opts, tempFile.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table1);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  // https://github.com/NVIDIA/spark-rapids-jni/issues/1338
+  // Need to remove this tag if #1338 is fixed.
+  @Tag("noSanitizer")
+  @Test
+  void testORCReadAndWriteForDecimal128() throws IOException {
+    File tempFile = File.createTempFile("test", ".orc");
+    String[] colNames = new String[]{Columns.DECIMAL64.name,
+        Columns.DECIMAL128.name, Columns.STRUCT_DEC128.name, Columns.LIST_DEC128.name};
+    try (Table table0 = getExpectedFileTable(colNames)) {
+      ORCWriterOptions.Builder optBuilder = ORCWriterOptions.builder();
+      WriteUtils.buildWriterOptions(optBuilder, colNames);
+      ORCWriterOptions options = optBuilder.build();
+      try (TableWriter writer = Table.writeORCChunked(options, tempFile.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      ORCOptions opts = ORCOptions.builder()
+          .includeColumn(colNames)
+          .decimal128Column(Columns.DECIMAL128.name,
+              String.format("%s.%s", Columns.STRUCT_DEC128.name, "ch_dec128"),
+              String.format("%s.1.%s", Columns.LIST_DEC128.name, "ch_dec128"))
+          .build();
+      try (Table table1 = Table.readORC(opts, tempFile.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table1);
+      }
+    } finally {
+      tempFile.delete();
+    }
+  }
+
+  @Test
+  void testORCWriteToFileUncompressed() throws IOException {
+    File tempFileUncompressed = File.createTempFile("test-uncompressed", ".orc");
+    try (Table table0 = getExpectedFileTable(WriteUtils.getNonNestedColumns(false))) {
+      String[] colNames = WriteUtils.getNonNestedColumns(false);
+      ORCWriterOptions.Builder optsBuilder = ORCWriterOptions.builder();
+      WriteUtils.buildWriterOptions(optsBuilder, colNames);
+      optsBuilder.withCompressionType(CompressionType.NONE);
+      ORCWriterOptions opts = optsBuilder.build();
+      try (TableWriter writer =
+               Table.writeORCChunked(opts,tempFileUncompressed.getAbsoluteFile())) {
+        writer.write(table0);
+      }
+      try (Table table2 = Table.readORC(tempFileUncompressed.getAbsoluteFile())) {
+        assertTablesAreEqual(table0, table2);
+      }
+    } finally {
+      tempFileUncompressed.delete();
+    }
+  }
+
+  @Test
+  void testStructColumnFilter() {
+    List<HostColumnVector.DataType> children =
+        Arrays.asList(new HostColumnVector.BasicType(true, DType.INT32),
+            new HostColumnVector.BasicType(true, DType.INT64));
+    HostColumnVector.StructType type = new HostColumnVector.StructType(true, children);
+    HostColumnVector.StructType expectedType = new HostColumnVector.StructType(true, children);
+    List data1 = Arrays.asList(10, 20L);
+    List data2 = Arrays.asList(50, 60L);
+    List data3 = Arrays.asList(null, 80L);
+    List data4 = null;
+    HostColumnVector.StructData structData1 = new HostColumnVector.StructData(data1);
+    HostColumnVector.StructData structData2 = new HostColumnVector.StructData(data2);
+    HostColumnVector.StructData structData3 = new HostColumnVector.StructData(data3);
+    HostColumnVector.StructData structData4 = new HostColumnVector.StructData(data4);
+    try (ColumnVector mask = ColumnVector.fromBoxedBooleans(true, false, true, false);
+         ColumnVector fromStructs = ColumnVector.fromStructs(type, Arrays.asList(structData1, structData2, structData3, structData4));
+         Table input = new Table(fromStructs);
+         Table filteredTable = input.filter(mask);
+         ColumnVector expectedStructs = ColumnVector.fromStructs(expectedType, Arrays.asList(structData1, structData3));
+         Table expected = new Table(expectedStructs)) {
+      assertTablesAreEqual(expected, filteredTable);
+    }
+  }
+
+  @Test
+  void testStructColumnFilterStrings() {
+    List<HostColumnVector.DataType> children =
+        Arrays.asList(new HostColumnVector.BasicType(true, DType.STRING),
+            new HostColumnVector.BasicType(true, DType.STRING));
+    HostColumnVector.StructType type = new HostColumnVector.StructType(true, children);
+    HostColumnVector.StructType expectedType = new HostColumnVector.StructType(true, children);
+    List data1 = Arrays.asList("10", "aliceAndBob");
+    List data2 = Arrays.asList("50", "foobar");
+    List data3 = Arrays.asList(null, "zombies");
+    List data4 = null;
+    HostColumnVector.StructData structData1 = new HostColumnVector.StructData(data1);
+    HostColumnVector.StructData structData2 = new HostColumnVector.StructData(data2);
+    HostColumnVector.StructData structData3 = new HostColumnVector.StructData(data3);
+    HostColumnVector.StructData structData4 = new HostColumnVector.StructData(data4);
+    try (ColumnVector mask = ColumnVector.fromBoxedBooleans(true, false, true, true);
+         ColumnVector fromStructs = ColumnVector.fromStructs(type, Arrays.asList(structData1, structData2, structData3, structData4));
+         Table input = new Table(fromStructs);
+         Table filteredTable = input.filter(mask);
+         ColumnVector expectedStructs = ColumnVector.fromStructs(expectedType, Arrays.asList(structData1, structData3, structData4));
+         Table expected = new Table(expectedStructs)) {
+      assertEquals(expected.getRowCount(), 3L, "Expected column row count is incorrect");
+      assertTablesAreEqual(expected, filteredTable);
+    }
+  }
+
+  @Test
+  void fixedWidthRowsRoundTripWide() {
+    TestBuilder tb = new TestBuilder();
+    IntStream.range(0, 10).forEach(i -> tb.column(3l, 9l, 4l, 2l, 20l, null));
+    IntStream.range(0, 10).forEach(i -> tb.column(5.0d, 9.5d, 0.9d, 7.23d, 2.8d, null));
+    IntStream.range(0, 10).forEach(i -> tb.column(5, 1, 0, 2, 7, null));
+    IntStream.range(0, 10).forEach(i -> tb.column(true, false, false, true, false, null));
+    IntStream.range(0, 10).forEach(i -> tb.column(1.0f, 3.5f, 5.9f, 7.1f, 9.8f, null));
+    IntStream.range(0, 10).forEach(i -> tb.column(new Byte[]{2, 3, 4, 5, 9, null}));
+    IntStream.range(0, 10).forEach(i -> tb.decimal32Column(-3, RoundingMode.UNNECESSARY, 5.0d,
+        9.5d, 0.9d, 7.23d, 2.8d, null));
+    IntStream.range(0, 10).forEach(i -> tb.decimal64Column(-8, 3L, 9L, 4L, 2L, 20L, null));
+    try (Table origTable = tb.build()) {
+      ColumnVector[] rowMajorTable = origTable.convertToRows();
+      try {
+        // We didn't overflow
+        assert rowMajorTable.length == 1;
+        ColumnVector cv = rowMajorTable[0];
+        assert cv.getRowCount() == origTable.getRowCount();
+        DType[] types = new DType[origTable.getNumberOfColumns()];
+        for (int i = 0; i < origTable.getNumberOfColumns(); i++) {
+          types[i] = origTable.getColumn(i).getType();
+        }
+        try (Table backAgain = Table.convertFromRows(cv, types)) {
+          assertTablesAreEqual(origTable, backAgain);
+        }
+      } finally {
+        for (ColumnVector cv : rowMajorTable) {
+          cv.close();
+        }
+      }
+    }
+  }
+
+  @Test
+  void fixedWidthRowsRoundTrip() {
+    try (Table origTable = new TestBuilder()
+        .column(3l, 9l, 4l, 2l, 20l, null)
+        .column(5.0d, 9.5d, 0.9d, 7.23d, 2.8d, null)
+        .column(5, 1, 0, 2, 7, null)
+        .column(true, false, false, true, false, null)
+        .column(1.0f, 3.5f, 5.9f, 7.1f, 9.8f, null)
+        .column(new Byte[]{2, 3, 4, 5, 9, null})
+        .decimal32Column(-3, RoundingMode.UNNECESSARY, 5.0d, 9.5d, 0.9d, 7.23d, 2.8d, null)
+        .decimal64Column(-8, 3L, 9L, 4L, 2L, 20L, null)
+        .build()) {
+      ColumnVector[] rowMajorTable = origTable.convertToRowsFixedWidthOptimized();
+      try {
+        // We didn't overflow
+        assert rowMajorTable.length == 1;
+        ColumnVector cv = rowMajorTable[0];
+        assert cv.getRowCount() == origTable.getRowCount();
+        DType[] types = new DType[origTable.getNumberOfColumns()];
+        for (int i = 0; i < origTable.getNumberOfColumns(); i++) {
+          types[i] = origTable.getColumn(i).getType();
+        }
+        try (Table backAgain = Table.convertFromRowsFixedWidthOptimized(cv, types)) {
+          assertTablesAreEqual(origTable, backAgain);
+        }
+      } finally {
+        for (ColumnVector cv : rowMajorTable) {
+          cv.close();
+        }
+      }
+    }
+  }
+
+  // utility methods to reduce typing
+
+  private static StructData struct(Object... values) {
+    return new StructData(values);
+  }
+
+  private StructData[] structs(StructData... values) {
+    return values;
+  }
+
+  private String[] strings(String... values) {
+    return values;
+  }
+
+  private static ColumnVector decimalFromBoxedInts(boolean isDec64, int scale, Integer... values) {
+    BigDecimal[] decimals = new BigDecimal[values.length];
+    for (int i = 0; i < values.length; i++) {
+      if (values[i] == null) {
+        decimals[i] = null;
+      } else {
+        decimals[i] = BigDecimal.valueOf(values[i], -scale);
+      }
+    }
+    DType type = isDec64 ? DType.create(DType.DTypeEnum.DECIMAL64, scale) : DType.create(DType.DTypeEnum.DECIMAL32, scale);
+    return ColumnVector.build(type, decimals.length, (b) -> b.appendBoxed(decimals));
+  }
+
+  private Table buildTestTable() {
+    StructType mapStructType = new StructType(true,
+        new BasicType(false, DType.STRING),
+        new BasicType(false, DType.STRING));
+    StructType structType = new StructType(true,
+        new BasicType(true, DType.INT32),
+        new BasicType(false, DType.FLOAT32));
+    return new Table.TestBuilder()
+        .column(     100,      202,      3003,    40004,        5,      -60,    1, null,    3,  null,     5, null,    7, null,   9,   null,    11, null,   13, null,  15)
+        .column(    true,     true,     false,    false,     true,     null, true, true, null, false, false, null, true, true, null, false, false, null, true, true, null)
+        .column( (byte)1,  (byte)2,      null,  (byte)4,  (byte)5,  (byte)6, (byte)1, (byte)2, (byte)3, null, (byte)5, (byte)6, (byte)7, null, (byte)9, (byte)10, (byte)11, null, (byte)13, (byte)14, (byte)15)
+        .column((short)6, (short)5,  (short)4,     null, (short)2, (short)1, (short)1, (short)2, (short)3, null, (short)5, (short)6, (short)7, null, (short)9, (short)10, null, (short)12, (short)13, (short)14, null)
+        .column(      1L,     null,     1001L,      50L,   -2000L,     null, 1L, 2L, 3L, 4L, null, 6L, 7L, 8L, 9L, null, 11L, 12L, 13L, 14L, null)
+        .column(   10.1f,      20f, Float.NaN,  3.1415f,     -60f,     null, 1f, 2f, 3f, 4f, 5f, null, 7f, 8f, 9f, 10f, 11f, null, 13f, 14f, 15f)
+        .column(   10.1f,      20f, Float.NaN,  3.1415f,     -60f,     -50f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, 10f, 11f, 12f, 13f, 14f, 15f)
+        .column(    10.1,     20.0,      33.1,   3.1415,    -60.5,     null, 1., 2., 3., 4., 5., 6., null, 8., 9., 10., 11., 12., null, 14., 15.)
+        .timestampDayColumn(99,      100,      101,      102,      103,      104, 1, 2, 3, 4, 5, 6, 7, null, 9, 10, 11, 12, 13, null, 15)
+        .timestampMillisecondsColumn(9L,    1006L,     101L,    5092L,     null,      88L, 1L, 2L, 3L, 4L, 5L ,6L, 7L, 8L, null, 10L, 11L, 12L, 13L, 14L, 15L)
+        .timestampSecondsColumn(1L, null, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L ,6L, 7L, 8L, 9L, null, 11L, 12L, 13L, 14L, 15L)
+        .decimal32Column(-3, 100,      202,      3003,    40004,        5,      -60,    1, null,    3,  null,     5, null,    7, null,   9,   null,    11, null,   13, null,  15)
+        .decimal64Column(-8,      1L,     null,     1001L,      50L,   -2000L,     null, 1L, 2L, 3L, 4L, null, 6L, 7L, 8L, 9L, null, 11L, 12L, 13L, 14L, null)
+        .column(     "A",      "B",      "C",      "D",     null,   "TESTING", "1", "2", "3", "4", "5", "6", "7", null, "9", "10", "11", "12", "13", null, "15")
+        .column(
+            strings("1", "2", "3"), strings("4"), strings("5"), strings("6, 7"),
+            strings("", "9", null), strings("11"), strings(""), strings(null, null),
+            strings("15", null), null, null, strings("18", "19", "20"),
+            null, strings("22"), strings("23", ""), null,
+            null, null, null, strings(),
+            strings("the end"))
+        .column(mapStructType,
+            structs(struct("1", "2")), structs(struct("3", "4")),
+            null, null,
+            structs(struct("key", "value"), struct("a", "b")), null,
+            null, structs(struct("3", "4"), struct("1", "2")),
+            structs(), structs(null, struct("foo", "bar")),
+            structs(null, null, null), null,
+            null, null,
+            null, null,
+            null, null,
+            null, null,
+            structs(struct("the", "end")))
+        .column(structType,
+            struct(1, 1f), null, struct(2, 3f), null, struct(8, 7f),
+            struct(0, 0f), null, null, struct(-1, -1f), struct(-100, -100f),
+            struct(Integer.MAX_VALUE, Float.MAX_VALUE), null, null, null, null,
+            null, null, null, null, null,
+            struct(Integer.MIN_VALUE, Float.MIN_VALUE))
+        .column(     "A",      "A",      "C",      "C",     null,   "TESTING", "1", "2", "3", "4", "5", "6", "7", null, "9", "10", "11", "12", "13", null, "15")
+        .build();
+  }
+
+  @Test
+  void testBuilderWithColumn() {
+    try (Table t1 = new Table.TestBuilder()
+        .decimal32Column(-3, 120, -230, null, 340)
+        .decimal64Column(-8, 1000L, 200L, null, 30L).build()) {
+      try (Table t2 = new Table.TestBuilder()
+          .decimal32Column(-3, RoundingMode.UNNECESSARY, 0.12, -0.23, null, 0.34)
+          .decimal64Column(-8, RoundingMode.UNNECESSARY, 1e-5, 2e-6, null, 3e-7).build()) {
+        try (Table t3 = new Table.TestBuilder()
+            .decimal32Column(-3, RoundingMode.UNNECESSARY, "0.12", "-000.23", null, ".34")
+            .decimal64Column(-8, RoundingMode.UNNECESSARY, "1e-5", "2e-6", null, "3e-7").build()) {
+          assertTablesAreEqual(t1, t2);
+          assertTablesAreEqual(t1, t3);
+        }
+      }
+    }
+  }
+
+  private Table[] buildExplodeTestTableWithPrimitiveTypes(boolean pos, boolean outer) {
+    try (Table input = new Table.TestBuilder()
+        .column(new ListType(true, new BasicType(true, DType.INT32)),
+            Arrays.asList(1, 2, 3),
+            Arrays.asList(4, 5),
+            Arrays.asList(6),
+            null,
+            Arrays.asList())
+        .column("s1", "s2", "s3", "s4", "s5")
+        .column(1, 3, 5, 7, 9)
+        .column(12.0, 14.0, 13.0, 11.0, 15.0)
+        .build()) {
+      Table.TestBuilder expectedBuilder = new Table.TestBuilder();
+      if (pos) {
+        Integer[] posData = outer ? new Integer[]{0, 1, 2, 0, 1, 0, null, null} : new Integer[]{0, 1, 2, 0, 1, 0};
+        expectedBuilder.column(posData);
+      }
+      List<Object[]> expectedData = new ArrayList<Object[]>(){{
+        if (!outer) {
+          this.add(new Integer[]{1, 2, 3, 4, 5, 6});
+          this.add(new String[]{"s1", "s1", "s1", "s2", "s2", "s3"});
+          this.add(new Integer[]{1, 1, 1, 3, 3, 5});
+          this.add(new Double[]{12.0, 12.0, 12.0, 14.0, 14.0, 13.0});
+        } else {
+          this.add(new Integer[]{1, 2, 3, 4, 5, 6, null, null});
+          this.add(new String[]{"s1", "s1", "s1", "s2", "s2", "s3", "s4", "s5"});
+          this.add(new Integer[]{1, 1, 1, 3, 3, 5, 7, 9});
+          this.add(new Double[]{12.0, 12.0, 12.0, 14.0, 14.0, 13.0, 11.0, 15.0});
+        }
+      }};
+      try (Table expected = expectedBuilder.column((Integer[]) expectedData.get(0))
+          .column((String[]) expectedData.get(1))
+          .column((Integer[]) expectedData.get(2))
+          .column((Double[]) expectedData.get(3))
+          .build()) {
+        return new Table[]{new Table(input.getColumns()), new Table(expected.getColumns())};
+      }
+    }
+  }
+
+  private Table[] buildExplodeTestTableWithNestedTypes(boolean pos, boolean outer) {
+    StructType nestedType = new StructType(true,
+        new BasicType(false, DType.INT32), new BasicType(false, DType.STRING));
+    try (Table input = new Table.TestBuilder()
+        .column(new ListType(false, nestedType),
+            Arrays.asList(struct(1, "k1"), struct(2, "k2"), struct(3, "k3")),
+            Arrays.asList(struct(4, "k4"), struct(5, "k5")),
+            Arrays.asList(struct(6, "k6")),
+            Arrays.asList(new HostColumnVector.StructData((List) null)),
+            null)
+        .column("s1", "s2", "s3", "s4", "s5")
+        .column(1, 3, 5, 7, 9)
+        .column(12.0, 14.0, 13.0, 11.0, 15.0)
+        .build()) {
+      Table.TestBuilder expectedBuilder = new Table.TestBuilder();
+      if (pos) {
+        if (outer) {
+          expectedBuilder.column(0, 1, 2, 0, 1, 0, 0, null);
+        } else {
+          expectedBuilder.column(0, 1, 2, 0, 1, 0, 0);
+        }
+      }
+      List<Object[]> expectedData = new ArrayList<Object[]>(){{
+        if (!outer) {
+          this.add(new HostColumnVector.StructData[]{
+              struct(1, "k1"), struct(2, "k2"), struct(3, "k3"),
+              struct(4, "k4"), struct(5, "k5"), struct(6, "k6"),
+              new HostColumnVector.StructData((List) null)});
+          this.add(new String[]{"s1", "s1", "s1", "s2", "s2", "s3", "s4"});
+          this.add(new Integer[]{1, 1, 1, 3, 3, 5, 7});
+          this.add(new Double[]{12.0, 12.0, 12.0, 14.0, 14.0, 13.0, 11.0});
+        } else {
+          this.add(new HostColumnVector.StructData[]{
+              struct(1, "k1"), struct(2, "k2"), struct(3, "k3"),
+              struct(4, "k4"), struct(5, "k5"), struct(6, "k6"),
+              new HostColumnVector.StructData((List) null), null});
+          this.add(new String[]{"s1", "s1", "s1", "s2", "s2", "s3", "s4", "s5"});
+          this.add(new Integer[]{1, 1, 1, 3, 3, 5, 7, 9});
+          this.add(new Double[]{12.0, 12.0, 12.0, 14.0, 14.0, 13.0, 11.0, 15.0});
+        }
+      }};
+      try (Table expected = expectedBuilder
+          .column(nestedType, (HostColumnVector.StructData[]) expectedData.get(0))
+          .column((String[]) expectedData.get(1))
+          .column((Integer[]) expectedData.get(2))
+          .column((Double[]) expectedData.get(3))
+          .build()) {
+        return new Table[]{new Table(input.getColumns()), new Table(expected.getColumns())};
+      }
+    }
+  }
+
+  @Test
+  void testExplode() {
+    // Child is primitive type
+    Table[] testTables = buildExplodeTestTableWithPrimitiveTypes(false, false);
+    try (Table input = testTables[0];
+         Table expected = testTables[1]) {
+      try (Table exploded = input.explode(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+
+    // Child is nested type
+    Table[] testTables2 = buildExplodeTestTableWithNestedTypes(false, false);
+    try (Table input = testTables2[0];
+         Table expected = testTables2[1]) {
+      try (Table exploded = input.explode(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+  }
+
+  @Test
+  void testExplodePosition() {
+    // Child is primitive type
+    Table[] testTables = buildExplodeTestTableWithPrimitiveTypes(true, false);
+    try (Table input = testTables[0];
+         Table expected = testTables[1]) {
+      try (Table exploded = input.explodePosition(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+
+    // Child is nested type
+    Table[] testTables2 = buildExplodeTestTableWithNestedTypes(true, false);
+    try (Table input = testTables2[0];
+         Table expected = testTables2[1]) {
+      try (Table exploded = input.explodePosition(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+  }
+
+  @Test
+  void testExplodeOuter() {
+    // Child is primitive type
+    Table[] testTables = buildExplodeTestTableWithPrimitiveTypes(false, true);
+    try (Table input = testTables[0];
+         Table expected = testTables[1]) {
+      try (Table exploded = input.explodeOuter(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+
+    // Child is nested type
+    Table[] testTables2 = buildExplodeTestTableWithNestedTypes(false, true);
+    try (Table input = testTables2[0];
+         Table expected = testTables2[1]) {
+      try (Table exploded = input.explodeOuter(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+  }
+
+  @Test
+  void testExplodeOuterPosition() {
+    // Child is primitive type
+    Table[] testTables = buildExplodeTestTableWithPrimitiveTypes(true, true);
+    try (Table input = testTables[0];
+         Table expected = testTables[1]) {
+      try (Table exploded = input.explodeOuterPosition(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+
+    // Child is nested type
+    Table[] testTables2 = buildExplodeTestTableWithNestedTypes(true, true);
+    try (Table input = testTables2[0];
+         Table expected = testTables2[1]) {
+      try (Table exploded = input.explodeOuterPosition(0)) {
+        assertTablesAreEqual(expected, exploded);
+      }
+    }
+  }
+
+  @Test
+  void testSample() {
+    try (Table t = new Table.TestBuilder().column("s1", "s2", "s3", "s4", "s5").build()) {
+      try (Table ret = t.sample(3, false, 0)) {
+        assertEquals(ret.getRowCount(), 3);
+      }
+
+      try (Table ret = t.sample(5, false, 0)) {
+        assertEquals(ret.getRowCount(), 5);
+      }
+
+      try (Table ret = t.sample(8, true, 0)) {
+        assertEquals(ret.getRowCount(), 8);
+      }
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/TestUtils.java b/java/src/test/java/ai/rapids/cudf/TestUtils.java
new file mode 100644
index 0000000..a1acab5
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/TestUtils.java
@@ -0,0 +1,229 @@
+/*
+ *
+ *  Copyright (c) 2020-2021, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import java.io.File;
+import java.net.URISyntaxException;
+import java.net.URL;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+import java.util.stream.IntStream;
+
+import static java.lang.Double.NEGATIVE_INFINITY;
+import static java.lang.Double.POSITIVE_INFINITY;
+
+/**
+ * Utility class for generating test data
+ */
+class TestUtils {
+
+  static int NULL = 0x00000001;
+  static int ZERO = 0x00000002;
+  static int INF = 0x00000004;
+  static int NAN = 0x00000008;
+  static int NEG_ZERO = 0x00000010;
+  static int ALL = NULL|ZERO|INF|NAN|NEG_ZERO;
+  static int NONE = 0;
+
+  private static boolean hasZero(int v) {
+    return (v & ZERO) > 0;
+  }
+
+  private static boolean hasNegativeZero(int v) {
+    return (v & NEG_ZERO) > 0;
+  }
+
+  private static boolean hasNan(int v) {
+    return (v & NAN) > 0;
+  }
+
+  private static boolean hasNull(int v) {
+    return (v & NULL) > 0;
+  }
+
+  private static boolean hasInf(int v) {
+    return (v & INF) > 0;
+  }
+
+  static Long[] getLongs(final long seed, final int size) {
+    return getLongs(seed, size, ALL);
+  }
+
+  static Double[] getDoubles(final long seed, final int size) {
+    return getDoubles(seed, size, ALL);
+  }
+
+  static Integer[] getIntegers(final long seed, final int size) {
+    return getIntegers(seed, size, ALL);
+  }
+
+  /**
+   * A convenience method for generating a fixed set of Integer values. This is by no means uniformly
+   * distributed. i.e. some values have more probability of occurrence than others.
+   *
+   * @param seed Seed value to be used to generate values
+   * @param size Number of values to be generated
+   * @param specialValues Values to include. Please refer to {@link TestUtils#ALL} for possible values
+   */
+  static Long[] getLongs(final long seed, final int size, int specialValues) {
+    Random r = new Random(seed);
+    Long[] result = new Long[size];
+    List<Long> v = new ArrayList();
+    if (hasZero(specialValues)) v.add(0L);
+    if (hasNull(specialValues)) v.add(null);
+
+    Long[] v_arr = v.stream().toArray(Long[]::new);
+
+    IntStream.range(0, size).forEach(index -> {
+      switch (r.nextInt(v_arr.length + 4)) {
+        case 0:
+          result[index] = (long) (Long.MAX_VALUE * r.nextDouble());
+          break;
+        case 1:
+          result[index] = (long) (Long.MIN_VALUE * r.nextDouble());
+          break;
+        case 2:
+          result[index] = Long.MIN_VALUE;
+          break;
+        case 3:
+          result[index] = Long.MAX_VALUE;
+          break;
+        case 4:
+          result[index] = v_arr[0];
+          break;
+        default:
+          result[index] = v_arr[1];
+      }
+    });
+    return result;
+  }
+
+  /**
+   * A convenience method for generating a fixed set of Integer values. This is by no means uniformly
+   * distributed. i.e. some values have more probability of occurrence than others.
+   *
+   * @param seed Seed value to be used to generate values
+   * @param size Number of values to be generated
+   * @param specialValues Values to include. Please refer to {@link TestUtils#ALL} for possible values
+   */
+  static Integer[] getIntegers(final long seed, final int size, int specialValues) {
+    Random r = new Random(seed);
+    Integer[] result = new Integer[size];
+    List<Integer> v = new ArrayList();
+    if (hasZero(specialValues)) v.add(0);
+    if (hasNull(specialValues)) v.add(null);
+
+    Integer[] v_arr = v.stream().toArray(Integer[]::new);
+
+    IntStream.range(0, size).forEach(index -> {
+      switch (r.nextInt(v_arr.length + 4)) {
+        case 0:
+          result[index] = (int) (Integer.MAX_VALUE * r.nextDouble());
+          break;
+        case 1:
+          result[index] = (int) (Integer.MIN_VALUE * r.nextDouble());
+          break;
+        case 2:
+          result[index] = Integer.MIN_VALUE;
+          break;
+        case 3:
+          result[index] = Integer.MAX_VALUE;
+          break;
+        case 4:
+          result[index] = v_arr[0];
+          break;
+        default:
+          result[index] = v_arr[1];
+      }
+    });
+    return result;
+  }
+
+  /**
+   * A convenience method for generating a fixed set of Double values. This is by no means uniformly
+   * distributed. i.e. some values have more probability of occurrence than others.
+   *
+   * @param seed Seed value to be used to generate values
+   * @param size Number of values to be generated
+   * @param specialValues Values to include. Please refer to {@link TestUtils#ALL} for possible values
+   */
+  static Double[] getDoubles(final long seed, final int size, int specialValues) {
+    Random r = new Random(seed);
+    Double[] result = new Double[size];
+    List<Double> v = new ArrayList();
+    if (hasZero(specialValues)) v.add(0.0);
+    if (hasNegativeZero(specialValues)) v.add(-0.0);
+    if (hasInf(specialValues)) {
+      v.add(POSITIVE_INFINITY);
+      v.add(NEGATIVE_INFINITY);
+    }
+    if (hasNan(specialValues)) v.add(Double.NaN);
+    if (hasNull(specialValues)) v.add(null);
+
+    Double[] v_arr = v.stream().toArray(Double[]::new);
+
+    IntStream.range(0, size).forEach(index -> {
+      switch (r.nextInt(v_arr.length + 4)) {
+        case 0:
+          result[index] = 1 + (Double.MAX_VALUE * r.nextDouble() - 2);
+          break;
+        case 1:
+          result[index] = 1 + (Double.MIN_VALUE * r.nextDouble() - 2);
+          break;
+        case 2:
+          result[index] = Double.MIN_VALUE;
+          break;
+        case 3:
+          result[index] = Double.MAX_VALUE;
+          break;
+        case 4:
+          result[index] = v_arr[0];
+          break;
+        case 5:
+          result[index] = v_arr[1];
+          break;
+        case 6:
+          result[index] = v_arr[2];
+          break;
+        case 7:
+          result[index] = v_arr[3];
+          break;
+        case 8:
+          result[index] = v_arr[4];
+          break;
+        default:
+          result[index] = v_arr[5];
+      }
+    });
+    return result;
+  }
+
+  public static File getResourceAsFile(String resourceName) {
+    URL url = TestUtils.class.getClassLoader().getResource(resourceName);
+    if (url == null) {
+      throw new IllegalArgumentException("Unable to locate resource: " + resourceName);
+    }
+    try {
+      return new File(url.toURI());
+    } catch (URISyntaxException e) {
+      throw new RuntimeException(e);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java b/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java
new file mode 100644
index 0000000..c22acac
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/TimestampColumnVectorTest.java
@@ -0,0 +1,492 @@
+/*
+ *
+ *  Copyright (c) 2019-2020, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Test;
+
+import java.util.function.Function;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+public class TimestampColumnVectorTest extends CudfTestBase {
+  static final int[] TIMES_DAY = {-1528,    //1965-10-26
+                                  17716,    //2018-07-04
+                                  19382,    //2023-01-25
+                                  -1528,    //1965-10-26
+                                  17716};   //2018-07-04
+
+  static final long[] TIMES_S = {-131968728L,   //'1965-10-26 14:01:12' Tuesday
+                                 1530705600L,   //'2018-07-04 12:00:00' Wednesday
+                                 1674631932L,   //'2023-01-25 07:32:12' Wednesday
+                                 -131968728L,   //'1965-10-26 14:01:12' Tuesday
+                                 1530705600L};  //'2018-07-04 12:00:00' Wednesday
+
+  static final long[] TIMES_MS = {-131968727762L,   //'1965-10-26 14:01:12.238' Tuesday
+                                  1530705600115L,   //'2018-07-04 12:00:00.115' Wednesday
+                                  1674631932929L,   //'2023-01-25 07:32:12.929' Wednesday
+                                  -131968727762L,   //'1965-10-26 14:01:12.238' Tuesday
+                                  1530705600115L};  //'2018-07-04 12:00:00.115' Wednesday
+
+  static final long[] TIMES_US = {-131968727761703L,   //'1965-10-26 14:01:12.238297'
+                                  1530705600115254L,   //'2018-07-04 12:00:00.115254'
+                                  1674631932929861L,   //'2023-01-25 07:32:12.929861'
+                                  -131968727761703L,   //'1965-10-26 14:01:12.238297'
+                                  1530705600115254L};  //'2018-07-04 12:00:00.115254'
+
+  static final long[] TIMES_NS = {-131968727761702469L,   //'1965-10-26 14:01:12.238297531'
+                                  1530705600115254330L,   //'2018-07-04 12:00:00.115254330'
+                                  1674631932929861604L,   //'2023-01-25 07:32:12.929861604'
+                                  -131968727761702469L,   //'1965-10-26 14:01:12.238297531'
+                                  1530705600115254330L};  //'2018-07-04 12:00:00.115254330'
+
+  static final String[] TIMES_S_STRING = {"1965-10-26 14:01:12",
+                                          "2018-07-04 12:00:00",
+                                          "2023-01-25 07:32:12",
+                                          "1965-10-26 14:01:12",
+                                          "2018-07-04 12:00:00"};
+
+  static final String[] TIMES_MS_STRING = {"1965-10-26 14:01:12.238000000",
+                                           "2018-07-04 12:00:00.115000000",
+                                           "2023-01-25 07:32:12.929000000",
+                                           "1965-10-26 14:01:12.238000000",
+                                           "2018-07-04 12:00:00.115000000"};
+
+  static final String[] TIMES_US_STRING = {"1965-10-26 14:01:12.238297000",
+                                           "2018-07-04 12:00:00.115254000",
+                                           "2023-01-25 07:32:12.929861000",
+                                           "1965-10-26 14:01:12.238297000",
+                                           "2018-07-04 12:00:00.115254000"};
+
+  static final String[] TIMES_NS_STRING = {"1965-10-26 14:01:12.238297531",
+                                           "2018-07-04 12:00:00.115254330",
+                                           "2023-01-25 07:32:12.929861604",
+                                           "1965-10-26 14:01:12.238297531",
+                                           "2018-07-04 12:00:00.115254330"};
+
+  static final long[] THOUSAND = {1000L, 1000L, 1000L, 1000L, 1000L};
+
+  public static ColumnVector mulThouAndClose(ColumnVector cv, int times) {
+    ColumnVector input = cv;
+    ColumnVector tmp = null;
+    try (ColumnVector THOU = ColumnVector.fromLongs(THOUSAND)) {
+      for (int i = 0; i < times; i++) {
+        tmp = input.mul(THOU);
+        input.close();
+        input = tmp;
+        tmp = null;
+      }
+      ColumnVector ret = input;
+      input = null;
+      return ret;
+    } finally {
+      if (tmp != null) {
+        tmp.close();
+      }
+      if (input != null) {
+        input.close();
+      }
+    }
+  }
+
+  public static ColumnVector applyAndClose(ColumnVector cv, Function<ColumnVector, ColumnVector> function) {
+    try {
+      return function.apply(cv);
+    } finally {
+      cv.close();
+    }
+  }
+
+  @Test
+  public void getYear() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector tmp = timestampColumnVector.year();
+         HostColumnVector result = tmp.copyToHost()) {
+      assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS);
+      assertEquals(1965, result.getShort(0));
+      assertEquals(2018, result.getShort(1));
+      assertEquals(2023, result.getShort(2));
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+      ColumnVector tmp = timestampColumnVector.year();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(1965, result.getShort(0));
+      assertEquals(2018, result.getShort(1));
+      assertEquals(2023, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getMonth() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector tmp = timestampColumnVector.month();
+         HostColumnVector result = tmp.copyToHost()) {
+      assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS);
+      assertEquals(10, result.getShort(0));
+      assertEquals(7, result.getShort(1));
+      assertEquals(1, result.getShort(2));
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector tmp = timestampColumnVector.month();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(10, result.getShort(0));
+      assertEquals(7, result.getShort(1));
+      assertEquals(1, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getDay() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS)) {
+      assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS);
+      try (ColumnVector tmp = timestampColumnVector.day();
+           HostColumnVector result = tmp.copyToHost()) {
+        assertEquals(26, result.getShort(0));
+        assertEquals(4, result.getShort(1));
+        assertEquals(25, result.getShort(2));
+      }
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector tmp = timestampColumnVector.day();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(26, result.getShort(0));
+      assertEquals(4, result.getShort(1));
+      assertEquals(25, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getHour() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS)) {
+      assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS);
+      try (ColumnVector tmp = timestampColumnVector.hour();
+           HostColumnVector result = tmp.copyToHost()) {
+        assertEquals(14, result.getShort(0));
+        assertEquals(12, result.getShort(1));
+        assertEquals(7, result.getShort(2));
+      }
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector tmp = timestampColumnVector.hour();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(14, result.getShort(0));
+      assertEquals(12, result.getShort(1));
+      assertEquals(7, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getMinute() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS)) {
+      assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS);
+      try (ColumnVector tmp = timestampColumnVector.minute();
+           HostColumnVector result = tmp.copyToHost()) {
+        assertEquals(1, result.getShort(0));
+        assertEquals(0, result.getShort(1));
+        assertEquals(32, result.getShort(2));
+      }
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector tmp = timestampColumnVector.minute();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(1, result.getShort(0));
+      assertEquals(0, result.getShort(1));
+      assertEquals(32, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void getSecond() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS)) {
+      assert timestampColumnVector.getType().equals(DType.TIMESTAMP_MILLISECONDS);
+      try (ColumnVector tmp = timestampColumnVector.second();
+           HostColumnVector result = tmp.copyToHost()) {
+        assertEquals(12, result.getShort(0));
+        assertEquals(0, result.getShort(1));
+        assertEquals(12, result.getShort(2));
+      }
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector tmp = timestampColumnVector.second();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(12, result.getShort(0));
+      assertEquals(0, result.getShort(1));
+      assertEquals(12, result.getShort(2));
+    }
+  }
+
+  @Test
+  public void testWeekDay() {
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector result = timestampColumnVector.weekDay();
+         ColumnVector expected = ColumnVector.fromBoxedShorts(
+                 (short)2, (short)3, (short)3, (short)2, (short)3)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector result = timestampColumnVector.weekDay();
+         ColumnVector expected = ColumnVector.fromBoxedShorts(
+                 (short)2, (short)3, (short)3, (short)2, (short)3)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampDaysFromBoxedInts(
+            17713, 17714, 17715, 17716, 17717, 17718, 17719, 17720);
+         ColumnVector result = timestampColumnVector.weekDay();
+         ColumnVector expected = ColumnVector.fromBoxedShorts(
+                 (short)7, (short)1, (short)2, (short)3, (short)4, (short)5, (short)6, (short)7)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  public void testLastDayOfMonth() {
+    int[] EXPECTED = new int[]{
+            -1523,    //1965-10-31
+            17743,    //2018-07-31
+            19388,    //2023-01-31
+            -1523,    //1965-10-31
+            17743};   //2018-07-31
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector result = timestampColumnVector.lastDayOfMonth();
+         ColumnVector expected = ColumnVector.daysFromInts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector result = timestampColumnVector.lastDayOfMonth();
+         ColumnVector expected = ColumnVector.daysFromInts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.daysFromInts(TIMES_DAY);
+         ColumnVector result = timestampColumnVector.lastDayOfMonth();
+         ColumnVector expected = ColumnVector.daysFromInts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  public void testDayOfYear() {
+    short[] EXPECTED = new short[]{299, 185, 25, 299, 185};
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector result = timestampColumnVector.dayOfYear();
+         ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector result = timestampColumnVector.dayOfYear();
+         ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.daysFromInts(TIMES_DAY);
+         ColumnVector result = timestampColumnVector.dayOfYear();
+         ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  public void testQuarterOfYear() {
+    short[] EXPECTED = new short[]{4, 3, 1, 4, 3};
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector result = timestampColumnVector.quarterOfYear();
+         ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector result = timestampColumnVector.quarterOfYear();
+         ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.daysFromInts(TIMES_DAY);
+         ColumnVector result = timestampColumnVector.quarterOfYear();
+         ColumnVector expected = ColumnVector.fromShorts(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  public void testAddMonths() {
+    long[] EXPECTED = new long[]{
+        -131968727762L,   //'1965-10-26 14:01:12.238' Tuesday
+        1533384000115L,   //'2018-08-04 12:00:00.115' Saturday
+        1679729532929L,   //'2023-03-25 07:32:12.929' Saturday
+        -124019927762L,   //'1966-01-26 14:01:12.238' Wednesday
+        1520164800115L};  //'2018-03-04 12:00:00.115' Sunday
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector months = ColumnVector.fromShorts(
+             (short)0, (short)1, (short)2, (short)3, (short)-4);
+         ColumnVector result = timestampColumnVector.addCalendricalMonths(months);
+         ColumnVector expected = ColumnVector.timestampMilliSecondsFromLongs(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  public void testIsLeapYear() {
+    Boolean[] EXPECTED = new Boolean[]{false, false, false, false, false};
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector result = timestampColumnVector.isLeapYear();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector result = timestampColumnVector.isLeapYear();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    try (ColumnVector timestampColumnVector = ColumnVector.daysFromInts(TIMES_DAY);
+         ColumnVector result = timestampColumnVector.isLeapYear();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(EXPECTED)) {
+      assertColumnsAreEqual(expected, result);
+    }
+
+    final long[] LEAP_TIMES_S = {1073865600L,   // Monday, January 12, 2004 0:00:00
+        947635200L, // Wednesday, January 12, 2000 0:00:00
+        -2208038400L // Friday, January 12, 1900 0:00:00
+    };
+
+    try (ColumnVector timestampColumnVector = ColumnVector.timestampSecondsFromLongs(LEAP_TIMES_S);
+         ColumnVector result = timestampColumnVector.isLeapYear();
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(true, true, false)) {
+      assertColumnsAreEqual(expected, result);
+    }
+  }
+
+  @Test
+  public void testCastToTimestamp() {
+    try (ColumnVector timestampMillis = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector tmp = timestampMillis.asTimestampSeconds();
+         HostColumnVector result = tmp.copyToHost()) {
+      assertEquals(-131968728L, result.getLong(0));
+      assertEquals(1530705600L, result.getLong(1));
+      assertEquals(1674631932L, result.getLong(2));
+    }
+  }
+
+  @Test
+  public void testTimestampToDays() {
+    try (ColumnVector s_string_times = ColumnVector.fromStrings(TIMES_S_STRING);
+         ColumnVector ms_string_times = ColumnVector.fromStrings(TIMES_MS_STRING);
+         ColumnVector us_string_times = ColumnVector.fromStrings(TIMES_US_STRING);
+         ColumnVector ns_string_times = ColumnVector.fromStrings(TIMES_NS_STRING);
+         ColumnVector day_expected = ColumnVector.daysFromInts(TIMES_DAY);
+         ColumnVector s_result = s_string_times.asTimestamp(DType.TIMESTAMP_DAYS, "%Y-%m-%d %H:%M:%S");
+         ColumnVector ms_result = ms_string_times.asTimestamp(DType.TIMESTAMP_DAYS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector us_result = us_string_times.asTimestamp(DType.TIMESTAMP_DAYS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector ns_result = ns_string_times.asTimestamp(DType.TIMESTAMP_DAYS, "%Y-%m-%d %H:%M:%S.%f")) {
+      assertColumnsAreEqual(day_expected, s_result);
+      assertColumnsAreEqual(day_expected, ms_result);
+      assertColumnsAreEqual(day_expected, us_result);
+      assertColumnsAreEqual(day_expected, ns_result);
+    }
+  }
+
+  @Test
+  public void testTimestampToLongSecond() {
+    try (ColumnVector s_string_times = ColumnVector.fromStrings(TIMES_S_STRING);
+         ColumnVector ms_string_times = ColumnVector.fromStrings(TIMES_MS_STRING);
+         ColumnVector us_string_times = ColumnVector.fromStrings(TIMES_US_STRING);
+         ColumnVector ns_string_times = ColumnVector.fromStrings(TIMES_NS_STRING);
+         ColumnVector s_expected = ColumnVector.timestampSecondsFromLongs(TIMES_S);
+         ColumnVector s_result = s_string_times.asTimestamp(DType.TIMESTAMP_SECONDS, "%Y-%m-%d %H:%M:%S");
+         ColumnVector ms_result = ms_string_times.asTimestamp(DType.TIMESTAMP_SECONDS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector us_result = us_string_times.asTimestamp(DType.TIMESTAMP_SECONDS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector ns_result = ns_string_times.asTimestamp(DType.TIMESTAMP_SECONDS, "%Y-%m-%d %H:%M:%S.%f")) {
+      assertColumnsAreEqual(s_expected, s_result);
+      assertColumnsAreEqual(s_expected, ms_result);
+      assertColumnsAreEqual(s_expected, us_result);
+      assertColumnsAreEqual(s_expected, ns_result);
+    }
+  }
+
+  @Test
+  public void testTimestampToLongMillisecond() {
+    try (ColumnVector s_string_times = ColumnVector.fromStrings(TIMES_S_STRING);
+         ColumnVector ms_string_times = ColumnVector.fromStrings(TIMES_MS_STRING);
+         ColumnVector us_string_times = ColumnVector.fromStrings(TIMES_US_STRING);
+         ColumnVector ns_string_times = ColumnVector.fromStrings(TIMES_NS_STRING);
+         ColumnVector s_expected = applyAndClose(mulThouAndClose(ColumnVector.fromLongs(TIMES_S), 1), cv -> cv.asTimestampMilliseconds());
+         ColumnVector ms_expected = ColumnVector.timestampMilliSecondsFromLongs(TIMES_MS);
+         ColumnVector s_result = s_string_times.asTimestamp(DType.TIMESTAMP_MILLISECONDS, "%Y-%m-%d %H:%M:%S");
+         ColumnVector ms_result = ms_string_times.asTimestamp(DType.TIMESTAMP_MILLISECONDS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector us_result = us_string_times.asTimestamp(DType.TIMESTAMP_MILLISECONDS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector ns_result = ns_string_times.asTimestamp(DType.TIMESTAMP_MILLISECONDS, "%Y-%m-%d %H:%M:%S.%f")) {
+      assertColumnsAreEqual(s_expected, s_result);
+      assertColumnsAreEqual(ms_expected, ms_result);
+      assertColumnsAreEqual(ms_expected, us_result);
+      assertColumnsAreEqual(ms_expected, ns_result);
+    }
+  }
+
+  @Test
+  public void testTimestampToLongMicrosecond() {
+    try (ColumnVector s_string_times = ColumnVector.fromStrings(TIMES_S_STRING);
+         ColumnVector ms_string_times = ColumnVector.fromStrings(TIMES_MS_STRING);
+         ColumnVector us_string_times = ColumnVector.fromStrings(TIMES_US_STRING);
+         ColumnVector ns_string_times = ColumnVector.fromStrings(TIMES_NS_STRING);
+         ColumnVector s_expected = applyAndClose(mulThouAndClose(ColumnVector.fromLongs(TIMES_S), 2), cv -> cv.asTimestampMicroseconds());
+         ColumnVector ms_expected = applyAndClose(mulThouAndClose(ColumnVector.fromLongs(TIMES_MS), 1), cv -> cv.asTimestampMicroseconds());
+         ColumnVector us_expected = ColumnVector.timestampMicroSecondsFromLongs(TIMES_US);
+         ColumnVector s_result = s_string_times.asTimestamp(DType.TIMESTAMP_MICROSECONDS, "%Y-%m-%d %H:%M:%S");
+         ColumnVector ms_result = ms_string_times.asTimestamp(DType.TIMESTAMP_MICROSECONDS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector us_result = us_string_times.asTimestamp(DType.TIMESTAMP_MICROSECONDS, "%Y-%m-%d %H:%M:%S.%f");
+         ColumnVector ns_result = ns_string_times.asTimestamp(DType.TIMESTAMP_MICROSECONDS, "%Y-%m-%d %H:%M:%S.%f")) {
+      assertColumnsAreEqual(s_expected, s_result);
+      assertColumnsAreEqual(ms_expected, ms_result);
+      assertColumnsAreEqual(us_expected, us_result);
+      assertColumnsAreEqual(us_expected, ns_result);
+    }
+  }
+
+  @Test
+  public void testTimestampToLongNanosecond() {
+    try (ColumnVector s_string_times = ColumnVector.fromStrings(TIMES_S_STRING);
+         ColumnVector ms_string_times = ColumnVector.fromStrings(TIMES_MS_STRING);
+         ColumnVector us_string_times = ColumnVector.fromStrings(TIMES_US_STRING);
+         ColumnVector ns_string_times = ColumnVector.fromStrings(TIMES_NS_STRING);
+         ColumnVector s_expected = applyAndClose(mulThouAndClose(ColumnVector.fromLongs(TIMES_S), 3), cv -> cv.asTimestampNanoseconds());
+         ColumnVector ms_expected = applyAndClose(mulThouAndClose(ColumnVector.fromLongs(TIMES_MS), 2), cv -> cv.asTimestampNanoseconds());
+         ColumnVector us_expected = applyAndClose(mulThouAndClose(ColumnVector.fromLongs(TIMES_US), 1), cv -> cv.asTimestampNanoseconds());
+         ColumnVector ns_expected = ColumnVector.timestampNanoSecondsFromLongs(TIMES_NS);
+         ColumnVector s_result = s_string_times.asTimestamp(DType.TIMESTAMP_NANOSECONDS, "%Y-%m-%d %H:%M:%S");
+         ColumnVector ms_result = ms_string_times.asTimestamp(DType.TIMESTAMP_NANOSECONDS, "%Y-%m-%d %H:%M:%S.%9f");
+         ColumnVector us_result = us_string_times.asTimestamp(DType.TIMESTAMP_NANOSECONDS, "%Y-%m-%d %H:%M:%S.%9f");
+         ColumnVector ns_result = ns_string_times.asTimestamp(DType.TIMESTAMP_NANOSECONDS, "%Y-%m-%d %H:%M:%S.%9f")) {
+      assertColumnsAreEqual(s_expected, s_result);
+      assertColumnsAreEqual(ms_expected, ms_result);
+      assertColumnsAreEqual(us_expected, us_result);
+      assertColumnsAreEqual(ns_expected, ns_result);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/UnaryOpTest.java b/java/src/test/java/ai/rapids/cudf/UnaryOpTest.java
new file mode 100644
index 0000000..7fcb7cb
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/UnaryOpTest.java
@@ -0,0 +1,345 @@
+
+/*
+ *
+ *  Copyright (c) 2019, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import ai.rapids.cudf.HostColumnVector.Builder;
+import org.junit.jupiter.api.Test;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+
+public class UnaryOpTest extends CudfTestBase {
+  private static final Double[] DOUBLES_1 = new Double[]{1.0, 10.0, -100.1, 5.3, 50.0, 100.0, null, Double.NaN, Double.POSITIVE_INFINITY, 1/9.0, Double.NEGATIVE_INFINITY, 500.0, -500.0};
+  private static final Integer[] INTS_1 = new Integer[]{1, 10, -100, 5, 50, 100, null};
+  private static final Boolean[] BOOLEANS_1 = new Boolean[]{true, false, true, false, true, false, null};
+
+  interface CpuOp {
+    void computeNullSafe(Builder ret, HostColumnVector input, int index);
+  }
+
+  interface DoubleFun {
+    double apply(double val);
+  }
+
+  static DoubleCpuOp doubleFun(DoubleFun fun) {
+    return new DoubleCpuOp(fun);
+  }
+
+  static class DoubleCpuOp implements CpuOp {
+    private final DoubleFun fun;
+
+    DoubleCpuOp(DoubleFun fun) {
+      this.fun = fun;
+    }
+
+    @Override
+    public void computeNullSafe(Builder ret, HostColumnVector input, int index) {
+      ret.append(fun.apply(input.getDouble(index)));
+    }
+  }
+
+  interface IntFun {
+    int apply(int val);
+  }
+
+  static IntCpuOp intFun(IntFun fun) {
+    return new IntCpuOp(fun);
+  }
+
+  static class IntCpuOp implements CpuOp {
+    private final IntFun fun;
+
+    IntCpuOp(IntFun fun) {
+      this.fun = fun;
+    }
+
+    @Override
+    public void computeNullSafe(Builder ret, HostColumnVector input, int index) {
+      ret.append(fun.apply(input.getInt(index)));
+    }
+  }
+
+  interface BoolFun {
+    boolean apply(boolean val);
+  }
+
+  static BoolCpuOp boolFun(BoolFun fun) {
+    return new BoolCpuOp(fun);
+  }
+
+  static class BoolCpuOp implements CpuOp {
+    private final BoolFun fun;
+
+    BoolCpuOp(BoolFun fun) {
+      this.fun = fun;
+    }
+
+    @Override
+    public void computeNullSafe(Builder ret, HostColumnVector input, int index) {
+      ret.append(fun.apply(input.getBoolean(index)));
+    }
+  }
+
+  public static ColumnVector forEach(ColumnVector input, CpuOp op) {
+    int len = (int)input.getRowCount();
+    try (HostColumnVector host = input.copyToHost();
+         Builder builder = HostColumnVector.builder(input.getType(), len)) {
+      for (int i = 0; i < len; i++) {
+        if (host.isNull(i)) {
+          builder.appendNull();
+        } else {
+          op.computeNullSafe(builder, host, i);
+        }
+      }
+      return builder.buildAndPutOnDevice();
+    }
+  }
+
+  // These tests are not for the correctness of the underlying implementation, but really just
+  // plumbing
+
+  @Test
+  public void testSin() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.sin();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::sin))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testCos() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.cos();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::cos))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testTan() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.tan();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::tan))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testArcsin() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.arcsin();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::asin))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testArccos() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.arccos();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::acos))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testArctan() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.arctan();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::atan))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testSinh() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.sinh();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::sinh))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testCosh() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.cosh();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::cosh))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testTanh() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.tanh();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::tanh))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  public static double asinh(double value) {
+    return value == Double.NEGATIVE_INFINITY ? Double.NEGATIVE_INFINITY :
+      java.lang.StrictMath.log(value + java.lang.Math.sqrt(value * value + 1.0));
+  }
+
+  @Test
+  public void testArcsinh() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.arcsinh();
+         ColumnVector expected = forEach(dcv, doubleFun(UnaryOpTest::asinh))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  public static double acosh(double value) {
+    return java.lang.StrictMath.log(value + java.lang.Math.sqrt(value * value - 1.0));
+  }
+
+  @Test
+  public void testArccosh() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.arccosh();
+         ColumnVector expected = forEach(dcv, doubleFun(UnaryOpTest::acosh))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  public static double atanh(double value) {
+    return 0.5 * (java.lang.StrictMath.log1p(value) - java.lang.StrictMath.log1p(- value));
+  }
+
+  @Test
+  public void testArctanh() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.arctanh();
+         ColumnVector expected = forEach(dcv, doubleFun(UnaryOpTest::atanh))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testExp() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.exp();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::exp))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testLog() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.log();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::log))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testLog2() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.log2();
+         ColumnVector expected = forEach(dcv, doubleFun(n -> Math.log(n) / Math.log(2)))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testLog10() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.log10();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::log10))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testSqrt() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.sqrt();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::sqrt))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testCbrt() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.cbrt();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::cbrt))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testCeil() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.ceil();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::ceil))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testFloor() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.floor();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::floor))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testAbs() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.abs();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::abs))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testRint() {
+    try (ColumnVector dcv = ColumnVector.fromBoxedDoubles(DOUBLES_1);
+         ColumnVector answer = dcv.rint();
+         ColumnVector expected = forEach(dcv, doubleFun(Math::rint))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testBitInvert() {
+    try (ColumnVector icv = ColumnVector.fromBoxedInts(INTS_1);
+         ColumnVector answer = icv.bitInvert();
+         ColumnVector expected = forEach(icv, intFun((i) -> ~i))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+
+  @Test
+  public void testNot() {
+    try (ColumnVector icv = ColumnVector.fromBoxedBooleans(BOOLEANS_1);
+         ColumnVector answer = icv.not();
+         ColumnVector expected = forEach(icv, boolFun((i) -> !i))) {
+      assertColumnsAreEqual(expected, answer);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/UnsafeMemoryAccessorTest.java b/java/src/test/java/ai/rapids/cudf/UnsafeMemoryAccessorTest.java
new file mode 100644
index 0000000..a3684cb
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/UnsafeMemoryAccessorTest.java
@@ -0,0 +1,100 @@
+/*
+ *
+ *  Copyright (c) 2019-2023, NVIDIA CORPORATION.
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ */
+
+package ai.rapids.cudf;
+
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotEquals;
+
+@Tag("noSanitizer")
+public class UnsafeMemoryAccessorTest {
+  @Test
+  public void testAllocate() {
+    long address = UnsafeMemoryAccessor.allocate(3);
+    try {
+      assertNotEquals(0, address);
+    } finally {
+      UnsafeMemoryAccessor.free(address);
+    }
+  }
+
+  @Test
+  public void setByteAndGetByte() {
+    long address = UnsafeMemoryAccessor.allocate(2);
+    try {
+      UnsafeMemoryAccessor.setByte(address, (byte) 34);
+      UnsafeMemoryAccessor.setByte(address + 1, (byte) 63);
+      Byte b = UnsafeMemoryAccessor.getByte(address);
+      assertEquals((byte) 34, b);
+      b = UnsafeMemoryAccessor.getByte(address + 1);
+      assertEquals((byte) 63, b);
+    } finally {
+      UnsafeMemoryAccessor.free(address);
+    }
+  }
+
+  @Test
+  public void setIntAndGetInt() {
+    long address = UnsafeMemoryAccessor.allocate(2 * 4);
+    try {
+      UnsafeMemoryAccessor.setInt(address, 2);
+      UnsafeMemoryAccessor.setInt(address + 4, 4);
+      int v = UnsafeMemoryAccessor.getInt(address);
+      assertEquals(2, v);
+      v = UnsafeMemoryAccessor.getInt(address + 4);
+      assertEquals(4, v);
+    } finally {
+      UnsafeMemoryAccessor.free(address);
+    }
+  }
+
+  @Test
+  public void setMemoryValue() {
+    long address = UnsafeMemoryAccessor.allocate(4);
+    try {
+      UnsafeMemoryAccessor.setMemory(address, 4, (byte) 1);
+      int v = UnsafeMemoryAccessor.getInt(address);
+      assertEquals(16843009, v);
+    } finally {
+      UnsafeMemoryAccessor.free(address);
+    }
+  }
+
+  @Test
+  public void testGetLongs() {
+    int numLongs = 257;
+    long address = UnsafeMemoryAccessor.allocate(numLongs * 8);
+    for (int i = 0; i < numLongs; ++i) {
+      UnsafeMemoryAccessor.setLong(address + (i * 8), i);
+    }
+    long[] result = new long[numLongs];
+    UnsafeMemoryAccessor.getLongs(result, 0, address, numLongs);
+    for (int i = 0; i < numLongs; ++i) {
+      assertEquals(i, result[i]);
+    }
+    UnsafeMemoryAccessor.getLongs(result, 1,
+        address + ((numLongs - 1) * 8), 1);
+    for (int i = 0; i < numLongs; ++i) {
+      long expected = (i == 1) ? numLongs - 1 : i;
+      assertEquals(expected, result[i]);
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/ast/CompiledExpressionTest.java b/java/src/test/java/ai/rapids/cudf/ast/CompiledExpressionTest.java
new file mode 100644
index 0000000..f6a17b4
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/ast/CompiledExpressionTest.java
@@ -0,0 +1,614 @@
+/*
+ * Copyright (c) 2021-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.ast;
+
+import ai.rapids.cudf.ColumnVector;
+import ai.rapids.cudf.CudfException;
+import ai.rapids.cudf.CudfTestBase;
+import ai.rapids.cudf.DType;
+import ai.rapids.cudf.Table;
+import org.junit.jupiter.api.Assertions;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.Arguments;
+import org.junit.jupiter.params.provider.MethodSource;
+import org.junit.jupiter.params.provider.ValueSource;
+import org.junit.jupiter.params.provider.NullSource;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.function.BiFunction;
+import java.util.function.Function;
+import java.util.stream.Stream;
+
+import static ai.rapids.cudf.AssertUtils.assertColumnsAreEqual;
+
+public class CompiledExpressionTest extends CudfTestBase {
+  @Test
+  public void testColumnReferenceTransform() {
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build()) {
+      // use an implicit table reference
+      ColumnReference expr = new ColumnReference(1);
+      try (CompiledExpression compiledExpr = expr.compile();
+           ColumnVector actual = compiledExpr.computeColumn(t)) {
+        assertColumnsAreEqual(t.getColumn(1), actual);
+      }
+
+      // use an explicit table reference
+      expr = new ColumnReference(1, TableReference.LEFT);
+      try (CompiledExpression compiledExpr = expr.compile();
+           ColumnVector actual = compiledExpr.computeColumn(t)) {
+        assertColumnsAreEqual(t.getColumn(1), actual);
+      }
+    }
+  }
+
+  @Test
+  public void testInvalidColumnReferenceTransform() {
+    // Verify that computeColumn throws when passed an expression operating on TableReference.RIGHT.
+    ColumnReference expr = new ColumnReference(1, TableReference.RIGHT);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile()) {
+      Assertions.assertThrows(CudfException.class, () -> compiledExpr.computeColumn(t).close());
+    }
+  }
+
+  @Test
+  public void testBooleanLiteralTransform() {
+    try (Table t = new Table.TestBuilder().column(true, false, null).build()) {
+      Literal expr = Literal.ofBoolean(true);
+      try (CompiledExpression trueCompiledExpr = expr.compile();
+           ColumnVector trueExprActual = trueCompiledExpr.computeColumn(t);
+           ColumnVector trueExprExpected = ColumnVector.fromBoxedBooleans(true, true, true)) {
+        assertColumnsAreEqual(trueExprExpected, trueExprActual);
+      }
+
+      Literal nullLiteral = Literal.ofBoolean(null);
+      UnaryOperation nullExpr = new UnaryOperation(UnaryOperator.IDENTITY, nullLiteral);
+      try (CompiledExpression nullCompiledExpr = nullExpr.compile();
+           ColumnVector nullExprActual = nullCompiledExpr.computeColumn(t);
+           ColumnVector nullExprExpected = ColumnVector.fromBoxedBooleans(null, null, null)) {
+        assertColumnsAreEqual(nullExprExpected, nullExprActual);
+      }
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(bytes = 0x12)
+  public void testByteLiteralTransform(Byte value) {
+    Literal expr = Literal.ofByte(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedBytes(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(shorts = 0x1234)
+  public void testShortLiteralTransform(Short value) {
+    Literal expr = Literal.ofShort(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedShorts(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(ints = 0x12345678)
+  public void testIntLiteralTransform(Integer value) {
+    Literal expr = Literal.ofInt(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedInts(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testLongLiteralTransform(Long value) {
+    Literal expr = Literal.ofLong(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(floats = { 123456.789f, Float.NaN, Float.POSITIVE_INFINITY, Float.NEGATIVE_INFINITY} )
+  public void testFloatLiteralTransform(Float value) {
+    Literal expr = Literal.ofFloat(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(doubles = { 123456.789f, Double.NaN, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY} )
+  public void testDoubleLiteralTransform(Double value) {
+    Literal expr = Literal.ofDouble(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(ints = 0x12345678)
+  public void testTimestampDaysLiteralTransform(Integer value) {
+    Literal expr = Literal.ofTimestampDaysFromInt(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.timestampDaysFromBoxedInts(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testTimestampSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofTimestampFromLong(DType.TIMESTAMP_SECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.timestampSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testTimestampMilliSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofTimestampFromLong(DType.TIMESTAMP_MILLISECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.timestampMilliSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testTimestampMicroSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofTimestampFromLong(DType.TIMESTAMP_MICROSECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.timestampMicroSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testTimestampNanoSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofTimestampFromLong(DType.TIMESTAMP_NANOSECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.timestampNanoSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(ints = 0x12345678)
+  public void testDurationDaysLiteralTransform(Integer value) {
+    Literal expr = Literal.ofDurationDaysFromInt(value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.durationDaysFromBoxedInts(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testDurationSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofDurationFromLong(DType.DURATION_SECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.durationSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testDurationMilliSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofDurationFromLong(DType.DURATION_MILLISECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.durationMilliSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testDurationMicroSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofDurationFromLong(DType.DURATION_MICROSECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.durationMicroSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @ParameterizedTest
+  @NullSource
+  @ValueSource(longs = 0x1234567890abcdefL)
+  public void testDurationNanoSecondsLiteralTransform(Long value) {
+    Literal expr = Literal.ofDurationFromLong(DType.DURATION_NANOSECONDS, value);
+    try (Table t = new Table.TestBuilder().column(5, 4, 3, 2, 1).column(6, 7, 8, null, 10).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected =
+             ColumnVector.durationNanoSecondsFromBoxedLongs(value, value, value, value, value)) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static <T, R> ArrayList<R> mapArray(T[] input, Function<T, R> func) {
+    ArrayList<R> result = new ArrayList<>(input.length);
+    for (T t : input) {
+      result.add(t == null ? null : func.apply(t));
+    }
+    return result;
+  }
+
+  private static <T, U, R> ArrayList<R> mapArray(T[] in1, U[] in2, BiFunction<T, U, R> func) {
+    assert in1.length == in2.length;
+    ArrayList<R> result = new ArrayList<>(in1.length);
+    for (int i = 0; i < in1.length; i++) {
+      result.add(in1[i] == null || in2[i] == null ? null : func.apply(in1[i], in2[i]));
+    }
+    return result;
+  }
+
+  private static Stream<Arguments> createUnaryDoubleOperationParams() {
+    Double[] input = new Double[] { -5., 4.5, null, 2.7, 1.5 };
+    return Stream.of(
+        Arguments.of(UnaryOperator.IDENTITY, input, Arrays.asList(input)),
+        Arguments.of(UnaryOperator.SIN, input, mapArray(input, Math::sin)),
+        Arguments.of(UnaryOperator.COS, input, mapArray(input, Math::cos)),
+        Arguments.of(UnaryOperator.TAN, input, mapArray(input, Math::tan)),
+        Arguments.of(UnaryOperator.ARCSIN, input, mapArray(input, Math::asin)),
+        Arguments.of(UnaryOperator.ARCCOS, input, mapArray(input, Math::acos)),
+        Arguments.of(UnaryOperator.ARCTAN, input, mapArray(input, Math::atan)),
+        Arguments.of(UnaryOperator.SINH, input, mapArray(input, Math::sinh)),
+        Arguments.of(UnaryOperator.COSH, input, mapArray(input, Math::cosh)),
+        Arguments.of(UnaryOperator.TANH, input, mapArray(input, Math::tanh)),
+        Arguments.of(UnaryOperator.EXP, input, mapArray(input, Math::exp)),
+        Arguments.of(UnaryOperator.LOG, input, mapArray(input, Math::log)),
+        Arguments.of(UnaryOperator.SQRT, input, mapArray(input, Math::sqrt)),
+        Arguments.of(UnaryOperator.CBRT, input, mapArray(input, Math::cbrt)),
+        Arguments.of(UnaryOperator.CEIL, input, mapArray(input, Math::ceil)),
+        Arguments.of(UnaryOperator.FLOOR, input, mapArray(input, Math::floor)),
+        Arguments.of(UnaryOperator.ABS, input, mapArray(input, Math::abs)),
+        Arguments.of(UnaryOperator.RINT, input, mapArray(input, Math::rint)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createUnaryDoubleOperationParams")
+  void testUnaryDoubleOperationTransform(UnaryOperator op, Double[] input,
+                                          List<Double> expectedValues) {
+    UnaryOperation expr = new UnaryOperation(op, new ColumnReference(0));
+    try (Table t = new Table.TestBuilder().column(input).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(
+             expectedValues.toArray(new Double[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testUnaryShortOperationTransform() {
+    Short[] input = new Short[] { -5, 4, null, 2, 1 };
+    try (Table t = new Table.TestBuilder().column(input).build()) {
+      ColumnReference expr = new ColumnReference(0);
+      try (CompiledExpression compiledExpr = expr.compile();
+           ColumnVector actual = compiledExpr.computeColumn(t)) {
+        assertColumnsAreEqual(t.getColumn(0), actual);
+      }
+
+      UnaryOperation expr2 = new UnaryOperation(UnaryOperator.BIT_INVERT, new ColumnReference(0));
+      try (CompiledExpression compiledExpr = expr2.compile();
+           ColumnVector actual = compiledExpr.computeColumn(t);
+           ColumnVector expected = ColumnVector.fromBoxedInts(4, -5, null, -3, -2)) {
+        assertColumnsAreEqual(expected, actual);
+      }
+    }
+  }
+
+  private static Stream<Arguments> createUnaryLogicalOperationParams() {
+    Long[] input = new Long[] { -5L, 0L, null, 2L, 1L };
+    return Stream.of(
+        Arguments.of(UnaryOperator.NOT, input, Arrays.asList(false, true, null, false, false)),
+        Arguments.of(UnaryOperator.IS_NULL, input, Arrays.asList(false, false, true, false, false)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createUnaryLogicalOperationParams")
+  void testUnaryLogicalOperationTransform(UnaryOperator op, Long[] input,
+                                          List<Boolean> expectedValues) {
+    UnaryOperation expr = new UnaryOperation(op, new ColumnReference(0));
+    try (Table t = new Table.TestBuilder().column(input).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(
+             expectedValues.toArray(new Boolean[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createBinaryFloatOperationParams() {
+    Float[] in1 = new Float[] { -5f, 4.5f, null, 2.7f };
+    Float[] in2 = new Float[] { 123f, -456f, null, 0f };
+    return Stream.of(
+        Arguments.of(BinaryOperator.ADD, in1, in2, mapArray(in1, in2, Float::sum)),
+        Arguments.of(BinaryOperator.SUB, in1, in2, mapArray(in1, in2, (a, b) -> a - b)),
+        Arguments.of(BinaryOperator.MUL, in1, in2, mapArray(in1, in2, (a, b) -> a * b)),
+        Arguments.of(BinaryOperator.DIV, in1, in2, mapArray(in1, in2, (a, b) -> a / b)),
+        Arguments.of(BinaryOperator.MOD, in1, in2, mapArray(in1, in2, (a, b) -> a % b)),
+        Arguments.of(BinaryOperator.PYMOD, in1, in2, mapArray(in1, in2,
+            (a, b) -> ((a % b) + b) % b)),
+        Arguments.of(BinaryOperator.POW, in1, in2, mapArray(in1, in2,
+            (a, b) -> (float) Math.pow(a, b))));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBinaryFloatOperationParams")
+  void testBinaryFloatOperationTransform(BinaryOperator op, Float[] in1, Float[] in2,
+                                          List<Float> expectedValues) {
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(in1).column(in2).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedFloats(
+             expectedValues.toArray(new Float[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createBinaryDoublePromotedOperationParams() {
+    Float[] in1 = new Float[] { -5f, 4.5f, null, 2.7f };
+    Float[] in2 = new Float[] { 123f, -456f, null, 0f };
+    return Stream.of(
+        Arguments.of(BinaryOperator.TRUE_DIV, in1, in2, mapArray(in1, in2,
+            (a, b) -> (double) a / b)),
+        Arguments.of(BinaryOperator.FLOOR_DIV, in1, in2, mapArray(in1, in2,
+            (a, b) -> Math.floor(a / b))));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBinaryDoublePromotedOperationParams")
+  void testBinaryDoublePromotedOperationTransform(BinaryOperator op, Float[] in1, Float[] in2,
+                                                   List<Double> expectedValues) {
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(in1).column(in2).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedDoubles(
+             expectedValues.toArray(new Double[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createBinaryComparisonOperationParams() {
+    Integer[] in1 = new Integer[] { -5, 4, null, 2, -3 };
+    Integer[] in2 = new Integer[] { 123, -456, null, 0, -3 };
+    return Stream.of(
+        // nulls compare as equal by default
+        Arguments.of(BinaryOperator.NULL_EQUAL, in1, in2, Arrays.asList(false, false, true, false, true)),
+        Arguments.of(BinaryOperator.NOT_EQUAL, in1, in2, mapArray(in1, in2, (a, b) -> !a.equals(b))),
+        Arguments.of(BinaryOperator.LESS, in1, in2, mapArray(in1, in2, (a, b) -> a < b)),
+        Arguments.of(BinaryOperator.GREATER, in1, in2, mapArray(in1, in2, (a, b) -> a > b)),
+        Arguments.of(BinaryOperator.LESS_EQUAL, in1, in2, mapArray(in1, in2, (a, b) -> a <= b)),
+        Arguments.of(BinaryOperator.GREATER_EQUAL, in1, in2, mapArray(in1, in2, (a, b) -> a >= b)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBinaryComparisonOperationParams")
+  void testBinaryComparisonOperationTransform(BinaryOperator op, Integer[] in1, Integer[] in2,
+                                               List<Boolean> expectedValues) {
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(in1).column(in2).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(
+             expectedValues.toArray(new Boolean[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createStringLiteralComparisonParams() {
+    String[] in1 = new String[] {"a", "bb", null, "ccc", "dddd"};
+    String in2 = "ccc";
+    return Stream.of(
+        // nulls compare as equal by default
+        Arguments.of(BinaryOperator.NULL_EQUAL, in1, in2, Arrays.asList(false, false, false, true, false)),
+        Arguments.of(BinaryOperator.NOT_EQUAL, in1, in2, mapArray(in1, (a) -> !a.equals(in2))),
+        Arguments.of(BinaryOperator.LESS, in1, in2, mapArray(in1, (a) -> a.compareTo(in2) < 0)),
+        Arguments.of(BinaryOperator.GREATER, in1, in2, mapArray(in1, (a) -> a.compareTo(in2) > 0)),
+        Arguments.of(BinaryOperator.LESS_EQUAL, in1, in2, mapArray(in1, (a) -> a.compareTo(in2) <= 0)),
+        Arguments.of(BinaryOperator.GREATER_EQUAL, in1, in2, mapArray(in1, (a) -> a.compareTo(in2) >= 0)),
+        // null literal
+        Arguments.of(BinaryOperator.NULL_EQUAL, in1, null, Arrays.asList(false, false, true, false, false)),
+        Arguments.of(BinaryOperator.NOT_EQUAL, in1, null, Arrays.asList(null, null, null, null, null)),
+        Arguments.of(BinaryOperator.LESS, in1, null, Arrays.asList(null, null, null, null, null)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createStringLiteralComparisonParams")
+  void testStringLiteralComparison(BinaryOperator op, String[] in1, String in2,
+                                               List<Boolean> expectedValues) {
+    Literal lit = Literal.ofString(in2);
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        lit);
+    try (Table t = new Table.TestBuilder().column(in1).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(
+             expectedValues.toArray(new Boolean[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createBinaryComparisonOperationStringParams() {
+    String[] in1 = new String[] {"a", "bb", null, "ccc", "dddd"};
+    String[] in2 = new String[] {"aa", "b", null, "ccc", "ddd"};
+    return Stream.of(
+        // nulls compare as equal by default
+        Arguments.of(BinaryOperator.NULL_EQUAL, in1, in2, Arrays.asList(false, false, true, true, false)),
+        Arguments.of(BinaryOperator.NOT_EQUAL, in1, in2, mapArray(in1, in2, (a, b) -> !a.equals(b))),
+        Arguments.of(BinaryOperator.LESS, in1, in2, mapArray(in1, in2, (a, b) -> a.compareTo(b) < 0)),
+        Arguments.of(BinaryOperator.GREATER, in1, in2, mapArray(in1, in2, (a, b) -> a.compareTo(b) > 0)),
+        Arguments.of(BinaryOperator.LESS_EQUAL, in1, in2, mapArray(in1, in2, (a, b) -> a.compareTo(b) <= 0)),
+        Arguments.of(BinaryOperator.GREATER_EQUAL, in1, in2, mapArray(in1, in2, (a, b) -> a.compareTo(b) >= 0)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBinaryComparisonOperationStringParams")
+  void testBinaryComparisonOperationStringTransform(BinaryOperator op, String[] in1, String[] in2,
+                                               List<Boolean> expectedValues) {
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(in1).column(in2).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(
+             expectedValues.toArray(new Boolean[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createBinaryBitwiseOperationParams() {
+    Integer[] in1 = new Integer[] { -5, 4, null, 2, -3 };
+    Integer[] in2 = new Integer[] { 123, -456, null, 0, -3 };
+    return Stream.of(
+        Arguments.of(BinaryOperator.BITWISE_AND, in1, in2, mapArray(in1, in2, (a, b) -> a & b)),
+        Arguments.of(BinaryOperator.BITWISE_OR, in1, in2, mapArray(in1, in2, (a, b) -> a | b)),
+        Arguments.of(BinaryOperator.BITWISE_XOR, in1, in2, mapArray(in1, in2, (a, b) -> a ^ b)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBinaryBitwiseOperationParams")
+  void testBinaryBitwiseOperationTransform(BinaryOperator op, Integer[] in1, Integer[] in2,
+                                            List<Integer> expectedValues) {
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(in1).column(in2).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedInts(
+             expectedValues.toArray(new Integer[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  private static Stream<Arguments> createBinaryBooleanOperationParams() {
+    Boolean[] in1 = new Boolean[] { false, true, false, null, true, false };
+    Boolean[] in2 = new Boolean[] { true, null, null, null, true, false };
+    return Stream.of(
+        Arguments.of(BinaryOperator.LOGICAL_AND, in1, in2, mapArray(in1, in2, (a, b) -> a && b)),
+        Arguments.of(BinaryOperator.LOGICAL_OR, in1, in2, mapArray(in1, in2, (a, b) -> a || b)),
+        Arguments.of(BinaryOperator.NULL_LOGICAL_AND, in1, in2, Arrays.asList(false, null, false, null, true, false)),
+        Arguments.of(BinaryOperator.NULL_LOGICAL_OR, in1, in2, Arrays.asList(true, true, null, null, true, false)));
+  }
+
+  @ParameterizedTest
+  @MethodSource("createBinaryBooleanOperationParams")
+  void testBinaryBooleanOperationTransform(BinaryOperator op, Boolean[] in1, Boolean[] in2,
+                                            List<Boolean> expectedValues) {
+    BinaryOperation expr = new BinaryOperation(op,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(in1).column(in2).build();
+         CompiledExpression compiledExpr = expr.compile();
+         ColumnVector actual = compiledExpr.computeColumn(t);
+         ColumnVector expected = ColumnVector.fromBoxedBooleans(
+             expectedValues.toArray(new Boolean[0]))) {
+      assertColumnsAreEqual(expected, actual);
+    }
+  }
+
+  @Test
+  void testMismatchedBinaryOperationTypes() {
+    // verify expression fails to transform if operands are not the same type
+    BinaryOperation expr = new BinaryOperation(BinaryOperator.ADD,
+        new ColumnReference(0),
+        new ColumnReference(1));
+    try (Table t = new Table.TestBuilder().column(1, 2, 3).column(1L, 2L, 3L).build();
+         CompiledExpression compiledExpr = expr.compile()) {
+      Assertions.assertThrows(CudfException.class, () -> compiledExpr.computeColumn(t).close());
+    }
+  }
+}
diff --git a/java/src/test/java/ai/rapids/cudf/nvcomp/NvcompTest.java b/java/src/test/java/ai/rapids/cudf/nvcomp/NvcompTest.java
new file mode 100644
index 0000000..66f4fe3
--- /dev/null
+++ b/java/src/test/java/ai/rapids/cudf/nvcomp/NvcompTest.java
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2020-2022, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.rapids.cudf.nvcomp;
+
+import ai.rapids.cudf.*;
+import org.junit.jupiter.api.Assertions;
+import org.junit.jupiter.api.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.Arrays;
+import java.util.Optional;
+
+public class NvcompTest {
+  private static final HostMemoryAllocator hostMemoryAllocator = DefaultHostMemoryAllocator.get();
+
+  private static final Logger log = LoggerFactory.getLogger(ColumnVector.class);
+
+  @Test
+  void testBatchedLZ4RoundTripAsync() {
+    final Cuda.Stream stream = Cuda.DEFAULT_STREAM;
+    final long chunkSize = 64 * 1024;
+    final long targetIntermediteSize = Long.MAX_VALUE;
+    final int maxElements = 1024 * 1024 + 1;
+    final int numBuffers = 200;
+    long[] data = new long[maxElements];
+    for (int i = 0; i < maxElements; ++i) {
+      data[i] = i;
+    }
+
+    try (CloseableArray<DeviceMemoryBuffer> originalBuffers =
+             CloseableArray.wrap(new DeviceMemoryBuffer[numBuffers])) {
+      // create the batched buffers to compress
+      for (int i = 0; i < originalBuffers.size(); i++) {
+        originalBuffers.set(i, initBatchBuffer(data, i));
+        // Increment the refcount since compression will try to close it
+        originalBuffers.get(i).incRefCount();
+      }
+
+      // compress and decompress the buffers
+      BatchedLZ4Compressor compressor = new BatchedLZ4Compressor(chunkSize, targetIntermediteSize);
+
+      try (CloseableArray<DeviceMemoryBuffer> compressedBuffers =
+               CloseableArray.wrap(compressor.compress(originalBuffers.getArray(), stream));
+           CloseableArray<DeviceMemoryBuffer> uncompressedBuffers =
+               CloseableArray.wrap(new DeviceMemoryBuffer[numBuffers])) {
+        for (int i = 0; i < numBuffers; i++) {
+          uncompressedBuffers.set(i,
+              DeviceMemoryBuffer.allocate(originalBuffers.get(i).getLength()));
+        }
+
+        // decompress takes ownership of the compressed buffers and will close them
+        BatchedLZ4Decompressor.decompressAsync(chunkSize, compressedBuffers.release(),
+            uncompressedBuffers.getArray(), stream);
+
+        // check the decompressed results against the original
+        for (int i = 0; i < numBuffers; ++i) {
+          try (HostMemoryBuffer expected =
+                   hostMemoryAllocator.allocate(originalBuffers.get(i).getLength());
+               HostMemoryBuffer actual =
+                   hostMemoryAllocator.allocate(uncompressedBuffers.get(i).getLength())) {
+            Assertions.assertTrue(expected.getLength() <= Integer.MAX_VALUE);
+            Assertions.assertTrue(actual.getLength() <= Integer.MAX_VALUE);
+            Assertions.assertEquals(expected.getLength(), actual.getLength(),
+                "uncompressed size mismatch at buffer " + i);
+            expected.copyFromDeviceBuffer(originalBuffers.get(i));
+            actual.copyFromDeviceBuffer(uncompressedBuffers.get(i));
+            byte[] expectedBytes = new byte[(int) expected.getLength()];
+            expected.getBytes(expectedBytes, 0, 0, expected.getLength());
+            byte[] actualBytes = new byte[(int) actual.getLength()];
+            actual.getBytes(actualBytes, 0, 0, actual.getLength());
+            Assertions.assertArrayEquals(expectedBytes, actualBytes,
+                "mismatch in batch buffer " + i);
+          }
+        }
+      }
+    }
+  }
+
+  private void closeBuffer(MemoryBuffer buffer) {
+    if (buffer != null) {
+      buffer.close();
+    }
+  }
+
+  private DeviceMemoryBuffer initBatchBuffer(long[] data, int bufferId) {
+    // grab a subsection of the data based on buffer ID
+    int dataStart = 0;
+    int dataLength = data.length / (bufferId + 1);
+    switch (bufferId % 3) {
+      case 0:
+        // take a portion of the first half
+        dataLength /= 2;
+        break;
+      case 1:
+        // take a portion of the last half
+        dataStart = data.length / 2;
+        dataLength /= 2;
+        break;
+      default:
+        break;
+    }
+    long[] bufferData = Arrays.copyOfRange(data, dataStart, dataStart + dataLength + 1);
+    DeviceMemoryBuffer devBuffer = null;
+    try (HostMemoryBuffer hmb = hostMemoryAllocator.allocate(bufferData.length * 8)) {
+      hmb.setLongs(0, bufferData, 0, bufferData.length);
+      devBuffer = DeviceMemoryBuffer.allocate(hmb.getLength());
+      devBuffer.copyFromHostBuffer(hmb);
+      return devBuffer;
+    } catch (Throwable t) {
+      closeBuffer(devBuffer);
+      throw new RuntimeException(t);
+    }
+  }
+}
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diff --git a/java/src/test/resources/people.json b/java/src/test/resources/people.json
new file mode 100644
index 0000000..50a859c
--- /dev/null
+++ b/java/src/test/resources/people.json
@@ -0,0 +1,3 @@
+{"name":"Michael"}
+{"name":"Andy", "age":30}
+{"name":"Justin", "age":19}
diff --git a/java/src/test/resources/people_with_invalid_lines.json b/java/src/test/resources/people_with_invalid_lines.json
new file mode 100644
index 0000000..a99592e
--- /dev/null
+++ b/java/src/test/resources/people_with_invalid_lines.json
@@ -0,0 +1,4 @@
+{"name":"Michael"}
+{"name":"Andy", "age":30}
+this_line_is_not_valid
+{"name":"Justin", "age":19}
diff --git a/java/src/test/resources/simple.csv b/java/src/test/resources/simple.csv
new file mode 100644
index 0000000..557bc68
--- /dev/null
+++ b/java/src/test/resources/simple.csv
@@ -0,0 +1,10 @@
+0,110.0,120,one
+1,111.0,121,two
+2,112.0,122,three
+3,113.0,123,four
+4,114.0,124,five
+5,115.0,125,six
+6,116.0,126,seven𐖸
+7,117.0,127,eight뽨
+8,118.2,128,nineϨ
+9,119.8,129,ten
diff --git a/java/src/test/resources/splittable.parquet b/java/src/test/resources/splittable.parquet
new file mode 100644
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GIT binary patch
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diff --git a/notebooks/10min.ipynb b/notebooks/10min.ipynb
new file mode 120000
index 0000000..bd57fc7
--- /dev/null
+++ b/notebooks/10min.ipynb
@@ -0,0 +1 @@
+../docs/cudf/source/user_guide/10min.ipynb
\ No newline at end of file
diff --git a/notebooks/README.md b/notebooks/README.md
new file mode 100644
index 0000000..df8d4c8
--- /dev/null
+++ b/notebooks/README.md
@@ -0,0 +1,6 @@
+# cuDF Notebooks
+## Intro
+These notebooks provide examples of how to use cuDF.  These notebooks are designed to be self-contained with the `runtime` version of the [RAPIDS Docker Container](https://hub.docker.com/r/rapidsai/rapidsai/) and [RAPIDS Nightly Docker Containers](https://hub.docker.com/r/rapidsai/rapidsai-nightly) and can run on air-gapped systems.  You can quickly get this container using the install guide from the [RAPIDS.ai Getting Started page](https://rapids.ai/start.html#get-rapids)
+
+## RAPIDS notebooks
+Visit the main RAPIDS [notebooks](https://github.com/rapidsai/notebooks) repo for a listing of all notebooks across all RAPIDS libraries.
diff --git a/notebooks/cupy-interop.ipynb b/notebooks/cupy-interop.ipynb
new file mode 120000
index 0000000..0ba8810
--- /dev/null
+++ b/notebooks/cupy-interop.ipynb
@@ -0,0 +1 @@
+../docs/cudf/source/user_guide/cupy-interop.ipynb
\ No newline at end of file
diff --git a/notebooks/guide-to-udfs.ipynb b/notebooks/guide-to-udfs.ipynb
new file mode 120000
index 0000000..a4bbe59
--- /dev/null
+++ b/notebooks/guide-to-udfs.ipynb
@@ -0,0 +1 @@
+../docs/cudf/source/user_guide/guide-to-udfs.ipynb
\ No newline at end of file
diff --git a/notebooks/missing-data.ipynb b/notebooks/missing-data.ipynb
new file mode 120000
index 0000000..7e3b01a
--- /dev/null
+++ b/notebooks/missing-data.ipynb
@@ -0,0 +1 @@
+../docs/cudf/source/user_guide/missing-data.ipynb
\ No newline at end of file
diff --git a/notebooks/performance-comparisons b/notebooks/performance-comparisons
new file mode 120000
index 0000000..10be3bc
--- /dev/null
+++ b/notebooks/performance-comparisons
@@ -0,0 +1 @@
+../docs/cudf/source/user_guide/performance-comparisons/
\ No newline at end of file
diff --git a/print_env.sh b/print_env.sh
new file mode 100755
index 0000000..6774f94
--- /dev/null
+++ b/print_env.sh
@@ -0,0 +1,88 @@
+#!/usr/bin/env bash
+# Copyright (c) 2022, NVIDIA CORPORATION.
+# Reports relevant environment information useful for diagnosing and
+# debugging cuDF issues.
+# Usage:
+# "./print_env.sh" - prints to stdout
+# "./print_env.sh > env.txt" - prints to file "env.txt"
+
+print_env() {
+echo "**git***"
+if [ "$(git rev-parse --is-inside-work-tree 2>/dev/null)" == "true" ]; then
+git log --decorate -n 1
+echo "**git submodules***"
+git submodule status --recursive
+else
+echo "Not inside a git repository"
+fi
+echo
+
+echo "***OS Information***"
+cat /etc/*-release
+uname -a
+echo
+
+echo "***GPU Information***"
+nvidia-smi
+echo
+
+echo "***CPU***"
+lscpu
+echo
+
+echo "***CMake***"
+which cmake && cmake --version
+echo
+
+echo "***g++***"
+which g++ && g++ --version
+echo
+
+echo "***nvcc***"
+which nvcc && nvcc --version
+echo
+
+echo "***Python***"
+which python && python -c "import sys; print('Python {0}.{1}.{2}'.format(sys.version_info[0], sys.version_info[1], sys.version_info[2]))"
+echo
+
+echo "***Environment Variables***"
+
+printf '%-32s: %s\n' PATH $PATH
+
+printf '%-32s: %s\n' LD_LIBRARY_PATH $LD_LIBRARY_PATH
+
+printf '%-32s: %s\n' NUMBAPRO_NVVM $NUMBAPRO_NVVM
+
+printf '%-32s: %s\n' NUMBAPRO_LIBDEVICE $NUMBAPRO_LIBDEVICE
+
+printf '%-32s: %s\n' CONDA_PREFIX $CONDA_PREFIX
+
+printf '%-32s: %s\n' PYTHON_PATH $PYTHON_PATH
+
+echo
+
+
+# Print conda packages if conda exists
+if type "conda" &> /dev/null; then
+echo '***conda packages***'
+which conda && conda list
+echo
+# Print pip packages if pip exists
+elif type "pip" &> /dev/null; then
+echo "conda not found"
+echo "***pip packages***"
+which pip && pip list
+echo
+else
+echo "conda not found"
+echo "pip not found"
+fi
+}
+
+echo "<details><summary>Click here to see environment details</summary><pre>"
+echo "     "
+print_env | while read -r line; do
+    echo "     $line"
+done
+echo "</pre></details>"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..c6096fb
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,74 @@
+[tool.black]
+line-length = 79
+target-version = ["py39"]
+include = '\.py?$'
+force-exclude = '''
+/(
+    thirdparty |
+    \.eggs |
+    \.git |
+    \.hg |
+    \.mypy_cache |
+    \.tox |
+    \.venv |
+    _build |
+    buck-out |
+    build |
+    dist
+)/
+'''
+
+[tool.pydocstyle]
+# Due to https://github.com/PyCQA/pydocstyle/issues/363, we must exclude rather
+# than include using match-dir. Note that as discussed in
+# https://stackoverflow.com/questions/65478393/how-to-filter-directories-using-the-match-dir-flag-for-pydocstyle,
+# unlike the match option above this match-dir will have no effect when
+# pydocstyle is invoked from pre-commit. Therefore this exclusion list must
+# also be maintained in the pre-commit config file.
+match-dir = "^(?!(ci|cpp|conda|docs|java|notebooks|python/cudf/cudf/pandas/scripts|python/cudf/cudf_pandas_tests)).*$"
+# Allow missing docstrings for docutils
+ignore-decorators = ".*(docutils|doc_apply|copy_docstring).*"
+select = "D201, D204, D206, D207, D208, D209, D210, D211, D214, D215, D300, D301, D302, D403, D405, D406, D407, D408, D409, D410, D411, D412, D414, D418"
+    # Would like to enable the following rules in the future:
+    # D200, D202, D205, D400
+
+[tool.mypy]
+ignore_missing_imports = true
+# If we don't specify this, then mypy will check excluded files if
+# they are imported by a checked file.
+follow_imports = "skip"
+exclude = [
+    "cudf/_lib/",
+    "cudf/cudf/benchmarks/",
+    "cudf/cudf/tests/",
+    "cudf/cudf/utils/metadata/orc_column_statistics_pb2.py",
+    "custreamz/custreamz/tests/",
+    "dask_cudf/dask_cudf/tests/",
+ ]
+
+[tool.codespell]
+# note: pre-commit passes explicit lists of files here, which this skip file list doesn't override -
+# this is only to allow you to run codespell interactively
+skip = "./.git,./.github,./cpp/build,.*egg-info.*,./.mypy_cache,./cpp/tests,./python/cudf/cudf/tests,./java/src/test,./cpp/include/cudf_test/cxxopts.hpp"
+# ignore short words, and typename parameters like OffsetT
+ignore-regex = "\\b(.{1,4}|[A-Z]\\w*T)\\b"
+ignore-words-list = "inout,unparseable,falsy"
+builtin = "clear"
+quiet-level = 3
+
+[tool.ruff]
+select = ["E", "F", "W"]
+ignore = [
+    # whitespace before :
+    "E203",
+]
+fixable = ["ALL"]
+exclude = [
+    # TODO: Remove this in a follow-up where we fix __all__.
+    "__init__.py",
+]
+line-length = 79
+
+[tool.ruff.per-file-ignores]
+# Lots of pytest implicitly injected attributes in conftest-patch.py
+"python/cudf/cudf/pandas/scripts/conftest-patch.py" = ["F821"]
diff --git a/python/cudf/.coveragerc b/python/cudf/.coveragerc
new file mode 100644
index 0000000..929ab87
--- /dev/null
+++ b/python/cudf/.coveragerc
@@ -0,0 +1,3 @@
+# Configuration file for Python coverage tests
+[run]
+source = cudf
diff --git a/python/cudf/CMakeLists.txt b/python/cudf/CMakeLists.txt
new file mode 100644
index 0000000..6f3e428
--- /dev/null
+++ b/python/cudf/CMakeLists.txt
@@ -0,0 +1,121 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR)
+
+set(cudf_version 23.10.00)
+
+include(../../fetch_rapids.cmake)
+include(rapids-cuda)
+rapids_cuda_init_architectures(cudf-python)
+
+project(
+  cudf-python
+  VERSION ${cudf_version}
+  LANGUAGES # TODO: Building Python extension modules via the python_extension_module requires the C
+            # language to be enabled here. The test project that is built in scikit-build to verify
+            # various linking options for the python library is hardcoded to build with C, so until
+            # that is fixed we need to keep C.
+            C CXX CUDA
+)
+
+option(FIND_CUDF_CPP "Search for existing CUDF C++ installations before defaulting to local files"
+       OFF
+)
+option(CUDF_BUILD_WHEELS "Whether this build is generating a Python wheel." OFF)
+option(USE_LIBARROW_FROM_PYARROW "Use the libarrow contained within pyarrow." OFF)
+mark_as_advanced(USE_LIBARROW_FROM_PYARROW)
+
+# Always build wheels against the pyarrow libarrow.
+if(CUDF_BUILD_WHEELS)
+  set(USE_LIBARROW_FROM_PYARROW ON)
+endif()
+
+# If the user requested it we attempt to find CUDF.
+if(FIND_CUDF_CPP)
+  if(USE_LIBARROW_FROM_PYARROW)
+    # We need to find arrow before libcudf since libcudf requires it but doesn't bundle it. TODO:
+    # These options should probably all become optional since in practice they aren't meaningful
+    # except in the case where we actually compile Arrow.
+    set(CUDF_USE_ARROW_STATIC OFF)
+    set(CUDF_ENABLE_ARROW_S3 OFF)
+    set(CUDF_ENABLE_ARROW_ORC OFF)
+    set(CUDF_ENABLE_ARROW_PYTHON OFF)
+    set(CUDF_ENABLE_ARROW_PARQUET OFF)
+    include(rapids-find)
+    include(rapids-export)
+    include(../../cpp/cmake/thirdparty/get_arrow.cmake)
+  endif()
+
+  find_package(cudf ${cudf_version} REQUIRED)
+
+  # an installed version of libcudf doesn't provide the dlpack headers so we need to download dlpack
+  # for the interop.pyx
+  include(rapids-cpm)
+  rapids_cpm_init()
+  include(../../cpp/cmake/thirdparty/get_dlpack.cmake)
+else()
+  set(cudf_FOUND OFF)
+endif()
+
+include(rapids-cython)
+
+if(NOT cudf_FOUND)
+  set(BUILD_TESTS OFF)
+  set(BUILD_BENCHMARKS OFF)
+
+  set(_exclude_from_all "")
+  if(CUDF_BUILD_WHEELS)
+    # We don't build C++ tests when building wheels, so we can also omit the test util and shrink
+    # the wheel by avoiding embedding GTest.
+    set(CUDF_BUILD_TESTUTIL OFF)
+    set(CUDF_BUILD_STREAMS_TEST_UTIL OFF)
+
+    # Statically link cudart if building wheels
+    set(CUDA_STATIC_RUNTIME ON)
+
+    # Need to set this so all the nvcomp targets are global, not only nvcomp::nvcomp
+    # https://cmake.org/cmake/help/latest/variable/CMAKE_FIND_PACKAGE_TARGETS_GLOBAL.html#variable:CMAKE_FIND_PACKAGE_TARGETS_GLOBAL
+    set(CMAKE_FIND_PACKAGE_TARGETS_GLOBAL ON)
+
+    # Don't install the cuDF C++ targets into wheels
+    set(_exclude_from_all EXCLUDE_FROM_ALL)
+  endif()
+
+  add_subdirectory(../../cpp cudf-cpp ${_exclude_from_all})
+
+  if(CUDF_BUILD_WHEELS)
+    include(cmake/Modules/WheelHelpers.cmake)
+    get_target_property(_nvcomp_link_libs nvcomp::nvcomp INTERFACE_LINK_LIBRARIES)
+    # Ensure all the shared objects we need at runtime are in the wheel
+    add_target_libs_to_wheel(LIB_DIR cudf TARGETS arrow_shared nvcomp::nvcomp ${_nvcomp_link_libs})
+  endif()
+  # Since there are multiple subpackages of cudf._lib that require access to libcudf, we place the
+  # library in the cudf directory as a single source of truth and modify the other rpaths
+  # appropriately.
+  set(cython_lib_dir cudf)
+  install(TARGETS cudf DESTINATION ${cython_lib_dir})
+endif()
+
+rapids_cython_init()
+
+add_subdirectory(cudf/_lib)
+add_subdirectory(udf_cpp)
+
+include(cmake/Modules/ProtobufHelpers.cmake)
+codegen_protoc(cudf/utils/metadata/orc_column_statistics.proto)
+
+if(DEFINED cython_lib_dir)
+  rapids_cython_add_rpath_entries(TARGET cudf PATHS "${cython_lib_dir}")
+endif()
diff --git a/python/cudf/LICENSE b/python/cudf/LICENSE
new file mode 120000
index 0000000..30cff74
--- /dev/null
+++ b/python/cudf/LICENSE
@@ -0,0 +1 @@
+../../LICENSE
\ No newline at end of file
diff --git a/python/cudf/README.md b/python/cudf/README.md
new file mode 120000
index 0000000..fe84005
--- /dev/null
+++ b/python/cudf/README.md
@@ -0,0 +1 @@
+../../README.md
\ No newline at end of file
diff --git a/python/cudf/benchmarks/API/bench_dataframe.py b/python/cudf/benchmarks/API/bench_dataframe.py
new file mode 100644
index 0000000..f908a99
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_dataframe.py
@@ -0,0 +1,183 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+"""Benchmarks of DataFrame methods."""
+
+import string
+
+import numpy
+import pytest
+import pytest_cases
+from config import cudf, cupy
+from utils import benchmark_with_object
+
+
+@pytest.mark.parametrize("N", [100, 1_000_000])
+def bench_construction(benchmark, N):
+    benchmark(cudf.DataFrame, {None: cupy.random.rand(N)})
+
+
+@benchmark_with_object(cls="dataframe", dtype="float", cols=6)
+@pytest.mark.parametrize(
+    "expr", ["a+b", "a+b+c+d+e", "a / (sin(a) + cos(b)) * tanh(d*e*f)"]
+)
+def bench_eval_func(benchmark, expr, dataframe):
+    benchmark(dataframe.eval, expr)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", nulls=False, cols=6)
+@pytest.mark.parametrize(
+    "num_key_cols",
+    [2, 3, 4],
+)
+def bench_merge(benchmark, dataframe, num_key_cols):
+    benchmark(
+        dataframe.merge, dataframe, on=list(dataframe.columns[:num_key_cols])
+    )
+
+
+# TODO: Some of these cases could be generalized to an IndexedFrame benchmark
+# instead of a DataFrame benchmark.
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize(
+    "values",
+    [
+        lambda: range(50),
+        lambda: {f"{string.ascii_lowercase[i]}": range(50) for i in range(10)},
+        lambda: cudf.DataFrame(
+            {f"{string.ascii_lowercase[i]}": range(50) for i in range(10)}
+        ),
+        lambda: cudf.Series(range(50)),
+    ],
+)
+def bench_isin(benchmark, dataframe, values):
+    benchmark(dataframe.isin, values())
+
+
+@pytest.fixture(
+    params=[0, numpy.random.RandomState, cupy.random.RandomState],
+    ids=["Seed", "NumpyRandomState", "CupyRandomState"],
+)
+def random_state(request):
+    rs = request.param
+    return rs if isinstance(rs, int) else rs(seed=42)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize("frac", [0.5])
+def bench_sample(benchmark, dataframe, axis, frac, random_state):
+    if axis == 1 and isinstance(random_state, cupy.random.RandomState):
+        pytest.skip("Unsupported params.")
+    benchmark(
+        dataframe.sample, frac=frac, axis=axis, random_state=random_state
+    )
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize("frac", [0, 0.25, 0.5, 0.75, 1])
+def bench_iloc_getitem_indices(benchmark, dataframe, frac):
+    rs = numpy.random.RandomState(seed=42)
+    n = int(len(dataframe) * frac)
+    values = rs.choice(len(dataframe), size=n, replace=False)
+    benchmark(dataframe.iloc.__getitem__, values)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize("frac", [0, 0.25, 0.5, 0.75, 1])
+def bench_iloc_getitem_mask(benchmark, dataframe, frac):
+    rs = numpy.random.RandomState(seed=42)
+    n = int(len(dataframe) * frac)
+    values = rs.choice(len(dataframe), size=n, replace=False)
+    mask = numpy.zeros(len(dataframe), dtype=bool)
+    mask[values] = True
+    benchmark(dataframe.iloc.__getitem__, mask)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize(
+    "slice",
+    [slice(None), slice(0, 0, 1), slice(1, None, 10), slice(None, -1, -1)],
+)
+def bench_iloc_getitem_slice(benchmark, dataframe, slice):
+    benchmark(dataframe.iloc.__getitem__, slice)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+def bench_iloc_getitem_scalar(benchmark, dataframe):
+    benchmark(dataframe.iloc.__getitem__, len(dataframe) // 2)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", nulls=False, cols=6)
+@pytest.mark.parametrize(
+    "num_key_cols",
+    [2, 3, 4],
+)
+def bench_groupby(benchmark, dataframe, num_key_cols):
+    benchmark(dataframe.groupby, by=list(dataframe.columns[:num_key_cols]))
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", nulls=False, cols=6)
+@pytest.mark.parametrize(
+    "agg",
+    [
+        "sum",
+        ["sum", "mean"],
+        {
+            f"{string.ascii_lowercase[i]}": ["sum", "mean", "count"]
+            for i in range(6)
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "num_key_cols",
+    [2, 3, 4],
+)
+@pytest.mark.parametrize("as_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+def bench_groupby_agg(benchmark, dataframe, agg, num_key_cols, as_index, sort):
+    by = list(dataframe.columns[:num_key_cols])
+    benchmark(dataframe.groupby(by=by, as_index=as_index, sort=sort).agg, agg)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", nulls=False, cols=6)
+@pytest.mark.parametrize(
+    "num_key_cols",
+    [2, 3, 4],
+)
+@pytest.mark.parametrize("use_frac", [True, False])
+@pytest.mark.parametrize("replace", [True, False])
+@pytest.mark.parametrize("target_sample_frac", [0.1, 0.5, 1])
+def bench_groupby_sample(
+    benchmark, dataframe, num_key_cols, use_frac, replace, target_sample_frac
+):
+    grouper = dataframe.groupby(by=list(dataframe.columns[:num_key_cols]))
+    if use_frac:
+        kwargs = {"frac": target_sample_frac, "replace": replace}
+    else:
+        minsize = grouper.size().min()
+        target_size = numpy.round(
+            target_sample_frac * minsize, decimals=0
+        ).astype(int)
+        kwargs = {"n": target_size, "replace": replace}
+
+    benchmark(grouper.sample, **kwargs)
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize("num_cols_to_sort", [1])
+def bench_sort_values(benchmark, dataframe, num_cols_to_sort):
+    benchmark(
+        dataframe.sort_values, list(dataframe.columns[:num_cols_to_sort])
+    )
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+@pytest.mark.parametrize("num_cols_to_sort", [1])
+@pytest.mark.parametrize("n", [10])
+def bench_nsmallest(benchmark, dataframe, num_cols_to_sort, n):
+    by = list(dataframe.columns[:num_cols_to_sort])
+    benchmark(dataframe.nsmallest, n, by)
+
+
+@pytest_cases.parametrize_with_cases("dataframe, cond, other", prefix="where")
+def bench_where(benchmark, dataframe, cond, other):
+    benchmark(dataframe.where, cond, other)
diff --git a/python/cudf/benchmarks/API/bench_dataframe_cases.py b/python/cudf/benchmarks/API/bench_dataframe_cases.py
new file mode 100644
index 0000000..fc41d14
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_dataframe_cases.py
@@ -0,0 +1,14 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from utils import benchmark_with_object
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", nulls=False)
+def where_case_1(dataframe):
+    return dataframe, dataframe % 2 == 0, 0
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", nulls=False)
+def where_case_2(dataframe):
+    cond = dataframe[dataframe.columns[0]] % 2 == 0
+    return dataframe, cond, 0
diff --git a/python/cudf/benchmarks/API/bench_frame_or_index.py b/python/cudf/benchmarks/API/bench_frame_or_index.py
new file mode 100644
index 0000000..42b73ce
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_frame_or_index.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of methods that exist for both Frame and BaseIndex."""
+
+import operator
+
+import numpy as np
+import pytest
+from utils import benchmark_with_object, make_gather_map
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+@pytest.mark.parametrize("gather_how", ["sequence", "reverse", "random"])
+@pytest.mark.parametrize("fraction", [0.4])
+def bench_take(benchmark, gather_how, fraction, frame_or_index):
+    nr = len(frame_or_index)
+    gather_map = make_gather_map(nr * fraction, nr, gather_how)
+    benchmark(frame_or_index.take, gather_map)
+
+
+@pytest.mark.pandas_incompatible  # Series/Index work, but not DataFrame
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_argsort(benchmark, frame_or_index):
+    benchmark(frame_or_index.argsort)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_min(benchmark, frame_or_index):
+    benchmark(frame_or_index.min)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_where(benchmark, frame_or_index):
+    cond = frame_or_index % 2 == 0
+    benchmark(frame_or_index.where, cond, 0)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int", nulls=False)
+@pytest.mark.pandas_incompatible
+def bench_values_host(benchmark, frame_or_index):
+    benchmark(lambda: frame_or_index.values_host)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int", nulls=False)
+def bench_values(benchmark, frame_or_index):
+    benchmark(lambda: frame_or_index.values)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_nunique(benchmark, frame_or_index):
+    benchmark(frame_or_index.nunique)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int", nulls=False)
+def bench_to_numpy(benchmark, frame_or_index):
+    benchmark(frame_or_index.to_numpy)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int", nulls=False)
+@pytest.mark.pandas_incompatible
+def bench_to_cupy(benchmark, frame_or_index):
+    benchmark(frame_or_index.to_cupy)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+@pytest.mark.pandas_incompatible
+def bench_to_arrow(benchmark, frame_or_index):
+    benchmark(frame_or_index.to_arrow)
+
+
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_astype(benchmark, frame_or_index):
+    benchmark(frame_or_index.astype, float)
+
+
+@pytest.mark.parametrize("ufunc", [np.add, np.logical_and])
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_ufunc_series_binary(benchmark, frame_or_index, ufunc):
+    benchmark(ufunc, frame_or_index, frame_or_index)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [operator.add, operator.mul, operator.eq],
+)
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_binops(benchmark, op, frame_or_index):
+    benchmark(op, frame_or_index, frame_or_index)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [operator.add, operator.mul, operator.eq],
+)
+@benchmark_with_object(cls="frame_or_index", dtype="int")
+def bench_scalar_binops(benchmark, op, frame_or_index):
+    benchmark(op, frame_or_index, 1)
diff --git a/python/cudf/benchmarks/API/bench_functions.py b/python/cudf/benchmarks/API/bench_functions.py
new file mode 100644
index 0000000..ec4be22
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_functions.py
@@ -0,0 +1,82 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of free functions that accept cudf objects."""
+
+import numpy as np
+import pytest
+import pytest_cases
+from config import NUM_ROWS, cudf, cupy
+from utils import benchmark_with_object
+
+
+@pytest_cases.parametrize_with_cases("objs", prefix="concat")
+@pytest.mark.parametrize(
+    "axis",
+    [
+        1,
+    ],
+)
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("ignore_index", [True, False])
+def bench_concat_axis_1(benchmark, objs, axis, join, ignore_index):
+    benchmark(
+        cudf.concat, objs=objs, axis=axis, join=join, ignore_index=ignore_index
+    )
+
+
+@pytest.mark.parametrize("size", [10_000, 100_000])
+@pytest.mark.parametrize("cardinality", [10, 100, 1000])
+@pytest.mark.parametrize("dtype", [cupy.bool_, cupy.float64])
+def bench_get_dummies_high_cardinality(benchmark, size, cardinality, dtype):
+    """Benchmark when the cardinality of column to encode is high."""
+    df = cudf.DataFrame(
+        {
+            "col": cudf.Series(
+                cupy.random.randint(low=0, high=cardinality, size=size)
+            ).astype("category")
+        }
+    )
+    benchmark(cudf.get_dummies, df, columns=["col"], dtype=dtype)
+
+
+@pytest.mark.parametrize("prefix", [None, "pre"])
+def bench_get_dummies_simple(benchmark, prefix):
+    """Benchmark with small input to test the efficiency of the API itself."""
+    df = cudf.DataFrame(
+        {
+            "col1": list(range(10)),
+            "col2": list("abcdefghij"),
+            "col3": cudf.Series(list(range(100, 110)), dtype="category"),
+        }
+    )
+    benchmark(
+        cudf.get_dummies, df, columns=["col1", "col2", "col3"], prefix=prefix
+    )
+
+
+@benchmark_with_object(cls="dataframe", dtype="int", cols=6)
+def bench_pivot_table_simple(benchmark, dataframe):
+    values = ["d", "e"]
+    index = ["a", "b"]
+    columns = ["c"]
+    benchmark(
+        cudf.pivot_table,
+        data=dataframe,
+        values=values,
+        index=index,
+        columns=columns,
+    )
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def bench_crosstab_simple(benchmark, nr):
+    series_a = np.array(["foo", "bar"] * nr)
+    series_b = np.array(["one", "two"] * nr)
+    series_c = np.array(["dull", "shiny"] * nr)
+    np.random.shuffle(series_a)
+    np.random.shuffle(series_b)
+    np.random.shuffle(series_c)
+    series_a = cudf.Series(series_a)
+    series_b = cudf.Series(series_b)
+    series_c = cudf.Series(series_c)
+    benchmark(cudf.crosstab, index=series_a, columns=[series_b, series_c])
diff --git a/python/cudf/benchmarks/API/bench_functions_cases.py b/python/cudf/benchmarks/API/bench_functions_cases.py
new file mode 100644
index 0000000..c81f8f2
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_functions_cases.py
@@ -0,0 +1,148 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Test cases for benchmarks in bench_functions.py."""
+
+import pytest_cases
+from config import NUM_ROWS, cudf, cupy
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_default_index(nr):
+    return [
+        cudf.DataFrame({"a": cupy.tile([1, 2, 3], nr)}),
+        cudf.DataFrame({"b": cupy.tile([4, 5, 7], nr)}),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_contiguous_indexes(nr):
+    return [
+        cudf.DataFrame({"a": cupy.tile([1, 2, 3], nr)}),
+        cudf.DataFrame(
+            {"b": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3),
+        ),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_contiguous_indexes_different_cols(nr):
+    return [
+        cudf.DataFrame(
+            {"a": cupy.tile([1, 2, 3], nr), "b": cupy.tile([4, 5, 7], nr)}
+        ),
+        cudf.DataFrame(
+            {"c": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3),
+        ),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_string_index(nr):
+    return [
+        cudf.DataFrame(
+            {"a": cupy.tile([1, 2, 3], nr), "b": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=0, stop=nr * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"c": [4, 5, 7] * nr},
+            index=cudf.RangeIndex(start=0, stop=nr * 3).astype("str"),
+        ),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_contiguous_string_index_different_col(nr):
+    return [
+        cudf.DataFrame(
+            {"a": cupy.tile([1, 2, 3], nr), "b": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=0, stop=nr * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"c": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3).astype("str"),
+        ),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_complex_string_index(nr):
+    return [
+        cudf.DataFrame(
+            {"a": cupy.tile([1, 2, 3], nr), "b": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=0, stop=nr * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"c": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"d": cupy.tile([1, 2, 3], nr), "e": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=0, stop=nr * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"f": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"g": cupy.tile([1, 2, 3], nr), "h": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=0, stop=nr * 3).astype("str"),
+        ),
+        cudf.DataFrame(
+            {"i": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3).astype("str"),
+        ),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_unique_columns(nr):
+    # To avoid any edge case bugs, always use at least 10 rows per DataFrame.
+    nr_actual = max(10, nr // 20)
+    return [
+        cudf.DataFrame({"a": cupy.tile([1, 2, 3], nr_actual)}),
+        cudf.DataFrame({"b": cupy.tile([4, 5, 7], nr_actual)}),
+        cudf.DataFrame({"c": cupy.tile([1, 2, 3], nr_actual)}),
+        cudf.DataFrame({"d": cupy.tile([4, 5, 7], nr_actual)}),
+        cudf.DataFrame({"e": cupy.tile([1, 2, 3], nr_actual)}),
+        cudf.DataFrame({"f": cupy.tile([4, 5, 7], nr_actual)}),
+        cudf.DataFrame({"g": cupy.tile([1, 2, 3], nr_actual)}),
+        cudf.DataFrame({"h": cupy.tile([4, 5, 7], nr_actual)}),
+        cudf.DataFrame({"i": cupy.tile([1, 2, 3], nr_actual)}),
+        cudf.DataFrame({"j": cupy.tile([4, 5, 7], nr_actual)}),
+    ]
+
+
+@pytest_cases.parametrize("nr", NUM_ROWS)
+def concat_case_unique_columns_with_different_range_index(nr):
+    return [
+        cudf.DataFrame(
+            {"a": cupy.tile([1, 2, 3], nr), "b": cupy.tile([4, 5, 7], nr)}
+        ),
+        cudf.DataFrame(
+            {"c": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3),
+        ),
+        cudf.DataFrame(
+            {"d": cupy.tile([1, 2, 3], nr), "e": cupy.tile([4, 5, 7], nr)}
+        ),
+        cudf.DataFrame(
+            {"f": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3),
+        ),
+        cudf.DataFrame(
+            {"g": cupy.tile([1, 2, 3], nr), "h": cupy.tile([4, 5, 7], nr)}
+        ),
+        cudf.DataFrame(
+            {"i": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3),
+        ),
+        cudf.DataFrame(
+            {"j": cupy.tile([1, 2, 3], nr), "k": cupy.tile([4, 5, 7], nr)}
+        ),
+        cudf.DataFrame(
+            {"l": cupy.tile([4, 5, 7], nr)},
+            index=cudf.RangeIndex(start=nr * 3, stop=nr * 2 * 3),
+        ),
+    ]
diff --git a/python/cudf/benchmarks/API/bench_index.py b/python/cudf/benchmarks/API/bench_index.py
new file mode 100644
index 0000000..53e6171
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_index.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of Index methods."""
+
+import pytest
+from config import cudf, cupy
+from utils import benchmark_with_object
+
+
+@pytest.mark.parametrize("N", [100, 1_000_000])
+def bench_construction(benchmark, N):
+    benchmark(cudf.Index, cupy.random.rand(N))
+
+
+@benchmark_with_object(cls="index", dtype="int", nulls=False)
+def bench_sort_values(benchmark, index):
+    benchmark(index.sort_values)
diff --git a/python/cudf/benchmarks/API/bench_indexed_frame.py b/python/cudf/benchmarks/API/bench_indexed_frame.py
new file mode 100644
index 0000000..6969121
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_indexed_frame.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of IndexedFrame methods."""
+
+import pytest
+from utils import benchmark_with_object
+
+
+@benchmark_with_object(cls="indexedframe", dtype="int")
+@pytest.mark.parametrize("op", ["cumsum", "cumprod", "cummax"])
+def bench_scans(benchmark, op, indexedframe):
+    benchmark(getattr(indexedframe, op))
+
+
+@benchmark_with_object(cls="indexedframe", dtype="int")
+@pytest.mark.parametrize("op", ["sum", "product", "mean"])
+def bench_reductions(benchmark, op, indexedframe):
+    benchmark(getattr(indexedframe, op))
+
+
+@benchmark_with_object(cls="indexedframe", dtype="int")
+def bench_drop_duplicates(benchmark, indexedframe):
+    benchmark(indexedframe.drop_duplicates)
+
+
+@benchmark_with_object(cls="indexedframe", dtype="int")
+def bench_rangeindex_replace(benchmark, indexedframe):
+    # TODO: Consider adding more DataFrame-specific benchmarks for different
+    # types of valid inputs (dicts, etc).
+    benchmark(indexedframe.replace, 0, 2)
diff --git a/python/cudf/benchmarks/API/bench_multiindex.py b/python/cudf/benchmarks/API/bench_multiindex.py
new file mode 100644
index 0000000..6268bcc
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_multiindex.py
@@ -0,0 +1,44 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of MultiIndex methods."""
+
+import numpy as np
+import pandas as pd
+import pytest
+from config import cudf
+
+
+@pytest.fixture
+def pidx():
+    num_elements = int(1e3)
+    a = np.random.randint(0, num_elements // 10, num_elements)
+    b = np.random.randint(0, num_elements // 10, num_elements)
+    return pd.MultiIndex.from_arrays([a, b], names=("a", "b"))
+
+
+@pytest.fixture
+def midx(pidx):
+    num_elements = int(1e3)
+    a = np.random.randint(0, num_elements // 10, num_elements)
+    b = np.random.randint(0, num_elements // 10, num_elements)
+    df = cudf.DataFrame({"a": a, "b": b})
+    return cudf.MultiIndex.from_frame(df)
+
+
+@pytest.mark.pandas_incompatible
+def bench_from_pandas(benchmark, pidx):
+    benchmark(cudf.MultiIndex.from_pandas, pidx)
+
+
+def bench_constructor(benchmark, midx):
+    benchmark(
+        cudf.MultiIndex, codes=midx.codes, levels=midx.levels, names=midx.names
+    )
+
+
+def bench_from_frame(benchmark, midx):
+    benchmark(cudf.MultiIndex.from_frame, midx.to_frame(index=False))
+
+
+def bench_copy(benchmark, midx):
+    benchmark(midx.copy, deep=False)
diff --git a/python/cudf/benchmarks/API/bench_rangeindex.py b/python/cudf/benchmarks/API/bench_rangeindex.py
new file mode 100644
index 0000000..42de5a8
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_rangeindex.py
@@ -0,0 +1,47 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import pytest
+
+
+@pytest.mark.pandas_incompatible
+def bench_values_host(benchmark, rangeindex):
+    benchmark(lambda: rangeindex.values_host)
+
+
+def bench_to_numpy(benchmark, rangeindex):
+    benchmark(rangeindex.to_numpy)
+
+
+@pytest.mark.pandas_incompatible
+def bench_to_arrow(benchmark, rangeindex):
+    benchmark(rangeindex.to_arrow)
+
+
+def bench_argsort(benchmark, rangeindex):
+    benchmark(rangeindex.argsort)
+
+
+def bench_nunique(benchmark, rangeindex):
+    benchmark(rangeindex.nunique)
+
+
+def bench_isna(benchmark, rangeindex):
+    benchmark(rangeindex.isna)
+
+
+def bench_max(benchmark, rangeindex):
+    benchmark(rangeindex.max)
+
+
+def bench_min(benchmark, rangeindex):
+    benchmark(rangeindex.min)
+
+
+def bench_where(benchmark, rangeindex):
+    cond = rangeindex % 2 == 0
+    benchmark(rangeindex.where, cond, 0)
+
+
+def bench_isin(benchmark, rangeindex):
+    values = [10, 100]
+    benchmark(rangeindex.isin, values)
diff --git a/python/cudf/benchmarks/API/bench_series.py b/python/cudf/benchmarks/API/bench_series.py
new file mode 100644
index 0000000..92032da
--- /dev/null
+++ b/python/cudf/benchmarks/API/bench_series.py
@@ -0,0 +1,23 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of Series methods."""
+
+import pytest
+from config import cudf, cupy
+from utils import benchmark_with_object
+
+
+@pytest.mark.parametrize("N", [100, 1_000_000])
+def bench_construction(benchmark, N):
+    benchmark(cudf.Series, cupy.random.rand(N))
+
+
+@benchmark_with_object(cls="series", dtype="int")
+def bench_sort_values(benchmark, series):
+    benchmark(series.sort_values)
+
+
+@benchmark_with_object(cls="series", dtype="int")
+@pytest.mark.parametrize("n", [10])
+def bench_series_nsmallest(benchmark, series, n):
+    benchmark(series.nsmallest, n)
diff --git a/python/cudf/benchmarks/common/config.py b/python/cudf/benchmarks/common/config.py
new file mode 100644
index 0000000..305a21d
--- /dev/null
+++ b/python/cudf/benchmarks/common/config.py
@@ -0,0 +1,69 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Module used for global configuration of benchmarks.
+
+This file contains global definitions that are important for configuring all
+benchmarks such as fixture sizes. In addition, this file supports the following
+features:
+    - Defining the CUDF_BENCHMARKS_USE_PANDAS environment variable will change
+      all benchmarks to run with pandas instead of cudf (and numpy instead of
+      cupy). This feature enables easy comparisons of benchmarks between cudf
+      and pandas. All common modules (cudf, cupy) should be imported from here
+      by benchmark modules to allow configuration if needed.
+    - Defining CUDF_BENCHMARKS_DEBUG_ONLY will set global configuration
+      variables to avoid running large benchmarks, instead using minimal values
+      to simply ensure that benchmarks are functional.
+
+This file is also where standard pytest hooks should be overridden. While these
+definitions typically belong in conftest.py, since any of the above environment
+variables could affect test collection or other properties, we must define them
+in this file and import them in conftest.py to ensure that they are handled
+appropriately.
+"""
+import os
+import sys
+
+# Environment variable-based configuration of benchmarking pandas or cudf.
+collect_ignore = []
+if "CUDF_BENCHMARKS_USE_PANDAS" in os.environ:
+    import numpy as cupy
+    import pandas as cudf
+
+    # cudf internals offer no pandas compatibility guarantees, and we also
+    # never need to compare those benchmarks to pandas.
+    collect_ignore.append("internal/")
+
+    # Also filter out benchmarks of APIs that are not compatible with pandas.
+    def is_pandas_compatible(item):
+        return all(m.name != "pandas_incompatible" for m in item.own_markers)
+
+    def pytest_collection_modifyitems(session, config, items):
+        items[:] = list(filter(is_pandas_compatible, items))
+
+else:
+    import cupy  # noqa: W0611, F401
+
+    import cudf  # noqa: W0611, F401
+
+    def pytest_collection_modifyitems(session, config, items):
+        pass
+
+
+def pytest_sessionstart(session):
+    """Add the common files to the path for all tests to import."""
+    sys.path.insert(0, os.path.join(os.getcwd(), "common"))
+
+
+def pytest_sessionfinish(session, exitstatus):
+    """Clean up sys.path after exit."""
+    if "common" in sys.path[0]:
+        del sys.path[0]
+
+
+# Constants used to define benchmarking standards.
+if "CUDF_BENCHMARKS_DEBUG_ONLY" in os.environ:
+    NUM_ROWS = [10, 20]
+    NUM_COLS = [1, 6]
+else:
+    NUM_ROWS = [100, 10_000, 1_000_000]
+    NUM_COLS = [1, 6]
diff --git a/python/cudf/benchmarks/common/utils.py b/python/cudf/benchmarks/common/utils.py
new file mode 100644
index 0000000..363316f
--- /dev/null
+++ b/python/cudf/benchmarks/common/utils.py
@@ -0,0 +1,257 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Common utilities for fixture creation and benchmarking."""
+
+import inspect
+import re
+import textwrap
+from collections.abc import MutableSet
+from itertools import groupby
+from numbers import Real
+
+import pytest_cases
+from config import NUM_COLS, NUM_ROWS, cudf, cupy
+
+
+def make_gather_map(len_gather_map: Real, len_column: Real, how: str):
+    """Create a gather map based on "how" you'd like to gather from input.
+    - sequence: gather the first `len_gather_map` rows, the first thread
+                collects the first element
+    - reverse:  gather the last `len_gather_map` rows, the first thread
+                collects the last element
+    - random:   create a pseudorandom gather map
+
+    `len_gather_map`, `len_column` gets rounded to integer.
+    """
+    len_gather_map = round(len_gather_map)
+    len_column = round(len_column)
+
+    rstate = cupy.random.RandomState(seed=0)
+    if how == "sequence":
+        return cudf.Series(cupy.arange(0, len_gather_map))
+    elif how == "reverse":
+        return cudf.Series(
+            cupy.arange(len_column - 1, len_column - len_gather_map - 1, -1)
+        )
+    elif how == "random":
+        return cudf.Series(rstate.randint(0, len_column, len_gather_map))
+
+
+def make_boolean_mask_column(size):
+    rstate = cupy.random.RandomState(seed=0)
+    return cudf.core.column.as_column(rstate.randint(0, 2, size).astype(bool))
+
+
+def benchmark_with_object(
+    cls, *, dtype="int", nulls=None, cols=None, rows=None
+):
+    """Pass "standard" cudf fixtures to functions without renaming parameters.
+
+    The fixture generation logic in conftest.py provides a plethora of useful
+    fixtures to allow developers to easily select an appropriate cross-section
+    of the space of objects to apply a particular benchmark to. However, the
+    usage of these fixtures is cumbersome because creating them in a principled
+    fashion results in long names and very specific naming schemes. This
+    decorator abstracts that naming logic away from the developer, allowing
+    them to instead focus on defining the fixture semantically by describing
+    its properties.
+
+    Parameters
+    ----------
+    cls : Union[str, Type]
+        The class of object to test. May either be specified as the type
+        itself, or using the name (as a string). If a string, the case is
+        irrelevant as the string will be converted to all lowercase.
+    dtype : Union[str, Iterable[str]], default 'int'
+        The dtype or set of dtypes to use.
+    nulls : Optional[bool], default None
+        Whether to test nullable or non-nullable data. If None, both nullable
+        and non-nullable data are included.
+    cols : Optional[int], None
+        The number of columns. Only valid if cls == 'dataframe'. If None, use
+        all possible numbers of columns. Specifying multiple values is
+        unsupported.
+    rows : Optional[int], None
+        The number of rows. If None, use all possible numbers of rows.
+        Specifying multiple values is unsupported.
+
+    Raises
+    ------
+    AssertionError
+        If any of the parameters do not correspond to extant fixtures.
+
+    Examples
+    --------
+    # Note: As an internal function, this example is not meant for doctesting.
+
+    @benchmark_with_object("dataframe", dtype="int", nulls=False)
+    def bench_columns(benchmark, df):
+        benchmark(df.columns)
+    """
+    if inspect.isclass(cls):
+        cls = cls.__name__
+    cls = cls.lower()
+
+    supported_classes = (
+        "column",
+        "series",
+        "index",
+        "dataframe",
+        "indexedframe",
+        "frame_or_index",
+    )
+    assert cls in supported_classes, (
+        f"cls {cls} is invalid, choose from " f"{', '.join(supported_classes)}"
+    )
+
+    if not isinstance(dtype, list):
+        dtype = [dtype]
+    assert all(dt in column_generators for dt in dtype), (
+        f"The only supported dtypes are " f"{', '.join(column_generators)}"
+    )
+
+    dtype_str = "_dtype_" + "_or_".join(dtype)
+
+    null_str = ""
+    if nulls is not None:
+        null_str = f"_nulls_{nulls}".lower()
+
+    col_str = ""
+    if cols is not None:
+        assert cols in NUM_COLS, (
+            f"You have requested a DataFrame with {cols} columns but fixtures "
+            f"only exist for the values {', '.join(NUM_COLS)}"
+        )
+        col_str = f"_cols_{cols}"
+
+    row_str = ""
+    if rows is not None:
+        assert rows in NUM_ROWS, (
+            f"You have requested a {cls} with {rows} rows but fixtures "
+            f"only exist for the values {', '.join(NUM_ROWS)}"
+        )
+        row_str = f"_rows_{rows}"
+
+    fixture_name = f"{cls}{dtype_str}{null_str}{col_str}{row_str}"
+
+    def deco(bm):
+        # pytest's test collection process relies on parsing the globals dict
+        # to find test functions and identify their parameters for the purpose
+        # of fixtures and parameters. Therefore, the primary purpose of this
+        # decorator is to define a new benchmark function with a signature
+        # identical to that of the decorated benchmark except with the user's
+        # fixture name replaced by the true fixture name based on the arguments
+        # to benchmark_with_object.
+        parameters = inspect.signature(bm).parameters
+
+        # Note: This logic assumes that any benchmark using this fixture has at
+        # least two parameters since they must be using both the
+        # pytest-benchmark `benchmark` fixture and the cudf object.
+        params_str = ", ".join(f"{p}" for p in parameters if p != cls)
+        arg_str = ", ".join(f"{p}={p}" for p in parameters if p != cls)
+
+        if params_str:
+            params_str += ", "
+        if arg_str:
+            arg_str += ", "
+
+        params_str += f"{fixture_name}"
+        arg_str += f"{cls}={fixture_name}"
+
+        src = textwrap.dedent(
+            f"""
+            import makefun
+            @makefun.wraps(
+                bm,
+                remove_args=("{cls}",),
+                prepend_args=("{fixture_name}",)
+            )
+            def wrapped_bm({params_str}):
+                return bm({arg_str})
+            """
+        )
+        globals_ = {"bm": bm}
+        exec(src, globals_)
+        wrapped_bm = globals_["wrapped_bm"]
+        # In case marks were applied to the original benchmark, copy them over.
+        if marks := getattr(bm, "pytestmark", None):
+            wrapped_bm.pytestmark = marks
+        wrapped_bm.place_as = bm
+        return wrapped_bm
+
+    return deco
+
+
+class OrderedSet(MutableSet):
+    """A minimal OrderedSet implementation built on a dict.
+
+    This implementation exploits the fact that dicts are ordered as of Python
+    3.7. It is not intended to be performant, so only the minimal set of
+    methods are implemented. We need this class to ensure that fixture names
+    are constructed deterministically, otherwise pytest-xdist will complain if
+    different threads have seemingly different tests.
+    """
+
+    def __init__(self, args=None):
+        args = args or []
+        self._data = {value: None for value in args}
+
+    def __contains__(self, key):
+        return key in self._data
+
+    def __iter__(self):
+        return iter(self._data)
+
+    def __len__(self):
+        return len(self._data)
+
+    def __repr__(self):
+        # Helpful for debugging.
+        data = ", ".join(str(i) for i in self._data)
+        return f"{self.__class__.__name__}({data})"
+
+    def add(self, value):
+        self._data[value] = None
+
+    def discard(self, value):
+        self._data.pop(value, None)
+
+
+def make_fixture(name, func, globals_, fixtures):
+    """Create a named fixture in `globals_` and save its name in `fixtures`.
+
+    https://github.com/pytest-dev/pytest/issues/2424#issuecomment-333387206
+    explains why this hack is necessary. Essentially, dynamically generated
+    fixtures must exist in globals() to be found by pytest.
+    """
+    globals_[name] = pytest_cases.fixture(name=name)(func)
+    fixtures.add(name)
+
+
+def collapse_fixtures(fixtures, pattern, repl, globals_, idfunc=None):
+    """Create unions of fixtures based on specific name mappings.
+
+    `fixtures` are grouped into unions according the regex replacement
+    `re.sub(pattern, repl)` and placed into `new_fixtures`.
+    """
+
+    def collapser(n):
+        return re.sub(pattern, repl, n)
+
+    # Note: sorted creates a new list, not a view, so it's OK to modify the
+    # list of fixtures while iterating over the sorted result.
+    for name, group in groupby(sorted(fixtures, key=collapser), key=collapser):
+        group = list(group)
+        if len(group) > 1 and name not in fixtures:
+            pytest_cases.fixture_union(name=name, fixtures=group, ids=idfunc)
+            # Need to assign back to the parent scope's globals.
+            globals_[name] = globals()[name]
+            fixtures.add(name)
+
+
+# A dictionary of callables that create a column of a specified length
+random_state = cupy.random.RandomState(42)
+column_generators = {
+    "int": (lambda nr: random_state.randint(low=0, high=100, size=nr)),
+    "float": (lambda nr: random_state.rand(nr)),
+}
diff --git a/python/cudf/benchmarks/conftest.py b/python/cudf/benchmarks/conftest.py
new file mode 100644
index 0000000..4f2bb96
--- /dev/null
+++ b/python/cudf/benchmarks/conftest.py
@@ -0,0 +1,234 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Defines pytest fixtures for all benchmarks.
+
+Most fixtures defined in this file represent one of the primary classes in the
+cuDF ecosystem such as DataFrame, Series, or Index. These fixtures may in turn
+be broken up into two categories: base fixtures and fixture unions. Each base
+fixture represents a specific type of object as well as certain of its
+properties crucial for benchmarking. Specifically, fixtures must account for
+the following different parameters:
+    - Class of object (DataFrame, Series, Index)
+    - Dtype
+    - Nullability
+    - Size (rows for all, rows/columns for DataFrame)
+
+One such fixture is a series of nullable integer data. Given that we generally
+want data across different sizes, we parametrize all fixtures across different
+numbers of rows rather than generating separate fixtures for each different
+possible number of rows. The number of columns is only relevant for DataFrame.
+
+While this core set of fixtures means that any benchmark can be run for any
+combination of these parameters, it also means that we would effectively have
+to parametrize our benchmarks with many fixtures. Not only is parametrizing
+tests with fixtures in this manner unsupported by pytest, it is also an
+inelegant solution leading to cumbersome parameter lists that must be
+maintained across all tests. Instead we make use of the
+`pytest_cases <https://smarie.github.io/python-pytest-cases/>_` pytest plugin,
+which supports the creation of fixture unions: fixtures that result from
+combining other fixtures together. The result is a set of well-defined fixtures
+that allow us to write benchmarks that naturally express the set of objects for
+which they are valid, e.g. `def bench_sort_values(frame_or_index)`.
+
+The generated fixtures are named according to the following convention:
+`{classname}_dtype_{dtype}[_nulls_{true|false}][_cols_{num_cols}][_rows_{num_rows}]`
+where classname is one of the following: index, series, dataframe,
+indexedframe, frame, frame_or_index. Note that in the case of indexes, to match
+Series/DataFrame we simply set `classname=index` and rely on the
+`dtype_{dtype}` component to delineate which index class is actually in use.
+
+In addition to the above fixtures, we also provide the following more
+specialized fixtures:
+    - rangeindex: Since RangeIndex always holds int64 data we cannot conflate
+      it with index_dtype_int64 (a true Int64Index), and it cannot hold nulls.
+      As a result, it is provided as a separate fixture.
+"""
+
+import os
+import string
+import sys
+
+import pytest_cases
+
+# TODO: Rather than doing this path hacking (including the sessionstart and
+# sessionfinish hooks), we could just make the benchmarks a (sub)package to
+# enable relative imports. A minor change to consider when these are ported
+# into the main repo.
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "common"))
+
+from config import cudf  # noqa: W0611, E402, F401
+from utils import (  # noqa: E402
+    OrderedSet,
+    collapse_fixtures,
+    column_generators,
+    make_fixture,
+)
+
+# Turn off isort until we upgrade to 5.8.0
+# https://github.com/pycqa/isort/issues/1594
+# isort: off
+from config import (  # noqa: W0611, E402, F401
+    NUM_COLS,
+    NUM_ROWS,
+    collect_ignore,
+    pytest_collection_modifyitems,
+    pytest_sessionfinish,
+    pytest_sessionstart,
+)
+
+# isort: on
+
+
+@pytest_cases.fixture(params=[0, 1], ids=["AxisIndex", "AxisColumn"])
+def axis(request):
+    return request.param
+
+
+# First generate all the base fixtures.
+fixtures = OrderedSet()
+for dtype, column_generator in column_generators.items():
+
+    def make_dataframe(nr, nc, column_generator=column_generator):
+        assert nc <= len(
+            string.ascii_lowercase
+        ), "make_dataframe only supports a maximum of 26 columns"
+        return cudf.DataFrame(
+            {
+                f"{string.ascii_lowercase[i]}": column_generator(nr)
+                for i in range(nc)
+            }
+        )
+
+    for nr in NUM_ROWS:
+        # TODO: pytest_cases.fixture doesn't appear to support lambdas where
+        # pytest does. https://github.com/smarie/python-pytest-cases/issues/278
+        # Once that is fixed we could use lambdas here.
+        # TODO: pytest_cases has a bug where the first argument being a
+        # defaulted kwarg e.g. (nr=nr, nc=nc) raises errors.
+        # https://github.com/smarie/python-pytest-cases/issues/278
+        # Once that is fixed we could remove all the extraneous `request`
+        # fixtures in these fixtures.
+        def series_nulls_false(
+            request, nr=nr, column_generator=column_generator
+        ):
+            return cudf.Series(column_generator(nr))
+
+        make_fixture(
+            f"series_dtype_{dtype}_nulls_false_rows_{nr}",
+            series_nulls_false,
+            globals(),
+            fixtures,
+        )
+
+        def series_nulls_true(
+            request, nr=nr, column_generator=column_generator
+        ):
+            s = cudf.Series(column_generator(nr))
+            s.iloc[::2] = None
+            return s
+
+        make_fixture(
+            f"series_dtype_{dtype}_nulls_true_rows_{nr}",
+            series_nulls_true,
+            globals(),
+            fixtures,
+        )
+
+        # For now, not bothering to include a nullable index fixture.
+        def index_nulls_false(
+            request, nr=nr, column_generator=column_generator
+        ):
+            return cudf.Index(column_generator(nr))
+
+        make_fixture(
+            f"index_dtype_{dtype}_nulls_false_rows_{nr}",
+            index_nulls_false,
+            globals(),
+            fixtures,
+        )
+
+        for nc in NUM_COLS:
+
+            def dataframe_nulls_false(
+                request, nr=nr, nc=nc, make_dataframe=make_dataframe
+            ):
+                return make_dataframe(nr, nc)
+
+            make_fixture(
+                f"dataframe_dtype_{dtype}_nulls_false_cols_{nc}_rows_{nr}",
+                dataframe_nulls_false,
+                globals(),
+                fixtures,
+            )
+
+            def dataframe_nulls_true(
+                request, nr=nr, nc=nc, make_dataframe=make_dataframe
+            ):
+                df = make_dataframe(nr, nc)
+                df.iloc[::2, :] = None
+                return df
+
+            make_fixture(
+                f"dataframe_dtype_{dtype}_nulls_true_cols_{nc}_rows_{nr}",
+                dataframe_nulls_true,
+                globals(),
+                fixtures,
+            )
+
+
+# We define some custom naming functions for use in the creation of fixture
+# unions to create more readable test function names that don't contain the
+# entire union, which quickly becomes intractably long.
+def unique_union_id(val):
+    return val.alternative_name
+
+
+def default_union_id(val):
+    return f"alt{val.get_alternative_idx()}"
+
+
+# Label the first level differently from others since there's no redundancy.
+idfunc = unique_union_id
+num_new_fixtures = len(fixtures)
+
+# Keep trying to merge existing fixtures until no new fixtures are added.
+while num_new_fixtures > 0:
+    num_fixtures = len(fixtures)
+
+    # Note: If we start also introducing unions across dtypes, most likely
+    # those will take the form `*int_and_float*` or similar since we won't want
+    # to union _all_ dtypes. In that case, the regexes will need to use
+    # suitable lookaheads etc to avoid infinite loops here.
+    for pat, repl in [
+        ("_nulls_(true|false)", ""),
+        ("series|dataframe", "indexedframe"),
+        ("indexedframe|index", "frame_or_index"),
+        (r"_rows_\d+", ""),
+        (r"_cols_\d+", ""),
+    ]:
+
+        collapse_fixtures(fixtures, pat, repl, globals(), idfunc)
+
+    num_new_fixtures = len(fixtures) - num_fixtures
+    # All subsequent levels get the same (collapsed) labels.
+    idfunc = default_union_id
+
+
+for dtype, column_generator in column_generators.items():
+    # We have to manually add this one because we aren't including nullable
+    # indexes but we want to be able to run some benchmarks on Series/DataFrame
+    # that may or may not be nullable as well as Index objects.
+    pytest_cases.fixture_union(
+        name=f"frame_or_index_dtype_{dtype}",
+        fixtures=(
+            f"indexedframe_dtype_{dtype}",
+            f"index_dtype_{dtype}_nulls_false",
+        ),
+        ids=["", f"index_dtype_{dtype}_nulls_false"],
+    )
+
+
+# TODO: Decide where to incorporate RangeIndex and MultiIndex fixtures.
+@pytest_cases.fixture(params=NUM_ROWS)
+def rangeindex(request):
+    return cudf.RangeIndex(request.param)
diff --git a/python/cudf/benchmarks/internal/bench_column.py b/python/cudf/benchmarks/internal/bench_column.py
new file mode 100644
index 0000000..d4969b3
--- /dev/null
+++ b/python/cudf/benchmarks/internal/bench_column.py
@@ -0,0 +1,115 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of Column methods."""
+
+import pytest
+import pytest_cases
+from utils import (
+    benchmark_with_object,
+    make_boolean_mask_column,
+    make_gather_map,
+)
+
+
+@benchmark_with_object(cls="column", dtype="float")
+def bench_apply_boolean_mask(benchmark, column):
+    mask = make_boolean_mask_column(column.size)
+    benchmark(column.apply_boolean_mask, mask)
+
+
+@benchmark_with_object(cls="column", dtype="float")
+@pytest.mark.parametrize("dropnan", [True, False])
+def bench_dropna(benchmark, column, dropnan):
+    benchmark(column.dropna, drop_nan=dropnan)
+
+
+@benchmark_with_object(cls="column", dtype="float")
+def bench_unique_single_column(benchmark, column):
+    benchmark(column.unique)
+
+
+@benchmark_with_object(cls="column", dtype="float")
+@pytest.mark.parametrize("nullify", [True, False])
+@pytest.mark.parametrize("gather_how", ["sequence", "reverse", "random"])
+def bench_take(benchmark, column, gather_how, nullify):
+    gather_map = make_gather_map(
+        column.size * 0.4, column.size, gather_how
+    )._column
+    benchmark(column.take, gather_map, nullify=nullify)
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_stride_1_slice_scalar(column):
+    return column, slice(None, None, 1), 42
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_stride_2_slice_scalar(column):
+    return column, slice(None, None, 2), 42
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_boolean_column_scalar(column):
+    column = column
+    return column, [True, False] * (len(column) // 2), 42
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_int_column_scalar(column):
+    column = column
+    return column, list(range(len(column))), 42
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_stride_1_slice_align_to_key_size(
+    column,
+):
+    column = column
+    key = slice(None, None, 1)
+    start, stop, stride = key.indices(len(column))
+    materialized_key_size = len(column.slice(start, stop, stride))
+    return column, key, [42] * materialized_key_size
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_stride_2_slice_align_to_key_size(
+    column,
+):
+    column = column
+    key = slice(None, None, 2)
+    start, stop, stride = key.indices(len(column))
+    materialized_key_size = len(column.slice(start, stop, stride))
+    return column, key, [42] * materialized_key_size
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_boolean_column_align_to_col_size(
+    column,
+):
+    column = column
+    size = len(column)
+    return column, [True, False] * (size // 2), [42] * size
+
+
+@benchmark_with_object(cls="column", dtype="int", nulls=False)
+def setitem_case_int_column_align_to_col_size(column):
+    column = column
+    size = len(column)
+    return column, list(range(size)), [42] * size
+
+
+# Benchmark Grid
+# key:  slice == 1  (fill or copy_range shortcut),
+#       slice != 1  (scatter),
+#       column(bool)    (boolean_mask_scatter),
+#       column(int) (scatter)
+# value:    scalar,
+#           column (len(val) == len(key)),
+#           column (len(val) != len(key) and len == num_true)
+
+
+@pytest_cases.parametrize_with_cases(
+    "column,key,value", cases=".", prefix="setitem"
+)
+def bench_setitem(benchmark, column, key, value):
+    benchmark(column.__setitem__, key, value)
diff --git a/python/cudf/benchmarks/internal/bench_dataframe_internal.py b/python/cudf/benchmarks/internal/bench_dataframe_internal.py
new file mode 100644
index 0000000..a262976
--- /dev/null
+++ b/python/cudf/benchmarks/internal/bench_dataframe_internal.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+"""Benchmarks of internal DataFrame methods."""
+
+from utils import benchmark_with_object, make_boolean_mask_column
+
+from cudf.core.copy_types import BooleanMask
+
+
+@benchmark_with_object(cls="dataframe", dtype="int")
+def bench_apply_boolean_mask(benchmark, dataframe):
+    mask = make_boolean_mask_column(len(dataframe))
+    benchmark(dataframe._apply_boolean_mask, BooleanMask(mask, len(dataframe)))
diff --git a/python/cudf/benchmarks/internal/bench_rangeindex_internal.py b/python/cudf/benchmarks/internal/bench_rangeindex_internal.py
new file mode 100644
index 0000000..c4cf1de
--- /dev/null
+++ b/python/cudf/benchmarks/internal/bench_rangeindex_internal.py
@@ -0,0 +1,11 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Benchmarks of internal RangeIndex methods."""
+
+
+def bench_column(benchmark, rangeindex):
+    benchmark(lambda: rangeindex._column)
+
+
+def bench_columns(benchmark, rangeindex):
+    benchmark(lambda: rangeindex._columns)
diff --git a/python/cudf/benchmarks/internal/conftest.py b/python/cudf/benchmarks/internal/conftest.py
new file mode 100644
index 0000000..7351f1d
--- /dev/null
+++ b/python/cudf/benchmarks/internal/conftest.py
@@ -0,0 +1,56 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+"""Defines pytest fixtures for internal benchmarks."""
+
+from config import NUM_ROWS, cudf
+from utils import (
+    OrderedSet,
+    collapse_fixtures,
+    column_generators,
+    make_fixture,
+)
+
+fixtures = OrderedSet()
+for dtype, column_generator in column_generators.items():
+    for nr in NUM_ROWS:
+
+        def column_nulls_false(request, nr=nr):
+            return cudf.core.column.as_column(column_generator(nr))
+
+        make_fixture(
+            f"column_dtype_{dtype}_nulls_false_rows_{nr}",
+            column_nulls_false,
+            globals(),
+            fixtures,
+        )
+
+        def column_nulls_true(request, nr=nr):
+            c = cudf.core.column.as_column(column_generator(nr))
+            c[::2] = None
+            return c
+
+        make_fixture(
+            f"column_dtype_{dtype}_nulls_true_rows_{nr}",
+            column_nulls_true,
+            globals(),
+            fixtures,
+        )
+
+num_new_fixtures = len(fixtures)
+
+# Keep trying to merge existing fixtures until no new fixtures are added.
+while num_new_fixtures > 0:
+    num_fixtures = len(fixtures)
+
+    # Note: If we start also introducing unions across dtypes, most likely
+    # those will take the form `*int_and_float*` or similar since we won't want
+    # to union _all_ dtypes. In that case, the regexes will need to use
+    # suitable lookaheads etc to avoid infinite loops here.
+    for pat, repl in [
+        ("_nulls_(true|false)", ""),
+        (r"_rows_\d+", ""),
+    ]:
+
+        collapse_fixtures(fixtures, pat, repl, globals())
+
+    num_new_fixtures = len(fixtures) - num_fixtures
diff --git a/python/cudf/benchmarks/pytest.ini b/python/cudf/benchmarks/pytest.ini
new file mode 100644
index 0000000..db24415
--- /dev/null
+++ b/python/cudf/benchmarks/pytest.ini
@@ -0,0 +1,8 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+[pytest]
+python_files = bench_*.py
+python_classes = Bench
+python_functions = bench_*
+markers =
+    pandas_incompatible: mark a benchmark that cannot be run with pandas
diff --git a/python/cudf/cmake/Modules/ProtobufHelpers.cmake b/python/cudf/cmake/Modules/ProtobufHelpers.cmake
new file mode 100644
index 0000000..70b8879
--- /dev/null
+++ b/python/cudf/cmake/Modules/ProtobufHelpers.cmake
@@ -0,0 +1,50 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+include_guard(GLOBAL)
+
+# Compile protobuf files to Python. All arguments are assumed to be .proto files.
+function(codegen_protoc)
+  # Allow user to provide path to protoc executable as an environment variable.
+  if(DEFINED ENV{PROTOC})
+    set(protoc_COMMAND $ENV{PROTOC})
+  else()
+    find_program(protoc_COMMAND protoc REQUIRED)
+  endif()
+
+  foreach(_proto_path IN LISTS ARGV)
+    string(REPLACE "\.proto" "_pb2\.py" pb2_py_path "${_proto_path}")
+    set(pb2_py_path "${CMAKE_CURRENT_SOURCE_DIR}/${pb2_py_path}")
+    # Note: If we ever need to process larger numbers of protobuf files we should consider switching
+    # to protobuf_generate_python from the FindProtobuf module.
+    execute_process(
+      COMMAND ${protoc_COMMAND} --python_out=. "${_proto_path}"
+      WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} COMMAND_ERROR_IS_FATAL ANY
+    )
+    # Mark entire file to skip formatting.
+    file(READ "${pb2_py_path}" pb2_py)
+    file(
+      WRITE "${pb2_py_path}"
+      [=[
+# fmt: off
+]=]
+    )
+    file(APPEND "${pb2_py_path}" "${pb2_py}")
+    file(
+      APPEND "${pb2_py_path}"
+      [=[
+# fmt: on
+]=]
+    )
+  endforeach()
+endfunction()
diff --git a/python/cudf/cmake/Modules/WheelHelpers.cmake b/python/cudf/cmake/Modules/WheelHelpers.cmake
new file mode 100644
index 0000000..c0351e8
--- /dev/null
+++ b/python/cudf/cmake/Modules/WheelHelpers.cmake
@@ -0,0 +1,71 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+include_guard(GLOBAL)
+
+# Making libraries available inside wheels by installing the associated targets.
+function(add_target_libs_to_wheel)
+  list(APPEND CMAKE_MESSAGE_CONTEXT "add_target_libs_to_wheel")
+
+  set(options "")
+  set(one_value "LIB_DIR")
+  set(multi_value "TARGETS")
+  cmake_parse_arguments(_ "${options}" "${one_value}" "${multi_value}" ${ARGN})
+
+  message(VERBOSE "Installing targets '${__TARGETS}' into lib_dir '${__LIB_DIR}'")
+
+  foreach(target IN LISTS __TARGETS)
+
+    if(NOT TARGET ${target})
+      message(VERBOSE "No target named ${target}")
+      continue()
+    endif()
+
+    get_target_property(alias_target ${target} ALIASED_TARGET)
+    if(alias_target)
+      set(target ${alias_target})
+    endif()
+
+    get_target_property(is_imported ${target} IMPORTED)
+    if(NOT is_imported)
+      # If the target isn't imported, install it into the wheel
+      install(TARGETS ${target} DESTINATION ${__LIB_DIR})
+      message(VERBOSE "install(TARGETS ${target} DESTINATION ${__LIB_DIR})")
+    else()
+      # If the target is imported, make sure it's global
+      get_target_property(already_global ${target} IMPORTED_GLOBAL)
+      if(NOT already_global)
+        set_target_properties(${target} PROPERTIES IMPORTED_GLOBAL TRUE)
+      endif()
+
+      # Find the imported target's library so we can copy it into the wheel
+      set(lib_loc)
+      foreach(prop IN ITEMS IMPORTED_LOCATION IMPORTED_LOCATION_RELEASE IMPORTED_LOCATION_DEBUG)
+        get_target_property(lib_loc ${target} ${prop})
+        if(lib_loc)
+          message(VERBOSE "Found ${prop} for ${target}: ${lib_loc}")
+          break()
+        endif()
+        message(VERBOSE "${target} has no value for property ${prop}")
+      endforeach()
+
+      if(NOT lib_loc)
+        message(FATAL_ERROR "Found no libs to install for target ${target}")
+      endif()
+
+      # Copy the imported library into the wheel
+      install(FILES ${lib_loc} DESTINATION ${__LIB_DIR})
+      message(VERBOSE "install(FILES ${lib_loc} DESTINATION ${__LIB_DIR})")
+    endif()
+  endforeach()
+endfunction()
diff --git a/python/cudf/cudf/__init__.py b/python/cudf/cudf/__init__.py
new file mode 100644
index 0000000..e5c78fc
--- /dev/null
+++ b/python/cudf/cudf/__init__.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+# _setup_numba _must be called before numba.cuda is imported, because
+# it sets the numba config variable responsible for enabling
+# Minor Version Compatibility. Setting it after importing numba.cuda has no effect.
+from cudf.utils._numba import _setup_numba
+from cudf.utils.gpu_utils import validate_setup
+
+_setup_numba()
+validate_setup()
+
+import cupy
+from numba import config as numba_config, cuda
+
+import rmm
+from rmm.allocators.cupy import rmm_cupy_allocator
+from rmm.allocators.numba import RMMNumbaManager
+
+from cudf import api, core, datasets, testing
+from cudf.api.extensions import (
+    register_dataframe_accessor,
+    register_index_accessor,
+    register_series_accessor,
+)
+from cudf.api.types import dtype
+from cudf.core.algorithms import factorize
+from cudf.core.cut import cut
+from cudf.core.dataframe import DataFrame, from_dataframe, from_pandas, merge
+from cudf.core.dtypes import (
+    CategoricalDtype,
+    Decimal32Dtype,
+    Decimal64Dtype,
+    Decimal128Dtype,
+    IntervalDtype,
+    ListDtype,
+    StructDtype,
+)
+from cudf.core.groupby import Grouper
+from cudf.core.index import (
+    BaseIndex,
+    CategoricalIndex,
+    DatetimeIndex,
+    Float32Index,
+    Float64Index,
+    GenericIndex,
+    Index,
+    Int8Index,
+    Int16Index,
+    Int32Index,
+    Int64Index,
+    IntervalIndex,
+    RangeIndex,
+    StringIndex,
+    TimedeltaIndex,
+    UInt8Index,
+    UInt16Index,
+    UInt32Index,
+    UInt64Index,
+    interval_range,
+)
+from cudf.core.missing import NA, NaT
+from cudf.core.multiindex import MultiIndex
+from cudf.core.reshape import (
+    concat,
+    crosstab,
+    get_dummies,
+    melt,
+    pivot,
+    pivot_table,
+    unstack,
+)
+from cudf.core.scalar import Scalar
+from cudf.core.series import Series, isclose
+from cudf.core.tools.datetimes import DateOffset, date_range, to_datetime
+from cudf.core.tools.numeric import to_numeric
+from cudf.io import (
+    from_dlpack,
+    read_avro,
+    read_csv,
+    read_feather,
+    read_hdf,
+    read_json,
+    read_orc,
+    read_parquet,
+    read_text,
+)
+from cudf.options import (
+    describe_option,
+    get_option,
+    option_context,
+    set_option,
+)
+from cudf.utils.utils import clear_cache
+
+cuda.set_memory_manager(RMMNumbaManager)
+cupy.cuda.set_allocator(rmm_cupy_allocator)
+
+
+rmm.register_reinitialize_hook(clear_cache)
+
+
+__version__ = "23.10.00"
+
+__all__ = [
+    "BaseIndex",
+    "CategoricalDtype",
+    "CategoricalIndex",
+    "DataFrame",
+    "DateOffset",
+    "DatetimeIndex",
+    "Decimal32Dtype",
+    "Decimal64Dtype",
+    "Float32Index",
+    "Float64Index",
+    "GenericIndex",
+    "Grouper",
+    "Index",
+    "Int16Index",
+    "Int32Index",
+    "Int64Index",
+    "Int8Index",
+    "IntervalDtype",
+    "IntervalIndex",
+    "ListDtype",
+    "MultiIndex",
+    "NA",
+    "NaT",
+    "RangeIndex",
+    "Scalar",
+    "Series",
+    "StringIndex",
+    "StructDtype",
+    "TimedeltaIndex",
+    "UInt16Index",
+    "UInt32Index",
+    "UInt64Index",
+    "UInt8Index",
+    "api",
+    "concat",
+    "crosstab",
+    "cut",
+    "date_range",
+    "describe_option",
+    "factorize",
+    "from_dataframe",
+    "from_dlpack",
+    "from_pandas",
+    "get_dummies",
+    "get_option",
+    "interval_range",
+    "isclose",
+    "melt",
+    "merge",
+    "pivot",
+    "pivot_table",
+    "read_avro",
+    "read_csv",
+    "read_feather",
+    "read_hdf",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_text",
+    "set_option",
+    "testing",
+    "to_datetime",
+    "to_numeric",
+    "unstack",
+]
diff --git a/python/cudf/cudf/_fuzz_testing/__init__.py b/python/cudf/cudf/_fuzz_testing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_fuzz_testing/avro.py b/python/cudf/cudf/_fuzz_testing/avro.py
new file mode 100644
index 0000000..d997403
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/avro.py
@@ -0,0 +1,116 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import copy
+import io
+import logging
+import random
+
+import numpy as np
+
+import cudf
+from cudf._fuzz_testing.io import IOFuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    _generate_rand_meta,
+    pandas_to_avro,
+    pyarrow_to_pandas,
+)
+from cudf.testing import dataset_generator as dg
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+class AvroReader(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+        self._df = None
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                - {"category"}
+                # No unsigned support in avro:
+                # https://avro.apache.org/docs/current/spec.html
+                - cudf.utils.dtypes.UNSIGNED_TYPES
+                # TODO: Remove DATETIME_TYPES once
+                # following bug is fixed:
+                # https://github.com/rapidsai/cudf/issues/6482
+                - cudf.utils.dtypes.DATETIME_TYPES
+                # TODO: Remove DURATION_TYPES once
+                # following bug is fixed:
+                # https://github.com/rapidsai/cudf/issues/6604
+                - cudf.utils.dtypes.TIMEDELTA_TYPES
+            )
+
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            seed = random.randint(0, 2**32 - 1)
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_cols"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+        self._df = df
+        logging.info(f"Shape of DataFrame generated: {table.shape}")
+
+        file_obj = io.BytesIO()
+        pandas_to_avro(df, file_io_obj=file_obj)
+        file_obj.seek(0)
+        buf = file_obj.read()
+        self._current_buffer = copy.copy(buf)
+        return (df, buf)
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            with open(file_name + "_crash.avro", "wb") as crash_dataset:
+                crash_dataset.write(self._current_buffer)
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if values == ALL_POSSIBLE_VALUES:
+                if param == "columns":
+                    col_size = self._rand(len(self._df.columns))
+                    params_dict[param] = list(
+                        np.unique(np.random.choice(self._df.columns, col_size))
+                    )
+                elif param in ("skiprows", "num_rows"):
+                    params_dict[param] = np.random.choice(
+                        [None, self._rand(len(self._df))]
+                    )
+            else:
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
diff --git a/python/cudf/cudf/_fuzz_testing/csv.py b/python/cudf/cudf/_fuzz_testing/csv.py
new file mode 100644
index 0000000..8ab7048
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/csv.py
@@ -0,0 +1,207 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import logging
+import random
+
+import numpy as np
+
+import cudf
+from cudf._fuzz_testing.io import IOFuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    _generate_rand_meta,
+    pyarrow_to_pandas,
+)
+from cudf.testing import dataset_generator as dg
+from cudf.utils.dtypes import pandas_dtypes_to_np_dtypes
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+class CSVReader(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            seed = random.randint(0, 2**32 - 1)
+            random.seed(seed)
+            dtypes_list = list(cudf.utils.dtypes.ALL_TYPES)
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_columns"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+
+        logging.info(f"Shape of DataFrame generated: {df.shape}")
+        self._current_buffer = df
+        return df.to_csv()
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            self._current_buffer.to_csv(file_name + "_crash.csv")
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if values == ALL_POSSIBLE_VALUES:
+                if param == "usecols":
+                    col_size = self._rand(len(self._df.columns))
+                    col_val = np.random.choice(
+                        [
+                            None,
+                            np.unique(
+                                np.random.choice(self._df.columns, col_size)
+                            ),
+                        ]
+                    )
+                    params_dict[param] = (
+                        col_val if col_val is None else list(col_val)
+                    )
+                elif param == "dtype":
+                    dtype_val = np.random.choice(
+                        [None, self._df.dtypes.to_dict()]
+                    )
+                    if dtype_val is not None:
+                        dtype_val = {
+                            col_name: "category"
+                            if cudf.utils.dtypes.is_categorical_dtype(dtype)
+                            else pandas_dtypes_to_np_dtypes[dtype]
+                            for col_name, dtype in dtype_val.items()
+                        }
+                    params_dict[param] = dtype_val
+                elif param == "header":
+                    header_val = np.random.choice(
+                        ["infer", np.random.randint(low=0, high=len(self._df))]
+                    )
+                    params_dict[param] = header_val
+                elif param == "skiprows":
+                    params_dict[param] = np.random.randint(
+                        low=0, high=len(self._df)
+                    )
+                elif param == "skipfooter":
+                    params_dict[param] = np.random.randint(
+                        low=0, high=len(self._df)
+                    )
+                elif param == "nrows":
+                    nrows_val = np.random.choice(
+                        [None, np.random.randint(low=0, high=len(self._df))]
+                    )
+                    params_dict[param] = nrows_val
+            else:
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
+
+
+class CSVWriter(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            seed = random.randint(0, 2**32 - 1)
+            random.seed(seed)
+            dtypes_list = list(cudf.utils.dtypes.ALL_TYPES)
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_columns"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+
+        logging.info(f"Shape of DataFrame generated: {df.shape}")
+        self._current_buffer = df
+        return df
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            self._current_buffer.to_csv(file_name + "_crash.csv")
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if values == ALL_POSSIBLE_VALUES:
+                if param == "columns":
+                    col_size = self._rand(len(self._current_buffer.columns))
+                    params_dict[param] = list(
+                        np.unique(
+                            np.random.choice(
+                                self._current_buffer.columns, col_size
+                            )
+                        )
+                    )
+                elif param == "chunksize":
+                    params_dict[param] = np.random.choice(
+                        [
+                            None,
+                            np.random.randint(
+                                low=1, high=max(1, len(self._current_buffer))
+                            ),
+                        ]
+                    )
+            else:
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
diff --git a/python/cudf/cudf/_fuzz_testing/fuzzer.py b/python/cudf/cudf/_fuzz_testing/fuzzer.py
new file mode 100644
index 0000000..59d6f19
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/fuzzer.py
@@ -0,0 +1,116 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import datetime
+import json
+import logging
+import os
+import sys
+import traceback
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+class Fuzzer:
+    def __init__(
+        self,
+        target,
+        data_handler_class,
+        dirs=None,
+        crash_reports_dir=None,
+        regression=False,
+        max_rows_size=100_000,
+        max_cols_size=1000,
+        runs=-1,
+        max_string_length=None,
+        params=None,
+        write_data_on_failure=True,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+
+        self._target = target
+        self._dirs = [] if dirs is None else dirs
+        self._crash_dir = crash_reports_dir
+        self._data_handler = data_handler_class(
+            dirs=self._dirs,
+            max_rows=max_rows_size,
+            max_columns=max_cols_size,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+        self._total_executions = 0
+        self._regression = regression
+        self._start_time = None
+        self.runs = runs
+        self.params = params
+        self.write_data_on_failure = write_data_on_failure
+
+    def log_stats(self):
+        end_time = datetime.datetime.now()
+        total_time_taken = end_time - self._start_time
+
+        logging.info(f"Run-Time elapsed (hh:mm:ss.ms) {total_time_taken}")
+
+    def write_crash(self, error):
+        error_file_name = str(datetime.datetime.now())
+        if self._crash_dir:
+            crash_path = os.path.join(
+                self._crash_dir,
+                error_file_name + "_crash.json",
+            )
+            crash_log_path = os.path.join(
+                self._crash_dir,
+                error_file_name + "_crash.log",
+            )
+        else:
+            crash_path = error_file_name + "_crash.json"
+            crash_log_path = error_file_name + "_crash.log"
+
+        with open(crash_path, "w") as f:
+            json.dump(
+                self._data_handler.current_params, f, sort_keys=True, indent=4
+            )
+
+        logging.info(f"Crash params was written to {crash_path}")
+
+        with open(crash_log_path, "w") as f:
+            f.write(str(error))
+        logging.info(f"Crash exception was written to {crash_log_path}")
+
+        if self.write_data_on_failure:
+            self._data_handler.write_data(error_file_name)
+
+    def start(self):
+
+        while True:
+            logging.info(f"Running test {self._total_executions}")
+            file_name = self._data_handler.generate_input()
+            try:
+                self._start_time = datetime.datetime.now()
+                if self.params is None:
+                    self._target(file_name)
+                else:
+                    self._data_handler.set_rand_params(self.params)
+                    kwargs = self._data_handler._current_params["test_kwargs"]
+                    logging.info(f"Parameters passed: {str(kwargs)}")
+                    self._target(file_name, **kwargs)
+            except KeyboardInterrupt:
+                logging.info(
+                    f"Keyboard Interrupt encountered, stopping after "
+                    f"{self.runs} runs."
+                )
+                sys.exit(0)
+            except Exception as e:
+                logging.exception(e)
+                self.write_crash(traceback.format_exc())
+            self.log_stats()
+            if self.runs != -1 and self._total_executions >= self.runs:
+                logging.info(f"Completed {self.runs}, stopping now.")
+                break
+
+            self._total_executions += 1
diff --git a/python/cudf/cudf/_fuzz_testing/io.py b/python/cudf/cudf/_fuzz_testing/io.py
new file mode 100644
index 0000000..ffb7171
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/io.py
@@ -0,0 +1,109 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import copy
+import json
+import logging
+import os
+import random
+import sys
+
+import numpy as np
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+class IOFuzz:
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+        max_structs_nesting_depth=None,
+        max_struct_null_frequency=None,
+        max_struct_types_at_each_level=None,
+    ):
+        dirs = [] if dirs is None else dirs
+        self._inputs = []
+        self._max_rows = max_rows
+        self._max_columns = max_columns
+        self._max_string_length = max_string_length
+        self._max_lists_length = max_lists_length
+        self._max_lists_nesting_depth = max_lists_nesting_depth
+        self._max_structs_nesting_depth = max_structs_nesting_depth
+        self._max_struct_null_frequency = max_struct_null_frequency
+        self._max_struct_types_at_each_level = max_struct_types_at_each_level
+
+        for i, path in enumerate(dirs):
+            if i == 0 and not os.path.exists(path):
+                raise FileNotFoundError(f"No {path} exists")
+
+            if os.path.isfile(path) and path.endswith("_crash.json"):
+                self._load_params(path)
+            else:
+                for i in os.listdir(path):
+                    file_name = os.path.join(path, i)
+                    if os.path.isfile(file_name) and file_name.endswith(
+                        "_crash.json"
+                    ):
+                        self._load_params(file_name)
+        self._regression = bool(self._inputs)
+        self._idx = 0
+        self._current_params = {}
+        self._current_buffer = None
+
+    def _load_params(self, path):
+        with open(path) as f:
+            params = json.load(f)
+        self._inputs.append(params)
+
+    @staticmethod
+    def _rand(n):
+        return random.randrange(0, n + 1)
+
+    def generate_input(self):
+        raise NotImplementedError("Must be implemented by inherited class")
+
+    @property
+    def current_params(self):
+        return self._current_params
+
+    def get_next_regression_params(self):
+        if self._idx >= len(self._inputs):
+            logging.info(
+                "Reached the end of all crash.json files to run..Exiting.."
+            )
+            sys.exit(0)
+        param = self._inputs[self._idx]
+        dtypes_meta = param["dtypes_meta"]
+        num_rows = param["num_rows"]
+        num_cols = param["num_columns"]
+        seed = param["seed"]
+        random.seed(seed)
+        self._idx += 1
+        self._current_params = copy.copy(param)
+        return dtypes_meta, num_rows, num_cols, seed
+
+    def set_rand_params(self, params):
+        params_dict = {
+            param: np.random.choice(values) for param, values in params.items()
+        }
+        self._current_params["test_kwargs"] = self.process_kwargs(
+            params_dict=params_dict
+        )
+
+    def process_kwargs(self, params_dict):
+        return {
+            key: bool(value)
+            if isinstance(value, np.bool_)
+            else str(value)
+            if isinstance(value, np.dtype)
+            else value
+            for key, value in params_dict.items()
+        }
diff --git a/python/cudf/cudf/_fuzz_testing/json.py b/python/cudf/cudf/_fuzz_testing/json.py
new file mode 100644
index 0000000..29e0aeb
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/json.py
@@ -0,0 +1,191 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import logging
+import random
+from collections import abc
+
+import numpy as np
+
+import cudf
+from cudf._fuzz_testing.io import IOFuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    _generate_rand_meta,
+    pyarrow_to_pandas,
+)
+from cudf.testing import dataset_generator as dg
+from cudf.utils.dtypes import pandas_dtypes_to_np_dtypes
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+def _get_dtype_param_value(dtype_val):
+    if dtype_val is not None and isinstance(dtype_val, abc.Mapping):
+        processed_dtypes = {}
+        for col_name, dtype in dtype_val.items():
+            if cudf.utils.dtypes.is_categorical_dtype(dtype):
+                processed_dtypes[col_name] = "category"
+            else:
+                processed_dtypes[col_name] = str(
+                    pandas_dtypes_to_np_dtypes.get(dtype, dtype)
+                )
+        return processed_dtypes
+    return dtype_val
+
+
+class JSONReader(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            seed = random.randint(0, 2**32 - 1)
+            random.seed(seed)
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                # https://github.com/pandas-dev/pandas/issues/20599
+                - {"uint64"}
+                # TODO: Remove DATETIME_TYPES after this is fixed:
+                # https://github.com/rapidsai/cudf/issues/6586
+                - set(cudf.utils.dtypes.DATETIME_TYPES)
+            )
+            # TODO: Uncomment following after following
+            # issue is fixed:
+            # https://github.com/rapidsai/cudf/issues/7086
+            # dtypes_list.extend(["list"])
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_columns"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+        self._current_buffer = df
+        logging.info(f"Shape of DataFrame generated: {df.shape}")
+
+        return df.to_json(orient="records", lines=True)
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            self._current_buffer.to_json(
+                file_name + "_crash_json.json", orient="records", lines=True
+            )
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if param == "dtype" and values == ALL_POSSIBLE_VALUES:
+                dtype_val = np.random.choice(
+                    [True, self._current_buffer.dtypes.to_dict()]
+                )
+                params_dict[param] = _get_dtype_param_value(dtype_val)
+            else:
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
+
+
+class JSONWriter(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+        )
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            seed = random.randint(0, 2**32 - 1)
+            random.seed(seed)
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                # https://github.com/pandas-dev/pandas/issues/20599
+                - {"uint64"}
+                # TODO: Remove DATETIME_TYPES after this is fixed:
+                # https://github.com/rapidsai/cudf/issues/6586
+                - set(cudf.utils.dtypes.DATETIME_TYPES)
+            )
+            # TODO: Uncomment following after following
+            # issue is fixed:
+            # https://github.com/rapidsai/cudf/issues/7086
+            # dtypes_list.extend(["list"])
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_columns"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+
+        logging.info(f"Shape of DataFrame generated: {df.shape}")
+        self._current_buffer = df
+        return df
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            self._current_buffer.to_json(
+                file_name + "_crash_json.json", lines=True, orient="records"
+            )
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if param == "dtype" and values == ALL_POSSIBLE_VALUES:
+                dtype_val = np.random.choice(
+                    [True, self._current_buffer.dtypes.to_dict()]
+                )
+                params_dict[param] = _get_dtype_param_value(dtype_val)
+            else:
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
diff --git a/python/cudf/cudf/_fuzz_testing/main.py b/python/cudf/cudf/_fuzz_testing/main.py
new file mode 100644
index 0000000..54e49b6
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/main.py
@@ -0,0 +1,45 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf._fuzz_testing import fuzzer
+
+
+class PythonFuzz:
+    def __init__(self, func, params=None, data_handle=None, **kwargs):
+        self.function = func
+        self.data_handler_class = data_handle
+        self.fuzz_worker = fuzzer.Fuzzer(
+            target=self.function,
+            data_handler_class=self.data_handler_class,
+            dirs=kwargs.get("dir", None),
+            crash_reports_dir=kwargs.get("crash_reports_dir", None),
+            regression=kwargs.get("regression", False),
+            max_rows_size=kwargs.get("max_rows_size", 100_000),
+            max_cols_size=kwargs.get("max_cols_size", 1000),
+            runs=kwargs.get("runs", -1),
+            max_string_length=kwargs.get("max_string_length", None),
+            params=params,
+            write_data_on_failure=kwargs.get("write_data_on_failure", True),
+            max_lists_length=kwargs.get("max_lists_length", None),
+            max_lists_nesting_depth=kwargs.get(
+                "max_lists_nesting_depth", None
+            ),
+        )
+
+    def __call__(self, *args, **kwargs):
+        self.fuzz_worker.start()
+
+
+# wrap PythonFuzz to allow for deferred calling
+def pythonfuzz(function=None, data_handle=None, params=None, **kwargs):
+    if function:
+        return PythonFuzz(function, params, **kwargs)
+    else:
+
+        def wrapper(function):
+            return PythonFuzz(function, params, data_handle, **kwargs)
+
+        return wrapper
+
+
+if __name__ == "__main__":
+    PythonFuzz(None)
diff --git a/python/cudf/cudf/_fuzz_testing/orc.py b/python/cudf/cudf/_fuzz_testing/orc.py
new file mode 100644
index 0000000..65d2e09
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/orc.py
@@ -0,0 +1,209 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import copy
+import io
+import logging
+import random
+
+import numpy as np
+import pyorc
+
+import cudf
+from cudf._fuzz_testing.io import IOFuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    _generate_rand_meta,
+    pandas_to_orc,
+    pyarrow_to_pandas,
+)
+from cudf.testing import dataset_generator as dg
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+class OrcReader(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+        self._df = None
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                - {"category"}
+                # Following dtypes are not supported by orc
+                # https://orc.apache.org/specification/ORCv0/
+                - cudf.utils.dtypes.TIMEDELTA_TYPES
+                - cudf.utils.dtypes.UNSIGNED_TYPES
+                - {"datetime64[ns]"}
+            )
+
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+
+            self._current_params["dtypes_meta"] = dtypes_meta
+            seed = random.randint(0, 2**32 - 1)
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_cols"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+        logging.info(f"Shape of DataFrame generated: {table.shape}")
+        self._df = df
+        file_obj = io.BytesIO()
+        pandas_to_orc(
+            df,
+            file_io_obj=file_obj,
+            stripe_size=self._rand(len(df)),
+            arrow_table_schema=table.schema,
+        )
+        file_obj.seek(0)
+        buf = file_obj.read()
+        self._current_buffer = copy.copy(buf)
+        return (df, buf)
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            with open(file_name + "_crash.orc", "wb") as crash_dataset:
+                crash_dataset.write(self._current_buffer)
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if values == ALL_POSSIBLE_VALUES:
+                if param == "columns":
+                    col_size = self._rand(len(self._df.columns))
+                    params_dict[param] = list(
+                        np.unique(np.random.choice(self._df.columns, col_size))
+                    )
+                elif param == "stripes":
+                    f = io.BytesIO(self._current_buffer)
+                    reader = pyorc.Reader(f)
+                    stripes = [i for i in range(reader.num_of_stripes)]
+                    params_dict[param] = np.random.choice(
+                        [
+                            None,
+                            list(
+                                map(
+                                    int,
+                                    np.unique(
+                                        np.random.choice(
+                                            stripes, reader.num_of_stripes
+                                        )
+                                    ),
+                                )
+                            ),
+                        ]
+                    )
+                elif param == "use_index":
+                    params_dict[param] = np.random.choice([True, False])
+                elif param in ("skiprows", "num_rows"):
+                    params_dict[param] = np.random.choice(
+                        [None, self._rand(len(self._df))]
+                    )
+            else:
+                if not isinstance(values, list):
+                    raise TypeError("values must be of type list")
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
+
+
+class OrcWriter(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+        self._df = None
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                # TODO: Remove "bool" from below
+                # list after following issue is fixed:
+                # https://github.com/rapidsai/cudf/issues/6763
+                - {"category", "bool"}
+                # Following dtypes are not supported by orc
+                # https://orc.apache.org/specification/ORCv0/
+                - cudf.utils.dtypes.TIMEDELTA_TYPES
+                - cudf.utils.dtypes.UNSIGNED_TYPES
+                # TODO: Remove `DATETIME_TYPES` once
+                # following bug is fixed:
+                # https://github.com/rapidsai/cudf/issues/7355
+                - cudf.utils.dtypes.DATETIME_TYPES
+            )
+
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            seed = random.randint(0, 2**32 - 1)
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_cols"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+        logging.info(f"Shape of DataFrame generated: {table.shape}")
+        self._df = df
+        return df
+
+    def write_data(self, file_name):
+        # Due to the lack of really fast reference writer we are dumping
+        # the dataframe to a parquet file
+        if self._df is not None:
+            self._df.to_parquet(file_name + "_crash.parquet")
diff --git a/python/cudf/cudf/_fuzz_testing/parquet.py b/python/cudf/cudf/_fuzz_testing/parquet.py
new file mode 100644
index 0000000..2d934e4
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/parquet.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import logging
+import random
+
+import numpy as np
+
+import cudf
+from cudf._fuzz_testing.io import IOFuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    _generate_rand_meta,
+    pyarrow_to_pandas,
+)
+from cudf.testing import dataset_generator as dg
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+class ParquetReader(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+        self._df = None
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                - {"category", "datetime64[ns]"}
+                - cudf.utils.dtypes.TIMEDELTA_TYPES
+                # TODO: Remove uint32 below after this bug is fixed
+                # https://github.com/pandas-dev/pandas/issues/37327
+                - {"uint32"}
+                | {"list", "decimal64"}
+            )
+
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            seed = random.randint(0, 2**32 - 1)
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_cols"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+        logging.info(f"Shape of DataFrame generated: {table.shape}")
+
+        # TODO: Change this to write into
+        # a BytesIO object once below issue is fixed
+        # https://issues.apache.org/jira/browse/ARROW-10123
+
+        # file = io.BytesIO()
+
+        df.to_parquet("temp_file")
+        # file.seek(0)
+        # self._current_buffer = copy.copy(file.read())
+        # return self._current_buffer
+        self._df = df
+        return "temp_file"
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            with open(file_name + "_crash.parquet", "wb") as crash_dataset:
+                crash_dataset.write(self._current_buffer)
+
+    def set_rand_params(self, params):
+        params_dict = {}
+        for param, values in params.items():
+            if param == "columns" and values == ALL_POSSIBLE_VALUES:
+                col_size = self._rand(len(self._df.columns))
+                params_dict[param] = list(
+                    np.unique(np.random.choice(self._df.columns, col_size))
+                )
+            else:
+                params_dict[param] = np.random.choice(values)
+        self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
+
+
+class ParquetWriter(IOFuzz):
+    def __init__(
+        self,
+        dirs=None,
+        max_rows=100_000,
+        max_columns=1000,
+        max_string_length=None,
+        max_lists_length=None,
+        max_lists_nesting_depth=None,
+    ):
+        super().__init__(
+            dirs=dirs,
+            max_rows=max_rows,
+            max_columns=max_columns,
+            max_string_length=max_string_length,
+            max_lists_length=max_lists_length,
+            max_lists_nesting_depth=max_lists_nesting_depth,
+        )
+
+    def generate_input(self):
+        if self._regression:
+            (
+                dtypes_meta,
+                num_rows,
+                num_cols,
+                seed,
+            ) = self.get_next_regression_params()
+        else:
+            seed = random.randint(0, 2**32 - 1)
+            random.seed(seed)
+            dtypes_list = list(
+                cudf.utils.dtypes.ALL_TYPES
+                - {"category", "timedelta64[ns]", "datetime64[ns]"}
+                # TODO: Remove uint32 below after this bug is fixed
+                # https://github.com/pandas-dev/pandas/issues/37327
+                - {"uint32"}
+                | {"list", "decimal64"}
+            )
+            dtypes_meta, num_rows, num_cols = _generate_rand_meta(
+                self, dtypes_list
+            )
+            self._current_params["dtypes_meta"] = dtypes_meta
+            self._current_params["seed"] = seed
+            self._current_params["num_rows"] = num_rows
+            self._current_params["num_columns"] = num_cols
+        logging.info(
+            f"Generating DataFrame with rows: {num_rows} "
+            f"and columns: {num_cols}"
+        )
+
+        table = dg.rand_dataframe(dtypes_meta, num_rows, seed)
+        df = pyarrow_to_pandas(table)
+
+        logging.info(f"Shape of DataFrame generated: {df.shape}")
+        self._current_buffer = df
+        return df
+
+    def write_data(self, file_name):
+        if self._current_buffer is not None:
+            self._current_buffer.to_parquet(file_name + "_crash.parquet")
diff --git a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_avro.py b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_avro.py
new file mode 100644
index 0000000..5a90aec
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_avro.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+import sys
+
+import cudf
+from cudf._fuzz_testing.avro import AvroReader
+from cudf._fuzz_testing.main import pythonfuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    compare_dataframe,
+    run_test,
+)
+
+
+@pythonfuzz(
+    data_handle=AvroReader,
+    params={
+        "columns": ALL_POSSIBLE_VALUES,
+        "skiprows": ALL_POSSIBLE_VALUES,
+        "num_rows": ALL_POSSIBLE_VALUES,
+    },
+)
+def avro_reader_test(input_tuple, columns, skiprows, num_rows):
+    pdf, parquet_buffer = input_tuple
+    expected_pdf = pdf[skiprows:]
+    if num_rows is not None:
+        expected_pdf = expected_pdf.head(num_rows)
+    if skiprows is not None or num_rows is not None:
+        expected_pdf = expected_pdf.reset_index(drop=True)
+
+    gdf = cudf.read_avro(
+        parquet_buffer, columns=columns, skiprows=skiprows, num_rows=num_rows
+    )
+    compare_dataframe(expected_pdf, gdf)
+
+
+if __name__ == "__main__":
+    run_test(globals(), sys.argv)
diff --git a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_csv.py b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_csv.py
new file mode 100644
index 0000000..f8f674f
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_csv.py
@@ -0,0 +1,132 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import sys
+from io import StringIO
+
+import pandas as pd
+
+import cudf
+from cudf._fuzz_testing.csv import CSVReader, CSVWriter
+from cudf._fuzz_testing.main import pythonfuzz
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    compare_content,
+    run_test,
+)
+from cudf.testing._utils import assert_eq
+
+
+@pythonfuzz(data_handle=CSVReader)
+def csv_reader_test(csv_buffer):
+    pdf = pd.read_csv(StringIO(csv_buffer))
+    gdf = cudf.read_csv(StringIO(csv_buffer))
+
+    assert_eq(gdf, pdf)
+
+
+@pythonfuzz(data_handle=CSVWriter)
+def csv_writer_test(pdf):
+    gdf = cudf.from_pandas(pdf)
+
+    pd_buffer = pdf.to_csv()
+    gd_buffer = gdf.to_csv()
+
+    compare_content(pd_buffer, gd_buffer)
+    actual = cudf.read_csv(StringIO(gd_buffer))
+    expected = pd.read_csv(StringIO(pd_buffer))
+    assert_eq(actual, expected)
+
+
+@pythonfuzz(
+    data_handle=CSVWriter,
+    params={
+        "sep": list([",", "|", "\t", "\r", "~"]),
+        "header": [True, False],
+        "na_rep": [
+            "",
+            "<NA>",
+            "NA",
+            "_NA_",
+            "__",
+            "<<<<>>>>>",
+            "--<>--",
+            "-+><+-",
+        ],
+        "columns": ALL_POSSIBLE_VALUES,
+        "index": [True, False],
+        "lineterminator": ["\n", "\r", "\r\n"],
+        "chunksize": ALL_POSSIBLE_VALUES,
+    },
+)
+def csv_writer_test_params(
+    pdf, sep, header, na_rep, columns, index, lineterminator, chunksize
+):
+    gdf = cudf.from_pandas(pdf)
+
+    pd_buffer = pdf.to_csv(
+        sep=sep,
+        header=header,
+        na_rep=na_rep,
+        columns=columns,
+        index=index,
+        lineterminator=lineterminator,
+        chunksize=chunksize,
+    )
+    gd_buffer = gdf.to_csv(
+        sep=sep,
+        header=header,
+        na_rep=na_rep,
+        columns=columns,
+        index=index,
+        lineterminator=lineterminator,
+        chunksize=chunksize,
+    )
+
+    # TODO: Uncomment once this issue is fixed
+    # https://github.com/rapidsai/cudf/issues/6418
+    # compare_content(pd_buffer, gd_buffer)
+
+    actual = cudf.read_csv(
+        StringIO(gd_buffer),
+        delimiter=sep,
+        na_values=na_rep,
+        lineterminator=lineterminator,
+    )
+    expected = pd.read_csv(
+        StringIO(pd_buffer),
+        delimiter=sep,
+        na_values=na_rep,
+        lineterminator=lineterminator,
+    )
+    if not header:
+        # TODO: Remove renaming columns once the following bug is fixed:
+        # https://github.com/rapidsai/cudf/issues/6418
+        actual.columns = expected.columns
+
+    assert_eq(actual, expected)
+
+
+@pythonfuzz(
+    data_handle=CSVReader,
+    params={
+        "dtype": ALL_POSSIBLE_VALUES,
+        "usecols": ALL_POSSIBLE_VALUES,
+        "header": ALL_POSSIBLE_VALUES,
+        "skiprows": ALL_POSSIBLE_VALUES,
+        "skipfooter": ALL_POSSIBLE_VALUES,
+        "nrows": ALL_POSSIBLE_VALUES,
+    },
+)
+def csv_reader_test_params(csv_buffer, dtype, header, skiprows):
+    pdf = pd.read_csv(
+        StringIO(csv_buffer), dtype=dtype, header=header, skiprows=skiprows
+    )
+    gdf = cudf.read_csv(
+        StringIO(csv_buffer), dtype=dtype, header=header, skiprows=skiprows
+    )
+
+    assert_eq(gdf, pdf)
+
+
+if __name__ == "__main__":
+    run_test(globals(), sys.argv)
diff --git a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_json.py b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_json.py
new file mode 100644
index 0000000..2f5e620
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_json.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+import io
+import sys
+
+import pandas as pd
+
+import cudf
+from cudf._fuzz_testing.json import JSONReader, JSONWriter
+from cudf._fuzz_testing.main import pythonfuzz
+from cudf._fuzz_testing.utils import ALL_POSSIBLE_VALUES, run_test
+from cudf.testing._utils import assert_eq
+
+
+@pythonfuzz(data_handle=JSONReader)
+def json_reader_test(json_buffer):
+    pdf = pd.read_json(io.StringIO(json_buffer), orient="records", lines=True)
+    # Difference in behaviour with pandas
+    # cudf reads column as strings only.
+    pdf.columns = pdf.columns.astype("str")
+    gdf = cudf.read_json(io.StringIO(json_buffer), engine="cudf", lines=True)
+
+    assert_eq(gdf, pdf)
+
+
+@pythonfuzz(data_handle=JSONReader, params={"dtype": ALL_POSSIBLE_VALUES})
+def json_reader_test_params(json_buffer, dtype):
+    pdf = pd.read_json(json_buffer, dtype=dtype, orient="records", lines=True)
+    pdf.columns = pdf.columns.astype("str")
+
+    gdf = cudf.read_json(json_buffer, dtype=dtype, engine="cudf", lines=True)
+
+    assert_eq(gdf, pdf)
+
+
+@pythonfuzz(data_handle=JSONWriter)
+def json_writer_test(pdf):
+    gdf = cudf.from_pandas(pdf)
+
+    pdf_buffer = pdf.to_json(lines=True, orient="records")
+    gdf_buffer = gdf.to_json(lines=True, orient="records")
+
+    # TODO: Uncomment once this is fixed:
+    # https://github.com/rapidsai/cudf/issues/6429
+    # compare_content(pdf_buffer, gdf_buffer)
+
+    actual = cudf.read_json(
+        gdf_buffer, engine="cudf", lines=True, orient="records"
+    )
+    expected = pd.read_json(pdf_buffer, lines=True, orient="records")
+    expected.columns = expected.columns.astype("str")
+    assert_eq(actual, expected)
+
+
+@pythonfuzz(
+    data_handle=JSONWriter,
+    params={
+        "compression": ["gzip", "bz2", "zip", "xz", None],
+        "dtype": ALL_POSSIBLE_VALUES,
+    },
+)
+def json_writer_test_params(pdf, compression, dtype):
+    gdf = cudf.from_pandas(pdf)
+
+    pdf_buffer = pdf.to_json(
+        lines=True, orient="records", compression=compression
+    )
+    gdf_buffer = gdf.to_json(
+        lines=True, orient="records", compression=compression
+    )
+
+    # TODO: Uncomment once this is fixed:
+    # https://github.com/rapidsai/cudf/issues/6429
+    # compare_content(pdf_buffer, gdf_buffer)
+
+    actual = cudf.read_json(
+        io.StringIO(gdf_buffer),
+        engine="cudf",
+        lines=True,
+        orient="records",
+        dtype=dtype,
+    )
+    expected = pd.read_json(
+        io.StringIO(pdf_buffer), lines=True, orient="records", dtype=dtype
+    )
+
+    # Difference in behaviour with pandas
+    # cudf reads column as strings only.
+    expected.columns = expected.columns.astype("str")
+    assert_eq(actual, expected)
+
+
+if __name__ == "__main__":
+    run_test(globals(), sys.argv)
diff --git a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_orc.py b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_orc.py
new file mode 100644
index 0000000..977038d
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_orc.py
@@ -0,0 +1,98 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import io
+import sys
+
+import cudf
+from cudf._fuzz_testing.main import pythonfuzz
+from cudf._fuzz_testing.orc import OrcReader, OrcWriter
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    compare_dataframe,
+    orc_to_pandas,
+    run_test,
+)
+
+
+@pythonfuzz(
+    data_handle=OrcReader,
+    params={
+        "columns": ALL_POSSIBLE_VALUES,
+        "skiprows": ALL_POSSIBLE_VALUES,
+        "num_rows": ALL_POSSIBLE_VALUES,
+        "use_index": ALL_POSSIBLE_VALUES,
+    },
+)
+def orc_reader_test(input_tuple, columns, skiprows, num_rows, use_index):
+    pdf, file_buffer = input_tuple
+    expected_pdf = pdf.iloc[skiprows:]
+    if num_rows is not None:
+        expected_pdf = expected_pdf.head(num_rows)
+    if skiprows is not None or num_rows is not None:
+        expected_pdf.reset_index(drop=True, inplace=True)
+    if columns is not None and len(columns) > 0:
+        # ORC reader picks columns if only
+        # there are any elements in `columns`
+        expected_pdf = expected_pdf[columns]
+    if use_index is False:
+        expected_pdf.reset_index(drop=True, inplace=True)
+
+    gdf = cudf.read_orc(
+        io.BytesIO(file_buffer),
+        columns=columns,
+        skiprows=skiprows,
+        num_rows=num_rows,
+        use_index=use_index,
+    )
+
+    compare_dataframe(expected_pdf, gdf)
+
+
+@pythonfuzz(
+    data_handle=OrcReader,
+    params={"columns": ALL_POSSIBLE_VALUES, "stripes": ALL_POSSIBLE_VALUES},
+)
+def orc_reader_stripes_test(input_tuple, columns, stripes):
+    _, file_buffer = input_tuple
+    expected_pdf = orc_to_pandas(
+        file_io_obj=io.BytesIO(file_buffer), stripes=stripes
+    )
+
+    if columns is not None and len(columns) > 0:
+        # ORC reader picks columns if only
+        # there are any elements in `columns`
+        expected_pdf = expected_pdf[columns]
+
+    gdf = cudf.read_orc(
+        io.BytesIO(file_buffer), columns=columns, stripes=stripes
+    )
+
+    compare_dataframe(expected_pdf, gdf)
+
+
+@pythonfuzz(
+    data_handle=OrcWriter,
+    params={
+        "compression": [None, "snappy"],
+        "enable_statistics": ["NONE", "STRIPE", "ROWGROUP"],
+    },
+)
+def orc_writer_test(pdf, compression, enable_statistics):
+    file_to_strore = io.BytesIO()
+
+    gdf = cudf.from_pandas(pdf)
+
+    gdf.to_orc(
+        file_to_strore,
+        compression=compression,
+        enable_statistics=enable_statistics,
+    )
+    file_to_strore.seek(0)
+
+    actual_df = cudf.read_orc(file_to_strore)
+
+    compare_dataframe(pdf, actual_df)
+
+
+if __name__ == "__main__":
+    run_test(globals(), sys.argv)
diff --git a/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py
new file mode 100644
index 0000000..3d07057
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/tests/fuzz_test_parquet.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import sys
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf._fuzz_testing.main import pythonfuzz
+from cudf._fuzz_testing.parquet import ParquetReader, ParquetWriter
+from cudf._fuzz_testing.utils import (
+    ALL_POSSIBLE_VALUES,
+    compare_dataframe,
+    run_test,
+)
+
+
+@pythonfuzz(data_handle=ParquetReader)
+def parquet_reader_test(parquet_buffer):
+    pdf = pd.read_parquet(parquet_buffer)
+    gdf = cudf.read_parquet(parquet_buffer)
+
+    compare_dataframe(gdf, pdf)
+
+
+@pythonfuzz(
+    data_handle=ParquetReader,
+    params={
+        "columns": ALL_POSSIBLE_VALUES,
+        "use_pandas_metadata": [True, False],
+    },
+)
+def parquet_reader_columns(parquet_buffer, columns, use_pandas_metadata):
+    pdf = pd.read_parquet(
+        parquet_buffer,
+        columns=columns,
+        use_pandas_metadata=use_pandas_metadata,
+    )
+
+    gdf = cudf.read_parquet(
+        parquet_buffer,
+        columns=columns,
+        use_pandas_metadata=use_pandas_metadata,
+    )
+
+    compare_dataframe(gdf, pdf)
+
+
+@pythonfuzz(data_handle=ParquetWriter)
+def parquet_writer_test(pdf):
+    pd_file_name = "cpu_pdf.parquet"
+    gd_file_name = "gpu_pdf.parquet"
+
+    gdf = cudf.from_pandas(pdf)
+
+    pdf.to_parquet(pd_file_name)
+    gdf.to_parquet(gd_file_name)
+
+    actual = cudf.read_parquet(gd_file_name)
+    expected = pd.read_parquet(pd_file_name)
+    compare_dataframe(actual, expected)
+
+    actual = cudf.read_parquet(pd_file_name)
+    expected = pd.read_parquet(gd_file_name)
+    compare_dataframe(actual, expected)
+
+
+@pythonfuzz(
+    data_handle=ParquetWriter,
+    params={
+        "row_group_size": np.random.random_integers(1, 10000, 100),
+        "compression": ["snappy", None],
+    },
+)
+def parquet_writer_test_rowgroup_index_compression(
+    pdf, compression, row_group_size
+):
+    pd_file_name = "cpu_pdf.parquet"
+    gd_file_name = "gpu_pdf.parquet"
+
+    gdf = cudf.from_pandas(pdf)
+
+    pdf.to_parquet(
+        pd_file_name,
+        compression=compression,
+        row_group_size=row_group_size,
+    )
+    gdf.to_parquet(
+        gd_file_name,
+        compression=compression,
+        row_group_size=row_group_size,
+    )
+
+    actual = cudf.read_parquet(gd_file_name)
+    expected = pd.read_parquet(pd_file_name)
+    compare_dataframe(actual, expected)
+
+    actual = cudf.read_parquet(pd_file_name)
+    expected = pd.read_parquet(gd_file_name)
+    compare_dataframe(actual, expected, nullable=False)
+
+
+if __name__ == "__main__":
+    run_test(globals(), sys.argv)
diff --git a/python/cudf/cudf/_fuzz_testing/tests/readme.md b/python/cudf/cudf/_fuzz_testing/tests/readme.md
new file mode 100644
index 0000000..f9ef111
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/tests/readme.md
@@ -0,0 +1,100 @@
+# Fuzz Tests
+
+This directory contains all the Fuzz tests for cudf library.
+
+
+## Steps to write a fuzz test
+
+1. Add a Data Handler class which actually generates the necessary random data according to your requirements. This class should be added in `cudf/cudf/testing/`. A sample data handler class is: `CSVWriter`: https://github.com/rapidsai/cudf/blob/branch-0.16/python/cudf/cudf/testing/csv.py
+2. Data Handlers are registered by the `pythonfuzz` decorator. At runtime, the Fuzzer will continuously run registered fuzz tests.
+
+```python
+from cudf.testing.csv import CSVWriter
+
+@pythonfuzz(data_handle=CSVWriter)
+def csv_writer_test(data_from_generate_input):
+    ...
+    ...
+    ...
+
+if __name__ == "__main__":
+    ...
+    ...
+
+```
+## Steps to run fuzz tests
+
+1. To run a fuzz test, for example a test(method) is in `write_csv.py`:
+
+```bash
+python write_csv.py your_function_name
+```
+
+To run a basic csv write test in `write_csv.py`:
+```bash
+python write_csv.py csv_writer_test
+```
+
+## Tips to run specific crash file/files
+
+Using the `pythonfuzz` decorator pass in `regression=True` with `dirs` having list of directories
+```python
+@pythonfuzz(data_handle=CSVWriter, regression=True, dir=["/cudf/python/cudf/cudf/_fuzz_testing"])
+```
+
+
+## Tips to run for varying parameter combinations
+
+In the `pythonfuzz` decorator you can pass in the function parameters you would like to pass to the
+fuzz-test being written via `params` as a dictionary. The values in dictionary are sampled randomly
+and passed to the `your_custom_fuzz_test`.
+
+If a parameter value depends the kind of input generated by the `data_handle`(in this case `CSVReader`),
+then you can assign `ALL_POSSIBLE_VALUES` constant to it. This constant is used as an identifier by the
+`data_handle` to generate random parameter values for that specific parameter purely based on data.
+To perform this customization `set_rand_params` should be implemented as shown in the below example.
+```python
+from cudf._fuzz_testing.main import pythonfuzz
+from cudf._fuzz_testing.utils import ALL_POSSIBLE_VALUES
+@pythonfuzz(
+    data_handle=CSVWriter,
+    params={
+        "columns": ALL_POSSIBLE_VALUES,
+        "is_folder": [True, False, None],
+        "chunksize": ALL_POSSIBLE_VALUES,
+    },
+)
+def your_custom_fuzz_test(data_from_data_handle, dtype, is_folder, header):
+    ...
+    ...
+    ...
+```
+
+A sample implementation of `set_rand_params` in a `data_handle` class:
+```
+def set_rand_params(self, params):
+    params_dict = {}
+    for param, values in params.items():
+        if values == ALL_POSSIBLE_VALUES:
+            if param == "columns":
+                col_size = self._rand(len(self._current_buffer.columns))
+                params_dict[param] = list(
+                    np.unique(
+                        np.random.choice(
+                            self._current_buffer.columns, col_size
+                        )
+                    )
+                )
+            elif param == "chunksize":
+                params_dict[param] = np.random.choice(
+                    [
+                        None,
+                        np.random.randint(
+                            low=1, high=max(1, len(self._current_buffer))
+                        ),
+                    ]
+                )
+        else:
+            params_dict[param] = np.random.choice(values)
+    self._current_params["test_kwargs"] = self.process_kwargs(params_dict)
+```
diff --git a/python/cudf/cudf/_fuzz_testing/utils.py b/python/cudf/cudf/_fuzz_testing/utils.py
new file mode 100644
index 0000000..03418e0
--- /dev/null
+++ b/python/cudf/cudf/_fuzz_testing/utils.py
@@ -0,0 +1,407 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import random
+from collections import OrderedDict
+
+import fastavro
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyorc
+
+import cudf
+from cudf.testing._utils import assert_eq
+from cudf.utils.dtypes import (
+    pandas_dtypes_to_np_dtypes,
+    pyarrow_dtypes_to_pandas_dtypes,
+)
+
+ALL_POSSIBLE_VALUES = "ALL_POSSIBLE_VALUES"
+
+_PANDAS_TO_AVRO_SCHEMA_MAP = {
+    cudf.dtype("int8"): "int",
+    pd.Int8Dtype(): ["int", "null"],
+    pd.Int16Dtype(): ["int", "null"],
+    pd.Int32Dtype(): ["int", "null"],
+    pd.Int64Dtype(): ["long", "null"],
+    pd.Float32Dtype(): ["float", "null"],
+    pd.Float64Dtype(): ["double", "null"],
+    pd.BooleanDtype(): ["boolean", "null"],
+    pd.StringDtype(): ["string", "null"],
+    cudf.dtype("bool_"): "boolean",
+    cudf.dtype("int16"): "int",
+    cudf.dtype("int32"): "int",
+    cudf.dtype("int64"): "long",
+    cudf.dtype("O"): "string",
+    cudf.dtype("str"): "string",
+    cudf.dtype("float32"): "float",
+    cudf.dtype("float64"): "double",
+    cudf.dtype("<M8[ns]"): {"type": "long", "logicalType": "timestamp-millis"},
+    cudf.dtype("<M8[ms]"): {"type": "long", "logicalType": "timestamp-millis"},
+    cudf.dtype("<M8[us]"): {"type": "long", "logicalType": "timestamp-micros"},
+}
+
+PANDAS_TO_ORC_TYPES = {
+    cudf.dtype("int8"): pyorc.TinyInt(),
+    pd.Int8Dtype(): pyorc.TinyInt(),
+    pd.Int16Dtype(): pyorc.SmallInt(),
+    pd.Int32Dtype(): pyorc.Int(),
+    pd.Int64Dtype(): pyorc.BigInt(),
+    pd.Float32Dtype(): pyorc.Float(),
+    pd.Float64Dtype(): pyorc.Double(),
+    pd.BooleanDtype(): pyorc.Boolean(),
+    cudf.dtype("bool_"): pyorc.Boolean(),
+    cudf.dtype("int16"): pyorc.SmallInt(),
+    cudf.dtype("int32"): pyorc.Int(),
+    cudf.dtype("int64"): pyorc.BigInt(),
+    cudf.dtype("O"): pyorc.String(),
+    pd.StringDtype(): pyorc.String(),
+    cudf.dtype("float32"): pyorc.Float(),
+    cudf.dtype("float64"): pyorc.Double(),
+    cudf.dtype("<M8[ns]"): pyorc.Timestamp(),
+    cudf.dtype("<M8[ms]"): pyorc.Timestamp(),
+    cudf.dtype("<M8[us]"): pyorc.Timestamp(),
+}
+
+ORC_TO_PANDAS_TYPES = {
+    pyorc.TinyInt().name: pd.Int8Dtype(),
+    pyorc.Int().name: pd.Int32Dtype(),
+    pyorc.Boolean().name: pd.BooleanDtype(),
+    pyorc.SmallInt().name: pd.Int16Dtype(),
+    pyorc.BigInt().name: pd.Int64Dtype(),
+    pyorc.String().name: pd.StringDtype(),
+    pyorc.Float().name: pd.Float32Dtype(),
+    pyorc.Double().name: pd.Float64Dtype(),
+    pyorc.Timestamp().name: cudf.dtype("<M8[ns]"),
+}
+
+
+def _generate_rand_meta(obj, dtypes_list, null_frequency_override=None):
+    obj._current_params = {}
+    num_rows = obj._rand(obj._max_rows)
+    num_cols = obj._rand(obj._max_columns)
+
+    dtypes_meta = []
+
+    for _ in range(num_cols):
+        dtype = random.choice(dtypes_list)
+        null_frequency = (
+            random.uniform(0, 1)
+            if null_frequency_override is None
+            else null_frequency_override
+        )
+        # `cardinality` has to be at least 1.
+        cardinality = max(1, obj._rand(obj._max_rows))
+        meta = dict()
+        if dtype == "str":
+            # We want to operate near the limits of string column
+            # Hence creating a string column of size almost
+            # equal to 2 Billion bytes(sizeof(int))
+            if obj._max_string_length is None:
+                meta["max_string_length"] = random.randrange(
+                    0, int(2000000000 / num_rows)
+                )
+            else:
+                meta["max_string_length"] = obj._max_string_length
+        elif dtype == "list":
+            if obj._max_lists_length is None:
+                meta["lists_max_length"] = np.random.randint(0, 2000000000)
+            else:
+                meta["lists_max_length"] = obj._max_lists_length
+
+            if obj._max_lists_nesting_depth is None:
+                meta["nesting_max_depth"] = np.random.randint(
+                    1, np.iinfo("int64").max
+                )
+            else:
+                meta["nesting_max_depth"] = obj._max_lists_nesting_depth
+
+            meta["value_type"] = random.choice(
+                list(cudf.utils.dtypes.ALL_TYPES - {"category"})
+            )
+        elif dtype == "struct":
+            if obj._max_lists_nesting_depth is None:
+                meta["nesting_max_depth"] = np.random.randint(2, 10)
+            else:
+                meta["nesting_max_depth"] = obj._max_lists_nesting_depth
+
+            if obj._max_struct_null_frequency is None:
+                meta["max_null_frequency"] = random.uniform(0, 1)
+            else:
+                meta["max_null_frequency"] = obj._max_struct_null_frequency
+
+            if obj._max_struct_types_at_each_level is None:
+                meta["max_types_at_each_level"] = np.random.randint(
+                    low=1, high=10
+                )
+            else:
+                meta[
+                    "max_types_at_each_level"
+                ] = obj._max_struct_types_at_each_level
+
+        elif dtype == "decimal64":
+            meta["max_precision"] = cudf.Decimal64Dtype.MAX_PRECISION
+        elif dtype == "decimal32":
+            meta["max_precision"] = cudf.Decimal32Dtype.MAX_PRECISION
+
+        meta["dtype"] = dtype
+        meta["null_frequency"] = null_frequency
+        meta["cardinality"] = cardinality
+        dtypes_meta.append(meta)
+    return dtypes_meta, num_rows, num_cols
+
+
+def run_test(funcs, args):
+    if len(args) != 2:
+        ValueError("Usage is python file_name.py function_name")
+
+    function_name_to_run = args[1]
+    try:
+        funcs[function_name_to_run]()
+    except KeyError:
+        print(
+            f"Provided function name({function_name_to_run}) does not exist."
+        )
+
+
+def pyarrow_to_pandas(table):
+    """
+    Converts a pyarrow table to a pandas dataframe
+    with Nullable dtypes.
+
+    Parameters
+    ----------
+    table: Pyarrow Table
+        Pyarrow table to be converted to pandas
+
+    Returns
+    -------
+    DataFrame
+        A Pandas dataframe with nullable dtypes.
+    """
+    df = pd.DataFrame()
+
+    for column in table.columns:
+        if column.type in pyarrow_dtypes_to_pandas_dtypes:
+            df[column._name] = pd.Series(
+                column, dtype=pyarrow_dtypes_to_pandas_dtypes[column.type]
+            )
+        elif isinstance(column.type, pa.StructType):
+            df[column._name] = column.to_pandas(integer_object_nulls=True)
+        else:
+            df[column._name] = column.to_pandas()
+
+    return df
+
+
+def compare_content(a, b):
+    if a == b:
+        return
+    else:
+        raise ValueError(
+            f"Contents of two files are different:\n left: {a} \n right: {b}"
+        )
+
+
+def get_avro_dtype_info(dtype):
+    if dtype in _PANDAS_TO_AVRO_SCHEMA_MAP:
+        return _PANDAS_TO_AVRO_SCHEMA_MAP[dtype]
+    else:
+        raise TypeError(
+            f"Unsupported dtype({dtype}) according to avro spec:"
+            f" https://avro.apache.org/docs/current/spec.html"
+        )
+
+
+def get_orc_dtype_info(dtype):
+    if dtype in PANDAS_TO_ORC_TYPES:
+        return PANDAS_TO_ORC_TYPES[dtype]
+    else:
+        raise TypeError(
+            f"Unsupported dtype({dtype}) according to orc spec:"
+            f" https://orc.apache.org/specification/"
+        )
+
+
+def get_arrow_dtype_info_for_pyorc(dtype):
+    if isinstance(dtype, pa.StructType):
+        return get_orc_schema(df=None, arrow_table_schema=dtype)
+    else:
+        pd_dtype = cudf.dtype(dtype.to_pandas_dtype())
+        return get_orc_dtype_info(pd_dtype)
+
+
+def get_avro_schema(df):
+    fields = [
+        {"name": col_name, "type": get_avro_dtype_info(col_dtype)}
+        for col_name, col_dtype in df.dtypes.items()
+    ]
+    schema = {"type": "record", "name": "Root", "fields": fields}
+    return schema
+
+
+def get_orc_schema(df, arrow_table_schema=None):
+    if arrow_table_schema is None:
+        ordered_dict = OrderedDict(
+            (col_name, get_orc_dtype_info(col_dtype))
+            for col_name, col_dtype in df.dtypes.items()
+        )
+    else:
+        ordered_dict = OrderedDict(
+            (field.name, get_arrow_dtype_info_for_pyorc(field.type))
+            for field in arrow_table_schema
+        )
+
+    schema = pyorc.Struct(**ordered_dict)
+    return schema
+
+
+def convert_nulls_to_none(records, df):
+    columns_with_nulls = {col for col in df.columns if df[col].isnull().any()}
+    scalar_columns_convert = [
+        col
+        for col in df.columns
+        if df[col].dtype in pandas_dtypes_to_np_dtypes
+        or pd.api.types.is_datetime64_dtype(df[col].dtype)
+        or pd.api.types.is_timedelta64_dtype(df[col].dtype)
+    ]
+
+    for record in records:
+        for col, value in record.items():
+            if col in scalar_columns_convert:
+                if col in columns_with_nulls and value in (pd.NA, pd.NaT):
+                    record[col] = None
+                else:
+                    if isinstance(value, str):
+                        record[col] = value
+                    elif isinstance(value, (pd.Timestamp, pd.Timedelta)):
+                        record[col] = int(value.value)
+                    else:
+                        record[col] = value.item()
+
+    return records
+
+
+def pandas_to_avro(df, file_name=None, file_io_obj=None):
+    schema = get_avro_schema(df)
+    avro_schema = fastavro.parse_schema(schema)
+
+    records = df.to_dict("records")
+    records = convert_nulls_to_none(records, df)
+
+    if file_name is not None:
+        with open(file_name, "wb") as out:
+            fastavro.writer(out, avro_schema, records)
+    elif file_io_obj is not None:
+        fastavro.writer(file_io_obj, avro_schema, records)
+
+
+def _preprocess_to_orc_tuple(df, arrow_table_schema):
+    def _null_to_None(value):
+        if value is pd.NA or value is pd.NaT:
+            return None
+        else:
+            return value
+
+    def sanitize(value, struct_type):
+        if value is None:
+            return None
+
+        values_list = []
+        for name, sub_type in struct_type.fields.items():
+            if isinstance(sub_type, cudf.StructDtype):
+                values_list.append(sanitize(value[name], sub_type))
+            else:
+                values_list.append(value[name])
+        return tuple(values_list)
+
+    has_nulls_or_nullable_dtype = any(
+        (col := df[colname]).dtype in pandas_dtypes_to_np_dtypes
+        or col.isnull().any()
+        for colname in df.columns
+    )
+    pdf = df.copy(deep=True)
+    for field in arrow_table_schema:
+        if isinstance(field.type, pa.StructType):
+            pdf[field.name] = pdf[field.name].apply(
+                sanitize, args=(cudf.StructDtype.from_arrow(field.type),)
+            )
+        else:
+            pdf[field.name] = pdf[field.name]
+
+    tuple_list = [
+        tuple(map(_null_to_None, tup)) if has_nulls_or_nullable_dtype else tup
+        for tup in pdf.itertuples(index=False, name=None)
+    ]
+
+    return tuple_list, pdf, df
+
+
+def pandas_to_orc(
+    df,
+    file_name=None,
+    file_io_obj=None,
+    stripe_size=67108864,
+    arrow_table_schema=None,
+):
+    schema = get_orc_schema(df, arrow_table_schema=arrow_table_schema)
+
+    tuple_list, pdf, df = _preprocess_to_orc_tuple(
+        df, arrow_table_schema=arrow_table_schema
+    )
+
+    if file_name is not None:
+        with open(file_name, "wb") as data:
+            with pyorc.Writer(data, schema, stripe_size=stripe_size) as writer:
+                writer.writerows(tuple_list)
+    elif file_io_obj is not None:
+        with pyorc.Writer(
+            file_io_obj, schema, stripe_size=stripe_size
+        ) as writer:
+            writer.writerows(tuple_list)
+
+
+def orc_to_pandas(file_name=None, file_io_obj=None, stripes=None):
+    if file_name is not None:
+        f = open(file_name, "rb")
+    elif file_io_obj is not None:
+        f = file_io_obj
+
+    reader = pyorc.Reader(f)
+
+    dtypes = {
+        col: ORC_TO_PANDAS_TYPES[pyorc_type.name]
+        for col, pyorc_type in reader.schema.fields.items()
+    }
+
+    if stripes is None:
+        df = pd.DataFrame.from_records(
+            reader, columns=reader.schema.fields.keys()
+        )
+    else:
+        records = [
+            record for i in stripes for record in list(reader.read_stripe(i))
+        ]
+        df = pd.DataFrame.from_records(
+            records, columns=reader.schema.fields.keys()
+        )
+
+    # Need to type-cast to extracted `dtypes` from pyorc schema because
+    # a fully empty/ full <NA> can result in incorrect dtype by pandas.
+    df = df.astype(dtypes)
+
+    return df
+
+
+def compare_dataframe(left, right, nullable=True):
+    if nullable and isinstance(left, cudf.DataFrame):
+        left = left.to_pandas(nullable=True)
+    if nullable and isinstance(right, cudf.DataFrame):
+        right = right.to_pandas(nullable=True)
+
+    if len(left.index) == 0 and len(right.index) == 0:
+        check_index_type = False
+    else:
+        check_index_type = True
+
+    return assert_eq(left, right, check_index_type=check_index_type)
diff --git a/python/cudf/cudf/_lib/CMakeLists.txt b/python/cudf/cudf/_lib/CMakeLists.txt
new file mode 100644
index 0000000..947659c
--- /dev/null
+++ b/python/cudf/cudf/_lib/CMakeLists.txt
@@ -0,0 +1,119 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources
+    aggregation.pyx
+    avro.pyx
+    binaryop.pyx
+    column.pyx
+    concat.pyx
+    copying.pyx
+    csv.pyx
+    datetime.pyx
+    expressions.pyx
+    filling.pyx
+    groupby.pyx
+    hash.pyx
+    interop.pyx
+    join.pyx
+    json.pyx
+    labeling.pyx
+    lists.pyx
+    merge.pyx
+    null_mask.pyx
+    orc.pyx
+    parquet.pyx
+    partitioning.pyx
+    quantiles.pyx
+    reduce.pyx
+    replace.pyx
+    reshape.pyx
+    rolling.pyx
+    round.pyx
+    scalar.pyx
+    search.pyx
+    sort.pyx
+    stream_compaction.pyx
+    string_casting.pyx
+    strings_udf.pyx
+    text.pyx
+    timezone.pyx
+    transform.pyx
+    transpose.pyx
+    types.pyx
+    unary.pyx
+    utils.pyx
+)
+set(linked_libraries cudf::cudf)
+
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cudf
+)
+
+target_link_libraries(strings_udf cudf_strings_udf)
+
+# TODO: Finding NumPy currently requires finding Development due to a bug in CMake. This bug was
+# fixed in https://gitlab.kitware.com/cmake/cmake/-/merge_requests/7410 and will be available in
+# CMake 3.24, so we can remove the Development component once we upgrade to CMake 3.24.
+# find_package(Python REQUIRED COMPONENTS Development NumPy)
+
+# Note: The bug noted above prevents us from finding NumPy successfully using FindPython.cmake
+# inside the manylinux images used to build wheels because manylinux images do not contain
+# libpython.so and therefore Development cannot be found. Until we upgrade to CMake 3.24, we should
+# use FindNumpy.cmake instead (provided by scikit-build). When we switch to 3.24 we can try
+# switching back, but it may not work if that implicitly still requires Python libraries. In that
+# case we'll need to follow up with the CMake team to remove that dependency.  The stopgap solution
+# is to unpack the static lib tarballs in the wheel building jobs so that there are at least static
+# libs to be found, but that should be a last resort since it implies a dependency that isn't really
+# necessary. The relevant command is tar -xf /opt/_internal/static-libs-for-embedding-only.tar.xz -C
+# /opt/_internal"
+find_package(NumPy REQUIRED)
+set(targets_using_numpy interop avro csv orc json parquet)
+foreach(target IN LISTS targets_using_numpy)
+  target_include_directories(${target} PRIVATE "${NumPy_INCLUDE_DIRS}")
+  # Switch to the line below when we switch back to FindPython.cmake in CMake 3.24.
+  # target_include_directories(${target} PRIVATE "${Python_NumPy_INCLUDE_DIRS}")
+endforeach()
+
+set(targets_using_dlpack interop)
+foreach(target IN LISTS targets_using_dlpack)
+  target_include_directories(${target} PRIVATE "${DLPACK_INCLUDE_DIR}")
+endforeach()
+
+find_package(Python 3.9 REQUIRED COMPONENTS Interpreter)
+
+execute_process(
+  COMMAND "${Python_EXECUTABLE}" -c "import pyarrow; print(pyarrow.get_include())"
+  OUTPUT_VARIABLE PYARROW_INCLUDE_DIR
+  ERROR_VARIABLE PYARROW_ERROR
+  RESULT_VARIABLE PYARROW_RESULT
+  OUTPUT_STRIP_TRAILING_WHITESPACE
+)
+
+if(${PYARROW_RESULT})
+  message(FATAL_ERROR "Error while trying to obtain pyarrow include directory:\n${PYARROW_ERROR}")
+endif()
+
+set(targets_using_arrow_headers interop avro csv orc json parquet)
+foreach(target IN LISTS targets_using_arrow_headers)
+  target_include_directories(${target} PRIVATE "${PYARROW_INCLUDE_DIR}")
+endforeach()
+
+add_subdirectory(cpp)
+add_subdirectory(io)
+add_subdirectory(nvtext)
+add_subdirectory(pylibcudf)
+add_subdirectory(strings)
diff --git a/python/cudf/cudf/_lib/__init__.pxd b/python/cudf/cudf/_lib/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/__init__.py b/python/cudf/cudf/_lib/__init__.py
new file mode 100644
index 0000000..18b95f5
--- /dev/null
+++ b/python/cudf/cudf/_lib/__init__.py
@@ -0,0 +1,47 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+import numpy as np
+
+from . import (
+    avro,
+    binaryop,
+    concat,
+    copying,
+    csv,
+    datetime,
+    expressions,
+    filling,
+    groupby,
+    hash,
+    interop,
+    join,
+    json,
+    labeling,
+    merge,
+    null_mask,
+    nvtext,
+    orc,
+    parquet,
+    partitioning,
+    pylibcudf,
+    quantiles,
+    reduce,
+    replace,
+    reshape,
+    rolling,
+    round,
+    search,
+    sort,
+    stream_compaction,
+    string_casting,
+    strings,
+    strings_udf,
+    text,
+    timezone,
+    transpose,
+    unary,
+)
+
+MAX_COLUMN_SIZE = np.iinfo(np.int32).max
+MAX_COLUMN_SIZE_STR = "INT32_MAX"
+MAX_STRING_COLUMN_BYTES = np.iinfo(np.int32).max
+MAX_STRING_COLUMN_BYTES_STR = "INT32_MAX"
diff --git a/python/cudf/cudf/_lib/aggregation.pxd b/python/cudf/cudf/_lib/aggregation.pxd
new file mode 100644
index 0000000..ad2c978
--- /dev/null
+++ b/python/cudf/cudf/_lib/aggregation.pxd
@@ -0,0 +1,33 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.aggregation cimport (
+    groupby_aggregation,
+    groupby_scan_aggregation,
+    reduce_aggregation,
+    rolling_aggregation,
+    scan_aggregation,
+)
+
+
+cdef class RollingAggregation:
+    cdef unique_ptr[rolling_aggregation] c_obj
+
+cdef class GroupbyAggregation:
+    cdef unique_ptr[groupby_aggregation] c_obj
+
+cdef class GroupbyScanAggregation:
+    cdef unique_ptr[groupby_scan_aggregation] c_obj
+
+cdef class ReduceAggregation:
+    cdef unique_ptr[reduce_aggregation] c_obj
+
+cdef class ScanAggregation:
+    cdef unique_ptr[scan_aggregation] c_obj
+
+cdef RollingAggregation make_rolling_aggregation(op, kwargs=*)
+cdef GroupbyAggregation make_groupby_aggregation(op, kwargs=*)
+cdef GroupbyScanAggregation make_groupby_scan_aggregation(op, kwargs=*)
+cdef ReduceAggregation make_reduce_aggregation(op, kwargs=*)
+cdef ScanAggregation make_scan_aggregation(op, kwargs=*)
diff --git a/python/cudf/cudf/_lib/aggregation.pyx b/python/cudf/cudf/_lib/aggregation.pyx
new file mode 100644
index 0000000..72c5e28
--- /dev/null
+++ b/python/cudf/cudf/_lib/aggregation.pyx
@@ -0,0 +1,983 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from enum import Enum, IntEnum
+
+import pandas as pd
+
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.types import SUPPORTED_NUMPY_TO_LIBCUDF_TYPES, NullHandling
+from cudf.utils import cudautils
+
+from cudf._lib.types cimport (
+    underlying_type_t_interpolation,
+    underlying_type_t_null_policy,
+    underlying_type_t_type_id,
+)
+
+from numba.np import numpy_support
+
+from cudf._lib.types import Interpolation
+
+cimport cudf._lib.cpp.aggregation as libcudf_aggregation
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.aggregation cimport (
+    underlying_type_t_correlation_type,
+    underlying_type_t_rank_method,
+)
+
+import cudf
+
+
+class AggregationKind(Enum):
+    SUM = libcudf_aggregation.aggregation.Kind.SUM
+    PRODUCT = libcudf_aggregation.aggregation.Kind.PRODUCT
+    MIN = libcudf_aggregation.aggregation.Kind.MIN
+    MAX = libcudf_aggregation.aggregation.Kind.MAX
+    COUNT = libcudf_aggregation.aggregation.Kind.COUNT_VALID
+    SIZE = libcudf_aggregation.aggregation.Kind.COUNT_ALL
+    ANY = libcudf_aggregation.aggregation.Kind.ANY
+    ALL = libcudf_aggregation.aggregation.Kind.ALL
+    SUM_OF_SQUARES = libcudf_aggregation.aggregation.Kind.SUM_OF_SQUARES
+    MEAN = libcudf_aggregation.aggregation.Kind.MEAN
+    VAR = libcudf_aggregation.aggregation.Kind.VARIANCE
+    STD = libcudf_aggregation.aggregation.Kind.STD
+    MEDIAN = libcudf_aggregation.aggregation.Kind.MEDIAN
+    QUANTILE = libcudf_aggregation.aggregation.Kind.QUANTILE
+    ARGMAX = libcudf_aggregation.aggregation.Kind.ARGMAX
+    ARGMIN = libcudf_aggregation.aggregation.Kind.ARGMIN
+    NUNIQUE = libcudf_aggregation.aggregation.Kind.NUNIQUE
+    NTH = libcudf_aggregation.aggregation.Kind.NTH_ELEMENT
+    RANK = libcudf_aggregation.aggregation.Kind.RANK
+    COLLECT = libcudf_aggregation.aggregation.Kind.COLLECT
+    UNIQUE = libcudf_aggregation.aggregation.Kind.COLLECT_SET
+    PTX = libcudf_aggregation.aggregation.Kind.PTX
+    CUDA = libcudf_aggregation.aggregation.Kind.CUDA
+    CORRELATION = libcudf_aggregation.aggregation.Kind.CORRELATION
+    COVARIANCE = libcudf_aggregation.aggregation.Kind.COVARIANCE
+
+
+class CorrelationType(IntEnum):
+    PEARSON = (
+        <underlying_type_t_correlation_type>
+        libcudf_aggregation.correlation_type.PEARSON
+    )
+    KENDALL = (
+        <underlying_type_t_correlation_type>
+        libcudf_aggregation.correlation_type.KENDALL
+    )
+    SPEARMAN = (
+        <underlying_type_t_correlation_type>
+        libcudf_aggregation.correlation_type.SPEARMAN
+    )
+
+
+class RankMethod(IntEnum):
+    FIRST = libcudf_aggregation.rank_method.FIRST
+    AVERAGE = libcudf_aggregation.rank_method.AVERAGE
+    MIN = libcudf_aggregation.rank_method.MIN
+    MAX = libcudf_aggregation.rank_method.MAX
+    DENSE = libcudf_aggregation.rank_method.DENSE
+
+
+cdef class RollingAggregation:
+    """A Cython wrapper for rolling window aggregations.
+
+    **This class should never be instantiated using a standard constructor,
+    only using one of its many factories.** These factories handle mapping
+    different cudf operations to their libcudf analogs, e.g.
+    `cudf.DataFrame.idxmin` -> `libcudf.argmin`. Additionally, they perform
+    any additional configuration needed to translate Python arguments into
+    their corresponding C++ types (for instance, C++ enumerations used for
+    flag arguments). The factory approach is necessary to support operations
+    like `df.agg(lambda x: x.sum())`; such functions are called with this
+    class as an argument to generation the desired aggregation.
+    """
+    @property
+    def kind(self):
+        return AggregationKind(self.c_obj.get()[0].kind).name
+
+    @classmethod
+    def sum(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_sum_aggregation[rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def min(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_min_aggregation[rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def max(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_max_aggregation[rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def idxmin(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_argmin_aggregation[
+                rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def idxmax(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_argmax_aggregation[
+                rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def mean(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_mean_aggregation[rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def var(cls, ddof=1):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_variance_aggregation[rolling_aggregation](
+                ddof
+            )
+        )
+        return agg
+
+    @classmethod
+    def std(cls, ddof=1):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_std_aggregation[rolling_aggregation](ddof)
+        )
+        return agg
+
+    @classmethod
+    def count(cls, dropna=True):
+        cdef libcudf_types.null_policy c_null_handling
+        if dropna:
+            c_null_handling = libcudf_types.null_policy.EXCLUDE
+        else:
+            c_null_handling = libcudf_types.null_policy.INCLUDE
+
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_count_aggregation[rolling_aggregation](
+                c_null_handling
+            ))
+        return agg
+
+    @classmethod
+    def size(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_count_aggregation[rolling_aggregation](
+                <libcudf_types.null_policy><underlying_type_t_null_policy>(
+                    NullHandling.INCLUDE)
+            ))
+        return agg
+
+    @classmethod
+    def collect(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_collect_list_aggregation[
+                rolling_aggregation]())
+        return agg
+
+    @classmethod
+    def from_udf(cls, op, *args, **kwargs):
+        cdef RollingAggregation agg = cls()
+
+        cdef libcudf_types.type_id tid
+        cdef libcudf_types.data_type out_dtype
+        cdef string cpp_str
+
+        # Handling UDF type
+        nb_type = numpy_support.from_dtype(kwargs['dtype'])
+        type_signature = (nb_type[:],)
+        compiled_op = cudautils.compile_udf(op, type_signature)
+        output_np_dtype = cudf.dtype(compiled_op[1])
+        cpp_str = compiled_op[0].encode('UTF-8')
+        if output_np_dtype not in SUPPORTED_NUMPY_TO_LIBCUDF_TYPES:
+            raise TypeError(
+                "Result of window function has unsupported dtype {}"
+                .format(op[1])
+            )
+        tid = (
+            <libcudf_types.type_id> (
+                <underlying_type_t_type_id> (
+                    SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[output_np_dtype]
+                )
+            )
+        )
+        out_dtype = libcudf_types.data_type(tid)
+
+        agg.c_obj = move(
+            libcudf_aggregation.make_udf_aggregation[rolling_aggregation](
+                libcudf_aggregation.udf_type.PTX, cpp_str, out_dtype
+            ))
+        return agg
+
+    # scan aggregations
+    # TODO: update this after adding per algorithm aggregation derived types
+    # https://github.com/rapidsai/cudf/issues/7106
+    cumsum = sum
+    cummin = min
+    cummax = max
+
+    @classmethod
+    def cumcount(cls):
+        cdef RollingAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_count_aggregation[rolling_aggregation](
+                libcudf_types.null_policy.INCLUDE
+            ))
+        return agg
+
+cdef class GroupbyAggregation:
+    """A Cython wrapper for groupby aggregations.
+
+    **This class should never be instantiated using a standard constructor,
+    only using one of its many factories.** These factories handle mapping
+    different cudf operations to their libcudf analogs, e.g.
+    `cudf.DataFrame.idxmin` -> `libcudf.argmin`. Additionally, they perform
+    any additional configuration needed to translate Python arguments into
+    their corresponding C++ types (for instance, C++ enumerations used for
+    flag arguments). The factory approach is necessary to support operations
+    like `df.agg(lambda x: x.sum())`; such functions are called with this
+    class as an argument to generation the desired aggregation.
+    """
+    @property
+    def kind(self):
+        return AggregationKind(self.c_obj.get()[0].kind).name
+
+    @classmethod
+    def sum(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_sum_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def min(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_min_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def max(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_max_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def idxmin(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_argmin_aggregation[
+                groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def idxmax(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_argmax_aggregation[
+                groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def mean(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_mean_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def count(cls, dropna=True):
+        cdef libcudf_types.null_policy c_null_handling
+        if dropna:
+            c_null_handling = libcudf_types.null_policy.EXCLUDE
+        else:
+            c_null_handling = libcudf_types.null_policy.INCLUDE
+
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_count_aggregation[groupby_aggregation](
+                c_null_handling
+            ))
+        return agg
+
+    @classmethod
+    def size(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_count_aggregation[groupby_aggregation](
+                <libcudf_types.null_policy><underlying_type_t_null_policy>(
+                    NullHandling.INCLUDE)
+            ))
+        return agg
+
+    @classmethod
+    def collect(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_collect_list_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def nunique(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_nunique_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def nth(cls, libcudf_types.size_type size):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_nth_element_aggregation[groupby_aggregation](size))
+        return agg
+
+    @classmethod
+    def product(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_product_aggregation[groupby_aggregation]())
+        return agg
+    prod = product
+
+    @classmethod
+    def sum_of_squares(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_sum_of_squares_aggregation[groupby_aggregation]()
+        )
+        return agg
+
+    @classmethod
+    def var(cls, ddof=1):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_variance_aggregation[groupby_aggregation](ddof))
+        return agg
+
+    @classmethod
+    def std(cls, ddof=1):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_std_aggregation[groupby_aggregation](ddof))
+        return agg
+
+    @classmethod
+    def median(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_median_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def quantile(cls, q=0.5, interpolation="linear"):
+        cdef GroupbyAggregation agg = cls()
+
+        if not pd.api.types.is_list_like(q):
+            q = [q]
+
+        cdef vector[double] c_q = q
+        cdef libcudf_types.interpolation c_interp = (
+            <libcudf_types.interpolation> (
+                <underlying_type_t_interpolation> (
+                    Interpolation[interpolation.upper()]
+                )
+            )
+        )
+        agg.c_obj = move(
+            libcudf_aggregation.make_quantile_aggregation[groupby_aggregation](
+                c_q, c_interp)
+        )
+        return agg
+
+    @classmethod
+    def unique(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_collect_set_aggregation[groupby_aggregation]())
+        return agg
+
+    @classmethod
+    def first(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_nth_element_aggregation[groupby_aggregation](
+                0,
+                <libcudf_types.null_policy><underlying_type_t_null_policy>(
+                    NullHandling.EXCLUDE
+                )
+            )
+        )
+        return agg
+
+    @classmethod
+    def last(cls):
+        cdef GroupbyAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_nth_element_aggregation[groupby_aggregation](
+                -1,
+                <libcudf_types.null_policy><underlying_type_t_null_policy>(
+                    NullHandling.EXCLUDE
+                )
+            )
+        )
+        return agg
+
+    @classmethod
+    def corr(cls, method, libcudf_types.size_type min_periods):
+        cdef GroupbyAggregation agg = cls()
+        cdef libcudf_aggregation.correlation_type c_method = (
+            <libcudf_aggregation.correlation_type> (
+                <underlying_type_t_correlation_type> (
+                    CorrelationType[method.upper()]
+                )
+            )
+        )
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_correlation_aggregation[groupby_aggregation](
+                c_method, min_periods
+            ))
+        return agg
+
+    @classmethod
+    def cov(
+        cls,
+        libcudf_types.size_type min_periods,
+        libcudf_types.size_type ddof=1
+    ):
+        cdef GroupbyAggregation agg = cls()
+
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_covariance_aggregation[groupby_aggregation](
+                min_periods, ddof
+            ))
+        return agg
+
+
+cdef class GroupbyScanAggregation:
+    """A Cython wrapper for groupby scan aggregations.
+
+    **This class should never be instantiated using a standard constructor,
+    only using one of its many factories.** These factories handle mapping
+    different cudf operations to their libcudf analogs, e.g.
+    `cudf.DataFrame.idxmin` -> `libcudf.argmin`. Additionally, they perform
+    any additional configuration needed to translate Python arguments into
+    their corresponding C++ types (for instance, C++ enumerations used for
+    flag arguments). The factory approach is necessary to support operations
+    like `df.agg(lambda x: x.sum())`; such functions are called with this
+    class as an argument to generation the desired aggregation.
+    """
+    @property
+    def kind(self):
+        return AggregationKind(self.c_obj.get()[0].kind).name
+
+    @classmethod
+    def sum(cls):
+        cdef GroupbyScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_sum_aggregation[groupby_scan_aggregation]())
+        return agg
+
+    @classmethod
+    def min(cls):
+        cdef GroupbyScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_min_aggregation[groupby_scan_aggregation]())
+        return agg
+
+    @classmethod
+    def max(cls):
+        cdef GroupbyScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_max_aggregation[groupby_scan_aggregation]())
+        return agg
+
+    @classmethod
+    def count(cls, dropna=True):
+        cdef libcudf_types.null_policy c_null_handling
+        if dropna:
+            c_null_handling = libcudf_types.null_policy.EXCLUDE
+        else:
+            c_null_handling = libcudf_types.null_policy.INCLUDE
+
+        cdef GroupbyScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_count_aggregation[groupby_scan_aggregation](c_null_handling))
+        return agg
+
+    @classmethod
+    def size(cls):
+        cdef GroupbyScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_count_aggregation[groupby_scan_aggregation](
+                <libcudf_types.null_policy><underlying_type_t_null_policy>(
+                    NullHandling.INCLUDE)
+            ))
+        return agg
+
+    @classmethod
+    def cumcount(cls):
+        cdef GroupbyScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_count_aggregation[groupby_scan_aggregation](
+                libcudf_types.null_policy.INCLUDE
+            ))
+        return agg
+
+    # scan aggregations
+    # TODO: update this after adding per algorithm aggregation derived types
+    # https://github.com/rapidsai/cudf/issues/7106
+    cumsum = sum
+    cummin = min
+    cummax = max
+
+    @classmethod
+    def rank(cls, method, ascending, na_option, pct):
+        cdef GroupbyScanAggregation agg = cls()
+        cdef libcudf_aggregation.rank_method c_method = (
+            <libcudf_aggregation.rank_method> (
+                <underlying_type_t_rank_method> (
+                    RankMethod[method.upper()]
+                )
+            )
+        )
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_rank_aggregation[groupby_scan_aggregation](
+                c_method,
+                (libcudf_types.order.ASCENDING if ascending else
+                    libcudf_types.order.DESCENDING),
+                (libcudf_types.null_policy.EXCLUDE if na_option == "keep" else
+                    libcudf_types.null_policy.INCLUDE),
+                (libcudf_types.null_order.BEFORE
+                    if (na_option == "top") == ascending else
+                    libcudf_types.null_order.AFTER),
+                (libcudf_aggregation.rank_percentage.ZERO_NORMALIZED
+                    if pct else
+                    libcudf_aggregation.rank_percentage.NONE)
+            ))
+        return agg
+
+
+cdef class ReduceAggregation:
+    """A Cython wrapper for reduce aggregations.
+
+    **This class should never be instantiated using a standard constructor,
+    only using one of its many factories.** These factories handle mapping
+    different cudf operations to their libcudf analogs, e.g.
+    `cudf.DataFrame.idxmin` -> `libcudf.argmin`. Additionally, they perform
+    any additional configuration needed to translate Python arguments into
+    their corresponding C++ types (for instance, C++ enumerations used for
+    flag arguments). The factory approach is necessary to support operations
+    like `df.agg(lambda x: x.sum())`; such functions are called with this
+    class as an argument to generation the desired aggregation.
+    """
+    @property
+    def kind(self):
+        return AggregationKind(self.c_obj.get()[0].kind).name
+
+    @classmethod
+    def sum(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_sum_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def product(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_product_aggregation[
+                reduce_aggregation]())
+        return agg
+    prod = product
+
+    @classmethod
+    def min(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_min_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def max(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_max_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def any(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_any_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def all(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_all_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def sum_of_squares(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_sum_of_squares_aggregation[
+                reduce_aggregation]()
+        )
+        return agg
+
+    @classmethod
+    def mean(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_mean_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def var(cls, ddof=1):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_variance_aggregation[
+                reduce_aggregation](ddof))
+        return agg
+
+    @classmethod
+    def std(cls, ddof=1):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_std_aggregation[reduce_aggregation](ddof))
+        return agg
+
+    @classmethod
+    def median(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_median_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def quantile(cls, q=0.5, interpolation="linear"):
+        cdef ReduceAggregation agg = cls()
+
+        if not pd.api.types.is_list_like(q):
+            q = [q]
+
+        cdef vector[double] c_q = q
+        cdef libcudf_types.interpolation c_interp = (
+            <libcudf_types.interpolation> (
+                <underlying_type_t_interpolation> (
+                    Interpolation[interpolation.upper()]
+                )
+            )
+        )
+        agg.c_obj = move(
+            libcudf_aggregation.make_quantile_aggregation[reduce_aggregation](
+                c_q, c_interp)
+        )
+        return agg
+
+    @classmethod
+    def nunique(cls):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_nunique_aggregation[reduce_aggregation]())
+        return agg
+
+    @classmethod
+    def nth(cls, libcudf_types.size_type size):
+        cdef ReduceAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_nth_element_aggregation[
+                reduce_aggregation](size))
+        return agg
+
+cdef class ScanAggregation:
+    """A Cython wrapper for scan aggregations.
+
+    **This class should never be instantiated using a standard constructor,
+    only using one of its many factories.** These factories handle mapping
+    different cudf operations to their libcudf analogs, e.g.
+    `cudf.DataFrame.idxmin` -> `libcudf.argmin`. Additionally, they perform
+    any additional configuration needed to translate Python arguments into
+    their corresponding C++ types (for instance, C++ enumerations used for
+    flag arguments). The factory approach is necessary to support operations
+    like `df.agg(lambda x: x.sum())`; such functions are called with this
+    class as an argument to generation the desired aggregation.
+    """
+    @property
+    def kind(self):
+        return AggregationKind(self.c_obj.get()[0].kind).name
+
+    @classmethod
+    def sum(cls):
+        cdef ScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_sum_aggregation[scan_aggregation]())
+        return agg
+
+    @classmethod
+    def product(cls):
+        cdef ScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.make_product_aggregation[scan_aggregation]())
+        return agg
+    prod = product
+
+    @classmethod
+    def min(cls):
+        cdef ScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_min_aggregation[scan_aggregation]())
+        return agg
+
+    @classmethod
+    def max(cls):
+        cdef ScanAggregation agg = cls()
+        agg.c_obj = move(
+            libcudf_aggregation.
+            make_max_aggregation[scan_aggregation]())
+        return agg
+
+    # scan aggregations
+    # TODO: update this after adding per algorithm aggregation derived types
+    # https://github.com/rapidsai/cudf/issues/7106
+    cumsum = sum
+    cummin = min
+    cummax = max
+
+
+cdef RollingAggregation make_rolling_aggregation(op, kwargs=None):
+    r"""
+    Parameters
+    ----------
+    op : str or callable
+        If callable, must meet one of the following requirements:
+
+        * Is of the form lambda x: x.agg(*args, **kwargs), where
+          `agg` is the name of a supported aggregation. Used to
+          to specify aggregations that take arguments, e.g.,
+          `lambda x: x.quantile(0.5)`.
+        * Is a user defined aggregation function that operates on
+          group values. In this case, the output dtype must be
+          specified in the `kwargs` dictionary.
+    \*\*kwargs : dict, optional
+        Any keyword arguments to be passed to the op.
+
+    Returns
+    -------
+    RollingAggregation
+    """
+    if kwargs is None:
+        kwargs = {}
+
+    cdef RollingAggregation agg
+    if isinstance(op, str):
+        agg = getattr(RollingAggregation, op)(**kwargs)
+    elif callable(op):
+        if op is list:
+            agg = RollingAggregation.collect()
+        elif "dtype" in kwargs:
+            agg = RollingAggregation.from_udf(op, **kwargs)
+        else:
+            agg = op(RollingAggregation)
+    else:
+        raise TypeError(f"Unknown aggregation {op}")
+    return agg
+
+cdef GroupbyAggregation make_groupby_aggregation(op, kwargs=None):
+    r"""
+    Parameters
+    ----------
+    op : str or callable
+        If callable, must meet one of the following requirements:
+
+        * Is of the form lambda x: x.agg(*args, **kwargs), where
+          `agg` is the name of a supported aggregation. Used to
+          to specify aggregations that take arguments, e.g.,
+          `lambda x: x.quantile(0.5)`.
+        * Is a user defined aggregation function that operates on
+          group values. In this case, the output dtype must be
+          specified in the `kwargs` dictionary.
+    \*\*kwargs : dict, optional
+        Any keyword arguments to be passed to the op.
+
+    Returns
+    -------
+    GroupbyAggregation
+    """
+    if kwargs is None:
+        kwargs = {}
+
+    cdef GroupbyAggregation agg
+    if isinstance(op, str):
+        agg = getattr(GroupbyAggregation, op)(**kwargs)
+    elif callable(op):
+        if op is list:
+            agg = GroupbyAggregation.collect()
+        elif "dtype" in kwargs:
+            agg = GroupbyAggregation.from_udf(op, **kwargs)
+        else:
+            agg = op(GroupbyAggregation)
+    else:
+        raise TypeError(f"Unknown aggregation {op}")
+    return agg
+
+cdef GroupbyScanAggregation make_groupby_scan_aggregation(op, kwargs=None):
+    r"""
+    Parameters
+    ----------
+    op : str or callable
+        If callable, must meet one of the following requirements:
+
+        * Is of the form lambda x: x.agg(*args, **kwargs), where
+          `agg` is the name of a supported aggregation. Used to
+          to specify aggregations that take arguments, e.g.,
+          `lambda x: x.quantile(0.5)`.
+        * Is a user defined aggregation function that operates on
+          grouped, scannable values. In this case, the output dtype must be
+          specified in the `kwargs` dictionary.
+    \*\*kwargs : dict, optional
+        Any keyword arguments to be passed to the op.
+
+    Returns
+    -------
+    GroupbyScanAggregation
+    """
+    if kwargs is None:
+        kwargs = {}
+
+    cdef GroupbyScanAggregation agg
+    if isinstance(op, str):
+        agg = getattr(GroupbyScanAggregation, op)(**kwargs)
+    elif callable(op):
+        if op is list:
+            agg = GroupbyScanAggregation.collect()
+        elif "dtype" in kwargs:
+            agg = GroupbyScanAggregation.from_udf(op, **kwargs)
+        else:
+            agg = op(GroupbyScanAggregation)
+    else:
+        raise TypeError(f"Unknown aggregation {op}")
+    return agg
+
+cdef ReduceAggregation make_reduce_aggregation(op, kwargs=None):
+    r"""
+    Parameters
+    ----------
+    op : str or callable
+        If callable, must meet one of the following requirements:
+
+        * Is of the form lambda x: x.agg(*args, **kwargs), where
+          `agg` is the name of a supported aggregation. Used to
+          to specify aggregations that take arguments, e.g.,
+          `lambda x: x.quantile(0.5)`.
+        * Is a user defined aggregation function that operates on
+          reducible values. In this case, the output dtype must be
+          specified in the `kwargs` dictionary.
+    \*\*kwargs : dict, optional
+        Any keyword arguments to be passed to the op.
+
+    Returns
+    -------
+    ReduceAggregation
+    """
+    if kwargs is None:
+        kwargs = {}
+
+    cdef ReduceAggregation agg
+    if isinstance(op, str):
+        agg = getattr(ReduceAggregation, op)(**kwargs)
+    elif callable(op):
+        if op is list:
+            agg = ReduceAggregation.collect()
+        elif "dtype" in kwargs:
+            agg = ReduceAggregation.from_udf(op, **kwargs)
+        else:
+            agg = op(ReduceAggregation)
+    else:
+        raise TypeError(f"Unknown aggregation {op}")
+    return agg
+
+cdef ScanAggregation make_scan_aggregation(op, kwargs=None):
+    r"""
+    Parameters
+    ----------
+    op : str or callable
+        If callable, must meet one of the following requirements:
+
+        * Is of the form lambda x: x.agg(*args, **kwargs), where
+          `agg` is the name of a supported aggregation. Used to
+          to specify aggregations that take arguments, e.g.,
+          `lambda x: x.quantile(0.5)`.
+        * Is a user defined aggregation function that operates on
+          scannable values. In this case, the output dtype must be
+          specified in the `kwargs` dictionary.
+    \*\*kwargs : dict, optional
+        Any keyword arguments to be passed to the op.
+
+    Returns
+    -------
+    ScanAggregation
+    """
+    if kwargs is None:
+        kwargs = {}
+
+    cdef ScanAggregation agg
+    if isinstance(op, str):
+        agg = getattr(ScanAggregation, op)(**kwargs)
+    elif callable(op):
+        if op is list:
+            agg = ScanAggregation.collect()
+        elif "dtype" in kwargs:
+            agg = ScanAggregation.from_udf(op, **kwargs)
+        else:
+            agg = op(ScanAggregation)
+    else:
+        raise TypeError(f"Unknown aggregation {op}")
+    return agg
diff --git a/python/cudf/cudf/_lib/avro.pyx b/python/cudf/cudf/_lib/avro.pyx
new file mode 100644
index 0000000..0e24b5b
--- /dev/null
+++ b/python/cudf/cudf/_lib/avro.pyx
@@ -0,0 +1,55 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.io.avro cimport (
+    avro_reader_options,
+    read_avro as libcudf_read_avro,
+)
+from cudf._lib.cpp.io.types cimport table_with_metadata
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.io.utils cimport make_source_info
+from cudf._lib.utils cimport data_from_unique_ptr
+
+
+cpdef read_avro(datasource, columns=None, skip_rows=-1, num_rows=-1):
+    """
+    Cython function to call libcudf read_avro, see `read_avro`.
+
+    See Also
+    --------
+    cudf.io.avro.read_avro
+    """
+
+    num_rows = -1 if num_rows is None else num_rows
+    skip_rows = 0 if skip_rows is None else skip_rows
+
+    if not isinstance(num_rows, int) or num_rows < -1:
+        raise TypeError("num_rows must be an int >= -1")
+    if not isinstance(skip_rows, int) or skip_rows < -1:
+        raise TypeError("skip_rows must be an int >= -1")
+
+    cdef vector[string] c_columns
+    if columns is not None and len(columns) > 0:
+        c_columns.reserve(len(columns))
+        for col in columns:
+            c_columns.push_back(str(col).encode())
+
+    cdef avro_reader_options options = move(
+        avro_reader_options.builder(make_source_info([datasource]))
+        .columns(c_columns)
+        .skip_rows(<size_type> skip_rows)
+        .num_rows(<size_type> num_rows)
+        .build()
+    )
+
+    cdef table_with_metadata c_result
+
+    with nogil:
+        c_result = move(libcudf_read_avro(options))
+
+    names = [info.name.decode() for info in c_result.metadata.schema_info]
+
+    return data_from_unique_ptr(move(c_result.tbl), column_names=names)
diff --git a/python/cudf/cudf/_lib/binaryop.pxd b/python/cudf/cudf/_lib/binaryop.pxd
new file mode 100644
index 0000000..1f60222
--- /dev/null
+++ b/python/cudf/cudf/_lib/binaryop.pxd
@@ -0,0 +1,5 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+
+ctypedef int32_t underlying_type_t_binary_operator
diff --git a/python/cudf/cudf/_lib/binaryop.pyx b/python/cudf/cudf/_lib/binaryop.pyx
new file mode 100644
index 0000000..6212347
--- /dev/null
+++ b/python/cudf/cudf/_lib/binaryop.pyx
@@ -0,0 +1,239 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from enum import IntEnum
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.binaryop cimport underlying_type_t_binary_operator
+from cudf._lib.column cimport Column
+
+from cudf._lib.scalar import as_device_scalar
+
+from cudf._lib.scalar cimport DeviceScalar
+
+from cudf._lib.types import SUPPORTED_NUMPY_TO_LIBCUDF_TYPES
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.types cimport data_type, type_id
+from cudf._lib.types cimport dtype_to_data_type, underlying_type_t_type_id
+
+from cudf.api.types import is_scalar
+from cudf.core.buffer import acquire_spill_lock
+
+cimport cudf._lib.cpp.binaryop as cpp_binaryop
+from cudf._lib.cpp.binaryop cimport binary_operator
+import cudf
+
+
+class BinaryOperation(IntEnum):
+    ADD = (
+        <underlying_type_t_binary_operator> binary_operator.ADD
+    )
+    SUB = (
+        <underlying_type_t_binary_operator> binary_operator.SUB
+    )
+    MUL = (
+        <underlying_type_t_binary_operator> binary_operator.MUL
+    )
+    DIV = (
+        <underlying_type_t_binary_operator> binary_operator.DIV
+    )
+    TRUEDIV = (
+        <underlying_type_t_binary_operator> binary_operator.TRUE_DIV
+    )
+    FLOORDIV = (
+        <underlying_type_t_binary_operator> binary_operator.FLOOR_DIV
+    )
+    MOD = (
+        <underlying_type_t_binary_operator> binary_operator.PYMOD
+    )
+    POW = (
+        <underlying_type_t_binary_operator> binary_operator.POW
+    )
+    INT_POW = (
+        <underlying_type_t_binary_operator> binary_operator.INT_POW
+    )
+    EQ = (
+        <underlying_type_t_binary_operator> binary_operator.EQUAL
+    )
+    NE = (
+        <underlying_type_t_binary_operator> binary_operator.NOT_EQUAL
+    )
+    LT = (
+        <underlying_type_t_binary_operator> binary_operator.LESS
+    )
+    GT = (
+        <underlying_type_t_binary_operator> binary_operator.GREATER
+    )
+    LE = (
+        <underlying_type_t_binary_operator> binary_operator.LESS_EQUAL
+    )
+    GE = (
+        <underlying_type_t_binary_operator> binary_operator.GREATER_EQUAL
+    )
+    AND = (
+        <underlying_type_t_binary_operator> binary_operator.BITWISE_AND
+    )
+    OR = (
+        <underlying_type_t_binary_operator> binary_operator.BITWISE_OR
+    )
+    XOR = (
+        <underlying_type_t_binary_operator> binary_operator.BITWISE_XOR
+    )
+    L_AND = (
+        <underlying_type_t_binary_operator> binary_operator.LOGICAL_AND
+    )
+    L_OR = (
+        <underlying_type_t_binary_operator> binary_operator.LOGICAL_OR
+    )
+    GENERIC_BINARY = (
+        <underlying_type_t_binary_operator> binary_operator.GENERIC_BINARY
+    )
+    NULL_EQUALS = (
+        <underlying_type_t_binary_operator> binary_operator.NULL_EQUALS
+    )
+
+
+cdef binaryop_v_v(Column lhs, Column rhs,
+                  binary_operator c_op, data_type c_dtype):
+    cdef column_view c_lhs = lhs.view()
+    cdef column_view c_rhs = rhs.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_binaryop.binary_operation(
+                c_lhs,
+                c_rhs,
+                c_op,
+                c_dtype
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+cdef binaryop_v_s(Column lhs, DeviceScalar rhs,
+                  binary_operator c_op, data_type c_dtype):
+    cdef column_view c_lhs = lhs.view()
+    cdef const scalar* c_rhs = rhs.get_raw_ptr()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_binaryop.binary_operation(
+                c_lhs,
+                c_rhs[0],
+                c_op,
+                c_dtype
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+cdef binaryop_s_v(DeviceScalar lhs, Column rhs,
+                  binary_operator c_op, data_type c_dtype):
+    cdef const scalar* c_lhs = lhs.get_raw_ptr()
+    cdef column_view c_rhs = rhs.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_binaryop.binary_operation(
+                c_lhs[0],
+                c_rhs,
+                c_op,
+                c_dtype
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def binaryop(lhs, rhs, op, dtype):
+    """
+    Dispatches a binary op call to the appropriate libcudf function:
+    """
+    # TODO: Shouldn't have to keep special-casing. We need to define a separate
+    # pipeline for libcudf binops that don't map to Python binops.
+    if op not in {"INT_POW", "NULL_EQUALS"}:
+        op = op[2:-2]
+
+    op = BinaryOperation[op.upper()]
+    cdef binary_operator c_op = <binary_operator> (
+        <underlying_type_t_binary_operator> op
+    )
+
+    cdef data_type c_dtype = dtype_to_data_type(dtype)
+
+    if is_scalar(lhs) or lhs is None:
+        s_lhs = as_device_scalar(lhs, dtype=rhs.dtype if lhs is None else None)
+        result = binaryop_s_v(
+            s_lhs,
+            rhs,
+            c_op,
+            c_dtype
+        )
+
+    elif is_scalar(rhs) or rhs is None:
+        s_rhs = as_device_scalar(rhs, dtype=lhs.dtype if rhs is None else None)
+        result = binaryop_v_s(
+            lhs,
+            s_rhs,
+            c_op,
+            c_dtype
+        )
+
+    else:
+        result = binaryop_v_v(
+            lhs,
+            rhs,
+            c_op,
+            c_dtype
+        )
+    return result
+
+
+@acquire_spill_lock()
+def binaryop_udf(Column lhs, Column rhs, udf_ptx, dtype):
+    """
+    Apply a user-defined binary operator (a UDF) defined in `udf_ptx` on
+    the two input columns `lhs` and `rhs`. The output type of the UDF
+    has to be specified in `dtype`, a numpy data type.
+    Currently ONLY int32, int64, float32 and float64 are supported.
+    """
+    cdef column_view c_lhs = lhs.view()
+    cdef column_view c_rhs = rhs.view()
+
+    cdef type_id tid = (
+        <type_id> (
+            <underlying_type_t_type_id> (
+                SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[cudf.dtype(dtype)]
+            )
+        )
+    )
+    cdef data_type c_dtype = data_type(tid)
+
+    cdef string cpp_str = udf_ptx.encode("UTF-8")
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_binaryop.binary_operation(
+                c_lhs,
+                c_rhs,
+                cpp_str,
+                c_dtype
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/column.pxd b/python/cudf/cudf/_lib/column.pxd
new file mode 100644
index 0000000..7ffb55a
--- /dev/null
+++ b/python/cudf/cudf/_lib/column.pxd
@@ -0,0 +1,42 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from typing import Literal
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef class Column:
+    cdef public:
+        cdef int _offset
+        cdef int _size
+        cdef object _dtype
+        cdef object _base_children
+        cdef object _base_data
+        cdef object _base_mask
+        cdef object _children
+        cdef object _data
+        cdef object _mask
+        cdef object _null_count
+        cdef object _distinct_count
+
+    cdef column_view _view(self, size_type null_count) except *
+    cdef column_view view(self) except *
+    cdef mutable_column_view mutable_view(self) except *
+    cpdef to_pylibcudf(self, mode: Literal["read", "write"])
+
+    @staticmethod
+    cdef Column from_unique_ptr(
+        unique_ptr[column] c_col, bint data_ptr_exposed=*
+    )
+
+    @staticmethod
+    cdef Column from_column_view(column_view, object)
+
+    cdef size_type compute_null_count(self) except? 0
diff --git a/python/cudf/cudf/_lib/column.pyi b/python/cudf/cudf/_lib/column.pyi
new file mode 100644
index 0000000..c667286
--- /dev/null
+++ b/python/cudf/cudf/_lib/column.pyi
@@ -0,0 +1,75 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+from typing import Dict, Optional, Tuple
+
+from typing_extensions import Self
+
+from cudf._typing import Dtype, DtypeObj, ScalarLike
+from cudf.core.buffer import Buffer
+from cudf.core.column import ColumnBase
+
+class Column:
+    _data: Optional[Buffer]
+    _mask: Optional[Buffer]
+    _base_data: Optional[Buffer]
+    _base_mask: Optional[Buffer]
+    _dtype: DtypeObj
+    _size: int
+    _offset: int
+    _null_count: int
+    _children: Tuple[ColumnBase, ...]
+    _base_children: Tuple[ColumnBase, ...]
+    _distinct_count: Dict[bool, int]
+
+    def __init__(
+        self,
+        data: Optional[Buffer],
+        size: int,
+        dtype: Dtype,
+        mask: Optional[Buffer] = None,
+        offset: Optional[int] = None,
+        null_count: Optional[int] = None,
+        children: Tuple[ColumnBase, ...] = (),
+    ) -> None: ...
+    @property
+    def base_size(self) -> int: ...
+    @property
+    def dtype(self) -> DtypeObj: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def base_data(self) -> Optional[Buffer]: ...
+    @property
+    def data(self) -> Optional[Buffer]: ...
+    @property
+    def data_ptr(self) -> int: ...
+    def set_base_data(self, value: Buffer) -> None: ...
+    @property
+    def nullable(self) -> bool: ...
+    def has_nulls(self, include_nan: bool = False) -> bool: ...
+    @property
+    def base_mask(self) -> Optional[Buffer]: ...
+    @property
+    def mask(self) -> Optional[Buffer]: ...
+    @property
+    def mask_ptr(self) -> int: ...
+    def set_base_mask(self, value: Optional[Buffer]) -> None: ...
+    def set_mask(self, value: Optional[Buffer]) -> Self: ...
+    @property
+    def null_count(self) -> int: ...
+    @property
+    def offset(self) -> int: ...
+    @property
+    def base_children(self) -> Tuple[ColumnBase, ...]: ...
+    @property
+    def children(self) -> Tuple[ColumnBase, ...]: ...
+    def set_base_children(self, value: Tuple[ColumnBase, ...]) -> None: ...
+    def _mimic_inplace(
+        self, other_col: ColumnBase, inplace=False
+    ) -> Optional[Self]: ...
+
+    # TODO: The val parameter should be Scalar, not ScalarLike
+    @staticmethod
+    def from_scalar(val: ScalarLike, size: int) -> ColumnBase: ...
diff --git a/python/cudf/cudf/_lib/column.pyx b/python/cudf/cudf/_lib/column.pyx
new file mode 100644
index 0000000..f751d73
--- /dev/null
+++ b/python/cudf/cudf/_lib/column.pyx
@@ -0,0 +1,788 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+
+from typing import Literal
+
+import cupy as cp
+import numpy as np
+
+import rmm
+
+import cudf
+import cudf._lib as libcudf
+from cudf._lib import pylibcudf
+from cudf.api.types import is_categorical_dtype, is_datetime64tz_dtype
+from cudf.core.buffer import (
+    Buffer,
+    ExposureTrackedBuffer,
+    SpillableBuffer,
+    acquire_spill_lock,
+    as_buffer,
+    cuda_array_interface_wrapper,
+)
+from cudf.utils.dtypes import _get_base_dtype
+
+from cpython.buffer cimport PyObject_CheckBuffer
+from libc.stdint cimport uintptr_t
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport DeviceBuffer
+
+from cudf._lib.types cimport (
+    dtype_from_column_view,
+    dtype_to_data_type,
+    dtype_to_pylibcudf_type,
+)
+
+from cudf._lib.null_mask import bitmask_allocation_size_bytes
+from cudf._lib.types import dtype_from_pylibcudf_column
+
+cimport cudf._lib.cpp.types as libcudf_types
+cimport cudf._lib.cpp.unary as libcudf_unary
+from cudf._lib.cpp.column.column cimport column, column_contents
+from cudf._lib.cpp.column.column_factories cimport (
+    make_column_from_scalar as cpp_make_column_from_scalar,
+    make_numeric_column,
+)
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.null_mask cimport null_count as cpp_null_count
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.scalar cimport DeviceScalar
+
+
+cdef class Column:
+    """
+    A Column stores columnar data in device memory.
+    A Column may be composed of:
+
+    * A *data* Buffer
+    * One or more (optional) *children* Columns
+    * An (optional) *mask* Buffer representing the nullmask
+
+    The *dtype* indicates the Column's element type.
+    """
+    def __init__(
+        self,
+        object data,
+        int size,
+        object dtype,
+        object mask=None,
+        int offset=0,
+        object null_count=None,
+        object children=()
+    ):
+        self._size = size
+        self._distinct_count = {}
+        self._dtype = dtype
+        self._offset = offset
+        self._null_count = null_count
+        self.set_base_children(children)
+        self.set_base_data(data)
+        self.set_base_mask(mask)
+
+    @property
+    def base_size(self):
+        return int(self.base_data.size / self.dtype.itemsize)
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @property
+    def size(self):
+        return self._size
+
+    @property
+    def base_data(self):
+        return self._base_data
+
+    @property
+    def data(self):
+        if self.base_data is None:
+            return None
+        if self._data is None:
+            start = self.offset * self.dtype.itemsize
+            end = start + self.size * self.dtype.itemsize
+            self._data = self.base_data[start:end]
+        return self._data
+
+    @property
+    def data_ptr(self):
+        if self.data is None:
+            return 0
+        else:
+            return self.data.get_ptr(mode="write")
+
+    def set_base_data(self, value):
+        if value is not None and not isinstance(value, Buffer):
+            raise TypeError(
+                "Expected a Buffer or None for data, "
+                f"got {type(value).__name__}"
+            )
+
+        self._data = None
+        self._base_data = value
+
+    @property
+    def nullable(self):
+        return self.base_mask is not None
+
+    def has_nulls(self, include_nan=False):
+        return int(self.null_count) != 0
+
+    @property
+    def base_mask(self):
+        return self._base_mask
+
+    @property
+    def mask(self):
+        if self._mask is None:
+            if self.base_mask is None or self.offset == 0:
+                self._mask = self.base_mask
+            else:
+                self._mask = libcudf.null_mask.copy_bitmask(self)
+        return self._mask
+
+    @property
+    def mask_ptr(self):
+        if self.mask is None:
+            return 0
+        else:
+            return self.mask.get_ptr(mode="write")
+
+    def set_base_mask(self, value):
+        """
+        Replaces the base mask buffer of the column inplace. This does not
+        modify size or offset in any way, so the passed mask is expected to be
+        compatible with the current offset.
+        """
+        if value is not None and not isinstance(value, Buffer):
+            raise TypeError(
+                "Expected a Buffer or None for mask, "
+                f"got {type(value).__name__}"
+            )
+
+        if value is not None:
+            # bitmask size must be relative to offset = 0 data.
+            required_size = bitmask_allocation_size_bytes(self.base_size)
+            if value.size < required_size:
+                error_msg = (
+                    "The Buffer for mask is smaller than expected, "
+                    f"got {value.size} bytes, expected {required_size} bytes."
+                )
+                if self.offset > 0 or self.size < self.base_size:
+                    error_msg += (
+                        "\n\nNote: The mask is expected to be sized according "
+                        "to the base allocation as opposed to the offsetted or"
+                        " sized allocation."
+                    )
+                raise ValueError(error_msg)
+
+        self._mask = None
+        self._children = None
+        self._base_mask = value
+        self._clear_cache()
+
+    def _clear_cache(self):
+        self._distinct_count = {}
+        try:
+            del self.memory_usage
+        except AttributeError:
+            # `self.memory_usage` was never called before, So ignore.
+            pass
+        self._null_count = None
+
+    def set_mask(self, value):
+        """
+        Replaces the mask buffer of the column and returns a new column. This
+        will zero the column offset, compute a new mask buffer if necessary,
+        and compute new data Buffers zero-copy that use pointer arithmetic to
+        properly adjust the pointer.
+        """
+        mask_size = bitmask_allocation_size_bytes(self.size)
+        required_num_bytes = -(-self.size // 8)  # ceiling divide
+        error_msg = (
+            "The value for mask is smaller than expected, got {}  bytes, "
+            "expected " + str(required_num_bytes) + " bytes."
+        )
+        if value is None:
+            mask = None
+        elif hasattr(value, "__cuda_array_interface__"):
+            if value.__cuda_array_interface__["typestr"] not in ("|i1", "|u1"):
+                if isinstance(value, Column):
+                    value = value.data_array_view(mode="write")
+                value = cp.asarray(value).view('|u1')
+            mask = as_buffer(value)
+            if mask.size < required_num_bytes:
+                raise ValueError(error_msg.format(str(value.size)))
+            if mask.size < mask_size:
+                dbuf = rmm.DeviceBuffer(size=mask_size)
+                dbuf.copy_from_device(value)
+                mask = as_buffer(dbuf)
+        elif hasattr(value, "__array_interface__"):
+            value = np.asarray(value).view("u1")[:mask_size]
+            if value.size < required_num_bytes:
+                raise ValueError(error_msg.format(str(value.size)))
+            dbuf = rmm.DeviceBuffer(size=mask_size)
+            dbuf.copy_from_host(value)
+            mask = as_buffer(dbuf)
+        elif PyObject_CheckBuffer(value):
+            value = np.asarray(value).view("u1")[:mask_size]
+            if value.size < required_num_bytes:
+                raise ValueError(error_msg.format(str(value.size)))
+            dbuf = rmm.DeviceBuffer(size=mask_size)
+            dbuf.copy_from_host(value)
+            mask = as_buffer(dbuf)
+        else:
+            raise TypeError(
+                "Expected a Buffer object or None for mask, "
+                f"got {type(value).__name__}"
+            )
+
+        return cudf.core.column.build_column(
+            data=self.data,
+            dtype=self.dtype,
+            mask=mask,
+            size=self.size,
+            offset=0,
+            children=self.children
+        )
+
+    @property
+    def null_count(self):
+        if self._null_count is None:
+            self._null_count = self.compute_null_count()
+        return self._null_count
+
+    @property
+    def offset(self):
+        return self._offset
+
+    @property
+    def base_children(self):
+        return self._base_children
+
+    @property
+    def children(self):
+        if (self.offset == 0) and (self.size == self.base_size):
+            self._children = self.base_children
+        if self._children is None:
+            if self.base_children == ():
+                self._children = ()
+            else:
+                children = Column.from_unique_ptr(
+                    move(make_unique[column](self.view()))
+                ).base_children
+                dtypes = [
+                    base_child.dtype for base_child in self.base_children
+                ]
+                self._children = [
+                    child._with_type_metadata(dtype) for child, dtype in zip(
+                        children, dtypes
+                    )
+                ]
+        return self._children
+
+    def set_base_children(self, value):
+        if not isinstance(value, tuple):
+            raise TypeError("Expected a tuple of Columns for children, got " +
+                            type(value).__name__)
+
+        for child in value:
+            if not isinstance(child, Column):
+                raise TypeError(
+                    "Expected each of children to be a  Column, got " +
+                    type(child).__name__
+                )
+
+        self._children = None
+        self._base_children = value
+
+    def _mimic_inplace(self, other_col, inplace=False):
+        """
+        Given another column, update the attributes of this column to mimic an
+        inplace operation. This does not modify the memory of Buffers, but
+        instead replaces the Buffers and other attributes underneath the column
+        object with the Buffers and attributes from the other column.
+        """
+        if inplace:
+            self._offset = other_col.offset
+            self._size = other_col.size
+            self._dtype = other_col._dtype
+            self.set_base_data(other_col.base_data)
+            self.set_base_children(other_col.base_children)
+            self.set_base_mask(other_col.base_mask)
+        else:
+            return other_col
+
+    cdef libcudf_types.size_type compute_null_count(self) except? 0:
+        with acquire_spill_lock():
+            if not self.nullable:
+                return 0
+            return cpp_null_count(
+                <libcudf_types.bitmask_type*><uintptr_t>(
+                    self.base_mask.get_ptr(mode="read")
+                ),
+                self.offset,
+                self.offset + self.size
+            )
+
+    cdef mutable_column_view mutable_view(self) except *:
+        if is_categorical_dtype(self.dtype):
+            col = self.base_children[0]
+            data_dtype = col.dtype
+        elif is_datetime64tz_dtype(self.dtype):
+            col = self
+            data_dtype = _get_base_dtype(col.dtype)
+        else:
+            col = self
+            data_dtype = col.dtype
+
+        cdef libcudf_types.data_type dtype = dtype_to_data_type(data_dtype)
+        cdef libcudf_types.size_type offset = self.offset
+        cdef vector[mutable_column_view] children
+        cdef void* data
+
+        if col.base_data is None:
+            data = NULL
+        else:
+            data = <void*><uintptr_t>(
+                col.base_data.get_ptr(mode="write")
+            )
+
+        cdef Column child_column
+        if col.base_children:
+            for child_column in col.base_children:
+                children.push_back(child_column.mutable_view())
+
+        cdef libcudf_types.bitmask_type* mask
+        if self.nullable:
+            mask = <libcudf_types.bitmask_type*><uintptr_t>(
+                self.base_mask.get_ptr(mode="write")
+            )
+        else:
+            mask = NULL
+
+        null_count = self._null_count
+
+        if null_count is None:
+            null_count = 0
+        cdef libcudf_types.size_type c_null_count = null_count
+
+        self._mask = None
+        self._null_count = None
+        self._children = None
+        self._data = None
+
+        return mutable_column_view(
+            dtype,
+            self.size,
+            data,
+            mask,
+            c_null_count,
+            offset,
+            children)
+
+    cdef column_view view(self) except *:
+        null_count = self.null_count
+        if null_count is None:
+            null_count = 0
+        cdef libcudf_types.size_type c_null_count = null_count
+        return self._view(c_null_count)
+
+    cdef column_view _view(self, libcudf_types.size_type null_count) except *:
+        if is_categorical_dtype(self.dtype):
+            col = self.base_children[0]
+            data_dtype = col.dtype
+        elif is_datetime64tz_dtype(self.dtype):
+            col = self
+            data_dtype = _get_base_dtype(col.dtype)
+        else:
+            col = self
+            data_dtype = col.dtype
+
+        cdef libcudf_types.data_type dtype = dtype_to_data_type(data_dtype)
+        cdef libcudf_types.size_type offset = self.offset
+        cdef vector[column_view] children
+        cdef void* data
+
+        if col.base_data is None:
+            data = NULL
+        else:
+            data = <void*><uintptr_t>(col.base_data.get_ptr(mode="read"))
+
+        cdef Column child_column
+        if col.base_children:
+            for child_column in col.base_children:
+                children.push_back(child_column.view())
+
+        cdef libcudf_types.bitmask_type* mask
+        if self.nullable:
+            mask = <libcudf_types.bitmask_type*><uintptr_t>(
+                self.base_mask.get_ptr(mode="read")
+            )
+        else:
+            mask = NULL
+
+        cdef libcudf_types.size_type c_null_count = null_count
+
+        return column_view(
+            dtype,
+            self.size,
+            data,
+            mask,
+            c_null_count,
+            offset,
+            children)
+
+    # TODO: Consider whether this function should support some sort of `copy`
+    # parameter. Not urgent until this functionality is moved up to the Frame
+    # layer and made public. This function will also need to mark the
+    # underlying buffers as exposed before this function can itself be exposed
+    # publicly.  User requests to convert to pylibcudf must assume that the
+    # data may be modified afterwards.
+    cpdef to_pylibcudf(self, mode: Literal["read", "write"]):
+        """Convert this Column to a pylibcudf.Column.
+
+        This function will generate a pylibcudf Column pointing to the same
+        data, mask, and children as this one.
+
+        Parameters
+        ----------
+        mode : str
+            Supported values are {"read", "write"} If "write", the data pointed
+            to may be modified by the caller. If "read", the data pointed to
+            must not be modified by the caller.  Failure to fulfill this
+            contract will cause incorrect behavior.
+
+        Returns
+        -------
+        pylibcudf.Column
+            A new pylibcudf.Column referencing the same data.
+        """
+
+        # TODO: Categoricals will need to be treated differently eventually.
+        # There is no 1-1 correspondence between cudf and libcudf for
+        # categoricals because cudf supports ordered and unordered categoricals
+        # while libcudf supports only unordered categoricals (see
+        # https://github.com/rapidsai/cudf/pull/8567).
+        if is_categorical_dtype(self.dtype):
+            col = self.base_children[0]
+        else:
+            col = self
+
+        dtype = dtype_to_pylibcudf_type(col.dtype)
+
+        data = None
+        if col.base_data is not None:
+            cai = cuda_array_interface_wrapper(
+                ptr=col.base_data.get_ptr(mode=mode),
+                size=col.base_data.size,
+                owner=col.base_data,
+            )
+            data = pylibcudf.gpumemoryview(cai)
+
+        mask = None
+        if self.nullable:
+            # TODO: Are we intentionally use self's mask instead of col's?
+            # Where is the mask stored for categoricals?
+            cai = cuda_array_interface_wrapper(
+                ptr=self.base_mask.get_ptr(mode=mode),
+                size=self.base_mask.size,
+                owner=self.base_mask,
+            )
+            mask = pylibcudf.gpumemoryview(cai)
+
+        cdef Column child_column
+        children = []
+        if col.base_children:
+            for child_column in col.base_children:
+                children.append(child_column.to_pylibcudf(mode=mode))
+
+        return pylibcudf.Column(
+            dtype,
+            self.size,
+            data,
+            mask,
+            self.null_count,
+            self.offset,
+            children,
+        )
+
+    @staticmethod
+    cdef Column from_unique_ptr(
+        unique_ptr[column] c_col, bint data_ptr_exposed=False
+    ):
+        """Create a Column from a column
+
+        Typically, this is called on the result of a libcudf operation.
+        If the data of the libcudf result has been exposed, set
+        `data_ptr_exposed=True` to expose the memory of the returned Column
+        as well.
+        """
+        cdef column_view view = c_col.get()[0].view()
+        cdef libcudf_types.type_id tid = view.type().id()
+        cdef libcudf_types.data_type c_dtype
+        cdef size_type length = view.size()
+        cdef libcudf_types.mask_state mask_state
+        if tid == libcudf_types.type_id.TIMESTAMP_DAYS:
+            c_dtype = libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_SECONDS
+            )
+            with nogil:
+                c_col = move(libcudf_unary.cast(view, c_dtype))
+        elif tid == libcudf_types.type_id.EMPTY:
+            c_dtype = libcudf_types.data_type(libcudf_types.type_id.INT8)
+            mask_state = libcudf_types.mask_state.ALL_NULL
+            with nogil:
+                c_col = move(make_numeric_column(c_dtype, length, mask_state))
+
+        size = c_col.get()[0].size()
+        dtype = dtype_from_column_view(c_col.get()[0].view())
+        null_count = c_col.get()[0].null_count()
+
+        # After call to release(), c_col is unusable
+        cdef column_contents contents = move(c_col.get()[0].release())
+
+        data = as_buffer(
+            DeviceBuffer.c_from_unique_ptr(move(contents.data)),
+            exposed=data_ptr_exposed
+        )
+
+        if null_count > 0:
+            mask = as_buffer(
+                DeviceBuffer.c_from_unique_ptr(move(contents.null_mask)),
+                exposed=data_ptr_exposed
+            )
+        else:
+            mask = None
+
+        cdef vector[unique_ptr[column]] c_children = move(contents.children)
+        children = []
+        if c_children.size() != 0:
+            # Because of a bug in Cython, we cannot set the optional
+            # `data_ptr_exposed` argument within a comprehension.
+            for i in range(c_children.size()):
+                child = Column.from_unique_ptr(
+                    move(c_children[i]),
+                    data_ptr_exposed=data_ptr_exposed
+                )
+                children.append(child)
+
+        return cudf.core.column.build_column(
+            data,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            null_count=null_count,
+            children=tuple(children)
+        )
+
+    #  TODO: Actually support exposed data pointers.
+    @staticmethod
+    def from_pylibcudf(
+        col, bint data_ptr_exposed=False
+    ):
+        """Create a Column from a pylibcudf.Column.
+
+        This function will generate a Column pointing to the provided pylibcudf
+        Column.  It will directly access the data and mask buffers of the
+        pylibcudf Column, so the newly created object is not tied to the
+        lifetime of the original pylibcudf.Column.
+
+        Parameters
+        ----------
+        col : pylibcudf.Column
+            The object to copy.
+        data_ptr_exposed : bool
+            This parameter is not yet supported
+
+        Returns
+        -------
+        pylibcudf.Column
+            A new pylibcudf.Column referencing the same data.
+        """
+        dtype = dtype_from_pylibcudf_column(col)
+
+        return cudf.core.column.build_column(
+            data=as_buffer(col.data().obj) if col.data() is not None else None,
+            dtype=dtype,
+            size=col.size(),
+            mask=as_buffer(
+                col.null_mask().obj
+            ) if col.null_mask() is not None else None,
+            offset=col.offset(),
+            null_count=col.null_count(),
+            children=tuple([
+                Column.from_pylibcudf(child)
+                for child in col.children()
+            ])
+        )
+
+    @staticmethod
+    cdef Column from_column_view(column_view cv, object owner):
+        """
+        Given a ``cudf::column_view``, constructs a ``cudf.Column`` from it,
+        along with referencing an ``owner`` Python object that owns the memory
+        lifetime. If ``owner`` is a ``cudf.Column``, we reach inside of it and
+        make the owner of each newly created ``Buffer`` the respective
+        ``Buffer`` from the ``owner`` ``cudf.Column``.
+        If ``owner`` is ``None``, we allocate new memory for the resulting
+        ``cudf.Column``.
+        """
+        column_owner = isinstance(owner, Column)
+        mask_owner = owner
+        if column_owner and is_categorical_dtype(owner.dtype):
+            owner = owner.base_children[0]
+
+        size = cv.size()
+        offset = cv.offset()
+        dtype = dtype_from_column_view(cv)
+        dtype_itemsize = getattr(dtype, "itemsize", 1)
+
+        data_ptr = <uintptr_t>(cv.head[void]())
+        data = None
+        base_size = size + offset
+        data_owner = owner
+
+        if column_owner:
+            data_owner = owner.base_data
+            mask_owner = mask_owner.base_mask
+            base_size = owner.base_size
+        base_nbytes = base_size * dtype_itemsize
+        if data_ptr:
+            if data_owner is None:
+                data = as_buffer(
+                    rmm.DeviceBuffer(ptr=data_ptr,
+                                     size=(size+offset) * dtype_itemsize)
+                )
+            elif (
+                column_owner and
+                isinstance(data_owner, ExposureTrackedBuffer)
+            ):
+                data = as_buffer(
+                    data=data_ptr,
+                    size=base_nbytes,
+                    owner=data_owner,
+                    exposed=False,
+                )
+            elif (
+                # This is an optimization of the most common case where
+                # from_column_view creates a "view" that is identical to
+                # the owner.
+                column_owner and
+                isinstance(data_owner, SpillableBuffer) and
+                # We check that `data_owner` is spill locked (not spillable)
+                # and that it points to the same memory as `data_ptr`.
+                not data_owner.spillable and
+                data_owner.memory_info() == (data_ptr, base_nbytes, "gpu")
+            ):
+                data = data_owner
+            else:
+                # At this point we don't know the relationship between data_ptr
+                # and data_owner thus we mark both of them exposed.
+                # TODO: try to discover their relationship and create a
+                #       SpillableBufferSlice instead.
+                data = as_buffer(
+                    data=data_ptr,
+                    size=base_nbytes,
+                    owner=data_owner,
+                    exposed=True,
+                )
+                if isinstance(data_owner, ExposureTrackedBuffer):
+                    # accessing the pointer marks it exposed permanently.
+                    data_owner.mark_exposed()
+                elif isinstance(data_owner, SpillableBuffer):
+                    if data_owner.is_spilled:
+                        raise ValueError(
+                            f"{data_owner} is spilled, which invalidates "
+                            f"the exposed data_ptr ({hex(data_ptr)})"
+                        )
+                    # accessing the pointer marks it exposed permanently.
+                    data_owner.mark_exposed()
+        else:
+            data = as_buffer(
+                rmm.DeviceBuffer(ptr=data_ptr, size=0)
+            )
+
+        mask = None
+        mask_ptr = <uintptr_t>(cv.null_mask())
+        if mask_ptr:
+            if mask_owner is None:
+                if column_owner:
+                    # if we reached here, it means `owner` is a `Column`
+                    # that does not have a null mask, but `cv` thinks it
+                    # should have a null mask. This can happen in the
+                    # following sequence of events:
+                    #
+                    # 1) `cv` is constructed as a view into a
+                    #    `cudf::column` that is nullable (i.e., it has
+                    #    a null mask), but contains no nulls.
+                    # 2) `owner`, a `Column`, is constructed from the
+                    #    same `cudf::column`. Because `cudf::column`
+                    #    is memory owning, `owner` takes ownership of
+                    #    the memory owned by the
+                    #    `cudf::column`. Because the column has a null
+                    #    count of 0, it may choose to discard the null
+                    #    mask.
+                    # 3) Now, `cv` points to a discarded null mask.
+                    #
+                    # TL;DR: we should not include a null mask in the
+                    # result:
+                    mask = None
+                else:
+                    mask = as_buffer(
+                        rmm.DeviceBuffer(
+                            ptr=mask_ptr,
+                            size=bitmask_allocation_size_bytes(base_size)
+                        )
+                    )
+            else:
+                mask = as_buffer(
+                    data=mask_ptr,
+                    size=bitmask_allocation_size_bytes(base_size),
+                    owner=mask_owner,
+                    exposed=True
+                )
+
+        if cv.has_nulls():
+            null_count = cv.null_count()
+        else:
+            null_count = 0
+
+        children = []
+        for child_index in range(cv.num_children()):
+            child_owner = owner
+            if column_owner:
+                child_owner = owner.base_children[child_index]
+            children.append(
+                Column.from_column_view(
+                    cv.child(child_index),
+                    child_owner
+                )
+            )
+        children = tuple(children)
+
+        result = cudf.core.column.build_column(
+            data=data,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+            children=tuple(children)
+        )
+
+        return result
+
+    @staticmethod
+    def from_scalar(py_val, size_type size):
+        cdef DeviceScalar val = py_val.device_value
+        cdef const scalar* c_val = val.get_raw_ptr()
+        cdef unique_ptr[column] c_result
+        with nogil:
+            c_result = move(cpp_make_column_from_scalar(c_val[0], size))
+        return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/concat.pyx b/python/cudf/cudf/_lib/concat.pyx
new file mode 100644
index 0000000..feaf75e
--- /dev/null
+++ b/python/cudf/cudf/_lib/concat.pyx
@@ -0,0 +1,63 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column, column_view
+from cudf._lib.cpp.concatenate cimport (
+    concatenate_columns as libcudf_concatenate_columns,
+    concatenate_masks as libcudf_concatenate_masks,
+    concatenate_tables as libcudf_concatenate_tables,
+)
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.table.table cimport table, table_view
+from cudf._lib.utils cimport (
+    data_from_unique_ptr,
+    make_column_views,
+    table_view_from_table,
+)
+
+from cudf.core.buffer import acquire_spill_lock, as_buffer
+
+from rmm._lib.device_buffer cimport DeviceBuffer, device_buffer
+
+
+cpdef concat_masks(object columns):
+    cdef device_buffer c_result
+    cdef unique_ptr[device_buffer] c_unique_result
+    cdef vector[column_view] c_views = make_column_views(columns)
+    with nogil:
+        c_result = move(libcudf_concatenate_masks(c_views))
+        c_unique_result = move(make_unique[device_buffer](move(c_result)))
+    return as_buffer(
+        DeviceBuffer.c_from_unique_ptr(move(c_unique_result))
+    )
+
+
+@acquire_spill_lock()
+def concat_columns(object columns):
+    cdef unique_ptr[column] c_result
+    cdef vector[column_view] c_views = make_column_views(columns)
+    with nogil:
+        c_result = move(libcudf_concatenate_columns(c_views))
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def concat_tables(object tables, bool ignore_index=False):
+    cdef unique_ptr[table] c_result
+    cdef vector[table_view] c_views
+    c_views.reserve(len(tables))
+    for tbl in tables:
+        c_views.push_back(table_view_from_table(tbl, ignore_index))
+    with nogil:
+        c_result = move(libcudf_concatenate_tables(c_views))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=tables[0]._column_names,
+        index_names=None if ignore_index else tables[0]._index_names
+    )
diff --git a/python/cudf/cudf/_lib/copying.pxd b/python/cudf/cudf/_lib/copying.pxd
new file mode 100644
index 0000000..599b9c5
--- /dev/null
+++ b/python/cudf/cudf/_lib/copying.pxd
@@ -0,0 +1,10 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from cudf._lib.cpp.contiguous_split cimport packed_columns
+
+
+cdef class _CPackedColumns:
+    cdef packed_columns c_obj
+    cdef object column_names
+    cdef object column_dtypes
+    cdef object index_names
diff --git a/python/cudf/cudf/_lib/copying.pyx b/python/cudf/cudf/_lib/copying.pyx
new file mode 100644
index 0000000..f57bc15
--- /dev/null
+++ b/python/cudf/cudf/_lib/copying.pyx
@@ -0,0 +1,852 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import pickle
+
+from libc.stdint cimport int32_t, uint8_t, uintptr_t
+from libcpp cimport bool
+from libcpp.memory cimport make_shared, shared_ptr, unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport DeviceBuffer
+
+import cudf
+from cudf._lib import pylibcudf
+from cudf.core.buffer import Buffer, acquire_spill_lock, as_buffer
+
+from cudf._lib.column cimport Column
+
+from cudf._lib.scalar import as_device_scalar
+
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport table_view_from_columns, table_view_from_table
+
+from cudf._lib.reduce import minmax
+from cudf.core.abc import Serializable
+
+cimport cudf._lib.cpp.contiguous_split as cpp_contiguous_split
+cimport cudf._lib.cpp.copying as cpp_copying
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.libcpp.functional cimport reference_wrapper
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.lists.gather cimport (
+    segmented_gather as cpp_segmented_gather,
+)
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.utils cimport (
+    columns_from_pylibcudf_table,
+    columns_from_table_view,
+    columns_from_unique_ptr,
+    data_from_table_view,
+    table_view_from_columns,
+)
+
+# workaround for https://github.com/cython/cython/issues/3885
+ctypedef const scalar constscalar
+
+
+def _gather_map_is_valid(
+    gather_map: "cudf.core.column.ColumnBase",
+    nrows: int,
+    check_bounds: bool,
+    nullify: bool,
+) -> bool:
+    """Returns true if gather map is valid.
+
+    A gather map is valid if empty or all indices are within the range
+    ``[-nrows, nrows)``, except when ``nullify`` is specified.
+    """
+    if not check_bounds or nullify or len(gather_map) == 0:
+        return True
+    gm_min, gm_max = minmax(gather_map)
+    return gm_min >= -nrows and gm_max < nrows
+
+
+@acquire_spill_lock()
+def copy_column(Column input_column):
+    """
+    Deep copies a column
+
+    Parameters
+    ----------
+    input_columns : column to be copied
+
+    Returns
+    -------
+    Deep copied column
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view input_column_view = input_column.view()
+    with nogil:
+        c_result = move(make_unique[column](input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def _copy_range_in_place(Column input_column,
+                         Column target_column,
+                         size_type input_begin,
+                         size_type input_end,
+                         size_type target_begin):
+
+    cdef column_view input_column_view = input_column.view()
+    cdef mutable_column_view target_column_view = target_column.mutable_view()
+    cdef size_type c_input_begin = input_begin
+    cdef size_type c_input_end = input_end
+    cdef size_type c_target_begin = target_begin
+
+    with nogil:
+        cpp_copying.copy_range_in_place(
+            input_column_view,
+            target_column_view,
+            c_input_begin,
+            c_input_end,
+            c_target_begin)
+
+
+def _copy_range(Column input_column,
+                Column target_column,
+                size_type input_begin,
+                size_type input_end,
+                size_type target_begin):
+
+    cdef column_view input_column_view = input_column.view()
+    cdef column_view target_column_view = target_column.view()
+    cdef size_type c_input_begin = input_begin
+    cdef size_type c_input_end = input_end
+    cdef size_type c_target_begin = target_begin
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_copying.copy_range(
+            input_column_view,
+            target_column_view,
+            c_input_begin,
+            c_input_end,
+            c_target_begin)
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def copy_range(Column source_column,
+               Column target_column,
+               size_type source_begin,
+               size_type source_end,
+               size_type target_begin,
+               size_type target_end,
+               bool inplace):
+    """
+    Copy a contiguous range from a source to a target column
+
+    Notes
+    -----
+    Expects the source and target ranges to have been sanitised to be
+    in-range for the source and target column respectively. For
+    example via ``slice.indices``.
+    """
+
+    msg = "Source and target ranges must be same length"
+    assert source_end - source_begin == target_end - target_begin, msg
+    if target_end >= target_begin and inplace:
+        # FIXME: Are we allowed to do this when inplace=False?
+        return target_column
+
+    if inplace:
+        _copy_range_in_place(source_column, target_column,
+                             source_begin, source_end, target_begin)
+    else:
+        return _copy_range(source_column, target_column,
+                           source_begin, source_end, target_begin)
+
+
+@acquire_spill_lock()
+def gather(
+    list columns,
+    Column gather_map,
+    bool nullify=False
+):
+    tbl = pylibcudf.copying.gather(
+        pylibcudf.Table([col.to_pylibcudf(mode="read") for col in columns]),
+        gather_map.to_pylibcudf(mode="read"),
+        pylibcudf.copying.OutOfBoundsPolicy.NULLIFY if nullify
+        else pylibcudf.copying.OutOfBoundsPolicy.DONT_CHECK
+    )
+    return columns_from_pylibcudf_table(tbl)
+
+
+cdef scatter_scalar(list source_device_slrs,
+                    column_view scatter_map,
+                    table_view target_table):
+    cdef vector[reference_wrapper[constscalar]] c_source
+    cdef DeviceScalar d_slr
+    cdef unique_ptr[table] c_result
+
+    c_source.reserve(len(source_device_slrs))
+    for d_slr in source_device_slrs:
+        c_source.push_back(
+            reference_wrapper[constscalar](d_slr.get_raw_ptr()[0])
+        )
+
+    with nogil:
+        c_result = move(
+            cpp_copying.scatter(
+                c_source,
+                scatter_map,
+                target_table,
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+cdef scatter_column(list source_columns,
+                    column_view scatter_map,
+                    table_view target_table):
+    cdef table_view c_source = table_view_from_columns(source_columns)
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.scatter(
+                c_source,
+                scatter_map,
+                target_table,
+            )
+        )
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def scatter(list sources, Column scatter_map, list target_columns,
+            bool bounds_check=True):
+    """
+    Scattering source into target as per the scatter map.
+    `source` can be a list of scalars, or a list of columns. The number of
+    items in `sources` must equal the number of `target_columns` to scatter.
+    """
+    # TODO: Only single column scatter is used, we should explore multi-column
+    # scatter for frames for performance increase.
+
+    if len(sources) != len(target_columns):
+        raise ValueError("Mismatched number of source and target columns.")
+
+    if len(sources) == 0:
+        return []
+
+    cdef column_view scatter_map_view = scatter_map.view()
+    cdef table_view target_table_view = table_view_from_columns(target_columns)
+
+    if bounds_check:
+        n_rows = len(target_columns[0])
+        if not (
+            (scatter_map >= -n_rows).all()
+            and (scatter_map < n_rows).all()
+        ):
+            raise IndexError(
+                f"index out of bounds for column of size {n_rows}"
+            )
+
+    if isinstance(sources[0], Column):
+        return scatter_column(
+            sources, scatter_map_view, target_table_view
+        )
+    else:
+        source_scalars = [as_device_scalar(slr) for slr in sources]
+        return scatter_scalar(
+            source_scalars, scatter_map_view, target_table_view
+        )
+
+
+@acquire_spill_lock()
+def column_empty_like(Column input_column):
+
+    cdef column_view input_column_view = input_column.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_copying.empty_like(input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def column_allocate_like(Column input_column, size=None):
+
+    cdef size_type c_size = 0
+    cdef column_view input_column_view = input_column.view()
+    cdef unique_ptr[column] c_result
+
+    if size is None:
+        with nogil:
+            c_result = move(cpp_copying.allocate_like(
+                input_column_view,
+                cpp_copying.mask_allocation_policy.RETAIN)
+            )
+    else:
+        c_size = size
+        with nogil:
+            c_result = move(cpp_copying.allocate_like(
+                input_column_view,
+                c_size,
+                cpp_copying.mask_allocation_policy.RETAIN)
+            )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def columns_empty_like(list input_columns):
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(cpp_copying.empty_like(input_table_view))
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def column_slice(Column input_column, object indices):
+
+    cdef column_view input_column_view = input_column.view()
+    cdef vector[size_type] c_indices
+    c_indices.reserve(len(indices))
+
+    cdef vector[column_view] c_result
+
+    cdef int index
+
+    for index in indices:
+        c_indices.push_back(index)
+
+    with nogil:
+        c_result = move(
+            cpp_copying.slice(
+                input_column_view,
+                c_indices)
+        )
+
+    num_of_result_cols = c_result.size()
+    result = [
+        Column.from_column_view(
+            c_result[i],
+            input_column) for i in range(num_of_result_cols)]
+
+    return result
+
+
+@acquire_spill_lock()
+def columns_slice(list input_columns, list indices):
+    """
+    Given a list of input columns, return columns sliced by ``indices``.
+
+    Returns a list of list of columns. The length of return is
+    `len(indices) / 2`. The `i`th item in return is a list of columns sliced
+    from ``input_columns`` with `slice(indices[i*2], indices[i*2 + 1])`.
+    """
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef vector[size_type] c_indices = indices
+    cdef vector[table_view] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.slice(
+                input_table_view,
+                c_indices)
+        )
+
+    return [
+        columns_from_table_view(
+            c_result[i], input_columns
+        ) for i in range(c_result.size())
+    ]
+
+
+@acquire_spill_lock()
+def column_split(Column input_column, object splits):
+
+    cdef column_view input_column_view = input_column.view()
+    cdef vector[size_type] c_splits
+    c_splits.reserve(len(splits))
+
+    cdef vector[column_view] c_result
+
+    cdef int split
+
+    for split in splits:
+        c_splits.push_back(split)
+
+    with nogil:
+        c_result = move(
+            cpp_copying.split(
+                input_column_view,
+                c_splits)
+        )
+
+    num_of_result_cols = c_result.size()
+    result = [
+        Column.from_column_view(
+            c_result[i],
+            input_column
+        ) for i in range(num_of_result_cols)
+    ]
+
+    return result
+
+
+@acquire_spill_lock()
+def columns_split(list input_columns, object splits):
+
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef vector[size_type] c_splits = splits
+    cdef vector[table_view] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.split(
+                input_table_view,
+                c_splits)
+        )
+
+    return [
+        columns_from_table_view(
+            c_result[i], input_columns
+        ) for i in range(c_result.size())
+    ]
+
+
+def _copy_if_else_column_column(Column lhs, Column rhs, Column boolean_mask):
+
+    cdef column_view lhs_view = lhs.view()
+    cdef column_view rhs_view = rhs.view()
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.copy_if_else(
+                lhs_view,
+                rhs_view,
+                boolean_mask_view
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def _copy_if_else_scalar_column(DeviceScalar lhs,
+                                Column rhs,
+                                Column boolean_mask):
+
+    cdef const scalar* lhs_scalar = lhs.get_raw_ptr()
+    cdef column_view rhs_view = rhs.view()
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.copy_if_else(
+                lhs_scalar[0],
+                rhs_view,
+                boolean_mask_view
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def _copy_if_else_column_scalar(Column lhs,
+                                DeviceScalar rhs,
+                                Column boolean_mask):
+
+    cdef column_view lhs_view = lhs.view()
+    cdef const scalar* rhs_scalar = rhs.get_raw_ptr()
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.copy_if_else(
+                lhs_view,
+                rhs_scalar[0],
+                boolean_mask_view
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def _copy_if_else_scalar_scalar(DeviceScalar lhs,
+                                DeviceScalar rhs,
+                                Column boolean_mask):
+
+    cdef const scalar* lhs_scalar = lhs.get_raw_ptr()
+    cdef const scalar* rhs_scalar = rhs.get_raw_ptr()
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.copy_if_else(
+                lhs_scalar[0],
+                rhs_scalar[0],
+                boolean_mask_view
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def copy_if_else(object lhs, object rhs, Column boolean_mask):
+
+    if isinstance(lhs, Column):
+        if isinstance(rhs, Column):
+            return _copy_if_else_column_column(lhs, rhs, boolean_mask)
+        else:
+            return _copy_if_else_column_scalar(
+                lhs, as_device_scalar(rhs), boolean_mask)
+    else:
+        if isinstance(rhs, Column):
+            return _copy_if_else_scalar_column(
+                as_device_scalar(lhs), rhs, boolean_mask)
+        else:
+            if lhs is None and rhs is None:
+                return lhs
+
+            return _copy_if_else_scalar_scalar(
+                as_device_scalar(lhs), as_device_scalar(rhs), boolean_mask)
+
+
+def _boolean_mask_scatter_columns(list input_columns, list target_columns,
+                                  Column boolean_mask):
+
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef table_view target_table_view = table_view_from_columns(target_columns)
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.boolean_mask_scatter(
+                input_table_view,
+                target_table_view,
+                boolean_mask_view
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+def _boolean_mask_scatter_scalar(list input_scalars, list target_columns,
+                                 Column boolean_mask):
+
+    cdef vector[reference_wrapper[constscalar]] input_scalar_vector
+    input_scalar_vector.reserve(len(input_scalars))
+    cdef DeviceScalar scl
+    for scl in input_scalars:
+        input_scalar_vector.push_back(reference_wrapper[constscalar](
+            scl.get_raw_ptr()[0]))
+    cdef table_view target_table_view = table_view_from_columns(target_columns)
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_copying.boolean_mask_scatter(
+                input_scalar_vector,
+                target_table_view,
+                boolean_mask_view
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def boolean_mask_scatter(list input_, list target_columns,
+                         Column boolean_mask):
+    """Copy the target columns, replacing masked rows with input data.
+
+    The ``input_`` data can be a list of columns or as a list of scalars.
+    A list of input columns will be used to replace corresponding rows in the
+    target columns for which the boolean mask is ``True``. For the nth ``True``
+    in the boolean mask, the nth row in ``input_`` is used to replace. A list
+    of input scalars will replace all rows in the target columns for which the
+    boolean mask is ``True``.
+    """
+    if len(input_) != len(target_columns):
+        raise ValueError("Mismatched number of input and target columns.")
+
+    if len(input_) == 0:
+        return []
+
+    if isinstance(input_[0], Column):
+        return _boolean_mask_scatter_columns(
+            input_,
+            target_columns,
+            boolean_mask
+        )
+    else:
+        scalar_list = [as_device_scalar(i) for i in input_]
+        return _boolean_mask_scatter_scalar(
+            scalar_list,
+            target_columns,
+            boolean_mask
+        )
+
+
+@acquire_spill_lock()
+def shift(Column input, int offset, object fill_value=None):
+
+    cdef DeviceScalar fill
+
+    if isinstance(fill_value, DeviceScalar):
+        fill = fill_value
+    else:
+        fill = as_device_scalar(fill_value, input.dtype)
+
+    cdef column_view c_input = input.view()
+    cdef int32_t c_offset = offset
+    cdef const scalar* c_fill_value = fill.get_raw_ptr()
+    cdef unique_ptr[column] c_output
+
+    with nogil:
+        c_output = move(
+            cpp_copying.shift(
+                c_input,
+                c_offset,
+                c_fill_value[0]
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_output))
+
+
+@acquire_spill_lock()
+def get_element(Column input_column, size_type index):
+    cdef column_view col_view = input_column.view()
+
+    cdef unique_ptr[scalar] c_output
+    with nogil:
+        c_output = move(
+            cpp_copying.get_element(col_view, index)
+        )
+
+    return DeviceScalar.from_unique_ptr(
+        move(c_output), dtype=input_column.dtype
+    )
+
+
+@acquire_spill_lock()
+def segmented_gather(Column source_column, Column gather_map):
+    cdef shared_ptr[lists_column_view] source_LCV = (
+        make_shared[lists_column_view](source_column.view())
+    )
+    cdef shared_ptr[lists_column_view] gather_map_LCV = (
+        make_shared[lists_column_view](gather_map.view())
+    )
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_segmented_gather(
+                source_LCV.get()[0], gather_map_LCV.get()[0])
+        )
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+cdef class _CPackedColumns:
+
+    @staticmethod
+    def from_py_table(input_table, keep_index=True):
+        """
+        Construct a ``PackedColumns`` object from a ``cudf.DataFrame``.
+        """
+        import cudf.core.dtypes
+
+        cdef _CPackedColumns p = _CPackedColumns.__new__(_CPackedColumns)
+
+        if keep_index and (
+            not isinstance(input_table.index, cudf.RangeIndex)
+            or input_table.index.start != 0
+            or input_table.index.stop != len(input_table)
+            or input_table.index.step != 1
+        ):
+            input_table_view = table_view_from_table(input_table)
+            p.index_names = input_table._index_names
+        else:
+            input_table_view = table_view_from_table(
+                input_table, ignore_index=True)
+
+        p.column_names = input_table._column_names
+        p.column_dtypes = {}
+        for name, col in input_table._data.items():
+            if isinstance(col.dtype, cudf.core.dtypes._BaseDtype):
+                p.column_dtypes[name] = col.dtype
+
+        p.c_obj = move(cpp_contiguous_split.pack(input_table_view))
+
+        return p
+
+    @property
+    def gpu_data_ptr(self):
+        return int(<uintptr_t>self.c_obj.gpu_data.get()[0].data())
+
+    @property
+    def gpu_data_size(self):
+        return int(<size_t>self.c_obj.gpu_data.get()[0].size())
+
+    def serialize(self):
+        header = {}
+        frames = []
+
+        gpu_data = as_buffer(
+            data=self.gpu_data_ptr,
+            size=self.gpu_data_size,
+            owner=self,
+            exposed=True
+        )
+        data_header, data_frames = gpu_data.serialize()
+        header["data"] = data_header
+        frames.extend(data_frames)
+
+        header["column-names"] = self.column_names
+        header["index-names"] = self.index_names
+        if self.c_obj.metadata.get()[0].data() != NULL:
+            header["metadata"] = list(
+                <uint8_t[:self.c_obj.metadata.get()[0].size()]>
+                self.c_obj.metadata.get()[0].data()
+            )
+
+        column_dtypes = {}
+        for name, dtype in self.column_dtypes.items():
+            dtype_header, dtype_frames = dtype.serialize()
+            column_dtypes[name] = (
+                dtype_header,
+                (len(frames), len(frames) + len(dtype_frames)),
+            )
+            frames.extend(dtype_frames)
+        header["column-dtypes"] = column_dtypes
+
+        return header, frames
+
+    @staticmethod
+    def deserialize(header, frames):
+        cdef _CPackedColumns p = _CPackedColumns.__new__(_CPackedColumns)
+
+        gpu_data = Buffer.deserialize(header["data"], frames)
+
+        dbuf = DeviceBuffer(
+            ptr=gpu_data.get_ptr(mode="write"),
+            size=gpu_data.nbytes
+        )
+
+        cdef cpp_contiguous_split.packed_columns data
+        data.metadata = move(
+            make_unique[vector[uint8_t]](
+                move(<vector[uint8_t]>header.get("metadata", []))
+            )
+        )
+        data.gpu_data = move(dbuf.c_obj)
+
+        p.c_obj = move(data)
+        p.column_names = header["column-names"]
+        p.index_names = header["index-names"]
+
+        column_dtypes = {}
+        for name, dtype in header["column-dtypes"].items():
+            dtype_header, (start, stop) = dtype
+            column_dtypes[name] = pickle.loads(
+                dtype_header["type-serialized"]
+            ).deserialize(dtype_header, frames[start:stop])
+        p.column_dtypes = column_dtypes
+
+        return p
+
+    def unpack(self):
+        output_table = cudf.DataFrame._from_data(*data_from_table_view(
+            cpp_contiguous_split.unpack(self.c_obj),
+            self,
+            self.column_names,
+            self.index_names
+        ))
+
+        for name, dtype in self.column_dtypes.items():
+            output_table._data[name] = (
+                output_table._data[name]._with_type_metadata(dtype)
+            )
+
+        return output_table
+
+
+class PackedColumns(Serializable):
+    """
+    A packed representation of a Frame, with all columns residing
+    in a single GPU memory buffer.
+    """
+
+    def __init__(self, data):
+        self._data = data
+
+    def __reduce__(self):
+        return self.deserialize, self.serialize()
+
+    @property
+    def __cuda_array_interface__(self):
+        return {
+            "data": (self._data.gpu_data_ptr, False),
+            "shape": (self._data.gpu_data_size,),
+            "strides": None,
+            "typestr": "|u1",
+            "version": 0
+        }
+
+    def serialize(self):
+        header, frames = self._data.serialize()
+        header["type-serialized"] = pickle.dumps(type(self))
+
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        return cls(_CPackedColumns.deserialize(header, frames))
+
+    @classmethod
+    def from_py_table(cls, input_table, keep_index=True):
+        return cls(_CPackedColumns.from_py_table(input_table, keep_index))
+
+    def unpack(self):
+        return self._data.unpack()
+
+
+def pack(input_table, keep_index=True):
+    """
+    Pack the columns of a cudf Frame into a single GPU memory buffer.
+    """
+    return PackedColumns.from_py_table(input_table, keep_index)
+
+
+def unpack(packed):
+    """
+    Unpack the results of packing a cudf Frame returning a new
+    cudf Frame in the process.
+    """
+    return packed.unpack()
diff --git a/python/cudf/cudf/_lib/cpp/CMakeLists.txt b/python/cudf/cudf/_lib/cpp/CMakeLists.txt
new file mode 100644
index 0000000..a99aa58
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/CMakeLists.txt
@@ -0,0 +1,23 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources copying.pyx types.pyx)
+
+set(linked_libraries cudf::cudf)
+
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" ASSOCIATED_TARGETS cudf MODULE_PREFIX cpp
+)
diff --git a/python/cudf/cudf/_lib/cpp/__init__.pxd b/python/cudf/cudf/_lib/cpp/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/__init__.py b/python/cudf/cudf/_lib/cpp/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/aggregation.pxd b/python/cudf/cudf/_lib/cpp/aggregation.pxd
new file mode 100644
index 0000000..a1d1485
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/aggregation.pxd
@@ -0,0 +1,153 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+from libc.stdint cimport int32_t
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.types cimport (
+    data_type,
+    interpolation,
+    null_order,
+    null_policy,
+    order,
+    size_type,
+)
+
+ctypedef int32_t underlying_type_t_correlation_type
+ctypedef int32_t underlying_type_t_rank_method
+
+cdef extern from "cudf/aggregation.hpp" namespace "cudf" nogil:
+
+    cdef cppclass aggregation:
+        ctypedef enum Kind:
+            SUM 'cudf::aggregation::SUM'
+            PRODUCT 'cudf::aggregation::PRODUCT'
+            MIN 'cudf::aggregation::MIN'
+            MAX 'cudf::aggregation::MAX'
+            COUNT_VALID 'cudf::aggregation::COUNT_VALID'
+            COUNT_ALL 'cudf::aggregation::COUNT_ALL'
+            ANY 'cudf::aggregation::ANY'
+            ALL 'cudf::aggregation::ALL'
+            SUM_OF_SQUARES 'cudf::aggregation::SUM_OF_SQUARES'
+            MEAN 'cudf::aggregation::MEAN'
+            VARIANCE 'cudf::aggregation::VARIANCE'
+            STD 'cudf::aggregation::STD'
+            MEDIAN 'cudf::aggregation::MEDIAN'
+            QUANTILE 'cudf::aggregation::QUANTILE'
+            ARGMAX 'cudf::aggregation::ARGMAX'
+            ARGMIN 'cudf::aggregation::ARGMIN'
+            NUNIQUE 'cudf::aggregation::NUNIQUE'
+            NTH_ELEMENT 'cudf::aggregation::NTH_ELEMENT'
+            RANK 'cudf::aggregation::RANK'
+            COLLECT 'cudf::aggregation::COLLECT_LIST'
+            COLLECT_SET 'cudf::aggregation::COLLECT_SET'
+            PTX 'cudf::aggregation::PTX'
+            CUDA 'cudf::aggregation::CUDA'
+            CORRELATION 'cudf::aggregation::CORRELATION'
+            COVARIANCE 'cudf::aggregation::COVARIANCE'
+
+        Kind kind
+
+    cdef cppclass rolling_aggregation:
+        aggregation.Kind kind
+
+    cdef cppclass groupby_aggregation:
+        aggregation.Kind kind
+
+    cdef cppclass groupby_scan_aggregation:
+        aggregation.Kind kind
+
+    cdef cppclass reduce_aggregation:
+        aggregation.Kind kind
+
+    cdef cppclass scan_aggregation:
+        aggregation.Kind kind
+
+    ctypedef enum udf_type:
+        CUDA 'cudf::udf_type::CUDA'
+        PTX 'cudf::udf_type::PTX'
+
+    ctypedef enum correlation_type:
+        PEARSON 'cudf::correlation_type::PEARSON'
+        KENDALL 'cudf::correlation_type::KENDALL'
+        SPEARMAN 'cudf::correlation_type::SPEARMAN'
+
+    ctypedef enum rank_method:
+        FIRST "cudf::rank_method::FIRST"
+        AVERAGE "cudf::rank_method::AVERAGE"
+        MIN "cudf::rank_method::MIN"
+        MAX "cudf::rank_method::MAX"
+        DENSE "cudf::rank_method::DENSE"
+
+    ctypedef enum rank_percentage:
+        NONE "cudf::rank_percentage::NONE"
+        ZERO_NORMALIZED "cudf::rank_percentage::ZERO_NORMALIZED"
+        ONE_NORMALIZED "cudf::rank_percentage::ONE_NORMALIZED"
+
+    cdef unique_ptr[T] make_sum_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_product_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_min_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_max_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_count_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_count_aggregation[T](null_policy) except +
+
+    cdef unique_ptr[T] make_any_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_all_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_sum_of_squares_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_mean_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_variance_aggregation[T](
+        size_type ddof) except +
+
+    cdef unique_ptr[T] make_std_aggregation[T](size_type ddof) except +
+
+    cdef unique_ptr[T] make_median_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_quantile_aggregation[T](
+        vector[double] q, interpolation i) except +
+
+    cdef unique_ptr[T] make_argmax_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_argmin_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_nunique_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_nth_element_aggregation[T](
+        size_type n
+    ) except +
+
+    cdef unique_ptr[T] make_nth_element_aggregation[T](
+        size_type n,
+        null_policy null_handling
+    ) except +
+
+    cdef unique_ptr[T] make_collect_list_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_collect_set_aggregation[T]() except +
+
+    cdef unique_ptr[T] make_udf_aggregation[T](
+        udf_type type,
+        string user_defined_aggregator,
+        data_type output_type) except +
+
+    cdef unique_ptr[T] make_correlation_aggregation[T](
+        correlation_type type, size_type min_periods) except +
+
+    cdef unique_ptr[T] make_covariance_aggregation[T](
+        size_type min_periods, size_type ddof) except +
+
+    cdef unique_ptr[T] make_rank_aggregation[T](
+        rank_method method,
+        order column_order,
+        null_policy null_handling,
+        null_order null_precedence,
+        rank_percentage percentage) except +
diff --git a/python/cudf/cudf/_lib/cpp/binaryop.pxd b/python/cudf/cudf/_lib/cpp/binaryop.pxd
new file mode 100644
index 0000000..f73a950
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/binaryop.pxd
@@ -0,0 +1,88 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/binaryop.hpp" namespace "cudf" nogil:
+    ctypedef enum binary_operator:
+        ADD "cudf::binary_operator::ADD"
+        SUB "cudf::binary_operator::SUB"
+        MUL "cudf::binary_operator::MUL"
+        DIV "cudf::binary_operator::DIV"
+        TRUE_DIV "cudf::binary_operator::TRUE_DIV"
+        FLOOR_DIV "cudf::binary_operator::FLOOR_DIV"
+        MOD "cudf::binary_operator::MOD"
+        PYMOD "cudf::binary_operator::PYMOD"
+        POW "cudf::binary_operator::POW"
+        INT_POW "cudf::binary_operator::INT_POW"
+        EQUAL "cudf::binary_operator::EQUAL"
+        NOT_EQUAL "cudf::binary_operator::NOT_EQUAL"
+        LESS "cudf::binary_operator::LESS"
+        GREATER "cudf::binary_operator::GREATER"
+        LESS_EQUAL "cudf::binary_operator::LESS_EQUAL"
+        GREATER_EQUAL "cudf::binary_operator::GREATER_EQUAL"
+        NULL_EQUALS "cudf::binary_operator::NULL_EQUALS"
+        BITWISE_AND "cudf::binary_operator::BITWISE_AND"
+        BITWISE_OR "cudf::binary_operator::BITWISE_OR"
+        BITWISE_XOR "cudf::binary_operator::BITWISE_XOR"
+        LOGICAL_AND "cudf::binary_operator::LOGICAL_AND"
+        LOGICAL_OR "cudf::binary_operator::LOGICAL_OR"
+        GENERIC_BINARY "cudf::binary_operator::GENERIC_BINARY"
+
+    cdef unique_ptr[column] binary_operation (
+        const scalar& lhs,
+        const column_view& rhs,
+        binary_operator op,
+        data_type output_type
+    ) except +
+
+    cdef unique_ptr[column] binary_operation (
+        const column_view& lhs,
+        const scalar& rhs,
+        binary_operator op,
+        data_type output_type
+    ) except +
+
+    cdef unique_ptr[column] binary_operation (
+        const column_view& lhs,
+        const column_view& rhs,
+        binary_operator op,
+        data_type output_type
+    ) except +
+
+    cdef unique_ptr[column] binary_operation (
+        const column_view& lhs,
+        const column_view& rhs,
+        const string& op,
+        data_type output_type
+    ) except +
+
+    unique_ptr[column] jit_binary_operation \
+        "cudf::jit::binary_operation" (
+        const column_view& lhs,
+        const column_view& rhs,
+        binary_operator op,
+        data_type output_type
+    ) except +
+
+    unique_ptr[column] jit_binary_operation \
+        "cudf::jit::binary_operation" (
+        const column_view& lhs,
+        const scalar& rhs,
+        binary_operator op,
+        data_type output_type
+    ) except +
+
+    unique_ptr[column] jit_binary_operation \
+        "cudf::jit::binary_operation" (
+        const scalar& lhs,
+        const column_view& rhs,
+        binary_operator op,
+        data_type output_type
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/column/__init__.pxd b/python/cudf/cudf/_lib/cpp/column/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/column/__init__.py b/python/cudf/cudf/_lib/cpp/column/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/column/column.pxd b/python/cudf/cudf/_lib/cpp/column/column.pxd
new file mode 100644
index 0000000..136f1d7
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/column/column.pxd
@@ -0,0 +1,31 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.types cimport data_type, size_type
+
+
+cdef extern from "cudf/column/column.hpp" namespace "cudf" nogil:
+    cdef cppclass column_contents "cudf::column::contents":
+        unique_ptr[device_buffer] data
+        unique_ptr[device_buffer] null_mask
+        vector[unique_ptr[column]] children
+
+    cdef cppclass column:
+        column() except +
+        column(const column& other) except +
+
+        column(column_view view) except +
+
+        size_type size() except +
+        size_type null_count() except +
+        bool has_nulls() except +
+        data_type type() except +
+        column_view view() except +
+        mutable_column_view mutable_view() except +
+        column_contents release() except +
diff --git a/python/cudf/cudf/_lib/cpp/column/column_factories.pxd b/python/cudf/cudf/_lib/cpp/column/column_factories.pxd
new file mode 100644
index 0000000..0f22e78
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/column/column_factories.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.types cimport data_type, mask_state, size_type
+
+
+cdef extern from "cudf/column/column_factories.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[column] make_numeric_column(data_type type,
+                                                size_type size,
+                                                mask_state state) except +
+
+    cdef unique_ptr[column] make_column_from_scalar (const scalar & s,
+                                                     size_type size) except +
diff --git a/python/cudf/cudf/_lib/cpp/column/column_view.pxd b/python/cudf/cudf/_lib/cpp/column/column_view.pxd
new file mode 100644
index 0000000..edd013d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/column/column_view.pxd
@@ -0,0 +1,119 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.types cimport bitmask_type, data_type, size_type
+
+
+cdef extern from "cudf/column/column_view.hpp" namespace "cudf" nogil:
+    cdef cppclass column_view:
+        column_view() except +
+        column_view(const column_view& other) except +
+
+        column_view& operator=(const column_view&) except +
+
+        column_view(
+            data_type type,
+            size_type size,
+            const void* data
+        ) except +
+
+        column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask
+        ) except +
+
+        column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask,
+            size_type null_count
+        ) except +
+
+        column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask,
+            size_type null_count,
+            size_type offset
+        ) except +
+
+        column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask,
+            size_type null_count,
+            size_type offset,
+            vector[column_view] children
+        ) except +
+
+        const T* data[T]() except +
+        const T* head[T]() except +
+        const bitmask_type* null_mask() except +
+        size_type size() except +
+        data_type type() except +
+        bool nullable() except +
+        size_type null_count() except +
+        bool has_nulls() except +
+        size_type offset() except +
+        size_type num_children() except +
+        column_view child(size_type) except +
+
+    cdef cppclass mutable_column_view:
+        mutable_column_view() except +
+        mutable_column_view(const mutable_column_view&) except +
+        mutable_column_view& operator=(const mutable_column_view&) except +
+
+        mutable_column_view(
+            data_type type,
+            size_type size,
+            const void* data
+        ) except +
+
+        mutable_column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask
+        ) except +
+
+        mutable_column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask,
+            size_type null_count
+        ) except +
+
+        mutable_column_view(
+            data_type type,
+            size_type size,
+            const void* data,
+            const bitmask_type* null_mask,
+            size_type null_count,
+            size_type offset
+        ) except +
+
+        mutable_column_view(
+            data_type type, size_type size, const void* data,
+            const bitmask_type* null_mask, size_type null_count,
+            size_type offset, vector[mutable_column_view] children
+        ) except +
+
+        T* data[T]() except +
+        T* head[T]() except +
+        bitmask_type* null_mask() except +
+        size_type size() except +
+        data_type type() except +
+        bool nullable() except +
+        size_type null_count() except +
+        bool has_nulls() except +
+        size_type offset() except +
+        size_type num_children() except +
+        mutable_column_view& child(size_type) except +
diff --git a/python/cudf/cudf/_lib/cpp/concatenate.pxd b/python/cudf/cudf/_lib/cpp/concatenate.pxd
new file mode 100644
index 0000000..0506831
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/concatenate.pxd
@@ -0,0 +1,31 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.column.column cimport column, column_view
+from cudf._lib.cpp.table.table cimport table, table_view
+from cudf._lib.cpp.utilities.host_span cimport host_span
+
+
+cdef extern from "cudf/concatenate.hpp" namespace "cudf" nogil:
+    # The versions of concatenate taking vectors don't exist in libcudf
+    # C++, but passing a vector works because a host_span is implicitly
+    # constructable from a vector. In case they are needed in the future,
+    # host_span versions can be added, e.g:
+    #
+    # cdef device_buffer concatenate_masks "cudf::concatenate_masks"(
+    #    host_span[column_view] views
+    # ) except +
+
+    cdef device_buffer concatenate_masks "cudf::concatenate_masks"(
+        const vector[column_view] views
+    ) except +
+    cdef unique_ptr[column] concatenate_columns "cudf::concatenate"(
+        const vector[column_view] columns
+    ) except +
+    cdef unique_ptr[table] concatenate_tables "cudf::concatenate"(
+        const vector[table_view] tables
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/contiguous_split.pxd b/python/cudf/cudf/_lib/cpp/contiguous_split.pxd
new file mode 100644
index 0000000..134e4ed
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/contiguous_split.pxd
@@ -0,0 +1,29 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/contiguous_split.hpp" namespace "cudf" nogil:
+    cdef cppclass packed_columns:
+        unique_ptr[vector[uint8_t]] metadata
+        unique_ptr[device_buffer] gpu_data
+
+    cdef struct contiguous_split_result:
+        table_view table
+        vector[device_buffer] all_data
+
+    cdef vector[contiguous_split_result] contiguous_split (
+        table_view input_table,
+        vector[size_type] splits
+    ) except +
+
+    cdef packed_columns pack (const table_view& input) except +
+
+    cdef table_view unpack (const packed_columns& input) except +
diff --git a/python/cudf/cudf/_lib/cpp/copying.pxd b/python/cudf/cudf/_lib/cpp/copying.pxd
new file mode 100644
index 0000000..20725c2
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/copying.pxd
@@ -0,0 +1,153 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, int64_t, uint8_t
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.libcpp.functional cimport reference_wrapper
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.exception_handler cimport cudf_exception_handler
+
+ctypedef const scalar constscalar
+
+cdef extern from "cudf/copying.hpp" namespace "cudf" nogil:
+    cpdef enum class out_of_bounds_policy(bool):
+        NULLIFY
+        DONT_CHECK
+
+    cdef unique_ptr[table] gather (
+        const table_view& source_table,
+        const column_view& gather_map,
+        out_of_bounds_policy policy
+    ) except +cudf_exception_handler
+
+    cdef unique_ptr[column] shift(
+        const column_view& input,
+        size_type offset,
+        const scalar& fill_values
+    ) except +
+
+    cdef unique_ptr[table] scatter (
+        table_view source_table,
+        column_view scatter_map,
+        table_view target_table,
+    ) except +
+
+    cdef unique_ptr[table] scatter (
+        vector[reference_wrapper[constscalar]] source_scalars,
+        column_view indices,
+        table_view target,
+    ) except +
+
+    ctypedef enum mask_allocation_policy:
+        NEVER 'cudf::mask_allocation_policy::NEVER',
+        RETAIN 'cudf::mask_allocation_policy::RETAIN',
+        ALWAYS 'cudf::mask_allocation_policy::ALWAYS'
+
+    cdef unique_ptr[column] empty_like (
+        column_view input_column
+    ) except +
+
+    cdef unique_ptr[column] allocate_like (
+        column_view input_column,
+        mask_allocation_policy policy
+    ) except +
+
+    cdef unique_ptr[column] allocate_like (
+        column_view input_column,
+        size_type size,
+        mask_allocation_policy policy
+    ) except +
+
+    cdef unique_ptr[table] empty_like (
+        table_view input_table
+    ) except +
+
+    cdef void copy_range_in_place (
+        column_view input_column,
+        mutable_column_view target_column,
+        size_type input_begin,
+        size_type input_end,
+        size_type target_begin
+    ) except +
+
+    cdef unique_ptr[column] copy_range (
+        column_view input_column,
+        column_view target_column,
+        size_type input_begin,
+        size_type input_end,
+        size_type target_begin
+    ) except +
+
+    cdef vector[column_view] slice (
+        column_view input_column,
+        vector[size_type] indices
+    ) except +
+
+    cdef vector[table_view] slice (
+        table_view input_table,
+        vector[size_type] indices
+    ) except +
+
+    cdef vector[column_view] split (
+        column_view input_column,
+        vector[size_type] splits
+    ) except +
+
+    cdef vector[table_view] split (
+        table_view input_table,
+        vector[size_type] splits
+    ) except +
+
+    cdef unique_ptr[column] copy_if_else (
+        column_view lhs,
+        column_view rhs,
+        column_view boolean_mask
+    ) except +
+
+    cdef unique_ptr[column] copy_if_else (
+        scalar lhs,
+        column_view rhs,
+        column_view boolean_mask
+    ) except +
+
+    cdef unique_ptr[column] copy_if_else (
+        column_view lhs,
+        scalar rhs,
+        column_view boolean_mask
+    ) except +
+
+    cdef unique_ptr[column] copy_if_else (
+        scalar lhs,
+        scalar rhs,
+        column_view boolean_mask
+    ) except +
+
+    cdef unique_ptr[table] boolean_mask_scatter (
+        table_view input,
+        table_view target,
+        column_view boolean_mask
+    ) except +
+
+    cdef unique_ptr[table] boolean_mask_scatter (
+        vector[reference_wrapper[constscalar]] input,
+        table_view target,
+        column_view boolean_mask
+    ) except +
+
+    cdef unique_ptr[scalar] get_element (
+        column_view input,
+        size_type index
+    ) except +
+
+    ctypedef enum sample_with_replacement:
+        FALSE 'cudf::sample_with_replacement::FALSE',
+        TRUE 'cudf::sample_with_replacement::TRUE',
diff --git a/python/cudf/cudf/_lib/cpp/copying.pyx b/python/cudf/cudf/_lib/cpp/copying.pyx
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/datetime.pxd b/python/cudf/cudf/_lib/cpp/datetime.pxd
new file mode 100644
index 0000000..d035877
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/datetime.pxd
@@ -0,0 +1,57 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+
+
+cdef extern from "cudf/datetime.hpp" namespace "cudf::datetime" nogil:
+    cdef unique_ptr[column] extract_year(const column_view& column) except +
+    cdef unique_ptr[column] extract_month(const column_view& column) except +
+    cdef unique_ptr[column] extract_day(const column_view& column) except +
+    cdef unique_ptr[column] extract_weekday(const column_view& column) except +
+    cdef unique_ptr[column] extract_hour(const column_view& column) except +
+    cdef unique_ptr[column] extract_minute(const column_view& column) except +
+    cdef unique_ptr[column] extract_second(const column_view& column) except +
+    cdef unique_ptr[column] extract_millisecond_fraction(
+        const column_view& column
+    ) except +
+    cdef unique_ptr[column] extract_microsecond_fraction(
+        const column_view& column
+    ) except +
+    cdef unique_ptr[column] extract_nanosecond_fraction(
+        const column_view& column
+    ) except +
+
+    ctypedef enum rounding_frequency "cudf::datetime::rounding_frequency":
+        DAY "cudf::datetime::rounding_frequency::DAY"
+        HOUR "cudf::datetime::rounding_frequency::HOUR"
+        MINUTE "cudf::datetime::rounding_frequency::MINUTE"
+        SECOND "cudf::datetime::rounding_frequency::SECOND"
+        MILLISECOND "cudf::datetime::rounding_frequency::MILLISECOND"
+        MICROSECOND "cudf::datetime::rounding_frequency::MICROSECOND"
+        NANOSECOND "cudf::datetime::rounding_frequency::NANOSECOND"
+
+    cdef unique_ptr[column] ceil_datetimes(
+        const column_view& column, rounding_frequency freq
+    ) except +
+    cdef unique_ptr[column] floor_datetimes(
+        const column_view& column, rounding_frequency freq
+    ) except +
+    cdef unique_ptr[column] round_datetimes(
+        const column_view& column, rounding_frequency freq
+    ) except +
+
+    cdef unique_ptr[column] add_calendrical_months(
+        const column_view& timestamps,
+        const column_view& months
+    ) except +
+    cdef unique_ptr[column] day_of_year(const column_view& column) except +
+    cdef unique_ptr[column] is_leap_year(const column_view& column) except +
+    cdef unique_ptr[column] last_day_of_month(
+        const column_view& column
+    ) except +
+    cdef unique_ptr[column] extract_quarter(const column_view& column) except +
+    cdef unique_ptr[column] days_in_month(const column_view& column) except +
diff --git a/python/cudf/cudf/_lib/cpp/expressions.pxd b/python/cudf/cudf/_lib/cpp/expressions.pxd
new file mode 100644
index 0000000..291afbc
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/expressions.pxd
@@ -0,0 +1,95 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.scalar.scalar cimport (
+    duration_scalar,
+    numeric_scalar,
+    timestamp_scalar,
+)
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/ast/expressions.hpp" namespace "cudf::ast" nogil:
+    ctypedef enum ast_operator:
+        # Binary operators
+        ADD "cudf::ast::ast_operator::ADD"
+        SUB "cudf::ast::ast_operator::SUB"
+        MUL "cudf::ast::ast_operator::MUL"
+        DIV "cudf::ast::ast_operator::DIV"
+        TRUE_DIV "cudf::ast::ast_operator::TRUE_DIV"
+        FLOOR_DIV "cudf::ast::ast_operator::FLOOR_DIV"
+        MOD "cudf::ast::ast_operator::MOD"
+        PYMOD "cudf::ast::ast_operator::PYMOD"
+        POW "cudf::ast::ast_operator::POW"
+        EQUAL "cudf::ast::ast_operator::EQUAL"
+        NULL_EQUAL "cudf::ast::ast_operator::NULL_EQUAL"
+        NOT_EQUAL "cudf::ast::ast_operator::NOT_EQUAL"
+        LESS "cudf::ast::ast_operator::LESS"
+        GREATER "cudf::ast::ast_operator::GREATER"
+        LESS_EQUAL "cudf::ast::ast_operator::LESS_EQUAL"
+        GREATER_EQUAL "cudf::ast::ast_operator::GREATER_EQUAL"
+        BITWISE_AND "cudf::ast::ast_operator::BITWISE_AND"
+        BITWISE_OR "cudf::ast::ast_operator::BITWISE_OR"
+        BITWISE_XOR "cudf::ast::ast_operator::BITWISE_XOR"
+        NULL_LOGICAL_AND "cudf::ast::ast_operator::NULL_LOGICAL_AND"
+        LOGICAL_AND "cudf::ast::ast_operator::LOGICAL_AND"
+        NULL_LOGICAL_OR "cudf::ast::ast_operator::NULL_LOGICAL_OR"
+        LOGICAL_OR "cudf::ast::ast_operator::LOGICAL_OR"
+        # Unary operators
+        IDENTITY "cudf::ast::ast_operator::IDENTITY"
+        IS_NULL "cudf::ast::ast_operator::IS_NULL"
+        SIN "cudf::ast::ast_operator::SIN"
+        COS "cudf::ast::ast_operator::COS"
+        TAN "cudf::ast::ast_operator::TAN"
+        ARCSIN "cudf::ast::ast_operator::ARCSIN"
+        ARCCOS "cudf::ast::ast_operator::ARCCOS"
+        ARCTAN "cudf::ast::ast_operator::ARCTAN"
+        SINH "cudf::ast::ast_operator::SINH"
+        COSH "cudf::ast::ast_operator::COSH"
+        TANH "cudf::ast::ast_operator::TANH"
+        ARCSINH "cudf::ast::ast_operator::ARCSINH"
+        ARCCOSH "cudf::ast::ast_operator::ARCCOSH"
+        ARCTANH "cudf::ast::ast_operator::ARCTANH"
+        EXP "cudf::ast::ast_operator::EXP"
+        LOG "cudf::ast::ast_operator::LOG"
+        SQRT "cudf::ast::ast_operator::SQRT"
+        CBRT "cudf::ast::ast_operator::CBRT"
+        CEIL "cudf::ast::ast_operator::CEIL"
+        FLOOR "cudf::ast::ast_operator::FLOOR"
+        ABS "cudf::ast::ast_operator::ABS"
+        RINT "cudf::ast::ast_operator::RINT"
+        BIT_INVERT "cudf::ast::ast_operator::BIT_INVERT"
+        NOT "cudf::ast::ast_operator::NOT"
+
+    cdef cppclass expression:
+        pass
+
+    ctypedef enum table_reference:
+        LEFT "cudf::ast::table_reference::LEFT"
+        RIGHT "cudf::ast::table_reference::RIGHT"
+
+    cdef cppclass literal(expression):
+        # Due to https://github.com/cython/cython/issues/3198, we need to
+        # specify a return type for templated constructors.
+        literal literal[T](numeric_scalar[T] &) except +
+        literal literal[T](timestamp_scalar[T] &) except +
+        literal literal[T](duration_scalar[T] &) except +
+
+    cdef cppclass column_reference(expression):
+        # Allow for default C++ parameters by declaring multiple constructors
+        # with the default parameters optionally omitted.
+        column_reference(size_type) except +
+        column_reference(size_type, table_reference) except +
+
+    cdef cppclass operation(expression):
+        operation(ast_operator, const expression &)
+        operation(ast_operator, const expression &, const expression&)
+
+    cdef cppclass column_name_reference(expression):
+        # column_name_reference is only meant for use in file I/O such as the
+        # Parquet reader.
+        column_name_reference(string) except +
diff --git a/python/cudf/cudf/_lib/cpp/filling.pxd b/python/cudf/cudf/_lib/cpp/filling.pxd
new file mode 100644
index 0000000..e412f29
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/filling.pxd
@@ -0,0 +1,49 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/filling.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[column] fill(
+        const column_view & input,
+        size_type begin,
+        size_type end,
+        const scalar & value
+    ) except +
+
+    cdef void fill_in_place(
+        const mutable_column_view & destination,
+        size_type beign,
+        size_type end,
+        const scalar & value
+    ) except +
+
+    cdef unique_ptr[table] repeat(
+        const table_view & input,
+        const column_view & count,
+    ) except +
+
+    cdef unique_ptr[table] repeat(
+        const table_view & input,
+        size_type count
+    ) except +
+
+    cdef unique_ptr[column] sequence(
+        size_type size,
+        const scalar & init,
+        const scalar & step
+    ) except +
+
+    cdef unique_ptr[column] calendrical_month_sequence(
+        size_type n,
+        const scalar& init,
+        size_type months,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/groupby.pxd b/python/cudf/cudf/_lib/cpp/groupby.pxd
new file mode 100644
index 0000000..2ecdf76
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/groupby.pxd
@@ -0,0 +1,105 @@
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.aggregation cimport (
+    groupby_aggregation,
+    groupby_scan_aggregation,
+)
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.libcpp.functional cimport reference_wrapper
+from cudf._lib.cpp.replace cimport replace_policy
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport null_order, null_policy, order, size_type
+from cudf._lib.cpp.utilities.host_span cimport host_span
+
+# workaround for https://github.com/cython/cython/issues/3885
+ctypedef const scalar constscalar
+
+
+cdef extern from "cudf/groupby.hpp" \
+        namespace "cudf::groupby" nogil:
+
+    cdef cppclass aggregation_request:
+        aggregation_request() except +
+        column_view values
+        vector[unique_ptr[groupby_aggregation]] aggregations
+
+    cdef cppclass scan_request:
+        scan_request() except +
+        column_view values
+        vector[unique_ptr[groupby_scan_aggregation]] aggregations
+
+    cdef cppclass aggregation_result:
+        vector[unique_ptr[column]] results
+
+    cdef cppclass groups \
+            "cudf::groupby::groupby::groups" nogil:
+        unique_ptr[table] keys
+        vector[size_type] offsets
+        unique_ptr[table] values
+
+    cdef cppclass groupby:
+        groupby(const table_view& keys) except +
+        groupby(
+            const table_view& keys,
+            null_policy include_null_keys
+        ) except +
+
+        groupby(
+            const table_view& keys,
+            null_policy include_null_keys,
+            bool keys_are_sorted,
+        ) except +
+
+        groupby(
+            const table_view& keys,
+            null_policy include_null_keys,
+            bool keys_are_sorted,
+            const vector[order]& column_order,
+        ) except +
+
+        groupby(
+            const table_view& keys,
+            null_policy include_null_keys,
+            bool keys_are_sorted,
+            const vector[order]& column_order,
+            const vector[null_order]& null_precedence
+        ) except +
+
+        pair[
+            unique_ptr[table],
+            vector[aggregation_result]
+        ] aggregate(
+            const vector[aggregation_request]& requests,
+        ) except +
+
+        pair[
+            unique_ptr[table],
+            vector[aggregation_result]
+        ] scan(
+            const vector[scan_request]& requests,
+        ) except +
+
+        pair[
+            unique_ptr[table],
+            unique_ptr[table]
+        ] shift(
+            const table_view values,
+            const vector[size_type] offset,
+            const vector[reference_wrapper[constscalar]] fill_values
+        ) except +
+
+        groups get_groups() except +
+        groups get_groups(table_view values) except +
+
+        pair[unique_ptr[table], unique_ptr[table]] replace_nulls(
+            const table_view& value,
+            const vector[replace_policy] replace_policy
+        ) except +
diff --git a/python/cudf/cudf/_lib/cpp/hash.pxd b/python/cudf/cudf/_lib/cpp/hash.pxd
new file mode 100644
index 0000000..ac5e1db
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/hash.pxd
@@ -0,0 +1,24 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/hashing.hpp" namespace "cudf" nogil:
+
+    ctypedef enum hash_id "cudf::hash_id":
+        HASH_IDENTITY "cudf::hash_id::HASH_IDENTITY"
+        HASH_MURMUR3 "cudf::hash_id::HASH_MURMUR3"
+        HASH_SPARK_MURMUR3 "cudf::hash_id::HASH_SPARK_MURMUR3"
+        HASH_MD5 "cudf::hash_id::HASH_MD5"
+
+    cdef unique_ptr[column] hash "cudf::hash" (
+        const table_view& input,
+        const hash_id hash_function,
+        const uint32_t seed
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/interop.pxd b/python/cudf/cudf/_lib/cpp/interop.pxd
new file mode 100644
index 0000000..88e9d83
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/interop.pxd
@@ -0,0 +1,44 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+from pyarrow.lib cimport CScalar, CTable
+
+from cudf._lib.types import cudf_to_np_types, np_to_cudf_types
+
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "dlpack/dlpack.h" nogil:
+    ctypedef struct DLManagedTensor:
+        void(*deleter)(DLManagedTensor*) except +
+
+cdef extern from "cudf/interop.hpp" namespace "cudf" \
+        nogil:
+    cdef unique_ptr[table] from_dlpack(const DLManagedTensor* tensor
+                                       ) except +
+
+    DLManagedTensor* to_dlpack(table_view input_table
+                               ) except +
+
+    cdef unique_ptr[table] from_arrow(CTable input) except +
+    cdef unique_ptr[scalar] from_arrow(CScalar input) except +
+
+    cdef cppclass column_metadata:
+        column_metadata() except +
+        column_metadata(string name_) except +
+        string name
+        vector[column_metadata] children_meta
+
+    cdef shared_ptr[CTable] to_arrow(
+        table_view input,
+        vector[column_metadata] metadata,
+    ) except +
+
+    cdef shared_ptr[CScalar] to_arrow(
+        const scalar& input,
+        column_metadata metadata,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/__init__.pxd b/python/cudf/cudf/_lib/cpp/io/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/io/__init__.py b/python/cudf/cudf/_lib/cpp/io/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/io/arrow_io_source.pxd b/python/cudf/cudf/_lib/cpp/io/arrow_io_source.pxd
new file mode 100644
index 0000000..4aef484
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/arrow_io_source.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport shared_ptr
+from libcpp.string cimport string
+from pyarrow.includes.libarrow cimport CRandomAccessFile
+
+cimport cudf._lib.cpp.io.datasource as cudf_io_datasource
+
+
+cdef extern from "cudf/io/arrow_io_source.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass arrow_io_source(cudf_io_datasource.datasource):
+        arrow_io_source(const string& arrow_uri) except +
+        arrow_io_source(shared_ptr[CRandomAccessFile]) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/avro.pxd b/python/cudf/cudf/_lib/cpp/io/avro.pxd
new file mode 100644
index 0000000..9b683e5
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/avro.pxd
@@ -0,0 +1,44 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/io/avro.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass avro_reader_options:
+        avro_reader_options() except +
+        cudf_io_types.source_info get_source() except +
+        vector[string] get_columns() except +
+        size_type get_skip_rows() except +
+        size_type get_num_rows() except +
+
+        # setters
+
+        void set_columns(vector[string] col_names) except +
+        void set_skip_rows(size_type val) except +
+        void set_num_rows(size_type val) except +
+
+        @staticmethod
+        avro_reader_options_builder builder(
+            cudf_io_types.source_info src
+        ) except +
+
+    cdef cppclass avro_reader_options_builder:
+        avro_reader_options_builder() except +
+        avro_reader_options_builder(
+            cudf_io_types.source_info src
+        ) except +
+        avro_reader_options_builder& columns(vector[string] col_names) except +
+        avro_reader_options_builder& skip_rows(size_type val) except +
+        avro_reader_options_builder& num_rows(size_type val) except +
+
+        avro_reader_options build() except +
+
+    cdef cudf_io_types.table_with_metadata read_avro(
+        avro_reader_options &options
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/csv.pxd b/python/cudf/cudf/_lib/cpp/io/csv.pxd
new file mode 100644
index 0000000..e7c0fec
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/csv.pxd
@@ -0,0 +1,239 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+cimport cudf._lib.cpp.table.table_view as cudf_table_view
+from cudf._lib.cpp.types cimport data_type, size_type
+
+
+cdef extern from "cudf/io/csv.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass csv_reader_options:
+        csv_reader_options() except +
+
+        # Getter
+
+        cudf_io_types.source_info get_source() except +
+        # Reader settings
+        cudf_io_types.compression_type get_compression() except +
+        size_t get_byte_range_offset() except +
+        size_t get_byte_range_size() except +
+        vector[string] get_names() except +
+        string get_prefix() except +
+        bool is_enabled_mangle_dupe_cols() except +
+
+        # Filter settings
+        vector[string] get_use_cols_names() except +
+        vector[int] get_use_cols_indexes() except +
+        size_type get_nrows() except +
+        size_type get_skiprows() except +
+        size_type get_skipfooter() except +
+        size_type get_header() except +
+
+        # Parsing settings
+        char get_lineterminator() except +
+        char get_delimiter() except +
+        char get_thousands() except +
+        char get_decimal() except +
+        char get_comment() except +
+        bool is_enabled_windowslinetermination() except +
+        bool is_enabled_delim_whitespace() except +
+        bool is_enabled_skipinitialspace() except +
+        bool is_enabled_skip_blank_lines() except +
+        cudf_io_types.quote_style get_quoting() except +
+        char get_quotechar() except +
+        bool is_enabled_doublequote() except +
+        vector[string] get_parse_dates_names() except +
+        vector[int] get_parse_dates_indexes() except +
+        vector[string] get_parse_hex_names() except +
+        vector[int] get_parse_hex_indexes() except +
+
+        # Conversion settings
+        vector[string] get_dtype() except +
+        vector[string] get_true_values() except +
+        vector[string] get_false_values() except +
+        vector[string] get_na_values() except +
+        bool is_enabled_keep_default_na() except +
+        bool is_enabled_na_filter() except +
+        bool is_enabled_dayfirst() except +
+
+        # setter
+
+        # Reader settings
+        void set_compression(cudf_io_types.compression_type comp) except +
+        void set_byte_range_offset(size_t val) except +
+        void set_byte_range_size(size_t val) except +
+        void set_names(vector[string] val) except +
+        void set_prefix(string pfx) except +
+        void set_mangle_dupe_cols(bool val) except +
+
+        # Filter settings
+        void set_use_cols_names(vector[string] col_names) except +
+        void set_use_cols_indexes(vector[int] col_ind) except +
+        void set_nrows(size_type n_rows) except +
+        void set_skiprows(size_type val) except +
+        void set_skipfooter(size_type val) except +
+        void set_header(size_type hdr) except +
+
+        # Parsing settings
+        void set_lineterminator(char val) except +
+        void set_delimiter(char val) except +
+        void set_thousands(char val) except +
+        void set_decimal(char val) except +
+        void set_comment(char val) except +
+        void enable_windowslinetermination(bool val) except +
+        void enable_delim_whitespace(bool val) except +
+        void enable_skipinitialspace(bool val) except +
+        void enable_skip_blank_lines(bool val) except +
+        void set_quoting(cudf_io_types.quote_style style) except +
+        void set_quotechar(char val) except +
+        void set_doublequote(bool val) except +
+        void set_parse_dates(vector[string]) except +
+        void set_parse_dates(vector[int]) except +
+        void set_parse_hex(vector[string]) except +
+        void set_parse_hex(vector[int]) except +
+
+        # Conversion settings
+        void set_dtypes(vector[data_type] types) except +
+        void set_dtypes(map[string, data_type] types) except +
+        void set_true_values(vector[string] vals) except +
+        void set_false_values(vector[string] vals) except +
+        void set_na_values(vector[string] vals) except +
+        void enable_keep_default_na(bool val) except +
+        void enable_na_filter(bool val) except +
+        void enable_dayfirst(bool val) except +
+        void set_timestamp_type(data_type type) except +
+
+        @staticmethod
+        csv_reader_options_builder builder(
+            cudf_io_types.source_info src
+        ) except +
+
+    cdef cppclass csv_reader_options_builder:
+
+        csv_reader_options_builder() except +
+        csv_reader_options_builder(
+            cudf_io_types.source_info src
+        ) except +
+
+        csv_reader_options_builder& source(
+            cudf_io_types.source_info info
+        ) except +
+        # Reader settings
+        csv_reader_options_builder& compression(
+            cudf_io_types.compression_type comp
+        ) except +
+        csv_reader_options_builder& byte_range_offset(size_t val) except +
+        csv_reader_options_builder& byte_range_size(size_t val) except +
+        csv_reader_options_builder& names(vector[string] val) except +
+        csv_reader_options_builder& prefix(string pfx) except +
+        csv_reader_options_builder& mangle_dupe_cols(bool val) except +
+
+        # Filter settings
+        csv_reader_options_builder& use_cols_names(
+            vector[string] col_names
+        ) except +
+        csv_reader_options_builder& use_cols_indexes(
+            vector[int] col_ind
+        ) except +
+        csv_reader_options_builder& nrows(size_type n_rows) except +
+        csv_reader_options_builder& skiprows(size_type val) except +
+        csv_reader_options_builder& skipfooter(size_type val) except +
+        csv_reader_options_builder& header(size_type hdr) except +
+
+        # Parsing settings
+        csv_reader_options_builder& lineterminator(char val) except +
+        csv_reader_options_builder& delimiter(char val) except +
+        csv_reader_options_builder& thousands(char val) except +
+        csv_reader_options_builder& decimal(char val) except +
+        csv_reader_options_builder& comment(char val) except +
+        csv_reader_options_builder& windowslinetermination(bool val) except +
+        csv_reader_options_builder& delim_whitespace(bool val) except +
+        csv_reader_options_builder& skipinitialspace(bool val) except +
+        csv_reader_options_builder& skip_blank_lines(bool val) except +
+        csv_reader_options_builder& quoting(
+            cudf_io_types.quote_style style
+        ) except +
+        csv_reader_options_builder& quotechar(char val) except +
+        csv_reader_options_builder& doublequote(bool val) except +
+        csv_reader_options_builder& parse_dates(vector[string]) except +
+        csv_reader_options_builder& parse_dates(vector[int]) except +
+
+        # Conversion settings
+        csv_reader_options_builder& dtypes(vector[string] types) except +
+        csv_reader_options_builder& dtypes(vector[data_type] types) except +
+        csv_reader_options_builder& dtypes(
+            map[string, data_type] types
+        ) except +
+        csv_reader_options_builder& true_values(vector[string] vals) except +
+        csv_reader_options_builder& false_values(vector[string] vals) except +
+        csv_reader_options_builder& na_values(vector[string] vals) except +
+        csv_reader_options_builder& keep_default_na(bool val) except +
+        csv_reader_options_builder& na_filter(bool val) except +
+        csv_reader_options_builder& dayfirst(bool val) except +
+        csv_reader_options_builder& timestamp_type(data_type type) except +
+
+        csv_reader_options build() except +
+
+    cdef cudf_io_types.table_with_metadata read_csv(
+        csv_reader_options &options
+    ) except +
+
+    cdef cppclass csv_writer_options:
+        csv_writer_options() except +
+
+        cudf_io_types.sink_info get_sink() except +
+        cudf_table_view.table_view get_table() except +
+        cudf_io_types.table_metadata get_metadata() except +
+        string get_na_rep() except +
+        bool is_enabled_include_header() except +
+        size_type get_rows_per_chunk() except +
+        string get_line_terminator() except +
+        char get_inter_column_delimiter() except +
+        string get_true_value() except +
+        string get_false_value() except +
+        vector[string] get_names() except +
+
+        # setter
+        void set_metadata(cudf_io_types.table_metadata* val) except +
+        void set_na_rep(string val) except +
+        void enable_include_header(bool val) except +
+        void set_rows_per_chunk(size_type val) except +
+        void set_line_terminator(string term) except +
+        void set_inter_column_delimiter(char delim) except +
+        void set_true_value(string val) except +
+        void set_false_value(string val) except +
+        void set_names(vector[string] val) except +
+
+        @staticmethod
+        csv_writer_options_builder builder(
+            cudf_io_types.sink_info sink,
+            cudf_table_view.table_view table
+        ) except +
+
+    cdef cppclass csv_writer_options_builder:
+        csv_writer_options_builder() except +
+        csv_writer_options_builder(
+            cudf_io_types.sink_info sink,
+            cudf_table_view.table_view table
+        ) except +
+
+        csv_writer_options_builder& names(vector[string] val) except +
+        csv_writer_options_builder& na_rep(string val) except +
+        csv_writer_options_builder& include_header(bool val) except +
+        csv_writer_options_builder& rows_per_chunk(size_type val) except +
+        csv_writer_options_builder& line_terminator(string term) except +
+        csv_writer_options_builder& inter_column_delimiter(char delim) except +
+        csv_writer_options_builder& true_value(string val) except +
+        csv_writer_options_builder& false_value(string val) except +
+
+        csv_writer_options build() except +
+
+    cdef void write_csv(csv_writer_options args) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/data_sink.pxd b/python/cudf/cudf/_lib/cpp/io/data_sink.pxd
new file mode 100644
index 0000000..e939a47
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/data_sink.pxd
@@ -0,0 +1,8 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+
+cdef extern from "cudf/io/data_sink.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass data_sink:
+        pass
diff --git a/python/cudf/cudf/_lib/cpp/io/datasource.pxd b/python/cudf/cudf/_lib/cpp/io/datasource.pxd
new file mode 100644
index 0000000..c69aa65
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/datasource.pxd
@@ -0,0 +1,8 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+
+cdef extern from "cudf/io/datasource.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass datasource:
+        pass
diff --git a/python/cudf/cudf/_lib/cpp/io/json.pxd b/python/cudf/cudf/_lib/cpp/io/json.pxd
new file mode 100644
index 0000000..ad618cc
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/json.pxd
@@ -0,0 +1,142 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+cimport cudf._lib.cpp.table.table_view as cudf_table_view
+from cudf._lib.cpp.types cimport data_type, size_type
+
+
+cdef extern from "cudf/io/json.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef struct schema_element:
+        data_type type
+        map[string, schema_element] child_types
+
+    cdef cppclass json_reader_options:
+        json_reader_options() except +
+        cudf_io_types.source_info get_source() except +
+        vector[string] get_dtypes() except +
+        cudf_io_types.compression_type get_compression() except +
+        size_type get_byte_range_offset() except +
+        size_type get_byte_range_size() except +
+        bool is_enabled_lines() except +
+        bool is_enabled_dayfirst() except +
+        bool is_enabled_experimental() except +
+
+        # setter
+        void set_dtypes(vector[data_type] types) except +
+        void set_dtypes(map[string, schema_element] types) except +
+        void set_compression(
+            cudf_io_types.compression_type compression
+        ) except +
+        void set_byte_range_offset(size_type offset) except +
+        void set_byte_range_size(size_type size) except +
+        void enable_lines(bool val) except +
+        void enable_dayfirst(bool val) except +
+        void enable_experimental(bool val) except +
+        void enable_keep_quotes(bool val) except +
+
+        @staticmethod
+        json_reader_options_builder builder(
+            cudf_io_types.source_info src
+        ) except +
+
+    cdef cppclass json_reader_options_builder:
+        json_reader_options_builder() except +
+        json_reader_options_builder(
+            cudf_io_types.source_info src
+        ) except +
+        json_reader_options_builder& dtypes(
+            vector[string] types
+        ) except +
+        json_reader_options_builder& dtypes(
+            vector[data_type] types
+        ) except +
+        json_reader_options_builder& dtypes(
+            map[string, schema_element] types
+        ) except +
+        json_reader_options_builder& compression(
+            cudf_io_types.compression_type compression
+        ) except +
+        json_reader_options_builder& byte_range_offset(
+            size_type offset
+        ) except +
+        json_reader_options_builder& byte_range_size(
+            size_type size
+        ) except +
+        json_reader_options_builder& lines(
+            bool val
+        ) except +
+        json_reader_options_builder& dayfirst(
+            bool val
+        ) except +
+        json_reader_options_builder& legacy(
+            bool val
+        ) except +
+        json_reader_options_builder& keep_quotes(
+            bool val
+        ) except +
+
+        json_reader_options build() except +
+
+    cdef cudf_io_types.table_with_metadata read_json(
+        json_reader_options &options) except +
+
+    cdef cppclass json_writer_options:
+        json_writer_options() except +
+        cudf_io_types.sink_info get_sink() except +
+        cudf_table_view.table_view get_table() except +
+        string get_na_rep() except +
+        bool is_enabled_include_nulls() except +
+        bool is_enabled_lines() except +
+        bool is_enabled_experimental() except +
+        size_type get_rows_per_chunk() except +
+        string get_true_value() except +
+        string get_false_value() except +
+
+        # setter
+        void set_table(cudf_table_view.table_view tbl) except +
+        void set_metadata(cudf_io_types.table_metadata meta) except +
+        void set_na_rep(string val) except +
+        void enable_include_nulls(bool val) except +
+        void enable_lines(bool val) except +
+        void set_rows_per_chunk(size_type val) except +
+        void set_true_value(string val) except +
+        void set_false_value(string val) except +
+
+        @staticmethod
+        json_writer_options_builder builder(
+            cudf_io_types.sink_info sink,
+            cudf_table_view.table_view tbl
+        ) except +
+
+    cdef cppclass json_writer_options_builder:
+        json_writer_options_builder() except +
+        json_writer_options_builder(
+            cudf_io_types.source_info src,
+            cudf_table_view.table_view tbl
+        ) except +
+        json_writer_options_builder& table(
+            cudf_table_view.table_view tbl
+        ) except +
+        json_writer_options_builder& metadata(
+            cudf_io_types.table_metadata meta
+        ) except +
+        json_writer_options_builder& na_rep(string val) except +
+        json_writer_options_builder& include_nulls(bool val) except +
+        json_writer_options_builder& lines(bool val) except +
+        json_writer_options_builder& rows_per_chunk(size_type val) except +
+        json_writer_options_builder& true_value(string val) except +
+        json_writer_options_builder& false_value(string val) except +
+
+        json_writer_options build() except +
+
+    cdef cudf_io_types.table_with_metadata write_json(
+        json_writer_options &options) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/orc.pxd b/python/cudf/cudf/_lib/cpp/io/orc.pxd
new file mode 100644
index 0000000..dd6f919
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/orc.pxd
@@ -0,0 +1,173 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+cimport cudf._lib.cpp.table.table_view as cudf_table_view
+from cudf._lib.cpp.libcpp.optional cimport optional
+from cudf._lib.cpp.types cimport data_type, size_type
+
+
+cdef extern from "cudf/io/orc.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass orc_reader_options:
+        orc_reader_options() except +
+
+        cudf_io_types.source_info get_source() except +
+        vector[vector[size_type]] get_stripes() except +
+        size_type get_skip_rows() except +
+        size_type get_num_rows() except +
+        bool is_enabled_use_index() except +
+        bool is_enabled_use_np_dtypes() except +
+        data_type get_timestamp_type() except +
+        bool is_enabled_decimals_as_float64() except +
+        int get_forced_decimals_scale() except +
+
+        void set_columns(vector[string] col_names) except +
+        void set_stripes(vector[vector[size_type]] strps) except +
+        void set_skip_rows(size_type rows) except +
+        void set_num_rows(size_type nrows) except +
+        void enable_use_index(bool val) except +
+        void enable_use_np_dtypes(bool val) except +
+        void set_timestamp_type(data_type type) except +
+
+        @staticmethod
+        orc_reader_options_builder builder(
+            cudf_io_types.source_info src
+        ) except +
+
+    cdef cppclass orc_reader_options_builder:
+        orc_reader_options_builder() except +
+        orc_reader_options_builder(cudf_io_types.source_info &src) except +
+
+        orc_reader_options_builder& columns(vector[string] col_names) except +
+        orc_reader_options_builder& \
+            stripes(vector[vector[size_type]] strps) except +
+        orc_reader_options_builder& skip_rows(size_type rows) except +
+        orc_reader_options_builder& num_rows(size_type nrows) except +
+        orc_reader_options_builder& use_index(bool val) except +
+        orc_reader_options_builder& use_np_dtypes(bool val) except +
+        orc_reader_options_builder& timestamp_type(data_type type) except +
+
+        orc_reader_options build() except +
+
+    cdef cudf_io_types.table_with_metadata read_orc(
+        orc_reader_options opts
+    ) except +
+
+    cdef cppclass orc_writer_options:
+        orc_writer_options()
+        cudf_io_types.sink_info get_sink() except +
+        cudf_io_types.compression_type get_compression() except +
+        bool is_enabled_statistics() except +
+        size_t get_stripe_size_bytes() except +
+        size_type get_stripe_size_rows() except +
+        size_type get_row_index_stride() except +
+        cudf_table_view.table_view get_table() except +
+        const optional[cudf_io_types.table_input_metadata]& get_metadata(
+        ) except +
+
+        # setter
+        void set_compression(cudf_io_types.compression_type comp) except +
+        void enable_statistics(bool val) except +
+        void set_stripe_size_bytes(size_t val) except +
+        void set_stripe_size_rows(size_type val) except +
+        void set_row_index_stride(size_type val) except +
+        void set_table(cudf_table_view.table_view tbl) except +
+        void set_metadata(cudf_io_types.table_input_metadata meta) except +
+        void set_key_value_metadata(map[string, string] kvm) except +
+
+        @staticmethod
+        orc_writer_options_builder builder(
+            cudf_io_types.sink_info &sink,
+            cudf_table_view.table_view &tbl
+        ) except +
+
+    cdef cppclass orc_writer_options_builder:
+        # setter
+        orc_writer_options_builder& compression(
+            cudf_io_types.compression_type comp
+        ) except +
+        orc_writer_options_builder& enable_statistics(bool val) except +
+        orc_writer_options_builder& stripe_size_bytes(size_t val) except +
+        orc_writer_options_builder& stripe_size_rows(size_type val) except +
+        orc_writer_options_builder& row_index_stride(size_type val) except +
+        orc_writer_options_builder& table(
+            cudf_table_view.table_view tbl
+        ) except +
+        orc_writer_options_builder& metadata(
+            cudf_io_types.table_input_metadata meta
+        ) except +
+        orc_writer_options_builder& key_value_metadata(
+            map[string, string] kvm
+        ) except +
+
+        orc_writer_options build() except +
+
+    cdef void write_orc(orc_writer_options options) except +
+
+    cdef cppclass chunked_orc_writer_options:
+        chunked_orc_writer_options() except +
+        cudf_io_types.sink_info get_sink() except +
+        cudf_io_types.compression_type get_compression() except +
+        bool enable_statistics() except +
+        size_t stripe_size_bytes() except +
+        size_type stripe_size_rows() except +
+        size_type row_index_stride() except +
+        cudf_table_view.table_view get_table() except +
+        const optional[cudf_io_types.table_input_metadata]& get_metadata(
+        ) except +
+
+        # setter
+        void set_compression(cudf_io_types.compression_type comp) except +
+        void enable_statistics(bool val) except +
+        void set_stripe_size_bytes(size_t val) except +
+        void set_stripe_size_rows(size_type val) except +
+        void set_row_index_stride(size_type val) except +
+        void set_table(cudf_table_view.table_view tbl) except +
+        void set_metadata(
+            cudf_io_types.table_input_metadata meta
+        ) except +
+        void set_key_value_metadata(map[string, string] kvm) except +
+
+        @staticmethod
+        chunked_orc_writer_options_builder builder(
+            cudf_io_types.sink_info &sink
+        ) except +
+
+    cdef cppclass chunked_orc_writer_options_builder:
+        # setter
+        chunked_orc_writer_options_builder& compression(
+            cudf_io_types.compression_type comp
+        ) except +
+        chunked_orc_writer_options_builder& enable_statistics(
+            bool val
+        ) except +
+        orc_writer_options_builder& stripe_size_bytes(size_t val) except +
+        orc_writer_options_builder& stripe_size_rows(size_type val) except +
+        orc_writer_options_builder& row_index_stride(size_type val) except +
+        chunked_orc_writer_options_builder& table(
+            cudf_table_view.table_view tbl
+        ) except +
+        chunked_orc_writer_options_builder& metadata(
+            cudf_io_types.table_input_metadata meta
+        ) except +
+        chunked_orc_writer_options_builder& key_value_metadata(
+            map[string, string] kvm
+        ) except +
+
+        chunked_orc_writer_options build() except +
+
+    cdef cppclass orc_chunked_writer:
+        orc_chunked_writer() except +
+        orc_chunked_writer(chunked_orc_writer_options args) except +
+        orc_chunked_writer& write(
+            cudf_table_view.table_view table_,
+        ) except +
+        void close() except +
diff --git a/python/cudf/cudf/_lib/cpp/io/orc_metadata.pxd b/python/cudf/cudf/_lib/cpp/io/orc_metadata.pxd
new file mode 100644
index 0000000..57be1b1
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/orc_metadata.pxd
@@ -0,0 +1,19 @@
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+
+
+cdef extern from "cudf/io/orc_metadata.hpp" \
+        namespace "cudf::io" nogil:
+
+    cdef cppclass raw_orc_statistics:
+        vector[string] column_names
+        vector[string] file_stats
+        vector[vector[string]] stripes_stats
+
+    cdef raw_orc_statistics read_raw_orc_statistics(
+        cudf_io_types.source_info src_info
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/parquet.pxd b/python/cudf/cudf/_lib/cpp/io/parquet.pxd
new file mode 100644
index 0000000..2b92b9b
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/parquet.pxd
@@ -0,0 +1,233 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+cimport cudf._lib.cpp.table.table_view as cudf_table_view
+from cudf._lib.cpp.expressions cimport expression
+from cudf._lib.cpp.libcpp.functional cimport reference_wrapper
+from cudf._lib.cpp.libcpp.optional cimport optional
+from cudf._lib.cpp.types cimport data_type, size_type
+
+
+cdef extern from "cudf/io/parquet.hpp" namespace "cudf::io" nogil:
+    cdef cppclass parquet_reader_options:
+        parquet_reader_options() except +
+        cudf_io_types.source_info get_source_info() except +
+        vector[vector[size_type]] get_row_groups() except +
+        const optional[reference_wrapper[expression]]& get_filter() except +
+        data_type get_timestamp_type() except +
+        bool is_enabled_use_pandas_metadata() except +
+
+        # setter
+
+        void set_columns(vector[string] col_names) except +
+        void set_row_groups(vector[vector[size_type]] row_grp) except +
+        void enable_use_pandas_metadata(bool val) except +
+        void set_timestamp_type(data_type type) except +
+
+        @staticmethod
+        parquet_reader_options_builder builder(
+            cudf_io_types.source_info src
+        ) except +
+
+    cdef cppclass parquet_reader_options_builder:
+        parquet_reader_options_builder() except +
+        parquet_reader_options_builder(
+            cudf_io_types.source_info src
+        ) except +
+        parquet_reader_options_builder& columns(
+            vector[string] col_names
+        ) except +
+        parquet_reader_options_builder& row_groups(
+            vector[vector[size_type]] row_grp
+        ) except +
+        parquet_reader_options_builder& use_pandas_metadata(
+            bool val
+        ) except +
+        parquet_reader_options_builder& timestamp_type(
+            data_type type
+        ) except +
+        parquet_reader_options build() except +
+
+    cdef cudf_io_types.table_with_metadata read_parquet(
+        parquet_reader_options args) except +
+
+    cdef cppclass parquet_writer_options:
+        parquet_writer_options() except +
+        cudf_io_types.sink_info get_sink_info() except +
+        cudf_io_types.compression_type get_compression() except +
+        cudf_io_types.statistics_freq get_stats_level() except +
+        cudf_table_view.table_view get_table() except +
+        const optional[cudf_io_types.table_input_metadata]& get_metadata(
+        ) except +
+        string get_column_chunks_file_paths() except +
+        size_t get_row_group_size_bytes() except +
+        size_type get_row_group_size_rows() except +
+        size_t get_max_page_size_bytes() except +
+        size_type get_max_page_size_rows() except +
+
+        void set_partitions(
+            vector[cudf_io_types.partition_info] partitions
+        ) except +
+        void set_metadata(
+            cudf_io_types.table_input_metadata m
+        ) except +
+        void set_key_value_metadata(
+            vector[map[string, string]] kvm
+        ) except +
+        void set_stats_level(
+            cudf_io_types.statistics_freq sf
+        ) except +
+        void set_compression(
+            cudf_io_types.compression_type compression
+        ) except +
+        void set_column_chunks_file_paths(
+            vector[string] column_chunks_file_paths
+        ) except +
+        void set_row_group_size_bytes(size_t val) except +
+        void set_row_group_size_rows(size_type val) except +
+        void set_max_page_size_bytes(size_t val) except +
+        void set_max_page_size_rows(size_type val) except +
+
+        @staticmethod
+        parquet_writer_options_builder builder(
+            cudf_io_types.sink_info sink_,
+            cudf_table_view.table_view table_
+        ) except +
+
+    cdef cppclass parquet_writer_options_builder:
+
+        parquet_writer_options_builder() except +
+        parquet_writer_options_builder(
+            cudf_io_types.sink_info sink_,
+            cudf_table_view.table_view table_
+        ) except +
+        parquet_writer_options_builder& partitions(
+            vector[cudf_io_types.partition_info] partitions
+        ) except +
+        parquet_writer_options_builder& metadata(
+            cudf_io_types.table_input_metadata m
+        ) except +
+        parquet_writer_options_builder& key_value_metadata(
+            vector[map[string, string]] kvm
+        ) except +
+        parquet_writer_options_builder& stats_level(
+            cudf_io_types.statistics_freq sf
+        ) except +
+        parquet_writer_options_builder& compression(
+            cudf_io_types.compression_type compression
+        ) except +
+        parquet_writer_options_builder& column_chunks_file_paths(
+            vector[string] column_chunks_file_paths
+        ) except +
+        parquet_writer_options_builder& int96_timestamps(
+            bool enabled
+        ) except +
+        parquet_writer_options_builder& row_group_size_bytes(
+            size_t val
+        ) except +
+        parquet_writer_options_builder& row_group_size_rows(
+            size_type val
+        ) except +
+        parquet_writer_options_builder& max_page_size_bytes(
+            size_t val
+        ) except +
+        parquet_writer_options_builder& max_page_size_rows(
+            size_type val
+        ) except +
+
+        parquet_writer_options build() except +
+
+    cdef unique_ptr[vector[uint8_t]] write_parquet(
+        parquet_writer_options args
+    ) except +
+
+    cdef cppclass chunked_parquet_writer_options:
+        chunked_parquet_writer_options() except +
+        cudf_io_types.sink_info get_sink() except +
+        cudf_io_types.compression_type get_compression() except +
+        cudf_io_types.statistics_freq get_stats_level() except +
+        const optional[cudf_io_types.table_input_metadata]& get_metadata(
+        ) except +
+        size_t get_row_group_size_bytes() except +
+        size_type get_row_group_size_rows() except +
+        size_t get_max_page_size_bytes() except +
+        size_type get_max_page_size_rows() except +
+
+        void set_metadata(
+            cudf_io_types.table_input_metadata m
+        ) except +
+        void set_key_value_metadata(
+            vector[map[string, string]] kvm
+        ) except +
+        void set_stats_level(
+            cudf_io_types.statistics_freq sf
+        ) except +
+        void set_compression(
+            cudf_io_types.compression_type compression
+        ) except +
+        void set_row_group_size_bytes(size_t val) except +
+        void set_row_group_size_rows(size_type val) except +
+        void set_max_page_size_bytes(size_t val) except +
+        void set_max_page_size_rows(size_type val) except +
+
+        @staticmethod
+        chunked_parquet_writer_options_builder builder(
+            cudf_io_types.sink_info sink_,
+        ) except +
+
+    cdef cppclass chunked_parquet_writer_options_builder:
+        chunked_parquet_writer_options_builder() except +
+        chunked_parquet_writer_options_builder(
+            cudf_io_types.sink_info sink_,
+        ) except +
+        chunked_parquet_writer_options_builder& metadata(
+            cudf_io_types.table_input_metadata m
+        ) except +
+        chunked_parquet_writer_options_builder& key_value_metadata(
+            vector[map[string, string]] kvm
+        ) except +
+        chunked_parquet_writer_options_builder& stats_level(
+            cudf_io_types.statistics_freq sf
+        ) except +
+        chunked_parquet_writer_options_builder& compression(
+            cudf_io_types.compression_type compression
+        ) except +
+        chunked_parquet_writer_options_builder& row_group_size_bytes(
+            size_t val
+        ) except +
+        chunked_parquet_writer_options_builder& row_group_size_rows(
+            size_type val
+        ) except +
+        chunked_parquet_writer_options_builder& max_page_size_bytes(
+            size_t val
+        ) except +
+        chunked_parquet_writer_options_builder& max_page_size_rows(
+            size_type val
+        ) except +
+
+        chunked_parquet_writer_options build() except +
+
+    cdef cppclass parquet_chunked_writer:
+        parquet_chunked_writer() except +
+        parquet_chunked_writer(chunked_parquet_writer_options args) except +
+        parquet_chunked_writer& write(
+            cudf_table_view.table_view table_,
+        ) except +
+        parquet_chunked_writer& write(
+            const cudf_table_view.table_view& table_,
+            const vector[cudf_io_types.partition_info]& partitions,
+        ) except +
+        unique_ptr[vector[uint8_t]] close(
+            vector[string] column_chunks_file_paths,
+        ) except +
+
+    cdef unique_ptr[vector[uint8_t]] merge_row_group_metadata(
+        const vector[unique_ptr[vector[uint8_t]]]& metadata_list
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/text.pxd b/python/cudf/cudf/_lib/cpp/io/text.pxd
new file mode 100644
index 0000000..368b014
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/text.pxd
@@ -0,0 +1,49 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint64_t
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+
+
+cdef extern from "cudf/io/text/byte_range_info.hpp" \
+        namespace "cudf::io::text" nogil:
+
+    cdef cppclass byte_range_info:
+        byte_range_info() except +
+        byte_range_info(size_t offset, size_t size) except +
+
+cdef extern from "cudf/io/text/data_chunk_source.hpp" \
+        namespace "cudf::io::text" nogil:
+
+    cdef cppclass data_chunk_source:
+        data_chunk_source() except +
+
+cdef extern from "cudf/io/text/data_chunk_source_factories.hpp" \
+        namespace "cudf::io::text" nogil:
+
+    unique_ptr[data_chunk_source] make_source(string data) except +
+    unique_ptr[data_chunk_source] \
+        make_source_from_file(string filename) except +
+    unique_ptr[data_chunk_source] \
+        make_source_from_bgzip_file(string filename) except +
+    unique_ptr[data_chunk_source] \
+        make_source_from_bgzip_file(string filename,
+                                    uint64_t virtual_begin,
+                                    uint64_t virtual_end) except +
+
+
+cdef extern from "cudf/io/text/multibyte_split.hpp" \
+        namespace "cudf::io::text" nogil:
+
+    cdef cppclass parse_options:
+        byte_range_info byte_range
+        bool strip_delimiters
+
+        parse_options() except +
+
+    unique_ptr[column] multibyte_split(data_chunk_source source,
+                                       string delimiter,
+                                       parse_options options) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/timezone.pxd b/python/cudf/cudf/_lib/cpp/io/timezone.pxd
new file mode 100644
index 0000000..ba481d9
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/timezone.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.libcpp.optional cimport optional
+from cudf._lib.cpp.table.table cimport table
+
+
+cdef extern from "cudf/timezone.hpp" namespace "cudf" nogil:
+    unique_ptr[table] make_timezone_transition_table(
+        optional[string] tzif_dir,
+        string timezone_name
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/io/types.pxd b/python/cudf/cudf/_lib/cpp/io/types.pxd
new file mode 100644
index 0000000..01eaca8
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/io/types.pxd
@@ -0,0 +1,124 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.pair cimport pair
+from libcpp.string cimport string
+from libcpp.unordered_map cimport unordered_map
+from libcpp.vector cimport vector
+from pyarrow.includes.libarrow cimport CRandomAccessFile
+
+cimport cudf._lib.cpp.io.data_sink as cudf_io_data_sink
+cimport cudf._lib.cpp.io.datasource as cudf_io_datasource
+cimport cudf._lib.cpp.table.table_view as cudf_table_view
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/io/types.hpp" \
+        namespace "cudf::io" nogil:
+
+    ctypedef enum quote_style:
+        QUOTE_MINIMAL "cudf::io::quote_style::MINIMAL"
+        QUOTE_ALL "cudf::io::quote_style::ALL"
+        QUOTE_NONNUMERIC "cudf::io::quote_style::NONNUMERIC"
+        QUOTE_NONE "cudf::io::quote_style::NONE"
+
+    ctypedef enum compression_type:
+        NONE "cudf::io::compression_type::NONE"
+        AUTO "cudf::io::compression_type::AUTO"
+        SNAPPY "cudf::io::compression_type::SNAPPY"
+        GZIP "cudf::io::compression_type::GZIP"
+        BZIP2 "cudf::io::compression_type::BZIP2"
+        BROTLI "cudf::io::compression_type::BROTLI"
+        ZIP "cudf::io::compression_type::ZIP"
+        XZ "cudf::io::compression_type::XZ"
+        ZLIB "cudf::io::compression_type::ZLIB"
+        LZ4 "cudf::io::compression_type::LZ4"
+        LZO "cudf::io::compression_type::LZO"
+        ZSTD "cudf::io::compression_type::ZSTD"
+
+    ctypedef enum io_type:
+        FILEPATH "cudf::io::io_type::FILEPATH"
+        HOST_BUFFER "cudf::io::io_type::HOST_BUFFER"
+        VOID "cudf::io::io_type::VOID"
+        USER_IMPLEMENTED "cudf::io::io_type::USER_IMPLEMENTED"
+
+    ctypedef enum statistics_freq:
+        STATISTICS_NONE = 0,
+        STATISTICS_ROWGROUP = 1,
+        STATISTICS_PAGE = 2,
+        STATISTICS_COLUMN = 3,
+
+    cdef cppclass column_name_info:
+        string name
+        vector[column_name_info] children
+
+    cdef cppclass table_metadata:
+        table_metadata() except +
+
+        vector[string] column_names
+        map[string, string] user_data
+        vector[unordered_map[string, string]] per_file_user_data
+        vector[column_name_info] schema_info
+
+    cdef cppclass table_with_metadata:
+        unique_ptr[table] tbl
+        table_metadata metadata
+
+    cdef cppclass column_in_metadata:
+        column_in_metadata& set_name(const string& name)
+        column_in_metadata& set_nullability(bool nullable)
+        column_in_metadata& set_list_column_as_map()
+        column_in_metadata& set_int96_timestamps(bool req)
+        column_in_metadata& set_decimal_precision(uint8_t precision)
+        column_in_metadata& child(size_type i)
+        column_in_metadata& set_output_as_binary(bool binary)
+        string get_name()
+
+    cdef cppclass table_input_metadata:
+        table_input_metadata() except +
+        table_input_metadata(const cudf_table_view.table_view& table) except +
+
+        vector[column_in_metadata] column_metadata
+
+    cdef cppclass partition_info:
+        size_type start_row
+        size_type num_rows
+
+        partition_info()
+        partition_info(size_type start_row, size_type num_rows) except +
+
+    cdef cppclass host_buffer:
+        const char* data
+        size_t size
+
+        host_buffer()
+        host_buffer(const char* data, size_t size)
+
+    cdef cppclass source_info:
+        io_type type
+        const vector[string]& filepaths() except +
+        const vector[host_buffer]& buffers() except +
+        vector[shared_ptr[CRandomAccessFile]] files
+
+        source_info() except +
+        source_info(const vector[string] &filepaths) except +
+        source_info(const vector[host_buffer] &host_buffers) except +
+        source_info(cudf_io_datasource.datasource *source) except +
+        source_info(const vector[cudf_io_datasource.datasource*] &datasources) except +
+
+    cdef cppclass sink_info:
+        io_type type
+        const vector[string]& filepaths()
+        const vector[vector[char] *]& buffers()
+        const vector[cudf_io_data_sink.data_sink *]& user_sinks()
+
+        sink_info() except +
+        sink_info(string file_path) except +
+        sink_info(vector[string] file_path) except +
+        sink_info(vector[char] * buffer) except +
+        sink_info(cudf_io_data_sink.data_sink * user_sink) except +
+        sink_info(vector[cudf_io_data_sink.data_sink *] user_sink) except +
diff --git a/python/cudf/cudf/_lib/cpp/join.pxd b/python/cudf/cudf/_lib/cpp/join.pxd
new file mode 100644
index 0000000..171658c
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/join.pxd
@@ -0,0 +1,41 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.vector cimport vector
+
+from rmm._lib.device_uvector cimport device_uvector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+ctypedef unique_ptr[device_uvector[size_type]] gather_map_type
+
+cdef extern from "cudf/join.hpp" namespace "cudf" nogil:
+    cdef pair[gather_map_type, gather_map_type] inner_join(
+        const table_view left_keys,
+        const table_view right_keys,
+    ) except +
+
+    cdef pair[gather_map_type, gather_map_type] left_join(
+        const table_view left_keys,
+        const table_view right_keys,
+    ) except +
+
+    cdef pair[gather_map_type, gather_map_type] full_join(
+        const table_view left_keys,
+        const table_view right_keys,
+    ) except +
+
+    cdef gather_map_type left_semi_join(
+        const table_view left_keys,
+        const table_view right_keys,
+    ) except +
+
+    cdef gather_map_type left_anti_join(
+        const table_view left_keys,
+        const table_view right_keys,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/labeling.pxd b/python/cudf/cudf/_lib/cpp/labeling.pxd
new file mode 100644
index 0000000..af9c4bb
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/labeling.pxd
@@ -0,0 +1,20 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/labeling/label_bins.hpp" namespace "cudf" nogil:
+    ctypedef enum inclusive:
+        YES "cudf::inclusive::YES"
+        NO "cudf::inclusive::NO"
+
+    cdef unique_ptr[column] label_bins (
+        const column_view &input,
+        const column_view &left_edges,
+        inclusive left_inclusive,
+        const column_view &right_edges,
+        inclusive right_inclusive
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/libcpp/__init__.pxd b/python/cudf/cudf/_lib/cpp/libcpp/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/libcpp/__init__.py b/python/cudf/cudf/_lib/cpp/libcpp/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/libcpp/functional.pxd b/python/cudf/cudf/_lib/cpp/libcpp/functional.pxd
new file mode 100644
index 0000000..f3e2d6d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/libcpp/functional.pxd
@@ -0,0 +1,7 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+
+cdef extern from "<functional>" namespace "std" nogil:
+    cdef cppclass reference_wrapper[T]:
+        reference_wrapper()
+        reference_wrapper(T)
diff --git a/python/cudf/cudf/_lib/cpp/libcpp/memory.pxd b/python/cudf/cudf/_lib/cpp/libcpp/memory.pxd
new file mode 100644
index 0000000..2178f1a
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/libcpp/memory.pxd
@@ -0,0 +1,12 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+
+cdef extern from "<memory>" namespace "std" nogil:
+    # The Cython standard header does not have except +, so C++
+    # exceptions from make_unique are not caught and translated to
+    # Python ones. This is not perfectly ergonomic, we always have to
+    # wrap make_unique in move, but at least we can catch exceptions.
+    # See https://github.com/cython/cython/issues/5560
+    unique_ptr[T] make_unique[T](...) except +
diff --git a/python/cudf/cudf/_lib/cpp/libcpp/optional.pxd b/python/cudf/cudf/_lib/cpp/libcpp/optional.pxd
new file mode 100644
index 0000000..a78c18f
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/libcpp/optional.pxd
@@ -0,0 +1,50 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023, NVIDIA CORPORATION &
+# AFFILIATES. All rights reserved.  SPDX-License-Identifier:
+# Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from libcpp cimport bool
+
+
+cdef extern from "<optional>" namespace "std" nogil:
+    cdef cppclass nullopt_t:
+        nullopt_t()
+
+    cdef nullopt_t nullopt
+
+    cdef cppclass optional[T]:
+        ctypedef T value_type
+        optional()
+        optional(nullopt_t)
+        optional(optional&) except +
+        optional(T&) except +
+        bool has_value()
+        T& value()
+        T& value_or[U](U& default_value)
+        void swap(optional&)
+        void reset()
+        T& emplace(...)
+        T& operator*()
+        optional& operator=(optional&)
+        optional& operator=[U](U&)
+        bool operator bool()
+        bool operator!()
+        bool operator==[U](optional&, U&)
+        bool operator!=[U](optional&, U&)
+        bool operator<[U](optional&, U&)
+        bool operator>[U](optional&, U&)
+        bool operator<=[U](optional&, U&)
+        bool operator>=[U](optional&, U&)
+
+    optional[T] make_optional[T](...) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/__init__.pxd b/python/cudf/cudf/_lib/cpp/lists/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/lists/__init__.py b/python/cudf/cudf/_lib/cpp/lists/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/lists/combine.pxd b/python/cudf/cudf/_lib/cpp/lists/combine.pxd
new file mode 100644
index 0000000..a7ad8e7
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/combine.pxd
@@ -0,0 +1,29 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/lists/combine.hpp" namespace \
+        "cudf::lists" nogil:
+
+    ctypedef enum concatenate_null_policy:
+        IGNORE "cudf::lists::concatenate_null_policy::IGNORE"
+        NULLIFY_OUTPUT_ROW \
+            "cudf::lists::concatenate_null_policy::NULLIFY_OUTPUT_ROW"
+
+    cdef unique_ptr[column] concatenate_rows(
+        const table_view input_table
+    ) except +
+
+    cdef unique_ptr[column] concatenate_list_elements(
+        const table_view input_table,
+    ) except +
+
+    cdef unique_ptr[column] concatenate_list_elements(
+        const column_view input_table,
+        concatenate_null_policy null_policy
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/contains.pxd b/python/cudf/cudf/_lib/cpp/lists/contains.pxd
new file mode 100644
index 0000000..e86c73d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/contains.pxd
@@ -0,0 +1,26 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.exception_handler cimport cudf_exception_handler
+
+
+cdef extern from "cudf/lists/contains.hpp" namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] contains(
+        lists_column_view lists,
+        scalar search_key,
+    ) except +cudf_exception_handler
+
+    cdef unique_ptr[column] index_of(
+        lists_column_view lists,
+        scalar search_key,
+    ) except +cudf_exception_handler
+
+    cdef unique_ptr[column] index_of(
+        lists_column_view lists,
+        column_view search_keys,
+    ) except +cudf_exception_handler
diff --git a/python/cudf/cudf/_lib/cpp/lists/count_elements.pxd b/python/cudf/cudf/_lib/cpp/lists/count_elements.pxd
new file mode 100644
index 0000000..9be38f2
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/count_elements.pxd
@@ -0,0 +1,10 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+
+
+cdef extern from "cudf/lists/count_elements.hpp" namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] count_elements(const lists_column_view) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/explode.pxd b/python/cudf/cudf/_lib/cpp/lists/explode.pxd
new file mode 100644
index 0000000..c3e15dd
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/explode.pxd
@@ -0,0 +1,14 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/lists/explode.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[table] explode_outer(
+        const table_view,
+        size_type explode_column_idx,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/extract.pxd b/python/cudf/cudf/_lib/cpp/lists/extract.pxd
new file mode 100644
index 0000000..93a886d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/extract.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column, column_view
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/lists/extract.hpp" namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] extract_list_element(
+        const lists_column_view,
+        size_type
+    ) except +
+    cdef unique_ptr[column] extract_list_element(
+        const lists_column_view,
+        column_view
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/gather.pxd b/python/cudf/cudf/_lib/cpp/lists/gather.pxd
new file mode 100644
index 0000000..ea664ee
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/gather.pxd
@@ -0,0 +1,13 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+
+
+cdef extern from "cudf/lists/gather.hpp" namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] segmented_gather(
+        const lists_column_view source_column,
+        const lists_column_view gather_map_list
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/lists_column_view.pxd b/python/cudf/cudf/_lib/cpp/lists/lists_column_view.pxd
new file mode 100644
index 0000000..793f4b8
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/lists_column_view.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/lists/lists_column_view.hpp" namespace "cudf" nogil:
+    cdef cppclass lists_column_view(column_view):
+        lists_column_view(const column_view& lists_column) except +
+        column_view parent() except +
+        column_view offsets() except +
+        column_view child() except +
+
+    cdef enum:
+        offsets_column_index "cudf::lists_column_view::offsets_column_index"
+        child_column_index "cudf::lists_column_view::child_column_index"
diff --git a/python/cudf/cudf/_lib/cpp/lists/sorting.pxd b/python/cudf/cudf/_lib/cpp/lists/sorting.pxd
new file mode 100644
index 0000000..2115885
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/sorting.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.types cimport null_order, order
+
+
+cdef extern from "cudf/lists/sorting.hpp" namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] sort_lists(
+        const lists_column_view source_column,
+        order column_order,
+        null_order null_precedence
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/lists/stream_compaction.pxd b/python/cudf/cudf/_lib/cpp/lists/stream_compaction.pxd
new file mode 100644
index 0000000..58c1ab1
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/lists/stream_compaction.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.types cimport nan_equality, null_equality
+
+
+cdef extern from "cudf/lists/stream_compaction.hpp" \
+        namespace "cudf::lists" nogil:
+    cdef unique_ptr[column] distinct(
+        const lists_column_view lists_column,
+        null_equality nulls_equal,
+        nan_equality nans_equal
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/merge.pxd b/python/cudf/cudf/_lib/cpp/merge.pxd
new file mode 100644
index 0000000..32fe14a
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/merge.pxd
@@ -0,0 +1,17 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/merge.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[table] merge (
+        vector[table_view] tables_to_merge,
+        vector[libcudf_types.size_type] key_cols,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/null_mask.pxd b/python/cudf/cudf/_lib/cpp/null_mask.pxd
new file mode 100644
index 0000000..bd0eb68
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/null_mask.pxd
@@ -0,0 +1,46 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp.pair cimport pair
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport bitmask_type, mask_state, size_type
+
+ctypedef int32_t underlying_type_t_mask_state
+
+
+cdef extern from "cudf/null_mask.hpp" namespace "cudf" nogil:
+    cdef device_buffer copy_bitmask "cudf::copy_bitmask" (
+        column_view view
+    ) except +
+
+    cdef size_t bitmask_allocation_size_bytes (
+        size_type number_of_bits,
+        size_t padding_boundary
+    ) except +
+
+    cdef size_t bitmask_allocation_size_bytes (
+        size_type number_of_bits
+    ) except +
+
+    cdef device_buffer create_null_mask (
+        size_type size,
+        mask_state state
+    ) except +
+
+    cdef pair[device_buffer, size_type] bitmask_and(
+        table_view view
+    )
+
+    cdef pair[device_buffer, size_type] bitmask_or(
+        table_view view
+    )
+
+    cdef size_type null_count(
+        const bitmask_type * bitmask,
+        size_type start,
+        size_type stop,
+    )
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/__init__.pxd b/python/cudf/cudf/_lib/cpp/nvtext/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/__init__.py b/python/cudf/cudf/_lib/cpp/nvtext/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/edit_distance.pxd b/python/cudf/cudf/_lib/cpp/nvtext/edit_distance.pxd
new file mode 100644
index 0000000..11de596
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/edit_distance.pxd
@@ -0,0 +1,19 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "nvtext/edit_distance.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] edit_distance(
+        const column_view & strings,
+        const column_view & targets
+    ) except +
+
+    cdef unique_ptr[column] edit_distance_matrix(
+        const column_view & strings
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/generate_ngrams.pxd b/python/cudf/cudf/_lib/cpp/nvtext/generate_ngrams.pxd
new file mode 100644
index 0000000..7582205
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/generate_ngrams.pxd
@@ -0,0 +1,27 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/generate_ngrams.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] generate_ngrams(
+        const column_view &strings,
+        size_type ngrams,
+        const string_scalar & separator
+    ) except +
+
+    cdef unique_ptr[column] generate_character_ngrams(
+        const column_view &strings,
+        size_type ngrams
+    ) except +
+
+    cdef unique_ptr[column] hash_character_ngrams(
+        const column_view &strings,
+        size_type ngrams
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/jaccard.pxd b/python/cudf/cudf/_lib/cpp/nvtext/jaccard.pxd
new file mode 100644
index 0000000..a77f95f
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/jaccard.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/jaccard.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] jaccard_index(
+        const column_view &input1,
+        const column_view &input2,
+        size_type width
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/minhash.pxd b/python/cudf/cudf/_lib/cpp/nvtext/minhash.pxd
new file mode 100644
index 0000000..08b3330
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/minhash.pxd
@@ -0,0 +1,22 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/minhash.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] minhash(
+        const column_view &strings,
+        const column_view &seeds,
+        const size_type width,
+    ) except +
+
+    cdef unique_ptr[column] minhash64(
+        const column_view &strings,
+        const column_view &seeds,
+        const size_type width,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/ngrams_tokenize.pxd b/python/cudf/cudf/_lib/cpp/nvtext/ngrams_tokenize.pxd
new file mode 100644
index 0000000..d716df2
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/ngrams_tokenize.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/ngrams_tokenize.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] ngrams_tokenize(
+        const column_view & strings,
+        size_type ngrams,
+        const string_scalar & delimiter,
+        const string_scalar & separator
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/normalize.pxd b/python/cudf/cudf/_lib/cpp/nvtext/normalize.pxd
new file mode 100644
index 0000000..f012670
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/normalize.pxd
@@ -0,0 +1,19 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "nvtext/normalize.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] normalize_spaces(
+        const column_view & strings
+    ) except +
+
+    cdef unique_ptr[column] normalize_characters(
+        const column_view & strings,
+        bool do_lower_case
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/replace.pxd b/python/cudf/cudf/_lib/cpp/nvtext/replace.pxd
new file mode 100644
index 0000000..c4e5258
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/replace.pxd
@@ -0,0 +1,25 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/replace.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] replace_tokens(
+        const column_view & strings,
+        const column_view & targets,
+        const column_view & replacements,
+        const string_scalar & delimiter
+    ) except +
+
+    cdef unique_ptr[column] filter_tokens(
+        const column_view & strings,
+        size_type min_token_length,
+        const string_scalar & replacement,
+        const string_scalar & delimiter
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/stemmer.pxd b/python/cudf/cudf/_lib/cpp/nvtext/stemmer.pxd
new file mode 100644
index 0000000..5a92b45
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/stemmer.pxd
@@ -0,0 +1,30 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/stemmer.hpp" namespace "nvtext" nogil:
+    ctypedef enum letter_type:
+        CONSONANT 'nvtext::letter_type::CONSONANT'
+        VOWEL 'nvtext::letter_type::VOWEL'
+
+    cdef unique_ptr[column] porter_stemmer_measure(
+        const column_view & strings
+    ) except +
+
+    cdef unique_ptr[column] is_letter(
+        column_view source_strings,
+        letter_type ltype,
+        size_type character_index) except +
+
+    cdef unique_ptr[column] is_letter(
+        column_view source_strings,
+        letter_type ltype,
+        column_view indices) except +
+
+ctypedef int32_t underlying_type_t_letter_type
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/subword_tokenize.pxd b/python/cudf/cudf/_lib/cpp/nvtext/subword_tokenize.pxd
new file mode 100644
index 0000000..226fa61
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/subword_tokenize.pxd
@@ -0,0 +1,54 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint16_t, uint32_t
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "nvtext/subword_tokenize.hpp" namespace "nvtext" nogil:
+    cdef cppclass tokenizer_result "nvtext::tokenizer_result":
+        uint32_t nrows_tensor
+        uint32_t sequence_length
+        unique_ptr[column] tensor_token_ids
+        unique_ptr[column] tensor_attention_mask
+        unique_ptr[column] tensor_metadata
+
+    cdef struct hashed_vocabulary "nvtext::hashed_vocabulary":
+        uint16_t first_token_id
+        uint16_t separator_token_id
+        uint16_t unknown_token_id
+        uint32_t outer_hash_a
+        uint32_t outer_hash_b
+        uint16_t num_bin
+        unique_ptr[column] table
+        unique_ptr[column] bin_coefficients
+        unique_ptr[column] bin_offsets
+
+    cdef unique_ptr[hashed_vocabulary] load_vocabulary_file(
+        const string &filename_hashed_vocabulary
+    ) except +
+
+    cdef tokenizer_result subword_tokenize(
+        const column_view & strings,
+        hashed_vocabulary & hashed_vocablary_obj,
+        uint32_t max_sequence_length,
+        uint32_t stride,
+        bool do_lower,
+        bool do_truncate
+    ) except +
+
+    cdef tokenizer_result subword_tokenize(
+        const column_view &strings,
+        const string &filename_hashed_vocabulary,
+        uint32_t max_sequence_length,
+        uint32_t stride,
+        bool do_lower,
+        bool do_truncate
+    ) except +
+
+cdef extern from "<utility>" namespace "std" nogil:
+    cdef tokenizer_result move(tokenizer_result)
diff --git a/python/cudf/cudf/_lib/cpp/nvtext/tokenize.pxd b/python/cudf/cudf/_lib/cpp/nvtext/tokenize.pxd
new file mode 100644
index 0000000..3cc3fd6
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/nvtext/tokenize.pxd
@@ -0,0 +1,55 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "nvtext/tokenize.hpp" namespace "nvtext" nogil:
+
+    cdef unique_ptr[column] tokenize(
+        const column_view & strings,
+        const string_scalar & delimiter
+    ) except +
+
+    cdef unique_ptr[column] tokenize(
+        const column_view & strings,
+        const column_view & delimiters
+    ) except +
+
+    cdef unique_ptr[column] count_tokens(
+        const column_view & strings,
+        const string_scalar & delimiter
+    ) except +
+
+    cdef unique_ptr[column] count_tokens(
+        const column_view & strings,
+        const column_view & delimiters
+    ) except +
+
+    cdef unique_ptr[column] character_tokenize(
+        const column_view & strings
+    ) except +
+
+    cdef unique_ptr[column] detokenize(
+        const column_view & strings,
+        const column_view & row_indices,
+        const string_scalar & separator
+    ) except +
+
+    cdef struct tokenize_vocabulary "nvtext::tokenize_vocabulary":
+        pass
+
+    cdef unique_ptr[tokenize_vocabulary] load_vocabulary(
+        const column_view & strings
+    ) except +
+
+    cdef unique_ptr[column] tokenize_with_vocabulary(
+        const column_view & strings,
+        const tokenize_vocabulary & vocabulary,
+        const string_scalar & delimiter,
+        size_type default_id
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/partitioning.pxd b/python/cudf/cudf/_lib/cpp/partitioning.pxd
new file mode 100644
index 0000000..5c58dbc
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/partitioning.pxd
@@ -0,0 +1,28 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/partitioning.hpp" namespace "cudf" nogil:
+    cdef pair[unique_ptr[table], vector[libcudf_types.size_type]] \
+        hash_partition "cudf::hash_partition" (
+        const table_view& input,
+        const vector[libcudf_types.size_type]& columns_to_hash,
+        int num_partitions
+    ) except +
+
+    cdef pair[unique_ptr[table], vector[libcudf_types.size_type]] \
+        partition "cudf::partition" (
+        const table_view& t,
+        const column_view& partition_map,
+        int num_partitions
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/quantiles.pxd b/python/cudf/cudf/_lib/cpp/quantiles.pxd
new file mode 100644
index 0000000..03fda16
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/quantiles.pxd
@@ -0,0 +1,37 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport (
+    interpolation,
+    null_order,
+    order,
+    order_info,
+    sorted,
+)
+
+
+cdef extern from "cudf/quantiles.hpp" namespace "cudf" nogil:
+
+    cdef unique_ptr[column] quantile (
+        column_view input,
+        vector[double] q,
+        interpolation interp,
+        column_view ordered_indices,
+        bool exact,
+    ) except +
+
+    cdef unique_ptr[table] quantiles (
+        table_view source_table,
+        vector[double] q,
+        interpolation interp,
+        sorted is_input_sorted,
+        vector[order] column_order,
+        vector[null_order] null_precedence,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/reduce.pxd b/python/cudf/cudf/_lib/cpp/reduce.pxd
new file mode 100644
index 0000000..997782d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/reduce.pxd
@@ -0,0 +1,33 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport pair
+
+from cudf._lib.cpp.aggregation cimport reduce_aggregation, scan_aggregation
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/reduction.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[scalar] cpp_reduce "cudf::reduce" (
+        column_view col,
+        const reduce_aggregation& agg,
+        data_type type
+    ) except +
+
+    ctypedef enum scan_type:
+        INCLUSIVE "cudf::scan_type::INCLUSIVE",
+        EXCLUSIVE "cudf::scan_type::EXCLUSIVE",
+
+    cdef unique_ptr[column] cpp_scan "cudf::scan" (
+        column_view col,
+        const scan_aggregation& agg,
+        scan_type inclusive
+    ) except +
+
+    cdef pair[unique_ptr[scalar],
+              unique_ptr[scalar]] cpp_minmax "cudf::minmax" (
+        column_view col
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/replace.pxd b/python/cudf/cudf/_lib/cpp/replace.pxd
new file mode 100644
index 0000000..c1ec89a
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/replace.pxd
@@ -0,0 +1,49 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.types import cudf_to_np_types, np_to_cudf_types
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+
+
+cdef extern from "cudf/replace.hpp" namespace "cudf" nogil:
+
+    ctypedef enum replace_policy:
+        PRECEDING 'cudf::replace_policy::PRECEDING',
+        FOLLOWING 'cudf::replace_policy::FOLLOWING'
+
+    cdef unique_ptr[column] replace_nulls(
+        column_view source_column,
+        column_view replacement_column) except +
+
+    cdef unique_ptr[column] replace_nulls(
+        column_view source_column,
+        scalar replacement) except +
+
+    cdef unique_ptr[column] replace_nulls(
+        column_view source_column,
+        replace_policy replace_policy) except +
+
+    cdef unique_ptr[column] find_and_replace_all(
+        column_view source_column,
+        column_view values_to_replace,
+        column_view replacement_values) except +
+
+    cdef unique_ptr[column] clamp(
+        column_view source_column,
+        scalar lo, scalar lo_replace,
+        scalar hi, scalar hi_replace) except +
+
+    cdef unique_ptr[column] clamp(
+        column_view source_column,
+        scalar lo, scalar hi) except +
+
+cdef extern from "cudf/replace.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[column] normalize_nans_and_zeros(
+        column_view source_column) except +
+
+    cdef void normalize_nans_and_zeros(
+        mutable_column_view source_column) except +
diff --git a/python/cudf/cudf/_lib/cpp/reshape.pxd b/python/cudf/cudf/_lib/cpp/reshape.pxd
new file mode 100644
index 0000000..5b9d40a
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/reshape.pxd
@@ -0,0 +1,17 @@
+# Copyright (c) 2019, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/reshape.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[column] interleave_columns(
+        table_view source_table
+    ) except +
+    cdef unique_ptr[table] tile(
+        table_view source_table, size_type count
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/rolling.pxd b/python/cudf/cudf/_lib/cpp/rolling.pxd
new file mode 100644
index 0000000..df2e833
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/rolling.pxd
@@ -0,0 +1,26 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.types import cudf_to_np_types, np_to_cudf_types
+
+from cudf._lib.cpp.aggregation cimport rolling_aggregation
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/rolling.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[column] rolling_window(
+        column_view source,
+        column_view preceding_window,
+        column_view following_window,
+        size_type min_periods,
+        rolling_aggregation agg) except +
+
+    cdef unique_ptr[column] rolling_window(
+        column_view source,
+        size_type preceding_window,
+        size_type following_window,
+        size_type min_periods,
+        rolling_aggregation agg) except +
diff --git a/python/cudf/cudf/_lib/cpp/round.pxd b/python/cudf/cudf/_lib/cpp/round.pxd
new file mode 100644
index 0000000..66d76c3
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/round.pxd
@@ -0,0 +1,20 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/round.hpp" namespace "cudf" nogil:
+
+    ctypedef enum rounding_method "cudf::rounding_method":
+        HALF_UP "cudf::rounding_method::HALF_UP"
+        HALF_EVEN "cudf::rounding_method::HALF_EVEN"
+
+    cdef unique_ptr[column] round (
+        const column_view& input,
+        int32_t decimal_places,
+        rounding_method method,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/scalar/__init__.pxd b/python/cudf/cudf/_lib/cpp/scalar/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/scalar/__init__.py b/python/cudf/cudf/_lib/cpp/scalar/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/scalar/scalar.pxd b/python/cudf/cudf/_lib/cpp/scalar/scalar.pxd
new file mode 100644
index 0000000..b5e9b0b
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/scalar/scalar.pxd
@@ -0,0 +1,75 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, int64_t
+from libcpp cimport bool
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport data_type
+from cudf._lib.cpp.wrappers.decimals cimport scale_type
+
+
+cdef extern from "cudf/scalar/scalar.hpp" namespace "cudf" nogil:
+    cdef cppclass scalar:
+        scalar() except +
+        scalar(scalar other) except +
+        data_type type() except +
+        void set_valid_async(bool is_valid) except +
+        bool is_valid() except +
+
+    cdef cppclass numeric_scalar[T](scalar):
+        numeric_scalar() except +
+        numeric_scalar(numeric_scalar other) except +
+        numeric_scalar(T value) except +
+        numeric_scalar(T value, bool is_valid) except +
+        void set_value(T value) except +
+        T value() except +
+
+    cdef cppclass timestamp_scalar[T](scalar):
+        timestamp_scalar() except +
+        timestamp_scalar(timestamp_scalar other) except +
+        timestamp_scalar(int64_t value) except +
+        timestamp_scalar(int64_t value, bool is_valid) except +
+        timestamp_scalar(int32_t value) except +
+        timestamp_scalar(int32_t value, bool is_valid) except +
+        int64_t ticks_since_epoch_64 "ticks_since_epoch"() except +
+        int32_t ticks_since_epoch_32 "ticks_since_epoch"() except +
+        T value() except +
+
+    cdef cppclass duration_scalar[T](scalar):
+        duration_scalar() except +
+        duration_scalar(duration_scalar other) except +
+        duration_scalar(int64_t value) except +
+        duration_scalar(int64_t value, bool is_valid) except +
+        duration_scalar(int32_t value) except +
+        duration_scalar(int32_t value, bool is_valid) except +
+        int64_t ticks "count"() except +
+        T value() except +
+
+    cdef cppclass string_scalar(scalar):
+        string_scalar() except +
+        string_scalar(string st) except +
+        string_scalar(string st, bool is_valid) except +
+        string_scalar(string_scalar other) except +
+        string to_string() except +
+
+    cdef cppclass fixed_point_scalar[T](scalar):
+        fixed_point_scalar() except +
+        fixed_point_scalar(int64_t value,
+                           scale_type scale,
+                           bool is_valid) except +
+        fixed_point_scalar(data_type value,
+                           scale_type scale,
+                           bool is_valid) except +
+        int64_t value() except +
+        # TODO: Figure out how to add an int32 overload of value()
+
+    cdef cppclass list_scalar(scalar):
+        list_scalar(column_view col) except +
+        list_scalar(column_view col, bool is_valid) except +
+        column_view view() except +
+
+    cdef cppclass struct_scalar(scalar):
+        struct_scalar(table_view cols, bool valid) except +
+        table_view view() except +
diff --git a/python/cudf/cudf/_lib/cpp/search.pxd b/python/cudf/cudf/_lib/cpp/search.pxd
new file mode 100644
index 0000000..8baef0a
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/search.pxd
@@ -0,0 +1,31 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/search.hpp" namespace "cudf" nogil:
+
+    cdef unique_ptr[column] lower_bound(
+        table_view haystack,
+        table_view needles,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence,
+    ) except +
+
+    cdef unique_ptr[column] upper_bound(
+        table_view haystack,
+        table_view needles,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence,
+    ) except +
+
+    cdef unique_ptr[column] contains(
+        column_view haystack,
+        column_view needles,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/sorting.pxd b/python/cudf/cudf/_lib/cpp/sorting.pxd
new file mode 100644
index 0000000..68f0100
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/sorting.pxd
@@ -0,0 +1,70 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.types import cudf_to_np_types, np_to_cudf_types
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.aggregation cimport rank_method
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/sorting.hpp" namespace "cudf" nogil:
+    cdef unique_ptr[column] sorted_order(
+        table_view source_table,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[column] stable_sorted_order(
+        table_view source_table,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[column] rank(
+        column_view input_view,
+        rank_method method,
+        libcudf_types.order column_order,
+        libcudf_types.null_policy null_handling,
+        libcudf_types.null_order null_precedence,
+        bool percentage) except +
+
+    cdef bool is_sorted(
+        const table_view& table,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[table] segmented_sort_by_key(
+        const table_view& values,
+        const table_view& keys,
+        const column_view& segment_offsets,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[table] stable_segmented_sort_by_key(
+        const table_view& values,
+        const table_view& keys,
+        const column_view& segment_offsets,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[table] sort_by_key(
+        const table_view& values,
+        const table_view& keys,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[table] stable_sort_by_key(
+        const table_view& values,
+        const table_view& keys,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
+
+    cdef unique_ptr[table] sort(
+        table_view source_table,
+        vector[libcudf_types.order] column_order,
+        vector[libcudf_types.null_order] null_precedence) except +
diff --git a/python/cudf/cudf/_lib/cpp/stream_compaction.pxd b/python/cudf/cudf/_lib/cpp/stream_compaction.pxd
new file mode 100644
index 0000000..bba2d1f
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/stream_compaction.pxd
@@ -0,0 +1,47 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.types import cudf_to_np_types, np_to_cudf_types
+
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport (
+    nan_policy,
+    null_equality,
+    null_policy,
+    size_type,
+)
+
+
+cdef extern from "cudf/stream_compaction.hpp" namespace "cudf" \
+        nogil:
+    ctypedef enum duplicate_keep_option:
+        KEEP_ANY 'cudf::duplicate_keep_option::KEEP_ANY'
+        KEEP_FIRST 'cudf::duplicate_keep_option::KEEP_FIRST'
+        KEEP_LAST 'cudf::duplicate_keep_option::KEEP_LAST'
+        KEEP_NONE 'cudf::duplicate_keep_option::KEEP_NONE'
+
+    cdef unique_ptr[table] drop_nulls(table_view source_table,
+                                      vector[size_type] keys,
+                                      size_type keep_threshold) except +
+
+    cdef unique_ptr[table] apply_boolean_mask(
+        table_view source_table,
+        column_view boolean_mask
+    ) except +
+
+    cdef size_type distinct_count(
+        column_view source_table,
+        null_policy null_handling,
+        nan_policy nan_handling) except +
+
+    cdef unique_ptr[table] stable_distinct(
+        table_view input,
+        vector[size_type] keys,
+        duplicate_keep_option keep,
+        null_equality nulls_equal,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/__init__.pxd b/python/cudf/cudf/_lib/cpp/strings/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/strings/__init__.py b/python/cudf/cudf/_lib/cpp/strings/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/strings/attributes.pxd b/python/cudf/cudf/_lib/cpp/strings/attributes.pxd
new file mode 100644
index 0000000..31133b4
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/attributes.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/strings/attributes.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] count_characters(
+        column_view source_strings) except +
+
+    cdef unique_ptr[column] count_bytes(
+        column_view source_strings) except +
+
+    cdef unique_ptr[column] code_points(
+        column_view source_strings) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/capitalize.pxd b/python/cudf/cudf/_lib/cpp/strings/capitalize.pxd
new file mode 100644
index 0000000..d193a82
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/capitalize.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/strings/capitalize.hpp" namespace "cudf::strings" nogil:
+    cdef unique_ptr[column] capitalize(
+        const column_view & strings) except +
+
+    cdef unique_ptr[column] title(
+        const column_view & strings) except +
+
+    cdef unique_ptr[column] is_title(
+        const column_view & strings) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/case.pxd b/python/cudf/cudf/_lib/cpp/strings/case.pxd
new file mode 100644
index 0000000..01cd08c
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/case.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/strings/case.hpp" namespace "cudf::strings" nogil:
+    cdef unique_ptr[column] to_lower(
+        const column_view & strings) except +
+
+    cdef unique_ptr[column] to_upper(
+        const column_view & strings) except +
+
+    cdef unique_ptr[column] swapcase(
+        const column_view & strings) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/char_types.pxd b/python/cudf/cudf/_lib/cpp/strings/char_types.pxd
new file mode 100644
index 0000000..ae921c6
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/char_types.pxd
@@ -0,0 +1,37 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+
+
+cdef extern from "cudf/strings/char_types/char_types.hpp" \
+        namespace "cudf::strings" nogil:
+
+    ctypedef enum string_character_types:
+        DECIMAL 'cudf::strings::string_character_types::DECIMAL'
+        NUMERIC  'cudf::strings::string_character_types::NUMERIC'
+        DIGIT 'cudf::strings::string_character_types::DIGIT'
+        ALPHA 'cudf::strings::string_character_types::ALPHA'
+        SPACE 'cudf::strings::string_character_types::SPACE'
+        UPPER 'cudf::strings::string_character_types::UPPER'
+        LOWER 'cudf::strings::string_character_types::LOWER'
+        ALPHANUM 'cudf::strings::string_character_types::ALPHANUM'
+        CASE_TYPES 'cudf::strings::string_character_types::CASE_TYPES'
+        ALL_TYPES 'cudf::strings::string_character_types::ALL_TYPES'
+
+cdef extern from "cudf/strings/char_types/char_types.hpp" \
+        namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] all_characters_of_type(
+        column_view source_strings,
+        string_character_types types,
+        string_character_types verify_types) except +
+
+    cdef unique_ptr[column] filter_characters_of_type(
+        column_view source_strings,
+        string_character_types types_to_remove,
+        string_scalar replacement,
+        string_character_types types_to_keep) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/combine.pxd b/python/cudf/cudf/_lib/cpp/strings/combine.pxd
new file mode 100644
index 0000000..2b10427
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/combine.pxd
@@ -0,0 +1,44 @@
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/strings/combine.hpp" namespace "cudf::strings" nogil:
+
+    ctypedef enum separator_on_nulls:
+        YES 'cudf::strings::separator_on_nulls::YES'
+        NO  'cudf::strings::separator_on_nulls::NO'
+
+    ctypedef enum output_if_empty_list:
+        EMPTY_STRING 'cudf::strings::output_if_empty_list::EMPTY_STRING'
+        NULL_ELEMENT 'cudf::strings::output_if_empty_list::NULL_ELEMENT'
+
+    cdef unique_ptr[column] concatenate(
+        table_view source_strings,
+        string_scalar separator,
+        string_scalar narep) except +
+
+    cdef unique_ptr[column] join_strings(
+        column_view source_strings,
+        string_scalar separator,
+        string_scalar narep) except +
+
+    cdef unique_ptr[column] join_list_elements(
+        column_view lists_strings_column,
+        column_view separators,
+        string_scalar separator_narep,
+        string_scalar string_narep,
+        separator_on_nulls separate_nulls,
+        output_if_empty_list empty_list_policy) except +
+
+    cdef unique_ptr[column] join_list_elements(
+        column_view lists_strings_column,
+        string_scalar separator,
+        string_scalar narep,
+        separator_on_nulls separate_nulls,
+        output_if_empty_list empty_list_policy) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/contains.pxd b/python/cudf/cudf/_lib/cpp/strings/contains.pxd
new file mode 100644
index 0000000..94c2fb2
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/contains.pxd
@@ -0,0 +1,28 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+
+
+cdef extern from "cudf/strings/contains.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] contains_re(
+        column_view source_strings,
+        regex_program) except +
+
+    cdef unique_ptr[column] count_re(
+        column_view source_strings,
+        regex_program) except +
+
+    cdef unique_ptr[column] matches_re(
+        column_view source_strings,
+        regex_program) except +
+
+    cdef unique_ptr[column] like(
+        column_view source_strings,
+        string_scalar pattern,
+        string_scalar escape) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/__init__.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/__init__.py b/python/cudf/cudf/_lib/cpp/strings/convert/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_booleans.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_booleans.pxd
new file mode 100644
index 0000000..96cb439
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_booleans.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+
+
+cdef extern from "cudf/strings/convert/convert_booleans.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] to_booleans(
+        column_view input_col,
+        string_scalar true_string) except +
+
+    cdef unique_ptr[column] from_booleans(
+        column_view input_col,
+        string_scalar true_string,
+        string_scalar false_string) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_datetime.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_datetime.pxd
new file mode 100644
index 0000000..5e7380c
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_datetime.pxd
@@ -0,0 +1,25 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/strings/convert/convert_datetime.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] to_timestamps(
+        column_view input_col,
+        data_type timestamp_type,
+        string format) except +
+
+    cdef unique_ptr[column] from_timestamps(
+        column_view input_col,
+        string format,
+        column_view input_strings_names) except +
+
+    cdef unique_ptr[column] is_timestamp(
+        column_view input_col,
+        string format) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_durations.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_durations.pxd
new file mode 100644
index 0000000..8c54fd5
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_durations.pxd
@@ -0,0 +1,20 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/strings/convert/convert_durations.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] to_durations(
+        const column_view & strings_col,
+        data_type duration_type,
+        const string & format) except +
+
+    cdef unique_ptr[column] from_durations(
+        const column_view & durations,
+        const string & format) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_fixed_point.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_fixed_point.pxd
new file mode 100644
index 0000000..a993c5b
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_fixed_point.pxd
@@ -0,0 +1,22 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/strings/convert/convert_fixed_point.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] to_fixed_point(
+        column_view input_col,
+        data_type output_type) except +
+
+    cdef unique_ptr[column] from_fixed_point(
+        column_view input_col) except +
+
+    cdef unique_ptr[column] is_fixed_point(
+        column_view source_strings,
+        data_type output_type
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_floats.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_floats.pxd
new file mode 100644
index 0000000..6388f43
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_floats.pxd
@@ -0,0 +1,21 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/strings/convert/convert_floats.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] to_floats(
+        column_view input_col,
+        data_type output_type) except +
+
+    cdef unique_ptr[column] from_floats(
+        column_view input_col) except +
+
+    cdef unique_ptr[column] is_float(
+        column_view source_strings
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_integers.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_integers.pxd
new file mode 100644
index 0000000..b544397
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_integers.pxd
@@ -0,0 +1,32 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+
+
+cdef extern from "cudf/strings/convert/convert_integers.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] to_integers(
+        column_view input_col,
+        data_type output_type) except +
+
+    cdef unique_ptr[column] from_integers(
+        column_view input_col) except +
+
+    cdef unique_ptr[column] is_integer(
+        column_view source_strings
+    ) except +
+
+    cdef unique_ptr[column] hex_to_integers(
+        column_view input_col,
+        data_type output_type) except +
+
+    cdef unique_ptr[column] is_hex(
+        column_view source_strings
+    ) except +
+
+    cdef unique_ptr[column] integers_to_hex(
+        column_view input_col) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_ipv4.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_ipv4.pxd
new file mode 100644
index 0000000..d6e881c
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_ipv4.pxd
@@ -0,0 +1,19 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/strings/convert/convert_ipv4.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] ipv4_to_integers(
+        column_view input_col) except +
+
+    cdef unique_ptr[column] integers_to_ipv4(
+        column_view input_col) except +
+
+    cdef unique_ptr[column] is_ipv4(
+        column_view source_strings
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_lists.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_lists.pxd
new file mode 100644
index 0000000..99bb80a
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_lists.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+
+
+cdef extern from "cudf/strings/convert/convert_lists.hpp" namespace \
+        "cudf::strings" nogil:
+
+    cdef unique_ptr[column] format_list_column(
+        column_view input_col,
+        string_scalar na_rep,
+        column_view separators) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/convert/convert_urls.pxd b/python/cudf/cudf/_lib/cpp/strings/convert/convert_urls.pxd
new file mode 100644
index 0000000..5d9991d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/convert/convert_urls.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/strings/convert/convert_urls.hpp" namespace \
+        "cudf::strings" nogil:
+    cdef unique_ptr[column] url_encode(
+        column_view input_col) except +
+
+    cdef unique_ptr[column] url_decode(
+        column_view input_col) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/extract.pxd b/python/cudf/cudf/_lib/cpp/strings/extract.pxd
new file mode 100644
index 0000000..384f0f0
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/extract.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.cpp.table.table cimport table
+
+
+cdef extern from "cudf/strings/extract.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[table] extract(
+        column_view source_strings,
+        regex_program) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/find.pxd b/python/cudf/cudf/_lib/cpp/strings/find.pxd
new file mode 100644
index 0000000..953d5c3
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/find.pxd
@@ -0,0 +1,48 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/find.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] contains(
+        column_view source_strings,
+        string_scalar target) except +
+
+    cdef unique_ptr[column] contains(
+        column_view source_strings,
+        column_view target_strings) except +
+
+    cdef unique_ptr[column] ends_with(
+        column_view source_strings,
+        string_scalar target) except +
+
+    cdef unique_ptr[column] ends_with(
+        column_view source_strings,
+        column_view target_strings) except +
+
+    cdef unique_ptr[column] starts_with(
+        column_view source_strings,
+        string_scalar target) except +
+
+    cdef unique_ptr[column] starts_with(
+        column_view source_strings,
+        column_view target_strings) except +
+
+    cdef unique_ptr[column] find(
+        column_view source_strings,
+        string_scalar target,
+        size_type start,
+        size_type stop) except +
+
+    cdef unique_ptr[column] rfind(
+        column_view source_strings,
+        string_scalar target,
+        size_type start,
+        size_type stop) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/find_multiple.pxd b/python/cudf/cudf/_lib/cpp/strings/find_multiple.pxd
new file mode 100644
index 0000000..27b1972
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/find_multiple.pxd
@@ -0,0 +1,14 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+
+
+cdef extern from "cudf/strings/find_multiple.hpp" namespace "cudf::strings" \
+        nogil:
+
+    cdef unique_ptr[column] find_multiple(
+        column_view source_strings,
+        column_view targets) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/findall.pxd b/python/cudf/cudf/_lib/cpp/strings/findall.pxd
new file mode 100644
index 0000000..8c878ad
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/findall.pxd
@@ -0,0 +1,14 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+
+
+cdef extern from "cudf/strings/findall.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] findall(
+        column_view source_strings,
+        regex_program) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/json.pxd b/python/cudf/cudf/_lib/cpp/strings/json.pxd
new file mode 100644
index 0000000..a017e1c
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/json.pxd
@@ -0,0 +1,28 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar, string_scalar
+
+
+cdef extern from "cudf/strings/json.hpp" namespace "cudf::strings" nogil:
+    cdef cppclass get_json_object_options:
+        get_json_object_options() except +
+        # getters
+        bool get_allow_single_quotes() except +
+        bool get_strip_quotes_from_single_strings() except +
+        bool get_missing_fields_as_nulls() except +
+        # setters
+        void set_allow_single_quotes(bool val) except +
+        void set_strip_quotes_from_single_strings(bool val) except +
+        void set_missing_fields_as_nulls(bool val) except +
+
+    cdef unique_ptr[column] get_json_object(
+        column_view col,
+        string_scalar json_path,
+        get_json_object_options options,
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/padding.pxd b/python/cudf/cudf/_lib/cpp/strings/padding.pxd
new file mode 100644
index 0000000..c3906a5
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/padding.pxd
@@ -0,0 +1,23 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+from libc.stdint cimport int32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.side_type cimport side_type
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/padding.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] pad(
+        column_view source_strings,
+        size_type width,
+        side_type side,
+        string fill_char) except +
+
+    cdef unique_ptr[column] zfill(
+        column_view source_strings,
+        size_type width) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/regex_flags.pxd b/python/cudf/cudf/_lib/cpp/strings/regex_flags.pxd
new file mode 100644
index 0000000..2a5701f
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/regex_flags.pxd
@@ -0,0 +1,9 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+cdef extern from "cudf/strings/regex/flags.hpp" \
+        namespace "cudf::strings" nogil:
+
+    ctypedef enum regex_flags:
+        DEFAULT 'cudf::strings::regex_flags::DEFAULT'
+        MULTILINE  'cudf::strings::regex_flags::MULTILINE'
+        DOTALL 'cudf::strings::regex_flags::DOTALL'
diff --git a/python/cudf/cudf/_lib/cpp/strings/regex_program.pxd b/python/cudf/cudf/_lib/cpp/strings/regex_program.pxd
new file mode 100644
index 0000000..7818c9c
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/regex_program.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.strings.regex_flags cimport regex_flags
+
+
+cdef extern from "cudf/strings/regex/regex_program.hpp" \
+        namespace "cudf::strings" nogil:
+
+    cdef cppclass regex_program:
+
+        @staticmethod
+        unique_ptr[regex_program] create(
+            string pattern,
+            regex_flags flags
+        ) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/repeat.pxd b/python/cudf/cudf/_lib/cpp/strings/repeat.pxd
new file mode 100644
index 0000000..2a6754b
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/repeat.pxd
@@ -0,0 +1,19 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/repeat_strings.hpp" namespace "cudf::strings" \
+        nogil:
+
+    cdef unique_ptr[column] repeat_strings(
+        column_view strings,
+        size_type repeat) except +
+
+    cdef unique_ptr[column] repeat_strings(
+        column_view strings,
+        column_view repeats) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/replace.pxd b/python/cudf/cudf/_lib/cpp/strings/replace.pxd
new file mode 100644
index 0000000..2a9c691
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/replace.pxd
@@ -0,0 +1,29 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/replace.hpp" namespace "cudf::strings" nogil:
+    cdef unique_ptr[column] replace_slice(
+        column_view source_strings,
+        string_scalar repl,
+        size_type start,
+        size_type stop) except +
+
+    cdef unique_ptr[column] replace(
+        column_view source_strings,
+        string_scalar target,
+        string_scalar repl,
+        int32_t maxrepl) except +
+
+    cdef unique_ptr[column] replace(
+        column_view source_strings,
+        column_view target_strings,
+        column_view repl_strings) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/replace_re.pxd b/python/cudf/cudf/_lib/cpp/strings/replace_re.pxd
new file mode 100644
index 0000000..94f3d05
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/replace_re.pxd
@@ -0,0 +1,31 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/replace_re.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] replace_re(
+        column_view source_strings,
+        regex_program,
+        string_scalar repl,
+        size_type maxrepl) except +
+
+    cdef unique_ptr[column] replace_with_backrefs(
+        column_view source_strings,
+        regex_program,
+        string repl) except +
+
+    cdef unique_ptr[column] replace_re(
+        column_view source_strings,
+        vector[string] patterns,
+        column_view repls) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/side_type.pxd b/python/cudf/cudf/_lib/cpp/strings/side_type.pxd
new file mode 100644
index 0000000..3a89299
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/side_type.pxd
@@ -0,0 +1,12 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+from libc.stdint cimport int32_t
+
+
+cdef extern from "cudf/strings/side_type.hpp" namespace "cudf::strings" nogil:
+
+    ctypedef enum side_type:
+        LEFT 'cudf::strings::side_type::LEFT'
+        RIGHT 'cudf::strings::side_type::RIGHT'
+        BOTH 'cudf::strings::side_type::BOTH'
+
+ctypedef int32_t underlying_type_t_side_type
diff --git a/python/cudf/cudf/_lib/cpp/strings/split/__init__.pxd b/python/cudf/cudf/_lib/cpp/strings/split/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/strings/split/__init__.py b/python/cudf/cudf/_lib/cpp/strings/split/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/strings/split/partition.pxd b/python/cudf/cudf/_lib/cpp/strings/split/partition.pxd
new file mode 100644
index 0000000..fb83512
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/split/partition.pxd
@@ -0,0 +1,21 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.table.table cimport table
+
+
+cdef extern from "cudf/strings/split/partition.hpp" namespace \
+        "cudf::strings" nogil:
+
+    cdef unique_ptr[table] partition(
+        column_view source_strings,
+        string_scalar delimiter) except +
+
+    cdef unique_ptr[table] rpartition(
+        column_view source_strings,
+        string_scalar delimiter) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/split/split.pxd b/python/cudf/cudf/_lib/cpp/strings/split/split.pxd
new file mode 100644
index 0000000..d6207cd
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/split/split.pxd
@@ -0,0 +1,59 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/split/split.hpp" namespace \
+        "cudf::strings" nogil:
+
+    cdef unique_ptr[table] split(
+        column_view source_strings,
+        string_scalar delimiter,
+        size_type maxsplit) except +
+
+    cdef unique_ptr[table] rsplit(
+        column_view source_strings,
+        string_scalar delimiter,
+        size_type maxsplit) except +
+
+    cdef unique_ptr[column] split_record(
+        column_view source_strings,
+        string_scalar delimiter,
+        size_type maxsplit) except +
+
+    cdef unique_ptr[column] rsplit_record(
+        column_view source_strings,
+        string_scalar delimiter,
+        size_type maxsplit) except +
+
+
+cdef extern from "cudf/strings/split/split_re.hpp" namespace \
+        "cudf::strings" nogil:
+
+    cdef unique_ptr[table] split_re(
+        const column_view& source_strings,
+        regex_program,
+        size_type maxsplit) except +
+
+    cdef unique_ptr[table] rsplit_re(
+        const column_view& source_strings,
+        regex_program,
+        size_type maxsplit) except +
+
+    cdef unique_ptr[column] split_record_re(
+        const column_view& source_strings,
+        regex_program,
+        size_type maxsplit) except +
+
+    cdef unique_ptr[column] rsplit_record_re(
+        const column_view& source_strings,
+        regex_program,
+        size_type maxsplit) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/strip.pxd b/python/cudf/cudf/_lib/cpp/strings/strip.pxd
new file mode 100644
index 0000000..3a86f80
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/strip.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.side_type cimport side_type
+
+
+cdef extern from "cudf/strings/strip.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] strip(
+        column_view source_strings,
+        side_type stype,
+        string_scalar to_strip) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/substring.pxd b/python/cudf/cudf/_lib/cpp/strings/substring.pxd
new file mode 100644
index 0000000..99ea8c7
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/substring.pxd
@@ -0,0 +1,21 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport numeric_scalar
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/slice.hpp" namespace "cudf::strings" nogil:
+    cdef unique_ptr[column] slice_strings(
+        column_view source_strings,
+        numeric_scalar[size_type] start,
+        numeric_scalar[size_type] end,
+        numeric_scalar[size_type] step) except +
+
+    cdef unique_ptr[column] slice_strings(
+        column_view source_strings,
+        column_view starts,
+        column_view stops) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/translate.pxd b/python/cudf/cudf/_lib/cpp/strings/translate.pxd
new file mode 100644
index 0000000..3239ba3
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/translate.pxd
@@ -0,0 +1,28 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport char_utf8
+
+
+cdef extern from "cudf/strings/translate.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] translate(
+        column_view source_strings,
+        vector[pair[char_utf8, char_utf8]] chars_table) except +
+
+    ctypedef enum filter_type:
+        KEEP 'cudf::strings::filter_type::KEEP',
+        REMOVE 'cudf::strings::filter_type::REMOVE'
+
+    cdef unique_ptr[column] filter_characters(
+        column_view source_strings,
+        vector[pair[char_utf8, char_utf8]] chars_table,
+        filter_type keep,
+        string_scalar replacement) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings/wrap.pxd b/python/cudf/cudf/_lib/cpp/strings/wrap.pxd
new file mode 100644
index 0000000..62c7917
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings/wrap.pxd
@@ -0,0 +1,14 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/wrap.hpp" namespace "cudf::strings" nogil:
+
+    cdef unique_ptr[column] wrap(
+        column_view source_strings,
+        size_type width) except +
diff --git a/python/cudf/cudf/_lib/cpp/strings_udf.pxd b/python/cudf/cudf/_lib/cpp/strings_udf.pxd
new file mode 100644
index 0000000..7d45bc8
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/strings_udf.pxd
@@ -0,0 +1,33 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t, uint16_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+from rmm._lib.device_buffer cimport DeviceBuffer, device_buffer
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/strings/udf/udf_string.hpp" namespace \
+        "cudf::strings::udf" nogil:
+    cdef cppclass udf_string
+
+cdef extern from "cudf/strings/udf/udf_apis.hpp"  namespace \
+        "cudf::strings::udf" nogil:
+    cdef unique_ptr[device_buffer] to_string_view_array(column_view) except +
+    cdef unique_ptr[column] column_from_udf_string_array(
+        udf_string* strings, size_type size,
+    ) except +
+    cdef void free_udf_string_array(
+        udf_string* strings, size_type size
+    ) except +
+
+cdef extern from "cudf/strings/detail/char_tables.hpp" namespace \
+        "cudf::strings::detail" nogil:
+    cdef const uint8_t* get_character_flags_table() except +
+    cdef const uint16_t* get_character_cases_table() except +
+    cdef const void* get_special_case_mapping_table() except +
diff --git a/python/cudf/cudf/_lib/cpp/table/__init__.pxd b/python/cudf/cudf/_lib/cpp/table/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/table/__init__.py b/python/cudf/cudf/_lib/cpp/table/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/table/table.pxd b/python/cudf/cudf/_lib/cpp/table/table.pxd
new file mode 100644
index 0000000..ac93e3d
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/table/table.pxd
@@ -0,0 +1,19 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.table.table_view cimport mutable_table_view, table_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/table/table.hpp" namespace "cudf" nogil:
+    cdef cppclass table:
+        table(const table&) except +
+        table(table_view) except +
+        size_type num_columns() except +
+        size_type num_rows() except +
+        table_view view() except +
+        mutable_table_view mutable_view() except +
+        vector[unique_ptr[column]] release() except +
diff --git a/python/cudf/cudf/_lib/cpp/table/table_view.pxd b/python/cudf/cudf/_lib/cpp/table/table_view.pxd
new file mode 100644
index 0000000..728b6d2
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/table/table_view.pxd
@@ -0,0 +1,23 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.types cimport size_type
+
+
+cdef extern from "cudf/table/table_view.hpp" namespace "cudf" nogil:
+    cdef cppclass table_view:
+        table_view() except +
+        table_view(const vector[column_view]) except +
+        column_view column(size_type column_index) except +
+        size_type num_columns() except +
+        size_type num_rows() except +
+        table_view select(vector[size_type] column_indices) except +
+
+    cdef cppclass mutable_table_view:
+        mutable_table_view() except +
+        mutable_table_view(const vector[mutable_column_view]) except +
+        mutable_column_view column(size_type column_index) except +
+        size_type num_columns() except +
+        size_type num_rows() except +
diff --git a/python/cudf/cudf/_lib/cpp/transform.pxd b/python/cudf/cudf/_lib/cpp/transform.pxd
new file mode 100644
index 0000000..d9de04b
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/transform.pxd
@@ -0,0 +1,50 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.string cimport string
+
+from rmm._lib.device_buffer cimport device_buffer
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.expressions cimport expression
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport bitmask_type, data_type, size_type
+
+
+cdef extern from "cudf/transform.hpp" namespace "cudf" nogil:
+    cdef pair[unique_ptr[device_buffer], size_type] bools_to_mask (
+        column_view input
+    ) except +
+
+    cdef unique_ptr[column] mask_to_bools (
+        bitmask_type* bitmask, size_type begin_bit, size_type end_bit
+    ) except +
+
+    cdef pair[unique_ptr[device_buffer], size_type] nans_to_nulls(
+        column_view input
+    ) except +
+
+    cdef unique_ptr[column] transform(
+        column_view input,
+        string unary_udf,
+        data_type output_type,
+        bool is_ptx
+    ) except +
+
+    cdef pair[unique_ptr[table], unique_ptr[column]] encode(
+        table_view input
+    ) except +
+
+    cdef pair[unique_ptr[column], table_view] one_hot_encode(
+        column_view input_column,
+        column_view categories
+    )
+
+    cdef unique_ptr[column] compute_column(
+        const table_view table,
+        const expression& expr
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/transpose.pxd b/python/cudf/cudf/_lib/cpp/transpose.pxd
new file mode 100644
index 0000000..8cbfb00
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/transpose.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef extern from "cudf/transpose.hpp" namespace "cudf" nogil:
+    cdef pair[
+        unique_ptr[column],
+        table_view
+    ] transpose(
+        table_view input_table
+    ) except +
diff --git a/python/cudf/cudf/_lib/cpp/types.pxd b/python/cudf/cudf/_lib/cpp/types.pxd
new file mode 100644
index 0000000..14bf8a8
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/types.pxd
@@ -0,0 +1,106 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, uint32_t
+
+
+cdef extern from "cudf/types.hpp" namespace "cudf" nogil:
+    # The declaration below is to work around
+    # https://github.com/cython/cython/issues/5637
+    """
+    #define __PYX_ENUM_CLASS_DECL enum
+    """
+    ctypedef int32_t size_type
+    ctypedef uint32_t bitmask_type
+    ctypedef uint32_t char_utf8
+
+    ctypedef enum mask_state:
+        UNALLOCATED "cudf::mask_state::UNALLOCATED"
+        UNINITIALIZED "cudf::mask_state::UNINITIALIZED"
+        ALL_VALID "cudf::mask_state::ALL_VALID"
+        ALL_NULL "cudf::mask_state::ALL_NULL"
+
+    ctypedef enum order "cudf::order":
+        ASCENDING "cudf::order::ASCENDING"
+        DESCENDING "cudf::order::DESCENDING"
+
+    ctypedef enum null_order "cudf::null_order":
+        AFTER "cudf::null_order::AFTER"
+        BEFORE "cudf::null_order::BEFORE"
+
+    ctypedef enum sorted "cudf::sorted":
+        NO "cudf::sorted::NO"
+        YES "cudf::sorted::YES"
+
+    cdef cppclass order_info:
+        sorted is_sorted
+        order ordering
+        null_order null_ordering
+
+    ctypedef enum null_policy "cudf::null_policy":
+        EXCLUDE "cudf::null_policy::EXCLUDE"
+        INCLUDE "cudf::null_policy::INCLUDE"
+
+    ctypedef enum nan_policy "cudf::nan_policy":
+        NAN_IS_NULL  "cudf::nan_policy::NAN_IS_NULL"
+        NAN_IS_VALID "cudf::nan_policy::NAN_IS_VALID"
+
+    ctypedef enum null_equality "cudf::null_equality":
+        EQUAL "cudf::null_equality::EQUAL"
+        UNEQUAL "cudf::null_equality::UNEQUAL"
+
+    ctypedef enum nan_equality "cudf::nan_equality":
+        # These names differ from the C++ names due to Cython warnings if
+        # "UNEQUAL" is declared by both null_equality and nan_equality.
+        ALL_EQUAL "cudf::nan_equality::ALL_EQUAL"
+        NANS_UNEQUAL "cudf::nan_equality::UNEQUAL"
+
+    cpdef enum class type_id(int32_t):
+        EMPTY
+        INT8
+        INT16
+        INT32
+        INT64
+        UINT8
+        UINT16
+        UINT32
+        UINT64
+        FLOAT32
+        FLOAT64
+        BOOL8
+        TIMESTAMP_DAYS
+        TIMESTAMP_SECONDS
+        TIMESTAMP_MILLISECONDS
+        TIMESTAMP_MICROSECONDS
+        TIMESTAMP_NANOSECONDS
+        DICTIONARY32
+        STRING
+        LIST
+        STRUCT
+        NUM_TYPE_IDS
+        DURATION_SECONDS
+        DURATION_MILLISECONDS
+        DURATION_MICROSECONDS
+        DURATION_NANOSECONDS
+        DECIMAL32
+        DECIMAL64
+        DECIMAL128
+
+    cdef cppclass data_type:
+        data_type() except +
+        data_type(const data_type&) except +
+        data_type(type_id id) except +
+        data_type(type_id id, int32_t scale) except +
+        type_id id() except +
+        int32_t scale() except +
+
+cdef extern from "cudf/types.hpp" namespace "cudf" nogil:
+    ctypedef enum interpolation:
+        LINEAR "cudf::interpolation::LINEAR"
+        LOWER "cudf::interpolation::LOWER"
+        HIGHER "cudf::interpolation::HIGHER"
+        MIDPOINT "cudf::interpolation::MIDPOINT"
+        NEAREST "cudf::interpolation::NEAREST"
+
+    # A Hack to let cython compile with __int128_t symbol
+    # https://stackoverflow.com/a/27609033
+    ctypedef int int128 "__int128_t"
diff --git a/python/cudf/cudf/_lib/cpp/types.pyx b/python/cudf/cudf/_lib/cpp/types.pyx
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/unary.pxd b/python/cudf/cudf/_lib/cpp/unary.pxd
new file mode 100644
index 0000000..83a5701
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/unary.pxd
@@ -0,0 +1,49 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+
+ctypedef int32_t underlying_type_t_unary_op
+
+
+cdef extern from "cudf/unary.hpp" namespace "cudf" nogil:
+
+    ctypedef enum unary_operator:
+        SIN "cudf::unary_operator::SIN"
+        COS "cudf::unary_operator::COS"
+        TAN "cudf::unary_operator::TAN"
+        ARCSIN "cudf::unary_operator::ARCSIN"
+        ARCCOS "cudf::unary_operator::ARCCOS"
+        ARCTAN "cudf::unary_operator::ARCTAN"
+        SINH "cudf::unary_operator::SINH"
+        COSH "cudf::unary_operator::COSH"
+        TANH "cudf::unary_operator::TANH"
+        ARCSINH "cudf::unary_operator::ARCSINH"
+        ARCCOSH "cudf::unary_operator::ARCCOSH"
+        ARCTANH "cudf::unary_operator::ARCTANH"
+        EXP "cudf::unary_operator::EXP"
+        LOG "cudf::unary_operator::LOG"
+        SQRT "cudf::unary_operator::SQRT"
+        CBRT "cudf::unary_operator::CBRT"
+        CEIL "cudf::unary_operator::CEIL"
+        FLOOR "cudf::unary_operator::FLOOR"
+        ABS "cudf::unary_operator::ABS"
+        RINT "cudf::unary_operator::RINT"
+        BIT_INVERT "cudf::unary_operator::BIT_INVERT"
+        NOT "cudf::unary_operator::NOT"
+
+    cdef extern unique_ptr[column] unary_operation(
+        column_view input,
+        unary_operator op) except +
+
+    cdef extern unique_ptr[column] is_null(column_view input) except +
+    cdef extern unique_ptr[column] is_valid(column_view input) except +
+    cdef extern unique_ptr[column] cast(
+        column_view input,
+        data_type out_type) except +
+    cdef extern unique_ptr[column] is_nan(column_view input) except +
+    cdef extern unique_ptr[column] is_not_nan(column_view input) except +
diff --git a/python/cudf/cudf/_lib/cpp/utilities/__init__.pxd b/python/cudf/cudf/_lib/cpp/utilities/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/utilities/__init__.py b/python/cudf/cudf/_lib/cpp/utilities/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/utilities/host_span.pxd b/python/cudf/cudf/_lib/cpp/utilities/host_span.pxd
new file mode 100644
index 0000000..7e591e9
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/utilities/host_span.pxd
@@ -0,0 +1,9 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from libcpp.vector cimport vector
+
+
+cdef extern from "cudf/utilities/span.hpp" namespace "cudf" nogil:
+    cdef cppclass host_span[T]:
+        host_span() except +
+        host_span(vector[T]) except +
diff --git a/python/cudf/cudf/_lib/cpp/wrappers/__init__.pxd b/python/cudf/cudf/_lib/cpp/wrappers/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/wrappers/__init__.py b/python/cudf/cudf/_lib/cpp/wrappers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/cpp/wrappers/decimals.pxd b/python/cudf/cudf/_lib/cpp/wrappers/decimals.pxd
new file mode 100644
index 0000000..858569f
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/wrappers/decimals.pxd
@@ -0,0 +1,17 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, int64_t
+
+from cudf._lib.cpp.types cimport int128
+
+
+cdef extern from "cudf/fixed_point/fixed_point.hpp" namespace "numeric" nogil:
+    # cython type stub to help resolve to numeric::decimal64
+    ctypedef int64_t decimal64
+    # cython type stub to help resolve to numeric::decimal32
+    ctypedef int64_t decimal32
+    # cython type stub to help resolve to numeric::decimal128
+    ctypedef int128 decimal128
+
+    cdef cppclass scale_type:
+        scale_type(int32_t)
diff --git a/python/cudf/cudf/_lib/cpp/wrappers/durations.pxd b/python/cudf/cudf/_lib/cpp/wrappers/durations.pxd
new file mode 100644
index 0000000..7c64842
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/wrappers/durations.pxd
@@ -0,0 +1,10 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport int64_t
+
+
+cdef extern from "cudf/wrappers/durations.hpp" namespace "cudf" nogil:
+    ctypedef int64_t duration_s
+    ctypedef int64_t duration_ms
+    ctypedef int64_t duration_us
+    ctypedef int64_t duration_ns
diff --git a/python/cudf/cudf/_lib/cpp/wrappers/timestamps.pxd b/python/cudf/cudf/_lib/cpp/wrappers/timestamps.pxd
new file mode 100644
index 0000000..50d37fd
--- /dev/null
+++ b/python/cudf/cudf/_lib/cpp/wrappers/timestamps.pxd
@@ -0,0 +1,10 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from libc.stdint cimport int64_t
+
+
+cdef extern from "cudf/wrappers/timestamps.hpp" namespace "cudf" nogil:
+    ctypedef int64_t timestamp_s
+    ctypedef int64_t timestamp_ms
+    ctypedef int64_t timestamp_us
+    ctypedef int64_t timestamp_ns
diff --git a/python/cudf/cudf/_lib/csv.pyx b/python/cudf/cudf/_lib/csv.pyx
new file mode 100644
index 0000000..359c9f8
--- /dev/null
+++ b/python/cudf/cudf/_lib/csv.pyx
@@ -0,0 +1,602 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.types cimport data_type
+from cudf._lib.io.datasource cimport Datasource, NativeFileDatasource
+from cudf._lib.types cimport dtype_to_data_type
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.cpp.types cimport size_type
+
+import errno
+import os
+from collections import abc
+from enum import IntEnum
+from io import BytesIO, StringIO
+
+from libc.stdint cimport int32_t
+from libcpp cimport bool
+
+from cudf._lib.cpp.io.csv cimport (
+    csv_reader_options,
+    csv_writer_options,
+    read_csv as cpp_read_csv,
+    write_csv as cpp_write_csv,
+)
+from cudf._lib.cpp.io.data_sink cimport data_sink
+from cudf._lib.cpp.io.types cimport (
+    compression_type,
+    quote_style,
+    sink_info,
+    source_info,
+    table_with_metadata,
+)
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.io.utils cimport make_sink_info, make_source_info
+from cudf._lib.utils cimport data_from_unique_ptr, table_view_from_table
+
+from pyarrow.lib import NativeFile
+
+from cudf.api.types import is_hashable
+
+ctypedef int32_t underlying_type_t_compression
+
+
+class Compression(IntEnum):
+    INFER = (
+        <underlying_type_t_compression> compression_type.AUTO
+    )
+    SNAPPY = (
+        <underlying_type_t_compression> compression_type.SNAPPY
+    )
+    GZIP = (
+        <underlying_type_t_compression> compression_type.GZIP
+    )
+    BZ2 = (
+        <underlying_type_t_compression> compression_type.BZIP2
+    )
+    BROTLI = (
+        <underlying_type_t_compression> compression_type.BROTLI
+    )
+    ZIP = (
+        <underlying_type_t_compression> compression_type.ZIP
+    )
+    XZ = (
+        <underlying_type_t_compression> compression_type.XZ
+    )
+
+
+CSV_HEX_TYPE_MAP = {
+    "hex": np.dtype("int64"),
+    "hex64": np.dtype("int64"),
+    "hex32": np.dtype("int32")
+}
+
+cdef csv_reader_options make_csv_reader_options(
+    object datasource,
+    object lineterminator,
+    object quotechar,
+    int quoting,
+    bool doublequote,
+    object header,
+    bool mangle_dupe_cols,
+    object usecols,
+    object delimiter,
+    bool delim_whitespace,
+    bool skipinitialspace,
+    object names,
+    object dtype,
+    int skipfooter,
+    int skiprows,
+    bool dayfirst,
+    object compression,
+    object thousands,
+    object decimal,
+    object true_values,
+    object false_values,
+    object nrows,
+    object byte_range,
+    bool skip_blank_lines,
+    object parse_dates,
+    object comment,
+    object na_values,
+    bool keep_default_na,
+    bool na_filter,
+    object prefix,
+    object index_col,
+) except *:
+    cdef source_info c_source_info = make_source_info([datasource])
+    cdef compression_type c_compression
+    cdef vector[string] c_names
+    cdef size_t c_byte_range_offset = (
+        byte_range[0] if byte_range is not None else 0
+    )
+    cdef size_t c_byte_range_size = (
+        byte_range[1] if byte_range is not None else 0
+    )
+    cdef vector[int] c_use_cols_indexes
+    cdef vector[string] c_use_cols_names
+    cdef size_type c_nrows = nrows if nrows is not None else -1
+    cdef quote_style c_quoting
+    cdef vector[string] c_parse_dates_names
+    cdef vector[int] c_parse_dates_indexes
+    cdef vector[string] c_hex_col_names
+    cdef vector[data_type] c_dtypes_list
+    cdef map[string, data_type] c_dtypes_map
+    cdef vector[int] c_hex_col_indexes
+    cdef vector[string] c_true_values
+    cdef vector[string] c_false_values
+    cdef vector[string] c_na_values
+
+    # Reader settings
+    if compression is None:
+        c_compression = compression_type.NONE
+    else:
+        compression = str(compression)
+        compression = Compression[compression.upper()]
+        c_compression = <compression_type> (
+            <underlying_type_t_compression> compression
+        )
+
+    if quoting == 1:
+        c_quoting = quote_style.QUOTE_ALL
+    elif quoting == 2:
+        c_quoting = quote_style.QUOTE_NONNUMERIC
+    elif quoting == 3:
+        c_quoting = quote_style.QUOTE_NONE
+    else:
+        # Default value
+        c_quoting = quote_style.QUOTE_MINIMAL
+
+    cdef csv_reader_options csv_reader_options_c = move(
+        csv_reader_options.builder(c_source_info)
+        .compression(c_compression)
+        .mangle_dupe_cols(mangle_dupe_cols)
+        .byte_range_offset(c_byte_range_offset)
+        .byte_range_size(c_byte_range_size)
+        .nrows(c_nrows)
+        .skiprows(skiprows)
+        .skipfooter(skipfooter)
+        .quoting(c_quoting)
+        .lineterminator(ord(lineterminator))
+        .quotechar(ord(quotechar))
+        .decimal(ord(decimal))
+        .delim_whitespace(delim_whitespace)
+        .skipinitialspace(skipinitialspace)
+        .skip_blank_lines(skip_blank_lines)
+        .doublequote(doublequote)
+        .keep_default_na(keep_default_na)
+        .na_filter(na_filter)
+        .dayfirst(dayfirst)
+        .build()
+    )
+
+    if names is not None:
+        # explicitly mentioned name, so don't check header
+        if header is None or header == 'infer':
+            csv_reader_options_c.set_header(-1)
+        else:
+            csv_reader_options_c.set_header(header)
+
+        c_names.reserve(len(names))
+        for name in names:
+            c_names.push_back(str(name).encode())
+        csv_reader_options_c.set_names(c_names)
+    else:
+        if header is None:
+            csv_reader_options_c.set_header(-1)
+        elif header == 'infer':
+            csv_reader_options_c.set_header(0)
+        else:
+            csv_reader_options_c.set_header(header)
+
+    if prefix is not None:
+        csv_reader_options_c.set_prefix(prefix.encode())
+
+    if usecols is not None:
+        all_int = all(isinstance(col, int) for col in usecols)
+        if all_int:
+            c_use_cols_indexes.reserve(len(usecols))
+            c_use_cols_indexes = usecols
+            csv_reader_options_c.set_use_cols_indexes(c_use_cols_indexes)
+        else:
+            c_use_cols_names.reserve(len(usecols))
+            for col_name in usecols:
+                c_use_cols_names.push_back(
+                    str(col_name).encode()
+                )
+            csv_reader_options_c.set_use_cols_names(c_use_cols_names)
+
+    if delimiter is not None:
+        csv_reader_options_c.set_delimiter(ord(delimiter))
+
+    if thousands is not None:
+        csv_reader_options_c.set_thousands(ord(thousands))
+
+    if comment is not None:
+        csv_reader_options_c.set_comment(ord(comment))
+
+    if parse_dates is not None:
+        if isinstance(parse_dates, abc.Mapping):
+            raise NotImplementedError(
+                "`parse_dates`: dictionaries are unsupported")
+        if not isinstance(parse_dates, abc.Iterable):
+            raise NotImplementedError(
+                "`parse_dates`: an iterable is required")
+        for col in parse_dates:
+            if isinstance(col, str):
+                c_parse_dates_names.push_back(str(col).encode())
+            elif isinstance(col, int):
+                c_parse_dates_indexes.push_back(col)
+            else:
+                raise NotImplementedError(
+                    "`parse_dates`: Nesting is unsupported")
+        csv_reader_options_c.set_parse_dates(c_parse_dates_names)
+        csv_reader_options_c.set_parse_dates(c_parse_dates_indexes)
+
+    if dtype is not None:
+        if isinstance(dtype, abc.Mapping):
+            for k, v in dtype.items():
+                col_type = v
+                if is_hashable(v) and v in CSV_HEX_TYPE_MAP:
+                    col_type = CSV_HEX_TYPE_MAP[v]
+                    c_hex_col_names.push_back(str(k).encode())
+
+                c_dtypes_map[str(k).encode()] = \
+                    _get_cudf_data_type_from_dtype(
+                        cudf.dtype(col_type))
+            csv_reader_options_c.set_dtypes(c_dtypes_map)
+            csv_reader_options_c.set_parse_hex(c_hex_col_names)
+        elif (
+            cudf.api.types.is_scalar(dtype) or
+            isinstance(dtype, (
+                np.dtype, pd.api.extensions.ExtensionDtype, type
+            ))
+        ):
+            c_dtypes_list.reserve(1)
+            if is_hashable(dtype) and dtype in CSV_HEX_TYPE_MAP:
+                dtype = CSV_HEX_TYPE_MAP[dtype]
+                c_hex_col_indexes.push_back(0)
+
+            c_dtypes_list.push_back(
+                _get_cudf_data_type_from_dtype(dtype)
+            )
+            csv_reader_options_c.set_dtypes(c_dtypes_list)
+            csv_reader_options_c.set_parse_hex(c_hex_col_indexes)
+        elif isinstance(dtype, abc.Collection):
+            c_dtypes_list.reserve(len(dtype))
+            for index, col_dtype in enumerate(dtype):
+                if is_hashable(col_dtype) and col_dtype in CSV_HEX_TYPE_MAP:
+                    col_dtype = CSV_HEX_TYPE_MAP[col_dtype]
+                    c_hex_col_indexes.push_back(index)
+
+                c_dtypes_list.push_back(
+                    _get_cudf_data_type_from_dtype(col_dtype)
+                )
+            csv_reader_options_c.set_dtypes(c_dtypes_list)
+            csv_reader_options_c.set_parse_hex(c_hex_col_indexes)
+        else:
+            raise ValueError(
+                "dtype should be a scalar/str/list-like/dict-like"
+            )
+
+    if true_values is not None:
+        c_true_values.reserve(len(true_values))
+        for tv in true_values:
+            c_true_values.push_back(tv.encode())
+        csv_reader_options_c.set_true_values(c_true_values)
+
+    if false_values is not None:
+        c_false_values.reserve(len(false_values))
+        for fv in false_values:
+            c_false_values.push_back(fv.encode())
+        csv_reader_options_c.set_false_values(c_false_values)
+
+    if na_values is not None:
+        c_na_values.reserve(len(na_values))
+        for nv in na_values:
+            c_na_values.push_back(nv.encode())
+        csv_reader_options_c.set_na_values(c_na_values)
+
+    return csv_reader_options_c
+
+
+def validate_args(
+    object delimiter,
+    object sep,
+    bool delim_whitespace,
+    object decimal,
+    object thousands,
+    object nrows,
+    int skipfooter,
+    object byte_range,
+    int skiprows
+):
+    if delim_whitespace:
+        if delimiter is not None:
+            raise ValueError("cannot set both delimiter and delim_whitespace")
+        if sep != ',':
+            raise ValueError("cannot set both sep and delim_whitespace")
+
+    # Alias sep -> delimiter.
+    actual_delimiter = delimiter if delimiter else sep
+
+    if decimal == actual_delimiter:
+        raise ValueError("decimal cannot be the same as delimiter")
+
+    if thousands == actual_delimiter:
+        raise ValueError("thousands cannot be the same as delimiter")
+
+    if nrows is not None and skipfooter != 0:
+        raise ValueError("cannot use both nrows and skipfooter parameters")
+
+    if byte_range is not None:
+        if skipfooter != 0 or skiprows != 0 or nrows is not None:
+            raise ValueError("""cannot manually limit rows to be read when
+                                using the byte range parameter""")
+
+
+def read_csv(
+    object datasource,
+    object lineterminator="\n",
+    object quotechar='"',
+    int quoting=0,
+    bool doublequote=True,
+    object header="infer",
+    bool mangle_dupe_cols=True,
+    object usecols=None,
+    object sep=",",
+    object delimiter=None,
+    bool delim_whitespace=False,
+    bool skipinitialspace=False,
+    object names=None,
+    object dtype=None,
+    int skipfooter=0,
+    int skiprows=0,
+    bool dayfirst=False,
+    object compression="infer",
+    object thousands=None,
+    object decimal=".",
+    object true_values=None,
+    object false_values=None,
+    object nrows=None,
+    object byte_range=None,
+    bool skip_blank_lines=True,
+    object parse_dates=None,
+    object comment=None,
+    object na_values=None,
+    bool keep_default_na=True,
+    bool na_filter=True,
+    object prefix=None,
+    object index_col=None,
+    **kwargs,
+):
+    """
+    Cython function to call into libcudf API, see `read_csv`.
+
+    See Also
+    --------
+    cudf.read_csv
+    """
+
+    if not isinstance(datasource, (BytesIO, StringIO, bytes,
+                                   Datasource,
+                                   NativeFile)):
+        if not os.path.isfile(datasource):
+            raise FileNotFoundError(
+                errno.ENOENT, os.strerror(errno.ENOENT), datasource
+            )
+
+    if isinstance(datasource, StringIO):
+        datasource = datasource.read().encode()
+    elif isinstance(datasource, str) and not os.path.isfile(datasource):
+        datasource = datasource.encode()
+    elif isinstance(datasource, NativeFile):
+        datasource = NativeFileDatasource(datasource)
+
+    validate_args(delimiter, sep, delim_whitespace, decimal, thousands,
+                  nrows, skipfooter, byte_range, skiprows)
+
+    # Alias sep -> delimiter.
+    if delimiter is None:
+        delimiter = sep
+
+    cdef csv_reader_options read_csv_options_c = make_csv_reader_options(
+        datasource, lineterminator, quotechar, quoting, doublequote,
+        header, mangle_dupe_cols, usecols, delimiter, delim_whitespace,
+        skipinitialspace, names, dtype, skipfooter, skiprows, dayfirst,
+        compression, thousands, decimal, true_values, false_values, nrows,
+        byte_range, skip_blank_lines, parse_dates, comment, na_values,
+        keep_default_na, na_filter, prefix, index_col)
+
+    cdef table_with_metadata c_result
+    with nogil:
+        c_result = move(cpp_read_csv(read_csv_options_c))
+
+    meta_names = [info.name.decode() for info in c_result.metadata.schema_info]
+    df = cudf.DataFrame._from_data(*data_from_unique_ptr(
+        move(c_result.tbl),
+        column_names=meta_names
+    ))
+
+    if dtype is not None:
+        if isinstance(dtype, abc.Mapping):
+            for k, v in dtype.items():
+                if cudf.api.types.is_categorical_dtype(v):
+                    df._data[str(k)] = df._data[str(k)].astype(v)
+        elif (
+            cudf.api.types.is_scalar(dtype) or
+            isinstance(dtype, (
+                np.dtype, pd.api.extensions.ExtensionDtype, type
+            ))
+        ):
+            if cudf.api.types.is_categorical_dtype(dtype):
+                df = df.astype(dtype)
+        elif isinstance(dtype, abc.Collection):
+            for index, col_dtype in enumerate(dtype):
+                if cudf.api.types.is_categorical_dtype(col_dtype):
+                    col_name = df._data.names[index]
+                    df._data[col_name] = df._data[col_name].astype(col_dtype)
+
+    if names is not None and isinstance(names[0], (int)):
+        df.columns = [int(x) for x in df._data]
+
+    # Set index if the index_col parameter is passed
+    if index_col is not None and index_col is not False:
+        if isinstance(index_col, int):
+            index_col_name = df._data.select_by_index(index_col).names[0]
+            df = df.set_index(index_col_name)
+            if isinstance(index_col_name, str) and \
+                    names is None and header in ("infer",):
+                if index_col_name.startswith("Unnamed:"):
+                    # TODO: Try to upstream it to libcudf
+                    # csv reader in future
+                    df._index.name = None
+            elif names is None:
+                df._index.name = index_col
+        else:
+            df = df.set_index(index_col)
+
+    return df
+
+
+@acquire_spill_lock()
+def write_csv(
+    table,
+    object path_or_buf=None,
+    object sep=",",
+    object na_rep="",
+    bool header=True,
+    object lineterminator="\n",
+    int rows_per_chunk=8,
+    bool index=True,
+):
+    """
+    Cython function to call into libcudf API, see `write_csv`.
+
+    See Also
+    --------
+    cudf.to_csv
+    """
+    cdef table_view input_table_view = table_view_from_table(
+        table, not index
+    )
+    cdef bool include_header_c = header
+    cdef char delim_c = ord(sep)
+    cdef string line_term_c = lineterminator.encode()
+    cdef string na_c = na_rep.encode()
+    cdef int rows_per_chunk_c = rows_per_chunk
+    cdef vector[string] col_names
+    cdef string true_value_c = 'True'.encode()
+    cdef string false_value_c = 'False'.encode()
+    cdef unique_ptr[data_sink] data_sink_c
+    cdef sink_info sink_info_c = make_sink_info(path_or_buf, data_sink_c)
+
+    if header is True:
+        all_names = columns_apply_na_rep(table._column_names, na_rep)
+        if index is True:
+            all_names = table._index.names + all_names
+
+        if len(all_names) > 0:
+            col_names.reserve(len(all_names))
+            if len(all_names) == 1:
+                if all_names[0] in (None, ''):
+                    col_names.push_back('""'.encode())
+                else:
+                    col_names.push_back(
+                        str(all_names[0]).encode()
+                    )
+            else:
+                for idx, col_name in enumerate(all_names):
+                    if col_name is None:
+                        col_names.push_back(''.encode())
+                    else:
+                        col_names.push_back(
+                            str(col_name).encode()
+                        )
+
+    cdef csv_writer_options options = move(
+        csv_writer_options.builder(sink_info_c, input_table_view)
+        .names(col_names)
+        .na_rep(na_c)
+        .include_header(include_header_c)
+        .rows_per_chunk(rows_per_chunk_c)
+        .line_terminator(line_term_c)
+        .inter_column_delimiter(delim_c)
+        .true_value(true_value_c)
+        .false_value(false_value_c)
+        .build()
+    )
+
+    try:
+        with nogil:
+            cpp_write_csv(options)
+    except OverflowError:
+        raise OverflowError(
+            f"Writing CSV file with chunksize={rows_per_chunk} failed. "
+            "Consider providing a smaller chunksize argument."
+        )
+
+
+cdef data_type _get_cudf_data_type_from_dtype(object dtype) except *:
+    # TODO: Remove this work-around Dictionary types
+    # in libcudf are fully mapped to categorical columns:
+    # https://github.com/rapidsai/cudf/issues/3960
+    if cudf.api.types.is_categorical_dtype(dtype):
+        if isinstance(dtype, str):
+            dtype = "str"
+        else:
+            dtype = dtype.categories.dtype
+
+    if isinstance(dtype, str):
+        if str(dtype) == "date32":
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_DAYS
+            )
+        elif str(dtype) in ("date", "date64"):
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_MILLISECONDS
+            )
+        elif str(dtype) == "timestamp":
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_MILLISECONDS
+            )
+        elif str(dtype) == "timestamp[us]":
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_MICROSECONDS
+            )
+        elif str(dtype) == "timestamp[s]":
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_SECONDS
+            )
+        elif str(dtype) == "timestamp[ms]":
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_MILLISECONDS
+            )
+        elif str(dtype) == "timestamp[ns]":
+            return libcudf_types.data_type(
+                libcudf_types.type_id.TIMESTAMP_NANOSECONDS
+            )
+
+    dtype = cudf.dtype(dtype)
+    return dtype_to_data_type(dtype)
+
+
+def columns_apply_na_rep(column_names, na_rep):
+    return tuple(
+        na_rep if pd.isnull(col_name)
+        else col_name
+        for col_name in column_names
+    )
diff --git a/python/cudf/cudf/_lib/datetime.pyx b/python/cudf/cudf/_lib/datetime.pyx
new file mode 100644
index 0000000..81949db
--- /dev/null
+++ b/python/cudf/cudf/_lib/datetime.pyx
@@ -0,0 +1,214 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+cimport cudf._lib.cpp.datetime as libcudf_datetime
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.filling cimport calendrical_month_sequence
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def add_months(Column col, Column months):
+    # months must be int16 dtype
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+    cdef column_view months_view = months.view()
+
+    with nogil:
+        c_result = move(
+            libcudf_datetime.add_calendrical_months(
+                col_view,
+                months_view
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def extract_datetime_component(Column col, object field):
+
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+
+    with nogil:
+        if field == "year":
+            c_result = move(libcudf_datetime.extract_year(col_view))
+        elif field == "month":
+            c_result = move(libcudf_datetime.extract_month(col_view))
+        elif field == "day":
+            c_result = move(libcudf_datetime.extract_day(col_view))
+        elif field == "weekday":
+            c_result = move(libcudf_datetime.extract_weekday(col_view))
+        elif field == "hour":
+            c_result = move(libcudf_datetime.extract_hour(col_view))
+        elif field == "minute":
+            c_result = move(libcudf_datetime.extract_minute(col_view))
+        elif field == "second":
+            c_result = move(libcudf_datetime.extract_second(col_view))
+        elif field == "millisecond":
+            c_result = move(
+                libcudf_datetime.extract_millisecond_fraction(col_view)
+            )
+        elif field == "microsecond":
+            c_result = move(
+                libcudf_datetime.extract_microsecond_fraction(col_view)
+            )
+        elif field == "nanosecond":
+            c_result = move(
+                libcudf_datetime.extract_nanosecond_fraction(col_view)
+            )
+        elif field == "day_of_year":
+            c_result = move(libcudf_datetime.day_of_year(col_view))
+        else:
+            raise ValueError(f"Invalid datetime field: '{field}'")
+
+    result = Column.from_unique_ptr(move(c_result))
+
+    if field == "weekday":
+        # Pandas counts Monday-Sunday as 0-6
+        # while libcudf counts Monday-Sunday as 1-7
+        result = result - result.dtype.type(1)
+
+    return result
+
+
+cdef libcudf_datetime.rounding_frequency _get_rounding_frequency(object freq):
+    cdef libcudf_datetime.rounding_frequency freq_val
+
+    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timedelta.resolution_string.html
+    if freq == "D":
+        freq_val = libcudf_datetime.rounding_frequency.DAY
+    elif freq == "H":
+        freq_val = libcudf_datetime.rounding_frequency.HOUR
+    elif freq in ("T", "min"):
+        freq_val = libcudf_datetime.rounding_frequency.MINUTE
+    elif freq == "S":
+        freq_val = libcudf_datetime.rounding_frequency.SECOND
+    elif freq in ("L", "ms"):
+        freq_val = libcudf_datetime.rounding_frequency.MILLISECOND
+    elif freq in ("U", "us"):
+        freq_val = libcudf_datetime.rounding_frequency.MICROSECOND
+    elif freq == "N":
+        freq_val = libcudf_datetime.rounding_frequency.NANOSECOND
+    else:
+        raise ValueError(f"Invalid resolution: '{freq}'")
+    return freq_val
+
+
+@acquire_spill_lock()
+def ceil_datetime(Column col, object freq):
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+    cdef libcudf_datetime.rounding_frequency freq_val = \
+        _get_rounding_frequency(freq)
+
+    with nogil:
+        c_result = move(libcudf_datetime.ceil_datetimes(col_view, freq_val))
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def floor_datetime(Column col, object freq):
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+    cdef libcudf_datetime.rounding_frequency freq_val = \
+        _get_rounding_frequency(freq)
+
+    with nogil:
+        c_result = move(libcudf_datetime.floor_datetimes(col_view, freq_val))
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def round_datetime(Column col, object freq):
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+    cdef libcudf_datetime.rounding_frequency freq_val = \
+        _get_rounding_frequency(freq)
+
+    with nogil:
+        c_result = move(libcudf_datetime.round_datetimes(col_view, freq_val))
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def is_leap_year(Column col):
+    """Returns a boolean indicator whether the year of the date is a leap year
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+
+    with nogil:
+        c_result = move(libcudf_datetime.is_leap_year(col_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def date_range(DeviceScalar start, size_type n, offset):
+    cdef unique_ptr[column] c_result
+    cdef size_type months = (
+        offset.kwds.get("years", 0) * 12
+        + offset.kwds.get("months", 0)
+    )
+
+    with nogil:
+        c_result = move(calendrical_month_sequence(
+            n,
+            start.c_value.get()[0],
+            months
+        ))
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def extract_quarter(Column col):
+    """
+    Returns a column which contains the corresponding quarter of the year
+    for every timestamp inside the input column.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+
+    with nogil:
+        c_result = move(libcudf_datetime.extract_quarter(col_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def days_in_month(Column col):
+    """Extracts the number of days in the month of the date
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+
+    with nogil:
+        c_result = move(libcudf_datetime.days_in_month(col_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def last_day_of_month(Column col):
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+
+    with nogil:
+        c_result = move(libcudf_datetime.last_day_of_month(col_view))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/exception_handler.pxd b/python/cudf/cudf/_lib/exception_handler.pxd
new file mode 100644
index 0000000..4337d8d
--- /dev/null
+++ b/python/cudf/cudf/_lib/exception_handler.pxd
@@ -0,0 +1,69 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+
+# See
+# https://github.com/cython/cython/blob/master/Cython/Utility/CppSupport.cpp
+# for the original Cython exception handler.
+cdef extern from *:
+    """
+    #include <Python.h>
+    #include <cudf/utilities/error.hpp>
+    #include <ios>
+    #include <stdexcept>
+
+    namespace {
+
+    /**
+     * @brief Exception handler to map C++ exceptions to Python ones in Cython
+     *
+     * This exception handler extends the base exception handler provided by
+     * Cython. In addition to the exceptions that Cython itself supports, this
+     * file adds support for additional exceptions thrown by libcudf that need
+     * to be mapped to specific Python exceptions.
+     *
+     * Since this function interoperates with Python's exception state, it
+     * does not throw any C++ exceptions.
+     */
+    void cudf_exception_handler()
+    {
+      // Catch a handful of different errors here and turn them into the
+      // equivalent Python errors.
+      try {
+        if (PyErr_Occurred())
+          ;  // let latest Python exn pass through and ignore the current one
+        throw;
+      } catch (const std::bad_alloc& exn) {
+        PyErr_SetString(PyExc_MemoryError, exn.what());
+      } catch (const std::bad_cast& exn) {
+        PyErr_SetString(PyExc_TypeError, exn.what());
+      } catch (const std::domain_error& exn) {
+        PyErr_SetString(PyExc_ValueError, exn.what());
+      } catch (const cudf::data_type_error& exn) {
+        // Catch subclass (data_type_error) before parent (invalid_argument)
+        PyErr_SetString(PyExc_TypeError, exn.what());
+      } catch (const std::invalid_argument& exn) {
+        PyErr_SetString(PyExc_ValueError, exn.what());
+      } catch (const std::ios_base::failure& exn) {
+        // Unfortunately, in standard C++ we have no way of distinguishing EOF
+        // from other errors here; be careful with the exception mask
+        PyErr_SetString(PyExc_IOError, exn.what());
+      } catch (const std::out_of_range& exn) {
+        // Change out_of_range to IndexError
+        PyErr_SetString(PyExc_IndexError, exn.what());
+      } catch (const std::overflow_error& exn) {
+        PyErr_SetString(PyExc_OverflowError, exn.what());
+      } catch (const std::range_error& exn) {
+        PyErr_SetString(PyExc_ArithmeticError, exn.what());
+      } catch (const std::underflow_error& exn) {
+        PyErr_SetString(PyExc_ArithmeticError, exn.what());
+        // The below is the default catch-all case.
+      } catch (const std::exception& exn) {
+        PyErr_SetString(PyExc_RuntimeError, exn.what());
+      } catch (...) {
+        PyErr_SetString(PyExc_RuntimeError, "Unknown exception");
+      }
+    }
+
+    }  // anonymous namespace
+    """
+    cdef void cudf_exception_handler()
diff --git a/python/cudf/cudf/_lib/expressions.pxd b/python/cudf/cudf/_lib/expressions.pxd
new file mode 100644
index 0000000..fc69dc1
--- /dev/null
+++ b/python/cudf/cudf/_lib/expressions.pxd
@@ -0,0 +1,31 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, int64_t
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.expressions cimport (
+    column_reference,
+    expression,
+    literal,
+    operation,
+)
+from cudf._lib.cpp.scalar.scalar cimport numeric_scalar, scalar, string_scalar
+
+
+cdef class Expression:
+    cdef unique_ptr[expression] c_obj
+
+
+cdef class Literal(Expression):
+    cdef unique_ptr[scalar] c_scalar
+
+
+cdef class ColumnReference(Expression):
+    pass
+
+
+cdef class Operation(Expression):
+    pass
+
+cdef class ColumnNameReference(Expression):
+    pass
diff --git a/python/cudf/cudf/_lib/expressions.pyx b/python/cudf/cudf/_lib/expressions.pyx
new file mode 100644
index 0000000..8d7545f
--- /dev/null
+++ b/python/cudf/cudf/_lib/expressions.pyx
@@ -0,0 +1,126 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from enum import Enum
+
+from cython.operator cimport dereference
+from libc.stdint cimport int64_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.cpp cimport expressions as libcudf_exp
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.types cimport size_type
+
+# Necessary for proper casting, see below.
+ctypedef int32_t underlying_type_ast_operator
+
+
+# Aliases for simplicity
+ctypedef unique_ptr[libcudf_exp.expression] expression_ptr
+
+
+class ASTOperator(Enum):
+    ADD = libcudf_exp.ast_operator.ADD
+    SUB = libcudf_exp.ast_operator.SUB
+    MUL = libcudf_exp.ast_operator.MUL
+    DIV = libcudf_exp.ast_operator.DIV
+    TRUE_DIV = libcudf_exp.ast_operator.TRUE_DIV
+    FLOOR_DIV = libcudf_exp.ast_operator.FLOOR_DIV
+    MOD = libcudf_exp.ast_operator.MOD
+    PYMOD = libcudf_exp.ast_operator.PYMOD
+    POW = libcudf_exp.ast_operator.POW
+    EQUAL = libcudf_exp.ast_operator.EQUAL
+    NULL_EQUAL = libcudf_exp.ast_operator.NULL_EQUAL
+    NOT_EQUAL = libcudf_exp.ast_operator.NOT_EQUAL
+    LESS = libcudf_exp.ast_operator.LESS
+    GREATER = libcudf_exp.ast_operator.GREATER
+    LESS_EQUAL = libcudf_exp.ast_operator.LESS_EQUAL
+    GREATER_EQUAL = libcudf_exp.ast_operator.GREATER_EQUAL
+    BITWISE_AND = libcudf_exp.ast_operator.BITWISE_AND
+    BITWISE_OR = libcudf_exp.ast_operator.BITWISE_OR
+    BITWISE_XOR = libcudf_exp.ast_operator.BITWISE_XOR
+    LOGICAL_AND = libcudf_exp.ast_operator.LOGICAL_AND
+    NULL_LOGICAL_AND = libcudf_exp.ast_operator.NULL_LOGICAL_AND
+    LOGICAL_OR = libcudf_exp.ast_operator.LOGICAL_OR
+    NULL_LOGICAL_OR = libcudf_exp.ast_operator.NULL_LOGICAL_OR
+    # Unary operators
+    IDENTITY = libcudf_exp.ast_operator.IDENTITY
+    IS_NULL = libcudf_exp.ast_operator.IS_NULL
+    SIN = libcudf_exp.ast_operator.SIN
+    COS = libcudf_exp.ast_operator.COS
+    TAN = libcudf_exp.ast_operator.TAN
+    ARCSIN = libcudf_exp.ast_operator.ARCSIN
+    ARCCOS = libcudf_exp.ast_operator.ARCCOS
+    ARCTAN = libcudf_exp.ast_operator.ARCTAN
+    SINH = libcudf_exp.ast_operator.SINH
+    COSH = libcudf_exp.ast_operator.COSH
+    TANH = libcudf_exp.ast_operator.TANH
+    ARCSINH = libcudf_exp.ast_operator.ARCSINH
+    ARCCOSH = libcudf_exp.ast_operator.ARCCOSH
+    ARCTANH = libcudf_exp.ast_operator.ARCTANH
+    EXP = libcudf_exp.ast_operator.EXP
+    LOG = libcudf_exp.ast_operator.LOG
+    SQRT = libcudf_exp.ast_operator.SQRT
+    CBRT = libcudf_exp.ast_operator.CBRT
+    CEIL = libcudf_exp.ast_operator.CEIL
+    FLOOR = libcudf_exp.ast_operator.FLOOR
+    ABS = libcudf_exp.ast_operator.ABS
+    RINT = libcudf_exp.ast_operator.RINT
+    BIT_INVERT = libcudf_exp.ast_operator.BIT_INVERT
+    NOT = libcudf_exp.ast_operator.NOT
+
+
+class TableReference(Enum):
+    LEFT = libcudf_exp.table_reference.LEFT
+    RIGHT = libcudf_exp.table_reference.RIGHT
+
+
+# Note that this function only currently supports numeric literals. libcudf
+# expressions don't really support other types yet though, so this isn't
+# restrictive at the moment.
+cdef class Literal(Expression):
+    def __cinit__(self, value):
+        if isinstance(value, int):
+            self.c_scalar.reset(new numeric_scalar[int64_t](value, True))
+            self.c_obj = <expression_ptr> move(make_unique[libcudf_exp.literal](
+                <numeric_scalar[int64_t] &>dereference(self.c_scalar)
+            ))
+        elif isinstance(value, float):
+            self.c_scalar.reset(new numeric_scalar[double](value, True))
+            self.c_obj = <expression_ptr> move(make_unique[libcudf_exp.literal](
+                <numeric_scalar[double] &>dereference(self.c_scalar)
+            ))
+        elif isinstance(value, str):
+            self.c_scalar.reset(new string_scalar(value.encode(), True))
+            self.c_obj = <expression_ptr> move(make_unique[libcudf_exp.literal](
+                <string_scalar &>dereference(self.c_scalar)
+            ))
+
+
+cdef class ColumnReference(Expression):
+    def __cinit__(self, size_type index):
+        self.c_obj = <expression_ptr>move(make_unique[libcudf_exp.column_reference](
+            index
+        ))
+
+
+cdef class Operation(Expression):
+    def __cinit__(self, op, Expression left, Expression right=None):
+        cdef libcudf_exp.ast_operator op_value = <libcudf_exp.ast_operator>(
+            <underlying_type_ast_operator> op.value
+        )
+
+        if right is None:
+            self.c_obj = <expression_ptr> move(make_unique[libcudf_exp.operation](
+                op_value, dereference(left.c_obj)
+            ))
+        else:
+            self.c_obj = <expression_ptr> move(make_unique[libcudf_exp.operation](
+                op_value, dereference(left.c_obj), dereference(right.c_obj)
+            ))
+
+cdef class ColumnNameReference(Expression):
+    def __cinit__(self, string name):
+        self.c_obj = <expression_ptr> \
+            move(make_unique[libcudf_exp.column_name_reference](name))
diff --git a/python/cudf/cudf/_lib/filling.pyx b/python/cudf/cudf/_lib/filling.pyx
new file mode 100644
index 0000000..63549f0
--- /dev/null
+++ b/python/cudf/cudf/_lib/filling.pyx
@@ -0,0 +1,103 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+cimport cudf._lib.cpp.filling as cpp_filling
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def fill_in_place(Column destination, int begin, int end, DeviceScalar value):
+    cdef mutable_column_view c_destination = destination.mutable_view()
+    cdef size_type c_begin = <size_type> begin
+    cdef size_type c_end = <size_type> end
+    cdef const scalar* c_value = value.get_raw_ptr()
+
+    cpp_filling.fill_in_place(
+        c_destination,
+        c_begin,
+        c_end,
+        c_value[0]
+    )
+
+
+@acquire_spill_lock()
+def fill(Column destination, int begin, int end, DeviceScalar value):
+    cdef column_view c_destination = destination.view()
+    cdef size_type c_begin = <size_type> begin
+    cdef size_type c_end = <size_type> end
+    cdef const scalar* c_value = value.get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_filling.fill(
+            c_destination,
+            c_begin,
+            c_end,
+            c_value[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def repeat(list inp, object count):
+    if isinstance(count, Column):
+        return _repeat_via_column(inp, count)
+    else:
+        return _repeat_via_size_type(inp, count)
+
+
+def _repeat_via_column(list inp, Column count):
+    cdef table_view c_inp = table_view_from_columns(inp)
+    cdef column_view c_count = count.view()
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(cpp_filling.repeat(
+            c_inp,
+            c_count,
+        ))
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+def _repeat_via_size_type(list inp, size_type count):
+    cdef table_view c_inp = table_view_from_columns(inp)
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(cpp_filling.repeat(
+            c_inp,
+            count
+        ))
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def sequence(int size, DeviceScalar init, DeviceScalar step):
+    cdef size_type c_size = size
+    cdef const scalar* c_init = init.get_raw_ptr()
+    cdef const scalar* c_step = step.get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_filling.sequence(
+            c_size,
+            c_init[0],
+            c_step[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/groupby.pyx b/python/cudf/cudf/_lib/groupby.pyx
new file mode 100644
index 0000000..a26d820
--- /dev/null
+++ b/python/cudf/cudf/_lib/groupby.pyx
@@ -0,0 +1,404 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from pandas.core.groupby.groupby import DataError
+
+from cudf.api.types import (
+    is_categorical_dtype,
+    is_decimal_dtype,
+    is_interval_dtype,
+    is_list_dtype,
+    is_string_dtype,
+    is_struct_dtype,
+)
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+from cudf._lib.scalar import as_device_scalar
+
+cimport cudf._lib.cpp.groupby as libcudf_groupby
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.aggregation cimport (
+    GroupbyAggregation,
+    GroupbyScanAggregation,
+    make_groupby_aggregation,
+    make_groupby_scan_aggregation,
+)
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.libcpp.functional cimport reference_wrapper
+from cudf._lib.cpp.replace cimport replace_policy
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table, table_view
+from cudf._lib.cpp.types cimport size_type
+
+# The sets below define the possible aggregations that can be performed on
+# different dtypes. These strings must be elements of the AggregationKind enum.
+# The libcudf infrastructure exists for "COLLECT" support on
+# categoricals, but the dtype support in python does not.
+_CATEGORICAL_AGGS = {"COUNT", "NUNIQUE", "SIZE", "UNIQUE"}
+_STRING_AGGS = {
+    "COLLECT",
+    "COUNT",
+    "MAX",
+    "MIN",
+    "NTH",
+    "NUNIQUE",
+    "SIZE",
+    "UNIQUE",
+}
+_LIST_AGGS = {"COLLECT"}
+_STRUCT_AGGS = {"COLLECT", "CORRELATION", "COVARIANCE"}
+_INTERVAL_AGGS = {"COLLECT"}
+_DECIMAL_AGGS = {
+    "ARGMIN",
+    "ARGMAX",
+    "COLLECT",
+    "COUNT",
+    "MAX",
+    "MIN",
+    "NTH",
+    "NUNIQUE",
+    "SUM",
+}
+# workaround for https://github.com/cython/cython/issues/3885
+ctypedef const scalar constscalar
+
+
+cdef _agg_result_from_columns(
+    vector[libcudf_groupby.aggregation_result]& c_result_columns,
+    set column_included,
+    int n_input_columns
+):
+    """Construct the list of result columns from libcudf result. The result
+    contains the same number of lists as the number of input columns. Result
+    for an input column that has no applicable aggregations is an empty list.
+    """
+    cdef:
+        int i
+        int j
+        int result_index = 0
+        vector[unique_ptr[column]]* c_result
+    result_columns = []
+    for i in range(n_input_columns):
+        if i in column_included:
+            c_result = &c_result_columns[result_index].results
+            result_columns.append([
+                Column.from_unique_ptr(move(c_result[0][j]))
+                for j in range(c_result[0].size())
+            ])
+            result_index += 1
+        else:
+            result_columns.append([])
+    return result_columns
+
+cdef class GroupBy:
+    cdef unique_ptr[libcudf_groupby.groupby] c_obj
+    cdef dict __dict__
+
+    def __cinit__(self, list keys, bool dropna=True, *args, **kwargs):
+        cdef libcudf_types.null_policy c_null_handling
+        cdef table_view keys_view
+
+        if dropna:
+            c_null_handling = libcudf_types.null_policy.EXCLUDE
+        else:
+            c_null_handling = libcudf_types.null_policy.INCLUDE
+
+        with acquire_spill_lock() as spill_lock:
+            keys_view = table_view_from_columns(keys)
+            # We spill lock the columns while this GroupBy instance is alive.
+            self._spill_lock = spill_lock
+
+        with nogil:
+            self.c_obj.reset(
+                new libcudf_groupby.groupby(
+                    keys_view,
+                    c_null_handling,
+                )
+            )
+
+    def __init__(self, list keys, bool dropna=True):
+        self.keys = keys
+        self.dropna = dropna
+
+    def groups(self, list values):
+        """
+        Perform a sort groupby, using ``self.keys`` as the key columns
+        and ``values`` as the value columns.
+
+        Parameters
+        ----------
+        values: list of Columns
+            The value columns
+
+        Returns
+        -------
+        grouped_keys: list of Columns
+            The grouped key columns
+        grouped_values: list of Columns
+            The grouped value columns
+        offsets: list of integers
+            Integer offsets such that offsets[i+1] - offsets[i]
+            represents the size of group `i`.
+        """
+        cdef table_view values_view = table_view_from_columns(values)
+
+        with nogil:
+            c_groups = move(self.c_obj.get()[0].get_groups(values_view))
+
+        grouped_key_cols = columns_from_unique_ptr(move(c_groups.keys))
+
+        if values:
+            grouped_value_cols = columns_from_unique_ptr(move(c_groups.values))
+        else:
+            grouped_value_cols = []
+        return grouped_key_cols, grouped_value_cols, c_groups.offsets
+
+    def aggregate_internal(self, values, aggregations):
+        """`values` is a list of columns and `aggregations` is a list of list
+        of aggregations. `aggregations[i]` is a list of aggregations for
+        `values[i]`. Returns a tuple containing 1) list of list of aggregation
+        results, 2) a list of grouped keys, and 3) a list of list of
+        aggregations performed.
+        """
+        cdef vector[libcudf_groupby.aggregation_request] c_agg_requests
+        cdef libcudf_groupby.aggregation_request c_agg_request
+        cdef Column col
+        cdef GroupbyAggregation agg_obj
+
+        cdef pair[
+            unique_ptr[table],
+            vector[libcudf_groupby.aggregation_result]
+        ] c_result
+
+        allow_empty = all(len(v) == 0 for v in aggregations)
+
+        included_aggregations = []
+        column_included = set()
+        for i, (col, aggs) in enumerate(zip(values, aggregations)):
+            dtype = col.dtype
+
+            valid_aggregations = (
+                _LIST_AGGS if is_list_dtype(dtype)
+                else _STRING_AGGS if is_string_dtype(dtype)
+                else _CATEGORICAL_AGGS if is_categorical_dtype(dtype)
+                else _STRUCT_AGGS if is_struct_dtype(dtype)
+                else _INTERVAL_AGGS if is_interval_dtype(dtype)
+                else _DECIMAL_AGGS if is_decimal_dtype(dtype)
+                else "ALL"
+            )
+            included_aggregations_i = []
+
+            c_agg_request = move(libcudf_groupby.aggregation_request())
+            for agg in aggs:
+                agg_obj = make_groupby_aggregation(agg)
+                if (valid_aggregations == "ALL"
+                        or agg_obj.kind in valid_aggregations):
+                    included_aggregations_i.append((agg, agg_obj.kind))
+                    c_agg_request.aggregations.push_back(
+                        move(agg_obj.c_obj)
+                    )
+            included_aggregations.append(included_aggregations_i)
+            if not c_agg_request.aggregations.empty():
+                c_agg_request.values = col.view()
+                c_agg_requests.push_back(
+                    move(c_agg_request)
+                )
+                column_included.add(i)
+        if c_agg_requests.empty() and not allow_empty:
+            raise DataError("All requested aggregations are unsupported.")
+
+        with nogil:
+            c_result = move(
+                self.c_obj.get()[0].aggregate(
+                    c_agg_requests
+                )
+            )
+
+        grouped_keys = columns_from_unique_ptr(
+            move(c_result.first)
+        )
+
+        result_columns = _agg_result_from_columns(
+            c_result.second, column_included, len(values)
+        )
+
+        return result_columns, grouped_keys, included_aggregations
+
+    def scan_internal(self, values, aggregations):
+        """`values` is a list of columns and `aggregations` is a list of list
+        of aggregations. `aggregations[i]` is a list of aggregations for
+        `values[i]`. Returns a tuple containing 1) list of list of aggregation
+        results, 2) a list of grouped keys, and 3) a list of list of
+        aggregations performed.
+        """
+        cdef vector[libcudf_groupby.scan_request] c_agg_requests
+        cdef libcudf_groupby.scan_request c_agg_request
+        cdef Column col
+        cdef GroupbyScanAggregation agg_obj
+
+        cdef pair[
+            unique_ptr[table],
+            vector[libcudf_groupby.aggregation_result]
+        ] c_result
+
+        allow_empty = all(len(v) == 0 for v in aggregations)
+
+        included_aggregations = []
+        column_included = set()
+        for i, (col, aggs) in enumerate(zip(values, aggregations)):
+            dtype = col.dtype
+
+            valid_aggregations = (
+                _LIST_AGGS if is_list_dtype(dtype)
+                else _STRING_AGGS if is_string_dtype(dtype)
+                else _CATEGORICAL_AGGS if is_categorical_dtype(dtype)
+                else _STRUCT_AGGS if is_struct_dtype(dtype)
+                else _INTERVAL_AGGS if is_interval_dtype(dtype)
+                else _DECIMAL_AGGS if is_decimal_dtype(dtype)
+                else "ALL"
+            )
+            included_aggregations_i = []
+
+            c_agg_request = move(libcudf_groupby.scan_request())
+            for agg in aggs:
+                agg_obj = make_groupby_scan_aggregation(agg)
+                if (valid_aggregations == "ALL"
+                        or agg_obj.kind in valid_aggregations):
+                    included_aggregations_i.append((agg, agg_obj.kind))
+                    c_agg_request.aggregations.push_back(
+                        move(agg_obj.c_obj)
+                    )
+            included_aggregations.append(included_aggregations_i)
+            if not c_agg_request.aggregations.empty():
+                c_agg_request.values = col.view()
+                c_agg_requests.push_back(
+                    move(c_agg_request)
+                )
+                column_included.add(i)
+        if c_agg_requests.empty() and not allow_empty:
+            raise DataError("All requested aggregations are unsupported.")
+
+        with nogil:
+            c_result = move(
+                self.c_obj.get()[0].scan(
+                    c_agg_requests
+                )
+            )
+
+        grouped_keys = columns_from_unique_ptr(
+            move(c_result.first)
+        )
+
+        result_columns = _agg_result_from_columns(
+            c_result.second, column_included, len(values)
+        )
+
+        return result_columns, grouped_keys, included_aggregations
+
+    def aggregate(self, values, aggregations):
+        """
+        Parameters
+        ----------
+        values : Frame
+        aggregations
+            A dict mapping column names in `Frame` to a list of aggregations
+            to perform on that column
+
+            Each aggregation may be specified as:
+            - a string (e.g., "max")
+            - a lambda/function
+
+        Returns
+        -------
+        Frame of aggregated values
+        """
+        if _is_all_scan_aggregate(aggregations):
+            return self.scan_internal(values, aggregations)
+
+        return self.aggregate_internal(values, aggregations)
+
+    def shift(self, list values, int periods, list fill_values):
+        cdef table_view view = table_view_from_columns(values)
+        cdef size_type num_col = view.num_columns()
+        cdef vector[size_type] offsets = vector[size_type](num_col, periods)
+
+        cdef vector[reference_wrapper[constscalar]] c_fill_values
+        cdef DeviceScalar d_slr
+        d_slrs = []
+        c_fill_values.reserve(num_col)
+        for val, col in zip(fill_values, values):
+            d_slr = as_device_scalar(val, dtype=col.dtype)
+            d_slrs.append(d_slr)
+            c_fill_values.push_back(
+                reference_wrapper[constscalar](d_slr.get_raw_ptr()[0])
+            )
+
+        cdef pair[unique_ptr[table], unique_ptr[table]] c_result
+
+        with nogil:
+            c_result = move(
+                self.c_obj.get()[0].shift(view, offsets, c_fill_values)
+            )
+
+        grouped_keys = columns_from_unique_ptr(move(c_result.first))
+        shifted = columns_from_unique_ptr(move(c_result.second))
+
+        return shifted, grouped_keys
+
+    def replace_nulls(self, list values, object method):
+        cdef table_view val_view = table_view_from_columns(values)
+        cdef pair[unique_ptr[table], unique_ptr[table]] c_result
+        cdef replace_policy policy = (
+            replace_policy.PRECEDING
+            if method == 'ffill' else replace_policy.FOLLOWING
+        )
+        cdef vector[replace_policy] policies = vector[replace_policy](
+            val_view.num_columns(), policy
+        )
+
+        with nogil:
+            c_result = move(
+                self.c_obj.get()[0].replace_nulls(val_view, policies)
+            )
+
+        return columns_from_unique_ptr(move(c_result.second))
+
+
+_GROUPBY_SCANS = {"cumcount", "cumsum", "cummin", "cummax", "rank"}
+
+
+def _is_all_scan_aggregate(all_aggs):
+    """
+    Returns true if all are scan aggregations.
+    Raises
+    ------
+    NotImplementedError
+        If both reduction aggregations and scan aggregations are present.
+    """
+
+    def get_name(agg):
+        return agg.__name__ if callable(agg) else agg
+
+    all_scan = all(
+        get_name(agg_name) in _GROUPBY_SCANS for aggs in all_aggs
+        for agg_name in aggs
+    )
+    any_scan = any(
+        get_name(agg_name) in _GROUPBY_SCANS for aggs in all_aggs
+        for agg_name in aggs
+    )
+
+    if not all_scan and any_scan:
+        raise NotImplementedError(
+            "Cannot perform both aggregation and scan in one operation"
+        )
+    return all_scan and any_scan
diff --git a/python/cudf/cudf/_lib/hash.pyx b/python/cudf/cudf/_lib/hash.pyx
new file mode 100644
index 0000000..1264a9b
--- /dev/null
+++ b/python/cudf/cudf/_lib/hash.pyx
@@ -0,0 +1,63 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.hash cimport hash as cpp_hash, hash_id as cpp_hash_id
+from cudf._lib.cpp.partitioning cimport hash_partition as cpp_hash_partition
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def hash_partition(list source_columns, object columns_to_hash,
+                   int num_partitions):
+    cdef vector[libcudf_types.size_type] c_columns_to_hash = columns_to_hash
+    cdef int c_num_partitions = num_partitions
+    cdef table_view c_source_view = table_view_from_columns(source_columns)
+
+    cdef pair[unique_ptr[table], vector[libcudf_types.size_type]] c_result
+    with nogil:
+        c_result = move(
+            cpp_hash_partition(
+                c_source_view,
+                c_columns_to_hash,
+                c_num_partitions
+            )
+        )
+
+    return (
+        columns_from_unique_ptr(move(c_result.first)),
+        list(c_result.second)
+    )
+
+
+@acquire_spill_lock()
+def hash(list source_columns, str method, int seed=0):
+    cdef table_view c_source_view = table_view_from_columns(source_columns)
+    cdef unique_ptr[column] c_result
+    cdef cpp_hash_id c_hash_function
+    if method == "murmur3":
+        c_hash_function = cpp_hash_id.HASH_MURMUR3
+    elif method == "md5":
+        c_hash_function = cpp_hash_id.HASH_MD5
+    else:
+        raise ValueError(f"Unsupported hash function: {method}")
+    with nogil:
+        c_result = move(
+            cpp_hash(
+                c_source_view,
+                c_hash_function,
+                seed
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/interop.pyx b/python/cudf/cudf/_lib/interop.pyx
new file mode 100644
index 0000000..639754f
--- /dev/null
+++ b/python/cudf/cudf/_lib/interop.pyx
@@ -0,0 +1,277 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cpython cimport pycapsule
+from libcpp.memory cimport shared_ptr, unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+from pyarrow.lib cimport (
+    CScalar,
+    CTable,
+    pyarrow_unwrap_scalar,
+    pyarrow_unwrap_table,
+    pyarrow_wrap_scalar,
+    pyarrow_wrap_table,
+)
+
+from cudf._lib.cpp.interop cimport (
+    DLManagedTensor,
+    column_metadata,
+    from_arrow as cpp_from_arrow,
+    from_dlpack as cpp_from_dlpack,
+    to_arrow as cpp_to_arrow,
+    to_dlpack as cpp_to_dlpack,
+)
+from cudf._lib.cpp.scalar.scalar cimport fixed_point_scalar, scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport type_id
+from cudf._lib.cpp.wrappers.decimals cimport (
+    decimal32,
+    decimal64,
+    decimal128,
+    scale_type,
+)
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+from cudf.api.types import is_list_dtype, is_struct_dtype
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype
+
+
+def from_dlpack(dlpack_capsule):
+    """
+    Converts a DLPack Tensor PyCapsule into a list of columns.
+
+    DLPack Tensor PyCapsule is expected to have the name "dltensor".
+    """
+    cdef DLManagedTensor* dlpack_tensor = <DLManagedTensor*>pycapsule.\
+        PyCapsule_GetPointer(dlpack_capsule, 'dltensor')
+    pycapsule.PyCapsule_SetName(dlpack_capsule, 'used_dltensor')
+
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_from_dlpack(dlpack_tensor)
+        )
+
+    res = columns_from_unique_ptr(move(c_result))
+    dlpack_tensor.deleter(dlpack_tensor)
+    return res
+
+
+def to_dlpack(list source_columns):
+    """
+    Converts a list of columns into a DLPack Tensor PyCapsule.
+
+    DLPack Tensor PyCapsule will have the name "dltensor".
+    """
+    if any(column.null_count for column in source_columns):
+        raise ValueError(
+            "Cannot create a DLPack tensor with null values. \
+                Input is required to have null count as zero."
+        )
+
+    cdef DLManagedTensor *dlpack_tensor
+    cdef table_view source_table_view = table_view_from_columns(source_columns)
+
+    with nogil:
+        dlpack_tensor = cpp_to_dlpack(
+            source_table_view
+        )
+
+    return pycapsule.PyCapsule_New(
+        dlpack_tensor,
+        'dltensor',
+        dlmanaged_tensor_pycapsule_deleter
+    )
+
+
+cdef void dlmanaged_tensor_pycapsule_deleter(object pycap_obj) noexcept:
+    cdef DLManagedTensor* dlpack_tensor = <DLManagedTensor*>0
+    try:
+        dlpack_tensor = <DLManagedTensor*>pycapsule.PyCapsule_GetPointer(
+            pycap_obj, 'used_dltensor')
+        return  # we do not call a used capsule's deleter
+    except Exception:
+        dlpack_tensor = <DLManagedTensor*>pycapsule.PyCapsule_GetPointer(
+            pycap_obj, 'dltensor')
+    dlpack_tensor.deleter(dlpack_tensor)
+
+
+cdef vector[column_metadata] gather_metadata(object cols_dtypes) except *:
+    """
+    Generates a column_metadata vector for each column.
+
+    Parameters
+    ----------
+    cols_dtypes : iterable
+        An iterable of ``(column_name, dtype)`` pairs.
+    """
+    cdef vector[column_metadata] cpp_metadata
+    cpp_metadata.reserve(len(cols_dtypes))
+
+    if cols_dtypes is not None:
+        for idx, (col_name, col_dtype) in enumerate(cols_dtypes):
+            cpp_metadata.push_back(column_metadata(col_name.encode()))
+            if is_struct_dtype(col_dtype) or is_list_dtype(col_dtype):
+                _set_col_children_metadata(col_dtype, cpp_metadata[idx])
+    else:
+        raise TypeError(
+            "An iterable of (column_name, dtype) pairs is required to "
+            "construct column_metadata"
+        )
+    return cpp_metadata
+
+
+cdef _set_col_children_metadata(dtype,
+                                column_metadata& col_meta):
+
+    cdef column_metadata element_metadata
+
+    if is_struct_dtype(dtype):
+        for name, value in dtype.fields.items():
+            element_metadata = column_metadata(name.encode())
+            _set_col_children_metadata(
+                value, element_metadata
+            )
+            col_meta.children_meta.push_back(element_metadata)
+    elif is_list_dtype(dtype):
+        col_meta.children_meta.reserve(2)
+        # Offsets - child 0
+        col_meta.children_meta.push_back(column_metadata())
+
+        # Element column - child 1
+        element_metadata = column_metadata()
+        _set_col_children_metadata(
+            dtype.element_type, element_metadata
+        )
+        col_meta.children_meta.push_back(element_metadata)
+    else:
+        col_meta.children_meta.push_back(column_metadata())
+
+
+@acquire_spill_lock()
+def to_arrow(list source_columns, object column_dtypes):
+    """Convert a list of columns from
+    cudf Frame to a PyArrow Table.
+
+    Parameters
+    ----------
+    source_columns : a list of columns to convert
+    column_dtypes : Iterable of ``(column_name, column_dtype)`` pairs
+
+    Returns
+    -------
+    pyarrow table
+    """
+    cdef vector[column_metadata] cpp_metadata = gather_metadata(column_dtypes)
+    cdef table_view input_table_view = table_view_from_columns(source_columns)
+
+    cdef shared_ptr[CTable] cpp_arrow_table
+    with nogil:
+        cpp_arrow_table = cpp_to_arrow(
+            input_table_view, cpp_metadata
+        )
+
+    return pyarrow_wrap_table(cpp_arrow_table)
+
+
+@acquire_spill_lock()
+def from_arrow(object input_table):
+    """Convert from PyArrow Table to a list of columns.
+
+    Parameters
+    ----------
+    input_table : PyArrow table
+
+    Returns
+    -------
+    A list of columns to construct Frame object
+    """
+    cdef shared_ptr[CTable] cpp_arrow_table = (
+        pyarrow_unwrap_table(input_table)
+    )
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(cpp_from_arrow(cpp_arrow_table.get()[0]))
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def to_arrow_scalar(DeviceScalar source_scalar):
+    """Convert a scalar to a PyArrow scalar.
+
+    Parameters
+    ----------
+    source_scalar : the scalar to convert
+
+    Returns
+    -------
+    pyarrow.lib.Scalar
+    """
+    cdef vector[column_metadata] cpp_metadata = gather_metadata(
+        [("", source_scalar.dtype)]
+    )
+    cdef const scalar* source_scalar_ptr = source_scalar.get_raw_ptr()
+
+    cdef shared_ptr[CScalar] cpp_arrow_scalar
+    with nogil:
+        cpp_arrow_scalar = cpp_to_arrow(
+            source_scalar_ptr[0], cpp_metadata[0]
+        )
+
+    return pyarrow_wrap_scalar(cpp_arrow_scalar)
+
+
+@acquire_spill_lock()
+def from_arrow_scalar(object input_scalar, output_dtype=None):
+    """Convert from PyArrow scalar to a cudf scalar.
+
+    Parameters
+    ----------
+    input_scalar : PyArrow scalar
+    output_dtype : output type to cast to, ignored except for decimals
+
+    Returns
+    -------
+    cudf._lib.DeviceScalar
+    """
+    cdef shared_ptr[CScalar] cpp_arrow_scalar = (
+        pyarrow_unwrap_scalar(input_scalar)
+    )
+    cdef unique_ptr[scalar] c_result
+
+    with nogil:
+        c_result = move(cpp_from_arrow(cpp_arrow_scalar.get()[0]))
+
+    cdef type_id ctype = c_result.get().type().id()
+    if ctype == type_id.DECIMAL128:
+        if output_dtype is None:
+            # Decimals must be cast to the cudf dtype of the right width
+            raise ValueError(
+                "Decimal scalars must be constructed with a dtype"
+            )
+
+        if isinstance(output_dtype, Decimal32Dtype):
+            c_result.reset(
+                new fixed_point_scalar[decimal32](
+                    (<fixed_point_scalar[decimal128]*> c_result.get()).value(),
+                    scale_type(-input_scalar.type.scale),
+                    c_result.get().is_valid()
+                )
+            )
+        elif isinstance(output_dtype, Decimal64Dtype):
+            c_result.reset(
+                new fixed_point_scalar[decimal64](
+                    (<fixed_point_scalar[decimal128]*> c_result.get()).value(),
+                    scale_type(-input_scalar.type.scale),
+                    c_result.get().is_valid()
+                )
+            )
+        # Decimal128Dtype is a no-op, no conversion needed.
+
+    return DeviceScalar.from_unique_ptr(move(c_result), output_dtype)
diff --git a/python/cudf/cudf/_lib/io/CMakeLists.txt b/python/cudf/cudf/_lib/io/CMakeLists.txt
new file mode 100644
index 0000000..1f93bfa
--- /dev/null
+++ b/python/cudf/cudf/_lib/io/CMakeLists.txt
@@ -0,0 +1,27 @@
+# =============================================================================
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources datasource.pyx utils.pyx)
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX io_ ASSOCIATED_TARGETS cudf
+)
+
+set(targets_using_numpy_and_arrow_headers io_datasource io_utils)
+foreach(target IN LISTS targets_using_numpy_and_arrow_headers)
+  target_include_directories(${target} PRIVATE "${NumPy_INCLUDE_DIRS}")
+  target_include_directories(${target} PRIVATE "${PYARROW_INCLUDE_DIR}")
+endforeach()
diff --git a/python/cudf/cudf/_lib/io/__init__.pxd b/python/cudf/cudf/_lib/io/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/io/__init__.py b/python/cudf/cudf/_lib/io/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/io/datasource.pxd b/python/cudf/cudf/_lib/io/datasource.pxd
new file mode 100644
index 0000000..bd5bf02
--- /dev/null
+++ b/python/cudf/cudf/_lib/io/datasource.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport shared_ptr
+
+from cudf._lib.cpp.io.arrow_io_source cimport arrow_io_source
+from cudf._lib.cpp.io.datasource cimport datasource
+
+
+cdef class Datasource:
+    cdef datasource* get_datasource(self) except * nogil
+
+
+cdef class NativeFileDatasource(Datasource):
+    cdef shared_ptr[arrow_io_source] c_datasource
+    cdef datasource* get_datasource(self) nogil
diff --git a/python/cudf/cudf/_lib/io/datasource.pyx b/python/cudf/cudf/_lib/io/datasource.pyx
new file mode 100644
index 0000000..5cadd58
--- /dev/null
+++ b/python/cudf/cudf/_lib/io/datasource.pyx
@@ -0,0 +1,27 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport shared_ptr
+from pyarrow.includes.libarrow cimport CRandomAccessFile
+from pyarrow.lib cimport NativeFile
+
+from cudf._lib.cpp.io.arrow_io_source cimport arrow_io_source
+from cudf._lib.cpp.io.datasource cimport datasource
+
+
+cdef class Datasource:
+    cdef datasource* get_datasource(self) except * nogil:
+        with gil:
+            raise NotImplementedError("get_datasource() should not "
+                                      + "be directly invoked here")
+
+cdef class NativeFileDatasource(Datasource):
+
+    def __cinit__(self, NativeFile native_file,):
+
+        cdef shared_ptr[CRandomAccessFile] ra_src
+
+        ra_src = native_file.get_random_access_file()
+        self.c_datasource.reset(new arrow_io_source(ra_src))
+
+    cdef datasource* get_datasource(self) nogil:
+        return <datasource *> (self.c_datasource.get())
diff --git a/python/cudf/cudf/_lib/io/utils.pxd b/python/cudf/cudf/_lib/io/utils.pxd
new file mode 100644
index 0000000..2c2d52b
--- /dev/null
+++ b/python/cudf/cudf/_lib/io/utils.pxd
@@ -0,0 +1,21 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.io.data_sink cimport data_sink
+from cudf._lib.cpp.io.types cimport column_name_info, sink_info, source_info
+
+
+cdef source_info make_source_info(list src) except*
+cdef sink_info make_sinks_info(
+    list src, vector[unique_ptr[data_sink]] & data) except*
+cdef sink_info make_sink_info(src, unique_ptr[data_sink] & data) except*
+cdef update_struct_field_names(
+    table,
+    vector[column_name_info]& schema_info)
+cdef Column update_column_struct_field_names(
+    Column col,
+    column_name_info& info
+)
diff --git a/python/cudf/cudf/_lib/io/utils.pyx b/python/cudf/cudf/_lib/io/utils.pyx
new file mode 100644
index 0000000..9b027a4
--- /dev/null
+++ b/python/cudf/cudf/_lib/io/utils.pyx
@@ -0,0 +1,183 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cpython.buffer cimport PyBUF_READ
+from cpython.memoryview cimport PyMemoryView_FromMemory
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.io.data_sink cimport data_sink
+from cudf._lib.cpp.io.datasource cimport datasource
+from cudf._lib.cpp.io.types cimport (
+    column_name_info,
+    host_buffer,
+    sink_info,
+    source_info,
+)
+from cudf._lib.io.datasource cimport Datasource
+
+import codecs
+import errno
+import io
+import os
+
+from cudf.api.types import is_struct_dtype
+
+
+# Converts the Python source input to libcudf IO source_info
+# with the appropriate type and source values
+cdef source_info make_source_info(list src) except*:
+    if not src:
+        raise ValueError("Need to pass at least one source")
+
+    cdef const unsigned char[::1] c_buffer
+    cdef vector[host_buffer] c_host_buffers
+    cdef vector[string] c_files
+    cdef Datasource csrc
+    cdef vector[datasource*] c_datasources
+    empty_buffer = False
+    if isinstance(src[0], bytes):
+        empty_buffer = True
+        for buffer in src:
+            if (len(buffer) > 0):
+                c_buffer = buffer
+                c_host_buffers.push_back(host_buffer(<char*>&c_buffer[0],
+                                                     c_buffer.shape[0]))
+                empty_buffer = False
+    elif isinstance(src[0], io.BytesIO):
+        for bio in src:
+            c_buffer = bio.getbuffer()  # check if empty?
+            c_host_buffers.push_back(host_buffer(<char*>&c_buffer[0],
+                                                 c_buffer.shape[0]))
+    # Otherwise src is expected to be a numeric fd, string path, or PathLike.
+    # TODO (ptaylor): Might need to update this check if accepted input types
+    #                 change when UCX and/or cuStreamz support is added.
+    elif isinstance(src[0], Datasource):
+        for csrc in src:
+            c_datasources.push_back(csrc.get_datasource())
+        return source_info(c_datasources)
+    elif isinstance(src[0], (int, float, complex, basestring, os.PathLike)):
+        # If source is a file, return source_info where type=FILEPATH
+        if not all(os.path.isfile(file) for file in src):
+            raise FileNotFoundError(errno.ENOENT,
+                                    os.strerror(errno.ENOENT),
+                                    src)
+
+        files = [<string> str(elem).encode() for elem in src]
+        c_files = files
+        return source_info(c_files)
+    else:
+        raise TypeError("Unrecognized input type: {}".format(type(src[0])))
+
+    if empty_buffer is True:
+        c_host_buffers.push_back(host_buffer(<char*>NULL, 0))
+
+    return source_info(c_host_buffers)
+
+# Converts the Python sink input to libcudf IO sink_info.
+cdef sink_info make_sinks_info(
+    list src, vector[unique_ptr[data_sink]] & sink
+) except*:
+    cdef vector[data_sink *] data_sinks
+    cdef vector[string] paths
+    if isinstance(src[0], io.StringIO):
+        data_sinks.reserve(len(src))
+        for s in src:
+            sink.push_back(unique_ptr[data_sink](new iobase_data_sink(s)))
+            data_sinks.push_back(sink.back().get())
+        return sink_info(data_sinks)
+    elif isinstance(src[0], io.TextIOBase):
+        data_sinks.reserve(len(src))
+        for s in src:
+            # Files opened in text mode expect writes to be str rather than
+            # bytes, which requires conversion from utf-8. If the underlying
+            # buffer is utf-8, we can bypass this conversion by writing
+            # directly to it.
+            if codecs.lookup(s.encoding).name not in {"utf-8", "ascii"}:
+                raise NotImplementedError(f"Unsupported encoding {s.encoding}")
+            sink.push_back(
+                unique_ptr[data_sink](new iobase_data_sink(s.buffer))
+            )
+            data_sinks.push_back(sink.back().get())
+        return sink_info(data_sinks)
+    elif isinstance(src[0], io.IOBase):
+        data_sinks.reserve(len(src))
+        for s in src:
+            sink.push_back(unique_ptr[data_sink](new iobase_data_sink(s)))
+            data_sinks.push_back(sink.back().get())
+        return sink_info(data_sinks)
+    elif isinstance(src[0], (basestring, os.PathLike)):
+        paths.reserve(len(src))
+        for s in src:
+            paths.push_back(<string> os.path.expanduser(s).encode())
+        return sink_info(move(paths))
+    else:
+        raise TypeError("Unrecognized input type: {}".format(type(src)))
+
+
+cdef sink_info make_sink_info(src, unique_ptr[data_sink] & sink) except*:
+    cdef vector[unique_ptr[data_sink]] datasinks
+    cdef sink_info info = make_sinks_info([src], datasinks)
+    if not datasinks.empty():
+        sink.swap(datasinks[0])
+    return info
+
+
+# Adapts a python io.IOBase object as a libcudf IO data_sink. This lets you
+# write from cudf to any python file-like object (File/BytesIO/SocketIO etc)
+cdef cppclass iobase_data_sink(data_sink):
+    object buf
+
+    iobase_data_sink(object buf_):
+        this.buf = buf_
+
+    void host_write(const void * data, size_t size) with gil:
+        if isinstance(buf, io.StringIO):
+            buf.write(PyMemoryView_FromMemory(<char*>data, size, PyBUF_READ)
+                      .tobytes().decode())
+        else:
+            buf.write(PyMemoryView_FromMemory(<char*>data, size, PyBUF_READ))
+
+    void flush() with gil:
+        buf.flush()
+
+    size_t bytes_written() with gil:
+        return buf.tell()
+
+
+cdef update_struct_field_names(
+    table,
+    vector[column_name_info]& schema_info
+):
+    for i, (name, col) in enumerate(table._data.items()):
+        table._data[name] = update_column_struct_field_names(
+            col, schema_info[i]
+        )
+
+
+cdef Column update_column_struct_field_names(
+    Column col,
+    column_name_info& info
+):
+    cdef vector[string] field_names
+
+    if col.children:
+        children = list(col.children)
+        for i, child in enumerate(children):
+            children[i] = update_column_struct_field_names(
+                child,
+                info.children[i]
+            )
+        col.set_base_children(tuple(children))
+
+    if is_struct_dtype(col):
+        field_names.reserve(len(col.base_children))
+        for i in range(info.children.size()):
+            field_names.push_back(info.children[i].name)
+        col = col._rename_fields(
+            field_names
+        )
+
+    return col
diff --git a/python/cudf/cudf/_lib/join.pyx b/python/cudf/cudf/_lib/join.pyx
new file mode 100644
index 0000000..416680a
--- /dev/null
+++ b/python/cudf/cudf/_lib/join.pyx
@@ -0,0 +1,74 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+
+from rmm._lib.device_buffer cimport device_buffer
+
+cimport cudf._lib.cpp.join as cpp_join
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport data_type, size_type, type_id
+from cudf._lib.utils cimport table_view_from_columns
+
+# The functions below return the *gathermaps* that represent
+# the join result when joining on the keys `lhs` and `rhs`.
+
+
+@acquire_spill_lock()
+def join(list lhs, list rhs, how=None):
+    cdef pair[cpp_join.gather_map_type, cpp_join.gather_map_type] c_result
+    cdef table_view c_lhs = table_view_from_columns(lhs)
+    cdef table_view c_rhs = table_view_from_columns(rhs)
+
+    if how == "inner":
+        with nogil:
+            c_result = move(cpp_join.inner_join(c_lhs, c_rhs))
+    elif how == "left":
+        with nogil:
+            c_result = move(cpp_join.left_join(c_lhs, c_rhs))
+    elif how == "outer":
+        with nogil:
+            c_result = move(cpp_join.full_join(c_lhs, c_rhs))
+    else:
+        raise ValueError(f"Invalid join type {how}")
+
+    cdef Column left_rows = _gather_map_as_column(move(c_result.first))
+    cdef Column right_rows = _gather_map_as_column(move(c_result.second))
+    return left_rows, right_rows
+
+
+@acquire_spill_lock()
+def semi_join(list lhs, list rhs, how=None):
+    # left-semi and left-anti joins
+    cdef cpp_join.gather_map_type c_result
+    cdef table_view c_lhs = table_view_from_columns(lhs)
+    cdef table_view c_rhs = table_view_from_columns(rhs)
+
+    if how == "leftsemi":
+        with nogil:
+            c_result = move(cpp_join.left_semi_join(c_lhs, c_rhs))
+    elif how == "leftanti":
+        with nogil:
+            c_result = move(cpp_join.left_anti_join(c_lhs, c_rhs))
+    else:
+        raise ValueError(f"Invalid join type {how}")
+
+    cdef Column left_rows = _gather_map_as_column(move(c_result))
+    return left_rows, None
+
+
+cdef Column _gather_map_as_column(cpp_join.gather_map_type gather_map):
+    # help to convert a gather map to a Column
+    cdef device_buffer c_empty
+    cdef size_type size = gather_map.get()[0].size()
+    cdef unique_ptr[column] c_col = move(make_unique[column](
+        data_type(type_id.INT32),
+        size,
+        gather_map.get()[0].release(), move(c_empty), 0))
+    return Column.from_unique_ptr(move(c_col))
diff --git a/python/cudf/cudf/_lib/json.pyx b/python/cudf/cudf/_lib/json.pyx
new file mode 100644
index 0000000..437c3ef
--- /dev/null
+++ b/python/cudf/cudf/_lib/json.pyx
@@ -0,0 +1,268 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+# cython: boundscheck = False
+
+import io
+import os
+from collections import abc
+
+import cudf
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+from cudf._lib.cpp.io.data_sink cimport data_sink
+from cudf._lib.cpp.io.json cimport (
+    json_reader_options,
+    json_writer_options,
+    read_json as libcudf_read_json,
+    schema_element,
+    write_json as libcudf_write_json,
+)
+from cudf._lib.cpp.io.types cimport (
+    column_name_info,
+    compression_type,
+    sink_info,
+    table_metadata,
+    table_with_metadata,
+)
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport data_type, size_type
+from cudf._lib.io.utils cimport (
+    make_sink_info,
+    make_source_info,
+    update_struct_field_names,
+)
+from cudf._lib.types cimport dtype_to_data_type
+from cudf._lib.utils cimport data_from_unique_ptr, table_view_from_table
+
+from cudf.api.types import is_list_dtype, is_struct_dtype
+
+from cudf._lib.column cimport Column
+
+
+cpdef read_json(object filepaths_or_buffers,
+                object dtype,
+                bool lines,
+                object compression,
+                object byte_range,
+                bool legacy,
+                bool keep_quotes):
+    """
+    Cython function to call into libcudf API, see `read_json`.
+
+    See Also
+    --------
+    cudf.io.json.read_json
+    cudf.io.json.to_json
+    """
+
+    # If input data is a JSON string (or StringIO), hold a reference to
+    # the encoded memoryview externally to ensure the encoded buffer
+    # isn't destroyed before calling libcudf `read_json()`
+    for idx in range(len(filepaths_or_buffers)):
+        if isinstance(filepaths_or_buffers[idx], io.StringIO):
+            filepaths_or_buffers[idx] = \
+                filepaths_or_buffers[idx].read().encode()
+        elif isinstance(filepaths_or_buffers[idx], str) and \
+                not os.path.isfile(filepaths_or_buffers[idx]):
+            filepaths_or_buffers[idx] = filepaths_or_buffers[idx].encode()
+
+    # Setup arguments
+    cdef vector[data_type] c_dtypes_list
+    cdef map[string, schema_element] c_dtypes_schema_map
+    cdef cudf_io_types.compression_type c_compression
+    # Determine byte read offsets if applicable
+    cdef size_type c_range_offset = (
+        byte_range[0] if byte_range is not None else 0
+    )
+    cdef size_type c_range_size = (
+        byte_range[1] if byte_range is not None else 0
+    )
+    cdef bool c_lines = lines
+
+    if compression is not None:
+        if compression == 'gzip':
+            c_compression = cudf_io_types.compression_type.GZIP
+        elif compression == 'bz2':
+            c_compression = cudf_io_types.compression_type.BZIP2
+        elif compression == 'zip':
+            c_compression = cudf_io_types.compression_type.ZIP
+        else:
+            c_compression = cudf_io_types.compression_type.AUTO
+    else:
+        c_compression = cudf_io_types.compression_type.NONE
+    is_list_like_dtypes = False
+    if dtype is False:
+        raise ValueError("False value is unsupported for `dtype`")
+    elif dtype is not True:
+        if isinstance(dtype, abc.Mapping):
+            for k, v in dtype.items():
+                c_dtypes_schema_map[str(k).encode()] = \
+                    _get_cudf_schema_element_from_dtype(v)
+        elif isinstance(dtype, abc.Collection):
+            is_list_like_dtypes = True
+            c_dtypes_list.reserve(len(dtype))
+            for col_dtype in dtype:
+                c_dtypes_list.push_back(
+                    _get_cudf_data_type_from_dtype(
+                        col_dtype))
+        else:
+            raise TypeError("`dtype` must be 'list like' or 'dict'")
+
+    cdef json_reader_options opts = move(
+        json_reader_options.builder(make_source_info(filepaths_or_buffers))
+        .compression(c_compression)
+        .lines(c_lines)
+        .byte_range_offset(c_range_offset)
+        .byte_range_size(c_range_size)
+        .legacy(legacy)
+        .build()
+    )
+    if is_list_like_dtypes:
+        opts.set_dtypes(c_dtypes_list)
+    else:
+        opts.set_dtypes(c_dtypes_schema_map)
+
+    opts.enable_keep_quotes(keep_quotes)
+    # Read JSON
+    cdef cudf_io_types.table_with_metadata c_result
+
+    with nogil:
+        c_result = move(libcudf_read_json(opts))
+
+    meta_names = [info.name.decode() for info in c_result.metadata.schema_info]
+    df = cudf.DataFrame._from_data(*data_from_unique_ptr(
+        move(c_result.tbl),
+        column_names=meta_names
+    ))
+
+    update_struct_field_names(df, c_result.metadata.schema_info)
+
+    return df
+
+
+@acquire_spill_lock()
+def write_json(
+    table,
+    object path_or_buf=None,
+    object na_rep="null",
+    bool include_nulls=True,
+    bool lines=False,
+    bool index=False,
+    int rows_per_chunk=1024*64,  # 64K rows
+):
+    """
+    Cython function to call into libcudf API, see `write_json`.
+
+    See Also
+    --------
+    cudf.to_json
+    """
+    cdef table_view input_table_view = table_view_from_table(
+        table, ignore_index=True
+    )
+
+    cdef unique_ptr[data_sink] data_sink_c
+    cdef sink_info sink_info_c = make_sink_info(path_or_buf, data_sink_c)
+    cdef string na_c = na_rep.encode()
+    cdef bool include_nulls_c = include_nulls
+    cdef bool lines_c = lines
+    cdef int rows_per_chunk_c = rows_per_chunk
+    cdef string true_value_c = 'true'.encode()
+    cdef string false_value_c = 'false'.encode()
+    cdef table_metadata tbl_meta
+
+    num_index_cols_meta = 0
+    cdef column_name_info child_info
+    for i, name in enumerate(table._column_names, num_index_cols_meta):
+        child_info.name = name.encode()
+        tbl_meta.schema_info.push_back(child_info)
+        _set_col_children_metadata(
+            table[name]._column,
+            tbl_meta.schema_info[i]
+        )
+
+    cdef json_writer_options options = move(
+        json_writer_options.builder(sink_info_c, input_table_view)
+        .metadata(tbl_meta)
+        .na_rep(na_c)
+        .include_nulls(include_nulls_c)
+        .lines(lines_c)
+        .rows_per_chunk(rows_per_chunk_c)
+        .true_value(true_value_c)
+        .false_value(false_value_c)
+        .build()
+    )
+
+    try:
+        with nogil:
+            libcudf_write_json(options)
+    except OverflowError:
+        raise OverflowError(
+            f"Writing JSON file with rows_per_chunk={rows_per_chunk} failed. "
+            "Consider providing a smaller rows_per_chunk argument."
+        )
+
+
+cdef schema_element _get_cudf_schema_element_from_dtype(object dtype) except *:
+    cdef schema_element s_element
+    cdef data_type lib_type
+    if cudf.api.types.is_categorical_dtype(dtype):
+        raise NotImplementedError(
+            "CategoricalDtype as dtype is not yet "
+            "supported in JSON reader"
+        )
+
+    dtype = cudf.dtype(dtype)
+    lib_type = dtype_to_data_type(dtype)
+    s_element.type = lib_type
+    if isinstance(dtype, cudf.StructDtype):
+        for name, child_type in dtype.fields.items():
+            s_element.child_types[name.encode()] = \
+                _get_cudf_schema_element_from_dtype(child_type)
+    elif isinstance(dtype, cudf.ListDtype):
+        s_element.child_types["offsets".encode()] = \
+            _get_cudf_schema_element_from_dtype(cudf.dtype("int32"))
+        s_element.child_types["element".encode()] = \
+            _get_cudf_schema_element_from_dtype(dtype.element_type)
+
+    return s_element
+
+
+cdef data_type _get_cudf_data_type_from_dtype(object dtype) except *:
+    if cudf.api.types.is_categorical_dtype(dtype):
+        raise NotImplementedError(
+            "CategoricalDtype as dtype is not yet "
+            "supported in JSON reader"
+        )
+
+    dtype = cudf.dtype(dtype)
+    return dtype_to_data_type(dtype)
+
+cdef _set_col_children_metadata(Column col,
+                                column_name_info& col_meta):
+    cdef column_name_info child_info
+    if is_struct_dtype(col):
+        for i, (child_col, name) in enumerate(
+            zip(col.children, list(col.dtype.fields))
+        ):
+            child_info.name = name.encode()
+            col_meta.children.push_back(child_info)
+            _set_col_children_metadata(
+                child_col, col_meta.children[i]
+            )
+    elif is_list_dtype(col):
+        for i, child_col in enumerate(col.children):
+            col_meta.children.push_back(child_info)
+            _set_col_children_metadata(
+                child_col, col_meta.children[i]
+            )
+    else:
+        return
diff --git a/python/cudf/cudf/_lib/labeling.pyx b/python/cudf/cudf/_lib/labeling.pyx
new file mode 100644
index 0000000..2c2538a
--- /dev/null
+++ b/python/cudf/cudf/_lib/labeling.pyx
@@ -0,0 +1,43 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool as cbool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.labeling cimport inclusive, label_bins as cpp_label_bins
+
+
+# Note that the parameter input shadows a Python built-in in the local scope,
+# but I'm not too concerned about that since there's no use-case for actual
+# input in this context.
+@acquire_spill_lock()
+def label_bins(Column input, Column left_edges, cbool left_inclusive,
+               Column right_edges, cbool right_inclusive):
+    cdef inclusive c_left_inclusive = \
+        inclusive.YES if left_inclusive else inclusive.NO
+    cdef inclusive c_right_inclusive = \
+        inclusive.YES if right_inclusive else inclusive.NO
+
+    cdef column_view input_view = input.view()
+    cdef column_view left_edges_view = left_edges.view()
+    cdef column_view right_edges_view = right_edges.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_label_bins(
+                input_view,
+                left_edges_view,
+                c_left_inclusive,
+                right_edges_view,
+                c_right_inclusive,
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/lists.pyx b/python/cudf/cudf/_lib/lists.pyx
new file mode 100644
index 0000000..199641f
--- /dev/null
+++ b/python/cudf/cudf/_lib/lists.pyx
@@ -0,0 +1,246 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport make_shared, shared_ptr, unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.lists.combine cimport (
+    concatenate_list_elements as cpp_concatenate_list_elements,
+    concatenate_null_policy,
+    concatenate_rows as cpp_concatenate_rows,
+)
+from cudf._lib.cpp.lists.contains cimport contains, index_of as cpp_index_of
+from cudf._lib.cpp.lists.count_elements cimport (
+    count_elements as cpp_count_elements,
+)
+from cudf._lib.cpp.lists.explode cimport explode_outer as cpp_explode_outer
+from cudf._lib.cpp.lists.extract cimport extract_list_element
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.cpp.lists.sorting cimport sort_lists as cpp_sort_lists
+from cudf._lib.cpp.lists.stream_compaction cimport distinct as cpp_distinct
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport (
+    nan_equality,
+    null_equality,
+    null_order,
+    order,
+    size_type,
+)
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def count_elements(Column col):
+
+    # shared_ptr required because lists_column_view has no default
+    # ctor
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_count_elements(list_view.get()[0]))
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def explode_outer(
+    list source_columns, int explode_column_idx
+):
+    cdef table_view c_table_view = table_view_from_columns(source_columns)
+    cdef size_type c_explode_column_idx = explode_column_idx
+
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(cpp_explode_outer(c_table_view, c_explode_column_idx))
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def distinct(Column col, bool nulls_equal, bool nans_all_equal):
+    """
+    nulls_equal == True indicates that libcudf should treat any two nulls as
+    equal, and as unequal otherwise.
+    nans_all_equal == True indicates that libcudf should treat any two
+    elements from {-nan, +nan} as equal, and as unequal otherwise.
+    """
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+    cdef null_equality c_nulls_equal = (
+        null_equality.EQUAL if nulls_equal else null_equality.UNEQUAL
+    )
+    cdef nan_equality c_nans_equal = (
+        nan_equality.ALL_EQUAL if nans_all_equal else nan_equality.NANS_UNEQUAL
+    )
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_distinct(list_view.get()[0],
+                         c_nulls_equal,
+                         c_nans_equal)
+        )
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def sort_lists(Column col, bool ascending, str na_position):
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+    cdef order c_sort_order = (
+        order.ASCENDING if ascending else order.DESCENDING
+    )
+    cdef null_order c_null_prec = (
+        null_order.BEFORE if na_position == "first" else null_order.AFTER
+    )
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_sort_lists(list_view.get()[0], c_sort_order, c_null_prec)
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def extract_element_scalar(Column col, size_type index):
+    # shared_ptr required because lists_column_view has no default
+    # ctor
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(extract_list_element(list_view.get()[0], index))
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def extract_element_column(Column col, Column index):
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+
+    cdef column_view index_view = index.view()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(extract_list_element(list_view.get()[0], index_view))
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def contains_scalar(Column col, object py_search_key):
+
+    cdef DeviceScalar search_key = py_search_key.device_value
+
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+    cdef const scalar* search_key_value = search_key.get_raw_ptr()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(contains(
+            list_view.get()[0],
+            search_key_value[0],
+        ))
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
+@acquire_spill_lock()
+def index_of_scalar(Column col, object py_search_key):
+
+    cdef DeviceScalar search_key = py_search_key.device_value
+
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+    cdef const scalar* search_key_value = search_key.get_raw_ptr()
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_index_of(
+            list_view.get()[0],
+            search_key_value[0],
+        ))
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def index_of_column(Column col, Column search_keys):
+
+    cdef column_view keys_view = search_keys.view()
+
+    cdef shared_ptr[lists_column_view] list_view = (
+        make_shared[lists_column_view](col.view())
+    )
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_index_of(
+            list_view.get()[0],
+            keys_view,
+        ))
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def concatenate_rows(list source_columns):
+    cdef unique_ptr[column] c_result
+
+    cdef table_view c_table_view = table_view_from_columns(source_columns)
+
+    with nogil:
+        c_result = move(cpp_concatenate_rows(
+            c_table_view,
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def concatenate_list_elements(Column input_column, dropna=False):
+    cdef concatenate_null_policy policy = (
+        concatenate_null_policy.IGNORE if dropna
+        else concatenate_null_policy.NULLIFY_OUTPUT_ROW
+    )
+    cdef column_view c_input = input_column.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_concatenate_list_elements(
+            c_input,
+            policy
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/merge.pyx b/python/cudf/cudf/_lib/merge.pyx
new file mode 100644
index 0000000..935d8c6
--- /dev/null
+++ b/python/cudf/cudf/_lib/merge.pyx
@@ -0,0 +1,66 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.merge cimport merge as cpp_merge
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+def merge_sorted(
+    list input_columns,
+    list key_columns_indices,
+    bool ascending=True,
+    str na_position="last",
+):
+    """Merge multiple lists of lexicographically sorted columns into one list
+    of sorted columns. `input_columns` is a list of lists of columns to be
+    merged.
+    """
+    cdef vector[libcudf_types.size_type] c_column_keys = key_columns_indices
+    cdef vector[table_view] c_input_tables
+    cdef vector[libcudf_types.order] c_column_order
+    cdef vector[libcudf_types.null_order] c_null_precedence
+
+    c_input_tables.reserve(len(input_columns))
+    for source_columns in input_columns:
+        c_input_tables.push_back(
+            table_view_from_columns(source_columns))
+
+    num_keys = len(key_columns_indices)
+
+    cdef libcudf_types.order column_order = (
+        libcudf_types.order.ASCENDING if ascending
+        else libcudf_types.order.DESCENDING
+    )
+    c_column_order = vector[libcudf_types.order](num_keys, column_order)
+
+    if not ascending:
+        na_position = "last" if na_position == "first" else "first"
+    cdef libcudf_types.null_order null_precedence = (
+        libcudf_types.null_order.BEFORE if na_position == "first"
+        else libcudf_types.null_order.AFTER
+    )
+    c_null_precedence = vector[libcudf_types.null_order](
+        num_keys,
+        null_precedence
+    )
+
+    # Perform sorted merge operation
+    cdef unique_ptr[table] c_result
+    with nogil:
+        c_result = move(
+            cpp_merge(
+                c_input_tables,
+                c_column_keys,
+                c_column_order,
+                c_null_precedence,
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/null_mask.pyx b/python/cudf/cudf/_lib/null_mask.pyx
new file mode 100644
index 0000000..5b45386
--- /dev/null
+++ b/python/cudf/cudf/_lib/null_mask.pyx
@@ -0,0 +1,130 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from enum import Enum
+
+from rmm._lib.device_buffer cimport DeviceBuffer, device_buffer
+
+from cudf.core.buffer import acquire_spill_lock, as_buffer
+
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.null_mask cimport (
+    bitmask_allocation_size_bytes as cpp_bitmask_allocation_size_bytes,
+    bitmask_and as cpp_bitmask_and,
+    bitmask_or as cpp_bitmask_or,
+    copy_bitmask as cpp_copy_bitmask,
+    create_null_mask as cpp_create_null_mask,
+    underlying_type_t_mask_state,
+)
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport mask_state, size_type
+from cudf._lib.utils cimport table_view_from_columns
+
+
+class MaskState(Enum):
+    """
+    Enum for null mask creation state
+    """
+    UNALLOCATED = <underlying_type_t_mask_state> mask_state.UNALLOCATED
+    UNINITIALIZED = <underlying_type_t_mask_state> mask_state.UNINITIALIZED
+    ALL_VALID = <underlying_type_t_mask_state> mask_state.ALL_VALID
+    ALL_NULL = <underlying_type_t_mask_state> mask_state.ALL_NULL
+
+
+@acquire_spill_lock()
+def copy_bitmask(Column col):
+    """
+    Copies column's validity mask buffer into a new buffer, shifting by the
+    offset if nonzero
+    """
+    if col.base_mask is None:
+        return None
+
+    cdef column_view col_view = col.view()
+    cdef device_buffer db
+    cdef unique_ptr[device_buffer] up_db
+
+    with nogil:
+        db = move(cpp_copy_bitmask(col_view))
+        up_db = move(make_unique[device_buffer](move(db)))
+
+    rmm_db = DeviceBuffer.c_from_unique_ptr(move(up_db))
+    buf = as_buffer(rmm_db)
+    return buf
+
+
+def bitmask_allocation_size_bytes(size_type num_bits):
+    """
+    Given a size, calculates the number of bytes that should be allocated for a
+    column validity mask
+    """
+    cdef size_t output_size
+
+    with nogil:
+        output_size = cpp_bitmask_allocation_size_bytes(num_bits)
+
+    return output_size
+
+
+def create_null_mask(size_type size, state=MaskState.UNINITIALIZED):
+    """
+    Given a size and a mask state, allocate a mask that can properly represent
+    the given size with the given mask state
+
+    Parameters
+    ----------
+    size : int
+        Number of elements the mask needs to be able to represent
+    state : ``MaskState``, default ``MaskState.UNINITIALIZED``
+        State the null mask should be created in
+    """
+    if not isinstance(state, MaskState):
+        raise TypeError(
+            "`state` is required to be of type `MaskState`, got "
+            + (type(state).__name__)
+        )
+
+    cdef device_buffer db
+    cdef unique_ptr[device_buffer] up_db
+    cdef mask_state c_mask_state = <mask_state>(
+        <underlying_type_t_mask_state>(state.value)
+    )
+
+    with nogil:
+        db = move(cpp_create_null_mask(size, c_mask_state))
+        up_db = move(make_unique[device_buffer](move(db)))
+
+    rmm_db = DeviceBuffer.c_from_unique_ptr(move(up_db))
+    buf = as_buffer(rmm_db)
+    return buf
+
+
+@acquire_spill_lock()
+def bitmask_and(columns: list):
+    cdef table_view c_view = table_view_from_columns(columns)
+    cdef pair[device_buffer, size_type] c_result
+    cdef unique_ptr[device_buffer] up_db
+    with nogil:
+        c_result = move(cpp_bitmask_and(c_view))
+        up_db = move(make_unique[device_buffer](move(c_result.first)))
+    dbuf = DeviceBuffer.c_from_unique_ptr(move(up_db))
+    buf = as_buffer(dbuf)
+    return buf, c_result.second
+
+
+@acquire_spill_lock()
+def bitmask_or(columns: list):
+    cdef table_view c_view = table_view_from_columns(columns)
+    cdef pair[device_buffer, size_type] c_result
+    cdef unique_ptr[device_buffer] up_db
+    with nogil:
+        c_result = move(cpp_bitmask_or(c_view))
+        up_db = move(make_unique[device_buffer](move(c_result.first)))
+    dbuf = DeviceBuffer.c_from_unique_ptr(move(up_db))
+    buf = as_buffer(dbuf)
+    return buf, c_result.second
diff --git a/python/cudf/cudf/_lib/nvtext/CMakeLists.txt b/python/cudf/cudf/_lib/nvtext/CMakeLists.txt
new file mode 100644
index 0000000..515b9c1
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources
+    edit_distance.pyx generate_ngrams.pyx jaccard.pyx minhash.pyx ngrams_tokenize.pyx normalize.pyx
+    replace.pyx stemmer.pyx subword_tokenize.pyx tokenize.pyx
+)
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX nvtext_ ASSOCIATED_TARGETS cudf
+)
diff --git a/python/cudf/cudf/_lib/nvtext/__init__.pxd b/python/cudf/cudf/_lib/nvtext/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/nvtext/__init__.py b/python/cudf/cudf/_lib/nvtext/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/nvtext/edit_distance.pyx b/python/cudf/cudf/_lib/nvtext/edit_distance.pyx
new file mode 100644
index 0000000..984c8e8
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/edit_distance.pyx
@@ -0,0 +1,37 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.edit_distance cimport (
+    edit_distance as cpp_edit_distance,
+    edit_distance_matrix as cpp_edit_distance_matrix,
+)
+
+
+@acquire_spill_lock()
+def edit_distance(Column strings, Column targets):
+    cdef column_view c_strings = strings.view()
+    cdef column_view c_targets = targets.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_edit_distance(c_strings, c_targets))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def edit_distance_matrix(Column strings):
+    cdef column_view c_strings = strings.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_edit_distance_matrix(c_strings))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/generate_ngrams.pyx b/python/cudf/cudf/_lib/nvtext/generate_ngrams.pyx
new file mode 100644
index 0000000..96b95c8
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/generate_ngrams.pyx
@@ -0,0 +1,75 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.generate_ngrams cimport (
+    generate_character_ngrams as cpp_generate_character_ngrams,
+    generate_ngrams as cpp_generate_ngrams,
+    hash_character_ngrams as cpp_hash_character_ngrams,
+)
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def generate_ngrams(Column strings, int ngrams, object py_separator):
+
+    cdef DeviceScalar separator = py_separator.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef size_type c_ngrams = ngrams
+    cdef const string_scalar* c_separator = <const string_scalar*>separator\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_generate_ngrams(
+                c_strings,
+                c_ngrams,
+                c_separator[0]
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def generate_character_ngrams(Column strings, int ngrams):
+    cdef column_view c_strings = strings.view()
+    cdef size_type c_ngrams = ngrams
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_generate_character_ngrams(
+                c_strings,
+                c_ngrams
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def hash_character_ngrams(Column strings, int ngrams):
+    cdef column_view c_strings = strings.view()
+    cdef size_type c_ngrams = ngrams
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_hash_character_ngrams(
+                c_strings,
+                c_ngrams
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/jaccard.pyx b/python/cudf/cudf/_lib/nvtext/jaccard.pyx
new file mode 100644
index 0000000..9035e74
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/jaccard.pyx
@@ -0,0 +1,31 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.jaccard cimport jaccard_index as cpp_jaccard_index
+from cudf._lib.cpp.types cimport size_type
+
+
+@acquire_spill_lock()
+def jaccard_index(Column input1, Column input2, int width):
+    cdef column_view c_input1 = input1.view()
+    cdef column_view c_input2 = input2.view()
+    cdef size_type c_width = width
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_jaccard_index(
+                c_input1,
+                c_input2,
+                c_width
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/minhash.pyx b/python/cudf/cudf/_lib/nvtext/minhash.pyx
new file mode 100644
index 0000000..6ed5ca8
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/minhash.pyx
@@ -0,0 +1,55 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.minhash cimport (
+    minhash as cpp_minhash,
+    minhash64 as cpp_minhash64,
+)
+from cudf._lib.cpp.types cimport size_type
+
+
+@acquire_spill_lock()
+def minhash(Column strings, Column seeds, int width):
+
+    cdef column_view c_strings = strings.view()
+    cdef size_type c_width = width
+    cdef column_view c_seeds = seeds.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_minhash(
+                c_strings,
+                c_seeds,
+                c_width
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def minhash64(Column strings, Column seeds, int width):
+
+    cdef column_view c_strings = strings.view()
+    cdef size_type c_width = width
+    cdef column_view c_seeds = seeds.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_minhash64(
+                c_strings,
+                c_seeds,
+                c_width
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/ngrams_tokenize.pyx b/python/cudf/cudf/_lib/nvtext/ngrams_tokenize.pyx
new file mode 100644
index 0000000..3e7911c
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/ngrams_tokenize.pyx
@@ -0,0 +1,48 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.ngrams_tokenize cimport (
+    ngrams_tokenize as cpp_ngrams_tokenize,
+)
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def ngrams_tokenize(
+    Column strings,
+    int ngrams,
+    object py_delimiter,
+    object py_separator
+):
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+    cdef DeviceScalar separator = py_separator.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef size_type c_ngrams = ngrams
+    cdef const string_scalar* c_separator = <const string_scalar*>separator\
+        .get_raw_ptr()
+    cdef const string_scalar* c_delimiter = <const string_scalar*>delimiter\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_ngrams_tokenize(
+                c_strings,
+                c_ngrams,
+                c_delimiter[0],
+                c_separator[0]
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/normalize.pyx b/python/cudf/cudf/_lib/nvtext/normalize.pyx
new file mode 100644
index 0000000..80c6ef7
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/normalize.pyx
@@ -0,0 +1,37 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.normalize cimport (
+    normalize_characters as cpp_normalize_characters,
+    normalize_spaces as cpp_normalize_spaces,
+)
+
+
+@acquire_spill_lock()
+def normalize_spaces(Column strings):
+    cdef column_view c_strings = strings.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_normalize_spaces(c_strings))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def normalize_characters(Column strings, bool do_lower=True):
+    cdef column_view c_strings = strings.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_normalize_characters(c_strings, do_lower))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/replace.pyx b/python/cudf/cudf/_lib/nvtext/replace.pyx
new file mode 100644
index 0000000..289e561
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/replace.pyx
@@ -0,0 +1,87 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.replace cimport (
+    filter_tokens as cpp_filter_tokens,
+    replace_tokens as cpp_replace_tokens,
+)
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def replace_tokens(Column strings,
+                   Column targets,
+                   Column replacements,
+                   object py_delimiter):
+    """
+    The `targets` tokens are searched for within each `strings`
+    in the Column and replaced with the corresponding `replacements`
+    if found. Tokens are identified by the `py_delimiter` character
+    provided.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef column_view c_targets = targets.view()
+    cdef column_view c_replacements = replacements.view()
+
+    cdef const string_scalar* c_delimiter = <const string_scalar*>delimiter\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_replace_tokens(
+                c_strings,
+                c_targets,
+                c_replacements,
+                c_delimiter[0],
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def filter_tokens(Column strings,
+                  size_type min_token_length,
+                  object py_replacement,
+                  object py_delimiter):
+    """
+    Tokens smaller than `min_token_length` are removed from `strings`
+    in the Column and optionally replaced with the corresponding
+    `py_replacement` string. Tokens are identified by the `py_delimiter`
+    character provided.
+    """
+
+    cdef DeviceScalar replacement = py_replacement.device_value
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef const string_scalar* c_repl = <const string_scalar*>replacement\
+        .get_raw_ptr()
+    cdef const string_scalar* c_delimiter = <const string_scalar*>delimiter\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_filter_tokens(
+                c_strings,
+                min_token_length,
+                c_repl[0],
+                c_delimiter[0],
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/stemmer.pyx b/python/cudf/cudf/_lib/nvtext/stemmer.pyx
new file mode 100644
index 0000000..c690aba
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/stemmer.pyx
@@ -0,0 +1,68 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from enum import IntEnum
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.stemmer cimport (
+    is_letter as cpp_is_letter,
+    letter_type,
+    porter_stemmer_measure as cpp_porter_stemmer_measure,
+    underlying_type_t_letter_type,
+)
+from cudf._lib.cpp.types cimport size_type
+
+
+class LetterType(IntEnum):
+    CONSONANT = <underlying_type_t_letter_type> letter_type.CONSONANT
+    VOWEL = <underlying_type_t_letter_type> letter_type.VOWEL
+
+
+@acquire_spill_lock()
+def porter_stemmer_measure(Column strings):
+    cdef column_view c_strings = strings.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_porter_stemmer_measure(c_strings))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_letter(Column strings,
+              object ltype,
+              size_type index):
+    cdef column_view c_strings = strings.view()
+    cdef letter_type c_ltype = <letter_type>(
+        <underlying_type_t_letter_type> ltype
+    )
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_is_letter(c_strings, c_ltype, index))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_letter_multi(Column strings,
+                    object ltype,
+                    Column indices):
+    cdef column_view c_strings = strings.view()
+    cdef column_view c_indices = indices.view()
+    cdef letter_type c_ltype = <letter_type>(
+        <underlying_type_t_letter_type> ltype
+    )
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_is_letter(c_strings, c_ltype, c_indices))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/nvtext/subword_tokenize.pyx b/python/cudf/cudf/_lib/nvtext/subword_tokenize.pyx
new file mode 100644
index 0000000..bf675a1
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/subword_tokenize.pyx
@@ -0,0 +1,61 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint32_t
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.subword_tokenize cimport (
+    hashed_vocabulary as cpp_hashed_vocabulary,
+    load_vocabulary_file as cpp_load_vocabulary_file,
+    move as tr_move,
+    subword_tokenize as cpp_subword_tokenize,
+    tokenizer_result as cpp_tokenizer_result,
+)
+
+
+cdef class Hashed_Vocabulary:
+    cdef unique_ptr[cpp_hashed_vocabulary] c_obj
+
+    def __cinit__(self, hash_file):
+        cdef string c_hash_file = <string>str(hash_file).encode()
+        with nogil:
+            self.c_obj = move(cpp_load_vocabulary_file(c_hash_file))
+
+
+@acquire_spill_lock()
+def subword_tokenize_inmem_hash(
+    Column strings,
+    Hashed_Vocabulary hashed_vocabulary,
+    uint32_t max_sequence_length=64,
+    uint32_t stride=48,
+    bool do_lower=True,
+    bool do_truncate=False,
+):
+    """
+    Subword tokenizes text series by using the pre-loaded hashed vocabulary
+    """
+    cdef column_view c_strings = strings.view()
+    cdef cpp_tokenizer_result c_result
+    with nogil:
+        c_result = tr_move(
+            cpp_subword_tokenize(
+                c_strings,
+                hashed_vocabulary.c_obj.get()[0],
+                max_sequence_length,
+                stride,
+                do_lower,
+                do_truncate,
+            )
+        )
+    # return the 3 tensor components
+    tokens = Column.from_unique_ptr(move(c_result.tensor_token_ids))
+    masks = Column.from_unique_ptr(move(c_result.tensor_attention_mask))
+    metadata = Column.from_unique_ptr(move(c_result.tensor_metadata))
+    return tokens, masks, metadata
diff --git a/python/cudf/cudf/_lib/nvtext/tokenize.pyx b/python/cudf/cudf/_lib/nvtext/tokenize.pyx
new file mode 100644
index 0000000..bee9d6f
--- /dev/null
+++ b/python/cudf/cudf/_lib/nvtext/tokenize.pyx
@@ -0,0 +1,162 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.nvtext.tokenize cimport (
+    character_tokenize as cpp_character_tokenize,
+    count_tokens as cpp_count_tokens,
+    detokenize as cpp_detokenize,
+    load_vocabulary as cpp_load_vocabulary,
+    tokenize as cpp_tokenize,
+    tokenize_vocabulary as cpp_tokenize_vocabulary,
+    tokenize_with_vocabulary as cpp_tokenize_with_vocabulary,
+)
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def _tokenize_scalar(Column strings, object py_delimiter):
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef const string_scalar* c_delimiter = <const string_scalar*>delimiter\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_tokenize(
+                c_strings,
+                c_delimiter[0],
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def _tokenize_column(Column strings, Column delimiters):
+    cdef column_view c_strings = strings.view()
+    cdef column_view c_delimiters = delimiters.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_tokenize(
+                c_strings,
+                c_delimiters
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def _count_tokens_scalar(Column strings, object py_delimiter):
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef const string_scalar* c_delimiter = <const string_scalar*>delimiter\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_count_tokens(
+                c_strings,
+                c_delimiter[0]
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def _count_tokens_column(Column strings, Column delimiters):
+    cdef column_view c_strings = strings.view()
+    cdef column_view c_delimiters = delimiters.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_count_tokens(
+                c_strings,
+                c_delimiters
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def character_tokenize(Column strings):
+    cdef column_view c_strings = strings.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_character_tokenize(c_strings)
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def detokenize(Column strings, Column indices, object py_separator):
+
+    cdef DeviceScalar separator = py_separator.device_value
+
+    cdef column_view c_strings = strings.view()
+    cdef column_view c_indices = indices.view()
+    cdef const string_scalar* c_separator = <const string_scalar*>separator\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_detokenize(c_strings, c_indices, c_separator[0])
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+cdef class TokenizeVocabulary:
+    cdef unique_ptr[cpp_tokenize_vocabulary] c_obj
+
+    def __cinit__(self, Column vocab):
+        cdef column_view c_vocab = vocab.view()
+        with nogil:
+            self.c_obj = move(cpp_load_vocabulary(c_vocab))
+
+
+@acquire_spill_lock()
+def tokenize_with_vocabulary(Column strings,
+                             TokenizeVocabulary vocabulary,
+                             object py_delimiter,
+                             size_type default_id):
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+    cdef column_view c_strings = strings.view()
+    cdef const string_scalar* c_delimiter = <const string_scalar*>delimiter\
+        .get_raw_ptr()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_tokenize_with_vocabulary(
+                c_strings,
+                vocabulary.c_obj.get()[0],
+                c_delimiter[0],
+                default_id
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/orc.pyx b/python/cudf/cudf/_lib/orc.pyx
new file mode 100644
index 0000000..0ae039b
--- /dev/null
+++ b/python/cudf/cudf/_lib/orc.pyx
@@ -0,0 +1,494 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import cudf
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool, int
+from libcpp.map cimport map
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from collections import OrderedDict
+
+cimport cudf._lib.cpp.lists.lists_column_view as cpp_lists_column_view
+
+try:
+    import ujson as json
+except ImportError:
+    import json
+
+cimport cudf._lib.cpp.io.types as cudf_io_types
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.io.data_sink cimport data_sink
+from cudf._lib.cpp.io.orc cimport (
+    chunked_orc_writer_options,
+    orc_chunked_writer,
+    orc_reader_options,
+    orc_writer_options,
+    read_orc as libcudf_read_orc,
+    write_orc as libcudf_write_orc,
+)
+from cudf._lib.cpp.io.orc_metadata cimport (
+    raw_orc_statistics,
+    read_raw_orc_statistics as libcudf_read_raw_orc_statistics,
+)
+from cudf._lib.cpp.io.types cimport (
+    column_in_metadata,
+    compression_type,
+    sink_info,
+    source_info,
+    table_input_metadata,
+    table_with_metadata,
+)
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport data_type, size_type, type_id
+from cudf._lib.io.datasource cimport NativeFileDatasource
+from cudf._lib.io.utils cimport (
+    make_sink_info,
+    make_source_info,
+    update_column_struct_field_names,
+)
+
+from cudf._lib.types import SUPPORTED_NUMPY_TO_LIBCUDF_TYPES
+
+from cudf._lib.types cimport underlying_type_t_type_id
+from cudf._lib.utils cimport data_from_unique_ptr, table_view_from_table
+
+from pyarrow.lib import NativeFile
+
+from cudf._lib.utils import _index_level_name, generate_pandas_metadata
+from cudf.api.types import is_list_dtype, is_struct_dtype
+
+
+cpdef read_raw_orc_statistics(filepath_or_buffer):
+    """
+    Cython function to call into libcudf API, see `read_raw_orc_statistics`.
+
+    See Also
+    --------
+    cudf.io.orc.read_orc_statistics
+    """
+
+    # Handle NativeFile input
+    if isinstance(filepath_or_buffer, NativeFile):
+        filepath_or_buffer = NativeFileDatasource(filepath_or_buffer)
+
+    cdef raw_orc_statistics raw = (
+        libcudf_read_raw_orc_statistics(make_source_info([filepath_or_buffer]))
+    )
+    return (raw.column_names, raw.file_stats, raw.stripes_stats)
+
+
+cpdef read_orc(object filepaths_or_buffers,
+               object columns=None,
+               object stripes=None,
+               object skip_rows=None,
+               object num_rows=None,
+               bool use_index=True,
+               object timestamp_type=None):
+    """
+    Cython function to call into libcudf API, see `read_orc`.
+
+    See Also
+    --------
+    cudf.read_orc
+    """
+    cdef orc_reader_options c_orc_reader_options = make_orc_reader_options(
+        filepaths_or_buffers,
+        columns,
+        stripes or [],
+        get_size_t_arg(skip_rows, "skip_rows"),
+        get_size_t_arg(num_rows, "num_rows"),
+        (
+            type_id.EMPTY
+            if timestamp_type is None else
+            <type_id>(
+                <underlying_type_t_type_id> (
+                    SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[
+                        cudf.dtype(timestamp_type)
+                    ]
+                )
+            )
+        ),
+        use_index,
+    )
+
+    cdef table_with_metadata c_result
+
+    with nogil:
+        c_result = move(libcudf_read_orc(c_orc_reader_options))
+
+    names = [info.name.decode() for info in c_result.metadata.schema_info]
+    actual_index_names, col_names, is_range_index, reset_index_name, \
+        range_idx = _get_index_from_metadata(c_result.metadata.user_data,
+                                             names,
+                                             skip_rows,
+                                             num_rows)
+
+    data, index = data_from_unique_ptr(
+        move(c_result.tbl),
+        col_names if columns is None else names,
+        actual_index_names
+    )
+
+    if is_range_index:
+        index = range_idx
+    elif reset_index_name:
+        index.names = [None] * len(index.names)
+
+    data = {
+        name: update_column_struct_field_names(
+            col, c_result.metadata.schema_info[i]
+        )
+        for i, (name, col) in enumerate(data.items())
+    }
+
+    return data, index
+
+
+cdef compression_type _get_comp_type(object compression):
+    if compression is None or compression is False:
+        return compression_type.NONE
+    elif compression == "snappy":
+        return compression_type.SNAPPY
+    elif compression == "ZLIB":
+        return compression_type.ZLIB
+    elif compression == "ZSTD":
+        return compression_type.ZSTD
+    else:
+        raise ValueError(f"Unsupported `compression` type {compression}")
+
+cdef tuple _get_index_from_metadata(
+        map[string, string] user_data,
+        object names,
+        object skip_rows,
+        object num_rows):
+    json_str = user_data[b'pandas'].decode('utf-8')
+    meta = None
+    index_col = None
+    is_range_index = False
+    reset_index_name = False
+    range_idx = None
+    if json_str != "":
+        meta = json.loads(json_str)
+
+        if 'index_columns' in meta and len(meta['index_columns']) > 0:
+            index_col = meta['index_columns']
+            if isinstance(index_col[0], dict) and \
+                    index_col[0]['kind'] == 'range':
+                is_range_index = True
+            else:
+                index_col_names = OrderedDict()
+                for idx_col in index_col:
+                    for c in meta['columns']:
+                        if c['field_name'] == idx_col:
+                            index_col_names[idx_col] = \
+                                c['name'] or c['field_name']
+                            if c['name'] is None:
+                                reset_index_name = True
+
+    actual_index_names = None
+    if index_col is not None and len(index_col) > 0:
+        if is_range_index:
+            range_index_meta = index_col[0]
+            range_idx = cudf.RangeIndex(
+                start=range_index_meta['start'],
+                stop=range_index_meta['stop'],
+                step=range_index_meta['step'],
+                name=range_index_meta['name']
+            )
+            if skip_rows is not None:
+                range_idx = range_idx[skip_rows:]
+            if num_rows is not None:
+                range_idx = range_idx[:num_rows]
+        else:
+            actual_index_names = list(index_col_names.values())
+            names = names[len(actual_index_names):]
+
+    return (
+        actual_index_names,
+        names,
+        is_range_index,
+        reset_index_name,
+        range_idx
+    )
+
+cdef cudf_io_types.statistics_freq _get_orc_stat_freq(object statistics):
+    """
+    Convert ORC statistics terms to CUDF convention:
+      - ORC "STRIPE"   == CUDF "ROWGROUP"
+      - ORC "ROWGROUP" == CUDF "PAGE"
+    """
+    statistics = str(statistics).upper()
+    if statistics == "NONE":
+        return cudf_io_types.statistics_freq.STATISTICS_NONE
+    elif statistics == "STRIPE":
+        return cudf_io_types.statistics_freq.STATISTICS_ROWGROUP
+    elif statistics == "ROWGROUP":
+        return cudf_io_types.statistics_freq.STATISTICS_PAGE
+    else:
+        raise ValueError(f"Unsupported `statistics_freq` type {statistics}")
+
+
+@acquire_spill_lock()
+def write_orc(
+    table,
+    object path_or_buf,
+    object compression="snappy",
+    object statistics="ROWGROUP",
+    object stripe_size_bytes=None,
+    object stripe_size_rows=None,
+    object row_index_stride=None,
+    object cols_as_map_type=None,
+    object index=None
+):
+    """
+    Cython function to call into libcudf API, see `cudf::io::write_orc`.
+
+    See Also
+    --------
+    cudf.read_orc
+    """
+    cdef compression_type compression_ = _get_comp_type(compression)
+    cdef unique_ptr[data_sink] data_sink_c
+    cdef sink_info sink_info_c = make_sink_info(path_or_buf, data_sink_c)
+    cdef table_input_metadata tbl_meta
+    cdef map[string, string] user_data
+    user_data[str.encode("pandas")] = str.encode(generate_pandas_metadata(
+        table, index)
+    )
+
+    if index is True or (
+        index is None and not isinstance(table._index, cudf.RangeIndex)
+    ):
+        tv = table_view_from_table(table)
+        tbl_meta = table_input_metadata(tv)
+        for level, idx_name in enumerate(table._index.names):
+            tbl_meta.column_metadata[level].set_name(
+                str.encode(
+                    _index_level_name(idx_name, level, table._column_names)
+                )
+            )
+        num_index_cols_meta = len(table._index.names)
+    else:
+        tv = table_view_from_table(table, ignore_index=True)
+        tbl_meta = table_input_metadata(tv)
+        num_index_cols_meta = 0
+
+    if cols_as_map_type is not None:
+        cols_as_map_type = set(cols_as_map_type)
+
+    for i, name in enumerate(table._column_names, num_index_cols_meta):
+        tbl_meta.column_metadata[i].set_name(name.encode())
+        _set_col_children_metadata(
+            table[name]._column,
+            tbl_meta.column_metadata[i],
+            (cols_as_map_type is not None)
+            and (name in cols_as_map_type),
+        )
+
+    cdef orc_writer_options c_orc_writer_options = move(
+        orc_writer_options.builder(
+            sink_info_c, tv
+        ).metadata(tbl_meta)
+        .key_value_metadata(move(user_data))
+        .compression(compression_)
+        .enable_statistics(_get_orc_stat_freq(statistics))
+        .build()
+    )
+    if stripe_size_bytes is not None:
+        c_orc_writer_options.set_stripe_size_bytes(stripe_size_bytes)
+    if stripe_size_rows is not None:
+        c_orc_writer_options.set_stripe_size_rows(stripe_size_rows)
+    if row_index_stride is not None:
+        c_orc_writer_options.set_row_index_stride(row_index_stride)
+
+    with nogil:
+        libcudf_write_orc(c_orc_writer_options)
+
+
+cdef size_type get_size_t_arg(object arg, str name) except*:
+    if name == "skip_rows":
+        arg = 0 if arg is None else arg
+        if not isinstance(arg, int) or arg < 0:
+            raise TypeError(f"{name} must be an int >= 0")
+    else:
+        arg = -1 if arg is None else arg
+        if not isinstance(arg, int) or arg < -1:
+            raise TypeError(f"{name} must be an int >= -1")
+    return <size_type> arg
+
+
+cdef orc_reader_options make_orc_reader_options(
+    object filepaths_or_buffers,
+    object column_names,
+    object stripes,
+    size_type skip_rows,
+    size_type num_rows,
+    type_id timestamp_type,
+    bool use_index
+) except*:
+
+    for i, datasource in enumerate(filepaths_or_buffers):
+        if isinstance(datasource, NativeFile):
+            filepaths_or_buffers[i] = NativeFileDatasource(datasource)
+    cdef vector[vector[size_type]] strps = stripes
+    cdef orc_reader_options opts
+    cdef source_info src = make_source_info(filepaths_or_buffers)
+    opts = move(
+        orc_reader_options.builder(src)
+        .stripes(strps)
+        .skip_rows(skip_rows)
+        .timestamp_type(data_type(timestamp_type))
+        .use_index(use_index)
+        .build()
+    )
+    if num_rows >= 0:
+        opts.set_num_rows(num_rows)
+
+    cdef vector[string] c_column_names
+    if column_names is not None:
+        c_column_names.reserve(len(column_names))
+        for col in column_names:
+            c_column_names.push_back(str(col).encode())
+        opts.set_columns(c_column_names)
+
+    return opts
+
+
+cdef class ORCWriter:
+    """
+    ORCWriter lets you you incrementally write out a ORC file from a series
+    of cudf tables
+
+    See Also
+    --------
+    cudf.io.orc.to_orc
+    """
+    cdef bool initialized
+    cdef unique_ptr[orc_chunked_writer] writer
+    cdef sink_info sink
+    cdef unique_ptr[data_sink] _data_sink
+    cdef cudf_io_types.statistics_freq stat_freq
+    cdef compression_type comp_type
+    cdef object index
+    cdef table_input_metadata tbl_meta
+    cdef object cols_as_map_type
+
+    def __cinit__(self,
+                  object path,
+                  object index=None,
+                  object compression="snappy",
+                  object statistics="ROWGROUP",
+                  object cols_as_map_type=None):
+
+        self.sink = make_sink_info(path, self._data_sink)
+        self.stat_freq = _get_orc_stat_freq(statistics)
+        self.comp_type = _get_comp_type(compression)
+        self.index = index
+        self.cols_as_map_type = cols_as_map_type \
+            if cols_as_map_type is None else set(cols_as_map_type)
+        self.initialized = False
+
+    def write_table(self, table):
+        """ Writes a single table to the file """
+        if not self.initialized:
+            self._initialize_chunked_state(table)
+
+        keep_index = self.index is not False and (
+            table._index.name is not None or
+            isinstance(table._index, cudf.core.multiindex.MultiIndex)
+        )
+        tv = table_view_from_table(table, not keep_index)
+
+        with nogil:
+            self.writer.get()[0].write(tv)
+
+    def close(self):
+        if not self.initialized:
+            return
+
+        with nogil:
+            self.writer.get()[0].close()
+
+    def __dealloc__(self):
+        self.close()
+
+    def _initialize_chunked_state(self, table):
+        """
+        Prepare all the values required to build the
+        chunked_orc_writer_options anb creates a writer"""
+        cdef table_view tv
+
+        num_index_cols_meta = 0
+        self.tbl_meta = table_input_metadata(
+            table_view_from_table(table, ignore_index=True),
+        )
+        if self.index is not False:
+            if isinstance(table._index, cudf.core.multiindex.MultiIndex):
+                tv = table_view_from_table(table)
+                self.tbl_meta = table_input_metadata(tv)
+                for level, idx_name in enumerate(table._index.names):
+                    self.tbl_meta.column_metadata[level].set_name(
+                        (str.encode(idx_name))
+                    )
+                num_index_cols_meta = len(table._index.names)
+            else:
+                if table._index.name is not None:
+                    tv = table_view_from_table(table)
+                    self.tbl_meta = table_input_metadata(tv)
+                    self.tbl_meta.column_metadata[0].set_name(
+                        str.encode(table._index.name)
+                    )
+                    num_index_cols_meta = 1
+
+        for i, name in enumerate(table._column_names, num_index_cols_meta):
+            self.tbl_meta.column_metadata[i].set_name(name.encode())
+            _set_col_children_metadata(
+                table[name]._column,
+                self.tbl_meta.column_metadata[i],
+                (self.cols_as_map_type is not None)
+                and (name in self.cols_as_map_type),
+            )
+
+        cdef map[string, string] user_data
+        pandas_metadata = generate_pandas_metadata(table, self.index)
+        user_data[str.encode("pandas")] = str.encode(pandas_metadata)
+
+        cdef chunked_orc_writer_options args
+        with nogil:
+            args = move(
+                chunked_orc_writer_options.builder(self.sink)
+                .metadata(self.tbl_meta)
+                .key_value_metadata(move(user_data))
+                .compression(self.comp_type)
+                .enable_statistics(self.stat_freq)
+                .build()
+            )
+            self.writer.reset(new orc_chunked_writer(args))
+
+        self.initialized = True
+
+cdef _set_col_children_metadata(Column col,
+                                column_in_metadata& col_meta,
+                                list_column_as_map=False):
+    if is_struct_dtype(col):
+        for i, (child_col, name) in enumerate(
+            zip(col.children, list(col.dtype.fields))
+        ):
+            col_meta.child(i).set_name(name.encode())
+            _set_col_children_metadata(
+                child_col, col_meta.child(i), list_column_as_map
+            )
+    elif is_list_dtype(col):
+        if list_column_as_map:
+            col_meta.set_list_column_as_map()
+        _set_col_children_metadata(
+            col.children[cpp_lists_column_view.child_column_index],
+            col_meta.child(cpp_lists_column_view.child_column_index),
+            list_column_as_map
+        )
+    else:
+        return
diff --git a/python/cudf/cudf/_lib/parquet.pyx b/python/cudf/cudf/_lib/parquet.pyx
new file mode 100644
index 0000000..85fd25c
--- /dev/null
+++ b/python/cudf/cudf/_lib/parquet.pyx
@@ -0,0 +1,709 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+# cython: boundscheck = False
+
+import io
+
+import pyarrow as pa
+
+import cudf
+from cudf.core.buffer import acquire_spill_lock
+
+try:
+    import ujson as json
+except ImportError:
+    import json
+
+import numpy as np
+
+from cython.operator cimport dereference
+
+from cudf.api.types import (
+    is_decimal_dtype,
+    is_list_dtype,
+    is_list_like,
+    is_struct_dtype,
+)
+
+from cudf._lib.utils cimport data_from_unique_ptr
+
+from cudf._lib.utils import _index_level_name, generate_pandas_metadata
+
+from libc.stdint cimport uint8_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.unordered_map cimport unordered_map
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.io.data_sink as cudf_io_data_sink
+cimport cudf._lib.cpp.io.types as cudf_io_types
+cimport cudf._lib.cpp.types as cudf_types
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.io.parquet cimport (
+    chunked_parquet_writer_options,
+    merge_row_group_metadata as parquet_merge_metadata,
+    parquet_chunked_writer as cpp_parquet_chunked_writer,
+    parquet_reader_options,
+    parquet_writer_options,
+    read_parquet as parquet_reader,
+    write_parquet as parquet_writer,
+)
+from cudf._lib.cpp.io.types cimport column_in_metadata, table_input_metadata
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport data_type, size_type
+from cudf._lib.io.datasource cimport NativeFileDatasource
+from cudf._lib.io.utils cimport (
+    make_sinks_info,
+    make_source_info,
+    update_struct_field_names,
+)
+from cudf._lib.utils cimport table_view_from_table
+
+from pyarrow.lib import NativeFile
+
+from cudf.utils.ioutils import _ROW_GROUP_SIZE_BYTES_DEFAULT
+
+
+cdef class BufferArrayFromVector:
+    cdef Py_ssize_t length
+    cdef unique_ptr[vector[uint8_t]] in_vec
+
+    # these two things declare part of the buffer interface
+    cdef Py_ssize_t shape[1]
+    cdef Py_ssize_t strides[1]
+
+    @staticmethod
+    cdef BufferArrayFromVector from_unique_ptr(
+        unique_ptr[vector[uint8_t]] in_vec
+    ):
+        cdef BufferArrayFromVector buf = BufferArrayFromVector()
+        buf.in_vec = move(in_vec)
+        buf.length = dereference(buf.in_vec).size()
+        return buf
+
+    def __getbuffer__(self, Py_buffer *buffer, int flags):
+        cdef Py_ssize_t itemsize = sizeof(uint8_t)
+
+        self.shape[0] = self.length
+        self.strides[0] = 1
+
+        buffer.buf = dereference(self.in_vec).data()
+
+        buffer.format = NULL  # byte
+        buffer.internal = NULL
+        buffer.itemsize = itemsize
+        buffer.len = self.length * itemsize   # product(shape) * itemsize
+        buffer.ndim = 1
+        buffer.obj = self
+        buffer.readonly = 0
+        buffer.shape = self.shape
+        buffer.strides = self.strides
+        buffer.suboffsets = NULL
+
+    def __releasebuffer__(self, Py_buffer *buffer):
+        pass
+
+
+def _parse_metadata(meta):
+    file_is_range_index = False
+    file_index_cols = None
+
+    if 'index_columns' in meta and len(meta['index_columns']) > 0:
+        file_index_cols = meta['index_columns']
+
+        if isinstance(file_index_cols[0], dict) and \
+                file_index_cols[0]['kind'] == 'range':
+            file_is_range_index = True
+    return file_is_range_index, file_index_cols
+
+
+cpdef read_parquet(filepaths_or_buffers, columns=None, row_groups=None,
+                   use_pandas_metadata=True):
+    """
+    Cython function to call into libcudf API, see `read_parquet`.
+
+    See Also
+    --------
+    cudf.io.parquet.read_parquet
+    cudf.io.parquet.to_parquet
+    """
+
+    # Convert NativeFile buffers to NativeFileDatasource,
+    # but save original buffers in case we need to use
+    # pyarrow for metadata processing
+    # (See: https://github.com/rapidsai/cudf/issues/9599)
+    pa_buffers = []
+    for i, datasource in enumerate(filepaths_or_buffers):
+        if isinstance(datasource, NativeFile):
+            pa_buffers.append(datasource)
+            filepaths_or_buffers[i] = NativeFileDatasource(datasource)
+
+    cdef cudf_io_types.source_info source = make_source_info(
+        filepaths_or_buffers)
+
+    cdef bool cpp_use_pandas_metadata = use_pandas_metadata
+
+    cdef vector[vector[size_type]] cpp_row_groups
+    cdef data_type cpp_timestamp_type = cudf_types.data_type(
+        cudf_types.type_id.EMPTY
+    )
+
+    if row_groups is not None:
+        cpp_row_groups = row_groups
+
+    cdef parquet_reader_options args
+    # Setup parquet reader arguments
+    args = move(
+        parquet_reader_options.builder(source)
+        .row_groups(cpp_row_groups)
+        .use_pandas_metadata(cpp_use_pandas_metadata)
+        .timestamp_type(cpp_timestamp_type)
+        .build()
+    )
+    cdef vector[string] cpp_columns
+    allow_range_index = True
+    if columns is not None:
+        cpp_columns.reserve(len(columns))
+        allow_range_index = len(columns) > 0
+        for col in columns:
+            cpp_columns.push_back(str(col).encode())
+        args.set_columns(cpp_columns)
+
+    # Read Parquet
+    cdef cudf_io_types.table_with_metadata c_result
+
+    with nogil:
+        c_result = move(parquet_reader(args))
+
+    names = [info.name.decode() for info in c_result.metadata.schema_info]
+
+    # Access the Parquet per_file_user_data to find the index
+    index_col = None
+    cdef vector[unordered_map[string, string]] per_file_user_data = \
+        c_result.metadata.per_file_user_data
+
+    index_col_names = None
+    is_range_index = True
+    for single_file in per_file_user_data:
+        json_str = single_file[b'pandas'].decode('utf-8')
+        meta = None
+        if json_str != "":
+            meta = json.loads(json_str)
+            file_is_range_index, index_col = _parse_metadata(meta)
+            is_range_index &= file_is_range_index
+
+            if not file_is_range_index and index_col is not None \
+                    and index_col_names is None:
+                index_col_names = {}
+                for idx_col in index_col:
+                    for c in meta['columns']:
+                        if c['field_name'] == idx_col:
+                            index_col_names[idx_col] = c['name']
+
+    df = cudf.DataFrame._from_data(*data_from_unique_ptr(
+        move(c_result.tbl),
+        column_names=names
+    ))
+
+    update_struct_field_names(df, c_result.metadata.schema_info)
+
+    if meta is not None:
+        # Book keep each column metadata as the order
+        # of `meta["columns"]` and `column_names` are not
+        # guaranteed to be deterministic and same always.
+        meta_data_per_column = {
+            col_meta['name']: col_meta for col_meta in meta["columns"]
+        }
+
+        # update the decimal precision of each column
+        for col in names:
+            if is_decimal_dtype(df._data[col].dtype):
+                df._data[col].dtype.precision = (
+                    meta_data_per_column[col]["metadata"]["precision"]
+                )
+
+    # Set the index column
+    if index_col is not None and len(index_col) > 0:
+        if is_range_index:
+            if not allow_range_index:
+                return df
+
+            if len(per_file_user_data) > 1:
+                range_index_meta = {
+                    "kind": "range",
+                    "name": None,
+                    "start": 0,
+                    "stop": len(df),
+                    "step": 1
+                }
+            else:
+                range_index_meta = index_col[0]
+
+            if row_groups is not None:
+                per_file_metadata = [
+                    pa.parquet.read_metadata(
+                        # Pyarrow cannot read directly from bytes
+                        io.BytesIO(s) if isinstance(s, bytes) else s
+                    ) for s in (
+                        pa_buffers or filepaths_or_buffers
+                    )
+                ]
+
+                filtered_idx = []
+                for i, file_meta in enumerate(per_file_metadata):
+                    row_groups_i = []
+                    start = 0
+                    for row_group in range(file_meta.num_row_groups):
+                        stop = start + file_meta.row_group(row_group).num_rows
+                        row_groups_i.append((start, stop))
+                        start = stop
+
+                    for rg in row_groups[i]:
+                        filtered_idx.append(
+                            cudf.RangeIndex(
+                                start=row_groups_i[rg][0],
+                                stop=row_groups_i[rg][1],
+                                step=range_index_meta['step']
+                            )
+                        )
+
+                if len(filtered_idx) > 0:
+                    idx = cudf.concat(filtered_idx)
+                else:
+                    idx = cudf.Index(cudf.core.column.column_empty(0))
+            else:
+                idx = cudf.RangeIndex(
+                    start=range_index_meta['start'],
+                    stop=range_index_meta['stop'],
+                    step=range_index_meta['step'],
+                    name=range_index_meta['name']
+                )
+
+            df._index = idx
+        elif set(index_col).issubset(names):
+            index_data = df[index_col]
+            actual_index_names = list(index_col_names.values())
+            if len(index_data._data) == 1:
+                idx = cudf.Index(
+                    index_data._data.columns[0],
+                    name=actual_index_names[0]
+                )
+            else:
+                idx = cudf.MultiIndex.from_frame(
+                    index_data,
+                    names=actual_index_names
+                )
+            df.drop(columns=index_col, inplace=True)
+            df._index = idx
+        else:
+            if use_pandas_metadata:
+                df.index.names = index_col
+
+    return df
+
+
+@acquire_spill_lock()
+def write_parquet(
+    table,
+    object filepaths_or_buffers,
+    object index=None,
+    object compression="snappy",
+    object statistics="ROWGROUP",
+    object metadata_file_path=None,
+    object int96_timestamps=False,
+    object row_group_size_bytes=_ROW_GROUP_SIZE_BYTES_DEFAULT,
+    object row_group_size_rows=None,
+    object max_page_size_bytes=None,
+    object max_page_size_rows=None,
+    object partitions_info=None,
+    object force_nullable_schema=False,
+):
+    """
+    Cython function to call into libcudf API, see `write_parquet`.
+
+    See Also
+    --------
+    cudf.io.parquet.write_parquet
+    """
+
+    # Create the write options
+    cdef table_input_metadata tbl_meta
+
+    cdef vector[map[string, string]] user_data
+    cdef table_view tv
+    cdef vector[unique_ptr[cudf_io_data_sink.data_sink]] _data_sinks
+    cdef cudf_io_types.sink_info sink = make_sinks_info(
+        filepaths_or_buffers, _data_sinks
+    )
+
+    if index is True or (
+        index is None and not isinstance(table._index, cudf.RangeIndex)
+    ):
+        tv = table_view_from_table(table)
+        tbl_meta = table_input_metadata(tv)
+        for level, idx_name in enumerate(table._index.names):
+            tbl_meta.column_metadata[level].set_name(
+                str.encode(
+                    _index_level_name(idx_name, level, table._column_names)
+                )
+            )
+        num_index_cols_meta = len(table._index.names)
+    else:
+        tv = table_view_from_table(table, ignore_index=True)
+        tbl_meta = table_input_metadata(tv)
+        num_index_cols_meta = 0
+
+    for i, name in enumerate(table._column_names, num_index_cols_meta):
+        if not isinstance(name, str):
+            raise ValueError("parquet must have string column names")
+
+        tbl_meta.column_metadata[i].set_name(name.encode())
+        _set_col_metadata(
+            table[name]._column,
+            tbl_meta.column_metadata[i],
+            force_nullable_schema
+        )
+
+    cdef map[string, string] tmp_user_data
+    if partitions_info is not None:
+        for start_row, num_row in partitions_info:
+            partitioned_df = table.iloc[start_row: start_row + num_row].copy(
+                deep=False
+            )
+            pandas_metadata = generate_pandas_metadata(partitioned_df, index)
+            tmp_user_data[str.encode("pandas")] = str.encode(pandas_metadata)
+            user_data.push_back(tmp_user_data)
+            tmp_user_data.clear()
+    else:
+        pandas_metadata = generate_pandas_metadata(table, index)
+        tmp_user_data[str.encode("pandas")] = str.encode(pandas_metadata)
+        user_data.push_back(tmp_user_data)
+
+    cdef cudf_io_types.compression_type comp_type = _get_comp_type(compression)
+    cdef cudf_io_types.statistics_freq stat_freq = _get_stat_freq(statistics)
+
+    cdef unique_ptr[vector[uint8_t]] out_metadata_c
+    cdef vector[string] c_column_chunks_file_paths
+    cdef bool _int96_timestamps = int96_timestamps
+    cdef vector[cudf_io_types.partition_info] partitions
+
+    # Perform write
+    cdef parquet_writer_options args = move(
+        parquet_writer_options.builder(sink, tv)
+        .metadata(tbl_meta)
+        .key_value_metadata(move(user_data))
+        .compression(comp_type)
+        .stats_level(stat_freq)
+        .int96_timestamps(_int96_timestamps)
+        .build()
+    )
+    if partitions_info is not None:
+        partitions.reserve(len(partitions_info))
+        for part in partitions_info:
+            partitions.push_back(
+                cudf_io_types.partition_info(part[0], part[1])
+            )
+        args.set_partitions(move(partitions))
+    if metadata_file_path is not None:
+        if is_list_like(metadata_file_path):
+            for path in metadata_file_path:
+                c_column_chunks_file_paths.push_back(str.encode(path))
+        else:
+            c_column_chunks_file_paths.push_back(
+                str.encode(metadata_file_path)
+            )
+        args.set_column_chunks_file_paths(move(c_column_chunks_file_paths))
+    if row_group_size_bytes is not None:
+        args.set_row_group_size_bytes(row_group_size_bytes)
+    if row_group_size_rows is not None:
+        args.set_row_group_size_rows(row_group_size_rows)
+    if max_page_size_bytes is not None:
+        args.set_max_page_size_bytes(max_page_size_bytes)
+    if max_page_size_rows is not None:
+        args.set_max_page_size_rows(max_page_size_rows)
+
+    with nogil:
+        out_metadata_c = move(parquet_writer(args))
+
+    if metadata_file_path is not None:
+        out_metadata_py = BufferArrayFromVector.from_unique_ptr(
+            move(out_metadata_c)
+        )
+        return np.asarray(out_metadata_py)
+    else:
+        return None
+
+
+cdef class ParquetWriter:
+    """
+    ParquetWriter lets you incrementally write out a Parquet file from a series
+    of cudf tables
+
+    Parameters
+    ----------
+    filepath_or_buffer : str, io.IOBase, os.PathLike, or list
+        File path or buffer to write to. The argument may also correspond
+        to a list of file paths or buffers.
+    index : bool or None, default None
+        If ``True``, include a dataframe's index(es) in the file output.
+        If ``False``, they will not be written to the file. If ``None``,
+        index(es) other than RangeIndex will be saved as columns.
+    compression : {'snappy', None}, default 'snappy'
+        Name of the compression to use. Use ``None`` for no compression.
+    statistics : {'ROWGROUP', 'PAGE', 'COLUMN', 'NONE'}, default 'ROWGROUP'
+        Level at which column statistics should be included in file.
+    row_group_size_bytes: int, default 134217728
+        Maximum size of each stripe of the output.
+        By default, 134217728 (128MB) will be used.
+    row_group_size_rows: int, default 1000000
+        Maximum number of rows of each stripe of the output.
+        By default, 1000000 (10^6 rows) will be used.
+    max_page_size_bytes: int, default 524288
+        Maximum uncompressed size of each page of the output.
+        By default, 524288 (512KB) will be used.
+    max_page_size_rows: int, default 20000
+        Maximum number of rows of each page of the output.
+        By default, 20000 will be used.
+
+    See Also
+    --------
+    cudf.io.parquet.write_parquet
+    """
+    cdef bool initialized
+    cdef unique_ptr[cpp_parquet_chunked_writer] writer
+    cdef table_input_metadata tbl_meta
+    cdef cudf_io_types.sink_info sink
+    cdef vector[unique_ptr[cudf_io_data_sink.data_sink]] _data_sink
+    cdef cudf_io_types.statistics_freq stat_freq
+    cdef cudf_io_types.compression_type comp_type
+    cdef object index
+    cdef size_t row_group_size_bytes
+    cdef size_type row_group_size_rows
+    cdef size_t max_page_size_bytes
+    cdef size_type max_page_size_rows
+
+    def __cinit__(self, object filepath_or_buffer, object index=None,
+                  object compression="snappy", str statistics="ROWGROUP",
+                  int row_group_size_bytes=_ROW_GROUP_SIZE_BYTES_DEFAULT,
+                  int row_group_size_rows=1000000,
+                  int max_page_size_bytes=524288,
+                  int max_page_size_rows=20000):
+        filepaths_or_buffers = (
+            list(filepath_or_buffer)
+            if is_list_like(filepath_or_buffer)
+            else [filepath_or_buffer]
+        )
+        self.sink = make_sinks_info(filepaths_or_buffers, self._data_sink)
+        self.stat_freq = _get_stat_freq(statistics)
+        self.comp_type = _get_comp_type(compression)
+        self.index = index
+        self.initialized = False
+        self.row_group_size_bytes = row_group_size_bytes
+        self.row_group_size_rows = row_group_size_rows
+        self.max_page_size_bytes = max_page_size_bytes
+        self.max_page_size_rows = max_page_size_rows
+
+    def write_table(self, table, object partitions_info=None):
+        """ Writes a single table to the file """
+        if not self.initialized:
+            self._initialize_chunked_state(
+                table,
+                num_partitions=len(partitions_info) if partitions_info else 1
+            )
+
+        cdef table_view tv
+        if self.index is not False and (
+            table._index.name is not None or
+                isinstance(table._index, cudf.core.multiindex.MultiIndex)):
+            tv = table_view_from_table(table)
+        else:
+            tv = table_view_from_table(table, ignore_index=True)
+
+        cdef vector[cudf_io_types.partition_info] partitions
+        if partitions_info is not None:
+            for part in partitions_info:
+                partitions.push_back(
+                    cudf_io_types.partition_info(part[0], part[1])
+                )
+
+        with nogil:
+            self.writer.get()[0].write(tv, partitions)
+
+    def close(self, object metadata_file_path=None):
+        cdef unique_ptr[vector[uint8_t]] out_metadata_c
+        cdef vector[string] column_chunks_file_paths
+
+        if not self.initialized:
+            return None
+
+        # Update metadata-collection options
+        if metadata_file_path is not None:
+            if is_list_like(metadata_file_path):
+                for path in metadata_file_path:
+                    column_chunks_file_paths.push_back(str.encode(path))
+            else:
+                column_chunks_file_paths.push_back(
+                    str.encode(metadata_file_path)
+                )
+
+        with nogil:
+            out_metadata_c = move(
+                self.writer.get()[0].close(column_chunks_file_paths)
+            )
+
+        if metadata_file_path is not None:
+            out_metadata_py = BufferArrayFromVector.from_unique_ptr(
+                move(out_metadata_c)
+            )
+            return np.asarray(out_metadata_py)
+        return None
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+    def _initialize_chunked_state(self, table, num_partitions=1):
+        """ Prepares all the values required to build the
+        chunked_parquet_writer_options and creates a writer"""
+        cdef table_view tv
+
+        # Set the table_metadata
+        num_index_cols_meta = 0
+        self.tbl_meta = table_input_metadata(
+            table_view_from_table(table, ignore_index=True))
+        if self.index is not False:
+            if isinstance(table._index, cudf.core.multiindex.MultiIndex):
+                tv = table_view_from_table(table)
+                self.tbl_meta = table_input_metadata(tv)
+                for level, idx_name in enumerate(table._index.names):
+                    self.tbl_meta.column_metadata[level].set_name(
+                        (str.encode(idx_name))
+                    )
+                num_index_cols_meta = len(table._index.names)
+            else:
+                if table._index.name is not None:
+                    tv = table_view_from_table(table)
+                    self.tbl_meta = table_input_metadata(tv)
+                    self.tbl_meta.column_metadata[0].set_name(
+                        str.encode(table._index.name)
+                    )
+                    num_index_cols_meta = 1
+
+        for i, name in enumerate(table._column_names, num_index_cols_meta):
+            self.tbl_meta.column_metadata[i].set_name(name.encode())
+            _set_col_metadata(
+                table[name]._column,
+                self.tbl_meta.column_metadata[i],
+            )
+
+        index = (
+            False if isinstance(table._index, cudf.RangeIndex) else self.index
+        )
+        pandas_metadata = generate_pandas_metadata(table, index)
+        cdef map[string, string] tmp_user_data
+        tmp_user_data[str.encode("pandas")] = str.encode(pandas_metadata)
+        cdef vector[map[string, string]] user_data
+        user_data = vector[map[string, string]](num_partitions, tmp_user_data)
+
+        cdef chunked_parquet_writer_options args
+        with nogil:
+            args = move(
+                chunked_parquet_writer_options.builder(self.sink)
+                .metadata(self.tbl_meta)
+                .key_value_metadata(move(user_data))
+                .compression(self.comp_type)
+                .stats_level(self.stat_freq)
+                .row_group_size_bytes(self.row_group_size_bytes)
+                .row_group_size_rows(self.row_group_size_rows)
+                .max_page_size_bytes(self.max_page_size_bytes)
+                .max_page_size_rows(self.max_page_size_rows)
+                .build()
+            )
+            self.writer.reset(new cpp_parquet_chunked_writer(args))
+        self.initialized = True
+
+
+cpdef merge_filemetadata(object filemetadata_list):
+    """
+    Cython function to call into libcudf API, see `merge_row_group_metadata`.
+
+    See Also
+    --------
+    cudf.io.parquet.merge_row_group_metadata
+    """
+    cdef vector[unique_ptr[vector[uint8_t]]] list_c
+    cdef vector[uint8_t] blob_c
+    cdef unique_ptr[vector[uint8_t]] output_c
+
+    for blob_py in filemetadata_list:
+        blob_c = blob_py
+        list_c.push_back(move(make_unique[vector[uint8_t]](blob_c)))
+
+    with nogil:
+        output_c = move(parquet_merge_metadata(list_c))
+
+    out_metadata_py = BufferArrayFromVector.from_unique_ptr(move(output_c))
+    return np.asarray(out_metadata_py)
+
+
+cdef cudf_io_types.statistics_freq _get_stat_freq(object statistics):
+    statistics = str(statistics).upper()
+    if statistics == "NONE":
+        return cudf_io_types.statistics_freq.STATISTICS_NONE
+    elif statistics == "ROWGROUP":
+        return cudf_io_types.statistics_freq.STATISTICS_ROWGROUP
+    elif statistics == "PAGE":
+        return cudf_io_types.statistics_freq.STATISTICS_PAGE
+    elif statistics == "COLUMN":
+        return cudf_io_types.statistics_freq.STATISTICS_COLUMN
+    else:
+        raise ValueError("Unsupported `statistics_freq` type")
+
+
+cdef cudf_io_types.compression_type _get_comp_type(object compression):
+    if compression is None:
+        return cudf_io_types.compression_type.NONE
+    elif compression == "snappy":
+        return cudf_io_types.compression_type.SNAPPY
+    elif compression == "ZSTD":
+        return cudf_io_types.compression_type.ZSTD
+    else:
+        raise ValueError("Unsupported `compression` type")
+
+
+cdef _set_col_metadata(
+    Column col,
+    column_in_metadata& col_meta,
+    bool force_nullable_schema=False,
+):
+    if force_nullable_schema:
+        # Only set nullability if `force_nullable_schema`
+        # is true.
+        col_meta.set_nullability(True)
+
+    if is_struct_dtype(col):
+        for i, (child_col, name) in enumerate(
+            zip(col.children, list(col.dtype.fields))
+        ):
+            col_meta.child(i).set_name(name.encode())
+            _set_col_metadata(
+                child_col,
+                col_meta.child(i),
+                force_nullable_schema
+            )
+    elif is_list_dtype(col):
+        _set_col_metadata(
+            col.children[1],
+            col_meta.child(1),
+            force_nullable_schema
+        )
+    else:
+        if is_decimal_dtype(col):
+            col_meta.set_decimal_precision(col.dtype.precision)
+        return
diff --git a/python/cudf/cudf/_lib/partitioning.pyx b/python/cudf/cudf/_lib/partitioning.pyx
new file mode 100644
index 0000000..4bf8b32
--- /dev/null
+++ b/python/cudf/cudf/_lib/partitioning.pyx
@@ -0,0 +1,73 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.partitioning cimport partition as cpp_partition
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+from cudf._lib.reduce import minmax
+from cudf._lib.stream_compaction import distinct_count as cpp_distinct_count
+
+cimport cudf._lib.cpp.types as libcudf_types
+
+
+@acquire_spill_lock()
+def partition(list source_columns, Column partition_map,
+              object num_partitions):
+    """Partition source columns given a partitioning map
+
+    Parameters
+    ----------
+    source_columns: list[Column]
+        Columns to partition
+    partition_map: Column
+        Column of integer values that map each row in the input to a
+        partition
+    num_partitions: Optional[int]
+        Number of output partitions (deduced from unique values in
+        partition_map if None)
+
+    Returns
+    -------
+    Pair of reordered columns and partition offsets
+
+    Raises
+    ------
+    ValueError
+        If the partition map has invalid entries (not all in [0,
+        num_partitions)).
+    """
+
+    if num_partitions is None:
+        num_partitions = cpp_distinct_count(partition_map, ignore_nulls=True)
+    cdef int c_num_partitions = num_partitions
+    cdef table_view c_source_view = table_view_from_columns(source_columns)
+
+    cdef column_view c_partition_map_view = partition_map.view()
+
+    cdef pair[unique_ptr[table], vector[libcudf_types.size_type]] c_result
+    if partition_map.size > 0:
+        lo, hi = minmax(partition_map)
+        if lo < 0 or hi >= num_partitions:
+            raise ValueError("Partition map has invalid values")
+    with nogil:
+        c_result = move(
+            cpp_partition(
+                c_source_view,
+                c_partition_map_view,
+                c_num_partitions
+            )
+        )
+
+    return (
+        columns_from_unique_ptr(move(c_result.first)), list(c_result.second)
+    )
diff --git a/python/cudf/cudf/_lib/pylibcudf/CMakeLists.txt b/python/cudf/cudf/_lib/pylibcudf/CMakeLists.txt
new file mode 100644
index 0000000..0ce42dc
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/CMakeLists.txt
@@ -0,0 +1,21 @@
+# =============================================================================
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources column.pyx copying.pyx gpumemoryview.pyx table.pyx types.pyx utils.pyx)
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX pylibcudf_ ASSOCIATED_TARGETS cudf
+)
diff --git a/python/cudf/cudf/_lib/pylibcudf/__init__.pxd b/python/cudf/cudf/_lib/pylibcudf/__init__.pxd
new file mode 100644
index 0000000..ba7822b
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/__init__.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+# TODO: Verify consistent usage of relative/absolute imports in pylibcudf.
+from . cimport copying
+from .column cimport Column
+from .gpumemoryview cimport gpumemoryview
+from .table cimport Table
+# TODO: cimport type_id once
+# https://github.com/cython/cython/issues/5609 is resolved
+from .types cimport DataType
+
+__all__ = [
+    "Column",
+    "DataType",
+    "Table",
+    "copying",
+    "gpumemoryview",
+]
diff --git a/python/cudf/cudf/_lib/pylibcudf/__init__.py b/python/cudf/cudf/_lib/pylibcudf/__init__.py
new file mode 100644
index 0000000..3edff9a
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from . import copying
+from .column import Column
+from .gpumemoryview import gpumemoryview
+from .table import Table
+from .types import DataType, TypeId
+
+__all__ = [
+    "Column",
+    "DataType",
+    "Table",
+    "TypeId",
+    "copying",
+    "gpumemoryview",
+]
diff --git a/python/cudf/cudf/_lib/pylibcudf/column.pxd b/python/cudf/cudf/_lib/pylibcudf/column.pxd
new file mode 100644
index 0000000..2af87db
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/column.pxd
@@ -0,0 +1,50 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport bitmask_type, size_type
+
+from .gpumemoryview cimport gpumemoryview
+from .types cimport DataType
+
+
+cdef class Column:
+    # TODO: Should we document these attributes? Should we mark them readonly?
+    cdef:
+        # Core data
+        DataType _data_type
+        size_type _size
+        gpumemoryview _data
+        gpumemoryview _mask
+        size_type _null_count
+        size_type _offset
+        # children: List[Column]
+        list _children
+        size_type _num_children
+
+    cdef column_view view(self) nogil
+
+    @staticmethod
+    cdef Column from_libcudf(unique_ptr[column] libcudf_col)
+
+    cpdef DataType type(self)
+    cpdef Column child(self, size_type index)
+    cpdef size_type num_children(self)
+    cpdef size_type size(self)
+    cpdef size_type null_count(self)
+    cpdef size_type offset(self)
+    cpdef gpumemoryview data(self)
+    cpdef gpumemoryview null_mask(self)
+    cpdef list children(self)
+
+    cpdef list_view(self)
+
+
+cdef class ListColumnView:
+    """Accessor for methods of a Column that are specific to lists."""
+    cdef Column _column
+    cpdef child(self)
+    cpdef offsets(self)
diff --git a/python/cudf/cudf/_lib/pylibcudf/column.pyx b/python/cudf/cudf/_lib/pylibcudf/column.pyx
new file mode 100644
index 0000000..40afc8a
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/column.pyx
@@ -0,0 +1,194 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from rmm._lib.device_buffer cimport DeviceBuffer
+
+from cudf._lib.cpp.column.column cimport column, column_contents
+from cudf._lib.cpp.types cimport size_type
+
+from .gpumemoryview cimport gpumemoryview
+from .types cimport DataType, type_id
+from .utils cimport int_to_bitmask_ptr, int_to_void_ptr
+
+
+cdef class Column:
+    """A container of nullable device data as a column of elements.
+
+    This class is an implementation of [Arrow columnar data
+    specification](https://arrow.apache.org/docs/format/Columnar.html) for data
+    stored on GPUs. It relies on Python memoryview-like semantics to maintain
+    shared ownership of the data it is constructed with, so any input data may
+    also be co-owned by other data structures. The Column is designed to be
+    operated on using algorithms backed by libcudf.
+
+    Parameters
+    ----------
+    data_type : DataType
+        The type of data in the column.
+    size : size_type
+        The number of rows in the column.
+    data : gpumemoryview
+        The data the column will refer to.
+    mask : gpumemoryview
+        The null mask for the column.
+    null_count : int
+        The number of null rows in the column.
+    offset : int
+        The offset into the data buffer where the column's data begins.
+    children : list
+        The children of this column if it is a compound column type.
+    """
+    def __init__(
+        self, DataType data_type not None, size_type size, gpumemoryview data,
+        gpumemoryview mask, size_type null_count, size_type offset,
+        list children
+    ):
+        self._data_type = data_type
+        self._size = size
+        self._data = data
+        self._mask = mask
+        self._null_count = null_count
+        self._offset = offset
+        self._children = children
+        self._num_children = len(children)
+
+    cdef column_view view(self) nogil:
+        """Generate a libcudf column_view to pass to libcudf algorithms.
+
+        This method is for pylibcudf's functions to use to generate inputs when
+        calling libcudf algorithms, and should generally not be needed by users
+        (even direct pylibcudf Cython users).
+        """
+        cdef const void * data = NULL
+        cdef const bitmask_type * null_mask = NULL
+
+        if self._data is not None:
+            data = int_to_void_ptr(self._data.ptr)
+        if self._mask is not None:
+            null_mask = int_to_bitmask_ptr(self._mask.ptr)
+
+        # TODO: Check if children can ever change. If not, this could be
+        # computed once in the constructor and always be reused.
+        cdef vector[column_view] c_children
+        with gil:
+            if self._children is not None:
+                for child in self._children:
+                    # Need to cast to Column here so that Cython knows that
+                    # `view` returns a typed object, not a Python object. We
+                    # cannot use a typed variable for `child` because cdef
+                    # declarations cannot be inside nested blocks (`if` or
+                    # `with` blocks) so we cannot declare it inside the `with
+                    # gil` block, but we also cannot declare it outside the
+                    # `with gil` block because it is erroneous to declare a
+                    # variable of a cdef class type in a `nogil` context (which
+                    # this whole function is).
+                    c_children.push_back((<Column> child).view())
+
+        return column_view(
+            self._data_type.c_obj, self._size, data, null_mask,
+            self._null_count, self._offset, c_children
+        )
+
+    @staticmethod
+    cdef Column from_libcudf(unique_ptr[column] libcudf_col):
+        """Create a Column from a libcudf column.
+
+        This method is for pylibcudf's functions to use to ingest outputs of
+        calling libcudf algorithms, and should generally not be needed by users
+        (even direct pylibcudf Cython users).
+        """
+        cdef DataType dtype = DataType.from_libcudf(libcudf_col.get().type())
+        cdef size_type size = libcudf_col.get().size()
+        cdef size_type null_count = libcudf_col.get().null_count()
+
+        cdef column_contents contents = move(libcudf_col.get().release())
+
+        # Note that when converting to cudf Column objects we'll need to pull
+        # out the base object.
+        cdef gpumemoryview data = gpumemoryview(
+            DeviceBuffer.c_from_unique_ptr(move(contents.data))
+        )
+
+        cdef gpumemoryview mask = None
+        if null_count > 0:
+            mask = gpumemoryview(
+                DeviceBuffer.c_from_unique_ptr(move(contents.null_mask))
+            )
+
+        children = []
+        if contents.children.size() != 0:
+            for i in range(contents.children.size()):
+                children.append(
+                    Column.from_libcudf(move(contents.children[i]))
+                )
+
+        return Column(
+            dtype,
+            size,
+            data,
+            mask,
+            null_count,
+            # Initial offset when capturing a C++ column is always 0.
+            0,
+            children,
+        )
+
+    cpdef DataType type(self):
+        """The type of data in the column."""
+        return self._data_type
+
+    cpdef Column child(self, size_type index):
+        """Get a child column of this column.
+
+        Parameters
+        ----------
+        index : size_type
+            The index of the child column to get.
+
+        Returns
+        -------
+        Column
+            The child column.
+        """
+        return self._children[index]
+
+    cpdef size_type num_children(self):
+        """The number of children of this column."""
+        return self._num_children
+
+    cpdef list_view(self):
+        return ListColumnView(self)
+
+    cpdef gpumemoryview data(self):
+        return self._data
+
+    cpdef gpumemoryview null_mask(self):
+        return self._mask
+
+    cpdef size_type size(self):
+        return self._size
+
+    cpdef size_type offset(self):
+        return self._offset
+
+    cpdef size_type null_count(self):
+        return self._null_count
+
+    cpdef list children(self):
+        return self._children
+
+
+cdef class ListColumnView:
+    """Accessor for methods of a Column that are specific to lists."""
+    def __init__(self, Column col):
+        if col.type().id() != type_id.LIST:
+            raise TypeError("Column is not a list type")
+        self._column = col
+
+    cpdef child(self):
+        return self._column.child(1)
+
+    cpdef offsets(self):
+        return self._column.child(1)
diff --git a/python/cudf/cudf/_lib/pylibcudf/copying.pxd b/python/cudf/cudf/_lib/pylibcudf/copying.pxd
new file mode 100644
index 0000000..d57be65
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/copying.pxd
@@ -0,0 +1,15 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool as cbool
+
+from cudf._lib.cpp.copying cimport out_of_bounds_policy
+
+from .column cimport Column
+from .table cimport Table
+
+
+cpdef Table gather(
+    Table source_table,
+    Column gather_map,
+    out_of_bounds_policy bounds_policy
+)
diff --git a/python/cudf/cudf/_lib/pylibcudf/copying.pyx b/python/cudf/cudf/_lib/pylibcudf/copying.pyx
new file mode 100644
index 0000000..a27b44b
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/copying.pyx
@@ -0,0 +1,57 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+# TODO: We want to make cpp a more full-featured package so that we can access
+# directly from that. It will make namespacing much cleaner in pylibcudf. What
+# we really want here would be
+# cimport libcudf... libcudf.copying.algo(...)
+from cudf._lib.cpp cimport copying as cpp_copying
+from cudf._lib.cpp.copying cimport out_of_bounds_policy
+
+from cudf._lib.cpp.copying import \
+    out_of_bounds_policy as OutOfBoundsPolicy  # no-cython-lint
+
+from cudf._lib.cpp.table.table cimport table
+
+from .column cimport Column
+from .table cimport Table
+
+
+# TODO: Is it OK to reference the corresponding libcudf algorithm in the
+# documentation? Otherwise there's a lot of room for duplication.
+cpdef Table gather(
+    Table source_table,
+    Column gather_map,
+    out_of_bounds_policy bounds_policy
+):
+    """Select rows from source_table according to the provided gather_map.
+
+    For details on the implementation, see cudf::gather in libcudf.
+
+    Parameters
+    ----------
+    source_table : Table
+        The table object from which to pull data.
+    gather_map : Column
+        The list of row indices to pull out of the source table.
+    bounds_policy : out_of_bounds_policy
+        Controls whether out of bounds indices are checked and nullified in the
+        output or if indices are assumed to be in bounds.
+
+    Returns
+    -------
+    pylibcudf.Table
+        The result of the gather
+    """
+    cdef unique_ptr[table] c_result
+    with nogil:
+        c_result = move(
+            cpp_copying.gather(
+                source_table.view(),
+                gather_map.view(),
+                bounds_policy
+            )
+        )
+    return Table.from_libcudf(move(c_result))
diff --git a/python/cudf/cudf/_lib/pylibcudf/gpumemoryview.pxd b/python/cudf/cudf/_lib/pylibcudf/gpumemoryview.pxd
new file mode 100644
index 0000000..713697b
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/gpumemoryview.pxd
@@ -0,0 +1,9 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+
+cdef class gpumemoryview:
+    # TODO: Eventually probably want to make this opaque, but for now it's fine
+    # to treat this object as something like a POD struct
+    cdef readonly:
+        Py_ssize_t ptr
+        object obj
diff --git a/python/cudf/cudf/_lib/pylibcudf/gpumemoryview.pyx b/python/cudf/cudf/_lib/pylibcudf/gpumemoryview.pyx
new file mode 100644
index 0000000..fc98f08
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/gpumemoryview.pyx
@@ -0,0 +1,27 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+
+cdef class gpumemoryview:
+    """Minimal representation of a memory buffer.
+
+    This class aspires to be a GPU equivalent of the [Python memoryview
+    type](https://docs.python.org/3/library/stdtypes.html#memoryview) for any
+    objects exposing a [CUDA Array
+    Interface](https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html).
+    It will be expanded to encompass more memoryview functionality over time.
+    """
+    # TODO: dlpack support
+    def __init__(self, object obj):
+        try:
+            cai = obj.__cuda_array_interface__
+        except AttributeError:
+            raise ValueError(
+                "gpumemoryview must be constructed from an object supporting "
+                "the CUDA array interface"
+            )
+        self.obj = obj
+        # TODO: Need to respect readonly
+        self.ptr = cai["data"][0]
+
+    def __cuda_array_interface__(self):
+        return self.obj.__cuda_array_interface__
diff --git a/python/cudf/cudf/_lib/pylibcudf/table.pxd b/python/cudf/cudf/_lib/pylibcudf/table.pxd
new file mode 100644
index 0000000..95f197b
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/table.pxd
@@ -0,0 +1,18 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+
+
+cdef class Table:
+    # List[pylibcudf.Column]
+    cdef list _columns
+
+    cdef table_view view(self) nogil
+
+    @staticmethod
+    cdef Table from_libcudf(unique_ptr[table] libcudf_tbl)
+
+    cpdef list columns(self)
diff --git a/python/cudf/cudf/_lib/pylibcudf/table.pyx b/python/cudf/cudf/_lib/pylibcudf/table.pyx
new file mode 100644
index 0000000..720f981
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/table.pyx
@@ -0,0 +1,62 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from cython.operator cimport dereference
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table cimport table
+
+from .column cimport Column
+
+
+cdef class Table:
+    """A list of columns of the same size.
+
+    Parameters
+    ----------
+    columns : list
+        The columns in this table.
+    """
+    def __init__(self, list columns):
+        self._columns = columns
+
+    cdef table_view view(self) nogil:
+        """Generate a libcudf table_view to pass to libcudf algorithms.
+
+        This method is for pylibcudf's functions to use to generate inputs when
+        calling libcudf algorithms, and should generally not be needed by users
+        (even direct pylibcudf Cython users).
+        """
+        # TODO: Make c_columns a class attribute that is updated along with
+        # self._columns whenever new columns are added or columns are removed.
+        cdef vector[column_view] c_columns
+
+        with gil:
+            for col in self._columns:
+                c_columns.push_back((<Column> col).view())
+
+        return table_view(c_columns)
+
+    @staticmethod
+    cdef Table from_libcudf(unique_ptr[table] libcudf_tbl):
+        """Create a Table from a libcudf table.
+
+        This method is for pylibcudf's functions to use to ingest outputs of
+        calling libcudf algorithms, and should generally not be needed by users
+        (even direct pylibcudf Cython users).
+        """
+        cdef vector[unique_ptr[column]] c_columns = move(
+            dereference(libcudf_tbl).release()
+        )
+
+        cdef vector[unique_ptr[column]].size_type i
+        return Table([
+            Column.from_libcudf(move(c_columns[i]))
+            for i in range(c_columns.size())
+        ])
+
+    cpdef list columns(self):
+        return self._columns
diff --git a/python/cudf/cudf/_lib/pylibcudf/types.pxd b/python/cudf/cudf/_lib/pylibcudf/types.pxd
new file mode 100644
index 0000000..80baa48
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/types.pxd
@@ -0,0 +1,16 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp cimport bool as cbool
+
+from cudf._lib.cpp.types cimport data_type, type_id
+
+
+cdef class DataType:
+    cdef data_type c_obj
+
+    cpdef type_id id(self)
+    cpdef int32_t scale(self)
+
+    @staticmethod
+    cdef DataType from_libcudf(data_type dt)
diff --git a/python/cudf/cudf/_lib/pylibcudf/types.pyx b/python/cudf/cudf/_lib/pylibcudf/types.pyx
new file mode 100644
index 0000000..b139172
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/types.pyx
@@ -0,0 +1,45 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+
+from cudf._lib.cpp.types cimport data_type, type_id
+
+from cudf._lib.cpp.types import type_id as TypeId  # no-cython-lint
+
+
+cdef class DataType:
+    """Indicator for the logical data type of an element in a column.
+
+    This is the Cython representation of libcudf's data_type.
+
+    Parameters
+    ----------
+    id : TypeId
+        The type's identifier
+    scale : int
+        The scale associated with the data. Only used for decimal data types.
+    """
+    def __cinit__(self, type_id id, int32_t scale=0):
+        self.c_obj = data_type(id, scale)
+
+    # TODO: Consider making both id and scale cached properties.
+    cpdef type_id id(self):
+        """Get the id associated with this data type."""
+        return self.c_obj.id()
+
+    cpdef int32_t scale(self):
+        """Get the scale associated with this data type."""
+        return self.c_obj.scale()
+
+    @staticmethod
+    cdef DataType from_libcudf(data_type dt):
+        """Create a DataType from a libcudf data_type.
+
+        This method is for pylibcudf's functions to use to ingest outputs of
+        calling libcudf algorithms, and should generally not be needed by users
+        (even direct pylibcudf Cython users).
+        """
+        # Spoof an empty data type then swap in the real one.
+        cdef DataType ret = DataType.__new__(DataType, type_id.EMPTY)
+        ret.c_obj = dt
+        return ret
diff --git a/python/cudf/cudf/_lib/pylibcudf/utils.pxd b/python/cudf/cudf/_lib/pylibcudf/utils.pxd
new file mode 100644
index 0000000..18bcd9c
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/utils.pxd
@@ -0,0 +1,7 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from cudf._lib.cpp.types cimport bitmask_type
+
+
+cdef void * int_to_void_ptr(Py_ssize_t ptr) nogil
+cdef bitmask_type * int_to_bitmask_ptr(Py_ssize_t ptr) nogil
diff --git a/python/cudf/cudf/_lib/pylibcudf/utils.pyx b/python/cudf/cudf/_lib/pylibcudf/utils.pyx
new file mode 100644
index 0000000..ccf9ea2
--- /dev/null
+++ b/python/cudf/cudf/_lib/pylibcudf/utils.pyx
@@ -0,0 +1,13 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uintptr_t
+
+from cudf._lib.cpp.types cimport bitmask_type
+
+
+cdef void * int_to_void_ptr(Py_ssize_t ptr) nogil:
+    return <void*><uintptr_t>(ptr)
+
+
+cdef bitmask_type * int_to_bitmask_ptr(Py_ssize_t ptr) nogil:
+    return <bitmask_type*><uintptr_t>(ptr)
diff --git a/python/cudf/cudf/_lib/quantiles.pyx b/python/cudf/cudf/_lib/quantiles.pyx
new file mode 100644
index 0000000..d3a02fa
--- /dev/null
+++ b/python/cudf/cudf/_lib/quantiles.pyx
@@ -0,0 +1,119 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.types cimport (
+    underlying_type_t_interpolation,
+    underlying_type_t_null_order,
+    underlying_type_t_order,
+    underlying_type_t_sorted,
+)
+
+from cudf._lib.types import Interpolation
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.quantiles cimport (
+    quantile as cpp_quantile,
+    quantiles as cpp_quantile_table,
+)
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport interpolation, null_order, order, sorted
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def quantile(
+    Column input,
+    object q,
+    str interp,
+    Column ordered_indices,
+    bool exact,
+
+):
+    cdef column_view c_input = input.view()
+    cdef column_view c_ordered_indices = (
+        column_view() if ordered_indices is None
+        else ordered_indices.view()
+    )
+    cdef interpolation c_interp = <interpolation>(
+        <underlying_type_t_interpolation> Interpolation[interp.upper()]
+    )
+    cdef bool c_exact = exact
+
+    cdef vector[double] c_q
+    c_q.reserve(len(q))
+
+    for value in q:
+        c_q.push_back(value)
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_quantile(
+                c_input,
+                c_q,
+                c_interp,
+                c_ordered_indices,
+                c_exact,
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def quantile_table(
+    list source_columns,
+    vector[double] q,
+    object interp,
+    object is_input_sorted,
+    list column_order,
+    list null_precedence,
+):
+    cdef table_view c_input = table_view_from_columns(source_columns)
+    cdef vector[double] c_q = q
+    cdef interpolation c_interp = <interpolation>(
+        <underlying_type_t_interpolation> interp
+    )
+    cdef sorted c_is_input_sorted = <sorted>(
+        <underlying_type_t_sorted> is_input_sorted
+    )
+    cdef vector[order] c_column_order
+    cdef vector[null_order] c_null_precedence
+
+    c_column_order.reserve(len(column_order))
+    c_null_precedence.reserve(len(null_precedence))
+
+    for value in column_order:
+        c_column_order.push_back(
+            <order>(<underlying_type_t_order> value)
+        )
+
+    for value in null_precedence:
+        c_null_precedence.push_back(
+            <null_order>(<underlying_type_t_null_order> value)
+        )
+
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_quantile_table(
+                c_input,
+                c_q,
+                c_interp,
+                c_is_input_sorted,
+                c_column_order,
+                c_null_precedence,
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/reduce.pyx b/python/cudf/cudf/_lib/reduce.pyx
new file mode 100644
index 0000000..f11bacd
--- /dev/null
+++ b/python/cudf/cudf/_lib/reduce.pyx
@@ -0,0 +1,163 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cython.operator import dereference
+
+import cudf
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move, pair
+
+from cudf._lib.aggregation cimport (
+    ReduceAggregation,
+    ScanAggregation,
+    make_reduce_aggregation,
+    make_scan_aggregation,
+)
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.reduce cimport cpp_minmax, cpp_reduce, cpp_scan, scan_type
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.cpp.types cimport data_type
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.types cimport dtype_to_data_type, is_decimal_type_id
+
+
+@acquire_spill_lock()
+def reduce(reduction_op, Column incol, dtype=None, **kwargs):
+    """
+    Top level Cython reduce function wrapping libcudf reductions.
+
+    Parameters
+    ----------
+    reduction_op : string
+        A string specifying the operation, e.g. sum, prod
+    incol : Column
+        A cuDF Column object
+    dtype: numpy.dtype, optional
+        A numpy data type to use for the output, defaults
+        to the same type as the input column
+    """
+
+    col_dtype = (
+        dtype if dtype is not None
+        else incol._reduction_result_dtype(reduction_op)
+    )
+
+    cdef column_view c_incol_view = incol.view()
+    cdef unique_ptr[scalar] c_result
+    cdef ReduceAggregation cython_agg = make_reduce_aggregation(
+        reduction_op, kwargs)
+
+    cdef data_type c_out_dtype = dtype_to_data_type(col_dtype)
+
+    # check empty case
+    if len(incol) <= incol.null_count:
+        if reduction_op == 'sum' or reduction_op == 'sum_of_squares':
+            return incol.dtype.type(0)
+        if reduction_op == 'product':
+            return incol.dtype.type(1)
+        if reduction_op == "any":
+            return False
+
+        return cudf.utils.dtypes._get_nan_for_dtype(col_dtype)
+
+    with nogil:
+        c_result = move(cpp_reduce(
+            c_incol_view,
+            dereference(cython_agg.c_obj),
+            c_out_dtype
+        ))
+
+    if is_decimal_type_id(c_result.get()[0].type().id()):
+        scale = -c_result.get()[0].type().scale()
+        precision = _reduce_precision(col_dtype, reduction_op, len(incol))
+        py_result = DeviceScalar.from_unique_ptr(
+            move(c_result), dtype=col_dtype.__class__(precision, scale)
+        )
+    else:
+        py_result = DeviceScalar.from_unique_ptr(move(c_result))
+    return py_result.value
+
+
+@acquire_spill_lock()
+def scan(scan_op, Column incol, inclusive, **kwargs):
+    """
+    Top level Cython scan function wrapping libcudf scans.
+
+    Parameters
+    ----------
+    incol : Column
+        A cuDF Column object
+    scan_op : string
+        A string specifying the operation, e.g. cumprod
+    inclusive: bool
+        Flag for including nulls in relevant scan
+    """
+    cdef column_view c_incol_view = incol.view()
+    cdef unique_ptr[column] c_result
+    cdef ScanAggregation cython_agg = make_scan_aggregation(scan_op, kwargs)
+
+    cdef scan_type c_inclusive = \
+        scan_type.INCLUSIVE if inclusive else scan_type.EXCLUSIVE
+
+    with nogil:
+        c_result = move(cpp_scan(
+            c_incol_view,
+            dereference(cython_agg.c_obj),
+            c_inclusive
+        ))
+
+    py_result = Column.from_unique_ptr(move(c_result))
+    return py_result
+
+
+@acquire_spill_lock()
+def minmax(Column incol):
+    """
+    Top level Cython minmax function wrapping libcudf minmax.
+
+    Parameters
+    ----------
+    incol : Column
+        A cuDF Column object
+
+    Returns
+    -------
+    A pair of ``(min, max)`` values of ``incol``
+    """
+    cdef column_view c_incol_view = incol.view()
+    cdef pair[unique_ptr[scalar], unique_ptr[scalar]] c_result
+
+    with nogil:
+        c_result = move(cpp_minmax(c_incol_view))
+
+    py_result_min = DeviceScalar.from_unique_ptr(move(c_result.first))
+    py_result_max = DeviceScalar.from_unique_ptr(move(c_result.second))
+
+    return (
+        cudf.Scalar.from_device_scalar(py_result_min),
+        cudf.Scalar.from_device_scalar(py_result_max)
+    )
+
+
+def _reduce_precision(dtype, op, nrows):
+    """
+    Returns the result precision when performing the reduce
+    operation `op` for the given dtype and column size.
+
+    See: https://docs.microsoft.com/en-us/sql/t-sql/data-types/precision-scale-and-length-transact-sql
+    """  # noqa: E501
+    p = dtype.precision
+    if op in ("min", "max"):
+        new_p = p
+    elif op == "sum":
+        new_p = p + nrows - 1
+    elif op == "product":
+        new_p = p * nrows + nrows - 1
+    elif op == "sum_of_squares":
+        new_p = 2 * p + nrows
+    else:
+        raise NotImplementedError()
+    return max(min(new_p, dtype.MAX_PRECISION), 0)
diff --git a/python/cudf/cudf/_lib/replace.pyx b/python/cudf/cudf/_lib/replace.pyx
new file mode 100644
index 0000000..c763a86
--- /dev/null
+++ b/python/cudf/cudf/_lib/replace.pyx
@@ -0,0 +1,261 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.api.types import is_scalar
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+
+from cudf._lib.scalar import as_device_scalar
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view, mutable_column_view
+from cudf._lib.cpp.replace cimport (
+    clamp as cpp_clamp,
+    find_and_replace_all as cpp_find_and_replace_all,
+    normalize_nans_and_zeros as cpp_normalize_nans_and_zeros,
+    replace_nulls as cpp_replace_nulls,
+    replace_policy as cpp_replace_policy,
+)
+from cudf._lib.cpp.scalar.scalar cimport scalar
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def replace(Column input_col, Column values_to_replace,
+            Column replacement_values):
+    """
+    Replaces values from values_to_replace with corresponding value from
+    replacement_values in input_col
+
+    Parameters
+    ----------
+    input_col : Column whose value will be updated
+    values_to_replace : Column with values which needs to be replaced
+    replacement_values : Column with values which will replace
+    """
+
+    cdef column_view input_col_view = input_col.view()
+    cdef column_view values_to_replace_view = values_to_replace.view()
+    cdef column_view replacement_values_view = replacement_values.view()
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(cpp_find_and_replace_all(input_col_view,
+                                                 values_to_replace_view,
+                                                 replacement_values_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace_nulls_column(Column input_col, Column replacement_values):
+    """
+    Replaces null values in input_col with corresponding values from
+    replacement_values
+
+    Parameters
+    ----------
+    input_col : Column whose value will be updated
+    replacement_values : Column with values which will replace nulls
+    """
+
+    cdef column_view input_col_view = input_col.view()
+    cdef column_view replacement_values_view = replacement_values.view()
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(cpp_replace_nulls(input_col_view,
+                                          replacement_values_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace_nulls_scalar(Column input_col, DeviceScalar replacement_value):
+    """
+    Replaces null values in input_col with replacement_value
+
+    Parameters
+    ----------
+    input_col : Column whose value will be updated
+    replacement_value : DeviceScalar with value which will replace nulls
+    """
+
+    cdef column_view input_col_view = input_col.view()
+    cdef const scalar* replacement_value_scalar = replacement_value\
+        .get_raw_ptr()
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(cpp_replace_nulls(input_col_view,
+                                          replacement_value_scalar[0]))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace_nulls_fill(Column input_col, object method):
+    """
+    Replaces null values in input_col with replacement_value
+
+    Parameters
+    ----------
+    input_col : Column whose value will be updated
+    method : 'ffill' or 'bfill'
+    """
+
+    cdef column_view input_col_view = input_col.view()
+
+    cdef unique_ptr[column] c_result
+    cdef cpp_replace_policy policy = (
+        cpp_replace_policy.PRECEDING
+        if method == 'ffill'
+        else cpp_replace_policy.FOLLOWING
+    )
+
+    with nogil:
+        c_result = move(cpp_replace_nulls(input_col_view, policy))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def replace_nulls(
+    Column input_col,
+    object replacement=None,
+    object method=None,
+    object dtype=None
+):
+    """
+    Calls one of the version of replace_nulls depending on type
+    of replacement
+    """
+
+    if replacement is None and method is None:
+        raise ValueError("Must specify a fill 'value' or 'method'.")
+
+    if replacement and method:
+        raise ValueError("Cannot specify both 'value' and 'method'.")
+
+    if method:
+        return replace_nulls_fill(input_col, method)
+    elif is_scalar(replacement):
+        return replace_nulls_scalar(
+            input_col,
+            as_device_scalar(replacement, dtype=dtype)
+        )
+    else:
+        return replace_nulls_column(input_col, replacement)
+
+
+@acquire_spill_lock()
+def clamp(Column input_col, DeviceScalar lo, DeviceScalar lo_replace,
+          DeviceScalar hi, DeviceScalar hi_replace):
+    """
+    Clip the input_col such that values < lo will be replaced by lo_replace
+    and > hi will be replaced by hi_replace
+
+    Parameters
+    ----------
+    input_col : Column whose value will be updated
+    lo : DeviceScalar value for clipping lower values
+    lo_replace : DeviceScalar value which will replace clipped with lo
+    hi : DeviceScalar value for clipping upper values
+    lo_replace : DeviceScalar value which will replace clipped with hi
+    """
+
+    cdef column_view input_col_view = input_col.view()
+    cdef const scalar* lo_value = lo.get_raw_ptr()
+    cdef const scalar* lo_replace_value = lo_replace.get_raw_ptr()
+    cdef const scalar* hi_value = hi.get_raw_ptr()
+    cdef const scalar* hi_replace_value = hi_replace.get_raw_ptr()
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(cpp_clamp(
+            input_col_view, lo_value[0],
+            lo_replace_value[0], hi_value[0], hi_replace_value[0]))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def clamp(Column input_col, DeviceScalar lo, DeviceScalar hi):
+    """
+    Clip the input_col such that values < lo will be replaced by lo
+    and > hi will be replaced by hi
+
+    Parameters
+    ----------
+    input_col : Column whose value will be updated
+    lo : DeviceScalar value for clipping lower values
+    hi : DeviceScalar value for clipping upper values
+    """
+
+    cdef column_view input_col_view = input_col.view()
+    cdef const scalar* lo_value = lo.get_raw_ptr()
+    cdef const scalar* hi_value = hi.get_raw_ptr()
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(cpp_clamp(input_col_view, lo_value[0], hi_value[0]))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def clip(Column input_col, object lo, object hi):
+    """
+    Clip the input_col such that values < lo will be replaced by lo
+    and > hi will be replaced by hi
+    """
+
+    lo_scalar = as_device_scalar(lo, dtype=input_col.dtype)
+    hi_scalar = as_device_scalar(hi, dtype=input_col.dtype)
+
+    return clamp(input_col, lo_scalar, hi_scalar)
+
+
+@acquire_spill_lock()
+def normalize_nans_and_zeros_inplace(Column input_col):
+    """
+    Inplace normalizing
+    """
+
+    cdef mutable_column_view input_col_view = input_col.mutable_view()
+    with nogil:
+        cpp_normalize_nans_and_zeros(input_col_view)
+
+
+@acquire_spill_lock()
+def normalize_nans_and_zeros_column(Column input_col):
+    """
+    Returns a new  normalized Column
+    """
+
+    cdef column_view input_col_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(cpp_normalize_nans_and_zeros(input_col_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def normalize_nans_and_zeros(Column input_col, in_place=False):
+    """
+    Normalize the NaN and zeros in input_col
+    Convert  -NaN  -> NaN
+    Convert  -0.0  -> 0.0
+
+    Parameters
+    ----------
+    input_col : Column that needs to be normalized
+    in_place : boolean whether to normalize in place or return new column
+    """
+
+    if in_place is True:
+        normalize_nans_and_zeros_inplace(input_col)
+    else:
+        return normalize_nans_and_zeros_column(input_col)
diff --git a/python/cudf/cudf/_lib/reshape.pyx b/python/cudf/cudf/_lib/reshape.pyx
new file mode 100644
index 0000000..c237b7b
--- /dev/null
+++ b/python/cudf/cudf/_lib/reshape.pyx
@@ -0,0 +1,40 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.reshape cimport (
+    interleave_columns as cpp_interleave_columns,
+    tile as cpp_tile,
+)
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def interleave_columns(list source_columns):
+    cdef table_view c_view = table_view_from_columns(source_columns)
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_interleave_columns(c_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def tile(list source_columns, size_type count):
+    cdef size_type c_count = count
+    cdef table_view c_view = table_view_from_columns(source_columns)
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(cpp_tile(c_view, c_count))
+
+    return columns_from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/rolling.pyx b/python/cudf/cudf/_lib/rolling.pyx
new file mode 100644
index 0000000..8c4751e
--- /dev/null
+++ b/python/cudf/cudf/_lib/rolling.pyx
@@ -0,0 +1,95 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.aggregation cimport RollingAggregation, make_rolling_aggregation
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.rolling cimport rolling_window as cpp_rolling_window
+from cudf._lib.cpp.types cimport size_type
+
+
+@acquire_spill_lock()
+def rolling(Column source_column,
+            Column pre_column_window,
+            Column fwd_column_window,
+            window,
+            min_periods,
+            center,
+            op,
+            agg_params):
+    """
+    Rolling on input executing operation within the given window for each row
+
+    Parameters
+    ----------
+    source_column : input column on which rolling operation is executed
+    pre_column_window : prior window for each element of source_column
+    fwd_column_window : forward window for each element of source_column
+    window : Size of the moving window, can be integer or None
+    min_periods : Minimum number of observations in window required to have
+                  a value (otherwise result is null)
+    center : Set the labels at the center of the window
+    op : operation to be executed
+    agg_params : dict, parameter for the aggregation (e.g. ddof for VAR/STD)
+
+    Returns
+    -------
+    A Column with rolling calculations
+    """
+    cdef size_type c_min_periods = min_periods
+    cdef size_type c_window = 0
+    cdef size_type c_forward_window = 0
+    cdef unique_ptr[column] c_result
+    cdef column_view source_column_view = source_column.view()
+    cdef column_view pre_column_window_view
+    cdef column_view fwd_column_window_view
+    cdef RollingAggregation cython_agg
+
+    if callable(op):
+        cython_agg = make_rolling_aggregation(
+            op, {'dtype': source_column.dtype})
+    else:
+        cython_agg = make_rolling_aggregation(op, agg_params)
+
+    if window is None:
+        if center:
+            # TODO: we can support this even though Pandas currently does not
+            raise NotImplementedError(
+                "center is not implemented for offset-based windows"
+            )
+        pre_column_window_view = pre_column_window.view()
+        fwd_column_window_view = fwd_column_window.view()
+        with nogil:
+            c_result = move(
+                cpp_rolling_window(
+                    source_column_view,
+                    pre_column_window_view,
+                    fwd_column_window_view,
+                    c_min_periods,
+                    cython_agg.c_obj.get()[0])
+            )
+    else:
+        c_min_periods = min_periods
+        if center:
+            c_window = (window // 2) + 1
+            c_forward_window = window - (c_window)
+        else:
+            c_window = window
+            c_forward_window = 0
+
+        with nogil:
+            c_result = move(
+                cpp_rolling_window(
+                    source_column_view,
+                    c_window,
+                    c_forward_window,
+                    c_min_periods,
+                    cython_agg.c_obj.get()[0])
+            )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/round.pyx b/python/cudf/cudf/_lib/round.pyx
new file mode 100644
index 0000000..7eddb1b
--- /dev/null
+++ b/python/cudf/cudf/_lib/round.pyx
@@ -0,0 +1,49 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.round cimport (
+    round as cpp_round,
+    rounding_method as cpp_rounding_method,
+)
+
+
+@acquire_spill_lock()
+def round(Column input_col, int decimal_places=0, how="half_even"):
+    """
+    Round column values to the given number of decimal places
+
+    Parameters
+    ----------
+    input_col : Column whose values will be rounded
+    decimal_places : The number or decimal places to round to
+
+    Returns
+    -------
+    A Column with values rounded to the given number of decimal places
+    """
+    if how not in {"half_even", "half_up"}:
+        raise ValueError("'how' must be either 'half_even' or 'half_up'")
+
+    cdef column_view input_col_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    cdef cpp_rounding_method c_how = (
+        cpp_rounding_method.HALF_EVEN if how == "half_even"
+        else cpp_rounding_method.HALF_UP
+    )
+    with nogil:
+        c_result = move(
+            cpp_round(
+                input_col_view,
+                decimal_places,
+                c_how
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/scalar.pxd b/python/cudf/cudf/_lib/scalar.pxd
new file mode 100644
index 0000000..1deed60
--- /dev/null
+++ b/python/cudf/cudf/_lib/scalar.pxd
@@ -0,0 +1,26 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+
+from rmm._lib.memory_resource cimport DeviceMemoryResource
+
+from cudf._lib.cpp.scalar.scalar cimport scalar
+
+
+cdef class DeviceScalar:
+    cdef unique_ptr[scalar] c_value
+
+    # Holds a reference to the DeviceMemoryResource used for allocation.
+    # Ensures the MR does not get destroyed before this DeviceBuffer. `mr` is
+    # needed for deallocation
+    cdef DeviceMemoryResource mr
+
+    cdef object _dtype
+
+    cdef const scalar* get_raw_ptr(self) except *
+
+    @staticmethod
+    cdef DeviceScalar from_unique_ptr(unique_ptr[scalar] ptr, dtype=*)
+
+    cpdef bool is_valid(DeviceScalar s)
diff --git a/python/cudf/cudf/_lib/scalar.pyx b/python/cudf/cudf/_lib/scalar.pyx
new file mode 100644
index 0000000..5ab286c
--- /dev/null
+++ b/python/cudf/cudf/_lib/scalar.pyx
@@ -0,0 +1,318 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+cimport cython
+
+import copy
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+
+from libc.stdint cimport int64_t
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from rmm._lib.memory_resource cimport get_current_device_resource
+
+import cudf
+from cudf._lib.types import LIBCUDF_TO_SUPPORTED_NUMPY_TYPES
+from cudf.core.dtypes import ListDtype, StructDtype
+from cudf.core.missing import NA, NaT
+
+from cudf._lib.types cimport dtype_from_column_view, underlying_type_t_type_id
+
+from cudf._lib.interop import from_arrow_scalar, to_arrow_scalar
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.scalar.scalar cimport (
+    duration_scalar,
+    list_scalar,
+    scalar,
+    struct_scalar,
+    timestamp_scalar,
+)
+from cudf._lib.cpp.wrappers.durations cimport (
+    duration_ms,
+    duration_ns,
+    duration_s,
+    duration_us,
+)
+from cudf._lib.cpp.wrappers.timestamps cimport (
+    timestamp_ms,
+    timestamp_ns,
+    timestamp_s,
+    timestamp_us,
+)
+
+
+def _replace_nested(obj, check, replacement):
+    if isinstance(obj, list):
+        for i, item in enumerate(obj):
+            if check(item):
+                obj[i] = replacement
+            elif isinstance(item, (dict, list)):
+                _replace_nested(item, check, replacement)
+    elif isinstance(obj, dict):
+        for k, v in obj.items():
+            if check(v):
+                obj[k] = replacement
+            elif isinstance(v, (dict, list)):
+                _replace_nested(v, check, replacement)
+
+
+# The DeviceMemoryResource attribute could be released prematurely
+# by the gc if the DeviceScalar is in a reference cycle. Removing
+# the tp_clear function with the no_gc_clear decoration prevents that.
+# See https://github.com/rapidsai/rmm/pull/931 for details.
+@cython.no_gc_clear
+cdef class DeviceScalar:
+
+    def __cinit__(self, *args, **kwargs):
+        self.mr = get_current_device_resource()
+
+    def __init__(self, value, dtype):
+        """
+        Type representing an *immutable* scalar value on the device
+
+        Parameters
+        ----------
+        value : scalar
+            An object of scalar type, i.e., one for which
+            `np.isscalar()` returns `True`. Can also be `None`,
+            to represent a "null" scalar. In this case,
+            dtype *must* be provided.
+        dtype : dtype
+            A NumPy dtype.
+        """
+        self._dtype = dtype if dtype.kind != 'U' else cudf.dtype('object')
+
+        if cudf.utils.utils.is_na_like(value):
+            value = None
+        else:
+            # TODO: For now we always deepcopy the input value to avoid
+            # overwriting the input values when replacing nulls. Since it's
+            # just host values it's not that expensive, but we could consider
+            # alternatives.
+            value = copy.deepcopy(value)
+            _replace_nested(value, cudf.utils.utils.is_na_like, None)
+
+        if isinstance(dtype, cudf.core.dtypes._BaseDtype):
+            pa_type = dtype.to_arrow()
+        elif pd.api.types.is_string_dtype(dtype):
+            # Have to manually convert object types, which we use internally
+            # for strings but pyarrow only supports as unicode 'U'
+            pa_type = pa.string()
+        else:
+            pa_type = pa.from_numpy_dtype(dtype)
+
+        pa_scalar = pa.scalar(value, type=pa_type)
+
+        # Note: This factory-like behavior in __init__ will be removed when
+        # migrating to pylibcudf.
+        cdef DeviceScalar obj = from_arrow_scalar(pa_scalar, self._dtype)
+        self.c_value.swap(obj.c_value)
+
+    def _to_host_scalar(self):
+        is_datetime = self.dtype.kind == "M"
+        is_timedelta = self.dtype.kind == "m"
+
+        null_type = NaT if is_datetime or is_timedelta else NA
+
+        ps = to_arrow_scalar(self)
+        if not ps.is_valid:
+            return null_type
+
+        # TODO: The special handling of specific types below does not currently
+        # extend to nested types containing those types (e.g. List[timedelta]
+        # where the timedelta would overflow). We should eventually account for
+        # those cases, but that will require more careful consideration of how
+        # to traverse the contents of the nested data.
+        if is_datetime or is_timedelta:
+            time_unit, _ = np.datetime_data(self.dtype)
+            # Cast to int64 to avoid overflow
+            ps_cast = ps.cast('int64').as_py()
+            out_type = np.datetime64 if is_datetime else np.timedelta64
+            ret = out_type(ps_cast, time_unit)
+        elif cudf.api.types.is_numeric_dtype(self.dtype):
+            ret = ps.type.to_pandas_dtype()(ps.as_py())
+        else:
+            ret = ps.as_py()
+
+        _replace_nested(ret, lambda item: item is None, NA)
+        return ret
+
+    @property
+    def dtype(self):
+        """
+        The NumPy dtype corresponding to the data type of the underlying
+        device scalar.
+        """
+        return self._dtype
+
+    @property
+    def value(self):
+        """
+        Returns a host copy of the underlying device scalar.
+        """
+        return self._to_host_scalar()
+
+    cdef const scalar* get_raw_ptr(self) except *:
+        return self.c_value.get()
+
+    cpdef bool is_valid(self):
+        """
+        Returns if the Scalar is valid or not(i.e., <NA>).
+        """
+        return self.get_raw_ptr()[0].is_valid()
+
+    def __repr__(self):
+        if cudf.utils.utils.is_na_like(self.value):
+            return (
+                f"{self.__class__.__name__}"
+                f"({self.value}, {repr(self.dtype)})"
+            )
+        else:
+            return f"{self.__class__.__name__}({repr(self.value)})"
+
+    @staticmethod
+    cdef DeviceScalar from_unique_ptr(unique_ptr[scalar] ptr, dtype=None):
+        """
+        Construct a Scalar object from a unique_ptr<cudf::scalar>.
+        """
+        cdef DeviceScalar s = DeviceScalar.__new__(DeviceScalar)
+        cdef libcudf_types.data_type cdtype
+
+        s.c_value = move(ptr)
+        cdtype = s.get_raw_ptr()[0].type()
+
+        if dtype is not None:
+            s._dtype = dtype
+        elif cdtype.id() in {
+            libcudf_types.type_id.DECIMAL32,
+            libcudf_types.type_id.DECIMAL64,
+            libcudf_types.type_id.DECIMAL128,
+        }:
+            raise TypeError(
+                "Must pass a dtype when constructing from a fixed-point scalar"
+            )
+        elif cdtype.id() == libcudf_types.type_id.STRUCT:
+            struct_table_view = (<struct_scalar*>s.get_raw_ptr())[0].view()
+            s._dtype = StructDtype({
+                str(i): dtype_from_column_view(struct_table_view.column(i))
+                for i in range(struct_table_view.num_columns())
+            })
+        elif cdtype.id() == libcudf_types.type_id.LIST:
+            if (
+                <list_scalar*>s.get_raw_ptr()
+            )[0].view().type().id() == libcudf_types.type_id.LIST:
+                s._dtype = dtype_from_column_view(
+                    (<list_scalar*>s.get_raw_ptr())[0].view()
+                )
+            else:
+                s._dtype = ListDtype(
+                    LIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
+                        <underlying_type_t_type_id>(
+                            (<list_scalar*>s.get_raw_ptr())[0]
+                            .view().type().id()
+                        )
+                    ]
+                )
+        else:
+            s._dtype = LIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
+                <underlying_type_t_type_id>(cdtype.id())
+            ]
+        return s
+
+
+# TODO: Currently the only uses of this function and the one below are in
+# _create_proxy_nat_scalar. See if that code path can be simplified to excise
+# or at least simplify these implementations.
+cdef _set_datetime64_from_np_scalar(unique_ptr[scalar]& s,
+                                    object value,
+                                    object dtype,
+                                    bool valid=True):
+
+    value = value if valid else 0
+
+    if dtype == "datetime64[s]":
+        s.reset(
+            new timestamp_scalar[timestamp_s](<int64_t>np.int64(value), valid)
+        )
+    elif dtype == "datetime64[ms]":
+        s.reset(
+            new timestamp_scalar[timestamp_ms](<int64_t>np.int64(value), valid)
+        )
+    elif dtype == "datetime64[us]":
+        s.reset(
+            new timestamp_scalar[timestamp_us](<int64_t>np.int64(value), valid)
+        )
+    elif dtype == "datetime64[ns]":
+        s.reset(
+            new timestamp_scalar[timestamp_ns](<int64_t>np.int64(value), valid)
+        )
+    else:
+        raise ValueError(f"dtype not supported: {dtype}")
+
+cdef _set_timedelta64_from_np_scalar(unique_ptr[scalar]& s,
+                                     object value,
+                                     object dtype,
+                                     bool valid=True):
+
+    value = value if valid else 0
+
+    if dtype == "timedelta64[s]":
+        s.reset(
+            new duration_scalar[duration_s](<int64_t>np.int64(value), valid)
+        )
+    elif dtype == "timedelta64[ms]":
+        s.reset(
+            new duration_scalar[duration_ms](<int64_t>np.int64(value), valid)
+        )
+    elif dtype == "timedelta64[us]":
+        s.reset(
+            new duration_scalar[duration_us](<int64_t>np.int64(value), valid)
+        )
+    elif dtype == "timedelta64[ns]":
+        s.reset(
+            new duration_scalar[duration_ns](<int64_t>np.int64(value), valid)
+        )
+    else:
+        raise ValueError(f"dtype not supported: {dtype}")
+
+
+def as_device_scalar(val, dtype=None):
+    if isinstance(val, (cudf.Scalar, DeviceScalar)):
+        if dtype == val.dtype or dtype is None:
+            if isinstance(val, DeviceScalar):
+                return val
+            else:
+                return val.device_value
+        else:
+            raise TypeError("Can't update dtype of existing GPU scalar")
+    else:
+        return cudf.Scalar(val, dtype=dtype).device_value
+
+
+def _is_null_host_scalar(slr):
+    if cudf.utils.utils.is_na_like(slr):
+        return True
+    elif isinstance(slr, (np.datetime64, np.timedelta64)) and np.isnat(slr):
+        return True
+    else:
+        return False
+
+
+def _create_proxy_nat_scalar(dtype):
+    cdef DeviceScalar result = DeviceScalar.__new__(DeviceScalar)
+
+    dtype = cudf.dtype(dtype)
+    if dtype.char in 'mM':
+        nat = dtype.type('NaT').astype(dtype)
+        if dtype.type == np.datetime64:
+            _set_datetime64_from_np_scalar(result.c_value, nat, dtype, True)
+        elif dtype.type == np.timedelta64:
+            _set_timedelta64_from_np_scalar(result.c_value, nat, dtype, True)
+        return result
+    else:
+        raise TypeError('NAT only valid for datetime and timedelta')
diff --git a/python/cudf/cudf/_lib/search.pyx b/python/cudf/cudf/_lib/search.pyx
new file mode 100644
index 0000000..fef3a08
--- /dev/null
+++ b/python/cudf/cudf/_lib/search.pyx
@@ -0,0 +1,101 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+cimport cudf._lib.cpp.search as cpp_search
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.utils cimport table_view_from_columns
+
+
+@acquire_spill_lock()
+def search_sorted(
+    list source, list values, side, ascending=True, na_position="last"
+):
+    """Find indices where elements should be inserted to maintain order
+
+    Parameters
+    ----------
+    source : list of columns
+        List of columns to search in
+    values : List of columns
+        List of value columns to search for
+    side : str {'left', 'right'} optional
+        If 'left', the index of the first suitable location is given.
+        If 'right', return the last such index
+    """
+    cdef unique_ptr[column] c_result
+    cdef vector[libcudf_types.order] c_column_order
+    cdef vector[libcudf_types.null_order] c_null_precedence
+    cdef libcudf_types.order c_order
+    cdef libcudf_types.null_order c_null_order
+    cdef table_view c_table_data = table_view_from_columns(source)
+    cdef table_view c_values_data = table_view_from_columns(values)
+
+    # Note: We are ignoring index columns here
+    c_order = (libcudf_types.order.ASCENDING
+               if ascending
+               else libcudf_types.order.DESCENDING)
+    c_null_order = (
+        libcudf_types.null_order.AFTER
+        if na_position=="last"
+        else libcudf_types.null_order.BEFORE
+    )
+    c_column_order = vector[libcudf_types.order](len(source), c_order)
+    c_null_precedence = vector[libcudf_types.null_order](
+        len(source), c_null_order
+    )
+
+    if side == 'left':
+        with nogil:
+            c_result = move(
+                cpp_search.lower_bound(
+                    c_table_data,
+                    c_values_data,
+                    c_column_order,
+                    c_null_precedence,
+                )
+            )
+    elif side == 'right':
+        with nogil:
+            c_result = move(
+                cpp_search.upper_bound(
+                    c_table_data,
+                    c_values_data,
+                    c_column_order,
+                    c_null_precedence,
+                )
+            )
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def contains(Column haystack, Column needles):
+    """Check whether column contains multiple values
+
+    Parameters
+    ----------
+    column : NumericalColumn
+        Column to search in
+    needles :
+        A column of values to search for
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view c_haystack = haystack.view()
+    cdef column_view c_needles = needles.view()
+
+    with nogil:
+        c_result = move(
+            cpp_search.contains(
+                c_haystack,
+                c_needles,
+            )
+        )
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/sort.pyx b/python/cudf/cudf/_lib/sort.pyx
new file mode 100644
index 0000000..b80ea9c
--- /dev/null
+++ b/python/cudf/cudf/_lib/sort.pyx
@@ -0,0 +1,477 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from itertools import repeat
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move, pair
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.aggregation cimport (
+    rank_method,
+    underlying_type_t_rank_method,
+)
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.search cimport lower_bound, upper_bound
+from cudf._lib.cpp.sorting cimport (
+    is_sorted as cpp_is_sorted,
+    rank,
+    segmented_sort_by_key as cpp_segmented_sort_by_key,
+    sort as cpp_sort,
+    sort_by_key as cpp_sort_by_key,
+    sorted_order,
+    stable_segmented_sort_by_key as cpp_stable_segmented_sort_by_key,
+    stable_sort_by_key as cpp_stable_sort_by_key,
+    stable_sorted_order,
+)
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport null_order, null_policy, order as cpp_order
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def is_sorted(
+    list source_columns, object ascending=None, object null_position=None
+):
+    """
+    Checks whether the rows of a `table` are sorted in lexicographical order.
+
+    Parameters
+    ----------
+    source_columns : list of columns
+        columns to be checked for sort order
+    ascending : None or list-like of booleans
+        None or list-like of boolean values indicating expected sort order of
+        each column. If list-like, size of list-like must be len(columns). If
+        None, all columns expected sort order is set to ascending. False (0) -
+        descending, True (1) - ascending.
+    null_position : None or list-like of booleans
+        None or list-like of boolean values indicating desired order of nulls
+        compared to other elements. If list-like, size of list-like must be
+        len(columns). If None, null order is set to before. False (0) - after,
+        True (1) - before.
+
+    Returns
+    -------
+    returns : boolean
+        Returns True, if sorted as expected by ``ascending`` and
+        ``null_position``, False otherwise.
+    """
+
+    cdef vector[cpp_order] column_order
+    cdef vector[null_order] null_precedence
+
+    if ascending is None:
+        column_order = vector[cpp_order](
+            len(source_columns), cpp_order.ASCENDING
+        )
+    else:
+        if len(ascending) != len(source_columns):
+            raise ValueError(
+                f"Expected a list-like of length {len(source_columns)}, "
+                f"got length {len(ascending)} for `ascending`"
+            )
+        column_order = vector[cpp_order](
+            len(source_columns), cpp_order.DESCENDING
+        )
+        for idx, val in enumerate(ascending):
+            if val:
+                column_order[idx] = cpp_order.ASCENDING
+
+    if null_position is None:
+        null_precedence = vector[null_order](
+            len(source_columns), null_order.AFTER
+        )
+    else:
+        if len(null_position) != len(source_columns):
+            raise ValueError(
+                f"Expected a list-like of length {len(source_columns)}, "
+                f"got length {len(null_position)} for `null_position`"
+            )
+        null_precedence = vector[null_order](
+            len(source_columns), null_order.AFTER
+        )
+        for idx, val in enumerate(null_position):
+            if val:
+                null_precedence[idx] = null_order.BEFORE
+
+    cdef bool c_result
+    cdef table_view source_table_view = table_view_from_columns(source_columns)
+    with nogil:
+        c_result = cpp_is_sorted(
+            source_table_view,
+            column_order,
+            null_precedence
+        )
+
+    return c_result
+
+
+cdef pair[vector[cpp_order], vector[null_order]] ordering(
+    column_order, null_precedence
+):
+    """
+    Construct order and null order vectors
+
+    Parameters
+    ----------
+    column_order
+        Iterable of bool (True for ascending order, False for descending)
+    null_precedence
+        Iterable string for null positions ("first" for start, "last" for end)
+
+    Both iterables must be the same length (not checked)
+
+    Returns
+    -------
+    pair of vectors (order, and null_order)
+    """
+    cdef vector[cpp_order] c_column_order
+    cdef vector[null_order] c_null_precedence
+    for asc, null in zip(column_order, null_precedence):
+        c_column_order.push_back(
+            cpp_order.ASCENDING if asc else cpp_order.DESCENDING
+        )
+        if asc ^ (null == "first"):
+            c_null_precedence.push_back(null_order.AFTER)
+        elif asc ^ (null == "last"):
+            c_null_precedence.push_back(null_order.BEFORE)
+        else:
+            raise ValueError(f"Invalid null precedence {null}")
+    return pair[vector[cpp_order], vector[null_order]](
+        c_column_order, c_null_precedence
+    )
+
+
+@acquire_spill_lock()
+def order_by(
+    list columns_from_table,
+    object ascending,
+    str na_position,
+    *,
+    bool stable
+):
+    """
+    Get index to sort the table in ascending/descending order.
+
+    Parameters
+    ----------
+    columns_from_table : list[Column]
+        Columns from the table which will be sorted
+    ascending : sequence[bool]
+         Sequence of boolean values which correspond to each column
+         in the table to be sorted signifying the order of each column
+         True - Ascending and False - Descending
+    na_position : str
+        Whether null values should show up at the "first" or "last"
+        position of **all** sorted column.
+    stable : bool
+        Should the sort be stable? (no default)
+
+    Returns
+    -------
+    Column of indices that sorts the table
+    """
+    cdef table_view source_table_view = table_view_from_columns(
+        columns_from_table
+    )
+    cdef pair[vector[cpp_order], vector[null_order]] order = ordering(
+        ascending, repeat(na_position)
+    )
+    cdef unique_ptr[column] c_result
+    if stable:
+        with nogil:
+            c_result = move(stable_sorted_order(source_table_view,
+                                                order.first,
+                                                order.second))
+    else:
+        with nogil:
+            c_result = move(sorted_order(source_table_view,
+                                         order.first,
+                                         order.second))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def sort(
+    list values,
+    list column_order=None,
+    list null_precedence=None,
+):
+    """
+    Sort the table in ascending/descending order.
+
+    Parameters
+    ----------
+    values : list[Column]
+        Columns of the table which will be sorted
+    column_order : list[bool], optional
+        Sequence of boolean values which correspond to each column in
+        keys providing the sort order (default all True).
+        With True <=> ascending; False <=> descending.
+    null_precedence : list[str], optional
+        Sequence of "first" or "last" values (default "first")
+        indicating the position of null values when sorting the keys.
+    """
+    cdef table_view values_view = table_view_from_columns(values)
+    cdef unique_ptr[table] result
+    ncol = len(values)
+    cdef pair[vector[cpp_order], vector[null_order]] order = ordering(
+        column_order or repeat(True, ncol),
+        null_precedence or repeat("first", ncol),
+    )
+    with nogil:
+        result = move(
+            cpp_sort(
+                values_view,
+                order.first,
+                order.second,
+            )
+        )
+    return columns_from_unique_ptr(move(result))
+
+
+@acquire_spill_lock()
+def sort_by_key(
+    list values,
+    list keys,
+    object ascending,
+    object na_position,
+    *,
+    bool stable,
+):
+    """
+    Sort a table by given keys
+
+    Parameters
+    ----------
+    values : list[Column]
+        Columns of the table which will be sorted
+    keys : list[Column]
+        Columns making up the sort key
+    ascending : list[bool]
+        Sequence of boolean values which correspond to each column
+        in the table to be sorted signifying the order of each column
+        True - Ascending and False - Descending
+    na_position : list[str]
+        Sequence of "first" or "last" values (default "first")
+        indicating the position of null values when sorting the keys.
+    stable : bool
+        Should the sort be stable? (no default)
+
+    Returns
+    -------
+    list[Column]
+        list of value columns sorted by keys
+    """
+    cdef table_view value_view = table_view_from_columns(values)
+    cdef table_view key_view = table_view_from_columns(keys)
+    cdef pair[vector[cpp_order], vector[null_order]] order = ordering(
+        ascending, na_position
+    )
+    cdef unique_ptr[table] c_result
+    if stable:
+        with nogil:
+            c_result = move(cpp_stable_sort_by_key(value_view,
+                                                   key_view,
+                                                   order.first,
+                                                   order.second))
+    else:
+        with nogil:
+            c_result = move(cpp_sort_by_key(value_view,
+                                            key_view,
+                                            order.first,
+                                            order.second))
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def segmented_sort_by_key(
+    list values,
+    list keys,
+    Column segment_offsets,
+    list column_order=None,
+    list null_precedence=None,
+    *,
+    bool stable,
+):
+    """
+    Sort segments of a table by given keys
+
+    Parameters
+    ----------
+    values : list[Column]
+        Columns of the table which will be sorted
+    keys : list[Column]
+        Columns making up the sort key
+    offsets : Column
+        Segment offsets
+    column_order : list[bool], optional
+        Sequence of boolean values which correspond to each column in
+        keys providing the sort order (default all True).
+        With True <=> ascending; False <=> descending.
+    null_precedence : list[str], optional
+        Sequence of "first" or "last" values (default "first")
+        indicating the position of null values when sorting the keys.
+    stable : bool
+        Should the sort be stable? (no default)
+
+    Returns
+    -------
+    list[Column]
+        list of value columns sorted by keys
+    """
+    cdef table_view values_view = table_view_from_columns(values)
+    cdef table_view keys_view = table_view_from_columns(keys)
+    cdef column_view offsets_view = segment_offsets.view()
+    cdef unique_ptr[table] result
+    ncol = len(values)
+    cdef pair[vector[cpp_order], vector[null_order]] order = ordering(
+        column_order or repeat(True, ncol),
+        null_precedence or repeat("first", ncol),
+    )
+    if stable:
+        with nogil:
+            result = move(
+                cpp_stable_segmented_sort_by_key(
+                    values_view,
+                    keys_view,
+                    offsets_view,
+                    order.first,
+                    order.second,
+                )
+            )
+    else:
+        with nogil:
+            result = move(
+                cpp_segmented_sort_by_key(
+                    values_view,
+                    keys_view,
+                    offsets_view,
+                    order.first,
+                    order.second,
+                )
+            )
+    return columns_from_unique_ptr(move(result))
+
+
+@acquire_spill_lock()
+def digitize(list source_columns, list bins, bool right=False):
+    """
+    Return the indices of the bins to which each value in source_table belongs.
+
+    Parameters
+    ----------
+    source_columns : Input columns to be binned.
+    bins : List containing columns of bins
+    right : Indicating whether the intervals include the
+            right or the left bin edge.
+    """
+
+    cdef table_view bins_view = table_view_from_columns(bins)
+    cdef table_view source_table_view = table_view_from_columns(
+        source_columns
+    )
+    cdef vector[cpp_order] column_order = (
+        vector[cpp_order](
+            bins_view.num_columns(),
+            cpp_order.ASCENDING
+        )
+    )
+    cdef vector[null_order] null_precedence = (
+        vector[null_order](
+            bins_view.num_columns(),
+            null_order.BEFORE
+        )
+    )
+
+    cdef unique_ptr[column] c_result
+    if right:
+        with nogil:
+            c_result = move(lower_bound(
+                bins_view,
+                source_table_view,
+                column_order,
+                null_precedence)
+            )
+    else:
+        with nogil:
+            c_result = move(upper_bound(
+                bins_view,
+                source_table_view,
+                column_order,
+                null_precedence)
+            )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def rank_columns(list source_columns, object method, str na_option,
+                 bool ascending, bool pct
+                 ):
+    """
+    Compute numerical data ranks (1 through n) of each column in the dataframe
+    """
+    cdef rank_method c_rank_method = < rank_method > (
+        < underlying_type_t_rank_method > method
+    )
+
+    cdef cpp_order column_order = (
+        cpp_order.ASCENDING
+        if ascending
+        else cpp_order.DESCENDING
+    )
+    # ascending
+    #    #top    = na_is_smallest
+    #    #bottom = na_is_largest
+    #    #keep   = na_is_largest
+    # descending
+    #    #top    = na_is_largest
+    #    #bottom = na_is_smallest
+    #    #keep   = na_is_smallest
+    cdef null_order null_precedence
+    if ascending:
+        if na_option == 'top':
+            null_precedence = null_order.BEFORE
+        else:
+            null_precedence = null_order.AFTER
+    else:
+        if na_option == 'top':
+            null_precedence = null_order.AFTER
+        else:
+            null_precedence = null_order.BEFORE
+    cdef null_policy c_null_handling = (
+        null_policy.EXCLUDE
+        if na_option == 'keep'
+        else null_policy.INCLUDE
+    )
+    cdef bool percentage = pct
+
+    cdef vector[unique_ptr[column]] c_results
+    cdef column_view c_view
+    cdef Column col
+    for col in source_columns:
+        c_view = col.view()
+        with nogil:
+            c_results.push_back(move(
+                rank(
+                    c_view,
+                    c_rank_method,
+                    column_order,
+                    c_null_handling,
+                    null_precedence,
+                    percentage
+                )
+            ))
+
+    return [Column.from_unique_ptr(
+        move(c_results[i])
+    ) for i in range(c_results.size())]
diff --git a/python/cudf/cudf/_lib/stream_compaction.pyx b/python/cudf/cudf/_lib/stream_compaction.pyx
new file mode 100644
index 0000000..37387b0
--- /dev/null
+++ b/python/cudf/cudf/_lib/stream_compaction.pyx
@@ -0,0 +1,200 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.stream_compaction cimport (
+    apply_boolean_mask as cpp_apply_boolean_mask,
+    distinct_count as cpp_distinct_count,
+    drop_nulls as cpp_drop_nulls,
+    duplicate_keep_option,
+    stable_distinct as cpp_stable_distinct,
+)
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport (
+    nan_policy,
+    null_equality,
+    null_policy,
+    size_type,
+)
+from cudf._lib.utils cimport columns_from_unique_ptr, table_view_from_columns
+
+
+@acquire_spill_lock()
+def drop_nulls(list columns, how="any", keys=None, thresh=None):
+    """
+    Drops null rows from cols depending on key columns.
+
+    Parameters
+    ----------
+    columns : list of columns
+    how  : "any" or "all". If thresh is None, drops rows of cols that have any
+           nulls or all nulls (respectively) in subset (default: "any")
+    keys : List of column indices. If set, then these columns are checked for
+           nulls rather than all of columns (optional)
+    thresh : Minimum number of non-nulls required to keep a row (optional)
+
+    Returns
+    -------
+    columns with null rows dropped
+    """
+
+    cdef vector[size_type] cpp_keys = (
+        keys if keys is not None else range(len(columns))
+    )
+
+    cdef size_type c_keep_threshold = cpp_keys.size()
+    if thresh is not None:
+        c_keep_threshold = thresh
+    elif how == "all":
+        c_keep_threshold = 1
+
+    cdef unique_ptr[table] c_result
+    cdef table_view source_table_view = table_view_from_columns(columns)
+
+    if how not in {"any", "all"}:
+        raise ValueError("how must be 'any' or 'all'")
+    with nogil:
+        c_result = move(
+            cpp_drop_nulls(
+                source_table_view,
+                cpp_keys,
+                c_keep_threshold
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def apply_boolean_mask(list columns, Column boolean_mask):
+    """
+    Drops the rows which correspond to False in boolean_mask.
+
+    Parameters
+    ----------
+    columns : list of columns whose rows are dropped as per boolean_mask
+    boolean_mask : a boolean column of same size as source_table
+
+    Returns
+    -------
+    columns obtained from applying mask
+    """
+
+    cdef unique_ptr[table] c_result
+    cdef table_view source_table_view = table_view_from_columns(columns)
+    cdef column_view boolean_mask_view = boolean_mask.view()
+
+    with nogil:
+        c_result = move(
+            cpp_apply_boolean_mask(
+                source_table_view,
+                boolean_mask_view
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def drop_duplicates(list columns,
+                    object keys=None,
+                    object keep='first',
+                    bool nulls_are_equal=True):
+    """
+    Drops rows in source_table as per duplicate rows in keys.
+
+    Parameters
+    ----------
+    columns : List of columns
+    keys : List of column indices. If set, then these columns are checked for
+           duplicates rather than all of columns (optional)
+    keep : keep 'first' or 'last' or none of the duplicate rows
+    nulls_are_equal : if True, nulls are treated equal else not.
+
+    Returns
+    -------
+    columns with duplicate dropped
+    """
+
+    cdef vector[size_type] cpp_keys = (
+        keys if keys is not None else range(len(columns))
+    )
+    cdef duplicate_keep_option cpp_keep_option
+
+    if keep == 'first':
+        cpp_keep_option = duplicate_keep_option.KEEP_FIRST
+    elif keep == 'last':
+        cpp_keep_option = duplicate_keep_option.KEEP_LAST
+    elif keep is False:
+        cpp_keep_option = duplicate_keep_option.KEEP_NONE
+    else:
+        raise ValueError('keep must be either "first", "last" or False')
+
+    # shifting the index number by number of index columns
+    cdef null_equality cpp_nulls_equal = (
+        null_equality.EQUAL
+        if nulls_are_equal
+        else null_equality.UNEQUAL
+    )
+    cdef table_view source_table_view = table_view_from_columns(columns)
+    cdef unique_ptr[table] c_result
+
+    with nogil:
+        c_result = move(
+            cpp_stable_distinct(
+                source_table_view,
+                cpp_keys,
+                cpp_keep_option,
+                cpp_nulls_equal
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def distinct_count(Column source_column, ignore_nulls=True, nan_as_null=False):
+    """
+    Finds number of unique rows in `source_column`
+
+    Parameters
+    ----------
+    source_column : source table checked for unique rows
+    ignore_nulls : If True nulls are ignored,
+                   else counted as one more distinct value
+    nan_as_null  : If True, NAN is considered NULL,
+                   else counted as one more distinct value
+
+    Returns
+    -------
+    Count of number of unique rows in `source_column`
+    """
+
+    cdef null_policy cpp_null_handling = (
+        null_policy.EXCLUDE
+        if ignore_nulls
+        else null_policy.INCLUDE
+    )
+    cdef nan_policy cpp_nan_handling = (
+        nan_policy.NAN_IS_NULL
+        if nan_as_null
+        else nan_policy.NAN_IS_VALID
+    )
+
+    cdef column_view source_column_view = source_column.view()
+    with nogil:
+        count = cpp_distinct_count(
+            source_column_view,
+            cpp_null_handling,
+            cpp_nan_handling
+        )
+
+    return count
diff --git a/python/cudf/cudf/_lib/string_casting.pyx b/python/cudf/cudf/_lib/string_casting.pyx
new file mode 100644
index 0000000..3b29255
--- /dev/null
+++ b/python/cudf/cudf/_lib/string_casting.pyx
@@ -0,0 +1,800 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from cudf._lib.column cimport Column
+
+from cudf._lib.scalar import as_device_scalar
+
+from cudf._lib.scalar cimport DeviceScalar
+
+from cudf._lib.types import SUPPORTED_NUMPY_TO_LIBCUDF_TYPES
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.convert.convert_booleans cimport (
+    from_booleans as cpp_from_booleans,
+    to_booleans as cpp_to_booleans,
+)
+from cudf._lib.cpp.strings.convert.convert_datetime cimport (
+    from_timestamps as cpp_from_timestamps,
+    is_timestamp as cpp_is_timestamp,
+    to_timestamps as cpp_to_timestamps,
+)
+from cudf._lib.cpp.strings.convert.convert_durations cimport (
+    from_durations as cpp_from_durations,
+    to_durations as cpp_to_durations,
+)
+from cudf._lib.cpp.strings.convert.convert_floats cimport (
+    from_floats as cpp_from_floats,
+    to_floats as cpp_to_floats,
+)
+from cudf._lib.cpp.strings.convert.convert_integers cimport (
+    from_integers as cpp_from_integers,
+    hex_to_integers as cpp_hex_to_integers,
+    integers_to_hex as cpp_integers_to_hex,
+    is_hex as cpp_is_hex,
+    to_integers as cpp_to_integers,
+)
+from cudf._lib.cpp.strings.convert.convert_ipv4 cimport (
+    integers_to_ipv4 as cpp_integers_to_ipv4,
+    ipv4_to_integers as cpp_ipv4_to_integers,
+    is_ipv4 as cpp_is_ipv4,
+)
+from cudf._lib.cpp.types cimport data_type, type_id
+from cudf._lib.types cimport underlying_type_t_type_id
+
+import cudf
+
+
+def floating_to_string(Column input_col):
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_from_floats(
+                input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def string_to_floating(Column input_col, object out_type):
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    cdef type_id tid = <type_id> (
+        <underlying_type_t_type_id> (
+            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[out_type]
+        )
+    )
+    cdef data_type c_out_type = data_type(tid)
+    with nogil:
+        c_result = move(
+            cpp_to_floats(
+                input_column_view,
+                c_out_type))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def dtos(Column input_col):
+    """
+    Converting/Casting input column of type double to string column
+
+    Parameters
+    ----------
+    input_col : input column of type double
+
+    Returns
+    -------
+    A Column with double values cast to string
+    """
+
+    return floating_to_string(input_col)
+
+
+def stod(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to double
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to double
+    """
+
+    return string_to_floating(input_col, cudf.dtype("float64"))
+
+
+def ftos(Column input_col):
+    """
+    Converting/Casting input column of type float to string column
+
+    Parameters
+    ----------
+    input_col : input column of type double
+
+    Returns
+    -------
+    A Column with float values cast to string
+    """
+
+    return floating_to_string(input_col)
+
+
+def stof(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to float
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to float
+    """
+
+    return string_to_floating(input_col, cudf.dtype("float32"))
+
+
+def integer_to_string(Column input_col):
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_from_integers(
+                input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def string_to_integer(Column input_col, object out_type):
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    cdef type_id tid = <type_id> (
+        <underlying_type_t_type_id> (
+            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[out_type]
+        )
+    )
+    cdef data_type c_out_type = data_type(tid)
+    with nogil:
+        c_result = move(
+            cpp_to_integers(
+                input_column_view,
+                c_out_type))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def i8tos(Column input_col):
+    """
+    Converting/Casting input column of type int8 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type int8
+
+    Returns
+    -------
+    A Column with int8 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoi8(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to int8
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to int8
+    """
+
+    return string_to_integer(input_col, cudf.dtype("int8"))
+
+
+def i16tos(Column input_col):
+    """
+    Converting/Casting input column of type int16 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type int16
+
+    Returns
+    -------
+    A Column with int16 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoi16(Column input_col):
+    """
+    Converting/Casting input column of type string to int16
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to int16
+    """
+
+    return string_to_integer(input_col, cudf.dtype("int16"))
+
+
+def itos(Column input_col):
+    """
+    Converting/Casting input column of type int32 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type int32
+
+    Returns
+    -------
+    A Column with int32 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoi(Column input_col):
+    """
+    Converting/Casting input column of type string to int32
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to int32
+    """
+
+    return string_to_integer(input_col, cudf.dtype("int32"))
+
+
+def ltos(Column input_col):
+    """
+    Converting/Casting input column of type int64 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type int64
+
+    Returns
+    -------
+    A Column with int64 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stol(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to int64
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to int64
+    """
+
+    return string_to_integer(input_col, cudf.dtype("int64"))
+
+
+def ui8tos(Column input_col):
+    """
+    Converting/Casting input column of type uint8 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type uint8
+
+    Returns
+    -------
+    A Column with uint8 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoui8(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to uint8
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to uint8
+    """
+
+    return string_to_integer(input_col, cudf.dtype("uint8"))
+
+
+def ui16tos(Column input_col):
+    """
+    Converting/Casting input column of type uint16 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type uint16
+
+    Returns
+    -------
+    A Column with uint16 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoui16(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to uint16
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to uint16
+    """
+
+    return string_to_integer(input_col, cudf.dtype("uint16"))
+
+
+def uitos(Column input_col):
+    """
+    Converting/Casting input column of type uint32 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type uint32
+
+    Returns
+    -------
+    A Column with uint32 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoui(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to uint32
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to uint32
+    """
+
+    return string_to_integer(input_col, cudf.dtype("uint32"))
+
+
+def ultos(Column input_col):
+    """
+    Converting/Casting input column of type uint64 to string column
+
+    Parameters
+    ----------
+    input_col : input column of type uint64
+
+    Returns
+    -------
+    A Column with uint64 values cast to string
+    """
+
+    return integer_to_string(input_col)
+
+
+def stoul(Column input_col, **kwargs):
+    """
+    Converting/Casting input column of type string to uint64
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with strings cast to uint64
+    """
+
+    return string_to_integer(input_col, cudf.dtype("uint64"))
+
+
+def _to_booleans(Column input_col, object string_true="True"):
+    """
+    Converting/Casting input column of type string to boolean column
+
+    Parameters
+    ----------
+    input_col : input column of type string
+    string_true : string that represents True
+
+    Returns
+    -------
+    A Column with string values cast to boolean
+    """
+
+    cdef DeviceScalar str_true = as_device_scalar(string_true)
+    cdef column_view input_column_view = input_col.view()
+    cdef const string_scalar* string_scalar_true = <const string_scalar*>(
+        str_true.get_raw_ptr())
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_to_booleans(
+                input_column_view,
+                string_scalar_true[0]))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def to_booleans(Column input_col, **kwargs):
+
+    return _to_booleans(input_col)
+
+
+def _from_booleans(
+        Column input_col,
+        object string_true="True",
+        object string_false="False"):
+    """
+    Converting/Casting input column of type boolean to string column
+
+    Parameters
+    ----------
+    input_col : input column of type boolean
+    string_true : string that represents True
+    string_false : string that represents False
+
+    Returns
+    -------
+    A Column with boolean values cast to string
+    """
+
+    cdef DeviceScalar str_true = as_device_scalar(string_true)
+    cdef DeviceScalar str_false = as_device_scalar(string_false)
+    cdef column_view input_column_view = input_col.view()
+    cdef const string_scalar* string_scalar_true = <const string_scalar*>(
+        str_true.get_raw_ptr())
+    cdef const string_scalar* string_scalar_false = <const string_scalar*>(
+        str_false.get_raw_ptr())
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_from_booleans(
+                input_column_view,
+                string_scalar_true[0],
+                string_scalar_false[0]))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def from_booleans(Column input_col):
+    return _from_booleans(input_col)
+
+
+def int2timestamp(
+        Column input_col,
+        str format,
+        Column names):
+    """
+    Converting/Casting input date-time column to string
+    column with specified format
+
+    Parameters
+    ----------
+    input_col : input column of type timestamp in integer format
+    format : The string specifying output format
+    names : The string names to use for weekdays ("%a", "%A") and
+    months ("%b", "%B")
+
+    Returns
+    -------
+    A Column with date-time represented in string format
+
+    """
+    cdef column_view input_column_view = input_col.view()
+    cdef string c_timestamp_format = format.encode("UTF-8")
+    cdef column_view input_strings_names = names.view()
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_from_timestamps(
+                input_column_view,
+                c_timestamp_format,
+                input_strings_names))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def timestamp2int(Column input_col, dtype, format):
+    """
+    Converting/Casting input string column to date-time column with specified
+    timestamp_format
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with string represented in date-time format
+
+    """
+    cdef column_view input_column_view = input_col.view()
+    cdef type_id tid = <type_id> (
+        <underlying_type_t_type_id> (
+            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[dtype]
+        )
+    )
+    cdef data_type out_type = data_type(tid)
+    cdef string c_timestamp_format = format.encode('UTF-8')
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_to_timestamps(
+                input_column_view,
+                out_type,
+                c_timestamp_format))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def istimestamp(
+        Column input_col,
+        object format,
+        **kwargs):
+    """
+    Check input string column matches the specified timestamp format
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    format : format string of timestamp specifiers
+
+    Returns
+    -------
+    A Column of boolean values identifying strings that matched the format.
+
+    """
+    if input_col.size == 0:
+        return cudf.core.column.as_column([], dtype=kwargs.get('dtype'))
+    cdef column_view input_column_view = input_col.view()
+    cdef string c_timestamp_format = <string>str(format).encode('UTF-8')
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_is_timestamp(
+                input_column_view,
+                c_timestamp_format))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def timedelta2int(Column input_col, dtype, format):
+    """
+    Converting/Casting input string column to TimeDelta column with specified
+    format
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    A Column with string represented in TimeDelta format
+
+    """
+    cdef column_view input_column_view = input_col.view()
+    cdef type_id tid = <type_id> (
+        <underlying_type_t_type_id> (
+            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[dtype]
+        )
+    )
+    cdef data_type out_type = data_type(tid)
+    cdef string c_duration_format = format.encode('UTF-8')
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_to_durations(
+                input_column_view,
+                out_type,
+                c_duration_format))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def int2timedelta(
+        Column input_col,
+        **kwargs):
+    """
+    Converting/Casting input Timedelta column to string
+    column with specified format
+
+    Parameters
+    ----------
+    input_col : input column of type Timedelta in integer format
+
+    Returns
+    -------
+    A Column with Timedelta represented in string format
+
+    """
+
+    cdef column_view input_column_view = input_col.view()
+    cdef string c_duration_format = kwargs.get(
+        'format', "%D days %H:%M:%S").encode('UTF-8')
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_from_durations(
+                input_column_view,
+                c_duration_format))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def int2ip(Column input_col, **kwargs):
+    """
+    Converting/Casting integer column to string column in ipv4 format
+
+    Parameters
+    ----------
+    input_col : input integer column
+
+    Returns
+    -------
+    A Column with integer represented in string ipv4 format
+
+    """
+
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_integers_to_ipv4(input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def ip2int(Column input_col, **kwargs):
+    """
+    Converting string ipv4 column to integer column
+
+    Parameters
+    ----------
+    input_col : input string column
+
+    Returns
+    -------
+    A Column with ipv4 represented as integer
+
+    """
+
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_ipv4_to_integers(input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def is_ipv4(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that have strings in IPv4 format. This format is nnn.nnn.nnn.nnn
+    where nnn is integer digits in [0,255].
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_is_ipv4(
+            source_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def htoi(Column input_col, **kwargs):
+    """
+    Converting input column of type string having hex values
+    to integer of out_type
+
+    Parameters
+    ----------
+    input_col : input column of type string
+    out_type : The type of integer column expected
+
+    Returns
+    -------
+    A Column of integers parsed from hexadecimal string values.
+    """
+
+    cdef column_view input_column_view = input_col.view()
+    cdef type_id tid = <type_id> (
+        <underlying_type_t_type_id> (
+            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[
+                kwargs.get('dtype', cudf.dtype("int64"))
+            ]
+        )
+    )
+    cdef data_type c_out_type = data_type(tid)
+
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_hex_to_integers(input_column_view,
+                                c_out_type))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def is_hex(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that have hex characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_is_hex(
+            source_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+def itoh(Column input_col):
+    """
+    Converting input column of type integer to a string
+    column with hexadecimal character digits.
+
+    Parameters
+    ----------
+    input_col : input column of type integer
+
+    Returns
+    -------
+    A Column of strings with hexadecimal characters.
+    """
+
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_integers_to_hex(input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/CMakeLists.txt b/python/cudf/cudf/_lib/strings/CMakeLists.txt
new file mode 100644
index 0000000..a5e87a4
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/CMakeLists.txt
@@ -0,0 +1,45 @@
+# =============================================================================
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources
+    attributes.pyx
+    capitalize.pyx
+    case.pyx
+    char_types.pyx
+    combine.pyx
+    contains.pyx
+    extract.pyx
+    find.pyx
+    find_multiple.pyx
+    findall.pyx
+    json.pyx
+    padding.pyx
+    repeat.pyx
+    replace.pyx
+    replace_re.pyx
+    strip.pyx
+    substring.pyx
+    translate.pyx
+    wrap.pyx
+)
+
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX strings_ ASSOCIATED_TARGETS cudf
+)
+
+add_subdirectory(convert)
+add_subdirectory(split)
diff --git a/python/cudf/cudf/_lib/strings/__init__.pxd b/python/cudf/cudf/_lib/strings/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/strings/__init__.py b/python/cudf/cudf/_lib/strings/__init__.py
new file mode 100644
index 0000000..47a194c
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/__init__.py
@@ -0,0 +1,105 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+from cudf._lib.nvtext.edit_distance import edit_distance, edit_distance_matrix
+from cudf._lib.nvtext.generate_ngrams import (
+    generate_character_ngrams,
+    generate_ngrams,
+    hash_character_ngrams,
+)
+from cudf._lib.nvtext.jaccard import jaccard_index
+from cudf._lib.nvtext.minhash import minhash, minhash64
+from cudf._lib.nvtext.ngrams_tokenize import ngrams_tokenize
+from cudf._lib.nvtext.normalize import normalize_characters, normalize_spaces
+from cudf._lib.nvtext.replace import filter_tokens, replace_tokens
+from cudf._lib.nvtext.stemmer import (
+    LetterType,
+    is_letter,
+    is_letter_multi,
+    porter_stemmer_measure,
+)
+from cudf._lib.nvtext.tokenize import (
+    _count_tokens_column,
+    _count_tokens_scalar,
+    _tokenize_column,
+    _tokenize_scalar,
+    character_tokenize,
+    detokenize,
+    tokenize_with_vocabulary,
+)
+from cudf._lib.strings.attributes import (
+    code_points,
+    count_bytes,
+    count_characters,
+)
+from cudf._lib.strings.capitalize import capitalize, is_title, title
+from cudf._lib.strings.case import swapcase, to_lower, to_upper
+from cudf._lib.strings.char_types import (
+    filter_alphanum,
+    is_alnum,
+    is_alpha,
+    is_decimal,
+    is_digit,
+    is_lower,
+    is_numeric,
+    is_space,
+    is_upper,
+)
+from cudf._lib.strings.combine import (
+    concatenate,
+    join,
+    join_lists_with_column,
+    join_lists_with_scalar,
+)
+from cudf._lib.strings.contains import contains_re, count_re, like, match_re
+from cudf._lib.strings.convert.convert_fixed_point import to_decimal
+from cudf._lib.strings.convert.convert_floats import is_float
+from cudf._lib.strings.convert.convert_integers import is_integer
+from cudf._lib.strings.convert.convert_urls import url_decode, url_encode
+from cudf._lib.strings.extract import extract
+from cudf._lib.strings.find import (
+    contains,
+    contains_multiple,
+    endswith,
+    endswith_multiple,
+    find,
+    rfind,
+    startswith,
+    startswith_multiple,
+)
+from cudf._lib.strings.find_multiple import find_multiple
+from cudf._lib.strings.findall import findall
+from cudf._lib.strings.json import GetJsonObjectOptions, get_json_object
+from cudf._lib.strings.padding import (
+    SideType,
+    center,
+    ljust,
+    pad,
+    rjust,
+    zfill,
+)
+from cudf._lib.strings.repeat import repeat_scalar, repeat_sequence
+from cudf._lib.strings.replace import (
+    insert,
+    replace,
+    replace_multi,
+    slice_replace,
+)
+from cudf._lib.strings.replace_re import (
+    replace_multi_re,
+    replace_re,
+    replace_with_backrefs,
+)
+from cudf._lib.strings.split.partition import partition, rpartition
+from cudf._lib.strings.split.split import (
+    rsplit,
+    rsplit_re,
+    rsplit_record,
+    rsplit_record_re,
+    split,
+    split_re,
+    split_record,
+    split_record_re,
+)
+from cudf._lib.strings.strip import lstrip, rstrip, strip
+from cudf._lib.strings.substring import get, slice_from, slice_strings
+from cudf._lib.strings.translate import filter_characters, translate
+from cudf._lib.strings.wrap import wrap
diff --git a/python/cudf/cudf/_lib/strings/attributes.pyx b/python/cudf/cudf/_lib/strings/attributes.pyx
new file mode 100644
index 0000000..c1b69dd
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/attributes.pyx
@@ -0,0 +1,60 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.attributes cimport (
+    code_points as cpp_code_points,
+    count_bytes as cpp_count_bytes,
+    count_characters as cpp_count_characters,
+)
+
+
+@acquire_spill_lock()
+def count_characters(Column source_strings):
+    """
+    Returns an integer numeric column containing the
+    length of each string in characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_count_characters(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def count_bytes(Column source_strings):
+    """
+    Returns an integer numeric column containing the
+    number of bytes of each string.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_count_bytes(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def code_points(Column source_strings):
+    """
+    Creates a numeric column with code point values (integers)
+    for each character of each string.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_code_points(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/capitalize.pyx b/python/cudf/cudf/_lib/strings/capitalize.pyx
new file mode 100644
index 0000000..f6a80ac
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/capitalize.pyx
@@ -0,0 +1,48 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.capitalize cimport (
+    capitalize as cpp_capitalize,
+    is_title as cpp_is_title,
+    title as cpp_title,
+)
+
+
+@acquire_spill_lock()
+def capitalize(Column source_strings):
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_capitalize(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def title(Column source_strings):
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_title(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_title(Column source_strings):
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_is_title(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/case.pyx b/python/cudf/cudf/_lib/strings/case.pyx
new file mode 100644
index 0000000..09af117
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/case.pyx
@@ -0,0 +1,48 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.case cimport (
+    swapcase as cpp_swapcase,
+    to_lower as cpp_to_lower,
+    to_upper as cpp_to_upper,
+)
+
+
+@acquire_spill_lock()
+def to_upper(Column source_strings):
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_to_upper(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def to_lower(Column source_strings):
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_to_lower(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def swapcase(Column source_strings):
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_swapcase(source_view))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/char_types.pyx b/python/cudf/cudf/_lib/strings/char_types.pyx
new file mode 100644
index 0000000..14d78cd
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/char_types.pyx
@@ -0,0 +1,202 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.char_types cimport (
+    all_characters_of_type as cpp_all_characters_of_type,
+    filter_characters_of_type as cpp_filter_characters_of_type,
+    string_character_types,
+)
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def filter_alphanum(Column source_strings, object py_repl, bool keep=True):
+    """
+    Returns a Column of strings keeping only alphanumeric character types.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_repl = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_filter_characters_of_type(
+            source_view,
+            string_character_types.ALL_TYPES if keep
+            else string_character_types.ALPHANUM,
+            scalar_repl[0],
+            string_character_types.ALPHANUM if keep
+            else string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_decimal(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only decimal characters -- those that can be used
+    to extract base10 numbers.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.DECIMAL,
+            string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_alnum(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only alphanumeric characters.
+
+    Equivalent to: is_alpha() or is_digit() or is_numeric() or is_decimal()
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.ALPHANUM,
+            string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_alpha(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only alphabetic characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.ALPHA,
+            string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_digit(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only decimal and digit characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.DIGIT,
+            string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_numeric(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only numeric characters. These include digit and
+    numeric characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.NUMERIC,
+            string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_upper(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only upper-case characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.UPPER,
+            string_character_types.CASE_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_lower(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain only lower-case characters.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.LOWER,
+            string_character_types.CASE_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_space(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contains all characters which are spaces only.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_all_characters_of_type(
+            source_view,
+            string_character_types.SPACE,
+            string_character_types.ALL_TYPES
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/combine.pyx b/python/cudf/cudf/_lib/strings/combine.pyx
new file mode 100644
index 0000000..7d86d34
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/combine.pyx
@@ -0,0 +1,161 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.combine cimport (
+    concatenate as cpp_concatenate,
+    join_list_elements as cpp_join_list_elements,
+    join_strings as cpp_join_strings,
+    output_if_empty_list,
+    separator_on_nulls,
+)
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport table_view_from_columns
+
+
+@acquire_spill_lock()
+def concatenate(list source_strings,
+                object sep,
+                object na_rep):
+    """
+    Returns a Column by concatenating strings column-wise in `source_strings`
+    with the specified `sep` between each column and
+    `na`/`None` values are replaced by `na_rep`
+    """
+    cdef DeviceScalar separator = sep.device_value
+    cdef DeviceScalar narep = na_rep.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef table_view source_view = table_view_from_columns(source_strings)
+
+    cdef const string_scalar* scalar_separator = \
+        <const string_scalar*>(separator.get_raw_ptr())
+    cdef const string_scalar* scalar_narep = <const string_scalar*>(
+        narep.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_concatenate(
+            source_view,
+            scalar_separator[0],
+            scalar_narep[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def join(Column source_strings,
+         object sep,
+         object na_rep):
+    """
+    Returns a Column by concatenating strings row-wise in `source_strings`
+    with the specified `sep` between each column and
+    `na`/`None` values are replaced by `na_rep`
+    """
+
+    cdef DeviceScalar separator = sep.device_value
+    cdef DeviceScalar narep = na_rep.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_separator = \
+        <const string_scalar*>(separator.get_raw_ptr())
+    cdef const string_scalar* scalar_narep = <const string_scalar*>(
+        narep.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_join_strings(
+            source_view,
+            scalar_separator[0],
+            scalar_narep[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def join_lists_with_scalar(
+        Column source_strings,
+        object py_separator,
+        object py_narep):
+    """
+    Returns a Column by concatenating Lists of strings row-wise
+    in `source_strings` with the specified `py_separator`
+    between each string in lists and `<NA>`/`None` values
+    are replaced by `py_narep`
+    """
+
+    cdef DeviceScalar separator = py_separator.device_value
+    cdef DeviceScalar narep = py_narep.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_separator = \
+        <const string_scalar*>(separator.get_raw_ptr())
+    cdef const string_scalar* scalar_narep = <const string_scalar*>(
+        narep.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_join_list_elements(
+            source_view,
+            scalar_separator[0],
+            scalar_narep[0],
+            separator_on_nulls.YES,
+            output_if_empty_list.NULL_ELEMENT
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def join_lists_with_column(
+        Column source_strings,
+        Column separator_strings,
+        object py_source_narep,
+        object py_separator_narep):
+    """
+    Returns a Column by concatenating Lists of strings row-wise in
+    `source_strings` with a corresponding separator at the same
+    position in `separator_strings` and `<NA>`/`None` values in
+    `source_strings` are replaced by `py_source_narep` and
+    `<NA>`/`None` values in `separator_strings` are replaced
+    by `py_separator_narep`
+    """
+
+    cdef DeviceScalar source_narep = py_source_narep.device_value
+    cdef DeviceScalar separator_narep = py_separator_narep.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view separator_view = separator_strings.view()
+
+    cdef const string_scalar* scalar_source_narep = \
+        <const string_scalar*>(source_narep.get_raw_ptr())
+    cdef const string_scalar* scalar_separator_narep = <const string_scalar*>(
+        separator_narep.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_join_list_elements(
+            source_view,
+            separator_view,
+            scalar_separator_narep[0],
+            scalar_source_narep[0],
+            separator_on_nulls.YES,
+            output_if_empty_list.NULL_ELEMENT
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/contains.pyx b/python/cudf/cudf/_lib/strings/contains.pyx
new file mode 100644
index 0000000..82034f7
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/contains.pyx
@@ -0,0 +1,122 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cython.operator cimport dereference
+from libc.stdint cimport uint32_t
+
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.contains cimport (
+    contains_re as cpp_contains_re,
+    count_re as cpp_count_re,
+    like as cpp_like,
+    matches_re as cpp_matches_re,
+)
+from cudf._lib.cpp.strings.regex_flags cimport regex_flags
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def contains_re(Column source_strings, object reg_ex, uint32_t flags):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain regular expression `reg_ex`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string reg_ex_string = <string>str(reg_ex).encode()
+    cdef regex_flags c_flags = <regex_flags>flags
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(reg_ex_string, c_flags))
+        c_result = move(cpp_contains_re(
+            source_view,
+            dereference(c_prog)
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def count_re(Column source_strings, object reg_ex, uint32_t flags):
+    """
+    Returns a Column with count of occurrences of `reg_ex` in
+    each string of `source_strings`
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string reg_ex_string = <string>str(reg_ex).encode()
+    cdef regex_flags c_flags = <regex_flags>flags
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(reg_ex_string, c_flags))
+        c_result = move(cpp_count_re(
+            source_view,
+            dereference(c_prog)
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def match_re(Column source_strings, object reg_ex, uint32_t flags):
+    """
+    Returns a Column with each value True if the string matches `reg_ex`
+    regular expression with each record of `source_strings`
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string reg_ex_string = <string>str(reg_ex).encode()
+    cdef regex_flags c_flags = <regex_flags>flags
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(reg_ex_string, c_flags))
+        c_result = move(cpp_matches_re(
+            source_view,
+            dereference(c_prog)
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def like(Column source_strings, object py_pattern, object py_escape):
+    """
+    Returns a Column with each value True if the string matches the
+    `py_pattern` like expression with each record of `source_strings`
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef DeviceScalar pattern = py_pattern.device_value
+    cdef DeviceScalar escape = py_escape.device_value
+
+    cdef const string_scalar* scalar_ptn = <const string_scalar*>(
+        pattern.get_raw_ptr()
+    )
+    cdef const string_scalar* scalar_esc = <const string_scalar*>(
+        escape.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_like(
+            source_view,
+            scalar_ptn[0],
+            scalar_esc[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/convert/CMakeLists.txt b/python/cudf/cudf/_lib/strings/convert/CMakeLists.txt
new file mode 100644
index 0000000..434f79d
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/convert/CMakeLists.txt
@@ -0,0 +1,24 @@
+# =============================================================================
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources convert_fixed_point.pyx convert_floats.pyx convert_integers.pyx
+                   convert_lists.pyx convert_urls.pyx
+)
+
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX strings_ ASSOCIATED_TARGETS cudf
+)
diff --git a/python/cudf/cudf/_lib/strings/convert/__init__.pxd b/python/cudf/cudf/_lib/strings/convert/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/strings/convert/__init__.py b/python/cudf/cudf/_lib/strings/convert/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/strings/convert/convert_fixed_point.pyx b/python/cudf/cudf/_lib/strings/convert/convert_fixed_point.pyx
new file mode 100644
index 0000000..2085d5c
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/convert/convert_fixed_point.pyx
@@ -0,0 +1,110 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import cudf
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.convert.convert_fixed_point cimport (
+    from_fixed_point as cpp_from_fixed_point,
+    is_fixed_point as cpp_is_fixed_point,
+    to_fixed_point as cpp_to_fixed_point,
+)
+from cudf._lib.cpp.types cimport data_type, type_id
+
+
+@acquire_spill_lock()
+def from_decimal(Column input_col):
+    """
+    Converts a `Decimal64Column` to a `StringColumn`.
+
+    Parameters
+    ----------
+    input_col : input column of type decimal
+
+    Returns
+    -------
+    A column of strings representing the input decimal values.
+    """
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    with nogil:
+        c_result = move(
+            cpp_from_fixed_point(
+                input_column_view))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def to_decimal(Column input_col, object out_type):
+    """
+    Returns a `Decimal64Column` from the provided `StringColumn`
+    using the scale in the `out_type`.
+
+    Parameters
+    ----------
+    input_col : input column of type string
+    out_type : The type and scale of the decimal column expected
+
+    Returns
+    -------
+    A column of decimals parsed from the string values.
+    """
+    cdef column_view input_column_view = input_col.view()
+    cdef unique_ptr[column] c_result
+    cdef int scale = out_type.scale
+    cdef data_type c_out_type
+    if isinstance(out_type, cudf.Decimal32Dtype):
+        c_out_type = data_type(type_id.DECIMAL32, -scale)
+    elif isinstance(out_type, cudf.Decimal64Dtype):
+        c_out_type = data_type(type_id.DECIMAL64, -scale)
+    elif isinstance(out_type, cudf.Decimal128Dtype):
+        c_out_type = data_type(type_id.DECIMAL128, -scale)
+    else:
+        raise TypeError("should be a decimal dtype")
+    with nogil:
+        c_result = move(
+            cpp_to_fixed_point(
+                input_column_view,
+                c_out_type))
+
+    result = Column.from_unique_ptr(move(c_result))
+    result.dtype.precision = out_type.precision
+    return result
+
+
+@acquire_spill_lock()
+def is_fixed_point(Column input_col, object dtype):
+    """
+    Returns a Column of boolean values with True for `input_col`
+    that have fixed-point characters. The output row also has a
+    False value if the corresponding string would cause an integer
+    overflow. The scale of the `dtype` is used to determine overflow
+    in the output row.
+
+    Parameters
+    ----------
+    input_col : input column of type string
+    dtype : The type and scale of a decimal column
+
+    Returns
+    -------
+    A Column of booleans indicating valid decimal conversion.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = input_col.view()
+    cdef int scale = dtype.scale
+    cdef data_type c_dtype = data_type(type_id.DECIMAL64, -scale)
+    with nogil:
+        c_result = move(cpp_is_fixed_point(
+            source_view,
+            c_dtype
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/convert/convert_floats.pyx b/python/cudf/cudf/_lib/strings/convert/convert_floats.pyx
new file mode 100644
index 0000000..d1617d8
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/convert/convert_floats.pyx
@@ -0,0 +1,30 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.convert.convert_floats cimport (
+    is_float as cpp_is_float,
+)
+
+
+@acquire_spill_lock()
+def is_float(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that have floats.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_is_float(
+            source_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/convert/convert_integers.pyx b/python/cudf/cudf/_lib/strings/convert/convert_integers.pyx
new file mode 100644
index 0000000..52a4791
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/convert/convert_integers.pyx
@@ -0,0 +1,30 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.convert.convert_integers cimport (
+    is_integer as cpp_is_integer,
+)
+
+
+@acquire_spill_lock()
+def is_integer(Column source_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that have integers.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_is_integer(
+            source_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/convert/convert_lists.pyx b/python/cudf/cudf/_lib/strings/convert/convert_lists.pyx
new file mode 100644
index 0000000..1a89fa7
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/convert/convert_lists.pyx
@@ -0,0 +1,51 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.convert.convert_lists cimport (
+    format_list_column as cpp_format_list_column,
+)
+
+from cudf._lib.scalar import as_device_scalar
+
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def format_list_column(Column source_list, Column separators):
+    """
+    Format a list column of strings into a strings column.
+
+    Parameters
+    ----------
+    input_col : input column of type list with strings child.
+
+    separators: strings used for formatting (', ', '[', ']')
+
+    Returns
+    -------
+    Formatted strings column
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_list.view()
+    cdef column_view separators_view = separators.view()
+    # Use 'None' as null-replacement string
+    cdef DeviceScalar str_na_rep = as_device_scalar("None")
+    cdef const string_scalar* string_scalar_na_rep = <const string_scalar*>(
+        str_na_rep.get_raw_ptr())
+
+    with nogil:
+        c_result = move(cpp_format_list_column(
+            source_view, string_scalar_na_rep[0], separators_view
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result)
+    )
diff --git a/python/cudf/cudf/_lib/strings/convert/convert_urls.pyx b/python/cudf/cudf/_lib/strings/convert/convert_urls.pyx
new file mode 100644
index 0000000..bc81232
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/convert/convert_urls.pyx
@@ -0,0 +1,69 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.convert.convert_urls cimport (
+    url_decode as cpp_url_decode,
+    url_encode as cpp_url_encode,
+)
+
+
+@acquire_spill_lock()
+def url_decode(Column source_strings):
+    """
+    Decode each string in column. No format checking is performed.
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    URL decoded string column
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_url_decode(
+            source_view
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result)
+    )
+
+
+@acquire_spill_lock()
+def url_encode(Column source_strings):
+    """
+    Encode each string in column. No format checking is performed.
+    All characters are encoded except for ASCII letters, digits,
+    and these characters: '.','_','-','~'. Encoding converts to
+    hex using UTF-8 encoded bytes.
+
+    Parameters
+    ----------
+    input_col : input column of type string
+
+    Returns
+    -------
+    URL encoded string column
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_url_encode(
+            source_view
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result)
+    )
diff --git a/python/cudf/cudf/_lib/strings/extract.pyx b/python/cudf/cudf/_lib/strings/extract.pyx
new file mode 100644
index 0000000..d3d8610
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/extract.pyx
@@ -0,0 +1,45 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cython.operator cimport dereference
+from libc.stdint cimport uint32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.extract cimport extract as cpp_extract
+from cudf._lib.cpp.strings.regex_flags cimport regex_flags
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.utils cimport data_from_unique_ptr
+
+
+@acquire_spill_lock()
+def extract(Column source_strings, object pattern, uint32_t flags):
+    """
+    Returns data which contains extracted capture groups provided in
+    `pattern` for all `source_strings`.
+    The returning data contains one row for each subject string,
+    and one column for each group.
+    """
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef regex_flags c_flags = <regex_flags>flags
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_extract(
+            source_view,
+            dereference(c_prog)
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
diff --git a/python/cudf/cudf/_lib/strings/find.pyx b/python/cudf/cudf/_lib/strings/find.pyx
new file mode 100644
index 0000000..f6dd3b8
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/find.pyx
@@ -0,0 +1,217 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.find cimport (
+    contains as cpp_contains,
+    ends_with as cpp_ends_with,
+    find as cpp_find,
+    rfind as cpp_rfind,
+    starts_with as cpp_starts_with,
+)
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def contains(Column source_strings, object py_target):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain the pattern given in `py_target`.
+    """
+    cdef DeviceScalar target = py_target.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        target.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_contains(
+            source_view,
+            scalar_str[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def contains_multiple(Column source_strings, Column target_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain the corresponding string in `target_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view target_view = target_strings.view()
+
+    with nogil:
+        c_result = move(cpp_contains(
+            source_view,
+            target_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def endswith(Column source_strings, object py_target):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain strings that end with the pattern given in `py_target`.
+    """
+
+    cdef DeviceScalar target = py_target.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        target.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_ends_with(
+            source_view,
+            scalar_str[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def endswith_multiple(Column source_strings, Column target_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain strings that end with corresponding location
+    in `target_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view target_view = target_strings.view()
+
+    with nogil:
+        c_result = move(cpp_ends_with(
+            source_view,
+            target_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def startswith(Column source_strings, object py_target):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain strings that start with the pattern given in `py_target`.
+    """
+
+    cdef DeviceScalar target = py_target.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        target.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_starts_with(
+            source_view,
+            scalar_str[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def startswith_multiple(Column source_strings, Column target_strings):
+    """
+    Returns a Column of boolean values with True for `source_strings`
+    that contain strings that begin with corresponding location
+    in `target_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view target_view = target_strings.view()
+
+    with nogil:
+        c_result = move(cpp_starts_with(
+            source_view,
+            target_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def find(Column source_strings,
+         object py_target,
+         size_type start,
+         size_type end):
+    """
+    Returns a Column containing lowest indexes in each string of
+    `source_strings` that fully contain `py_target` string.
+    Scan portion of strings in `source_strings` can be
+    controlled by setting `start` and `end` values.
+    """
+
+    cdef DeviceScalar target = py_target.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        target.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_find(
+            source_view,
+            scalar_str[0],
+            start,
+            end
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def rfind(Column source_strings,
+          object py_target,
+          size_type start,
+          size_type end):
+    """
+    Returns a Column containing highest indexes in each string of
+    `source_strings` that fully contain `py_target` string.
+    Scan portion of strings in `source_strings` can be
+    controlled by setting `start` and `end` values.
+    """
+
+    cdef DeviceScalar target = py_target.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        target.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_rfind(
+            source_view,
+            scalar_str[0],
+            start,
+            end
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/find_multiple.pyx b/python/cudf/cudf/_lib/strings/find_multiple.pyx
new file mode 100644
index 0000000..c2a97a4
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/find_multiple.pyx
@@ -0,0 +1,32 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.find_multiple cimport (
+    find_multiple as cpp_find_multiple,
+)
+
+
+@acquire_spill_lock()
+def find_multiple(Column source_strings, Column target_strings):
+    """
+    Returns a column with character position values where each
+    of the `target_strings` are found in each string of `source_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view target_view = target_strings.view()
+
+    with nogil:
+        c_result = move(cpp_find_multiple(
+            source_view,
+            target_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/findall.pyx b/python/cudf/cudf/_lib/strings/findall.pyx
new file mode 100644
index 0000000..6df1d32
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/findall.pyx
@@ -0,0 +1,39 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from cython.operator cimport dereference
+from libc.stdint cimport uint32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.findall cimport findall as cpp_findall
+from cudf._lib.cpp.strings.regex_flags cimport regex_flags
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+
+
+@acquire_spill_lock()
+def findall(Column source_strings, object pattern, uint32_t flags):
+    """
+    Returns data with all non-overlapping matches of `pattern`
+    in each string of `source_strings` as a lists column.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef regex_flags c_flags = <regex_flags>flags
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_findall(
+            source_view,
+            dereference(c_prog)
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/json.pyx b/python/cudf/cudf/_lib/strings/json.pyx
new file mode 100644
index 0000000..861e0da
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/json.pyx
@@ -0,0 +1,83 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.json cimport (
+    get_json_object as cpp_get_json_object,
+    get_json_object_options,
+)
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def get_json_object(
+        Column col, object py_json_path, GetJsonObjectOptions options):
+    """
+    Apply a JSONPath string to all rows in an input column
+    of json strings.
+    """
+    cdef unique_ptr[column] c_result
+
+    cdef column_view col_view = col.view()
+    cdef DeviceScalar json_path = py_json_path.device_value
+
+    cdef const string_scalar* scalar_json_path = <const string_scalar*>(
+        json_path.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_get_json_object(
+            col_view,
+            scalar_json_path[0],
+            options.options,
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+cdef class GetJsonObjectOptions:
+    cdef get_json_object_options options
+
+    def __init__(
+        self,
+        *,
+        allow_single_quotes=False,
+        strip_quotes_from_single_strings=True,
+        missing_fields_as_nulls=False
+    ):
+        self.options.set_allow_single_quotes(allow_single_quotes)
+        self.options.set_strip_quotes_from_single_strings(
+            strip_quotes_from_single_strings
+        )
+        self.options.set_missing_fields_as_nulls(missing_fields_as_nulls)
+
+    @property
+    def allow_single_quotes(self):
+        return self.options.get_allow_single_quotes()
+
+    @property
+    def strip_quotes_from_single_strings(self):
+        return self.options.get_strip_quotes_from_single_strings()
+
+    @property
+    def missing_fields_as_nulls(self):
+        return self.options.get_missing_fields_as_nulls()
+
+    @allow_single_quotes.setter
+    def allow_single_quotes(self, val):
+        self.options.set_allow_single_quotes(val)
+
+    @strip_quotes_from_single_strings.setter
+    def strip_quotes_from_single_strings(self, val):
+        self.options.set_strip_quotes_from_single_strings(val)
+
+    @missing_fields_as_nulls.setter
+    def missing_fields_as_nulls(self, val):
+        self.options.set_missing_fields_as_nulls(val)
diff --git a/python/cudf/cudf/_lib/strings/padding.pyx b/python/cudf/cudf/_lib/strings/padding.pyx
new file mode 100644
index 0000000..340d7eb
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/padding.pyx
@@ -0,0 +1,149 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport size_type
+
+from enum import IntEnum
+
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.strings.padding cimport pad as cpp_pad, zfill as cpp_zfill
+from cudf._lib.cpp.strings.side_type cimport (
+    side_type,
+    underlying_type_t_side_type,
+)
+
+
+class SideType(IntEnum):
+    LEFT = <underlying_type_t_side_type> side_type.LEFT
+    RIGHT = <underlying_type_t_side_type> side_type.RIGHT
+    BOTH = <underlying_type_t_side_type> side_type.BOTH
+
+
+@acquire_spill_lock()
+def pad(Column source_strings,
+        size_type width,
+        fill_char,
+        side=SideType.LEFT):
+    """
+    Returns a Column by padding strings in `source_strings`
+    up to the given `width`. Direction of padding is to be specified by `side`.
+    The additional characters being filled can be changed by specifying
+    `fill_char`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string f_char = <string>str(fill_char).encode()
+
+    cdef side_type pad_direction = <side_type>(
+        <underlying_type_t_side_type> side
+    )
+
+    with nogil:
+        c_result = move(cpp_pad(
+            source_view,
+            width,
+            pad_direction,
+            f_char
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def zfill(Column source_strings,
+          size_type width):
+    """
+    Returns a Column by prepending strings in `source_strings`
+    with '0' characters up to the given `width`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_zfill(
+            source_view,
+            width
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def center(Column source_strings,
+           size_type width,
+           fill_char):
+    """
+    Returns a Column by filling left and right side of strings
+    in `source_strings` with additional character, `fill_char`
+    up to the given `width`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string f_char = <string>str(fill_char).encode()
+
+    with nogil:
+        c_result = move(cpp_pad(
+            source_view,
+            width,
+            side_type.BOTH,
+            f_char
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def ljust(Column source_strings,
+          size_type width,
+          fill_char):
+    """
+    Returns a Column by filling right side of strings in `source_strings`
+    with additional character, `fill_char` up to the given `width`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string f_char = <string>str(fill_char).encode()
+
+    with nogil:
+        c_result = move(cpp_pad(
+            source_view,
+            width,
+            side_type.RIGHT,
+            f_char
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def rjust(Column source_strings,
+          size_type width,
+          fill_char):
+    """
+    Returns a Column by filling left side of strings in `source_strings`
+    with additional character, `fill_char` up to the given `width`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string f_char = <string>str(fill_char).encode()
+
+    with nogil:
+        c_result = move(cpp_pad(
+            source_view,
+            width,
+            side_type.LEFT,
+            f_char
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/repeat.pyx b/python/cudf/cudf/_lib/strings/repeat.pyx
new file mode 100644
index 0000000..4896fb7
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/repeat.pyx
@@ -0,0 +1,53 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings cimport repeat as cpp_repeat
+from cudf._lib.cpp.types cimport size_type
+
+
+@acquire_spill_lock()
+def repeat_scalar(Column source_strings,
+                  size_type repeats):
+    """
+    Returns a Column after repeating
+    each string in `source_strings`
+    `repeats` number of times.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_repeat.repeat_strings(
+            source_view,
+            repeats
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def repeat_sequence(Column source_strings,
+                    Column repeats):
+    """
+    Returns a Column after repeating
+    each string in `source_strings`
+    `repeats` number of times.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view repeats_view = repeats.view()
+
+    with nogil:
+        c_result = move(cpp_repeat.repeat_strings(
+            source_view,
+            repeats_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/replace.pyx b/python/cudf/cudf/_lib/strings/replace.pyx
new file mode 100644
index 0000000..80c9ba9
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/replace.pyx
@@ -0,0 +1,135 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.replace cimport (
+    replace as cpp_replace,
+    replace_slice as cpp_replace_slice,
+)
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def slice_replace(Column source_strings,
+                  size_type start,
+                  size_type stop,
+                  object py_repl):
+    """
+    Returns a Column by replacing specified section
+    of each string with `py_repl`. Positions can be
+    specified with `start` and `stop` params.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_replace_slice(
+            source_view,
+            scalar_str[0],
+            start,
+            stop
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def insert(Column source_strings,
+           size_type start,
+           object py_repl):
+    """
+    Returns a Column by inserting a specified
+    string `py_repl` at a specific position in all strings.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_replace_slice(
+            source_view,
+            scalar_str[0],
+            start,
+            start
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace(Column source_strings,
+            object py_target,
+            object py_repl,
+            int32_t maxrepl):
+    """
+    Returns a Column after replacing occurrences of
+    patterns `py_target` with `py_repl` in `source_strings`.
+    `maxrepl` indicates number of replacements to make from start.
+    """
+    cdef DeviceScalar target = py_target.device_value
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_target = <const string_scalar*>(
+        target.get_raw_ptr()
+    )
+    cdef const string_scalar* scalar_repl = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_replace(
+            source_view,
+            scalar_target[0],
+            scalar_repl[0],
+            maxrepl
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace_multi(Column source_strings,
+                  Column target_strings,
+                  Column repl_strings):
+    """
+    Returns a Column after replacing occurrences of
+    patterns `target_strings` with `repl_strings` in `source_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view target_view = target_strings.view()
+    cdef column_view repl_view = repl_strings.view()
+
+    with nogil:
+        c_result = move(cpp_replace(
+            source_view,
+            target_view,
+            repl_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/replace_re.pyx b/python/cudf/cudf/_lib/strings/replace_re.pyx
new file mode 100644
index 0000000..1fbbaa8
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/replace_re.pyx
@@ -0,0 +1,116 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cython.operator cimport dereference
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.regex_flags cimport regex_flags
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.cpp.strings.replace_re cimport (
+    replace_re as cpp_replace_re,
+    replace_with_backrefs as cpp_replace_with_backrefs,
+)
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def replace_re(Column source_strings,
+               object pattern,
+               object py_repl,
+               size_type n):
+    """
+    Returns a Column after replacing occurrences regular
+    expressions `pattern` with `py_repl` in `source_strings`.
+    `n` indicates the number of resplacements to be made from
+    start. (-1 indicates all)
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef const string_scalar* scalar_repl = \
+        <const string_scalar*>(repl.get_raw_ptr())
+    cdef regex_flags c_flags = regex_flags.DEFAULT
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_replace_re(
+            source_view,
+            dereference(c_prog),
+            scalar_repl[0],
+            n
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace_with_backrefs(
+        Column source_strings,
+        object pattern,
+        object repl):
+    """
+    Returns a Column after using the `repl` back-ref template to create
+    new string with the extracted elements found using
+    `pattern` regular expression in `source_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef string repl_string = <string>str(repl).encode()
+    cdef regex_flags c_flags = regex_flags.DEFAULT
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_replace_with_backrefs(
+            source_view,
+            dereference(c_prog),
+            repl_string
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def replace_multi_re(Column source_strings,
+                     object patterns,
+                     Column repl_strings):
+    """
+    Returns a Column after replacing occurrences of multiple
+    regular expressions `patterns` with their corresponding
+    strings in `repl_strings` in `source_strings`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view repl_view = repl_strings.view()
+
+    cdef int pattern_size = len(patterns)
+    cdef vector[string] patterns_vector
+    patterns_vector.reserve(pattern_size)
+
+    for pattern in patterns:
+        patterns_vector.push_back(str.encode(pattern))
+
+    with nogil:
+        c_result = move(cpp_replace_re(
+            source_view,
+            patterns_vector,
+            repl_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/split/CMakeLists.txt b/python/cudf/cudf/_lib/strings/split/CMakeLists.txt
new file mode 100644
index 0000000..59a22c0
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/split/CMakeLists.txt
@@ -0,0 +1,22 @@
+# =============================================================================
+# Copyright (c) 2022, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources partition.pyx split.pyx)
+
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX strings_ ASSOCIATED_TARGETS cudf
+)
diff --git a/python/cudf/cudf/_lib/strings/split/__init__.pxd b/python/cudf/cudf/_lib/strings/split/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/strings/split/__init__.py b/python/cudf/cudf/_lib/strings/split/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/_lib/strings/split/partition.pyx b/python/cudf/cudf/_lib/strings/split/partition.pyx
new file mode 100644
index 0000000..281d131
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/split/partition.pyx
@@ -0,0 +1,73 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.split.partition cimport (
+    partition as cpp_partition,
+    rpartition as cpp_rpartition,
+)
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport data_from_unique_ptr
+
+
+@acquire_spill_lock()
+def partition(Column source_strings,
+              object py_delimiter):
+    """
+    Returns data by splitting the `source_strings`
+    column at the first occurrence of the specified `py_delimiter`.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        delimiter.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_partition(
+            source_view,
+            scalar_str[0]
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
+
+
+@acquire_spill_lock()
+def rpartition(Column source_strings,
+               object py_delimiter):
+    """
+    Returns a Column by splitting the `source_strings`
+    column at the last occurrence of the specified `py_delimiter`.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        delimiter.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_rpartition(
+            source_view,
+            scalar_str[0]
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
diff --git a/python/cudf/cudf/_lib/strings/split/split.pyx b/python/cudf/cudf/_lib/strings/split/split.pyx
new file mode 100644
index 0000000..08c7dde
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/split/split.pyx
@@ -0,0 +1,263 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cython.operator cimport dereference
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.regex_flags cimport regex_flags
+from cudf._lib.cpp.strings.regex_program cimport regex_program
+from cudf._lib.cpp.strings.split.split cimport (
+    rsplit as cpp_rsplit,
+    rsplit_re as cpp_rsplit_re,
+    rsplit_record as cpp_rsplit_record,
+    rsplit_record_re as cpp_rsplit_record_re,
+    split as cpp_split,
+    split_re as cpp_split_re,
+    split_record as cpp_split_record,
+    split_record_re as cpp_split_record_re,
+)
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.types cimport size_type
+from cudf._lib.scalar cimport DeviceScalar
+from cudf._lib.utils cimport data_from_unique_ptr
+
+
+@acquire_spill_lock()
+def split(Column source_strings,
+          object py_delimiter,
+          size_type maxsplit):
+    """
+    Returns data by splitting the `source_strings`
+    column around the specified `py_delimiter`.
+    The split happens from beginning.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        delimiter.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_split(
+            source_view,
+            scalar_str[0],
+            maxsplit
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
+
+
+@acquire_spill_lock()
+def split_record(Column source_strings,
+                 object py_delimiter,
+                 size_type maxsplit):
+    """
+    Returns a Column by splitting the `source_strings`
+    column around the specified `py_delimiter`.
+    The split happens from beginning.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        delimiter.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_split_record(
+            source_view,
+            scalar_str[0],
+            maxsplit
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result),
+    )
+
+
+@acquire_spill_lock()
+def rsplit(Column source_strings,
+           object py_delimiter,
+           size_type maxsplit):
+    """
+    Returns data by splitting the `source_strings`
+    column around the specified `py_delimiter`.
+    The split happens from the end.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        delimiter.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_rsplit(
+            source_view,
+            scalar_str[0],
+            maxsplit
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
+
+
+@acquire_spill_lock()
+def rsplit_record(Column source_strings,
+                  object py_delimiter,
+                  size_type maxsplit):
+    """
+    Returns a Column by splitting the `source_strings`
+    column around the specified `py_delimiter`.
+    The split happens from the end.
+    """
+
+    cdef DeviceScalar delimiter = py_delimiter.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        delimiter.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_rsplit_record(
+            source_view,
+            scalar_str[0],
+            maxsplit
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result),
+    )
+
+
+@acquire_spill_lock()
+def split_re(Column source_strings,
+             object pattern,
+             size_type maxsplit):
+    """
+    Returns data by splitting the `source_strings`
+    column around the delimiters identified by `pattern`.
+    """
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef regex_flags c_flags = regex_flags.DEFAULT
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_split_re(
+            source_view,
+            dereference(c_prog),
+            maxsplit
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
+
+
+@acquire_spill_lock()
+def rsplit_re(Column source_strings,
+              object pattern,
+              size_type maxsplit):
+    """
+    Returns data by splitting the `source_strings`
+    column around the delimiters identified by `pattern`.
+    The delimiters are searched starting from the end of each string.
+    """
+    cdef unique_ptr[table] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef regex_flags c_flags = regex_flags.DEFAULT
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_rsplit_re(
+            source_view,
+            dereference(c_prog),
+            maxsplit
+        ))
+
+    return data_from_unique_ptr(
+        move(c_result),
+        column_names=range(0, c_result.get()[0].num_columns())
+    )
+
+
+@acquire_spill_lock()
+def split_record_re(Column source_strings,
+                    object pattern,
+                    size_type maxsplit):
+    """
+    Returns a Column by splitting the `source_strings`
+    column around the delimiters identified by `pattern`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef regex_flags c_flags = regex_flags.DEFAULT
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_split_record_re(
+            source_view,
+            dereference(c_prog),
+            maxsplit
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result),
+    )
+
+
+@acquire_spill_lock()
+def rsplit_record_re(Column source_strings,
+                     object pattern,
+                     size_type maxsplit):
+    """
+    Returns a Column by splitting the `source_strings`
+    column around the delimiters identified by `pattern`.
+    The delimiters are searched starting from the end of each string.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef string pattern_string = <string>str(pattern).encode()
+    cdef regex_flags c_flags = regex_flags.DEFAULT
+    cdef unique_ptr[regex_program] c_prog
+
+    with nogil:
+        c_prog = move(regex_program.create(pattern_string, c_flags))
+        c_result = move(cpp_rsplit_record_re(
+            source_view,
+            dereference(c_prog),
+            maxsplit
+        ))
+
+    return Column.from_unique_ptr(
+        move(c_result),
+    )
diff --git a/python/cudf/cudf/_lib/strings/strip.pyx b/python/cudf/cudf/_lib/strings/strip.pyx
new file mode 100644
index 0000000..2c53782
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/strip.pyx
@@ -0,0 +1,98 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.side_type cimport side_type
+from cudf._lib.cpp.strings.strip cimport strip as cpp_strip
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def strip(Column source_strings,
+          object py_repl):
+    """
+    Returns a Column by removing leading and trailing characters.
+    The set of characters need be stripped from left and right side
+    can be specified by `py_repl`.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_strip(
+            source_view,
+            side_type.BOTH,
+            scalar_str[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def lstrip(Column source_strings,
+           object py_repl):
+    """
+    Returns a Column by removing leading and trailing characters.
+    The set of characters need be stripped from left side can
+    be specified by `py_repl`.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_strip(
+            source_view,
+            side_type.LEFT,
+            scalar_str[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def rstrip(Column source_strings,
+           object py_repl):
+    """
+    Returns a Column by removing leading and trailing characters.
+    The set of characters need be stripped from right side can
+    be specified by `py_repl`.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef const string_scalar* scalar_str = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+
+    with nogil:
+        c_result = move(cpp_strip(
+            source_view,
+            side_type.RIGHT,
+            scalar_str[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/substring.pyx b/python/cudf/cudf/_lib/strings/substring.pyx
new file mode 100644
index 0000000..e6b8cdd
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/substring.pyx
@@ -0,0 +1,119 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import numpy as np
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.substring cimport slice_strings as cpp_slice_strings
+from cudf._lib.cpp.types cimport size_type
+
+from cudf._lib.scalar import as_device_scalar
+
+from cudf._lib.cpp.scalar.scalar cimport numeric_scalar
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def slice_strings(Column source_strings,
+                  object start,
+                  object end,
+                  object step):
+    """
+    Returns a Column by extracting a substring of each string
+    at given start and end positions. Slicing can also be
+    performed in steps by skipping `step` number of
+    characters in a string.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    cdef DeviceScalar start_scalar = as_device_scalar(start, np.int32)
+    cdef DeviceScalar end_scalar = as_device_scalar(end, np.int32)
+    cdef DeviceScalar step_scalar = as_device_scalar(step, np.int32)
+
+    cdef numeric_scalar[size_type]* start_numeric_scalar = \
+        <numeric_scalar[size_type]*>(
+            start_scalar.get_raw_ptr())
+    cdef numeric_scalar[size_type]* end_numeric_scalar = \
+        <numeric_scalar[size_type]*>(end_scalar.get_raw_ptr())
+    cdef numeric_scalar[size_type]* step_numeric_scalar = \
+        <numeric_scalar[size_type]*>(step_scalar.get_raw_ptr())
+
+    with nogil:
+        c_result = move(cpp_slice_strings(
+            source_view,
+            start_numeric_scalar[0],
+            end_numeric_scalar[0],
+            step_numeric_scalar[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def slice_from(Column source_strings,
+               Column starts,
+               Column stops):
+    """
+    Returns a Column by extracting a substring of each string
+    at given starts and stops positions. `starts` and `stops`
+    here are positions per element in the string-column.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef column_view starts_view = starts.view()
+    cdef column_view stops_view = stops.view()
+
+    with nogil:
+        c_result = move(cpp_slice_strings(
+            source_view,
+            starts_view,
+            stops_view
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def get(Column source_strings,
+        object index):
+    """
+    Returns a Column which contains only single
+    character from each input string. The index of
+    characters required can be controlled by passing `index`.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    if index < 0:
+        next_index = index - 1
+        step = -1
+    else:
+        next_index = index + 1
+        step = 1
+    cdef DeviceScalar start_scalar = as_device_scalar(index, np.int32)
+    cdef DeviceScalar end_scalar = as_device_scalar(next_index, np.int32)
+    cdef DeviceScalar step_scalar = as_device_scalar(step, np.int32)
+
+    cdef numeric_scalar[size_type]* start_numeric_scalar = \
+        <numeric_scalar[size_type]*>(
+            start_scalar.get_raw_ptr())
+    cdef numeric_scalar[size_type]* end_numeric_scalar = \
+        <numeric_scalar[size_type]*>(end_scalar.get_raw_ptr())
+    cdef numeric_scalar[size_type]* step_numeric_scalar = \
+        <numeric_scalar[size_type]*>(step_scalar.get_raw_ptr())
+
+    with nogil:
+        c_result = move(cpp_slice_strings(
+            source_view,
+            start_numeric_scalar[0],
+            end_numeric_scalar[0],
+            step_numeric_scalar[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/translate.pyx b/python/cudf/cudf/_lib/strings/translate.pyx
new file mode 100644
index 0000000..55659e9
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/translate.pyx
@@ -0,0 +1,106 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from libcpp cimport bool
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.scalar.scalar cimport string_scalar
+from cudf._lib.cpp.strings.translate cimport (
+    filter_characters as cpp_filter_characters,
+    filter_type,
+    translate as cpp_translate,
+)
+from cudf._lib.cpp.types cimport char_utf8
+from cudf._lib.scalar cimport DeviceScalar
+
+
+@acquire_spill_lock()
+def translate(Column source_strings,
+              object mapping_table):
+    """
+    Translates individual characters within each string
+    if present in the mapping_table.
+    """
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef int table_size
+    table_size = len(mapping_table)
+
+    cdef vector[pair[char_utf8, char_utf8]] c_mapping_table
+    c_mapping_table.reserve(table_size)
+
+    for key in mapping_table:
+        value = mapping_table[key]
+        if type(value) is int:
+            value = chr(value)
+        if type(value) is str:
+            value = int.from_bytes(value.encode(), byteorder='big')
+        if type(key) is int:
+            key = chr(key)
+        if type(key) is str:
+            key = int.from_bytes(key.encode(), byteorder='big')
+        c_mapping_table.push_back((key, value))
+
+    with nogil:
+        c_result = move(cpp_translate(source_view, c_mapping_table))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def filter_characters(Column source_strings,
+                      object mapping_table,
+                      bool keep,
+                      object py_repl):
+    """
+    Removes or keeps individual characters within each string
+    using the provided mapping_table.
+    """
+
+    cdef DeviceScalar repl = py_repl.device_value
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+    cdef const string_scalar* scalar_repl = <const string_scalar*>(
+        repl.get_raw_ptr()
+    )
+    cdef int table_size
+    table_size = len(mapping_table)
+
+    cdef vector[pair[char_utf8, char_utf8]] c_mapping_table
+    c_mapping_table.reserve(table_size)
+
+    for key in mapping_table:
+        value = mapping_table[key]
+        if type(value) is int:
+            value = chr(value)
+        if type(value) is str:
+            value = int.from_bytes(value.encode(), byteorder='big')
+        if type(key) is int:
+            key = chr(key)
+        if type(key) is str:
+            key = int.from_bytes(key.encode(), byteorder='big')
+        c_mapping_table.push_back((key, value))
+
+    cdef filter_type c_keep
+    if keep is True:
+        c_keep = filter_type.KEEP
+    else:
+        c_keep = filter_type.REMOVE
+
+    with nogil:
+        c_result = move(cpp_filter_characters(
+            source_view,
+            c_mapping_table,
+            c_keep,
+            scalar_repl[0]
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings/wrap.pyx b/python/cudf/cudf/_lib/strings/wrap.pyx
new file mode 100644
index 0000000..8b0c367
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings/wrap.pyx
@@ -0,0 +1,33 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import acquire_spill_lock
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.strings.wrap cimport wrap as cpp_wrap
+from cudf._lib.cpp.types cimport size_type
+
+
+@acquire_spill_lock()
+def wrap(Column source_strings,
+         size_type width):
+    """
+    Returns a Column by wrapping long strings
+    in the Column to be formatted in paragraphs
+    with length less than a given `width`.
+    """
+
+    cdef unique_ptr[column] c_result
+    cdef column_view source_view = source_strings.view()
+
+    with nogil:
+        c_result = move(cpp_wrap(
+            source_view,
+            width
+        ))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/strings_udf.pyx b/python/cudf/cudf/_lib/strings_udf.pyx
new file mode 100644
index 0000000..a59e6db
--- /dev/null
+++ b/python/cudf/cudf/_lib/strings_udf.pyx
@@ -0,0 +1,66 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport uint8_t, uint16_t, uintptr_t
+
+from cudf._lib.cpp.strings_udf cimport (
+    get_character_cases_table as cpp_get_character_cases_table,
+    get_character_flags_table as cpp_get_character_flags_table,
+    get_special_case_mapping_table as cpp_get_special_case_mapping_table,
+)
+
+import numpy as np
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+from cudf.core.buffer import as_buffer
+
+from rmm._lib.device_buffer cimport DeviceBuffer, device_buffer
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column, column_view
+from cudf._lib.cpp.strings_udf cimport (
+    column_from_udf_string_array as cpp_column_from_udf_string_array,
+    free_udf_string_array as cpp_free_udf_string_array,
+    to_string_view_array as cpp_to_string_view_array,
+    udf_string,
+)
+
+
+def column_to_string_view_array(Column strings_col):
+    cdef unique_ptr[device_buffer] c_buffer
+    cdef column_view input_view = strings_col.view()
+    with nogil:
+        c_buffer = move(cpp_to_string_view_array(input_view))
+
+    db = DeviceBuffer.c_from_unique_ptr(move(c_buffer))
+    return as_buffer(db, exposed=True)
+
+
+def column_from_udf_string_array(DeviceBuffer d_buffer):
+    cdef size_t size = int(d_buffer.c_size() / sizeof(udf_string))
+    cdef udf_string* data = <udf_string*>d_buffer.c_data()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(cpp_column_from_udf_string_array(data, size))
+        cpp_free_udf_string_array(data, size)
+
+    result = Column.from_unique_ptr(move(c_result))
+
+    return result
+
+
+def get_character_flags_table_ptr():
+    cdef const uint8_t* tbl_ptr = cpp_get_character_flags_table()
+    return np.uintp(<uintptr_t>tbl_ptr)
+
+
+def get_character_cases_table_ptr():
+    cdef const uint16_t* tbl_ptr = cpp_get_character_cases_table()
+    return np.uintp(<uintptr_t>tbl_ptr)
+
+
+def get_special_case_mapping_table_ptr():
+    cdef const void* tbl_ptr = cpp_get_special_case_mapping_table()
+    return np.uintp(<uintptr_t>tbl_ptr)
diff --git a/python/cudf/cudf/_lib/text.pyx b/python/cudf/cudf/_lib/text.pyx
new file mode 100644
index 0000000..a7346cd
--- /dev/null
+++ b/python/cudf/cudf/_lib/text.pyx
@@ -0,0 +1,88 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from io import TextIOBase
+
+from cython.operator cimport dereference
+from libc.stdint cimport uint64_t
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.io.text cimport (
+    byte_range_info,
+    data_chunk_source,
+    make_source,
+    make_source_from_bgzip_file,
+    make_source_from_file,
+    multibyte_split,
+    parse_options,
+)
+
+
+def read_text(object filepaths_or_buffers,
+              object delimiter=None,
+              object byte_range=None,
+              object strip_delimiters=False,
+              object compression=None,
+              object compression_offsets=None):
+    """
+    Cython function to call into libcudf API, see `multibyte_split`.
+
+    See Also
+    --------
+    cudf.io.text.read_text
+    """
+    cdef string delim = delimiter.encode()
+
+    cdef unique_ptr[data_chunk_source] datasource
+    cdef unique_ptr[column] c_col
+
+    cdef size_t c_byte_range_offset
+    cdef size_t c_byte_range_size
+    cdef uint64_t c_compression_begin_offset
+    cdef uint64_t c_compression_end_offset
+    cdef parse_options c_options
+
+    if compression is None:
+        if isinstance(filepaths_or_buffers, TextIOBase):
+            datasource = move(make_source(
+                filepaths_or_buffers.read().encode()))
+        else:
+            datasource = move(make_source_from_file(
+                filepaths_or_buffers.encode()))
+    elif compression == "bgzip":
+        if isinstance(filepaths_or_buffers, TextIOBase):
+            raise ValueError("bgzip compression requires a file path")
+        if compression_offsets is not None:
+            if len(compression_offsets) != 2:
+                raise ValueError(
+                    "compression offsets need to consist of two elements")
+            c_compression_begin_offset = compression_offsets[0]
+            c_compression_end_offset = compression_offsets[1]
+            datasource = move(make_source_from_bgzip_file(
+                filepaths_or_buffers.encode(),
+                c_compression_begin_offset,
+                c_compression_end_offset))
+        else:
+            datasource = move(make_source_from_bgzip_file(
+                filepaths_or_buffers.encode()))
+    else:
+        raise ValueError("Only bgzip compression is supported at the moment")
+
+    c_options = parse_options()
+    if byte_range is not None:
+        c_byte_range_offset = byte_range[0]
+        c_byte_range_size = byte_range[1]
+        c_options.byte_range = byte_range_info(
+            c_byte_range_offset,
+            c_byte_range_size)
+    c_options.strip_delimiters = strip_delimiters
+    with nogil:
+        c_col = move(multibyte_split(
+            dereference(datasource),
+            delim,
+            c_options))
+
+    return {None: Column.from_unique_ptr(move(c_col))}
diff --git a/python/cudf/cudf/_lib/timezone.pyx b/python/cudf/cudf/_lib/timezone.pyx
new file mode 100644
index 0000000..4d76cbf
--- /dev/null
+++ b/python/cudf/cudf/_lib/timezone.pyx
@@ -0,0 +1,28 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.cpp.io.timezone cimport (
+    make_timezone_transition_table as cpp_make_timezone_transition_table,
+)
+from cudf._lib.cpp.libcpp.optional cimport make_optional
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.utils cimport columns_from_unique_ptr
+
+
+def make_timezone_transition_table(tzdir, tzname):
+    cdef unique_ptr[table] c_result
+    cdef string c_tzdir = tzdir.encode()
+    cdef string c_tzname = tzname.encode()
+
+    with nogil:
+        c_result = move(
+            cpp_make_timezone_transition_table(
+                make_optional[string](c_tzdir),
+                c_tzname
+            )
+        )
+
+    return columns_from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/transform.pyx b/python/cudf/cudf/_lib/transform.pyx
new file mode 100644
index 0000000..d8eb613
--- /dev/null
+++ b/python/cudf/cudf/_lib/transform.pyx
@@ -0,0 +1,201 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from numba.np import numpy_support
+
+import cudf
+from cudf._lib.types import SUPPORTED_NUMPY_TO_LIBCUDF_TYPES
+from cudf.core._internals.expressions import parse_expression
+from cudf.core.buffer import acquire_spill_lock, as_buffer
+from cudf.utils import cudautils
+
+from cython.operator cimport dereference
+from libc.stdint cimport uintptr_t
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from rmm._lib.device_buffer cimport DeviceBuffer, device_buffer
+
+cimport cudf._lib.cpp.transform as libcudf_transform
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.expressions cimport expression
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport bitmask_type, data_type, size_type, type_id
+from cudf._lib.expressions cimport Expression
+from cudf._lib.types cimport underlying_type_t_type_id
+from cudf._lib.utils cimport (
+    columns_from_unique_ptr,
+    data_from_table_view,
+    table_view_from_columns,
+)
+
+
+@acquire_spill_lock()
+def bools_to_mask(Column col):
+    """
+    Given an int8 (boolean) column, compress the data from booleans to bits and
+    return a Buffer
+    """
+    cdef column_view col_view = col.view()
+    cdef pair[unique_ptr[device_buffer], size_type] cpp_out
+    cdef unique_ptr[device_buffer] up_db
+
+    with nogil:
+        cpp_out = move(libcudf_transform.bools_to_mask(col_view))
+        up_db = move(cpp_out.first)
+
+    rmm_db = DeviceBuffer.c_from_unique_ptr(move(up_db))
+    buf = as_buffer(rmm_db)
+    return buf
+
+
+@acquire_spill_lock()
+def mask_to_bools(object mask_buffer, size_type begin_bit, size_type end_bit):
+    """
+    Given a mask buffer, returns a boolean column representng bit 0 -> False
+    and 1 -> True within range of [begin_bit, end_bit),
+    """
+    if not isinstance(mask_buffer, cudf.core.buffer.Buffer):
+        raise TypeError("mask_buffer is not an instance of "
+                        "cudf.core.buffer.Buffer")
+    cdef bitmask_type* bit_mask = <bitmask_type*><uintptr_t>(
+        mask_buffer.get_ptr(mode="read")
+    )
+
+    cdef unique_ptr[column] result
+    with nogil:
+        result = move(
+            libcudf_transform.mask_to_bools(bit_mask, begin_bit, end_bit)
+        )
+
+    return Column.from_unique_ptr(move(result))
+
+
+@acquire_spill_lock()
+def nans_to_nulls(Column input):
+    cdef column_view c_input = input.view()
+    cdef pair[unique_ptr[device_buffer], size_type] c_output
+    cdef unique_ptr[device_buffer] c_buffer
+
+    with nogil:
+        c_output = move(libcudf_transform.nans_to_nulls(c_input))
+        c_buffer = move(c_output.first)
+
+    if c_output.second == 0:
+        return None
+
+    return as_buffer(DeviceBuffer.c_from_unique_ptr(move(c_buffer)))
+
+
+@acquire_spill_lock()
+def transform(Column input, op):
+    cdef column_view c_input = input.view()
+    cdef string c_str
+    cdef type_id c_tid
+    cdef data_type c_dtype
+
+    nb_type = numpy_support.from_dtype(input.dtype)
+    nb_signature = (nb_type,)
+    compiled_op = cudautils.compile_udf(op, nb_signature)
+    c_str = compiled_op[0].encode('UTF-8')
+    np_dtype = cudf.dtype(compiled_op[1])
+
+    try:
+        c_tid = <type_id> (
+            <underlying_type_t_type_id> SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[
+                np_dtype
+            ]
+        )
+        c_dtype = data_type(c_tid)
+
+    except KeyError:
+        raise TypeError(
+            "Result of window function has unsupported dtype {}"
+            .format(np_dtype)
+        )
+
+    with nogil:
+        c_output = move(libcudf_transform.transform(
+            c_input,
+            c_str,
+            c_dtype,
+            True
+        ))
+
+    return Column.from_unique_ptr(move(c_output))
+
+
+def table_encode(list source_columns):
+    cdef table_view c_input = table_view_from_columns(source_columns)
+    cdef pair[unique_ptr[table], unique_ptr[column]] c_result
+
+    with nogil:
+        c_result = move(libcudf_transform.encode(c_input))
+
+    return (
+        columns_from_unique_ptr(move(c_result.first)),
+        Column.from_unique_ptr(move(c_result.second))
+    )
+
+
+def one_hot_encode(Column input_column, Column categories):
+    cdef column_view c_view_input = input_column.view()
+    cdef column_view c_view_categories = categories.view()
+    cdef pair[unique_ptr[column], table_view] c_result
+
+    with nogil:
+        c_result = move(
+            libcudf_transform.one_hot_encode(c_view_input, c_view_categories)
+        )
+
+    # Notice, the data pointer of `owner` has been exposed
+    # through `c_result.second` at this point.
+    owner = Column.from_unique_ptr(
+        move(c_result.first), data_ptr_exposed=True
+    )
+
+    pylist_categories = categories.to_arrow().to_pylist()
+    encodings, _ = data_from_table_view(
+        move(c_result.second),
+        owner=owner,
+        column_names=[
+            x if x is not None else '<NA>' for x in pylist_categories
+        ]
+    )
+    return encodings
+
+
+@acquire_spill_lock()
+def compute_column(list columns, tuple column_names, expr: str):
+    """Compute a new column by evaluating an expression on a set of columns.
+
+    Parameters
+    ----------
+    columns : list
+        The set of columns forming the table to evaluate the expression on.
+    column_names : tuple[str]
+        The names associated with each column. These names are necessary to map
+        column names in the expression to indices in the provided list of
+        columns, which are what will be used by libcudf to evaluate the
+        expression on the table.
+    expr : str
+        The expression to evaluate.
+    """
+    visitor = parse_expression(expr, column_names)
+
+    # At the end, all the stack contains is the expression to evaluate.
+    cdef Expression cudf_expr = visitor.expression
+    cdef table_view tbl = table_view_from_columns(columns)
+    cdef unique_ptr[column] col
+    with nogil:
+        col = move(
+            libcudf_transform.compute_column(
+                tbl,
+                <expression &> dereference(cudf_expr.c_obj.get())
+            )
+        )
+    return Column.from_unique_ptr(move(col))
diff --git a/python/cudf/cudf/_lib/transpose.pyx b/python/cudf/cudf/_lib/transpose.pyx
new file mode 100644
index 0000000..51e49b1
--- /dev/null
+++ b/python/cudf/cudf/_lib/transpose.pyx
@@ -0,0 +1,31 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.pair cimport pair
+from libcpp.utility cimport move
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.transpose cimport transpose as cpp_transpose
+from cudf._lib.utils cimport columns_from_table_view, table_view_from_columns
+
+
+def transpose(list source_columns):
+    """Transpose m n-row columns into n m-row columns
+    """
+    cdef pair[unique_ptr[column], table_view] c_result
+    cdef table_view c_input = table_view_from_columns(source_columns)
+
+    with nogil:
+        c_result = move(cpp_transpose(c_input))
+
+    # Notice, the data pointer of `result_owner` has been exposed
+    # through `c_result.second` at this point.
+    result_owner = Column.from_unique_ptr(
+        move(c_result.first), data_ptr_exposed=True
+    )
+    return columns_from_table_view(
+        c_result.second,
+        owners=[result_owner] * c_result.second.num_columns()
+    )
diff --git a/python/cudf/cudf/_lib/types.pxd b/python/cudf/cudf/_lib/types.pxd
new file mode 100644
index 0000000..a95db84
--- /dev/null
+++ b/python/cudf/cudf/_lib/types.pxd
@@ -0,0 +1,21 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t
+from libcpp cimport bool
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+
+ctypedef bool underlying_type_t_order
+ctypedef bool underlying_type_t_null_order
+ctypedef bool underlying_type_t_sorted
+ctypedef int32_t underlying_type_t_interpolation
+ctypedef int32_t underlying_type_t_type_id
+ctypedef bool underlying_type_t_null_policy
+
+cdef dtype_from_column_view(column_view cv)
+
+cdef libcudf_types.data_type dtype_to_data_type(dtype) except *
+cpdef dtype_to_pylibcudf_type(dtype)
+cdef bool is_decimal_type_id(libcudf_types.type_id tid) except *
diff --git a/python/cudf/cudf/_lib/types.pyx b/python/cudf/cudf/_lib/types.pyx
new file mode 100644
index 0000000..929f8b4
--- /dev/null
+++ b/python/cudf/cudf/_lib/types.pyx
@@ -0,0 +1,333 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from enum import IntEnum
+
+import numpy as np
+
+from libcpp.memory cimport make_shared, shared_ptr
+
+cimport cudf._lib.cpp.types as libcudf_types
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.lists.lists_column_view cimport lists_column_view
+from cudf._lib.types cimport (
+    underlying_type_t_interpolation,
+    underlying_type_t_order,
+    underlying_type_t_sorted,
+)
+
+import cudf
+from cudf._lib import pylibcudf
+
+size_type_dtype = np.dtype("int32")
+
+
+class TypeId(IntEnum):
+    EMPTY = <underlying_type_t_type_id> libcudf_types.type_id.EMPTY
+    INT8 = <underlying_type_t_type_id> libcudf_types.type_id.INT8
+    INT16 = <underlying_type_t_type_id> libcudf_types.type_id.INT16
+    INT32 = <underlying_type_t_type_id> libcudf_types.type_id.INT32
+    INT64 = <underlying_type_t_type_id> libcudf_types.type_id.INT64
+    UINT8 = <underlying_type_t_type_id> libcudf_types.type_id.UINT8
+    UINT16 = <underlying_type_t_type_id> libcudf_types.type_id.UINT16
+    UINT32 = <underlying_type_t_type_id> libcudf_types.type_id.UINT32
+    UINT64 = <underlying_type_t_type_id> libcudf_types.type_id.UINT64
+    FLOAT32 = <underlying_type_t_type_id> libcudf_types.type_id.FLOAT32
+    FLOAT64 = <underlying_type_t_type_id> libcudf_types.type_id.FLOAT64
+    BOOL8 = <underlying_type_t_type_id> libcudf_types.type_id.BOOL8
+    TIMESTAMP_DAYS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.TIMESTAMP_DAYS
+    )
+    TIMESTAMP_SECONDS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.TIMESTAMP_SECONDS
+    )
+    TIMESTAMP_MILLISECONDS = (
+        <underlying_type_t_type_id> (
+            libcudf_types.type_id.TIMESTAMP_MILLISECONDS
+        )
+    )
+    TIMESTAMP_MICROSECONDS = (
+        <underlying_type_t_type_id> (
+            libcudf_types.type_id.TIMESTAMP_MICROSECONDS
+        )
+    )
+    TIMESTAMP_NANOSECONDS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.TIMESTAMP_NANOSECONDS
+    )
+    DURATION_SECONDS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.DURATION_SECONDS
+    )
+    DURATION_MILLISECONDS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.DURATION_MILLISECONDS
+    )
+    DURATION_MICROSECONDS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.DURATION_MICROSECONDS
+    )
+    DURATION_NANOSECONDS = (
+        <underlying_type_t_type_id> libcudf_types.type_id.DURATION_NANOSECONDS
+    )
+    STRING = <underlying_type_t_type_id> libcudf_types.type_id.STRING
+    DECIMAL32 = <underlying_type_t_type_id> libcudf_types.type_id.DECIMAL32
+    DECIMAL64 = <underlying_type_t_type_id> libcudf_types.type_id.DECIMAL64
+    DECIMAL128 = <underlying_type_t_type_id> libcudf_types.type_id.DECIMAL128
+    STRUCT = <underlying_type_t_type_id> libcudf_types.type_id.STRUCT
+
+
+SUPPORTED_NUMPY_TO_LIBCUDF_TYPES = {
+    np.dtype("int8"): TypeId.INT8,
+    np.dtype("int16"): TypeId.INT16,
+    np.dtype("int32"): TypeId.INT32,
+    np.dtype("int64"): TypeId.INT64,
+    np.dtype("uint8"): TypeId.UINT8,
+    np.dtype("uint16"): TypeId.UINT16,
+    np.dtype("uint32"): TypeId.UINT32,
+    np.dtype("uint64"): TypeId.UINT64,
+    np.dtype("float32"): TypeId.FLOAT32,
+    np.dtype("float64"): TypeId.FLOAT64,
+    np.dtype("datetime64[s]"): TypeId.TIMESTAMP_SECONDS,
+    np.dtype("datetime64[ms]"): TypeId.TIMESTAMP_MILLISECONDS,
+    np.dtype("datetime64[us]"): TypeId.TIMESTAMP_MICROSECONDS,
+    np.dtype("datetime64[ns]"): TypeId.TIMESTAMP_NANOSECONDS,
+    np.dtype("object"): TypeId.STRING,
+    np.dtype("bool"): TypeId.BOOL8,
+    np.dtype("timedelta64[s]"): TypeId.DURATION_SECONDS,
+    np.dtype("timedelta64[ms]"): TypeId.DURATION_MILLISECONDS,
+    np.dtype("timedelta64[us]"): TypeId.DURATION_MICROSECONDS,
+    np.dtype("timedelta64[ns]"): TypeId.DURATION_NANOSECONDS,
+}
+
+SUPPORTED_NUMPY_TO_PYLIBCUDF_TYPES = {
+    k: pylibcudf.TypeId(v).value
+    for k, v in SUPPORTED_NUMPY_TO_LIBCUDF_TYPES.items()
+}
+
+LIBCUDF_TO_SUPPORTED_NUMPY_TYPES = {
+    # There's no equivalent to EMPTY in cudf.  We translate EMPTY
+    # columns from libcudf to ``int8`` columns of all nulls in Python.
+    # ``int8`` is chosen because it uses the least amount of memory.
+    TypeId.EMPTY: np.dtype("int8"),
+    TypeId.INT8: np.dtype("int8"),
+    TypeId.INT16: np.dtype("int16"),
+    TypeId.INT32: np.dtype("int32"),
+    TypeId.INT64: np.dtype("int64"),
+    TypeId.UINT8: np.dtype("uint8"),
+    TypeId.UINT16: np.dtype("uint16"),
+    TypeId.UINT32: np.dtype("uint32"),
+    TypeId.UINT64: np.dtype("uint64"),
+    TypeId.FLOAT32: np.dtype("float32"),
+    TypeId.FLOAT64: np.dtype("float64"),
+    TypeId.BOOL8: np.dtype("bool"),
+    TypeId.TIMESTAMP_SECONDS: np.dtype("datetime64[s]"),
+    TypeId.TIMESTAMP_MILLISECONDS: np.dtype("datetime64[ms]"),
+    TypeId.TIMESTAMP_MICROSECONDS: np.dtype("datetime64[us]"),
+    TypeId.TIMESTAMP_NANOSECONDS: np.dtype("datetime64[ns]"),
+    TypeId.DURATION_SECONDS: np.dtype("timedelta64[s]"),
+    TypeId.DURATION_MILLISECONDS: np.dtype("timedelta64[ms]"),
+    TypeId.DURATION_MICROSECONDS: np.dtype("timedelta64[us]"),
+    TypeId.DURATION_NANOSECONDS: np.dtype("timedelta64[ns]"),
+    TypeId.STRING: np.dtype("object"),
+    TypeId.STRUCT: np.dtype("object"),
+}
+
+PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES = {
+    pylibcudf.TypeId(k).value: v
+    for k, v in LIBCUDF_TO_SUPPORTED_NUMPY_TYPES.items()
+}
+
+duration_unit_map = {
+    TypeId.DURATION_SECONDS: "s",
+    TypeId.DURATION_MILLISECONDS: "ms",
+    TypeId.DURATION_MICROSECONDS: "us",
+    TypeId.DURATION_NANOSECONDS: "ns"
+}
+
+datetime_unit_map = {
+    TypeId.TIMESTAMP_SECONDS: "s",
+    TypeId.TIMESTAMP_MILLISECONDS: "ms",
+    TypeId.TIMESTAMP_MICROSECONDS: "us",
+    TypeId.TIMESTAMP_NANOSECONDS: "ns",
+}
+
+
+class Interpolation(IntEnum):
+    LINEAR = (
+        <underlying_type_t_interpolation> libcudf_types.interpolation.LINEAR
+    )
+    LOWER = (
+        <underlying_type_t_interpolation> libcudf_types.interpolation.LOWER
+    )
+    HIGHER = (
+        <underlying_type_t_interpolation> libcudf_types.interpolation.HIGHER
+    )
+    MIDPOINT = (
+        <underlying_type_t_interpolation> libcudf_types.interpolation.MIDPOINT
+    )
+    NEAREST = (
+        <underlying_type_t_interpolation> libcudf_types.interpolation.NEAREST
+    )
+
+
+class Order(IntEnum):
+    ASCENDING = <underlying_type_t_order> libcudf_types.order.ASCENDING
+    DESCENDING = <underlying_type_t_order> libcudf_types.order.DESCENDING
+
+
+class Sorted(IntEnum):
+    YES = <underlying_type_t_sorted> libcudf_types.sorted.YES
+    NO = <underlying_type_t_sorted> libcudf_types.sorted.NO
+
+
+class NullOrder(IntEnum):
+    BEFORE = <underlying_type_t_order> libcudf_types.null_order.BEFORE
+    AFTER = <underlying_type_t_order> libcudf_types.null_order.AFTER
+
+
+class NullHandling(IntEnum):
+    INCLUDE = <underlying_type_t_null_policy> libcudf_types.null_policy.INCLUDE
+    EXCLUDE = <underlying_type_t_null_policy> libcudf_types.null_policy.EXCLUDE
+
+
+cdef dtype_from_lists_column_view(column_view cv):
+    # lists_column_view have no default constructor, so we heap
+    # allocate it to get around Cython's limitation of requiring
+    # default constructors for stack allocated objects
+    cdef shared_ptr[lists_column_view] lv = make_shared[lists_column_view](cv)
+    cdef column_view child = lv.get()[0].child()
+
+    if child.type().id() == libcudf_types.type_id.LIST:
+        return cudf.ListDtype(dtype_from_lists_column_view(child))
+    elif child.type().id() == libcudf_types.type_id.EMPTY:
+        return cudf.ListDtype("int8")
+    else:
+        return cudf.ListDtype(
+            dtype_from_column_view(child)
+        )
+
+cdef dtype_from_structs_column_view(column_view cv):
+    fields = {
+        str(i): dtype_from_column_view(cv.child(i))
+        for i in range(cv.num_children())
+    }
+    return cudf.StructDtype(fields)
+
+cdef dtype_from_column_view(column_view cv):
+    cdef libcudf_types.type_id tid = cv.type().id()
+    if tid == libcudf_types.type_id.LIST:
+        return dtype_from_lists_column_view(cv)
+    elif tid == libcudf_types.type_id.STRUCT:
+        return dtype_from_structs_column_view(cv)
+    elif tid == libcudf_types.type_id.DECIMAL64:
+        return cudf.Decimal64Dtype(
+            precision=cudf.Decimal64Dtype.MAX_PRECISION,
+            scale=-cv.type().scale()
+        )
+    elif tid == libcudf_types.type_id.DECIMAL32:
+        return cudf.Decimal32Dtype(
+            precision=cudf.Decimal32Dtype.MAX_PRECISION,
+            scale=-cv.type().scale()
+        )
+    elif tid == libcudf_types.type_id.DECIMAL128:
+        return cudf.Decimal128Dtype(
+            precision=cudf.Decimal128Dtype.MAX_PRECISION,
+            scale=-cv.type().scale()
+        )
+    else:
+        return LIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
+            <underlying_type_t_type_id>(tid)
+        ]
+
+cdef libcudf_types.data_type dtype_to_data_type(dtype) except *:
+    cdef libcudf_types.type_id tid
+    if cudf.api.types.is_list_dtype(dtype):
+        tid = libcudf_types.type_id.LIST
+    elif cudf.api.types.is_struct_dtype(dtype):
+        tid = libcudf_types.type_id.STRUCT
+    elif cudf.api.types.is_decimal128_dtype(dtype):
+        tid = libcudf_types.type_id.DECIMAL128
+    elif cudf.api.types.is_decimal64_dtype(dtype):
+        tid = libcudf_types.type_id.DECIMAL64
+    elif cudf.api.types.is_decimal32_dtype(dtype):
+        tid = libcudf_types.type_id.DECIMAL32
+    else:
+        tid = <libcudf_types.type_id> (
+            <underlying_type_t_type_id> (
+                SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[np.dtype(dtype)]))
+
+    if is_decimal_type_id(tid):
+        return libcudf_types.data_type(tid, -dtype.scale)
+    else:
+        return libcudf_types.data_type(tid)
+
+cpdef dtype_to_pylibcudf_type(dtype):
+    if cudf.api.types.is_list_dtype(dtype):
+        return pylibcudf.DataType(pylibcudf.TypeId.LIST)
+    elif cudf.api.types.is_struct_dtype(dtype):
+        return pylibcudf.DataType(pylibcudf.TypeId.STRUCT)
+    elif cudf.api.types.is_decimal_dtype(dtype):
+        if cudf.api.types.is_decimal128_dtype(dtype):
+            tid = pylibcudf.TypeId.DECIMAL128
+        elif cudf.api.types.is_decimal64_dtype(dtype):
+            tid = pylibcudf.TypeId.DECIMAL64
+        else:
+            tid = pylibcudf.TypeId.DECIMAL32
+        return pylibcudf.DataType(tid, -dtype.scale)
+    return pylibcudf.DataType(
+        SUPPORTED_NUMPY_TO_PYLIBCUDF_TYPES[np.dtype(dtype)]
+    )
+
+cdef bool is_decimal_type_id(libcudf_types.type_id tid) except *:
+    return tid in (
+        libcudf_types.type_id.DECIMAL128,
+        libcudf_types.type_id.DECIMAL64,
+        libcudf_types.type_id.DECIMAL32,
+    )
+
+
+def dtype_from_pylibcudf_lists_column(col):
+    child = col.list_view().child()
+    tid = child.type().id()
+
+    if tid == pylibcudf.TypeId.LIST:
+        return cudf.ListDtype(dtype_from_pylibcudf_lists_column(child))
+    elif tid == pylibcudf.TypeId.EMPTY:
+        return cudf.ListDtype("int8")
+    else:
+        return cudf.ListDtype(
+            dtype_from_pylibcudf_column(child)
+        )
+
+
+def dtype_from_pylibcudf_structs_column(col):
+    fields = {
+        str(i): dtype_from_pylibcudf_column(col.child(i))
+        for i in range(col.num_children())
+    }
+    return cudf.StructDtype(fields)
+
+
+def dtype_from_pylibcudf_column(col):
+    type_ = col.type()
+    tid = type_.id()
+
+    if tid == pylibcudf.TypeId.LIST:
+        return dtype_from_pylibcudf_lists_column(col)
+    elif tid == pylibcudf.TypeId.STRUCT:
+        return dtype_from_pylibcudf_structs_column(col)
+    elif tid == pylibcudf.TypeId.DECIMAL64:
+        return cudf.Decimal64Dtype(
+            precision=cudf.Decimal64Dtype.MAX_PRECISION,
+            scale=-type_.scale()
+        )
+    elif tid == pylibcudf.TypeId.DECIMAL32:
+        return cudf.Decimal32Dtype(
+            precision=cudf.Decimal32Dtype.MAX_PRECISION,
+            scale=-type_.scale()
+        )
+    elif tid == pylibcudf.TypeId.DECIMAL128:
+        return cudf.Decimal128Dtype(
+            precision=cudf.Decimal128Dtype.MAX_PRECISION,
+            scale=-type_.scale()
+        )
+    else:
+        return PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
+            <underlying_type_t_type_id>(tid)
+        ]
diff --git a/python/cudf/cudf/_lib/unary.pyx b/python/cudf/cudf/_lib/unary.pyx
new file mode 100644
index 0000000..7ef4d00
--- /dev/null
+++ b/python/cudf/cudf/_lib/unary.pyx
@@ -0,0 +1,122 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+from enum import IntEnum
+
+from cudf.api.types import is_decimal_dtype
+from cudf.core.buffer import acquire_spill_lock
+
+from libcpp.memory cimport unique_ptr
+from libcpp.utility cimport move
+
+import numpy as np
+
+cimport cudf._lib.cpp.unary as libcudf_unary
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column
+from cudf._lib.cpp.column.column_view cimport column_view
+from cudf._lib.cpp.types cimport data_type
+from cudf._lib.cpp.unary cimport unary_operator, underlying_type_t_unary_op
+from cudf._lib.types cimport dtype_to_data_type
+
+
+class UnaryOp(IntEnum):
+    SIN = <underlying_type_t_unary_op> unary_operator.SIN
+    COS = <underlying_type_t_unary_op> unary_operator.COS
+    TAN = <underlying_type_t_unary_op> unary_operator.TAN
+    ASIN = <underlying_type_t_unary_op> unary_operator.ARCSIN
+    ACOS = <underlying_type_t_unary_op> unary_operator.ARCCOS
+    ATAN = <underlying_type_t_unary_op> unary_operator.ARCTAN
+    SINH = <underlying_type_t_unary_op> unary_operator.SINH
+    COSH = <underlying_type_t_unary_op> unary_operator.COSH
+    TANH = <underlying_type_t_unary_op> unary_operator.TANH
+    ARCSINH = <underlying_type_t_unary_op> unary_operator.ARCSINH
+    ARCCOSH = <underlying_type_t_unary_op> unary_operator.ARCCOSH
+    ARCTANH = <underlying_type_t_unary_op> unary_operator.ARCTANH
+    EXP = <underlying_type_t_unary_op> unary_operator.EXP
+    LOG = <underlying_type_t_unary_op> unary_operator.LOG
+    SQRT = <underlying_type_t_unary_op> unary_operator.SQRT
+    CBRT = <underlying_type_t_unary_op> unary_operator.CBRT
+    CEIL = <underlying_type_t_unary_op> unary_operator.CEIL
+    FLOOR = <underlying_type_t_unary_op> unary_operator.FLOOR
+    ABS = <underlying_type_t_unary_op> unary_operator.ABS
+    RINT = <underlying_type_t_unary_op> unary_operator.RINT
+    INVERT = <underlying_type_t_unary_op> unary_operator.BIT_INVERT
+    NOT = <underlying_type_t_unary_op> unary_operator.NOT
+
+
+@acquire_spill_lock()
+def unary_operation(Column input, object op):
+    cdef column_view c_input = input.view()
+    cdef unary_operator c_op = <unary_operator>(<underlying_type_t_unary_op>
+                                                op)
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(
+            libcudf_unary.unary_operation(
+                c_input,
+                c_op
+            )
+        )
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_null(Column input):
+    cdef column_view c_input = input.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(libcudf_unary.is_null(c_input))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_valid(Column input):
+    cdef column_view c_input = input.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(libcudf_unary.is_valid(c_input))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def cast(Column input, object dtype=np.float64):
+    cdef column_view c_input = input.view()
+    cdef data_type c_dtype = dtype_to_data_type(dtype)
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(libcudf_unary.cast(c_input, c_dtype))
+
+    result = Column.from_unique_ptr(move(c_result))
+    if is_decimal_dtype(result.dtype):
+        result.dtype.precision = dtype.precision
+    return result
+
+
+@acquire_spill_lock()
+def is_nan(Column input):
+    cdef column_view c_input = input.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(libcudf_unary.is_nan(c_input))
+
+    return Column.from_unique_ptr(move(c_result))
+
+
+@acquire_spill_lock()
+def is_non_nan(Column input):
+    cdef column_view c_input = input.view()
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = move(libcudf_unary.is_not_nan(c_input))
+
+    return Column.from_unique_ptr(move(c_result))
diff --git a/python/cudf/cudf/_lib/utils.pxd b/python/cudf/cudf/_lib/utils.pxd
new file mode 100644
index 0000000..653fa8f
--- /dev/null
+++ b/python/cudf/cudf/_lib/utils.pxd
@@ -0,0 +1,21 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.column.column cimport column_view
+from cudf._lib.cpp.table.table cimport table, table_view
+
+
+cdef vector[column_view] make_column_views(object columns) except*
+cdef vector[string] get_column_names(object table, object index) except*
+cdef data_from_unique_ptr(
+    unique_ptr[table] c_tbl, column_names, index_names=*)
+cdef data_from_table_view(
+    table_view tv, object owner, object column_names, object index_names=*)
+cdef table_view table_view_from_columns(columns) except *
+cdef table_view table_view_from_table(tbl, ignore_index=*) except*
+cdef columns_from_unique_ptr(unique_ptr[table] c_tbl)
+cdef columns_from_table_view(table_view tv, object owners)
+cdef columns_from_pylibcudf_table(tbl)
diff --git a/python/cudf/cudf/_lib/utils.pyx b/python/cudf/cudf/_lib/utils.pyx
new file mode 100644
index 0000000..03982a5
--- /dev/null
+++ b/python/cudf/cudf/_lib/utils.pyx
@@ -0,0 +1,397 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pyarrow as pa
+
+import cudf
+
+from cython.operator cimport dereference
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+from libcpp.vector cimport vector
+
+from cudf._lib.column cimport Column
+from cudf._lib.cpp.column.column cimport column, column_view
+from cudf._lib.cpp.table.table cimport table
+from cudf._lib.cpp.table.table_view cimport table_view
+from cudf._lib.cpp.types cimport size_type
+
+try:
+    import ujson as json
+except ImportError:
+    import json
+
+from cudf.api.types import (
+    is_categorical_dtype,
+    is_decimal_dtype,
+    is_list_dtype,
+    is_struct_dtype,
+)
+from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes, np_to_pa_dtype
+
+PARQUET_META_TYPE_MAP = {
+    str(cudf_dtype): str(pandas_dtype)
+    for cudf_dtype, pandas_dtype in np_dtypes_to_pandas_dtypes.items()
+}
+
+cdef table_view table_view_from_columns(columns) except*:
+    """Create a cudf::table_view from an iterable of Columns."""
+    cdef vector[column_view] column_views
+
+    cdef Column col
+    for col in columns:
+        column_views.push_back(col.view())
+
+    return table_view(column_views)
+
+
+cdef table_view table_view_from_table(tbl, ignore_index=False) except*:
+    """Create a cudf::table_view from a Table.
+
+    Parameters
+    ----------
+    ignore_index : bool, default False
+        If True, don't include the index in the columns.
+    """
+    return table_view_from_columns(
+        tbl._index._data.columns + tbl._data.columns
+        if not ignore_index and tbl._index is not None
+        else tbl._data.columns
+    )
+cdef vector[column_view] make_column_views(object columns):
+    cdef vector[column_view] views
+    views.reserve(len(columns))
+    for col in columns:
+        views.push_back((<Column> col).view())
+    return views
+
+
+cdef vector[string] get_column_names(object tbl, object index):
+    cdef vector[string] column_names
+    if index is not False:
+        if isinstance(tbl._index, cudf.core.multiindex.MultiIndex):
+            for idx_name in tbl._index.names:
+                column_names.push_back(str.encode(idx_name))
+        else:
+            if tbl._index.name is not None:
+                column_names.push_back(str.encode(tbl._index.name))
+
+    for col_name in tbl._column_names:
+        column_names.push_back(str.encode(col_name))
+
+    return column_names
+
+
+cpdef generate_pandas_metadata(table, index):
+    col_names = []
+    types = []
+    index_levels = []
+    index_descriptors = []
+
+    # Columns
+    for name, col in table._data.items():
+        col_names.append(name)
+        if is_categorical_dtype(col):
+            raise ValueError(
+                "'category' column dtypes are currently not "
+                + "supported by the gpu accelerated parquet writer"
+            )
+        elif (
+            is_list_dtype(col)
+            or is_struct_dtype(col)
+            or is_decimal_dtype(col)
+        ):
+            types.append(col.dtype.to_arrow())
+        else:
+            # A boolean element takes 8 bits in cudf and 1 bit in
+            # pyarrow. To make sure the cudf format is interperable
+            # in arrow, we use `int8` type when converting from a
+            # cudf boolean array.
+            if col.dtype.type == np.bool_:
+                types.append(pa.int8())
+            else:
+                types.append(np_to_pa_dtype(col.dtype))
+
+    # Indexes
+    if index is not False:
+        for level, name in enumerate(table._index.names):
+            if isinstance(table._index, cudf.core.multiindex.MultiIndex):
+                idx = table.index.get_level_values(level)
+            else:
+                idx = table.index
+
+            if isinstance(idx, cudf.core.index.RangeIndex):
+                if index is None:
+                    descr = {
+                        "kind": "range",
+                        "name": table.index.name,
+                        "start": table.index.start,
+                        "stop": table.index.stop,
+                        "step": table.index.step,
+                    }
+                else:
+                    # When `index=True`, RangeIndex needs to be materialized.
+                    materialized_idx = cudf.Index(idx._values, name=idx.name)
+                    descr = \
+                        _index_level_name(
+                            index_name=materialized_idx.name,
+                            level=level,
+                            column_names=col_names
+                        )
+                    index_levels.append(materialized_idx)
+            else:
+                descr = \
+                    _index_level_name(
+                        index_name=idx.name,
+                        level=level,
+                        column_names=col_names
+                    )
+                if is_categorical_dtype(idx):
+                    raise ValueError(
+                        "'category' column dtypes are currently not "
+                        + "supported by the gpu accelerated parquet writer"
+                    )
+                elif is_list_dtype(idx):
+                    types.append(col.dtype.to_arrow())
+                else:
+                    # A boolean element takes 8 bits in cudf and 1 bit in
+                    # pyarrow. To make sure the cudf format is interperable
+                    # in arrow, we use `int8` type when converting from a
+                    # cudf boolean array.
+                    if idx.dtype.type == np.bool_:
+                        types.append(pa.int8())
+                    else:
+                        types.append(np_to_pa_dtype(idx.dtype))
+
+                index_levels.append(idx)
+            col_names.append(name)
+            index_descriptors.append(descr)
+
+    metadata = pa.pandas_compat.construct_metadata(
+        columns_to_convert=[
+            col
+            for col in table._columns
+        ],
+        df=table,
+        column_names=col_names,
+        index_levels=index_levels,
+        index_descriptors=index_descriptors,
+        preserve_index=index,
+        types=types,
+    )
+
+    md_dict = json.loads(metadata[b"pandas"])
+
+    # correct metadata for list and struct and nullable numeric types
+    for col_meta in md_dict["columns"]:
+        if (
+            col_meta["name"] in table._column_names
+            and table._data[col_meta["name"]].nullable
+            and col_meta["numpy_type"] in PARQUET_META_TYPE_MAP
+            and col_meta["pandas_type"] != "decimal"
+        ):
+            col_meta["numpy_type"] = PARQUET_META_TYPE_MAP[
+                col_meta["numpy_type"]
+            ]
+        if col_meta["numpy_type"] in ("list", "struct"):
+            col_meta["numpy_type"] = "object"
+
+    return json.dumps(md_dict)
+
+
+def _index_level_name(index_name, level, column_names):
+    """
+    Return the name of an index level or a default name
+    if `index_name` is None or is already a column name.
+
+    Parameters
+    ----------
+    index_name : name of an Index object
+    level : level of the Index object
+
+    Returns
+    -------
+    name : str
+    """
+    if index_name is not None and index_name not in column_names:
+        return index_name
+    else:
+        return f"__index_level_{level}__"
+
+
+cdef columns_from_unique_ptr(
+    unique_ptr[table] c_tbl
+):
+    """Convert a libcudf table into list of columns.
+
+    Parameters
+    ----------
+    c_tbl : unique_ptr[cudf::table]
+        The libcudf table whose columns will be extracted
+
+    Returns
+    -------
+    list[Column]
+        A list of columns.
+    """
+    cdef vector[unique_ptr[column]] c_columns = move(c_tbl.get().release())
+    cdef vector[unique_ptr[column]].iterator it = c_columns.begin()
+
+    cdef size_t i
+
+    columns = [Column.from_unique_ptr(move(dereference(it+i)))
+               for i in range(c_columns.size())]
+
+    return columns
+
+
+cdef columns_from_pylibcudf_table(tbl):
+    """Convert a pylibcudf table into list of columns.
+
+    Parameters
+    ----------
+    tbl : pylibcudf.Table
+        The pylibcudf table whose columns will be extracted
+
+    Returns
+    -------
+    list[Column]
+        A list of columns.
+    """
+    return [Column.from_pylibcudf(plc) for plc in tbl.columns()]
+
+
+cdef data_from_unique_ptr(
+    unique_ptr[table] c_tbl, column_names, index_names=None
+):
+    """Convert a libcudf table into a dict with an index.
+
+    This method is intended to provide the bridge between the columns returned
+    from calls to libcudf APIs and the cuDF Python Frame objects, which require
+    named columns and a separate index.
+
+    Since cuDF Python has an independent representation of a table as a
+    collection of columns, this function simply returns a dict of columns
+    suitable for conversion into data to be passed to cuDF constructors.
+    This method returns the columns of the table in the order they are
+    stored in libcudf, but calling code is responsible for partitioning and
+    labeling them as needed.
+
+    Parameters
+    ----------
+    c_tbl : unique_ptr[cudf::table]
+        The libcudf table whose columns will be extracted
+    column_names : iterable
+        The keys associated with the columns in the output data.
+    index_names : iterable, optional
+        If provided, an iterable of strings that will be used to label the
+        corresponding first set of columns into a (Multi)Index. If this
+        argument is omitted, all columns are assumed to be part of the output
+        table and no index is constructed.
+
+
+    Returns
+    -------
+    tuple(Dict[str, Column], Optional[Index])
+        A dict of the columns in the output table.
+    """
+
+    columns = columns_from_unique_ptr(move(c_tbl))
+
+    # First construct the index, if any
+    index = (
+        # TODO: For performance, the _from_data methods of Frame types assume
+        # that the passed index object is already an Index because cudf.Index
+        # and cudf.as_index are expensive. As a result, this function is
+        # currently somewhat inconsistent in returning a dict of columns for
+        # the data while actually constructing the Index object here (instead
+        # of just returning a dict for that as well). As we clean up the
+        # Frame factories we may want to look for a less dissonant approach
+        # that does not impose performance penalties. The same applies to
+        # data_from_table_view below.
+        cudf.core.index._index_from_data(
+            {
+                name: columns[i]
+                for i, name in enumerate(index_names)
+            }
+        )
+        if index_names is not None
+        else None
+    )
+    n_index_columns = len(index_names) if index_names is not None else 0
+    data = {
+        name: columns[i + n_index_columns]
+        for i, name in enumerate(column_names)
+    }
+    return data, index
+
+cdef columns_from_table_view(
+    table_view tv,
+    object owners,
+):
+    """
+    Given a ``cudf::table_view``, constructs a list of columns from it,
+    along with referencing an owner Python object that owns the memory
+    lifetime. owner must be either None or a list of column. If owner
+    is a list of columns, the owner of the `i`th ``cudf::column_view``
+    in the table view is ``owners[i]``. For more about memory ownership,
+    see ``Column.from_column_view``.
+    """
+
+    return [
+        Column.from_column_view(
+            tv.column(i), owners[i] if isinstance(owners, list) else None
+        ) for i in range(tv.num_columns())
+    ]
+
+cdef data_from_table_view(
+    table_view tv,
+    object owner,
+    object column_names,
+    object index_names=None
+):
+    """
+    Given a ``cudf::table_view``, constructs a Frame from it,
+    along with referencing an ``owner`` Python object that owns the memory
+    lifetime. If ``owner`` is a Frame we reach inside of it and
+    reach inside of each ``cudf.Column`` to make the owner of each newly
+    created ``Buffer`` underneath the ``cudf.Column`` objects of the
+    created Frame the respective ``Buffer`` from the relevant
+    ``cudf.Column`` of the ``owner`` Frame
+    """
+    cdef size_type column_idx = 0
+    table_owner = isinstance(owner, cudf.core.frame.Frame)
+
+    # First construct the index, if any
+    index = None
+    if index_names is not None:
+        index_columns = []
+        for _ in index_names:
+            column_owner = owner
+            if table_owner:
+                column_owner = owner._index._columns[column_idx]
+            index_columns.append(
+                Column.from_column_view(
+                    tv.column(column_idx),
+                    column_owner
+                )
+            )
+            column_idx += 1
+        index = cudf.core.index._index_from_data(
+            dict(zip(index_names, index_columns)))
+
+    # Construct the data dict
+    cdef size_type source_column_idx = 0
+    data_columns = []
+    for _ in column_names:
+        column_owner = owner
+        if table_owner:
+            column_owner = owner._columns[source_column_idx]
+        data_columns.append(
+            Column.from_column_view(tv.column(column_idx), column_owner)
+        )
+        column_idx += 1
+        source_column_idx += 1
+
+    return dict(zip(column_names, data_columns)), index
diff --git a/python/cudf/cudf/_typing.py b/python/cudf/cudf/_typing.py
new file mode 100644
index 0000000..e2ea12a
--- /dev/null
+++ b/python/cudf/cudf/_typing.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+import sys
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, TypeVar, Union
+
+import numpy as np
+from pandas import Period, Timedelta, Timestamp
+from pandas.api.extensions import ExtensionDtype
+
+if TYPE_CHECKING:
+    import cudf
+
+# Backwards compat: mypy >= 0.790 rejects Type[NotImplemented], but
+# NotImplementedType is only introduced in 3.10
+if sys.version_info >= (3, 10):
+    from types import NotImplementedType
+else:
+    NotImplementedType = Any
+
+# Many of these are from
+# https://github.com/pandas-dev/pandas/blob/master/pandas/_typing.py
+
+Dtype = Union["ExtensionDtype", str, np.dtype]
+DtypeObj = Union["ExtensionDtype", np.dtype]
+
+# scalars
+DatetimeLikeScalar = TypeVar(
+    "DatetimeLikeScalar", Period, Timestamp, Timedelta
+)
+ScalarLike = Any
+
+# columns
+ColumnLike = Any
+
+# binary operation
+ColumnBinaryOperand = Union["cudf.Scalar", "cudf.core.column.ColumnBase"]
+
+DataFrameOrSeries = Union["cudf.Series", "cudf.DataFrame"]
+SeriesOrIndex = Union["cudf.Series", "cudf.core.index.BaseIndex"]
+SeriesOrSingleColumnIndex = Union[
+    "cudf.Series", "cudf.core.index.GenericIndex"
+]
+
+# Groupby aggregation
+AggType = Union[str, Callable]
+MultiColumnAggType = Union[
+    AggType, Iterable[AggType], Dict[Any, Iterable[AggType]]
+]
diff --git a/python/cudf/cudf/api/__init__.py b/python/cudf/cudf/api/__init__.py
new file mode 100644
index 0000000..c66bfb4
--- /dev/null
+++ b/python/cudf/cudf/api/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from cudf.api import extensions, types
+
+__all__ = ["extensions", "types"]
diff --git a/python/cudf/cudf/api/extensions/__init__.py b/python/cudf/cudf/api/extensions/__init__.py
new file mode 100644
index 0000000..6118b6b
--- /dev/null
+++ b/python/cudf/cudf/api/extensions/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from pandas.api.extensions import no_default
+
+from cudf.api.extensions.accessor import (
+    register_dataframe_accessor,
+    register_index_accessor,
+    register_series_accessor,
+)
+
+__all__ = [
+    "no_default",
+    "register_dataframe_accessor",
+    "register_index_accessor",
+    "register_series_accessor",
+]
diff --git a/python/cudf/cudf/api/extensions/accessor.py b/python/cudf/cudf/api/extensions/accessor.py
new file mode 100644
index 0000000..311b33a
--- /dev/null
+++ b/python/cudf/cudf/api/extensions/accessor.py
@@ -0,0 +1,161 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import warnings
+
+from pandas.core.accessor import CachedAccessor
+
+import cudf
+from cudf.utils.docutils import docfmt_partial
+
+_docstring_register_accessor = """
+    Extends `cudf.{klass}` with custom defined accessor
+
+    Parameters
+    ----------
+    name : str
+        The name to be registered in `{klass}` for the custom accessor
+
+    Returns
+    -------
+    decorator : callable
+        Decorator function for accessor
+
+    Notes
+    -----
+    The `{klass}` object will be passed to your custom accessor upon first
+    invocation. And will be cached for future calls.
+
+    If the data passed to your accessor is of wrong datatype, you should
+    raise an `AttributeError` in consistent with other cudf methods.
+
+
+    Examples
+    --------
+    {example}
+"""
+
+_dataframe_example = """
+    In your library code:
+
+        >>> import cudf as gd
+        >>> @gd.api.extensions.register_dataframe_accessor("point")
+        ... class PointsAccessor:
+        ...     def __init__(self, obj):
+        ...         self._validate(obj)
+        ...         self._obj = obj
+        ...     @staticmethod
+        ...     def _validate(obj):
+        ...         cols = obj.columns
+        ...         if not all([vertex in cols for vertex in ["x", "y"]]):
+        ...             raise AttributeError("Must have vertices 'x', 'y'.")
+        ...     @property
+        ...     def bounding_box(self):
+        ...         xs, ys = self._obj["x"], self._obj["y"]
+        ...         min_x, min_y = xs.min(), ys.min()
+        ...         max_x, max_y = xs.max(), ys.max()
+        ...         return (min_x, min_y, max_x, max_y)
+
+    Then in user code:
+
+        >>> df = gd.DataFrame({'x': [1,2,3,4,5,6], 'y':[7,6,5,4,3,2]})
+        >>> df.point.bounding_box
+        (1, 2, 6, 7)
+
+"""
+
+_index_example = """
+    In your library code:
+
+        >>> import cudf as gd
+        >>> @gd.api.extensions.register_index_accessor("odd")
+        ... class OddRowAccessor:
+        ...     def __init__(self, obj):
+        ...         self._obj = obj
+        ...     def __getitem__(self, i):
+        ...         return self._obj[2 * i - 1]
+
+    Then in user code:
+
+        >>> gs = gd.Index(list(range(0, 50)))
+        >>> gs.odd[1]
+        1
+        >>> gs.odd[2]
+        3
+        >>> gs.odd[3]
+        5
+
+"""
+
+_series_example = """
+    In your library code:
+
+        >>> import cudf as gd
+        >>> @gd.api.extensions.register_series_accessor("odd")
+        ... class OddRowAccessor:
+        ...     def __init__(self, obj):
+        ...         self._obj = obj
+        ...     def __getitem__(self, i):
+        ...         return self._obj[2 * i - 1]
+
+    Then in user code:
+
+        >>> gs = gd.Series(list(range(0, 50)))
+        >>> gs.odd[1]
+        1
+        >>> gs.odd[2]
+        3
+        >>> gs.odd[3]
+        5
+
+"""
+
+
+doc_register_dataframe_accessor = docfmt_partial(
+    docstring=_docstring_register_accessor.format(
+        klass="DataFrame", example=_dataframe_example
+    )
+)
+
+doc_register_index_accessor = docfmt_partial(
+    docstring=_docstring_register_accessor.format(
+        klass="Index", example=_index_example
+    )
+)
+
+doc_register_series_accessor = docfmt_partial(
+    docstring=_docstring_register_accessor.format(
+        klass="Series", example=_series_example
+    )
+)
+
+
+def _register_accessor(name, cls):
+    def decorator(accessor):
+        if hasattr(cls, name):
+            msg = f"Attribute {name} will be overridden in {cls.__name__}"
+            warnings.warn(msg)
+        cached_accessor = CachedAccessor(name, accessor)
+        cls._accessors.add(name)
+        setattr(cls, name, cached_accessor)
+
+        return accessor
+
+    return decorator
+
+
+@doc_register_dataframe_accessor()
+def register_dataframe_accessor(name):
+    """{docstring}"""
+    return _register_accessor(name, cudf.DataFrame)
+
+
+@doc_register_index_accessor()
+def register_index_accessor(name):
+    """{docstring}"""
+    return _register_accessor(name, cudf.BaseIndex)
+
+
+@doc_register_series_accessor()
+def register_series_accessor(name):
+    """{docstring}"""
+    return _register_accessor(name, cudf.Series)
diff --git a/python/cudf/cudf/api/types.py b/python/cudf/cudf/api/types.py
new file mode 100644
index 0000000..399e045
--- /dev/null
+++ b/python/cudf/cudf/api/types.py
@@ -0,0 +1,506 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+"""Define common type operations."""
+
+from __future__ import annotations
+
+from collections import abc
+from functools import wraps
+from inspect import isclass
+from typing import List, Union
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+from pandas.api import types as pd_types
+
+import cudf
+from cudf.core.dtypes import (  # noqa: F401
+    _BaseDtype,
+    dtype,
+    is_categorical_dtype,
+    is_decimal32_dtype,
+    is_decimal64_dtype,
+    is_decimal128_dtype,
+    is_decimal_dtype,
+    is_interval_dtype,
+    is_list_dtype,
+    is_struct_dtype,
+)
+
+
+def is_numeric_dtype(obj):
+    """Check whether the provided array or dtype is of a numeric dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array or dtype is of a numeric dtype.
+    """
+    if isclass(obj):
+        if issubclass(obj, cudf.core.dtypes.DecimalDtype):
+            return True
+        if issubclass(obj, _BaseDtype):
+            return False
+    else:
+        if isinstance(
+            obj,
+            (cudf.Decimal128Dtype, cudf.Decimal64Dtype, cudf.Decimal32Dtype),
+        ) or isinstance(
+            getattr(obj, "dtype", None),
+            (cudf.Decimal128Dtype, cudf.Decimal64Dtype, cudf.Decimal32Dtype),
+        ):
+            return True
+        if isinstance(obj, _BaseDtype) or isinstance(
+            getattr(obj, "dtype", None), _BaseDtype
+        ):
+            return False
+    if isinstance(obj, cudf.BaseIndex):
+        return obj._is_numeric()
+    return pd_types.is_numeric_dtype(obj)
+
+
+# A version of numerical type check that does not include cudf decimals for
+# places where we need to distinguish fixed and floating point numbers.
+def _is_non_decimal_numeric_dtype(obj):
+    if isinstance(obj, _BaseDtype) or isinstance(
+        getattr(obj, "dtype", None), _BaseDtype
+    ):
+        return False
+    try:
+        return pd_types.is_numeric_dtype(obj)
+    except TypeError:
+        return False
+
+
+def is_integer(obj):
+    """Return True if given object is integer.
+
+    Returns
+    -------
+    bool
+    """
+    if isinstance(obj, cudf.Scalar):
+        return pd.api.types.is_integer_dtype(obj.dtype)
+    return pd.api.types.is_integer(obj)
+
+
+def is_string_dtype(obj):
+    """Check whether the provided array or dtype is of the string dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array or dtype is of the string dtype.
+    """
+    return (
+        pd.api.types.is_string_dtype(obj)
+        # Reject all cudf extension types.
+        and not is_categorical_dtype(obj)
+        and not is_decimal_dtype(obj)
+        and not is_list_dtype(obj)
+        and not is_struct_dtype(obj)
+        and not is_interval_dtype(obj)
+    )
+
+
+def is_scalar(val):
+    """Return True if given object is scalar.
+
+    Parameters
+    ----------
+    val : object
+        Possibly scalar object.
+
+    Returns
+    -------
+    bool
+        Return True if given object is scalar.
+    """
+    return isinstance(
+        val,
+        (
+            cudf.Scalar,
+            cudf._lib.scalar.DeviceScalar,
+            cudf.core.tools.datetimes.DateOffset,
+        ),
+    ) or pd_types.is_scalar(val)
+
+
+def _is_scalar_or_zero_d_array(val):
+    """Return True if given object is scalar or a 0d array.
+
+    This is an internal function primarily used by indexing applications that
+    need to flatten dimensions that are indexed by 0d arrays.
+
+    Parameters
+    ----------
+    val : object
+        Possibly scalar object.
+
+    Returns
+    -------
+    bool
+        Return True if given object is scalar.
+    """
+    return (
+        isinstance(val, (np.ndarray, cp.ndarray)) and val.ndim == 0
+    ) or is_scalar(val)
+
+
+# TODO: We should be able to reuse the pandas function for this, need to figure
+# out why we can't.
+def is_list_like(obj):
+    """Return `True` if the given `obj` is list-like (list, tuple, Series...).
+
+    Parameters
+    ----------
+    obj : object of any type which needs to be validated.
+
+    Returns
+    -------
+    bool
+        Return True if given object is list-like.
+    """
+    return isinstance(obj, (abc.Sequence, np.ndarray)) and not isinstance(
+        obj, (str, bytes)
+    )
+
+
+# These methods are aliased directly into this namespace, but can be modified
+# later if we determine that there is a need.
+
+
+def _wrap_pandas_is_dtype_api(func):
+    """Wrap a pandas dtype checking function to ignore cudf types."""
+
+    @wraps(func)
+    def wrapped_func(obj):
+        if (
+            (isclass(obj) and issubclass(obj, _BaseDtype))
+            or isinstance(obj, _BaseDtype)
+            or isinstance(getattr(obj, "dtype", None), _BaseDtype)
+        ):
+            return False
+        return func(obj)
+
+    return wrapped_func
+
+
+def _union_categoricals(
+    to_union: List[Union[cudf.Series, cudf.CategoricalIndex]],
+    sort_categories: bool = False,
+    ignore_order: bool = False,
+):
+    """Combine categorical data.
+
+    This API is currently internal but should be exposed once full support for
+    cudf.Categorical is ready.
+    """
+    # TODO(s) in the order specified :
+    # 1. The return type needs to be changed
+    #    to cudf.Categorical once it is implemented.
+    # 2. Make this API public (i.e., to resemble
+    #    pd.api.types.union_categoricals)
+
+    if ignore_order:
+        raise TypeError("ignore_order is not yet implemented")
+
+    result_col = cudf.core.column.CategoricalColumn._concat(
+        [obj._column for obj in to_union]
+    )
+    if sort_categories:
+        sorted_categories = result_col.categories.sort_values(ascending=True)
+        result_col = result_col.reorder_categories(
+            new_categories=sorted_categories
+        )
+
+    return cudf.Index(result_col)
+
+
+def is_bool_dtype(arr_or_dtype):
+    """
+    Check whether the provided array or dtype is of a boolean dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a boolean dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_bool_dtype
+    >>> import numpy as np
+    >>> import cudf
+    >>> is_bool_dtype(str)
+    False
+    >>> is_bool_dtype(int)
+    False
+    >>> is_bool_dtype(bool)
+    True
+    >>> is_bool_dtype(np.bool_)
+    True
+    >>> is_bool_dtype(np.array(['a', 'b']))
+    False
+    >>> is_bool_dtype(cudf.Series([1, 2]))
+    False
+    >>> is_bool_dtype(np.array([True, False]))
+    True
+    >>> is_bool_dtype(cudf.Series([True, False], dtype='category'))
+    True
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_boolean()
+    elif isinstance(arr_or_dtype, cudf.Series):
+        if isinstance(arr_or_dtype.dtype, cudf.CategoricalDtype):
+            return is_bool_dtype(arr_or_dtype=arr_or_dtype.dtype)
+        else:
+            return pd_types.is_bool_dtype(arr_or_dtype=arr_or_dtype.dtype)
+    elif isinstance(arr_or_dtype, cudf.CategoricalDtype):
+        return pd_types.is_bool_dtype(
+            arr_or_dtype=arr_or_dtype.categories.dtype
+        )
+    else:
+        return pd_types.is_bool_dtype(arr_or_dtype=arr_or_dtype)
+
+
+def is_object_dtype(arr_or_dtype):
+    """
+    Check whether an array-like or dtype is of the object dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the object dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_object_dtype
+    >>> import numpy as np
+    >>> is_object_dtype(object)
+    True
+    >>> is_object_dtype(int)
+    False
+    >>> is_object_dtype(np.array([], dtype=object))
+    True
+    >>> is_object_dtype(np.array([], dtype=int))
+    False
+    >>> is_object_dtype([1, 2, 3])
+    False
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_object()
+    elif isinstance(arr_or_dtype, cudf.Series):
+        return pd_types.is_object_dtype(arr_or_dtype=arr_or_dtype.dtype)
+    else:
+        return pd_types.is_object_dtype(arr_or_dtype=arr_or_dtype)
+
+
+def is_float_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a float dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a float dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_float_dtype
+    >>> import numpy as np
+    >>> import cudf
+    >>> is_float_dtype(str)
+    False
+    >>> is_float_dtype(int)
+    False
+    >>> is_float_dtype(float)
+    True
+    >>> is_float_dtype(np.array(['a', 'b']))
+    False
+    >>> is_float_dtype(cudf.Series([1, 2]))
+    False
+    >>> is_float_dtype(cudf.Index([1, 2.]))
+    True
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_floating()
+    return _wrap_pandas_is_dtype_api(pd_types.is_float_dtype)(arr_or_dtype)
+
+
+def is_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of an integer dtype.
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of an integer dtype and
+        not an instance of timedelta64.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_integer_dtype
+    >>> import numpy as np
+    >>> import cudf
+    >>> is_integer_dtype(str)
+    False
+    >>> is_integer_dtype(int)
+    True
+    >>> is_integer_dtype(float)
+    False
+    >>> is_integer_dtype(np.uint64)
+    True
+    >>> is_integer_dtype('int8')
+    True
+    >>> is_integer_dtype('Int8')
+    True
+    >>> is_integer_dtype(np.datetime64)
+    False
+    >>> is_integer_dtype(np.timedelta64)
+    False
+    >>> is_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_integer_dtype(cudf.Series([1, 2]))
+    True
+    >>> is_integer_dtype(np.array([], dtype=np.timedelta64))
+    False
+    >>> is_integer_dtype(cudf.Index([1, 2.]))  # float
+    False
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_integer()
+    return _wrap_pandas_is_dtype_api(pd_types.is_integer_dtype)(arr_or_dtype)
+
+
+def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a real number dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a real number dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_any_real_numeric_dtype
+    >>> import cudf
+    >>> is_any_real_numeric_dtype(int)
+    True
+    >>> is_any_real_numeric_dtype(float)
+    True
+    >>> is_any_real_numeric_dtype(object)
+    False
+    >>> is_any_real_numeric_dtype(str)
+    False
+    >>> is_any_real_numeric_dtype(complex(1, 2))
+    False
+    >>> is_any_real_numeric_dtype(bool)
+    False
+    >>> is_any_real_numeric_dtype(cudf.Index([1, 2, 3]))
+    True
+    """
+    return (
+        is_numeric_dtype(arr_or_dtype)
+        and not is_complex_dtype(arr_or_dtype)
+        and not is_bool_dtype(arr_or_dtype)
+    )
+
+
+def _is_pandas_nullable_extension_dtype(dtype_to_check):
+    if isinstance(
+        dtype_to_check, pd.api.extensions.ExtensionDtype
+    ) and not isinstance(dtype_to_check, pd.core.dtypes.dtypes.PandasDtype):
+        if isinstance(dtype_to_check, pd.CategoricalDtype):
+            return _is_pandas_nullable_extension_dtype(
+                dtype_to_check.categories.dtype
+            )
+        return True
+    return False
+
+
+# TODO: The below alias is removed for now since improving cudf categorical
+# support is ongoing and we don't want to introduce any ambiguities. The above
+# method _union_categoricals will take its place once exposed.
+# union_categoricals = pd_types.union_categoricals
+infer_dtype = pd_types.infer_dtype
+pandas_dtype = pd_types.pandas_dtype
+is_complex_dtype = pd_types.is_complex_dtype
+# TODO: Evaluate which of the datetime types need special handling for cudf.
+is_datetime_dtype = _wrap_pandas_is_dtype_api(pd_types.is_datetime64_dtype)
+is_datetime64_any_dtype = pd_types.is_datetime64_any_dtype
+is_datetime64_dtype = _wrap_pandas_is_dtype_api(pd_types.is_datetime64_dtype)
+is_datetime64_ns_dtype = _wrap_pandas_is_dtype_api(
+    pd_types.is_datetime64_ns_dtype
+)
+is_datetime64tz_dtype = _wrap_pandas_is_dtype_api(
+    pd_types.is_datetime64tz_dtype
+)
+is_extension_type = pd_types.is_extension_type
+is_extension_array_dtype = pd_types.is_extension_array_dtype
+is_int64_dtype = pd_types.is_int64_dtype
+is_period_dtype = pd_types.is_period_dtype
+is_signed_integer_dtype = pd_types.is_signed_integer_dtype
+is_timedelta_dtype = _wrap_pandas_is_dtype_api(pd_types.is_timedelta64_dtype)
+is_timedelta64_dtype = _wrap_pandas_is_dtype_api(pd_types.is_timedelta64_dtype)
+is_timedelta64_ns_dtype = _wrap_pandas_is_dtype_api(
+    pd_types.is_timedelta64_ns_dtype
+)
+is_unsigned_integer_dtype = pd_types.is_unsigned_integer_dtype
+is_sparse = pd_types.is_sparse
+# is_list_like = pd_types.is_list_like
+is_dict_like = pd_types.is_dict_like
+is_file_like = pd_types.is_file_like
+is_named_tuple = pd_types.is_named_tuple
+is_iterator = pd_types.is_iterator
+is_bool = pd_types.is_bool
+is_categorical = pd_types.is_categorical
+is_complex = pd_types.is_complex
+is_float = pd_types.is_float
+is_hashable = pd_types.is_hashable
+is_interval = pd_types.is_interval
+is_number = pd_types.is_number
+is_re = pd_types.is_re
+is_re_compilable = pd_types.is_re_compilable
+is_dtype_equal = pd_types.is_dtype_equal
+
+
+# Aliases of numpy dtype functionality.
+issubdtype = np.issubdtype
diff --git a/python/cudf/cudf/benchmarks/README.md b/python/cudf/cudf/benchmarks/README.md
new file mode 100644
index 0000000..5486176
--- /dev/null
+++ b/python/cudf/cudf/benchmarks/README.md
@@ -0,0 +1,32 @@
+# cuDF benchmarks
+
+## Overview
+
+This directory contains source and configuration files for benchmarking
+`cuDF`. The sources are currently intended to benchmark `cuDF` via the
+python API, but this is not a requirement.
+
+## Prerequisites
+### Datasets
+* Download datasets using `get_datasets.sh` shell file. Currently there are
+  only avro and json datasets.
+
+## Usage
+### Python
+* Run benchmarks using pytest as shown below
+
+```
+pytest cudf/benchmarks/
+```
+* cuIO benchmarks have option of using file path directly or memory buffers,
+  by default file path option is enabled. To enable memory buffer usage, use
+  `--use_buffer True` with pytest as shown below.
+```
+pytest --use_buffer True cudf/benchmarks/
+```
+
+* If the datasets directory is different from default, it can be
+  provided with option "--dataset_dir directory_path" as shown below.
+```
+pytest --dataset_dir directory_path cudf/benchmarks/
+```
diff --git a/python/cudf/cudf/benchmarks/bench_cudf_io.py b/python/cudf/cudf/benchmarks/bench_cudf_io.py
new file mode 100644
index 0000000..20f5afa
--- /dev/null
+++ b/python/cudf/cudf/benchmarks/bench_cudf_io.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+import glob
+import io
+
+import pytest
+from conftest import option
+
+import cudf
+
+
+def get_dataset_dir():
+    if option.dataset_dir == "NONE":
+        return "cudf/benchmarks/cuio_data/datasets/"
+    return option.dataset_dir
+
+
+@pytest.mark.parametrize("skiprows", [None, 100000, 200000])
+@pytest.mark.parametrize("file_path", glob.glob(get_dataset_dir() + "avro_*"))
+def bench_avro(benchmark, file_path, use_buffer, skiprows):
+
+    if use_buffer == "True":
+        with open(file_path, "rb") as f:
+            file_path = io.BytesIO(f.read())
+    benchmark(cudf.read_avro, file_path, skiprows=skiprows)
+
+
+def get_dtypes(file_path):
+    if "_unsigned_int_" in file_path:
+        return ["uint8", "uint16", "uint32", "uint64"] * 16
+    elif "_int_" in file_path:
+        return ["int8", "int16", "int32", "int64"] * 16
+    elif "_float_" in file_path:
+        return ["float32", "float64"] * 32
+    elif "_str_" in file_path:
+        return ["str"] * 64
+    elif "_datetime64_" in file_path:
+        return [
+            "timestamp[s]",
+            "timestamp[ms]",
+            "timestamp[us]",
+            "timestamp[ns]",
+        ] * 16
+    elif "_timedelta64_" in file_path:
+        return [
+            "timedelta64[s]",
+            "timedelta64[ms]",
+            "timedelta64[us]",
+            "timedelta64[ns]",
+        ] * 16
+    elif "_bool_" in file_path:
+        return ["bool"] * 64
+    else:
+        raise TypeError("Unsupported dtype file")
+
+
+@pytest.mark.parametrize("dtype", ["infer", "provide"])
+@pytest.mark.parametrize("file_path", glob.glob(get_dataset_dir() + "json_*"))
+def bench_json(benchmark, file_path, use_buffer, dtype):
+    if "bz2" in file_path:
+        compression = "bz2"
+    elif "gzip" in file_path:
+        compression = "gzip"
+    elif "infer" in file_path:
+        compression = "infer"
+    else:
+        raise TypeError("Unsupported compression type")
+
+    if dtype == "infer":
+        dtype = True
+    else:
+        dtype = get_dtypes(file_path)
+
+    if use_buffer == "True":
+        with open(file_path, "rb") as f:
+            file_path = io.BytesIO(f.read())
+
+    benchmark(
+        cudf.read_json,
+        file_path,
+        engine="cudf",
+        compression=compression,
+        lines=True,
+        orient="records",
+        dtype=dtype,
+    )
diff --git a/python/cudf/cudf/benchmarks/conftest.py b/python/cudf/cudf/benchmarks/conftest.py
new file mode 100644
index 0000000..bb63965
--- /dev/null
+++ b/python/cudf/cudf/benchmarks/conftest.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+option = None
+
+
+def pytest_addoption(parser):
+    parser.addoption("--use_buffer", action="store", default=False)
+    parser.addoption("--dataset_dir", action="store", default="NONE")
+
+
+def pytest_generate_tests(metafunc):
+    # This is called for every test. Only get/set command line arguments
+    # if the argument is specified in the list of test "fixturenames".
+    option_value = metafunc.config.option.use_buffer
+    if "use_buffer" in metafunc.fixturenames and option_value is not None:
+        metafunc.parametrize("use_buffer", [option_value])
+
+
+def pytest_configure(config):
+    global option
+    option = config.option
diff --git a/python/cudf/cudf/benchmarks/get_datasets.py b/python/cudf/cudf/benchmarks/get_datasets.py
new file mode 100644
index 0000000..7090539
--- /dev/null
+++ b/python/cudf/cudf/benchmarks/get_datasets.py
@@ -0,0 +1,91 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import argparse
+import os
+import shutil
+from collections import namedtuple
+
+# Update url and dir where datasets needs to be copied
+Dataset = namedtuple("Dataset", ["url", "dir"])
+datasets = {
+    "cuio_dataset": Dataset(
+        "https://data.rapids.ai/cudf/benchmark/avro_json_datasets.zip",
+        "cudf/benchmarks/cuio_data/",
+    ),
+}
+
+
+def delete_dir(path):
+    if path == "/" or path == "~":
+        raise ValueError("Trying to delete root/home directory")
+
+    shutil.rmtree(path, ignore_errors=True)
+
+
+def fetch_datasets(urls, dirs):
+    tmp_path = os.path.join(os.getcwd(), "tmp_benchmark/")
+    delete_dir(tmp_path)
+    os.mkdir(tmp_path)
+    for url, path in zip(urls, dirs):
+        path = os.path.join(os.getcwd(), path)
+
+        delete_dir(path)
+        os.mkdir(path)
+
+        os.system("wget " + url + " -P " + tmp_path)
+        os.system(
+            "unzip " + tmp_path + "/" + url.split("/")[-1] + " -d " + path
+        )
+
+    delete_dir(tmp_path)
+
+
+urls = []
+dirs = []
+
+parser = argparse.ArgumentParser(
+    description="""
+    Fetches datasets as per given option.
+    By default it will download all available datasets
+    """
+)
+
+parser.add_argument("-u", nargs=1, help="url of a dataset")
+parser.add_argument(
+    "-d",
+    nargs=1,
+    help="path where downloaded dataset from given url will be unzipped",
+)
+parser.add_argument(
+    "--datasets",
+    nargs="+",
+    help="Currently supported datasets are: "
+    + ", ".join(list(datasets.keys())),
+)
+args = parser.parse_args()
+
+if (args.u is None and args.d is not None) or (
+    args.u is not None and args.d is None
+):
+    raise ValueError(
+        "option -u and -d should be used together, can't use only one"
+    )
+
+if args.u and args.d:
+    urls.append(args.u[0])
+    dirs.append(args.d[0])
+
+if args.datasets:
+    for dataset in args.datasets:
+        urls.append(datasets[dataset].url)
+        dirs.append(datasets[dataset].dir)
+
+if len(dirs) != len(set(dirs)):
+    raise ValueError("Duplicate destination paths are provided")
+
+if len(urls) == 0:
+    for _, val in datasets.items():
+        urls.append(val.url)
+        dirs.append(val.dir)
+
+fetch_datasets(urls, dirs)
diff --git a/python/cudf/cudf/comm/__init__.py b/python/cudf/cudf/comm/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/comm/serialize.py b/python/cudf/cudf/comm/serialize.py
new file mode 100644
index 0000000..9fb2890
--- /dev/null
+++ b/python/cudf/cudf/comm/serialize.py
@@ -0,0 +1,33 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import cudf  # noqa: F401
+from cudf.core.abc import Serializable
+
+try:
+    from distributed.protocol import dask_deserialize, dask_serialize
+    from distributed.protocol.cuda import cuda_deserialize, cuda_serialize
+    from distributed.utils import log_errors
+
+    @cuda_serialize.register(Serializable)
+    def cuda_serialize_cudf_object(x):
+        with log_errors():
+            return x.device_serialize()
+
+    @dask_serialize.register(Serializable)
+    def dask_serialize_cudf_object(x):
+        with log_errors():
+            return x.host_serialize()
+
+    @cuda_deserialize.register(Serializable)
+    def cuda_deserialize_cudf_object(header, frames):
+        with log_errors():
+            return Serializable.device_deserialize(header, frames)
+
+    @dask_deserialize.register(Serializable)
+    def dask_deserialize_cudf_object(header, frames):
+        with log_errors():
+            return Serializable.host_deserialize(header, frames)
+
+except ImportError:
+    # distributed is probably not installed on the system
+    pass
diff --git a/python/cudf/cudf/core/__init__.py b/python/cudf/cudf/core/__init__.py
new file mode 100644
index 0000000..ec4878b
--- /dev/null
+++ b/python/cudf/cudf/core/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2018-2021, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/core/_base_index.py b/python/cudf/cudf/core/_base_index.py
new file mode 100644
index 0000000..3616ec1
--- /dev/null
+++ b/python/cudf/cudf/core/_base_index.py
@@ -0,0 +1,2117 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import builtins
+import pickle
+import warnings
+from functools import cached_property
+from typing import Any, Set, Tuple
+
+import pandas as pd
+from typing_extensions import Self
+
+import cudf
+from cudf._lib.copying import _gather_map_is_valid, gather
+from cudf._lib.stream_compaction import (
+    apply_boolean_mask,
+    drop_duplicates,
+    drop_nulls,
+)
+from cudf._lib.types import size_type_dtype
+from cudf.api.extensions import no_default
+from cudf.api.types import (
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_scalar,
+    is_signed_integer_dtype,
+    is_unsigned_integer_dtype,
+)
+from cudf.core.abc import Serializable
+from cudf.core.column import ColumnBase, column
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.errors import MixedTypeError
+from cudf.utils import ioutils
+from cudf.utils.dtypes import can_convert_to_column, is_mixed_with_object_dtype
+from cudf.utils.utils import _is_same_name
+
+
+class BaseIndex(Serializable):
+    """Base class for all cudf Index types."""
+
+    _accessors: Set[Any] = set()
+    _data: ColumnAccessor
+
+    @property
+    def _columns(self) -> Tuple[Any, ...]:
+        raise NotImplementedError
+
+    @cached_property
+    def _values(self) -> ColumnBase:
+        raise NotImplementedError
+
+    def copy(self, deep: bool = True) -> Self:
+        raise NotImplementedError
+
+    def __len__(self):
+        raise NotImplementedError
+
+    @property
+    def size(self):
+        # The size of an index is always its length irrespective of dimension.
+        return len(self)
+
+    def astype(self, dtype, copy: bool = True):
+        """Create an Index with values cast to dtypes.
+
+        The class of a new Index is determined by dtype. When conversion is
+        impossible, a ValueError exception is raised.
+
+        Parameters
+        ----------
+        dtype : :class:`numpy.dtype`
+            Use a :class:`numpy.dtype` to cast entire Index object to.
+        copy : bool, default False
+            By default, astype always returns a newly allocated object.
+            If copy is set to False and internal requirements on dtype are
+            satisfied, the original data is used to create a new Index
+            or the original Index is returned.
+
+        Returns
+        -------
+        Index
+            Index with values cast to specified dtype.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> index = cudf.Index([1, 2, 3])
+        >>> index
+        Int64Index([1, 2, 3], dtype='int64')
+        >>> index.astype('float64')
+        Float64Index([1.0, 2.0, 3.0], dtype='float64')
+        """
+        raise NotImplementedError
+
+    def argsort(self, *args, **kwargs):
+        """Return the integer indices that would sort the index.
+
+        Parameters vary by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def dtype(self):
+        raise NotImplementedError
+
+    @property
+    def empty(self):
+        return self.size == 0
+
+    @property
+    def is_unique(self):
+        """Return if the index has unique values."""
+        raise NotImplementedError
+
+    def memory_usage(self, deep=False):
+        """Return the memory usage of an object.
+
+        Parameters
+        ----------
+        deep : bool
+            The deep parameter is ignored and is only included for pandas
+            compatibility.
+
+        Returns
+        -------
+        The total bytes used.
+        """
+        raise NotImplementedError
+
+    def tolist(self):  # noqa: D102
+        raise TypeError(
+            "cuDF does not support conversion to host memory "
+            "via the `tolist()` method. Consider using "
+            "`.to_arrow().to_pylist()` to construct a Python list."
+        )
+
+    to_list = tolist
+
+    @property
+    def name(self):
+        """Returns the name of the Index."""
+        raise NotImplementedError
+
+    @property  # type: ignore
+    def ndim(self):  # noqa: D401
+        """Number of dimensions of the underlying data, by definition 1."""
+        return 1
+
+    def equals(self, other):
+        """
+        Determine if two Index objects contain the same elements.
+
+        Returns
+        -------
+        out: bool
+            True if "other" is an Index and it has the same elements
+            as calling index; False otherwise.
+        """
+        raise NotImplementedError
+
+    def shift(self, periods=1, freq=None):
+        """Not yet implemented"""
+        raise NotImplementedError
+
+    @property
+    def shape(self):
+        """Get a tuple representing the dimensionality of the data."""
+        return (len(self),)
+
+    @property
+    def str(self):
+        """Not yet implemented."""
+        raise NotImplementedError
+
+    @property
+    def values(self):
+        raise NotImplementedError
+
+    def max(self):
+        """The maximum value of the index."""
+        raise NotImplementedError
+
+    def min(self):
+        """The minimum value of the index."""
+        raise NotImplementedError
+
+    def get_loc(self, key, method=None, tolerance=None):
+        raise NotImplementedError
+
+    def __getitem__(self, key):
+        raise NotImplementedError()
+
+    def __contains__(self, item):
+        return item in self._values
+
+    def _copy_type_metadata(
+        self, other: Self, *, override_dtypes=None
+    ) -> Self:
+        raise NotImplementedError
+
+    def get_level_values(self, level):
+        """
+        Return an Index of values for requested level.
+
+        This is primarily useful to get an individual level of values from a
+        MultiIndex, but is provided on Index as well for compatibility.
+
+        Parameters
+        ----------
+        level : int or str
+            It is either the integer position or the name of the level.
+
+        Returns
+        -------
+        Index
+            Calling object, as there is only one level in the Index.
+
+        See Also
+        --------
+        cudf.MultiIndex.get_level_values : Get values for
+            a level of a MultiIndex.
+
+        Notes
+        -----
+        For Index, level should be 0, since there are no multiple levels.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index(["a", "b", "c"])
+        >>> idx.get_level_values(0)
+        StringIndex(['a' 'b' 'c'], dtype='object')
+        """
+
+        if level == self.name:
+            return self
+        elif is_integer(level):
+            if level != 0:
+                raise IndexError(
+                    f"Cannot get level: {level} " f"for index with 1 level"
+                )
+            return self
+        else:
+            raise KeyError(f"Requested level with name {level} " "not found")
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        # Dispatch deserialization to the appropriate index type in case
+        # deserialization is ever attempted with the base class directly.
+        idx_type = pickle.loads(header["type-serialized"])
+        return idx_type.deserialize(header, frames)
+
+    @property
+    def names(self):
+        """
+        Returns a tuple containing the name of the Index.
+        """
+        return (self.name,)
+
+    @names.setter
+    def names(self, values):
+        if not is_list_like(values):
+            raise ValueError("Names must be a list-like")
+
+        num_values = len(values)
+        if num_values > 1:
+            raise ValueError(
+                "Length of new names must be 1, got %d" % num_values
+            )
+
+        self.name = values[0]
+
+    def _clean_nulls_from_index(self):
+        """
+        Convert all na values(if any) in Index object
+        to `<NA>` as a preprocessing step to `__repr__` methods.
+
+        This will involve changing type of Index object
+        to StringIndex but it is the responsibility of the `__repr__`
+        methods using this method to replace or handle representation
+        of the actual types correctly.
+        """
+        raise NotImplementedError
+
+    @property
+    def is_monotonic(self):
+        """Return boolean if values in the object are monotonic_increasing.
+
+        This property is an alias for :attr:`is_monotonic_increasing`.
+
+        Returns
+        -------
+        bool
+        """
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            "is_monotonic is deprecated and will be removed in a future "
+            "version. Use is_monotonic_increasing instead.",
+            FutureWarning,
+        )
+
+        return self.is_monotonic_increasing
+
+    @property
+    def is_monotonic_increasing(self):
+        """Return boolean if values in the object are monotonically increasing.
+
+        Returns
+        -------
+        bool
+        """
+        raise NotImplementedError
+
+    @property
+    def is_monotonic_decreasing(self):
+        """Return boolean if values in the object are monotonically decreasing.
+
+        Returns
+        -------
+        bool
+        """
+        raise NotImplementedError
+
+    @property
+    def hasnans(self):
+        """
+        Return True if there are any NaNs or nulls.
+
+        Returns
+        -------
+        out : bool
+            If Series has at least one NaN or null value, return True,
+            if not return False.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import numpy as np
+        >>> index = cudf.Index([1, 2, np.nan, 3, 4], nan_as_null=False)
+        >>> index
+        Float64Index([1.0, 2.0, nan, 3.0, 4.0], dtype='float64')
+        >>> index.hasnans
+        True
+
+        `hasnans` returns `True` for the presence of any `NA` values:
+
+        >>> index = cudf.Index([1, 2, None, 3, 4])
+        >>> index
+        Int64Index([1, 2, <NA>, 3, 4], dtype='int64')
+        >>> index.hasnans
+        True
+        """
+        raise NotImplementedError
+
+    @property
+    def nlevels(self):
+        """
+        Number of levels.
+        """
+        return 1
+
+    def _set_names(self, names, inplace=False):
+        if inplace:
+            idx = self
+        else:
+            idx = self.copy(deep=False)
+
+        idx.names = names
+        if not inplace:
+            return idx
+
+    def set_names(self, names, level=None, inplace=False):
+        """
+        Set Index or MultiIndex name.
+        Able to set new names partially and by level.
+
+        Parameters
+        ----------
+        names : label or list of label
+            Name(s) to set.
+        level : int, label or list of int or label, optional
+            If the index is a MultiIndex, level(s) to set (None for all
+            levels). Otherwise level must be None.
+        inplace : bool, default False
+            Modifies the object directly, instead of creating a new Index or
+            MultiIndex.
+
+        Returns
+        -------
+        Index
+            The same type as the caller or None if inplace is True.
+
+        See Also
+        --------
+        cudf.Index.rename : Able to set new names without level.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([1, 2, 3, 4])
+        >>> idx
+        Int64Index([1, 2, 3, 4], dtype='int64')
+        >>> idx.set_names('quarter')
+        Int64Index([1, 2, 3, 4], dtype='int64', name='quarter')
+        >>> idx = cudf.MultiIndex.from_product([['python', 'cobra'],
+        ... [2018, 2019]])
+        >>> idx
+        MultiIndex([('python', 2018),
+                    ('python', 2019),
+                    ( 'cobra', 2018),
+                    ( 'cobra', 2019)],
+                   )
+        >>> idx.names
+        FrozenList([None, None])
+        >>> idx.set_names(['kind', 'year'], inplace=True)
+        >>> idx.names
+        FrozenList(['kind', 'year'])
+        >>> idx.set_names('species', level=0, inplace=True)
+        >>> idx.names
+        FrozenList(['species', 'year'])
+        """
+        if level is not None:
+            raise ValueError("Level must be None for non-MultiIndex")
+
+        if not is_list_like(names):
+            names = [names]
+
+        return self._set_names(names=names, inplace=inplace)
+
+    @property
+    def has_duplicates(self):
+        return not self.is_unique
+
+    def where(self, cond, other=None, inplace=False):
+        """
+        Replace values where the condition is False.
+
+        The replacement is taken from other.
+
+        Parameters
+        ----------
+        cond : bool array-like with the same length as self
+            Condition to select the values on.
+        other : scalar, or array-like, default None
+            Replacement if the condition is False.
+
+        Returns
+        -------
+        cudf.Index
+            A copy of self with values replaced from other
+            where the condition is False.
+        """
+        raise NotImplementedError
+
+    def factorize(self, sort=False, na_sentinel=None, use_na_sentinel=None):
+        raise NotImplementedError
+
+    def union(self, other, sort=None):
+        """
+        Form the union of two Index objects.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : bool or None, default None
+            Whether to sort the resulting Index.
+
+            * None : Sort the result, except when
+
+              1. `self` and `other` are equal.
+              2. `self` or `other` has length 0.
+
+            * False : do not sort the result.
+
+        Returns
+        -------
+        union : Index
+
+        Examples
+        --------
+        Union of an Index
+        >>> import cudf
+        >>> import pandas as pd
+        >>> idx1 = cudf.Index([1, 2, 3, 4])
+        >>> idx2 = cudf.Index([3, 4, 5, 6])
+        >>> idx1.union(idx2)
+        Int64Index([1, 2, 3, 4, 5, 6], dtype='int64')
+
+        MultiIndex case
+
+        >>> idx1 = cudf.MultiIndex.from_pandas(
+        ...    pd.MultiIndex.from_arrays(
+        ...         [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+        ...    )
+        ... )
+        >>> idx1
+        MultiIndex([(1,  'Red'),
+                    (1, 'Blue'),
+                    (2,  'Red'),
+                    (2, 'Blue')],
+                   )
+        >>> idx2 = cudf.MultiIndex.from_pandas(
+        ...    pd.MultiIndex.from_arrays(
+        ...         [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
+        ...    )
+        ... )
+        >>> idx2
+        MultiIndex([(3,   'Red'),
+                    (3, 'Green'),
+                    (2,   'Red'),
+                    (2, 'Green')],
+                   )
+        >>> idx1.union(idx2)
+        MultiIndex([(1,  'Blue'),
+                    (1,   'Red'),
+                    (2,  'Blue'),
+                    (2, 'Green'),
+                    (2,   'Red'),
+                    (3, 'Green'),
+                    (3,   'Red')],
+                   )
+        >>> idx1.union(idx2, sort=False)
+        MultiIndex([(1,   'Red'),
+                    (1,  'Blue'),
+                    (2,   'Red'),
+                    (2,  'Blue'),
+                    (3,   'Red'),
+                    (3, 'Green'),
+                    (2, 'Green')],
+                   )
+        """
+        if not isinstance(other, BaseIndex):
+            other = cudf.Index(other, name=self.name)
+
+        if sort not in {None, False}:
+            raise ValueError(
+                f"The 'sort' keyword only takes the values of "
+                f"None or False; {sort} was passed."
+            )
+
+        if cudf.get_option("mode.pandas_compatible"):
+            if (
+                is_bool_dtype(self.dtype) and not is_bool_dtype(other.dtype)
+            ) or (
+                not is_bool_dtype(self.dtype) and is_bool_dtype(other.dtype)
+            ):
+                # Bools + other types will result in mixed type.
+                # This is not yet consistent in pandas and specific to APIs.
+                raise MixedTypeError("Cannot perform union with mixed types")
+            if (
+                is_signed_integer_dtype(self.dtype)
+                and is_unsigned_integer_dtype(other.dtype)
+            ) or (
+                is_unsigned_integer_dtype(self.dtype)
+                and is_signed_integer_dtype(other.dtype)
+            ):
+                # signed + unsigned types will result in
+                # mixed type for union in pandas.
+                raise MixedTypeError("Cannot perform union with mixed types")
+
+        if not len(other) or self.equals(other):
+            common_dtype = cudf.utils.dtypes.find_common_type(
+                [self.dtype, other.dtype]
+            )
+            return self._get_reconciled_name_object(other).astype(common_dtype)
+        elif not len(self):
+            common_dtype = cudf.utils.dtypes.find_common_type(
+                [self.dtype, other.dtype]
+            )
+            return other._get_reconciled_name_object(self).astype(common_dtype)
+
+        result = self._union(other, sort=sort)
+        result.name = _get_result_name(self.name, other.name)
+        return result
+
+    def intersection(self, other, sort=False):
+        """
+        Form the intersection of two Index objects.
+
+        This returns a new Index with elements common to the index and `other`.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : False or None, default False
+            Whether to sort the resulting index.
+
+            * False : do not sort the result.
+            * None : sort the result, except when `self` and `other` are equal
+              or when the values cannot be compared.
+
+        Returns
+        -------
+        intersection : Index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> idx1 = cudf.Index([1, 2, 3, 4])
+        >>> idx2 = cudf.Index([3, 4, 5, 6])
+        >>> idx1.intersection(idx2)
+        Int64Index([3, 4], dtype='int64')
+
+        MultiIndex case
+
+        >>> idx1 = cudf.MultiIndex.from_pandas(
+        ...    pd.MultiIndex.from_arrays(
+        ...         [[1, 1, 3, 4], ["Red", "Blue", "Red", "Blue"]]
+        ...    )
+        ... )
+        >>> idx2 = cudf.MultiIndex.from_pandas(
+        ...    pd.MultiIndex.from_arrays(
+        ...         [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+        ...    )
+        ... )
+        >>> idx1
+        MultiIndex([(1,  'Red'),
+                    (1, 'Blue'),
+                    (3,  'Red'),
+                    (4, 'Blue')],
+                )
+        >>> idx2
+        MultiIndex([(1,  'Red'),
+                    (1, 'Blue'),
+                    (2,  'Red'),
+                    (2, 'Blue')],
+                )
+        >>> idx1.intersection(idx2)
+        MultiIndex([(1,  'Red'),
+                    (1, 'Blue')],
+                )
+        >>> idx1.intersection(idx2, sort=False)
+        MultiIndex([(1,  'Red'),
+                    (1, 'Blue')],
+                )
+        """
+        if not can_convert_to_column(other):
+            raise TypeError("Input must be Index or array-like")
+
+        if not isinstance(other, BaseIndex):
+            other = cudf.Index(
+                other,
+                name=getattr(other, "name", self.name),
+            )
+
+        if sort not in {None, False}:
+            raise ValueError(
+                f"The 'sort' keyword only takes the values of "
+                f"None or False; {sort} was passed."
+            )
+
+        if not len(self) or not len(other) or self.equals(other):
+            common_dtype = cudf.utils.dtypes._dtype_pandas_compatible(
+                cudf.utils.dtypes.find_common_type([self.dtype, other.dtype])
+            )
+
+            lhs = self.unique() if self.has_duplicates else self
+            rhs = other
+            if not len(other):
+                lhs, rhs = rhs, lhs
+
+            return lhs._get_reconciled_name_object(rhs).astype(common_dtype)
+
+        res_name = _get_result_name(self.name, other.name)
+
+        if (self._is_boolean() and other._is_numeric()) or (
+            self._is_numeric() and other._is_boolean()
+        ):
+            if isinstance(self, cudf.MultiIndex):
+                return self[:0].rename(res_name)
+            else:
+                return cudf.Index([], name=res_name)
+
+        if self.has_duplicates:
+            lhs = self.unique()
+        else:
+            lhs = self
+        if other.has_duplicates:
+            rhs = other.unique()
+        else:
+            rhs = other
+        result = lhs._intersection(rhs, sort=sort)
+        result.name = res_name
+        return result
+
+    def _get_reconciled_name_object(self, other):
+        """
+        If the result of a set operation will be self,
+        return self, unless the name changes, in which
+        case make a shallow copy of self.
+        """
+        name = _get_result_name(self.name, other.name)
+        if not _is_same_name(self.name, name):
+            return self.rename(name)
+        return self
+
+    def fillna(self, value, downcast=None):
+        """
+        Fill null values with the specified value.
+
+        Parameters
+        ----------
+        value : scalar
+            Scalar value to use to fill nulls. This value cannot be a
+            list-likes.
+
+        downcast : dict, default is None
+            This Parameter is currently NON-FUNCTIONAL.
+
+        Returns
+        -------
+        filled : Index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> index = cudf.Index([1, 2, None, 4])
+        >>> index
+        Int64Index([1, 2, <NA>, 4], dtype='int64')
+        >>> index.fillna(3)
+        Int64Index([1, 2, 3, 4], dtype='int64')
+        """
+        if downcast is not None:
+            raise NotImplementedError(
+                "`downcast` parameter is not yet supported"
+            )
+
+        return super().fillna(value=value)
+
+    def to_frame(self, index=True, name=no_default):
+        """Create a DataFrame with a column containing this Index
+
+        Parameters
+        ----------
+        index : boolean, default True
+            Set the index of the returned DataFrame as the original Index
+        name : object, defaults to index.name
+            The passed name should substitute for the index name (if it has
+            one).
+
+        Returns
+        -------
+        DataFrame
+            DataFrame containing the original Index data.
+
+        See Also
+        --------
+        Index.to_series : Convert an Index to a Series.
+        Series.to_frame : Convert Series to DataFrame.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index(['Ant', 'Bear', 'Cow'], name='animal')
+        >>> idx.to_frame()
+               animal
+        animal
+        Ant       Ant
+        Bear     Bear
+        Cow       Cow
+
+        By default, the original Index is reused. To enforce a new Index:
+
+        >>> idx.to_frame(index=False)
+            animal
+        0   Ant
+        1  Bear
+        2   Cow
+
+        To override the name of the resulting column, specify `name`:
+
+        >>> idx.to_frame(index=False, name='zoo')
+            zoo
+        0   Ant
+        1  Bear
+        2   Cow
+        """
+        if name is None:
+            warnings.warn(
+                "Explicitly passing `name=None` currently preserves "
+                "the Index's name or uses a default name of 0. This "
+                "behaviour is deprecated, and in the future `None` "
+                "will be used as the name of the "
+                "resulting DataFrame column.",
+                FutureWarning,
+            )
+            name = no_default
+        if name is not no_default:
+            col_name = name
+        elif self.name is None:
+            col_name = 0
+        else:
+            col_name = self.name
+        return cudf.DataFrame(
+            {col_name: self._values}, index=self if index else None
+        )
+
+    def to_arrow(self):
+        """Convert to a suitable Arrow object."""
+        raise NotImplementedError
+
+    def to_cupy(self):
+        """Convert to a cupy array."""
+        raise NotImplementedError
+
+    def to_numpy(self):
+        """Convert to a numpy array."""
+        raise NotImplementedError
+
+    def any(self):
+        """
+        Return whether any elements is True in Index.
+        """
+        raise NotImplementedError
+
+    def isna(self):
+        """
+        Detect missing values.
+
+        Return a boolean same-sized object indicating if the values are NA.
+        NA values, such as ``None``, `numpy.NAN` or `cudf.NA`, get
+        mapped to ``True`` values.
+        Everything else get mapped to ``False`` values.
+
+        Returns
+        -------
+        numpy.ndarray[bool]
+            A boolean array to indicate which entries are NA.
+
+        """
+        raise NotImplementedError
+
+    def notna(self):
+        """
+        Detect existing (non-missing) values.
+
+        Return a boolean same-sized object indicating if the values are not NA.
+        Non-missing values get mapped to ``True``.
+        NA values, such as None or `numpy.NAN`, get mapped to ``False``
+        values.
+
+        Returns
+        -------
+        numpy.ndarray[bool]
+            A boolean array to indicate which entries are not NA.
+        """
+        raise NotImplementedError
+
+    def to_pandas(self, nullable=False):
+        """
+        Convert to a Pandas Index.
+
+        Parameters
+        ----------
+        nullable : bool, Default False
+            If ``nullable`` is ``True``, the resulting index will have
+            a corresponding nullable Pandas dtype.
+            If there is no corresponding nullable Pandas dtype present,
+            the resulting dtype will be a regular pandas dtype.
+            If ``nullable`` is ``False``, the resulting index will
+            either convert null values to ``np.nan`` or ``None``
+            depending on the dtype.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([-3, 10, 15, 20])
+        >>> idx
+        Int64Index([-3, 10, 15, 20], dtype='int64')
+        >>> idx.to_pandas()
+        Int64Index([-3, 10, 15, 20], dtype='int64')
+        >>> type(idx.to_pandas())
+        <class 'pandas.core.indexes.numeric.Int64Index'>
+        >>> type(idx)
+        <class 'cudf.core.index.Int64Index'>
+        """
+        raise NotImplementedError
+
+    def isin(self, values):
+        """Return a boolean array where the index values are in values.
+
+        Compute boolean array of whether each index value is found in
+        the passed set of values. The length of the returned boolean
+        array matches the length of the index.
+
+        Parameters
+        ----------
+        values : set, list-like, Index
+            Sought values.
+
+        Returns
+        -------
+        is_contained : cupy array
+            CuPy array of boolean values.
+
+        Examples
+        --------
+        >>> idx = cudf.Index([1,2,3])
+        >>> idx
+        Int64Index([1, 2, 3], dtype='int64')
+
+        Check whether each index value in a list of values.
+
+        >>> idx.isin([1, 4])
+        array([ True, False, False])
+        """
+        # To match pandas behavior, even though only list-like objects are
+        # supposed to be passed, only scalars throw errors. Other types (like
+        # dicts) just transparently return False (see the implementation of
+        # ColumnBase.isin).
+        raise NotImplementedError
+
+    def unique(self):
+        """
+        Return unique values in the index.
+
+        Returns
+        -------
+        Index without duplicates
+        """
+        raise NotImplementedError
+
+    def to_series(self, index=None, name=None):
+        """
+        Create a Series with both index and values equal to the index keys.
+        Useful with map for returning an indexer based on an index.
+
+        Parameters
+        ----------
+        index : Index, optional
+            Index of resulting Series. If None, defaults to original index.
+        name : str, optional
+            Name of resulting Series. If None, defaults to name of original
+            index.
+
+        Returns
+        -------
+        Series
+            The dtype will be based on the type of the Index values.
+        """
+        return cudf.Series._from_data(
+            self._data,
+            index=self.copy(deep=False) if index is None else index,
+            name=self.name if name is None else name,
+        )
+
+    @ioutils.doc_to_dlpack()
+    def to_dlpack(self):
+        """{docstring}"""
+
+        return cudf.io.dlpack.to_dlpack(self)
+
+    def append(self, other):
+        """
+        Append a collection of Index objects together.
+
+        Parameters
+        ----------
+        other : Index or list/tuple of indices
+
+        Returns
+        -------
+        appended : Index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([1, 2, 10, 100])
+        >>> idx
+        Int64Index([1, 2, 10, 100], dtype='int64')
+        >>> other = cudf.Index([200, 400, 50])
+        >>> other
+        Int64Index([200, 400, 50], dtype='int64')
+        >>> idx.append(other)
+        Int64Index([1, 2, 10, 100, 200, 400, 50], dtype='int64')
+
+        append accepts list of Index objects
+
+        >>> idx.append([other, other])
+        Int64Index([1, 2, 10, 100, 200, 400, 50, 200, 400, 50], dtype='int64')
+        """
+        raise NotImplementedError
+
+    def difference(self, other, sort=None):
+        """
+        Return a new Index with elements from the index that are not in
+        `other`.
+
+        This is the set difference of two Index objects.
+
+        Parameters
+        ----------
+        other : Index or array-like
+        sort : False or None, default None
+            Whether to sort the resulting index. By default, the
+            values are attempted to be sorted, but any TypeError from
+            incomparable elements is caught by cudf.
+
+            * None : Attempt to sort the result, but catch any TypeErrors
+              from comparing incomparable elements.
+            * False : Do not sort the result.
+
+        Returns
+        -------
+        difference : Index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx1 = cudf.Index([2, 1, 3, 4])
+        >>> idx1
+        Int64Index([2, 1, 3, 4], dtype='int64')
+        >>> idx2 = cudf.Index([3, 4, 5, 6])
+        >>> idx2
+        Int64Index([3, 4, 5, 6], dtype='int64')
+        >>> idx1.difference(idx2)
+        Int64Index([1, 2], dtype='int64')
+        >>> idx1.difference(idx2, sort=False)
+        Int64Index([2, 1], dtype='int64')
+        """
+        if not can_convert_to_column(other):
+            raise TypeError("Input must be Index or array-like")
+
+        if sort not in {None, False}:
+            raise ValueError(
+                f"The 'sort' keyword only takes the values "
+                f"of None or False; {sort} was passed."
+            )
+
+        other = cudf.Index(other, name=getattr(other, "name", self.name))
+
+        if not len(other):
+            return self._get_reconciled_name_object(other)
+        elif self.equals(other):
+            return self[:0]._get_reconciled_name_object(other)
+
+        res_name = _get_result_name(self.name, other.name)
+
+        if is_mixed_with_object_dtype(self, other):
+            difference = self.copy()
+        else:
+            other = other.copy(deep=False)
+            difference = cudf.core.index._index_from_data(
+                cudf.DataFrame._from_data({"None": self._column})
+                .merge(
+                    cudf.DataFrame._from_data({"None": other._column}),
+                    how="leftanti",
+                    on="None",
+                )
+                ._data
+            )
+
+            if self.dtype != other.dtype:
+                difference = difference.astype(self.dtype)
+
+        difference.name = res_name
+
+        if sort is None and len(other):
+            return difference.sort_values()
+
+        return difference
+
+    def is_numeric(self):
+        """
+        Check if the Index only consists of numeric data.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_any_real_numeric_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of numeric data.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans.
+        is_integer : Check if the Index only consists of integers.
+        is_floating : Check if the Index is a floating type.
+        is_object : Check if the Index is of the object dtype.
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_numeric()
+        True
+        >>> idx = cudf.Index([1, 2, 3, 4.0])
+        >>> idx.is_numeric()
+        True
+        >>> idx = cudf.Index([1, 2, 3, 4])
+        >>> idx.is_numeric()
+        True
+        >>> idx = cudf.Index([1, 2, 3, 4.0, np.nan])
+        >>> idx.is_numeric()
+        True
+        >>> idx = cudf.Index(["Apple", "cold"])
+        >>> idx.is_numeric()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_numeric is deprecated. "
+            "Use cudf.api.types.is_any_real_numeric_dtype instead",
+            FutureWarning,
+        )
+        return self._is_numeric()
+
+    def _is_numeric(self):
+        raise NotImplementedError
+
+    def is_boolean(self):
+        """
+        Check if the Index only consists of booleans.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_bool_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of booleans.
+
+        See Also
+        --------
+        is_integer : Check if the Index only consists of integers.
+        is_floating : Check if the Index is a floating type.
+        is_numeric : Check if the Index only consists of numeric data.
+        is_object : Check if the Index is of the object dtype.
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([True, False, True])
+        >>> idx.is_boolean()
+        True
+        >>> idx = cudf.Index(["True", "False", "True"])
+        >>> idx.is_boolean()
+        False
+        >>> idx = cudf.Index([1, 2, 3])
+        >>> idx.is_boolean()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_boolean is deprecated. "
+            "Use cudf.api.types.is_bool_dtype instead",
+            FutureWarning,
+        )
+        return self._is_boolean()
+
+    def _is_boolean(self):
+        raise NotImplementedError
+
+    def is_integer(self):
+        """
+        Check if the Index only consists of integers.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_integer_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of integers.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans.
+        is_floating : Check if the Index is a floating type.
+        is_numeric : Check if the Index only consists of numeric data.
+        is_object : Check if the Index is of the object dtype.
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([1, 2, 3, 4])
+        >>> idx.is_integer()
+        True
+        >>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_integer()
+        False
+        >>> idx = cudf.Index(["Apple", "Mango", "Watermelon"])
+        >>> idx.is_integer()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_integer is deprecated. "
+            "Use cudf.api.types.is_integer_dtype instead",
+            FutureWarning,
+        )
+        return self._is_integer()
+
+    def _is_integer(self):
+        raise NotImplementedError
+
+    def is_floating(self):
+        """
+        Check if the Index is a floating type.
+
+        The Index may consist of only floats, NaNs, or a mix of floats,
+        integers, or NaNs.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_float_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index only consists of only consists
+            of floats, NaNs, or a mix of floats, integers, or NaNs.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans.
+        is_integer : Check if the Index only consists of integers.
+        is_numeric : Check if the Index only consists of numeric data.
+        is_object : Check if the Index is of the object dtype.
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_floating()
+        True
+        >>> idx = cudf.Index([1.0, 2.0, np.nan, 4.0])
+        >>> idx.is_floating()
+        True
+        >>> idx = cudf.Index([1, 2, 3, 4, np.nan], nan_as_null=False)
+        >>> idx.is_floating()
+        True
+        >>> idx = cudf.Index([1, 2, 3, 4])
+        >>> idx.is_floating()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_floating is deprecated. "
+            "Use cudf.api.types.is_float_dtype instead",
+            FutureWarning,
+        )
+        return self._is_floating()
+
+    def _is_floating(self):
+        raise NotImplementedError
+
+    def is_object(self):
+        """
+        Check if the Index is of the object dtype.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_object_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index is of the object dtype.
+
+        See Also
+        --------
+        is_boolean : Check if the Index only consists of booleans.
+        is_integer : Check if the Index only consists of integers.
+        is_floating : Check if the Index is a floating type.
+        is_numeric : Check if the Index only consists of numeric data.
+        is_categorical : Check if the Index holds categorical data.
+        is_interval : Check if the Index holds Interval objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index(["Apple", "Mango", "Watermelon"])
+        >>> idx.is_object()
+        True
+        >>> idx = cudf.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_object()
+        False
+        >>> idx = cudf.Index([1.0, 2.0, 3.0, 4.0])
+        >>> idx.is_object()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_object is deprecated. "
+            "Use cudf.api.types.is_object_dtype instead",
+            FutureWarning,
+        )
+        return self._is_object()
+
+    def _is_object(self):
+        raise NotImplementedError
+
+    def is_categorical(self):
+        """
+        Check if the Index holds categorical data.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_categorical_dtype` instead.
+
+        Returns
+        -------
+        bool
+            True if the Index is categorical.
+
+        See Also
+        --------
+        CategoricalIndex : Index for categorical data.
+        is_boolean : Check if the Index only consists of booleans.
+        is_integer : Check if the Index only consists of integers.
+        is_floating : Check if the Index is a floating type.
+        is_numeric : Check if the Index only consists of numeric data.
+        is_object : Check if the Index is of the object dtype.
+        is_interval : Check if the Index holds Interval objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index(["Watermelon", "Orange", "Apple",
+        ...                 "Watermelon"]).astype("category")
+        >>> idx.is_categorical()
+        True
+        >>> idx = cudf.Index([1, 3, 5, 7])
+        >>> idx.is_categorical()
+        False
+        >>> s = cudf.Series(["Peter", "Victor", "Elisabeth", "Mar"])
+        >>> s
+        0        Peter
+        1       Victor
+        2    Elisabeth
+        3          Mar
+        dtype: object
+        >>> s.index.is_categorical()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_categorical is deprecated. "
+            "Use cudf.api.types.is_categorical_dtype instead",
+            FutureWarning,
+        )
+        return self._is_categorical()
+
+    def _is_categorical(self):
+        raise NotImplementedError
+
+    def is_interval(self):
+        """
+        Check if the Index holds Interval objects.
+
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_interval_dtype` instead.
+
+        Returns
+        -------
+        bool
+            Whether or not the Index holds Interval objects.
+
+        See Also
+        --------
+        IntervalIndex : Index for Interval objects.
+        is_boolean : Check if the Index only consists of booleans.
+        is_integer : Check if the Index only consists of integers.
+        is_floating : Check if the Index is a floating type.
+        is_numeric : Check if the Index only consists of numeric data.
+        is_object : Check if the Index is of the object dtype.
+        is_categorical : Check if the Index holds categorical data.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> idx = cudf.from_pandas(
+        ...     pd.Index([pd.Interval(left=0, right=5),
+        ...               pd.Interval(left=5, right=10)])
+        ... )
+        >>> idx.is_interval()
+        True
+        >>> idx = cudf.Index([1, 3, 5, 7])
+        >>> idx.is_interval()
+        False
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            f"{type(self).__name__}.is_interval is deprecated. "
+            "Use cudf.api.types.is_interval_dtype instead",
+            FutureWarning,
+        )
+        return self._is_interval()
+
+    def _is_interval(self):
+        raise NotImplementedError
+
+    def _union(self, other, sort=None):
+        # TODO: As a future optimization we should explore
+        # not doing `to_frame`
+        self_df = self.to_frame(index=False, name=0)
+        other_df = other.to_frame(index=False, name=0)
+        self_df["order"] = self_df.index
+        other_df["order"] = other_df.index
+        res = self_df.merge(other_df, on=[0], how="outer")
+        res = res.sort_values(
+            by=res._data.to_pandas_index()[1:], ignore_index=True
+        )
+        union_result = cudf.core.index._index_from_data({0: res._data[0]})
+
+        if sort is None and len(other):
+            return union_result.sort_values()
+        return union_result
+
+    def _intersection(self, other, sort=None):
+        intersection_result = cudf.core.index._index_from_data(
+            cudf.DataFrame._from_data({"None": self.unique()._column})
+            .merge(
+                cudf.DataFrame._from_data({"None": other.unique()._column}),
+                how="inner",
+                on="None",
+            )
+            ._data
+        )
+
+        if sort is None and len(other):
+            return intersection_result.sort_values()
+        return intersection_result
+
+    def sort_values(
+        self,
+        return_indexer=False,
+        ascending=True,
+        na_position="last",
+        key=None,
+    ):
+        """
+        Return a sorted copy of the index, and optionally return the indices
+        that sorted the index itself.
+
+        Parameters
+        ----------
+        return_indexer : bool, default False
+            Should the indices that would sort the index be returned.
+        ascending : bool, default True
+            Should the index values be sorted in an ascending order.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+        key : None, optional
+            This parameter is NON-FUNCTIONAL.
+
+        Returns
+        -------
+        sorted_index : Index
+            Sorted copy of the index.
+        indexer : cupy.ndarray, optional
+            The indices that the index itself was sorted by.
+
+        See Also
+        --------
+        cudf.Series.min : Sort values of a Series.
+        cudf.DataFrame.sort_values : Sort values in a DataFrame.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.Index([10, 100, 1, 1000])
+        >>> idx
+        Int64Index([10, 100, 1, 1000], dtype='int64')
+
+        Sort values in ascending order (default behavior).
+
+        >>> idx.sort_values()
+        Int64Index([1, 10, 100, 1000], dtype='int64')
+
+        Sort values in descending order, and also get the indices `idx` was
+        sorted by.
+
+        >>> idx.sort_values(ascending=False, return_indexer=True)
+        (Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2],
+                                                            dtype=int32))
+
+        Sorting values in a MultiIndex:
+
+        >>> midx = cudf.MultiIndex(
+        ...      levels=[[1, 3, 4, -10], [1, 11, 5]],
+        ...      codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        ...      names=["x", "y"],
+        ... )
+        >>> midx
+        MultiIndex([(  1,  1),
+                    (  1,  5),
+                    (  3, 11),
+                    (  4, 11),
+                    (-10,  1)],
+                   names=['x', 'y'])
+        >>> midx.sort_values()
+        MultiIndex([(-10,  1),
+                    (  1,  1),
+                    (  1,  5),
+                    (  3, 11),
+                    (  4, 11)],
+                   names=['x', 'y'])
+        >>> midx.sort_values(ascending=False)
+        MultiIndex([(  4, 11),
+                    (  3, 11),
+                    (  1,  5),
+                    (  1,  1),
+                    (-10,  1)],
+                   names=['x', 'y'])
+        """
+        if key is not None:
+            raise NotImplementedError("key parameter is not yet implemented.")
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        indices = self.argsort(ascending=ascending, na_position=na_position)
+        index_sorted = self.take(indices)
+
+        if return_indexer:
+            return index_sorted, indices
+        else:
+            return index_sorted
+
+    def join(
+        self, other, how="left", level=None, return_indexers=False, sort=False
+    ):
+        """
+        Compute join_index and indexers to conform data structures
+        to the new index.
+
+        Parameters
+        ----------
+        other : Index.
+        how : {'left', 'right', 'inner', 'outer'}
+        return_indexers : bool, default False
+        sort : bool, default False
+            Sort the join keys lexicographically in the result Index. If False,
+            the order of the join keys depends on the join type (how keyword).
+
+        Returns: index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> lhs = cudf.DataFrame({
+        ...     "a": [2, 3, 1],
+        ...     "b": [3, 4, 2],
+        ... }).set_index(['a', 'b']).index
+        >>> lhs
+        MultiIndex([(2, 3),
+                    (3, 4),
+                    (1, 2)],
+                   names=['a', 'b'])
+        >>> rhs = cudf.DataFrame({"a": [1, 4, 3]}).set_index('a').index
+        >>> rhs
+        Int64Index([1, 4, 3], dtype='int64', name='a')
+        >>> lhs.join(rhs, how='inner')
+        MultiIndex([(3, 4),
+                    (1, 2)],
+                   names=['a', 'b'])
+        """
+        if return_indexers is not False:
+            raise NotImplementedError("return_indexers is not implemented")
+        self_is_multi = isinstance(self, cudf.MultiIndex)
+        other_is_multi = isinstance(other, cudf.MultiIndex)
+        if level is not None:
+            if self_is_multi and other_is_multi:
+                raise TypeError(
+                    "Join on level between two MultiIndex objects is ambiguous"
+                )
+
+            if not is_scalar(level):
+                raise ValueError("level should be an int or a label only")
+
+        if other_is_multi:
+            if how == "left":
+                how = "right"
+            elif how == "right":
+                how = "left"
+            rhs = self.copy(deep=False)
+            lhs = other.copy(deep=False)
+        else:
+            lhs = self.copy(deep=False)
+            rhs = other.copy(deep=False)
+        same_names = lhs.names == rhs.names
+        # There should be no `None` values in Joined indices,
+        # so essentially it would be `left/right` or 'inner'
+        # in case of MultiIndex
+        if isinstance(lhs, cudf.MultiIndex):
+            on = (
+                lhs._data.select_by_index(level).names[0]
+                if isinstance(level, int)
+                else level
+            )
+
+            if on is not None:
+                rhs.names = (on,)
+            on = rhs.names[0]
+            if how == "outer":
+                how = "left"
+            elif how == "right":
+                how = "inner"
+        else:
+            # Both are normal indices
+            on = lhs.names[0]
+            rhs.names = lhs.names
+
+        lhs = lhs.to_frame()
+        rhs = rhs.to_frame()
+
+        output = lhs.merge(rhs, how=how, on=on, sort=sort)
+
+        # If both inputs were MultiIndexes, the output is a MultiIndex.
+        # Otherwise, the output is only a MultiIndex if there are multiple
+        # columns
+        if self_is_multi and other_is_multi:
+            return cudf.MultiIndex._from_data(output._data)
+        else:
+            idx = cudf.core.index._index_from_data(output._data)
+            idx.name = self.name if same_names else None
+            return idx
+
+    def rename(self, name, inplace=False):
+        """
+        Alter Index name.
+
+        Defaults to returning new index.
+
+        Parameters
+        ----------
+        name : label
+            Name(s) to set.
+
+        Returns
+        -------
+        Index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> index = cudf.Index([1, 2, 3], name='one')
+        >>> index
+        Int64Index([1, 2, 3], dtype='int64', name='one')
+        >>> index.name
+        'one'
+        >>> renamed_index = index.rename('two')
+        >>> renamed_index
+        Int64Index([1, 2, 3], dtype='int64', name='two')
+        >>> renamed_index.name
+        'two'
+        """
+        if inplace is True:
+            self.name = name
+            return None
+        else:
+            out = self.copy(deep=True)
+            out.name = name
+            return out
+
+    def _indices_of(self, value) -> cudf.core.column.NumericalColumn:
+        """
+        Return indices corresponding to value
+
+        Parameters
+        ----------
+        value
+            Value to look for in index
+
+        Returns
+        -------
+        Column of indices
+        """
+        raise NotImplementedError
+
+    def find_label_range(self, loc: slice) -> slice:
+        """
+        Translate a label-based slice to an index-based slice
+
+        Parameters
+        ----------
+        loc
+            slice to search for.
+
+        Notes
+        -----
+        As with all label-based searches, the slice is right-closed.
+
+        Returns
+        -------
+        New slice translated into integer indices of the index (right-open).
+        """
+        start = loc.start
+        stop = loc.stop
+        step = 1 if loc.step is None else loc.step
+        if step < 0:
+            start_side, stop_side = "right", "left"
+        else:
+            start_side, stop_side = "left", "right"
+        istart = (
+            None
+            if start is None
+            else self.get_slice_bound(start, side=start_side)
+        )
+        istop = (
+            None
+            if stop is None
+            else self.get_slice_bound(stop, side=stop_side)
+        )
+        if step < 0:
+            # Fencepost
+            istart = None if istart is None else max(istart - 1, 0)
+            istop = None if (istop is None or istop == 0) else istop - 1
+        return slice(istart, istop, step)
+
+    def searchsorted(
+        self,
+        value,
+        side: builtins.str = "left",
+        ascending: bool = True,
+        na_position: builtins.str = "last",
+    ):
+        """Find index where elements should be inserted to maintain order
+
+        Parameters
+        ----------
+        value :
+            Value to be hypothetically inserted into Self
+        side : str {'left', 'right'} optional, default 'left'
+            If 'left', the index of the first suitable location found is given
+            If 'right', return the last such index
+        ascending : bool optional, default True
+            Index is in ascending order (otherwise descending)
+        na_position : str {'last', 'first'} optional, default 'last'
+            Position of null values in sorted order
+
+        Returns
+        -------
+        Insertion point.
+
+        Notes
+        -----
+        As a precondition the index must be sorted in the same order
+        as requested by the `ascending` flag.
+        """
+        raise NotImplementedError
+
+    def get_slice_bound(self, label, side: builtins.str, kind=None) -> int:
+        """
+        Calculate slice bound that corresponds to given label.
+        Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
+        of given label.
+
+        Parameters
+        ----------
+        label : object
+        side : {'left', 'right'}
+        kind : {'ix', 'loc', 'getitem'}
+
+        Returns
+        -------
+        int
+            Index of label.
+        """
+        if kind is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "'kind' argument in get_slice_bound is deprecated and will be "
+                "removed in a future version.",
+                FutureWarning,
+            )
+        if side not in {"left", "right"}:
+            raise ValueError(f"Invalid side argument {side}")
+        if self.is_monotonic_increasing or self.is_monotonic_decreasing:
+            return self.searchsorted(
+                label, side=side, ascending=self.is_monotonic_increasing
+            )
+        else:
+            try:
+                left, right = self._values._find_first_and_last(label)
+            except ValueError:
+                raise KeyError(f"{label=} not in index")
+            if left != right:
+                raise KeyError(
+                    f"Cannot get slice bound for non-unique label {label=}"
+                )
+            if side == "left":
+                return left
+            else:
+                return right + 1
+
+    def __array_function__(self, func, types, args, kwargs):
+        # check if the function is implemented for the current type
+        cudf_index_module = type(self)
+        for submodule in func.__module__.split(".")[1:]:
+            # point cudf_index_module to the correct submodule
+            if hasattr(cudf_index_module, submodule):
+                cudf_index_module = getattr(cudf_index_module, submodule)
+            else:
+                return NotImplemented
+
+        fname = func.__name__
+
+        handled_types = [BaseIndex, cudf.Series]
+
+        # check if  we don't handle any of the types (including sub-class)
+        for t in types:
+            if not any(
+                issubclass(t, handled_type) for handled_type in handled_types
+            ):
+                return NotImplemented
+
+        if hasattr(cudf_index_module, fname):
+            cudf_func = getattr(cudf_index_module, fname)
+            # Handle case if cudf_func is same as numpy function
+            if cudf_func is func:
+                return NotImplemented
+            else:
+                result = cudf_func(*args, **kwargs)
+                if fname == "unique":
+                    # NumPy expects a sorted result for `unique`, which is not
+                    # guaranteed by cudf.Index.unique.
+                    result = result.sort_values()
+                return result
+
+        else:
+            return NotImplemented
+
+    @classmethod
+    def from_pandas(cls, index, nan_as_null=no_default):
+        """
+        Convert from a Pandas Index.
+
+        Parameters
+        ----------
+        index : Pandas Index object
+            A Pandas Index object which has to be converted
+            to cuDF Index.
+        nan_as_null : bool, Default None
+            If ``None``/``True``, converts ``np.nan`` values
+            to ``null`` values.
+            If ``False``, leaves ``np.nan`` values as is.
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> import numpy as np
+        >>> data = [10, 20, 30, np.nan]
+        >>> pdi = pd.Index(data)
+        >>> cudf.Index.from_pandas(pdi)
+        Float64Index([10.0, 20.0, 30.0, <NA>], dtype='float64')
+        >>> cudf.Index.from_pandas(pdi, nan_as_null=False)
+        Float64Index([10.0, 20.0, 30.0, nan], dtype='float64')
+        """
+        if nan_as_null is no_default:
+            nan_as_null = (
+                False if cudf.get_option("mode.pandas_compatible") else None
+            )
+
+        if not isinstance(index, pd.Index):
+            raise TypeError("not a pandas.Index")
+
+        ind = cudf.Index(column.as_column(index, nan_as_null=nan_as_null))
+        ind.name = index.name
+        return ind
+
+    @property
+    def _constructor_expanddim(self):
+        return cudf.MultiIndex
+
+    def drop_duplicates(
+        self,
+        keep="first",
+        nulls_are_equal=True,
+    ):
+        """
+        Drop duplicate rows in index.
+
+        keep : {"first", "last", False}, default "first"
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+        nulls_are_equal: bool, default True
+            Null elements are considered equal to other null elements.
+        """
+
+        # This utilizes the fact that all `Index` is also a `Frame`.
+        # Except RangeIndex.
+        return self._from_columns_like_self(
+            drop_duplicates(
+                list(self._columns),
+                keys=range(len(self._data)),
+                keep=keep,
+                nulls_are_equal=nulls_are_equal,
+            ),
+            self._column_names,
+        )
+
+    def duplicated(self, keep="first"):
+        """
+        Indicate duplicate index values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        array. Either all duplicates, all except the first, or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            The value or values in a set of duplicates to mark as missing.
+
+            - ``'first'`` : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - ``'last'`` : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        cupy.ndarray[bool]
+
+        See Also
+        --------
+        Series.duplicated : Equivalent method on cudf.Series.
+        DataFrame.duplicated : Equivalent method on cudf.DataFrame.
+        Index.drop_duplicates : Remove duplicate values from Index.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set to False and all others to True:
+
+        >>> import cudf
+        >>> idx = cudf.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> idx.duplicated()
+        array([False, False,  True, False,  True])
+
+        which is equivalent to
+
+        >>> idx.duplicated(keep='first')
+        array([False, False,  True, False,  True])
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set to False and all others to True:
+
+        >>> idx.duplicated(keep='last')
+        array([ True, False,  True, False, False])
+
+        By setting keep to ``False``, all duplicates are True:
+
+        >>> idx.duplicated(keep=False)
+        array([ True, False,  True, False,  True])
+        """
+        return self.to_series().duplicated(keep=keep).to_cupy()
+
+    def dropna(self, how="any"):
+        """
+        Drop null rows from Index.
+
+        how : {"any", "all"}, default "any"
+            Specifies how to decide whether to drop a row.
+            "any" (default) drops rows containing at least
+            one null value. "all" drops only rows containing
+            *all* null values.
+        """
+
+        # This is to be consistent with IndexedFrame.dropna to handle nans
+        # as nulls by default
+        data_columns = [
+            col.nans_to_nulls()
+            if isinstance(col, cudf.core.column.NumericalColumn)
+            else col
+            for col in self._columns
+        ]
+
+        return self._from_columns_like_self(
+            drop_nulls(
+                data_columns,
+                how=how,
+                keys=range(len(data_columns)),
+            ),
+            self._column_names,
+        )
+
+    def _gather(self, gather_map, nullify=False, check_bounds=True):
+        """Gather rows of index specified by indices in `gather_map`.
+
+        Skip bounds checking if check_bounds is False.
+        Set rows to null for all out of bound indices if nullify is `True`.
+        """
+        gather_map = cudf.core.column.as_column(gather_map)
+
+        # TODO: For performance, the check and conversion of gather map should
+        # be done by the caller. This check will be removed in future release.
+        if not is_integer_dtype(gather_map.dtype):
+            gather_map = gather_map.astype(size_type_dtype)
+
+        if not _gather_map_is_valid(
+            gather_map, len(self), check_bounds, nullify
+        ):
+            raise IndexError("Gather map index is out of bounds.")
+
+        return self._from_columns_like_self(
+            gather(list(self._columns), gather_map, nullify=nullify),
+            self._column_names,
+        )
+
+    def take(self, indices, axis=0, allow_fill=True, fill_value=None):
+        """Return a new index containing the rows specified by *indices*
+
+        Parameters
+        ----------
+        indices : array-like
+            Array of ints indicating which positions to take.
+        axis : int
+            The axis over which to select values, always 0.
+        allow_fill : Unsupported
+        fill_value : Unsupported
+
+        Returns
+        -------
+        out : Index
+            New object with desired subset of rows.
+
+        Examples
+        --------
+        >>> idx = cudf.Index(['a', 'b', 'c', 'd', 'e'])
+        >>> idx.take([2, 0, 4, 3])
+        StringIndex(['c' 'a' 'e' 'd'], dtype='object')
+        """
+
+        if axis not in {0, "index"}:
+            raise NotImplementedError(
+                "Gather along column axis is not yet supported."
+            )
+        if not allow_fill or fill_value is not None:
+            raise NotImplementedError(
+                "`allow_fill` and `fill_value` are unsupported."
+            )
+
+        return self._gather(indices)
+
+    def _apply_boolean_mask(self, boolean_mask):
+        """Apply boolean mask to each row of `self`.
+
+        Rows corresponding to `False` is dropped.
+        """
+        boolean_mask = cudf.core.column.as_column(boolean_mask)
+        if not is_bool_dtype(boolean_mask.dtype):
+            raise ValueError("boolean_mask is not boolean type.")
+
+        return self._from_columns_like_self(
+            apply_boolean_mask(list(self._columns), boolean_mask),
+            column_names=self._column_names,
+        )
+
+    def repeat(self, repeats, axis=None):
+        """Repeat elements of a Index.
+
+        Returns a new Index where each element of the current Index is repeated
+        consecutively a given number of times.
+
+        Parameters
+        ----------
+        repeats : int, or array of ints
+            The number of repetitions for each element. This should
+            be a non-negative integer. Repeating 0 times will return
+            an empty object.
+
+        Returns
+        -------
+        Index
+            A newly created object of same type as caller with repeated
+            elements.
+
+        Examples
+        --------
+        >>> index = cudf.Index([10, 22, 33, 55])
+        >>> index
+        Int64Index([10, 22, 33, 55], dtype='int64')
+        >>> index.repeat(5)
+        Int64Index([10, 10, 10, 10, 10, 22, 22, 22, 22, 22, 33,
+                    33, 33, 33, 33, 55, 55, 55, 55, 55],
+                dtype='int64')
+        """
+        raise NotImplementedError
+
+    def _split_columns_by_levels(self, levels):
+        if isinstance(levels, int) and levels > 0:
+            raise ValueError(f"Out of bound level: {levels}")
+        return (
+            [self._data[self.name]],
+            [],
+            ["index" if self.name is None else self.name],
+            [],
+        )
+
+    def _split(self, splits):
+        raise NotImplementedError
+
+
+def _get_result_name(left_name, right_name):
+    return left_name if _is_same_name(left_name, right_name) else None
diff --git a/python/cudf/cudf/core/_compat.py b/python/cudf/cudf/core/_compat.py
new file mode 100644
index 0000000..888b94e
--- /dev/null
+++ b/python/cudf/cudf/core/_compat.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import pandas as pd
+from packaging import version
+
+PANDAS_VERSION = version.parse(pd.__version__)
+PANDAS_GE_133 = PANDAS_VERSION >= version.parse("1.3.3")
+PANDAS_GE_134 = PANDAS_VERSION >= version.parse("1.3.4")
+PANDAS_LT_140 = PANDAS_VERSION < version.parse("1.4.0")
+PANDAS_GE_150 = PANDAS_VERSION >= version.parse("1.5.0")
+PANDAS_LT_153 = PANDAS_VERSION < version.parse("1.5.3")
+PANDAS_GE_200 = PANDAS_VERSION >= version.parse("2.0.0")
+PANDAS_GE_210 = PANDAS_VERSION >= version.parse("2.1.0")
diff --git a/python/cudf/cudf/core/_internals/__init__.py b/python/cudf/cudf/core/_internals/__init__.py
new file mode 100644
index 0000000..6faeeff
--- /dev/null
+++ b/python/cudf/cudf/core/_internals/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/core/_internals/expressions.py b/python/cudf/cudf/core/_internals/expressions.py
new file mode 100644
index 0000000..5cb9f03
--- /dev/null
+++ b/python/cudf/cudf/core/_internals/expressions.py
@@ -0,0 +1,224 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import ast
+import functools
+from typing import List, Tuple
+
+from cudf._lib.expressions import (
+    ASTOperator,
+    ColumnReference,
+    Expression,
+    Literal,
+    Operation,
+)
+
+# This dictionary encodes the mapping from Python AST operators to their cudf
+# counterparts.
+python_cudf_operator_map = {
+    # Binary operators
+    ast.Add: ASTOperator.ADD,
+    ast.Sub: ASTOperator.SUB,
+    ast.Mult: ASTOperator.MUL,
+    ast.Div: ASTOperator.DIV,
+    ast.FloorDiv: ASTOperator.FLOOR_DIV,
+    ast.Mod: ASTOperator.PYMOD,
+    ast.Pow: ASTOperator.POW,
+    ast.Eq: ASTOperator.EQUAL,
+    ast.NotEq: ASTOperator.NOT_EQUAL,
+    ast.Lt: ASTOperator.LESS,
+    ast.Gt: ASTOperator.GREATER,
+    ast.LtE: ASTOperator.LESS_EQUAL,
+    ast.GtE: ASTOperator.GREATER_EQUAL,
+    ast.BitXor: ASTOperator.BITWISE_XOR,
+    # TODO: The mapping of logical/bitwise operators here is inconsistent with
+    # pandas. In pandas, Both `BitAnd` and `And` map to
+    # `ASTOperator.LOGICAL_AND` for booleans, while they map to
+    # `ASTOperator.BITWISE_AND` for integers. However, there is no good way to
+    # encode this at present because expressions can be arbitrarily nested so
+    # we won't know the dtype of the input without inserting a much more
+    # complex traversal of the expression tree to determine the output types at
+    # each node. For now, we'll rely on users to use the appropriate operator.
+    ast.BitAnd: ASTOperator.BITWISE_AND,
+    ast.BitOr: ASTOperator.BITWISE_OR,
+    ast.And: ASTOperator.LOGICAL_AND,
+    ast.Or: ASTOperator.LOGICAL_OR,
+    # Unary operators
+    ast.Invert: ASTOperator.BIT_INVERT,
+    ast.Not: ASTOperator.NOT,
+    # TODO: Missing USub, possibility other unary ops?
+}
+
+
+# Mapping between Python function names encode in an ast.Call node and the
+# corresponding libcudf C++ AST operators.
+python_cudf_function_map = {
+    # TODO: Operators listed on
+    # https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#expression-evaluation-via-eval  # noqa: E501
+    # that we don't support yet:
+    # expm1, log1p, arctan2 and log10.
+    "isnull": ASTOperator.IS_NULL,
+    "isna": ASTOperator.IS_NULL,
+    "sin": ASTOperator.SIN,
+    "cos": ASTOperator.COS,
+    "tan": ASTOperator.TAN,
+    "arcsin": ASTOperator.ARCSIN,
+    "arccos": ASTOperator.ARCCOS,
+    "arctan": ASTOperator.ARCTAN,
+    "sinh": ASTOperator.SINH,
+    "cosh": ASTOperator.COSH,
+    "tanh": ASTOperator.TANH,
+    "arcsinh": ASTOperator.ARCSINH,
+    "arccosh": ASTOperator.ARCCOSH,
+    "arctanh": ASTOperator.ARCTANH,
+    "exp": ASTOperator.EXP,
+    "log": ASTOperator.LOG,
+    "sqrt": ASTOperator.SQRT,
+    "abs": ASTOperator.ABS,
+    "ceil": ASTOperator.CEIL,
+    "floor": ASTOperator.FLOOR,
+    # TODO: Operators supported by libcudf with no Python function analog.
+    # ast.rint: ASTOperator.RINT,
+    # ast.cbrt: ASTOperator.CBRT,
+}
+
+
+class libcudfASTVisitor(ast.NodeVisitor):
+    """A NodeVisitor specialized for constructing a libcudf expression tree.
+
+    This visitor is designed to handle AST nodes that have libcudf equivalents.
+    It constructs column references from names and literals from constants,
+    then builds up operations. The final result can be accessed using the
+    `expression` property. The visitor must be kept in scope for as long as the
+    expression is needed because all of the underlying libcudf expressions will
+    be destroyed when the libcudfASTVisitor is.
+
+    Parameters
+    ----------
+    col_names : Tuple[str]
+        The column names used to map the names in an expression.
+    """
+
+    def __init__(self, col_names: Tuple[str]):
+        self.stack: List[Expression] = []
+        self.nodes: List[Expression] = []
+        self.col_names = col_names
+
+    @property
+    def expression(self):
+        """Expression: The result of parsing an AST."""
+        assert len(self.stack) == 1
+        return self.stack[-1]
+
+    def visit_Name(self, node):
+        try:
+            col_id = self.col_names.index(node.id)
+        except ValueError:
+            raise ValueError(f"Unknown column name {node.id}")
+        self.stack.append(ColumnReference(col_id))
+
+    def visit_Constant(self, node):
+        if not isinstance(node, (ast.Num, ast.Str)):
+            raise ValueError(
+                f"Unsupported literal {repr(node.value)} of type "
+                "{type(node.value).__name__}"
+            )
+        self.stack.append(Literal(node.value))
+
+    def visit_UnaryOp(self, node):
+        self.visit(node.operand)
+        self.nodes.append(self.stack.pop())
+        if isinstance(node.op, ast.USub):
+            # TODO: Except for leaf nodes, we won't know the type of the
+            # operand, so there's no way to know whether this should be a float
+            # or an int. We should maybe see what Spark does, and this will
+            # probably require casting.
+            self.nodes.append(Literal(-1))
+            op = ASTOperator.MUL
+            self.stack.append(Operation(op, self.nodes[-1], self.nodes[-2]))
+        elif isinstance(node.op, ast.UAdd):
+            self.stack.append(self.nodes[-1])
+        else:
+            op = python_cudf_operator_map[type(node.op)]
+            self.stack.append(Operation(op, self.nodes[-1]))
+
+    def visit_BinOp(self, node):
+        self.visit(node.left)
+        self.visit(node.right)
+        self.nodes.append(self.stack.pop())
+        self.nodes.append(self.stack.pop())
+
+        op = python_cudf_operator_map[type(node.op)]
+        self.stack.append(Operation(op, self.nodes[-1], self.nodes[-2]))
+
+    def _visit_BoolOp_Compare(self, operators, operands, has_multiple_ops):
+        # Helper function handling the common components of parsing BoolOp and
+        # Compare AST nodes. These two types of nodes both support chaining
+        # (e.g. `a > b > c` is equivalent to `a > b and b > c`, so this
+        # function helps standardize that.
+
+        # TODO: Whether And/Or and BitAnd/BitOr actually correspond to
+        # logical or bitwise operators depends on the data types that they
+        # are applied to. We'll need to add logic to map to that.
+        inner_ops = []
+        for op, (left, right) in zip(operators, operands):
+            # Note that this will lead to duplicate nodes, e.g. if
+            # the comparison is `a < b < c` that will be encoded as
+            # `a < b and b < c`. We could potentially optimize by caching
+            # expressions by name so that we only construct them once.
+            self.visit(left)
+            self.visit(right)
+
+            self.nodes.append(self.stack.pop())
+            self.nodes.append(self.stack.pop())
+
+            op = python_cudf_operator_map[type(op)]
+            inner_ops.append(Operation(op, self.nodes[-1], self.nodes[-2]))
+
+        self.nodes.extend(inner_ops)
+
+        # If we have more than one comparator, we need to link them
+        # together with LOGICAL_AND operators.
+        if has_multiple_ops:
+            op = ASTOperator.LOGICAL_AND
+
+            def _combine_compare_ops(left, right):
+                self.nodes.append(Operation(op, left, right))
+                return self.nodes[-1]
+
+            functools.reduce(_combine_compare_ops, inner_ops)
+
+        self.stack.append(self.nodes[-1])
+
+    def visit_BoolOp(self, node):
+        operators = [node.op] * (len(node.values) - 1)
+        operands = zip(node.values[:-1], node.values[1:])
+        self._visit_BoolOp_Compare(operators, operands, len(node.values) > 2)
+
+    def visit_Compare(self, node):
+        operands = (node.left, *node.comparators)
+        has_multiple_ops = len(operands) > 2
+        operands = zip(operands[:-1], operands[1:])
+        self._visit_BoolOp_Compare(node.ops, operands, has_multiple_ops)
+
+    def visit_Call(self, node):
+        try:
+            op = python_cudf_function_map[node.func.id]
+        except KeyError:
+            raise ValueError(f"Unsupported function {node.func}.")
+        # Assuming only unary functions are supported, which is checked above.
+        if len(node.args) != 1 or node.keywords:
+            raise ValueError(
+                f"Function {node.func} only accepts one positional "
+                "argument."
+            )
+        self.visit(node.args[0])
+
+        self.nodes.append(self.stack.pop())
+        self.stack.append(Operation(op, self.nodes[-1]))
+
+
+@functools.lru_cache(256)
+def parse_expression(expr: str, col_names: Tuple[str]):
+    visitor = libcudfASTVisitor(col_names)
+    visitor.visit(ast.parse(expr))
+    return visitor
diff --git a/python/cudf/cudf/core/_internals/timezones.py b/python/cudf/cudf/core/_internals/timezones.py
new file mode 100644
index 0000000..67043d3
--- /dev/null
+++ b/python/cudf/cudf/core/_internals/timezones.py
@@ -0,0 +1,254 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+import os
+import zoneinfo
+from functools import lru_cache
+from typing import Tuple, cast
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf._lib.labeling import label_bins
+from cudf._lib.search import search_sorted
+from cudf._lib.timezone import make_timezone_transition_table
+from cudf.core.column.column import as_column, build_column
+from cudf.core.column.datetime import DatetimeColumn, DatetimeTZColumn
+from cudf.core.dataframe import DataFrame
+from cudf.utils.dtypes import _get_base_dtype
+
+
+@lru_cache(maxsize=20)
+def get_tz_data(zone_name):
+    """
+    Return timezone data (transition times and UTC offsets) for the
+    given IANA time zone.
+
+    Parameters
+    ----------
+    zone_name: str
+        IANA time zone name
+
+    Returns
+    -------
+    DataFrame with two columns containing the transition times
+    ("transition_times") and corresponding UTC offsets ("offsets").
+    """
+    try:
+        # like zoneinfo, we first look in TZPATH
+        tz_table = _find_and_read_tzfile_tzpath(zone_name)
+    except zoneinfo.ZoneInfoNotFoundError:
+        # if that fails, we fall back to using `tzdata`
+        tz_table = _find_and_read_tzfile_tzdata(zone_name)
+    return tz_table
+
+
+def _find_and_read_tzfile_tzpath(zone_name):
+    for search_path in zoneinfo.TZPATH:
+        if os.path.isfile(os.path.join(search_path, zone_name)):
+            return _read_tzfile_as_frame(search_path, zone_name)
+    raise zoneinfo.ZoneInfoNotFoundError(zone_name)
+
+
+def _find_and_read_tzfile_tzdata(zone_name):
+    import importlib.resources
+
+    package_base = "tzdata.zoneinfo"
+    try:
+        return _read_tzfile_as_frame(
+            str(importlib.resources.files(package_base)), zone_name
+        )
+    # TODO: make it so that the call to libcudf raises a
+    # FileNotFoundError instead of a RuntimeError
+    except (ImportError, FileNotFoundError, UnicodeEncodeError, RuntimeError):
+        # the "except" part of this try-except is basically vendored
+        # from the zoneinfo library.
+        #
+        # There are three types of exception that can be raised that all amount
+        # to "we cannot find this key":
+        #
+        # ImportError: If package_name doesn't exist (e.g. if tzdata is not
+        #   installed, or if there's an error in the folder name like
+        #   Amrica/New_York)
+        # FileNotFoundError: If resource_name doesn't exist in the package
+        #   (e.g. Europe/Krasnoy)
+        # UnicodeEncodeError: If package_name or resource_name are not UTF-8,
+        #   such as keys containing a surrogate character.
+        raise zoneinfo.ZoneInfoNotFoundError(zone_name)
+
+
+def _read_tzfile_as_frame(tzdir, zone_name):
+    transition_times_and_offsets = make_timezone_transition_table(
+        tzdir, zone_name
+    )
+
+    if not transition_times_and_offsets:
+        # this happens for UTC-like zones
+        min_date = np.int64(np.iinfo("int64").min + 1).astype("M8[s]")
+        transition_times_and_offsets = as_column([min_date]), as_column(
+            [np.timedelta64(0, "s")]
+        )
+
+    return DataFrame._from_columns(
+        transition_times_and_offsets, ["transition_times", "offsets"]
+    )
+
+
+def _find_ambiguous_and_nonexistent(
+    data: DatetimeColumn, zone_name: str
+) -> Tuple:
+    """
+    Recognize ambiguous and nonexistent timestamps for the given timezone.
+
+    Returns a tuple of columns, both of "bool" dtype and of the same
+    size as `data`, that respectively indicate ambiguous and
+    nonexistent timestamps in `data` with the value `True`.
+
+    Ambiguous and/or nonexistent timestamps are only possible if any
+    transitions occur in the time zone database for the given timezone.
+    If no transitions occur, the tuple `(False, False)` is returned.
+    """
+    tz_data_for_zone = get_tz_data(zone_name)
+    transition_times = tz_data_for_zone["transition_times"]
+    offsets = tz_data_for_zone["offsets"].astype(
+        f"timedelta64[{data._time_unit}]"
+    )
+
+    if len(offsets) == 1:  # no transitions
+        return False, False
+
+    transition_times, offsets, old_offsets = (
+        transition_times[1:]._column,
+        offsets[1:]._column,
+        offsets[:-1]._column,
+    )
+
+    # Assume we have two clocks at the moment of transition:
+    # - Clock 1 is turned forward or backwards correctly
+    # - Clock 2 makes no changes
+    clock_1 = transition_times + offsets
+    clock_2 = transition_times + old_offsets
+
+    # At the start of an ambiguous time period, Clock 1 (which has
+    # been turned back) reads less than Clock 2:
+    cond = clock_1 < clock_2
+    ambiguous_begin = clock_1.apply_boolean_mask(cond)
+
+    # The end of an ambiguous time period is what Clock 2 reads at
+    # the moment of transition:
+    ambiguous_end = clock_2.apply_boolean_mask(cond)
+    ambiguous = label_bins(
+        data,
+        left_edges=ambiguous_begin,
+        left_inclusive=True,
+        right_edges=ambiguous_end,
+        right_inclusive=False,
+    ).notnull()
+
+    # At the start of a non-existent time period, Clock 2 reads less
+    # than Clock 1 (which has been turned forward):
+    cond = clock_1 > clock_2
+    nonexistent_begin = clock_2.apply_boolean_mask(cond)
+
+    # The end of the non-existent time period is what Clock 1 reads
+    # at the moment of transition:
+    nonexistent_end = clock_1.apply_boolean_mask(cond)
+    nonexistent = label_bins(
+        data,
+        left_edges=nonexistent_begin,
+        left_inclusive=True,
+        right_edges=nonexistent_end,
+        right_inclusive=False,
+    ).notnull()
+
+    return ambiguous, nonexistent
+
+
+def localize(
+    data: DatetimeColumn, zone_name: str, ambiguous, nonexistent
+) -> DatetimeTZColumn:
+    if ambiguous != "NaT":
+        raise NotImplementedError(
+            "Only ambiguous='NaT' is currently supported"
+        )
+    if nonexistent != "NaT":
+        raise NotImplementedError(
+            "Only nonexistent='NaT' is currently supported"
+        )
+    if isinstance(data, DatetimeTZColumn):
+        raise ValueError(
+            "Already localized. "
+            "Use `tz_convert` to convert between time zones."
+        )
+    dtype = pd.DatetimeTZDtype(data._time_unit, zone_name)
+    ambiguous, nonexistent = _find_ambiguous_and_nonexistent(data, zone_name)
+    localized = cast(
+        DatetimeColumn,
+        data._scatter_by_column(
+            data.isnull() | (ambiguous | nonexistent),
+            cudf.Scalar(cudf.NaT, dtype=data.dtype),
+        ),
+    )
+    gmt_data = local_to_utc(localized, zone_name)
+    return cast(
+        DatetimeTZColumn,
+        build_column(
+            data=gmt_data.base_data,
+            dtype=dtype,
+            mask=localized.base_mask,
+            size=gmt_data.size,
+            offset=gmt_data.offset,
+        ),
+    )
+
+
+def delocalize(data: DatetimeColumn) -> DatetimeColumn:
+    """
+    Convert a timezone-aware datetime column to a timezone-naive one.
+    If the column is already timezone-naive, return it as is.
+    """
+    if isinstance(data, DatetimeTZColumn):
+        return data._local_time
+    # already timezone-naive:
+    return data
+
+
+def convert(data: DatetimeTZColumn, zone_name: str) -> DatetimeTZColumn:
+    if not isinstance(data, DatetimeTZColumn):
+        raise TypeError(
+            "Cannot convert from timezone-naive timestamps to "
+            "timezone-aware timestamps. For that, "
+            "use `tz_localize`."
+        )
+    if zone_name == str(data.dtype.tz):
+        return data.copy()
+    utc_time = data._utc_time
+    out = cast(
+        DatetimeTZColumn,
+        build_column(
+            data=utc_time.base_data,
+            dtype=pd.DatetimeTZDtype(data._time_unit, zone_name),
+            mask=utc_time.base_mask,
+            size=utc_time.size,
+            offset=utc_time.offset,
+        ),
+    )
+    return out
+
+
+def utc_to_local(data: DatetimeColumn, zone_name: str) -> DatetimeColumn:
+    tz_data_for_zone = get_tz_data(zone_name)
+    transition_times, offsets = tz_data_for_zone._columns
+    transition_times = transition_times.astype(_get_base_dtype(data.dtype))
+    indices = search_sorted([transition_times], [data], "right") - 1
+    offsets_from_utc = offsets.take(indices, nullify=True)
+    return data + offsets_from_utc
+
+
+def local_to_utc(data: DatetimeColumn, zone_name: str) -> DatetimeColumn:
+    tz_data_for_zone = get_tz_data(zone_name)
+    transition_times, offsets = tz_data_for_zone._columns
+    transition_times_local = (transition_times + offsets).astype(data.dtype)
+    indices = search_sorted([transition_times_local], [data], "right") - 1
+    offsets_to_utc = offsets.take(indices, nullify=True)
+    return data - offsets_to_utc
diff --git a/python/cudf/cudf/core/_internals/where.py b/python/cudf/cudf/core/_internals/where.py
new file mode 100644
index 0000000..e9131fd
--- /dev/null
+++ b/python/cudf/cudf/core/_internals/where.py
@@ -0,0 +1,127 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import warnings
+from typing import Tuple, Union
+
+import numpy as np
+
+import cudf
+from cudf._typing import ScalarLike
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    is_bool_dtype,
+    is_categorical_dtype,
+    is_scalar,
+)
+from cudf.core.column import ColumnBase
+from cudf.utils.dtypes import (
+    _can_cast,
+    _dtype_can_hold_element,
+    find_common_type,
+    is_mixed_with_object_dtype,
+)
+
+
+def _normalize_categorical(input_col, other):
+    if isinstance(input_col, cudf.core.column.CategoricalColumn):
+        if cudf.api.types.is_scalar(other):
+            try:
+                other = input_col._encode(other)
+            except ValueError:
+                # When other is not present in categories,
+                # fill with Null.
+                other = None
+            other = cudf.Scalar(other, dtype=input_col.codes.dtype)
+        elif isinstance(other, cudf.core.column.CategoricalColumn):
+            other = other.codes
+
+        input_col = input_col.codes
+    return input_col, other
+
+
+def _check_and_cast_columns_with_other(
+    source_col: ColumnBase,
+    other: Union[ScalarLike, ColumnBase],
+    inplace: bool,
+) -> Tuple[ColumnBase, Union[ScalarLike, ColumnBase]]:
+    # Returns type-casted `source_col` & `other` based on `inplace`.
+    source_dtype = source_col.dtype
+    if is_categorical_dtype(source_dtype):
+        return _normalize_categorical(source_col, other)
+
+    other_is_scalar = is_scalar(other)
+    if other_is_scalar:
+        if (isinstance(other, float) and not np.isnan(other)) and (
+            source_dtype.type(other) != other
+        ):
+            raise TypeError(
+                f"Cannot safely cast non-equivalent "
+                f"{type(other).__name__} to {source_dtype.name}"
+            )
+
+        if cudf.utils.utils.is_na_like(other):
+            return _normalize_categorical(
+                source_col, cudf.Scalar(other, dtype=source_dtype)
+            )
+
+    mixed_err = (
+        "cudf does not support mixed types, please type-cast the column of "
+        "dataframe/series and other to same dtypes."
+    )
+
+    if inplace:
+        other = cudf.Scalar(other) if other_is_scalar else other
+        if is_mixed_with_object_dtype(other, source_col):
+            raise TypeError(mixed_err)
+
+        if not _can_cast(other.dtype, source_dtype):
+            warnings.warn(
+                f"Type-casting from {other.dtype} "
+                f"to {source_dtype}, there could be potential data loss"
+            )
+        return _normalize_categorical(source_col, other.astype(source_dtype))
+
+    if _is_non_decimal_numeric_dtype(source_dtype) and _can_cast(
+        other, source_dtype
+    ):
+        common_dtype = source_dtype
+    elif (
+        isinstance(source_col, cudf.core.column.NumericalColumn)
+        and other_is_scalar
+        and _dtype_can_hold_element(source_dtype, other)
+    ):
+        common_dtype = source_dtype
+    else:
+        common_dtype = find_common_type(
+            [
+                source_dtype,
+                np.min_scalar_type(other) if other_is_scalar else other.dtype,
+            ]
+        )
+
+    if other_is_scalar:
+        other = cudf.Scalar(other)
+
+    if is_mixed_with_object_dtype(other, source_col) or (
+        is_bool_dtype(source_col) and not is_bool_dtype(common_dtype)
+    ):
+        raise TypeError(mixed_err)
+
+    other = other.astype(common_dtype)
+
+    return _normalize_categorical(source_col.astype(common_dtype), other)
+
+
+def _make_categorical_like(result, column):
+    if isinstance(column, cudf.core.column.CategoricalColumn):
+        result = cudf.core.column.build_categorical_column(
+            categories=column.categories,
+            codes=cudf.core.column.build_column(
+                result.base_data, dtype=result.dtype
+            ),
+            mask=result.base_mask,
+            size=result.size,
+            offset=result.offset,
+            ordered=column.ordered,
+        )
+    return result
diff --git a/python/cudf/cudf/core/abc.py b/python/cudf/cudf/core/abc.py
new file mode 100644
index 0000000..adf9fe3
--- /dev/null
+++ b/python/cudf/cudf/core/abc.py
@@ -0,0 +1,186 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+"""Common abstract base classes for cudf."""
+
+import pickle
+
+import numpy
+
+import cudf
+
+
+class Serializable:
+    """A serializable object composed of device memory buffers.
+
+    This base class defines a standard serialization protocol for objects
+    encapsulating device memory buffers. Serialization proceeds by copying
+    device data onto the host, then returning it along with suitable metadata
+    for reconstruction of the object. Deserialization performs the reverse
+    process, copying the serialized data from the host to new device buffers.
+    Subclasses must define the abstract methods :meth:`~.serialize` and
+    :meth:`~.deserialize`. The former defines the conversion of the object
+    into a representative collection of metadata and data buffers, while the
+    latter converts back from that representation into an equivalent object.
+    """
+
+    def serialize(self):
+        """Generate an equivalent serializable representation of an object.
+
+        Subclasses must implement this method to define how the attributes of
+        the object are converted into a serializable representation. A common
+        solution is to construct a list containing device buffer attributes in
+        a well-defined order that can be reinterpreted upon deserialization,
+        then place all other lightweight attributes into the metadata
+        dictionary.
+
+        Returns
+        -------
+        Tuple[Dict, List]
+            The first element of the returned tuple is a dict containing any
+            serializable metadata required to reconstruct the object. The
+            second element is a list containing the device data buffers
+            or memoryviews of the object.
+
+        :meta private:
+        """
+        raise NotImplementedError(
+            "Subclasses of Serializable must implement serialize"
+        )
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        """Generate an object from a serialized representation.
+
+        Subclasses must implement this method to define how objects of that
+        class can be constructed from a serialized representation generalized
+        by :meth:`serialize`.
+
+        Parameters
+        ----------
+        header : dict
+            The metadata required to reconstruct the object.
+        frames : list
+            The Buffers or memoryviews that the object should contain.
+
+        Returns
+        -------
+        Serializable
+            A new instance of `cls` (a subclass of `Serializable`) equivalent
+            to the instance that was serialized to produce the header and
+            frames.
+
+        :meta private:
+        """
+        raise NotImplementedError(
+            "Subclasses of Serializable must implement deserialize"
+        )
+
+    def device_serialize(self):
+        """Serialize data and metadata associated with device memory.
+
+        Returns
+        -------
+        header : dict
+            The metadata required to reconstruct the object.
+        frames : list
+            The Buffer or memoryview objects that the object
+            should contain.
+
+        :meta private:
+        """
+        header, frames = self.serialize()
+        assert all(
+            isinstance(f, (cudf.core.buffer.Buffer, memoryview))
+            for f in frames
+        )
+        header["type-serialized"] = pickle.dumps(type(self))
+        header["is-cuda"] = [
+            hasattr(f, "__cuda_array_interface__") for f in frames
+        ]
+        header["lengths"] = [f.nbytes for f in frames]
+        return header, frames
+
+    @classmethod
+    def device_deserialize(cls, header, frames):
+        """Perform device-side deserialization tasks.
+
+        The primary purpose of this method is the creation of device memory
+        buffers from host buffers where necessary.
+
+        Parameters
+        ----------
+        header : dict
+            The metadata required to reconstruct the object.
+        frames : list
+            The Buffers or memoryviews that the object should contain.
+
+        Returns
+        -------
+        Serializable
+            A new instance of `cls` (a subclass of `Serializable`) equivalent
+            to the instance that was serialized to produce the header and
+            frames.
+
+        :meta private:
+        """
+        typ = pickle.loads(header["type-serialized"])
+        frames = [
+            cudf.core.buffer.as_buffer(f) if c else memoryview(f)
+            for c, f in zip(header["is-cuda"], frames)
+        ]
+        return typ.deserialize(header, frames)
+
+    def host_serialize(self):
+        """Serialize data and metadata associated with host memory.
+
+        Returns
+        -------
+        header : dict
+            The metadata required to reconstruct the object.
+        frames : list
+            The Buffers or memoryviews that the object should contain.
+
+        :meta private:
+        """
+        header, frames = self.device_serialize()
+        header["writeable"] = len(frames) * (None,)
+        frames = [
+            f.memoryview() if c else memoryview(f)
+            for c, f in zip(header["is-cuda"], frames)
+        ]
+        return header, frames
+
+    @classmethod
+    def host_deserialize(cls, header, frames):
+        """Perform device-side deserialization tasks.
+
+        Parameters
+        ----------
+        header : dict
+            The metadata required to reconstruct the object.
+        frames : list
+            The Buffers or memoryviews that the object should contain.
+
+        Returns
+        -------
+        Serializable
+            A new instance of `cls` (a subclass of `Serializable`) equivalent
+            to the instance that was serialized to produce the header and
+            frames.
+
+        :meta private:
+        """
+        frames = [
+            cudf.core.buffer.as_buffer(f) if c else f
+            for c, f in zip(header["is-cuda"], map(memoryview, frames))
+        ]
+        obj = cls.device_deserialize(header, frames)
+        return obj
+
+    def __reduce_ex__(self, protocol):
+        header, frames = self.host_serialize()
+
+        # Since memoryviews are not pickable, we convert them to numpy
+        # arrays (zero-copy). This works seamlessly because host_deserialize
+        # converts the frames back into memoryviews.
+        frames = [numpy.asarray(f) for f in frames]
+        return self.host_deserialize, (header, frames)
diff --git a/python/cudf/cudf/core/algorithms.py b/python/cudf/cudf/core/algorithms.py
new file mode 100644
index 0000000..25d5802
--- /dev/null
+++ b/python/cudf/cudf/core/algorithms.py
@@ -0,0 +1,207 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+import warnings
+
+import cupy as cp
+import numpy as np
+
+from cudf.core.column import as_column
+from cudf.core.copy_types import BooleanMask
+from cudf.core.index import RangeIndex, as_index
+from cudf.core.indexed_frame import IndexedFrame
+from cudf.core.scalar import Scalar
+from cudf.options import get_option
+from cudf.utils.dtypes import can_convert_to_column
+
+
+def factorize(
+    values, sort=False, na_sentinel=None, use_na_sentinel=None, size_hint=None
+):
+    """Encode the input values as integer labels
+
+    Parameters
+    ----------
+    values: Series, Index, or CuPy array
+        The data to be factorized.
+    sort : bool, default True
+        Sort uniques and shuffle codes to maintain the relationship.
+    na_sentinel : number, default -1
+        Value to indicate missing category.
+
+        .. deprecated:: 23.04
+
+           The na_sentinel argument is deprecated and will be removed in
+           a future version of cudf. Specify use_na_sentinel as
+           either True or False.
+    use_na_sentinel : bool, default True
+        If True, the sentinel -1 will be used for NA values.
+        If False, NA values will be encoded as non-negative
+        integers and will not drop the NA from the uniques
+        of the values.
+
+    Returns
+    -------
+    (labels, cats) : (cupy.ndarray, cupy.ndarray or Index)
+        - *labels* contains the encoded values
+        - *cats* contains the categories in order that the N-th
+            item corresponds to the (N-1) code.
+
+    See Also
+    --------
+    cudf.Series.factorize : Encode the input values of Series.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> import numpy as np
+    >>> data = cudf.Series(['a', 'c', 'c'])
+    >>> codes, uniques = cudf.factorize(data)
+    >>> codes
+    array([0, 1, 1], dtype=int8)
+    >>> uniques
+    StringIndex(['a' 'c'], dtype='object')
+
+    When ``use_na_sentinel=True`` (the default), missing values are indicated
+    in the `codes` with the sentinel value ``-1`` and missing values are not
+    included in `uniques`.
+
+    >>> codes, uniques = cudf.factorize(['b', None, 'a', 'c', 'b'])
+    >>> codes
+    array([ 1, -1,  0,  2,  1], dtype=int8)
+    >>> uniques
+    StringIndex(['a' 'b' 'c'], dtype='object')
+
+    If NA is in the values, and we want to include NA in the uniques of the
+    values, it can be achieved by setting ``use_na_sentinel=False``.
+
+    >>> values = np.array([1, 2, 1, np.nan])
+    >>> codes, uniques = cudf.factorize(values)
+    >>> codes
+    array([ 0,  1,  0, -1], dtype=int8)
+    >>> uniques
+    Float64Index([1.0, 2.0], dtype='float64')
+    >>> codes, uniques = cudf.factorize(values, use_na_sentinel=False)
+    >>> codes
+    array([1, 2, 1, 0], dtype=int8)
+    >>> uniques
+    Float64Index([<NA>, 1.0, 2.0], dtype='float64')
+    """
+    # TODO: Drop `na_sentinel` in the next release immediately after
+    # pandas 2.0 upgrade.
+    if na_sentinel is not None and use_na_sentinel is not None:
+        raise ValueError(
+            "Cannot specify both `na_sentinel` and `use_na_sentile`; "
+            f"got `na_sentinel={na_sentinel}` and "
+            f"`use_na_sentinel={use_na_sentinel}`"
+        )
+
+    return_cupy_array = isinstance(values, cp.ndarray)
+
+    if not can_convert_to_column(values):
+        raise TypeError(
+            "'values' can only be a Series, Index, or CuPy array, "
+            f"got {type(values)}"
+        )
+
+    values = as_column(values)
+
+    if na_sentinel is None:
+        na_sentinel = (
+            -1
+            if use_na_sentinel is None or use_na_sentinel
+            else Scalar(None, dtype=values.dtype)
+        )
+    else:
+        if na_sentinel is None:
+            msg = (
+                "Specifying `na_sentinel=None` is deprecated, specify "
+                "`use_na_sentinel=False` instead."
+            )
+        elif na_sentinel == -1:
+            msg = (
+                "Specifying `na_sentinel=-1` is deprecated, specify "
+                "`use_na_sentinel=True` instead."
+            )
+        else:
+            msg = (
+                "Specifying the specific value to use for `na_sentinel` is "
+                "deprecated and will be removed in a future version of cudf. "
+                "Specify `use_na_sentinel=True` to use the sentinel value -1, "
+                "and `use_na_sentinel=False` to encode NA values.",
+            )
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(msg, FutureWarning)
+
+    if size_hint:
+        warnings.warn("size_hint is not applicable for cudf.factorize")
+
+    if use_na_sentinel is None or use_na_sentinel:
+        cats = values.dropna()
+    else:
+        cats = values
+
+    cats = cats.unique().astype(values.dtype)
+
+    if sort:
+        cats = cats.sort_values()
+
+    labels = values._label_encoding(
+        cats=cats,
+        na_sentinel=Scalar(na_sentinel),
+        dtype="int64" if get_option("mode.pandas_compatible") else None,
+    ).values
+
+    return labels, cats.values if return_cupy_array else as_index(cats)
+
+
+def _linear_interpolation(column, index=None):
+    """
+    Interpolate over a float column. Implicitly assumes that values are
+    evenly spaced with respect to the x-axis, for example the data
+    [1.0, NaN, 3.0] will be interpolated assuming the NaN is half way
+    between the two valid values, yielding [1.0, 2.0, 3.0]
+    """
+
+    index = RangeIndex(start=0, stop=len(column), step=1)
+    return _index_or_values_interpolation(column, index=index)
+
+
+def _index_or_values_interpolation(column, index=None):
+    """
+    Interpolate over a float column. assumes a linear interpolation
+    strategy using the index of the data to denote spacing of the x
+    values. For example the data and index [1.0, NaN, 4.0], [1, 3, 4]
+    would result in [1.0, 3.0, 4.0]
+    """
+    # figure out where the nans are
+    mask = cp.isnan(column)
+
+    # trivial cases, all nan or no nans
+    num_nan = mask.sum()
+    if num_nan == 0 or num_nan == len(column):
+        return column
+
+    to_interp = IndexedFrame(data={None: column}, index=index)
+    known_x_and_y = to_interp._apply_boolean_mask(
+        BooleanMask(~mask, len(to_interp))
+    )
+
+    known_x = known_x_and_y._index._column.values
+    known_y = known_x_and_y._data.columns[0].values
+
+    result = cp.interp(to_interp._index.values, known_x, known_y)
+
+    # find the first nan
+    first_nan_idx = (mask == 0).argmax().item()
+    result[:first_nan_idx] = np.nan
+    return result
+
+
+def get_column_interpolator(method):
+    interpolator = {
+        "linear": _linear_interpolation,
+        "index": _index_or_values_interpolation,
+        "values": _index_or_values_interpolation,
+    }.get(method, None)
+    if not interpolator:
+        raise ValueError(f"Interpolation method `{method}` not found")
+    return interpolator
diff --git a/python/cudf/cudf/core/buffer/__init__.py b/python/cudf/cudf/core/buffer/__init__.py
new file mode 100644
index 0000000..d8883bd
--- /dev/null
+++ b/python/cudf/cudf/core/buffer/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from cudf.core.buffer.buffer import Buffer, cuda_array_interface_wrapper
+from cudf.core.buffer.exposure_tracked_buffer import ExposureTrackedBuffer
+from cudf.core.buffer.spillable_buffer import SpillableBuffer, SpillLock
+from cudf.core.buffer.utils import (
+    acquire_spill_lock,
+    as_buffer,
+    get_spill_lock,
+)
diff --git a/python/cudf/cudf/core/buffer/buffer.py b/python/cudf/cudf/core/buffer/buffer.py
new file mode 100644
index 0000000..59d20a2
--- /dev/null
+++ b/python/cudf/cudf/core/buffer/buffer.py
@@ -0,0 +1,388 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import math
+import pickle
+from types import SimpleNamespace
+from typing import Any, Dict, Literal, Mapping, Optional, Sequence, Tuple
+
+import numpy
+from typing_extensions import Self
+
+import rmm
+
+import cudf
+from cudf.core.abc import Serializable
+from cudf.utils.string import format_bytes
+
+
+def host_memory_allocation(nbytes: int) -> memoryview:
+    """Allocate host memory using NumPy
+
+    This is an alternative to `bytearray` to avoid memory initialization cost.
+    A `bytearray` is zero-initialized using `calloc`, which we don't need.
+    Additionally, `numpy.empty` both skips the zero-initialization and uses
+    hugepages when available <https://github.com/numpy/numpy/pull/14216>.
+
+    Parameters
+    ----------
+    nbytes : int
+        Size of the new host allocation in bytes.
+
+    Return
+    ------
+    memoryview
+        The new host allocation.
+    """
+    return numpy.empty((nbytes,), dtype="u1").data
+
+
+def cuda_array_interface_wrapper(
+    ptr: int,
+    size: int,
+    owner: Optional[object] = None,
+    readonly=False,
+    typestr="|u1",
+    version=0,
+):
+    """Wrap device pointer in an object that exposes `__cuda_array_interface__`
+
+    See <https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html>
+
+    Parameters
+    ----------
+    ptr : int
+        An integer representing a pointer to device memory.
+    size : int, optional
+        Size of device memory in bytes.
+    owner : object, optional
+        Python object to which the lifetime of the memory allocation is tied.
+        A reference to this object is kept in the returned wrapper object.
+    readonly: bool, optional
+        Mark the interface read-only.
+    typestr: str, optional
+        The type string of the interface. By default this is "|u1", which
+        means "an unsigned integer with a not relevant byteorder". See:
+        <https://numpy.org/doc/stable/reference/arrays.interface.html>
+    version : bool, optional
+        The version of the interface.
+
+    Return
+    ------
+    SimpleNamespace
+        An object that exposes `__cuda_array_interface__` and keeps a reference
+        to `owner`.
+    """
+
+    if size < 0:
+        raise ValueError("size cannot be negative")
+
+    return SimpleNamespace(
+        __cuda_array_interface__={
+            "data": (ptr, readonly),
+            "shape": (size,),
+            "strides": None,
+            "typestr": typestr,
+            "version": version,
+        },
+        owner=owner,
+    )
+
+
+class Buffer(Serializable):
+    """A Buffer represents device memory.
+
+    Use the factory function `as_buffer` to create a Buffer instance.
+    """
+
+    _ptr: int
+    _size: int
+    _owner: object
+
+    def __init__(self):
+        raise ValueError(
+            f"do not create a {self.__class__} directly, please "
+            "use the factory function `cudf.core.buffer.as_buffer`"
+        )
+
+    @classmethod
+    def _from_device_memory(cls, data: Any) -> Self:
+        """Create a Buffer from an object exposing `__cuda_array_interface__`.
+
+        No data is being copied.
+
+        Parameters
+        ----------
+        data : device-buffer-like
+            An object implementing the CUDA Array Interface.
+
+        Returns
+        -------
+        Buffer
+            Buffer representing the same device memory as `data`
+        """
+
+        # Bypass `__init__` and initialize attributes manually
+        ret = cls.__new__(cls)
+        ret._owner = data
+        if isinstance(data, rmm.DeviceBuffer):  # Common case shortcut
+            ret._ptr = data.ptr
+            ret._size = data.size
+        else:
+            ret._ptr, ret._size = get_ptr_and_size(
+                data.__cuda_array_interface__
+            )
+        if ret.size < 0:
+            raise ValueError("size cannot be negative")
+        return ret
+
+    @classmethod
+    def _from_host_memory(cls, data: Any) -> Self:
+        """Create a Buffer from a buffer or array like object
+
+        Data must implement `__array_interface__`, the buffer protocol, and/or
+        be convertible to a buffer object using `numpy.array()`
+
+        The host memory is copied to a new device allocation.
+
+        Raises ValueError if array isn't C-contiguous.
+
+        Parameters
+        ----------
+        data : Any
+            An object that represens host memory.
+
+        Returns
+        -------
+        Buffer
+            Buffer representing a copy of `data`.
+        """
+
+        # Convert to numpy array, this will not copy data in most cases.
+        ary = numpy.array(data, copy=False, subok=True)
+        # Extract pointer and size
+        ptr, size = get_ptr_and_size(ary.__array_interface__)
+        # Copy to device memory
+        buf = rmm.DeviceBuffer(ptr=ptr, size=size)
+        # Create from device memory
+        return cls._from_device_memory(buf)
+
+    def _getitem(self, offset: int, size: int) -> Self:
+        """
+        Sub-classes can overwrite this to implement __getitem__
+        without having to handle non-slice inputs.
+        """
+        return self._from_device_memory(
+            cuda_array_interface_wrapper(
+                ptr=self.get_ptr(mode="read") + offset,
+                size=size,
+                owner=self.owner,
+            )
+        )
+
+    def __getitem__(self, key: slice) -> Self:
+        """Create a new slice of the buffer."""
+        if not isinstance(key, slice):
+            raise TypeError(
+                "Argument 'key' has incorrect type "
+                f"(expected slice, got {key.__class__.__name__})"
+            )
+        start, stop, step = key.indices(self.size)
+        if step != 1:
+            raise ValueError("slice must be C-contiguous")
+        return self._getitem(offset=start, size=stop - start)
+
+    def copy(self, deep: bool = True) -> Self:
+        """
+        Return a copy of Buffer.
+
+        Parameters
+        ----------
+        deep : bool, default True
+            If True, returns a deep copy of the underlying Buffer data.
+            If False, returns a shallow copy of the Buffer pointing to
+            the same underlying data.
+
+        Returns
+        -------
+        Buffer
+        """
+        if deep:
+            return self._from_device_memory(
+                rmm.DeviceBuffer(ptr=self.get_ptr(mode="read"), size=self.size)
+            )
+        else:
+            return self[:]
+
+    @property
+    def size(self) -> int:
+        """Size of the buffer in bytes."""
+        return self._size
+
+    @property
+    def nbytes(self) -> int:
+        """Size of the buffer in bytes."""
+        return self._size
+
+    @property
+    def owner(self) -> Any:
+        """Object owning the memory of the buffer."""
+        return self._owner
+
+    @property
+    def __cuda_array_interface__(self) -> Mapping:
+        """Implementation of the CUDA Array Interface."""
+        return {
+            "data": (self.get_ptr(mode="write"), False),
+            "shape": (self.size,),
+            "strides": None,
+            "typestr": "|u1",
+            "version": 0,
+        }
+
+    def get_ptr(self, *, mode: Literal["read", "write"]) -> int:
+        """Device pointer to the start of the buffer.
+
+        Parameters
+        ----------
+        mode : str
+            Supported values are {"read", "write"}
+            If "write", the data pointed to may be modified
+            by the caller. If "read", the data pointed to
+            must not be modified by the caller.
+            Failure to fulfill this contract will cause
+            incorrect behavior.
+
+        Returns
+        -------
+        int
+            The device pointer as an integer
+
+        See Also
+        --------
+        SpillableBuffer.get_ptr
+        ExposureTrackedBuffer.get_ptr
+        """
+        return self._ptr
+
+    def memoryview(
+        self, *, offset: int = 0, size: Optional[int] = None
+    ) -> memoryview:
+        """Read-only access to the buffer through host memory."""
+        size = self._size if size is None else size
+        host_buf = host_memory_allocation(size)
+        rmm._lib.device_buffer.copy_ptr_to_host(
+            self.get_ptr(mode="read") + offset, host_buf
+        )
+        return memoryview(host_buf).toreadonly()
+
+    def serialize(self) -> Tuple[dict, list]:
+        """Serialize the buffer into header and frames.
+
+        The frames can be a mixture of memoryview and Buffer objects.
+
+        Returns
+        -------
+        Tuple[dict, List]
+            The first element of the returned tuple is a dict containing any
+            serializable metadata required to reconstruct the object. The
+            second element is a list containing Buffers and memoryviews.
+        """
+        header: Dict[str, Any] = {}
+        header["type-serialized"] = pickle.dumps(type(self))
+        header["frame_count"] = 1
+        frames = [self]
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list) -> Self:
+        """Create an Buffer from a serialized representation.
+
+        Parameters
+        ----------
+        header : dict
+            The metadata required to reconstruct the object.
+        frames : list
+            The Buffer and memoryview that makes up the Buffer.
+
+        Returns
+        -------
+        Buffer
+            The deserialized Buffer.
+        """
+        if header["frame_count"] != 1:
+            raise ValueError("Deserializing a Buffer expect a single frame")
+        frame = frames[0]
+        if isinstance(frame, cls):
+            return frame  # The frame is already deserialized
+
+        if hasattr(frame, "__cuda_array_interface__"):
+            return cls._from_device_memory(frame)
+        return cls._from_host_memory(frame)
+
+    def __repr__(self) -> str:
+        klass = self.__class__
+        name = f"{klass.__module__}.{klass.__qualname__}"
+        return (
+            f"<{name} size={format_bytes(self._size)} "
+            f"ptr={hex(self._ptr)} owner={repr(self._owner)}>"
+        )
+
+
+def is_c_contiguous(
+    shape: Sequence[int], strides: Sequence[int], itemsize: int
+) -> bool:
+    """Determine if shape and strides are C-contiguous
+
+    Parameters
+    ----------
+    shape : Sequence[int]
+        Number of elements in each dimension.
+    strides : Sequence[int]
+        The stride of each dimension in bytes.
+    itemsize : int
+        Size of an element in bytes.
+
+    Return
+    ------
+    bool
+        The boolean answer.
+    """
+
+    if any(dim == 0 for dim in shape):
+        return True
+    cumulative_stride = itemsize
+    for dim, stride in zip(reversed(shape), reversed(strides)):
+        if dim > 1 and stride != cumulative_stride:
+            return False
+        cumulative_stride *= dim
+    return True
+
+
+def get_ptr_and_size(array_interface: Mapping) -> Tuple[int, int]:
+    """Retrieve the pointer and size from an array interface.
+
+    Raises ValueError if array isn't C-contiguous.
+
+    Parameters
+    ----------
+    array_interface : Mapping
+        The array interface metadata.
+
+    Return
+    ------
+    pointer : int
+        The pointer to device or host memory
+    size : int
+        The size in bytes
+    """
+
+    shape = array_interface["shape"] or (1,)
+    strides = array_interface["strides"]
+    itemsize = cudf.dtype(array_interface["typestr"]).itemsize
+    if strides is None or is_c_contiguous(shape, strides, itemsize):
+        nelem = math.prod(shape)
+        ptr = array_interface["data"][0] or 0
+        return ptr, nelem * itemsize
+    raise ValueError("Buffer data must be C-contiguous")
diff --git a/python/cudf/cudf/core/buffer/exposure_tracked_buffer.py b/python/cudf/cudf/core/buffer/exposure_tracked_buffer.py
new file mode 100644
index 0000000..f2ac630
--- /dev/null
+++ b/python/cudf/cudf/core/buffer/exposure_tracked_buffer.py
@@ -0,0 +1,311 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import weakref
+from typing import (
+    Any,
+    Container,
+    Literal,
+    Mapping,
+    Optional,
+    Type,
+    TypeVar,
+    cast,
+)
+
+from typing_extensions import Self
+
+import cudf
+from cudf.core.buffer.buffer import Buffer, get_ptr_and_size
+from cudf.utils.string import format_bytes
+
+T = TypeVar("T", bound="ExposureTrackedBuffer")
+
+
+def get_owner(data, klass: Type[T]) -> Optional[T]:
+    """Get the owner of `data`, if any exist
+
+    Search through the stack of data owners in order to find an
+    owner of type `klass` (not subclasses).
+
+    Parameters
+    ----------
+    data
+        The data object
+
+    Return
+    ------
+    klass or None
+        The owner of `data` if `klass` or None.
+    """
+
+    if type(data) is klass:
+        return data
+    if hasattr(data, "owner"):
+        return get_owner(data.owner, klass)
+    return None
+
+
+def as_exposure_tracked_buffer(
+    data, exposed: bool, subclass: Optional[Type[T]] = None
+) -> BufferSlice:
+    """Factory function to wrap `data` in a slice of an exposure tracked buffer
+
+    If `subclass` is None, a new ExposureTrackedBuffer that points to the
+    memory of `data` is created and a BufferSlice that points to all of the
+    new ExposureTrackedBuffer is returned.
+
+    If `subclass` is not None, a new `subclass` is created instead. Still,
+    a BufferSlice that points to all of the new `subclass` is returned
+
+    It is illegal for an exposure tracked buffer to own another exposure
+    tracked buffer. When representing the same memory, we should have a single
+    exposure tracked buffer and multiple buffer slices.
+
+    Developer Notes
+    ---------------
+    This function always returns slices thus all buffers in cudf will use
+    `BufferSlice` when copy-on-write is enabled. The slices implement
+    copy-on-write by trigging deep copies when write access is detected
+    and multiple slices points to the same exposure tracked buffer.
+
+    Parameters
+    ----------
+    data : buffer-like or array-like
+        A buffer-like or array-like object that represents C-contiguous memory.
+    exposed
+        Mark the buffer as permanently exposed.
+    subclass
+        If not None, a subclass of ExposureTrackedBuffer to wrap `data`.
+
+    Return
+    ------
+    BufferSlice
+        A buffer slice that points to a ExposureTrackedBuffer (or `subclass`),
+        which in turn wraps `data`.
+    """
+
+    if not hasattr(data, "__cuda_array_interface__"):
+        if exposed:
+            raise ValueError("cannot created exposed host memory")
+        return cast(
+            BufferSlice, ExposureTrackedBuffer._from_host_memory(data)[:]
+        )
+
+    owner = get_owner(data, subclass or ExposureTrackedBuffer)
+    if owner is None:
+        return cast(
+            BufferSlice,
+            ExposureTrackedBuffer._from_device_memory(data, exposed=exposed)[
+                :
+            ],
+        )
+
+    # At this point, we know that `data` is owned by a exposure tracked buffer
+    ptr, size = get_ptr_and_size(data.__cuda_array_interface__)
+    if size > 0 and owner._ptr == 0:
+        raise ValueError("Cannot create a non-empty slice of a null buffer")
+    return BufferSlice(base=owner, offset=ptr - owner._ptr, size=size)
+
+
+class ExposureTrackedBuffer(Buffer):
+    """A Buffer that tracks its "expose" status.
+
+    In order to implement copy-on-write and spillable buffers, we need the
+    ability to detect external access to the underlying memory. We say that
+    the buffer has been exposed if the device pointer (integer or void*) has
+    been accessed outside of ExposureTrackedBuffer. In this case, we have no
+    control over knowing if the data is being modified by a third-party.
+
+    Attributes
+    ----------
+    _exposed
+        The current exposure status of the buffer. Notice, once the exposure
+        status becomes True, it should never change back.
+    _slices
+        The set of BufferSlice instances that point to this buffer.
+    """
+
+    _exposed: bool
+    _slices: weakref.WeakSet[BufferSlice]
+
+    @property
+    def exposed(self) -> bool:
+        return self._exposed
+
+    def mark_exposed(self) -> None:
+        """Mark the buffer as "exposed" permanently"""
+        self._exposed = True
+
+    @classmethod
+    def _from_device_memory(cls, data: Any, *, exposed: bool = False) -> Self:
+        """Create an exposure tracked buffer from device memory.
+
+        No data is being copied.
+
+        Parameters
+        ----------
+        data : device-buffer-like
+            An object implementing the CUDA Array Interface.
+        exposed : bool, optional
+            Mark the buffer as permanently exposed.
+
+        Returns
+        -------
+        ExposureTrackedBuffer
+            Buffer representing the same device memory as `data`
+        """
+        ret = super()._from_device_memory(data)
+        ret._exposed = exposed
+        ret._slices = weakref.WeakSet()
+        return ret
+
+    def _getitem(self, offset: int, size: int) -> BufferSlice:
+        return BufferSlice(base=self, offset=offset, size=size)
+
+    @property
+    def __cuda_array_interface__(self) -> Mapping:
+        self.mark_exposed()
+        return super().__cuda_array_interface__
+
+    def __repr__(self) -> str:
+        return (
+            f"<ExposureTrackedBuffer exposed={self.exposed} "
+            f"size={format_bytes(self._size)} "
+            f"ptr={hex(self._ptr)} owner={repr(self._owner)}>"
+        )
+
+
+class BufferSlice(ExposureTrackedBuffer):
+    """A slice (aka. a view) of a exposure tracked buffer.
+
+    Parameters
+    ----------
+    base
+        The exposure tracked buffer this slice refers to.
+    offset
+        The offset relative to the start memory of base (in bytes).
+    size
+        The size of the slice (in bytes)
+    passthrough_attributes
+        Name of attributes that are passed through to the base as-is.
+    """
+
+    def __init__(
+        self,
+        base: ExposureTrackedBuffer,
+        offset: int,
+        size: int,
+        *,
+        passthrough_attributes: Container[str] = ("exposed",),
+    ) -> None:
+        if size < 0:
+            raise ValueError("size cannot be negative")
+        if offset < 0:
+            raise ValueError("offset cannot be negative")
+        if offset + size > base.size:
+            raise ValueError(
+                "offset+size cannot be greater than the size of base"
+            )
+        self._base = base
+        self._offset = offset
+        self._size = size
+        self._owner = base
+        self._passthrough_attributes = passthrough_attributes
+        base._slices.add(self)
+
+    def __getattr__(self, name):
+        if name in self._passthrough_attributes:
+            return getattr(self._base, name)
+        raise AttributeError(
+            f"{self.__class__.__name__} object has no attribute {name}"
+        )
+
+    def _getitem(self, offset: int, size: int) -> BufferSlice:
+        return BufferSlice(
+            base=self._base, offset=offset + self._offset, size=size
+        )
+
+    def get_ptr(self, *, mode: Literal["read", "write"]) -> int:
+        if mode == "write" and cudf.get_option("copy_on_write"):
+            self.make_single_owner_inplace()
+        return self._base.get_ptr(mode=mode) + self._offset
+
+    def memoryview(
+        self, *, offset: int = 0, size: Optional[int] = None
+    ) -> memoryview:
+        return self._base.memoryview(offset=self._offset + offset, size=size)
+
+    def copy(self, deep: bool = True) -> Self:
+        """Return a copy of Buffer.
+
+        What actually happens when `deep == False` depends on the
+        "copy_on_write" option. When copy-on-write is enabled, a shallow copy
+        becomes a deep copy if the buffer has been exposed. This is because we
+        have no control over knowing if the data is being modified when the
+        buffer has been exposed to third-party.
+
+        Parameters
+        ----------
+        deep : bool, default True
+            The semantics when copy-on-write is disabled:
+                - If deep=True, returns a deep copy of the underlying data.
+                - If deep=False, returns a shallow copy of the Buffer pointing
+                  to the same underlying data.
+            The semantics when copy-on-write is enabled:
+                - From the users perspective, always a deep copy of the
+                  underlying data. However, the data isn't actually copied
+                  until someone writers to the returned buffer.
+
+        Returns
+        -------
+        BufferSlice
+            A slice pointing to either a new or the existing base buffer
+            depending on the expose status of the base buffer and the
+            copy-on-write option (see above).
+        """
+        if cudf.get_option("copy_on_write"):
+            base_copy = self._base.copy(deep=deep or self.exposed)
+        else:
+            base_copy = self._base.copy(deep=deep)
+        return cast(Self, base_copy[self._offset : self._offset + self._size])
+
+    @property
+    def __cuda_array_interface__(self) -> Mapping:
+        if cudf.get_option("copy_on_write"):
+            self.make_single_owner_inplace()
+        return super().__cuda_array_interface__
+
+    def make_single_owner_inplace(self) -> None:
+        """Make sure this slice is the only one pointing to the base.
+
+        This is used by copy-on-write to trigger a deep copy when write
+        access is detected.
+
+        Parameters
+        ----------
+        data : device-buffer-like
+            An object implementing the CUDA Array Interface.
+
+        Returns
+        -------
+        Buffer
+            Buffer representing the same device memory as `data`
+        """
+
+        if len(self._base._slices) > 1:
+            # If this is not the only slice pointing to `self._base`, we
+            # point to a new deep copy of the base.
+            t = self.copy(deep=True)
+            self._base = t._base
+            self._offset = t._offset
+            self._size = t._size
+            self._owner = t._base
+            self._base._slices.add(self)
+
+    def __repr__(self) -> str:
+        return (
+            f"<BufferSlice size={format_bytes(self._size)} "
+            f"offset={format_bytes(self._offset)} of {self._base}>"
+        )
diff --git a/python/cudf/cudf/core/buffer/spill_manager.py b/python/cudf/cudf/core/buffer/spill_manager.py
new file mode 100644
index 0000000..f056a0f
--- /dev/null
+++ b/python/cudf/cudf/core/buffer/spill_manager.py
@@ -0,0 +1,446 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import gc
+import io
+import textwrap
+import threading
+import traceback
+import warnings
+import weakref
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+
+import rmm.mr
+
+from cudf.core.buffer.spillable_buffer import SpillableBuffer
+from cudf.options import get_option
+from cudf.utils.string import format_bytes
+
+
+def get_traceback() -> str:
+    """Pretty print current traceback to a string"""
+    with io.StringIO() as f:
+        traceback.print_stack(file=f)
+        f.seek(0)
+        return f.read()
+
+
+def get_rmm_memory_resource_stack(
+    mr: rmm.mr.DeviceMemoryResource,
+) -> List[rmm.mr.DeviceMemoryResource]:
+    """Get the RMM resource stack
+
+    Parameters
+    ----------
+    mr : rmm.mr.DeviceMemoryResource
+        Top of the resource stack
+
+    Return
+    ------
+    list
+        List of RMM resources
+    """
+
+    if hasattr(mr, "upstream_mr"):
+        return [mr] + get_rmm_memory_resource_stack(mr.upstream_mr)
+    return [mr]
+
+
+class SpillStatistics:
+    """Gather spill statistics
+
+    Levels of information gathered:
+      0  - disabled (no overhead).
+      1+ - duration and number of bytes spilled (very low overhead).
+      2+ - a traceback for each time a spillable buffer is exposed
+           permanently (potential high overhead).
+
+    The statistics are printed when spilling-on-demand fails to find
+    any buffer to spill. It is possible to retrieve the statistics
+    manually through the spill manager, see example below.
+
+    Parameters
+    ----------
+    level : int
+        If not 0, enables statistics at the specified level.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> from cudf.core.buffer.spill_manager import get_global_manager
+    >>> manager = get_global_manager()
+    >>> manager.statistics
+    <SpillStatistics level=1>
+    >>> df = cudf.DataFrame({"a": [1,2,3]})
+    >>> manager.spill_to_device_limit(1)  # Spill df
+    24
+    >>> print(get_global_manager().statistics)
+    Spill Statistics (level=1):
+     Spilling (level >= 1):
+      gpu => cpu: 24B in 0.0033579860000827466s
+    """
+
+    @dataclass
+    class Expose:
+        traceback: str
+        count: int = 1
+        total_nbytes: int = 0
+        spilled_nbytes: int = 0
+
+    spill_totals: Dict[Tuple[str, str], Tuple[int, float]]
+
+    def __init__(self, level) -> None:
+        self.lock = threading.Lock()
+        self.level = level
+        self.spill_totals = defaultdict(lambda: (0, 0))
+        # Maps each traceback to a Expose
+        self.exposes: Dict[str, SpillStatistics.Expose] = {}
+
+    def log_spill(self, src: str, dst: str, nbytes: int, time: float) -> None:
+        """Log a (un-)spilling event
+
+        Parameters
+        ----------
+        src : str
+            The memory location before spilling.
+        dst : str
+            The memory location after spilling.
+        nbytes : int
+            Number of bytes (un-)spilled.
+        nbytes : float
+            Elapsed time the event took in seconds.
+        """
+        if self.level < 1:
+            return
+        with self.lock:
+            total_nbytes, total_time = self.spill_totals[(src, dst)]
+            self.spill_totals[(src, dst)] = (
+                total_nbytes + nbytes,
+                total_time + time,
+            )
+
+    def log_expose(self, buf: SpillableBuffer) -> None:
+        """Log an expose event
+
+        We track logged exposes by grouping them by their traceback such
+        that `self.exposes` maps tracebacks (as strings) to their logged
+        data (as `Expose`).
+
+        Parameters
+        ----------
+        buf : spillabe-buffer
+            The buffer being exposed.
+        """
+        if self.level < 2:
+            return
+        with self.lock:
+            tb = get_traceback()
+            stat = self.exposes.get(tb, None)
+            spilled_nbytes = buf.nbytes if buf.is_spilled else 0
+            if stat is None:
+                self.exposes[tb] = self.Expose(
+                    traceback=tb,
+                    total_nbytes=buf.nbytes,
+                    spilled_nbytes=spilled_nbytes,
+                )
+            else:
+                stat.count += 1
+                stat.total_nbytes += buf.nbytes
+                stat.spilled_nbytes += spilled_nbytes
+
+    def __repr__(self) -> str:
+        return f"<SpillStatistics level={self.level}>"
+
+    def __str__(self) -> str:
+        with self.lock:
+            ret = f"Spill Statistics (level={self.level}):\n"
+            if self.level == 0:
+                return ret[:-1] + " N/A"
+
+            # Print spilling stats
+            ret += "  Spilling (level >= 1):"
+            if len(self.spill_totals) == 0:
+                ret += " None"
+            ret += "\n"
+            for (src, dst), (nbytes, time) in self.spill_totals.items():
+                ret += f"    {src} => {dst}: "
+                ret += f"{format_bytes(nbytes)} in {time:.3f}s\n"
+
+            # Print expose stats
+            ret += "  Exposed buffers (level >= 2): "
+            if self.level < 2:
+                return ret + "disabled"
+            if len(self.exposes) == 0:
+                ret += "None"
+            ret += "\n"
+            for s in sorted(self.exposes.values(), key=lambda x: -x.count):
+                ret += textwrap.indent(
+                    (
+                        f"exposed {s.count} times, "
+                        f"total: {format_bytes(s.total_nbytes)}, "
+                        f"spilled: {format_bytes(s.spilled_nbytes)}, "
+                        f"traceback:\n{s.traceback}"
+                    ),
+                    prefix=" " * 4,
+                )
+            return ret[:-1]  # Remove last `\n`
+
+
+class SpillManager:
+    """Manager of spillable buffers.
+
+    This class implements tracking of all known spillable buffers, on-demand
+    spilling of said buffers, and (optionally) maintains a memory usage limit.
+
+    When `spill_on_demand=True`, the manager registers an RMM out-of-memory
+    error handler, which will spill spillable buffers in order to free up
+    memory.
+
+    When `device_memory_limit=<limit-in-bytes>`, the manager will try keep
+    the device memory usage below the specified limit by spilling of spillable
+    buffers continuously, which will introduce a modest overhead.
+    Notice, this is a soft limit. The memory usage might exceed the limit if
+    too many buffers are unspillable.
+
+    Parameters
+    ----------
+    spill_on_demand : bool
+        Enable spill on demand.
+    device_memory_limit: int, optional
+        If not None, this is the device memory limit in bytes that triggers
+        device to host spilling. The global manager sets this to the value
+        of `CUDF_SPILL_DEVICE_LIMIT` or None.
+    statistic_level: int, optional
+        If not 0, enables statistics at the specified level. See
+        SpillStatistics for the different levels.
+    """
+
+    _buffers: weakref.WeakValueDictionary[int, SpillableBuffer]
+    statistics: SpillStatistics
+
+    def __init__(
+        self,
+        *,
+        spill_on_demand: bool = False,
+        device_memory_limit: Optional[int] = None,
+        statistic_level: int = 0,
+    ) -> None:
+        self._lock = threading.Lock()
+        self._buffers = weakref.WeakValueDictionary()
+        self._id_counter = 0
+        self._spill_on_demand = spill_on_demand
+        self._device_memory_limit = device_memory_limit
+        self.statistics = SpillStatistics(statistic_level)
+
+        if self._spill_on_demand:
+            # Set the RMM out-of-memory handle if not already set
+            mr = rmm.mr.get_current_device_resource()
+            if all(
+                not isinstance(m, rmm.mr.FailureCallbackResourceAdaptor)
+                for m in get_rmm_memory_resource_stack(mr)
+            ):
+                rmm.mr.set_current_device_resource(
+                    rmm.mr.FailureCallbackResourceAdaptor(
+                        mr, self._out_of_memory_handle
+                    )
+                )
+
+    def _out_of_memory_handle(self, nbytes: int, *, retry_once=True) -> bool:
+        """Try to handle an out-of-memory error by spilling
+
+        This can by used as the callback function to RMM's
+        `FailureCallbackResourceAdaptor`
+
+        Parameters
+        ----------
+        nbytes : int
+            Number of bytes to try to spill.
+        retry_once : bool, optional
+            If True, call `gc.collect()` and retry once.
+
+        Return
+        ------
+        bool
+            True if any buffers were freed otherwise False.
+
+        Warning
+        -------
+        In order to avoid deadlock, this function should not lock
+        already locked buffers.
+        """
+        # Let's try to spill device memory
+
+        spilled = self.spill_device_memory(nbytes=nbytes)
+
+        if spilled > 0:
+            return True  # Ask RMM to retry the allocation
+
+        if retry_once:
+            # Let's collect garbage and try one more time
+            gc.collect()
+            return self._out_of_memory_handle(nbytes, retry_once=False)
+
+        # TODO: write to log instead of stdout
+        print(
+            f"[WARNING] RMM allocation of {format_bytes(nbytes)} bytes "
+            "failed, spill-on-demand couldn't find any device memory to "
+            f"spill:\n{repr(self)}\ntraceback:\n{get_traceback()}\n"
+            f"{self.statistics}"
+        )
+        return False  # Since we didn't find anything to spill, we give up
+
+    def add(self, buffer: SpillableBuffer) -> None:
+        """Add buffer to the set of managed buffers
+
+        The manager keeps a weak reference to the buffer
+
+        Parameters
+        ----------
+        buffer : SpillableBuffer
+            The buffer to manage
+        """
+        if buffer.size > 0 and not buffer.exposed:
+            with self._lock:
+                self._buffers[self._id_counter] = buffer
+                self._id_counter += 1
+        self.spill_to_device_limit()
+
+    def buffers(
+        self, order_by_access_time: bool = False
+    ) -> Tuple[SpillableBuffer, ...]:
+        """Get all managed buffers
+
+        Parameters
+        ----------
+        order_by_access_time : bool, optional
+            Order the buffer by access time (ascending order)
+
+        Return
+        ------
+        tuple
+            Tuple of buffers
+        """
+        with self._lock:
+            ret = tuple(self._buffers.values())
+        if order_by_access_time:
+            ret = tuple(sorted(ret, key=lambda b: b.last_accessed))
+        return ret
+
+    def spill_device_memory(self, nbytes: int) -> int:
+        """Try to spill device memory
+
+        This function is safe to call doing spill-on-demand
+        since it does not lock buffers already locked.
+
+        Parameters
+        ----------
+        nbytes : int
+            Number of bytes to try to spill
+
+        Return
+        ------
+        int
+            Number of actually bytes spilled.
+        """
+        spilled = 0
+        for buf in self.buffers(order_by_access_time=True):
+            if buf.lock.acquire(blocking=False):
+                try:
+                    if not buf.is_spilled and buf.spillable:
+                        buf.spill(target="cpu")
+                        spilled += buf.size
+                        if spilled >= nbytes:
+                            break
+                finally:
+                    buf.lock.release()
+        return spilled
+
+    def spill_to_device_limit(self, device_limit: Optional[int] = None) -> int:
+        """Try to spill device memory until device limit
+
+        Notice, by default this is a no-op.
+
+        Parameters
+        ----------
+        device_limit : int, optional
+            Limit in bytes. If None, the value of the environment variable
+            `CUDF_SPILL_DEVICE_LIMIT` is used. If this is not set, the method
+            does nothing and returns 0.
+
+        Return
+        ------
+        int
+            The number of bytes spilled.
+        """
+        limit = (
+            self._device_memory_limit if device_limit is None else device_limit
+        )
+        if limit is None:
+            return 0
+        unspilled = sum(
+            buf.size for buf in self.buffers() if not buf.is_spilled
+        )
+        return self.spill_device_memory(nbytes=unspilled - limit)
+
+    def __repr__(self) -> str:
+        spilled = sum(buf.size for buf in self.buffers() if buf.is_spilled)
+        unspilled = sum(
+            buf.size for buf in self.buffers() if not buf.is_spilled
+        )
+        unspillable = 0
+        for buf in self.buffers():
+            if not (buf.is_spilled or buf.spillable):
+                unspillable += buf.size
+        unspillable_ratio = unspillable / unspilled if unspilled else 0
+
+        dev_limit = "N/A"
+        if self._device_memory_limit is not None:
+            dev_limit = format_bytes(self._device_memory_limit)
+
+        return (
+            f"<SpillManager spill_on_demand={self._spill_on_demand} "
+            f"device_memory_limit={dev_limit} | "
+            f"{format_bytes(spilled)} spilled | "
+            f"{format_bytes(unspilled)} ({unspillable_ratio:.0%}) "
+            f"unspilled (unspillable)>"
+        )
+
+
+# The global manager has three states:
+#   - Uninitialized
+#   - Initialized to None (spilling disabled)
+#   - Initialized to a SpillManager instance (spilling enabled)
+_global_manager_uninitialized: bool = True
+_global_manager: Optional[SpillManager] = None
+
+
+def set_global_manager(manager: Optional[SpillManager]) -> None:
+    """Set the global manager, which if None disables spilling"""
+
+    global _global_manager, _global_manager_uninitialized
+    if _global_manager is not None:
+        gc.collect()
+        buffers = _global_manager.buffers()
+        if len(buffers) > 0:
+            warnings.warn(f"overwriting non-empty manager: {buffers}")
+
+    _global_manager = manager
+    _global_manager_uninitialized = False
+
+
+def get_global_manager() -> Optional[SpillManager]:
+    """Get the global manager or None if spilling is disabled"""
+    global _global_manager_uninitialized
+    if _global_manager_uninitialized:
+        manager = None
+        if get_option("spill"):
+            manager = SpillManager(
+                spill_on_demand=get_option("spill_on_demand"),
+                device_memory_limit=get_option("spill_device_limit"),
+                statistic_level=get_option("spill_stats"),
+            )
+        set_global_manager(manager)
+    return _global_manager
diff --git a/python/cudf/cudf/core/buffer/spillable_buffer.py b/python/cudf/cudf/core/buffer/spillable_buffer.py
new file mode 100644
index 0000000..84fb204
--- /dev/null
+++ b/python/cudf/cudf/core/buffer/spillable_buffer.py
@@ -0,0 +1,598 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import collections.abc
+import pickle
+import time
+import weakref
+from threading import RLock
+from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple
+
+import numpy
+from typing_extensions import Self
+
+import rmm
+
+from cudf.core.buffer.buffer import (
+    Buffer,
+    cuda_array_interface_wrapper,
+    get_ptr_and_size,
+    host_memory_allocation,
+)
+from cudf.utils.string import format_bytes
+
+if TYPE_CHECKING:
+    from cudf.core.buffer.spill_manager import SpillManager
+
+
+def get_spillable_owner(data) -> Optional[SpillableBuffer]:
+    """Get the spillable owner of `data`, if any exist
+
+    Search through the stack of data owners in order to find an
+    owner of type `SpillableBuffer` (not subclasses).
+
+    Parameters
+    ----------
+    data : buffer-like or array-like
+        A buffer-like or array-like object that represent C-contiguous memory.
+
+    Return
+    ------
+    SpillableBuffer or None
+        The owner of `data` if spillable or None.
+    """
+
+    if type(data) is SpillableBuffer:
+        return data
+    if hasattr(data, "owner"):
+        return get_spillable_owner(data.owner)
+    return None
+
+
+def as_spillable_buffer(data, exposed: bool) -> SpillableBuffer:
+    """Factory function to wrap `data` in a SpillableBuffer object.
+
+    If `data` isn't a buffer already, a new buffer that points to the memory of
+    `data` is created. If `data` represents host memory, it is copied to a new
+    `rmm.DeviceBuffer` device allocation. Otherwise, the memory of `data` is
+    **not** copied, instead the new buffer keeps a reference to `data` in order
+    to retain its lifetime.
+
+    If `data` is owned by a spillable buffer, a "slice" of the buffer is
+    returned. In this case, the spillable buffer must either be "exposed" or
+    spilled locked (called within an acquire_spill_lock context). This is to
+    guarantee that the memory of `data` isn't spilled before this function gets
+    to calculate the offset of the new slice.
+
+    It is illegal for a spillable buffer to own another spillable buffer.
+
+    Parameters
+    ----------
+    data : buffer-like or array-like
+        A buffer-like or array-like object that represent C-contiguous memory.
+    exposed : bool, optional
+        Mark the buffer as permanently exposed (unspillable).
+
+    Return
+    ------
+    SpillableBuffer
+        A spillabe buffer instance that represents the device memory of `data`.
+    """
+
+    from cudf.core.buffer.utils import get_spill_lock
+
+    if not hasattr(data, "__cuda_array_interface__"):
+        if exposed:
+            raise ValueError("cannot created exposed host memory")
+        return SpillableBuffer._from_host_memory(data)
+
+    spillable_owner = get_spillable_owner(data)
+    if spillable_owner is None:
+        return SpillableBuffer._from_device_memory(data, exposed=exposed)
+
+    if not spillable_owner.exposed and get_spill_lock() is None:
+        raise ValueError(
+            "A owning spillable buffer must "
+            "either be exposed or spilled locked."
+        )
+
+    # At this point, we know that `data` is owned by a spillable buffer,
+    # which is exposed or spilled locked.
+    ptr, size = get_ptr_and_size(data.__cuda_array_interface__)
+    base_ptr = spillable_owner.memory_info()[0]
+    return SpillableBufferSlice(
+        spillable_owner, offset=ptr - base_ptr, size=size
+    )
+
+
+class SpillLock:
+    pass
+
+
+class DelayedPointerTuple(collections.abc.Sequence):
+    """
+    A delayed version of the "data" field in __cuda_array_interface__.
+
+    The idea is to delay the access to `Buffer.ptr` until the user
+    actually accesses the data pointer.
+
+    For instance, in many cases __cuda_array_interface__ is accessed
+    only to determine whether an object is a CUDA object or not.
+
+    TODO: this doesn't support libraries such as PyTorch that declare
+    the tuple of __cuda_array_interface__["data"] in Cython. In such
+    cases, Cython will raise an error because DelayedPointerTuple
+    isn't a "real" tuple.
+    """
+
+    def __init__(self, buffer) -> None:
+        self._buf = buffer
+
+    def __len__(self):
+        return 2
+
+    def __getitem__(self, i):
+        if i == 0:
+            return self._buf.get_ptr(mode="write")
+        elif i == 1:
+            return False
+        raise IndexError("tuple index out of range")
+
+
+class SpillableBuffer(Buffer):
+    """A Buffer that supports spilling memory off the GPU to avoid OOMs.
+
+    This buffer supports spilling the represented data to host memory.
+    Spilling can be done manually by calling `.spill(target="cpu")` but
+    usually the associated spilling manager triggers spilling based on current
+    device memory usage see `cudf.core.buffer.spill_manager.SpillManager`.
+    Unspill is triggered automatically when accessing the data of the buffer.
+
+    The buffer might not be spillable, which is based on the "expose" status
+    of the buffer. We say that the buffer has been exposed if the device
+    pointer (integer or void*) has been accessed outside of SpillableBuffer.
+    In this case, we cannot invalidate the device pointer by moving the data
+    to host.
+
+    A buffer can be exposed permanently at creation or by accessing the `.ptr`
+    property. To avoid this, one can use `.get_ptr()` instead, which support
+    exposing the buffer temporarily.
+
+    Use the factory function `as_buffer` to create a SpillableBuffer instance.
+    """
+
+    lock: RLock
+    _spill_locks: weakref.WeakSet
+    _last_accessed: float
+    _ptr_desc: Dict[str, Any]
+    _exposed: bool
+    _manager: SpillManager
+
+    def _finalize_init(self, ptr_desc: Dict[str, Any], exposed: bool) -> None:
+        """Finish initialization of the spillable buffer
+
+        This implements the common initialization that `_from_device_memory`
+        and `_from_host_memory` are missing.
+
+        Parameters
+        ----------
+        ptr_desc : dict
+            Description of the memory.
+        exposed : bool, optional
+            Mark the buffer as permanently exposed (unspillable).
+        """
+
+        from cudf.core.buffer.spill_manager import get_global_manager
+
+        self.lock = RLock()
+        self._spill_locks = weakref.WeakSet()
+        self._last_accessed = time.monotonic()
+        self._ptr_desc = ptr_desc
+        self._exposed = exposed
+        manager = get_global_manager()
+        if manager is None:
+            raise ValueError(
+                f"cannot create {self.__class__} without "
+                "a global spill manager"
+            )
+
+        self._manager = manager
+        self._manager.add(self)
+
+    @classmethod
+    def _from_device_memory(cls, data: Any, *, exposed: bool = False) -> Self:
+        """Create a spillabe buffer from device memory.
+
+        No data is being copied.
+
+        Parameters
+        ----------
+        data : device-buffer-like
+            An object implementing the CUDA Array Interface.
+        exposed : bool, optional
+            Mark the buffer as permanently exposed (unspillable).
+
+        Returns
+        -------
+        SpillableBuffer
+            Buffer representing the same device memory as `data`
+        """
+        ret = super()._from_device_memory(data)
+        ret._finalize_init(ptr_desc={"type": "gpu"}, exposed=exposed)
+        return ret
+
+    @classmethod
+    def _from_host_memory(cls, data: Any) -> Self:
+        """Create a spillabe buffer from host memory.
+
+        Data must implement `__array_interface__`, the buffer protocol, and/or
+        be convertible to a buffer object using `numpy.array()`
+
+        The new buffer is marked as spilled to host memory already.
+
+        Raises ValueError if array isn't C-contiguous.
+
+        Parameters
+        ----------
+        data : Any
+            An object that represens host memory.
+
+        Returns
+        -------
+        SpillableBuffer
+            Buffer representing a copy of `data`.
+        """
+
+        # Convert to a memoryview using numpy array, this will not copy data
+        # in most cases.
+        data = memoryview(numpy.array(data, copy=False, subok=True))
+        if not data.c_contiguous:
+            raise ValueError("Buffer data must be C-contiguous")
+        data = data.cast("B")  # Make sure itemsize==1
+
+        # Create an already spilled buffer
+        ret = cls.__new__(cls)
+        ret._owner = None
+        ret._ptr = 0
+        ret._size = data.nbytes
+        ret._finalize_init(
+            ptr_desc={"type": "cpu", "memoryview": data}, exposed=False
+        )
+        return ret
+
+    @property
+    def is_spilled(self) -> bool:
+        return self._ptr_desc["type"] != "gpu"
+
+    def copy(self, deep: bool = True) -> Self:
+        spill_lock = SpillLock()
+        self.spill_lock(spill_lock=spill_lock)
+        return super().copy(deep=deep)
+
+    def spill(self, target: str = "cpu") -> None:
+        """Spill or un-spill this buffer in-place
+
+        Parameters
+        ----------
+        target : str
+            The target of the spilling.
+        """
+
+        time_start = time.perf_counter()
+        with self.lock:
+            ptr_type = self._ptr_desc["type"]
+            if ptr_type == target:
+                return
+
+            if not self.spillable:
+                raise ValueError(
+                    f"Cannot in-place move an unspillable buffer: {self}"
+                )
+
+            if (ptr_type, target) == ("gpu", "cpu"):
+                host_mem = host_memory_allocation(self.size)
+                rmm._lib.device_buffer.copy_ptr_to_host(self._ptr, host_mem)
+                self._ptr_desc["memoryview"] = host_mem
+                self._ptr = 0
+                self._owner = None
+            elif (ptr_type, target) == ("cpu", "gpu"):
+                # Notice, this operation is prone to deadlock because the RMM
+                # allocation might trigger spilling-on-demand which in turn
+                # trigger a new call to this buffer's `spill()`.
+                # Therefore, it is important that spilling-on-demand doesn't
+                # try to unspill an already locked buffer!
+                dev_mem = rmm.DeviceBuffer.to_device(
+                    self._ptr_desc.pop("memoryview")
+                )
+                self._ptr = dev_mem.ptr
+                self._owner = dev_mem
+                assert self._size == dev_mem.size
+            else:
+                # TODO: support moving to disk
+                raise ValueError(f"Unknown target: {target}")
+            self._ptr_desc["type"] = target
+
+        time_end = time.perf_counter()
+        self._manager.statistics.log_spill(
+            src=ptr_type,
+            dst=target,
+            nbytes=self.size,
+            time=time_end - time_start,
+        )
+
+    def mark_exposed(self) -> None:
+        """Mark the buffer as "exposed" and make it unspillable permanently.
+
+        This also unspills the buffer (unspillable buffers cannot be spilled!).
+        """
+
+        self._manager.spill_to_device_limit()
+        with self.lock:
+            if not self._exposed:
+                self._manager.statistics.log_expose(self)
+            self.spill(target="gpu")
+            self._exposed = True
+            self._last_accessed = time.monotonic()
+
+    def spill_lock(self, spill_lock: SpillLock) -> None:
+        """Spill lock the buffer
+
+        Mark the buffer as unspillable while `spill_lock` is alive,
+        which is tracked by monitoring a weakref to `spill_lock`.
+
+        Parameters
+        ----------
+        spill_lock : SpillLock
+            The object that defines the scope of the lock.
+        """
+
+        with self.lock:
+            self.spill(target="gpu")
+            self._spill_locks.add(spill_lock)
+
+    def get_ptr(self, *, mode: Literal["read", "write"]) -> int:
+        """Get a device pointer to the memory of the buffer.
+
+        If this is called within an `acquire_spill_lock` context,
+        a reference to this buffer is added to spill_lock, which
+        disable spilling of this buffer while in the context.
+
+        If this is *not* called within a `acquire_spill_lock` context,
+        this buffer is marked as unspillable permanently.
+
+        Returns
+        -------
+        int
+            The device pointer as an integer
+        """
+        from cudf.core.buffer.utils import get_spill_lock
+
+        spill_lock = get_spill_lock()
+        if spill_lock is None:
+            self.mark_exposed()
+        else:
+            self.spill_lock(spill_lock)
+            self._last_accessed = time.monotonic()
+        return self._ptr
+
+    def memory_info(self) -> Tuple[int, int, str]:
+        """Get pointer, size, and device type of this buffer.
+
+        Warning, it is not safe to access the pointer value without
+        spill lock the buffer manually. This method neither exposes
+        nor spill locks the buffer.
+
+        Return
+        ------
+        int
+            The memory pointer as an integer (device or host memory)
+        int
+            The size of the memory in bytes
+        str
+            The device type as a string ("cpu" or "gpu")
+        """
+
+        if self._ptr_desc["type"] == "gpu":
+            ptr = self._ptr
+        elif self._ptr_desc["type"] == "cpu":
+            ptr = numpy.array(
+                self._ptr_desc["memoryview"], copy=False
+            ).__array_interface__["data"][0]
+        return (ptr, self.nbytes, self._ptr_desc["type"])
+
+    @property
+    def owner(self) -> Any:
+        return self._owner
+
+    @property
+    def exposed(self) -> bool:
+        return self._exposed
+
+    @property
+    def spillable(self) -> bool:
+        return not self._exposed and len(self._spill_locks) == 0
+
+    @property
+    def size(self) -> int:
+        return self._size
+
+    @property
+    def nbytes(self) -> int:
+        return self._size
+
+    @property
+    def last_accessed(self) -> float:
+        return self._last_accessed
+
+    @property
+    def __cuda_array_interface__(self) -> dict:
+        return {
+            "data": DelayedPointerTuple(self),
+            "shape": (self.size,),
+            "strides": None,
+            "typestr": "|u1",
+            "version": 0,
+        }
+
+    def memoryview(
+        self, *, offset: int = 0, size: Optional[int] = None
+    ) -> memoryview:
+        size = self._size if size is None else size
+        with self.lock:
+            if self.spillable:
+                self.spill(target="cpu")
+                return self._ptr_desc["memoryview"][offset : offset + size]
+            else:
+                assert self._ptr_desc["type"] == "gpu"
+                ret = host_memory_allocation(size)
+                rmm._lib.device_buffer.copy_ptr_to_host(
+                    self._ptr + offset, ret
+                )
+                return ret
+
+    def _getitem(self, offset: int, size: int) -> SpillableBufferSlice:
+        return SpillableBufferSlice(base=self, offset=offset, size=size)
+
+    def serialize(self) -> Tuple[dict, list]:
+        """Serialize the Buffer
+
+        Normally, we would use `[self]` as the frames. This would work but
+        also mean that `self` becomes exposed permanently if the frames are
+        later accessed through `__cuda_array_interface__`, which is exactly
+        what libraries like Dask+UCX would do when communicating!
+
+        The sound solution is to modify Dask et al. so that they access the
+        frames through `.get_ptr()` and holds on to the `spill_lock` until
+        the frame has been transferred. However, until this adaptation we
+        use a hack where the frame is a `Buffer` with a `spill_lock` as the
+        owner, which makes `self` unspillable while the frame is alive but
+        doesn't expose `self` when `__cuda_array_interface__` is accessed.
+
+        Warning, this hack means that the returned frame must be copied before
+        given to `.deserialize()`, otherwise we would have a `Buffer` pointing
+        to memory already owned by an existing `SpillableBuffer`.
+        """
+        header: Dict[Any, Any]
+        frames: List[Buffer | memoryview]
+        with self.lock:
+            header = {}
+            header["type-serialized"] = pickle.dumps(self.__class__)
+            header["frame_count"] = 1
+            if self.is_spilled:
+                frames = [self.memoryview()]
+            else:
+                # TODO: Use `frames=[self]` instead of this hack, see doc above
+                spill_lock = SpillLock()
+                self.spill_lock(spill_lock)
+                ptr, size, _ = self.memory_info()
+                frames = [
+                    Buffer._from_device_memory(
+                        cuda_array_interface_wrapper(
+                            ptr=ptr,
+                            size=size,
+                            owner=(self._owner, spill_lock),
+                        )
+                    )
+                ]
+            return header, frames
+
+    def __repr__(self) -> str:
+        if self._ptr_desc["type"] != "gpu":
+            ptr_info = str(self._ptr_desc)
+        else:
+            ptr_info = str(hex(self._ptr))
+        return (
+            f"<SpillableBuffer size={format_bytes(self._size)} "
+            f"spillable={self.spillable} exposed={self.exposed} "
+            f"num-spill-locks={len(self._spill_locks)} "
+            f"ptr={ptr_info} owner={repr(self._owner)}>"
+        )
+
+
+class SpillableBufferSlice(SpillableBuffer):
+    """A slice of a spillable buffer
+
+    This buffer applies the slicing and then delegates all
+    operations to its base buffer.
+
+    Parameters
+    ----------
+    base : SpillableBuffer
+        The base of the view
+    offset : int
+        Memory offset into the base buffer
+    size : int
+        Size of the view (in bytes)
+    """
+
+    def __init__(self, base: SpillableBuffer, offset: int, size: int) -> None:
+        if size < 0:
+            raise ValueError("size cannot be negative")
+        if offset < 0:
+            raise ValueError("offset cannot be negative")
+        if offset + size > base.size:
+            raise ValueError(
+                "offset+size cannot be greater than the size of base"
+            )
+        self._base = base
+        self._offset = offset
+        self._size = size
+        self._owner = base
+        self.lock = base.lock
+
+    def get_ptr(self, *, mode: Literal["read", "write"]) -> int:
+        """
+        A passthrough method to `SpillableBuffer.get_ptr`
+        with factoring in the `offset`.
+        """
+        return self._base.get_ptr(mode=mode) + self._offset
+
+    def _getitem(self, offset: int, size: int) -> SpillableBufferSlice:
+        return SpillableBufferSlice(
+            base=self._base, offset=offset + self._offset, size=size
+        )
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list):
+        # TODO: because of the hack in `SpillableBuffer.serialize()` where
+        # frames are of type `Buffer`, we always deserialize as if they are
+        # `SpillableBuffer`. In the future, we should be able to
+        # deserialize into `SpillableBufferSlice` when the frames hasn't been
+        # copied.
+        return SpillableBuffer.deserialize(header, frames)
+
+    def memoryview(
+        self, *, offset: int = 0, size: Optional[int] = None
+    ) -> memoryview:
+        size = self._size if size is None else size
+        return self._base.memoryview(offset=self._offset + offset, size=size)
+
+    def __repr__(self) -> str:
+        return (
+            f"<SpillableBufferSlice size={format_bytes(self._size)} "
+            f"offset={format_bytes(self._offset)} of {self._base} "
+        )
+
+    # The rest of the methods delegate to the base buffer.
+    def spill(self, target: str = "cpu") -> None:
+        return self._base.spill(target=target)
+
+    @property
+    def is_spilled(self) -> bool:
+        return self._base.is_spilled
+
+    @property
+    def exposed(self) -> bool:
+        return self._base.exposed
+
+    @property
+    def spillable(self) -> bool:
+        return self._base.spillable
+
+    def spill_lock(self, spill_lock: SpillLock) -> None:
+        self._base.spill_lock(spill_lock=spill_lock)
+
+    def memory_info(self) -> Tuple[int, int, str]:
+        (ptr, _, device_type) = self._base.memory_info()
+        return (ptr + self._offset, self.nbytes, device_type)
diff --git a/python/cudf/cudf/core/buffer/utils.py b/python/cudf/cudf/core/buffer/utils.py
new file mode 100644
index 0000000..373be99
--- /dev/null
+++ b/python/cudf/cudf/core/buffer/utils.py
@@ -0,0 +1,135 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import threading
+from contextlib import ContextDecorator
+from typing import Any, Dict, Optional, Tuple, Union
+
+from cudf.core.buffer.buffer import Buffer, cuda_array_interface_wrapper
+from cudf.core.buffer.exposure_tracked_buffer import as_exposure_tracked_buffer
+from cudf.core.buffer.spill_manager import get_global_manager
+from cudf.core.buffer.spillable_buffer import SpillLock, as_spillable_buffer
+from cudf.options import get_option
+
+
+def as_buffer(
+    data: Union[int, Any],
+    *,
+    size: Optional[int] = None,
+    owner: Optional[object] = None,
+    exposed: bool = False,
+) -> Buffer:
+    """Factory function to wrap `data` in a Buffer object.
+
+    If `data` isn't a buffer already, a new buffer that points to the memory of
+    `data` is created. If `data` represents host memory, it is copied to a new
+    `rmm.DeviceBuffer` device allocation. Otherwise, the memory of `data` is
+    **not** copied, instead the new buffer keeps a reference to `data` in order
+    to retain its lifetime.
+
+    If `data` is an integer, it is assumed to point to device memory.
+
+    Raises ValueError if data isn't C-contiguous.
+
+    Parameters
+    ----------
+    data : int or buffer-like or array-like
+        An integer representing a pointer to device memory or a buffer-like
+        or array-like object. When not an integer, `size` and `owner` must
+        be None.
+    size : int, optional
+        Size of device memory in bytes. Must be specified if `data` is an
+        integer.
+    owner : object, optional
+        Python object to which the lifetime of the memory allocation is tied.
+        A reference to this object is kept in the returned Buffer.
+    exposed : bool, optional
+        Mark the buffer as permanently exposed. This is used by
+        ExposureTrackedBuffer to determine when a deep copy is required and
+        by SpillableBuffer to mark the buffer unspillable.
+
+    Return
+    ------
+    Buffer
+        A buffer instance that represents the device memory of `data`.
+    """
+
+    if isinstance(data, Buffer):
+        return data
+
+    # We handle the integer argument in the factory function by wrapping
+    # the pointer in a `__cuda_array_interface__` exposing object so that
+    # the Buffer (and its sub-classes) do not have to.
+    if isinstance(data, int):
+        if size is None:
+            raise ValueError(
+                "size must be specified when `data` is an integer"
+            )
+        data = cuda_array_interface_wrapper(ptr=data, size=size, owner=owner)
+    elif size is not None or owner is not None:
+        raise ValueError(
+            "`size` and `owner` must be None when "
+            "`data` is a buffer-like or array-like object"
+        )
+
+    if get_option("copy_on_write"):
+        return as_exposure_tracked_buffer(data, exposed=exposed)
+    if get_global_manager() is not None:
+        return as_spillable_buffer(data, exposed=exposed)
+    if hasattr(data, "__cuda_array_interface__"):
+        return Buffer._from_device_memory(data)
+    return Buffer._from_host_memory(data)
+
+
+_thread_spill_locks: Dict[int, Tuple[Optional[SpillLock], int]] = {}
+
+
+def _push_thread_spill_lock() -> None:
+    _id = threading.get_ident()
+    spill_lock, count = _thread_spill_locks.get(_id, (None, 0))
+    if spill_lock is None:
+        spill_lock = SpillLock()
+    _thread_spill_locks[_id] = (spill_lock, count + 1)
+
+
+def _pop_thread_spill_lock() -> None:
+    _id = threading.get_ident()
+    spill_lock, count = _thread_spill_locks[_id]
+    if count == 1:
+        spill_lock = None
+    _thread_spill_locks[_id] = (spill_lock, count - 1)
+
+
+class acquire_spill_lock(ContextDecorator):
+    """Decorator and context to set spill lock automatically.
+
+    All calls to `get_spill_lock()` within the decorated function or context
+    will return a spill lock with a lifetime bound to the function or context.
+
+    Developer Notes
+    ---------------
+    We use the global variable `_thread_spill_locks` to track the global spill
+    lock state. To support concurrency, each thread tracks its own state by
+    pushing and popping from `_thread_spill_locks` using its thread ID.
+    """
+
+    def __enter__(self) -> Optional[SpillLock]:
+        _push_thread_spill_lock()
+        return get_spill_lock()
+
+    def __exit__(self, *exc):
+        _pop_thread_spill_lock()
+
+
+def get_spill_lock() -> Union[SpillLock, None]:
+    """Return a spill lock within the context of `acquire_spill_lock` or None
+
+    Returns None, if spilling is disabled.
+    """
+
+    if get_global_manager() is None:
+        return None
+    _id = threading.get_ident()
+    spill_lock, _ = _thread_spill_locks.get(_id, (None, 0))
+    return spill_lock
diff --git a/python/cudf/cudf/core/column/__init__.py b/python/cudf/cudf/core/column/__init__.py
new file mode 100644
index 0000000..aba4ded
--- /dev/null
+++ b/python/cudf/cudf/core/column/__init__.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+"""
+isort: skip_file
+"""
+
+
+from cudf.core.column.categorical import CategoricalColumn
+from cudf.core.column.column import (
+    ColumnBase,
+    arange,
+    as_column,
+    build_categorical_column,
+    build_column,
+    build_list_column,
+    build_struct_column,
+    column_empty,
+    column_empty_like,
+    column_empty_like_same_mask,
+    concat_columns,
+    deserialize_columns,
+    full,
+    serialize_columns,
+)
+from cudf.core.column.datetime import DatetimeColumn  # noqa: F401
+from cudf.core.column.datetime import DatetimeTZColumn  # noqa: F401
+from cudf.core.column.lists import ListColumn  # noqa: F401
+from cudf.core.column.numerical import NumericalColumn  # noqa: F401
+from cudf.core.column.string import StringColumn  # noqa: F401
+from cudf.core.column.struct import StructColumn  # noqa: F401
+from cudf.core.column.timedelta import TimeDeltaColumn  # noqa: F401
+from cudf.core.column.interval import IntervalColumn  # noqa: F401
+from cudf.core.column.decimal import (  # noqa: F401
+    Decimal32Column,
+    Decimal64Column,
+    Decimal128Column,
+    DecimalBaseColumn,
+)
+from cudf.core.column.interval import IntervalColumn  # noqa: F401
diff --git a/python/cudf/cudf/core/column/categorical.py b/python/cudf/cudf/core/column/categorical.py
new file mode 100644
index 0000000..bab0762
--- /dev/null
+++ b/python/cudf/cudf/core/column/categorical.py
@@ -0,0 +1,1654 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import warnings
+from collections import abc
+from functools import cached_property
+from typing import TYPE_CHECKING, Any, Mapping, Optional, Sequence, Tuple, cast
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from numba import cuda
+from typing_extensions import Self
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib.transform import bools_to_mask
+from cudf._typing import ColumnBinaryOperand, ColumnLike, Dtype, ScalarLike
+from cudf.api.types import is_categorical_dtype, is_interval_dtype
+from cudf.core.buffer import Buffer
+from cudf.core.column import column
+from cudf.core.column.methods import ColumnMethods
+from cudf.core.dtypes import CategoricalDtype
+from cudf.utils.dtypes import (
+    is_mixed_with_object_dtype,
+    min_signed_type,
+    min_unsigned_type,
+)
+
+if TYPE_CHECKING:
+    from cudf._typing import SeriesOrIndex, SeriesOrSingleColumnIndex
+    from cudf.core.column import (
+        ColumnBase,
+        DatetimeColumn,
+        NumericalColumn,
+        StringColumn,
+        TimeDeltaColumn,
+    )
+
+
+_DEFAULT_CATEGORICAL_VALUE = -1
+
+
+class CategoricalAccessor(ColumnMethods):
+    """
+    Accessor object for categorical properties of the Series values.
+    Be aware that assigning to `categories` is a inplace operation,
+    while all methods return new categorical data per default.
+
+    Parameters
+    ----------
+    column : Column
+    parent : Series or CategoricalIndex
+
+    Examples
+    --------
+    >>> s = cudf.Series([1,2,3], dtype='category')
+    >>> s
+    0    1
+    1    2
+    2    3
+    dtype: category
+    Categories (3, int64): [1, 2, 3]
+    >>> s.cat.categories
+    Int64Index([1, 2, 3], dtype='int64')
+    >>> s.cat.reorder_categories([3,2,1])
+    0    1
+    1    2
+    2    3
+    dtype: category
+    Categories (3, int64): [3, 2, 1]
+    >>> s.cat.remove_categories([1])
+    0    <NA>
+    1       2
+    2       3
+    dtype: category
+    Categories (2, int64): [2, 3]
+    >>> s.cat.set_categories(list('abcde'))
+    0    <NA>
+    1    <NA>
+    2    <NA>
+    dtype: category
+    Categories (5, object): ['a', 'b', 'c', 'd', 'e']
+    >>> s.cat.as_ordered()
+    0    1
+    1    2
+    2    3
+    dtype: category
+    Categories (3, int64): [1 < 2 < 3]
+    >>> s.cat.as_unordered()
+    0    1
+    1    2
+    2    3
+    dtype: category
+    Categories (3, int64): [1, 2, 3]
+    """
+
+    _column: CategoricalColumn
+
+    def __init__(self, parent: SeriesOrSingleColumnIndex):
+        if not is_categorical_dtype(parent.dtype):
+            raise AttributeError(
+                "Can only use .cat accessor with a 'category' dtype"
+            )
+        super().__init__(parent=parent)
+
+    @property
+    def categories(self) -> "cudf.core.index.GenericIndex":
+        """
+        The categories of this categorical.
+        """
+        return cudf.core.index.as_index(self._column.categories)
+
+    @property
+    def codes(self) -> "cudf.Series":
+        """
+        Return Series of codes as well as the index.
+        """
+        index = (
+            self._parent.index
+            if isinstance(self._parent, cudf.Series)
+            else None
+        )
+        return cudf.Series(self._column.codes, index=index)
+
+    @property
+    def ordered(self) -> bool:
+        """
+        Whether the categories have an ordered relationship.
+        """
+        return self._column.ordered
+
+    def as_ordered(self, inplace: bool = False) -> Optional[SeriesOrIndex]:
+        """
+        Set the Categorical to be ordered.
+
+        Parameters
+        ----------
+        inplace : bool, default False
+            Whether or not to add the categories inplace
+            or return a copy of this categorical with
+            added categories.
+
+            .. deprecated:: 23.02
+
+               The `inplace` parameter is is deprecated and
+               will be removed in a future version of cudf.
+               Setting categories as ordered will always
+               return a new Categorical object.
+
+        Returns
+        -------
+        Categorical
+            Ordered Categorical or None if inplace.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([10, 1, 1, 2, 10, 2, 10], dtype="category")
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s.cat.as_ordered()
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1 < 2 < 10]
+        >>> s.cat.as_ordered(inplace=True)
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1 < 2 < 10]
+        """
+        if inplace:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "The inplace parameter is deprecated and will be removed in a "
+                "future release. set_ordered will always return a new Series "
+                "in the future.",
+                FutureWarning,
+            )
+        return self._return_or_inplace(
+            self._column.as_ordered(), inplace=inplace
+        )
+
+    def as_unordered(self, inplace: bool = False) -> Optional[SeriesOrIndex]:
+        """
+        Set the Categorical to be unordered.
+
+        Parameters
+        ----------
+        inplace : bool, default False
+            Whether or not to set the ordered attribute
+            in-place or return a copy of this
+            categorical with ordered set to False.
+
+            .. deprecated:: 23.02
+
+               The `inplace` parameter is is deprecated and
+               will be removed in a future version of cudf.
+               Setting categories as unordered will always
+               return a new Categorical object.
+
+        Returns
+        -------
+        Categorical
+            Unordered Categorical or None if inplace.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([10, 1, 1, 2, 10, 2, 10], dtype="category")
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s = s.cat.as_ordered()
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1 < 2 < 10]
+        >>> s.cat.as_unordered()
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s.cat.as_unordered(inplace=True)
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        """
+        if inplace:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "The inplace parameter is deprecated and will be removed in a "
+                "future release. set_ordered will always return a new Series "
+                "in the future.",
+                FutureWarning,
+            )
+        return self._return_or_inplace(
+            self._column.as_unordered(), inplace=inplace
+        )
+
+    def add_categories(
+        self, new_categories: Any, inplace: bool = False
+    ) -> Optional[SeriesOrIndex]:
+        """
+        Add new categories.
+
+        `new_categories` will be included at the last/highest
+        place in the categories and will be unused directly
+        after this call.
+
+        Parameters
+        ----------
+        new_categories : category or list-like of category
+            The new categories to be included.
+        inplace : bool, default False
+            Whether or not to add the categories inplace
+            or return a copy of this categorical with
+            added categories.
+
+            .. deprecated:: 23.04
+
+               The `inplace` parameter is is deprecated and
+               will be removed in a future version of cudf.
+               Adding categories will always return a
+               new Categorical object.
+
+        Returns
+        -------
+        cat
+            Categorical with new categories added or
+            None if inplace.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([1, 2], dtype="category")
+        >>> s
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int64): [1, 2]
+        >>> s.cat.add_categories([0, 3, 4])
+        0    1
+        1    2
+        dtype: category
+        Categories (5, int64): [1, 2, 0, 3, 4]
+        >>> s
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int64): [1, 2]
+        >>> s.cat.add_categories([0, 3, 4], inplace=True)
+        >>> s
+        0    1
+        1    2
+        dtype: category
+        Categories (5, int64): [1, 2, 0, 3, 4]
+        """
+        if inplace:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "The `inplace` parameter in cudf.Series.cat.add_categories "
+                "is deprecated and will be removed in a future version of "
+                "cudf. Adding categories will always return a new "
+                "Categorical object.",
+                FutureWarning,
+            )
+        old_categories = self._column.categories
+        new_categories = column.as_column(
+            new_categories,
+            dtype=old_categories.dtype if len(new_categories) == 0 else None,
+        )
+
+        if is_mixed_with_object_dtype(old_categories, new_categories):
+            raise TypeError(
+                f"cudf does not support adding categories with existing "
+                f"categories of dtype `{old_categories.dtype}` and new "
+                f"categories of dtype `{new_categories.dtype}`, please "
+                f"type-cast new_categories to the same type as "
+                f"existing categories."
+            )
+        common_dtype = np.find_common_type(
+            [old_categories.dtype, new_categories.dtype], []
+        )
+
+        new_categories = new_categories.astype(common_dtype)
+        old_categories = old_categories.astype(common_dtype)
+
+        if old_categories.isin(new_categories).any():
+            raise ValueError("new categories must not include old categories")
+
+        new_categories = old_categories.append(new_categories)
+        out_col = self._column
+        if not out_col._categories_equal(new_categories):
+            out_col = out_col._set_categories(new_categories)
+
+        return self._return_or_inplace(out_col, inplace=inplace)
+
+    def remove_categories(
+        self,
+        removals: Any,
+        inplace: bool = False,
+    ) -> Optional[SeriesOrIndex]:
+        """
+        Remove the specified categories.
+
+        `removals` must be included in the
+        old categories. Values which were in the
+        removed categories will be set to null.
+
+        Parameters
+        ----------
+        removals : category or list-like of category
+            The categories which should be removed.
+        inplace : bool, default False
+            Whether or not to remove the categories
+            inplace or return a copy of this categorical
+            with removed categories.
+
+            .. deprecated:: 23.04
+
+               The `inplace` parameter is is deprecated and
+               will be removed in a future version of cudf.
+               Removing categories will always return a
+               new Categorical object.
+
+        Returns
+        -------
+        cat
+            Categorical with removed categories or None
+            if inplace.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([10, 1, 1, 2, 10, 2, 10], dtype="category")
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s.cat.remove_categories([1])
+        0      10
+        1    <NA>
+        2    <NA>
+        3       2
+        4      10
+        5       2
+        6      10
+        dtype: category
+        Categories (2, int64): [2, 10]
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s.cat.remove_categories([10], inplace=True)
+        >>> s
+        0    <NA>
+        1       1
+        2       1
+        3       2
+        4    <NA>
+        5       2
+        6    <NA>
+        dtype: category
+        Categories (2, int64): [1, 2]
+        """
+        if inplace:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "The `inplace` parameter in "
+                "cudf.Series.cat.remove_categories is deprecated and "
+                "will be removed in a future version of cudf. "
+                "Removing categories will always return a new "
+                "Categorical object.",
+                FutureWarning,
+            )
+
+        cats = self.categories.to_series()
+        removals = cudf.Series(removals, dtype=cats.dtype)
+        removals_mask = removals.isin(cats)
+
+        # ensure all the removals are in the current categories
+        # list. If not, raise an error to match Pandas behavior
+        if not removals_mask.all():
+            vals = removals[~removals_mask].to_numpy()
+            raise ValueError(f"removals must all be in old categories: {vals}")
+
+        new_categories = cats[~cats.isin(removals)]._column
+        out_col = self._column
+        if not out_col._categories_equal(new_categories):
+            out_col = out_col._set_categories(new_categories)
+
+        return self._return_or_inplace(out_col, inplace=inplace)
+
+    def set_categories(
+        self,
+        new_categories: Any,
+        ordered: bool = False,
+        rename: bool = False,
+        inplace: bool = False,
+    ) -> Optional[SeriesOrIndex]:
+        """
+        Set the categories to the specified new_categories.
+
+
+        `new_categories` can include new categories (which
+        will result in unused categories) or remove old categories
+        (which results in values set to null). If `rename==True`,
+        the categories will simple be renamed (less or more items
+        than in old categories will result in values set to null or
+        in unused categories respectively).
+
+        This method can be used to perform more than one action
+        of adding, removing, and reordering simultaneously and
+        is therefore faster than performing the individual steps
+        via the more specialised methods.
+
+        On the other hand this methods does not do checks
+        (e.g., whether the old categories are included in the
+        new categories on a reorder), which can result in
+        surprising changes.
+
+        Parameters
+        ----------
+        new_categories : list-like
+            The categories in new order.
+        ordered : bool, default None
+            Whether or not the categorical is treated as
+            a ordered categorical. If not given, do
+            not change the ordered information.
+        rename : bool, default False
+            Whether or not the `new_categories` should be
+            considered as a rename of the old categories
+            or as reordered categories.
+        inplace : bool, default False
+            Whether or not to reorder the categories in-place
+            or return a copy of this categorical with
+            reordered categories.
+
+            .. deprecated:: 23.04
+
+               The `inplace` parameter is is deprecated and
+               will be removed in a future version of cudf.
+               Setting categories will always return a
+               new Categorical object.
+
+        Returns
+        -------
+        cat
+            Categorical with reordered categories
+            or None if inplace.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([1, 1, 2, 10, 2, 10], dtype='category')
+        >>> s
+        0     1
+        1     1
+        2     2
+        3    10
+        4     2
+        5    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s.cat.set_categories([1, 10])
+        0       1
+        1       1
+        2    <NA>
+        3      10
+        4    <NA>
+        5      10
+        dtype: category
+        Categories (2, int64): [1, 10]
+        >>> s.cat.set_categories([1, 10], inplace=True)
+        >>> s
+        0       1
+        1       1
+        2    <NA>
+        3      10
+        4    <NA>
+        5      10
+        dtype: category
+        Categories (2, int64): [1, 10]
+        """
+        if inplace:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "The `inplace` parameter in cudf.Series.cat.set_categories is "
+                "deprecated and will be removed in a future version of cudf. "
+                "Setting categories will always return a new Categorical "
+                "object.",
+                FutureWarning,
+            )
+        return self._return_or_inplace(
+            self._column.set_categories(
+                new_categories=new_categories, ordered=ordered, rename=rename
+            ),
+            inplace=inplace,
+        )
+
+    def reorder_categories(
+        self,
+        new_categories: Any,
+        ordered: bool = False,
+        inplace: bool = False,
+    ) -> Optional[SeriesOrIndex]:
+        """
+        Reorder categories as specified in new_categories.
+
+        `new_categories` need to include all old categories
+        and no new category items.
+
+        Parameters
+        ----------
+        new_categories : Index-like
+            The categories in new order.
+        ordered : bool, optional
+            Whether or not the categorical is treated
+            as a ordered categorical. If not given, do
+            not change the ordered information.
+        inplace : bool, default False
+            Whether or not to reorder the categories
+            inplace or return a copy of this categorical
+            with reordered categories.
+
+            .. deprecated:: 23.04
+
+               The `inplace` parameter is is deprecated and
+               will be removed in a future version of cudf.
+               Reordering categories will always return a
+               new Categorical object.
+
+        Returns
+        -------
+        cat
+            Categorical with reordered categories or
+            None if inplace.
+
+        Raises
+        ------
+        ValueError
+            If the new categories do not contain all old
+            category items or any new ones.
+
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([10, 1, 1, 2, 10, 2, 10], dtype="category")
+        >>> s
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [1, 2, 10]
+        >>> s.cat.reorder_categories([10, 1, 2])
+        0    10
+        1     1
+        2     1
+        3     2
+        4    10
+        5     2
+        6    10
+        dtype: category
+        Categories (3, int64): [10, 1, 2]
+        >>> s.cat.reorder_categories([10, 1])
+        ValueError: items in new_categories are not the same as in
+        old categories
+        """
+        if inplace:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "The `inplace` parameter in "
+                "cudf.Series.cat.reorder_categories is deprecated "
+                "and will be removed in a future version of cudf. "
+                "Reordering categories will always return a new "
+                "Categorical object.",
+                FutureWarning,
+            )
+        return self._return_or_inplace(
+            self._column.reorder_categories(new_categories, ordered=ordered),
+            inplace=inplace,
+        )
+
+
+class CategoricalColumn(column.ColumnBase):
+    """
+    Implements operations for Columns of Categorical type
+
+    Parameters
+    ----------
+    dtype : CategoricalDtype
+    mask : Buffer
+        The validity mask
+    offset : int
+        Data offset
+    children : Tuple[ColumnBase]
+        Two non-null columns containing the categories and codes
+        respectively
+    """
+
+    dtype: cudf.core.dtypes.CategoricalDtype
+    _codes: Optional[NumericalColumn]
+    _children: Tuple[NumericalColumn]
+    _VALID_BINARY_OPERATIONS = {
+        "__eq__",
+        "__ne__",
+        "__lt__",
+        "__le__",
+        "__gt__",
+        "__ge__",
+    }
+
+    def __init__(
+        self,
+        dtype: CategoricalDtype,
+        mask: Optional[Buffer] = None,
+        size: Optional[int] = None,
+        offset: int = 0,
+        null_count: Optional[int] = None,
+        children: Tuple["column.ColumnBase", ...] = (),
+    ):
+        if size is None:
+            for child in children:
+                assert child.offset == 0
+                assert child.base_mask is None
+            size = children[0].size
+            size = size - offset
+        if isinstance(dtype, pd.api.types.CategoricalDtype):
+            dtype = CategoricalDtype.from_pandas(dtype)
+        if not isinstance(dtype, CategoricalDtype):
+            raise ValueError("dtype must be instance of CategoricalDtype")
+        super().__init__(
+            data=None,
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+        self._codes = None
+
+    @property
+    def base_size(self) -> int:
+        return int(
+            (self.base_children[0].size) / self.base_children[0].dtype.itemsize
+        )
+
+    def __contains__(self, item: ScalarLike) -> bool:
+        try:
+            self._encode(item)
+        except ValueError:
+            return False
+        return self._encode(item) in self.as_numerical
+
+    def set_base_data(self, value):
+        if value is not None:
+            raise RuntimeError(
+                "CategoricalColumns do not use data attribute of Column, use "
+                "`set_base_children` instead"
+            )
+        else:
+            super().set_base_data(value)
+
+    def _process_values_for_isin(
+        self, values: Sequence
+    ) -> Tuple[ColumnBase, ColumnBase]:
+        lhs = self
+        # We need to convert values to same type as self,
+        # hence passing dtype=self.dtype
+        rhs = cudf.core.column.as_column(values, dtype=self.dtype)
+        return lhs, rhs
+
+    def set_base_mask(self, value: Optional[Buffer]):
+        super().set_base_mask(value)
+        self._codes = None
+
+    def set_base_children(self, value: Tuple[ColumnBase, ...]):
+        super().set_base_children(value)
+        self._codes = None
+
+    @property
+    def children(self) -> Tuple[NumericalColumn]:
+        if self._children is None:
+            codes_column = self.base_children[0]
+            start = self.offset * codes_column.dtype.itemsize
+            end = start + self.size * codes_column.dtype.itemsize
+            codes_column = cast(
+                cudf.core.column.NumericalColumn,
+                column.build_column(
+                    data=codes_column.base_data[start:end],
+                    dtype=codes_column.dtype,
+                    size=self.size,
+                ),
+            )
+            self._children = (codes_column,)
+        return self._children
+
+    @property
+    def as_numerical(self) -> NumericalColumn:
+        return cast(
+            cudf.core.column.NumericalColumn,
+            column.build_column(
+                data=self.codes.data, dtype=self.codes.dtype, mask=self.mask
+            ),
+        )
+
+    @property
+    def categories(self) -> ColumnBase:
+        return self.dtype.categories._values
+
+    @categories.setter
+    def categories(self, value):
+        self._dtype = CategoricalDtype(
+            categories=value, ordered=self.dtype.ordered
+        )
+
+    @property
+    def codes(self) -> NumericalColumn:
+        if self._codes is None:
+            self._codes = self.children[0].set_mask(self.mask)
+        return cast(cudf.core.column.NumericalColumn, self._codes)
+
+    @property
+    def ordered(self) -> bool:
+        return self.dtype.ordered
+
+    @ordered.setter
+    def ordered(self, value: bool):
+        self.dtype.ordered = value
+
+    def unary_operator(self, unaryop: str):
+        raise TypeError(
+            f"Series of dtype `category` cannot perform the operation: "
+            f"{unaryop}"
+        )
+
+    def __setitem__(self, key, value):
+        if cudf.api.types.is_scalar(
+            value
+        ) and cudf._lib.scalar._is_null_host_scalar(value):
+            to_add_categories = 0
+        else:
+            if cudf.api.types.is_scalar(value):
+                arr = [value]
+            else:
+                arr = value
+            to_add_categories = len(
+                cudf.Index(arr, nan_as_null=False).difference(self.categories)
+            )
+
+        if to_add_categories > 0:
+            raise TypeError(
+                "Cannot setitem on a Categorical with a new "
+                "category, set the categories first"
+            )
+
+        if cudf.api.types.is_scalar(value):
+            value = self._encode(value) if value is not None else value
+        else:
+            value = cudf.core.column.as_column(value).astype(self.dtype)
+            value = value.codes
+        codes = self.codes
+        codes[key] = value
+        out = cudf.core.column.build_categorical_column(
+            categories=self.categories,
+            codes=codes,
+            mask=codes.base_mask,
+            size=codes.size,
+            offset=self.offset,
+            ordered=self.ordered,
+        )
+        self._mimic_inplace(out, inplace=True)
+
+    def _fill(
+        self,
+        fill_value: ScalarLike,
+        begin: int,
+        end: int,
+        inplace: bool = False,
+    ) -> Self:
+        if end <= begin or begin >= self.size:
+            return self if inplace else self.copy()
+
+        fill_code = self._encode(fill_value)
+        fill_scalar = cudf._lib.scalar.as_device_scalar(
+            fill_code, self.codes.dtype
+        )
+
+        result = self if inplace else self.copy()
+
+        libcudf.filling.fill_in_place(result.codes, begin, end, fill_scalar)
+        return result
+
+    def slice(
+        self, start: int, stop: int, stride: Optional[int] = None
+    ) -> Self:
+        codes = self.codes.slice(start, stop, stride)
+        return cast(
+            Self,
+            cudf.core.column.build_categorical_column(
+                categories=self.categories,
+                codes=cudf.core.column.build_column(
+                    codes.base_data, dtype=codes.dtype
+                ),
+                mask=codes.base_mask,
+                ordered=self.ordered,
+                size=codes.size,
+                offset=codes.offset,
+            ),
+        )
+
+    def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase:
+        other = self._wrap_binop_normalization(other)
+        # TODO: This is currently just here to make mypy happy, but eventually
+        # we'll need to properly establish the APIs for these methods.
+        if not isinstance(other, CategoricalColumn):
+            raise ValueError
+        # Note: at this stage we are guaranteed that the dtypes are equal.
+        if not self.ordered and op not in {"__eq__", "__ne__", "NULL_EQUALS"}:
+            raise TypeError(
+                "The only binary operations supported by unordered "
+                "categorical columns are equality and inequality."
+            )
+        return self.as_numerical._binaryop(other.as_numerical, op)
+
+    def normalize_binop_value(self, other: ScalarLike) -> CategoricalColumn:
+        if isinstance(other, column.ColumnBase):
+            if not isinstance(other, CategoricalColumn):
+                return NotImplemented
+            if other.dtype != self.dtype:
+                raise TypeError(
+                    "Categoricals can only compare with the same type"
+                )
+            return other
+
+        ary = column.full(
+            len(self), self._encode(other), dtype=self.codes.dtype
+        )
+        return column.build_categorical_column(
+            categories=self.dtype.categories._values,
+            codes=column.as_column(ary),
+            mask=self.base_mask,
+            ordered=self.dtype.ordered,
+        )
+
+    def sort_values(
+        self, ascending: bool = True, na_position="last"
+    ) -> CategoricalColumn:
+        codes = self.as_numerical.sort_values(ascending, na_position)
+        col = column.build_categorical_column(
+            categories=self.dtype.categories._values,
+            codes=column.build_column(codes.base_data, dtype=codes.dtype),
+            mask=codes.base_mask,
+            size=codes.size,
+            ordered=self.dtype.ordered,
+        )
+        return col
+
+    def element_indexing(self, index: int) -> ScalarLike:
+        val = self.as_numerical.element_indexing(index)
+        return self._decode(int(val)) if val is not None else val
+
+    @property
+    def __cuda_array_interface__(self) -> Mapping[str, Any]:
+        raise TypeError(
+            "Categorical does not support `__cuda_array_interface__`."
+            " Please consider using `.codes` or `.categories`"
+            " if you need this functionality."
+        )
+
+    def to_pandas(
+        self, index: Optional[pd.Index] = None, **kwargs
+    ) -> pd.Series:
+        if self.categories.dtype.kind == "f":
+            new_mask = bools_to_mask(self.notnull())
+            col = column.build_categorical_column(
+                categories=self.categories,
+                codes=column.as_column(self.codes, dtype=self.codes.dtype),
+                mask=new_mask,
+                ordered=self.dtype.ordered,
+                size=self.codes.size,
+            )
+        else:
+            col = self
+
+        signed_dtype = min_signed_type(len(col.categories))
+        codes = (
+            col.codes.astype(signed_dtype)
+            .fillna(_DEFAULT_CATEGORICAL_VALUE)
+            .values_host
+        )
+        if is_interval_dtype(col.categories.dtype):
+            # leaving out dropna because it temporarily changes an interval
+            # index into a struct and throws off results.
+            # TODO: work on interval index dropna
+            categories = col.categories.to_pandas()
+        else:
+            categories = col.categories.dropna(drop_nan=True).to_pandas()
+        data = pd.Categorical.from_codes(
+            codes, categories=categories, ordered=col.ordered
+        )
+        return pd.Series(data, index=index)
+
+    def to_arrow(self) -> pa.Array:
+        """Convert to PyArrow Array."""
+        # arrow doesn't support unsigned codes
+        signed_type = (
+            min_signed_type(self.codes.max())
+            if self.codes.size > 0
+            else np.int8
+        )
+        codes = self.codes.astype(signed_type)
+        categories = self.categories
+
+        out_indices = codes.to_arrow()
+        out_dictionary = categories.to_arrow()
+
+        return pa.DictionaryArray.from_arrays(
+            out_indices,
+            out_dictionary,
+            ordered=self.ordered,
+        )
+
+    @property
+    def values_host(self) -> np.ndarray:
+        """
+        Return a numpy representation of the CategoricalColumn.
+        """
+        return self.to_pandas().values
+
+    @property
+    def values(self):
+        """
+        Return a CuPy representation of the CategoricalColumn.
+        """
+        raise NotImplementedError("cudf.Categorical is not yet implemented")
+
+    def clip(self, lo: ScalarLike, hi: ScalarLike) -> "column.ColumnBase":
+        return (
+            self.astype(self.categories.dtype).clip(lo, hi).astype(self.dtype)
+        )
+
+    def data_array_view(
+        self, *, mode="write"
+    ) -> cuda.devicearray.DeviceNDArray:
+        return self.codes.data_array_view(mode=mode)
+
+    def unique(self) -> CategoricalColumn:
+        codes = self.as_numerical.unique()
+        return column.build_categorical_column(
+            categories=self.categories,
+            codes=column.build_column(codes.base_data, dtype=codes.dtype),
+            mask=codes.base_mask,
+            offset=codes.offset,
+            size=codes.size,
+            ordered=self.ordered,
+        )
+
+    def _encode(self, value) -> ScalarLike:
+        return self.categories.find_first_value(value)
+
+    def _decode(self, value: int) -> ScalarLike:
+        if value == _DEFAULT_CATEGORICAL_VALUE:
+            return None
+        return self.categories.element_indexing(value)
+
+    def find_and_replace(
+        self,
+        to_replace: ColumnLike,
+        replacement: ColumnLike,
+        all_nan: bool = False,
+    ) -> CategoricalColumn:
+        """
+        Return col with *to_replace* replaced with *replacement*.
+        """
+        to_replace_col = column.as_column(to_replace)
+        if len(to_replace_col) == to_replace_col.null_count:
+            to_replace_col = to_replace_col.astype(self.categories.dtype)
+        replacement_col = column.as_column(replacement)
+        if len(replacement_col) == replacement_col.null_count:
+            replacement_col = replacement_col.astype(self.categories.dtype)
+
+        if type(to_replace_col) != type(replacement_col):
+            raise TypeError(
+                f"to_replace and value should be of same types,"
+                f"got to_replace dtype: {to_replace_col.dtype} and "
+                f"value dtype: {replacement_col.dtype}"
+            )
+        df = cudf.DataFrame._from_data(
+            {"old": to_replace_col, "new": replacement_col}
+        )
+        df = df.drop_duplicates(subset=["old"], keep="last", ignore_index=True)
+        if df._data["old"].null_count == 1:
+            fill_value = (
+                df._data["new"]
+                .apply_boolean_mask(df._data["old"].isnull())
+                .element_indexing(0)
+            )
+            # TODO: This line of code does not work because we cannot use the
+            # `in` operator on self.categories (which is a column). mypy
+            # realizes that this is wrong because __iter__ is not implemented.
+            # However, it seems that this functionality has been broken for a
+            # long time so for now we're just having mypy ignore and we'll come
+            # back to this.
+            if fill_value in self.categories:  # type: ignore
+                replaced = self.fillna(fill_value)
+            else:
+                new_categories = self.categories.append(
+                    column.as_column([fill_value])
+                )
+                replaced = self._set_categories(new_categories)
+                replaced = replaced.fillna(fill_value)
+            df = df.dropna(subset=["old"])
+            to_replace_col = df._data["old"]
+            replacement_col = df._data["new"]
+        else:
+            replaced = self
+        if df._data["new"].null_count > 0:
+            drop_values = df._data["old"].apply_boolean_mask(
+                df._data["new"].isnull()
+            )
+            cur_categories = replaced.categories
+            new_categories = cur_categories.apply_boolean_mask(
+                ~cudf.Series(cur_categories.isin(drop_values))
+            )
+            replaced = replaced._set_categories(new_categories)
+            df = df.dropna(subset=["new"])
+            to_replace_col = df._data["old"]
+            replacement_col = df._data["new"]
+
+        # create a dataframe containing the pre-replacement categories
+        # and a column with the appropriate labels replaced.
+        # The index of this dataframe represents the original
+        # ints that map to the categories
+        cats_col = column.as_column(replaced.dtype.categories)
+        old_cats = cudf.DataFrame._from_data(
+            {
+                "cats": cats_col,
+                "cats_replace": cats_col.find_and_replace(
+                    to_replace_col, replacement_col
+                ),
+            }
+        )
+
+        # Construct the new categorical labels
+        # If a category is being replaced by an existing one, we
+        # want to map it to None. If it's totally new, we want to
+        # map it to the new label it is to be replaced by
+        dtype_replace = cudf.Series._from_data({None: replacement_col})
+        dtype_replace[dtype_replace.isin(cats_col)] = None
+        new_cats_col = cats_col.find_and_replace(
+            to_replace_col, dtype_replace._column
+        )
+
+        # anything we mapped to None, we want to now filter out since
+        # those categories don't exist anymore
+        # Resetting the index creates a column 'index' that associates
+        # the original integers to the new labels
+        bmask = new_cats_col.notnull()
+        new_cats_col = new_cats_col.apply_boolean_mask(bmask)
+        new_cats = cudf.DataFrame._from_data(
+            {
+                "index": cudf.core.column.arange(len(new_cats_col)),
+                "cats": new_cats_col,
+            }
+        )
+
+        # old_cats contains replaced categories and the ints that
+        # previously mapped to those categories and the index of
+        # new_cats is a RangeIndex that contains the new ints
+        catmap = old_cats.merge(
+            new_cats, left_on="cats_replace", right_on="cats", how="inner"
+        )
+
+        # The index of this frame is now the old ints, but the column
+        # named 'index', which came from the filtered categories,
+        # contains the new ints that we need to map to
+        to_replace_col = column.as_column(catmap.index).astype(
+            replaced.codes.dtype
+        )
+        replacement_col = catmap._data["index"].astype(replaced.codes.dtype)
+
+        replaced = column.as_column(replaced.codes)
+        output = libcudf.replace.replace(
+            replaced, to_replace_col, replacement_col
+        )
+
+        return column.build_categorical_column(
+            categories=new_cats["cats"],
+            codes=column.build_column(output.base_data, dtype=output.dtype),
+            mask=output.base_mask,
+            offset=output.offset,
+            size=output.size,
+            ordered=self.dtype.ordered,
+        )
+
+    def isnull(self) -> ColumnBase:
+        """
+        Identify missing values in a CategoricalColumn.
+        """
+        result = libcudf.unary.is_null(self)
+
+        if self.categories.dtype.kind == "f":
+            # Need to consider `np.nan` values in case
+            # of an underlying float column
+            categories = libcudf.unary.is_nan(self.categories)
+            if categories.any():
+                code = self._encode(np.nan)
+                result = result | (self.codes == cudf.Scalar(code))
+
+        return result
+
+    def notnull(self) -> ColumnBase:
+        """
+        Identify non-missing values in a CategoricalColumn.
+        """
+        result = libcudf.unary.is_valid(self)
+
+        if self.categories.dtype.kind == "f":
+            # Need to consider `np.nan` values in case
+            # of an underlying float column
+            categories = libcudf.unary.is_nan(self.categories)
+            if categories.any():
+                code = self._encode(np.nan)
+                result = result & (self.codes != cudf.Scalar(code))
+
+        return result
+
+    def fillna(
+        self,
+        fill_value: Any = None,
+        method: Any = None,
+        dtype: Optional[Dtype] = None,
+    ) -> CategoricalColumn:
+        """
+        Fill null values with *fill_value*
+        """
+        if not self.nullable:
+            return self
+
+        if fill_value is not None:
+            fill_is_scalar = np.isscalar(fill_value)
+
+            if fill_is_scalar:
+                if fill_value == _DEFAULT_CATEGORICAL_VALUE:
+                    fill_value = self.codes.dtype.type(fill_value)
+                else:
+                    try:
+                        fill_value = self._encode(fill_value)
+                        fill_value = self.codes.dtype.type(fill_value)
+                    except ValueError as err:
+                        err_msg = "fill value must be in categories"
+                        raise ValueError(err_msg) from err
+            else:
+                fill_value = column.as_column(fill_value, nan_as_null=False)
+                if isinstance(fill_value, CategoricalColumn):
+                    if self.dtype != fill_value.dtype:
+                        raise TypeError(
+                            "Cannot set a Categorical with another, "
+                            "without identical categories"
+                        )
+                # TODO: only required if fill_value has a subset of the
+                # categories:
+                fill_value = fill_value._set_categories(
+                    self.categories,
+                    is_unique=True,
+                )
+                fill_value = column.as_column(fill_value.codes).astype(
+                    self.codes.dtype
+                )
+
+        return super().fillna(value=fill_value, method=method)
+
+    def indices_of(
+        self, value: ScalarLike
+    ) -> cudf.core.column.NumericalColumn:
+        return self.as_numerical.indices_of(self._encode(value))
+
+    @property
+    def is_monotonic_increasing(self) -> bool:
+        return bool(self.ordered) and self.as_numerical.is_monotonic_increasing
+
+    @property
+    def is_monotonic_decreasing(self) -> bool:
+        return bool(self.ordered) and self.as_numerical.is_monotonic_decreasing
+
+    def as_categorical_column(self, dtype: Dtype) -> CategoricalColumn:
+        if isinstance(dtype, str) and dtype == "category":
+            return self
+        if (
+            isinstance(
+                dtype, (cudf.core.dtypes.CategoricalDtype, pd.CategoricalDtype)
+            )
+            and (dtype.categories is None)
+            and (dtype.ordered is None)
+        ):
+            return self
+
+        if isinstance(dtype, pd.CategoricalDtype):
+            dtype = CategoricalDtype(
+                categories=dtype.categories, ordered=dtype.ordered
+            )
+
+        if not isinstance(dtype, CategoricalDtype):
+            raise ValueError("dtype must be CategoricalDtype")
+
+        if not isinstance(self.categories, type(dtype.categories._values)):
+            # If both categories are of different Column types,
+            # return a column full of Nulls.
+            return _create_empty_categorical_column(self, dtype)
+
+        return self.set_categories(
+            new_categories=dtype.categories, ordered=bool(dtype.ordered)
+        )
+
+    def as_numerical_column(self, dtype: Dtype, **kwargs) -> NumericalColumn:
+        return self._get_decategorized_column().as_numerical_column(dtype)
+
+    def as_string_column(self, dtype, format=None, **kwargs) -> StringColumn:
+        return self._get_decategorized_column().as_string_column(
+            dtype, format=format
+        )
+
+    def as_datetime_column(self, dtype, **kwargs) -> DatetimeColumn:
+        return self._get_decategorized_column().as_datetime_column(
+            dtype, **kwargs
+        )
+
+    def as_timedelta_column(self, dtype, **kwargs) -> TimeDeltaColumn:
+        return self._get_decategorized_column().as_timedelta_column(
+            dtype, **kwargs
+        )
+
+    def _get_decategorized_column(self) -> ColumnBase:
+        if self.null_count == len(self):
+            # self.categories is empty; just return codes
+            return self.codes
+        gather_map = self.codes.astype(libcudf.types.size_type_dtype).fillna(0)
+        out = self.categories.take(gather_map)
+        out = out.set_mask(self.mask)
+        return out
+
+    def copy(self, deep: bool = True) -> Self:
+        result_col = super().copy(deep=deep)
+        if deep:
+            result_col.categories = libcudf.copying.copy_column(
+                self.dtype._categories
+            )
+        return result_col
+
+    @cached_property
+    def memory_usage(self) -> int:
+        return self.categories.memory_usage + self.codes.memory_usage
+
+    def _mimic_inplace(
+        self, other_col: ColumnBase, inplace: bool = False
+    ) -> Optional[Self]:
+        out = super()._mimic_inplace(other_col, inplace=inplace)
+        if inplace and isinstance(other_col, CategoricalColumn):
+            self._codes = other_col._codes
+        return out
+
+    def view(self, dtype: Dtype) -> ColumnBase:
+        raise NotImplementedError(
+            "Categorical column views are not currently supported"
+        )
+
+    @staticmethod
+    def _concat(
+        objs: abc.MutableSequence[CategoricalColumn],
+    ) -> CategoricalColumn:
+        # TODO: This function currently assumes it is being called from
+        # column.concat_columns, at least to the extent that all the
+        # preprocessing in that function has already been done. That should be
+        # improved as the concatenation API is solidified.
+
+        # Find the first non-null column:
+        head = next((obj for obj in objs if obj.valid_count), objs[0])
+
+        # Combine and de-dupe the categories
+        cats = column.concat_columns([o.categories for o in objs]).unique()
+        objs = [o._set_categories(cats, is_unique=True) for o in objs]
+        codes = [o.codes for o in objs]
+
+        newsize = sum(map(len, codes))
+        if newsize > libcudf.MAX_COLUMN_SIZE:
+            raise MemoryError(
+                f"Result of concat cannot have "
+                f"size > {libcudf.MAX_COLUMN_SIZE_STR}"
+            )
+        elif newsize == 0:
+            codes_col = column.column_empty(0, head.codes.dtype, masked=True)
+        else:
+            # Filter out inputs that have 0 length, then concatenate.
+            codes = [o for o in codes if len(o)]
+            codes_col = libcudf.concat.concat_columns(objs)
+
+        return column.build_categorical_column(
+            categories=column.as_column(cats),
+            codes=column.build_column(
+                codes_col.base_data, dtype=codes_col.dtype
+            ),
+            mask=codes_col.base_mask,
+            size=codes_col.size,
+            offset=codes_col.offset,
+        )
+
+    def _with_type_metadata(
+        self: CategoricalColumn, dtype: Dtype
+    ) -> CategoricalColumn:
+        if isinstance(dtype, CategoricalDtype):
+            return column.build_categorical_column(
+                categories=dtype.categories._values,
+                codes=column.build_column(
+                    self.codes.base_data, dtype=self.codes.dtype
+                ),
+                mask=self.codes.base_mask,
+                ordered=dtype.ordered,
+                size=self.codes.size,
+                offset=self.codes.offset,
+                null_count=self.codes.null_count,
+            )
+        return self
+
+    def set_categories(
+        self,
+        new_categories: Any,
+        ordered: bool = False,
+        rename: bool = False,
+    ) -> CategoricalColumn:
+        # See CategoricalAccessor.set_categories.
+
+        ordered = ordered if ordered is not None else self.ordered
+        new_categories = column.as_column(new_categories)
+
+        if isinstance(new_categories, CategoricalColumn):
+            new_categories = new_categories.categories
+
+        # when called with rename=True, the pandas behavior is
+        # to replace the current category values with the new
+        # categories.
+        if rename:
+            # enforce same length
+            if len(new_categories) != len(self.categories):
+                raise ValueError(
+                    "new_categories must have the same "
+                    "number of items as old categories"
+                )
+
+            out_col = column.build_categorical_column(
+                categories=new_categories,
+                codes=self.base_children[0],
+                mask=self.base_mask,
+                size=self.size,
+                offset=self.offset,
+                ordered=ordered,
+            )
+        else:
+            out_col = self
+            if type(out_col.categories) is not type(new_categories):
+                # If both categories are of different Column types,
+                # return a column full of Nulls.
+                out_col = _create_empty_categorical_column(
+                    self,
+                    CategoricalDtype(
+                        categories=new_categories, ordered=ordered
+                    ),
+                )
+            elif (
+                not out_col._categories_equal(new_categories, ordered=True)
+                or not self.ordered == ordered
+            ):
+                out_col = out_col._set_categories(
+                    new_categories,
+                    ordered=ordered,
+                )
+        return out_col
+
+    def _categories_equal(
+        self, new_categories: ColumnBase, ordered=False
+    ) -> bool:
+        cur_categories = self.categories
+        if len(new_categories) != len(cur_categories):
+            return False
+        if new_categories.dtype != cur_categories.dtype:
+            return False
+        # if order doesn't matter, sort before the equals call below
+        if not ordered:
+            cur_categories = cudf.Series(cur_categories).sort_values(
+                ignore_index=True
+            )
+            new_categories = cudf.Series(new_categories).sort_values(
+                ignore_index=True
+            )
+        return cur_categories.equals(new_categories)
+
+    def _set_categories(
+        self,
+        new_categories: Any,
+        is_unique: bool = False,
+        ordered: bool = False,
+    ) -> CategoricalColumn:
+        """Returns a new CategoricalColumn with the categories set to the
+        specified *new_categories*.
+
+        Notes
+        -----
+        Assumes ``new_categories`` is the same dtype as the current categories
+        """
+
+        cur_cats = column.as_column(self.categories)
+        new_cats = column.as_column(new_categories)
+
+        # Join the old and new categories to build a map from
+        # old to new codes, inserting na_sentinel for any old
+        # categories that don't exist in the new categories
+
+        # Ensure new_categories is unique first
+        if not (is_unique or new_cats.is_unique):
+            new_cats = cudf.Series(new_cats)._column.unique()
+
+        cur_codes = self.codes
+        max_cat_size = (
+            len(cur_cats) if len(cur_cats) > len(new_cats) else len(new_cats)
+        )
+        out_code_dtype = min_unsigned_type(max_cat_size)
+
+        cur_order = column.arange(len(cur_codes))
+        old_codes = column.arange(len(cur_cats), dtype=out_code_dtype)
+        new_codes = column.arange(len(new_cats), dtype=out_code_dtype)
+
+        new_df = cudf.DataFrame._from_data(
+            data={"new_codes": new_codes, "cats": new_cats}
+        )
+        old_df = cudf.DataFrame._from_data(
+            data={"old_codes": old_codes, "cats": cur_cats}
+        )
+        cur_df = cudf.DataFrame._from_data(
+            data={"old_codes": cur_codes, "order": cur_order}
+        )
+
+        # Join the old and new categories and line up their codes
+        df = old_df.merge(new_df, on="cats", how="left")
+        # Join the old and new codes to "recode" the codes data buffer
+        df = cur_df.merge(df, on="old_codes", how="left")
+        df = df.sort_values(by="order")
+        df.reset_index(drop=True, inplace=True)
+
+        ordered = ordered if ordered is not None else self.ordered
+        new_codes = df._data["new_codes"]
+
+        # codes can't have masks, so take mask out before moving in
+        return column.build_categorical_column(
+            categories=new_cats,
+            codes=column.build_column(
+                new_codes.base_data, dtype=new_codes.dtype
+            ),
+            mask=new_codes.base_mask,
+            size=new_codes.size,
+            offset=new_codes.offset,
+            ordered=ordered,
+        )
+
+    def reorder_categories(
+        self,
+        new_categories: Any,
+        ordered: bool = False,
+    ) -> CategoricalColumn:
+        new_categories = column.as_column(new_categories)
+        # Compare new_categories against current categories.
+        # Ignore order for comparison because we're only interested
+        # in whether new_categories has all the same values as the
+        # current set of categories.
+        if not self._categories_equal(new_categories, ordered=False):
+            raise ValueError(
+                "items in new_categories are not the same as in "
+                "old categories"
+            )
+        return self._set_categories(new_categories, ordered=ordered)
+
+    def as_ordered(self):
+        out_col = self
+        if not out_col.ordered:
+            out_col = column.build_categorical_column(
+                categories=self.categories,
+                codes=self.codes,
+                mask=self.base_mask,
+                size=self.base_size,
+                offset=self.offset,
+                ordered=True,
+            )
+        return out_col
+
+    def as_unordered(self):
+        out_col = self
+        if out_col.ordered:
+            out_col = column.build_categorical_column(
+                categories=self.categories,
+                codes=self.codes,
+                mask=self.base_mask,
+                size=self.base_size,
+                offset=self.offset,
+                ordered=False,
+            )
+        return out_col
+
+
+def _create_empty_categorical_column(
+    categorical_column: CategoricalColumn, dtype: "CategoricalDtype"
+) -> CategoricalColumn:
+    return column.build_categorical_column(
+        categories=column.as_column(dtype.categories),
+        codes=column.as_column(
+            column.full(
+                categorical_column.size,
+                _DEFAULT_CATEGORICAL_VALUE,
+                categorical_column.codes.dtype,
+            )
+        ),
+        offset=categorical_column.offset,
+        size=categorical_column.size,
+        mask=categorical_column.base_mask,
+        ordered=dtype.ordered,
+    )
+
+
+def pandas_categorical_as_column(
+    categorical: ColumnLike, codes: Optional[ColumnLike] = None
+) -> CategoricalColumn:
+    """Creates a CategoricalColumn from a pandas.Categorical
+
+    If ``codes`` is defined, use it instead of ``categorical.codes``
+    """
+    codes = categorical.codes if codes is None else codes
+    codes = column.as_column(codes)
+
+    valid_codes = codes != codes.dtype.type(_DEFAULT_CATEGORICAL_VALUE)
+
+    mask = None
+    if not valid_codes.all():
+        mask = bools_to_mask(valid_codes)
+
+    return column.build_categorical_column(
+        categories=categorical.categories,
+        codes=column.build_column(codes.base_data, codes.dtype),
+        size=codes.size,
+        mask=mask,
+        ordered=categorical.ordered,
+    )
diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
new file mode 100644
index 0000000..a5e99ab
--- /dev/null
+++ b/python/cudf/cudf/core/column/column.py
@@ -0,0 +1,2835 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import builtins
+import pickle
+from collections import abc
+from functools import cached_property
+from itertools import chain
+from types import SimpleNamespace
+from typing import (
+    Any,
+    Dict,
+    List,
+    MutableSequence,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+)
+
+import cupy
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from numba import cuda
+from typing_extensions import Self
+
+import rmm
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib.column import Column
+from cudf._lib.null_mask import (
+    MaskState,
+    bitmask_allocation_size_bytes,
+    create_null_mask,
+)
+from cudf._lib.scalar import as_device_scalar
+from cudf._lib.stream_compaction import (
+    apply_boolean_mask,
+    distinct_count as cpp_distinct_count,
+    drop_duplicates,
+    drop_nulls,
+)
+from cudf._lib.transform import bools_to_mask
+from cudf._lib.types import size_type_dtype
+from cudf._typing import ColumnLike, Dtype, ScalarLike
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    _is_pandas_nullable_extension_dtype,
+    infer_dtype,
+    is_bool_dtype,
+    is_categorical_dtype,
+    is_datetime64_dtype,
+    is_datetime64tz_dtype,
+    is_decimal32_dtype,
+    is_decimal64_dtype,
+    is_decimal128_dtype,
+    is_decimal_dtype,
+    is_dtype_equal,
+    is_integer_dtype,
+    is_interval_dtype,
+    is_list_dtype,
+    is_scalar,
+    is_string_dtype,
+    is_struct_dtype,
+)
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.abc import Serializable
+from cudf.core.buffer import (
+    Buffer,
+    acquire_spill_lock,
+    as_buffer,
+    cuda_array_interface_wrapper,
+)
+from cudf.core.dtypes import (
+    CategoricalDtype,
+    IntervalDtype,
+    ListDtype,
+    StructDtype,
+)
+from cudf.core.mixins import BinaryOperand, Reducible
+from cudf.errors import MixedTypeError
+from cudf.utils.dtypes import (
+    _maybe_convert_to_default_type,
+    cudf_dtype_from_pa_type,
+    get_time_unit,
+    is_mixed_with_object_dtype,
+    min_scalar_type,
+    min_unsigned_type,
+    np_to_pa_dtype,
+    pandas_dtypes_alias_to_cudf_alias,
+    pandas_dtypes_to_np_dtypes,
+)
+from cudf.utils.utils import _array_ufunc, mask_dtype
+
+if PANDAS_GE_150:
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+else:
+    from pandas.core.arrays._arrow_utils import ArrowIntervalType
+
+
+class ColumnBase(Column, Serializable, BinaryOperand, Reducible):
+    _VALID_REDUCTIONS = {
+        "any",
+        "all",
+        "max",
+        "min",
+    }
+
+    def as_frame(self) -> "cudf.core.frame.Frame":
+        """
+        Converts a Column to Frame
+        """
+        return cudf.core.single_column_frame.SingleColumnFrame(
+            {None: self.copy(deep=False)}
+        )
+
+    def data_array_view(
+        self, *, mode="write"
+    ) -> "cuda.devicearray.DeviceNDArray":
+        """
+        View the data as a device array object
+
+        Parameters
+        ----------
+        mode : str, default 'write'
+            Supported values are {'read', 'write'}
+            If 'write' is passed, a device array object
+            with readonly flag set to False in CAI is returned.
+            If 'read' is passed, a device array object
+            with readonly flag set to True in CAI is returned.
+            This also means, If the caller wishes to modify
+            the data returned through this view, they must
+            pass mode="write", else pass mode="read".
+
+        Returns
+        -------
+        numba.cuda.cudadrv.devicearray.DeviceNDArray
+        """
+        if self.data is not None:
+            if mode == "read":
+                obj = cuda_array_interface_wrapper(
+                    ptr=self.data.get_ptr(mode="read"),
+                    size=self.data.size,
+                    owner=self.data,
+                )
+            elif mode == "write":
+                obj = self.data
+            else:
+                raise ValueError(f"Unsupported mode: {mode}")
+        else:
+            obj = None
+        return cuda.as_cuda_array(obj).view(self.dtype)
+
+    def mask_array_view(
+        self, *, mode="write"
+    ) -> "cuda.devicearray.DeviceNDArray":
+        """
+        View the mask as a device array
+
+        Parameters
+        ----------
+        mode : str, default 'write'
+            Supported values are {'read', 'write'}
+            If 'write' is passed, a device array object
+            with readonly flag set to False in CAI is returned.
+            If 'read' is passed, a device array object
+            with readonly flag set to True in CAI is returned.
+            This also means, If the caller wishes to modify
+            the data returned through this view, they must
+            pass mode="write", else pass mode="read".
+
+        Returns
+        -------
+        numba.cuda.cudadrv.devicearray.DeviceNDArray
+        """
+        if self.mask is not None:
+            if mode == "read":
+                obj = cuda_array_interface_wrapper(
+                    ptr=self.mask.get_ptr(mode="read"),
+                    size=self.mask.size,
+                    owner=self.mask,
+                )
+            elif mode == "write":
+                obj = self.mask
+            else:
+                raise ValueError(f"Unsupported mode: {mode}")
+        else:
+            obj = None
+        return cuda.as_cuda_array(obj).view(mask_dtype)
+
+    def __len__(self) -> int:
+        return self.size
+
+    def __repr__(self):
+        return (
+            f"{object.__repr__(self)}\n"
+            f"{self.to_arrow().to_string()}\n"
+            f"dtype: {self.dtype}"
+        )
+
+    def to_pandas(
+        self, index: Optional[pd.Index] = None, **kwargs
+    ) -> pd.Series:
+        """Convert object to pandas type.
+
+        The default implementation falls back to PyArrow for the conversion.
+        """
+        # This default implementation does not handle nulls in any meaningful
+        # way, but must consume the parameter to avoid passing it to PyArrow
+        # (which does not recognize it).
+        kwargs.pop("nullable", None)
+        pd_series = self.to_arrow().to_pandas(**kwargs)
+
+        if index is not None:
+            pd_series.index = index
+        return pd_series
+
+    @property
+    def values_host(self) -> "np.ndarray":
+        """
+        Return a numpy representation of the Column.
+        """
+        if len(self) == 0:
+            return np.array([], dtype=self.dtype)
+
+        if self.has_nulls():
+            raise ValueError("Column must have no nulls.")
+
+        with acquire_spill_lock():
+            return self.data_array_view(mode="read").copy_to_host()
+
+    @property
+    def values(self) -> "cupy.ndarray":
+        """
+        Return a CuPy representation of the Column.
+        """
+        if len(self) == 0:
+            return cupy.array([], dtype=self.dtype)
+
+        if self.has_nulls():
+            raise ValueError("Column must have no nulls.")
+
+        return cupy.asarray(self.data_array_view(mode="write"))
+
+    def find_and_replace(
+        self,
+        to_replace: ColumnLike,
+        replacement: ColumnLike,
+        all_nan: bool = False,
+    ) -> Self:
+        raise NotImplementedError
+
+    def clip(self, lo: ScalarLike, hi: ScalarLike) -> ColumnBase:
+        return libcudf.replace.clip(self, lo, hi)
+
+    def equals(self, other: ColumnBase, check_dtypes: bool = False) -> bool:
+        if self is other:
+            return True
+        if other is None or len(self) != len(other):
+            return False
+        if check_dtypes and (self.dtype != other.dtype):
+            return False
+        ret = self._binaryop(other, "NULL_EQUALS")
+        if ret is NotImplemented:
+            raise TypeError(f"Cannot compare equality with {type(other)}")
+        return ret.all()
+
+    def all(self, skipna: bool = True) -> bool:
+        # The skipna argument is only used for numerical columns.
+        # If all entries are null the result is True, including when the column
+        # is empty.
+
+        if self.null_count == self.size:
+            return True
+
+        return libcudf.reduce.reduce("all", self, dtype=np.bool_)
+
+    def any(self, skipna: bool = True) -> bool:
+        # Early exit for fast cases.
+
+        if not skipna and self.has_nulls():
+            return True
+        elif skipna and self.null_count == self.size:
+            return False
+
+        return libcudf.reduce.reduce("any", self, dtype=np.bool_)
+
+    def dropna(self, drop_nan: bool = False) -> ColumnBase:
+        # The drop_nan argument is only used for numerical columns.
+        return drop_nulls([self])[0]._with_type_metadata(self.dtype)
+
+    def to_arrow(self) -> pa.Array:
+        """Convert to PyArrow Array
+
+        Examples
+        --------
+        >>> import cudf
+        >>> col = cudf.core.column.as_column([1, 2, 3, 4])
+        >>> col.to_arrow()
+        <pyarrow.lib.Int64Array object at 0x7f886547f830>
+        [
+          1,
+          2,
+          3,
+          4
+        ]
+        """
+        return libcudf.interop.to_arrow([self], [("None", self.dtype)])[
+            "None"
+        ].chunk(0)
+
+    @classmethod
+    def from_arrow(cls, array: pa.Array) -> ColumnBase:
+        """
+        Convert PyArrow Array/ChunkedArray to column
+
+        Parameters
+        ----------
+        array : PyArrow Array/ChunkedArray
+
+        Returns
+        -------
+        column
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> import cudf
+        >>> cudf.core.column.ColumnBase.from_arrow(pa.array([1, 2, 3, 4]))
+        <cudf.core.column.numerical.NumericalColumn object at 0x7f8865497ef0>
+        """
+        if not isinstance(array, (pa.Array, pa.ChunkedArray)):
+            raise TypeError("array should be PyArrow array or chunked array")
+
+        data = pa.table([array], [None])
+
+        if (
+            isinstance(array.type, pa.TimestampType)
+            and array.type.tz is not None
+        ):
+            raise NotImplementedError(
+                "cuDF does not yet support timezone-aware datetimes"
+            )
+        if isinstance(array.type, pa.DictionaryType):
+            indices_table = pa.table(
+                {
+                    "None": pa.chunked_array(
+                        [chunk.indices for chunk in data["None"].chunks],
+                        type=array.type.index_type,
+                    )
+                }
+            )
+            dictionaries_table = pa.table(
+                {
+                    "None": pa.chunked_array(
+                        [chunk.dictionary for chunk in data["None"].chunks],
+                        type=array.type.value_type,
+                    )
+                }
+            )
+
+            codes = libcudf.interop.from_arrow(indices_table)[0]
+            categories = libcudf.interop.from_arrow(dictionaries_table)[0]
+
+            return build_categorical_column(
+                categories=categories,
+                codes=codes,
+                mask=codes.base_mask,
+                size=codes.size,
+                ordered=array.type.ordered,
+            )
+        elif isinstance(array.type, ArrowIntervalType):
+            return cudf.core.column.IntervalColumn.from_arrow(array)
+
+        result = libcudf.interop.from_arrow(data)[0]
+
+        return result._with_type_metadata(cudf_dtype_from_pa_type(array.type))
+
+    def _get_mask_as_column(self) -> ColumnBase:
+        return libcudf.transform.mask_to_bools(
+            self.base_mask, self.offset, self.offset + len(self)
+        )
+
+    @cached_property
+    def memory_usage(self) -> int:
+        n = 0
+        if self.data is not None:
+            n += self.data.size
+        if self.nullable:
+            n += bitmask_allocation_size_bytes(self.size)
+        return n
+
+    def _fill(
+        self,
+        fill_value: ScalarLike,
+        begin: int,
+        end: int,
+        inplace: bool = False,
+    ) -> Optional[Self]:
+        if end <= begin or begin >= self.size:
+            return self if inplace else self.copy()
+
+        # Constructing a cuDF scalar can cut unnecessary DtoH copy if
+        # the scalar is None when calling `is_valid`.
+        slr = cudf.Scalar(fill_value, dtype=self.dtype)
+
+        if not inplace:
+            return libcudf.filling.fill(self, begin, end, slr.device_value)
+
+        if is_string_dtype(self.dtype):
+            return self._mimic_inplace(
+                libcudf.filling.fill(self, begin, end, slr.device_value),
+                inplace=True,
+            )
+
+        if not slr.is_valid() and not self.nullable:
+            mask = create_null_mask(self.size, state=MaskState.ALL_VALID)
+            self.set_base_mask(mask)
+
+        libcudf.filling.fill_in_place(self, begin, end, slr.device_value)
+
+        return self
+
+    def shift(self, offset: int, fill_value: ScalarLike) -> ColumnBase:
+        return libcudf.copying.shift(self, offset, fill_value)
+
+    @property
+    def valid_count(self) -> int:
+        """Number of non-null values"""
+        return len(self) - self.null_count
+
+    @property
+    def nullmask(self) -> Buffer:
+        """The gpu buffer for the null-mask"""
+        if not self.nullable:
+            raise ValueError("Column has no null mask")
+        return self.mask_array_view(mode="read")
+
+    def force_deep_copy(self) -> Self:
+        """
+        A method to create deep copy irrespective of whether
+        `copy-on-write` is enabled.
+        """
+        result = libcudf.copying.copy_column(self)
+        return result._with_type_metadata(self.dtype)
+
+    def copy(self, deep: bool = True) -> Self:
+        """
+        Makes a copy of the Column.
+
+        Parameters
+        ----------
+        deep : bool, default True
+            If True, a true physical copy of the column
+            is made.
+            If False and `copy_on_write` is False, the same
+            memory is shared between the buffers of the Column
+            and changes made to one Column will propagate to
+            its copy and vice-versa.
+            If False and `copy_on_write` is True, the same
+            memory is shared between the buffers of the Column
+            until there is a write operation being performed on
+            them.
+        """
+        if deep:
+            return self.force_deep_copy()
+        else:
+            return cast(
+                Self,
+                build_column(
+                    data=self.base_data
+                    if self.base_data is None
+                    else self.base_data.copy(deep=False),
+                    dtype=self.dtype,
+                    mask=self.base_mask
+                    if self.base_mask is None
+                    else self.base_mask.copy(deep=False),
+                    size=self.size,
+                    offset=self.offset,
+                    children=tuple(
+                        col.copy(deep=False) for col in self.base_children
+                    ),
+                ),
+            )
+
+    def view(self, dtype: Dtype) -> ColumnBase:
+        """
+        View the data underlying a column as different dtype.
+        The source column must divide evenly into the size of
+        the desired data type. Columns with nulls may only be
+        viewed as dtypes with size equal to source dtype size
+
+        Parameters
+        ----------
+        dtype : NumPy dtype, string
+            The dtype to view the data as
+
+        """
+
+        dtype = cudf.dtype(dtype)
+
+        if dtype.kind in ("o", "u", "s"):
+            raise TypeError(
+                "Bytes viewed as str without metadata is ambiguous"
+            )
+
+        if self.dtype.itemsize == dtype.itemsize:
+            return build_column(
+                self.base_data,
+                dtype=dtype,
+                mask=self.base_mask,
+                size=self.size,
+                offset=self.offset,
+            )
+
+        else:
+            if self.null_count > 0:
+                raise ValueError(
+                    "Can not produce a view of a column with nulls"
+                )
+
+            if (self.size * self.dtype.itemsize) % dtype.itemsize:
+                raise ValueError(
+                    f"Can not divide {self.size * self.dtype.itemsize}"
+                    + f" total bytes into {dtype} with size {dtype.itemsize}"
+                )
+
+            # This assertion prevents mypy errors below.
+            assert self.base_data is not None
+
+            start = self.offset * self.dtype.itemsize
+            end = start + self.size * self.dtype.itemsize
+            return build_column(self.base_data[start:end], dtype=dtype)
+
+    def element_indexing(self, index: int):
+        """Default implementation for indexing to an element
+
+        Raises
+        ------
+        ``IndexError`` if out-of-bound
+        """
+        idx = np.int32(index)
+        if idx < 0:
+            idx = len(self) + idx
+        if idx > len(self) - 1 or idx < 0:
+            raise IndexError("single positional indexer is out-of-bounds")
+        result = libcudf.copying.get_element(self, idx).value
+        if cudf.get_option("mode.pandas_compatible"):
+            if isinstance(result, np.datetime64):
+                return pd.Timestamp(result)
+            elif isinstance(result, np.timedelta64):
+                return pd.Timedelta(result)
+        return result
+
+    def slice(
+        self, start: int, stop: int, stride: Optional[int] = None
+    ) -> Self:
+        stride = 1 if stride is None else stride
+        if start < 0:
+            start = start + len(self)
+        if stop < 0 and not (stride < 0 and stop == -1):
+            stop = stop + len(self)
+        if (stride > 0 and start >= stop) or (stride < 0 and start <= stop):
+            return cast(Self, column_empty(0, self.dtype, masked=True))
+        # compute mask slice
+        if stride == 1:
+            return libcudf.copying.column_slice(self, [start, stop])[
+                0
+            ]._with_type_metadata(self.dtype)
+        else:
+            # Need to create a gather map for given slice with stride
+            gather_map = arange(
+                start=start,
+                stop=stop,
+                step=stride,
+                dtype=cudf.dtype(np.int32),
+            )
+            return self.take(gather_map)
+
+    def __setitem__(self, key: Any, value: Any):
+        """
+        Set the value of ``self[key]`` to ``value``.
+
+        If ``value`` and ``self`` are of different types, ``value`` is coerced
+        to ``self.dtype``. Assumes ``self`` and ``value`` are index-aligned.
+        """
+
+        # Normalize value to scalar/column
+        value_normalized = (
+            cudf.Scalar(value, dtype=self.dtype)
+            if is_scalar(value)
+            else as_column(value, dtype=self.dtype)
+        )
+
+        out: Optional[ColumnBase]  # If None, no need to perform mimic inplace.
+        if isinstance(key, slice):
+            out = self._scatter_by_slice(key, value_normalized)
+        else:
+            key = as_column(key)
+            if not isinstance(key, cudf.core.column.NumericalColumn):
+                raise ValueError(f"Invalid scatter map type {key.dtype}.")
+            out = self._scatter_by_column(key, value_normalized)
+
+        if out:
+            self._mimic_inplace(out, inplace=True)
+
+    def _wrap_binop_normalization(self, other):
+        if cudf.utils.utils.is_na_like(other):
+            return cudf.Scalar(other, dtype=self.dtype)
+        if isinstance(other, np.ndarray) and other.ndim == 0:
+            # Try and maintain the dtype
+            other = other.dtype.type(other.item())
+        return self.normalize_binop_value(other)
+
+    def _scatter_by_slice(
+        self,
+        key: builtins.slice,
+        value: Union[cudf.core.scalar.Scalar, ColumnBase],
+    ) -> Optional[Self]:
+        """If this function returns None, it's either a no-op (slice is empty),
+        or the inplace replacement is already performed (fill-in-place).
+        """
+        start, stop, step = key.indices(len(self))
+        if start >= stop:
+            return None
+        num_keys = len(range(start, stop, step))
+
+        self._check_scatter_key_length(num_keys, value)
+
+        if step == 1 and not isinstance(
+            self, (cudf.core.column.StructColumn, cudf.core.column.ListColumn)
+        ):
+            # NOTE: List & Struct dtypes aren't supported by both
+            # inplace & out-of-place fill. Hence we need to use scatter for
+            # these two types.
+            if isinstance(value, cudf.core.scalar.Scalar):
+                return self._fill(value, start, stop, inplace=True)
+            else:
+                return libcudf.copying.copy_range(
+                    value, self, 0, num_keys, start, stop, False
+                )
+
+        # step != 1, create a scatter map with arange
+        scatter_map = arange(
+            start=start,
+            stop=stop,
+            step=step,
+            dtype=cudf.dtype(np.int32),
+        )
+
+        return self._scatter_by_column(scatter_map, value)
+
+    def _scatter_by_column(
+        self,
+        key: cudf.core.column.NumericalColumn,
+        value: Union[cudf.core.scalar.Scalar, ColumnBase],
+    ) -> Self:
+        if is_bool_dtype(key.dtype):
+            # `key` is boolean mask
+            if len(key) != len(self):
+                raise ValueError(
+                    "Boolean mask must be of same length as column"
+                )
+            if isinstance(value, ColumnBase) and len(self) == len(value):
+                # Both value and key are aligned to self. Thus, the values
+                # corresponding to the false values in key should be
+                # ignored.
+                value = value.apply_boolean_mask(key)
+                # After applying boolean mask, the length of value equals
+                # the number of elements to scatter, we can skip computing
+                # the sum of ``key`` below.
+                num_keys = len(value)
+            else:
+                # Compute the number of element to scatter by summing all
+                # `True`s in the boolean mask.
+                num_keys = key.sum()
+        else:
+            # `key` is integer scatter map
+            num_keys = len(key)
+
+        self._check_scatter_key_length(num_keys, value)
+
+        if is_bool_dtype(key.dtype):
+            return libcudf.copying.boolean_mask_scatter([value], [self], key)[
+                0
+            ]._with_type_metadata(self.dtype)
+        else:
+            return libcudf.copying.scatter([value], key, [self])[
+                0
+            ]._with_type_metadata(self.dtype)
+
+    def _check_scatter_key_length(
+        self, num_keys: int, value: Union[cudf.core.scalar.Scalar, ColumnBase]
+    ):
+        """`num_keys` is the number of keys to scatter. Should equal to the
+        number of rows in ``value`` if ``value`` is a column.
+        """
+        if isinstance(value, ColumnBase):
+            if len(value) != num_keys:
+                msg = (
+                    f"Size mismatch: cannot set value "
+                    f"of size {len(value)} to indexing result of size "
+                    f"{num_keys}"
+                )
+                raise ValueError(msg)
+
+    def fillna(
+        self,
+        value: Any = None,
+        method: Optional[str] = None,
+        dtype: Optional[Dtype] = None,
+    ) -> Self:
+        """Fill null values with ``value``.
+
+        Returns a copy with null filled.
+        """
+        return libcudf.replace.replace_nulls(
+            input_col=self, replacement=value, method=method, dtype=dtype
+        )._with_type_metadata(self.dtype)
+
+    def isnull(self) -> ColumnBase:
+        """Identify missing values in a Column."""
+        result = libcudf.unary.is_null(self)
+
+        if self.dtype.kind == "f":
+            # Need to consider `np.nan` values in case
+            # of a float column
+            result = result | libcudf.unary.is_nan(self)
+
+        return result
+
+    def notnull(self) -> ColumnBase:
+        """Identify non-missing values in a Column."""
+        result = libcudf.unary.is_valid(self)
+
+        if self.dtype.kind == "f":
+            # Need to consider `np.nan` values in case
+            # of a float column
+            result = result & libcudf.unary.is_non_nan(self)
+
+        return result
+
+    def indices_of(
+        self, value: ScalarLike | Self
+    ) -> cudf.core.column.NumericalColumn:
+        """
+        Find locations of value in the column
+
+        Parameters
+        ----------
+        value
+            Scalar to look for (cast to dtype of column), or a length-1 column
+
+        Returns
+        -------
+        Column of indices that match value
+        """
+        if not isinstance(value, ColumnBase):
+            value = as_column([value], dtype=self.dtype)
+        else:
+            assert len(value) == 1
+        mask = libcudf.search.contains(value, self)
+        return apply_boolean_mask(
+            [arange(0, len(self), dtype=size_type_dtype)], mask
+        )[0]
+
+    def _find_first_and_last(self, value: ScalarLike) -> Tuple[int, int]:
+        indices = self.indices_of(value)
+        if n := len(indices):
+            return (
+                indices.element_indexing(0),
+                indices.element_indexing(n - 1),
+            )
+        else:
+            raise ValueError(f"Value {value} not found in column")
+
+    def find_first_value(self, value: ScalarLike) -> int:
+        """
+        Return index of first value that matches
+
+        Parameters
+        ----------
+        value
+            Value to search for (cast to dtype of column)
+
+        Returns
+        -------
+        Index of value
+
+        Raises
+        ------
+        ValueError if value is not found
+        """
+        first, _ = self._find_first_and_last(value)
+        return first
+
+    def find_last_value(self, value: ScalarLike) -> int:
+        """
+        Return index of last value that matches
+
+        Parameters
+        ----------
+        value
+            Value to search for (cast to dtype of column)
+
+        Returns
+        -------
+        Index of value
+
+        Raises
+        ------
+        ValueError if value is not found
+        """
+        _, last = self._find_first_and_last(value)
+        return last
+
+    def append(self, other: ColumnBase) -> ColumnBase:
+        return concat_columns([self, as_column(other)])
+
+    def quantile(
+        self,
+        q: np.ndarray,
+        interpolation: str,
+        exact: bool,
+        return_scalar: bool,
+    ) -> ColumnBase:
+        raise TypeError(f"cannot perform quantile with type {self.dtype}")
+
+    def take(
+        self, indices: ColumnBase, nullify: bool = False, check_bounds=True
+    ) -> Self:
+        """Return Column by taking values from the corresponding *indices*.
+
+        Skip bounds checking if check_bounds is False.
+        Set rows to null for all out of bound indices if nullify is `True`.
+        """
+        # Handle zero size
+        if indices.size == 0:
+            return cast(Self, column_empty_like(self, newsize=0))
+
+        # TODO: For performance, the check and conversion of gather map should
+        # be done by the caller. This check will be removed in future release.
+        if not is_integer_dtype(indices.dtype):
+            indices = indices.astype(libcudf.types.size_type_dtype)
+        if not libcudf.copying._gather_map_is_valid(
+            indices, len(self), check_bounds, nullify
+        ):
+            raise IndexError("Gather map index is out of bounds.")
+
+        return libcudf.copying.gather([self], indices, nullify=nullify)[
+            0
+        ]._with_type_metadata(self.dtype)
+
+    def isin(self, values: Sequence) -> ColumnBase:
+        """Check whether values are contained in the Column.
+
+        Parameters
+        ----------
+        values : set or list-like
+            The sequence of values to test. Passing in a single string will
+            raise a TypeError. Instead, turn a single string into a list
+            of one element.
+
+        Returns
+        -------
+        result: Column
+            Column of booleans indicating if each element is in values.
+        """
+        try:
+            lhs, rhs = self._process_values_for_isin(values)
+            res = lhs._isin_earlystop(rhs)
+            if res is not None:
+                return res
+        except ValueError:
+            # pandas functionally returns all False when cleansing via
+            # typecasting fails
+            return full(len(self), False, dtype="bool")
+
+        return lhs._obtain_isin_result(rhs)
+
+    def _process_values_for_isin(
+        self, values: Sequence
+    ) -> Tuple[ColumnBase, ColumnBase]:
+        """
+        Helper function for `isin` which pre-process `values` based on `self`.
+        """
+        lhs = self
+        rhs = as_column(values, nan_as_null=False)
+        if lhs.null_count == len(lhs):
+            lhs = lhs.astype(rhs.dtype)
+        elif rhs.null_count == len(rhs):
+            rhs = rhs.astype(lhs.dtype)
+        return lhs, rhs
+
+    def _isin_earlystop(self, rhs: ColumnBase) -> Union[ColumnBase, None]:
+        """
+        Helper function for `isin` which determines possibility of
+        early-stopping or not.
+        """
+        if self.dtype != rhs.dtype:
+            if self.null_count and rhs.null_count:
+                return self.isnull()
+            else:
+                return cudf.core.column.full(len(self), False, dtype="bool")
+        elif self.null_count == 0 and (rhs.null_count == len(rhs)):
+            return cudf.core.column.full(len(self), False, dtype="bool")
+        else:
+            return None
+
+    def _obtain_isin_result(self, rhs: ColumnBase) -> ColumnBase:
+        """
+        Helper function for `isin` which merges `self` & `rhs`
+        to determine what values of `rhs` exist in `self`.
+        """
+        ldf = cudf.DataFrame({"x": self, "orig_order": arange(len(self))})
+        rdf = cudf.DataFrame(
+            {"x": rhs, "bool": full(len(rhs), True, dtype="bool")}
+        )
+        res = ldf.merge(rdf, on="x", how="left").sort_values(by="orig_order")
+        res = res.drop_duplicates(subset="orig_order", ignore_index=True)
+        return res._data["bool"].fillna(False)
+
+    def as_mask(self) -> Buffer:
+        """Convert booleans to bitmask
+
+        Returns
+        -------
+        Buffer
+        """
+
+        if self.has_nulls():
+            raise ValueError("Column must have no nulls.")
+
+        return bools_to_mask(self)
+
+    @property
+    def is_unique(self) -> bool:
+        return self.distinct_count(dropna=False) == len(self)
+
+    @property
+    def is_monotonic_increasing(self) -> bool:
+        return not self.has_nulls() and self.as_frame()._is_sorted(
+            ascending=None, null_position=None
+        )
+
+    @property
+    def is_monotonic_decreasing(self) -> bool:
+        return not self.has_nulls() and self.as_frame()._is_sorted(
+            ascending=[False], null_position=None
+        )
+
+    def sort_values(
+        self: ColumnBase,
+        ascending: bool = True,
+        na_position: str = "last",
+    ) -> ColumnBase:
+        return libcudf.sort.sort(
+            [self], column_order=[ascending], null_precedence=[na_position]
+        )[0]
+
+    def distinct_count(self, dropna: bool = True) -> int:
+        try:
+            return self._distinct_count[dropna]
+        except KeyError:
+            self._distinct_count[dropna] = cpp_distinct_count(
+                self, ignore_nulls=dropna
+            )
+            return self._distinct_count[dropna]
+
+    def can_cast_safely(self, to_dtype: Dtype) -> bool:
+        raise NotImplementedError()
+
+    def astype(self, dtype: Dtype, **kwargs) -> ColumnBase:
+        if self.dtype == dtype:
+            return self
+        if is_categorical_dtype(dtype):
+            return self.as_categorical_column(dtype)
+
+        if (
+            isinstance(dtype, str)
+            and dtype in pandas_dtypes_alias_to_cudf_alias
+        ):
+            if cudf.get_option("mode.pandas_compatible"):
+                raise NotImplementedError("not supported")
+            else:
+                dtype = pandas_dtypes_alias_to_cudf_alias[dtype]
+        elif _is_pandas_nullable_extension_dtype(dtype) and cudf.get_option(
+            "mode.pandas_compatible"
+        ):
+            raise NotImplementedError("not supported")
+        else:
+            dtype = pandas_dtypes_to_np_dtypes.get(dtype, dtype)
+        if _is_non_decimal_numeric_dtype(dtype):
+            return self.as_numerical_column(dtype, **kwargs)
+        elif is_categorical_dtype(dtype):
+            return self.as_categorical_column(dtype)
+        elif cudf.dtype(dtype).type in {
+            np.str_,
+            np.object_,
+            str,
+        }:
+            if cudf.get_option("mode.pandas_compatible") and np.dtype(
+                dtype
+            ).type in {np.object_}:
+                raise ValueError(
+                    f"Casting to {dtype} is not supported, use "
+                    "`.astype('str')` instead."
+                )
+            return self.as_string_column(dtype, **kwargs)
+        elif is_list_dtype(dtype):
+            if not self.dtype == dtype:
+                raise NotImplementedError(
+                    "Casting list columns not currently supported"
+                )
+            return self
+        elif is_struct_dtype(dtype):
+            if not self.dtype == dtype:
+                raise NotImplementedError(
+                    "Casting struct columns not currently supported"
+                )
+            return self
+        elif is_interval_dtype(self.dtype):
+            return self.as_interval_column(dtype, **kwargs)
+        elif is_decimal_dtype(dtype):
+            return self.as_decimal_column(dtype, **kwargs)
+        elif np.issubdtype(cast(Any, dtype), np.datetime64):
+            return self.as_datetime_column(dtype, **kwargs)
+        elif np.issubdtype(cast(Any, dtype), np.timedelta64):
+            return self.as_timedelta_column(dtype, **kwargs)
+        else:
+            return self.as_numerical_column(dtype, **kwargs)
+
+    def as_categorical_column(self, dtype) -> ColumnBase:
+        if isinstance(dtype, (cudf.CategoricalDtype, pd.CategoricalDtype)):
+            ordered = dtype.ordered
+        else:
+            ordered = False
+
+        # Re-label self w.r.t. the provided categories
+        if (
+            isinstance(dtype, cudf.CategoricalDtype)
+            and dtype._categories is not None
+        ) or (
+            isinstance(dtype, pd.CategoricalDtype)
+            and dtype.categories is not None
+        ):
+            labels = self._label_encoding(cats=as_column(dtype.categories))
+
+            return build_categorical_column(
+                categories=as_column(dtype.categories),
+                codes=labels,
+                mask=self.mask,
+                ordered=dtype.ordered,
+            )
+
+        # Categories must be unique and sorted in ascending order.
+        cats = self.unique().sort_values().astype(self.dtype)
+        label_dtype = min_unsigned_type(len(cats))
+        labels = self._label_encoding(
+            cats=cats, dtype=label_dtype, na_sentinel=cudf.Scalar(1)
+        )
+        # columns include null index in factorization; remove:
+        if self.has_nulls():
+            cats = cats.dropna(drop_nan=False)
+            min_type = min_unsigned_type(len(cats), 8)
+            if cudf.dtype(min_type).itemsize < labels.dtype.itemsize:
+                labels = labels.astype(min_type)
+
+        return build_categorical_column(
+            categories=cats,
+            codes=labels,
+            mask=self.mask,
+            ordered=ordered,
+        )
+
+    def as_numerical_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.NumericalColumn":
+        raise NotImplementedError
+
+    def as_datetime_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.DatetimeColumn":
+        raise NotImplementedError
+
+    def as_interval_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.IntervalColumn":
+        raise NotImplementedError
+
+    def as_timedelta_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.TimeDeltaColumn":
+        raise NotImplementedError
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        raise NotImplementedError
+
+    def as_decimal_column(
+        self, dtype: Dtype, **kwargs
+    ) -> Union["cudf.core.column.decimal.DecimalBaseColumn"]:
+        raise NotImplementedError
+
+    def as_decimal128_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.Decimal128Column":
+        raise NotImplementedError
+
+    def as_decimal64_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.Decimal64Column":
+        raise NotImplementedError
+
+    def as_decimal32_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.Decimal32Column":
+        raise NotImplementedError
+
+    def apply_boolean_mask(self, mask) -> ColumnBase:
+        mask = as_column(mask)
+        if not is_bool_dtype(mask.dtype):
+            raise ValueError("boolean_mask is not boolean type.")
+
+        return apply_boolean_mask([self], mask)[0]._with_type_metadata(
+            self.dtype
+        )
+
+    def argsort(
+        self, ascending: bool = True, na_position: str = "last"
+    ) -> "cudf.core.column.NumericalColumn":
+        return self.as_frame()._get_sorted_inds(
+            ascending=ascending, na_position=na_position
+        )
+
+    def __arrow_array__(self, type=None):
+        raise TypeError(
+            "Implicit conversion to a host PyArrow Array via __arrow_array__ "
+            "is not allowed, To explicitly construct a PyArrow Array, "
+            "consider using .to_arrow()"
+        )
+
+    @property
+    def __cuda_array_interface__(self):
+        raise NotImplementedError(
+            f"dtype {self.dtype} is not yet supported via "
+            "`__cuda_array_interface__`"
+        )
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        return _array_ufunc(self, ufunc, method, inputs, kwargs)
+
+    def searchsorted(
+        self,
+        value,
+        side: str = "left",
+        ascending: bool = True,
+        na_position: str = "last",
+    ):
+        values = as_column(value).as_frame()
+        return self.as_frame().searchsorted(
+            values, side, ascending=ascending, na_position=na_position
+        )
+
+    def unique(self) -> ColumnBase:
+        """
+        Get unique values in the data
+        """
+        return drop_duplicates([self], keep="first")[0]._with_type_metadata(
+            self.dtype
+        )
+
+    def serialize(self) -> Tuple[dict, list]:
+        # data model:
+
+        # Serialization produces a nested metadata "header" and a flattened
+        # list of memoryviews/buffers that reference data (frames).  Each
+        # header advertises a frame_count slot which indicates how many
+        # frames deserialization will consume. The class used to construct
+        # an object is named under the key "type-serialized" to match with
+        # Dask's serialization protocol (see
+        # distributed.protocol.serialize). Since column dtypes may either be
+        # cudf native or foreign some special-casing is required here for
+        # serialization.
+
+        header: Dict[Any, Any] = {}
+        frames = []
+        header["type-serialized"] = pickle.dumps(type(self))
+        try:
+            dtype, dtype_frames = self.dtype.serialize()
+            header["dtype"] = dtype
+            frames.extend(dtype_frames)
+            header["dtype-is-cudf-serialized"] = True
+        except AttributeError:
+            header["dtype"] = pickle.dumps(self.dtype)
+            header["dtype-is-cudf-serialized"] = False
+
+        if self.data is not None:
+            data_header, data_frames = self.data.serialize()
+            header["data"] = data_header
+            frames.extend(data_frames)
+
+        if self.mask is not None:
+            mask_header, mask_frames = self.mask.serialize()
+            header["mask"] = mask_header
+            frames.extend(mask_frames)
+        if self.children:
+            child_headers, child_frames = zip(
+                *(c.serialize() for c in self.children)
+            )
+            header["subheaders"] = list(child_headers)
+            frames.extend(chain(*child_frames))
+        header["size"] = self.size
+        header["frame_count"] = len(frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list) -> ColumnBase:
+        def unpack(header, frames) -> Tuple[Any, list]:
+            count = header["frame_count"]
+            klass = pickle.loads(header["type-serialized"])
+            obj = klass.deserialize(header, frames[:count])
+            return obj, frames[count:]
+
+        assert header["frame_count"] == len(frames), (
+            f"Deserialization expected {header['frame_count']} frames, "
+            f"but received {len(frames)}"
+        )
+        if header["dtype-is-cudf-serialized"]:
+            dtype, frames = unpack(header["dtype"], frames)
+        else:
+            dtype = pickle.loads(header["dtype"])
+        if "data" in header:
+            data, frames = unpack(header["data"], frames)
+        else:
+            data = None
+        if "mask" in header:
+            mask, frames = unpack(header["mask"], frames)
+        else:
+            mask = None
+        children = []
+        if "subheaders" in header:
+            for h in header["subheaders"]:
+                child, frames = unpack(h, frames)
+                children.append(child)
+        assert len(frames) == 0, "Deserialization did not consume all frames"
+        return build_column(
+            data=data,
+            dtype=dtype,
+            mask=mask,
+            size=header.get("size", None),
+            children=tuple(children),
+        )
+
+    def unary_operator(self, unaryop: str):
+        raise TypeError(
+            f"Operation {unaryop} not supported for dtype {self.dtype}."
+        )
+
+    def normalize_binop_value(
+        self, other: ScalarLike
+    ) -> Union[ColumnBase, ScalarLike]:
+        raise NotImplementedError
+
+    def _minmax(self, skipna: Optional[bool] = None):
+        result_col = self._process_for_reduction(skipna=skipna)
+        if isinstance(result_col, ColumnBase):
+            return libcudf.reduce.minmax(result_col)
+        return result_col
+
+    def _reduce(
+        self,
+        op: str,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ) -> ScalarLike:
+        """Compute {op} of column values.
+
+        skipna : bool
+            Whether or not na values must be skipped.
+        min_count : int, default 0
+            The minimum number of entries for the reduction, otherwise the
+            reduction returns NaN.
+        """
+        preprocessed = self._process_for_reduction(
+            skipna=skipna, min_count=min_count
+        )
+        if isinstance(preprocessed, ColumnBase):
+            return libcudf.reduce.reduce(op, preprocessed, **kwargs)
+        return preprocessed
+
+    @property
+    def contains_na_entries(self) -> bool:
+        return self.null_count != 0
+
+    def _process_for_reduction(
+        self, skipna: Optional[bool] = None, min_count: int = 0
+    ) -> Union[ColumnBase, ScalarLike]:
+        skipna = True if skipna is None else skipna
+
+        if skipna:
+            if self.has_nulls():
+                result_col = self.dropna()
+        else:
+            if self.has_nulls():
+                return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        result_col = self
+
+        # TODO: If and when pandas decides to validate that `min_count` >= 0 we
+        # should insert comparable behavior.
+        # https://github.com/pandas-dev/pandas/issues/50022
+        if min_count > 0:
+            valid_count = len(result_col) - result_col.null_count
+            if valid_count < min_count:
+                return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+        return result_col
+
+    def _reduction_result_dtype(self, reduction_op: str) -> Dtype:
+        """
+        Determine the correct dtype to pass to libcudf based on
+        the input dtype, data dtype, and specific reduction op
+        """
+        return self.dtype
+
+    def _with_type_metadata(self: ColumnBase, dtype: Dtype) -> ColumnBase:
+        """
+        Copies type metadata from self onto other, returning a new column.
+
+        When ``self`` is a nested column, recursively apply this function on
+        the children of ``self``.
+        """
+        return self
+
+    def _label_encoding(
+        self,
+        cats: ColumnBase,
+        dtype: Optional[Dtype] = None,
+        na_sentinel: Optional[ScalarLike] = None,
+    ):
+        """
+        Convert each value in `self` into an integer code, with `cats`
+        providing the mapping between codes and values.
+
+        Examples
+        --------
+        >>> from cudf.core.column import as_column
+        >>> col = as_column(['foo', 'bar', 'foo', 'baz'])
+        >>> cats = as_column(['foo', 'bar', 'baz'])
+        >>> col._label_encoding(cats)
+        <cudf.core.column.numerical.NumericalColumn object at 0x7f99bf3155c0>
+        [
+          0,
+          1,
+          0,
+          2
+        ]
+        dtype: int8
+        >>> cats = as_column(['foo', 'bar'])
+        >>> col._label_encoding(cats)
+        <cudf.core.column.numerical.NumericalColumn object at 0x7f99bfde0e40>
+        [
+          0,
+          1,
+          0,
+          -1
+        ]
+        dtype: int8
+        """
+        from cudf._lib.join import join as cpp_join
+
+        if na_sentinel is None or na_sentinel.value is cudf.NA:
+            na_sentinel = cudf.Scalar(-1)
+
+        def _return_sentinel_column():
+            return cudf.core.column.full(
+                size=len(self), fill_value=na_sentinel, dtype=dtype
+            )
+
+        if dtype is None:
+            dtype = min_scalar_type(max(len(cats), na_sentinel), 8)
+
+        if is_mixed_with_object_dtype(self, cats):
+            return _return_sentinel_column()
+
+        try:
+            # Where there is a type-cast failure, we have
+            # to catch the exception and return encoded labels
+            # with na_sentinel values as there would be no corresponding
+            # encoded values of cats in self.
+            cats = cats.astype(self.dtype)
+        except ValueError:
+            return _return_sentinel_column()
+
+        left_gather_map, right_gather_map = cpp_join(
+            [self], [cats], how="left"
+        )
+        codes = libcudf.copying.gather(
+            [arange(len(cats), dtype=dtype)], right_gather_map, nullify=True
+        )
+        del right_gather_map
+        # reorder `codes` so that its values correspond to the
+        # values of `self`:
+        (codes,) = libcudf.sort.sort_by_key(
+            codes, [left_gather_map], [True], ["last"], stable=True
+        )
+        return codes.fillna(na_sentinel.value)
+
+
+def column_empty_like(
+    column: ColumnBase,
+    dtype: Optional[Dtype] = None,
+    masked: bool = False,
+    newsize: Optional[int] = None,
+) -> ColumnBase:
+    """Allocate a new column like the given *column*"""
+    if dtype is None:
+        dtype = column.dtype
+    row_count = len(column) if newsize is None else newsize
+
+    if (
+        hasattr(column, "dtype")
+        and is_categorical_dtype(column.dtype)
+        and dtype == column.dtype
+    ):
+        catcolumn = cast("cudf.core.column.CategoricalColumn", column)
+        codes = column_empty_like(
+            catcolumn.codes, masked=masked, newsize=newsize
+        )
+        return build_column(
+            data=None,
+            dtype=dtype,
+            mask=codes.base_mask,
+            children=(codes,),
+            size=codes.size,
+        )
+
+    return column_empty(row_count, dtype, masked)
+
+
+def column_empty_like_same_mask(
+    column: ColumnBase, dtype: Dtype
+) -> ColumnBase:
+    """Create a new empty Column with the same length and the same mask.
+
+    Parameters
+    ----------
+    dtype : np.dtype like
+        The dtype of the data buffer.
+    """
+    result = column_empty_like(column, dtype)
+    if column.nullable:
+        result = result.set_mask(column.mask)
+    return result
+
+
+def column_empty(
+    row_count: int, dtype: Dtype = "object", masked: bool = False
+) -> ColumnBase:
+    """Allocate a new column like the given row_count and dtype."""
+    dtype = cudf.dtype(dtype)
+    children = ()  # type: Tuple[ColumnBase, ...]
+
+    if is_struct_dtype(dtype):
+        data = None
+        children = tuple(
+            column_empty(row_count, field_dtype)
+            for field_dtype in dtype.fields.values()
+        )
+    elif is_list_dtype(dtype):
+        data = None
+        children = (
+            full(row_count + 1, 0, dtype=libcudf.types.size_type_dtype),
+            column_empty(row_count, dtype=dtype.element_type),
+        )
+    elif is_categorical_dtype(dtype):
+        data = None
+        children = (
+            build_column(
+                data=as_buffer(
+                    rmm.DeviceBuffer(
+                        size=row_count
+                        * cudf.dtype(libcudf.types.size_type_dtype).itemsize
+                    )
+                ),
+                dtype=libcudf.types.size_type_dtype,
+            ),
+        )
+    elif dtype.kind in "OU" and not is_decimal_dtype(dtype):
+        data = None
+        children = (
+            full(row_count + 1, 0, dtype=libcudf.types.size_type_dtype),
+            build_column(
+                data=as_buffer(
+                    rmm.DeviceBuffer(
+                        size=row_count * cudf.dtype("int8").itemsize
+                    )
+                ),
+                dtype="int8",
+            ),
+        )
+    else:
+        data = as_buffer(rmm.DeviceBuffer(size=row_count * dtype.itemsize))
+
+    if masked:
+        mask = create_null_mask(row_count, state=MaskState.ALL_NULL)
+    else:
+        mask = None
+
+    return build_column(
+        data, dtype, mask=mask, size=row_count, children=children
+    )
+
+
+def build_column(
+    data: Union[Buffer, None],
+    dtype: Dtype,
+    *,
+    size: Optional[int] = None,
+    mask: Optional[Buffer] = None,
+    offset: int = 0,
+    null_count: Optional[int] = None,
+    children: Tuple[ColumnBase, ...] = (),
+) -> ColumnBase:
+    """
+    Build a Column of the appropriate type from the given parameters
+
+    Parameters
+    ----------
+    data : Buffer
+        The data buffer (can be None if constructing certain Column
+        types like StringColumn, ListColumn, or CategoricalColumn)
+    dtype
+        The dtype associated with the Column to construct
+    mask : Buffer, optional
+        The mask buffer
+    size : int, optional
+    offset : int, optional
+    children : tuple, optional
+    """
+    dtype = cudf.dtype(dtype)
+
+    if _is_non_decimal_numeric_dtype(dtype):
+        assert data is not None
+        col = cudf.core.column.NumericalColumn(
+            data=data,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+        )
+        return col
+
+    if is_categorical_dtype(dtype):
+        if not len(children) == 1:
+            raise ValueError(
+                "Must specify exactly one child column for CategoricalColumn"
+            )
+        if not isinstance(children[0], ColumnBase):
+            raise TypeError("children must be a tuple of Columns")
+        return cudf.core.column.CategoricalColumn(
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+    elif dtype.type is np.datetime64:
+        if data is None:
+            raise TypeError("Must specify data buffer")
+        return cudf.core.column.DatetimeColumn(
+            data=data,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+        )
+    elif is_datetime64tz_dtype(dtype):
+        if data is None:
+            raise TypeError("Must specify data buffer")
+        return cudf.core.column.datetime.DatetimeTZColumn(
+            data=data,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+        )
+    elif dtype.type is np.timedelta64:
+        if data is None:
+            raise TypeError("Must specify data buffer")
+        return cudf.core.column.TimeDeltaColumn(
+            data=data,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+        )
+    elif dtype.type in (np.object_, np.str_):
+        return cudf.core.column.StringColumn(
+            mask=mask,
+            size=size,
+            offset=offset,
+            children=children,
+            null_count=null_count,
+        )
+    elif is_list_dtype(dtype):
+        return cudf.core.column.ListColumn(
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+    elif is_interval_dtype(dtype):
+        return cudf.core.column.IntervalColumn(
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            children=children,
+            null_count=null_count,
+        )
+    elif is_struct_dtype(dtype):
+        if size is None:
+            raise TypeError("Must specify size")
+        return cudf.core.column.StructColumn(
+            data=data,
+            dtype=dtype,
+            size=size,
+            offset=offset,
+            mask=mask,
+            null_count=null_count,
+            children=children,
+        )
+    elif is_decimal64_dtype(dtype):
+        if size is None:
+            raise TypeError("Must specify size")
+        return cudf.core.column.Decimal64Column(
+            data=data,
+            size=size,
+            offset=offset,
+            dtype=dtype,
+            mask=mask,
+            null_count=null_count,
+            children=children,
+        )
+    elif is_decimal32_dtype(dtype):
+        if size is None:
+            raise TypeError("Must specify size")
+        return cudf.core.column.Decimal32Column(
+            data=data,
+            size=size,
+            offset=offset,
+            dtype=dtype,
+            mask=mask,
+            null_count=null_count,
+            children=children,
+        )
+    elif is_decimal128_dtype(dtype):
+        if size is None:
+            raise TypeError("Must specify size")
+        return cudf.core.column.Decimal128Column(
+            data=data,
+            size=size,
+            offset=offset,
+            dtype=dtype,
+            mask=mask,
+            null_count=null_count,
+            children=children,
+        )
+    elif is_interval_dtype(dtype):
+        return cudf.core.column.IntervalColumn(
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+    else:
+        raise TypeError(f"Unrecognized dtype: {dtype}")
+
+
+def build_categorical_column(
+    categories: ColumnBase,
+    codes: ColumnBase,
+    mask: Optional[Buffer] = None,
+    size: Optional[int] = None,
+    offset: int = 0,
+    null_count: Optional[int] = None,
+    ordered: bool = False,
+) -> "cudf.core.column.CategoricalColumn":
+    """
+    Build a CategoricalColumn
+
+    Parameters
+    ----------
+    categories : Column
+        Column of categories
+    codes : Column
+        Column of codes, the size of the resulting Column will be
+        the size of `codes`
+    mask : Buffer
+        Null mask
+    size : int, optional
+    offset : int, optional
+    ordered : bool, default False
+        Indicates whether the categories are ordered
+    """
+    codes_dtype = min_unsigned_type(len(categories))
+    codes = as_column(codes)
+    if codes.dtype != codes_dtype:
+        codes = codes.astype(codes_dtype)
+
+    dtype = CategoricalDtype(categories=categories, ordered=ordered)
+
+    result = build_column(
+        data=None,
+        dtype=dtype,
+        mask=mask,
+        size=size,
+        offset=offset,
+        null_count=null_count,
+        children=(codes,),
+    )
+    return cast("cudf.core.column.CategoricalColumn", result)
+
+
+def build_interval_column(
+    left_col,
+    right_col,
+    mask=None,
+    size=None,
+    offset=0,
+    null_count=None,
+    closed="right",
+):
+    """
+    Build an IntervalColumn
+
+    Parameters
+    ----------
+    left_col : Column
+        Column of values representing the left of the interval
+    right_col : Column
+        Column of representing the right of the interval
+    mask : Buffer
+        Null mask
+    size : int, optional
+    offset : int, optional
+    closed : {"left", "right", "both", "neither"}, default "right"
+            Whether the intervals are closed on the left-side, right-side,
+            both or neither.
+    """
+    left = as_column(left_col)
+    right = as_column(right_col)
+    if closed not in {"left", "right", "both", "neither"}:
+        closed = "right"
+    if type(left_col) is not list:
+        dtype = IntervalDtype(left_col.dtype, closed)
+    else:
+        dtype = IntervalDtype("int64", closed)
+    size = len(left)
+    return build_column(
+        data=None,
+        dtype=dtype,
+        mask=mask,
+        size=size,
+        offset=offset,
+        null_count=null_count,
+        children=(left, right),
+    )
+
+
+def build_list_column(
+    indices: ColumnBase,
+    elements: ColumnBase,
+    mask: Optional[Buffer] = None,
+    size: Optional[int] = None,
+    offset: int = 0,
+    null_count: Optional[int] = None,
+) -> "cudf.core.column.ListColumn":
+    """
+    Build a ListColumn
+
+    Parameters
+    ----------
+    indices : ColumnBase
+        Column of list indices
+    elements : ColumnBase
+        Column of list elements
+    mask: Buffer
+        Null mask
+    size: int, optional
+    offset: int, optional
+    """
+    dtype = ListDtype(element_type=elements.dtype)
+    if size is None:
+        if indices.size == 0:
+            size = 0
+        else:
+            # one less because the last element of offsets is the number of
+            # bytes in the data buffer
+            size = indices.size - 1
+        size = size - offset
+
+    result = build_column(
+        data=None,
+        dtype=dtype,
+        mask=mask,
+        size=size,
+        offset=offset,
+        null_count=null_count,
+        children=(indices, elements),
+    )
+
+    return cast("cudf.core.column.ListColumn", result)
+
+
+def build_struct_column(
+    names: Sequence[str],
+    children: Tuple[ColumnBase, ...],
+    dtype: Optional[Dtype] = None,
+    mask: Optional[Buffer] = None,
+    size: Optional[int] = None,
+    offset: int = 0,
+    null_count: Optional[int] = None,
+) -> "cudf.core.column.StructColumn":
+    """
+    Build a StructColumn
+
+    Parameters
+    ----------
+    names : sequence of strings
+        Field names to map to children dtypes, must be strings.
+    children : tuple
+
+    mask: Buffer
+        Null mask
+    size: int, optional
+    offset: int, optional
+    """
+    if dtype is None:
+        dtype = StructDtype(
+            fields={name: col.dtype for name, col in zip(names, children)}
+        )
+
+    result = build_column(
+        data=None,
+        dtype=dtype,
+        mask=mask,
+        size=size,
+        offset=offset,
+        null_count=null_count,
+        children=children,
+    )
+
+    return cast("cudf.core.column.StructColumn", result)
+
+
+def _make_copy_replacing_NaT_with_null(column):
+    """Return a copy with NaT values replaced with nulls."""
+    if np.issubdtype(column.dtype, np.timedelta64):
+        na_value = np.timedelta64("NaT", column.time_unit)
+    elif np.issubdtype(column.dtype, np.datetime64):
+        na_value = np.datetime64("NaT", column.time_unit)
+    else:
+        raise ValueError("This type does not support replacing NaT with null.")
+
+    null = column_empty_like(column, masked=True, newsize=1)
+    out_col = cudf._lib.replace.replace(
+        column,
+        build_column(
+            as_buffer(np.array([na_value], dtype=column.dtype).view("|u1")),
+            dtype=column.dtype,
+        ),
+        null,
+    )
+    return out_col
+
+
+def as_column(
+    arbitrary: Any,
+    nan_as_null: Optional[bool] = None,
+    dtype: Optional[Dtype] = None,
+    length: Optional[int] = None,
+):
+    """Create a Column from an arbitrary object
+
+    Parameters
+    ----------
+    arbitrary : object
+        Object to construct the Column from. See *Notes*.
+    nan_as_null : bool, optional, default None
+        If None (default), treats NaN values in arbitrary as null if there is
+        no mask passed along with it. If True, combines the mask and NaNs to
+        form a new validity mask. If False, leaves NaN values as is.
+    dtype : optional
+        Optionally typecast the constructed Column to the given
+        dtype.
+    length : int, optional
+        If `arbitrary` is a scalar, broadcast into a Column of
+        the given length.
+
+    Returns
+    -------
+    A Column of the appropriate type and size.
+
+    Notes
+    -----
+    Currently support inputs are:
+
+    * ``Column``
+    * ``Series``
+    * ``Index``
+    * Scalars (can be broadcasted to a specified `length`)
+    * Objects exposing ``__cuda_array_interface__`` (e.g., numba device arrays)
+    * Objects exposing ``__array_interface__``(e.g., numpy arrays)
+    * pyarrow array
+    * pandas.Categorical objects
+    """
+    if isinstance(arbitrary, ColumnBase):
+        if dtype is not None:
+            return arbitrary.astype(dtype)
+        else:
+            return arbitrary
+
+    elif isinstance(arbitrary, cudf.Series):
+        data = arbitrary._column
+        if dtype is not None:
+            data = data.astype(dtype)
+    elif isinstance(arbitrary, cudf.BaseIndex):
+        data = arbitrary._values
+        if dtype is not None:
+            data = data.astype(dtype)
+
+    elif hasattr(arbitrary, "__cuda_array_interface__"):
+        desc = arbitrary.__cuda_array_interface__
+        shape = desc["shape"]
+        if len(shape) > 1:
+            raise ValueError("Data must be 1-dimensional")
+        current_dtype = np.dtype(desc["typestr"])
+
+        arb_dtype = (
+            np.dtype("float32")
+            if current_dtype == "float16"
+            else cudf.dtype(current_dtype)
+        )
+
+        if desc.get("mask", None) is not None:
+            # Extract and remove the mask from arbitrary before
+            # passing to cupy.asarray
+            mask = _mask_from_cuda_array_interface_desc(arbitrary)
+            arbitrary = SimpleNamespace(__cuda_array_interface__=desc.copy())
+            arbitrary.__cuda_array_interface__["mask"] = None
+            desc = arbitrary.__cuda_array_interface__
+        else:
+            mask = None
+
+        arbitrary = cupy.asarray(arbitrary)
+
+        if arb_dtype != current_dtype:
+            arbitrary = arbitrary.astype(arb_dtype)
+            current_dtype = arb_dtype
+
+        if (
+            desc["strides"] is not None
+            and not (arbitrary.itemsize,) == arbitrary.strides
+        ):
+            arbitrary = cupy.ascontiguousarray(arbitrary)
+
+        data = as_buffer(arbitrary, exposed=cudf.get_option("copy_on_write"))
+        col = build_column(data, dtype=current_dtype, mask=mask)
+
+        if dtype is not None:
+            col = col.astype(dtype)
+
+        if isinstance(col, cudf.core.column.CategoricalColumn):
+            return col
+        elif np.issubdtype(col.dtype, np.floating):
+            if nan_as_null or (mask is None and nan_as_null is None):
+                mask = libcudf.transform.nans_to_nulls(col.fillna(np.nan))
+                col = col.set_mask(mask)
+        elif np.issubdtype(col.dtype, np.datetime64):
+            if nan_as_null or (mask is None and nan_as_null is None):
+                col = _make_copy_replacing_NaT_with_null(col)
+        return col
+
+    elif isinstance(arbitrary, (pa.Array, pa.ChunkedArray)):
+        if isinstance(arbitrary, pa.lib.HalfFloatArray):
+            raise NotImplementedError(
+                "Type casting from `float16` to `float32` is not "
+                "yet supported in pyarrow, see: "
+                "https://issues.apache.org/jira/browse/ARROW-3802"
+            )
+        col = ColumnBase.from_arrow(arbitrary)
+
+        if isinstance(arbitrary, pa.NullArray):
+            if dtype is not None:
+                # Cast the column to the `dtype` if specified.
+                new_dtype = dtype
+            elif len(arbitrary) == 0:
+                # If the column is empty, it has to be
+                # a `float64` dtype.
+                new_dtype = cudf.dtype("float64")
+            else:
+                # If the null column is not empty, it has to
+                # be of `object` dtype.
+                new_dtype = cudf.dtype(arbitrary.type.to_pandas_dtype())
+
+            if cudf.get_option(
+                "mode.pandas_compatible"
+            ) and new_dtype == cudf.dtype("O"):
+                # We internally raise if we do `astype("object")`, hence
+                # need to cast to `str` since this is safe to do so because
+                # it is a null-array.
+                new_dtype = "str"
+
+            col = col.astype(new_dtype)
+
+        return col
+
+    elif isinstance(arbitrary, (pd.Series, pd.Categorical)):
+        if isinstance(arbitrary, pd.Series):
+            if isinstance(
+                arbitrary.array, pd.core.arrays.masked.BaseMaskedArray
+            ):
+                return as_column(arbitrary.array)
+            elif PANDAS_GE_150 and isinstance(arbitrary.dtype, pd.ArrowDtype):
+                if cudf.get_option("mode.pandas_compatible"):
+                    raise NotImplementedError("not supported")
+                return as_column(pa.array(arbitrary.array, from_pandas=True))
+            elif isinstance(arbitrary.dtype, pd.SparseDtype):
+                raise NotImplementedError(
+                    f"{arbitrary.dtype} is not supported. Convert first to "
+                    f"{arbitrary.dtype.subtype}."
+                )
+        if is_categorical_dtype(arbitrary.dtype):
+            if isinstance(
+                arbitrary.dtype.categories.dtype, pd.DatetimeTZDtype
+            ):
+                raise NotImplementedError(
+                    "cuDF does not yet support timezone-aware datetimes"
+                )
+            data = as_column(pa.array(arbitrary, from_pandas=True))
+        elif is_interval_dtype(arbitrary.dtype):
+            if isinstance(arbitrary.dtype.subtype, pd.DatetimeTZDtype):
+                raise NotImplementedError(
+                    "cuDF does not yet support timezone-aware datetimes"
+                )
+            data = as_column(pa.array(arbitrary, from_pandas=True))
+        elif arbitrary.dtype == np.bool_:
+            data = as_column(cupy.asarray(arbitrary), dtype=arbitrary.dtype)
+        elif arbitrary.dtype.kind in ("f"):
+            arb_dtype = np.dtype(arbitrary.dtype)
+            data = as_column(
+                cupy.asarray(arbitrary, dtype=arb_dtype),
+                nan_as_null=nan_as_null,
+                dtype=dtype,
+            )
+        elif arbitrary.dtype.kind in ("u", "i"):
+            data = as_column(
+                cupy.asarray(arbitrary), nan_as_null=nan_as_null, dtype=dtype
+            )
+        elif isinstance(arbitrary.dtype, pd.PeriodDtype):
+            raise NotImplementedError(
+                "cuDF does not yet support `PeriodDtype`"
+            )
+        else:
+            if cudf.get_option(
+                "mode.pandas_compatible"
+            ) and _is_pandas_nullable_extension_dtype(arbitrary.dtype):
+                raise NotImplementedError("not supported")
+            pyarrow_array = pa.array(arbitrary, from_pandas=nan_as_null)
+            if arbitrary.dtype == cudf.dtype("object") and cudf.dtype(
+                pyarrow_array.type.to_pandas_dtype()
+            ) != cudf.dtype(arbitrary.dtype):
+                raise MixedTypeError("Cannot create column with mixed types")
+            if isinstance(pyarrow_array.type, pa.Decimal128Type):
+                pyarrow_type = cudf.Decimal128Dtype.from_arrow(
+                    pyarrow_array.type
+                )
+            else:
+                pyarrow_type = arbitrary.dtype
+            data = as_column(pyarrow_array, dtype=pyarrow_type)
+        if dtype is not None:
+            data = data.astype(dtype)
+
+    elif isinstance(arbitrary, (pd.Timestamp, pd.Timedelta)):
+        # This will always treat NaTs as nulls since it's not technically a
+        # discrete value like NaN
+        data = as_column(pa.array(pd.Series([arbitrary]), from_pandas=True))
+        if dtype is not None:
+            data = data.astype(dtype)
+
+    elif np.isscalar(arbitrary) and not isinstance(arbitrary, memoryview):
+        length = length or 1
+        if (
+            (nan_as_null is True)
+            and isinstance(arbitrary, (np.floating, float))
+            and np.isnan(arbitrary)
+        ):
+            arbitrary = None
+            if dtype is None:
+                dtype = cudf.dtype("float64")
+
+        data = as_column(full(length, arbitrary, dtype=dtype))
+        if not nan_as_null and not is_decimal_dtype(data.dtype):
+            if np.issubdtype(data.dtype, np.floating):
+                data = data.fillna(np.nan)
+            elif np.issubdtype(data.dtype, np.datetime64):
+                data = data.fillna(np.datetime64("NaT"))
+
+    elif hasattr(arbitrary, "__array_interface__"):
+        # CUDF assumes values are always contiguous
+        desc = arbitrary.__array_interface__
+        shape = desc["shape"]
+        arb_dtype = np.dtype(desc["typestr"])
+        # CUDF assumes values are always contiguous
+        if len(shape) > 1:
+            raise ValueError("Data must be 1-dimensional")
+
+        arbitrary = np.asarray(arbitrary)
+
+        # Handle case that `arbitrary` elements are cupy arrays
+        if (
+            shape
+            and shape[0]
+            and hasattr(arbitrary[0], "__cuda_array_interface__")
+        ):
+            return as_column(
+                cupy.asarray(arbitrary, dtype=arbitrary[0].dtype),
+                nan_as_null=nan_as_null,
+                dtype=dtype,
+                length=length,
+            )
+
+        if not arbitrary.flags["C_CONTIGUOUS"]:
+            arbitrary = np.ascontiguousarray(arbitrary)
+
+        delayed_cast = False
+        if dtype is not None:
+            try:
+                dtype = np.dtype(dtype)
+            except TypeError:
+                # Some `dtype`'s can't be parsed by `np.dtype`
+                # for which we will have to cast after the column
+                # has been constructed.
+                delayed_cast = True
+            else:
+                arbitrary = arbitrary.astype(dtype)
+
+        if arb_dtype.kind == "M":
+            time_unit = get_time_unit(arbitrary)
+            cast_dtype = time_unit in ("D", "W", "M", "Y")
+
+            if cast_dtype:
+                arbitrary = arbitrary.astype(cudf.dtype("datetime64[s]"))
+
+            buffer = as_buffer(arbitrary.view("|u1"))
+            mask = None
+            if nan_as_null is None or nan_as_null is True:
+                data = build_column(buffer, dtype=arbitrary.dtype)
+                data = _make_copy_replacing_NaT_with_null(data)
+                mask = data.mask
+
+            data = build_column(data=buffer, mask=mask, dtype=arbitrary.dtype)
+        elif arb_dtype.kind == "m":
+            time_unit = get_time_unit(arbitrary)
+            cast_dtype = time_unit in ("D", "W", "M", "Y")
+
+            if cast_dtype:
+                arbitrary = arbitrary.astype(cudf.dtype("timedelta64[s]"))
+
+            buffer = as_buffer(arbitrary.view("|u1"))
+            mask = None
+            if nan_as_null is None or nan_as_null is True:
+                data = build_column(buffer, dtype=arbitrary.dtype)
+                data = _make_copy_replacing_NaT_with_null(data)
+                mask = data.mask
+
+            data = cudf.core.column.timedelta.TimeDeltaColumn(
+                data=buffer,
+                size=len(arbitrary),
+                mask=mask,
+                dtype=arbitrary.dtype,
+            )
+        elif (
+            arbitrary.size != 0
+            and arb_dtype.kind in ("O")
+            and isinstance(arbitrary[0], pd._libs.interval.Interval)
+        ):
+            # changing from pd array to series,possible arrow bug
+            interval_series = pd.Series(arbitrary)
+            data = as_column(
+                pa.Array.from_pandas(interval_series),
+                dtype=arbitrary.dtype,
+            )
+            if dtype is not None:
+                data = data.astype(dtype)
+        elif arb_dtype.kind in ("O", "U"):
+            data = as_column(pa.array(arbitrary), dtype=arbitrary.dtype)
+            # There is no cast operation available for pa.Array from int to
+            # str, Hence instead of handling in pa.Array block, we
+            # will have to type-cast here.
+            if dtype is not None:
+                data = data.astype(dtype)
+        elif arb_dtype.kind in ("f"):
+            if arb_dtype == np.dtype("float16"):
+                arb_dtype = np.dtype("float32")
+            arb_dtype = cudf.dtype(arb_dtype if dtype is None else dtype)
+            data = as_column(
+                cupy.asarray(arbitrary, dtype=arb_dtype),
+                nan_as_null=nan_as_null,
+            )
+        else:
+            data = as_column(cupy.asarray(arbitrary), nan_as_null=nan_as_null)
+
+        if delayed_cast:
+            data = data.astype(cudf.dtype(dtype))
+
+    elif isinstance(arbitrary, pd.arrays.PandasArray):
+        if cudf.get_option(
+            "mode.pandas_compatible"
+        ) and _is_pandas_nullable_extension_dtype(arbitrary.dtype):
+            raise NotImplementedError("not supported")
+        if is_categorical_dtype(arbitrary.dtype):
+            arb_dtype = arbitrary.dtype
+        else:
+            if arbitrary.dtype == pd.StringDtype():
+                arb_dtype = cudf.dtype("O")
+            else:
+                arb_dtype = (
+                    cudf.dtype("float32")
+                    if arbitrary.dtype == "float16"
+                    else cudf.dtype(arbitrary.dtype)
+                )
+                if arb_dtype != arbitrary.dtype.numpy_dtype:
+                    arbitrary = arbitrary.astype(arb_dtype)
+        if (
+            arbitrary.size != 0
+            and isinstance(arbitrary[0], pd._libs.interval.Interval)
+            and arb_dtype.kind in ("O")
+        ):
+            # changing from pd array to series,possible arrow bug
+            interval_series = pd.Series(arbitrary)
+            data = as_column(
+                pa.Array.from_pandas(interval_series), dtype=arb_dtype
+            )
+        elif arb_dtype.kind in ("O", "U"):
+            pyarrow_array = pa.Array.from_pandas(arbitrary)
+            if not isinstance(
+                pyarrow_array,
+                (
+                    pa.ListArray,
+                    pa.StructArray,
+                    pa.NullArray,
+                    pa.Decimal128Array,
+                    pa.StringArray,
+                    pa.BooleanArray,
+                ),
+            ):
+                raise MixedTypeError("Cannot create column with mixed types")
+            data = as_column(pyarrow_array, dtype=arb_dtype)
+        else:
+            data = as_column(
+                pa.array(
+                    arbitrary,
+                    from_pandas=True if nan_as_null is None else nan_as_null,
+                ),
+                nan_as_null=nan_as_null,
+            )
+        if dtype is not None:
+            data = data.astype(dtype)
+    elif isinstance(arbitrary, pd.arrays.SparseArray):
+        raise NotImplementedError(
+            f"{arbitrary.dtype} is not supported. Convert first to "
+            f"{arbitrary.dtype.subtype}."
+        )
+    elif isinstance(arbitrary, memoryview):
+        data = as_column(
+            np.asarray(arbitrary), dtype=dtype, nan_as_null=nan_as_null
+        )
+    elif isinstance(arbitrary, cudf.Scalar):
+        data = ColumnBase.from_scalar(arbitrary, length if length else 1)
+    elif isinstance(arbitrary, pd.core.arrays.masked.BaseMaskedArray):
+        if cudf.get_option("mode.pandas_compatible"):
+            raise NotImplementedError("not supported")
+        data = as_column(pa.Array.from_pandas(arbitrary), dtype=dtype)
+    elif (
+        (
+            isinstance(arbitrary, pd.DatetimeIndex)
+            and isinstance(arbitrary.dtype, pd.DatetimeTZDtype)
+        )
+        or (
+            isinstance(arbitrary, pd.IntervalIndex)
+            and is_datetime64tz_dtype(arbitrary.dtype.subtype)
+        )
+        or (
+            isinstance(arbitrary, pd.CategoricalIndex)
+            and isinstance(
+                arbitrary.dtype.categories.dtype, pd.DatetimeTZDtype
+            )
+        )
+    ):
+        raise NotImplementedError(
+            "cuDF does not yet support timezone-aware datetimes"
+        )
+    elif isinstance(
+        arbitrary, (pd.core.arrays.period.PeriodArray, pd.PeriodIndex)
+    ):
+        raise NotImplementedError(
+            f"cuDF does not yet support {type(arbitrary).__name__}"
+        )
+    elif (
+        cudf.get_option("mode.pandas_compatible")
+        and isinstance(arbitrary, (pd.DatetimeIndex, pd.TimedeltaIndex))
+        and arbitrary.freq is not None
+    ):
+        raise NotImplementedError("freq is not implemented yet")
+    else:
+        try:
+            data = as_column(
+                memoryview(arbitrary), dtype=dtype, nan_as_null=nan_as_null
+            )
+        except TypeError:
+            if dtype is not None:
+                # Arrow throws a type error if the input is of
+                # mixed-precision and cannot fit into the provided
+                # decimal type properly, see:
+                # https://github.com/apache/arrow/pull/9948
+                # Hence we should let the exception propagate to
+                # the user.
+                if isinstance(dtype, cudf.core.dtypes.Decimal128Dtype):
+                    data = pa.array(
+                        arbitrary,
+                        type=pa.decimal128(
+                            precision=dtype.precision, scale=dtype.scale
+                        ),
+                    )
+                    return cudf.core.column.Decimal128Column.from_arrow(data)
+                elif isinstance(dtype, cudf.core.dtypes.Decimal64Dtype):
+                    data = pa.array(
+                        arbitrary,
+                        type=pa.decimal128(
+                            precision=dtype.precision, scale=dtype.scale
+                        ),
+                    )
+                    return cudf.core.column.Decimal64Column.from_arrow(data)
+                elif isinstance(dtype, cudf.core.dtypes.Decimal32Dtype):
+                    data = pa.array(
+                        arbitrary,
+                        type=pa.decimal128(
+                            precision=dtype.precision, scale=dtype.scale
+                        ),
+                    )
+                    return cudf.core.column.Decimal32Column.from_arrow(data)
+
+            pa_type = None
+            np_type = None
+            try:
+                if dtype is not None:
+                    if is_categorical_dtype(dtype) or is_interval_dtype(dtype):
+                        raise TypeError
+                    if is_datetime64tz_dtype(dtype):
+                        raise NotImplementedError(
+                            "Use `tz_localize()` to construct "
+                            "timezone aware data."
+                        )
+                    elif is_datetime64_dtype(dtype):
+                        # Error checking only, actual construction happens
+                        # below.
+                        pa_array = pa.array(arbitrary)
+                        if (
+                            isinstance(pa_array.type, pa.TimestampType)
+                            and pa_array.type.tz is not None
+                        ):
+                            raise NotImplementedError(
+                                "cuDF does not yet support timezone-aware "
+                                "datetimes"
+                            )
+                    if is_list_dtype(dtype):
+                        data = pa.array(arbitrary)
+                        if type(data) not in (pa.ListArray, pa.NullArray):
+                            raise ValueError(
+                                "Cannot create list column from given data"
+                            )
+                        return as_column(data, nan_as_null=nan_as_null)
+                    elif isinstance(
+                        dtype, cudf.StructDtype
+                    ) and not isinstance(dtype, cudf.IntervalDtype):
+                        data = pa.array(arbitrary, type=dtype.to_arrow())
+                        return as_column(data, nan_as_null=nan_as_null)
+                    elif isinstance(dtype, cudf.core.dtypes.Decimal128Dtype):
+                        data = pa.array(
+                            arbitrary,
+                            type=pa.decimal128(
+                                precision=dtype.precision, scale=dtype.scale
+                            ),
+                        )
+                        return cudf.core.column.Decimal128Column.from_arrow(
+                            data
+                        )
+                    elif isinstance(dtype, cudf.core.dtypes.Decimal64Dtype):
+                        data = pa.array(
+                            arbitrary,
+                            type=pa.decimal128(
+                                precision=dtype.precision, scale=dtype.scale
+                            ),
+                        )
+                        return cudf.core.column.Decimal64Column.from_arrow(
+                            data
+                        )
+                    elif isinstance(dtype, cudf.core.dtypes.Decimal32Dtype):
+                        data = pa.array(
+                            arbitrary,
+                            type=pa.decimal128(
+                                precision=dtype.precision, scale=dtype.scale
+                            ),
+                        )
+                        return cudf.core.column.Decimal32Column.from_arrow(
+                            data
+                        )
+                    if is_bool_dtype(dtype):
+                        # Need this special case handling for bool dtypes,
+                        # since 'boolean' & 'pd.BooleanDtype' are not
+                        # understood by np.dtype below.
+                        dtype = "bool"
+                    np_dtype = np.dtype(dtype)
+                    if np_dtype.kind in {"m", "M"}:
+                        unit = np.datetime_data(np_dtype)[0]
+                        if unit not in {"ns", "us", "ms", "s", "D"}:
+                            raise NotImplementedError(
+                                f"{dtype=} is not supported."
+                            )
+                    np_type = np_dtype.type
+                    pa_type = np_to_pa_dtype(np_dtype)
+                else:
+                    # By default cudf constructs a 64-bit column. Setting
+                    # the `default_*_bitwidth` to 32 will result in a 32-bit
+                    # column being created.
+                    if (
+                        cudf.get_option("default_integer_bitwidth")
+                        and infer_dtype(arbitrary) == "integer"
+                    ):
+                        pa_type = np_to_pa_dtype(
+                            _maybe_convert_to_default_type("int")
+                        )
+                    if cudf.get_option(
+                        "default_float_bitwidth"
+                    ) and infer_dtype(arbitrary) in (
+                        "floating",
+                        "mixed-integer-float",
+                    ):
+                        pa_type = np_to_pa_dtype(
+                            _maybe_convert_to_default_type("float")
+                        )
+
+                if (
+                    cudf.get_option("mode.pandas_compatible")
+                    and isinstance(
+                        arbitrary, (pd.Index, pd.api.extensions.ExtensionArray)
+                    )
+                    and _is_pandas_nullable_extension_dtype(arbitrary.dtype)
+                ):
+                    raise NotImplementedError("not supported")
+
+                pyarrow_array = pa.array(
+                    arbitrary,
+                    type=pa_type,
+                    from_pandas=True if nan_as_null is None else nan_as_null,
+                )
+
+                if (
+                    isinstance(pyarrow_array, pa.NullArray)
+                    and pa_type is None
+                    and dtype is None
+                    and getattr(arbitrary, "dtype", None)
+                    == cudf.dtype("object")
+                ):
+                    # pa.array constructor returns a NullArray
+                    # for empty arrays, instead of a StringArray.
+                    # This issue is only specific to this dtype,
+                    # all other dtypes, result in their corresponding
+                    # arrow array creation.
+                    dtype = cudf.dtype("str")
+                    pyarrow_array = pyarrow_array.cast(np_to_pa_dtype(dtype))
+
+                if (
+                    isinstance(arbitrary, pd.Index)
+                    and arbitrary.dtype == cudf.dtype("object")
+                    and (
+                        cudf.dtype(pyarrow_array.type.to_pandas_dtype())
+                        != cudf.dtype(arbitrary.dtype)
+                    )
+                ):
+                    raise MixedTypeError(
+                        "Cannot create column with mixed types"
+                    )
+
+                if (
+                    cudf.get_option("mode.pandas_compatible")
+                    and pa.types.is_integer(pyarrow_array.type)
+                    and pyarrow_array.null_count
+                ):
+                    pyarrow_array = pyarrow_array.cast("float64").fill_null(
+                        np.nan
+                    )
+
+                data = as_column(
+                    pyarrow_array,
+                    dtype=dtype,
+                    nan_as_null=nan_as_null,
+                )
+            except (pa.ArrowInvalid, pa.ArrowTypeError, TypeError) as e:
+                if isinstance(e, MixedTypeError):
+                    raise TypeError(str(e))
+                if is_categorical_dtype(dtype):
+                    sr = pd.Series(arbitrary, dtype="category")
+                    data = as_column(sr, nan_as_null=nan_as_null, dtype=dtype)
+                elif np_type == np.str_:
+                    sr = pd.Series(arbitrary, dtype="str")
+                    data = as_column(sr, nan_as_null=nan_as_null)
+                elif is_interval_dtype(dtype):
+                    sr = pd.Series(arbitrary, dtype="interval")
+                    data = as_column(sr, nan_as_null=nan_as_null, dtype=dtype)
+                elif (
+                    isinstance(arbitrary, Sequence)
+                    and len(arbitrary) > 0
+                    and any(
+                        cudf.utils.dtypes.is_column_like(arb)
+                        for arb in arbitrary
+                    )
+                ):
+                    return cudf.core.column.ListColumn.from_sequences(
+                        arbitrary
+                    )
+                elif isinstance(arbitrary, abc.Iterable) or isinstance(
+                    arbitrary, abc.Sequence
+                ):
+                    data = as_column(
+                        _construct_array(arbitrary, dtype),
+                        dtype=dtype,
+                        nan_as_null=nan_as_null,
+                    )
+                else:
+                    raise e
+    return data
+
+
+def _construct_array(
+    arbitrary: Any, dtype: Optional[Dtype]
+) -> Union[np.ndarray, cupy.ndarray, pd.api.extensions.ExtensionArray]:
+    """
+    Construct a CuPy/NumPy/Pandas array from `arbitrary`
+    """
+    try:
+        dtype = dtype if dtype is None else cudf.dtype(dtype)
+        arbitrary = cupy.asarray(arbitrary, dtype=dtype)
+    except (TypeError, ValueError):
+        native_dtype = dtype
+        inferred_dtype = infer_dtype(arbitrary, skipna=False)
+        if (
+            dtype is None
+            and not cudf._lib.scalar._is_null_host_scalar(arbitrary)
+            and inferred_dtype
+            in (
+                "mixed",
+                "mixed-integer",
+            )
+        ):
+            native_dtype = "object"
+        if inferred_dtype == "interval":
+            # Only way to construct an Interval column.
+            return pd.array(arbitrary)
+        elif (
+            inferred_dtype == "string" and getattr(dtype, "kind", None) == "M"
+        ):
+            # We may have date-like strings with timezones
+            try:
+                pd_arbitrary = pd.to_datetime(arbitrary)
+                if isinstance(pd_arbitrary.dtype, pd.DatetimeTZDtype):
+                    raise NotImplementedError(
+                        "cuDF does not yet support timezone-aware datetimes"
+                    )
+                return pd_arbitrary.to_numpy()
+            except pd.errors.OutOfBoundsDatetime:
+                # https://github.com/pandas-dev/pandas/issues/55096
+                pass
+
+        arbitrary = np.asarray(
+            arbitrary,
+            dtype=native_dtype
+            if native_dtype is None
+            else np.dtype(native_dtype),
+        )
+    return arbitrary
+
+
+def _mask_from_cuda_array_interface_desc(obj) -> Union[Buffer, None]:
+    desc = obj.__cuda_array_interface__
+    mask = desc.get("mask", None)
+
+    if mask is not None:
+        desc = mask.__cuda_array_interface__
+        ptr = desc["data"][0]
+        nelem = desc["shape"][0]
+        typestr = desc["typestr"]
+        typecode = typestr[1]
+        if typecode == "t":
+            mask_size = bitmask_allocation_size_bytes(nelem)
+            mask = as_buffer(data=ptr, size=mask_size, owner=obj)
+        elif typecode == "b":
+            col = as_column(mask)
+            mask = bools_to_mask(col)
+        else:
+            raise NotImplementedError(
+                f"Cannot infer mask from typestr {typestr}"
+            )
+    return mask
+
+
+def serialize_columns(columns) -> Tuple[List[dict], List]:
+    """
+    Return the headers and frames resulting
+    from serializing a list of Column
+    Parameters
+    ----------
+    columns : list
+        list of Columns to serialize
+    Returns
+    -------
+    headers : list
+        list of header metadata for each Column
+    frames : list
+        list of frames
+    """
+    headers: List[Dict[Any, Any]] = []
+    frames = []
+
+    if len(columns) > 0:
+        header_columns = [c.serialize() for c in columns]
+        headers, column_frames = zip(*header_columns)
+        for f in column_frames:
+            frames.extend(f)
+
+    return headers, frames
+
+
+def deserialize_columns(headers: List[dict], frames: List) -> List[ColumnBase]:
+    """
+    Construct a list of Columns from a list of headers
+    and frames.
+    """
+    columns = []
+
+    for meta in headers:
+        col_frame_count = meta["frame_count"]
+        col_typ = pickle.loads(meta["type-serialized"])
+        colobj = col_typ.deserialize(meta, frames[:col_frame_count])
+        columns.append(colobj)
+        # Advance frames
+        frames = frames[col_frame_count:]
+
+    return columns
+
+
+def arange(
+    start: Union[int, float],
+    stop: Optional[Union[int, float]] = None,
+    step: Union[int, float] = 1,
+    dtype=None,
+) -> cudf.core.column.NumericalColumn:
+    """
+    Returns a column with evenly spaced values within a given interval.
+
+    Values are generated within the half-open interval [start, stop).
+    The first three arguments are mapped like the range built-in function,
+    i.e. start and step are optional.
+
+    Parameters
+    ----------
+    start : int/float
+        Start of the interval.
+    stop : int/float, default is None
+        Stop of the interval.
+    step : int/float, default 1
+        Step width between each pair of consecutive values.
+    dtype : default None
+        Data type specifier. It is inferred from other arguments by default.
+
+    Returns
+    -------
+    cudf.core.column.NumericalColumn
+
+    Examples
+    --------
+    >>> import cudf
+    >>> col = cudf.core.column.arange(2, 7, 1, dtype='int16')
+    >>> col
+    <cudf.core.column.numerical.NumericalColumn object at 0x7ff7998f8b90>
+    >>> cudf.Series(col)
+    0    2
+    1    3
+    2    4
+    3    5
+    4    6
+    dtype: int16
+    """
+    if stop is None:
+        stop = start
+        start = 0
+
+    if step is None:
+        step = 1
+
+    size = len(range(int(start), int(stop), int(step)))
+    if size == 0:
+        return as_column([], dtype=dtype)
+
+    return libcudf.filling.sequence(
+        size,
+        as_device_scalar(start, dtype=dtype),
+        as_device_scalar(step, dtype=dtype),
+    )
+
+
+def full(
+    size: int, fill_value: ScalarLike, dtype: Optional[Dtype] = None
+) -> ColumnBase:
+    """
+    Returns a column of given size and dtype, filled with a given value.
+
+    Parameters
+    ----------
+    size : int
+        size of the expected column.
+    fill_value : scalar
+         A scalar value to fill a new array.
+    dtype : default None
+        Data type specifier. It is inferred from other arguments by default.
+
+    Returns
+    -------
+    Column
+
+    Examples
+    --------
+    >>> import cudf
+    >>> col = cudf.core.column.full(size=5, fill_value=7, dtype='int8')
+    >>> col
+    <cudf.core.column.numerical.NumericalColumn object at 0x7fa0912e8b90>
+    >>> cudf.Series(col)
+    0    7
+    1    7
+    2    7
+    3    7
+    4    7
+    dtype: int8
+    """
+    return ColumnBase.from_scalar(cudf.Scalar(fill_value, dtype), size)
+
+
+def concat_columns(objs: "MutableSequence[ColumnBase]") -> ColumnBase:
+    """Concatenate a sequence of columns."""
+    if len(objs) == 0:
+        dtype = cudf.dtype(None)
+        return column_empty(0, dtype=dtype, masked=True)
+
+    # If all columns are `NumericalColumn` with different dtypes,
+    # we cast them to a common dtype.
+    # Notice, we can always cast pure null columns
+    not_null_col_dtypes = [o.dtype for o in objs if o.valid_count]
+    if len(not_null_col_dtypes) and all(
+        _is_non_decimal_numeric_dtype(dtyp)
+        and np.issubdtype(dtyp, np.datetime64)
+        for dtyp in not_null_col_dtypes
+    ):
+        # Use NumPy to find a common dtype
+        common_dtype = np.find_common_type(not_null_col_dtypes, [])
+        # Cast all columns to the common dtype
+        objs = [obj.astype(common_dtype) for obj in objs]
+
+    # Find the first non-null column:
+    head = next((obj for obj in objs if obj.valid_count), objs[0])
+
+    for i, obj in enumerate(objs):
+        # Check that all columns are the same type:
+        if not is_dtype_equal(obj.dtype, head.dtype):
+            # if all null, cast to appropriate dtype
+            if obj.valid_count == 0:
+                objs[i] = column_empty_like(
+                    head, dtype=head.dtype, masked=True, newsize=len(obj)
+                )
+            else:
+                raise ValueError("All columns must be the same type")
+
+    # TODO: This logic should be generalized to a dispatch to
+    # ColumnBase._concat so that all subclasses can override necessary
+    # behavior. However, at the moment it's not clear what that API should look
+    # like, so CategoricalColumn simply implements a minimal working API.
+    if all(is_categorical_dtype(o.dtype) for o in objs):
+        return cudf.core.column.categorical.CategoricalColumn._concat(
+            cast(
+                MutableSequence[
+                    cudf.core.column.categorical.CategoricalColumn
+                ],
+                objs,
+            )
+        )
+
+    newsize = sum(map(len, objs))
+    if newsize > libcudf.MAX_COLUMN_SIZE:
+        raise MemoryError(
+            f"Result of concat cannot have "
+            f"size > {libcudf.MAX_COLUMN_SIZE_STR}"
+        )
+    elif newsize == 0:
+        return column_empty(0, head.dtype, masked=True)
+
+    # Filter out inputs that have 0 length, then concatenate.
+    return libcudf.concat.concat_columns([o for o in objs if len(o)])
diff --git a/python/cudf/cudf/core/column/datetime.py b/python/cudf/cudf/core/column/datetime.py
new file mode 100644
index 0000000..b03b3c9
--- /dev/null
+++ b/python/cudf/cudf/core/column/datetime.py
@@ -0,0 +1,737 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import datetime
+import locale
+import re
+from locale import nl_langinfo
+from typing import Any, Mapping, Optional, Sequence, cast
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._typing import (
+    ColumnBinaryOperand,
+    DatetimeLikeScalar,
+    Dtype,
+    DtypeObj,
+    ScalarLike,
+)
+from cudf.api.types import (
+    is_datetime64_dtype,
+    is_datetime64tz_dtype,
+    is_scalar,
+    is_timedelta64_dtype,
+)
+from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
+from cudf.core.column import ColumnBase, as_column, column, string
+from cudf.core.column.timedelta import _unit_to_nanoseconds_conversion
+from cudf.utils.dtypes import _get_base_dtype
+from cudf.utils.utils import _all_bools_with_nulls
+
+_guess_datetime_format = pd.core.tools.datetimes.guess_datetime_format
+
+# nanoseconds per time_unit
+_dtype_to_format_conversion = {
+    "datetime64[ns]": "%Y-%m-%d %H:%M:%S.%9f",
+    "datetime64[us]": "%Y-%m-%d %H:%M:%S.%6f",
+    "datetime64[ms]": "%Y-%m-%d %H:%M:%S.%3f",
+    "datetime64[s]": "%Y-%m-%d %H:%M:%S",
+}
+
+_DATETIME_SPECIAL_FORMATS = {
+    "%b",
+    "%B",
+    "%A",
+    "%a",
+}
+
+_DATETIME_NAMES = [
+    nl_langinfo(locale.AM_STR),  # type: ignore
+    nl_langinfo(locale.PM_STR),  # type: ignore
+    nl_langinfo(locale.DAY_1),
+    nl_langinfo(locale.DAY_2),
+    nl_langinfo(locale.DAY_3),
+    nl_langinfo(locale.DAY_4),
+    nl_langinfo(locale.DAY_5),
+    nl_langinfo(locale.DAY_6),
+    nl_langinfo(locale.DAY_7),
+    nl_langinfo(locale.ABDAY_1),
+    nl_langinfo(locale.ABDAY_2),
+    nl_langinfo(locale.ABDAY_3),
+    nl_langinfo(locale.ABDAY_4),
+    nl_langinfo(locale.ABDAY_5),
+    nl_langinfo(locale.ABDAY_6),
+    nl_langinfo(locale.ABDAY_7),
+    nl_langinfo(locale.MON_1),
+    nl_langinfo(locale.MON_2),
+    nl_langinfo(locale.MON_3),
+    nl_langinfo(locale.MON_4),
+    nl_langinfo(locale.MON_5),
+    nl_langinfo(locale.MON_6),
+    nl_langinfo(locale.MON_7),
+    nl_langinfo(locale.MON_8),
+    nl_langinfo(locale.MON_9),
+    nl_langinfo(locale.MON_10),
+    nl_langinfo(locale.MON_11),
+    nl_langinfo(locale.MON_12),
+    nl_langinfo(locale.ABMON_1),
+    nl_langinfo(locale.ABMON_2),
+    nl_langinfo(locale.ABMON_3),
+    nl_langinfo(locale.ABMON_4),
+    nl_langinfo(locale.ABMON_5),
+    nl_langinfo(locale.ABMON_6),
+    nl_langinfo(locale.ABMON_7),
+    nl_langinfo(locale.ABMON_8),
+    nl_langinfo(locale.ABMON_9),
+    nl_langinfo(locale.ABMON_10),
+    nl_langinfo(locale.ABMON_11),
+    nl_langinfo(locale.ABMON_12),
+]
+
+
+def infer_format(element: str, **kwargs) -> str:
+    """
+    Infers datetime format from a string, also takes cares for `ms` and `ns`
+    """
+    fmt = _guess_datetime_format(element, **kwargs)
+
+    if fmt is not None:
+        if "%z" in fmt or "%Z" in fmt:
+            raise NotImplementedError(
+                "cuDF does not yet support timezone-aware datetimes"
+            )
+        return fmt
+
+    element_parts = element.split(".")
+    if len(element_parts) != 2:
+        raise ValueError("Given date string not likely a datetime.")
+
+    # There is possibility that the element is of following format
+    # '00:00:03.333333 2016-01-01'
+    second_parts = re.split(r"(\D+)", element_parts[1], maxsplit=1)
+    subsecond_fmt = ".%" + str(len(second_parts[0])) + "f"
+
+    first_part = _guess_datetime_format(element_parts[0], **kwargs)
+    # For the case where first_part is '00:00:03'
+    if first_part is None:
+        tmp = "1970-01-01 " + element_parts[0]
+        first_part = _guess_datetime_format(tmp, **kwargs).split(" ", 1)[1]
+    if first_part is None:
+        raise ValueError("Unable to infer the timestamp format from the data")
+
+    if len(second_parts) > 1:
+        # We may have a non-digit, timezone-like component
+        # like Z, UTC-3, +01:00
+        if any(re.search(r"\D", part) for part in second_parts):
+            raise NotImplementedError(
+                "cuDF does not yet support timezone-aware datetimes"
+            )
+        second_part = "".join(second_parts[1:])
+
+        if len(second_part) > 1:
+            # Only infer if second_parts is not an empty string.
+            second_part = _guess_datetime_format(second_part, **kwargs)
+    else:
+        second_part = ""
+
+    try:
+        fmt = first_part + subsecond_fmt + second_part
+    except Exception:
+        raise ValueError("Unable to infer the timestamp format from the data")
+
+    return fmt
+
+
+def _resolve_mixed_dtypes(
+    lhs: ColumnBinaryOperand, rhs: ColumnBinaryOperand, base_type: str
+) -> Dtype:
+    units = ["s", "ms", "us", "ns"]
+    lhs_time_unit = cudf.utils.dtypes.get_time_unit(lhs)
+    lhs_unit = units.index(lhs_time_unit)
+    rhs_time_unit = cudf.utils.dtypes.get_time_unit(rhs)
+    rhs_unit = units.index(rhs_time_unit)
+    return cudf.dtype(f"{base_type}[{units[max(lhs_unit, rhs_unit)]}]")
+
+
+def _get_datetime_format(col, dtype, time_unit):
+    format = _dtype_to_format_conversion.get(dtype.name, "%Y-%m-%d %H:%M:%S")
+    if format.endswith("f"):
+        sub_second_res_len = 3
+    else:
+        sub_second_res_len = 0
+
+    has_nanos = time_unit in {"ns"} and col.get_dt_field("nanosecond").any()
+    has_micros = (
+        time_unit in {"ns", "us"} and col.get_dt_field("microsecond").any()
+    )
+    has_millis = (
+        time_unit in {"ns", "us", "ms"}
+        and col.get_dt_field("millisecond").any()
+    )
+    has_seconds = col.get_dt_field("second").any()
+    has_minutes = col.get_dt_field("minute").any()
+    has_hours = col.get_dt_field("hour").any()
+    if sub_second_res_len:
+        if has_nanos:
+            # format should be intact and rest of the
+            # following conditions shouldn't execute.
+            pass
+        elif has_micros:
+            format = format[:-sub_second_res_len] + "%6f"
+        elif has_millis:
+            format = format[:-sub_second_res_len] + "%3f"
+        elif has_seconds or has_minutes or has_hours:
+            format = format[:-4]
+        else:
+            format = format.split(" ")[0]
+    else:
+        if not (has_seconds or has_minutes or has_hours):
+            format = format.split(" ")[0]
+    return format
+
+
+class DatetimeColumn(column.ColumnBase):
+    """
+    A Column implementation for Date-time types.
+
+    Parameters
+    ----------
+    data : Buffer
+        The datetime values
+    dtype : np.dtype
+        The data type
+    mask : Buffer; optional
+        The validity mask
+    """
+
+    _VALID_BINARY_OPERATIONS = {
+        "__eq__",
+        "__ne__",
+        "__lt__",
+        "__le__",
+        "__gt__",
+        "__ge__",
+        "__add__",
+        "__sub__",
+        "__radd__",
+        "__rsub__",
+    }
+
+    def __init__(
+        self,
+        data: Buffer,
+        dtype: DtypeObj,
+        mask: Optional[Buffer] = None,
+        size: Optional[int] = None,  # TODO: make non-optional
+        offset: int = 0,
+        null_count: Optional[int] = None,
+    ):
+        dtype = cudf.dtype(dtype)
+
+        if data.size % dtype.itemsize:
+            raise ValueError("Buffer size must be divisible by element size")
+        if size is None:
+            size = data.size // dtype.itemsize
+            size = size - offset
+        super().__init__(
+            data,
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+        )
+
+        if self.dtype.type is not np.datetime64:
+            raise TypeError(f"{self.dtype} is not a supported datetime type")
+
+        self._time_unit, _ = np.datetime_data(self.dtype)
+
+    def __contains__(self, item: ScalarLike) -> bool:
+        try:
+            item_as_dt64 = np.datetime64(item, self._time_unit)
+        except ValueError:
+            # If item cannot be converted to datetime type
+            # np.datetime64 raises ValueError, hence `item`
+            # cannot exist in `self`.
+            return False
+        return item_as_dt64.astype("int64") in self.as_numerical
+
+    @property
+    def time_unit(self) -> str:
+        return self._time_unit
+
+    @property
+    def year(self) -> ColumnBase:
+        return self.get_dt_field("year")
+
+    @property
+    def month(self) -> ColumnBase:
+        return self.get_dt_field("month")
+
+    @property
+    def day(self) -> ColumnBase:
+        return self.get_dt_field("day")
+
+    @property
+    def hour(self) -> ColumnBase:
+        return self.get_dt_field("hour")
+
+    @property
+    def minute(self) -> ColumnBase:
+        return self.get_dt_field("minute")
+
+    @property
+    def second(self) -> ColumnBase:
+        return self.get_dt_field("second")
+
+    @property
+    def weekday(self) -> ColumnBase:
+        return self.get_dt_field("weekday")
+
+    @property
+    def dayofyear(self) -> ColumnBase:
+        return self.get_dt_field("day_of_year")
+
+    @property
+    def day_of_year(self) -> ColumnBase:
+        return self.get_dt_field("day_of_year")
+
+    def to_pandas(
+        self,
+        index: Optional[pd.Index] = None,
+        nullable: bool = False,
+        **kwargs,
+    ) -> "cudf.Series":
+        # `copy=True` workaround until following issue is fixed:
+        # https://issues.apache.org/jira/browse/ARROW-9772
+
+        # Pandas only supports `datetime64[ns]` dtype
+        # and conversion to this type is necessary to make
+        # arrow to pandas conversion happen for large values.
+        return pd.Series(
+            self.astype("datetime64[ns]").to_arrow(),
+            copy=True,
+            dtype=self.dtype,
+            index=index,
+        )
+
+    @property
+    def values(self):
+        """
+        Return a CuPy representation of the DateTimeColumn.
+        """
+        raise NotImplementedError(
+            "DateTime Arrays is not yet implemented in cudf"
+        )
+
+    def get_dt_field(self, field: str) -> ColumnBase:
+        return libcudf.datetime.extract_datetime_component(self, field)
+
+    def ceil(self, freq: str) -> ColumnBase:
+        return libcudf.datetime.ceil_datetime(self, freq)
+
+    def floor(self, freq: str) -> ColumnBase:
+        return libcudf.datetime.floor_datetime(self, freq)
+
+    def round(self, freq: str) -> ColumnBase:
+        return libcudf.datetime.round_datetime(self, freq)
+
+    def normalize_binop_value(self, other: DatetimeLikeScalar) -> ScalarLike:
+        if isinstance(other, (cudf.Scalar, ColumnBase, cudf.DateOffset)):
+            return other
+
+        tz_error_msg = (
+            "Cannot perform binary operation on timezone-naive columns"
+            " and timezone-aware timestamps."
+        )
+        if isinstance(other, pd.Timestamp):
+            if other.tz is not None:
+                raise NotImplementedError(tz_error_msg)
+            other = other.to_datetime64()
+        elif isinstance(other, pd.Timedelta):
+            other = other.to_timedelta64()
+        elif isinstance(other, datetime.datetime):
+            if other.tzinfo is not None:
+                raise NotImplementedError(tz_error_msg)
+            other = np.datetime64(other)
+        elif isinstance(other, datetime.timedelta):
+            other = np.timedelta64(other)
+
+        if isinstance(other, np.datetime64):
+            if np.isnat(other):
+                return cudf.Scalar(None, dtype=self.dtype)
+
+            other = other.astype(self.dtype)
+            return cudf.Scalar(other)
+        elif isinstance(other, np.timedelta64):
+            other_time_unit = cudf.utils.dtypes.get_time_unit(other)
+
+            if other_time_unit not in {"s", "ms", "ns", "us"}:
+                other = other.astype("timedelta64[s]")
+
+            if np.isnat(other):
+                return cudf.Scalar(None, dtype=other.dtype)
+
+            return cudf.Scalar(other)
+        elif isinstance(other, str):
+            try:
+                return cudf.Scalar(other, dtype=self.dtype)
+            except ValueError:
+                pass
+
+        return NotImplemented
+
+    @property
+    def as_numerical(self) -> "cudf.core.column.NumericalColumn":
+        return cast(
+            "cudf.core.column.NumericalColumn",
+            column.build_column(
+                data=self.base_data,
+                dtype=np.int64,
+                mask=self.base_mask,
+                offset=self.offset,
+                size=self.size,
+            ),
+        )
+
+    @property
+    def __cuda_array_interface__(self) -> Mapping[str, Any]:
+        output = {
+            "shape": (len(self),),
+            "strides": (self.dtype.itemsize,),
+            "typestr": self.dtype.str,
+            "data": (self.data_ptr, False),
+            "version": 1,
+        }
+
+        if self.nullable and self.has_nulls():
+            # Create a simple Python object that exposes the
+            # `__cuda_array_interface__` attribute here since we need to modify
+            # some of the attributes from the numba device array
+            output["mask"] = cuda_array_interface_wrapper(
+                ptr=self.mask_ptr,
+                size=len(self),
+                owner=self.mask,
+                readonly=True,
+                typestr="<t1",
+            )
+        return output
+
+    def as_datetime_column(self, dtype: Dtype, **kwargs) -> DatetimeColumn:
+        dtype = cudf.dtype(dtype)
+        if dtype == self.dtype:
+            return self
+        return libcudf.unary.cast(self, dtype=dtype)
+
+    def as_timedelta_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.TimeDeltaColumn":
+        raise TypeError(
+            f"cannot astype a datetimelike from {self.dtype} to {dtype}"
+        )
+
+    def as_numerical_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.NumericalColumn":
+        return cast(
+            "cudf.core.column.NumericalColumn", self.as_numerical.astype(dtype)
+        )
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        if format is None:
+            format = _dtype_to_format_conversion.get(
+                self.dtype.name, "%Y-%m-%d %H:%M:%S"
+            )
+            if cudf.get_option("mode.pandas_compatible"):
+                format = _get_datetime_format(
+                    self, dtype=self.dtype, time_unit=self.time_unit
+                )
+        if format in _DATETIME_SPECIAL_FORMATS:
+            names = as_column(_DATETIME_NAMES)
+        else:
+            names = cudf.core.column.column_empty(
+                0, dtype="object", masked=False
+            )
+        if len(self) > 0:
+            return string._datetime_to_str_typecast_functions[
+                cudf.dtype(self.dtype)
+            ](self, format, names)
+        else:
+            return cast(
+                "cudf.core.column.StringColumn",
+                column.column_empty(0, dtype="object", masked=False),
+            )
+
+    def mean(
+        self, skipna=None, min_count: int = 0, dtype=np.float64
+    ) -> ScalarLike:
+        return pd.Timestamp(
+            self.as_numerical.mean(
+                skipna=skipna, min_count=min_count, dtype=dtype
+            ),
+            unit=self.time_unit,
+        )
+
+    def std(
+        self,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        dtype: Dtype = np.float64,
+        ddof: int = 1,
+    ) -> pd.Timedelta:
+        return pd.Timedelta(
+            self.as_numerical.std(
+                skipna=skipna, min_count=min_count, dtype=dtype, ddof=ddof
+            )
+            * _unit_to_nanoseconds_conversion[self.time_unit],
+        )
+
+    def median(self, skipna: Optional[bool] = None) -> pd.Timestamp:
+        return pd.Timestamp(
+            self.as_numerical.median(skipna=skipna), unit=self.time_unit
+        )
+
+    def quantile(
+        self,
+        q: np.ndarray,
+        interpolation: str,
+        exact: bool,
+        return_scalar: bool,
+    ) -> ColumnBase:
+        result = self.as_numerical.quantile(
+            q=q,
+            interpolation=interpolation,
+            exact=exact,
+            return_scalar=return_scalar,
+        )
+        if return_scalar:
+            return pd.Timestamp(result, unit=self.time_unit)
+        return result.astype(self.dtype)
+
+    def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase:
+        reflect, op = self._check_reflected_op(op)
+        other = self._wrap_binop_normalization(other)
+        if other is NotImplemented:
+            return NotImplemented
+        if isinstance(other, cudf.DateOffset):
+            return other._datetime_binop(self, op, reflect=reflect)
+
+        # We check this on `other` before reflection since we already know the
+        # dtype of `self`.
+        other_is_timedelta = is_timedelta64_dtype(other.dtype)
+        other_is_datetime64 = not other_is_timedelta and is_datetime64_dtype(
+            other.dtype
+        )
+        lhs, rhs = (other, self) if reflect else (self, other)
+        out_dtype = None
+
+        if (
+            op
+            in {
+                "__ne__",
+                "__lt__",
+                "__gt__",
+                "__le__",
+                "__ge__",
+            }
+            and other_is_datetime64
+        ):
+            out_dtype = cudf.dtype(np.bool_)
+        elif op == "__add__" and other_is_timedelta:
+            # The only thing we can add to a datetime is a timedelta. This
+            # operation is symmetric, i.e. we allow `datetime + timedelta` or
+            # `timedelta + datetime`. Both result in DatetimeColumns.
+            out_dtype = _resolve_mixed_dtypes(lhs, rhs, "datetime64")
+        elif op == "__sub__":
+            # Subtracting a datetime from a datetime results in a timedelta.
+            if other_is_datetime64:
+                out_dtype = _resolve_mixed_dtypes(lhs, rhs, "timedelta64")
+            # We can subtract a timedelta from a datetime, but not vice versa.
+            # Not only is subtraction antisymmetric (as is normal), it is only
+            # well-defined if this operation was not invoked via reflection.
+            elif other_is_timedelta and not reflect:
+                out_dtype = _resolve_mixed_dtypes(lhs, rhs, "datetime64")
+        elif op in {
+            "__eq__",
+            "NULL_EQUALS",
+            "__ne__",
+        }:
+            out_dtype = cudf.dtype(np.bool_)
+            if isinstance(other, ColumnBase) and not isinstance(
+                other, DatetimeColumn
+            ):
+                result = _all_bools_with_nulls(
+                    self, other, bool_fill_value=op == "__ne__"
+                )
+                if cudf.get_option("mode.pandas_compatible"):
+                    result = result.fillna(op == "__ne__")
+                return result
+
+        if out_dtype is None:
+            return NotImplemented
+
+        result = libcudf.binaryop.binaryop(lhs, rhs, op, out_dtype)
+        if cudf.get_option(
+            "mode.pandas_compatible"
+        ) and out_dtype == cudf.dtype(np.bool_):
+            result = result.fillna(op == "__ne__")
+        return result
+
+    def fillna(
+        self,
+        fill_value: Any = None,
+        method: Optional[str] = None,
+        dtype: Optional[Dtype] = None,
+    ) -> DatetimeColumn:
+        if fill_value is not None:
+            if cudf.utils.utils._isnat(fill_value):
+                return self.copy(deep=True)
+            if is_scalar(fill_value):
+                if not isinstance(fill_value, cudf.Scalar):
+                    fill_value = cudf.Scalar(fill_value, dtype=self.dtype)
+            else:
+                fill_value = column.as_column(fill_value, nan_as_null=False)
+
+        return super().fillna(fill_value, method)
+
+    def indices_of(
+        self, value: ScalarLike
+    ) -> cudf.core.column.NumericalColumn:
+        value = column.as_column(
+            pd.to_datetime(value), dtype=self.dtype
+        ).as_numerical
+        return self.as_numerical.indices_of(value)
+
+    @property
+    def is_unique(self) -> bool:
+        return self.as_numerical.is_unique
+
+    def isin(self, values: Sequence) -> ColumnBase:
+        return cudf.core.tools.datetimes._isin_datetimelike(self, values)
+
+    def can_cast_safely(self, to_dtype: Dtype) -> bool:
+        if np.issubdtype(to_dtype, np.datetime64):
+            to_res, _ = np.datetime_data(to_dtype)
+            self_res, _ = np.datetime_data(self.dtype)
+
+            max_int = np.iinfo(cudf.dtype("int64")).max
+
+            max_dist = np.timedelta64(
+                self.max().astype(cudf.dtype("int64"), copy=False), self_res
+            )
+            min_dist = np.timedelta64(
+                self.min().astype(cudf.dtype("int64"), copy=False), self_res
+            )
+
+            self_delta_dtype = np.timedelta64(0, self_res).dtype
+
+            if max_dist <= np.timedelta64(max_int, to_res).astype(
+                self_delta_dtype
+            ) and min_dist <= np.timedelta64(max_int, to_res).astype(
+                self_delta_dtype
+            ):
+                return True
+            else:
+                return False
+        elif to_dtype == cudf.dtype("int64") or to_dtype == cudf.dtype("O"):
+            # can safely cast to representation, or string
+            return True
+        else:
+            return False
+
+    def _with_type_metadata(self, dtype):
+        if is_datetime64tz_dtype(dtype):
+            return DatetimeTZColumn(
+                data=self.base_data,
+                dtype=dtype,
+                mask=self.base_mask,
+                size=self.size,
+                offset=self.offset,
+                null_count=self.null_count,
+            )
+        return self
+
+
+class DatetimeTZColumn(DatetimeColumn):
+    def __init__(
+        self,
+        data: Buffer,
+        dtype: pd.DatetimeTZDtype,
+        mask: Optional[Buffer] = None,
+        size: Optional[int] = None,
+        offset: int = 0,
+        null_count: Optional[int] = None,
+    ):
+        super().__init__(
+            data=data,
+            dtype=_get_base_dtype(dtype),
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+        )
+        self._dtype = dtype
+
+    def to_pandas(
+        self,
+        index: Optional[pd.Index] = None,
+        nullable: bool = False,
+        **kwargs,
+    ) -> "cudf.Series":
+        return self._local_time.to_pandas().dt.tz_localize(
+            self.dtype.tz, ambiguous="NaT", nonexistent="NaT"
+        )
+
+    def to_arrow(self):
+        return pa.compute.assume_timezone(
+            self._local_time.to_arrow(), str(self.dtype.tz)
+        )
+
+    @property
+    def _utc_time(self):
+        """Return UTC time as naive timestamps."""
+        return DatetimeColumn(
+            data=self.base_data,
+            dtype=_get_base_dtype(self.dtype),
+            mask=self.base_mask,
+            size=self.size,
+            offset=self.offset,
+            null_count=self.null_count,
+        )
+
+    @property
+    def _local_time(self):
+        """Return the local time as naive timestamps."""
+        from cudf.core._internals.timezones import utc_to_local
+
+        return utc_to_local(self, str(self.dtype.tz))
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        return self._local_time.as_string_column(dtype, format, **kwargs)
+
+    def get_dt_field(self, field: str) -> ColumnBase:
+        return libcudf.datetime.extract_datetime_component(
+            self._local_time, field
+        )
+
+    def __repr__(self):
+        # Arrow prints the UTC timestamps, but we want to print the
+        # local timestamps:
+        arr = self._local_time.to_arrow().cast(
+            pa.timestamp(self.dtype.unit, str(self.dtype.tz))
+        )
+        return (
+            f"{object.__repr__(self)}\n"
+            f"{arr.to_string()}\n"
+            f"dtype: {self.dtype}"
+        )
diff --git a/python/cudf/cudf/core/column/decimal.py b/python/cudf/cudf/core/column/decimal.py
new file mode 100644
index 0000000..5a823c5
--- /dev/null
+++ b/python/cudf/cudf/core/column/decimal.py
@@ -0,0 +1,443 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import warnings
+from decimal import Decimal
+from typing import Any, Optional, Sequence, Union, cast
+
+import cupy as cp
+import numpy as np
+import pyarrow as pa
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib.quantiles import quantile as cpp_quantile
+from cudf._lib.strings.convert.convert_fixed_point import (
+    from_decimal as cpp_from_decimal,
+)
+from cudf._typing import ColumnBinaryOperand, Dtype
+from cudf.api.types import is_integer_dtype, is_scalar
+from cudf.core.buffer import as_buffer
+from cudf.core.column import ColumnBase, as_column
+from cudf.core.dtypes import (
+    Decimal32Dtype,
+    Decimal64Dtype,
+    Decimal128Dtype,
+    DecimalDtype,
+)
+from cudf.core.mixins import BinaryOperand
+from cudf.utils.utils import pa_mask_buffer_to_mask
+
+from .numerical_base import NumericalBaseColumn
+
+
+class DecimalBaseColumn(NumericalBaseColumn):
+    """Base column for decimal32, decimal64 or decimal128 columns"""
+
+    dtype: DecimalDtype
+    _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
+
+    def as_decimal_column(
+        self, dtype: Dtype, **kwargs
+    ) -> Union["DecimalBaseColumn"]:
+        if (
+            isinstance(dtype, cudf.core.dtypes.DecimalDtype)
+            and dtype.scale < self.dtype.scale
+        ):
+            warnings.warn(
+                "cuDF truncates when downcasting decimals to a lower scale. "
+                "To round, use Series.round() or DataFrame.round()."
+            )
+
+        if dtype == self.dtype:
+            return self
+        return libcudf.unary.cast(self, dtype)
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        if len(self) > 0:
+            return cpp_from_decimal(self)
+        else:
+            return cast(
+                "cudf.core.column.StringColumn", as_column([], dtype="object")
+            )
+
+    def __pow__(self, other):
+        if isinstance(other, int):
+            if other == 0:
+                res = cudf.core.column.full(
+                    size=len(self), fill_value=1, dtype=self.dtype
+                )
+                if self.nullable:
+                    res = res.set_mask(self.mask)
+                return res
+            elif other < 0:
+                raise TypeError("Power of negative integers not supported.")
+            res = self
+            for _ in range(other - 1):
+                res = self * res
+            return res
+        else:
+            raise NotImplementedError(
+                f"__pow__ of types {self.dtype} and {type(other)} is "
+                "not yet implemented."
+            )
+
+    # Decimals in libcudf don't support truediv, see
+    # https://github.com/rapidsai/cudf/pull/7435 for explanation.
+    def __truediv__(self, other):
+        return self._binaryop(other, "__div__")
+
+    def __rtruediv__(self, other):
+        return self._binaryop(other, "__rdiv__")
+
+    def _binaryop(self, other: ColumnBinaryOperand, op: str):
+        reflect, op = self._check_reflected_op(op)
+        other = self._wrap_binop_normalization(other)
+        if other is NotImplemented:
+            return NotImplemented
+        lhs, rhs = (other, self) if reflect else (self, other)
+
+        # Binary Arithmetics between decimal columns. `Scale` and `precision`
+        # are computed outside of libcudf
+        if op in {"__add__", "__sub__", "__mul__", "__div__"}:
+            output_type = _get_decimal_type(lhs.dtype, rhs.dtype, op)
+            result = libcudf.binaryop.binaryop(lhs, rhs, op, output_type)
+            # TODO:  Why is this necessary? Why isn't the result's
+            # precision already set correctly based on output_type?
+            result.dtype.precision = output_type.precision
+        elif op in {
+            "__eq__",
+            "__ne__",
+            "__lt__",
+            "__gt__",
+            "__le__",
+            "__ge__",
+        }:
+            result = libcudf.binaryop.binaryop(lhs, rhs, op, bool)
+        else:
+            raise TypeError(
+                f"{op} not supported for the following dtypes: "
+                f"{self.dtype}, {other.dtype}"
+            )
+
+        return result
+
+    def fillna(
+        self,
+        value: Any = None,
+        method: Optional[str] = None,
+        dtype: Optional[Dtype] = None,
+    ):
+        """Fill null values with ``value``.
+
+        Returns a copy with null filled.
+        """
+        if isinstance(value, (int, Decimal)):
+            value = cudf.Scalar(value, dtype=self.dtype)
+        elif (
+            isinstance(value, DecimalBaseColumn)
+            or isinstance(value, cudf.core.column.NumericalColumn)
+            and is_integer_dtype(value.dtype)
+        ):
+            value = value.astype(self.dtype)
+        else:
+            raise TypeError(
+                "Decimal columns only support using fillna with decimal and "
+                "integer values"
+            )
+
+        return super().fillna(value=value, method=method)
+
+    def normalize_binop_value(self, other):
+        if isinstance(other, ColumnBase):
+            if isinstance(other, cudf.core.column.NumericalColumn):
+                if not is_integer_dtype(other.dtype):
+                    raise TypeError(
+                        "Decimal columns only support binary operations with "
+                        "integer numerical columns."
+                    )
+                other = other.as_decimal_column(
+                    self.dtype.__class__(self.dtype.__class__.MAX_PRECISION, 0)
+                )
+            elif not isinstance(other, DecimalBaseColumn):
+                return NotImplemented
+            elif not isinstance(self.dtype, other.dtype.__class__):
+                # This branch occurs if we have a DecimalBaseColumn of a
+                # different size (e.g. 64 instead of 32).
+                if _same_precision_and_scale(self.dtype, other.dtype):
+                    other = other.astype(self.dtype)
+            return other
+        if isinstance(other, cudf.Scalar) and isinstance(
+            # TODO: Should it be possible to cast scalars of other numerical
+            # types to decimal?
+            other.dtype,
+            cudf.core.dtypes.DecimalDtype,
+        ):
+            if _same_precision_and_scale(self.dtype, other.dtype):
+                other = other.astype(self.dtype)
+            return other
+        elif is_scalar(other) and isinstance(other, (int, Decimal)):
+            other = Decimal(other)
+            metadata = other.as_tuple()
+            precision = max(len(metadata.digits), metadata.exponent)
+            scale = -metadata.exponent
+            return cudf.Scalar(
+                other, dtype=self.dtype.__class__(precision, scale)
+            )
+        return NotImplemented
+
+    def _decimal_quantile(
+        self, q: Union[float, Sequence[float]], interpolation: str, exact: bool
+    ) -> ColumnBase:
+        quant = [float(q)] if not isinstance(q, (Sequence, np.ndarray)) else q
+        # get sorted indices and exclude nulls
+        sorted_indices = self.as_frame()._get_sorted_inds(
+            ascending=True, na_position="first"
+        )
+        sorted_indices = sorted_indices[self.null_count :]
+
+        result = cpp_quantile(
+            self, quant, interpolation, sorted_indices, exact
+        )
+
+        return result._with_type_metadata(self.dtype)
+
+    def as_numerical_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.NumericalColumn":
+        return libcudf.unary.cast(self, dtype)
+
+
+class Decimal32Column(DecimalBaseColumn):
+    dtype: Decimal32Dtype
+
+    @classmethod
+    def from_arrow(cls, data: pa.Array):
+        dtype = Decimal32Dtype.from_arrow(data.type)
+        mask_buf = data.buffers()[0]
+        mask = (
+            mask_buf
+            if mask_buf is None
+            else pa_mask_buffer_to_mask(mask_buf, len(data))
+        )
+        data_128 = cp.array(np.frombuffer(data.buffers()[1]).view("int32"))
+        data_32 = data_128[::4].copy()
+        return cls(
+            data=as_buffer(data_32.view("uint8")),
+            size=len(data),
+            dtype=dtype,
+            offset=data.offset,
+            mask=mask,
+        )
+
+    def to_arrow(self):
+        data_buf_32 = np.array(self.base_data.memoryview()).view("int32")
+        data_buf_128 = np.empty(len(data_buf_32) * 4, dtype="int32")
+
+        # use striding to set the first 32 bits of each 128-bit chunk:
+        data_buf_128[::4] = data_buf_32
+        # use striding again to set the remaining bits of each 128-bit chunk:
+        # 0 for non-negative values, -1 for negative values:
+        data_buf_128[1::4] = np.piecewise(
+            data_buf_32, [data_buf_32 < 0], [-1, 0]
+        )
+        data_buf_128[2::4] = np.piecewise(
+            data_buf_32, [data_buf_32 < 0], [-1, 0]
+        )
+        data_buf_128[3::4] = np.piecewise(
+            data_buf_32, [data_buf_32 < 0], [-1, 0]
+        )
+        data_buf = pa.py_buffer(data_buf_128)
+        mask_buf = (
+            self.base_mask
+            if self.base_mask is None
+            else pa.py_buffer(self.base_mask.memoryview())
+        )
+        return pa.Array.from_buffers(
+            type=self.dtype.to_arrow(),
+            offset=self._offset,
+            length=self.size,
+            buffers=[mask_buf, data_buf],
+        )
+
+    def _with_type_metadata(
+        self: "cudf.core.column.Decimal32Column", dtype: Dtype
+    ) -> "cudf.core.column.Decimal32Column":
+        if isinstance(dtype, Decimal32Dtype):
+            self.dtype.precision = dtype.precision
+
+        return self
+
+
+class Decimal128Column(DecimalBaseColumn):
+    dtype: Decimal128Dtype
+
+    @classmethod
+    def from_arrow(cls, data: pa.Array):
+        result = cast(Decimal128Dtype, super().from_arrow(data))
+        result.dtype.precision = data.type.precision
+        return result
+
+    def to_arrow(self):
+        return super().to_arrow().cast(self.dtype.to_arrow())
+
+    def _with_type_metadata(
+        self: "cudf.core.column.Decimal128Column", dtype: Dtype
+    ) -> "cudf.core.column.Decimal128Column":
+        if isinstance(dtype, Decimal128Dtype):
+            self.dtype.precision = dtype.precision
+
+        return self
+
+
+class Decimal64Column(DecimalBaseColumn):
+    dtype: Decimal64Dtype
+
+    def __setitem__(self, key, value):
+        if isinstance(value, np.integer):
+            value = int(value)
+        super().__setitem__(key, value)
+
+    @classmethod
+    def from_arrow(cls, data: pa.Array):
+        dtype = Decimal64Dtype.from_arrow(data.type)
+        mask_buf = data.buffers()[0]
+        mask = (
+            mask_buf
+            if mask_buf is None
+            else pa_mask_buffer_to_mask(mask_buf, len(data))
+        )
+        data_128 = cp.array(np.frombuffer(data.buffers()[1]).view("int64"))
+        data_64 = data_128[::2].copy()
+        return cls(
+            data=as_buffer(data_64.view("uint8")),
+            size=len(data),
+            dtype=dtype,
+            offset=data.offset,
+            mask=mask,
+        )
+
+    def to_arrow(self):
+        data_buf_64 = np.array(self.base_data.memoryview()).view("int64")
+        data_buf_128 = np.empty(len(data_buf_64) * 2, dtype="int64")
+
+        # use striding to set the first 64 bits of each 128-bit chunk:
+        data_buf_128[::2] = data_buf_64
+        # use striding again to set the remaining bits of each 128-bit chunk:
+        # 0 for non-negative values, -1 for negative values:
+        data_buf_128[1::2] = np.piecewise(
+            data_buf_64, [data_buf_64 < 0], [-1, 0]
+        )
+        data_buf = pa.py_buffer(data_buf_128)
+        mask_buf = (
+            self.base_mask
+            if self.base_mask is None
+            else pa.py_buffer(self.base_mask.memoryview())
+        )
+        return pa.Array.from_buffers(
+            type=self.dtype.to_arrow(),
+            offset=self._offset,
+            length=self.size,
+            buffers=[mask_buf, data_buf],
+        )
+
+    @property
+    def __cuda_array_interface__(self):
+        raise NotImplementedError(
+            "Decimals are not yet supported via `__cuda_array_interface__`"
+        )
+
+    def _with_type_metadata(
+        self: "cudf.core.column.Decimal64Column", dtype: Dtype
+    ) -> "cudf.core.column.Decimal64Column":
+        if isinstance(dtype, Decimal64Dtype):
+            self.dtype.precision = dtype.precision
+
+        return self
+
+
+def _get_decimal_type(lhs_dtype, rhs_dtype, op):
+    """
+    Returns the resulting decimal type after calculating
+    precision & scale when performing the binary operation
+    `op` for the given dtypes.
+
+    For precision & scale calculations see : https://docs.microsoft.com/en-us/sql/t-sql/data-types/precision-scale-and-length-transact-sql
+    """  # noqa: E501
+
+    # This should at some point be hooked up to libcudf's
+    # binary_operation_fixed_point_scale
+
+    p1, p2 = lhs_dtype.precision, rhs_dtype.precision
+    s1, s2 = lhs_dtype.scale, rhs_dtype.scale
+
+    if op in {"__add__", "__sub__"}:
+        scale = max(s1, s2)
+        precision = scale + max(p1 - s1, p2 - s2) + 1
+        if precision > Decimal128Dtype.MAX_PRECISION:
+            precision = Decimal128Dtype.MAX_PRECISION
+            scale = Decimal128Dtype.MAX_PRECISION - max(p1 - s1, p2 - s2)
+    elif op in {"__mul__", "__div__"}:
+        if op == "__mul__":
+            scale = s1 + s2
+            precision = p1 + p2 + 1
+        else:
+            scale = max(6, s1 + p2 + 1)
+            precision = p1 - s1 + s2 + scale
+        if precision > Decimal128Dtype.MAX_PRECISION:
+            integral = precision - scale
+            if integral < 32:
+                scale = min(scale, Decimal128Dtype.MAX_PRECISION - integral)
+            elif scale > 6 and integral > 32:
+                scale = 6
+            precision = Decimal128Dtype.MAX_PRECISION
+    else:
+        raise NotImplementedError()
+
+    try:
+        if isinstance(lhs_dtype, type(rhs_dtype)):
+            # SCENARIO 1: If `lhs_dtype` & `rhs_dtype` are same, then try to
+            # see if `precision` & `scale` can be fit into this type.
+            return lhs_dtype.__class__(precision=precision, scale=scale)
+        else:
+            # SCENARIO 2: If `lhs_dtype` & `rhs_dtype` are of different dtypes,
+            # then try to see if `precision` & `scale` can be fit into the type
+            # with greater MAX_PRECISION (i.e., the bigger dtype).
+            if lhs_dtype.MAX_PRECISION >= rhs_dtype.MAX_PRECISION:
+                return lhs_dtype.__class__(precision=precision, scale=scale)
+            else:
+                return rhs_dtype.__class__(precision=precision, scale=scale)
+    except ValueError:
+        # Call to _validate fails, which means we need
+        # to goto SCENARIO 3.
+        pass
+
+    # SCENARIO 3: If either of the above two scenarios fail, then get the
+    # MAX_PRECISION of `lhs_dtype` & `rhs_dtype` so that we can only check
+    # and return a dtype that is greater than or equal to input dtype that
+    # can fit `precision` & `scale`.
+    max_precision = max(lhs_dtype.MAX_PRECISION, rhs_dtype.MAX_PRECISION)
+    for decimal_type in (
+        Decimal32Dtype,
+        Decimal64Dtype,
+        Decimal128Dtype,
+    ):
+        if decimal_type.MAX_PRECISION >= max_precision:
+            try:
+                return decimal_type(precision=precision, scale=scale)
+            except ValueError:
+                # Call to _validate fails, which means we need
+                # to try the next dtype
+                continue
+
+    # if we've reached this point, we cannot create a decimal type without
+    # overflow; raise an informative error
+    raise ValueError(
+        f"Performing {op} between columns of type {repr(lhs_dtype)} and "
+        f"{repr(rhs_dtype)} would result in overflow"
+    )
+
+
+def _same_precision_and_scale(lhs: DecimalDtype, rhs: DecimalDtype) -> bool:
+    return lhs.precision == rhs.precision and lhs.scale == rhs.scale
diff --git a/python/cudf/cudf/core/column/interval.py b/python/cudf/cudf/core/column/interval.py
new file mode 100644
index 0000000..d4855de
--- /dev/null
+++ b/python/cudf/cudf/core/column/interval.py
@@ -0,0 +1,147 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+from typing import Optional
+
+import pandas as pd
+import pyarrow as pa
+
+import cudf
+from cudf.api.types import is_categorical_dtype, is_interval_dtype
+from cudf.core.column import StructColumn
+from cudf.core.dtypes import IntervalDtype
+
+
+class IntervalColumn(StructColumn):
+    def __init__(
+        self,
+        dtype,
+        mask=None,
+        size=None,
+        offset=0,
+        null_count=None,
+        children=(),
+        closed="right",
+    ):
+        super().__init__(
+            data=None,
+            dtype=dtype,
+            mask=mask,
+            size=size,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+        if closed in ["left", "right", "neither", "both"]:
+            self._closed = closed
+        else:
+            raise ValueError("closed value is not valid")
+
+    @property
+    def closed(self):
+        return self._closed
+
+    @classmethod
+    def from_arrow(cls, data):
+        new_col = super().from_arrow(data.storage)
+        size = len(data)
+        dtype = IntervalDtype.from_arrow(data.type)
+        mask = data.buffers()[0]
+        if mask is not None:
+            mask = cudf.utils.utils.pa_mask_buffer_to_mask(mask, len(data))
+
+        offset = data.offset
+        null_count = data.null_count
+        children = new_col.children
+        closed = dtype.closed
+
+        return IntervalColumn(
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+            closed=closed,
+        )
+
+    def to_arrow(self):
+        typ = self.dtype.to_arrow()
+        struct_arrow = super().to_arrow()
+        if len(struct_arrow) == 0:
+            # struct arrow is pa.struct array with null children types
+            # we need to make sure its children have non-null type
+            struct_arrow = pa.array([], typ.storage_type)
+        return pa.ExtensionArray.from_storage(typ, struct_arrow)
+
+    @classmethod
+    def from_struct_column(cls, struct_column: StructColumn, closed="right"):
+        first_field_name = list(struct_column.dtype.fields.keys())[0]
+        return IntervalColumn(
+            size=struct_column.size,
+            dtype=IntervalDtype(
+                struct_column.dtype.fields[first_field_name], closed
+            ),
+            mask=struct_column.base_mask,
+            offset=struct_column.offset,
+            null_count=struct_column.null_count,
+            children=struct_column.base_children,
+            closed=closed,
+        )
+
+    def copy(self, deep=True):
+        closed = self.closed
+        struct_copy = super().copy(deep=deep)
+        return IntervalColumn(
+            size=struct_copy.size,
+            dtype=IntervalDtype(struct_copy.dtype.fields["left"], closed),
+            mask=struct_copy.base_mask,
+            offset=struct_copy.offset,
+            null_count=struct_copy.null_count,
+            children=struct_copy.base_children,
+            closed=closed,
+        )
+
+    def as_interval_column(self, dtype, **kwargs):
+        if is_interval_dtype(dtype):
+            if is_categorical_dtype(self):
+                new_struct = self._get_decategorized_column()
+                return IntervalColumn.from_struct_column(new_struct)
+            if is_interval_dtype(dtype):
+                # a user can directly input the string `interval` as the dtype
+                # when creating an interval series or interval dataframe
+                if dtype == "interval":
+                    dtype = IntervalDtype(
+                        self.dtype.fields["left"], self.closed
+                    )
+                children = self.children
+                return IntervalColumn(
+                    size=self.size,
+                    dtype=dtype,
+                    mask=self.mask,
+                    offset=self.offset,
+                    null_count=self.null_count,
+                    children=children,
+                    closed=dtype.closed,
+                )
+        else:
+            raise ValueError("dtype must be IntervalDtype")
+
+    def to_pandas(
+        self, index: Optional[pd.Index] = None, **kwargs
+    ) -> pd.Series:
+        # Note: This does not handle null values in the interval column.
+        # However, this exact sequence (calling __from_arrow__ on the output of
+        # self.to_arrow) is currently the best known way to convert interval
+        # types into pandas (trying to convert the underlying numerical columns
+        # directly is problematic), so we're stuck with this for now.
+        return pd.Series(
+            self.dtype.to_pandas().__from_arrow__(self.to_arrow()), index=index
+        )
+
+    def element_indexing(self, index: int):
+        result = super().element_indexing(index)
+        if cudf.get_option("mode.pandas_compatible"):
+            return pd.Interval(**result, closed=self._closed)
+        return {
+            field: value
+            for field, value in zip(self.dtype.fields, result.values())
+        }
diff --git a/python/cudf/cudf/core/column/lists.py b/python/cudf/cudf/core/column/lists.py
new file mode 100644
index 0000000..0bb9f70
--- /dev/null
+++ b/python/cudf/cudf/core/column/lists.py
@@ -0,0 +1,728 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from functools import cached_property
+from typing import List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import pyarrow as pa
+from typing_extensions import Self
+
+import cudf
+from cudf._lib.copying import segmented_gather
+from cudf._lib.lists import (
+    concatenate_list_elements,
+    concatenate_rows,
+    contains_scalar,
+    count_elements,
+    distinct,
+    extract_element_column,
+    extract_element_scalar,
+    index_of_column,
+    index_of_scalar,
+    sort_lists,
+)
+from cudf._lib.strings.convert.convert_lists import format_list_column
+from cudf._lib.types import size_type_dtype
+from cudf._typing import ColumnBinaryOperand, ColumnLike, Dtype, ScalarLike
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    is_list_dtype,
+    is_scalar,
+)
+from cudf.core.column import ColumnBase, as_column, column
+from cudf.core.column.methods import ColumnMethods, ParentType
+from cudf.core.dtypes import ListDtype
+from cudf.core.missing import NA
+
+
+class ListColumn(ColumnBase):
+    dtype: ListDtype
+    _VALID_BINARY_OPERATIONS = {"__add__", "__radd__"}
+
+    def __init__(
+        self,
+        size,
+        dtype,
+        mask=None,
+        offset=0,
+        null_count=None,
+        children=(),
+    ):
+        super().__init__(
+            None,
+            size,
+            dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+
+    @cached_property
+    def memory_usage(self):
+        n = 0
+        if self.nullable:
+            n += cudf._lib.null_mask.bitmask_allocation_size_bytes(self.size)
+
+        child0_size = (self.size + 1) * self.base_children[0].dtype.itemsize
+        current_base_child = self.base_children[1]
+        current_offset = self.offset
+        n += child0_size
+        while type(current_base_child) is ListColumn:
+            child0_size = (
+                current_base_child.size + 1 - current_offset
+            ) * current_base_child.base_children[0].dtype.itemsize
+            current_offset = current_base_child.base_children[
+                0
+            ].element_indexing(current_offset)
+            n += child0_size
+            current_base_child = current_base_child.base_children[1]
+
+        n += (
+            current_base_child.size - current_offset
+        ) * current_base_child.dtype.itemsize
+
+        if current_base_child.nullable:
+            n += cudf._lib.null_mask.bitmask_allocation_size_bytes(
+                current_base_child.size
+            )
+        return n
+
+    def __setitem__(self, key, value):
+        if isinstance(value, list):
+            value = cudf.Scalar(value)
+        if isinstance(value, cudf.Scalar):
+            if value.dtype != self.dtype:
+                raise TypeError("list nesting level mismatch")
+        elif value is NA:
+            value = cudf.Scalar(value, dtype=self.dtype)
+        else:
+            raise ValueError(f"Can not set {value} into ListColumn")
+        super().__setitem__(key, value)
+
+    @property
+    def base_size(self):
+        # in some cases, libcudf will return an empty ListColumn with no
+        # indices; in these cases, we must manually set the base_size to 0 to
+        # avoid it being negative
+        return max(0, len(self.base_children[0]) - 1)
+
+    def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase:
+        # Lists only support __add__, which concatenates lists.
+        reflect, op = self._check_reflected_op(op)
+        other = self._wrap_binop_normalization(other)
+        if other is NotImplemented:
+            return NotImplemented
+        if isinstance(other.dtype, ListDtype):
+            if op == "__add__":
+                return concatenate_rows([self, other])
+            else:
+                raise NotImplementedError(
+                    "Lists concatenation for this operation is not yet"
+                    "supported"
+                )
+        else:
+            raise TypeError("can only concatenate list to list")
+
+    @property
+    def elements(self):
+        """
+        Column containing the elements of each list (may itself be a
+        ListColumn)
+        """
+        return self.children[1]
+
+    @property
+    def offsets(self):
+        """
+        Integer offsets to elements specifying each row of the ListColumn
+        """
+        return self.children[0]
+
+    def to_arrow(self):
+        offsets = self.offsets.to_arrow()
+        elements = (
+            pa.nulls(len(self.elements))
+            if len(self.elements) == self.elements.null_count
+            else self.elements.to_arrow()
+        )
+        pa_type = pa.list_(elements.type)
+
+        if self.nullable:
+            nbuf = pa.py_buffer(self.mask.memoryview())
+            buffers = (nbuf, offsets.buffers()[1])
+        else:
+            buffers = offsets.buffers()
+        return pa.ListArray.from_buffers(
+            pa_type, len(self), buffers, children=[elements]
+        )
+
+    def set_base_data(self, value):
+        if value is not None:
+            raise RuntimeError(
+                "ListColumn's do not use data attribute of Column, use "
+                "`set_base_children` instead"
+            )
+        else:
+            super().set_base_data(value)
+
+    def set_base_children(self, value: Tuple[ColumnBase, ...]):
+        super().set_base_children(value)
+        _, values = value
+        self._dtype = cudf.ListDtype(element_type=values.dtype)
+
+    @property
+    def __cuda_array_interface__(self):
+        raise NotImplementedError(
+            "Lists are not yet supported via `__cuda_array_interface__`"
+        )
+
+    def normalize_binop_value(self, other):
+        if not isinstance(other, ListColumn):
+            return NotImplemented
+        return other
+
+    def _with_type_metadata(
+        self: "cudf.core.column.ListColumn", dtype: Dtype
+    ) -> "cudf.core.column.ListColumn":
+        if isinstance(dtype, ListDtype):
+            return column.build_list_column(
+                indices=self.base_children[0],
+                elements=self.base_children[1]._with_type_metadata(
+                    dtype.element_type
+                ),
+                mask=self.base_mask,
+                size=self.size,
+                offset=self.offset,
+                null_count=self.null_count,
+            )
+
+        return self
+
+    def copy(self, deep: bool = True):
+        # Since list columns are immutable, both deep and shallow copies share
+        # the underlying device data and mask.
+        return super().copy(deep=False)
+
+    def leaves(self):
+        if isinstance(self.elements, ListColumn):
+            return self.elements.leaves()
+        else:
+            return self.elements
+
+    @classmethod
+    def from_sequences(
+        cls, arbitrary: Sequence[ColumnLike]
+    ) -> "cudf.core.column.ListColumn":
+        """
+        Create a list column for list of column-like sequences
+        """
+        data_col = column.column_empty(0)
+        mask_col = []
+        offset_col = [0]
+        offset = 0
+
+        # Build Data, Mask & Offsets
+        for data in arbitrary:
+            if cudf._lib.scalar._is_null_host_scalar(data):
+                mask_col.append(False)
+                offset_col.append(offset)
+            else:
+                mask_col.append(True)
+                data_col = data_col.append(as_column(data))
+                offset += len(data)
+                offset_col.append(offset)
+
+        offset_col = column.as_column(offset_col, dtype=size_type_dtype)
+
+        # Build ListColumn
+        res = cls(
+            size=len(arbitrary),
+            dtype=cudf.ListDtype(data_col.dtype),
+            mask=cudf._lib.transform.bools_to_mask(as_column(mask_col)),
+            offset=0,
+            null_count=0,
+            children=(offset_col, data_col),
+        )
+        return res
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        """
+        Create a strings column from a list column
+        """
+        lc = self._transform_leaves(
+            lambda col, dtype: col.as_string_column(dtype), dtype
+        )
+
+        # Separator strings to match the Python format
+        separators = as_column([", ", "[", "]"])
+
+        # Call libcudf to format the list column
+        return format_list_column(lc, separators)
+
+    def _transform_leaves(self, func, *args, **kwargs) -> Self:
+        # return a new list column with the same nested structure
+        # as ``self``, but with the leaf column transformed
+        # by applying ``func`` to it
+
+        cc: List[ListColumn] = []
+        c: ColumnBase = self
+
+        while isinstance(c, ListColumn):
+            cc.insert(0, c)
+            c = c.children[1]
+
+        lc = func(c, *args, **kwargs)
+
+        # Rebuild the list column replacing just the leaf child
+        for c in cc:
+            o = c.children[0]
+            lc = cudf.core.column.ListColumn(  # type: ignore
+                size=c.size,
+                dtype=cudf.ListDtype(lc.dtype),
+                mask=c.mask,
+                offset=c.offset,
+                null_count=c.null_count,
+                children=(o, lc),
+            )
+        return lc
+
+
+class ListMethods(ColumnMethods):
+    """
+    List methods for Series
+    """
+
+    _column: ListColumn
+
+    def __init__(self, parent: ParentType):
+        if not is_list_dtype(parent.dtype):
+            raise AttributeError(
+                "Can only use .list accessor with a 'list' dtype"
+            )
+        super().__init__(parent=parent)
+
+    def get(
+        self,
+        index: int,
+        default: Optional[Union[ScalarLike, ColumnLike]] = None,
+    ) -> ParentType:
+        """
+        Extract element at the given index from each list in a Series of lists.
+
+        ``index`` can be an integer or a sequence of integers.  If
+        ``index`` is an integer, the element at position ``index`` is
+        extracted from each list.  If ``index`` is a sequence, it must
+        be of the same length as the Series, and ``index[i]``
+        specifies the position of the element to extract from the
+        ``i``-th list in the Series.
+
+        If the index is out of bounds for any list, return <NA> or, if
+        provided, ``default``.  Thus, this method never raises an
+        ``IndexError``.
+
+        Parameters
+        ----------
+        index : int or sequence of ints
+        default : scalar, optional
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], [3, 4, 5], [4, 5, 6]])
+        >>> s.list.get(-1)
+        0    3
+        1    5
+        2    6
+        dtype: int64
+
+        >>> s = cudf.Series([[1, 2], [3, 4, 5], [4, 5, 6]])
+        >>> s.list.get(2)
+        0    <NA>
+        1       5
+        2       6
+        dtype: int64
+
+        >>> s.list.get(2, default=0)
+        0   0
+        1   5
+        2   6
+        dtype: int64
+
+        >>> s.list.get([0, 1, 2])
+        0   1
+        1   4
+        2   6
+        dtype: int64
+        """
+        if is_scalar(index):
+            out = extract_element_scalar(self._column, cudf.Scalar(index))
+        else:
+            index = as_column(index)
+            out = extract_element_column(self._column, as_column(index))
+
+        if not (default is None or default is NA):
+            # determine rows for which `index` is out-of-bounds
+            lengths = count_elements(self._column)
+            out_of_bounds_mask = (np.negative(index) > lengths) | (
+                index >= lengths
+            )
+
+            # replace the value in those rows (should be NA) with `default`
+            if out_of_bounds_mask.any():
+                out = out._scatter_by_column(
+                    out_of_bounds_mask, cudf.Scalar(default)
+                )
+        if out.dtype != self._column.dtype.element_type:
+            # libcudf doesn't maintain struct labels so we must transfer over
+            # manually from the input column if we lost some information
+            # somewhere. Not doing this unilaterally since the cost is
+            # non-zero..
+            out = out._with_type_metadata(self._column.dtype.element_type)
+        return self._return_or_inplace(out)
+
+    def contains(self, search_key: ScalarLike) -> ParentType:
+        """
+        Returns boolean values indicating whether the specified scalar
+        is an element of each row.
+
+        Parameters
+        ----------
+        search_key : scalar
+            element being searched for in each row of the list column
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], [3, 4, 5], [4, 5, 6]])
+        >>> s.list.contains(4)
+        Series([False, True, True])
+        dtype: bool
+        """
+        return self._return_or_inplace(
+            contains_scalar(self._column, cudf.Scalar(search_key))
+        )
+
+    def index(self, search_key: Union[ScalarLike, ColumnLike]) -> ParentType:
+        """
+        Returns integers representing the index of the search key for each row.
+
+        If ``search_key`` is a sequence, it must be the same length as the
+        Series and ``search_key[i]`` represents the search key for the
+        ``i``-th row of the Series.
+
+        If the search key is not contained in a row, -1 is returned. If either
+        the row or the search key are null, <NA> is returned. If the search key
+        is contained multiple times, the smallest matching index is returned.
+
+        Parameters
+        ----------
+        search_key : scalar or sequence of scalars
+            Element or elements being searched for in each row of the list
+            column
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], [3, 4, 5], [4, 5, 6]])
+        >>> s.list.index(4)
+        0   -1
+        1    1
+        2    0
+        dtype: int32
+
+        >>> s = cudf.Series([["a", "b", "c"], ["x", "y", "z"]])
+        >>> s.list.index(["b", "z"])
+        0    1
+        1    2
+        dtype: int32
+
+        >>> s = cudf.Series([[4, 5, 6], None, [-3, -2, -1]])
+        >>> s.list.index([None, 3, -2])
+        0    <NA>
+        1    <NA>
+        2       1
+        dtype: int32
+        """
+
+        if is_scalar(search_key):
+            return self._return_or_inplace(
+                index_of_scalar(self._column, cudf.Scalar(search_key))
+            )
+        else:
+            return self._return_or_inplace(
+                index_of_column(self._column, as_column(search_key))
+            )
+
+    @property
+    def leaves(self) -> ParentType:
+        """
+        From a Series of (possibly nested) lists, obtain the elements from
+        the innermost lists as a flat Series (one value per row).
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> a = cudf.Series([[[1, None], [3, 4]], None, [[5, 6]]])
+        >>> a.list.leaves
+        0       1
+        1    <NA>
+        2       3
+        3       4
+        4       5
+        5       6
+        dtype: int64
+        """
+        return self._return_or_inplace(
+            self._column.leaves(), retain_index=False
+        )
+
+    def len(self) -> ParentType:
+        """
+        Computes the length of each element in the Series/Index.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
+        >>> s
+        0    [1, 2, 3]
+        1         None
+        2       [4, 5]
+        dtype: list
+        >>> s.list.len()
+        0       3
+        1    <NA>
+        2       2
+        dtype: int32
+        """
+        return self._return_or_inplace(count_elements(self._column))
+
+    def take(self, lists_indices: ColumnLike) -> ParentType:
+        """
+        Collect list elements based on given indices.
+
+        Parameters
+        ----------
+        lists_indices: Series-like of lists
+            Specifies what to collect from each row
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
+        >>> s
+        0    [1, 2, 3]
+        1         None
+        2       [4, 5]
+        dtype: list
+        >>> s.list.take([[0, 1], [], []])
+        0    [1, 2]
+        1      None
+        2        []
+        dtype: list
+        """
+
+        lists_indices_col = as_column(lists_indices)
+        if not isinstance(lists_indices_col, ListColumn):
+            raise ValueError("lists_indices should be list type array.")
+        if not lists_indices_col.size == self._column.size:
+            raise ValueError(
+                "lists_indices and list column is of different " "size."
+            )
+        if not _is_non_decimal_numeric_dtype(
+            lists_indices_col.children[1].dtype
+        ) or not np.issubdtype(
+            lists_indices_col.children[1].dtype, np.integer
+        ):
+            raise TypeError(
+                "lists_indices should be column of values of index types."
+            )
+
+        return self._return_or_inplace(
+            segmented_gather(self._column, lists_indices_col)
+        )
+
+    def unique(self) -> ParentType:
+        """
+        Returns the unique elements in each list.
+        The ordering of elements is not guaranteed.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 1, 2, None, None], None, [4, 4], []])
+        >>> s
+        0    [1.0, 1.0, 2.0, nan, nan]
+        1                         None
+        2                   [4.0, 4.0]
+        3                           []
+        dtype: list
+        >>> s.list.unique() # Order of list element is not guaranteed
+        0              [1.0, 2.0, nan]
+        1                         None
+        2                        [4.0]
+        3                           []
+        dtype: list
+        """
+
+        if is_list_dtype(self._column.children[1].dtype):
+            raise NotImplementedError("Nested lists unique is not supported.")
+
+        return self._return_or_inplace(
+            distinct(self._column, nulls_equal=True, nans_all_equal=True)
+        )
+
+    def sort_values(
+        self,
+        ascending: bool = True,
+        inplace: bool = False,
+        kind: str = "quicksort",
+        na_position: str = "last",
+        ignore_index: bool = False,
+    ) -> ParentType:
+        """
+        Sort each list by the values.
+
+        Sort the lists in ascending or descending order by some criterion.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If True, sort values in ascending order, otherwise descending.
+        na_position : {'first', 'last'}, default 'last'
+            'first' puts nulls at the beginning, 'last' puts nulls at the end.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, ..., n - 1.
+
+        Returns
+        -------
+        Series or Index with each list sorted
+
+        Notes
+        -----
+        Difference from pandas:
+          * Not supporting: `inplace`, `kind`
+
+        Examples
+        --------
+        >>> s = cudf.Series([[4, 2, None, 9], [8, 8, 2], [2, 1]])
+        >>> s.list.sort_values(ascending=True, na_position="last")
+        0    [2.0, 4.0, 9.0, nan]
+        1         [2.0, 8.0, 8.0]
+        2              [1.0, 2.0]
+        dtype: list
+        """
+        if inplace:
+            raise NotImplementedError("`inplace` not currently implemented.")
+        if kind != "quicksort":
+            raise NotImplementedError("`kind` not currently implemented.")
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"Unknown `na_position` value {na_position}")
+        if is_list_dtype(self._column.children[1].dtype):
+            raise NotImplementedError("Nested lists sort is not supported.")
+
+        return self._return_or_inplace(
+            sort_lists(self._column, ascending, na_position),
+            retain_index=not ignore_index,
+        )
+
+    def concat(self, dropna=True) -> ParentType:
+        """
+        For a column with at least one level of nesting, concatenate the
+        lists in each row.
+
+        Parameters
+        ----------
+        dropna: bool, optional
+            If True (default), ignores top-level null elements in each row.
+            If False, and top-level null elements are present, the resulting
+            row in the output is null.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s1
+        0      [[1.0, 2.0], [3.0, 4.0, 5.0]]
+        1    [[6.0, None], [7.0], [8.0, 9.0]]
+        dtype: list
+        >>> s1.list.concat()
+        0    [1.0, 2.0, 3.0, 4.0, 5.0]
+        1    [6.0, None, 7.0, 8.0, 9.0]
+        dtype: list
+
+        Null values at the top-level in each row are dropped by default:
+
+        >>> s2
+        0    [[1.0, 2.0], None, [3.0, 4.0, 5.0]]
+        1        [[6.0, None], [7.0], [8.0, 9.0]]
+        dtype: list
+        >>> s2.list.concat()
+        0    [1.0, 2.0, 3.0, 4.0, 5.0]
+        1    [6.0, None, 7.0, 8.0, 9.0]
+        dtype: list
+
+        Use ``dropna=False`` to produce a null instead:
+
+        >>> s2.list.concat(dropna=False)
+        0                         None
+        1    [6.0, nan, 7.0, 8.0, 9.0]
+        dtype: list
+        """
+        return self._return_or_inplace(
+            concatenate_list_elements(self._column, dropna=dropna)
+        )
+
+    def astype(self, dtype):
+        """
+        Return a new list Series with the leaf values casted
+        to the specified data type.
+
+        Parameters
+        ----------
+        dtype: data type to cast leaves values to
+
+        Returns
+        -------
+        A new Series of lists
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2], [3, 4]])
+        >>> s.dtype
+        ListDtype(int64)
+        >>> s2 = s.list.astype("float64")
+        >>> s2.dtype
+        ListDtype(float64)
+        """
+        return self._return_or_inplace(
+            self._column._transform_leaves(
+                lambda col, dtype: col.astype(dtype), dtype
+            )
+        )
diff --git a/python/cudf/cudf/core/column/methods.py b/python/cudf/cudf/core/column/methods.py
new file mode 100644
index 0000000..741d18c
--- /dev/null
+++ b/python/cudf/cudf/core/column/methods.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+from typing import Optional, Union, overload
+
+from typing_extensions import Literal
+
+import cudf
+from cudf.utils.utils import NotIterable
+
+ParentType = Union["cudf.Series", "cudf.core.index.GenericIndex"]
+
+
+class ColumnMethods(NotIterable):
+    _parent: ParentType
+
+    def __init__(self, parent: ParentType):
+        self._parent = parent
+        self._column = self._parent._column
+
+    @overload
+    def _return_or_inplace(
+        self,
+        new_col,
+        inplace: Literal[True],
+        expand: bool = False,
+        retain_index: bool = True,
+    ) -> None:
+        ...
+
+    @overload
+    def _return_or_inplace(
+        self,
+        new_col,
+        inplace: Literal[False],
+        expand: bool = False,
+        retain_index: bool = True,
+    ) -> ParentType:
+        ...
+
+    @overload
+    def _return_or_inplace(
+        self,
+        new_col,
+        expand: bool = False,
+        retain_index: bool = True,
+    ) -> ParentType:
+        ...
+
+    @overload
+    def _return_or_inplace(
+        self,
+        new_col,
+        inplace: bool = False,
+        expand: bool = False,
+        retain_index: bool = True,
+    ) -> Optional[ParentType]:
+        ...
+
+    def _return_or_inplace(
+        self, new_col, inplace=False, expand=False, retain_index=True
+    ):
+        """
+        Returns an object of the type of the column owner or updates the column
+        of the owner (Series or Index) to mimic an inplace operation
+        """
+        if inplace:
+            self._parent._mimic_inplace(
+                self._parent.__class__._from_data(
+                    {self._parent.name: new_col}
+                ),
+                inplace=True,
+            )
+            return None
+        else:
+            if expand:
+                # This branch indicates the passed as new_col
+                # is a Table
+                table = new_col
+
+                if isinstance(self._parent, cudf.BaseIndex):
+                    idx = self._parent._constructor_expanddim._from_data(table)
+                    idx.names = None
+                    return idx
+                else:
+                    return self._parent._constructor_expanddim._from_data(
+                        data=table, index=self._parent.index
+                    )
+            elif isinstance(self._parent, cudf.Series):
+                if retain_index:
+                    return cudf.Series(
+                        new_col,
+                        name=self._parent.name,
+                        index=self._parent.index,
+                    )
+                else:
+                    return cudf.Series(new_col, name=self._parent.name)
+            elif isinstance(self._parent, cudf.BaseIndex):
+                return cudf.core.index.as_index(
+                    new_col, name=self._parent.name
+                )
+            else:
+                return self._parent._mimic_inplace(new_col, inplace=False)
diff --git a/python/cudf/cudf/core/column/numerical.py b/python/cudf/cudf/core/column/numerical.py
new file mode 100644
index 0000000..4ea49c8
--- /dev/null
+++ b/python/cudf/cudf/core/column/numerical.py
@@ -0,0 +1,791 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Callable,
+    Mapping,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+)
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib.stream_compaction import drop_nulls
+from cudf._lib.types import size_type_dtype
+from cudf._typing import (
+    ColumnBinaryOperand,
+    ColumnLike,
+    Dtype,
+    DtypeObj,
+    ScalarLike,
+)
+from cudf.api.types import (
+    is_bool_dtype,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_scalar,
+)
+from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
+from cudf.core.column import (
+    ColumnBase,
+    as_column,
+    build_column,
+    column,
+    full,
+    string,
+)
+from cudf.core.dtypes import CategoricalDtype
+from cudf.core.mixins import BinaryOperand
+from cudf.utils.dtypes import (
+    NUMERIC_TYPES,
+    min_column_type,
+    min_signed_type,
+    np_dtypes_to_pandas_dtypes,
+    numeric_normalize_types,
+)
+
+from .numerical_base import NumericalBaseColumn
+
+
+class NumericalColumn(NumericalBaseColumn):
+    """
+    A Column object for Numeric types.
+
+    Parameters
+    ----------
+    data : Buffer
+    dtype : np.dtype
+        The dtype associated with the data Buffer
+    mask : Buffer, optional
+    """
+
+    _nan_count: Optional[int]
+    _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
+
+    def __init__(
+        self,
+        data: Buffer,
+        dtype: DtypeObj,
+        mask: Optional[Buffer] = None,
+        size: Optional[int] = None,  # TODO: make this non-optional
+        offset: int = 0,
+        null_count: Optional[int] = None,
+    ):
+        dtype = cudf.dtype(dtype)
+
+        if data.size % dtype.itemsize:
+            raise ValueError("Buffer size must be divisible by element size")
+        if size is None:
+            size = (data.size // dtype.itemsize) - offset
+        self._nan_count = None
+        super().__init__(
+            data,
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+        )
+
+    def _clear_cache(self):
+        super()._clear_cache()
+        self._nan_count = None
+
+    def __contains__(self, item: ScalarLike) -> bool:
+        """
+        Returns True if column contains item, else False.
+        """
+        # Handles improper item types
+        # Fails if item is of type None, so the handler.
+        try:
+            if np.can_cast(item, self.dtype):
+                item = self.dtype.type(item)
+            else:
+                return False
+        except (TypeError, ValueError):
+            return False
+        # TODO: Use `scalar`-based `contains` wrapper
+        return libcudf.search.contains(
+            self, column.as_column([item], dtype=self.dtype)
+        ).any()
+
+    def indices_of(self, value: ScalarLike) -> NumericalColumn:
+        if isinstance(value, (bool, np.bool_)) and self.dtype.kind != "b":
+            raise ValueError(
+                f"Cannot use a {type(value).__name__} to find an index of "
+                f"a {self.dtype} Index."
+            )
+        if (
+            value is not None
+            and self.dtype.kind in {"c", "f"}
+            and np.isnan(value)
+        ):
+            return column.as_column(
+                cp.argwhere(
+                    cp.isnan(self.data_array_view(mode="read"))
+                ).flatten(),
+                dtype=size_type_dtype,
+            )
+        else:
+            return super().indices_of(value)
+
+    def has_nulls(self, include_nan=False):
+        return bool(self.null_count != 0) or (
+            include_nan and bool(self.nan_count != 0)
+        )
+
+    def __setitem__(self, key: Any, value: Any):
+        """
+        Set the value of ``self[key]`` to ``value``.
+
+        If ``value`` and ``self`` are of different types, ``value`` is coerced
+        to ``self.dtype``.
+        """
+
+        # Normalize value to scalar/column
+        device_value = (
+            cudf.Scalar(
+                value,
+                dtype=self.dtype
+                if cudf._lib.scalar._is_null_host_scalar(value)
+                else None,
+            )
+            if is_scalar(value)
+            else as_column(value)
+        )
+
+        if not is_bool_dtype(self.dtype) and is_bool_dtype(device_value.dtype):
+            raise TypeError(f"Invalid value {value} for dtype {self.dtype}")
+        else:
+            device_value = device_value.astype(self.dtype)
+
+        out: Optional[ColumnBase]  # If None, no need to perform mimic inplace.
+        if isinstance(key, slice):
+            out = self._scatter_by_slice(key, device_value)
+        else:
+            key = as_column(key)
+            if not isinstance(key, cudf.core.column.NumericalColumn):
+                raise ValueError(f"Invalid scatter map type {key.dtype}.")
+            out = self._scatter_by_column(key, device_value)
+
+        if out:
+            self._mimic_inplace(out, inplace=True)
+
+    @property
+    def __cuda_array_interface__(self) -> Mapping[str, Any]:
+        output = {
+            "shape": (len(self),),
+            "strides": (self.dtype.itemsize,),
+            "typestr": self.dtype.str,
+            "data": (self.data_ptr, False),
+            "version": 1,
+        }
+
+        if self.nullable and self.has_nulls():
+            # Create a simple Python object that exposes the
+            # `__cuda_array_interface__` attribute here since we need to modify
+            # some of the attributes from the numba device array
+            output["mask"] = cuda_array_interface_wrapper(
+                ptr=self.mask_ptr,
+                size=len(self),
+                owner=self.mask,
+                readonly=True,
+                typestr="<t1",
+            )
+
+        return output
+
+    def unary_operator(self, unaryop: Union[str, Callable]) -> ColumnBase:
+        if callable(unaryop):
+            return libcudf.transform.transform(self, unaryop)
+
+        unaryop = libcudf.unary.UnaryOp[unaryop.upper()]
+        return libcudf.unary.unary_operation(self, unaryop)
+
+    def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase:
+        int_float_dtype_mapping = {
+            np.int8: np.float32,
+            np.int16: np.float32,
+            np.int32: np.float32,
+            np.int64: np.float64,
+            np.uint8: np.float32,
+            np.uint16: np.float32,
+            np.uint32: np.float64,
+            np.uint64: np.float64,
+            np.bool_: np.float32,
+        }
+
+        if op in {"__truediv__", "__rtruediv__"}:
+            # Division with integer types results in a suitable float.
+            if truediv_type := int_float_dtype_mapping.get(self.dtype.type):
+                return self.astype(truediv_type)._binaryop(other, op)
+
+        reflect, op = self._check_reflected_op(op)
+        if (other := self._wrap_binop_normalization(other)) is NotImplemented:
+            return NotImplemented
+        out_dtype = self.dtype
+        if other is not None:
+            out_dtype = np.result_type(self.dtype, other.dtype)
+            if op in {"__mod__", "__floordiv__"}:
+                tmp = self if reflect else other
+                # Guard against division by zero for integers.
+                if (
+                    (tmp.dtype.type in int_float_dtype_mapping)
+                    and (tmp.dtype.type != np.bool_)
+                    and (
+                        (
+                            (
+                                np.isscalar(tmp)
+                                or (
+                                    isinstance(tmp, cudf.Scalar)
+                                    # host to device copy
+                                    and tmp.is_valid()
+                                )
+                            )
+                            and (0 == tmp)
+                        )
+                        or ((isinstance(tmp, NumericalColumn)) and (0 in tmp))
+                    )
+                ):
+                    out_dtype = cudf.dtype("float64")
+
+        if op in {
+            "__lt__",
+            "__gt__",
+            "__le__",
+            "__ge__",
+            "__eq__",
+            "__ne__",
+            "NULL_EQUALS",
+        }:
+            out_dtype = "bool"
+
+        if op in {"__and__", "__or__", "__xor__"}:
+            if is_float_dtype(self.dtype) or is_float_dtype(other):
+                raise TypeError(
+                    f"Operation 'bitwise {op[2:-2]}' not supported between "
+                    f"{self.dtype.type.__name__} and "
+                    f"{other.dtype.type.__name__}"
+                )
+            if is_bool_dtype(self.dtype) or is_bool_dtype(other):
+                out_dtype = "bool"
+
+        if (
+            op == "__pow__"
+            and is_integer_dtype(self.dtype)
+            and (is_integer(other) or is_integer_dtype(other.dtype))
+        ):
+            op = "INT_POW"
+
+        lhs, rhs = (other, self) if reflect else (self, other)
+
+        return libcudf.binaryop.binaryop(lhs, rhs, op, out_dtype)
+
+    def nans_to_nulls(self: NumericalColumn) -> NumericalColumn:
+        # Only floats can contain nan.
+        if self.dtype.kind != "f" or self.nan_count == 0:
+            return self
+        newmask = libcudf.transform.nans_to_nulls(self)
+        return self.set_mask(newmask)
+
+    def normalize_binop_value(
+        self, other: ScalarLike
+    ) -> Union[ColumnBase, cudf.Scalar]:
+        if isinstance(other, ColumnBase):
+            if not isinstance(other, NumericalColumn):
+                return NotImplemented
+            return other
+        if isinstance(other, cudf.Scalar):
+            if self.dtype == other.dtype:
+                return other
+            # expensive device-host transfer just to
+            # adjust the dtype
+            other = other.value
+        # Try and match pandas and hence numpy. Deduce the common
+        # dtype via the _value_ of other, and the dtype of self. TODO:
+        # When NEP50 is accepted, this might want changed or
+        # simplified.
+        # This is not at all simple:
+        # np.result_type(np.int64(0), np.uint8)
+        #   => np.uint8
+        # np.result_type(np.asarray([0], dtype=np.int64), np.uint8)
+        #   => np.int64
+        # np.promote_types(np.int64(0), np.uint8)
+        #   => np.int64
+        # np.promote_types(np.asarray([0], dtype=np.int64).dtype, np.uint8)
+        #   => np.int64
+        common_dtype = np.result_type(self.dtype, other)
+        if common_dtype.kind in {"b", "i", "u", "f"}:
+            if self.dtype.kind == "b":
+                common_dtype = min_signed_type(other)
+            return cudf.Scalar(other, dtype=common_dtype)
+        else:
+            return NotImplemented
+
+    def int2ip(self) -> "cudf.core.column.StringColumn":
+        if self.dtype != cudf.dtype("int64"):
+            raise TypeError("Only int64 type can be converted to ip")
+
+        return libcudf.string_casting.int2ip(self)
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        if len(self) > 0:
+            return string._numeric_to_str_typecast_functions[
+                cudf.dtype(self.dtype)
+            ](self)
+        else:
+            return cast(
+                "cudf.core.column.StringColumn", as_column([], dtype="object")
+            )
+
+    def as_datetime_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.DatetimeColumn":
+        return cast(
+            "cudf.core.column.DatetimeColumn",
+            build_column(
+                data=self.astype("int64").base_data,
+                dtype=dtype,
+                mask=self.base_mask,
+                offset=self.offset,
+                size=self.size,
+            ),
+        )
+
+    def as_timedelta_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.TimeDeltaColumn":
+        return cast(
+            "cudf.core.column.TimeDeltaColumn",
+            build_column(
+                data=self.astype("int64").base_data,
+                dtype=dtype,
+                mask=self.base_mask,
+                offset=self.offset,
+                size=self.size,
+            ),
+        )
+
+    def as_decimal_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.DecimalBaseColumn":
+        return libcudf.unary.cast(self, dtype)
+
+    def as_numerical_column(self, dtype: Dtype, **kwargs) -> NumericalColumn:
+        dtype = cudf.dtype(dtype)
+        if dtype == self.dtype:
+            return self
+        return libcudf.unary.cast(self, dtype)
+
+    def all(self, skipna: bool = True) -> bool:
+        # If all entries are null the result is True, including when the column
+        # is empty.
+        result_col = self.nans_to_nulls() if skipna else self
+
+        if result_col.null_count == result_col.size:
+            return True
+
+        return libcudf.reduce.reduce("all", result_col, dtype=np.bool_)
+
+    def any(self, skipna: bool = True) -> bool:
+        # Early exit for fast cases.
+        result_col = self.nans_to_nulls() if skipna else self
+
+        if not skipna and result_col.has_nulls():
+            return True
+        elif skipna and result_col.null_count == result_col.size:
+            return False
+
+        return libcudf.reduce.reduce("any", result_col, dtype=np.bool_)
+
+    @property
+    def nan_count(self) -> int:
+        if self.dtype.kind != "f":
+            self._nan_count = 0
+        elif self._nan_count is None:
+            nan_col = libcudf.unary.is_nan(self)
+            self._nan_count = nan_col.sum()
+        return self._nan_count
+
+    def dropna(self, drop_nan: bool = False) -> NumericalColumn:
+        col = self.nans_to_nulls() if drop_nan else self
+        return drop_nulls([col])[0]
+
+    @property
+    def contains_na_entries(self) -> bool:
+        return (self.nan_count != 0) or (self.null_count != 0)
+
+    def _process_values_for_isin(
+        self, values: Sequence
+    ) -> Tuple[ColumnBase, ColumnBase]:
+        lhs = cast("cudf.core.column.ColumnBase", self)
+        rhs = as_column(values, nan_as_null=False)
+
+        if isinstance(rhs, NumericalColumn):
+            rhs = rhs.astype(dtype=self.dtype)
+
+        if lhs.null_count == len(lhs):
+            lhs = lhs.astype(rhs.dtype)
+        elif rhs.null_count == len(rhs):
+            rhs = rhs.astype(lhs.dtype)
+
+        return lhs, rhs
+
+    def _can_return_nan(self, skipna: Optional[bool] = None) -> bool:
+        return not skipna and self.has_nulls(include_nan=True)
+
+    def _process_for_reduction(
+        self, skipna: Optional[bool] = None, min_count: int = 0
+    ) -> Union[NumericalColumn, ScalarLike]:
+        skipna = True if skipna is None else skipna
+
+        if self._can_return_nan(skipna=skipna):
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        col = self.nans_to_nulls() if skipna else self
+        return super(NumericalColumn, col)._process_for_reduction(
+            skipna=skipna, min_count=min_count
+        )
+
+    def find_and_replace(
+        self,
+        to_replace: ColumnLike,
+        replacement: ColumnLike,
+        all_nan: bool = False,
+    ) -> NumericalColumn:
+        """
+        Return col with *to_replace* replaced with *value*.
+        """
+
+        # If all of `to_replace`/`replacement` are `None`,
+        # dtype of `to_replace_col`/`replacement_col`
+        # is inferred as `string`, but this is a valid
+        # float64 column too, Hence we will need to type-cast
+        # to self.dtype.
+        to_replace_col = column.as_column(to_replace)
+        if to_replace_col.null_count == len(to_replace_col):
+            to_replace_col = to_replace_col.astype(self.dtype)
+
+        replacement_col = column.as_column(replacement)
+        if replacement_col.null_count == len(replacement_col):
+            replacement_col = replacement_col.astype(self.dtype)
+
+        if not isinstance(to_replace_col, type(replacement_col)):
+            raise TypeError(
+                f"to_replace and value should be of same types,"
+                f"got to_replace dtype: {to_replace_col.dtype} and "
+                f"value dtype: {replacement_col.dtype}"
+            )
+
+        if not isinstance(to_replace_col, NumericalColumn) and not isinstance(
+            replacement_col, NumericalColumn
+        ):
+            return self.copy()
+
+        to_replace_col = _normalize_find_and_replace_input(
+            self.dtype, to_replace
+        )
+        if all_nan:
+            replacement_col = column.as_column(replacement, dtype=self.dtype)
+        else:
+            replacement_col = _normalize_find_and_replace_input(
+                self.dtype, replacement
+            )
+        if len(replacement_col) == 1 and len(to_replace_col) > 1:
+            replacement_col = column.as_column(
+                full(len(to_replace_col), replacement[0], self.dtype)
+            )
+        elif len(replacement_col) == 1 and len(to_replace_col) == 0:
+            return self.copy()
+        to_replace_col, replacement_col, replaced = numeric_normalize_types(
+            to_replace_col, replacement_col, self
+        )
+        df = cudf.DataFrame._from_data(
+            {"old": to_replace_col, "new": replacement_col}
+        )
+        df = df.drop_duplicates(subset=["old"], keep="last", ignore_index=True)
+        if df._data["old"].null_count == 1:
+            replaced = replaced.fillna(
+                df._data["new"]
+                .apply_boolean_mask(df._data["old"].isnull())
+                .element_indexing(0)
+            )
+            df = df.dropna(subset=["old"])
+
+        return libcudf.replace.replace(
+            replaced, df._data["old"], df._data["new"]
+        )
+
+    def fillna(
+        self,
+        fill_value: Any = None,
+        method: Optional[str] = None,
+        dtype: Optional[Dtype] = None,
+        fill_nan: bool = True,
+    ) -> NumericalColumn:
+        """
+        Fill null values with *fill_value*
+        """
+        col = self.nans_to_nulls() if fill_nan else self
+
+        if col.null_count == 0:
+            return col
+
+        if method is not None:
+            return super(NumericalColumn, col).fillna(fill_value, method)
+
+        if fill_value is None:
+            raise ValueError("Must specify either 'fill_value' or 'method'")
+
+        if (
+            isinstance(fill_value, cudf.Scalar)
+            and fill_value.dtype == col.dtype
+        ):
+            return super(NumericalColumn, col).fillna(fill_value, method)
+
+        if np.isscalar(fill_value):
+            # cast safely to the same dtype as self
+            fill_value_casted = col.dtype.type(fill_value)
+            if not np.isnan(fill_value) and (fill_value_casted != fill_value):
+                raise TypeError(
+                    f"Cannot safely cast non-equivalent "
+                    f"{type(fill_value).__name__} to {col.dtype.name}"
+                )
+            fill_value = cudf.Scalar(fill_value_casted)
+        else:
+            fill_value = column.as_column(fill_value, nan_as_null=False)
+            if is_integer_dtype(col.dtype):
+                # cast safely to the same dtype as self
+                if fill_value.dtype != col.dtype:
+                    new_fill_value = fill_value.astype(col.dtype)
+                    if not (new_fill_value == fill_value).all():
+                        raise TypeError(
+                            f"Cannot safely cast non-equivalent "
+                            f"{col.dtype.type.__name__} to "
+                            f"{cudf.dtype(dtype).type.__name__}"
+                        )
+                    fill_value = new_fill_value
+            else:
+                fill_value = fill_value.astype(col.dtype)
+
+        return super(NumericalColumn, col).fillna(fill_value, method)
+
+    def can_cast_safely(self, to_dtype: DtypeObj) -> bool:
+        """
+        Returns true if all the values in self can be
+        safely cast to dtype
+        """
+        if self.dtype.kind == to_dtype.kind:
+            if self.dtype <= to_dtype:
+                return True
+            else:
+                # Kinds are the same but to_dtype is smaller
+                if "float" in to_dtype.name:
+                    finfo = np.finfo(to_dtype)
+                    lower_, upper_ = finfo.min, finfo.max
+                elif "int" in to_dtype.name:
+                    iinfo = np.iinfo(to_dtype)
+                    lower_, upper_ = iinfo.min, iinfo.max
+
+                if self.dtype.kind == "f":
+                    # Exclude 'np.inf', '-np.inf'
+                    s = cudf.Series(self)
+                    # TODO: replace np.inf with cudf scalar when
+                    # https://github.com/rapidsai/cudf/pull/6297 merges
+                    non_infs = s[~((s == np.inf) | (s == -np.inf))]
+                    col = non_infs._column
+                else:
+                    col = self
+
+                min_ = col.min()
+                # TODO: depending on implementation of cudf scalar and future
+                # refactor of min/max, change the test method
+                if np.isnan(min_):
+                    # Column contains only infs
+                    return True
+
+                return (min_ >= lower_) and (col.max() < upper_)
+
+        # want to cast int to uint
+        elif self.dtype.kind == "i" and to_dtype.kind == "u":
+            i_max_ = np.iinfo(self.dtype).max
+            u_max_ = np.iinfo(to_dtype).max
+
+            return (self.min() >= 0) and (
+                (i_max_ <= u_max_) or (self.max() < u_max_)
+            )
+
+        # want to cast uint to int
+        elif self.dtype.kind == "u" and to_dtype.kind == "i":
+            u_max_ = np.iinfo(self.dtype).max
+            i_max_ = np.iinfo(to_dtype).max
+
+            return (u_max_ <= i_max_) or (self.max() < i_max_)
+
+        # want to cast int to float
+        elif self.dtype.kind in {"i", "u"} and to_dtype.kind == "f":
+            info = np.finfo(to_dtype)
+            biggest_exact_int = 2 ** (info.nmant + 1)
+            if (self.min() >= -biggest_exact_int) and (
+                self.max() <= biggest_exact_int
+            ):
+                return True
+            else:
+                filled = self.fillna(0)
+                return (
+                    cudf.Series(filled).astype(to_dtype).astype(filled.dtype)
+                    == cudf.Series(filled)
+                ).all()
+
+        # want to cast float to int:
+        elif self.dtype.kind == "f" and to_dtype.kind in {"i", "u"}:
+            iinfo = np.iinfo(to_dtype)
+            min_, max_ = iinfo.min, iinfo.max
+
+            # best we can do is hope to catch it here and avoid compare
+            if (self.min() >= min_) and (self.max() <= max_):
+                filled = self.fillna(0, fill_nan=False)
+                return (cudf.Series(filled) % 1 == 0).all()
+            else:
+                return False
+
+        return False
+
+    def _with_type_metadata(self: ColumnBase, dtype: Dtype) -> ColumnBase:
+        if isinstance(dtype, CategoricalDtype):
+            return column.build_categorical_column(
+                categories=dtype.categories._values,
+                codes=build_column(self.base_data, dtype=self.dtype),
+                mask=self.base_mask,
+                ordered=dtype.ordered,
+                size=self.size,
+                offset=self.offset,
+                null_count=self.null_count,
+            )
+
+        return self
+
+    def to_pandas(
+        self,
+        index: Optional[pd.Index] = None,
+        nullable: bool = False,
+        **kwargs,
+    ) -> pd.Series:
+        if nullable and self.dtype in np_dtypes_to_pandas_dtypes:
+            pandas_nullable_dtype = np_dtypes_to_pandas_dtypes[self.dtype]
+            arrow_array = self.to_arrow()
+            pandas_array = pandas_nullable_dtype.__from_arrow__(arrow_array)
+            pd_series = pd.Series(pandas_array, copy=False)
+        elif str(self.dtype) in NUMERIC_TYPES and not self.has_nulls():
+            pd_series = pd.Series(self.values_host, copy=False)
+        else:
+            pd_series = self.to_arrow().to_pandas(**kwargs)
+
+        if index is not None:
+            pd_series.index = index
+        return pd_series
+
+    def _reduction_result_dtype(self, reduction_op: str) -> Dtype:
+        col_dtype = self.dtype
+        if reduction_op in {"sum", "product"}:
+            col_dtype = (
+                col_dtype if col_dtype.kind == "f" else np.dtype("int64")
+            )
+        elif reduction_op == "sum_of_squares":
+            col_dtype = np.find_common_type([col_dtype], [np.dtype("uint64")])
+
+        return col_dtype
+
+
+def _normalize_find_and_replace_input(
+    input_column_dtype: DtypeObj, col_to_normalize: Union[ColumnBase, list]
+) -> ColumnBase:
+    normalized_column = column.as_column(
+        col_to_normalize,
+        dtype=input_column_dtype if len(col_to_normalize) <= 0 else None,
+    )
+    col_to_normalize_dtype = normalized_column.dtype
+    if isinstance(col_to_normalize, list):
+        if normalized_column.null_count == len(normalized_column):
+            normalized_column = normalized_column.astype(input_column_dtype)
+        col_to_normalize_dtype = min_column_type(
+            normalized_column, input_column_dtype
+        )
+        # Scalar case
+        if len(col_to_normalize) == 1:
+            if cudf._lib.scalar._is_null_host_scalar(col_to_normalize[0]):
+                return normalized_column.astype(input_column_dtype)
+            if np.isinf(col_to_normalize[0]):
+                return normalized_column
+            col_to_normalize_casted = np.array(col_to_normalize[0]).astype(
+                input_column_dtype
+            )
+
+            if not np.isnan(col_to_normalize_casted) and (
+                col_to_normalize_casted != col_to_normalize[0]
+            ):
+                raise TypeError(
+                    f"Cannot safely cast non-equivalent "
+                    f"{col_to_normalize[0]} "
+                    f"to {input_column_dtype.name}"
+                )
+            else:
+                col_to_normalize_dtype = input_column_dtype
+    elif hasattr(col_to_normalize, "dtype"):
+        col_to_normalize_dtype = col_to_normalize.dtype
+    else:
+        raise TypeError(f"Type {type(col_to_normalize)} not supported")
+
+    if (
+        col_to_normalize_dtype.kind == "f"
+        and input_column_dtype.kind in {"i", "u"}
+    ) or (col_to_normalize_dtype.num > input_column_dtype.num):
+        raise TypeError(
+            f"Potentially unsafe cast for non-equivalent "
+            f"{col_to_normalize_dtype.name} "
+            f"to {input_column_dtype.name}"
+        )
+    return normalized_column.astype(input_column_dtype)
+
+
+def digitize(
+    column: ColumnBase, bins: np.ndarray, right: bool = False
+) -> ColumnBase:
+    """Return the indices of the bins to which each value in column belongs.
+
+    Parameters
+    ----------
+    column : Column
+        Input column.
+    bins : Column-like
+        1-D column-like object of bins with same type as `column`, should be
+        monotonically increasing.
+    right : bool
+        Indicates whether interval contains the right or left bin edge.
+
+    Returns
+    -------
+    A column containing the indices
+    """
+    if not column.dtype == bins.dtype:
+        raise ValueError(
+            "Digitize() expects bins and input column have the same dtype."
+        )
+
+    bin_col = as_column(bins, dtype=bins.dtype)
+    if bin_col.nullable:
+        raise ValueError("`bins` cannot contain null entries.")
+
+    return as_column(libcudf.sort.digitize([column], [bin_col], right))
diff --git a/python/cudf/cudf/core/column/numerical_base.py b/python/cudf/cudf/core/column/numerical_base.py
new file mode 100644
index 0000000..f3c5f99
--- /dev/null
+++ b/python/cudf/cudf/core/column/numerical_base.py
@@ -0,0 +1,230 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+"""Define an interface for columns that can perform numerical operations."""
+
+from __future__ import annotations
+
+from typing import Optional, cast
+
+import numpy as np
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._typing import ScalarLike
+from cudf.core.column import ColumnBase
+from cudf.core.missing import NA
+from cudf.core.mixins import Scannable
+
+
+class NumericalBaseColumn(ColumnBase, Scannable):
+    """A column composed of numerical data.
+
+    This class encodes a standard interface for different types of columns
+    containing numerical types of data. In particular, mathematical operations
+    that make sense whether a column is integral or real, fixed or floating
+    point, should be encoded here.
+    """
+
+    _VALID_REDUCTIONS = {
+        "sum",
+        "product",
+        "sum_of_squares",
+        "mean",
+        "var",
+        "std",
+    }
+
+    _VALID_SCANS = {
+        "cumsum",
+        "cumprod",
+        "cummin",
+        "cummax",
+    }
+
+    def _can_return_nan(self, skipna: Optional[bool] = None) -> bool:
+        return not skipna and self.has_nulls()
+
+    def kurtosis(self, skipna: Optional[bool] = None) -> float:
+        skipna = True if skipna is None else skipna
+
+        if len(self) == 0 or self._can_return_nan(skipna=skipna):
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        self = self.nans_to_nulls().dropna()  # type: ignore
+
+        if len(self) < 4:
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        n = len(self)
+        miu = self.mean()
+        m4_numerator = ((self - miu) ** self.normalize_binop_value(4)).sum()
+        V = self.var()
+
+        if V == 0:
+            return 0
+
+        term_one_section_one = (n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3))
+        term_one_section_two = m4_numerator / (V**2)
+        term_two = ((n - 1) ** 2) / ((n - 2) * (n - 3))
+        kurt = term_one_section_one * term_one_section_two - 3 * term_two
+        return kurt
+
+    def skew(self, skipna: Optional[bool] = None) -> ScalarLike:
+        skipna = True if skipna is None else skipna
+
+        if len(self) == 0 or self._can_return_nan(skipna=skipna):
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        self = self.nans_to_nulls().dropna()  # type: ignore
+
+        if len(self) < 3:
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        n = len(self)
+        miu = self.mean()
+        m3 = (((self - miu) ** self.normalize_binop_value(3)).sum()) / n
+        m2 = self.var(ddof=0)
+
+        if m2 == 0:
+            return 0
+
+        unbiased_coef = ((n * (n - 1)) ** 0.5) / (n - 2)
+        skew = unbiased_coef * m3 / (m2 ** (3 / 2))
+        return skew
+
+    def quantile(
+        self,
+        q: np.ndarray,
+        interpolation: str,
+        exact: bool,
+        return_scalar: bool,
+    ) -> NumericalBaseColumn:
+        if np.logical_or(q < 0, q > 1).any():
+            raise ValueError(
+                "percentiles should all be in the interval [0, 1]"
+            )
+        # Beyond this point, q either being scalar or list-like
+        # will only have values in range [0, 1]
+        if len(self) == 0:
+            result = cast(
+                NumericalBaseColumn,
+                cudf.core.column.column_empty(
+                    row_count=len(q), dtype=self.dtype, masked=True
+                ),
+            )
+        else:
+            result = self._numeric_quantile(q, interpolation, exact)
+        if return_scalar:
+            scalar_result = result.element_indexing(0)
+            if interpolation in {"lower", "higher", "nearest"}:
+                try:
+                    new_scalar = self.dtype.type(scalar_result)
+                    scalar_result = (
+                        new_scalar
+                        if new_scalar == scalar_result
+                        else scalar_result
+                    )
+                except (TypeError, ValueError):
+                    pass
+            return (
+                cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+                if scalar_result is NA
+                else scalar_result
+            )
+        return result
+
+    def mean(
+        self,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        dtype=np.float64,
+    ):
+        return self._reduce(
+            "mean", skipna=skipna, min_count=min_count, dtype=dtype
+        )
+
+    def var(
+        self,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        dtype=np.float64,
+        ddof=1,
+    ):
+        return self._reduce(
+            "var", skipna=skipna, min_count=min_count, dtype=dtype, ddof=ddof
+        )
+
+    def std(
+        self,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        dtype=np.float64,
+        ddof=1,
+    ):
+        return self._reduce(
+            "std", skipna=skipna, min_count=min_count, dtype=dtype, ddof=ddof
+        )
+
+    def median(self, skipna: Optional[bool] = None) -> NumericalBaseColumn:
+        skipna = True if skipna is None else skipna
+
+        if self._can_return_nan(skipna=skipna):
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        # enforce linear in case the default ever changes
+        return self.quantile(
+            np.array([0.5]),
+            interpolation="linear",
+            exact=True,
+            return_scalar=True,
+        )
+
+    def _numeric_quantile(
+        self, q: np.ndarray, interpolation: str, exact: bool
+    ) -> NumericalBaseColumn:
+        # get sorted indices and exclude nulls
+        sorted_indices = self.as_frame()._get_sorted_inds(
+            ascending=True, na_position="first"
+        )
+        sorted_indices = sorted_indices.slice(
+            self.null_count, len(sorted_indices)
+        )
+
+        return libcudf.quantiles.quantile(
+            self, q, interpolation, sorted_indices, exact
+        )
+
+    def cov(self, other: NumericalBaseColumn) -> float:
+        if (
+            len(self) == 0
+            or len(other) == 0
+            or (len(self) == 1 and len(other) == 1)
+        ):
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        result = (self - self.mean()) * (other - other.mean())
+        cov_sample = result.sum() / (len(self) - 1)
+        return cov_sample
+
+    def corr(self, other: NumericalBaseColumn) -> float:
+        if len(self) == 0 or len(other) == 0:
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        cov = self.cov(other)
+        lhs_std, rhs_std = self.std(), other.std()
+
+        if not cov or lhs_std == 0 or rhs_std == 0:
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+        return cov / lhs_std / rhs_std
+
+    def round(
+        self, decimals: int = 0, how: str = "half_even"
+    ) -> NumericalBaseColumn:
+        if not cudf.api.types.is_integer(decimals):
+            raise TypeError("Values in decimals must be integers")
+        """Round the values in the Column to the given number of decimals."""
+        return libcudf.round.round(self, decimal_places=decimals, how=how)
+
+    def _scan(self, op: str) -> ColumnBase:
+        return libcudf.reduce.scan(
+            op.replace("cum", ""), self, True
+        )._with_type_metadata(self.dtype)
diff --git a/python/cudf/cudf/core/column/string.py b/python/cudf/cudf/core/column/string.py
new file mode 100644
index 0000000..eb86f55
--- /dev/null
+++ b/python/cudf/cudf/core/column/string.py
@@ -0,0 +1,6009 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import re
+import warnings
+from functools import cached_property
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+    overload,
+)
+
+import cupy
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from numba import cuda
+
+import cudf
+import cudf.api.types
+from cudf import _lib as libcudf
+from cudf._lib import string_casting as str_cast, strings as libstrings
+from cudf._lib.column import Column
+from cudf._lib.types import size_type_dtype
+from cudf.api.types import (
+    is_integer,
+    is_list_dtype,
+    is_scalar,
+    is_string_dtype,
+)
+from cudf.core.buffer import Buffer
+from cudf.core.column import column, datetime
+from cudf.core.column.column import ColumnBase
+from cudf.core.column.methods import ColumnMethods
+from cudf.utils.docutils import copy_docstring
+from cudf.utils.dtypes import can_convert_to_column
+
+
+def str_to_boolean(column: StringColumn):
+    """Takes in string column and returns boolean column"""
+    return (
+        libstrings.count_characters(column) > cudf.Scalar(0, dtype="int8")
+    ).fillna(False)
+
+
+if TYPE_CHECKING:
+    from cudf._typing import (
+        ColumnBinaryOperand,
+        ColumnLike,
+        Dtype,
+        ScalarLike,
+        SeriesOrIndex,
+    )
+
+
+_str_to_numeric_typecast_functions = {
+    cudf.api.types.dtype("int8"): str_cast.stoi8,
+    cudf.api.types.dtype("int16"): str_cast.stoi16,
+    cudf.api.types.dtype("int32"): str_cast.stoi,
+    cudf.api.types.dtype("int64"): str_cast.stol,
+    cudf.api.types.dtype("uint8"): str_cast.stoui8,
+    cudf.api.types.dtype("uint16"): str_cast.stoui16,
+    cudf.api.types.dtype("uint32"): str_cast.stoui,
+    cudf.api.types.dtype("uint64"): str_cast.stoul,
+    cudf.api.types.dtype("float32"): str_cast.stof,
+    cudf.api.types.dtype("float64"): str_cast.stod,
+    cudf.api.types.dtype("bool"): str_to_boolean,
+}
+
+_numeric_to_str_typecast_functions = {
+    cudf.api.types.dtype("int8"): str_cast.i8tos,
+    cudf.api.types.dtype("int16"): str_cast.i16tos,
+    cudf.api.types.dtype("int32"): str_cast.itos,
+    cudf.api.types.dtype("int64"): str_cast.ltos,
+    cudf.api.types.dtype("uint8"): str_cast.ui8tos,
+    cudf.api.types.dtype("uint16"): str_cast.ui16tos,
+    cudf.api.types.dtype("uint32"): str_cast.uitos,
+    cudf.api.types.dtype("uint64"): str_cast.ultos,
+    cudf.api.types.dtype("float32"): str_cast.ftos,
+    cudf.api.types.dtype("float64"): str_cast.dtos,
+    cudf.api.types.dtype("bool"): str_cast.from_booleans,
+}
+
+_datetime_to_str_typecast_functions = {
+    # TODO: support Date32 UNIX days
+    # cudf.api.types.dtype("datetime64[D]"): str_cast.int2timestamp,
+    cudf.api.types.dtype("datetime64[s]"): str_cast.int2timestamp,
+    cudf.api.types.dtype("datetime64[ms]"): str_cast.int2timestamp,
+    cudf.api.types.dtype("datetime64[us]"): str_cast.int2timestamp,
+    cudf.api.types.dtype("datetime64[ns]"): str_cast.int2timestamp,
+}
+
+_timedelta_to_str_typecast_functions = {
+    cudf.api.types.dtype("timedelta64[s]"): str_cast.int2timedelta,
+    cudf.api.types.dtype("timedelta64[ms]"): str_cast.int2timedelta,
+    cudf.api.types.dtype("timedelta64[us]"): str_cast.int2timedelta,
+    cudf.api.types.dtype("timedelta64[ns]"): str_cast.int2timedelta,
+}
+
+
+def _is_supported_regex_flags(flags):
+    return flags == 0 or (
+        (flags & (re.MULTILINE | re.DOTALL) != 0)
+        and (flags & ~(re.MULTILINE | re.DOTALL) == 0)
+    )
+
+
+class StringMethods(ColumnMethods):
+    """
+    Vectorized string functions for Series and Index.
+
+    This mimics pandas ``df.str`` interface. nulls stay null
+    unless handled otherwise by a particular method.
+    Patterned after Python's string methods, with some
+    inspiration from R's stringr package.
+    """
+
+    _column: StringColumn
+
+    def __init__(self, parent):
+        value_type = (
+            parent.dtype.leaf_type
+            if is_list_dtype(parent.dtype)
+            else parent.dtype
+        )
+        if not is_string_dtype(value_type):
+            raise AttributeError(
+                "Can only use .str accessor with string values"
+            )
+        super().__init__(parent=parent)
+
+    def htoi(self) -> SeriesOrIndex:
+        """
+        Returns integer value represented by each hex string.
+        String is interpreted to have hex (base-16) characters.
+
+        Returns
+        -------
+        Series/Index of str dtype
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["1234", "ABCDEF", "1A2", "cafe"])
+        >>> s.str.htoi()
+        0        4660
+        1    11259375
+        2         418
+        3       51966
+        dtype: int64
+        """
+
+        out = str_cast.htoi(self._column)
+
+        return self._return_or_inplace(out, inplace=False)
+
+    hex_to_int = htoi
+
+    def ip2int(self) -> SeriesOrIndex:
+        """
+        This converts ip strings to integers
+
+        Returns
+        -------
+        Series/Index of str dtype
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["12.168.1.1", "10.0.0.1"])
+        >>> s.str.ip2int()
+        0    212336897
+        1    167772161
+        dtype: int64
+
+        Returns 0's if any string is not an IP.
+
+        >>> s = cudf.Series(["12.168.1.1", "10.0.0.1", "abc"])
+        >>> s.str.ip2int()
+        0    212336897
+        1    167772161
+        2            0
+        dtype: int64
+        """
+
+        out = str_cast.ip2int(self._column)
+
+        return self._return_or_inplace(out, inplace=False)
+
+    ip_to_int = ip2int
+
+    def __getitem__(self, key):
+        if isinstance(key, slice):
+            return self.slice(start=key.start, stop=key.stop, step=key.step)
+        else:
+            return self.get(key)
+
+    def len(self) -> SeriesOrIndex:
+        r"""
+        Computes the length of each element in the Series/Index.
+
+        Returns
+        -------
+        Series or Index of int
+            A Series or Index of integer values
+            indicating the length of each element in the Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["dog", "", "\n", None])
+        >>> s.str.len()
+        0       3
+        1       0
+        2       1
+        3    <NA>
+        dtype: int32
+        """
+
+        return self._return_or_inplace(
+            libstrings.count_characters(self._column)
+        )
+
+    def byte_count(self) -> SeriesOrIndex:
+        """
+        Computes the number of bytes of each string in the Series/Index.
+
+        Returns
+        -------
+        Series or Index of int
+            A Series or Index of integer values
+            indicating the number of bytes of each strings in the
+            Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["abc","d","ef"])
+        >>> s.str.byte_count()
+        0    3
+        1    1
+        2    2
+        dtype: int32
+        >>> s = cudf.Series(["Hello", "Bye", "Thanks 😊"])
+        >>> s.str.byte_count()
+        0     5
+        1     3
+        2    11
+        dtype: int32
+        """
+        return self._return_or_inplace(
+            libstrings.count_bytes(self._column),
+        )
+
+    @overload
+    def cat(
+        self, sep: Optional[str] = None, na_rep: Optional[str] = None
+    ) -> str:
+        ...
+
+    @overload
+    def cat(
+        self, others, sep: Optional[str] = None, na_rep: Optional[str] = None
+    ) -> Union[SeriesOrIndex, "cudf.core.column.string.StringColumn"]:
+        ...
+
+    def cat(self, others=None, sep=None, na_rep=None):
+        """
+        Concatenate strings in the Series/Index with given separator.
+
+        If ``others`` is specified, this function concatenates the Series/Index
+        and elements of others element-wise. If others is not passed, then all
+        values in the Series/Index are concatenated into a single string with
+        a given sep.
+
+        Parameters
+        ----------
+        others : Series or List of str
+            Strings to be appended.
+            The number of strings must match ``size()`` of this instance.
+            This must be either a Series of string dtype or a Python
+            list of strings.
+
+        sep : str
+            If specified, this separator will be appended to each string
+            before appending the others.
+
+        na_rep : str
+            This character will take the place of any null strings
+            (not empty strings) in either list.
+
+            -  If ``na_rep`` is ``None``, and ``others`` is ``None``,
+               missing values in the Series/Index are
+               omitted from the result.
+
+            -  If ``na_rep`` is ``None``, and ``others`` is
+               not ``None``, a row containing a missing value
+               in any of the columns (before concatenation)
+               will have a missing value in the result.
+
+        Returns
+        -------
+        concat : str or Series/Index of str dtype
+            If ``others`` is ``None``, ``str`` is returned,
+            otherwise a ``Series/Index`` (same type as caller)
+            of str dtype is returned.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['a', 'b', None, 'd'])
+        >>> s.str.cat(sep=' ')
+        'a b d'
+
+        By default, NA values in the Series are ignored. Using na_rep, they
+        can be given a representation:
+
+        >>> s.str.cat(sep=' ', na_rep='?')
+        'a b ? d'
+
+        If others is specified, corresponding values are concatenated with
+        the separator. Result will be a Series of strings.
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',')
+        0     a,A
+        1     b,B
+        2    <NA>
+        3     d,D
+        dtype: object
+
+        Missing values will remain missing in the result, but can again be
+        represented using na_rep
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')
+        0    a,A
+        1    b,B
+        2    -,C
+        3    d,D
+        dtype: object
+
+        If sep is not specified, the values are concatenated without
+        separation.
+
+        >>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')
+        0    aA
+        1    bB
+        2    -C
+        3    dD
+        dtype: object
+        """
+        if sep is None:
+            sep = ""
+
+        if others is None:
+            data = libstrings.join(
+                self._column,
+                cudf.Scalar(sep),
+                cudf.Scalar(na_rep, "str"),
+            )
+        else:
+            other_cols = _get_cols_list(self._parent, others)
+            all_cols = [self._column] + other_cols
+            data = libstrings.concatenate(
+                all_cols,
+                cudf.Scalar(sep),
+                cudf.Scalar(na_rep, "str"),
+            )
+
+        if len(data) == 1 and data.null_count == 1:
+            data = [""]
+        # We only want to keep the index if we are adding something to each
+        # row, not if we are joining all the rows into a single string.
+        out = self._return_or_inplace(data, retain_index=others is not None)
+        if len(out) == 1 and others is None:
+            if isinstance(out, cudf.Series):
+                out = out.iloc[0]
+            else:
+                out = out[0]
+        return out
+
+    def join(
+        self, sep=None, string_na_rep=None, sep_na_rep=None
+    ) -> SeriesOrIndex:
+        """
+        Join lists contained as elements in the Series/Index with passed
+        delimiter.
+
+        If the elements of a Series are lists themselves, join the content of
+        these lists using the delimiter passed to the function.
+        This function is an equivalent to :meth:`str.join`.
+        In the special case that the lists in the Series contain only ``None``,
+        a `<NA>`/`None` value will always be returned.
+
+        Parameters
+        ----------
+        sep : str or array-like
+            If str, the delimiter is used between list entries.
+            If array-like, the string at a position is used as a
+            delimiter for corresponding row of the list entries.
+        string_na_rep : str, default None
+            This character will take the place of null strings
+            (not empty strings) in the Series but will be considered
+            only if the Series contains list elements and those lists have
+            at least one non-null string. If ``string_na_rep`` is ``None``,
+            it defaults to empty space "".
+        sep_na_rep : str, default None
+            This character will take the place of any null strings
+            (not empty strings) in `sep`. This parameter can be used
+            only if `sep` is array-like. If ``sep_na_rep`` is ``None``,
+            it defaults to empty space "".
+
+        Returns
+        -------
+        Series/Index: object
+            The list entries concatenated by intervening occurrences of
+            the delimiter.
+
+        Raises
+        ------
+        ValueError
+            - If ``sep_na_rep`` is supplied when ``sep`` is str.
+            - If ``sep`` is array-like and not of equal length with Series/Index.
+        TypeError
+            - If ``string_na_rep`` or ``sep_na_rep`` are not scalar values.
+            - If ``sep`` is not of following types: str or array-like.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series([['a', 'b', 'c'], ['d', 'e'], ['f'], ['g', ' ', 'h']])
+        >>> ser
+        0    [a, b, c]
+        1       [d, e]
+        2          [f]
+        3    [g,  , h]
+        dtype: list
+        >>> ser.str.join(sep='-')
+        0    a-b-c
+        1      d-e
+        2        f
+        3    g- -h
+        dtype: object
+
+        ``sep`` can an array-like input:
+
+        >>> ser.str.join(sep=['-', '+', '.', '='])
+        0    a-b-c
+        1      d+e
+        2        f
+        3    g= =h
+        dtype: object
+
+        If the actual series doesn't have lists, each character is joined
+        by `sep`:
+
+        >>> ser = cudf.Series(['abc', 'def', 'ghi'])
+        >>> ser
+        0    abc
+        1    def
+        2    ghi
+        dtype: object
+        >>> ser.str.join(sep='_')
+        0    a_b_c
+        1    d_e_f
+        2    g_h_i
+        dtype: object
+
+        We can replace `<NA>`/`None` values present in lists using
+        ``string_na_rep`` if the lists contain at least one valid string
+        (lists containing all `None` will result in a `<NA>`/`None` value):
+
+        >>> ser = cudf.Series([['a', 'b', None], [None, None, None], None, ['c', 'd']])
+        >>> ser
+        0          [a, b, None]
+        1    [None, None, None]
+        2                  None
+        3                [c, d]
+        dtype: list
+        >>> ser.str.join(sep='_', string_na_rep='k')
+        0    a_b_k
+        1     <NA>
+        2     <NA>
+        3      c_d
+        dtype: object
+
+        We can replace `<NA>`/`None` values present in lists of ``sep``
+        using ``sep_na_rep``:
+
+        >>> ser.str.join(sep=[None, '^', '.', '-'], sep_na_rep='+')
+        0    a+b+
+        1    <NA>
+        2    <NA>
+        3     c-d
+        dtype: object
+        """  # noqa E501
+        if sep is None:
+            sep = ""
+
+        if string_na_rep is None:
+            string_na_rep = ""
+
+        if is_scalar(sep) and sep_na_rep:
+            raise ValueError(
+                "sep_na_rep cannot be defined when `sep` is scalar."
+            )
+
+        if sep_na_rep is None:
+            sep_na_rep = ""
+
+        if not is_scalar(string_na_rep):
+            raise TypeError(
+                f"string_na_rep should be a string scalar, got {string_na_rep}"
+                f" of type : {type(string_na_rep)}"
+            )
+
+        if isinstance(self._column, cudf.core.column.ListColumn):
+            strings_column = self._column
+        else:
+            # If self._column is not a ListColumn, we will have to
+            # split each row by character and create a ListColumn out of it.
+            strings_column = self._split_by_character()
+
+        if is_scalar(sep):
+            data = libstrings.join_lists_with_scalar(
+                strings_column, cudf.Scalar(sep), cudf.Scalar(string_na_rep)
+            )
+        elif can_convert_to_column(sep):
+            sep_column = column.as_column(sep)
+            if len(sep_column) != len(strings_column):
+                raise ValueError(
+                    f"sep should be of similar size to the series, "
+                    f"got: {len(sep_column)}, expected: {len(strings_column)}"
+                )
+            if not is_scalar(sep_na_rep):
+                raise TypeError(
+                    f"sep_na_rep should be a string scalar, got {sep_na_rep} "
+                    f"of type: {type(sep_na_rep)}"
+                )
+
+            data = libstrings.join_lists_with_column(
+                strings_column,
+                sep_column,
+                cudf.Scalar(string_na_rep),
+                cudf.Scalar(sep_na_rep),
+            )
+        else:
+            raise TypeError(
+                f"sep should be an str, array-like or Series object, "
+                f"found {type(sep)}"
+            )
+
+        return self._return_or_inplace(data)
+
+    def _split_by_character(self):
+        result_col = libstrings.character_tokenize(self._column)
+
+        offset_col = self._column.children[0]
+
+        return cudf.core.column.ListColumn(
+            size=len(self._column),
+            dtype=cudf.ListDtype(self._column.dtype),
+            mask=self._column.mask,
+            offset=0,
+            null_count=self._column.null_count,
+            children=(offset_col, result_col),
+        )
+
+    def extract(
+        self, pat: str, flags: int = 0, expand: bool = True
+    ) -> SeriesOrIndex:
+        r"""
+        Extract capture groups in the regex `pat` as columns in a DataFrame.
+
+        For each subject string in the Series, extract groups from the first
+        match of regular expression `pat`.
+
+        Parameters
+        ----------
+        pat : str
+            Regular expression pattern with capturing groups.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the regex engine (e.g. re.MULTILINE)
+        expand : bool, default True
+            If True, return DataFrame with one column per capture group.
+            If False, return a Series/Index if there is one capture group or
+            DataFrame if there are multiple capture groups.
+
+        Returns
+        -------
+        DataFrame or Series/Index
+            A DataFrame with one row for each subject string, and one column
+            for each group. If `expand=False` and `pat` has only one capture
+            group, then return a Series/Index.
+
+        Notes
+        -----
+        The `flags` parameter currently only supports re.DOTALL and
+        re.MULTILINE.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['a1', 'b2', 'c3'])
+        >>> s.str.extract(r'([ab])(\d)')
+              0     1
+        0     a     1
+        1     b     2
+        2  <NA>  <NA>
+
+        A pattern with one group will return a DataFrame with one
+        column if expand=True.
+
+        >>> s.str.extract(r'[ab](\d)', expand=True)
+              0
+        0     1
+        1     2
+        2  <NA>
+
+        A pattern with one group will return a Series if expand=False.
+
+        >>> s.str.extract(r'[ab](\d)', expand=False)
+        0       1
+        1       2
+        2    <NA>
+        dtype: object
+        """  # noqa W605
+        if not _is_supported_regex_flags(flags):
+            raise NotImplementedError(
+                "unsupported value for `flags` parameter"
+            )
+
+        data, _ = libstrings.extract(self._column, pat, flags)
+        if len(data) == 1 and expand is False:
+            data = next(iter(data.values()))
+        else:
+            data = data
+        return self._return_or_inplace(data, expand=expand)
+
+    def contains(
+        self,
+        pat: Union[str, Sequence],
+        case: bool = True,
+        flags: int = 0,
+        na=np.nan,
+        regex: bool = True,
+    ) -> SeriesOrIndex:
+        r"""
+        Test if pattern or regex is contained within a string of a Series or
+        Index.
+
+        Return boolean Series or Index based on whether a given pattern or
+        regex is contained within a string of a Series or Index.
+
+        Parameters
+        ----------
+        pat : str or list-like
+            Character sequence or regular expression.
+            If ``pat`` is list-like then regular expressions are not
+            accepted.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the regex engine (e.g. re.MULTILINE)
+        regex : bool, default True
+            If True, assumes the pattern is a regular expression.
+            If False, treats the pattern as a literal string.
+
+        Returns
+        -------
+        Series/Index of bool dtype
+            A Series/Index of boolean dtype indicating whether the given
+            pattern is contained within the string of each element of the
+            Series/Index.
+
+        Notes
+        -----
+        The parameters `case` and `na` are not yet supported and will
+        raise a NotImplementedError if anything other than the default
+        value is set.
+        The `flags` parameter currently only supports re.DOTALL and
+        re.MULTILINE.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s1 = cudf.Series(['Mouse', 'dog', 'house and parrot', '23', None])
+        >>> s1
+        0               Mouse
+        1                 dog
+        2    house and parrot
+        3                  23
+        4                <NA>
+        dtype: object
+        >>> s1.str.contains('og', regex=False)
+        0    False
+        1     True
+        2    False
+        3    False
+        4     <NA>
+        dtype: bool
+
+        Returning an Index of booleans using only a literal pattern.
+
+        >>> data = ['Mouse', 'dog', 'house and parrot', '23.0', np.NaN]
+        >>> idx = cudf.Index(data)
+        >>> idx
+        StringIndex(['Mouse' 'dog' 'house and parrot' '23.0' None], dtype='object')
+        >>> idx.str.contains('23', regex=False)
+        GenericIndex([False, False, False, True, <NA>], dtype='bool')
+
+        Returning 'house' or 'dog' when either expression occurs in a string.
+
+        >>> s1.str.contains('house|dog', regex=True)
+        0    False
+        1     True
+        2     True
+        3    False
+        4     <NA>
+        dtype: bool
+
+        Returning any digit using regular expression.
+
+        >>> s1.str.contains('\d', regex=True)
+        0    False
+        1    False
+        2    False
+        3     True
+        4     <NA>
+        dtype: bool
+
+        Ensure ``pat`` is a not a literal pattern when ``regex`` is set
+        to True. Note in the following example one might expect
+        only `s2[1]` and `s2[3]` to return True. However,
+        '.0' as a regex matches any character followed by a 0.
+
+        >>> s2 = cudf.Series(['40', '40.0', '41', '41.0', '35'])
+        >>> s2.str.contains('.0', regex=True)
+        0     True
+        1     True
+        2    False
+        3     True
+        4    False
+        dtype: bool
+
+        The ``pat`` may also be a sequence of strings in which case
+        the individual strings are searched in corresponding rows.
+
+        >>> s2 = cudf.Series(['house', 'dog', 'and', '', ''])
+        >>> s1.str.contains(s2)
+        0    False
+        1     True
+        2     True
+        3     True
+        4     <NA>
+        dtype: bool
+        """  # noqa W605
+        if na is not np.nan:
+            raise NotImplementedError("`na` parameter is not yet supported")
+        if regex and isinstance(pat, re.Pattern):
+            flags = pat.flags & ~re.U
+            pat = pat.pattern
+        if not _is_supported_regex_flags(flags):
+            raise NotImplementedError(
+                "unsupported value for `flags` parameter"
+            )
+        if regex and not case:
+            raise NotImplementedError(
+                "`case=False` only supported when `regex=False`"
+            )
+
+        if is_scalar(pat):
+            if regex:
+                result_col = libstrings.contains_re(self._column, pat, flags)
+            else:
+                if case is False:
+                    input_column = libstrings.to_lower(self._column)
+                    pat = cudf.Scalar(pat.lower(), dtype="str")  # type: ignore
+                else:
+                    input_column = self._column
+                    pat = cudf.Scalar(pat, dtype="str")  # type: ignore
+                result_col = libstrings.contains(input_column, pat)
+        else:
+            # TODO: we silently ignore the `regex=` flag here
+            if case is False:
+                input_column = libstrings.to_lower(self._column)
+                pat = libstrings.to_lower(column.as_column(pat, dtype="str"))
+            else:
+                input_column = self._column
+                pat = column.as_column(pat, dtype="str")
+            result_col = libstrings.contains_multiple(input_column, pat)
+        return self._return_or_inplace(result_col)
+
+    def like(self, pat: str, esc: Optional[str] = None) -> SeriesOrIndex:
+        """
+        Test if a like pattern matches a string of a Series or Index.
+
+        Return boolean Series or Index based on whether a given pattern
+        matches strings in a Series or Index.
+
+        Parameters
+        ----------
+        pat : str
+            Pattern for matching. Use '%' for any number of any character
+            including no characters. Use '_' for any single character.
+
+        esc : str
+            Character to use if escape is necessary to match '%' or '_'
+            literals.
+
+        Returns
+        -------
+        Series/Index of bool dtype
+            A Series/Index of boolean dtype indicating whether the given
+            pattern matches the string of each element of the Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['abc', 'a', 'b' ,'ddbc', '%bb'])
+        >>> s.str.like('%b_')
+        0   False
+        1   False
+        2   False
+        3   True
+        4   True
+        dtype: boolean
+
+        Parameter `esc` can be used to match a wildcard literal.
+
+        >>> s.str.like('/%b_', esc='/' )
+        0   False
+        1   False
+        2   False
+        3   False
+        4   True
+        dtype: boolean
+        """
+        if not isinstance(pat, str):
+            raise TypeError(
+                f"expected a string object, not {type(pat).__name__}"
+            )
+
+        if esc is None:
+            esc = ""
+
+        if not isinstance(esc, str):
+            raise TypeError(
+                f"expected a string object, not {type(esc).__name__}"
+            )
+
+        if len(esc) > 1:
+            raise ValueError(
+                "expected esc to contain less than or equal to 1 characters"
+            )
+
+        result_col = libstrings.like(
+            self._column, cudf.Scalar(pat, "str"), cudf.Scalar(esc, "str")
+        )
+
+        return self._return_or_inplace(result_col)
+
+    def repeat(
+        self,
+        repeats: Union[int, Sequence],
+    ) -> SeriesOrIndex:
+        """
+        Duplicate each string in the Series or Index.
+        Equivalent to `str.repeat()
+        <https://pandas.pydata.org/docs/reference/api/pandas.Series.str.repeat.html>`_.
+
+        Parameters
+        ----------
+        repeats : int or sequence of int
+            Same value for all (int) or different value per (sequence).
+
+        Returns
+        -------
+        Series or Index of object
+            Series or Index of repeated string objects specified by
+            input parameter repeats.
+
+        Examples
+        --------
+        >>> s = cudf.Series(['a', 'b', 'c'])
+        >>> s
+        0    a
+        1    b
+        2    c
+        dtype: object
+
+        Single int repeats string in Series
+
+        >>> s.str.repeat(repeats=2)
+        0    aa
+        1    bb
+        2    cc
+        dtype: object
+
+        Sequence of int repeats corresponding string in Series
+
+        >>> s.str.repeat(repeats=[1, 2, 3])
+        0      a
+        1     bb
+        2    ccc
+        dtype: object
+        """
+        if can_convert_to_column(repeats):
+            return self._return_or_inplace(
+                libstrings.repeat_sequence(
+                    self._column,
+                    column.as_column(repeats, dtype="int"),
+                ),
+            )
+
+        return self._return_or_inplace(
+            libstrings.repeat_scalar(self._column, repeats)
+        )
+
+    def replace(
+        self,
+        pat: Union[str, Sequence],
+        repl: Union[str, Sequence],
+        n: int = -1,
+        case=None,
+        flags: int = 0,
+        regex: bool = True,
+    ) -> SeriesOrIndex:
+        """
+        Replace occurrences of pattern/regex in the Series/Index with some
+        other string. Equivalent to `str.replace()
+        <https://docs.python.org/3/library/stdtypes.html#str.replace>`_
+        or `re.sub()
+        <https://docs.python.org/3/library/re.html#re.sub>`_.
+
+        Parameters
+        ----------
+        pat : str or list-like
+            String(s) to be replaced as a character sequence or regular
+            expression.
+        repl : str or list-like
+            String(s) to be used as replacement.
+        n : int, default -1 (all)
+            Number of replacements to make from the start.
+        regex : bool, default True
+            If True, assumes the pattern is a regular expression.
+            If False, treats the pattern as a literal string.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            A copy of the object with all matching occurrences of pat replaced
+            by repl.
+
+        Notes
+        -----
+        The parameters `case` and `flags` are not yet supported and will raise
+        a `NotImplementedError` if anything other than the default value
+        is set.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['foo', 'fuz', None])
+        >>> s
+        0     foo
+        1     fuz
+        2    <NA>
+        dtype: object
+
+        When pat is a string and regex is True (the default), the given pat
+        is compiled as a regex. When repl is a string, it replaces matching
+        regex patterns as with ``re.sub()``. NaN value(s) in the Series
+        are left as is:
+
+        >>> s.str.replace('f.', 'ba', regex=True)
+        0     bao
+        1     baz
+        2    <NA>
+        dtype: object
+
+        When pat is a string and `regex` is False, every pat is replaced
+        with repl as with ``str.replace()``:
+
+        >>> s.str.replace('f.', 'ba', regex=False)
+        0     foo
+        1     fuz
+        2    <NA>
+        dtype: object
+        """
+        if case is not None:
+            raise NotImplementedError("`case` parameter is not yet supported")
+        if flags != 0:
+            raise NotImplementedError("`flags` parameter is not yet supported")
+
+        if can_convert_to_column(pat) and can_convert_to_column(repl):
+            if n != -1:
+                warnings.warn(
+                    "`n` parameter is not supported when "
+                    "`pat` and `repl` are list-like inputs"
+                )
+
+            return self._return_or_inplace(
+                libstrings.replace_multi_re(
+                    self._column,
+                    pat,
+                    column.as_column(repl, dtype="str"),
+                )
+                if regex
+                else libstrings.replace_multi(
+                    self._column,
+                    column.as_column(pat, dtype="str"),
+                    column.as_column(repl, dtype="str"),
+                ),
+            )
+        # Pandas treats 0 as all
+        if n == 0:
+            n = -1
+
+        # If 'pat' is re.Pattern then get the pattern string from it
+        if regex and isinstance(pat, re.Pattern):
+            pat = pat.pattern
+
+        # Pandas forces non-regex replace when pat is a single-character
+        return self._return_or_inplace(
+            libstrings.replace_re(
+                self._column, pat, cudf.Scalar(repl, "str"), n
+            )
+            if regex is True and len(pat) > 1
+            else libstrings.replace(
+                self._column,
+                cudf.Scalar(pat, "str"),
+                cudf.Scalar(repl, "str"),
+                n,
+            ),
+        )
+
+    def replace_with_backrefs(self, pat: str, repl: str) -> SeriesOrIndex:
+        r"""
+        Use the ``repl`` back-ref template to create a new string
+        with the extracted elements found using the ``pat`` expression.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            Regex with groupings to identify extract sections.
+            This should not be a compiled regex.
+        repl : str
+            String template containing back-reference indicators.
+
+        Returns
+        -------
+        Series/Index of str dtype
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["A543","Z756"])
+        >>> s.str.replace_with_backrefs('(\\d)(\\d)', 'V\\2\\1')
+        0    AV453
+        1    ZV576
+        dtype: object
+        """
+
+        # If 'pat' is re.Pattern then get the pattern string from it
+        if isinstance(pat, re.Pattern):
+            pat = pat.pattern
+
+        return self._return_or_inplace(
+            libstrings.replace_with_backrefs(self._column, pat, repl)
+        )
+
+    def slice(
+        self,
+        start: Optional[int] = None,
+        stop: Optional[int] = None,
+        step: Optional[int] = None,
+    ) -> SeriesOrIndex:
+        """
+        Slice substrings from each element in the Series or Index.
+
+        Parameters
+        ----------
+        start : int, optional
+            Start position for slice operation.
+        stop : int, optional
+            Stop position for slice operation.
+        step : int, optional
+            Step size for slice operation.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Series or Index from sliced substring from
+            original string object.
+
+        See Also
+        --------
+        slice_replace
+            Replace a slice with a string.
+
+        get
+            Return element at position. Equivalent
+            to ``Series.str.slice(start=i, stop=i+1)``
+            with ``i`` being the position.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["koala", "fox", "chameleon"])
+        >>> s
+        0        koala
+        1          fox
+        2    chameleon
+        dtype: object
+        >>> s.str.slice(start=1)
+        0        oala
+        1          ox
+        2    hameleon
+        dtype: object
+        >>> s.str.slice(start=-1)
+        0    a
+        1    x
+        2    n
+        dtype: object
+        >>> s.str.slice(stop=2)
+        0    ko
+        1    fo
+        2    ch
+        dtype: object
+        >>> s.str.slice(step=2)
+        0      kaa
+        1       fx
+        2    caeen
+        dtype: object
+        >>> s.str.slice(start=0, stop=5, step=3)
+        0    kl
+        1     f
+        2    cm
+        dtype: object
+        """
+
+        return self._return_or_inplace(
+            libstrings.slice_strings(self._column, start, stop, step),
+        )
+
+    def isinteger(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string form integer.
+
+        If a string has zero characters, False is returned for
+        that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["1", "0.1", "+100", "-15", "abc"])
+        >>> s.str.isinteger()
+        0     True
+        1    False
+        2     True
+        3     True
+        4    False
+        dtype: bool
+        >>> s = cudf.Series(["this is plan text", "", "10 10"])
+        >>> s.str.isinteger()
+        0    False
+        1    False
+        2    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_integer(self._column))
+
+    def ishex(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string form a hex integer.
+
+        If a string has zero characters, False is returned for
+        that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["", "123DEF", "0x2D3", "-15", "abc"])
+        >>> s.str.ishex()
+        0    False
+        1     True
+        2     True
+        3    False
+        4     True
+        dtype: bool
+        """
+        return self._return_or_inplace(str_cast.is_hex(self._column))
+
+    def istimestamp(self, format: str) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string can be converted to
+        a timestamp using the given format.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["20201101", "192011", "18200111", "2120-11-01"])
+        >>> s.str.istimestamp("%Y%m%d")
+        0     True
+        1    False
+        2     True
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(
+            str_cast.istimestamp(self._column, format)
+        )
+
+    def isfloat(self) -> SeriesOrIndex:
+        r"""
+        Check whether all characters in each string form floating value.
+
+        If a string has zero characters, False is returned for
+        that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["1.1", "0.123213", "+0.123", "-100.0001", "234",
+        ... "3-"])
+        >>> s.str.isfloat()
+        0     True
+        1     True
+        2     True
+        3     True
+        4     True
+        5    False
+        dtype: bool
+        >>> s = cudf.Series(["this is plain text", "\t\n", "9.9", "9.9.9"])
+        >>> s.str.isfloat()
+        0    False
+        1    False
+        2     True
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_float(self._column))
+
+    def isdecimal(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are decimal.
+
+        This is equivalent to running the Python string method
+        `str.isdecimal()
+        <https://docs.python.org/3/library/stdtypes.html#str.isdecimal>`_
+        for each element of the Series/Index.
+        If a string has zero characters, False is returned for
+        that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s3 = cudf.Series(['23', '³', '⅕', ''])
+
+        The s3.str.isdecimal method checks for characters used to form
+        numbers in base 10.
+
+        >>> s3.str.isdecimal()
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_decimal(self._column))
+
+    def isalnum(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are alphanumeric.
+
+        This is equivalent to running the Python string method
+        `str.isalnum()
+        <https://docs.python.org/3/library/stdtypes.html#str.isalnum>`_
+        for each element of the Series/Index. If a string has zero
+        characters, False is returned for that check.
+
+        Equivalent to: ``isalpha() or isdigit() or isnumeric() or isdecimal()``
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the
+            same length as the original Series/Index.
+
+        See Also
+        --------
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s1 = cudf.Series(['one', 'one1', '1', ''])
+        >>> s1.str.isalnum()
+        0     True
+        1     True
+        2     True
+        3    False
+        dtype: bool
+
+        Note that checks against characters mixed with
+        any additional punctuation or whitespace will
+        evaluate to false for an alphanumeric check.
+
+        >>> s2 = cudf.Series(['A B', '1.5', '3,000'])
+        >>> s2.str.isalnum()
+        0    False
+        1    False
+        2    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_alnum(self._column))
+
+    def isalpha(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are alphabetic.
+
+        This is equivalent to running the Python string method
+        `str.isalpha()
+        <https://docs.python.org/3/library/stdtypes.html#str.isalpha>`_
+        for each element of the Series/Index.
+        If a string has zero characters, False is returned for that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same length
+            as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s1 = cudf.Series(['one', 'one1', '1', ''])
+        >>> s1.str.isalpha()
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_alpha(self._column))
+
+    def isdigit(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are digits.
+
+        This is equivalent to running the Python string method
+        `str.isdigit()
+        <https://docs.python.org/3/library/stdtypes.html#str.isdigit>`_
+        for each element of the Series/Index.
+        If a string has zero characters, False is returned
+        for that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['23', '³', '⅕', ''])
+
+        The ``s.str.isdigit`` method is the same as ``s.str.isdecimal`` but
+        also includes special digits, like superscripted and
+        subscripted digits in unicode.
+
+        >>> s.str.isdigit()
+        0     True
+        1     True
+        2    False
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_digit(self._column))
+
+    def isnumeric(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are numeric.
+
+        This is equivalent to running the Python string method
+        `str.isnumeric()
+        <https://docs.python.org/3/library/stdtypes.html#str.isnumeric>`_
+        for each element of the Series/Index. If a
+        string has zero characters, False is returned for that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s1 = cudf.Series(['one', 'one1', '1', ''])
+        >>> s1.str.isnumeric()
+        0    False
+        1    False
+        2     True
+        3    False
+        dtype: bool
+
+        The ``s1.str.isnumeric`` method is the same as ``s2.str.isdigit`` but
+        also includes other characters that can represent
+        quantities such as unicode fractions.
+
+        >>> s2 = pd.Series(['23', '³', '⅕', ''], dtype='str')
+        >>> s2.str.isnumeric()
+        0     True
+        1     True
+        2     True
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_numeric(self._column))
+
+    def isupper(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are uppercase.
+
+        This is equivalent to running the Python string method
+        `str.isupper()
+        <https://docs.python.org/3/library/stdtypes.html#str.isupper>`_
+        for each element of the Series/Index.
+        If a string has zero characters, False is returned
+        for that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['leopard', 'Golden Eagle', 'SNAKE', ''])
+        >>> s.str.isupper()
+        0    False
+        1    False
+        2     True
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_upper(self._column))
+
+    def islower(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string are lowercase.
+
+        This is equivalent to running the Python string method
+        `str.islower()
+        <https://docs.python.org/3/library/stdtypes.html#str.islower>`_
+        for each element of the Series/Index.
+        If a string has zero characters, False is returned
+        for that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        isspace
+            Check whether all characters are whitespace.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['leopard', 'Golden Eagle', 'SNAKE', ''])
+        >>> s.str.islower()
+        0     True
+        1    False
+        2    False
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_lower(self._column))
+
+    def isipv4(self) -> SeriesOrIndex:
+        """
+        Check whether all characters in each string form an IPv4 address.
+
+        If a string has zero characters, False is returned for
+        that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same
+            length as the original Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["", "127.0.0.1", "255.255.255.255", "123.456"])
+        >>> s.str.isipv4()
+        0    False
+        1     True
+        2     True
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(str_cast.is_ipv4(self._column))
+
+    def lower(self) -> SeriesOrIndex:
+        """
+        Converts all characters to lowercase.
+
+        Equivalent to `str.lower()
+        <https://docs.python.org/3/library/stdtypes.html#str.lower>`_.
+
+        Returns
+        -------
+        Series or Index of object
+            A copy of the object with all strings converted to lowercase.
+
+        See Also
+        --------
+        upper
+            Converts all characters to uppercase.
+
+        title
+            Converts first character of each word to uppercase and remaining
+            to lowercase.
+
+        capitalize
+            Converts first character to uppercase and remaining to lowercase.
+
+        swapcase
+            Converts uppercase to lowercase and lowercase to uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']
+        >>> s = cudf.Series(data)
+        >>> s.str.lower()
+        0                 lower
+        1              capitals
+        2    this is a sentence
+        3              swapcase
+        dtype: object
+        """
+        return self._return_or_inplace(libstrings.to_lower(self._column))
+
+    def upper(self) -> SeriesOrIndex:
+        """
+        Convert each string to uppercase.
+        This only applies to ASCII characters at this time.
+
+        Equivalent to `str.upper()
+        <https://docs.python.org/3/library/stdtypes.html#str.upper>`_.
+
+        Returns
+        -------
+        Series or Index of object
+
+        See Also
+        --------
+        lower
+            Converts all characters to lowercase.
+
+        upper
+            Converts all characters to uppercase.
+
+        title
+            Converts first character of each word to uppercase and
+            remaining to lowercase.
+
+        capitalize
+            Converts first character to uppercase and remaining to
+            lowercase.
+
+        swapcase
+            Converts uppercase to lowercase and lowercase to uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']
+        >>> s = cudf.Series(data)
+        >>> s
+        0                 lower
+        1              CAPITALS
+        2    this is a sentence
+        3              SwApCaSe
+        dtype: object
+        >>> s.str.upper()
+        0                 LOWER
+        1              CAPITALS
+        2    THIS IS A SENTENCE
+        3              SWAPCASE
+        dtype: object
+        """
+        return self._return_or_inplace(libstrings.to_upper(self._column))
+
+    def capitalize(self) -> SeriesOrIndex:
+        """
+        Convert strings in the Series/Index to be capitalized.
+        This only applies to ASCII characters at this time.
+
+        Returns
+        -------
+        Series or Index of object
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']
+        >>> s = cudf.Series(data)
+        >>> s.str.capitalize()
+        0                 Lower
+        1              Capitals
+        2    This is a sentence
+        3              Swapcase
+        dtype: object
+        >>> s = cudf.Series(["hello, friend","goodbye, friend"])
+        >>> s.str.capitalize()
+        0      Hello, friend
+        1    Goodbye, friend
+        dtype: object
+        """
+        return self._return_or_inplace(libstrings.capitalize(self._column))
+
+    def swapcase(self) -> SeriesOrIndex:
+        """
+        Change each lowercase character to uppercase and vice versa.
+        This only applies to ASCII characters at this time.
+
+        Equivalent to `str.swapcase()
+        <https://docs.python.org/3/library/stdtypes.html#str.swapcase>`_.
+
+        Returns
+        -------
+        Series or Index of object
+
+        See Also
+        --------
+        lower
+            Converts all characters to lowercase.
+
+        upper
+            Converts all characters to uppercase.
+
+        title
+            Converts first character of each word to uppercase and remaining
+            to lowercase.
+
+        capitalize
+            Converts first character to uppercase and remaining to lowercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']
+        >>> s = cudf.Series(data)
+        >>> s
+        0                 lower
+        1              CAPITALS
+        2    this is a sentence
+        3              SwApCaSe
+        dtype: object
+        >>> s.str.swapcase()
+        0                 LOWER
+        1              capitals
+        2    THIS IS A SENTENCE
+        3              sWaPcAsE
+        dtype: object
+        """
+        return self._return_or_inplace(libstrings.swapcase(self._column))
+
+    def title(self) -> SeriesOrIndex:
+        """
+        Uppercase the first letter of each letter after a space
+        and lowercase the rest.
+        This only applies to ASCII characters at this time.
+
+        Equivalent to `str.title()
+        <https://docs.python.org/3/library/stdtypes.html#str.title>`_.
+
+        Returns
+        -------
+        Series or Index of object
+
+        See Also
+        --------
+        lower
+            Converts all characters to lowercase.
+
+        upper
+            Converts all characters to uppercase.
+
+        capitalize
+            Converts first character to uppercase and remaining to lowercase.
+
+        swapcase
+            Converts uppercase to lowercase and lowercase to uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe'])
+        >>> s = cudf.Series(data)
+        >>> s
+        0                 lower
+        1              CAPITALS
+        2    this is a sentence
+        3              SwApCaSe
+        dtype: object
+        >>> s.str.title()
+        0                 Lower
+        1              Capitals
+        2    This Is A Sentence
+        3              Swapcase
+        dtype: object
+        """
+        return self._return_or_inplace(libstrings.title(self._column))
+
+    def istitle(self) -> SeriesOrIndex:
+        """
+        Check whether each string is title formatted.
+        The first letter of each word should be uppercase and the rest
+        should be lowercase.
+
+        Equivalent to :meth:`str.istitle`.
+
+        Returns
+        -------
+        Series or Index of object
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['leopard', 'Golden Eagle', 'SNAKE', ''])
+        >>> s = cudf.Series(data)
+        >>> s.str.istitle()
+        0    False
+        1     True
+        2    False
+        3    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_title(self._column))
+
+    def filter_alphanum(
+        self, repl: Optional[str] = None, keep: bool = True
+    ) -> SeriesOrIndex:
+        """
+        Remove non-alphanumeric characters from strings in this column.
+
+        Parameters
+        ----------
+        repl : str
+            Optional string to use in place of removed characters.
+        keep : bool
+            Set to False to remove all alphanumeric characters instead
+            of keeping them.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Strings with only alphanumeric characters.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["pears £12", "plums $34", "Temp 72℉", "100K℧"])
+        >>> s.str.filter_alphanum(" ")
+        0    pears  12
+        1    plums  34
+        2     Temp 72
+        3        100K
+        dtype: object
+        """
+        if repl is None:
+            repl = ""
+
+        return self._return_or_inplace(
+            libstrings.filter_alphanum(
+                self._column, cudf.Scalar(repl, "str"), keep
+            ),
+        )
+
+    def slice_from(
+        self, starts: "cudf.Series", stops: "cudf.Series"
+    ) -> SeriesOrIndex:
+        """
+        Return substring of each string using positions for each string.
+
+        The starts and stops parameters are of Column type.
+
+        Parameters
+        ----------
+        starts : Series
+            Beginning position of each the string to extract.
+            Default is beginning of the each string.
+        stops : Series
+            Ending position of the each string to extract.
+            Default is end of each string.
+            Use -1 to specify to the end of that string.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            A substring of each string using positions for each string.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["hello","there"])
+        >>> s
+        0    hello
+        1    there
+        dtype: object
+        >>> starts = cudf.Series([1, 3])
+        >>> stops = cudf.Series([5, 5])
+        >>> s.str.slice_from(starts, stops)
+        0    ello
+        1      re
+        dtype: object
+        """
+
+        return self._return_or_inplace(
+            libstrings.slice_from(
+                self._column,
+                column.as_column(starts),
+                column.as_column(stops),
+            ),
+        )
+
+    def slice_replace(
+        self,
+        start: Optional[int] = None,
+        stop: Optional[int] = None,
+        repl: Optional[str] = None,
+    ) -> SeriesOrIndex:
+        """
+        Replace the specified section of each string with a new string.
+
+        Parameters
+        ----------
+        start : int, optional
+            Beginning position of the string to replace.
+            Default is beginning of the each string.
+        stop : int, optional
+            Ending position of the string to replace.
+            Default is end of each string.
+        repl : str, optional
+            String to insert into the specified position values.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            A new string with the specified section of the string
+            replaced with `repl` string.
+
+        See Also
+        --------
+        slice
+            Just slicing without replacement.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['a', 'ab', 'abc', 'abdc', 'abcde'])
+        >>> s
+        0        a
+        1       ab
+        2      abc
+        3     abdc
+        4    abcde
+        dtype: object
+
+        Specify just `start`, meaning replace `start` until the `end` of
+        the string with `repl`.
+
+        >>> s.str.slice_replace(1, repl='X')
+        0    aX
+        1    aX
+        2    aX
+        3    aX
+        4    aX
+        dtype: object
+
+        Specify just `stop`, meaning the `start` of the string to `stop`
+        is replaced with `repl`, and the rest of the string is included.
+
+        >>> s.str.slice_replace(stop=2, repl='X')
+        0       X
+        1       X
+        2      Xc
+        3     Xdc
+        4    Xcde
+        dtype: object
+
+        Specify `start` and `stop`, meaning the slice from `start`
+        to `stop` is replaced with `repl`. Everything before or
+        after `start` and `stop` is included as is.
+
+        >>> s.str.slice_replace(start=1, stop=3, repl='X')
+        0      aX
+        1      aX
+        2      aX
+        3     aXc
+        4    aXde
+        dtype: object
+        """
+        if start is None:
+            start = 0
+
+        if stop is None:
+            stop = -1
+
+        if repl is None:
+            repl = ""
+
+        return self._return_or_inplace(
+            libstrings.slice_replace(
+                self._column, start, stop, cudf.Scalar(repl, "str")
+            ),
+        )
+
+    def insert(
+        self, start: int = 0, repl: Optional[str] = None
+    ) -> SeriesOrIndex:
+        """
+        Insert the specified string into each string in the specified
+        position.
+
+        Parameters
+        ----------
+        start : int
+            Beginning position of the string to replace.
+            Default is beginning of the each string.
+            Specify -1 to insert at the end of each string.
+        repl : str
+            String to insert into the specified position value.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            A new string series with the specified string
+            inserted at the specified position.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["abcdefghij", "0123456789"])
+        >>> s.str.insert(2, '_')
+        0    ab_cdefghij
+        1    01_23456789
+        dtype: object
+
+        When no `repl` is passed, nothing is inserted.
+
+        >>> s.str.insert(2)
+        0    abcdefghij
+        1    0123456789
+        dtype: object
+
+        Negative values are also supported for `start`.
+
+        >>> s.str.insert(-1,'_')
+        0    abcdefghij_
+        1    0123456789_
+        dtype: object
+        """
+        if repl is None:
+            repl = ""
+
+        return self._return_or_inplace(
+            libstrings.insert(self._column, start, cudf.Scalar(repl, "str")),
+        )
+
+    def get(self, i: int = 0) -> SeriesOrIndex:
+        """
+        Extract element from each component at specified position.
+
+        Parameters
+        ----------
+        i : int
+            Position of element to extract.
+
+        Returns
+        -------
+        Series/Index of str dtype
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["hello world", "rapids", "cudf"])
+        >>> s
+        0    hello world
+        1         rapids
+        2           cudf
+        dtype: object
+        >>> s.str.get(10)
+        0    d
+        1
+        2
+        dtype: object
+        >>> s.str.get(1)
+        0    e
+        1    a
+        2    u
+        dtype: object
+
+        ``get`` also accepts negative index number.
+
+        >>> s.str.get(-1)
+        0    d
+        1    s
+        2    f
+        dtype: object
+        """
+
+        return self._return_or_inplace(libstrings.get(self._column, i))
+
+    def get_json_object(
+        self,
+        json_path,
+        *,
+        allow_single_quotes=False,
+        strip_quotes_from_single_strings=True,
+        missing_fields_as_nulls=False,
+    ):
+        r"""
+        Applies a JSONPath string to an input strings column
+        where each row in the column is a valid json string
+
+        Parameters
+        ----------
+        json_path : str
+            The JSONPath string to be applied to each row
+            of the input column
+        allow_single_quotes : bool, default False
+            If True, representing strings with single
+            quotes is allowed.
+            If False, strings must only be represented
+            with double quotes.
+        strip_quotes_from_single_strings : bool, default True
+            If True, strip the quotes from the return value of
+            a given row if it is a string.
+            If False, values returned for a given row include
+            quotes if they are strings.
+        missing_fields_as_nulls : bool, default False
+            If True, when an object is queried for a field
+            it does not contain, "null" is returned.
+            If False, when an object is queried for a field
+            it does not contain, None is returned.
+
+        Returns
+        -------
+        Column: New strings column containing the retrieved json object strings
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(
+            [
+                \"\"\"
+                {
+                    "store":{
+                        "book":[
+                            {
+                                "category":"reference",
+                                "author":"Nigel Rees",
+                                "title":"Sayings of the Century",
+                                "price":8.95
+                            },
+                            {
+                                "category":"fiction",
+                                "author":"Evelyn Waugh",
+                                "title":"Sword of Honour",
+                                "price":12.99
+                            }
+                        ]
+                    }
+                }
+                \"\"\"
+            ])
+        >>> s
+            0    {"store": {\n        "book": [\n        { "cat...
+            dtype: object
+        >>> s.str.get_json_object("$.store.book")
+            0    [\n        { "category": "reference",\n       ...
+            dtype: object
+        """
+
+        options = libstrings.GetJsonObjectOptions(
+            allow_single_quotes=allow_single_quotes,
+            strip_quotes_from_single_strings=(
+                strip_quotes_from_single_strings
+            ),
+            missing_fields_as_nulls=missing_fields_as_nulls,
+        )
+        return self._return_or_inplace(
+            libstrings.get_json_object(
+                self._column, cudf.Scalar(json_path, "str"), options
+            )
+        )
+
+    def split(
+        self,
+        pat: Optional[str] = None,
+        n: int = -1,
+        expand: bool = False,
+        regex: Optional[bool] = None,
+    ) -> SeriesOrIndex:
+        """
+        Split strings around given separator/delimiter.
+
+        Splits the string in the Series/Index from the beginning, at the
+        specified delimiter string. Similar to `str.split()
+        <https://docs.python.org/3/library/stdtypes.html#str.split>`_.
+
+        Parameters
+        ----------
+        pat : str, default None
+            String or regular expression to split on. If not specified, split
+            on whitespace.
+        n : int, default -1 (all)
+            Limit number of splits in output. `None`, 0, and -1 will all be
+            interpreted as "all splits".
+        expand : bool, default False
+            Expand the split strings into separate columns.
+
+            * If ``True``, return DataFrame/MultiIndex expanding
+              dimensionality.
+            * If ``False``, return Series/Index, containing lists
+              of strings.
+        regex : bool, default None
+            Determines if the passed-in pattern is a regular expression:
+
+            * If ``True``, assumes the passed-in pattern is a regular
+              expression
+            * If ``False``, treats the pattern as a literal string.
+            * If pat length is 1, treats pat as a literal string.
+
+        Returns
+        -------
+        Series, Index, DataFrame or MultiIndex
+            Type matches caller unless ``expand=True`` (see Notes).
+
+        See Also
+        --------
+        rsplit
+            Splits string around given separator/delimiter, starting from
+            the right.
+
+        str.split
+            Standard library version for split.
+
+        str.rsplit
+            Standard library version for rsplit.
+
+        Notes
+        -----
+        The handling of the n keyword depends on the number
+        of found splits:
+
+            - If found splits > n, make first n splits only
+            - If found splits <= n, make all splits
+            - If for a certain row the number of found
+              splits < n, append None for padding up to n
+              if ``expand=True``.
+
+        If using ``expand=True``, Series and Index callers return
+        DataFrame and MultiIndex objects, respectively.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ["this is a regular sentence",
+        ...     "https://docs.python.org/index.html", None]
+        >>> s = cudf.Series(data)
+        >>> s
+        0            this is a regular sentence
+        1    https://docs.python.org/index.html
+        2                                  <NA>
+        dtype: object
+
+        In the default setting, the string is split by whitespace.
+
+        >>> s.str.split()
+        0        [this, is, a, regular, sentence]
+        1    [https://docs.python.org/index.html]
+        2                                    None
+        dtype: list
+
+        Without the ``n`` parameter, the outputs of ``rsplit``
+        and ``split`` are identical.
+
+        >>> s.str.rsplit()
+        0        [this, is, a, regular, sentence]
+        1    [https://docs.python.org/index.html]
+        2                                    None
+        dtype: list
+
+        The `n` parameter can be used to limit the number of
+        splits on the delimiter.
+
+        >>> s.str.split(n=2)
+        0          [this, is, a regular sentence]
+        1    [https://docs.python.org/index.html]
+        2                                    None
+        dtype: list
+
+        The `pat` parameter can be used to split by other characters.
+
+        >>> s.str.split(pat="/")
+        0               [this is a regular sentence]
+        1    [https:, , docs.python.org, index.html]
+        2                                       None
+        dtype: list
+
+        When using ``expand=True``, the split elements will expand out
+        into separate columns. If ``<NA>`` value is present, it is propagated
+        throughout the columns during the split.
+
+        >>> s.str.split(expand=True)
+                                            0     1     2        3         4
+        0                                this    is     a  regular  sentence
+        1  https://docs.python.org/index.html  <NA>  <NA>     <NA>      <NA>
+        2                                <NA>  <NA>  <NA>     <NA>      <NA>
+        """
+
+        if expand not in (True, False):
+            raise ValueError(
+                f"expand parameter accepts only : [True, False], "
+                f"got {expand}"
+            )
+
+        # Pandas treats 0 as all
+        if n is None or n == 0:
+            n = -1
+
+        if pat is None:
+            pat = ""
+
+        if regex and isinstance(pat, re.Pattern):
+            pat = pat.pattern
+
+        if len(str(pat)) <= 1:
+            regex = False
+
+        if expand:
+            if self._column.null_count == len(self._column):
+                result_table = {0: self._column.copy()}
+            else:
+                if regex is True:
+                    data, _ = libstrings.split_re(self._column, pat, n)
+                else:
+                    data, _ = libstrings.split(
+                        self._column, cudf.Scalar(pat, "str"), n
+                    )
+                if len(data) == 1 and data[0].null_count == len(self._column):
+                    result_table = {}
+                else:
+                    result_table = data
+        else:
+            if regex is True:
+                result_table = libstrings.split_record_re(self._column, pat, n)
+            else:
+                result_table = libstrings.split_record(
+                    self._column, cudf.Scalar(pat, "str"), n
+                )
+
+        return self._return_or_inplace(result_table, expand=expand)
+
+    def rsplit(
+        self,
+        pat: Optional[str] = None,
+        n: int = -1,
+        expand: bool = False,
+        regex: Optional[bool] = None,
+    ) -> SeriesOrIndex:
+        """
+        Split strings around given separator/delimiter.
+
+        Splits the string in the Series/Index from the end, at the
+        specified delimiter string. Similar to `str.rsplit()
+        <https://docs.python.org/3/library/stdtypes.html#str.rsplit>`_.
+
+        Parameters
+        ----------
+        pat : str, default ' ' (space)
+            String to split on, does not yet support regular expressions.
+        n : int, default -1 (all)
+            Limit number of splits in output. `None`, 0, and -1 will all be
+            interpreted as "all splits".
+        expand : bool, default False
+            Expand the split strings into separate columns.
+
+            * If ``True``, return DataFrame/MultiIndex expanding
+              dimensionality.
+            * If ``False``, return Series/Index, containing lists
+              of strings.
+        regex : bool, default None
+            Determines if the passed-in pattern is a regular expression:
+
+            * If ``True``, assumes the passed-in pattern is a regular
+              expression
+            * If ``False``, treats the pattern as a literal string.
+            * If pat length is 1, treats pat as a literal string.
+
+        Returns
+        -------
+        Series, Index, DataFrame or MultiIndex
+            Type matches caller unless ``expand=True`` (see Notes).
+
+        See Also
+        --------
+        split
+            Split strings around given separator/delimiter.
+
+        str.split
+            Standard library version for split.
+
+        str.rsplit
+            Standard library version for rsplit.
+
+        Notes
+        -----
+        The handling of the n keyword depends on the number of
+        found splits:
+
+        - If found splits > n, make first n splits only
+        - If found splits <= n, make all splits
+        - If for a certain row the number of found splits < n,
+          append None for padding up to n if ``expand=True``.
+
+        If using ``expand=True``, Series and Index callers return
+        DataFrame and MultiIndex objects, respectively.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(
+        ...     [
+        ...         "this is a regular sentence",
+        ...         "https://docs.python.org/3/tutorial/index.html",
+        ...         None
+        ...     ]
+        ... )
+        >>> s
+        0                       this is a regular sentence
+        1    https://docs.python.org/3/tutorial/index.html
+        2                                             <NA>
+        dtype: object
+
+        In the default setting, the string is split by whitespace.
+
+        >>> s.str.rsplit()
+        0                   [this, is, a, regular, sentence]
+        1    [https://docs.python.org/3/tutorial/index.html]
+        2                                               None
+        dtype: list
+
+        Without the ``n`` parameter, the outputs of ``rsplit``
+        and ``split`` are identical.
+
+        >>> s.str.split()
+        0                   [this, is, a, regular, sentence]
+        1    [https://docs.python.org/3/tutorial/index.html]
+        2                                               None
+        dtype: list
+
+        The n parameter can be used to limit the number of
+        splits on the delimiter. The outputs of split and rsplit are different.
+
+        >>> s.str.rsplit(n=2)
+        0                     [this is a, regular, sentence]
+        1    [https://docs.python.org/3/tutorial/index.html]
+        2                                               None
+        dtype: list
+        >>> s.str.split(n=2)
+        0                     [this, is, a regular sentence]
+        1    [https://docs.python.org/3/tutorial/index.html]
+        2                                               None
+        dtype: list
+
+        When using ``expand=True``, the split elements will expand
+        out into separate columns. If ``<NA>`` value is present,
+        it is propagated throughout the columns during the split.
+
+        >>> s.str.rsplit(n=2, expand=True)
+                                                       0        1         2
+        0                                      this is a  regular  sentence
+        1  https://docs.python.org/3/tutorial/index.html     <NA>      <NA>
+        2                                           <NA>     <NA>      <NA>
+
+        For slightly more complex use cases like splitting the
+        html document name from a url, a combination of parameter
+        settings can be used.
+
+        >>> s.str.rsplit("/", n=1, expand=True)
+                                            0           1
+        0          this is a regular sentence        <NA>
+        1  https://docs.python.org/3/tutorial  index.html
+        2                                <NA>        <NA>
+        """
+
+        if expand not in (True, False):
+            raise ValueError(
+                f"expand parameter accepts only : [True, False], "
+                f"got {expand}"
+            )
+
+        # Pandas treats 0 as all
+        if n == 0:
+            n = -1
+
+        if pat is None:
+            pat = ""
+
+        if regex and isinstance(pat, re.Pattern):
+            pat = pat.pattern
+
+        if expand:
+            if self._column.null_count == len(self._column):
+                result_table = {0: self._column.copy()}
+            else:
+                if regex is True:
+                    data, _ = libstrings.rsplit_re(self._column, pat, n)
+                else:
+                    data, _ = libstrings.rsplit(
+                        self._column, cudf.Scalar(pat, "str"), n
+                    )
+                if len(data) == 1 and data[0].null_count == len(self._column):
+                    result_table = {}
+                else:
+                    result_table = data
+        else:
+            if regex is True:
+                result_table = libstrings.rsplit_record_re(
+                    self._column, pat, n
+                )
+            else:
+                result_table = libstrings.rsplit_record(
+                    self._column, cudf.Scalar(pat, "str"), n
+                )
+
+        return self._return_or_inplace(result_table, expand=expand)
+
+    def partition(self, sep: str = " ", expand: bool = True) -> SeriesOrIndex:
+        """
+        Split the string at the first occurrence of sep.
+
+        This method splits the string at the first occurrence
+        of sep, and returns 3 elements containing the part
+        before the separator, the separator itself, and the
+        part after the separator. If the separator is not found,
+        return 3 elements containing the string itself, followed
+        by two empty strings.
+
+        Parameters
+        ----------
+        sep : str, default ' ' (whitespace)
+            String to split on.
+
+        Returns
+        -------
+        DataFrame or MultiIndex
+            Returns a DataFrame / MultiIndex
+
+        Notes
+        -----
+        The parameter `expand` is not yet supported and will raise a
+        `NotImplementedError` if anything other than the default value is set.
+
+        See Also
+        --------
+        rpartition
+            Split the string at the last occurrence of sep.
+
+        split
+            Split strings around given separators.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['Linda van der Berg', 'George Pitt-Rivers'])
+        >>> s
+        0    Linda van der Berg
+        1    George Pitt-Rivers
+        dtype: object
+
+        >>> s.str.partition()
+                0  1             2
+        0   Linda     van der Berg
+        1  George      Pitt-Rivers
+
+        To partition by something different than a space:
+
+        >>> s.str.partition('-')
+                            0  1       2
+        0  Linda van der Berg
+        1         George Pitt  -  Rivers
+
+        Also available on indices:
+
+        >>> idx = cudf.Index(['X 123', 'Y 999'])
+        >>> idx
+        StringIndex(['X 123' 'Y 999'], dtype='object')
+
+        Which will create a MultiIndex:
+
+        >>> idx.str.partition()
+        MultiIndex([('X', ' ', '123'),
+                    ('Y', ' ', '999')],
+                   )
+        """
+        if expand is not True:
+            raise NotImplementedError(
+                "`expand=False` is currently not supported"
+            )
+
+        if sep is None:
+            sep = " "
+
+        return self._return_or_inplace(
+            libstrings.partition(self._column, cudf.Scalar(sep, "str"))[0],
+            expand=expand,
+        )
+
+    def rpartition(self, sep: str = " ", expand: bool = True) -> SeriesOrIndex:
+        """
+        Split the string at the last occurrence of sep.
+
+        This method splits the string at the last occurrence
+        of sep, and returns 3 elements containing the part
+        before the separator, the separator itself, and the
+        part after the separator. If the separator is not
+        found, return 3 elements containing two empty strings,
+        followed by the string itself.
+
+        Parameters
+        ----------
+        sep : str, default ' ' (whitespace)
+            String to split on.
+
+        Returns
+        -------
+        DataFrame or MultiIndex
+            Returns a DataFrame / MultiIndex
+
+        Notes
+        -----
+        The parameter `expand` is not yet supported and will raise a
+        `NotImplementedError` if anything other than the default value is set.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['Linda van der Berg', 'George Pitt-Rivers'])
+        >>> s
+        0    Linda van der Berg
+        1    George Pitt-Rivers
+        dtype: object
+        >>> s.str.rpartition()
+                    0  1            2
+        0  Linda van der            Berg
+        1         George     Pitt-Rivers
+
+        Also available on indices:
+
+        >>> idx = cudf.Index(['X 123', 'Y 999'])
+        >>> idx
+        StringIndex(['X 123' 'Y 999'], dtype='object')
+
+        Which will create a MultiIndex:
+
+        >>> idx.str.rpartition()
+        MultiIndex([('X', ' ', '123'),
+                    ('Y', ' ', '999')],
+                   )
+        """
+        if expand is not True:
+            raise NotImplementedError(
+                "`expand=False` is currently not supported"
+            )
+
+        if sep is None:
+            sep = " "
+
+        return self._return_or_inplace(
+            libstrings.rpartition(self._column, cudf.Scalar(sep, "str"))[0],
+            expand=expand,
+        )
+
+    def pad(
+        self, width: int, side: str = "left", fillchar: str = " "
+    ) -> SeriesOrIndex:
+        """
+        Pad strings in the Series/Index up to width.
+
+        Parameters
+        ----------
+        width : int
+            Minimum width of resulting string;
+            additional characters will be filled with
+            character defined in fillchar.
+
+        side : {'left', 'right', 'both'}, default 'left'
+            Side from which to fill resulting string.
+
+        fillchar : str,  default ' ' (whitespace)
+            Additional character for filling, default is whitespace.
+
+        Returns
+        -------
+        Series/Index of object
+            Returns Series or Index with minimum number
+            of char in object.
+
+        See Also
+        --------
+        rjust
+            Fills the left side of strings with an arbitrary character.
+            Equivalent to ``Series.str.pad(side='left')``.
+
+        ljust
+            Fills the right side of strings with an arbitrary character.
+            Equivalent to ``Series.str.pad(side='right')``.
+
+        center
+            Fills both sides of strings with an arbitrary character.
+            Equivalent to ``Series.str.pad(side='both')``.
+
+        zfill
+            Pad strings in the Series/Index by prepending '0' character.
+            Equivalent to ``Series.str.pad(side='left', fillchar='0')``.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["caribou", "tiger"])
+
+        >>> s.str.pad(width=10)
+        0       caribou
+        1         tiger
+        dtype: object
+
+        >>> s.str.pad(width=10, side='right', fillchar='-')
+        0    caribou---
+        1    tiger-----
+        dtype: object
+
+        >>> s.str.pad(width=10, side='both', fillchar='-')
+        0    -caribou--
+        1    --tiger---
+        dtype: object
+        """
+        if not isinstance(fillchar, str):
+            msg = (
+                f"fillchar must be a character, not {type(fillchar).__name__}"
+            )
+            raise TypeError(msg)
+
+        if len(fillchar) != 1:
+            raise TypeError("fillchar must be a character, not str")
+
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        try:
+            side = libstrings.SideType[side.upper()]
+        except KeyError:
+            raise ValueError(
+                "side has to be either one of {'left', 'right', 'both'}"
+            )
+
+        return self._return_or_inplace(
+            libstrings.pad(self._column, width, fillchar, side)
+        )
+
+    def zfill(self, width: int) -> SeriesOrIndex:
+        """
+        Pad strings in the Series/Index by prepending '0' characters.
+
+        Strings in the Series/Index are padded with '0' characters
+        on the left of the string to reach a total string length
+        width. Strings in the Series/Index with length greater
+        or equal to width are unchanged.
+
+        The sign character is preserved if it appears in the first
+        position of the string.
+
+        Parameters
+        ----------
+        width : int
+            Minimum length of resulting string;
+            strings with length less than width
+            be prepended with '0' characters.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Returns Series or Index with prepended '0' characters.
+
+        See Also
+        --------
+        rjust
+            Fills the left side of strings with an arbitrary character.
+
+        ljust
+            Fills the right side of strings with an arbitrary character.
+
+        pad
+            Fills the specified sides of strings with an arbitrary character.
+
+        center
+            Fills both sides of strings with an arbitrary character.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['-1', '1', '1000',  None])
+        >>> s
+        0      -1
+        1       1
+        2    1000
+        3    <NA>
+        dtype: object
+
+        Note that ``None`` is not string, therefore it is converted
+        to ``None``. ``1000`` remains unchanged as
+        it is longer than width.
+
+        >>> s.str.zfill(3)
+        0     -01
+        1     001
+        2    1000
+        3    <NA>
+        dtype: object
+        """
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        return self._return_or_inplace(libstrings.zfill(self._column, width))
+
+    def center(self, width: int, fillchar: str = " ") -> SeriesOrIndex:
+        """
+        Filling left and right side of strings in the Series/Index with an
+        additional character.
+
+        Parameters
+        ----------
+        width : int
+            Minimum width of resulting string;
+            additional characters will be filled
+            with fillchar.
+
+        fillchar : str, default is ' ' (whitespace)
+            Additional character for filling.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Returns Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['a', 'b', None, 'd'])
+        >>> s.str.center(1)
+        0       a
+        1       b
+        2    <NA>
+        3       d
+        dtype: object
+        >>> s.str.center(1, fillchar='-')
+        0       a
+        1       b
+        2    <NA>
+        3       d
+        dtype: object
+        >>> s.str.center(2, fillchar='-')
+        0      a-
+        1      b-
+        2    <NA>
+        3      d-
+        dtype: object
+        >>> s.str.center(5, fillchar='-')
+        0    --a--
+        1    --b--
+        2     <NA>
+        3    --d--
+        dtype: object
+        >>> s.str.center(6, fillchar='-')
+        0    --a---
+        1    --b---
+        2      <NA>
+        3    --d---
+        dtype: object
+        """
+        if not isinstance(fillchar, str):
+            msg = (
+                f"fillchar must be a character, not {type(fillchar).__name__}"
+            )
+            raise TypeError(msg)
+
+        if len(fillchar) != 1:
+            raise TypeError("fillchar must be a character, not str")
+
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        return self._return_or_inplace(
+            libstrings.center(self._column, width, fillchar)
+        )
+
+    def ljust(self, width: int, fillchar: str = " ") -> SeriesOrIndex:
+        """
+        Filling right side of strings in the Series/Index with an additional
+        character. Equivalent to `str.ljust()
+        <https://docs.python.org/3/library/stdtypes.html#str.ljust>`_.
+
+        Parameters
+        ----------
+        width : int
+            Minimum width of resulting string;
+            additional characters will be filled
+            with ``fillchar``.
+
+        fillchar : str, default ' ' (whitespace)
+            Additional character for filling, default is whitespace.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Returns Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["hello world", "rapids ai"])
+        >>> s.str.ljust(10, fillchar="_")
+        0    hello world
+        1     rapids ai_
+        dtype: object
+        >>> s = cudf.Series(["a", "",  "ab", "__"])
+        >>> s.str.ljust(1, fillchar="-")
+        0     a
+        1     -
+        2    ab
+        3    __
+        dtype: object
+        """
+        if not isinstance(fillchar, str):
+            msg = (
+                f"fillchar must be a character, not {type(fillchar).__name__}"
+            )
+            raise TypeError(msg)
+
+        if len(fillchar) != 1:
+            raise TypeError("fillchar must be a character, not str")
+
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        return self._return_or_inplace(
+            libstrings.ljust(self._column, width, fillchar)
+        )
+
+    def rjust(self, width: int, fillchar: str = " ") -> SeriesOrIndex:
+        """
+        Filling left side of strings in the Series/Index with an additional
+        character. Equivalent to `str.rjust()
+        <https://docs.python.org/3/library/stdtypes.html#str.rjust>`_.
+
+        Parameters
+        ----------
+        width : int
+            Minimum width of resulting string;
+            additional characters will be filled
+            with fillchar.
+
+        fillchar : str, default ' ' (whitespace)
+            Additional character for filling, default is whitespace.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Returns Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["hello world", "rapids ai"])
+        >>> s.str.rjust(20, fillchar="_")
+        0    _________hello world
+        1    ___________rapids ai
+        dtype: object
+        >>> s = cudf.Series(["a", "",  "ab", "__"])
+        >>> s.str.rjust(1, fillchar="-")
+        0     a
+        1     -
+        2    ab
+        3    __
+        dtype: object
+        """
+        if not isinstance(fillchar, str):
+            msg = (
+                f"fillchar must be a character, not {type(fillchar).__name__}"
+            )
+            raise TypeError(msg)
+
+        if len(fillchar) != 1:
+            raise TypeError("fillchar must be a character, not str")
+
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        return self._return_or_inplace(
+            libstrings.rjust(self._column, width, fillchar)
+        )
+
+    def strip(self, to_strip: Optional[str] = None) -> SeriesOrIndex:
+        r"""
+        Remove leading and trailing characters.
+
+        Strip whitespaces (including newlines) or a set of
+        specified characters from each string in the Series/Index
+        from left and right sides. Equivalent to `str.strip()
+        <https://docs.python.org/3/library/stdtypes.html#str.strip>`_.
+
+        Parameters
+        ----------
+        to_strip : str or None, default None
+            Specifying the set of characters to be removed.
+            All combinations of this set of characters
+            will be stripped. If None then whitespaces are removed.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Returns Series or Index.
+
+        See Also
+        --------
+        lstrip
+            Remove leading characters in Series/Index.
+
+        rstrip
+            Remove trailing characters in Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['1. Ant.  ', '2. Bee!\n', '3. Cat?\t', None])
+        >>> s
+        0    1. Ant.
+        1    2. Bee!\n
+        2    3. Cat?\t
+        3         <NA>
+        dtype: object
+        >>> s.str.strip()
+        0    1. Ant.
+        1    2. Bee!
+        2    3. Cat?
+        3       <NA>
+        dtype: object
+        >>> s.str.strip('123.!? \n\t')
+        0     Ant
+        1     Bee
+        2     Cat
+        3    <NA>
+        dtype: object
+        """
+        if to_strip is None:
+            to_strip = ""
+
+        return self._return_or_inplace(
+            libstrings.strip(self._column, cudf.Scalar(to_strip, "str"))
+        )
+
+    def lstrip(self, to_strip: Optional[str] = None) -> SeriesOrIndex:
+        r"""
+        Remove leading and trailing characters.
+
+        Strip whitespaces (including newlines)
+        or a set of specified characters from
+        each string in the Series/Index from left side.
+        Equivalent to `str.lstrip()
+        <https://docs.python.org/3/library/stdtypes.html#str.lstrip>`_.
+
+        Parameters
+        ----------
+        to_strip : str or None, default None
+            Specifying the set of characters to be removed.
+            All combinations of this set of characters will
+            be stripped. If None then whitespaces are removed.
+
+        Returns
+        -------
+            Series or Index of object
+
+        See Also
+        --------
+        strip
+            Remove leading and trailing characters in Series/Index.
+
+        rstrip
+            Remove trailing characters in Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['1. Ant.  ', '2. Bee!\n', '3. Cat?\t', None])
+        >>> s.str.lstrip('123.')
+        0     Ant.
+        1     Bee!\n
+        2     Cat?\t
+        3       <NA>
+        dtype: object
+        """
+        if to_strip is None:
+            to_strip = ""
+
+        return self._return_or_inplace(
+            libstrings.lstrip(self._column, cudf.Scalar(to_strip, "str"))
+        )
+
+    def rstrip(self, to_strip: Optional[str] = None) -> SeriesOrIndex:
+        r"""
+        Remove leading and trailing characters.
+
+        Strip whitespaces (including newlines)
+        or a set of specified characters from each
+        string in the Series/Index from right side.
+        Equivalent to `str.rstrip()
+        <https://docs.python.org/3/library/stdtypes.html#str.rstrip>`_.
+
+        Parameters
+        ----------
+        to_strip : str or None, default None
+            Specifying the set of characters to
+            be removed. All combinations of this
+            set of characters will be stripped.
+            If None then whitespaces are removed.
+
+        Returns
+        -------
+        Series/Index of str dtype
+            Returns Series or Index.
+
+        See Also
+        --------
+        strip
+            Remove leading and trailing characters in Series/Index.
+
+        lstrip
+            Remove leading characters in Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['1. Ant.  ', '2. Bee!\n', '3. Cat?\t', None])
+        >>> s
+        0    1. Ant.
+        1    2. Bee!\n
+        2    3. Cat?\t
+        3         <NA>
+        dtype: object
+        >>> s.str.rstrip('.!? \n\t')
+        0    1. Ant
+        1    2. Bee
+        2    3. Cat
+        3      <NA>
+        dtype: object
+        """
+        if to_strip is None:
+            to_strip = ""
+
+        return self._return_or_inplace(
+            libstrings.rstrip(self._column, cudf.Scalar(to_strip, "str"))
+        )
+
+    def wrap(self, width: int, **kwargs) -> SeriesOrIndex:
+        r"""
+        Wrap long strings in the Series/Index to be formatted in
+        paragraphs with length less than a given width.
+
+        Parameters
+        ----------
+        width : int
+            Maximum line width.
+
+        Returns
+        -------
+        Series or Index
+
+        Notes
+        -----
+        The parameters `expand_tabsbool`, `replace_whitespace`,
+        `drop_whitespace`, `break_long_words`, `break_on_hyphens`,
+        `expand_tabsbool` are not yet supported and will raise a
+        NotImplementedError if they are set to any value.
+
+        This method currently achieves behavior matching R's
+        stringr library ``str_wrap`` function, the equivalent
+        pandas implementation can be obtained using the
+        following parameter setting:
+
+            expand_tabs = False
+
+            replace_whitespace = True
+
+            drop_whitespace = True
+
+            break_long_words = False
+
+            break_on_hyphens = False
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['line to be wrapped', 'another line to be wrapped']
+        >>> s = cudf.Series(data)
+        >>> s.str.wrap(12)
+        0             line to be\nwrapped
+        1    another line\nto be\nwrapped
+        dtype: object
+        """
+        if not is_integer(width):
+            msg = f"width must be of integer type, not {type(width).__name__}"
+            raise TypeError(msg)
+
+        expand_tabs = kwargs.get("expand_tabs", None)
+        if expand_tabs is True:
+            raise NotImplementedError("`expand_tabs=True` is not supported")
+        elif expand_tabs is None:
+            warnings.warn(
+                "wrap current implementation defaults to `expand_tabs`=False"
+            )
+
+        replace_whitespace = kwargs.get("replace_whitespace", True)
+        if not replace_whitespace:
+            raise NotImplementedError(
+                "`replace_whitespace=False` is not supported"
+            )
+
+        drop_whitespace = kwargs.get("drop_whitespace", True)
+        if not drop_whitespace:
+            raise NotImplementedError(
+                "`drop_whitespace=False` is not supported"
+            )
+
+        break_long_words = kwargs.get("break_long_words", None)
+        if break_long_words is True:
+            raise NotImplementedError(
+                "`break_long_words=True` is not supported"
+            )
+        elif break_long_words is None:
+            warnings.warn(
+                "wrap current implementation defaults to "
+                "`break_long_words`=False"
+            )
+
+        break_on_hyphens = kwargs.get("break_on_hyphens", None)
+        if break_long_words is True:
+            raise NotImplementedError(
+                "`break_on_hyphens=True` is not supported"
+            )
+        elif break_on_hyphens is None:
+            warnings.warn(
+                "wrap current implementation defaults to "
+                "`break_on_hyphens`=False"
+            )
+
+        return self._return_or_inplace(libstrings.wrap(self._column, width))
+
+    def count(self, pat: str, flags: int = 0) -> SeriesOrIndex:
+        r"""
+        Count occurrences of pattern in each string of the Series/Index.
+
+        This function is used to count the number of times a particular
+        regex pattern is repeated in each of the string elements of the Series.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            Valid regular expression.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the regex engine (e.g. re.MULTILINE)
+
+        Returns
+        -------
+        Series or Index
+
+        Notes
+        -----
+            -  `flags` parameter currently only supports re.DOTALL
+               and re.MULTILINE.
+            -  Some characters need to be escaped when passing
+               in pat. e.g. ``'$'`` has a special meaning in regex
+               and must be escaped when finding this literal character.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['A', 'B', 'Aaba', 'Baca', None, 'CABA', 'cat'])
+        >>> s.str.count('a')
+        0       0
+        1       0
+        2       2
+        3       2
+        4    <NA>
+        5       0
+        6       1
+        dtype: int32
+
+        Escape ``'$'`` to find the literal dollar sign.
+
+        >>> s = cudf.Series(['$', 'B', 'Aab$', '$$ca', 'C$B$', 'cat'])
+        >>> s.str.count('\$')
+        0    1
+        1    0
+        2    1
+        3    2
+        4    2
+        5    0
+        dtype: int32
+
+        This is also available on Index.
+
+        >>> index = cudf.Index(['A', 'A', 'Aaba', 'cat'])
+        >>> index.str.count('a')
+        Int64Index([0, 0, 2, 1], dtype='int64')
+        """  # noqa W605
+        if isinstance(pat, re.Pattern):
+            flags = pat.flags & ~re.U
+            pat = pat.pattern
+        if not _is_supported_regex_flags(flags):
+            raise NotImplementedError(
+                "unsupported value for `flags` parameter"
+            )
+
+        return self._return_or_inplace(
+            libstrings.count_re(self._column, pat, flags)
+        )
+
+    def findall(self, pat: str, flags: int = 0) -> SeriesOrIndex:
+        """
+        Find all occurrences of pattern or regular expression in the
+        Series/Index.
+
+        Parameters
+        ----------
+        pat : str
+            Pattern or regular expression.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the regex engine (e.g. re.MULTILINE)
+
+        Returns
+        -------
+        DataFrame
+            All non-overlapping matches of pattern or
+            regular expression in each string of this Series/Index.
+
+        Notes
+        -----
+        The `flags` parameter currently only supports re.DOTALL and
+        re.MULTILINE.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['Lion', 'Monkey', 'Rabbit'])
+
+        The search for the pattern 'Monkey' returns one match:
+
+        >>> s.str.findall('Monkey')
+        0          []
+        1    [Monkey]
+        2          []
+        dtype: list
+
+        When the pattern matches more than one string
+        in the Series, all matches are returned:
+
+        >>> s.str.findall('on')
+        0    [on]
+        1    [on]
+        2      []
+        dtype: list
+
+        Regular expressions are supported too. For instance,
+        the search for all the strings ending with
+        the word 'on' is shown next:
+
+        >>> s.str.findall('on$')
+        0    [on]
+        1      []
+        2      []
+        dtype: list
+
+        If the pattern is found more than once in the same
+        string, then multiple strings are returned:
+
+        >>> s.str.findall('b')
+        0        []
+        1        []
+        2    [b, b]
+        dtype: list
+        """
+        if isinstance(pat, re.Pattern):
+            flags = pat.flags & ~re.U
+            pat = pat.pattern
+        if not _is_supported_regex_flags(flags):
+            raise NotImplementedError(
+                "unsupported value for `flags` parameter"
+            )
+
+        data = libstrings.findall(self._column, pat, flags)
+        return self._return_or_inplace(data)
+
+    def find_multiple(self, patterns: SeriesOrIndex) -> "cudf.Series":
+        """
+        Find all first occurrences of patterns in the Series/Index.
+
+        Parameters
+        ----------
+        patterns : array-like, Sequence or Series
+            Patterns to search for in the given Series/Index.
+
+        Returns
+        -------
+        Series
+            A Series with a list of indices of each pattern's first occurrence.
+            If a pattern is not found, -1 is returned for that index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["strings", "to", "search", "in"])
+        >>> s
+        0    strings
+        1         to
+        2     search
+        3         in
+        dtype: object
+        >>> t = cudf.Series(["a", "string", "g", "inn", "o", "r", "sea"])
+        >>> t
+        0         a
+        1    string
+        2         g
+        3       inn
+        4         o
+        5         r
+        6       sea
+        dtype: object
+        >>> s.str.find_multiple(t)
+        0       [-1, 0, 5, -1, -1, 2, -1]
+        1     [-1, -1, -1, -1, 1, -1, -1]
+        2       [2, -1, -1, -1, -1, 3, 0]
+        3    [-1, -1, -1, -1, -1, -1, -1]
+        dtype: list
+        """
+        if can_convert_to_column(patterns):
+            patterns_column = column.as_column(patterns)
+        else:
+            raise TypeError(
+                "patterns should be an array-like or a Series object, "
+                f"found {type(patterns)}"
+            )
+
+        if not isinstance(patterns_column, StringColumn):
+            raise TypeError(
+                "patterns can only be of 'string' dtype, "
+                f"got: {patterns_column.dtype}"
+            )
+
+        return cudf.Series(
+            libstrings.find_multiple(self._column, patterns_column),
+            index=self._parent.index
+            if isinstance(self._parent, cudf.Series)
+            else self._parent,
+            name=self._parent.name,
+        )
+
+    def isempty(self) -> SeriesOrIndex:
+        """
+        Check whether each string is an empty string.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same length as
+            the original Series/Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["1", "abc", "", " ", None])
+        >>> s.str.isempty()
+        0    False
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+        """
+        return self._return_or_inplace(
+            # mypy can't deduce that the return value of
+            # StringColumn.__eq__ is ColumnBase because the binops are
+            # dynamically added by a mixin class
+            cast(ColumnBase, self._column == "").fillna(False)
+        )
+
+    def isspace(self) -> SeriesOrIndex:
+        r"""
+        Check whether all characters in each string are whitespace.
+
+        This is equivalent to running the Python string method
+        `str.isspace()
+        <https://docs.python.org/3/library/stdtypes.html#str.isspace>`_
+        for each element of the Series/Index.
+        If a string has zero characters, False is returned
+        for that check.
+
+        Returns
+        -------
+        Series or Index of bool
+            Series or Index of boolean values with the same length as
+            the original Series/Index.
+
+        See Also
+        --------
+        isalnum
+            Check whether all characters are alphanumeric.
+
+        isalpha
+            Check whether all characters are alphabetic.
+
+        isdecimal
+            Check whether all characters are decimal.
+
+        isdigit
+            Check whether all characters are digits.
+
+        isinteger
+            Check whether all characters are integer.
+
+        isnumeric
+            Check whether all characters are numeric.
+
+        isfloat
+            Check whether all characters are float.
+
+        islower
+            Check whether all characters are lowercase.
+
+        isupper
+            Check whether all characters are uppercase.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([' ', '\t\r\n ', ''])
+        >>> s.str.isspace()
+        0     True
+        1     True
+        2    False
+        dtype: bool
+        """
+        return self._return_or_inplace(libstrings.is_space(self._column))
+
+    def endswith(self, pat: str) -> SeriesOrIndex:
+        """
+        Test if the end of each string element matches a pattern.
+
+        Parameters
+        ----------
+        pat : str or list-like
+            If `str` is an `str`, evaluates whether each string of
+            series ends with `pat`.
+            If `pat` is a list-like, evaluates whether `self[i]`
+            ends with `pat[i]`.
+            Regular expressions are not accepted.
+
+        Returns
+        -------
+        Series or Index of bool
+            A Series of booleans indicating whether the given
+            pattern matches the end of each string element.
+
+        Notes
+        -----
+        `na` parameter is not yet supported, as cudf uses
+        native strings instead of Python objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['bat', 'bear', 'caT', None])
+        >>> s
+        0     bat
+        1    bear
+        2     caT
+        3    <NA>
+        dtype: object
+        >>> s.str.endswith('t')
+        0     True
+        1    False
+        2    False
+        3     <NA>
+        dtype: bool
+        """
+        if pat is None:
+            raise TypeError(
+                f"expected a string or a sequence-like object, not "
+                f"{type(pat).__name__}"
+            )
+        elif is_scalar(pat):
+            result_col = libstrings.endswith(
+                self._column, cudf.Scalar(pat, "str")
+            )
+        else:
+            result_col = libstrings.endswith_multiple(
+                self._column, column.as_column(pat, dtype="str")
+            )
+
+        return self._return_or_inplace(result_col)
+
+    def startswith(self, pat: Union[str, Sequence]) -> SeriesOrIndex:
+        """
+        Test if the start of each string element matches a pattern.
+
+        Equivalent to `str.startswith()
+        <https://docs.python.org/3/library/stdtypes.html#str.startswith>`_.
+
+        Parameters
+        ----------
+        pat : str or list-like
+            If `str` is an `str`, evaluates whether each string of
+            series starts with `pat`.
+            If `pat` is a list-like, evaluates whether `self[i]`
+            starts with `pat[i]`.
+            Regular expressions are not accepted.
+
+        Returns
+        -------
+        Series or Index of bool
+            A Series of booleans indicating whether the given
+            pattern matches the start of each string element.
+
+        See Also
+        --------
+        endswith
+            Same as startswith, but tests the end of string.
+
+        contains
+            Tests if string element contains a pattern.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['bat', 'Bear', 'cat', None])
+        >>> s
+        0     bat
+        1    Bear
+        2     cat
+        3    <NA>
+        dtype: object
+        >>> s.str.startswith('b')
+        0     True
+        1    False
+        2    False
+        3     <NA>
+        dtype: bool
+        """
+        if pat is None:
+            raise TypeError(
+                f"expected a string or a sequence-like object, not "
+                f"{type(pat).__name__}"
+            )
+        elif is_scalar(pat):
+            result_col = libstrings.startswith(
+                self._column, cudf.Scalar(pat, "str")
+            )
+        else:
+            result_col = libstrings.startswith_multiple(
+                self._column, column.as_column(pat, dtype="str")
+            )
+
+        return self._return_or_inplace(result_col)
+
+    def removesuffix(self, suffix: str) -> SeriesOrIndex:
+        """
+        Remove a suffix from an object series.
+
+        If the suffix is not present, the original string will be returned.
+
+        Parameters
+        ----------
+        suffix : str
+            Remove the suffix of the string.
+
+        Returns
+        -------
+        Series/Index: object
+            The Series or Index with given suffix removed.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["foo_str", "bar_str", "no_suffix"])
+        >>> s
+        0    foo_str
+        1    bar_str
+        2    no_suffix
+        dtype: object
+        >>> s.str.removesuffix("_str")
+        0    foo
+        1    bar
+        2    no_suffix
+        dtype: object
+        """
+        if suffix is None or len(suffix) == 0:
+            return self._return_or_inplace(self._column)
+        ends_column = libstrings.endswith(
+            self._column, cudf.Scalar(suffix, "str")
+        )
+        removed_column = libstrings.slice_strings(
+            self._column, 0, -len(suffix), None
+        )
+        result = cudf._lib.copying.copy_if_else(
+            removed_column, self._column, ends_column
+        )
+        return self._return_or_inplace(result)
+
+    def removeprefix(self, prefix: str) -> SeriesOrIndex:
+        """
+        Remove a prefix from an object series.
+
+        If the prefix is not present, the original string will be returned.
+
+        Parameters
+        ----------
+        prefix : str
+            Remove the prefix of the string.
+
+        Returns
+        -------
+        Series/Index: object
+            The Series or Index with given prefix removed.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["str_foo", "str_bar", "no_prefix"])
+        >>> s
+        0    str_foo
+        1    str_bar
+        2    no_prefix
+        dtype: object
+        >>> s.str.removeprefix("str_")
+        0    foo
+        1    bar
+        2    no_prefix
+        dtype: object
+        """
+        if prefix is None or len(prefix) == 0:
+            return self._return_or_inplace(self._column)
+        starts_column = libstrings.startswith(
+            self._column, cudf.Scalar(prefix, "str")
+        )
+        removed_column = libstrings.slice_strings(
+            self._column, len(prefix), None, None
+        )
+        result = cudf._lib.copying.copy_if_else(
+            removed_column, self._column, starts_column
+        )
+        return self._return_or_inplace(result)
+
+    def find(
+        self, sub: str, start: int = 0, end: Optional[int] = None
+    ) -> SeriesOrIndex:
+        """
+        Return lowest indexes in each strings in the Series/Index
+        where the substring is fully contained between ``[start:end]``.
+        Return -1 on failure.
+
+        Parameters
+        ----------
+        sub : str
+            Substring being searched.
+
+        start : int
+            Left edge index.
+
+        end : int
+            Right edge index.
+
+        Returns
+        -------
+        Series or Index of int
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['abc', 'a','b' ,'ddb'])
+        >>> s.str.find('b')
+        0    1
+        1   -1
+        2    0
+        3    2
+        dtype: int32
+
+        Parameters such as `start` and `end` can also be used.
+
+        >>> s.str.find('b', start=1, end=5)
+        0    1
+        1   -1
+        2   -1
+        3    2
+        dtype: int32
+        """
+        if not isinstance(sub, str):
+            raise TypeError(
+                f"expected a string object, not {type(sub).__name__}"
+            )
+
+        if end is None:
+            end = -1
+
+        result_col = libstrings.find(
+            self._column, cudf.Scalar(sub, "str"), start, end
+        )
+
+        return self._return_or_inplace(result_col)
+
+    def rfind(
+        self, sub: str, start: int = 0, end: Optional[int] = None
+    ) -> SeriesOrIndex:
+        """
+        Return highest indexes in each strings in the Series/Index
+        where the substring is fully contained between ``[start:end]``.
+        Return -1 on failure. Equivalent to standard `str.rfind()
+        <https://docs.python.org/3/library/stdtypes.html#str.rfind>`_.
+
+        Parameters
+        ----------
+        sub : str
+            Substring being searched.
+
+        start : int
+            Left edge index.
+
+        end : int
+            Right edge index.
+
+        Returns
+        -------
+        Series or Index of int
+
+        See Also
+        --------
+        find
+            Return lowest indexes in each strings.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["abc", "hello world", "rapids ai"])
+        >>> s.str.rfind('a')
+        0    0
+        1   -1
+        2    7
+        dtype: int32
+
+        Using `start` and `end` parameters.
+
+        >>> s.str.rfind('a', start=2, end=5)
+        0   -1
+        1   -1
+        2   -1
+        dtype: int32
+        """
+        if not isinstance(sub, str):
+            raise TypeError(
+                f"expected a string object, not {type(sub).__name__}"
+            )
+
+        if end is None:
+            end = -1
+
+        result_col = libstrings.rfind(
+            self._column, cudf.Scalar(sub, "str"), start, end
+        )
+
+        return self._return_or_inplace(result_col)
+
+    def index(
+        self, sub: str, start: int = 0, end: Optional[int] = None
+    ) -> SeriesOrIndex:
+        """
+        Return lowest indexes in each strings where the substring
+        is fully contained between ``[start:end]``. This is the same
+        as str.find except instead of returning -1, it raises a ValueError
+        when the substring is not found.
+
+        Parameters
+        ----------
+        sub : str
+            Substring being searched.
+
+        start : int
+            Left edge index.
+
+        end : int
+            Right edge index.
+
+        Returns
+        -------
+        Series or Index of object
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['abc', 'a','b' ,'ddb'])
+        >>> s.str.index('b')
+        Traceback (most recent call last):
+        File "<stdin>", line 1, in <module>
+        ValueError: substring not found
+
+        Parameters such as `start` and `end` can also be used.
+
+        >>> s = cudf.Series(['abc', 'abb','ab' ,'ddb'])
+        >>> s.str.index('b', start=1, end=5)
+        0    1
+        1    1
+        2    1
+        3    2
+        dtype: int32
+        """
+        if not isinstance(sub, str):
+            raise TypeError(
+                f"expected a string object, not {type(sub).__name__}"
+            )
+
+        if end is None:
+            end = -1
+
+        result_col = libstrings.find(
+            self._column, cudf.Scalar(sub, "str"), start, end
+        )
+
+        result = self._return_or_inplace(result_col)
+
+        if (result == -1).any():
+            raise ValueError("substring not found")
+        else:
+            return result
+
+    def rindex(
+        self, sub: str, start: int = 0, end: Optional[int] = None
+    ) -> SeriesOrIndex:
+        """
+        Return highest indexes in each strings where the substring
+        is fully contained between ``[start:end]``. This is the same
+        as ``str.rfind`` except instead of returning -1, it raises a
+        ``ValueError`` when the substring is not found.
+
+        Parameters
+        ----------
+        sub : str
+            Substring being searched.
+
+        start : int
+            Left edge index.
+
+        end : int
+            Right edge index.
+
+        Returns
+        -------
+        Series or Index of object
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['abc', 'a','b' ,'ddb'])
+        >>> s.str.rindex('b')
+        Traceback (most recent call last):
+        File "<stdin>", line 1, in <module>
+        ValueError: substring not found
+
+        Parameters such as `start` and `end` can also be used.
+
+        >>> s = cudf.Series(['abc', 'abb','ab' ,'ddb'])
+        >>> s.str.rindex('b', start=1, end=5)
+        0    1
+        1    2
+        2    1
+        3    2
+        dtype: int32
+        """
+        if not isinstance(sub, str):
+            raise TypeError(
+                f"expected a string object, not {type(sub).__name__}"
+            )
+
+        if end is None:
+            end = -1
+
+        result_col = libstrings.rfind(
+            self._column, cudf.Scalar(sub, "str"), start, end
+        )
+
+        result = self._return_or_inplace(result_col)
+
+        if (result == -1).any():
+            raise ValueError("substring not found")
+        else:
+            return result
+
+    def match(
+        self, pat: str, case: bool = True, flags: int = 0
+    ) -> SeriesOrIndex:
+        """
+        Determine if each string matches a regular expression.
+
+        Parameters
+        ----------
+        pat : str or compiled regex
+            Character sequence or regular expression.
+        flags : int, default 0 (no flags)
+            Flags to pass through to the regex engine (e.g. re.MULTILINE)
+
+        Returns
+        -------
+        Series or Index of boolean values.
+
+        Notes
+        -----
+        Parameters `case` and `na` are currently not supported.
+        The `flags` parameter currently only supports re.DOTALL and
+        re.MULTILINE.
+
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["rapids", "ai", "cudf"])
+
+        Checking for strings starting with `a`.
+
+        >>> s.str.match('a')
+        0    False
+        1     True
+        2    False
+        dtype: bool
+
+        Checking for strings starting with any of `a` or `c`.
+
+        >>> s.str.match('[ac]')
+        0    False
+        1     True
+        2     True
+        dtype: bool
+        """
+        if case is not True:
+            raise NotImplementedError("`case` parameter is not yet supported")
+        if isinstance(pat, re.Pattern):
+            flags = pat.flags & ~re.U
+            pat = pat.pattern
+        if not _is_supported_regex_flags(flags):
+            raise NotImplementedError(
+                "unsupported value for `flags` parameter"
+            )
+
+        return self._return_or_inplace(
+            libstrings.match_re(self._column, pat, flags)
+        )
+
+    def url_decode(self) -> SeriesOrIndex:
+        """
+        Returns a URL-decoded format of each string.
+        No format checking is performed. All characters
+        are expected to be encoded as UTF-8 hex values.
+
+        Returns
+        -------
+        Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['A%2FB-C%2FD', 'e%20f.g', '4-5%2C6'])
+        >>> s.str.url_decode()
+        0    A/B-C/D
+        1      e f.g
+        2      4-5,6
+        dtype: object
+        >>> data = ["https%3A%2F%2Frapids.ai%2Fstart.html",
+        ...     "https%3A%2F%2Fmedium.com%2Frapids-ai"]
+        >>> s = cudf.Series(data)
+        >>> s.str.url_decode()
+        0    https://rapids.ai/start.html
+        1    https://medium.com/rapids-ai
+        dtype: object
+        """
+
+        return self._return_or_inplace(libstrings.url_decode(self._column))
+
+    def url_encode(self) -> SeriesOrIndex:
+        """
+        Returns a URL-encoded format of each string.
+        No format checking is performed.
+        All characters are encoded except for ASCII letters,
+        digits, and these characters: ``'.','_','-','~'``.
+        Encoding converts to hex using UTF-8 encoded bytes.
+
+        Returns
+        -------
+        Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['A/B-C/D', 'e f.g', '4-5,6'])
+        >>> s.str.url_encode()
+        0    A%2FB-C%2FD
+        1        e%20f.g
+        2        4-5%2C6
+        dtype: object
+        >>> data = ["https://rapids.ai/start.html",
+        ...     "https://medium.com/rapids-ai"]
+        >>> s = cudf.Series(data)
+        >>> s.str.url_encode()
+        0    https%3A%2F%2Frapids.ai%2Fstart.html
+        1    https%3A%2F%2Fmedium.com%2Frapids-ai
+        dtype: object
+        """
+        return self._return_or_inplace(libstrings.url_encode(self._column))
+
+    def code_points(self) -> SeriesOrIndex:
+        """
+        Returns an array by filling it with the UTF-8 code point
+        values for each character of each string.
+        This function uses the ``len()`` method to determine
+        the size of each sub-array of integers.
+
+        Returns
+        -------
+        Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["a","xyz", "éee"])
+        >>> s.str.code_points()
+        0       97
+        1      120
+        2      121
+        3      122
+        4    50089
+        5      101
+        6      101
+        dtype: int32
+        >>> s = cudf.Series(["abc"])
+        >>> s.str.code_points()
+        0    97
+        1    98
+        2    99
+        dtype: int32
+        """
+
+        new_col = libstrings.code_points(self._column)
+        if isinstance(self._parent, cudf.Series):
+            return cudf.Series(new_col, name=self._parent.name)
+        elif isinstance(self._parent, cudf.BaseIndex):
+            return cudf.core.index.as_index(new_col, name=self._parent.name)
+        else:
+            return new_col
+
+    def translate(self, table: dict) -> SeriesOrIndex:
+        """
+        Map all characters in the string through the given
+        mapping table.
+
+        Equivalent to standard `str.translate()
+        <https://docs.python.org/3/library/stdtypes.html#str.translate>`_.
+
+        Parameters
+        ----------
+        table : dict
+            Table is a mapping of Unicode ordinals to Unicode
+            ordinals, strings, or None.
+            Unmapped characters are left untouched.
+            `str.maketrans()
+            <https://docs.python.org/3/library/stdtypes.html#str.maketrans>`_
+            is a helper function for making translation tables.
+
+        Returns
+        -------
+        Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['lower', 'CAPITALS', 'this is a sentence','SwApCaSe']
+        >>> s = cudf.Series(data)
+        >>> s.str.translate({'a': "1"})
+        0                 lower
+        1              CAPITALS
+        2    this is 1 sentence
+        3              SwApC1Se
+        dtype: object
+        >>> s.str.translate({'a': "1", "e":"#"})
+        0                 low#r
+        1              CAPITALS
+        2    this is 1 s#nt#nc#
+        3              SwApC1S#
+        dtype: object
+        """
+        table = str.maketrans(table)
+        return self._return_or_inplace(
+            libstrings.translate(self._column, table)
+        )
+
+    def filter_characters(
+        self, table: dict, keep: bool = True, repl: Optional[str] = None
+    ) -> SeriesOrIndex:
+        """
+        Remove characters from each string using the character ranges
+        in the given mapping table.
+
+        Parameters
+        ----------
+        table : dict
+            This table is a range of Unicode ordinals to filter.
+            The minimum value is the key and the maximum value is the value.
+            You can use `str.maketrans()
+            <https://docs.python.org/3/library/stdtypes.html#str.maketrans>`_
+            as a helper function for making the filter table.
+            Overlapping ranges will cause undefined results.
+            Range values are inclusive.
+        keep : boolean
+            If False, the character ranges in the ``table`` are removed.
+            If True, the character ranges not in the ``table`` are removed.
+            Default is True.
+        repl : str
+            Optional replacement string to use in place of removed characters.
+
+        Returns
+        -------
+        Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ['aeiou', 'AEIOU', '0123456789']
+        >>> s = cudf.Series(data)
+        >>> s.str.filter_characters({'a':'l', 'M':'Z', '4':'6'})
+        0    aei
+        1     OU
+        2    456
+        dtype: object
+        >>> s.str.filter_characters({'a':'l', 'M':'Z', '4':'6'}, False, "_")
+        0         ___ou
+        1         AEI__
+        2    0123___789
+        dtype: object
+        """
+        if repl is None:
+            repl = ""
+        table = str.maketrans(table)
+        return self._return_or_inplace(
+            libstrings.filter_characters(
+                self._column, table, keep, cudf.Scalar(repl, "str")
+            ),
+        )
+
+    def normalize_spaces(self) -> SeriesOrIndex:
+        r"""
+        Remove extra whitespace between tokens and trim whitespace
+        from the beginning and the end of each string.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(["hello \\t world"," test string  "])
+        >>> ser.str.normalize_spaces()
+        0    hello world
+        1    test string
+        dtype: object
+        """
+        return self._return_or_inplace(
+            libstrings.normalize_spaces(self._column)
+        )
+
+    def normalize_characters(self, do_lower: bool = True) -> SeriesOrIndex:
+        r"""
+        Normalizes strings characters for tokenizing.
+
+        This uses the normalizer that is built into the
+        subword_tokenize function which includes:
+
+            - adding padding around punctuation (unicode category starts with
+              "P") as well as certain ASCII symbols like "^" and "$"
+            - adding padding around the CJK Unicode block characters
+            - changing whitespace (e.g. ``\t``, ``\n``, ``\r``) to space
+            - removing control characters (unicode categories "Cc" and "Cf")
+
+        If `do_lower_case = true`, lower-casing also removes the accents.
+        The accents cannot be removed from upper-case characters without
+        lower-casing and lower-casing cannot be performed without also
+        removing accents. However, if the accented character is already
+        lower-case, then only the accent is removed.
+
+        Parameters
+        ----------
+        do_lower : bool, Default is True
+            If set to True, characters will be lower-cased and accents
+            will be removed. If False, accented and upper-case characters
+            are not transformed.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(["héllo, \tworld","ĂĆCĖÑTED","$99"])
+        >>> ser.str.normalize_characters()
+        0    hello ,  world
+        1          accented
+        2              $ 99
+        dtype: object
+        >>> ser.str.normalize_characters(do_lower=False)
+        0    héllo ,  world
+        1          ĂĆCĖÑTED
+        2              $ 99
+        dtype: object
+        """
+        return self._return_or_inplace(
+            libstrings.normalize_characters(self._column, do_lower)
+        )
+
+    def tokenize(self, delimiter: str = " ") -> SeriesOrIndex:
+        """
+        Each string is split into tokens using the provided delimiter(s).
+        The sequence returned contains the tokens in the order
+        they were found.
+
+        Parameters
+        ----------
+        delimiter : str or list of strs, Default is whitespace.
+            The string used to locate the split points of each string.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ["hello world", "goodbye world", "hello goodbye"]
+        >>> ser = cudf.Series(data)
+        >>> ser.str.tokenize()
+        0      hello
+        0      world
+        1    goodbye
+        1      world
+        2      hello
+        2    goodbye
+        dtype: object
+        """
+        delimiter = _massage_string_arg(delimiter, "delimiter", allow_col=True)
+
+        if isinstance(delimiter, Column):
+            result = self._return_or_inplace(
+                libstrings._tokenize_column(self._column, delimiter),
+                retain_index=False,
+            )
+        elif isinstance(delimiter, cudf.Scalar):
+            result = self._return_or_inplace(
+                libstrings._tokenize_scalar(self._column, delimiter),
+                retain_index=False,
+            )
+        else:
+            raise TypeError(
+                f"Expected a Scalar or Column\
+                for delimiters, but got {type(delimiter)}"
+            )
+        if isinstance(self._parent, cudf.Series):
+            result.index = self._parent.index.repeat(  # type: ignore
+                self.token_count(delimiter=delimiter)
+            )
+        return result
+
+    def detokenize(
+        self, indices: "cudf.Series", separator: str = " "
+    ) -> SeriesOrIndex:
+        """
+        Combines tokens into strings by concatenating them in the order
+        in which they appear in the ``indices`` column. The ``separator`` is
+        concatenated between each token.
+
+        Parameters
+        ----------
+        indices : Series
+            Each value identifies the output row for the corresponding token.
+        separator : str
+            The string concatenated between each token in an output row.
+            Default is space.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> strs = cudf.Series(["hello", "world", "one", "two", "three"])
+        >>> indices = cudf.Series([0, 0, 1, 1, 2])
+        >>> strs.str.detokenize(indices)
+        0    hello world
+        1        one two
+        2          three
+        dtype: object
+        """
+        separator = _massage_string_arg(separator, "separator")
+        return self._return_or_inplace(
+            libstrings.detokenize(self._column, indices._column, separator),
+            retain_index=False,
+        )
+
+    def character_tokenize(self) -> SeriesOrIndex:
+        """
+        Each string is split into individual characters.
+        The sequence returned contains each character as an individual string.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> data = ["hello world", None, "goodbye, thank you."]
+        >>> ser = cudf.Series(data)
+        >>> ser.str.character_tokenize()
+        0    h
+        0    e
+        0    l
+        0    l
+        0    o
+        0
+        0    w
+        0    o
+        0    r
+        0    l
+        0    d
+        2    g
+        2    o
+        2    o
+        2    d
+        2    b
+        2    y
+        2    e
+        2    ,
+        2
+        2    t
+        2    h
+        2    a
+        2    n
+        2    k
+        2
+        2    y
+        2    o
+        2    u
+        2    .
+        dtype: object
+        """
+        result_col = libstrings.character_tokenize(self._column)
+        if isinstance(self._parent, cudf.Series):
+            lengths = self.len().fillna(0)
+            index = self._parent.index.repeat(lengths)
+            return cudf.Series(result_col, name=self._parent.name, index=index)
+        elif isinstance(self._parent, cudf.BaseIndex):
+            return cudf.core.index.as_index(result_col, name=self._parent.name)
+        else:
+            return result_col
+
+    def token_count(self, delimiter: str = " ") -> SeriesOrIndex:
+        """
+        Each string is split into tokens using the provided delimiter.
+        The returned integer sequence is the number of tokens in each string.
+
+        Parameters
+        ----------
+        delimiter : str or list of strs, Default is whitespace.
+            The characters or strings used to locate the
+            split points of each string.
+
+        Returns
+        -------
+        Series or Index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(["hello world","goodbye",""])
+        >>> ser.str.token_count()
+        0    2
+        1    1
+        2    0
+        dtype: int32
+        """
+        delimiter = _massage_string_arg(delimiter, "delimiter", allow_col=True)
+        if isinstance(delimiter, Column):
+            return self._return_or_inplace(
+                libstrings._count_tokens_column(self._column, delimiter)
+            )
+
+        elif isinstance(delimiter, cudf.Scalar):
+            return self._return_or_inplace(
+                libstrings._count_tokens_scalar(self._column, delimiter)
+            )
+        else:
+            raise TypeError(
+                f"Expected a Scalar or Column\
+                for delimiters, but got {type(delimiter)}"
+            )
+
+    def ngrams(self, n: int = 2, separator: str = "_") -> SeriesOrIndex:
+        """
+        Generate the n-grams from a set of tokens, each record
+        in series is treated a token.
+
+        You can generate tokens from a Series instance using
+        the ``Series.str.tokenize()`` function.
+
+        Parameters
+        ----------
+        n : int
+            The degree of the n-gram (number of consecutive tokens).
+            Default of 2 for bigrams.
+        separator : str
+            The separator to use between within an n-gram.
+            Default is '_'.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> str_series = cudf.Series(['this is my', 'favorite book'])
+        >>> str_series.str.ngrams(2, "_")
+        0    this is my_favorite book
+        dtype: object
+        >>> str_series = cudf.Series(['abc','def','xyz','hhh'])
+        >>> str_series.str.ngrams(2, "_")
+        0    abc_def
+        1    def_xyz
+        2    xyz_hhh
+        dtype: object
+        """
+        separator = _massage_string_arg(separator, "separator")
+        return self._return_or_inplace(
+            libstrings.generate_ngrams(self._column, n, separator),
+            retain_index=False,
+        )
+
+    def character_ngrams(
+        self, n: int = 2, as_list: bool = False
+    ) -> SeriesOrIndex:
+        """
+        Generate the n-grams from characters in a column of strings.
+
+        Parameters
+        ----------
+        n : int
+            The degree of the n-gram (number of consecutive characters).
+            Default of 2 for bigrams.
+        as_list : bool
+            Set to True to return ngrams in a list column where each
+            list element is the ngrams for each string.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> str_series = cudf.Series(['abcd','efgh','xyz'])
+        >>> str_series.str.character_ngrams(2)
+        0    ab
+        0    bc
+        0    cd
+        1    ef
+        1    fg
+        1    gh
+        2    xy
+        2    yz
+        dtype: object
+        >>> str_series.str.character_ngrams(3)
+        0    abc
+        0    bcd
+        1    efg
+        1    fgh
+        2    xyz
+        dtype: object
+        >>> str_series.str.character_ngrams(3,True)
+        0    [abc, bcd]
+        1    [efg, fgh]
+        2         [xyz]
+        dtype: list
+        """
+        ngrams = libstrings.generate_character_ngrams(self._column, n)
+
+        # convert the output to a list by just generating the
+        # offsets for the output list column
+        sn = (self.len() - (n - 1)).clip(0, None).fillna(0)  # type: ignore
+        sizes = libcudf.concat.concat_columns(
+            [column.as_column(0, dtype=np.int32, length=1), sn._column]
+        )
+        oc = libcudf.reduce.scan("cumsum", sizes, True)
+        lc = cudf.core.column.ListColumn(
+            size=self._column.size,
+            dtype=cudf.ListDtype(self._column.dtype),
+            mask=self._column.mask,
+            offset=0,
+            null_count=self._column.null_count,
+            children=(oc, ngrams),
+        )
+        result = self._return_or_inplace(lc, retain_index=True)
+
+        if isinstance(result, cudf.Series) and not as_list:
+            return result.explode()
+        return result
+
+    def hash_character_ngrams(
+        self, n: int = 5, as_list: bool = False
+    ) -> SeriesOrIndex:
+        """
+        Generate hashes of n-grams from characters in a column of strings.
+        The MurmurHash32 algorithm is used to produce the hash results.
+
+        Parameters
+        ----------
+        n : int
+            The degree of the n-gram (number of consecutive characters).
+            Default is 5.
+        as_list : bool
+            Set to True to return the hashes in a list column where each
+            list element is the hashes for each string.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> str_series = cudf.Series(['abcdefg','stuvwxyz'])
+        >>> str_series.str.hash_character_ngrams(5, True)
+        0               [3902511862, 570445242, 4202475763]
+        1    [556054766, 3166857694, 3760633458, 192452857]
+        dtype: list
+        >>> str_series.str.hash_character_ngrams(5)
+        0    3902511862
+        0     570445242
+        0    4202475763
+        1     556054766
+        1    3166857694
+        1    3760633458
+        1     192452857
+        dtype: uint32
+        """
+
+        result = self._return_or_inplace(
+            libstrings.hash_character_ngrams(self._column, n),
+            retain_index=True,
+        )
+        if isinstance(result, cudf.Series) and not as_list:
+            return result.explode()
+        return result
+
+    def ngrams_tokenize(
+        self, n: int = 2, delimiter: str = " ", separator: str = "_"
+    ) -> SeriesOrIndex:
+        """
+        Generate the n-grams using tokens from each string.
+        This will tokenize each string and then generate ngrams for each
+        string.
+
+        Parameters
+        ----------
+        n : int, Default 2.
+            The degree of the n-gram (number of consecutive tokens).
+        delimiter : str, Default is white-space.
+            The character used to locate the split points of each string.
+        sep : str, Default is '_'.
+            The separator to use between tokens within an n-gram.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(['this is the', 'best book'])
+        >>> ser.str.ngrams_tokenize(n=2, sep='_')
+        0      this_is
+        1       is_the
+        2    best_book
+        dtype: object
+        """
+        delimiter = _massage_string_arg(delimiter, "delimiter")
+        separator = _massage_string_arg(separator, "separator")
+        return self._return_or_inplace(
+            libstrings.ngrams_tokenize(self._column, n, delimiter, separator),
+            retain_index=False,
+        )
+
+    def replace_tokens(
+        self, targets, replacements, delimiter: Optional[str] = None
+    ) -> SeriesOrIndex:
+        """
+        The targets tokens are searched for within each string in the series
+        and replaced with the corresponding replacements if found.
+        Tokens are identified by the delimiter character provided.
+
+        Parameters
+        ----------
+        targets : array-like, Sequence or Series
+            The tokens to search for inside each string.
+
+        replacements : array-like, Sequence, Series or str
+            The strings to replace for each found target token found.
+            Alternately, this can be a single str instance and would be
+            used as replacement for each string found.
+
+        delimiter : str
+            The character used to locate the tokens of each string.
+            Default is whitespace.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> sr = cudf.Series(["this is me", "theme music", ""])
+        >>> targets = cudf.Series(["is", "me"])
+        >>> sr.str.replace_tokens(targets=targets, replacements="_")
+        0       this _ _
+        1    theme music
+        2
+        dtype: object
+        >>> sr = cudf.Series(["this;is;me", "theme;music", ""])
+        >>> sr.str.replace_tokens(targets=targets, replacements=":")
+        0     this;is;me
+        1    theme;music
+        2
+        dtype: object
+        """
+        if can_convert_to_column(targets):
+            targets_column = column.as_column(targets)
+        else:
+            raise TypeError(
+                f"targets should be an array-like or a Series object, "
+                f"found {type(targets)}"
+            )
+
+        if is_scalar(replacements):
+            replacements_column = column.as_column([replacements])
+        elif can_convert_to_column(replacements):
+            replacements_column = column.as_column(replacements)
+            if len(targets_column) != len(replacements_column):
+                raise ValueError(
+                    "targets and replacements should be same size"
+                    " sequences unless replacements is a string."
+                )
+        else:
+            raise TypeError(
+                f"replacements should be an str, array-like or Series object, "
+                f"found {type(replacements)}"
+            )
+
+        if delimiter is None:
+            delimiter = ""
+        elif not is_scalar(delimiter):
+            raise TypeError(
+                f"Type of delimiter should be a string,"
+                f" found {type(delimiter)}"
+            )
+
+        return self._return_or_inplace(
+            libstrings.replace_tokens(
+                self._column,
+                targets_column,
+                replacements_column,
+                cudf.Scalar(delimiter, dtype="str"),
+            ),
+        )
+
+    def filter_tokens(
+        self,
+        min_token_length: int,
+        replacement: Optional[str] = None,
+        delimiter: Optional[str] = None,
+    ) -> SeriesOrIndex:
+        """
+        Remove tokens from within each string in the series that are
+        smaller than min_token_length and optionally replace them
+        with the replacement string.
+        Tokens are identified by the delimiter character provided.
+
+        Parameters
+        ----------
+        min_token_length: int
+            Minimum number of characters for a token to be retained
+            in the output string.
+
+        replacement : str
+            String used in place of removed tokens.
+
+        delimiter : str
+            The character(s) used to locate the tokens of each string.
+            Default is whitespace.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> sr = cudf.Series(["this is me", "theme music", ""])
+        >>> sr.str.filter_tokens(3, replacement="_")
+        0       this _ _
+        1    theme music
+        2
+        dtype: object
+        >>> sr = cudf.Series(["this;is;me", "theme;music", ""])
+        >>> sr.str.filter_tokens(5,None,";")
+        0             ;;
+        1    theme;music
+        2
+        dtype: object
+        """
+
+        if replacement is None:
+            replacement = ""
+        elif not is_scalar(replacement):
+            raise TypeError(
+                f"Type of replacement should be a string,"
+                f" found {type(replacement)}"
+            )
+
+        if delimiter is None:
+            delimiter = ""
+        elif not is_scalar(delimiter):
+            raise TypeError(
+                f"Type of delimiter should be a string,"
+                f" found {type(delimiter)}"
+            )
+
+        return self._return_or_inplace(
+            libstrings.filter_tokens(
+                self._column,
+                min_token_length,
+                cudf.Scalar(replacement, dtype="str"),
+                cudf.Scalar(delimiter, dtype="str"),
+            ),
+        )
+
+    def porter_stemmer_measure(self) -> SeriesOrIndex:
+        """
+        Compute the Porter Stemmer measure for each string.
+        The Porter Stemmer algorithm is described `here
+        <https://tartarus.org/martin/PorterStemmer/def.txt>`_.
+
+        Returns
+        -------
+        Series or Index of object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(["hello", "super"])
+        >>> ser.str.porter_stemmer_measure()
+        0    1
+        1    2
+        dtype: int32
+        """
+        return self._return_or_inplace(
+            libstrings.porter_stemmer_measure(self._column)
+        )
+
+    def is_consonant(self, position) -> SeriesOrIndex:
+        """
+        Return true for strings where the character at ``position`` is a
+        consonant. The ``position`` parameter may also be a list of integers
+        to check different characters per string.
+        If the ``position`` is larger than the string length, False is
+        returned for that string.
+
+        Parameters
+        ----------
+        position: int or list-like
+           The character position to check within each string.
+
+        Returns
+        -------
+        Series or Index of bool dtype.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(["toy", "trouble"])
+        >>> ser.str.is_consonant(1)
+        0    False
+        1     True
+        dtype: bool
+        >>> positions = cudf.Series([2, 3])
+        >>> ser.str.is_consonant(positions)
+        0     True
+        1    False
+        dtype: bool
+        """
+        ltype = libstrings.LetterType.CONSONANT
+
+        if can_convert_to_column(position):
+            return self._return_or_inplace(
+                libstrings.is_letter_multi(
+                    self._column, ltype, column.as_column(position)
+                ),
+            )
+
+        return self._return_or_inplace(
+            libstrings.is_letter(self._column, ltype, position)
+        )
+
+    def is_vowel(self, position) -> SeriesOrIndex:
+        """
+        Return true for strings where the character at ``position`` is a
+        vowel -- not a consonant. The ``position`` parameter may also be
+        a list of integers to check different characters per string.
+        If the ``position`` is larger than the string length, False is
+        returned for that string.
+
+        Parameters
+        ----------
+        position: int or list-like
+           The character position to check within each string.
+
+        Returns
+        -------
+        Series or Index of bool dtype.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series(["toy", "trouble"])
+        >>> ser.str.is_vowel(1)
+        0     True
+        1    False
+        dtype: bool
+        >>> positions = cudf.Series([2, 3])
+        >>> ser.str.is_vowel(positions)
+        0    False
+        1     True
+        dtype: bool
+        """
+        ltype = libstrings.LetterType.VOWEL
+
+        if can_convert_to_column(position):
+            return self._return_or_inplace(
+                libstrings.is_letter_multi(
+                    self._column, ltype, column.as_column(position)
+                ),
+            )
+
+        return self._return_or_inplace(
+            libstrings.is_letter(self._column, ltype, position)
+        )
+
+    def edit_distance(self, targets) -> SeriesOrIndex:
+        """
+        The ``targets`` strings are measured against the strings in this
+        instance using the Levenshtein edit distance algorithm.
+        https://www.cuelogic.com/blog/the-levenshtein-algorithm
+
+        The ``targets`` parameter may also be a single string in which
+        case the edit distance is computed for all the strings against
+        that single string.
+
+        Parameters
+        ----------
+        targets : array-like, Sequence or Series or str
+            The string(s) to measure against each string.
+
+        Returns
+        -------
+        Series or Index of int32.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> sr = cudf.Series(["puppy", "doggy", "kitty"])
+        >>> targets = cudf.Series(["pup", "dogie", "kitten"])
+        >>> sr.str.edit_distance(targets=targets)
+        0    2
+        1    2
+        2    2
+        dtype: int32
+        >>> sr.str.edit_distance("puppy")
+        0    0
+        1    4
+        2    4
+        dtype: int32
+        """
+        if is_scalar(targets):
+            targets_column = column.as_column([targets])
+        elif can_convert_to_column(targets):
+            targets_column = column.as_column(targets)
+        else:
+            raise TypeError(
+                f"targets should be an str, array-like or Series object, "
+                f"found {type(targets)}"
+            )
+
+        return self._return_or_inplace(
+            libstrings.edit_distance(self._column, targets_column)
+        )
+
+    def edit_distance_matrix(self) -> SeriesOrIndex:
+        """Computes the edit distance between strings in the series.
+
+        The series to compute the matrix should have more than 2 strings and
+        should not contain nulls.
+
+        Edit distance is measured based on the `Levenshtein edit distance
+        algorithm <https://www.cuelogic.com/blog/the-levenshtein-algorithm>`_.
+
+        Returns
+        -------
+        Series of ListDtype(int64)
+            Assume ``N`` is the length of this series. The return series
+            contains ``N`` lists of size ``N``, where the ``j`` th number in
+            the ``i`` th row of the series tells the edit distance between the
+            ``i`` th string and the ``j`` th string of this series.  The matrix
+            is symmetric. Diagonal elements are 0.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['abc', 'bc', 'cba'])
+        >>> s.str.edit_distance_matrix()
+        0    [0, 1, 2]
+        1    [1, 0, 2]
+        2    [2, 2, 0]
+        dtype: list
+        """
+        if self._column.size < 2:
+            raise ValueError(
+                "Require size >= 2 to compute edit distance matrix."
+            )
+        if self._column.has_nulls():
+            raise ValueError(
+                "Cannot compute edit distance between null strings. "
+                "Consider removing them using `dropna` or fill with `fillna`."
+            )
+        return self._return_or_inplace(
+            libstrings.edit_distance_matrix(self._column)
+        )
+
+    def minhash(
+        self, seeds: Optional[ColumnLike] = None, width: int = 4
+    ) -> SeriesOrIndex:
+        """
+        Compute the minhash of a strings column.
+        This uses the MurmurHash3_x86_32 algorithm for the hash function.
+
+        Parameters
+        ----------
+        seeds : ColumnLike
+            The seeds used for the hash algorithm.
+            Must be of type uint32.
+        width : int
+            The width of the substring to hash.
+            Default is 4 characters.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> str_series = cudf.Series(['this is my', 'favorite book'])
+        >>> seeds = cudf.Series([0], dtype=np.uint32)
+        >>> str_series.str.minhash(seeds)
+        0     [21141582]
+        1    [962346254]
+        dtype: list
+        >>> seeds = cudf.Series([0, 1, 2], dtype=np.uint32)
+        >>> str_series.str.minhash(seeds)
+        0    [21141582, 403093213, 1258052021]
+        1    [962346254, 677440381, 122618762]
+        dtype: list
+        """
+        if seeds is None:
+            seeds_column = column.as_column(0, dtype=np.uint32, length=1)
+        else:
+            seeds_column = column.as_column(seeds)
+            if seeds_column.dtype != np.uint32:
+                raise ValueError(
+                    f"Expecting a Series with dtype uint32, got {type(seeds)}"
+                )
+        return self._return_or_inplace(
+            libstrings.minhash(self._column, seeds_column, width)
+        )
+
+    def minhash64(
+        self, seeds: Optional[ColumnLike] = None, width: int = 4
+    ) -> SeriesOrIndex:
+        """
+        Compute the minhash of a strings column.
+        This uses the MurmurHash3_x64_128 algorithm for the hash function.
+        This function generates 2 uint64 values but only the first
+        uint64 value is used.
+
+        Parameters
+        ----------
+        seeds : ColumnLike
+            The seeds used for the hash algorithm.
+            Must be of type uint64.
+        width : int
+            The width of the substring to hash.
+            Default is 4 characters.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> str_series = cudf.Series(['this is my', 'favorite book'])
+        >>> seeds = cudf.Series([0, 1, 2], dtype=np.uint64)
+        >>> str_series.str.minhash64(seeds)
+        0    [3232308021562742685, 4445611509348165860, 586435843695903598]
+        1    [23008204270530356, 1281229757012344693, 153762819128779913]
+        dtype: list
+        """
+        if seeds is None:
+            seeds_column = column.as_column(0, dtype=np.uint64, length=1)
+        else:
+            seeds_column = column.as_column(seeds)
+            if seeds_column.dtype != np.uint64:
+                raise ValueError(
+                    f"Expecting a Series with dtype uint64, got {type(seeds)}"
+                )
+        return self._return_or_inplace(
+            libstrings.minhash64(self._column, seeds_column, width)
+        )
+
+    def jaccard_index(self, input: cudf.Series, width: int) -> SeriesOrIndex:
+        """
+        Compute the Jaccard index between this column and the given
+        input strings column.
+
+        Parameters
+        ----------
+        input : Series
+            The input strings column to compute the Jaccard index against.
+            Must have the same number of strings as this column.
+        width : int
+            The number of characters for the sliding window calculation.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> str1 = cudf.Series(["the brown dog", "jumped about"])
+        >>> str2 = cudf.Series(["the black cat", "jumped around"])
+        >>> str1.str.jaccard_index(str2, 5)
+        0    0.058824
+        1    0.307692
+        dtype: float32
+        """
+
+        return self._return_or_inplace(
+            libstrings.jaccard_index(self._column, input._column, width),
+        )
+
+
+def _massage_string_arg(value, name, allow_col=False):
+    if isinstance(value, cudf.Scalar):
+        return value
+
+    if isinstance(value, str):
+        return cudf.Scalar(value, dtype="str")
+
+    allowed_types = ["Scalar"]
+
+    if allow_col:
+        if isinstance(value, list):
+            return column.as_column(value, dtype="str")
+
+        if isinstance(value, Column) and is_string_dtype(value.dtype):
+            return value
+
+        allowed_types.append("Column")
+
+    raise ValueError(
+        f"Expected {_expected_types_format(allowed_types)} "
+        f"for {name} but got {type(value)}"
+    )
+
+
+def _expected_types_format(types):
+    if len(types) == 1:
+        return types[0]
+
+    return ", ".join(types[:-1]) + ", or " + types[-1]
+
+
+class StringColumn(column.ColumnBase):
+    """
+    Implements operations for Columns of String type
+
+    Parameters
+    ----------
+    mask : Buffer
+        The validity mask
+    offset : int
+        Data offset
+    children : Tuple[Column]
+        Two non-null columns containing the string data and offsets
+        respectively
+    """
+
+    _start_offset: Optional[int]
+    _end_offset: Optional[int]
+
+    _VALID_BINARY_OPERATIONS = {
+        "__eq__",
+        "__ne__",
+        "__lt__",
+        "__le__",
+        "__gt__",
+        "__ge__",
+        "__add__",
+        "__radd__",
+        # These operators aren't actually supported, they only exist to allow
+        # empty column binops with scalars of arbitrary other dtypes. See
+        # the _binaryop method for more information.
+        "__sub__",
+        "__mul__",
+        "__mod__",
+        "__pow__",
+        "__truediv__",
+        "__floordiv__",
+    }
+
+    def __init__(
+        self,
+        mask: Optional[Buffer] = None,
+        size: Optional[int] = None,  # TODO: make non-optional
+        offset: int = 0,
+        null_count: Optional[int] = None,
+        children: Tuple["column.ColumnBase", ...] = (),
+    ):
+        dtype = cudf.api.types.dtype("object")
+
+        if size is None:
+            for child in children:
+                assert child.offset == 0
+
+            if len(children) == 0:
+                size = 0
+            elif children[0].size == 0:
+                size = 0
+            else:
+                # one less because the last element of offsets is the number of
+                # bytes in the data buffer
+                size = children[0].size - 1
+            size = size - offset
+
+        if len(children) == 0 and size != 0:
+            # all nulls-column:
+            offsets = column.full(size + 1, 0, dtype=size_type_dtype)
+
+            chars = cudf.core.column.as_column([], dtype="int8")
+            children = (offsets, chars)
+
+        super().__init__(
+            data=None,
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+            children=children,
+        )
+
+        self._start_offset = None
+        self._end_offset = None
+
+    def copy(self, deep: bool = True):
+        # Since string columns are immutable, both deep
+        # and shallow copies share the underlying device data and mask.
+        return super().copy(deep=False)
+
+    @property
+    def start_offset(self) -> int:
+        if self._start_offset is None:
+            if (
+                len(self.base_children) == 2
+                and self.offset < self.base_children[0].size
+            ):
+                self._start_offset = int(
+                    self.base_children[0].element_indexing(self.offset)
+                )
+            else:
+                self._start_offset = 0
+
+        return self._start_offset
+
+    @property
+    def end_offset(self) -> int:
+        if self._end_offset is None:
+            if (
+                len(self.base_children) == 2
+                and (self.offset + self.size) < self.base_children[0].size
+            ):
+                self._end_offset = int(
+                    self.base_children[0].element_indexing(
+                        self.offset + self.size
+                    )
+                )
+            else:
+                self._end_offset = 0
+
+        return self._end_offset
+
+    @cached_property
+    def memory_usage(self) -> int:
+        n = 0
+        if len(self.base_children) == 2:
+            child0_size = (self.size + 1) * self.base_children[
+                0
+            ].dtype.itemsize
+
+            child1_size = (
+                self.end_offset - self.start_offset
+            ) * self.base_children[1].dtype.itemsize
+
+            n += child0_size + child1_size
+        if self.nullable:
+            n += cudf._lib.null_mask.bitmask_allocation_size_bytes(self.size)
+        return n
+
+    @property
+    def base_size(self) -> int:
+        if len(self.base_children) == 0:
+            return 0
+        else:
+            return self.base_children[0].size - 1
+
+    def data_array_view(
+        self, *, mode="write"
+    ) -> cuda.devicearray.DeviceNDArray:
+        raise ValueError("Cannot get an array view of a StringColumn")
+
+    def to_arrow(self) -> pa.Array:
+        """Convert to PyArrow Array
+
+        Examples
+        --------
+        >>> import cudf
+        >>> col = cudf.core.column.as_column([1, 2, 3, 4])
+        >>> col.to_arrow()
+        <pyarrow.lib.Int64Array object at 0x7f886547f830>
+        [
+          1,
+          2,
+          3,
+          4
+        ]
+        """
+        if self.null_count == len(self):
+            return pa.NullArray.from_buffers(
+                pa.null(), len(self), [pa.py_buffer(b"")]
+            )
+        else:
+            return super().to_arrow()
+
+    def sum(
+        self,
+        skipna: Optional[bool] = None,
+        dtype: Optional[Dtype] = None,
+        min_count: int = 0,
+    ):
+        result_col = self._process_for_reduction(
+            skipna=skipna, min_count=min_count
+        )
+        if isinstance(result_col, type(self)):
+            return libstrings.join(
+                result_col,
+                sep=cudf.Scalar(""),
+                na_rep=cudf.Scalar(None, "str"),
+            ).element_indexing(0)
+        else:
+            return result_col
+
+    def set_base_data(self, value):
+        if value is not None:
+            raise RuntimeError(
+                "StringColumns do not use data attribute of Column, use "
+                "`set_base_children` instead"
+            )
+        super().set_base_data(value)
+
+    def __contains__(self, item: ScalarLike) -> bool:
+        if is_scalar(item):
+            return True in libcudf.search.contains(
+                self, column.as_column([item], dtype=self.dtype)
+            )
+        else:
+            return True in libcudf.search.contains(
+                self, column.as_column(item, dtype=self.dtype)
+            )
+
+    def as_numerical_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.NumericalColumn":
+        out_dtype = cudf.api.types.dtype(dtype)
+        string_col = self
+        if out_dtype.kind in {"i", "u"}:
+            if not libstrings.is_integer(string_col).all():
+                raise ValueError(
+                    "Could not convert strings to integer "
+                    "type due to presence of non-integer values."
+                )
+        elif out_dtype.kind == "f":
+            if not libstrings.is_float(string_col).all():
+                raise ValueError(
+                    "Could not convert strings to float "
+                    "type due to presence of non-floating values."
+                )
+
+        result_col = _str_to_numeric_typecast_functions[out_dtype](string_col)
+        return result_col
+
+    def _as_datetime_or_timedelta_column(self, dtype, format):
+        if len(self) == 0:
+            return cudf.core.column.as_column([], dtype=dtype)
+
+        # Check for None strings
+        if (self == "None").any():
+            raise ValueError("Could not convert `None` value to datetime")
+
+        is_nat = self == "NaT"
+        if dtype.kind == "M":
+            without_nat = self.apply_boolean_mask(is_nat.unary_operator("not"))
+            all_same_length = (
+                libstrings.count_characters(without_nat).distinct_count(
+                    dropna=True
+                )
+                == 1
+            )
+            if not all_same_length:
+                # Unfortunately disables OK cases like:
+                # ["2020-01-01", "2020-01-01 00:00:00"]
+                # But currently incorrect for cases like (drops 10):
+                # ["2020-01-01", "2020-01-01 10:00:00"]
+                raise NotImplementedError(
+                    "Cannot parse date-like strings with different formats"
+                )
+            valid_ts = str_cast.istimestamp(self, format)
+            valid = valid_ts | is_nat
+            if not valid.all():
+                raise ValueError(f"Column contains invalid data for {format=}")
+
+        casting_func = (
+            str_cast.timestamp2int
+            if dtype.type == np.datetime64
+            else str_cast.timedelta2int
+        )
+        result_col = casting_func(self, dtype, format)
+
+        if is_nat.any():
+            result_col[is_nat] = None
+
+        return result_col
+
+    def as_datetime_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.DatetimeColumn":
+        out_dtype = cudf.api.types.dtype(dtype)
+
+        # infer on host from the first not na element
+        # or return all null column if all values
+        # are null in current column
+        format = kwargs.get("format", None)
+        if format is None:
+            if self.null_count == len(self):
+                return cast(
+                    "cudf.core.column.DatetimeColumn",
+                    column.column_empty(
+                        len(self), dtype=out_dtype, masked=True
+                    ),
+                )
+            else:
+                format = datetime.infer_format(
+                    self.apply_boolean_mask(self.notnull()).element_indexing(0)
+                )
+
+        return self._as_datetime_or_timedelta_column(out_dtype, format)
+
+    def as_timedelta_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.TimeDeltaColumn":
+        out_dtype = cudf.api.types.dtype(dtype)
+        format = "%D days %H:%M:%S"
+        return self._as_datetime_or_timedelta_column(out_dtype, format)
+
+    def as_decimal_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.DecimalBaseColumn":
+        return libstrings.to_decimal(self, dtype)
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> StringColumn:
+        return self
+
+    @property
+    def values_host(self) -> np.ndarray:
+        """
+        Return a numpy representation of the StringColumn.
+        """
+        return self.to_pandas().values
+
+    @property
+    def values(self) -> cupy.ndarray:
+        """
+        Return a CuPy representation of the StringColumn.
+        """
+        raise TypeError("String Arrays is not yet implemented in cudf")
+
+    def to_pandas(
+        self,
+        index: Optional[pd.Index] = None,
+        nullable: bool = False,
+        **kwargs,
+    ) -> pd.Series:
+        if nullable:
+            pandas_array = pd.StringDtype().__from_arrow__(self.to_arrow())
+            pd_series = pd.Series(pandas_array, copy=False)
+        else:
+            pd_series = self.to_arrow().to_pandas(**kwargs)
+
+        if index is not None:
+            pd_series.index = index
+        return pd_series
+
+    def can_cast_safely(self, to_dtype: Dtype) -> bool:
+        to_dtype = cudf.api.types.dtype(to_dtype)
+
+        if self.dtype == to_dtype:
+            return True
+        elif (
+            to_dtype.kind in {"i", "u"}
+            and not libstrings.is_integer(self).all()
+        ):
+            return False
+        elif to_dtype.kind == "f" and not libstrings.is_float(self).all():
+            return False
+        else:
+            return True
+
+    def find_and_replace(
+        self,
+        to_replace: ColumnLike,
+        replacement: ColumnLike,
+        all_nan: bool = False,
+    ) -> StringColumn:
+        """
+        Return col with *to_replace* replaced with *value*
+        """
+
+        to_replace_col = column.as_column(to_replace)
+        replacement_col = column.as_column(replacement)
+
+        if type(to_replace_col) != type(replacement_col):
+            raise TypeError(
+                f"to_replace and value should be of same types,"
+                f"got to_replace dtype: {to_replace_col.dtype} and "
+                f"value dtype: {replacement_col.dtype}"
+            )
+
+        if (
+            to_replace_col.dtype != self.dtype
+            and replacement_col.dtype != self.dtype
+        ):
+            return self.copy()
+        df = cudf.DataFrame._from_data(
+            {"old": to_replace_col, "new": replacement_col}
+        )
+        df = df.drop_duplicates(subset=["old"], keep="last", ignore_index=True)
+        if df._data["old"].null_count == 1:
+            res = self.fillna(
+                df._data["new"]
+                .apply_boolean_mask(df._data["old"].isnull())
+                .element_indexing(0)
+            )
+            df = df.dropna(subset=["old"])
+        else:
+            res = self
+        return libcudf.replace.replace(res, df._data["old"], df._data["new"])
+
+    def fillna(
+        self,
+        fill_value: Any = None,
+        method: Optional[str] = None,
+        dtype: Optional[Dtype] = None,
+    ) -> StringColumn:
+        if fill_value is not None:
+            if not is_scalar(fill_value):
+                fill_value = column.as_column(fill_value, dtype=self.dtype)
+            elif cudf._lib.scalar._is_null_host_scalar(fill_value):
+                # Trying to fill <NA> with <NA> value? Return copy.
+                return self.copy(deep=True)
+            return super().fillna(value=fill_value, dtype="object")
+        else:
+            return super().fillna(method=method)
+
+    def normalize_binop_value(
+        self, other
+    ) -> Union[column.ColumnBase, cudf.Scalar]:
+        if (
+            isinstance(other, (column.ColumnBase, cudf.Scalar))
+            and other.dtype == "object"
+        ):
+            return other
+        if is_scalar(other):
+            return cudf.Scalar(other)
+        return NotImplemented
+
+    def _binaryop(
+        self, other: ColumnBinaryOperand, op: str
+    ) -> "column.ColumnBase":
+        reflect, op = self._check_reflected_op(op)
+        # Due to https://github.com/pandas-dev/pandas/issues/46332 we need to
+        # support binary operations between empty or all null string columns
+        # and columns of other dtypes, even if those operations would otherwise
+        # be invalid. For example, you cannot divide strings, but pandas allows
+        # division between an empty string column and a (nonempty) integer
+        # column. Ideally we would disable these operators entirely, but until
+        # the above issue is resolved we cannot avoid this problem.
+        if self.null_count == len(self):
+            if op in {
+                "__add__",
+                "__sub__",
+                "__mul__",
+                "__mod__",
+                "__pow__",
+                "__truediv__",
+                "__floordiv__",
+            }:
+                return self
+            elif op in {"__eq__", "__lt__", "__le__", "__gt__", "__ge__"}:
+                return self.notnull()
+            elif op == "__ne__":
+                return self.isnull()
+
+        other = self._wrap_binop_normalization(other)
+        if other is NotImplemented:
+            return NotImplemented
+
+        if isinstance(other, (StringColumn, str, cudf.Scalar)):
+            if isinstance(other, cudf.Scalar) and other.dtype != "O":
+                if op in {
+                    "__eq__",
+                    "__ne__",
+                }:
+                    return column.full(
+                        len(self), op == "__ne__", dtype="bool"
+                    ).set_mask(self.mask)
+                else:
+                    return NotImplemented
+
+            if op == "__add__":
+                if isinstance(other, cudf.Scalar):
+                    other = cast(
+                        StringColumn,
+                        column.full(len(self), other, dtype="object"),
+                    )
+
+                # Explicit types are necessary because mypy infers ColumnBase
+                # rather than StringColumn and sometimes forgets Scalar.
+                lhs: Union[cudf.Scalar, StringColumn]
+                rhs: Union[cudf.Scalar, StringColumn]
+                lhs, rhs = (other, self) if reflect else (self, other)
+
+                return cast(
+                    "column.ColumnBase",
+                    libstrings.concatenate(
+                        [lhs, rhs],
+                        sep=cudf.Scalar(""),
+                        na_rep=cudf.Scalar(None, "str"),
+                    ),
+                )
+            elif op in {
+                "__eq__",
+                "__ne__",
+                "__gt__",
+                "__lt__",
+                "__ge__",
+                "__le__",
+                "NULL_EQUALS",
+            }:
+                lhs, rhs = (other, self) if reflect else (self, other)
+                return libcudf.binaryop.binaryop(
+                    lhs=lhs, rhs=rhs, op=op, dtype="bool"
+                )
+        return NotImplemented
+
+    @copy_docstring(column.ColumnBase.view)
+    def view(self, dtype) -> "cudf.core.column.ColumnBase":
+        if self.null_count > 0:
+            raise ValueError(
+                "Can not produce a view of a string column with nulls"
+            )
+        dtype = cudf.api.types.dtype(dtype)
+        str_byte_offset = self.base_children[0].element_indexing(self.offset)
+        str_end_byte_offset = self.base_children[0].element_indexing(
+            self.offset + self.size
+        )
+        char_dtype_size = self.base_children[1].dtype.itemsize
+
+        n_bytes_to_view = (
+            str_end_byte_offset - str_byte_offset
+        ) * char_dtype_size
+
+        to_view = column.build_column(
+            self.base_children[1].data,
+            dtype=self.base_children[1].dtype,
+            offset=str_byte_offset,
+            size=n_bytes_to_view,
+        )
+
+        return to_view.view(dtype)
+
+
+def _get_cols_list(parent_obj, others):
+    parent_index = (
+        parent_obj.index if isinstance(parent_obj, cudf.Series) else parent_obj
+    )
+
+    if (
+        can_convert_to_column(others)
+        and len(others) > 0
+        and (
+            can_convert_to_column(
+                others.iloc[0]
+                if isinstance(others, cudf.Series)
+                else others[0]
+            )
+        )
+    ):
+        """
+        If others is a list-like object (in our case lists & tuples)
+        just another Series/Index, great go ahead with concatenation.
+        """
+        cols_list = [
+            column.as_column(frame.reindex(parent_index), dtype="str")
+            if (
+                parent_index is not None
+                and isinstance(frame, cudf.Series)
+                and not frame.index.equals(parent_index)
+            )
+            else column.as_column(frame, dtype="str")
+            for frame in others
+        ]
+
+        return cols_list
+    elif others is not None and not isinstance(others, StringMethods):
+        if (
+            parent_index is not None
+            and isinstance(others, cudf.Series)
+            and not others.index.equals(parent_index)
+        ):
+            others = others.reindex(parent_index)
+
+        return [column.as_column(others, dtype="str")]
+    else:
+        raise TypeError(
+            "others must be Series, Index, DataFrame, np.ndarrary "
+            "or list-like (either containing only strings or "
+            "containing only objects of type Series/Index/"
+            "np.ndarray[1-dim])"
+        )
diff --git a/python/cudf/cudf/core/column/struct.py b/python/cudf/cudf/core/column/struct.py
new file mode 100644
index 0000000..0bb21f4
--- /dev/null
+++ b/python/cudf/cudf/core/column/struct.py
@@ -0,0 +1,244 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+from __future__ import annotations
+
+from functools import cached_property
+from typing import Optional
+
+import pandas as pd
+import pyarrow as pa
+
+import cudf
+from cudf._typing import Dtype
+from cudf.api.types import is_struct_dtype
+from cudf.core.column import ColumnBase, build_struct_column
+from cudf.core.column.methods import ColumnMethods
+from cudf.core.dtypes import StructDtype
+from cudf.core.missing import NA
+
+
+class StructColumn(ColumnBase):
+    """
+    Column that stores fields of values.
+
+    Every column has n children, where n is
+    the number of fields in the Struct Dtype.
+
+    """
+
+    dtype: StructDtype
+
+    @property
+    def base_size(self):
+        if self.base_children:
+            return len(self.base_children[0])
+        else:
+            return self.size + self.offset
+
+    def to_arrow(self):
+        children = [
+            pa.nulls(len(child))
+            if len(child) == child.null_count
+            else child.to_arrow()
+            for child in self.children
+        ]
+
+        pa_type = pa.struct(
+            {
+                field: child.type
+                for field, child in zip(self.dtype.fields, children)
+            }
+        )
+
+        if self.nullable:
+            buffers = (pa.py_buffer(self.mask.memoryview()),)
+        else:
+            buffers = (None,)
+
+        return pa.StructArray.from_buffers(
+            pa_type, len(self), buffers, children=children
+        )
+
+    def to_pandas(
+        self, index: Optional[pd.Index] = None, **kwargs
+    ) -> pd.Series:
+        # We cannot go via Arrow's `to_pandas` because of the following issue:
+        # https://issues.apache.org/jira/browse/ARROW-12680
+
+        pd_series = pd.Series(self.to_arrow().tolist(), dtype="object")
+
+        if index is not None:
+            pd_series.index = index
+        return pd_series
+
+    @cached_property
+    def memory_usage(self):
+        n = 0
+        if self.nullable:
+            n += cudf._lib.null_mask.bitmask_allocation_size_bytes(self.size)
+
+        for child in self.children:
+            n += child.memory_usage
+
+        return n
+
+    def element_indexing(self, index: int):
+        result = super().element_indexing(index)
+        return {
+            field: value
+            for field, value in zip(self.dtype.fields, result.values())
+        }
+
+    def __setitem__(self, key, value):
+        if isinstance(value, dict):
+            # filling in fields not in dict
+            for field in self.dtype.fields:
+                value[field] = value.get(field, NA)
+
+            value = cudf.Scalar(value, self.dtype)
+        super().__setitem__(key, value)
+
+    def copy(self, deep=True):
+        # Since struct columns are immutable, both deep and
+        # shallow copies share the underlying device data and mask.
+        result = super().copy(deep=False)
+        if deep:
+            result = result._rename_fields(self.dtype.fields.keys())
+        return result
+
+    def _rename_fields(self, names):
+        """
+        Return a StructColumn with the same field values as this StructColumn,
+        but with the field names equal to `names`.
+        """
+        dtype = cudf.core.dtypes.StructDtype(
+            {name: col.dtype for name, col in zip(names, self.children)}
+        )
+        return StructColumn(
+            data=None,
+            size=self.size,
+            dtype=dtype,
+            mask=self.base_mask,
+            offset=self.offset,
+            null_count=self.null_count,
+            children=self.base_children,
+        )
+
+    @property
+    def __cuda_array_interface__(self):
+        raise NotImplementedError(
+            "Structs are not yet supported via `__cuda_array_interface__`"
+        )
+
+    def _with_type_metadata(self: StructColumn, dtype: Dtype) -> StructColumn:
+        from cudf.core.column import IntervalColumn
+        from cudf.core.dtypes import IntervalDtype
+
+        # Check IntervalDtype first because it's a subclass of StructDtype
+        if isinstance(dtype, IntervalDtype):
+            return IntervalColumn.from_struct_column(self, closed=dtype.closed)
+        elif isinstance(dtype, StructDtype):
+            return build_struct_column(
+                names=dtype.fields.keys(),
+                children=tuple(
+                    self.base_children[i]._with_type_metadata(dtype.fields[f])
+                    for i, f in enumerate(dtype.fields.keys())
+                ),
+                mask=self.base_mask,
+                size=self.size,
+                offset=self.offset,
+                null_count=self.null_count,
+            )
+
+        return self
+
+
+class StructMethods(ColumnMethods):
+    """
+    Struct methods for Series
+    """
+
+    _column: StructColumn
+
+    def __init__(self, parent=None):
+        if not is_struct_dtype(parent.dtype):
+            raise AttributeError(
+                "Can only use .struct accessor with a 'struct' dtype"
+            )
+        super().__init__(parent=parent)
+
+    def field(self, key):
+        """
+        Extract children of the specified struct column
+        in the Series
+
+        Parameters
+        ----------
+        key: int or str
+            index/position or field name of the respective
+            struct column
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = cudf.Series([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
+        >>> s.struct.field(0)
+        0    1
+        1    3
+        dtype: int64
+        >>> s.struct.field('a')
+        0    1
+        1    3
+        dtype: int64
+        """
+        fields = list(self._column.dtype.fields.keys())
+        if key in fields:
+            pos = fields.index(key)
+            return self._return_or_inplace(self._column.children[pos])
+        else:
+            if isinstance(key, int):
+                try:
+                    return self._return_or_inplace(self._column.children[key])
+                except IndexError:
+                    raise IndexError(f"Index {key} out of range")
+            else:
+                raise KeyError(
+                    f"Field '{key}' is not found in the set of existing keys."
+                )
+
+    def explode(self):
+        """
+        Return a DataFrame whose columns are the fields of this struct Series.
+
+        Notes
+        -----
+        Note that a copy of the columns is made.
+
+        Examples
+        --------
+        >>> s
+        0    {'a': 1, 'b': 'x'}
+        1    {'a': 2, 'b': 'y'}
+        2    {'a': 3, 'b': 'z'}
+        3    {'a': 4, 'b': 'a'}
+        dtype: struct
+
+        >>> s.struct.explode()
+           a  b
+        0  1  x
+        1  2  y
+        2  3  z
+        3  4  a
+        """
+        return cudf.DataFrame._from_data(
+            cudf.core.column_accessor.ColumnAccessor(
+                {
+                    name: col.copy(deep=True)
+                    for name, col in zip(
+                        self._column.dtype.fields, self._column.children
+                    )
+                }
+            )
+        )
diff --git a/python/cudf/cudf/core/column/timedelta.py b/python/cudf/cudf/core/column/timedelta.py
new file mode 100644
index 0000000..13bb97b
--- /dev/null
+++ b/python/cudf/cudf/core/column/timedelta.py
@@ -0,0 +1,590 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import datetime
+from typing import Any, Optional, Sequence, cast
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._typing import ColumnBinaryOperand, DatetimeLikeScalar, Dtype
+from cudf.api.types import is_scalar, is_timedelta64_dtype
+from cudf.core.buffer import Buffer, acquire_spill_lock
+from cudf.core.column import ColumnBase, column, string
+from cudf.utils.dtypes import np_to_pa_dtype
+from cudf.utils.utils import _all_bools_with_nulls
+
+_dtype_to_format_conversion = {
+    "timedelta64[ns]": "%D days %H:%M:%S",
+    "timedelta64[us]": "%D days %H:%M:%S",
+    "timedelta64[ms]": "%D days %H:%M:%S",
+    "timedelta64[s]": "%D days %H:%M:%S",
+}
+
+_unit_to_nanoseconds_conversion = {
+    "ns": 1,
+    "us": 1_000,
+    "ms": 1_000_000,
+    "s": 1_000_000_000,
+    "m": 60_000_000_000,
+    "h": 3_600_000_000_000,
+    "D": 86_400_000_000_000,
+}
+
+
+class TimeDeltaColumn(ColumnBase):
+    """
+    Parameters
+    ----------
+    data : Buffer
+        The Timedelta values
+    dtype : np.dtype
+        The data type
+    size : int
+        Size of memory allocation.
+    mask : Buffer; optional
+        The validity mask
+    offset : int
+        Data offset
+    null_count : int, optional
+        The number of null values.
+        If None, it is calculated automatically.
+    """
+
+    _VALID_BINARY_OPERATIONS = {
+        "__eq__",
+        "__ne__",
+        "__lt__",
+        "__le__",
+        "__gt__",
+        "__ge__",
+        "__add__",
+        "__sub__",
+        "__mul__",
+        "__mod__",
+        "__truediv__",
+        "__floordiv__",
+        "__radd__",
+        "__rsub__",
+        "__rmul__",
+        "__rmod__",
+        "__rtruediv__",
+        "__rfloordiv__",
+    }
+
+    def __init__(
+        self,
+        data: Buffer,
+        dtype: Dtype,
+        size: Optional[int] = None,  # TODO: make non-optional
+        mask: Optional[Buffer] = None,
+        offset: int = 0,
+        null_count: Optional[int] = None,
+    ):
+        dtype = cudf.dtype(dtype)
+
+        if data.size % dtype.itemsize:
+            raise ValueError("Buffer size must be divisible by element size")
+        if size is None:
+            size = data.size // dtype.itemsize
+            size = size - offset
+        super().__init__(
+            data,
+            size=size,
+            dtype=dtype,
+            mask=mask,
+            offset=offset,
+            null_count=null_count,
+        )
+
+        if self.dtype.type is not np.timedelta64:
+            raise TypeError(f"{self.dtype} is not a supported duration type")
+
+        self._time_unit, _ = np.datetime_data(self.dtype)
+
+    def __contains__(self, item: DatetimeLikeScalar) -> bool:
+        try:
+            item = np.timedelta64(item, self._time_unit)
+        except ValueError:
+            # If item cannot be converted to duration type
+            # np.timedelta64 raises ValueError, hence `item`
+            # cannot exist in `self`.
+            return False
+        return item.view("int64") in self.as_numerical
+
+    @property
+    def values(self):
+        """
+        Return a CuPy representation of the TimeDeltaColumn.
+        """
+        raise NotImplementedError(
+            "TimeDelta Arrays is not yet implemented in cudf"
+        )
+
+    @acquire_spill_lock()
+    def to_arrow(self) -> pa.Array:
+        mask = None
+        if self.nullable:
+            mask = pa.py_buffer(
+                self.mask_array_view(mode="read").copy_to_host()
+            )
+        data = pa.py_buffer(
+            self.as_numerical.data_array_view(mode="read").copy_to_host()
+        )
+        pa_dtype = np_to_pa_dtype(self.dtype)
+        return pa.Array.from_buffers(
+            type=pa_dtype,
+            length=len(self),
+            buffers=[mask, data],
+            null_count=self.null_count,
+        )
+
+    def to_pandas(
+        self, index=None, nullable: bool = False, **kwargs
+    ) -> pd.Series:
+        # `copy=True` workaround until following issue is fixed:
+        # https://issues.apache.org/jira/browse/ARROW-9772
+
+        # Pandas only supports `timedelta64[ns]` dtype
+        # and conversion to this type is necessary to make
+        # arrow to pandas conversion happen for large values.
+        return pd.Series(
+            self.astype("timedelta64[ns]").to_arrow(),
+            copy=True,
+            dtype=self.dtype,
+            index=index,
+        )
+
+    def _binaryop(self, other: ColumnBinaryOperand, op: str) -> ColumnBase:
+        reflect, op = self._check_reflected_op(op)
+        other = self._wrap_binop_normalization(other)
+        if other is NotImplemented:
+            return NotImplemented
+
+        this: ColumnBinaryOperand = self
+        out_dtype = None
+
+        if is_timedelta64_dtype(other.dtype):
+            # TODO: pandas will allow these operators to work but return false
+            # when comparing to non-timedelta dtypes. We should do the same.
+            if op in {
+                "__eq__",
+                "__ne__",
+                "__lt__",
+                "__gt__",
+                "__le__",
+                "__ge__",
+                "NULL_EQUALS",
+            }:
+                out_dtype = cudf.dtype(np.bool_)
+            elif op == "__mod__":
+                out_dtype = determine_out_dtype(self.dtype, other.dtype)
+            elif op in {"__truediv__", "__floordiv__"}:
+                common_dtype = determine_out_dtype(self.dtype, other.dtype)
+                out_dtype = np.float64 if op == "__truediv__" else np.int64
+                this = self.astype(common_dtype).astype(out_dtype)
+                if isinstance(other, cudf.Scalar):
+                    if other.is_valid():
+                        other = other.value.astype(common_dtype).astype(
+                            out_dtype
+                        )
+                    else:
+                        other = cudf.Scalar(None, out_dtype)
+                else:
+                    other = other.astype(common_dtype).astype(out_dtype)
+            elif op in {"__add__", "__sub__"}:
+                out_dtype = determine_out_dtype(self.dtype, other.dtype)
+        elif other.dtype.kind in {"f", "i", "u"}:
+            if op in {"__mul__", "__mod__", "__truediv__", "__floordiv__"}:
+                out_dtype = self.dtype
+            elif op in {"__eq__", "NULL_EQUALS", "__ne__"}:
+                if isinstance(other, ColumnBase) and not isinstance(
+                    other, TimeDeltaColumn
+                ):
+                    result = _all_bools_with_nulls(
+                        self, other, bool_fill_value=op == "__ne__"
+                    )
+                    if cudf.get_option("mode.pandas_compatible"):
+                        result = result.fillna(op == "__ne__")
+                    return result
+
+        if out_dtype is None:
+            return NotImplemented
+
+        lhs, rhs = (other, this) if reflect else (this, other)
+
+        result = libcudf.binaryop.binaryop(lhs, rhs, op, out_dtype)
+        if cudf.get_option(
+            "mode.pandas_compatible"
+        ) and out_dtype == cudf.dtype(np.bool_):
+            result = result.fillna(op == "__ne__")
+        return result
+
+    def normalize_binop_value(self, other) -> ColumnBinaryOperand:
+        if isinstance(other, (ColumnBase, cudf.Scalar)):
+            return other
+
+        tz_error_msg = (
+            "Cannot perform binary operation on timezone-naive columns"
+            " and timezone-aware timestamps."
+        )
+        if isinstance(other, pd.Timestamp):
+            if other.tz is not None:
+                raise NotImplementedError(tz_error_msg)
+            other = other.to_datetime64()
+        elif isinstance(other, pd.Timedelta):
+            other = other.to_timedelta64()
+        elif isinstance(other, datetime.timedelta):
+            other = np.timedelta64(other)
+        elif isinstance(other, datetime.datetime) and other.tzinfo is not None:
+            raise NotImplementedError(tz_error_msg)
+
+        if isinstance(other, np.timedelta64):
+            other_time_unit = cudf.utils.dtypes.get_time_unit(other)
+            if np.isnat(other):
+                return cudf.Scalar(None, dtype=self.dtype)
+
+            if other_time_unit not in {"s", "ms", "ns", "us"}:
+                common_dtype = "timedelta64[s]"
+            else:
+                common_dtype = determine_out_dtype(self.dtype, other.dtype)
+            return cudf.Scalar(other.astype(common_dtype))
+        elif np.isscalar(other):
+            return cudf.Scalar(other)
+        return NotImplemented
+
+    @property
+    def as_numerical(self) -> "cudf.core.column.NumericalColumn":
+        return cast(
+            "cudf.core.column.NumericalColumn",
+            column.build_column(
+                data=self.base_data,
+                dtype=np.int64,
+                mask=self.base_mask,
+                offset=self.offset,
+                size=self.size,
+            ),
+        )
+
+    @property
+    def time_unit(self) -> str:
+        return self._time_unit
+
+    def fillna(
+        self,
+        fill_value: Any = None,
+        method: Optional[str] = None,
+        dtype: Optional[Dtype] = None,
+    ) -> TimeDeltaColumn:
+        if fill_value is not None:
+            if cudf.utils.utils._isnat(fill_value):
+                return self.copy(deep=True)
+            col: ColumnBase = self
+            if is_scalar(fill_value):
+                if isinstance(fill_value, np.timedelta64):
+                    dtype = determine_out_dtype(self.dtype, fill_value.dtype)
+                    fill_value = fill_value.astype(dtype)
+                    col = col.astype(dtype)
+                if not isinstance(fill_value, cudf.Scalar):
+                    fill_value = cudf.Scalar(fill_value, dtype=dtype)
+            else:
+                fill_value = column.as_column(fill_value, nan_as_null=False)
+            return cast(TimeDeltaColumn, ColumnBase.fillna(col, fill_value))
+        else:
+            return super().fillna(method=method)
+
+    def as_numerical_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.NumericalColumn":
+        return cast(
+            "cudf.core.column.NumericalColumn", self.as_numerical.astype(dtype)
+        )
+
+    def as_datetime_column(
+        self, dtype: Dtype, **kwargs
+    ) -> "cudf.core.column.DatetimeColumn":
+        raise TypeError(
+            f"cannot astype a timedelta from {self.dtype} to {dtype}"
+        )
+
+    def as_string_column(
+        self, dtype: Dtype, format=None, **kwargs
+    ) -> "cudf.core.column.StringColumn":
+        if format is None:
+            format = _dtype_to_format_conversion.get(
+                self.dtype.name, "%D days %H:%M:%S"
+            )
+        if len(self) > 0:
+            return string._timedelta_to_str_typecast_functions[
+                cudf.dtype(self.dtype)
+            ](self, format=format)
+        else:
+            return cast(
+                "cudf.core.column.StringColumn",
+                column.column_empty(0, dtype="object", masked=False),
+            )
+
+    def as_timedelta_column(self, dtype: Dtype, **kwargs) -> TimeDeltaColumn:
+        dtype = cudf.dtype(dtype)
+        if dtype == self.dtype:
+            return self
+        return libcudf.unary.cast(self, dtype=dtype)
+
+    def mean(self, skipna=None, dtype: Dtype = np.float64) -> pd.Timedelta:
+        return pd.Timedelta(
+            self.as_numerical.mean(skipna=skipna, dtype=dtype),
+            unit=self.time_unit,
+        )
+
+    def median(self, skipna: Optional[bool] = None) -> pd.Timedelta:
+        return pd.Timedelta(
+            self.as_numerical.median(skipna=skipna), unit=self.time_unit
+        )
+
+    def isin(self, values: Sequence) -> ColumnBase:
+        return cudf.core.tools.datetimes._isin_datetimelike(self, values)
+
+    def quantile(
+        self,
+        q: np.ndarray,
+        interpolation: str,
+        exact: bool,
+        return_scalar: bool,
+    ) -> ColumnBase:
+        result = self.as_numerical.quantile(
+            q=q,
+            interpolation=interpolation,
+            exact=exact,
+            return_scalar=return_scalar,
+        )
+        if return_scalar:
+            return pd.Timedelta(result, unit=self.time_unit)
+        return result.astype(self.dtype)
+
+    def sum(
+        self,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        dtype: Optional[Dtype] = None,
+    ) -> pd.Timedelta:
+        return pd.Timedelta(
+            # Since sum isn't overridden in Numerical[Base]Column, mypy only
+            # sees the signature from Reducible (which doesn't have the extra
+            # parameters from ColumnBase._reduce) so we have to ignore this.
+            self.as_numerical.sum(  # type: ignore
+                skipna=skipna, min_count=min_count, dtype=dtype
+            ),
+            unit=self.time_unit,
+        )
+
+    def std(
+        self,
+        skipna: Optional[bool] = None,
+        min_count: int = 0,
+        dtype: Dtype = np.float64,
+        ddof: int = 1,
+    ) -> pd.Timedelta:
+        return pd.Timedelta(
+            self.as_numerical.std(
+                skipna=skipna, min_count=min_count, ddof=ddof, dtype=dtype
+            ),
+            unit=self.time_unit,
+        )
+
+    def components(self, index=None) -> "cudf.DataFrame":
+        """
+        Return a Dataframe of the components of the Timedeltas.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
+        >>> s = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656,
+        ...     3244334234], dtype='timedelta64[ms]')
+        >>> s
+        0      141 days 13:35:12.123
+        1       14 days 06:00:31.231
+        2    13000 days 10:12:48.712
+        3        0 days 00:35:35.656
+        4       37 days 13:12:14.234
+        dtype: timedelta64[ms]
+        >>> s.dt.components
+            days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0    141     13       35       12           123             0            0
+        1     14      6        0       31           231             0            0
+        2  13000     10       12       48           712             0            0
+        3      0      0       35       35           656             0            0
+        4     37     13       12       14           234             0            0
+        """  # noqa: E501
+
+        return cudf.DataFrame(
+            data={
+                "days": self
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["D"], "ns")
+                ),
+                "hours": (
+                    self
+                    % cudf.Scalar(
+                        np.timedelta64(
+                            _unit_to_nanoseconds_conversion["D"], "ns"
+                        )
+                    )
+                )
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["h"], "ns")
+                ),
+                "minutes": (
+                    self
+                    % cudf.Scalar(
+                        np.timedelta64(
+                            _unit_to_nanoseconds_conversion["h"], "ns"
+                        )
+                    )
+                )
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["m"], "ns")
+                ),
+                "seconds": (
+                    self
+                    % cudf.Scalar(
+                        np.timedelta64(
+                            _unit_to_nanoseconds_conversion["m"], "ns"
+                        )
+                    )
+                )
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["s"], "ns")
+                ),
+                "milliseconds": (
+                    self
+                    % cudf.Scalar(
+                        np.timedelta64(
+                            _unit_to_nanoseconds_conversion["s"], "ns"
+                        )
+                    )
+                )
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["ms"], "ns")
+                ),
+                "microseconds": (
+                    self
+                    % cudf.Scalar(
+                        np.timedelta64(
+                            _unit_to_nanoseconds_conversion["ms"], "ns"
+                        )
+                    )
+                )
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["us"], "ns")
+                ),
+                "nanoseconds": (
+                    self
+                    % cudf.Scalar(
+                        np.timedelta64(
+                            _unit_to_nanoseconds_conversion["us"], "ns"
+                        )
+                    )
+                )
+                // cudf.Scalar(
+                    np.timedelta64(_unit_to_nanoseconds_conversion["ns"], "ns")
+                ),
+            },
+            index=index,
+        )
+
+    @property
+    def days(self) -> "cudf.core.column.NumericalColumn":
+        """
+        Number of days for each element.
+
+        Returns
+        -------
+        NumericalColumn
+        """
+        return self // cudf.Scalar(
+            np.timedelta64(_unit_to_nanoseconds_conversion["D"], "ns")
+        )
+
+    @property
+    def seconds(self) -> "cudf.core.column.NumericalColumn":
+        """
+        Number of seconds (>= 0 and less than 1 day).
+
+        Returns
+        -------
+        NumericalColumn
+        """
+        # This property must return the number of seconds (>= 0 and
+        # less than 1 day) for each element, hence first performing
+        # mod operation to remove the number of days and then performing
+        # division operation to extract the number of seconds.
+
+        return (
+            self
+            % cudf.Scalar(
+                np.timedelta64(_unit_to_nanoseconds_conversion["D"], "ns")
+            )
+        ) // cudf.Scalar(
+            np.timedelta64(_unit_to_nanoseconds_conversion["s"], "ns")
+        )
+
+    @property
+    def microseconds(self) -> "cudf.core.column.NumericalColumn":
+        """
+        Number of microseconds (>= 0 and less than 1 second).
+
+        Returns
+        -------
+        NumericalColumn
+        """
+        # This property must return the number of microseconds (>= 0 and
+        # less than 1 second) for each element, hence first performing
+        # mod operation to remove the number of seconds and then performing
+        # division operation to extract the number of microseconds.
+
+        return (
+            self % np.timedelta64(_unit_to_nanoseconds_conversion["s"], "ns")
+        ) // cudf.Scalar(
+            np.timedelta64(_unit_to_nanoseconds_conversion["us"], "ns")
+        )
+
+    @property
+    def nanoseconds(self) -> "cudf.core.column.NumericalColumn":
+        """
+        Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
+
+        Returns
+        -------
+        NumericalColumn
+        """
+        # This property must return the number of nanoseconds (>= 0 and
+        # less than 1 microsecond) for each element, hence first performing
+        # mod operation to remove the number of microseconds and then
+        # performing division operation to extract the number
+        # of nanoseconds.
+
+        return (
+            self
+            % cudf.Scalar(
+                np.timedelta64(_unit_to_nanoseconds_conversion["us"], "ns")
+            )
+        ) // cudf.Scalar(
+            np.timedelta64(_unit_to_nanoseconds_conversion["ns"], "ns")
+        )
+
+
+def determine_out_dtype(lhs_dtype: Dtype, rhs_dtype: Dtype) -> Dtype:
+    if np.can_cast(np.dtype(lhs_dtype), np.dtype(rhs_dtype)):
+        return rhs_dtype
+    elif np.can_cast(np.dtype(rhs_dtype), np.dtype(lhs_dtype)):
+        return lhs_dtype
+    else:
+        raise TypeError(f"Cannot type-cast {lhs_dtype} and {rhs_dtype}")
diff --git a/python/cudf/cudf/core/column_accessor.py b/python/cudf/cudf/core/column_accessor.py
new file mode 100644
index 0000000..93105b4
--- /dev/null
+++ b/python/cudf/cudf/core/column_accessor.py
@@ -0,0 +1,750 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import itertools
+import warnings
+from collections import abc
+from functools import cached_property, reduce
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Mapping,
+    Optional,
+    Tuple,
+    Union,
+)
+
+import numpy as np
+import pandas as pd
+from packaging.version import Version
+from pandas.api.types import is_bool
+from typing_extensions import Self
+
+import cudf
+from cudf.core import column
+
+if TYPE_CHECKING:
+    from cudf.core.column import ColumnBase
+
+
+class _NestedGetItemDict(dict):
+    """A dictionary whose __getitem__ method accesses nested dicts.
+
+    This class directly subclasses dict for performance, so there are a number
+    of gotchas: 1) the only safe accessor for nested elements is
+    `__getitem__` (all other accessors will fail to perform nested lookups), 2)
+    nested mappings will not exhibit the same behavior (they will be raw
+    dictionaries unless explicitly created to be of this class), and 3) to
+    construct this class you _must_ use `from_zip` to get appropriate treatment
+    of tuple keys.
+    """
+
+    @classmethod
+    def from_zip(cls, data):
+        """Create from zip, specialized factory for nesting."""
+        obj = cls()
+        for key, value in data:
+            d = obj
+            for k in key[:-1]:
+                d = d.setdefault(k, {})
+            d[key[-1]] = value
+        return obj
+
+    def __getitem__(self, key):
+        """Recursively apply dict.__getitem__ for nested elements."""
+        # As described in the pandas docs
+        # https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-indexing-with-hierarchical-index  # noqa: E501
+        # accessing nested elements of a multiindex must be done using a tuple.
+        # Lists and other sequences are treated as accessing multiple elements
+        # at the top level of the index.
+        if isinstance(key, tuple):
+            return reduce(dict.__getitem__, key, self)
+        return super().__getitem__(key)
+
+
+def _to_flat_dict_inner(d, parents=()):
+    for k, v in d.items():
+        if not isinstance(v, d.__class__):
+            if parents:
+                k = parents + (k,)
+            yield (k, v)
+        else:
+            yield from _to_flat_dict_inner(d=v, parents=parents + (k,))
+
+
+def _to_flat_dict(d):
+    """
+    Convert the given nested dictionary to a flat dictionary
+    with tuple keys.
+    """
+    return {k: v for k, v in _to_flat_dict_inner(d)}
+
+
+class ColumnAccessor(abc.MutableMapping):
+    """
+    Parameters
+    ----------
+    data : mapping
+        Mapping of keys to column values.
+    multiindex : bool, optional
+        Whether tuple keys represent a hierarchical
+        index with multiple "levels" (default=False).
+    level_names : tuple, optional
+        Tuple containing names for each of the levels.
+        For a non-hierarchical index, a tuple of size 1
+        may be passe.
+    rangeindex : bool, optional
+        Whether the keys should be returned as a RangeIndex
+        in `to_pandas_index` (default=False).
+    """
+
+    _data: "Dict[Any, ColumnBase]"
+    multiindex: bool
+    _level_names: Tuple[Any, ...]
+
+    def __init__(
+        self,
+        data: Union[abc.MutableMapping, ColumnAccessor, None] = None,
+        multiindex: bool = False,
+        level_names=None,
+        rangeindex: bool = False,
+    ):
+        self.rangeindex = rangeindex
+        if data is None:
+            data = {}
+        # TODO: we should validate the keys of `data`
+        if isinstance(data, ColumnAccessor):
+            multiindex = multiindex or data.multiindex
+            level_names = level_names or data.level_names
+            self._data = data._data
+            self.multiindex = multiindex
+            self._level_names = level_names
+            self.rangeindex = data.rangeindex
+        else:
+            # This code path is performance-critical for copies and should be
+            # modified with care.
+            self._data = {}
+            if data:
+                data = dict(data)
+                # Faster than next(iter(data.values()))
+                column_length = len(data[next(iter(data))])
+                for k, v in data.items():
+                    # Much faster to avoid the function call if possible; the
+                    # extra isinstance is negligible if we do have to make a
+                    # column from something else.
+                    if not isinstance(v, column.ColumnBase):
+                        v = column.as_column(v)
+                    if len(v) != column_length:
+                        raise ValueError("All columns must be of equal length")
+                    self._data[k] = v
+
+            self.multiindex = multiindex
+            self._level_names = level_names
+
+    @classmethod
+    def _create_unsafe(
+        cls,
+        data: Dict[Any, ColumnBase],
+        multiindex: bool = False,
+        level_names=None,
+    ) -> ColumnAccessor:
+        # create a ColumnAccessor without verifying column
+        # type or size
+        obj = cls()
+        obj._data = data
+        obj.multiindex = multiindex
+        obj._level_names = level_names
+        return obj
+
+    def __iter__(self):
+        return iter(self._data)
+
+    def __getitem__(self, key: Any) -> ColumnBase:
+        return self._data[key]
+
+    def __setitem__(self, key: Any, value: Any):
+        self.set_by_label(key, value)
+
+    def __delitem__(self, key: Any):
+        del self._data[key]
+        self._clear_cache()
+
+    def __len__(self) -> int:
+        return len(self._data)
+
+    def __repr__(self) -> str:
+        type_info = (
+            f"{self.__class__.__name__}("
+            f"multiindex={self.multiindex}, "
+            f"level_names={self.level_names})"
+        )
+        column_info = "\n".join(
+            [f"{name}: {col.dtype}" for name, col in self.items()]
+        )
+        return f"{type_info}\n{column_info}"
+
+    @property
+    def level_names(self) -> Tuple[Any, ...]:
+        if self._level_names is None or len(self._level_names) == 0:
+            return tuple((None,) * max(1, self.nlevels))
+        else:
+            return self._level_names
+
+    @property
+    def nlevels(self) -> int:
+        if len(self._data) == 0:
+            return 0
+        if not self.multiindex:
+            return 1
+        else:
+            return len(next(iter(self.keys())))
+
+    @property
+    def name(self) -> Any:
+        return self.level_names[-1]
+
+    @property
+    def nrows(self) -> int:
+        if len(self._data) == 0:
+            return 0
+        else:
+            return len(next(iter(self.values())))
+
+    @cached_property
+    def names(self) -> Tuple[Any, ...]:
+        return tuple(self.keys())
+
+    @cached_property
+    def columns(self) -> Tuple[ColumnBase, ...]:
+        return tuple(self.values())
+
+    @cached_property
+    def _grouped_data(self) -> abc.MutableMapping:
+        """
+        If self.multiindex is True,
+        return the underlying mapping as a nested mapping.
+        """
+        if self.multiindex:
+            return _NestedGetItemDict.from_zip(zip(self.names, self.columns))
+        else:
+            return self._data
+
+    @cached_property
+    def _column_length(self):
+        try:
+            return len(self._data[next(iter(self._data))])
+        except StopIteration:
+            return 0
+
+    def _clear_cache(self):
+        cached_properties = ("columns", "names", "_grouped_data")
+        for attr in cached_properties:
+            try:
+                self.__delattr__(attr)
+            except AttributeError:
+                pass
+
+        # Column length should only be cleared if no data is present.
+        if len(self._data) == 0 and hasattr(self, "_column_length"):
+            del self._column_length
+
+    def to_pandas_index(self) -> pd.Index:
+        """Convert the keys of the ColumnAccessor to a Pandas Index object."""
+        if self.multiindex and len(self.level_names) > 0:
+            # Using `from_frame()` instead of `from_tuples`
+            # prevents coercion of values to a different type
+            # (e.g., ''->NaT)
+            with warnings.catch_warnings():
+                # Specifying `dtype="object"` here and passing that to
+                # `from_frame` is deprecated in pandas, but we cannot remove
+                # that without also losing compatibility with other current
+                # pandas behaviors like the NaT inference above. For now we
+                # must catch the warnings internally, but we will need to
+                # remove this when we implement compatibility with pandas 2.0,
+                # which will remove these compatibility layers.
+                assert Version(pd.__version__) < Version("2.0.0")
+                warnings.simplefilter("ignore")
+                result = pd.MultiIndex.from_frame(
+                    pd.DataFrame(
+                        self.names, columns=self.level_names, dtype="object"
+                    ),
+                )
+        else:
+            # Determine if we can return a RangeIndex
+            if self.rangeindex:
+                if not self.names:
+                    return pd.RangeIndex(
+                        start=0, stop=0, step=1, name=self.name
+                    )
+                elif cudf.api.types.infer_dtype(self.names) == "integer":
+                    if len(self.names) == 1:
+                        start = self.names[0]
+                        return pd.RangeIndex(
+                            start=start, stop=start + 1, step=1, name=self.name
+                        )
+                    uniques = np.unique(np.diff(np.array(self.names)))
+                    if len(uniques) == 1 and uniques[0] != 0:
+                        diff = uniques[0]
+                        new_range = range(
+                            self.names[0], self.names[-1] + diff, diff
+                        )
+                        return pd.RangeIndex(new_range, name=self.name)
+            result = pd.Index(self.names, name=self.name, tupleize_cols=False)
+        return result
+
+    def insert(
+        self, name: Any, value: Any, loc: int = -1, validate: bool = True
+    ):
+        """
+        Insert column into the ColumnAccessor at the specified location.
+
+        Parameters
+        ----------
+        name : Name corresponding to the new column
+        value : column-like
+        loc : int, optional
+            The location to insert the new value at.
+            Must be (0 <= loc <= ncols). By default, the column is added
+            to the end.
+
+        Returns
+        -------
+        None, this function operates in-place.
+        """
+        name = self._pad_key(name)
+
+        ncols = len(self._data)
+        if loc == -1:
+            loc = ncols
+        if not (0 <= loc <= ncols):
+            raise ValueError(
+                "insert: loc out of bounds: must be  0 <= loc <= ncols"
+            )
+        # TODO: we should move all insert logic here
+        if name in self._data:
+            raise ValueError(f"Cannot insert '{name}', already exists")
+        if loc == len(self._data):
+            if validate:
+                value = column.as_column(value)
+                if len(self._data) > 0:
+                    if len(value) != self._column_length:
+                        raise ValueError("All columns must be of equal length")
+                else:
+                    self._column_length = len(value)
+            self._data[name] = value
+        else:
+            new_keys = self.names[:loc] + (name,) + self.names[loc:]
+            new_values = self.columns[:loc] + (value,) + self.columns[loc:]
+            self._data = self._data.__class__(zip(new_keys, new_values))
+        self._clear_cache()
+
+    def copy(self, deep=False) -> ColumnAccessor:
+        """
+        Make a copy of this ColumnAccessor.
+        """
+        if deep or cudf.get_option("copy_on_write"):
+            return self.__class__(
+                {k: v.copy(deep=deep) for k, v in self._data.items()},
+                multiindex=self.multiindex,
+                level_names=self.level_names,
+            )
+        return self.__class__(
+            self._data.copy(),
+            multiindex=self.multiindex,
+            level_names=self.level_names,
+        )
+
+    def select_by_label(self, key: Any) -> ColumnAccessor:
+        """
+        Return a subset of this column accessor,
+        composed of the keys specified by `key`.
+
+        Parameters
+        ----------
+        key : slice, list-like, tuple or scalar
+
+        Returns
+        -------
+        ColumnAccessor
+        """
+        if isinstance(key, slice):
+            return self._select_by_label_slice(key)
+        elif pd.api.types.is_list_like(key) and not isinstance(key, tuple):
+            return self._select_by_label_list_like(key)
+        else:
+            if isinstance(key, tuple):
+                if any(isinstance(k, slice) for k in key):
+                    return self._select_by_label_with_wildcard(key)
+            return self._select_by_label_grouped(key)
+
+    def get_labels_by_index(self, index: Any) -> tuple:
+        """Get the labels corresponding to the provided column indices.
+
+        Parameters
+        ----------
+        index : integer, integer slice, boolean mask,
+            or list-like of integers
+            The column indexes.
+
+        Returns
+        -------
+        tuple
+        """
+        if isinstance(index, slice):
+            start, stop, step = index.indices(len(self._data))
+            return self.names[start:stop:step]
+        elif pd.api.types.is_integer(index):
+            return (self.names[index],)
+        elif (bn := len(index)) > 0 and all(map(is_bool, index)):
+            if bn != (n := len(self.names)):
+                raise IndexError(
+                    f"Boolean mask has wrong length: {bn} not {n}"
+                )
+            if isinstance(index, (pd.Series, cudf.Series)):
+                # Don't allow iloc indexing with series
+                raise NotImplementedError(
+                    "Cannot use Series object for mask iloc indexing"
+                )
+            # TODO: Doesn't handle on-device columns
+            return tuple(n for n, keep in zip(self.names, index) if keep)
+        else:
+            return tuple(self.names[i] for i in index)
+
+    def select_by_index(self, index: Any) -> ColumnAccessor:
+        """
+        Return a ColumnAccessor composed of the columns
+        specified by index.
+
+        Parameters
+        ----------
+        key : integer, integer slice, boolean mask,
+            or list-like of integers
+
+        Returns
+        -------
+        ColumnAccessor
+        """
+        keys = self.get_labels_by_index(index)
+        data = {k: self._data[k] for k in keys}
+        return self.__class__(
+            data,
+            multiindex=self.multiindex,
+            level_names=self.level_names,
+        )
+
+    def swaplevel(self, i=-2, j=-1):
+        """
+        Swap level i with level j.
+        Calling this method does not change the ordering of the values.
+
+        Parameters
+        ----------
+        i : int or str, default -2
+            First level of index to be swapped.
+        j : int or str, default -1
+            Second level of index to be swapped.
+
+        Returns
+        -------
+        ColumnAccessor
+        """
+
+        i = _get_level(i, self.nlevels, self.level_names)
+        j = _get_level(j, self.nlevels, self.level_names)
+
+        new_keys = [list(row) for row in self]
+        new_dict = {}
+
+        # swap old keys for i and j
+        for n, row in enumerate(self.names):
+            new_keys[n][i], new_keys[n][j] = row[j], row[i]
+            new_dict.update({row: tuple(new_keys[n])})
+
+        new_data = {new_dict[k]: v.copy(deep=True) for k, v in self.items()}
+
+        # swap level_names for i and j
+        new_names = list(self.level_names)
+        new_names[i], new_names[j] = new_names[j], new_names[i]
+
+        return self.__class__(
+            new_data,
+            multiindex=True,
+            level_names=new_names,
+        )
+
+    def set_by_label(self, key: Any, value: Any, validate: bool = True):
+        """
+        Add (or modify) column by name.
+
+        Parameters
+        ----------
+        key
+            name of the column
+        value : column-like
+            The value to insert into the column.
+        validate : bool
+            If True, the provided value will be coerced to a column and
+            validated before setting (Default value = True).
+        """
+        key = self._pad_key(key)
+        if validate:
+            value = column.as_column(value)
+            if len(self._data) > 0:
+                if len(value) != self._column_length:
+                    raise ValueError("All columns must be of equal length")
+            else:
+                self._column_length = len(value)
+
+        self._data[key] = value
+        self._clear_cache()
+
+    def _select_by_names(self, names: abc.Sequence) -> Self:
+        return self.__class__(
+            {key: self[key] for key in names},
+            multiindex=self.multiindex,
+            level_names=self.level_names,
+        )
+
+    def _select_by_label_list_like(self, key: Any) -> ColumnAccessor:
+        # Might be a generator
+        key = tuple(key)
+        # Special-casing for boolean mask
+        if (bn := len(key)) > 0 and all(map(is_bool, key)):
+            if bn != (n := len(self.names)):
+                raise IndexError(
+                    f"Boolean mask has wrong length: {bn} not {n}"
+                )
+            data = dict(
+                item
+                for item, keep in zip(self._grouped_data.items(), key)
+                if keep
+            )
+        else:
+            data = {k: self._grouped_data[k] for k in key}
+        if self.multiindex:
+            data = _to_flat_dict(data)
+        return self.__class__(
+            data,
+            multiindex=self.multiindex,
+            level_names=self.level_names,
+        )
+
+    def _select_by_label_grouped(self, key: Any) -> ColumnAccessor:
+        result = self._grouped_data[key]
+        if isinstance(result, cudf.core.column.ColumnBase):
+            return self.__class__({key: result}, multiindex=self.multiindex)
+        else:
+            if self.multiindex:
+                result = _to_flat_dict(result)
+            if not isinstance(key, tuple):
+                key = (key,)
+            return self.__class__(
+                result,
+                multiindex=self.nlevels - len(key) > 1,
+                level_names=self.level_names[len(key) :],
+            )
+
+    def _select_by_label_slice(self, key: slice) -> ColumnAccessor:
+        start, stop = key.start, key.stop
+        if key.step is not None:
+            raise TypeError("Label slicing with step is not supported")
+
+        if start is None:
+            start = self.names[0]
+        if stop is None:
+            stop = self.names[-1]
+        start = self._pad_key(start, slice(None))
+        stop = self._pad_key(stop, slice(None))
+        for idx, name in enumerate(self.names):
+            if _compare_keys(name, start):
+                start_idx = idx
+                break
+        for idx, name in enumerate(reversed(self.names)):
+            if _compare_keys(name, stop):
+                stop_idx = len(self.names) - idx
+                break
+        keys = self.names[start_idx:stop_idx]
+        return self.__class__(
+            {k: self._data[k] for k in keys},
+            multiindex=self.multiindex,
+            level_names=self.level_names,
+        )
+
+    def _select_by_label_with_wildcard(self, key: Any) -> ColumnAccessor:
+        key = self._pad_key(key, slice(None))
+        return self.__class__(
+            {k: self._data[k] for k in self._data if _compare_keys(k, key)},
+            multiindex=self.multiindex,
+            level_names=self.level_names,
+        )
+
+    def _pad_key(self, key: Any, pad_value="") -> Any:
+        """
+        Pad the provided key to a length equal to the number
+        of levels.
+        """
+        if not self.multiindex:
+            return key
+        if not isinstance(key, tuple):
+            key = (key,)
+        return key + (pad_value,) * (self.nlevels - len(key))
+
+    def rename_levels(
+        self, mapper: Union[Mapping[Any, Any], Callable], level: Optional[int]
+    ) -> ColumnAccessor:
+        """
+        Rename the specified levels of the given ColumnAccessor
+
+        Parameters
+        ----------
+        self : ColumnAccessor of a given dataframe
+
+        mapper : dict-like or function transformations to apply to
+            the column label values depending on selected ``level``.
+
+            If dict-like, only replace the specified level of the
+            ColumnAccessor's keys (that match the mapper's keys) with
+            mapper's values
+
+            If callable, the function is applied only to the specified level
+            of the ColumnAccessor's keys.
+
+        level : int
+            In case of RangeIndex, only supported level is [0, None].
+            In case of a MultiColumn, only the column labels in the specified
+            level of the ColumnAccessor's keys will be transformed.
+
+        Returns
+        -------
+        A new ColumnAccessor with values in the keys replaced according
+        to the given mapper and level.
+
+        """
+        if self.multiindex:
+
+            def rename_column(x):
+                x = list(x)
+                if isinstance(mapper, Mapping):
+                    x[level] = mapper.get(x[level], x[level])
+                else:
+                    x[level] = mapper(x[level])
+                x = tuple(x)
+                return x
+
+            if level is None:
+                raise NotImplementedError(
+                    "Renaming columns with a MultiIndex and level=None is"
+                    "not supported"
+                )
+            new_names = map(rename_column, self.keys())
+            ca = ColumnAccessor(
+                dict(zip(new_names, self.values())),
+                level_names=self.level_names,
+                multiindex=self.multiindex,
+            )
+
+        else:
+            if level is None:
+                level = 0
+            if level != 0:
+                raise IndexError(
+                    f"Too many levels: Index has only 1 level, not {level+1}"
+                )
+
+            if isinstance(mapper, Mapping):
+                new_col_names = [
+                    mapper.get(col_name, col_name) for col_name in self.keys()
+                ]
+            else:
+                new_col_names = [mapper(col_name) for col_name in self.keys()]
+
+            if len(new_col_names) != len(set(new_col_names)):
+                raise ValueError("Duplicate column names are not allowed")
+
+            ca = ColumnAccessor(
+                dict(zip(new_col_names, self.values())),
+                level_names=self.level_names,
+                multiindex=self.multiindex,
+            )
+
+        return self.__class__(ca)
+
+    def droplevel(self, level):
+        # drop the nth level
+        if level < 0:
+            level += self.nlevels
+
+        self._data = {
+            _remove_key_level(key, level): value
+            for key, value in self._data.items()
+        }
+        self._level_names = (
+            self._level_names[:level] + self._level_names[level + 1 :]
+        )
+
+        if (
+            len(self._level_names) == 1
+        ):  # can't use nlevels, as it depends on multiindex
+            self.multiindex = False
+        self._clear_cache()
+
+
+def _compare_keys(target: Any, key: Any) -> bool:
+    """
+    Compare `key` to `target`.
+
+    Return True if each value in `key` == corresponding value in `target`.
+    If any value in `key` is slice(None), it is considered equal
+    to the corresponding value in `target`.
+    """
+    if not isinstance(target, tuple):
+        return target == key
+    for k1, k2 in itertools.zip_longest(target, key, fillvalue=None):
+        if k2 == slice(None):
+            continue
+        if k1 != k2:
+            return False
+    return True
+
+
+def _remove_key_level(key: Any, level: int) -> Any:
+    """
+    Remove a level from key. If detupleize is True, and if only a
+    single level remains, convert the tuple to a scalar.
+    """
+    result = key[:level] + key[level + 1 :]
+    if len(result) == 1:
+        return result[0]
+    return result
+
+
+def _get_level(x, nlevels, level_names):
+    """Get the level index from a level number or name.
+
+    If given an integer, this function will handle wraparound for
+    negative values. If given a string (the level name), this function
+    will extract the index of that level from `level_names`.
+
+    Parameters
+    ----------
+    x
+        The level number to validate
+    nlevels
+        The total available levels in the MultiIndex
+    level_names
+        The names of the levels.
+    """
+    if isinstance(x, int):
+        if x < 0:
+            x += nlevels
+        if x >= nlevels:
+            raise IndexError(
+                f"Level {x} out of bounds. Index has {nlevels} levels."
+            )
+        return x
+    else:
+        x = level_names.index(x)
+        return x
diff --git a/python/cudf/cudf/core/common.py b/python/cudf/cudf/core/common.py
new file mode 100644
index 0000000..5276cd5
--- /dev/null
+++ b/python/cudf/cudf/core/common.py
@@ -0,0 +1,37 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+
+def pipe(obj, func, *args, **kwargs):
+    """
+    Apply a function ``func`` to object ``obj`` either by passing obj as the
+    first argument to the function or, in the case that the func is a tuple,
+    interpret the first element of the tuple as a function and pass the obj to
+    that function as a keyword argument whose key is the value of the second
+    element of the tuple.
+
+    Parameters
+    ----------
+    func : callable or tuple of (callable, str)
+        Function to apply to this object or, alternatively, a
+        ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
+        string indicating the keyword of `callable`` that expects the
+        object.
+    *args : iterable, optional
+        Positional arguments passed into ``func``.
+    **kwargs : dict, optional
+        A dictionary of keyword arguments passed into ``func``.
+
+    Returns
+    -------
+    object : the return type of ``func``.
+    """
+    if isinstance(func, tuple):
+        func, target = func
+        if target in kwargs:
+            raise ValueError(
+                f"{target} is both the pipe target and a keyword argument"
+            )
+        kwargs[target] = obj
+        return func(*args, **kwargs)
+    else:
+        return func(obj, *args, **kwargs)
diff --git a/python/cudf/cudf/core/copy_types.py b/python/cudf/cudf/core/copy_types.py
new file mode 100644
index 0000000..6afbc0b
--- /dev/null
+++ b/python/cudf/cudf/core/copy_types.py
@@ -0,0 +1,171 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, cast
+
+from typing_extensions import Self
+
+import cudf
+import cudf._lib as libcudf
+from cudf._lib.types import size_type_dtype
+
+if TYPE_CHECKING:
+    from cudf.core.column import NumericalColumn
+
+
+@dataclass
+class GatherMap:
+    """A representation of a column as a gather map.
+
+    This object augments the column with the information that it
+    is valid as a gather map for the specified number of rows with
+    the given nullification flag.
+
+    Parameters
+    ----------
+    column
+        The data to turn into a column and then verify
+    nrows
+        The number of rows to verify against
+    nullify
+        Will the gather map be used nullifying out of bounds
+        accesses?
+
+    Returns
+    -------
+    GatherMap
+        New object wrapping the column bearing witness to its
+        suitability as a gather map for columns with nrows.
+
+    Raises
+    ------
+    TypeError
+        If the column is of unsuitable dtype
+    IndexError
+        If the map is not in bounds.
+    """
+
+    #: The gather map
+    column: "NumericalColumn"
+    #: The number of rows the gather map has been validated for
+    nrows: int
+    #: Was the validation for nullify=True?
+    nullify: bool
+
+    def __init__(self, column: Any, nrows: int, *, nullify: bool):
+        self.column = cudf.core.column.as_column(column)
+        self.nrows = nrows
+        self.nullify = nullify
+        if len(self.column) == 0:
+            # Any empty column is valid as a gather map
+            # This is necessary because as_column([]) defaults to float64
+            # TODO: we should fix this further up.
+            # Alternately we can have an Optional[Column] and handle None
+            # specially in _gather.
+            self.column = cast(
+                "NumericalColumn", self.column.astype(size_type_dtype)
+            )
+        else:
+            if self.column.dtype.kind not in {"i", "u"}:
+                raise TypeError("Gather map must have integer dtype")
+            if not nullify:
+                lo, hi = libcudf.reduce.minmax(self.column)
+                if lo.value < -nrows or hi.value >= nrows:
+                    raise IndexError(
+                        f"Gather map is out of bounds for [0, {nrows})"
+                    )
+
+    @classmethod
+    def from_column_unchecked(
+        cls, column: "NumericalColumn", nrows: int, *, nullify: bool
+    ) -> Self:
+        """Construct a new GatherMap from a column without checks.
+
+        Parameters
+        ----------
+        column
+           The column that will be used as a gather map
+        nrows
+           The number of rows the gather map will be used for
+        nullify
+           Will the gather map be used nullifying out of bounds
+           accesses?
+
+        Returns
+        -------
+        GatherMap
+
+        Notes
+        -----
+        This method asserts, by fiat, that the column is valid.
+        Behaviour is undefined if it is not.
+        """
+        self = cls.__new__(cls)
+        self.column = column
+        self.nrows = nrows
+        self.nullify = nullify
+        return self
+
+
+@dataclass
+class BooleanMask:
+    """A representation of a column as a boolean mask.
+
+    This augments the column with information that it is valid as a
+    boolean mask for columns with a given number of rows
+
+    Parameters
+    ----------
+    column
+        The data to turn into a column to then verify
+    nrows
+        the number of rows to verify against
+
+    Returns
+    -------
+    BooleanMask
+        New object wrapping the column bearing witness to its
+        suitability as a boolean mask for columns with matching
+        row count.
+
+    Raises
+    ------
+    TypeError
+        If the column is of unsuitable dtype
+    IndexError
+        If the mask has the wrong number of rows
+    """
+
+    #: The boolean mask
+    column: "NumericalColumn"
+
+    def __init__(self, column: Any, nrows: int):
+        self.column = cudf.core.column.as_column(column)
+        if self.column.dtype.kind != "b":
+            raise TypeError("Boolean mask must have bool dtype")
+        if len(column) != nrows:
+            raise IndexError(
+                f"Column with {len(column)} rows not suitable "
+                f"as a boolean mask for {nrows} rows"
+            )
+
+    @classmethod
+    def from_column_unchecked(cls, column: "NumericalColumn") -> Self:
+        """Construct a new BooleanMask from a column without checks.
+
+        Parameters
+        ----------
+        column
+           The column that will be used as a boolean mask
+
+        Returns
+        -------
+        BooleanMask
+
+        Notes
+        -----
+        This method asserts, by fiat, that the column is valid.
+        Behaviour is undefined if it is not.
+        """
+        self = cls.__new__(cls)
+        self.column = column
+        return self
diff --git a/python/cudf/cudf/core/cut.py b/python/cudf/cudf/core/cut.py
new file mode 100644
index 0000000..ccf730c
--- /dev/null
+++ b/python/cudf/cudf/core/cut.py
@@ -0,0 +1,308 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from collections import abc
+
+import cupy
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf.api.types import is_list_like
+from cudf.core.column import as_column, build_categorical_column
+from cudf.core.index import IntervalIndex, interval_range
+
+
+def cut(
+    x,
+    bins,
+    right: bool = True,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+):
+    """Bin values into discrete intervals.
+
+    Use cut when you need to segment and sort data values into bins. This
+    function is also useful for going from a continuous variable to a
+    categorical variable.
+
+    Parameters
+    ----------
+    x : array-like
+        The input array to be binned. Must be 1-dimensional.
+    bins : int, sequence of scalars, or IntervalIndex
+        The criteria to bin by.
+
+        * int : Defines the number of equal-width bins in the range of `x`. The
+          range of `x` is extended by .1% on each side to include the minimum
+          and maximum values of `x`.
+        * sequence of scalars : Defines the bin edges allowing for non-uniform
+          width. No extension of the range of `x` is done.
+        * IntervalIndex : Defines the exact bins to be used. Note that
+          IntervalIndex for `bins` must be non-overlapping.
+
+    right : bool, default True
+        Indicates whether bins includes the rightmost edge or not.
+    labels : array or False, default None
+        Specifies the labels for the returned bins. Must be the same
+        length as the resulting bins. If False, returns only integer
+        indicators of the bins. If True,raises an error. When ordered=False,
+        labels must be provided.
+    retbins : bool, default False
+        Whether to return the bins or not.
+    precision : int, default 3
+        The precision at which to store and display the bins labels.
+    include_lowest : bool, default False
+        Whether the first interval should be left-inclusive or not.
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+    ordered : bool, default True
+        Whether the labels are ordered or not. Applies to returned types
+        Categorical and Series (with Categorical dtype). If True,
+        the resulting categorical will be ordered. If False, the resulting
+        categorical will be unordered (labels must be provided).
+
+    Returns
+    -------
+    out : CategoricalIndex
+        An array-like object representing the respective bin for each value
+        of x. The type depends on the value of labels.
+    bins : numpy.ndarray or IntervalIndex.
+        The computed or specified bins. Only returned when retbins=True.
+        For scalar or sequence bins, this is an ndarray with the computed
+        bins. If set duplicates=drop, bins will drop non-unique bin. For
+        an IntervalIndex bins, this is equal to bins.
+
+    Examples
+    --------
+    Discretize into three equal-sized bins.
+
+    >>> cudf.cut(np.array([1, 7, 5, 4, 6, 3]), 3)
+    CategoricalIndex([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0],
+                (5.0, 7.0], (0.994, 3.0]], categories=[(0.994, 3.0],
+                (3.0, 5.0], (5.0, 7.0]], ordered=True, dtype='category')
+
+    >>> cudf.cut(np.array([1, 7, 5, 4, 6, 3]), 3, retbins=True)
+    (CategoricalIndex([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0],
+                (5.0, 7.0], (0.994, 3.0]], categories=[(0.994, 3.0],
+                (3.0, 5.0], (5.0, 7.0]], ordered=True, dtype='category'),
+     array([0.994, 3.   , 5.   , 7.   ]))
+
+    >>> cudf.cut(np.array([1, 7, 5, 4, 6, 3]),
+    ...          3, labels=["bad", "medium", "good"])
+    CategoricalIndex(['bad', 'good', 'medium', 'medium', 'good', 'bad'],
+                     categories=['bad', 'medium', 'good'],ordered=True,
+                     dtype='category')
+
+    >>> cudf.cut(np.array([1, 7, 5, 4, 6, 3]), 3,
+    ...          labels=["B", "A", "B"], ordered=False)
+    CategoricalIndex(['B', 'B', 'A', 'A', 'B', 'B'], categories=['A', 'B'],
+               ordered=False, dtype='category')
+
+    >>> cudf.cut([0, 1, 1, 2], bins=4, labels=False)
+    array([0, 1, 1, 3], dtype=int32)
+
+    Passing a Series as an input returns a Series with categorical dtype:
+
+    >>> s = cudf.Series(np.array([2, 4, 6, 8, 10]),
+    ...        index=['a', 'b', 'c', 'd', 'e'])
+    >>> cudf.cut(s, 3)
+    """
+    left_inclusive = False
+    right_inclusive = True
+    # saving the original input x for use in case its a series
+    orig_x = x
+    old_bins = bins
+
+    if not ordered and labels is None:
+        raise ValueError("'labels' must be provided if 'ordered = False'")
+
+    if duplicates not in ["raise", "drop"]:
+        raise ValueError(
+            "invalid value for 'duplicates' parameter, valid options are: "
+            "raise, drop"
+        )
+
+    if labels is not False:
+        if not (labels is None or is_list_like(labels)):
+            raise ValueError(
+                "Bin labels must either be False, None or passed in as a "
+                "list-like argument"
+            )
+        if ordered and labels is not None:
+            if len(set(labels)) != len(labels):
+                raise ValueError(
+                    "labels must be unique if ordered=True;"
+                    "pass ordered=False for duplicate labels"
+                )
+
+    # bins can either be an int, sequence of scalars or an intervalIndex
+    if isinstance(bins, abc.Sequence):
+        if len(set(bins)) is not len(bins):
+            if duplicates == "raise":
+                raise ValueError(
+                    f"Bin edges must be unique: {repr(bins)}.\n"
+                    f"You can drop duplicate edges by setting the 'duplicates'"
+                    "kwarg"
+                )
+            elif duplicates == "drop":
+                # get unique values but maintain list dtype
+                bins = list(dict.fromkeys(bins))
+
+    # if bins is an intervalIndex we ignore the value of right
+    elif isinstance(bins, (pd.IntervalIndex, cudf.IntervalIndex)):
+        right = bins.closed == "right"
+
+    # create bins if given an int or single scalar
+    if not isinstance(bins, pd.IntervalIndex):
+        if not isinstance(bins, (abc.Sequence)):
+            if isinstance(
+                x, (pd.Series, cudf.Series, np.ndarray, cupy.ndarray)
+            ):
+                mn = x.min()
+                mx = x.max()
+            else:
+                mn = min(x)
+                mx = max(x)
+            bins = np.linspace(mn, mx, bins + 1, endpoint=True)
+            adj = (mx - mn) * 0.001
+            if right:
+                bins[0] -= adj
+            else:
+                bins[-1] += adj
+
+        # if right and include lowest we adjust the first
+        # bin edge to make sure it is included
+        if right and include_lowest:
+            bins[0] = bins[0] - 10 ** (-precision)
+
+        # if right is false the last bin edge is not included
+        if not right:
+            right_edge = bins[-1]
+            x = cupy.asarray(x)
+            x[x == right_edge] = right_edge + 1
+
+        # adjust bin edges decimal precision
+        int_label_bins = np.around(bins, precision)
+
+    # the inputs is a column of the values in the array x
+    input_arr = as_column(x)
+
+    # checking for the correct inclusivity values
+    if right:
+        closed = "right"
+    else:
+        closed = "left"
+        left_inclusive = True
+
+    if isinstance(bins, pd.IntervalIndex):
+        interval_labels = bins
+    elif labels is None:
+        if duplicates == "drop" and len(bins) == 1 and len(old_bins) != 1:
+            if right and include_lowest:
+                old_bins[0] = old_bins[0] - 10 ** (-precision)
+                interval_labels = interval_range(
+                    old_bins[0], old_bins[1], periods=1, closed=closed
+                )
+            else:
+                interval_labels = IntervalIndex.from_breaks(
+                    old_bins, closed=closed
+                )
+        else:
+            # get labels for categories
+            interval_labels = IntervalIndex.from_breaks(
+                int_label_bins, closed=closed
+            )
+    elif labels is not False:
+        if not (is_list_like(labels)):
+            raise ValueError(
+                "Bin labels must either be False, None or passed in as a "
+                "list-like argument"
+            )
+        if ordered and len(set(labels)) != len(labels):
+            raise ValueError(
+                "labels must be unique if ordered=True; "
+                "pass ordered=False for"
+                "duplicate labels"
+            )
+
+        if len(labels) != len(bins) - 1:
+            raise ValueError(
+                "Bin labels must be one fewer than the number of bin edges"
+            )
+        if not ordered and len(set(labels)) != len(labels):
+            interval_labels = cudf.CategoricalIndex(
+                labels, categories=None, ordered=False
+            )
+        else:
+            interval_labels = (
+                labels if len(set(labels)) == len(labels) else None
+            )
+
+    if isinstance(bins, pd.IntervalIndex):
+        # get the left and right edges of the bins as columns
+        # we cannot typecast an IntervalIndex, so we need to
+        # make the edges the same type as the input array
+        left_edges = as_column(bins.left).astype(input_arr.dtype)
+        right_edges = as_column(bins.right).astype(input_arr.dtype)
+    else:
+        # get the left and right edges of the bins as columns
+        left_edges = as_column(bins[:-1:], dtype="float64")
+        right_edges = as_column(bins[+1::], dtype="float64")
+        # the input arr must be changed to the same type as the edges
+        input_arr = input_arr.astype(left_edges.dtype)
+    # get the indexes for the appropriate number
+    index_labels = cudf._lib.labeling.label_bins(
+        input_arr, left_edges, left_inclusive, right_edges, right_inclusive
+    )
+
+    if labels is False:
+        # if labels is false we return the index labels, we return them
+        # as a series if we have a series input
+        if isinstance(orig_x, (pd.Series, cudf.Series)):
+            # need to run more tests but looks like in this case pandas
+            # always returns a float64 dtype
+            indx_arr_series = cudf.Series(index_labels, dtype="float64")
+            # if retbins we return the bins as well
+            if retbins:
+                return indx_arr_series, bins
+            else:
+                return indx_arr_series
+        elif retbins:
+            return index_labels.values, bins
+        else:
+            return index_labels.values
+
+    if labels is not None:
+        if labels is not ordered and len(set(labels)) != len(labels):
+            # when we have duplicate labels and ordered is False, we
+            # should allow duplicate categories.
+            return interval_labels[index_labels]
+
+    col = build_categorical_column(
+        categories=interval_labels,
+        codes=index_labels,
+        mask=index_labels.base_mask,
+        offset=index_labels.offset,
+        size=index_labels.size,
+        ordered=ordered,
+    )
+
+    # we return a categorical index, as we don't have a Categorical method
+    categorical_index = cudf.core.index.as_index(col)
+
+    if isinstance(orig_x, (pd.Series, cudf.Series)):
+        # if we have a series input we return a series output
+        res_series = cudf.Series(categorical_index, index=orig_x.index)
+        if retbins:
+            return res_series, bins
+        else:
+            return res_series
+    elif retbins:
+        # if retbins is true we return the bins as well
+        return categorical_index, bins
+    else:
+        return categorical_index
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
new file mode 100644
index 0000000..e3d4b20
--- /dev/null
+++ b/python/cudf/cudf/core/dataframe.py
@@ -0,0 +1,8163 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import functools
+import inspect
+import itertools
+import numbers
+import os
+import pickle
+import re
+import sys
+import textwrap
+import warnings
+from collections import abc, defaultdict
+from collections.abc import Iterator
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    MutableMapping,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+)
+
+import cupy
+import numba
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from nvtx import annotate
+from packaging.version import Version
+from pandas._config import get_option
+from pandas.core.dtypes.common import is_float, is_integer
+from pandas.io.formats import console
+from pandas.io.formats.printing import pprint_thing
+from typing_extensions import Self, assert_never
+
+import cudf
+import cudf.core.common
+from cudf import _lib as libcudf
+from cudf._typing import ColumnLike, Dtype, NotImplementedType
+from cudf.api.extensions import no_default
+from cudf.api.types import (
+    _is_scalar_or_zero_d_array,
+    is_bool_dtype,
+    is_categorical_dtype,
+    is_datetime_dtype,
+    is_dict_like,
+    is_dtype_equal,
+    is_list_dtype,
+    is_list_like,
+    is_numeric_dtype,
+    is_object_dtype,
+    is_scalar,
+    is_string_dtype,
+    is_struct_dtype,
+)
+from cudf.core import column, df_protocol, indexing_utils, reshape
+from cudf.core.abc import Serializable
+from cudf.core.column import (
+    CategoricalColumn,
+    ColumnBase,
+    as_column,
+    build_categorical_column,
+    build_column,
+    column_empty,
+    concat_columns,
+)
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.copy_types import BooleanMask
+from cudf.core.groupby.groupby import DataFrameGroupBy, groupby_doc_template
+from cudf.core.index import BaseIndex, RangeIndex, _index_from_data, as_index
+from cudf.core.indexed_frame import (
+    IndexedFrame,
+    _FrameIndexer,
+    _get_label_range_or_mask,
+    _indices_from_labels,
+    doc_reset_index_template,
+)
+from cudf.core.join import Merge, MergeSemi
+from cudf.core.missing import NA
+from cudf.core.multiindex import MultiIndex
+from cudf.core.resample import DataFrameResampler
+from cudf.core.series import Series
+from cudf.core.udf.row_function import _get_row_kernel
+from cudf.utils import applyutils, docutils, ioutils, queryutils
+from cudf.utils.docutils import copy_docstring
+from cudf.utils.dtypes import (
+    can_convert_to_column,
+    cudf_dtype_from_pydata_dtype,
+    find_common_type,
+    is_column_like,
+    min_scalar_type,
+    numeric_normalize_types,
+)
+from cudf.utils.utils import (
+    GetAttrGetItemMixin,
+    _cudf_nvtx_annotate,
+    _external_only_api,
+)
+
+_cupy_nan_methods_map = {
+    "min": "nanmin",
+    "max": "nanmax",
+    "sum": "nansum",
+    "prod": "nanprod",
+    "product": "nanprod",
+    "mean": "nanmean",
+    "std": "nanstd",
+    "var": "nanvar",
+}
+
+_numeric_reduction_ops = (
+    "mean",
+    "min",
+    "max",
+    "sum",
+    "product",
+    "prod",
+    "std",
+    "var",
+    "kurtosis",
+    "kurt",
+    "skew",
+)
+
+
+def _shape_mismatch_error(x, y):
+    raise ValueError(
+        f"shape mismatch: value array of shape {x} "
+        f"could not be broadcast to indexing result of "
+        f"shape {y}"
+    )
+
+
+class _DataFrameIndexer(_FrameIndexer):
+    def __getitem__(self, arg):
+        if (
+            isinstance(self._frame.index, MultiIndex)
+            or self._frame._data.multiindex
+        ):
+            # This try/except block allows the use of pandas-like
+            # tuple arguments into MultiIndex dataframes.
+            try:
+                return self._getitem_tuple_arg(arg)
+            except (TypeError, KeyError, IndexError, ValueError):
+                return self._getitem_tuple_arg((arg, slice(None)))
+        else:
+            if not isinstance(arg, tuple):
+                arg = (arg, slice(None))
+            return self._getitem_tuple_arg(arg)
+
+    def __setitem__(self, key, value):
+        if not isinstance(key, tuple):
+            key = (key, slice(None))
+        return self._setitem_tuple_arg(key, value)
+
+    @_cudf_nvtx_annotate
+    def _can_downcast_to_series(self, df, arg):
+        """
+        This method encapsulates the logic used
+        to determine whether or not the result of a loc/iloc
+        operation should be "downcasted" from a DataFrame to a
+        Series
+        """
+        if isinstance(df, cudf.Series):
+            return False
+        nrows, ncols = df.shape
+        if nrows == 1:
+            if type(arg[0]) is slice:
+                if not is_scalar(arg[1]):
+                    return False
+            elif (is_list_like(arg[0]) or is_column_like(arg[0])) and (
+                is_list_like(arg[1])
+                or is_column_like(arg[0])
+                or type(arg[1]) is slice
+            ):
+                return False
+            else:
+                if is_bool_dtype(as_column(arg[0]).dtype) and not isinstance(
+                    arg[1], slice
+                ):
+                    return True
+            dtypes = df.dtypes.values.tolist()
+            all_numeric = all(is_numeric_dtype(t) for t in dtypes)
+            if all_numeric or (
+                len(dtypes) and all(t == dtypes[0] for t in dtypes)
+            ):
+                return True
+            if isinstance(arg[1], tuple):
+                return True
+        if ncols == 1:
+            if type(arg[1]) is slice:
+                return False
+            if isinstance(arg[1], tuple):
+                return len(arg[1]) == df._data.nlevels
+            if not (is_list_like(arg[1]) or is_column_like(arg[1])):
+                return True
+        return False
+
+    @_cudf_nvtx_annotate
+    def _downcast_to_series(self, df, arg):
+        """
+        "Downcast" from a DataFrame to a Series
+        based on Pandas indexing rules
+        """
+        nrows, ncols = df.shape
+        # determine the axis along which the Series is taken:
+        if nrows == 1 and ncols == 1:
+            if is_scalar(arg[0]) and (
+                is_scalar(arg[1])
+                or (df._data.multiindex and arg[1] in df._column_names)
+            ):
+                return df[df._column_names[0]].iloc[0]
+            elif not is_scalar(arg[0]):
+                axis = 1
+            else:
+                axis = 0
+
+        elif nrows == 1:
+            axis = 0
+        elif ncols == 1:
+            axis = 1
+        else:
+            raise ValueError("Cannot downcast DataFrame selection to Series")
+
+        # take series along the axis:
+        if axis == 1:
+            return df[df._data.names[0]]
+        else:
+            if df._num_columns > 0:
+                dtypes = df.dtypes.values.tolist()
+                normalized_dtype = np.result_type(*dtypes)
+                for name, col in df._data.items():
+                    df[name] = col.astype(normalized_dtype)
+
+            sr = df.T
+            return sr[sr._data.names[0]]
+
+
+class _DataFrameLocIndexer(_DataFrameIndexer):
+    """
+    For selection by label.
+    """
+
+    @_cudf_nvtx_annotate
+    def _getitem_scalar(self, arg):
+        return self._frame[arg[1]].loc[arg[0]]
+
+    @_cudf_nvtx_annotate
+    def _getitem_tuple_arg(self, arg):
+        from uuid import uuid4
+
+        # Step 1: Gather columns
+        if isinstance(arg, tuple):
+            columns_df = self._frame._get_columns_by_label(arg[1])
+            columns_df._index = self._frame._index
+        else:
+            columns_df = self._frame
+
+        # Step 2: Gather rows
+        if isinstance(columns_df.index, MultiIndex):
+            if isinstance(arg, (MultiIndex, pd.MultiIndex)):
+                if isinstance(arg, pd.MultiIndex):
+                    arg = MultiIndex.from_pandas(arg)
+
+                indices = _indices_from_labels(columns_df, arg)
+                return columns_df.take(indices)
+
+            else:
+                if isinstance(arg, tuple):
+                    row_arg = arg[0]
+                elif is_scalar(arg):
+                    row_arg = (arg,)
+                else:
+                    row_arg = arg
+                result = columns_df.index._get_row_major(columns_df, row_arg)
+                if (
+                    len(result) == 1
+                    and isinstance(arg, tuple)
+                    and len(arg) > 1
+                    and is_scalar(arg[1])
+                ):
+                    return result._data.columns[0].element_indexing(0)
+                return result
+        else:
+            if isinstance(arg[0], slice):
+                out = _get_label_range_or_mask(
+                    columns_df.index, arg[0].start, arg[0].stop, arg[0].step
+                )
+                if isinstance(out, slice):
+                    df = columns_df._slice(out)
+                else:
+                    df = columns_df._apply_boolean_mask(
+                        BooleanMask.from_column_unchecked(
+                            cudf.core.column.as_column(out)
+                        )
+                    )
+            else:
+                tmp_arg = arg
+                if is_scalar(arg[0]):
+                    # If a scalar, there is possibility of having duplicates.
+                    # Join would get all the duplicates. So, converting it to
+                    # an array kind.
+                    if cudf.get_option("mode.pandas_compatible"):
+                        if any(
+                            c.dtype != columns_df._columns[0].dtype
+                            for c in columns_df._columns
+                        ):
+                            raise TypeError(
+                                "All columns need to be of same type, please "
+                                "typecast to common dtype."
+                            )
+                    tmp_arg = ([tmp_arg[0]], tmp_arg[1])
+                if len(tmp_arg[0]) == 0:
+                    return columns_df._empty_like(keep_index=True)
+                tmp_arg = (
+                    as_column(
+                        tmp_arg[0],
+                        dtype=self._frame.index.dtype
+                        if is_categorical_dtype(self._frame.index.dtype)
+                        else None,
+                    ),
+                    tmp_arg[1],
+                )
+
+                if is_bool_dtype(tmp_arg[0]):
+                    df = columns_df._apply_boolean_mask(
+                        BooleanMask(tmp_arg[0], len(columns_df))
+                    )
+                else:
+                    tmp_col_name = str(uuid4())
+                    cantor_name = "_" + "_".join(
+                        map(str, columns_df._data.names)
+                    )
+                    if columns_df._data.multiindex:
+                        # column names must be appropriate length tuples
+                        extra = tuple(
+                            "" for _ in range(columns_df._data.nlevels - 1)
+                        )
+                        tmp_col_name = (tmp_col_name, *extra)
+                        cantor_name = (cantor_name, *extra)
+                    other_df = DataFrame(
+                        {tmp_col_name: column.arange(len(tmp_arg[0]))},
+                        index=as_index(tmp_arg[0]),
+                    )
+                    columns_df[cantor_name] = column.arange(len(columns_df))
+                    df = other_df.join(columns_df, how="inner")
+                    # as join is not assigning any names to index,
+                    # update it over here
+                    df.index.name = columns_df.index.name
+                    df = df.sort_values(by=[tmp_col_name, cantor_name])
+                    df.drop(columns=[tmp_col_name, cantor_name], inplace=True)
+                    # There were no indices found
+                    if len(df) == 0:
+                        raise KeyError(arg)
+
+        # Step 3: Downcast
+        if self._can_downcast_to_series(df, arg):
+            return self._downcast_to_series(df, arg)
+        return df
+
+    @_cudf_nvtx_annotate
+    def _setitem_tuple_arg(self, key, value):
+        if (
+            isinstance(self._frame.index, MultiIndex)
+            or self._frame._data.multiindex
+        ):
+            raise NotImplementedError(
+                "Setting values using df.loc[] not supported on "
+                "DataFrames with a MultiIndex"
+            )
+
+        try:
+            columns_df = self._frame._get_columns_by_label(key[1])
+        except KeyError:
+            if not self._frame.empty and isinstance(key[0], slice):
+                pos_range = _get_label_range_or_mask(
+                    self._frame.index, key[0].start, key[0].stop, key[0].step
+                )
+                idx = self._frame.index[pos_range]
+            elif self._frame.empty and isinstance(key[0], slice):
+                idx = None
+            else:
+                if is_scalar(key[0]):
+                    arr = [key[0]]
+                else:
+                    arr = key[0]
+                idx = cudf.Index(arr)
+            if is_scalar(value):
+                length = len(idx) if idx is not None else 1
+                value = as_column(value, length=length)
+
+            new_col = cudf.Series(value, index=idx)
+            if not self._frame.empty:
+                new_col = new_col._align_to_index(
+                    self._frame.index, how="right"
+                )
+
+            if self._frame.empty:
+                self._frame.index = (
+                    idx if idx is not None else cudf.RangeIndex(len(new_col))
+                )
+            self._frame._data.insert(key[1], new_col)
+        else:
+            if is_scalar(value):
+                for col in columns_df._column_names:
+                    self._frame[col].loc[key[0]] = value
+
+            elif isinstance(value, cudf.DataFrame):
+                if value.shape != self._frame.loc[key[0]].shape:
+                    _shape_mismatch_error(
+                        value.shape,
+                        self._frame.loc[key[0]].shape,
+                    )
+                value_column_names = set(value._column_names)
+                scatter_map = _indices_from_labels(self._frame, key[0])
+                for col in columns_df._column_names:
+                    columns_df[col][scatter_map] = (
+                        value._data[col] if col in value_column_names else NA
+                    )
+
+            else:
+                value = cupy.asarray(value)
+                if cupy.ndim(value) == 2:
+                    # If the inner dimension is 1, it's broadcastable to
+                    # all columns of the dataframe.
+                    indexed_shape = columns_df.loc[key[0]].shape
+                    if value.shape[1] == 1:
+                        if value.shape[0] != indexed_shape[0]:
+                            _shape_mismatch_error(value.shape, indexed_shape)
+                        for i, col in enumerate(columns_df._column_names):
+                            self._frame[col].loc[key[0]] = value[:, 0]
+                    else:
+                        if value.shape != indexed_shape:
+                            _shape_mismatch_error(value.shape, indexed_shape)
+                        for i, col in enumerate(columns_df._column_names):
+                            self._frame[col].loc[key[0]] = value[:, i]
+                else:
+                    # handle cases where value is 1d object:
+                    # If the key on column axis is a scalar, we indexed
+                    # a single column; The 1d value should assign along
+                    # the columns.
+                    if is_scalar(key[1]):
+                        for col in columns_df._column_names:
+                            self._frame[col].loc[key[0]] = value
+                    # Otherwise, there are two situations. The key on row axis
+                    # can be a scalar or 1d. In either of the situation, the
+                    # ith element in value corresponds to the ith row in
+                    # the indexed object.
+                    # If the key is 1d, a broadcast will happen.
+                    else:
+                        for i, col in enumerate(columns_df._column_names):
+                            self._frame[col].loc[key[0]] = value[i]
+
+
+class _DataFrameIlocIndexer(_DataFrameIndexer):
+    """
+    For selection by index.
+    """
+
+    _frame: DataFrame
+
+    def __getitem__(self, arg):
+        row_key, (
+            col_is_scalar,
+            column_names,
+        ) = indexing_utils.destructure_dataframe_iloc_indexer(arg, self._frame)
+        row_spec = indexing_utils.parse_row_iloc_indexer(
+            row_key, len(self._frame)
+        )
+        ca = self._frame._data
+        index = self._frame.index
+        if col_is_scalar:
+            s = Series._from_data(
+                ca._select_by_names(column_names), index=index
+            )
+            return s._getitem_preprocessed(row_spec)
+        if column_names != list(self._frame._column_names):
+            frame = self._frame._from_data(
+                ca._select_by_names(column_names), index=index
+            )
+        else:
+            frame = self._frame
+        if isinstance(row_spec, indexing_utils.MapIndexer):
+            return frame._gather(row_spec.key, keep_index=True)
+        elif isinstance(row_spec, indexing_utils.MaskIndexer):
+            return frame._apply_boolean_mask(row_spec.key, keep_index=True)
+        elif isinstance(row_spec, indexing_utils.SliceIndexer):
+            return frame._slice(row_spec.key)
+        elif isinstance(row_spec, indexing_utils.ScalarIndexer):
+            result = frame._gather(row_spec.key, keep_index=True)
+            # Attempt to turn into series.
+            try:
+                # Behaviour difference from pandas, which will merrily
+                # turn any heterogeneous set of columns into a series if
+                # you only ask for one row.
+                new_name = result.index[0]
+                result = Series._concat(
+                    [result[name] for name in column_names],
+                    index=result.keys(),
+                )
+                result.name = new_name
+                return result
+            except TypeError:
+                # Couldn't find a common type, Hence:
+                # Raise in pandas compatibility mode,
+                # or just return a 1xN dataframe otherwise
+                if cudf.get_option("mode.pandas_compatible"):
+                    raise TypeError(
+                        "All columns need to be of same type, please "
+                        "typecast to common dtype."
+                    )
+                return result
+        elif isinstance(row_spec, indexing_utils.EmptyIndexer):
+            return frame._empty_like(keep_index=True)
+        assert_never(row_spec)
+
+    @_cudf_nvtx_annotate
+    def _setitem_tuple_arg(self, key, value):
+        columns_df = self._frame._from_data(
+            self._frame._data.select_by_index(key[1]), self._frame._index
+        )
+
+        if is_scalar(value):
+            for col in columns_df._column_names:
+                self._frame[col].iloc[key[0]] = value
+
+        elif isinstance(value, cudf.DataFrame):
+            if value.shape != self._frame.iloc[key[0]].shape:
+                _shape_mismatch_error(
+                    value.shape,
+                    self._frame.loc[key[0]].shape,
+                )
+            value_column_names = set(value._column_names)
+            for col in columns_df._column_names:
+                columns_df[col][key[0]] = (
+                    value._data[col] if col in value_column_names else NA
+                )
+
+        else:
+            # TODO: consolidate code path with identical counterpart
+            # in `_DataFrameLocIndexer._setitem_tuple_arg`
+            value = cupy.asarray(value)
+            if cupy.ndim(value) == 2:
+                indexed_shape = columns_df.iloc[key[0]].shape
+                if value.shape[1] == 1:
+                    if value.shape[0] != indexed_shape[0]:
+                        _shape_mismatch_error(value.shape, indexed_shape)
+                    for i, col in enumerate(columns_df._column_names):
+                        self._frame[col].iloc[key[0]] = value[:, 0]
+                else:
+                    if value.shape != indexed_shape:
+                        _shape_mismatch_error(value.shape, indexed_shape)
+                    for i, col in enumerate(columns_df._column_names):
+                        self._frame._data[col][key[0]] = value[:, i]
+            else:
+                if is_scalar(key[1]):
+                    for col in columns_df._column_names:
+                        self._frame[col].iloc[key[0]] = value
+                else:
+                    for i, col in enumerate(columns_df._column_names):
+                        self._frame[col].iloc[key[0]] = value[i]
+
+
+class DataFrame(IndexedFrame, Serializable, GetAttrGetItemMixin):
+    """
+    A GPU Dataframe object.
+
+    Parameters
+    ----------
+    data : array-like, Iterable, dict, or DataFrame.
+        Dict can contain Series, arrays, constants, or list-like objects.
+    index : Index or array-like
+        Index to use for resulting frame. Will default to
+        RangeIndex if no indexing information part of input data and
+        no index provided.
+    columns : Index or array-like
+        Column labels to use for resulting frame.
+        Will default to RangeIndex (0, 1, 2, …, n) if no column
+        labels are provided.
+    dtype : dtype, default None
+        Data type to force. Only a single dtype is allowed.
+        If None, infer.
+    nan_as_null : bool, Default True
+        If ``None``/``True``, converts ``np.nan`` values to
+        ``null`` values.
+        If ``False``, leaves ``np.nan`` values as is.
+
+    Examples
+    --------
+    Build dataframe with ``__setitem__``:
+
+    >>> import cudf
+    >>> df = cudf.DataFrame()
+    >>> df['key'] = [0, 1, 2, 3, 4]
+    >>> df['val'] = [float(i + 10) for i in range(5)]  # insert column
+    >>> df
+       key   val
+    0    0  10.0
+    1    1  11.0
+    2    2  12.0
+    3    3  13.0
+    4    4  14.0
+
+    Build DataFrame via dict of columns:
+
+    >>> import numpy as np
+    >>> from datetime import datetime, timedelta
+    >>> t0 = datetime.strptime('2018-10-07 12:00:00', '%Y-%m-%d %H:%M:%S')
+    >>> n = 5
+    >>> df = cudf.DataFrame({
+    ...     'id': np.arange(n),
+    ...     'datetimes': np.array(
+    ...     [(t0+ timedelta(seconds=x)) for x in range(n)])
+    ... })
+    >>> df
+        id            datetimes
+    0    0  2018-10-07 12:00:00
+    1    1  2018-10-07 12:00:01
+    2    2  2018-10-07 12:00:02
+    3    3  2018-10-07 12:00:03
+    4    4  2018-10-07 12:00:04
+
+    Build DataFrame via list of rows as tuples:
+
+    >>> df = cudf.DataFrame([
+    ...     (5, "cats", "jump", np.nan),
+    ...     (2, "dogs", "dig", 7.5),
+    ...     (3, "cows", "moo", -2.1, "occasionally"),
+    ... ])
+    >>> df
+       0     1     2     3             4
+    0  5  cats  jump  <NA>          <NA>
+    1  2  dogs   dig   7.5          <NA>
+    2  3  cows   moo  -2.1  occasionally
+
+    Convert from a Pandas DataFrame:
+
+    >>> import pandas as pd
+    >>> pdf = pd.DataFrame({'a': [0, 1, 2, 3],'b': [0.1, 0.2, None, 0.3]})
+    >>> pdf
+       a    b
+    0  0  0.1
+    1  1  0.2
+    2  2  NaN
+    3  3  0.3
+    >>> df = cudf.from_pandas(pdf)
+    >>> df
+       a     b
+    0  0   0.1
+    1  1   0.2
+    2  2  <NA>
+    3  3   0.3
+    """
+
+    _PROTECTED_KEYS = frozenset(("_data", "_index"))
+    _accessors: Set[Any] = set()
+    _loc_indexer_type = _DataFrameLocIndexer
+    _iloc_indexer_type = _DataFrameIlocIndexer
+    _groupby = DataFrameGroupBy
+    _resampler = DataFrameResampler
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self, data=None, index=None, columns=None, dtype=None, nan_as_null=True
+    ):
+        super().__init__()
+
+        if isinstance(columns, (Series, cudf.BaseIndex)):
+            columns = columns.to_pandas()
+
+        if isinstance(data, (DataFrame, pd.DataFrame)):
+            if isinstance(data, pd.DataFrame):
+                data = self.from_pandas(data, nan_as_null=nan_as_null)
+
+            if index is not None:
+                if not data.index.equals(index):
+                    data = data.reindex(index)
+                    index = data._index
+                else:
+                    index = as_index(index)
+            else:
+                index = data._index
+
+            self._index = index
+
+            if columns is not None:
+                self._data = data._data
+                self._reindex(
+                    column_names=columns, index=index, deep=False, inplace=True
+                )
+                if isinstance(
+                    columns, (range, pd.RangeIndex, cudf.RangeIndex)
+                ):
+                    self._data.rangeindex = True
+            else:
+                self._data = data._data
+                self._data.rangeindex = True
+        elif isinstance(data, (cudf.Series, pd.Series)):
+            if isinstance(data, pd.Series):
+                data = cudf.Series.from_pandas(data, nan_as_null=nan_as_null)
+
+            # Series.name is not None and Series.name in columns
+            #   -> align
+            # Series.name is not None and Series.name not in columns
+            #   -> return empty DataFrame
+            # Series.name is None and no columns
+            #   -> return 1 column DataFrame
+            # Series.name is None and columns
+            #   -> return 1 column DataFrame if len(columns) in {0, 1}
+            if data.name is None and columns is not None:
+                if len(columns) > 1:
+                    raise ValueError(
+                        "Length of columns must be less than 2 if "
+                        f"{type(data).__name__}.name is None."
+                    )
+                name = columns[0]
+            else:
+                name = data.name or 0
+            self._init_from_dict_like(
+                {name: data},
+                index=index,
+                columns=columns,
+                nan_as_null=nan_as_null,
+            )
+        elif data is None:
+            if index is None:
+                self._index = RangeIndex(0)
+            else:
+                self._index = as_index(index)
+            if columns is not None:
+                rangeindex = isinstance(
+                    columns, (range, pd.RangeIndex, cudf.RangeIndex)
+                )
+                self._data = ColumnAccessor(
+                    {
+                        k: column.column_empty(
+                            len(self), dtype="object", masked=True
+                        )
+                        for k in columns
+                    },
+                    level_names=tuple(columns.names)
+                    if isinstance(columns, pd.Index)
+                    else None,
+                    rangeindex=rangeindex,
+                )
+        elif isinstance(data, ColumnAccessor):
+            raise TypeError(
+                "Use cudf.Series._from_data for constructing a Series from "
+                "ColumnAccessor"
+            )
+        elif hasattr(data, "__cuda_array_interface__"):
+            arr_interface = data.__cuda_array_interface__
+
+            # descr is an optional field of the _cuda_ary_iface_
+            if "descr" in arr_interface:
+                if len(arr_interface["descr"]) == 1:
+                    new_df = self._from_arrays(
+                        data, index=index, columns=columns
+                    )
+                else:
+                    new_df = self.from_records(
+                        data, index=index, columns=columns
+                    )
+            else:
+                new_df = self._from_arrays(data, index=index, columns=columns)
+
+            self._data = new_df._data
+            self._index = new_df._index
+            self._check_data_index_length_match()
+        elif hasattr(data, "__array_interface__"):
+            arr_interface = data.__array_interface__
+            if len(arr_interface["descr"]) == 1:
+                # not record arrays
+                new_df = self._from_arrays(data, index=index, columns=columns)
+            else:
+                new_df = self.from_records(data, index=index, columns=columns)
+            self._data = new_df._data
+            self._index = new_df._index
+            self._check_data_index_length_match()
+        else:
+            if isinstance(data, Iterator):
+                data = list(data)
+            if is_list_like(data):
+                if len(data) > 0 and is_scalar(data[0]):
+                    if columns is not None:
+                        data = dict(zip(columns, [data]))
+                        rangeindex = isinstance(
+                            columns, (range, pd.RangeIndex, cudf.RangeIndex)
+                        )
+                    else:
+                        data = dict(enumerate([data]))
+                        rangeindex = True
+                    new_df = DataFrame(data=data, index=index)
+
+                    self._data = new_df._data
+                    self._index = new_df._index
+                    self._data._level_names = (
+                        tuple(columns.names)
+                        if isinstance(columns, pd.Index)
+                        else self._data._level_names
+                    )
+                    self._data.rangeindex = rangeindex
+                elif len(data) > 0 and isinstance(data[0], Series):
+                    self._init_from_series_list(
+                        data=data, columns=columns, index=index
+                    )
+                else:
+                    self._init_from_list_like(
+                        data, index=index, columns=columns
+                    )
+                self._check_data_index_length_match()
+            else:
+                if not is_dict_like(data):
+                    raise TypeError("data must be list or dict-like")
+
+                self._init_from_dict_like(
+                    data, index=index, columns=columns, nan_as_null=nan_as_null
+                )
+                self._check_data_index_length_match()
+
+        if dtype:
+            self._data = self.astype(dtype)._data
+
+        self._data.multiindex = self._data.multiindex or isinstance(
+            columns, pd.MultiIndex
+        )
+
+    @_cudf_nvtx_annotate
+    def _init_from_series_list(self, data, columns, index):
+        if index is None:
+            # When `index` is `None`, the final index of
+            # resulting dataframe will be union of
+            # all Series's names.
+            final_index = as_index(_get_union_of_series_names(data))
+        else:
+            # When an `index` is passed, the final index of
+            # resulting dataframe will be whatever
+            # index passed, but will need
+            # shape validations - explained below
+            data_length = len(data)
+            index_length = len(index)
+            if data_length != index_length:
+                # If the passed `index` length doesn't match
+                # length of Series objects in `data`, we must
+                # check if `data` can be duplicated/expanded
+                # to match the length of index. For that we
+                # check if the length of index is a factor
+                # of length of data.
+                #
+                # 1. If yes, we extend data
+                # until length of data is equal to length of index.
+                # 2. If no, we throw an error stating the
+                # shape of resulting `data` and `index`
+
+                # Simple example
+                # >>> import pandas as pd
+                # >>> s = pd.Series([1, 2, 3])
+                # >>> pd.DataFrame([s], index=['a', 'b'])
+                #    0  1  2
+                # a  1  2  3
+                # b  1  2  3
+                # >>> pd.DataFrame([s], index=['a', 'b', 'c'])
+                #    0  1  2
+                # a  1  2  3
+                # b  1  2  3
+                # c  1  2  3
+                if index_length % data_length == 0:
+                    initial_data = data
+                    data = []
+                    for _ in range(int(index_length / data_length)):
+                        data.extend([o for o in initial_data])
+                else:
+                    raise ValueError(
+                        f"Length of values ({data_length}) does "
+                        f"not match length of index ({index_length})"
+                    )
+
+            final_index = as_index(index)
+
+        series_lengths = list(map(len, data))
+        data = numeric_normalize_types(*data)
+        if series_lengths.count(series_lengths[0]) == len(series_lengths):
+            # Calculating the final dataframe columns by
+            # getting union of all `index` of the Series objects.
+            final_columns = _get_union_of_indices([d.index for d in data])
+            if isinstance(final_columns, cudf.RangeIndex):
+                self._data.rangeindex = True
+
+            for idx, series in enumerate(data):
+                if not series.index.is_unique:
+                    raise ValueError(
+                        "Reindexing only valid with uniquely valued Index "
+                        "objects"
+                    )
+                if not series.index.equals(final_columns):
+                    series = series.reindex(final_columns)
+                self._data[idx] = column.as_column(series._column)
+
+            # Setting `final_columns` to self._index so
+            # that the resulting `transpose` will be have
+            # columns set to `final_columns`
+            self._index = as_index(final_columns)
+
+            transpose = self.T
+        else:
+            concat_df = cudf.concat(data, axis=1)
+
+            cols = concat_df._data.to_pandas_index()
+            if cols.dtype == "object":
+                concat_df.columns = cols.astype("str")
+
+            transpose = concat_df.T
+
+        transpose._index = final_index
+        self._data = transpose._data
+        self._index = transpose._index
+
+        # If `columns` is passed, the result dataframe
+        # contain a dataframe with only the
+        # specified `columns` in the same order.
+        if columns is not None:
+            for col_name in columns:
+                if col_name not in self._data:
+                    self._data[col_name] = column.column_empty(
+                        row_count=len(self), dtype=None, masked=True
+                    )
+            self._data._level_names = (
+                tuple(columns.names)
+                if isinstance(columns, pd.Index)
+                else self._data._level_names
+            )
+            self._data = self._data.select_by_label(columns)
+            self._data.rangeindex = isinstance(
+                columns, (range, cudf.RangeIndex, pd.RangeIndex)
+            )
+        else:
+            self._data.rangeindex = True
+
+    @_cudf_nvtx_annotate
+    def _init_from_list_like(self, data, index=None, columns=None):
+        if index is None:
+            index = RangeIndex(start=0, stop=len(data))
+        else:
+            index = as_index(index)
+
+        self._index = as_index(index)
+        # list-of-dicts case
+        if len(data) > 0 and isinstance(data[0], dict):
+            data = DataFrame.from_pandas(pd.DataFrame(data))
+            self._data = data._data
+        # interval in a list
+        elif len(data) > 0 and isinstance(data[0], pd.Interval):
+            data = DataFrame.from_pandas(pd.DataFrame(data))
+            self._data = data._data
+        elif any(
+            not isinstance(col, (abc.Iterable, abc.Sequence)) for col in data
+        ):
+            raise TypeError("Inputs should be an iterable or sequence.")
+        elif len(data) > 0 and not can_convert_to_column(data[0]):
+            raise ValueError("Must pass 2-d input.")
+        else:
+            if (
+                len(data) > 0
+                and columns is None
+                and isinstance(data[0], tuple)
+                and hasattr(data[0], "_fields")
+            ):
+                # pandas behavior is to use the fields from the first
+                # namedtuple as the column names
+                columns = data[0]._fields
+
+            data = list(itertools.zip_longest(*data))
+
+            if columns is not None and len(data) == 0:
+                data = [
+                    cudf.core.column.column_empty(row_count=0, dtype=None)
+                    for _ in columns
+                ]
+
+            for col_name, col in enumerate(data):
+                self._data[col_name] = column.as_column(col)
+            self._data.rangeindex = True
+
+        if columns is not None:
+            if len(columns) != len(data):
+                raise ValueError(
+                    f"Shape of passed values is ({len(index)}, {len(data)}), "
+                    f"indices imply ({len(index)}, {len(columns)})."
+                )
+
+            self.columns = columns
+            self._data.rangeindex = isinstance(
+                columns, (range, pd.RangeIndex, cudf.RangeIndex)
+            )
+
+    @_cudf_nvtx_annotate
+    def _init_from_dict_like(
+        self, data, index=None, columns=None, nan_as_null=None
+    ):
+        if columns is not None:
+            # remove all entries in data that are not in columns,
+            # inserting new empty columns for entries in columns that
+            # are not in data
+            if any(c in data for c in columns):
+                # Let the downstream logic determine the length of the
+                # empty columns here
+                empty_column = lambda: None  # noqa: E731
+            else:
+                # If keys is empty, none of the data keys match the
+                # columns, so we need to create an empty DataFrame. To
+                # match pandas, the size of the dataframe must match
+                # the provided index, so we need to return a masked
+                # array of nulls if an index is given.
+                empty_column = functools.partial(
+                    cudf.core.column.column_empty,
+                    row_count=(0 if index is None else len(index)),
+                    dtype=None,
+                    masked=index is not None,
+                )
+
+            data = {
+                c: data[c] if c in data else empty_column() for c in columns
+            }
+
+        data, index = self._align_input_series_indices(data, index=index)
+
+        if index is None:
+            num_rows = 0
+            if data:
+                keys, values, lengths = zip(
+                    *(
+                        (k, v, 1)
+                        if is_scalar(v)
+                        else (
+                            k,
+                            vc := as_column(v, nan_as_null=nan_as_null),
+                            len(vc),
+                        )
+                        for k, v in data.items()
+                    )
+                )
+                data = dict(zip(keys, values))
+                try:
+                    (num_rows,) = (set(lengths) - {1}) or {1}
+                except ValueError:
+                    raise ValueError("All arrays must be the same length")
+
+            self._index = RangeIndex(0, num_rows)
+        else:
+            self._index = as_index(index)
+
+        if len(data):
+            self._data.multiindex = True
+            for i, col_name in enumerate(data):
+                self._data.multiindex = self._data.multiindex and isinstance(
+                    col_name, tuple
+                )
+                self._insert(
+                    i,
+                    col_name,
+                    data[col_name],
+                    nan_as_null=nan_as_null,
+                )
+        self._data._level_names = (
+            tuple(columns.names)
+            if isinstance(columns, pd.Index)
+            else self._data._level_names
+        )
+
+    @classmethod
+    def _from_data(
+        cls,
+        data: MutableMapping,
+        index: Optional[BaseIndex] = None,
+        columns: Any = None,
+    ) -> DataFrame:
+        out = super()._from_data(data=data, index=index)
+        if columns is not None:
+            out.columns = columns
+        return out
+
+    @staticmethod
+    @_cudf_nvtx_annotate
+    def _align_input_series_indices(data, index):
+        data = data.copy()
+
+        input_series = [
+            Series(val)
+            for val in data.values()
+            if isinstance(val, (pd.Series, Series, dict))
+        ]
+
+        if input_series:
+            if index is not None:
+                aligned_input_series = [
+                    sr._align_to_index(index, how="right", sort=False)
+                    for sr in input_series
+                ]
+
+            else:
+                aligned_input_series = cudf.core.series._align_indices(
+                    input_series
+                )
+                index = aligned_input_series[0].index
+
+            for name, val in data.items():
+                if isinstance(val, (pd.Series, Series, dict)):
+                    data[name] = aligned_input_series.pop(0)
+
+        return data, index
+
+    # The `constructor*` properties are used by `dask` (and `dask_cudf`)
+    @property
+    def _constructor(self):
+        return DataFrame
+
+    @property
+    def _constructor_sliced(self):
+        return Series
+
+    @property
+    def _constructor_expanddim(self):
+        raise NotImplementedError(
+            "_constructor_expanddim not supported for DataFrames!"
+        )
+
+    def serialize(self):
+        header, frames = super().serialize()
+
+        header["index"], index_frames = self._index.serialize()
+        header["index_frame_count"] = len(index_frames)
+        # For backwards compatibility with older versions of cuDF, index
+        # columns are placed before data columns.
+        frames = index_frames + frames
+
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        index_nframes = header["index_frame_count"]
+        obj = super().deserialize(
+            header, frames[header["index_frame_count"] :]
+        )
+
+        idx_typ = pickle.loads(header["index"]["type-serialized"])
+        index = idx_typ.deserialize(header["index"], frames[:index_nframes])
+        obj._index = index
+
+        return obj
+
+    @property
+    @_cudf_nvtx_annotate
+    def shape(self):
+        """Returns a tuple representing the dimensionality of the DataFrame."""
+        return self._num_rows, self._num_columns
+
+    @property
+    def dtypes(self):
+        """
+        Return the dtypes in this object.
+
+        Returns
+        -------
+        pandas.Series
+            The data type of each column.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> df = cudf.DataFrame({'float': [1.0],
+        ...                    'int': [1],
+        ...                    'datetime': [pd.Timestamp('20180310')],
+        ...                    'string': ['foo']})
+        >>> df
+           float  int   datetime string
+        0    1.0    1 2018-03-10    foo
+        >>> df.dtypes
+        float              float64
+        int                  int64
+        datetime    datetime64[us]
+        string              object
+        dtype: object
+        """
+        return pd.Series(self._dtypes, dtype="object")
+
+    @property
+    def ndim(self):
+        """Dimension of the data. DataFrame ndim is always 2."""
+        return 2
+
+    def __dir__(self):
+        # Add the columns of the DataFrame to the dir output.
+        o = set(dir(type(self)))
+        o.update(self.__dict__)
+        o.update(
+            c
+            for c in self._column_names
+            if isinstance(c, str) and c.isidentifier()
+        )
+        return list(o)
+
+    def __setattr__(self, key, col):
+        try:
+            # Preexisting attributes may be set. We cannot rely on checking the
+            # `_PROTECTED_KEYS` because we must also allow for settable
+            # properties, and we must call object.__getattribute__ to bypass
+            # the `__getitem__` behavior inherited from `GetAttrGetItemMixin`.
+            object.__getattribute__(self, key)
+        except AttributeError:
+            if key not in self._PROTECTED_KEYS:
+                try:
+                    # Check key existence.
+                    self[key]
+                    # If a column already exists, set it.
+                    self[key] = col
+                    return
+                except KeyError:
+                    pass
+
+            # Set a new attribute that is not already a column.
+            super().__setattr__(key, col)
+
+        except RuntimeError as e:
+            # TODO: This allows setting properties that are marked as forbidden
+            # for internal usage. It is necessary because the __getattribute__
+            # call in the try block will trigger the error. We should see if
+            # setting these variables can also always be disabled
+            if "External-only API" not in str(e):
+                raise
+            super().__setattr__(key, col)
+        else:
+            super().__setattr__(key, col)
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, arg):
+        """
+        If *arg* is a ``str`` or ``int`` type, return the column Series.
+        If *arg* is a ``slice``, return a new DataFrame with all columns
+        sliced to the specified range.
+        If *arg* is an ``array`` containing column names, return a new
+        DataFrame with the corresponding columns.
+        If *arg* is a ``dtype.bool array``, return the rows marked True
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame({
+        ...     'a': list(range(10)),
+        ...     'b': list(range(10)),
+        ...     'c': list(range(10)),
+        ... })
+
+        Get first 4 rows of all columns.
+
+        >>> df[:4]
+           a  b  c
+        0  0  0  0
+        1  1  1  1
+        2  2  2  2
+        3  3  3  3
+
+        Get last 5 rows of all columns.
+
+        >>> df[-5:]
+           a  b  c
+        5  5  5  5
+        6  6  6  6
+        7  7  7  7
+        8  8  8  8
+        9  9  9  9
+
+        Get columns a and c.
+
+        >>> df[['a', 'c']]
+           a  c
+        0  0  0
+        1  1  1
+        2  2  2
+        3  3  3
+        4  4  4
+        5  5  5
+        6  6  6
+        7  7  7
+        8  8  8
+        9  9  9
+
+        Return the rows specified in the boolean mask.
+
+        >>> df[[True, False, True, False, True,
+        ...     False, True, False, True, False]]
+           a  b  c
+        0  0  0  0
+        2  2  2  2
+        4  4  4  4
+        6  6  6  6
+        8  8  8  8
+        """
+        if _is_scalar_or_zero_d_array(arg) or isinstance(arg, tuple):
+            return self._get_columns_by_label(arg, downcast=True)
+
+        elif isinstance(arg, slice):
+            return self._slice(arg)
+
+        elif can_convert_to_column(arg):
+            mask = arg
+            if is_list_like(mask):
+                # An explicit dtype is needed to avoid pandas warnings from
+                # empty sets of columns. This shouldn't be needed in pandas
+                # 2.0, we don't need to specify a dtype when we know we're not
+                # trying to match any columns so the default is fine.
+                dtype = None
+                if len(mask) == 0:
+                    assert Version(pd.__version__) < Version("2.0.0")
+                    dtype = "float64"
+                mask = pd.Series(mask, dtype=dtype)
+            if mask.dtype == "bool":
+                return self._apply_boolean_mask(BooleanMask(mask, len(self)))
+            else:
+                return self._get_columns_by_label(mask)
+        elif isinstance(arg, DataFrame):
+            return self.where(arg)
+        else:
+            raise TypeError(
+                f"__getitem__ on type {type(arg)} is not supported"
+            )
+
+    @_cudf_nvtx_annotate
+    def __setitem__(self, arg, value):
+        """Add/set column by *arg or DataFrame*"""
+        if isinstance(arg, DataFrame):
+            # not handling set_item where arg = df & value = df
+            if isinstance(value, DataFrame):
+                raise TypeError(
+                    f"__setitem__ with arg = {type(value)} and "
+                    f"value = {type(arg)} is not supported"
+                )
+            else:
+                for col_name in self._data:
+                    scatter_map = arg._data[col_name]
+                    if is_scalar(value):
+                        self._data[col_name][scatter_map] = value
+                    else:
+                        self._data[col_name][scatter_map] = column.as_column(
+                            value
+                        )[scatter_map]
+        elif is_scalar(arg) or isinstance(arg, tuple):
+            if isinstance(value, DataFrame):
+                _setitem_with_dataframe(
+                    input_df=self,
+                    replace_df=value,
+                    input_cols=[arg],
+                    mask=None,
+                )
+            else:
+                if arg in self._data:
+                    if not is_scalar(value) and len(self) == 0:
+                        if isinstance(value, (pd.Series, Series)):
+                            self._index = as_index(value.index)
+                        elif len(value) > 0:
+                            self._index = RangeIndex(start=0, stop=len(value))
+                        value = column.as_column(value)
+                        new_data = self._data.__class__()
+                        for key in self._data:
+                            if key == arg:
+                                new_data[key] = value
+                            else:
+                                new_data[key] = column.column_empty_like(
+                                    self._data[key],
+                                    masked=True,
+                                    newsize=len(value),
+                                )
+
+                        self._data = new_data
+                        return
+                    elif isinstance(value, (pd.Series, Series)):
+                        value = Series(value)._align_to_index(
+                            self._index,
+                            how="right",
+                            sort=False,
+                            allow_non_unique=True,
+                        )
+                    if is_scalar(value):
+                        self._data[arg] = column.full(len(self), value)
+                    else:
+                        value = as_column(value)
+                        self._data[arg] = value
+                else:
+                    # disc. with pandas here
+                    # pandas raises key error here
+                    self.insert(len(self._data), arg, value)
+
+        elif can_convert_to_column(arg):
+            mask = arg
+            if is_list_like(mask):
+                mask = np.array(mask)
+
+            if mask.dtype == "bool":
+                mask = column.as_column(arg)
+
+                if isinstance(value, DataFrame):
+                    _setitem_with_dataframe(
+                        input_df=self,
+                        replace_df=value,
+                        input_cols=None,
+                        mask=mask,
+                    )
+                else:
+                    if not is_scalar(value):
+                        value = column.as_column(value)[mask]
+                    for col_name in self._data:
+                        self._data[col_name][mask] = value
+            else:
+                if isinstance(value, (cupy.ndarray, np.ndarray)):
+                    _setitem_with_dataframe(
+                        input_df=self,
+                        replace_df=cudf.DataFrame(value),
+                        input_cols=arg,
+                        mask=None,
+                        ignore_index=True,
+                    )
+                elif isinstance(value, DataFrame):
+                    _setitem_with_dataframe(
+                        input_df=self,
+                        replace_df=value,
+                        input_cols=arg,
+                        mask=None,
+                    )
+                else:
+                    for col in arg:
+                        if is_scalar(value):
+                            self._data[col] = column.full(
+                                size=len(self), fill_value=value
+                            )
+                        else:
+                            self._data[col] = column.as_column(value)
+
+        else:
+            raise TypeError(
+                f"__setitem__ on type {type(arg)} is not supported"
+            )
+
+    def __delitem__(self, name):
+        self._drop_column(name)
+
+    @_cudf_nvtx_annotate
+    def memory_usage(self, index=True, deep=False):
+        mem_usage = [col.memory_usage for col in self._data.columns]
+        names = [str(name) for name in self._data.names]
+        if index:
+            mem_usage.append(self._index.memory_usage())
+            names.append("Index")
+        return Series._from_data(
+            data={None: as_column(mem_usage)},
+            index=as_index(names),
+        )
+
+    @_cudf_nvtx_annotate
+    def __array_function__(self, func, types, args, kwargs):
+        if "out" in kwargs or not all(
+            issubclass(t, (Series, DataFrame)) for t in types
+        ):
+            return NotImplemented
+
+        try:
+            if func.__name__ in {"any", "all"}:
+                # NumPy default for `axis` is
+                # different from `cudf`/`pandas`
+                # hence need this special handling.
+                kwargs.setdefault("axis", None)
+            if cudf_func := getattr(self.__class__, func.__name__, None):
+                out = cudf_func(*args, **kwargs)
+                # The dot product of two DataFrames returns an array in pandas.
+                if (
+                    func is np.dot
+                    and isinstance(args[0], (DataFrame, pd.DataFrame))
+                    and isinstance(args[1], (DataFrame, pd.DataFrame))
+                ):
+                    return out.values
+                return out
+        except Exception:
+            # The rare instance where a "silent" failure is preferable. Except
+            # in the (highly unlikely) case that some other library
+            # interoperates with cudf objects, the result will be that numpy
+            # raises a TypeError indicating that the operation is not
+            # implemented, which is much friendlier than an arbitrary internal
+            # cudf error.
+            pass
+        return NotImplemented
+
+    # The _get_numeric_data method is necessary for dask compatibility.
+    @_cudf_nvtx_annotate
+    def _get_numeric_data(self):
+        """Return a dataframe with only numeric data types"""
+        columns = [
+            c
+            for c, dt in self.dtypes.items()
+            if dt != object and not is_categorical_dtype(dt)
+        ]
+        return self[columns]
+
+    @_cudf_nvtx_annotate
+    def assign(self, **kwargs: Union[Callable[[Self], Any], Any]):
+        """
+        Assign columns to DataFrame from keyword arguments.
+
+        Parameters
+        ----------
+        **kwargs: dict mapping string column names to values
+            The value for each key can either be a literal column (or
+            something that can be converted to a column), or
+            a callable of one argument that will be given the
+            dataframe as an argument and should return the new column
+            (without modifying the input argument).
+            Columns are added in-order, so callables can refer to
+            column names constructed in the assignment.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame()
+        >>> df = df.assign(a=[0, 1, 2], b=[3, 4, 5])
+        >>> df
+           a  b
+        0  0  3
+        1  1  4
+        2  2  5
+        """
+        new_df = self.copy(deep=False)
+        for k, v in kwargs.items():
+            new_df[k] = v(new_df) if callable(v) else v
+        return new_df
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _concat(
+        cls, objs, axis=0, join="outer", ignore_index=False, sort=False
+    ):
+        # flag to indicate at least one empty input frame also has an index
+        empty_has_index = False
+        # length of output frame's RangeIndex if all input frames are empty,
+        # and at least one has an index
+        result_index_length = 0
+        # the number of empty input frames
+        num_empty_input_frames = 0
+
+        # flag to indicate if all DataFrame's have
+        # RangeIndex as their index
+        are_all_range_index = False
+
+        for i, obj in enumerate(objs):
+            # shallow-copy the input DFs in case the same DF instance
+            # is concatenated with itself
+            objs[i] = obj.copy(deep=False)
+
+            # If ignore_index is true, determine if
+            # all or some objs are empty(and have index).
+            # 1. If all objects are empty(and have index), we
+            # should set the index separately using RangeIndex.
+            # 2. If some objects are empty(and have index), we
+            # create empty columns later while populating `columns`
+            # variable. Detailed explanation of second case before
+            # allocation of `columns` variable below.
+            if ignore_index and obj.empty:
+                num_empty_input_frames += 1
+                result_index_length += len(obj)
+                empty_has_index = empty_has_index or len(obj) > 0
+
+            are_all_range_index = (
+                True if i == 0 else are_all_range_index
+            ) and isinstance(obj.index, cudf.RangeIndex)
+
+        if join == "inner":
+            sets_of_column_names = [set(obj._column_names) for obj in objs]
+
+            intersecting_columns = functools.reduce(
+                set.intersection, sets_of_column_names
+            )
+            union_of_columns = functools.reduce(
+                set.union, sets_of_column_names
+            )
+            non_intersecting_columns = union_of_columns.symmetric_difference(
+                intersecting_columns
+            )
+
+            # Get an ordered list of the intersecting columns to preserve input
+            # order, which is promised by pandas for inner joins.
+            ordered_intersecting_columns = [
+                name
+                for obj in objs
+                for name in obj._column_names
+                if name in intersecting_columns
+            ]
+
+            names = dict.fromkeys(ordered_intersecting_columns).keys()
+
+            if axis == 0:
+                if ignore_index and (
+                    num_empty_input_frames > 0
+                    or len(intersecting_columns) == 0
+                ):
+                    # When ignore_index is True and if there is
+                    # at least 1 empty dataframe and no
+                    # intersecting columns are present, an empty dataframe
+                    # needs to be returned just with an Index.
+                    empty_has_index = True
+                    num_empty_input_frames = len(objs)
+                    result_index_length = sum(len(obj) for obj in objs)
+
+                # remove columns not present in all objs
+                for obj in objs:
+                    obj.drop(
+                        columns=non_intersecting_columns,
+                        inplace=True,
+                        errors="ignore",
+                    )
+        elif join == "outer":
+            # Get a list of the unique table column names
+            names = [name for f in objs for name in f._column_names]
+            names = dict.fromkeys(names).keys()
+
+        else:
+            raise ValueError(
+                "Only can inner (intersect) or outer (union) when joining"
+                "the other axis"
+            )
+
+        if sort:
+            try:
+                # Sorted always returns a list, but will fail to sort if names
+                # include different types that are not comparable.
+                names = sorted(names)
+            except TypeError:
+                # For pandas compatibility, we also try to handle the case
+                # where some column names are strings and others are ints. Just
+                # assume that everything that isn't a str is numerical, we
+                # can't sort anything else.
+                try:
+                    str_names = sorted(n for n in names if isinstance(n, str))
+                    non_str_names = sorted(
+                        n for n in names if not isinstance(n, str)
+                    )
+                    names = non_str_names + str_names
+                except TypeError:
+                    names = list(names)
+        else:
+            names = list(names)
+
+        # Combine the index and table columns for each Frame into a list of
+        # [...index_cols, ...table_cols].
+        #
+        # If any of the input frames have a non-empty index, include these
+        # columns in the list of columns to concatenate, even if the input
+        # frames are empty and `ignore_index=True`.
+        columns = [
+            (
+                []
+                if are_all_range_index
+                or (ignore_index and not empty_has_index)
+                else list(f._index._data.columns)
+            )
+            + [f._data[name] if name in f._data else None for name in names]
+            for f in objs
+        ]
+
+        # Get a list of the combined index and table column indices
+        indices = list(range(functools.reduce(max, map(len, columns))))
+        # The position of the first table column in each
+        # combined index + table columns list
+        first_data_column_position = len(indices) - len(names)
+
+        # Get the non-null columns and their dtypes
+        non_null_cols, dtypes = _get_non_null_cols_and_dtypes(indices, columns)
+
+        # Infer common dtypes between numeric columns
+        # and combine CategoricalColumn categories
+        categories = _find_common_dtypes_and_categories(non_null_cols, dtypes)
+
+        # Cast all columns to a common dtype, assign combined categories,
+        # and back-fill missing columns with all-null columns
+        _cast_cols_to_common_dtypes(indices, columns, dtypes, categories)
+
+        # Construct input tables with the index and data columns in the same
+        # order. This strips the given index/column names and replaces the
+        # names with their integer positions in the `cols` list
+        tables = []
+        for cols in columns:
+            table_index = None
+            if 1 == first_data_column_position:
+                table_index = cudf.core.index.as_index(cols[0])
+            elif first_data_column_position > 1:
+                table_index = DataFrame._from_data(
+                    data=dict(
+                        zip(
+                            indices[:first_data_column_position],
+                            cols[:first_data_column_position],
+                        )
+                    )
+                )
+            tables.append(
+                DataFrame._from_data(
+                    data=dict(
+                        zip(
+                            indices[first_data_column_position:],
+                            cols[first_data_column_position:],
+                        )
+                    ),
+                    index=table_index,
+                )
+            )
+
+        # Concatenate the Tables
+        out = cls._from_data(
+            *libcudf.concat.concat_tables(
+                tables, ignore_index=ignore_index or are_all_range_index
+            )
+        )
+
+        # If ignore_index is True, all input frames are empty, and at
+        # least one input frame has an index, assign a new RangeIndex
+        # to the result frame.
+        if empty_has_index and num_empty_input_frames == len(objs):
+            out._index = cudf.RangeIndex(result_index_length)
+        elif are_all_range_index and not ignore_index:
+            out._index = cudf.core.index.GenericIndex._concat(
+                [o._index for o in objs]
+            )
+
+        # Reassign the categories for any categorical table cols
+        _reassign_categories(
+            categories, out._data, indices[first_data_column_position:]
+        )
+
+        # Reassign the categories for any categorical index cols
+        if not isinstance(out._index, cudf.RangeIndex):
+            _reassign_categories(
+                categories,
+                out._index._data,
+                indices[:first_data_column_position],
+            )
+            if not isinstance(out._index, MultiIndex) and is_categorical_dtype(
+                out._index._values.dtype
+            ):
+                out = out.set_index(
+                    cudf.core.index.as_index(out.index._values)
+                )
+        for name, col in out._data.items():
+            out._data[name] = col._with_type_metadata(
+                tables[0]._data[name].dtype
+            )
+
+        # Reassign index and column names
+        if objs[0]._data.multiindex:
+            out._set_column_names_like(objs[0])
+        else:
+            out.columns = names
+        if not ignore_index:
+            out._index.name = objs[0]._index.name
+            out._index.names = objs[0]._index.names
+
+        return out
+
+    def astype(self, dtype, copy=False, errors="raise", **kwargs):
+        if is_dict_like(dtype):
+            if len(set(dtype.keys()) - set(self._data.names)) > 0:
+                raise KeyError(
+                    "Only a column name can be used for the "
+                    "key in a dtype mappings argument."
+                )
+        else:
+            dtype = {cc: dtype for cc in self._data.names}
+        return super().astype(dtype, copy, errors, **kwargs)
+
+    def _clean_renderable_dataframe(self, output):
+        """
+        This method takes in partial/preprocessed dataframe
+        and returns correct representation of it with correct
+        dimensions (rows x columns)
+        """
+
+        max_rows = get_option("display.max_rows")
+        min_rows = get_option("display.min_rows")
+        max_cols = get_option("display.max_columns")
+        max_colwidth = get_option("display.max_colwidth")
+        show_dimensions = get_option("display.show_dimensions")
+        if get_option("display.expand_frame_repr"):
+            width, _ = console.get_console_size()
+        else:
+            width = None
+
+        output = output.to_pandas().to_string(
+            max_rows=max_rows,
+            min_rows=min_rows,
+            max_cols=max_cols,
+            line_width=width,
+            max_colwidth=max_colwidth,
+            show_dimensions=show_dimensions,
+        )
+
+        lines = output.split("\n")
+
+        if lines[-1].startswith("["):
+            lines = lines[:-1]
+            lines.append(
+                "[%d rows x %d columns]" % (len(self), len(self._data.names))
+            )
+        return "\n".join(lines)
+
+    def _clean_nulls_from_dataframe(self, df):
+        """
+        This function converts all ``null`` values to ``<NA>`` for
+        representation as a string in `__repr__`.
+
+        Since we utilize Pandas `__repr__` at all places in our code
+        for formatting purposes, we convert columns to `str` dtype for
+        filling with `<NA>` values.
+        """
+        for col in df._data:
+            if is_list_dtype(df._data[col]) or is_struct_dtype(df._data[col]):
+                # TODO we need to handle this
+                pass
+            elif df._data[col].has_nulls():
+                fill_value = (
+                    str(cudf.NaT)
+                    if isinstance(
+                        df._data[col],
+                        (
+                            cudf.core.column.DatetimeColumn,
+                            cudf.core.column.TimeDeltaColumn,
+                        ),
+                    )
+                    else str(cudf.NA)
+                )
+
+                df[col] = df._data[col].astype("str").fillna(fill_value)
+            else:
+                df[col] = df._data[col]
+
+        return df
+
+    def _get_renderable_dataframe(self):
+        """
+        Takes rows and columns from pandas settings or estimation from size.
+        pulls quadrants based off of some known parameters then style for
+        multiindex as well producing an efficient representative string
+        for printing with the dataframe.
+        """
+        max_rows = pd.options.display.max_rows
+        nrows = np.max([len(self) if max_rows is None else max_rows, 1])
+        if pd.options.display.max_rows == 0:
+            nrows = len(self)
+        ncols = (
+            pd.options.display.max_columns
+            if pd.options.display.max_columns
+            else pd.options.display.width / 2
+        )
+
+        if len(self) <= nrows and len(self._data.names) <= ncols:
+            output = self.copy(deep=False)
+        elif self.empty and len(self.index) > 0:
+            max_seq_items = pd.options.display.max_seq_items
+            # In case of Empty DataFrame with index, Pandas prints
+            # first `pd.options.display.max_seq_items` index values
+            # followed by ... To obtain ... at the end of index list,
+            # adding 1 extra value.
+            # If `pd.options.display.max_seq_items` is None,
+            # entire sequence/Index is to be printed.
+            # Note : Pandas truncates the dimensions at the end of
+            # the resulting dataframe when `display.show_dimensions`
+            # is set to truncate. Hence to display the dimensions we
+            # need to extract maximum of `max_seq_items` and `nrows`
+            # and have 1 extra value for ... to show up in the output
+            # string.
+            if max_seq_items is not None:
+                output = self.head(max(max_seq_items, nrows) + 1)
+            else:
+                output = self.copy(deep=False)
+        else:
+            left_cols = len(self._data.names)
+            right_cols = 0
+            upper_rows = len(self)
+            lower_rows = 0
+            if len(self) > nrows and nrows > 0:
+                upper_rows = int(nrows / 2.0) + 1
+                lower_rows = upper_rows + (nrows % 2)
+            if len(self._data.names) > ncols:
+                right_cols = len(self._data.names) - int(ncols / 2.0)
+                # adjust right columns for output if multiindex.
+                right_cols = (
+                    right_cols - 1
+                    if isinstance(self.index, MultiIndex)
+                    else right_cols
+                )
+                left_cols = int(ncols / 2.0) + 1
+            if right_cols > 0:
+                # Pick ncols - left_cols number of columns
+                # from the right side/from the end.
+                right_cols = -(int(ncols) - left_cols + 1)
+            else:
+                # If right_cols is 0 or negative, it means
+                # self has lesser number of columns than ncols.
+                # Hence assign len(self._data.names) which
+                # will result in empty `*_right` quadrants.
+                # This is because `*_left` quadrants will
+                # contain all columns.
+                right_cols = len(self._data.names)
+
+            upper_left = self.head(upper_rows).iloc[:, :left_cols]
+            upper_right = self.head(upper_rows).iloc[:, right_cols:]
+            lower_left = self.tail(lower_rows).iloc[:, :left_cols]
+            lower_right = self.tail(lower_rows).iloc[:, right_cols:]
+
+            upper = cudf.concat([upper_left, upper_right], axis=1)
+            lower = cudf.concat([lower_left, lower_right], axis=1)
+            output = cudf.concat([upper, lower])
+
+        output = self._clean_nulls_from_dataframe(output)
+        output._index = output._index._clean_nulls_from_index()
+
+        return output
+
+    @_cudf_nvtx_annotate
+    def __repr__(self):
+        output = self._get_renderable_dataframe()
+        return self._clean_renderable_dataframe(output)
+
+    @_cudf_nvtx_annotate
+    def _repr_html_(self):
+        lines = (
+            self._get_renderable_dataframe()
+            .to_pandas()
+            ._repr_html_()
+            .split("\n")
+        )
+        if lines[-2].startswith("<p>"):
+            lines = lines[:-2]
+            lines.append(
+                "<p>%d rows × %d columns</p>"
+                % (len(self), len(self._data.names))
+            )
+            lines.append("</div>")
+        return "\n".join(lines)
+
+    @_cudf_nvtx_annotate
+    def _repr_latex_(self):
+        return self._get_renderable_dataframe().to_pandas()._repr_latex_()
+
+    @_cudf_nvtx_annotate
+    def _get_columns_by_label(
+        self, labels, *, downcast=False
+    ) -> Self | Series:
+        """
+        Return columns of dataframe by `labels`
+
+        If downcast is True, try and downcast from a DataFrame to a Series
+        """
+        ca = self._data.select_by_label(labels)
+        if downcast:
+            if is_scalar(labels):
+                nlevels = 1
+            elif isinstance(labels, tuple):
+                nlevels = len(labels)
+            if self._data.multiindex is False or nlevels == self._data.nlevels:
+                out = self._constructor_sliced._from_data(
+                    ca, index=self.index, name=labels
+                )
+                return out
+        out = self.__class__._from_data(
+            ca, index=self.index, columns=ca.to_pandas_index()
+        )
+        return out
+
+    def _make_operands_and_index_for_binop(
+        self,
+        other: Any,
+        fn: str,
+        fill_value: Any = None,
+        reflect: bool = False,
+        can_reindex: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tuple[
+        Union[
+            Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
+            NotImplementedType,
+        ],
+        Optional[BaseIndex],
+        bool,
+    ]:
+        lhs, rhs = self._data, other
+        index = self._index
+        fill_requires_key = False
+        left_default: Any = False
+        equal_columns = False
+        can_use_self_column_name = True
+
+        if _is_scalar_or_zero_d_array(other):
+            rhs = {name: other for name in self._data}
+            equal_columns = True
+        elif isinstance(other, Series):
+            rhs = dict(zip(other.index.to_pandas(), other.values_host))
+            # For keys in right but not left, perform binops between NaN (not
+            # NULL!) and the right value (result is NaN).
+            left_default = as_column(np.nan, length=len(self))
+            equal_columns = other.index.to_pandas().equals(
+                self._data.to_pandas_index()
+            )
+            can_use_self_column_name = (
+                equal_columns
+                or list(other._index._data.names) == self._data._level_names
+            )
+        elif isinstance(other, DataFrame):
+            if (
+                not can_reindex
+                and fn in cudf.utils.utils._EQUALITY_OPS
+                and (
+                    not self.index.equals(other.index)
+                    or not self._data.to_pandas_index().equals(
+                        other._data.to_pandas_index()
+                    )
+                )
+            ):
+                raise ValueError(
+                    "Can only compare identically-labeled DataFrame objects"
+                )
+            new_lhs, new_rhs = _align_indices(self, other)
+            index = new_lhs._index
+            lhs, rhs = new_lhs._data, new_rhs._data
+            fill_requires_key = True
+            # For DataFrame-DataFrame ops, always default to operating against
+            # the fill value.
+            left_default = fill_value
+            equal_columns = self._column_names == other._column_names
+            can_use_self_column_name = (
+                equal_columns
+                or self._data._level_names == other._data._level_names
+            )
+        elif isinstance(other, (dict, abc.Mapping)):
+            # Need to fail early on host mapping types because we ultimately
+            # convert everything to a dict.
+            return NotImplemented, None, True
+
+        if not isinstance(rhs, (dict, abc.Mapping)):
+            return NotImplemented, None, True
+
+        operands = {
+            k: (
+                v,
+                rhs.get(k, fill_value),
+                reflect,
+                fill_value if (not fill_requires_key or k in rhs) else None,
+            )
+            for k, v in lhs.items()
+        }
+
+        if left_default is not False:
+            for k, v in rhs.items():
+                if k not in lhs:
+                    operands[k] = (left_default, v, reflect, None)
+
+        if not equal_columns:
+            if isinstance(other, DataFrame):
+                column_names_list = self._data.to_pandas_index().join(
+                    other._data.to_pandas_index(), how="outer"
+                )
+            elif isinstance(other, Series):
+                column_names_list = self._data.to_pandas_index().join(
+                    other.index.to_pandas(), how="outer"
+                )
+            else:
+                raise ValueError("other must be a DataFrame or Series.")
+
+            sorted_dict = {key: operands[key] for key in column_names_list}
+            return sorted_dict, index, can_use_self_column_name
+        return operands, index, can_use_self_column_name
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_dict(
+        cls,
+        data: dict,
+        orient: str = "columns",
+        dtype: Optional[Dtype] = None,
+        columns: Optional[list] = None,
+    ) -> DataFrame:
+        """
+        Construct DataFrame from dict of array-like or dicts.
+        Creates DataFrame object from dictionary by columns or by index
+        allowing dtype specification.
+
+        Parameters
+        ----------
+        data : dict
+            Of the form {field : array-like} or {field : dict}.
+        orient : {'columns', 'index', 'tight'}, default 'columns'
+            The "orientation" of the data. If the keys of the passed dict
+            should be the columns of the resulting DataFrame, pass 'columns'
+            (default). Otherwise if the keys should be rows, pass 'index'.
+            If 'tight', assume a dict with keys ['index', 'columns', 'data',
+            'index_names', 'column_names'].
+        dtype : dtype, default None
+            Data type to force, otherwise infer.
+        columns : list, default None
+            Column labels to use when ``orient='index'``. Raises a ``ValueError``
+            if used with ``orient='columns'`` or ``orient='tight'``.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        DataFrame.from_records : DataFrame from structured ndarray, sequence
+            of tuples or dicts, or DataFrame.
+        DataFrame : DataFrame object creation using constructor.
+        DataFrame.to_dict : Convert the DataFrame to a dictionary.
+
+        Examples
+        --------
+        By default the keys of the dict become the DataFrame columns:
+
+        >>> import cudf
+        >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
+        >>> cudf.DataFrame.from_dict(data)
+           col_1 col_2
+        0      3     a
+        1      2     b
+        2      1     c
+        3      0     d
+
+        Specify ``orient='index'`` to create the DataFrame using dictionary
+        keys as rows:
+
+        >>> data = {'row_1': [3, 2, 1, 0], 'row_2': [10, 11, 12, 13]}
+        >>> cudf.DataFrame.from_dict(data, orient='index')
+                0   1   2   3
+        row_1   3   2   1   0
+        row_2  10  11  12  13
+
+        When using the 'index' orientation, the column names can be
+        specified manually:
+
+        >>> cudf.DataFrame.from_dict(data, orient='index',
+        ...                          columns=['A', 'B', 'C', 'D'])
+                A   B   C   D
+        row_1   3   2   1   0
+        row_2  10  11  12  13
+
+        Specify ``orient='tight'`` to create the DataFrame using a 'tight'
+        format:
+
+        >>> data = {'index': [('a', 'b'), ('a', 'c')],
+        ...         'columns': [('x', 1), ('y', 2)],
+        ...         'data': [[1, 3], [2, 4]],
+        ...         'index_names': ['n1', 'n2'],
+        ...         'column_names': ['z1', 'z2']}
+        >>> cudf.DataFrame.from_dict(data, orient='tight')
+        z1     x  y
+        z2     1  2
+        n1 n2
+        a  b   1  3
+           c   2  4
+        """  # noqa: E501
+
+        orient = orient.lower()
+        if orient == "index":
+            if len(data) > 0 and isinstance(
+                next(iter(data.values())), (cudf.Series, cupy.ndarray)
+            ):
+                result = cls(data).T
+                result.columns = columns
+                if dtype is not None:
+                    result = result.astype(dtype)
+                return result
+            else:
+                return cls.from_pandas(
+                    pd.DataFrame.from_dict(
+                        data=data,
+                        orient=orient,
+                        dtype=dtype,
+                        columns=columns,
+                    )
+                )
+        elif orient == "columns":
+            if columns is not None:
+                raise ValueError(
+                    "Cannot use columns parameter with orient='columns'"
+                )
+            return cls(data, columns=None, dtype=dtype)
+        elif orient == "tight":
+            if columns is not None:
+                raise ValueError(
+                    "Cannot use columns parameter with orient='right'"
+                )
+
+            index = _from_dict_create_index(
+                data["index"], data["index_names"], cudf
+            )
+            columns = _from_dict_create_index(
+                data["columns"], data["column_names"], pd
+            )
+            return cls(data["data"], index=index, columns=columns, dtype=dtype)
+        else:
+            raise ValueError(
+                "Expected 'index', 'columns' or 'tight' for orient "
+                f"parameter. Got '{orient}' instead"
+            )
+
+    @_cudf_nvtx_annotate
+    def to_dict(
+        self,
+        orient: str = "dict",
+        into: type[dict] = dict,
+    ) -> dict | list[dict]:
+        """
+        Convert the DataFrame to a dictionary.
+
+        The type of the key-value pairs can be customized with the parameters
+        (see below).
+
+        Parameters
+        ----------
+        orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
+            Determines the type of the values of the dictionary.
+
+            - 'dict' (default) : dict like {column -> {index -> value}}
+            - 'list' : dict like {column -> [values]}
+            - 'series' : dict like {column -> Series(values)}
+            - 'split' : dict like
+              {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
+            - 'tight' : dict like
+              {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
+              'index_names' -> [index.names], 'column_names' -> [column.names]}
+            - 'records' : list like
+              [{column -> value}, ... , {column -> value}]
+            - 'index' : dict like {index -> {column -> value}}
+
+            Abbreviations are allowed. `s` indicates `series` and `sp`
+            indicates `split`.
+
+        into : class, default dict
+            The collections.abc.Mapping subclass used for all Mappings
+            in the return value.  Can be the actual class or an empty
+            instance of the mapping type you want.  If you want a
+            collections.defaultdict, you must pass it initialized.
+
+        Returns
+        -------
+        dict, list or collections.abc.Mapping
+            Return a collections.abc.Mapping object representing the DataFrame.
+            The resulting transformation depends on the `orient` parameter.
+
+        See Also
+        --------
+        DataFrame.from_dict: Create a DataFrame from a dictionary.
+        DataFrame.to_json: Convert a DataFrame to JSON format.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'col1': [1, 2],
+        ...                      'col2': [0.5, 0.75]},
+        ...                     index=['row1', 'row2'])
+        >>> df
+              col1  col2
+        row1     1  0.50
+        row2     2  0.75
+        >>> df.to_dict()
+        {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
+
+        You can specify the return orientation.
+
+        >>> df.to_dict('series')
+        {'col1': row1    1
+                 row2    2
+        Name: col1, dtype: int64,
+        'col2': row1    0.50
+                row2    0.75
+        Name: col2, dtype: float64}
+
+        >>> df.to_dict('split')
+        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
+         'data': [[1, 0.5], [2, 0.75]]}
+
+        >>> df.to_dict('records')
+        [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
+
+        >>> df.to_dict('index')
+        {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
+
+        >>> df.to_dict('tight')
+        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
+         'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}
+
+        You can also specify the mapping type.
+
+        >>> from collections import OrderedDict, defaultdict
+        >>> df.to_dict(into=OrderedDict)
+        OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
+                     ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
+
+        If you want a `defaultdict`, you need to initialize it:
+
+        >>> dd = defaultdict(list)
+        >>> df.to_dict('records', into=dd)
+        [defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
+         defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
+        """  # noqa: E501
+        orient = orient.lower()
+
+        if orient == "series":
+            # Special case needed to avoid converting
+            # cudf.Series objects into pd.Series
+            into_c = pd.core.common.standardize_mapping(into)
+            return into_c((k, v) for k, v in self.items())
+
+        return self.to_pandas().to_dict(orient=orient, into=into)
+
+    @_cudf_nvtx_annotate
+    def scatter_by_map(
+        self, map_index, map_size=None, keep_index=True, **kwargs
+    ):
+        """Scatter to a list of dataframes.
+
+        Uses map_index to determine the destination
+        of each row of the original DataFrame.
+
+        Parameters
+        ----------
+        map_index : Series, str or list-like
+            Scatter assignment for each row
+        map_size : int
+            Length of output list. Must be >= uniques in map_index
+        keep_index : bool
+            Conserve original index values for each row
+
+        Returns
+        -------
+        A list of cudf.DataFrame objects.
+
+        Raises
+        ------
+        ValueError
+            If the map_index has invalid entries (not all in [0,
+            num_partitions)).
+        """
+        # map_index might be a column name or array,
+        # make it a Column
+        if isinstance(map_index, str):
+            map_index = self._data[map_index]
+        elif isinstance(map_index, cudf.Series):
+            map_index = map_index._column
+        else:
+            map_index = as_column(map_index)
+
+        # Convert float to integer
+        if map_index.dtype.kind == "f":
+            map_index = map_index.astype(np.int32)
+
+        # Convert string or categorical to integer
+        if isinstance(map_index, cudf.core.column.StringColumn):
+            map_index = map_index.as_categorical_column(
+                "category"
+            ).as_numerical
+            warnings.warn(
+                "Using StringColumn for map_index in scatter_by_map. "
+                "Use an integer array/column for better performance."
+            )
+        elif isinstance(map_index, cudf.core.column.CategoricalColumn):
+            map_index = map_index.as_numerical
+            warnings.warn(
+                "Using CategoricalColumn for map_index in scatter_by_map. "
+                "Use an integer array/column for better performance."
+            )
+
+        if kwargs.get("debug", False) == 1 and map_size is not None:
+            count = map_index.distinct_count()
+            if map_size < count:
+                raise ValueError(
+                    f"ERROR: map_size must be >= {count} (got {map_size})."
+                )
+
+        partitioned_columns, output_offsets = libcudf.partitioning.partition(
+            [*(self._index._columns if keep_index else ()), *self._columns],
+            map_index,
+            map_size,
+        )
+        partitioned = self._from_columns_like_self(
+            partitioned_columns,
+            column_names=self._column_names,
+            index_names=self._index_names if keep_index else None,
+        )
+
+        # due to the split limitation mentioned
+        # here: https://github.com/rapidsai/cudf/issues/4607
+        # we need to remove first & last elements in offsets.
+        # TODO: Remove this after the above issue is fixed.
+        output_offsets = output_offsets[1:-1]
+
+        result = partitioned._split(output_offsets, keep_index=keep_index)
+
+        if map_size:
+            result += [
+                self._empty_like(keep_index)
+                for _ in range(map_size - len(result))
+            ]
+
+        return result
+
+    @_cudf_nvtx_annotate
+    def update(
+        self,
+        other,
+        join="left",
+        overwrite=True,
+        filter_func=None,
+        errors="ignore",
+    ):
+        """
+        Modify a DataFrame in place using non-NA values from another DataFrame.
+
+        Aligns on indices. There is no return value.
+
+        Parameters
+        ----------
+        other : DataFrame, or object coercible into a DataFrame
+            Should have at least one matching index/column label with the
+            original DataFrame. If a Series is passed, its name attribute must
+            be set, and that will be used as the column name to align with the
+            original DataFrame.
+
+        join : {'left'}, default 'left'
+            Only left join is implemented, keeping the index and
+            columns of the original object.
+
+        overwrite : {True, False}, default True
+            How to handle non-NA values for overlapping keys:
+            True: overwrite original DataFrame's values with values from other.
+            False: only update values that are NA in the original DataFrame.
+
+        filter_func : None
+            filter_func is not supported yet
+            Return True for values that should be updated.S
+
+        errors : {'raise', 'ignore'}, default 'ignore'
+            If 'raise', will raise a ValueError if the DataFrame and other
+            both contain non-NA data in the same place.
+
+
+        Returns
+        -------
+        None : method directly changes calling object
+
+        Raises
+        ------
+        ValueError
+            - When ``errors`` = 'raise' and there's overlapping non-NA data.
+            - When ``errors`` is not either 'ignore' or 'raise'
+
+        NotImplementedError
+            - If ``join`` != 'left'
+        """
+        # TODO: Support other joins
+        if join != "left":
+            raise NotImplementedError("Only left join is supported")
+        if errors not in {"ignore", "raise"}:
+            raise ValueError(
+                "The parameter errors must be either 'ignore' or 'raise'"
+            )
+        if filter_func is not None:
+            raise NotImplementedError("filter_func is not supported yet")
+
+        if not isinstance(other, DataFrame):
+            other = DataFrame(other)
+
+        self_cols = self._data.to_pandas_index()
+        if not self_cols.equals(other._data.to_pandas_index()):
+            other = other.reindex(self_cols, axis=1)
+        if not self.index.equals(other.index):
+            other = other.reindex(self.index, axis=0)
+
+        source_df = self.copy(deep=False)
+        for col in source_df._column_names:
+            this = source_df[col]
+            that = other[col]
+
+            if errors == "raise":
+                mask_this = that.notna()
+                mask_that = this.notna()
+                if (mask_this & mask_that).any():
+                    raise ValueError("Data overlaps.")
+
+            if overwrite:
+                mask = that.isna()
+            else:
+                mask = this.notna()
+
+            # don't overwrite columns unnecessarily
+            if mask.all():
+                continue
+            source_df[col] = source_df[col].where(mask, that)
+
+        self._mimic_inplace(source_df, inplace=True)
+
+    @_cudf_nvtx_annotate
+    def __iter__(self):
+        return iter(self._column_names)
+
+    @_cudf_nvtx_annotate
+    def __contains__(self, item):
+        # This must check against containment in the pandas Index and not
+        # self._column_names to handle NA, None, nan, etc. correctly.
+        return item in self._data.to_pandas_index()
+
+    @_cudf_nvtx_annotate
+    def items(self):
+        """Iterate over column names and series pairs"""
+        for k in self:
+            yield (k, self[k])
+
+    @_cudf_nvtx_annotate
+    def equals(self, other, **kwargs):
+        ret = super().equals(other)
+        # If all other checks matched, validate names.
+        if ret:
+            for self_name, other_name in zip(
+                self._data.names, other._data.names
+            ):
+                if self_name != other_name:
+                    ret = False
+                    break
+        return ret
+
+    @property
+    def iat(self):
+        """
+        Alias for ``DataFrame.iloc``; provided for compatibility with Pandas.
+        """
+        return self.iloc
+
+    @property
+    def at(self):
+        """
+        Alias for ``DataFrame.loc``; provided for compatibility with Pandas.
+        """
+        return self.loc
+
+    @property  # type: ignore
+    @_external_only_api(
+        "Use _column_names instead, or _data.to_pandas_index() if a pandas "
+        "index is absolutely necessary. For checking if the columns are a "
+        "MultiIndex, use _data.multiindex."
+    )
+    @_cudf_nvtx_annotate
+    def columns(self):
+        """Returns a tuple of columns"""
+        return self._data.to_pandas_index()
+
+    @columns.setter  # type: ignore
+    @_cudf_nvtx_annotate
+    def columns(self, columns):
+        if isinstance(columns, cudf.BaseIndex):
+            columns = columns.to_pandas()
+        if columns is None:
+            columns = pd.Index(range(len(self._data.columns)))
+        is_multiindex = isinstance(columns, pd.MultiIndex)
+
+        if isinstance(columns, (Series, cudf.Index, ColumnBase)):
+            columns = pd.Index(columns.to_numpy(), tupleize_cols=is_multiindex)
+        elif not isinstance(columns, pd.Index):
+            columns = pd.Index(columns, tupleize_cols=is_multiindex)
+
+        if not len(columns) == len(self._data.names):
+            raise ValueError(
+                f"Length mismatch: expected {len(self._data.names)} elements, "
+                f"got {len(columns)} elements"
+            )
+
+        self._set_column_names(columns, is_multiindex, columns.names)
+
+    def _set_column_names(self, names, multiindex=False, level_names=None):
+        data = dict(zip(names, self._data.columns))
+        if len(names) != len(data):
+            raise ValueError("Duplicate column names are not allowed")
+
+        self._data = ColumnAccessor(
+            data,
+            multiindex=multiindex,
+            level_names=level_names,
+        )
+
+    def _set_column_names_like(self, other):
+        self._set_column_names(
+            other._data.names, other._data.multiindex, other._data.level_names
+        )
+
+    @_cudf_nvtx_annotate
+    def reindex(
+        self,
+        labels=None,
+        index=None,
+        columns=None,
+        axis=None,
+        method=None,
+        copy=True,
+        level=None,
+        fill_value=NA,
+        limit=None,
+        tolerance=None,
+    ):
+        """
+        Conform DataFrame to new index. Places NA/NaN in locations
+        having no value in the previous index. A new object is produced
+        unless the new index is equivalent to the current one and copy=False.
+
+        Parameters
+        ----------
+        labels : Index, Series-convertible, optional, default None
+            New labels / index to conform the axis specified by ``axis`` to.
+        index : Index, Series-convertible, optional, default None
+            The index labels specifying the index to conform to.
+        columns : array-like, optional, default None
+            The column labels specifying the columns to conform to.
+        axis : Axis to target.
+            Can be either the axis name
+            (``index``, ``columns``) or number (0, 1).
+        method : Not supported
+        copy : boolean, default True
+            Return a new object, even if the passed indexes are the same.
+        level : Not supported
+        fill_value : Value to use for missing values.
+            Defaults to ``NA``, but can be any "compatible" value.
+        limit : Not supported
+        tolerance : Not supported
+
+        Returns
+        -------
+        DataFrame with changed index.
+
+        Examples
+        --------
+        ``DataFrame.reindex`` supports two calling conventions
+        * ``(index=index_labels, columns=column_labels, ...)``
+        * ``(labels, axis={'index', 'columns'}, ...)``
+        We _highly_ recommend using keyword arguments to clarify your intent.
+
+        Create a dataframe with some fictional data.
+
+        >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
+        >>> df = cudf.DataFrame({'http_status': [200, 200, 404, 404, 301],
+        ...                    'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
+        ...                      index=index)
+        >>> df
+                http_status  response_time
+        Firefox            200           0.04
+        Chrome             200           0.02
+        Safari             404           0.07
+        IE10               404           0.08
+        Konqueror          301           1.00
+        >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
+        ...              'Chrome']
+        >>> df.reindex(new_index)
+                    http_status response_time
+        Safari                404          0.07
+        Iceweasel            <NA>          <NA>
+        Comodo Dragon        <NA>          <NA>
+        IE10                  404          0.08
+        Chrome                200          0.02
+
+        .. pandas-compat::
+            **DataFrame.reindex**
+
+            Note: One difference from Pandas is that ``NA`` is used for rows
+            that do not match, rather than ``NaN``. One side effect of this is
+            that the column ``http_status`` retains an integer dtype in cuDF
+            where it is cast to float in Pandas.
+
+        We can fill in the missing values by
+        passing a value to the keyword ``fill_value``.
+
+        >>> df.reindex(new_index, fill_value=0)
+                    http_status  response_time
+        Safari                 404           0.07
+        Iceweasel                0           0.00
+        Comodo Dragon            0           0.00
+        IE10                   404           0.08
+        Chrome                 200           0.02
+
+        We can also reindex the columns.
+
+        >>> df.reindex(columns=['http_status', 'user_agent'])
+                http_status user_agent
+        Firefox            200       <NA>
+        Chrome             200       <NA>
+        Safari             404       <NA>
+        IE10               404       <NA>
+        Konqueror          301       <NA>
+
+        Or we can use "axis-style" keyword arguments
+
+        >>> df.reindex(columns=['http_status', 'user_agent'])
+                http_status user_agent
+        Firefox            200       <NA>
+        Chrome             200       <NA>
+        Safari             404       <NA>
+        IE10               404       <NA>
+        Konqueror          301       <NA>
+        """
+
+        if labels is None and index is None and columns is None:
+            return self.copy(deep=copy)
+
+        # pandas simply ignores the labels keyword if it is provided in
+        # addition to index and columns, but it prohibits the axis arg.
+        if (index is not None or columns is not None) and axis is not None:
+            raise TypeError(
+                "Cannot specify both 'axis' and any of 'index' or 'columns'."
+            )
+
+        axis = 0 if axis is None else self._get_axis_from_axis_arg(axis)
+        if axis == 0:
+            if index is None:
+                index = labels
+        else:
+            if columns is None:
+                columns = labels
+        df = (
+            self
+            if columns is None
+            else self[list(set(self._column_names) & set(columns))]
+        )
+
+        return df._reindex(
+            column_names=columns,
+            dtypes=self._dtypes,
+            deep=copy,
+            index=index,
+            inplace=False,
+            fill_value=fill_value,
+        )
+
+    @_cudf_nvtx_annotate
+    def set_index(
+        self,
+        keys,
+        drop=True,
+        append=False,
+        inplace=False,
+        verify_integrity=False,
+    ):
+        """Return a new DataFrame with a new index
+
+        Parameters
+        ----------
+        keys : Index, Series-convertible, label-like, or list
+            Index : the new index.
+            Series-convertible : values for the new index.
+            Label-like : Label of column to be used as index.
+            List : List of items from above.
+        drop : boolean, default True
+            Whether to drop corresponding column for str index argument
+        append : boolean, default True
+            Whether to append columns to the existing index,
+            resulting in a MultiIndex.
+        inplace : boolean, default False
+            Modify the DataFrame in place (do not create a new object).
+        verify_integrity : boolean, default False
+            Check for duplicates in the new index.
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame({
+        ...     "a": [1, 2, 3, 4, 5],
+        ...     "b": ["a", "b", "c", "d","e"],
+        ...     "c": [1.0, 2.0, 3.0, 4.0, 5.0]
+        ... })
+        >>> df
+           a  b    c
+        0  1  a  1.0
+        1  2  b  2.0
+        2  3  c  3.0
+        3  4  d  4.0
+        4  5  e  5.0
+
+        Set the index to become the 'b' column:
+
+        >>> df.set_index('b')
+           a    c
+        b
+        a  1  1.0
+        b  2  2.0
+        c  3  3.0
+        d  4  4.0
+        e  5  5.0
+
+        Create a MultiIndex using columns 'a' and 'b':
+
+        >>> df.set_index(["a", "b"])
+               c
+        a b
+        1 a  1.0
+        2 b  2.0
+        3 c  3.0
+        4 d  4.0
+        5 e  5.0
+
+        Set new Index instance as index:
+
+        >>> df.set_index(cudf.RangeIndex(10, 15))
+            a  b    c
+        10  1  a  1.0
+        11  2  b  2.0
+        12  3  c  3.0
+        13  4  d  4.0
+        14  5  e  5.0
+
+        Setting `append=True` will combine current index with column `a`:
+
+        >>> df.set_index("a", append=True)
+             b    c
+          a
+        0 1  a  1.0
+        1 2  b  2.0
+        2 3  c  3.0
+        3 4  d  4.0
+        4 5  e  5.0
+
+        `set_index` supports `inplace` parameter too:
+
+        >>> df.set_index("a", inplace=True)
+        >>> df
+           b    c
+        a
+        1  a  1.0
+        2  b  2.0
+        3  c  3.0
+        4  d  4.0
+        5  e  5.0
+        """
+
+        if not isinstance(keys, list):
+            keys = [keys]
+
+        # Preliminary type check
+        col_not_found = []
+        columns_to_add = []
+        names = []
+        to_drop = []
+        for col in keys:
+            # Is column label
+            if is_scalar(col) or isinstance(col, tuple):
+                if col in self._column_names:
+                    columns_to_add.append(self[col])
+                    names.append(col)
+                    if drop:
+                        to_drop.append(col)
+                else:
+                    col_not_found.append(col)
+            else:
+                # Try coerce into column
+                if not is_column_like(col):
+                    try:
+                        col = as_column(col)
+                    except TypeError:
+                        msg = f"{col} cannot be converted to column-like."
+                        raise TypeError(msg)
+                if isinstance(col, (MultiIndex, pd.MultiIndex)):
+                    col = (
+                        cudf.from_pandas(col)
+                        if isinstance(col, pd.MultiIndex)
+                        else col
+                    )
+                    cols = [col._data[x] for x in col._data]
+                    columns_to_add.extend(cols)
+                    names.extend(col.names)
+                else:
+                    if isinstance(col, (pd.RangeIndex, cudf.RangeIndex)):
+                        # Corner case: RangeIndex does not need to instantiate
+                        columns_to_add.append(col)
+                    else:
+                        # For pandas obj, convert to gpu obj
+                        columns_to_add.append(as_column(col))
+                    if isinstance(
+                        col, (cudf.Series, cudf.Index, pd.Series, pd.Index)
+                    ):
+                        names.append(col.name)
+                    else:
+                        names.append(None)
+
+        if col_not_found:
+            raise KeyError(f"None of {col_not_found} are in the columns")
+
+        if append:
+            idx_cols = [self.index._data[x] for x in self.index._data]
+            if isinstance(self.index, MultiIndex):
+                idx_names = self.index.names
+            else:
+                idx_names = [self.index.name]
+            columns_to_add = idx_cols + columns_to_add
+            names = idx_names + names
+
+        if len(columns_to_add) == 0:
+            raise ValueError("No valid columns to be added to index.")
+        elif (
+            len(columns_to_add) == 1
+            and len(keys) == 1
+            and not isinstance(keys[0], (cudf.MultiIndex, pd.MultiIndex))
+        ):
+            idx = cudf.Index(columns_to_add[0], name=names[0])
+        else:
+            idx = MultiIndex._from_data(
+                {i: col for i, col in enumerate(columns_to_add)}
+            )
+            idx.names = names
+
+        if not isinstance(idx, BaseIndex):
+            raise ValueError("Parameter index should be type `Index`.")
+
+        df = self if inplace else self.copy(deep=True)
+
+        if verify_integrity and not idx.is_unique:
+            raise ValueError(f"Values in Index are not unique: {idx}")
+
+        if to_drop:
+            df.drop(columns=to_drop, inplace=True)
+
+        df.index = idx
+        return df if not inplace else None
+
+    @_cudf_nvtx_annotate
+    def where(self, cond, other=None, inplace=False):
+        from cudf.core._internals.where import (
+            _check_and_cast_columns_with_other,
+            _make_categorical_like,
+        )
+
+        # First process the condition.
+        if isinstance(cond, Series):
+            cond = self._from_data_like_self(
+                {name: cond._column for name in self._column_names},
+            )
+        elif hasattr(cond, "__cuda_array_interface__"):
+            cond = DataFrame(
+                cond, columns=self._column_names, index=self.index
+            )
+        elif (
+            hasattr(cond, "__array_interface__")
+            and cond.__array_interface__["shape"] != self.shape
+        ):
+            raise ValueError("conditional must be same shape as self")
+        elif not isinstance(cond, DataFrame):
+            cond = cudf.DataFrame(cond)
+
+        if set(self._column_names).intersection(set(cond._column_names)):
+            if not self.index.equals(cond.index):
+                cond = cond.reindex(self.index)
+        else:
+            if cond.shape != self.shape:
+                raise ValueError(
+                    "Array conditional must be same shape as self"
+                )
+            # Setting `self` column names to `cond` as it has no column names.
+            cond._set_column_names_like(self)
+
+        # If other was provided, process that next.
+        if isinstance(other, DataFrame):
+            other_cols = [other._data[col] for col in self._column_names]
+        elif cudf.api.types.is_scalar(other):
+            other_cols = [other] * len(self._column_names)
+        elif isinstance(other, cudf.Series):
+            other_cols = other.to_pandas()
+        else:
+            other_cols = other
+
+        if len(self._columns) != len(other_cols):
+            raise ValueError(
+                """Replacement list length or number of data columns
+                should be equal to number of columns of self"""
+            )
+
+        out = {}
+        for (name, col), other_col in zip(self._data.items(), other_cols):
+            col, other_col = _check_and_cast_columns_with_other(
+                source_col=col,
+                other=other_col,
+                inplace=inplace,
+            )
+
+            if cond_col := cond._data.get(name):
+                result = cudf._lib.copying.copy_if_else(
+                    col, other_col, cond_col
+                )
+
+                out[name] = _make_categorical_like(result, self._data[name])
+            else:
+                out_mask = cudf._lib.null_mask.create_null_mask(
+                    len(col),
+                    state=cudf._lib.null_mask.MaskState.ALL_NULL,
+                )
+                out[name] = col.set_mask(out_mask)
+
+        return self._mimic_inplace(
+            self._from_data_like_self(out), inplace=inplace
+        )
+
+    @docutils.doc_apply(
+        doc_reset_index_template.format(
+            klass="DataFrame",
+            argument="",
+            return_type="DataFrame or None",
+            return_doc="",
+            example="""
+        >>> df = cudf.DataFrame([('bird', 389.0),
+        ...                    ('bird', 24.0),
+        ...                    ('mammal', 80.5),
+        ...                    ('mammal', np.nan)],
+        ...                   index=['falcon', 'parrot', 'lion', 'monkey'],
+        ...                   columns=('class', 'max_speed'))
+        >>> df
+                 class max_speed
+        falcon    bird     389.0
+        parrot    bird      24.0
+        lion    mammal      80.5
+        monkey  mammal      <NA>
+        >>> df.reset_index()
+            index   class max_speed
+        0  falcon    bird     389.0
+        1  parrot    bird      24.0
+        2    lion  mammal      80.5
+        3  monkey  mammal      <NA>
+        >>> df.reset_index(drop=True)
+            class max_speed
+        0    bird     389.0
+        1    bird      24.0
+        2  mammal      80.5
+        3  mammal      <NA>
+
+        You can also use ``reset_index`` with MultiIndex.
+
+        >>> index = cudf.MultiIndex.from_tuples([('bird', 'falcon'),
+        ...                                     ('bird', 'parrot'),
+        ...                                     ('mammal', 'lion'),
+        ...                                     ('mammal', 'monkey')],
+        ...                                     names=['class', 'name'])
+        >>> df = cudf.DataFrame([(389.0, 'fly'),
+        ...                      ( 24.0, 'fly'),
+        ...                      ( 80.5, 'run'),
+        ...                      (np.nan, 'jump')],
+        ...                      index=index,
+        ...                      columns=('speed', 'type'))
+        >>> df
+                       speed  type
+        class  name
+        bird   falcon  389.0   fly
+               parrot   24.0   fly
+        mammal lion     80.5   run
+               monkey   <NA>  jump
+        >>> df.reset_index(level='class')
+                 class  speed  type
+        name
+        falcon    bird  389.0   fly
+        parrot    bird   24.0   fly
+        lion    mammal   80.5   run
+        monkey  mammal   <NA>  jump
+        """,
+        )
+    )
+    def reset_index(
+        self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
+    ):
+        return self._mimic_inplace(
+            DataFrame._from_data(
+                *self._reset_index(
+                    level=level,
+                    drop=drop,
+                    col_level=col_level,
+                    col_fill=col_fill,
+                )
+            ),
+            inplace=inplace,
+        )
+
+    @_cudf_nvtx_annotate
+    def insert(self, loc, name, value, nan_as_null=None):
+        """Add a column to DataFrame at the index specified by loc.
+
+        Parameters
+        ----------
+        loc : int
+            location to insert by index, cannot be greater then num columns + 1
+        name : number or string
+            name or label of column to be inserted
+        value : Series or array-like
+        nan_as_null : bool, Default None
+            If ``None``/``True``, converts ``np.nan`` values to
+            ``null`` values.
+            If ``False``, leaves ``np.nan`` values as is.
+        """
+        return self._insert(
+            loc=loc,
+            name=name,
+            value=value,
+            nan_as_null=nan_as_null,
+            ignore_index=False,
+        )
+
+    @_cudf_nvtx_annotate
+    def _insert(self, loc, name, value, nan_as_null=None, ignore_index=True):
+        """
+        Same as `insert`, with additional `ignore_index` param.
+
+        ignore_index : bool, default True
+            If True, there will be no index equality check & reindexing
+            happening.
+            If False, a reindexing operation is performed if
+            `value.index` is not equal to `self.index`.
+        """
+        if name in self._data:
+            raise NameError(f"duplicated column name {name}")
+
+        num_cols = len(self._data)
+        if loc < 0:
+            loc += num_cols + 1
+
+        if not (0 <= loc <= num_cols):
+            raise ValueError(
+                f"insert location must be within range "
+                f"{-(num_cols + 1) * (num_cols > 0)}, "
+                f"{num_cols * (num_cols > 0)}"
+            )
+
+        # TODO: This check is currently necessary because
+        # _is_scalar_or_zero_d_array below will treat a length 1 pd.Categorical
+        # as a scalar and attempt to use column.full, which can't handle it.
+        # Maybe _is_scalar_or_zero_d_array should be changed, or maybe we just
+        # shouldn't support pd.Categorical at all, but those changes will at
+        # least require a deprecation cycle because we currently support
+        # inserting a pd.Categorical.
+        if isinstance(value, pd.Categorical):
+            value = cudf.core.column.categorical.pandas_categorical_as_column(
+                value
+            )
+
+        if _is_scalar_or_zero_d_array(value):
+            value = column.full(
+                len(self),
+                value,
+                "str" if libcudf.scalar._is_null_host_scalar(value) else None,
+            )
+
+        if len(self) == 0:
+            if isinstance(value, (pd.Series, Series)):
+                if not ignore_index:
+                    self._index = as_index(value.index)
+            elif len(value) > 0:
+                self._index = RangeIndex(start=0, stop=len(value))
+                new_data = self._data.__class__()
+                if num_cols != 0:
+                    for col_name in self._data:
+                        new_data[col_name] = column.column_empty_like(
+                            self._data[col_name],
+                            masked=True,
+                            newsize=len(value),
+                        )
+                self._data = new_data
+        elif isinstance(value, (pd.Series, Series)):
+            value = Series(value, nan_as_null=nan_as_null)
+            if not ignore_index:
+                value = value._align_to_index(
+                    self._index, how="right", sort=False
+                )
+
+        value = column.as_column(value, nan_as_null=nan_as_null)
+
+        self._data.insert(name, value, loc=loc)
+
+    @property  # type:ignore
+    @_cudf_nvtx_annotate
+    def axes(self):
+        """
+        Return a list representing the axes of the DataFrame.
+
+        DataFrame.axes returns a list of two elements:
+        element zero is the row index and element one is the columns.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> cdf1 = cudf.DataFrame()
+        >>> cdf1["key"] = [0,0,1,1]
+        >>> cdf1["k2"] = [1,2,2,3]
+        >>> cdf1["val"] = [1,2,3,4]
+        >>> cdf1["temp"] = [-1,2,2,3]
+        >>> cdf1.axes
+        [RangeIndex(start=0, stop=4, step=1),
+            Index(['key', 'k2', 'val', 'temp'], dtype='object')]
+
+        """
+        return [self._index, self._data.to_pandas_index()]
+
+    def diff(self, periods=1, axis=0):
+        """
+        First discrete difference of element.
+
+        Calculates the difference of a DataFrame element compared with another
+        element in the DataFrame (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference,
+            accepts negative values.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Take difference over rows (0) or columns (1).
+            Only row-wise (0) shift is supported.
+
+        Returns
+        -------
+        DataFrame
+            First differences of the DataFrame.
+
+        Notes
+        -----
+        Diff currently only supports numeric dtype columns.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gdf = cudf.DataFrame({'a': [1, 2, 3, 4, 5, 6],
+        ...                       'b': [1, 1, 2, 3, 5, 8],
+        ...                       'c': [1, 4, 9, 16, 25, 36]})
+        >>> gdf
+           a  b   c
+        0  1  1   1
+        1  2  1   4
+        2  3  2   9
+        3  4  3  16
+        4  5  5  25
+        5  6  8  36
+        >>> gdf.diff(periods=2)
+              a     b     c
+        0  <NA>  <NA>  <NA>
+        1  <NA>  <NA>  <NA>
+        2     2     1     8
+        3     2     2    12
+        4     2     3    16
+        5     2     5    20
+
+        """
+        if not is_integer(periods):
+            if not (is_float(periods) and periods.is_integer()):
+                raise ValueError("periods must be an integer")
+            periods = int(periods)
+
+        axis = self._get_axis_from_axis_arg(axis)
+        if axis != 0:
+            raise NotImplementedError("Only axis=0 is supported.")
+
+        if abs(periods) > len(self):
+            df = cudf.DataFrame._from_data(
+                {
+                    name: column_empty(len(self), dtype=dtype, masked=True)
+                    for name, dtype in zip(self._column_names, self.dtypes)
+                }
+            )
+            return df
+
+        return self - self.shift(periods=periods)
+
+    @_cudf_nvtx_annotate
+    def drop_duplicates(
+        self,
+        subset=None,
+        keep="first",
+        inplace=False,
+        ignore_index=False,
+    ):
+        """
+        Return DataFrame with duplicate rows removed.
+
+        Considering certain columns is optional. Indexes, including time
+        indexes are ignored.
+
+        Parameters
+        ----------
+        subset : column label or sequence of labels, optional
+            Only consider certain columns for identifying duplicates, by
+            default use all of the columns.
+        keep : {'first', 'last', ``False``}, default 'first'
+            Determines which duplicates (if any) to keep.
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+        inplace : bool, default ``False``
+            Whether to drop duplicates in place or to return a copy.
+        ignore_index : bool, default ``False``
+            If True, the resulting axis will be labeled 0, 1, ..., n - 1.
+
+        Returns
+        -------
+        DataFrame or None
+            DataFrame with duplicates removed or None if ``inplace=True``.
+
+        See Also
+        --------
+        DataFrame.value_counts: Count unique combinations of columns.
+
+        Examples
+        --------
+        Consider a dataset containing ramen ratings.
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({
+        ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
+        ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
+        ...     'rating': [4, 4, 3.5, 15, 5]
+        ... })
+        >>> df
+             brand style  rating
+        0  Yum Yum   cup     4.0
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        By default, it removes duplicate rows based on all columns.
+
+        >>> df.drop_duplicates()
+             brand style  rating
+        0  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        3  Indomie  pack    15.0
+        4  Indomie  pack     5.0
+
+        To remove duplicates on specific column(s), use ``subset``.
+
+        >>> df.drop_duplicates(subset=['brand'])
+             brand style  rating
+        0  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+
+        To remove duplicates and keep last occurrences, use ``keep``.
+
+        >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
+             brand style  rating
+        1  Yum Yum   cup     4.0
+        2  Indomie   cup     3.5
+        4  Indomie  pack     5.0
+        """  # noqa: E501
+        outdf = super().drop_duplicates(
+            subset=subset,
+            keep=keep,
+            ignore_index=ignore_index,
+        )
+
+        return self._mimic_inplace(outdf, inplace=inplace)
+
+    @_cudf_nvtx_annotate
+    def pop(self, item):
+        """Return a column and drop it from the DataFrame."""
+        popped = self[item]
+        del self[item]
+        return popped
+
+    @_cudf_nvtx_annotate
+    def rename(
+        self,
+        mapper=None,
+        index=None,
+        columns=None,
+        axis=0,
+        copy=True,
+        inplace=False,
+        level=None,
+        errors="ignore",
+    ):
+        """Alter column and index labels.
+
+        Function / dict values must be unique (1-to-1). Labels not contained in
+        a dict / Series will be left as-is. Extra labels listed don't throw an
+        error.
+
+        ``DataFrame.rename`` supports two calling conventions:
+            - ``(index=index_mapper, columns=columns_mapper, ...)``
+            - ``(mapper, axis={0/'index' or 1/'column'}, ...)``
+
+        We highly recommend using keyword arguments to clarify your intent.
+
+        Parameters
+        ----------
+        mapper : dict-like or function, default None
+            optional dict-like or functions transformations to apply to
+            the index/column values depending on selected ``axis``.
+        index : dict-like, default None
+            Optional dict-like transformations to apply to the index axis'
+            values. Does not support functions for axis 0 yet.
+        columns : dict-like or function, default None
+            optional dict-like or functions transformations to apply to
+            the columns axis' values.
+        axis : int, default 0
+            Axis to rename with mapper.
+            0 or 'index' for index
+            1  or 'columns' for columns
+        copy : boolean, default True
+            Also copy underlying data
+        inplace : boolean, default False
+            Return new DataFrame.  If True, assign columns without copy
+        level : int or level name, default None
+            In case of a MultiIndex, only rename labels in the specified level.
+        errors : {'raise', 'ignore', 'warn'}, default 'ignore'
+            *Only 'ignore' supported*
+            Control raising of exceptions on invalid data for provided dtype.
+
+            -   ``raise`` : allow exceptions to be raised
+            -   ``ignore`` : suppress exceptions. On error return original
+                object.
+            -   ``warn`` : prints last exceptions as warnings and
+                return original object.
+
+        Returns
+        -------
+        DataFrame
+
+        Notes
+        -----
+        Difference from pandas:
+            * Not supporting: level
+
+        Rename will not overwrite column names. If a list with duplicates is
+        passed, column names will be postfixed with a number.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        >>> df
+           A  B
+        0  1  4
+        1  2  5
+        2  3  6
+
+        Rename columns using a mapping:
+
+        >>> df.rename(columns={"A": "a", "B": "c"})
+           a  c
+        0  1  4
+        1  2  5
+        2  3  6
+
+        Rename index using a mapping:
+
+        >>> df.rename(index={0: 10, 1: 20, 2: 30})
+            A  B
+        10  1  4
+        20  2  5
+        30  3  6
+        """
+        if errors != "ignore":
+            raise NotImplementedError(
+                "Only errors='ignore' is currently supported"
+            )
+
+        if mapper is None and index is None and columns is None:
+            return self.copy(deep=copy)
+
+        index = mapper if index is None and axis in (0, "index") else index
+        columns = (
+            mapper if columns is None and axis in (1, "columns") else columns
+        )
+
+        if index:
+            if (
+                any(type(item) == str for item in index.values())
+                and type(self.index) != cudf.StringIndex
+            ):
+                raise NotImplementedError(
+                    "Implicit conversion of index to "
+                    "mixed type is not yet supported."
+                )
+
+            if level is not None and isinstance(self.index, MultiIndex):
+                out_index = self.index.copy(deep=copy)
+                out_index.get_level_values(level).to_frame().replace(
+                    to_replace=list(index.keys()),
+                    value=list(index.values()),
+                    inplace=True,
+                )
+                out = DataFrame(index=out_index)
+            else:
+                to_replace = list(index.keys())
+                vals = list(index.values())
+                is_all_na = vals.count(None) == len(vals)
+
+                try:
+                    index_data = {
+                        name: col.find_and_replace(to_replace, vals, is_all_na)
+                        for name, col in self.index._data.items()
+                    }
+                except OverflowError:
+                    index_data = self.index._data.copy(deep=True)
+
+                out = DataFrame(index=_index_from_data(index_data))
+        else:
+            out = DataFrame(index=self.index)
+
+        if columns:
+            out._data = self._data.rename_levels(mapper=columns, level=level)
+        else:
+            out._data = self._data.copy(deep=copy)
+
+        if inplace:
+            self._data = out._data
+        else:
+            return out.copy(deep=copy)
+
+    @_cudf_nvtx_annotate
+    def add_prefix(self, prefix):
+        out = self.copy(deep=True)
+        out.columns = [
+            prefix + col_name for col_name in list(self._data.keys())
+        ]
+        return out
+
+    @_cudf_nvtx_annotate
+    def add_suffix(self, suffix):
+        out = self.copy(deep=True)
+        out.columns = [
+            col_name + suffix for col_name in list(self._data.keys())
+        ]
+        return out
+
+    @_cudf_nvtx_annotate
+    def agg(self, aggs, axis=None):
+        """
+        Aggregate using one or more operations over the specified axis.
+
+        Parameters
+        ----------
+        aggs : Iterable (set, list, string, tuple or dict)
+            Function to use for aggregating data. Accepted types are:
+             * string name, e.g. ``"sum"``
+             * list of functions, e.g. ``["sum", "min", "max"]``
+             * dict of axis labels specified operations per column,
+               e.g. ``{"a": "sum"}``
+
+        axis : not yet supported
+
+        Returns
+        -------
+        Aggregation Result : ``Series`` or ``DataFrame``
+            When ``DataFrame.agg`` is called with single agg,
+            ``Series`` is returned.
+            When ``DataFrame.agg`` is called with several aggs,
+            ``DataFrame`` is returned.
+
+        Notes
+        -----
+        Difference from pandas:
+          * Not supporting: ``axis``, ``*args``, ``**kwargs``
+
+        """
+        # TODO: Remove the typecasting below once issue #6846 is fixed
+        # link <https://github.com/rapidsai/cudf/issues/6846>
+        dtypes = [self[col].dtype for col in self._column_names]
+        common_dtype = find_common_type(dtypes)
+        df_normalized = self.astype(common_dtype)
+
+        if any(is_string_dtype(dt) for dt in dtypes):
+            raise NotImplementedError(
+                "DataFrame.agg() is not supported for "
+                "frames containing string columns"
+            )
+
+        if axis == 0 or axis is not None:
+            raise NotImplementedError("axis not implemented yet")
+
+        if isinstance(aggs, abc.Iterable) and not isinstance(
+            aggs, (str, dict)
+        ):
+            result = DataFrame()
+            # TODO : Allow simultaneous pass for multi-aggregation as
+            # a future optimization
+            for agg in aggs:
+                result[agg] = getattr(df_normalized, agg)()
+            return result.T.sort_index(axis=1, ascending=True)
+
+        elif isinstance(aggs, str):
+            if not hasattr(df_normalized, aggs):
+                raise AttributeError(
+                    f"{aggs} is not a valid function for "
+                    f"'DataFrame' object"
+                )
+            result = DataFrame()
+            result[aggs] = getattr(df_normalized, aggs)()
+            result = result.iloc[:, 0]
+            result.name = None
+            return result
+
+        elif isinstance(aggs, dict):
+            cols = aggs.keys()
+            if any(callable(val) for val in aggs.values()):
+                raise NotImplementedError(
+                    "callable parameter is not implemented yet"
+                )
+            elif all(isinstance(val, str) for val in aggs.values()):
+                result = cudf.Series(index=cols)
+                for key, value in aggs.items():
+                    col = df_normalized[key]
+                    if not hasattr(col, value):
+                        raise AttributeError(
+                            f"{value} is not a valid function for "
+                            f"'Series' object"
+                        )
+                    result[key] = getattr(col, value)()
+            elif all(isinstance(val, abc.Iterable) for val in aggs.values()):
+                idxs = set()
+                for val in aggs.values():
+                    if isinstance(val, str):
+                        idxs.add(val)
+                    elif isinstance(val, abc.Iterable):
+                        idxs.update(val)
+                idxs = sorted(list(idxs))
+                for agg in idxs:
+                    if agg is callable:
+                        raise NotImplementedError(
+                            "callable parameter is not implemented yet"
+                        )
+                result = DataFrame(index=idxs, columns=cols)
+                for key in aggs.keys():
+                    col = df_normalized[key]
+                    col_empty = column_empty(
+                        len(idxs), dtype=col.dtype, masked=True
+                    )
+                    ans = cudf.Series(data=col_empty, index=idxs)
+                    if isinstance(aggs.get(key), abc.Iterable):
+                        # TODO : Allow simultaneous pass for multi-aggregation
+                        # as a future optimization
+                        for agg in aggs.get(key):
+                            if not hasattr(col, agg):
+                                raise AttributeError(
+                                    f"{agg} is not a valid function for "
+                                    f"'Series' object"
+                                )
+                            ans[agg] = getattr(col, agg)()
+                    elif isinstance(aggs.get(key), str):
+                        if not hasattr(col, aggs.get(key)):
+                            raise AttributeError(
+                                f"{aggs.get(key)} is not a valid function for "
+                                f"'Series' object"
+                            )
+                        ans[aggs.get(key)] = getattr(col, agg)()
+                    result[key] = ans
+            else:
+                raise ValueError("values of dict must be a string or list")
+
+            return result
+
+        elif callable(aggs):
+            raise NotImplementedError(
+                "callable parameter is not implemented yet"
+            )
+
+        else:
+            raise ValueError("argument must be a string, list or dict")
+
+    @_cudf_nvtx_annotate
+    def nlargest(self, n, columns, keep="first"):
+        """Return the first *n* rows ordered by *columns* in descending order.
+
+        Return the first *n* rows with the largest values in *columns*, in
+        descending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        Parameters
+        ----------
+        n : int
+            Number of rows to return.
+        columns : label or list of labels
+            Column label(s) to order by.
+        keep : {'first', 'last'}, default 'first'
+            Where there are duplicate values:
+
+            - `first` : prioritize the first occurrence(s)
+            - `last` : prioritize the last occurrence(s)
+
+        Returns
+        -------
+        DataFrame
+            The first `n` rows ordered by the given columns in descending
+            order.
+
+        Notes
+        -----
+        Difference from pandas:
+            - Only a single column is supported in *columns*
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'population': [59000000, 65000000, 434000,
+        ...                                   434000, 434000, 337000, 11300,
+        ...                                   11300, 11300],
+        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
+        ...                            17036, 182, 38, 311],
+        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
+        ...                                "IS", "NR", "TV", "AI"]},
+        ...                   index=["Italy", "France", "Malta",
+        ...                          "Maldives", "Brunei", "Iceland",
+        ...                          "Nauru", "Tuvalu", "Anguilla"])
+        >>> df
+                  population      GDP alpha-2
+        Italy       59000000  1937894      IT
+        France      65000000  2583560      FR
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+        Iceland       337000    17036      IS
+        Nauru          11300      182      NR
+        Tuvalu         11300       38      TV
+        Anguilla       11300      311      AI
+        >>> df.nlargest(3, 'population')
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Malta       434000    12011      MT
+        >>> df.nlargest(3, 'population', keep='last')
+                population      GDP alpha-2
+        France    65000000  2583560      FR
+        Italy     59000000  1937894      IT
+        Brunei      434000    12128      BN
+        """
+        return self._n_largest_or_smallest(True, n, columns, keep)
+
+    def nsmallest(self, n, columns, keep="first"):
+        """Return the first *n* rows ordered by *columns* in ascending order.
+
+        Return the first *n* rows with the smallest values in *columns*, in
+        ascending order. The columns that are not specified are returned as
+        well, but not used for ordering.
+
+        Parameters
+        ----------
+        n : int
+            Number of items to retrieve.
+        columns : list or str
+            Column name or names to order by.
+        keep : {'first', 'last'}, default 'first'
+            Where there are duplicate values:
+
+            - ``first`` : take the first occurrence.
+            - ``last`` : take the last occurrence.
+
+        Returns
+        -------
+        DataFrame
+
+        Notes
+        -----
+        Difference from pandas:
+            - Only a single column is supported in *columns*
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'population': [59000000, 65000000, 434000,
+        ...                                   434000, 434000, 337000, 337000,
+        ...                                   11300, 11300],
+        ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
+        ...                            17036, 182, 38, 311],
+        ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
+        ...                                "IS", "NR", "TV", "AI"]},
+        ...                   index=["Italy", "France", "Malta",
+        ...                          "Maldives", "Brunei", "Iceland",
+        ...                          "Nauru", "Tuvalu", "Anguilla"])
+        >>> df
+                  population      GDP alpha-2
+        Italy       59000000  1937894      IT
+        France      65000000  2583560      FR
+        Malta         434000    12011      MT
+        Maldives      434000     4520      MV
+        Brunei        434000    12128      BN
+        Iceland       337000    17036      IS
+        Nauru         337000      182      NR
+        Tuvalu         11300       38      TV
+        Anguilla       11300      311      AI
+
+        In the following example, we will use ``nsmallest`` to select the
+        three rows having the smallest values in column "population".
+
+        >>> df.nsmallest(3, 'population')
+                  population    GDP alpha-2
+        Tuvalu         11300     38      TV
+        Anguilla       11300    311      AI
+        Iceland       337000  17036      IS
+
+        When using ``keep='last'``, ties are resolved in reverse order:
+
+        >>> df.nsmallest(3, 'population', keep='last')
+                  population  GDP alpha-2
+        Anguilla       11300  311      AI
+        Tuvalu         11300   38      TV
+        Nauru         337000  182      NR
+        """
+        return self._n_largest_or_smallest(False, n, columns, keep)
+
+    @_cudf_nvtx_annotate
+    def swaplevel(self, i=-2, j=-1, axis=0):
+        """
+        Swap level i with level j.
+        Calling this method does not change the ordering of the values.
+
+        Parameters
+        ----------
+        i : int or str, default -2
+            First level of index to be swapped.
+        j : int or str, default -1
+            Second level of index to be swapped.
+        axis : The axis to swap levels on.
+            0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> midx = cudf.MultiIndex(levels=[['llama', 'cow', 'falcon'],
+        ...   ['speed', 'weight', 'length'],['first','second']],
+        ...   codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2],
+        ...             [0, 0, 0, 0, 0, 0, 1, 1, 1]])
+        >>> cdf = cudf.DataFrame(index=midx, columns=['big', 'small'],
+        ...  data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
+        ...         [250, 150], [1.5, 0.8], [320, 250], [1, 0.8], [0.3, 0.2]])
+
+        >>> cdf
+                                     big  small
+             llama  speed  first    45.0   30.0
+                    weight first   200.0  100.0
+                    length first     1.5    1.0
+             cow    speed  first    30.0   20.0
+                    weight first   250.0  150.0
+                    length first     1.5    0.8
+             falcon speed  second  320.0  250.0
+                    weight second    1.0    0.8
+                    length second    0.3    0.2
+
+        >>> cdf.swaplevel()
+                                     big  small
+             llama  first  speed    45.0   30.0
+                           weight  200.0  100.0
+                           length    1.5    1.0
+             cow    first  speed    30.0   20.0
+                           weight  250.0  150.0
+                           length    1.5    0.8
+             falcon second speed   320.0  250.0
+                           weight    1.0    0.8
+                           length    0.3    0.2
+        """
+        result = self.copy()
+
+        # To get axis number
+        axis = self._get_axis_from_axis_arg(axis)
+
+        if axis == 0:
+            if not isinstance(result.index, MultiIndex):
+                raise TypeError("Can only swap levels on a hierarchical axis.")
+            result.index = result.index.swaplevel(i, j)
+        else:
+            if not result._data.multiindex:
+                raise TypeError("Can only swap levels on a hierarchical axis.")
+            result._data = result._data.swaplevel(i, j)
+
+        return result
+
+    @_cudf_nvtx_annotate
+    def transpose(self):
+        """Transpose index and columns.
+
+        Returns
+        -------
+        a new (ncol x nrow) dataframe. self is (nrow x ncol)
+
+        Notes
+        -----
+        Difference from pandas:
+        Not supporting *copy* because default and only behavior is copy=True
+        """
+
+        index = self._data.to_pandas_index()
+        columns = self.index.copy(deep=False)
+        if self._num_columns == 0 or self._num_rows == 0:
+            return DataFrame(index=index, columns=columns)
+
+        # No column from index is transposed with libcudf.
+        source_columns = [*self._columns]
+        source_dtype = source_columns[0].dtype
+        if is_categorical_dtype(source_dtype):
+            if any(not is_categorical_dtype(c.dtype) for c in source_columns):
+                raise ValueError("Columns must all have the same dtype")
+            cats = list(c.categories for c in source_columns)
+            cats = cudf.core.column.concat_columns(cats).unique()
+            source_columns = [
+                col._set_categories(cats, is_unique=True).codes
+                for col in source_columns
+            ]
+
+        if any(c.dtype != source_columns[0].dtype for c in source_columns):
+            raise ValueError("Columns must all have the same dtype")
+
+        result_columns = libcudf.transpose.transpose(source_columns)
+
+        if is_categorical_dtype(source_dtype):
+            result_columns = [
+                codes._with_type_metadata(
+                    cudf.core.dtypes.CategoricalDtype(categories=cats)
+                )
+                for codes in result_columns
+            ]
+        else:
+            result_columns = [
+                result_column._with_type_metadata(source_dtype)
+                for result_column in result_columns
+            ]
+
+        # Set the old column names as the new index
+        result = self.__class__._from_data(
+            {i: col for i, col in enumerate(result_columns)},
+            index=as_index(index),
+        )
+        # Set the old index as the new column names
+        result.columns = columns
+        return result
+
+    T = property(transpose, doc=transpose.__doc__)
+
+    @_cudf_nvtx_annotate
+    def melt(self, **kwargs):
+        """Unpivots a DataFrame from wide format to long format,
+        optionally leaving identifier variables set.
+
+        Parameters
+        ----------
+        frame : DataFrame
+        id_vars : tuple, list, or ndarray, optional
+            Column(s) to use as identifier variables.
+            default: None
+        value_vars : tuple, list, or ndarray, optional
+            Column(s) to unpivot.
+            default: all columns that are not set as `id_vars`.
+        var_name : scalar
+            Name to use for the `variable` column.
+            default: frame.columns.name or 'variable'
+        value_name : str
+            Name to use for the `value` column.
+            default: 'value'
+
+        Returns
+        -------
+        out : DataFrame
+            Melted result
+        """
+        from cudf.core.reshape import melt
+
+        return melt(self, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def merge(
+        self,
+        right,
+        on=None,
+        left_on=None,
+        right_on=None,
+        left_index=False,
+        right_index=False,
+        how="inner",
+        sort=False,
+        lsuffix=None,
+        rsuffix=None,
+        indicator=False,
+        suffixes=("_x", "_y"),
+    ):
+        """Merge GPU DataFrame objects by performing a database-style join
+        operation by columns or indexes.
+
+        Parameters
+        ----------
+        right : DataFrame
+        on : label or list; defaults to None
+            Column or index level names to join on. These must be found in
+            both DataFrames.
+
+            If on is None and not merging on indexes then
+            this defaults to the intersection of the columns
+            in both DataFrames.
+        how : {'left', 'outer', 'inner', 'leftsemi', 'leftanti'}, \
+            default 'inner'
+            Type of merge to be performed.
+
+            - left : use only keys from left frame, similar to a SQL left
+              outer join.
+            - right : not supported.
+            - outer : use union of keys from both frames, similar to a SQL
+              full outer join.
+            - inner : use intersection of keys from both frames, similar to
+              a SQL inner join.
+            - leftsemi : similar to ``inner`` join, but only returns columns
+               from the left dataframe and ignores all columns from the
+               right dataframe.
+            - leftanti : returns only rows columns from the left dataframe
+              for non-matched records. This is exact opposite to ``leftsemi``
+              join.
+        left_on : label or list, or array-like
+            Column or index level names to join on in the left DataFrame.
+            Can also be an array or list of arrays of the length of the
+            left DataFrame. These arrays are treated as if they are columns.
+        right_on : label or list, or array-like
+            Column or index level names to join on in the right DataFrame.
+            Can also be an array or list of arrays of the length of the
+            right DataFrame. These arrays are treated as if they are columns.
+        left_index : bool, default False
+            Use the index from the left DataFrame as the join key(s).
+        right_index : bool, default False
+            Use the index from the right DataFrame as the join key.
+        sort : bool, default False
+            Sort the resulting dataframe by the columns that were merged on,
+            starting from the left.
+        suffixes: Tuple[str, str], defaults to ('_x', '_y')
+            Suffixes applied to overlapping column names on the left and right
+            sides
+
+        Returns
+        -------
+            merged : DataFrame
+
+        Notes
+        -----
+        **DataFrames merges in cuDF result in non-deterministic row ordering.**
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df_a = cudf.DataFrame()
+        >>> df_a['key'] = [0, 1, 2, 3, 4]
+        >>> df_a['vals_a'] = [float(i + 10) for i in range(5)]
+        >>> df_b = cudf.DataFrame()
+        >>> df_b['key'] = [1, 2, 4]
+        >>> df_b['vals_b'] = [float(i+10) for i in range(3)]
+        >>> df_merged = df_a.merge(df_b, on=['key'], how='left')
+        >>> df_merged.sort_values('key')  # doctest: +SKIP
+           key  vals_a  vals_b
+        3    0    10.0
+        0    1    11.0    10.0
+        1    2    12.0    11.0
+        4    3    13.0
+        2    4    14.0    12.0
+
+        **Merging on categorical variables is only allowed in certain cases**
+
+        Categorical variable typecasting logic depends on both `how`
+        and the specifics of the categorical variables to be merged.
+        Merging categorical variables when only one side is ordered
+        is ambiguous and not allowed. Merging when both categoricals
+        are ordered is allowed, but only when the categories are
+        exactly equal and have equal ordering, and will result in the
+        common dtype.
+        When both sides are unordered, the result categorical depends
+        on the kind of join:
+        - For inner joins, the result will be the intersection of the
+        categories
+        - For left or right joins, the result will be the left or
+        right dtype respectively. This extends to semi and anti joins.
+        - For outer joins, the result will be the union of categories
+        from both sides.
+        """
+        if indicator:
+            raise NotImplementedError(
+                "Only indicator=False is currently supported"
+            )
+
+        if lsuffix or rsuffix:
+            raise ValueError(
+                "The lsuffix and rsuffix keywords have been replaced with the "
+                "``suffixes=`` keyword.  "
+                "Please provide the following instead: \n\n"
+                "    suffixes=('%s', '%s')"
+                % (lsuffix or "_x", rsuffix or "_y")
+            )
+        else:
+            lsuffix, rsuffix = suffixes
+
+        lhs, rhs = self, right
+        merge_cls = Merge
+        if how == "right":
+            # Merge doesn't support right, so just swap
+            how = "left"
+            lhs, rhs = right, self
+            left_on, right_on = right_on, left_on
+            left_index, right_index = right_index, left_index
+            suffixes = (suffixes[1], suffixes[0])
+        elif how in {"leftsemi", "leftanti"}:
+            merge_cls = MergeSemi
+
+        return merge_cls(
+            lhs,
+            rhs,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            how=how,
+            sort=sort,
+            indicator=indicator,
+            suffixes=suffixes,
+        ).perform_merge()
+
+    @_cudf_nvtx_annotate
+    def join(
+        self,
+        other,
+        on=None,
+        how="left",
+        lsuffix="",
+        rsuffix="",
+        sort=False,
+    ):
+        """Join columns with other DataFrame on index or on a key column.
+
+        Parameters
+        ----------
+        other : DataFrame
+        how : str
+            Only accepts "left", "right", "inner", "outer"
+        lsuffix, rsuffix : str
+            The suffices to add to the left (*lsuffix*) and right (*rsuffix*)
+            column names when avoiding conflicts.
+        sort : bool
+            Set to True to ensure sorted ordering.
+
+        Returns
+        -------
+        joined : DataFrame
+
+        Notes
+        -----
+        Difference from pandas:
+
+        - *other* must be a single DataFrame for now.
+        - *on* is not supported yet due to lack of multi-index support.
+        """
+        if on is not None:
+            raise NotImplementedError("The on parameter is not yet supported")
+
+        df = self.merge(
+            other,
+            left_index=True,
+            right_index=True,
+            how=how,
+            suffixes=(lsuffix, rsuffix),
+            sort=sort,
+        )
+        df.index.name = (
+            None if self.index.name != other.index.name else self.index.name
+        )
+        return df
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        groupby_doc_template.format(
+            ret=textwrap.dedent(
+                """
+                Returns
+                -------
+                DataFrameGroupBy
+                    Returns a DataFrameGroupBy object that contains
+                    information about the groups.
+                """
+            )
+        )
+    )
+    def groupby(
+        self,
+        by=None,
+        axis=0,
+        level=None,
+        as_index=True,
+        sort=no_default,
+        group_keys=False,
+        squeeze=False,
+        observed=True,
+        dropna=True,
+    ):
+        return super().groupby(
+            by,
+            axis,
+            level,
+            as_index,
+            sort,
+            group_keys,
+            squeeze,
+            observed,
+            dropna,
+        )
+
+    def query(self, expr, local_dict=None):
+        """
+        Query with a boolean expression using Numba to compile a GPU kernel.
+
+        See pandas.DataFrame.query.
+
+        Parameters
+        ----------
+        expr : str
+            A boolean expression. Names in expression refer to columns.
+            `index` can be used instead of index name, but this is not
+            supported for MultiIndex.
+
+            Names starting with `@` refer to Python variables.
+
+            An output value will be `null` if any of the input values are
+            `null` regardless of expression.
+
+        local_dict : dict
+            Containing the local variable to be used in query.
+
+        Returns
+        -------
+        filtered : DataFrame
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame({
+        ...     "a": [1, 2, 2],
+        ...     "b": [3, 4, 5],
+        ... })
+        >>> expr = "(a == 2 and b == 4) or (b == 3)"
+        >>> df.query(expr)
+           a  b
+        0  1  3
+        1  2  4
+
+        DateTime conditionals:
+
+        >>> import numpy as np
+        >>> import datetime
+        >>> df = cudf.DataFrame()
+        >>> data = np.array(['2018-10-07', '2018-10-08'], dtype='datetime64')
+        >>> df['datetimes'] = data
+        >>> search_date = datetime.datetime.strptime('2018-10-08', '%Y-%m-%d')
+        >>> df.query('datetimes==@search_date')
+           datetimes
+        1 2018-10-08
+
+        Using local_dict:
+
+        >>> import numpy as np
+        >>> import datetime
+        >>> df = cudf.DataFrame()
+        >>> data = np.array(['2018-10-07', '2018-10-08'], dtype='datetime64')
+        >>> df['datetimes'] = data
+        >>> search_date2 = datetime.datetime.strptime('2018-10-08', '%Y-%m-%d')
+        >>> df.query('datetimes==@search_date',
+        ...          local_dict={'search_date': search_date2})
+           datetimes
+        1 2018-10-08
+
+        .. pandas-compat::
+            **DataFrame.query**
+
+            One difference from pandas is that ``query`` currently only
+            supports numeric, datetime, timedelta, or bool dtypes.
+        """
+        # can't use `annotate` decorator here as we inspect the calling
+        # environment.
+        with annotate("DATAFRAME_QUERY", color="purple", domain="cudf_python"):
+            if local_dict is None:
+                local_dict = {}
+
+            if self.empty:
+                return self.copy()
+
+            if not isinstance(local_dict, dict):
+                raise TypeError(
+                    f"local_dict type: expected dict but found "
+                    f"{type(local_dict)}"
+                )
+
+            # Get calling environment
+            callframe = inspect.currentframe().f_back
+            callenv = {
+                "locals": callframe.f_locals,
+                "globals": callframe.f_globals,
+                "local_dict": local_dict,
+            }
+            # Run query
+            boolmask = queryutils.query_execute(self, expr, callenv)
+            return self._apply_boolean_mask(
+                BooleanMask.from_column_unchecked(boolmask)
+            )
+
+    @_cudf_nvtx_annotate
+    def apply(
+        self, func, axis=1, raw=False, result_type=None, args=(), **kwargs
+    ):
+        """
+        Apply a function along an axis of the DataFrame.
+        ``apply`` relies on Numba to JIT compile ``func``.
+        Thus the allowed operations within ``func`` are limited to `those
+        supported by the CUDA Python Numba target
+        <https://numba.readthedocs.io/en/stable/cuda/cudapysupported.html>`__.
+        For more information, see the `cuDF guide to user defined functions
+        <https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html>`__.
+
+        Some string functions and methods are supported. Refer to the guide
+        to UDFs for details.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to each row.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Axis along which the function is applied.
+            - 0 or 'index': apply function to each column (not yet supported).
+            - 1 or 'columns': apply function to each row.
+        raw: bool, default False
+            Not yet supported
+        result_type: {'expand', 'reduce', 'broadcast', None}, default None
+            Not yet supported
+        args: tuple
+            Positional arguments to pass to func in addition to the dataframe.
+
+        Examples
+        --------
+        Simple function of a single variable which could be NA:
+
+        >>> def f(row):
+        ...     if row['a'] is cudf.NA:
+        ...             return 0
+        ...     else:
+        ...             return row['a'] + 1
+        ...
+        >>> df = cudf.DataFrame({'a': [1, cudf.NA, 3]})
+        >>> df.apply(f, axis=1)
+        0    2
+        1    0
+        2    4
+        dtype: int64
+
+        Function of multiple variables will operate in
+        a null aware manner:
+
+        >>> def f(row):
+        ...     return row['a'] - row['b']
+        ...
+        >>> df = cudf.DataFrame({
+        ...     'a': [1, cudf.NA, 3, cudf.NA],
+        ...     'b': [5, 6, cudf.NA, cudf.NA]
+        ... })
+        >>> df.apply(f)
+        0      -4
+        1    <NA>
+        2    <NA>
+        3    <NA>
+        dtype: int64
+
+        Functions may conditionally return NA as in pandas:
+
+        >>> def f(row):
+        ...     if row['a'] + row['b'] > 3:
+        ...             return cudf.NA
+        ...     else:
+        ...             return row['a'] + row['b']
+        ...
+        >>> df = cudf.DataFrame({
+        ...     'a': [1, 2, 3],
+        ...     'b': [2, 1, 1]
+        ... })
+        >>> df.apply(f, axis=1)
+        0       3
+        1       3
+        2    <NA>
+        dtype: int64
+
+        Mixed types are allowed, but will return the common
+        type, rather than object as in pandas:
+
+        >>> def f(row):
+        ...     return row['a'] + row['b']
+        ...
+        >>> df = cudf.DataFrame({
+        ...     'a': [1, 2, 3],
+        ...     'b': [0.5, cudf.NA, 3.14]
+        ... })
+        >>> df.apply(f, axis=1)
+        0     1.5
+        1    <NA>
+        2    6.14
+        dtype: float64
+
+        Functions may also return scalar values, however the
+        result will be promoted to a safe type regardless of
+        the data:
+
+        >>> def f(row):
+        ...     if row['a'] > 3:
+        ...             return row['a']
+        ...     else:
+        ...             return 1.5
+        ...
+        >>> df = cudf.DataFrame({
+        ...     'a': [1, 3, 5]
+        ... })
+        >>> df.apply(f, axis=1)
+        0    1.5
+        1    1.5
+        2    5.0
+        dtype: float64
+
+        Ops against N columns are supported generally:
+
+        >>> def f(row):
+        ...     v, w, x, y, z = (
+        ...         row['a'], row['b'], row['c'], row['d'], row['e']
+        ...     )
+        ...     return x + (y - (z / w)) % v
+        ...
+        >>> df = cudf.DataFrame({
+        ...     'a': [1, 2, 3],
+        ...     'b': [4, 5, 6],
+        ...     'c': [cudf.NA, 4, 4],
+        ...     'd': [8, 7, 8],
+        ...     'e': [7, 1, 6]
+        ... })
+        >>> df.apply(f, axis=1)
+        0    <NA>
+        1     4.8
+        2     5.0
+        dtype: float64
+
+        UDFs manipulating string data are allowed, as long as
+        they neither modify strings in place nor create new strings.
+        For example, the following UDF is allowed:
+
+        >>> def f(row):
+        ...     st = row['str_col']
+        ...     scale = row['scale']
+        ...     if len(st) == 0:
+        ...             return -1
+        ...     elif st.startswith('a'):
+        ...             return 1 - scale
+        ...     elif 'example' in st:
+        ...             return 1 + scale
+        ...     else:
+        ...             return 42
+        ...
+        >>> df = cudf.DataFrame({
+        ...     'str_col': ['', 'abc', 'some_example'],
+        ...     'scale': [1, 2, 3]
+        ... })
+        >>> df.apply(f, axis=1)  # doctest: +SKIP
+        0   -1
+        1   -1
+        2    4
+        dtype: int64
+
+        However, the following UDF is not allowed since it includes an
+        operation that requires the creation of a new string: a call to the
+        ``upper`` method. Methods that are not supported in this manner
+        will raise an ``AttributeError``.
+
+        >>> def f(row):
+        ...     st = row['str_col'].upper()
+        ...     return 'ABC' in st
+        >>> df.apply(f, axis=1)  # doctest: +SKIP
+
+        For a complete list of supported functions and methods that may be
+        used to manipulate string data, see the UDF guide,
+        <https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html>
+        """
+        if axis != 1:
+            raise ValueError(
+                "DataFrame.apply currently only supports row wise ops"
+            )
+        if raw:
+            raise ValueError("The `raw` kwarg is not yet supported.")
+        if result_type is not None:
+            raise ValueError("The `result_type` kwarg is not yet supported.")
+
+        return self._apply(func, _get_row_kernel, *args, **kwargs)
+
+    def applymap(
+        self,
+        func: Callable[[Any], Any],
+        na_action: Union[str, None] = None,
+        **kwargs,
+    ) -> DataFrame:
+        """
+        Apply a function to a Dataframe elementwise.
+
+        This method applies a function that accepts and returns a scalar
+        to every element of a DataFrame.
+
+        Parameters
+        ----------
+        func : callable
+            Python function, returns a single value from a single value.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to func.
+
+        Returns
+        -------
+        DataFrame
+            Transformed DataFrame.
+        """
+
+        if kwargs:
+            raise NotImplementedError(
+                "DataFrame.applymap does not yet support **kwargs."
+            )
+
+        if na_action not in {"ignore", None}:
+            raise ValueError(
+                f"na_action must be 'ignore' or None. Got {repr(na_action)}"
+            )
+
+        if na_action == "ignore":
+            devfunc = numba.cuda.jit(device=True)(func)
+
+            # promote to a null-ignoring function
+            # this code is never run in python, it only
+            # exists to provide numba with the correct
+            # bytecode to generate the equivalent PTX
+            # as a null-ignoring version of the function
+            def _func(x):  # pragma: no cover
+                if x is NA:
+                    return NA
+                else:
+                    return devfunc(x)
+
+        else:
+            _func = func
+
+        # TODO: naive implementation
+        # this could be written as a single kernel
+        result = {}
+        for name, col in self._data.items():
+            apply_sr = Series._from_data({None: col})
+            result[name] = apply_sr.apply(_func)
+
+        return DataFrame._from_data(result, index=self.index)
+
+    @_cudf_nvtx_annotate
+    @applyutils.doc_apply()
+    def apply_rows(
+        self,
+        func,
+        incols,
+        outcols,
+        kwargs,
+        pessimistic_nulls=True,
+        cache_key=None,
+    ):
+        """
+        Apply a row-wise user defined function.
+
+        Parameters
+        ----------
+        {params}
+
+        Examples
+        --------
+        The user function should loop over the columns and set the output for
+        each row. Loop execution order is arbitrary, so each iteration of
+        the loop **MUST** be independent of each other.
+
+        When ``func`` is invoked, the array args corresponding to the
+        input/output are strided so as to improve GPU parallelism.
+        The loop in the function resembles serial code, but executes
+        concurrently in multiple threads.
+
+        >>> import cudf
+        >>> import numpy as np
+        >>> df = cudf.DataFrame()
+        >>> nelem = 3
+        >>> df['in1'] = np.arange(nelem)
+        >>> df['in2'] = np.arange(nelem)
+        >>> df['in3'] = np.arange(nelem)
+
+        Define input columns for the kernel
+
+        >>> in1 = df['in1']
+        >>> in2 = df['in2']
+        >>> in3 = df['in3']
+        >>> def kernel(in1, in2, in3, out1, out2, kwarg1, kwarg2):
+        ...     for i, (x, y, z) in enumerate(zip(in1, in2, in3)):
+        ...         out1[i] = kwarg2 * x - kwarg1 * y
+        ...         out2[i] = y - kwarg1 * z
+
+        Call ``.apply_rows`` with the name of the input columns, the name and
+        dtype of the output columns, and, optionally, a dict of extra
+        arguments.
+
+        >>> df.apply_rows(kernel,
+        ...               incols=['in1', 'in2', 'in3'],
+        ...               outcols=dict(out1=np.float64, out2=np.float64),
+        ...               kwargs=dict(kwarg1=3, kwarg2=4))
+           in1  in2  in3 out1 out2
+        0    0    0    0  0.0  0.0
+        1    1    1    1  1.0 -2.0
+        2    2    2    2  2.0 -4.0
+        """
+        for col in incols:
+            current_col_dtype = self._data[col].dtype
+            if is_string_dtype(current_col_dtype) or is_categorical_dtype(
+                current_col_dtype
+            ):
+                raise TypeError(
+                    "User defined functions are currently not "
+                    "supported on Series with dtypes `str` and `category`."
+                )
+        return applyutils.apply_rows(
+            self,
+            func,
+            incols,
+            outcols,
+            kwargs,
+            pessimistic_nulls,
+            cache_key=cache_key,
+        )
+
+    @_cudf_nvtx_annotate
+    @applyutils.doc_applychunks()
+    def apply_chunks(
+        self,
+        func,
+        incols,
+        outcols,
+        kwargs=None,
+        pessimistic_nulls=True,
+        chunks=None,
+        blkct=None,
+        tpb=None,
+    ):
+        """
+        Transform user-specified chunks using the user-provided function.
+
+        Parameters
+        ----------
+        {params}
+        {params_chunks}
+
+        Examples
+        --------
+
+        For ``tpb > 1``, ``func`` is executed by ``tpb`` number of threads
+        concurrently.  To access the thread id and count,
+        use ``numba.cuda.threadIdx.x`` and ``numba.cuda.blockDim.x``,
+        respectively (See `numba CUDA kernel documentation`_).
+
+        .. _numba CUDA kernel documentation:\
+        https://numba.readthedocs.io/en/stable/cuda/kernels.html
+
+        In the example below, the *kernel* is invoked concurrently on each
+        specified chunk. The *kernel* computes the corresponding output
+        for the chunk.
+
+        By looping over the range
+        ``range(cuda.threadIdx.x, in1.size, cuda.blockDim.x)``, the *kernel*
+        function can be used with any *tpb* in an efficient manner.
+
+        >>> from numba import cuda
+        >>> @cuda.jit
+        ... def kernel(in1, in2, in3, out1):
+        ...      for i in range(cuda.threadIdx.x, in1.size, cuda.blockDim.x):
+        ...          x = in1[i]
+        ...          y = in2[i]
+        ...          z = in3[i]
+        ...          out1[i] = x * y + z
+
+        See also
+        --------
+        DataFrame.apply_rows
+        """
+        if kwargs is None:
+            kwargs = {}
+        if chunks is None:
+            raise ValueError("*chunks* must be defined")
+        return applyutils.apply_chunks(
+            self,
+            func,
+            incols,
+            outcols,
+            kwargs,
+            pessimistic_nulls,
+            chunks,
+            tpb=tpb,
+        )
+
+    @_cudf_nvtx_annotate
+    def partition_by_hash(self, columns, nparts, keep_index=True):
+        """Partition the dataframe by the hashed value of data in *columns*.
+
+        Parameters
+        ----------
+        columns : sequence of str
+            The names of the columns to be hashed.
+            Must have at least one name.
+        nparts : int
+            Number of output partitions
+        keep_index : boolean
+            Whether to keep the index or drop it
+
+        Returns
+        -------
+        partitioned: list of DataFrame
+        """
+        key_indices = [self._column_names.index(k) for k in columns]
+        if keep_index:
+            cols = [*self._index._columns, *self._columns]
+            key_indices = [i + len(self._index._columns) for i in key_indices]
+        else:
+            cols = [*self._columns]
+
+        output_columns, offsets = libcudf.hash.hash_partition(
+            cols, key_indices, nparts
+        )
+        outdf = self._from_columns_like_self(
+            output_columns,
+            self._column_names,
+            self._index_names if keep_index else None,
+        )
+        # Slice into partitions. Notice, `hash_partition` returns the start
+        # offset of each partition thus we skip the first offset
+        ret = outdf._split(offsets[1:], keep_index=keep_index)
+
+        # Calling `_split()` on an empty dataframe returns an empty list
+        # so we add empty partitions here
+        ret += [self._empty_like(keep_index) for _ in range(nparts - len(ret))]
+        return ret
+
+    def info(
+        self,
+        verbose=None,
+        buf=None,
+        max_cols=None,
+        memory_usage=None,
+        null_counts=None,
+    ):
+        """
+        Print a concise summary of a DataFrame.
+
+        This method prints information about a DataFrame including
+        the index dtype and column dtypes, non-null values and memory usage.
+
+        Parameters
+        ----------
+        verbose : bool, optional
+            Whether to print the full summary. By default, the setting in
+            ``pandas.options.display.max_info_columns`` is followed.
+        buf : writable buffer, defaults to sys.stdout
+            Where to send the output. By default, the output is printed to
+            sys.stdout. Pass a writable buffer if you need to further process
+            the output.
+        max_cols : int, optional
+            When to switch from the verbose to the truncated output. If the
+            DataFrame has more than `max_cols` columns, the truncated output
+            is used. By default, the setting in
+            ``pandas.options.display.max_info_columns`` is used.
+        memory_usage : bool, str, optional
+            Specifies whether total memory usage of the DataFrame
+            elements (including the index) should be displayed. By default,
+            this follows the ``pandas.options.display.memory_usage`` setting.
+            True always show memory usage. False never shows memory usage.
+            A value of 'deep' is equivalent to "True with deep introspection".
+            Memory usage is shown in human-readable units (base-2
+            representation). Without deep introspection a memory estimation is
+            made based in column dtype and number of rows assuming values
+            consume the same memory amount for corresponding dtypes. With deep
+            memory introspection, a real memory usage calculation is performed
+            at the cost of computational resources.
+        null_counts : bool, optional
+            Whether to show the non-null counts. By default, this is shown
+            only if the frame is smaller than
+            ``pandas.options.display.max_info_rows`` and
+            ``pandas.options.display.max_info_columns``. A value of True always
+            shows the counts, and False never shows the counts.
+
+        Returns
+        -------
+        None
+            This method prints a summary of a DataFrame and returns None.
+
+        See Also
+        --------
+        DataFrame.describe: Generate descriptive statistics of DataFrame
+            columns.
+        DataFrame.memory_usage: Memory usage of DataFrame columns.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> int_values = [1, 2, 3, 4, 5]
+        >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+        >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
+        >>> df = cudf.DataFrame({"int_col": int_values,
+        ...                     "text_col": text_values,
+        ...                     "float_col": float_values})
+        >>> df
+           int_col text_col  float_col
+        0        1    alpha       0.00
+        1        2     beta       0.25
+        2        3    gamma       0.50
+        3        4    delta       0.75
+        4        5  epsilon       1.00
+
+        Prints information of all columns:
+
+        >>> df.info(verbose=True)
+        <class 'cudf.core.dataframe.DataFrame'>
+        RangeIndex: 5 entries, 0 to 4
+        Data columns (total 3 columns):
+         #   Column     Non-Null Count  Dtype
+        ---  ------     --------------  -----
+         0   int_col    5 non-null      int64
+         1   text_col   5 non-null      object
+         2   float_col  5 non-null      float64
+        dtypes: float64(1), int64(1), object(1)
+        memory usage: 130.0+ bytes
+
+        Prints a summary of columns count and its dtypes but not per column
+        information:
+
+        >>> df.info(verbose=False)
+        <class 'cudf.core.dataframe.DataFrame'>
+        RangeIndex: 5 entries, 0 to 4
+        Columns: 3 entries, int_col to float_col
+        dtypes: float64(1), int64(1), object(1)
+        memory usage: 130.0+ bytes
+
+        Pipe output of DataFrame.info to a buffer instead of sys.stdout and
+        print buffer contents:
+
+        >>> import io
+        >>> buffer = io.StringIO()
+        >>> df.info(buf=buffer)
+        >>> print(buffer.getvalue())
+        <class 'cudf.core.dataframe.DataFrame'>
+        RangeIndex: 5 entries, 0 to 4
+        Data columns (total 3 columns):
+         #   Column     Non-Null Count  Dtype
+        ---  ------     --------------  -----
+         0   int_col    5 non-null      int64
+         1   text_col   5 non-null      object
+         2   float_col  5 non-null      float64
+        dtypes: float64(1), int64(1), object(1)
+        memory usage: 130.0+ bytes
+
+        The `memory_usage` parameter allows deep introspection mode, specially
+        useful for big DataFrames and fine-tune memory optimization:
+
+        >>> import numpy as np
+        >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+        >>> df = cudf.DataFrame({
+        ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+        ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+        ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
+        ... })
+        >>> df.info(memory_usage='deep')
+        <class 'cudf.core.dataframe.DataFrame'>
+        RangeIndex: 1000000 entries, 0 to 999999
+        Data columns (total 3 columns):
+         #   Column    Non-Null Count    Dtype
+        ---  ------    --------------    -----
+         0   column_1  1000000 non-null  object
+         1   column_2  1000000 non-null  object
+         2   column_3  1000000 non-null  object
+        dtypes: object(3)
+        memory usage: 14.3 MB
+        """
+        if buf is None:
+            buf = sys.stdout
+
+        lines = [str(type(self))]
+
+        index_name = type(self._index).__name__
+        if len(self._index) > 0:
+            entries_summary = f", {self._index[0]} to {self._index[-1]}"
+        else:
+            entries_summary = ""
+        index_summary = (
+            f"{index_name}: {len(self._index)} entries{entries_summary}"
+        )
+        lines.append(index_summary)
+
+        if len(self._data) == 0:
+            lines.append(f"Empty {type(self).__name__}")
+            cudf.utils.ioutils.buffer_write_lines(buf, lines)
+            return
+
+        cols = self._column_names
+        col_count = len(cols)
+
+        if max_cols is None:
+            max_cols = pd.options.display.max_info_columns
+
+        max_rows = pd.options.display.max_info_rows
+
+        if null_counts is None:
+            show_counts = (col_count <= max_cols) and (len(self) < max_rows)
+        else:
+            show_counts = null_counts
+
+        exceeds_info_cols = col_count > max_cols
+
+        def _put_str(s, space):
+            return str(s)[:space].ljust(space)
+
+        def _verbose_repr():
+            lines.append(f"Data columns (total {col_count} columns):")
+
+            id_head = " # "
+            column_head = "Column"
+            col_space = 2
+
+            max_col = max(len(pprint_thing(k)) for k in cols)
+            len_column = len(pprint_thing(column_head))
+            space = max(max_col, len_column) + col_space
+
+            max_id = len(pprint_thing(col_count))
+            len_id = len(pprint_thing(id_head))
+            space_num = max(max_id, len_id) + col_space
+            counts = None
+
+            header = _put_str(id_head, space_num) + _put_str(
+                column_head, space
+            )
+            if show_counts:
+                counts = self.count().to_pandas().tolist()
+                if col_count != len(counts):
+                    raise AssertionError(
+                        f"Columns must equal "
+                        f"counts ({col_count} != {len(counts)})"
+                    )
+                count_header = "Non-Null Count"
+                len_count = len(count_header)
+                non_null = " non-null"
+                max_count = max(len(pprint_thing(k)) for k in counts) + len(
+                    non_null
+                )
+                space_count = max(len_count, max_count) + col_space
+                count_temp = "{count}" + non_null
+            else:
+                count_header = ""
+                space_count = len(count_header)
+                len_count = space_count
+                count_temp = "{count}"
+
+            dtype_header = "Dtype"
+            len_dtype = len(dtype_header)
+            max_dtypes = max(len(pprint_thing(k)) for k in self.dtypes)
+            space_dtype = max(len_dtype, max_dtypes)
+            header += (
+                _put_str(count_header, space_count)
+                + _put_str(dtype_header, space_dtype).rstrip()
+            )
+
+            lines.append(header)
+            lines.append(
+                _put_str("-" * len_id, space_num)
+                + _put_str("-" * len_column, space)
+                + _put_str("-" * len_count, space_count)
+                + _put_str("-" * len_dtype, space_dtype).rstrip()
+            )
+
+            for i, col in enumerate(self._column_names):
+                dtype = self.dtypes.iloc[i]
+                col = pprint_thing(col)
+
+                line_no = _put_str(f" {i}", space_num)
+                count = ""
+                if show_counts:
+                    count = counts[i]
+
+                lines.append(
+                    line_no
+                    + _put_str(col, space)
+                    + _put_str(count_temp.format(count=count), space_count)
+                    + _put_str(dtype, space_dtype).rstrip()
+                )
+
+        def _non_verbose_repr():
+            if col_count > 0:
+                entries_summary = f", {cols[0]} to {cols[-1]}"
+            else:
+                entries_summary = ""
+            columns_summary = f"Columns: {col_count} entries{entries_summary}"
+            lines.append(columns_summary)
+
+        def _sizeof_fmt(num, size_qualifier):
+            # returns size in human readable format
+            for x in ["bytes", "KB", "MB", "GB", "TB"]:
+                if num < 1024.0:
+                    return f"{num:3.1f}{size_qualifier} {x}"
+                num /= 1024.0
+            return f"{num:3.1f}{size_qualifier} PB"
+
+        if verbose:
+            _verbose_repr()
+        elif verbose is False:  # specifically set to False, not nesc None
+            _non_verbose_repr()
+        else:
+            if exceeds_info_cols:
+                _non_verbose_repr()
+            else:
+                _verbose_repr()
+
+        dtype_counts = defaultdict(int)
+        for col in self._data:
+            dtype_counts[self._data[col].dtype.name] += 1
+
+        dtypes = [f"{k[0]}({k[1]:d})" for k in sorted(dtype_counts.items())]
+        lines.append(f"dtypes: {', '.join(dtypes)}")
+
+        if memory_usage is None:
+            memory_usage = pd.options.display.memory_usage
+
+        if memory_usage:
+            # append memory usage of df to display
+            size_qualifier = ""
+            if memory_usage == "deep":
+                deep = True
+            else:
+                deep = False
+                if "object" in dtype_counts or self.index.dtype == "object":
+                    size_qualifier = "+"
+            mem_usage = self.memory_usage(index=True, deep=deep).sum()
+            lines.append(
+                f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n"
+            )
+
+        cudf.utils.ioutils.buffer_write_lines(buf, lines)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_describe()
+    def describe(
+        self,
+        percentiles=None,
+        include=None,
+        exclude=None,
+        datetime_is_numeric=False,
+    ):
+        """{docstring}"""
+
+        if not include and not exclude:
+            default_include = [np.number]
+            if datetime_is_numeric:
+                default_include.append("datetime")
+            else:
+                # Do not remove until pandas 2.0 support is added.
+                warnings.warn(
+                    "`datetime_is_numeric` is deprecated. Specify "
+                    "`datetime_is_numeric=True` to silence this "
+                    "warning and adopt the future behavior now.",
+                    FutureWarning,
+                )
+            data_to_describe = self.select_dtypes(include=default_include)
+            if data_to_describe._num_columns == 0:
+                data_to_describe = self
+
+        elif include == "all":
+            if exclude is not None:
+                raise ValueError("exclude must be None when include is 'all'")
+
+            data_to_describe = self
+        else:
+            data_to_describe = self.select_dtypes(
+                include=include, exclude=exclude
+            )
+
+            if data_to_describe.empty:
+                raise ValueError("No data of included types.")
+
+        describe_series_list = [
+            data_to_describe[col].describe(
+                percentiles=percentiles,
+                datetime_is_numeric=datetime_is_numeric,
+            )
+            for col in data_to_describe._column_names
+        ]
+        if len(describe_series_list) == 1:
+            return describe_series_list[0].to_frame()
+        else:
+            ldesc_indexes = sorted(
+                (x.index for x in describe_series_list), key=len
+            )
+            names = dict.fromkeys(
+                [
+                    name
+                    for idxnames in ldesc_indexes
+                    for name in idxnames.to_pandas()
+                ],
+                None,
+            )
+
+            return cudf.concat(
+                [
+                    series.reindex(names, copy=False)
+                    for series in describe_series_list
+                ],
+                axis=1,
+                sort=False,
+            )
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, nullable=False, **kwargs):
+        """
+        Convert to a Pandas DataFrame.
+
+        Parameters
+        ----------
+        nullable : Boolean, Default False
+            If ``nullable`` is ``True``, the resulting columns
+            in the dataframe will be having a corresponding
+            nullable Pandas dtype. If there is no corresponding
+            nullable Pandas dtype present, the resulting dtype
+            will be a regular pandas dtype.
+            If ``nullable`` is ``False``,
+            the resulting columns will either convert null
+            values to ``np.nan`` or ``None`` depending on the dtype.
+
+        Returns
+        -------
+        out : Pandas DataFrame
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [0, 1, 2], 'b': [-3, 2, 0]})
+        >>> pdf = df.to_pandas()
+        >>> pdf
+           a  b
+        0  0 -3
+        1  1  2
+        2  2  0
+        >>> type(pdf)
+        <class 'pandas.core.frame.DataFrame'>
+
+        ``nullable`` parameter can be used to control
+        whether dtype can be Pandas Nullable or not:
+
+        >>> df = cudf.DataFrame({'a': [0, None, 2], 'b': [True, False, None]})
+        >>> df
+              a      b
+        0     0   True
+        1  <NA>  False
+        2     2   <NA>
+        >>> pdf = df.to_pandas(nullable=True)
+        >>> pdf
+              a      b
+        0     0   True
+        1  <NA>  False
+        2     2   <NA>
+        >>> pdf.dtypes
+        a      Int64
+        b    boolean
+        dtype: object
+        >>> pdf = df.to_pandas(nullable=False)
+        >>> pdf
+             a      b
+        0  0.0   True
+        1  NaN  False
+        2  2.0   None
+        >>> pdf.dtypes
+        a    float64
+        b     object
+        dtype: object
+        """
+        out_data = {}
+        out_index = self.index.to_pandas()
+
+        for i, col_key in enumerate(self._data):
+            out_data[i] = self._data[col_key].to_pandas(
+                index=out_index, nullable=nullable
+            )
+
+        out_df = pd.DataFrame(out_data, index=out_index)
+        out_df.columns = self._data.to_pandas_index()
+
+        return out_df
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_pandas(cls, dataframe, nan_as_null=no_default):
+        """
+        Convert from a Pandas DataFrame.
+
+        Parameters
+        ----------
+        dataframe : Pandas DataFrame object
+            A Pandas DataFrame object which has to be converted
+            to cuDF DataFrame.
+        nan_as_null : bool, Default True
+            If ``True``, converts ``np.nan`` values to ``null`` values.
+            If ``False``, leaves ``np.nan`` values as is.
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> data = [[0,1], [1,2], [3,4]]
+        >>> pdf = pd.DataFrame(data, columns=['a', 'b'], dtype=int)
+        >>> cudf.from_pandas(pdf)
+           a  b
+        0  0  1
+        1  1  2
+        2  3  4
+        """
+        if nan_as_null is no_default:
+            nan_as_null = (
+                False if cudf.get_option("mode.pandas_compatible") else None
+            )
+
+        if isinstance(dataframe, pd.DataFrame):
+
+            if not dataframe.columns.is_unique:
+                raise ValueError("Duplicate column names are not allowed")
+
+            # Set columns
+            data = {}
+            for col_name, col_value in dataframe.items():
+                # necessary because multi-index can return multiple
+                # columns for a single key
+                if len(col_value.shape) == 1:
+                    data[col_name] = column.as_column(
+                        col_value.array, nan_as_null=nan_as_null
+                    )
+                else:
+                    vals = col_value.values.T
+                    if vals.shape[0] == 1:
+                        data[col_name] = column.as_column(
+                            vals.flatten(), nan_as_null=nan_as_null
+                        )
+                    else:
+                        if isinstance(col_name, tuple):
+                            col_name = str(col_name)
+                        for idx in range(len(vals.shape)):
+                            data[col_name] = column.as_column(
+                                vals[idx], nan_as_null=nan_as_null
+                            )
+
+            index = cudf.from_pandas(dataframe.index, nan_as_null=nan_as_null)
+            df = cls._from_data(data, index)
+            df._data._level_names = tuple(dataframe.columns.names)
+
+            if isinstance(dataframe.columns, pd.RangeIndex):
+                df._data.rangeindex = True
+            # Set columns only if it is a MultiIndex
+            elif isinstance(dataframe.columns, pd.MultiIndex):
+                df.columns = dataframe.columns
+
+            return df
+        else:
+            try:
+                return from_dataframe(dataframe, allow_copy=True)
+            except Exception:
+                raise TypeError(
+                    f"Could not construct DataFrame from {type(dataframe)}"
+                )
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_arrow(cls, table):
+        """
+        Convert from PyArrow Table to DataFrame.
+
+        Parameters
+        ----------
+        table : PyArrow Table Object
+            PyArrow Table Object which has to be converted to cudf DataFrame.
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Returns
+        -------
+        cudf DataFrame
+
+        Notes
+        -----
+        -   Does not support automatically setting index column(s) similar
+            to how ``to_pandas`` works for PyArrow Tables.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pyarrow as pa
+        >>> data = pa.table({"a":[1, 2, 3], "b":[4, 5, 6]})
+        >>> cudf.DataFrame.from_arrow(data)
+           a  b
+        0  1  4
+        1  2  5
+        2  3  6
+        """
+        index_col = None
+        col_index_names = None
+        if isinstance(table, pa.Table) and isinstance(
+            table.schema.pandas_metadata, dict
+        ):
+            index_col = table.schema.pandas_metadata["index_columns"]
+            if "column_indexes" in table.schema.pandas_metadata:
+                col_index_names = []
+                for col_meta in table.schema.pandas_metadata["column_indexes"]:
+                    col_index_names.append(col_meta["name"])
+
+        out = super().from_arrow(table)
+        if col_index_names is not None:
+            out._data._level_names = col_index_names
+        if index_col:
+            if isinstance(index_col[0], dict):
+                idx = cudf.RangeIndex(
+                    index_col[0]["start"],
+                    index_col[0]["stop"],
+                    name=index_col[0]["name"],
+                )
+                if len(idx) == len(out):
+                    # `idx` is generated from arrow `pandas_metadata`
+                    # which can get out of date with many of the
+                    # arrow operations. Hence verifying if the
+                    # lengths match, or else don't need to set
+                    # an index at all i.e., Default RangeIndex
+                    # will be set.
+                    # See more about the discussion here:
+                    # https://github.com/apache/arrow/issues/15178
+                    out = out.set_index(idx)
+            else:
+                out = out.set_index(index_col[0])
+
+        return out
+
+    @_cudf_nvtx_annotate
+    def to_arrow(self, preserve_index=True):
+        """
+        Convert to a PyArrow Table.
+
+        Parameters
+        ----------
+        preserve_index : bool, default True
+            whether index column and its meta data needs to be saved or not
+
+        Returns
+        -------
+        PyArrow Table
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame(
+        ...     {"a":[1, 2, 3], "b":[4, 5, 6]}, index=[1, 2, 3])
+        >>> df.to_arrow()
+        pyarrow.Table
+        a: int64
+        b: int64
+        index: int64
+        ----
+        a: [[1,2,3]]
+        b: [[4,5,6]]
+        index: [[1,2,3]]
+        >>> df.to_arrow(preserve_index=False)
+        pyarrow.Table
+        a: int64
+        b: int64
+        ----
+        a: [[1,2,3]]
+        b: [[4,5,6]]
+        """
+
+        data = self.copy(deep=False)
+        index_descr = []
+        if preserve_index:
+            if isinstance(self.index, cudf.RangeIndex):
+                descr = {
+                    "kind": "range",
+                    "name": self.index.name,
+                    "start": self.index._start,
+                    "stop": self.index._stop,
+                    "step": 1,
+                }
+            else:
+                if isinstance(self.index, MultiIndex):
+                    gen_names = tuple(
+                        f"level_{i}"
+                        for i, _ in enumerate(self.index._data.names)
+                    )
+                else:
+                    gen_names = (
+                        self.index.names
+                        if self.index.name is not None
+                        else ("index",)
+                    )
+                for gen_name, col_name in zip(
+                    gen_names, self.index._data.names
+                ):
+                    data._insert(
+                        data.shape[1],
+                        gen_name,
+                        self.index._data[col_name],
+                    )
+                descr = gen_names[0]
+            index_descr.append(descr)
+
+        out = super(DataFrame, data).to_arrow()
+        metadata = pa.pandas_compat.construct_metadata(
+            columns_to_convert=[self[col] for col in self._data.names],
+            df=self,
+            column_names=out.schema.names,
+            index_levels=[self.index],
+            index_descriptors=index_descr,
+            preserve_index=preserve_index,
+            types=out.schema.types,
+        )
+
+        return out.replace_schema_metadata(metadata)
+
+    @_cudf_nvtx_annotate
+    def to_records(self, index=True):
+        """Convert to a numpy recarray
+
+        Parameters
+        ----------
+        index : bool
+            Whether to include the index in the output.
+
+        Returns
+        -------
+        numpy recarray
+        """
+        members = [("index", self.index.dtype)] if index else []
+        members += [(col, self[col].dtype) for col in self._data.names]
+        dtype = np.dtype(members)
+        ret = np.recarray(len(self), dtype=dtype)
+        if index:
+            ret["index"] = self.index.to_numpy()
+        for col in self._data.names:
+            ret[col] = self[col].to_numpy()
+        return ret
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_records(cls, data, index=None, columns=None, nan_as_null=False):
+        """
+        Convert structured or record ndarray to DataFrame.
+
+        Parameters
+        ----------
+        data : numpy structured dtype or recarray of ndim=2
+        index : str, array-like
+            The name of the index column in *data*.
+            If None, the default index is used.
+        columns : list of str
+            List of column names to include.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if data.ndim != 1 and data.ndim != 2:
+            raise ValueError(
+                f"records dimension expected 1 or 2 but found {data.ndim}"
+            )
+
+        num_cols = len(data[0])
+
+        if columns is None and data.dtype.names is None:
+            names = [i for i in range(num_cols)]
+
+        elif data.dtype.names is not None:
+            names = data.dtype.names
+
+        else:
+            if len(columns) != num_cols:
+                raise ValueError(
+                    f"columns length expected {num_cols} "
+                    f"but found {len(columns)}"
+                )
+            names = columns
+
+        df = DataFrame()
+
+        if data.ndim == 2:
+            for i, k in enumerate(names):
+                df._data[k] = column.as_column(
+                    data[:, i], nan_as_null=nan_as_null
+                )
+        elif data.ndim == 1:
+            for k in names:
+                df._data[k] = column.as_column(
+                    data[k], nan_as_null=nan_as_null
+                )
+
+        if index is None:
+            df._index = RangeIndex(start=0, stop=len(data))
+        elif is_scalar(index):
+            df._index = RangeIndex(start=0, stop=len(data))
+            df = df.set_index(index)
+        else:
+            df._index = as_index(index)
+        if isinstance(columns, pd.Index):
+            df._data._level_names = tuple(columns.names)
+        return df
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_arrays(cls, data, index=None, columns=None, nan_as_null=False):
+        """Convert a numpy/cupy array to DataFrame.
+
+        Parameters
+        ----------
+        data : numpy/cupy array of ndim 1 or 2,
+            dimensions greater than 2 are not supported yet.
+        index : Index or array-like
+            Index to use for resulting frame. Will default to
+            RangeIndex if no indexing information part of input data and
+            no index provided.
+        columns : list of str
+            List of column names to include.
+
+        Returns
+        -------
+        DataFrame
+        """
+
+        data = cupy.asarray(data)
+        if data.ndim != 1 and data.ndim != 2:
+            raise ValueError(
+                f"records dimension expected 1 or 2 but found: {data.ndim}"
+            )
+
+        if data.ndim == 2:
+            num_cols = data.shape[1]
+        else:
+            # Since we validate ndim to be either 1 or 2 above,
+            # this case can be assumed to be ndim == 1.
+            num_cols = 1
+
+        if columns is None:
+            names = range(num_cols)
+        else:
+            if len(columns) != num_cols:
+                raise ValueError(
+                    f"columns length expected {num_cols} but "
+                    f"found {len(columns)}"
+                )
+            elif len(columns) != len(set(columns)):
+                raise ValueError("Duplicate column names are not allowed")
+            names = columns
+
+        df = cls()
+        if data.ndim == 2:
+            for i, k in enumerate(names):
+                df._data[k] = column.as_column(
+                    data[:, i], nan_as_null=nan_as_null
+                )
+        elif data.ndim == 1:
+            df._data[names[0]] = column.as_column(
+                data, nan_as_null=nan_as_null
+            )
+        if isinstance(columns, pd.Index):
+            df._data._level_names = tuple(columns.names)
+        if isinstance(columns, (range, pd.RangeIndex, cudf.RangeIndex)):
+            df._data.rangeindex = True
+
+        if index is None:
+            df._index = RangeIndex(start=0, stop=len(data))
+        else:
+            df._index = as_index(index)
+        return df
+
+    @_cudf_nvtx_annotate
+    def interpolate(
+        self,
+        method="linear",
+        axis=0,
+        limit=None,
+        inplace=False,
+        limit_direction=None,
+        limit_area=None,
+        downcast=None,
+        **kwargs,
+    ):
+        if all(dt == np.dtype("object") for dt in self.dtypes):
+            raise TypeError(
+                "Cannot interpolate with all object-dtype "
+                "columns in the DataFrame. Try setting at "
+                "least one column to a numeric dtype."
+            )
+
+        return super().interpolate(
+            method=method,
+            axis=axis,
+            limit=limit,
+            inplace=inplace,
+            limit_direction=limit_direction,
+            limit_area=limit_area,
+            downcast=downcast,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def quantile(
+        self,
+        q=0.5,
+        axis=0,
+        numeric_only=True,
+        interpolation=None,
+        columns=None,
+        exact=True,
+        method="single",
+    ):
+        """
+        Return values at the given quantile.
+
+        Parameters
+        ----------
+        q : float or array-like
+            0 <= q <= 1, the quantile(s) to compute
+        axis : int
+            axis is a NON-FUNCTIONAL parameter
+        numeric_only : bool, default True
+            If False, the quantile of datetime and timedelta data will be
+            computed as well.
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            This parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points i and j.
+            Default is ``'linear'`` for ``method="single"``, and ``'nearest'``
+            for ``method="table"``.
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        columns : list of str
+            List of column names to include.
+        exact : boolean
+            Whether to use approximate or exact quantile algorithm.
+        method : {'single', 'table'}, default `'single'`
+            Whether to compute quantiles per-column ('single') or over all
+            columns ('table'). When 'table', the only allowed interpolation
+            methods are 'nearest', 'lower', and 'higher'.
+
+        Returns
+        -------
+        Series or DataFrame
+            If q is an array or numeric_only is set to False, a DataFrame
+            will be returned where index is q, the columns are the columns
+            of self, and the values are the quantile.
+
+            If q is a float, a Series will be returned where the index is
+            the columns of self and the values are the quantiles.
+
+        .. pandas-compat::
+            **DataFrame.quantile**
+
+            One notable difference from Pandas is when DataFrame is of
+            non-numeric types and result is expected to be a Series in case of
+            Pandas. cuDF will return a DataFrame as it doesn't support mixed
+            types under Series.
+
+        Examples
+        --------
+        >>> import cupy as cp
+        >>> import cudf
+        >>> df = cudf.DataFrame(cp.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
+        ...                   columns=['a', 'b'])
+        >>> df
+           a    b
+        0  1    1
+        1  2   10
+        2  3  100
+        3  4  100
+        >>> df.quantile(0.1)
+        a    1.3
+        b    3.7
+        Name: 0.1, dtype: float64
+        >>> df.quantile([.1, .5])
+               a     b
+        0.1  1.3   3.7
+        0.5  2.5  55.0
+        """  # noqa: E501
+        if axis not in (0, None):
+            raise NotImplementedError("axis is not implemented yet")
+
+        data_df = self
+        if numeric_only:
+            data_df = data_df.select_dtypes(
+                include=[np.number], exclude=["datetime64", "timedelta64"]
+            )
+
+        if columns is None:
+            columns = data_df._data.names
+
+        if isinstance(q, numbers.Number):
+            q_is_number = True
+            qs = [float(q)]
+        elif pd.api.types.is_list_like(q):
+            q_is_number = False
+            qs = q
+        else:
+            msg = "`q` must be either a single element or list"
+            raise TypeError(msg)
+
+        if method == "table":
+            interpolation = interpolation or "nearest"
+            result = self._quantile_table(qs, interpolation.upper())
+
+            if q_is_number:
+                result = result.transpose()
+                return Series(
+                    data=result._columns[0], index=result.index, name=q
+                )
+        else:
+            # Ensure that qs is non-scalar so that we always get a column back.
+            interpolation = interpolation or "linear"
+            result = {}
+            for k in data_df._data.names:
+                if k in columns:
+                    ser = data_df[k]
+                    res = ser.quantile(
+                        qs,
+                        interpolation=interpolation,
+                        exact=exact,
+                        quant_index=False,
+                    )._column
+                    if len(res) == 0:
+                        res = column.column_empty_like(
+                            qs, dtype=ser.dtype, masked=True, newsize=len(qs)
+                        )
+                    result[k] = res
+            result = DataFrame._from_data(result)
+
+            if q_is_number and numeric_only:
+                result = result.fillna(np.nan).iloc[0]
+                result.index = data_df.keys()
+                result.name = q
+                return result
+
+        result.index = cudf.Index(list(map(float, qs)), dtype="float64")
+        return result
+
+    @_cudf_nvtx_annotate
+    def isin(self, values):
+        """
+        Whether each element in the DataFrame is contained in values.
+
+        Parameters
+        ----------
+        values : iterable, Series, DataFrame or dict
+            The result will only be true at a location if all
+            the labels match. If values is a Series, that's the index.
+            If values is a dict, the keys must be the column names,
+            which must match. If values is a DataFrame, then both the
+            index and column labels must match.
+
+        Returns
+        -------
+        DataFrame:
+            DataFrame of booleans showing whether each element in
+            the DataFrame is contained in values.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
+        ...                     index=['falcon', 'dog'])
+        >>> df
+                num_legs  num_wings
+        falcon         2          2
+        dog            4          0
+
+        When ``values`` is a list check whether every value in the DataFrame
+        is present in the list (which animals have 0 or 2 legs or wings)
+
+        >>> df.isin([0, 2])
+                num_legs  num_wings
+        falcon      True       True
+        dog        False       True
+
+        When ``values`` is a dict, we can pass values to check for each
+        column separately:
+
+        >>> df.isin({'num_wings': [0, 3]})
+                num_legs  num_wings
+        falcon     False      False
+        dog        False       True
+
+        When ``values`` is a Series or DataFrame the index and column must
+        match. Note that 'falcon' does not match based on the number of legs
+        in other.
+
+        >>> other = cudf.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
+        ...                         index=['spider', 'falcon'])
+        >>> df.isin(other)
+                num_legs  num_wings
+        falcon      True       True
+        dog        False      False
+        """
+        # TODO: propagate nulls through isin
+        # https://github.com/rapidsai/cudf/issues/7556
+
+        fill_value = cudf.Scalar(False)
+
+        def make_false_column_like_self():
+            return column.full(len(self), fill_value, "bool")
+
+        # Preprocess different input types into a mapping from column names to
+        # a list of values to check.
+        result = {}
+        if isinstance(values, IndexedFrame):
+            # Note: In the case where values is a Series, computing some
+            # information about the values column outside the loop may result
+            # in performance gains.  However, since categorical conversion
+            # depends on the current column in the loop, using the correct
+            # precomputed variables inside the loop requires nontrivial logic.
+            # This optimization could be attempted if `isin` ever becomes a
+            # bottleneck.
+            if (
+                isinstance(values, (Series, DataFrame))
+                and not values.index.is_unique
+            ):
+                # if DataFrame ever supports duplicate columns
+                # would need to check that here
+                raise ValueError("cannot compute isin with a duplicate axis.")
+            values = values.reindex(self.index)
+            other_cols = (
+                values._data
+                if isinstance(values, DataFrame)
+                else {name: values._column for name in self._data}
+            )
+            for col, self_col in self._data.items():
+                if col in other_cols:
+                    other_col = other_cols[col]
+                    self_is_cat = isinstance(self_col, CategoricalColumn)
+                    other_is_cat = isinstance(other_col, CategoricalColumn)
+
+                    if self_is_cat != other_is_cat:
+                        # It is valid to compare the levels of a categorical
+                        # column to a non-categorical column.
+                        if self_is_cat:
+                            self_col = self_col._get_decategorized_column()
+                        else:
+                            other_col = other_col._get_decategorized_column()
+
+                    # We use the type checks from _before_ the conversion
+                    # because if only one was categorical then it's already
+                    # been converted and we have to check if they're strings.
+                    if self_is_cat and other_is_cat:
+                        self_is_str = other_is_str = False
+                    else:
+                        # These checks must happen after the conversions above
+                        # since numpy can't handle categorical dtypes.
+                        self_is_str = is_string_dtype(self_col.dtype)
+                        other_is_str = is_string_dtype(other_col.dtype)
+
+                    if self_is_str != other_is_str:
+                        # Strings can't compare to anything else.
+                        result[col] = make_false_column_like_self()
+                    else:
+                        result[col] = (self_col == other_col).fillna(False)
+                else:
+                    result[col] = make_false_column_like_self()
+        elif is_dict_like(values):
+            for name, col in self._data.items():
+                if name in values:
+                    result[name] = col.isin(values[name])
+                else:
+                    result[name] = make_false_column_like_self()
+        elif is_list_like(values):
+            for name, col in self._data.items():
+                result[name] = col.isin(values)
+        else:
+            raise TypeError(
+                "only list-like or dict-like objects are "
+                "allowed to be passed to DataFrame.isin(), "
+                "you passed a "
+                f"'{type(values).__name__}'"
+            )
+
+        # TODO: Update this logic to properly preserve MultiIndex columns.
+        return DataFrame._from_data(result, self.index)
+
+    #
+    # Stats
+    #
+    @_cudf_nvtx_annotate
+    def _prepare_for_rowwise_op(self, method, skipna):
+        """Prepare a DataFrame for CuPy-based row-wise operations."""
+
+        if method not in _cupy_nan_methods_map and any(
+            col.nullable for col in self._columns
+        ):
+            msg = (
+                f"Row-wise operations to calculate '{method}' do not "
+                f"currently support columns with null values. "
+                f"Consider removing them with .dropna() "
+                f"or using .fillna()."
+            )
+            raise ValueError(msg)
+
+        is_pure_dt = all(is_datetime_dtype(dt) for dt in self.dtypes)
+
+        if not is_pure_dt:
+            filtered = self.select_dtypes(include=[np.number, np.bool_])
+        else:
+            filtered = self.copy(deep=False)
+
+        common_dtype = find_common_type(filtered.dtypes)
+
+        if filtered._num_columns < self._num_columns:
+            # When we update our pandas compatibility target to 2.0, pandas
+            # will stop supporting numeric_only=None and users will have to
+            # specify True/False. At that time we should also top our implicit
+            # removal of non-numeric columns here.
+            assert Version(pd.__version__) < Version("2.0.0")
+            msg = (
+                "Row-wise operations currently only support int, float "
+                "and bool dtypes. Non numeric columns are ignored."
+            )
+            warnings.warn(msg)
+
+        if not skipna and any(col.nullable for col in filtered._columns):
+            mask = DataFrame(
+                {
+                    name: filtered._data[name]._get_mask_as_column()
+                    if filtered._data[name].nullable
+                    else column.full(len(filtered._data[name]), True)
+                    for name in filtered._data.names
+                }
+            )
+            mask = mask.all(axis=1)
+        else:
+            mask = None
+
+        coerced = filtered.astype(common_dtype, copy=False)
+        if is_pure_dt:
+            # Further convert into cupy friendly types
+            coerced = coerced.astype("int64", copy=False)
+        return coerced, mask, common_dtype
+
+    @_cudf_nvtx_annotate
+    def count(self, axis=0, level=None, numeric_only=False, **kwargs):
+        """
+        Count ``non-NA`` cells for each column or row.
+
+        The values ``None``, ``NaN``, ``NaT`` are considered ``NA``.
+
+        Returns
+        -------
+        Series
+            For each column/row the number of non-NA/null entries.
+
+        Notes
+        -----
+        Parameters currently not supported are `axis`, `level`, `numeric_only`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import numpy as np
+        >>> df = cudf.DataFrame({"Person":
+        ...        ["John", "Myla", "Lewis", "John", "Myla"],
+        ...        "Age": [24., np.nan, 21., 33, 26],
+        ...        "Single": [False, True, True, True, False]})
+        >>> df.count()
+        Person    5
+        Age       4
+        Single    5
+        dtype: int64
+        """
+        axis = self._get_axis_from_axis_arg(axis)
+        if axis != 0:
+            raise NotImplementedError("Only axis=0 is currently supported.")
+
+        return Series._from_data(
+            {None: [self._data[col].valid_count for col in self._data.names]},
+            as_index(self._data.names),
+        )
+
+    _SUPPORT_AXIS_LOOKUP = {
+        0: 0,
+        1: 1,
+        "index": 0,
+        "columns": 1,
+    }
+
+    @_cudf_nvtx_annotate
+    def _reduce(
+        self,
+        op,
+        axis=None,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        if level is not None:
+            raise NotImplementedError("level parameter is not implemented yet")
+
+        source = self
+        if numeric_only:
+            numeric_cols = (
+                name
+                for name in self._data.names
+                if is_numeric_dtype(self._data[name])
+            )
+            source = self._get_columns_by_label(numeric_cols)
+            if source.empty:
+                return Series(index=cudf.Index([], dtype="str"))
+
+        if axis is None:
+            if op in {"any", "all"}:
+                axis = 2
+            else:
+                # Do not remove until pandas 2.0 support is added.
+                warnings.warn(
+                    f"In a future version, {type(self).__name__}"
+                    f".{op}(axis=None) will return a scalar {op} over "
+                    "the entire DataFrame. To retain the old behavior, "
+                    f"use '{type(self).__name__}.{op}(axis=0)' or "
+                    f"just '{type(self)}.{op}()'",
+                    FutureWarning,
+                )
+                axis = 0
+        elif axis is no_default:
+            axis = 0
+        else:
+            axis = source._get_axis_from_axis_arg(axis)
+
+        if axis in {0, 2}:
+            try:
+                result = [
+                    getattr(source._data[col], op)(**kwargs)
+                    for col in source._data.names
+                ]
+            except AttributeError:
+                if numeric_only is None and op in _numeric_reduction_ops:
+                    # Do not remove until pandas 2.0 support is added.
+                    warnings.warn(
+                        f"The default value of numeric_only in DataFrame.{op} "
+                        "is deprecated. In a future version, it will default "
+                        "to False. In addition, specifying "
+                        "'numeric_only=None' is deprecated. Select only valid "
+                        "columns or specify the value of numeric_only to "
+                        "silence this warning.",
+                        FutureWarning,
+                    )
+                    numeric_cols = (
+                        name
+                        for name in self._data.names
+                        if is_numeric_dtype(self._data[name])
+                    )
+                    source = self._get_columns_by_label(numeric_cols)
+                    if source.empty:
+                        if axis == 2:
+                            return getattr(as_column([]), op)(**kwargs)
+                        else:
+                            return Series(index=cudf.Index([], dtype="str"))
+                    try:
+                        result = [
+                            getattr(source._data[col], op)(**kwargs)
+                            for col in source._data.names
+                        ]
+                    except AttributeError:
+                        raise TypeError(
+                            f"Not all column dtypes support op {op}"
+                        )
+                else:
+                    raise
+            if axis == 2:
+                return getattr(as_column(result), op)(**kwargs)
+            else:
+                source_dtypes = [c.dtype for c in source._data.columns]
+                common_dtype = find_common_type(source_dtypes)
+                if is_object_dtype(common_dtype) and any(
+                    not is_object_dtype(dtype) for dtype in source_dtypes
+                ):
+                    raise TypeError(
+                        "Columns must all have the same dtype to "
+                        f"perform {op=} with {axis=}"
+                    )
+                return Series._from_data(
+                    {None: as_column(result)}, as_index(source._data.names)
+                )
+        elif axis == 1:
+            return source._apply_cupy_method_axis_1(op, **kwargs)
+        else:
+            raise ValueError(f"Invalid value of {axis=} received for {op}")
+
+    @_cudf_nvtx_annotate
+    def _scan(
+        self,
+        op,
+        axis=None,
+        *args,
+        **kwargs,
+    ):
+        if axis is None:
+            axis = 0
+        axis = self._get_axis_from_axis_arg(axis)
+
+        if axis == 0:
+            return super()._scan(op, axis=axis, *args, **kwargs)
+        elif axis == 1:
+            return self._apply_cupy_method_axis_1(op, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def mode(self, axis=0, numeric_only=False, dropna=True):
+        """
+        Get the mode(s) of each element along the selected axis.
+
+        The mode of a set of values is the value that appears most often.
+        It can be multiple values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to iterate over while searching for the mode:
+
+            - 0 or 'index' : get mode of each column
+            - 1 or 'columns' : get mode of each row.
+        numeric_only : bool, default False
+            If True, only apply to numeric columns.
+        dropna : bool, default True
+            Don't consider counts of NA/NaN/NaT.
+
+        Returns
+        -------
+        DataFrame
+            The modes of each column or row.
+
+        See Also
+        --------
+        cudf.Series.mode : Return the highest frequency value
+            in a Series.
+        cudf.Series.value_counts : Return the counts of values
+            in a Series.
+
+        Notes
+        -----
+        ``axis`` parameter is currently not supported.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({
+        ...     "species": ["bird", "mammal", "arthropod", "bird"],
+        ...     "legs": [2, 4, 8, 2],
+        ...     "wings": [2.0, None, 0.0, None]
+        ... })
+        >>> df
+             species  legs wings
+        0       bird     2   2.0
+        1     mammal     4  <NA>
+        2  arthropod     8   0.0
+        3       bird     2  <NA>
+
+        By default, missing values are not considered, and the mode of wings
+        are both 0 and 2. The second row of species and legs contains ``NA``,
+        because they have only one mode, but the DataFrame has two rows.
+
+        >>> df.mode()
+          species  legs  wings
+        0    bird     2    0.0
+        1    <NA>  <NA>    2.0
+
+        Setting ``dropna=False``, ``NA`` values are considered and they can be
+        the mode (like for wings).
+
+        >>> df.mode(dropna=False)
+          species  legs wings
+        0    bird     2  <NA>
+
+        Setting ``numeric_only=True``, only the mode of numeric columns is
+        computed, and columns of other types are ignored.
+
+        >>> df.mode(numeric_only=True)
+           legs  wings
+        0     2    0.0
+        1  <NA>    2.0
+        """
+        if axis not in (0, "index"):
+            raise NotImplementedError("Only axis=0 is currently supported")
+
+        if numeric_only:
+            data_df = self.select_dtypes(
+                include=[np.number], exclude=["datetime64", "timedelta64"]
+            )
+        else:
+            data_df = self
+
+        mode_results = [
+            data_df[col].mode(dropna=dropna) for col in data_df._data
+        ]
+
+        if len(mode_results) == 0:
+            return DataFrame()
+
+        df = cudf.concat(mode_results, axis=1)
+        if isinstance(df, Series):
+            df = df.to_frame()
+
+        df._set_column_names_like(data_df)
+
+        return df
+
+    @_cudf_nvtx_annotate
+    def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
+        obj = self.select_dtypes(include="bool") if bool_only else self
+        return super(DataFrame, obj).all(axis, skipna, level, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
+        obj = self.select_dtypes(include="bool") if bool_only else self
+        return super(DataFrame, obj).any(axis, skipna, level, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def _apply_cupy_method_axis_1(self, method, *args, **kwargs):
+        # This method uses cupy to perform scans and reductions along rows of a
+        # DataFrame. Since cuDF is designed around columnar storage and
+        # operations, we convert DataFrames to 2D cupy arrays for these ops.
+
+        # for dask metadata compatibility
+        skipna = kwargs.pop("skipna", None)
+        skipna = True if skipna is None else skipna
+        if method not in _cupy_nan_methods_map and skipna not in (
+            None,
+            True,
+            1,
+        ):
+            raise NotImplementedError(
+                f"Row-wise operations to calculate '{method}'"
+                f" currently do not support `skipna=False`."
+            )
+
+        level = kwargs.pop("level", None)
+        if level not in (None,):
+            raise NotImplementedError(
+                "Row-wise operations currently do not support `level`."
+            )
+
+        numeric_only = kwargs.pop("numeric_only", None)
+        if numeric_only not in (None, True):
+            raise NotImplementedError(
+                "Row-wise operations currently do not "
+                "support `numeric_only=False`."
+            )
+
+        min_count = kwargs.pop("min_count", None)
+        if min_count not in (None, 0):
+            raise NotImplementedError(
+                "Row-wise operations currently do not support `min_count`."
+            )
+
+        bool_only = kwargs.pop("bool_only", None)
+        if bool_only not in (None, True):
+            raise NotImplementedError(
+                "Row-wise operations currently do not support `bool_only`."
+            )
+
+        # This parameter is only necessary for axis 0 reductions that cuDF
+        # performs internally. cupy already upcasts smaller integer/bool types
+        # to int64 when accumulating.
+        kwargs.pop("cast_to_int", None)
+
+        prepared, mask, common_dtype = self._prepare_for_rowwise_op(
+            method, skipna
+        )
+        for col in prepared._data.names:
+            if prepared._data[col].nullable:
+                prepared._data[col] = (
+                    prepared._data[col]
+                    .astype(
+                        cudf.utils.dtypes.get_min_float_dtype(
+                            prepared._data[col]
+                        )
+                        if not is_datetime_dtype(common_dtype)
+                        else cudf.dtype("float64")
+                    )
+                    .fillna(np.nan)
+                )
+        arr = prepared.to_cupy()
+
+        if skipna is not False and method in _cupy_nan_methods_map:
+            method = _cupy_nan_methods_map[method]
+
+        result = getattr(cupy, method)(arr, axis=1, **kwargs)
+
+        if result.ndim == 1:
+            type_coerced_methods = {
+                "count",
+                "min",
+                "max",
+                "sum",
+                "prod",
+                "cummin",
+                "cummax",
+                "cumsum",
+                "cumprod",
+            }
+            result_dtype = (
+                common_dtype
+                if method in type_coerced_methods
+                or is_datetime_dtype(common_dtype)
+                else None
+            )
+            result = column.as_column(result, dtype=result_dtype)
+            if mask is not None:
+                result = result.set_mask(
+                    cudf._lib.transform.bools_to_mask(mask._column)
+                )
+            return Series(
+                result,
+                index=self.index,
+                dtype=result_dtype,
+            )
+        else:
+            result_df = DataFrame(result).set_index(self.index)
+            result_df._set_column_names_like(prepared)
+            return result_df
+
+    @_cudf_nvtx_annotate
+    def _columns_view(self, columns):
+        """
+        Return a subset of the DataFrame's columns as a view.
+        """
+        return DataFrame(
+            {col: self._data[col] for col in columns}, index=self.index
+        )
+
+    @_cudf_nvtx_annotate
+    def select_dtypes(self, include=None, exclude=None):
+        """Return a subset of the DataFrame's columns based on the column dtypes.
+
+        Parameters
+        ----------
+        include : str or list
+            which columns to include based on dtypes
+        exclude : str or list
+            which columns to exclude based on dtypes
+
+        Returns
+        -------
+        DataFrame
+            The subset of the frame including the dtypes
+            in ``include`` and excluding the dtypes in ``exclude``.
+
+        Raises
+        ------
+        ValueError
+            - If both of ``include`` and ``exclude`` are empty
+            - If ``include`` and ``exclude`` have overlapping elements
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2] * 3,
+        ...                    'b': [True, False] * 3,
+        ...                    'c': [1.0, 2.0] * 3})
+        >>> df
+           a      b    c
+        0  1   True  1.0
+        1  2  False  2.0
+        2  1   True  1.0
+        3  2  False  2.0
+        4  1   True  1.0
+        5  2  False  2.0
+        >>> df.select_dtypes(include='bool')
+               b
+        0   True
+        1  False
+        2   True
+        3  False
+        4   True
+        5  False
+        >>> df.select_dtypes(include=['float64'])
+             c
+        0  1.0
+        1  2.0
+        2  1.0
+        3  2.0
+        4  1.0
+        5  2.0
+        >>> df.select_dtypes(exclude=['int'])
+               b    c
+        0   True  1.0
+        1  False  2.0
+        2   True  1.0
+        3  False  2.0
+        4   True  1.0
+        5  False  2.0
+        """  # noqa: E501
+
+        # code modified from:
+        # https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L3196
+
+        if not isinstance(include, (list, tuple)):
+            include = (include,) if include is not None else ()
+        if not isinstance(exclude, (list, tuple)):
+            exclude = (exclude,) if exclude is not None else ()
+
+        df = DataFrame(index=self.index)
+
+        # cudf_dtype_from_pydata_dtype can distinguish between
+        # np.float and np.number
+        selection = tuple(map(frozenset, (include, exclude)))
+
+        if not any(selection):
+            raise ValueError(
+                "at least one of include or exclude must be nonempty"
+            )
+
+        include, exclude = map(
+            lambda x: frozenset(map(cudf_dtype_from_pydata_dtype, x)),
+            selection,
+        )
+
+        # can't both include AND exclude!
+        if not include.isdisjoint(exclude):
+            raise ValueError(
+                f"include and exclude overlap on {(include & exclude)}"
+            )
+
+        # include all subtypes
+        include_subtypes = set()
+        for dtype in self.dtypes:
+            for i_dtype in include:
+                # category handling
+                if is_categorical_dtype(i_dtype):
+                    include_subtypes.add(i_dtype)
+                elif inspect.isclass(dtype.type):
+                    if issubclass(dtype.type, i_dtype):
+                        include_subtypes.add(dtype.type)
+
+        # exclude all subtypes
+        exclude_subtypes = set()
+        for dtype in self.dtypes:
+            for e_dtype in exclude:
+                # category handling
+                if is_categorical_dtype(e_dtype):
+                    exclude_subtypes.add(e_dtype)
+                elif inspect.isclass(dtype.type):
+                    if issubclass(dtype.type, e_dtype):
+                        exclude_subtypes.add(dtype.type)
+
+        include_all = {cudf_dtype_from_pydata_dtype(d) for d in self.dtypes}
+
+        if include:
+            inclusion = include_all & include_subtypes
+        elif exclude:
+            inclusion = include_all
+        else:
+            inclusion = set()
+        # remove all exclude types
+        inclusion = inclusion - exclude_subtypes
+
+        for k, col in self._data.items():
+            infered_type = cudf_dtype_from_pydata_dtype(col.dtype)
+            if infered_type in inclusion:
+                df._insert(len(df._data), k, col)
+
+        return df
+
+    @ioutils.doc_to_parquet()
+    def to_parquet(
+        self,
+        path,
+        engine="cudf",
+        compression="snappy",
+        index=None,
+        partition_cols=None,
+        partition_file_name=None,
+        partition_offsets=None,
+        statistics="ROWGROUP",
+        metadata_file_path=None,
+        int96_timestamps=False,
+        row_group_size_bytes=ioutils._ROW_GROUP_SIZE_BYTES_DEFAULT,
+        row_group_size_rows=None,
+        max_page_size_bytes=None,
+        max_page_size_rows=None,
+        storage_options=None,
+        return_metadata=False,
+        *args,
+        **kwargs,
+    ):
+        """{docstring}"""
+        from cudf.io import parquet
+
+        return parquet.to_parquet(
+            self,
+            path=path,
+            engine=engine,
+            compression=compression,
+            index=index,
+            partition_cols=partition_cols,
+            partition_file_name=partition_file_name,
+            partition_offsets=partition_offsets,
+            statistics=statistics,
+            metadata_file_path=metadata_file_path,
+            int96_timestamps=int96_timestamps,
+            row_group_size_bytes=row_group_size_bytes,
+            row_group_size_rows=row_group_size_rows,
+            max_page_size_bytes=max_page_size_bytes,
+            max_page_size_rows=max_page_size_rows,
+            storage_options=storage_options,
+            return_metadata=return_metadata,
+            *args,
+            **kwargs,
+        )
+
+    @ioutils.doc_to_feather()
+    def to_feather(self, path, *args, **kwargs):
+        """{docstring}"""
+        from cudf.io import feather
+
+        feather.to_feather(self, path, *args, **kwargs)
+
+    @ioutils.doc_dataframe_to_csv()
+    def to_csv(
+        self,
+        path_or_buf=None,
+        sep=",",
+        na_rep="",
+        columns=None,
+        header=True,
+        index=True,
+        encoding=None,
+        compression=None,
+        lineterminator=None,
+        chunksize=None,
+        storage_options=None,
+    ):
+        """{docstring}"""
+        from cudf.io import csv
+
+        if lineterminator is None:
+            lineterminator = os.linesep
+        return csv.to_csv(
+            self,
+            path_or_buf=path_or_buf,
+            sep=sep,
+            na_rep=na_rep,
+            columns=columns,
+            header=header,
+            index=index,
+            lineterminator=lineterminator,
+            chunksize=chunksize,
+            encoding=encoding,
+            compression=compression,
+            storage_options=storage_options,
+        )
+
+    @ioutils.doc_to_orc()
+    def to_orc(
+        self,
+        fname,
+        compression="snappy",
+        statistics="ROWGROUP",
+        stripe_size_bytes=None,
+        stripe_size_rows=None,
+        row_index_stride=None,
+        cols_as_map_type=None,
+        storage_options=None,
+        index=None,
+    ):
+        """{docstring}"""
+        from cudf.io import orc
+
+        return orc.to_orc(
+            df=self,
+            fname=fname,
+            compression=compression,
+            statistics=statistics,
+            stripe_size_bytes=stripe_size_bytes,
+            stripe_size_rows=stripe_size_rows,
+            row_index_stride=row_index_stride,
+            cols_as_map_type=cols_as_map_type,
+            storage_options=storage_options,
+            index=index,
+        )
+
+    @_cudf_nvtx_annotate
+    def stack(self, level=-1, dropna=True):
+        """Stack the prescribed level(s) from columns to index
+
+        Return a reshaped DataFrame or Series having a multi-level
+        index with one or more new inner-most levels compared to
+        the current DataFrame. The new inner-most levels are created
+        by pivoting the columns of the current dataframe:
+
+          - if the columns have a single level, the output is a Series;
+          - if the columns have multiple levels, the new index
+            level(s) is (are) taken from the prescribed level(s) and
+            the output is a DataFrame.
+
+        Parameters
+        ----------
+        level : int, str, list default -1
+            Level(s) to stack from the column axis onto the index axis,
+            defined as one index or label, or a list of indices or labels.
+        dropna : bool, default True
+            Whether to drop rows in the resulting Frame/Series with missing
+            values. When multiple levels are specified, `dropna==False` is
+            unsupported.
+
+        Returns
+        -------
+        DataFrame or Series
+            Stacked dataframe or series.
+
+        See Also
+        --------
+        DataFrame.unstack : Unstack prescribed level(s) from index axis
+             onto column axis.
+        DataFrame.pivot : Reshape dataframe from long format to wide
+             format.
+        DataFrame.pivot_table : Create a spreadsheet-style pivot table
+             as a DataFrame.
+
+        Notes
+        -----
+        The function is named by analogy with a collection of books
+        being reorganized from being side by side on a horizontal
+        position (the columns of the dataframe) to being stacked
+        vertically on top of each other (in the index of the
+        dataframe).
+
+        Examples
+        --------
+        **Single level columns**
+
+        >>> df_single_level_cols = cudf.DataFrame([[0, 1], [2, 3]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=['weight', 'height'])
+
+        Stacking a dataframe with a single level column axis returns a Series:
+
+        >>> df_single_level_cols
+             weight height
+        cat       0      1
+        dog       2      3
+        >>> df_single_level_cols.stack()
+        cat  height    1
+             weight    0
+        dog  height    3
+             weight    2
+        dtype: int64
+
+        **Multi level columns: simple case**
+
+        >>> import pandas as pd
+        >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
+        ...                                        ('weight', 'pounds')])
+        >>> df_multi_level_cols1 = cudf.DataFrame([[1, 2], [2, 4]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=multicol1)
+
+        Stacking a dataframe with a multi-level column axis:
+
+        >>> df_multi_level_cols1
+             weight
+                 kg    pounds
+        cat       1        2
+        dog       2        4
+        >>> df_multi_level_cols1.stack()
+                    weight
+        cat kg           1
+            pounds       2
+        dog kg           2
+            pounds       4
+
+        **Missing values**
+
+        >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
+        ...                                        ('height', 'm')])
+        >>> df_multi_level_cols2 = cudf.DataFrame([[1.0, 2.0], [3.0, 4.0]],
+        ...                                     index=['cat', 'dog'],
+        ...                                     columns=multicol2)
+
+        It is common to have missing values when stacking a dataframe
+        with multi-level columns, as the stacked dataframe typically
+        has more values than the original dataframe. Missing values
+        are filled with NULLs:
+
+        >>> df_multi_level_cols2
+            weight height
+                kg      m
+        cat    1.0    2.0
+        dog    3.0    4.0
+        >>> df_multi_level_cols2.stack()
+            height weight
+        cat kg   <NA>    1.0
+            m     2.0   <NA>
+        dog kg   <NA>    3.0
+            m     4.0   <NA>
+
+        **Prescribing the level(s) to be stacked**
+
+        The first parameter controls which level or levels are stacked:
+
+        >>> df_multi_level_cols2.stack(0)
+                    kg     m
+        cat height  <NA>   2.0
+            weight   1.0  <NA>
+        dog height  <NA>   4.0
+            weight   3.0  <NA>
+
+        >>> df_multi_level_cols2.stack([0, 1])
+        cat  height  m     2.0
+             weight  kg    1.0
+        dog  height  m     4.0
+             weight  kg    3.0
+        dtype: float64
+        """
+
+        if isinstance(level, (int, str)):
+            level = [level]
+        elif isinstance(level, list):
+            if not all(isinstance(lv, (int, str)) for lv in level):
+                raise ValueError(
+                    "level must be either an int/str, or a list of int/str."
+                )
+        else:
+            raise ValueError(
+                "level must be either an int/str, or a list of int/str."
+            )
+
+        level = [level] if not isinstance(level, list) else level
+
+        if len(level) > 1 and not dropna:
+            raise NotImplementedError(
+                "When stacking multiple levels, setting `dropna` to False "
+                "will generate new column combination that does not exist "
+                "in original dataframe. This behavior is unsupported in "
+                "cuDF. See pandas deprecation note: "
+                "https://github.com/pandas-dev/pandas/issues/53515"
+            )
+
+        # Compute the columns to stack based on specified levels
+
+        level_indices: list[int] = []
+
+        # If all passed in level names match up to the dataframe column's level
+        # names, cast them to indices
+        if all(lv in self._data.level_names for lv in level):
+            level_indices = [self._data.level_names.index(lv) for lv in level]
+        elif not all(isinstance(lv, int) for lv in level):
+            raise ValueError(
+                "`level` must either be a list of names or positions, not a "
+                "mixture of both."
+            )
+        else:
+            # Must be a list of positions, normalize negative positions
+            level_indices = [
+                lv + self._data.nlevels if lv < 0 else lv for lv in level
+            ]
+
+        unnamed_levels_indices = [
+            i for i in range(self._data.nlevels) if i not in level_indices
+        ]
+        has_unnamed_levels = len(unnamed_levels_indices) > 0
+
+        column_name_idx = self._data.to_pandas_index()
+        # Construct new index from the levels specified by `level`
+        named_levels = pd.MultiIndex.from_arrays(
+            [column_name_idx.get_level_values(lv) for lv in level_indices]
+        )
+
+        # Since `level` may only specify a subset of all levels, `unique()` is
+        # required to remove duplicates. In pandas, the order of the keys in
+        # the specified levels are always sorted.
+        unique_named_levels = named_levels.unique().sort_values()
+
+        # Each index from the original dataframe should repeat by the number
+        # of unique values in the named_levels
+        repeated_index = self.index.repeat(len(unique_named_levels))
+
+        # Each column name should tile itself by len(df) times
+        tiled_index = libcudf.reshape.tile(
+            [
+                as_column(unique_named_levels.get_level_values(i))
+                for i in range(unique_named_levels.nlevels)
+            ],
+            self.shape[0],
+        )
+
+        # Assemble the final index
+        new_index_columns = [*repeated_index._columns, *tiled_index]
+        index_names = [*self._index.names, *unique_named_levels.names]
+        new_index = MultiIndex.from_frame(
+            DataFrame._from_data(
+                dict(zip(range(0, len(new_index_columns)), new_index_columns))
+            ),
+            names=index_names,
+        )
+
+        # Compute the column indices that serves as the input for
+        # `interleave_columns`
+        column_idx_df = pd.DataFrame(
+            data=range(len(self._data)), index=named_levels
+        )
+
+        column_indices: list[list[int]] = []
+        if has_unnamed_levels:
+            unnamed_level_values = list(
+                map(column_name_idx.get_level_values, unnamed_levels_indices)
+            )
+            unnamed_level_values = pd.MultiIndex.from_arrays(
+                unnamed_level_values
+            )
+
+        def unnamed_group_generator():
+            if has_unnamed_levels:
+                for _, grpdf in column_idx_df.groupby(by=unnamed_level_values):
+                    # When stacking part of the levels, some combinations
+                    # of keys may not be present in this group but can be
+                    # present in others. Reindexing with the globally computed
+                    # `unique_named_levels` assigns -1 to these key
+                    # combinations, representing an all-null column that
+                    # is used in the subsequent libcudf call.
+                    yield grpdf.reindex(
+                        unique_named_levels, axis=0, fill_value=-1
+                    ).sort_index().values
+            else:
+                yield column_idx_df.sort_index().values
+
+        column_indices = list(unnamed_group_generator())
+
+        # For each of the group constructed from the unnamed levels,
+        # invoke `interleave_columns` to stack the values.
+        stacked = []
+
+        for column_idx in column_indices:
+            # Collect columns based on indices, append None for -1 indices.
+            columns = [
+                None if i == -1 else self._data.select_by_index(i).columns[0]
+                for i in column_idx
+            ]
+
+            # Collect datatypes and cast columns as that type
+            common_type = np.result_type(
+                *(col.dtype for col in columns if col is not None)
+            )
+
+            all_nulls = functools.cache(
+                functools.partial(
+                    column_empty, self.shape[0], common_type, masked=True
+                )
+            )
+
+            # homogenize the dtypes of the columns
+            homogenized = [
+                col.astype(common_type) if col is not None else all_nulls()
+                for col in columns
+            ]
+
+            stacked.append(libcudf.reshape.interleave_columns(homogenized))
+
+        # Construct the resulting dataframe / series
+        if not has_unnamed_levels:
+            result = Series._from_data(
+                data={None: stacked[0]}, index=new_index
+            )
+        else:
+            if unnamed_level_values.nlevels == 1:
+                unnamed_level_values = unnamed_level_values.get_level_values(0)
+            unnamed_level_values = unnamed_level_values.unique().sort_values()
+
+            data = ColumnAccessor(
+                dict(zip(unnamed_level_values, stacked)),
+                isinstance(unnamed_level_values, pd.MultiIndex),
+                unnamed_level_values.names,
+            )
+
+            result = DataFrame._from_data(data, index=new_index)
+
+        if dropna:
+            return result.dropna(how="all")
+        else:
+            return result
+
+    @_cudf_nvtx_annotate
+    def cov(self, **kwargs):
+        """Compute the covariance matrix of a DataFrame.
+
+        Parameters
+        ----------
+        **kwargs
+            Keyword arguments to be passed to cupy.cov
+
+        Returns
+        -------
+        cov : DataFrame
+        """
+        cov = cupy.cov(self.values, rowvar=False)
+        cols = self._data.to_pandas_index()
+        df = DataFrame(cupy.asfortranarray(cov)).set_index(cols)
+        df._set_column_names_like(self)
+        return df
+
+    def corr(self, method="pearson", min_periods=None):
+        """Compute the correlation matrix of a DataFrame.
+
+        Parameters
+        ----------
+        method : {'pearson', 'spearman'}, default 'pearson'
+            Method used to compute correlation:
+
+            - pearson : Standard correlation coefficient
+            - spearman : Spearman rank correlation
+
+        min_periods : int, optional
+            Minimum number of observations required per pair of columns to
+            have a valid result.
+
+        Returns
+        -------
+        DataFrame
+            The requested correlation matrix.
+        """
+        if method == "pearson":
+            values = self.values
+        elif method == "spearman":
+            values = self.rank().values
+        else:
+            raise ValueError("method must be either 'pearson', 'spearman'")
+
+        if min_periods is not None:
+            raise NotImplementedError("Unsupported argument 'min_periods'")
+
+        corr = cupy.corrcoef(values, rowvar=False)
+        cols = self._data.to_pandas_index()
+        df = DataFrame(cupy.asfortranarray(corr)).set_index(cols)
+        df._set_column_names_like(self)
+        return df
+
+    @_cudf_nvtx_annotate
+    def to_struct(self, name=None):
+        """
+        Return a struct Series composed of the columns of the DataFrame.
+
+        Parameters
+        ----------
+        name: optional
+            Name of the resulting Series
+
+        Notes
+        -----
+        Note that a copy of the columns is made.
+        """
+        if not all(isinstance(name, str) for name in self._data.names):
+            warnings.warn(
+                "DataFrame contains non-string column name(s). Struct column "
+                "requires field name to be string. Non-string column names "
+                "will be casted to string as the field name."
+            )
+        field_names = [str(name) for name in self._data.names]
+
+        col = cudf.core.column.build_struct_column(
+            names=field_names,
+            children=tuple(col.copy(deep=True) for col in self._data.columns),
+            size=len(self),
+        )
+        return cudf.Series._from_data(
+            cudf.core.column_accessor.ColumnAccessor({name: col}),
+            index=self.index,
+            name=name,
+        )
+
+    @_cudf_nvtx_annotate
+    def keys(self):
+        """
+        Get the columns.
+        This is index for Series, columns for DataFrame.
+
+        Returns
+        -------
+        Index
+            Columns of DataFrame.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'one' : [1, 2, 3], 'five' : ['a', 'b', 'c']})
+        >>> df
+           one five
+        0    1    a
+        1    2    b
+        2    3    c
+        >>> df.keys()
+        Index(['one', 'five'], dtype='object')
+        >>> df = cudf.DataFrame(columns=[0, 1, 2, 3])
+        >>> df
+        Empty DataFrame
+        Columns: [0, 1, 2, 3]
+        Index: []
+        >>> df.keys()
+        Int64Index([0, 1, 2, 3], dtype='int64')
+        """
+        return self._data.to_pandas_index()
+
+    def itertuples(self, index=True, name="Pandas"):
+        """
+        Iteration is unsupported.
+
+        See :ref:`iteration <pandas-comparison/iteration>` for more
+        information.
+        """
+        raise TypeError(
+            "cuDF does not support iteration of DataFrame "
+            "via itertuples. Consider using "
+            "`.to_pandas().itertuples()` "
+            "if you wish to iterate over namedtuples."
+        )
+
+    def iterrows(self):
+        """
+        Iteration is unsupported.
+
+        See :ref:`iteration <pandas-comparison/iteration>` for more
+        information.
+        """
+        raise TypeError(
+            "cuDF does not support iteration of DataFrame "
+            "via iterrows. Consider using "
+            "`.to_pandas().iterrows()` "
+            "if you wish to iterate over each row."
+        )
+
+    @_cudf_nvtx_annotate
+    def append(
+        self, other, ignore_index=False, verify_integrity=False, sort=False
+    ):
+        """
+        Append rows of `other` to the end of caller, returning a new object.
+        Columns in `other` that are not in the caller are added as new columns.
+
+        Parameters
+        ----------
+        other : DataFrame or Series/dict-like object, or list of these
+            The data to append.
+        ignore_index : bool, default False
+            If True, do not use the index labels.
+        sort : bool, default False
+            Sort columns ordering if the columns of
+            `self` and `other` are not aligned.
+        verify_integrity : bool, default False
+            This Parameter is currently not supported.
+
+        Returns
+        -------
+        DataFrame
+
+        See Also
+        --------
+        cudf.concat : General function to concatenate DataFrame or
+            objects.
+
+        Notes
+        -----
+        If a list of dict/series is passed and the keys are all contained in
+        the DataFrame's index, the order of the columns in the resulting
+        DataFrame will be unchanged.
+        Iteratively appending rows to a cudf DataFrame can be more
+        computationally intensive than a single concatenate. A better
+        solution is to append those rows to a list and then concatenate
+        the list with the original DataFrame all at once.
+        `verify_integrity` parameter is not supported yet.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
+        >>> df
+           A  B
+        0  1  2
+        1  3  4
+        >>> df2 = cudf.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
+        >>> df2
+           A  B
+        0  5  6
+        1  7  8
+        >>> df.append(df2)
+           A  B
+        0  1  2
+        1  3  4
+        0  5  6
+        1  7  8
+
+        With `ignore_index` set to True:
+
+        >>> df.append(df2, ignore_index=True)
+           A  B
+        0  1  2
+        1  3  4
+        2  5  6
+        3  7  8
+
+        The following, while not recommended methods for generating DataFrames,
+        show two ways to generate a DataFrame from multiple data sources.
+        Less efficient:
+
+        >>> df = cudf.DataFrame(columns=['A'])
+        >>> for i in range(5):
+        ...     df = df.append({'A': i}, ignore_index=True)
+        >>> df
+           A
+        0  0
+        1  1
+        2  2
+        3  3
+        4  4
+
+        More efficient than above:
+
+        >>> cudf.concat([cudf.DataFrame([i], columns=['A']) for i in range(5)],
+        ...           ignore_index=True)
+           A
+        0  0
+        1  1
+        2  2
+        3  3
+        4  4
+        """
+        if isinstance(other, dict):
+            if not ignore_index:
+                raise TypeError("Can only append a dict if ignore_index=True")
+            other = DataFrame(other)
+        elif isinstance(other, Series):
+            if other.name is None and not ignore_index:
+                raise TypeError(
+                    "Can only append a Series if ignore_index=True "
+                    "or if the Series has a name"
+                )
+
+            current_cols = self._data.to_pandas_index()
+            combined_columns = other.index.to_pandas()
+            if len(current_cols):
+                if cudf.utils.dtypes.is_mixed_with_object_dtype(
+                    current_cols, combined_columns
+                ):
+                    raise TypeError(
+                        "cudf does not support mixed types, please type-cast "
+                        "the column index of dataframe and index of series "
+                        "to same dtypes."
+                    )
+
+                combined_columns = current_cols.union(
+                    combined_columns, sort=False
+                )
+
+            if sort:
+                combined_columns = combined_columns.sort_values()
+
+            other = other.reindex(combined_columns, copy=False).to_frame().T
+            if not current_cols.equals(combined_columns):
+                self = self.reindex(columns=combined_columns)
+        elif (
+            isinstance(other, list)
+            and other
+            and not isinstance(other[0], DataFrame)
+        ):
+            other = DataFrame(other)
+            cols = self._data.to_pandas_index()
+            if (cols.get_indexer(other._data.to_pandas_index()) >= 0).all():
+                other = other.reindex(columns=cols)
+
+        return super()._append(other, ignore_index, verify_integrity, sort)
+
+    @_cudf_nvtx_annotate
+    @copy_docstring(reshape.pivot)
+    def pivot(self, index, columns, values=None):
+        return cudf.core.reshape.pivot(
+            self, index=index, columns=columns, values=values
+        )
+
+    @_cudf_nvtx_annotate
+    @copy_docstring(reshape.pivot_table)
+    def pivot_table(
+        self,
+        values=None,
+        index=None,
+        columns=None,
+        aggfunc="mean",
+        fill_value=None,
+        margins=False,
+        dropna=None,
+        margins_name="All",
+        observed=False,
+        sort=True,
+    ):
+        return cudf.core.reshape.pivot_table(
+            self,
+            values=values,
+            index=index,
+            columns=columns,
+            aggfunc=aggfunc,
+            fill_value=fill_value,
+            margins=margins,
+            dropna=dropna,
+            margins_name=margins_name,
+            observed=observed,
+            sort=sort,
+        )
+
+    @_cudf_nvtx_annotate
+    @copy_docstring(reshape.unstack)
+    def unstack(self, level=-1, fill_value=None):
+        return cudf.core.reshape.unstack(
+            self, level=level, fill_value=fill_value
+        )
+
+    @_cudf_nvtx_annotate
+    def explode(self, column, ignore_index=False):
+        """
+        Transform each element of a list-like to a row, replicating index
+        values.
+
+        Parameters
+        ----------
+        column : str
+            Column to explode.
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({
+        ...     "a": [[1, 2, 3], [], None, [4, 5]],
+        ...     "b": [11, 22, 33, 44],
+        ... })
+        >>> df
+                   a   b
+        0  [1, 2, 3]  11
+        1         []  22
+        2       None  33
+        3     [4, 5]  44
+        >>> df.explode('a')
+              a   b
+        0     1  11
+        0     2  11
+        0     3  11
+        1  <NA>  22
+        2  <NA>  33
+        3     4  44
+        3     5  44
+        """
+        if column not in self._column_names:
+            raise KeyError(column)
+
+        return super()._explode(column, ignore_index)
+
+    def pct_change(
+        self, periods=1, fill_method="ffill", limit=None, freq=None
+    ):
+        """
+        Calculates the percent change between sequential elements
+        in the DataFrame.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for forming percent change.
+        fill_method : str, default 'ffill'
+            How to handle NAs before computing percent changes.
+        limit : int, optional
+            The number of consecutive NAs to fill before stopping.
+            Not yet implemented.
+        freq : str, optional
+            Increment to use from time series API.
+            Not yet implemented.
+
+        Returns
+        -------
+        DataFrame
+        """
+        if limit is not None:
+            raise NotImplementedError("limit parameter not supported yet.")
+        if freq is not None:
+            raise NotImplementedError("freq parameter not supported yet.")
+        elif fill_method not in {"ffill", "pad", "bfill", "backfill"}:
+            raise ValueError(
+                "fill_method must be one of 'ffill', 'pad', "
+                "'bfill', or 'backfill'."
+            )
+
+        data = self.fillna(method=fill_method, limit=limit)
+
+        return data.diff(periods=periods) / data.shift(
+            periods=periods, freq=freq
+        )
+
+    def __dataframe__(
+        self, nan_as_null: bool = False, allow_copy: bool = True
+    ):
+        return df_protocol.__dataframe__(
+            self, nan_as_null=nan_as_null, allow_copy=allow_copy
+        )
+
+    def nunique(self, axis=0, dropna=True):
+        """
+        Count number of distinct elements in specified axis.
+        Return Series with number of distinct elements. Can ignore NaN values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
+            column-wise.
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
+        >>> df.nunique()
+        A    3
+        B    2
+        dtype: int64
+        """
+        if axis != 0:
+            raise NotImplementedError("axis parameter is not supported yet.")
+
+        return cudf.Series(super().nunique(dropna=dropna))
+
+    def _sample_axis_1(
+        self,
+        n: int,
+        weights: Optional[ColumnLike],
+        replace: bool,
+        random_state: np.random.RandomState,
+        ignore_index: bool,
+    ):
+        if replace:
+            # Since cuDF does not support multiple columns with same name,
+            # sample with replace=True at axis 1 is unsupported.
+            raise NotImplementedError(
+                "Sample is not supported for axis 1/`columns` when"
+                "`replace=True`."
+            )
+
+        sampled_column_labels = random_state.choice(
+            self._column_names, size=n, replace=False, p=weights
+        )
+
+        result = self._get_columns_by_label(sampled_column_labels)
+        if ignore_index:
+            result.reset_index(drop=True)
+
+        return result
+
+    def _from_columns_like_self(
+        self,
+        columns: List[ColumnBase],
+        column_names: abc.Iterable[str],
+        index_names: Optional[List[str]] = None,
+        *,
+        override_dtypes: Optional[abc.Iterable[Optional[Dtype]]] = None,
+    ) -> DataFrame:
+        result = super()._from_columns_like_self(
+            columns,
+            column_names,
+            index_names,
+            override_dtypes=override_dtypes,
+        )
+        result._set_column_names_like(self)
+        return result
+
+    @_cudf_nvtx_annotate
+    def interleave_columns(self):
+        """
+        Interleave Series columns of a table into a single column.
+
+        Converts the column major table `cols` into a row major column.
+
+        Parameters
+        ----------
+        cols : input Table containing columns to interleave.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({0: ['A1', 'A2', 'A3'], 1: ['B1', 'B2', 'B3']})
+        >>> df
+            0   1
+        0  A1  B1
+        1  A2  B2
+        2  A3  B3
+        >>> df.interleave_columns()
+        0    A1
+        1    B1
+        2    A2
+        3    B2
+        4    A3
+        5    B3
+        dtype: object
+
+        Returns
+        -------
+        The interleaved columns as a single column
+        """
+        if ("category" == self.dtypes).any():
+            raise ValueError(
+                "interleave_columns does not support 'category' dtype."
+            )
+
+        return self._constructor_sliced._from_data(
+            {None: libcudf.reshape.interleave_columns([*self._columns])}
+        )
+
+    @_cudf_nvtx_annotate
+    def eval(self, expr: str, inplace: bool = False, **kwargs):
+        """Evaluate a string describing operations on DataFrame columns.
+
+        Operates on columns only, not specific rows or elements.
+
+        Parameters
+        ----------
+        expr : str
+            The expression string to evaluate.
+        inplace : bool, default False
+            If the expression contains an assignment, whether to perform the
+            operation inplace and mutate the existing DataFrame. Otherwise,
+            a new DataFrame is returned.
+        **kwargs
+            Not supported.
+
+        Returns
+        -------
+        DataFrame, Series, or None
+            Series if a single column is returned (the typical use case),
+            DataFrame if any assignment statements are included in
+            ``expr``, or None if ``inplace=True``.
+
+        Notes
+        -----
+        Difference from pandas:
+            * Additional kwargs are not supported.
+            * Bitwise and logical operators are not dtype-dependent.
+              Specifically, `&` must be used for bitwise operators on integers,
+              not `and`, which is specifically for the logical and between
+              booleans.
+            * Only numerical types currently support all operators.
+            * String types currently support comparison operators.
+            * Operators generally will not cast automatically. Users are
+              responsible for casting columns to suitable types before
+              evaluating a function.
+            * Multiple assignments to the same name (i.e. a sequence of
+              assignment statements where later statements are conditioned upon
+              the output of earlier statements) is not supported.
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
+        >>> df
+           A   B
+        0  1  10
+        1  2   8
+        2  3   6
+        3  4   4
+        4  5   2
+        >>> df.eval('A + B')
+        0    11
+        1    10
+        2     9
+        3     8
+        4     7
+        dtype: int64
+
+        Assignment is allowed though by default the original DataFrame is not
+        modified.
+
+        >>> df.eval('C = A + B')
+           A   B   C
+        0  1  10  11
+        1  2   8  10
+        2  3   6   9
+        3  4   4   8
+        4  5   2   7
+        >>> df
+           A   B
+        0  1  10
+        1  2   8
+        2  3   6
+        3  4   4
+        4  5   2
+
+        Use ``inplace=True`` to modify the original DataFrame.
+
+        >>> df.eval('C = A + B', inplace=True)
+        >>> df
+           A   B   C
+        0  1  10  11
+        1  2   8  10
+        2  3   6   9
+        3  4   4   8
+        4  5   2   7
+
+        Multiple columns can be assigned to using multi-line expressions:
+
+        >>> df.eval(
+        ...     '''
+        ... C = A + B
+        ... D = A - B
+        ... '''
+        ... )
+           A   B   C  D
+        0  1  10  11 -9
+        1  2   8  10 -6
+        2  3   6   9 -3
+        3  4   4   8  0
+        4  5   2   7  3
+        """
+        if kwargs:
+            raise ValueError(
+                "Keyword arguments other than `inplace` are not supported"
+            )
+
+        # Have to use a regex match to avoid capturing ==, >=, or <=
+        equals_sign_regex = "[^=><]=[^=]"
+        includes_assignment = re.search(equals_sign_regex, expr) is not None
+
+        # Check if there were multiple statements. Filter out empty lines.
+        statements = tuple(filter(None, expr.strip().split("\n")))
+        if len(statements) > 1 and any(
+            re.search(equals_sign_regex, st) is None for st in statements
+        ):
+            raise ValueError(
+                "Multi-line expressions are only valid if all expressions "
+                "contain an assignment."
+            )
+
+        if not includes_assignment:
+            if inplace:
+                raise ValueError(
+                    "Cannot operate inplace if there is no assignment"
+                )
+            return Series._from_data(
+                {
+                    None: libcudf.transform.compute_column(
+                        [*self._columns], self._column_names, statements[0]
+                    )
+                }
+            )
+
+        targets = []
+        exprs = []
+        for st in statements:
+            try:
+                t, e = re.split("[^=]=[^=]", st)
+            except ValueError as err:
+                if "too many values" in str(err):
+                    raise ValueError(
+                        f"Statement {st} contains too many assignments ('=')"
+                    )
+                raise
+            targets.append(t.strip())
+            exprs.append(e.strip())
+
+        cols = (
+            libcudf.transform.compute_column(
+                [*self._columns], self._column_names, e
+            )
+            for e in exprs
+        )
+        ret = self if inplace else self.copy(deep=False)
+        for name, col in zip(targets, cols):
+            ret._data[name] = col
+        if not inplace:
+            return ret
+
+    def value_counts(
+        self,
+        subset=None,
+        normalize=False,
+        sort=True,
+        ascending=False,
+        dropna=True,
+    ):
+        """
+        Return a Series containing counts of unique rows in the DataFrame.
+
+        Parameters
+        ----------
+        subset: list-like, optional
+            Columns to use when counting unique combinations.
+        normalize: bool, default False
+            Return proportions rather than frequencies.
+        sort: bool, default True
+            Sort by frequencies.
+        ascending: bool, default False
+            Sort in ascending order.
+        dropna: bool, default True
+            Don't include counts of rows that contain NA values.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        The returned Series will have a MultiIndex with one level per input
+        column. By default, rows that contain any NA values are omitted from
+        the result. By default, the resulting Series will be in descending
+        order so that the first element is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'num_legs': [2, 4, 4, 6],
+        ...                    'num_wings': [2, 0, 0, 0]},
+        ...                    index=['falcon', 'dog', 'cat', 'ant'])
+        >>> df.value_counts()
+        num_legs  num_wings
+        4         0            2
+        2         2            1
+        6         0            1
+        dtype: int64
+        """
+        if subset:
+            diff = set(subset) - set(self._data)
+            if len(diff) != 0:
+                raise KeyError(f"columns {diff} do not exist")
+        columns = list(self._data.names) if subset is None else subset
+        result = (
+            self.groupby(
+                by=columns,
+                dropna=dropna,
+            )
+            .size()
+            .astype("int64")
+        )
+        if sort:
+            result = result.sort_values(ascending=ascending)
+        if normalize:
+            result = result / result._column.sum()
+        # Pandas always returns MultiIndex even if only one column.
+        if not isinstance(result.index, MultiIndex):
+            result.index = MultiIndex._from_data(result._index._data)
+        return result
+
+
+def from_dataframe(df, allow_copy=False):
+    return df_protocol.from_dataframe(df, allow_copy=allow_copy)
+
+
+def make_binop_func(op, postprocess=None):
+    # This function is used to wrap binary operations in Frame with an
+    # appropriate API for DataFrame as required for pandas compatibility. The
+    # main effect is reordering and error-checking parameters in
+    # DataFrame-specific ways. The postprocess argument is a callable that may
+    # optionally be provided to modify the result of the binop if additional
+    # processing is needed for pandas compatibility. The callable must have the
+    # signature
+    # def postprocess(left, right, output)
+    # where left and right are the inputs to the binop and output is the result
+    # of calling the wrapped Frame binop.
+    wrapped_func = getattr(IndexedFrame, op)
+
+    @functools.wraps(wrapped_func)
+    def wrapper(self, other, axis="columns", level=None, fill_value=None):
+        if axis not in (1, "columns"):
+            raise NotImplementedError("Only axis=1 supported at this time.")
+        output = wrapped_func(self, other, axis, level, fill_value)
+        if postprocess is None:
+            return output
+        return postprocess(self, other, output)
+
+    # functools.wraps copies module level attributes to `wrapper` and sets
+    # __wrapped__ attributes to `wrapped_func`. Cpython looks up the signature
+    # string of a function by recursively delving into __wrapped__ until
+    # it hits the first function that has __signature__ attribute set. To make
+    # the signature string of `wrapper` matches with its actual parameter list,
+    # we directly set the __signature__ attribute of `wrapper` below.
+
+    new_sig = inspect.signature(
+        lambda self, other, axis="columns", level=None, fill_value=None: None
+    )
+
+    wrapper.__signature__ = new_sig
+    return wrapper
+
+
+# Wrap arithmetic Frame binop functions with the expected API for Series.
+for binop in [
+    "add",
+    "radd",
+    "subtract",
+    "sub",
+    "rsub",
+    "multiply",
+    "mul",
+    "rmul",
+    "mod",
+    "rmod",
+    "pow",
+    "rpow",
+    "floordiv",
+    "rfloordiv",
+    "truediv",
+    "div",
+    "divide",
+    "rtruediv",
+    "rdiv",
+]:
+    setattr(DataFrame, binop, make_binop_func(binop))
+
+
+def _make_replacement_func(value):
+    # This function generates a postprocessing function suitable for use with
+    # make_binop_func that fills null columns with the desired fill value.
+
+    def func(left, right, output):
+        # This function may be passed as the postprocess argument to
+        # make_binop_func. Columns that are only present in one of the inputs
+        # will be null in the output. This function postprocesses the output to
+        # replace those nulls with some desired output.
+        if isinstance(right, Series):
+            uncommon_columns = set(left._column_names) ^ set(right.index)
+        elif isinstance(right, DataFrame):
+            uncommon_columns = set(left._column_names) ^ set(
+                right._column_names
+            )
+        elif _is_scalar_or_zero_d_array(right):
+            for name, col in output._data.items():
+                output._data[name] = col.fillna(value)
+            return output
+        else:
+            return output
+
+        for name in uncommon_columns:
+            output._data[name] = column.full(
+                size=len(output), fill_value=value, dtype="bool"
+            )
+        return output
+
+    return func
+
+
+# The ne comparator needs special postprocessing because elements that missing
+# in one operand should be treated as null and result in True in the output
+# rather than simply propagating nulls.
+DataFrame.ne = make_binop_func("ne", _make_replacement_func(True))
+
+
+# All other comparison operators needs return False when one of the operands is
+# missing in the input.
+for binop in [
+    "eq",
+    "lt",
+    "le",
+    "gt",
+    "ge",
+]:
+    setattr(
+        DataFrame, binop, make_binop_func(binop, _make_replacement_func(False))
+    )
+
+
+@_cudf_nvtx_annotate
+def from_pandas(obj, nan_as_null=no_default):
+    """
+    Convert certain Pandas objects into the cudf equivalent.
+
+    Supports DataFrame, Series, Index, or MultiIndex.
+
+    Returns
+    -------
+    DataFrame/Series/Index/MultiIndex
+        Return type depends on the passed input.
+
+    Raises
+    ------
+    TypeError for invalid input type.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> import pandas as pd
+    >>> data = [[0, 1], [1, 2], [3, 4]]
+    >>> pdf = pd.DataFrame(data, columns=['a', 'b'], dtype=int)
+    >>> pdf
+       a  b
+    0  0  1
+    1  1  2
+    2  3  4
+    >>> gdf = cudf.from_pandas(pdf)
+    >>> gdf
+       a  b
+    0  0  1
+    1  1  2
+    2  3  4
+    >>> type(gdf)
+    <class 'cudf.core.dataframe.DataFrame'>
+    >>> type(pdf)
+    <class 'pandas.core.frame.DataFrame'>
+
+    Converting a Pandas Series to cuDF Series:
+
+    >>> psr = pd.Series(['a', 'b', 'c', 'd'], name='apple', dtype='str')
+    >>> psr
+    0    a
+    1    b
+    2    c
+    3    d
+    Name: apple, dtype: object
+    >>> gsr = cudf.from_pandas(psr)
+    >>> gsr
+    0    a
+    1    b
+    2    c
+    3    d
+    Name: apple, dtype: object
+    >>> type(gsr)
+    <class 'cudf.core.series.Series'>
+    >>> type(psr)
+    <class 'pandas.core.series.Series'>
+
+    Converting a Pandas Index to cuDF Index:
+
+    >>> pidx = pd.Index([1, 2, 10, 20])
+    >>> pidx
+    Int64Index([1, 2, 10, 20], dtype='int64')
+    >>> gidx = cudf.from_pandas(pidx)
+    >>> gidx
+    Int64Index([1, 2, 10, 20], dtype='int64')
+    >>> type(gidx)
+    <class 'cudf.core.index.Int64Index'>
+    >>> type(pidx)
+    <class 'pandas.core.indexes.numeric.Int64Index'>
+
+    Converting a Pandas MultiIndex to cuDF MultiIndex:
+
+    >>> pmidx = pd.MultiIndex(
+    ...         levels=[[1, 3, 4, 5], [1, 2, 5]],
+    ...         codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+    ...         names=["x", "y"],
+    ...     )
+    >>> pmidx
+    MultiIndex([(1, 1),
+                (1, 5),
+                (3, 2),
+                (4, 2),
+                (5, 1)],
+               names=['x', 'y'])
+    >>> gmidx = cudf.from_pandas(pmidx)
+    >>> gmidx
+    MultiIndex([(1, 1),
+                (1, 5),
+                (3, 2),
+                (4, 2),
+                (5, 1)],
+               names=['x', 'y'])
+    >>> type(gmidx)
+    <class 'cudf.core.multiindex.MultiIndex'>
+    >>> type(pmidx)
+    <class 'pandas.core.indexes.multi.MultiIndex'>
+    """
+    if nan_as_null is no_default:
+        nan_as_null = (
+            False if cudf.get_option("mode.pandas_compatible") else None
+        )
+
+    if isinstance(obj, pd.DataFrame):
+        return DataFrame.from_pandas(obj, nan_as_null=nan_as_null)
+    elif isinstance(obj, pd.Series):
+        return Series.from_pandas(obj, nan_as_null=nan_as_null)
+    # This carveout for cudf.pandas is undesirable, but fixes crucial issues
+    # for core RAPIDS projects like cuML and cuGraph that rely on
+    # `cudf.from_pandas`, so we allow it for now.
+    elif (ret := getattr(obj, "_fsproxy_wrapped", None)) is not None:
+        return ret
+    elif isinstance(obj, pd.MultiIndex):
+        return MultiIndex.from_pandas(obj, nan_as_null=nan_as_null)
+    elif isinstance(obj, pd.RangeIndex):
+        return cudf.core.index.RangeIndex(
+            start=obj.start, stop=obj.stop, step=obj.step, name=obj.name
+        )
+    elif isinstance(obj, pd.Index):
+        return cudf.Index.from_pandas(obj, nan_as_null=nan_as_null)
+    elif isinstance(obj, pd.CategoricalDtype):
+        return cudf.CategoricalDtype.from_pandas(obj)
+    else:
+        raise TypeError(
+            "from_pandas only accepts Pandas Dataframes, Series, "
+            "Index, RangeIndex and MultiIndex objects. "
+            "Got %s" % type(obj)
+        )
+
+
+@_cudf_nvtx_annotate
+def merge(left, right, *args, **kwargs):
+    if isinstance(left, Series):
+        left = left.to_frame()
+    return left.merge(right, *args, **kwargs)
+
+
+# a bit of fanciness to inject docstring with left parameter
+merge_doc = DataFrame.merge.__doc__
+if merge_doc is not None:
+    idx = merge_doc.find("right")
+    merge.__doc__ = "".join(
+        [
+            merge_doc[:idx],
+            "\n\tleft : Series or DataFrame\n\t",
+            merge_doc[idx:],
+        ]
+    )
+
+
+def _align_indices(lhs, rhs):
+    """
+    Internal util to align the indices of two DataFrames. Returns a tuple of
+    the aligned dataframes, or the original arguments if the indices are the
+    same, or if rhs isn't a DataFrame.
+    """
+    lhs_out, rhs_out = lhs, rhs
+    if isinstance(rhs, DataFrame) and not lhs.index.equals(rhs.index):
+        df = lhs.merge(
+            rhs,
+            sort=True,
+            how="outer",
+            left_index=True,
+            right_index=True,
+            suffixes=("_x", "_y"),
+        )
+        df = df.sort_index()
+        lhs_out = DataFrame(index=df.index)
+        rhs_out = DataFrame(index=df.index)
+        common = set(lhs._column_names) & set(rhs._column_names)
+        common_x = {f"{x}_x": x for x in common}
+        common_y = {f"{x}_y": x for x in common}
+        for col in df._column_names:
+            if col in common_x:
+                lhs_out[common_x[col]] = df[col]
+            elif col in common_y:
+                rhs_out[common_y[col]] = df[col]
+            elif col in lhs:
+                lhs_out[col] = df[col]
+            elif col in rhs:
+                rhs_out[col] = df[col]
+
+    return lhs_out, rhs_out
+
+
+def _setitem_with_dataframe(
+    input_df: DataFrame,
+    replace_df: DataFrame,
+    input_cols: Any = None,
+    mask: Optional[ColumnBase] = None,
+    ignore_index: bool = False,
+):
+    """
+    This function sets item dataframes relevant columns with replacement df
+    :param input_df: Dataframe to be modified inplace
+    :param replace_df: Replacement DataFrame to replace values with
+    :param input_cols: columns to replace in the input dataframe
+    :param mask: boolean mask in case of masked replacing
+    :param ignore_index: Whether to conduct index equality and reindex
+    """
+
+    if input_cols is None:
+        input_cols = input_df._column_names
+
+    if len(input_cols) != len(replace_df._column_names):
+        raise ValueError(
+            "Number of Input Columns must be same replacement Dataframe"
+        )
+
+    if (
+        not ignore_index
+        and len(input_df) != 0
+        and not input_df.index.equals(replace_df.index)
+    ):
+        replace_df = replace_df.reindex(input_df.index)
+
+    for col_1, col_2 in zip(input_cols, replace_df._column_names):
+        if col_1 in input_df._column_names:
+            if mask is not None:
+                input_df._data[col_1][mask] = column.as_column(
+                    replace_df[col_2]
+                )
+            else:
+                input_df._data[col_1] = column.as_column(replace_df[col_2])
+        else:
+            if mask is not None:
+                raise ValueError("Can not insert new column with a bool mask")
+            else:
+                # handle append case
+                input_df._insert(
+                    loc=len(input_df._data),
+                    name=col_1,
+                    value=replace_df[col_2],
+                )
+
+
+def extract_col(df, col):
+    """
+    Extract column from dataframe `df` with their name `col`.
+    If `col` is index and there are no columns with name `index`,
+    then this will return index column.
+    """
+    try:
+        return df._data[col]
+    except KeyError:
+        if (
+            col == "index"
+            and col not in df.index._data
+            and not isinstance(df.index, MultiIndex)
+        ):
+            return df.index._data.columns[0]
+        return df.index._data[col]
+
+
+def _get_union_of_indices(indexes):
+    if len(indexes) == 1:
+        return indexes[0]
+    else:
+        merged_index = cudf.core.index.GenericIndex._concat(indexes)
+        return merged_index.drop_duplicates()
+
+
+def _get_union_of_series_names(series_list):
+    names_list = []
+    unnamed_count = 0
+    for series in series_list:
+        if series.name is None:
+            names_list.append(f"Unnamed {unnamed_count}")
+            unnamed_count += 1
+        else:
+            names_list.append(series.name)
+    if unnamed_count == len(series_list):
+        names_list = range(len(series_list))
+
+    return names_list
+
+
+# Create a dictionary of the common, non-null columns
+def _get_non_null_cols_and_dtypes(col_idxs, list_of_columns):
+    # A mapping of {idx: np.dtype}
+    dtypes = dict()
+    # A mapping of {idx: [...columns]}, where `[...columns]`
+    # is a list of columns with at least one valid value for each
+    # column name across all input frames
+    non_null_columns = dict()
+    for idx in col_idxs:
+        for cols in list_of_columns:
+            # Skip columns not in this frame
+            if idx >= len(cols) or cols[idx] is None:
+                continue
+            # Store the first dtype we find for a column, even if it's
+            # all-null. This ensures we always have at least one dtype
+            # for each name. This dtype will be overwritten later if a
+            # non-null Column with the same name is found.
+            if idx not in dtypes:
+                dtypes[idx] = cols[idx].dtype
+            if cols[idx].valid_count > 0:
+                if idx not in non_null_columns:
+                    non_null_columns[idx] = [cols[idx]]
+                else:
+                    non_null_columns[idx].append(cols[idx])
+    return non_null_columns, dtypes
+
+
+def _find_common_dtypes_and_categories(non_null_columns, dtypes):
+    # A mapping of {idx: categories}, where `categories` is a
+    # column of all the unique categorical values from each
+    # categorical column across all input frames
+    categories = dict()
+    for idx, cols in non_null_columns.items():
+        # default to the first non-null dtype
+        dtypes[idx] = cols[0].dtype
+        # If all the non-null dtypes are int/float, find a common dtype
+        if all(is_numeric_dtype(col.dtype) for col in cols):
+            dtypes[idx] = find_common_type([col.dtype for col in cols])
+        # If all categorical dtypes, combine the categories
+        elif all(
+            isinstance(col, cudf.core.column.CategoricalColumn) for col in cols
+        ):
+            # Combine and de-dupe the categories
+            categories[idx] = cudf.Series(
+                concat_columns([col.categories for col in cols])
+            )._column.unique()
+            # Set the column dtype to the codes' dtype. The categories
+            # will be re-assigned at the end
+            dtypes[idx] = min_scalar_type(len(categories[idx]))
+        # Otherwise raise an error if columns have different dtypes
+        elif not all(is_dtype_equal(c.dtype, dtypes[idx]) for c in cols):
+            raise ValueError("All columns must be the same type")
+    return categories
+
+
+def _cast_cols_to_common_dtypes(col_idxs, list_of_columns, dtypes, categories):
+    # Cast all columns to a common dtype, assign combined categories,
+    # and back-fill missing columns with all-null columns
+    for idx in col_idxs:
+        dtype = dtypes[idx]
+        for cols in list_of_columns:
+            # If column not in this df, fill with an all-null column
+            if idx >= len(cols) or cols[idx] is None:
+                n = len(next(x for x in cols if x is not None))
+                cols[idx] = column_empty(row_count=n, dtype=dtype, masked=True)
+            else:
+                # If column is categorical, rebase the codes with the
+                # combined categories, and cast the new codes to the
+                # min-scalar-sized dtype
+                if idx in categories:
+                    cols[idx] = (
+                        cols[idx]
+                        ._set_categories(
+                            categories[idx],
+                            is_unique=True,
+                        )
+                        .codes
+                    )
+                cols[idx] = cols[idx].astype(dtype)
+
+
+def _reassign_categories(categories, cols, col_idxs):
+    for name, idx in zip(cols, col_idxs):
+        if idx in categories:
+            cols[name] = build_categorical_column(
+                categories=categories[idx],
+                codes=build_column(
+                    cols[name].base_data, dtype=cols[name].dtype
+                ),
+                mask=cols[name].base_mask,
+                offset=cols[name].offset,
+                size=cols[name].size,
+            )
+
+
+def _from_dict_create_index(indexlist, namelist, library):
+    if len(namelist) > 1:
+        index = library.MultiIndex.from_tuples(indexlist, names=namelist)
+    else:
+        index = library.Index(indexlist, name=namelist[0])
+    return index
diff --git a/python/cudf/cudf/core/df_protocol.py b/python/cudf/cudf/core/df_protocol.py
new file mode 100644
index 0000000..6e1c5f6
--- /dev/null
+++ b/python/cudf/cudf/core/df_protocol.py
@@ -0,0 +1,900 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import enum
+from collections import abc
+from typing import (
+    Any,
+    Dict,
+    Iterable,
+    Mapping,
+    Optional,
+    Sequence,
+    Tuple,
+    cast,
+)
+
+import cupy as cp
+import numpy as np
+from numba.cuda import as_cuda_array
+
+import rmm
+
+import cudf
+from cudf.core.buffer import Buffer, as_buffer
+from cudf.core.column import as_column, build_categorical_column, build_column
+
+# Implementation of interchange protocol classes
+# ----------------------------------------------
+
+
+class _DtypeKind(enum.IntEnum):
+    INT = 0
+    UINT = 1
+    FLOAT = 2
+    BOOL = 20
+    STRING = 21  # UTF-8
+    DATETIME = 22
+    CATEGORICAL = 23
+
+
+class _Device(enum.IntEnum):
+    CPU = 1
+    CUDA = 2
+    CPU_PINNED = 3
+    OPENCL = 4
+    VULKAN = 7
+    METAL = 8
+    VPI = 9
+    ROCM = 10
+
+
+class _MaskKind(enum.IntEnum):
+    NON_NULLABLE = 0
+    NAN = 1
+    SENTINEL = 2
+    BITMASK = 3
+    BYTEMASK = 4
+
+
+_SUPPORTED_KINDS = {
+    _DtypeKind.INT,
+    _DtypeKind.UINT,
+    _DtypeKind.FLOAT,
+    _DtypeKind.CATEGORICAL,
+    _DtypeKind.BOOL,
+    _DtypeKind.STRING,
+}
+ProtoDtype = Tuple[_DtypeKind, int, str, str]
+
+
+class _CuDFBuffer:
+    """
+    Data in the buffer is guaranteed to be contiguous in memory.
+    """
+
+    def __init__(
+        self,
+        buf: Buffer,
+        dtype: np.dtype,
+        allow_copy: bool = True,
+    ) -> None:
+        """
+        Use Buffer object.
+        """
+        # Store the cudf buffer where the data resides as a private
+        # attribute, so we can use it to retrieve the public attributes
+        self._buf = buf
+        self._dtype = dtype
+        self._allow_copy = allow_copy
+
+    @property
+    def bufsize(self) -> int:
+        """
+        The Buffer size in bytes.
+        """
+        return self._buf.size
+
+    @property
+    def ptr(self) -> int:
+        """
+        Pointer to start of the buffer as an integer.
+        """
+        return self._buf.get_ptr(mode="write")
+
+    def __dlpack__(self):
+        # DLPack not implemented in NumPy yet, so leave it out here.
+        try:
+            cuda_array = as_cuda_array(self._buf).view(self._dtype)
+            return cp.asarray(cuda_array).toDlpack()
+        except ValueError:
+            raise TypeError(f"dtype {self._dtype} unsupported by `dlpack`")
+
+    def __dlpack_device__(self) -> Tuple[_Device, int]:
+        """
+        _Device type and _Device ID for where the data in the buffer resides.
+        """
+        return (_Device.CUDA, cp.asarray(self._buf).device.id)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(" + str(
+            {
+                "bufsize": self.bufsize,
+                "ptr": self.ptr,
+                "device": self.__dlpack_device__()[0].name,
+            }
+        )
+        +")"
+
+
+class _CuDFColumn:
+    """
+    A column object, with only the methods and properties required by the
+    interchange protocol defined.
+
+    A column can contain one or more chunks. Each chunk can contain up to three
+    buffers - a data buffer, a mask buffer (depending on null representation),
+    and an offsets buffer (if variable-size binary; e.g., variable-length
+    strings).
+
+    Note: this Column object can only be produced by ``__dataframe__``, so
+          doesn't need its own version or ``__column__`` protocol.
+
+    """
+
+    def __init__(
+        self,
+        column: cudf.core.column.ColumnBase,
+        nan_as_null: bool = True,
+        allow_copy: bool = True,
+    ) -> None:
+        """
+        Note: doesn't deal with extension arrays yet, just assume a regular
+        Series/ndarray for now.
+        """
+        if not isinstance(column, cudf.core.column.ColumnBase):
+            raise TypeError(
+                "column must be a subtype of df.core.column.ColumnBase,"
+                f"got {type(column)}"
+            )
+        self._col = column
+        self._nan_as_null = nan_as_null
+        self._allow_copy = allow_copy
+
+    def size(self) -> int:
+        """
+        Size of the column, in elements.
+        """
+        return self._col.size
+
+    @property
+    def offset(self) -> int:
+        """
+        Offset of first element. Always zero.
+        """
+        return 0
+
+    @property
+    def dtype(self) -> ProtoDtype:
+        """
+        Dtype description as a tuple
+        ``(kind, bit-width, format string, endianness)``
+
+        Kind :
+
+            - INT = 0
+            - UINT = 1
+            - FLOAT = 2
+            - BOOL = 20
+            - STRING = 21   # UTF-8
+            - DATETIME = 22
+            - CATEGORICAL = 23
+
+        Bit-width : the number of bits as an integer
+        Format string : data type description format string in Apache Arrow C
+                        Data Interface format.
+        Endianness : current only native endianness (``=``) is supported
+
+        Notes
+        -----
+        - Kind specifiers are aligned with DLPack where possible
+         (hence the jump to 20, leave enough room for future extension)
+        - Masks must be specified as boolean with either bit width 1
+         (for bit masks) or 8 (for byte masks).
+        - Dtype width in bits was preferred over bytes
+        - Endianness isn't too useful, but included now in case
+          in the future we need to support non-native endianness
+        - Went with Apache Arrow format strings over NumPy format strings
+          because they're more complete from a dataframe perspective
+        - Format strings are mostly useful for datetime specification,
+          and for categoricals.
+        - For categoricals, the format string describes the type of the
+          categorical in the data buffer. In case of a separate encoding
+          of the categorical (e.g. an integer to string mapping),
+          this can be derived from ``self.describe_categorical``.
+        - Data types not included: complex, Arrow-style null,
+          binary, decimal, and nested (list, struct, map, union) dtypes.
+        """
+        dtype = self._col.dtype
+
+        # For now, assume that, if the column dtype is 'O' (i.e., `object`),
+        # then we have an array of strings
+        if not isinstance(dtype, cudf.CategoricalDtype) and dtype.kind == "O":
+            return (_DtypeKind.STRING, 8, "u", "=")
+
+        return self._dtype_from_cudfdtype(dtype)
+
+    def _dtype_from_cudfdtype(self, dtype) -> ProtoDtype:
+        """
+        See `self.dtype` for details.
+        """
+        # Note: 'c' (complex) not handled yet (not in array spec v1).
+        #       'b', 'B' (bytes), 'S', 'a', (old-style string) 'V' (void)
+        #       not handled datetime and timedelta both map to datetime
+        #       (is timedelta handled?)
+        _np_kinds = {
+            "i": _DtypeKind.INT,
+            "u": _DtypeKind.UINT,
+            "f": _DtypeKind.FLOAT,
+            "b": _DtypeKind.BOOL,
+            "U": _DtypeKind.STRING,
+            "M": _DtypeKind.DATETIME,
+            "m": _DtypeKind.DATETIME,
+        }
+        kind = _np_kinds.get(dtype.kind, None)
+        if kind is None:
+            # Not a NumPy/CuPy dtype. Check if it's a categorical maybe
+            if isinstance(dtype, cudf.CategoricalDtype):
+                kind = _DtypeKind.CATEGORICAL
+                # Codes and categories' dtypes are different.
+                # We use codes' dtype as these are stored in the buffer.
+                codes = cast(
+                    cudf.core.column.CategoricalColumn, self._col
+                ).codes
+                dtype = codes.dtype
+            else:
+                raise ValueError(
+                    f"Data type {dtype} not supported by exchange protocol"
+                )
+
+        if kind not in _SUPPORTED_KINDS:
+            raise NotImplementedError(f"Data type {dtype} not handled yet")
+
+        bitwidth = dtype.itemsize * 8
+        format_str = dtype.str
+        endianness = dtype.byteorder if kind != _DtypeKind.CATEGORICAL else "="
+        return (kind, bitwidth, format_str, endianness)
+
+    @property
+    def describe_categorical(self) -> Tuple[bool, bool, Dict[int, Any]]:
+        """
+        If the dtype is categorical, there are two options:
+
+        - There are only values in the data buffer.
+        - There is a separate dictionary-style encoding for categorical values.
+
+        Raises TypeError if the dtype is not categorical
+
+        Content of returned dict:
+
+            - "is_ordered" : bool, whether the ordering of dictionary
+                             indices is semantically meaningful.
+            - "is_dictionary" : bool, whether a dictionary-style mapping of
+                                categorical values to other objects exists
+            - "mapping" : dict, Python-level only (e.g. ``{int: str}``).
+                          None if not a dictionary-style categorical.
+        """
+        if not self.dtype[0] == _DtypeKind.CATEGORICAL:
+            raise TypeError(
+                "`describe_categorical only works on "
+                "a column with categorical dtype!"
+            )
+        categ_col = cast(cudf.core.column.CategoricalColumn, self._col)
+        ordered = bool(categ_col.dtype.ordered)
+        is_dictionary = True
+        # NOTE: this shows the children approach is better, transforming
+        # `categories` to a "mapping" dict is inefficient
+        categories = categ_col.categories
+        mapping = {ix: val for ix, val in enumerate(categories.values_host)}
+        return ordered, is_dictionary, mapping
+
+    @property
+    def describe_null(self) -> Tuple[int, Any]:
+        """
+        Return the missing value (or "null") representation the column dtype
+        uses, as a tuple ``(kind, value)``.
+
+        Kind:
+
+            - 0 : non-nullable
+            - 1 : NaN/NaT
+            - 2 : sentinel value
+            - 3 : bit mask
+            - 4 : byte mask
+
+        Value : if kind is "sentinel value", the actual value.
+        If kind is a bit mask or a byte mask, the value (0 or 1)
+        indicating a missing value.
+        None otherwise.
+        """
+        kind = self.dtype[0]
+        if self.null_count == 0:
+            # there is no validity mask so it is non-nullable
+            return _MaskKind.NON_NULLABLE, None
+
+        elif kind in _SUPPORTED_KINDS:
+            # currently, we return a bit mask
+            return _MaskKind.BITMASK, 0
+
+        else:
+            raise NotImplementedError(
+                f"Data type {self.dtype} not yet supported"
+            )
+
+    @property
+    def null_count(self) -> int:
+        """
+        Number of null elements. Should always be known.
+        """
+        return self._col.null_count
+
+    @property
+    def metadata(self) -> Dict[str, Any]:
+        """
+        Store specific metadata of the column.
+        """
+        return {}
+
+    def num_chunks(self) -> int:
+        """
+        Return the number of chunks the column consists of.
+        """
+        return 1
+
+    def get_chunks(
+        self, n_chunks: Optional[int] = None
+    ) -> Iterable["_CuDFColumn"]:
+        """
+        Return an iterable yielding the chunks.
+
+        See `DataFrame.get_chunks` for details on ``n_chunks``.
+        """
+        return (self,)
+
+    def get_buffers(
+        self,
+    ) -> Mapping[str, Optional[Tuple[_CuDFBuffer, ProtoDtype]]]:
+        """
+        Return a dictionary containing the underlying buffers.
+
+        The returned dictionary has the following contents:
+
+            - "data": a two-element tuple whose first element is a buffer
+                      containing the data and whose second element is the data
+                      buffer's associated dtype.
+            - "validity": a two-element tuple whose first element is a buffer
+                          containing mask values indicating missing data and
+                          whose second element is the mask value buffer's
+                          associated dtype. None if the null representation is
+                          not a bit or byte mask.
+            - "offsets": a two-element tuple whose first element is a buffer
+                         containing the offset values for variable-size binary
+                         data (e.g., variable-length strings) and whose second
+                         element is the offsets buffer's associated dtype. None
+                         if the data buffer does not have an associated offsets
+                         buffer.
+        """
+        buffers = {}
+        try:
+            buffers["validity"] = self._get_validity_buffer()
+        except RuntimeError:
+            buffers["validity"] = None
+
+        try:
+            buffers["offsets"] = self._get_offsets_buffer()
+        except RuntimeError:
+            buffers["offsets"] = None
+
+        buffers["data"] = self._get_data_buffer()
+
+        return buffers
+
+    def _get_validity_buffer(
+        self,
+    ) -> Optional[Tuple[_CuDFBuffer, ProtoDtype]]:
+        """
+        Return the buffer containing the mask values
+        indicating missing data and the buffer's associated dtype.
+
+        Raises RuntimeError if null representation is not a bit or byte mask.
+        """
+        null, invalid = self.describe_null
+
+        if null == _MaskKind.BITMASK:
+            assert self._col.mask is not None
+            buffer = _CuDFBuffer(
+                self._col.mask, cp.uint8, allow_copy=self._allow_copy
+            )
+            dtype = (_DtypeKind.UINT, 8, "C", "=")
+            return buffer, dtype
+
+        elif null == _MaskKind.NAN:
+            raise RuntimeError(
+                "This column uses NaN as null "
+                "so does not have a separate mask"
+            )
+        elif null == _MaskKind.NON_NULLABLE:
+            raise RuntimeError(
+                "This column is non-nullable so does not have a mask"
+            )
+        else:
+            raise NotImplementedError(
+                f"See {self.__class__.__name__}.describe_null method."
+            )
+
+    def _get_offsets_buffer(
+        self,
+    ) -> Optional[Tuple[_CuDFBuffer, ProtoDtype]]:
+        """
+        Return the buffer containing the offset values for
+        variable-size binary data (e.g., variable-length strings)
+        and the buffer's associated dtype.
+
+        Raises RuntimeError if the data buffer does not have an associated
+        offsets buffer.
+        """
+        if self.dtype[0] == _DtypeKind.STRING:
+            offsets = self._col.children[0]
+            assert (offsets is not None) and (offsets.data is not None), " "
+            "offsets(.data) should not be None for string column"
+
+            buffer = _CuDFBuffer(
+                offsets.data, offsets.dtype, allow_copy=self._allow_copy
+            )
+            dtype = self._dtype_from_cudfdtype(offsets.dtype)
+        else:
+            raise RuntimeError(
+                "This column has a fixed-length dtype "
+                "so does not have an offsets buffer"
+            )
+
+        return buffer, dtype
+
+    def _get_data_buffer(
+        self,
+    ) -> Tuple[_CuDFBuffer, ProtoDtype]:
+        """
+        Return the buffer containing the data and
+               the buffer's associated dtype.
+        """
+        if self.dtype[0] in (
+            _DtypeKind.INT,
+            _DtypeKind.UINT,
+            _DtypeKind.FLOAT,
+            _DtypeKind.BOOL,
+        ):
+            col_data = self._col
+            dtype = self.dtype
+
+        elif self.dtype[0] == _DtypeKind.CATEGORICAL:
+            col_data = cast(
+                cudf.core.column.CategoricalColumn, self._col
+            ).codes
+            dtype = self._dtype_from_cudfdtype(col_data.dtype)
+
+        elif self.dtype[0] == _DtypeKind.STRING:
+            col_data = self._col.children[1]
+            dtype = self._dtype_from_cudfdtype(col_data.dtype)
+
+        else:
+            raise NotImplementedError(
+                f"Data type {self._col.dtype} not handled yet"
+            )
+        assert (col_data is not None) and (col_data.data is not None), " "
+        f"col_data(.data) should not be None when dtype = {dtype}"
+        buffer = _CuDFBuffer(
+            col_data.data, col_data.dtype, allow_copy=self._allow_copy
+        )
+
+        return buffer, dtype
+
+
+class _CuDFDataFrame:
+    """
+    A data frame class, with only the methods required by the interchange
+    protocol defined.
+
+    Instances of this (private) class are returned from
+    ``cudf.DataFrame.__dataframe__`` as objects with the methods and
+    attributes defined on this class.
+    """
+
+    def __init__(
+        self,
+        df: "cudf.core.dataframe.DataFrame",
+        nan_as_null: bool = True,
+        allow_copy: bool = True,
+    ) -> None:
+        """
+        Constructor - an instance of this (private) class is returned from
+        `cudf.DataFrame.__dataframe__`.
+        """
+        self._df = df
+        # ``nan_as_null`` is a keyword intended for the consumer to tell the
+        # producer to overwrite null values in the data with
+        # ``NaN`` (or ``NaT``).
+        # This currently has no effect; once support for nullable extension
+        # dtypes is added, this value should be propagated to columns.
+        self._nan_as_null = nan_as_null
+        self._allow_copy = allow_copy
+
+    def __dataframe__(
+        self, nan_as_null: bool = False, allow_copy: bool = True
+    ) -> "_CuDFDataFrame":
+        """
+        See the docstring of the `cudf.DataFrame.__dataframe__` for details
+        """
+        return _CuDFDataFrame(
+            self._df, nan_as_null=nan_as_null, allow_copy=allow_copy
+        )
+
+    @property
+    def metadata(self):
+        # `index` isn't a regular column, and the protocol doesn't support row
+        # labels - so we export it as cuDF-specific metadata here.
+        return {"cudf.index": self._df.index}
+
+    def num_columns(self) -> int:
+        return len(self._df._column_names)
+
+    def num_rows(self) -> int:
+        return len(self._df)
+
+    def num_chunks(self) -> int:
+        return 1
+
+    def column_names(self) -> Iterable[str]:
+        return self._df._column_names
+
+    def get_column(self, i: int) -> _CuDFColumn:
+        return _CuDFColumn(
+            as_column(self._df.iloc[:, i]), allow_copy=self._allow_copy
+        )
+
+    def get_column_by_name(self, name: str) -> _CuDFColumn:
+        return _CuDFColumn(
+            as_column(self._df[name]), allow_copy=self._allow_copy
+        )
+
+    def get_columns(self) -> Iterable[_CuDFColumn]:
+        return [
+            _CuDFColumn(as_column(self._df[name]), allow_copy=self._allow_copy)
+            for name in self._df.columns
+        ]
+
+    def select_columns(self, indices: Sequence[int]) -> "_CuDFDataFrame":
+        if not isinstance(indices, abc.Sequence):
+            raise ValueError("`indices` is not a sequence")
+
+        return _CuDFDataFrame(self._df.iloc[:, indices])
+
+    def select_columns_by_name(self, names: Sequence[str]) -> "_CuDFDataFrame":
+        if not isinstance(names, abc.Sequence):
+            raise ValueError("`names` is not a sequence")
+
+        return _CuDFDataFrame(
+            self._df.loc[:, names], self._nan_as_null, self._allow_copy
+        )
+
+    def get_chunks(
+        self, n_chunks: Optional[int] = None
+    ) -> Iterable["_CuDFDataFrame"]:
+        """
+        Return an iterator yielding the chunks.
+        """
+        return (self,)
+
+
+def __dataframe__(
+    self, nan_as_null: bool = False, allow_copy: bool = True
+) -> _CuDFDataFrame:
+    """
+    The public method to attach to cudf.DataFrame.
+
+    ``nan_as_null`` is a keyword intended for the consumer to tell the
+    producer to overwrite null values in the data with ``NaN`` (or ``NaT``).
+    This currently has no effect; once support for nullable extension
+    dtypes is added, this value should be propagated to columns.
+
+    ``allow_copy`` is a keyword that defines whether or not the library is
+    allowed to make a copy of the data. For example, copying data would be
+    necessary if a library supports strided buffers, given that this protocol
+    specifies contiguous buffers.
+    """
+    return _CuDFDataFrame(self, nan_as_null=nan_as_null, allow_copy=allow_copy)
+
+
+"""
+Implementation of the dataframe exchange protocol.
+
+Public API
+----------
+
+from_dataframe : construct a cudf.DataFrame from an input data frame which
+                 implements the exchange protocol
+
+Notes
+-----
+
+- Interpreting a raw pointer (as in ``Buffer.ptr``) is annoying and
+  unsafe to do in pure Python. It's more general but definitely less friendly
+  than having ``to_arrow`` and ``to_numpy`` methods. So for the buffers which
+  lack ``__dlpack__`` (e.g., because the column dtype isn't supported by
+  DLPack), this is worth looking at again.
+
+"""
+
+
+# A typing protocol could be added later to let Mypy validate code using
+# `from_dataframe` better.
+DataFrameObject = Any
+ColumnObject = Any
+
+
+_INTS = {8: cp.int8, 16: cp.int16, 32: cp.int32, 64: cp.int64}
+_UINTS = {8: cp.uint8, 16: cp.uint16, 32: cp.uint32, 64: cp.uint64}
+_FLOATS = {32: cp.float32, 64: cp.float64}
+_CP_DTYPES = {
+    0: _INTS,
+    1: _UINTS,
+    2: _FLOATS,
+    20: {8: bool},
+    21: {8: cp.uint8},
+}
+
+
+def from_dataframe(
+    df: DataFrameObject, allow_copy: bool = False
+) -> _CuDFDataFrame:
+    """
+    Construct a ``DataFrame`` from ``df`` if it supports the
+    dataframe interchange protocol (``__dataframe__``).
+
+    Parameters
+    ----------
+    df : DataFrameObject
+        Object supporting dataframe interchange protocol
+    allow_copy : bool
+        If ``True``, allow copying of the data. If ``False``, a
+        ``TypeError`` is raised if data copying is required to
+        construct the ``DataFrame`` (e.g., if ``df`` lives in CPU
+        memory).
+
+    Returns
+    -------
+    DataFrame
+
+    Examples
+    --------
+    >>> import pandas as pd
+    >>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': ['x', 'y', 'z']})
+    >>> df = cudf.from_dataframe(pdf, allow_copy=True)
+    >>> type(df)
+    cudf.core.dataframe.DataFrame
+    >>> df
+       a  b
+    0  1  x
+    1  2  y
+    2  3  z
+
+    Notes
+    -----
+    See https://data-apis.org/dataframe-protocol/latest/index.html
+    for the dataframe interchange protocol spec and API
+    """
+    if isinstance(df, cudf.DataFrame):
+        return df
+
+    if not hasattr(df, "__dataframe__"):
+        raise ValueError("`df` does not support __dataframe__")
+
+    df = df.__dataframe__(allow_copy=allow_copy)
+
+    # Check number of chunks, if there's more than one we need to iterate
+    if df.num_chunks() > 1:
+        raise NotImplementedError("More than one chunk not handled yet")
+
+    # We need a dict of columns here, with each column being a cudf column.
+    columns = dict()
+    _buffers = []  # hold on to buffers, keeps memory alive
+    for name in df.column_names():
+        col = df.get_column_by_name(name)
+
+        if col.dtype[0] in (
+            _DtypeKind.INT,
+            _DtypeKind.UINT,
+            _DtypeKind.FLOAT,
+            _DtypeKind.BOOL,
+        ):
+            columns[name], _buf = _protocol_to_cudf_column_numeric(
+                col, allow_copy
+            )
+
+        elif col.dtype[0] == _DtypeKind.CATEGORICAL:
+            columns[name], _buf = _protocol_to_cudf_column_categorical(
+                col, allow_copy
+            )
+
+        elif col.dtype[0] == _DtypeKind.STRING:
+            columns[name], _buf = _protocol_to_cudf_column_string(
+                col, allow_copy
+            )
+
+        else:
+            raise NotImplementedError(
+                f"Data type {col.dtype[0]} not handled yet"
+            )
+
+        _buffers.append(_buf)
+
+    df_new = cudf.DataFrame._from_data(columns)
+    df_new._buffers = _buffers
+    return df_new
+
+
+def _protocol_to_cudf_column_numeric(
+    col, allow_copy: bool
+) -> Tuple[
+    cudf.core.column.ColumnBase,
+    Mapping[str, Optional[Tuple[_CuDFBuffer, ProtoDtype]]],
+]:
+    """
+    Convert an int, uint, float or bool protocol column
+    to the corresponding cudf column
+    """
+    if col.offset != 0:
+        raise NotImplementedError("column.offset > 0 not handled yet")
+
+    buffers = col.get_buffers()
+    assert buffers["data"] is not None, "data buffer should not be None"
+    _dbuffer, _ddtype = buffers["data"]
+    _dbuffer = _ensure_gpu_buffer(_dbuffer, _ddtype, allow_copy)
+    cudfcol_num = build_column(
+        _dbuffer._buf,
+        protocol_dtype_to_cupy_dtype(_ddtype),
+    )
+    return _set_missing_values(col, cudfcol_num, allow_copy), buffers
+
+
+def _ensure_gpu_buffer(buf, data_type, allow_copy: bool) -> _CuDFBuffer:
+    # if `buf` is a (protocol) buffer that lives on the GPU already,
+    # return it as is.  Otherwise, copy it to the device and return
+    # the resulting buffer.
+    if buf.__dlpack_device__()[0] != _Device.CUDA:
+        if allow_copy:
+            dbuf = rmm.DeviceBuffer(ptr=buf.ptr, size=buf.bufsize)
+            return _CuDFBuffer(
+                as_buffer(dbuf, exposed=True),
+                protocol_dtype_to_cupy_dtype(data_type),
+                allow_copy,
+            )
+        else:
+            raise TypeError(
+                "This operation must copy data from CPU to GPU. "
+                "Set `allow_copy=True` to allow it."
+            )
+    return buf
+
+
+def _set_missing_values(
+    protocol_col,
+    cudf_col: cudf.core.column.ColumnBase,
+    allow_copy: bool,
+) -> cudf.core.column.ColumnBase:
+
+    valid_mask = protocol_col.get_buffers()["validity"]
+    if valid_mask is not None:
+        null, invalid = protocol_col.describe_null
+        if null == _MaskKind.BYTEMASK:
+            valid_mask = _ensure_gpu_buffer(
+                valid_mask[0], valid_mask[1], allow_copy
+            )
+            boolmask = as_column(valid_mask._buf, dtype="bool")
+            bitmask = cudf._lib.transform.bools_to_mask(boolmask)
+            return cudf_col.set_mask(bitmask)
+        elif null == _MaskKind.BITMASK:
+            valid_mask = _ensure_gpu_buffer(
+                valid_mask[0], valid_mask[1], allow_copy
+            )
+            bitmask = valid_mask._buf
+            return cudf_col.set_mask(bitmask)
+    return cudf_col
+
+
+def protocol_dtype_to_cupy_dtype(_dtype: ProtoDtype) -> cp.dtype:
+    kind = _dtype[0]
+    bitwidth = _dtype[1]
+    if _dtype[0] not in _SUPPORTED_KINDS:
+        raise RuntimeError(f"Data type {_dtype[0]} not handled yet")
+
+    return _CP_DTYPES[kind][bitwidth]
+
+
+def _protocol_to_cudf_column_categorical(
+    col, allow_copy: bool
+) -> Tuple[
+    cudf.core.column.ColumnBase,
+    Mapping[str, Optional[Tuple[_CuDFBuffer, ProtoDtype]]],
+]:
+    """
+    Convert a categorical column to a Series instance
+    """
+    ordered, is_dict, categories = col.describe_categorical
+    if not is_dict:
+        raise NotImplementedError(
+            "Non-dictionary categoricals not supported yet"
+        )
+    buffers = col.get_buffers()
+    assert buffers["data"] is not None, "data buffer should not be None"
+    codes_buffer, codes_dtype = buffers["data"]
+    codes_buffer = _ensure_gpu_buffer(codes_buffer, codes_dtype, allow_copy)
+    cdtype = protocol_dtype_to_cupy_dtype(codes_dtype)
+    codes = build_column(
+        codes_buffer._buf,
+        cdtype,
+    )
+
+    cudfcol = build_categorical_column(
+        categories=categories,
+        codes=codes,
+        mask=codes.base_mask,
+        size=codes.size,
+        ordered=ordered,
+    )
+
+    return _set_missing_values(col, cudfcol, allow_copy), buffers
+
+
+def _protocol_to_cudf_column_string(
+    col, allow_copy: bool
+) -> Tuple[
+    cudf.core.column.ColumnBase,
+    Mapping[str, Optional[Tuple[_CuDFBuffer, ProtoDtype]]],
+]:
+    """
+    Convert a string ColumnObject to cudf Column object.
+    """
+    # Retrieve the data buffers
+    buffers = col.get_buffers()
+
+    # Retrieve the data buffer containing the UTF-8 code units
+    assert buffers["data"] is not None, "data buffer should never be None"
+    data_buffer, data_dtype = buffers["data"]
+    data_buffer = _ensure_gpu_buffer(data_buffer, data_dtype, allow_copy)
+    encoded_string = build_column(
+        data_buffer._buf,
+        protocol_dtype_to_cupy_dtype(data_dtype),
+    )
+
+    # Retrieve the offsets buffer containing the index offsets demarcating
+    # the beginning and end of each string
+    assert buffers["offsets"] is not None, "not possible for string column"
+    offset_buffer, offset_dtype = buffers["offsets"]
+    offset_buffer = _ensure_gpu_buffer(offset_buffer, offset_dtype, allow_copy)
+    offsets = build_column(
+        offset_buffer._buf,
+        protocol_dtype_to_cupy_dtype(offset_dtype),
+    )
+    offsets = offsets.astype("int32")
+    cudfcol_str = build_column(
+        None, dtype=cp.dtype("O"), children=(offsets, encoded_string)
+    )
+    return _set_missing_values(col, cudfcol_str, allow_copy), buffers
+
+
+def _protocol_buffer_to_cudf_buffer(protocol_buffer):
+    return as_buffer(
+        rmm.DeviceBuffer(
+            ptr=protocol_buffer.ptr, size=protocol_buffer.bufsize
+        ),
+        exposed=True,
+    )
diff --git a/python/cudf/cudf/core/dtypes.py b/python/cudf/cudf/core/dtypes.py
new file mode 100644
index 0000000..12d8884
--- /dev/null
+++ b/python/cudf/cudf/core/dtypes.py
@@ -0,0 +1,1168 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import decimal
+import operator
+import pickle
+import textwrap
+from functools import cached_property
+from typing import Any, Callable, Dict, List, Tuple, Type, Union
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from pandas.api import types as pd_types
+from pandas.api.extensions import ExtensionDtype
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype as pd_CategoricalDtype,
+    CategoricalDtypeType as pd_CategoricalDtypeType,
+)
+
+import cudf
+from cudf._typing import Dtype
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.abc import Serializable
+from cudf.core.buffer import Buffer
+from cudf.utils.docutils import doc_apply
+
+if PANDAS_GE_150:
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+else:
+    from pandas.core.arrays._arrow_utils import ArrowIntervalType
+
+
+def dtype(arbitrary):
+    """
+    Return the cuDF-supported dtype corresponding to `arbitrary`.
+
+    Parameters
+    ----------
+    arbitrary: dtype or scalar-like
+
+    Returns
+    -------
+    dtype: the cuDF-supported dtype that best matches `arbitrary`
+    """
+    #  first, check if `arbitrary` is one of our extension types:
+    if isinstance(arbitrary, cudf.core.dtypes._BaseDtype):
+        return arbitrary
+
+    # next, try interpreting arbitrary as a NumPy dtype that we support:
+    try:
+        np_dtype = np.dtype(arbitrary)
+        if np_dtype.kind in ("OU"):
+            return np.dtype("object")
+    except TypeError:
+        pass
+    else:
+        if np_dtype not in cudf._lib.types.SUPPORTED_NUMPY_TO_LIBCUDF_TYPES:
+            raise TypeError(f"Unsupported type {np_dtype}")
+        return np_dtype
+
+    # use `pandas_dtype` to try and interpret
+    # `arbitrary` as a Pandas extension type.
+    #  Return the corresponding NumPy/cuDF type.
+    pd_dtype = pd.api.types.pandas_dtype(arbitrary)
+    if cudf.get_option(
+        "mode.pandas_compatible"
+    ) and cudf.api.types._is_pandas_nullable_extension_dtype(pd_dtype):
+        raise NotImplementedError("not supported")
+    try:
+        return dtype(pd_dtype.numpy_dtype)
+    except AttributeError:
+        if isinstance(pd_dtype, pd.CategoricalDtype):
+            return cudf.CategoricalDtype.from_pandas(pd_dtype)
+        elif isinstance(pd_dtype, pd.StringDtype):
+            return np.dtype("object")
+        elif isinstance(pd_dtype, pd.IntervalDtype):
+            return cudf.IntervalDtype.from_pandas(pd_dtype)
+        elif isinstance(pd_dtype, pd.DatetimeTZDtype):
+            return pd_dtype
+        else:
+            raise TypeError(
+                f"Cannot interpret {arbitrary} as a valid cuDF dtype"
+            )
+
+
+def _decode_type(
+    cls: Type,
+    header: dict,
+    frames: list,
+    is_valid_class: Callable[[Type, Type], bool] = operator.is_,
+) -> Tuple[dict, list, Type]:
+    """Decode metadata-encoded type and check validity
+
+    Parameters
+    ----------
+    cls : type
+        class performing deserialization
+    header : dict
+        metadata for deserialization
+    frames : list
+        buffers containing data for deserialization
+    is_valid_class : Callable
+        function to call to check if the encoded class type is valid for
+        serialization by `cls` (default is to check type equality), called
+        as `is_valid_class(decoded_class, cls)`.
+
+    Returns
+    -------
+    tuple
+        Tuple of validated headers, frames, and the decoded class
+        constructor.
+
+    Raises
+    ------
+    AssertionError
+        if the number of frames doesn't match the count encoded in the
+        headers, or `is_valid_class` is not true.
+    """
+    assert header["frame_count"] == len(frames), (
+        f"Deserialization expected {header['frame_count']} frames, "
+        f"but received {len(frames)}."
+    )
+    klass = pickle.loads(header["type-serialized"])
+    assert is_valid_class(
+        klass, cls
+    ), f"Header-encoded {klass=} does not match decoding {cls=}."
+    return header, frames, klass
+
+
+class _BaseDtype(ExtensionDtype, Serializable):
+    # Base type for all cudf-specific dtypes
+    pass
+
+
+class CategoricalDtype(_BaseDtype):
+    """
+    Type for categorical data with the categories and orderedness.
+
+    Parameters
+    ----------
+    categories : sequence, optional
+        Must be unique, and must not contain any nulls.
+        The categories are stored in an Index,
+        and if an index is provided the dtype of that index will be used.
+    ordered : bool or None, default False
+        Whether or not this categorical is treated as a ordered categorical.
+        None can be used to maintain the ordered value of existing categoricals
+        when used in operations that combine categoricals, e.g. astype, and
+        will resolve to False if there is no existing ordered to maintain.
+
+    Attributes
+    ----------
+    categories
+    ordered
+
+    Methods
+    -------
+    from_pandas
+    to_pandas
+
+    Examples
+    --------
+    >>> import cudf
+    >>> dtype = cudf.CategoricalDtype(categories=['b', 'a'], ordered=True)
+    >>> cudf.Series(['a', 'b', 'a', 'c'], dtype=dtype)
+    0       a
+    1       b
+    2       a
+    3    <NA>
+    dtype: category
+    Categories (2, object): ['b' < 'a']
+    """
+
+    def __init__(self, categories=None, ordered: bool = False) -> None:
+        self._categories = self._init_categories(categories)
+        self._ordered = ordered
+
+    @property
+    def categories(self) -> "cudf.core.index.GenericIndex":
+        """
+        An ``Index`` containing the unique categories allowed.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> dtype = cudf.CategoricalDtype(categories=['b', 'a'], ordered=True)
+        >>> dtype.categories
+        StringIndex(['b' 'a'], dtype='object')
+        """
+        if self._categories is None:
+            return cudf.core.index.as_index(
+                cudf.core.column.column_empty(0, dtype="object", masked=False)
+            )
+        return cudf.core.index.as_index(self._categories, copy=False)
+
+    @property
+    def type(self):
+        return self._categories.dtype.type
+
+    @property
+    def name(self):
+        return "category"
+
+    @property
+    def str(self):
+        return "|O08"
+
+    @property
+    def ordered(self) -> bool:
+        """
+        Whether the categories have an ordered relationship.
+        """
+        return self._ordered
+
+    @ordered.setter
+    def ordered(self, value) -> None:
+        self._ordered = value
+
+    @classmethod
+    def from_pandas(cls, dtype: pd.CategoricalDtype) -> "CategoricalDtype":
+        """
+        Convert a ``pandas.CategrocialDtype`` to ``cudf.CategoricalDtype``
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> pd_dtype = pd.CategoricalDtype(categories=['b', 'a'], ordered=True)
+        >>> pd_dtype
+        CategoricalDtype(categories=['b', 'a'], ordered=True)
+        >>> cudf_dtype = cudf.CategoricalDtype.from_pandas(pd_dtype)
+        >>> cudf_dtype
+        CategoricalDtype(categories=['b', 'a'], ordered=True)
+        """
+        return CategoricalDtype(
+            categories=dtype.categories, ordered=dtype.ordered
+        )
+
+    def to_pandas(self) -> pd.CategoricalDtype:
+        """
+        Convert a ``cudf.CategoricalDtype`` to ``pandas.CategoricalDtype``
+
+        Examples
+        --------
+        >>> import cudf
+        >>> dtype = cudf.CategoricalDtype(categories=['b', 'a'], ordered=True)
+        >>> dtype
+        CategoricalDtype(categories=['b', 'a'], ordered=True)
+        >>> dtype.to_pandas()
+        CategoricalDtype(categories=['b', 'a'], ordered=True)
+        """
+        if self._categories is None:
+            categories = None
+        else:
+            if isinstance(
+                self._categories, (cudf.Float32Index, cudf.Float64Index)
+            ):
+                categories = self._categories.dropna().to_pandas()
+            else:
+                categories = self._categories.to_pandas()
+        return pd.CategoricalDtype(categories=categories, ordered=self.ordered)
+
+    def _init_categories(self, categories: Any):
+        if categories is None:
+            return categories
+        if len(categories) == 0 and not is_interval_dtype(categories):
+            dtype = "object"  # type: Any
+        else:
+            dtype = None
+
+        column = cudf.core.column.as_column(categories, dtype=dtype)
+
+        if isinstance(column, cudf.core.column.CategoricalColumn):
+            return column.categories
+        else:
+            return column
+
+    def __eq__(self, other: Dtype) -> bool:
+        if isinstance(other, str):
+            return other == self.name
+        elif other is self:
+            return True
+        elif not isinstance(other, self.__class__):
+            return False
+        elif self.ordered != other.ordered:
+            return False
+        elif self._categories is None or other._categories is None:
+            return True
+        else:
+            return (
+                self._categories.dtype == other._categories.dtype
+                and self._categories.equals(other._categories)
+            )
+
+    def construct_from_string(self):
+        raise NotImplementedError()
+
+    def serialize(self):
+        header = {}
+        header["type-serialized"] = pickle.dumps(type(self))
+        header["ordered"] = self.ordered
+
+        frames = []
+
+        if self.categories is not None:
+            categories_header, categories_frames = self.categories.serialize()
+        header["categories"] = categories_header
+        frames.extend(categories_frames)
+        header["frame_count"] = len(frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        header, frames, klass = _decode_type(cls, header, frames)
+        ordered = header["ordered"]
+        categories_header = header["categories"]
+        categories_frames = frames
+        categories_type = pickle.loads(categories_header["type-serialized"])
+        categories = categories_type.deserialize(
+            categories_header, categories_frames
+        )
+        return klass(categories=categories, ordered=ordered)
+
+    def __repr__(self):
+        return self.to_pandas().__repr__()
+
+
+class ListDtype(_BaseDtype):
+    """
+    Type to represent list data.
+
+    Parameters
+    ----------
+    element_type : object
+        A dtype with which represents the element types in the list.
+
+    Attributes
+    ----------
+    element_type
+    leaf_type
+
+    Methods
+    -------
+    from_arrow
+    to_arrow
+
+    Examples
+    --------
+    >>> import cudf
+    >>> list_dtype = cudf.ListDtype("int32")
+    >>> list_dtype
+    ListDtype(int32)
+
+    A nested list dtype can be created by:
+
+    >>> nested_list_dtype = cudf.ListDtype(list_dtype)
+    >>> nested_list_dtype
+    ListDtype(ListDtype(int32))
+    """
+
+    _typ: pa.ListType
+    name: str = "list"
+
+    def __init__(self, element_type: Any) -> None:
+        if isinstance(element_type, ListDtype):
+            self._typ = pa.list_(element_type._typ)
+        else:
+            element_type = cudf.utils.dtypes.cudf_dtype_to_pa_type(
+                element_type
+            )
+            self._typ = pa.list_(element_type)
+
+    @cached_property
+    def element_type(self) -> Dtype:
+        """
+        Returns the element type of the ``ListDtype``.
+
+        Returns
+        -------
+        Dtype
+
+        Examples
+        --------
+        >>> import cudf
+        >>> deep_nested_type = cudf.ListDtype(cudf.ListDtype(cudf.ListDtype("float32")))
+        >>> deep_nested_type
+        ListDtype(ListDtype(ListDtype(float32)))
+        >>> deep_nested_type.element_type
+        ListDtype(ListDtype(float32))
+        >>> deep_nested_type.element_type.element_type
+        ListDtype(float32)
+        >>> deep_nested_type.element_type.element_type.element_type
+        'float32'
+        """  # noqa: E501
+        if isinstance(self._typ.value_type, pa.ListType):
+            return ListDtype.from_arrow(self._typ.value_type)
+        elif isinstance(self._typ.value_type, pa.StructType):
+            return StructDtype.from_arrow(self._typ.value_type)
+        else:
+            return cudf.dtype(self._typ.value_type.to_pandas_dtype())
+
+    @cached_property
+    def leaf_type(self):
+        """
+        Returns the type of the leaf values.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> deep_nested_type = cudf.ListDtype(cudf.ListDtype(cudf.ListDtype("float32")))
+        >>> deep_nested_type
+        ListDtype(ListDtype(ListDtype(float32)))
+        >>> deep_nested_type.leaf_type
+        'float32'
+        """  # noqa: E501
+        if isinstance(self.element_type, ListDtype):
+            return self.element_type.leaf_type
+        else:
+            return self.element_type
+
+    @property
+    def type(self):
+        # TODO: we should change this to return something like a
+        # ListDtypeType, once we figure out what that should look like
+        return pa.array
+
+    @classmethod
+    def from_arrow(cls, typ):
+        """
+        Creates a ``ListDtype`` from ``pyarrow.ListType``.
+
+        Parameters
+        ----------
+        typ : pyarrow.ListType
+            A ``pyarrow.ListType`` that has to be converted to
+            ``ListDtype``.
+
+        Returns
+        -------
+        obj : ``ListDtype``
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pyarrow as pa
+        >>> arrow_type = pa.infer_type([[1]])
+        >>> arrow_type
+        ListType(list<item: int64>)
+        >>> list_dtype = cudf.ListDtype.from_arrow(arrow_type)
+        >>> list_dtype
+        ListDtype(int64)
+        """
+        obj = object.__new__(cls)
+        obj._typ = typ
+        return obj
+
+    def to_arrow(self):
+        """
+        Convert to a ``pyarrow.ListType``
+
+        Examples
+        --------
+        >>> import cudf
+        >>> list_dtype = cudf.ListDtype(cudf.ListDtype("float32"))
+        >>> list_dtype
+        ListDtype(ListDtype(float32))
+        >>> list_dtype.to_arrow()
+        ListType(list<item: list<item: float>>)
+        """
+        return self._typ
+
+    def __eq__(self, other):
+        if isinstance(other, str):
+            return other == self.name
+        if not isinstance(other, ListDtype):
+            return False
+        return self._typ.equals(other._typ)
+
+    def __repr__(self):
+        if isinstance(self.element_type, (ListDtype, StructDtype)):
+            return f"{type(self).__name__}({repr(self.element_type)})"
+        else:
+            return f"{type(self).__name__}({self.element_type})"
+
+    def __hash__(self):
+        return hash(self._typ)
+
+    def serialize(self) -> Tuple[dict, list]:
+        header: Dict[str, Dtype] = {}
+        header["type-serialized"] = pickle.dumps(type(self))
+
+        frames = []
+
+        if isinstance(self.element_type, _BaseDtype):
+            header["element-type"], frames = self.element_type.serialize()
+        else:
+            header["element-type"] = getattr(
+                self.element_type, "name", self.element_type
+            )
+        header["frame_count"] = len(frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list):
+        header, frames, klass = _decode_type(cls, header, frames)
+        if isinstance(header["element-type"], dict):
+            element_type = pickle.loads(
+                header["element-type"]["type-serialized"]
+            ).deserialize(header["element-type"], frames)
+        else:
+            element_type = header["element-type"]
+        return klass(element_type=element_type)
+
+    @cached_property
+    def itemsize(self):
+        return self.element_type.itemsize
+
+
+class StructDtype(_BaseDtype):
+    """
+    Type to represent a struct data.
+
+    Parameters
+    ----------
+    fields : dict
+        A mapping of field names to dtypes, the dtypes can themselves
+        be of ``StructDtype`` too.
+
+    Attributes
+    ----------
+    fields
+    itemsize
+
+    Methods
+    -------
+    from_arrow
+    to_arrow
+
+    Examples
+    --------
+    >>> import cudf
+    >>> struct_dtype = cudf.StructDtype({"a": "int64", "b": "string"})
+    >>> struct_dtype
+    StructDtype({'a': dtype('int64'), 'b': dtype('O')})
+
+    A nested ``StructDtype`` can also be constructed in the following way:
+
+    >>> nested_struct_dtype = cudf.StructDtype({"dict_data": struct_dtype, "c": "uint8"})
+    >>> nested_struct_dtype
+    StructDtype({'dict_data': StructDtype({'a': dtype('int64'), 'b': dtype('O')}), 'c': dtype('uint8')})
+    """  # noqa: E501
+
+    name = "struct"
+
+    def __init__(self, fields):
+        pa_fields = {
+            k: cudf.utils.dtypes.cudf_dtype_to_pa_type(v)
+            for k, v in fields.items()
+        }
+        self._typ = pa.struct(pa_fields)
+
+    @property
+    def fields(self):
+        """
+        Returns an ordered dict of column name and dtype key-value.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> struct_dtype = cudf.StructDtype({"a": "int64", "b": "string"})
+        >>> struct_dtype
+        StructDtype({'a': dtype('int64'), 'b': dtype('O')})
+        >>> struct_dtype.fields
+        {'a': dtype('int64'), 'b': dtype('O')}
+        """
+        return {
+            field.name: cudf.utils.dtypes.cudf_dtype_from_pa_type(field.type)
+            for field in self._typ
+        }
+
+    @property
+    def type(self):
+        # TODO: we should change this to return something like a
+        # StructDtypeType, once we figure out what that should look like
+        return dict
+
+    @classmethod
+    def from_arrow(cls, typ):
+        """
+        Convert a ``pyarrow.StructType`` to ``StructDtype``.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pyarrow as pa
+        >>> pa_struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()})
+        >>> pa_struct_type
+        StructType(struct<x: int32, y: string>)
+        >>> cudf.StructDtype.from_arrow(pa_struct_type)
+        StructDtype({'x': dtype('int32'), 'y': dtype('O')})
+        """
+        obj = object.__new__(cls)
+        obj._typ = typ
+        return obj
+
+    def to_arrow(self):
+        """
+        Convert a ``StructDtype`` to a ``pyarrow.StructType``.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> struct_type = cudf.StructDtype({"x": "int32", "y": "string"})
+        >>> struct_type
+        StructDtype({'x': dtype('int32'), 'y': dtype('O')})
+        >>> struct_type.to_arrow()
+        StructType(struct<x: int32, y: string>)
+        """
+        return self._typ
+
+    def __eq__(self, other):
+        if isinstance(other, str):
+            return other == self.name
+        if not isinstance(other, StructDtype):
+            return False
+        return self._typ.equals(other._typ)
+
+    def __repr__(self):
+        return f"{type(self).__name__}({self.fields})"
+
+    def __hash__(self):
+        return hash(self._typ)
+
+    def serialize(self) -> Tuple[dict, list]:
+        header: Dict[str, Any] = {}
+        header["type-serialized"] = pickle.dumps(type(self))
+
+        frames: List[Buffer] = []
+
+        fields: Dict[str, Union[bytes, Tuple[Any, Tuple[int, int]]]] = {}
+
+        for k, dtype in self.fields.items():
+            if isinstance(dtype, _BaseDtype):
+                dtype_header, dtype_frames = dtype.serialize()
+                fields[k] = (
+                    dtype_header,
+                    (len(frames), len(frames) + len(dtype_frames)),
+                )
+                frames.extend(dtype_frames)
+            else:
+                fields[k] = pickle.dumps(dtype)
+        header["fields"] = fields
+        header["frame_count"] = len(frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list):
+        header, frames, klass = _decode_type(cls, header, frames)
+        fields = {}
+        for k, dtype in header["fields"].items():
+            if isinstance(dtype, tuple):
+                dtype_header, (start, stop) = dtype
+                fields[k] = pickle.loads(
+                    dtype_header["type-serialized"]
+                ).deserialize(
+                    dtype_header,
+                    frames[start:stop],
+                )
+            else:
+                fields[k] = pickle.loads(dtype)
+        return cls(fields)
+
+    @cached_property
+    def itemsize(self):
+        return sum(
+            cudf.utils.dtypes.cudf_dtype_from_pa_type(field.type).itemsize
+            for field in self._typ
+        )
+
+
+decimal_dtype_template = textwrap.dedent(
+    """
+        Type to represent a ``decimal{size}`` data.
+
+        Parameters
+        ----------
+        precision : int
+            The total number of digits in each value of this dtype
+        scale : int, optional
+            The scale of the dtype. See Notes below.
+
+        Attributes
+        ----------
+        precision
+        scale
+        itemsize
+
+        Methods
+        -------
+        to_arrow
+        from_arrow
+
+        Notes
+        -----
+        When the scale is positive:
+            - numbers with fractional parts (e.g., 0.0042) can be represented
+            - the scale is the total number of digits to the right of the
+              decimal point
+
+        When the scale is negative:
+            - only multiples of powers of 10 (including 10**0) can be
+              represented (e.g., 1729, 4200, 1000000)
+            - the scale represents the number of trailing zeros in the value.
+
+        For example, 42 is representable with precision=2 and scale=0.
+        13.0051 is representable with precision=6 and scale=4,
+        and *not* representable with precision<6 or scale<4.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> decimal{size}_dtype = cudf.Decimal{size}Dtype(precision=9, scale=2)
+        >>> decimal{size}_dtype
+        Decimal{size}Dtype(precision=9, scale=2)
+    """  # noqa: E501
+)
+
+
+class DecimalDtype(_BaseDtype):
+    _metadata = ("precision", "scale")
+
+    def __init__(self, precision, scale=0):
+        self._validate(precision, scale)
+        self._typ = pa.decimal128(precision, scale)
+
+    @property
+    def str(self):
+        return f"{str(self.name)}({self.precision}, {self.scale})"
+
+    @property
+    def precision(self):
+        """
+        The decimal precision, in number of decimal digits (an integer).
+        """
+        return self._typ.precision
+
+    @precision.setter
+    def precision(self, value):
+        self._validate(value, self.scale)
+        self._typ = pa.decimal128(precision=value, scale=self.scale)
+
+    @property
+    def scale(self):
+        """
+        The decimal scale (an integer).
+        """
+        return self._typ.scale
+
+    @property
+    def itemsize(self):
+        """
+        Length of one column element in bytes.
+        """
+        return self.ITEMSIZE
+
+    @property
+    def type(self):
+        # might need to account for precision and scale here
+        return decimal.Decimal
+
+    def to_arrow(self):
+        """
+        Return the equivalent ``pyarrow`` dtype.
+        """
+        return self._typ
+
+    @classmethod
+    def from_arrow(cls, typ):
+        """
+        Construct a cudf decimal dtype from a ``pyarrow`` dtype
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pyarrow as pa
+        >>> pa_type = pa.decimal128(precision=9, scale=2)
+
+        Constructing a ``Decimal32Dtype``:
+
+        >>> cudf.Decimal32Dtype.from_arrow(pa_type)
+        Decimal64Dtype(precision=9, scale=2)
+
+        Constructing a ``Decimal64Dtype``:
+
+        >>> cudf.Decimal64Dtype.from_arrow(pa_type)
+        Decimal64Dtype(precision=9, scale=2)
+
+        Constructing a ``Decimal128Dtype``:
+
+        >>> cudf.Decimal128Dtype.from_arrow(pa_type)
+        Decimal128Dtype(precision=9, scale=2)
+        """
+        return cls(typ.precision, typ.scale)
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}"
+            f"(precision={self.precision}, scale={self.scale})"
+        )
+
+    @classmethod
+    def _validate(cls, precision, scale=0):
+        if precision > cls.MAX_PRECISION:
+            raise ValueError(
+                f"Cannot construct a {cls.__name__}"
+                f" with precision > {cls.MAX_PRECISION}"
+            )
+        if abs(scale) > precision:
+            raise ValueError(f"scale={scale} exceeds precision={precision}")
+
+    @classmethod
+    def _from_decimal(cls, decimal):
+        """
+        Create a cudf.DecimalDtype from a decimal.Decimal object
+        """
+        metadata = decimal.as_tuple()
+        precision = max(len(metadata.digits), -metadata.exponent)
+        return cls(precision, -metadata.exponent)
+
+    def serialize(self) -> Tuple[dict, list]:
+        return (
+            {
+                "type-serialized": pickle.dumps(type(self)),
+                "precision": self.precision,
+                "scale": self.scale,
+                "frame_count": 0,
+            },
+            [],
+        )
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list):
+        header, frames, klass = _decode_type(
+            cls, header, frames, is_valid_class=issubclass
+        )
+        klass = pickle.loads(header["type-serialized"])
+        return klass(header["precision"], header["scale"])
+
+    def __eq__(self, other: Dtype) -> bool:
+        if other is self:
+            return True
+        elif not isinstance(other, self.__class__):
+            return False
+        return self.precision == other.precision and self.scale == other.scale
+
+    def __hash__(self):
+        return hash(self._typ)
+
+
+@doc_apply(
+    decimal_dtype_template.format(
+        size="32",
+    )
+)
+class Decimal32Dtype(DecimalDtype):
+    name = "decimal32"
+    MAX_PRECISION = np.floor(np.log10(np.iinfo("int32").max))
+    ITEMSIZE = 4
+
+
+@doc_apply(
+    decimal_dtype_template.format(
+        size="64",
+    )
+)
+class Decimal64Dtype(DecimalDtype):
+    name = "decimal64"
+    MAX_PRECISION = np.floor(np.log10(np.iinfo("int64").max))
+    ITEMSIZE = 8
+
+
+@doc_apply(
+    decimal_dtype_template.format(
+        size="128",
+    )
+)
+class Decimal128Dtype(DecimalDtype):
+    name = "decimal128"
+    MAX_PRECISION = 38
+    ITEMSIZE = 16
+
+
+class IntervalDtype(StructDtype):
+    """
+    subtype: str, np.dtype
+        The dtype of the Interval bounds.
+    closed: {'right', 'left', 'both', 'neither'}, default 'right'
+        Whether the interval is closed on the left-side, right-side,
+        both or neither. See the Notes for more detailed explanation.
+    """
+
+    name = "interval"
+
+    def __init__(self, subtype, closed="right"):
+        super().__init__(fields={"left": subtype, "right": subtype})
+
+        if closed is None:
+            closed = "right"
+        if closed in ["left", "right", "neither", "both"]:
+            self.closed = closed
+        else:
+            raise ValueError("closed value is not valid")
+
+    @property
+    def subtype(self):
+        return self.fields["left"]
+
+    def __repr__(self) -> str:
+        return f"interval[{self.subtype}, {self.closed}]"
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+    @classmethod
+    def from_arrow(cls, typ):
+        return IntervalDtype(typ.subtype.to_pandas_dtype(), typ.closed)
+
+    def to_arrow(self):
+        return ArrowIntervalType(
+            pa.from_numpy_dtype(self.subtype), self.closed
+        )
+
+    @classmethod
+    def from_pandas(cls, pd_dtype: pd.IntervalDtype) -> "IntervalDtype":
+        return cls(subtype=pd_dtype.subtype, closed=pd_dtype.closed)
+
+    def to_pandas(self) -> pd.IntervalDtype:
+        return pd.IntervalDtype(subtype=self.subtype, closed=self.closed)
+
+    def __eq__(self, other):
+        if isinstance(other, str):
+            # This means equality isn't transitive but mimics pandas
+            return other == self.name
+        return (
+            type(self) == type(other)
+            and self.subtype == other.subtype
+            and self.closed == other.closed
+        )
+
+    def __hash__(self):
+        return hash((self.subtype, self.closed))
+
+    def serialize(self) -> Tuple[dict, list]:
+        header = {
+            "type-serialized": pickle.dumps(type(self)),
+            "fields": pickle.dumps((self.subtype, self.closed)),
+            "frame_count": 0,
+        }
+        return header, []
+
+    @classmethod
+    def deserialize(cls, header: dict, frames: list):
+        header, frames, klass = _decode_type(cls, header, frames)
+        klass = pickle.loads(header["type-serialized"])
+        subtype, closed = pickle.loads(header["fields"])
+        return klass(subtype, closed=closed)
+
+
+def is_categorical_dtype(obj):
+    """Check whether an array-like or dtype is of the Categorical dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like or dtype is of a categorical dtype.
+    """
+    if obj is None:
+        return False
+
+    if isinstance(
+        obj,
+        (
+            pd_CategoricalDtype,
+            cudf.CategoricalDtype,
+            cudf.core.index.CategoricalIndex,
+            cudf.core.column.CategoricalColumn,
+            pd.Categorical,
+            pd.CategoricalIndex,
+        ),
+    ):
+        return True
+    # Note that we cannot directly use `obj in (...)`  because that triggers
+    # equality as well as identity checks and pandas extension dtypes won't
+    # allow converting that equality check to a boolean; `__nonzero__` is
+    # disabled because they treat dtypes as "array-like".
+    if any(
+        obj is t
+        for t in (
+            cudf.CategoricalDtype,
+            pd_CategoricalDtype,
+            pd_CategoricalDtypeType,
+        )
+    ):
+        return True
+    if isinstance(obj, (np.ndarray, np.dtype)):
+        return False
+    if isinstance(obj, str) and obj == "category":
+        return True
+    if isinstance(obj, cudf.core.index.BaseIndex):
+        return obj._is_categorical()
+    if isinstance(
+        obj,
+        (
+            cudf.Series,
+            cudf.core.column.ColumnBase,
+            pd.Index,
+            pd.Series,
+        ),
+    ):
+        return is_categorical_dtype(obj.dtype)
+    if hasattr(obj, "type"):
+        if obj.type is pd_CategoricalDtypeType:
+            return True
+    # TODO: A lot of the above checks are probably redundant and should be
+    # farmed out to this function here instead.
+    return pd_types.is_categorical_dtype(obj)
+
+
+def is_list_dtype(obj):
+    """Check whether an array-like or dtype is of the list dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like or dtype is of the list dtype.
+    """
+    return (
+        type(obj) is cudf.core.dtypes.ListDtype
+        or obj is cudf.core.dtypes.ListDtype
+        or type(obj) is cudf.core.column.ListColumn
+        or obj is cudf.core.column.ListColumn
+        or (isinstance(obj, str) and obj == cudf.core.dtypes.ListDtype.name)
+        or (hasattr(obj, "dtype") and is_list_dtype(obj.dtype))
+    )
+
+
+def is_struct_dtype(obj):
+    """Check whether an array-like or dtype is of the struct dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like or dtype is of the struct dtype.
+    """
+    # TODO: This behavior is currently inconsistent for interval types. the
+    # actual class IntervalDtype will return False, but instances (e.g.
+    # IntervalDtype(int)) will return True. For now this is not being changed
+    # since the interval dtype is being modified as part of the array refactor,
+    # but this behavior should be made consistent afterwards.
+    return (
+        isinstance(obj, cudf.core.dtypes.StructDtype)
+        or obj is cudf.core.dtypes.StructDtype
+        or (isinstance(obj, str) and obj == cudf.core.dtypes.StructDtype.name)
+        or (hasattr(obj, "dtype") and is_struct_dtype(obj.dtype))
+    )
+
+
+def is_decimal_dtype(obj):
+    """Check whether an array-like or dtype is of the decimal dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like or dtype is of the decimal dtype.
+    """
+    return (
+        is_decimal32_dtype(obj)
+        or is_decimal64_dtype(obj)
+        or is_decimal128_dtype(obj)
+    )
+
+
+def is_interval_dtype(obj):
+    """Check whether an array-like or dtype is of the interval dtype.
+
+    Parameters
+    ----------
+    obj : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the array-like or dtype is of the interval dtype.
+    """
+    # TODO: Should there be any branch in this function that calls
+    # pd.api.types.is_interval_dtype?
+    return (
+        isinstance(
+            obj,
+            (
+                cudf.core.dtypes.IntervalDtype,
+                pd.IntervalDtype,
+            ),
+        )
+        or obj is cudf.core.dtypes.IntervalDtype
+        or (isinstance(obj, cudf.core.index.BaseIndex) and obj._is_interval())
+        or (
+            isinstance(obj, str) and obj == cudf.core.dtypes.IntervalDtype.name
+        )
+        or (hasattr(obj, "dtype") and is_interval_dtype(obj.dtype))
+    )
+
+
+def is_decimal32_dtype(obj):
+    return (
+        type(obj) is cudf.core.dtypes.Decimal32Dtype
+        or obj is cudf.core.dtypes.Decimal32Dtype
+        or (
+            isinstance(obj, str)
+            and obj == cudf.core.dtypes.Decimal32Dtype.name
+        )
+        or (hasattr(obj, "dtype") and is_decimal32_dtype(obj.dtype))
+    )
+
+
+def is_decimal64_dtype(obj):
+    return (
+        type(obj) is cudf.core.dtypes.Decimal64Dtype
+        or obj is cudf.core.dtypes.Decimal64Dtype
+        or (
+            isinstance(obj, str)
+            and obj == cudf.core.dtypes.Decimal64Dtype.name
+        )
+        or (hasattr(obj, "dtype") and is_decimal64_dtype(obj.dtype))
+    )
+
+
+def is_decimal128_dtype(obj):
+    return (
+        type(obj) is cudf.core.dtypes.Decimal128Dtype
+        or obj is cudf.core.dtypes.Decimal128Dtype
+        or (
+            isinstance(obj, str)
+            and obj == cudf.core.dtypes.Decimal128Dtype.name
+        )
+        or (hasattr(obj, "dtype") and is_decimal128_dtype(obj.dtype))
+    )
diff --git a/python/cudf/cudf/core/frame.py b/python/cudf/cudf/core/frame.py
new file mode 100644
index 0000000..f7329d4
--- /dev/null
+++ b/python/cudf/cudf/core/frame.py
@@ -0,0 +1,2884 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import copy
+import itertools
+import operator
+import pickle
+import warnings
+from collections import abc
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    MutableMapping,
+    Optional,
+    Tuple,
+    Union,
+)
+
+# TODO: The `numpy` import is needed for typing purposes during doc builds
+# only, need to figure out why the `np` alias is insufficient then remove.
+import cupy
+import numpy
+import numpy as np
+import pyarrow as pa
+from typing_extensions import Self
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._typing import Dtype
+from cudf.api.extensions import no_default
+from cudf.api.types import is_bool_dtype, is_dtype_equal, is_scalar
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.column import (
+    ColumnBase,
+    as_column,
+    build_categorical_column,
+    deserialize_columns,
+    serialize_columns,
+)
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.mixins import BinaryOperand, Scannable
+from cudf.core.window import Rolling
+from cudf.utils import ioutils
+from cudf.utils.docutils import copy_docstring
+from cudf.utils.dtypes import can_convert_to_column, find_common_type
+from cudf.utils.utils import (
+    _array_ufunc,
+    _cudf_nvtx_annotate,
+    _warn_no_dask_cudf,
+)
+
+
+# TODO: It looks like Frame is missing a declaration of `copy`, need to add
+class Frame(BinaryOperand, Scannable):
+    """A collection of Column objects with an optional index.
+
+    Parameters
+    ----------
+    data : dict
+        An dict mapping column names to Columns
+    index : Table
+        A Frame representing the (optional) index columns.
+    """
+
+    _data: "ColumnAccessor"
+
+    _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
+
+    def __init__(self, data=None):
+        if data is None:
+            data = {}
+        self._data = cudf.core.column_accessor.ColumnAccessor(data)
+
+    @property
+    def _num_columns(self) -> int:
+        return len(self._data)
+
+    @property
+    def _num_rows(self) -> int:
+        return 0 if self._num_columns == 0 else len(self._data.columns[0])
+
+    @property
+    def _column_names(self) -> Tuple[Any, ...]:  # TODO: Tuple[str]?
+        return tuple(self._data.names)
+
+    @property
+    def _columns(self) -> Tuple[Any, ...]:  # TODO: Tuple[Column]?
+        return tuple(self._data.columns)
+
+    @property
+    def _dtypes(self):
+        return dict(
+            zip(self._data.names, (col.dtype for col in self._data.columns))
+        )
+
+    @property
+    def _has_nulls(self):
+        return any(col.has_nulls() for col in self._data.values())
+
+    @_cudf_nvtx_annotate
+    def serialize(self):
+        header = {
+            "type-serialized": pickle.dumps(type(self)),
+            "column_names": pickle.dumps(tuple(self._data.names)),
+        }
+        header["columns"], frames = serialize_columns(self._columns)
+        return header, frames
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def deserialize(cls, header, frames):
+        cls_deserialize = pickle.loads(header["type-serialized"])
+        column_names = pickle.loads(header["column_names"])
+        columns = deserialize_columns(header["columns"], frames)
+        return cls_deserialize._from_data(dict(zip(column_names, columns)))
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_data(cls, data: MutableMapping):
+        obj = cls.__new__(cls)
+        Frame.__init__(obj, data)
+        return obj
+
+    @_cudf_nvtx_annotate
+    def _from_data_like_self(self, data: MutableMapping):
+        return self._from_data(data)
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_columns(
+        cls,
+        columns: List[ColumnBase],
+        column_names: abc.Iterable[str],
+    ):
+        """Construct a `Frame` object from a list of columns."""
+        data = {name: columns[i] for i, name in enumerate(column_names)}
+        return cls._from_data(data)
+
+    @_cudf_nvtx_annotate
+    def _from_columns_like_self(
+        self,
+        columns: List[ColumnBase],
+        column_names: Optional[abc.Iterable[str]] = None,
+        *,
+        override_dtypes: Optional[abc.Iterable[Optional[Dtype]]] = None,
+    ):
+        """Construct a Frame from a list of columns with metadata from self.
+
+        If `column_names` is None, use column names from self.
+        """
+        if column_names is None:
+            column_names = self._column_names
+        frame = self.__class__._from_columns(columns, column_names)
+        return frame._copy_type_metadata(self, override_dtypes=override_dtypes)
+
+    @_cudf_nvtx_annotate
+    def _mimic_inplace(
+        self, result: Self, inplace: bool = False
+    ) -> Optional[Self]:
+        if inplace:
+            for col in self._data:
+                if col in result._data:
+                    self._data[col]._mimic_inplace(
+                        result._data[col], inplace=True
+                    )
+            self._data = result._data
+            return None
+        else:
+            return result
+
+    @property
+    @_cudf_nvtx_annotate
+    def size(self):
+        """
+        Return the number of elements in the underlying data.
+
+        Returns
+        -------
+        size : Size of the DataFrame / Index / Series / MultiIndex
+
+        Examples
+        --------
+        Size of an empty dataframe is 0.
+
+        >>> import cudf
+        >>> df = cudf.DataFrame()
+        >>> df
+        Empty DataFrame
+        Columns: []
+        Index: []
+        >>> df.size
+        0
+        >>> df = cudf.DataFrame(index=[1, 2, 3])
+        >>> df
+        Empty DataFrame
+        Columns: []
+        Index: [1, 2, 3]
+        >>> df.size
+        0
+
+        DataFrame with values
+
+        >>> df = cudf.DataFrame({'a': [10, 11, 12],
+        ...         'b': ['hello', 'rapids', 'ai']})
+        >>> df
+            a       b
+        0  10   hello
+        1  11  rapids
+        2  12      ai
+        >>> df.size
+        6
+        >>> df.index
+        RangeIndex(start=0, stop=3)
+        >>> df.index.size
+        3
+
+        Size of an Index
+
+        >>> index = cudf.Index([])
+        >>> index
+        Float64Index([], dtype='float64')
+        >>> index.size
+        0
+        >>> index = cudf.Index([1, 2, 3, 10])
+        >>> index
+        Int64Index([1, 2, 3, 10], dtype='int64')
+        >>> index.size
+        4
+
+        Size of a MultiIndex
+
+        >>> midx = cudf.MultiIndex(
+        ...                 levels=[["a", "b", "c", None], ["1", None, "5"]],
+        ...                 codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        ...                 names=["x", "y"],
+        ...             )
+        >>> midx
+        MultiIndex([( 'a',  '1'),
+                    ( 'a',  '5'),
+                    ( 'b', <NA>),
+                    ( 'c', <NA>),
+                    (<NA>,  '1')],
+                   names=['x', 'y'])
+        >>> midx.size
+        5
+        """
+        return self._num_columns * self._num_rows
+
+    @_cudf_nvtx_annotate
+    def memory_usage(self, deep=False):
+        """Return the memory usage of an object.
+
+        Parameters
+        ----------
+        deep : bool
+            The deep parameter is ignored and is only included for pandas
+            compatibility.
+
+        Returns
+        -------
+        The total bytes used.
+        """
+        raise NotImplementedError
+
+    @_cudf_nvtx_annotate
+    def __len__(self):
+        return self._num_rows
+
+    @_cudf_nvtx_annotate
+    def astype(self, dtype, copy=False, **kwargs):
+        result_data = {}
+        for col_name, col in self._data.items():
+            dt = dtype.get(col_name, col.dtype)
+            if not is_dtype_equal(dt, col.dtype):
+                result_data[col_name] = col.astype(dt, copy=copy, **kwargs)
+            else:
+                result_data[col_name] = col.copy() if copy else col
+
+        return ColumnAccessor._create_unsafe(
+            data=result_data,
+            multiindex=self._data.multiindex,
+            level_names=self._data.level_names,
+        )
+
+    @_cudf_nvtx_annotate
+    def equals(self, other):
+        """
+        Test whether two objects contain the same elements.
+
+        This function allows two objects to be compared against
+        each other to see if they have the same shape and elements. NaNs in
+        the same location are considered equal. The column headers do not
+        need to have the same type.
+
+        Parameters
+        ----------
+        other : Index, Series, DataFrame
+            The other object to be compared with.
+
+        Returns
+        -------
+        bool
+            True if all elements are the same in both objects, False
+            otherwise.
+
+        Examples
+        --------
+        >>> import cudf
+
+        Comparing Series with `equals`:
+
+        >>> s = cudf.Series([1, 2, 3])
+        >>> other = cudf.Series([1, 2, 3])
+        >>> s.equals(other)
+        True
+        >>> different = cudf.Series([1.5, 2, 3])
+        >>> s.equals(different)
+        False
+
+        Comparing DataFrames with `equals`:
+
+        >>> df = cudf.DataFrame({1: [10], 2: [20]})
+        >>> df
+            1   2
+        0  10  20
+        >>> exactly_equal = cudf.DataFrame({1: [10], 2: [20]})
+        >>> exactly_equal
+            1   2
+        0  10  20
+        >>> df.equals(exactly_equal)
+        True
+
+        For two DataFrames to compare equal, the types of column
+        values must be equal, but the types of column labels
+        need not:
+
+        >>> different_column_type = cudf.DataFrame({1.0: [10], 2.0: [20]})
+        >>> different_column_type
+           1.0  2.0
+        0   10   20
+        >>> df.equals(different_column_type)
+        True
+        """
+        if self is other:
+            return True
+        if (
+            other is None
+            or not isinstance(other, type(self))
+            or len(self) != len(other)
+        ):
+            return False
+
+        return all(
+            self_col.equals(other_col, check_dtypes=True)
+            for self_col, other_col in zip(
+                self._data.values(), other._data.values()
+            )
+        )
+
+    @_cudf_nvtx_annotate
+    def _get_columns_by_label(self, labels, *, downcast=False) -> Self:
+        """
+        Returns columns of the Frame specified by `labels`
+
+        """
+        return self.__class__._from_data(self._data.select_by_label(labels))
+
+    @property
+    @_cudf_nvtx_annotate
+    def values(self):
+        """
+        Return a CuPy representation of the DataFrame.
+
+        Only the values in the DataFrame will be returned, the axes labels will
+        be removed.
+
+        Returns
+        -------
+        cupy.ndarray
+            The values of the DataFrame.
+        """
+        return self.to_cupy()
+
+    @property
+    @_cudf_nvtx_annotate
+    def values_host(self):
+        """
+        Return a NumPy representation of the data.
+
+        Only the values in the DataFrame will be returned, the axes labels will
+        be removed.
+
+        Returns
+        -------
+        numpy.ndarray
+            A host representation of the underlying data.
+        """
+        return self.to_numpy()
+
+    @_cudf_nvtx_annotate
+    def __array__(self, dtype=None):
+        raise TypeError(
+            "Implicit conversion to a host NumPy array via __array__ is not "
+            "allowed, To explicitly construct a GPU matrix, consider using "
+            ".to_cupy()\nTo explicitly construct a host matrix, consider "
+            "using .to_numpy()."
+        )
+
+    @_cudf_nvtx_annotate
+    def __arrow_array__(self, type=None):
+        raise TypeError(
+            "Implicit conversion to a host PyArrow object via __arrow_array__ "
+            "is not allowed. Consider using .to_arrow()"
+        )
+
+    @_cudf_nvtx_annotate
+    def _to_array(
+        self,
+        get_column_values: Callable,
+        make_empty_matrix: Callable,
+        dtype: Union[Dtype, None] = None,
+        na_value=None,
+    ) -> Union[cupy.ndarray, np.ndarray]:
+        # Internal function to implement to_cupy and to_numpy, which are nearly
+        # identical except for the attribute they access to generate values.
+
+        def get_column_values_na(col):
+            if na_value is not None:
+                col = col.fillna(na_value)
+            return get_column_values(col)
+
+        # Early exit for an empty Frame.
+        ncol = self._num_columns
+        if ncol == 0:
+            return make_empty_matrix(
+                shape=(len(self), ncol), dtype=np.dtype("float64"), order="F"
+            )
+
+        if dtype is None:
+            dtypes = [col.dtype for col in self._data.values()]
+            for dtype in dtypes:
+                if isinstance(
+                    dtype,
+                    (
+                        cudf.ListDtype,
+                        cudf.core.dtypes.DecimalDtype,
+                        cudf.StructDtype,
+                    ),
+                ):
+                    raise NotImplementedError(
+                        f"{dtype} cannot be exposed as a cupy array"
+                    )
+            dtype = find_common_type(dtypes)
+
+        matrix = make_empty_matrix(
+            shape=(len(self), ncol), dtype=dtype, order="F"
+        )
+        for i, col in enumerate(self._data.values()):
+            # TODO: col.values may fail if there is nullable data or an
+            # unsupported dtype. We may want to catch and provide a more
+            # suitable error.
+            matrix[:, i] = get_column_values_na(col)
+        return matrix
+
+    # TODO: As of now, calling cupy.asarray is _much_ faster than calling
+    # to_cupy. We should investigate the reasons why and whether we can provide
+    # a more efficient method here by exploiting __cuda_array_interface__. In
+    # particular, we need to benchmark how much of the overhead is coming from
+    # (potentially unavoidable) local copies in to_cupy and how much comes from
+    # inefficiencies in the implementation.
+    @_cudf_nvtx_annotate
+    def to_cupy(
+        self,
+        dtype: Union[Dtype, None] = None,
+        copy: bool = False,
+        na_value=None,
+    ) -> cupy.ndarray:
+        """Convert the Frame to a CuPy array.
+
+        Parameters
+        ----------
+        dtype : str or :class:`numpy.dtype`, optional
+            The dtype to pass to :func:`numpy.asarray`.
+        copy : bool, default False
+            Whether to ensure that the returned value is not a view on
+            another array. Note that ``copy=False`` does not *ensure* that
+            ``to_cupy()`` is no-copy. Rather, ``copy=True`` ensure that
+            a copy is made, even if not strictly necessary.
+        na_value : Any, default None
+            The value to use for missing values. The default value depends on
+            dtype and the dtypes of the DataFrame columns.
+
+        Returns
+        -------
+        cupy.ndarray
+        """
+        return self._to_array(
+            (lambda col: col.values.copy())
+            if copy
+            else (lambda col: col.values),
+            cupy.empty,
+            dtype,
+            na_value,
+        )
+
+    @_cudf_nvtx_annotate
+    def to_numpy(
+        self,
+        dtype: Union[Dtype, None] = None,
+        copy: bool = True,
+        na_value=None,
+    ) -> numpy.ndarray:
+        """Convert the Frame to a NumPy array.
+
+        Parameters
+        ----------
+        dtype : str or :class:`numpy.dtype`, optional
+            The dtype to pass to :func:`numpy.asarray`.
+        copy : bool, default True
+            Whether to ensure that the returned value is not a view on
+            another array. This parameter must be ``True`` since cuDF must copy
+            device memory to host to provide a numpy array.
+        na_value : Any, default None
+            The value to use for missing values. The default value depends on
+            dtype and the dtypes of the DataFrame columns.
+
+        Returns
+        -------
+        numpy.ndarray
+        """
+        if not copy:
+            raise ValueError(
+                "copy=False is not supported because conversion to a numpy "
+                "array always copies the data."
+            )
+
+        return self._to_array(
+            (lambda col: col.values_host), np.empty, dtype, na_value
+        )
+
+    @_cudf_nvtx_annotate
+    def where(self, cond, other=None, inplace=False):
+        """
+        Replace values where the condition is False.
+
+        Parameters
+        ----------
+        cond : bool Series/DataFrame, array-like
+            Where cond is True, keep the original value.
+            Where False, replace with corresponding value from other.
+            Callables are not supported.
+        other: scalar, list of scalars, Series/DataFrame
+            Entries where cond is False are replaced with
+            corresponding value from other. Callables are not
+            supported. Default is None.
+
+            DataFrame expects only Scalar or array like with scalars or
+            dataframe with same dimension as self.
+
+            Series expects only scalar or series like with same length
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+
+        Returns
+        -------
+        Same type as caller
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({"A":[1, 4, 5], "B":[3, 5, 8]})
+        >>> df.where(df % 2 == 0, [-1, -1])
+           A  B
+        0 -1 -1
+        1  4 -1
+        2 -1  8
+
+        >>> ser = cudf.Series([4, 3, 2, 1, 0])
+        >>> ser.where(ser > 2, 10)
+        0     4
+        1     3
+        2    10
+        3    10
+        4    10
+        dtype: int64
+        >>> ser.where(ser > 2)
+        0       4
+        1       3
+        2    <NA>
+        3    <NA>
+        4    <NA>
+        dtype: int64
+
+        .. pandas-compat::
+            Note that ``where`` treats missing values as falsy,
+            in parallel with pandas treatment of nullable data:
+
+            >>> gsr = cudf.Series([1, 2, 3])
+            >>> gsr.where([True, False, cudf.NA])
+            0       1
+            1    <NA>
+            2    <NA>
+            dtype: int64
+            >>> gsr.where([True, False, False])
+            0       1
+            1    <NA>
+            2    <NA>
+            dtype: int64
+        """
+        raise NotImplementedError
+
+    @_cudf_nvtx_annotate
+    def mask(self, cond, other=None, inplace=False):
+        """
+        Replace values where the condition is True.
+
+        Parameters
+        ----------
+        cond : bool Series/DataFrame, array-like
+            Where cond is False, keep the original value.
+            Where True, replace with corresponding value from other.
+            Callables are not supported.
+        other: scalar, list of scalars, Series/DataFrame
+            Entries where cond is True are replaced with
+            corresponding value from other. Callables are not
+            supported. Default is None.
+
+            DataFrame expects only Scalar or array like with scalars or
+            dataframe with same dimension as self.
+
+            Series expects only scalar or series like with same length
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+
+        Returns
+        -------
+        Same type as caller
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({"A":[1, 4, 5], "B":[3, 5, 8]})
+        >>> df.mask(df % 2 == 0, [-1, -1])
+           A  B
+        0  1  3
+        1 -1  5
+        2  5 -1
+
+        >>> ser = cudf.Series([4, 3, 2, 1, 0])
+        >>> ser.mask(ser > 2, 10)
+        0    10
+        1    10
+        2     2
+        3     1
+        4     0
+        dtype: int64
+        >>> ser.mask(ser > 2)
+        0    <NA>
+        1    <NA>
+        2       2
+        3       1
+        4       0
+        dtype: int64
+        """
+
+        if not hasattr(cond, "__invert__"):
+            # We Invert `cond` below and call `where`, so
+            # making sure the object supports
+            # `~`(inversion) operator or `__invert__` method
+            cond = cupy.asarray(cond)
+
+        return self.where(cond=~cond, other=other, inplace=inplace)
+
+    @_cudf_nvtx_annotate
+    def pipe(self, func, *args, **kwargs):
+        """
+        Apply ``func(self, *args, **kwargs)``.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to the Series/DataFrame/Index.
+            ``args``, and ``kwargs`` are passed into ``func``.
+            Alternatively a ``(callable, data_keyword)`` tuple where
+            ``data_keyword`` is a string indicating the keyword of
+            ``callable`` that expects the Series/DataFrame/Index.
+        args : iterable, optional
+            Positional arguments passed into ``func``.
+        kwargs : mapping, optional
+            A dictionary of keyword arguments passed into ``func``.
+
+        Returns
+        -------
+        object : the return type of ``func``.
+
+        Examples
+        --------
+        Use ``.pipe`` when chaining together functions that expect
+        Series, DataFrames or GroupBy objects. Instead of writing
+
+        >>> func(g(h(df), arg1=a), arg2=b, arg3=c)
+
+        You can write
+
+        >>> (df.pipe(h)
+        ...    .pipe(g, arg1=a)
+        ...    .pipe(func, arg2=b, arg3=c)
+        ... )
+
+        If you have a function that takes the data as (say) the second
+        argument, pass a tuple indicating which keyword expects the
+        data. For example, suppose ``f`` takes its data as ``arg2``:
+
+        >>> (df.pipe(h)
+        ...    .pipe(g, arg1=a)
+        ...    .pipe((func, 'arg2'), arg1=a, arg3=c)
+        ...  )
+        """
+        return cudf.core.common.pipe(self, func, *args, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def fillna(
+        self, value=None, method=None, axis=None, inplace=False, limit=None
+    ):
+        """Fill null values with ``value`` or specified ``method``.
+
+        Parameters
+        ----------
+        value : scalar, Series-like or dict
+            Value to use to fill nulls. If Series-like, null values
+            are filled with values in corresponding indices.
+            A dict can be used to provide different values to fill nulls
+            in different columns. Cannot be used with ``method``.
+
+        method : {'ffill', 'bfill'}, default None
+            Method to use for filling null values in the dataframe or series.
+            `ffill` propagates the last non-null values forward to the next
+            non-null value. `bfill` propagates backward with the next non-null
+            value. Cannot be used with ``value``.
+
+        Returns
+        -------
+        result : DataFrame, Series, or Index
+            Copy with nulls filled.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, None], 'b': [3, None, 5]})
+        >>> df
+              a     b
+        0     1     3
+        1     2  <NA>
+        2  <NA>     5
+        >>> df.fillna(4)
+           a  b
+        0  1  3
+        1  2  4
+        2  4  5
+        >>> df.fillna({'a': 3, 'b': 4})
+           a  b
+        0  1  3
+        1  2  4
+        2  3  5
+
+        ``fillna`` on a Series object:
+
+        >>> ser = cudf.Series(['a', 'b', None, 'c'])
+        >>> ser
+        0       a
+        1       b
+        2    <NA>
+        3       c
+        dtype: object
+        >>> ser.fillna('z')
+        0    a
+        1    b
+        2    z
+        3    c
+        dtype: object
+
+        ``fillna`` can also supports inplace operation:
+
+        >>> ser.fillna('z', inplace=True)
+        >>> ser
+        0    a
+        1    b
+        2    z
+        3    c
+        dtype: object
+        >>> df.fillna({'a': 3, 'b': 4}, inplace=True)
+        >>> df
+           a  b
+        0  1  3
+        1  2  4
+        2  3  5
+
+        ``fillna`` specified with fill ``method``
+
+        >>> ser = cudf.Series([1, None, None, 2, 3, None, None])
+        >>> ser.fillna(method='ffill')
+        0    1
+        1    1
+        2    1
+        3    2
+        4    3
+        5    3
+        6    3
+        dtype: int64
+        >>> ser.fillna(method='bfill')
+        0       1
+        1       2
+        2       2
+        3       2
+        4       3
+        5    <NA>
+        6    <NA>
+        dtype: int64
+        """
+        if limit is not None:
+            raise NotImplementedError("The limit keyword is not supported")
+        if axis:
+            raise NotImplementedError("The axis keyword is not supported")
+
+        if value is not None and method is not None:
+            raise ValueError("Cannot specify both 'value' and 'method'.")
+
+        if method:
+            if method not in {"ffill", "bfill", "pad", "backfill"}:
+                raise NotImplementedError(
+                    f"Fill method {method} is not supported"
+                )
+            if method == "pad":
+                method = "ffill"
+            elif method == "backfill":
+                method = "bfill"
+
+        # TODO: This logic should be handled in different subclasses since
+        # different Frames support different types of values.
+        if isinstance(value, cudf.Series):
+            value = value.reindex(self._data.names)
+        elif isinstance(value, cudf.DataFrame):
+            if not self.index.equals(value.index):
+                value = value.reindex(self.index)
+            else:
+                value = value
+        elif not isinstance(value, abc.Mapping):
+            value = {name: copy.deepcopy(value) for name in self._data.names}
+        else:
+            value = {
+                key: value.reindex(self.index)
+                if isinstance(value, cudf.Series)
+                else value
+                for key, value in value.items()
+            }
+
+        filled_data = {}
+        for col_name, col in self._data.items():
+            if col_name in value and method is None:
+                replace_val = value[col_name]
+            else:
+                replace_val = None
+            should_fill = (
+                col_name in value
+                and col.contains_na_entries
+                and not libcudf.scalar._is_null_host_scalar(replace_val)
+            ) or method is not None
+            if should_fill:
+                filled_data[col_name] = col.fillna(replace_val, method)
+            else:
+                filled_data[col_name] = col.copy(deep=True)
+
+        return self._mimic_inplace(
+            self._from_data(
+                data=ColumnAccessor._create_unsafe(
+                    data=filled_data,
+                    multiindex=self._data.multiindex,
+                    level_names=self._data.level_names,
+                )
+            ),
+            inplace=inplace,
+        )
+
+    @_cudf_nvtx_annotate
+    def _drop_column(self, name):
+        """Drop a column by *name*"""
+        if name not in self._data:
+            raise KeyError(f"column '{name}' does not exist")
+        del self._data[name]
+
+    @_cudf_nvtx_annotate
+    def _drop_na_columns(self, how="any", subset=None, thresh=None):
+        """
+        Drop columns containing nulls
+        """
+        out_cols = []
+
+        if subset is None:
+            df = self
+        else:
+            df = self.take(subset)
+
+        if thresh is None:
+            if how == "all":
+                thresh = 1
+            else:
+                thresh = len(df)
+
+        for name, col in df._data.items():
+            try:
+                check_col = col.nans_to_nulls()
+            except AttributeError:
+                check_col = col
+            no_threshold_valid_count = (
+                len(col) - check_col.null_count
+            ) < thresh
+            if no_threshold_valid_count:
+                continue
+            out_cols.append(name)
+
+        return self[out_cols]
+
+    @_cudf_nvtx_annotate
+    def _quantile_table(
+        self,
+        q,
+        interpolation="LINEAR",
+        is_sorted=False,
+        column_order=(),
+        null_precedence=(),
+    ):
+        interpolation = libcudf.types.Interpolation[interpolation]
+
+        is_sorted = libcudf.types.Sorted["YES" if is_sorted else "NO"]
+
+        column_order = [libcudf.types.Order[key] for key in column_order]
+
+        null_precedence = [
+            libcudf.types.NullOrder[key] for key in null_precedence
+        ]
+
+        return self._from_columns_like_self(
+            libcudf.quantiles.quantile_table(
+                [*self._columns],
+                q,
+                interpolation,
+                is_sorted,
+                column_order,
+                null_precedence,
+            ),
+            column_names=self._column_names,
+        )
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_arrow(cls, data):
+        """Convert from PyArrow Table to Frame
+
+        Parameters
+        ----------
+        data : PyArrow Table
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pyarrow as pa
+        >>> data = pa.table({"a":[1, 2, 3], "b":[4, 5, 6]})
+        >>> cudf.core.frame.Frame.from_arrow(data)
+           a  b
+        0  1  4
+        1  2  5
+        2  3  6
+        """
+
+        if not isinstance(data, (pa.Table)):
+            raise TypeError(
+                "To create a multicolumn cudf data, "
+                "the data should be an arrow Table"
+            )
+
+        column_names = data.column_names
+        pandas_dtypes = {}
+        np_dtypes = {}
+        if isinstance(data.schema.pandas_metadata, dict):
+            metadata = data.schema.pandas_metadata
+            pandas_dtypes = {
+                col["field_name"]: col["pandas_type"]
+                for col in metadata["columns"]
+                if "field_name" in col
+            }
+            np_dtypes = {
+                col["field_name"]: col["numpy_type"]
+                for col in metadata["columns"]
+                if "field_name" in col
+            }
+
+        # Currently we don't have support for
+        # pyarrow.DictionaryArray -> cudf Categorical column,
+        # so handling indices and dictionary as two different columns.
+        # This needs be removed once we have hooked libcudf dictionary32
+        # with categorical.
+        dict_indices = {}
+        dict_dictionaries = {}
+        dict_ordered = {}
+        for field in data.schema:
+            if isinstance(field.type, pa.DictionaryType):
+                dict_ordered[field.name] = field.type.ordered
+                dict_indices[field.name] = pa.chunked_array(
+                    [chunk.indices for chunk in data[field.name].chunks],
+                    type=field.type.index_type,
+                )
+                dict_dictionaries[field.name] = pa.chunked_array(
+                    [chunk.dictionary for chunk in data[field.name].chunks],
+                    type=field.type.value_type,
+                )
+
+        # Handle dict arrays
+        cudf_category_frame = {}
+        if len(dict_indices):
+            dict_indices_table = pa.table(dict_indices)
+            data = data.drop(dict_indices_table.column_names)
+            indices_columns = libcudf.interop.from_arrow(dict_indices_table)
+            # as dictionary size can vary, it can't be a single table
+            cudf_dictionaries_columns = {
+                name: ColumnBase.from_arrow(dict_dictionaries[name])
+                for name in dict_dictionaries.keys()
+            }
+
+            cudf_category_frame = {
+                name: build_categorical_column(
+                    cudf_dictionaries_columns[name],
+                    codes,
+                    mask=codes.base_mask,
+                    size=codes.size,
+                    ordered=dict_ordered[name],
+                )
+                for name, codes in zip(
+                    dict_indices_table.column_names, indices_columns
+                )
+            }
+
+        # Handle non-dict arrays
+        cudf_non_category_frame = {
+            name: col
+            for name, col in zip(
+                data.column_names, libcudf.interop.from_arrow(data)
+            )
+        }
+
+        result = {**cudf_non_category_frame, **cudf_category_frame}
+
+        # There are some special cases that need to be handled
+        # based on metadata.
+        for name in result:
+            if (
+                len(result[name]) == 0
+                and pandas_dtypes.get(name) == "categorical"
+            ):
+                # When pandas_dtype is a categorical column and the size
+                # of column is 0 (i.e., empty) then we will have an
+                # int8 column in result._data[name] returned by libcudf,
+                # which needs to be type-casted to 'category' dtype.
+                result[name] = result[name].as_categorical_column("category")
+            elif (
+                pandas_dtypes.get(name) == "empty"
+                and np_dtypes.get(name) == "object"
+            ):
+                # When a string column has all null values, pandas_dtype is
+                # is specified as 'empty' and np_dtypes as 'object',
+                # hence handling this special case to type-cast the empty
+                # float column to str column.
+                result[name] = result[name].as_string_column(cudf.dtype("str"))
+            elif name in data.column_names and isinstance(
+                data[name].type,
+                (
+                    pa.StructType,
+                    pa.ListType,
+                    pa.Decimal128Type,
+                    pa.TimestampType,
+                ),
+            ):
+                # In case of struct column, libcudf is not aware of names of
+                # struct fields, hence renaming the struct fields is
+                # necessary by extracting the field names from arrow
+                # struct types.
+
+                # In case of decimal column, libcudf is not aware of the
+                # decimal precision.
+
+                # In case of list column, there is a possibility of nested
+                # list columns to have struct or decimal columns inside them.
+
+                # Datetimes ("timestamps") may need timezone metadata
+                # attached to them, as libcudf is timezone-unaware
+
+                # All of these cases are handled by calling the
+                # _with_type_metadata method on the column.
+                result[name] = result[name]._with_type_metadata(
+                    cudf.utils.dtypes.cudf_dtype_from_pa_type(data[name].type)
+                )
+
+        return cls._from_data({name: result[name] for name in column_names})
+
+    @_cudf_nvtx_annotate
+    def to_arrow(self):
+        """
+        Convert to arrow Table
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame(
+        ...     {"a":[1, 2, 3], "b":[4, 5, 6]}, index=[1, 2, 3])
+        >>> df.to_arrow()
+        pyarrow.Table
+        a: int64
+        b: int64
+        index: int64
+        ----
+        a: [[1,2,3]]
+        b: [[4,5,6]]
+        index: [[1,2,3]]
+        """
+        return pa.Table.from_pydict(
+            {str(name): col.to_arrow() for name, col in self._data.items()}
+        )
+
+    @_cudf_nvtx_annotate
+    def _positions_from_column_names(self, column_names):
+        """Map each column name into their positions in the frame.
+
+        The order of indices returned corresponds to the column order in this
+        Frame.
+        """
+        return [
+            i
+            for i, name in enumerate(self._column_names)
+            if name in set(column_names)
+        ]
+
+    @_cudf_nvtx_annotate
+    def _copy_type_metadata(
+        self,
+        other: Self,
+        *,
+        override_dtypes: Optional[abc.Iterable[Optional[Dtype]]] = None,
+    ) -> Self:
+        """
+        Copy type metadata from each column of `other` to the corresponding
+        column of `self`.
+
+        If override_dtypes is provided, any non-None entry
+        will be used in preference to the relevant column of other to
+        provide the new dtype.
+
+        See `ColumnBase._with_type_metadata` for more information.
+        """
+        if override_dtypes is None:
+            override_dtypes = itertools.repeat(None)
+        dtypes = (
+            dtype if dtype is not None else col.dtype
+            for (dtype, col) in zip(override_dtypes, other._data.values())
+        )
+        for (name, col), dtype in zip(self._data.items(), dtypes):
+            self._data.set_by_label(
+                name, col._with_type_metadata(dtype), validate=False
+            )
+
+        return self
+
+    @_cudf_nvtx_annotate
+    def isna(self):
+        """
+        Identify missing values.
+
+        Return a boolean same-sized object indicating if
+        the values are ``<NA>``. ``<NA>`` values gets mapped to
+        ``True`` values. Everything else gets mapped to
+        ``False`` values. ``<NA>`` values include:
+
+        * Values where null mask is set.
+        * ``NaN`` in float dtype.
+        * ``NaT`` in datetime64 and timedelta64 types.
+
+        Characters such as empty strings ``''`` or
+        ``inf`` in case of float are not
+        considered ``<NA>`` values.
+
+        Returns
+        -------
+        DataFrame/Series/Index
+            Mask of bool values for each element in
+            the object that indicates whether an element is an NA value.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are NA.
+
+        >>> import cudf
+        >>> import numpy as np
+        >>> import pandas as pd
+        >>> df = cudf.DataFrame({'age': [5, 6, np.NaN],
+        ...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
+        ...                             pd.Timestamp('1940-04-25')],
+        ...                    'name': ['Alfred', 'Batman', ''],
+        ...                    'toy': [None, 'Batmobile', 'Joker']})
+        >>> df
+            age                        born    name        toy
+        0     5                        <NA>  Alfred       <NA>
+        1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
+        2  <NA>  1940-04-25 00:00:00.000000              Joker
+        >>> df.isna()
+             age   born   name    toy
+        0  False   True  False   True
+        1  False  False  False  False
+        2   True  False  False  False
+
+        Show which entries in a Series are NA.
+
+        >>> ser = cudf.Series([5, 6, np.NaN, np.inf, -np.inf])
+        >>> ser
+        0     5.0
+        1     6.0
+        2    <NA>
+        3     Inf
+        4    -Inf
+        dtype: float64
+        >>> ser.isna()
+        0    False
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        Show which entries in an Index are NA.
+
+        >>> idx = cudf.Index([1, 2, None, np.NaN, 0.32, np.inf])
+        >>> idx
+        Float64Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
+        >>> idx.isna()
+        array([False, False,  True,  True, False, False])
+        """
+        data_columns = (col.isnull() for col in self._columns)
+        return self._from_data_like_self(zip(self._column_names, data_columns))
+
+    # Alias for isna
+    isnull = isna
+
+    @_cudf_nvtx_annotate
+    def notna(self):
+        """
+        Identify non-missing values.
+
+        Return a boolean same-sized object indicating if
+        the values are not ``<NA>``. Non-missing values get
+        mapped to ``True``. ``<NA>`` values get mapped to
+        ``False`` values. ``<NA>`` values include:
+
+        * Values where null mask is set.
+        * ``NaN`` in float dtype.
+        * ``NaT`` in datetime64 and timedelta64 types.
+
+        Characters such as empty strings ``''`` or
+        ``inf`` in case of float are not
+        considered ``<NA>`` values.
+
+        Returns
+        -------
+        DataFrame/Series/Index
+            Mask of bool values for each element in
+            the object that indicates whether an element is not an NA value.
+
+        Examples
+        --------
+        Show which entries in a DataFrame are NA.
+
+        >>> import cudf
+        >>> import numpy as np
+        >>> import pandas as pd
+        >>> df = cudf.DataFrame({'age': [5, 6, np.NaN],
+        ...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
+        ...                             pd.Timestamp('1940-04-25')],
+        ...                    'name': ['Alfred', 'Batman', ''],
+        ...                    'toy': [None, 'Batmobile', 'Joker']})
+        >>> df
+            age                        born    name        toy
+        0     5                        <NA>  Alfred       <NA>
+        1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
+        2  <NA>  1940-04-25 00:00:00.000000              Joker
+        >>> df.notna()
+             age   born  name    toy
+        0   True  False  True  False
+        1   True   True  True   True
+        2  False   True  True   True
+
+        Show which entries in a Series are NA.
+
+        >>> ser = cudf.Series([5, 6, np.NaN, np.inf, -np.inf])
+        >>> ser
+        0     5.0
+        1     6.0
+        2    <NA>
+        3     Inf
+        4    -Inf
+        dtype: float64
+        >>> ser.notna()
+        0     True
+        1     True
+        2    False
+        3     True
+        4     True
+        dtype: bool
+
+        Show which entries in an Index are NA.
+
+        >>> idx = cudf.Index([1, 2, None, np.NaN, 0.32, np.inf])
+        >>> idx
+        Float64Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
+        >>> idx.notna()
+        array([ True,  True, False, False,  True,  True])
+        """
+        data_columns = (col.notnull() for col in self._columns)
+        return self._from_data_like_self(zip(self._column_names, data_columns))
+
+    # Alias for notna
+    notnull = notna
+
+    @_cudf_nvtx_annotate
+    def searchsorted(
+        self, values, side="left", ascending=True, na_position="last"
+    ):
+        """Find indices where elements should be inserted to maintain order
+
+        Parameters
+        ----------
+        value : Frame (Shape must be consistent with self)
+            Values to be hypothetically inserted into Self
+        side : str {'left', 'right'} optional, default 'left'
+            If 'left', the index of the first suitable location found is given
+            If 'right', return the last such index
+        ascending : bool optional, default True
+            Sorted Frame is in ascending order (otherwise descending)
+        na_position : str {'last', 'first'} optional, default 'last'
+            Position of null values in sorted order
+
+        Returns
+        -------
+        1-D cupy array of insertion points
+
+        Examples
+        --------
+        >>> s = cudf.Series([1, 2, 3])
+        >>> s.searchsorted(4)
+        3
+        >>> s.searchsorted([0, 4])
+        array([0, 3], dtype=int32)
+        >>> s.searchsorted([1, 3], side='left')
+        array([0, 2], dtype=int32)
+        >>> s.searchsorted([1, 3], side='right')
+        array([1, 3], dtype=int32)
+
+        If the values are not monotonically sorted, wrong
+        locations may be returned:
+
+        >>> s = cudf.Series([2, 1, 3])
+        >>> s.searchsorted(1)
+        0   # wrong result, correct would be 1
+
+        >>> df = cudf.DataFrame({'a': [1, 3, 5, 7], 'b': [10, 12, 14, 16]})
+        >>> df
+           a   b
+        0  1  10
+        1  3  12
+        2  5  14
+        3  7  16
+        >>> values_df = cudf.DataFrame({'a': [0, 2, 5, 6],
+        ... 'b': [10, 11, 13, 15]})
+        >>> values_df
+           a   b
+        0  0  10
+        1  2  17
+        2  5  13
+        3  6  15
+        >>> df.searchsorted(values_df, ascending=False)
+        array([4, 4, 4, 0], dtype=int32)
+        """
+        # Call libcudf search_sorted primitive
+
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        scalar_flag = None
+        if is_scalar(values):
+            scalar_flag = True
+
+        if not isinstance(values, Frame):
+            values = [as_column(values)]
+        else:
+            values = [*values._columns]
+        if len(values) != len(self._data):
+            raise ValueError("Mismatch number of columns to search for.")
+
+        # TODO: Change behavior based on the decision in
+        # https://github.com/pandas-dev/pandas/issues/54668
+        common_dtype_list = [
+            find_common_type([col.dtype, val.dtype])
+            for col, val in zip(self._columns, values)
+        ]
+        sources = [
+            col
+            if is_dtype_equal(col.dtype, common_dtype)
+            else col.astype(common_dtype)
+            for col, common_dtype in zip(self._columns, common_dtype_list)
+        ]
+        values = [
+            val
+            if is_dtype_equal(val.dtype, common_dtype)
+            else val.astype(common_dtype)
+            for val, common_dtype in zip(values, common_dtype_list)
+        ]
+
+        outcol = libcudf.search.search_sorted(
+            sources,
+            values,
+            side,
+            ascending=ascending,
+            na_position=na_position,
+        )
+
+        # Return result as cupy array if the values is non-scalar
+        # If values is scalar, result is expected to be scalar.
+        result = cupy.asarray(outcol.data_array_view(mode="read"))
+        if scalar_flag:
+            return result[0].item()
+        else:
+            return result
+
+    @_cudf_nvtx_annotate
+    def argsort(
+        self,
+        by=None,
+        axis=0,
+        kind="quicksort",
+        order=None,
+        ascending=True,
+        na_position="last",
+    ):
+        """Return the integer indices that would sort the Series values.
+
+        Parameters
+        ----------
+        by : str or list of str, default None
+            Name or list of names to sort by. If None, sort by all columns.
+        axis : {0 or "index"}
+            Has no effect but is accepted for compatibility with numpy.
+        kind : {'mergesort', 'quicksort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable
+            algorithms. Only quicksort is supported in cuDF.
+        order : None
+            Has no effect but is accepted for compatibility with numpy.
+        ascending : bool or list of bool, default True
+            If True, sort values in ascending order, otherwise descending.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs
+            at the end.
+
+        Returns
+        -------
+        cupy.ndarray: The indices sorted based on input.
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> s = cudf.Series([3, 1, 2])
+        >>> s
+        0    3
+        1    1
+        2    2
+        dtype: int64
+        >>> s.argsort()
+        0    1
+        1    2
+        2    0
+        dtype: int32
+        >>> s[s.argsort()]
+        1    1
+        2    2
+        0    3
+        dtype: int64
+
+        **DataFrame**
+        >>> import cudf
+        >>> df = cudf.DataFrame({'foo': [3, 1, 2]})
+        >>> df.argsort()
+        array([1, 2, 0], dtype=int32)
+
+        **Index**
+        >>> import cudf
+        >>> idx = cudf.Index([3, 1, 2])
+        >>> idx.argsort()
+        array([1, 2, 0], dtype=int32)
+        """  # noqa: E501
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+        if kind != "quicksort":
+            if kind not in {"mergesort", "heapsort", "stable"}:
+                raise AttributeError(
+                    f"{kind} is not a valid sorting algorithm for "
+                    f"'DataFrame' object"
+                )
+            warnings.warn(
+                f"GPU-accelerated {kind} is currently not supported, "
+                "defaulting to quicksort."
+            )
+
+        if isinstance(by, str):
+            by = [by]
+        return self._get_sorted_inds(
+            by=by, ascending=ascending, na_position=na_position
+        ).values
+
+    @_cudf_nvtx_annotate
+    def _get_sorted_inds(self, by=None, ascending=True, na_position="last"):
+        """
+        Get the indices required to sort self according to the columns
+        specified in by.
+        """
+
+        to_sort = [
+            *(
+                self
+                if by is None
+                else self._get_columns_by_label(list(by), downcast=False)
+            )._columns
+        ]
+
+        # If given a scalar need to construct a sequence of length # of columns
+        if np.isscalar(ascending):
+            ascending = [ascending] * len(to_sort)
+
+        return libcudf.sort.order_by(
+            to_sort,
+            ascending,
+            na_position,
+            stable=True,
+        )
+
+    @_cudf_nvtx_annotate
+    def abs(self):
+        """
+        Return a Series/DataFrame with absolute numeric value of each element.
+
+        This function only applies to elements that are all numeric.
+
+        Returns
+        -------
+        DataFrame/Series
+            Absolute value of each element.
+
+        Examples
+        --------
+        Absolute numeric values in a Series
+
+        >>> s = cudf.Series([-1.10, 2, -3.33, 4])
+        >>> s.abs()
+        0    1.10
+        1    2.00
+        2    3.33
+        3    4.00
+        dtype: float64
+        """
+        return self._unaryop("abs")
+
+    @_cudf_nvtx_annotate
+    def _is_sorted(self, ascending=None, null_position=None):
+        """
+        Returns a boolean indicating whether the data of the Frame are sorted
+        based on the parameters given. Does not account for the index.
+
+        Parameters
+        ----------
+        self : Frame
+            Frame whose columns are to be checked for sort order
+        ascending : None or list-like of booleans
+            None or list-like of boolean values indicating expected sort order
+            of each column. If list-like, size of list-like must be
+            len(columns). If None, all columns expected sort order is set to
+            ascending. False (0) - ascending, True (1) - descending.
+        null_position : None or list-like of booleans
+            None or list-like of boolean values indicating desired order of
+            nulls compared to other elements. If list-like, size of list-like
+            must be len(columns). If None, null order is set to before. False
+            (0) - before, True (1) - after.
+
+        Returns
+        -------
+        returns : boolean
+            Returns True, if sorted as expected by ``ascending`` and
+            ``null_position``, False otherwise.
+        """
+        if ascending is not None and not cudf.api.types.is_list_like(
+            ascending
+        ):
+            raise TypeError(
+                f"Expected a list-like or None for `ascending`, got "
+                f"{type(ascending)}"
+            )
+        if null_position is not None and not cudf.api.types.is_list_like(
+            null_position
+        ):
+            raise TypeError(
+                f"Expected a list-like or None for `null_position`, got "
+                f"{type(null_position)}"
+            )
+        return libcudf.sort.is_sorted(
+            [*self._columns], ascending=ascending, null_position=null_position
+        )
+
+    @_cudf_nvtx_annotate
+    def _split(self, splits):
+        """Split a frame with split points in ``splits``. Returns a list of
+        Frames of length `len(splits) + 1`.
+        """
+        return [
+            self._from_columns_like_self(
+                libcudf.copying.columns_split([*self._data.columns], splits)[
+                    split_idx
+                ],
+                self._column_names,
+            )
+            for split_idx in range(len(splits) + 1)
+        ]
+
+    @_cudf_nvtx_annotate
+    def _encode(self):
+        columns, indices = libcudf.transform.table_encode([*self._columns])
+        keys = self._from_columns_like_self(columns)
+        return keys, indices
+
+    @_cudf_nvtx_annotate
+    def _unaryop(self, op):
+        data_columns = (col.unary_operator(op) for col in self._columns)
+        return self._from_data_like_self(zip(self._column_names, data_columns))
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _colwise_binop(
+        cls,
+        operands: Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
+        fn: str,
+    ):
+        """Implement binary ops between two frame-like objects.
+
+        Binary operations for Frames can be reduced to a sequence of binary
+        operations between column-like objects. Different types of frames need
+        to preprocess different inputs, so subclasses should implement binary
+        operations as a preprocessing step that calls this method.
+
+        Parameters
+        ----------
+        operands : Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]]
+            A mapping from column names to a tuple containing left and right
+            operands as well as a boolean indicating whether or not to reflect
+            an operation and fill value for nulls.
+        fn : str
+            The operation to perform.
+
+        Returns
+        -------
+        Dict[ColumnBase]
+            A dict of columns constructed from the result of performing the
+            requested operation on the operands.
+        """
+        # Now actually perform the binop on the columns in left and right.
+        output = {}
+        for (
+            col,
+            (left_column, right_column, reflect, fill_value),
+        ) in operands.items():
+            output_mask = None
+            if fill_value is not None:
+                left_is_column = isinstance(left_column, ColumnBase)
+                right_is_column = isinstance(right_column, ColumnBase)
+
+                if left_is_column and right_is_column:
+                    # If both columns are nullable, pandas semantics dictate
+                    # that nulls that are present in both left_column and
+                    # right_column are not filled.
+                    if left_column.nullable and right_column.nullable:
+                        with acquire_spill_lock():
+                            lmask = as_column(left_column.nullmask)
+                            rmask = as_column(right_column.nullmask)
+                            output_mask = (lmask | rmask).data
+                        left_column = left_column.fillna(fill_value)
+                        right_column = right_column.fillna(fill_value)
+                    elif left_column.nullable:
+                        left_column = left_column.fillna(fill_value)
+                    elif right_column.nullable:
+                        right_column = right_column.fillna(fill_value)
+                elif left_is_column:
+                    if left_column.nullable:
+                        left_column = left_column.fillna(fill_value)
+                elif right_is_column:
+                    if right_column.nullable:
+                        right_column = right_column.fillna(fill_value)
+                else:
+                    assert False, "At least one operand must be a column."
+
+            # TODO: Disable logical and binary operators between columns that
+            # are not numerical using the new binops mixin.
+
+            outcol = (
+                getattr(operator, fn)(right_column, left_column)
+                if reflect
+                else getattr(operator, fn)(left_column, right_column)
+            )
+
+            if output_mask is not None:
+                outcol = outcol.set_mask(output_mask)
+
+            output[col] = outcol
+
+        return output
+
+    @_cudf_nvtx_annotate
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        return _array_ufunc(self, ufunc, method, inputs, kwargs)
+
+    @_cudf_nvtx_annotate
+    @acquire_spill_lock()
+    def _apply_cupy_ufunc_to_operands(
+        self, ufunc, cupy_func, operands, **kwargs
+    ):
+        # Note: There are some operations that may be supported by libcudf but
+        # are not supported by pandas APIs. In particular, libcudf binary
+        # operations support logical and/or operations as well as
+        # trigonometric, but those operations are not defined on
+        # pd.Series/DataFrame. For now those operations will dispatch to cupy,
+        # but if ufuncs are ever a bottleneck we could add special handling to
+        # dispatch those (or any other) functions that we could implement
+        # without cupy.
+
+        mask = None
+        data = [{} for _ in range(ufunc.nout)]
+        for name, (left, right, _, _) in operands.items():
+            cupy_inputs = []
+            for inp in (left, right) if ufunc.nin == 2 else (left,):
+                if isinstance(inp, ColumnBase) and inp.has_nulls():
+                    new_mask = as_column(inp.nullmask)
+
+                    # TODO: This is a hackish way to perform a bitwise and
+                    # of bitmasks. Once we expose
+                    # cudf::detail::bitwise_and, then we can use that
+                    # instead.
+                    mask = new_mask if mask is None else (mask & new_mask)
+
+                    # Arbitrarily fill with zeros. For ufuncs, we assume
+                    # that the end result propagates nulls via a bitwise
+                    # and, so these elements are irrelevant.
+                    inp = inp.fillna(0)
+                cupy_inputs.append(cupy.asarray(inp))
+
+            cp_output = cupy_func(*cupy_inputs, **kwargs)
+            if ufunc.nout == 1:
+                cp_output = (cp_output,)
+            for i, out in enumerate(cp_output):
+                data[i][name] = as_column(out).set_mask(mask)
+        return data
+
+    @_cudf_nvtx_annotate
+    def dot(self, other, reflect=False):
+        """
+        Get dot product of frame and other, (binary operator `dot`).
+
+        Among flexible wrappers (`add`, `sub`, `mul`, `div`, `mod`, `pow`,
+        `dot`) to arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`,
+        `@`.
+
+        Parameters
+        ----------
+        other : Sequence, Series, or DataFrame
+            Any multiple element data structure, or list-like object.
+        reflect : bool, default False
+            If ``True``, swap the order of the operands. See
+            https://docs.python.org/3/reference/datamodel.html#object.__ror__
+            for more information on when this is necessary.
+
+        Returns
+        -------
+        scalar, Series, or DataFrame
+            The result of the operation.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame([[1, 2, 3, 4],
+        ...                      [5, 6, 7, 8]])
+        >>> df @ df.T
+            0    1
+        0  30   70
+        1  70  174
+        >>> s = cudf.Series([1, 1, 1, 1])
+        >>> df @ s
+        0    10
+        1    26
+        dtype: int64
+        >>> [1, 2, 3, 4] @ s
+        10
+        """
+        # TODO: This function does not currently support nulls.
+        lhs = self.values
+        result_index = None
+        result_cols = None
+        if isinstance(self, cudf.Series) and isinstance(
+            other, (cudf.Series, cudf.DataFrame)
+        ):
+            common = self.index.union(other.index)
+            if len(common) > len(self.index) or len(common) > len(other.index):
+                raise ValueError("matrices are not aligned")
+
+            lhs = self.reindex(index=common, copy=False).values
+            rhs = other.reindex(index=common, copy=False).values
+            if isinstance(other, cudf.DataFrame):
+                result_index = other._data.to_pandas_index()
+        elif isinstance(self, cudf.DataFrame) and isinstance(
+            other, (cudf.Series, cudf.DataFrame)
+        ):
+            common = self._data.to_pandas_index().union(
+                other.index.to_pandas()
+            )
+            if len(common) > len(self._data.names) or len(common) > len(
+                other.index
+            ):
+                raise ValueError("matrices are not aligned")
+
+            lhs = self.reindex(columns=common, copy=False)
+            result_index = lhs.index
+
+            rhs = other.reindex(index=common, copy=False).values
+            lhs = lhs.values
+            if isinstance(other, cudf.DataFrame):
+                result_cols = other._data.to_pandas_index()
+
+        elif isinstance(
+            other, (cupy.ndarray, np.ndarray)
+        ) or can_convert_to_column(other):
+            rhs = cupy.asarray(other)
+        else:
+            # TODO: This should raise an exception, not return NotImplemented,
+            # but __matmul__ relies on the current behavior. We should either
+            # move this implementation to __matmul__ and call it from here
+            # (checking for NotImplemented and raising NotImplementedError if
+            # that's what's returned), or __matmul__ should catch a
+            # NotImplementedError from here and return NotImplemented. The
+            # latter feels cleaner (putting the implementation in this method
+            # rather than in the operator) but will be slower in the (highly
+            # unlikely) case that we're multiplying a cudf object with another
+            # type of object that somehow supports this behavior.
+            return NotImplemented
+        if reflect:
+            lhs, rhs = rhs, lhs
+
+        result = lhs.dot(rhs)
+        if len(result.shape) == 1:
+            return cudf.Series(
+                result,
+                index=self.index if result_index is None else result_index,
+            )
+        if len(result.shape) == 2:
+            return cudf.DataFrame(
+                result,
+                index=self.index if result_index is None else result_index,
+                columns=result_cols,
+            )
+        return result.item()
+
+    @_cudf_nvtx_annotate
+    def __matmul__(self, other):
+        return self.dot(other)
+
+    @_cudf_nvtx_annotate
+    def __rmatmul__(self, other):
+        return self.dot(other, reflect=True)
+
+    # Unary logical operators
+    @_cudf_nvtx_annotate
+    def __neg__(self):
+        """Negate for integral dtypes, logical NOT for bools."""
+        return self._from_data_like_self(
+            {
+                name: col.unary_operator("not")
+                if is_bool_dtype(col.dtype)
+                else -1 * col
+                for name, col in self._data.items()
+            }
+        )
+
+    @_cudf_nvtx_annotate
+    def __pos__(self):
+        return self.copy(deep=True)
+
+    @_cudf_nvtx_annotate
+    def __abs__(self):
+        return self._unaryop("abs")
+
+    # Reductions
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _get_axis_from_axis_arg(cls, axis):
+        try:
+            return cls._SUPPORT_AXIS_LOOKUP[axis]
+        except KeyError:
+            raise ValueError(f"No axis named {axis} for object type {cls}")
+
+    @_cudf_nvtx_annotate
+    def _reduce(self, *args, **kwargs):
+        raise NotImplementedError(
+            f"Reductions are not supported for objects of type {type(self)}."
+        )
+
+    @_cudf_nvtx_annotate
+    def min(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return the minimum of the values in the DataFrame.
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values when computing the result.
+        level: int or level name, default None
+            If the axis is a MultiIndex (hierarchical), count along a
+            particular level, collapsing into a Series.
+        numeric_only: bool, default None
+            Include only float, int, boolean columns. If None, will attempt to
+            use everything, then use only numeric data.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `level`, `numeric_only`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.min()
+        a    1
+        b    7
+        dtype: int64
+        """
+        return self._reduce(
+            "min",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def max(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return the maximum of the values in the DataFrame.
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values when computing the result.
+        level: int or level name, default None
+            If the axis is a MultiIndex (hierarchical), count along a
+            particular level, collapsing into a Series.
+        numeric_only: bool, default None
+            Include only float, int, boolean columns. If None, will attempt to
+            use everything, then use only numeric data.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `level`, `numeric_only`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.max()
+        a     4
+        b    10
+        dtype: int64
+        """
+        return self._reduce(
+            "max",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def sum(
+        self,
+        axis=no_default,
+        skipna=True,
+        dtype=None,
+        level=None,
+        numeric_only=None,
+        min_count=0,
+        **kwargs,
+    ):
+        """
+        Return sum of the values in the DataFrame.
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values when computing the result.
+        dtype: data type
+            Data type to cast the result to.
+        min_count: int, default 0
+            The required number of valid values to perform the operation.
+            If fewer than min_count non-NA values are present the result
+            will be NA.
+
+            The default being 0. This means the sum of an all-NA or empty
+            Series is 0, and the product of an all-NA or empty Series is 1.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `level`, `numeric_only`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.sum()
+        a    10
+        b    34
+        dtype: int64
+        """
+        return self._reduce(
+            "sum",
+            axis=axis,
+            skipna=skipna,
+            dtype=dtype,
+            level=level,
+            numeric_only=numeric_only,
+            min_count=min_count,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def product(
+        self,
+        axis=no_default,
+        skipna=True,
+        dtype=None,
+        level=None,
+        numeric_only=None,
+        min_count=0,
+        **kwargs,
+    ):
+        """
+        Return product of the values in the DataFrame.
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values when computing the result.
+        dtype: data type
+            Data type to cast the result to.
+        min_count: int, default 0
+            The required number of valid values to perform the operation.
+            If fewer than min_count non-NA values are present the result
+            will be NA.
+
+            The default being 0. This means the sum of an all-NA or empty
+            Series is 0, and the product of an all-NA or empty Series is 1.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are level`, `numeric_only`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.product()
+        a      24
+        b    5040
+        dtype: int64
+        """
+
+        return self._reduce(
+            # cuDF columns use "product" as the op name, but cupy uses "prod"
+            # and we need cupy if axis == 1.
+            "prod" if axis in {1, "columns"} else "product",
+            axis=axis,
+            skipna=skipna,
+            dtype=dtype,
+            level=level,
+            numeric_only=numeric_only,
+            min_count=min_count,
+            **kwargs,
+        )
+
+    # Alias for pandas compatibility.
+    prod = product
+
+    @_cudf_nvtx_annotate
+    def mean(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return the mean of the values for the requested axis.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}
+            Axis for the function to be applied on.
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+        level : int or level name, default None
+            If the axis is a MultiIndex (hierarchical), count along a
+            particular level, collapsing into a Series.
+        numeric_only : bool, default None
+            Include only float, int, boolean columns. If None, will attempt to
+            use everything, then use only numeric data. Not implemented for
+            Series.
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        mean : Series or DataFrame (if level specified)
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.mean()
+        a    2.5
+        b    8.5
+        dtype: float64
+        """
+        return self._reduce(
+            "mean",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def std(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        ddof=1,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return sample standard deviation of the DataFrame.
+
+        Normalized by N-1 by default. This can be changed using
+        the `ddof` argument
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        ddof: int, default 1
+            Delta Degrees of Freedom. The divisor used in calculations
+            is N - ddof, where N represents the number of elements.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `level` and
+        `numeric_only`
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.std()
+        a    1.290994
+        b    1.290994
+        dtype: float64
+        """
+
+        return self._reduce(
+            "std",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            ddof=ddof,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def var(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        ddof=1,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return unbiased variance of the DataFrame.
+
+        Normalized by N-1 by default. This can be changed using the
+        ddof argument.
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values. If an entire row/column is NA, the result
+            will be NA.
+        ddof: int, default 1
+            Delta Degrees of Freedom. The divisor used in calculations is
+            N - ddof, where N represents the number of elements.
+
+        Returns
+        -------
+        scalar
+
+        Notes
+        -----
+        Parameters currently not supported are `level` and
+        `numeric_only`
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.var()
+        a    1.666667
+        b    1.666667
+        dtype: float64
+        """
+        return self._reduce(
+            "var",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            ddof=ddof,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def kurtosis(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return Fisher's unbiased kurtosis of a sample.
+
+        Kurtosis obtained using Fisher's definition of
+        kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
+
+        Parameters
+        ----------
+        axis: {index (0), columns(1)}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values when computing the result.
+
+        Returns
+        -------
+        Series or scalar
+
+        Notes
+        -----
+        Parameters currently not supported are `level` and `numeric_only`
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> series = cudf.Series([1, 2, 3, 4])
+        >>> series.kurtosis()
+        -1.1999999999999904
+
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]})
+        >>> df.kurt()
+        a   -1.2
+        b   -1.2
+        dtype: float64
+        """
+        if axis not in (0, "index", None, no_default):
+            raise NotImplementedError("Only axis=0 is currently supported.")
+
+        return self._reduce(
+            "kurtosis",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    # Alias for kurtosis.
+    kurt = kurtosis
+
+    @_cudf_nvtx_annotate
+    def skew(
+        self,
+        axis=no_default,
+        skipna=True,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        """
+        Return unbiased Fisher-Pearson skew of a sample.
+
+        Parameters
+        ----------
+        skipna: bool, default True
+            Exclude NA/null values when computing the result.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `axis`, `level` and
+        `numeric_only`
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> series = cudf.Series([1, 2, 3, 4, 5, 6, 6])
+        >>> series
+        0    1
+        1    2
+        2    3
+        3    4
+        4    5
+        5    6
+        6    6
+        dtype: int64
+
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [3, 2, 3, 4], 'b': [7, 8, 10, 10]})
+        >>> df.skew()
+        a    0.00000
+        b   -0.37037
+        dtype: float64
+        """
+        if axis not in (0, "index", None, no_default):
+            raise NotImplementedError("Only axis=0 is currently supported.")
+
+        return self._reduce(
+            "skew",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def all(self, axis=0, skipna=True, level=None, **kwargs):
+        """
+        Return whether all elements are True in DataFrame.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Indicate which axis or axes should be reduced. For `Series`
+            this parameter is unused and defaults to `0`.
+
+            - 0 or 'index' : reduce the index, return a Series
+                whose index is the original column labels.
+            - 1 or 'columns' : reduce the columns, return a Series
+                whose index is the original index.
+            - None : reduce all axes, return a scalar.
+        skipna: bool, default True
+            Exclude NA/null values. If the entire row/column is NA and
+            skipna is True, then the result will be True, as for an
+            empty row/column.
+            If skipna is False, then NA are treated as True, because
+            these are not equal to zero.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `bool_only`, `level`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [3, 2, 3, 4], 'b': [7, 0, 10, 10]})
+        >>> df.all()
+        a     True
+        b    False
+        dtype: bool
+        """
+        return self._reduce(
+            "all",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def any(self, axis=0, skipna=True, level=None, **kwargs):
+        """
+        Return whether any elements is True in DataFrame.
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns', None}, default 0
+            Indicate which axis or axes should be reduced. For `Series`
+            this parameter is unused and defaults to `0`.
+
+            - 0 or 'index' : reduce the index, return a Series
+                whose index is the original column labels.
+            - 1 or 'columns' : reduce the columns, return a Series
+                whose index is the original index.
+            - None : reduce all axes, return a scalar.
+        skipna: bool, default True
+            Exclude NA/null values. If the entire row/column is NA and
+            skipna is True, then the result will be False, as for an
+            empty row/column.
+            If skipna is False, then NA are treated as True, because
+            these are not equal to zero.
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Parameters currently not supported are `bool_only`, `level`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [3, 2, 3, 4], 'b': [7, 0, 10, 10]})
+        >>> df.any()
+        a    True
+        b    True
+        dtype: bool
+        """
+        return self._reduce(
+            "any",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    def median(
+        self, axis=None, skipna=True, level=None, numeric_only=None, **kwargs
+    ):
+        """
+        Return the median of the values for the requested axis.
+
+        Parameters
+        ----------
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+
+        Returns
+        -------
+        scalar
+
+        Notes
+        -----
+        Parameters currently not supported are `level` and `numeric_only`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series([10, 25, 3, 25, 24, 6])
+        >>> ser
+        0    10
+        1    25
+        2     3
+        3    25
+        4    24
+        5     6
+        dtype: int64
+        >>> ser.median()
+        17.0
+        """
+        return self._reduce(
+            "median",
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )
+
+    @_cudf_nvtx_annotate
+    @ioutils.doc_to_json()
+    def to_json(self, path_or_buf=None, *args, **kwargs):
+        """{docstring}"""
+
+        return cudf.io.json.to_json(
+            self, path_or_buf=path_or_buf, *args, **kwargs
+        )
+
+    @_cudf_nvtx_annotate
+    @ioutils.doc_to_hdf()
+    def to_hdf(self, path_or_buf, key, *args, **kwargs):
+        """{docstring}"""
+
+        cudf.io.hdf.to_hdf(path_or_buf, key, self, *args, **kwargs)
+
+    @_cudf_nvtx_annotate
+    @ioutils.doc_to_dlpack()
+    def to_dlpack(self):
+        """{docstring}"""
+
+        return cudf.io.dlpack.to_dlpack(self)
+
+    @_cudf_nvtx_annotate
+    def to_string(self):
+        r"""
+        Convert to string
+
+        cuDF uses Pandas internals for efficient string formatting.
+        Set formatting options using pandas string formatting options and
+        cuDF objects will print identically to Pandas objects.
+
+        cuDF supports `null/None` as a value in any column type, which
+        is transparently supported during this output process.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame()
+        >>> df['key'] = [0, 1, 2]
+        >>> df['val'] = [float(i + 10) for i in range(3)]
+        >>> df.to_string()
+        '   key   val\n0    0  10.0\n1    1  11.0\n2    2  12.0'
+        """
+        return repr(self)
+
+    @_cudf_nvtx_annotate
+    def __str__(self):
+        return self.to_string()
+
+    @_cudf_nvtx_annotate
+    def __deepcopy__(self, memo):
+        return self.copy(deep=True)
+
+    @_cudf_nvtx_annotate
+    def __copy__(self):
+        return self.copy(deep=False)
+
+    @_cudf_nvtx_annotate
+    def head(self, n=5):
+        """
+        Return the first `n` rows.
+        This function returns the first `n` rows for the object based
+        on position. It is useful for quickly testing if your object
+        has the right type of data in it.
+        For negative values of `n`, this function returns all rows except
+        the last `n` rows, equivalent to ``df[:-n]``.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Number of rows to select.
+
+        Returns
+        -------
+        DataFrame or Series
+            The first `n` rows of the caller object.
+
+        Examples
+        --------
+        **Series**
+
+        >>> ser = cudf.Series(['alligator', 'bee', 'falcon',
+        ... 'lion', 'monkey', 'parrot', 'shark', 'whale', 'zebra'])
+        >>> ser
+        0    alligator
+        1          bee
+        2       falcon
+        3         lion
+        4       monkey
+        5       parrot
+        6        shark
+        7        whale
+        8        zebra
+        dtype: object
+
+        Viewing the first 5 lines
+
+        >>> ser.head()
+        0    alligator
+        1          bee
+        2       falcon
+        3         lion
+        4       monkey
+        dtype: object
+
+        Viewing the first `n` lines (three in this case)
+
+        >>> ser.head(3)
+        0    alligator
+        1          bee
+        2       falcon
+        dtype: object
+
+        For negative values of `n`
+
+        >>> ser.head(-3)
+        0    alligator
+        1          bee
+        2       falcon
+        3         lion
+        4       monkey
+        5       parrot
+        dtype: object
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame()
+        >>> df['key'] = [0, 1, 2, 3, 4]
+        >>> df['val'] = [float(i + 10) for i in range(5)]  # insert column
+        >>> df.head(2)
+           key   val
+        0    0  10.0
+        1    1  11.0
+        """
+        return self.iloc[:n]
+
+    @_cudf_nvtx_annotate
+    def tail(self, n=5):
+        """
+        Returns the last n rows as a new DataFrame or Series
+
+        Examples
+        --------
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame()
+        >>> df['key'] = [0, 1, 2, 3, 4]
+        >>> df['val'] = [float(i + 10) for i in range(5)]  # insert column
+        >>> df.tail(2)
+           key   val
+        3    3  13.0
+        4    4  14.0
+
+        **Series**
+
+        >>> import cudf
+        >>> ser = cudf.Series([4, 3, 2, 1, 0])
+        >>> ser.tail(2)
+        3    1
+        4    0
+        """
+        if n == 0:
+            return self.iloc[0:0]
+
+        return self.iloc[-n:]
+
+    @_cudf_nvtx_annotate
+    @copy_docstring(Rolling)
+    def rolling(
+        self, window, min_periods=None, center=False, axis=0, win_type=None
+    ):
+        return Rolling(
+            self,
+            window,
+            min_periods=min_periods,
+            center=center,
+            axis=axis,
+            win_type=win_type,
+        )
+
+    @_cudf_nvtx_annotate
+    def nans_to_nulls(self):
+        """
+        Convert nans (if any) to nulls
+
+        Returns
+        -------
+        DataFrame or Series
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf, numpy as np
+        >>> series = cudf.Series([1, 2, np.nan, None, 10], nan_as_null=False)
+        >>> series
+        0     1.0
+        1     2.0
+        2     NaN
+        3    <NA>
+        4    10.0
+        dtype: float64
+        >>> series.nans_to_nulls()
+        0     1.0
+        1     2.0
+        2    <NA>
+        3    <NA>
+        4    10.0
+        dtype: float64
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame()
+        >>> df['a'] = cudf.Series([1, None, np.nan], nan_as_null=False)
+        >>> df['b'] = cudf.Series([None, 3.14, np.nan], nan_as_null=False)
+        >>> df
+              a     b
+        0   1.0  <NA>
+        1  <NA>  3.14
+        2   NaN   NaN
+        >>> df.nans_to_nulls()
+              a     b
+        0   1.0  <NA>
+        1  <NA>  3.14
+        2  <NA>  <NA>
+        """
+        result_data = {}
+        for name, col in self._data.items():
+            try:
+                result_data[name] = col.nans_to_nulls()
+            except AttributeError:
+                result_data[name] = col.copy()
+        return self._from_data_like_self(result_data)
+
+    @_cudf_nvtx_annotate
+    def __invert__(self):
+        """Bitwise invert (~) for integral dtypes, logical NOT for bools."""
+        return self._from_data_like_self(
+            {
+                name: _apply_inverse_column(col)
+                for name, col in self._data.items()
+            }
+        )
+
+    @_cudf_nvtx_annotate
+    def nunique(self, dropna: bool = True):
+        """
+        Returns a per column mapping with counts of unique values for
+        each column.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        dict
+            Name and unique value counts of each column in frame.
+        """
+        return {
+            name: col.distinct_count(dropna=dropna)
+            for name, col in self._data.items()
+        }
+
+    @staticmethod
+    @_cudf_nvtx_annotate
+    def _repeat(
+        columns: List[ColumnBase], repeats, axis=None
+    ) -> List[ColumnBase]:
+        if axis is not None:
+            raise NotImplementedError(
+                "Only axis=`None` supported at this time."
+            )
+
+        if not is_scalar(repeats):
+            repeats = as_column(repeats)
+
+        return libcudf.filling.repeat(columns, repeats)
+
+    @_cudf_nvtx_annotate
+    @_warn_no_dask_cudf
+    def __dask_tokenize__(self):
+        return [
+            type(self),
+            self._dtypes,
+            self.to_pandas(),
+        ]
+
+
+def _apply_inverse_column(col: ColumnBase) -> ColumnBase:
+    """Bitwise invert (~) for integral dtypes, logical NOT for bools."""
+    if np.issubdtype(col.dtype, np.integer):
+        return col.unary_operator("invert")
+    elif is_bool_dtype(col.dtype):
+        return col.unary_operator("not")
+    else:
+        raise TypeError(
+            f"Operation `~` not supported on {col.dtype.type.__name__}"
+        )
diff --git a/python/cudf/cudf/core/groupby/__init__.py b/python/cudf/cudf/core/groupby/__init__.py
new file mode 100644
index 0000000..4375ed3
--- /dev/null
+++ b/python/cudf/cudf/core/groupby/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from cudf.core.groupby.groupby import GroupBy, Grouper
+
+__all__ = [
+    "GroupBy",
+    "Grouper",
+]
diff --git a/python/cudf/cudf/core/groupby/groupby.py b/python/cudf/cudf/core/groupby/groupby.py
new file mode 100644
index 0000000..4b715e9
--- /dev/null
+++ b/python/cudf/cudf/core/groupby/groupby.py
@@ -0,0 +1,2800 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import copy
+import itertools
+import pickle
+import textwrap
+import warnings
+from collections import abc
+from functools import cached_property
+from typing import Any, Iterable, List, Optional, Tuple, Union
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib import groupby as libgroupby
+from cudf._lib.null_mask import bitmask_or
+from cudf._lib.reshape import interleave_columns
+from cudf._lib.sort import segmented_sort_by_key
+from cudf._lib.types import size_type_dtype
+from cudf._typing import AggType, DataFrameOrSeries, MultiColumnAggType
+from cudf.api.types import is_bool_dtype, is_float_dtype, is_list_like
+from cudf.core.abc import Serializable
+from cudf.core.column.column import ColumnBase, arange, as_column
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.join._join_helpers import _match_join_keys
+from cudf.core.mixins import Reducible, Scannable
+from cudf.core.multiindex import MultiIndex
+from cudf.core.udf.groupby_utils import _can_be_jitted, jit_groupby_apply
+from cudf.utils.utils import GetAttrGetItemMixin, _cudf_nvtx_annotate
+
+
+# The three functions below return the quantiles [25%, 50%, 75%]
+# respectively, which are called in the describe() method to output
+# the summary stats of a GroupBy object
+def _quantile_25(x):
+    return x.quantile(0.25)
+
+
+def _quantile_50(x):
+    return x.quantile(0.50)
+
+
+def _quantile_75(x):
+    return x.quantile(0.75)
+
+
+def _is_row_of(chunk, obj):
+    return (
+        isinstance(chunk, cudf.Series)
+        and isinstance(obj, cudf.DataFrame)
+        and len(chunk.index) == len(obj._column_names)
+        and (chunk.index.to_pandas() == pd.Index(obj._column_names)).all()
+    )
+
+
+groupby_doc_template = textwrap.dedent(
+    """Group using a mapper or by a Series of columns.
+
+A groupby operation involves some combination of splitting the object,
+applying a function, and combining the results. This can be used to
+group large amounts of data and compute operations on these groups.
+
+Parameters
+----------
+by : mapping, function, label, or list of labels
+    Used to determine the groups for the groupby. If by is a
+    function, it's called on each value of the object's index.
+    If a dict or Series is passed, the Series or dict VALUES will
+    be used to determine the groups (the Series' values are first
+    aligned; see .align() method). If an cupy array is passed, the
+    values are used as-is determine the groups. A label or list
+    of labels may be passed to group by the columns in self.
+    Notice that a tuple is interpreted as a (single) key.
+level : int, level name, or sequence of such, default None
+    If the axis is a MultiIndex (hierarchical), group by a particular
+    level or levels.
+as_index : bool, default True
+    For aggregated output, return object with group labels as
+    the index. Only relevant for DataFrame input.
+    as_index=False is effectively "SQL-style" grouped output.
+sort : bool, default False
+    Sort result by group key. Differ from Pandas, cudf defaults to
+    ``False`` for better performance. Note this does not influence
+    the order of observations within each group. Groupby preserves
+    the order of rows within each group.
+group_keys : bool, optional
+    When calling apply and the ``by`` argument produces a like-indexed
+    result, add group keys to index to identify pieces. By default group
+    keys are not included when the result's index (and column) labels match
+    the inputs, and are included otherwise. This argument has no effect if
+    the result produced is not like-indexed with respect to the input.
+{ret}
+Examples
+--------
+**Series**
+
+>>> ser = cudf.Series([390., 350., 30., 20.],
+...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+...                 name="Max Speed")
+>>> ser
+Falcon    390.0
+Falcon    350.0
+Parrot     30.0
+Parrot     20.0
+Name: Max Speed, dtype: float64
+>>> ser.groupby(level=0).mean()
+Falcon    370.0
+Parrot     25.0
+Name: Max Speed, dtype: float64
+>>> ser.groupby(ser > 100).mean()
+Max Speed
+False     25.0
+True     370.0
+Name: Max Speed, dtype: float64
+
+**DataFrame**
+
+>>> import cudf
+>>> import pandas as pd
+>>> df = cudf.DataFrame({{
+...     'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+...     'Max Speed': [380., 370., 24., 26.],
+... }})
+>>> df
+   Animal  Max Speed
+0  Falcon      380.0
+1  Falcon      370.0
+2  Parrot       24.0
+3  Parrot       26.0
+>>> df.groupby(['Animal']).mean()
+        Max Speed
+Animal
+Falcon      375.0
+Parrot       25.0
+
+>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+...           ['Captive', 'Wild', 'Captive', 'Wild']]
+>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
+>>> df = cudf.DataFrame({{'Max Speed': [390., 350., 30., 20.]}},
+...     index=index)
+>>> df
+                Max Speed
+Animal Type
+Falcon Captive      390.0
+        Wild         350.0
+Parrot Captive       30.0
+        Wild          20.0
+>>> df.groupby(level=0).mean()
+        Max Speed
+Animal
+Falcon      370.0
+Parrot       25.0
+>>> df.groupby(level="Type").mean()
+        Max Speed
+Type
+Wild         185.0
+Captive      210.0
+
+>>> df = cudf.DataFrame({{'A': 'a a b'.split(),
+...                      'B': [1,2,3],
+...                      'C': [4,6,5]}})
+>>> g1 = df.groupby('A', group_keys=False)
+>>> g2 = df.groupby('A', group_keys=True)
+
+Notice that ``g1`` have ``g2`` have two groups, ``a`` and ``b``, and only
+differ in their ``group_keys`` argument. Calling `apply` in various ways,
+we can get different grouping results:
+
+>>> g1[['B', 'C']].apply(lambda x: x / x.sum())
+          B    C
+0  0.333333  0.4
+1  0.666667  0.6
+2  1.000000  1.0
+
+In the above, the groups are not part of the index. We can have them included
+by using ``g2`` where ``group_keys=True``:
+
+>>> g2[['B', 'C']].apply(lambda x: x / x.sum())
+            B    C
+A
+a 0  0.333333  0.4
+  1  0.666667  0.6
+b 2  1.000000  1.0
+"""
+)
+
+
+class GroupBy(Serializable, Reducible, Scannable):
+    obj: "cudf.core.indexed_frame.IndexedFrame"
+
+    _VALID_REDUCTIONS = {
+        "sum",
+        "prod",
+        "idxmin",
+        "idxmax",
+        "min",
+        "max",
+        "mean",
+        "median",
+        "nunique",
+        "first",
+        "last",
+        "var",
+        "std",
+    }
+
+    _VALID_SCANS = {
+        "cumsum",
+        "cummin",
+        "cummax",
+    }
+
+    # Necessary because the function names don't directly map to the docs.
+    _SCAN_DOCSTRINGS = {
+        "cumsum": {"op_name": "Cumulative sum"},
+        "cummin": {"op_name": "Cumulative min"},
+        "cummax": {"op_name": "Cumulative max"},
+    }
+
+    _MAX_GROUPS_BEFORE_WARN = 100
+
+    def __init__(
+        self,
+        obj,
+        by=None,
+        level=None,
+        sort=False,
+        as_index=True,
+        dropna=True,
+        group_keys=True,
+    ):
+        """
+        Group a DataFrame or Series by a set of columns.
+
+        Parameters
+        ----------
+        by : optional
+            Specifies the grouping columns. Can be any of the following:
+            - A Python function called on each value of the object's index
+            - A dict or Series that maps index labels to group names
+            - A cudf.Index object
+            - A str indicating a column name
+            - An array of the same length as the object
+            - A Grouper object
+            - A list of the above
+        level : int, level_name or list, optional
+            For objects with a MultiIndex, `level` can be used to specify
+            grouping by one or more levels of the MultiIndex.
+        sort : bool, default False
+            Sort the result by group keys. Differ from Pandas, cudf defaults
+            to False for better performance.
+        as_index : bool, optional
+            If as_index=True (default), the group names appear
+            as the keys of the resulting DataFrame.
+            If as_index=False, the groups are returned as ordinary
+            columns of the resulting DataFrame, *if they are named columns*.
+        dropna : bool, optional
+            If True (default), do not include the "null" group.
+        """
+        self.obj = obj
+        self._as_index = as_index
+        self._by = by.copy(deep=True) if isinstance(by, _Grouping) else by
+        self._level = level
+        self._sort = sort
+        self._dropna = dropna
+        self._group_keys = group_keys
+
+        if isinstance(self._by, _Grouping):
+            self._by._obj = self.obj
+            self.grouping = self._by
+        else:
+            self.grouping = _Grouping(obj, self._by, level)
+
+    def __iter__(self):
+        if isinstance(self._by, list) and len(self._by) == 1:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "In a future version of cudf, a length 1 tuple will be "
+                "returned when iterating over a groupby with a grouper equal "
+                "to a list of length 1. To avoid this warning, do not supply "
+                "a list with a single grouper.",
+                FutureWarning,
+            )
+        group_names, offsets, _, grouped_values = self._grouped()
+        if isinstance(group_names, cudf.BaseIndex):
+            group_names = group_names.to_pandas()
+        for i, name in enumerate(group_names):
+            yield name, grouped_values[offsets[i] : offsets[i + 1]]
+
+    @property
+    def dtypes(self):
+        """
+        Return the dtypes in this group.
+
+        Returns
+        -------
+        pandas.DataFrame
+            The data type of each column of the group.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3, 3], 'b': ['x', 'y', 'z', 'a'],
+        ...                      'c':[10, 11, 12, 12]})
+        >>> df.groupby("a").dtypes
+                b      c
+        a
+        1  object  int64
+        2  object  int64
+        3  object  int64
+        """
+        index = self.grouping.keys.unique().sort_values().to_pandas()
+        obj_dtypes = self.obj._dtypes
+        return pd.DataFrame(
+            {
+                name: [obj_dtypes[name]] * len(index)
+                for name in self.grouping.values._column_names
+            },
+            index=index,
+        )
+
+    @cached_property
+    def groups(self):
+        """
+        Returns a dictionary mapping group keys to row labels.
+        """
+        group_names, offsets, _, grouped_values = self._grouped()
+        grouped_index = grouped_values.index
+
+        if len(group_names) > self._MAX_GROUPS_BEFORE_WARN:
+            warnings.warn(
+                f"GroupBy.groups() performance scales poorly with "
+                f"number of groups. Got {len(group_names)} groups."
+            )
+
+        return dict(
+            zip(group_names.to_pandas(), grouped_index._split(offsets[1:-1]))
+        )
+
+    @_cudf_nvtx_annotate
+    def get_group(self, name, obj=None):
+        """
+        Construct DataFrame from group with provided name.
+
+        Parameters
+        ----------
+        name : object
+            The name of the group to get as a DataFrame.
+        obj : DataFrame, default None
+            The DataFrame to take the DataFrame out of.  If
+            it is None, the object groupby was called on will
+            be used.
+
+        Returns
+        -------
+        group : same type as obj
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]})
+        >>> df
+           X  Y
+        0  A  1
+        1  B  4
+        2  A  3
+        3  B  2
+        >>> df.groupby("X").get_group("A")
+           X  Y
+        0  A  1
+        2  A  3
+        """
+        if obj is None:
+            obj = self.obj
+
+        return obj.loc[self.groups[name]]
+
+    @_cudf_nvtx_annotate
+    def size(self):
+        """
+        Return the size of each group.
+        """
+        return (
+            cudf.Series(
+                cudf.core.column.column_empty(
+                    len(self.obj), "int8", masked=False
+                )
+            )
+            .groupby(self.grouping, sort=self._sort, dropna=self._dropna)
+            .agg("size")
+        )
+
+    @_cudf_nvtx_annotate
+    def cumcount(self):
+        """
+        Return the cumulative count of keys in each group.
+        """
+        return (
+            cudf.Series(
+                cudf.core.column.column_empty(
+                    len(self.obj), "int8", masked=False
+                ),
+                index=self.obj.index,
+            )
+            .groupby(self.grouping, sort=self._sort)
+            .agg("cumcount")
+        )
+
+    @_cudf_nvtx_annotate
+    def rank(
+        self,
+        method="average",
+        ascending=True,
+        na_option="keep",
+        pct=False,
+        axis=0,
+    ):
+        """
+        Return the rank of values within each group.
+        """
+        if not axis == 0:
+            raise NotImplementedError("Only axis=0 is supported.")
+
+        if na_option not in {"keep", "top", "bottom"}:
+            raise ValueError(
+                f"na_option must be one of 'keep', 'top', or 'bottom', "
+                f"but got {na_option}"
+            )
+
+        # TODO: in pandas compatibility mode, we should convert any
+        # NaNs to nulls in any float value columns, as Pandas
+        # treats NaNs the way we treat nulls.
+        if cudf.get_option("mode.pandas_compatible"):
+            if any(
+                is_float_dtype(typ)
+                for typ in self.grouping.values._dtypes.values()
+            ):
+                raise NotImplementedError(
+                    "NaNs are not supported in groupby.rank."
+                )
+
+        def rank(x):
+            return getattr(x, "rank")(
+                method=method,
+                ascending=ascending,
+                na_option=na_option,
+                pct=pct,
+            )
+
+        result = self.agg(rank)
+
+        if cudf.get_option("mode.pandas_compatible"):
+            # pandas always returns floats:
+            return result.astype("float64")
+
+        return result
+
+    @cached_property
+    def _groupby(self):
+        return libgroupby.GroupBy(
+            [*self.grouping.keys._columns], dropna=self._dropna
+        )
+
+    @_cudf_nvtx_annotate
+    def agg(self, func):
+        """
+        Apply aggregation(s) to the groups.
+
+        Parameters
+        ----------
+        func : str, callable, list or dict
+            Argument specifying the aggregation(s) to perform on the
+            groups. `func` can be any of the following:
+
+              - string: the name of a supported aggregation
+              - callable: a function that accepts a Series/DataFrame and
+                performs a supported operation on it.
+              - list: a list of strings/callables specifying the
+                aggregations to perform on every column.
+              - dict: a mapping of column names to string/callable
+                specifying the aggregations to perform on those
+                columns.
+
+        See :ref:`the user guide <basics.groupby>` for supported
+        aggregations.
+
+        Returns
+        -------
+        A Series or DataFrame containing the combined results of the
+        aggregation(s).
+
+        Examples
+        --------
+        >>> import cudf
+        >>> a = cudf.DataFrame({
+        ...     'a': [1, 1, 2],
+        ...     'b': [1, 2, 3],
+        ...     'c': [2, 2, 1]
+        ... })
+        >>> a.groupby('a').agg('sum')
+           b  c
+        a
+        2  3  1
+        1  3  4
+
+        Specifying a list of aggregations to perform on each column.
+
+        >>> import cudf
+        >>> a = cudf.DataFrame({
+        ...     'a': [1, 1, 2],
+        ...     'b': [1, 2, 3],
+        ...     'c': [2, 2, 1]
+        ... })
+        >>> a.groupby('a').agg(['sum', 'min'])
+            b       c
+          sum min sum min
+        a
+        2   3   3   1   1
+        1   3   1   4   2
+
+        Using a dict to specify aggregations to perform per column.
+
+        >>> import cudf
+        >>> a = cudf.DataFrame({
+        ...     'a': [1, 1, 2],
+        ...     'b': [1, 2, 3],
+        ...     'c': [2, 2, 1]
+        ... })
+        >>> a.groupby('a').agg({'a': 'max', 'b': ['min', 'mean']})
+            a   b
+          max min mean
+        a
+        2   2   3  3.0
+        1   1   1  1.5
+
+        Using lambdas/callables to specify aggregations taking parameters.
+
+        >>> import cudf
+        >>> a = cudf.DataFrame({
+        ...     'a': [1, 1, 2],
+        ...     'b': [1, 2, 3],
+        ...     'c': [2, 2, 1]
+        ... })
+        >>> f1 = lambda x: x.quantile(0.5); f1.__name__ = "q0.5"
+        >>> f2 = lambda x: x.quantile(0.75); f2.__name__ = "q0.75"
+        >>> a.groupby('a').agg([f1, f2])
+             b          c
+          q0.5 q0.75 q0.5 q0.75
+        a
+        1  1.5  1.75  2.0   2.0
+        2  3.0  3.00  1.0   1.0
+        """
+        column_names, columns, normalized_aggs = self._normalize_aggs(func)
+        orig_dtypes = tuple(c.dtype for c in columns)
+
+        # Note: When there are no key columns, the below produces
+        # a Float64Index, while Pandas returns an Int64Index
+        # (GH: 6945)
+        (
+            result_columns,
+            grouped_key_cols,
+            included_aggregations,
+        ) = self._groupby.aggregate(columns, normalized_aggs)
+
+        result_index = self.grouping.keys._from_columns_like_self(
+            grouped_key_cols,
+        )
+
+        multilevel = _is_multi_agg(func)
+        data = {}
+        for col_name, aggs, cols, orig_dtype in zip(
+            column_names,
+            included_aggregations,
+            result_columns,
+            orig_dtypes,
+        ):
+            for agg_tuple, col in zip(aggs, cols):
+                agg, agg_kind = agg_tuple
+                agg_name = agg.__name__ if callable(agg) else agg
+                if multilevel:
+                    key = (col_name, agg_name)
+                else:
+                    key = col_name
+                if (
+                    agg in {list, "collect"}
+                    and orig_dtype != col.dtype.element_type
+                ):
+                    # Structs lose their labels which we reconstruct here
+                    col = col._with_type_metadata(cudf.ListDtype(orig_dtype))
+
+                if (
+                    self.obj.empty
+                    and (
+                        isinstance(agg_name, str)
+                        and agg_name in Reducible._SUPPORTED_REDUCTIONS
+                    )
+                    and len(col) == 0
+                    and not isinstance(
+                        col,
+                        (
+                            cudf.core.column.ListColumn,
+                            cudf.core.column.StructColumn,
+                            cudf.core.column.DecimalBaseColumn,
+                        ),
+                    )
+                ):
+                    data[key] = col.astype(orig_dtype)
+                elif agg_kind in {"COUNT", "SIZE"}:
+                    data[key] = col.astype("int64")
+                else:
+                    data[key] = col
+        data = ColumnAccessor(data, multiindex=multilevel)
+        if not multilevel:
+            data = data.rename_levels({np.nan: None}, level=0)
+        result = cudf.DataFrame._from_data(data, index=result_index)
+
+        if self._sort:
+            result = result.sort_index()
+        else:
+            if cudf.get_option(
+                "mode.pandas_compatible"
+            ) and not libgroupby._is_all_scan_aggregate(normalized_aggs):
+                # Even with `sort=False`, pandas guarantees that
+                # groupby preserves the order of rows within each group.
+                left_cols = list(
+                    self.grouping.keys.drop_duplicates()._data.columns
+                )
+                right_cols = list(result_index._data.columns)
+                join_keys = [
+                    _match_join_keys(lcol, rcol, "left")
+                    for lcol, rcol in zip(left_cols, right_cols)
+                ]
+                # TODO: In future, see if we can centralize
+                # logic else where that has similar patterns.
+                join_keys = map(list, zip(*join_keys))
+                _, indices = libcudf.join.join(
+                    *join_keys,
+                    how="left",
+                )
+                result = result.take(indices)
+                if isinstance(result._index, cudf.CategoricalIndex):
+                    # Needs re-ordering the categories in the order
+                    # they are after grouping.
+                    result._index = cudf.Index(
+                        result._index._column.reorder_categories(
+                            result._index._column._get_decategorized_column()
+                        ),
+                        name=result._index.name,
+                    )
+
+        if not self._as_index:
+            result = result.reset_index()
+        if libgroupby._is_all_scan_aggregate(normalized_aggs):
+            # Scan aggregations return rows in original index order
+            return self._mimic_pandas_order(result)
+
+        return result
+
+    def _reduce(
+        self,
+        op: str,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        """Compute {op} of group values.
+
+        Parameters
+        ----------
+        numeric_only : bool, default None
+            Include only float, int, boolean columns. If None, will attempt to
+            use everything, then use only numeric data.
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If
+            fewer than ``min_count`` non-NA values are present the result will
+            be NA.
+
+        Returns
+        -------
+        Series or DataFrame
+            Computed {op} of values within each group.
+
+        Notes
+        -----
+        Difference from pandas:
+            * Not supporting: numeric_only, min_count
+        """
+        if numeric_only:
+            raise NotImplementedError(
+                "numeric_only parameter is not implemented yet"
+            )
+        if min_count != 0:
+            raise NotImplementedError(
+                "min_count parameter is not implemented yet"
+            )
+        return self.agg(op)
+
+    def _scan(self, op: str, *args, **kwargs):
+        """{op_name} for each group."""
+        return self.agg(op)
+
+    aggregate = agg
+
+    def _head_tail(self, n, *, take_head: bool, preserve_order: bool):
+        """Return the head or tail of each group
+
+        Parameters
+        ----------
+        n
+           Number of entries to include (if negative, number of
+           entries to exclude)
+        take_head
+           Do we want the head or the tail of the group
+        preserve_order
+            If True, return the n rows from each group in original
+            dataframe order (this mimics pandas behavior though is
+            more expensive).
+
+        Returns
+        -------
+        New DataFrame or Series
+
+        Notes
+        -----
+        Unlike pandas, this returns an object in group order, not
+        original order, unless ``preserve_order`` is ``True``.
+        """
+        # A more memory-efficient implementation would merge the take
+        # into the grouping, but that probably requires a new
+        # aggregation scheme in libcudf. This is probably "fast
+        # enough" for most reasonable input sizes.
+        _, offsets, _, group_values = self._grouped()
+        group_offsets = np.asarray(offsets, dtype=size_type_dtype)
+        size_per_group = np.diff(group_offsets)
+        # "Out of bounds" n for the group size either means no entries
+        # (negative) or all the entries (positive)
+        if n < 0:
+            size_per_group = np.maximum(
+                size_per_group + n, 0, out=size_per_group
+            )
+        else:
+            size_per_group = np.minimum(size_per_group, n, out=size_per_group)
+        if take_head:
+            group_offsets = group_offsets[:-1]
+        else:
+            group_offsets = group_offsets[1:] - size_per_group
+        to_take = np.arange(size_per_group.sum(), dtype=size_type_dtype)
+        fixup = np.empty_like(size_per_group)
+        fixup[0] = 0
+        np.cumsum(size_per_group[:-1], out=fixup[1:])
+        to_take += np.repeat(group_offsets - fixup, size_per_group)
+        to_take = as_column(to_take)
+        result = group_values.iloc[to_take]
+        if preserve_order:
+            # Can't use _mimic_pandas_order because we need to
+            # subsample the gather map from the full input ordering,
+            # rather than permuting the gather map of the output.
+            _, (ordering,), _ = self._groupby.groups(
+                [arange(0, len(self.obj))]
+            )
+            # Invert permutation from original order to groups on the
+            # subset of entries we want.
+            gather_map = ordering.take(to_take).argsort()
+            return result.take(gather_map)
+        else:
+            return result
+
+    @_cudf_nvtx_annotate
+    def head(self, n: int = 5, *, preserve_order: bool = True):
+        """Return first n rows of each group
+
+        Parameters
+        ----------
+        n
+            If positive: number of entries to include from start of group
+            If negative: number of entries to exclude from end of group
+
+        preserve_order
+            If True (default), return the n rows from each group in
+            original dataframe order (this mimics pandas behavior
+            though is more expensive). If you don't need rows in
+            original dataframe order you will see a performance
+            improvement by setting ``preserve_order=False``. In both
+            cases, the original index is preserved, so ``.loc``-based
+            indexing will work identically.
+
+        Returns
+        -------
+        Series or DataFrame
+            Subset of the original grouped object as determined by n
+
+        See Also
+        --------
+        .tail
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame(
+        ...     {
+        ...         "a": [1, 0, 1, 2, 2, 1, 3, 2, 3, 3, 3],
+        ...         "b": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        ...     }
+        ... )
+        >>> df.groupby("a").head(1)
+           a  b
+        0  1  0
+        1  0  1
+        3  2  3
+        6  3  6
+        >>> df.groupby("a").head(-2)
+           a  b
+        0  1  0
+        3  2  3
+        6  3  6
+        8  3  8
+        """
+        return self._head_tail(
+            n, take_head=True, preserve_order=preserve_order
+        )
+
+    @_cudf_nvtx_annotate
+    def tail(self, n: int = 5, *, preserve_order: bool = True):
+        """Return last n rows of each group
+
+        Parameters
+        ----------
+        n
+            If positive: number of entries to include from end of group
+            If negative: number of entries to exclude from start of group
+
+        preserve_order
+            If True (default), return the n rows from each group in
+            original dataframe order (this mimics pandas behavior
+            though is more expensive). If you don't need rows in
+            original dataframe order you will see a performance
+            improvement by setting ``preserve_order=False``. In both
+            cases, the original index is preserved, so ``.loc``-based
+            indexing will work identically.
+
+        Returns
+        -------
+        Series or DataFrame
+            Subset of the original grouped object as determined by n
+
+
+        See Also
+        --------
+        .head
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame(
+        ...     {
+        ...         "a": [1, 0, 1, 2, 2, 1, 3, 2, 3, 3, 3],
+        ...         "b": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        ...     }
+        ... )
+        >>> df.groupby("a").tail(1)
+            a   b
+        1   0   1
+        5   1   5
+        7   2   7
+        10  3  10
+        >>> df.groupby("a").tail(-2)
+            a   b
+        5   1   5
+        7   2   7
+        9   3   9
+        10  3  10
+        """
+        return self._head_tail(
+            n, take_head=False, preserve_order=preserve_order
+        )
+
+    @_cudf_nvtx_annotate
+    def nth(self, n):
+        """
+        Return the nth row from each group.
+        """
+        result = self.agg(lambda x: x.nth(n)).sort_index()
+        sizes = self.size().sort_index()
+
+        return result[sizes > n]
+
+    @_cudf_nvtx_annotate
+    def ngroup(self, ascending=True):
+        """
+        Number each group from 0 to the number of groups - 1.
+
+        This is the enumerative complement of cumcount. Note that the
+        numbers given to the groups match the order in which the groups
+        would be seen when iterating over the groupby object, not the
+        order they are first observed.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If False, number in reverse, from number of group - 1 to 0.
+
+        Returns
+        -------
+        Series
+            Unique numbers for each group.
+
+        See Also
+        --------
+        .cumcount : Number the rows in each group.
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame({"A": list("aaabba")})
+        >>> df
+           A
+        0  a
+        1  a
+        2  a
+        3  b
+        4  b
+        5  a
+        >>> df.groupby('A').ngroup()
+        0    0
+        1    0
+        2    0
+        3    1
+        4    1
+        5    0
+        dtype: int64
+        >>> df.groupby('A').ngroup(ascending=False)
+        0    1
+        1    1
+        2    1
+        3    0
+        4    0
+        5    1
+        dtype: int64
+        >>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
+        0    0
+        1    0
+        2    1
+        3    3
+        4    2
+        5    0
+        dtype: int64
+        """
+        index = self.grouping.keys.unique().sort_values()
+        num_groups = len(index)
+        _, has_null_group = bitmask_or([*index._columns])
+
+        if ascending:
+            # Count ascending from 0 to num_groups - 1
+            group_ids = cudf.Series._from_data({None: cp.arange(num_groups)})
+        elif has_null_group:
+            # Count descending from num_groups - 1 to 0, but subtract one more
+            # for the null group making it num_groups - 2 to -1.
+            group_ids = cudf.Series._from_data(
+                {None: cp.arange(num_groups - 2, -2, -1)}
+            )
+        else:
+            # Count descending from num_groups - 1 to 0
+            group_ids = cudf.Series._from_data(
+                {None: cp.arange(num_groups - 1, -1, -1)}
+            )
+
+        if has_null_group:
+            group_ids.iloc[-1] = cudf.NA
+
+        group_ids._index = index
+        return self._broadcast(group_ids)
+
+    def sample(
+        self,
+        n: Optional[int] = None,
+        frac: Optional[float] = None,
+        replace: bool = False,
+        weights: Union[abc.Sequence, "cudf.Series", None] = None,
+        random_state: Union[np.random.RandomState, int, None] = None,
+    ):
+        """Return a random sample of items in each group.
+
+        Parameters
+        ----------
+        n
+            Number of items to return for each group, if sampling
+            without replacement must be at most the size of the
+            smallest group. Cannot be used with frac. Default is
+            ``n=1`` if frac is None.
+        frac
+            Fraction of items to return. Cannot be used with n.
+        replace
+            Should sampling occur with or without replacement?
+        weights
+            Sampling probability for each element. Must be the same
+            length as the grouped frame. Not currently supported.
+        random_state
+            Seed for random number generation.
+
+        Returns
+        -------
+        New dataframe or series with samples of appropriate size drawn
+        from each group.
+
+        """
+        if weights is not None:
+            # To implement this case again needs different algorithms
+            # in both cases.
+            #
+            # Without replacement, use the weighted reservoir sampling
+            # approach of Efraimidas and Spirakis (2006)
+            # https://doi.org/10.1016/j.ipl.2005.11.003, essentially,
+            # do a segmented argsort sorting on weight-scaled
+            # logarithmic deviates. See
+            # https://timvieira.github.io/blog/post/
+            # 2019/09/16/algorithms-for-sampling-without-replacement/
+            #
+            # With replacement is trickier, one might be able to use
+            # the alias method, otherwise we're back to bucketed
+            # rejection sampling.
+            raise NotImplementedError("Sampling with weights is not supported")
+        if frac is not None and n is not None:
+            raise ValueError("Cannot supply both of frac and n")
+        elif n is None and frac is None:
+            n = 1
+        elif frac is not None and not (0 <= frac <= 1):
+            raise ValueError(
+                "Sampling with fraction must provide fraction in "
+                f"[0, 1], got {frac=}"
+            )
+        # TODO: handle random states properly.
+        if random_state is not None and not isinstance(random_state, int):
+            raise NotImplementedError(
+                "Only integer seeds are supported for random_state "
+                "in this case"
+            )
+        # Get the groups
+        # TODO: convince Cython to convert the std::vector offsets
+        # into a numpy array directly, rather than a list.
+        # TODO: this uses the sort-based groupby, could one use hash-based?
+        _, offsets, _, group_values = self._grouped()
+        group_offsets = np.asarray(offsets, dtype=size_type_dtype)
+        size_per_group = np.diff(group_offsets)
+        if n is not None:
+            samples_per_group = np.broadcast_to(
+                size_type_dtype.type(n), size_per_group.shape
+            )
+            if not replace and (minsize := size_per_group.min()) < n:
+                raise ValueError(
+                    f"Cannot sample {n=} without replacement, "
+                    f"smallest group is {minsize}"
+                )
+        else:
+            # Pandas uses round-to-nearest, ties to even to
+            # pick sample sizes for the fractional case (unlike IEEE
+            # which is round-to-nearest, ties to sgn(x) * inf).
+            samples_per_group = np.round(
+                size_per_group * frac, decimals=0
+            ).astype(size_type_dtype)
+        if replace:
+            # We would prefer to use cupy here, but their rng.integers
+            # interface doesn't take array-based low and high
+            # arguments.
+            low = 0
+            high = np.repeat(size_per_group, samples_per_group)
+            rng = np.random.default_rng(seed=random_state)
+            indices = rng.integers(low, high, dtype=size_type_dtype)
+            indices += np.repeat(group_offsets[:-1], samples_per_group)
+        else:
+            # Approach: do a segmented argsort of the index array and take
+            # the first samples_per_group entries from sorted array.
+            # We will shuffle the group indices and then pick them out
+            # from the grouped dataframe index.
+            nrows = len(group_values)
+            indices = cp.arange(nrows, dtype=size_type_dtype)
+            if len(size_per_group) < 500:
+                # Empirically shuffling with cupy is faster at this scale
+                rs = cp.random.get_random_state()
+                rs.seed(seed=random_state)
+                for off, size in zip(group_offsets, size_per_group):
+                    rs.shuffle(indices[off : off + size])
+            else:
+                rng = cp.random.default_rng(seed=random_state)
+                (indices,) = segmented_sort_by_key(
+                    [as_column(indices)],
+                    [as_column(rng.random(size=nrows))],
+                    as_column(group_offsets),
+                    [],
+                    [],
+                    stable=True,
+                )
+                indices = cp.asarray(indices.data_array_view(mode="read"))
+            # Which indices are we going to want?
+            want = np.arange(samples_per_group.sum(), dtype=size_type_dtype)
+            scan = np.empty_like(samples_per_group)
+            scan[0] = 0
+            np.cumsum(samples_per_group[:-1], out=scan[1:])
+            want += np.repeat(group_offsets[:-1] - scan, samples_per_group)
+            indices = indices[want]
+        return group_values.iloc[indices]
+
+    def serialize(self):
+        header = {}
+        frames = []
+
+        header["kwargs"] = {
+            "sort": self._sort,
+            "dropna": self._dropna,
+            "as_index": self._as_index,
+        }
+
+        obj_header, obj_frames = self.obj.serialize()
+        header["obj"] = obj_header
+        header["obj_type"] = pickle.dumps(type(self.obj))
+        header["num_obj_frames"] = len(obj_frames)
+        frames.extend(obj_frames)
+
+        grouping_header, grouping_frames = self.grouping.serialize()
+        header["grouping"] = grouping_header
+        header["num_grouping_frames"] = len(grouping_frames)
+        frames.extend(grouping_frames)
+
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        kwargs = header["kwargs"]
+
+        obj_type = pickle.loads(header["obj_type"])
+        obj = obj_type.deserialize(
+            header["obj"], frames[: header["num_obj_frames"]]
+        )
+        grouping = _Grouping.deserialize(
+            header["grouping"], frames[header["num_obj_frames"] :]
+        )
+        return cls(obj, grouping, **kwargs)
+
+    def _grouped(self):
+        grouped_key_cols, grouped_value_cols, offsets = self._groupby.groups(
+            [*self.obj._index._columns, *self.obj._columns]
+        )
+        grouped_keys = cudf.core.index._index_from_columns(grouped_key_cols)
+        if isinstance(self.grouping.keys, cudf.MultiIndex):
+            grouped_keys.names = self.grouping.keys.names
+        else:
+            grouped_keys.name = self.grouping.keys.name
+        grouped_values = self.obj._from_columns_like_self(
+            grouped_value_cols,
+            column_names=self.obj._column_names,
+            index_names=self.obj._index_names,
+        )
+        group_names = grouped_keys.unique().sort_values()
+        return (group_names, offsets, grouped_keys, grouped_values)
+
+    def _normalize_aggs(
+        self, aggs: MultiColumnAggType
+    ) -> Tuple[Iterable[Any], Tuple[ColumnBase, ...], List[List[AggType]]]:
+        """
+        Normalize aggs to a list of list of aggregations, where `out[i]`
+        is a list of aggregations for column `self.obj[i]`. We support three
+        different form of `aggs` input here:
+        - A single agg, such as "sum". This agg is applied to all value
+        columns.
+        - A list of aggs, such as ["sum", "mean"]. All aggs are applied to all
+        value columns.
+        - A mapping of column name to aggs, such as
+        {"a": ["sum"], "b": ["mean"]}, the aggs are applied to specified
+        column.
+        Each agg can be string or lambda functions.
+        """
+
+        aggs_per_column: Iterable[Union[AggType, Iterable[AggType]]]
+        if isinstance(aggs, dict):
+            column_names, aggs_per_column = aggs.keys(), aggs.values()
+            columns = tuple(self.obj._data[col] for col in column_names)
+        else:
+            values = self.grouping.values
+            column_names = values._column_names
+            columns = values._columns
+            aggs_per_column = (aggs,) * len(columns)
+
+        # is_list_like performs type narrowing but type-checkers don't
+        # know it. One could add a TypeGuard annotation to
+        # is_list_like (see PEP647), but that is less useful than it
+        # seems because unlike the builtin narrowings it only performs
+        # narrowing in the positive case.
+        normalized_aggs = [
+            list(agg) if is_list_like(agg) else [agg]  # type: ignore
+            for agg in aggs_per_column
+        ]
+        return column_names, columns, normalized_aggs
+
+    @_cudf_nvtx_annotate
+    def pipe(self, func, *args, **kwargs):
+        """
+        Apply a function `func` with arguments to this GroupBy
+        object and return the function's result.
+
+        Parameters
+        ----------
+        func : function
+            Function to apply to this GroupBy object or,
+            alternatively, a ``(callable, data_keyword)`` tuple where
+            ``data_keyword`` is a string indicating the keyword of
+            ``callable`` that expects the GroupBy object.
+        args : iterable, optional
+            Positional arguments passed into ``func``.
+        kwargs : mapping, optional
+            A dictionary of keyword arguments passed into ``func``.
+
+        Returns
+        -------
+        object : the return type of ``func``.
+
+        See Also
+        --------
+        cudf.Series.pipe
+            Apply a function with arguments to a series.
+
+        cudf.DataFrame.pipe
+            Apply a function with arguments to a dataframe.
+
+        apply
+            Apply function to each group instead of to the full GroupBy object.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'A': ['a', 'b', 'a', 'b'], 'B': [1, 2, 3, 4]})
+        >>> df
+           A  B
+        0  a  1
+        1  b  2
+        2  a  3
+        3  b  4
+
+        To get the difference between each groups maximum and minimum value
+        in one pass, you can do
+
+        >>> df.groupby('A').pipe(lambda x: x.max() - x.min())
+           B
+        A
+        a  2
+        b  2
+        """
+        return cudf.core.common.pipe(self, func, *args, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def _jit_groupby_apply(
+        self, function, group_names, offsets, group_keys, grouped_values, *args
+    ):
+        # Nulls are not yet supported
+        if self.grouping._obj._has_nulls:
+            raise ValueError("Nulls not yet supported with groupby JIT engine")
+
+        chunk_results = jit_groupby_apply(
+            offsets, grouped_values, function, *args
+        )
+        result = cudf.Series._from_data(
+            {None: chunk_results}, index=group_names
+        )
+        result.index.names = self.grouping.names
+
+        return result
+
+    @_cudf_nvtx_annotate
+    def _iterative_groupby_apply(
+        self, function, group_names, offsets, group_keys, grouped_values, *args
+    ):
+        ngroups = len(offsets) - 1
+        if ngroups > self._MAX_GROUPS_BEFORE_WARN:
+            warnings.warn(
+                f"GroupBy.apply() performance scales poorly with "
+                f"number of groups. Got {ngroups} groups. Some functions "
+                "may perform better by passing engine='jit'",
+                RuntimeWarning,
+            )
+
+        chunks = [
+            grouped_values[s:e] for s, e in zip(offsets[:-1], offsets[1:])
+        ]
+        chunk_results = [function(chk, *args) for chk in chunks]
+        return self._post_process_chunk_results(
+            chunk_results, group_names, group_keys, grouped_values
+        )
+
+    def _post_process_chunk_results(
+        self, chunk_results, group_names, group_keys, grouped_values
+    ):
+        if not len(chunk_results):
+            return self.obj.head(0)
+        if cudf.api.types.is_scalar(chunk_results[0]):
+            result = cudf.Series._from_data(
+                {None: chunk_results}, index=group_names
+            )
+            result.index.names = self.grouping.names
+            return result
+        elif isinstance(chunk_results[0], cudf.Series) and isinstance(
+            self.obj, cudf.DataFrame
+        ):
+            # When the UDF is like df.sum(), the result for each
+            # group is a row-like "Series" where the index labels
+            # are the same as the original calling DataFrame
+            if _is_row_of(chunk_results[0], self.obj):
+                result = cudf.concat(chunk_results, axis=1).T
+                result.index = group_names
+                result.index.names = self.grouping.names
+            # When the UDF is like df.x + df.y, the result for each
+            # group is the same length as the original group
+            elif len(self.obj) == sum(len(chk) for chk in chunk_results):
+                result = cudf.concat(chunk_results)
+                index_data = group_keys._data.copy(deep=True)
+                index_data[None] = grouped_values.index._column
+                result.index = cudf.MultiIndex._from_data(index_data)
+            else:
+                raise TypeError(
+                    "Error handling Groupby apply output with input of "
+                    f"type {type(self.obj)} and output of "
+                    f"type {type(chunk_results[0])}"
+                )
+        else:
+            result = cudf.concat(chunk_results)
+            if self._group_keys:
+                index_data = group_keys._data.copy(deep=True)
+                index_data[None] = grouped_values.index._column
+                result.index = cudf.MultiIndex._from_data(index_data)
+        return result
+
+    @_cudf_nvtx_annotate
+    def apply(self, function, *args, engine="auto"):
+        """Apply a python transformation function over the grouped chunk.
+
+        Parameters
+        ----------
+        function : callable
+          The python transformation function that will be applied
+          on the grouped chunk.
+        args : tuple
+            Optional positional arguments to pass to the function.
+        engine: 'auto', 'cudf', or 'jit', default 'auto'
+          Selects the GroupBy.apply implementation. Use `jit` to
+          select the numba JIT pipeline. Only certain operations are allowed
+          within the function when using this option: min, max, sum, mean, var,
+          std, idxmax, and idxmin and any arithmetic formula involving them are
+          allowed. Binary operations are not yet supported, so syntax like
+          `df['x'] * 2` is not yet allowed.
+          For more information, see the `cuDF guide to user defined functions
+          <https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html>`__.
+          Use `cudf` to select the iterative groupby apply algorithm which aims
+          to provide maximum flexibility at the expense of performance.
+          The default value `auto` will attempt to use the numba JIT pipeline
+          where possible and will fall back to the iterative algorithm if
+          necessary.
+
+        Examples
+        --------
+        .. code-block:: python
+
+          from cudf import DataFrame
+          df = DataFrame()
+          df['key'] = [0, 0, 1, 1, 2, 2, 2]
+          df['val'] = [0, 1, 2, 3, 4, 5, 6]
+          groups = df.groupby(['key'])
+
+          # Define a function to apply to each row in a group
+          def mult(df):
+            df['out'] = df['key'] * df['val']
+            return df
+
+          result = groups.apply(mult)
+          print(result)
+
+        Output:
+
+        .. code-block:: python
+
+             key  val  out
+          0    0    0    0
+          1    0    1    0
+          2    1    2    2
+          3    1    3    3
+          4    2    4    8
+          5    2    5   10
+          6    2    6   12
+
+        .. pandas-compat::
+            **groupby.apply**
+
+            cuDF's ``groupby.apply`` is limited compared to pandas.
+            In some situations, Pandas returns the grouped keys as part of
+            the index while cudf does not due to redundancy. For example:
+
+            .. code-block::
+
+                >>> import pandas as pd
+                >>> df = pd.DataFrame({
+                ...     'a': [1, 1, 2, 2],
+                ...     'b': [1, 2, 1, 2],
+                ...     'c': [1, 2, 3, 4],
+                ... })
+                >>> gdf = cudf.from_pandas(df)
+                >>> df.groupby('a').apply(lambda x: x.iloc[[0]])
+                     a  b  c
+                a
+                1 0  1  1  1
+                2 2  2  1  3
+                >>> gdf.groupby('a').apply(lambda x: x.iloc[[0]])
+                   a  b  c
+                0  1  1  1
+                2  2  1  3
+
+        ``engine='jit'`` may be used to accelerate certain functions,
+        initially those that contain reductions and arithmetic operations
+        between results of those reductions:
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a':[1,1,2,2,3,3], 'b':[1,2,3,4,5,6]})
+        >>> df.groupby('a').apply(
+        ...   lambda group: group['b'].max() - group['b'].min(),
+        ...   engine='jit'
+        ... )
+        a
+        1    1
+        2    1
+        3    1
+        dtype: int64
+
+        """
+
+        if self.obj.empty:
+            res = self.obj.copy(deep=True)
+            res.index = self.grouping.keys
+            if function in {"sum", "product"}:
+                # For `sum` & `product`, boolean types
+                # will need to result in `int64` type.
+                for name, col in res._data.items():
+                    if is_bool_dtype(col.dtype):
+                        res._data[name] = col.astype("int")
+            return res
+
+        if not callable(function):
+            raise TypeError(f"type {type(function)} is not callable")
+        group_names, offsets, group_keys, grouped_values = self._grouped()
+
+        if engine == "auto":
+            if (not grouped_values._has_nulls) and _can_be_jitted(
+                grouped_values, function, args
+            ):
+                engine = "jit"
+            else:
+                engine = "cudf"
+        if engine == "jit":
+            result = self._jit_groupby_apply(
+                function,
+                group_names,
+                offsets,
+                group_keys,
+                grouped_values,
+                *args,
+            )
+        elif engine == "cudf":
+            result = self._iterative_groupby_apply(
+                function,
+                group_names,
+                offsets,
+                group_keys,
+                grouped_values,
+                *args,
+            )
+        else:
+            raise ValueError(f"Unsupported engine '{engine}'")
+
+        if self._sort:
+            result = result.sort_index()
+        if self._as_index is False:
+            result = result.reset_index()
+            result[None] = result.pop(0)
+        return result
+
+    @_cudf_nvtx_annotate
+    def apply_grouped(self, function, **kwargs):
+        """Apply a transformation function over the grouped chunk.
+
+        This uses numba's CUDA JIT compiler to convert the Python
+        transformation function into a CUDA kernel, thus will have a
+        compilation overhead during the first run.
+
+        Parameters
+        ----------
+        func : function
+          The transformation function that will be executed on the CUDA GPU.
+        incols: list
+          A list of names of input columns.
+        outcols: list
+          A dictionary of output column names and their dtype.
+        kwargs : dict
+          name-value of extra arguments. These values are passed directly into
+          the function.
+
+        Examples
+        --------
+        .. code-block:: python
+
+            from cudf import DataFrame
+            from numba import cuda
+            import numpy as np
+
+            df = DataFrame()
+            df['key'] = [0, 0, 1, 1, 2, 2, 2]
+            df['val'] = [0, 1, 2, 3, 4, 5, 6]
+            groups = df.groupby(['key'])
+
+            # Define a function to apply to each group
+            def mult_add(key, val, out1, out2):
+                for i in range(cuda.threadIdx.x, len(key), cuda.blockDim.x):
+                    out1[i] = key[i] * val[i]
+                    out2[i] = key[i] + val[i]
+
+            result = groups.apply_grouped(mult_add,
+                                          incols=['key', 'val'],
+                                          outcols={'out1': np.int32,
+                                                   'out2': np.int32},
+                                          # threads per block
+                                          tpb=8)
+
+            print(result)
+
+        Output:
+
+        .. code-block:: python
+
+               key  val out1 out2
+            0    0    0    0    0
+            1    0    1    0    1
+            2    1    2    2    3
+            3    1    3    3    4
+            4    2    4    8    6
+            5    2    5   10    7
+            6    2    6   12    8
+
+
+
+        .. code-block:: python
+
+            import cudf
+            import numpy as np
+            from numba import cuda
+            import pandas as pd
+            from random import randint
+
+
+            # Create a random 15 row dataframe with one categorical
+            # feature and one random integer valued feature
+            df = cudf.DataFrame(
+                    {
+                        "cat": [1] * 5 + [2] * 5 + [3] * 5,
+                        "val": [randint(0, 100) for _ in range(15)],
+                    }
+                 )
+
+            # Group the dataframe by its categorical feature
+            groups = df.groupby("cat")
+
+            # Define a kernel which takes the moving average of a
+            # sliding window
+            def rolling_avg(val, avg):
+                win_size = 3
+                for i in range(cuda.threadIdx.x, len(val), cuda.blockDim.x):
+                    if i < win_size - 1:
+                        # If there is not enough data to fill the window,
+                        # take the average to be NaN
+                        avg[i] = np.nan
+                    else:
+                        total = 0
+                        for j in range(i - win_size + 1, i + 1):
+                            total += val[j]
+                        avg[i] = total / win_size
+
+            # Compute moving averages on all groups
+            results = groups.apply_grouped(rolling_avg,
+                                           incols=['val'],
+                                           outcols=dict(avg=np.float64))
+            print("Results:", results)
+
+            # Note this gives the same result as its pandas equivalent
+            pdf = df.to_pandas()
+            pd_results = pdf.groupby('cat')['val'].rolling(3).mean()
+
+
+        Output:
+
+        .. code-block:: python
+
+            Results:
+               cat  val                 avg
+            0    1   16
+            1    1   45
+            2    1   62                41.0
+            3    1   45  50.666666666666664
+            4    1   26  44.333333333333336
+            5    2    5
+            6    2   51
+            7    2   77  44.333333333333336
+            8    2    1                43.0
+            9    2   46  41.333333333333336
+            [5 more rows]
+
+        This is functionally equivalent to `pandas.DataFrame.Rolling
+        <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html>`_
+
+        """
+        if not callable(function):
+            raise TypeError(f"type {type(function)} is not callable")
+
+        _, offsets, _, grouped_values = self._grouped()
+        kwargs.update({"chunks": offsets})
+        return grouped_values.apply_chunks(function, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def _broadcast(self, values):
+        """
+        Broadcast the results of an aggregation to the group
+
+        Parameters
+        ----------
+        values: Series
+            A Series representing the results of an aggregation.  The
+            index of the Series must be the (unique) values
+            representing the group keys.
+
+        Returns
+        -------
+        A Series of the same size and with the same index as
+        ``self.obj``.
+        """
+        if not values.index.equals(self.grouping.keys):
+            values = values._align_to_index(
+                self.grouping.keys, how="right", allow_non_unique=True
+            )
+            values.index = self.obj.index
+        return values
+
+    @_cudf_nvtx_annotate
+    def transform(self, function):
+        """Apply an aggregation, then broadcast the result to the group size.
+
+        Parameters
+        ----------
+        function: str or callable
+            Aggregation to apply to each group. Note that the set of
+            operations currently supported by `transform` is identical
+            to that supported by the `agg` method.
+
+        Returns
+        -------
+        A Series or DataFrame of the same size as the input, with the
+        result of the aggregation per group broadcasted to the group
+        size.
+
+        Examples
+        --------
+        .. code-block:: python
+
+          import cudf
+          df = cudf.DataFrame({'a': [2, 1, 1, 2, 2], 'b': [1, 2, 3, 4, 5]})
+          df.groupby('a').transform('max')
+             b
+          0  5
+          1  3
+          2  3
+          3  5
+          4  5
+
+        See Also
+        --------
+        agg
+        """
+        try:
+            result = self.agg(function)
+        except TypeError as e:
+            raise NotImplementedError(
+                "Currently, `transform()` supports only aggregations."
+            ) from e
+
+        return self._broadcast(result)
+
+    def rolling(self, *args, **kwargs):
+        """
+        Returns a `RollingGroupby` object that enables rolling window
+        calculations on the groups.
+
+        See Also
+        --------
+        cudf.core.window.Rolling
+        """
+        return cudf.core.window.rolling.RollingGroupby(self, *args, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def count(self, dropna=True):
+        """Compute the number of values in each column.
+
+        Parameters
+        ----------
+        dropna : bool
+            If ``True``, don't include null values in the count.
+        """
+
+        def func(x):
+            return getattr(x, "count")(dropna=dropna)
+
+        return self.agg(func)
+
+    @_cudf_nvtx_annotate
+    def describe(self, include=None, exclude=None):
+        """
+        Generate descriptive statistics that summarizes the central tendency,
+        dispersion and shape of a dataset's distribution, excluding NaN values.
+
+        Analyzes numeric DataFrames only
+
+        Parameters
+        ----------
+        include: 'all', list-like of dtypes or None (default), optional
+            list of data types to include in the result.
+            Ignored for Series.
+
+        exclude: list-like of dtypes or None (default), optional,
+            list of data types to omit from the result.
+            Ignored for Series.
+
+        Returns
+        -------
+        Series or DataFrame
+            Summary statistics of the Dataframe provided.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gdf = cudf.DataFrame({
+        ...     "Speed": [380.0, 370.0, 24.0, 26.0],
+        ...      "Score": [50, 30, 90, 80],
+        ... })
+        >>> gdf
+           Speed  Score
+        0  380.0     50
+        1  370.0     30
+        2   24.0     90
+        3   26.0     80
+        >>> gdf.groupby('Score').describe()
+             Speed
+             count   mean   std    min    25%    50%    75%     max
+        Score
+        30        1  370.0  <NA>  370.0  370.0  370.0  370.0  370.0
+        50        1  380.0  <NA>  380.0  380.0  380.0  380.0  380.0
+        80        1   26.0  <NA>   26.0   26.0   26.0   26.0   26.0
+        90        1   24.0  <NA>   24.0   24.0   24.0   24.0   24.0
+
+        """
+        if exclude is not None and include is not None:
+            raise NotImplementedError
+
+        res = self.agg(
+            [
+                "count",
+                "mean",
+                "std",
+                "min",
+                _quantile_25,
+                _quantile_50,
+                _quantile_75,
+                "max",
+            ]
+        )
+        res.rename(
+            columns={
+                "_quantile_25": "25%",
+                "_quantile_50": "50%",
+                "_quantile_75": "75%",
+            },
+            level=1,
+            inplace=True,
+        )
+        return res
+
+    @_cudf_nvtx_annotate
+    def corr(self, method="pearson", min_periods=1):
+        """
+        Compute pairwise correlation of columns, excluding NA/null values.
+
+        Parameters
+        ----------
+        method: {"pearson", "kendall", "spearman"} or callable,
+            default "pearson". Currently only the pearson correlation
+            coefficient is supported.
+
+        min_periods: int, optional
+            Minimum number of observations required per pair of columns
+            to have a valid result.
+
+        Returns
+        -------
+        DataFrame
+            Correlation matrix.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gdf = cudf.DataFrame({
+        ...             "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+        ...             "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
+        ...             "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+        ...             "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1]})
+        >>> gdf
+           id  val1  val2  val3
+        0  a     5     4     4
+        1  a     4     5     5
+        2  a     6     6     6
+        3  b     4     1     1
+        4  b     8     2     2
+        5  b     7     9     9
+        6  c     4     8     8
+        7  c     5     5     5
+        8  c     2     1     1
+        >>> gdf.groupby("id").corr(method="pearson")
+                    val1      val2      val3
+        id
+        a   val1  1.000000  0.500000  0.500000
+            val2  0.500000  1.000000  1.000000
+            val3  0.500000  1.000000  1.000000
+        b   val1  1.000000  0.385727  0.385727
+            val2  0.385727  1.000000  1.000000
+            val3  0.385727  1.000000  1.000000
+        c   val1  1.000000  0.714575  0.714575
+            val2  0.714575  1.000000  1.000000
+            val3  0.714575  1.000000  1.000000
+        """
+
+        if method.lower() not in ("pearson",):
+            raise NotImplementedError(
+                "Only pearson correlation is currently supported"
+            )
+
+        return self._cov_or_corr(
+            lambda x: x.corr(method, min_periods), "Correlation"
+        )
+
+    @_cudf_nvtx_annotate
+    def cov(self, min_periods=0, ddof=1):
+        """
+        Compute the pairwise covariance among the columns of a DataFrame,
+        excluding NA/null values.
+
+        The returned DataFrame is the covariance matrix of the columns of
+        the DataFrame.
+
+        Both NA and null values are automatically excluded from the
+        calculation. See the note below about bias from missing values.
+
+        A threshold can be set for the minimum number of observations
+        for each value created. Comparisons with observations below this
+        threshold will be returned as `NA`.
+
+        This method is generally used for the analysis of time series data to
+        understand the relationship between different measures across time.
+
+        Parameters
+        ----------
+        min_periods: int, optional
+            Minimum number of observations required per pair of columns
+            to have a valid result.
+
+        ddof: int, optional
+            Delta degrees of freedom, default is 1.
+
+        Returns
+        -------
+        DataFrame
+            Covariance matrix.
+
+        Notes
+        -----
+        Returns the covariance matrix of the DataFrame's time series.
+        The covariance is normalized by N-ddof.
+
+        For DataFrames that have Series that are missing data
+        (assuming that data is missing at random) the returned covariance
+        matrix will be an unbiased estimate of the variance and covariance
+        between the member Series.
+
+        However, for many applications this estimate may not be acceptable
+        because the estimate covariance matrix is not guaranteed to be
+        positive semi-definite. This could lead to estimate correlations
+        having absolute values which are greater than one, and/or a
+        non-invertible covariance matrix. See
+        `Estimation of covariance matrices
+        <https://en.wikipedia.org/wiki/Estimation_of_covariance_matrices>`
+        for more details.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gdf = cudf.DataFrame({
+        ...     "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+        ...     "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
+        ...     "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+        ...     "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+        ... })
+        >>> gdf
+          id  val1  val2  val3
+        0  a     5     4     4
+        1  a     4     5     5
+        2  a     6     6     6
+        3  b     4     1     1
+        4  b     8     2     2
+        5  b     7     9     9
+        6  c     4     8     8
+        7  c     5     5     5
+        8  c     2     1     1
+        >>> gdf.groupby("id").cov()
+                    val1       val2       val3
+        id
+        a  val1  1.000000   0.500000   0.500000
+           val2  0.500000   1.000000   1.000000
+           val3  0.500000   1.000000   1.000000
+        b  val1  4.333333   3.500000   3.500000
+           val2  3.500000  19.000000  19.000000
+           val3  3.500000  19.000000  19.000000
+        c  val1  2.333333   3.833333   3.833333
+           val2  3.833333  12.333333  12.333333
+           val3  3.833333  12.333333  12.333333
+        """
+
+        return self._cov_or_corr(
+            lambda x: x.cov(min_periods, ddof), "Covariance"
+        )
+
+    def _cov_or_corr(self, func, method_name):
+        """
+        Internal function that is called by either corr() or cov()
+        for sort groupby correlation and covariance computations,
+        respectively.
+        """
+        # create expanded dataframe consisting all combinations of the
+        # struct columns-pairs to be used in the correlation or covariance
+        # i.e. (('col1', 'col1'), ('col1', 'col2'), ('col2', 'col2'))
+        column_names = self.grouping.values._column_names
+        num_cols = len(column_names)
+
+        column_pair_structs = {}
+        for x, y in itertools.combinations_with_replacement(column_names, 2):
+            # The number of output columns is the number of input columns
+            # squared. We directly call the struct column factory here to
+            # reduce overhead and avoid copying data. Since libcudf groupby
+            # maintains a cache of aggregation requests, reusing the same
+            # column also makes use of previously cached column means and
+            # reduces kernel costs.
+
+            # checks if input column names are string, raise a warning if
+            # not so and cast them to strings
+            if not (isinstance(x, str) and isinstance(y, str)):
+                warnings.warn(
+                    "DataFrame contains non-string column name(s). "
+                    "Struct columns require field names to be strings. "
+                    "Non-string column names will be cast to strings "
+                    "in the result's field names."
+                )
+                x, y = str(x), str(y)
+
+            column_pair_structs[(x, y)] = cudf.core.column.build_struct_column(
+                names=(x, y),
+                children=(self.obj._data[x], self.obj._data[y]),
+                size=len(self.obj),
+            )
+
+        column_pair_groupby = cudf.DataFrame._from_data(
+            column_pair_structs
+        ).groupby(by=self.grouping.keys)
+
+        try:
+            gb_cov_corr = column_pair_groupby.agg(func)
+        except RuntimeError as e:
+            if "Unsupported groupby reduction type-agg combination" in str(e):
+                raise TypeError(
+                    f"{method_name} accepts only numerical column-pairs"
+                )
+            raise
+
+        # ensure that column-pair labels are arranged in ascending order
+        cols_list = [
+            (y, x) if i > j else (x, y)
+            for j, y in enumerate(column_names)
+            for i, x in enumerate(column_names)
+        ]
+        cols_split = [
+            cols_list[i : i + num_cols]
+            for i in range(0, len(cols_list), num_cols)
+        ]
+
+        # interleave: combines the correlation or covariance results for each
+        # column-pair into a single column
+        res = cudf.DataFrame._from_data(
+            {
+                x: interleave_columns([gb_cov_corr._data[y] for y in ys])
+                for ys, x in zip(cols_split, column_names)
+            }
+        )
+
+        # create a multiindex for the groupby covariance or correlation
+        # dataframe, to match pandas behavior
+        unsorted_idx = gb_cov_corr.index.repeat(num_cols)
+        idx_sort_order = unsorted_idx._get_sorted_inds()
+        sorted_idx = unsorted_idx._gather(idx_sort_order)
+        if len(gb_cov_corr):
+            # TO-DO: Should the operation below be done on the CPU instead?
+            sorted_idx._data[None] = as_column(
+                np.tile(column_names, len(gb_cov_corr.index))
+            )
+        res.index = MultiIndex._from_data(sorted_idx._data)
+
+        return res
+
+    @_cudf_nvtx_annotate
+    def var(self, ddof=1):
+        """Compute the column-wise variance of the values in each group.
+
+        Parameters
+        ----------
+        ddof : int
+            The delta degrees of freedom. N - ddof is the divisor used to
+            normalize the variance.
+        """
+
+        def func(x):
+            return getattr(x, "var")(ddof=ddof)
+
+        return self.agg(func)
+
+    @_cudf_nvtx_annotate
+    def std(self, ddof=1):
+        """Compute the column-wise std of the values in each group.
+
+        Parameters
+        ----------
+        ddof : int
+            The delta degrees of freedom. N - ddof is the divisor used to
+            normalize the standard deviation.
+        """
+
+        def func(x):
+            return getattr(x, "std")(ddof=ddof)
+
+        return self.agg(func)
+
+    @_cudf_nvtx_annotate
+    def quantile(self, q=0.5, interpolation="linear"):
+        """Compute the column-wise quantiles of the values in each group.
+
+        Parameters
+        ----------
+        q : float or array-like
+            The quantiles to compute.
+        interpolation : {"linear", "lower", "higher", "midpoint", "nearest"}
+            The interpolation method to use when the desired quantile lies
+            between two data points. Defaults to "linear".
+        """
+
+        def func(x):
+            return getattr(x, "quantile")(q=q, interpolation=interpolation)
+
+        return self.agg(func)
+
+    @_cudf_nvtx_annotate
+    def collect(self):
+        """Get a list of all the values for each column in each group."""
+        return self.agg("collect")
+
+    @_cudf_nvtx_annotate
+    def unique(self):
+        """Get a list of the unique values for each column in each group."""
+        return self.agg("unique")
+
+    @_cudf_nvtx_annotate
+    def diff(self, periods=1, axis=0):
+        """Get the difference between the values in each group.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference,
+            accepts negative values.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Take difference over rows (0) or columns (1).
+            Only row-wise (0) shift is supported.
+
+        Returns
+        -------
+        Series or DataFrame
+            First differences of the Series or DataFrame.
+        """
+
+        if not axis == 0:
+            raise NotImplementedError("Only axis=0 is supported.")
+
+        values = self.obj.__class__._from_data(
+            self.grouping.values._data, self.obj.index
+        )
+        return values - self.shift(periods=periods)
+
+    def _scan_fill(self, method: str, limit: int) -> DataFrameOrSeries:
+        """Internal implementation for `ffill` and `bfill`"""
+        values = self.grouping.values
+        result = self.obj._from_columns(
+            self._groupby.replace_nulls([*values._columns], method),
+            values._column_names,
+        )
+        result = self._mimic_pandas_order(result)
+        return result._copy_type_metadata(values)
+
+    @_cudf_nvtx_annotate
+    def pad(self, limit=None):
+        """Forward fill NA values.
+
+        .. deprecated:: 23.06
+           `pad` is deprecated, use `ffill` instead.
+
+        Parameters
+        ----------
+        limit : int, default None
+            Unsupported
+        """
+
+        if limit is not None:
+            raise NotImplementedError("Does not support limit param yet.")
+
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            "pad is deprecated and will be removed in a future version. "
+            "Use ffill instead.",
+            FutureWarning,
+        )
+        return self._scan_fill("ffill", limit)
+
+    def ffill(self, limit=None):
+        """Forward fill NA values.
+
+        Parameters
+        ----------
+        limit : int, default None
+            Unsupported
+        """
+
+        if limit is not None:
+            raise NotImplementedError("Does not support limit param yet.")
+
+        return self._scan_fill("ffill", limit)
+
+    @_cudf_nvtx_annotate
+    def backfill(self, limit=None):
+        """Backward fill NA values.
+
+        .. deprecated:: 23.06
+           `backfill` is deprecated, use `bfill` instead.
+
+        Parameters
+        ----------
+        limit : int, default None
+            Unsupported
+        """
+        if limit is not None:
+            raise NotImplementedError("Does not support limit param yet.")
+
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            "backfill is deprecated and will be removed in a future version. "
+            "Use bfill instead.",
+            FutureWarning,
+        )
+        return self._scan_fill("bfill", limit)
+
+    def bfill(self, limit=None):
+        """Backward fill NA values.
+
+        Parameters
+        ----------
+        limit : int, default None
+            Unsupported
+        """
+        if limit is not None:
+            raise NotImplementedError("Does not support limit param yet.")
+
+        return self._scan_fill("bfill", limit)
+
+    @_cudf_nvtx_annotate
+    def fillna(
+        self,
+        value=None,
+        method=None,
+        axis=0,
+        inplace=False,
+        limit=None,
+        downcast=None,
+    ):
+        """Fill NA values using the specified method.
+
+        Parameters
+        ----------
+        value : scalar, dict
+            Value to use to fill the holes. Cannot be specified with method.
+        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+            Method to use for filling holes in reindexed Series
+
+            - pad/ffill: propagate last valid observation forward to next valid
+            - backfill/bfill: use next valid observation to fill gap
+        axis : {0 or 'index', 1 or 'columns'}
+            Unsupported
+        inplace : bool, default False
+            If `True`, fill inplace. Note: this will modify other views on this
+            object.
+        limit : int, default None
+            Unsupported
+        downcast : dict, default None
+            Unsupported
+
+        Returns
+        -------
+        DataFrame or Series
+        """
+        if inplace:
+            raise NotImplementedError("Does not support inplace yet.")
+        if limit is not None:
+            raise NotImplementedError("Does not support limit param yet.")
+        if downcast is not None:
+            raise NotImplementedError("Does not support downcast yet.")
+        if not axis == 0:
+            raise NotImplementedError("Only support axis == 0.")
+
+        if value is None and method is None:
+            raise ValueError("Must specify a fill 'value' or 'method'.")
+        if value is not None and method is not None:
+            raise ValueError("Cannot specify both 'value' and 'method'.")
+
+        if method is not None:
+            if method not in {"pad", "ffill", "backfill", "bfill"}:
+                raise ValueError(
+                    "Method can only be of 'pad', 'ffill',"
+                    "'backfill', 'bfill'."
+                )
+            return getattr(self, method, limit)()
+
+        values = self.obj.__class__._from_data(
+            self.grouping.values._data, self.obj.index
+        )
+        return values.fillna(
+            value=value, inplace=inplace, axis=axis, limit=limit
+        )
+
+    @_cudf_nvtx_annotate
+    def shift(self, periods=1, freq=None, axis=0, fill_value=None):
+        """
+        Shift each group by ``periods`` positions.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Number of periods to shift.
+        freq : str, unsupported
+        axis : 0, axis to shift
+            Shift direction. Only row-wise shift is supported
+        fill_value : scalar or list of scalars, optional
+            The scalar value to use for newly introduced missing values. Can be
+            specified with `None`, a single value or multiple values:
+
+            - `None` (default): sets all indeterminable values to null.
+            - Single value: fill all shifted columns with this value. Should
+              match the data type of all columns.
+            - List of values: fill shifted columns with corresponding value in
+              the list. The length of the list should match the number of
+              columns shifted. Each value should match the data type of the
+              column to fill.
+
+        Returns
+        -------
+        Series or DataFrame
+            Object shifted within each group.
+
+        Notes
+        -----
+        Parameter ``freq`` is unsupported.
+        """
+
+        if freq is not None:
+            raise NotImplementedError("Parameter freq is unsupported.")
+
+        if not axis == 0:
+            raise NotImplementedError("Only axis=0 is supported.")
+
+        values = self.grouping.values
+        if is_list_like(fill_value):
+            if len(fill_value) != len(values._data):
+                raise ValueError(
+                    "Mismatched number of columns and values to fill."
+                )
+        else:
+            fill_value = [fill_value] * len(values._data)
+
+        result = self.obj.__class__._from_columns(
+            self._groupby.shift([*values._columns], periods, fill_value)[0],
+            values._column_names,
+        )
+        result = self._mimic_pandas_order(result)
+        return result._copy_type_metadata(values)
+
+    @_cudf_nvtx_annotate
+    def pct_change(
+        self, periods=1, fill_method="ffill", axis=0, limit=None, freq=None
+    ):
+        """
+        Calculates the percent change between sequential elements
+        in the group.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for forming percent change.
+        fill_method : str, default 'ffill'
+            How to handle NAs before computing percent changes.
+        limit : int, optional
+            The number of consecutive NAs to fill before stopping.
+            Not yet implemented.
+        freq : str, optional
+            Increment to use from time series API.
+            Not yet implemented.
+
+        Returns
+        -------
+        Series or DataFrame
+            Percentage changes within each group
+        """
+        if not axis == 0:
+            raise NotImplementedError("Only axis=0 is supported.")
+        if limit is not None:
+            raise NotImplementedError("limit parameter not supported yet.")
+        if freq is not None:
+            raise NotImplementedError("freq parameter not supported yet.")
+        elif fill_method not in {"ffill", "pad", "bfill", "backfill"}:
+            raise ValueError(
+                "fill_method must be one of 'ffill', 'pad', "
+                "'bfill', or 'backfill'."
+            )
+
+        if fill_method in ("pad", "backfill"):
+            alternative = "ffill" if fill_method == "pad" else "bfill"
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                f"{fill_method} is deprecated and will be removed in a future "
+                f"version. Use f{alternative} instead.",
+                FutureWarning,
+            )
+
+        filled = self.fillna(method=fill_method, limit=limit)
+        fill_grp = filled.groupby(self.grouping)
+        shifted = fill_grp.shift(periods=periods, freq=freq)
+        return (filled / shifted) - 1
+
+    def value_counts(
+        self,
+        subset=None,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        dropna: bool = True,
+    ) -> DataFrameOrSeries:
+        """
+        Return a Series or DataFrame containing counts of unique rows.
+
+        Parameters
+        ----------
+        subset : list-like, optional
+            Columns to use when counting unique combinations.
+        normalize : bool, default False
+            Return proportions rather than frequencies.
+        sort : bool, default True
+            Sort by frequencies.
+        ascending : bool, default False
+            Sort in ascending order.
+        dropna : bool, default True
+            Don't include counts of rows that contain NA values.
+
+        Returns
+        -------
+        Series or DataFrame
+            Series if the groupby as_index is True, otherwise DataFrame.
+
+        See Also
+        --------
+        Series.value_counts: Equivalent method on Series.
+        DataFrame.value_counts: Equivalent method on DataFrame.
+        SeriesGroupBy.value_counts: Equivalent method on SeriesGroupBy.
+
+        Notes
+        -----
+        - If the groupby as_index is True then the returned Series will have a
+          MultiIndex with one level per input column.
+        - If the groupby as_index is False then the returned DataFrame will
+          have an additional column with the value_counts. The column is
+          labelled 'count' or 'proportion', depending on the ``normalize``
+          parameter.
+
+        By default, rows that contain any NA values are omitted from
+        the result.
+
+        By default, the result will be in descending order so that the
+        first element of each group is the most frequently-occurring row.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({
+        ...    'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
+        ...    'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
+        ...    'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
+        ... })
+
+        >>> df
+                gender  education   country
+        0       male    low         US
+        1       male    medium      FR
+        2       female  high        US
+        3       male    low         FR
+        4       female  high        FR
+        5       male    low         FR
+
+        >>> df.groupby('gender').value_counts()
+        gender  education  country
+        female  high       FR         1
+                           US         1
+        male    low        FR         2
+                           US         1
+                medium     FR         1
+        Name: count, dtype: int64
+
+        >>> df.groupby('gender').value_counts(ascending=True)
+        gender  education  country
+        female  high       FR         1
+                           US         1
+        male    low        US         1
+                medium     FR         1
+                low        FR         2
+        Name: count, dtype: int64
+
+        >>> df.groupby('gender').value_counts(normalize=True)
+        gender  education  country
+        female  high       FR         0.50
+                           US         0.50
+        male    low        FR         0.50
+                           US         0.25
+                medium     FR         0.25
+        Name: proportion, dtype: float64
+
+        >>> df.groupby('gender', as_index=False).value_counts()
+           gender education country  count
+        0  female      high      FR      1
+        1  female      high      US      1
+        2    male       low      FR      2
+        3    male       low      US      1
+        4    male    medium      FR      1
+
+        >>> df.groupby('gender', as_index=False).value_counts(normalize=True)
+           gender education country  proportion
+        0  female      high      FR        0.50
+        1  female      high      US        0.50
+        2    male       low      FR        0.50
+        3    male       low      US        0.25
+        4    male    medium      FR        0.25
+        """
+
+        df = cudf.DataFrame.copy(self.obj)
+        groupings = self.grouping.names
+        name = "proportion" if normalize else "count"
+
+        if subset is None:
+            subset = [i for i in df._column_names if i not in groupings]
+        # Check subset exists in dataframe
+        elif set(subset) - set(df._column_names):
+            raise ValueError(
+                f"Keys {set(subset) - set(df._column_names)} in subset "
+                f"do not exist in the DataFrame."
+            )
+        # Catch case where groupby and subset share an element
+        elif set(subset) & set(groupings):
+            raise ValueError(
+                f"Keys {set(subset) & set(groupings)} in subset "
+                "cannot be in the groupby column keys."
+            )
+
+        df["__placeholder"] = 1
+        result = (
+            df.groupby(groupings + list(subset), dropna=dropna)[
+                "__placeholder"
+            ]
+            .count()
+            .sort_index()
+            .astype(np.int64)
+        )
+
+        if normalize:
+            levels = list(range(len(groupings), result.index.nlevels))
+            result /= result.groupby(
+                result.index.droplevel(levels),
+            ).transform("sum")
+
+        if sort:
+            result = result.sort_values(ascending=ascending).sort_index(
+                level=range(len(groupings)), sort_remaining=False
+            )
+
+        if not self._as_index:
+            if name in df._column_names:
+                raise ValueError(
+                    f"Column label '{name}' is duplicate of result column"
+                )
+            result.name = name
+            result = result.to_frame().reset_index()
+        else:
+            result.name = name
+
+        return result
+
+    def _mimic_pandas_order(
+        self, result: DataFrameOrSeries
+    ) -> DataFrameOrSeries:
+        """Given a groupby result from libcudf, reconstruct the row orders
+        matching that of pandas. This also adds appropriate indices.
+        """
+        # TODO: copy metadata after this method is a common pattern, should
+        # merge in this method.
+
+        # This function is used to reorder the results of scan-based
+        # groupbys which have the same output size as input size.
+        # However, if the grouping key has NAs and dropna=True, the
+        # result coming back from libcudf has null_count few rows than
+        # the input, so we must produce an ordering from the full
+        # input range.
+        _, (ordering,), _ = self._groupby.groups([arange(0, len(self.obj))])
+        if self._dropna and any(
+            c.has_nulls(include_nan=True) > 0
+            for c in self.grouping._key_columns
+        ):
+            # Scan aggregations with null/nan keys put nulls in the
+            # corresponding output rows in pandas, to do that here
+            # expand the result by reindexing.
+            ri = cudf.RangeIndex(0, len(self.obj))
+            result.index = cudf.Index(ordering)
+            # This reorders and expands
+            result = result.reindex(ri)
+        else:
+            # Just reorder according to the groupings
+            result = result.take(ordering.argsort())
+        # Now produce the actual index we first thought of
+        result.index = self.obj.index
+        return result
+
+
+class DataFrameGroupBy(GroupBy, GetAttrGetItemMixin):
+    obj: "cudf.core.dataframe.DataFrame"
+
+    _PROTECTED_KEYS = frozenset(("obj",))
+
+    def __getitem__(self, key):
+        return self.obj[key].groupby(
+            by=self.grouping.keys,
+            dropna=self._dropna,
+            sort=self._sort,
+            group_keys=self._group_keys,
+            as_index=self._as_index,
+        )
+
+
+DataFrameGroupBy.__doc__ = groupby_doc_template.format(ret="")
+
+
+class SeriesGroupBy(GroupBy):
+    obj: "cudf.core.series.Series"
+
+    def agg(self, func):
+        result = super().agg(func)
+
+        # downcast the result to a Series:
+        if len(result._data):
+            if result.shape[1] == 1 and not is_list_like(func):
+                return result.iloc[:, 0]
+
+        # drop the first level if we have a multiindex
+        if result._data.nlevels > 1:
+            result.columns = result._data.to_pandas_index().droplevel(0)
+
+        return result
+
+    def apply(self, func, *args):
+        result = super().apply(func, *args)
+
+        # apply Series name to result
+        result.name = self.obj.name
+
+        return result
+
+
+SeriesGroupBy.__doc__ = groupby_doc_template.format(ret="")
+
+
+# TODO: should we define this as a dataclass instead?
+class Grouper:
+    def __init__(
+        self, key=None, level=None, freq=None, closed=None, label=None
+    ):
+        if key is not None and level is not None:
+            raise ValueError("Grouper cannot specify both key and level")
+        if (key, level) == (None, None) and not freq:
+            raise ValueError("Grouper must specify either key or level")
+        self.key = key
+        self.level = level
+        self.freq = freq
+        self.closed = closed
+        self.label = label
+
+
+class _Grouping(Serializable):
+    def __init__(self, obj, by=None, level=None):
+        self._obj = obj
+        self._key_columns = []
+        self.names = []
+
+        # Need to keep track of named key columns
+        # to support `as_index=False` correctly
+        self._named_columns = []
+        self._handle_by_or_level(by, level)
+
+        if len(obj) and not len(self._key_columns):
+            raise ValueError("No group keys passed")
+
+    def _handle_by_or_level(self, by=None, level=None):
+        if level is not None:
+            if by is not None:
+                raise ValueError("Cannot specify both by and level")
+            level_list = level if isinstance(level, list) else [level]
+            for level in level_list:
+                self._handle_level(level)
+        else:
+            by_list = by if isinstance(by, list) else [by]
+
+            for by in by_list:
+                if callable(by):
+                    self._handle_callable(by)
+                elif isinstance(by, cudf.Series):
+                    self._handle_series(by)
+                elif isinstance(by, cudf.BaseIndex):
+                    self._handle_index(by)
+                elif isinstance(by, abc.Mapping):
+                    self._handle_mapping(by)
+                elif isinstance(by, Grouper):
+                    self._handle_grouper(by)
+                elif isinstance(by, pd.Series):
+                    self._handle_series(cudf.Series.from_pandas(by))
+                elif isinstance(by, pd.Index):
+                    self._handle_index(cudf.Index.from_pandas(by))
+                else:
+                    try:
+                        self._handle_label(by)
+                    except (KeyError, TypeError):
+                        self._handle_misc(by)
+
+    @property
+    def keys(self):
+        """Return grouping key columns as index"""
+        nkeys = len(self._key_columns)
+
+        if nkeys == 0:
+            return cudf.core.index.as_index([], name=None)
+        elif nkeys > 1:
+            return cudf.MultiIndex._from_data(
+                dict(zip(range(nkeys), self._key_columns))
+            )._set_names(self.names)
+        else:
+            return cudf.core.index.as_index(
+                self._key_columns[0], name=self.names[0]
+            )
+
+    @property
+    def values(self) -> cudf.core.frame.Frame:
+        """Return value columns as a frame.
+
+        Note that in aggregation, value columns can be arbitrarily
+        specified. While this method returns all non-key columns from `obj` as
+        a frame.
+
+        This is mainly used in transform-like operations.
+        """
+        # If the key columns are in `obj`, filter them out
+        value_column_names = [
+            x for x in self._obj._data.names if x not in self._named_columns
+        ]
+        value_columns = self._obj._data.select_by_label(value_column_names)
+        return self._obj.__class__._from_data(value_columns)
+
+    def _handle_callable(self, by):
+        by = by(self._obj.index)
+        self.__init__(self._obj, by)
+
+    def _handle_series(self, by):
+        by = by._align_to_index(self._obj.index, how="right")
+        self._key_columns.append(by._column)
+        self.names.append(by.name)
+
+    def _handle_index(self, by):
+        self._key_columns.extend(by._data.columns)
+        self.names.extend(by._data.names)
+
+    def _handle_mapping(self, by):
+        by = cudf.Series(by.values(), index=by.keys())
+        self._handle_series(by)
+
+    def _handle_label(self, by):
+        try:
+            self._key_columns.append(self._obj._data[by])
+        except KeyError as e:
+            # `by` can be index name(label) too.
+            if by in self._obj._index.names:
+                self._key_columns.append(self._obj._index._data[by])
+            else:
+                raise e
+        self.names.append(by)
+        self._named_columns.append(by)
+
+    def _handle_grouper(self, by):
+        if by.freq:
+            self._handle_frequency_grouper(by)
+        elif by.key:
+            self._handle_label(by.key)
+        else:
+            self._handle_level(by.level)
+
+    def _handle_frequency_grouper(self, by):
+        raise NotImplementedError()
+
+    def _handle_level(self, by):
+        level_values = self._obj.index.get_level_values(by)
+        self._key_columns.append(level_values._values)
+        self.names.append(level_values.name)
+
+    def _handle_misc(self, by):
+        by = cudf.core.column.as_column(by)
+        if len(by) != len(self._obj):
+            raise ValueError("Grouper and object must have same length")
+        self._key_columns.append(by)
+        self.names.append(None)
+
+    def serialize(self):
+        header = {}
+        frames = []
+        header["names"] = pickle.dumps(self.names)
+        header["_named_columns"] = pickle.dumps(self._named_columns)
+        column_header, column_frames = cudf.core.column.serialize_columns(
+            self._key_columns
+        )
+        header["columns"] = column_header
+        frames.extend(column_frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        names = pickle.loads(header["names"])
+        _named_columns = pickle.loads(header["_named_columns"])
+        key_columns = cudf.core.column.deserialize_columns(
+            header["columns"], frames
+        )
+        out = _Grouping.__new__(_Grouping)
+        out.names = names
+        out._named_columns = _named_columns
+        out._key_columns = key_columns
+        return out
+
+    def copy(self, deep=True):
+        out = _Grouping.__new__(_Grouping)
+        out.names = copy.deepcopy(self.names)
+        out._named_columns = copy.deepcopy(self._named_columns)
+        out._key_columns = [col.copy(deep=deep) for col in self._key_columns]
+        return out
+
+
+def _is_multi_agg(aggs):
+    """
+    Returns True if more than one aggregation is performed
+    on any of the columns as specified in `aggs`.
+    """
+    if isinstance(aggs, abc.Mapping):
+        return any(is_list_like(agg) for agg in aggs.values())
+    if is_list_like(aggs):
+        return True
+    return False
diff --git a/python/cudf/cudf/core/index.py b/python/cudf/cudf/core/index.py
new file mode 100644
index 0000000..51a7e9d
--- /dev/null
+++ b/python/cudf/cudf/core/index.py
@@ -0,0 +1,3627 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import math
+import pickle
+import warnings
+from functools import cache, cached_property
+from numbers import Number
+from typing import (
+    Any,
+    Dict,
+    List,
+    MutableMapping,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+
+import cupy
+import numpy as np
+import pandas as pd
+from pandas._config import get_option
+
+import cudf
+from cudf._lib.datetime import extract_quarter, is_leap_year
+from cudf._lib.filling import sequence
+from cudf._lib.search import search_sorted
+from cudf._lib.types import size_type_dtype
+from cudf.api.extensions import no_default
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    is_categorical_dtype,
+    is_dtype_equal,
+    is_integer,
+    is_interval_dtype,
+    is_list_like,
+    is_scalar,
+    is_signed_integer_dtype,
+    is_string_dtype,
+)
+from cudf.core._base_index import BaseIndex
+from cudf.core.column import (
+    CategoricalColumn,
+    ColumnBase,
+    DatetimeColumn,
+    IntervalColumn,
+    NumericalColumn,
+    StringColumn,
+    StructColumn,
+    TimeDeltaColumn,
+    column,
+)
+from cudf.core.column.column import as_column, concat_columns
+from cudf.core.column.string import StringMethods as StringMethods
+from cudf.core.dtypes import IntervalDtype
+from cudf.core.frame import Frame
+from cudf.core.mixins import BinaryOperand
+from cudf.core.single_column_frame import SingleColumnFrame
+from cudf.utils.docutils import copy_docstring
+from cudf.utils.dtypes import (
+    _maybe_convert_to_default_type,
+    find_common_type,
+    is_mixed_with_object_dtype,
+    numeric_normalize_types,
+)
+from cudf.utils.utils import (
+    _cudf_nvtx_annotate,
+    _warn_no_dask_cudf,
+    search_range,
+)
+
+
+def _lexsorted_equal_range(
+    idx: Union[GenericIndex, cudf.MultiIndex],
+    key_as_table: Frame,
+    is_sorted: bool,
+) -> Tuple[int, int, Optional[ColumnBase]]:
+    """Get equal range for key in lexicographically sorted index. If index
+    is not sorted when called, a sort will take place and `sort_inds` is
+    returned. Otherwise `None` is returned in that position.
+    """
+    if not is_sorted:
+        sort_inds = idx._get_sorted_inds()
+        sort_vals = idx._gather(sort_inds)
+    else:
+        sort_inds = None
+        sort_vals = idx
+    lower_bound = search_sorted(
+        [*sort_vals._data.columns], [*key_as_table._columns], side="left"
+    ).element_indexing(0)
+    upper_bound = search_sorted(
+        [*sort_vals._data.columns], [*key_as_table._columns], side="right"
+    ).element_indexing(0)
+
+    return lower_bound, upper_bound, sort_inds
+
+
+def _index_from_data(data: MutableMapping, name: Any = no_default):
+    """Construct an index of the appropriate type from some data."""
+
+    if len(data) == 0:
+        raise ValueError("Cannot construct Index from any empty Table")
+    if len(data) == 1:
+        values = next(iter(data.values()))
+
+        if isinstance(values, NumericalColumn):
+            try:
+                index_class_type: Type[
+                    Union[GenericIndex, cudf.MultiIndex]
+                ] = _dtype_to_index[values.dtype.type]
+            except KeyError:
+                index_class_type = GenericIndex
+        elif isinstance(values, DatetimeColumn):
+            index_class_type = DatetimeIndex
+        elif isinstance(values, TimeDeltaColumn):
+            index_class_type = TimedeltaIndex
+        elif isinstance(values, StringColumn):
+            index_class_type = StringIndex
+        elif isinstance(values, CategoricalColumn):
+            index_class_type = CategoricalIndex
+        elif isinstance(values, (IntervalColumn, StructColumn)):
+            index_class_type = IntervalIndex
+        else:
+            raise NotImplementedError(
+                "Unsupported column type passed to "
+                f"create an Index: {type(values)}"
+            )
+    else:
+        index_class_type = cudf.MultiIndex
+    return index_class_type._from_data(data, name)
+
+
+def _index_from_columns(
+    columns: List[cudf.core.column.ColumnBase], name: Any = no_default
+):
+    """Construct an index from ``columns``, with levels named 0, 1, 2..."""
+    return _index_from_data(dict(zip(range(len(columns)), columns)), name=name)
+
+
+class RangeIndex(BaseIndex, BinaryOperand):
+    """
+    Immutable Index implementing a monotonic integer range.
+
+    This is the default index type used by DataFrame and Series
+    when no explicit index is provided by the user.
+
+    Parameters
+    ----------
+    start : int (default: 0), or other range instance
+    stop : int (default: 0)
+    step : int (default: 1)
+    name : object, optional
+        Name to be stored in the index.
+    dtype : numpy dtype
+        Unused, accepted for homogeneity with other index types.
+    copy : bool, default False
+        Unused, accepted for homogeneity with other index types.
+
+    Attributes
+    ----------
+    start
+    stop
+    step
+
+    Methods
+    -------
+    to_numpy
+    to_arrow
+
+    Examples
+    --------
+    >>> import cudf
+    >>> cudf.RangeIndex(0, 10, 1, name="a")
+    RangeIndex(start=0, stop=10, step=1, name='a')
+
+    >>> cudf.RangeIndex(range(1, 10, 1), name="a")
+    RangeIndex(start=1, stop=10, step=1, name='a')
+    """
+
+    _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
+
+    _range: range
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self, start, stop=None, step=1, dtype=None, copy=False, name=None
+    ):
+        if step == 0:
+            raise ValueError("Step must not be zero.")
+        if not cudf.api.types.is_hashable(name):
+            raise ValueError("Name must be a hashable value.")
+        if dtype is not None and not is_signed_integer_dtype(dtype):
+            raise ValueError(f"{dtype=} must be a signed integer type")
+
+        if isinstance(start, range):
+            therange = start
+            start = therange.start
+            stop = therange.stop
+            step = therange.step
+        if stop is None:
+            start, stop = 0, start
+        if not is_integer(start):
+            raise TypeError(
+                f"start must be an integer, not {type(start).__name__}"
+            )
+        self._start = int(start)
+        if not is_integer(stop):
+            raise TypeError(
+                f"stop must be an integer, not {type(stop).__name__}"
+            )
+        self._stop = int(stop)
+        if step is not None:
+            if not is_integer(step):
+                raise TypeError(
+                    f"step must be an integer, not {type(step).__name__}"
+                )
+            self._step = int(step)
+        else:
+            self._step = 1
+        self._index = None
+        self._name = name
+        self._range = range(self._start, self._stop, self._step)
+        # _end is the actual last element of RangeIndex,
+        # whereas _stop is an upper bound.
+        self._end = self._start + self._step * (len(self._range) - 1)
+
+    def _copy_type_metadata(
+        self: RangeIndex, other: RangeIndex, *, override_dtypes=None
+    ) -> RangeIndex:
+        # There is no metadata to be copied for RangeIndex since it does not
+        # have an underlying column.
+        return self
+
+    def searchsorted(
+        self,
+        value: int,
+        side: str = "left",
+        ascending: bool = True,
+        na_position: str = "last",
+    ):
+        assert (len(self) <= 1) or (
+            ascending == (self._step > 0)
+        ), "Invalid ascending flag"
+        return search_range(value, self.as_range, side=side)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def name(self):
+        return self._name
+
+    @name.setter  # type: ignore
+    @_cudf_nvtx_annotate
+    def name(self, value):
+        self._name = value
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def start(self):
+        """
+        The value of the `start` parameter (0 if this was not supplied).
+        """
+        return self._start
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def stop(self):
+        """
+        The value of the stop parameter.
+        """
+        return self._stop
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def step(self):
+        """
+        The value of the step parameter.
+        """
+        return self._step
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _num_rows(self):
+        return len(self)
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _values(self):
+        if len(self) > 0:
+            return column.arange(
+                self._start, self._stop, self._step, dtype=self.dtype
+            )
+        else:
+            return column.column_empty(0, masked=False, dtype=self.dtype)
+
+    def _clean_nulls_from_index(self):
+        return self
+
+    def _is_numeric(self):
+        return True
+
+    def _is_boolean(self):
+        return False
+
+    def _is_integer(self):
+        return True
+
+    def _is_floating(self):
+        return False
+
+    def _is_object(self):
+        return False
+
+    def _is_categorical(self):
+        return False
+
+    def _is_interval(self):
+        return False
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def hasnans(self):
+        return False
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _data(self):
+        return cudf.core.column_accessor.ColumnAccessor(
+            {self.name: self._values}
+        )
+
+    @_cudf_nvtx_annotate
+    def __contains__(self, item):
+        if not isinstance(
+            item, tuple(np.sctypes["int"] + np.sctypes["float"] + [int, float])
+        ):
+            return False
+        try:
+            item = pd.core.dtypes.common.ensure_python_int(item)
+        except TypeError:
+            return False
+        if not item % 1 == 0:
+            return False
+        return item in range(self._start, self._stop, self._step)
+
+    @_cudf_nvtx_annotate
+    def copy(self, name=None, deep=False, dtype=None, names=None):
+        """
+        Make a copy of this object.
+
+        Parameters
+        ----------
+        name : object optional (default: None), name of index
+        deep : Bool (default: False)
+            Ignored for RangeIndex
+        dtype : numpy dtype optional (default: None)
+            Target dtype for underlying range data
+
+            .. deprecated:: 23.02
+
+               The `dtype` parameter is deprecated and will be removed in
+               a future version of cudf. Use the `astype` method instead.
+
+        names : list-like optional (default: False)
+            Kept compatibility with MultiIndex. Should not be used.
+
+            .. deprecated:: 23.04
+
+               The parameter `names` is deprecated and will be removed in
+               a future version of cudf. Use the `name` parameter instead.
+
+        Returns
+        -------
+        New RangeIndex instance with same range, casted to new dtype
+        """
+        if dtype is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter dtype is deprecated and will be removed in a "
+                "future version. Use the astype method instead.",
+                FutureWarning,
+            )
+
+        if names is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter names is deprecated and will be removed in a "
+                "future version. Use the name parameter instead.",
+                FutureWarning,
+            )
+
+        dtype = self.dtype if dtype is None else dtype
+
+        if not np.issubdtype(dtype, np.signedinteger):
+            raise ValueError(f"Expected Signed Integer Type, Got {dtype}")
+
+        name = self.name if name is None else name
+
+        return RangeIndex(
+            start=self._start, stop=self._stop, step=self._step, name=name
+        )
+
+    @_cudf_nvtx_annotate
+    def astype(self, dtype, copy: bool = True):
+        if is_dtype_equal(dtype, self.dtype):
+            return self
+        return self._as_int_index().astype(dtype, copy=copy)
+
+    @_cudf_nvtx_annotate
+    def drop_duplicates(self, keep="first"):
+        return self
+
+    @_cudf_nvtx_annotate
+    def duplicated(self, keep="first"):
+        return cupy.zeros(len(self), dtype=bool)
+
+    @_cudf_nvtx_annotate
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}(start={self._start}, stop={self._stop}"
+            f", step={self._step}"
+            + (
+                f", name={pd.io.formats.printing.default_pprint(self.name)}"
+                if self.name is not None
+                else ""
+            )
+            + ")"
+        )
+
+    @_cudf_nvtx_annotate
+    def __len__(self):
+        return len(range(self._start, self._stop, self._step))
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, index):
+        if isinstance(index, slice):
+            sl_start, sl_stop, sl_step = index.indices(len(self))
+
+            lo = self._start + sl_start * self._step
+            hi = self._start + sl_stop * self._step
+            st = self._step * sl_step
+            return RangeIndex(start=lo, stop=hi, step=st, name=self._name)
+
+        elif isinstance(index, Number):
+            len_self = len(self)
+            if index < 0:
+                index += len_self
+            if not (0 <= index < len_self):
+                raise IndexError("Index out of bounds")
+            return self._start + index * self._step
+        return self._as_int_index()[index]
+
+    @_cudf_nvtx_annotate
+    def equals(self, other):
+        if isinstance(other, RangeIndex):
+            if (self._start, self._stop, self._step) == (
+                other._start,
+                other._stop,
+                other._step,
+            ):
+                return True
+        return self._as_int_index().equals(other)
+
+    @_cudf_nvtx_annotate
+    def serialize(self):
+        header = {}
+        header["index_column"] = {}
+
+        # store metadata values of index separately
+        # We don't need to store the GPU buffer for RangeIndexes
+        # cuDF only needs to store start/stop and rehydrate
+        # during de-serialization
+        header["index_column"]["start"] = self._start
+        header["index_column"]["stop"] = self._stop
+        header["index_column"]["step"] = self._step
+        frames = []
+
+        header["name"] = pickle.dumps(self.name)
+        header["dtype"] = pickle.dumps(self.dtype)
+        header["type-serialized"] = pickle.dumps(type(self))
+        header["frame_count"] = 0
+        return header, frames
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def deserialize(cls, header, frames):
+        h = header["index_column"]
+        name = pickle.loads(header["name"])
+        start = h["start"]
+        stop = h["stop"]
+        step = h.get("step", 1)
+        return RangeIndex(start=start, stop=stop, step=step, name=name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dtype(self):
+        """
+        `dtype` of the range of values in RangeIndex.
+
+        By default the dtype is 64 bit signed integer. This is configurable
+        via `default_integer_bitwidth` as 32 bit in `cudf.options`
+        """
+        dtype = np.dtype(np.int64)
+        return _maybe_convert_to_default_type(dtype)
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, nullable=False):
+        return pd.RangeIndex(
+            start=self._start,
+            stop=self._stop,
+            step=self._step,
+            dtype=self.dtype,
+            name=self.name,
+        )
+
+    @property
+    def is_unique(self):
+        return True
+
+    @cached_property
+    def as_range(self):
+        return range(self._start, self._stop, self._step)
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic_increasing(self):
+        return self._step > 0 or len(self) <= 1
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic_decreasing(self):
+        return self._step < 0 or len(self) <= 1
+
+    @_cudf_nvtx_annotate
+    def memory_usage(self, deep=False):
+        if deep:
+            warnings.warn(
+                "The deep parameter is ignored and is only included "
+                "for pandas compatibility."
+            )
+        return 0
+
+    def unique(self):
+        # RangeIndex always has unique values
+        return self
+
+    @_cudf_nvtx_annotate
+    def __mul__(self, other):
+        # Multiplication by raw ints must return a RangeIndex to match pandas.
+        if isinstance(other, cudf.Scalar) and other.dtype.kind in "iu":
+            other = other.value
+        elif (
+            isinstance(other, (np.ndarray, cupy.ndarray))
+            and other.ndim == 0
+            and other.dtype.kind in "iu"
+        ):
+            other = other.item()
+        if isinstance(other, (int, np.integer)):
+            return RangeIndex(
+                self.start * other, self.stop * other, self.step * other
+            )
+        return self._as_int_index().__mul__(other)
+
+    @_cudf_nvtx_annotate
+    def __rmul__(self, other):
+        # Multiplication is commutative.
+        return self.__mul__(other)
+
+    @_cudf_nvtx_annotate
+    def _as_int_index(self):
+        # Convert self to an integer index. This method is used to perform ops
+        # that are not defined directly on RangeIndex.
+        return _dtype_to_index[self.dtype.type]._from_data(self._data)
+
+    @_cudf_nvtx_annotate
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        return self._as_int_index().__array_ufunc__(
+            ufunc, method, *inputs, **kwargs
+        )
+
+    @_cudf_nvtx_annotate
+    def get_loc(self, key, method=None, tolerance=None):
+        # We should not actually remove this code until we have implemented the
+        # get_indexers method as an alternative, see
+        # https://github.com/rapidsai/cudf/issues/12312
+        if method is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                f"Passing method to {self.__class__.__name__}.get_loc is "
+                "deprecated and will raise in a future version.",
+                FutureWarning,
+            )
+
+        # Given an actual integer,
+        idx = (key - self._start) / self._step
+        idx_int_upper_bound = (self._stop - self._start) // self._step
+        if method is None:
+            if tolerance is not None:
+                raise ValueError(
+                    "tolerance argument only valid if using pad, "
+                    "backfill or nearest lookups"
+                )
+
+            if idx > idx_int_upper_bound or idx < 0:
+                raise KeyError(key)
+
+            idx_int = (key - self._start) // self._step
+            if idx_int != idx:
+                raise KeyError(key)
+            return idx_int
+
+        if (method == "ffill" and idx < 0) or (
+            method == "bfill" and idx > idx_int_upper_bound
+        ):
+            raise KeyError(key)
+
+        round_method = {
+            "ffill": math.floor,
+            "bfill": math.ceil,
+            "nearest": round,
+        }[method]
+        if tolerance is not None and (abs(idx) * self._step > tolerance):
+            raise KeyError(key)
+        return np.clip(round_method(idx), 0, idx_int_upper_bound, dtype=int)
+
+    @_cudf_nvtx_annotate
+    def _union(self, other, sort=None):
+        if isinstance(other, RangeIndex):
+            # Variable suffixes are of the
+            # following notation: *_o -> other, *_s -> self,
+            # and *_r -> result
+            start_s, step_s = self.start, self.step
+            end_s = self._end
+            start_o, step_o = other.start, other.step
+            end_o = other._end
+            if self.step < 0:
+                start_s, step_s, end_s = end_s, -step_s, start_s
+            if other.step < 0:
+                start_o, step_o, end_o = end_o, -step_o, start_o
+            if len(self) == 1 and len(other) == 1:
+                step_s = step_o = abs(self.start - other.start)
+            elif len(self) == 1:
+                step_s = step_o
+            elif len(other) == 1:
+                step_o = step_s
+
+            # Determine minimum start value of the result.
+            start_r = min(start_s, start_o)
+            # Determine maximum end value of the result.
+            end_r = max(end_s, end_o)
+            result = None
+            min_step = min(step_o, step_s)
+
+            if ((start_s - start_o) % min_step) == 0:
+                # Checking to determine other is a subset of self with
+                # equal step size.
+                if (
+                    step_o == step_s
+                    and (start_s - end_o) <= step_s
+                    and (start_o - end_s) <= step_s
+                ):
+                    result = type(self)(start_r, end_r + step_s, step_s)
+                # Checking if self is a subset of other with unequal
+                # step sizes.
+                elif (
+                    step_o % step_s == 0
+                    and (start_o + step_s >= start_s)
+                    and (end_o - step_s <= end_s)
+                ):
+                    result = type(self)(start_r, end_r + step_s, step_s)
+                # Checking if other is a subset of self with unequal
+                # step sizes.
+                elif (
+                    step_s % step_o == 0
+                    and (start_s + step_o >= start_o)
+                    and (end_s - step_o <= end_o)
+                ):
+                    result = type(self)(start_r, end_r + step_o, step_o)
+            # Checking to determine when the steps are even but one of
+            # the inputs spans across is near half or less then half
+            # the other input. This case needs manipulation to step
+            # size.
+            elif (
+                step_o == step_s
+                and (step_s % 2 == 0)
+                and (abs(start_s - start_o) <= step_s / 2)
+                and (abs(end_s - end_o) <= step_s / 2)
+            ):
+                result = type(self)(start_r, end_r + step_s / 2, step_s / 2)
+            if result is not None:
+                if sort is None and not result.is_monotonic_increasing:
+                    return result.sort_values()
+                else:
+                    return result
+
+        # If all the above optimizations don't cater to the inputs,
+        # we materialize RangeIndexes into integer indexes and
+        # then perform `union`.
+        return self._try_reconstruct_range_index(
+            self._as_int_index()._union(other, sort=sort)
+        )
+
+    @_cudf_nvtx_annotate
+    def _intersection(self, other, sort=False):
+        if not isinstance(other, RangeIndex):
+            return self._try_reconstruct_range_index(
+                super()._intersection(other, sort=sort)
+            )
+
+        if not len(self) or not len(other):
+            return RangeIndex(0)
+
+        first = self._range[::-1] if self.step < 0 else self._range
+        second = other._range[::-1] if other.step < 0 else other._range
+
+        # check whether intervals intersect
+        # deals with in- and decreasing ranges
+        int_low = max(first.start, second.start)
+        int_high = min(first.stop, second.stop)
+        if int_high <= int_low:
+            return RangeIndex(0)
+
+        # Method hint: linear Diophantine equation
+        # solve intersection problem
+        # performance hint: for identical step sizes, could use
+        # cheaper alternative
+        gcd, s, _ = _extended_gcd(first.step, second.step)
+
+        # check whether element sets intersect
+        if (first.start - second.start) % gcd:
+            return RangeIndex(0)
+
+        # calculate parameters for the RangeIndex describing the
+        # intersection disregarding the lower bounds
+        tmp_start = (
+            first.start + (second.start - first.start) * first.step // gcd * s
+        )
+        new_step = first.step * second.step // gcd
+        no_steps = -(-(int_low - tmp_start) // abs(new_step))
+        new_start = tmp_start + abs(new_step) * no_steps
+        new_range = range(new_start, int_high, new_step)
+        new_index = RangeIndex(new_range)
+
+        if (self.step < 0 and other.step < 0) is not (new_index.step < 0):
+            new_index = new_index[::-1]
+        if sort is None:
+            new_index = new_index.sort_values()
+
+        return self._try_reconstruct_range_index(new_index)
+
+    @_cudf_nvtx_annotate
+    def difference(self, other, sort=None):
+        if isinstance(other, RangeIndex) and self.equals(other):
+            return self[:0]._get_reconciled_name_object(other)
+
+        return self._try_reconstruct_range_index(
+            super().difference(other, sort=sort)
+        )
+
+    def _try_reconstruct_range_index(self, index):
+        if isinstance(index, RangeIndex) or index.dtype.kind == "f":
+            return index
+        # Evenly spaced values can return a
+        # RangeIndex instead of a materialized Index.
+        if not index._column.has_nulls():
+            uniques = cupy.unique(cupy.diff(index.values))
+            if len(uniques) == 1 and uniques[0].get() != 0:
+                diff = uniques[0].get()
+                new_range = range(index[0], index[-1] + diff, diff)
+                return type(self)(new_range, name=index.name)
+        return index
+
+    def sort_values(
+        self,
+        return_indexer=False,
+        ascending=True,
+        na_position="last",
+        key=None,
+    ):
+        if key is not None:
+            raise NotImplementedError("key parameter is not yet implemented.")
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        sorted_index = self
+        indexer = RangeIndex(range(len(self)))
+
+        sorted_index = self
+        if ascending:
+            if self.step < 0:
+                sorted_index = self[::-1]
+                indexer = indexer[::-1]
+        else:
+            if self.step > 0:
+                sorted_index = self[::-1]
+                indexer = indexer = indexer[::-1]
+
+        if return_indexer:
+            return sorted_index, indexer
+        else:
+            return sorted_index
+
+    @_cudf_nvtx_annotate
+    def _gather(self, gather_map, nullify=False, check_bounds=True):
+        gather_map = cudf.core.column.as_column(gather_map)
+        return _dtype_to_index[self.dtype.type]._from_columns(
+            [self._values.take(gather_map, nullify, check_bounds)], [self.name]
+        )
+
+    @_cudf_nvtx_annotate
+    def _apply_boolean_mask(self, boolean_mask):
+        return _dtype_to_index[self.dtype.type]._from_columns(
+            [self._values.apply_boolean_mask(boolean_mask)], [self.name]
+        )
+
+    def repeat(self, repeats, axis=None):
+        return self._as_int_index().repeat(repeats, axis)
+
+    def _split(self, splits):
+        return _dtype_to_index[self.dtype.type]._from_columns(
+            [self._as_int_index()._split(splits)], [self.name]
+        )
+
+    def _binaryop(self, other, op: str):
+        # TODO: certain binops don't require materializing range index and
+        # could use some optimization.
+        return self._as_int_index()._binaryop(other, op=op)
+
+    def join(
+        self, other, how="left", level=None, return_indexers=False, sort=False
+    ):
+        if how in {"left", "right"} or self.equals(other):
+            # pandas supports directly merging RangeIndex objects and can
+            # intelligently create RangeIndex outputs depending on the type of
+            # join. Hence falling back to performing a merge on pd.RangeIndex
+            # since the conversion is cheap.
+            if isinstance(other, RangeIndex):
+                result = self.to_pandas().join(
+                    other.to_pandas(),
+                    how=how,
+                    level=level,
+                    return_indexers=return_indexers,
+                    sort=sort,
+                )
+                if return_indexers:
+                    return tuple(
+                        cudf.from_pandas(result[0]), result[1], result[2]
+                    )
+                else:
+                    return cudf.from_pandas(result)
+        return self._as_int_index().join(
+            other, how, level, return_indexers, sort
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _column(self):
+        return self._as_int_index()._column
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _columns(self):
+        return self._as_int_index()._columns
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def values_host(self):
+        return self.to_pandas().values
+
+    @_cudf_nvtx_annotate
+    def argsort(
+        self,
+        ascending=True,
+        na_position="last",
+    ):
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        indices = cupy.arange(0, len(self))
+        if (ascending and self._step < 0) or (
+            not ascending and self._step > 0
+        ):
+            indices = indices[::-1]
+        return indices
+
+    @_cudf_nvtx_annotate
+    def where(self, cond, other=None, inplace=False):
+        return self._as_int_index().where(cond, other, inplace)
+
+    @_cudf_nvtx_annotate
+    def to_numpy(self):
+        return self.values_host
+
+    @_cudf_nvtx_annotate
+    def to_arrow(self):
+        return self._as_int_index().to_arrow()
+
+    def __array__(self, dtype=None):
+        raise TypeError(
+            "Implicit conversion to a host NumPy array via __array__ is not "
+            "allowed, To explicitly construct a GPU matrix, consider using "
+            ".to_cupy()\nTo explicitly construct a host matrix, consider "
+            "using .to_numpy()."
+        )
+
+    @_cudf_nvtx_annotate
+    def nunique(self):
+        return len(self)
+
+    @_cudf_nvtx_annotate
+    def isna(self):
+        return cupy.zeros(len(self), dtype=bool)
+
+    isnull = isna
+
+    @_cudf_nvtx_annotate
+    def notna(self):
+        return cupy.ones(len(self), dtype=bool)
+
+    notnull = isna
+
+    @_cudf_nvtx_annotate
+    def _minmax(self, meth: str):
+        no_steps = len(self) - 1
+        if no_steps == -1:
+            return np.nan
+        elif (meth == "min" and self.step > 0) or (
+            meth == "max" and self.step < 0
+        ):
+            return self.start
+
+        return self.start + self.step * no_steps
+
+    def min(self):
+        return self._minmax("min")
+
+    def max(self):
+        return self._minmax("max")
+
+    @property
+    def values(self):
+        return cupy.arange(self.start, self.stop, self.step)
+
+    def any(self):
+        return any(self._range)
+
+    def append(self, other):
+        result = self._as_int_index().append(other)
+        return self._try_reconstruct_range_index(result)
+
+    def _indices_of(self, value) -> cudf.core.column.NumericalColumn:
+        if isinstance(value, (bool, np.bool_)):
+            raise ValueError(
+                f"Cannot use {type(value).__name__} to get an index of a "
+                f"{type(self).__name__}."
+            )
+        try:
+            i = [self._range.index(value)]
+        except ValueError:
+            i = []
+        return as_column(i, dtype=size_type_dtype)
+
+    def isin(self, values):
+        if is_scalar(values):
+            raise TypeError(
+                "only list-like objects are allowed to be passed "
+                f"to isin(), you passed a {type(values).__name__}"
+            )
+
+        return self._values.isin(values).values
+
+    def __neg__(self):
+        return -self._as_int_index()
+
+    def __pos__(self):
+        return +self._as_int_index()
+
+    def __abs__(self):
+        return abs(self._as_int_index())
+
+    @_warn_no_dask_cudf
+    def __dask_tokenize__(self):
+        return (type(self), self.start, self.stop, self.step)
+
+
+class GenericIndex(SingleColumnFrame, BaseIndex):
+    """
+    An array of orderable values that represent the indices of another Column
+
+    Attributes
+    ----------
+    _values: A Column object
+    name: A string
+
+    Parameters
+    ----------
+    data : Column
+        The Column of data for this index
+    name : str optional
+        The name of the Index. If not provided, the Index adopts the value
+        Column's name. Otherwise if this name is different from the value
+        Column's, the data Column will be cloned to adopt this name.
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(self, data, **kwargs):
+        kwargs = _setdefault_name(data, **kwargs)
+
+        # normalize the input
+        if isinstance(data, cudf.Series):
+            data = data._column
+        elif isinstance(data, column.ColumnBase):
+            data = data
+        else:
+            if isinstance(data, (list, tuple)):
+                if len(data) == 0:
+                    data = np.asarray([], dtype="int64")
+                else:
+                    data = np.asarray(data)
+            data = column.as_column(data)
+            assert isinstance(data, (NumericalColumn, StringColumn))
+
+        name = kwargs.get("name")
+        super().__init__({name: data})
+
+    @_cudf_nvtx_annotate
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        ret = super().__array_ufunc__(ufunc, method, *inputs, **kwargs)
+
+        if ret is not None:
+            return ret
+
+        # Attempt to dispatch all other functions to cupy.
+        cupy_func = getattr(cupy, ufunc.__name__)
+        if cupy_func:
+            if ufunc.nin == 2:
+                other = inputs[self is inputs[0]]
+                inputs = self._make_operands_for_binop(other)
+            else:
+                inputs = {
+                    name: (col, None, False, None)
+                    for name, col in self._data.items()
+                }
+
+            data = self._apply_cupy_ufunc_to_operands(
+                ufunc, cupy_func, inputs, **kwargs
+            )
+
+            out = [_index_from_data(out) for out in data]
+
+            # pandas returns numpy arrays when the outputs are boolean.
+            for i, o in enumerate(out):
+                # We explicitly _do not_ use isinstance here: we want only
+                # boolean GenericIndexes, not dtype-specific subclasses.
+                if type(o) is GenericIndex and o.dtype.kind == "b":
+                    out[i] = o.values
+
+            return out[0] if ufunc.nout == 1 else tuple(out)
+
+        return NotImplemented
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_data(
+        cls, data: MutableMapping, name: Any = no_default
+    ) -> GenericIndex:
+        out = super()._from_data(data=data)
+        if name is not no_default:
+            out.name = name
+        return out
+
+    def _binaryop(
+        self,
+        other: Frame,
+        op: str,
+        fill_value: Any = None,
+        *args,
+        **kwargs,
+    ) -> SingleColumnFrame:
+        reflect, op = self._check_reflected_op(op)
+        operands = self._make_operands_for_binop(other, fill_value, reflect)
+        if operands is NotImplemented:
+            return NotImplemented
+        binop_result = self._colwise_binop(operands, op)
+
+        if isinstance(other, cudf.Series):
+            ret = other._from_data_like_self(binop_result)
+            other_name = other.name
+        else:
+            ret = _index_from_data(binop_result)
+            other_name = getattr(other, "name", self.name)
+
+        ret.name = (
+            self.name
+            if cudf.utils.utils._is_same_name(self.name, other_name)
+            else None
+        )
+
+        # pandas returns numpy arrays when the outputs are boolean. We
+        # explicitly _do not_ use isinstance here: we want only boolean
+        # GenericIndexes, not dtype-specific subclasses.
+        if (
+            isinstance(ret, (GenericIndex, cudf.Series))
+            and ret.dtype.kind == "b"
+        ):
+            if ret._column.has_nulls():
+                ret = ret.fillna(op == "__ne__")
+            return ret.values
+        return ret
+
+    # Override just to make mypy happy.
+    @_cudf_nvtx_annotate
+    def _copy_type_metadata(
+        self: GenericIndex, other: GenericIndex, *, override_dtypes=None
+    ) -> GenericIndex:
+        return super()._copy_type_metadata(
+            other, override_dtypes=override_dtypes
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _values(self):
+        return self._column
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _concat(cls, objs):
+        non_empties = [index for index in objs if len(index)]
+        if all(isinstance(obj, RangeIndex) for obj in non_empties):
+            result = _concat_range_index(non_empties)
+        else:
+            data = concat_columns([o._values for o in non_empties])
+            result = as_index(data)
+
+        names = {obj.name for obj in objs}
+        if len(names) == 1:
+            name = names.pop()
+        else:
+            name = None
+
+        result.name = name
+        return result
+
+    @_cudf_nvtx_annotate
+    def memory_usage(self, deep=False):
+        return self._column.memory_usage
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_unique(self):
+        return self._column.is_unique
+
+    @_cudf_nvtx_annotate
+    def equals(self, other):
+        if (
+            other is None
+            or not isinstance(other, BaseIndex)
+            or len(self) != len(other)
+        ):
+            return False
+
+        check_dtypes = False
+
+        self_is_categorical = isinstance(self, CategoricalIndex)
+        other_is_categorical = isinstance(other, CategoricalIndex)
+        if self_is_categorical and not other_is_categorical:
+            other = other.astype(self.dtype)
+            check_dtypes = True
+        elif other_is_categorical and not self_is_categorical:
+            self = self.astype(other.dtype)
+            check_dtypes = True
+
+        try:
+            return self._column.equals(
+                other._column, check_dtypes=check_dtypes
+            )
+        except TypeError:
+            return False
+
+    @_cudf_nvtx_annotate
+    def copy(self, name=None, deep=False, dtype=None, names=None):
+        """
+        Make a copy of this object.
+
+        Parameters
+        ----------
+        name : object, default None
+            Name of index, use original name when None
+        deep : bool, default True
+            Make a deep copy of the data.
+            With ``deep=False`` the original data is used
+        dtype : numpy dtype, default None
+            Target datatype to cast into, use original dtype when None
+
+            .. deprecated:: 23.02
+
+               The `dtype` parameter is deprecated and will be removed in
+               a future version of cudf. Use the `astype` method instead.
+
+        names : list-like, default False
+            Kept compatibility with MultiIndex. Should not be used.
+
+            .. deprecated:: 23.04
+
+               The parameter `names` is deprecated and will be removed in
+               a future version of cudf. Use the `name` parameter instead.
+
+        Returns
+        -------
+        New index instance, casted to new dtype
+        """
+        if dtype is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter dtype is deprecated and will be removed in a "
+                "future version. Use the astype method instead.",
+                FutureWarning,
+            )
+
+        if names is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter names is deprecated and will be removed in a "
+                "future version. Use the name parameter instead.",
+                FutureWarning,
+            )
+
+        dtype = self.dtype if dtype is None else dtype
+        name = self.name if name is None else name
+
+        col = self._values.astype(dtype)
+        return _index_from_data({name: col.copy(True) if deep else col})
+
+    @_cudf_nvtx_annotate
+    def astype(self, dtype, copy: bool = True):
+        return _index_from_data(super().astype({self.name: dtype}, copy))
+
+    @_cudf_nvtx_annotate
+    def get_loc(self, key, method=None, tolerance=None):
+        """Get integer location, slice or boolean mask for requested label.
+
+        Parameters
+        ----------
+        key : label
+        method : {None, 'pad'/'fill', 'backfill'/'bfill', 'nearest'}, optional
+            - default: exact matches only.
+            - pad / ffill: find the PREVIOUS index value if no exact match.
+            - backfill / bfill: use NEXT index value if no exact match.
+            - nearest: use the NEAREST index value if no exact match. Tied
+              distances are broken by preferring the larger index
+              value.
+        tolerance : int or float, optional
+            Maximum distance from index value for inexact matches. The value
+            of the index at the matching location must satisfy the equation
+            ``abs(index[loc] - key) <= tolerance``.
+
+        Returns
+        -------
+        int or slice or boolean mask
+            - If result is unique, return integer index
+            - If index is monotonic, loc is returned as a slice object
+            - Otherwise, a boolean mask is returned
+
+        Examples
+        --------
+        >>> unique_index = cudf.Index(list('abc'))
+        >>> unique_index.get_loc('b')
+        1
+        >>> monotonic_index = cudf.Index(list('abbc'))
+        >>> monotonic_index.get_loc('b')
+        slice(1, 3, None)
+        >>> non_monotonic_index = cudf.Index(list('abcb'))
+        >>> non_monotonic_index.get_loc('b')
+        array([False,  True, False,  True])
+        >>> numeric_unique_index = cudf.Index([1, 2, 3])
+        >>> numeric_unique_index.get_loc(3)
+        2
+        """
+        # We should not actually remove this code until we have implemented the
+        # get_indexers method as an alternative, see
+        # https://github.com/rapidsai/cudf/issues/12312
+        if method is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                f"Passing method to {self.__class__.__name__}.get_loc is "
+                "deprecated and will raise in a future version.",
+                FutureWarning,
+            )
+        if tolerance is not None:
+            raise NotImplementedError(
+                "Parameter tolerance is not supported yet."
+            )
+        if method not in {
+            None,
+            "ffill",
+            "bfill",
+            "pad",
+            "backfill",
+            "nearest",
+        }:
+            raise ValueError(
+                f"Invalid fill method. Expecting pad (ffill), backfill (bfill)"
+                f" or nearest. Got {method}"
+            )
+
+        is_sorted = (
+            self.is_monotonic_increasing or self.is_monotonic_decreasing
+        )
+
+        if not is_sorted and method is not None:
+            raise ValueError(
+                "index must be monotonic increasing or decreasing if `method`"
+                "is specified."
+            )
+
+        key_as_table = cudf.core.frame.Frame(
+            {"None": as_column(key, length=1)}
+        )
+        lower_bound, upper_bound, sort_inds = _lexsorted_equal_range(
+            self, key_as_table, is_sorted
+        )
+
+        if lower_bound == upper_bound:
+            # Key not found, apply method
+            if method in ("pad", "ffill"):
+                if lower_bound == 0:
+                    raise KeyError(key)
+                return lower_bound - 1
+            elif method in ("backfill", "bfill"):
+                if lower_bound == self._data.nrows:
+                    raise KeyError(key)
+                return lower_bound
+            elif method == "nearest":
+                if lower_bound == self._data.nrows:
+                    return lower_bound - 1
+                elif lower_bound == 0:
+                    return 0
+                lower_val = self._column.element_indexing(lower_bound - 1)
+                upper_val = self._column.element_indexing(lower_bound)
+                return (
+                    lower_bound - 1
+                    if abs(lower_val - key) < abs(upper_val - key)
+                    else lower_bound
+                )
+            else:
+                raise KeyError(key)
+
+        if lower_bound + 1 == upper_bound:
+            # Search result is unique, return int.
+            return (
+                lower_bound
+                if is_sorted
+                else sort_inds.element_indexing(lower_bound)
+            )
+
+        if is_sorted:
+            # In monotonic index, lex search result is continuous. A slice for
+            # the range is returned.
+            return slice(lower_bound, upper_bound)
+
+        # Not sorted and not unique. Return a boolean mask
+        mask = cupy.full(self._data.nrows, False)
+        true_inds = sort_inds.slice(lower_bound, upper_bound).values
+        mask[true_inds] = True
+        return mask
+
+    @_cudf_nvtx_annotate
+    def __repr__(self):
+        max_seq_items = get_option("max_seq_items") or len(self)
+        mr = 0
+        if 2 * max_seq_items < len(self):
+            mr = max_seq_items + 1
+
+        if len(self) > mr and mr != 0:
+            top = self[0:mr]
+            bottom = self[-1 * mr :]
+
+            preprocess = cudf.concat([top, bottom])
+        else:
+            preprocess = self
+
+        # TODO: Change below usages accordingly to
+        # utilize `Index.to_string` once it is implemented
+        # related issue : https://github.com/pandas-dev/pandas/issues/35389
+        if isinstance(preprocess, CategoricalIndex):
+            if preprocess.categories.dtype.kind == "f":
+                output = repr(
+                    preprocess.astype("str")
+                    .to_pandas()
+                    .astype(
+                        dtype=pd.CategoricalDtype(
+                            categories=preprocess.dtype.categories.astype(
+                                "str"
+                            ).to_pandas(),
+                            ordered=preprocess.dtype.ordered,
+                        )
+                    )
+                )
+                break_idx = output.find("ordered=")
+                output = (
+                    output[:break_idx].replace("'", "") + output[break_idx:]
+                )
+            else:
+                output = repr(preprocess.to_pandas())
+
+            output = output.replace("nan", str(cudf.NA))
+        elif preprocess._values.nullable:
+            output = repr(self._clean_nulls_from_index().to_pandas())
+
+            if not isinstance(self, StringIndex):
+                # We should remove all the single quotes
+                # from the output due to the type-cast to
+                # object dtype happening above.
+                # Note : The replacing of single quotes has
+                # to happen only in case of non-StringIndex types,
+                # as we want to preserve single quotes in case
+                # of StringIndex and it is valid to have them.
+                output = output.replace("'", "")
+        else:
+            output = repr(preprocess.to_pandas())
+
+        # Fix and correct the class name of the output
+        # string by finding first occurrence of "(" in the output
+        index_class_split_index = output.find("(")
+        output = self.__class__.__name__ + output[index_class_split_index:]
+
+        lines = output.split("\n")
+
+        tmp_meta = lines[-1]
+        dtype_index = tmp_meta.rfind(" dtype=")
+        prior_to_dtype = tmp_meta[:dtype_index]
+        lines = lines[:-1]
+        lines.append(prior_to_dtype + " dtype='%s'" % self.dtype)
+        if self.name is not None:
+            lines[-1] = lines[-1] + ", name='%s'" % self.name
+        if "length" in tmp_meta:
+            lines[-1] = lines[-1] + ", length=%d)" % len(self)
+        else:
+            lines[-1] = lines[-1] + ")"
+
+        return "\n".join(lines)
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, index):
+        res = self._get_elements_from_column(index)
+        if isinstance(res, ColumnBase):
+            res = as_index(res)
+            res.name = self.name
+        return res
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dtype(self):
+        """
+        `dtype` of the underlying values in GenericIndex.
+        """
+        return self._values.dtype
+
+    @_cudf_nvtx_annotate
+    def isna(self):
+        return self._column.isnull().values
+
+    isnull = isna
+
+    @_cudf_nvtx_annotate
+    def notna(self):
+        return self._column.notnull().values
+
+    notnull = notna
+
+    def _is_numeric(self):
+        return False
+
+    def _is_boolean(self):
+        return True
+
+    def _is_integer(self):
+        return False
+
+    def _is_floating(self):
+        return False
+
+    def _is_object(self):
+        return False
+
+    def _is_categorical(self):
+        return False
+
+    def _is_interval(self):
+        return False
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def hasnans(self):
+        return self._column.has_nulls(include_nan=True)
+
+    @_cudf_nvtx_annotate
+    def argsort(
+        self,
+        axis=0,
+        kind="quicksort",
+        order=None,
+        ascending=True,
+        na_position="last",
+    ):
+        """Return the integer indices that would sort the index.
+
+        Parameters
+        ----------
+        axis : {0 or "index"}
+            Has no effect but is accepted for compatibility with numpy.
+        kind : {'mergesort', 'quicksort', 'heapsort', 'stable'}, default 'quicksort'
+            Choice of sorting algorithm. See :func:`numpy.sort` for more
+            information. 'mergesort' and 'stable' are the only stable
+            algorithms. Only quicksort is supported in cuDF.
+        order : None
+            Has no effect but is accepted for compatibility with numpy.
+        ascending : bool or list of bool, default True
+            If True, sort values in ascending order, otherwise descending.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs
+            at the end.
+
+        Returns
+        -------
+        cupy.ndarray: The indices sorted based on input.
+        """  # noqa: E501
+        return super().argsort(
+            axis=axis,
+            kind=kind,
+            order=order,
+            ascending=ascending,
+            na_position=na_position,
+        )
+
+    def repeat(self, repeats, axis=None):
+        return self._from_columns_like_self(
+            Frame._repeat([*self._columns], repeats, axis), self._column_names
+        )
+
+    @_cudf_nvtx_annotate
+    def where(self, cond, other=None, inplace=False):
+        result_col = super().where(cond, other, inplace)
+        return self._mimic_inplace(
+            _index_from_data({self.name: result_col}),
+            inplace=inplace,
+        )
+
+    @property
+    def values(self):
+        return self._column.values
+
+    def __contains__(self, item):
+        return item in self._values
+
+    def _clean_nulls_from_index(self):
+        if self._values.has_nulls():
+            fill_value = (
+                str(cudf.NaT)
+                if isinstance(self, (DatetimeIndex, TimedeltaIndex))
+                else str(cudf.NA)
+            )
+            return cudf.Index(
+                self._values.astype("str").fillna(fill_value),
+                name=self.name,
+            )
+
+        return self
+
+    def any(self):
+        return self._values.any()
+
+    def to_pandas(self, nullable=False):
+        return pd.Index(
+            self._values.to_pandas(nullable=nullable), name=self.name
+        )
+
+    def append(self, other):
+        if is_list_like(other):
+            to_concat = [self]
+            for obj in other:
+                if not isinstance(obj, BaseIndex):
+                    raise TypeError("all inputs must be Index")
+                to_concat.append(obj)
+        else:
+            this = self
+            other = cudf.Index(other)
+
+            if len(this) == 0 or len(other) == 0:
+                # we'll filter out empties later in ._concat
+                to_concat = [this, other]
+            else:
+                if is_mixed_with_object_dtype(this, other):
+                    got_dtype = (
+                        other.dtype
+                        if this.dtype == cudf.dtype("object")
+                        else this.dtype
+                    )
+                    raise TypeError(
+                        f"cudf does not support appending an Index of "
+                        f"dtype `{cudf.dtype('object')}` with an Index "
+                        f"of dtype `{got_dtype}`, please type-cast "
+                        f"either one of them to same dtypes."
+                    )
+
+                if isinstance(self._values, cudf.core.column.NumericalColumn):
+                    if self.dtype != other.dtype:
+                        this, other = numeric_normalize_types(self, other)
+                to_concat = [this, other]
+
+        return self._concat(to_concat)
+
+    def unique(self):
+        return cudf.core.index._index_from_data(
+            {self.name: self._values.unique()}, name=self.name
+        )
+
+    def isin(self, values):
+        if is_scalar(values):
+            raise TypeError(
+                "only list-like objects are allowed to be passed "
+                f"to isin(), you passed a {type(values).__name__}"
+            )
+
+        return self._values.isin(values).values
+
+    def _indices_of(self, value):
+        """Return indices of value in index"""
+        return self._column.indices_of(value)
+
+    @cache
+    @_warn_no_dask_cudf
+    def __dask_tokenize__(self):
+        # We can use caching, because an index is immutable
+        return super().__dask_tokenize__()
+
+
+class NumericIndex(GenericIndex):
+    """Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Returns
+    -------
+    Index
+    """
+
+    # Subclasses must define the dtype they are associated with.
+    _dtype: Union[None, Type[np.number]] = None
+
+    @_cudf_nvtx_annotate
+    def __init__(self, data=None, dtype=None, copy=False, name=None):
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            f"cudf.{self.__class__.__name__} is deprecated and will be "
+            "removed from cudf in a future version. Use cudf.Index with the "
+            "appropriate dtype instead.",
+            FutureWarning,
+        )
+
+        dtype = type(self)._dtype
+        if copy:
+            data = column.as_column(data, dtype=dtype).copy()
+
+        kwargs = _setdefault_name(data, name=name)
+
+        data = column.as_column(data, dtype=dtype)
+
+        super().__init__(data, **kwargs)
+
+    def _is_numeric(self):
+        return True
+
+    def _is_boolean(self):
+        return False
+
+    def _is_integer(self):
+        return True
+
+    def _is_floating(self):
+        return False
+
+    def _is_object(self):
+        return False
+
+    def _is_categorical(self):
+        return False
+
+    def _is_interval(self):
+        return False
+
+
+class Int8Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    Int8Index is a special case of Index with purely
+    integer(``int8``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    Int8Index
+    """
+
+    _dtype = np.int8
+
+
+class Int16Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    Int16Index is a special case of Index with purely
+    integer(``int16``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    Int16Index
+    """
+
+    _dtype = np.int16
+
+
+class Int32Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    Int32Index is a special case of Index with purely
+    integer(``int32``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    Int32Index
+    """
+
+    _dtype = np.int32
+
+
+class Int64Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    Int64Index is a special case of Index with purely
+    integer(``int64``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    Int64Index
+    """
+
+    _dtype = np.int64
+
+
+class UInt8Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    UInt8Index is a special case of Index with purely
+    integer(``uint64``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    UInt8Index
+    """
+
+    _dtype = np.uint8
+
+
+class UInt16Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    UInt16Index is a special case of Index with purely
+    integer(``uint16``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    UInt16Index
+    """
+
+    _dtype = np.uint16
+
+
+class UInt32Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    UInt32Index is a special case of Index with purely
+    integer(``uint32``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    UInt32Index
+    """
+
+    _dtype = np.uint32
+
+
+class UInt64Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    UInt64Index is a special case of Index with purely
+    integer(``uint64``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    UInt64Index
+    """
+
+    _dtype = np.uint64
+
+
+class Float32Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    Float32Index is a special case of Index with purely
+    float(``float32``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    Float32Index
+    """
+
+    _dtype = np.float32
+
+    def _is_integer(self):
+        return False
+
+    def _is_floating(self):
+        return True
+
+
+class Float64Index(NumericIndex):
+    """
+    Immutable, ordered and sliceable sequence of labels.
+    The basic object storing row labels for all cuDF objects.
+    Float64Index is a special case of Index with purely
+    float(``float64``) labels.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+    dtype : NumPy dtype,
+            but not used.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    None
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    Float64Index
+    """
+
+    _dtype = np.float64
+
+    def _is_integer(self):
+        return False
+
+    def _is_floating(self):
+        return True
+
+
+class DatetimeIndex(GenericIndex):
+    """
+    Immutable , ordered and sliceable sequence of datetime64 data,
+    represented internally as int64.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional), optional
+        Optional datetime-like data to construct index with.
+    copy : bool
+        Make a copy of input.
+    freq : str, optional
+        This is not yet supported
+    tz : pytz.timezone or dateutil.tz.tzfile
+        This is not yet supported
+    ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
+        This is not yet supported
+    name : object
+        Name to be stored in the index.
+    dayfirst : bool, default False
+        If True, parse dates in data with the day first order.
+        This is not yet supported
+    yearfirst : bool, default False
+        If True parse dates in data with the year first order.
+        This is not yet supported
+
+    Attributes
+    ----------
+    year
+    month
+    day
+    hour
+    minute
+    second
+    microsecond
+    nanosecond
+    date
+    time
+    dayofyear
+    day_of_year
+    weekday
+    quarter
+    freq
+
+    Methods
+    -------
+    ceil
+    floor
+    round
+    tz_convert
+    tz_localize
+
+    Returns
+    -------
+    DatetimeIndex
+
+    Examples
+    --------
+    >>> import cudf
+    >>> cudf.DatetimeIndex([1, 2, 3, 4], name="a")
+    DatetimeIndex(['1970-01-01 00:00:00.000000001',
+                   '1970-01-01 00:00:00.000000002',
+                   '1970-01-01 00:00:00.000000003',
+                   '1970-01-01 00:00:00.000000004'],
+                  dtype='datetime64[ns]', name='a')
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        data=None,
+        freq=None,
+        tz=None,
+        normalize=False,
+        closed=None,
+        ambiguous="raise",
+        dayfirst=False,
+        yearfirst=False,
+        dtype=None,
+        copy=False,
+        name=None,
+    ):
+        # we should be more strict on what we accept here but
+        # we'd have to go and figure out all the semantics around
+        # pandas dtindex creation first which.  For now
+        # just make sure we handle np.datetime64 arrays
+        # and then just dispatch upstream
+        if freq is not None:
+            raise NotImplementedError("Freq is not yet supported")
+        if tz is not None:
+            raise NotImplementedError("tz is not yet supported")
+        if normalize is not False:
+            raise NotImplementedError("normalize == True is not yet supported")
+        if closed is not None:
+            raise NotImplementedError("closed is not yet supported")
+        if ambiguous != "raise":
+            raise NotImplementedError("ambiguous is not yet supported")
+        if dayfirst is not False:
+            raise NotImplementedError("dayfirst == True is not yet supported")
+        if yearfirst is not False:
+            raise NotImplementedError("yearfirst == True is not yet supported")
+
+        valid_dtypes = tuple(
+            f"datetime64[{res}]" for res in ("s", "ms", "us", "ns")
+        )
+        if dtype is None:
+            # nanosecond default matches pandas
+            dtype = "datetime64[ns]"
+        elif dtype not in valid_dtypes:
+            raise TypeError("Invalid dtype")
+
+        kwargs = _setdefault_name(data, name=name)
+        data = column.as_column(data, dtype=dtype)
+
+        if copy:
+            data = data.copy()
+
+        super().__init__(data, **kwargs)
+
+    def __getitem__(self, index):
+        value = super().__getitem__(index)
+        if cudf.get_option("mode.pandas_compatible") and isinstance(
+            value, np.datetime64
+        ):
+            return pd.Timestamp(value)
+        return value
+
+    def searchsorted(
+        self,
+        value,
+        side: str = "left",
+        ascending: bool = True,
+        na_position: str = "last",
+    ):
+        value = self.dtype.type(value)
+        return super().searchsorted(
+            value, side=side, ascending=ascending, na_position=na_position
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def year(self):
+        """
+        The year of the datetime.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="Y"))
+        >>> datetime_index
+        DatetimeIndex(['2000-12-31', '2001-12-31', '2002-12-31'], dtype='datetime64[ns]')
+        >>> datetime_index.year
+        Int16Index([2000, 2001, 2002], dtype='int16')
+        """  # noqa: E501
+        return self._get_dt_field("year")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def month(self):
+        """
+        The month as January=1, December=12.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="M"))
+        >>> datetime_index
+        DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31'], dtype='datetime64[ns]')
+        >>> datetime_index.month
+        Int16Index([1, 2, 3], dtype='int16')
+        """  # noqa: E501
+        return self._get_dt_field("month")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def day(self):
+        """
+        The day of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="D"))
+        >>> datetime_index
+        DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03'], dtype='datetime64[ns]')
+        >>> datetime_index.day
+        Int16Index([1, 2, 3], dtype='int16')
+        """  # noqa: E501
+        return self._get_dt_field("day")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def hour(self):
+        """
+        The hours of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="h"))
+        >>> datetime_index
+        DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:00:00',
+                    '2000-01-01 02:00:00'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.hour
+        Int16Index([0, 1, 2], dtype='int16')
+        """
+        return self._get_dt_field("hour")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def minute(self):
+        """
+        The minutes of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="T"))
+        >>> datetime_index
+        DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00',
+                    '2000-01-01 00:02:00'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.minute
+        Int16Index([0, 1, 2], dtype='int16')
+        """
+        return self._get_dt_field("minute")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def second(self):
+        """
+        The seconds of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="s"))
+        >>> datetime_index
+        DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:00:01',
+                    '2000-01-01 00:00:02'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.second
+        Int16Index([0, 1, 2], dtype='int16')
+        """
+        return self._get_dt_field("second")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def microsecond(self):
+        """
+        The microseconds of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="us"))
+        >>> datetime_index
+        DatetimeIndex([       '2000-01-01 00:00:00', '2000-01-01 00:00:00.000001',
+               '2000-01-01 00:00:00.000002'],
+              dtype='datetime64[ns]')
+        >>> datetime_index.microsecond
+        Int32Index([0, 1, 2], dtype='int32')
+        """  # noqa: E501
+        return as_index(
+            (
+                # Need to manually promote column to int32 because
+                # pandas-matching binop behaviour requires that this
+                # __mul__ returns an int16 column.
+                self._values.get_dt_field("millisecond").astype("int32")
+                * cudf.Scalar(1000, dtype="int32")
+            )
+            + self._values.get_dt_field("microsecond"),
+            name=self.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nanosecond(self):
+        """
+        The nanoseconds of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
+        ...             periods=3, freq="ns"))
+        >>> datetime_index
+        DatetimeIndex([          '2000-01-01 00:00:00',
+                       '2000-01-01 00:00:00.000000001',
+                       '2000-01-01 00:00:00.000000002'],
+                      dtype='datetime64[ns]')
+        >>> datetime_index.nanosecond
+        Int16Index([0, 1, 2], dtype='int16')
+        """
+        return self._get_dt_field("nanosecond")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def weekday(self):
+        """
+        The day of the week with Monday=0, Sunday=6.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2016-12-31",
+        ...     "2017-01-08", freq="D"))
+        >>> datetime_index
+        DatetimeIndex(['2016-12-31', '2017-01-01', '2017-01-02', '2017-01-03',
+                    '2017-01-04', '2017-01-05', '2017-01-06', '2017-01-07',
+                    '2017-01-08'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.weekday
+        Int16Index([5, 6, 0, 1, 2, 3, 4, 5, 6], dtype='int16')
+        """
+        return self._get_dt_field("weekday")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dayofweek(self):
+        """
+        The day of the week with Monday=0, Sunday=6.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2016-12-31",
+        ...     "2017-01-08", freq="D"))
+        >>> datetime_index
+        DatetimeIndex(['2016-12-31', '2017-01-01', '2017-01-02', '2017-01-03',
+                    '2017-01-04', '2017-01-05', '2017-01-06', '2017-01-07',
+                    '2017-01-08'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.dayofweek
+        Int16Index([5, 6, 0, 1, 2, 3, 4, 5, 6], dtype='int16')
+        """
+        return self._get_dt_field("weekday")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dayofyear(self):
+        """
+        The day of the year, from 1-365 in non-leap years and
+        from 1-366 in leap years.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2016-12-31",
+        ...     "2017-01-08", freq="D"))
+        >>> datetime_index
+        DatetimeIndex(['2016-12-31', '2017-01-01', '2017-01-02', '2017-01-03',
+                    '2017-01-04', '2017-01-05', '2017-01-06', '2017-01-07',
+                    '2017-01-08'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.dayofyear
+        Int16Index([366, 1, 2, 3, 4, 5, 6, 7, 8], dtype='int16')
+        """
+        return self._get_dt_field("day_of_year")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def day_of_year(self):
+        """
+        The day of the year, from 1-365 in non-leap years and
+        from 1-366 in leap years.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_index = cudf.Index(pd.date_range("2016-12-31",
+        ...     "2017-01-08", freq="D"))
+        >>> datetime_index
+        DatetimeIndex(['2016-12-31', '2017-01-01', '2017-01-02', '2017-01-03',
+                    '2017-01-04', '2017-01-05', '2017-01-06', '2017-01-07',
+                    '2017-01-08'],
+                    dtype='datetime64[ns]')
+        >>> datetime_index.day_of_year
+        Int16Index([366, 1, 2, 3, 4, 5, 6, 7, 8], dtype='int16')
+        """
+        return self._get_dt_field("day_of_year")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_leap_year(self):
+        """
+        Boolean indicator if the date belongs to a leap year.
+
+        A leap year is a year, which has 366 days (instead of 365) including
+        29th of February as an intercalary day. Leap years are years which are
+        multiples of four with the exception of years divisible by 100 but not
+        by 400.
+
+        Returns
+        -------
+        ndarray
+        Booleans indicating if dates belong to a leap year.
+        """
+        res = is_leap_year(self._values).fillna(False)
+        return cupy.asarray(res)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def quarter(self):
+        """
+        Integer indicator for which quarter of the year the date belongs in.
+
+        There are 4 quarters in a year. With the first quarter being from
+        January - March, second quarter being April - June, third quarter
+        being July - September and fourth quarter being October - December.
+
+        Returns
+        -------
+        Int8Index
+        Integer indicating which quarter the date belongs to.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gIndex = cudf.DatetimeIndex(["2020-05-31 08:00:00",
+        ...    "1999-12-31 18:40:00"])
+        >>> gIndex.quarter
+        Int8Index([2, 4], dtype='int8')
+        """
+        res = extract_quarter(self._values)
+        return Index(res, dtype="int8")
+
+    @_cudf_nvtx_annotate
+    def isocalendar(self):
+        """
+        Returns a DataFrame with the year, week, and day
+        calculated according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+        with columns year, week and day
+
+        Examples
+        --------
+        >>> gIndex = cudf.DatetimeIndex(["2020-05-31 08:00:00",
+        ...    "1999-12-31 18:40:00"])
+        >>> gIndex.isocalendar()
+                             year  week  day
+        2020-05-31 08:00:00  2020    22    7
+        1999-12-31 18:40:00  1999    52    5
+        """
+        return cudf.core.tools.datetimes._to_iso_calendar(self)
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, nullable=False):
+        # TODO: no need to convert to nanos with Pandas 2.x
+        if isinstance(self.dtype, pd.DatetimeTZDtype):
+            nanos = self._values.astype(
+                pd.DatetimeTZDtype("ns", self.dtype.tz)
+            )
+        else:
+            nanos = self._values.astype("datetime64[ns]")
+        return pd.DatetimeIndex(nanos.to_pandas(), name=self.name)
+
+    @_cudf_nvtx_annotate
+    def _get_dt_field(self, field):
+        out_column = self._values.get_dt_field(field)
+        # column.column_empty_like always returns a Column object
+        # but we need a NumericalColumn for GenericIndex..
+        # how should this be handled?
+        out_column = column.build_column(
+            data=out_column.base_data,
+            dtype=out_column.dtype,
+            mask=out_column.base_mask,
+            offset=out_column.offset,
+        )
+        return as_index(out_column, name=self.name)
+
+    def _is_boolean(self):
+        return False
+
+    @_cudf_nvtx_annotate
+    def ceil(self, freq):
+        """
+        Perform ceil operation on the data to the specified freq.
+
+        Parameters
+        ----------
+        freq : str
+            One of ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"].
+            Must be a fixed frequency like 'S' (second) not 'ME' (month end).
+            See `frequency aliases <https://pandas.pydata.org/docs/\
+                user_guide/timeseries.html#timeseries-offset-aliases>`__
+            for more details on these aliases.
+
+        Returns
+        -------
+        DatetimeIndex
+            Index of the same type for a DatetimeIndex
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gIndex = cudf.DatetimeIndex([
+        ...     "2020-05-31 08:05:42",
+        ...     "1999-12-31 18:40:30",
+        ... ])
+        >>> gIndex.ceil("T")
+        DatetimeIndex(['2020-05-31 08:06:00', '1999-12-31 18:41:00'], dtype='datetime64[ns]')
+        """  # noqa: E501
+        out_column = self._values.ceil(freq)
+
+        return self.__class__._from_data({self.name: out_column})
+
+    @_cudf_nvtx_annotate
+    def floor(self, freq):
+        """
+        Perform floor operation on the data to the specified freq.
+
+        Parameters
+        ----------
+        freq : str
+            One of ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"].
+            Must be a fixed frequency like 'S' (second) not 'ME' (month end).
+            See `frequency aliases <https://pandas.pydata.org/docs/\
+                user_guide/timeseries.html#timeseries-offset-aliases>`__
+            for more details on these aliases.
+
+        Returns
+        -------
+        DatetimeIndex
+            Index of the same type for a DatetimeIndex
+
+        Examples
+        --------
+        >>> import cudf
+        >>> gIndex = cudf.DatetimeIndex([
+        ...     "2020-05-31 08:59:59",
+        ...     "1999-12-31 18:44:59",
+        ... ])
+        >>> gIndex.floor("T")
+        DatetimeIndex(['2020-05-31 08:59:00', '1999-12-31 18:44:00'], dtype='datetime64[ns]')
+        """  # noqa: E501
+        out_column = self._values.floor(freq)
+
+        return self.__class__._from_data({self.name: out_column})
+
+    @_cudf_nvtx_annotate
+    def round(self, freq):
+        """
+        Perform round operation on the data to the specified freq.
+
+        Parameters
+        ----------
+        freq : str
+            One of ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"].
+            Must be a fixed frequency like 'S' (second) not 'ME' (month end).
+            See `frequency aliases <https://pandas.pydata.org/docs/\
+                user_guide/timeseries.html#timeseries-offset-aliases>`__
+            for more details on these aliases.
+
+        Returns
+        -------
+        DatetimeIndex
+            Index containing rounded datetimes.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> dt_idx = cudf.Index([
+        ...     "2001-01-01 00:04:45",
+        ...     "2001-01-01 00:04:58",
+        ...     "2001-01-01 00:05:04",
+        ... ], dtype="datetime64[ns]")
+        >>> dt_idx
+        DatetimeIndex(['2001-01-01 00:04:45', '2001-01-01 00:04:58',
+                       '2001-01-01 00:05:04'],
+                      dtype='datetime64[ns]')
+        >>> dt_idx.round('H')
+        DatetimeIndex(['2001-01-01', '2001-01-01', '2001-01-01'], dtype='datetime64[ns]')
+        >>> dt_idx.round('T')
+        DatetimeIndex(['2001-01-01 00:05:00', '2001-01-01 00:05:00', '2001-01-01 00:05:00'], dtype='datetime64[ns]')
+        """  # noqa: E501
+        out_column = self._values.round(freq)
+
+        return self.__class__._from_data({self.name: out_column})
+
+    def tz_localize(self, tz, ambiguous="NaT", nonexistent="NaT"):
+        """
+        Localize timezone-naive data to timezone-aware data.
+
+        Parameters
+        ----------
+        tz : str
+            Timezone to convert timestamps to.
+
+        Returns
+        -------
+        DatetimeIndex containing timezone aware timestamps.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> tz_naive = cudf.date_range('2018-03-01 09:00', periods=3, freq='D')
+        >>> tz_aware = tz_naive.tz_localize("America/New_York")
+        >>> tz_aware
+        DatetimeIndex(['2018-03-01 09:00:00-05:00',
+                       '2018-03-02 09:00:00-05:00',
+                       '2018-03-03 09:00:00-05:00'],
+                      dtype='datetime64[ns, America/New_York]')
+
+        Ambiguous or nonexistent datetimes are converted to NaT.
+
+        >>> s = cudf.to_datetime(cudf.Series(['2018-10-28 01:20:00',
+        ...                                   '2018-10-28 02:36:00',
+        ...                                   '2018-10-28 03:46:00']))
+        >>> s.dt.tz_localize("CET")
+        0    2018-10-28 01:20:00.000000000
+        1                              NaT
+        2    2018-10-28 03:46:00.000000000
+        dtype: datetime64[ns, CET]
+
+        Notes
+        -----
+        'NaT' is currently the only supported option for the
+        ``ambiguous`` and ``nonexistent`` arguments. Any
+        ambiguous or nonexistent timestamps are converted
+        to 'NaT'.
+        """
+        from cudf.core._internals.timezones import delocalize, localize
+
+        if tz is None:
+            result_col = delocalize(self._column)
+        else:
+            result_col = localize(self._column, tz, ambiguous, nonexistent)
+        return DatetimeIndex._from_data({self.name: result_col})
+
+    def tz_convert(self, tz):
+        """
+        Convert tz-aware datetimes from one time zone to another.
+
+        Parameters
+        ----------
+        tz : str
+            Time zone for time. Corresponding timestamps would be converted
+            to this time zone of the Datetime Array/Index.
+            A `tz` of None will convert to UTC and remove the timezone
+            information.
+
+        Returns
+        -------
+        DatetimeIndex containing timestamps corresponding to the timezone
+        `tz`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> dti = cudf.date_range('2018-03-01 09:00', periods=3, freq='D')
+        >>> dti = dti.tz_localize("America/New_York")
+        >>> dti
+        DatetimeIndex(['2018-03-01 09:00:00-05:00',
+                       '2018-03-02 09:00:00-05:00',
+                       '2018-03-03 09:00:00-05:00'],
+                      dtype='datetime64[ns, America/New_York]')
+        >>> dti.tz_convert("Europe/London")
+        DatetimeIndex(['2018-03-01 14:00:00+00:00',
+                       '2018-03-02 14:00:00+00:00',
+                       '2018-03-03 14:00:00+00:00'],
+                      dtype='datetime64[ns, Europe/London]')
+        """
+        from cudf.core._internals.timezones import convert
+
+        if tz is None:
+            result_col = self._column._utc_time
+        else:
+            result_col = convert(self._column, tz)
+        return DatetimeIndex._from_data({self.name: result_col})
+
+
+class TimedeltaIndex(GenericIndex):
+    """
+    Immutable, ordered and sliceable sequence of timedelta64 data,
+    represented internally as int64.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional), optional
+        Optional datetime-like data to construct index with.
+    unit : str, optional
+        This is not yet supported
+    copy : bool
+        Make a copy of input.
+    freq : str, optional
+        This is not yet supported
+    closed : str, optional
+        This is not yet supported
+    dtype : str or :class:`numpy.dtype`, optional
+        Data type for the output Index. If not specified, the
+        default dtype will be ``timedelta64[ns]``.
+    name : object
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    days
+    seconds
+    microseconds
+    nanoseconds
+    components
+    inferred_freq
+
+    Methods
+    -------
+    None
+
+    Returns
+    -------
+    TimedeltaIndex
+
+    Examples
+    --------
+    >>> import cudf
+    >>> cudf.TimedeltaIndex([1132223, 2023232, 342234324, 4234324],
+    ...     dtype="timedelta64[ns]")
+    TimedeltaIndex(['0 days 00:00:00.001132223', '0 days 00:00:00.002023232',
+                    '0 days 00:00:00.342234324', '0 days 00:00:00.004234324'],
+                  dtype='timedelta64[ns]')
+    >>> cudf.TimedeltaIndex([1, 2, 3, 4], dtype="timedelta64[s]",
+    ...     name="delta-index")
+    TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03',
+                    '0 days 00:00:04'],
+                  dtype='timedelta64[s]', name='delta-index')
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        data=None,
+        unit=None,
+        freq=None,
+        closed=None,
+        dtype="timedelta64[ns]",
+        copy=False,
+        name=None,
+    ):
+        if freq is not None:
+            raise NotImplementedError("freq is not yet supported")
+
+        if unit is not None:
+            raise NotImplementedError(
+                "unit is not yet supported, alternatively "
+                "dtype parameter is supported"
+            )
+
+        valid_dtypes = tuple(
+            f"timedelta64[{res}]" for res in ("s", "ms", "us", "ns")
+        )
+        if dtype not in valid_dtypes:
+            raise TypeError("Invalid dtype")
+
+        kwargs = _setdefault_name(data, name=name)
+        data = column.as_column(data, dtype=dtype)
+
+        if copy:
+            data = data.copy()
+
+        super().__init__(data, **kwargs)
+
+    def __getitem__(self, index):
+        value = super().__getitem__(index)
+        if cudf.get_option("mode.pandas_compatible") and isinstance(
+            value, np.timedelta64
+        ):
+            return pd.Timedelta(value)
+        return value
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, nullable=False):
+        return pd.TimedeltaIndex(
+            self._values.to_pandas(),
+            name=self.name,
+            unit=self._values.time_unit,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def days(self):
+        """
+        Number of days for each element.
+        """
+        return as_index(arbitrary=self._values.days, name=self.name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def seconds(self):
+        """
+        Number of seconds (>= 0 and less than 1 day) for each element.
+        """
+        return as_index(arbitrary=self._values.seconds, name=self.name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def microseconds(self):
+        """
+        Number of microseconds (>= 0 and less than 1 second) for each element.
+        """
+        return as_index(arbitrary=self._values.microseconds, name=self.name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nanoseconds(self):
+        """
+        Number of nanoseconds (>= 0 and less than 1 microsecond) for each
+        element.
+        """
+        return as_index(arbitrary=self._values.nanoseconds, name=self.name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def components(self):
+        """
+        Return a dataframe of the components (days, hours, minutes,
+        seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.
+        """
+        return self._values.components()
+
+    @property
+    def inferred_freq(self):
+        """
+        Infers frequency of TimedeltaIndex.
+
+        Notes
+        -----
+        This property is currently not supported.
+        """
+        raise NotImplementedError("inferred_freq is not yet supported")
+
+    def _is_boolean(self):
+        return False
+
+
+class CategoricalIndex(GenericIndex):
+    """
+    A categorical of orderable values that represent the indices of another
+    Column
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+        The values of the categorical. If categories are given,
+        values not in categories will be replaced with None/NaN.
+    categories : list-like, optional
+        The categories for the categorical. Items need to be unique.
+        If the categories are not given here (and also not in dtype),
+        they will be inferred from the data.
+    ordered : bool, optional
+        Whether or not this categorical is treated as an ordered categorical.
+        If not given here or in dtype, the resulting categorical will be
+        unordered.
+    dtype : CategoricalDtype or "category", optional
+        If CategoricalDtype, cannot be used together with categories or
+        ordered.
+    copy : bool, default False
+        Make a copy of input.
+    name : object, optional
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    codes
+    categories
+
+    Methods
+    -------
+    equals
+
+    Returns
+    -------
+    CategoricalIndex
+
+    Examples
+    --------
+    >>> import cudf
+    >>> import pandas as pd
+    >>> cudf.CategoricalIndex(
+    ... data=[1, 2, 3, 4], categories=[1, 2], ordered=False, name="a")
+    CategoricalIndex([1, 2, <NA>, <NA>], categories=[1, 2], ordered=False, dtype='category', name='a')
+
+    >>> cudf.CategoricalIndex(
+    ... data=[1, 2, 3, 4], dtype=pd.CategoricalDtype([1, 2, 3]), name="a")
+    CategoricalIndex([1, 2, 3, <NA>], categories=[1, 2, 3], ordered=False, dtype='category', name='a')
+    """  # noqa: E501
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        data=None,
+        categories=None,
+        ordered=None,
+        dtype=None,
+        copy=False,
+        name=None,
+    ):
+        if isinstance(dtype, (pd.CategoricalDtype, cudf.CategoricalDtype)):
+            if categories is not None or ordered is not None:
+                raise ValueError(
+                    "Cannot specify `categories` or "
+                    "`ordered` together with `dtype`."
+                )
+        if copy:
+            data = column.as_column(data, dtype=dtype).copy(deep=True)
+        kwargs = _setdefault_name(data, name=name)
+        if isinstance(data, CategoricalColumn):
+            data = data
+        elif isinstance(data, pd.Series) and (
+            is_categorical_dtype(data.dtype)
+        ):
+            codes_data = column.as_column(data.cat.codes.values)
+            data = column.build_categorical_column(
+                categories=data.cat.categories,
+                codes=codes_data,
+                ordered=data.cat.ordered,
+            )
+        elif isinstance(data, (pd.Categorical, pd.CategoricalIndex)):
+            codes_data = column.as_column(data.codes)
+            data = column.build_categorical_column(
+                categories=data.categories,
+                codes=codes_data,
+                ordered=data.ordered,
+            )
+        else:
+            data = column.as_column(
+                data, dtype="category" if dtype is None else dtype
+            )
+            # dtype has already been taken care
+            dtype = None
+
+        if categories is not None:
+            data = data.set_categories(categories, ordered=ordered)
+        elif isinstance(dtype, (pd.CategoricalDtype, cudf.CategoricalDtype)):
+            data = data.set_categories(dtype.categories, ordered=ordered)
+        elif ordered is True and data.ordered is False:
+            data = data.as_ordered()
+        elif ordered is False and data.ordered is True:
+            data = data.as_unordered()
+
+        super().__init__(data, **kwargs)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def codes(self):
+        """
+        The category codes of this categorical.
+        """
+        return as_index(self._values.codes)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def categories(self):
+        """
+        The categories of this categorical.
+        """
+        return as_index(self._values.categories)
+
+    def _is_boolean(self):
+        return False
+
+    def _is_categorical(self):
+        return True
+
+
+@_cudf_nvtx_annotate
+def interval_range(
+    start=None,
+    end=None,
+    periods=None,
+    freq=None,
+    name=None,
+    closed="right",
+) -> "IntervalIndex":
+    """
+    Returns a fixed frequency IntervalIndex.
+
+    Parameters
+    ----------
+    start : numeric, default None
+        Left bound for generating intervals.
+    end : numeric , default None
+        Right bound for generating intervals.
+    periods : int, default None
+        Number of periods to generate
+    freq : numeric, default None
+        The length of each interval. Must be consistent
+        with the type of start and end
+    name : str, default None
+        Name of the resulting IntervalIndex.
+    closed : {"left", "right", "both", "neither"}, default "right"
+        Whether the intervals are closed on the left-side, right-side,
+        both or neither.
+
+    Returns
+    -------
+    IntervalIndex
+
+    Examples
+    --------
+    >>> import cudf
+    >>> import pandas as pd
+    >>> cudf.interval_range(start=0,end=5)
+    IntervalIndex([(0, 0], (1, 1], (2, 2], (3, 3], (4, 4], (5, 5]],
+    ...closed='right',dtype='interval')
+    >>> cudf.interval_range(start=0,end=10, freq=2,closed='left')
+    IntervalIndex([[0, 2), [2, 4), [4, 6), [6, 8), [8, 10)],
+    ...closed='left',dtype='interval')
+    >>> cudf.interval_range(start=0,end=10, periods=3,closed='left')
+    ...IntervalIndex([[0.0, 3.3333333333333335),
+            [3.3333333333333335, 6.666666666666667),
+            [6.666666666666667, 10.0)],
+            closed='left',
+            dtype='interval')
+    """
+    nargs = sum(_ is not None for _ in (start, end, periods, freq))
+
+    # we need at least three of (start, end, periods, freq)
+    if nargs == 2 and freq is None:
+        freq = 1
+        nargs += 1
+
+    if nargs != 3:
+        raise ValueError(
+            "Of the four parameters: start, end, periods, and "
+            "freq, exactly three must be specified"
+        )
+
+    start = cudf.Scalar(start) if start is not None else start
+    end = cudf.Scalar(end) if end is not None else end
+    periods = cudf.Scalar(int(periods)) if periods is not None else periods
+    freq = cudf.Scalar(freq) if freq is not None else freq
+
+    if start is None:
+        start = end - freq * periods
+    elif freq is None:
+        quotient, remainder = divmod((end - start).value, periods.value)
+        if remainder:
+            freq = (end - start) / periods
+        else:
+            freq = cudf.Scalar(int(quotient))
+    elif periods is None:
+        periods = cudf.Scalar(int((end - start) / freq))
+    elif end is None:
+        end = start + periods * freq
+
+    if any(
+        not _is_non_decimal_numeric_dtype(x.dtype)
+        for x in (start, periods, freq, end)
+    ):
+        raise ValueError("start, end, periods, freq must be numeric values.")
+
+    periods = periods.astype("int64")
+    common_dtype = find_common_type((start.dtype, freq.dtype, end.dtype))
+    start = start.astype(common_dtype)
+    freq = freq.astype(common_dtype)
+
+    bin_edges = sequence(
+        size=periods + 1,
+        init=start.device_value,
+        step=freq.device_value,
+    )
+    left_col = bin_edges.slice(0, len(bin_edges) - 1)
+    right_col = bin_edges.slice(1, len(bin_edges))
+
+    if len(right_col) == 0 or len(left_col) == 0:
+        dtype = IntervalDtype("int64", closed)
+        data = column.column_empty_like_same_mask(left_col, dtype)
+        return IntervalIndex(data, closed=closed)
+
+    interval_col = column.build_interval_column(
+        left_col, right_col, closed=closed
+    )
+    return IntervalIndex(interval_col)
+
+
+class IntervalIndex(GenericIndex):
+    """
+    Immutable index of intervals that are closed on the same side.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)
+        Array-like containing Interval objects from which to build the
+        IntervalIndex.
+    closed : {"left", "right", "both", "neither"}, default "right"
+        Whether the intervals are closed on the left-side, right-side,
+        both or neither.
+    dtype : dtype or None, default None
+        If None, dtype will be inferred.
+    copy : bool, default False
+        Copy the input data.
+    name : object, optional
+        Name to be stored in the index.
+
+    Attributes
+    ----------
+    values
+
+    Methods
+    -------
+    from_breaks
+    get_loc
+
+    Returns
+    -------
+    IntervalIndex
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        data,
+        closed=None,
+        dtype=None,
+        copy=False,
+        name=None,
+    ):
+        if copy:
+            data = column.as_column(data, dtype=dtype).copy()
+        kwargs = _setdefault_name(data, name=name)
+
+        if closed is None:
+            closed = "right"
+
+        if isinstance(data, IntervalColumn):
+            data = data
+        elif isinstance(data, pd.Series) and (is_interval_dtype(data.dtype)):
+            data = column.as_column(data, data.dtype)
+        elif isinstance(data, (pd.Interval, pd.IntervalIndex)):
+            data = column.as_column(
+                data,
+                dtype=dtype,
+            )
+        elif len(data) == 0:
+            subtype = getattr(data, "dtype", "int64")
+            dtype = IntervalDtype(subtype, closed)
+            data = column.column_empty_like_same_mask(
+                column.as_column(data), dtype
+            )
+        else:
+            data = column.as_column(data)
+            data.dtype.closed = closed
+
+        self.closed = closed
+        super().__init__(data, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def from_breaks(breaks, closed="right", name=None, copy=False, dtype=None):
+        """
+        Construct an IntervalIndex from an array of splits.
+
+        Parameters
+        ----------
+        breaks : array-like (1-dimensional)
+            Left and right bounds for each interval.
+        closed : {"left", "right", "both", "neither"}, default "right"
+            Whether the intervals are closed on the left-side, right-side,
+            both or neither.
+        copy : bool, default False
+            Copy the input data.
+        name : object, optional
+            Name to be stored in the index.
+        dtype : dtype or None, default None
+            If None, dtype will be inferred.
+
+        Returns
+        -------
+        IntervalIndex
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> cudf.IntervalIndex.from_breaks([0, 1, 2, 3])
+        IntervalIndex([(0, 1], (1, 2], (2, 3]], dtype='interval[int64, right]')
+        """
+        if copy:
+            breaks = column.as_column(breaks, dtype=dtype).copy()
+        left_col = breaks[:-1:]
+        right_col = breaks[+1::]
+
+        interval_col = column.build_interval_column(
+            left_col, right_col, closed=closed
+        )
+
+        return IntervalIndex(interval_col, name=name)
+
+    def __getitem__(self, index):
+        raise NotImplementedError(
+            "Getting a scalar from an IntervalIndex is not yet supported"
+        )
+
+    def _is_interval(self):
+        return True
+
+    def _is_boolean(self):
+        return False
+
+    def _clean_nulls_from_index(self):
+        return self
+
+
+class StringIndex(GenericIndex):
+    """String defined indices into another Column
+
+    .. deprecated:: 23.06
+        `StringIndex` is deprecated, use `Index` instead.
+
+    Attributes
+    ----------
+    _values: A StringColumn object or NDArray of strings
+    name: A string
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(self, values, copy=False, **kwargs):
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            f"cudf.{self.__class__.__name__} is deprecated and will be "
+            "removed from cudf in a future version. Use cudf.Index with the "
+            "appropriate dtype instead.",
+            FutureWarning,
+        )
+        kwargs = _setdefault_name(values, **kwargs)
+        if isinstance(values, StringColumn):
+            values = values.copy(deep=copy)
+        elif isinstance(values, StringIndex):
+            values = values._values.copy(deep=copy)
+        else:
+            values = column.as_column(values, dtype="str")
+            if not is_string_dtype(values.dtype):
+                raise ValueError(
+                    "Couldn't create StringIndex from passed in object"
+                )
+
+        super().__init__(values, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, nullable=False):
+        return pd.Index(
+            self.to_numpy(na_value=None),
+            name=self.name,
+            dtype=pd.StringDtype() if nullable else "object",
+        )
+
+    @_cudf_nvtx_annotate
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}({self._values.values_host},"
+            f" dtype='object'"
+            + (
+                f", name={pd.io.formats.printing.default_pprint(self.name)}"
+                if self.name is not None
+                else ""
+            )
+            + ")"
+        )
+
+    @copy_docstring(StringMethods)  # type: ignore
+    @property
+    @_cudf_nvtx_annotate
+    def str(self):
+        return StringMethods(parent=self)
+
+    def _clean_nulls_from_index(self):
+        if self._values.has_nulls():
+            return self.fillna(str(cudf.NA))
+        else:
+            return self
+
+    def _is_boolean(self):
+        return False
+
+    def _is_object(self):
+        return True
+
+
+@_cudf_nvtx_annotate
+def as_index(
+    arbitrary, nan_as_null=None, copy=False, name=no_default, dtype=None
+) -> BaseIndex:
+    """Create an Index from an arbitrary object
+
+    Parameters
+    ----------
+    arbitrary : object
+        Object to construct the Index from. See *Notes*.
+    nan_as_null : bool, optional, default None
+        If None (default), treats NaN values in arbitrary as null.
+        If True, combines the mask and NaNs to
+        form a new validity mask. If False, leaves NaN values as is.
+    copy : bool, default False
+        If True, Make copies of `arbitrary` if possible and create an
+        Index out of it.
+        If False, `arbitrary` will be shallow-copied if it is a
+        device-object to construct an Index.
+    name : object, optional
+        Name of the index being created, by default it is `None`.
+    dtype : optional
+        Optionally typecast the constructed Index to the given
+        dtype.
+
+    Returns
+    -------
+    result : subclass of Index
+        - CategoricalIndex for Categorical input.
+        - DatetimeIndex for Datetime input.
+        - GenericIndex for all other inputs.
+
+    Notes
+    -----
+    Currently supported inputs are:
+
+    * ``Column``
+    * ``Buffer``
+    * ``Series``
+    * ``Index``
+    * numba device array
+    * numpy array
+    * pyarrow array
+    * pandas.Categorical
+    """
+
+    if name is no_default:
+        name = getattr(arbitrary, "name", None)
+
+    if isinstance(arbitrary, cudf.MultiIndex):
+        if dtype is not None:
+            raise TypeError(
+                "dtype must be `None` for inputs of type: "
+                f"{type(arbitrary).__name__}, found {dtype=} "
+            )
+        return arbitrary.copy(deep=copy)
+    elif isinstance(arbitrary, BaseIndex):
+        idx = arbitrary.copy(deep=copy).rename(name)
+    elif isinstance(arbitrary, ColumnBase):
+        idx = _index_from_data({name: arbitrary})
+    elif isinstance(arbitrary, cudf.Series):
+        return as_index(
+            arbitrary._column,
+            nan_as_null=nan_as_null,
+            copy=copy,
+            name=name,
+            dtype=dtype,
+        )
+    elif isinstance(arbitrary, (pd.RangeIndex, range)):
+        idx = RangeIndex(
+            start=arbitrary.start,
+            stop=arbitrary.stop,
+            step=arbitrary.step,
+            name=name,
+        )
+    elif isinstance(arbitrary, pd.MultiIndex):
+        if dtype is not None:
+            raise TypeError(
+                "dtype must be `None` for inputs of type: "
+                f"{type(arbitrary).__name__}, found {dtype=} "
+            )
+        return cudf.MultiIndex.from_pandas(
+            arbitrary.copy(deep=copy), nan_as_null=nan_as_null
+        )
+    elif isinstance(arbitrary, cudf.DataFrame) or is_scalar(arbitrary):
+        raise ValueError("Index data must be 1-dimensional and list-like")
+    else:
+        return as_index(
+            column.as_column(arbitrary, dtype=dtype, nan_as_null=nan_as_null),
+            copy=copy,
+            name=name,
+            dtype=dtype,
+        )
+    if dtype is not None:
+        idx = idx.astype(dtype)
+    return idx
+
+
+_dtype_to_index: Dict[Any, Type[NumericIndex]] = {
+    np.int8: Int8Index,
+    np.int16: Int16Index,
+    np.int32: Int32Index,
+    np.int64: Int64Index,
+    np.uint8: UInt8Index,
+    np.uint16: UInt16Index,
+    np.uint32: UInt32Index,
+    np.uint64: UInt64Index,
+    np.float32: Float32Index,
+    np.float64: Float64Index,
+}
+
+
+def _setdefault_name(values, **kwargs):
+    if kwargs.get("name") is None:
+        kwargs["name"] = getattr(values, "name", None)
+    return kwargs
+
+
+class IndexMeta(type):
+    """Custom metaclass for Index that overrides instance/subclass tests."""
+
+    def __instancecheck__(self, instance):
+        return isinstance(instance, BaseIndex)
+
+    def __subclasscheck__(self, subclass):
+        return issubclass(subclass, BaseIndex)
+
+
+class Index(BaseIndex, metaclass=IndexMeta):
+    """The basic object storing row labels for all cuDF objects.
+
+    Parameters
+    ----------
+    data : array-like (1-dimensional)/ DataFrame
+        If it is a DataFrame, it will return a MultiIndex
+    dtype : NumPy dtype (default: object)
+        If dtype is None, we find the dtype that best fits the data.
+    copy : bool
+        Make a copy of input data.
+    name : object
+        Name to be stored in the index.
+    tupleize_cols : bool (default: True)
+        When True, attempt to create a MultiIndex if possible.
+        tupleize_cols == False is not yet supported.
+    nan_as_null : bool, Default True
+        If ``None``/``True``, converts ``np.nan`` values to
+        ``null`` values.
+        If ``False``, leaves ``np.nan`` values as is.
+
+    Returns
+    -------
+    Index
+        cudf Index
+
+    Warnings
+    --------
+    This class should not be subclassed. It is designed as a factory for
+    different subclasses of `BaseIndex` depending on the provided input.
+    If you absolutely must, and if you're intimately familiar with the
+    internals of cuDF, subclass `BaseIndex` instead.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> cudf.Index([1, 2, 3], dtype="uint64", name="a")
+    UInt64Index([1, 2, 3], dtype='uint64', name='a')
+    """
+
+    @_cudf_nvtx_annotate
+    def __new__(
+        cls,
+        data=None,
+        dtype=None,
+        copy=False,
+        name=no_default,
+        tupleize_cols=True,
+        nan_as_null=True,
+        **kwargs,
+    ):
+        assert (
+            cls is Index
+        ), "Index cannot be subclassed, extend BaseIndex instead."
+        if tupleize_cols is not True:
+            raise NotImplementedError(
+                "tupleize_cols != True is not yet supported"
+            )
+
+        res = as_index(
+            data,
+            copy=copy,
+            dtype=dtype,
+            name=name,
+            nan_as_null=nan_as_null,
+            **kwargs,
+        )
+        if (
+            isinstance(data, Sequence)
+            and not isinstance(data, range)
+            and len(data) == 0
+            and dtype is None
+            and getattr(data, "dtype", None) is None
+        ):
+            return res.astype("str")
+        return res
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_arrow(cls, obj):
+        try:
+            return cls(ColumnBase.from_arrow(obj))
+        except TypeError:
+            # Try interpreting object as a MultiIndex before failing.
+            return cudf.MultiIndex.from_arrow(obj)
+
+    @cached_property
+    def is_monotonic_increasing(self):
+        return super().is_monotonic_increasing
+
+    @cached_property
+    def is_monotonic_decreasing(self):
+        return super().is_monotonic_decreasing
+
+
+@_cudf_nvtx_annotate
+def _concat_range_index(indexes: List[RangeIndex]) -> BaseIndex:
+    """
+    An internal Utility function to concat RangeIndex objects.
+    """
+    start = step = next_ = None
+
+    # Filter the empty indexes
+    non_empty_indexes = [obj for obj in indexes if len(obj)]
+
+    if not non_empty_indexes:
+        # Here all "indexes" had 0 length, i.e. were empty.
+        # In this case return an empty range index.
+        return RangeIndex(0, 0)
+
+    for obj in non_empty_indexes:
+        if start is None:
+            # This is set by the first non-empty index
+            start = obj.start
+            if step is None and len(obj) > 1:
+                step = obj.step
+        elif step is None:
+            # First non-empty index had only one element
+            if obj.start == start:
+                result = as_index(concat_columns([x._values for x in indexes]))
+                return result
+            step = obj.start - start
+
+        non_consecutive = (step != obj.step and len(obj) > 1) or (
+            next_ is not None and obj.start != next_
+        )
+        if non_consecutive:
+            result = as_index(concat_columns([x._values for x in indexes]))
+            return result
+        if step is not None:
+            next_ = obj[-1] + step
+
+    stop = non_empty_indexes[-1].stop if next_ is None else next_
+    return RangeIndex(start, stop, step)
+
+
+@_cudf_nvtx_annotate
+def _extended_gcd(a: int, b: int) -> Tuple[int, int, int]:
+    """
+    Extended Euclidean algorithms to solve Bezout's identity:
+       a*x + b*y = gcd(x, y)
+    Finds one particular solution for x, y: s, t
+    Returns: gcd, s, t
+    """
+    s, old_s = 0, 1
+    t, old_t = 1, 0
+    r, old_r = b, a
+    while r:
+        quotient = old_r // r
+        old_r, r = r, old_r - quotient * r
+        old_s, s = s, old_s - quotient * s
+        old_t, t = t, old_t - quotient * t
+    return old_r, old_s, old_t
diff --git a/python/cudf/cudf/core/indexed_frame.py b/python/cudf/cudf/core/indexed_frame.py
new file mode 100644
index 0000000..fef6259
--- /dev/null
+++ b/python/cudf/cudf/core/indexed_frame.py
@@ -0,0 +1,5476 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+"""Base class for Frame types that have an index."""
+
+from __future__ import annotations
+
+import numbers
+import operator
+import textwrap
+import warnings
+from collections import Counter, abc
+from functools import cached_property
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    MutableMapping,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+from uuid import uuid4
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+from typing_extensions import Self
+
+import cudf
+import cudf._lib as libcudf
+from cudf._lib.types import size_type_dtype
+from cudf._typing import (
+    ColumnLike,
+    DataFrameOrSeries,
+    Dtype,
+    NotImplementedType,
+)
+from cudf.api.extensions import no_default
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    is_bool_dtype,
+    is_categorical_dtype,
+    is_decimal_dtype,
+    is_dict_like,
+    is_list_dtype,
+    is_list_like,
+    is_scalar,
+)
+from cudf.core._base_index import BaseIndex
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.column import ColumnBase, as_column, full
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.copy_types import BooleanMask, GatherMap
+from cudf.core.dtypes import ListDtype
+from cudf.core.frame import Frame
+from cudf.core.groupby.groupby import GroupBy
+from cudf.core.index import Index, RangeIndex, _index_from_columns
+from cudf.core.missing import NA
+from cudf.core.multiindex import MultiIndex
+from cudf.core.resample import _Resampler
+from cudf.core.udf.utils import (
+    _compile_or_get,
+    _get_input_args_from_frame,
+    _post_process_output_col,
+    _return_arr_from_dtype,
+)
+from cudf.utils import docutils
+from cudf.utils._numba import _CUDFNumbaConfig
+from cudf.utils.utils import _cudf_nvtx_annotate, _warn_no_dask_cudf
+
+doc_reset_index_template = """
+        Reset the index of the {klass}, or a level of it.
+
+        Parameters
+        ----------
+        level : int, str, tuple, or list, default None
+            Only remove the given levels from the index. Removes all levels by
+            default.
+        drop : bool, default False
+            Do not try to insert index into dataframe columns. This resets
+            the index to the default integer index.
+{argument}
+        inplace : bool, default False
+            Modify the DataFrame in place (do not create a new object).
+
+        Returns
+        -------
+        {return_type}
+            {klass} with the new index or None if ``inplace=True``.{return_doc}
+
+        Examples
+        --------
+        {example}
+"""
+
+
+doc_binop_template = textwrap.dedent(
+    """
+    Get {operation} of DataFrame or Series and other, element-wise (binary
+    operator `{op_name}`).
+
+    Equivalent to ``frame + other``, but with support to substitute a
+    ``fill_value`` for missing data in one of the inputs.
+
+    Parameters
+    ----------
+    other : scalar, sequence, Series, or DataFrame
+        Any single or multiple element data structure, or list-like object.
+    axis : int or string
+        Only ``0`` is supported for series, ``1`` or ``columns`` supported
+        for dataframe
+    level : int or name
+        Broadcast across a level, matching Index values on the
+        passed MultiIndex level. Not yet supported.
+    fill_value  : float or None, default None
+        Fill existing missing (NaN) values, and any new element needed
+        for successful DataFrame alignment, with this value before
+        computation. If data in both corresponding DataFrame locations
+        is missing the result will be missing.
+
+    Returns
+    -------
+    DataFrame or Series
+        Result of the arithmetic operation.
+
+    Examples
+    --------
+
+    **DataFrame**
+
+    >>> df = cudf.DataFrame(
+    ...     {{'angles': [0, 3, 4], 'degrees': [360, 180, 360]}},
+    ...     index=['circle', 'triangle', 'rectangle']
+    ... )
+    {df_op_example}
+
+    **Series**
+
+    >>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])
+    >>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])
+    {ser_op_example}
+    """
+)
+
+
+def _get_host_unique(array):
+    if isinstance(array, (cudf.Series, cudf.Index, ColumnBase)):
+        return array.unique.to_pandas()
+    elif isinstance(array, (str, numbers.Number)):
+        return [array]
+    else:
+        return set(array)
+
+
+def _drop_columns(f: Frame, columns: abc.Iterable, errors: str):
+    for c in columns:
+        try:
+            f._drop_column(c)
+        except KeyError as e:
+            if errors == "ignore":
+                pass
+            else:
+                raise e
+
+
+def _indices_from_labels(obj, labels):
+    if not isinstance(labels, cudf.MultiIndex):
+        labels = cudf.core.column.as_column(labels)
+
+        if is_categorical_dtype(obj.index):
+            labels = labels.astype("category")
+            codes = labels.codes.astype(obj.index._values.codes.dtype)
+            labels = cudf.core.column.build_categorical_column(
+                categories=labels.dtype.categories,
+                codes=codes,
+                ordered=labels.dtype.ordered,
+            )
+        else:
+            labels = labels.astype(obj.index.dtype)
+
+    # join is not guaranteed to maintain the index ordering
+    # so we will sort it with its initial ordering which is stored
+    # in column "__"
+    lhs = cudf.DataFrame(
+        {"__": cudf.core.column.arange(len(labels))}, index=labels
+    )
+    rhs = cudf.DataFrame(
+        {"_": cudf.core.column.arange(len(obj))}, index=obj.index
+    )
+    return lhs.join(rhs).sort_values(by=["__", "_"])["_"]
+
+
+def _get_label_range_or_mask(index, start, stop, step):
+    if (
+        not (start is None and stop is None)
+        and type(index) is cudf.core.index.DatetimeIndex
+        and index.is_monotonic_increasing is False
+    ):
+        start = pd.to_datetime(start)
+        stop = pd.to_datetime(stop)
+        if start is not None and stop is not None:
+            if start > stop:
+                return slice(0, 0, None)
+            # TODO: Once Index binary ops are updated to support logical_and,
+            # can use that instead of using cupy.
+            boolean_mask = cp.logical_and((index >= start), (index <= stop))
+        elif start is not None:
+            boolean_mask = index >= start
+        else:
+            boolean_mask = index <= stop
+        return boolean_mask
+    else:
+        return index.find_label_range(slice(start, stop, step))
+
+
+class _FrameIndexer:
+    """Parent class for indexers."""
+
+    def __init__(self, frame):
+        self._frame = frame
+
+
+_LocIndexerClass = TypeVar("_LocIndexerClass", bound="_FrameIndexer")
+_IlocIndexerClass = TypeVar("_IlocIndexerClass", bound="_FrameIndexer")
+
+
+class IndexedFrame(Frame):
+    """A frame containing an index.
+
+    This class encodes the common behaviors for core user-facing classes like
+    DataFrame and Series that consist of a sequence of columns along with a
+    special set of index columns.
+
+    Parameters
+    ----------
+    data : dict
+        An dict mapping column names to Columns
+    index : Table
+        A Frame representing the (optional) index columns.
+    """
+
+    # mypy can't handle bound type variables as class members
+    _loc_indexer_type: Type[_LocIndexerClass]  # type: ignore
+    _iloc_indexer_type: Type[_IlocIndexerClass]  # type: ignore
+    _index: cudf.core.index.BaseIndex
+    _groupby = GroupBy
+    _resampler = _Resampler
+
+    _VALID_SCANS = {
+        "cumsum",
+        "cumprod",
+        "cummin",
+        "cummax",
+    }
+
+    # Necessary because the function names don't directly map to the docs.
+    _SCAN_DOCSTRINGS = {
+        "cumsum": {"op_name": "cumulative sum"},
+        "cumprod": {"op_name": "cumulative product"},
+        "cummin": {"op_name": "cumulative min"},
+        "cummax": {"op_name": "cumulative max"},
+    }
+
+    def __init__(self, data=None, index=None):
+        super().__init__(data=data)
+        # TODO: Right now it is possible to initialize an IndexedFrame without
+        # an index. The code's correctness relies on the subclass constructors
+        # assigning the attribute after the fact. We should restructure those
+        # to ensure that this constructor is always invoked with an index.
+        self._index = index
+
+    @property
+    def _num_rows(self) -> int:
+        # Important to use the index because the data may be empty.
+        return len(self._index)
+
+    @property
+    def _index_names(self) -> Tuple[Any, ...]:  # TODO: Tuple[str]?
+        return self._index._data.names
+
+    @classmethod
+    def _from_data(
+        cls,
+        data: MutableMapping,
+        index: Optional[BaseIndex] = None,
+    ):
+        out = super()._from_data(data)
+        out._index = RangeIndex(out._data.nrows) if index is None else index
+        return out
+
+    @_cudf_nvtx_annotate
+    def _from_data_like_self(self, data: MutableMapping):
+        out = self._from_data(data, self._index)
+        out._data._level_names = self._data._level_names
+        return out
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_columns(
+        cls,
+        columns: List[ColumnBase],
+        column_names: List[str],
+        index_names: Optional[List[str]] = None,
+    ):
+        """Construct a `Frame` object from a list of columns.
+
+        If `index_names` is set, the first `len(index_names)` columns are
+        used to construct the index of the frame.
+        """
+        data_columns = columns
+        index = None
+
+        if index_names is not None:
+            n_index_columns = len(index_names)
+            data_columns = columns[n_index_columns:]
+            index = _index_from_columns(columns[:n_index_columns])
+            if isinstance(index, cudf.MultiIndex):
+                index.names = index_names
+            else:
+                index.name = index_names[0]
+
+        out = super()._from_columns(data_columns, column_names)
+
+        if index is not None:
+            out._index = index
+
+        return out
+
+    @_cudf_nvtx_annotate
+    def _from_columns_like_self(
+        self,
+        columns: List[ColumnBase],
+        column_names: Optional[abc.Iterable[str]] = None,
+        index_names: Optional[List[str]] = None,
+        *,
+        override_dtypes: Optional[abc.Iterable[Optional[Dtype]]] = None,
+    ) -> Self:
+        """Construct a `Frame` from a list of columns with metadata from self.
+
+        If `index_names` is set, the first `len(index_names)` columns are
+        used to construct the index of the frame.
+
+        If override_dtypes is provided then any non-None entry will be
+        used for the dtype of the matching column in preference to the
+        dtype of the column in self.
+        """
+        if column_names is None:
+            column_names = self._column_names
+        frame = self.__class__._from_columns(
+            columns, column_names, index_names
+        )
+        return frame._copy_type_metadata(
+            self,
+            include_index=bool(index_names),
+            override_dtypes=override_dtypes,
+        )
+
+    def __round__(self, digits=0):
+        # Shouldn't be added to BinaryOperand
+        # because pandas Index doesn't implement
+        # this method.
+        return self.round(decimals=digits)
+
+    def _mimic_inplace(
+        self, result: Self, inplace: bool = False
+    ) -> Optional[Self]:
+        if inplace:
+            self._index = result._index
+        return super()._mimic_inplace(result, inplace)
+
+    # Scans
+    @_cudf_nvtx_annotate
+    def _scan(self, op, axis=None, skipna=True):
+        """
+        Return {op_name} of the {cls}.
+
+        Parameters
+        ----------
+        axis: {{index (0), columns(1)}}
+            Axis for the function to be applied on.
+        skipna: bool, default True
+            Exclude NA/null values. If an entire row/column is NA,
+            the result will be NA.
+
+        Returns
+        -------
+        {cls}
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> ser = cudf.Series([1, 5, 2, 4, 3])
+        >>> ser.cumsum()
+        0    1
+        1    6
+        2    8
+        3    12
+        4    15
+
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({{'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]}})
+        >>> s.cumsum()
+            a   b
+        0   1   7
+        1   3  15
+        2   6  24
+        3  10  34
+        """
+        cast_to_int = op in ("cumsum", "cumprod")
+        skipna = True if skipna is None else skipna
+
+        results = {}
+        for name, col in self._data.items():
+            if skipna:
+                try:
+                    result_col = col.nans_to_nulls()
+                except AttributeError:
+                    result_col = col
+            else:
+                if col.has_nulls(include_nan=True):
+                    first_index = col.isnull().find_first_value(True)
+                    result_col = col.copy()
+                    result_col[first_index:] = None
+                else:
+                    result_col = col
+
+            if (
+                cast_to_int
+                and not is_decimal_dtype(result_col.dtype)
+                and (
+                    np.issubdtype(result_col.dtype, np.integer)
+                    or np.issubdtype(result_col.dtype, np.bool_)
+                )
+            ):
+                # For reductions that accumulate a value (e.g. sum, not max)
+                # pandas returns an int64 dtype for all int or bool dtypes.
+                result_col = result_col.astype(np.int64)
+            results[name] = getattr(result_col, op)()
+        return self._from_data(results, self._index)
+
+    def _check_data_index_length_match(self) -> None:
+        # Validate that the number of rows in the data matches the index if the
+        # data is not empty. This is a helper for the constructor.
+        if self._data.nrows > 0 and self._data.nrows != len(self._index):
+            raise ValueError(
+                f"Length of values ({self._data.nrows}) does not "
+                f"match length of index ({len(self._index)})"
+            )
+
+    @property
+    @_cudf_nvtx_annotate
+    def empty(self):
+        """
+        Indicator whether DataFrame or Series is empty.
+
+        True if DataFrame/Series is entirely empty (no items),
+        meaning any of the axes are of length 0.
+
+        Returns
+        -------
+        out : bool
+            If DataFrame/Series is empty, return True, if not return False.
+
+        Notes
+        -----
+        If DataFrame/Series contains only `null` values, it is still not
+        considered empty. See the example below.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'A' : []})
+        >>> df
+        Empty DataFrame
+        Columns: [A]
+        Index: []
+        >>> df.empty
+        True
+
+        If we only have `null` values in our DataFrame, it is
+        not considered empty! We will need to drop
+        the `null`'s to make the DataFrame empty:
+
+        >>> df = cudf.DataFrame({'A' : [None, None]})
+        >>> df
+              A
+        0  <NA>
+        1  <NA>
+        >>> df.empty
+        False
+        >>> df.dropna().empty
+        True
+
+        Non-empty and empty Series example:
+
+        >>> s = cudf.Series([1, 2, None])
+        >>> s
+        0       1
+        1       2
+        2    <NA>
+        dtype: int64
+        >>> s.empty
+        False
+        >>> s = cudf.Series([])
+        >>> s
+        Series([], dtype: float64)
+        >>> s.empty
+        True
+        """
+        return self.size == 0
+
+    def copy(self, deep: bool = True) -> Self:
+        """Make a copy of this object's indices and data.
+
+        When ``deep=True`` (default), a new object will be created with a
+        copy of the calling object's data and indices. Modifications to
+        the data or indices of the copy will not be reflected in the
+        original object (see notes below).
+        When ``deep=False``, a new object will be created without copying
+        the calling object's data or index (only references to the data
+        and index are copied). Any changes to the data of the original
+        will be reflected in the shallow copy (and vice versa).
+
+        Parameters
+        ----------
+        deep : bool, default True
+            Make a deep copy, including a copy of the data and the indices.
+            With ``deep=False`` neither the indices nor the data are copied.
+
+        Returns
+        -------
+        copy : Series or DataFrame
+            Object type matches caller.
+
+        Examples
+        --------
+        >>> s = cudf.Series([1, 2], index=["a", "b"])
+        >>> s
+        a    1
+        b    2
+        dtype: int64
+        >>> s_copy = s.copy()
+        >>> s_copy
+        a    1
+        b    2
+        dtype: int64
+
+        **Shallow copy versus default (deep) copy:**
+
+        >>> s = cudf.Series([1, 2], index=["a", "b"])
+        >>> deep = s.copy()
+        >>> shallow = s.copy(deep=False)
+
+        Updates to the data shared by shallow copy and original is reflected
+        in both; deep copy remains unchanged.
+
+        >>> s['a'] = 3
+        >>> shallow['b'] = 4
+        >>> s
+        a    3
+        b    4
+        dtype: int64
+        >>> shallow
+        a    3
+        b    4
+        dtype: int64
+        >>> deep
+        a    1
+        b    2
+        dtype: int64
+        """
+        return self._from_data(
+            self._data.copy(deep=deep),
+            # Indexes are immutable so copies can always be shallow.
+            self._index.copy(deep=False),
+        )
+
+    @_cudf_nvtx_annotate
+    def equals(self, other):  # noqa: D102
+        if not super().equals(other):
+            return False
+        return self._index.equals(other._index)
+
+    @property
+    def index(self):
+        """Get the labels for the rows."""
+        return self._index
+
+    @index.setter
+    def index(self, value):
+        old_length = len(self)
+        new_length = len(value)
+
+        # A DataFrame with 0 columns can have an index of arbitrary length.
+        if len(self._data) > 0 and new_length != old_length:
+            raise ValueError(
+                f"Length mismatch: Expected axis has {old_length} elements, "
+                f"new values have {len(value)} elements"
+            )
+        self._index = Index(value)
+
+    @_cudf_nvtx_annotate
+    def replace(
+        self,
+        to_replace=None,
+        value=None,
+        inplace=False,
+        limit=None,
+        regex=False,
+        method=None,
+    ):
+        """Replace values given in ``to_replace`` with ``value``.
+
+        Parameters
+        ----------
+        to_replace : numeric, str or list-like
+            Value(s) to replace.
+
+            * numeric or str:
+                - values equal to ``to_replace`` will be replaced
+                  with ``value``
+            * list of numeric or str:
+                - If ``value`` is also list-like, ``to_replace`` and
+                  ``value`` must be of same length.
+            * dict:
+                - Dicts can be used to specify different replacement values
+                  for different existing values. For example, {'a': 'b',
+                  'y': 'z'} replaces the value 'a' with 'b' and
+                  'y' with 'z'.
+                  To use a dict in this way the ``value`` parameter should
+                  be ``None``.
+        value : scalar, dict, list-like, str, default None
+            Value to replace any values matching ``to_replace`` with.
+        inplace : bool, default False
+            If True, in place.
+
+        See Also
+        --------
+        Series.fillna
+
+        Raises
+        ------
+        TypeError
+            - If ``to_replace`` is not a scalar, array-like, dict, or None
+            - If ``to_replace`` is a dict and value is not a list, dict,
+              or Series
+        ValueError
+            - If a list is passed to ``to_replace`` and ``value`` but they
+              are not the same length.
+
+        Returns
+        -------
+        result : Series
+            Series after replacement. The mask and index are preserved.
+
+        Notes
+        -----
+        Parameters that are currently not supported are: `limit`, `regex`,
+        `method`
+
+        Examples
+        --------
+        **Series**
+
+        Scalar ``to_replace`` and ``value``
+
+        >>> import cudf
+        >>> s = cudf.Series([0, 1, 2, 3, 4])
+        >>> s
+        0    0
+        1    1
+        2    2
+        3    3
+        4    4
+        dtype: int64
+        >>> s.replace(0, 5)
+        0    5
+        1    1
+        2    2
+        3    3
+        4    4
+        dtype: int64
+
+        List-like ``to_replace``
+
+        >>> s.replace([1, 2], 10)
+        0     0
+        1    10
+        2    10
+        3     3
+        4     4
+        dtype: int64
+
+        dict-like ``to_replace``
+
+        >>> s.replace({1:5, 3:50})
+        0     0
+        1     5
+        2     2
+        3    50
+        4     4
+        dtype: int64
+        >>> s = cudf.Series(['b', 'a', 'a', 'b', 'a'])
+        >>> s
+        0     b
+        1     a
+        2     a
+        3     b
+        4     a
+        dtype: object
+        >>> s.replace({'a': None})
+        0       b
+        1    <NA>
+        2    <NA>
+        3       b
+        4    <NA>
+        dtype: object
+
+        If there is a mismatch in types of the values in
+        ``to_replace`` & ``value`` with the actual series, then
+        cudf exhibits different behavior with respect to pandas
+        and the pairs are ignored silently:
+
+        >>> s = cudf.Series(['b', 'a', 'a', 'b', 'a'])
+        >>> s
+        0    b
+        1    a
+        2    a
+        3    b
+        4    a
+        dtype: object
+        >>> s.replace('a', 1)
+        0    b
+        1    a
+        2    a
+        3    b
+        4    a
+        dtype: object
+        >>> s.replace(['a', 'c'], [1, 2])
+        0    b
+        1    a
+        2    a
+        3    b
+        4    a
+        dtype: object
+
+        **DataFrame**
+
+        Scalar ``to_replace`` and ``value``
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({'A': [0, 1, 2, 3, 4],
+        ...                    'B': [5, 6, 7, 8, 9],
+        ...                    'C': ['a', 'b', 'c', 'd', 'e']})
+        >>> df
+           A  B  C
+        0  0  5  a
+        1  1  6  b
+        2  2  7  c
+        3  3  8  d
+        4  4  9  e
+        >>> df.replace(0, 5)
+           A  B  C
+        0  5  5  a
+        1  1  6  b
+        2  2  7  c
+        3  3  8  d
+        4  4  9  e
+
+        List-like ``to_replace``
+
+        >>> df.replace([0, 1, 2, 3], 4)
+           A  B  C
+        0  4  5  a
+        1  4  6  b
+        2  4  7  c
+        3  4  8  d
+        4  4  9  e
+        >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
+           A  B  C
+        0  4  5  a
+        1  3  6  b
+        2  2  7  c
+        3  1  8  d
+        4  4  9  e
+
+        dict-like ``to_replace``
+
+        >>> df.replace({0: 10, 1: 100})
+             A  B  C
+        0   10  5  a
+        1  100  6  b
+        2    2  7  c
+        3    3  8  d
+        4    4  9  e
+        >>> df.replace({'A': 0, 'B': 5}, 100)
+             A    B  C
+        0  100  100  a
+        1    1    6  b
+        2    2    7  c
+        3    3    8  d
+        4    4    9  e
+        """
+        if limit is not None:
+            raise NotImplementedError("limit parameter is not implemented yet")
+
+        if regex:
+            raise NotImplementedError("regex parameter is not implemented yet")
+
+        if method not in ("pad", None):
+            raise NotImplementedError(
+                "method parameter is not implemented yet"
+            )
+
+        if not (to_replace is None and value is None):
+            copy_data = {}
+            (
+                all_na_per_column,
+                to_replace_per_column,
+                replacements_per_column,
+            ) = _get_replacement_values_for_columns(
+                to_replace=to_replace,
+                value=value,
+                columns_dtype_map=self._dtypes,
+            )
+
+            for name, col in self._data.items():
+                try:
+                    copy_data[name] = col.find_and_replace(
+                        to_replace_per_column[name],
+                        replacements_per_column[name],
+                        all_na_per_column[name],
+                    )
+                except (KeyError, OverflowError):
+                    # We need to create a deep copy if:
+                    # i. `find_and_replace` was not successful or any of
+                    #    `to_replace_per_column`, `replacements_per_column`,
+                    #    `all_na_per_column` don't contain the `name`
+                    #    that exists in `copy_data`.
+                    # ii. There is an OverflowError while trying to cast
+                    #     `to_replace_per_column` to `replacements_per_column`.
+                    copy_data[name] = col.copy(deep=True)
+        else:
+            copy_data = self._data.copy(deep=True)
+
+        result = self._from_data(copy_data, self._index)
+
+        return self._mimic_inplace(result, inplace=inplace)
+
+    @_cudf_nvtx_annotate
+    def clip(self, lower=None, upper=None, inplace=False, axis=1):
+        """
+        Trim values at input threshold(s).
+
+        Assigns values outside boundary to boundary values.
+        Thresholds can be singular values or array like,
+        and in the latter case the clipping is performed
+        element-wise in the specified axis. Currently only
+        `axis=1` is supported.
+
+        Parameters
+        ----------
+        lower : scalar or array_like, default None
+            Minimum threshold value. All values below this
+            threshold will be set to it. If it is None,
+            there will be no clipping based on lower.
+            In case of Series/Index, lower is expected to be
+            a scalar or an array of size 1.
+        upper : scalar or array_like, default None
+            Maximum threshold value. All values below this
+            threshold will be set to it. If it is None,
+            there will be no clipping based on upper.
+            In case of Series, upper is expected to be
+            a scalar or an array of size 1.
+        inplace : bool, default False
+
+        Returns
+        -------
+        Clipped DataFrame/Series/Index/MultiIndex
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({"a":[1, 2, 3, 4], "b":['a', 'b', 'c', 'd']})
+        >>> df.clip(lower=[2, 'b'], upper=[3, 'c'])
+           a  b
+        0  2  b
+        1  2  b
+        2  3  c
+        3  3  c
+
+        >>> df.clip(lower=None, upper=[3, 'c'])
+           a  b
+        0  1  a
+        1  2  b
+        2  3  c
+        3  3  c
+
+        >>> df.clip(lower=[2, 'b'], upper=None)
+           a  b
+        0  2  b
+        1  2  b
+        2  3  c
+        3  4  d
+
+        >>> df.clip(lower=2, upper=3, inplace=True)
+        >>> df
+           a  b
+        0  2  2
+        1  2  3
+        2  3  3
+        3  3  3
+
+        >>> import cudf
+        >>> sr = cudf.Series([1, 2, 3, 4])
+        >>> sr.clip(lower=2, upper=3)
+        0    2
+        1    2
+        2    3
+        3    3
+        dtype: int64
+
+        >>> sr.clip(lower=None, upper=3)
+        0    1
+        1    2
+        2    3
+        3    3
+        dtype: int64
+
+        >>> sr.clip(lower=2, upper=None, inplace=True)
+        >>> sr
+        0    2
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        """
+        if axis != 1:
+            raise NotImplementedError("`axis is not yet supported in clip`")
+
+        if lower is None and upper is None:
+            return None if inplace is True else self.copy(deep=True)
+
+        if is_scalar(lower):
+            lower = np.full(self._num_columns, lower)
+        if is_scalar(upper):
+            upper = np.full(self._num_columns, upper)
+
+        if len(lower) != len(upper):
+            raise ValueError("Length of lower and upper should be equal")
+
+        if len(lower) != self._num_columns:
+            raise ValueError(
+                "Length of lower/upper should be equal to number of columns"
+            )
+
+        if self.ndim == 1:
+            # In case of series and Index,
+            # swap lower and upper if lower > upper
+            if (
+                lower[0] is not None
+                and upper[0] is not None
+                and (lower[0] > upper[0])
+            ):
+                lower[0], upper[0] = upper[0], lower[0]
+
+        data = {
+            name: col.clip(lower[i], upper[i])
+            for i, (name, col) in enumerate(self._data.items())
+        }
+        output = self._from_data(data, self._index)
+        output._copy_type_metadata(self, include_index=False)
+        return self._mimic_inplace(output, inplace=inplace)
+
+    def _copy_type_metadata(
+        self,
+        other: Self,
+        include_index: bool = True,
+        *,
+        override_dtypes: Optional[abc.Iterable[Optional[Dtype]]] = None,
+    ) -> Self:
+        """
+        Copy type metadata from each column of `other` to the corresponding
+        column of `self`.
+        See `ColumnBase._with_type_metadata` for more information.
+        """
+        super()._copy_type_metadata(other, override_dtypes=override_dtypes)
+        if (
+            include_index
+            and self._index is not None
+            and other._index is not None
+        ):
+            self._index._copy_type_metadata(other._index)
+            # When other._index is a CategoricalIndex, the current index
+            # will be a NumericalIndex with an underlying CategoricalColumn
+            # (the above _copy_type_metadata call will have converted the
+            # column). Calling cudf.Index on that column generates the
+            # appropriate index.
+            if isinstance(
+                other._index, cudf.core.index.CategoricalIndex
+            ) and not isinstance(
+                self._index, cudf.core.index.CategoricalIndex
+            ):
+                self._index = cudf.Index(
+                    cast(cudf.core.index.NumericIndex, self._index)._column,
+                    name=self._index.name,
+                )
+            elif isinstance(other._index, cudf.MultiIndex) and not isinstance(
+                self._index, cudf.MultiIndex
+            ):
+                self._index = cudf.MultiIndex._from_data(
+                    self._index._data, name=self._index.name
+                )
+        return self
+
+    @_cudf_nvtx_annotate
+    def interpolate(
+        self,
+        method="linear",
+        axis=0,
+        limit=None,
+        inplace=False,
+        limit_direction=None,
+        limit_area=None,
+        downcast=None,
+        **kwargs,
+    ):
+        """
+        Interpolate data values between some points.
+
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. Currently,
+            only 'linear` is supported.
+            * 'linear': Ignore the index and treat the values as
+            equally spaced. This is the only method supported on MultiIndexes.
+            * 'index', 'values': linearly interpolate using the index as
+            an x-axis. Unsorted indices can lead to erroneous results.
+        axis : int, default 0
+            Axis to interpolate along. Currently,
+            only 'axis=0' is supported.
+        inplace : bool, default False
+            Update the data in place if possible.
+
+        Returns
+        -------
+        Series or DataFrame
+            Returns the same object type as the caller, interpolated at
+            some or all ``NaN`` values
+
+        """
+        if method in {"pad", "ffill"} and limit_direction != "forward":
+            raise ValueError(
+                f"`limit_direction` must be 'forward' for method `{method}`"
+            )
+        if method in {"backfill", "bfill"} and limit_direction != "backward":
+            raise ValueError(
+                f"`limit_direction` must be 'backward' for method `{method}`"
+            )
+
+        data = self
+
+        if not isinstance(data._index, cudf.RangeIndex):
+            perm_sort = data._index.argsort()
+            data = data._gather(
+                GatherMap.from_column_unchecked(
+                    cudf.core.column.as_column(perm_sort),
+                    len(data),
+                    nullify=False,
+                )
+            )
+
+        interpolator = cudf.core.algorithms.get_column_interpolator(method)
+        columns = {}
+        for colname, col in data._data.items():
+            if col.nullable:
+                col = col.astype("float64").fillna(np.nan)
+
+            # Interpolation methods may or may not need the index
+            columns[colname] = interpolator(col, index=data._index)
+
+        result = self._from_data(columns, index=data._index)
+
+        return (
+            result
+            if isinstance(data._index, cudf.RangeIndex)
+            # TODO: This should be a scatter, avoiding an argsort.
+            else result._gather(
+                GatherMap.from_column_unchecked(
+                    cudf.core.column.as_column(perm_sort.argsort()),
+                    len(result),
+                    nullify=False,
+                )
+            )
+        )
+
+    @_cudf_nvtx_annotate
+    def shift(self, periods=1, freq=None, axis=0, fill_value=None):
+        """Shift values by `periods` positions."""
+        axis = self._get_axis_from_axis_arg(axis)
+        if axis != 0:
+            raise ValueError("Only axis=0 is supported.")
+        if freq is not None:
+            raise ValueError("The freq argument is not yet supported.")
+
+        data_columns = (
+            col.shift(periods, fill_value) for col in self._columns
+        )
+        return self.__class__._from_data(
+            zip(self._column_names, data_columns), self._index
+        )
+
+    @_cudf_nvtx_annotate
+    def truncate(self, before=None, after=None, axis=0, copy=True):
+        """
+        Truncate a Series or DataFrame before and after some index value.
+
+        This is a useful shorthand for boolean indexing based on index
+        values above or below certain thresholds.
+
+        Parameters
+        ----------
+        before : date, str, int
+            Truncate all rows before this index value.
+        after : date, str, int
+            Truncate all rows after this index value.
+        axis : {0 or 'index', 1 or 'columns'}, optional
+            Axis to truncate. Truncates the index (rows) by default.
+        copy : bool, default is True,
+            Return a copy of the truncated section.
+
+        Returns
+        -------
+            The truncated Series or DataFrame.
+
+        Notes
+        -----
+        If the index being truncated contains only datetime values,
+        `before` and `after` may be specified as strings instead of
+        Timestamps.
+
+        .. pandas-compat::
+            **DataFrame.truncate, Series.truncate**
+
+            The ``copy`` parameter is only present for API compatibility, but
+            ``copy=False`` is not supported. This method always generates a
+            copy.
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> cs1 = cudf.Series([1, 2, 3, 4])
+        >>> cs1
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+
+        >>> cs1.truncate(before=1, after=2)
+        1    2
+        2    3
+        dtype: int64
+
+        >>> import cudf
+        >>> dates = cudf.date_range(
+        ...     '2021-01-01 23:45:00', '2021-01-01 23:46:00', freq='s'
+        ... )
+        >>> cs2 = cudf.Series(range(len(dates)), index=dates)
+        >>> cs2
+        2021-01-01 23:45:00     0
+        2021-01-01 23:45:01     1
+        2021-01-01 23:45:02     2
+        2021-01-01 23:45:03     3
+        2021-01-01 23:45:04     4
+        2021-01-01 23:45:05     5
+        2021-01-01 23:45:06     6
+        2021-01-01 23:45:07     7
+        2021-01-01 23:45:08     8
+        2021-01-01 23:45:09     9
+        2021-01-01 23:45:10    10
+        2021-01-01 23:45:11    11
+        2021-01-01 23:45:12    12
+        2021-01-01 23:45:13    13
+        2021-01-01 23:45:14    14
+        2021-01-01 23:45:15    15
+        2021-01-01 23:45:16    16
+        2021-01-01 23:45:17    17
+        2021-01-01 23:45:18    18
+        2021-01-01 23:45:19    19
+        2021-01-01 23:45:20    20
+        2021-01-01 23:45:21    21
+        2021-01-01 23:45:22    22
+        2021-01-01 23:45:23    23
+        2021-01-01 23:45:24    24
+        ...
+        2021-01-01 23:45:56    56
+        2021-01-01 23:45:57    57
+        2021-01-01 23:45:58    58
+        2021-01-01 23:45:59    59
+        dtype: int64
+
+
+        >>> cs2.truncate(
+        ...     before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
+        ... )
+        2021-01-01 23:45:18    18
+        2021-01-01 23:45:19    19
+        2021-01-01 23:45:20    20
+        2021-01-01 23:45:21    21
+        2021-01-01 23:45:22    22
+        2021-01-01 23:45:23    23
+        2021-01-01 23:45:24    24
+        2021-01-01 23:45:25    25
+        2021-01-01 23:45:26    26
+        2021-01-01 23:45:27    27
+        dtype: int64
+
+        >>> cs3 = cudf.Series({'A': 1, 'B': 2, 'C': 3, 'D': 4})
+        >>> cs3
+        A    1
+        B    2
+        C    3
+        D    4
+        dtype: int64
+
+        >>> cs3.truncate(before='B', after='C')
+        B    2
+        C    3
+        dtype: int64
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame({
+        ...     'A': ['a', 'b', 'c', 'd', 'e'],
+        ...     'B': ['f', 'g', 'h', 'i', 'j'],
+        ...     'C': ['k', 'l', 'm', 'n', 'o']
+        ... }, index=[1, 2, 3, 4, 5])
+        >>> df
+           A  B  C
+        1  a  f  k
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+        5  e  j  o
+
+        >>> df.truncate(before=2, after=4)
+           A  B  C
+        2  b  g  l
+        3  c  h  m
+        4  d  i  n
+
+        >>> df.truncate(before="A", after="B", axis="columns")
+           A  B
+        1  a  f
+        2  b  g
+        3  c  h
+        4  d  i
+        5  e  j
+
+        >>> import cudf
+        >>> dates = cudf.date_range(
+        ...     '2021-01-01 23:45:00', '2021-01-01 23:46:00', freq='s'
+        ... )
+        >>> df2 = cudf.DataFrame(data={'A': 1, 'B': 2}, index=dates)
+        >>> df2.head()
+                             A  B
+        2021-01-01 23:45:00  1  2
+        2021-01-01 23:45:01  1  2
+        2021-01-01 23:45:02  1  2
+        2021-01-01 23:45:03  1  2
+        2021-01-01 23:45:04  1  2
+
+        >>> df2.truncate(
+        ...     before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
+        ... )
+                             A  B
+        2021-01-01 23:45:18  1  2
+        2021-01-01 23:45:19  1  2
+        2021-01-01 23:45:20  1  2
+        2021-01-01 23:45:21  1  2
+        2021-01-01 23:45:22  1  2
+        2021-01-01 23:45:23  1  2
+        2021-01-01 23:45:24  1  2
+        2021-01-01 23:45:25  1  2
+        2021-01-01 23:45:26  1  2
+        2021-01-01 23:45:27  1  2
+        """
+        if not copy:
+            raise ValueError("Truncating with copy=False is not supported.")
+        axis = self._get_axis_from_axis_arg(axis)
+        ax = self._index if axis == 0 else self._data.to_pandas_index()
+
+        if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
+            raise ValueError("truncate requires a sorted index")
+
+        if type(ax) is cudf.core.index.DatetimeIndex:
+            before = pd.to_datetime(before)
+            after = pd.to_datetime(after)
+
+        if before is not None and after is not None and before > after:
+            raise ValueError(f"Truncate: {after} must be after {before}")
+
+        if len(ax) > 1 and ax.is_monotonic_decreasing and ax.nunique() > 1:
+            before, after = after, before
+
+        slicer = [slice(None, None)] * self.ndim
+        slicer[axis] = slice(before, after)
+        return self.loc[tuple(slicer)].copy()
+
+    @cached_property
+    def loc(self):
+        """Select rows and columns by label or boolean mask.
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> series = cudf.Series([10, 11, 12], index=['a', 'b', 'c'])
+        >>> series
+        a    10
+        b    11
+        c    12
+        dtype: int64
+        >>> series.loc['b']
+        11
+
+        **DataFrame**
+
+        DataFrame with string index.
+
+        >>> df
+           a  b
+        a  0  5
+        b  1  6
+        c  2  7
+        d  3  8
+        e  4  9
+
+        Select a single row by label.
+
+        >>> df.loc['a']
+        a    0
+        b    5
+        Name: a, dtype: int64
+
+        Select multiple rows and a single column.
+
+        >>> df.loc[['a', 'c', 'e'], 'b']
+        a    5
+        c    7
+        e    9
+        Name: b, dtype: int64
+
+        Selection by boolean mask.
+
+        >>> df.loc[df.a > 2]
+           a  b
+        d  3  8
+        e  4  9
+
+        Setting values using loc.
+
+        >>> df.loc[['a', 'c', 'e'], 'a'] = 0
+        >>> df
+           a  b
+        a  0  5
+        b  1  6
+        c  0  7
+        d  3  8
+        e  0  9
+
+        """
+        return self._loc_indexer_type(self)
+
+    @cached_property
+    def iloc(self):
+        """Select values by position.
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> s = cudf.Series([10, 20, 30])
+        >>> s
+        0    10
+        1    20
+        2    30
+        dtype: int64
+        >>> s.iloc[2]
+        30
+
+        **DataFrame**
+
+        Selecting rows and column by position.
+
+        >>> df = cudf.DataFrame({'a': range(20),
+        ...                      'b': range(20),
+        ...                      'c': range(20)})
+
+        Select a single row using an integer index.
+
+        >>> df.iloc[1]
+        a    1
+        b    1
+        c    1
+        Name: 1, dtype: int64
+
+        Select multiple rows using a list of integers.
+
+        >>> df.iloc[[0, 2, 9, 18]]
+              a    b    c
+         0    0    0    0
+         2    2    2    2
+         9    9    9    9
+        18   18   18   18
+
+        Select rows using a slice.
+
+        >>> df.iloc[3:10:2]
+             a    b    c
+        3    3    3    3
+        5    5    5    5
+        7    7    7    7
+        9    9    9    9
+
+        Select both rows and columns.
+
+        >>> df.iloc[[1, 3, 5, 7], 2]
+        1    1
+        3    3
+        5    5
+        7    7
+        Name: c, dtype: int64
+
+        Setting values in a column using iloc.
+
+        >>> df.iloc[:4] = 0
+        >>> df
+           a  b  c
+        0  0  0  0
+        1  0  0  0
+        2  0  0  0
+        3  0  0  0
+        4  4  4  4
+        5  5  5  5
+        6  6  6  6
+        7  7  7  7
+        8  8  8  8
+        9  9  9  9
+        [10 more rows]
+
+        """
+        return self._iloc_indexer_type(self)
+
+    @_cudf_nvtx_annotate
+    def scale(self):
+        """
+        Scale values to [0, 1] in float64
+
+        Returns
+        -------
+        DataFrame or Series
+            Values scaled to [0, 1].
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([10, 11, 12, 0.5, 1])
+        >>> series
+        0    10.0
+        1    11.0
+        2    12.0
+        3     0.5
+        4     1.0
+        dtype: float64
+        >>> series.scale()
+        0    0.826087
+        1    0.913043
+        2    1.000000
+        3    0.000000
+        4    0.043478
+        dtype: float64
+        """
+        vmin = self.min()
+        vmax = self.max()
+        scaled = (self - vmin) / (vmax - vmin)
+        scaled._index = self._index.copy(deep=False)
+        return scaled
+
+    @_cudf_nvtx_annotate
+    def sort_index(
+        self,
+        axis=0,
+        level=None,
+        ascending=True,
+        inplace=False,
+        kind=None,
+        na_position="last",
+        sort_remaining=True,
+        ignore_index=False,
+        key=None,
+    ):
+        """Sort object by labels (along an axis).
+
+        Parameters
+        ----------
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis along which to sort. The value 0 identifies the rows,
+            and 1 identifies the columns.
+        level : int or level name or list of ints or list of level names
+            If not None, sort on values in specified index level(s).
+            This is only useful in the case of MultiIndex.
+        ascending : bool, default True
+            Sort ascending vs. descending.
+        inplace : bool, default False
+            If True, perform operation in-place.
+        kind : sorting method such as `quick sort` and others.
+            Not yet supported.
+        na_position : {'first', 'last'}, default 'last'
+            Puts NaNs at the beginning if first; last puts NaNs at the end.
+        sort_remaining : bool, default True
+            When sorting a multiindex on a subset of its levels,
+            should entries be lexsorted by the remaining
+            (non-specified) levels as well?
+        ignore_index : bool, default False
+            if True, index will be replaced with RangeIndex.
+        key : callable, optional
+            If not None, apply the key function to the index values before
+            sorting. This is similar to the key argument in the builtin
+            sorted() function, with the notable difference that this key
+            function should be vectorized. It should expect an Index and return
+            an Index of the same shape. For MultiIndex inputs, the key is
+            applied per level.
+
+        Returns
+        -------
+        Frame or None
+
+        Notes
+        -----
+        Difference from pandas:
+          * Not supporting: kind, sort_remaining=False
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> series = cudf.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
+        >>> series
+        3    a
+        2    b
+        1    c
+        4    d
+        dtype: object
+        >>> series.sort_index()
+        1    c
+        2    b
+        3    a
+        4    d
+        dtype: object
+
+        Sort Descending
+
+        >>> series.sort_index(ascending=False)
+        4    d
+        3    a
+        2    b
+        1    c
+        dtype: object
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame(
+        ... {"b":[3, 2, 1], "a":[2, 1, 3]}, index=[1, 3, 2])
+        >>> df.sort_index(axis=0)
+           b  a
+        1  3  2
+        2  1  3
+        3  2  1
+        >>> df.sort_index(axis=1)
+           a  b
+        1  2  3
+        3  1  2
+        2  3  1
+        """
+        if kind is not None:
+            raise NotImplementedError("kind is not yet supported")
+
+        if key is not None:
+            raise NotImplementedError("key is not yet supported.")
+
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+
+        if axis in (0, "index"):
+            idx = self.index
+            if isinstance(idx, MultiIndex):
+                if level is not None:
+                    # Pandas doesn't handle na_position in case of MultiIndex.
+                    na_position = "first" if ascending is True else "last"
+                    if not is_list_like(level):
+                        level = [level]
+                    by = list(map(idx._get_level_label, level))
+                    if sort_remaining:
+                        handled = set(by)
+                        by.extend(
+                            filter(
+                                lambda n: n not in handled,
+                                self.index._data.names,
+                            )
+                        )
+                else:
+                    by = list(idx._data.names)
+
+                inds = idx._get_sorted_inds(
+                    by=by, ascending=ascending, na_position=na_position
+                )
+                out = self._gather(
+                    GatherMap.from_column_unchecked(
+                        inds, len(self), nullify=False
+                    )
+                )
+                # TODO: frame factory function should handle multilevel column
+                # names
+                if (
+                    isinstance(self, cudf.core.dataframe.DataFrame)
+                    and self._data.multiindex
+                ):
+                    out._set_column_names_like(self)
+            elif (ascending and idx.is_monotonic_increasing) or (
+                not ascending and idx.is_monotonic_decreasing
+            ):
+                out = self.copy()
+            else:
+                inds = idx.argsort(
+                    ascending=ascending, na_position=na_position
+                )
+                out = self._gather(
+                    GatherMap.from_column_unchecked(
+                        cudf.core.column.as_column(inds),
+                        len(self),
+                        nullify=False,
+                    )
+                )
+                if (
+                    isinstance(self, cudf.core.dataframe.DataFrame)
+                    and self._data.multiindex
+                ):
+                    out._set_column_names_like(self)
+        else:
+            labels = sorted(self._data.names, reverse=not ascending)
+            out = self[labels]
+
+        if ignore_index is True:
+            out = out.reset_index(drop=True)
+        return self._mimic_inplace(out, inplace=inplace)
+
+    def memory_usage(self, index=True, deep=False):
+        """Return the memory usage of an object.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Specifies whether to include the memory usage of the index.
+        deep : bool, default False
+            The deep parameter is ignored and is only included for pandas
+            compatibility.
+
+        Returns
+        -------
+        Series or scalar
+            For DataFrame, a Series whose index is the original column names
+            and whose values is the memory usage of each column in bytes. For a
+            Series the total memory usage.
+
+        Examples
+        --------
+        **DataFrame**
+
+        >>> dtypes = ['int64', 'float64', 'object', 'bool']
+        >>> data = dict([(t, np.ones(shape=5000).astype(t))
+        ...              for t in dtypes])
+        >>> df = cudf.DataFrame(data)
+        >>> df.head()
+           int64  float64  object  bool
+        0      1      1.0     1.0  True
+        1      1      1.0     1.0  True
+        2      1      1.0     1.0  True
+        3      1      1.0     1.0  True
+        4      1      1.0     1.0  True
+        >>> df.memory_usage(index=False)
+        int64      40000
+        float64    40000
+        object     40000
+        bool        5000
+        dtype: int64
+
+        Use a Categorical for efficient storage of an object-dtype column with
+        many repeated values.
+
+        >>> df['object'].astype('category').memory_usage(deep=True)
+        5008
+
+        **Series**
+        >>> s = cudf.Series(range(3), index=['a','b','c'])
+        >>> s.memory_usage()
+        43
+
+        Not including the index gives the size of the rest of the data, which
+        is necessarily smaller:
+
+        >>> s.memory_usage(index=False)
+        24
+        """
+        raise NotImplementedError
+
+    def hash_values(self, method="murmur3", seed=None):
+        """Compute the hash of values in this column.
+
+        Parameters
+        ----------
+        method : {'murmur3', 'md5'}, default 'murmur3'
+            Hash function to use:
+            * murmur3: MurmurHash3 hash function.
+            * md5: MD5 hash function.
+
+        seed : int, optional
+            Seed value to use for the hash function.
+            Note - This only has effect for the following supported
+            hash functions:
+            * murmur3: MurmurHash3 hash function.
+
+        Returns
+        -------
+        Series
+            A Series with hash values.
+
+        Examples
+        --------
+        **Series**
+
+        >>> import cudf
+        >>> series = cudf.Series([10, 120, 30])
+        >>> series
+        0     10
+        1    120
+        2     30
+        dtype: int64
+        >>> series.hash_values(method="murmur3")
+        0   -1930516747
+        1     422619251
+        2    -941520876
+        dtype: int32
+        >>> series.hash_values(method="md5")
+        0    7be4bbacbfdb05fb3044e36c22b41e8b
+        1    947ca8d2c5f0f27437f156cfbfab0969
+        2    d0580ef52d27c043c8e341fd5039b166
+        dtype: object
+        >>> series.hash_values(method="murmur3", seed=42)
+        0    2364453205
+        1     422621911
+        2    3353449140
+        dtype: uint32
+
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({"a": [10, 120, 30], "b": [0.0, 0.25, 0.50]})
+        >>> df
+             a     b
+        0   10  0.00
+        1  120  0.25
+        2   30  0.50
+        >>> df.hash_values(method="murmur3")
+        0    -330519225
+        1    -397962448
+        2   -1345834934
+        dtype: int32
+        >>> df.hash_values(method="md5")
+        0    57ce879751b5169c525907d5c563fae1
+        1    948d6221a7c4963d4be411bcead7e32b
+        2    fe061786ea286a515b772d91b0dfcd70
+        dtype: object
+        """
+        seed_hash_methods = {"murmur3"}
+        if seed is None:
+            seed = 0
+        elif method not in seed_hash_methods:
+            warnings.warn(
+                "Provided seed value has no effect for hash method"
+                f" `{method}`. Refer to the docstring for information"
+                " on hash methods that support the `seed` param"
+            )
+        # Note that both Series and DataFrame return Series objects from this
+        # calculation, necessitating the unfortunate circular reference to the
+        # child class here.
+        return cudf.Series._from_data(
+            {None: libcudf.hash.hash([*self._columns], method, seed)},
+            index=self.index,
+        )
+
+    def _gather(
+        self,
+        gather_map: GatherMap,
+        keep_index=True,
+    ):
+        """Gather rows of frame specified by indices in `gather_map`.
+
+        Maintain the index if keep_index is True.
+
+        This function does no expensive bounds checking, but does
+        check that the number of rows of self matches the validated
+        number of rows.
+        """
+        if not gather_map.nullify and len(self) != gather_map.nrows:
+            raise IndexError("Gather map is out of bounds")
+        return self._from_columns_like_self(
+            libcudf.copying.gather(
+                list(self._index._columns + self._columns)
+                if keep_index
+                else list(self._columns),
+                gather_map.column,
+                nullify=gather_map.nullify,
+            ),
+            self._column_names,
+            self._index.names if keep_index else None,
+        )
+
+    def _slice(self, arg: slice, keep_index: bool = True) -> Self:
+        """Slice a frame.
+
+        Parameters
+        ----------
+        arg
+            The slice
+        keep_index
+            Preserve the index when slicing?
+
+        Returns
+        -------
+        Sliced frame
+
+        Notes
+        -----
+        This slicing has normal python semantics.
+        """
+        num_rows = len(self)
+        if num_rows == 0:
+            return self
+        start, stop, stride = arg.indices(num_rows)
+        index = self.index
+        has_range_index = isinstance(index, RangeIndex)
+        if len(range(start, stop, stride)) == 0:
+            # Avoid materialising the range index column
+            result = self._empty_like(
+                keep_index=keep_index and not has_range_index
+            )
+            if keep_index and has_range_index:
+                lo = index.start + start * index.step
+                hi = index.start + stop * index.step
+                step = index.step * stride
+                result.index = RangeIndex(
+                    start=lo, stop=hi, step=step, name=index.name
+                )
+            return result
+        if start < 0:
+            start = start + num_rows
+
+        # At this point, we have converted slice arguments into
+        # indices that no longer wrap around.
+        # For example slice(4, None, -1) will produce the
+        # start, stop, stride tuple (4, -1, -1)
+        # This check makes sure -1 is not wrapped (again) to
+        # produce -1 + num_rows.
+        if stop < 0 and not (stride < 0 and stop == -1):
+            stop = stop + num_rows
+        stride = 1 if stride is None else stride
+
+        if (stop - start) * stride <= 0:
+            return self._empty_like(keep_index=True)
+
+        start = min(start, num_rows)
+        stop = min(stop, num_rows)
+
+        if stride != 1:
+            return self._gather(
+                GatherMap.from_column_unchecked(
+                    cudf.core.column.arange(
+                        start,
+                        stop=stop,
+                        step=stride,
+                        dtype=libcudf.types.size_type_dtype,
+                    ),
+                    len(self),
+                    nullify=False,
+                ),
+                keep_index=keep_index,
+            )
+
+        columns_to_slice = [
+            *(
+                self._index._data.columns
+                if keep_index and not has_range_index
+                else []
+            ),
+            *self._columns,
+        ]
+        result = self._from_columns_like_self(
+            libcudf.copying.columns_slice(columns_to_slice, [start, stop])[0],
+            self._column_names,
+            None if has_range_index or not keep_index else self._index.names,
+        )
+
+        if keep_index and has_range_index:
+            result.index = self.index[start:stop]
+        return result
+
+    def _positions_from_column_names(
+        self, column_names, offset_by_index_columns=False
+    ):
+        """Map each column name into their positions in the frame.
+
+        Return positions of the provided column names, offset by the number of
+        index columns if `offset_by_index_columns` is True. The order of
+        indices returned corresponds to the column order in this Frame.
+        """
+        num_index_columns = (
+            len(self._index._data) if offset_by_index_columns else 0
+        )
+        return [
+            i + num_index_columns
+            for i, name in enumerate(self._column_names)
+            if name in set(column_names)
+        ]
+
+    def drop_duplicates(
+        self,
+        subset=None,
+        keep="first",
+        nulls_are_equal=True,
+        ignore_index=False,
+    ):
+        """
+        Drop duplicate rows in frame.
+
+        subset : list, optional
+            List of columns to consider when dropping rows.
+        keep : ["first", "last", False]
+            "first" will keep the first duplicate entry, "last" will keep the
+            last duplicate entry, and False will drop all duplicates.
+        nulls_are_equal: bool, default True
+            Null elements are considered equal to other null elements.
+        ignore_index: bool, default False
+            If True, the resulting axis will be labeled 0, 1, ..., n - 1.
+        """
+        if not isinstance(ignore_index, (np.bool_, bool)):
+            raise ValueError(
+                f"{ignore_index=} must be bool, "
+                f"not {type(ignore_index).__name__}"
+            )
+        subset = self._preprocess_subset(subset)
+        subset_cols = [name for name in self._column_names if name in subset]
+        if len(subset_cols) == 0:
+            return self.copy(deep=True)
+
+        keys = self._positions_from_column_names(
+            subset, offset_by_index_columns=not ignore_index
+        )
+        return self._from_columns_like_self(
+            libcudf.stream_compaction.drop_duplicates(
+                list(self._columns)
+                if ignore_index
+                else list(self._index._columns + self._columns),
+                keys=keys,
+                keep=keep,
+                nulls_are_equal=nulls_are_equal,
+            ),
+            self._column_names,
+            self._index.names if not ignore_index else None,
+        )
+
+    @_cudf_nvtx_annotate
+    def duplicated(self, subset=None, keep="first"):
+        """
+        Return boolean Series denoting duplicate rows.
+
+        Considering certain columns is optional.
+
+        Parameters
+        ----------
+        subset : column label or sequence of labels, optional
+            Only consider certain columns for identifying duplicates, by
+            default use all of the columns.
+        keep : {'first', 'last', False}, default 'first'
+            Determines which duplicates (if any) to mark.
+
+            - ``'first'`` : Mark duplicates as ``True`` except for the first
+                occurrence.
+            - ``'last'`` : Mark duplicates as ``True`` except for the last
+                occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        Series
+            Boolean series indicating duplicated rows.
+
+        See Also
+        --------
+        Index.duplicated : Equivalent method on index.
+        Series.duplicated : Equivalent method on Series.
+        Series.drop_duplicates : Remove duplicate values from Series.
+        DataFrame.drop_duplicates : Remove duplicate values from DataFrame.
+
+        Examples
+        --------
+        Consider a dataset containing ramen product ratings.
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({
+        ...     'brand': ['Yum Yum', 'Yum Yum', 'Maggie', 'Maggie', 'Maggie'],
+        ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
+        ...     'rating': [4, 4, 3.5, 15, 5]
+        ... })
+        >>> df
+             brand style  rating
+        0  Yum Yum   cup     4.0
+        1  Yum Yum   cup     4.0
+        2   Maggie   cup     3.5
+        3   Maggie  pack    15.0
+        4   Maggie  pack     5.0
+
+        By default, for each set of duplicated values, the first occurrence
+        is set to False and all others to True.
+
+        >>> df.duplicated()
+        0    False
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set to False and all others to True.
+
+        >>> df.duplicated(keep='last')
+        0     True
+        1    False
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        By setting ``keep`` to False, all duplicates are True.
+
+        >>> df.duplicated(keep=False)
+        0     True
+        1     True
+        2    False
+        3    False
+        4    False
+        dtype: bool
+
+        To find duplicates on specific column(s), use ``subset``.
+
+        >>> df.duplicated(subset=['brand'])
+        0    False
+        1     True
+        2    False
+        3     True
+        4     True
+        dtype: bool
+        """
+        subset = self._preprocess_subset(subset)
+
+        if isinstance(self, cudf.Series):
+            df = self.to_frame(name="None")
+            subset = ["None"]
+        else:
+            df = self.copy(deep=False)
+        df._data["index"] = cudf.core.column.arange(
+            0, len(self), dtype=size_type_dtype
+        )
+
+        new_df = df.drop_duplicates(subset=subset, keep=keep)
+        idx = df.merge(new_df, how="inner")["index"]
+        s = cudf.Series._from_data(
+            {
+                None: cudf.core.column.full(
+                    size=len(self), fill_value=True, dtype="bool"
+                )
+            },
+            index=self.index,
+        )
+        s.iloc[idx] = False
+        return s
+
+    @_cudf_nvtx_annotate
+    def _empty_like(self, keep_index=True) -> Self:
+        return self._from_columns_like_self(
+            libcudf.copying.columns_empty_like(
+                [
+                    *(self._index._data.columns if keep_index else ()),
+                    *self._columns,
+                ]
+            ),
+            self._column_names,
+            self._index.names if keep_index else None,
+        )
+
+    def _split(self, splits, keep_index=True):
+        if self._num_rows == 0:
+            return []
+
+        columns_split = libcudf.copying.columns_split(
+            [
+                *(self._index._data.columns if keep_index else []),
+                *self._columns,
+            ],
+            splits,
+        )
+
+        return [
+            self._from_columns_like_self(
+                columns_split[i],
+                self._column_names,
+                self._index.names if keep_index else None,
+            )
+            for i in range(len(splits) + 1)
+        ]
+
+    @_cudf_nvtx_annotate
+    def fillna(
+        self, value=None, method=None, axis=None, inplace=False, limit=None
+    ):  # noqa: D102
+        old_index = self._index
+        ret = super().fillna(value, method, axis, inplace, limit)
+        if inplace:
+            self._index = old_index
+        else:
+            ret._index = old_index
+        return ret
+
+    @_cudf_nvtx_annotate
+    def bfill(self, value=None, axis=None, inplace=None, limit=None):
+        """
+        Synonym for :meth:`Series.fillna` with ``method='bfill'``.
+
+        Returns
+        -------
+            Object with missing values filled or None if ``inplace=True``.
+        """
+        return self.fillna(
+            method="bfill",
+            value=value,
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+        )
+
+    @_cudf_nvtx_annotate
+    def backfill(self, value=None, axis=None, inplace=None, limit=None):
+        """
+        Synonym for :meth:`Series.fillna` with ``method='bfill'``.
+
+        .. deprecated:: 23.06
+           Use `DataFrame.bfill/Series.bfill` instead.
+
+        Returns
+        -------
+            Object with missing values filled or None if ``inplace=True``.
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            "DataFrame.backfill/Series.backfill is deprecated. Use "
+            "DataFrame.bfill/Series.bfill instead",
+            FutureWarning,
+        )
+        return self.bfill(value=value, axis=axis, inplace=inplace, limit=limit)
+
+    @_cudf_nvtx_annotate
+    def ffill(self, value=None, axis=None, inplace=None, limit=None):
+        """
+        Synonym for :meth:`Series.fillna` with ``method='ffill'``.
+
+        Returns
+        -------
+            Object with missing values filled or None if ``inplace=True``.
+        """
+        return self.fillna(
+            method="ffill",
+            value=value,
+            axis=axis,
+            inplace=inplace,
+            limit=limit,
+        )
+
+    @_cudf_nvtx_annotate
+    def pad(self, value=None, axis=None, inplace=None, limit=None):
+        """
+        Synonym for :meth:`Series.fillna` with ``method='ffill'``.
+
+        .. deprecated:: 23.06
+           Use `DataFrame.ffill/Series.ffill` instead.
+
+        Returns
+        -------
+            Object with missing values filled or None if ``inplace=True``.
+        """
+        # Do not remove until pandas removes this.
+        warnings.warn(
+            "DataFrame.pad/Series.pad is deprecated. Use "
+            "DataFrame.ffill/Series.ffill instead",
+            FutureWarning,
+        )
+        return self.ffill(value=value, axis=axis, inplace=inplace, limit=limit)
+
+    def add_prefix(self, prefix):
+        """
+        Prefix labels with string `prefix`.
+
+        For Series, the row labels are prefixed.
+        For DataFrame, the column labels are prefixed.
+
+        Parameters
+        ----------
+        prefix : str
+            The string to add before each label.
+
+        Returns
+        -------
+        Series or DataFrame
+            New Series with updated labels or DataFrame with updated labels.
+
+        See Also
+        --------
+        Series.add_suffix: Suffix row labels with string 'suffix'.
+        DataFrame.add_suffix: Suffix column labels with string 'suffix'.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = cudf.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        >>> s.add_prefix('item_')
+        item_0    1
+        item_1    2
+        item_2    3
+        item_3    4
+        dtype: int64
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
+        >>> df
+           A  B
+        0  1  3
+        1  2  4
+        2  3  5
+        3  4  6
+        >>> df.add_prefix('col_')
+             col_A  col_B
+        0       1       3
+        1       2       4
+        2       3       5
+        3       4       6
+        """
+        raise NotImplementedError(
+            "`IndexedFrame.add_prefix` not currently implemented. \
+                Use `Series.add_prefix` or `DataFrame.add_prefix`"
+        )
+
+    def add_suffix(self, suffix):
+        """
+        Suffix labels with string `suffix`.
+
+        For Series, the row labels are suffixed.
+        For DataFrame, the column labels are suffixed.
+
+        Parameters
+        ----------
+        prefix : str
+            The string to add after each label.
+
+        Returns
+        -------
+        Series or DataFrame
+            New Series with updated labels or DataFrame with updated labels.
+
+        See Also
+        --------
+        Series.add_prefix: prefix row labels with string 'prefix'.
+        DataFrame.add_prefix: Prefix column labels with string 'prefix'.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = cudf.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        >>> s.add_suffix('_item')
+        0_item    1
+        1_item    2
+        2_item    3
+        3_item    4
+        dtype: int64
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
+        >>> df
+           A  B
+        0  1  3
+        1  2  4
+        2  3  5
+        3  4  6
+        >>> df.add_suffix('_col')
+             A_col  B_col
+        0       1       3
+        1       2       4
+        2       3       5
+        3       4       6
+        """
+        raise NotImplementedError
+
+    @acquire_spill_lock()
+    @_cudf_nvtx_annotate
+    def _apply(self, func, kernel_getter, *args, **kwargs):
+        """Apply `func` across the rows of the frame."""
+        if kwargs:
+            raise ValueError("UDFs using **kwargs are not yet supported.")
+        try:
+            kernel, retty = _compile_or_get(
+                self, func, args, kernel_getter=kernel_getter
+            )
+        except Exception as e:
+            raise ValueError(
+                "user defined function compilation failed."
+            ) from e
+
+        # Mask and data column preallocated
+        ans_col = _return_arr_from_dtype(retty, len(self))
+        ans_mask = cudf.core.column.full(
+            size=len(self), fill_value=True, dtype="bool"
+        )
+        output_args = [(ans_col, ans_mask), len(self)]
+        input_args = _get_input_args_from_frame(self)
+        launch_args = output_args + input_args + list(args)
+        try:
+            with _CUDFNumbaConfig():
+                kernel.forall(len(self))(*launch_args)
+        except Exception as e:
+            raise RuntimeError("UDF kernel execution failed.") from e
+
+        col = _post_process_output_col(ans_col, retty)
+
+        col.set_base_mask(libcudf.transform.bools_to_mask(ans_mask))
+        result = cudf.Series._from_data({None: col}, self._index)
+
+        return result
+
+    def sort_values(
+        self,
+        by,
+        axis=0,
+        ascending=True,
+        inplace=False,
+        kind="quicksort",
+        na_position="last",
+        ignore_index=False,
+    ):
+        """Sort by the values along either axis.
+
+        Parameters
+        ----------
+        by : str or list of str
+            Name or list of names to sort by.
+        ascending : bool or list of bool, default True
+            Sort ascending vs. descending. Specify list for multiple sort
+            orders. If this is a list of bools, must match the length of the
+            by.
+        na_position : {'first', 'last'}, default 'last'
+            'first' puts nulls at the beginning, 'last' puts nulls at the end
+        ignore_index : bool, default False
+            If True, index will not be sorted.
+
+        Returns
+        -------
+        Frame : Frame with sorted values.
+
+        Notes
+        -----
+        Difference from pandas:
+          * Support axis='index' only.
+          * Not supporting: inplace, kind
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame()
+        >>> df['a'] = [0, 1, 2]
+        >>> df['b'] = [-3, 2, 0]
+        >>> df.sort_values('b')
+           a  b
+        0  0 -3
+        2  2  0
+        1  1  2
+        """
+        if na_position not in {"first", "last"}:
+            raise ValueError(f"invalid na_position: {na_position}")
+        if inplace:
+            raise NotImplementedError("`inplace` not currently implemented.")
+        if kind != "quicksort":
+            if kind not in {"mergesort", "heapsort", "stable"}:
+                raise AttributeError(
+                    f"{kind} is not a valid sorting algorithm for "
+                    f"'DataFrame' object"
+                )
+            warnings.warn(
+                f"GPU-accelerated {kind} is currently not supported, "
+                f"defaulting to quicksort."
+            )
+        if axis != 0:
+            raise NotImplementedError("`axis` not currently implemented.")
+
+        if len(self) == 0:
+            return self
+
+        # argsort the `by` column
+        out = self._gather(
+            GatherMap.from_column_unchecked(
+                self._get_columns_by_label(by)._get_sorted_inds(
+                    ascending=ascending, na_position=na_position
+                ),
+                len(self),
+                nullify=False,
+            ),
+            keep_index=not ignore_index,
+        )
+        if (
+            isinstance(self, cudf.core.dataframe.DataFrame)
+            and self._data.multiindex
+        ):
+            out.columns = self._data.to_pandas_index()
+        return out
+
+    def _n_largest_or_smallest(self, largest, n, columns, keep):
+        # Get column to operate on
+        if isinstance(columns, str):
+            columns = [columns]
+
+        method = "nlargest" if largest else "nsmallest"
+        for col in columns:
+            if isinstance(self._data[col], cudf.core.column.StringColumn):
+                if isinstance(self, cudf.DataFrame):
+                    error_msg = (
+                        f"Column '{col}' has dtype {self._data[col].dtype}, "
+                        f"cannot use method '{method}' with this dtype"
+                    )
+                else:
+                    error_msg = (
+                        f"Cannot use method '{method}' with "
+                        f"dtype {self._data[col].dtype}"
+                    )
+                raise TypeError(error_msg)
+        if len(self) == 0:
+            return self
+
+        if keep == "first":
+            if n < 0:
+                n = 0
+
+            # argsort the `by` column
+            return self._gather(
+                GatherMap.from_column_unchecked(
+                    self._get_columns_by_label(columns)
+                    ._get_sorted_inds(ascending=not largest)
+                    .slice(*slice(None, n).indices(len(self))),
+                    len(self),
+                    nullify=False,
+                ),
+                keep_index=True,
+            )
+        elif keep == "last":
+            indices = self._get_columns_by_label(columns)._get_sorted_inds(
+                ascending=largest
+            )
+
+            if n <= 0:
+                # Empty slice.
+                indices = indices.slice(0, 0)
+            else:
+                indices = indices.slice(
+                    *slice(None, -n - 1, -1).indices(len(self))
+                )
+            return self._gather(
+                GatherMap.from_column_unchecked(
+                    indices, len(self), nullify=False
+                ),
+                keep_index=True,
+            )
+        else:
+            raise ValueError('keep must be either "first", "last"')
+
+    def _align_to_index(
+        self,
+        index: ColumnLike,
+        how: str = "outer",
+        sort: bool = True,
+        allow_non_unique: bool = False,
+    ) -> Self:
+        index = cudf.core.index.as_index(index)
+
+        if self.index.equals(index):
+            return self
+        if not allow_non_unique:
+            if not self.index.is_unique or not index.is_unique:
+                raise ValueError("Cannot align indices with non-unique values")
+
+        lhs = cudf.DataFrame._from_data(self._data, index=self.index)
+        rhs = cudf.DataFrame._from_data({}, index=index)
+
+        # create a temporary column that we will later sort by
+        # to recover ordering after index alignment.
+        sort_col_id = str(uuid4())
+        if how == "left":
+            lhs[sort_col_id] = cudf.core.column.arange(len(lhs))
+        elif how == "right":
+            rhs[sort_col_id] = cudf.core.column.arange(len(rhs))
+
+        result = lhs.join(rhs, how=how, sort=sort)
+        if how in ("left", "right"):
+            result = result.sort_values(sort_col_id)
+            del result[sort_col_id]
+
+        result = self.__class__._from_data(
+            data=result._data, index=result.index
+        )
+        result._data.multiindex = self._data.multiindex
+        result._data._level_names = self._data._level_names
+        result.index.names = self.index.names
+
+        return result
+
+    @_cudf_nvtx_annotate
+    def _reindex(
+        self,
+        column_names,
+        dtypes=None,
+        deep=False,
+        index=None,
+        inplace=False,
+        fill_value=NA,
+    ):
+        """
+        Helper for `.reindex`
+
+        Parameters
+        ----------
+        columns_names : array-like
+            The list of columns to select from the Frame,
+            if ``columns`` is a superset of ``Frame.columns`` new
+            columns are created.
+        dtypes : dict
+            Mapping of dtypes for the empty columns being created.
+        deep : boolean, optional, default False
+            Whether to make deep copy or shallow copy of the columns.
+        index : Index or array-like, default None
+            The ``index`` to be used to reindex the Frame with.
+        inplace : bool, default False
+            Whether to perform the operation in place on the data.
+        fill_value : value with which to replace nulls in the result
+
+        Returns
+        -------
+        Series or DataFrame
+        """
+        if dtypes is None:
+            dtypes = {}
+
+        df = self
+        if index is not None:
+            if not df._index.is_unique:
+                raise ValueError(
+                    "cannot reindex on an axis with duplicate labels"
+                )
+            index = cudf.core.index.as_index(
+                index, name=getattr(index, "name", self._index.name)
+            )
+
+            idx_dtype_match = (df.index.nlevels == index.nlevels) and all(
+                _is_same_dtype(left_dtype, right_dtype)
+                for left_dtype, right_dtype in zip(
+                    (col.dtype for col in df.index._data.columns),
+                    (col.dtype for col in index._data.columns),
+                )
+            )
+
+            if not idx_dtype_match:
+                column_names = (
+                    column_names
+                    if column_names is not None
+                    else list(df._column_names)
+                )
+                df = cudf.DataFrame()
+            else:
+                lhs = cudf.DataFrame._from_data({}, index=index)
+                rhs = cudf.DataFrame._from_data(
+                    {
+                        # bookkeeping workaround for unnamed series
+                        (name or 0)
+                        if isinstance(self, cudf.Series)
+                        else name: col
+                        for name, col in df._data.items()
+                    },
+                    index=df._index,
+                )
+                df = lhs.join(rhs, how="left", sort=True)
+                # double-argsort to map back from sorted to unsorted positions
+                df = df.take(index.argsort(ascending=True).argsort())
+
+        index = index if index is not None else df.index
+        names = (
+            column_names if column_names is not None else list(df._data.names)
+        )
+        cols = {
+            name: (
+                df._data[name].copy(deep=deep)
+                if name in df._data
+                else cudf.core.column.column.column_empty(
+                    dtype=dtypes.get(name, np.float64),
+                    masked=True,
+                    row_count=len(index),
+                )
+            )
+            for name in names
+        }
+        result = self.__class__._from_data(
+            data=cudf.core.column_accessor.ColumnAccessor(
+                cols,
+                multiindex=self._data.multiindex,
+                level_names=tuple(column_names.names)
+                if isinstance(column_names, pd.Index)
+                else None,
+            ),
+            index=index,
+        )
+
+        result.fillna(fill_value, inplace=True)
+        return self._mimic_inplace(result, inplace=inplace)
+
+    def round(self, decimals=0, how="half_even"):
+        """
+        Round to a variable number of decimal places.
+
+        Parameters
+        ----------
+        decimals : int, dict, Series
+            Number of decimal places to round each column to. This parameter
+            must be an int for a Series. For a DataFrame, a dict or a Series
+            are also valid inputs. If an int is given, round each column to the
+            same number of places. Otherwise dict and Series round to variable
+            numbers of places. Column names should be in the keys if
+            `decimals` is a dict-like, or in the index if `decimals` is a
+            Series. Any columns not included in `decimals` will be left as is.
+            Elements of `decimals` which are not columns of the input will be
+            ignored.
+        how : str, optional
+            Type of rounding. Can be either "half_even" (default)
+            or "half_up" rounding.
+
+        Returns
+        -------
+        Series or DataFrame
+            A Series or DataFrame with the affected columns rounded to the
+            specified number of decimal places.
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = cudf.Series([0.1, 1.4, 2.9])
+        >>> s.round()
+        0    0.0
+        1    1.0
+        2    3.0
+        dtype: float64
+
+        **DataFrame**
+
+        >>> df = cudf.DataFrame(
+        ...     [(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
+        ...     columns=['dogs', 'cats'],
+        ... )
+        >>> df
+           dogs  cats
+        0  0.21  0.32
+        1  0.01  0.67
+        2  0.66  0.03
+        3  0.21  0.18
+
+        By providing an integer each column is rounded to the same number
+        of decimal places.
+
+        >>> df.round(1)
+           dogs  cats
+        0   0.2   0.3
+        1   0.0   0.7
+        2   0.7   0.0
+        3   0.2   0.2
+
+        With a dict, the number of places for specific columns can be
+        specified with the column names as keys and the number of decimal
+        places as values.
+
+        >>> df.round({'dogs': 1, 'cats': 0})
+           dogs  cats
+        0   0.2   0.0
+        1   0.0   1.0
+        2   0.7   0.0
+        3   0.2   0.0
+
+        Using a Series, the number of places for specific columns can be
+        specified with the column names as the index and the number of
+        decimal places as the values.
+
+        >>> decimals = cudf.Series([0, 1], index=['cats', 'dogs'])
+        >>> df.round(decimals)
+           dogs  cats
+        0   0.2   0.0
+        1   0.0   1.0
+        2   0.7   0.0
+        3   0.2   0.0
+        """
+        if isinstance(decimals, cudf.Series):
+            decimals = decimals.to_pandas()
+
+        if isinstance(decimals, pd.Series):
+            if not decimals.index.is_unique:
+                raise ValueError("Index of decimals must be unique")
+            decimals = decimals.to_dict()
+        elif isinstance(decimals, int):
+            decimals = {name: decimals for name in self._column_names}
+        elif not isinstance(decimals, abc.Mapping):
+            raise TypeError(
+                "decimals must be an integer, a dict-like or a Series"
+            )
+
+        cols = {
+            name: col.round(decimals[name], how=how)
+            if (
+                name in decimals
+                and _is_non_decimal_numeric_dtype(col.dtype)
+                and not is_bool_dtype(col.dtype)
+            )
+            else col.copy(deep=True)
+            for name, col in self._data.items()
+        }
+
+        return self.__class__._from_data(
+            data=cudf.core.column_accessor.ColumnAccessor(
+                cols,
+                multiindex=self._data.multiindex,
+                level_names=self._data.level_names,
+            ),
+            index=self._index,
+        )
+
+    def resample(
+        self,
+        rule,
+        axis=0,
+        closed=None,
+        label=None,
+        convention="start",
+        kind=None,
+        loffset=None,
+        base=None,
+        on=None,
+        level=None,
+        origin="start_day",
+        offset=None,
+    ):
+        """
+        Convert the frequency of ("resample") the given time series data.
+
+        Parameters
+        ----------
+        rule: str
+            The offset string representing the frequency to use.
+            Note that DateOffset objects are not yet supported.
+        closed: {"right", "left"}, default None
+            Which side of bin interval is closed. The default is
+            "left" for all frequency offsets except for "M" and "W",
+            which have a default of "right".
+        label: {"right", "left"}, default None
+            Which bin edge label to label bucket with. The default is
+            "left" for all frequency offsets except for "M" and "W",
+            which have a default of "right".
+        on: str, optional
+            For a DataFrame, column to use instead of the index for
+            resampling.  Column must be a datetime-like.
+        level: str or int, optional
+            For a MultiIndex, level to use instead of the index for
+            resampling.  The level must be a datetime-like.
+
+        Returns
+        -------
+        A Resampler object
+
+        Examples
+        --------
+        First, we create a time series with 1 minute intervals:
+
+        >>> index = cudf.date_range(start="2001-01-01", periods=10, freq="1T")
+        >>> sr = cudf.Series(range(10), index=index)
+        >>> sr
+        2001-01-01 00:00:00    0
+        2001-01-01 00:01:00    1
+        2001-01-01 00:02:00    2
+        2001-01-01 00:03:00    3
+        2001-01-01 00:04:00    4
+        2001-01-01 00:05:00    5
+        2001-01-01 00:06:00    6
+        2001-01-01 00:07:00    7
+        2001-01-01 00:08:00    8
+        2001-01-01 00:09:00    9
+        dtype: int64
+
+        Downsampling to 3 minute intervals, followed by a "sum" aggregation:
+
+        >>> sr.resample("3T").sum()
+        2001-01-01 00:00:00     3
+        2001-01-01 00:03:00    12
+        2001-01-01 00:06:00    21
+        2001-01-01 00:09:00     9
+        dtype: int64
+
+        Use the right side of each interval to label the bins:
+
+        >>> sr.resample("3T", label="right").sum()
+        2001-01-01 00:03:00     3
+        2001-01-01 00:06:00    12
+        2001-01-01 00:09:00    21
+        2001-01-01 00:12:00     9
+        dtype: int64
+
+        Close the right side of the interval instead of the left:
+
+        >>> sr.resample("3T", closed="right").sum()
+        2000-12-31 23:57:00     0
+        2001-01-01 00:00:00     6
+        2001-01-01 00:03:00    15
+        2001-01-01 00:06:00    24
+        dtype: int64
+
+        Upsampling to 30 second intervals:
+
+        >>> sr.resample("30s").asfreq()[:5]  # show the first 5 rows
+        2001-01-01 00:00:00       0
+        2001-01-01 00:00:30    <NA>
+        2001-01-01 00:01:00       1
+        2001-01-01 00:01:30    <NA>
+        2001-01-01 00:02:00       2
+        dtype: int64
+
+        Upsample and fill nulls using the "bfill" method:
+
+        >>> sr.resample("30s").bfill()[:5]
+        2001-01-01 00:00:00    0
+        2001-01-01 00:00:30    1
+        2001-01-01 00:01:00    1
+        2001-01-01 00:01:30    2
+        2001-01-01 00:02:00    2
+        dtype: int64
+
+        Resampling by a specified column of a Dataframe:
+
+        >>> df = cudf.DataFrame({
+        ...     "price": [10, 11, 9, 13, 14, 18, 17, 19],
+        ...     "volume": [50, 60, 40, 100, 50, 100, 40, 50],
+        ...     "week_starting": cudf.date_range(
+        ...         "2018-01-01", periods=8, freq="7D"
+        ...     )
+        ... })
+        >>> df
+        price  volume week_starting
+        0     10      50    2018-01-01
+        1     11      60    2018-01-08
+        2      9      40    2018-01-15
+        3     13     100    2018-01-22
+        4     14      50    2018-01-29
+        5     18     100    2018-02-05
+        6     17      40    2018-02-12
+        7     19      50    2018-02-19
+        >>> df.resample("M", on="week_starting").mean()
+                       price     volume
+        week_starting
+        2018-01-31      11.4  60.000000
+        2018-02-28      18.0  63.333333
+
+
+        Notes
+        -----
+        Note that the dtype of the index (or the 'on' column if using
+        'on=') in the result will be of a frequency closest to the
+        resampled frequency.  For example, if resampling from
+        nanoseconds to milliseconds, the index will be of dtype
+        'datetime64[ms]'.
+        """
+        import cudf.core.resample
+
+        if (axis, convention, kind, loffset, base, origin, offset) != (
+            0,
+            "start",
+            None,
+            None,
+            None,
+            "start_day",
+            None,
+        ):
+            raise NotImplementedError(
+                "The following arguments are not "
+                "currently supported by resample:\n\n"
+                "- axis\n"
+                "- convention\n"
+                "- kind\n"
+                "- loffset\n"
+                "- base\n"
+                "- origin\n"
+                "- offset"
+            )
+        by = cudf.Grouper(
+            key=on, freq=rule, closed=closed, label=label, level=level
+        )
+        return (
+            cudf.core.resample.SeriesResampler(self, by=by)
+            if isinstance(self, cudf.Series)
+            else cudf.core.resample.DataFrameResampler(self, by=by)
+        )
+
+    def dropna(
+        self, axis=0, how="any", thresh=None, subset=None, inplace=False
+    ):
+        """
+        Drop rows (or columns) containing nulls from a Column.
+
+        Parameters
+        ----------
+        axis : {0, 1}, optional
+            Whether to drop rows (axis=0, default) or columns (axis=1)
+            containing nulls.
+        how : {"any", "all"}, optional
+            Specifies how to decide whether to drop a row (or column).
+            any (default) drops rows (or columns) containing at least
+            one null value. all drops only rows (or columns) containing
+            *all* null values.
+        thresh: int, optional
+            If specified, then drops every row (or column) containing
+            less than `thresh` non-null values
+        subset : list, optional
+            List of columns to consider when dropping rows (all columns
+            are considered by default). Alternatively, when dropping
+            columns, subset is a list of rows to consider.
+        inplace : bool, default False
+            If True, do operation inplace and return None.
+
+        Returns
+        -------
+        Copy of the DataFrame with rows/columns containing nulls dropped.
+
+        See Also
+        --------
+        cudf.DataFrame.isna
+            Indicate null values.
+        cudf.DataFrame.notna
+            Indicate non-null values.
+        cudf.DataFrame.fillna
+            Replace null values.
+        cudf.Series.dropna
+            Drop null values.
+        cudf.Index.dropna
+            Drop null indices.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
+        ...                    "toy": ['Batmobile', None, 'Bullwhip'],
+        ...                    "born": [np.datetime64("1940-04-25"),
+        ...                             np.datetime64("NaT"),
+        ...                             np.datetime64("NaT")]})
+        >>> df
+               name        toy                 born
+        0    Alfred  Batmobile  1940-04-25 00:00:00
+        1    Batman       <NA>                 <NA>
+        2  Catwoman   Bullwhip                 <NA>
+
+        Drop the rows where at least one element is null.
+
+        >>> df.dropna()
+             name        toy       born
+        0  Alfred  Batmobile 1940-04-25
+
+        Drop the columns where at least one element is null.
+
+        >>> df.dropna(axis='columns')
+               name
+        0    Alfred
+        1    Batman
+        2  Catwoman
+
+        Drop the rows where all elements are null.
+
+        >>> df.dropna(how='all')
+               name        toy                 born
+        0    Alfred  Batmobile  1940-04-25 00:00:00
+        1    Batman       <NA>                 <NA>
+        2  Catwoman   Bullwhip                 <NA>
+
+        Keep only the rows with at least 2 non-null values.
+
+        >>> df.dropna(thresh=2)
+               name        toy                 born
+        0    Alfred  Batmobile  1940-04-25 00:00:00
+        2  Catwoman   Bullwhip                 <NA>
+
+        Define in which columns to look for null values.
+
+        >>> df.dropna(subset=['name', 'born'])
+             name        toy       born
+        0  Alfred  Batmobile 1940-04-25
+
+        Keep the DataFrame with valid entries in the same variable.
+
+        >>> df.dropna(inplace=True)
+        >>> df
+             name        toy       born
+        0  Alfred  Batmobile 1940-04-25
+        """
+        if axis == 0:
+            result = self._drop_na_rows(how=how, subset=subset, thresh=thresh)
+        else:
+            result = self._drop_na_columns(
+                how=how, subset=subset, thresh=thresh
+            )
+
+        return self._mimic_inplace(result, inplace=inplace)
+
+    def _drop_na_rows(self, how="any", subset=None, thresh=None):
+        """
+        Drop null rows from `self`.
+
+        how : {"any", "all"}, optional
+            Specifies how to decide whether to drop a row.
+            any (default) drops rows containing at least
+            one null value. all drops only rows containing
+            *all* null values.
+        subset : list, optional
+            List of columns to consider when dropping rows.
+        thresh : int, optional
+            If specified, then drops every row containing
+            less than `thresh` non-null values.
+        """
+        subset = self._preprocess_subset(subset)
+
+        if len(subset) == 0:
+            return self.copy(deep=True)
+
+        data_columns = [
+            col.nans_to_nulls()
+            if isinstance(col, cudf.core.column.NumericalColumn)
+            else col
+            for col in self._columns
+        ]
+
+        return self._from_columns_like_self(
+            libcudf.stream_compaction.drop_nulls(
+                [*self._index._data.columns, *data_columns],
+                how=how,
+                keys=self._positions_from_column_names(
+                    subset, offset_by_index_columns=True
+                ),
+                thresh=thresh,
+            ),
+            self._column_names,
+            self._index.names,
+        )
+
+    def _apply_boolean_mask(self, boolean_mask: BooleanMask, keep_index=True):
+        """Apply boolean mask to each row of `self`.
+
+        Rows corresponding to `False` is dropped.
+
+        If keep_index is False, the index is not preserved.
+        """
+        if len(boolean_mask.column) != len(self):
+            raise IndexError(
+                "Boolean mask has wrong length: "
+                f"{len(boolean_mask.column)} not {len(self)}"
+            )
+        return self._from_columns_like_self(
+            libcudf.stream_compaction.apply_boolean_mask(
+                list(self._index._columns + self._columns)
+                if keep_index
+                else list(self._columns),
+                boolean_mask.column,
+            ),
+            column_names=self._column_names,
+            index_names=self._index.names if keep_index else None,
+        )
+
+    def take(self, indices, axis=0):
+        """Return a new frame containing the rows specified by *indices*.
+
+        Parameters
+        ----------
+        indices : array-like
+            Array of ints indicating which positions to take.
+        axis : Unsupported
+
+        Returns
+        -------
+        out : Series or DataFrame
+            New object with desired subset of rows.
+
+        Examples
+        --------
+        **Series**
+        >>> s = cudf.Series(['a', 'b', 'c', 'd', 'e'])
+        >>> s.take([2, 0, 4, 3])
+        2    c
+        0    a
+        4    e
+        3    d
+        dtype: object
+
+        **DataFrame**
+
+        >>> a = cudf.DataFrame({'a': [1.0, 2.0, 3.0],
+        ...                    'b': cudf.Series(['a', 'b', 'c'])})
+        >>> a.take([0, 2, 2])
+             a  b
+        0  1.0  a
+        2  3.0  c
+        2  3.0  c
+        >>> a.take([True, False, True])
+             a  b
+        0  1.0  a
+        2  3.0  c
+        """
+        if self._get_axis_from_axis_arg(axis) != 0:
+            raise NotImplementedError("Only axis=0 is supported.")
+
+        return self._gather(GatherMap(indices, len(self), nullify=False))
+
+    def _reset_index(self, level, drop, col_level=0, col_fill=""):
+        """Shared path for DataFrame.reset_index and Series.reset_index."""
+        if level is not None and not isinstance(level, (tuple, list)):
+            level = (level,)
+        _check_duplicate_level_names(level, self._index.names)
+
+        # Split the columns in the index into data and index columns
+        (
+            data_columns,
+            index_columns,
+            data_names,
+            index_names,
+        ) = self._index._split_columns_by_levels(level)
+        if index_columns:
+            index = _index_from_columns(
+                index_columns,
+                name=self._index.name,
+            )
+            if isinstance(index, MultiIndex):
+                index.names = index_names
+            else:
+                index.name = index_names[0]
+        else:
+            index = RangeIndex(len(self))
+
+        if drop:
+            return self._data, index
+
+        new_column_data = {}
+        for name, col in zip(data_names, data_columns):
+            if name == "index" and "index" in self._data:
+                name = "level_0"
+            name = (
+                tuple(
+                    name if i == col_level else col_fill
+                    for i in range(self._data.nlevels)
+                )
+                if self._data.multiindex
+                else name
+            )
+            new_column_data[name] = col
+        # This is to match pandas where the new data columns are always
+        # inserted to the left of existing data columns.
+        return (
+            ColumnAccessor(
+                {**new_column_data, **self._data},
+                self._data.multiindex,
+                self._data._level_names,
+            ),
+            index,
+        )
+
+    def _first_or_last(
+        self, offset, idx: int, op: Callable, side: str, slice_func: Callable
+    ) -> "IndexedFrame":
+        """Shared code path for ``first`` and ``last``."""
+        if not isinstance(self._index, cudf.core.index.DatetimeIndex):
+            raise TypeError("'first' only supports a DatetimeIndex index.")
+        if not isinstance(offset, str):
+            raise NotImplementedError(
+                f"Unsupported offset type {type(offset)}."
+            )
+
+        if len(self) == 0:
+            return self.copy()
+
+        pd_offset = pd.tseries.frequencies.to_offset(offset)
+        to_search = op(
+            pd.Timestamp(self._index._column.element_indexing(idx)), pd_offset
+        )
+        if (
+            idx == 0
+            and not isinstance(pd_offset, pd.tseries.offsets.Tick)
+            and pd_offset.is_on_offset(pd.Timestamp(self._index[0]))
+        ):
+            # Special handle is required when the start time of the index
+            # is on the end of the offset. See pandas gh29623 for detail.
+            to_search = to_search - pd_offset.base
+            return self.loc[:to_search]
+        end_point = int(
+            self._index._column.searchsorted(to_search, side=side)[0]
+        )
+        return slice_func(end_point)
+
+    def first(self, offset):
+        """Select initial periods of time series data based on a date offset.
+
+        When having a DataFrame with **sorted** dates as index, this function
+        can select the first few rows based on a date offset.
+
+        Parameters
+        ----------
+        offset: str
+            The offset length of the data that will be selected. For instance,
+            '1M' will display all rows having their index within the first
+            month.
+
+        Returns
+        -------
+        Series or DataFrame
+            A subset of the caller.
+
+        Raises
+        ------
+        TypeError
+            If the index is not a ``DatetimeIndex``
+
+        Examples
+        --------
+        >>> i = cudf.date_range('2018-04-09', periods=4, freq='2D')
+        >>> ts = cudf.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        2018-04-13  3
+        2018-04-15  4
+        >>> ts.first('3D')
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        """
+        return self._first_or_last(
+            offset,
+            idx=0,
+            op=operator.__add__,
+            side="left",
+            slice_func=lambda i: self.iloc[:i],
+        )
+
+    def last(self, offset):
+        """Select final periods of time series data based on a date offset.
+
+        When having a DataFrame with **sorted** dates as index, this function
+        can select the last few rows based on a date offset.
+
+        Parameters
+        ----------
+        offset: str
+            The offset length of the data that will be selected. For instance,
+            '3D' will display all rows having their index within the last 3
+            days.
+
+        Returns
+        -------
+        Series or DataFrame
+            A subset of the caller.
+
+        Raises
+        ------
+        TypeError
+            If the index is not a ``DatetimeIndex``
+
+        Examples
+        --------
+        >>> i = cudf.date_range('2018-04-09', periods=4, freq='2D')
+        >>> ts = cudf.DataFrame({'A': [1, 2, 3, 4]}, index=i)
+        >>> ts
+                    A
+        2018-04-09  1
+        2018-04-11  2
+        2018-04-13  3
+        2018-04-15  4
+        >>> ts.last('3D')
+                    A
+        2018-04-13  3
+        2018-04-15  4
+        """
+        return self._first_or_last(
+            offset,
+            idx=-1,
+            op=operator.__sub__,
+            side="right",
+            slice_func=lambda i: self.iloc[i:],
+        )
+
+    @_cudf_nvtx_annotate
+    def sample(
+        self,
+        n=None,
+        frac=None,
+        replace=False,
+        weights=None,
+        random_state=None,
+        axis=None,
+        ignore_index=False,
+    ):
+        """Return a random sample of items from an axis of object.
+
+        If reproducible results are required, a random number generator may be
+        provided via the `random_state` parameter. This function will always
+        produce the same sample given an identical `random_state`.
+
+        Notes
+        -----
+        When sampling from ``axis=0/'index'``, ``random_state`` can be either
+        a numpy random state (``numpy.random.RandomState``) or a cupy random
+        state (``cupy.random.RandomState``). When a numpy random state is
+        used, the output is guaranteed to match the output of the corresponding
+        pandas method call, but generating the sample may be slow. If exact
+        pandas equivalence is not required, using a cupy random state will
+        achieve better performance, especially when sampling large number of
+        items. It's advised to use the matching `ndarray` type to the random
+        state for the `weights` array.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of items from axis to return. Cannot be used with `frac`.
+            Default = 1 if frac = None.
+        frac : float, optional
+            Fraction of axis items to return. Cannot be used with n.
+        replace : bool, default False
+            Allow or disallow sampling of the same row more than once.
+            `replace == True` is not supported for axis = 1/"columns".
+            `replace == False` is not supported for axis = 0/"index" given
+            `random_state` is `None` or a cupy random state, and `weights` is
+            specified.
+        weights : ndarray-like, optional
+            Default `None` for uniform probability distribution over rows to
+            sample from. If `ndarray` is passed, the length of `weights` should
+            equal to the number of rows to sample from, and will be normalized
+            to have a sum of 1. Unlike pandas, index alignment is not currently
+            not performed.
+        random_state : int, numpy/cupy RandomState, or None, default None
+            If None, default cupy random state is chosen.
+            If int, the seed for the default cupy random state.
+            If RandomState, rows-to-sample are generated from the RandomState.
+        axis : {0 or `index`, 1 or `columns`, None}, default None
+            Axis to sample. Accepts axis number or name.
+            Default is stat axis for given data type
+            (0 for Series and DataFrames). Series doesn't support axis=1.
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        Series or DataFrame
+            A new object of same type as caller containing n items
+            randomly sampled from the caller object.
+
+        Examples
+        --------
+        >>> import cudf as cudf
+        >>> df = cudf.DataFrame({"a":{1, 2, 3, 4, 5}})
+        >>> df.sample(3)
+           a
+        1  2
+        3  4
+        0  1
+
+        >>> sr = cudf.Series([1, 2, 3, 4, 5])
+        >>> sr.sample(10, replace=True)
+        1    4
+        3    1
+        2    4
+        0    5
+        0    1
+        4    5
+        4    1
+        0    2
+        0    3
+        3    2
+        dtype: int64
+
+        >>> df = cudf.DataFrame(
+        ...     {"a": [1, 2], "b": [2, 3], "c": [3, 4], "d": [4, 5]}
+        ... )
+        >>> df.sample(2, axis=1)
+           a  c
+        0  1  3
+        1  2  4
+        """
+        axis = 0 if axis is None else self._get_axis_from_axis_arg(axis)
+        size = self.shape[axis]
+
+        # Compute `n` from parameter `frac`.
+        if frac is None:
+            n = 1 if n is None else n
+        else:
+            if frac > 1 and not replace:
+                raise ValueError(
+                    "Replace has to be set to `True` when upsampling the "
+                    "population `frac` > 1."
+                )
+            if n is not None:
+                raise ValueError(
+                    "Please enter a value for `frac` OR `n`, not both."
+                )
+            n = int(round(size * frac))
+
+        if n > 0 and size == 0:
+            raise ValueError(
+                "Cannot take a sample larger than 0 when axis is empty."
+            )
+
+        if isinstance(random_state, cp.random.RandomState):
+            lib = cp
+        elif isinstance(random_state, np.random.RandomState):
+            lib = np
+        else:
+            # Construct random state if `random_state` parameter is None or a
+            # seed. By default, cupy random state is used to sample rows
+            # and numpy is used to sample columns. This is because row data
+            # is stored on device, and the column objects are stored on host.
+            lib = cp if axis == 0 else np
+            random_state = lib.random.RandomState(seed=random_state)
+
+        # Normalize `weights` array.
+        if weights is not None:
+            if isinstance(weights, str):
+                raise NotImplementedError(
+                    "Weights specified by string is unsupported yet."
+                )
+
+            if size != len(weights):
+                raise ValueError(
+                    "Weights and axis to be sampled must be of same length."
+                )
+
+            weights = lib.asarray(weights)
+            weights = weights / weights.sum()
+
+        if axis == 0:
+            return self._sample_axis_0(
+                n, weights, replace, random_state, ignore_index
+            )
+        else:
+            if isinstance(random_state, cp.random.RandomState):
+                raise ValueError(
+                    "Sampling from `axis=1`/`columns` with cupy random state"
+                    "isn't supported."
+                )
+            return self._sample_axis_1(
+                n, weights, replace, random_state, ignore_index
+            )
+
+    def _sample_axis_0(
+        self,
+        n: int,
+        weights: Optional[ColumnLike],
+        replace: bool,
+        random_state: Union[np.random.RandomState, cp.random.RandomState],
+        ignore_index: bool,
+    ):
+        try:
+            gather_map = GatherMap.from_column_unchecked(
+                cudf.core.column.as_column(
+                    random_state.choice(
+                        len(self), size=n, replace=replace, p=weights
+                    )
+                ),
+                len(self),
+                nullify=False,
+            )
+        except NotImplementedError as e:
+            raise NotImplementedError(
+                "Random sampling with cupy does not support these inputs."
+            ) from e
+
+        return self._gather(gather_map, keep_index=not ignore_index)
+
+    def _sample_axis_1(
+        self,
+        n: int,
+        weights: Optional[ColumnLike],
+        replace: bool,
+        random_state: np.random.RandomState,
+        ignore_index: bool,
+    ):
+        raise NotImplementedError(
+            f"Sampling from axis 1 is not implemented for {self.__class__}."
+        )
+
+    def _binaryop(
+        self,
+        other: Any,
+        op: str,
+        fill_value: Any = None,
+        can_reindex: bool = False,
+        *args,
+        **kwargs,
+    ):
+        reflect, op = self._check_reflected_op(op)
+        (
+            operands,
+            out_index,
+            can_use_self_column_name,
+        ) = self._make_operands_and_index_for_binop(
+            other, op, fill_value, reflect, can_reindex
+        )
+        if operands is NotImplemented:
+            return NotImplemented
+
+        level_names = (
+            self._data._level_names if can_use_self_column_name else None
+        )
+        return self._from_data(
+            ColumnAccessor(
+                type(self)._colwise_binop(operands, op),
+                level_names=level_names,
+            ),
+            index=out_index,
+        )
+
+    def _make_operands_and_index_for_binop(
+        self,
+        other: Any,
+        fn: str,
+        fill_value: Any = None,
+        reflect: bool = False,
+        can_reindex: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tuple[
+        Union[
+            Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
+            NotImplementedType,
+        ],
+        Optional[cudf.BaseIndex],
+        bool,
+    ]:
+        raise NotImplementedError(
+            f"Binary operations are not supported for {self.__class__}"
+        )
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        ret = super().__array_ufunc__(ufunc, method, *inputs, **kwargs)
+        fname = ufunc.__name__
+
+        if ret is not None:
+            # pandas bitwise operations return bools if indexes are misaligned.
+            if "bitwise" in fname:
+                reflect = self is not inputs[0]
+                other = inputs[0] if reflect else inputs[1]
+                if isinstance(other, self.__class__) and not self.index.equals(
+                    other.index
+                ):
+                    ret = ret.astype(bool)
+            return ret
+
+        # Attempt to dispatch all other functions to cupy.
+        cupy_func = getattr(cp, fname)
+        if cupy_func:
+            if ufunc.nin == 2:
+                other = inputs[self is inputs[0]]
+                inputs, index, _ = self._make_operands_and_index_for_binop(
+                    other, fname
+                )
+            else:
+                # This works for Index too
+                inputs = {
+                    name: (col, None, False, None)
+                    for name, col in self._data.items()
+                }
+                index = self._index
+
+            data = self._apply_cupy_ufunc_to_operands(
+                ufunc, cupy_func, inputs, **kwargs
+            )
+
+            out = tuple(self._from_data(out, index=index) for out in data)
+            return out[0] if ufunc.nout == 1 else out
+
+        return NotImplemented
+
+    @_cudf_nvtx_annotate
+    def repeat(self, repeats, axis=None):
+        """Repeats elements consecutively.
+
+        Returns a new object of caller type(DataFrame/Series) where each
+        element of the current object is repeated consecutively a given
+        number of times.
+
+        Parameters
+        ----------
+        repeats : int, or array of ints
+            The number of repetitions for each element. This should
+            be a non-negative integer. Repeating 0 times will return
+            an empty object.
+
+        Returns
+        -------
+        Series/DataFrame
+            A newly created object of same type as caller
+            with repeated elements.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30]})
+        >>> df
+           a   b
+        0  1  10
+        1  2  20
+        2  3  30
+        >>> df.repeat(3)
+           a   b
+        0  1  10
+        0  1  10
+        0  1  10
+        1  2  20
+        1  2  20
+        1  2  20
+        2  3  30
+        2  3  30
+        2  3  30
+
+        Repeat on Series
+
+        >>> s = cudf.Series([0, 2])
+        >>> s
+        0    0
+        1    2
+        dtype: int64
+        >>> s.repeat([3, 4])
+        0    0
+        0    0
+        0    0
+        1    2
+        1    2
+        1    2
+        1    2
+        dtype: int64
+        >>> s.repeat(2)
+        0    0
+        0    0
+        1    2
+        1    2
+        dtype: int64
+        """
+        return self._from_columns_like_self(
+            Frame._repeat(
+                [*self._index._data.columns, *self._columns], repeats, axis
+            ),
+            self._column_names,
+            self._index_names,
+        )
+
+    def _append(
+        self, other, ignore_index=False, verify_integrity=False, sort=None
+    ):
+        # Note: Do not remove this function until pandas does. This warning is
+        # to clean up cudf but to match a deprecation in pandas
+        warnings.warn(
+            "The append method is deprecated and will be removed in a future "
+            "version. Use cudf.concat instead.",
+            FutureWarning,
+        )
+        if verify_integrity not in (None, False):
+            raise NotImplementedError(
+                "verify_integrity parameter is not supported yet."
+            )
+
+        if is_list_like(other):
+            to_concat = [self, *other]
+        else:
+            to_concat = [self, other]
+
+        return cudf.concat(to_concat, ignore_index=ignore_index, sort=sort)
+
+    def astype(self, dtype, copy=False, errors="raise", **kwargs):
+        """Cast the object to the given dtype.
+
+        Parameters
+        ----------
+        dtype : data type, or dict of column name -> data type
+            Use a :class:`numpy.dtype` or Python type to cast entire DataFrame
+            object to the same type. Alternatively, use ``{col: dtype, ...}``,
+            where col is a column label and dtype is a :class:`numpy.dtype`
+            or Python type to cast one or more of the DataFrame's columns to
+            column-specific types.
+        copy : bool, default False
+            Return a deep-copy when ``copy=True``. Note by default
+            ``copy=False`` setting is used and hence changes to
+            values then may propagate to other cudf objects.
+        errors : {'raise', 'ignore', 'warn'}, default 'raise'
+            Control raising of exceptions on invalid data for provided dtype.
+
+            -   ``raise`` : allow exceptions to be raised
+            -   ``ignore`` : suppress exceptions. On error return original
+                object.
+        **kwargs : extra arguments to pass on to the constructor
+
+        Returns
+        -------
+        DataFrame/Series
+
+        Examples
+        --------
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a': [10, 20, 30], 'b': [1, 2, 3]})
+        >>> df
+            a  b
+        0  10  1
+        1  20  2
+        2  30  3
+        >>> df.dtypes
+        a    int64
+        b    int64
+        dtype: object
+
+        Cast all columns to `int32`:
+
+        >>> df.astype('int32').dtypes
+        a    int32
+        b    int32
+        dtype: object
+
+        Cast `a` to `float32` using a dictionary:
+
+        >>> df.astype({'a': 'float32'}).dtypes
+        a    float32
+        b      int64
+        dtype: object
+        >>> df.astype({'a': 'float32'})
+              a  b
+        0  10.0  1
+        1  20.0  2
+        2  30.0  3
+
+        **Series**
+
+        >>> import cudf
+        >>> series = cudf.Series([1, 2], dtype='int32')
+        >>> series
+        0    1
+        1    2
+        dtype: int32
+        >>> series.astype('int64')
+        0    1
+        1    2
+        dtype: int64
+
+        Convert to categorical type:
+
+        >>> series.astype('category')
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int64): [1, 2]
+
+        Convert to ordered categorical type with custom ordering:
+
+        >>> cat_dtype = cudf.CategoricalDtype(categories=[2, 1], ordered=True)
+        >>> series.astype(cat_dtype)
+        0    1
+        1    2
+        dtype: category
+        Categories (2, int64): [2 < 1]
+
+        Note that using ``copy=False`` (enabled by default)
+        and changing data on a new Series will
+        propagate changes:
+
+        >>> s1 = cudf.Series([1, 2])
+        >>> s1
+        0    1
+        1    2
+        dtype: int64
+        >>> s2 = s1.astype('int64', copy=False)
+        >>> s2[0] = 10
+        >>> s1
+        0    10
+        1     2
+        dtype: int64
+        """
+        if errors not in ("ignore", "raise"):
+            raise ValueError("invalid error value specified")
+
+        try:
+            data = super().astype(dtype, copy, **kwargs)
+        except Exception as e:
+            if errors == "raise":
+                raise e
+            return self
+
+        return self._from_data(data, index=self._index)
+
+    @_cudf_nvtx_annotate
+    def drop(
+        self,
+        labels=None,
+        axis=0,
+        index=None,
+        columns=None,
+        level=None,
+        inplace=False,
+        errors="raise",
+    ):
+        """Drop specified labels from rows or columns.
+
+        Remove rows or columns by specifying label names and corresponding
+        axis, or by specifying directly index or column names. When using a
+        multi-index, labels on different levels can be removed by specifying
+        the level.
+
+        Parameters
+        ----------
+        labels : single label or list-like
+            Index or column labels to drop.
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            Whether to drop labels from the index (0 or 'index') or
+            columns (1 or 'columns').
+        index : single label or list-like
+            Alternative to specifying axis (``labels, axis=0``
+            is equivalent to ``index=labels``).
+        columns : single label or list-like
+            Alternative to specifying axis (``labels, axis=1``
+            is equivalent to ``columns=labels``).
+        level : int or level name, optional
+            For MultiIndex, level from which the labels will be removed.
+        inplace : bool, default False
+            If False, return a copy. Otherwise, do operation
+            inplace and return None.
+        errors : {'ignore', 'raise'}, default 'raise'
+            If 'ignore', suppress error and only existing labels are
+            dropped.
+
+        Returns
+        -------
+        DataFrame or Series
+            DataFrame or Series without the removed index or column labels.
+
+        Raises
+        ------
+        KeyError
+            If any of the labels is not found in the selected axis.
+
+        See Also
+        --------
+        DataFrame.loc : Label-location based indexer for selection by label.
+        DataFrame.dropna : Return DataFrame with labels on given axis omitted
+            where (all or any) data are missing.
+        DataFrame.drop_duplicates : Return DataFrame with duplicate rows
+            removed, optionally only considering certain columns.
+        Series.reindex
+            Return only specified index labels of Series
+        Series.dropna
+            Return series without null values
+        Series.drop_duplicates
+            Return series with duplicate values removed
+
+        Examples
+        --------
+        **Series**
+
+        >>> s = cudf.Series([1,2,3], index=['x', 'y', 'z'])
+        >>> s
+        x    1
+        y    2
+        z    3
+        dtype: int64
+
+        Drop labels x and z
+
+        >>> s.drop(labels=['x', 'z'])
+        y    2
+        dtype: int64
+
+        Drop a label from the second level in MultiIndex Series.
+
+        >>> midx = cudf.MultiIndex.from_product([[0, 1, 2], ['x', 'y']])
+        >>> s = cudf.Series(range(6), index=midx)
+        >>> s
+        0  x    0
+           y    1
+        1  x    2
+           y    3
+        2  x    4
+           y    5
+        dtype: int64
+        >>> s.drop(labels='y', level=1)
+        0  x    0
+        1  x    2
+        2  x    4
+        Name: 2, dtype: int64
+
+        **DataFrame**
+
+        >>> import cudf
+        >>> df = cudf.DataFrame({"A": [1, 2, 3, 4],
+        ...                      "B": [5, 6, 7, 8],
+        ...                      "C": [10, 11, 12, 13],
+        ...                      "D": [20, 30, 40, 50]})
+        >>> df
+           A  B   C   D
+        0  1  5  10  20
+        1  2  6  11  30
+        2  3  7  12  40
+        3  4  8  13  50
+
+        Drop columns
+
+        >>> df.drop(['B', 'C'], axis=1)
+           A   D
+        0  1  20
+        1  2  30
+        2  3  40
+        3  4  50
+        >>> df.drop(columns=['B', 'C'])
+           A   D
+        0  1  20
+        1  2  30
+        2  3  40
+        3  4  50
+
+        Drop a row by index
+
+        >>> df.drop([0, 1])
+           A  B   C   D
+        2  3  7  12  40
+        3  4  8  13  50
+
+        Drop columns and/or rows of MultiIndex DataFrame
+
+        >>> midx = cudf.MultiIndex(levels=[['lama', 'cow', 'falcon'],
+        ...                              ['speed', 'weight', 'length']],
+        ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
+        ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
+        >>> df = cudf.DataFrame(index=midx, columns=['big', 'small'],
+        ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
+        ...                         [250, 150], [1.5, 0.8], [320, 250],
+        ...                         [1, 0.8], [0.3, 0.2]])
+        >>> df
+                         big  small
+        lama   speed    45.0   30.0
+               weight  200.0  100.0
+               length    1.5    1.0
+        cow    speed    30.0   20.0
+               weight  250.0  150.0
+               length    1.5    0.8
+        falcon speed   320.0  250.0
+               weight    1.0    0.8
+               length    0.3    0.2
+        >>> df.drop(index='cow', columns='small')
+                         big
+        lama   speed    45.0
+               weight  200.0
+               length    1.5
+        falcon speed   320.0
+               weight    1.0
+               length    0.3
+        >>> df.drop(index='length', level=1)
+                         big  small
+        lama   speed    45.0   30.0
+               weight  200.0  100.0
+        cow    speed    30.0   20.0
+               weight  250.0  150.0
+        falcon speed   320.0  250.0
+               weight    1.0    0.8
+        """
+        if labels is not None:
+            if index is not None or columns is not None:
+                raise ValueError(
+                    "Cannot specify both 'labels' and 'index'/'columns'"
+                )
+            target = labels
+        elif index is not None:
+            target = index
+            axis = 0
+        elif columns is not None:
+            target = columns
+            axis = 1
+        else:
+            raise ValueError(
+                "Need to specify at least one of 'labels', "
+                "'index' or 'columns'"
+            )
+
+        if inplace:
+            out = self
+        else:
+            out = self.copy()
+
+        if axis in (1, "columns"):
+            target = _get_host_unique(target)
+
+            _drop_columns(out, target, errors)
+        elif axis in (0, "index"):
+            dropped = _drop_rows_by_labels(out, target, level, errors)
+
+            if columns is not None:
+                columns = _get_host_unique(columns)
+                _drop_columns(dropped, columns, errors)
+
+            out._data = dropped._data
+            out._index = dropped._index
+
+        if not inplace:
+            return out
+
+    @_cudf_nvtx_annotate
+    def _explode(self, explode_column: Any, ignore_index: bool):
+        # Helper function for `explode` in `Series` and `Dataframe`, explodes a
+        # specified nested column. Other columns' corresponding rows are
+        # duplicated. If ignore_index is set, the original index is not
+        # exploded and will be replaced with a `RangeIndex`.
+        if not is_list_dtype(self._data[explode_column].dtype):
+            data = self._data.copy(deep=True)
+            idx = None if ignore_index else self._index.copy(deep=True)
+            return self.__class__._from_data(data, index=idx)
+
+        column_index = self._column_names.index(explode_column)
+        if not ignore_index and self._index is not None:
+            index_offset = self._index.nlevels
+        else:
+            index_offset = 0
+
+        exploded = libcudf.lists.explode_outer(
+            [
+                *(self._index._data.columns if not ignore_index else ()),
+                *self._columns,
+            ],
+            column_index + index_offset,
+        )
+        # We must copy inner datatype of the exploded list column to
+        # maintain struct dtype key names
+        exploded_dtype = cast(
+            ListDtype, self._columns[column_index].dtype
+        ).element_type
+        return self._from_columns_like_self(
+            exploded,
+            self._column_names,
+            self._index_names if not ignore_index else None,
+            override_dtypes=(
+                exploded_dtype if i == column_index else None
+                for i in range(len(self._columns))
+            ),
+        )
+
+    @_cudf_nvtx_annotate
+    def tile(self, count):
+        """Repeats the rows `count` times to form a new Frame.
+
+        Parameters
+        ----------
+        self : input Table containing columns to interleave.
+        count : Number of times to tile "rows". Must be non-negative.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df  = cudf.Dataframe([[8, 4, 7], [5, 2, 3]])
+        >>> count = 2
+        >>> df.tile(df, count)
+           0  1  2
+        0  8  4  7
+        1  5  2  3
+        0  8  4  7
+        1  5  2  3
+
+        Returns
+        -------
+        The indexed frame containing the tiled "rows".
+        """
+        return self._from_columns_like_self(
+            libcudf.reshape.tile(
+                [*self._index._columns, *self._columns], count
+            ),
+            column_names=self._column_names,
+            index_names=self._index_names,
+        )
+
+    @_cudf_nvtx_annotate
+    def groupby(
+        self,
+        by=None,
+        axis=0,
+        level=None,
+        as_index=True,
+        sort=no_default,
+        group_keys=False,
+        squeeze=False,
+        observed=True,
+        dropna=True,
+    ):
+        if sort is no_default:
+            sort = cudf.get_option("mode.pandas_compatible")
+
+        if axis not in (0, "index"):
+            raise NotImplementedError("axis parameter is not yet implemented")
+
+        if squeeze is not False:
+            raise NotImplementedError(
+                "squeeze parameter is not yet implemented"
+            )
+
+        if not observed:
+            raise NotImplementedError(
+                "observed parameter is not yet implemented"
+            )
+
+        if by is None and level is None:
+            raise TypeError(
+                "groupby() requires either by or level to be specified."
+            )
+        if group_keys is None:
+            group_keys = False
+
+        return (
+            self.__class__._resampler(self, by=by)
+            if isinstance(by, cudf.Grouper) and by.freq
+            else self.__class__._groupby(
+                self,
+                by=by,
+                level=level,
+                as_index=as_index,
+                dropna=dropna,
+                sort=sort,
+                group_keys=group_keys,
+            )
+        )
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Addition",
+            op_name="add",
+            equivalent_op="frame + other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.add(1)
+                        angles  degrees
+                circle          1      361
+                triangle        4      181
+                rectangle       5      361
+                """,
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.add(b)
+                a       2
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.add(b, fill_value=0)
+                a       2
+                b       1
+                c       1
+                d       1
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def add(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__add__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Addition",
+            op_name="radd",
+            equivalent_op="other + frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.radd(1)
+                        angles  degrees
+                circle          1      361
+                triangle        4      181
+                rectangle       5      361
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.radd(b)
+                a       2
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.radd(b, fill_value=0)
+                a       2
+                b       1
+                c       1
+                d       1
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def radd(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__radd__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Subtraction",
+            op_name="sub",
+            equivalent_op="frame - other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.sub(1)
+                        angles  degrees
+                circle         -1      359
+                triangle        2      179
+                rectangle       3      359
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.sub(b)
+                a       0
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.sub(b, fill_value=0)
+                a       2
+                b       1
+                c       1
+                d      -1
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def subtract(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__sub__", fill_value)
+
+    sub = subtract
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Subtraction",
+            op_name="rsub",
+            equivalent_op="other - frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.rsub(1)
+                        angles  degrees
+                circle          1     -359
+                triangle       -2     -179
+                rectangle      -3     -359
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.rsub(b)
+                a       0
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.rsub(b, fill_value=0)
+                a       0
+                b      -1
+                c      -1
+                d       1
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def rsub(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__rsub__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Multiplication",
+            op_name="mul",
+            equivalent_op="frame * other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.multiply(1)
+                        angles  degrees
+                circle          0      360
+                triangle        3      180
+                rectangle       4      360
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.multiply(b)
+                a       1
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.multiply(b, fill_value=0)
+                a       1
+                b       0
+                c       0
+                d       0
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def multiply(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__mul__", fill_value)
+
+    mul = multiply
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Multiplication",
+            op_name="rmul",
+            equivalent_op="other * frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.rmul(1)
+                        angles  degrees
+                circle          0      360
+                triangle        3      180
+                rectangle       4      360
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.rmul(b)
+                a       1
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.rmul(b, fill_value=0)
+                a       1
+                b       0
+                c       0
+                d       0
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def rmul(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__rmul__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Modulo",
+            op_name="mod",
+            equivalent_op="frame % other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.mod(1)
+                        angles  degrees
+                circle          0        0
+                triangle        0        0
+                rectangle       0        0
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.mod(b)
+                a       0
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.mod(b, fill_value=0)
+                a             0
+                b    4294967295
+                c    4294967295
+                d             0
+                e          <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def mod(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__mod__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Modulo",
+            op_name="rmod",
+            equivalent_op="other % frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.rmod(1)
+                            angles  degrees
+                circle     4294967295        1
+                triangle            1        1
+                rectangle           1        1
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.rmod(b)
+                a       0
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.rmod(b, fill_value=0)
+                a             0
+                b             0
+                c             0
+                d    4294967295
+                e          <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def rmod(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__rmod__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Exponential",
+            op_name="pow",
+            equivalent_op="frame ** other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.pow(1)
+                        angles  degrees
+                circle          0      360
+                triangle        2      180
+                rectangle       4      360
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.pow(b)
+                a       1
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.pow(b, fill_value=0)
+                a       1
+                b       1
+                c       1
+                d       0
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def pow(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__pow__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Exponential",
+            op_name="rpow",
+            equivalent_op="other ** frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.rpow(1)
+                        angles  degrees
+                circle          1        1
+                triangle        1        1
+                rectangle       1        1
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.rpow(b)
+                a       1
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.rpow(b, fill_value=0)
+                a       1
+                b       0
+                c       0
+                d       1
+                e    <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def rpow(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__rpow__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Integer division",
+            op_name="floordiv",
+            equivalent_op="frame // other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.floordiv(1)
+                        angles  degrees
+                circle          0      360
+                triangle        3      180
+                rectangle       4      360
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.floordiv(b)
+                a       1
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.floordiv(b, fill_value=0)
+                a                      1
+                b    9223372036854775807
+                c    9223372036854775807
+                d                      0
+                e                   <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def floordiv(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__floordiv__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Integer division",
+            op_name="rfloordiv",
+            equivalent_op="other // frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.rfloordiv(1)
+                                        angles  degrees
+                circle     9223372036854775807        0
+                triangle                     0        0
+                rectangle                    0        0
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.rfloordiv(b)
+                a       1
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: int64
+                >>> a.rfloordiv(b, fill_value=0)
+                a                      1
+                b                      0
+                c                      0
+                d    9223372036854775807
+                e                   <NA>
+                dtype: int64
+                """
+            ),
+        )
+    )
+    def rfloordiv(
+        self, other, axis, level=None, fill_value=None
+    ):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__rfloordiv__", fill_value)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Floating division",
+            op_name="truediv",
+            equivalent_op="frame / other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.truediv(1)
+                        angles  degrees
+                circle        0.0    360.0
+                triangle      3.0    180.0
+                rectangle     4.0    360.0
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.truediv(b)
+                a     1.0
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: float64
+                >>> a.truediv(b, fill_value=0)
+                a     1.0
+                b     Inf
+                c     Inf
+                d     0.0
+                e    <NA>
+                dtype: float64
+                """
+            ),
+        )
+    )
+    def truediv(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__truediv__", fill_value)
+
+    # Alias for truediv
+    div = truediv
+    divide = truediv
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Floating division",
+            op_name="rtruediv",
+            equivalent_op="other / frame",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.rtruediv(1)
+                            angles   degrees
+                circle          inf  0.002778
+                triangle   0.333333  0.005556
+                rectangle  0.250000  0.002778
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.rtruediv(b)
+                a     1.0
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: float64
+                >>> a.rtruediv(b, fill_value=0)
+                a     1.0
+                b     0.0
+                c     0.0
+                d     Inf
+                e    <NA>
+                dtype: float64
+                """
+            ),
+        )
+    )
+    def rtruediv(self, other, axis, level=None, fill_value=None):  # noqa: D102
+        if level is not None:
+            raise NotImplementedError("level parameter is not supported yet.")
+
+        return self._binaryop(other, "__rtruediv__", fill_value)
+
+    # Alias for rtruediv
+    rdiv = rtruediv
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Equal to",
+            op_name="eq",
+            equivalent_op="frame == other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.eq(1)
+                        angles  degrees
+                circle      False    False
+                triangle    False    False
+                rectangle   False    False
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.eq(b)
+                a    True
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: bool
+                >>> a.eq(b, fill_value=0)
+                a    True
+                b   False
+                c   False
+                d   False
+                e    <NA>
+                dtype: bool
+                """
+            ),
+        )
+    )
+    def eq(
+        self, other, axis="columns", level=None, fill_value=None
+    ):  # noqa: D102
+        return self._binaryop(
+            other=other, op="__eq__", fill_value=fill_value, can_reindex=True
+        )
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Not equal to",
+            op_name="ne",
+            equivalent_op="frame != other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.ne(1)
+                        angles  degrees
+                circle       True     True
+                triangle     True     True
+                rectangle    True     True
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.ne(b)
+                a    False
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: bool
+                >>> a.ne(b, fill_value=0)
+                a   False
+                b    True
+                c    True
+                d    True
+                e    <NA>
+                dtype: bool
+                """
+            ),
+        )
+    )
+    def ne(
+        self, other, axis="columns", level=None, fill_value=None
+    ):  # noqa: D102
+        return self._binaryop(
+            other=other, op="__ne__", fill_value=fill_value, can_reindex=True
+        )
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Less than",
+            op_name="lt",
+            equivalent_op="frame < other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.lt(1)
+                        angles  degrees
+                circle       True    False
+                triangle    False    False
+                rectangle   False    False
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.lt(b)
+                a   False
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: bool
+                >>> a.lt(b, fill_value=0)
+                a   False
+                b   False
+                c   False
+                d    True
+                e    <NA>
+                dtype: bool
+                """
+            ),
+        )
+    )
+    def lt(
+        self, other, axis="columns", level=None, fill_value=None
+    ):  # noqa: D102
+        return self._binaryop(
+            other=other, op="__lt__", fill_value=fill_value, can_reindex=True
+        )
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Less than or equal to",
+            op_name="le",
+            equivalent_op="frame <= other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.le(1)
+                        angles  degrees
+                circle       True    False
+                triangle    False    False
+                rectangle   False    False
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.le(b)
+                a    True
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: bool
+                >>> a.le(b, fill_value=0)
+                a    True
+                b   False
+                c   False
+                d    True
+                e    <NA>
+                dtype: bool
+                """
+            ),
+        )
+    )
+    def le(
+        self, other, axis="columns", level=None, fill_value=None
+    ):  # noqa: D102
+        return self._binaryop(
+            other=other, op="__le__", fill_value=fill_value, can_reindex=True
+        )
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Greater than",
+            op_name="gt",
+            equivalent_op="frame > other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.gt(1)
+                        angles  degrees
+                circle      False     True
+                triangle     True     True
+                rectangle    True     True
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.gt(b)
+                a   False
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: bool
+                >>> a.gt(b, fill_value=0)
+                a   False
+                b    True
+                c    True
+                d   False
+                e    <NA>
+                dtype: bool
+                """
+            ),
+        )
+    )
+    def gt(
+        self, other, axis="columns", level=None, fill_value=None
+    ):  # noqa: D102
+        return self._binaryop(
+            other=other, op="__gt__", fill_value=fill_value, can_reindex=True
+        )
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_binop_template.format(
+            operation="Greater than or equal to",
+            op_name="ge",
+            equivalent_op="frame >= other",
+            df_op_example=textwrap.dedent(
+                """
+                >>> df.ge(1)
+                        angles  degrees
+                circle      False     True
+                triangle     True     True
+                rectangle    True     True
+                """
+            ),
+            ser_op_example=textwrap.dedent(
+                """
+                >>> a.ge(b)
+                a    True
+                b    <NA>
+                c    <NA>
+                d    <NA>
+                e    <NA>
+                dtype: bool
+                >>> a.ge(b, fill_value=0)
+                a   True
+                b    True
+                c    True
+                d   False
+                e    <NA>
+                dtype: bool
+                """
+            ),
+        )
+    )
+    def ge(
+        self, other, axis="columns", level=None, fill_value=None
+    ):  # noqa: D102
+        return self._binaryop(
+            other=other, op="__ge__", fill_value=fill_value, can_reindex=True
+        )
+
+    def _preprocess_subset(self, subset):
+        if subset is None:
+            subset = self._column_names
+        elif (
+            not np.iterable(subset)
+            or isinstance(subset, str)
+            or isinstance(subset, tuple)
+            and subset in self._data.names
+        ):
+            subset = (subset,)
+        diff = set(subset) - set(self._data)
+        if len(diff) != 0:
+            raise KeyError(f"columns {diff} do not exist")
+        return subset
+
+    @_cudf_nvtx_annotate
+    def rank(
+        self,
+        axis=0,
+        method="average",
+        numeric_only=None,
+        na_option="keep",
+        ascending=True,
+        pct=False,
+    ):
+        """
+        Compute numerical data ranks (1 through n) along axis.
+
+        By default, equal values are assigned a rank that is the average of the
+        ranks of those values.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}, default 0
+            Index to direct ranking.
+        method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
+            How to rank the group of records that have the same value
+            (i.e. ties):
+            * average: average rank of the group
+            * min: lowest rank in the group
+            * max: highest rank in the group
+            * first: ranks assigned in order they appear in the array
+            * dense: like 'min', but rank always increases by 1 between groups.
+        numeric_only : bool, optional
+            For DataFrame objects, rank only numeric columns if set to True.
+        na_option : {'keep', 'top', 'bottom'}, default 'keep'
+            How to rank NaN values:
+            * keep: assign NaN rank to NaN values
+            * top: assign smallest rank to NaN values if ascending
+            * bottom: assign highest rank to NaN values if ascending.
+        ascending : bool, default True
+            Whether or not the elements should be ranked in ascending order.
+        pct : bool, default False
+            Whether or not to display the returned rankings in percentile
+            form.
+
+        Returns
+        -------
+        same type as caller
+            Return a Series or DataFrame with data ranks as values.
+        """
+        if method not in {"average", "min", "max", "first", "dense"}:
+            raise KeyError(method)
+
+        method_enum = libcudf.aggregation.RankMethod[method.upper()]
+        if na_option not in {"keep", "top", "bottom"}:
+            raise ValueError(
+                "na_option must be one of 'keep', 'top', or 'bottom'"
+            )
+
+        if axis not in (0, "index"):
+            raise NotImplementedError(
+                f"axis must be `0`/`index`, "
+                f"axis={axis} is not yet supported in rank"
+            )
+
+        source = self
+        if numeric_only:
+            numeric_cols = (
+                name
+                for name in self._data.names
+                if _is_non_decimal_numeric_dtype(self._data[name])
+            )
+            source = self._get_columns_by_label(numeric_cols)
+            if source.empty:
+                return source.astype("float64")
+
+        result_columns = libcudf.sort.rank_columns(
+            [*source._columns], method_enum, na_option, ascending, pct
+        )
+
+        return self.__class__._from_data(
+            dict(zip(source._column_names, result_columns)),
+            index=source._index,
+        ).astype(np.float64)
+
+    def convert_dtypes(
+        self,
+        infer_objects=True,
+        convert_string=True,
+        convert_integer=True,
+        convert_boolean=True,
+        convert_floating=True,
+        dtype_backend=None,
+    ):
+        """
+        Convert columns to the best possible nullable dtypes.
+
+        If the dtype is numeric, and consists of all integers, convert
+        to an appropriate integer extension type. Otherwise, convert
+        to an appropriate floating type.
+
+        All other dtypes are always returned as-is as all dtypes in
+        cudf are nullable.
+        """
+        result = self.copy()
+
+        if convert_floating:
+            # cast any floating columns to int64 if
+            # they are all integer data:
+            for name, col in result._data.items():
+                if col.dtype.kind == "f":
+                    col = col.fillna(0)
+                    if cp.allclose(col, col.astype("int64")):
+                        result._data[name] = col.astype("int64")
+        return result
+
+    @_warn_no_dask_cudf
+    def __dask_tokenize__(self):
+        return [
+            type(self),
+            self._dtypes,
+            self.index,
+            self.hash_values().values_host,
+        ]
+
+
+def _check_duplicate_level_names(specified, level_names):
+    """Raise if any of `specified` has duplicates in `level_names`."""
+    if specified is None:
+        return
+    if len(set(level_names)) == len(level_names):
+        return
+    duplicates = {key for key, val in Counter(level_names).items() if val > 1}
+
+    duplicates_specified = [spec for spec in specified if spec in duplicates]
+    if not len(duplicates_specified) == 0:
+        # Note: pandas raises first encountered duplicates, cuDF raises all.
+        raise ValueError(
+            f"The names {duplicates_specified} occurs multiple times, use a"
+            " level number"
+        )
+
+
+@_cudf_nvtx_annotate
+def _get_replacement_values_for_columns(
+    to_replace: Any, value: Any, columns_dtype_map: Dict[Any, Any]
+) -> Tuple[Dict[Any, bool], Dict[Any, Any], Dict[Any, Any]]:
+    """
+    Returns a per column mapping for the values to be replaced, new
+    values to be replaced with and if all the values are empty.
+
+    Parameters
+    ----------
+    to_replace : numeric, str, list-like or dict
+        Contains the values to be replaced.
+    value : numeric, str, list-like, or dict
+        Contains the values to replace `to_replace` with.
+    columns_dtype_map : dict
+        A column to dtype mapping representing dtype of columns.
+
+    Returns
+    -------
+    all_na_columns : dict
+        A dict mapping of all columns if they contain all na values
+    to_replace_columns : dict
+        A dict mapping of all columns and the existing values that
+        have to be replaced.
+    values_columns : dict
+        A dict mapping of all columns and the corresponding values
+        to be replaced with.
+    """
+    to_replace_columns: Dict[Any, Any] = {}
+    values_columns: Dict[Any, Any] = {}
+    all_na_columns: Dict[Any, Any] = {}
+
+    if is_scalar(to_replace) and is_scalar(value):
+        to_replace_columns = {col: [to_replace] for col in columns_dtype_map}
+        values_columns = {col: [value] for col in columns_dtype_map}
+    elif cudf.api.types.is_list_like(to_replace) or isinstance(
+        to_replace, ColumnBase
+    ):
+        if is_scalar(value):
+            to_replace_columns = {col: to_replace for col in columns_dtype_map}
+            values_columns = {
+                col: [value]
+                if _is_non_decimal_numeric_dtype(columns_dtype_map[col])
+                else full(
+                    len(to_replace),
+                    value,
+                    cudf.dtype(type(value)),
+                )
+                for col in columns_dtype_map
+            }
+        elif cudf.api.types.is_list_like(value):
+            if len(to_replace) != len(value):
+                raise ValueError(
+                    f"Replacement lists must be "
+                    f"of same length."
+                    f" Expected {len(to_replace)}, got {len(value)}."
+                )
+            else:
+                to_replace_columns = {
+                    col: to_replace for col in columns_dtype_map
+                }
+                values_columns = {col: value for col in columns_dtype_map}
+        elif cudf.utils.dtypes.is_column_like(value):
+            to_replace_columns = {col: to_replace for col in columns_dtype_map}
+            values_columns = {col: value for col in columns_dtype_map}
+        else:
+            raise TypeError(
+                "value argument must be scalar, list-like or Series"
+            )
+    elif _is_series(to_replace):
+        if value is None:
+            to_replace_columns = {
+                col: as_column(to_replace.index) for col in columns_dtype_map
+            }
+            values_columns = {col: to_replace for col in columns_dtype_map}
+        elif is_dict_like(value):
+            to_replace_columns = {
+                col: to_replace[col]
+                for col in columns_dtype_map
+                if col in to_replace
+            }
+            values_columns = {
+                col: value[col] for col in to_replace_columns if col in value
+            }
+        elif is_scalar(value) or _is_series(value):
+            to_replace_columns = {
+                col: to_replace[col]
+                for col in columns_dtype_map
+                if col in to_replace
+            }
+            values_columns = {
+                col: [value] if is_scalar(value) else value[col]
+                for col in to_replace_columns
+                if col in value
+            }
+        else:
+            raise ValueError(
+                "Series.replace cannot use dict-like to_replace and non-None "
+                "value"
+            )
+    elif is_dict_like(to_replace):
+        if value is None:
+            to_replace_columns = {
+                col: list(to_replace.keys()) for col in columns_dtype_map
+            }
+            values_columns = {
+                col: list(to_replace.values()) for col in columns_dtype_map
+            }
+        elif is_dict_like(value):
+            to_replace_columns = {
+                col: to_replace[col]
+                for col in columns_dtype_map
+                if col in to_replace
+            }
+            values_columns = {
+                col: value[col] for col in columns_dtype_map if col in value
+            }
+        elif is_scalar(value) or _is_series(value):
+            to_replace_columns = {
+                col: to_replace[col]
+                for col in columns_dtype_map
+                if col in to_replace
+            }
+            values_columns = {
+                col: [value] if is_scalar(value) else value
+                for col in columns_dtype_map
+                if col in to_replace
+            }
+        else:
+            raise TypeError("value argument must be scalar, dict, or Series")
+    else:
+        raise TypeError(
+            "Expecting 'to_replace' to be either a scalar, array-like, "
+            "dict or None, got invalid type "
+            f"'{type(to_replace).__name__}'"
+        )
+
+    to_replace_columns = {
+        key: [value] if is_scalar(value) else value
+        for key, value in to_replace_columns.items()
+    }
+    values_columns = {
+        key: [value] if is_scalar(value) else value
+        for key, value in values_columns.items()
+    }
+
+    for i in to_replace_columns:
+        if i in values_columns:
+            if isinstance(values_columns[i], list):
+                all_na = values_columns[i].count(None) == len(
+                    values_columns[i]
+                )
+            else:
+                all_na = False
+            all_na_columns[i] = all_na
+
+    return all_na_columns, to_replace_columns, values_columns
+
+
+def _is_series(obj):
+    """
+    Checks if the `obj` is of type `cudf.Series`
+    instead of checking for isinstance(obj, cudf.Series)
+    """
+    return isinstance(obj, Frame) and obj.ndim == 1 and obj._index is not None
+
+
+@_cudf_nvtx_annotate
+def _drop_rows_by_labels(
+    obj: DataFrameOrSeries,
+    labels: Union[ColumnLike, abc.Iterable, str],
+    level: Union[int, str],
+    errors: str,
+) -> DataFrameOrSeries:
+    """Remove rows specified by `labels`.
+
+    If `errors="raise"`, an error is raised if some items in `labels` do not
+    exist in `obj._index`.
+
+    Will raise if level(int) is greater or equal to index nlevels.
+    """
+    if isinstance(level, int) and level >= obj.index.nlevels:
+        raise ValueError("Param level out of bounds.")
+
+    if not isinstance(labels, cudf.core.single_column_frame.SingleColumnFrame):
+        labels = as_column(labels)
+
+    if isinstance(obj.index, cudf.MultiIndex):
+        if level is None:
+            level = 0
+
+        levels_index = obj.index.get_level_values(level)
+        if errors == "raise" and not labels.isin(levels_index).all():
+            raise KeyError("One or more values not found in axis")
+
+        if isinstance(level, int):
+            ilevel = level
+        else:
+            ilevel = obj._index.names.index(level)
+
+        # 1. Merge Index df and data df along column axis:
+        # | id | ._index df | data column(s) |
+        idx_nlv = obj._index.nlevels
+        working_df = obj._index.to_frame(index=False)
+        working_df.columns = list(range(idx_nlv))
+        for i, col in enumerate(obj._data):
+            working_df[idx_nlv + i] = obj._data[col]
+        # 2. Set `level` as common index:
+        # | level | ._index df w/o level | data column(s) |
+        working_df = working_df.set_index(level)
+
+        # 3. Use "leftanti" join to drop
+        # TODO: use internal API with "leftanti" and specify left and right
+        # join keys to bypass logic check
+        to_join = cudf.DataFrame(index=cudf.Index(labels, name=level))
+        join_res = working_df.join(to_join, how="leftanti")
+
+        # 4. Reconstruct original layout, and rename
+        join_res._insert(
+            ilevel, name=join_res._index.name, value=join_res._index
+        )
+
+        midx = cudf.MultiIndex.from_frame(
+            join_res.iloc[:, 0:idx_nlv], names=obj._index.names
+        )
+
+        if isinstance(obj, cudf.Series):
+            return obj.__class__._from_data(
+                join_res.iloc[:, idx_nlv:]._data, index=midx, name=obj.name
+            )
+        else:
+            return obj.__class__._from_data(
+                join_res.iloc[:, idx_nlv:]._data,
+                index=midx,
+                columns=obj._data.to_pandas_index(),
+            )
+
+    else:
+        if errors == "raise" and not labels.isin(obj.index).all():
+            raise KeyError("One or more values not found in axis")
+
+        key_df = cudf.DataFrame._from_data(
+            data={},
+            index=cudf.Index(
+                labels, name=getattr(labels, "name", obj.index.name)
+            ),
+        )
+        if isinstance(obj, cudf.DataFrame):
+            res = obj.join(key_df, how="leftanti")
+        else:
+            res = obj.to_frame(name="tmp").join(key_df, how="leftanti")["tmp"]
+            res.name = obj.name
+        # Join changes the index to common type,
+        # but we need to preserve the type of
+        # index being returned, Hence this type-cast.
+        res._index = res.index.astype(obj.index.dtype)
+        return res
+
+
+def _is_same_dtype(lhs_dtype, rhs_dtype):
+    # Utility specific to `_reindex` to check
+    # for matching column dtype.
+    if lhs_dtype == rhs_dtype:
+        return True
+    elif (
+        is_categorical_dtype(lhs_dtype)
+        and is_categorical_dtype(rhs_dtype)
+        and lhs_dtype.categories.dtype == rhs_dtype.categories.dtype
+    ):
+        # OK if categories are not all the same
+        return True
+    elif (
+        is_categorical_dtype(lhs_dtype)
+        and not is_categorical_dtype(rhs_dtype)
+        and lhs_dtype.categories.dtype == rhs_dtype
+    ):
+        return True
+    elif (
+        is_categorical_dtype(rhs_dtype)
+        and not is_categorical_dtype(lhs_dtype)
+        and rhs_dtype.categories.dtype == lhs_dtype
+    ):
+        return True
+    else:
+        return False
diff --git a/python/cudf/cudf/core/indexing_utils.py b/python/cudf/cudf/core/indexing_utils.py
new file mode 100644
index 0000000..7242de9
--- /dev/null
+++ b/python/cudf/cudf/core/indexing_utils.py
@@ -0,0 +1,243 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, List, Tuple, Union
+
+from typing_extensions import TypeAlias
+
+import cudf
+from cudf.api.types import (
+    _is_scalar_or_zero_d_array,
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+)
+from cudf.core.copy_types import BooleanMask, GatherMap
+
+
+class EmptyIndexer:
+    """An indexer that will produce an empty result."""
+
+    pass
+
+
+@dataclass
+class MapIndexer:
+    """An indexer for a gather map."""
+
+    key: GatherMap
+
+
+@dataclass
+class MaskIndexer:
+    """An indexer for a boolean mask."""
+
+    key: BooleanMask
+
+
+@dataclass
+class SliceIndexer:
+    """An indexer for a slice."""
+
+    key: slice
+
+
+@dataclass
+class ScalarIndexer:
+    """An indexer for a scalar value."""
+
+    key: GatherMap
+
+
+IndexingSpec: TypeAlias = Union[
+    EmptyIndexer, MapIndexer, MaskIndexer, ScalarIndexer, SliceIndexer
+]
+
+ColumnLabels: TypeAlias = List[str]
+
+
+def destructure_iloc_key(
+    key: Any, frame: Union[cudf.Series, cudf.DataFrame]
+) -> tuple[Any, ...]:
+    """
+    Destructure a potentially tuple-typed key into row and column indexers.
+
+    Tuple arguments to iloc indexing are treated specially. They are
+    picked apart into indexers for the row and column. If the number
+    of entries is less than the number of modes of the frame, missing
+    entries are slice-expanded.
+
+    If the user-provided key is not a tuple, it is treated as if it
+    were a singleton tuple, and then slice-expanded.
+
+    Once this destructuring has occurred, any entries that are
+    callables are then called with the indexed frame. This should
+    return a valid indexing object for the rows (respectively
+    columns), namely one of:
+
+    - A boolean mask of the same length as the frame in the given
+      dimension
+    - A scalar integer that indexes the frame
+    - An array-like of integers that index the frame
+    - A slice that indexes the frame
+
+    Integer and slice-based indexing follows usual Python conventions.
+
+    Parameters
+    ----------
+    key
+        The key to destructure
+    frame
+        DataFrame or Series to provide context
+
+    Returns
+    -------
+    tuple
+        Indexers with length equal to the dimension of the frame
+
+    Raises
+    ------
+    IndexError
+        If there are too many indexers, or any individual indexer is a tuple.
+    """
+    n = len(frame.shape)
+    if isinstance(key, tuple):
+        # Key potentially indexes rows and columns, slice-expand to
+        # shape of frame
+        indexers = key + (slice(None),) * (n - len(key))
+        if len(indexers) > n:
+            raise IndexError(
+                f"Too many indexers: got {len(indexers)} expected {n}"
+            )
+    else:
+        # Key indexes rows, slice-expand to shape of frame
+        indexers = (key, *(slice(None),) * (n - 1))
+    indexers = tuple(k(frame) if callable(k) else k for k in indexers)
+    if any(isinstance(k, tuple) for k in indexers):
+        raise IndexError(
+            "Too many indexers: can't have nested tuples in iloc indexing"
+        )
+    return indexers
+
+
+def destructure_dataframe_iloc_indexer(
+    key: Any, frame: cudf.DataFrame
+) -> Tuple[Any, Tuple[bool, ColumnLabels]]:
+    """Destructure an index key for DataFrame iloc getitem.
+
+    Parameters
+    ----------
+    key
+        Key to destructure
+    frame
+        DataFrame to provide context context
+
+    Returns
+    -------
+    tuple
+        2-tuple of a key for the rows and tuple of
+        (column_index_is_scalar, column_names) for the columns
+
+    Raises
+    ------
+    TypeError
+        If the column indexer is invalid
+    IndexError
+        If the provided key does not destructure correctly
+    NotImplementedError
+        If the requested column indexer repeats columns
+    """
+    rows, cols = destructure_iloc_key(key, frame)
+    if cols is Ellipsis:
+        cols = slice(None)
+    scalar = is_integer(cols)
+    try:
+        column_names: ColumnLabels = list(
+            frame._data.get_labels_by_index(cols)
+        )
+        if len(set(column_names)) != len(column_names):
+            raise NotImplementedError(
+                "cudf DataFrames do not support repeated column names"
+            )
+    except TypeError:
+        raise TypeError(
+            "Column indices must be integers, slices, "
+            "or list-like of integers"
+        )
+    if scalar:
+        assert (
+            len(column_names) == 1
+        ), "Scalar column indexer should not produce more than one column"
+
+    return rows, (scalar, column_names)
+
+
+def destructure_series_iloc_indexer(key: Any, frame: cudf.Series) -> Any:
+    """Destructure an index key for Series iloc getitem.
+
+    Parameters
+    ----------
+    key
+        Key to destructure
+    frame
+        Series for unpacking context
+
+    Returns
+    -------
+    Single key that will index the rows
+    """
+    (rows,) = destructure_iloc_key(key, frame)
+    return rows
+
+
+def parse_row_iloc_indexer(key: Any, n: int) -> IndexingSpec:
+    """
+    Normalize and produce structured information about a row indexer.
+
+    Given a row indexer that has already been destructured by
+    :func:`destructure_iloc_key`, inspect further and produce structured
+    information for indexing operations to act upon.
+
+    Parameters
+    ----------
+    key
+        Suitably destructured key for row indexing
+    n
+        Length of frame to index
+
+    Returns
+    -------
+    IndexingSpec
+        Structured data for indexing. A tag + parsed data.
+
+    Raises
+    ------
+    IndexError
+        If a valid type of indexer is provided, but it is out of
+        bounds
+    TypeError
+        If the indexing key is otherwise invalid.
+    """
+    if key is Ellipsis:
+        return SliceIndexer(slice(None))
+    elif isinstance(key, slice):
+        return SliceIndexer(key)
+    elif _is_scalar_or_zero_d_array(key):
+        return ScalarIndexer(GatherMap(key, n, nullify=False))
+    else:
+        key = cudf.core.column.as_column(key)
+        if isinstance(key, cudf.core.column.CategoricalColumn):
+            key = key.as_numerical_column(key.codes.dtype)
+        if is_bool_dtype(key.dtype):
+            return MaskIndexer(BooleanMask(key, n))
+        elif len(key) == 0:
+            return EmptyIndexer()
+        elif is_integer_dtype(key.dtype):
+            return MapIndexer(GatherMap(key, n, nullify=False))
+        else:
+            raise TypeError(
+                "Cannot index by location "
+                f"with non-integer key of type {type(key)}"
+            )
diff --git a/python/cudf/cudf/core/join/__init__.py b/python/cudf/cudf/core/join/__init__.py
new file mode 100644
index 0000000..71a91c3
--- /dev/null
+++ b/python/cudf/cudf/core/join/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+
+from cudf.core.join.join import Merge, MergeSemi
diff --git a/python/cudf/cudf/core/join/_join_helpers.py b/python/cudf/cudf/core/join/_join_helpers.py
new file mode 100644
index 0000000..1071261
--- /dev/null
+++ b/python/cudf/cudf/core/join/_join_helpers.py
@@ -0,0 +1,186 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+from collections import abc
+from typing import TYPE_CHECKING, Any, Tuple, cast
+
+import numpy as np
+
+import cudf
+from cudf.api.types import is_decimal_dtype, is_dtype_equal
+from cudf.core.column import CategoricalColumn
+from cudf.core.dtypes import CategoricalDtype
+
+if TYPE_CHECKING:
+    from cudf.core.column import ColumnBase
+
+
+class _Indexer:
+    # Indexer into a column (either a data column or index level).
+    #
+    # >>> df
+    #    a
+    # b
+    # 4  1
+    # 5  2
+    # 6  3
+    # >>> _Indexer("a", column=True).get(df)  # returns column "a" of df
+    # >>> _Indexer("b", index=True).get(df)  # returns index level "b" of df
+
+    def __init__(self, name: Any):
+        self.name = name
+
+
+class _ColumnIndexer(_Indexer):
+    def get(self, obj: cudf.DataFrame) -> ColumnBase:
+        return obj._data[self.name]
+
+    def set(self, obj: cudf.DataFrame, value: ColumnBase, validate=False):
+        obj._data.set_by_label(self.name, value, validate=validate)
+
+
+class _IndexIndexer(_Indexer):
+    def get(self, obj: cudf.DataFrame) -> ColumnBase:
+        return obj._index._data[self.name]
+
+    def set(self, obj: cudf.DataFrame, value: ColumnBase, validate=False):
+        obj._index._data.set_by_label(self.name, value, validate=validate)
+
+
+def _match_join_keys(
+    lcol: ColumnBase, rcol: ColumnBase, how: str
+) -> Tuple[ColumnBase, ColumnBase]:
+    # Casts lcol and rcol to a common dtype for use as join keys. If no casting
+    # is necessary, they are returned as is.
+
+    common_type = None
+
+    # cast the keys lcol and rcol to a common dtype
+    ltype = lcol.dtype
+    rtype = rcol.dtype
+
+    # if either side is categorical, different logic
+    left_is_categorical = isinstance(ltype, CategoricalDtype)
+    right_is_categorical = isinstance(rtype, CategoricalDtype)
+    if left_is_categorical and right_is_categorical:
+        return _match_categorical_dtypes_both(
+            cast(CategoricalColumn, lcol), cast(CategoricalColumn, rcol), how
+        )
+    elif left_is_categorical or right_is_categorical:
+        if left_is_categorical:
+            if how in {"left", "leftsemi", "leftanti"}:
+                return lcol, rcol.astype(ltype)
+            common_type = ltype.categories.dtype
+        else:
+            common_type = rtype.categories.dtype
+        common_type = cudf.utils.dtypes._dtype_pandas_compatible(common_type)
+        return lcol.astype(common_type), rcol.astype(common_type)
+
+    if is_dtype_equal(ltype, rtype):
+        return lcol, rcol
+
+    if is_decimal_dtype(ltype) or is_decimal_dtype(rtype):
+        raise TypeError(
+            "Decimal columns can only be merged with decimal columns "
+            "of the same precision and scale"
+        )
+
+    if (
+        np.issubdtype(ltype, np.number)
+        and np.issubdtype(rtype, np.number)
+        and not (
+            np.issubdtype(ltype, np.timedelta64)
+            or np.issubdtype(rtype, np.timedelta64)
+        )
+    ):
+        common_type = (
+            max(ltype, rtype)
+            if ltype.kind == rtype.kind
+            else np.find_common_type([], (ltype, rtype))
+        )
+    elif (
+        np.issubdtype(ltype, np.datetime64)
+        and np.issubdtype(rtype, np.datetime64)
+    ) or (
+        np.issubdtype(ltype, np.timedelta64)
+        and np.issubdtype(rtype, np.timedelta64)
+    ):
+        common_type = max(ltype, rtype)
+    elif (
+        np.issubdtype(ltype, np.datetime64)
+        or np.issubdtype(ltype, np.timedelta64)
+    ) and not rcol.fillna(0).can_cast_safely(ltype):
+        raise TypeError(
+            f"Cannot join between {ltype} and {rtype}, please type-cast both "
+            "columns to the same type."
+        )
+    elif (
+        np.issubdtype(rtype, np.datetime64)
+        or np.issubdtype(rtype, np.timedelta64)
+    ) and not lcol.fillna(0).can_cast_safely(rtype):
+        raise TypeError(
+            f"Cannot join between {rtype} and {ltype}, please type-cast both "
+            "columns to the same type."
+        )
+
+    if how == "left" and rcol.fillna(0).can_cast_safely(ltype):
+        return lcol, rcol.astype(ltype)
+
+    return lcol.astype(common_type), rcol.astype(common_type)
+
+
+def _match_categorical_dtypes_both(
+    lcol: CategoricalColumn, rcol: CategoricalColumn, how: str
+) -> Tuple[ColumnBase, ColumnBase]:
+    ltype, rtype = lcol.dtype, rcol.dtype
+
+    # when both are ordered and both have the same categories,
+    # no casting required:
+    if ltype == rtype:
+        return lcol, rcol
+
+    # Merging categorical variables when only one side is ordered is
+    # ambiguous and not allowed.
+    if ltype.ordered != rtype.ordered:
+        raise TypeError(
+            "Merging on categorical variables with mismatched"
+            " ordering is ambiguous"
+        )
+
+    if ltype.ordered and rtype.ordered:
+        # if we get to here, categories must be what causes the
+        # dtype equality check to fail. And we can never merge
+        # two ordered categoricals with different categories
+        raise TypeError(
+            f"{how} merge between categoricals with "
+            "different categories is only valid when "
+            "neither side is ordered"
+        )
+
+    if how == "inner":
+        # cast to category types -- we must cast them back later
+        return _match_join_keys(
+            lcol._get_decategorized_column(),
+            rcol._get_decategorized_column(),
+            how,
+        )
+    elif how in {"left", "leftanti", "leftsemi"}:
+        # always cast to left type
+        return lcol, rcol.astype(ltype)
+    else:
+        # merge categories
+        merged_categories = cudf.concat(
+            [ltype.categories, rtype.categories]
+        ).unique()
+        common_type = cudf.CategoricalDtype(
+            categories=merged_categories, ordered=False
+        )
+        return lcol.astype(common_type), rcol.astype(common_type)
+
+
+def _coerce_to_tuple(obj):
+    if isinstance(obj, abc.Iterable) and not isinstance(obj, str):
+        return tuple(obj)
+    else:
+        return (obj,)
diff --git a/python/cudf/cudf/core/join/join.py b/python/cudf/cudf/core/join/join.py
new file mode 100644
index 0000000..6a6e371
--- /dev/null
+++ b/python/cudf/cudf/core/join/join.py
@@ -0,0 +1,451 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+from __future__ import annotations
+
+import warnings
+from typing import Any, ClassVar, List, Optional
+
+import cudf
+from cudf import _lib as libcudf
+from cudf.core.copy_types import GatherMap
+from cudf.core.join._join_helpers import (
+    _coerce_to_tuple,
+    _ColumnIndexer,
+    _IndexIndexer,
+    _match_join_keys,
+)
+
+
+class Merge:
+    # The joiner function must have the following signature:
+    #
+    #     def joiner(
+    #         lhs: Frame,
+    #         rhs: Frame
+    #     ) -> Tuple[Optional[Column], Optional[Column]]:
+    #          ...
+    #
+    # where `lhs` and `rhs` are Frames composed of the left and right
+    # join key. The `joiner` returns a tuple of two Columns
+    # representing the rows to gather from the left- and right- side
+    # tables respectively.
+    _joiner: ClassVar[staticmethod] = staticmethod(libcudf.join.join)
+
+    def __init__(
+        self,
+        lhs,
+        rhs,
+        *,
+        on,
+        left_on,
+        right_on,
+        left_index,
+        right_index,
+        how,
+        sort,
+        indicator,
+        suffixes,
+    ):
+        """
+        Manage the merging of two Frames.
+
+        Parameters
+        ----------
+        lhs : DataFrame
+            The left operand of the merge
+        rhs : DataFrame
+            The right operand of the merge
+        on : string or list like
+            A set of key columns in the left and right operands
+            elements must be common to both frames
+        left_on : string or list like
+            A set of key columns in the left operand. Must be
+            specified with right_on or right_index concurrently
+        right_on : string or list like
+            A set of key columns in the right operand. Must be
+            specified with left_on or left_index concurrently
+        left_index : bool
+            Boolean flag indicating the left index column or columns
+            are to be used as join keys in order.
+        right_index : bool
+            Boolean flag indicating the right index column or columns
+            are to be used as join keys in order.
+        how : string
+            The type of join. Possible values are
+            'inner', 'outer', 'left', 'leftsemi' and 'leftanti'
+        sort : bool
+            Boolean flag indicating if the output Frame is to be
+            sorted on the output's join keys, in left to right order.
+        suffixes : list like
+            Left and right suffixes specified together, unpacked into lsuffix
+            and rsuffix.
+        """
+        self._validate_merge_params(
+            lhs,
+            rhs,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+            how=how,
+            suffixes=suffixes,
+        )
+
+        self.lhs = lhs.copy(deep=False)
+        self.rhs = rhs.copy(deep=False)
+        self.how = how
+        self.sort = sort
+        self.lsuffix, self.rsuffix = suffixes
+
+        # At this point validation guarantees that if on is not None we
+        # don't have any other args, so we can apply it directly to left_on and
+        # right_on.
+        self._using_left_index = bool(left_index)
+        left_on = (
+            lhs.index._data.names if left_index else left_on if left_on else on
+        )
+        self._using_right_index = bool(right_index)
+        right_on = (
+            rhs.index._data.names
+            if right_index
+            else right_on
+            if right_on
+            else on
+        )
+
+        if left_on or right_on:
+            self._left_keys = [
+                _ColumnIndexer(name=on)
+                if not self._using_left_index and on in lhs._data
+                else _IndexIndexer(name=on)
+                for on in (_coerce_to_tuple(left_on) if left_on else [])
+            ]
+            self._right_keys = [
+                _ColumnIndexer(name=on)
+                if not self._using_right_index and on in rhs._data
+                else _IndexIndexer(name=on)
+                for on in (_coerce_to_tuple(right_on) if right_on else [])
+            ]
+            if len(self._left_keys) != len(self._right_keys):
+                raise ValueError(
+                    "Merge operands must have same number of join key columns"
+                )
+            self._using_left_index = any(
+                isinstance(idx, _IndexIndexer) for idx in self._left_keys
+            )
+            self._using_right_index = any(
+                isinstance(idx, _IndexIndexer) for idx in self._right_keys
+            )
+        else:
+            # if `on` is not provided and we're not merging
+            # index with column or on both indexes, then use
+            # the intersection  of columns in both frames
+            on_names = set(lhs._data) & set(rhs._data)
+            self._left_keys = [_ColumnIndexer(name=on) for on in on_names]
+            self._right_keys = [_ColumnIndexer(name=on) for on in on_names]
+            self._using_left_index = False
+            self._using_right_index = False
+
+        self._key_columns_with_same_name = (
+            set(_coerce_to_tuple(on))
+            if on
+            else {
+                lkey.name
+                for lkey, rkey in zip(self._left_keys, self._right_keys)
+                if lkey.name == rkey.name
+                and not (
+                    isinstance(lkey, _IndexIndexer)
+                    or isinstance(rkey, _IndexIndexer)
+                )
+            }
+        )
+
+    def perform_merge(self) -> cudf.DataFrame:
+        left_join_cols = []
+        right_join_cols = []
+
+        for left_key, right_key in zip(self._left_keys, self._right_keys):
+            lcol = left_key.get(self.lhs)
+            rcol = right_key.get(self.rhs)
+            lcol_casted, rcol_casted = _match_join_keys(lcol, rcol, self.how)
+            left_join_cols.append(lcol_casted)
+            right_join_cols.append(rcol_casted)
+
+            # Categorical dtypes must be cast back from the underlying codes
+            # type that was returned by _match_join_keys.
+            if (
+                self.how == "inner"
+                and isinstance(lcol.dtype, cudf.CategoricalDtype)
+                and isinstance(rcol.dtype, cudf.CategoricalDtype)
+            ):
+                lcol_casted = lcol_casted.astype("category")
+                rcol_casted = rcol_casted.astype("category")
+
+            left_key.set(self.lhs, lcol_casted, validate=False)
+            right_key.set(self.rhs, rcol_casted, validate=False)
+
+        left_rows, right_rows = self._joiner(
+            left_join_cols,
+            right_join_cols,
+            how=self.how,
+        )
+
+        gather_kwargs = {
+            "keep_index": self._using_left_index or self._using_right_index,
+        }
+        left_result = (
+            self.lhs._gather(
+                GatherMap.from_column_unchecked(
+                    left_rows, len(self.lhs), nullify=True
+                ),
+                **gather_kwargs,
+            )
+            if left_rows is not None
+            else cudf.DataFrame._from_data({})
+        )
+        right_result = (
+            self.rhs._gather(
+                GatherMap.from_column_unchecked(
+                    right_rows, len(self.rhs), nullify=True
+                ),
+                **gather_kwargs,
+            )
+            if right_rows is not None
+            else cudf.DataFrame._from_data({})
+        )
+
+        result = cudf.DataFrame._from_data(
+            *self._merge_results(left_result, right_result)
+        )
+
+        if self.sort:
+            result = self._sort_result(result)
+        return result
+
+    def _merge_results(
+        self, left_result: cudf.DataFrame, right_result: cudf.DataFrame
+    ):
+        # Merge the DataFrames `left_result` and `right_result` into a single
+        # `DataFrame`, suffixing column names if necessary.
+
+        # If two key columns have the same name, a single output column appears
+        # in the result. For all non-outer join types, the key column from the
+        # rhs is simply dropped. For outer joins, the two key columns are
+        # combined by filling nulls in the left key column with corresponding
+        # values from the right key column:
+        if self.how == "outer":
+            for lkey, rkey in zip(self._left_keys, self._right_keys):
+                if lkey.name == rkey.name:
+                    # fill nulls in lhs from values in the rhs
+                    lkey.set(
+                        left_result,
+                        lkey.get(left_result).fillna(rkey.get(right_result)),
+                        validate=False,
+                    )
+
+        # All columns from the left table make it into the output. Non-key
+        # columns that share a name with a column in the right table are
+        # suffixed with the provided suffix.
+        common_names = set(left_result._data.names) & set(
+            right_result._data.names
+        )
+        cols_to_suffix = common_names - self._key_columns_with_same_name
+        data = {
+            (f"{name}{self.lsuffix}" if name in cols_to_suffix else name): col
+            for name, col in left_result._data.items()
+        }
+
+        # The right table follows the same rule as the left table except that
+        # key columns from the right table are removed.
+        for name, col in right_result._data.items():
+            if name in common_names:
+                if name not in self._key_columns_with_same_name:
+                    data[f"{name}{self.rsuffix}"] = col
+            else:
+                data[name] = col
+
+        # determine if the result has multiindex columns.  The result
+        # of a join has a MultiIndex as its columns if:
+        # - both the `lhs` and `rhs` have a MultiIndex columns
+        # OR
+        # - either one of `lhs` or `rhs` have a MultiIndex columns,
+        #   and the other is empty (i.e., no columns)
+        if self.lhs._data and self.rhs._data:
+            multiindex_columns = (
+                self.lhs._data.multiindex and self.rhs._data.multiindex
+            )
+        elif self.lhs._data:
+            multiindex_columns = self.lhs._data.multiindex
+        elif self.rhs._data:
+            multiindex_columns = self.rhs._data.multiindex
+        else:
+            multiindex_columns = False
+
+        index: Optional[cudf.BaseIndex]
+        if self._using_right_index:
+            # right_index and left_on
+            index = left_result._index
+        elif self._using_left_index:
+            # left_index and right_on
+            index = right_result._index
+        else:
+            index = None
+
+        # Construct result from data and index:
+        return (
+            left_result._data.__class__(
+                data=data, multiindex=multiindex_columns
+            ),
+            index,
+        )
+
+    def _sort_result(self, result: cudf.DataFrame) -> cudf.DataFrame:
+        # Pandas sorts on the key columns in the
+        # same order as given in 'on'. If the indices are used as
+        # keys, the index will be sorted. If one index is specified,
+        # the key columns on the other side will be used to sort.
+        by: List[Any] = []
+        if self._using_left_index and self._using_right_index:
+            by.extend(result._index._data.columns)
+        if not self._using_left_index:
+            by.extend([result._data[col.name] for col in self._left_keys])
+        if not self._using_right_index:
+            by.extend([result._data[col.name] for col in self._right_keys])
+        if by:
+            to_sort = cudf.DataFrame._from_data(dict(enumerate(by)))
+            sort_order = GatherMap.from_column_unchecked(
+                cudf.core.column.as_column(to_sort.argsort()),
+                len(result),
+                nullify=False,
+            )
+            result = result._gather(
+                sort_order,
+                keep_index=self._using_left_index or self._using_right_index,
+            )
+        return result
+
+    @staticmethod
+    def _validate_merge_params(
+        lhs,
+        rhs,
+        on,
+        left_on,
+        right_on,
+        left_index,
+        right_index,
+        how,
+        suffixes,
+    ):
+        # Error for various invalid combinations of merge input parameters
+
+        # We must actually support the requested merge type
+        if how not in {"left", "inner", "outer", "leftanti", "leftsemi"}:
+            raise NotImplementedError(f"{how} merge not supported yet")
+
+        if on:
+            if left_on or right_on:
+                # Passing 'on' with 'left_on' or 'right_on' is ambiguous
+                raise ValueError(
+                    'Can only pass argument "on" OR "left_on" '
+                    'and "right_on", not a combination of both.'
+                )
+            elif left_index or right_index:
+                # Passing 'on' with 'left_index' or 'right_index' is ambiguous
+                raise ValueError(
+                    'Can only pass argument "on" OR "left_index" '
+                    'and "right_index", not a combination of both.'
+                )
+            else:
+                # the validity of 'on' being checked by _Indexer
+                return
+        elif left_on and left_index:
+            raise ValueError(
+                'Can only pass argument "left_on" OR "left_index" not both.'
+            )
+        elif right_on and right_index:
+            raise ValueError(
+                'Can only pass argument "right_on" OR "right_index" not both.'
+            )
+
+        # Can't merge on a column name that is present in both a frame and its
+        # indexes.
+        if on:
+            for key in on:
+                if (key in lhs._data and key in lhs.index._data) or (
+                    key in rhs._data and key in rhs.index._data
+                ):
+                    raise ValueError(
+                        f"{key} is both an index level and a "
+                        "column label, which is ambiguous."
+                    )
+        if left_on:
+            for key in left_on:
+                if key in lhs._data and key in lhs.index._data:
+                    raise ValueError(
+                        f"{key} is both an index level and a "
+                        "column label, which is ambiguous."
+                    )
+        if right_on:
+            for key in right_on:
+                if key in rhs._data and key in rhs.index._data:
+                    raise ValueError(
+                        f"{key} is both an index level and a "
+                        "column label, which is ambiguous."
+                    )
+
+        # Can't merge on unnamed Series
+        if (isinstance(lhs, cudf.Series) and not lhs.name) or (
+            isinstance(rhs, cudf.Series) and not rhs.name
+        ):
+            raise ValueError("Cannot merge on unnamed Series")
+
+        # If nothing specified, must have common cols to use implicitly
+        same_named_columns = set(lhs._data) & set(rhs._data)
+        if (
+            not (left_index or right_index)
+            and not (left_on or right_on)
+            and len(same_named_columns) == 0
+        ):
+            raise ValueError("No common columns to perform merge on")
+
+        lsuffix, rsuffix = suffixes
+        for name in same_named_columns:
+            if name == left_on == right_on:
+                continue
+            elif left_on and right_on:
+                if (name in left_on and name in right_on) and (
+                    left_on.index(name) == right_on.index(name)
+                ):
+                    continue
+            else:
+                if not (lsuffix or rsuffix):
+                    raise ValueError(
+                        "there are overlapping columns but "
+                        "lsuffix and rsuffix are not defined"
+                    )
+
+        if (
+            isinstance(lhs, cudf.DataFrame)
+            and isinstance(rhs, cudf.DataFrame)
+            # An empty column is considered to have 1 level by pandas (can be
+            # seen by using lhs.columns.nlevels, but we don't want to use
+            # columns internally because it's expensive).
+            # TODO: Investigate whether ColumnAccessor.nlevels should be
+            # modified in the size 0 case.
+            and max(lhs._data.nlevels, 1) != max(rhs._data.nlevels, 1)
+        ):
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "merging between different levels is deprecated and will be "
+                f"removed in a future version. ({lhs._data.nlevels} levels on "
+                f"the left, {rhs._data.nlevels} on the right)",
+                FutureWarning,
+            )
+
+
+class MergeSemi(Merge):
+    _joiner: ClassVar[staticmethod] = staticmethod(libcudf.join.semi_join)
+
+    def _merge_results(self, lhs: cudf.DataFrame, rhs: cudf.DataFrame):
+        # semi-join result includes only lhs columns
+        return lhs._data, lhs._index
diff --git a/python/cudf/cudf/core/missing.py b/python/cudf/cudf/core/missing.py
new file mode 100644
index 0000000..0d48a1d
--- /dev/null
+++ b/python/cudf/cudf/core/missing.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+
+# Pandas NAType enforces a single instance exists at a time
+# instantiating this class will yield the existing instance
+# of pandas._libs.missing.NAType, id(cudf.NA) == id(pd.NA).
+from pandas import NA, NaT
+
+__all__ = ["NA", "NaT"]
diff --git a/python/cudf/cudf/core/mixins/__init__.py b/python/cudf/cudf/core/mixins/__init__.py
new file mode 100644
index 0000000..8306f3f
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from .binops import BinaryOperand
+from .reductions import Reducible
+from .scans import Scannable
+
+__all__ = ["BinaryOperand", "Reducible", "Scannable"]
diff --git a/python/cudf/cudf/core/mixins/binops.py b/python/cudf/cudf/core/mixins/binops.py
new file mode 100644
index 0000000..eaabc00
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/binops.py
@@ -0,0 +1,72 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from .mixin_factory import _create_delegating_mixin
+
+BinaryOperand = _create_delegating_mixin(
+    "BinaryOperand",
+    "Mixin encapsulating binary operations.",
+    "BINARY_OPERATION",
+    "_binaryop",
+    {
+        # Numeric operations.
+        "__add__",
+        "__sub__",
+        "__mul__",
+        "__matmul__",
+        "__truediv__",
+        "__floordiv__",
+        "__mod__",
+        # "__divmod__", # Not yet implemented
+        "__pow__",
+        # "__lshift__", # Not yet implemented
+        # "__rshift__", # Not yet implemented
+        "__and__",
+        "__xor__",
+        "__or__",
+        # Reflected numeric operations.
+        "__radd__",
+        "__rsub__",
+        "__rmul__",
+        "__rmatmul__",
+        "__rtruediv__",
+        "__rfloordiv__",
+        "__rmod__",
+        # "__rdivmod__", # Not yet implemented
+        "__rpow__",
+        # "__rlshift__", # Not yet implemented
+        # "__rrshift__", # Not yet implemented
+        "__rand__",
+        "__rxor__",
+        "__ror__",
+        # Rich comparison operations.
+        "__lt__",
+        "__le__",
+        "__eq__",
+        "__ne__",
+        "__gt__",
+        "__ge__",
+    },
+)
+
+# TODO: See if there is a better approach to these two issues: 1) The mixin
+# assumes a single standard parameter, whereas binops have two, and 2) we need
+# a way to determine reflected vs normal ops.
+
+
+def _binaryop(self, other, op: str):
+    """The core binary_operation function.
+
+    Must be overridden by subclasses, the default implementation raises a
+    NotImplementedError.
+    """
+    raise NotImplementedError
+
+
+def _check_reflected_op(op):
+    if reflect := op[2] == "r" and op != "__rshift__":
+        op = op[:2] + op[3:]
+    return reflect, op
+
+
+BinaryOperand._binaryop = _binaryop
+BinaryOperand._check_reflected_op = staticmethod(_check_reflected_op)
diff --git a/python/cudf/cudf/core/mixins/binops.pyi b/python/cudf/cudf/core/mixins/binops.pyi
new file mode 100644
index 0000000..8587b2d
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/binops.pyi
@@ -0,0 +1,39 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from typing import Any, Set, Tuple, TypeVar
+
+# Note: It may be possible to define a narrower bound here eventually.
+BinaryOperandType = TypeVar("BinaryOperandType", bound="Any")
+
+class BinaryOperand:
+    _SUPPORTED_BINARY_OPERATIONS: Set
+
+    def _binaryop(self, other: BinaryOperandType, op: str): ...
+    def __add__(self, other): ...
+    def __sub__(self, other): ...
+    def __mul__(self, other): ...
+    def __truediv__(self, other): ...
+    def __floordiv__(self, other): ...
+    def __mod__(self, other): ...
+    def __pow__(self, other): ...
+    def __and__(self, other): ...
+    def __xor__(self, other): ...
+    def __or__(self, other): ...
+    def __radd__(self, other): ...
+    def __rsub__(self, other): ...
+    def __rmul__(self, other): ...
+    def __rtruediv__(self, other): ...
+    def __rfloordiv__(self, other): ...
+    def __rmod__(self, other): ...
+    def __rpow__(self, other): ...
+    def __rand__(self, other): ...
+    def __rxor__(self, other): ...
+    def __ror__(self, other): ...
+    def __lt__(self, other): ...
+    def __le__(self, other): ...
+    def __eq__(self, other): ...
+    def __ne__(self, other): ...
+    def __gt__(self, other): ...
+    def __ge__(self, other): ...
+    @staticmethod
+    def _check_reflected_op(op) -> Tuple[bool, str]: ...
diff --git a/python/cudf/cudf/core/mixins/mixin_factory.py b/python/cudf/cudf/core/mixins/mixin_factory.py
new file mode 100644
index 0000000..7bbb299
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/mixin_factory.py
@@ -0,0 +1,263 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import inspect
+
+
+# `functools.partialmethod` does not allow setting attributes such as
+# __doc__ on the resulting method. So we use a simple alternative to
+# it here:
+def _partialmethod(method, *args1, **kwargs1):
+    def wrapper(self, *args2, **kwargs2):
+        return method(self, *args1, *args2, **kwargs1, **kwargs2)
+
+    return wrapper
+
+
+class Operation:
+    """Descriptor used to define operations for delegating mixins.
+
+    This class is designed to be assigned to the attributes (the delegating
+    methods) defined by the OperationMixin. This class will create the method
+    and mimic all the expected attributes for that method to appear as though
+    it was originally designed on the class. The use of the descriptor pattern
+    ensures that the method is only created the first time it is invoked, after
+    which all further calls use the callable generated on the first invocation.
+
+    Parameters
+    ----------
+    name : str
+        The name of the operation.
+    docstring_format_args : str
+        The attribute of the owning class from which to pull format parameters
+        for this operation's docstring.
+    base_operation : str
+        The underlying operation function to be invoked when operation `name`
+        is called on the owning class.
+    """
+
+    def __init__(self, name, docstring_format_args, base_operation):
+        self._name = name
+        self._docstring_format_args = docstring_format_args
+        self._base_operation = base_operation
+
+    def __get__(self, obj, owner=None):
+        retfunc = _partialmethod(self._base_operation, op=self._name)
+
+        # Required attributes that will exist.
+        retfunc.__name__ = self._name
+        retfunc.__qualname__ = ".".join([owner.__name__, self._name])
+        retfunc.__module__ = self._base_operation.__module__
+
+        if self._base_operation.__doc__ is not None:
+            retfunc.__doc__ = self._base_operation.__doc__.format(
+                cls=owner.__name__,
+                op=self._name,
+                **self._docstring_format_args,
+            )
+
+        retfunc.__annotations__ = self._base_operation.__annotations__.copy()
+        retfunc.__annotations__.pop("op", None)
+        retfunc_params = [
+            v
+            for k, v in inspect.signature(
+                self._base_operation
+            ).parameters.items()
+            if k != "op"
+        ]
+        retfunc.__signature__ = inspect.Signature(retfunc_params)
+
+        setattr(owner, self._name, retfunc)
+
+        if obj is None:
+            return getattr(owner, self._name)
+        else:
+            return getattr(obj, self._name)
+
+
+def _should_define_operation(cls, operation, base_operation_name):
+    if operation not in dir(cls):
+        return True
+
+    # If the class doesn't override the base operation we stick to whatever
+    # parent implementation exists.
+    if base_operation_name not in cls.__dict__:
+        return False
+
+    # At this point we know that the class has the operation defined but it
+    # also overrides the base operation. Since this function is called before
+    # the operation is defined on the current class, we know that it inherited
+    # the operation from a parent. We therefore have three possibilities:
+    # 1. A parent class manually defined the operation. That override takes
+    #    precedence even if the current class defined the base operation.
+    # 2. A parent class has an auto-generated operation, i.e. it is of type
+    #    Operation and was created by OperationMixin.__init_subclass__. The
+    #    current class must override it so that its base operation is used
+    #    rather than the parent's base operation.
+    # 3. The method is defined for all classes, i.e. it is a method of object.
+    for base_cls in cls.__mro__:
+        # We always override methods defined for object.
+        if base_cls is object:
+            return True
+        # The first attribute in the MRO is the one that will be used.
+        if operation in base_cls.__dict__:
+            return isinstance(base_cls.__dict__[operation], Operation)
+
+    # This line should be unreachable since we know the attribute exists
+    # somewhere in the MRO if the for loop was entered.
+    assert False, "Operation attribute not found in hierarchy."
+
+
+def _create_delegating_mixin(
+    mixin_name,
+    docstring,
+    category_name,
+    base_operation_name,
+    supported_operations,
+):
+    """Factory for mixins defining collections of delegated operations.
+
+    This function generates mixins based on two common paradigms in cuDF:
+
+    1. libcudf groups many operations into categories using a common API. These
+       APIs usually accept an enum to delineate the specific operation to
+       perform, e.g. binary operations use the `binary_operator` enum when
+       calling the `binary_operation` function. cuDF Python mimics this
+       structure by having operations within a category delegate to a common
+       internal function (e.g. DataFrame.__add__ calls DataFrame._binaryop).
+    2. Many cuDF classes implement similar operations (e.g. `sum`) via
+       delegation to lower-level APIs before reaching a libcudf C++ function
+       call. As a result, many API function calls actually involve multiple
+       delegations to lower-level APIs that can look essentially identical. An
+       example of such a sequence would be DataFrame.sum -> DataFrame._reduce
+       -> Column.sum -> Column._reduce -> libcudf.
+
+    This factory creates mixins for a category of operations implemented by via
+    this delegator pattern. The resulting mixins make it easy to share common
+    functions across various classes while also providing a common entrypoint
+    for implementing the centralized logic for a given category of operations.
+    Its usage is best demonstrated by example below.
+
+    Parameters
+    ----------
+    mixin_name : str
+        The name of the class. This argument should be the same as the object
+        that this function's output is assigned to, e.g.
+        :code:`Baz = _create_delegating_mixin("Baz", ...)`.
+    docstring : str
+        The documentation string for the mixin class.
+    category_name : str
+        The category of operations for which a mixin is being created. This
+        name will be used to define or access the following attributes as shown
+        in the example below:
+            - f'_{category_name}_DOCSTRINGS'
+            - f'_VALID_{category_name}S'  # The subset of ops a subclass allows
+            - f'_SUPPORTED_{category_name}S'  # The ops supported by the mixin
+    base_operation_name : str
+        The name given to the core function implementing this category of
+        operations.  The corresponding function is the entrypoint for child
+        classes.
+    supported_ops : List[str]
+        The list of valid operations that subclasses of the resulting mixin may
+        request to be implemented.
+
+    Examples
+    --------
+    >>> # The class below:
+    >>> class Person:
+    ...     def _greet(self, op):
+    ...         print(op)
+    ...
+    ...     def hello(self):
+    ...         self._greet("hello")
+    ...
+    ...     def goodbye(self):
+    ...         self._greet("goodbye")
+    >>> # can  be rewritten using a delegating mixin as follows:
+    >>> Greeter = _create_delegating_mixin(
+    ...     "Greeter", "", "GREETING", "_greet", {"hello", "goodbye", "hey"}
+    ... )
+    >>> # The `hello` and `goodbye` methods will now be automatically generated
+    >>> # for the Person class below.
+    >>> class Person(Greeter):
+    ...     _VALID_GREETINGS = {"hello", "goodbye"}
+    ...
+    ...     def _greet(self, op: str):
+    ...         '''Say {op}.'''
+    ...         print(op)
+    >>> mom = Person()
+    >>> mom.hello()
+    hello
+    >>> # The Greeter class could also enable the `hey` method, but Person did
+    >>> # not include it in the _VALID_GREETINGS set so it will not exist.
+    >>> mom.hey()
+    Traceback (most recent call last):
+        ...
+    AttributeError: 'Person' object has no attribute 'hey'
+    >>> # The docstrings for each method are generated by formatting the _greet
+    >>> # docstring with the operation name as well as any additional keys
+    >>> # provided via the _GREETING_DOCSTRINGS parameter.
+    >>> print(mom.hello.__doc__)
+    Say hello.
+    """
+    # The first two attributes may be defined on subclasses of the generated
+    # OperationMixin to indicate valid attributes and parameters to use when
+    # formatting docstrings. The supported_attr will be defined on the
+    # OperationMixin itself to indicate what operations its subclass may
+    # inherit from it.
+    validity_attr = f"_VALID_{category_name}S"
+    docstring_attr = f"_{category_name}_DOCSTRINGS"
+    supported_attr = f"_SUPPORTED_{category_name}S"
+
+    class OperationMixin:
+        @classmethod
+        def __init_subclass__(cls):
+            # Support composition of various OperationMixins. Note that since
+            # this __init_subclass__ is defined on mixins, it does not prohibit
+            # classes that inherit it from implementing this method as well as
+            # long as those implementations also include this super call.
+            super().__init_subclass__()
+
+            # Only add the valid set of operations for a particular class.
+            valid_operations = set()
+            for base_cls in cls.__mro__:
+                # Check for sentinel indicating that all operations are valid.
+                valid_operations |= getattr(base_cls, validity_attr, set())
+
+            invalid_operations = valid_operations - supported_operations
+            assert (
+                len(invalid_operations) == 0
+            ), f"Invalid requested operations: {invalid_operations}"
+
+            base_operation = getattr(cls, base_operation_name)
+            for operation in valid_operations:
+                if _should_define_operation(
+                    cls, operation, base_operation_name
+                ):
+                    docstring_format_args = getattr(
+                        cls, docstring_attr, {}
+                    ).get(operation, {})
+                    op_attr = Operation(
+                        operation, docstring_format_args, base_operation
+                    )
+                    setattr(cls, operation, op_attr)
+
+    OperationMixin.__name__ = mixin_name
+    OperationMixin.__qualname__ = mixin_name
+    OperationMixin.__doc__ = docstring
+
+    def _operation(self, op: str, *args, **kwargs):
+        raise NotImplementedError
+
+    _operation.__name__ = base_operation_name
+    _operation.__qualname__ = ".".join([mixin_name, base_operation_name])
+    _operation.__doc__ = (
+        f"The core {category_name.lower()} function. Must be overridden by "
+        "subclasses, the default implementation raises a NotImplementedError."
+    )
+
+    setattr(OperationMixin, base_operation_name, _operation)
+    # Making this attribute available makes it easy for subclasses to indicate
+    # that all supported operations for this mixin are valid.
+    setattr(OperationMixin, supported_attr, supported_operations)
+
+    return OperationMixin
diff --git a/python/cudf/cudf/core/mixins/reductions.py b/python/cudf/cudf/core/mixins/reductions.py
new file mode 100644
index 0000000..f73f0e8
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/reductions.py
@@ -0,0 +1,35 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from .mixin_factory import _create_delegating_mixin
+
+Reducible = _create_delegating_mixin(
+    "Reducible",
+    "Mixin encapsulating reduction operations.",
+    "REDUCTION",
+    "_reduce",
+    {
+        "sum",
+        "product",
+        "min",
+        "max",
+        "count",
+        "any",
+        "all",
+        "sum_of_squares",
+        "mean",
+        "var",
+        "std",
+        "median",
+        "argmax",
+        "argmin",
+        "nunique",
+        "nth",
+        "collect",
+        "unique",
+        "prod",
+        "idxmin",
+        "idxmax",
+        "first",
+        "last",
+    },
+)
diff --git a/python/cudf/cudf/core/mixins/reductions.pyi b/python/cudf/cudf/core/mixins/reductions.pyi
new file mode 100644
index 0000000..dbaafdb
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/reductions.pyi
@@ -0,0 +1,29 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from typing import Set
+
+class Reducible:
+    _SUPPORTED_REDUCTIONS: Set
+
+    def sum(self): ...
+    def product(self): ...
+    def min(self): ...
+    def max(self): ...
+    def count(self): ...
+    def any(self): ...
+    def all(self): ...
+    def sum_of_squares(self): ...
+    def mean(self): ...
+    def var(self): ...
+    def std(self): ...
+    def median(self): ...
+    def argmax(self): ...
+    def argmin(self): ...
+    def nunique(self): ...
+    def nth(self): ...
+    def collect(self): ...
+    def prod(self): ...
+    def idxmin(self): ...
+    def idxmax(self): ...
+    def first(self): ...
+    def last(self): ...
diff --git a/python/cudf/cudf/core/mixins/scans.py b/python/cudf/cudf/core/mixins/scans.py
new file mode 100644
index 0000000..b0f606e
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/scans.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from .mixin_factory import _create_delegating_mixin
+
+Scannable = _create_delegating_mixin(
+    "Scannable",
+    "Mixin encapsulating scan operations.",
+    "SCAN",
+    "_scan",
+    {
+        "cumsum",
+        "cumprod",
+        "cummin",
+        "cummax",
+    },  # noqa: E231
+)
diff --git a/python/cudf/cudf/core/mixins/scans.pyi b/python/cudf/cudf/core/mixins/scans.pyi
new file mode 100644
index 0000000..3799524
--- /dev/null
+++ b/python/cudf/cudf/core/mixins/scans.pyi
@@ -0,0 +1,11 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+from typing import Set
+
+class Scannable:
+    _SUPPORTED_SCANS: Set
+
+    def cumsum(self): ...
+    def cumprod(self): ...
+    def cummin(self): ...
+    def cummax(self): ...
diff --git a/python/cudf/cudf/core/multiindex.py b/python/cudf/cudf/core/multiindex.py
new file mode 100644
index 0000000..b4bbd0a
--- /dev/null
+++ b/python/cudf/cudf/core/multiindex.py
@@ -0,0 +1,2117 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import itertools
+import numbers
+import operator
+import pickle
+import warnings
+from collections import abc
+from functools import cached_property
+from numbers import Integral
+from typing import Any, List, MutableMapping, Tuple, Union
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+from pandas._config import get_option
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._typing import DataFrameOrSeries
+from cudf.api.extensions import no_default
+from cudf.api.types import is_integer, is_list_like, is_object_dtype
+from cudf.core import column
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.frame import Frame
+from cudf.core.index import BaseIndex, _lexsorted_equal_range, as_index
+from cudf.utils.utils import (
+    NotIterable,
+    _cudf_nvtx_annotate,
+    _external_only_api,
+    _is_same_name,
+)
+
+
+def _maybe_indices_to_slice(indices: cp.ndarray) -> Union[slice, cp.ndarray]:
+    """Makes best effort to convert an array of indices into a python slice.
+    If the conversion is not possible, return input. `indices` are expected
+    to be valid.
+    """
+    # TODO: improve efficiency by avoiding sync.
+    if len(indices) == 1:
+        x = indices[0].item()
+        return slice(x, x + 1)
+    if len(indices) == 2:
+        x1, x2 = indices[0].item(), indices[1].item()
+        return slice(x1, x2 + 1, x2 - x1)
+    start, step = indices[0].item(), (indices[1] - indices[0]).item()
+    stop = start + step * len(indices)
+    if (indices == cp.arange(start, stop, step)).all():
+        return slice(start, stop, step)
+    return indices
+
+
+class MultiIndex(Frame, BaseIndex, NotIterable):
+    """A multi-level or hierarchical index.
+
+    Provides N-Dimensional indexing into Series and DataFrame objects.
+
+    Parameters
+    ----------
+    levels : sequence of arrays
+        The unique labels for each level.
+    codes: sequence of arrays
+        Integers for each level designating which label at each location.
+    sortorder : optional int
+        Not yet supported
+    names: optional sequence of objects
+        Names for each of the index levels.
+    copy : bool, default False
+        Copy the levels and codes.
+    verify_integrity : bool, default True
+        Check that the levels/codes are consistent and valid.
+        Not yet supported
+
+    Attributes
+    ----------
+    names
+    nlevels
+    dtypes
+    levels
+    codes
+
+    Methods
+    -------
+    from_arrays
+    from_tuples
+    from_product
+    from_frame
+    set_levels
+    set_codes
+    to_frame
+    to_flat_index
+    sortlevel
+    droplevel
+    swaplevel
+    reorder_levels
+    remove_unused_levels
+    get_level_values
+    get_loc
+    drop
+
+    Returns
+    -------
+    MultiIndex
+
+    Examples
+    --------
+    >>> import cudf
+    >>> cudf.MultiIndex(
+    ... levels=[[1, 2], ['blue', 'red']], codes=[[0, 0, 1, 1], [1, 0, 1, 0]])
+    MultiIndex([(1,  'red'),
+                (1, 'blue'),
+                (2,  'red'),
+                (2, 'blue')],
+               )
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        levels=None,
+        codes=None,
+        sortorder=None,
+        names=None,
+        dtype=None,
+        copy=False,
+        name=None,
+        **kwargs,
+    ):
+        if sortorder is not None:
+            raise NotImplementedError("sortorder is not yet supported")
+        if name is not None:
+            raise NotImplementedError(
+                "Use `names`, `name` is not yet supported"
+            )
+        if len(levels) == 0:
+            raise ValueError("Must pass non-zero number of levels/codes")
+        if not isinstance(codes, cudf.DataFrame) and not isinstance(
+            codes[0], (abc.Sequence, np.ndarray, cp.ndarray)
+        ):
+            raise TypeError("Codes is not a Sequence of sequences")
+
+        if copy:
+            if isinstance(codes, cudf.DataFrame):
+                codes = codes.copy(deep=True)
+            if len(levels) > 0 and isinstance(levels[0], cudf.Series):
+                levels = [level.copy(deep=True) for level in levels]
+
+        if not isinstance(codes, cudf.DataFrame):
+            if len(levels) == len(codes):
+                codes = cudf.DataFrame._from_data(
+                    {
+                        i: column.as_column(code).astype(np.int64)
+                        for i, code in enumerate(codes)
+                    }
+                )
+            else:
+                raise ValueError(
+                    "MultiIndex has unequal number of levels and "
+                    "codes and is inconsistent!"
+                )
+
+        levels = [cudf.Series(level) for level in levels]
+
+        if len(levels) != len(codes._data):
+            raise ValueError(
+                "MultiIndex has unequal number of levels and "
+                "codes and is inconsistent!"
+            )
+        if len({c.size for c in codes._data.columns}) != 1:
+            raise ValueError(
+                "MultiIndex length of codes does not match "
+                "and is inconsistent!"
+            )
+        for level, code in zip(levels, codes._data.columns):
+            if code.max() > len(level) - 1:
+                raise ValueError(
+                    "MultiIndex code %d contains value %d larger "
+                    "than maximum level size at this position"
+                )
+
+        source_data = {}
+        for i, (column_name, col) in enumerate(codes._data.items()):
+            if -1 in col:
+                level = cudf.DataFrame(
+                    {column_name: [None] + list(levels[i])},
+                    index=range(-1, len(levels[i])),
+                )
+            else:
+                level = cudf.DataFrame({column_name: levels[i]})
+
+            source_data[column_name] = libcudf.copying.gather(
+                [level._data[column_name]], col
+            )[0]
+
+        super().__init__(source_data)
+        self._levels = levels
+        self._codes = codes
+        self._name = None
+        self.names = names
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def names(self):
+        return self._names
+
+    @names.setter  # type: ignore
+    @_cudf_nvtx_annotate
+    def names(self, value):
+        if value is None:
+            value = [None] * self.nlevels
+        elif not is_list_like(value):
+            raise ValueError("Names should be list-like for a MultiIndex")
+        elif len(value) != self.nlevels:
+            raise ValueError(
+                "Length of names must match number of levels in MultiIndex."
+            )
+
+        if len(value) == len(set(value)):
+            # IMPORTANT: if the provided names are unique,
+            # we reconstruct self._data with the names as keys.
+            # If they are not unique, the keys of self._data
+            # and self._names will be different, which can lead
+            # to unexpected behavior in some cases. This is
+            # definitely buggy, but we can't disallow non-unique
+            # names either...
+            self._data = self._data.__class__._create_unsafe(
+                dict(zip(value, self._data.values())),
+                level_names=self._data.level_names,
+            )
+        self._names = pd.core.indexes.frozen.FrozenList(value)
+
+    @_cudf_nvtx_annotate
+    def to_series(self, index=None, name=None):
+        raise NotImplementedError(
+            "MultiIndex.to_series isn't implemented yet."
+        )
+
+    @_cudf_nvtx_annotate
+    def astype(self, dtype, copy: bool = True):
+        if not is_object_dtype(dtype):
+            raise TypeError(
+                "Setting a MultiIndex dtype to anything other than object is "
+                "not supported"
+            )
+        return self
+
+    @_cudf_nvtx_annotate
+    def rename(self, names, inplace=False):
+        """
+        Alter MultiIndex level names
+
+        Parameters
+        ----------
+        names : list of label
+            Names to set, length must be the same as number of levels
+        inplace : bool, default False
+            If True, modifies objects directly, otherwise returns a new
+            ``MultiIndex`` instance
+
+        Returns
+        -------
+        None or MultiIndex
+
+        Examples
+        --------
+        Renaming each levels of a MultiIndex to specified name:
+
+        >>> midx = cudf.MultiIndex.from_product(
+        ...     [('A', 'B'), (2020, 2021)], names=['c1', 'c2'])
+        >>> midx.rename(['lv1', 'lv2'])
+        MultiIndex([('A', 2020),
+                    ('A', 2021),
+                    ('B', 2020),
+                    ('B', 2021)],
+                names=['lv1', 'lv2'])
+        >>> midx.rename(['lv1', 'lv2'], inplace=True)
+        >>> midx
+        MultiIndex([('A', 2020),
+                    ('A', 2021),
+                    ('B', 2020),
+                    ('B', 2021)],
+                names=['lv1', 'lv2'])
+
+        ``names`` argument must be a list, and must have same length as
+        ``MultiIndex.levels``:
+
+        >>> midx.rename(['lv0'])
+        Traceback (most recent call last):
+        ValueError: Length of names must match number of levels in MultiIndex.
+
+        """
+        return self.set_names(names, level=None, inplace=inplace)
+
+    @_cudf_nvtx_annotate
+    def set_names(self, names, level=None, inplace=False):
+        names_is_list_like = is_list_like(names)
+        level_is_list_like = is_list_like(level)
+
+        if level is not None and not level_is_list_like and names_is_list_like:
+            raise TypeError(
+                "Names must be a string when a single level is provided."
+            )
+
+        if not names_is_list_like and level is None and self.nlevels > 1:
+            raise TypeError("Must pass list-like as `names`.")
+
+        if not names_is_list_like:
+            names = [names]
+        if level is not None and not level_is_list_like:
+            level = [level]
+
+        if level is not None and len(names) != len(level):
+            raise ValueError("Length of names must match length of level.")
+        if level is None and len(names) != self.nlevels:
+            raise ValueError(
+                "Length of names must match number of levels in MultiIndex."
+            )
+
+        if level is None:
+            level = range(self.nlevels)
+        else:
+            level = [self._level_index_from_level(lev) for lev in level]
+
+        existing_names = list(self.names)
+        for i, lev in enumerate(level):
+            existing_names[lev] = names[i]
+        names = existing_names
+
+        return self._set_names(names=names, inplace=inplace)
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_data(
+        cls,
+        data: MutableMapping,
+        name: Any = None,
+    ) -> MultiIndex:
+        obj = cls.from_frame(cudf.DataFrame._from_data(data=data))
+        if name is not None:
+            obj.name = name
+        return obj
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def name(self):
+        return self._name
+
+    @name.setter  # type: ignore
+    @_cudf_nvtx_annotate
+    def name(self, value):
+        self._name = value
+
+    @_cudf_nvtx_annotate
+    def copy(
+        self,
+        names=None,
+        dtype=None,
+        levels=None,
+        codes=None,
+        deep=False,
+        name=None,
+    ):
+        """Returns copy of MultiIndex object.
+
+        Returns a copy of `MultiIndex`. The `levels` and `codes` value can be
+        set to the provided parameters. When they are provided, the returned
+        MultiIndex is always newly constructed.
+
+        Parameters
+        ----------
+        names : sequence of objects, optional (default None)
+            Names for each of the index levels.
+        dtype : object, optional (default None)
+            MultiIndex dtype, only supports None or object type
+
+            .. deprecated:: 23.02
+
+               The `dtype` parameter is deprecated and will be removed in
+               a future version of cudf. Use the `astype` method instead.
+
+        levels : sequence of arrays, optional (default None)
+            The unique labels for each level. Original values used if None.
+
+            .. deprecated:: 23.02
+
+               The `levels` parameter is deprecated and will be removed in
+               a future version of cudf.
+
+        codes : sequence of arrays, optional (default None)
+            Integers for each level designating which label at each location.
+            Original values used if None.
+
+            .. deprecated:: 23.02
+
+               The `codes` parameter is deprecated and will be removed in
+               a future version of cudf.
+
+        deep : Bool (default False)
+            If True, `._data`, `._levels`, `._codes` will be copied. Ignored if
+            `levels` or `codes` are specified.
+        name : object, optional (default None)
+            To keep consistent with `Index.copy`, should not be used.
+
+        Returns
+        -------
+        Copy of MultiIndex Instance
+
+        Examples
+        --------
+        >>> df = cudf.DataFrame({'Close': [3400.00, 226.58, 3401.80, 228.91]})
+        >>> idx1 = cudf.MultiIndex(
+        ... levels=[['2020-08-27', '2020-08-28'], ['AMZN', 'MSFT']],
+        ... codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+        ... names=['Date', 'Symbol'])
+        >>> idx2 = idx1.copy(
+        ... levels=[['day1', 'day2'], ['com1', 'com2']],
+        ... codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
+        ... names=['col1', 'col2'])
+
+        >>> df.index = idx1
+        >>> df
+                             Close
+        Date       Symbol
+        2020-08-27 AMZN    3400.00
+                   MSFT     226.58
+        2020-08-28 AMZN    3401.80
+                   MSFT     228.91
+
+        >>> df.index = idx2
+        >>> df
+                     Close
+        col1 col2
+        day1 com1  3400.00
+             com2   226.58
+        day2 com1  3401.80
+             com2   228.91
+
+        """
+
+        # TODO: Update message when set_levels is implemented.
+        # https://github.com/rapidsai/cudf/issues/12307
+        if levels is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter levels is deprecated and will be removed in a "
+                "future version.",
+                FutureWarning,
+            )
+
+        # TODO: Update message when set_codes is implemented.
+        # https://github.com/rapidsai/cudf/issues/12308
+        if codes is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter codes is deprecated and will be removed in a "
+                "future version.",
+                FutureWarning,
+            )
+
+        if dtype is not None:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "parameter dtype is deprecated and will be removed in a "
+                "future version. Use the astype method instead.",
+                FutureWarning,
+            )
+
+        dtype = object if dtype is None else dtype
+        if not pd.api.types.is_object_dtype(dtype):
+            raise TypeError("Dtype for MultiIndex only supports object type.")
+
+        # ._data needs to be rebuilt
+        if levels is not None or codes is not None:
+            if self._levels is None or self._codes is None:
+                self._compute_levels_and_codes()
+            levels = self._levels if levels is None else levels
+            codes = self._codes if codes is None else codes
+            names = self.names if names is None else names
+
+            mi = MultiIndex(levels=levels, codes=codes, names=names, copy=deep)
+            return mi
+
+        mi = MultiIndex._from_data(self._data.copy(deep=deep))
+        if self._levels is not None:
+            mi._levels = [s.copy(deep) for s in self._levels]
+        if self._codes is not None:
+            mi._codes = self._codes.copy(deep)
+        if names is not None:
+            mi.names = names
+        elif self.names is not None:
+            mi.names = self.names.copy()
+
+        return mi
+
+    @_cudf_nvtx_annotate
+    def __repr__(self):
+        max_seq_items = get_option("display.max_seq_items") or len(self)
+
+        if len(self) > max_seq_items:
+            n = int(max_seq_items / 2) + 1
+            # TODO: Update the following two arange calls to
+            # a single arange call once arange has support for
+            # a vector start/end points.
+            indices = column.arange(start=0, stop=n, step=1)
+            indices = indices.append(
+                column.arange(start=len(self) - n, stop=len(self), step=1)
+            )
+            preprocess = self.take(indices)
+        else:
+            preprocess = self.copy(deep=False)
+
+        if any(col.has_nulls() for col in preprocess._data.columns):
+            preprocess_df = preprocess.to_frame(index=False)
+            for name, col in preprocess._data.items():
+                if isinstance(
+                    col,
+                    (
+                        column.datetime.DatetimeColumn,
+                        column.timedelta.TimeDeltaColumn,
+                    ),
+                ):
+                    preprocess_df[name] = col.astype("str").fillna(
+                        str(cudf.NaT)
+                    )
+
+            tuples_list = list(
+                zip(
+                    *list(
+                        map(lambda val: pd.NA if val is None else val, col)
+                        for col in preprocess_df.to_arrow()
+                        .to_pydict()
+                        .values()
+                    )
+                )
+            )
+
+            if not PANDAS_GE_150:
+                # Need this whole `if` block,
+                # this is a workaround for the following issue:
+                # https://github.com/pandas-dev/pandas/issues/39984
+                preprocess_pdf = pd.DataFrame(
+                    {
+                        name: col.to_pandas(nullable=(col.dtype.kind != "f"))
+                        for name, col in preprocess._data.items()
+                    }
+                )
+
+                preprocess_pdf.columns = preprocess.names
+                preprocess = pd.MultiIndex.from_frame(preprocess_pdf)
+            else:
+                preprocess = preprocess.to_pandas(nullable=True)
+            preprocess.values[:] = tuples_list
+        else:
+            preprocess = preprocess.to_pandas(nullable=True)
+
+        output = repr(preprocess)
+        output_prefix = self.__class__.__name__ + "("
+        output = output.lstrip(output_prefix)
+        lines = output.split("\n")
+
+        if len(lines) > 1:
+            if "length=" in lines[-1] and len(self) != len(preprocess):
+                last_line = lines[-1]
+                length_index = last_line.index("length=")
+                last_line = last_line[:length_index] + f"length={len(self)})"
+                lines = lines[:-1]
+                lines.append(last_line)
+
+        data_output = "\n".join(lines)
+        return output_prefix + data_output
+
+    @property
+    def _codes_frame(self):
+        if self._codes is None:
+            self._compute_levels_and_codes()
+        return self._codes
+
+    @property  # type: ignore
+    @_external_only_api("Use ._codes_frame instead")
+    @_cudf_nvtx_annotate
+    def codes(self):
+        """
+        Returns the codes of the underlying MultiIndex.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a':[1, 2, 3], 'b':[10, 11, 12]})
+        >>> midx = cudf.MultiIndex.from_frame(df)
+        >>> midx
+        MultiIndex([(1, 10),
+                    (2, 11),
+                    (3, 12)],
+                names=['a', 'b'])
+        >>> midx.codes
+        FrozenList([[0, 1, 2], [0, 1, 2]])
+        """
+        return pd.core.indexes.frozen.FrozenList(
+            col.values for col in self._codes_frame._columns
+        )
+
+    def get_slice_bound(self, label, side, kind=None):
+        raise NotImplementedError()
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nlevels(self):
+        """Integer number of levels in this MultiIndex."""
+        return len(self._data)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def levels(self):
+        """
+        Returns list of levels in the MultiIndex
+
+        Returns
+        -------
+        List of Series objects
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame({'a':[1, 2, 3], 'b':[10, 11, 12]})
+        >>> cudf.MultiIndex.from_frame(df)
+        MultiIndex([(1, 10),
+                    (2, 11),
+                    (3, 12)],
+                names=['a', 'b'])
+        >>> midx = cudf.MultiIndex.from_frame(df)
+        >>> midx
+        MultiIndex([(1, 10),
+                    (2, 11),
+                    (3, 12)],
+                names=['a', 'b'])
+        >>> midx.levels
+        [Int64Index([1, 2, 3], dtype='int64', name='a'), Int64Index([10, 11, 12], dtype='int64', name='b')]
+        """  # noqa: E501
+        if self._levels is None:
+            self._compute_levels_and_codes()
+        return self._levels
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def ndim(self):
+        """Dimension of the data. For MultiIndex ndim is always 2."""
+        return 2
+
+    @_cudf_nvtx_annotate
+    def _get_level_label(self, level):
+        """Get name of the level.
+
+        Parameters
+        ----------
+        level : int or level name
+            if level is name, it will be returned as it is
+            else if level is index of the level, then level
+            label will be returned as per the index.
+        """
+
+        if level in self._data.names:
+            return level
+        else:
+            return self._data.names[level]
+
+    @_cudf_nvtx_annotate
+    def isin(self, values, level=None):
+        """Return a boolean array where the index values are in values.
+
+        Compute boolean array of whether each index value is found in
+        the passed set of values. The length of the returned boolean
+        array matches the length of the index.
+
+        Parameters
+        ----------
+        values : set, list-like, Index or Multi-Index
+            Sought values.
+        level : str or int, optional
+            Name or position of the index level to use (if the index
+            is a MultiIndex).
+
+        Returns
+        -------
+        is_contained : cupy array
+            CuPy array of boolean values.
+
+        Notes
+        -----
+        When `level` is None, `values` can only be MultiIndex, or a
+        set/list-like tuples.
+        When `level` is provided, `values` can be Index or MultiIndex,
+        or a set/list-like tuples.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> midx = cudf.from_pandas(pd.MultiIndex.from_arrays([[1,2,3],
+        ...                                  ['red', 'blue', 'green']],
+        ...                                  names=('number', 'color')))
+        >>> midx
+        MultiIndex([(1,   'red'),
+                    (2,  'blue'),
+                    (3, 'green')],
+                   names=['number', 'color'])
+
+        Check whether the strings in the 'color' level of the MultiIndex
+        are in a list of colors.
+
+        >>> midx.isin(['red', 'orange', 'yellow'], level='color')
+        array([ True, False, False])
+
+        To check across the levels of a MultiIndex, pass a list of tuples:
+
+        >>> midx.isin([(1, 'red'), (3, 'red')])
+        array([ True, False, False])
+        """
+        if level is None:
+            if isinstance(values, cudf.MultiIndex):
+                values_idx = values
+            elif (
+                (
+                    isinstance(
+                        values,
+                        (
+                            cudf.Series,
+                            cudf.Index,
+                            cudf.DataFrame,
+                            column.ColumnBase,
+                        ),
+                    )
+                )
+                or (not is_list_like(values))
+                or (
+                    is_list_like(values)
+                    and len(values) > 0
+                    and not isinstance(values[0], tuple)
+                )
+            ):
+                raise TypeError(
+                    "values need to be a Multi-Index or set/list-like tuple "
+                    "squences  when `level=None`."
+                )
+            else:
+                values_idx = cudf.MultiIndex.from_tuples(
+                    values, names=self.names
+                )
+            self_df = self.to_frame(index=False).reset_index()
+            values_df = values_idx.to_frame(index=False)
+            idx = self_df.merge(values_df)._data["index"]
+            res = cudf.core.column.full(size=len(self), fill_value=False)
+            res[idx] = True
+            result = res.values
+        else:
+            level_series = self.get_level_values(level)
+            result = level_series.isin(values)
+
+        return result
+
+    def where(self, cond, other=None, inplace=False):
+        raise NotImplementedError(
+            ".where is not supported for MultiIndex operations"
+        )
+
+    @_cudf_nvtx_annotate
+    def _compute_levels_and_codes(self):
+        levels = []
+
+        codes = {}
+        for name, col in self._data.items():
+            with warnings.catch_warnings():
+                # TODO: Remove this filter when
+                # `na_sentinel` is removed from `factorize`.
+                # This is a filter to not let the warnings from
+                # `factorize` show up in other parts of public APIs.
+                warnings.simplefilter("ignore")
+                code, cats = cudf.Series._from_data({None: col}).factorize()
+            cats.name = name
+            codes[name] = code.astype(np.int64)
+            levels.append(cats)
+
+        self._levels = levels
+        self._codes = cudf.DataFrame._from_data(codes)
+
+    @_cudf_nvtx_annotate
+    def _compute_validity_mask(self, index, row_tuple, max_length):
+        """Computes the valid set of indices of values in the lookup"""
+        lookup = cudf.DataFrame()
+        for i, row in enumerate(row_tuple):
+            if isinstance(row, slice) and row == slice(None):
+                continue
+            lookup[i] = cudf.Series(row)
+        frame = cudf.DataFrame(dict(enumerate(index._data.columns)))
+        data_table = cudf.concat(
+            [
+                frame,
+                cudf.DataFrame(
+                    {"idx": cudf.Series(column.arange(len(frame)))}
+                ),
+            ],
+            axis=1,
+        )
+        # Sort indices in pandas compatible mode
+        # because we want the indices to be fetched
+        # in a deterministic order.
+        # TODO: Remove this after merge/join
+        # obtain deterministic ordering.
+        if cudf.get_option("mode.pandas_compatible"):
+            lookup_order = "_" + "_".join(map(str, lookup._data.names))
+            lookup[lookup_order] = column.arange(len(lookup))
+            postprocess = operator.methodcaller(
+                "sort_values", by=[lookup_order, "idx"]
+            )
+        else:
+            postprocess = lambda r: r  # noqa: E731
+        result = postprocess(lookup.merge(data_table))["idx"]
+        # Avoid computing levels unless the result of the merge is empty,
+        # which suggests that a KeyError should be raised.
+        if len(result) == 0:
+            for idx, row in enumerate(row_tuple):
+                if row == slice(None):
+                    continue
+                if row not in index.levels[idx]._column:
+                    raise KeyError(row)
+        return result
+
+    @_cudf_nvtx_annotate
+    def _get_valid_indices_by_tuple(self, index, row_tuple, max_length):
+        # Instructions for Slicing
+        # if tuple, get first and last elements of tuple
+        # if open beginning tuple, get 0 to highest valid_index
+        # if open ending tuple, get highest valid_index to len()
+        # if not open end or beginning, get range lowest beginning index
+        # to highest ending index
+        if isinstance(row_tuple, slice):
+            if (
+                isinstance(row_tuple.start, numbers.Number)
+                or isinstance(row_tuple.stop, numbers.Number)
+                or row_tuple == slice(None)
+            ):
+                stop = row_tuple.stop or max_length
+                start, stop, step = row_tuple.indices(stop)
+                return column.arange(start, stop, step)
+            start_values = self._compute_validity_mask(
+                index, row_tuple.start, max_length
+            )
+            stop_values = self._compute_validity_mask(
+                index, row_tuple.stop, max_length
+            )
+            return column.arange(start_values.min(), stop_values.max() + 1)
+        elif isinstance(row_tuple, numbers.Number):
+            return row_tuple
+        return self._compute_validity_mask(index, row_tuple, max_length)
+
+    @_cudf_nvtx_annotate
+    def _index_and_downcast(self, result, index, index_key):
+        if isinstance(index_key, (numbers.Number, slice)):
+            index_key = [index_key]
+        if (
+            len(index_key) > 0 and not isinstance(index_key, tuple)
+        ) or isinstance(index_key[0], slice):
+            index_key = index_key[0]
+
+        slice_access = isinstance(index_key, slice)
+        out_index = cudf.DataFrame()
+        # Select the last n-k columns where n is the number of columns and k is
+        # the length of the indexing tuple
+        size = 0
+        if not isinstance(index_key, (numbers.Number, slice)):
+            size = len(index_key)
+        for k in range(size, len(index._data)):
+            out_index.insert(
+                out_index._num_columns,
+                k,
+                cudf.Series._from_data({None: index._data.columns[k]}),
+            )
+
+        # determine if we should downcast from a DataFrame to a Series
+        need_downcast = (
+            isinstance(result, cudf.DataFrame)
+            and len(result) == 1  # only downcast if we have a single row
+            and not slice_access  # never downcast if we sliced
+            and (
+                size == 0  # index_key was an integer
+                # we indexed into a single row directly, using its label:
+                or len(index_key) == self.nlevels
+            )
+        )
+        if need_downcast:
+            result = result.T
+            return result[result._data.names[0]]
+
+        if len(result) == 0 and not slice_access:
+            # Pandas returns an empty Series with a tuple as name
+            # the one expected result column
+            result = cudf.Series._from_data(
+                {}, name=tuple(col[0] for col in index._data.columns)
+            )
+        elif out_index._num_columns == 1:
+            # If there's only one column remaining in the output index, convert
+            # it into an Index and name the final index values according
+            # to that column's name.
+            *_, last_column = index._data.columns
+            out_index = as_index(last_column)
+            out_index.name = index.names[-1]
+            index = out_index
+        elif out_index._num_columns > 1:
+            # Otherwise pop the leftmost levels, names, and codes from the
+            # source index until it has the correct number of columns (n-k)
+            result.reset_index(drop=True)
+            if index.names is not None:
+                result.names = index.names[size:]
+            index = MultiIndex(
+                levels=index.levels[size:],
+                codes=index._codes_frame.iloc[:, size:],
+                names=index.names[size:],
+            )
+
+        if isinstance(index_key, tuple):
+            result.index = index
+        return result
+
+    @_cudf_nvtx_annotate
+    def _get_row_major(
+        self,
+        df: DataFrameOrSeries,
+        row_tuple: Union[
+            numbers.Number, slice, Tuple[Any, ...], List[Tuple[Any, ...]]
+        ],
+    ) -> DataFrameOrSeries:
+        if pd.api.types.is_bool_dtype(
+            list(row_tuple) if isinstance(row_tuple, tuple) else row_tuple
+        ):
+            return df[row_tuple]
+        if isinstance(row_tuple, slice):
+            if row_tuple.start is None:
+                row_tuple = slice(self[0], row_tuple.stop, row_tuple.step)
+            if row_tuple.stop is None:
+                row_tuple = slice(row_tuple.start, self[-1], row_tuple.step)
+        self._validate_indexer(row_tuple)
+        valid_indices = self._get_valid_indices_by_tuple(
+            df.index, row_tuple, len(df.index)
+        )
+        indices = cudf.Series(valid_indices)
+        result = df.take(indices)
+        final = self._index_and_downcast(result, result.index, row_tuple)
+        return final
+
+    @_cudf_nvtx_annotate
+    def _validate_indexer(
+        self,
+        indexer: Union[
+            numbers.Number, slice, Tuple[Any, ...], List[Tuple[Any, ...]]
+        ],
+    ):
+        if isinstance(indexer, numbers.Number):
+            return
+        if isinstance(indexer, tuple):
+            # drop any slice(None) from the end:
+            indexer = tuple(
+                itertools.dropwhile(
+                    lambda x: x == slice(None), reversed(indexer)
+                )
+            )[::-1]
+
+            # now check for size
+            if len(indexer) > self.nlevels:
+                raise IndexError("Indexer size exceeds number of levels")
+        elif isinstance(indexer, slice):
+            self._validate_indexer(indexer.start)
+            self._validate_indexer(indexer.stop)
+        else:
+            for i in indexer:
+                self._validate_indexer(i)
+
+    @_cudf_nvtx_annotate
+    def __eq__(self, other):
+        if isinstance(other, MultiIndex):
+            return np.array(
+                [
+                    self_col.equals(other_col)
+                    for self_col, other_col in zip(
+                        self._data.values(), other._data.values()
+                    )
+                ]
+            )
+        return NotImplemented
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def size(self):
+        # The size of a MultiIndex is only dependent on the number of rows.
+        return self._num_rows
+
+    @_cudf_nvtx_annotate
+    def take(self, indices):
+        if isinstance(indices, cudf.Series) and indices.has_nulls:
+            raise ValueError("Column must have no nulls.")
+        obj = super().take(indices)
+        obj.names = self.names
+        return obj
+
+    @_cudf_nvtx_annotate
+    def serialize(self):
+        header, frames = super().serialize()
+        # Overwrite the names in _data with the true names.
+        header["column_names"] = pickle.dumps(self.names)
+        return header, frames
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def deserialize(cls, header, frames):
+        # Spoof the column names to construct the frame, then set manually.
+        column_names = pickle.loads(header["column_names"])
+        header["column_names"] = pickle.dumps(range(0, len(column_names)))
+        obj = super().deserialize(header, frames)
+        return obj._set_names(column_names)
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, index):
+        flatten = isinstance(index, int)
+
+        if isinstance(index, (Integral, abc.Sequence)):
+            index = np.array(index)
+        elif isinstance(index, slice):
+            start, stop, step = index.indices(len(self))
+            index = column.arange(start, stop, step)
+        result = MultiIndex.from_frame(
+            self.to_frame(index=False, name=range(0, self.nlevels)).take(
+                index
+            ),
+            names=self.names,
+        )
+
+        # we are indexing into a single row of the MultiIndex,
+        # return that row as a tuple:
+        if flatten:
+            return result.to_pandas()[0]
+
+        if self._codes_frame is not None:
+            result._codes = self._codes_frame.take(index)
+        if self._levels is not None:
+            result._levels = self._levels
+        return result
+
+    @_cudf_nvtx_annotate
+    def to_frame(self, index=True, name=no_default, allow_duplicates=False):
+        """
+        Create a DataFrame with the levels of the MultiIndex as columns.
+
+        Column ordering is determined by the DataFrame constructor with data as
+        a dict.
+
+        Parameters
+        ----------
+        index : bool, default True
+            Set the index of the returned DataFrame as the original MultiIndex.
+        name : list / sequence of str, optional
+            The passed names should substitute index level names.
+        allow_duplicates : bool, optional default False
+            Allow duplicate column labels to be created. Note
+            that this parameter is non-functional because
+            duplicates column labels aren't supported in cudf.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> import cudf
+        >>> mi = cudf.MultiIndex.from_tuples([('a', 'c'), ('b', 'd')])
+        >>> mi
+        MultiIndex([('a', 'c'),
+                    ('b', 'd')],
+                   )
+
+        >>> df = mi.to_frame()
+        >>> df
+             0  1
+        a c  a  c
+        b d  b  d
+
+        >>> df = mi.to_frame(index=False)
+        >>> df
+           0  1
+        0  a  c
+        1  b  d
+
+        >>> df = mi.to_frame(name=['x', 'y'])
+        >>> df
+             x  y
+        a c  a  c
+        b d  b  d
+        """
+        # TODO: Currently this function makes a shallow copy, which is
+        # incorrect. We want to make a deep copy, otherwise further
+        # modifications of the resulting DataFrame will affect the MultiIndex.
+        if name is None:
+            warnings.warn(
+                "Explicitly passing `name=None` currently preserves the "
+                "Index's name or uses a default name of 0. This behaviour "
+                "is deprecated, and in the future `None` will be used "
+                "as the name of the resulting DataFrame column.",
+                FutureWarning,
+            )
+            name = no_default
+
+        if name is not no_default:
+            if len(name) != len(self.levels):
+                raise ValueError(
+                    "'name' should have the same length as "
+                    "number of levels on index."
+                )
+            column_names = name
+        else:
+            column_names = self.names
+        all_none_names = None
+        if not (
+            all_none_names := all(x is None for x in column_names)
+        ) and len(column_names) != len(set(column_names)):
+            raise ValueError("Duplicate column names are not allowed")
+        df = cudf.DataFrame._from_data(
+            data=self._data,
+            columns=column_names
+            if name is not no_default and not all_none_names
+            else None,
+        )
+
+        if index:
+            df = df.set_index(self)
+
+        return df
+
+    @_cudf_nvtx_annotate
+    def get_level_values(self, level):
+        """
+        Return the values at the requested level
+
+        Parameters
+        ----------
+        level : int or label
+
+        Returns
+        -------
+        An Index containing the values at the requested level.
+        """
+        colnames = self._data.names
+        if level not in colnames:
+            if isinstance(level, int):
+                if level < 0:
+                    level = level + len(colnames)
+                if level < 0 or level >= len(colnames):
+                    raise IndexError(f"Invalid level number: '{level}'")
+                level_idx = level
+                level = colnames[level_idx]
+            elif level in self.names:
+                level_idx = list(self.names).index(level)
+                level = colnames[level_idx]
+            else:
+                raise KeyError(f"Level not found: '{level}'")
+        else:
+            level_idx = colnames.index(level)
+        level_values = as_index(self._data[level], name=self.names[level_idx])
+        return level_values
+
+    def _is_numeric(self):
+        return False
+
+    def _is_boolean(self):
+        return False
+
+    def _is_integer(self):
+        return False
+
+    def _is_floating(self):
+        return False
+
+    def _is_object(self):
+        return False
+
+    def _is_categorical(self):
+        return False
+
+    def _is_interval(self):
+        return False
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _concat(cls, objs):
+        source_data = [o.to_frame(index=False) for o in objs]
+
+        # TODO: Verify if this is really necessary or if we can rely on
+        # DataFrame._concat.
+        if len(source_data) > 1:
+            colnames = source_data[0]._data.to_pandas_index()
+            for obj in source_data[1:]:
+                obj.columns = colnames
+
+        source_data = cudf.DataFrame._concat(source_data)
+        names = [None] * source_data._num_columns
+        objs = list(filter(lambda o: o.names is not None, objs))
+        for o in range(len(objs)):
+            for i, name in enumerate(objs[o].names):
+                names[i] = names[i] or name
+        return cudf.MultiIndex.from_frame(source_data, names=names)
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_tuples(cls, tuples, names=None):
+        """
+        Convert list of tuples to MultiIndex.
+
+        Parameters
+        ----------
+        tuples : list / sequence of tuple-likes
+            Each tuple is the index of one row/column.
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> tuples = [(1, 'red'), (1, 'blue'),
+        ...           (2, 'red'), (2, 'blue')]
+        >>> cudf.MultiIndex.from_tuples(tuples, names=('number', 'color'))
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue')],
+                   names=['number', 'color'])
+        """
+        # Use Pandas for handling Python host objects
+        pdi = pd.MultiIndex.from_tuples(tuples, names=names)
+        return cls.from_pandas(pdi)
+
+    @_cudf_nvtx_annotate
+    def to_numpy(self):
+        return self.values_host
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def values_host(self):
+        """
+        Return a numpy representation of the MultiIndex.
+
+        Only the values in the MultiIndex will be returned.
+
+        Returns
+        -------
+        out : numpy.ndarray
+            The values of the MultiIndex.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> midx = cudf.MultiIndex(
+        ...         levels=[[1, 3, 4, 5], [1, 2, 5]],
+        ...         codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        ...         names=["x", "y"],
+        ...     )
+        >>> midx.values_host
+        array([(1, 1), (1, 5), (3, 2), (4, 2), (5, 1)], dtype=object)
+        >>> type(midx.values_host)
+        <class 'numpy.ndarray'>
+        """
+        return self.to_pandas().values
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def values(self):
+        """
+        Return a CuPy representation of the MultiIndex.
+
+        Only the values in the MultiIndex will be returned.
+
+        Returns
+        -------
+        out: cupy.ndarray
+            The values of the MultiIndex.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> midx = cudf.MultiIndex(
+        ...         levels=[[1, 3, 4, 5], [1, 2, 5]],
+        ...         codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        ...         names=["x", "y"],
+        ...     )
+        >>> midx.values
+        array([[1, 1],
+            [1, 5],
+            [3, 2],
+            [4, 2],
+            [5, 1]])
+        >>> type(midx.values)
+        <class 'cupy...ndarray'>
+        """
+        if cudf.get_option("mode.pandas_compatible"):
+            raise NotImplementedError(
+                "Unable to create a cupy array with tuples."
+            )
+        return self.to_frame(index=False).values
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_frame(cls, df, names=None):
+        """
+        Make a MultiIndex from a DataFrame.
+
+        Parameters
+        ----------
+        df : DataFrame
+            DataFrame to be converted to MultiIndex.
+        names : list-like, optional
+            If no names are provided, use the column names, or tuple of column
+            names if the columns is a MultiIndex. If a sequence, overwrite
+            names with the given sequence.
+
+        Returns
+        -------
+        MultiIndex
+            The MultiIndex representation of the given DataFrame.
+
+        See Also
+        --------
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_product : Make a MultiIndex from cartesian product
+                                  of iterables.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> df = cudf.DataFrame([['HI', 'Temp'], ['HI', 'Precip'],
+        ...                    ['NJ', 'Temp'], ['NJ', 'Precip']],
+        ...                   columns=['a', 'b'])
+        >>> df
+              a       b
+        0    HI    Temp
+        1    HI  Precip
+        2    NJ    Temp
+        3    NJ  Precip
+        >>> cudf.MultiIndex.from_frame(df)
+        MultiIndex([('HI',   'Temp'),
+                    ('HI', 'Precip'),
+                    ('NJ',   'Temp'),
+                    ('NJ', 'Precip')],
+                   names=['a', 'b'])
+
+        Using explicit names, instead of the column names
+
+        >>> cudf.MultiIndex.from_frame(df, names=['state', 'observation'])
+        MultiIndex([('HI',   'Temp'),
+                    ('HI', 'Precip'),
+                    ('NJ',   'Temp'),
+                    ('NJ', 'Precip')],
+                   names=['state', 'observation'])
+        """
+        obj = cls.__new__(cls)
+        super(cls, obj).__init__()
+
+        source_data = df.copy(deep=False)
+        source_data.reset_index(drop=True, inplace=True)
+        if isinstance(source_data, pd.DataFrame):
+            source_data = cudf.DataFrame.from_pandas(source_data)
+
+        names = names if names is not None else source_data._data.names
+        # if names are unique
+        # try using those as the source_data column names:
+        if len(dict.fromkeys(names)) == len(names):
+            source_data.columns = names
+        obj._name = None
+        obj._data = source_data._data
+        obj.names = names
+        obj._codes = None
+        obj._levels = None
+        return obj
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_product(cls, arrays, names=None):
+        """
+        Make a MultiIndex from the cartesian product of multiple iterables.
+
+        Parameters
+        ----------
+        iterables : list / sequence of iterables
+            Each iterable has unique labels for each level of the index.
+        names : list / sequence of str, optional
+            Names for the levels in the index.
+            If not explicitly provided, names will be inferred from the
+            elements of iterables if an element has a name attribute
+
+        Returns
+        -------
+        MultiIndex
+
+        See Also
+        --------
+        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
+        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.
+
+        Examples
+        --------
+        >>> numbers = [0, 1, 2]
+        >>> colors = ['green', 'purple']
+        >>> cudf.MultiIndex.from_product([numbers, colors],
+        ...                            names=['number', 'color'])
+        MultiIndex([(0,  'green'),
+                    (0, 'purple'),
+                    (1,  'green'),
+                    (1, 'purple'),
+                    (2,  'green'),
+                    (2, 'purple')],
+                   names=['number', 'color'])
+        """
+        # Use Pandas for handling Python host objects
+        pdi = pd.MultiIndex.from_product(arrays, names=names)
+        return cls.from_pandas(pdi)
+
+    @_cudf_nvtx_annotate
+    def _poplevels(self, level):
+        """
+        Remove and return the specified levels from self.
+
+        Parameters
+        ----------
+        level : level name or index, list
+            One or more levels to remove
+
+        Returns
+        -------
+        Index composed of the removed levels. If only a single level
+        is removed, a flat index is returned. If no levels are specified
+        (empty list), None is returned.
+        """
+        if not pd.api.types.is_list_like(level):
+            level = (level,)
+
+        ilevels = sorted(self._level_index_from_level(lev) for lev in level)
+
+        if not ilevels:
+            return None
+
+        popped_data = {}
+        popped_names = []
+        names = list(self.names)
+
+        # build the popped data and names
+        for i in ilevels:
+            n = self._data.names[i]
+            popped_data[n] = self._data[n]
+            popped_names.append(self.names[i])
+
+        # pop the levels out from self
+        # this must be done iterating backwards
+        for i in reversed(ilevels):
+            n = self._data.names[i]
+            names.pop(i)
+            popped_data[n] = self._data.pop(n)
+
+        # construct the popped result
+        popped = cudf.core.index._index_from_data(popped_data)
+        popped.names = popped_names
+
+        # update self
+        self.names = names
+        self._compute_levels_and_codes()
+
+        return popped
+
+    @_cudf_nvtx_annotate
+    def swaplevel(self, i=-2, j=-1):
+        """
+        Swap level i with level j.
+        Calling this method does not change the ordering of the values.
+
+        Parameters
+        ----------
+        i : int or str, default -2
+            First level of index to be swapped.
+        j : int or str, default -1
+            Second level of index to be swapped.
+
+        Returns
+        -------
+        MultiIndex
+            A new MultiIndex.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> mi = cudf.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],
+        ...                    codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        >>> mi
+        MultiIndex([('a', 'bb'),
+            ('a', 'aa'),
+            ('b', 'bb'),
+            ('b', 'aa')],
+           )
+        >>> mi.swaplevel(0, 1)
+        MultiIndex([('bb', 'a'),
+            ('aa', 'a'),
+            ('bb', 'b'),
+            ('aa', 'b')],
+           )
+        """
+        name_i = self._data.names[i] if isinstance(i, int) else i
+        name_j = self._data.names[j] if isinstance(j, int) else j
+        new_data = {}
+        for k, v in self._data.items():
+            if k not in (name_i, name_j):
+                new_data[k] = v
+            elif k == name_i:
+                new_data[name_j] = self._data[name_j]
+            elif k == name_j:
+                new_data[name_i] = self._data[name_i]
+        midx = MultiIndex._from_data(new_data)
+        if all(n is None for n in self.names):
+            midx = midx.set_names(self.names)
+        return midx
+
+    @_cudf_nvtx_annotate
+    def droplevel(self, level=-1):
+        """
+        Removes the specified levels from the MultiIndex.
+
+        Parameters
+        ----------
+        level : level name or index, list-like
+            Integer, name or list of such, specifying one or more
+            levels to drop from the MultiIndex
+
+        Returns
+        -------
+        A MultiIndex or Index object, depending on the number of remaining
+        levels.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx = cudf.MultiIndex.from_frame(
+        ...     cudf.DataFrame(
+        ...         {
+        ...             "first": ["a", "a", "a", "b", "b", "b"],
+        ...             "second": [1, 1, 2, 2, 3, 3],
+        ...             "third": [0, 1, 2, 0, 1, 2],
+        ...         }
+        ...     )
+        ... )
+
+        Dropping level by index:
+
+        >>> idx.droplevel(0)
+        MultiIndex([(1, 0),
+                    (1, 1),
+                    (2, 2),
+                    (2, 0),
+                    (3, 1),
+                    (3, 2)],
+                   names=['second', 'third'])
+
+        Dropping level by name:
+
+        >>> idx.droplevel("first")
+        MultiIndex([(1, 0),
+                    (1, 1),
+                    (2, 2),
+                    (2, 0),
+                    (3, 1),
+                    (3, 2)],
+                   names=['second', 'third'])
+
+        Dropping multiple levels:
+
+        >>> idx.droplevel(["first", "second"])
+        Int64Index([0, 1, 2, 0, 1, 2], dtype='int64', name='third')
+        """
+        mi = self.copy(deep=False)
+        mi._poplevels(level)
+        if mi.nlevels == 1:
+            return mi.get_level_values(mi.names[0])
+        else:
+            return mi
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, nullable=False, **kwargs):
+        result = self.to_frame(
+            index=False, name=list(range(self.nlevels))
+        ).to_pandas(nullable=nullable)
+        return pd.MultiIndex.from_frame(result, names=self.names)
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_pandas(cls, multiindex, nan_as_null=no_default):
+        """
+        Convert from a Pandas MultiIndex
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> pmi = pd.MultiIndex(levels=[['a', 'b'], ['c', 'd']],
+        ...                     codes=[[0, 1], [1, 1]])
+        >>> cudf.from_pandas(pmi)
+        MultiIndex([('a', 'd'),
+                    ('b', 'd')],
+                   )
+        """
+        if not isinstance(multiindex, pd.MultiIndex):
+            raise TypeError("not a pandas.MultiIndex")
+        if nan_as_null is no_default:
+            nan_as_null = (
+                False if cudf.get_option("mode.pandas_compatible") else None
+            )
+
+        # if `multiindex` has two or more levels that
+        # have the same name, then `multiindex.to_frame()`
+        # results in a DataFrame containing only one of those
+        # levels. Thus, set `names` to some tuple of unique values
+        # and then call `multiindex.to_frame(name=names)`,
+        # which preserves all levels of `multiindex`.
+        names = tuple(range(len(multiindex.names)))
+
+        df = cudf.DataFrame.from_pandas(
+            multiindex.to_frame(index=False, name=names), nan_as_null
+        )
+        return cls.from_frame(df, names=multiindex.names)
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_unique(self):
+        return len(self) == len(self.unique())
+
+    @property
+    def dtype(self):
+        return np.dtype("O")
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic_increasing(self):
+        """
+        Return if the index is monotonic increasing
+        (only equal or increasing) values.
+        """
+        return self._is_sorted(ascending=None, null_position=None)
+
+    @cached_property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic_decreasing(self):
+        """
+        Return if the index is monotonic decreasing
+        (only equal or decreasing) values.
+        """
+        return self._is_sorted(
+            ascending=[False] * len(self.levels), null_position=None
+        )
+
+    @_cudf_nvtx_annotate
+    def fillna(self, value):
+        """
+        Fill null values with the specified value.
+
+        Parameters
+        ----------
+        value : scalar
+            Scalar value to use to fill nulls. This value cannot be a
+            list-likes.
+
+        Returns
+        -------
+        filled : MultiIndex
+
+        Examples
+        --------
+        >>> import cudf
+        >>> index = cudf.MultiIndex(
+        ...         levels=[["a", "b", "c", None], ["1", None, "5"]],
+        ...         codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        ...         names=["x", "y"],
+        ...       )
+        >>> index
+        MultiIndex([( 'a',  '1'),
+                    ( 'a',  '5'),
+                    ( 'b', <NA>),
+                    ( 'c', <NA>),
+                    (<NA>,  '1')],
+                   names=['x', 'y'])
+        >>> index.fillna('hello')
+        MultiIndex([(    'a',     '1'),
+                    (    'a',     '5'),
+                    (    'b', 'hello'),
+                    (    'c', 'hello'),
+                    ('hello',     '1')],
+                   names=['x', 'y'])
+        """
+
+        return super().fillna(value=value)
+
+    @_cudf_nvtx_annotate
+    def unique(self):
+        return self.drop_duplicates(keep="first")
+
+    def _clean_nulls_from_index(self):
+        """
+        Convert all na values(if any) in MultiIndex object
+        to `<NA>` as a preprocessing step to `__repr__` methods.
+        """
+        index_df = self.to_frame(index=False, name=list(range(self.nlevels)))
+        return MultiIndex.from_frame(
+            index_df._clean_nulls_from_dataframe(index_df), names=self.names
+        )
+
+    @_cudf_nvtx_annotate
+    def memory_usage(self, deep=False):
+        usage = sum(col.memory_usage for col in self._data.columns)
+        if self.levels:
+            for level in self.levels:
+                usage += level.memory_usage(deep=deep)
+        if self._codes_frame:
+            for col in self._codes_frame._data.columns:
+                usage += col.memory_usage
+        return usage
+
+    @_cudf_nvtx_annotate
+    def difference(self, other, sort=None):
+        if hasattr(other, "to_pandas"):
+            other = other.to_pandas()
+        return cudf.from_pandas(self.to_pandas().difference(other, sort))
+
+    @_cudf_nvtx_annotate
+    def append(self, other):
+        """
+        Append a collection of MultiIndex objects together
+
+        Parameters
+        ----------
+        other : MultiIndex or list/tuple of MultiIndex objects
+
+        Returns
+        -------
+        appended : Index
+
+        Examples
+        --------
+        >>> import cudf
+        >>> idx1 = cudf.MultiIndex(
+        ...     levels=[[1, 2], ['blue', 'red']],
+        ...     codes=[[0, 0, 1, 1], [1, 0, 1, 0]]
+        ... )
+        >>> idx2 = cudf.MultiIndex(
+        ...     levels=[[3, 4], ['blue', 'red']],
+        ...     codes=[[0, 0, 1, 1], [1, 0, 1, 0]]
+        ... )
+        >>> idx1
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue')],
+                   )
+        >>> idx2
+        MultiIndex([(3,  'red'),
+                    (3, 'blue'),
+                    (4,  'red'),
+                    (4, 'blue')],
+                   )
+        >>> idx1.append(idx2)
+        MultiIndex([(1,  'red'),
+                    (1, 'blue'),
+                    (2,  'red'),
+                    (2, 'blue'),
+                    (3,  'red'),
+                    (3, 'blue'),
+                    (4,  'red'),
+                    (4, 'blue')],
+                   )
+        """
+        if isinstance(other, (list, tuple)):
+            to_concat = [self]
+            to_concat.extend(other)
+        else:
+            to_concat = [self, other]
+
+        for obj in to_concat:
+            if not isinstance(obj, MultiIndex):
+                raise TypeError(
+                    f"all objects should be of type "
+                    f"MultiIndex for MultiIndex.append, "
+                    f"found object of type: {type(obj)}"
+                )
+
+        return MultiIndex._concat(to_concat)
+
+    @_cudf_nvtx_annotate
+    def __array_function__(self, func, types, args, kwargs):
+        cudf_df_module = MultiIndex
+
+        for submodule in func.__module__.split(".")[1:]:
+            # point cudf to the correct submodule
+            if hasattr(cudf_df_module, submodule):
+                cudf_df_module = getattr(cudf_df_module, submodule)
+            else:
+                return NotImplemented
+
+        fname = func.__name__
+
+        handled_types = [cudf_df_module, np.ndarray]
+
+        for t in types:
+            if t not in handled_types:
+                return NotImplemented
+
+        if hasattr(cudf_df_module, fname):
+            cudf_func = getattr(cudf_df_module, fname)
+            # Handle case if cudf_func is same as numpy function
+            if cudf_func is func:
+                return NotImplemented
+            else:
+                return cudf_func(*args, **kwargs)
+        else:
+            return NotImplemented
+
+    def _level_index_from_level(self, level):
+        """
+        Return level index from given level name or index
+        """
+        try:
+            return self.names.index(level)
+        except ValueError:
+            if not is_integer(level):
+                raise KeyError(f"Level {level} not found")
+            if level < 0:
+                level += self.nlevels
+            if level >= self.nlevels:
+                raise IndexError(
+                    f"Level {level} out of bounds. "
+                    f"Index has {self.nlevels} levels."
+                ) from None
+            return level
+
+    @_cudf_nvtx_annotate
+    def get_loc(self, key, method=None, tolerance=None):
+        """
+        Get location for a label or a tuple of labels.
+
+        The location is returned as an integer/slice or boolean mask.
+
+        Parameters
+        ----------
+        key : label or tuple of labels (one for each level)
+        method : None
+
+        Returns
+        -------
+        loc : int, slice object or boolean mask
+            - If index is unique, search result is unique, return a single int.
+            - If index is monotonic, index is returned as a slice object.
+            - Otherwise, cudf attempts a best effort to convert the search
+              result into a slice object, and will return a boolean mask if
+              failed to do so. Notice this can deviate from Pandas behavior
+              in some situations.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> mi = cudf.MultiIndex.from_tuples(
+        ...     [('a', 'd'), ('b', 'e'), ('b', 'f')])
+        >>> mi.get_loc('b')
+        slice(1, 3, None)
+        >>> mi.get_loc(('b', 'e'))
+        1
+        >>> non_monotonic_non_unique_idx = cudf.MultiIndex.from_tuples(
+        ...     [('c', 'd'), ('b', 'e'), ('a', 'f'), ('b', 'e')])
+        >>> non_monotonic_non_unique_idx.get_loc('b') # differ from pandas
+        slice(1, 4, 2)
+
+        .. pandas-compat::
+            **MultiIndex.get_loc**
+
+            The return types of this function may deviates from the
+            method provided by Pandas. If the index is neither
+            lexicographically sorted nor unique, a best effort attempt is made
+            to coerce the found indices into a slice. For example:
+
+            .. code-block::
+
+                >>> import pandas as pd
+                >>> import cudf
+                >>> x = pd.MultiIndex.from_tuples([
+                ...     (2, 1, 1), (1, 2, 3), (1, 2, 1),
+                ...     (1, 1, 1), (1, 1, 1), (2, 2, 1),
+                ... ])
+                >>> x.get_loc(1)
+                array([False,  True,  True,  True,  True, False])
+                >>> cudf.from_pandas(x).get_loc(1)
+                slice(1, 5, 1)
+        """
+        if tolerance is not None:
+            raise NotImplementedError(
+                "Parameter tolerance is not supported yet."
+            )
+        if method is not None:
+            raise NotImplementedError(
+                "only the default get_loc method is currently supported for"
+                " MultiIndex"
+            )
+
+        is_sorted = (
+            self.is_monotonic_increasing or self.is_monotonic_decreasing
+        )
+        is_unique = self.is_unique
+        key = (key,) if not isinstance(key, tuple) else key
+
+        # Handle partial key search. If length of `key` is less than `nlevels`,
+        # Only search levels up to `len(key)` level.
+        key_as_table = cudf.core.frame.Frame(
+            {i: column.as_column(k, length=1) for i, k in enumerate(key)}
+        )
+        partial_index = self.__class__._from_data(
+            data=self._data.select_by_index(slice(key_as_table._num_columns))
+        )
+        (
+            lower_bound,
+            upper_bound,
+            sort_inds,
+        ) = _lexsorted_equal_range(partial_index, key_as_table, is_sorted)
+
+        if lower_bound == upper_bound:
+            raise KeyError(key)
+
+        if is_unique and lower_bound + 1 == upper_bound:
+            # Indices are unique (Pandas constraint), search result is unique,
+            # return int.
+            return (
+                lower_bound
+                if is_sorted
+                else sort_inds.element_indexing(lower_bound)
+            )
+
+        if is_sorted:
+            # In monotonic index, lex search result is continuous. A slice for
+            # the range is returned.
+            return slice(lower_bound, upper_bound)
+
+        true_inds = sort_inds.slice(lower_bound, upper_bound).values
+        true_inds = _maybe_indices_to_slice(true_inds)
+        if isinstance(true_inds, slice):
+            return true_inds
+
+        # Not sorted and not unique. Return a boolean mask
+        mask = cp.full(self._data.nrows, False)
+        mask[true_inds] = True
+        return mask
+
+    def _get_reconciled_name_object(self, other) -> MultiIndex:
+        """
+        If the result of a set operation will be self,
+        return self, unless the names change, in which
+        case make a shallow copy of self.
+        """
+        names = self._maybe_match_names(other)
+        if self.names != names:
+            return self.rename(names)
+        return self
+
+    def _maybe_match_names(self, other):
+        """
+        Try to find common names to attach to the result of an operation
+        between a and b. Return a consensus list of names if they match
+        at least partly or list of None if they have completely
+        different names.
+        """
+        if len(self.names) != len(other.names):
+            return [None] * len(self.names)
+        return [
+            self_name if _is_same_name(self_name, other_name) else None
+            for self_name, other_name in zip(self.names, other.names)
+        ]
+
+    @_cudf_nvtx_annotate
+    def union(self, other, sort=None):
+        if not isinstance(other, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            try:
+                other = MultiIndex.from_tuples(other, names=self.names)
+            except (ValueError, TypeError) as err:
+                # ValueError raised by tuples_to_object_array if we
+                #  have non-object dtype
+                raise TypeError(msg) from err
+
+        if sort not in {None, False}:
+            raise ValueError(
+                f"The 'sort' keyword only takes the values of "
+                f"None or False; {sort} was passed."
+            )
+
+        if not len(other) or self.equals(other):
+            return self._get_reconciled_name_object(other)
+        elif not len(self):
+            return other._get_reconciled_name_object(self)
+
+        return self._union(other, sort=sort)
+
+    @_cudf_nvtx_annotate
+    def _union(self, other, sort=None):
+        # TODO: When to_frame is refactored to return a
+        # deep copy in future, we should push most of the common
+        # logic between MultiIndex._union & BaseIndex._union into
+        # GenericIndex._union.
+        other_df = other.copy(deep=True).to_frame(index=False)
+        self_df = self.copy(deep=True).to_frame(index=False)
+        col_names = list(range(0, self.nlevels))
+        self_df.columns = col_names
+        other_df.columns = col_names
+        self_df["order"] = self_df.index
+        other_df["order"] = other_df.index
+
+        result_df = self_df.merge(other_df, on=col_names, how="outer")
+        result_df = result_df.sort_values(
+            by=result_df._data.to_pandas_index()[self.nlevels :],
+            ignore_index=True,
+        )
+
+        midx = MultiIndex.from_frame(result_df.iloc[:, : self.nlevels])
+        midx.names = self.names if self.names == other.names else None
+        if sort is None and len(other):
+            return midx.sort_values()
+        return midx
+
+    @_cudf_nvtx_annotate
+    def _intersection(self, other, sort=None):
+        if self.names != other.names:
+            deep = True
+            col_names = list(range(0, self.nlevels))
+            res_name = (None,) * self.nlevels
+        else:
+            deep = False
+            col_names = None
+            res_name = self.names
+
+        other_df = other.copy(deep=deep).to_frame(index=False)
+        self_df = self.copy(deep=deep).to_frame(index=False)
+        if col_names is not None:
+            other_df.columns = col_names
+            self_df.columns = col_names
+
+        result_df = cudf.merge(self_df, other_df, how="inner")
+        midx = self.__class__.from_frame(result_df, names=res_name)
+        if sort is None and len(other):
+            return midx.sort_values()
+        return midx
+
+    @_cudf_nvtx_annotate
+    def _copy_type_metadata(
+        self: MultiIndex, other: MultiIndex, *, override_dtypes=None
+    ) -> MultiIndex:
+        res = super()._copy_type_metadata(other)
+        res._names = other._names
+        return res
+
+    @_cudf_nvtx_annotate
+    def _split_columns_by_levels(self, levels):
+        # This function assumes that for levels with duplicate names, they are
+        # specified by indices, not name by ``levels``. E.g. [None, None] can
+        # only be specified by 0, 1, not "None".
+
+        if levels is None:
+            return (
+                list(self._data.columns),
+                [],
+                [
+                    f"level_{i}" if name is None else name
+                    for i, name in enumerate(self.names)
+                ],
+                [],
+            )
+
+        # Normalize named levels into indices
+        level_names = list(self.names)
+        level_indices = {
+            lv if isinstance(lv, int) else level_names.index(lv)
+            for lv in levels
+        }
+
+        # Split the columns
+        data_columns, index_columns = [], []
+        data_names, index_names = [], []
+        for i, (name, col) in enumerate(zip(self.names, self._data.columns)):
+            if i in level_indices:
+                name = f"level_{i}" if name is None else name
+                data_columns.append(col)
+                data_names.append(name)
+            else:
+                index_columns.append(col)
+                index_names.append(name)
+        return data_columns, index_columns, data_names, index_names
+
+    def repeat(self, repeats, axis=None):
+        return self._from_columns_like_self(
+            Frame._repeat([*self._columns], repeats, axis), self._column_names
+        )
diff --git a/python/cudf/cudf/core/resample.py b/python/cudf/cudf/core/resample.py
new file mode 100644
index 0000000..eb59cf8
--- /dev/null
+++ b/python/cudf/cudf/core/resample.py
@@ -0,0 +1,421 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021-2023, NVIDIA CORPORATION &
+# AFFILIATES. All rights reserved.  SPDX-License-Identifier:
+# Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pickle
+
+import numpy as np
+import pandas as pd
+
+import cudf
+import cudf._lib.labeling
+import cudf.core.index
+from cudf._typing import DataFrameOrSeries
+from cudf.core.groupby.groupby import (
+    DataFrameGroupBy,
+    GroupBy,
+    SeriesGroupBy,
+    _Grouping,
+)
+from cudf.core.tools.datetimes import _offset_alias_to_code, _unit_dtype_map
+
+
+class _Resampler(GroupBy):
+
+    grouping: "_ResampleGrouping"
+
+    def __init__(self, obj, by, axis=None, kind=None):
+        by = _ResampleGrouping(obj, by)
+        super().__init__(obj, by=by)
+
+    def agg(self, func):
+        result = super().agg(func)
+        if len(self.grouping.bin_labels) != len(result):
+            index = cudf.core.index.Index(
+                self.grouping.bin_labels, name=self.grouping.names[0]
+            )
+            return result._align_to_index(
+                index, how="right", sort=False, allow_non_unique=True
+            )
+        else:
+            return result.sort_index()
+
+    def asfreq(self):
+        return self.obj._align_to_index(
+            self.grouping.bin_labels,
+            how="right",
+            sort=False,
+            allow_non_unique=True,
+        )
+
+    def _scan_fill(self, method: str, limit: int) -> DataFrameOrSeries:
+        # TODO: can this be more efficient?
+
+        # first, compute the outer join between `self.obj` and the `bin_labels`
+        # to get the sampling "gaps":
+        upsampled = self.obj._align_to_index(
+            self.grouping.bin_labels,
+            how="outer",
+            sort=True,
+            allow_non_unique=True,
+        )
+
+        # fill the gaps:
+        filled = upsampled.fillna(method=method)
+
+        # filter the result to only include the values corresponding
+        # to the bin labels:
+        return filled._align_to_index(
+            self.grouping.bin_labels,
+            how="right",
+            sort=False,
+            allow_non_unique=True,
+        )
+
+    def serialize(self):
+        header, frames = super().serialize()
+        grouping_head, grouping_frames = self.grouping.serialize()
+        header["grouping"] = grouping_head
+        header["resampler_type"] = pickle.dumps(type(self))
+        header["grouping_frames_count"] = len(grouping_frames)
+        frames.extend(grouping_frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        obj_type = pickle.loads(header["obj_type"])
+        obj = obj_type.deserialize(
+            header["obj"], frames[: header["num_obj_frames"]]
+        )
+        grouping = _ResampleGrouping.deserialize(
+            header["grouping"], frames[header["num_obj_frames"] :]
+        )
+        resampler_cls = pickle.loads(header["resampler_type"])
+        out = resampler_cls.__new__(resampler_cls)
+        out.grouping = grouping
+        super().__init__(out, obj, by=grouping)
+        return out
+
+
+class DataFrameResampler(_Resampler, DataFrameGroupBy):
+    pass
+
+
+class SeriesResampler(_Resampler, SeriesGroupBy):
+    pass
+
+
+class _ResampleGrouping(_Grouping):
+
+    bin_labels: cudf.core.index.Index
+
+    def copy(self, deep=True):
+        out = super().copy(deep=deep)
+        result = _ResampleGrouping.__new__(_ResampleGrouping)
+        result.names = out.names
+        result._named_columns = out._named_columns
+        result._key_columns = out._key_columns
+        result.bin_labels = self.bin_labels.copy(deep=deep)
+        return result
+
+    def serialize(self):
+        header, frames = super().serialize()
+        labels_head, labels_frames = self.bin_labels.serialize()
+        header["__bin_labels"] = labels_head
+        header["__bin_labels_count"] = len(labels_frames)
+        frames.extend(labels_frames)
+        return header, frames
+
+    @classmethod
+    def deserialize(cls, header, frames):
+        names = pickle.loads(header["names"])
+        _named_columns = pickle.loads(header["_named_columns"])
+        key_columns = cudf.core.column.deserialize_columns(
+            header["columns"], frames[: -header["__bin_labels_count"]]
+        )
+        out = _ResampleGrouping.__new__(_ResampleGrouping)
+        out.names = names
+        out._named_columns = _named_columns
+        out._key_columns = key_columns
+        out.bin_labels = cudf.core.index.Index.deserialize(
+            header["__bin_labels"], frames[-header["__bin_labels_count"] :]
+        )
+        return out
+
+    def _handle_frequency_grouper(self, by):
+        # if `by` is a time frequency grouper, we bin the key column
+        # using bin intervals specified by `by.freq`, then use *that*
+        # as the groupby key
+
+        freq = by.freq
+        label = by.label
+        closed = by.closed
+
+        if isinstance(freq, (cudf.DateOffset, pd.DateOffset)):
+            raise NotImplementedError(
+                "Resampling by DateOffset objects is not yet supported."
+            )
+        if not isinstance(freq, str):
+            raise TypeError(
+                f"Unsupported type for freq: {type(freq).__name__}"
+            )
+        # convert freq to a pd.DateOffset:
+        offset = pd.tseries.frequencies.to_offset(freq)
+
+        if offset.freqstr == "M" or offset.freqstr.startswith("W-"):
+            label = "right" if label is None else label
+            closed = "right" if closed is None else closed
+        else:
+            label = "left" if label is None else label
+            closed = "left" if closed is None else closed
+
+        # determine the key column
+        if by.key is None and by.level is None:
+            # then assume that the key is the index of `self._obj`:
+            self._handle_index(self._obj.index)
+        elif by.key:
+            self._handle_label(by.key)
+        elif by.level:
+            self._handle_level(by.level)
+
+        if not len(self._key_columns) == 1:
+            raise ValueError("Must resample on exactly one column")
+
+        key_column = self._key_columns[0]
+
+        if not isinstance(key_column, cudf.core.column.DatetimeColumn):
+            raise TypeError(
+                f"Can only resample on a DatetimeIndex or datetime column, "
+                f"got column of type {key_column.dtype}"
+            )
+
+        # get the start and end values that will be used to generate
+        # the bin labels
+        min_date, max_date = key_column._minmax()
+        start, end = _get_timestamp_range_edges(
+            pd.Timestamp(min_date.value),
+            pd.Timestamp(max_date.value),
+            offset,
+            closed=closed,
+        )
+
+        # in some cases, an extra time stamp is required in order to
+        # bin all the values. It's OK if we generate more labels than
+        # we need, as we remove any unused labels below
+        end += offset
+
+        # generate the labels for binning the key column:
+        bin_labels = cudf.date_range(
+            start=start,
+            end=end,
+            freq=freq,
+        )
+
+        # We want the (resampled) column of timestamps in the result
+        # to have a resolution closest to the resampling
+        # frequency. For example, if resampling from '1T' to '1s', we
+        # want the resulting timestamp column to by of dtype
+        # 'datetime64[s]'.  libcudf requires the bin labels and key
+        # column to have the same dtype, so we compute a `result_type`
+        # and cast them both to that type.
+        try:
+            result_type = np.dtype(
+                _unit_dtype_map[_offset_alias_to_code[offset.name]]
+            )
+        except KeyError:
+            # unsupported resolution (we don't support resolutions >s)
+            # fall back to using datetime64[s]
+            result_type = np.dtype("datetime64[s]")
+
+        # TODO: Ideally, we can avoid one cast by having `date_range`
+        # generate timestamps of a given dtype.  Currently, it can
+        # only generate timestamps with 'ns' precision
+        key_column = key_column.astype(result_type)
+        bin_labels = bin_labels.astype(result_type)
+
+        # bin the key column:
+        bin_numbers = cudf._lib.labeling.label_bins(
+            key_column,
+            left_edges=bin_labels[:-1]._column,
+            left_inclusive=(closed == "left"),
+            right_edges=bin_labels[1:]._column,
+            right_inclusive=(closed == "right"),
+        )
+
+        if label == "right":
+            bin_labels = bin_labels[1:]
+        else:
+            bin_labels = bin_labels[:-1]
+
+        # if we have more labels than bins, remove the extras labels:
+        nbins = bin_numbers.max() + 1
+        if len(bin_labels) > nbins:
+            bin_labels = bin_labels[:nbins]
+
+        bin_labels.name = self.names[0]
+        self.bin_labels = bin_labels
+
+        # replace self._key_columns with the binned key column:
+        self._key_columns = [
+            bin_labels._gather(bin_numbers, check_bounds=False)._column.astype(
+                result_type
+            )
+        ]
+
+
+# NOTE: this function is vendored from Pandas
+def _get_timestamp_range_edges(
+    first, last, freq, closed="left", origin="start_day", offset=None
+):
+    """
+    Adjust the `first` Timestamp to the preceding Timestamp that resides on
+    the provided offset. Adjust the `last` Timestamp to the following
+    Timestamp that resides on the provided offset. Input Timestamps that
+    already reside on the offset will be adjusted depending on the type of
+    offset and the `closed` parameter.
+
+    Parameters
+    ----------
+    first : pd.Timestamp
+        The beginning Timestamp of the range to be adjusted.
+    last : pd.Timestamp
+        The ending Timestamp of the range to be adjusted.
+    freq : pd.DateOffset
+        The dateoffset to which the Timestamps will be adjusted.
+    closed : {'right', 'left'}, default None
+        Which side of bin interval is closed.
+    origin : {'epoch', 'start', 'start_day'} or Timestamp, default 'start_day'
+        The timestamp on which to adjust the grouping. The timezone of origin
+        must match the timezone of the index.  If a timestamp is not used,
+        these values are also supported:
+
+        - 'epoch': `origin` is 1970-01-01
+        - 'start': `origin` is the first value of the timeseries
+        - 'start_day': `origin` is the first day at midnight of the timeseries
+    offset : pd.Timedelta, default is None
+        An offset timedelta added to the origin.
+
+    Returns
+    -------
+    A tuple of length 2, containing the adjusted pd.Timestamp objects.
+    """
+    from pandas.tseries.offsets import Day, Tick
+
+    if isinstance(freq, Tick):
+        index_tz = first.tz
+        if isinstance(origin, pd.Timestamp) and (origin.tz is None) != (
+            index_tz is None
+        ):
+            raise ValueError(
+                "The origin must have the same timezone as the index."
+            )
+        elif origin == "epoch":
+            # set the epoch based on the timezone to have similar bins results
+            # when resampling on the same kind of indexes on different
+            # timezones
+            origin = pd.Timestamp("1970-01-01", tz=index_tz)
+
+        if isinstance(freq, Day):
+            # _adjust_dates_anchored assumes 'D' means 24H, but first/last
+            # might contain a DST transition (23H, 24H, or 25H).
+            # So "pretend" the dates are naive when adjusting the endpoints
+            first = first.tz_localize(None)
+            last = last.tz_localize(None)
+            if isinstance(origin, pd.Timestamp):
+                origin = origin.tz_localize(None)
+
+        first, last = _adjust_dates_anchored(
+            first, last, freq, closed=closed, origin=origin, offset=offset
+        )
+        if isinstance(freq, Day):
+            first = first.tz_localize(index_tz)
+            last = last.tz_localize(index_tz)
+    else:
+        first = first.normalize()
+        last = last.normalize()
+
+        if closed == "left":
+            first = pd.Timestamp(freq.rollback(first))
+        else:
+            first = pd.Timestamp(first - freq)
+
+        last = pd.Timestamp(last + freq)
+
+    return first, last
+
+
+# NOTE: this function is vendored from Pandas
+def _adjust_dates_anchored(
+    first, last, freq, closed="right", origin="start_day", offset=None
+):
+    # First and last offsets should be calculated from the start day to fix an
+    # error cause by resampling across multiple days when a one day period is
+    # not a multiple of the frequency. See GH 8683
+    # To handle frequencies that are not multiple or divisible by a day we let
+    # the possibility to define a fixed origin timestamp. See GH 31809
+    origin_nanos = 0  # origin == "epoch"
+    if origin == "start_day":
+        origin_nanos = first.normalize().value
+    elif origin == "start":
+        origin_nanos = first.value
+    elif isinstance(origin, pd.Timestamp):
+        origin_nanos = origin.value
+    origin_nanos += offset.value if offset else 0
+
+    # GH 10117 & GH 19375. If first and last contain timezone information,
+    # Perform the calculation in UTC in order to avoid localizing on an
+    # Ambiguous or Nonexistent time.
+    first_tzinfo = first.tzinfo
+    last_tzinfo = last.tzinfo
+    if first_tzinfo is not None:
+        first = first.tz_convert("UTC")
+    if last_tzinfo is not None:
+        last = last.tz_convert("UTC")
+
+    foffset = (first.value - origin_nanos) % freq.nanos
+    loffset = (last.value - origin_nanos) % freq.nanos
+
+    if closed == "right":
+        if foffset > 0:
+            # roll back
+            fresult = first.value - foffset
+        else:
+            fresult = first.value - freq.nanos
+
+        if loffset > 0:
+            # roll forward
+            lresult = last.value + (freq.nanos - loffset)
+        else:
+            # already the end of the road
+            lresult = last.value
+    else:  # closed == 'left'
+        if foffset > 0:
+            fresult = first.value - foffset
+        else:
+            # start of the road
+            fresult = first.value
+
+        if loffset > 0:
+            # roll forward
+            lresult = last.value + (freq.nanos - loffset)
+        else:
+            lresult = last.value + freq.nanos
+    fresult = pd.Timestamp(fresult)
+    lresult = pd.Timestamp(lresult)
+    if first_tzinfo is not None:
+        fresult = fresult.tz_localize("UTC").tz_convert(first_tzinfo)
+    if last_tzinfo is not None:
+        lresult = lresult.tz_localize("UTC").tz_convert(last_tzinfo)
+    return fresult, lresult
diff --git a/python/cudf/cudf/core/reshape.py b/python/cudf/cudf/core/reshape.py
new file mode 100644
index 0000000..3c84894
--- /dev/null
+++ b/python/cudf/cudf/core/reshape.py
@@ -0,0 +1,1455 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import itertools
+import warnings
+from collections import abc
+from typing import Dict, Optional
+
+import cupy
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf._lib.transform import one_hot_encode
+from cudf._lib.types import size_type_dtype
+from cudf._typing import Dtype
+from cudf.api.extensions import no_default
+from cudf.core.column import ColumnBase, as_column, column_empty_like
+from cudf.core.column.categorical import CategoricalColumn
+from cudf.utils.dtypes import min_unsigned_type
+
+_AXIS_MAP = {0: 0, 1: 1, "index": 0, "columns": 1}
+
+
+def _align_objs(objs, how="outer", sort=None):
+    """Align a set of Series or Dataframe objects.
+
+    Parameters
+    ----------
+    objs : list of DataFrame, Series, or Index
+    how : How to handle indexes on other axis (or axes),
+    similar to join in concat
+    sort : Whether to sort the resulting Index
+    Returns
+    -------
+    A list of reindexed and aligned objects
+    ready for concatenation
+    """
+    # Check if multiindex then check if indexes match. GenericIndex
+    # returns ndarray tuple of bools requiring additional filter.
+    # Then check for duplicate index value.
+    i_objs = iter(objs)
+    first = next(i_objs)
+
+    not_matching_index = any(
+        not first.index.equals(rest.index) for rest in i_objs
+    )
+
+    if not_matching_index:
+        if not all(o.index.is_unique for o in objs):
+            raise ValueError("cannot reindex on an axis with duplicate labels")
+
+        index = objs[0].index
+        name = index.name
+
+        final_index = _get_combined_index(
+            [obj.index for obj in objs], intersect=how == "inner", sort=sort
+        )
+
+        final_index.name = name
+        return [
+            obj.reindex(final_index)
+            if not final_index.equals(obj.index)
+            else obj
+            for obj in objs
+        ]
+    else:
+        if sort:
+            if not first.index.is_monotonic_increasing:
+                final_index = first.index.sort_values()
+                return [obj.reindex(final_index) for obj in objs]
+        return objs
+
+
+def _get_combined_index(indexes, intersect: bool = False, sort=None):
+    if len(indexes) == 0:
+        index = cudf.Index([])
+    elif len(indexes) == 1:
+        index = indexes[0]
+    elif intersect:
+        sort = True
+        index = indexes[0]
+        for other in indexes[1:]:
+            # Don't sort for every intersection,
+            # let the sorting happen in the end.
+            index = index.intersection(other, sort=False)
+    else:
+        index = indexes[0]
+        if sort is None:
+            sort = not isinstance(index, cudf.StringIndex)
+        for other in indexes[1:]:
+            index = index.union(other, sort=False)
+
+    if sort:
+        if not index.is_monotonic_increasing:
+            index = index.sort_values()
+
+    return index
+
+
+def _normalize_series_and_dataframe(objs, axis):
+    """Convert any cudf.Series objects in objs to DataFrames in place."""
+    # Default to naming series by a numerical id if they are not named.
+    sr_name = 0
+    for idx, o in enumerate(objs):
+        if isinstance(o, cudf.Series):
+            if axis == 1:
+                name = o.name
+                if name is None:
+                    name = sr_name
+                    sr_name += 1
+            else:
+                name = sr_name
+
+            objs[idx] = o.to_frame(name=name)
+
+
+def concat(objs, axis=0, join="outer", ignore_index=False, sort=None):
+    """Concatenate DataFrames, Series, or Indices row-wise.
+
+    Parameters
+    ----------
+    objs : list of DataFrame, Series, or Index
+    axis : {0/'index', 1/'columns'}, default 0
+        The axis to concatenate along.
+    join : {'inner', 'outer'}, default 'outer'
+        How to handle indexes on other axis (or axes).
+    ignore_index : bool, default False
+        Set True to ignore the index of the *objs* and provide a
+        default range index instead.
+    sort : bool, default False
+        Sort non-concatenation axis if it is not already aligned.
+
+    Returns
+    -------
+    A new object of like type with rows from each object in ``objs``.
+
+    Examples
+    --------
+    Combine two ``Series``.
+
+    >>> import cudf
+    >>> s1 = cudf.Series(['a', 'b'])
+    >>> s2 = cudf.Series(['c', 'd'])
+    >>> s1
+    0    a
+    1    b
+    dtype: object
+    >>> s2
+    0    c
+    1    d
+    dtype: object
+    >>> cudf.concat([s1, s2])
+    0    a
+    1    b
+    0    c
+    1    d
+    dtype: object
+
+    Clear the existing index and reset it in the
+    result by setting the ``ignore_index`` option to ``True``.
+
+    >>> cudf.concat([s1, s2], ignore_index=True)
+    0    a
+    1    b
+    2    c
+    3    d
+    dtype: object
+
+    Combine two DataFrame objects with identical columns.
+
+    >>> df1 = cudf.DataFrame([['a', 1], ['b', 2]],
+    ...                    columns=['letter', 'number'])
+    >>> df1
+      letter  number
+    0      a       1
+    1      b       2
+    >>> df2 = cudf.DataFrame([['c', 3], ['d', 4]],
+    ...                    columns=['letter', 'number'])
+    >>> df2
+      letter  number
+    0      c       3
+    1      d       4
+    >>> cudf.concat([df1, df2])
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine DataFrame objects with overlapping columns and return
+    everything. Columns outside the intersection will
+    be filled with ``null`` values.
+
+    >>> df3 = cudf.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
+    ...                    columns=['letter', 'number', 'animal'])
+    >>> df3
+      letter  number animal
+    0      c       3    cat
+    1      d       4    dog
+    >>> cudf.concat([df1, df3], sort=False)
+      letter  number animal
+    0      a       1   <NA>
+    1      b       2   <NA>
+    0      c       3    cat
+    1      d       4    dog
+
+    Combine ``DataFrame`` objects with overlapping columns
+    and return only those that are shared by passing ``inner`` to
+    the ``join`` keyword argument.
+
+    >>> cudf.concat([df1, df3], join="inner")
+      letter  number
+    0      a       1
+    1      b       2
+    0      c       3
+    1      d       4
+
+    Combine ``DataFrame`` objects horizontally along the
+    x axis by passing in ``axis=1``.
+
+    >>> df4 = cudf.DataFrame([['bird', 'polly'], ['monkey', 'george']],
+    ...                    columns=['animal', 'name'])
+    >>> df4
+       animal    name
+    0    bird   polly
+    1  monkey  george
+    >>> cudf.concat([df1, df4], axis=1)
+      letter  number  animal    name
+    0      a       1    bird   polly
+    1      b       2  monkey  george
+    """
+    # TODO: Do we really need to have different error messages for an empty
+    # list and a list of None?
+    if not objs:
+        raise ValueError("No objects to concatenate")
+
+    objs = [obj for obj in objs if obj is not None]
+
+    if not objs:
+        raise ValueError("All objects passed were None")
+
+    axis = _AXIS_MAP.get(axis, None)
+    if axis is None:
+        raise ValueError(
+            f'`axis` must be 0 / "index" or 1 / "columns", got: {axis}'
+        )
+
+    # Return for single object
+    if len(objs) == 1:
+        obj = objs[0]
+
+        if ignore_index:
+            if axis == 1:
+                result = cudf.DataFrame._from_data(
+                    data=obj._data.copy(deep=True),
+                    index=obj.index.copy(deep=True),
+                )
+                # The DataFrame constructor for dict-like data (such as the
+                # ColumnAccessor given by obj._data here) will drop any columns
+                # in the data that are not in `columns`, so we have to rename
+                # after construction.
+                result.columns = pd.RangeIndex(len(obj._data.names))
+            else:
+                if isinstance(obj, cudf.Series):
+                    result = cudf.Series._from_data(
+                        data=obj._data.copy(deep=True),
+                        index=cudf.RangeIndex(len(obj)),
+                    )
+                elif isinstance(obj, pd.Series):
+                    result = cudf.Series(
+                        data=obj,
+                        index=cudf.RangeIndex(len(obj)),
+                    )
+                else:
+                    result = cudf.DataFrame._from_data(
+                        data=obj._data.copy(deep=True),
+                        index=cudf.RangeIndex(len(obj)),
+                    )
+        else:
+            if axis == 0:
+                result = obj.copy()
+            else:
+                data = obj._data.copy(deep=True)
+                if isinstance(obj, cudf.Series) and obj.name is None:
+                    # If the Series has no name, pandas renames it to 0.
+                    data[0] = data.pop(None)
+                result = cudf.DataFrame._from_data(
+                    data, index=obj.index.copy(deep=True)
+                )
+
+        if isinstance(result, cudf.Series) and axis == 0:
+            # sort has no effect for series concatted along axis 0
+            return result
+        else:
+            return result.sort_index(axis=(1 - axis)) if sort else result
+
+    # Retrieve the base types of `objs`. In order to support sub-types
+    # and object wrappers, we use `isinstance()` instead of comparing
+    # types directly
+    typs = set()
+    for o in objs:
+        if isinstance(o, cudf.MultiIndex):
+            typs.add(cudf.MultiIndex)
+        elif isinstance(o, cudf.BaseIndex):
+            typs.add(type(o))
+        elif isinstance(o, cudf.DataFrame):
+            typs.add(cudf.DataFrame)
+        elif isinstance(o, cudf.Series):
+            typs.add(cudf.Series)
+        else:
+            raise TypeError(f"cannot concatenate object of type {type(o)}")
+
+    allowed_typs = {cudf.Series, cudf.DataFrame}
+
+    # when axis is 1 (column) we can concat with Series and Dataframes
+    if axis == 1:
+        if not typs.issubset(allowed_typs):
+            raise TypeError(
+                "Can only concatenate Series and DataFrame objects when axis=1"
+            )
+        df = cudf.DataFrame()
+        _normalize_series_and_dataframe(objs, axis=axis)
+
+        # Inner joins involving empty data frames always return empty dfs, but
+        # We must delay returning until we have set the column names.
+        empty_inner = any(obj.empty for obj in objs) and join == "inner"
+
+        objs = [obj for obj in objs if obj.shape != (0, 0)]
+
+        if len(objs) == 0:
+            return df
+
+        # Don't need to align indices of all `objs` since we
+        # would anyway return an empty dataframe below
+        if not empty_inner:
+            objs = _align_objs(objs, how=join, sort=sort)
+            df.index = objs[0].index
+
+        for o in objs:
+            for name, col in o._data.items():
+                if name in df._data:
+                    raise NotImplementedError(
+                        f"A Column with duplicate name found: {name}, cuDF "
+                        f"doesn't support having multiple columns with "
+                        f"same names yet."
+                    )
+                if empty_inner:
+                    # if join is inner and it contains an empty df
+                    # we return an empty df, hence creating an empty
+                    # column with dtype metadata retained.
+                    df[name] = cudf.core.column.column_empty_like(
+                        col, newsize=0
+                    )
+                else:
+                    df[name] = col
+
+        result_columns = (
+            objs[0]
+            ._data.to_pandas_index()
+            .append([obj._data.to_pandas_index() for obj in objs[1:]])
+        )
+
+        if ignore_index:
+            # with ignore_index the column names change to numbers
+            df.columns = pd.RangeIndex(len(result_columns.unique()))
+        else:
+            df.columns = result_columns.unique()
+
+        if empty_inner:
+            # if join is inner and it contains an empty df
+            # we return an empty df
+            return df.head(0)
+
+        return df
+
+    # If we get here, we are always concatenating along axis 0 (the rows).
+    typ = list(typs)[0]
+    if len(typs) > 1:
+        if allowed_typs == typs:
+            # This block of code will run when `objs` has
+            # both Series & DataFrame kind of inputs.
+            _normalize_series_and_dataframe(objs, axis=axis)
+            typ = cudf.DataFrame
+        else:
+            raise TypeError(
+                f"`concat` cannot concatenate objects of "
+                f"types: {sorted([t.__name__ for t in typs])}."
+            )
+
+    if typ is cudf.DataFrame:
+        old_objs = objs
+        objs = [obj for obj in objs if obj.shape != (0, 0)]
+        if len(objs) == 0:
+            # If objs is empty, that indicates all of
+            # objs are empty dataframes.
+            return cudf.DataFrame()
+        elif len(objs) == 1:
+            obj = objs[0]
+            result = cudf.DataFrame._from_data(
+                data=None if join == "inner" else obj._data.copy(deep=True),
+                index=cudf.RangeIndex(len(obj))
+                if ignore_index
+                else obj.index.copy(deep=True),
+            )
+            return result
+        else:
+            if join == "inner" and len(old_objs) != len(objs):
+                # don't filter out empty df's
+                objs = old_objs
+            result = cudf.DataFrame._concat(
+                objs,
+                axis=axis,
+                join=join,
+                ignore_index=ignore_index,
+                # Explicitly cast rather than relying on None being falsy.
+                sort=bool(sort),
+            )
+        return result
+
+    elif typ is cudf.Series:
+        objs = [obj for obj in objs if len(obj)]
+        if len(objs) == 0:
+            return cudf.Series()
+        elif len(objs) == 1 and not ignore_index:
+            return objs[0]
+        else:
+            return cudf.Series._concat(
+                objs, axis=axis, index=None if ignore_index else True
+            )
+    elif typ is cudf.MultiIndex:
+        return cudf.MultiIndex._concat(objs)
+    elif issubclass(typ, cudf.Index):
+        return cudf.core.index.GenericIndex._concat(objs)
+    else:
+        raise TypeError(f"cannot concatenate object of type {typ}")
+
+
+def melt(
+    frame,
+    id_vars=None,
+    value_vars=None,
+    var_name=None,
+    value_name="value",
+    col_level=None,
+):
+    """Unpivots a DataFrame from wide format to long format,
+    optionally leaving identifier variables set.
+
+    Parameters
+    ----------
+    frame : DataFrame
+    id_vars : tuple, list, or ndarray, optional
+        Column(s) to use as identifier variables.
+        default: None
+    value_vars : tuple, list, or ndarray, optional
+        Column(s) to unpivot.
+        default: all columns that are not set as `id_vars`.
+    var_name : scalar
+        Name to use for the `variable` column.
+        default: frame.columns.name or 'variable'
+    value_name : str
+        Name to use for the `value` column.
+        default: 'value'
+
+    Returns
+    -------
+    out : DataFrame
+        Melted result
+
+    Difference from pandas:
+     * Does not support 'col_level' because cuDF does not have multi-index
+
+    Examples
+    --------
+    >>> import cudf
+    >>> df = cudf.DataFrame({'A': ['a', 'b', 'c'],
+    ...                      'B': [1, 3, 5],
+    ...                      'C': [2, 4, 6]})
+    >>> df
+       A  B  C
+    0  a  1  2
+    1  b  3  4
+    2  c  5  6
+    >>> cudf.melt(df, id_vars=['A'], value_vars=['B'])
+       A variable  value
+    0  a        B      1
+    1  b        B      3
+    2  c        B      5
+    >>> cudf.melt(df, id_vars=['A'], value_vars=['B', 'C'])
+       A variable  value
+    0  a        B      1
+    1  b        B      3
+    2  c        B      5
+    3  a        C      2
+    4  b        C      4
+    5  c        C      6
+
+    The names of 'variable' and 'value' columns can be customized:
+
+    >>> cudf.melt(df, id_vars=['A'], value_vars=['B'],
+    ...         var_name='myVarname', value_name='myValname')
+       A myVarname  myValname
+    0  a         B          1
+    1  b         B          3
+    2  c         B          5
+    """
+    if col_level is not None:
+        raise NotImplementedError("col_level != None is not supported yet.")
+
+    # Arg cleaning
+
+    # id_vars
+    if id_vars is not None:
+        if not isinstance(id_vars, abc.Sequence):
+            id_vars = [id_vars]
+        id_vars = list(id_vars)
+        missing = set(id_vars) - set(frame._column_names)
+        if not len(missing) == 0:
+            raise KeyError(
+                f"The following 'id_vars' are not present"
+                f" in the DataFrame: {list(missing)}"
+            )
+    else:
+        id_vars = []
+
+    # value_vars
+    if value_vars is not None:
+        if not isinstance(value_vars, abc.Sequence):
+            value_vars = [value_vars]
+        value_vars = list(value_vars)
+        missing = set(value_vars) - set(frame._column_names)
+        if not len(missing) == 0:
+            raise KeyError(
+                f"The following 'value_vars' are not present"
+                f" in the DataFrame: {list(missing)}"
+            )
+    else:
+        # then all remaining columns in frame
+        unique_id = set(id_vars)
+        value_vars = [c for c in frame._column_names if c not in unique_id]
+
+    # Error for unimplemented support for datatype
+    dtypes = [frame[col].dtype for col in id_vars + value_vars]
+    if any(cudf.api.types.is_categorical_dtype(t) for t in dtypes):
+        raise NotImplementedError(
+            "Categorical columns are not yet supported for function"
+        )
+
+    # Check dtype homogeneity in value_var
+    # Because heterogeneous concat is unimplemented
+    dtypes = [frame[col].dtype for col in value_vars]
+    if len(dtypes) > 0:
+        dtype = dtypes[0]
+        if any(t != dtype for t in dtypes):
+            raise ValueError("all cols in value_vars must have the same dtype")
+
+    # overlap
+    overlap = set(id_vars).intersection(set(value_vars))
+    if not len(overlap) == 0:
+        raise KeyError(
+            f"'value_vars' and 'id_vars' cannot have overlap."
+            f" The following 'value_vars' are ALSO present"
+            f" in 'id_vars': {list(overlap)}"
+        )
+
+    N = len(frame)
+    K = len(value_vars)
+
+    def _tile(A, reps):
+        series_list = [A] * reps
+        if reps > 0:
+            return cudf.Series._concat(objs=series_list, index=None)
+        else:
+            return cudf.Series([], dtype=A.dtype)
+
+    # Step 1: tile id_vars
+    mdata = {col: _tile(frame[col], K) for col in id_vars}
+
+    # Step 2: add variable
+    nval = len(value_vars)
+    dtype = min_unsigned_type(nval)
+    temp = cudf.Series(cupy.repeat(cupy.arange(nval, dtype=dtype), N))
+
+    if not var_name:
+        var_name = "variable"
+
+    mdata[var_name] = cudf.Series(
+        cudf.core.column.build_categorical_column(
+            categories=value_vars,
+            codes=temp._column,
+            mask=temp._column.base_mask,
+            size=temp._column.size,
+            offset=temp._column.offset,
+            ordered=False,
+        )
+    )
+
+    # Step 3: add values
+    mdata[value_name] = cudf.Series._concat(
+        objs=[frame[val] for val in value_vars], index=None
+    )
+
+    return cudf.DataFrame(mdata)
+
+
+def get_dummies(
+    df,
+    prefix=None,
+    prefix_sep="_",
+    dummy_na=False,
+    columns=None,
+    cats=None,
+    sparse=False,
+    drop_first=False,
+    dtype=no_default,
+):
+    """Returns a dataframe whose columns are the one hot encodings of all
+    columns in `df`
+
+    Parameters
+    ----------
+    df : array-like, Series, or DataFrame
+        Data of which to get dummy indicators.
+    prefix : str, dict, or sequence, optional
+        Prefix to append. Either a str (to apply a constant prefix), dict
+        mapping column names to prefixes, or sequence of prefixes to apply with
+        the same length as the number of columns. If not supplied, defaults
+        to the empty string
+    prefix_sep : str, dict, or sequence, optional, default '_'
+        Separator to use when appending prefixes
+    dummy_na : boolean, optional
+        Add a column to indicate Nones, if False Nones are ignored.
+    cats : dict, optional
+        Dictionary mapping column names to sequences of values representing
+        that column's category. If not supplied, it is computed as the unique
+        values of the column.
+    sparse : boolean, optional
+        Right now this is NON-FUNCTIONAL argument in rapids.
+    drop_first : boolean, optional
+        Right now this is NON-FUNCTIONAL argument in rapids.
+    columns : sequence of str, optional
+        Names of columns to encode. If not provided, will attempt to encode all
+        columns. Note this is different from pandas default behavior, which
+        encodes all columns with dtype object or categorical
+    dtype : str, optional
+        Output dtype, default 'uint8'
+
+    Examples
+    --------
+    >>> import cudf
+    >>> df = cudf.DataFrame({"a": ["value1", "value2", None], "b": [0, 0, 0]})
+    >>> cudf.get_dummies(df)
+       b  a_value1  a_value2
+    0  0         1         0
+    1  0         0         1
+    2  0         0         0
+
+    >>> cudf.get_dummies(df, dummy_na=True)
+       b  a_None  a_value1  a_value2
+    0  0       0         1         0
+    1  0       0         0         1
+    2  0       1         0         0
+
+    >>> import numpy as np
+    >>> df = cudf.DataFrame({"a":cudf.Series([1, 2, np.nan, None],
+    ...                     nan_as_null=False)})
+    >>> df
+          a
+    0   1.0
+    1   2.0
+    2   NaN
+    3  <NA>
+
+    >>> cudf.get_dummies(df, dummy_na=True, columns=["a"])
+       a_1.0  a_2.0  a_nan  a_null
+    0      1      0      0       0
+    1      0      1      0       0
+    2      0      0      1       0
+    3      0      0      0       1
+
+    >>> series = cudf.Series([1, 2, None, 2, 4])
+    >>> series
+    0       1
+    1       2
+    2    <NA>
+    3       2
+    4       4
+    dtype: int64
+    >>> cudf.get_dummies(series, dummy_na=True)
+       null  1  2  4
+    0     0  1  0  0
+    1     0  0  1  0
+    2     1  0  0  0
+    3     0  0  1  0
+    4     0  0  0  1
+    """
+
+    if cats is None:
+        cats = {}
+    if sparse:
+        raise NotImplementedError("sparse is not supported yet")
+
+    if drop_first:
+        raise NotImplementedError("drop_first is not supported yet")
+
+    if dtype is no_default:
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            "Default `dtype` value will be changed to 'bool' in a future "
+            "release, please update `dtype='bool'` to adapt for "
+            "future behavior.",
+            FutureWarning,
+        )
+        dtype = "uint8"
+
+    if isinstance(df, cudf.DataFrame):
+        encode_fallback_dtypes = ["object", "category"]
+
+        if columns is None or len(columns) == 0:
+            columns = df.select_dtypes(
+                include=encode_fallback_dtypes
+            )._column_names
+
+        _length_check_params(prefix, columns, "prefix")
+        _length_check_params(prefix_sep, columns, "prefix_sep")
+
+        if prefix is None:
+            prefix = columns
+
+        if isinstance(prefix, str):
+            prefix_map = {}
+        elif isinstance(prefix, dict):
+            prefix_map = prefix
+        else:
+            prefix_map = dict(zip(columns, prefix))
+
+        if isinstance(prefix_sep, str):
+            prefix_sep_map = {}
+        elif isinstance(prefix_sep, dict):
+            prefix_sep_map = prefix_sep
+        else:
+            prefix_sep_map = dict(zip(columns, prefix_sep))
+
+        # If we have no columns to encode, we need to drop
+        # fallback columns(if any)
+        if len(columns) == 0:
+            return df.select_dtypes(exclude=encode_fallback_dtypes)
+        else:
+            result_data = {
+                col_name: col
+                for col_name, col in df._data.items()
+                if col_name not in columns
+            }
+
+            for name in columns:
+                if name not in cats:
+                    unique = _get_unique(
+                        column=df._data[name], dummy_na=dummy_na
+                    )
+                else:
+                    unique = as_column(cats[name])
+
+                col_enc_data = _one_hot_encode_column(
+                    column=df._data[name],
+                    categories=unique,
+                    prefix=prefix_map.get(name, prefix),
+                    prefix_sep=prefix_sep_map.get(name, prefix_sep),
+                    dtype=dtype,
+                )
+                result_data.update(col_enc_data)
+            return cudf.DataFrame._from_data(result_data, index=df._index)
+    else:
+        ser = cudf.Series(df)
+        unique = _get_unique(column=ser._column, dummy_na=dummy_na)
+        data = _one_hot_encode_column(
+            column=ser._column,
+            categories=unique,
+            prefix=prefix,
+            prefix_sep=prefix_sep,
+            dtype=dtype,
+        )
+        return cudf.DataFrame._from_data(data, index=ser._index)
+
+
+def _merge_sorted(
+    objs,
+    keys=None,
+    by_index=False,
+    ignore_index=False,
+    ascending=True,
+    na_position="last",
+):
+    """Merge a list of sorted DataFrame or Series objects.
+
+    Dataframes/Series in objs list MUST be pre-sorted by columns
+    listed in `keys`, or by the index (if `by_index=True`).
+
+    Parameters
+    ----------
+    objs : list of DataFrame or Series
+    keys : list, default None
+        List of Column names to sort by. If None, all columns used
+        (Ignored if `by_index=True`)
+    by_index : bool, default False
+        Use index for sorting. `keys` input will be ignored if True
+    ignore_index : bool, default False
+        Drop and ignore index during merge. Default range index will
+        be used in the output dataframe.
+    ascending : bool, default True
+        Sorting is in ascending order, otherwise it is descending
+    na_position : {'first', 'last'}, default 'last'
+        'first' nulls at the beginning, 'last' nulls at the end
+
+    Returns
+    -------
+    A new, lexicographically sorted, DataFrame/Series.
+    """
+    if not pd.api.types.is_list_like(objs):
+        raise TypeError("objs must be a list-like of Frame-like objects")
+
+    if len(objs) < 1:
+        raise ValueError("objs must be non-empty")
+
+    if not all(isinstance(table, cudf.core.frame.Frame) for table in objs):
+        raise TypeError("Elements of objs must be Frame-like")
+
+    if len(objs) == 1:
+        return objs[0]
+
+    if by_index and ignore_index:
+        raise ValueError("`by_index` and `ignore_index` cannot both be True")
+
+    if by_index:
+        key_columns_indices = list(range(0, objs[0]._index.nlevels))
+    else:
+        if keys is None:
+            key_columns_indices = list(range(0, objs[0]._num_columns))
+        else:
+            key_columns_indices = [
+                objs[0]._column_names.index(key) for key in keys
+            ]
+        if not ignore_index:
+            key_columns_indices = [
+                idx + objs[0]._index.nlevels for idx in key_columns_indices
+            ]
+
+    columns = [
+        [
+            *(obj._index._data.columns if not ignore_index else ()),
+            *obj._columns,
+        ]
+        for obj in objs
+    ]
+
+    return objs[0]._from_columns_like_self(
+        cudf._lib.merge.merge_sorted(
+            input_columns=columns,
+            key_columns_indices=key_columns_indices,
+            ascending=ascending,
+            na_position=na_position,
+        ),
+        column_names=objs[0]._column_names,
+        index_names=None if ignore_index else objs[0]._index_names,
+    )
+
+
+def _pivot(df, index, columns):
+    """
+    Reorganize the values of the DataFrame according to the given
+    index and columns.
+
+    Parameters
+    ----------
+    df : DataFrame
+    index : cudf.Index
+        Index labels of the result
+    columns : cudf.Index
+        Column labels of the result
+    """
+    columns_labels, columns_idx = columns._encode()
+    index_labels, index_idx = index._encode()
+    column_labels = columns_labels.to_pandas().to_flat_index()
+
+    # the result of pivot always has a multicolumn
+    result = cudf.core.column_accessor.ColumnAccessor(
+        multiindex=True, level_names=(None,) + columns._data.names
+    )
+
+    def as_tuple(x):
+        return x if isinstance(x, tuple) else (x,)
+
+    for v in df:
+        names = [as_tuple(v) + as_tuple(name) for name in column_labels]
+        nrows = len(index_labels)
+        ncols = len(names)
+        num_elements = nrows * ncols
+        if num_elements > 0:
+            col = df._data[v]
+            scatter_map = (columns_idx * np.int32(nrows)) + index_idx
+            target = cudf.DataFrame._from_data(
+                {
+                    None: cudf.core.column.column_empty_like(
+                        col, masked=True, newsize=nrows * ncols
+                    )
+                }
+            )
+            target._data[None][scatter_map] = col
+            result_frames = target._split(range(nrows, nrows * ncols, nrows))
+            result.update(
+                {
+                    name: next(iter(f._columns))
+                    for name, f in zip(names, result_frames)
+                }
+            )
+
+    return cudf.DataFrame._from_data(
+        result, index=cudf.Index(index_labels, name=index.name)
+    )
+
+
+def pivot(data, index=None, columns=None, values=None):
+    """
+    Return reshaped DataFrame organized by the given index and column values.
+
+    Reshape data (produce a "pivot" table) based on column values. Uses
+    unique values from specified `index` / `columns` to form axes of the
+    resulting DataFrame.
+
+    Parameters
+    ----------
+    index : column name, optional
+        Column used to construct the index of the result.
+    columns : column name, optional
+        Column used to construct the columns of the result.
+    values : column name or list of column names, optional
+        Column(s) whose values are rearranged to produce the result.
+        If not specified, all remaining columns of the DataFrame
+        are used.
+
+    Returns
+    -------
+    DataFrame
+
+    Examples
+    --------
+    >>> a = cudf.DataFrame()
+    >>> a['a'] = [1, 1, 2, 2]
+    >>> a['b'] = ['a', 'b', 'a', 'b']
+    >>> a['c'] = [1, 2, 3, 4]
+    >>> a.pivot(index='a', columns='b')
+       c
+    b  a  b
+    a
+    1  1  2
+    2  3  4
+
+    Pivot with missing values in result:
+
+    >>> a = cudf.DataFrame()
+    >>> a['a'] = [1, 1, 2]
+    >>> a['b'] = [1, 2, 3]
+    >>> a['c'] = ['one', 'two', 'three']
+    >>> a.pivot(index='a', columns='b')
+              c
+        b     1     2      3
+        a
+        1   one   two   <NA>
+        2  <NA>  <NA>  three
+
+    """
+    df = data
+    values_is_list = True
+    if values is None:
+        values = df._columns_view(
+            col for col in df._column_names if col not in (index, columns)
+        )
+    else:
+        if not isinstance(values, (list, tuple)):
+            values = [values]
+            values_is_list = False
+        values = df._columns_view(values)
+    if index is None:
+        index = df.index
+    else:
+        index = cudf.core.index.Index(df.loc[:, index])
+    columns = cudf.Index(df.loc[:, columns])
+
+    # Create a DataFrame composed of columns from both
+    # columns and index
+    columns_index = {}
+    columns_index = {
+        i: col
+        for i, col in enumerate(
+            itertools.chain(index._data.columns, columns._data.columns)
+        )
+    }
+    columns_index = cudf.DataFrame(columns_index)
+
+    # Check that each row is unique:
+    if len(columns_index) != len(columns_index.drop_duplicates()):
+        raise ValueError("Duplicate index-column pairs found. Cannot reshape.")
+
+    result = _pivot(values, index, columns)
+
+    # MultiIndex to Index
+    if not values_is_list:
+        result._data.droplevel(0)
+
+    return result
+
+
+def unstack(df, level, fill_value=None):
+    """
+    Pivot one or more levels of the (necessarily hierarchical) index labels.
+
+    Pivots the specified levels of the index labels of df to the innermost
+    levels of the columns labels of the result.
+
+    * If the index of ``df`` has multiple levels, returns a ``Dataframe`` with
+      specified level of the index pivoted to the column levels.
+    * If the index of ``df`` has single level, returns a ``Series`` with all
+      column levels pivoted to the index levels.
+
+    Parameters
+    ----------
+    df : DataFrame
+    level : level name or index, list-like
+        Integer, name or list of such, specifying one or more
+        levels of the index to pivot
+    fill_value
+        Non-functional argument provided for compatibility with Pandas.
+
+    Returns
+    -------
+    Series or DataFrame
+
+    Examples
+    --------
+    >>> df = cudf.DataFrame()
+    >>> df['a'] = [1, 1, 1, 2, 2]
+    >>> df['b'] = [1, 2, 3, 1, 2]
+    >>> df['c'] = [5, 6, 7, 8, 9]
+    >>> df['d'] = ['a', 'b', 'a', 'd', 'e']
+    >>> df = df.set_index(['a', 'b', 'd'])
+    >>> df
+           c
+    a b d
+    1 1 a  5
+      2 b  6
+      3 a  7
+    2 1 d  8
+      2 e  9
+
+    Unstacking level 'a':
+
+    >>> df.unstack('a')
+            c
+    a       1     2
+    b d
+    1 a     5  <NA>
+      d  <NA>     8
+    2 b     6  <NA>
+      e  <NA>     9
+    3 a     7  <NA>
+
+    Unstacking level 'd' :
+
+    >>> df.unstack('d')
+            c
+    d       a     b     d     e
+    a b
+    1 1     5  <NA>  <NA>  <NA>
+      2  <NA>     6  <NA>  <NA>
+      3     7  <NA>  <NA>  <NA>
+    2 1  <NA>  <NA>     8  <NA>
+      2  <NA>  <NA>  <NA>     9
+
+    Unstacking multiple levels:
+
+    >>> df.unstack(['b', 'd'])
+          c
+    b     1           2           3
+    d     a     d     b     e     a
+    a
+    1     5  <NA>     6  <NA>     7
+    2  <NA>     8  <NA>     9  <NA>
+
+    Unstacking single level index dataframe:
+
+    >>> df = cudf.DataFrame({('c', 1): [1, 2, 3], ('c', 2):[9, 8, 7]})
+    >>> df.unstack()
+    c  1  0    1
+          1    2
+          2    3
+       2  0    9
+          1    8
+          2    7
+    dtype: int64
+    """
+    if not isinstance(df, cudf.DataFrame):
+        raise ValueError("`df` should be a cudf Dataframe object.")
+
+    if df.empty:
+        raise ValueError("Cannot unstack an empty dataframe.")
+
+    if fill_value is not None:
+        raise NotImplementedError("fill_value is not supported.")
+    if pd.api.types.is_list_like(level):
+        if not level:
+            return df
+    df = df.copy(deep=False)
+    if not isinstance(df.index, cudf.MultiIndex):
+        dtype = df._columns[0].dtype
+        for col in df._columns:
+            if not col.dtype == dtype:
+                raise ValueError(
+                    "Calling unstack() on single index dataframe"
+                    " with different column datatype is not supported."
+                )
+        res = df.T.stack(dropna=False)
+        # Result's index is a multiindex
+        res.index.names = (
+            tuple(df._data.to_pandas_index().names) + df.index.names
+        )
+        return res
+    else:
+        columns = df.index._poplevels(level)
+        index = df.index
+    result = _pivot(df, index, columns)
+    if result.index.nlevels == 1:
+        result.index = result.index.get_level_values(result.index.names[0])
+    return result
+
+
+def _get_unique(column, dummy_na):
+    """
+    Returns unique values in a column, if
+    dummy_na is False, nan's are also dropped.
+    """
+    if isinstance(column, cudf.core.column.CategoricalColumn):
+        unique = column.categories
+    else:
+        unique = column.unique().sort_values()
+    if not dummy_na:
+        if np.issubdtype(unique.dtype, np.floating):
+            unique = unique.nans_to_nulls()
+        unique = unique.dropna()
+    return unique
+
+
+def _one_hot_encode_column(
+    column: ColumnBase,
+    categories: ColumnBase,
+    prefix: Optional[str],
+    prefix_sep: Optional[str],
+    dtype: Optional[Dtype],
+) -> Dict[str, ColumnBase]:
+    """Encode a single column with one hot encoding. The return dictionary
+    contains pairs of (category, encodings). The keys may be prefixed with
+    `prefix`, separated with category name with `prefix_sep`. The encoding
+    columns maybe coerced into `dtype`.
+    """
+    if isinstance(column, CategoricalColumn):
+        if column.size == column.null_count:
+            column = column_empty_like(categories, newsize=column.size)
+        else:
+            column = column._get_decategorized_column()
+
+    if column.size * categories.size >= np.iinfo(size_type_dtype).max:
+        raise ValueError(
+            "Size limitation exceeded: column.size * category.size < "
+            f"np.iinfo({size_type_dtype}).max. Consider reducing "
+            "size of category"
+        )
+    data = one_hot_encode(column, categories)
+
+    if prefix is not None and prefix_sep is not None:
+        data = {f"{prefix}{prefix_sep}{col}": enc for col, enc in data.items()}
+    if dtype:
+        data = {k: v.astype(dtype) for k, v in data.items()}
+    return data
+
+
+def _length_check_params(obj, columns, name):
+    if cudf.api.types.is_list_like(obj):
+        if len(obj) != len(columns):
+            raise ValueError(
+                f"Length of '{name}' ({len(obj)}) did not match the "
+                f"length of the columns being "
+                f"encoded ({len(columns)})."
+            )
+
+
+def _get_pivot_names(arrs, names, prefix):
+    """
+    Generates unique names for rows/columns
+    """
+    if names is None:
+        names = []
+        for i, arr in enumerate(arrs):
+            if isinstance(arr, cudf.Series) and arr.name is not None:
+                names.append(arr.name)
+            else:
+                names.append(f"{prefix}_{i}")
+    else:
+        if len(names) != len(arrs):
+            raise ValueError("arrays and names must have the same length")
+        if not isinstance(names, list):
+            names = list(names)
+
+    return names
+
+
+def crosstab(
+    index,
+    columns,
+    values=None,
+    rownames=None,
+    colnames=None,
+    aggfunc=None,
+    margins=False,
+    margins_name="All",
+    dropna=None,
+    normalize=False,
+):
+    """
+    Compute a simple cross tabulation of two (or more) factors. By default
+    computes a frequency table of the factors unless an array of values and an
+    aggregation function are passed.
+
+    Parameters
+    ----------
+    index : array-like, Series, or list of arrays/Series
+        Values to group by in the rows.
+    columns : array-like, Series, or list of arrays/Series
+        Values to group by in the columns.
+    values : array-like, optional
+        Array of values to aggregate according to the factors.
+        Requires `aggfunc` be specified.
+    rownames : list of str, default None
+        If passed, must match number of row arrays passed.
+    colnames : list of str, default None
+        If passed, must match number of column arrays passed.
+    aggfunc : function, optional
+        If specified, requires `values` be specified as well.
+    margins : Not supported
+    margins_name : Not supported
+    dropna : Not supported
+    normalize : Not supported
+
+    Returns
+    -------
+    DataFrame
+        Cross tabulation of the data.
+
+    Examples
+    --------
+    >>> a = cudf.Series(["foo", "foo", "foo", "foo", "bar", "bar",
+    ...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
+    >>> b = cudf.Series(["one", "one", "one", "two", "one", "one",
+    ...               "one", "two", "two", "two", "one"], dtype=object)
+    >>> c = cudf.Series(["dull", "dull", "shiny", "dull", "dull", "shiny",
+    ...               "shiny", "dull", "shiny", "shiny", "shiny"],
+    ...              dtype=object)
+    >>> cudf.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
+    b   one        two
+    c   dull shiny dull shiny
+    a
+    bar    1     2    1     0
+    foo    2     2    1     2
+    """
+    if normalize is not False:
+        raise NotImplementedError("normalize is not supported yet")
+
+    if values is None and aggfunc is not None:
+        raise ValueError("aggfunc cannot be used without values.")
+
+    if values is not None and aggfunc is None:
+        raise ValueError("values cannot be used without an aggfunc.")
+
+    if not isinstance(index, (list, tuple)):
+        index = [index]
+    if not isinstance(columns, (list, tuple)):
+        columns = [columns]
+
+    if not rownames:
+        rownames = _get_pivot_names(index, rownames, prefix="row")
+    if not colnames:
+        colnames = _get_pivot_names(columns, colnames, prefix="col")
+
+    if len(index) != len(rownames):
+        raise ValueError("index and rownames must have same length")
+    if len(columns) != len(colnames):
+        raise ValueError("columns and colnames must have same length")
+
+    if len(set(rownames)) != len(rownames):
+        raise ValueError("rownames must be unique")
+    if len(set(colnames)) != len(colnames):
+        raise ValueError("colnames must be unique")
+
+    data = {
+        **dict(zip(rownames, map(as_column, index))),
+        **dict(zip(colnames, map(as_column, columns))),
+    }
+
+    df = cudf.DataFrame._from_data(data)
+
+    if values is None:
+        df["__dummy__"] = 0
+        kwargs = {"aggfunc": "count", "fill_value": 0}
+    else:
+        df["__dummy__"] = values
+        kwargs = {"aggfunc": aggfunc}
+
+    table = pivot_table(
+        data=df,
+        index=rownames,
+        columns=colnames,
+        values="__dummy__",
+        margins=margins,
+        margins_name=margins_name,
+        dropna=dropna,
+        **kwargs,
+    )
+
+    return table
+
+
+def pivot_table(
+    data,
+    values=None,
+    index=None,
+    columns=None,
+    aggfunc="mean",
+    fill_value=None,
+    margins=False,
+    dropna=None,
+    margins_name="All",
+    observed=False,
+    sort=True,
+):
+    """
+    Create a spreadsheet-style pivot table as a DataFrame.
+
+    Parameters
+    ----------
+    data : DataFrame
+    values : column name or list of column names to aggregate, optional
+    index : list of column names
+            Values to group by in the rows.
+    columns : list of column names
+            Values to group by in the columns.
+    aggfunc : str or dict, default "mean"
+            If dict is passed, the key is column to aggregate
+            and value is function name.
+    fill_value : scalar, default None
+        Value to replace missing values with
+        (in the resulting pivot table, after aggregation).
+    margins : Not supported
+    dropna : Not supported
+    margins_name : Not supported
+    observed : Not supported
+    sort : Not supported
+
+    Returns
+    -------
+    DataFrame
+        An Excel style pivot table.
+    """
+    if margins is not False:
+        raise NotImplementedError("margins is not supported yet")
+
+    if margins_name != "All":
+        raise NotImplementedError("margins_name is not supported yet")
+
+    if dropna is not None:
+        raise NotImplementedError("dropna is not supported yet")
+
+    if observed is not False:
+        raise NotImplementedError("observed is not supported yet")
+
+    if sort is not True:
+        raise NotImplementedError("sort is not supported yet")
+
+    keys = index + columns
+
+    values_passed = values is not None
+    if values_passed:
+        if pd.api.types.is_list_like(values):
+            values_multi = True
+            values = list(values)
+        else:
+            values_multi = False
+            values = [values]
+
+        for i in values:
+            if i not in data:
+                raise KeyError(i)
+
+        to_filter = []
+        for x in keys + values:
+            if isinstance(x, cudf.Grouper):
+                x = x.key
+            try:
+                if x in data:
+                    to_filter.append(x)
+            except TypeError:
+                pass
+        if len(to_filter) < len(data._column_names):
+            data = data[to_filter]
+
+    else:
+        values = data.columns
+        for key in keys:
+            try:
+                values = values.drop(key)
+            except (TypeError, ValueError, KeyError):
+                pass
+        values = list(values)
+
+    grouped = data.groupby(keys)
+    agged = grouped.agg(aggfunc)
+
+    table = agged
+
+    if table.index.nlevels > 1 and index:
+        # If index_names are integers, determine whether the integers refer
+        # to the level position or name.
+        index_names = agged.index.names[: len(index)]
+        to_unstack = []
+        for i in range(len(index), len(keys)):
+            name = agged.index.names[i]
+            if name is None or name in index_names:
+                to_unstack.append(i)
+            else:
+                to_unstack.append(name)
+        table = agged.unstack(to_unstack)
+
+    if fill_value is not None:
+        table = table.fillna(fill_value)
+
+    # discard the top level
+    if values_passed and not values_multi and table._data.multiindex:
+        column_names = table._data.level_names[1:]
+        table_columns = tuple(
+            map(lambda column: column[1:], table._data.names)
+        )
+        table.columns = cudf.MultiIndex.from_tuples(
+            tuples=table_columns, names=column_names
+        )
+
+    if len(index) == 0 and len(columns) > 0:
+        table = table.T
+
+    return table
diff --git a/python/cudf/cudf/core/scalar.py b/python/cudf/cudf/core/scalar.py
new file mode 100644
index 0000000..a20628f
--- /dev/null
+++ b/python/cudf/cudf/core/scalar.py
@@ -0,0 +1,404 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import decimal
+import operator
+from collections import OrderedDict
+
+import numpy as np
+import pyarrow as pa
+
+import cudf
+from cudf.api.types import is_datetime64_dtype, is_scalar, is_timedelta64_dtype
+from cudf.core.dtypes import ListDtype, StructDtype
+from cudf.core.missing import NA, NaT
+from cudf.core.mixins import BinaryOperand
+from cudf.utils.dtypes import (
+    get_allowed_combinations_for_operator,
+    to_cudf_compatible_scalar,
+)
+
+
+# Note that the metaclass below can easily be generalized for use with
+# other classes, if needed in the future. Simply replace the arguments
+# of the `__call__` method with `*args` and `**kwargs`. This will
+# result in additional overhead when constructing the cache key, as
+# unpacking *args and **kwargs is not cheap. See the discussion in
+# https://github.com/rapidsai/cudf/pull/11246#discussion_r955843532
+# for details.
+class CachedScalarInstanceMeta(type):
+    """
+    Metaclass for Scalar that caches `maxsize` instances.
+
+    After `maxsize` is reached, evicts the least recently used
+    instances to make room for new values.
+    """
+
+    def __new__(cls, names, bases, attrs, **kwargs):
+        return type.__new__(cls, names, bases, attrs)
+
+    # choose 128 because that's the default `maxsize` for
+    # `functools.lru_cache`:
+    def __init__(self, names, bases, attrs, maxsize=128):
+        self.__maxsize = maxsize
+        self.__instances = OrderedDict()
+
+    def __call__(self, value, dtype=None):
+        # the cache key is constructed from the arguments, and also
+        # the _types_ of the arguments, since objects of different
+        # types can compare equal
+        cache_key = (value, type(value), dtype, type(dtype))
+        try:
+            # try retrieving an instance from the cache:
+            self.__instances.move_to_end(cache_key)
+            return self.__instances[cache_key]
+        except KeyError:
+            # if an instance couldn't be found in the cache,
+            # construct it and add to cache:
+            obj = super().__call__(value, dtype=dtype)
+            try:
+                self.__instances[cache_key] = obj
+            except TypeError:
+                # couldn't hash the arguments, don't cache:
+                return obj
+            if len(self.__instances) > self.__maxsize:
+                self.__instances.popitem(last=False)
+            return obj
+        except TypeError:
+            # couldn't hash the arguments, don't cache:
+            return super().__call__(value, dtype=dtype)
+
+    def _clear_instance_cache(self):
+        self.__instances.clear()
+
+
+class Scalar(BinaryOperand, metaclass=CachedScalarInstanceMeta):
+    """
+    A GPU-backed scalar object with NumPy scalar like properties
+    May be used in binary operations against other scalars, cuDF
+    Series, DataFrame, and Index objects.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> cudf.Scalar(42, dtype='int64')
+    Scalar(42, dtype=int64)
+    >>> cudf.Scalar(42, dtype='int32') + cudf.Scalar(42, dtype='float64')
+    Scalar(84.0, dtype=float64)
+    >>> cudf.Scalar(42, dtype='int64') + np.int8(21)
+    Scalar(63, dtype=int64)
+    >>> x = cudf.Scalar(42, dtype='datetime64[s]')
+    >>> y = cudf.Scalar(21, dtype='timedelta64[ns]')
+    >>> x - y
+    Scalar(1970-01-01T00:00:41.999999979, dtype=datetime64[ns])
+    >>> cudf.Series([1,2,3]) + cudf.Scalar(1)
+    0    2
+    1    3
+    2    4
+    dtype: int64
+    >>> df = cudf.DataFrame({'a':[1,2,3], 'b':[4.5, 5.5, 6.5]})
+    >>> slr = cudf.Scalar(10, dtype='uint8')
+    >>> df - slr
+       a    b
+    0 -9 -5.5
+    1 -8 -4.5
+    2 -7 -3.5
+
+    Parameters
+    ----------
+    value : Python Scalar, NumPy Scalar, or cuDF Scalar
+        The scalar value to be converted to a GPU backed scalar object
+    dtype : np.dtype or string specifier
+        The data type
+    """
+
+    _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
+
+    def __init__(self, value, dtype=None):
+
+        self._host_value = None
+        self._host_dtype = None
+        self._device_value = None
+
+        if isinstance(value, Scalar):
+            if value._is_host_value_current:
+                self._host_value = value._host_value
+                self._host_dtype = value._host_dtype
+            else:
+                self._device_value = value._device_value
+        else:
+            self._host_value, self._host_dtype = self._preprocess_host_value(
+                value, dtype
+            )
+
+    @classmethod
+    def from_device_scalar(cls, device_scalar):
+        if not isinstance(device_scalar, cudf._lib.scalar.DeviceScalar):
+            raise TypeError(
+                "Expected an instance of DeviceScalar, "
+                f"got {type(device_scalar).__name__}"
+            )
+        obj = object.__new__(cls)
+        obj._host_value = None
+        obj._host_dtype = None
+        obj._device_value = device_scalar
+        return obj
+
+    @property
+    def _is_host_value_current(self):
+        return self._host_value is not None
+
+    @property
+    def _is_device_value_current(self):
+        return self._device_value is not None
+
+    @property
+    def device_value(self):
+        if self._device_value is None:
+            self._device_value = cudf._lib.scalar.DeviceScalar(
+                self._host_value, self._host_dtype
+            )
+        return self._device_value
+
+    @property
+    def value(self):
+        if not self._is_host_value_current:
+            self._device_value_to_host()
+        return self._host_value
+
+    # todo: change to cached property
+    @property
+    def dtype(self):
+        if self._is_host_value_current:
+            if isinstance(self._host_value, str):
+                return cudf.dtype("object")
+            else:
+                return self._host_dtype
+        else:
+            return self.device_value.dtype
+
+    def is_valid(self):
+        if not self._is_host_value_current:
+            self._device_value_to_host()
+        return not cudf._lib.scalar._is_null_host_scalar(self._host_value)
+
+    def _device_value_to_host(self):
+        self._host_value = self._device_value._to_host_scalar()
+
+    def _preprocess_host_value(self, value, dtype):
+        valid = not cudf._lib.scalar._is_null_host_scalar(value)
+
+        if isinstance(value, list):
+            if dtype is not None:
+                raise TypeError("Lists may not be cast to a different dtype")
+            else:
+                dtype = ListDtype.from_arrow(
+                    pa.infer_type([value], from_pandas=True)
+                )
+                return value, dtype
+        elif isinstance(dtype, ListDtype):
+            if value not in {None, NA}:
+                raise ValueError(f"Can not coerce {value} to ListDtype")
+            else:
+                return NA, dtype
+
+        if isinstance(value, dict):
+            if dtype is None:
+                dtype = StructDtype.from_arrow(
+                    pa.infer_type([value], from_pandas=True)
+                )
+            return value, dtype
+        elif isinstance(dtype, StructDtype):
+            if value not in {None, NA}:
+                raise ValueError(f"Can not coerce {value} to StructDType")
+            else:
+                return NA, dtype
+
+        if isinstance(dtype, cudf.core.dtypes.DecimalDtype):
+            value = pa.scalar(
+                value, type=pa.decimal128(dtype.precision, dtype.scale)
+            ).as_py()
+        if isinstance(value, decimal.Decimal) and dtype is None:
+            dtype = cudf.Decimal128Dtype._from_decimal(value)
+
+        value = to_cudf_compatible_scalar(value, dtype=dtype)
+
+        if dtype is None:
+            if not valid:
+                if isinstance(value, (np.datetime64, np.timedelta64)):
+                    unit, _ = np.datetime_data(value)
+                    if unit == "generic":
+                        raise TypeError(
+                            "Cant convert generic NaT to null scalar"
+                        )
+                    else:
+                        dtype = value.dtype
+                else:
+                    raise TypeError(
+                        "dtype required when constructing a null scalar"
+                    )
+            else:
+                dtype = value.dtype
+
+        if not isinstance(dtype, cudf.core.dtypes.DecimalDtype):
+            dtype = cudf.dtype(dtype)
+
+        if not valid:
+            value = (
+                NaT
+                if is_datetime64_dtype(dtype) or is_timedelta64_dtype(dtype)
+                else NA
+            )
+
+        return value, dtype
+
+    def _sync(self):
+        """
+        If the cache is not synched, copy either the device or host value
+        to the host or device respectively. If cache is valid, do nothing
+        """
+        if self._is_host_value_current and self._is_device_value_current:
+            return
+        elif self._is_host_value_current and not self._is_device_value_current:
+            self._device_value = cudf._lib.scalar.DeviceScalar(
+                self._host_value, self._host_dtype
+            )
+        elif self._is_device_value_current and not self._is_host_value_current:
+            self._host_value = self._device_value.value
+            self._host_dtype = self._host_value.dtype
+        else:
+            raise ValueError("Invalid cudf.Scalar")
+
+    def __index__(self):
+        if self.dtype.kind not in {"u", "i"}:
+            raise TypeError("Only Integer typed scalars may be used in slices")
+        return int(self)
+
+    def __int__(self):
+        return int(self.value)
+
+    def __float__(self):
+        return float(self.value)
+
+    def __bool__(self):
+        return bool(self.value)
+
+    def __round__(self, n):
+        return self._binaryop(n, "__round__")
+
+    # Scalar Unary Operations
+    def __abs__(self):
+        return self._scalar_unaop("__abs__")
+
+    def __ceil__(self):
+        return self._scalar_unaop("__ceil__")
+
+    def __floor__(self):
+        return self._scalar_unaop("__floor__")
+
+    def __invert__(self):
+        return self._scalar_unaop("__invert__")
+
+    def __neg__(self):
+        return self._scalar_unaop("__neg__")
+
+    def __repr__(self):
+        # str() fixes a numpy bug with NaT
+        # https://github.com/numpy/numpy/issues/17552
+        return (
+            f"{self.__class__.__name__}"
+            f"({str(self.value)}, dtype={self.dtype})"
+        )
+
+    def _binop_result_dtype_or_error(self, other, op):
+        if op in {"__eq__", "__ne__", "__lt__", "__gt__", "__le__", "__ge__"}:
+            return np.bool_
+
+        out_dtype = get_allowed_combinations_for_operator(
+            self.dtype, other.dtype, op
+        )
+
+        # datetime handling
+        if out_dtype in {"M", "m"}:
+            if self.dtype.char in {"M", "m"} and other.dtype.char not in {
+                "M",
+                "m",
+            }:
+                return self.dtype
+            if other.dtype.char in {"M", "m"} and self.dtype.char not in {
+                "M",
+                "m",
+            }:
+                return other.dtype
+            else:
+                if (
+                    op == "__sub__"
+                    and self.dtype.char == other.dtype.char == "M"
+                ):
+                    res, _ = np.datetime_data(max(self.dtype, other.dtype))
+                    return cudf.dtype("m8" + f"[{res}]")
+                return np.result_type(self.dtype, other.dtype)
+
+        return cudf.dtype(out_dtype)
+
+    def _binaryop(self, other, op: str):
+        if is_scalar(other):
+            other = to_cudf_compatible_scalar(other)
+            out_dtype = self._binop_result_dtype_or_error(other, op)
+            valid = self.is_valid() and (
+                isinstance(other, np.generic) or other.is_valid()
+            )
+            if not valid:
+                return Scalar(None, dtype=out_dtype)
+            else:
+                result = self._dispatch_scalar_binop(other, op)
+                return Scalar(result, dtype=out_dtype)
+        else:
+            return NotImplemented
+
+    def _dispatch_scalar_binop(self, other, op):
+        if isinstance(other, Scalar):
+            rhs = other.value
+        else:
+            rhs = other
+        lhs = self.value
+        reflect, op = self._check_reflected_op(op)
+        if reflect:
+            lhs, rhs = rhs, lhs
+        try:
+            return getattr(operator, op)(lhs, rhs)
+        except AttributeError:
+            return getattr(lhs, op)(rhs)
+
+    def _unaop_result_type_or_error(self, op):
+        if op == "__neg__" and self.dtype == "bool":
+            raise TypeError(
+                "Boolean scalars in cuDF do not support"
+                " negation, use logical not"
+            )
+
+        if op in {"__ceil__", "__floor__"}:
+            if self.dtype.char in "bBhHf?":
+                return cudf.dtype("float32")
+            else:
+                return cudf.dtype("float64")
+        return self.dtype
+
+    def _scalar_unaop(self, op):
+        out_dtype = self._unaop_result_type_or_error(op)
+        if not self.is_valid():
+            result = None
+        else:
+            result = self._dispatch_scalar_unaop(op)
+            return Scalar(result, dtype=out_dtype)
+
+    def _dispatch_scalar_unaop(self, op):
+        if op == "__floor__":
+            return np.floor(self.value)
+        if op == "__ceil__":
+            return np.ceil(self.value)
+        return getattr(self.value, op)()
+
+    def astype(self, dtype):
+        if self.dtype == dtype:
+            return self
+        return Scalar(self.value, dtype)
diff --git a/python/cudf/cudf/core/series.py b/python/cudf/cudf/core/series.py
new file mode 100644
index 0000000..6fa5a8f
--- /dev/null
+++ b/python/cudf/cudf/core/series.py
@@ -0,0 +1,5282 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import functools
+import inspect
+import pickle
+import textwrap
+import warnings
+from collections import abc
+from shutil import get_terminal_size
+from typing import (
+    Any,
+    Dict,
+    MutableMapping,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Union,
+)
+
+import cupy
+import numpy as np
+import pandas as pd
+from pandas._config import get_option
+from pandas.core.dtypes.common import is_float
+from typing_extensions import Self, assert_never
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._typing import (
+    ColumnLike,
+    DataFrameOrSeries,
+    NotImplementedType,
+    ScalarLike,
+)
+from cudf.api.extensions import no_default
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    _is_scalar_or_zero_d_array,
+    is_bool_dtype,
+    is_decimal_dtype,
+    is_dict_like,
+    is_float_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_list_dtype,
+    is_scalar,
+    is_string_dtype,
+    is_struct_dtype,
+)
+from cudf.core import indexing_utils
+from cudf.core.abc import Serializable
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.column import (
+    ColumnBase,
+    DatetimeColumn,
+    IntervalColumn,
+    TimeDeltaColumn,
+    arange,
+    as_column,
+    column,
+    full,
+)
+from cudf.core.column.categorical import (
+    CategoricalAccessor as CategoricalAccessor,
+)
+from cudf.core.column.column import concat_columns
+from cudf.core.column.lists import ListMethods
+from cudf.core.column.string import StringMethods
+from cudf.core.column.struct import StructMethods
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.groupby.groupby import SeriesGroupBy, groupby_doc_template
+from cudf.core.index import BaseIndex, DatetimeIndex, RangeIndex, as_index
+from cudf.core.indexed_frame import (
+    IndexedFrame,
+    _FrameIndexer,
+    _get_label_range_or_mask,
+    _indices_from_labels,
+    doc_reset_index_template,
+)
+from cudf.core.resample import SeriesResampler
+from cudf.core.single_column_frame import SingleColumnFrame
+from cudf.core.udf.scalar_function import _get_scalar_kernel
+from cudf.errors import MixedTypeError
+from cudf.utils import docutils
+from cudf.utils.docutils import copy_docstring
+from cudf.utils.dtypes import (
+    can_convert_to_column,
+    find_common_type,
+    is_mixed_with_object_dtype,
+    to_cudf_compatible_scalar,
+)
+from cudf.utils.utils import _cudf_nvtx_annotate
+
+
+def _format_percentile_names(percentiles):
+    return [f"{int(x * 100)}%" for x in percentiles]
+
+
+def _describe_numeric(obj, percentiles):
+    # Helper for Series.describe with numerical data.
+    data = {
+        "count": obj.count(),
+        "mean": obj.mean(),
+        "std": obj.std(),
+        "min": obj.min(),
+        **dict(
+            zip(
+                _format_percentile_names(percentiles),
+                obj.quantile(percentiles).to_numpy(na_value=np.nan).tolist(),
+            )
+        ),
+        "max": obj.max(),
+    }
+    return {k: round(v, 6) for k, v in data.items()}
+
+
+def _describe_timetype(obj, percentiles, typ):
+    # Common helper for Series.describe with timedelta/timestamp data.
+    data = {
+        "count": str(obj.count()),
+        "mean": str(typ(obj.mean())),
+        "std": "",
+        "min": str(typ(obj.min())),
+        **dict(
+            zip(
+                _format_percentile_names(percentiles),
+                obj.quantile(percentiles)
+                .astype("str")
+                .to_numpy(na_value=np.nan)
+                .tolist(),
+            )
+        ),
+        "max": str(typ(obj.max())),
+    }
+
+    if typ is pd.Timedelta:
+        data["std"] = str(obj.std())
+    else:
+        data.pop("std")
+    return data
+
+
+def _describe_timedelta(obj, percentiles):
+    # Helper for Series.describe with timedelta data.
+    return _describe_timetype(obj, percentiles, pd.Timedelta)
+
+
+def _describe_timestamp(obj, percentiles):
+    # Helper for Series.describe with timestamp data.
+    return _describe_timetype(obj, percentiles, pd.Timestamp)
+
+
+def _describe_categorical(obj, percentiles):
+    # Helper for Series.describe with categorical data.
+    data = {
+        "count": obj.count(),
+        "unique": len(obj.unique()),
+        "top": None,
+        "freq": None,
+    }
+    if data["count"] > 0:
+        # In case there's a tie, break the tie by sorting the index
+        # and take the top.
+        val_counts = obj.value_counts(ascending=False)
+        tied_val_counts = val_counts[
+            val_counts == val_counts.iloc[0]
+        ].sort_index()
+        data.update(
+            {
+                "top": tied_val_counts.index[0],
+                "freq": tied_val_counts.iloc[0],
+            }
+        )
+    return data
+
+
+def _append_new_row_inplace(col: ColumnLike, value: ScalarLike):
+    """Append a scalar `value` to the end of `col` inplace.
+    Cast to common type if possible
+    """
+    to_type = find_common_type([type(value), col.dtype])
+    val_col = as_column(value, dtype=to_type)
+    old_col = col.astype(to_type)
+
+    col._mimic_inplace(concat_columns([old_col, val_col]), inplace=True)
+
+
+class _SeriesIlocIndexer(_FrameIndexer):
+    """
+    For integer-location based selection.
+    """
+
+    _frame: cudf.Series
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, arg):
+        indexing_spec = indexing_utils.parse_row_iloc_indexer(
+            indexing_utils.destructure_series_iloc_indexer(arg, self._frame),
+            len(self._frame),
+        )
+        return self._frame._getitem_preprocessed(indexing_spec)
+
+    @_cudf_nvtx_annotate
+    def __setitem__(self, key, value):
+        from cudf.core.column import column
+
+        if isinstance(key, tuple):
+            key = list(key)
+
+        # coerce value into a scalar or column
+        if is_scalar(value):
+            value = to_cudf_compatible_scalar(value)
+            if (
+                not isinstance(
+                    self._frame._column,
+                    (
+                        cudf.core.column.DatetimeColumn,
+                        cudf.core.column.TimeDeltaColumn,
+                    ),
+                )
+                and cudf.utils.utils._isnat(value)
+                and not (
+                    isinstance(
+                        self._frame._column, cudf.core.column.StringColumn
+                    )
+                    and isinstance(value, str)
+                )
+            ):
+                raise MixedTypeError(
+                    f"Cannot assign {value=} to non-datetime/non-timedelta "
+                    "columns"
+                )
+            elif (
+                not (
+                    is_float_dtype(self._frame._column.dtype)
+                    or (
+                        isinstance(
+                            self._frame._column.dtype, cudf.CategoricalDtype
+                        )
+                        and is_float_dtype(
+                            self._frame._column.dtype.categories.dtype
+                        )
+                    )
+                )
+                and isinstance(value, (np.float32, np.float64))
+                and np.isnan(value)
+            ):
+                raise MixedTypeError(
+                    f"Cannot assign {value=} to "
+                    f"non-float dtype={self._frame._column.dtype}"
+                )
+            elif (
+                is_bool_dtype(self._frame._column.dtype)
+                and not is_bool_dtype(value)
+                and value not in {None, cudf.NA}
+            ):
+                raise MixedTypeError(
+                    f"Cannot assign {value=} to "
+                    f"bool dtype={self._frame._column.dtype}"
+                )
+        elif not (
+            isinstance(value, (list, dict))
+            and isinstance(
+                self._frame._column.dtype, (cudf.ListDtype, cudf.StructDtype)
+            )
+        ):
+            value = column.as_column(value)
+
+        if (
+            (
+                _is_non_decimal_numeric_dtype(self._frame._column.dtype)
+                or is_string_dtype(self._frame._column.dtype)
+            )
+            and hasattr(value, "dtype")
+            and _is_non_decimal_numeric_dtype(value.dtype)
+        ):
+            # normalize types if necessary:
+            # In contrast to Column.__setitem__ (which downcasts the value to
+            # the dtype of the column) here we upcast the series to the
+            # larger data type mimicking pandas
+            to_dtype = np.result_type(value.dtype, self._frame._column.dtype)
+            value = value.astype(to_dtype)
+            if to_dtype != self._frame._column.dtype:
+                self._frame._column._mimic_inplace(
+                    self._frame._column.astype(to_dtype), inplace=True
+                )
+
+        self._frame._column[key] = value
+
+
+class _SeriesLocIndexer(_FrameIndexer):
+    """
+    Label-based selection
+    """
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, arg: Any) -> Union[ScalarLike, DataFrameOrSeries]:
+        if isinstance(arg, pd.MultiIndex):
+            arg = cudf.from_pandas(arg)
+
+        if isinstance(self._frame.index, cudf.MultiIndex) and not isinstance(
+            arg, cudf.MultiIndex
+        ):
+            if is_scalar(arg):
+                row_arg = (arg,)
+            else:
+                row_arg = arg
+            result = self._frame.index._get_row_major(self._frame, row_arg)
+            if (
+                isinstance(arg, tuple)
+                and len(arg) == self._frame._index.nlevels
+                and not any(isinstance(x, slice) for x in arg)
+            ):
+                result = result.iloc[0]
+            return result
+        try:
+            arg = self._loc_to_iloc(arg)
+        except (TypeError, KeyError, IndexError, ValueError):
+            raise KeyError(arg)
+
+        return self._frame.iloc[arg]
+
+    @_cudf_nvtx_annotate
+    def __setitem__(self, key, value):
+        try:
+            key = self._loc_to_iloc(key)
+        except KeyError as e:
+            if (
+                is_scalar(key)
+                and not isinstance(self._frame.index, cudf.MultiIndex)
+                and is_scalar(value)
+            ):
+                # TODO: Modifying index in place is bad because
+                # our index are immutable, but columns are not (which
+                # means our index are mutable with internal APIs).
+                # Get rid of the deep copy once columns too are
+                # immutable.
+                idx_copy = self._frame._index.copy(deep=True)
+                if (
+                    isinstance(idx_copy, cudf.RangeIndex)
+                    and isinstance(key, int)
+                    and (key == idx_copy[-1] + idx_copy.step)
+                ):
+                    idx_copy = cudf.RangeIndex(
+                        start=idx_copy.start,
+                        stop=idx_copy.stop + idx_copy.step,
+                        step=idx_copy.step,
+                        name=idx_copy.name,
+                    )
+                else:
+                    if isinstance(idx_copy, cudf.RangeIndex):
+                        idx_copy = idx_copy._as_int_index()
+                    _append_new_row_inplace(idx_copy._values, key)
+                self._frame._index = idx_copy
+                _append_new_row_inplace(self._frame._column, value)
+                return
+            else:
+                raise e
+        if isinstance(value, (pd.Series, cudf.Series)):
+            value = cudf.Series(value)
+            value = value._align_to_index(self._frame.index, how="right")
+        self._frame.iloc[key] = value
+
+    def _loc_to_iloc(self, arg):
+        if isinstance(arg, tuple) and arg and isinstance(arg[0], slice):
+            if len(arg) > 1:
+                raise IndexError("Too many Indexers")
+            arg = arg[0]
+        if _is_scalar_or_zero_d_array(arg):
+            index_dtype = self._frame.index.dtype
+            if not _is_non_decimal_numeric_dtype(index_dtype) and not (
+                isinstance(index_dtype, cudf.CategoricalDtype)
+                and is_integer_dtype(index_dtype.categories.dtype)
+            ):
+                # TODO: switch to cudf.utils.dtypes.is_integer(arg)
+                if isinstance(arg, cudf.Scalar) and is_integer_dtype(
+                    arg.dtype
+                ):
+                    found_index = arg.value
+                    return found_index
+                elif is_integer(arg):
+                    found_index = arg
+                    return found_index
+            try:
+                indices = self._frame.index._indices_of(arg)
+                if (n := len(indices)) == 0:
+                    raise KeyError("Label scalar is out of bounds")
+                elif n == 1:
+                    return indices.element_indexing(0)
+                else:
+                    return indices
+            except (TypeError, KeyError, IndexError, ValueError):
+                raise KeyError("Label scalar is out of bounds")
+
+        elif isinstance(arg, slice):
+            return _get_label_range_or_mask(
+                self._frame.index, arg.start, arg.stop, arg.step
+            )
+        elif isinstance(arg, (cudf.MultiIndex, pd.MultiIndex)):
+            if isinstance(arg, pd.MultiIndex):
+                arg = cudf.MultiIndex.from_pandas(arg)
+
+            return _indices_from_labels(self._frame, arg)
+
+        else:
+            arg = cudf.core.series.Series(cudf.core.column.as_column(arg))
+            if arg.dtype in (bool, np.bool_):
+                return arg
+            else:
+                indices = _indices_from_labels(self._frame, arg)
+                if indices.null_count > 0:
+                    raise KeyError("label scalar is out of bound")
+                return indices
+
+
+class Series(SingleColumnFrame, IndexedFrame, Serializable):
+    """
+    One-dimensional GPU array (including time series).
+
+    Labels need not be unique but must be a hashable type. The object
+    supports both integer- and label-based indexing and provides a
+    host of methods for performing operations involving the index.
+    Statistical methods from ndarray have been overridden to
+    automatically exclude missing data (currently represented
+    as null/NaN).
+
+    Operations between Series (`+`, `-`, `/`, `*`, `**`) align
+    values based on their associated index values, they need
+    not be the same length. The result index will be the
+    sorted union of the two indexes.
+
+    ``Series`` objects are used as columns of ``DataFrame``.
+
+    Parameters
+    ----------
+    data : array-like, Iterable, dict, or scalar value
+        Contains data stored in Series.
+
+    index : array-like or Index (1d)
+        Values must be hashable and have the same length
+        as data. Non-unique index values are allowed. Will
+        default to RangeIndex (0, 1, 2, ..., n) if not provided.
+        If both a dict and index sequence are used, the index will
+        override the keys found in the dict.
+
+    dtype : str, :class:`numpy.dtype`, or ExtensionDtype, optional
+        Data type for the output Series. If not specified,
+        this will be inferred from data.
+
+    name : str, optional
+        The name to give to the Series.
+
+    copy : bool, default False
+        Copy input data. Only affects Series or 1d ndarray input.
+
+    nan_as_null : bool, Default True
+        If ``None``/``True``, converts ``np.nan`` values to
+        ``null`` values.
+        If ``False``, leaves ``np.nan`` values as is.
+    """
+
+    _accessors: Set[Any] = set()
+    _loc_indexer_type = _SeriesLocIndexer
+    _iloc_indexer_type = _SeriesIlocIndexer
+    _groupby = SeriesGroupBy
+    _resampler = SeriesResampler
+
+    # The `constructor*` properties are used by `dask` (and `dask_cudf`)
+    @property
+    def _constructor(self):
+        return Series
+
+    @property
+    def _constructor_sliced(self):
+        raise NotImplementedError(
+            "_constructor_sliced not supported for Series!"
+        )
+
+    @property
+    def _constructor_expanddim(self):
+        return cudf.DataFrame
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_categorical(cls, categorical, codes=None):
+        """Creates from a pandas.Categorical
+
+        Parameters
+        ----------
+        categorical : pandas.Categorical
+            Contains data stored in a pandas Categorical.
+
+        codes : array-like, optional.
+            The category codes of this categorical. If ``codes`` are
+            defined, they are used instead of ``categorical.codes``
+
+        Returns
+        -------
+        Series
+            A cudf categorical series.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> pd_categorical = pd.Categorical(pd.Series(['a', 'b', 'c', 'a'], dtype='category'))
+        >>> pd_categorical
+        ['a', 'b', 'c', 'a']
+        Categories (3, object): ['a', 'b', 'c']
+        >>> series = cudf.Series.from_categorical(pd_categorical)
+        >>> series
+        0    a
+        1    b
+        2    c
+        3    a
+        dtype: category
+        Categories (3, object): ['a', 'b', 'c']
+        """  # noqa: E501
+        col = cudf.core.column.categorical.pandas_categorical_as_column(
+            categorical, codes=codes
+        )
+        return Series(data=col)
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_masked_array(cls, data, mask, null_count=None):
+        """Create a Series with null-mask.
+        This is equivalent to:
+
+            Series(data).set_mask(mask, null_count=null_count)
+
+        Parameters
+        ----------
+        data : 1D array-like
+            The values.  Null values must not be skipped.  They can appear
+            as garbage values.
+        mask : 1D array-like
+            The null-mask.  Valid values are marked as ``1``; otherwise ``0``.
+            The mask bit given the data index ``idx`` is computed as::
+
+                (mask[idx // 8] >> (idx % 8)) & 1
+        null_count : int, optional
+            The number of null values.
+            If None, it is calculated automatically.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> a = cudf.Series([1, 2, 3, None, 4, None])
+        >>> a
+        0       1
+        1       2
+        2       3
+        3    <NA>
+        4       4
+        5    <NA>
+        dtype: int64
+        >>> b = cudf.Series([10, 11, 12, 13, 14])
+        >>> cudf.Series.from_masked_array(data=b, mask=a._column.mask)
+        0      10
+        1      11
+        2      12
+        3    <NA>
+        4      14
+        dtype: int64
+        """
+        col = column.as_column(data).set_mask(mask)
+        return cls(data=col)
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        data=None,
+        index=None,
+        dtype=None,
+        name=None,
+        copy=False,
+        nan_as_null=True,
+    ):
+        if (
+            isinstance(data, Sequence)
+            and len(data) == 0
+            and dtype is None
+            and getattr(data, "dtype", None) is None
+        ):
+            warnings.warn(
+                "The default dtype for empty Series will be 'object' instead "
+                "of 'float64' in a future version. Specify a dtype explicitly "
+                "to silence this warning.",
+                FutureWarning,
+            )
+        if isinstance(data, pd.Series):
+            if name is None:
+                name = data.name
+            if isinstance(data.index, pd.MultiIndex):
+                index = cudf.from_pandas(data.index)
+            else:
+                index = as_index(data.index)
+        elif isinstance(data, pd.Index):
+            if name is None:
+                name = data.name
+            data = as_column(data, nan_as_null=nan_as_null, dtype=dtype)
+        elif isinstance(data, BaseIndex):
+            if name is None:
+                name = data.name
+            data = data._values
+            if dtype is not None:
+                data = data.astype(dtype)
+        elif isinstance(data, ColumnAccessor):
+            raise TypeError(
+                "Use cudf.Series._from_data for constructing a Series from "
+                "ColumnAccessor"
+            )
+
+        if isinstance(data, Series):
+            if index is not None:
+                data = data.reindex(index)
+            else:
+                index = data._index
+            if name is None:
+                name = data.name
+            data = data._column
+            if copy:
+                data = data.copy(deep=True)
+            if dtype is not None:
+                data = data.astype(dtype)
+
+        if isinstance(data, dict):
+            if not data:
+                current_index = RangeIndex(0)
+            else:
+                current_index = data.keys()
+            if index is not None:
+                series = Series(
+                    list(data.values()),
+                    nan_as_null=nan_as_null,
+                    dtype=dtype,
+                    index=current_index,
+                )
+                new_index = as_index(index)
+                if not series.index.equals(new_index):
+                    series = series.reindex(new_index)
+                data = series._column
+                index = series._index
+            else:
+                data = column.as_column(
+                    list(data.values()), nan_as_null=nan_as_null, dtype=dtype
+                )
+                index = current_index
+        if data is None:
+            if index is not None:
+                data = column.column_empty(
+                    row_count=len(index), dtype=None, masked=True
+                )
+            else:
+                data = {}
+
+        if not isinstance(data, ColumnBase):
+            # Using `getattr_static` to check if
+            # `data` is on device memory and perform
+            # a deep copy later. This is different
+            # from `hasattr` because, it doesn't
+            # invoke the property we are looking
+            # for and the latter actually invokes
+            # the property, which in this case could
+            # be expensive or mark a buffer as
+            # unspillable.
+            has_cai = (
+                type(
+                    inspect.getattr_static(
+                        data, "__cuda_array_interface__", None
+                    )
+                )
+                is property
+            )
+            data = column.as_column(
+                data,
+                nan_as_null=nan_as_null,
+                dtype=dtype,
+                length=len(index) if index is not None else None,
+            )
+            if copy and has_cai:
+                data = data.copy(deep=True)
+        else:
+            if dtype is not None:
+                data = data.astype(dtype)
+
+        if index is not None and not isinstance(index, BaseIndex):
+            index = as_index(index)
+
+        assert isinstance(data, ColumnBase)
+
+        super().__init__({name: data})
+        self._index = RangeIndex(len(data)) if index is None else index
+        self._check_data_index_length_match()
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _from_data(
+        cls,
+        data: MutableMapping,
+        index: Optional[BaseIndex] = None,
+        name: Any = no_default,
+    ) -> Series:
+        out = super()._from_data(data=data, index=index)
+        if name is not no_default:
+            out.name = name
+        return out
+
+    @_cudf_nvtx_annotate
+    def __contains__(self, item):
+        return item in self._index
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_pandas(cls, s, nan_as_null=no_default):
+        """
+        Convert from a Pandas Series.
+
+        Parameters
+        ----------
+        s : Pandas Series object
+            A Pandas Series object which has to be converted
+            to cuDF Series.
+        nan_as_null : bool, Default None
+            If ``None``/``True``, converts ``np.nan`` values to
+            ``null`` values.
+            If ``False``, leaves ``np.nan`` values as is.
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> import numpy as np
+        >>> data = [10, 20, 30, np.nan]
+        >>> pds = pd.Series(data, dtype='float64')
+        >>> cudf.Series.from_pandas(pds)
+        0    10.0
+        1    20.0
+        2    30.0
+        3    <NA>
+        dtype: float64
+        >>> cudf.Series.from_pandas(pds, nan_as_null=False)
+        0    10.0
+        1    20.0
+        2    30.0
+        3     NaN
+        dtype: float64
+        """
+        if nan_as_null is no_default:
+            nan_as_null = (
+                False if cudf.get_option("mode.pandas_compatible") else None
+            )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            result = cls(s, nan_as_null=nan_as_null)
+        return result
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_unique(self):
+        """Return boolean if values in the object are unique.
+
+        Returns
+        -------
+        bool
+        """
+        return self._column.is_unique
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dt(self):
+        """
+        Accessor object for datetime-like properties of the Series values.
+
+        Examples
+        --------
+        >>> s = cudf.Series(cudf.date_range(
+        ...   start='2001-02-03 12:00:00',
+        ...   end='2001-02-03 14:00:00',
+        ...   freq='1H'))
+        >>> s.dt.hour
+        0    12
+        1    13
+        dtype: int16
+        >>> s.dt.second
+        0    0
+        1    0
+        dtype: int16
+        >>> s.dt.day
+        0    3
+        1    3
+        dtype: int16
+
+        Returns
+        -------
+            A Series indexed like the original Series.
+
+        Raises
+        ------
+            TypeError if the Series does not contain datetimelike values.
+        """
+        if isinstance(self._column, DatetimeColumn):
+            return DatetimeProperties(self)
+        elif isinstance(self._column, TimeDeltaColumn):
+            return TimedeltaProperties(self)
+        else:
+            raise AttributeError(
+                "Can only use .dt accessor with datetimelike values"
+            )
+
+    @property  # type:ignore
+    @_cudf_nvtx_annotate
+    def axes(self):
+        """
+        Return a list representing the axes of the Series.
+
+        Series.axes returns a list containing the row index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> csf1 = cudf.Series([1, 2, 3, 4])
+        >>> csf1.axes
+        [RangeIndex(start=0, stop=4, step=1)]
+
+        """
+        return [self.index]
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def hasnans(self):
+        """
+        Return True if there are any NaNs or nulls.
+
+        Returns
+        -------
+        out : bool
+            If Series has at least one NaN or null value, return True,
+            if not return False.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import numpy as np
+        >>> series = cudf.Series([1, 2, np.nan, 3, 4], nan_as_null=False)
+        >>> series
+        0    1.0
+        1    2.0
+        2    NaN
+        3    3.0
+        4    4.0
+        dtype: float64
+        >>> series.hasnans
+        True
+
+        `hasnans` returns `True` for the presence of any `NA` values:
+
+        >>> series = cudf.Series([1, 2, 3, None, 4])
+        >>> series
+        0       1
+        1       2
+        2       3
+        3    <NA>
+        4       4
+        dtype: int64
+        >>> series.hasnans
+        True
+        """
+        return self._column.has_nulls(include_nan=True)
+
+    @_cudf_nvtx_annotate
+    def serialize(self):
+        header, frames = super().serialize()
+
+        header["index"], index_frames = self._index.serialize()
+        header["index_frame_count"] = len(index_frames)
+        # For backwards compatibility with older versions of cuDF, index
+        # columns are placed before data columns.
+        frames = index_frames + frames
+
+        return header, frames
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def deserialize(cls, header, frames):
+        index_nframes = header["index_frame_count"]
+        obj = super().deserialize(
+            header, frames[header["index_frame_count"] :]
+        )
+
+        idx_typ = pickle.loads(header["index"]["type-serialized"])
+        index = idx_typ.deserialize(header["index"], frames[:index_nframes])
+        obj._index = index
+
+        return obj
+
+    def _get_columns_by_label(self, labels, *, downcast=False) -> Self:
+        """Return the column specified by `labels`
+
+        For cudf.Series, either the column, or an empty series is returned.
+        Parameter `downcast` does not have effects.
+        """
+        ca = self._data.select_by_label(labels)
+
+        return (
+            self.__class__._from_data(data=ca, index=self.index)
+            if len(ca) > 0
+            else self.__class__(dtype=self.dtype, name=self.name)
+        )
+
+    @_cudf_nvtx_annotate
+    def drop(
+        self,
+        labels=None,
+        axis=0,
+        index=None,
+        columns=None,
+        level=None,
+        inplace=False,
+        errors="raise",
+    ):
+        if axis == 1:
+            raise ValueError("No axis named 1 for object type Series")
+        # Ignore columns for Series
+        if columns is not None:
+            columns = []
+        return super().drop(
+            labels, axis, index, columns, level, inplace, errors
+        )
+
+    def tolist(self):  # noqa: D102
+        raise TypeError(
+            "cuDF does not support conversion to host memory "
+            "via the `tolist()` method. Consider using "
+            "`.to_arrow().to_pylist()` to construct a Python list."
+        )
+
+    to_list = tolist
+
+    @_cudf_nvtx_annotate
+    def to_dict(self, into: type[dict] = dict) -> dict:
+        """
+        Convert Series to {label -> value} dict or dict-like object.
+
+        Parameters
+        ----------
+        into : class, default dict
+            The collections.abc.Mapping subclass to use as the return
+            object. Can be the actual class or an empty
+            instance of the mapping type you want.  If you want a
+            collections.defaultdict, you must pass it initialized.
+
+        Returns
+        -------
+        collections.abc.Mapping
+            Key-value representation of Series.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([1, 2, 3, 4])
+        >>> s
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        >>> s.to_dict()
+        {0: 1, 1: 2, 2: 3, 3: 4}
+        >>> from collections import OrderedDict, defaultdict
+        >>> s.to_dict(OrderedDict)
+        OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])
+        >>> dd = defaultdict(list)
+        >>> s.to_dict(dd)
+        defaultdict(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
+        """
+        return self.to_pandas().to_dict(into=into)
+
+    @_cudf_nvtx_annotate
+    def append(self, to_append, ignore_index=False, verify_integrity=False):
+        """Append values from another ``Series`` or array-like object.
+        If ``ignore_index=True``, the index is reset.
+
+        Parameters
+        ----------
+        to_append : Series or list/tuple of Series
+            Series to append with self.
+        ignore_index : boolean, default False.
+            If True, do not use the index.
+        verify_integrity : bool, default False
+            This Parameter is currently not supported.
+
+        Returns
+        -------
+        Series
+            A new concatenated series
+
+        See Also
+        --------
+        cudf.concat : General function to concatenate DataFrame or
+            Series objects.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s1 = cudf.Series([1, 2, 3])
+        >>> s2 = cudf.Series([4, 5, 6])
+        >>> s1
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s2
+        0    4
+        1    5
+        2    6
+        dtype: int64
+        >>> s1.append(s2)
+        0    1
+        1    2
+        2    3
+        0    4
+        1    5
+        2    6
+        dtype: int64
+
+        >>> s3 = cudf.Series([4, 5, 6], index=[3, 4, 5])
+        >>> s3
+        3    4
+        4    5
+        5    6
+        dtype: int64
+        >>> s1.append(s3)
+        0    1
+        1    2
+        2    3
+        3    4
+        4    5
+        5    6
+        dtype: int64
+
+        With `ignore_index` set to True:
+
+        >>> s1.append(s2, ignore_index=True)
+        0    1
+        1    2
+        2    3
+        3    4
+        4    5
+        5    6
+        dtype: int64
+        """
+        return super()._append(to_append, ignore_index, verify_integrity)
+
+    @_cudf_nvtx_annotate
+    def reindex(self, *args, **kwargs):
+        """
+        Conform Series to new index.
+
+        Parameters
+        ----------
+        index : Index, Series-convertible, default None
+            New labels / index to conform to,
+            should be specified using keywords.
+        method: Not Supported
+        copy : boolean, default True
+        level: Not Supported
+        fill_value : Value to use for missing values.
+            Defaults to ``NA``, but can be any "compatible" value.
+        limit: Not Supported
+        tolerance: Not Supported
+
+        Returns
+        -------
+        Series with changed index.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
+        >>> series
+        a    10
+        b    20
+        c    30
+        d    40
+        dtype: int64
+        >>> series.reindex(['a', 'b', 'y', 'z'])
+        a      10
+        b      20
+        y    <NA>
+        z    <NA>
+        dtype: int64
+
+        .. pandas-compat::
+            **Series.reindex**
+
+            Note: One difference from Pandas is that ``NA`` is used for rows
+            that do not match, rather than ``NaN``. One side effect of this is
+            that the series retains an integer dtype in cuDF
+            where it is cast to float in Pandas.
+
+        """
+        if len(args) > 1:
+            raise TypeError(
+                "Only one positional argument ('index') is allowed"
+            )
+        if args:
+            (index,) = args
+            if "index" in kwargs:
+                raise TypeError(
+                    "'index' passed as both positional and keyword argument"
+                )
+        else:
+            index = kwargs.get("index", self._index)
+
+        name = self.name or 0
+        series = self._reindex(
+            deep=kwargs.get("copy", True),
+            dtypes={name: self.dtype},
+            index=index,
+            column_names=[name],
+            inplace=False,
+            fill_value=kwargs.get("fill_value", cudf.NA),
+        )
+        series.name = self.name
+        return series
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        doc_reset_index_template.format(
+            klass="Series",
+            argument="""
+        name : object, optional
+            The name to use for the column containing the original Series
+            values. Uses self.name by default. This argument is ignored when
+            ``drop`` is True.""",
+            return_type="Series or DataFrame or None",
+            return_doc=""" For Series, When drop is False (the default), a DataFrame
+            is returned. The newly created columns will come first in the
+            DataFrame, followed by the original Series values. When `drop` is
+            True, a `Series` is returned. In either case, if ``inplace=True``,
+            no value is returned.
+""",  # noqa: E501
+            example="""
+        >>> series = cudf.Series(['a', 'b', 'c', 'd'], index=[10, 11, 12, 13])
+        >>> series
+        10    a
+        11    b
+        12    c
+        13    d
+        dtype: object
+        >>> series.reset_index()
+           index  0
+        0     10  a
+        1     11  b
+        2     12  c
+        3     13  d
+        >>> series.reset_index(drop=True)
+        0    a
+        1    b
+        2    c
+        3    d
+        dtype: object
+
+        You can also use ``reset_index`` with MultiIndex.
+
+        >>> s2 = cudf.Series(
+        ...             range(4), name='foo',
+        ...             index=cudf.MultiIndex.from_tuples([
+        ...                     ('bar', 'one'), ('bar', 'two'),
+        ...                     ('baz', 'one'), ('baz', 'two')],
+        ...                     names=['a', 'b']
+        ...      ))
+        >>> s2
+        a    b
+        bar  one    0
+             two    1
+        baz  one    2
+             two    3
+        Name: foo, dtype: int64
+        >>> s2.reset_index(level='a')
+               a  foo
+        b
+        one  bar    0
+        two  bar    1
+        one  baz    2
+        two  baz    3
+""",
+        )
+    )
+    def reset_index(self, level=None, drop=False, name=None, inplace=False):
+        if not drop and inplace:
+            raise TypeError(
+                "Cannot reset_index inplace on a Series "
+                "to create a DataFrame"
+            )
+        data, index = self._reset_index(level=level, drop=drop)
+        if not drop:
+            if name is None:
+                name = 0 if self.name is None else self.name
+            data[name] = data.pop(self.name)
+            return cudf.core.dataframe.DataFrame._from_data(data, index)
+        # For ``name`` behavior, see:
+        # https://github.com/pandas-dev/pandas/issues/44575
+        # ``name`` has to be ignored when `drop=True`
+        return self._mimic_inplace(
+            Series._from_data(data, index, self.name),
+            inplace=inplace,
+        )
+
+    @_cudf_nvtx_annotate
+    def to_frame(self, name=None):
+        """Convert Series into a DataFrame
+
+        Parameters
+        ----------
+        name : str, default None
+            Name to be used for the column
+
+        Returns
+        -------
+        DataFrame
+            cudf DataFrame
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series(['a', 'b', 'c', None, 'd'], name='sample', index=[10, 11, 12, 13, 15])
+        >>> series
+        10       a
+        11       b
+        12       c
+        13    <NA>
+        15       d
+        Name: sample, dtype: object
+        >>> series.to_frame()
+           sample
+        10      a
+        11      b
+        12      c
+        13   <NA>
+        15      d
+        """  # noqa: E501
+
+        if name is not None:
+            col = name
+        elif self.name is None:
+            col = 0
+        else:
+            col = self.name
+
+        return cudf.DataFrame({col: self._column}, index=self.index)
+
+    @_cudf_nvtx_annotate
+    def memory_usage(self, index=True, deep=False):
+        return self._column.memory_usage + (
+            self._index.memory_usage() if index else 0
+        )
+
+    @_cudf_nvtx_annotate
+    def __array_function__(self, func, types, args, kwargs):
+        if "out" in kwargs or not all(issubclass(t, Series) for t in types):
+            return NotImplemented
+
+        try:
+            # Apply a Series method if one exists.
+            if cudf_func := getattr(Series, func.__name__, None):
+                result = cudf_func(*args, **kwargs)
+                if func.__name__ == "unique":
+                    # NumPy expects a sorted result for `unique`, which is not
+                    # guaranteed by cudf.Series.unique.
+                    result = result.sort_values()
+                return result
+
+            # Assume that cupy subpackages match numpy and search the
+            # corresponding cupy submodule based on the func's __module__.
+            numpy_submodule = func.__module__.split(".")[1:]
+            cupy_func = cupy
+            for name in (*numpy_submodule, func.__name__):
+                cupy_func = getattr(cupy_func, name, None)
+
+            # Handle case if cupy does not implement the function or just
+            # aliases the numpy function.
+            if not cupy_func or cupy_func is func:
+                return NotImplemented
+
+            # For now just fail on cases with mismatched indices. There is
+            # almost certainly no general solution for all array functions.
+            index = args[0].index
+            if not all(s.index.equals(index) for s in args):
+                return NotImplemented
+            out = cupy_func(*(s.values for s in args), **kwargs)
+
+            # Return (host) scalar values immediately.
+            if not isinstance(out, cupy.ndarray):
+                return out
+
+            # 0D array (scalar)
+            if out.ndim == 0:
+                return to_cudf_compatible_scalar(out)
+            # 1D array
+            elif (
+                # Only allow 1D arrays
+                ((out.ndim == 1) or (out.ndim == 2 and out.shape[1] == 1))
+                # If we have an index, it must be the same length as the
+                # output for cupy dispatching to be well-defined.
+                and len(index) == len(out)
+            ):
+                return Series(out, index=index)
+        except Exception:
+            # The rare instance where a "silent" failure is preferable. Except
+            # in the (highly unlikely) case that some other library
+            # interoperates with cudf objects, the result will be that numpy
+            # raises a TypeError indicating that the operation is not
+            # implemented, which is much friendlier than an arbitrary internal
+            # cudf error.
+            pass
+
+        return NotImplemented
+
+    @_cudf_nvtx_annotate
+    def map(self, arg, na_action=None) -> "Series":
+        """
+        Map values of Series according to input correspondence.
+
+        Used for substituting each value in a Series with another value,
+        that may be derived from a function, a ``dict`` or
+        a :class:`Series`.
+
+        Parameters
+        ----------
+        arg : function, collections.abc.Mapping subclass or Series
+            Mapping correspondence.
+        na_action : {None, 'ignore'}, default None
+            If 'ignore', propagate NaN values, without passing them to the
+            mapping correspondence.
+
+        Returns
+        -------
+        Series
+            Same index as caller.
+
+        Examples
+        --------
+        >>> s = cudf.Series(['cat', 'dog', np.nan, 'rabbit'])
+        >>> s
+        0      cat
+        1      dog
+        2     <NA>
+        3   rabbit
+        dtype: object
+
+        ``map`` accepts a ``dict`` or a ``Series``. Values that are not found
+        in the ``dict`` are converted to ``NaN``, default values in dicts are
+        currently not supported.:
+
+        >>> s.map({'cat': 'kitten', 'dog': 'puppy'})
+        0   kitten
+        1    puppy
+        2     <NA>
+        3     <NA>
+        dtype: object
+
+        It also accepts numeric functions:
+
+        >>> s = cudf.Series([1, 2, 3, 4, np.nan])
+        >>> s.map(lambda x: x ** 2)
+        0       1
+        1       4
+        2       9
+        3       16
+        4     <NA>
+        dtype: int64
+
+        Notes
+        -----
+        Please note map currently only supports fixed-width numeric
+        type functions.
+        """
+        if isinstance(arg, dict):
+            if hasattr(arg, "__missing__"):
+                raise NotImplementedError(
+                    "default values in dicts are currently not supported."
+                )
+            lhs = cudf.DataFrame({"x": self, "orig_order": arange(len(self))})
+            rhs = cudf.DataFrame(
+                {
+                    "x": arg.keys(),
+                    "s": arg.values(),
+                    "bool": full(len(arg), True, dtype=self.dtype),
+                }
+            )
+            res = lhs.merge(rhs, on="x", how="left").sort_values(
+                by="orig_order"
+            )
+            result = res["s"]
+            result.name = self.name
+            result.index = self.index
+        elif isinstance(arg, cudf.Series):
+            if not arg.index.is_unique:
+                raise ValueError(
+                    "Reindexing only valid with"
+                    " uniquely valued Index objects"
+                )
+            lhs = cudf.DataFrame({"x": self, "orig_order": arange(len(self))})
+            rhs = cudf.DataFrame(
+                {
+                    "x": arg.keys(),
+                    "s": arg,
+                    "bool": full(len(arg), True, dtype=self.dtype),
+                }
+            )
+            res = lhs.merge(rhs, on="x", how="left").sort_values(
+                by="orig_order"
+            )
+            result = res["s"]
+            result.name = self.name
+            result.index = self.index
+        else:
+            result = self.apply(arg)
+        return result
+
+    def _getitem_preprocessed(
+        self,
+        spec: indexing_utils.IndexingSpec,
+    ) -> Union[Self, ScalarLike]:
+        """Get subset of entries given structured data
+
+        Parameters
+        ----------
+        spec
+            Indexing specification
+
+        Returns
+        -------
+        Subsetted Series or else scalar (if a scalar entry is
+        requested)
+
+        Notes
+        -----
+        This function performs no bounds-checking or massaging of the
+        inputs.
+        """
+        if isinstance(spec, indexing_utils.MapIndexer):
+            return self._gather(spec.key, keep_index=True)
+        elif isinstance(spec, indexing_utils.MaskIndexer):
+            return self._apply_boolean_mask(spec.key, keep_index=True)
+        elif isinstance(spec, indexing_utils.SliceIndexer):
+            return self._slice(spec.key)
+        elif isinstance(spec, indexing_utils.ScalarIndexer):
+            return self._gather(
+                spec.key, keep_index=False
+            )._column.element_indexing(0)
+        elif isinstance(spec, indexing_utils.EmptyIndexer):
+            return self._empty_like(keep_index=True)
+        assert_never(spec)
+
+    @_cudf_nvtx_annotate
+    def __getitem__(self, arg):
+        if isinstance(arg, slice):
+            return self.iloc[arg]
+        else:
+            return self.loc[arg]
+
+    iteritems = SingleColumnFrame.__iter__
+
+    items = SingleColumnFrame.__iter__
+
+    @_cudf_nvtx_annotate
+    def __setitem__(self, key, value):
+        if isinstance(key, slice):
+            self.iloc[key] = value
+        else:
+            self.loc[key] = value
+
+    def __repr__(self):
+        _, height = get_terminal_size()
+        max_rows = (
+            height
+            if get_option("display.max_rows") == 0
+            else get_option("display.max_rows")
+        )
+        if max_rows not in (0, None) and len(self) > max_rows:
+            top = self.head(int(max_rows / 2 + 1))
+            bottom = self.tail(int(max_rows / 2 + 1))
+            preprocess = cudf.concat([top, bottom])
+        else:
+            preprocess = self.copy()
+        preprocess.index = preprocess.index._clean_nulls_from_index()
+        if (
+            preprocess.nullable
+            and not isinstance(
+                preprocess._column, cudf.core.column.CategoricalColumn
+            )
+            and not is_list_dtype(preprocess.dtype)
+            and not is_struct_dtype(preprocess.dtype)
+            and not is_decimal_dtype(preprocess.dtype)
+            and not is_struct_dtype(preprocess.dtype)
+        ) or isinstance(
+            preprocess._column,
+            cudf.core.column.timedelta.TimeDeltaColumn,
+        ):
+            fill_value = (
+                str(cudf.NaT)
+                if isinstance(
+                    preprocess._column,
+                    (
+                        cudf.core.column.TimeDeltaColumn,
+                        cudf.core.column.DatetimeColumn,
+                    ),
+                )
+                else str(cudf.NA)
+            )
+            output = repr(
+                preprocess.astype("str").fillna(fill_value).to_pandas()
+            )
+        elif isinstance(
+            preprocess._column, cudf.core.column.CategoricalColumn
+        ):
+            min_rows = (
+                height
+                if get_option("display.min_rows") == 0
+                else get_option("display.min_rows")
+            )
+            show_dimensions = get_option("display.show_dimensions")
+            if preprocess._column.categories.dtype.kind == "f":
+                pd_series = (
+                    preprocess.astype("str")
+                    .to_pandas()
+                    .astype(
+                        dtype=pd.CategoricalDtype(
+                            categories=preprocess.dtype.categories.astype(
+                                "str"
+                            ).to_pandas(),
+                            ordered=preprocess.dtype.ordered,
+                        )
+                    )
+                )
+            else:
+                pd_series = preprocess.to_pandas()
+            output = pd_series.to_string(
+                name=self.name,
+                dtype=self.dtype,
+                min_rows=min_rows,
+                max_rows=max_rows,
+                length=show_dimensions,
+                na_rep=str(cudf.NA),
+            )
+        else:
+            output = repr(preprocess.to_pandas())
+
+        lines = output.split("\n")
+        if isinstance(preprocess._column, cudf.core.column.CategoricalColumn):
+            category_memory = lines[-1]
+            if preprocess._column.categories.dtype.kind == "f":
+                category_memory = category_memory.replace("'", "").split(": ")
+                category_memory = (
+                    category_memory[0].replace(
+                        "object", preprocess._column.categories.dtype.name
+                    )
+                    + ": "
+                    + category_memory[1]
+                )
+            lines = lines[:-1]
+        if len(lines) > 1:
+            if lines[-1].startswith("Name: "):
+                lines = lines[:-1]
+                lines.append("Name: %s" % str(self.name))
+                if len(self) > len(preprocess):
+                    lines[-1] = lines[-1] + ", Length: %d" % len(self)
+                lines[-1] = lines[-1] + ", "
+            elif lines[-1].startswith("Length: "):
+                lines = lines[:-1]
+                lines.append("Length: %d" % len(self))
+                lines[-1] = lines[-1] + ", "
+            else:
+                lines = lines[:-1]
+                lines[-1] = lines[-1] + "\n"
+            lines[-1] = lines[-1] + "dtype: %s" % self.dtype
+        else:
+            lines = output.split(",")
+            lines[-1] = " dtype: %s)" % self.dtype
+            return ",".join(lines)
+        if isinstance(preprocess._column, cudf.core.column.CategoricalColumn):
+            lines.append(category_memory)
+        return "\n".join(lines)
+
+    def _make_operands_and_index_for_binop(
+        self,
+        other: Any,
+        fn: str,
+        fill_value: Any = None,
+        reflect: bool = False,
+        can_reindex: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tuple[
+        Union[
+            Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
+            NotImplementedType,
+        ],
+        Optional[BaseIndex],
+        bool,
+    ]:
+        # Specialize binops to align indices.
+        if isinstance(other, Series):
+            if (
+                not can_reindex
+                and fn in cudf.utils.utils._EQUALITY_OPS
+                and not self.index.equals(other.index)
+            ):
+                raise ValueError(
+                    "Can only compare identically-labeled Series objects"
+                )
+            lhs, other = _align_indices([self, other], allow_non_unique=True)
+        else:
+            lhs = self
+
+        try:
+            can_use_self_column_name = cudf.utils.utils._is_same_name(
+                self.name, other.name
+            )
+        except AttributeError:
+            can_use_self_column_name = False
+
+        operands = lhs._make_operands_for_binop(other, fill_value, reflect)
+        return operands, lhs._index, can_use_self_column_name
+
+    @copy_docstring(CategoricalAccessor)  # type: ignore
+    @property
+    @_cudf_nvtx_annotate
+    def cat(self):
+        return CategoricalAccessor(parent=self)
+
+    @copy_docstring(StringMethods)  # type: ignore
+    @property
+    @_cudf_nvtx_annotate
+    def str(self):
+        return StringMethods(parent=self)
+
+    @copy_docstring(ListMethods)  # type: ignore
+    @property
+    @_cudf_nvtx_annotate
+    def list(self):
+        return ListMethods(parent=self)
+
+    @copy_docstring(StructMethods)  # type: ignore
+    @property
+    @_cudf_nvtx_annotate
+    def struct(self):
+        return StructMethods(parent=self)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dtype(self):
+        """The dtype of the Series."""
+        return self._column.dtype
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def _concat(cls, objs, axis=0, index=True):
+        # Concatenate index if not provided
+        if index is True:
+            if isinstance(objs[0].index, cudf.MultiIndex):
+                index = cudf.MultiIndex._concat([o.index for o in objs])
+            else:
+                index = cudf.core.index.GenericIndex._concat(
+                    [o.index for o in objs]
+                )
+
+        names = {obj.name for obj in objs}
+        if len(names) == 1:
+            [name] = names
+        else:
+            name = None
+
+        if len(objs) > 1:
+            dtype_mismatch = False
+            for obj in objs[1:]:
+                if (
+                    obj.null_count == len(obj)
+                    or len(obj) == 0
+                    or isinstance(
+                        obj._column, cudf.core.column.CategoricalColumn
+                    )
+                    or isinstance(
+                        objs[0]._column, cudf.core.column.CategoricalColumn
+                    )
+                ):
+                    continue
+
+                if (
+                    not dtype_mismatch
+                    and (
+                        not isinstance(
+                            objs[0]._column, cudf.core.column.CategoricalColumn
+                        )
+                        and not isinstance(
+                            obj._column, cudf.core.column.CategoricalColumn
+                        )
+                    )
+                    and objs[0].dtype != obj.dtype
+                ):
+                    dtype_mismatch = True
+
+                if is_mixed_with_object_dtype(objs[0], obj):
+                    raise TypeError(
+                        "cudf does not support mixed types, please type-cast "
+                        "both series to same dtypes."
+                    )
+
+            if dtype_mismatch:
+                common_dtype = find_common_type([obj.dtype for obj in objs])
+                objs = [obj.astype(common_dtype) for obj in objs]
+
+        col = concat_columns([o._column for o in objs])
+
+        if len(objs):
+            col = col._with_type_metadata(objs[0].dtype)
+
+        return cls(data=col, index=index, name=name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def valid_count(self):
+        """Number of non-null values"""
+        return self._column.valid_count
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def null_count(self):
+        """Number of null values"""
+        return self._column.null_count
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nullable(self):
+        """A boolean indicating whether a null-mask is needed"""
+        return self._column.nullable
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def has_nulls(self):
+        """
+        Indicator whether Series contains null values.
+
+        Returns
+        -------
+        out : bool
+            If Series has at least one null value, return True, if not
+            return False.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([1, 2, None, 3, 4])
+        >>> series
+        0       1
+        1       2
+        2    <NA>
+        3       3
+        4       4
+        dtype: int64
+        >>> series.has_nulls
+        True
+        >>> series.dropna().has_nulls
+        False
+        """
+        return self._column.has_nulls()
+
+    @_cudf_nvtx_annotate
+    def dropna(self, axis=0, inplace=False, how=None):
+        """
+        Return a Series with null values removed.
+
+        Parameters
+        ----------
+        axis : {0 or 'index'}, default 0
+            There is only one axis to drop values from.
+        inplace : bool, default False
+            If True, do operation inplace and return None.
+        how : str, optional
+            Not in use. Kept for compatibility.
+
+        Returns
+        -------
+        Series
+            Series with null entries dropped from it.
+
+        See Also
+        --------
+        Series.isna : Indicate null values.
+
+        Series.notna : Indicate non-null values.
+
+        Series.fillna : Replace null values.
+
+        cudf.DataFrame.dropna : Drop rows or columns which
+            contain null values.
+
+        cudf.Index.dropna : Drop null indices.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series([1, 2, None])
+        >>> ser
+        0       1
+        1       2
+        2    <NA>
+        dtype: int64
+
+        Drop null values from a Series.
+
+        >>> ser.dropna()
+        0    1
+        1    2
+        dtype: int64
+
+        Keep the Series with valid entries in the same variable.
+
+        >>> ser.dropna(inplace=True)
+        >>> ser
+        0    1
+        1    2
+        dtype: int64
+
+        Empty strings are not considered null values.
+        `None` is considered a null value.
+
+        >>> ser = cudf.Series(['', None, 'abc'])
+        >>> ser
+        0
+        1    <NA>
+        2     abc
+        dtype: object
+        >>> ser.dropna()
+        0
+        2    abc
+        dtype: object
+        """
+        if axis not in (0, "index"):
+            raise ValueError(
+                "Series.dropna supports only one axis to drop values from"
+            )
+
+        result = super().dropna(axis=axis)
+
+        return self._mimic_inplace(result, inplace=inplace)
+
+    @_cudf_nvtx_annotate
+    def drop_duplicates(self, keep="first", inplace=False, ignore_index=False):
+        """
+        Return Series with duplicate values removed.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', ``False``}, default 'first'
+            Method to handle dropping duplicates:
+
+            - 'first' : Drop duplicates except for the first occurrence.
+            - 'last' : Drop duplicates except for the last occurrence.
+            - ``False`` : Drop all duplicates.
+
+        inplace : bool, default ``False``
+            If ``True``, performs operation inplace and returns None.
+
+        Returns
+        -------
+        Series or None
+            Series with duplicates dropped or None if ``inplace=True``.
+
+        Examples
+        --------
+        >>> s = cudf.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
+        ...               name='animal')
+        >>> s
+        0      lama
+        1       cow
+        2      lama
+        3    beetle
+        4      lama
+        5     hippo
+        Name: animal, dtype: object
+
+        With the `keep` parameter, the selection behavior of duplicated
+        values can be changed. The value 'first' keeps the first
+        occurrence for each set of duplicated entries.
+        The default value of keep is 'first'. Note that order of
+        the rows being returned is not guaranteed
+        to be sorted.
+
+        >>> s.drop_duplicates()
+        0      lama
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+
+        The value 'last' for parameter `keep` keeps the last occurrence
+        for each set of duplicated entries.
+
+        >>> s.drop_duplicates(keep='last')
+        1       cow
+        3    beetle
+        4      lama
+        5     hippo
+        Name: animal, dtype: object
+
+        The value `False` for parameter `keep` discards all sets
+        of duplicated entries. Setting the value of 'inplace' to
+        `True` performs the operation inplace and returns `None`.
+
+        >>> s.drop_duplicates(keep=False, inplace=True)
+        >>> s
+        1       cow
+        3    beetle
+        5     hippo
+        Name: animal, dtype: object
+        """
+        result = super().drop_duplicates(keep=keep, ignore_index=ignore_index)
+
+        return self._mimic_inplace(result, inplace=inplace)
+
+    @_cudf_nvtx_annotate
+    def fillna(
+        self, value=None, method=None, axis=None, inplace=False, limit=None
+    ):
+        if isinstance(value, pd.Series):
+            value = Series.from_pandas(value)
+
+        if not (is_scalar(value) or isinstance(value, (abc.Mapping, Series))):
+            raise TypeError(
+                f'"value" parameter must be a scalar, dict '
+                f"or Series, but you passed a "
+                f'"{type(value).__name__}"'
+            )
+
+        if isinstance(value, (abc.Mapping, Series)):
+            value = Series(value)
+            if not self.index.equals(value.index):
+                value = value.reindex(self.index)
+            value = value._column
+
+        return super().fillna(
+            value=value, method=method, axis=axis, inplace=inplace, limit=limit
+        )
+
+    def between(self, left, right, inclusive="both") -> Series:
+        """
+        Return boolean Series equivalent to left <= series <= right.
+
+        This function returns a boolean vector containing `True` wherever the
+        corresponding Series element is between the boundary values `left` and
+        `right`. NA values are treated as `False`.
+
+        Parameters
+        ----------
+        left : scalar or list-like
+            Left boundary.
+        right : scalar or list-like
+            Right boundary.
+        inclusive : {"both", "neither", "left", "right"}
+            Include boundaries. Whether to set each bound as closed or open.
+
+        Returns
+        -------
+        Series
+            Series representing whether each element is between left and
+            right (inclusive).
+
+        See Also
+        --------
+        Series.gt : Greater than of series and other.
+        Series.lt : Less than of series and other.
+
+        Notes
+        -----
+        This function is equivalent to ``(left <= ser) & (ser <= right)``
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([2, 0, 4, 8, None])
+
+        Boundary values are included by default:
+
+        >>> s.between(1, 4)
+        0     True
+        1    False
+        2     True
+        3    False
+        4     <NA>
+        dtype: bool
+
+        With `inclusive` set to ``"neither"`` boundary values are excluded:
+
+        >>> s.between(1, 4, inclusive="neither")
+        0     True
+        1    False
+        2    False
+        3    False
+        4     <NA>
+        dtype: bool
+
+        `left` and `right` can be any scalar value:
+
+        >>> s = cudf.Series(['Alice', 'Bob', 'Carol', 'Eve'])
+        >>> s.between('Anna', 'Daniel')
+        0    False
+        1     True
+        2     True
+        3    False
+        dtype: bool
+        """
+        left_operand = left if is_scalar(left) else as_column(left)
+        right_operand = right if is_scalar(right) else as_column(right)
+
+        if inclusive == "both":
+            lmask = self._column >= left_operand
+            rmask = self._column <= right_operand
+        elif inclusive == "left":
+            lmask = self._column >= left_operand
+            rmask = self._column < right_operand
+        elif inclusive == "right":
+            lmask = self._column > left_operand
+            rmask = self._column <= right_operand
+        elif inclusive == "neither":
+            lmask = self._column > left_operand
+            rmask = self._column < right_operand
+        else:
+            raise ValueError(
+                "Inclusive has to be either string of 'both', "
+                "'left', 'right', or 'neither'."
+            )
+        return self._from_data({self.name: lmask & rmask}, self._index)
+
+    @_cudf_nvtx_annotate
+    def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
+        if bool_only not in (None, True):
+            raise NotImplementedError(
+                "The bool_only parameter is not supported for Series."
+            )
+        return super().all(axis, skipna, level, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
+        if bool_only not in (None, True):
+            raise NotImplementedError(
+                "The bool_only parameter is not supported for Series."
+            )
+        return super().any(axis, skipna, level, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def to_pandas(self, index=True, nullable=False, **kwargs):
+        """
+        Convert to a Pandas Series.
+
+        Parameters
+        ----------
+        index : Boolean, Default True
+            If ``index`` is ``True``, converts the index of cudf.Series
+            and sets it to the pandas.Series. If ``index`` is ``False``,
+            no index conversion is performed and pandas.Series will assign
+            a default index.
+        nullable : Boolean, Default False
+            If ``nullable`` is ``True``, the resulting series will be
+            having a corresponding nullable Pandas dtype.
+            If there is no corresponding nullable Pandas dtype present,
+            the resulting dtype will be a regular pandas dtype.
+            If ``nullable`` is ``False``, the resulting series will
+            either convert null values to ``np.nan`` or ``None``
+            depending on the dtype.
+
+        Returns
+        -------
+        out : Pandas Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series([-3, 2, 0])
+        >>> pds = ser.to_pandas()
+        >>> pds
+        0   -3
+        1    2
+        2    0
+        dtype: int64
+        >>> type(pds)
+        <class 'pandas.core.series.Series'>
+
+        ``nullable`` parameter can be used to control
+        whether dtype can be Pandas Nullable or not:
+
+        >>> ser = cudf.Series([10, 20, None, 30])
+        >>> ser
+        0      10
+        1      20
+        2    <NA>
+        3      30
+        dtype: int64
+        >>> ser.to_pandas(nullable=True)
+        0      10
+        1      20
+        2    <NA>
+        3      30
+        dtype: Int64
+        >>> ser.to_pandas(nullable=False)
+        0    10.0
+        1    20.0
+        2     NaN
+        3    30.0
+        dtype: float64
+        """
+        if index is True:
+            index = self.index.to_pandas()
+        s = self._column.to_pandas(index=index, nullable=nullable)
+        s.name = self.name
+        return s
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def data(self):
+        """The gpu buffer for the data
+
+        Returns
+        -------
+        out : The GPU buffer of the Series.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([1, 2, 3, 4])
+        >>> series
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        >>> np.array(series.data.memoryview())
+        array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
+               0, 0, 4, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)
+        """  # noqa: E501
+        return self._column.data
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nullmask(self):
+        """The gpu buffer for the null-mask"""
+        return cudf.Series(self._column.nullmask)
+
+    @_cudf_nvtx_annotate
+    def astype(self, dtype, copy=False, errors="raise", **kwargs):
+        if is_dict_like(dtype):
+            if len(dtype) > 1 or self.name not in dtype:
+                raise KeyError(
+                    "Only the Series name can be used for the key in Series "
+                    "dtype mappings."
+                )
+        else:
+            dtype = {self.name: dtype}
+        return super().astype(dtype, copy, errors, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def sort_index(self, axis=0, *args, **kwargs):
+        if axis not in (0, "index"):
+            raise ValueError("Only axis=0 is valid for Series.")
+        return super().sort_index(axis=axis, *args, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def sort_values(
+        self,
+        axis=0,
+        ascending=True,
+        inplace=False,
+        kind="quicksort",
+        na_position="last",
+        ignore_index=False,
+    ):
+        """Sort by the values along either axis.
+
+        Parameters
+        ----------
+        ascending : bool or list of bool, default True
+            Sort ascending vs. descending. Specify list for multiple sort
+            orders. If this is a list of bools, must match the length of the
+            by.
+        na_position : {'first', 'last'}, default 'last'
+            'first' puts nulls at the beginning, 'last' puts nulls at the end
+        ignore_index : bool, default False
+            If True, index will not be sorted.
+
+        Returns
+        -------
+        Series : Series with sorted values.
+
+        Notes
+        -----
+        Difference from pandas:
+          * Support axis='index' only.
+          * Not supporting: inplace, kind
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([1, 5, 2, 4, 3])
+        >>> s.sort_values()
+        0    1
+        2    2
+        4    3
+        3    4
+        1    5
+        dtype: int64
+        """
+        return super().sort_values(
+            by=self.name,
+            axis=axis,
+            ascending=ascending,
+            inplace=inplace,
+            kind=kind,
+            na_position=na_position,
+            ignore_index=ignore_index,
+        )
+
+    @_cudf_nvtx_annotate
+    def nlargest(self, n=5, keep="first"):
+        """Returns a new Series of the *n* largest element.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many descending sorted values.
+        keep : {'first', 'last'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+
+        Returns
+        -------
+        Series
+            The `n` largest values in the Series, sorted in decreasing order.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> countries_population = {"Italy": 59000000, "France": 65000000,
+        ...                         "Malta": 434000, "Maldives": 434000,
+        ...                         "Brunei": 434000, "Iceland": 337000,
+        ...                         "Nauru": 11300, "Tuvalu": 11300,
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
+        >>> series = cudf.Series(countries_population)
+        >>> series
+        Italy         59000000
+        France        65000000
+        Malta           434000
+        Maldives        434000
+        Brunei          434000
+        Iceland         337000
+        Nauru            11300
+        Tuvalu           11300
+        Anguilla         11300
+        Montserrat        5200
+        dtype: int64
+        >>> series.nlargest()
+        France      65000000
+        Italy       59000000
+        Malta         434000
+        Maldives      434000
+        Brunei        434000
+        dtype: int64
+        >>> series.nlargest(3)
+        France    65000000
+        Italy     59000000
+        Malta       434000
+        dtype: int64
+        >>> series.nlargest(3, keep='last')
+        France    65000000
+        Italy     59000000
+        Brunei      434000
+        dtype: int64
+        """
+        return self._n_largest_or_smallest(True, n, [self.name], keep)
+
+    @_cudf_nvtx_annotate
+    def nsmallest(self, n=5, keep="first"):
+        """
+        Returns a new Series of the *n* smallest element.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many ascending sorted values.
+        keep : {'first', 'last'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+
+        Returns
+        -------
+        Series
+            The `n` smallest values in the Series, sorted in increasing order.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> countries_population = {"Italy": 59000000, "France": 65000000,
+        ...                         "Brunei": 434000, "Malta": 434000,
+        ...                         "Maldives": 434000, "Iceland": 337000,
+        ...                         "Nauru": 11300, "Tuvalu": 11300,
+        ...                         "Anguilla": 11300, "Montserrat": 5200}
+        >>> s = cudf.Series(countries_population)
+        >>> s
+        Italy       59000000
+        France      65000000
+        Brunei        434000
+        Malta         434000
+        Maldives      434000
+        Iceland       337000
+        Nauru          11300
+        Tuvalu         11300
+        Anguilla       11300
+        Montserrat      5200
+        dtype: int64
+
+        The `n` smallest elements where ``n=5`` by default.
+
+        >>> s.nsmallest()
+        Montserrat    5200
+        Nauru        11300
+        Tuvalu       11300
+        Anguilla     11300
+        Iceland     337000
+        dtype: int64
+
+        The `n` smallest elements where ``n=3``. Default `keep` value is
+        'first' so Nauru and Tuvalu will be kept.
+
+        >>> s.nsmallest(3)
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` and keeping the last
+        duplicates. Anguilla and Tuvalu will be kept since they are the last
+        with value 11300 based on the index order.
+
+        >>> s.nsmallest(3, keep='last')
+        Montserrat   5200
+        Anguilla    11300
+        Tuvalu      11300
+        dtype: int64
+        """
+        return self._n_largest_or_smallest(False, n, [self.name], keep)
+
+    @_cudf_nvtx_annotate
+    def argsort(
+        self,
+        axis=0,
+        kind="quicksort",
+        order=None,
+        ascending=True,
+        na_position="last",
+    ):
+        obj = self.__class__._from_data(
+            {
+                None: super().argsort(
+                    axis=axis,
+                    kind=kind,
+                    order=order,
+                    ascending=ascending,
+                    na_position=na_position,
+                )
+            }
+        )
+        obj.name = self.name
+        return obj
+
+    @_cudf_nvtx_annotate
+    def replace(self, to_replace=None, value=None, *args, **kwargs):
+        if is_dict_like(to_replace) and value is not None:
+            raise ValueError(
+                "Series.replace cannot use dict-like to_replace and non-None "
+                "value"
+            )
+
+        return super().replace(to_replace, value, *args, **kwargs)
+
+    @_cudf_nvtx_annotate
+    def update(self, other):
+        """
+        Modify Series in place using values from passed Series.
+        Uses non-NA values from passed Series to make updates. Aligns
+        on index.
+
+        Parameters
+        ----------
+        other : Series, or object coercible into Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.update(cudf.Series([4, 5, 6]))
+        >>> s
+        0    4
+        1    5
+        2    6
+        dtype: int64
+        >>> s = cudf.Series(['a', 'b', 'c'])
+        >>> s
+        0    a
+        1    b
+        2    c
+        dtype: object
+        >>> s.update(cudf.Series(['d', 'e'], index=[0, 2]))
+        >>> s
+        0    d
+        1    b
+        2    e
+        dtype: object
+        >>> s = cudf.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.update(cudf.Series([4, 5, 6, 7, 8]))
+        >>> s
+        0    4
+        1    5
+        2    6
+        dtype: int64
+
+        If ``other`` contains NaNs the corresponding values are not updated
+        in the original Series.
+
+        >>> s = cudf.Series([1.0, 2.0, 3.0])
+        >>> s
+        0    1.0
+        1    2.0
+        2    3.0
+        dtype: float64
+        >>> s.update(cudf.Series([4.0, np.nan, 6.0], nan_as_null=False))
+        >>> s
+        0    4.0
+        1    2.0
+        2    6.0
+        dtype: float64
+
+        ``other`` can also be a non-Series object type
+        that is coercible into a Series
+
+        >>> s = cudf.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.update([4, np.nan, 6])
+        >>> s
+        0    4
+        1    2
+        2    6
+        dtype: int64
+        >>> s = cudf.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.update({1: 9})
+        >>> s
+        0    1
+        1    9
+        2    3
+        dtype: int64
+        """
+
+        if not isinstance(other, cudf.Series):
+            other = cudf.Series(other)
+
+        if not self.index.equals(other.index):
+            other = other.reindex(index=self.index)
+        mask = other.notna()
+
+        self.mask(mask, other, inplace=True)
+
+    # UDF related
+    @_cudf_nvtx_annotate
+    def apply(self, func, convert_dtype=True, args=(), **kwargs):
+        """
+        Apply a scalar function to the values of a Series.
+        Similar to ``pandas.Series.apply``.
+
+        ``apply`` relies on Numba to JIT compile ``func``.
+        Thus the allowed operations within ``func`` are limited to `those
+        supported by the CUDA Python Numba target
+        <https://numba.readthedocs.io/en/stable/cuda/cudapysupported.html>`__.
+        For more information, see the `cuDF guide to user defined functions
+        <https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html>`__.
+
+        Some string functions and methods are supported. Refer to the guide
+        to UDFs for details.
+
+        Parameters
+        ----------
+        func : function
+            Scalar Python function to apply.
+        convert_dtype : bool, default True
+            In cuDF, this parameter is always True. Because
+            cuDF does not support arbitrary object dtypes,
+            the result will always be the common type as determined
+            by numba based on the function logic and argument types.
+            See examples for details.
+        args : tuple
+            Positional arguments passed to func after the series value.
+        **kwargs
+            Not supported
+
+        Returns
+        -------
+        result : Series
+            The mask and index are preserved.
+
+        Notes
+        -----
+        UDFs are cached in memory to avoid recompilation. The first
+        call to the UDF will incur compilation overhead. `func` may
+        call nested functions that are decorated with the decorator
+        `numba.cuda.jit(device=True)`, otherwise numba will raise a
+        typing error.
+
+        Examples
+        --------
+        Apply a basic function to a series:
+
+        >>> sr = cudf.Series([1,2,3])
+        >>> def f(x):
+        ...     return x + 1
+        >>> sr.apply(f)
+        0    2
+        1    3
+        2    4
+        dtype: int64
+
+        Apply a basic function to a series with nulls:
+
+        >>> sr = cudf.Series([1,cudf.NA,3])
+        >>> def f(x):
+        ...     return x + 1
+        >>> sr.apply(f)
+        0       2
+        1    <NA>
+        2       4
+        dtype: int64
+
+        Use a function that does something conditionally,
+        based on if the value is or is not null:
+
+        >>> sr = cudf.Series([1,cudf.NA,3])
+        >>> def f(x):
+        ...     if x is cudf.NA:
+        ...         return 42
+        ...     else:
+        ...         return x - 1
+        >>> sr.apply(f)
+        0     0
+        1    42
+        2     2
+        dtype: int64
+
+        Results will be upcast to the common dtype required
+        as derived from the UDFs logic. Note that this means
+        the common type will be returned even if such data
+        is passed that would not result in any values of that
+        dtype:
+
+        >>> sr = cudf.Series([1,cudf.NA,3])
+        >>> def f(x):
+        ...     return x + 1.5
+        >>> sr.apply(f)
+        0     2.5
+        1    <NA>
+        2     4.5
+        dtype: float64
+
+        UDFs manipulating string data are allowed, as long as
+        they neither modify strings in place nor create new strings.
+        For example, the following UDF is allowed:
+
+        >>> def f(st):
+        ...     if len(st) == 0:
+        ...             return -1
+        ...     elif st.startswith('a'):
+        ...             return 1
+        ...     elif 'example' in st:
+        ...             return 2
+        ...     else:
+        ...             return 3
+        ...
+        >>> sr = cudf.Series(['', 'abc', 'some_example'])
+        >>> sr.apply(f)  # doctest: +SKIP
+        0   -1
+        1    1
+        2    2
+        dtype: int64
+
+        However, the following UDF is not allowed since it includes an
+        operation that requires the creation of a new string: a call to the
+        ``upper`` method. Methods that are not supported in this manner
+        will raise an ``AttributeError``.
+
+        >>> def f(st):
+        ...     new = st.upper()
+        ...     return 'ABC' in new
+        ...
+        >>> sr.apply(f)  # doctest: +SKIP
+
+        For a complete list of supported functions and methods that may be
+        used to manipulate string data, see the UDF guide,
+        <https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html>
+
+        """
+        if convert_dtype is not True:
+            raise ValueError("Series.apply only supports convert_dtype=True")
+
+        result = self._apply(func, _get_scalar_kernel, *args, **kwargs)
+        result.name = self.name
+        return result
+
+    #
+    # Stats
+    #
+    @_cudf_nvtx_annotate
+    def count(self, level=None):
+        """
+        Return number of non-NA/null observations in the Series
+
+        Returns
+        -------
+        int
+            Number of non-null values in the Series.
+
+        Notes
+        -----
+        Parameters currently not supported is `level`.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser = cudf.Series([1, 5, 2, 4, 3])
+        >>> ser.count()
+        5
+        """
+
+        if level is not None:
+            raise NotImplementedError("level parameter is not implemented yet")
+
+        return self.valid_count
+
+    @_cudf_nvtx_annotate
+    def mode(self, dropna=True):
+        """
+        Return the mode(s) of the dataset.
+
+        Always returns Series even if only one value is returned.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't consider counts of NA/NaN/NaT.
+
+        Returns
+        -------
+        Series
+            Modes of the Series in sorted order.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([7, 6, 5, 4, 3, 2, 1])
+        >>> series
+        0    7
+        1    6
+        2    5
+        3    4
+        4    3
+        5    2
+        6    1
+        dtype: int64
+        >>> series.mode()
+        0    1
+        1    2
+        2    3
+        3    4
+        4    5
+        5    6
+        6    7
+        dtype: int64
+
+        We can include ``<NA>`` values in mode by
+        passing ``dropna=False``.
+
+        >>> series = cudf.Series([7, 4, 3, 3, 7, None, None])
+        >>> series
+        0       7
+        1       4
+        2       3
+        3       3
+        4       7
+        5    <NA>
+        6    <NA>
+        dtype: int64
+        >>> series.mode()
+        0    3
+        1    7
+        dtype: int64
+        >>> series.mode(dropna=False)
+        0       3
+        1       7
+        2    <NA>
+        dtype: int64
+        """
+        val_counts = self.value_counts(ascending=False, dropna=dropna)
+        if len(val_counts) > 0:
+            val_counts = val_counts[val_counts == val_counts.iloc[0]]
+
+        return Series._from_data(
+            {self.name: val_counts.index.sort_values()}, name=self.name
+        )
+
+    @_cudf_nvtx_annotate
+    def round(self, decimals=0, how="half_even"):
+        if not is_integer(decimals):
+            raise ValueError(
+                f"decimals must be an int, got {type(decimals).__name__}"
+            )
+        decimals = int(decimals)
+        return super().round(decimals, how)
+
+    @_cudf_nvtx_annotate
+    def cov(self, other, min_periods=None):
+        """
+        Compute covariance with Series, excluding missing values.
+
+        Parameters
+        ----------
+        other : Series
+            Series with which to compute the covariance.
+
+        Returns
+        -------
+        float
+            Covariance between Series and other normalized by N-1
+            (unbiased estimator).
+
+        Notes
+        -----
+        `min_periods` parameter is not yet supported.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser1 = cudf.Series([0.9, 0.13, 0.62])
+        >>> ser2 = cudf.Series([0.12, 0.26, 0.51])
+        >>> ser1.cov(ser2)
+        -0.015750000000000004
+        """
+
+        if min_periods is not None:
+            raise NotImplementedError(
+                "min_periods parameter is not implemented yet"
+            )
+
+        if self.empty or other.empty:
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        lhs = self.nans_to_nulls().dropna()
+        rhs = other.nans_to_nulls().dropna()
+
+        lhs, rhs = _align_indices([lhs, rhs], how="inner")
+
+        try:
+            return lhs._column.cov(rhs._column)
+        except AttributeError:
+            raise TypeError(
+                f"cannot perform covariance with types {self.dtype}, "
+                f"{other.dtype}"
+            )
+
+    @_cudf_nvtx_annotate
+    def transpose(self):
+        """Return the transpose, which is by definition self."""
+
+        return self
+
+    T = property(transpose, doc=transpose.__doc__)
+
+    @_cudf_nvtx_annotate
+    def duplicated(self, keep="first"):
+        """
+        Indicate duplicate Series values.
+
+        Duplicated values are indicated as ``True`` values in the resulting
+        Series. Either all duplicates, all except the first or all except the
+        last occurrence of duplicates can be indicated.
+
+        Parameters
+        ----------
+        keep : {'first', 'last', False}, default 'first'
+            Method to handle dropping duplicates:
+
+            - ``'first'`` : Mark duplicates as ``True`` except for the first
+              occurrence.
+            - ``'last'`` : Mark duplicates as ``True`` except for the last
+              occurrence.
+            - ``False`` : Mark all duplicates as ``True``.
+
+        Returns
+        -------
+        Series[bool]
+            Series indicating whether each value has occurred in the
+            preceding values.
+
+        See Also
+        --------
+        Index.duplicated : Equivalent method on cudf.Index.
+        DataFrame.duplicated : Equivalent method on cudf.DataFrame.
+        Series.drop_duplicates : Remove duplicate values from Series.
+
+        Examples
+        --------
+        By default, for each set of duplicated values, the first occurrence is
+        set on False and all others on True:
+
+        >>> import cudf
+        >>> animals = cudf.Series(['lama', 'cow', 'lama', 'beetle', 'lama'])
+        >>> animals.duplicated()
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        which is equivalent to
+
+        >>> animals.duplicated(keep='first')
+        0    False
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+
+        By using 'last', the last occurrence of each set of duplicated values
+        is set on False and all others on True:
+
+        >>> animals.duplicated(keep='last')
+        0     True
+        1    False
+        2     True
+        3    False
+        4    False
+        dtype: bool
+
+        By setting keep on ``False``, all duplicates are True:
+
+        >>> animals.duplicated(keep=False)
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        dtype: bool
+        """
+        return super().duplicated(keep=keep)
+
+    @_cudf_nvtx_annotate
+    def corr(self, other, method="pearson", min_periods=None):
+        """Calculates the sample correlation between two Series,
+        excluding missing values.
+
+        Parameters
+        ----------
+        other : Series
+            Series with which to compute the correlation.
+        method : {'pearson', 'spearman'}, default 'pearson'
+            Method used to compute correlation:
+
+            - pearson : Standard correlation coefficient
+            - spearman : Spearman rank correlation
+
+        min_periods : int, optional
+            Minimum number of observations needed to have a valid result.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> ser1 = cudf.Series([0.9, 0.13, 0.62])
+        >>> ser2 = cudf.Series([0.12, 0.26, 0.51])
+        >>> ser1.corr(ser2, method="pearson")
+        -0.20454263717316112
+        >>> ser1.corr(ser2, method="spearman")
+        -0.5
+        """
+
+        if method not in {"pearson", "spearman"}:
+            raise ValueError(f"Unknown method {method}")
+
+        if min_periods is not None:
+            raise NotImplementedError("Unsupported argument 'min_periods'")
+
+        if self.empty or other.empty:
+            return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
+
+        lhs = self.nans_to_nulls().dropna()
+        rhs = other.nans_to_nulls().dropna()
+        lhs, rhs = _align_indices([lhs, rhs], how="inner")
+        if method == "spearman":
+            lhs = lhs.rank()
+            rhs = rhs.rank()
+
+        try:
+            return lhs._column.corr(rhs._column)
+        except AttributeError:
+            raise TypeError(
+                f"cannot perform corr with types {self.dtype}, {other.dtype}"
+            )
+
+    @_cudf_nvtx_annotate
+    def autocorr(self, lag=1):
+        """Compute the lag-N autocorrelation. This method computes the Pearson
+        correlation between the Series and its shifted self.
+
+        Parameters
+        ----------
+        lag : int, default 1
+            Number of lags to apply before performing autocorrelation.
+
+        Returns
+        -------
+        result : float
+            The Pearson correlation between self and self.shift(lag).
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([0.25, 0.5, 0.2, -0.05, 0.17])
+        >>> s.autocorr()
+        0.1438853844...
+        >>> s.autocorr(lag=2)
+        -0.9647548490...
+        """
+        return self.corr(self.shift(lag))
+
+    @_cudf_nvtx_annotate
+    def isin(self, values):
+        """Check whether values are contained in Series.
+
+        Parameters
+        ----------
+        values : set or list-like
+            The sequence of values to test. Passing in a single string will
+            raise a TypeError. Instead, turn a single string into a list
+            of one element.
+
+        Returns
+        -------
+        result : Series
+            Series of booleans indicating if each element is in values.
+
+        Raises
+        ------
+        TypeError
+            If values is a string
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['lama', 'cow', 'lama', 'beetle', 'lama',
+        ...                'hippo'], name='animal')
+        >>> s.isin(['cow', 'lama'])
+        0     True
+        1     True
+        2     True
+        3    False
+        4     True
+        5    False
+        Name: animal, dtype: bool
+
+        Passing a single string as ``s.isin('lama')`` will raise an error. Use
+        a list of one element instead:
+
+        >>> s.isin(['lama'])
+        0     True
+        1    False
+        2     True
+        3    False
+        4     True
+        5    False
+        Name: animal, dtype: bool
+
+        Strings and integers are distinct and are therefore not comparable:
+
+        >>> cudf.Series([1]).isin(['1'])
+        0    False
+        dtype: bool
+        >>> cudf.Series([1.1]).isin(['1.1'])
+        0    False
+        dtype: bool
+        """
+
+        # Even though only list-like objects are supposed to be passed, only
+        # scalars throw errors. Other types (like dicts) just transparently
+        # return False (see the implementation of ColumnBase.isin).
+        if is_scalar(values):
+            raise TypeError(
+                "only list-like objects are allowed to be passed "
+                f"to isin(), you passed a [{type(values).__name__}]"
+            )
+
+        return Series._from_data(
+            {self.name: self._column.isin(values)}, index=self.index
+        )
+
+    @_cudf_nvtx_annotate
+    def unique(self):
+        """
+        Returns unique values of this Series.
+
+        Returns
+        -------
+        Series
+            A series with only the unique values.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series(['a', 'a', 'b', None, 'b', None, 'c'])
+        >>> series
+        0       a
+        1       a
+        2       b
+        3    <NA>
+        4       b
+        5    <NA>
+        6       c
+        dtype: object
+        >>> series.unique()
+        0       a
+        1       b
+        2    <NA>
+        3       c
+        dtype: object
+        """
+        res = self._column.unique()
+        if cudf.get_option("mode.pandas_compatible"):
+            return res.values
+        return Series(res, name=self.name)
+
+    @_cudf_nvtx_annotate
+    def value_counts(
+        self,
+        normalize=False,
+        sort=True,
+        ascending=False,
+        bins=None,
+        dropna=True,
+    ):
+        """Return a Series containing counts of unique values.
+
+        The resulting object will be in descending order so that
+        the first element is the most frequently-occurring element.
+        Excludes NA values by default.
+
+        Parameters
+        ----------
+        normalize : bool, default False
+            If True then the object returned will contain
+            the relative frequencies of the unique values.
+
+        sort : bool, default True
+            Sort by frequencies.
+
+        ascending : bool, default False
+            Sort in ascending order.
+
+        bins : int, optional
+            Rather than count values, group them into half-open bins,
+            only works with numeric data.
+
+        dropna : bool, default True
+            Don't include counts of NaN and None.
+
+        Returns
+        -------
+        result : Series containing counts of unique values.
+
+        See Also
+        --------
+        Series.count
+            Number of non-NA elements in a Series.
+
+        cudf.DataFrame.count
+            Number of non-NA elements in a DataFrame.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> sr = cudf.Series([1.0, 2.0, 2.0, 3.0, 3.0, 3.0, None])
+        >>> sr
+        0     1.0
+        1     2.0
+        2     2.0
+        3     3.0
+        4     3.0
+        5     3.0
+        6    <NA>
+        dtype: float64
+        >>> sr.value_counts()
+        3.0    3
+        2.0    2
+        1.0    1
+        dtype: int64
+
+        The order of the counts can be changed by passing ``ascending=True``:
+
+        >>> sr.value_counts(ascending=True)
+        1.0    1
+        2.0    2
+        3.0    3
+        dtype: int64
+
+        With ``normalize`` set to True, returns the relative frequency
+        by dividing all values by the sum of values.
+
+        >>> sr.value_counts(normalize=True)
+        3.0    0.500000
+        2.0    0.333333
+        1.0    0.166667
+        dtype: float64
+
+        To include ``NA`` value counts, pass ``dropna=False``:
+
+        >>> sr = cudf.Series([1.0, 2.0, 2.0, 3.0, None, 3.0, 3.0, None])
+        >>> sr
+        0     1.0
+        1     2.0
+        2     2.0
+        3     3.0
+        4    <NA>
+        5     3.0
+        6     3.0
+        7    <NA>
+        dtype: float64
+        >>> sr.value_counts(dropna=False)
+        3.0     3
+        2.0     2
+        <NA>    2
+        1.0     1
+        dtype: int64
+
+        >>> s = cudf.Series([3, 1, 2, 3, 4, np.nan])
+        >>> s.value_counts(bins=3)
+        (2.0, 3.0]      2
+        (0.996, 2.0]    2
+        (3.0, 4.0]      1
+        dtype: int64
+        """
+        if bins is not None:
+            series_bins = cudf.cut(self, bins, include_lowest=True)
+
+        if dropna and self.null_count == len(self):
+            return Series(
+                [],
+                dtype=np.int64,
+                name=self.name,
+                index=cudf.Index([], dtype=self.dtype),
+            )
+
+        if bins is not None:
+            res = self.groupby(series_bins, dropna=dropna).count(dropna=dropna)
+            res = res[res.index.notna()]
+        else:
+            res = self.groupby(self, dropna=dropna).count(dropna=dropna)
+            if isinstance(self.dtype, cudf.CategoricalDtype) and len(
+                res
+            ) != len(self.dtype.categories):
+                # For categorical dtypes: When there exists
+                # categories in dtypes and they are missing in the
+                # column, `value_counts` will have to return
+                # their occurrences as 0.
+                # TODO: Remove this workaround once `observed`
+                # parameter support is added to `groupby`
+                res = res.reindex(self.dtype.categories).fillna(0)
+                res._index = res._index.astype(self.dtype)
+
+        res.index.name = None
+
+        if sort:
+            res = res.sort_values(ascending=ascending)
+
+        if normalize:
+            res = res / float(res._column.sum())
+
+        # Pandas returns an IntervalIndex as the index of res
+        # this condition makes sure we do too if bins is given
+        if bins is not None and len(res) == len(res.index.categories):
+            int_index = IntervalColumn.as_interval_column(
+                res.index._column, res.index.categories.dtype
+            )
+            res.index = int_index
+
+        return res
+
+    @_cudf_nvtx_annotate
+    def quantile(
+        self, q=0.5, interpolation="linear", exact=True, quant_index=True
+    ):
+        """
+        Return values at the given quantile.
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+            0 <= q <= 1, the quantile(s) to compute
+        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
+            This optional parameter specifies the interpolation method to use,
+            when the desired quantile lies between two data points i and j:
+
+                * linear: `i + (j - i) * fraction`, where `fraction` is the
+                  fractional part of the index surrounded by `i` and `j`.
+                * lower: `i`.
+                * higher: `j`.
+                * nearest: `i` or `j` whichever is nearest.
+                * midpoint: (`i` + `j`) / 2.
+        exact : boolean
+            Whether to use approximate or exact quantile algorithm.
+        quant_index : boolean
+            Whether to use the list of quantiles as index.
+
+        Returns
+        -------
+        float or Series
+            If ``q`` is an array, a Series will be returned where the
+            index is ``q`` and the values are the quantiles, otherwise
+            a float will be returned.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([1, 2, 3, 4])
+        >>> series
+        0    1
+        1    2
+        2    3
+        3    4
+        dtype: int64
+        >>> series.quantile(0.5)
+        2.5
+        >>> series.quantile([0.25, 0.5, 0.75])
+        0.25    1.75
+        0.50    2.50
+        0.75    3.25
+        dtype: float64
+        """
+
+        return_scalar = is_scalar(q)
+        if return_scalar:
+            np_array_q = np.asarray([float(q)])
+        else:
+            try:
+                np_array_q = np.asarray(q)
+            except TypeError:
+                try:
+                    np_array_q = cudf.core.column.as_column(q).values_host
+                except TypeError:
+                    raise TypeError(
+                        f"q must be a scalar or array-like, got {type(q)}"
+                    )
+
+        result = self._column.quantile(
+            np_array_q, interpolation, exact, return_scalar=return_scalar
+        )
+
+        if return_scalar:
+            return result
+
+        return Series._from_data(
+            data={self.name: result},
+            index=as_index(np_array_q) if quant_index else None,
+        )
+
+    @docutils.doc_describe()
+    @_cudf_nvtx_annotate
+    def describe(
+        self,
+        percentiles=None,
+        include=None,
+        exclude=None,
+        datetime_is_numeric=False,
+    ):
+        """{docstring}"""
+
+        if not datetime_is_numeric:
+            # Do not remove until pandas 2.0 support is added.
+            warnings.warn(
+                "`datetime_is_numeric` is deprecated and will be removed in "
+                "a future release. Specify `datetime_is_numeric=True` to "
+                "silence this warning and adopt the future behavior now.",
+                FutureWarning,
+            )
+        if percentiles is not None:
+            if not all(0 <= x <= 1 for x in percentiles):
+                raise ValueError(
+                    "All percentiles must be between 0 and 1, " "inclusive."
+                )
+
+            # describe always includes 50th percentile
+            percentiles = list(percentiles)
+            if 0.5 not in percentiles:
+                percentiles.append(0.5)
+
+            percentiles = np.sort(percentiles)
+        else:
+            # pandas defaults
+            percentiles = np.array([0.25, 0.5, 0.75])
+
+        dtype = "str"
+        if is_bool_dtype(self.dtype):
+            data = _describe_categorical(self, percentiles)
+        elif isinstance(self._column, cudf.core.column.NumericalColumn):
+            data = _describe_numeric(self, percentiles)
+            dtype = None
+        elif isinstance(self._column, TimeDeltaColumn):
+            data = _describe_timedelta(self, percentiles)
+        elif isinstance(self._column, DatetimeColumn):
+            data = _describe_timestamp(self, percentiles)
+        else:
+            data = _describe_categorical(self, percentiles)
+
+        return Series(
+            data=data.values(),
+            index=data.keys(),
+            dtype=dtype,
+            nan_as_null=False,
+            name=self.name,
+        )
+
+    @_cudf_nvtx_annotate
+    def digitize(self, bins, right=False):
+        """Return the indices of the bins to which each value belongs.
+
+        Notes
+        -----
+        Monotonicity of bins is assumed and not checked.
+
+        Parameters
+        ----------
+        bins : np.array
+            1-D monotonically, increasing array with same type as this series.
+        right : bool
+            Indicates whether interval contains the right or left bin edge.
+
+        Returns
+        -------
+        A new Series containing the indices.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([0.2, 6.4, 3.0, 1.6])
+        >>> bins = cudf.Series([0.0, 1.0, 2.5, 4.0, 10.0])
+        >>> inds = s.digitize(bins)
+        >>> inds
+        0    1
+        1    4
+        2    3
+        3    2
+        dtype: int32
+        """
+        return Series(
+            cudf.core.column.numerical.digitize(self._column, bins, right)
+        )
+
+    @_cudf_nvtx_annotate
+    def diff(self, periods=1):
+        """First discrete difference of element.
+
+        Calculates the difference of a Series element compared with another
+        element in the Series (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference,
+            accepts negative values.
+
+        Returns
+        -------
+        Series
+            First differences of the Series.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([1, 1, 2, 3, 5, 8])
+        >>> series
+        0    1
+        1    1
+        2    2
+        3    3
+        4    5
+        5    8
+        dtype: int64
+
+        Difference with previous row
+
+        >>> series.diff()
+        0    <NA>
+        1       0
+        2       1
+        3       1
+        4       2
+        5       3
+        dtype: int64
+
+        Difference with 3rd previous row
+
+        >>> series.diff(periods=3)
+        0    <NA>
+        1    <NA>
+        2    <NA>
+        3       2
+        4       4
+        5       6
+        dtype: int64
+
+        Difference with following row
+
+        >>> series.diff(periods=-1)
+        0       0
+        1      -1
+        2      -1
+        3      -2
+        4      -3
+        5    <NA>
+        dtype: int64
+        """
+        if not is_integer(periods):
+            if not (is_float(periods) and periods.is_integer()):
+                raise ValueError("periods must be an integer")
+            periods = int(periods)
+
+        return self - self.shift(periods=periods)
+
+    @_cudf_nvtx_annotate
+    @docutils.doc_apply(
+        groupby_doc_template.format(
+            ret=textwrap.dedent(
+                """
+                Returns
+                -------
+                SeriesGroupBy
+                    Returns a SeriesGroupBy object that contains
+                    information about the groups.
+                """
+            )
+        )
+    )
+    def groupby(
+        self,
+        by=None,
+        axis=0,
+        level=None,
+        as_index=True,
+        sort=no_default,
+        group_keys=False,
+        squeeze=False,
+        observed=True,
+        dropna=True,
+    ):
+        return super().groupby(
+            by,
+            axis,
+            level,
+            as_index,
+            sort,
+            group_keys,
+            squeeze,
+            observed,
+            dropna,
+        )
+
+    @_cudf_nvtx_annotate
+    def rename(self, index=None, copy=True):
+        """
+        Alter Series name
+
+        Change Series.name with a scalar value
+
+        Parameters
+        ----------
+        index : Scalar, optional
+            Scalar to alter the Series.name attribute
+        copy : boolean, default True
+            Also copy underlying data
+
+        Returns
+        -------
+        Series
+
+        Notes
+        -----
+        Difference from pandas:
+          - Supports scalar values only for changing name attribute
+          - Not supporting : inplace, level
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([10, 20, 30])
+        >>> series
+        0    10
+        1    20
+        2    30
+        dtype: int64
+        >>> series.name
+        >>> renamed_series = series.rename('numeric_series')
+        >>> renamed_series
+        0    10
+        1    20
+        2    30
+        Name: numeric_series, dtype: int64
+        >>> renamed_series.name
+        'numeric_series'
+        """
+        out_data = self._data.copy(deep=copy)
+        return Series._from_data(out_data, self.index, name=index)
+
+    @_cudf_nvtx_annotate
+    def add_prefix(self, prefix):
+        return Series._from_data(
+            data=self._data.copy(deep=True),
+            index=prefix + self.index.astype(str),
+        )
+
+    @_cudf_nvtx_annotate
+    def add_suffix(self, suffix):
+        return Series._from_data(
+            data=self._data.copy(deep=True),
+            index=self.index.astype(str) + suffix,
+        )
+
+    @_cudf_nvtx_annotate
+    def keys(self):
+        """
+        Return alias for index.
+
+        Returns
+        -------
+        Index
+            Index of the Series.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> sr = cudf.Series([10, 11, 12, 13, 14, 15])
+        >>> sr
+        0    10
+        1    11
+        2    12
+        3    13
+        4    14
+        5    15
+        dtype: int64
+
+        >>> sr.keys()
+        RangeIndex(start=0, stop=6, step=1)
+        >>> sr = cudf.Series(['a', 'b', 'c'])
+        >>> sr
+        0    a
+        1    b
+        2    c
+        dtype: object
+        >>> sr.keys()
+        RangeIndex(start=0, stop=3, step=1)
+        >>> sr = cudf.Series([1, 2, 3], index=['a', 'b', 'c'])
+        >>> sr
+        a    1
+        b    2
+        c    3
+        dtype: int64
+        >>> sr.keys()
+        StringIndex(['a' 'b' 'c'], dtype='object')
+        """
+        return self.index
+
+    @_cudf_nvtx_annotate
+    def explode(self, ignore_index=False):
+        """
+        Transform each element of a list-like to a row, replicating index
+        values.
+
+        Parameters
+        ----------
+        ignore_index : bool, default False
+            If True, the resulting index will be labeled 0, 1, …, n - 1.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([[1, 2, 3], [], None, [4, 5]])
+        >>> s
+        0    [1, 2, 3]
+        1           []
+        2         None
+        3       [4, 5]
+        dtype: list
+        >>> s.explode()
+        0       1
+        0       2
+        0       3
+        1    <NA>
+        2    <NA>
+        3       4
+        3       5
+        dtype: int64
+        """
+        return super()._explode(self.name, ignore_index)
+
+    @_cudf_nvtx_annotate
+    def pct_change(
+        self, periods=1, fill_method="ffill", limit=None, freq=None
+    ):
+        """
+        Calculates the percent change between sequential elements
+        in the Series.
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for forming percent change.
+        fill_method : str, default 'ffill'
+            How to handle NAs before computing percent changes.
+        limit : int, optional
+            The number of consecutive NAs to fill before stopping.
+            Not yet implemented.
+        freq : str, optional
+            Increment to use from time series API.
+            Not yet implemented.
+
+        Returns
+        -------
+        Series
+        """
+        if limit is not None:
+            raise NotImplementedError("limit parameter not supported yet.")
+        if freq is not None:
+            raise NotImplementedError("freq parameter not supported yet.")
+        elif fill_method not in {"ffill", "pad", "bfill", "backfill"}:
+            raise ValueError(
+                "fill_method must be one of 'ffill', 'pad', "
+                "'bfill', or 'backfill'."
+            )
+
+        data = self.fillna(method=fill_method, limit=limit)
+        diff = data.diff(periods=periods)
+        change = diff / data.shift(periods=periods, freq=freq)
+        return change
+
+    @_cudf_nvtx_annotate
+    def where(self, cond, other=None, inplace=False):
+        result_col = super().where(cond, other, inplace)
+        return self._mimic_inplace(
+            self._from_data_like_self({self.name: result_col}),
+            inplace=inplace,
+        )
+
+
+def make_binop_func(op):
+    # This function is used to wrap binary operations in Frame with an
+    # appropriate API for Series as required for pandas compatibility. The
+    # main effect is reordering and error-checking parameters in
+    # Series-specific ways.
+    wrapped_func = getattr(IndexedFrame, op)
+
+    @functools.wraps(wrapped_func)
+    def wrapper(self, other, level=None, fill_value=None, axis=0):
+        if axis != 0:
+            raise NotImplementedError("Only axis=0 supported at this time.")
+        return wrapped_func(self, other, axis, level, fill_value)
+
+    # functools.wraps copies module level attributes to `wrapper` and sets
+    # __wrapped__ attributes to `wrapped_func`. Cpython looks up the signature
+    # string of a function by recursively delving into __wrapped__ until
+    # it hits the first function that has __signature__ attribute set. To make
+    # the signature string of `wrapper` matches with its actual parameter list,
+    # we directly set the __signature__ attribute of `wrapper` below.
+
+    new_sig = inspect.signature(
+        lambda self, other, level=None, fill_value=None, axis=0: None
+    )
+    wrapper.__signature__ = new_sig
+    return wrapper
+
+
+# Wrap all Frame binop functions with the expected API for Series.
+for binop in (
+    "add",
+    "radd",
+    "subtract",
+    "sub",
+    "rsub",
+    "multiply",
+    "mul",
+    "rmul",
+    "mod",
+    "rmod",
+    "pow",
+    "rpow",
+    "floordiv",
+    "rfloordiv",
+    "truediv",
+    "div",
+    "divide",
+    "rtruediv",
+    "rdiv",
+    "eq",
+    "ne",
+    "lt",
+    "le",
+    "gt",
+    "ge",
+):
+    setattr(Series, binop, make_binop_func(binop))
+
+
+class DatetimeProperties:
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns
+    -------
+    Returns a Series indexed like the original Series.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> import pandas as pd
+    >>> seconds_series = cudf.Series(pd.date_range("2000-01-01", periods=3,
+    ...     freq="s"))
+    >>> seconds_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 00:00:01
+    2   2000-01-01 00:00:02
+    dtype: datetime64[ns]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    dtype: int16
+    >>> hours_series = cudf.Series(pd.date_range("2000-01-01", periods=3,
+    ...     freq="h"))
+    >>> hours_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 01:00:00
+    2   2000-01-01 02:00:00
+    dtype: datetime64[ns]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    dtype: int16
+    >>> weekday_series = cudf.Series(pd.date_range("2000-01-01", periods=3,
+    ...     freq="q"))
+    >>> weekday_series
+    0   2000-03-31
+    1   2000-06-30
+    2   2000-09-30
+    dtype: datetime64[ns]
+    >>> weekday_series.dt.weekday
+    0    4
+    1    4
+    2    5
+    dtype: int16
+    """
+
+    def __init__(self, series):
+        self.series = series
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def year(self):
+        """
+        The year of the datetime.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="Y"))
+        >>> datetime_series
+        0   2000-12-31
+        1   2001-12-31
+        2   2002-12-31
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.year
+        0    2000
+        1    2001
+        2    2002
+        dtype: int16
+        """
+        return self._get_dt_field("year")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def month(self):
+        """
+        The month as January=1, December=12.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="M"))
+        >>> datetime_series
+        0   2000-01-31
+        1   2000-02-29
+        2   2000-03-31
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.month
+        0    1
+        1    2
+        2    3
+        dtype: int16
+        """
+        return self._get_dt_field("month")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def day(self):
+        """
+        The day of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="D"))
+        >>> datetime_series
+        0   2000-01-01
+        1   2000-01-02
+        2   2000-01-03
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.day
+        0    1
+        1    2
+        2    3
+        dtype: int16
+        """
+        return self._get_dt_field("day")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def hour(self):
+        """
+        The hours of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="h"))
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 01:00:00
+        2   2000-01-01 02:00:00
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.hour
+        0    0
+        1    1
+        2    2
+        dtype: int16
+        """
+        return self._get_dt_field("hour")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def minute(self):
+        """
+        The minutes of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="T"))
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 00:01:00
+        2   2000-01-01 00:02:00
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.minute
+        0    0
+        1    1
+        2    2
+        dtype: int16
+        """
+        return self._get_dt_field("minute")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def second(self):
+        """
+        The seconds of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="s"))
+        >>> datetime_series
+        0   2000-01-01 00:00:00
+        1   2000-01-01 00:00:01
+        2   2000-01-01 00:00:02
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.second
+        0    0
+        1    1
+        2    2
+        dtype: int16
+        """
+        return self._get_dt_field("second")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def microsecond(self):
+        """
+        The microseconds of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="us"))
+        >>> datetime_series
+        0    2000-01-01 00:00:00.000000
+        1    2000-01-01 00:00:00.000001
+        2    2000-01-01 00:00:00.000002
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.microsecond
+        0    0
+        1    1
+        2    2
+        dtype: int32
+        """
+        return Series(
+            data=(
+                # Need to manually promote column to int32 because
+                # pandas-matching binop behaviour requires that this
+                # __mul__ returns an int16 column.
+                self.series._column.get_dt_field("millisecond").astype("int32")
+                * cudf.Scalar(1000, dtype="int32")
+            )
+            + self.series._column.get_dt_field("microsecond"),
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nanosecond(self):
+        """
+        The nanoseconds of the datetime.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range("2000-01-01",
+        ...         periods=3, freq="ns"))
+        >>> datetime_series
+        0    2000-01-01 00:00:00.000000000
+        1    2000-01-01 00:00:00.000000001
+        2    2000-01-01 00:00:00.000000002
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.nanosecond
+        0    0
+        1    1
+        2    2
+        dtype: int16
+        """
+        return self._get_dt_field("nanosecond")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def weekday(self):
+        """
+        The day of the week with Monday=0, Sunday=6.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range('2016-12-31',
+        ...     '2017-01-08', freq='D'))
+        >>> datetime_series
+        0   2016-12-31
+        1   2017-01-01
+        2   2017-01-02
+        3   2017-01-03
+        4   2017-01-04
+        5   2017-01-05
+        6   2017-01-06
+        7   2017-01-07
+        8   2017-01-08
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.weekday
+        0    5
+        1    6
+        2    0
+        3    1
+        4    2
+        5    3
+        6    4
+        7    5
+        8    6
+        dtype: int16
+        """
+        return self._get_dt_field("weekday")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dayofweek(self):
+        """
+        The day of the week with Monday=0, Sunday=6.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range('2016-12-31',
+        ...     '2017-01-08', freq='D'))
+        >>> datetime_series
+        0   2016-12-31
+        1   2017-01-01
+        2   2017-01-02
+        3   2017-01-03
+        4   2017-01-04
+        5   2017-01-05
+        6   2017-01-06
+        7   2017-01-07
+        8   2017-01-08
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.dayofweek
+        0    5
+        1    6
+        2    0
+        3    1
+        4    2
+        5    3
+        6    4
+        7    5
+        8    6
+        dtype: int16
+        """
+        return self._get_dt_field("weekday")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def dayofyear(self):
+        """
+        The day of the year, from 1-365 in non-leap years and
+        from 1-366 in leap years.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range('2016-12-31',
+        ...     '2017-01-08', freq='D'))
+        >>> datetime_series
+        0   2016-12-31
+        1   2017-01-01
+        2   2017-01-02
+        3   2017-01-03
+        4   2017-01-04
+        5   2017-01-05
+        6   2017-01-06
+        7   2017-01-07
+        8   2017-01-08
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.dayofyear
+        0    366
+        1      1
+        2      2
+        3      3
+        4      4
+        5      5
+        6      6
+        7      7
+        8      8
+        dtype: int16
+        """
+        return self._get_dt_field("day_of_year")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def day_of_year(self):
+        """
+        The day of the year, from 1-365 in non-leap years and
+        from 1-366 in leap years.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import cudf
+        >>> datetime_series = cudf.Series(pd.date_range('2016-12-31',
+        ...     '2017-01-08', freq='D'))
+        >>> datetime_series
+        0   2016-12-31
+        1   2017-01-01
+        2   2017-01-02
+        3   2017-01-03
+        4   2017-01-04
+        5   2017-01-05
+        6   2017-01-06
+        7   2017-01-07
+        8   2017-01-08
+        dtype: datetime64[ns]
+        >>> datetime_series.dt.day_of_year
+        0    366
+        1      1
+        2      2
+        3      3
+        4      4
+        5      5
+        6      6
+        7      7
+        8      8
+        dtype: int16
+        """
+        return self._get_dt_field("day_of_year")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_leap_year(self):
+        """
+        Boolean indicator if the date belongs to a leap year.
+
+        A leap year is a year, which has 366 days (instead of 365) including
+        29th of February as an intercalary day. Leap years are years which are
+        multiples of four with the exception of years divisible by 100 but not
+        by 400.
+
+        Returns
+        -------
+        Series
+        Booleans indicating if dates belong to a leap year.
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> s = cudf.Series(
+        ...     pd.date_range(start='2000-02-01', end='2013-02-01', freq='1Y'))
+        >>> s
+        0    2000-12-31
+        1    2001-12-31
+        2    2002-12-31
+        3    2003-12-31
+        4    2004-12-31
+        5    2005-12-31
+        6    2006-12-31
+        7    2007-12-31
+        8    2008-12-31
+        9    2009-12-31
+        10   2010-12-31
+        11   2011-12-31
+        12   2012-12-31
+        dtype: datetime64[ns]
+        >>> s.dt.is_leap_year
+        0      True
+        1     False
+        2     False
+        3     False
+        4      True
+        5     False
+        6     False
+        7     False
+        8      True
+        9     False
+        10    False
+        11    False
+        12     True
+        dtype: bool
+        """
+        res = libcudf.datetime.is_leap_year(self.series._column).fillna(False)
+        return Series._from_data(
+            ColumnAccessor({None: res}),
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def quarter(self):
+        """
+        Integer indicator for which quarter of the year the date belongs in.
+
+        There are 4 quarters in a year. With the first quarter being from
+        January - March, second quarter being April - June, third quarter
+        being July - September and fourth quarter being October - December.
+
+        Returns
+        -------
+        Series
+        Integer indicating which quarter the date belongs to.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(["2020-05-31 08:00:00","1999-12-31 18:40:00"],
+        ...     dtype="datetime64[ms]")
+        >>> s.dt.quarter
+        0    2
+        1    4
+        dtype: int8
+        """
+        res = libcudf.datetime.extract_quarter(self.series._column).astype(
+            np.int8
+        )
+        return Series._from_data(
+            {None: res},
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @_cudf_nvtx_annotate
+    def isocalendar(self):
+        """
+        Returns a DataFrame with the year, week, and day
+        calculated according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+        with columns year, week and day
+
+        Examples
+        --------
+        >>> ser = cudf.Series(pd.date_range(start="2021-07-25",
+        ... end="2021-07-30"))
+        >>> ser.dt.isocalendar()
+           year  week  day
+        0  2021    29    7
+        1  2021    30    1
+        2  2021    30    2
+        3  2021    30    3
+        4  2021    30    4
+        5  2021    30    5
+        >>> ser.dt.isocalendar().week
+        0    29
+        1    30
+        2    30
+        3    30
+        4    30
+        5    30
+        Name: week, dtype: object
+
+        >>> serIndex = cudf.to_datetime(pd.Series(["2010-01-01", pd.NaT]))
+        >>> serIndex.dt.isocalendar()
+            year  week  day
+        0  2009    53     5
+        1  <NA>  <NA>  <NA>
+        >>> serIndex.dt.isocalendar().year
+        0    2009
+        1    <NA>
+        Name: year, dtype: object
+        """
+        return cudf.core.tools.datetimes._to_iso_calendar(self)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_month_start(self):
+        """
+        Booleans indicating if dates are the first day of the month.
+        """
+        return (self.day == 1).fillna(False)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def days_in_month(self):
+        """
+        Get the total number of days in the month that the date falls on.
+
+        Returns
+        -------
+        Series
+        Integers representing the number of days in month
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> s = cudf.Series(
+        ...     pd.date_range(start='2000-08-01', end='2001-08-01', freq='1M'))
+        >>> s
+        0    2000-08-31
+        1    2000-09-30
+        2    2000-10-31
+        3    2000-11-30
+        4    2000-12-31
+        5    2001-01-31
+        6    2001-02-28
+        7    2001-03-31
+        8    2001-04-30
+        9    2001-05-31
+        10   2001-06-30
+        11   2001-07-31
+        dtype: datetime64[ns]
+        >>> s.dt.days_in_month
+        0     31
+        1     30
+        2     31
+        3     30
+        4     31
+        5     31
+        6     28
+        7     31
+        8     30
+        9     31
+        10    30
+        11    31
+        dtype: int16
+        """
+        res = libcudf.datetime.days_in_month(self.series._column)
+        return Series._from_data(
+            ColumnAccessor({None: res}),
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_month_end(self):
+        """
+        Boolean indicator if the date is the last day of the month.
+
+        Returns
+        -------
+        Series
+        Booleans indicating if dates are the last day of the month.
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> s = cudf.Series(
+        ...     pd.date_range(start='2000-08-26', end='2000-09-03', freq='1D'))
+        >>> s
+        0   2000-08-26
+        1   2000-08-27
+        2   2000-08-28
+        3   2000-08-29
+        4   2000-08-30
+        5   2000-08-31
+        6   2000-09-01
+        7   2000-09-02
+        8   2000-09-03
+        dtype: datetime64[ns]
+        >>> s.dt.is_month_end
+        0    False
+        1    False
+        2    False
+        3    False
+        4    False
+        5     True
+        6    False
+        7    False
+        8    False
+        dtype: bool
+        """  # noqa: E501
+        last_day = libcudf.datetime.last_day_of_month(self.series._column)
+        last_day = Series._from_data(
+            ColumnAccessor({None: last_day}),
+            index=self.series._index,
+            name=self.series.name,
+        )
+        return (self.day == last_day.dt.day).fillna(False)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_quarter_start(self):
+        """
+        Boolean indicator if the date is the first day of a quarter.
+
+        Returns
+        -------
+        Series
+        Booleans indicating if dates are the beginning of a quarter
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> s = cudf.Series(
+        ...     pd.date_range(start='2000-09-26', end='2000-10-03', freq='1D'))
+        >>> s
+        0   2000-09-26
+        1   2000-09-27
+        2   2000-09-28
+        3   2000-09-29
+        4   2000-09-30
+        5   2000-10-01
+        6   2000-10-02
+        7   2000-10-03
+        dtype: datetime64[ns]
+        >>> s.dt.is_quarter_start
+        0    False
+        1    False
+        2    False
+        3    False
+        4    False
+        5     True
+        6    False
+        7    False
+        dtype: bool
+        """
+        day = self.series._column.get_dt_field("day")
+        first_month = self.series._column.get_dt_field("month").isin(
+            [1, 4, 7, 10]
+        )
+
+        result = ((day == cudf.Scalar(1)) & first_month).fillna(False)
+        return Series._from_data(
+            {None: result},
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_quarter_end(self):
+        """
+        Boolean indicator if the date is the last day of a quarter.
+
+        Returns
+        -------
+        Series
+        Booleans indicating if dates are the end of a quarter
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> s = cudf.Series(
+        ...     pd.date_range(start='2000-09-26', end='2000-10-03', freq='1D'))
+        >>> s
+        0   2000-09-26
+        1   2000-09-27
+        2   2000-09-28
+        3   2000-09-29
+        4   2000-09-30
+        5   2000-10-01
+        6   2000-10-02
+        7   2000-10-03
+        dtype: datetime64[ns]
+        >>> s.dt.is_quarter_end
+        0    False
+        1    False
+        2    False
+        3    False
+        4     True
+        5    False
+        6    False
+        7    False
+        dtype: bool
+        """
+        day = self.series._column.get_dt_field("day")
+        last_day = libcudf.datetime.last_day_of_month(self.series._column)
+        last_day = last_day.get_dt_field("day")
+        last_month = self.series._column.get_dt_field("month").isin(
+            [3, 6, 9, 12]
+        )
+
+        result = ((day == last_day) & last_month).fillna(False)
+        return Series._from_data(
+            {None: result},
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_year_start(self):
+        """
+        Boolean indicator if the date is the first day of the year.
+
+        Returns
+        -------
+        Series
+        Booleans indicating if dates are the first day of the year.
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> s = cudf.Series(pd.date_range("2017-12-30", periods=3))
+        >>> dates
+        0   2017-12-30
+        1   2017-12-31
+        2   2018-01-01
+        dtype: datetime64[ns]
+        >>> dates.dt.is_year_start
+        0    False
+        1    False
+        2    True
+        dtype: bool
+        """
+        outcol = self.series._column.get_dt_field(
+            "day_of_year"
+        ) == cudf.Scalar(1)
+        return Series._from_data(
+            {None: outcol.fillna(False)},
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_year_end(self):
+        """
+        Boolean indicator if the date is the last day of the year.
+
+        Returns
+        -------
+        Series
+        Booleans indicating if dates are the last day of the year.
+
+        Examples
+        --------
+        >>> import pandas as pd, cudf
+        >>> dates = cudf.Series(pd.date_range("2017-12-30", periods=3))
+        >>> dates
+        0   2017-12-30
+        1   2017-12-31
+        2   2018-01-01
+        dtype: datetime64[ns]
+        >>> dates.dt.is_year_end
+        0    False
+        1     True
+        2    False
+        dtype: bool
+        """
+        day_of_year = self.series._column.get_dt_field("day_of_year")
+        leap_dates = libcudf.datetime.is_leap_year(self.series._column)
+
+        leap = day_of_year == cudf.Scalar(366)
+        non_leap = day_of_year == cudf.Scalar(365)
+        result = cudf._lib.copying.copy_if_else(leap, non_leap, leap_dates)
+        result = result.fillna(False)
+        return Series._from_data(
+            {None: result},
+            index=self.series._index,
+            name=self.series.name,
+        )
+
+    @_cudf_nvtx_annotate
+    def _get_dt_field(self, field):
+        out_column = self.series._column.get_dt_field(field)
+        return Series(
+            data=out_column, index=self.series._index, name=self.series.name
+        )
+
+    @_cudf_nvtx_annotate
+    def ceil(self, freq):
+        """
+        Perform ceil operation on the data to the specified freq.
+
+        Parameters
+        ----------
+        freq : str
+            One of ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"].
+            Must be a fixed frequency like 'S' (second) not 'ME' (month end).
+            See `frequency aliases <https://pandas.pydata.org/docs/\
+                user_guide/timeseries.html#timeseries-offset-aliases>`__
+            for more details on these aliases.
+
+        Returns
+        -------
+        Series
+            Series with all timestamps rounded up to the specified frequency.
+            The index is preserved.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> t = cudf.Series(["2001-01-01 00:04:45", "2001-01-01 00:04:58",
+        ... "2001-01-01 00:05:04"], dtype="datetime64[ns]")
+        >>> t.dt.ceil("T")
+        0   2001-01-01 00:05:00
+        1   2001-01-01 00:05:00
+        2   2001-01-01 00:06:00
+        dtype: datetime64[ns]
+        """
+        out_column = self.series._column.ceil(freq)
+
+        return Series._from_data(
+            data={self.series.name: out_column}, index=self.series._index
+        )
+
+    @_cudf_nvtx_annotate
+    def floor(self, freq):
+        """
+        Perform floor operation on the data to the specified freq.
+
+        Parameters
+        ----------
+        freq : str
+            One of ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"].
+            Must be a fixed frequency like 'S' (second) not 'ME' (month end).
+            See `frequency aliases <https://pandas.pydata.org/docs/\
+                user_guide/timeseries.html#timeseries-offset-aliases>`__
+            for more details on these aliases.
+
+        Returns
+        -------
+        Series
+            Series with all timestamps rounded up to the specified frequency.
+            The index is preserved.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> t = cudf.Series(["2001-01-01 00:04:45", "2001-01-01 00:04:58",
+        ... "2001-01-01 00:05:04"], dtype="datetime64[ns]")
+        >>> t.dt.floor("T")
+        0   2001-01-01 00:04:00
+        1   2001-01-01 00:04:00
+        2   2001-01-01 00:05:00
+        dtype: datetime64[ns]
+        """
+        out_column = self.series._column.floor(freq)
+
+        return Series._from_data(
+            data={self.series.name: out_column}, index=self.series._index
+        )
+
+    @_cudf_nvtx_annotate
+    def round(self, freq):
+        """
+        Perform round operation on the data to the specified freq.
+
+        Parameters
+        ----------
+        freq : str
+            One of ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"].
+            Must be a fixed frequency like 'S' (second) not 'ME' (month end).
+            See `frequency aliases <https://pandas.pydata.org/docs/\
+                user_guide/timeseries.html#timeseries-offset-aliases>`__
+            for more details on these aliases.
+
+        Returns
+        -------
+        Series
+            Series with all timestamps rounded to the specified frequency.
+            The index is preserved.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> dt_sr = cudf.Series([
+        ...     "2001-01-01 00:04:45",
+        ...     "2001-01-01 00:04:58",
+        ...     "2001-01-01 00:05:04",
+        ... ], dtype="datetime64[ns]")
+        >>> dt_sr.dt.round("T")
+        0   2001-01-01 00:05:00
+        1   2001-01-01 00:05:00
+        2   2001-01-01 00:05:00
+        dtype: datetime64[ns]
+        """
+        out_column = self.series._column.round(freq)
+
+        return Series._from_data(
+            data={self.series.name: out_column}, index=self.series._index
+        )
+
+    @_cudf_nvtx_annotate
+    def strftime(self, date_format, *args, **kwargs):
+        """
+        Convert to Series using specified ``date_format``.
+
+        Return a Series of formatted strings specified by ``date_format``,
+        which supports the same string format as the python standard library.
+        Details of the string format can be found in `python string format doc
+        <https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior>`_.
+
+        Parameters
+        ----------
+        date_format : str
+            Date format string (e.g. "%Y-%m-%d").
+
+        Returns
+        -------
+        Series
+            Series of formatted strings.
+
+        Notes
+        -----
+        The following date format identifiers are not yet
+        supported: ``%c``, ``%x``,``%X``
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pandas as pd
+        >>> weekday_series = cudf.Series(pd.date_range("2000-01-01", periods=3,
+        ...      freq="q"))
+        >>> weekday_series.dt.strftime("%Y-%m-%d")
+        >>> weekday_series
+        0   2000-03-31
+        1   2000-06-30
+        2   2000-09-30
+        dtype: datetime64[ns]
+        0    2000-03-31
+        1    2000-06-30
+        2    2000-09-30
+        dtype: object
+        >>> weekday_series.dt.strftime("%Y %d %m")
+        0    2000 31 03
+        1    2000 30 06
+        2    2000 30 09
+        dtype: object
+        >>> weekday_series.dt.strftime("%Y / %d / %m")
+        0    2000 / 31 / 03
+        1    2000 / 30 / 06
+        2    2000 / 30 / 09
+        dtype: object
+        """
+
+        if not isinstance(date_format, str):
+            raise TypeError(
+                f"'date_format' must be str, not {type(date_format)}"
+            )
+
+        # TODO: Remove following validations
+        # once https://github.com/rapidsai/cudf/issues/5991
+        # is implemented
+        not_implemented_formats = {
+            "%c",
+            "%x",
+            "%X",
+        }
+        for d_format in not_implemented_formats:
+            if d_format in date_format:
+                raise NotImplementedError(
+                    f"{d_format} date-time format is not "
+                    f"supported yet, Please follow this issue "
+                    f"https://github.com/rapidsai/cudf/issues/5991 "
+                    f"for tracking purposes."
+                )
+        str_col = self.series._column.as_string_column(
+            dtype="str", format=date_format
+        )
+        return Series(
+            data=str_col, index=self.series._index, name=self.series.name
+        )
+
+    @copy_docstring(DatetimeIndex.tz_localize)
+    def tz_localize(self, tz, ambiguous="NaT", nonexistent="NaT"):
+        from cudf.core._internals.timezones import delocalize, localize
+
+        if tz is None:
+            result_col = delocalize(self.series._column)
+        else:
+            result_col = localize(
+                self.series._column, tz, ambiguous, nonexistent
+            )
+        return Series._from_data(
+            data={self.series.name: result_col},
+            index=self.series._index,
+        )
+
+    @copy_docstring(DatetimeIndex.tz_convert)
+    def tz_convert(self, tz):
+        """
+        Parameters
+        ----------
+        tz : str
+            Time zone for time. Corresponding timestamps would be converted
+            to this time zone of the Datetime Array/Index.
+            A `tz` of None will convert to UTC and remove the
+            timezone information.
+        """
+        from cudf.core._internals.timezones import convert
+
+        if tz is None:
+            result_col = self.series._column._utc_time
+        else:
+            result_col = convert(self.series._column, tz)
+        return Series._from_data(
+            {self.series.name: result_col}, index=self.series._index
+        )
+
+
+class TimedeltaProperties:
+    """
+    Accessor object for timedelta-like properties of the Series values.
+
+    Returns
+    -------
+    Returns a Series indexed like the original Series.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> seconds_series = cudf.Series([1, 2, 3], dtype='timedelta64[s]')
+    >>> seconds_series
+    0    00:00:01
+    1    00:00:02
+    2    00:00:03
+    dtype: timedelta64[s]
+    >>> seconds_series.dt.seconds
+    0    1
+    1    2
+    2    3
+    dtype: int64
+    >>> series = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656,
+    ...     3244334234], dtype='timedelta64[ms]')
+    >>> series
+    0      141 days 13:35:12.123
+    1       14 days 06:00:31.231
+    2    13000 days 10:12:48.712
+    3        0 days 00:35:35.656
+    4       37 days 13:12:14.234
+    dtype: timedelta64[ms]
+    >>> series.dt.components
+        days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+    0    141     13       35       12           123             0            0
+    1     14      6        0       31           231             0            0
+    2  13000     10       12       48           712             0            0
+    3      0      0       35       35           656             0            0
+    4     37     13       12       14           234             0            0
+    >>> series.dt.days
+    0      141
+    1       14
+    2    13000
+    3        0
+    4       37
+    dtype: int64
+    >>> series.dt.seconds
+    0    48912
+    1    21631
+    2    36768
+    3     2135
+    4    47534
+    dtype: int64
+    >>> series.dt.microseconds
+    0    123000
+    1    231000
+    2    712000
+    3    656000
+    4    234000
+    dtype: int64
+    >>> s.dt.nanoseconds
+    0    0
+    1    0
+    2    0
+    3    0
+    4    0
+    dtype: int64
+    """
+
+    def __init__(self, series):
+        self.series = series
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def days(self):
+        """
+        Number of days.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656,
+        ...     3244334234], dtype='timedelta64[ms]')
+        >>> s
+        0      141 days 13:35:12.123
+        1       14 days 06:00:31.231
+        2    13000 days 10:12:48.712
+        3        0 days 00:35:35.656
+        4       37 days 13:12:14.234
+        dtype: timedelta64[ms]
+        >>> s.dt.days
+        0      141
+        1       14
+        2    13000
+        3        0
+        4       37
+        dtype: int64
+        """
+        return self._get_td_field("days")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def seconds(self):
+        """
+        Number of seconds (>= 0 and less than 1 day).
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656,
+        ...     3244334234], dtype='timedelta64[ms]')
+        >>> s
+        0      141 days 13:35:12.123
+        1       14 days 06:00:31.231
+        2    13000 days 10:12:48.712
+        3        0 days 00:35:35.656
+        4       37 days 13:12:14.234
+        dtype: timedelta64[ms]
+        >>> s.dt.seconds
+        0    48912
+        1    21631
+        2    36768
+        3     2135
+        4    47534
+        dtype: int64
+        >>> s.dt.microseconds
+        0    123000
+        1    231000
+        2    712000
+        3    656000
+        4    234000
+        dtype: int64
+        """
+        return self._get_td_field("seconds")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def microseconds(self):
+        """
+        Number of microseconds (>= 0 and less than 1 second).
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656,
+        ...     3244334234], dtype='timedelta64[ms]')
+        >>> s
+        0      141 days 13:35:12.123
+        1       14 days 06:00:31.231
+        2    13000 days 10:12:48.712
+        3        0 days 00:35:35.656
+        4       37 days 13:12:14.234
+        dtype: timedelta64[ms]
+        >>> s.dt.microseconds
+        0    123000
+        1    231000
+        2    712000
+        3    656000
+        4    234000
+        dtype: int64
+        """
+        return self._get_td_field("microseconds")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def nanoseconds(self):
+        """
+        Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656,
+        ...     3244334234], dtype='timedelta64[ns]')
+        >>> s
+        0    00:00:12.231312123
+        1    00:00:01.231231231
+        2    00:18:43.236768712
+        3    00:00:00.002135656
+        4    00:00:03.244334234
+        dtype: timedelta64[ns]
+        >>> s.dt.nanoseconds
+        0    123
+        1    231
+        2    712
+        3    656
+        4    234
+        dtype: int64
+        """
+        return self._get_td_field("nanoseconds")
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def components(self):
+        """
+        Return a Dataframe of the components of the Timedeltas.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> s = cudf.Series([12231312123, 1231231231, 1123236768712, 2135656, 3244334234], dtype='timedelta64[ms]')
+        >>> s
+        0      141 days 13:35:12.123
+        1       14 days 06:00:31.231
+        2    13000 days 10:12:48.712
+        3        0 days 00:35:35.656
+        4       37 days 13:12:14.234
+        dtype: timedelta64[ms]
+        >>> s.dt.components
+            days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0    141     13       35       12           123             0            0
+        1     14      6        0       31           231             0            0
+        2  13000     10       12       48           712             0            0
+        3      0      0       35       35           656             0            0
+        4     37     13       12       14           234             0            0
+        """  # noqa: E501
+        return self.series._column.components(index=self.series._index)
+
+    @_cudf_nvtx_annotate
+    def _get_td_field(self, field):
+        out_column = getattr(self.series._column, field)
+        return Series(
+            data=out_column, index=self.series._index, name=self.series.name
+        )
+
+
+@_cudf_nvtx_annotate
+def _align_indices(series_list, how="outer", allow_non_unique=False):
+    """
+    Internal util to align the indices of a list of Series objects
+
+    series_list : list of Series objects
+    how : {"outer", "inner"}
+        If "outer", the values of the resulting index are the
+        unique values of the index obtained by concatenating
+        the indices of all the series.
+        If "inner", the values of the resulting index are
+        the values common to the indices of all series.
+    allow_non_unique : bool
+        Whether or not to allow non-unique valued indices in the input
+        series.
+    """
+    if len(series_list) <= 1:
+        return series_list
+
+    # check if all indices are the same
+    head = series_list[0].index
+
+    all_index_equal = True
+    for sr in series_list[1:]:
+        if not sr.index.equals(head):
+            all_index_equal = False
+            break
+
+    # check if all names are the same
+    all_names_equal = True
+    for sr in series_list[1:]:
+        if not sr.index.names == head.names:
+            all_names_equal = False
+    new_index_names = [None] * head.nlevels
+    if all_names_equal:
+        new_index_names = head.names
+
+    if all_index_equal:
+        return series_list
+
+    combined_index = series_list[0].index
+    for sr in series_list[1:]:
+        combined_index = (
+            cudf.DataFrame(index=sr.index).join(
+                cudf.DataFrame(index=combined_index),
+                sort=True,
+                how=how,
+            )
+        ).index
+    combined_index.names = new_index_names
+
+    # align all Series to the combined index
+    result = [
+        sr._align_to_index(
+            combined_index, how=how, allow_non_unique=allow_non_unique
+        )
+        for sr in series_list
+    ]
+
+    return result
+
+
+@acquire_spill_lock()
+@_cudf_nvtx_annotate
+def isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
+    r"""Returns a boolean array where two arrays are equal within a tolerance.
+
+    Two values in ``a`` and ``b`` are  considered equal when the following
+    equation is satisfied.
+
+    .. math::
+       |a - b| \le \mathrm{atol} + \mathrm{rtol} |b|
+
+    Parameters
+    ----------
+    a : list-like, array-like or cudf.Series
+        Input sequence to compare.
+    b : list-like, array-like or cudf.Series
+        Input sequence to compare.
+    rtol : float
+        The relative tolerance.
+    atol : float
+        The absolute tolerance.
+    equal_nan : bool
+        If ``True``, null's in ``a`` will be considered equal
+        to null's in ``b``.
+
+    Returns
+    -------
+    Series
+
+    See Also
+    --------
+    np.isclose : Returns a boolean array where two arrays are element-wise
+        equal within a tolerance.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> s1 = cudf.Series([1.9876543,   2.9876654,   3.9876543, None, 9.9, 1.0])
+    >>> s2 = cudf.Series([1.987654321, 2.987654321, 3.987654321, None, 19.9,
+    ... None])
+    >>> s1
+    0    1.9876543
+    1    2.9876654
+    2    3.9876543
+    3         <NA>
+    4          9.9
+    5          1.0
+    dtype: float64
+    >>> s2
+    0    1.987654321
+    1    2.987654321
+    2    3.987654321
+    3           <NA>
+    4           19.9
+    5           <NA>
+    dtype: float64
+    >>> cudf.isclose(s1, s2)
+    0     True
+    1     True
+    2     True
+    3    False
+    4    False
+    5    False
+    dtype: bool
+    >>> cudf.isclose(s1, s2, equal_nan=True)
+    0     True
+    1     True
+    2     True
+    3     True
+    4    False
+    5    False
+    dtype: bool
+    >>> cudf.isclose(s1, s2, equal_nan=False)
+    0     True
+    1     True
+    2     True
+    3    False
+    4    False
+    5    False
+    dtype: bool
+    """
+
+    if not can_convert_to_column(a):
+        raise TypeError(
+            f"Parameter `a` is expected to be a "
+            f"list-like or Series object, found:{type(a)}"
+        )
+    if not can_convert_to_column(b):
+        raise TypeError(
+            f"Parameter `b` is expected to be a "
+            f"list-like or Series object, found:{type(a)}"
+        )
+
+    if isinstance(a, pd.Series):
+        a = Series.from_pandas(a)
+    if isinstance(b, pd.Series):
+        b = Series.from_pandas(b)
+
+    index = None
+
+    if isinstance(a, cudf.Series) and isinstance(b, cudf.Series):
+        b = b.reindex(a.index)
+        index = as_index(a.index)
+
+    a_col = column.as_column(a)
+    a_array = cupy.asarray(a_col.data_array_view(mode="read"))
+
+    b_col = column.as_column(b)
+    b_array = cupy.asarray(b_col.data_array_view(mode="read"))
+
+    result = cupy.isclose(
+        a=a_array, b=b_array, rtol=rtol, atol=atol, equal_nan=equal_nan
+    )
+    result_col = column.as_column(result)
+
+    if a_col.null_count and b_col.null_count:
+        a_nulls = a_col.isnull()
+        b_nulls = b_col.isnull()
+        null_values = a_nulls | b_nulls
+
+        if equal_nan is True:
+            equal_nulls = a_nulls & b_nulls
+
+        del a_nulls, b_nulls
+    elif a_col.null_count:
+        null_values = a_col.isnull()
+    elif b_col.null_count:
+        null_values = b_col.isnull()
+    else:
+        return Series(result_col, index=index)
+
+    result_col[null_values] = False
+    if equal_nan is True and a_col.null_count and b_col.null_count:
+        result_col[equal_nulls] = True
+
+    return Series(result_col, index=index)
diff --git a/python/cudf/cudf/core/single_column_frame.py b/python/cudf/cudf/core/single_column_frame.py
new file mode 100644
index 0000000..d35762c
--- /dev/null
+++ b/python/cudf/cudf/core/single_column_frame.py
@@ -0,0 +1,438 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+"""Base class for Frame types that only have a single column."""
+
+from __future__ import annotations
+
+import warnings
+from typing import Any, Dict, Optional, Tuple, Union
+
+import cupy
+import numpy
+
+import cudf
+from cudf._typing import Dtype, NotImplementedType, ScalarLike
+from cudf.api.extensions import no_default
+from cudf.api.types import (
+    _is_scalar_or_zero_d_array,
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+)
+from cudf.core.column import ColumnBase, as_column
+from cudf.core.frame import Frame
+from cudf.utils.utils import NotIterable, _cudf_nvtx_annotate
+
+
+class SingleColumnFrame(Frame, NotIterable):
+    """A one-dimensional frame.
+
+    Frames with only a single column share certain logic that is encoded in
+    this class.
+    """
+
+    _SUPPORT_AXIS_LOOKUP = {
+        0: 0,
+        "index": 0,
+    }
+
+    @_cudf_nvtx_annotate
+    def _reduce(
+        self,
+        op,
+        axis=no_default,
+        level=None,
+        numeric_only=None,
+        **kwargs,
+    ):
+        if axis not in (None, 0, no_default):
+            raise NotImplementedError("axis parameter is not implemented yet")
+
+        if level is not None:
+            raise NotImplementedError("level parameter is not implemented yet")
+
+        if numeric_only and not isinstance(
+            self._column, cudf.core.column.numerical_base.NumericalBaseColumn
+        ):
+            raise NotImplementedError(
+                f"Series.{op} does not implement numeric_only."
+            )
+        try:
+            return getattr(self._column, op)(**kwargs)
+        except AttributeError:
+            raise TypeError(f"cannot perform {op} with type {self.dtype}")
+
+    @_cudf_nvtx_annotate
+    def _scan(self, op, axis=None, *args, **kwargs):
+        if axis not in (None, 0):
+            raise NotImplementedError("axis parameter is not implemented yet")
+
+        return super()._scan(op, axis=axis, *args, **kwargs)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def name(self):
+        """Get the name of this object."""
+        return next(iter(self._data.names))
+
+    @name.setter  # type: ignore
+    @_cudf_nvtx_annotate
+    def name(self, value):
+        self._data[value] = self._data.pop(self.name)
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def ndim(self):  # noqa: D401
+        """Number of dimensions of the underlying data, by definition 1."""
+        return 1
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def shape(self):
+        """Get a tuple representing the dimensionality of the Index."""
+        return (len(self),)
+
+    def __bool__(self):
+        raise TypeError(
+            f"The truth value of a {type(self)} is ambiguous. Use "
+            "a.empty, a.bool(), a.item(), a.any() or a.all()."
+        )
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _num_columns(self):
+        return 1
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def _column(self):
+        return self._data[self.name]
+
+    @_column.setter  # type: ignore
+    @_cudf_nvtx_annotate
+    def _column(self, value):
+        self._data[self.name] = value
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def values(self):  # noqa: D102
+        return self._column.values
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def values_host(self):  # noqa: D102
+        return self._column.values_host
+
+    @_cudf_nvtx_annotate
+    def to_cupy(
+        self,
+        dtype: Union[Dtype, None] = None,
+        copy: bool = True,
+        na_value=None,
+    ) -> cupy.ndarray:  # noqa: D102
+        return super().to_cupy(dtype, copy, na_value).flatten()
+
+    @_cudf_nvtx_annotate
+    def to_numpy(
+        self,
+        dtype: Union[Dtype, None] = None,
+        copy: bool = True,
+        na_value=None,
+    ) -> numpy.ndarray:  # noqa: D102
+        return super().to_numpy(dtype, copy, na_value).flatten()
+
+    @classmethod
+    @_cudf_nvtx_annotate
+    def from_arrow(cls, array):
+        """Create from PyArrow Array/ChunkedArray.
+
+        Parameters
+        ----------
+        array : PyArrow Array/ChunkedArray
+            PyArrow Object which has to be converted.
+
+        Raises
+        ------
+        TypeError for invalid input type.
+
+        Returns
+        -------
+        SingleColumnFrame
+
+        Examples
+        --------
+        >>> import cudf
+        >>> import pyarrow as pa
+        >>> cudf.Index.from_arrow(pa.array(["a", "b", None]))
+        StringIndex(['a' 'b' None], dtype='object')
+        >>> cudf.Series.from_arrow(pa.array(["a", "b", None]))
+        0       a
+        1       b
+        2    <NA>
+        dtype: object
+        """
+        return cls(ColumnBase.from_arrow(array))
+
+    @_cudf_nvtx_annotate
+    def to_arrow(self):
+        """
+        Convert to a PyArrow Array.
+
+        Returns
+        -------
+        PyArrow Array
+
+        Examples
+        --------
+        >>> import cudf
+        >>> sr = cudf.Series(["a", "b", None])
+        >>> sr.to_arrow()
+        <pyarrow.lib.StringArray object at 0x7f796b0e7600>
+        [
+          "a",
+          "b",
+          null
+        ]
+        >>> ind = cudf.Index(["a", "b", None])
+        >>> ind.to_arrow()
+        <pyarrow.lib.StringArray object at 0x7f796b0e7750>
+        [
+          "a",
+          "b",
+          null
+        ]
+        """
+        return self._column.to_arrow()
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic(self):
+        """Return boolean if values in the object are monotonically increasing.
+
+        This property is an alias for :attr:`is_monotonic_increasing`.
+
+        Returns
+        -------
+        bool
+        """
+        # Do not remove until pandas 2.0 support is added.
+        warnings.warn(
+            "is_monotonic is deprecated and will be removed in a future "
+            "version. Use is_monotonic_increasing instead.",
+            FutureWarning,
+        )
+
+        return self.is_monotonic_increasing
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic_increasing(self):
+        """Return boolean if values in the object are monotonically increasing.
+
+        Returns
+        -------
+        bool
+        """
+        return self._column.is_monotonic_increasing
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def is_monotonic_decreasing(self):
+        """Return boolean if values in the object are monotonically decreasing.
+
+        Returns
+        -------
+        bool
+        """
+        return self._column.is_monotonic_decreasing
+
+    @property  # type: ignore
+    @_cudf_nvtx_annotate
+    def __cuda_array_interface__(self):
+        # While the parent column class has a `__cuda_array_interface__` method
+        # defined, it is not implemented for all column types. When it is not
+        # implemented, though, at the Frame level we really want to throw an
+        # AttributeError.
+        try:
+            return self._column.__cuda_array_interface__
+        except NotImplementedError:
+            raise AttributeError
+
+    @_cudf_nvtx_annotate
+    def factorize(self, sort=False, na_sentinel=None, use_na_sentinel=None):
+        """Encode the input values as integer labels.
+
+        Parameters
+        ----------
+        sort : bool, default True
+            Sort uniques and shuffle codes to maintain the relationship.
+        na_sentinel : number, default -1
+            Value to indicate missing category.
+
+            .. deprecated:: 23.04
+
+               The na_sentinel argument is deprecated and will be removed in
+               a future version of cudf. Specify use_na_sentinel as
+               either True or False.
+        use_na_sentinel : bool, default True
+            If True, the sentinel -1 will be used for NA values.
+            If False, NA values will be encoded as non-negative
+            integers and will not drop the NA from the uniques
+            of the values.
+
+        Returns
+        -------
+        (labels, cats) : (cupy.ndarray, cupy.ndarray or Index)
+            - *labels* contains the encoded values
+            - *cats* contains the categories in order that the N-th
+              item corresponds to the (N-1) code.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> s = cudf.Series(['a', 'a', 'c'])
+        >>> codes, uniques = s.factorize()
+        >>> codes
+        array([0, 0, 1], dtype=int8)
+        >>> uniques
+        StringIndex(['a' 'c'], dtype='object')
+        """
+        return cudf.core.algorithms.factorize(
+            self,
+            sort=sort,
+            na_sentinel=na_sentinel,
+            use_na_sentinel=use_na_sentinel,
+        )
+
+    @_cudf_nvtx_annotate
+    def _make_operands_for_binop(
+        self,
+        other: Any,
+        fill_value: Any = None,
+        reflect: bool = False,
+        *args,
+        **kwargs,
+    ) -> Union[
+        Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
+        NotImplementedType,
+    ]:
+        """Generate the dictionary of operands used for a binary operation.
+
+        Parameters
+        ----------
+        other : SingleColumnFrame
+            The second operand.
+        fill_value : Any, default None
+            The value to replace null values with. If ``None``, nulls are not
+            filled before the operation.
+        reflect : bool, default False
+            If ``True``, swap the order of the operands. See
+            https://docs.python.org/3/reference/datamodel.html#object.__ror__
+            for more information on when this is necessary.
+
+        Returns
+        -------
+        Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]]
+            The operands to be passed to _colwise_binop.
+        """
+        # Get the appropriate name for output operations involving two objects
+        # that are Series-like objects. The output shares the lhs's name unless
+        # the rhs is a _differently_ named Series-like object.
+        if isinstance(
+            other, SingleColumnFrame
+        ) and not cudf.utils.utils._is_same_name(self.name, other.name):
+            result_name = None
+        else:
+            result_name = self.name
+
+        if isinstance(other, SingleColumnFrame):
+            other = other._column
+        elif not _is_scalar_or_zero_d_array(other):
+            if not hasattr(
+                other, "__cuda_array_interface__"
+            ) and not isinstance(other, cudf.RangeIndex):
+                return NotImplemented
+
+            # Non-scalar right operands are valid iff they convert to columns.
+            try:
+                other = as_column(other)
+            except Exception:
+                return NotImplemented
+
+        return {result_name: (self._column, other, reflect, fill_value)}
+
+    @_cudf_nvtx_annotate
+    def nunique(self, dropna: bool = True):
+        """
+        Return count of unique values for the column.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include NaN in the counts.
+
+        Returns
+        -------
+        int
+            Number of unique values in the column.
+        """
+        if self._column.null_count == len(self):
+            return 0
+        return self._column.distinct_count(dropna=dropna)
+
+    def _get_elements_from_column(self, arg) -> Union[ScalarLike, ColumnBase]:
+        # A generic method for getting elements from a column that supports a
+        # wide range of different inputs. This method should only used where
+        # _absolutely_ necessary, since in almost all cases a more specific
+        # method can be used e.g. element_indexing or slice.
+        if _is_scalar_or_zero_d_array(arg):
+            if not is_integer(arg):
+                raise ValueError(
+                    "Can only select elements with an integer, "
+                    f"not a {type(arg).__name__}"
+                )
+            return self._column.element_indexing(int(arg))
+        elif isinstance(arg, slice):
+            start, stop, stride = arg.indices(len(self))
+            return self._column.slice(start, stop, stride)
+        else:
+            arg = as_column(arg)
+            if len(arg) == 0:
+                arg = as_column([], dtype="int32")
+            if is_integer_dtype(arg.dtype):
+                return self._column.take(arg)
+            if is_bool_dtype(arg.dtype):
+                if (bn := len(arg)) != (n := len(self)):
+                    raise IndexError(
+                        f"Boolean mask has wrong length: {bn} not {n}"
+                    )
+                return self._column.apply_boolean_mask(arg)
+            raise NotImplementedError(f"Unknown indexer {type(arg)}")
+
+    @_cudf_nvtx_annotate
+    def where(self, cond, other=None, inplace=False):
+        from cudf.core._internals.where import (
+            _check_and_cast_columns_with_other,
+            _make_categorical_like,
+        )
+
+        if isinstance(other, cudf.DataFrame):
+            raise NotImplementedError(
+                "cannot align with a higher dimensional Frame"
+            )
+        cond = as_column(cond)
+        if len(cond) != len(self):
+            raise ValueError(
+                """Array conditional must be same shape as self"""
+            )
+
+        if not cudf.api.types.is_scalar(other):
+            other = cudf.core.column.as_column(other)
+
+        self_column = self._column
+        input_col, other = _check_and_cast_columns_with_other(
+            source_col=self_column, other=other, inplace=inplace
+        )
+
+        result = cudf._lib.copying.copy_if_else(input_col, other, cond)
+
+        return _make_categorical_like(result, self_column)
diff --git a/python/cudf/cudf/core/subword_tokenizer.py b/python/cudf/cudf/core/subword_tokenizer.py
new file mode 100644
index 0000000..821afa2
--- /dev/null
+++ b/python/cudf/cudf/core/subword_tokenizer.py
@@ -0,0 +1,298 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import warnings
+from typing import Union
+
+import cupy as cp
+
+from cudf._lib.nvtext.subword_tokenize import (
+    Hashed_Vocabulary as cpp_hashed_vocabulary,
+    subword_tokenize_inmem_hash as cpp_subword_tokenize,
+)
+
+
+def _cast_to_appropriate_type(ar, cast_type):
+    if cast_type == "cp":
+        return ar
+
+    if cast_type == "pt":
+        from torch.utils.dlpack import from_dlpack
+
+    elif cast_type == "tf":
+        from tensorflow.experimental.dlpack import from_dlpack
+
+    return from_dlpack(ar.astype("int32").toDlpack())
+
+
+class SubwordTokenizer:
+    """
+    Run CUDA BERT subword tokenizer on cuDF strings column.
+    Encodes words to token ids using vocabulary from a pretrained
+    tokenizer.
+    This function requires about 21x the number of character bytes
+    in the input strings column as working memory.
+
+    Parameters
+    ----------
+    hash_file : str
+        Path to hash file containing vocabulary of words with token-ids.
+        This can be created from the raw vocabulary
+        using the ``cudf.utils.hash_vocab_utils.hash_vocab`` function
+
+    do_lower : bool, Default is True
+        If set to True, original text will be lowercased before encoding.
+
+    Returns
+    -------
+    SubwordTokenizer
+    """
+
+    def __init__(self, hash_file: str, do_lower_case: bool = True):
+
+        self.do_lower_case = do_lower_case
+        self.vocab_file = cpp_hashed_vocabulary(hash_file)
+
+    def __call__(
+        self,
+        text,
+        max_length: int,
+        max_num_rows: int,
+        add_special_tokens: bool = True,
+        padding: str = "max_length",
+        truncation: Union[bool, str] = False,
+        stride: int = 0,
+        return_tensors: str = "cp",
+        return_token_type_ids: bool = False,
+    ):
+        """
+        Run CUDA BERT subword tokenizer on cuDF strings column.
+        Encodes words to token ids using vocabulary from a
+        pretrained tokenizer.
+
+        Parameters
+        ----------
+        text : cudf string series
+            The batch of sequences to be encoded.
+
+        max_length : int
+            Controls the maximum length to use or pad to.
+
+        max_num_rows : int
+            Maximum number of rows for the output token-ids expected to
+            be generated by the tokenizer.
+            Used for allocating temporary working memory on the GPU device.
+            If the output generates a larger number of rows,
+            behavior is undefined.
+            This will vary based on stride, truncation, and max_length.
+            For example, for non-overlapping sequences output rows will be
+            the same as input rows.
+            A good default can be twice the max_length
+
+        add_special_tokens : bool, optional, defaults to True
+            Whether or not to encode the sequences with the special tokens
+            of the BERT classification model
+
+        padding : "max_length"
+            Pad to a maximum length specified with the argument max_length
+
+        truncation : bool, defaults to False
+            True:
+            Truncate to a maximum length specified with the argument max_length
+            False or 'do_not_truncate': default
+            No truncation (Output differs from HuggingFace)
+
+        stride : int, optional, defaults to 0
+            The value of this argument defines the number of
+            overlapping tokens.
+            The information about the overlapping tokens is
+            present in the metadata outputted.
+
+        return_tensors : str, {"cp", "pt", "tf"} defaults to "cp"
+            "cp" : Return cupy cp.ndarray objects
+            "tf" : Return TensorFlow tf.constant objects
+            "pt" : Return PyTorch torch.Tensor objects
+
+
+        return_token_type_ids : bool, optional
+            Only False currently supported
+
+        Returns
+        -------
+        An encoding with the following fields:
+            input_ids:(type defined by return_tensors)
+                A tensor of token ids to be fed to the model.
+            attention_mask: (type defined by return_tensors)
+                A tensor of indices specifying which tokens
+                should be attended to by the model
+            metadata: (type defined by return_tensors)
+                Each row contains the index id of the original string and the
+                first and last index of the token-ids that are non-padded and
+                non-overlapping
+
+        Examples
+        --------
+        >>> import cudf
+        >>> from cudf.utils.hash_vocab_utils import hash_vocab
+        >>> hash_vocab('bert-base-cased-vocab.txt', 'voc_hash.txt')
+
+
+        >>> from cudf.core.subword_tokenizer import SubwordTokenizer
+        >>> cudf_tokenizer = SubwordTokenizer('voc_hash.txt',
+        ...                                    do_lower_case=True)
+        >>> str_series = cudf.Series(['This is the', 'best book'])
+        >>> tokenizer_output = cudf_tokenizer(str_series,
+        ...                                   max_length=8,
+        ...                                   max_num_rows=len(str_series),
+        ...                                   padding='max_length',
+        ...                                   return_tensors='pt',
+        ...                                   truncation=True)
+        >>> tokenizer_output['input_ids']
+        tensor([[ 101, 1142, 1110, 1103,  102,    0,    0,    0],
+                [ 101, 1436, 1520,  102,    0,    0,    0,    0]],
+                device='cuda:0',
+               dtype=torch.int32)
+        >>> tokenizer_output['attention_mask']
+        tensor([[1, 1, 1, 1, 1, 0, 0, 0],
+                [1, 1, 1, 1, 0, 0, 0, 0]],
+                device='cuda:0', dtype=torch.int32)
+        >>> tokenizer_output['metadata']
+        tensor([[0, 1, 3],
+                [1, 1, 2]], device='cuda:0', dtype=torch.int32)
+        """
+
+        if return_token_type_ids:
+            # raise not currently supported
+            # Can also return zeros
+            error_msg = "Returning token_type_ids is currently supported"
+            raise NotImplementedError(error_msg)
+
+        if truncation in (False, "do_not_truncate"):
+            if add_special_tokens:
+                error_msg = (
+                    "Adding special tokens is not supported "
+                    f"with truncation = {truncation}. "
+                )
+                recommendation = (
+                    "Custom Cupy kernel can potentially "
+                    "be used to add it. For reference "
+                    "see: _bert_add_special_tokens"
+                )
+                raise NotImplementedError(error_msg + recommendation)
+
+            truncation = False
+            warning_msg = (
+                "When truncation is not True, the behavior currently differs "
+                "from HuggingFace as cudf always returns overflowing tokens"
+            )
+            warnings.warn(warning_msg)
+
+        if padding != "max_length":
+            error_msg = (
+                "Only padding to the provided max_length"
+                "is currently supported"
+            )
+            raise NotImplementedError(error_msg)
+
+        if max_length <= stride:
+            error_msg = "Stride should be less than max_length"
+            raise ValueError(error_msg)
+
+        if return_tensors not in {"cp", "pt", "tf"}:
+            error_msg = (
+                "Only cupy(cp), pytorch(pt) and tensorflow(tf) "
+                "tensors are supported"
+            )
+            raise NotImplementedError(error_msg)
+
+        stride = max_length - stride
+        # behavior varies from subword_tokenize but maps with huggingface
+
+        input_ids, attention_mask, metadata = cpp_subword_tokenize(
+            text._column,
+            self.vocab_file,
+            max_sequence_length=max_length,
+            stride=stride,
+            do_lower=self.do_lower_case,
+            do_truncate=truncation,
+        )
+
+        tokenizer_output = {
+            "input_ids": cp.asarray(input_ids).reshape(-1, max_length),
+            "attention_mask": cp.asarray(attention_mask).reshape(
+                -1, max_length
+            ),
+            "metadata": cp.asarray(metadata).reshape(-1, 3),
+        }
+
+        if add_special_tokens:
+            tokenizer_output = _bert_add_special_tokens(tokenizer_output)
+
+        tokenizer_output = {
+            k: _cast_to_appropriate_type(v, return_tensors)
+            for k, v in tokenizer_output.items()
+        }
+
+        return tokenizer_output
+
+
+def _bert_add_special_tokens(token_o):
+    """
+    Adds special tokens (CLS,SEP) which are often used by pre-trained BERT
+    models to input_ids and adjusts attention_mask and metadata to account
+    for them.
+    """
+    max_length = token_o["input_ids"].shape[1]
+    seq_end_col = max_length - (token_o["input_ids"][:, ::-1] != 0).argmax(1)
+    # clipping to take overflow into account
+    seq_end_col = cp.clip(seq_end_col + 1, a_min=None, a_max=max_length - 1)
+
+    _bert_add_special_tokens_input_ids(token_o["input_ids"], seq_end_col)
+    _bert_add_special_tokens_attention_mask(
+        token_o["attention_mask"], seq_end_col
+    )
+    _bert_add_special_tokens_metadata(token_o["metadata"], max_length)
+
+    return token_o
+
+
+def _bert_add_special_tokens_input_ids(input_ids, seq_end_col):
+    """
+    Add token ids for special tokens ([CLS] and [SEP]) to
+    the start and end of each sequence
+    """
+    # Mark sequence start with [CLS] token mapping to the start of sequence
+    input_ids[:, 1:-1] = input_ids[:, 0:-2]
+    input_ids[:, 0] = 101
+    # Mark end of sequence [SEP]
+
+    input_ids[
+        cp.arange(0, input_ids.shape[0], dtype=cp.uint32), seq_end_col
+    ] = 102
+
+
+def _bert_add_special_tokens_attention_mask(attention_mask, seq_end_col):
+    """
+    Mark attention mask for special tokens ([CLS] and [SEP]) with 1
+    """
+    # Copy attention masks for all but last two
+    attention_mask[:, 1:-1] = attention_mask[:, 0:-2]
+    # Mark [CLS] token with 1
+    attention_mask[:, 0] = 1
+    # Mark [SEP] token with 1
+    attention_mask[
+        cp.arange(0, attention_mask.shape[0], dtype=cp.uint32), seq_end_col
+    ] = 1
+
+
+def _bert_add_special_tokens_metadata(metadata, max_length):
+    """
+    Edit metadata to account for the added special tokens ([CLS] and [SEP])
+    """
+    # metadata seq starts from plus 1
+    metadata[:, 1] = metadata[:, 1] + 1
+    # clip done to take overflow into account
+    metadata[:, 2] = cp.clip(
+        metadata[:, 2] + 1, a_min=None, a_max=max_length - 2
+    )
diff --git a/python/cudf/cudf/core/tokenize_vocabulary.py b/python/cudf/cudf/core/tokenize_vocabulary.py
new file mode 100644
index 0000000..afb3496
--- /dev/null
+++ b/python/cudf/cudf/core/tokenize_vocabulary.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+import cudf
+from cudf._lib.nvtext.tokenize import (
+    TokenizeVocabulary as cpp_tokenize_vocabulary,
+    tokenize_with_vocabulary as cpp_tokenize_with_vocabulary,
+)
+
+
+class TokenizeVocabulary:
+    """
+    A vocabulary object used to tokenize input text.
+
+    Parameters
+    ----------
+    vocabulary : str
+        Strings column of vocabulary terms
+    """
+
+    def __init__(self, vocabulary: "cudf.Series"):
+        self.vocabulary = cpp_tokenize_vocabulary(vocabulary._column)
+
+    def tokenize(self, text, delimiter: str = "", default_id: int = -1):
+        """
+        Parameters
+        ----------
+        text : cudf string series
+            The strings to be tokenized.
+        delimiter : str
+            Delimiter to identify tokens. Default is whitespace.
+        default_id : int
+            Value to use for tokens not found in the vocabulary.
+            Default is -1.
+
+        Returns
+        -------
+        Tokenized strings
+        """
+        if delimiter is None:
+            delimiter = ""
+        delim = cudf.Scalar(delimiter, dtype="str")
+        result = cpp_tokenize_with_vocabulary(
+            text._column, self.vocabulary, delim, default_id
+        )
+
+        return cudf.Series(result)
diff --git a/python/cudf/cudf/core/tools/__init__.py b/python/cudf/cudf/core/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/core/tools/datetimes.py b/python/cudf/cudf/core/tools/datetimes.py
new file mode 100644
index 0000000..14b27e1
--- /dev/null
+++ b/python/cudf/cudf/core/tools/datetimes.py
@@ -0,0 +1,1022 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import math
+import re
+import warnings
+from typing import Sequence, Union
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pandas.tseries.offsets as pd_offset
+from pandas.core.tools.datetimes import _unit_map
+from typing_extensions import Self
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib.strings.convert.convert_integers import (
+    is_integer as cpp_is_integer,
+)
+from cudf.api.types import is_integer, is_scalar
+from cudf.core import column
+from cudf.core.index import as_index
+
+_unit_dtype_map = {
+    "ns": "datetime64[ns]",
+    "us": "datetime64[us]",
+    "ms": "datetime64[ms]",
+    "m": "datetime64[s]",
+    "h": "datetime64[s]",
+    "s": "datetime64[s]",
+    "D": "datetime64[s]",
+}
+
+_offset_alias_to_code = {
+    "W": "W",
+    "D": "D",
+    "H": "h",
+    "h": "h",
+    "T": "m",
+    "min": "m",
+    "s": "s",
+    "S": "s",
+    "U": "us",
+    "us": "us",
+    "N": "ns",
+    "ns": "ns",
+}
+
+
+def to_datetime(
+    arg,
+    errors="raise",
+    dayfirst=False,
+    yearfirst=False,
+    utc=None,
+    format=None,
+    exact=True,
+    unit="ns",
+    infer_datetime_format=False,
+    origin="unix",
+    cache=True,
+):
+    """
+    Convert argument to datetime.
+
+    Parameters
+    ----------
+    arg : int, float, str, datetime, list, tuple, 1-d array,
+        Series DataFrame/dict-like
+        The object to convert to a datetime.
+    errors : {'ignore', 'raise', 'coerce', 'warn'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception.
+        - If 'coerce', then invalid parsing will be set as NaT.
+        - If 'warn' : prints last exceptions as warnings and
+            return the input.
+        - If 'ignore', then invalid parsing will return the input.
+    dayfirst : bool, default False
+        Specify a date parse order if `arg` is str or its list-likes.
+        If True, parses dates with the day first, eg 10/11/12 is parsed as
+        2012-11-10.
+        Warning: dayfirst=True is not strict, but will prefer to parse
+        with day first (this is a known bug, based on dateutil behavior).
+    format : str, default None
+        The strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
+        all the way up to nanoseconds.
+        See strftime documentation for more information on choices:
+        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.
+    unit : str, default 'ns'
+        The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
+        integer or float number. This will be based off the
+        origin(unix epoch start).
+        Example, with unit='ms' and origin='unix' (the default), this
+        would calculate the number of milliseconds to the unix epoch start.
+    infer_datetime_format : bool, default False
+        If True and no `format` is given, attempt to infer the format of the
+        datetime strings, and if it can be inferred, switch to a faster
+        method of parsing them. In some cases this can increase the parsing
+        speed by ~5-10x.
+
+    Returns
+    -------
+    datetime
+        If parsing succeeded.
+        Return type depends on input:
+        - list-like: DatetimeIndex
+        - Series: Series of datetime64 dtype
+        - scalar: Timestamp
+
+    Examples
+    --------
+    Assembling a datetime from multiple columns of a DataFrame. The keys can be
+    common abbreviations like ['year', 'month', 'day', 'minute', 'second',
+    'ms', 'us', 'ns']) or plurals of the same
+
+    >>> import cudf
+    >>> df = cudf.DataFrame({'year': [2015, 2016],
+    ...                    'month': [2, 3],
+    ...                    'day': [4, 5]})
+    >>> cudf.to_datetime(df)
+    0   2015-02-04
+    1   2016-03-05
+    dtype: datetime64[ns]
+    >>> cudf.to_datetime(1490195805, unit='s')
+    numpy.datetime64('2017-03-22T15:16:45.000000000')
+    >>> cudf.to_datetime(1490195805433502912, unit='ns')
+    numpy.datetime64('1780-11-20T01:02:30.494253056')
+    """
+    if errors not in {"ignore", "raise", "coerce", "warn"}:
+        raise ValueError(
+            f"errors parameter has to be either one of: "
+            f"{['ignore', 'raise', 'coerce', 'warn']}, found: "
+            f"{errors}"
+        )
+    elif errors in {"ignore", "coerce"} and not is_scalar(arg):
+        raise NotImplementedError(
+            f"{errors=} is not implemented when arg is not scalar-like"
+        )
+
+    if arg is None:
+        return None
+
+    if exact is False:
+        raise NotImplementedError("exact support is not yet implemented")
+
+    if origin != "unix":
+        raise NotImplementedError("origin support is not yet implemented")
+
+    if yearfirst:
+        raise NotImplementedError("yearfirst support is not yet implemented")
+
+    if utc:
+        raise NotImplementedError("utc is not yet implemented")
+
+    if format is not None:
+        if "%Z" in format or "%z" in format:
+            raise NotImplementedError(
+                "cuDF does not yet support timezone-aware datetimes"
+            )
+        elif "%f" in format:
+            format = format.replace("%f", "%9f")
+
+    try:
+        if isinstance(arg, cudf.DataFrame):
+            # we require at least Ymd
+            required = ["year", "month", "day"]
+            req = list(set(required) - set(arg._data.names))
+            if len(req):
+                req = ",".join(req)
+                raise ValueError(
+                    f"to assemble mappings requires at least that "
+                    f"[year, month, day] be specified: [{req}] "
+                    f"is missing"
+                )
+
+            # replace passed column name with values in _unit_map
+            unit = {k: get_units(k) for k in arg._data.names}
+            unit_rev = {v: k for k, v in unit.items()}
+
+            # keys we don't recognize
+            excess = set(unit_rev.keys()) - set(_unit_map.values())
+            if len(excess):
+                excess = ",".join(excess)
+                raise ValueError(
+                    f"extra keys have been passed to the "
+                    f"datetime assemblage: [{excess}]"
+                )
+
+            new_series = (
+                arg[unit_rev["year"]].astype("str")
+                + "-"
+                + arg[unit_rev["month"]].astype("str").str.zfill(2)
+                + "-"
+                + arg[unit_rev["day"]].astype("str").str.zfill(2)
+            )
+            format = "%Y-%m-%d"
+            col = new_series._column.as_datetime_column(
+                "datetime64[s]", format=format
+            )
+
+            for u in ["h", "m", "s", "ms", "us", "ns"]:
+                value = unit_rev.get(u)
+                if value is not None and value in arg:
+                    arg_col = arg._data[value]
+                    if arg_col.dtype.kind in ("f"):
+                        col = new_series._column.as_datetime_column(
+                            "datetime64[ns]", format=format
+                        )
+                        break
+                    elif arg_col.dtype.kind in ("O"):
+                        if not cpp_is_integer(arg_col).all():
+                            col = new_series._column.as_datetime_column(
+                                "datetime64[ns]", format=format
+                            )
+                            break
+
+            times_column = None
+            for u in ["h", "m", "s", "ms", "us", "ns"]:
+                value = unit_rev.get(u)
+                if value is not None and value in arg:
+                    current_col = arg._data[value]
+                    # If the arg[value] is of int or
+                    # float dtype we don't want to type-cast
+                    if current_col.dtype.kind in ("O"):
+                        try:
+                            current_col = current_col.astype(dtype="int64")
+                        except ValueError:
+                            current_col = current_col.astype(dtype="float64")
+
+                    factor = cudf.Scalar(
+                        column.datetime._unit_to_nanoseconds_conversion[u]
+                        / (
+                            column.datetime._unit_to_nanoseconds_conversion[
+                                "s"
+                            ]
+                            if np.datetime_data(col.dtype)[0] == "s"
+                            else 1
+                        )
+                    )
+
+                    if times_column is None:
+                        times_column = current_col * factor
+                    else:
+                        times_column = times_column + (current_col * factor)
+            if times_column is not None:
+                col = (col.astype(dtype="int64") + times_column).astype(
+                    dtype=col.dtype
+                )
+            return cudf.Series(col, index=arg.index)
+        elif isinstance(arg, cudf.BaseIndex):
+            col = arg._values
+            col = _process_col(
+                col=col,
+                unit=unit,
+                dayfirst=dayfirst,
+                infer_datetime_format=infer_datetime_format,
+                format=format,
+            )
+            return as_index(col, name=arg.name)
+        elif isinstance(arg, (cudf.Series, pd.Series)):
+            col = column.as_column(arg)
+            col = _process_col(
+                col=col,
+                unit=unit,
+                dayfirst=dayfirst,
+                infer_datetime_format=infer_datetime_format,
+                format=format,
+            )
+            return cudf.Series(col, index=arg.index, name=arg.name)
+        else:
+            col = column.as_column(arg)
+            col = _process_col(
+                col=col,
+                unit=unit,
+                dayfirst=dayfirst,
+                infer_datetime_format=infer_datetime_format,
+                format=format,
+            )
+
+            if is_scalar(arg):
+                return col.element_indexing(0)
+            else:
+                return as_index(col)
+    except Exception as e:
+        if errors == "raise":
+            raise e
+        elif errors == "warn":
+            import traceback
+
+            tb = traceback.format_exc()
+            warnings.warn(tb)
+        elif errors == "ignore":
+            pass
+        elif errors == "coerce":
+            return np.datetime64("nat", "ns" if unit is None else unit)
+        return arg
+
+
+def _process_col(col, unit, dayfirst, infer_datetime_format, format):
+    if col.dtype.kind == "M":
+        return col
+
+    elif col.dtype.kind in ("f"):
+        if unit not in (None, "ns"):
+            factor = cudf.Scalar(
+                column.datetime._unit_to_nanoseconds_conversion[unit]
+            )
+            col = col * factor
+
+        if format is not None:
+            # Converting to int because,
+            # pandas actually creates a datetime column
+            # out of float values and then creates an
+            # int column out of it to parse against `format`.
+            # Instead we directly cast to int and perform
+            # parsing against `format`.
+            col = (
+                col.astype("int")
+                .astype("str")
+                .as_datetime_column(
+                    dtype="datetime64[us]"
+                    if "%f" in format
+                    else "datetime64[s]",
+                    format=format,
+                )
+            )
+        else:
+            col = col.as_datetime_column(dtype="datetime64[ns]")
+        return col
+
+    elif col.dtype.kind in ("i"):
+        if unit in ("D", "h", "m"):
+            factor = cudf.Scalar(
+                column.datetime._unit_to_nanoseconds_conversion[unit]
+                / column.datetime._unit_to_nanoseconds_conversion["s"]
+            )
+            col = col * factor
+
+        if format is not None:
+            col = col.astype("str").as_datetime_column(
+                dtype=_unit_dtype_map[unit], format=format
+            )
+        else:
+            col = col.as_datetime_column(dtype=_unit_dtype_map[unit])
+        return col
+
+    elif col.dtype.kind in ("O"):
+        if unit not in (None, "ns") or col.null_count == len(col):
+            try:
+                col = col.astype(dtype="int64")
+            except ValueError:
+                col = col.astype(dtype="float64")
+            return _process_col(
+                col=col,
+                unit=unit,
+                dayfirst=dayfirst,
+                infer_datetime_format=infer_datetime_format,
+                format=format,
+            )
+        else:
+            if format is None:
+                if not infer_datetime_format and dayfirst:
+                    raise NotImplementedError(
+                        f"{dayfirst=} not implemented "
+                        f"when {format=} and {infer_datetime_format=}."
+                    )
+                format = column.datetime.infer_format(
+                    element=col.element_indexing(0),
+                    dayfirst=dayfirst,
+                )
+            return col.as_datetime_column(
+                dtype=_unit_dtype_map[unit],
+                format=format,
+            )
+    raise TypeError(
+        f"dtype {col.dtype} cannot be converted to {_unit_dtype_map[unit]}"
+    )
+
+
+def get_units(value):
+    if value in _unit_map:
+        return _unit_map[value]
+
+    # m is case significant
+    if value.lower() in _unit_map:
+        return _unit_map[value.lower()]
+
+    return value
+
+
+class DateOffset:
+    """
+    An object used for binary ops where calendrical arithmetic
+    is desired rather than absolute time arithmetic. Used to
+    add or subtract a whole number of periods, such as several
+    months or years, to a series or index of datetime dtype.
+    Works similarly to pd.DateOffset, but stores the offset
+    on the device (GPU).
+
+    Parameters
+    ----------
+    n : int, default 1
+        The number of time periods the offset represents.
+    **kwds
+        Temporal parameter that add to or replace the offset value.
+        Parameters that **add** to the offset (like Timedelta):
+        - months
+
+    See Also
+    --------
+    pandas.DateOffset : The equivalent Pandas object that this
+    object replicates
+
+    Examples
+    --------
+    >>> from cudf import DateOffset
+    >>> ts = cudf.Series([
+    ...     "2000-01-01 00:00:00.012345678",
+    ...     "2000-01-31 00:00:00.012345678",
+    ...     "2000-02-29 00:00:00.012345678",
+    ... ], dtype='datetime64[ns]')
+    >>> ts + DateOffset(months=3)
+    0   2000-04-01 00:00:00.012345678
+    1   2000-04-30 00:00:00.012345678
+    2   2000-05-29 00:00:00.012345678
+    dtype: datetime64[ns]
+    >>> ts - DateOffset(months=12)
+    0   1999-01-01 00:00:00.012345678
+    1   1999-01-31 00:00:00.012345678
+    2   1999-02-28 00:00:00.012345678
+    dtype: datetime64[ns]
+
+    Notes
+    -----
+    Note that cuDF does not yet support DateOffset arguments
+    that 'replace' units in the datetime data being operated on
+    such as
+        - year
+        - month
+        - week
+        - day
+        - hour
+        - minute
+        - second
+        - microsecond
+        - millisecond
+        - nanosecond
+
+    cuDF does not yet support rounding via a `normalize`
+    keyword argument.
+    """
+
+    _UNITS_TO_CODES = {
+        "nanoseconds": "ns",
+        "microseconds": "us",
+        "milliseconds": "ms",
+        "seconds": "s",
+        "minutes": "m",
+        "hours": "h",
+        "days": "D",
+        "weeks": "W",
+        "months": "M",
+        "years": "Y",
+    }
+
+    _CODES_TO_UNITS = {
+        "ns": "nanoseconds",
+        "us": "microseconds",
+        "ms": "milliseconds",
+        "L": "milliseconds",
+        "s": "seconds",
+        "m": "minutes",
+        "h": "hours",
+        "D": "days",
+        "W": "weeks",
+        "M": "months",
+        "Y": "years",
+    }
+
+    _TICK_OR_WEEK_TO_UNITS = {
+        pd_offset.Week: "weeks",
+        pd_offset.Day: "days",
+        pd_offset.Hour: "hours",
+        pd_offset.Minute: "minutes",
+        pd_offset.Second: "seconds",
+        pd_offset.Milli: "milliseconds",
+        pd_offset.Micro: "microseconds",
+        pd_offset.Nano: "nanoseconds",
+    }
+
+    _FREQSTR_REGEX = re.compile("([0-9]*)([a-zA-Z]+)")
+
+    def __init__(self, n=1, normalize=False, **kwds):
+        if normalize:
+            raise NotImplementedError(
+                "normalize not yet supported for DateOffset"
+            )
+
+        all_possible_units = {
+            "years",
+            "months",
+            "weeks",
+            "days",
+            "hours",
+            "minutes",
+            "seconds",
+            "milliseconds",
+            "microseconds",
+            "nanoseconds",
+            "year",
+            "month",
+            "week",
+            "day",
+            "hour",
+            "minute",
+            "second",
+            "microsecond",
+            "millisecond",
+            "nanosecond",
+        }
+
+        supported_units = {
+            "years",
+            "months",
+            "weeks",
+            "days",
+            "hours",
+            "minutes",
+            "seconds",
+            "milliseconds",
+            "microseconds",
+            "nanoseconds",
+        }
+
+        unsupported_units = all_possible_units - supported_units
+
+        invalid_kwds = set(kwds) - supported_units - unsupported_units
+        if invalid_kwds:
+            raise TypeError(
+                f"Keyword arguments '{','.join(list(invalid_kwds))}'"
+                " are not recognized"
+            )
+
+        unsupported_kwds = set(kwds) & unsupported_units
+        if unsupported_kwds:
+            raise NotImplementedError(
+                f"Keyword arguments '{','.join(list(unsupported_kwds))}'"
+                " are not yet supported."
+            )
+
+        if any(not is_integer(val) for val in kwds.values()):
+            raise ValueError("Non-integer periods not supported")
+
+        self._kwds = kwds
+        kwds = self._combine_months_and_years(**kwds)
+        kwds = self._combine_kwargs_to_seconds(**kwds)
+
+        scalars = {}
+        for k, v in kwds.items():
+            if k in all_possible_units:
+                # Months must be int16
+                if k == "months":
+                    # TODO: throw for out-of-bounds int16 values
+                    dtype = "int16"
+                else:
+                    unit = self._UNITS_TO_CODES[k]
+                    dtype = cudf.dtype(f"timedelta64[{unit}]")
+                scalars[k] = cudf.Scalar(v, dtype=dtype)
+
+        self._scalars = scalars
+
+    @property
+    def kwds(self):
+        return self._kwds
+
+    def _combine_months_and_years(self, **kwargs):
+        # TODO: if months is zero, don't do a binop
+        kwargs["months"] = kwargs.pop("years", 0) * 12 + kwargs.pop(
+            "months", 0
+        )
+        return kwargs
+
+    def _combine_kwargs_to_seconds(self, **kwargs):
+        """
+        Combine days, weeks, hours and minutes to a single
+        scalar representing the total seconds
+        """
+        seconds = 0
+        seconds += kwargs.pop("weeks", 0) * 604800
+        seconds += kwargs.pop("days", 0) * 86400
+        seconds += kwargs.pop("hours", 0) * 3600
+        seconds += kwargs.pop("minutes", 0) * 60
+        seconds += kwargs.pop("seconds", 0)
+
+        if seconds > np.iinfo("int64").max:
+            raise NotImplementedError(
+                "Total days + weeks + hours + minutes + seconds can not exceed"
+                f" {np.iinfo('int64').max} seconds"
+            )
+
+        if seconds != 0:
+            kwargs["seconds"] = seconds
+        return kwargs
+
+    def _datetime_binop(
+        self, datetime_col, op, reflect=False
+    ) -> column.DatetimeColumn:
+        if reflect and op == "__sub__":
+            raise TypeError(
+                f"Can not subtract a {type(datetime_col).__name__}"
+                f" from a {type(self).__name__}"
+            )
+        if op not in {"__add__", "__sub__"}:
+            raise TypeError(
+                f"{op} not supported between {type(self).__name__}"
+                f" and {type(datetime_col).__name__}"
+            )
+        if not self._is_no_op:
+            if "months" in self._scalars:
+                rhs = self._generate_months_column(len(datetime_col), op)
+                datetime_col = libcudf.datetime.add_months(datetime_col, rhs)
+
+            for unit, value in self._scalars.items():
+                if unit != "months":
+                    value = -value if op == "__sub__" else value
+                    datetime_col += cudf.core.column.as_column(
+                        value, length=len(datetime_col)
+                    )
+
+        return datetime_col
+
+    def _generate_months_column(self, size, op):
+        months = self._scalars["months"]
+        months = -months if op == "__sub__" else months
+        # TODO: pass a scalar instead of constructing a column
+        # https://github.com/rapidsai/cudf/issues/6990
+        col = cudf.core.column.as_column(months, length=size)
+        return col
+
+    @property
+    def _is_no_op(self) -> bool:
+        # some logic could be implemented here for more complex cases
+        # such as +1 year, -12 months
+        return all(i == 0 for i in self._kwds.values())
+
+    def __neg__(self):
+        new_scalars = {k: -v for k, v in self._kwds.items()}
+        return DateOffset(**new_scalars)
+
+    def __repr__(self):
+        includes = []
+        for unit in sorted(self._UNITS_TO_CODES):
+            val = self._kwds.get(unit, None)
+            if val is not None:
+                includes.append(f"{unit}={val}")
+        unit_data = ", ".join(includes)
+        repr_str = f"<{self.__class__.__name__}: {unit_data}>"
+
+        return repr_str
+
+    @classmethod
+    def _from_freqstr(cls, freqstr: str) -> Self:
+        """
+        Parse a string and return a DateOffset object
+        expects strings of the form 3D, 25W, 10ms, 42ns, etc.
+        """
+        match = cls._FREQSTR_REGEX.match(freqstr)
+
+        if match is None:
+            raise ValueError(f"Invalid frequency string: {freqstr}")
+
+        numeric_part = match.group(1)
+        if numeric_part == "":
+            numeric_part = "1"
+        freq_part = match.group(2)
+
+        if freq_part not in cls._CODES_TO_UNITS:
+            raise ValueError(f"Cannot interpret frequency str: {freqstr}")
+
+        return cls(**{cls._CODES_TO_UNITS[freq_part]: int(numeric_part)})
+
+    @classmethod
+    def _from_pandas_ticks_or_weeks(
+        cls,
+        tick: Union[pd.tseries.offsets.Tick, pd.tseries.offsets.Week],
+    ) -> Self:
+        return cls(**{cls._TICK_OR_WEEK_TO_UNITS[type(tick)]: tick.n})
+
+    def _maybe_as_fast_pandas_offset(self):
+        if (
+            len(self.kwds) == 1
+            and _has_fixed_frequency(self)
+            and not _has_non_fixed_frequency(self)
+        ):
+            # Pandas computation between `n*offsets.Minute()` is faster than
+            # `n*DateOffset`. If only single offset unit is in use, we return
+            # the base offset for faster binary ops.
+            return pd.tseries.frequencies.to_offset(pd.Timedelta(**self.kwds))
+        return pd.DateOffset(**self.kwds, n=1)
+
+
+def _isin_datetimelike(
+    lhs: Union[column.TimeDeltaColumn, column.DatetimeColumn], values: Sequence
+) -> column.ColumnBase:
+    """
+    Check whether values are contained in the
+    DateTimeColumn or TimeDeltaColumn.
+
+    Parameters
+    ----------
+    lhs : TimeDeltaColumn or DatetimeColumn
+        Column to check whether the `values` exist in.
+    values : set or list-like
+        The sequence of values to test. Passing in a single string will
+        raise a TypeError. Instead, turn a single string into a list
+        of one element.
+
+    Returns
+    -------
+    result: Column
+        Column of booleans indicating if each element is in values.
+    """
+    rhs = None
+    try:
+        rhs = cudf.core.column.as_column(values)
+
+        if rhs.dtype.kind in {"f", "i", "u"}:
+            return cudf.core.column.full(len(lhs), False, dtype="bool")
+        rhs = rhs.astype(lhs.dtype)
+        res = lhs._isin_earlystop(rhs)
+        if res is not None:
+            return res
+    except ValueError:
+        # pandas functionally returns all False when cleansing via
+        # typecasting fails
+        return cudf.core.column.full(len(lhs), False, dtype="bool")
+
+    res = lhs._obtain_isin_result(rhs)
+    return res
+
+
+def date_range(
+    start=None,
+    end=None,
+    periods=None,
+    freq=None,
+    tz=None,
+    normalize=False,
+    name=None,
+    closed=None,
+):
+    """Return a fixed frequency DatetimeIndex.
+
+    Returns the range of equally spaced time points (where the difference
+    between any two adjacent points is specified by the given frequency)
+    such that they all satisfy `start` <[=] x <[=] `end`, where the first one
+    and the last one are, resp., the first and last time points in that range
+    that are valid for `freq`.
+
+    Parameters
+    ----------
+    start : str or datetime-like, optional
+        Left bound for generating dates.
+
+    end : str or datetime-like, optional
+        Right bound for generating dates.
+
+    periods : int, optional
+        Number of periods to generate.
+
+    freq : str or DateOffset
+        Frequencies to generate the datetime series. Mixed fixed-frequency and
+        non-fixed frequency offset is unsupported. See notes for detail.
+        Supported offset alias: ``D``, ``h``, ``H``, ``T``, ``min``, ``S``,
+        ``U``, ``us``, ``N``, ``ns``.
+
+    tz : str or tzinfo, optional
+        Not Supported
+
+    normalize : bool, default False
+        Not Supported
+
+    name : str, default None
+        Name of the resulting DatetimeIndex
+
+    closed : {None, 'left', 'right'}, optional
+        Not Supported
+
+    Returns
+    -------
+    DatetimeIndex
+
+    Notes
+    -----
+    Of the four parameters `start`, `end`, `periods`, and `freq`, exactly three
+    must be specified. If `freq` is omitted, the resulting DatetimeIndex will
+    have periods linearly spaced elements between start and end (closed on both
+    sides).
+
+    cudf supports `freq` specified with either fixed-frequency offset
+    (such as weeks, days, hours, minutes...) or non-fixed frequency offset
+    (such as years and months). Specifying `freq` with a mixed fixed and
+    non-fixed frequency is currently unsupported. For example:
+
+    >>> cudf.date_range(
+    ...     start='2021-08-23 08:00:00',
+    ...     freq=cudf.DateOffset(months=2, days=5),
+    ...     periods=5)
+    ...
+    NotImplementedError: Mixing fixed and non-fixed frequency offset is
+    unsupported.
+
+    Examples
+    --------
+    >>> cudf.date_range(
+    ...     start='2021-08-23 08:00:00',
+    ...     freq=cudf.DateOffset(years=1, months=2),
+    ...     periods=5)
+    DatetimeIndex(['2021-08-23 08:00:00', '2022-10-23 08:00:00',
+                '2023-12-23 08:00:00', '2025-02-23 08:00:00',
+                '2026-04-23 08:00:00'],
+                dtype='datetime64[ns]')
+
+    """
+    if tz is not None:
+        raise NotImplementedError("tz is currently unsupported.")
+
+    if closed is not None:
+        raise NotImplementedError("closed is currently unsupported.")
+
+    if (start, end, periods, freq).count(None) > 1:
+        raise ValueError(
+            "Of the four parameters: start, end, periods, and freq, exactly "
+            "three must be specified"
+        )
+
+    dtype = np.dtype("<M8[ns]")
+
+    if freq is None:
+        # `start`, `end`, `periods` is specified, we treat the timestamps as
+        # integers and divide the number range evenly with `periods` elements.
+        start = cudf.Scalar(start, dtype=dtype).value.astype("int64")
+        end = cudf.Scalar(end, dtype=dtype).value.astype("int64")
+        arr = cp.linspace(start=start, stop=end, num=periods)
+        result = cudf.core.column.as_column(arr).astype("datetime64[ns]")
+        return cudf.DatetimeIndex._from_data({name: result})
+    elif cudf.get_option("mode.pandas_compatible"):
+        raise NotImplementedError(
+            "`DatetimeIndex` with `freq` cannot be constructed."
+        )
+
+    # The code logic below assumes `freq` is defined. It is first normalized
+    # into `DateOffset` for further computation with timestamps.
+
+    if isinstance(freq, DateOffset):
+        offset = freq
+    elif isinstance(freq, str):
+        offset = pd.tseries.frequencies.to_offset(freq)
+        if not isinstance(offset, pd.tseries.offsets.Tick) and not isinstance(
+            offset, pd.tseries.offsets.Week
+        ):
+            raise ValueError(
+                f"Unrecognized frequency string {freq}. cuDF does "
+                "not yet support month, quarter, year-anchored frequency."
+            )
+        offset = DateOffset._from_pandas_ticks_or_weeks(offset)
+    else:
+        raise TypeError("`freq` must be a `str` or cudf.DateOffset object.")
+
+    if _has_mixed_freqeuency(offset):
+        raise NotImplementedError(
+            "Mixing fixed and non-fixed frequency offset is unsupported."
+        )
+
+    # Depending on different combinations of `start`, `end`, `offset`,
+    # `periods`, the following logic makes sure before computing the sequence,
+    # `start`, `periods`, `offset` is defined
+
+    _periods_not_specified = False
+
+    if start is None:
+        end = cudf.Scalar(end, dtype=dtype)
+        start = cudf.Scalar(
+            pd.Timestamp(end.value)
+            - (periods - 1) * offset._maybe_as_fast_pandas_offset(),
+            dtype=dtype,
+        )
+    elif end is None:
+        start = cudf.Scalar(start, dtype=dtype)
+    elif periods is None:
+        # When `periods` is unspecified, its upper bound estimated by
+        # dividing the number of nanoseconds between two timestamps with
+        # the lower bound of `freq` in nanoseconds. While the final result
+        # may contain extra elements that exceeds `end`, they are trimmed
+        # as a post processing step. [1]
+        _periods_not_specified = True
+        start = cudf.Scalar(start, dtype=dtype)
+        end = cudf.Scalar(end, dtype=dtype)
+        _is_increment_sequence = end >= start
+
+        periods = math.ceil(
+            int(end - start) / _offset_to_nanoseconds_lower_bound(offset)
+        )
+
+        if periods < 0:
+            # Mismatched sign between (end-start) and offset, return empty
+            # column
+            periods = 0
+        elif periods == 0:
+            # end == start, return exactly 1 timestamp (start)
+            periods = 1
+
+    # We compute `end_estim` (the estimated upper bound of the date
+    # range) below, but don't always use it.  We do this to ensure
+    # that the appropriate OverflowError is raised by Pandas in case
+    # of overflow.
+    # FIXME: when `end_estim` is out of bound, but the actual `end` is not,
+    # we shouldn't raise but compute the sequence as is. The trailing overflow
+    # part should get trimmed at the end.
+    end_estim = (
+        pd.Timestamp(start.value)
+        + periods * offset._maybe_as_fast_pandas_offset()
+    ).to_datetime64()
+
+    if "months" in offset.kwds or "years" in offset.kwds:
+        # If `offset` is non-fixed frequency, resort to libcudf.
+        res = libcudf.datetime.date_range(start.device_value, periods, offset)
+        if _periods_not_specified:
+            # As mentioned in [1], this is a post processing step to trim extra
+            # elements when `periods` is an estimated value. Only offset
+            # specified with non fixed frequencies requires trimming.
+            res = res.apply_boolean_mask(
+                (res <= end) if _is_increment_sequence else (res <= start)
+            )
+    else:
+        # If `offset` is fixed frequency, we generate a range of
+        # treating `start`, `stop` and `step` as ints:
+        stop = end_estim.astype("int64")
+        start = start.value.astype("int64")
+        step = _offset_to_nanoseconds_lower_bound(offset)
+        arr = cp.arange(start=start, stop=stop, step=step, dtype="int64")
+        res = cudf.core.column.as_column(arr).astype("datetime64[ns]")
+
+    return cudf.DatetimeIndex._from_data({name: res})
+
+
+def _has_fixed_frequency(freq: DateOffset) -> bool:
+    """Utility to determine if `freq` contains fixed frequency offset"""
+    fixed_frequencies = {
+        "weeks",
+        "days",
+        "hours",
+        "minutes",
+        "seconds",
+        "milliseconds",
+        "microseconds",
+        "nanoseconds",
+    }
+
+    return len(freq.kwds.keys() & fixed_frequencies) > 0
+
+
+def _has_non_fixed_frequency(freq: DateOffset) -> bool:
+    """Utility to determine if `freq` contains non-fixed frequency offset"""
+    non_fixed_frequencies = {"years", "months"}
+    return len(freq.kwds.keys() & non_fixed_frequencies) > 0
+
+
+def _has_mixed_freqeuency(freq: DateOffset) -> bool:
+    """Utility to determine if `freq` contains mixed fixed and non-fixed
+    frequency offset. e.g. {months=1, days=5}
+    """
+
+    return _has_fixed_frequency(freq) and _has_non_fixed_frequency(freq)
+
+
+def _offset_to_nanoseconds_lower_bound(offset: DateOffset) -> int:
+    """Given a DateOffset, which can consist of either fixed frequency or
+    non-fixed frequency offset, convert to the smallest possible fixed
+    frequency offset based in nanoseconds.
+
+    Specifically, the smallest fixed frequency conversion for {months=1}
+    is 28 * nano_seconds_per_day, because 1 month contains at least 28 days.
+    Similarly, the smallest fixed frequency conversion for {year=1} is
+    365 * nano_seconds_per_day.
+
+    This utility is used to compute the upper bound of the count of timestamps
+    given a range of datetime and an offset.
+    """
+    nanoseconds_per_day = 24 * 60 * 60 * 10**9
+    kwds = offset.kwds
+    return (
+        kwds.get("years", 0) * (365 * nanoseconds_per_day)
+        + kwds.get("months", 0) * (28 * nanoseconds_per_day)
+        + kwds.get("weeks", 0) * (7 * nanoseconds_per_day)
+        + kwds.get("days", 0) * nanoseconds_per_day
+        + kwds.get("hours", 0) * 3600 * 10**9
+        + kwds.get("minutes", 0) * 60 * 10**9
+        + kwds.get("seconds", 0) * 10**9
+        + kwds.get("milliseconds", 0) * 10**6
+        + kwds.get("microseconds", 0) * 10**3
+        + kwds.get("nanoseconds", 0)
+    )
+
+
+def _to_iso_calendar(arg):
+    formats = ["%G", "%V", "%u"]
+    if not isinstance(arg, (cudf.Index, cudf.core.series.DatetimeProperties)):
+        raise AttributeError(
+            "Can only use .isocalendar accessor with series or index"
+        )
+    if isinstance(arg, cudf.Index):
+        iso_params = [
+            arg._column.as_string_column(arg._values.dtype, fmt)
+            for fmt in formats
+        ]
+        index = arg._column
+    elif isinstance(arg.series, cudf.Series):
+        iso_params = [arg.strftime(fmt) for fmt in formats]
+        index = arg.series.index
+
+    data = dict(zip(["year", "week", "day"], iso_params))
+    return cudf.DataFrame(data, index=index, dtype=np.int32)
diff --git a/python/cudf/cudf/core/tools/numeric.py b/python/cudf/cudf/core/tools/numeric.py
new file mode 100644
index 0000000..0273227
--- /dev/null
+++ b/python/cudf/cudf/core/tools/numeric.py
@@ -0,0 +1,254 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+import warnings
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf import _lib as libcudf
+from cudf._lib import strings as libstrings
+from cudf.api.types import (
+    _is_non_decimal_numeric_dtype,
+    is_categorical_dtype,
+    is_datetime_dtype,
+    is_list_dtype,
+    is_string_dtype,
+    is_struct_dtype,
+    is_timedelta_dtype,
+)
+from cudf.core.column import as_column
+from cudf.utils.dtypes import can_convert_to_column
+
+
+def to_numeric(arg, errors="raise", downcast=None):
+    """
+    Convert argument into numerical types.
+
+    Parameters
+    ----------
+    arg : column-convertible
+        The object to convert to numeric types
+    errors : {'raise', 'ignore', 'coerce'}, defaults 'raise'
+        Policy to handle errors during parsing.
+
+        * 'raise' will notify user all errors encountered.
+        * 'ignore' will skip error and returns ``arg``.
+        * 'coerce' will leave invalid values as nulls.
+    downcast : {'integer', 'signed', 'unsigned', 'float'}, defaults None
+        If set, will try to down-convert the datatype of the
+        parsed results to smallest possible type. For each `downcast`
+        type, this method will determine the smallest possible
+        dtype from the following sets:
+
+        * {'integer', 'signed'}: all integer types greater or equal to
+          `np.int8`
+        * {'unsigned'}: all unsigned types greater or equal to `np.uint8`
+        * {'float'}: all floating types greater or equal to `np.float32`
+
+        Note that downcast behavior is decoupled from parsing. Errors
+        encountered during downcast is raised regardless of ``errors``
+        parameter.
+
+    Returns
+    -------
+    Series or ndarray
+        Depending on the input, if series is passed in, series is returned,
+        otherwise ndarray
+
+    Notes
+    -----
+    An important difference from pandas is that this function does not accept
+    mixed numeric/non-numeric type sequences. For example ``[1, 'a']``.
+    A ``TypeError`` will be raised when such input is received, regardless of
+    ``errors`` parameter.
+
+    Examples
+    --------
+    >>> s = cudf.Series(['1', '2.0', '3e3'])
+    >>> cudf.to_numeric(s)
+    0       1.0
+    1       2.0
+    2    3000.0
+    dtype: float64
+    >>> cudf.to_numeric(s, downcast='float')
+    0       1.0
+    1       2.0
+    2    3000.0
+    dtype: float32
+    >>> cudf.to_numeric(s, downcast='signed')
+    0       1
+    1       2
+    2    3000
+    dtype: int16
+    >>> s = cudf.Series(['apple', '1.0', '3e3'])
+    >>> cudf.to_numeric(s, errors='ignore')
+    0    apple
+    1      1.0
+    2      3e3
+    dtype: object
+    >>> cudf.to_numeric(s, errors='coerce')
+    0      <NA>
+    1       1.0
+    2    3000.0
+    dtype: float64
+    """
+
+    if errors not in {"raise", "ignore", "coerce"}:
+        raise ValueError("invalid error value specified")
+
+    if downcast not in {None, "integer", "signed", "unsigned", "float"}:
+        raise ValueError("invalid downcasting method provided")
+
+    if not can_convert_to_column(arg) or (
+        hasattr(arg, "ndim") and arg.ndim > 1
+    ):
+        raise ValueError("arg must be column convertible")
+
+    col = as_column(arg)
+    dtype = col.dtype
+
+    if is_datetime_dtype(dtype) or is_timedelta_dtype(dtype):
+        col = col.as_numerical_column(cudf.dtype("int64"))
+    elif is_categorical_dtype(dtype):
+        cat_dtype = col.dtype.type
+        if _is_non_decimal_numeric_dtype(cat_dtype):
+            col = col.as_numerical_column(cat_dtype)
+        else:
+            try:
+                col = _convert_str_col(
+                    col._get_decategorized_column(), errors, downcast
+                )
+            except ValueError as e:
+                if errors == "ignore":
+                    return arg
+                else:
+                    raise e
+    elif is_string_dtype(dtype):
+        try:
+            col = _convert_str_col(col, errors, downcast)
+        except ValueError as e:
+            if errors == "ignore":
+                return arg
+            else:
+                raise e
+    elif is_list_dtype(dtype) or is_struct_dtype(dtype):
+        raise ValueError("Input does not support nested datatypes")
+    elif _is_non_decimal_numeric_dtype(dtype):
+        pass
+    else:
+        raise ValueError("Unrecognized datatype")
+
+    # str->float conversion may require lower precision
+    if col.dtype == cudf.dtype("f"):
+        col = col.as_numerical_column("d")
+
+    if downcast:
+        if downcast == "float":
+            # we support only float32 & float64
+            type_set = [
+                cudf.dtype(np.float32).char,
+                cudf.dtype(np.float64).char,
+            ]
+        elif downcast in ("integer", "signed"):
+            type_set = list(np.typecodes["Integer"])
+        elif downcast == "unsigned":
+            type_set = list(np.typecodes["UnsignedInteger"])
+
+        for t in type_set:
+            downcast_dtype = cudf.dtype(t)
+            if downcast_dtype.itemsize <= col.dtype.itemsize:
+                if col.can_cast_safely(downcast_dtype):
+                    col = libcudf.unary.cast(col, downcast_dtype)
+                    break
+
+    if isinstance(arg, (cudf.Series, pd.Series)):
+        return cudf.Series(col)
+    else:
+        if col.has_nulls():
+            # To match pandas, always return a floating type filled with nan.
+            col = col.astype(float).fillna(np.nan)
+        return col.values
+
+
+def _convert_str_col(col, errors, _downcast=None):
+    """
+    Converts a string column to numeric column
+
+    Converts to integer column if all strings are integer-like (isinteger.all)
+    Otherwise, converts to float column if all strings are float-like (
+    isfloat.all)
+
+    If error == 'coerce', fill non-numerics strings with null
+
+    Looks ahead to ``downcast`` parameter, if the float may be casted to
+    integer, then only process in float32 pipeline.
+
+    Parameters
+    ----------
+    col : The string column to convert, must be string dtype
+    errors : {'raise', 'ignore', 'coerce'}, same as ``to_numeric``
+    _downcast : Same as ``to_numeric``, see description for use
+
+    Returns
+    -------
+    Converted numeric column
+    """
+    if not is_string_dtype(col):
+        raise TypeError("col must be string dtype.")
+
+    is_integer = libstrings.is_integer(col)
+    if is_integer.all():
+        return col.as_numerical_column(dtype=cudf.dtype("i8"))
+
+    col = _proc_inf_empty_strings(col)
+
+    is_float = libstrings.is_float(col)
+    if is_float.all():
+        if _downcast in {"unsigned", "signed", "integer"}:
+            warnings.warn(
+                UserWarning(
+                    "Downcasting from float to int will be "
+                    "limited by float32 precision."
+                )
+            )
+            return col.as_numerical_column(dtype=cudf.dtype("f"))
+        else:
+            return col.as_numerical_column(dtype=cudf.dtype("d"))
+    else:
+        if errors == "coerce":
+            col = libcudf.string_casting.stod(col)
+            non_numerics = is_float.unary_operator("not")
+            col[non_numerics] = None
+            return col
+        else:
+            raise ValueError("Unable to convert some strings to numerics.")
+
+
+def _proc_inf_empty_strings(col):
+    """Handles empty and infinity strings"""
+    col = libstrings.to_lower(col)
+    col = _proc_empty_strings(col)
+    col = _proc_inf_strings(col)
+    return col
+
+
+def _proc_empty_strings(col):
+    """Replaces empty strings with NaN"""
+    s = cudf.Series(col)
+    s = s.where(s != "", "NaN")
+    return s._column
+
+
+def _proc_inf_strings(col):
+    """Convert "inf/infinity" strings into "Inf", the native string
+    representing infinity in libcudf
+    """
+    # TODO: This can be handled by libcudf in
+    # future see StringColumn.as_numerical_column
+    col = libstrings.replace_multi(
+        col,
+        as_column(["+", "inf", "inity"]),
+        as_column(["", "Inf", ""]),
+    )
+    return col
diff --git a/python/cudf/cudf/core/udf/__init__.py b/python/cudf/cudf/core/udf/__init__.py
new file mode 100644
index 0000000..85d4546
--- /dev/null
+++ b/python/cudf/cudf/core/udf/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+from . import (
+    groupby_lowering,
+    groupby_typing,
+    masked_lowering,
+    masked_typing,
+    strings_lowering,
+    strings_typing,
+)
diff --git a/python/cudf/cudf/core/udf/_ops.py b/python/cudf/cudf/core/udf/_ops.py
new file mode 100644
index 0000000..6b0640b
--- /dev/null
+++ b/python/cudf/cudf/core/udf/_ops.py
@@ -0,0 +1,66 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+import math
+import operator
+
+arith_ops = [
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.truediv,
+    operator.floordiv,
+    operator.mod,
+    operator.pow,
+    operator.iadd,
+    operator.isub,
+    operator.imul,
+    operator.itruediv,
+    operator.floordiv,
+    operator.ipow,
+    operator.imod,
+]
+
+bitwise_ops = [operator.and_, operator.or_, operator.xor]
+
+unary_ops = [
+    math.acos,
+    math.acosh,
+    math.asin,
+    math.asinh,
+    math.atan,
+    math.atanh,
+    math.ceil,
+    math.cos,
+    math.degrees,
+    math.erf,
+    math.erfc,
+    math.exp,
+    math.expm1,
+    math.fabs,
+    math.floor,
+    math.gamma,
+    math.lgamma,
+    math.log,
+    math.log10,
+    math.log1p,
+    math.log2,
+    math.radians,
+    math.sin,
+    math.sinh,
+    math.sqrt,
+    math.tan,
+    math.tanh,
+    operator.pos,
+    operator.neg,
+    operator.not_,
+    operator.invert,
+]
+
+comparison_ops = [
+    operator.eq,
+    operator.ne,
+    operator.lt,
+    operator.le,
+    operator.gt,
+    operator.ge,
+]
diff --git a/python/cudf/cudf/core/udf/api.py b/python/cudf/cudf/core/udf/api.py
new file mode 100644
index 0000000..7a68cff
--- /dev/null
+++ b/python/cudf/cudf/core/udf/api.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+
+class Masked:
+    """
+    Most of the time, MaskedType as defined in typing.py
+    combined with the ops defined to operate on them are
+    enough to fulfill the obligations of DataFrame.apply
+    However sometimes we need to refer to an instance of
+    a masked scalar outside the context of a UDF like as
+    a global variable. To get numba to identify that var
+    a of type MaskedType and treat it as such we need to
+    have an actual python class we can tie to MaskedType
+    This is that class
+    """
+
+    def __init__(self, value, valid):
+        self.value = value
+        self.valid = valid
+
+
+def pack_return(masked_or_scalar):
+    # Blank function to give us something for the typing and
+    # lowering to grab onto. Just a dummy function for us to
+    # call within kernels that will get replaced later by the
+    # lowered implementation
+    pass
diff --git a/python/cudf/cudf/core/udf/groupby_lowering.py b/python/cudf/cudf/core/udf/groupby_lowering.py
new file mode 100644
index 0000000..fe0637c
--- /dev/null
+++ b/python/cudf/cudf/core/udf/groupby_lowering.py
@@ -0,0 +1,190 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from functools import partial
+
+from numba import types
+from numba.core import cgutils
+from numba.core.extending import lower_builtin
+from numba.core.typing import signature as nb_signature
+from numba.cuda.cudaimpl import lower as cuda_lower
+
+from cudf.core.udf.groupby_typing import (
+    SUPPORTED_GROUPBY_NUMBA_TYPES,
+    Group,
+    GroupType,
+    call_cuda_functions,
+    group_size_type,
+    index_default_type,
+)
+
+
+def group_reduction_impl_basic(context, builder, sig, args, function):
+    """
+    Instruction boilerplate used for calling a groupby reduction
+    __device__ function. Centers around a forward declaration of
+    this function and adds the pre/post processing instructions
+    necessary for calling it.
+    """
+    # return type
+    retty = sig.return_type
+
+    # a variable logically corresponding to the calling `Group`
+    grp = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    )
+
+    # what specific (numba) GroupType
+    grp_type = sig.args[0]
+    group_dataty = grp_type.group_data_type
+
+    # obtain the correct forward declaration from registry
+    type_key = (sig.return_type, grp_type.group_scalar_type)
+    func = call_cuda_functions[function][type_key]
+
+    # insert the forward declaration and return its result
+    # pass it the data pointer and the group's size
+    return context.compile_internal(
+        builder,
+        func,
+        nb_signature(retty, group_dataty, grp_type.group_size_type),
+        (grp.group_data, grp.size),
+    )
+
+
+def group_corr(context, builder, sig, args):
+    """
+    Instruction boilerplate used for calling a groupby correlation
+    """
+    lhs_grp = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    )
+    rhs_grp = cgutils.create_struct_proxy(sig.args[1])(
+        context, builder, value=args[1]
+    )
+
+    device_func = call_cuda_functions["corr"][
+        (
+            sig.return_type,
+            sig.args[0].group_scalar_type,
+            sig.args[1].group_scalar_type,
+        )
+    ]
+    result = context.compile_internal(
+        builder,
+        device_func,
+        nb_signature(
+            types.float64,
+            types.CPointer(
+                sig.args[0].group_scalar_type
+            ),  # this group calls corr
+            types.CPointer(
+                sig.args[1].group_scalar_type
+            ),  # this group is passed
+            group_size_type,
+        ),
+        (
+            lhs_grp.group_data,
+            rhs_grp.group_data,
+            lhs_grp.size,
+        ),
+    )
+    return result
+
+
+@lower_builtin(Group, types.Array, group_size_type, types.Array)
+def group_constructor(context, builder, sig, args):
+    """
+    Instruction boilerplate used for instantiating a Group
+    struct from a data pointer, an index pointer, and a size
+    """
+    # a variable logically corresponding to the calling `Group`
+    grp = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    grp.group_data = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    ).data
+    grp.index = cgutils.create_struct_proxy(sig.args[2])(
+        context, builder, value=args[2]
+    ).data
+    grp.size = args[1]
+    return grp._getvalue()
+
+
+def group_reduction_impl_idx_max_or_min(context, builder, sig, args, function):
+    """
+    Instruction boilerplate used for calling a groupby reduction
+    __device__ function in the case where the function is either
+    `idxmax` or `idxmin`. See `group_reduction_impl_basic` for
+    details. This lowering differs from other reductions due to
+    the presence of the index. This results in the forward
+    declaration expecting an extra arg.
+    """
+    retty = sig.return_type
+
+    grp = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    )
+    grp_type = sig.args[0]
+
+    if grp_type.index_type != index_default_type:
+        raise TypeError(
+            f"Only inputs with default index dtype {index_default_type} "
+            "are supported."
+        )
+
+    type_key = (index_default_type, grp_type.group_scalar_type)
+    func = call_cuda_functions[function][type_key]
+
+    return context.compile_internal(
+        builder,
+        func,
+        nb_signature(
+            retty,
+            grp_type.group_data_type,
+            grp_type.group_index_type,
+            grp_type.group_size_type,
+        ),
+        (grp.group_data, grp.index, grp.size),
+    )
+
+
+cuda_Group_max = partial(group_reduction_impl_basic, function="max")
+cuda_Group_min = partial(group_reduction_impl_basic, function="min")
+cuda_Group_sum = partial(group_reduction_impl_basic, function="sum")
+cuda_Group_mean = partial(group_reduction_impl_basic, function="mean")
+cuda_Group_std = partial(group_reduction_impl_basic, function="std")
+cuda_Group_var = partial(group_reduction_impl_basic, function="var")
+
+cuda_Group_idxmax = partial(
+    group_reduction_impl_idx_max_or_min, function="idxmax"
+)
+cuda_Group_idxmin = partial(
+    group_reduction_impl_idx_max_or_min, function="idxmin"
+)
+
+
+def cuda_Group_size(context, builder, sig, args):
+    grp = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    )
+    return grp.size
+
+
+cuda_Group_count = cuda_Group_size
+
+
+for ty in SUPPORTED_GROUPBY_NUMBA_TYPES:
+    cuda_lower("GroupType.max", GroupType(ty))(cuda_Group_max)
+    cuda_lower("GroupType.min", GroupType(ty))(cuda_Group_min)
+    cuda_lower("GroupType.sum", GroupType(ty))(cuda_Group_sum)
+    cuda_lower("GroupType.count", GroupType(ty))(cuda_Group_count)
+    cuda_lower("GroupType.size", GroupType(ty))(cuda_Group_size)
+    cuda_lower("GroupType.mean", GroupType(ty))(cuda_Group_mean)
+    cuda_lower("GroupType.std", GroupType(ty))(cuda_Group_std)
+    cuda_lower("GroupType.var", GroupType(ty))(cuda_Group_var)
+    cuda_lower("GroupType.idxmax", GroupType(ty, types.int64))(
+        cuda_Group_idxmax
+    )
+    cuda_lower("GroupType.idxmin", GroupType(ty, types.int64))(
+        cuda_Group_idxmin
+    )
+    cuda_lower("GroupType.corr", GroupType(ty), GroupType(ty))(group_corr)
diff --git a/python/cudf/cudf/core/udf/groupby_typing.py b/python/cudf/cudf/core/udf/groupby_typing.py
new file mode 100644
index 0000000..97afdd1
--- /dev/null
+++ b/python/cudf/cudf/core/udf/groupby_typing.py
@@ -0,0 +1,284 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+from typing import Any, Dict
+
+import numba
+from numba import cuda, types
+from numba.core.extending import (
+    make_attribute_wrapper,
+    models,
+    register_model,
+    type_callable,
+    typeof_impl,
+)
+from numba.core.typing import signature as nb_signature
+from numba.core.typing.templates import AbstractTemplate, AttributeTemplate
+from numba.cuda.cudadecl import registry as cuda_registry
+from numba.np import numpy_support
+
+index_default_type = types.int64
+group_size_type = types.int64
+SUPPORTED_GROUPBY_NUMBA_TYPES = [
+    types.int32,
+    types.int64,
+    types.float32,
+    types.float64,
+]
+SUPPORTED_GROUPBY_NUMPY_TYPES = [
+    numpy_support.as_dtype(dt) for dt in SUPPORTED_GROUPBY_NUMBA_TYPES
+]
+
+
+class Group:
+    """
+    A piece of python code whose purpose is to be replaced
+    during compilation. After being registered to GroupType,
+    serves as a handle for instantiating GroupType objects
+    in python code and accessing their attributes
+    """
+
+    pass
+
+
+class GroupType(numba.types.Type):
+    """
+    Numba extension type carrying metadata associated with a single
+    GroupBy group. This metadata ultimately is passed to the CUDA
+    __device__ function which actually performs the work.
+    """
+
+    def __init__(self, group_scalar_type, index_type=index_default_type):
+        self.group_scalar_type = group_scalar_type
+        self.index_type = index_type
+        self.group_data_type = types.CPointer(group_scalar_type)
+        self.group_size_type = group_size_type
+        self.group_index_type = types.CPointer(index_type)
+        super().__init__(
+            name=f"Group({self.group_scalar_type}, {self.index_type})"
+        )
+
+
+@typeof_impl.register(Group)
+def typeof_group(val, c):
+    """
+    Tie Group and GroupType together such that when Numba
+    sees usage of Group in raw python code, it knows to
+    treat those usages as uses of GroupType
+    """
+    return GroupType(
+        numba.np.numpy_support.from_dtype(val.dtype),
+        numba.np.numpy_support.from_dtype(val.index_dtype),
+    )
+
+
+# The typing of the python "function" Group.__init__
+# as it appears in python code
+@type_callable(Group)
+def type_group(context):
+    def typer(group_data, size, index):
+        if (
+            isinstance(group_data, types.Array)
+            and isinstance(size, types.Integer)
+            and isinstance(index, types.Array)
+        ):
+            return GroupType(group_data.dtype, index.dtype)
+
+    return typer
+
+
+@register_model(GroupType)
+class GroupModel(models.StructModel):
+    """
+    Model backing GroupType instances. See the link below for details.
+    https://github.com/numba/numba/blob/main/numba/core/datamodel/models.py
+    """
+
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("group_data", types.CPointer(fe_type.group_scalar_type)),
+            ("size", group_size_type),
+            ("index", types.CPointer(fe_type.index_type)),
+        ]
+        super().__init__(dmm, fe_type, members)
+
+
+call_cuda_functions: Dict[Any, Any] = {}
+
+
+def _register_cuda_binary_reduction_caller(funcname, lty, rty, retty):
+    cuda_func = cuda.declare_device(
+        f"Block{funcname}_{lty}_{rty}",
+        retty(types.CPointer(lty), types.CPointer(rty), group_size_type),
+    )
+
+    def caller(lhs, rhs, size):
+        return cuda_func(lhs, rhs, size)
+
+    call_cuda_functions.setdefault(funcname.lower(), {})
+
+    type_key = retty, lty, rty
+    call_cuda_functions[funcname.lower()][type_key] = caller
+
+
+def _register_cuda_unary_reduction_caller(funcname, inputty, retty):
+    cuda_func = cuda.declare_device(
+        f"Block{funcname}_{inputty}",
+        retty(types.CPointer(inputty), group_size_type),
+    )
+
+    def caller(data, size):
+        return cuda_func(data, size)
+
+    call_cuda_functions.setdefault(funcname.lower(), {})
+
+    type_key = (retty, inputty)
+    call_cuda_functions[funcname.lower()][type_key] = caller
+
+
+def _register_cuda_idx_reduction_caller(funcname, inputty):
+    cuda_func = cuda.declare_device(
+        f"Block{funcname}_{inputty}",
+        types.int64(
+            types.CPointer(inputty),
+            types.CPointer(index_default_type),
+            group_size_type,
+        ),
+    )
+
+    def caller(data, index, size):
+        return cuda_func(data, index, size)
+
+    # only support default index type right now
+    type_key = (index_default_type, inputty)
+    call_cuda_functions.setdefault(funcname.lower(), {})
+    call_cuda_functions[funcname.lower()][type_key] = caller
+
+
+def _make_unary_attr(funcname):
+    class GroupUnaryReductionAttrTyping(AbstractTemplate):
+        key = f"GroupType.{funcname}"
+
+        def generic(self, args, kws):
+            for retty, inputty in call_cuda_functions[funcname.lower()].keys():
+                if self.this.group_scalar_type == inputty:
+                    return nb_signature(retty, recvr=self.this)
+            return None
+
+    def _attr(self, mod):
+        return types.BoundFunction(
+            GroupUnaryReductionAttrTyping,
+            GroupType(mod.group_scalar_type, mod.index_type),
+        )
+
+    return _attr
+
+
+def _create_reduction_attr(name, retty=None):
+    class Attr(AbstractTemplate):
+        key = name
+
+    def generic(self, args, kws):
+        return nb_signature(
+            self.this.group_scalar_type if not retty else retty,
+            recvr=self.this,
+        )
+
+    Attr.generic = generic
+
+    def _attr(self, mod):
+        return types.BoundFunction(
+            Attr, GroupType(mod.group_scalar_type, mod.index_type)
+        )
+
+    return _attr
+
+
+class GroupIdxMax(AbstractTemplate):
+    key = "GroupType.idxmax"
+
+    def generic(self, args, kws):
+        return nb_signature(self.this.index_type, recvr=self.this)
+
+
+class GroupIdxMin(AbstractTemplate):
+    key = "GroupType.idxmin"
+
+    def generic(self, args, kws):
+        return nb_signature(self.this.index_type, recvr=self.this)
+
+
+class GroupCorr(AbstractTemplate):
+    key = "GroupType.corr"
+
+    def generic(self, args, kws):
+        return nb_signature(types.float64, args[0], recvr=self.this)
+
+
+@cuda_registry.register_attr
+class GroupAttr(AttributeTemplate):
+    key = GroupType
+
+    resolve_max = _make_unary_attr("max")
+    resolve_min = _make_unary_attr("min")
+    resolve_sum = _make_unary_attr("sum")
+
+    resolve_mean = _make_unary_attr("mean")
+    resolve_var = _make_unary_attr("var")
+    resolve_std = _make_unary_attr("std")
+
+    resolve_size = _create_reduction_attr(
+        "GroupType.size", retty=group_size_type
+    )
+    resolve_count = _create_reduction_attr(
+        "GroupType.count", retty=types.int64
+    )
+
+    def resolve_idxmax(self, mod):
+        return types.BoundFunction(
+            GroupIdxMax, GroupType(mod.group_scalar_type, mod.index_type)
+        )
+
+    def resolve_idxmin(self, mod):
+        return types.BoundFunction(
+            GroupIdxMin, GroupType(mod.group_scalar_type, mod.index_type)
+        )
+
+    def resolve_corr(self, mod):
+        return types.BoundFunction(
+            GroupCorr, GroupType(mod.group_scalar_type, mod.index_type)
+        )
+
+
+for ty in SUPPORTED_GROUPBY_NUMBA_TYPES:
+    _register_cuda_unary_reduction_caller("Max", ty, ty)
+    _register_cuda_unary_reduction_caller("Min", ty, ty)
+    _register_cuda_idx_reduction_caller("IdxMax", ty)
+    _register_cuda_idx_reduction_caller("IdxMin", ty)
+
+    if ty in types.integer_domain:
+        _register_cuda_binary_reduction_caller("Corr", ty, ty, types.float64)
+
+
+_register_cuda_unary_reduction_caller("Sum", types.int32, types.int64)
+_register_cuda_unary_reduction_caller("Sum", types.int64, types.int64)
+_register_cuda_unary_reduction_caller("Sum", types.float32, types.float32)
+_register_cuda_unary_reduction_caller("Sum", types.float64, types.float64)
+
+
+_register_cuda_unary_reduction_caller("Mean", types.int32, types.float64)
+_register_cuda_unary_reduction_caller("Mean", types.int64, types.float64)
+_register_cuda_unary_reduction_caller("Mean", types.float32, types.float32)
+_register_cuda_unary_reduction_caller("Mean", types.float64, types.float64)
+
+_register_cuda_unary_reduction_caller("Std", types.int32, types.float64)
+_register_cuda_unary_reduction_caller("Std", types.int64, types.float64)
+_register_cuda_unary_reduction_caller("Std", types.float32, types.float32)
+_register_cuda_unary_reduction_caller("Std", types.float64, types.float64)
+
+_register_cuda_unary_reduction_caller("Var", types.int32, types.float64)
+_register_cuda_unary_reduction_caller("Var", types.int64, types.float64)
+_register_cuda_unary_reduction_caller("Var", types.float32, types.float32)
+_register_cuda_unary_reduction_caller("Var", types.float64, types.float64)
+
+
+for attr in ("group_data", "index", "size"):
+    make_attribute_wrapper(GroupType, attr, attr)
diff --git a/python/cudf/cudf/core/udf/groupby_utils.py b/python/cudf/cudf/core/udf/groupby_utils.py
new file mode 100644
index 0000000..b18720f
--- /dev/null
+++ b/python/cudf/cudf/core/udf/groupby_utils.py
@@ -0,0 +1,229 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+
+import cupy as cp
+import numpy as np
+from numba import cuda, types
+from numba.core.errors import TypingError
+from numba.cuda.cudadrv.devices import get_context
+from numba.np import numpy_support
+
+import cudf.core.udf.utils
+from cudf.core.udf.groupby_typing import (
+    SUPPORTED_GROUPBY_NUMPY_TYPES,
+    Group,
+    GroupType,
+)
+from cudf.core.udf.templates import (
+    group_initializer_template,
+    groupby_apply_kernel_template,
+)
+from cudf.core.udf.utils import (
+    Row,
+    _compile_or_get,
+    _get_extensionty_size,
+    _get_kernel,
+    _get_udf_return_type,
+    _supported_cols_from_frame,
+    _supported_dtypes_from_frame,
+)
+from cudf.utils._numba import _CUDFNumbaConfig
+from cudf.utils.utils import _cudf_nvtx_annotate
+
+
+def _get_frame_groupby_type(dtype, index_dtype):
+    """
+    Get the Numba type corresponding to a row of grouped data. Models the
+    column as a Record-like data structure containing GroupTypes. See
+    numba.np.numpy_support.from_struct_dtype for details.
+
+    Parameters
+    ----------
+    level : np.dtype
+        A numpy structured array dtype associating field names
+        to scalar dtypes
+    index_dtype : np.dtype
+        A numpy scalar dtype associated with the index of the
+        incoming grouped data
+    """
+    # Create the numpy structured type corresponding to the numpy dtype.
+    fields = []
+    offset = 0
+
+    sizes = [val[0].itemsize for val in dtype.fields.values()]
+    for i, (name, info) in enumerate(dtype.fields.items()):
+        elemdtype = info[0]
+        title = info[2] if len(info) == 3 else None
+        ty = numpy_support.from_dtype(elemdtype)
+        indexty = numpy_support.from_dtype(index_dtype)
+        groupty = GroupType(ty, indexty)
+        infos = {
+            "type": groupty,
+            "offset": offset,
+            "title": title,
+        }
+        fields.append((name, infos))
+        offset += _get_extensionty_size(groupty)
+
+        # Align the next member of the struct to be a multiple of the
+        # memory access size, per PTX ISA 7.4/5.4.5
+        if i < len(sizes) - 1:
+            alignment = offset % 8
+            if alignment != 0:
+                offset += 8 - alignment
+
+    # Numba requires that structures are aligned for the CUDA target
+    _is_aligned_struct = True
+    return Row(fields, offset, _is_aligned_struct)
+
+
+def _groupby_apply_kernel_string_from_template(frame, args):
+    """
+    Function to write numba kernels for `Groupby.apply` as a string.
+    Workaround until numba supports functions that use `*args`
+    """
+    # Create argument list for kernel
+    frame = _supported_cols_from_frame(
+        frame, supported_types=SUPPORTED_GROUPBY_NUMPY_TYPES
+    )
+    input_columns = ", ".join([f"input_col_{i}" for i in range(len(frame))])
+    extra_args = ", ".join([f"extra_arg_{i}" for i in range(len(args))])
+
+    # Generate the initializers for each device function argument
+    initializers = []
+    for i, colname in enumerate(frame.keys()):
+        initializers.append(
+            group_initializer_template.format(idx=i, name=colname)
+        )
+
+    return groupby_apply_kernel_template.format(
+        input_columns=input_columns,
+        extra_args=extra_args,
+        group_initializers="\n".join(initializers),
+    )
+
+
+def _get_groupby_apply_kernel(frame, func, args):
+    np_field_types = np.dtype(
+        list(
+            _supported_dtypes_from_frame(
+                frame, supported_types=SUPPORTED_GROUPBY_NUMPY_TYPES
+            ).items()
+        )
+    )
+    dataframe_group_type = _get_frame_groupby_type(
+        np_field_types, frame.index.dtype
+    )
+    return_type = _get_udf_return_type(dataframe_group_type, func, args)
+
+    # Dict of 'local' variables into which `_kernel` is defined
+    global_exec_context = {
+        "cuda": cuda,
+        "Group": Group,
+        "dataframe_group_type": dataframe_group_type,
+        "types": types,
+    }
+    kernel_string = _groupby_apply_kernel_string_from_template(frame, args)
+    kernel = _get_kernel(kernel_string, global_exec_context, None, func)
+
+    return kernel, return_type
+
+
+@_cudf_nvtx_annotate
+def jit_groupby_apply(offsets, grouped_values, function, *args):
+    """
+    Main entrypoint for JIT Groupby.apply via Numba.
+
+    Parameters
+    ----------
+    offsets : list
+        A list of integers denoting the indices of the group
+        boundaries in grouped_values
+    grouped_values : DataFrame
+        A DataFrame representing the source data
+        sorted by group keys
+    function : callable
+        The user-defined function to execute
+    """
+
+    kernel, return_type = _compile_or_get(
+        grouped_values,
+        function,
+        args,
+        kernel_getter=_get_groupby_apply_kernel,
+        suffix="__GROUPBY_APPLY_UDF",
+    )
+
+    offsets = cp.asarray(offsets)
+    ngroups = len(offsets) - 1
+
+    output = cudf.core.column.column_empty(ngroups, dtype=return_type)
+
+    launch_args = [
+        offsets,
+        output,
+        grouped_values.index,
+    ]
+    launch_args += list(
+        _supported_cols_from_frame(
+            grouped_values, supported_types=SUPPORTED_GROUPBY_NUMPY_TYPES
+        ).values()
+    )
+    launch_args += list(args)
+
+    max_group_size = cp.diff(offsets).max()
+
+    if max_group_size >= 256:
+        blocklim = 256
+    else:
+        blocklim = ((max_group_size + 32 - 1) // 32) * 32
+
+    if kernel.specialized:
+        specialized = kernel
+    else:
+        specialized = kernel.specialize(*launch_args)
+
+    # Ask the driver to give a good config
+    ctx = get_context()
+    # Dispatcher is specialized, so there's only one definition - get
+    # it so we can get the cufunc from the code library
+    (kern_def,) = specialized.overloads.values()
+    grid, tpb = ctx.get_max_potential_block_size(
+        func=kern_def._codelibrary.get_cufunc(),
+        b2d_func=0,
+        memsize=0,
+        blocksizelimit=int(blocklim),
+    )
+
+    # Launch kernel
+    with _CUDFNumbaConfig():
+        specialized[ngroups, tpb](*launch_args)
+
+    return output
+
+
+def _can_be_jitted(frame, func, args):
+    """
+    Determine if this UDF is supported through the JIT engine
+    by attempting to compile just the function to PTX using the
+    target set of types
+    """
+    if not hasattr(func, "__code__"):
+        # Numba requires bytecode to be present to proceed.
+        # See https://github.com/numba/numba/issues/4587
+        return False
+    np_field_types = np.dtype(
+        list(
+            _supported_dtypes_from_frame(
+                frame, supported_types=SUPPORTED_GROUPBY_NUMPY_TYPES
+            ).items()
+        )
+    )
+    dataframe_group_type = _get_frame_groupby_type(
+        np_field_types, frame.index.dtype
+    )
+    try:
+        _get_udf_return_type(dataframe_group_type, func, args)
+        return True
+    except TypingError:
+        return False
diff --git a/python/cudf/cudf/core/udf/masked_lowering.py b/python/cudf/cudf/core/udf/masked_lowering.py
new file mode 100644
index 0000000..ae09294
--- /dev/null
+++ b/python/cudf/cudf/core/udf/masked_lowering.py
@@ -0,0 +1,400 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import operator
+
+from llvmlite import ir
+from numba.core import cgutils
+from numba.core.typing import signature as nb_signature
+from numba.cuda.cudaimpl import (
+    lower as cuda_lower,
+    registry as cuda_lowering_registry,
+)
+from numba.extending import lower_builtin, types
+
+from cudf.core.udf import api
+from cudf.core.udf._ops import (
+    arith_ops,
+    bitwise_ops,
+    comparison_ops,
+    unary_ops,
+)
+from cudf.core.udf.masked_typing import (
+    MaskedType,
+    NAType,
+    _supported_masked_types,
+)
+
+
+@cuda_lowering_registry.lower_constant(NAType)
+def constant_na(context, builder, ty, pyval):
+    # This handles None, etc.
+    return context.get_dummy_value()
+
+
+# In the typing phase, we declared that a `MaskedType` can be
+# added to another `MaskedType` and specified what kind of a
+# `MaskedType` would result. Now we have to actually fill in
+# the implementation details of how to do that. This is where
+# we can involve both validities in constructing the answer
+
+
+def make_arithmetic_op(op):
+    """
+    Make closures that implement arithmetic operations. See
+    register_arithmetic_op for details.
+    """
+
+    def masked_scalar_op_impl(context, builder, sig, args):
+        """
+        Implement `MaskedType` <op> `MaskedType`
+        """
+        # MaskedType(...), MaskedType(...)
+        masked_type_1, masked_type_2 = sig.args
+        # MaskedType(...)
+        masked_return_type = sig.return_type
+
+        # Let there be two actual LLVM structs backing the two inputs
+        # https://mapping-high-level-constructs-to-llvm-ir.readthedocs.io/en/latest/basic-constructs/structures.html
+        m1 = cgutils.create_struct_proxy(masked_type_1)(
+            context, builder, value=args[0]
+        )
+        m2 = cgutils.create_struct_proxy(masked_type_2)(
+            context, builder, value=args[1]
+        )
+
+        # we will return an output struct
+        result = cgutils.create_struct_proxy(masked_return_type)(
+            context, builder
+        )
+        # compute output validity
+        valid = builder.and_(m1.valid, m2.valid)
+        result.valid = valid
+        with builder.if_then(valid):
+            # Let numba handle generating the extra IR needed to perform
+            # operations on mixed types, by compiling the final core op between
+            # the two primitive values as a separate function and calling it
+            result.value = context.compile_internal(
+                builder,
+                lambda x, y: op(x, y),
+                nb_signature(
+                    masked_return_type.value_type,
+                    masked_type_1.value_type,
+                    masked_type_2.value_type,
+                ),
+                (m1.value, m2.value),
+            )
+        return result._getvalue()
+
+    return masked_scalar_op_impl
+
+
+def make_unary_op(op):
+    """
+    Make closures that implement unary operations. See register_unary_op for
+    details.
+    """
+
+    def masked_scalar_unary_op_impl(context, builder, sig, args):
+        """
+        Implement <op> `MaskedType`
+        """
+        # MaskedType(...)
+        masked_type_1 = sig.args[0]
+        # MaskedType(...)
+        masked_return_type = sig.return_type
+
+        m1 = cgutils.create_struct_proxy(masked_type_1)(
+            context, builder, value=args[0]
+        )
+
+        # we will return an output struct
+        result = cgutils.create_struct_proxy(masked_return_type)(
+            context, builder
+        )
+
+        # compute output validity
+        result.valid = m1.valid
+        with builder.if_then(m1.valid):
+            # Let numba handle generating the extra IR needed to perform
+            # operations on mixed types, by compiling the final core op between
+            # the two primitive values as a separate function and calling it
+            result.value = context.compile_internal(
+                builder,
+                lambda x: op(x),
+                nb_signature(
+                    masked_return_type.value_type,
+                    masked_type_1.value_type,
+                ),
+                (m1.value,),
+            )
+        return result._getvalue()
+
+    return masked_scalar_unary_op_impl
+
+
+def register_arithmetic_op(op):
+    """
+    Register a lowering implementation for the
+    arithmetic op `op`.
+
+    Because the lowering implementations compile the final
+    op separately using a lambda and compile_internal, `op`
+    needs to be tied to each lowering implementation using
+    a closure.
+
+    This function makes and lowers a closure for one op.
+
+    """
+    to_lower_op = make_arithmetic_op(op)
+    cuda_lower(op, MaskedType, MaskedType)(to_lower_op)
+
+
+def register_unary_op(op):
+    """
+    Register a lowering implementation for the
+    unary op `op`.
+
+    Because the lowering implementations compile the final
+    op separately using a lambda and compile_internal, `op`
+    needs to be tied to each lowering implementation using
+    a closure.
+
+    This function makes and lowers a closure for one op.
+
+    """
+    to_lower_op = make_unary_op(op)
+    cuda_lower(op, MaskedType)(to_lower_op)
+
+
+def masked_scalar_null_op_impl(context, builder, sig, args):
+    """
+    Implement `MaskedType` <op> `NAType`
+    or `NAType` <op> `MaskedType`
+    The answer to this is known up front so no actual operation
+    needs to take place
+    """
+
+    return_type = sig.return_type  # MaskedType(...)
+    result = cgutils.create_struct_proxy(MaskedType(return_type.value_type))(
+        context, builder
+    )
+
+    # Invalidate the struct and leave `value` uninitialized
+    result.valid = context.get_constant(types.boolean, 0)
+    return result._getvalue()
+
+
+def make_const_op(op):
+    def masked_scalar_const_op_impl(context, builder, sig, args):
+        return_type = sig.return_type
+        result = cgutils.create_struct_proxy(return_type)(context, builder)
+        result.valid = context.get_constant(types.boolean, 0)
+        if isinstance(sig.args[0], MaskedType):
+            masked_type, const_type = sig.args
+            masked_value, const_value = args
+
+            indata = cgutils.create_struct_proxy(masked_type)(
+                context, builder, value=masked_value
+            )
+            nb_sig = nb_signature(
+                return_type.value_type, masked_type.value_type, const_type
+            )
+            compile_args = (indata.value, const_value)
+        else:
+            const_type, masked_type = sig.args
+            const_value, masked_value = args
+            indata = cgutils.create_struct_proxy(masked_type)(
+                context, builder, value=masked_value
+            )
+            nb_sig = nb_signature(
+                return_type.value_type, const_type, masked_type.value_type
+            )
+            compile_args = (const_value, indata.value)
+        with builder.if_then(indata.valid):
+            result.value = context.compile_internal(
+                builder, lambda x, y: op(x, y), nb_sig, compile_args
+            )
+            result.valid = context.get_constant(types.boolean, 1)
+        return result._getvalue()
+
+    return masked_scalar_const_op_impl
+
+
+def register_const_op(op):
+    to_lower_op = make_const_op(op)
+    cuda_lower(op, MaskedType, types.Number)(to_lower_op)
+    cuda_lower(op, types.Number, MaskedType)(to_lower_op)
+    cuda_lower(op, MaskedType, types.Boolean)(to_lower_op)
+    cuda_lower(op, types.Boolean, MaskedType)(to_lower_op)
+    cuda_lower(op, MaskedType, types.NPDatetime)(to_lower_op)
+    cuda_lower(op, types.NPDatetime, MaskedType)(to_lower_op)
+    cuda_lower(op, MaskedType, types.NPTimedelta)(to_lower_op)
+    cuda_lower(op, types.NPTimedelta, MaskedType)(to_lower_op)
+
+
+# register all lowering at init
+for binary_op in arith_ops + bitwise_ops + comparison_ops:
+    register_arithmetic_op(binary_op)
+    register_const_op(binary_op)
+    # null op impl can be shared between all ops
+    cuda_lower(binary_op, MaskedType, NAType)(masked_scalar_null_op_impl)
+    cuda_lower(binary_op, NAType, MaskedType)(masked_scalar_null_op_impl)
+
+# register all lowering at init
+for unary_op in unary_ops:
+    register_unary_op(unary_op)
+register_unary_op(abs)
+
+
+@cuda_lower(operator.is_, MaskedType, NAType)
+@cuda_lower(operator.is_, NAType, MaskedType)
+def masked_scalar_is_null_impl(context, builder, sig, args):
+    """
+    Implement `MaskedType` is `NA`
+    """
+    if isinstance(sig.args[1], NAType):
+        masked_type, na = sig.args
+        value = args[0]
+    else:
+        na, masked_type = sig.args
+        value = args[1]
+
+    indata = cgutils.create_struct_proxy(masked_type)(
+        context, builder, value=value
+    )
+    result = cgutils.alloca_once(builder, ir.IntType(1))
+    with builder.if_else(indata.valid) as (then, otherwise):
+        with then:
+            builder.store(context.get_constant(types.boolean, 0), result)
+        with otherwise:
+            builder.store(context.get_constant(types.boolean, 1), result)
+
+    return builder.load(result)
+
+
+# Main kernel always calls `pack_return` on whatever the user defined
+# function returned. This returns the same data if its already a `Masked`
+# else packs it up into a new one that is valid from the get go
+@cuda_lower(api.pack_return, MaskedType)
+def pack_return_masked_impl(context, builder, sig, args):
+    return args[0]
+
+
+@cuda_lower(api.pack_return, types.Boolean)
+@cuda_lower(api.pack_return, types.Number)
+@cuda_lower(api.pack_return, types.NPDatetime)
+@cuda_lower(api.pack_return, types.NPTimedelta)
+def pack_return_scalar_impl(context, builder, sig, args):
+    outdata = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    outdata.value = args[0]
+    outdata.valid = context.get_constant(types.boolean, 1)
+
+    return outdata._getvalue()
+
+
+@cuda_lower(operator.truth, MaskedType)
+@cuda_lower(bool, MaskedType)
+def masked_scalar_bool_impl(context, builder, sig, args):
+    indata = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    )
+    result = cgutils.alloca_once(builder, ir.IntType(1))
+    with builder.if_else(indata.valid) as (then, otherwise):
+        with then:
+            builder.store(
+                context.cast(
+                    builder,
+                    indata.value,
+                    sig.args[0].value_type,
+                    types.boolean,
+                ),
+                result,
+            )
+        with otherwise:
+            builder.store(context.get_constant(types.boolean, 0), result)
+    return builder.load(result)
+
+
+@cuda_lower(float, MaskedType)
+@cuda_lower(int, MaskedType)
+def masked_scalar_cast_impl(context, builder, sig, args):
+    input = cgutils.create_struct_proxy(sig.args[0])(
+        context, builder, value=args[0]
+    )
+    result = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+    casted = context.cast(
+        builder,
+        input.value,
+        sig.args[0].value_type,
+        sig.return_type.value_type,
+    )
+    result.value = casted
+    result.valid = input.valid
+    return result._getvalue()
+
+
+# To handle the unification, we need to support casting from any type to a
+# masked type. The cast implementation takes the value passed in and returns
+# a masked type struct wrapping that value.
+@cuda_lowering_registry.lower_cast(types.Any, MaskedType)
+def cast_primitive_to_masked(context, builder, fromty, toty, val):
+    casted = context.cast(builder, val, fromty, toty.value_type)
+    ext = cgutils.create_struct_proxy(toty)(context, builder)
+    ext.value = casted
+    ext.valid = context.get_constant(types.boolean, 1)
+    return ext._getvalue()
+
+
+@cuda_lowering_registry.lower_cast(NAType, MaskedType)
+def cast_na_to_masked(context, builder, fromty, toty, val):
+    result = cgutils.create_struct_proxy(toty)(context, builder)
+    result.valid = context.get_constant(types.boolean, 0)
+
+    return result._getvalue()
+
+
+@cuda_lowering_registry.lower_cast(MaskedType, MaskedType)
+def cast_masked_to_masked(context, builder, fromty, toty, val):
+    """
+    When numba encounters an op that expects a certain type and
+    the input to the op is not of the expected type it will try
+    to cast the input to the appropriate type. But, in our case
+    the input may be a MaskedType, which numba doesn't natively
+    know how to cast to a different MaskedType with a different
+    `value_type`. This implements and registers that cast.
+    """
+
+    # We will
+    operand = cgutils.create_struct_proxy(fromty)(context, builder, value=val)
+    casted = context.cast(
+        builder, operand.value, fromty.value_type, toty.value_type
+    )
+    ext = cgutils.create_struct_proxy(toty)(context, builder)
+    ext.value = casted
+    ext.valid = operand.valid
+    return ext._getvalue()
+
+
+# Masked constructor for use in a kernel for testing
+def masked_constructor(context, builder, sig, args):
+    ty = sig.return_type
+    value, valid = args
+    masked = cgutils.create_struct_proxy(ty)(context, builder)
+    masked.value = value
+    masked.valid = valid
+    return masked._getvalue()
+
+
+for ty in _supported_masked_types:
+    lower_builtin(api.Masked, ty, types.boolean)(masked_constructor)
+
+
+# Allows us to make an instance of MaskedType a global variable
+# and properly use it inside functions we will later compile
+@cuda_lowering_registry.lower_constant(MaskedType)
+def lower_constant_masked(context, builder, ty, val):
+    masked = cgutils.create_struct_proxy(ty)(context, builder)
+    masked.value = context.get_constant(ty.value_type, val.value)
+    masked.valid = context.get_constant(types.boolean, val.valid)
+    return masked._getvalue()
diff --git a/python/cudf/cudf/core/udf/masked_typing.py b/python/cudf/cudf/core/udf/masked_typing.py
new file mode 100644
index 0000000..4c90c5b
--- /dev/null
+++ b/python/cudf/cudf/core/udf/masked_typing.py
@@ -0,0 +1,677 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import operator
+
+import numpy as np
+from numba import types
+from numba.core.extending import (
+    make_attribute_wrapper,
+    models,
+    register_model,
+    typeof_impl,
+)
+from numba.core.typing import signature as nb_signature
+from numba.core.typing.templates import (
+    AbstractTemplate,
+    AttributeTemplate,
+    ConcreteTemplate,
+)
+from numba.core.typing.typeof import typeof
+from numba.cuda.cudadecl import registry as cuda_decl_registry
+from numba.np.numpy_support import from_dtype
+
+from cudf.core.missing import NA
+from cudf.core.udf import api
+from cudf.core.udf._ops import (
+    arith_ops,
+    bitwise_ops,
+    comparison_ops,
+    unary_ops,
+)
+from cudf.core.udf.strings_typing import (
+    StringView,
+    UDFString,
+    bool_binary_funcs,
+    id_unary_funcs,
+    int_binary_funcs,
+    size_type,
+    string_return_attrs,
+    string_unary_funcs,
+    string_view,
+    udf_string,
+)
+from cudf.utils.dtypes import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    STRING_TYPES,
+    TIMEDELTA_TYPES,
+)
+
+SUPPORTED_NUMPY_TYPES = (
+    NUMERIC_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | STRING_TYPES
+)
+supported_type_str = "\n".join(sorted(list(SUPPORTED_NUMPY_TYPES) + ["bool"]))
+
+_units = ["ns", "ms", "us", "s"]
+_datetime_cases = {types.NPDatetime(u) for u in _units}
+_timedelta_cases = {types.NPTimedelta(u) for u in _units}
+_supported_masked_types = (
+    types.integer_domain
+    | types.real_domain
+    | _datetime_cases
+    | _timedelta_cases
+    | {types.boolean}
+    | {string_view, udf_string}
+)
+
+
+SUPPORTED_NUMBA_TYPES = (
+    types.Number,
+    types.Boolean,
+    types.NPDatetime,
+    types.NPTimedelta,
+    StringView,
+    UDFString,
+)
+
+
+def _format_error_string(err):
+    """
+    Wrap an error message in newlines and color it red.
+    """
+    return "\033[91m" + "\n" + err + "\n" + "\033[0m"
+
+
+def _type_to_masked_type(t):
+    if isinstance(t, SUPPORTED_NUMBA_TYPES):
+        return t
+    else:
+        # Unsupported Dtype. Numba tends to print out the type info
+        # for whatever operands and operation failed to type and then
+        # output its own error message. Putting the message in the repr
+        # then is one way of getting the true cause to the user
+        err = _format_error_string(
+            "Unsupported MaskedType. This is usually caused by "
+            "attempting to use a column of unsupported dtype in a UDF. "
+            f"Supported dtypes are:\n{supported_type_str}"
+        )
+        return types.Poison(err)
+
+
+# Masked scalars of all types
+class MaskedType(types.Type):
+    """
+    A Numba type consisting of a value of some primitive type
+    and a validity boolean, over which we can define math ops
+    """
+
+    def __init__(self, value):
+        # MaskedType in Numba shall be parameterized
+        # with a value type
+        self.value_type = _type_to_masked_type(value)
+        super().__init__(name=f"Masked({self.value_type})")
+
+    def __hash__(self):
+        """
+        Needed so that numba caches type instances with different
+        `value_type` separately.
+        """
+        return hash(repr(self))
+
+    def unify(self, context, other):
+        """
+        Often within a UDF an instance arises where a variable could
+        be a `MaskedType`, an `NAType`, or a literal based off
+        the data at runtime, for example the variable `ret` here:
+
+        def f(x):
+            if x == 1:
+                ret = x
+            elif x > 2:
+                ret = 1
+            else:
+                ret = cudf.NA
+            return ret
+
+        When numba analyzes this function it will eventually figure
+        out that the variable `ret` could be any of the three types
+        from above. This scenario will only work if numba knows how
+        to find some kind of common type between the possibilities,
+        and this function implements that - the goal is to return a
+        common type when comparing `self` to other.
+
+        """
+
+        # If we have Masked and NA, the output should be a
+        # MaskedType with the original type as its value_type
+        if isinstance(other, NAType):
+            return self
+
+        # two MaskedType unify to a new MaskedType whose value_type
+        # is the result of unifying `self` and `other` `value_type`
+        elif isinstance(other, MaskedType):
+            return MaskedType(
+                context.unify_pairs(self.value_type, other.value_type)
+            )
+
+        # if we have MaskedType and something that results in a
+        # scalar, unify between the MaskedType's value_type
+        # and that other thing
+        unified = context.unify_pairs(self.value_type, other)
+        if unified is None:
+            # The value types don't unify, so there is no unified masked type
+            return None
+
+        return MaskedType(unified)
+
+    def __eq__(self, other):
+        # Equality is required for determining whether a cast is required
+        # between two different types.
+        if not isinstance(other, MaskedType):
+            # Require a cast when the other type is not masked
+            return False
+
+        # Require a cast for another masked with a different value type
+        return self.value_type == other.value_type
+
+
+# For typing a Masked constant value defined outside a kernel (e.g. captured in
+# a closure).
+@typeof_impl.register(api.Masked)
+def typeof_masked(val, c):
+    return MaskedType(typeof(val.value))
+
+
+# Implemented typing for Masked(value, valid) - the construction of a Masked
+# type in a kernel.
+@cuda_decl_registry.register
+class MaskedConstructor(ConcreteTemplate):
+    key = api.Masked
+    cases = [
+        nb_signature(MaskedType(t), t, types.boolean)
+        for t in _supported_masked_types
+    ]
+
+
+# Typing for `api.Masked`
+@cuda_decl_registry.register_attr
+class ClassesTemplate(AttributeTemplate):
+    key = types.Module(api)
+
+    def resolve_Masked(self, mod):
+        return types.Function(MaskedConstructor)
+
+
+# Registration of the global is also needed for Numba to type api.Masked
+cuda_decl_registry.register_global(api, types.Module(api))
+# For typing bare Masked (as in `from .api import Masked`
+cuda_decl_registry.register_global(
+    api.Masked, types.Function(MaskedConstructor)
+)
+
+
+# Provide access to `m.value` and `m.valid` in a kernel for a Masked `m`.
+make_attribute_wrapper(MaskedType, "value", "value")
+make_attribute_wrapper(MaskedType, "valid", "valid")
+
+
+# Tell numba how `MaskedType` is constructed on the backend in terms
+# of primitive things that exist at the LLVM level
+@register_model(MaskedType)
+class MaskedModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        # This struct has two members, a value and a validity
+        # let the type of the `value` field be the same as the
+        # `value_type` and let `valid` be a boolean
+        members = [("value", fe_type.value_type), ("valid", types.bool_)]
+        models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+class NAType(types.Type):
+    """
+    A type for handling ops against nulls
+    Exists so we can:
+    1. Teach numba that all occurrences of `cudf.NA` are
+       to be read as instances of this type instead
+    2. Define ops like `if x is cudf.NA` where `x` is of
+       type `Masked` to mean `if x.valid is False`
+    """
+
+    def __init__(self):
+        super().__init__(name="NA")
+
+    def unify(self, context, other):
+        """
+        Masked  <-> NA is deferred to MaskedType.unify()
+        Literal <-> NA -> Masked
+        """
+        if isinstance(other, MaskedType):
+            # bounce to MaskedType.unify
+            return None
+        elif isinstance(other, NAType):
+            # unify {NA, NA} -> NA
+            return self
+        else:
+            return MaskedType(other)
+
+
+na_type = NAType()
+
+
+@typeof_impl.register(type(NA))
+def typeof_na(val, c):
+    """
+    Tie instances of _NAType (cudf.NA) to our NAType.
+    Effectively make it so numba sees `cudf.NA` as an
+    instance of this NAType -> handle it accordingly.
+    """
+    return na_type
+
+
+register_model(NAType)(models.OpaqueModel)
+
+
+# Ultimately, we want numba to produce PTX code that specifies how to implement
+# an operation on two singular `Masked` structs together, which is defined
+# as producing a new `Masked` with the right validity and if valid,
+# the correct value. This happens in two phases:
+#   1. Specify that `Masked` <op> `Masked` exists and what it should return
+#   2. Implement how to actually do (1) at the LLVM level
+# The following code accomplishes (1) - it is really just a way of specifying
+# that the <op> has a CUDA overload that accepts two `Masked` that
+# are parameterized with `value_type` and what flavor of `Masked` to return.
+class MaskedScalarArithOp(AbstractTemplate):
+    def generic(self, args, kws):
+        """
+        Typing for `Masked` <op> `Masked`
+        Numba expects a valid numba type to be returned if typing is successful
+        else `None` signifies the error state (this pattern is commonly used
+        in Numba)
+        """
+        if isinstance(args[0], MaskedType) and isinstance(args[1], MaskedType):
+            # In the case of op(Masked, Masked), the return type is a Masked
+            # such that Masked.value is the primitive type that would have
+            # been resolved if we were just operating on the
+            # `value_type`s.
+            return_type = self.context.resolve_function_type(
+                self.key, (args[0].value_type, args[1].value_type), kws
+            ).return_type
+            return nb_signature(MaskedType(return_type), args[0], args[1])
+
+
+class MaskedScalarUnaryOp(AbstractTemplate):
+    def generic(self, args, kws):
+        if len(args) == 1 and isinstance(args[0], MaskedType):
+            return_type = self.context.resolve_function_type(
+                self.key, (args[0].value_type,), kws
+            ).return_type
+            return nb_signature(MaskedType(return_type), args[0])
+
+
+class MaskedScalarNullOp(AbstractTemplate):
+    def generic(self, args, kws):
+        """
+        Typing for `Masked` + `NA`
+        Handles situations like `x + cudf.NA`
+        """
+        if isinstance(args[0], MaskedType) and isinstance(args[1], NAType):
+            # In the case of op(Masked, NA), the result has the same
+            # dtype as the original regardless of what it is
+            return nb_signature(
+                args[0],
+                args[0],
+                na_type,
+            )
+        elif isinstance(args[0], NAType) and isinstance(args[1], MaskedType):
+            return nb_signature(args[1], na_type, args[1])
+
+
+class MaskedScalarScalarOp(AbstractTemplate):
+    def generic(self, args, kws):
+        """
+        Typing for `Masked` <op> a scalar (and vice-versa).
+        handles situations like `x + 1`
+        """
+        # In the case of op(Masked, scalar), we resolve the type between
+        # the Masked value_type and the scalar's type directly
+        to_resolve_types = None
+        if isinstance(args[0], MaskedType) and isinstance(
+            args[1], SUPPORTED_NUMBA_TYPES
+        ):
+            to_resolve_types = (args[0].value_type, args[1])
+        elif isinstance(args[0], SUPPORTED_NUMBA_TYPES) and isinstance(
+            args[1], MaskedType
+        ):
+            to_resolve_types = (args[1].value_type, args[0])
+        else:
+            # fail typing
+            return None
+        return_type = self.context.resolve_function_type(
+            self.key, to_resolve_types, kws
+        ).return_type
+        return nb_signature(
+            MaskedType(return_type),
+            args[0],
+            args[1],
+        )
+
+
+@cuda_decl_registry.register_global(operator.is_)
+class MaskedScalarIsNull(AbstractTemplate):
+    """
+    Typing for `Masked is cudf.NA`
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], MaskedType) and isinstance(args[1], NAType):
+            return nb_signature(types.boolean, args[0], na_type)
+        elif isinstance(args[1], MaskedType) and isinstance(args[0], NAType):
+            return nb_signature(types.boolean, na_type, args[1])
+
+
+@cuda_decl_registry.register_global(operator.truth)
+class MaskedScalarTruth(AbstractTemplate):
+    """
+    Typing for `if Masked`
+    Used for `if x > y`
+    The truthiness of a MaskedType shall be the truthiness
+    of the `value` stored therein
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], MaskedType):
+            return nb_signature(types.boolean, MaskedType(types.boolean))
+
+
+@cuda_decl_registry.register_global(float)
+class MaskedScalarFloatCast(AbstractTemplate):
+    """
+    Typing for float(Masked)
+    returns the result of calling "float" on the input
+    TODO: retains the validity of the input rather than
+    raising as in float(pd.NA)
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], MaskedType):
+            # following numpy convention np.dtype(float) -> dtype('float64')
+            return nb_signature(MaskedType(types.float64), args[0])
+
+
+@cuda_decl_registry.register_global(int)
+class MaskedScalarIntCast(AbstractTemplate):
+    """
+    Typing for int(Masked)
+    returns the result of calling "int" on the input
+    TODO: retains the validity of the input rather than
+    raising as in int(pd.NA)
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], MaskedType):
+            # following numpy convention np.dtype(int) -> dtype('int64')
+            return nb_signature(MaskedType(types.int64), args[0])
+
+
+@cuda_decl_registry.register_global(abs)
+class MaskedScalarAbsoluteValue(AbstractTemplate):
+    """
+    Typing for the builtin function abs. Returns the same
+    type as input except for boolean values which are converted
+    to integer.
+
+    This follows the expected result from the builtin abs function
+    which differs from numpy - np.abs returns a bool whereas abs
+    itself performs the cast.
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], MaskedType):
+            if isinstance(args[0].value_type, (StringView, UDFString)):
+                # reject string types
+                return
+            else:
+                return_type = self.context.resolve_function_type(
+                    self.key, (args[0].value_type,), kws
+                ).return_type
+                if return_type in types.signed_domain:
+                    # promote to unsigned to avoid overflow
+                    return_type = from_dtype(np.dtype("u" + return_type.name))
+                return nb_signature(MaskedType(return_type), args[0])
+
+
+@cuda_decl_registry.register_global(api.pack_return)
+class UnpackReturnToMasked(AbstractTemplate):
+    """
+    Turn a returned MaskedType into its value and validity
+    or turn a scalar into the tuple (scalar, True).
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], MaskedType):
+            # MaskedType(dtype, valid) -> MaskedType(dtype, valid)
+            return nb_signature(args[0], args[0])
+        elif isinstance(args[0], SUPPORTED_NUMBA_TYPES):
+            # scalar_type -> MaskedType(scalar_type, True)
+            return_type = MaskedType(args[0])
+            return nb_signature(return_type, args[0])
+
+
+for binary_op in arith_ops + bitwise_ops + comparison_ops:
+    # Every op shares the same typing class
+    cuda_decl_registry.register_global(binary_op)(MaskedScalarArithOp)
+    cuda_decl_registry.register_global(binary_op)(MaskedScalarNullOp)
+    cuda_decl_registry.register_global(binary_op)(MaskedScalarScalarOp)
+
+for unary_op in unary_ops:
+    cuda_decl_registry.register_global(unary_op)(MaskedScalarUnaryOp)
+
+
+# Strings functions and utilities
+def _is_valid_string_arg(ty):
+    return (
+        isinstance(ty, MaskedType)
+        and isinstance(ty.value_type, (StringView, UDFString))
+    ) or isinstance(ty, types.StringLiteral)
+
+
+def register_masked_string_function(func):
+    """
+    Helper function wrapping numba's low level extension API. Provides
+    the boilerplate needed to associate a signature with a function or
+    operator to be overloaded.
+    """
+
+    def deco(generic):
+        class MaskedStringFunction(AbstractTemplate):
+            pass
+
+        MaskedStringFunction.generic = generic
+        cuda_decl_registry.register_global(func)(MaskedStringFunction)
+
+    return deco
+
+
+@register_masked_string_function(len)
+def len_typing(self, args, kws):
+    if isinstance(args[0], MaskedType) and isinstance(
+        args[0].value_type, (StringView, UDFString)
+    ):
+        return nb_signature(MaskedType(size_type), MaskedType(string_view))
+    elif isinstance(args[0], types.StringLiteral) and len(args) == 1:
+        return nb_signature(size_type, args[0])
+
+
+@register_masked_string_function(operator.add)
+def concat_typing(self, args, kws):
+    if _is_valid_string_arg(args[0]) and _is_valid_string_arg(args[1]):
+        return nb_signature(
+            MaskedType(udf_string),
+            MaskedType(string_view),
+            MaskedType(string_view),
+        )
+
+
+@register_masked_string_function(operator.contains)
+def contains_typing(self, args, kws):
+    if _is_valid_string_arg(args[0]) and _is_valid_string_arg(args[1]):
+        return nb_signature(
+            MaskedType(types.boolean),
+            MaskedType(string_view),
+            MaskedType(string_view),
+        )
+
+
+class MaskedStringViewCmpOp(AbstractTemplate):
+    """
+    return the boolean result of `cmpop` between to strings
+    since the typing is the same for every comparison operator,
+    we can reuse this class for all of them.
+    """
+
+    def generic(self, args, kws):
+        if _is_valid_string_arg(args[0]) and _is_valid_string_arg(args[1]):
+            return nb_signature(
+                MaskedType(types.boolean),
+                MaskedType(string_view),
+                MaskedType(string_view),
+            )
+
+
+for op in comparison_ops:
+    cuda_decl_registry.register_global(op)(MaskedStringViewCmpOp)
+
+
+def create_masked_binary_attr(attrname, retty):
+    """
+    Helper function wrapping numba's low level extension API. Provides
+    the boilerplate needed to register a binary function of two masked
+    string objects as an attribute of one, e.g. `string.func(other)`.
+    """
+
+    class MaskedStringViewBinaryAttr(AbstractTemplate):
+        key = attrname
+
+        def generic(self, args, kws):
+            return nb_signature(
+                MaskedType(retty), MaskedType(string_view), recvr=self.this
+            )
+
+    def attr(self, mod):
+        return types.BoundFunction(
+            MaskedStringViewBinaryAttr,
+            MaskedType(string_view),
+        )
+
+    return attr
+
+
+def create_masked_unary_attr(attrname, retty):
+    """
+    Helper function wrapping numba's low level extension API. Provides
+    the boilerplate needed to register a unary function of a masked
+    string object as an attribute, e.g. `string.func()`.
+    """
+
+    class MaskedStringViewIdentifierAttr(AbstractTemplate):
+        key = attrname
+
+        def generic(self, args, kws):
+            return nb_signature(MaskedType(retty), recvr=self.this)
+
+    def attr(self, mod):
+        return types.BoundFunction(
+            MaskedStringViewIdentifierAttr,
+            MaskedType(string_view),
+        )
+
+    return attr
+
+
+class MaskedStringViewCount(AbstractTemplate):
+    key = "MaskedType.count"
+
+    def generic(self, args, kws):
+        return nb_signature(
+            MaskedType(size_type), MaskedType(string_view), recvr=self.this
+        )
+
+
+class MaskedStringViewReplace(AbstractTemplate):
+    key = "MaskedType.replace"
+
+    def generic(self, args, kws):
+        return nb_signature(
+            MaskedType(udf_string),
+            MaskedType(string_view),
+            MaskedType(string_view),
+            recvr=self.this,
+        )
+
+
+class MaskedStringViewAttrs(AttributeTemplate):
+    key = MaskedType(string_view)
+
+    def resolve_replace(self, mod):
+        return types.BoundFunction(
+            MaskedStringViewReplace, MaskedType(string_view)
+        )
+
+    def resolve_count(self, mod):
+        return types.BoundFunction(
+            MaskedStringViewCount, MaskedType(string_view)
+        )
+
+    def resolve_value(self, mod):
+        return string_view
+
+    def resolve_valid(self, mod):
+        return types.boolean
+
+
+# Build attributes for `MaskedType(string_view)`
+for func in bool_binary_funcs:
+    setattr(
+        MaskedStringViewAttrs,
+        f"resolve_{func}",
+        create_masked_binary_attr(f"MaskedType.{func}", types.boolean),
+    )
+
+for func in int_binary_funcs:
+    setattr(
+        MaskedStringViewAttrs,
+        f"resolve_{func}",
+        create_masked_binary_attr(f"MaskedType.{func}", size_type),
+    )
+
+for func in string_return_attrs:
+    setattr(
+        MaskedStringViewAttrs,
+        f"resolve_{func}",
+        create_masked_binary_attr(f"MaskedType.{func}", udf_string),
+    )
+
+for func in id_unary_funcs:
+    setattr(
+        MaskedStringViewAttrs,
+        f"resolve_{func}",
+        create_masked_unary_attr(f"MaskedType.{func}", types.boolean),
+    )
+
+for func in string_unary_funcs:
+    setattr(
+        MaskedStringViewAttrs,
+        f"resolve_{func}",
+        create_masked_unary_attr(f"MaskedType.{func}", udf_string),
+    )
+
+
+class MaskedUDFStringAttrs(MaskedStringViewAttrs):
+    key = MaskedType(udf_string)
+
+    def resolve_value(self, mod):
+        return udf_string
+
+
+cuda_decl_registry.register_attr(MaskedStringViewAttrs)
+cuda_decl_registry.register_attr(MaskedUDFStringAttrs)
diff --git a/python/cudf/cudf/core/udf/row_function.py b/python/cudf/cudf/core/udf/row_function.py
new file mode 100644
index 0000000..e040836
--- /dev/null
+++ b/python/cudf/cudf/core/udf/row_function.py
@@ -0,0 +1,164 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+import math
+
+import numpy as np
+from numba import cuda
+from numba.np import numpy_support
+
+from cudf.core.udf.api import Masked, pack_return
+from cudf.core.udf.masked_typing import MaskedType
+from cudf.core.udf.strings_typing import string_view
+from cudf.core.udf.templates import (
+    masked_input_initializer_template,
+    row_initializer_template,
+    row_kernel_template,
+    unmasked_input_initializer_template,
+)
+from cudf.core.udf.utils import (
+    Row,
+    _all_dtypes_from_frame,
+    _construct_signature,
+    _get_extensionty_size,
+    _get_kernel,
+    _get_udf_return_type,
+    _mask_get,
+    _supported_cols_from_frame,
+    _supported_dtypes_from_frame,
+)
+
+
+def _get_frame_row_type(dtype):
+    """
+    Get the Numba type of a row in a frame. Models each column and its mask as
+    a MaskedType and models the row as a dictionary like data structure
+    containing these MaskedTypes. Large parts of this function are copied with
+    comments from the Numba internals and slightly modified to account for
+    validity bools to be present in the final struct. See
+    numba.np.numpy_support.from_struct_dtype for details.
+    """
+
+    # Create the numpy structured type corresponding to the numpy dtype.
+
+    fields = []
+    offset = 0
+
+    sizes = [
+        _get_extensionty_size(string_view)
+        if val[0] == np.dtype("O")
+        else val[0].itemsize
+        for val in dtype.fields.values()
+    ]
+
+    for i, (name, info) in enumerate(dtype.fields.items()):
+        # *info* consists of the element dtype, its offset from the beginning
+        # of the record, and an optional "title" containing metadata.
+        # We ignore the offset in info because its value assumes no masking;
+        # instead, we compute the correct offset based on the masked type.
+        elemdtype = info[0]
+        title = info[2] if len(info) == 3 else None
+
+        ty = (
+            # columns of dtype string start life as string_view
+            string_view
+            if elemdtype == np.dtype("O")
+            else numpy_support.from_dtype(elemdtype)
+        )
+        infos = {
+            "type": MaskedType(ty),
+            "offset": offset,
+            "title": title,
+        }
+        fields.append((name, infos))
+
+        # increment offset by itemsize plus one byte for validity
+        itemsize = (
+            _get_extensionty_size(string_view)
+            if elemdtype == np.dtype("O")
+            else elemdtype.itemsize
+        )
+        offset += itemsize + 1
+
+        # Align the next member of the struct to be a multiple of the
+        # memory access size, per PTX ISA 7.4/5.4.5
+        if i < len(sizes) - 1:
+            next_itemsize = sizes[i + 1]
+            offset = int(math.ceil(offset / next_itemsize) * next_itemsize)
+
+    # Numba requires that structures are aligned for the CUDA target
+    _is_aligned_struct = True
+    return Row(fields, offset, _is_aligned_struct)
+
+
+def _row_kernel_string_from_template(frame, row_type, args):
+    """
+    Function to write numba kernels for `DataFrame.apply` as a string.
+    Workaround until numba supports functions that use `*args`
+
+    `DataFrame.apply` expects functions of a dict like row as well as
+    possibly one or more scalar arguments
+
+    def f(row, c, k):
+        return (row['x'] + c) / k
+
+    Both the number of input columns as well as their nullability and any
+    scalar arguments may vary, so the kernels vary significantly. See
+    templates.py for the full row kernel template and more details.
+    """
+    # Create argument list for kernel
+    frame = _supported_cols_from_frame(frame)
+
+    input_columns = ", ".join([f"input_col_{i}" for i in range(len(frame))])
+    input_offsets = ", ".join([f"offset_{i}" for i in range(len(frame))])
+    extra_args = ", ".join([f"extra_arg_{i}" for i in range(len(args))])
+
+    # Generate the initializers for each device function argument
+    initializers = []
+    row_initializers = []
+    for i, (colname, col) in enumerate(frame.items()):
+        idx = str(i)
+        template = (
+            masked_input_initializer_template
+            if col.mask is not None
+            else unmasked_input_initializer_template
+        )
+        initializers.append(template.format(idx=idx))
+        row_initializers.append(
+            row_initializer_template.format(idx=idx, name=colname)
+        )
+
+    return row_kernel_template.format(
+        input_columns=input_columns,
+        input_offsets=input_offsets,
+        extra_args=extra_args,
+        masked_input_initializers="\n".join(initializers),
+        row_initializers="\n".join(row_initializers),
+        numba_rectype=row_type,
+    )
+
+
+def _get_row_kernel(frame, func, args):
+    row_type = _get_frame_row_type(
+        np.dtype(list(_all_dtypes_from_frame(frame).items()))
+    )
+    scalar_return_type = _get_udf_return_type(row_type, func, args)
+    # this is the signature for the final full kernel compilation
+    sig = _construct_signature(frame, scalar_return_type, args)
+    # this row type is used within the kernel to pack up the column and
+    # mask data into the dict like data structure the user udf expects
+    np_field_types = np.dtype(
+        list(_supported_dtypes_from_frame(frame).items())
+    )
+    row_type = _get_frame_row_type(np_field_types)
+
+    # Dict of 'local' variables into which `_kernel` is defined
+    global_exec_context = {
+        "cuda": cuda,
+        "Masked": Masked,
+        "_mask_get": _mask_get,
+        "pack_return": pack_return,
+        "row_type": row_type,
+    }
+    kernel_string = _row_kernel_string_from_template(frame, row_type, args)
+    kernel = _get_kernel(kernel_string, global_exec_context, sig, func)
+
+    return kernel, scalar_return_type
diff --git a/python/cudf/cudf/core/udf/scalar_function.py b/python/cudf/cudf/core/udf/scalar_function.py
new file mode 100644
index 0000000..ff7fad3
--- /dev/null
+++ b/python/cudf/cudf/core/udf/scalar_function.py
@@ -0,0 +1,69 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from numba import cuda
+from numba.np import numpy_support
+
+from cudf.core.udf.api import Masked, pack_return
+from cudf.core.udf.masked_typing import MaskedType
+from cudf.core.udf.strings_typing import string_view
+from cudf.core.udf.templates import (
+    masked_input_initializer_template,
+    scalar_kernel_template,
+    unmasked_input_initializer_template,
+)
+from cudf.core.udf.utils import (
+    _construct_signature,
+    _get_kernel,
+    _get_udf_return_type,
+    _mask_get,
+)
+
+
+def _scalar_kernel_string_from_template(sr, args):
+    """
+    Function to write numba kernels for `Series.apply` as a string.
+    Workaround until numba supports functions that use `*args`
+
+    `Series.apply` expects functions of a single variable and possibly
+    one or more constants, such as:
+
+    def f(x, c, k):
+        return (x + c) / k
+
+    where the `x` are meant to be the values of the series. Since there
+    can be only one column, the only thing that varies in the kinds of
+    kernels that we want is the number of extra_args. See templates.py
+    for the full kernel template.
+    """
+    extra_args = ", ".join([f"extra_arg_{i}" for i in range(len(args))])
+
+    masked_initializer = (
+        masked_input_initializer_template
+        if sr._column.mask
+        else unmasked_input_initializer_template
+    ).format(idx=0)
+
+    return scalar_kernel_template.format(
+        extra_args=extra_args, masked_initializer=masked_initializer
+    )
+
+
+def _get_scalar_kernel(sr, func, args):
+    sr_type = MaskedType(
+        string_view if sr.dtype == "O" else numpy_support.from_dtype(sr.dtype)
+    )
+    scalar_return_type = _get_udf_return_type(sr_type, func, args)
+
+    sig = _construct_signature(sr, scalar_return_type, args=args)
+    f_ = cuda.jit(device=True)(func)
+    global_exec_context = {
+        "f_": f_,
+        "cuda": cuda,
+        "Masked": Masked,
+        "_mask_get": _mask_get,
+        "pack_return": pack_return,
+    }
+    kernel_string = _scalar_kernel_string_from_template(sr, args=args)
+    kernel = _get_kernel(kernel_string, global_exec_context, sig, func)
+
+    return kernel, scalar_return_type
diff --git a/python/cudf/cudf/core/udf/strings_lowering.py b/python/cudf/cudf/core/udf/strings_lowering.py
new file mode 100644
index 0000000..fdce404
--- /dev/null
+++ b/python/cudf/cudf/core/udf/strings_lowering.py
@@ -0,0 +1,723 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import operator
+from functools import partial
+
+from numba import cuda, types
+from numba.core import cgutils
+from numba.core.datamodel import default_manager
+from numba.core.typing import signature as nb_signature
+from numba.cuda.cudaimpl import (
+    lower as cuda_lower,
+    registry as cuda_lowering_registry,
+)
+
+from cudf._lib.strings_udf import (
+    get_character_cases_table_ptr,
+    get_character_flags_table_ptr,
+    get_special_case_mapping_table_ptr,
+)
+from cudf.core.udf.masked_typing import MaskedType
+from cudf.core.udf.strings_typing import size_type, string_view, udf_string
+
+_STR_VIEW_PTR = types.CPointer(string_view)
+_UDF_STRING_PTR = types.CPointer(udf_string)
+
+
+# CUDA function declarations
+# read-only (input is a string_view, output is a fixed with type)
+_string_view_len = cuda.declare_device("len", size_type(_STR_VIEW_PTR))
+_concat_string_view = cuda.declare_device(
+    "concat", types.void(_UDF_STRING_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR)
+)
+
+_string_view_replace = cuda.declare_device(
+    "replace",
+    types.void(_UDF_STRING_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR),
+)
+
+
+def _declare_binary_func(lhs, rhs, out, name):
+    # Declare a binary function
+    return cuda.declare_device(
+        name,
+        out(lhs, rhs),
+    )
+
+
+def _declare_strip_func(name):
+    return cuda.declare_device(
+        name, size_type(_UDF_STRING_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR)
+    )
+
+
+# A binary function of the form f(string, string) -> bool
+_declare_bool_str_str_func = partial(
+    _declare_binary_func, _STR_VIEW_PTR, _STR_VIEW_PTR, types.boolean
+)
+
+_declare_size_type_str_str_func = partial(
+    _declare_binary_func, _STR_VIEW_PTR, _STR_VIEW_PTR, size_type
+)
+
+_string_view_contains = _declare_bool_str_str_func("contains")
+_string_view_eq = _declare_bool_str_str_func("eq")
+_string_view_ne = _declare_bool_str_str_func("ne")
+_string_view_ge = _declare_bool_str_str_func("ge")
+_string_view_le = _declare_bool_str_str_func("le")
+_string_view_gt = _declare_bool_str_str_func("gt")
+_string_view_lt = _declare_bool_str_str_func("lt")
+_string_view_startswith = _declare_bool_str_str_func("startswith")
+_string_view_endswith = _declare_bool_str_str_func("endswith")
+_string_view_find = _declare_size_type_str_str_func("find")
+_string_view_rfind = _declare_size_type_str_str_func("rfind")
+_string_view_contains = _declare_bool_str_str_func("contains")
+_string_view_strip = _declare_strip_func("strip")
+_string_view_lstrip = _declare_strip_func("lstrip")
+_string_view_rstrip = _declare_strip_func("rstrip")
+
+
+# A binary function of the form f(string, int) -> bool
+_declare_bool_str_int_func = partial(
+    _declare_binary_func, _STR_VIEW_PTR, types.int64, types.boolean
+)
+
+
+def _declare_upper_or_lower(func):
+    return cuda.declare_device(
+        func,
+        types.void(
+            _UDF_STRING_PTR,
+            _STR_VIEW_PTR,
+            types.uintp,
+            types.uintp,
+            types.uintp,
+        ),
+    )
+
+
+_string_view_isdigit = _declare_bool_str_int_func("pyisdigit")
+_string_view_isalnum = _declare_bool_str_int_func("pyisalnum")
+_string_view_isalpha = _declare_bool_str_int_func("pyisalpha")
+_string_view_isdecimal = _declare_bool_str_int_func("pyisdecimal")
+_string_view_isnumeric = _declare_bool_str_int_func("pyisnumeric")
+_string_view_isspace = _declare_bool_str_int_func("pyisspace")
+_string_view_isupper = _declare_bool_str_int_func("pyisupper")
+_string_view_islower = _declare_bool_str_int_func("pyislower")
+_string_view_istitle = _declare_bool_str_int_func("pyistitle")
+_string_view_upper = _declare_upper_or_lower("upper")
+_string_view_lower = _declare_upper_or_lower("lower")
+
+
+_string_view_count = cuda.declare_device(
+    "pycount",
+    size_type(_STR_VIEW_PTR, _STR_VIEW_PTR),
+)
+
+
+# casts
+@cuda_lowering_registry.lower_cast(types.StringLiteral, string_view)
+def cast_string_literal_to_string_view(context, builder, fromty, toty, val):
+    """
+    Cast a literal to a string_view
+    """
+    # create an empty string_view
+    sv = cgutils.create_struct_proxy(string_view)(context, builder)
+
+    # set the empty strview data pointer to point to the literal value
+    sv.data = context.insert_string_const_addrspace(
+        builder, fromty.literal_value
+    )
+    sv.length = context.get_constant(size_type, len(fromty.literal_value))
+    sv.bytes = context.get_constant(
+        size_type, len(fromty.literal_value.encode("UTF-8"))
+    )
+
+    return sv._getvalue()
+
+
+@cuda_lowering_registry.lower_cast(string_view, udf_string)
+def cast_string_view_to_udf_string(context, builder, fromty, toty, val):
+    sv_ptr = builder.alloca(default_manager[fromty].get_value_type())
+    udf_str_ptr = builder.alloca(default_manager[toty].get_value_type())
+    builder.store(val, sv_ptr)
+    _ = context.compile_internal(
+        builder,
+        call_create_udf_string_from_string_view,
+        nb_signature(types.void, _STR_VIEW_PTR, types.CPointer(udf_string)),
+        (sv_ptr, udf_str_ptr),
+    )
+    result = cgutils.create_struct_proxy(udf_string)(
+        context, builder, value=builder.load(udf_str_ptr)
+    )
+
+    return result._getvalue()
+
+
+@cuda_lowering_registry.lower_cast(udf_string, string_view)
+def cast_udf_string_to_string_view(context, builder, fromty, toty, val):
+    udf_str_ptr = builder.alloca(default_manager[fromty].get_value_type())
+    sv_ptr = builder.alloca(default_manager[toty].get_value_type())
+    builder.store(val, udf_str_ptr)
+
+    context.compile_internal(
+        builder,
+        call_create_string_view_from_udf_string,
+        nb_signature(types.void, _UDF_STRING_PTR, _STR_VIEW_PTR),
+        (udf_str_ptr, sv_ptr),
+    )
+
+    result = cgutils.create_struct_proxy(string_view)(
+        context, builder, value=builder.load(sv_ptr)
+    )
+
+    return result._getvalue()
+
+
+# utilities
+_create_udf_string_from_string_view = cuda.declare_device(
+    "udf_string_from_string_view",
+    types.void(_STR_VIEW_PTR, _UDF_STRING_PTR),
+)
+_create_string_view_from_udf_string = cuda.declare_device(
+    "string_view_from_udf_string",
+    types.void(_UDF_STRING_PTR, _STR_VIEW_PTR),
+)
+
+
+def call_create_udf_string_from_string_view(sv, udf_str):
+    _create_udf_string_from_string_view(sv, udf_str)
+
+
+def call_create_string_view_from_udf_string(udf_str, sv):
+    _create_string_view_from_udf_string(udf_str, sv)
+
+
+# String function implementations
+def call_len_string_view(st):
+    return _string_view_len(st)
+
+
+@cuda_lower(len, string_view)
+def len_impl(context, builder, sig, args):
+    sv_ptr = builder.alloca(args[0].type)
+    builder.store(args[0], sv_ptr)
+    result = context.compile_internal(
+        builder,
+        call_len_string_view,
+        nb_signature(size_type, _STR_VIEW_PTR),
+        (sv_ptr,),
+    )
+
+    return result
+
+
+def call_concat_string_view(result, lhs, rhs):
+    return _concat_string_view(result, lhs, rhs)
+
+
+@cuda_lower(operator.add, string_view, string_view)
+def concat_impl(context, builder, sig, args):
+    lhs_ptr = builder.alloca(args[0].type)
+    rhs_ptr = builder.alloca(args[1].type)
+    builder.store(args[0], lhs_ptr)
+    builder.store(args[1], rhs_ptr)
+
+    udf_str_ptr = builder.alloca(default_manager[udf_string].get_value_type())
+    _ = context.compile_internal(
+        builder,
+        call_concat_string_view,
+        types.void(_UDF_STRING_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR),
+        (udf_str_ptr, lhs_ptr, rhs_ptr),
+    )
+
+    result = cgutils.create_struct_proxy(udf_string)(
+        context, builder, value=builder.load(udf_str_ptr)
+    )
+    return result._getvalue()
+
+
+def call_string_view_replace(result, src, to_replace, replacement):
+    return _string_view_replace(result, src, to_replace, replacement)
+
+
+@cuda_lower("StringView.replace", string_view, string_view, string_view)
+@cuda_lower("UDFString.replace", string_view, string_view, string_view)
+def replace_impl(context, builder, sig, args):
+    src_ptr = builder.alloca(args[0].type)
+    to_replace_ptr = builder.alloca(args[1].type)
+    replacement_ptr = builder.alloca(args[2].type)
+
+    builder.store(args[0], src_ptr)
+    builder.store(args[1], to_replace_ptr),
+    builder.store(args[2], replacement_ptr)
+
+    udf_str_ptr = builder.alloca(default_manager[udf_string].get_value_type())
+
+    _ = context.compile_internal(
+        builder,
+        call_string_view_replace,
+        types.void(
+            _UDF_STRING_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR
+        ),
+        (udf_str_ptr, src_ptr, to_replace_ptr, replacement_ptr),
+    )
+
+    result = cgutils.create_struct_proxy(udf_string)(
+        context, builder, value=builder.load(udf_str_ptr)
+    )
+    return result._getvalue()
+
+
+def create_binary_string_func(binary_func, retty):
+    """
+    Provide a wrapper around numba's low-level extension API which
+    produces the boilerplate needed to implement a binary function
+    of two strings.
+    """
+
+    def deco(cuda_func):
+        @cuda_lower(binary_func, string_view, string_view)
+        def binary_func_impl(context, builder, sig, args):
+            lhs_ptr = builder.alloca(args[0].type)
+            rhs_ptr = builder.alloca(args[1].type)
+            builder.store(args[0], lhs_ptr)
+            builder.store(args[1], rhs_ptr)
+
+            # these conditional statements should compile out
+            if retty != udf_string:
+                # binary function of two strings yielding a fixed-width type
+                # example: str.startswith(other) -> bool
+                # shim functions can return the value through nb_retval
+                result = context.compile_internal(
+                    builder,
+                    cuda_func,
+                    nb_signature(retty, _STR_VIEW_PTR, _STR_VIEW_PTR),
+                    (lhs_ptr, rhs_ptr),
+                )
+                return result
+            else:
+                # binary function of two strings yielding a new string
+                # example: str.strip(other) -> str
+                # shim functions can not return a struct due to C linkage
+                # so we create a new udf_string and pass a pointer to it
+                # for the shim function to write the output to. The return
+                # value of compile_internal is therefore discarded (although
+                # this may change in the future if we need to return error
+                # codes, for instance).
+                udf_str_ptr = builder.alloca(
+                    default_manager[udf_string].get_value_type()
+                )
+                _ = context.compile_internal(
+                    builder,
+                    cuda_func,
+                    size_type(_UDF_STRING_PTR, _STR_VIEW_PTR, _STR_VIEW_PTR),
+                    (udf_str_ptr, lhs_ptr, rhs_ptr),
+                )
+                result = cgutils.create_struct_proxy(udf_string)(
+                    context, builder, value=builder.load(udf_str_ptr)
+                )
+                return result._getvalue()
+
+        # binary_func can be attribute-like: str.binary_func
+        # or operator-like: binary_func(str, other)
+        if isinstance(binary_func, str):
+            binary_func_impl = cuda_lower(
+                f"StringView.{binary_func}", string_view, string_view
+            )(binary_func_impl)
+            binary_func_impl = cuda_lower(
+                f"UDFString.{binary_func}", string_view, string_view
+            )(binary_func_impl)
+        else:
+            binary_func_impl = cuda_lower(
+                binary_func, string_view, string_view
+            )(binary_func_impl)
+
+        return binary_func_impl
+
+    return deco
+
+
+@create_binary_string_func(operator.contains, types.boolean)
+def contains_impl(st, substr):
+    return _string_view_contains(st, substr)
+
+
+@create_binary_string_func(operator.eq, types.boolean)
+def eq_impl(st, rhs):
+    return _string_view_eq(st, rhs)
+
+
+@create_binary_string_func(operator.ne, types.boolean)
+def ne_impl(st, rhs):
+    return _string_view_ne(st, rhs)
+
+
+@create_binary_string_func(operator.ge, types.boolean)
+def ge_impl(st, rhs):
+    return _string_view_ge(st, rhs)
+
+
+@create_binary_string_func(operator.le, types.boolean)
+def le_impl(st, rhs):
+    return _string_view_le(st, rhs)
+
+
+@create_binary_string_func(operator.gt, types.boolean)
+def gt_impl(st, rhs):
+    return _string_view_gt(st, rhs)
+
+
+@create_binary_string_func(operator.lt, types.boolean)
+def lt_impl(st, rhs):
+    return _string_view_lt(st, rhs)
+
+
+@create_binary_string_func("strip", udf_string)
+def strip_impl(result, to_strip, strip_char):
+    return _string_view_strip(result, to_strip, strip_char)
+
+
+@create_binary_string_func("lstrip", udf_string)
+def lstrip_impl(result, to_strip, strip_char):
+    return _string_view_lstrip(result, to_strip, strip_char)
+
+
+@create_binary_string_func("rstrip", udf_string)
+def rstrip_impl(result, to_strip, strip_char):
+    return _string_view_rstrip(result, to_strip, strip_char)
+
+
+@create_binary_string_func("startswith", types.boolean)
+def startswith_impl(sv, substr):
+    return _string_view_startswith(sv, substr)
+
+
+@create_binary_string_func("endswith", types.boolean)
+def endswith_impl(sv, substr):
+    return _string_view_endswith(sv, substr)
+
+
+@create_binary_string_func("count", size_type)
+def count_impl(st, substr):
+    return _string_view_count(st, substr)
+
+
+@create_binary_string_func("find", size_type)
+def find_impl(sv, substr):
+    return _string_view_find(sv, substr)
+
+
+@create_binary_string_func("rfind", size_type)
+def rfind_impl(sv, substr):
+    return _string_view_rfind(sv, substr)
+
+
+def create_unary_identifier_func(id_func):
+    """
+    Provide a wrapper around numba's low-level extension API which
+    produces the boilerplate needed to implement a unary function
+    of a string.
+    """
+
+    def deco(cuda_func):
+        @cuda_lower(f"StringView.{id_func}", string_view)
+        @cuda_lower(f"UDFString.{id_func}", string_view)
+        def id_func_impl(context, builder, sig, args):
+            str_ptr = builder.alloca(args[0].type)
+            builder.store(args[0], str_ptr)
+
+            # Lookup table required for conversion functions
+            # must be resolved at runtime after context initialization,
+            # therefore cannot be a global variable
+            tbl_ptr = context.get_constant(
+                types.uintp, get_character_flags_table_ptr()
+            )
+            result = context.compile_internal(
+                builder,
+                cuda_func,
+                nb_signature(types.boolean, _STR_VIEW_PTR, types.uintp),
+                (str_ptr, tbl_ptr),
+            )
+
+            return result
+
+        return id_func_impl
+
+    return deco
+
+
+def create_upper_or_lower(id_func):
+    """
+    Provide a wrapper around numba's low-level extension API which
+    produces the boilerplate needed to implement either the upper
+    or lower attrs of a string view.
+    """
+
+    def deco(cuda_func):
+        @cuda_lower(f"StringView.{id_func}", string_view)
+        @cuda_lower(f"UDFString.{id_func}", string_view)
+        def id_func_impl(context, builder, sig, args):
+            str_ptr = builder.alloca(args[0].type)
+            builder.store(args[0], str_ptr)
+
+            # Lookup table required for conversion functions
+            # must be resolved at runtime after context initialization,
+            # therefore cannot be a global variable
+            flags_tbl_ptr = context.get_constant(
+                types.uintp, get_character_flags_table_ptr()
+            )
+            cases_tbl_ptr = context.get_constant(
+                types.uintp, get_character_cases_table_ptr()
+            )
+            special_tbl_ptr = context.get_constant(
+                types.uintp, get_special_case_mapping_table_ptr()
+            )
+            udf_str_ptr = builder.alloca(
+                default_manager[udf_string].get_value_type()
+            )
+
+            _ = context.compile_internal(
+                builder,
+                cuda_func,
+                types.void(
+                    _UDF_STRING_PTR,
+                    _STR_VIEW_PTR,
+                    types.uintp,
+                    types.uintp,
+                    types.uintp,
+                ),
+                (
+                    udf_str_ptr,
+                    str_ptr,
+                    flags_tbl_ptr,
+                    cases_tbl_ptr,
+                    special_tbl_ptr,
+                ),
+            )
+
+            result = cgutils.create_struct_proxy(udf_string)(
+                context, builder, value=builder.load(udf_str_ptr)
+            )
+            return result._getvalue()
+
+        return id_func_impl
+
+    return deco
+
+
+@create_upper_or_lower("upper")
+def upper_impl(result, st, flags, cases, special):
+    return _string_view_upper(result, st, flags, cases, special)
+
+
+@create_upper_or_lower("lower")
+def lower_impl(result, st, flags, cases, special):
+    return _string_view_lower(result, st, flags, cases, special)
+
+
+@create_unary_identifier_func("isdigit")
+def isdigit_impl(st, tbl):
+    return _string_view_isdigit(st, tbl)
+
+
+@create_unary_identifier_func("isalnum")
+def isalnum_impl(st, tbl):
+    return _string_view_isalnum(st, tbl)
+
+
+@create_unary_identifier_func("isalpha")
+def isalpha_impl(st, tbl):
+    return _string_view_isalpha(st, tbl)
+
+
+@create_unary_identifier_func("isnumeric")
+def isnumeric_impl(st, tbl):
+    return _string_view_isnumeric(st, tbl)
+
+
+@create_unary_identifier_func("isdecimal")
+def isdecimal_impl(st, tbl):
+    return _string_view_isdecimal(st, tbl)
+
+
+@create_unary_identifier_func("isspace")
+def isspace_impl(st, tbl):
+    return _string_view_isspace(st, tbl)
+
+
+@create_unary_identifier_func("isupper")
+def isupper_impl(st, tbl):
+    return _string_view_isupper(st, tbl)
+
+
+@create_unary_identifier_func("islower")
+def islower_impl(st, tbl):
+    return _string_view_islower(st, tbl)
+
+
+@create_unary_identifier_func("istitle")
+def istitle_impl(st, tbl):
+    return _string_view_istitle(st, tbl)
+
+
+@cuda_lower(len, MaskedType(string_view))
+@cuda_lower(len, MaskedType(udf_string))
+def masked_len_impl(context, builder, sig, args):
+    ret = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    masked_sv_ty = sig.args[0]
+    masked_sv = cgutils.create_struct_proxy(masked_sv_ty)(
+        context, builder, value=args[0]
+    )
+    result = len_impl(
+        context, builder, size_type(string_view), (masked_sv.value,)
+    )
+    ret.value = result
+    ret.valid = masked_sv.valid
+
+    return ret._getvalue()
+
+
+def _masked_proxies(context, builder, maskedty, *args):
+    return tuple(
+        cgutils.create_struct_proxy(maskedty)(context, builder, value=arg)
+        for arg in args
+    )
+
+
+@cuda_lower(
+    "MaskedType.replace",
+    MaskedType(string_view),
+    MaskedType(string_view),
+    MaskedType(string_view),
+)
+def masked_string_view_replace_impl(context, builder, sig, args):
+    ret = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+    src_masked, to_replace_masked, replacement_masked = _masked_proxies(
+        context, builder, MaskedType(string_view), *args
+    )
+    result = replace_impl(
+        context,
+        builder,
+        nb_signature(udf_string, string_view, string_view, string_view),
+        (src_masked.value, to_replace_masked.value, replacement_masked.value),
+    )
+
+    ret.value = result
+    ret.valid = builder.and_(
+        builder.and_(src_masked.valid, to_replace_masked.valid),
+        replacement_masked.valid,
+    )
+
+    return ret._getvalue()
+
+
+def create_masked_binary_string_func(op, cuda_func, retty):
+    """
+    Provide a wrapper around numba's low-level extension API which
+    produces the boilerplate needed to implement a binary function
+    of two masked strings.
+    """
+
+    def masked_binary_func_impl(context, builder, sig, args):
+        ret = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+        lhs_masked = cgutils.create_struct_proxy(sig.args[0])(
+            context, builder, value=args[0]
+        )
+        rhs_masked = cgutils.create_struct_proxy(sig.args[0])(
+            context, builder, value=args[1]
+        )
+
+        result = cuda_func(
+            context,
+            builder,
+            nb_signature(retty, string_view, string_view),
+            (lhs_masked.value, rhs_masked.value),
+        )
+
+        ret.value = result
+        ret.valid = builder.and_(lhs_masked.valid, rhs_masked.valid)
+
+        return ret._getvalue()
+
+    cuda_lower(op, MaskedType(string_view), MaskedType(string_view))(
+        masked_binary_func_impl
+    )
+
+
+def create_masked_unary_identifier_func(op, cuda_func):
+    """
+    Provide a wrapper around numba's low-level extension API which
+    produces the boilerplate needed to implement a unary function
+    of a masked string.
+    """
+
+    def masked_unary_func_impl(context, builder, sig, args):
+        ret = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+        masked_str = cgutils.create_struct_proxy(sig.args[0])(
+            context, builder, value=args[0]
+        )
+
+        result = cuda_func(
+            context,
+            builder,
+            types.boolean(string_view, string_view),
+            (masked_str.value,),
+        )
+        ret.value = result
+        ret.valid = masked_str.valid
+        return ret._getvalue()
+
+    cuda_lower(op, MaskedType(string_view))(masked_unary_func_impl)
+
+
+def create_masked_upper_or_lower(op, cuda_func):
+    def upper_or_lower_impl(context, builder, sig, args):
+        ret = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+        masked_str = cgutils.create_struct_proxy(sig.args[0])(
+            context, builder, value=args[0]
+        )
+
+        result = cuda_func(
+            context,
+            builder,
+            udf_string(string_view),
+            (masked_str.value,),
+        )
+        ret.value = result
+        ret.valid = masked_str.valid
+        return ret._getvalue()
+
+    cuda_lower(op, MaskedType(string_view))(upper_or_lower_impl)
+
+
+create_masked_binary_string_func("MaskedType.strip", strip_impl, udf_string)
+create_masked_binary_string_func("MaskedType.lstrip", lstrip_impl, udf_string)
+create_masked_binary_string_func("MaskedType.rstrip", rstrip_impl, udf_string)
+create_masked_binary_string_func(
+    "MaskedType.startswith",
+    startswith_impl,
+    types.boolean,
+)
+create_masked_binary_string_func(
+    "MaskedType.endswith", endswith_impl, types.boolean
+)
+create_masked_binary_string_func("MaskedType.find", find_impl, size_type)
+create_masked_binary_string_func("MaskedType.rfind", rfind_impl, size_type)
+create_masked_binary_string_func("MaskedType.count", count_impl, size_type)
+create_masked_binary_string_func(
+    operator.contains, contains_impl, types.boolean
+)
+
+
+create_masked_unary_identifier_func("MaskedType.isalnum", isalnum_impl)
+create_masked_unary_identifier_func("MaskedType.isalpha", isalpha_impl)
+create_masked_unary_identifier_func("MaskedType.isdigit", isdigit_impl)
+create_masked_unary_identifier_func("MaskedType.isupper", isupper_impl)
+create_masked_unary_identifier_func("MaskedType.islower", islower_impl)
+create_masked_unary_identifier_func("MaskedType.isspace", isspace_impl)
+create_masked_unary_identifier_func("MaskedType.isdecimal", isdecimal_impl)
+create_masked_unary_identifier_func("MaskedType.istitle", istitle_impl)
+create_masked_upper_or_lower("MaskedType.upper", upper_impl)
+create_masked_upper_or_lower("MaskedType.lower", lower_impl)
diff --git a/python/cudf/cudf/core/udf/strings_typing.py b/python/cudf/cudf/core/udf/strings_typing.py
new file mode 100644
index 0000000..50d34be
--- /dev/null
+++ b/python/cudf/cudf/core/udf/strings_typing.py
@@ -0,0 +1,280 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import operator
+
+import numpy as np
+from numba import types
+from numba.core.extending import models, register_model
+from numba.core.typing import signature as nb_signature
+from numba.core.typing.templates import AbstractTemplate, AttributeTemplate
+from numba.cuda.cudadecl import registry as cuda_decl_registry
+
+import rmm
+
+# libcudf size_type
+size_type = types.int32
+
+
+# String object definitions
+class UDFString(types.Type):
+
+    np_dtype = np.dtype("object")
+
+    def __init__(self):
+        super().__init__(name="udf_string")
+
+    @property
+    def return_type(self):
+        return self
+
+
+class StringView(types.Type):
+
+    np_dtype = np.dtype("object")
+
+    def __init__(self):
+        super().__init__(name="string_view")
+
+    @property
+    def return_type(self):
+        return UDFString()
+
+
+@register_model(StringView)
+class stringview_model(models.StructModel):
+    # from string_view.hpp:
+    _members = (
+        # const char* _data{}
+        # Pointer to device memory contain char array for this string
+        ("data", types.CPointer(types.char)),
+        # size_type _bytes{};
+        # Number of bytes in _data for this string
+        ("bytes", size_type),
+        # mutable size_type _length{};
+        # Number of characters in this string (computed)
+        ("length", size_type),
+    )
+
+    def __init__(self, dmm, fe_type):
+        super().__init__(dmm, fe_type, self._members)
+
+
+@register_model(UDFString)
+class udf_string_model(models.StructModel):
+    # from udf_string.hpp:
+    # private:
+    #   char* m_data{};
+    #   cudf::size_type m_bytes{};
+    #   cudf::size_type m_size{};
+
+    _members = (
+        ("m_data", types.CPointer(types.char)),
+        ("m_bytes", size_type),
+        ("m_size", size_type),
+    )
+
+    def __init__(self, dmm, fe_type):
+        super().__init__(dmm, fe_type, self._members)
+
+
+any_string_ty = (StringView, UDFString, types.StringLiteral)
+string_view = StringView()
+udf_string = UDFString()
+
+
+class StrViewArgHandler:
+    """
+    As part of Numba's preprocessing step, incoming function arguments are
+    modified based on the associated type for that argument that was used
+    to JIT the kernel. However it only knows how to handle built in array
+    types natively. With string UDFs, the jitted type is string_view*,
+    which numba does not know how to handle.
+
+    This class converts string_view* to raw pointer arguments, which Numba
+    knows how to use.
+
+    See numba.cuda.compiler._prepare_args for details.
+    """
+
+    def prepare_args(self, ty, val, **kwargs):
+        if isinstance(ty, types.CPointer) and isinstance(
+            ty.dtype, (StringView, UDFString)
+        ):
+            return types.uint64, val.ptr if isinstance(
+                val, rmm._lib.device_buffer.DeviceBuffer
+            ) else val.get_ptr(mode="read")
+        else:
+            return ty, val
+
+
+str_view_arg_handler = StrViewArgHandler()
+
+
+# String functions
+@cuda_decl_registry.register_global(len)
+class StringLength(AbstractTemplate):
+    """
+    provide the length of a cudf::string_view like struct
+    """
+
+    def generic(self, args, kws):
+        if isinstance(args[0], any_string_ty) and len(args) == 1:
+            # length:
+            # string_view -> int32
+            # udf_string -> int32
+            # literal -> int32
+            return nb_signature(size_type, string_view)
+
+
+def register_stringview_binaryop(op, retty):
+    """
+    Helper function wrapping numba's low level extension API. Provides
+    the boilerplate needed to associate a signature with a function or
+    operator expecting a string.
+    """
+
+    class StringViewBinaryOp(AbstractTemplate):
+        def generic(self, args, kws):
+            if isinstance(args[0], any_string_ty) and isinstance(
+                args[1], any_string_ty
+            ):
+                return nb_signature(retty, string_view, string_view)
+
+    cuda_decl_registry.register_global(op)(StringViewBinaryOp)
+
+
+def create_binary_attr(attrname, retty):
+    """
+    Helper function wrapping numba's low level extension API. Provides
+    the boilerplate needed to register a binary function of two string
+    objects as an attribute of one, e.g. `string.func(other)`.
+    """
+
+    class StringViewBinaryAttr(AbstractTemplate):
+        key = f"StringView.{attrname}"
+
+        def generic(self, args, kws):
+            return nb_signature(retty, string_view, recvr=self.this)
+
+    def attr(self, mod):
+        return types.BoundFunction(StringViewBinaryAttr, string_view)
+
+    return attr
+
+
+def create_identifier_attr(attrname, retty):
+    """
+    Helper function wrapping numba's low level extension API. Provides
+    the boilerplate needed to register a unary function of a string
+    object as an attribute, e.g. `string.func()`.
+    """
+
+    class StringViewIdentifierAttr(AbstractTemplate):
+        key = f"StringView.{attrname}"
+
+        def generic(self, args, kws):
+            return nb_signature(retty, recvr=self.this)
+
+    def attr(self, mod):
+        return types.BoundFunction(StringViewIdentifierAttr, string_view)
+
+    return attr
+
+
+class StringViewCount(AbstractTemplate):
+    key = "StringView.count"
+
+    def generic(self, args, kws):
+        return nb_signature(size_type, string_view, recvr=self.this)
+
+
+class StringViewReplace(AbstractTemplate):
+    key = "StringView.replace"
+
+    def generic(self, args, kws):
+        return nb_signature(
+            udf_string, string_view, string_view, recvr=self.this
+        )
+
+
+class StringViewAttrs(AttributeTemplate):
+    key = string_view
+
+    def resolve_count(self, mod):
+        return types.BoundFunction(StringViewCount, string_view)
+
+    def resolve_replace(self, mod):
+        return types.BoundFunction(StringViewReplace, string_view)
+
+
+bool_binary_funcs = ["startswith", "endswith"]
+int_binary_funcs = ["find", "rfind"]
+id_unary_funcs = [
+    "isalpha",
+    "isalnum",
+    "isdecimal",
+    "isdigit",
+    "isupper",
+    "islower",
+    "isspace",
+    "isnumeric",
+    "istitle",
+]
+string_unary_funcs = ["upper", "lower"]
+string_return_attrs = ["strip", "lstrip", "rstrip"]
+
+for func in bool_binary_funcs:
+    setattr(
+        StringViewAttrs,
+        f"resolve_{func}",
+        create_binary_attr(func, types.boolean),
+    )
+
+for func in string_return_attrs:
+    setattr(
+        StringViewAttrs,
+        f"resolve_{func}",
+        create_binary_attr(func, udf_string),
+    )
+
+
+for func in int_binary_funcs:
+    setattr(
+        StringViewAttrs, f"resolve_{func}", create_binary_attr(func, size_type)
+    )
+
+for func in id_unary_funcs:
+    setattr(
+        StringViewAttrs,
+        f"resolve_{func}",
+        create_identifier_attr(func, types.boolean),
+    )
+
+for func in string_unary_funcs:
+    setattr(
+        StringViewAttrs,
+        f"resolve_{func}",
+        create_identifier_attr(func, udf_string),
+    )
+
+
+@cuda_decl_registry.register_attr
+class UDFStringAttrs(StringViewAttrs):
+    key = udf_string
+
+
+cuda_decl_registry.register_attr(StringViewAttrs)
+cuda_decl_registry.register_attr(UDFStringAttrs)
+
+register_stringview_binaryop(operator.eq, types.boolean)
+register_stringview_binaryop(operator.ne, types.boolean)
+register_stringview_binaryop(operator.lt, types.boolean)
+register_stringview_binaryop(operator.gt, types.boolean)
+register_stringview_binaryop(operator.le, types.boolean)
+register_stringview_binaryop(operator.ge, types.boolean)
+
+# st in other
+register_stringview_binaryop(operator.contains, types.boolean)
+
+# st + other
+register_stringview_binaryop(operator.add, udf_string)
diff --git a/python/cudf/cudf/core/udf/strings_utils.py b/python/cudf/cudf/core/udf/strings_utils.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/core/udf/templates.py b/python/cudf/cudf/core/udf/templates.py
new file mode 100644
index 0000000..9a03214
--- /dev/null
+++ b/python/cudf/cudf/core/udf/templates.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+unmasked_input_initializer_template = """\
+        d_{idx} = input_col_{idx}
+        masked_{idx} = Masked(d_{idx}[i], True)
+"""
+
+masked_input_initializer_template = """\
+        d_{idx}, m_{idx} = input_col_{idx}
+        masked_{idx} = Masked(d_{idx}[i], _mask_get(m_{idx}, i + offset_{idx}))
+"""
+
+row_initializer_template = """\
+        row["{name}"] = masked_{idx}
+"""
+
+group_initializer_template = """\
+        arr_{idx} = input_col_{idx}[offset[block_id]:offset[block_id+1]]
+        dataframe_group["{name}"] = Group(arr_{idx}, size, arr_index)
+"""
+
+row_kernel_template = """\
+def _kernel(retval, size, {input_columns}, {input_offsets}, {extra_args}):
+    i = cuda.grid(1)
+    ret_data_arr, ret_mask_arr = retval
+    if i < size:
+        # Create a structured array with the desired fields
+        rows = cuda.local.array(1, dtype=row_type)
+
+        # one element of that array
+        row = rows[0]
+
+{masked_input_initializers}
+{row_initializers}
+
+        # pass the assembled row into the udf
+        ret = f_(row, {extra_args})
+
+        # pack up the return values and set them
+        ret_masked = pack_return(ret)
+        ret_data_arr[i] = ret_masked.value
+        ret_mask_arr[i] = ret_masked.valid
+"""
+
+scalar_kernel_template = """
+def _kernel(retval, size, input_col_0, offset_0, {extra_args}):
+    i = cuda.grid(1)
+    ret_data_arr, ret_mask_arr = retval
+
+    if i < size:
+
+{masked_initializer}
+
+        ret = f_(masked_0, {extra_args})
+
+        ret_masked = pack_return(ret)
+        ret_data_arr[i] = ret_masked.value
+        ret_mask_arr[i] = ret_masked.valid
+"""
+
+groupby_apply_kernel_template = """
+def _kernel(offset, out, index, {input_columns}, {extra_args}):
+    tid = cuda.threadIdx.x
+    block_id = cuda.blockIdx.x
+    tb_size = cuda.blockDim.x
+
+    recarray = cuda.local.array(1, dtype=dataframe_group_type)
+    dataframe_group = recarray[0]
+
+    if block_id < (len(offset) - 1):
+
+        size = offset[block_id+1] - offset[block_id]
+        arr_index = index[offset[block_id]:offset[block_id+1]]
+
+{group_initializers}
+
+        result = f_(dataframe_group, {extra_args})
+        if cuda.threadIdx.x == 0:
+                out[block_id] = result
+"""
diff --git a/python/cudf/cudf/core/udf/utils.py b/python/cudf/cudf/core/udf/utils.py
new file mode 100644
index 0000000..35a3f6c
--- /dev/null
+++ b/python/cudf/cudf/core/udf/utils.py
@@ -0,0 +1,362 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import os
+from typing import Any, Callable, Dict
+
+import cachetools
+import cupy as cp
+import llvmlite.binding as ll
+import numpy as np
+from cuda import cudart
+from numba import cuda, typeof
+from numba.core.datamodel import default_manager, models
+from numba.core.errors import TypingError
+from numba.core.extending import register_model
+from numba.np import numpy_support
+from numba.types import CPointer, Poison, Record, Tuple, boolean, int64, void
+
+import rmm
+
+from cudf._lib.strings_udf import (
+    column_from_udf_string_array,
+    column_to_string_view_array,
+)
+from cudf.api.types import is_scalar
+from cudf.core.column.column import as_column
+from cudf.core.dtypes import dtype
+from cudf.core.udf.masked_typing import MaskedType
+from cudf.core.udf.strings_typing import (
+    str_view_arg_handler,
+    string_view,
+    udf_string,
+)
+from cudf.utils import cudautils
+from cudf.utils._numba import _get_ptx_file
+from cudf.utils.dtypes import (
+    BOOL_TYPES,
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    STRING_TYPES,
+    TIMEDELTA_TYPES,
+)
+from cudf.utils.utils import _cudf_nvtx_annotate, initfunc
+
+# Maximum size of a string column is 2 GiB
+_STRINGS_UDF_DEFAULT_HEAP_SIZE = os.environ.get(
+    "STRINGS_UDF_HEAP_SIZE", 2**31
+)
+_heap_size = 0
+_cudf_str_dtype = dtype(str)
+
+
+JIT_SUPPORTED_TYPES = (
+    NUMERIC_TYPES
+    | BOOL_TYPES
+    | DATETIME_TYPES
+    | TIMEDELTA_TYPES
+    | STRING_TYPES
+)
+libcudf_bitmask_type = numpy_support.from_dtype(np.dtype("int32"))
+MASK_BITSIZE = np.dtype("int32").itemsize * 8
+
+precompiled: cachetools.LRUCache = cachetools.LRUCache(maxsize=32)
+launch_arg_getters: Dict[Any, Any] = {}
+
+_PTX_FILE = _get_ptx_file(os.path.dirname(__file__), "shim_")
+
+
+@_cudf_nvtx_annotate
+def _get_udf_return_type(argty, func: Callable, args=()):
+    """
+    Get the return type of a masked UDF for a given set of argument dtypes. It
+    is assumed that the function consumes a dictionary whose keys are strings
+    and whose values are of MaskedType. Initially assume that the UDF may be
+    written to utilize any field in the row - including those containing an
+    unsupported dtype. If an unsupported dtype is actually used in the function
+    the compilation should fail at `compile_udf`. If compilation succeeds, one
+    can infer that the function does not use any of the columns of unsupported
+    dtype - meaning we can drop them going forward and the UDF will still end
+    up getting fed rows containing all the fields it actually needs to use to
+    compute the answer for that row.
+    """
+
+    # present a row containing all fields to the UDF and try and compile
+    compile_sig = (argty, *(typeof(arg) for arg in args))
+
+    # Get the return type. The PTX is also returned by compile_udf, but is not
+    # needed here.
+    ptx, output_type = cudautils.compile_udf(func, compile_sig)
+
+    if not isinstance(output_type, MaskedType):
+        numba_output_type = numpy_support.from_dtype(np.dtype(output_type))
+    else:
+        numba_output_type = output_type
+
+    result = (
+        numba_output_type
+        if not isinstance(numba_output_type, MaskedType)
+        else numba_output_type.value_type
+    )
+    result = result if result.is_internal else result.return_type
+
+    # _get_udf_return_type will throw a TypingError if the user tries to use
+    # a field in the row containing an unsupported dtype, except in the
+    # edge case where all the function does is return that element:
+
+    # def f(row):
+    #    return row[<bad dtype key>]
+    # In this case numba is happy to return MaskedType(<bad dtype key>)
+    # because it relies on not finding overloaded operators for types to raise
+    # the exception, so we have to explicitly check for that case.
+    if isinstance(result, Poison):
+        raise TypingError(str(result))
+
+    return result
+
+
+def _all_dtypes_from_frame(frame, supported_types=JIT_SUPPORTED_TYPES):
+    return {
+        colname: col.dtype
+        if str(col.dtype) in supported_types
+        else np.dtype("O")
+        for colname, col in frame._data.items()
+    }
+
+
+def _supported_dtypes_from_frame(frame, supported_types=JIT_SUPPORTED_TYPES):
+    return {
+        colname: col.dtype
+        for colname, col in frame._data.items()
+        if str(col.dtype) in supported_types
+    }
+
+
+def _supported_cols_from_frame(frame, supported_types=JIT_SUPPORTED_TYPES):
+    return {
+        colname: col
+        for colname, col in frame._data.items()
+        if str(col.dtype) in supported_types
+    }
+
+
+def _masked_array_type_from_col(col):
+    """
+    Return a type representing a tuple of arrays,
+    the first element an array of the numba type
+    corresponding to `dtype`, and the second an
+    array of bools representing a mask.
+    """
+
+    if col.dtype == _cudf_str_dtype:
+        col_type = CPointer(string_view)
+    else:
+        nb_scalar_ty = numpy_support.from_dtype(col.dtype)
+        col_type = nb_scalar_ty[::1]
+
+    if col.mask is None:
+        return col_type
+    else:
+        return Tuple((col_type, libcudf_bitmask_type[::1]))
+
+
+def _construct_signature(frame, return_type, args):
+    """
+    Build the signature of numba types that will be used to
+    actually JIT the kernel itself later, accounting for types
+    and offsets. Skips columns with unsupported dtypes.
+    """
+    if not return_type.is_internal:
+        return_type = CPointer(return_type)
+    else:
+        return_type = return_type[::1]
+    # Tuple of arrays, first the output data array, then the mask
+    return_type = Tuple((return_type, boolean[::1]))
+    offsets = []
+    sig = [return_type, int64]
+    for col in _supported_cols_from_frame(frame).values():
+        sig.append(_masked_array_type_from_col(col))
+        offsets.append(int64)
+
+    # return_type, size, data, masks, offsets, extra args
+    sig = void(*(sig + offsets + [typeof(arg) for arg in args]))
+
+    return sig
+
+
+class Row(Record):
+    # Numba's Record type provides a convenient abstraction for representing a
+    # row, in that it provides a mapping from strings (column / field names) to
+    # types. However, it cannot be used directly since it assumes that all its
+    # fields can be converted to NumPy types by Numba's internal conversion
+    # mechanism (`numba.np_support.as_dtype). This is not the case for cuDF
+    # extension types that might be the column types (e.g. masked types, string
+    # types or group types).
+    #
+    # We use this type for type inference and type checking, but not in code
+    # generation. For this use case, it is sufficient to provide a dtype for a
+    # row that corresponds to any Python object.
+    @property
+    def dtype(self):
+        return np.dtype("object")
+
+
+register_model(Row)(models.RecordModel)
+
+
+@cuda.jit(device=True)
+def _mask_get(mask, pos):
+    """Return the validity of mask[pos] as a word."""
+    return (mask[pos // MASK_BITSIZE] >> (pos % MASK_BITSIZE)) & 1
+
+
+def _generate_cache_key(frame, func: Callable, args, suffix="__APPLY_UDF"):
+    """Create a cache key that uniquely identifies a compilation.
+
+    A new compilation is needed any time any of the following things change:
+    - The UDF itself as defined in python by the user
+    - The types of the columns utilized by the UDF
+    - The existence of the input columns masks
+    """
+    scalar_argtypes = tuple(typeof(arg) for arg in args)
+    return (
+        *cudautils.make_cache_key(
+            func, tuple(_all_dtypes_from_frame(frame).values())
+        ),
+        *(col.mask is None for col in frame._data.values()),
+        *frame._data.keys(),
+        scalar_argtypes,
+        suffix,
+    )
+
+
+@_cudf_nvtx_annotate
+def _compile_or_get(
+    frame, func, args, kernel_getter=None, suffix="__APPLY_UDF"
+):
+    """
+    Return a compiled kernel in terms of MaskedTypes that launches a
+    kernel equivalent of `f` for the dtypes of `df`. The kernel uses
+    a thread for each row and calls `f` using that rows data / mask
+    to produce an output value and output validity for each row.
+
+    If the UDF has already been compiled for this requested dtypes,
+    a cached version will be returned instead of running compilation.
+
+    CUDA kernels are void and do not return values. Thus, we need to
+    preallocate a column of the correct dtype and pass it in as one of
+    the kernel arguments. This creates a chicken-and-egg problem where
+    we need the column type to compile the kernel, but normally we would
+    be getting that type FROM compiling the kernel (and letting numba
+    determine it as a return value). As a workaround, we compile the UDF
+    itself outside the final kernel to invoke a full typing pass, which
+    unfortunately is difficult to do without running full compilation.
+    we then obtain the return type from that separate compilation and
+    use it to allocate an output column of the right dtype.
+    """
+    if not all(is_scalar(arg) for arg in args):
+        raise TypeError("only scalar valued args are supported by apply")
+
+    # check to see if we already compiled this function
+    cache_key = _generate_cache_key(frame, func, args, suffix=suffix)
+    if precompiled.get(cache_key) is not None:
+        kernel, masked_or_scalar = precompiled[cache_key]
+        return kernel, masked_or_scalar
+
+    # precompile the user udf to get the right return type.
+    # could be a MaskedType or a scalar type.
+
+    kernel, scalar_return_type = kernel_getter(frame, func, args)
+    np_return_type = (
+        numpy_support.as_dtype(scalar_return_type)
+        if scalar_return_type.is_internal
+        else scalar_return_type.np_dtype
+    )
+
+    precompiled[cache_key] = (kernel, np_return_type)
+
+    return kernel, np_return_type
+
+
+def _get_kernel(kernel_string, globals_, sig, func):
+    """Template kernel compilation helper function."""
+    f_ = cuda.jit(device=True)(func)
+    globals_["f_"] = f_
+    exec(kernel_string, globals_)
+    _kernel = globals_["_kernel"]
+    kernel = cuda.jit(
+        sig, link=[_PTX_FILE], extensions=[str_view_arg_handler]
+    )(_kernel)
+
+    return kernel
+
+
+def _get_input_args_from_frame(fr):
+    args = []
+    offsets = []
+    for col in _supported_cols_from_frame(fr).values():
+        if col.dtype == _cudf_str_dtype:
+            data = column_to_string_view_array_init_heap(col)
+        else:
+            data = col.data
+        if col.mask is not None:
+            # argument is a tuple of data, mask
+            args.append((data, col.mask))
+        else:
+            # argument is just the data pointer
+            args.append(data)
+        offsets.append(col.offset)
+
+    return args + offsets
+
+
+def _return_arr_from_dtype(dtype, size):
+    if dtype == _cudf_str_dtype:
+        return rmm.DeviceBuffer(size=size * _get_extensionty_size(udf_string))
+    return cp.empty(size, dtype=dtype)
+
+
+def _post_process_output_col(col, retty):
+    if retty == _cudf_str_dtype:
+        return column_from_udf_string_array(col)
+    return as_column(col, retty)
+
+
+# The only supported data layout in NVVM.
+# See: https://docs.nvidia.com/cuda/nvvm-ir-spec/index.html?#data-layout
+_nvvm_data_layout = (
+    "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-"
+    "i128:128:128-f32:32:32-f64:64:64-v16:16:16-v32:32:32-"
+    "v64:64:64-v128:128:128-n16:32:64"
+)
+
+
+def _get_extensionty_size(ty):
+    """
+    Return the size of an extension type in bytes
+    """
+    target_data = ll.create_target_data(_nvvm_data_layout)
+    llty = default_manager[ty].get_value_type()
+    return llty.get_abi_size(target_data)
+
+
+@initfunc
+def set_malloc_heap_size(size=None):
+    """
+    Heap size control for strings_udf, size in bytes.
+    """
+    global _heap_size
+    if size is None:
+        size = _STRINGS_UDF_DEFAULT_HEAP_SIZE
+    if size != _heap_size:
+        (ret,) = cudart.cudaDeviceSetLimit(
+            cudart.cudaLimit.cudaLimitMallocHeapSize, size
+        )
+        if ret.value != 0:
+            raise RuntimeError("Unable to set cudaMalloc heap size")
+
+        _heap_size = size
+
+
+def column_to_string_view_array_init_heap(col):
+    # lazily allocate heap only when a string needs to be returned
+    return column_to_string_view_array(col)
diff --git a/python/cudf/cudf/core/window/__init__.py b/python/cudf/cudf/core/window/__init__.py
new file mode 100644
index 0000000..8ea3eb0
--- /dev/null
+++ b/python/cudf/cudf/core/window/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION
+
+from cudf.core.window.rolling import Rolling
diff --git a/python/cudf/cudf/core/window/rolling.py b/python/cudf/cudf/core/window/rolling.py
new file mode 100644
index 0000000..8a92ea8
--- /dev/null
+++ b/python/cudf/cudf/core/window/rolling.py
@@ -0,0 +1,562 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION
+
+import itertools
+
+import numba
+import pandas as pd
+from pandas.api.indexers import BaseIndexer
+
+import cudf
+from cudf import _lib as libcudf
+from cudf.api.types import is_integer, is_number
+from cudf.core import column
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.column.column import as_column
+from cudf.core.mixins import Reducible
+from cudf.utils import cudautils
+from cudf.utils.utils import GetAttrGetItemMixin
+
+
+class Rolling(GetAttrGetItemMixin, Reducible):
+    """
+    Rolling window calculations.
+
+    Parameters
+    ----------
+    window : int, offset or a BaseIndexer subclass
+        Size of the window, i.e., the number of observations used
+        to calculate the statistic.
+        For datetime indexes, an offset can be provided instead
+        of an int. The offset must be convertible to a timedelta.
+        As opposed to a fixed window size, each window will be
+        sized to accommodate observations within the time period
+        specified by the offset.
+        If a BaseIndexer subclass is passed, calculates the window
+        boundaries based on the defined ``get_window_bounds`` method.
+    min_periods : int, optional
+        The minimum number of observations in the window that are
+        required to be non-null, so that the result is non-null.
+        If not provided or ``None``, ``min_periods`` is equal to
+        the window size.
+    center : bool, optional
+        If ``True``, the result is set at the center of the window.
+        If ``False`` (default), the result is set at the right edge
+        of the window.
+
+    Returns
+    -------
+    ``Rolling`` object.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> a = cudf.Series([1, 2, 3, None, 4])
+
+    Rolling sum with window size 2.
+
+    >>> print(a.rolling(2).sum())
+    0
+    1    3
+    2    5
+    3
+    4
+    dtype: int64
+
+    Rolling sum with window size 2 and min_periods 1.
+
+    >>> print(a.rolling(2, min_periods=1).sum())
+    0    1
+    1    3
+    2    5
+    3    3
+    4    4
+    dtype: int64
+
+    Rolling count with window size 3.
+
+    >>> print(a.rolling(3).count())
+    0    1
+    1    2
+    2    3
+    3    2
+    4    2
+    dtype: int64
+
+    Rolling count with window size 3, but with the result set at the
+    center of the window.
+
+    >>> print(a.rolling(3, center=True).count())
+    0    2
+    1    3
+    2    2
+    3    2
+    4    1 dtype: int64
+
+    Rolling max with variable window size specified by an offset;
+    only valid for datetime index.
+
+    >>> a = cudf.Series(
+    ...     [1, 9, 5, 4, np.nan, 1],
+    ...     index=[
+    ...         pd.Timestamp('20190101 09:00:00'),
+    ...         pd.Timestamp('20190101 09:00:01'),
+    ...         pd.Timestamp('20190101 09:00:02'),
+    ...         pd.Timestamp('20190101 09:00:04'),
+    ...         pd.Timestamp('20190101 09:00:07'),
+    ...         pd.Timestamp('20190101 09:00:08')
+    ...     ]
+    ... )
+
+    >>> print(a.rolling('2s').max())
+    2019-01-01T09:00:00.000    1
+    2019-01-01T09:00:01.000    9
+    2019-01-01T09:00:02.000    9
+    2019-01-01T09:00:04.000    4
+    2019-01-01T09:00:07.000
+    2019-01-01T09:00:08.000    1
+    dtype: int64
+
+    Apply custom function on the window with the *apply* method
+
+    >>> import numpy as np
+    >>> import math
+    >>> b = cudf.Series([16, 25, 36, 49, 64, 81], dtype=np.float64)
+    >>> def some_func(A):
+    ...     b = 0
+    ...     for a in A:
+    ...         b = b + math.sqrt(a)
+    ...     return b
+    ...
+    >>> print(b.rolling(3, min_periods=1).apply(some_func))
+    0     4.0
+    1     9.0
+    2    15.0
+    3    18.0
+    4    21.0
+    5    24.0
+    dtype: float64
+
+    And this also works for window rolling set by an offset
+
+    >>> import pandas as pd
+    >>> c = cudf.Series(
+    ...     [16, 25, 36, 49, 64, 81],
+    ...     index=[
+    ...          pd.Timestamp('20190101 09:00:00'),
+    ...          pd.Timestamp('20190101 09:00:01'),
+    ...          pd.Timestamp('20190101 09:00:02'),
+    ...          pd.Timestamp('20190101 09:00:04'),
+    ...          pd.Timestamp('20190101 09:00:07'),
+    ...          pd.Timestamp('20190101 09:00:08')
+    ...      ],
+    ...     dtype=np.float64
+    ... )
+    >>> print(c.rolling('2s').apply(some_func))
+    2019-01-01T09:00:00.000     4.0
+    2019-01-01T09:00:01.000     9.0
+    2019-01-01T09:00:02.000    11.0
+    2019-01-01T09:00:04.000     7.0
+    2019-01-01T09:00:07.000     8.0
+    2019-01-01T09:00:08.000    17.0
+    dtype: float64
+    """
+
+    _PROTECTED_KEYS = frozenset(("obj",))
+
+    _time_window = False
+
+    _VALID_REDUCTIONS = {
+        "sum",
+        "min",
+        "max",
+        "mean",
+        "var",
+        "std",
+    }
+
+    def __init__(
+        self,
+        obj,
+        window,
+        min_periods=None,
+        center=False,
+        axis=0,
+        win_type=None,
+    ):
+        self.obj = obj
+        self.window = window
+        self.min_periods = min_periods
+        self.center = center
+        self._normalize()
+        self.agg_params = {}
+        if axis != 0:
+            raise NotImplementedError("axis != 0 is not supported yet.")
+        self.axis = axis
+
+        if win_type is not None:
+            if win_type != "boxcar":
+                raise NotImplementedError(
+                    "Only the default win_type 'boxcar' is currently supported"
+                )
+        self.win_type = win_type
+
+    def __getitem__(self, arg):
+        if isinstance(arg, tuple):
+            arg = list(arg)
+        return self.obj[arg].rolling(
+            window=self.window,
+            min_periods=self.min_periods,
+            center=self.center,
+        )
+
+    def _apply_agg_column(self, source_column, agg_name):
+        min_periods = self.min_periods or 1
+        if isinstance(self.window, int):
+            preceding_window = None
+            following_window = None
+            window = self.window
+        elif isinstance(self.window, BaseIndexer):
+            if PANDAS_GE_150:
+                start, end = self.window.get_window_bounds(
+                    num_values=len(self.obj),
+                    min_periods=self.min_periods,
+                    center=self.center,
+                    closed=None,
+                    step=None,
+                )
+            else:
+                start, end = self.window.get_window_bounds(
+                    num_values=len(self.obj),
+                    min_periods=self.min_periods,
+                    center=self.center,
+                    closed=None,
+                )
+            start = as_column(start, dtype="int32")
+            end = as_column(end, dtype="int32")
+
+            idx = cudf.core.column.arange(len(start))
+            preceding_window = (idx - start + cudf.Scalar(1, "int32")).astype(
+                "int32"
+            )
+            following_window = (end - idx - cudf.Scalar(1, "int32")).astype(
+                "int32"
+            )
+            window = None
+        else:
+            preceding_window = as_column(self.window)
+            following_window = column.full(
+                self.window.size, 0, dtype=self.window.dtype
+            )
+            window = None
+
+        return libcudf.rolling.rolling(
+            source_column=source_column,
+            pre_column_window=preceding_window,
+            fwd_column_window=following_window,
+            window=window,
+            min_periods=min_periods,
+            center=self.center,
+            op=agg_name,
+            agg_params=self.agg_params,
+        )
+
+    def _apply_agg_dataframe(self, df, agg_name):
+        return cudf.DataFrame._from_data(
+            {
+                col_name: self._apply_agg_column(col, agg_name)
+                for col_name, col in df._data.items()
+            },
+            index=df.index,
+        )
+
+    def _apply_agg(self, agg_name):
+        if isinstance(self.obj, cudf.Series):
+            return cudf.Series._from_data(
+                {
+                    self.obj.name: self._apply_agg_column(
+                        self.obj._column, agg_name
+                    )
+                },
+                index=self.obj.index,
+            )
+        else:
+            return self._apply_agg_dataframe(self.obj, agg_name)
+
+    def _reduce(
+        self,
+        op: str,
+        *args,
+        **kwargs,
+    ):
+        """Calculate the rolling {op}.
+
+        Returns
+        -------
+        Series or DataFrame
+            Return type is the same as the original object.
+        """
+        return self._apply_agg(op)
+
+    def var(self, ddof=1):
+        """Calculate the rolling variance.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of
+            elements.
+
+        Returns
+        -------
+        Series or DataFrame
+            Return type is the same as the original object.
+        """
+        self.agg_params["ddof"] = ddof
+        return self._apply_agg("var")
+
+    def std(self, ddof=1):
+        """Calculate the rolling standard deviation.
+
+        Parameters
+        ----------
+        ddof : int, default 1
+            Delta Degrees of Freedom.  The divisor used in calculations
+            is ``N - ddof``, where ``N`` represents the number of
+            elements.
+
+        Returns
+        -------
+        Series or DataFrame
+            Return type is the same as the original object.
+        """
+        self.agg_params["ddof"] = ddof
+        return self._apply_agg("std")
+
+    def count(self):
+        """Calculate the rolling count of non NaN observations.
+
+        Returns
+        -------
+        Series or DataFrame
+            Return type is the same as the original object.
+        """
+        return self._apply_agg("count")
+
+    def apply(self, func, *args, **kwargs):
+        """
+        Calculate the rolling custom aggregation function.
+
+        Parameters
+        ----------
+        func : function
+            A user defined function that takes an 1D array as input
+        args : tuple
+            unsupported.
+        kwargs
+            unsupported
+
+        See Also
+        --------
+        cudf.Series.apply: Apply an elementwise function to
+            transform the values in the Column.
+
+        Notes
+        -----
+        The supported Python features are listed in
+
+        https://numba.readthedocs.io/en/stable/cuda/cudapysupported.html
+
+        with these exceptions:
+
+        * Math functions in `cmath` are not supported since `libcudf` does not
+          have complex number support and output of `cmath` functions are most
+          likely complex numbers.
+
+        * These five functions in `math` are not supported since numba
+          generates multiple PTX functions from them:
+
+          * math.sin()
+          * math.cos()
+          * math.tan()
+          * math.gamma()
+          * math.lgamma()
+
+        * Series with string dtypes are not supported.
+
+        * Global variables need to be re-defined explicitly inside
+          the udf, as numba considers them to be compile-time constants
+          and there is no known way to obtain value of the global variable.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> def count_if_gt_3(window):
+        ...     count = 0
+        ...     for i in window:
+        ...             if i > 3:
+        ...                     count += 1
+        ...     return count
+        ...
+        >>> s = cudf.Series([0, 1.1, 5.8, 3.1, 6.2, 2.0, 1.5])
+        >>> s.rolling(3, min_periods=1).apply(count_if_gt_3)
+        0    0
+        1    0
+        2    1
+        3    2
+        4    3
+        5    2
+        6    1
+        dtype: int64
+        """
+        has_nulls = False
+        if isinstance(self.obj, cudf.Series):
+            if self.obj._column.has_nulls():
+                has_nulls = True
+        else:
+            for col in self.obj._data:
+                if self.obj[col].has_nulls:
+                    has_nulls = True
+        if has_nulls:
+            raise NotImplementedError(
+                "Handling UDF with null values is not yet supported"
+            )
+        return self._apply_agg(func)
+
+    def _normalize(self):
+        """
+        Normalize the *window* and *min_periods* args
+
+        *window* can be:
+
+        * An integer, in which case it is the window size.
+          If *min_periods* is unspecified, it is set to be equal to
+          the window size.
+
+        * A timedelta offset, in which case it is used to generate
+          a column of window sizes to use for each element.
+          If *min_periods* is unspecified, it is set to 1.
+          Only valid for datetime index.
+        """
+        window, min_periods = self.window, self.min_periods
+        if is_number(window):
+            # only allow integers
+            if not is_integer(window):
+                raise ValueError("window must be an integer")
+            if window <= 0:
+                raise ValueError("window cannot be zero or negative")
+            if self.min_periods is None:
+                min_periods = window
+        else:
+            if isinstance(
+                window, (numba.cuda.devicearray.DeviceNDArray, BaseIndexer)
+            ):
+                # window is a device_array of window sizes or BaseIndexer
+                self.window = window
+                self.min_periods = min_periods
+                return
+
+            if not isinstance(self.obj.index, cudf.core.index.DatetimeIndex):
+                raise ValueError(
+                    "window must be an integer for non datetime index"
+                )
+
+            self._time_window = True
+
+            try:
+                window = pd.to_timedelta(window)
+                # to_timedelta will also convert np.arrays etc.,
+                if not isinstance(window, pd.Timedelta):
+                    raise ValueError
+                window = window.to_timedelta64()
+            except ValueError as e:
+                raise ValueError(
+                    "window must be integer or convertible to a timedelta"
+                ) from e
+            if self.min_periods is None:
+                min_periods = 1
+
+        self.window = self._window_to_window_sizes(window)
+        self.min_periods = min_periods
+
+    def _window_to_window_sizes(self, window):
+        """
+        For non-fixed width windows,
+        convert the window argument into window sizes.
+        """
+        if is_integer(window):
+            return window
+        else:
+            with acquire_spill_lock():
+                return cudautils.window_sizes_from_offset(
+                    self.obj.index._values.data_array_view(mode="write"),
+                    window,
+                )
+
+    def __repr__(self):
+        return "{} [window={},min_periods={},center={}]".format(
+            self.__class__.__name__, self.window, self.min_periods, self.center
+        )
+
+
+class RollingGroupby(Rolling):
+    """
+    Grouped rolling window calculation.
+
+    See Also
+    --------
+    cudf.core.window.Rolling
+    """
+
+    def __init__(self, groupby, window, min_periods=None, center=False):
+        sort_order = groupby.grouping.keys.argsort()
+
+        # TODO: there may be overlap between the columns
+        # of `groupby.grouping.keys` and `groupby.obj`.
+        # As an optimization, avoid gathering those twice.
+        self._group_keys = groupby.grouping.keys.take(sort_order)
+        obj = groupby.obj.drop(columns=groupby.grouping._named_columns).take(
+            sort_order
+        )
+
+        gb_size = groupby.size().sort_index()
+        self._group_starts = (
+            gb_size.cumsum().shift(1).fillna(0).repeat(gb_size)
+        )
+
+        super().__init__(obj, window, min_periods=min_periods, center=center)
+
+    @acquire_spill_lock()
+    def _window_to_window_sizes(self, window):
+        if is_integer(window):
+            return cudautils.grouped_window_sizes_from_offset(
+                column.arange(len(self.obj)).data_array_view(mode="read"),
+                self._group_starts,
+                window,
+            )
+        else:
+            return cudautils.grouped_window_sizes_from_offset(
+                self.obj.index._values.data_array_view(mode="read"),
+                self._group_starts,
+                window,
+            )
+
+    def _apply_agg(self, agg_name):
+        if agg_name == "count" and not self._time_window:
+            self.min_periods = 0
+        index = cudf.MultiIndex.from_frame(
+            cudf.DataFrame(
+                {
+                    key: value
+                    for key, value in itertools.chain(
+                        self._group_keys._data.items(),
+                        self.obj.index._data.items(),
+                    )
+                }
+            )
+        )
+
+        result = super()._apply_agg(agg_name)
+        result.index = index
+        return result
diff --git a/python/cudf/cudf/datasets.py b/python/cudf/cudf/datasets.py
new file mode 100644
index 0000000..c6091ab
--- /dev/null
+++ b/python/cudf/cudf/datasets.py
@@ -0,0 +1,177 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf._lib.transform import bools_to_mask
+from cudf.core.column_accessor import ColumnAccessor
+
+__all__ = ["timeseries", "randomdata"]
+
+
+# TODO:
+# change default of name from category to str type when nvstring are merged
+def timeseries(
+    start="2000-01-01",
+    end="2000-01-31",
+    freq="1s",
+    dtypes=None,
+    nulls_frequency=0,
+    seed=None,
+):
+    """Create timeseries dataframe with random data
+
+    Parameters
+    ----------
+    start : datetime (or datetime-like string)
+        Start of time series
+    end : datetime (or datetime-like string)
+        End of time series
+    dtypes : dict
+        Mapping of column names to types.
+        Valid types include {float, int, str, 'category'}.
+        If none is provided, this defaults to
+        ``{"name": "category", "id": int, "x": float, "y": float}``
+    freq : string
+        String like '2s' or '1H' or '12W' for the time series frequency
+    nulls_frequency : float
+        Fill the series with the specified proportion of nulls. Default is 0.
+    seed : int (optional)
+        Randomstate seed
+
+    Examples
+    --------
+    >>> import cudf as gd
+    >>> gdf = gd.datasets.timeseries()
+    >>> gdf.head()  # doctest: +SKIP
+              timestamp    id     name         x         y
+    2000-01-01 00:00:00   967    Jerry -0.031348 -0.040633
+    2000-01-01 00:00:01  1066  Michael -0.262136  0.307107
+    2000-01-01 00:00:02   988    Wendy -0.526331  0.128641
+    2000-01-01 00:00:03  1016   Yvonne  0.620456  0.767270
+    2000-01-01 00:00:04   998   Ursula  0.684902 -0.463278
+    """
+    if dtypes is None:
+        dtypes = {"name": "category", "id": int, "x": float, "y": float}
+
+    index = pd.DatetimeIndex(
+        pd.date_range(start, end, freq=freq, name="timestamp")
+    )
+    state = np.random.RandomState(seed)
+    columns = {k: make[dt](len(index), state) for k, dt in dtypes.items()}
+    df = pd.DataFrame(columns, index=index, columns=sorted(columns))
+    if df.index[-1] == end:
+        df = df.iloc[:-1]
+
+    gdf = cudf.from_pandas(df)
+    for col in gdf:
+        mask = state.choice(
+            [True, False],
+            size=len(index),
+            p=[1 - nulls_frequency, nulls_frequency],
+        )
+        mask_buf = bools_to_mask(cudf.core.column.as_column(mask))
+        masked_col = gdf[col]._column.set_mask(mask_buf)
+        gdf[col] = cudf.Series._from_data(
+            ColumnAccessor({None: masked_col}), index=gdf.index
+        )
+
+    return gdf
+
+
+def randomdata(nrows=10, dtypes=None, seed=None):
+    """Create a dataframe with random data
+
+    Parameters
+    ----------
+    nrows : int
+        number of rows in the dataframe
+    dtypes : dict
+        Mapping of column names to types.
+        Valid types include {float, int, str, 'category'}
+        If none is provided, this defaults to
+        ``{"id": int, "x": float, "y": float}``
+    seed : int (optional)
+        Randomstate seed
+
+    Examples
+    --------
+    >>> import cudf as gd
+    >>> gdf = gd.datasets.randomdata()
+    >>> cdf.head()  # doctest: +SKIP
+            id                  x                   y
+    0  1014 0.28361267466770146 -0.44274170661264334
+    1  1026 -0.9937981936047235 -0.09433464773262323
+    2  1038 -0.1266722796765325 0.20971126368240123
+    3  1002 0.9280495300010041  0.5137701393017848
+    4   976 0.9089527839187654  0.9881063385586304
+    """
+    if dtypes is None:
+        dtypes = {"id": int, "x": float, "y": float}
+    state = np.random.RandomState(seed)
+    columns = {k: make[dt](nrows, state) for k, dt in dtypes.items()}
+    df = pd.DataFrame(columns, columns=sorted(columns))
+    return cudf.from_pandas(df)
+
+
+def make_float(n, rstate):
+    return rstate.rand(n) * 2 - 1
+
+
+def make_int(n, rstate):
+    return rstate.poisson(1000, size=n)
+
+
+names = [
+    "Alice",
+    "Bob",
+    "Charlie",
+    "Dan",
+    "Edith",
+    "Frank",
+    "George",
+    "Hannah",
+    "Ingrid",
+    "Jerry",
+    "Kevin",
+    "Laura",
+    "Michael",
+    "Norbert",
+    "Oliver",
+    "Patricia",
+    "Quinn",
+    "Ray",
+    "Sarah",
+    "Tim",
+    "Ursula",
+    "Victor",
+    "Wendy",
+    "Xavier",
+    "Yvonne",
+    "Zelda",
+]
+
+
+def make_string(n, rstate):
+    return rstate.choice(names, size=n)
+
+
+def make_categorical(n, rstate):
+    return pd.Categorical.from_codes(
+        rstate.randint(0, len(names), size=n), names
+    )
+
+
+def make_bool(n, rstate):
+    return rstate.choice([True, False], size=n)
+
+
+make = {
+    float: make_float,
+    int: make_int,
+    str: make_string,
+    object: make_string,
+    "category": make_categorical,
+    bool: make_bool,
+}
diff --git a/python/cudf/cudf/errors.py b/python/cudf/cudf/errors.py
new file mode 100644
index 0000000..973e5b9
--- /dev/null
+++ b/python/cudf/cudf/errors.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+
+class UnsupportedCUDAError(Exception):
+    pass
+
+
+class MixedTypeError(TypeError):
+    pass
diff --git a/python/cudf/cudf/io/__init__.py b/python/cudf/cudf/io/__init__.py
new file mode 100644
index 0000000..6d4b44d
--- /dev/null
+++ b/python/cudf/cudf/io/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+from cudf.io.avro import read_avro
+from cudf.io.csv import read_csv, to_csv
+from cudf.io.dlpack import from_dlpack
+from cudf.io.feather import read_feather
+from cudf.io.hdf import read_hdf
+from cudf.io.json import read_json
+from cudf.io.orc import read_orc, read_orc_metadata, to_orc
+from cudf.io.parquet import (
+    ParquetDatasetWriter,
+    merge_parquet_filemetadata,
+    read_parquet,
+    read_parquet_metadata,
+    write_to_dataset,
+)
+from cudf.io.text import read_text
diff --git a/python/cudf/cudf/io/avro.py b/python/cudf/cudf/io/avro.py
new file mode 100644
index 0000000..728b340
--- /dev/null
+++ b/python/cudf/cudf/io/avro.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import cudf
+from cudf import _lib as libcudf
+from cudf.utils import ioutils
+
+
+@ioutils.doc_read_avro()
+def read_avro(
+    filepath_or_buffer,
+    columns=None,
+    skiprows=None,
+    num_rows=None,
+    storage_options=None,
+):
+    """{docstring}"""
+
+    is_single_filepath_or_buffer = ioutils.ensure_single_filepath_or_buffer(
+        path_or_data=filepath_or_buffer,
+        storage_options=storage_options,
+    )
+    if not is_single_filepath_or_buffer:
+        raise NotImplementedError(
+            "`read_avro` does not yet support reading multiple files"
+        )
+
+    filepath_or_buffer, compression = ioutils.get_reader_filepath_or_buffer(
+        path_or_data=filepath_or_buffer,
+        compression=None,
+        storage_options=storage_options,
+    )
+    if compression is not None:
+        ValueError("URL content-encoding decompression is not supported")
+
+    return cudf.DataFrame._from_data(
+        *libcudf.avro.read_avro(
+            filepath_or_buffer, columns, skiprows, num_rows
+        )
+    )
diff --git a/python/cudf/cudf/io/csv.py b/python/cudf/cudf/io/csv.py
new file mode 100644
index 0000000..bacc064
--- /dev/null
+++ b/python/cudf/cudf/io/csv.py
@@ -0,0 +1,255 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from collections import abc
+from io import BytesIO, StringIO
+
+import numpy as np
+from pyarrow.lib import NativeFile
+
+import cudf
+from cudf import _lib as libcudf
+from cudf.api.types import is_scalar
+from cudf.utils import ioutils
+from cudf.utils.dtypes import _maybe_convert_to_default_type
+from cudf.utils.utils import _cudf_nvtx_annotate
+
+
+@_cudf_nvtx_annotate
+@ioutils.doc_read_csv()
+def read_csv(
+    filepath_or_buffer,
+    sep=",",
+    delimiter=None,
+    header="infer",
+    names=None,
+    index_col=None,
+    usecols=None,
+    prefix=None,
+    mangle_dupe_cols=True,
+    dtype=None,
+    true_values=None,
+    false_values=None,
+    skipinitialspace=False,
+    skiprows=0,
+    skipfooter=0,
+    nrows=None,
+    na_values=None,
+    keep_default_na=True,
+    na_filter=True,
+    skip_blank_lines=True,
+    parse_dates=None,
+    dayfirst=False,
+    compression="infer",
+    thousands=None,
+    decimal=".",
+    lineterminator="\n",
+    quotechar='"',
+    quoting=0,
+    doublequote=True,
+    comment=None,
+    delim_whitespace=False,
+    byte_range=None,
+    use_python_file_object=True,
+    storage_options=None,
+    bytes_per_thread=None,
+):
+    """{docstring}"""
+
+    if use_python_file_object and bytes_per_thread is not None:
+        raise ValueError(
+            "bytes_per_thread is only supported when "
+            "`use_python_file_object=False`"
+        )
+
+    if bytes_per_thread is None:
+        bytes_per_thread = ioutils._BYTES_PER_THREAD_DEFAULT
+
+    is_single_filepath_or_buffer = ioutils.ensure_single_filepath_or_buffer(
+        path_or_data=filepath_or_buffer,
+        storage_options=storage_options,
+    )
+    if not is_single_filepath_or_buffer:
+        raise NotImplementedError(
+            "`read_csv` does not yet support reading multiple files"
+        )
+
+    filepath_or_buffer, compression = ioutils.get_reader_filepath_or_buffer(
+        path_or_data=filepath_or_buffer,
+        compression=compression,
+        iotypes=(BytesIO, StringIO, NativeFile),
+        use_python_file_object=use_python_file_object,
+        storage_options=storage_options,
+        bytes_per_thread=bytes_per_thread,
+    )
+
+    if na_values is not None and is_scalar(na_values):
+        na_values = [na_values]
+
+    df = libcudf.csv.read_csv(
+        filepath_or_buffer,
+        lineterminator=lineterminator,
+        quotechar=quotechar,
+        quoting=quoting,
+        doublequote=doublequote,
+        header=header,
+        mangle_dupe_cols=mangle_dupe_cols,
+        usecols=usecols,
+        sep=sep,
+        delimiter=delimiter,
+        delim_whitespace=delim_whitespace,
+        skipinitialspace=skipinitialspace,
+        names=names,
+        dtype=dtype,
+        skipfooter=skipfooter,
+        skiprows=skiprows,
+        dayfirst=dayfirst,
+        compression=compression,
+        thousands=thousands,
+        decimal=decimal,
+        true_values=true_values,
+        false_values=false_values,
+        nrows=nrows,
+        byte_range=byte_range,
+        skip_blank_lines=skip_blank_lines,
+        parse_dates=parse_dates,
+        comment=comment,
+        na_values=na_values,
+        keep_default_na=keep_default_na,
+        na_filter=na_filter,
+        prefix=prefix,
+        index_col=index_col,
+    )
+
+    if dtype is None or isinstance(dtype, abc.Mapping):
+        # There exists some dtypes in the result columns that is inferred.
+        # Find them and map them to the default dtypes.
+        specified_dtypes = {} if dtype is None else dtype
+        df_dtypes = df._dtypes
+        unspecified_dtypes = {
+            name: df_dtypes[name]
+            for name in df._column_names
+            if name not in specified_dtypes
+        }
+        default_dtypes = {}
+
+        for name, dt in unspecified_dtypes.items():
+            if dt == np.dtype("i1"):
+                # csv reader reads all null column as int8.
+                # The dtype should remain int8.
+                default_dtypes[name] = dt
+            else:
+                default_dtypes[name] = _maybe_convert_to_default_type(dt)
+        df = df.astype(default_dtypes)
+
+    return df
+
+
+@_cudf_nvtx_annotate
+@ioutils.doc_to_csv()
+def to_csv(
+    df,
+    path_or_buf=None,
+    sep=",",
+    na_rep="",
+    columns=None,
+    header=True,
+    index=True,
+    encoding=None,
+    compression=None,
+    lineterminator="\n",
+    chunksize=None,
+    storage_options=None,
+):
+    """{docstring}"""
+
+    if not isinstance(sep, str):
+        raise TypeError(f'"sep" must be string, not {type(sep).__name__}')
+    elif len(sep) > 1:
+        raise TypeError('"sep" must be a 1-character string')
+
+    if encoding and encoding != "utf-8":
+        error_msg = (
+            f"Encoding {encoding} is not supported. "
+            + "Currently, only utf-8 encoding is supported."
+        )
+        raise NotImplementedError(error_msg)
+
+    if compression:
+        error_msg = "Writing compressed csv is not currently supported in cudf"
+        raise NotImplementedError(error_msg)
+
+    return_as_string = False
+    if path_or_buf is None:
+        path_or_buf = StringIO()
+        return_as_string = True
+
+    path_or_buf = ioutils.get_writer_filepath_or_buffer(
+        path_or_data=path_or_buf, mode="w", storage_options=storage_options
+    )
+
+    if columns is not None:
+        try:
+            df = df[columns]
+        except KeyError:
+            raise NameError(
+                "Dataframe doesn't have the labels provided in columns"
+            )
+
+    for col in df._data.columns:
+        if isinstance(col, cudf.core.column.ListColumn):
+            raise NotImplementedError(
+                "Writing to csv format is not yet supported with "
+                "list columns."
+            )
+        elif isinstance(col, cudf.core.column.StructColumn):
+            raise NotImplementedError(
+                "Writing to csv format is not yet supported with "
+                "Struct columns."
+            )
+
+    # TODO: Need to typecast categorical columns to the underlying
+    # categories dtype to write the actual data to csv. Remove this
+    # workaround once following issue is fixed:
+    # https://github.com/rapidsai/cudf/issues/6661
+    if any(
+        isinstance(col, cudf.core.column.CategoricalColumn)
+        for col in df._data.columns
+    ) or isinstance(df.index, cudf.CategoricalIndex):
+        df = df.copy(deep=False)
+        for col_name, col in df._data.items():
+            if isinstance(col, cudf.core.column.CategoricalColumn):
+                df._data[col_name] = col.astype(col.categories.dtype)
+
+        if isinstance(df.index, cudf.CategoricalIndex):
+            df.index = df.index.astype(df.index.categories.dtype)
+
+    rows_per_chunk = chunksize if chunksize else len(df)
+
+    if ioutils.is_fsspec_open_file(path_or_buf):
+        with path_or_buf as file_obj:
+            file_obj = ioutils.get_IOBase_writer(file_obj)
+            libcudf.csv.write_csv(
+                df,
+                path_or_buf=file_obj,
+                sep=sep,
+                na_rep=na_rep,
+                header=header,
+                lineterminator=lineterminator,
+                rows_per_chunk=rows_per_chunk,
+                index=index,
+            )
+    else:
+        libcudf.csv.write_csv(
+            df,
+            path_or_buf=path_or_buf,
+            sep=sep,
+            na_rep=na_rep,
+            header=header,
+            lineterminator=lineterminator,
+            rows_per_chunk=rows_per_chunk,
+            index=index,
+        )
+
+    if return_as_string:
+        path_or_buf.seek(0)
+        return path_or_buf.read()
diff --git a/python/cudf/cudf/io/dlpack.py b/python/cudf/cudf/io/dlpack.py
new file mode 100644
index 0000000..e1950c9
--- /dev/null
+++ b/python/cudf/cudf/io/dlpack.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+
+import cudf
+from cudf._lib import interop as libdlpack
+from cudf.core.column import ColumnBase
+from cudf.utils import ioutils
+
+
+def from_dlpack(pycapsule_obj):
+    """Converts from a DLPack tensor to a cuDF object.
+
+    DLPack is an open-source memory tensor structure:
+    `dmlc/dlpack <https://github.com/dmlc/dlpack>`_.
+
+    This function takes a PyCapsule object which contains a pointer to
+    a DLPack tensor as input, and returns a cuDF object. This function deep
+    copies the data in the DLPack tensor into a cuDF object.
+
+    Parameters
+    ----------
+    pycapsule_obj : PyCapsule
+        Input DLPack tensor pointer which is encapsulated in a PyCapsule
+        object.
+
+    Returns
+    -------
+    A cuDF DataFrame or Series depending on if the input DLPack tensor is 1D
+    or 2D.
+
+    Notes
+    -----
+    cuDF from_dlpack() assumes column-major (Fortran order) input. If the input
+    tensor is row-major, transpose it before passing it to this function.
+    """
+
+    columns = libdlpack.from_dlpack(pycapsule_obj)
+    column_names = range(len(columns))
+
+    if len(columns) == 1:
+        return cudf.Series._from_columns(columns, column_names=column_names)
+    else:
+        return cudf.DataFrame._from_columns(columns, column_names=column_names)
+
+
+@ioutils.doc_to_dlpack()
+def to_dlpack(cudf_obj):
+    """Converts a cuDF object to a DLPack tensor.
+
+    DLPack is an open-source memory tensor structure:
+    `dmlc/dlpack <https://github.com/dmlc/dlpack>`_.
+
+    This function takes a cuDF object as input, and returns a PyCapsule object
+    which contains a pointer to DLPack tensor. This function deep copies
+    the data in the cuDF object into the DLPack tensor.
+
+    Parameters
+    ----------
+    cudf_obj : cuDF Object
+        Input cuDF object.
+
+    Returns
+    -------
+    A  DLPack tensor pointer which is encapsulated in a PyCapsule object.
+
+    Notes
+    -----
+    cuDF to_dlpack() produces column-major (Fortran order) output. If the
+    output tensor needs to be row major, transpose the output of this function.
+    """
+    if isinstance(cudf_obj, (cudf.DataFrame, cudf.Series, cudf.BaseIndex)):
+        gdf = cudf_obj
+    elif isinstance(cudf_obj, ColumnBase):
+        gdf = cudf_obj.as_frame()
+    else:
+        raise TypeError(
+            f"Input of type {type(cudf_obj)} cannot be converted "
+            "to DLPack tensor"
+        )
+
+    if any(
+        not cudf.api.types._is_non_decimal_numeric_dtype(col.dtype)
+        for col in gdf._data.columns
+    ):
+        raise TypeError("non-numeric data not yet supported")
+
+    dtype = cudf.utils.dtypes.find_common_type(
+        [col.dtype for col in gdf._data.columns]
+    )
+    gdf = gdf.astype(dtype)
+
+    return libdlpack.to_dlpack([*gdf._columns])
diff --git a/python/cudf/cudf/io/feather.py b/python/cudf/cudf/io/feather.py
new file mode 100644
index 0000000..3ba16c3
--- /dev/null
+++ b/python/cudf/cudf/io/feather.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2019, NVIDIA CORPORATION.
+
+import warnings
+
+from pyarrow import feather
+
+from cudf.core.dataframe import DataFrame
+from cudf.utils import ioutils
+
+
+@ioutils.doc_read_feather()
+def read_feather(path, *args, **kwargs):
+    """{docstring}"""
+
+    warnings.warn(
+        "Using CPU via PyArrow to read feather dataset, this may "
+        "be GPU accelerated in the future"
+    )
+    pa_table = feather.read_table(path, *args, **kwargs)
+    return DataFrame.from_arrow(pa_table)
+
+
+@ioutils.doc_to_feather()
+def to_feather(df, path, *args, **kwargs):
+    """{docstring}"""
+    warnings.warn(
+        "Using CPU via PyArrow to write Feather dataset, this may "
+        "be GPU accelerated in the future"
+    )
+    # Feather doesn't support using an index
+    pa_table = df.to_arrow(preserve_index=False)
+    feather.write_feather(pa_table, path, *args, **kwargs)
diff --git a/python/cudf/cudf/io/hdf.py b/python/cudf/cudf/io/hdf.py
new file mode 100644
index 0000000..8cf8c01
--- /dev/null
+++ b/python/cudf/cudf/io/hdf.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2019, NVIDIA CORPORATION.
+
+import warnings
+
+import pandas as pd
+
+import cudf
+from cudf.utils import ioutils
+
+
+@ioutils.doc_read_hdf()
+def read_hdf(path_or_buf, *args, **kwargs):
+    """{docstring}"""
+    warnings.warn(
+        "Using CPU via Pandas to read HDF dataset, this may "
+        "be GPU accelerated in the future"
+    )
+    pd_value = pd.read_hdf(path_or_buf, *args, **kwargs)
+    return cudf.from_pandas(pd_value)
+
+
+@ioutils.doc_to_hdf()
+def to_hdf(path_or_buf, key, value, *args, **kwargs):
+    """{docstring}"""
+    warnings.warn(
+        "Using CPU via Pandas to write HDF dataset, this may "
+        "be GPU accelerated in the future"
+    )
+    pd_value = value.to_pandas()
+    pd.io.pytables.to_hdf(path_or_buf, key, pd_value, *args, **kwargs)
diff --git a/python/cudf/cudf/io/json.py b/python/cudf/cudf/io/json.py
new file mode 100644
index 0000000..efac24a
--- /dev/null
+++ b/python/cudf/cudf/io/json.py
@@ -0,0 +1,244 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import warnings
+from collections import abc
+from io import BytesIO, StringIO
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf._lib import json as libjson
+from cudf.api.types import is_list_like
+from cudf.utils import ioutils
+from cudf.utils.dtypes import _maybe_convert_to_default_type
+
+
+@ioutils.doc_read_json()
+def read_json(
+    path_or_buf,
+    engine="auto",
+    orient=None,
+    dtype=None,
+    lines=False,
+    compression="infer",
+    byte_range=None,
+    keep_quotes=False,
+    storage_options=None,
+    *args,
+    **kwargs,
+):
+    """{docstring}"""
+
+    if dtype is not None and not isinstance(dtype, (abc.Mapping, bool)):
+        raise TypeError(
+            "'dtype' parameter only supports "
+            "a dict of column names and types as key-value pairs, "
+            f"or a bool, or None. Got {type(dtype)}"
+        )
+
+    if engine == "cudf_experimental":
+        raise ValueError(
+            "engine='cudf_experimental' support has been removed, "
+            "use `engine='cudf'`"
+        )
+
+    if engine == "cudf_legacy":
+        # TODO: Deprecated in 23.02, please
+        # give some time until(more than couple of
+        # releases from now) `cudf_legacy`
+        # support can be removed completely.
+        warnings.warn(
+            "engine='cudf_legacy' is a deprecated engine."
+            "This will be removed in a future release."
+            "Please switch to using engine='cudf'.",
+            FutureWarning,
+        )
+    if engine == "cudf_legacy" and not lines:
+        raise ValueError(f"{engine} engine only supports JSON Lines format")
+    if engine == "auto":
+        engine = "cudf" if lines else "pandas"
+    if engine != "cudf" and keep_quotes:
+        raise ValueError(
+            "keep_quotes='True' is supported only with engine='cudf'"
+        )
+
+    if engine == "cudf_legacy" or engine == "cudf":
+        if dtype is None:
+            dtype = True
+
+        if kwargs:
+            raise ValueError(
+                "cudf engine doesn't support the "
+                f"following keyword arguments: {list(kwargs.keys())}"
+            )
+        if args:
+            raise ValueError(
+                "cudf engine doesn't support the "
+                f"following positional arguments: {list(args)}"
+            )
+
+        # Multiple sources are passed as a list. If a single source is passed,
+        # wrap it in a list for unified processing downstream.
+        if not is_list_like(path_or_buf):
+            path_or_buf = [path_or_buf]
+
+        filepaths_or_buffers = []
+        for source in path_or_buf:
+            if ioutils.is_directory(
+                path_or_data=source, storage_options=storage_options
+            ):
+                fs = ioutils._ensure_filesystem(
+                    passed_filesystem=None,
+                    path=source,
+                    storage_options=storage_options,
+                )
+                source = ioutils.stringify_pathlike(source)
+                source = fs.sep.join([source, "*.json"])
+
+            tmp_source, compression = ioutils.get_reader_filepath_or_buffer(
+                path_or_data=source,
+                compression=compression,
+                iotypes=(BytesIO, StringIO),
+                allow_raw_text_input=True,
+                storage_options=storage_options,
+            )
+            if isinstance(tmp_source, list):
+                filepaths_or_buffers.extend(tmp_source)
+            else:
+                filepaths_or_buffers.append(tmp_source)
+
+        df = libjson.read_json(
+            filepaths_or_buffers,
+            dtype,
+            lines,
+            compression,
+            byte_range,
+            engine == "cudf_legacy",
+            keep_quotes,
+        )
+    else:
+        warnings.warn(
+            "Using CPU via Pandas to read JSON dataset, this may "
+            "be GPU accelerated in the future"
+        )
+
+        if not ioutils.ensure_single_filepath_or_buffer(
+            path_or_data=path_or_buf,
+            storage_options=storage_options,
+        ):
+            raise NotImplementedError(
+                "`read_json` does not yet support reading "
+                "multiple files via pandas"
+            )
+
+        path_or_buf, compression = ioutils.get_reader_filepath_or_buffer(
+            path_or_data=path_or_buf,
+            compression=compression,
+            iotypes=(BytesIO, StringIO),
+            allow_raw_text_input=True,
+            storage_options=storage_options,
+        )
+
+        pd_value = pd.read_json(
+            path_or_buf,
+            lines=lines,
+            dtype=dtype,
+            compression=compression,
+            storage_options=storage_options,
+            orient=orient,
+            *args,
+            **kwargs,
+        )
+        df = cudf.from_pandas(pd_value)
+
+    if dtype is None:
+        dtype = True
+
+    if dtype is True or isinstance(dtype, abc.Mapping):
+        # There exists some dtypes in the result columns that is inferred.
+        # Find them and map them to the default dtypes.
+        specified_dtypes = {} if dtype is True else dtype
+        df_dtypes = df._dtypes
+        unspecified_dtypes = {
+            name: df_dtypes[name]
+            for name in df._column_names
+            if name not in specified_dtypes
+        }
+        default_dtypes = {}
+
+        for name, dt in unspecified_dtypes.items():
+            if dt == np.dtype("i1"):
+                # csv reader reads all null column as int8.
+                # The dtype should remain int8.
+                default_dtypes[name] = dt
+            else:
+                default_dtypes[name] = _maybe_convert_to_default_type(dt)
+        df = df.astype(default_dtypes)
+
+    return df
+
+
+@ioutils.doc_to_json()
+def to_json(
+    cudf_val,
+    path_or_buf=None,
+    engine="auto",
+    orient=None,
+    storage_options=None,
+    *args,
+    **kwargs,
+):
+    """{docstring}"""
+
+    if engine == "auto":
+        engine = "pandas"
+
+    if engine == "cudf":
+        if orient not in {"records", None}:
+            raise ValueError(
+                f"Only the `orient='records'` is supported for JSON writer"
+                f" with `engine='cudf'`, got {orient}"
+            )
+
+        if path_or_buf is None:
+            path_or_buf = StringIO()
+            return_as_string = True
+        else:
+            path_or_buf = ioutils.get_writer_filepath_or_buffer(
+                path_or_data=path_or_buf,
+                mode="w",
+                storage_options=storage_options,
+            )
+            return_as_string = False
+
+        if ioutils.is_fsspec_open_file(path_or_buf):
+            with path_or_buf as file_obj:
+                file_obj = ioutils.get_IOBase_writer(file_obj)
+                libjson.write_json(
+                    cudf_val, path_or_buf=file_obj, *args, **kwargs
+                )
+        else:
+            libjson.write_json(
+                cudf_val, path_or_buf=path_or_buf, *args, **kwargs
+            )
+
+        if return_as_string:
+            path_or_buf.seek(0)
+            return path_or_buf.read()
+    elif engine == "pandas":
+        warnings.warn("Using CPU via Pandas to write JSON dataset")
+        pd_value = cudf_val.to_pandas(nullable=True)
+        return pd.io.json.to_json(
+            path_or_buf,
+            pd_value,
+            orient=orient,
+            storage_options=storage_options,
+            *args,
+            **kwargs,
+        )
+    else:
+        raise ValueError(
+            f"`engine` only support {{'auto', 'cudf', 'pandas'}}, "
+            f"got: {engine}"
+        )
diff --git a/python/cudf/cudf/io/orc.py b/python/cudf/cudf/io/orc.py
new file mode 100644
index 0000000..f51952d
--- /dev/null
+++ b/python/cudf/cudf/io/orc.py
@@ -0,0 +1,474 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import datetime
+import warnings
+
+import pyarrow as pa
+from fsspec.utils import stringify_path
+from pyarrow import orc as orc
+
+import cudf
+from cudf._lib import orc as liborc
+from cudf.api.types import is_list_like
+from cudf.utils import ioutils
+from cudf.utils.metadata import (  # type: ignore
+    orc_column_statistics_pb2 as cs_pb2,
+)
+
+
+def _make_empty_df(filepath_or_buffer, columns):
+    orc_file = orc.ORCFile(filepath_or_buffer)
+    schema = orc_file.schema
+    col_names = schema.names if columns is None else columns
+    return cudf.DataFrame._from_data(
+        data={
+            col_name: cudf.core.column.column_empty(
+                row_count=0,
+                dtype=schema.field(col_name).type.to_pandas_dtype(),
+            )
+            for col_name in col_names
+        }
+    )
+
+
+def _parse_column_statistics(cs, column_statistics_blob):
+    # Initialize stats to return and parse stats blob
+    column_statistics = {}
+    cs.ParseFromString(column_statistics_blob)
+
+    # Load from parsed stats blob into stats to return
+    if cs.HasField("numberOfValues"):
+        column_statistics["number_of_values"] = cs.numberOfValues
+    if cs.HasField("hasNull"):
+        column_statistics["has_null"] = cs.hasNull
+
+    if cs.HasField("intStatistics"):
+        column_statistics["minimum"] = (
+            cs.intStatistics.minimum
+            if cs.intStatistics.HasField("minimum")
+            else None
+        )
+        column_statistics["maximum"] = (
+            cs.intStatistics.maximum
+            if cs.intStatistics.HasField("maximum")
+            else None
+        )
+        column_statistics["sum"] = (
+            cs.intStatistics.sum if cs.intStatistics.HasField("sum") else None
+        )
+
+    elif cs.HasField("doubleStatistics"):
+        column_statistics["minimum"] = (
+            cs.doubleStatistics.minimum
+            if cs.doubleStatistics.HasField("minimum")
+            else None
+        )
+        column_statistics["maximum"] = (
+            cs.doubleStatistics.maximum
+            if cs.doubleStatistics.HasField("maximum")
+            else None
+        )
+        column_statistics["sum"] = (
+            cs.doubleStatistics.sum
+            if cs.doubleStatistics.HasField("sum")
+            else None
+        )
+
+    elif cs.HasField("stringStatistics"):
+        column_statistics["minimum"] = (
+            cs.stringStatistics.minimum
+            if cs.stringStatistics.HasField("minimum")
+            else None
+        )
+        column_statistics["maximum"] = (
+            cs.stringStatistics.maximum
+            if cs.stringStatistics.HasField("maximum")
+            else None
+        )
+        column_statistics["sum"] = cs.stringStatistics.sum
+
+    elif cs.HasField("bucketStatistics"):
+        column_statistics["true_count"] = cs.bucketStatistics.count[0]
+        column_statistics["false_count"] = (
+            column_statistics["number_of_values"]
+            - column_statistics["true_count"]
+        )
+
+    elif cs.HasField("decimalStatistics"):
+        column_statistics["minimum"] = (
+            cs.decimalStatistics.minimum
+            if cs.decimalStatistics.HasField("minimum")
+            else None
+        )
+        column_statistics["maximum"] = (
+            cs.decimalStatistics.maximum
+            if cs.decimalStatistics.HasField("maximum")
+            else None
+        )
+        column_statistics["sum"] = cs.decimalStatistics.sum
+
+    elif cs.HasField("dateStatistics"):
+        column_statistics["minimum"] = (
+            datetime.datetime.fromtimestamp(
+                datetime.timedelta(cs.dateStatistics.minimum).total_seconds(),
+                datetime.timezone.utc,
+            )
+            if cs.dateStatistics.HasField("minimum")
+            else None
+        )
+        column_statistics["maximum"] = (
+            datetime.datetime.fromtimestamp(
+                datetime.timedelta(cs.dateStatistics.maximum).total_seconds(),
+                datetime.timezone.utc,
+            )
+            if cs.dateStatistics.HasField("maximum")
+            else None
+        )
+
+    elif cs.HasField("timestampStatistics"):
+        # Before ORC-135, the local timezone offset was included and they were
+        # stored as minimum and maximum. After ORC-135, the timestamp is
+        # adjusted to UTC before being converted to milliseconds and stored
+        # in minimumUtc and maximumUtc.
+        # TODO: Support minimum and maximum by reading writer's local timezone
+        if cs.timestampStatistics.HasField(
+            "minimumUtc"
+        ) and cs.timestampStatistics.HasField("maximumUtc"):
+            column_statistics["minimum"] = datetime.datetime.fromtimestamp(
+                cs.timestampStatistics.minimumUtc / 1000, datetime.timezone.utc
+            )
+            column_statistics["maximum"] = datetime.datetime.fromtimestamp(
+                cs.timestampStatistics.maximumUtc / 1000, datetime.timezone.utc
+            )
+
+    elif cs.HasField("binaryStatistics"):
+        column_statistics["sum"] = cs.binaryStatistics.sum
+
+    return column_statistics
+
+
+@ioutils.doc_read_orc_metadata()
+def read_orc_metadata(path):
+    """{docstring}"""
+
+    orc_file = orc.ORCFile(path)
+
+    num_rows = orc_file.nrows
+    num_stripes = orc_file.nstripes
+    col_names = orc_file.schema.names
+
+    return num_rows, num_stripes, col_names
+
+
+@ioutils.doc_read_orc_statistics()
+def read_orc_statistics(
+    filepaths_or_buffers,
+    columns=None,
+    **kwargs,
+):
+    """{docstring}"""
+
+    files_statistics = []
+    stripes_statistics = []
+    for source in filepaths_or_buffers:
+        path_or_buf, compression = ioutils.get_reader_filepath_or_buffer(
+            path_or_data=source, compression=None, **kwargs
+        )
+        if compression is not None:
+            ValueError("URL content-encoding decompression is not supported")
+
+        # Read in statistics and unpack
+        (
+            column_names,
+            raw_file_statistics,
+            raw_stripes_statistics,
+        ) = liborc.read_raw_orc_statistics(path_or_buf)
+
+        # Parse column names
+        column_names = [
+            column_name.decode("utf-8") for column_name in column_names
+        ]
+
+        # Parse statistics
+        cs = cs_pb2.ColumnStatistics()
+
+        file_statistics = {
+            column_names[i]: _parse_column_statistics(cs, raw_file_stats)
+            for i, raw_file_stats in enumerate(raw_file_statistics)
+            if columns is None or column_names[i] in columns
+        }
+        if any(
+            not parsed_statistics
+            for parsed_statistics in file_statistics.values()
+        ):
+            continue
+        else:
+            files_statistics.append(file_statistics)
+
+        for raw_stripe_statistics in raw_stripes_statistics:
+            stripe_statistics = {
+                column_names[i]: _parse_column_statistics(cs, raw_file_stats)
+                for i, raw_file_stats in enumerate(raw_stripe_statistics)
+                if columns is None or column_names[i] in columns
+            }
+            if any(
+                not parsed_statistics
+                for parsed_statistics in stripe_statistics.values()
+            ):
+                continue
+            else:
+                stripes_statistics.append(stripe_statistics)
+
+    return files_statistics, stripes_statistics
+
+
+def _filter_stripes(
+    filters, filepath_or_buffer, stripes=None, skip_rows=None, num_rows=None
+):
+    # Multiple sources are passed as a list. If a single source is passed,
+    # wrap it in a list for unified processing downstream.
+    if not is_list_like(filepath_or_buffer):
+        filepath_or_buffer = [filepath_or_buffer]
+
+    # Prepare filters
+    filters = ioutils._prepare_filters(filters)
+
+    # Get columns relevant to filtering
+    columns_in_predicate = [
+        col for conjunction in filters for (col, op, val) in conjunction
+    ]
+
+    # Read and parse file-level and stripe-level statistics
+    file_statistics, stripes_statistics = read_orc_statistics(
+        filepath_or_buffer, columns_in_predicate
+    )
+
+    file_stripe_map = []
+    for file_stat in file_statistics:
+        # Filter using file-level statistics
+        if not ioutils._apply_filters(filters, file_stat):
+            continue
+
+        # Filter using stripe-level statistics
+        selected_stripes = []
+        num_rows_scanned = 0
+        for i, stripe_statistics in enumerate(stripes_statistics):
+            num_rows_before_stripe = num_rows_scanned
+            num_rows_scanned += next(iter(stripe_statistics.values()))[
+                "number_of_values"
+            ]
+            if stripes is not None and i not in stripes:
+                continue
+            if skip_rows is not None and num_rows_scanned <= skip_rows:
+                continue
+            else:
+                skip_rows = 0
+            if (
+                skip_rows is not None
+                and num_rows is not None
+                and num_rows_before_stripe >= skip_rows + num_rows
+            ):
+                continue
+            if ioutils._apply_filters(filters, stripe_statistics):
+                selected_stripes.append(i)
+
+        file_stripe_map.append(selected_stripes)
+
+    return file_stripe_map
+
+
+@ioutils.doc_read_orc()
+def read_orc(
+    filepath_or_buffer,
+    engine="cudf",
+    columns=None,
+    filters=None,
+    stripes=None,
+    skiprows=None,
+    num_rows=None,
+    use_index=True,
+    timestamp_type=None,
+    use_python_file_object=True,
+    storage_options=None,
+    bytes_per_thread=None,
+):
+    """{docstring}"""
+    from cudf import DataFrame
+
+    if skiprows is not None:
+        # Do not remove until cuIO team approves its removal.
+        warnings.warn(
+            "skiprows is deprecated and will be removed.",
+            FutureWarning,
+        )
+
+    if num_rows is not None:
+        # Do not remove until cuIO team approves its removal.
+        warnings.warn(
+            "num_rows is deprecated and will be removed.",
+            FutureWarning,
+        )
+
+    # Multiple sources are passed as a list. If a single source is passed,
+    # wrap it in a list for unified processing downstream.
+    if not is_list_like(filepath_or_buffer):
+        filepath_or_buffer = [filepath_or_buffer]
+
+    # Each source must have a correlating stripe list. If a single stripe list
+    # is provided rather than a list of list of stripes then extrapolate that
+    # stripe list across all input sources
+    if stripes is not None:
+        if any(not isinstance(stripe, list) for stripe in stripes):
+            stripes = [stripes]
+
+        # Must ensure a stripe for each source is specified, unless None
+        if not len(stripes) == len(filepath_or_buffer):
+            raise ValueError(
+                "A list of stripes must be provided for each input source"
+            )
+
+    filepaths_or_buffers = []
+    for source in filepath_or_buffer:
+        if ioutils.is_directory(
+            path_or_data=source, storage_options=storage_options
+        ):
+            fs = ioutils._ensure_filesystem(
+                passed_filesystem=None,
+                path=source,
+                storage_options=storage_options,
+            )
+            source = stringify_path(source)
+            source = fs.sep.join([source, "*.orc"])
+
+        tmp_source, compression = ioutils.get_reader_filepath_or_buffer(
+            path_or_data=source,
+            compression=None,
+            use_python_file_object=use_python_file_object,
+            storage_options=storage_options,
+            bytes_per_thread=bytes_per_thread,
+        )
+        if compression is not None:
+            raise ValueError(
+                "URL content-encoding decompression is not supported"
+            )
+        if isinstance(tmp_source, list):
+            filepaths_or_buffers.extend(tmp_source)
+        else:
+            filepaths_or_buffers.append(tmp_source)
+
+    if filters is not None:
+        selected_stripes = _filter_stripes(
+            filters, filepaths_or_buffers, stripes, skiprows, num_rows
+        )
+
+        # Return empty if everything was filtered
+        if len(selected_stripes) == 0:
+            return _make_empty_df(filepaths_or_buffers[0], columns)
+        else:
+            stripes = selected_stripes
+
+    if engine == "cudf":
+        return DataFrame._from_data(
+            *liborc.read_orc(
+                filepaths_or_buffers,
+                columns,
+                stripes,
+                skiprows,
+                num_rows,
+                use_index,
+                timestamp_type,
+            )
+        )
+    else:
+
+        def read_orc_stripe(orc_file, stripe, columns):
+            pa_table = orc_file.read_stripe(stripe, columns)
+            if isinstance(pa_table, pa.RecordBatch):
+                pa_table = pa.Table.from_batches([pa_table])
+            return pa_table
+
+        warnings.warn("Using CPU via PyArrow to read ORC dataset.")
+        if len(filepath_or_buffer) > 1:
+            raise NotImplementedError(
+                "Using CPU via PyArrow only supports a single a "
+                "single input source"
+            )
+
+        orc_file = orc.ORCFile(filepath_or_buffer[0])
+        if stripes is not None and len(stripes) > 0:
+            for stripe_source_file in stripes:
+                pa_tables = [
+                    read_orc_stripe(orc_file, i, columns)
+                    for i in stripe_source_file
+                ]
+                pa_table = pa.concat_tables(pa_tables)
+        else:
+            pa_table = orc_file.read(columns=columns)
+        df = cudf.DataFrame.from_arrow(pa_table)
+
+    return df
+
+
+@ioutils.doc_to_orc()
+def to_orc(
+    df,
+    fname,
+    compression="snappy",
+    statistics="ROWGROUP",
+    stripe_size_bytes=None,
+    stripe_size_rows=None,
+    row_index_stride=None,
+    cols_as_map_type=None,
+    storage_options=None,
+    index=None,
+):
+    """{docstring}"""
+
+    for col in df._data.columns:
+        if isinstance(col, cudf.core.column.CategoricalColumn):
+            raise NotImplementedError(
+                "Writing to ORC format is not yet supported with "
+                "Categorical columns."
+            )
+
+    if isinstance(df.index, cudf.CategoricalIndex):
+        raise NotImplementedError(
+            "Writing to ORC format is not yet supported with "
+            "Categorical columns."
+        )
+
+    if cols_as_map_type is not None and not isinstance(cols_as_map_type, list):
+        raise TypeError("cols_as_map_type must be a list of column names.")
+
+    path_or_buf = ioutils.get_writer_filepath_or_buffer(
+        path_or_data=fname, mode="wb", storage_options=storage_options
+    )
+    if ioutils.is_fsspec_open_file(path_or_buf):
+        with path_or_buf as file_obj:
+            file_obj = ioutils.get_IOBase_writer(file_obj)
+            liborc.write_orc(
+                df,
+                file_obj,
+                compression,
+                statistics,
+                stripe_size_bytes,
+                stripe_size_rows,
+                row_index_stride,
+                cols_as_map_type,
+                index,
+            )
+    else:
+        liborc.write_orc(
+            df,
+            path_or_buf,
+            compression,
+            statistics,
+            stripe_size_bytes,
+            stripe_size_rows,
+            row_index_stride,
+            cols_as_map_type,
+            index,
+        )
+
+
+ORCWriter = liborc.ORCWriter
diff --git a/python/cudf/cudf/io/parquet.py b/python/cudf/cudf/io/parquet.py
new file mode 100644
index 0000000..81021a5
--- /dev/null
+++ b/python/cudf/cudf/io/parquet.py
@@ -0,0 +1,1458 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+from __future__ import annotations
+
+import itertools
+import math
+import operator
+import shutil
+import tempfile
+import warnings
+from collections import defaultdict
+from contextlib import ExitStack
+from functools import partial, reduce
+from typing import Callable, Dict, List, Optional, Tuple
+from uuid import uuid4
+
+import numpy as np
+import pandas as pd
+from pyarrow import dataset as ds, parquet as pq
+
+import cudf
+from cudf._lib import parquet as libparquet
+from cudf.api.types import is_list_like
+from cudf.core.column import build_categorical_column, column_empty, full
+from cudf.utils import ioutils
+from cudf.utils.utils import _cudf_nvtx_annotate
+
+BYTE_SIZES = {
+    "kb": 1000,
+    "mb": 1000000,
+    "gb": 1000000000,
+    "tb": 1000000000000,
+    "pb": 1000000000000000,
+    "kib": 1024,
+    "mib": 1048576,
+    "gib": 1073741824,
+    "tib": 1099511627776,
+    "pib": 1125899906842624,
+    "b": 1,
+    "": 1,
+    "k": 1000,
+    "m": 1000000,
+    "g": 1000000000,
+    "t": 1000000000000,
+    "p": 1000000000000000,
+    "ki": 1024,
+    "mi": 1048576,
+    "gi": 1073741824,
+    "ti": 1099511627776,
+    "pi": 1125899906842624,
+}
+
+
+@_cudf_nvtx_annotate
+def _write_parquet(
+    df,
+    paths,
+    compression="snappy",
+    index=None,
+    statistics="ROWGROUP",
+    metadata_file_path=None,
+    int96_timestamps=False,
+    row_group_size_bytes=ioutils._ROW_GROUP_SIZE_BYTES_DEFAULT,
+    row_group_size_rows=None,
+    max_page_size_bytes=None,
+    max_page_size_rows=None,
+    partitions_info=None,
+    storage_options=None,
+    force_nullable_schema=False,
+):
+    if is_list_like(paths) and len(paths) > 1:
+        if partitions_info is None:
+            ValueError("partition info is required for multiple paths")
+        elif not is_list_like(partitions_info):
+            ValueError("partition info must be list-like for multiple paths")
+        elif not len(paths) == len(partitions_info):
+            ValueError("partitions_info and paths must be of same size")
+    if is_list_like(partitions_info) and len(partitions_info) > 1:
+        if not is_list_like(paths):
+            ValueError("paths must be list-like when partitions_info provided")
+
+    paths_or_bufs = [
+        ioutils.get_writer_filepath_or_buffer(
+            path_or_data=path, mode="wb", storage_options=storage_options
+        )
+        for path in paths
+    ]
+    common_args = {
+        "index": index,
+        "compression": compression,
+        "statistics": statistics,
+        "metadata_file_path": metadata_file_path,
+        "int96_timestamps": int96_timestamps,
+        "row_group_size_bytes": row_group_size_bytes,
+        "row_group_size_rows": row_group_size_rows,
+        "max_page_size_bytes": max_page_size_bytes,
+        "max_page_size_rows": max_page_size_rows,
+        "partitions_info": partitions_info,
+        "force_nullable_schema": force_nullable_schema,
+    }
+    if all(ioutils.is_fsspec_open_file(buf) for buf in paths_or_bufs):
+        with ExitStack() as stack:
+            fsspec_objs = [stack.enter_context(file) for file in paths_or_bufs]
+            file_objs = [
+                ioutils.get_IOBase_writer(file_obj) for file_obj in fsspec_objs
+            ]
+            write_parquet_res = libparquet.write_parquet(
+                df, filepaths_or_buffers=file_objs, **common_args
+            )
+    else:
+        write_parquet_res = libparquet.write_parquet(
+            df, filepaths_or_buffers=paths_or_bufs, **common_args
+        )
+
+    return write_parquet_res
+
+
+# Logic chosen to match: https://arrow.apache.org/
+# docs/_modules/pyarrow/parquet.html#write_to_dataset
+@_cudf_nvtx_annotate
+def write_to_dataset(
+    df,
+    root_path,
+    compression="snappy",
+    filename=None,
+    partition_cols=None,
+    fs=None,
+    preserve_index=False,
+    return_metadata=False,
+    statistics="ROWGROUP",
+    int96_timestamps=False,
+    row_group_size_bytes=ioutils._ROW_GROUP_SIZE_BYTES_DEFAULT,
+    row_group_size_rows=None,
+    max_page_size_bytes=None,
+    max_page_size_rows=None,
+    storage_options=None,
+    force_nullable_schema=False,
+):
+    """Wraps `to_parquet` to write partitioned Parquet datasets.
+    For each combination of partition group and value,
+    subdirectories are created as follows:
+
+    .. code-block:: bash
+
+        root_dir/
+            group=value1
+                <filename>.parquet
+            ...
+            group=valueN
+                <filename>.parquet
+
+    Parameters
+    ----------
+    df : cudf.DataFrame
+    root_path : string,
+        The root directory of the dataset
+    compression : {'snappy', 'ZSTD', None}, default 'snappy'
+        Name of the compression to use. Use ``None`` for no compression.
+    filename : string, default None
+        The file name to use (within each partition directory). If None,
+        a random uuid4 hex string will be used for each file name.
+    partition_cols : list,
+        Column names by which to partition the dataset.
+        Columns are partitioned in the order they are given.
+    fs : FileSystem, default None
+        If nothing passed, paths assumed to be found in the local on-disk
+        filesystem
+    preserve_index : bool, default False
+        Preserve index values in each parquet file.
+    return_metadata : bool, default False
+        Return parquet metadata for written data. Returned metadata will
+        include the file-path metadata (relative to `root_path`).
+    int96_timestamps : bool, default False
+        If ``True``, write timestamps in int96 format. This will convert
+        timestamps from timestamp[ns], timestamp[ms], timestamp[s], and
+        timestamp[us] to the int96 format, which is the number of Julian
+        days and the number of nanoseconds since midnight of 1970-01-01.
+        If ``False``, timestamps will not be altered.
+    row_group_size_bytes: integer or None, default None
+        Maximum size of each stripe of the output.
+        If None, 134217728 (128MB) will be used.
+    row_group_size_rows: integer or None, default None
+        Maximum number of rows of each stripe of the output.
+        If None, 1000000 will be used.
+    max_page_size_bytes: integer or None, default None
+        Maximum uncompressed size of each page of the output.
+        If None, 524288 (512KB) will be used.
+    max_page_size_rows: integer or None, default None
+        Maximum number of rows of each page of the output.
+        If None, 20000 will be used.
+    storage_options : dict, optional, default None
+        Extra options that make sense for a particular storage connection,
+        e.g. host, port, username, password, etc. For HTTP(S) URLs the
+        key-value pairs are forwarded to ``urllib.request.Request`` as
+        header options. For other URLs (e.g. starting with "s3://", and
+        "gcs://") the key-value pairs are forwarded to ``fsspec.open``.
+        Please see ``fsspec`` and ``urllib`` for more details.
+    force_nullable_schema : bool, default False.
+        If True, writes all columns as `null` in schema.
+        If False, columns are written as `null` if they contain null values,
+        otherwise as `not null`.
+    """
+
+    fs = ioutils._ensure_filesystem(fs, root_path, storage_options)
+    fs.mkdirs(root_path, exist_ok=True)
+
+    if partition_cols is not None and len(partition_cols) > 0:
+
+        (
+            full_paths,
+            metadata_file_paths,
+            grouped_df,
+            part_offsets,
+            _,
+        ) = _get_partitioned(
+            df=df,
+            root_path=root_path,
+            partition_cols=partition_cols,
+            filename=filename,
+            fs=fs,
+            preserve_index=preserve_index,
+            storage_options=storage_options,
+        )
+        metadata_file_path = metadata_file_paths if return_metadata else None
+        metadata = to_parquet(
+            df=grouped_df,
+            path=full_paths,
+            compression=compression,
+            index=preserve_index,
+            partition_offsets=part_offsets,
+            storage_options=storage_options,
+            metadata_file_path=metadata_file_path,
+            statistics=statistics,
+            int96_timestamps=int96_timestamps,
+            row_group_size_bytes=row_group_size_bytes,
+            row_group_size_rows=row_group_size_rows,
+            max_page_size_bytes=max_page_size_bytes,
+            max_page_size_rows=max_page_size_rows,
+            force_nullable_schema=force_nullable_schema,
+        )
+
+    else:
+        filename = filename or _generate_filename()
+        full_path = fs.sep.join([root_path, filename])
+
+        metadata_file_path = filename if return_metadata else None
+
+        metadata = df.to_parquet(
+            path=full_path,
+            compression=compression,
+            index=preserve_index,
+            storage_options=storage_options,
+            metadata_file_path=metadata_file_path,
+            statistics=statistics,
+            int96_timestamps=int96_timestamps,
+            row_group_size_bytes=row_group_size_bytes,
+            row_group_size_rows=row_group_size_rows,
+            max_page_size_bytes=max_page_size_bytes,
+            max_page_size_rows=max_page_size_rows,
+            force_nullable_schema=force_nullable_schema,
+        )
+
+    return metadata
+
+
+@ioutils.doc_read_parquet_metadata()
+@_cudf_nvtx_annotate
+def read_parquet_metadata(path):
+    """{docstring}"""
+
+    pq_file = pq.ParquetFile(path)
+
+    num_rows = pq_file.metadata.num_rows
+    num_row_groups = pq_file.num_row_groups
+    col_names = pq_file.schema.names
+
+    return num_rows, num_row_groups, col_names
+
+
+@_cudf_nvtx_annotate
+def _process_dataset(
+    paths,
+    fs,
+    filters=None,
+    row_groups=None,
+    categorical_partitions=True,
+    dataset_kwargs=None,
+):
+    # Returns:
+    #     file_list - Expanded/filtered list of paths
+    #     row_groups - Filtered list of row-group selections
+    #     partition_keys - list of partition keys for each file
+    #     partition_categories - Categories for each partition
+
+    # The general purpose of this function is to (1) expand
+    # directory input into a list of paths (using the pyarrow
+    # dataset API), (2) to apply row-group filters, and (3)
+    # to discover directory-partitioning information
+
+    # Deal with case that the user passed in a directory name
+    file_list = paths
+    if len(paths) == 1 and ioutils.is_directory(paths[0]):
+        paths = ioutils.stringify_pathlike(paths[0])
+
+    # Convert filters to ds.Expression
+    if filters is not None:
+        filters = pq.filters_to_expression(filters)
+
+    # Initialize ds.FilesystemDataset
+    # TODO: Remove the if len(paths) workaround after following bug is fixed:
+    # https://issues.apache.org/jira/browse/ARROW-16438
+    dataset = ds.dataset(
+        source=paths[0] if len(paths) == 1 else paths,
+        filesystem=fs,
+        **(
+            dataset_kwargs
+            or {
+                "format": "parquet",
+                "partitioning": "hive",
+            }
+        ),
+    )
+
+    file_list = dataset.files
+    if len(file_list) == 0:
+        raise FileNotFoundError(f"{paths} could not be resolved to any files")
+
+    # Deal with directory partitioning
+    # Get all partition keys (without filters)
+    partition_categories = defaultdict(list)
+    file_fragment = None
+    for file_fragment in dataset.get_fragments():
+        keys = ds._get_partition_keys(file_fragment.partition_expression)
+        if not (keys or partition_categories):
+            # Bail - This is not a directory-partitioned dataset
+            break
+        for k, v in keys.items():
+            if v not in partition_categories[k]:
+                partition_categories[k].append(v)
+        if not categorical_partitions:
+            # Bail - We don't need to discover all categories.
+            # We only need to save the partition keys from this
+            # first `file_fragment`
+            break
+
+    if partition_categories and file_fragment is not None:
+        # Check/correct order of `categories` using last file_frag,
+        # because `_get_partition_keys` does NOT preserve the
+        # partition-hierarchy order of the keys.
+        cat_keys = [
+            part.split("=")[0]
+            for part in file_fragment.path.split(fs.sep)
+            if "=" in part
+        ]
+        if set(partition_categories) == set(cat_keys):
+            partition_categories = {
+                k: partition_categories[k]
+                for k in cat_keys
+                if k in partition_categories
+            }
+
+    # If we do not have partitioned data and
+    # are not filtering, we can return here
+    if filters is None and not partition_categories:
+        return file_list, row_groups, [], {}
+
+    # Record initial row_groups input
+    row_groups_map = {}
+    if row_groups is not None:
+        # Make sure paths and row_groups map 1:1
+        # and save the initial mapping
+        if len(paths) != len(file_list):
+            raise ValueError(
+                "Cannot specify a row_group selection for a directory path."
+            )
+        row_groups_map = {path: rgs for path, rgs in zip(paths, row_groups)}
+
+    # Apply filters and discover partition columns
+    partition_keys = []
+    if partition_categories or filters is not None:
+        file_list = []
+        if filters is not None:
+            row_groups = []
+        for file_fragment in dataset.get_fragments(filter=filters):
+            path = file_fragment.path
+
+            # Extract hive-partition keys, and make sure they
+            # are ordered the same as they are in `partition_categories`
+            if partition_categories:
+                raw_keys = ds._get_partition_keys(
+                    file_fragment.partition_expression
+                )
+                partition_keys.append(
+                    [
+                        (name, raw_keys[name])
+                        for name in partition_categories.keys()
+                    ]
+                )
+
+            # Apply row-group filtering
+            selection = row_groups_map.get(path, None)
+            if selection is not None or filters is not None:
+                filtered_row_groups = [
+                    rg_info.id
+                    for rg_fragment in file_fragment.split_by_row_group(
+                        filters,
+                        schema=dataset.schema,
+                    )
+                    for rg_info in rg_fragment.row_groups
+                ]
+            file_list.append(path)
+            if filters is not None:
+                if selection is None:
+                    row_groups.append(filtered_row_groups)
+                else:
+                    row_groups.append(
+                        [
+                            rg_id
+                            for rg_id in filtered_row_groups
+                            if rg_id in selection
+                        ]
+                    )
+
+    return (
+        file_list,
+        row_groups,
+        partition_keys,
+        partition_categories if categorical_partitions else {},
+    )
+
+
+@ioutils.doc_read_parquet()
+@_cudf_nvtx_annotate
+def read_parquet(
+    filepath_or_buffer,
+    engine="cudf",
+    columns=None,
+    storage_options=None,
+    filters=None,
+    row_groups=None,
+    use_pandas_metadata=True,
+    use_python_file_object=True,
+    categorical_partitions=True,
+    open_file_options=None,
+    bytes_per_thread=None,
+    dataset_kwargs=None,
+    *args,
+    **kwargs,
+):
+    """{docstring}"""
+    if engine not in {"cudf", "pyarrow"}:
+        raise ValueError(
+            f"Only supported engines are {{'cudf', 'pyarrow'}}, got {engine=}"
+        )
+    # Do not allow the user to set file-opening options
+    # when `use_python_file_object=False` is specified
+    if use_python_file_object is False:
+        if open_file_options:
+            raise ValueError(
+                "open_file_options is not currently supported when "
+                "use_python_file_object is set to False."
+            )
+        open_file_options = {}
+
+    if bytes_per_thread is None:
+        bytes_per_thread = ioutils._BYTES_PER_THREAD_DEFAULT
+
+    # Multiple sources are passed as a list. If a single source is passed,
+    # wrap it in a list for unified processing downstream.
+    if not is_list_like(filepath_or_buffer):
+        filepath_or_buffer = [filepath_or_buffer]
+
+    # a list of row groups per source should be passed. make the list of
+    # lists that is expected for multiple sources
+    if row_groups is not None:
+        if not is_list_like(row_groups):
+            row_groups = [[row_groups]]
+        elif not is_list_like(row_groups[0]):
+            row_groups = [row_groups]
+
+    # Check columns input
+    if columns is not None:
+        if not is_list_like(columns):
+            raise ValueError("Expected list like for columns")
+
+    # Start by trying construct a filesystem object, so we
+    # can apply filters on remote file-systems
+    fs, paths = ioutils._get_filesystem_and_paths(
+        path_or_data=filepath_or_buffer, storage_options=storage_options
+    )
+
+    # Normalize and validate filters
+    filters = _normalize_filters(filters)
+
+    # Use pyarrow dataset to detect/process directory-partitioned
+    # data and apply filters. Note that we can only support partitioned
+    # data and filtering if the input is a single directory or list of
+    # paths.
+    partition_keys = []
+    partition_categories = {}
+    if fs and paths:
+        (
+            paths,
+            row_groups,
+            partition_keys,
+            partition_categories,
+        ) = _process_dataset(
+            paths=paths,
+            fs=fs,
+            filters=filters,
+            row_groups=row_groups,
+            categorical_partitions=categorical_partitions,
+            dataset_kwargs=dataset_kwargs,
+        )
+    filepath_or_buffer = paths if paths else filepath_or_buffer
+
+    filepaths_or_buffers = []
+    if use_python_file_object:
+        open_file_options = _default_open_file_options(
+            open_file_options=open_file_options,
+            columns=columns,
+            row_groups=row_groups,
+            fs=fs,
+        )
+    for source in filepath_or_buffer:
+        tmp_source, compression = ioutils.get_reader_filepath_or_buffer(
+            path_or_data=source,
+            compression=None,
+            fs=fs,
+            use_python_file_object=use_python_file_object,
+            open_file_options=open_file_options,
+            storage_options=storage_options,
+            bytes_per_thread=bytes_per_thread,
+        )
+
+        if compression is not None:
+            raise ValueError(
+                "URL content-encoding decompression is not supported"
+            )
+        if isinstance(tmp_source, list):
+            filepath_or_buffer.extend(tmp_source)
+        else:
+            filepaths_or_buffers.append(tmp_source)
+
+    # Warn user if they are not using cudf for IO
+    # (There is a good chance this was not the intention)
+    if engine != "cudf":
+        warnings.warn(
+            "Using CPU via PyArrow to read Parquet dataset. "
+            "This option is both inefficient and unstable!"
+        )
+        if filters is not None:
+            warnings.warn(
+                "Parquet row-group filtering is only supported with "
+                "'engine=cudf'. Use pandas or pyarrow API directly "
+                "for full CPU-based filtering functionality."
+            )
+
+    # Make sure we read in the columns needed for row-wise
+    # filtering after IO. This means that one or more columns
+    # will be dropped almost immediately after IO. However,
+    # we do NEED these columns for accurate filtering.
+    projected_columns = None
+    if columns and filters:
+        projected_columns = columns
+        columns = sorted(
+            set(v[0] for v in itertools.chain.from_iterable(filters))
+            | set(columns)
+        )
+
+    # Convert parquet data to a cudf.DataFrame
+    df = _parquet_to_frame(
+        filepaths_or_buffers,
+        engine,
+        *args,
+        columns=columns,
+        row_groups=row_groups,
+        use_pandas_metadata=use_pandas_metadata,
+        partition_keys=partition_keys,
+        partition_categories=partition_categories,
+        dataset_kwargs=dataset_kwargs,
+        **kwargs,
+    )
+
+    # Apply filters row-wise (if any are defined), and return
+    df = _apply_post_filters(df, filters)
+    if projected_columns:
+        # Elements of `projected_columns` may now be in the index.
+        # We must filter these names from our projection
+        projected_columns = [
+            col for col in projected_columns if col in df._column_names
+        ]
+        return df[projected_columns]
+    return df
+
+
+def _normalize_filters(filters: list | None) -> List[List[tuple]] | None:
+    # Utility to normalize and validate the `filters`
+    # argument to `read_parquet`
+    if not filters:
+        return None
+
+    msg = (
+        f"filters must be None, or non-empty List[Tuple] "
+        f"or List[List[Tuple]]. Got {filters}"
+    )
+    if not isinstance(filters, list):
+        raise TypeError(msg)
+
+    def _validate_predicate(item):
+        if not isinstance(item, tuple) or len(item) != 3:
+            raise TypeError(
+                f"Predicate must be Tuple[str, str, Any], " f"got {predicate}."
+            )
+
+    filters = filters if isinstance(filters[0], list) else [filters]
+    for conjunction in filters:
+        if not conjunction or not isinstance(conjunction, list):
+            raise TypeError(msg)
+        for predicate in conjunction:
+            _validate_predicate(predicate)
+
+    return filters
+
+
+def _apply_post_filters(
+    df: cudf.DataFrame, filters: List[List[tuple]] | None
+) -> cudf.DataFrame:
+    """Apply DNF filters to an in-memory DataFrame
+
+    Disjunctive normal form (DNF) means that the inner-most
+    tuple describes a single column predicate. These inner
+    predicates are combined with an AND conjunction into a
+    larger predicate. The outer-most list then combines all
+    of the combined filters with an OR disjunction.
+    """
+
+    if not filters:
+        # No filters to apply
+        return df
+
+    def _handle_in(column: cudf.Series, value, *, negate) -> cudf.Series:
+        if not isinstance(value, (list, set, tuple)):
+            raise TypeError(
+                "Value of 'in'/'not in' filter must be a list, set, or tuple."
+            )
+        return ~column.isin(value) if negate else column.isin(value)
+
+    def _handle_is(column: cudf.Series, value, *, negate) -> cudf.Series:
+        if value not in {np.nan, None}:
+            raise TypeError(
+                "Value of 'is'/'is not' filter must be np.nan or None."
+            )
+        return ~column.isna() if negate else column.isna()
+
+    handlers: Dict[str, Callable] = {
+        "==": operator.eq,
+        "!=": operator.ne,
+        "<": operator.lt,
+        "<=": operator.le,
+        ">": operator.gt,
+        ">=": operator.ge,
+        "in": partial(_handle_in, negate=False),
+        "not in": partial(_handle_in, negate=True),
+        "is": partial(_handle_is, negate=False),
+        "is not": partial(_handle_is, negate=True),
+    }
+
+    # Can re-set the index before returning if we filter
+    # out rows from a DataFrame with a default RangeIndex
+    # (to reduce memory usage)
+    reset_index = (
+        isinstance(df.index, cudf.RangeIndex)
+        and df.index.name is None
+        and df.index.start == 0
+        and df.index.step == 1
+    )
+
+    try:
+        selection: cudf.Series = reduce(
+            operator.or_,
+            (
+                reduce(
+                    operator.and_,
+                    (
+                        handlers[op](df[column], value)
+                        for (column, op, value) in expr
+                    ),
+                )
+                for expr in filters
+            ),
+        )
+        if reset_index:
+            return df[selection].reset_index(drop=True)
+        return df[selection]
+    except (KeyError, TypeError):
+        warnings.warn(
+            f"Row-wise filtering failed in read_parquet for {filters}"
+        )
+        return df
+
+
+@_cudf_nvtx_annotate
+def _parquet_to_frame(
+    paths_or_buffers,
+    *args,
+    row_groups=None,
+    partition_keys=None,
+    partition_categories=None,
+    dataset_kwargs=None,
+    **kwargs,
+):
+
+    # If this is not a partitioned read, only need
+    # one call to `_read_parquet`
+    if not partition_keys:
+        return _read_parquet(
+            paths_or_buffers,
+            *args,
+            row_groups=row_groups,
+            **kwargs,
+        )
+
+    partition_meta = None
+    partitioning = (dataset_kwargs or {}).get("partitioning", None)
+    if hasattr(partitioning, "schema"):
+        partition_meta = cudf.DataFrame.from_arrow(
+            partitioning.schema.empty_table()
+        )
+
+    # For partitioned data, we need a distinct read for each
+    # unique set of partition keys. Therefore, we start by
+    # aggregating all paths with matching keys using a dict
+    plan = {}
+    for i, (keys, path) in enumerate(zip(partition_keys, paths_or_buffers)):
+        rgs = row_groups[i] if row_groups else None
+        tkeys = tuple(keys)
+        if tkeys in plan:
+            plan[tkeys][0].append(path)
+            if rgs is not None:
+                plan[tkeys][1].append(rgs)
+        else:
+            plan[tkeys] = ([path], None if rgs is None else [rgs])
+
+    dfs = []
+    for part_key, (key_paths, key_row_groups) in plan.items():
+        # Add new DataFrame to our list
+        dfs.append(
+            _read_parquet(
+                key_paths,
+                *args,
+                row_groups=key_row_groups,
+                **kwargs,
+            )
+        )
+        # Add partition columns to the last DataFrame
+        for (name, value) in part_key:
+            _len = len(dfs[-1])
+            if partition_categories and name in partition_categories:
+                # Build the categorical column from `codes`
+                codes = full(
+                    size=_len,
+                    fill_value=partition_categories[name].index(value),
+                )
+                dfs[-1][name] = build_categorical_column(
+                    categories=partition_categories[name],
+                    codes=codes,
+                    size=codes.size,
+                    offset=codes.offset,
+                    ordered=False,
+                )
+            else:
+                # Not building categorical columns, so
+                # `value` is already what we want
+                _dtype = (
+                    partition_meta[name].dtype
+                    if partition_meta is not None
+                    else None
+                )
+                if pd.isna(value):
+                    dfs[-1][name] = column_empty(
+                        row_count=_len,
+                        dtype=_dtype,
+                        masked=True,
+                    )
+                else:
+                    dfs[-1][name] = full(
+                        size=_len,
+                        fill_value=value,
+                        dtype=_dtype,
+                    )
+
+    # Concatenate dfs and return.
+    # Assume we can ignore the index if it has no name.
+    return (
+        cudf.concat(dfs, ignore_index=dfs[-1].index.name is None)
+        if len(dfs) > 1
+        else dfs[0]
+    )
+
+
+@_cudf_nvtx_annotate
+def _read_parquet(
+    filepaths_or_buffers,
+    engine,
+    columns=None,
+    row_groups=None,
+    use_pandas_metadata=None,
+    *args,
+    **kwargs,
+):
+    # Simple helper function to dispatch between
+    # cudf and pyarrow to read parquet data
+    if engine == "cudf":
+        if kwargs:
+            raise ValueError(
+                "cudf engine doesn't support the "
+                f"following keyword arguments: {list(kwargs.keys())}"
+            )
+        if args:
+            raise ValueError(
+                "cudf engine doesn't support the "
+                f"following positional arguments: {list(args)}"
+            )
+        return libparquet.read_parquet(
+            filepaths_or_buffers,
+            columns=columns,
+            row_groups=row_groups,
+            use_pandas_metadata=use_pandas_metadata,
+        )
+    else:
+        if (
+            isinstance(filepaths_or_buffers, list)
+            and len(filepaths_or_buffers) == 1
+        ):
+            filepaths_or_buffers = filepaths_or_buffers[0]
+
+        return cudf.DataFrame.from_pandas(
+            pd.read_parquet(
+                filepaths_or_buffers,
+                columns=columns,
+                engine=engine,
+                *args,
+                **kwargs,
+            )
+        )
+
+
+@ioutils.doc_to_parquet()
+@_cudf_nvtx_annotate
+def to_parquet(
+    df,
+    path,
+    engine="cudf",
+    compression="snappy",
+    index=None,
+    partition_cols=None,
+    partition_file_name=None,
+    partition_offsets=None,
+    statistics="ROWGROUP",
+    metadata_file_path=None,
+    int96_timestamps=False,
+    row_group_size_bytes=ioutils._ROW_GROUP_SIZE_BYTES_DEFAULT,
+    row_group_size_rows=None,
+    max_page_size_bytes=None,
+    max_page_size_rows=None,
+    storage_options=None,
+    return_metadata=False,
+    force_nullable_schema=False,
+    *args,
+    **kwargs,
+):
+    """{docstring}"""
+
+    if engine == "cudf":
+        if kwargs:
+            raise ValueError(
+                "cudf engine doesn't support the "
+                f"following keyword arguments: {list(kwargs.keys())}"
+            )
+        if args:
+            raise ValueError(
+                "cudf engine doesn't support the "
+                f"following positional arguments: {list(args)}"
+            )
+        # Ensure that no columns dtype is 'category'
+        for col in df._column_names:
+            if partition_cols is None or col not in partition_cols:
+                if df[col].dtype.name == "category":
+                    raise ValueError(
+                        "'category' column dtypes are currently not "
+                        + "supported by the gpu accelerated parquet writer"
+                    )
+
+        if partition_cols:
+            if metadata_file_path is not None:
+                warnings.warn(
+                    "metadata_file_path will be ignored/overwritten when "
+                    "partition_cols are provided. To request returning the "
+                    "metadata binary blob, pass `return_metadata=True`"
+                )
+
+            return write_to_dataset(
+                df,
+                filename=partition_file_name,
+                partition_cols=partition_cols,
+                root_path=path,
+                preserve_index=index,
+                compression=compression,
+                statistics=statistics,
+                int96_timestamps=int96_timestamps,
+                row_group_size_bytes=row_group_size_bytes,
+                row_group_size_rows=row_group_size_rows,
+                max_page_size_bytes=max_page_size_bytes,
+                max_page_size_rows=max_page_size_rows,
+                return_metadata=return_metadata,
+                storage_options=storage_options,
+                force_nullable_schema=force_nullable_schema,
+            )
+
+        partition_info = (
+            [
+                (i, j - i)
+                for i, j in zip(partition_offsets, partition_offsets[1:])
+            ]
+            if partition_offsets is not None
+            else None
+        )
+        return _write_parquet(
+            df,
+            paths=path if is_list_like(path) else [path],
+            compression=compression,
+            index=index,
+            statistics=statistics,
+            metadata_file_path=metadata_file_path,
+            int96_timestamps=int96_timestamps,
+            row_group_size_bytes=row_group_size_bytes,
+            row_group_size_rows=row_group_size_rows,
+            max_page_size_bytes=max_page_size_bytes,
+            max_page_size_rows=max_page_size_rows,
+            partitions_info=partition_info,
+            storage_options=storage_options,
+            force_nullable_schema=force_nullable_schema,
+        )
+
+    else:
+        if partition_offsets is not None:
+            warnings.warn(
+                "partition_offsets will be ignored when engine is not cudf"
+            )
+
+        # If index is empty set it to the expected default value of True
+        if index is None:
+            index = True
+
+        # Convert partition_file_name to a call back
+        if partition_file_name:
+            partition_file_name = lambda x: partition_file_name  # noqa: E731
+
+        pa_table = df.to_arrow(preserve_index=index)
+        return pq.write_to_dataset(
+            pa_table,
+            root_path=path,
+            partition_filename_cb=partition_file_name,
+            partition_cols=partition_cols,
+            *args,
+            **kwargs,
+        )
+
+
+@ioutils.doc_merge_parquet_filemetadata()
+def merge_parquet_filemetadata(filemetadata_list):
+    """{docstring}"""
+
+    return libparquet.merge_filemetadata(filemetadata_list)
+
+
+def _generate_filename():
+    return uuid4().hex + ".parquet"
+
+
+def _get_estimated_file_size(df):
+    # NOTE: This is purely a guesstimation method
+    # and the y = mx+c has been arrived
+    # after extensive experimentation of parquet file size
+    # vs dataframe sizes.
+    df_mem_usage = df.memory_usage().sum()
+    # Parquet file size of a dataframe with all unique values
+    # seems to be 1/1.5 times as that of on GPU for >10000 rows
+    # and 0.6 times else-wise.
+    # Y(file_size) = M(0.6) * X(df_mem_usage) + C(705)
+    file_size = int((df_mem_usage * 0.6) + 705)
+    # 1000 Bytes accounted for row-group metadata.
+    # A parquet file takes roughly ~810 Bytes of metadata per column.
+    file_size = file_size + 1000 + (810 * df.shape[1])
+    return file_size
+
+
+@_cudf_nvtx_annotate
+def _get_partitioned(
+    df,
+    root_path,
+    partition_cols,
+    filename=None,
+    fs=None,
+    preserve_index=False,
+    storage_options=None,
+):
+    fs = ioutils._ensure_filesystem(
+        fs, root_path, storage_options=storage_options
+    )
+    fs.mkdirs(root_path, exist_ok=True)
+
+    part_names, grouped_df, part_offsets = _get_groups_and_offsets(
+        df, partition_cols, preserve_index
+    )
+
+    full_paths = []
+    metadata_file_paths = []
+    for keys in part_names.itertuples(index=False):
+        subdir = fs.sep.join(
+            [
+                _hive_dirname(name, val)
+                for name, val in zip(partition_cols, keys)
+            ]
+        )
+        prefix = fs.sep.join([root_path, subdir])
+        fs.mkdirs(prefix, exist_ok=True)
+        filename = filename or _generate_filename()
+        full_path = fs.sep.join([prefix, filename])
+        full_paths.append(full_path)
+        metadata_file_paths.append(fs.sep.join([subdir, filename]))
+
+    return full_paths, metadata_file_paths, grouped_df, part_offsets, filename
+
+
+@_cudf_nvtx_annotate
+def _get_groups_and_offsets(
+    df,
+    partition_cols,
+    preserve_index=False,
+    **kwargs,
+):
+
+    if not (set(df._data) - set(partition_cols)):
+        warnings.warn("No data left to save outside partition columns")
+
+    _, part_offsets, part_keys, grouped_df = df.groupby(
+        partition_cols,
+        dropna=False,
+    )._grouped()
+    if not preserve_index:
+        grouped_df.reset_index(drop=True, inplace=True)
+    grouped_df.drop(columns=partition_cols, inplace=True)
+    # Copy the entire keys df in one operation rather than using iloc
+    part_names = (
+        part_keys.take(part_offsets[:-1])
+        .to_pandas(nullable=True)
+        .to_frame(index=False)
+    )
+    return part_names, grouped_df, part_offsets
+
+
+ParquetWriter = libparquet.ParquetWriter
+
+
+def _parse_bytes(s):
+    """Parse byte string to numbers
+
+    Utility function vendored from Dask.
+
+    >>> _parse_bytes('100')
+    100
+    >>> _parse_bytes('100 MB')
+    100000000
+    >>> _parse_bytes('100M')
+    100000000
+    >>> _parse_bytes('5kB')
+    5000
+    >>> _parse_bytes('5.4 kB')
+    5400
+    >>> _parse_bytes('1kiB')
+    1024
+    >>> _parse_bytes('1e6')
+    1000000
+    >>> _parse_bytes('1e6 kB')
+    1000000000
+    >>> _parse_bytes('MB')
+    1000000
+    >>> _parse_bytes(123)
+    123
+    >>> _parse_bytes('5 foos')
+    Traceback (most recent call last):
+        ...
+    ValueError: Could not interpret 'foos' as a byte unit
+    """
+    if isinstance(s, (int, float)):
+        return int(s)
+    s = s.replace(" ", "")
+    if not any(char.isdigit() for char in s):
+        s = "1" + s
+
+    for i in range(len(s) - 1, -1, -1):
+        if not s[i].isalpha():
+            break
+    index = i + 1
+
+    prefix = s[:index]
+    suffix = s[index:]
+
+    try:
+        n = float(prefix)
+    except ValueError as e:
+        raise ValueError(
+            "Could not interpret '%s' as a number" % prefix
+        ) from e
+
+    try:
+        multiplier = BYTE_SIZES[suffix.lower()]
+    except KeyError as e:
+        raise ValueError(
+            "Could not interpret '%s' as a byte unit" % suffix
+        ) from e
+
+    result = n * multiplier
+    return int(result)
+
+
+class ParquetDatasetWriter:
+    """
+    Write a parquet file or dataset incrementally
+
+    Parameters
+    ----------
+    path : str
+        A local directory path or S3 URL. Will be used as root directory
+        path while writing a partitioned dataset.
+    partition_cols : list
+        Column names by which to partition the dataset
+        Columns are partitioned in the order they are given
+    index : bool, default None
+        If ``True``, include the dataframe's index(es) in the file output.
+        If ``False``, they will not be written to the file. If ``None``,
+        index(es) other than RangeIndex will be saved as columns.
+    compression : {'snappy', None}, default 'snappy'
+        Name of the compression to use. Use ``None`` for no compression.
+    statistics : {'ROWGROUP', 'PAGE', 'COLUMN', 'NONE'}, default 'ROWGROUP'
+        Level at which column statistics should be included in file.
+    max_file_size : int or str, default None
+        A file size that cannot be exceeded by the writer.
+        It is in bytes, if the input is int.
+        Size can also be a str in form or "10 MB", "1 GB", etc.
+        If this parameter is used, it is mandatory to pass
+        `file_name_prefix`.
+    file_name_prefix : str
+        This is a prefix to file names generated only when
+        `max_file_size` is specified.
+    storage_options : dict, optional, default None
+        Extra options that make sense for a particular storage connection,
+        e.g. host, port, username, password, etc. For HTTP(S) URLs the
+        key-value pairs are forwarded to ``urllib.request.Request`` as
+        header options. For other URLs (e.g. starting with "s3://", and
+        "gcs://") the key-value pairs are forwarded to ``fsspec.open``.
+        Please see ``fsspec`` and ``urllib`` for more details.
+
+
+    Examples
+    --------
+    Using a context
+
+    >>> df1 = cudf.DataFrame({"a": [1, 1, 2, 2, 1], "b": [9, 8, 7, 6, 5]})
+    >>> df2 = cudf.DataFrame({"a": [1, 3, 3, 1, 3], "b": [4, 3, 2, 1, 0]})
+    >>> with ParquetDatasetWriter("./dataset", partition_cols=["a"]) as cw:
+    ...     cw.write_table(df1)
+    ...     cw.write_table(df2)
+
+    By manually calling ``close()``
+
+    >>> cw = ParquetDatasetWriter("./dataset", partition_cols=["a"])
+    >>> cw.write_table(df1)
+    >>> cw.write_table(df2)
+    >>> cw.close()
+
+    Both the methods will generate the same directory structure
+
+    .. code-block:: none
+
+        dataset/
+            a=1
+                <filename>.parquet
+            a=2
+                <filename>.parquet
+            a=3
+                <filename>.parquet
+
+    """
+
+    @_cudf_nvtx_annotate
+    def __init__(
+        self,
+        path,
+        partition_cols,
+        index=None,
+        compression="snappy",
+        statistics="ROWGROUP",
+        max_file_size=None,
+        file_name_prefix=None,
+        storage_options=None,
+    ) -> None:
+        if isinstance(path, str) and path.startswith("s3://"):
+            self.fs_meta = {"is_s3": True, "actual_path": path}
+            self.dir_: Optional[
+                tempfile.TemporaryDirectory
+            ] = tempfile.TemporaryDirectory()
+            self.path = self.dir_.name
+        else:
+            self.fs_meta = {}
+            self.dir_ = None
+            self.path = path
+
+        self.common_args = {
+            "index": index,
+            "compression": compression,
+            "statistics": statistics,
+        }
+        self.partition_cols = partition_cols
+        # Collection of `ParquetWriter`s, and the corresponding
+        # partition_col values they're responsible for
+        self._chunked_writers: List[
+            Tuple[libparquet.ParquetWriter, List[str], str]
+        ] = []
+        # Map of partition_col values to their ParquetWriter's index
+        # in self._chunked_writers for reverse lookup
+        self.path_cw_map: Dict[str, int] = {}
+        self.storage_options = storage_options
+        self.filename = file_name_prefix
+        self.max_file_size = max_file_size
+        if max_file_size is not None:
+            if file_name_prefix is None:
+                raise ValueError(
+                    "file_name_prefix cannot be None if max_file_size is "
+                    "passed"
+                )
+            self.max_file_size = _parse_bytes(max_file_size)
+
+        self._file_sizes: Dict[str, int] = {}
+
+    @_cudf_nvtx_annotate
+    def write_table(self, df):
+        """
+        Write a dataframe to the file/dataset
+        """
+        (part_names, grouped_df, part_offsets,) = _get_groups_and_offsets(
+            df=df,
+            partition_cols=self.partition_cols,
+            preserve_index=self.common_args["index"],
+        )
+        fs = ioutils._ensure_filesystem(None, self.path, None)
+        fs.mkdirs(self.path, exist_ok=True)
+
+        full_paths = []
+        metadata_file_paths = []
+        full_offsets = [0]
+
+        for idx, keys in enumerate(part_names.itertuples(index=False)):
+            subdir = fs.sep.join(
+                [
+                    f"{name}={val}"
+                    for name, val in zip(self.partition_cols, keys)
+                ]
+            )
+            prefix = fs.sep.join([self.path, subdir])
+            fs.mkdirs(prefix, exist_ok=True)
+            current_offset = (part_offsets[idx], part_offsets[idx + 1])
+            num_chunks = 1
+            parts = 1
+
+            if self.max_file_size is not None:
+                # get the current partition
+                start, end = current_offset
+                sliced_df = grouped_df[start:end]
+
+                current_file_size = _get_estimated_file_size(sliced_df)
+                if current_file_size > self.max_file_size:
+                    # if the file is too large, compute metadata for
+                    # smaller chunks
+                    parts = math.ceil(current_file_size / self.max_file_size)
+                    new_offsets = list(
+                        range(start, end, int((end - start) / parts))
+                    )[1:]
+                    new_offsets.append(end)
+                    num_chunks = len(new_offsets)
+                    parts = len(new_offsets)
+                    full_offsets.extend(new_offsets)
+                else:
+                    full_offsets.append(end)
+
+                curr_file_num = 0
+                num_chunks = 0
+                while num_chunks < parts:
+                    new_file_name = f"{self.filename}_{curr_file_num}.parquet"
+                    new_full_path = fs.sep.join([prefix, new_file_name])
+
+                    # Check if the same `new_file_name` exists and
+                    # generate a `new_file_name`
+                    while new_full_path in self._file_sizes and (
+                        self._file_sizes[new_full_path]
+                        + (current_file_size / parts)
+                    ) > (self.max_file_size):
+                        curr_file_num += 1
+                        new_file_name = (
+                            f"{self.filename}_{curr_file_num}.parquet"
+                        )
+                        new_full_path = fs.sep.join([prefix, new_file_name])
+
+                    self._file_sizes[new_full_path] = self._file_sizes.get(
+                        new_full_path, 0
+                    ) + (current_file_size / parts)
+                    full_paths.append(new_full_path)
+                    metadata_file_paths.append(
+                        fs.sep.join([subdir, new_file_name])
+                    )
+                    num_chunks += 1
+                    curr_file_num += 1
+            else:
+                self.filename = self.filename or _generate_filename()
+                full_path = fs.sep.join([prefix, self.filename])
+                full_paths.append(full_path)
+                metadata_file_paths.append(
+                    fs.sep.join([subdir, self.filename])
+                )
+                full_offsets.append(current_offset[1])
+
+        paths, metadata_file_paths, offsets = (
+            full_paths,
+            metadata_file_paths,
+            full_offsets,
+        )
+        existing_cw_batch = defaultdict(dict)
+        new_cw_paths = []
+        partition_info = [(i, j - i) for i, j in zip(offsets, offsets[1:])]
+
+        for path, part_info, meta_path in zip(
+            paths,
+            partition_info,
+            metadata_file_paths,
+        ):
+            if path in self.path_cw_map:  # path is a currently open file
+                cw_idx = self.path_cw_map[path]
+                existing_cw_batch[cw_idx][path] = part_info
+            else:  # path not currently handled by any chunked writer
+                new_cw_paths.append((path, part_info, meta_path))
+
+        # Write out the parts of grouped_df currently handled by existing cw's
+        for cw_idx, path_to_part_info_map in existing_cw_batch.items():
+            cw = self._chunked_writers[cw_idx][0]
+            # match found paths with this cw's paths and nullify partition info
+            # for partition_col values not in this batch
+            this_cw_part_info = [
+                path_to_part_info_map.get(path, (0, 0))
+                for path in self._chunked_writers[cw_idx][1]
+            ]
+            cw.write_table(grouped_df, this_cw_part_info)
+
+        if new_cw_paths:
+            # Create new cw for unhandled paths encountered in this write_table
+            new_paths, part_info, meta_paths = zip(*new_cw_paths)
+            self._chunked_writers.append(
+                (
+                    ParquetWriter(new_paths, **self.common_args),
+                    new_paths,
+                    meta_paths,
+                )
+            )
+            new_cw_idx = len(self._chunked_writers) - 1
+            self.path_cw_map.update({k: new_cw_idx for k in new_paths})
+            self._chunked_writers[-1][0].write_table(grouped_df, part_info)
+
+    @_cudf_nvtx_annotate
+    def close(self, return_metadata=False):
+        """
+        Close all open files and optionally return footer metadata as a binary
+        blob
+        """
+
+        metadata = [
+            cw.close(metadata_file_path=meta_path if return_metadata else None)
+            for cw, _, meta_path in self._chunked_writers
+        ]
+
+        if self.fs_meta.get("is_s3", False):
+            local_path = self.path
+            s3_path = self.fs_meta["actual_path"]
+            s3_file, _ = ioutils._get_filesystem_and_paths(
+                s3_path, storage_options=self.storage_options
+            )
+            s3_file.put(local_path, s3_path, recursive=True)
+            shutil.rmtree(self.path)
+
+        if self.dir_ is not None:
+            self.dir_.cleanup()
+
+        if return_metadata:
+            return (
+                merge_parquet_filemetadata(metadata)
+                if len(metadata) > 1
+                else metadata[0]
+            )
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+
+def _default_open_file_options(
+    open_file_options, columns, row_groups, fs=None
+):
+    """
+    Set default fields in open_file_options.
+
+    Copies and updates `open_file_options` to
+    include column and row-group information
+    under the "precache_options" key. By default,
+    we set "method" to "parquet", but precaching
+    will be disabled if the user chooses `method=None`
+
+    Parameters
+    ----------
+    open_file_options : dict or None
+    columns : list
+    row_groups : list
+    fs : fsspec.AbstractFileSystem, Optional
+    """
+    if fs and ioutils._is_local_filesystem(fs):
+        # Quick return for local fs
+        return open_file_options or {}
+    # Assume remote storage if `fs` was not specified
+    open_file_options = (open_file_options or {}).copy()
+    precache_options = open_file_options.pop("precache_options", {}).copy()
+    if precache_options.get("method", "parquet") == "parquet":
+        precache_options.update(
+            {
+                "method": "parquet",
+                "engine": precache_options.get("engine", "pyarrow"),
+                "columns": columns,
+                "row_groups": row_groups,
+            }
+        )
+    open_file_options["precache_options"] = precache_options
+    return open_file_options
+
+
+def _hive_dirname(name, val):
+    # Simple utility to produce hive directory name
+    if pd.isna(val):
+        val = "__HIVE_DEFAULT_PARTITION__"
+    return f"{name}={val}"
diff --git a/python/cudf/cudf/io/text.py b/python/cudf/cudf/io/text.py
new file mode 100644
index 0000000..eb2c7fa
--- /dev/null
+++ b/python/cudf/cudf/io/text.py
@@ -0,0 +1,43 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from io import BytesIO, StringIO
+
+import cudf
+from cudf._lib import text as libtext
+from cudf.utils import ioutils
+from cudf.utils.utils import _cudf_nvtx_annotate
+
+
+@_cudf_nvtx_annotate
+@ioutils.doc_read_text()
+def read_text(
+    filepath_or_buffer,
+    delimiter=None,
+    byte_range=None,
+    strip_delimiters=False,
+    compression=None,
+    compression_offsets=None,
+    storage_options=None,
+):
+    """{docstring}"""
+
+    if delimiter is None:
+        raise ValueError("delimiter needs to be provided")
+
+    filepath_or_buffer, _ = ioutils.get_reader_filepath_or_buffer(
+        path_or_data=filepath_or_buffer,
+        compression=None,
+        iotypes=(BytesIO, StringIO),
+        storage_options=storage_options,
+    )
+
+    return cudf.Series._from_data(
+        libtext.read_text(
+            filepath_or_buffer,
+            delimiter=delimiter,
+            byte_range=byte_range,
+            strip_delimiters=strip_delimiters,
+            compression=compression,
+            compression_offsets=compression_offsets,
+        )
+    )
diff --git a/python/cudf/cudf/options.py b/python/cudf/cudf/options.py
new file mode 100644
index 0000000..7a0db49
--- /dev/null
+++ b/python/cudf/cudf/options.py
@@ -0,0 +1,355 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import os
+import textwrap
+from collections.abc import Container
+from contextlib import ContextDecorator
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, Optional
+
+
+@dataclass
+class Option:
+    default: Any
+    value: Any
+    description: str
+    validator: Callable
+
+
+_OPTIONS: Dict[str, Option] = {}
+
+
+def _env_get_int(name, default):
+    try:
+        return int(os.getenv(name, default))
+    except (ValueError, TypeError):
+        return default
+
+
+def _env_get_bool(name, default):
+    env = os.getenv(name)
+    if env is None:
+        return default
+    as_a_int = _env_get_int(name, None)
+    env = env.lower().strip()
+    if env == "true" or env == "on" or as_a_int:
+        return True
+    if env == "false" or env == "off" or as_a_int == 0:
+        return False
+    return default
+
+
+def _register_option(
+    name: str, default_value: Any, description: str, validator: Callable
+):
+    """Register an option.
+
+    Parameters
+    ----------
+    name : str
+        The name of the option.
+    default_value : Any
+        The default value of the option.
+    description : str
+        A text description of the option.
+    validator : Callable
+        Called on the option value to check its validity. Should raise an
+        error if the value is invalid.
+
+    Raises
+    ------
+    BaseException
+        Raised by validator if the value is invalid.
+    """
+    validator(default_value)
+    _OPTIONS[name] = Option(
+        default_value, default_value, description, validator
+    )
+
+
+def get_option(name: str) -> Any:
+    """Get the value of option.
+
+    Parameters
+    ----------
+    key : str
+        The name of the option.
+
+    Returns
+    -------
+    The value of the option.
+
+    Raises
+    ------
+    KeyError
+        If option ``name`` does not exist.
+    """
+    try:
+        return _OPTIONS[name].value
+    except KeyError:
+        raise KeyError(f'"{name}" does not exist.')
+
+
+def set_option(name: str, val: Any):
+    """Set the value of option.
+
+    Parameters
+    ----------
+    name : str
+        The name of the option.
+    val : Any
+        The value to set.
+
+    Raises
+    ------
+    KeyError
+        If option ``name`` does not exist.
+    BaseException
+        Raised by validator if the value is invalid.
+    """
+    try:
+        option = _OPTIONS[name]
+    except KeyError:
+        raise KeyError(f'"{name}" does not exist.')
+    option.validator(val)
+    option.value = val
+
+
+def _build_option_description(name, opt):
+    return (
+        f"{name}:\n"
+        f"\t{opt.description}\n"
+        f"\t[Default: {opt.default}] [Current: {opt.value}]"
+    )
+
+
+def describe_option(name: Optional[str] = None):
+    """Prints the description of an option.
+
+    If `name` is unspecified, prints the description of all available options.
+
+    Parameters
+    ----------
+    name : Optional[str]
+        The name of the option.
+    """
+    names = _OPTIONS.keys() if name is None else [name]
+    for name in names:
+        print(_build_option_description(name, _OPTIONS[name]))
+
+
+def _make_contains_validator(valid_options: Container) -> Callable:
+    """Return a validator that checks if a value is in `valid_options`."""
+
+    def _validator(val):
+        if val not in valid_options:
+            raise ValueError(
+                f"{val} is not a valid option. "
+                f"Must be one of {set(valid_options)}."
+            )
+
+    return _validator
+
+
+def _cow_validator(val):
+    if get_option("spill") and val:
+        raise ValueError(
+            "Copy-on-write is not supported when spilling is enabled. "
+            "Please set `spill` to `False`"
+        )
+    if val not in {False, True}:
+        raise ValueError(
+            f"{val} is not a valid option. Must be one of {{False, True}}."
+        )
+
+
+def _spill_validator(val):
+    try:
+        if get_option("copy_on_write") and val:
+            raise ValueError(
+                "Spilling is not supported when copy-on-write is enabled. "
+                "Please set `copy_on_write` to `False`"
+            )
+    except KeyError:
+        pass
+    if val not in {False, True}:
+        raise ValueError(
+            f"{val} is not a valid option. Must be one of {{False, True}}."
+        )
+
+
+def _integer_validator(val):
+    try:
+        int(val)
+        return True
+    except ValueError:
+        raise ValueError(
+            f"{val} is not a valid option. " f"Must be an integer."
+        )
+
+
+def _integer_and_none_validator(val):
+    try:
+        if val is None or int(val):
+            return
+    except ValueError:
+        raise ValueError(
+            f"{val} is not a valid option. " f"Must be an integer or None."
+        )
+
+
+_register_option(
+    "default_integer_bitwidth",
+    None,
+    textwrap.dedent(
+        """
+        Default bitwidth when the dtype of an integer needs to be
+        inferred. If set to `None`, the API will align dtype with pandas.
+        APIs that respect this option include:
+        \t- cudf object constructors
+        \t- cudf.read_csv and cudf.read_json when `dtype` is not specified.
+        \t- APIs that require implicit conversion of cudf.RangeIndex to an
+        \t  integer index.
+        \tValid values are  None, 32 or 64. Default is None.
+    """
+    ),
+    _make_contains_validator([None, 32, 64]),
+)
+
+
+_register_option(
+    "default_float_bitwidth",
+    None,
+    textwrap.dedent(
+        """
+        Default bitwidth when the dtype of a float needs to be
+        inferred. If set to `None`, the API will align dtype with pandas.
+        APIs that respect this option include:
+        \t- cudf object constructors
+        \t- cudf.read_csv and cudf.read_json when `dtype` is not specified.
+        \tValid values are None, 32 or 64. Default is None.
+    """
+    ),
+    _make_contains_validator([None, 32, 64]),
+)
+
+_register_option(
+    "spill",
+    _env_get_bool("CUDF_SPILL", False),
+    textwrap.dedent(
+        """
+        Enables spilling.
+        \tValid values are True or False. Default is False.
+        """
+    ),
+    _spill_validator,
+)
+
+
+_register_option(
+    "copy_on_write",
+    _env_get_bool("CUDF_COPY_ON_WRITE", False),
+    textwrap.dedent(
+        """
+        If set to `False`, disables copy-on-write.
+        If set to `True`, enables copy-on-write.
+        Read more at: :ref:`copy-on-write-user-doc`
+        \tValid values are True or False. Default is False.
+    """
+    ),
+    _cow_validator,
+)
+
+
+_register_option(
+    "spill_on_demand",
+    _env_get_bool("CUDF_SPILL_ON_DEMAND", True),
+    textwrap.dedent(
+        """
+        Enables spilling on demand using an RMM out-of-memory error handler.
+        This has no effect if spilling is disabled, see the "spill" option.
+        \tValid values are True or False. Default is True.
+        """
+    ),
+    _make_contains_validator([False, True]),
+)
+
+_register_option(
+    "spill_device_limit",
+    _env_get_int("CUDF_SPILL_DEVICE_LIMIT", None),
+    textwrap.dedent(
+        """
+        Enforce a device memory limit in bytes.
+        This has no effect if spilling is disabled, see the "spill" option.
+        \tValid values are any positive integer or None (disabled).
+        \tDefault is None.
+        """
+    ),
+    _integer_and_none_validator,
+)
+
+_register_option(
+    "spill_stats",
+    _env_get_int("CUDF_SPILL_STATS", 0),
+    textwrap.dedent(
+        """
+        If not 0, enables statistics at the specified level:
+            0  - disabled (no overhead).
+            1+ - duration and number of bytes spilled (very low overhead).
+            2+ - a traceback for each time a spillable buffer is exposed
+                permanently (potential high overhead).
+
+        Valid values are any positive integer.
+        Default is 0 (disabled).
+        """
+    ),
+    _integer_validator,
+)
+
+_register_option(
+    "mode.pandas_compatible",
+    False,
+    textwrap.dedent(
+        """
+        If set to `False`, retains `cudf` specific behavior.
+        If set to `True`, enables pandas compatibility mode,
+        which will try to match pandas API behaviors in case of
+        any inconsistency.
+        \tValid values are True or False. Default is False.
+    """
+    ),
+    _make_contains_validator([False, True]),
+)
+
+
+class option_context(ContextDecorator):
+    """
+    Context manager to temporarily set options in the `with` statement context.
+
+    You need to invoke as ``option_context(pat, val, [(pat, val), ...])``.
+
+
+    Examples
+    --------
+    >>> from cudf import option_context
+    >>> with option_context('mode.pandas_compatible', True, 'default_float_bitwidth', 32):
+    ...     pass
+    """  # noqa: E501
+
+    def __init__(self, *args) -> None:
+        if len(args) % 2 != 0:
+            raise ValueError(
+                "Need to invoke as option_context(pat, val, "
+                "[(pat, val), ...])."
+            )
+
+        self.ops = tuple(zip(args[::2], args[1::2]))
+
+    def __enter__(self) -> None:
+        self.undo = tuple((pat, get_option(pat)) for pat, _ in self.ops)
+        for pat, val in self.ops:
+            set_option(pat, val)
+
+    def __exit__(self, *args) -> None:
+        for pat, val in self.undo:
+            set_option(pat, val)
diff --git a/python/cudf/cudf/pandas/__init__.py b/python/cudf/cudf/pandas/__init__.py
new file mode 100644
index 0000000..f2e855a
--- /dev/null
+++ b/python/cudf/cudf/pandas/__init__.py
@@ -0,0 +1,34 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from .magics import load_ipython_extension
+from .profiler import Profiler
+
+__all__ = ["Profiler", "load_ipython_extension", "install"]
+
+
+LOADED = False
+
+
+def install():
+    """Enable Pandas Accelerator Mode."""
+    from .module_accelerator import ModuleAccelerator
+
+    loader = ModuleAccelerator.install("pandas", "cudf", "pandas")
+    global LOADED
+    LOADED = loader is not None
+
+
+def pytest_load_initial_conftests(early_config, parser, args):
+    # We need to install ourselves before conftest.py import (which
+    # might import pandas) This hook is guaranteed to run before that
+    # happens see
+    # https://docs.pytest.org/en/7.1.x/reference/\
+    # reference.html#pytest.hookspec.pytest_load_initial_conftests
+    try:
+        install()
+    except RuntimeError:
+        raise RuntimeError(
+            "An existing plugin has already loaded pandas. Interposing failed."
+        )
diff --git a/python/cudf/cudf/pandas/__main__.py b/python/cudf/cudf/pandas/__main__.py
new file mode 100644
index 0000000..02e8e96
--- /dev/null
+++ b/python/cudf/cudf/pandas/__main__.py
@@ -0,0 +1,91 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Usage:
+
+python -m cudf.pandas <script.py> <args>
+python -m cudf.pandas -m module <args>
+"""
+
+import argparse
+import runpy
+import sys
+import tempfile
+from contextlib import contextmanager
+
+from . import install
+from .profiler import Profiler, lines_with_profiling
+
+
+@contextmanager
+def profile(function_profile, line_profile, fn):
+    if line_profile:
+        with open(fn) as f:
+            lines = f.readlines()
+
+        with tempfile.NamedTemporaryFile(mode="w+b", suffix=".py") as f:
+            f.write(lines_with_profiling(lines, function_profile).encode())
+            f.seek(0)
+
+            yield f.name
+    elif function_profile:
+        with Profiler() as profiler:
+            yield fn
+        profiler.print_per_func_stats()
+    else:
+        yield fn
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        prog="python -m cudf.pandas",
+        description=(
+            "Run a Python script with Pandas Accelerator Mode enabled. "
+            "In Pandas Accelerator Mode, all imports of pandas will "
+            "automatically use GPU accelerated cuDF equivalents where "
+            "possible."
+        ),
+    )
+
+    parser.add_argument(
+        "-m",
+        dest="module",
+        nargs=1,
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Perform per-function profiling of this script.",
+    )
+    parser.add_argument(
+        "--line-profile",
+        action="store_true",
+        help="Perform per-line profiling of this script.",
+    )
+    parser.add_argument(
+        "args",
+        nargs=argparse.REMAINDER,
+        help="Arguments to pass on to the script",
+    )
+
+    args = parser.parse_args()
+
+    install()
+    with profile(args.profile, args.line_profile, args.args[0]) as fn:
+        args.args[0] = fn
+        if args.module:
+            (module,) = args.module
+            # run the module passing the remaining arguments
+            # as if it were run with python -m <module> <args>
+            sys.argv[:] = [module] + args.args  # not thread safe?
+            runpy.run_module(module, run_name="__main__")
+        elif len(args.args) >= 1:
+            # Remove ourself from argv and continue
+            sys.argv[:] = args.args
+            runpy.run_path(args.args[0], run_name="__main__")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cudf/cudf/pandas/_wrappers/__init__.py b/python/cudf/cudf/pandas/_wrappers/__init__.py
new file mode 100644
index 0000000..1f7fe18
--- /dev/null
+++ b/python/cudf/cudf/pandas/_wrappers/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from . import numpy, pandas
diff --git a/python/cudf/cudf/pandas/_wrappers/common.py b/python/cudf/cudf/pandas/_wrappers/common.py
new file mode 100644
index 0000000..1669882
--- /dev/null
+++ b/python/cudf/cudf/pandas/_wrappers/common.py
@@ -0,0 +1,50 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Utility custom overrides for special methods/properties
+from ..fast_slow_proxy import (
+    _FastSlowAttribute,
+    _FastSlowProxy,
+    _maybe_wrap_result,
+    _slow_arg,
+)
+
+
+def array_method(self: _FastSlowProxy, *args, **kwargs):
+    return self._fsproxy_slow.__array__(*args, **kwargs)
+
+
+def array_function_method(self, func, types, args, kwargs):
+    try:
+        return _FastSlowAttribute("__array_function__").__get__(self)(
+            func, types, args, kwargs
+        )
+    except Exception:
+        # if something went wrong with __array_function__ we
+        # attempt to call the function directly on the slow
+        # object.  This ensures that the function call is
+        # handled in the same way as if the slow object was
+        # passed directly to the function.
+        slow_args, slow_kwargs = _slow_arg(args), _slow_arg(kwargs)
+        return _maybe_wrap_result(
+            func(*slow_args, **slow_kwargs), func, *args, **kwargs
+        )
+
+
+def arrow_array_method(self: _FastSlowProxy, *args, **kwargs):
+    import pyarrow as pa
+
+    try:
+        return self._fsproxy_fast.to_arrow(*args, **kwargs)
+    except Exception:
+        return pa.array(self._fsproxy_slow, *args, **kwargs)
+
+
+@property  # type: ignore
+def cuda_array_interface(self: _FastSlowProxy):
+    return self._fsproxy_fast.__cuda_array_interface__
+
+
+def custom_iter(self: _FastSlowProxy):
+    return iter(self._fsproxy_slow)
diff --git a/python/cudf/cudf/pandas/_wrappers/numpy.py b/python/cudf/cudf/pandas/_wrappers/numpy.py
new file mode 100644
index 0000000..9955550
--- /dev/null
+++ b/python/cudf/cudf/pandas/_wrappers/numpy.py
@@ -0,0 +1,133 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import cupy
+import cupy._core.flags
+import numpy
+import numpy.core.multiarray
+
+from ..fast_slow_proxy import (
+    make_final_proxy_type,
+    make_intermediate_proxy_type,
+)
+from .common import (
+    array_method,
+    arrow_array_method,
+    cuda_array_interface,
+    custom_iter,
+)
+
+# https://docs.cupy.dev/en/stable/reference/creation.html
+_CONSTRUCTORS = frozenset(
+    [
+        cupy.empty,
+        cupy.empty_like,
+        cupy.eye,
+        cupy.identity,
+        cupy.ones,
+        cupy.ones_like,
+        cupy.zeros,
+        cupy.zeros_like,
+        cupy.full,
+        cupy.full_like,
+        cupy.array,
+        cupy.asarray,
+        cupy.asanyarray,
+        cupy.ascontiguousarray,
+        cupy.copy,
+        cupy.frombuffer,
+        cupy.fromfile,
+        cupy.fromfunction,
+        cupy.fromiter,
+        cupy.fromstring,
+        cupy.loadtxt,
+        cupy.arange,
+        cupy.linspace,
+        cupy.logspace,
+        cupy.meshgrid,
+        cupy.diag,
+        cupy.diagflat,
+        cupy.tri,
+        cupy.tril,
+        cupy.triu,
+        cupy.vander,
+    ]
+)
+
+
+def wrap_ndarray(cls, arr: cupy.ndarray | numpy.ndarray, constructor):
+    """Wrap an ndarray in a proxy type
+
+    Parameters
+    ----------
+    cls
+        Proxy type for ndarray
+    arr
+        Concrete result ndarray (cupy or numpy)
+    constructor
+        Function that was called to construct the concrete array, used
+        to check against a denylist to avoid unwrapping.
+
+    Returns
+    -------
+    The scalar .item() wrapped in its numpy dtype if arr is a
+    zero-dimensional cupy array (and wasn't just constructed as such),
+    a new proxy type otherwise.
+
+    Notes
+    -----
+    Axis-reducing operations in numpy return scalar objects but
+    zero-dimensional arrays in cupy. This confuses downstream
+    libraries when they get a fast (device-based) zero-dim array when
+    they were expecting a scalar. To avoid this, if the provided array
+    is a cupy array, and its shape is zero, unwrap it.
+    """
+    if (
+        isinstance(arr, cupy.ndarray)
+        and arr.shape == ()
+        and constructor not in _CONSTRUCTORS
+    ):
+        return arr.dtype.type(arr.item())
+    else:
+        # Note, this super call means that the constructed ndarray
+        # class cannot be subclassed (because then super(cls,
+        # cls)._fsproxy_wrap produces an infinite loop). Really this
+        # should be super(ndarray, cls), but we don't have access to
+        # the ndarray type until after we need to pass this function
+        # in. So it works for now since without subclassing,
+        # super(ndarray, cls) == super(ndarray, ndarray) == super(cls,
+        # cls)
+        return super(cls, cls)._fsproxy_wrap(arr, constructor)
+
+
+ndarray = make_final_proxy_type(
+    "ndarray",
+    cupy.ndarray,
+    numpy.ndarray,
+    fast_to_slow=cupy.ndarray.get,
+    slow_to_fast=cupy.asarray,
+    additional_attributes={
+        "__array__": array_method,
+        # So that pa.array(wrapped-numpy-array) works
+        "__arrow_array__": arrow_array_method,
+        "__cuda_array_interface__": cuda_array_interface,
+        # ndarrays are unhashable
+        "__hash__": None,
+        # iter(cupy-array) produces an iterable of zero-dim device
+        # arrays, which is not usable in many settings (whereas
+        # iter(numpy-array) produces an iterable of scalars)
+        "__iter__": custom_iter,
+        # Special wrapping to handle scalar values
+        "_fsproxy_wrap": classmethod(wrap_ndarray),
+    },
+)
+
+# Mapping flags between slow and fast types
+_ndarray_flags = make_intermediate_proxy_type(
+    "_ndarray_flags",
+    cupy._core.flags.Flags,
+    numpy.core.multiarray.flagsobj,
+)
diff --git a/python/cudf/cudf/pandas/_wrappers/pandas.py b/python/cudf/cudf/pandas/_wrappers/pandas.py
new file mode 100644
index 0000000..71daf1e
--- /dev/null
+++ b/python/cudf/cudf/pandas/_wrappers/pandas.py
@@ -0,0 +1,1306 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+
+import pandas as pd
+
+import cudf
+
+from ..annotation import nvtx
+from ..fast_slow_proxy import (
+    _CUDF_PANDAS_NVTX_COLORS,
+    _DELETE,
+    _fast_slow_function_call,
+    _FastSlowAttribute,
+    _FunctionProxy,
+    _Unusable,
+    get_final_type_map,
+    make_final_proxy_type as _make_final_proxy_type,
+    make_intermediate_proxy_type as _make_intermediate_proxy_type,
+    register_proxy_func,
+)
+from .common import (
+    array_function_method,
+    array_method,
+    arrow_array_method,
+    cuda_array_interface,
+    custom_iter,
+)
+
+from pandas.io.sas.sas7bdat import (  # isort: skip
+    SAS7BDATReader as pd_SAS7BDATReader,
+)
+from pandas.io.sas.sas_xport import (  # isort: skip
+    XportReader as pd_XportReader,
+)
+
+
+# TODO(pandas2.1): Can import from pandas.api.typing
+from pandas.core.resample import (  # isort: skip
+    Resampler as pd_Resampler,
+    TimeGrouper as pd_TimeGrouper,
+)
+
+
+cudf.set_option("mode.pandas_compatible", True)
+
+
+def make_final_proxy_type(
+    name,
+    fast_type,
+    slow_type,
+    **kwargs,
+):
+    assert "module" not in kwargs
+    return _make_final_proxy_type(
+        name, fast_type, slow_type, module=slow_type.__module__, **kwargs
+    )
+
+
+def make_intermediate_proxy_type(name, fast_type, slow_type):
+    return _make_intermediate_proxy_type(
+        name, fast_type, slow_type, module=slow_type.__module__
+    )
+
+
+class _AccessorAttr:
+    """
+    Descriptor that ensures that accessors like `.dt` and `.str`
+    return the corresponding accessor types when accessed on `Series`
+    and `Index` _types_ (not instances).n
+
+    Attribute access for _instances_ uses the regular fast-then-slow
+    lookup defined in `__getattr__`.
+    """
+
+    def __init__(self, typ):
+        self.__typ = typ
+
+    def __get__(self, obj, cls=None):
+        if obj is None:
+            return self.__typ
+        else:
+            # allow __getattr__ to handle this
+            raise AttributeError()
+
+
+DatetimeProperties = make_intermediate_proxy_type(
+    "DatetimeProperties",
+    cudf.core.series.DatetimeProperties,
+    pd.core.indexes.accessors.DatetimeProperties,
+)
+
+TimedeltaProperties = make_intermediate_proxy_type(
+    "TimedeltaProperties",
+    cudf.core.series.TimedeltaProperties,
+    pd.core.indexes.accessors.TimedeltaProperties,
+)
+
+CombinedDatetimelikeProperties = make_intermediate_proxy_type(
+    "CombinedDatetimelikeProperties",
+    cudf.core.series.DatetimeProperties,
+    pd.core.indexes.accessors.CombinedDatetimelikeProperties,
+)
+
+StringMethods = make_intermediate_proxy_type(
+    "StringMethods",
+    cudf.core.column.string.StringMethods,
+    pd.core.strings.accessor.StringMethods,
+)
+
+_CategoricalAccessor = make_intermediate_proxy_type(
+    "CategoricalAccessor",
+    cudf.core.column.categorical.CategoricalAccessor,
+    pd.core.arrays.categorical.CategoricalAccessor,
+)
+
+
+def _DataFrame__dir__(self):
+    # Column names that are string identifiers are added to the dir of the
+    # DataFrame
+    # See https://github.com/pandas-dev/pandas/blob/43691a2f5d235b08f0f3aa813d8fdcb7c4ce1e47/pandas/core/indexes/base.py#L878  # noqa: E501
+    _pd_df_dir = dir(pd.DataFrame)
+    return _pd_df_dir + [
+        colname
+        for colname in self.columns
+        if isinstance(colname, str) and colname.isidentifier()
+    ]
+
+
+DataFrame = make_final_proxy_type(
+    "DataFrame",
+    cudf.DataFrame,
+    pd.DataFrame,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    additional_attributes={
+        "__array__": array_method,
+        "__dir__": _DataFrame__dir__,
+        "_constructor": _FastSlowAttribute("_constructor"),
+        "_constructor_sliced": _FastSlowAttribute("_constructor_sliced"),
+    },
+)
+
+
+Series = make_final_proxy_type(
+    "Series",
+    cudf.Series,
+    pd.Series,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    additional_attributes={
+        "__array__": array_method,
+        "__array_function__": array_function_method,
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+        "__arrow_array__": arrow_array_method,
+        "__cuda_array_interface__": cuda_array_interface,
+        "__iter__": custom_iter,
+        "dt": _AccessorAttr(DatetimeProperties),
+        "str": _AccessorAttr(StringMethods),
+        "cat": _AccessorAttr(_CategoricalAccessor),
+        "_constructor": _FastSlowAttribute("_constructor"),
+        "_constructor_expanddim": _FastSlowAttribute("_constructor_expanddim"),
+    },
+)
+
+
+def Index__new__(cls, *args, **kwargs):
+    # Call fast/slow constructor
+    # This takes care of running __init__ as well, but must be paired
+    # with a removal of the defaulted __init__ that
+    # make_final_proxy_type provides.
+    self, _ = _fast_slow_function_call(
+        lambda cls, args, kwargs: cls(*args, **kwargs),
+        cls,
+        args,
+        kwargs,
+    )
+    return self
+
+
+Index = make_final_proxy_type(
+    "Index",
+    cudf.Index,
+    pd.Index,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    additional_attributes={
+        "__array__": array_method,
+        "__array_function__": array_function_method,
+        "__arrow_array__": arrow_array_method,
+        "__cuda_array_interface__": cuda_array_interface,
+        "dt": _AccessorAttr(DatetimeProperties),
+        "str": _AccessorAttr(StringMethods),
+        "cat": _AccessorAttr(_CategoricalAccessor),
+        "__iter__": custom_iter,
+        "__init__": _DELETE,
+        "__new__": Index__new__,
+        "_constructor": _FastSlowAttribute("_constructor"),
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+    },
+)
+
+get_final_type_map()[cudf.StringIndex] = Index
+get_final_type_map()[cudf.Int8Index] = Index
+get_final_type_map()[cudf.Int8Index] = Index
+get_final_type_map()[cudf.Int16Index] = Index
+get_final_type_map()[cudf.Int32Index] = Index
+get_final_type_map()[cudf.UInt8Index] = Index
+get_final_type_map()[cudf.UInt16Index] = Index
+get_final_type_map()[cudf.UInt32Index] = Index
+get_final_type_map()[cudf.UInt64Index] = Index
+get_final_type_map()[cudf.Float32Index] = Index
+get_final_type_map()[cudf.GenericIndex] = Index
+
+
+RangeIndex = make_final_proxy_type(
+    "RangeIndex",
+    cudf.RangeIndex,
+    pd.RangeIndex,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+SparseDtype = make_final_proxy_type(
+    "SparseDtype",
+    _Unusable,
+    pd.SparseDtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+SparseArray = make_final_proxy_type(
+    "SparseDtype",
+    _Unusable,
+    pd.arrays.SparseArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+CategoricalIndex = make_final_proxy_type(
+    "CategoricalIndex",
+    cudf.CategoricalIndex,
+    pd.CategoricalIndex,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+Categorical = make_final_proxy_type(
+    "Categorical",
+    _Unusable,
+    pd.Categorical,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+CategoricalDtype = make_final_proxy_type(
+    "CategoricalDtype",
+    cudf.CategoricalDtype,
+    pd.CategoricalDtype,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+DatetimeIndex = make_final_proxy_type(
+    "DatetimeIndex",
+    cudf.DatetimeIndex,
+    pd.DatetimeIndex,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+DatetimeArray = make_final_proxy_type(
+    "DatetimeArray",
+    _Unusable,
+    pd.arrays.DatetimeArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+DatetimeTZDtype = make_final_proxy_type(
+    "DatetimeTZDtype",
+    _Unusable,
+    pd.DatetimeTZDtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+TimedeltaIndex = make_final_proxy_type(
+    "TimedeltaIndex",
+    cudf.TimedeltaIndex,
+    pd.TimedeltaIndex,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+TimedeltaArray = make_final_proxy_type(
+    "TimedeltaArray",
+    _Unusable,
+    pd.arrays.TimedeltaArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+PeriodIndex = make_final_proxy_type(
+    "PeriodIndex",
+    _Unusable,
+    pd.PeriodIndex,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+PeriodArray = make_final_proxy_type(
+    "PeriodArray",
+    _Unusable,
+    pd.arrays.PeriodArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+PeriodDtype = make_final_proxy_type(
+    "PeriodDtype",
+    _Unusable,
+    pd.PeriodDtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+Period = make_final_proxy_type(
+    "Period",
+    _Unusable,
+    pd.Period,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+MultiIndex = make_final_proxy_type(
+    "MultiIndex",
+    cudf.MultiIndex,
+    pd.MultiIndex,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+TimeGrouper = make_intermediate_proxy_type(
+    "TimeGrouper",
+    _Unusable,
+    pd_TimeGrouper,
+)
+
+Grouper = make_final_proxy_type(
+    "Grouper",
+    cudf.Grouper,
+    pd.Grouper,
+    fast_to_slow=lambda fast: pd.Grouper(
+        **{
+            k: getattr(fast, k)
+            for k in {"key", "level", "freq", "closed", "label"}
+            if getattr(fast, k) is not None
+        }
+    ),
+    slow_to_fast=lambda slow: cudf.Grouper(
+        **{
+            k: getattr(slow, k)
+            for k in {"key", "level", "freq", "closed", "label"}
+            if getattr(slow, k) is not None
+        }
+    ),
+)
+
+StringArray = make_final_proxy_type(
+    "StringArray",
+    _Unusable,
+    pd.arrays.StringArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+StringDtype = make_final_proxy_type(
+    "StringDtype",
+    _Unusable,
+    pd.StringDtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BooleanArray = make_final_proxy_type(
+    "BooleanArray",
+    _Unusable,
+    pd.arrays.BooleanArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__")
+    },
+)
+
+BooleanDtype = make_final_proxy_type(
+    "BooleanDtype",
+    _Unusable,
+    pd.BooleanDtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+IntegerArray = make_final_proxy_type(
+    "IntegerArray",
+    _Unusable,
+    pd.arrays.IntegerArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__")
+    },
+)
+
+Int8Dtype = make_final_proxy_type(
+    "Int8Dtype",
+    _Unusable,
+    pd.Int8Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+
+Int16Dtype = make_final_proxy_type(
+    "Int16Dtype",
+    _Unusable,
+    pd.Int16Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Int32Dtype = make_final_proxy_type(
+    "Int32Dtype",
+    _Unusable,
+    pd.Int32Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Int64Dtype = make_final_proxy_type(
+    "Int64Dtype",
+    _Unusable,
+    pd.Int64Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+
+Int64Index = make_final_proxy_type(
+    "Int64Index",
+    cudf.Int64Index,
+    pd.core.indexes.numeric.Int64Index,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+UInt8Dtype = make_final_proxy_type(
+    "UInt8Dtype",
+    _Unusable,
+    pd.UInt8Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+UInt16Dtype = make_final_proxy_type(
+    "UInt16Dtype",
+    _Unusable,
+    pd.UInt16Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+UInt32Dtype = make_final_proxy_type(
+    "UInt32Dtype",
+    _Unusable,
+    pd.UInt32Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+UInt64Dtype = make_final_proxy_type(
+    "UInt64Dtype",
+    _Unusable,
+    pd.UInt64Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+UInt64Index = make_final_proxy_type(
+    "UInt64Index",
+    cudf.UInt64Index,
+    pd.core.indexes.numeric.UInt64Index,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+IntervalIndex = make_final_proxy_type(
+    "IntervalIndex",
+    cudf.IntervalIndex,
+    pd.IntervalIndex,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+IntervalArray = make_final_proxy_type(
+    "IntervalArray",
+    _Unusable,
+    pd.arrays.IntervalArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+IntervalDtype = make_final_proxy_type(
+    "IntervalDtype",
+    cudf.IntervalDtype,
+    pd.IntervalDtype,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Interval = make_final_proxy_type(
+    "Interval",
+    _Unusable,
+    pd.Interval,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+FloatingArray = make_final_proxy_type(
+    "FloatingArray",
+    _Unusable,
+    pd.arrays.FloatingArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={
+        "__array_ufunc__": _FastSlowAttribute("__array_ufunc__")
+    },
+)
+
+Float32Dtype = make_final_proxy_type(
+    "Float32Dtype",
+    _Unusable,
+    pd.Float32Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Float64Dtype = make_final_proxy_type(
+    "Float64Dtype",
+    _Unusable,
+    pd.Float64Dtype,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Float64Index = make_final_proxy_type(
+    "Float64Index",
+    cudf.Float64Index,
+    pd.core.indexes.numeric.Float64Index,
+    fast_to_slow=lambda fast: fast.to_pandas(),
+    slow_to_fast=cudf.from_pandas,
+    bases=(Index,),
+    additional_attributes={"__init__": _DELETE},
+)
+
+SeriesGroupBy = make_intermediate_proxy_type(
+    "SeriesGroupBy",
+    cudf.core.groupby.groupby.SeriesGroupBy,
+    pd.core.groupby.SeriesGroupBy,
+)
+
+DataFrameGroupBy = make_intermediate_proxy_type(
+    "DataFrameGroupBy",
+    cudf.core.groupby.groupby.DataFrameGroupBy,
+    pd.core.groupby.DataFrameGroupBy,
+)
+
+RollingGroupBy = make_intermediate_proxy_type(
+    "RollingGroupBy",
+    cudf.core.window.rolling.RollingGroupby,
+    pd.core.window.rolling.RollingGroupby,
+)
+
+_SeriesIlocIndexer = make_intermediate_proxy_type(
+    "_SeriesIlocIndexer",
+    cudf.core.series._SeriesIlocIndexer,
+    pd.core.indexing._iLocIndexer,
+)
+
+_DataFrameIlocIndexer = make_intermediate_proxy_type(
+    "_SeriesIlocIndexer",
+    cudf.core.dataframe._DataFrameIlocIndexer,
+    pd.core.indexing._iLocIndexer,
+)
+
+_SeriesLocIndexer = make_intermediate_proxy_type(
+    "_SeriesLocIndexer",
+    cudf.core.series._SeriesLocIndexer,
+    pd.core.indexing._LocIndexer,
+)
+
+_DataFrameLocIndexer = make_intermediate_proxy_type(
+    "_DataFrameLocIndexer",
+    cudf.core.dataframe._DataFrameLocIndexer,
+    pd.core.indexing._LocIndexer,
+)
+
+FixedForwardWindowIndexer = make_final_proxy_type(
+    "FixedForwardWindowIndexer",
+    _Unusable,
+    pd.api.indexers.FixedForwardWindowIndexer,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+VariableOffsetWindowIndexer = make_final_proxy_type(
+    "VariableOffsetWindowIndexer",
+    _Unusable,
+    pd.api.indexers.VariableOffsetWindowIndexer,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+Window = make_intermediate_proxy_type(
+    "Window",
+    _Unusable,
+    pd.core.window.rolling.Window,
+)
+
+Rolling = make_intermediate_proxy_type(
+    "Rolling",
+    cudf.core.window.Rolling,
+    pd.core.window.Rolling,
+)
+
+ExponentialMovingWindow = make_intermediate_proxy_type(
+    "ExponentialMovingWindow",
+    _Unusable,
+    pd.core.window.ewm.ExponentialMovingWindow,
+)
+
+ExponentialMovingWindowGroupby = make_intermediate_proxy_type(
+    "ExponentialMovingWindowGroupby",
+    _Unusable,
+    pd.core.window.ewm.ExponentialMovingWindowGroupby,
+)
+
+EWMMeanState = make_intermediate_proxy_type(
+    "EWMMeanState",
+    _Unusable,
+    pd.core.window.online.EWMMeanState,
+)
+
+Expanding = make_intermediate_proxy_type(
+    "Expanding",
+    _Unusable,
+    pd.core.window.expanding.Expanding,
+)
+
+ExpandingGroupby = make_intermediate_proxy_type(
+    "ExpandingGroupby",
+    _Unusable,
+    pd.core.window.expanding.ExpandingGroupby,
+)
+
+Resampler = make_intermediate_proxy_type(
+    "Resampler", cudf.core.resample._Resampler, pd_Resampler
+)
+
+StataReader = make_intermediate_proxy_type(
+    "StataReader",
+    _Unusable,
+    pd.io.stata.StataReader,
+)
+
+HDFStore = make_final_proxy_type(
+    "HDFStore",
+    _Unusable,
+    pd.HDFStore,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+ExcelFile = make_final_proxy_type(
+    "ExcelFile",
+    _Unusable,
+    pd.ExcelFile,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+ExcelWriter = make_final_proxy_type(
+    "ExcelWriter",
+    _Unusable,
+    pd.ExcelWriter,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+try:
+    from pandas.io.formats.style import Styler as pd_Styler  # isort: skip
+
+    Styler = make_final_proxy_type(
+        "Styler",
+        _Unusable,
+        pd_Styler,
+        fast_to_slow=_Unusable(),
+        slow_to_fast=_Unusable(),
+    )
+except ImportError:
+    # Styler requires Jinja to be installed
+    pass
+
+_eval_func = _FunctionProxy(_Unusable(), pd.eval)
+
+
+def _get_eval_locals_and_globals(level, local_dict=None, global_dict=None):
+    frame = sys._getframe(level + 3)
+    local_dict = frame.f_locals if local_dict is None else local_dict
+    global_dict = frame.f_globals if global_dict is None else global_dict
+    return local_dict, global_dict
+
+
+@register_proxy_func(pd.eval)
+@nvtx.annotate(
+    "CUDF_PANDAS_EVAL",
+    color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
+    domain="cudf_pandas",
+)
+def _eval(
+    *args,
+    parser="pandas",
+    engine=None,
+    local_dict=None,
+    global_dict=None,
+    **kwargs,
+):
+    # Custom implementation of to pre-process globals and
+    # locals before calling pd.eval.
+    level = kwargs.get("level", 0)
+    local_dict, global_dict = _get_eval_locals_and_globals(
+        level, local_dict, global_dict
+    )
+    return _eval_func(
+        *args,
+        parser=parser,
+        engine=engine,
+        local_dict=local_dict,
+        global_dict=global_dict,
+        **kwargs,
+    )
+
+
+@nvtx.annotate(
+    "CUDF_PANDAS_DATAFRAME_EVAL",
+    color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
+    domain="cudf_pandas",
+)
+def _df_eval_method(self, *args, local_dict=None, global_dict=None, **kwargs):
+    level = kwargs.get("level", 0)
+    local_dict, global_dict = _get_eval_locals_and_globals(
+        level, local_dict, global_dict
+    )
+    return super(type(self), self).__getattr__("eval")(
+        *args, local_dict=local_dict, global_dict=global_dict, **kwargs
+    )
+
+
+@nvtx.annotate(
+    "CUDF_PANDAS_DATAFRAME_QUERY",
+    color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
+    domain="cudf_pandas",
+)
+def _df_query_method(self, *args, local_dict=None, global_dict=None, **kwargs):
+    # `query` API internally calls `eval`, hence we are making use of
+    # helps of `eval` to populate locals and globals dict.
+    level = kwargs.get("level", 0)
+    local_dict, global_dict = _get_eval_locals_and_globals(
+        level, local_dict, global_dict
+    )
+    return super(type(self), self).__getattr__("query")(
+        *args, local_dict=local_dict, global_dict=global_dict, **kwargs
+    )
+
+
+DataFrame.eval = _df_eval_method  # type: ignore
+DataFrame.query = _df_query_method  # type: ignore
+
+_JsonReader = make_intermediate_proxy_type(
+    "_JsonReader",
+    _Unusable,
+    pd.io.json._json.JsonReader,
+)
+
+_TextFileReader = make_intermediate_proxy_type(
+    "_TextFileReader", _Unusable, pd.io.parsers.readers.TextFileReader
+)
+
+_XportReader = make_intermediate_proxy_type(
+    "_XportReader", _Unusable, pd_XportReader
+)
+
+_SAS7BDATReader = make_intermediate_proxy_type(
+    "_SAS7BDATReader", _Unusable, pd_SAS7BDATReader
+)
+
+
+FY5253 = make_final_proxy_type(
+    "FY5253",
+    _Unusable,
+    pd.offsets.FY5253,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BDay = make_final_proxy_type(
+    "BDay",
+    _Unusable,
+    pd.offsets.BDay,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BMonthBegin = make_final_proxy_type(
+    "BMonthBegin",
+    _Unusable,
+    pd.offsets.BMonthBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BMonthEnd = make_final_proxy_type(
+    "BMonthEnd",
+    _Unusable,
+    pd.offsets.BMonthEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BQuarterBegin = make_final_proxy_type(
+    "BQuarterBegin",
+    _Unusable,
+    pd.offsets.BQuarterBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BQuarterEnd = make_final_proxy_type(
+    "BQuarterEnd",
+    _Unusable,
+    pd.offsets.BQuarterEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BusinessDay = make_final_proxy_type(
+    "BusinessDay",
+    _Unusable,
+    pd.offsets.BusinessDay,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BusinessHour = make_final_proxy_type(
+    "BusinessHour",
+    _Unusable,
+    pd.offsets.BusinessHour,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BusinessMonthBegin = make_final_proxy_type(
+    "BusinessMonthBegin",
+    _Unusable,
+    pd.offsets.BusinessMonthBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BusinessMonthEnd = make_final_proxy_type(
+    "BusinessMonthEnd",
+    _Unusable,
+    pd.offsets.BusinessMonthEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BYearBegin = make_final_proxy_type(
+    "BYearBegin",
+    _Unusable,
+    pd.offsets.BYearBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+BYearEnd = make_final_proxy_type(
+    "BYearEnd",
+    _Unusable,
+    pd.offsets.BYearEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CBMonthBegin = make_final_proxy_type(
+    "CBMonthBegin",
+    _Unusable,
+    pd.offsets.CBMonthBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CBMonthEnd = make_final_proxy_type(
+    "CBMonthEnd",
+    _Unusable,
+    pd.offsets.CBMonthEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CDay = make_final_proxy_type(
+    "CDay",
+    _Unusable,
+    pd.offsets.CDay,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CustomBusinessDay = make_final_proxy_type(
+    "CustomBusinessDay",
+    _Unusable,
+    pd.offsets.CustomBusinessDay,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CustomBusinessHour = make_final_proxy_type(
+    "CustomBusinessHour",
+    _Unusable,
+    pd.offsets.CustomBusinessHour,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CustomBusinessMonthBegin = make_final_proxy_type(
+    "CustomBusinessMonthBegin",
+    _Unusable,
+    pd.offsets.CustomBusinessMonthBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+CustomBusinessMonthEnd = make_final_proxy_type(
+    "CustomBusinessMonthEnd",
+    _Unusable,
+    pd.offsets.CustomBusinessMonthEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+DateOffset = make_final_proxy_type(
+    "DateOffset",
+    _Unusable,
+    pd.offsets.DateOffset,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Day = make_final_proxy_type(
+    "Day",
+    _Unusable,
+    pd.offsets.Day,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Easter = make_final_proxy_type(
+    "Easter",
+    _Unusable,
+    pd.offsets.Easter,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+FY5253Quarter = make_final_proxy_type(
+    "FY5253Quarter",
+    _Unusable,
+    pd.offsets.FY5253Quarter,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Hour = make_final_proxy_type(
+    "Hour",
+    _Unusable,
+    pd.offsets.Hour,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+LastWeekOfMonth = make_final_proxy_type(
+    "LastWeekOfMonth",
+    _Unusable,
+    pd.offsets.LastWeekOfMonth,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Micro = make_final_proxy_type(
+    "Micro",
+    _Unusable,
+    pd.offsets.Micro,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Milli = make_final_proxy_type(
+    "Milli",
+    _Unusable,
+    pd.offsets.Milli,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Minute = make_final_proxy_type(
+    "Minute",
+    _Unusable,
+    pd.offsets.Minute,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+MonthBegin = make_final_proxy_type(
+    "MonthBegin",
+    _Unusable,
+    pd.offsets.MonthBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+MonthEnd = make_final_proxy_type(
+    "MonthEnd",
+    _Unusable,
+    pd.offsets.MonthEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Nano = make_final_proxy_type(
+    "Nano",
+    _Unusable,
+    pd.offsets.Nano,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+QuarterBegin = make_final_proxy_type(
+    "QuarterBegin",
+    _Unusable,
+    pd.offsets.QuarterBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+QuarterEnd = make_final_proxy_type(
+    "QuarterEnd",
+    _Unusable,
+    pd.offsets.QuarterEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Second = make_final_proxy_type(
+    "Second",
+    _Unusable,
+    pd.offsets.Second,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+SemiMonthBegin = make_final_proxy_type(
+    "SemiMonthBegin",
+    _Unusable,
+    pd.offsets.SemiMonthBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+SemiMonthEnd = make_final_proxy_type(
+    "SemiMonthEnd",
+    _Unusable,
+    pd.offsets.SemiMonthEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Tick = make_final_proxy_type(
+    "Tick",
+    _Unusable,
+    pd.offsets.Tick,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Week = make_final_proxy_type(
+    "Week",
+    _Unusable,
+    pd.offsets.Week,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+WeekOfMonth = make_final_proxy_type(
+    "WeekOfMonth",
+    _Unusable,
+    pd.offsets.WeekOfMonth,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+YearBegin = make_final_proxy_type(
+    "YearBegin",
+    _Unusable,
+    pd.offsets.YearBegin,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+YearEnd = make_final_proxy_type(
+    "YearEnd",
+    _Unusable,
+    pd.offsets.YearEnd,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+Flags = make_final_proxy_type(
+    "Flags",
+    _Unusable,
+    pd.Flags,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+NamedAgg = make_final_proxy_type(
+    "NamedAgg",
+    _Unusable,
+    pd.NamedAgg,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+    additional_attributes={"__hash__": _FastSlowAttribute("__hash__")},
+)
+
+ArrowExtensionArray = make_final_proxy_type(
+    "ExtensionArray",
+    _Unusable,
+    pd.arrays.ArrowExtensionArray,
+    fast_to_slow=_Unusable(),
+    slow_to_fast=_Unusable(),
+)
+
+
+# The following are subclasses of `pandas.core.base.PandasObj`,
+# excluding subclasses defined in `pandas.core.internals`.  These are
+# not strictly part of the Pandas public API, but they do appear as
+# return types.
+
+_PANDAS_OBJ_FINAL_TYPES = [
+    pd.core.arrays.sparse.array.SparseArray,
+    pd.core.indexes.frozen.FrozenList,
+    pd.core.indexes.category.CategoricalIndex,
+    pd.core.indexes.datetimelike.DatetimeTimedeltaMixin,
+    pd.core.indexes.datetimelike.DatetimeIndexOpsMixin,
+    pd.core.indexes.extension.NDArrayBackedExtensionIndex,
+    pd.core.indexes.numeric.IntegerIndex,
+    pd.core.indexes.numeric.NumericIndex,
+    pd.core.generic.NDFrame,
+    pd.core.indexes.accessors.PeriodProperties,
+    pd.core.indexes.accessors.Properties,
+    pd.plotting._core.PlotAccessor,
+    pd.io.sql.SQLiteTable,
+    pd.io.sql.SQLTable,
+    pd.io.sql.SQLDatabase,
+    pd.io.sql.SQLiteDatabase,
+    pd.io.sql.PandasSQL,
+]
+
+_PANDAS_OBJ_INTERMEDIATE_TYPES = [
+    pd.core.groupby.groupby.GroupByPlot,
+    pd.core.groupby.groupby.GroupBy,
+    pd.core.groupby.groupby.BaseGroupBy,
+]
+
+for typ in _PANDAS_OBJ_FINAL_TYPES:
+    if typ.__name__ in globals():
+        # if we already defined a proxy type
+        # corresponding to this type, use that.
+        continue
+    globals()[typ.__name__] = make_final_proxy_type(
+        typ.__name__,
+        _Unusable,
+        typ,
+        fast_to_slow=_Unusable(),
+        slow_to_fast=_Unusable(),
+        additional_attributes={
+            "__array__": array_method,
+            "__array_function__": array_function_method,
+            "__array_ufunc__": _FastSlowAttribute("__array_ufunc__"),
+            "__hash__": _FastSlowAttribute("__hash__"),
+        },
+    )
+
+
+for typ in _PANDAS_OBJ_INTERMEDIATE_TYPES:
+    if typ.__name__ in globals():
+        # if we already defined a proxy type
+        # corresponding to this type, use that.
+        continue
+    globals()[typ.__name__] = make_intermediate_proxy_type(
+        typ.__name__,
+        _Unusable,
+        typ,
+    )
diff --git a/python/cudf/cudf/pandas/annotation.py b/python/cudf/cudf/pandas/annotation.py
new file mode 100644
index 0000000..30e2f3c
--- /dev/null
+++ b/python/cudf/cudf/pandas/annotation.py
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+try:
+    import nvtx
+except ImportError:
+
+    class nvtx:  # type: ignore
+        """Noop-stub with the same API as nvtx."""
+
+        push_range = lambda *args, **kwargs: None  # noqa: E731
+        pop_range = lambda *args, **kwargs: None  # noqa: E731
+
+        class annotate:
+            """No-op annotation/context-manager"""
+
+            def __init__(
+                self,
+                message: str | None = None,
+                color: str | None = None,
+                domain: str | None = None,
+                category: str | int | None = None,
+            ):
+                pass
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, *exc):
+                return False
+
+            __call__ = lambda self, fn: fn  # noqa: E731
diff --git a/python/cudf/cudf/pandas/fast_slow_proxy.py b/python/cudf/cudf/pandas/fast_slow_proxy.py
new file mode 100644
index 0000000..3dc6a59
--- /dev/null
+++ b/python/cudf/cudf/pandas/fast_slow_proxy.py
@@ -0,0 +1,1121 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import functools
+import inspect
+import operator
+import pickle
+import types
+from collections.abc import Iterator
+from enum import IntEnum
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Literal,
+    Mapping,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+)
+
+from .annotation import nvtx
+
+_CUDF_PANDAS_NVTX_COLORS = {
+    "COPY_SLOW_TO_FAST": 0xCA0020,
+    "COPY_FAST_TO_SLOW": 0xF4A582,
+    "EXECUTE_FAST": 0x92C5DE,
+    "EXECUTE_SLOW": 0x0571B0,
+}
+
+
+_WRAPPER_ASSIGNMENTS = tuple(
+    attr
+    for attr in functools.WRAPPER_ASSIGNMENTS
+    # Skip __doc__ because we assign it on class creation using exec_body
+    # callable that updates the namespace of the class.
+    # Skip __annotations__ because there are differences between Python
+    # versions on how it is initialized for a class that doesn't explicitly
+    # define it and we don't want to force eager evaluation of anything that
+    # would normally be lazy (mostly for consistency, shouldn't cause any
+    # significant issues).
+    if attr not in ("__annotations__", "__doc__")
+)
+
+
+def callers_module_name():
+    # Call f_back twice since this function adds an extra frame
+    return inspect.currentframe().f_back.f_back.f_globals["__name__"]
+
+
+class _State(IntEnum):
+    """Simple enum to track the type of wrapped object of a final proxy"""
+
+    SLOW = 0
+    FAST = 1
+
+
+class _Unusable:
+    """
+    A totally unusable type. When a "fast" object is not available,
+    it's useful to set it to _Unusable() so that any operations
+    on it fail, and ensure fallback to the corresponding
+    "slow" object.
+    """
+
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        raise NotImplementedError(
+            "Fast implementation not available. "
+            "Falling back to the slow implementation"
+        )
+
+    def __getattribute__(self, name: str) -> Any:
+        if name in {"__class__"}:  # needed for type introspection
+            return super().__getattribute__(name)
+        raise TypeError("Unusable type. Falling back to the slow object")
+
+
+class _PickleConstructor:
+    """A pickleable object to support construction in __reduce__.
+
+    This object is used to avoid having unpickling call __init__ on the
+    objects, instead only invoking __new__. __init__ may have required
+    arguments or otherwise perform invalid initialization that we could skip
+    altogether since we're going to overwrite the wrapped object.
+    """
+
+    def __init__(self, type_):
+        self._type = type_
+
+    def __call__(self):
+        return object.__new__(self._type)
+
+
+_DELETE = object()
+
+
+def make_final_proxy_type(
+    name: str,
+    fast_type: type,
+    slow_type: type,
+    *,
+    fast_to_slow: Callable,
+    slow_to_fast: Callable,
+    module: Optional[str] = None,
+    additional_attributes: Mapping[str, Any] | None = None,
+    postprocess: Callable[[_FinalProxy, Any, Any], Any] | None = None,
+    bases: Tuple = (),
+) -> Type[_FinalProxy]:
+    """
+    Defines a fast-slow proxy type for a pair of "final" fast and slow
+    types. Final types are types for which known operations exist for
+    converting an object of "fast" type to "slow" and vice-versa.
+
+    Parameters
+    ----------
+    name: str
+        The name of the class returned
+    fast_type: type
+    slow_type: type
+    fast_to_slow: callable
+        Function that accepts a single argument of type `fast_type`
+        and returns an object of type `slow_type`
+    slow_to_fast: callable
+        Function that accepts a single argument of type `slow_type`
+        and returns an object of type `fast_type`
+    additional_attributes
+        Mapping of additional attributes to add to the class
+       (optional), these will override any defaulted attributes (e.g.
+       ``__init__`). If you want to remove a defaulted attribute
+       completely, pass the special sentinel ``_DELETE`` as a value.
+    postprocess
+        Optional function called to allow the proxy to postprocess
+        itself when being wrapped up, called with the proxy object,
+        the unwrapped result object, and the function that was used to
+        construct said unwrapped object. See also `_maybe_wrap_result`.
+    bases
+        Optional tuple of base classes to insert into the mro.
+
+    Notes
+    -----
+    As a side-effect, this function adds `fast_type` and `slow_type`
+    to a global mapping of final types to their corresponding proxy
+    types, accessible via `get_final_type_map()`.
+    """
+
+    def __init__(self, *args, **kwargs):
+        _fast_slow_function_call(
+            lambda cls, args, kwargs: setattr(
+                self, "_fsproxy_wrapped", cls(*args, **kwargs)
+            ),
+            type(self),
+            args,
+            kwargs,
+        )
+
+    @nvtx.annotate(
+        "COPY_SLOW_TO_FAST",
+        color=_CUDF_PANDAS_NVTX_COLORS["COPY_SLOW_TO_FAST"],
+        domain="cudf_pandas",
+    )
+    def _fsproxy_slow_to_fast(self):
+        # if we are wrapping a slow object,
+        # convert it to a fast one
+        if self._fsproxy_state is _State.SLOW:
+            return slow_to_fast(self._fsproxy_wrapped)
+        return self._fsproxy_wrapped
+
+    @nvtx.annotate(
+        "COPY_FAST_TO_SLOW",
+        color=_CUDF_PANDAS_NVTX_COLORS["COPY_FAST_TO_SLOW"],
+        domain="cudf_pandas",
+    )
+    def _fsproxy_fast_to_slow(self):
+        # if we are wrapping a fast object,
+        # convert it to a slow one
+        if self._fsproxy_state is _State.FAST:
+            return fast_to_slow(self._fsproxy_wrapped)
+        return self._fsproxy_wrapped
+
+    @property  # type: ignore
+    def _fsproxy_state(self) -> _State:
+        return (
+            _State.FAST
+            if isinstance(self._fsproxy_wrapped, self._fsproxy_fast_type)
+            else _State.SLOW
+        )
+
+    def __reduce__(self):
+        # Need a local import to avoid circular import issues
+        from .module_accelerator import disable_module_accelerator
+
+        with disable_module_accelerator():
+            pickled_wrapped_obj = pickle.dumps(self._fsproxy_wrapped)
+        return (_PickleConstructor(type(self)), (), pickled_wrapped_obj)
+
+    def __setstate__(self, state):
+        # Need a local import to avoid circular import issues
+        from .module_accelerator import disable_module_accelerator
+
+        with disable_module_accelerator():
+            unpickled_wrapped_obj = pickle.loads(state)
+        self._fsproxy_wrapped = unpickled_wrapped_obj
+
+    slow_dir = dir(slow_type)
+    cls_dict = {
+        "__init__": __init__,
+        "__doc__": inspect.getdoc(slow_type),
+        "_fsproxy_slow_dir": slow_dir,
+        "_fsproxy_fast_type": fast_type,
+        "_fsproxy_slow_type": slow_type,
+        "_fsproxy_slow_to_fast": _fsproxy_slow_to_fast,
+        "_fsproxy_fast_to_slow": _fsproxy_fast_to_slow,
+        "_fsproxy_state": _fsproxy_state,
+        "__reduce__": __reduce__,
+        "__setstate__": __setstate__,
+    }
+    if additional_attributes is None:
+        additional_attributes = {}
+    for method in _SPECIAL_METHODS:
+        if getattr(slow_type, method, False):
+            cls_dict[method] = _FastSlowAttribute(method)
+    for k, v in additional_attributes.items():
+        if v is _DELETE and k in cls_dict:
+            del cls_dict[k]
+        elif v is not _DELETE:
+            cls_dict[k] = v
+
+    cls = types.new_class(
+        name,
+        (*bases, _FinalProxy),
+        {"metaclass": _FastSlowProxyMeta},
+        lambda ns: ns.update(cls_dict),
+    )
+    functools.update_wrapper(
+        cls,
+        slow_type,
+        assigned=_WRAPPER_ASSIGNMENTS,
+        updated=(),
+    )
+    cls.__module__ = module if module is not None else callers_module_name()
+
+    final_type_map = get_final_type_map()
+    if fast_type is not _Unusable:
+        final_type_map[fast_type] = cls
+    final_type_map[slow_type] = cls
+
+    return cls
+
+
+def make_intermediate_proxy_type(
+    name: str,
+    fast_type: type,
+    slow_type: type,
+    *,
+    module: Optional[str] = None,
+) -> Type[_IntermediateProxy]:
+    """
+    Defines a proxy type for a pair of "intermediate" fast and slow
+    types. Intermediate types are the types of the results of
+    operations invoked on final types.
+
+    As a side-effect, this function adds `fast_type` and `slow_type`
+    to a global mapping of intermediate types to their corresponding
+    proxy types, accessible via `get_intermediate_type_map()`.
+
+    Parameters
+    ----------
+    name: str
+        The name of the class returned
+    fast_type: type
+    slow_type: type
+    """
+
+    def __init__(self, *args, **kwargs):
+        # disallow __init__. An intermediate proxy type can only be
+        # instantiated from (possibly chained) operations on a final
+        # proxy type.
+        raise TypeError(
+            f"Cannot directly instantiate object of type {type(self)}"
+        )
+
+    @property  # type: ignore
+    def _fsproxy_state(self):
+        return (
+            _State.FAST
+            if isinstance(self._fsproxy_wrapped, self._fsproxy_fast_type)
+            else _State.SLOW
+        )
+
+    @nvtx.annotate(
+        "COPY_SLOW_TO_FAST",
+        color=_CUDF_PANDAS_NVTX_COLORS["COPY_SLOW_TO_FAST"],
+        domain="cudf_pandas",
+    )
+    def _fsproxy_slow_to_fast(self):
+        if self._fsproxy_state is _State.SLOW:
+            return super(type(self), self)._fsproxy_slow_to_fast()
+        return self._fsproxy_wrapped
+
+    @nvtx.annotate(
+        "COPY_FAST_TO_SLOW",
+        color=_CUDF_PANDAS_NVTX_COLORS["COPY_FAST_TO_SLOW"],
+        domain="cudf_pandas",
+    )
+    def _fsproxy_fast_to_slow(self):
+        if self._fsproxy_state is _State.FAST:
+            return super(type(self), self)._fsproxy_fast_to_slow()
+        return self._fsproxy_wrapped
+
+    slow_dir = dir(slow_type)
+    cls_dict = {
+        "__init__": __init__,
+        "__doc__": inspect.getdoc(slow_type),
+        "_fsproxy_slow_dir": slow_dir,
+        "_fsproxy_fast_type": fast_type,
+        "_fsproxy_slow_type": slow_type,
+        "_fsproxy_slow_to_fast": _fsproxy_slow_to_fast,
+        "_fsproxy_fast_to_slow": _fsproxy_fast_to_slow,
+        "_fsproxy_state": _fsproxy_state,
+    }
+
+    for method in _SPECIAL_METHODS:
+        if getattr(slow_type, method, False):
+            cls_dict[method] = _FastSlowAttribute(method)
+
+    cls = types.new_class(
+        name,
+        (_IntermediateProxy,),
+        {"metaclass": _FastSlowProxyMeta},
+        lambda ns: ns.update(cls_dict),
+    )
+    functools.update_wrapper(
+        cls,
+        slow_type,
+        assigned=_WRAPPER_ASSIGNMENTS,
+        updated=(),
+    )
+    cls.__module__ = module if module is not None else callers_module_name()
+
+    intermediate_type_map = get_intermediate_type_map()
+    if fast_type is not _Unusable:
+        intermediate_type_map[fast_type] = cls
+    intermediate_type_map[slow_type] = cls
+
+    return cls
+
+
+def register_proxy_func(slow_func: Callable):
+    """
+    Decorator to register custom function as a proxy for slow_func.
+
+    Parameters
+    ----------
+    slow_func: Callable
+        The function to register a wrapper for.
+
+    Returns
+    -------
+    Callable
+    """
+
+    def wrapper(func):
+        registered_functions = get_registered_functions()
+        registered_functions[slow_func] = func
+        functools.update_wrapper(func, slow_func)
+        return func
+
+    return wrapper
+
+
+@functools.lru_cache(maxsize=None)
+def get_final_type_map():
+    """
+    Return the mapping of all known fast and slow final types to their
+    corresponding proxy types.
+    """
+    return dict()
+
+
+@functools.lru_cache(maxsize=None)
+def get_intermediate_type_map():
+    """
+    Return a mapping of all known fast and slow intermediate types to their
+    corresponding proxy types.
+    """
+    return dict()
+
+
+@functools.lru_cache(maxsize=None)
+def get_registered_functions():
+    return dict()
+
+
+def _raise_attribute_error(obj, name):
+    """
+    Raise an AttributeError with a message that is consistent with
+    the error raised by Python for a non-existent attribute on a
+    proxy object.
+    """
+    raise AttributeError(f"'{obj}' object has no attribute '{name}'")
+
+
+class _FastSlowAttribute:
+    """
+    A descriptor type used to define attributes of fast-slow proxies.
+    """
+
+    def __init__(self, name: str):
+        self._name = name
+
+    def __get__(self, obj, owner=None) -> Any:
+        if obj is None:
+            # class attribute
+            obj = owner
+
+        if not (
+            isinstance(obj, _FastSlowProxy)
+            or issubclass(type(obj), _FastSlowProxyMeta)
+        ):
+            # we only want to look up attributes on the underlying
+            # fast/slow objects for instances of _FastSlowProxy or
+            # subtypes of _FastSlowProxyMeta:
+            _raise_attribute_error(owner if owner else obj, self._name)
+
+        result, _ = _fast_slow_function_call(getattr, obj, self._name)
+
+        if isinstance(result, functools.cached_property):
+            # TODO: temporary workaround until dask is able
+            # to correctly inspect cached_property objects.
+            # GH: 264
+            result = property(result.func)
+
+        if isinstance(result, (_MethodProxy, property)):
+            from .module_accelerator import disable_module_accelerator
+
+            type_ = owner if owner else type(obj)
+            slow_result_type = getattr(type_._fsproxy_slow, self._name)
+            with disable_module_accelerator():
+                result.__doc__ = inspect.getdoc(  # type: ignore
+                    slow_result_type
+                )
+
+            if isinstance(result, _MethodProxy):
+                # Note that this will produce the wrong result for bound
+                # methods because dir for the method won't be the same as for
+                # the pure unbound function, but the alternative is
+                # materializing the slow object when we don't really want to.
+                result._fsproxy_slow_dir = dir(
+                    slow_result_type
+                )  # type: ignore
+
+        return result
+
+
+class _FastSlowProxyMeta(type):
+    """
+    Metaclass used to dynamically find class attributes and
+    classmethods of fast-slow proxy types.
+    """
+
+    @property
+    def _fsproxy_slow(self) -> type:
+        return self._fsproxy_slow_type
+
+    @property
+    def _fsproxy_fast(self) -> type:
+        return self._fsproxy_fast_type
+
+    def __dir__(self):
+        # Try to return the cached dir of the slow object, but if it
+        # doesn't exist, fall back to the default implementation.
+        try:
+            return self._fsproxy_slow_dir
+        except AttributeError:
+            return type.__dir__(self)
+
+    def __getattr__(self, name: str) -> Any:
+        if name.startswith("_fsproxy") or name.startswith("__"):
+            # an AttributeError was raised when trying to evaluate
+            # an internal attribute, we just need to propagate this
+            _raise_attribute_error(self.__class__.__name__, name)
+
+        attr = _FastSlowAttribute(name)
+        return attr.__get__(None, owner=self)
+
+    def __subclasscheck__(self, __subclass: type) -> bool:
+        if super().__subclasscheck__(__subclass):
+            return True
+        if hasattr(__subclass, "_fsproxy_slow"):
+            return issubclass(__subclass._fsproxy_slow, self._fsproxy_slow)
+        return False
+
+    def __instancecheck__(self, __instance: Any) -> bool:
+        if super().__instancecheck__(__instance):
+            return True
+        elif hasattr(type(__instance), "_fsproxy_slow"):
+            return issubclass(type(__instance), self)
+        return False
+
+
+class _FastSlowProxy:
+    """
+    Base class for all fast=slow proxy types.
+
+    A fast-slow proxy is proxy for a pair of types that provide "fast"
+    and "slow" implementations of the same API.  At any time, a
+    fast-slow proxy wraps an object of either "fast" type, or "slow"
+    type. Operations invoked on the fast-slow proxy are first
+    delegated to the "fast" type, and if that fails, to the "slow"
+    type.
+    """
+
+    _fsproxy_wrapped: Any
+
+    def _fsproxy_fast_to_slow(self) -> Any:
+        """
+        If the wrapped object is of "fast" type, returns the
+        corresponding "slow" object. Otherwise, returns the wrapped
+        object as-is.
+        """
+        raise NotImplementedError("Abstract base class")
+
+    def _fsproxy_slow_to_fast(self) -> Any:
+        """
+        If the wrapped object is of "slow" type, returns the
+        corresponding "fast" object. Otherwise, returns the wrapped
+        object as-is.
+        """
+        raise NotImplementedError("Abstract base class")
+
+    @property
+    def _fsproxy_fast(self) -> Any:
+        """
+        Returns the wrapped object. If the wrapped object is of "slow"
+        type, replaces it with the corresponding "fast" object before
+        returning it.
+        """
+        self._fsproxy_wrapped = self._fsproxy_slow_to_fast()
+        return self._fsproxy_wrapped
+
+    @property
+    def _fsproxy_slow(self) -> Any:
+        """
+        Returns the wrapped object. If the wrapped object is of "fast"
+        type, replaces it with the corresponding "slow" object before
+        returning it.
+        """
+        self._fsproxy_wrapped = self._fsproxy_fast_to_slow()
+        return self._fsproxy_wrapped
+
+    def __dir__(self):
+        # Try to return the cached dir of the slow object, but if it
+        # doesn't exist, fall back to the default implementation.
+        try:
+            return self._fsproxy_slow_dir
+        except AttributeError:
+            return object.__dir__(self)
+
+    def __getattr__(self, name: str) -> Any:
+        if name.startswith("_fsproxy"):
+            # an AttributeError was raised when trying to evaluate
+            # an internal attribute, we just need to propagate this
+            _raise_attribute_error(self.__class__.__name__, name)
+        if name in {
+            "_ipython_canary_method_should_not_exist_",
+            "_ipython_display_",
+            "_repr_mimebundle_",
+            # Workaround for https://github.com/numpy/numpy/issues/5350
+            # see GH:216 for details
+            "__array_struct__",
+        }:
+            # IPython always looks for these names in its display
+            # logic. See #GH:70 and #GH:172 for more details but the
+            # gist is that not raising an AttributeError immediately
+            # results in slow display in IPython (since the fast
+            # object will be copied to the slow one to look for
+            # attributes there which then also won't exist).
+            # This is somewhat delicate to the order in which IPython
+            # implements special display fallbacks.
+            _raise_attribute_error(self.__class__.__name__, name)
+        if name.startswith("_"):
+            # private attributes always come from `._fsproxy_slow`:
+            return getattr(self._fsproxy_slow, name)
+        attr = _FastSlowAttribute(name)
+        return attr.__get__(self)
+
+    def __setattr__(self, name, value):
+        if name.startswith("_"):
+            object.__setattr__(self, name, value)
+            return
+        return _FastSlowAttribute("__setattr__").__get__(self)(name, value)
+
+    def __add__(self, other):
+        return _fast_slow_function_call(operator.add, self, other)[0]
+
+    def __radd__(self, other):
+        return _fast_slow_function_call(operator.add, other, self)[0]
+
+    def __sub__(self, other):
+        return _fast_slow_function_call(operator.sub, self, other)[0]
+
+    def __rsub__(self, other):
+        return _fast_slow_function_call(operator.sub, other, self)[0]
+
+    def __mul__(self, other):
+        return _fast_slow_function_call(operator.mul, self, other)[0]
+
+    def __rmul__(self, other):
+        return _fast_slow_function_call(operator.mul, other, self)[0]
+
+    def __truediv__(self, other):
+        return _fast_slow_function_call(operator.truediv, self, other)[0]
+
+    def __rtruediv__(self, other):
+        return _fast_slow_function_call(operator.truediv, other, self)[0]
+
+    def __floordiv__(self, other):
+        return _fast_slow_function_call(operator.floordiv, self, other)[0]
+
+    def __rfloordiv__(self, other):
+        return _fast_slow_function_call(operator.floordiv, other, self)[0]
+
+    def __mod__(self, other):
+        return _fast_slow_function_call(operator.mod, self, other)[0]
+
+    def __rmod__(self, other):
+        return _fast_slow_function_call(operator.mod, other, self)[0]
+
+    def __divmod__(self, other):
+        return _fast_slow_function_call(divmod, self, other)[0]
+
+    def __rdivmod__(self, other):
+        return _fast_slow_function_call(divmod, other, self)[0]
+
+    def __pow__(self, other):
+        return _fast_slow_function_call(operator.pow, self, other)[0]
+
+    def __rpow__(self, other):
+        return _fast_slow_function_call(operator.pow, other, self)[0]
+
+    def __lshift__(self, other):
+        return _fast_slow_function_call(operator.lshift, self, other)[0]
+
+    def __rlshift__(self, other):
+        return _fast_slow_function_call(operator.lshift, other, self)[0]
+
+    def __rshift__(self, other):
+        return _fast_slow_function_call(operator.rshift, self, other)[0]
+
+    def __rrshift__(self, other):
+        return _fast_slow_function_call(operator.rshift, other, self)[0]
+
+    def __and__(self, other):
+        return _fast_slow_function_call(operator.and_, self, other)[0]
+
+    def __rand__(self, other):
+        return _fast_slow_function_call(operator.and_, other, self)[0]
+
+    def __xor__(self, other):
+        return _fast_slow_function_call(operator.xor, self, other)[0]
+
+    def __rxor__(self, other):
+        return _fast_slow_function_call(operator.xor, other, self)[0]
+
+    def __or__(self, other):
+        return _fast_slow_function_call(operator.or_, self, other)[0]
+
+    def __ror__(self, other):
+        return _fast_slow_function_call(operator.or_, other, self)[0]
+
+    def __matmul__(self, other):
+        return _fast_slow_function_call(operator.matmul, self, other)[0]
+
+    def __rmatmul__(self, other):
+        return _fast_slow_function_call(operator.matmul, other, self)[0]
+
+
+class _FinalProxy(_FastSlowProxy):
+    """
+    Proxy type for a pair of fast and slow "final" types for which
+    there is a known conversion from fast to slow, and vice-versa.
+    The conversion between fast and slow types is done using
+    user-provided conversion functions.
+
+    Do not attempt to use this class directly. Instead, use
+    `make_final_proxy_type` to create subtypes.
+    """
+
+    @classmethod
+    def _fsproxy_wrap(cls, value, func):
+        """Default mechanism to wrap a value in a proxy type
+
+        Parameters
+        ----------
+        cls
+            The proxy type
+        value
+            The value to wrap up
+        func
+            The function called that constructed value
+
+        Returns
+        -------
+        A new proxied object
+
+        Notes
+        -----
+        _FinalProxy subclasses can override this classmethod if they
+        need particular behaviour when wrapped up.
+        """
+        proxy = object.__new__(cls)
+        proxy._fsproxy_wrapped = value
+        return proxy
+
+
+class _IntermediateProxy(_FastSlowProxy):
+    """
+    Proxy type for a pair of "intermediate" types that appear as
+    intermediate values when invoking operations on "final" types.
+    The conversion between fast and slow types is done by keeping
+    track of the sequence of operations that created the wrapped
+    object, and "playing back" that sequence starting from the "slow"
+    version of the originating _FinalProxy.
+
+    Do not attempt to use this class directly. Instead, use
+    `make_intermediate_proxy_type` to create subtypes.
+    """
+
+    _method_chain: Tuple[Callable, Tuple, Dict]
+
+    @classmethod
+    def _fsproxy_wrap(
+        cls,
+        obj: Any,
+        method_chain: Tuple[Callable, Tuple, Dict],
+    ):
+        """
+        Parameters
+        ----------
+        obj: The object to wrap
+        method_chain: A tuple of the form (func, args, kwargs) where
+            `func` is the function that was called to create `obj`,
+            and `args` and `kwargs` are the arguments that were passed
+            to `func`.
+        """
+        proxy = object.__new__(cls)
+        proxy._fsproxy_wrapped = obj
+        proxy._method_chain = method_chain
+        return proxy
+
+    @nvtx.annotate(
+        "COPY_SLOW_TO_FAST",
+        color=_CUDF_PANDAS_NVTX_COLORS["COPY_SLOW_TO_FAST"],
+        domain="cudf_pandas",
+    )
+    def _fsproxy_slow_to_fast(self) -> Any:
+        func, args, kwargs = self._method_chain
+        args, kwargs = _fast_arg(args), _fast_arg(kwargs)
+        return func(*args, **kwargs)
+
+    @nvtx.annotate(
+        "COPY_FAST_TO_SLOW",
+        color=_CUDF_PANDAS_NVTX_COLORS["COPY_FAST_TO_SLOW"],
+        domain="cudf_pandas",
+    )
+    def _fsproxy_fast_to_slow(self) -> Any:
+        func, args, kwargs = self._method_chain
+        args, kwargs = _slow_arg(args), _slow_arg(kwargs)
+        return func(*args, **kwargs)
+
+
+class _CallableProxyMixin:
+    """
+    Mixin class that implements __call__ for fast-slow proxies.
+    """
+
+    # For wrapped callables isinstance(self, FunctionType) should return True
+    __class__ = types.FunctionType  # type: ignore
+
+    def __call__(self, *args, **kwargs) -> Any:
+        result, _ = _fast_slow_function_call(
+            # We cannot directly call self here because we need it to be
+            # converted into either the fast or slow object (by
+            # _fast_slow_function_call) to avoid infinite recursion.
+            # TODO: When Python 3.11 is the minimum supported Python version
+            # this can use operator.call
+            lambda fn, args, kwargs: fn(*args, **kwargs),
+            self,
+            args,
+            kwargs,
+        )
+        return result
+
+
+class _FunctionProxy(_CallableProxyMixin):
+    """
+    Proxy for a pair of fast and slow functions.
+    """
+
+    __name__: str
+
+    def __init__(self, fast: Callable | _Unusable, slow: Callable):
+        self._fsproxy_fast = fast
+        self._fsproxy_slow = slow
+        functools.update_wrapper(self, slow)
+
+
+class _MethodProxy(_CallableProxyMixin, _IntermediateProxy):
+    """
+    Methods of fast-slow proxies are of type _MethodProxy.
+    """
+
+
+def _fast_slow_function_call(func: Callable, /, *args, **kwargs) -> Any:
+    """
+    Call `func` with all `args` and `kwargs` converted to their
+    respective fast type. If that fails, call `func` with all
+    `args` and `kwargs` converted to their slow type.
+
+    Wrap the result in a fast-slow proxy if it is a type we know how
+    to wrap.
+    """
+    from .module_accelerator import disable_module_accelerator
+
+    fast = False
+    try:
+        with nvtx.annotate(
+            "EXECUTE_FAST",
+            color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_FAST"],
+            domain="cudf_pandas",
+        ):
+            fast_args, fast_kwargs = _fast_arg(args), _fast_arg(kwargs)
+            result = func(*fast_args, **fast_kwargs)
+            if result is NotImplemented:
+                # try slow path
+                raise Exception()
+            fast = True
+    except Exception:
+        with nvtx.annotate(
+            "EXECUTE_SLOW",
+            color=_CUDF_PANDAS_NVTX_COLORS["EXECUTE_SLOW"],
+            domain="cudf_pandas",
+        ):
+            slow_args, slow_kwargs = _slow_arg(args), _slow_arg(kwargs)
+            with disable_module_accelerator():
+                result = func(*slow_args, **slow_kwargs)
+    return _maybe_wrap_result(result, func, *args, **kwargs), fast
+
+
+def _transform_arg(
+    arg: Any,
+    attribute_name: Literal["_fsproxy_slow", "_fsproxy_fast"],
+    seen: Set[int],
+) -> Any:
+    """
+    Transform "arg" into its corresponding slow (or fast) type.
+    """
+    import numpy as np
+
+    if isinstance(arg, (_FastSlowProxy, _FastSlowProxyMeta, _FunctionProxy)):
+        typ = getattr(arg, attribute_name)
+        if typ is _Unusable:
+            raise Exception("Cannot transform _Unusable")
+        return typ
+    elif isinstance(arg, types.ModuleType) and attribute_name in arg.__dict__:
+        return arg.__dict__[attribute_name]
+    elif isinstance(arg, list):
+        return type(arg)(_transform_arg(a, attribute_name, seen) for a in arg)
+    elif isinstance(arg, tuple):
+        # This attempts to handle arbitrary subclasses of tuple by
+        # assuming that if you've subclassed tuple with some special
+        # behaviour you'll also make the object pickleable by
+        # implementing the custom pickle protocol interface (either
+        # __getnewargs_ex__ or __getnewargs__). Perhaps this should
+        # use __reduce_ex__ instead...
+        if type(arg) is tuple:
+            # Must come first to avoid infinite recursion
+            return tuple(_transform_arg(a, attribute_name, seen) for a in arg)
+        elif hasattr(arg, "__getnewargs_ex__"):
+            # Partial implementation of to reconstruct with
+            # transformed pieces
+            # This handles scipy._lib._bunch._make_tuple_bunch
+            args, kwargs = (
+                _transform_arg(a, attribute_name, seen)
+                for a in arg.__getnewargs_ex__()
+            )
+            obj = type(arg).__new__(type(arg), *args, **kwargs)
+            if hasattr(obj, "__setstate__"):
+                raise NotImplementedError(
+                    "Transforming tuple-like with __getnewargs_ex__ and "
+                    "__setstate__ not implemented"
+                )
+            if not hasattr(obj, "__dict__") and kwargs:
+                raise NotImplementedError(
+                    "Transforming tuple-like with kwargs from "
+                    "__getnewargs_ex__ and no __dict__ not implemented"
+                )
+            obj.__dict__.update(kwargs)
+            return obj
+        elif hasattr(arg, "__getnewargs__"):
+            # This handles namedtuple, and would catch tuple if we
+            # didn't handle it above.
+            args = _transform_arg(arg.__getnewargs__(), attribute_name, seen)
+            return type(arg).__new__(type(arg), *args)
+        else:
+            # Hope we can just call the constructor with transformed entries.
+            return type(arg)(
+                _transform_arg(a, attribute_name, seen) for a in args
+            )
+    elif isinstance(arg, dict):
+        return {
+            _transform_arg(k, attribute_name, seen): _transform_arg(
+                a, attribute_name, seen
+            )
+            for k, a in arg.items()
+        }
+    elif isinstance(arg, np.ndarray) and arg.dtype == "O":
+        transformed = [
+            _transform_arg(a, attribute_name, seen) for a in arg.flat
+        ]
+        # Keep the same memory layout as arg (the default is C_CONTIGUOUS)
+        if arg.flags["F_CONTIGUOUS"] and not arg.flags["C_CONTIGUOUS"]:
+            order = "F"
+        else:
+            order = "C"
+        result = np.empty(int(np.prod(arg.shape)), dtype=object, order=order)
+        result[...] = transformed
+        return result.reshape(arg.shape)
+    elif isinstance(arg, Iterator) and attribute_name == "_fsproxy_fast":
+        # this may include consumable objects like generators or
+        # IOBase objects, which we don't want unavailable to the slow
+        # path in case of fallback. So, we raise here and ensure the
+        # slow path is taken:
+        raise Exception()
+    elif isinstance(arg, types.FunctionType):
+        if id(arg) in seen:
+            # `arg` is mutually recursive with another function.  We
+            # can't handle these cases yet:
+            return arg
+        seen.add(id(arg))
+        return _replace_closurevars(arg, attribute_name, seen)
+    else:
+        return arg
+
+
+def _fast_arg(arg: Any) -> Any:
+    """
+    Transform "arg" into its corresponding fast type.
+    """
+    seen: Set[int] = set()
+    return _transform_arg(arg, "_fsproxy_fast", seen)
+
+
+def _slow_arg(arg: Any) -> Any:
+    """
+    Transform "arg" into its corresponding slow type.
+    """
+    seen: Set[int] = set()
+    return _transform_arg(arg, "_fsproxy_slow", seen)
+
+
+def _maybe_wrap_result(result: Any, func: Callable, /, *args, **kwargs) -> Any:
+    """
+    Wraps "result" in a fast-slow proxy if is a "proxiable" object.
+    """
+    if _is_final_type(result):
+        typ = get_final_type_map()[type(result)]
+        return typ._fsproxy_wrap(result, func)
+    elif _is_intermediate_type(result):
+        typ = get_intermediate_type_map()[type(result)]
+        return typ._fsproxy_wrap(result, method_chain=(func, args, kwargs))
+    elif _is_final_class(result):
+        return get_final_type_map()[result]
+    elif isinstance(result, list):
+        return type(result)(
+            [
+                _maybe_wrap_result(r, operator.getitem, result, i)
+                for i, r in enumerate(result)
+            ]
+        )
+    elif isinstance(result, tuple):
+        wrapped = (
+            _maybe_wrap_result(r, operator.getitem, result, i)
+            for i, r in enumerate(result)
+        )
+        if hasattr(result, "_make"):
+            # namedtuple
+            return type(result)._make(wrapped)
+        else:
+            return type(result)(wrapped)
+    elif isinstance(result, Iterator):
+        return (_maybe_wrap_result(r, lambda x: x, r) for r in result)
+    elif _is_function_or_method(result):
+        return _MethodProxy._fsproxy_wrap(
+            result, method_chain=(func, args, kwargs)
+        )
+    else:
+        return result
+
+
+def _is_final_type(result: Any) -> bool:
+    return type(result) in get_final_type_map()
+
+
+def _is_final_class(result: Any) -> bool:
+    if not isinstance(result, type):
+        return False
+    return result in get_final_type_map()
+
+
+def _is_intermediate_type(result: Any) -> bool:
+    return type(result) in get_intermediate_type_map()
+
+
+def _is_function_or_method(obj: Any) -> bool:
+    return isinstance(
+        obj,
+        (
+            types.FunctionType,
+            types.BuiltinFunctionType,
+            types.MethodType,
+            types.WrapperDescriptorType,
+            types.MethodWrapperType,
+            types.MethodDescriptorType,
+            types.BuiltinMethodType,
+        ),
+    )
+
+
+def _replace_closurevars(
+    f: types.FunctionType,
+    attribute_name: Literal["_fsproxy_slow", "_fsproxy_fast"],
+    seen: Set[int],
+) -> types.FunctionType:
+    """
+    Return a copy of `f` with its closure variables replaced with
+    their corresponding slow (or fast) types.
+    """
+    if f.__closure__:
+        # GH #254: If empty cells are present - which can happen in
+        # situations like when `f` is a method that invokes the
+        # "empty" `super()` - the call to `getclosurevars` below will
+        # fail.  For now, we just return `f` in this case.  If needed,
+        # we can consider populating empty cells with a placeholder
+        # value to allow the call to `getclosurevars` to succeed.
+        if any(c == types.CellType() for c in f.__closure__):
+            return f
+
+    f_nonlocals, f_globals, f_builtins, _ = inspect.getclosurevars(f)
+
+    g_globals = _transform_arg(f_globals, attribute_name, seen)
+    g_nonlocals = _transform_arg(f_nonlocals, attribute_name, seen)
+
+    # if none of the globals/nonlocals were transformed, we
+    # can just return f:
+    if all(f_globals[k] is g_globals[k] for k in f_globals) and all(
+        g_nonlocals[k] is f_nonlocals[k] for k in f_nonlocals
+    ):
+        return f
+
+    g_closure = tuple(types.CellType(val) for val in g_nonlocals.values())
+    g_globals["__builtins__"] = f_builtins
+
+    g = types.FunctionType(
+        f.__code__,
+        g_globals,
+        name=f.__name__,
+        argdefs=f.__defaults__,
+        closure=g_closure,
+    )
+    g = functools.update_wrapper(
+        g,
+        f,
+        assigned=functools.WRAPPER_ASSIGNMENTS + ("__kwdefaults__",),
+    )
+    return g
+
+
+_SPECIAL_METHODS: Set[str] = {
+    "__repr__",
+    "__str__",
+    "__len__",
+    "__contains__",
+    "__getitem__",
+    "__setitem__",
+    "__delitem__",
+    "__getslice__",
+    "__setslice__",
+    "__delslice__",
+    "__iter__",
+    "__lt__",
+    "__le__",
+    "__eq__",
+    "__ne__",
+    "__gt__",
+    "__ge__",
+    "__pos__",
+    "__neg__",
+    "__invert__",
+    "__abs__",
+    "__round__",
+    "__format__",
+    "__bool__",
+    "__float__",
+    "__int__",
+    "__complex__",
+    "__enter__",
+    "__exit__",
+    "__next__",
+    "__copy__",
+    "__deepcopy__",
+    "__dataframe__",
+    "__call__",
+    # Added on a per-proxy basis
+    # https://github.com/rapidsai/xdf/pull/306#pullrequestreview-1636155428
+    # "__hash__",
+}
diff --git a/python/cudf/cudf/pandas/magics.py b/python/cudf/cudf/pandas/magics.py
new file mode 100644
index 0000000..1573682
--- /dev/null
+++ b/python/cudf/cudf/pandas/magics.py
@@ -0,0 +1,33 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+
+try:
+    from IPython.core.magic import Magics, cell_magic, magics_class
+
+    from .profiler import Profiler, lines_with_profiling
+
+    @magics_class
+    class CudfPandasMagics(Magics):
+        @cell_magic("cudf.pandas.profile")
+        def profile(self, _, cell):
+            with Profiler() as profiler:
+                get_ipython().run_cell(cell)  # noqa: F821
+            profiler.print_per_function_stats()
+
+        @cell_magic("cudf.pandas.line_profile")
+        def line_profile(self, _, cell):
+            new_cell = lines_with_profiling(cell.split("\n"))
+            get_ipython().run_cell(new_cell)  # noqa: F821
+
+    def load_ipython_extension(ip):
+        from . import install
+
+        install()
+        ip.register_magics(CudfPandasMagics)
+
+except ImportError:
+
+    def load_ipython_extension(ip):
+        pass
diff --git a/python/cudf/cudf/pandas/module_accelerator.py b/python/cudf/cudf/pandas/module_accelerator.py
new file mode 100644
index 0000000..eb35c4a
--- /dev/null
+++ b/python/cudf/cudf/pandas/module_accelerator.py
@@ -0,0 +1,620 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import contextlib
+import functools
+import importlib
+import importlib.abc
+import importlib.machinery
+import os
+import sys
+import threading
+import warnings
+from abc import abstractmethod
+from importlib._bootstrap import _ImportLockContext as ImportLock
+from types import ModuleType
+from typing import Any, ContextManager, Dict, List, NamedTuple
+
+from typing_extensions import Self
+
+from .fast_slow_proxy import (
+    _FunctionProxy,
+    _is_function_or_method,
+    _Unusable,
+    get_final_type_map,
+    get_intermediate_type_map,
+    get_registered_functions,
+)
+
+
+def rename_root_module(module: str, root: str, new_root: str) -> str:
+    """
+    Rename a module to a new root.
+
+    Parameters
+    ----------
+    module
+        Module to rename
+    root
+        Original root
+    new_root
+        New root
+
+    Returns
+    -------
+    New module name (if it matches root) otherwise original name.
+    """
+    if module.startswith(root):
+        return new_root + module[len(root) :]
+    else:
+        return module
+
+
+class DeducedMode(NamedTuple):
+    use_fast_lib: bool
+    slow_lib: str
+    fast_lib: str
+
+
+def deduce_cudf_pandas_mode(slow_lib: str, fast_lib: str) -> DeducedMode:
+    """
+    Determine if cudf.pandas should use the requested fast library.
+
+    Parameters
+    ----------
+    slow_lib
+        Name of the slow library
+    fast_lib
+        Name of the fast library
+
+    Returns
+    -------
+    Whether the fast library is being used, and the resulting names of
+    the "slow" and "fast" libraries.
+    """
+    if "CUDF_PANDAS_FALLBACK_MODE" not in os.environ:
+        try:
+            importlib.import_module(fast_lib)
+            return DeducedMode(
+                use_fast_lib=True, slow_lib=slow_lib, fast_lib=fast_lib
+            )
+        except Exception as e:
+            warnings.warn(
+                f"Exception encountered importing {fast_lib}: {e}."
+                f"Falling back to only using {slow_lib}."
+            )
+    return DeducedMode(
+        use_fast_lib=False, slow_lib=slow_lib, fast_lib=slow_lib
+    )
+
+
+class ModuleAcceleratorBase(
+    importlib.abc.MetaPathFinder, importlib.abc.Loader
+):
+    _instance: ModuleAcceleratorBase | None = None
+    mod_name: str
+    fast_lib: str
+    slow_lib: str
+
+    # When walking the module tree and wrapping module attributes,
+    # we often will come across the same object more than once. We
+    # don't want to create separate wrappers for each
+    # instance, so we keep a registry of all module attributes
+    # that we can look up to see if we have already wrapped an
+    # attribute before
+    _wrapped_objs: dict[Any, Any]
+
+    def __new__(
+        cls,
+        mod_name: str,
+        fast_lib: str,
+        slow_lib: str,
+    ):
+        """Build a custom module finder that will provide wrapped modules
+        on demand.
+
+        Parameters
+        ----------
+        mod_name
+             Import name to deliver modules under.
+        fast_lib
+             Name of package that provides "fast" implementation
+        slow_lib
+             Name of package that provides "slow" fallback implementation
+        """
+        if ModuleAcceleratorBase._instance is not None:
+            raise RuntimeError(
+                "Only one instance of ModuleAcceleratorBase allowed"
+            )
+        self = object.__new__(cls)
+        self.mod_name = mod_name
+        self.fast_lib = fast_lib
+        self.slow_lib = slow_lib
+
+        # When walking the module tree and wrapping module attributes,
+        # we often will come across the same object more than once. We
+        # don't want to create separate wrappers for each
+        # instance, so we keep a registry of all module attributes
+        # that we can look up to see if we have already wrapped an
+        # attribute before
+        self._wrapped_objs = {}
+        self._wrapped_objs.update(get_final_type_map())
+        self._wrapped_objs.update(get_intermediate_type_map())
+        self._wrapped_objs.update(get_registered_functions())
+
+        ModuleAcceleratorBase._instance = self
+        return self
+
+    def __repr__(self) -> str:
+        return (
+            f"{self.__class__.__name__}"
+            f"(fast={self.fast_lib}, slow={self.slow_lib})"
+        )
+
+    def find_spec(
+        self, fullname: str, path, target=None
+    ) -> importlib.machinery.ModuleSpec | None:
+        """Provide ourselves as a module loader.
+
+        Parameters
+        ----------
+        fullname
+            Name of module to be imported, if it starts with the name
+            that we are using to wrap, we will deliver ourselves as a
+            loader, otherwise defer to the standard Python loaders.
+
+        Returns
+        -------
+        A ModuleSpec with ourself as loader if we're interposing,
+        otherwise None to pass off to the next loader.
+        """
+        if fullname == self.mod_name or fullname.startswith(
+            f"{self.mod_name}."
+        ):
+            return importlib.machinery.ModuleSpec(
+                name=fullname,
+                loader=self,
+                # Note, this influences the repr of the module, so we may want
+                # to change it if we ever want to control that.
+                origin=None,
+                loader_state=None,
+                is_package=True,
+            )
+        return None
+
+    def create_module(self, spec) -> ModuleType | None:
+        return None
+
+    def exec_module(self, mod: ModuleType):
+        # importlib calls this function with the global import lock held.
+        self._populate_module(mod)
+
+    @abstractmethod
+    def disabled(self) -> ContextManager:
+        pass
+
+    def _postprocess_module(
+        self,
+        mod: ModuleType,
+        slow_mod: ModuleType,
+        fast_mod: ModuleType | None,
+    ) -> ModuleType:
+        """Ensure that the wrapped module satisfies required invariants.
+
+        Parameters
+        ----------
+        mod
+            Wrapped module to postprocess
+        slow_mod
+            Slow version that we are mimicking
+        fast_mod
+            Fast module that provides accelerated implementations (may
+            be None
+
+        Returns
+        -------
+        Checked and validated module
+
+        Notes
+        -----
+        The implementation of fast-slow proxies imposes certain
+        requirements on the wrapped modules that it delivers. This
+        function encodes those requirements and raises if the module
+        does not satisfy them.
+
+        This post-processing routine should be kept up to date with any
+        requirements encoded by fast_slow_proxy.py
+        """
+        mod.__dict__["_fsproxy_slow"] = slow_mod
+        if fast_mod is not None:
+            mod.__dict__["_fsproxy_fast"] = fast_mod
+        return mod
+
+    @abstractmethod
+    def _populate_module(self, mod: ModuleType) -> ModuleType:
+        """Populate given module with appropriate attributes.
+
+        This traverses the attributes of the slow module corresponding
+        to mod and mirrors those in the provided module in a wrapped
+        mode that attempts to execute them using the fast module first.
+
+        Parameters
+        ----------
+        mod
+            Module to populate
+
+        Returns
+        -------
+        ModuleType
+            Populated module
+
+        Notes
+        -----
+        In addition to the attributes of the slow module,
+        the returned module must have the following attributes:
+
+        - '_fsproxy_slow': the corresponding slow module
+        - '_fsproxy_fast': the corresponding fast module
+
+        This is necessary for correct rewriting of UDFs when calling
+        to the respective fast/slow libraries.
+
+        The necessary invariants are checked and applied in
+        :meth:`_postprocess_module`.
+        """
+        pass
+
+    def _wrap_attribute(
+        self,
+        slow_attr: Any,
+        fast_attr: Any | _Unusable,
+        name: str,
+    ) -> Any:
+        """
+        Return the wrapped version of an attribute.
+
+        Parameters
+        ----------
+        slow_attr : Any
+            The attribute from the slow module
+        fast_mod : Any (or None)
+            The same attribute from the fast module, if it exists
+        name
+            Name of attribute
+
+        Returns
+        -------
+        Wrapped attribute
+        """
+        wrapped_attr: Any
+        # TODO: what else should we make sure not to get from the fast
+        # library?
+        if name in {"__all__", "__dir__", "__file__", "__doc__"}:
+            wrapped_attr = slow_attr
+        elif self.fast_lib == self.slow_lib:
+            # no need to create a fast-slow wrapper
+            wrapped_attr = slow_attr
+        if any(
+            [
+                slow_attr in get_registered_functions(),
+                slow_attr in get_final_type_map(),
+                slow_attr in get_intermediate_type_map(),
+            ]
+        ):
+            # attribute already registered in self._wrapped_objs
+            return self._wrapped_objs[slow_attr]
+        if isinstance(slow_attr, ModuleType) and slow_attr.__name__.startswith(
+            self.slow_lib
+        ):
+            # attribute is a submodule of the slow library,
+            # replace the string "{slow_lib}" in the submodule's
+            # name with "{self.mod_name}"
+            # now, attempt to import the wrapped module, which will
+            # recursively wrap all of its attributes:
+            return importlib.import_module(
+                rename_root_module(
+                    slow_attr.__name__, self.slow_lib, self.mod_name
+                )
+            )
+        if slow_attr in self._wrapped_objs:
+            if type(fast_attr) is _Unusable:
+                # we don't want to replace a wrapped object that
+                # has a usable fast object with a wrapped object
+                # with a an unusable fast object.
+                return self._wrapped_objs[slow_attr]
+        if _is_function_or_method(slow_attr):
+            wrapped_attr = _FunctionProxy(fast_attr, slow_attr)
+        else:
+            wrapped_attr = slow_attr
+        return wrapped_attr
+
+    @classmethod
+    @abstractmethod
+    def install(
+        cls, destination_module: str, fast_lib: str, slow_lib: str
+    ) -> Self | None:
+        """
+        Install the loader in sys.meta_path.
+
+        Parameters
+        ----------
+        destination_module
+            Name under which the importer will kick in
+        fast_lib
+            Name of fast module
+        slow_lib
+            Name of slow module we are trying to mimic
+
+        Returns
+        -------
+        Instance of the class (or None if the loader was not installed)
+
+        Notes
+        -----
+        This function is idempotent. If called with the same arguments
+        a second time, it does not create a new loader, but instead
+        returns the existing loader from ``sys.meta_path``.
+
+        """
+        pass
+
+
+class ModuleAccelerator(ModuleAcceleratorBase):
+    """
+    A finder and loader that produces "accelerated" modules.
+
+    When someone attempts to import the specified slow library with
+    this finder enabled, we intercept the import and deliver an
+    equivalent, accelerated, version of the module. This provides
+    attributes and modules that check if they are being used from
+    "within" the slow (or fast) library themselves. If this is the
+    case, the implementation is forwarded to the actual slow library
+    implementation, otherwise a proxy implementation is used (which
+    attempts to call the fast version first).
+    """
+
+    _denylist: List[str]
+    _use_fast_lib: bool
+    _use_fast_lib_lock: threading.RLock
+    _module_cache_prefix: str = "_slow_lib_"
+
+    # TODO: Add possibility for either an explicit allow-list of
+    # libraries where the slow_lib should be wrapped, or, more likely
+    # a block-list that adds to the set of libraries where no proxying occurs.
+    def __new__(
+        cls,
+        fast_lib,
+        slow_lib,
+    ):
+        self = super().__new__(
+            cls,
+            slow_lib,
+            fast_lib,
+            slow_lib,
+        )
+        # Import the real versions of the modules so that we can
+        # rewrite the sys.modules cache.
+        slow_module = importlib.import_module(slow_lib)
+        fast_module = importlib.import_module(fast_lib)
+        # Note, this is not thread safe, but install() below grabs the
+        # lock for the whole initialisation and modification of
+        # sys.meta_path.
+        for mod in sys.modules.copy():
+            if mod.startswith(self.slow_lib):
+                sys.modules[self._module_cache_prefix + mod] = sys.modules[mod]
+                del sys.modules[mod]
+        self._denylist = [*slow_module.__path__, *fast_module.__path__]
+
+        # Lock to manage temporarily disabling delivering wrapped attributes
+        self._use_fast_lib_lock = threading.RLock()
+        self._use_fast_lib = True
+        return self
+
+    def _populate_module(self, mod: ModuleType):
+        mod_name = mod.__name__
+
+        # Here we attempt to import "_fsproxy_slow_lib.x.y.z", but
+        # "_fsproxy_slow_lib" does not exist anywhere as a real file, so
+        # how does this work?
+        # The importer attempts to import ".z" by first importing
+        # "_fsproxy_slow_lib.x.y", this recurses until we find
+        # "_fsproxy_slow_lib.x" (say), which does exist because we set that up
+        # in __init__. Now the importer looks at the __path__
+        # attribute of "x" and uses that to find the relative location
+        # to look for "y". This __path__ points to the real location
+        # of "slow_lib.x". So, as long as we rewire the _already imported_
+        # slow_lib modules in sys.modules to _fsproxy_slow_lib, when we
+        # get here this will find the right thing.
+        # The above exposition is for lazily imported submodules (e.g.
+        # avoiding circular imports by putting an import at function
+        # level). For everything that is eagerly imported when we do
+        # "import slow_lib" this import line is trivial because we
+        # immediately pull the correct result out of sys.modules.
+        slow_mod = importlib.import_module(
+            rename_root_module(
+                mod_name,
+                self.slow_lib,
+                self._module_cache_prefix + self.slow_lib,
+            )
+        )
+        try:
+            fast_mod = importlib.import_module(
+                rename_root_module(mod_name, self.slow_lib, self.fast_lib)
+            )
+        except Exception:
+            fast_mod = None
+
+        # The version that will be used if called within a denylist
+        # package
+        real_attributes = {}
+        # The version that will be used outside denylist packages
+        for key in slow_mod.__dir__():
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", FutureWarning)
+                slow_attr = getattr(slow_mod, key)
+            fast_attr = getattr(fast_mod, key, _Unusable())
+            real_attributes[key] = slow_attr
+            try:
+                wrapped_attr = self._wrap_attribute(slow_attr, fast_attr, key)
+                self._wrapped_objs[slow_attr] = wrapped_attr
+            except TypeError:
+                # slow_attr is not hashable
+                pass
+
+        # Our module has (basically) no static attributes and instead
+        # always delivers them dynamically where the behaviour is
+        # dependent on the calling module.
+        setattr(
+            mod,
+            "__getattr__",
+            functools.partial(
+                self.getattr_real_or_wrapped,
+                real=real_attributes,
+                wrapped_objs=self._wrapped_objs,
+                loader=self,
+            ),
+        )
+
+        # ...but, we want to pretend like we expose the same attributes
+        # as the equivalent slow module
+        setattr(mod, "__dir__", slow_mod.__dir__)
+
+        # We set __path__ to the real path so that importers like
+        # jinja2.PackageLoader("slow_mod") work correctly.
+        if getattr(slow_mod, "__path__", False):
+            assert mod.__spec__
+            mod.__path__ = slow_mod.__path__
+            mod.__spec__.submodule_search_locations = [*slow_mod.__path__]
+        return self._postprocess_module(mod, slow_mod, fast_mod)
+
+    @contextlib.contextmanager
+    def disabled(self):
+        """Return a context manager for disabling the module accelerator.
+
+        Within the block, any wrapped objects will instead deliver
+        attributes from their real counterparts (as if the current
+        nested block were in the denylist).
+
+        Returns
+        -------
+        Context manager for disabling things
+        """
+        try:
+            self._use_fast_lib_lock.acquire()
+            # The same thread might enter this context manager
+            # multiple times, so we need to remember the previous
+            # value
+            saved = self._use_fast_lib
+            self._use_fast_lib = False
+            yield
+        finally:
+            self._use_fast_lib = saved
+            self._use_fast_lib_lock.release()
+
+    @staticmethod
+    def getattr_real_or_wrapped(
+        name: str,
+        *,
+        real: Dict[str, Any],
+        wrapped_objs,
+        loader: ModuleAccelerator,
+    ) -> Any:
+        """
+        Obtain an attribute from a module from either the real or
+        wrapped namespace.
+
+        Parameters
+        ----------
+        name
+            Attribute to return
+        real
+            Unwrapped "original" attributes
+        wrapped
+            Wrapped attributes
+        loader
+            Loader object that manages denylist and other skipping
+
+        Returns
+        -------
+        The requested attribute (either real or wrapped)
+        """
+        with loader._use_fast_lib_lock:
+            # Have to hold the lock to read this variable since
+            # another thread might modify it.
+            # Modification has to happen with the lock held for the
+            # duration, so if someone else has modified things, then
+            # we block trying to acquire the lock (hence it is safe to
+            # release the lock after reading this value)
+            use_real = not loader._use_fast_lib
+        if not use_real:
+            # Only need to check the denylist if we're not turned off.
+            frame = sys._getframe()
+            # We cannot possibly be at the top level.
+            assert frame.f_back
+            calling_module = frame.f_back.f_code.co_filename
+            use_real = any(
+                calling_module.startswith(path) for path in loader._denylist
+            )
+        try:
+            if use_real:
+                return real[name]
+            else:
+                return wrapped_objs[real[name]]
+        except KeyError:
+            raise AttributeError(f"No attribute '{name}'")
+        except TypeError:
+            # real[name] is an unhashable type
+            return real[name]
+
+    @classmethod
+    def install(
+        cls,
+        destination_module: str,
+        fast_lib: str,
+        slow_lib: str,
+    ) -> Self | None:
+        # This grabs the global _import_ lock to avoid concurrent
+        # threads modifying sys.modules.
+        # We also make sure that we finish installing ourselves in
+        # sys.meta_path before releasing the lock so that there isn't
+        # a race between our modification of sys.modules and someone
+        # else importing the slow_lib before we have added ourselves
+        # to the meta_path
+        with ImportLock():
+            if destination_module != slow_lib:
+                raise RuntimeError(
+                    f"Destination module '{destination_module}' must match"
+                    f"'{slow_lib}' for this to work."
+                )
+            mode = deduce_cudf_pandas_mode(slow_lib, fast_lib)
+            if mode.use_fast_lib:
+                importlib.import_module(
+                    f".._wrappers.{mode.slow_lib}", __name__
+                )
+            try:
+                (self,) = (
+                    p
+                    for p in sys.meta_path
+                    if isinstance(p, cls)
+                    and p.slow_lib == mode.slow_lib
+                    and p.fast_lib == mode.fast_lib
+                )
+            except ValueError:
+                self = cls(mode.fast_lib, mode.slow_lib)
+                sys.meta_path.insert(0, self)
+            return self
+
+
+def disable_module_accelerator() -> contextlib.ExitStack:
+    """
+    Temporarily disable any module acceleration.
+    """
+    with contextlib.ExitStack() as stack:
+        for finder in sys.meta_path:
+            if isinstance(finder, ModuleAcceleratorBase):
+                stack.enter_context(finder.disabled())
+        return stack.pop_all()
+    assert False  # pacify type checker
diff --git a/python/cudf/cudf/pandas/profiler.py b/python/cudf/cudf/pandas/profiler.py
new file mode 100644
index 0000000..c5662d0
--- /dev/null
+++ b/python/cudf/cudf/pandas/profiler.py
@@ -0,0 +1,322 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import inspect
+import operator
+import pickle
+import sys
+import time
+from collections import defaultdict
+from typing import Union
+
+from rich.console import Console
+from rich.syntax import Syntax
+from rich.table import Table
+
+from .fast_slow_proxy import (
+    _FinalProxy,
+    _FunctionProxy,
+    _IntermediateProxy,
+    _MethodProxy,
+)
+
+# This text is used in contexts where the profiler is injected into the
+# original code. The profiler is injected at the top of the cell, so the line
+# numbers in the profiler results are offset by 2.
+_profile_injection_text = """\
+from cudf.pandas import Profiler
+with Profiler() as profiler:
+{original_lines}
+
+# Patch the results to shift the line numbers back to the original before the
+# profiler injection.
+new_results = {{}}
+
+for (lineno, currfile, line), v in profiler._results.items():
+    new_results[(lineno - 2, currfile, line)] = v
+
+profiler._results = new_results
+profiler.print_per_line_stats()
+{function_profile_printer}
+"""
+
+_cpu_issue_text = """\
+Not all pandas operations ran on the GPU. \
+The following functions required CPU fallback:
+
+{cpu_functions_used}
+"""
+
+
+def format_cpu_functions_used(cpu_funcs):
+    output_str = ""
+    for each in cpu_funcs:
+        output_str += f"- {each}\n"
+
+    # remove final newline character
+    output_str = output_str[:-1]
+    return output_str
+
+
+def lines_with_profiling(lines, print_function_profile=False):
+    """Inject profiling code into the given lines of code."""
+    cleaned_lines = "\n".join(
+        [(" " * 4) + line.replace("\t", " " * 4) for line in lines]
+    )
+    return _profile_injection_text.format(
+        original_lines=cleaned_lines,
+        function_profile_printer="profiler.print_per_function_stats()"
+        if print_function_profile
+        else "",
+    )
+
+
+class Profiler:
+    _IGNORE_LIST = ["Profiler()", "settrace(None)"]
+
+    def __init__(self):
+        self._results = {}
+        # Map func-name to list of calls (was_fast, time)
+        self._per_func_results = defaultdict(lambda: defaultdict(list))
+        # Current fast_slow_function_call stack frame recording name
+        # and start time
+        self._call_stack = []
+        self._currkey = None
+        self._timer = {}
+        self._currfile = None
+        self.start_time = None
+        self.end_time = None
+
+    def __enter__(self, *args, **kwargs):
+        self.start_time = time.perf_counter()
+        self._oldtrace = sys.gettrace()
+        # Setting the global trace function with sys.settrace does not affect
+        # the current call stack, so in addition to this we must also set the
+        # current frame's f_trace attribute as done below.
+        sys.settrace(self._tracefunc)
+
+        # Following excerpt from:
+        # https://docs.python.org/3/library/sys.html#sys.settrace
+        # For more fine-grained usage, it is possible
+        # to set a trace function by assigning
+        # frame.f_trace = tracefunc explicitly, rather than
+        # relying on it being set indirectly via the return
+        # value from an already installed trace function
+        # Hence we need to perform `f_trace = self._tracefunc`
+        # we need to `f_back` because current frame will be
+        # of this file.
+        frame = inspect.currentframe().f_back
+        self._currfile = frame.f_code.co_filename
+        self._f_back_oldtrace = frame.f_trace
+        frame.f_trace = self._tracefunc
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        sys.settrace(self._oldtrace)
+        inspect.currentframe().f_back.f_trace = self._f_back_oldtrace
+        self.end_time = time.perf_counter()
+
+    @staticmethod
+    def get_namespaced_function_name(
+        func_obj: Union[
+            _FunctionProxy,
+            _MethodProxy,
+            type[_FinalProxy],
+            type[_IntermediateProxy],
+        ]
+    ):
+        if isinstance(func_obj, _MethodProxy):
+            # Extract classname from method object
+            type_name = type(func_obj._fsproxy_wrapped.__self__).__name__
+            # Explicitly ask for __name__ on _fsproxy_wrapped to avoid
+            # getting a private attribute and forcing a slow-path copy
+            func_name = func_obj._fsproxy_wrapped.__name__
+            return ".".join([type_name, func_name])
+        elif isinstance(func_obj, _FunctionProxy) or issubclass(
+            func_obj, (_FinalProxy, _IntermediateProxy)
+        ):
+            return func_obj.__name__
+        else:
+            raise NotImplementedError(
+                f"Don't know how to get namespaced name for {func_obj}"
+            )
+
+    def _tracefunc(self, frame, event, arg):
+        if event == "line" and frame.f_code.co_filename == self._currfile:
+            key = "".join(inspect.stack()[1].code_context)
+            if not any(
+                ignore_word in key for ignore_word in Profiler._IGNORE_LIST
+            ):
+                self._currkey = (frame.f_lineno, self._currfile, key)
+                self._results.setdefault(self._currkey, {})
+                self._timer[self._currkey] = time.perf_counter()
+        elif (
+            event == "call"
+            and frame.f_code.co_name == "_fast_slow_function_call"
+        ):
+            if self._currkey is not None:
+                self._timer[self._currkey] = time.perf_counter()
+
+            # Store per-function information for free functions and methods
+            frame_locals = inspect.getargvalues(frame).locals
+            if (
+                isinstance(
+                    func_obj := frame_locals["args"][0],
+                    (_MethodProxy, _FunctionProxy),
+                )
+                or isinstance(func_obj, type)
+                and issubclass(func_obj, (_FinalProxy, _IntermediateProxy))
+            ):
+                func_name = self.get_namespaced_function_name(func_obj)
+                self._call_stack.append((func_name, time.perf_counter()))
+        elif (
+            event == "return"
+            and frame.f_code.co_name == "_fast_slow_function_call"
+        ):
+            if self._currkey is not None and arg is not None:
+                if arg[1]:  # fast
+                    run_time = time.perf_counter() - self._timer[self._currkey]
+                    self._results[self._currkey][
+                        "gpu_time"
+                    ] = run_time + self._results[self._currkey].get(
+                        "gpu_time", 0
+                    )
+                else:
+                    run_time = time.perf_counter() - self._timer[self._currkey]
+                    self._results[self._currkey][
+                        "cpu_time"
+                    ] = run_time + self._results[self._currkey].get(
+                        "cpu_time", 0
+                    )
+
+            frame_locals = inspect.getargvalues(frame).locals
+            if (
+                isinstance(
+                    func_obj := frame_locals["args"][0],
+                    (_MethodProxy, _FunctionProxy),
+                )
+                or isinstance(func_obj, type)
+                and issubclass(func_obj, (_FinalProxy, _IntermediateProxy))
+            ):
+                func_name, start = self._call_stack.pop()
+                if arg is not None:
+                    key = "gpu" if arg[1] else "cpu"
+                    self._per_func_results[func_name][key].append(
+                        time.perf_counter() - start
+                    )
+
+        return self._tracefunc
+
+    @property
+    def per_line_stats(self):
+        list_data = []
+        for key, val in self._results.items():
+            cpu_time = val.get("cpu_time", 0)
+            gpu_time = val.get("gpu_time", 0)
+            line_no, _, line = key
+            list_data.append([line_no, line, gpu_time, cpu_time])
+
+        return sorted(list_data, key=operator.itemgetter(0))
+
+    @property
+    def per_function_stats(self):
+        return self._per_func_results
+
+    def print_per_line_stats(self):
+        table = Table()
+        table.add_column("Line no.")
+        table.add_column("Line")
+        table.add_column("GPU TIME(s)")
+        table.add_column("CPU TIME(s)")
+        for line_no, line, gpu_time, cpu_time in self.per_line_stats:
+            table.add_row(
+                str(line_no),
+                Syntax(str(line), "python"),
+                "" if gpu_time == 0 else "{:.9f}".format(gpu_time),
+                "" if cpu_time == 0 else "{:.9f}".format(cpu_time),
+            )
+        time_elapsed = self.end_time - self.start_time
+        table.title = f"""\n\
+        Total time elapsed: {time_elapsed:.3f} seconds
+
+        Stats
+        """
+        console = Console()
+        console.print(table)
+
+    def print_per_function_stats(self):
+        cpu_funcs = []
+        n_gpu_func_calls = 0
+        n_cpu_func_calls = 0
+        total_gpu_time = 0
+        total_cpu_time = 0
+
+        table = Table()
+        for col in (
+            "Function",
+            "GPU ncalls",
+            "GPU cumtime",
+            "GPU percall",
+            "CPU ncalls",
+            "CPU cumtime",
+            "CPU percall",
+        ):
+            table.add_column(col)
+
+        for func_name, func_data in self.per_function_stats.items():
+            gpu_times = func_data["gpu"]
+            cpu_times = func_data["cpu"]
+            table.add_row(
+                func_name,
+                f"{len(gpu_times)}",
+                f"{sum(gpu_times):.3f}",
+                f"{sum(gpu_times) / max(len(gpu_times), 1):.3f}",
+                f"{len(cpu_times)}",
+                f"{sum(cpu_times):.3f}",
+                f"{sum(cpu_times) / max(len(cpu_times), 1):.3f}",
+            )
+            total_gpu_time += sum(gpu_times)
+            total_cpu_time += sum(cpu_times)
+            n_gpu_func_calls += len(gpu_times)
+            n_cpu_func_calls += len(cpu_times)
+
+            if cpu_times and func_name not in cpu_funcs:
+                cpu_funcs.append(func_name)
+
+        time_elapsed = self.end_time - self.start_time
+        table.title = f"""\n\
+        Total time elapsed: {time_elapsed:.3f} seconds
+        {n_gpu_func_calls} GPU function calls in {total_gpu_time:.3f} seconds
+        {n_cpu_func_calls} CPU function calls in {total_cpu_time:.3f} seconds
+
+        Stats
+        """
+        console = Console()
+        console.print(table)
+
+        if cpu_funcs:
+            call_to_action = (
+                "To request GPU support for any of these functions, "
+                "please file a Github issue here: "
+                "[link=https://github.com/rapidsai/cudf/issues/new?assignees"
+                "=&labels=%3F+-+Needs+Triage%2C+feature+request&projects="
+                "&template=pandas_function_request.md&title=%5BFEA%5D]"
+                "https://github.com/rapidsai/cudf/issues/new/choose"
+                "[/link]."
+            )
+            console.print(
+                _cpu_issue_text.format(
+                    cpu_functions_used=format_cpu_functions_used(cpu_funcs)
+                )
+            )
+            console.print(call_to_action)
+
+    def dump_stats(self, file_name):
+        with open(file_name, "wb") as f:
+            pickle.dump(self, f)
+
+
+def load_stats(file_name):
+    with open(file_name, "rb") as f:
+        return pickle.load(f)
diff --git a/python/cudf/cudf/pandas/scripts/analyze-test-failures.py b/python/cudf/cudf/pandas/scripts/analyze-test-failures.py
new file mode 100644
index 0000000..f1744c9
--- /dev/null
+++ b/python/cudf/cudf/pandas/scripts/analyze-test-failures.py
@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Prints the most common test failures for the given tests.
+
+Usage:
+    python analyze-test-failures.py <path-to-test-log> <file-or-pattern>
+
+Example:
+    python analyze-test-failures.py log.json frame/*
+"""
+import json
+import sys
+from collections import Counter
+from fnmatch import fnmatch
+
+from rich.console import Console
+from rich.table import Table
+
+PANDAS_TEST_PREFIX = "pandas-tests/"
+
+
+def count_failures(log_file_name, pattern):
+    counter = Counter()
+    with open(log_file_name) as f:
+        for line in f:
+            try:
+                line = json.loads(line)
+            except Exception:
+                continue
+            if (
+                "location" in line
+                and line["when"] == "call"
+                and line["outcome"] == "failed"
+            ):
+                line_module_name = line["location"][0].removeprefix(
+                    PANDAS_TEST_PREFIX
+                )
+                if fnmatch(line_module_name, pattern):
+                    if "longrepr" in line and line["longrepr"]:
+                        if isinstance(line["longrepr"], (tuple, list)):
+                            message = line["longrepr"][2].splitlines()[0]
+                        elif isinstance(line["longrepr"], str):
+                            message = line["longrepr"]
+                        else:
+                            message = line["longrepr"]["reprcrash"][
+                                "message"
+                            ].splitlines()[0]
+                        counter[message] += 1
+    return counter
+
+
+def render_results(results, num_rows=20):
+    table = Table()
+    table.add_column("Failure message")
+    table.add_column("Number of occurences")
+
+    for msg, num in results.most_common(20):
+        table.add_row(msg, str(num))
+
+    console = Console()
+    console.print(table)
+
+
+if __name__ == "__main__":
+    log_file_name = sys.argv[1]
+    pattern = sys.argv[2]
+    render_results(count_failures(log_file_name, pattern), num_rows=20)
diff --git a/python/cudf/cudf/pandas/scripts/conftest-patch.py b/python/cudf/cudf/pandas/scripts/conftest-patch.py
new file mode 100644
index 0000000..505a40b
--- /dev/null
+++ b/python/cudf/cudf/pandas/scripts/conftest-patch.py
@@ -0,0 +1,39 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import contextlib
+import os
+import sys
+from functools import wraps
+
+import pytest
+
+
+def replace_kwargs(new_kwargs):
+    def wrapper(func):
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            kwargs.update(new_kwargs)
+            return func(*args, **kwargs)
+
+        return wrapped
+
+    return wrapper
+
+
+@contextlib.contextmanager
+def null_assert_warnings(*args, **kwargs):
+    try:
+        yield []
+    finally:
+        pass
+
+
+@pytest.fixture(scope="session", autouse=True)  # type: ignore
+def patch_testing_functions():
+    tm.assert_produces_warning = null_assert_warnings
+    pytest.raises = replace_kwargs({"match": None})(pytest.raises)
+
+
+sys.path.append(os.path.dirname(__file__))
diff --git a/python/cudf/cudf/pandas/scripts/run-pandas-tests.sh b/python/cudf/cudf/pandas/scripts/run-pandas-tests.sh
new file mode 100755
index 0000000..c4dfe42
--- /dev/null
+++ b/python/cudf/cudf/pandas/scripts/run-pandas-tests.sh
@@ -0,0 +1,198 @@
+#!/usr/bin/env bash
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Run Pandas unit tests with cudf.pandas.
+#
+# Usage:
+#   run-pandas-tests.sh <pytest args> <path to pandas tests (optional)>
+#
+# Examples
+# Run a single test
+#   run-pandas-tests.sh -n auto -v tests/groupby/test_groupby_dropna.py
+# Run all tests
+#   run-pandas-tests.sh --tb=line --report-log=log.json
+#
+# This script creates a `pandas-testing` directory if it doesn't exist
+
+
+# Grab the Pandas source corresponding to the version
+# of Pandas installed.
+PANDAS_VERSION=$(python -c "import pandas; print(pandas.__version__)")
+
+PYTEST_IGNORES="--ignore=tests/io/test_user_agent.py"
+
+mkdir -p pandas-testing
+cd pandas-testing
+
+if [ ! -d "pandas" ]; then
+    git clone https://github.com/pandas-dev/pandas
+fi
+cd pandas && git clean -fdx && git checkout v$PANDAS_VERSION && cd ../
+
+
+if [ ! -d "pandas-tests" ]; then
+    # Copy just the tests out of the Pandas source tree.
+    # Not exactly sure why this is needed but Pandas
+    # imports fail if we don't do this:
+    mkdir -p pandas-tests
+    cp -r pandas/pandas/tests pandas-tests/
+    # directory layout requirement
+    # conftest.py
+    # pyproject.toml
+    # tests/
+    cp pandas/pandas/conftest.py pandas-tests/conftest.py
+    # Vendored from pandas/pyproject.toml
+    cat > pandas-tests/pyproject.toml << \EOF
+[tool.pytest.ini_options]
+xfail_strict = true
+filterwarnings = [
+  "error:Sparse:FutureWarning",
+  "error:The SparseArray:FutureWarning",
+  # Deprecation gives warning on import during pytest collection
+  "ignore:pandas.core.index is deprecated:FutureWarning:importlib",
+  "ignore:pandas.util.testing is deprecated:FutureWarning:importlib",
+  # Will be fixed in numba 0.56: https://github.com/numba/numba/issues/7758
+  "ignore:`np.MachAr` is deprecated:DeprecationWarning:numba",
+]
+markers = [
+  "single_cpu: tests that should run on a single cpu only",
+  "slow: mark a test as slow",
+  "network: mark a test as network",
+  "db: tests requiring a database (mysql or postgres)",
+  "clipboard: mark a pd.read_clipboard test",
+  "arm_slow: mark a test as slow for arm64 architecture",
+  "arraymanager: mark a test to run with ArrayManager enabled",
+]
+EOF
+    # append the contents of patch-confest.py to conftest.py
+    cat ../python/cudf/cudf/pandas/scripts/conftest-patch.py >> pandas-tests/conftest.py
+
+    # Substitute `pandas.tests` with a relative import.
+    # This will depend on the location of the test module relative to
+    # the pandas-tests directory.
+    for hit in $(find . -iname '*.py' | xargs grep "pandas.tests" | cut -d ":" -f 1 | sort | uniq); do
+        # Get the relative path to the test module
+        test_module=$(echo $hit | cut -d "/" -f 2-)
+        # Get the number of directories to go up
+        num_dirs=$(echo $test_module | grep -o "/" | wc -l)
+        num_dots=$(($num_dirs - 2))
+        # Construct the relative import
+        relative_import=$(printf "%0.s." $(seq 1 $num_dots))
+        # Replace the import
+        sed -i "s/pandas.tests/${relative_import}/g" $hit
+    done
+fi
+
+# append the contents of patch-confest.py to conftest.py
+cat ../python/cudf/cudf/pandas/scripts/conftest-patch.py >> pandas-tests/conftest.py
+
+# Run the tests
+cd pandas-tests/
+
+# TODO: Get a postgres & mysql container set up on the CI
+# test_overwrite_warns unsafely patchs over Series.mean affecting other tests when run in parallel
+# test_complex_series_frame_alignment randomly selects a DataFrames and axis to test but particular random selection(s) always fails
+# test_numpy_ufuncs_basic compares floating point values to unbounded precision, sometimes leading to failures
+TEST_NUMPY_UFUNCS_BASIC_FLAKY="test_numpy_ufuncs_basic[float-exp] \
+and not test_numpy_ufuncs_basic[float-exp2] \
+and not test_numpy_ufuncs_basic[float-expm1] \
+and not test_numpy_ufuncs_basic[float-log] \
+and not test_numpy_ufuncs_basic[float-log2] \
+and not test_numpy_ufuncs_basic[float-log10] \
+and not test_numpy_ufuncs_basic[float-log1p] \
+and not test_numpy_ufuncs_basic[float-sqrt] \
+and not test_numpy_ufuncs_basic[float-sin] \
+and not test_numpy_ufuncs_basic[float-cos] \
+and not test_numpy_ufuncs_basic[float-tan] \
+and not test_numpy_ufuncs_basic[float-arcsin] \
+and not test_numpy_ufuncs_basic[float-arccos] \
+and not test_numpy_ufuncs_basic[float-arctan] \
+and not test_numpy_ufuncs_basic[float-sinh] \
+and not test_numpy_ufuncs_basic[float-cosh] \
+and not test_numpy_ufuncs_basic[float-tanh] \
+and not test_numpy_ufuncs_basic[float-arcsinh] \
+and not test_numpy_ufuncs_basic[float-arccosh] \
+and not test_numpy_ufuncs_basic[float-arctanh] \
+and not test_numpy_ufuncs_basic[float-deg2rad] \
+and not test_numpy_ufuncs_basic[float-rad2deg] \
+and not test_numpy_ufuncs_basic[num_float64-exp] \
+and not test_numpy_ufuncs_basic[num_float64-exp2] \
+and not test_numpy_ufuncs_basic[num_float64-expm1] \
+and not test_numpy_ufuncs_basic[num_float64-log] \
+and not test_numpy_ufuncs_basic[num_float64-log2] \
+and not test_numpy_ufuncs_basic[num_float64-log10] \
+and not test_numpy_ufuncs_basic[num_float64-log1p] \
+and not test_numpy_ufuncs_basic[num_float64-sqrt] \
+and not test_numpy_ufuncs_basic[num_float64-sin] \
+and not test_numpy_ufuncs_basic[num_float64-cos] \
+and not test_numpy_ufuncs_basic[num_float64-tan] \
+and not test_numpy_ufuncs_basic[num_float64-arcsin] \
+and not test_numpy_ufuncs_basic[num_float64-arccos] \
+and not test_numpy_ufuncs_basic[num_float64-arctan] \
+and not test_numpy_ufuncs_basic[num_float64-sinh] \
+and not test_numpy_ufuncs_basic[num_float64-cosh] \
+and not test_numpy_ufuncs_basic[num_float64-tanh] \
+and not test_numpy_ufuncs_basic[num_float64-arcsinh] \
+and not test_numpy_ufuncs_basic[num_float64-arccosh] \
+and not test_numpy_ufuncs_basic[num_float64-arctanh] \
+and not test_numpy_ufuncs_basic[num_float64-deg2rad] \
+and not test_numpy_ufuncs_basic[num_float64-rad2deg] \
+and not test_numpy_ufuncs_basic[num_float32-exp] \
+and not test_numpy_ufuncs_basic[num_float32-exp2] \
+and not test_numpy_ufuncs_basic[num_float32-expm1] \
+and not test_numpy_ufuncs_basic[num_float32-log] \
+and not test_numpy_ufuncs_basic[num_float32-log2] \
+and not test_numpy_ufuncs_basic[num_float32-log10] \
+and not test_numpy_ufuncs_basic[num_float32-log1p] \
+and not test_numpy_ufuncs_basic[num_float32-sqrt] \
+and not test_numpy_ufuncs_basic[num_float32-sin] \
+and not test_numpy_ufuncs_basic[num_float32-cos] \
+and not test_numpy_ufuncs_basic[num_float32-tan] \
+and not test_numpy_ufuncs_basic[num_float32-arcsin] \
+and not test_numpy_ufuncs_basic[num_float32-arccos] \
+and not test_numpy_ufuncs_basic[num_float32-arctan] \
+and not test_numpy_ufuncs_basic[num_float32-sinh] \
+and not test_numpy_ufuncs_basic[num_float32-cosh] \
+and not test_numpy_ufuncs_basic[num_float32-tanh] \
+and not test_numpy_ufuncs_basic[num_float32-arcsinh] \
+and not test_numpy_ufuncs_basic[num_float32-arccosh] \
+and not test_numpy_ufuncs_basic[num_float32-arctanh] \
+and not test_numpy_ufuncs_basic[num_float32-deg2rad] \
+and not test_numpy_ufuncs_basic[num_float32-rad2deg] \
+and not test_numpy_ufuncs_basic[nullable_float-exp] \
+and not test_numpy_ufuncs_basic[nullable_float-exp2] \
+and not test_numpy_ufuncs_basic[nullable_float-expm1] \
+and not test_numpy_ufuncs_basic[nullable_float-log] \
+and not test_numpy_ufuncs_basic[nullable_float-log2] \
+and not test_numpy_ufuncs_basic[nullable_float-log10] \
+and not test_numpy_ufuncs_basic[nullable_float-log1p] \
+and not test_numpy_ufuncs_basic[nullable_float-sqrt] \
+and not test_numpy_ufuncs_basic[nullable_float-sin] \
+and not test_numpy_ufuncs_basic[nullable_float-cos] \
+and not test_numpy_ufuncs_basic[nullable_float-tan] \
+and not test_numpy_ufuncs_basic[nullable_float-arcsin] \
+and not test_numpy_ufuncs_basic[nullable_float-arccos] \
+and not test_numpy_ufuncs_basic[nullable_float-arctan] \
+and not test_numpy_ufuncs_basic[nullable_float-sinh] \
+and not test_numpy_ufuncs_basic[nullable_float-cosh] \
+and not test_numpy_ufuncs_basic[nullable_float-tanh] \
+and not test_numpy_ufuncs_basic[nullable_float-arcsinh] \
+and not test_numpy_ufuncs_basic[nullable_float-arccosh] \
+and not test_numpy_ufuncs_basic[nullable_float-arctanh] \
+and not test_numpy_ufuncs_basic[nullable_float-deg2rad] \
+and not test_numpy_ufuncs_basic[nullable_float-rad2deg]"
+
+PANDAS_CI="1" python -m pytest -p cudf.pandas \
+    -m "not single_cpu and not db" \
+    -k "not test_overwrite_warns and not test_complex_series_frame_alignment and not $TEST_NUMPY_UFUNCS_BASIC_FLAKY" \
+    --durations=50 \
+    --import-mode=importlib \
+    -o xfail_strict=True \
+    ${PYTEST_IGNORES} $@
+
+mv *.json ..
+cd ..
+
+rm -rf pandas-testing/pandas-tests/
diff --git a/python/cudf/cudf/pandas/scripts/summarize-test-results.py b/python/cudf/cudf/pandas/scripts/summarize-test-results.py
new file mode 100644
index 0000000..bfc5631
--- /dev/null
+++ b/python/cudf/cudf/pandas/scripts/summarize-test-results.py
@@ -0,0 +1,115 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Summarizes the test results per module.
+
+Examples:
+    python summarize-test-results.py log.json
+    python summarize-test-results.py log.json --output json
+    python summarize-test-results.py log.json --output table
+"""
+import argparse
+import json
+
+from rich.console import Console
+from rich.table import Table
+
+PANDAS_TEST_PREFIX = "pandas-tests/"
+
+
+def get_per_module_results(log_file_name):
+    per_module_results = {}
+    with open(log_file_name) as f:
+        for line in f:
+            try:
+                line = json.loads(line)
+            except Exception:
+                line = {}
+            if "outcome" in line:
+                outcome = line["outcome"]
+                # outcome can be "passed", "failed", or "skipped".
+                # Depending on other fields, it can indicate
+                # an errored, xpassed, or xfailed test.
+                if line.get("when", None) != "call":
+                    # when != call indicates test setup or teardown
+                    if outcome == "failed":
+                        # if the test failed during setup or teardown,
+                        # it counts as an "errored" test:
+                        outcome = "errored"
+                    else:
+                        # we don't care about other outcomes during
+                        # setup or teardown
+                        continue
+                else:
+                    if line.get("wasxfail", False) and outcome == "passed":
+                        # it's an xpassed test
+                        outcome = "failed"
+                module_name = (
+                    line["nodeid"]
+                    .split("::")[0]
+                    .removeprefix(PANDAS_TEST_PREFIX)
+                )
+                per_module_results.setdefault(module_name, {})
+                per_module_results[module_name].setdefault("total", 0)
+                per_module_results[module_name].setdefault(outcome, 0)
+                per_module_results[module_name]["total"] += 1
+                per_module_results[module_name][outcome] += 1
+    return per_module_results
+
+
+def sort_results(results):
+    sorted_keys = sorted(
+        results, key=lambda key: results[key].get("failed", 0)
+    )
+    return {key: results[key] for key in sorted_keys}
+
+
+def print_results_as_json(results):
+    print(json.dumps(results, indent=4))
+
+
+def print_results_as_table(results):
+    table = Table()
+    table.add_column("Test module")
+    table.add_column("Total tests")
+    table.add_column("Passed tests")
+    table.add_column("Failed tests")
+    table.add_column("Errored tests")
+    table.add_column("Skipped tests")
+    totals = {"total": 0, "passed": 0, "failed": 0, "errored": 0, "skipped": 0}
+    for module_name, row in results.items():
+        values = []
+        for key in ("total", "passed", "failed", "errored", "skipped"):
+            totals[key] += row.get(key, 0)
+            values.append(row.get(key, 0))
+        table.add_row(module_name, *map(str, values))
+    table.add_section()
+    table.add_row(
+        "total={}, passed={}, failed={}, errored={}, skipped={}".format(
+            *map(str, totals.values())
+        )
+    )
+    console = Console()
+    console.print(table)
+
+
+if __name__ == "__main__":
+    # parse arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "log_file_name", nargs=1, help="The input log file name"
+    )
+    parser.add_argument(
+        "--output",
+        choices=["json", "table"],
+        default="table",
+        help="The output format",
+    )
+    args = parser.parse_args()
+    results = sort_results(get_per_module_results(args.log_file_name[0]))
+    if args.output == "json":
+        print_results_as_json(results)
+    else:
+        print_results_as_table(results)
diff --git a/python/cudf/cudf/testing/__init__.py b/python/cudf/cudf/testing/__init__.py
new file mode 100644
index 0000000..1843344
--- /dev/null
+++ b/python/cudf/cudf/testing/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from cudf.testing.testing import (
+    assert_frame_equal,
+    assert_index_equal,
+    assert_series_equal,
+)
diff --git a/python/cudf/cudf/testing/_utils.py b/python/cudf/cudf/testing/_utils.py
new file mode 100644
index 0000000..9182246
--- /dev/null
+++ b/python/cudf/cudf/testing/_utils.py
@@ -0,0 +1,475 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import itertools
+import string
+import warnings
+from collections import abc
+from contextlib import contextmanager
+from decimal import Decimal
+
+import cupy
+import numpy as np
+import pandas as pd
+import pytest
+from numba.core.typing import signature as nb_signature
+from numba.core.typing.templates import AbstractTemplate
+from numba.cuda.cudadecl import registry as cuda_decl_registry
+from numba.cuda.cudaimpl import lower as cuda_lower
+from pandas import testing as tm
+
+import cudf
+from cudf._lib.null_mask import bitmask_allocation_size_bytes
+from cudf.api.types import is_scalar
+from cudf.core.column.timedelta import _unit_to_nanoseconds_conversion
+from cudf.core.udf.strings_lowering import cast_string_view_to_udf_string
+from cudf.core.udf.strings_typing import StringView, string_view, udf_string
+from cudf.utils import dtypes as dtypeutils
+
+supported_numpy_dtypes = [
+    "bool",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "float32",
+    "float64",
+    "datetime64[ms]",
+    "datetime64[us]",
+]
+
+SIGNED_INTEGER_TYPES = sorted(list(dtypeutils.SIGNED_INTEGER_TYPES))
+UNSIGNED_TYPES = sorted(list(dtypeutils.UNSIGNED_TYPES))
+INTEGER_TYPES = sorted(list(dtypeutils.INTEGER_TYPES))
+FLOAT_TYPES = sorted(list(dtypeutils.FLOAT_TYPES))
+SIGNED_TYPES = sorted(list(dtypeutils.SIGNED_TYPES))
+NUMERIC_TYPES = sorted(list(dtypeutils.NUMERIC_TYPES))
+DATETIME_TYPES = sorted(list(dtypeutils.DATETIME_TYPES))
+TIMEDELTA_TYPES = sorted(list(dtypeutils.TIMEDELTA_TYPES))
+OTHER_TYPES = sorted(list(dtypeutils.OTHER_TYPES))
+ALL_TYPES = sorted(list(dtypeutils.ALL_TYPES))
+
+SERIES_OR_INDEX_NAMES = [
+    None,
+    pd.NA,
+    cudf.NA,
+    np.nan,
+    float("NaN"),
+    "abc",
+    1,
+    pd.NaT,
+    np.datetime64("nat"),
+    np.timedelta64("NaT"),
+    np.timedelta64(10, "D"),
+    np.timedelta64(5, "D"),
+    np.datetime64("1970-01-01 00:00:00.000000001"),
+    np.datetime64("1970-01-01 00:00:00.000000002"),
+    pd.Timestamp(1),
+    pd.Timestamp(2),
+    pd.Timedelta(1),
+    pd.Timedelta(2),
+    Decimal("NaN"),
+    Decimal("1.2"),
+    np.int64(1),
+    np.int32(1),
+    np.float32(1),
+    pd.Timestamp(1),
+]
+
+
+def set_random_null_mask_inplace(series, null_probability=0.5, seed=None):
+    """Randomly nullify elements in series with the provided probability."""
+    probs = [null_probability, 1 - null_probability]
+    rng = np.random.default_rng(seed=seed)
+    mask = rng.choice([False, True], size=len(series), p=probs)
+    series.iloc[mask] = None
+
+
+# TODO: This function should be removed. Anywhere that it is being used should
+# instead be generating a random boolean array (bytemask) and use the public
+# APIs to set those elements to None.
+def random_bitmask(size):
+    """
+    Parameters
+    ----------
+    size : int
+        number of bits
+    """
+    sz = bitmask_allocation_size_bytes(size)
+    data = np.random.randint(0, 255, dtype="u1", size=sz)
+    return data.view("i1")
+
+
+def expand_bits_to_bytes(arr):
+    def fix_binary(bstr):
+        bstr = bstr[2:]
+        diff = 8 - len(bstr)
+        return ("0" * diff + bstr)[::-1]
+
+    ba = bytearray(arr.data)
+    return list(map(int, "".join(map(fix_binary, map(bin, ba)))))
+
+
+def count_zero(arr):
+    arr = np.asarray(arr)
+    return np.count_nonzero(arr == 0)
+
+
+def assert_eq(left, right, **kwargs):
+    """Assert that two cudf-like things are equivalent
+
+    This equality test works for pandas/cudf dataframes/series/indexes/scalars
+    in the same way, and so makes it easier to perform parametrized testing
+    without switching between assert_frame_equal/assert_series_equal/...
+    functions.
+    """
+    # dtypes that we support but Pandas doesn't will convert to
+    # `object`. Check equality before that happens:
+    if kwargs.get("check_dtype", True):
+        if hasattr(left, "dtype") and hasattr(right, "dtype"):
+            if isinstance(
+                left.dtype, cudf.core.dtypes._BaseDtype
+            ) and not isinstance(
+                left.dtype, cudf.CategoricalDtype
+            ):  # leave categorical comparison to Pandas
+                assert_eq(left.dtype, right.dtype)
+
+    if hasattr(left, "to_pandas"):
+        left = left.to_pandas()
+    if hasattr(right, "to_pandas"):
+        right = right.to_pandas()
+    if isinstance(left, cupy.ndarray):
+        left = cupy.asnumpy(left)
+    if isinstance(right, cupy.ndarray):
+        right = cupy.asnumpy(right)
+
+    if isinstance(left, (pd.DataFrame, pd.Series, pd.Index)):
+        # TODO: A warning is emitted from the function
+        # pandas.testing.assert_[series, frame, index]_equal for some inputs:
+        # "DeprecationWarning: elementwise comparison failed; this will raise
+        # an error in the future."
+        # or "FutureWarning: elementwise ..."
+        # This warning comes from a call from pandas to numpy. It is ignored
+        # here because it cannot be fixed within cudf.
+        with warnings.catch_warnings():
+            warnings.simplefilter(
+                "ignore", (DeprecationWarning, FutureWarning)
+            )
+            if isinstance(left, pd.DataFrame):
+                tm.assert_frame_equal(left, right, **kwargs)
+            elif isinstance(left, pd.Series):
+                tm.assert_series_equal(left, right, **kwargs)
+            else:
+                tm.assert_index_equal(left, right, **kwargs)
+
+    elif isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
+        if np.issubdtype(left.dtype, np.floating) and np.issubdtype(
+            right.dtype, np.floating
+        ):
+            assert np.allclose(left, right, equal_nan=True)
+        else:
+            assert np.array_equal(left, right)
+    else:
+        # Use the overloaded __eq__ of the operands
+        if left == right:
+            return True
+        elif any(np.issubdtype(type(x), np.floating) for x in (left, right)):
+            np.testing.assert_almost_equal(left, right)
+        else:
+            np.testing.assert_equal(left, right)
+    return True
+
+
+def assert_neq(left, right, **kwargs):
+    __tracebackhide__ = True
+    try:
+        assert_eq(left, right, **kwargs)
+    except AssertionError:
+        pass
+    else:
+        raise AssertionError
+
+
+def assert_exceptions_equal(
+    lfunc,
+    rfunc,
+    lfunc_args_and_kwargs=None,
+    rfunc_args_and_kwargs=None,
+    check_exception_type=True,
+):
+    """Compares if two functions ``lfunc`` and ``rfunc`` raise
+    same exception or not.
+
+    Parameters
+    ----------
+    lfunc : callable
+        A callable function to obtain the Exception.
+    rfunc : callable
+        A callable function to compare the Exception
+        obtained by calling ``rfunc``.
+    lfunc_args_and_kwargs : tuple, default None
+        Tuple containing positional arguments at first position,
+        and key-word arguments at second position that need to be passed into
+        ``lfunc``. If the tuple is of length 1, it must either contain
+        positional arguments(as a Sequence) or key-word arguments(as a Mapping
+        dict).
+    rfunc_args_and_kwargs : tuple, default None
+        Tuple containing positional arguments at first position,
+        and key-word arguments at second position that need to be passed into
+        ``rfunc``. If the tuple is of length 1, it must either contain
+        positional arguments(as a Sequence) or key-word arguments(as a Mapping
+        dict).
+    check_exception_type : boolean, default True
+        Whether to compare the exception types raised by ``lfunc``
+        with ``rfunc`` exception type or not. If False, ``rfunc``
+        is simply evaluated against `Exception` type.
+
+    Returns
+    -------
+    None
+        If exceptions raised by ``lfunc`` and
+        ``rfunc`` match.
+
+    Raises
+    ------
+    AssertionError
+        If call to ``lfunc`` doesn't raise any Exception.
+    """
+
+    lfunc_args, lfunc_kwargs = _get_args_kwars_for_assert_exceptions(
+        lfunc_args_and_kwargs
+    )
+    rfunc_args, rfunc_kwargs = _get_args_kwars_for_assert_exceptions(
+        rfunc_args_and_kwargs
+    )
+
+    try:
+        lfunc(*lfunc_args, **lfunc_kwargs)
+    except KeyboardInterrupt:
+        raise
+    except Exception as e:
+        with pytest.raises(type(e) if check_exception_type else Exception):
+            rfunc(*rfunc_args, **rfunc_kwargs)
+    else:
+        raise AssertionError("Expected to fail with an Exception.")
+
+
+def _get_args_kwars_for_assert_exceptions(func_args_and_kwargs):
+    if func_args_and_kwargs is None:
+        return [], {}
+    else:
+        if len(func_args_and_kwargs) == 1:
+            func_args, func_kwargs = [], {}
+            if isinstance(func_args_and_kwargs[0], abc.Sequence):
+                func_args = func_args_and_kwargs[0]
+            elif isinstance(func_args_and_kwargs[0], abc.Mapping):
+                func_kwargs = func_args_and_kwargs[0]
+            else:
+                raise ValueError(
+                    "length 1 func_args_and_kwargs must be "
+                    "either a Sequence or a Mapping"
+                )
+        elif len(func_args_and_kwargs) == 2:
+            if not isinstance(func_args_and_kwargs[0], abc.Sequence):
+                raise ValueError(
+                    "Positional argument at 1st position of "
+                    "func_args_and_kwargs should be a sequence."
+                )
+            if not isinstance(func_args_and_kwargs[1], abc.Mapping):
+                raise ValueError(
+                    "Key-word argument at 2nd position of "
+                    "func_args_and_kwargs should be a dictionary mapping."
+                )
+
+            func_args, func_kwargs = func_args_and_kwargs
+        else:
+            raise ValueError("func_args_and_kwargs must be of length 1 or 2")
+        return func_args, func_kwargs
+
+
+def gen_rand(dtype, size, **kwargs):
+    dtype = cudf.dtype(dtype)
+    if dtype.kind == "f":
+        res = np.random.random(size=size).astype(dtype)
+        if kwargs.get("positive_only", False):
+            return res
+        else:
+            return res * 2 - 1
+    elif dtype == np.int8 or dtype == np.int16:
+        low = kwargs.get("low", -32)
+        high = kwargs.get("high", 32)
+        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+    elif dtype.kind == "i":
+        low = kwargs.get("low", -10000)
+        high = kwargs.get("high", 10000)
+        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+    elif dtype == np.uint8 or dtype == np.uint16:
+        low = kwargs.get("low", 0)
+        high = kwargs.get("high", 32)
+        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+    elif dtype.kind == "u":
+        low = kwargs.get("low", 0)
+        high = kwargs.get("high", 128)
+        return np.random.randint(low=low, high=high, size=size).astype(dtype)
+    elif dtype.kind == "b":
+        low = kwargs.get("low", 0)
+        high = kwargs.get("high", 2)
+        return np.random.randint(low=low, high=high, size=size).astype(
+            np.bool_
+        )
+    elif dtype.kind == "M":
+        low = kwargs.get("low", 0)
+        time_unit, _ = np.datetime_data(dtype)
+        high = kwargs.get(
+            "high",
+            int(1e18) / _unit_to_nanoseconds_conversion[time_unit],
+        )
+        return pd.to_datetime(
+            np.random.randint(low=low, high=high, size=size), unit=time_unit
+        )
+    elif dtype.kind in ("O", "U"):
+        low = kwargs.get("low", 10)
+        high = kwargs.get("high", 11)
+        nchars = np.random.randint(low=low, high=high, size=1)[0]
+        char_options = np.array(list(string.ascii_letters + string.digits))
+        all_chars = "".join(np.random.choice(char_options, nchars * size))
+        return np.array(
+            [all_chars[nchars * i : nchars * (i + 1)] for i in range(size)]
+        )
+
+    raise NotImplementedError(f"dtype.kind={dtype.kind}")
+
+
+def gen_rand_series(dtype, size, **kwargs):
+    values = gen_rand(dtype, size, **kwargs)
+    if kwargs.get("has_nulls", False):
+        return cudf.Series.from_masked_array(values, random_bitmask(size))
+
+    return cudf.Series(values)
+
+
+def _decimal_series(input, dtype):
+    return cudf.Series(
+        [x if x is None else Decimal(x) for x in input],
+        dtype=dtype,
+    )
+
+
+@contextmanager
+def does_not_raise():
+    yield
+
+
+def assert_column_memory_eq(
+    lhs: cudf.core.column.ColumnBase, rhs: cudf.core.column.ColumnBase
+):
+    """Assert the memory location and size of `lhs` and `rhs` are equivalent.
+
+    Both data pointer and mask pointer are checked. Also recursively check for
+    children to the same constraints. Also fails check if the number of
+    children mismatches at any level.
+    """
+
+    def get_ptr(x) -> int:
+        return x.get_ptr(mode="read") if x else 0
+
+    assert get_ptr(lhs.base_data) == get_ptr(rhs.base_data)
+    assert get_ptr(lhs.base_mask) == get_ptr(rhs.base_mask)
+    assert lhs.base_size == rhs.base_size
+    assert lhs.offset == rhs.offset
+    assert lhs.size == rhs.size
+    assert len(lhs.base_children) == len(rhs.base_children)
+    for lhs_child, rhs_child in zip(lhs.base_children, rhs.base_children):
+        assert_column_memory_eq(lhs_child, rhs_child)
+    if isinstance(lhs, cudf.core.column.CategoricalColumn) and isinstance(
+        rhs, cudf.core.column.CategoricalColumn
+    ):
+        assert_column_memory_eq(lhs.categories, rhs.categories)
+        assert_column_memory_eq(lhs.codes, rhs.codes)
+
+
+def assert_column_memory_ne(
+    lhs: cudf.core.column.ColumnBase, rhs: cudf.core.column.ColumnBase
+):
+    try:
+        assert_column_memory_eq(lhs, rhs)
+    except AssertionError:
+        return
+    raise AssertionError("lhs and rhs holds the same memory.")
+
+
+def _create_pandas_series_float64_default(
+    data=None, index=None, dtype=None, *args, **kwargs
+):
+    # Wrapper around pd.Series using a float64
+    # default dtype for empty data to silence warnings.
+    # TODO: Remove this in pandas-2.0 upgrade
+    if dtype is None and (
+        data is None or (not is_scalar(data) and len(data) == 0)
+    ):
+        dtype = "float64"
+    return pd.Series(data=data, index=index, dtype=dtype, *args, **kwargs)
+
+
+def _create_cudf_series_float64_default(
+    data=None, index=None, dtype=None, *args, **kwargs
+):
+    # Wrapper around cudf.Series using a float64
+    # default dtype for empty data to silence warnings.
+    # TODO: Remove this in pandas-2.0 upgrade
+    if dtype is None and (
+        data is None or (not is_scalar(data) and len(data) == 0)
+    ):
+        dtype = "float64"
+    return cudf.Series(data=data, index=index, dtype=dtype, *args, **kwargs)
+
+
+parametrize_numeric_dtypes_pairwise = pytest.mark.parametrize(
+    "left_dtype,right_dtype",
+    list(itertools.combinations_with_replacement(NUMERIC_TYPES, 2)),
+)
+
+
+@contextmanager
+def expect_warning_if(condition, warning=FutureWarning, *args, **kwargs):
+    """Catch a warning using pytest.warns if the expect_warning is True.
+
+    All arguments are forwarded to pytest.warns if expect_warning is True.
+    """
+    if condition:
+        with pytest.warns(warning, *args, **kwargs):
+            yield
+    else:
+        yield
+
+
+def sv_to_udf_str(sv):
+    """
+    Cast a string_view object to a udf_string object
+
+    This placeholder function never runs in python
+    It exists only for numba to have something to replace
+    with the typing and lowering code below
+
+    This is similar conceptually to needing a translation
+    engine to emit an expression in target language "B" when
+    there is no equivalent in the source language "A" to
+    translate from. This function effectively defines the
+    expression in language "A" and the associated typing
+    and lowering describe the translation process, despite
+    the expression having no meaning in language "A"
+    """
+    pass
+
+
+@cuda_decl_registry.register_global(sv_to_udf_str)
+class StringViewToUDFStringDecl(AbstractTemplate):
+    def generic(self, args, kws):
+        if isinstance(args[0], StringView) and len(args) == 1:
+            return nb_signature(udf_string, string_view)
+
+
+@cuda_lower(sv_to_udf_str, string_view)
+def sv_to_udf_str_testing_lowering(context, builder, sig, args):
+    return cast_string_view_to_udf_string(
+        context, builder, sig.args[0], sig.return_type, args[0]
+    )
diff --git a/python/cudf/cudf/testing/dataset_generator.py b/python/cudf/cudf/testing/dataset_generator.py
new file mode 100644
index 0000000..1ba2052
--- /dev/null
+++ b/python/cudf/cudf/testing/dataset_generator.py
@@ -0,0 +1,857 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+# This module is for generating "synthetic" datasets. It was originally
+# designed for testing filtered reading. Generally, it should be useful
+# if you want to generate data where certain phenomena (e.g., cardinality)
+# are exaggerated.
+
+import copy
+import random
+import string
+import uuid
+from multiprocessing import Pool
+
+import mimesis
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from mimesis import Generic
+from pyarrow import parquet as pq
+
+import cudf
+from cudf.utils.dtypes import np_to_pa_dtype
+
+
+class ColumnParameters:
+    """Parameters for generating column of data
+
+    Attributes
+    ----------
+    cardinality : int or None
+        Size of a random set of values that generated data is sampled from.
+        The values in the random set are derived from the given generator.
+        If cardinality is None, the Iterable returned by the given generator
+        is invoked for each value to be generated.
+    null_frequency : 0.1
+        Probability of a generated value being null
+    generator : Callable
+        Function for generating random data. It is passed a Mimesis Generic
+        provider and returns an Iterable that generates data.
+    is_sorted : bool
+        Sort this column. Columns are sorted in same order as ColumnParameters
+        instances stored in column_params of Parameters. If there are one or
+        more columns marked as sorted, the generated PyArrow Table will be
+        converted to a Pandas DataFrame to do the sorting. This may implicitly
+        convert numbers to floats in the presence of nulls.
+    dtype : optional
+        a numpy dtype to control the format of the data
+    """
+
+    def __init__(
+        self,
+        cardinality=100,
+        null_frequency=0.1,
+        generator=lambda g: [g.address.country for _ in range(100)],
+        is_sorted=True,
+        dtype=None,
+    ):
+        self.cardinality = cardinality
+        self.null_frequency = null_frequency
+        self.generator = generator
+        self.is_sorted = is_sorted
+        self.dtype = dtype
+
+
+class Parameters:
+    """Parameters for random dataset generation
+
+    Attributes
+    ----------
+    num_rows : int
+        Number of rows to generate
+    column_parameters : List[ColumnParams]
+        ColumnParams for each column
+    seed : int or None, default None
+        Seed for random data generation
+    """
+
+    def __init__(
+        self,
+        num_rows=2048,
+        column_parameters=None,
+        seed=None,
+    ):
+        self.num_rows = num_rows
+        if column_parameters is None:
+            column_parameters = []
+        self.column_parameters = column_parameters
+        self.seed = seed
+
+
+def _write(tbl, path, format):
+    if format["name"] == "parquet":
+        if isinstance(tbl, pa.Table):
+            pq.write_table(tbl, path, row_group_size=format["row_group_size"])
+        elif isinstance(tbl, pd.DataFrame):
+            tbl.to_parquet(path, row_group_size=format["row_group_size"])
+
+
+def _generate_column(column_params, num_rows):
+    # If cardinality is specified, we create a set to sample from.
+    # Otherwise, we simply use the given generator to generate each value.
+
+    if column_params.cardinality is not None:
+        # Construct set of values to sample from where
+        # set size = cardinality
+
+        if (
+            isinstance(column_params.dtype, str)
+            and column_params.dtype == "category"
+        ):
+            vals = pa.array(
+                column_params.generator,
+                size=column_params.cardinality,
+                safe=False,
+            )
+            return pa.DictionaryArray.from_arrays(
+                dictionary=vals,
+                indices=np.random.randint(
+                    low=0, high=len(vals), size=num_rows
+                ),
+                mask=np.random.choice(
+                    [True, False],
+                    size=num_rows,
+                    p=[
+                        column_params.null_frequency,
+                        1 - column_params.null_frequency,
+                    ],
+                )
+                if column_params.null_frequency > 0.0
+                else None,
+            )
+
+        if hasattr(column_params.dtype, "to_arrow"):
+            arrow_type = column_params.dtype.to_arrow()
+        elif column_params.dtype is not None:
+            arrow_type = np_to_pa_dtype(cudf.dtype(column_params.dtype))
+        else:
+            arrow_type = None
+
+        if isinstance(column_params.dtype, cudf.StructDtype):
+            vals = pa.StructArray.from_arrays(
+                column_params.generator,
+                names=column_params.dtype.fields.keys(),
+                mask=pa.array(
+                    np.random.choice(
+                        [True, False],
+                        size=num_rows,
+                        p=[
+                            column_params.null_frequency,
+                            1 - column_params.null_frequency,
+                        ],
+                    )
+                )
+                if column_params.null_frequency > 0.0
+                else None,
+            )
+            return vals
+        elif not isinstance(arrow_type, pa.lib.Decimal128Type):
+            vals = pa.array(
+                column_params.generator,
+                size=column_params.cardinality,
+                safe=False,
+                type=arrow_type,
+            )
+        vals = pa.array(
+            np.random.choice(column_params.generator, size=num_rows)
+            if isinstance(arrow_type, pa.lib.Decimal128Type)
+            else np.random.choice(vals, size=num_rows),
+            mask=np.random.choice(
+                [True, False],
+                size=num_rows,
+                p=[
+                    column_params.null_frequency,
+                    1 - column_params.null_frequency,
+                ],
+            )
+            if column_params.null_frequency > 0.0
+            else None,
+            size=num_rows,
+            safe=False,
+            type=None
+            if isinstance(arrow_type, pa.lib.Decimal128Type)
+            else arrow_type,
+        )
+        if isinstance(arrow_type, pa.lib.Decimal128Type):
+            vals = vals.cast(arrow_type, safe=False)
+        return vals
+    else:
+        # Generate data for current column
+        return pa.array(
+            column_params.generator,
+            mask=np.random.choice(
+                [True, False],
+                size=num_rows,
+                p=[
+                    column_params.null_frequency,
+                    1 - column_params.null_frequency,
+                ],
+            )
+            if column_params.null_frequency > 0.0
+            else None,
+            size=num_rows,
+            safe=False,
+        )
+
+
+def generate(
+    path,
+    parameters,
+    format=None,
+    use_threads=True,
+):
+    """
+    Generate dataset using given parameters and write to given format
+
+    Parameters
+    ----------
+    path : str or file-like object
+        Path to write to
+    parameters : Parameters
+        Parameters specifying how to randomly generate data
+    format : Dict
+        Format to write
+    """
+    if format is None:
+        format = {"name": "parquet", "row_group_size": 64}
+    df = get_dataframe(parameters, use_threads)
+
+    # Write
+    _write(df, path, format)
+
+
+def get_dataframe(parameters, use_threads):
+    # Initialize seeds
+    if parameters.seed is not None:
+        np.random.seed(parameters.seed)
+
+    # For each column, use a generic Mimesis producer to create an Iterable
+    # for generating data
+    for i, column_params in enumerate(parameters.column_parameters):
+        if column_params.dtype is None:
+            column_params.generator = column_params.generator(
+                Generic("en", seed=parameters.seed)
+            )
+        else:
+            column_params.generator = column_params.generator()
+
+    # Get schema for each column
+    table_fields = []
+    for i, column_params in enumerate(parameters.column_parameters):
+        if (
+            isinstance(column_params.dtype, str)
+            and column_params.dtype == "category"
+        ):
+            arrow_type = pa.dictionary(
+                index_type=pa.int64(),
+                value_type=np_to_pa_dtype(
+                    cudf.dtype(type(next(iter(column_params.generator))))
+                ),
+            )
+        elif hasattr(column_params.dtype, "to_arrow"):
+            arrow_type = column_params.dtype.to_arrow()
+        else:
+            arrow_type = np_to_pa_dtype(
+                cudf.dtype(type(next(iter(column_params.generator))))
+                if column_params.dtype is None
+                else column_params.dtype
+            )
+        table_fields.append(
+            pa.field(
+                name=str(i),
+                type=arrow_type,
+                nullable=column_params.null_frequency > 0,
+            )
+        )
+
+    schema = pa.schema(table_fields)
+
+    # Initialize column data and which columns should be sorted
+    column_data = [None] * len(parameters.column_parameters)
+    columns_to_sort = [
+        str(i)
+        for i, column_params in enumerate(parameters.column_parameters)
+        if column_params.is_sorted
+    ]
+    # Generate data
+    if not use_threads:
+        for i, column_params in enumerate(parameters.column_parameters):
+            column_data[i] = _generate_column(
+                column_params, parameters.num_rows
+            )
+    else:
+        pool = Pool(pa.cpu_count())
+        column_data = pool.starmap(
+            _generate_column,
+            [
+                (column_params, parameters.num_rows)
+                for i, column_params in enumerate(parameters.column_parameters)
+            ],
+        )
+        pool.close()
+        pool.join()
+    # Convert to Pandas DataFrame and sort columns appropriately
+    tbl = pa.Table.from_arrays(
+        column_data,
+        schema=schema,
+    )
+    if columns_to_sort:
+        tbl = tbl.to_pandas()
+        tbl = tbl.sort_values(columns_to_sort)
+        tbl = pa.Table.from_pandas(tbl, schema)
+    return tbl
+
+
+def rand_dataframe(
+    dtypes_meta, rows, seed=random.randint(0, 2**32 - 1), use_threads=True
+):
+    """
+    Generates a random table.
+
+    Parameters
+    ----------
+    dtypes_meta : List of dict
+        Specifies list of dtype meta data. dtype meta data should
+        be a dictionary of the form example:
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10}
+        `"str"` dtype can contain an extra key `max_string_length` to
+        control the maximum size of the strings being generated in each row.
+        If not specified, it will default to 1000.
+    rows : int
+        Specifies the number of rows to be generated.
+    seed : int
+        Specifies the `seed` value to be utilized by all downstream
+        random data generation APIs.
+    use_threads : bool
+        Indicates whether to use threads pools to build the columns
+
+    Returns
+    -------
+    PyArrow Table
+        A Table with columns of corresponding dtypes mentioned in `dtypes_meta`
+    """
+    # Apply seed
+    random.seed(seed)
+    np.random.seed(seed)
+    mimesis.random.random.seed(seed)
+
+    column_params = []
+    for meta in dtypes_meta:
+        dtype = copy.deepcopy(meta["dtype"])
+        null_frequency = copy.deepcopy(meta["null_frequency"])
+        cardinality = copy.deepcopy(meta["cardinality"])
+
+        if dtype == "list":
+            lists_max_length = meta["lists_max_length"]
+            nesting_max_depth = meta["nesting_max_depth"]
+            value_type = meta["value_type"]
+            nesting_depth = np.random.randint(1, nesting_max_depth)
+
+            dtype = cudf.core.dtypes.ListDtype(value_type)
+
+            # Determining the `dtype` from the `value_type`
+            # and the nesting_depth
+            i = 1
+            while i < nesting_depth:
+                dtype = cudf.core.dtypes.ListDtype(dtype)
+                i += 1
+
+            column_params.append(
+                ColumnParameters(
+                    cardinality=cardinality,
+                    null_frequency=null_frequency,
+                    generator=list_generator(
+                        dtype=value_type,
+                        size=cardinality,
+                        nesting_depth=nesting_depth,
+                        lists_max_length=lists_max_length,
+                    ),
+                    is_sorted=False,
+                    dtype=dtype,
+                )
+            )
+        elif dtype == "struct":
+            nesting_max_depth = meta["nesting_max_depth"]
+            max_types_at_each_level = meta["max_types_at_each_level"]
+            max_null_frequency = meta["max_null_frequency"]
+            nesting_depth = np.random.randint(1, nesting_max_depth)
+            structDtype = create_nested_struct_type(
+                max_types_at_each_level=max_types_at_each_level,
+                nesting_level=nesting_depth,
+            )
+
+            column_params.append(
+                ColumnParameters(
+                    cardinality=cardinality,
+                    null_frequency=null_frequency,
+                    generator=struct_generator(
+                        dtype=structDtype,
+                        cardinality=cardinality,
+                        size=rows,
+                        max_null_frequency=max_null_frequency,
+                    ),
+                    is_sorted=False,
+                    dtype=structDtype,
+                )
+            )
+        elif dtype == "decimal64":
+            max_precision = meta.get(
+                "max_precision", cudf.Decimal64Dtype.MAX_PRECISION
+            )
+            precision = np.random.randint(1, max_precision)
+            scale = np.random.randint(0, precision)
+            dtype = cudf.Decimal64Dtype(precision=precision, scale=scale)
+            column_params.append(
+                ColumnParameters(
+                    cardinality=cardinality,
+                    null_frequency=null_frequency,
+                    generator=decimal_generator(dtype=dtype, size=cardinality),
+                    is_sorted=False,
+                    dtype=dtype,
+                )
+            )
+        elif dtype == "decimal32":
+            max_precision = meta.get(
+                "max_precision", cudf.Decimal32Dtype.MAX_PRECISION
+            )
+            precision = np.random.randint(1, max_precision)
+            scale = np.random.randint(0, precision)
+            dtype = cudf.Decimal32Dtype(precision=precision, scale=scale)
+            column_params.append(
+                ColumnParameters(
+                    cardinality=cardinality,
+                    null_frequency=null_frequency,
+                    generator=decimal_generator(dtype=dtype, size=cardinality),
+                    is_sorted=False,
+                    dtype=dtype,
+                )
+            )
+        elif dtype == "decimal128":
+            max_precision = meta.get(
+                "max_precision", cudf.Decimal128Dtype.MAX_PRECISION
+            )
+            precision = np.random.randint(1, max_precision)
+            scale = np.random.randint(0, precision)
+            dtype = cudf.Decimal128Dtype(precision=precision, scale=scale)
+            column_params.append(
+                ColumnParameters(
+                    cardinality=cardinality,
+                    null_frequency=null_frequency,
+                    generator=decimal_generator(dtype=dtype, size=cardinality),
+                    is_sorted=False,
+                    dtype=dtype,
+                )
+            )
+        elif dtype == "category":
+            column_params.append(
+                ColumnParameters(
+                    cardinality=cardinality,
+                    null_frequency=null_frequency,
+                    generator=lambda cardinality=cardinality: [
+                        _unique_string() for _ in range(cardinality)
+                    ],
+                    is_sorted=False,
+                    dtype="category",
+                )
+            )
+        else:
+            dtype = cudf.dtype(dtype)
+            if dtype.kind in ("i", "u"):
+                column_params.append(
+                    ColumnParameters(
+                        cardinality=cardinality,
+                        null_frequency=null_frequency,
+                        generator=int_generator(
+                            dtype=dtype,
+                            size=cardinality,
+                            min_bound=meta.get("min_bound", None),
+                            max_bound=meta.get("max_bound", None),
+                        ),
+                        is_sorted=False,
+                        dtype=dtype,
+                    )
+                )
+            elif dtype.kind == "f":
+                column_params.append(
+                    ColumnParameters(
+                        cardinality=cardinality,
+                        null_frequency=null_frequency,
+                        generator=float_generator(
+                            dtype=dtype,
+                            size=cardinality,
+                            min_bound=meta.get("min_bound", None),
+                            max_bound=meta.get("max_bound", None),
+                        ),
+                        is_sorted=False,
+                        dtype=dtype,
+                    )
+                )
+            elif dtype.kind in ("U", "O"):
+                column_params.append(
+                    ColumnParameters(
+                        cardinality=cardinality,
+                        null_frequency=null_frequency,
+                        generator=lambda cardinality=cardinality: [
+                            _generate_string(
+                                string.printable,
+                                np.random.randint(
+                                    low=0,
+                                    high=meta.get("max_string_length", 1000),
+                                    size=1,
+                                )[0],
+                            )
+                            for _ in range(cardinality)
+                        ],
+                        is_sorted=False,
+                        dtype=dtype,
+                    )
+                )
+            elif dtype.kind == "M":
+                column_params.append(
+                    ColumnParameters(
+                        cardinality=cardinality,
+                        null_frequency=null_frequency,
+                        generator=datetime_generator(
+                            dtype=dtype,
+                            size=cardinality,
+                            min_bound=meta.get("min_bound", None),
+                            max_bound=meta.get("max_bound", None),
+                        ),
+                        is_sorted=False,
+                        dtype=cudf.dtype(dtype),
+                    )
+                )
+            elif dtype.kind == "m":
+                column_params.append(
+                    ColumnParameters(
+                        cardinality=cardinality,
+                        null_frequency=null_frequency,
+                        generator=timedelta_generator(
+                            dtype=dtype,
+                            size=cardinality,
+                            min_bound=meta.get("min_bound", None),
+                            max_bound=meta.get("max_bound", None),
+                        ),
+                        is_sorted=False,
+                        dtype=cudf.dtype(dtype),
+                    )
+                )
+            elif dtype.kind == "b":
+                column_params.append(
+                    ColumnParameters(
+                        cardinality=cardinality,
+                        null_frequency=null_frequency,
+                        generator=boolean_generator(cardinality),
+                        is_sorted=False,
+                        dtype=cudf.dtype(dtype),
+                    )
+                )
+            else:
+                raise TypeError(f"Unsupported dtype: {dtype}")
+            # TODO: Add List column support once
+            # https://github.com/rapidsai/cudf/pull/6075
+            # is merged.
+
+    df = get_dataframe(
+        Parameters(
+            num_rows=rows,
+            column_parameters=column_params,
+            seed=seed,
+        ),
+        use_threads=use_threads,
+    )
+
+    return df
+
+
+def int_generator(dtype, size, min_bound=None, max_bound=None):
+    """
+    Generator for int data
+    """
+    if min_bound is not None and max_bound is not None:
+        low, high = min_bound, max_bound
+    else:
+        iinfo = np.iinfo(dtype)
+        low, high = iinfo.min, iinfo.max
+
+    return lambda: np.random.randint(
+        low=low,
+        high=high,
+        size=size,
+        dtype=dtype,
+    )
+
+
+def float_generator(dtype, size, min_bound=None, max_bound=None):
+    """
+    Generator for float data
+    """
+    if min_bound is not None and max_bound is not None:
+        low, high = min_bound, max_bound
+        return lambda: np.random.uniform(
+            low=low,
+            high=high,
+            size=size,
+        )
+    else:
+        finfo = np.finfo(dtype)
+        return (
+            lambda: np.random.uniform(
+                low=finfo.min / 2,
+                high=finfo.max / 2,
+                size=size,
+            )
+            * 2
+        )
+
+
+def datetime_generator(dtype, size, min_bound=None, max_bound=None):
+    """
+    Generator for datetime data
+    """
+    if min_bound is not None and max_bound is not None:
+        low, high = min_bound, max_bound
+    else:
+        iinfo = np.iinfo("int64")
+        low, high = iinfo.min + 1, iinfo.max
+
+    return lambda: np.random.randint(
+        low=np.datetime64(low, "ns").astype(dtype).astype("int"),
+        high=np.datetime64(high, "ns").astype(dtype).astype("int"),
+        size=size,
+    )
+
+
+def timedelta_generator(dtype, size, min_bound=None, max_bound=None):
+    """
+    Generator for timedelta data
+    """
+    if min_bound is not None and max_bound is not None:
+        low, high = min_bound, max_bound
+    else:
+        iinfo = np.iinfo("int64")
+        low, high = iinfo.min + 1, iinfo.max
+
+    return lambda: np.random.randint(
+        low=np.timedelta64(low, "ns").astype(dtype).astype("int"),
+        high=np.timedelta64(high, "ns").astype(dtype).astype("int"),
+        size=size,
+    )
+
+
+def boolean_generator(size):
+    """
+    Generator for bool data
+    """
+    return lambda: np.random.choice(a=[False, True], size=size)
+
+
+def decimal_generator(dtype, size):
+    max_integral = 10 ** (dtype.precision - dtype.scale) - 1
+    max_float = (10**dtype.scale - 1) if dtype.scale != 0 else 0
+    return lambda: (
+        np.random.uniform(
+            low=-max_integral,
+            high=max_integral + (max_float / 10**dtype.scale),
+            size=size,
+        )
+    )
+
+
+def get_values_for_nested_data(dtype, lists_max_length=None, size=None):
+    """
+    Returns list of values based on dtype.
+    """
+    if size is None:
+        cardinality = np.random.randint(0, lists_max_length)
+    else:
+        cardinality = size
+
+    dtype = cudf.dtype(dtype)
+    if dtype.kind in ("i", "u"):
+        values = int_generator(dtype=dtype, size=cardinality)()
+    elif dtype.kind == "f":
+        values = float_generator(dtype=dtype, size=cardinality)()
+    elif dtype.kind in ("U", "O"):
+        values = [
+            _generate_string(
+                string.printable,
+                100,
+            )
+            for _ in range(cardinality)
+        ]
+    elif dtype.kind == "M":
+        values = datetime_generator(dtype=dtype, size=cardinality)().astype(
+            dtype
+        )
+    elif dtype.kind == "m":
+        values = timedelta_generator(dtype=dtype, size=cardinality)().astype(
+            dtype
+        )
+    elif dtype.kind == "b":
+        values = boolean_generator(cardinality)().astype(dtype)
+    else:
+        raise TypeError(f"Unsupported dtype: {dtype}")
+
+    return values
+
+
+def make_lists(dtype, lists_max_length, nesting_depth, top_level_list):
+    """
+    Helper to create random list of lists with `nesting_depth` and
+    specified value type `dtype`.
+    """
+    nesting_depth -= 1
+    if nesting_depth >= 0:
+        L = np.random.randint(1, lists_max_length)
+        for i in range(L):
+            top_level_list.append(
+                make_lists(
+                    dtype=dtype,
+                    lists_max_length=lists_max_length,
+                    nesting_depth=nesting_depth,
+                    top_level_list=[],
+                )
+            )
+    else:
+        top_level_list = get_values_for_nested_data(
+            dtype=dtype, lists_max_length=lists_max_length
+        )
+        # To ensure numpy arrays are not passed as input to
+        # list constructor, returning a python list object here.
+        if isinstance(top_level_list, np.ndarray):
+            top_level_list = top_level_list.tolist()
+
+    return top_level_list
+
+
+def make_array_for_struct(dtype, cardinality, size, max_null_frequency):
+    """
+    Helper to create a pa.array with `size` and `dtype`
+    for a `StructArray`.
+    """
+
+    null_frequency = np.random.uniform(low=0, high=max_null_frequency)
+    local_cardinality = max(np.random.randint(low=0, high=cardinality), 1)
+    data = get_values_for_nested_data(
+        dtype=dtype.type.to_pandas_dtype(), size=local_cardinality
+    )
+    vals = np.random.choice(data, size=size)
+
+    return pa.array(
+        vals,
+        mask=np.random.choice(
+            [True, False],
+            size=size,
+            p=[null_frequency, 1 - null_frequency],
+        )
+        if null_frequency > 0.0
+        else None,
+        size=size,
+        safe=False,
+        type=dtype.type,
+    )
+
+
+def get_nested_lists(dtype, size, nesting_depth, lists_max_length):
+    """
+    Returns a list of nested lists with random nesting
+    depth and random nested lists length.
+    """
+    list_of_lists = []
+
+    while len(list_of_lists) <= size:
+        list_of_lists.extend(
+            make_lists(
+                dtype=dtype,
+                lists_max_length=lists_max_length,
+                nesting_depth=nesting_depth,
+                top_level_list=[],
+            )
+        )
+
+    return list_of_lists
+
+
+def get_nested_structs(dtype, cardinality, size, max_null_frequency):
+    """
+    Returns a list of arrays with random data
+    corresponding to the dtype provided.
+    ``dtype`` here should be a ``cudf.StructDtype``
+    """
+    list_of_arrays = []
+
+    for name, col_dtype in dtype.fields.items():
+        if isinstance(col_dtype, cudf.StructDtype):
+            result_arrays = get_nested_structs(
+                col_dtype, cardinality, size, max_null_frequency
+            )
+            result_arrays = pa.StructArray.from_arrays(
+                result_arrays, names=col_dtype.fields.keys()
+            )
+        else:
+            result_arrays = make_array_for_struct(
+                dtype=dtype._typ[name],
+                cardinality=cardinality,
+                size=size,
+                max_null_frequency=max_null_frequency,
+            )
+        list_of_arrays.append(result_arrays)
+
+    return list_of_arrays
+
+
+def list_generator(dtype, size, nesting_depth, lists_max_length):
+    """
+    Generator for list data
+    """
+    return lambda: get_nested_lists(
+        dtype=dtype,
+        size=size,
+        nesting_depth=nesting_depth,
+        lists_max_length=lists_max_length,
+    )
+
+
+def struct_generator(dtype, cardinality, size, max_null_frequency):
+    """
+    Generator for struct data
+    """
+    return lambda: get_nested_structs(
+        dtype=dtype,
+        cardinality=cardinality,
+        size=size,
+        max_null_frequency=max_null_frequency,
+    )
+
+
+def create_nested_struct_type(max_types_at_each_level, nesting_level):
+    dtypes_list = cudf.utils.dtypes.ALL_TYPES
+    picked_types = np.random.choice(list(dtypes_list), max_types_at_each_level)
+    type_dict = {}
+    for name, type_ in enumerate(picked_types):
+        if type_ == "struct":
+            type_dict[str(name)] = create_nested_struct_type(
+                max_types_at_each_level, nesting_level - 1
+            )
+        else:
+            type_dict[str(name)] = cudf.dtype(type_)
+    return cudf.StructDtype(type_dict)
+
+
+def _generate_string(str_seq: str, length: int = 10) -> str:
+    return "".join(random.choices(str_seq, k=length))
+
+
+def _unique_string() -> str:
+    return str(uuid.uuid4()).replace("-", "")
diff --git a/python/cudf/cudf/testing/testing.py b/python/cudf/cudf/testing/testing.py
new file mode 100644
index 0000000..a9c54dd
--- /dev/null
+++ b/python/cudf/cudf/testing/testing.py
@@ -0,0 +1,724 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from __future__ import annotations
+
+from typing import Union
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf._lib.unary import is_nan
+from cudf.api.types import (
+    is_categorical_dtype,
+    is_decimal_dtype,
+    is_interval_dtype,
+    is_list_dtype,
+    is_numeric_dtype,
+    is_string_dtype,
+    is_struct_dtype,
+)
+from cudf.core.missing import NA, NaT
+
+
+def dtype_can_compare_equal_to_other(dtype):
+    # return True if values of this dtype can compare
+    # as equal to equal values of a different dtype
+    return not (
+        is_string_dtype(dtype)
+        or is_list_dtype(dtype)
+        or is_struct_dtype(dtype)
+        or is_decimal_dtype(dtype)
+        or is_interval_dtype(dtype)
+    )
+
+
+def _check_isinstance(left, right, obj):
+    if not isinstance(left, obj):
+        raise AssertionError(
+            f"{obj} Expected type {obj}, found {type(left)} instead"
+        )
+    elif not isinstance(right, obj):
+        raise AssertionError(
+            f"{obj} Expected type {obj}, found {type(right)} instead"
+        )
+
+
+def raise_assert_detail(obj, message, left, right, diff=None):
+
+    msg = f"""{obj} are different
+
+{message}
+[left]:  {left}
+[right]: {right}"""
+
+    if diff is not None:
+        msg += f"\n[diff]: {diff}"
+
+    raise AssertionError(msg)
+
+
+def _check_types(
+    left, right, check_categorical=True, exact="equiv", obj="Index"
+):
+    if not exact or exact == "equiv":
+        if (
+            isinstance(left, cudf.RangeIndex)
+            and isinstance(
+                right,
+                (
+                    cudf.Int8Index,
+                    cudf.Int16Index,
+                    cudf.Int32Index,
+                    cudf.Int64Index,
+                ),
+            )
+        ) or (
+            isinstance(right, cudf.RangeIndex)
+            and isinstance(
+                left,
+                (
+                    cudf.Int8Index,
+                    cudf.Int16Index,
+                    cudf.Int32Index,
+                    cudf.Int64Index,
+                ),
+            )
+        ):
+            return
+
+    if type(left) != type(right):
+        raise_assert_detail(
+            obj, "Class types are different", f"{type(left)}", f"{type(right)}"
+        )
+
+    if (
+        exact
+        and not isinstance(left, cudf.MultiIndex)
+        and is_categorical_dtype(left)
+    ):
+        if left.dtype != right.dtype:
+            raise_assert_detail(
+                obj, "Categorical difference", f"{left}", f"{right}"
+            )
+
+
+def assert_column_equal(
+    left,
+    right,
+    check_dtype=True,
+    check_column_type="equiv",
+    check_less_precise=False,
+    check_exact=False,
+    check_datetimelike_compat=False,
+    check_categorical=True,
+    check_category_order=True,
+    rtol=1e-05,
+    atol=1e-08,
+    obj="ColumnBase",
+):
+    """
+    Check that left and right columns are equal
+
+    This function is intended to compare two columns and output
+    any differences. Additional parameters allow varying the strictness
+    of the equality checks performed.
+
+    Parameters
+    ----------
+    left : Column
+        left Column to compare
+    right : Column
+        right Column to compare
+    check_dtype : bool, default True
+        Whether to check the Column dtype is identical.
+    check_column_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the columns class, dtype and
+        inferred_type are identical. Currently it is idle,
+        and similar to pandas.
+    check_less_precise : bool or int, default False
+        Not yet supported
+    check_exact : bool, default False
+        Whether to compare number exactly.
+    check_datetime_like_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_category_order : bool, default True
+        Whether to compare category order of internal Categoricals
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when `check_exact` is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when `check_exact` is False.
+    obj : str, default 'ColumnBase'
+        Specify object name being compared, internally used to
+        show appropriate assertion message.
+    """
+    if check_dtype is True:
+        if (
+            is_categorical_dtype(left)
+            and is_categorical_dtype(right)
+            and not check_categorical
+        ):
+            pass
+        else:
+            if type(left) != type(right) or left.dtype != right.dtype:
+                msg1 = f"{left.dtype}"
+                msg2 = f"{right.dtype}"
+                raise_assert_detail(obj, "Dtypes are different", msg1, msg2)
+    else:
+        if left.null_count == len(left) and right.null_count == len(right):
+            return True
+
+    if check_datetimelike_compat:
+        if np.issubdtype(left.dtype, np.datetime64):
+            right = right.astype(left.dtype)
+        elif np.issubdtype(right.dtype, np.datetime64):
+            left = left.astype(right.dtype)
+
+        if np.issubdtype(left.dtype, np.datetime64):
+            if not left.equals(right):
+                raise AssertionError(
+                    f"[datetimelike_compat=True] {left.values} "
+                    f"is not equal to {right.values}."
+                )
+            return
+
+    if check_exact and check_categorical:
+        if is_categorical_dtype(left) and is_categorical_dtype(right):
+            left_cat = left.categories
+            right_cat = right.categories
+
+            if check_category_order:
+                assert_index_equal(
+                    left_cat,
+                    right_cat,
+                    exact=check_dtype,
+                    check_exact=True,
+                    check_categorical=False,
+                    rtol=rtol,
+                    atol=atol,
+                )
+                assert_column_equal(
+                    left.codes,
+                    right.codes,
+                    check_dtype=check_dtype,
+                    check_exact=True,
+                    check_categorical=False,
+                    check_category_order=False,
+                    rtol=rtol,
+                    atol=atol,
+                )
+
+            if left.ordered != right.ordered:
+                msg1 = f"{left.ordered}"
+                msg2 = f"{right.ordered}"
+                raise_assert_detail(
+                    f"{obj} category", "Orders are different", msg1, msg2
+                )
+
+    if (
+        not check_dtype
+        and is_categorical_dtype(left)
+        and is_categorical_dtype(right)
+    ):
+        left = left.astype(left.categories.dtype)
+        right = right.astype(right.categories.dtype)
+    columns_equal = False
+    if left.size == right.size == 0:
+        columns_equal = True
+    elif not (
+        (
+            not dtype_can_compare_equal_to_other(left.dtype)
+            and is_numeric_dtype(right)
+        )
+        or (
+            is_numeric_dtype(left)
+            and not dtype_can_compare_equal_to_other(right)
+        )
+    ):
+        try:
+            # nulls must be in the same places for all dtypes
+            columns_equal = cp.all(
+                left.isnull().values == right.isnull().values
+            )
+
+            if columns_equal and not check_exact and is_numeric_dtype(left):
+                # non-null values must be the same
+                columns_equal = cp.allclose(
+                    left.apply_boolean_mask(
+                        left.isnull().unary_operator("not")
+                    ).values,
+                    right.apply_boolean_mask(
+                        right.isnull().unary_operator("not")
+                    ).values,
+                )
+                if columns_equal and (
+                    left.dtype.kind == right.dtype.kind == "f"
+                ):
+                    columns_equal = cp.all(
+                        is_nan(left).values == is_nan(right).values
+                    )
+            else:
+                columns_equal = left.equals(right)
+        except TypeError as e:
+            if str(e) != "Categoricals can only compare with the same type":
+                raise e
+            else:
+                columns_equal = False
+            if is_categorical_dtype(left) and is_categorical_dtype(right):
+                left = left.astype(left.categories.dtype)
+                right = right.astype(right.categories.dtype)
+    if not columns_equal:
+        ldata = str([val for val in left.to_pandas(nullable=True)])
+        rdata = str([val for val in right.to_pandas(nullable=True)])
+        try:
+            diff = 0
+            for i in range(left.size):
+                if not null_safe_scalar_equals(left[i], right[i]):
+                    diff += 1
+            diff = diff * 100.0 / left.size
+        except BaseException:
+            diff = 100.0
+        raise_assert_detail(
+            obj,
+            f"values are different ({np.round(diff, 5)} %)",
+            {ldata},
+            {rdata},
+        )
+
+
+def null_safe_scalar_equals(left, right):
+    if left in {NA, NaT, np.nan} or right in {NA, NaT, np.nan}:
+        return left is right
+    return left == right
+
+
+def assert_index_equal(
+    left,
+    right,
+    exact="equiv",
+    check_names: bool = True,
+    check_less_precise: Union[bool, int] = False,
+    check_exact: bool = True,
+    check_categorical: bool = True,
+    check_order: bool = True,
+    rtol: float = 1e-5,
+    atol: float = 1e-8,
+    obj: str = "Index",
+):
+    """
+    Check that left and right Index are equal
+
+    This function is intended to compare two Index and output
+    any differences. Additional parameters allow varying the strictness
+    of the equality checks performed.
+
+    Parameters
+    ----------
+    left : Index
+        left Index to compare
+    right : Index
+        right Index to compare
+    exact : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical. If 'equiv', then RangeIndex can be substituted
+        for Int8Index, Int16Index, Int32Index, Int64Index as well.
+    check_names : bool, default True
+        Whether to check the names attribute.
+    check_less_precise : bool or int, default False
+        Not yet supported
+    check_exact : bool, default False
+        Whether to compare number exactly.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_order : bool, default True
+        Whether to compare the order of index entries as
+        well as their values.
+        If True, both indexes must contain the same elements,
+        in the same order.
+        If False, both indexes must contain the same elements,
+        but in any order.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when `check_exact` is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when `check_exact` is False.
+    obj : str, default 'Index'
+        Specify object name being compared, internally used to
+        show appropriate assertion message.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> id1 = cudf.Index([1, 2, 3, 4])
+    >>> id2 = cudf.Index([1, 2, 3, 5])
+    >>> cudf.testing.assert_index_equal(id1, id2)
+    ......
+    ......
+    AssertionError: ColumnBase are different
+    <BLANKLINE>
+    values are different (25.0 %)
+    [left]:  [1 2 3 4]
+    [right]: [1 2 3 5]
+
+    >>> id2 = cudf.Index([1, 2, 3, 4], name="b")
+    >>> cudf.testing.assert_index_equal(id1, id2)
+    ......
+    ......
+    AssertionError: Index are different
+    <BLANKLINE>
+    name mismatch
+    [left]:  a
+    [right]: b
+
+    This will pass without any hitch:
+
+    >>> id2 = cudf.Index([1, 2, 3, 4], name="a")
+    >>> cudf.testing.assert_index_equal(id1, id2)
+    """
+
+    # instance validation
+    _check_isinstance(left, right, cudf.BaseIndex)
+
+    _check_types(
+        left, right, exact=exact, check_categorical=check_categorical, obj=obj
+    )
+
+    if len(left) != len(right):
+        raise_assert_detail(
+            obj, "lengths are different", f"{len(left)}", f"{len(right)}"
+        )
+
+    # If order doesn't matter then sort the index entries
+    if not check_order:
+        left = left.sort_values()
+        right = right.sort_values()
+
+    if isinstance(left, cudf.MultiIndex):
+        if left.nlevels != right.nlevels:
+            raise AssertionError(
+                "Number of levels mismatch, "
+                f"left has {left.nlevels} levels and right has {right.nlevels}"
+            )
+
+        for level in range(left.nlevels):
+            llevel = cudf.Index(left._columns[level], name=left.names[level])
+            rlevel = cudf.Index(right._columns[level], name=right.names[level])
+            mul_obj = f"MultiIndex level [{level}]"
+            assert_index_equal(
+                llevel,
+                rlevel,
+                exact=check_exact,
+                check_names=check_names,
+                check_exact=check_exact,
+                check_less_precise=check_less_precise,
+                check_order=check_order,
+                rtol=rtol,
+                atol=atol,
+                obj=mul_obj,
+            )
+        return
+    assert_column_equal(
+        left._columns[0],
+        right._columns[0],
+        check_dtype=exact,
+        check_exact=check_exact,
+        check_categorical=check_categorical,
+        obj=obj,
+    )
+
+    # metadata comparison
+    if check_names and (left.name != right.name):
+        raise_assert_detail(
+            obj, "name mismatch", f"{left.name}", f"{right.name}"
+        )
+
+
+def assert_series_equal(
+    left,
+    right,
+    check_dtype=True,
+    check_index_type="equiv",
+    check_series_type=True,
+    check_less_precise=False,
+    check_names=True,
+    check_exact=False,
+    check_datetimelike_compat=False,
+    check_categorical=True,
+    check_category_order=True,
+    rtol=1e-5,
+    atol=1e-8,
+    obj="Series",
+):
+    """
+    Check that left and right Series are equal
+
+    This function is intended to compare two Series and output
+    any differences. Additional parameters allow varying the strictness
+    of the equality checks performed.
+
+    Parameters
+    ----------
+    left : Series
+        left Series to compare
+    right : Series
+        right Series to compare
+    check_dtype : bool, default True
+        Whether to check the Series dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_series_type : bool, default True
+        Whether to check the series class, dtype and
+        inferred_type are identical. Currently it is idle,
+        and similar to pandas.
+    check_less_precise : bool or int, default False
+        Not yet supported
+    check_names : bool, default True
+        Whether to check that the names attribute for both the index
+        and column attributes of the Series is identical.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+    check_datetime_like_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_category_order : bool, default True
+        Whether to compare category order of internal Categoricals
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when `check_exact` is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when `check_exact` is False.
+    obj : str, default 'Series'
+        Specify object name being compared, internally used to
+        show appropriate assertion message.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> sr1 = cudf.Series([1, 2, 3, 4], name="a")
+    >>> sr2 = cudf.Series([1, 2, 3, 5], name="b")
+    >>> cudf.testing.assert_series_equal(sr1, sr2)
+    ......
+    ......
+    AssertionError: ColumnBase are different
+    <BLANKLINE>
+    values are different (25.0 %)
+    [left]:  [1 2 3 4]
+    [right]: [1 2 3 5]
+
+    >>> sr2 = cudf.Series([1, 2, 3, 4], name="b")
+    >>> cudf.testing.assert_series_equal(sr1, sr2)
+    ......
+    ......
+    AssertionError: Series are different
+    <BLANKLINE>
+    name mismatch
+    [left]:  a
+    [right]: b
+
+    This will pass without any hitch:
+
+    >>> sr2 = cudf.Series([1, 2, 3, 4], name="a")
+    >>> cudf.testing.assert_series_equal(sr1, sr2)
+    """
+
+    # instance validation
+    _check_isinstance(left, right, cudf.Series)
+
+    if len(left) != len(right):
+        msg1 = f"{len(left)}, {left.index}"
+        msg2 = f"{len(right)}, {right.index}"
+        raise_assert_detail(obj, "Series length are different", msg1, msg2)
+
+    # index comparison
+    assert_index_equal(
+        left.index,
+        right.index,
+        exact=check_index_type,
+        check_names=check_names,
+        check_less_precise=check_less_precise,
+        check_exact=check_exact,
+        check_categorical=check_categorical,
+        rtol=rtol,
+        atol=atol,
+        obj=f"{obj}.index",
+    )
+
+    assert_column_equal(
+        left._column,
+        right._column,
+        check_dtype=check_dtype,
+        check_column_type=check_series_type,
+        check_less_precise=check_less_precise,
+        check_exact=check_exact,
+        check_datetimelike_compat=check_datetimelike_compat,
+        check_categorical=check_categorical,
+        check_category_order=check_category_order,
+        rtol=rtol,
+        atol=atol,
+    )
+
+    # metadata comparison
+    if check_names and (left.name != right.name):
+        raise_assert_detail(
+            obj, "name mismatch", f"{left.name}", f"{right.name}"
+        )
+
+
+def assert_frame_equal(
+    left,
+    right,
+    check_dtype=True,
+    check_index_type="equiv",
+    check_column_type="equiv",
+    check_frame_type=True,
+    check_names=True,
+    by_blocks=False,
+    check_exact=False,
+    check_datetimelike_compat=False,
+    check_categorical=True,
+    check_like=False,
+    rtol=1e-5,
+    atol=1e-8,
+    obj="DataFrame",
+):
+    """
+    Check that left and right DataFrame are equal
+
+    This function is intended to compare two DataFrame and output
+    any differences. Additional parameters allow varying the strictness
+    of the equality checks performed.
+
+    Parameters
+    ----------
+    left : DataFrame
+        left DataFrame to compare
+    right : DataFrame
+        right DataFrame to compare
+    check_dtype : bool, default True
+        Whether to check the DataFrame dtype is identical.
+    check_index_type : bool or {'equiv'}, default 'equiv'
+        Whether to check the Index class, dtype and inferred_type
+        are identical.
+    check_column_type : bool, default True
+        Whether to check the column class, dtype and
+        inferred_type are identical. Currently it is idle,
+        and similar to pandas.
+    check_frame_type : bool, default True
+        Whether to check the DataFrame class is identical.
+    check_names : bool, default True
+        Whether to check that the names attribute for both the index and
+        column attributes of the DataFrame is identical.
+    check_exact : bool, default False
+        Whether to compare number exactly.
+    by_blocks : bool, default False
+        Not supported
+    check_exact : bool, default False
+        Whether to compare number exactly.
+    check_datetime_like_compat : bool, default False
+        Compare datetime-like which is comparable ignoring dtype.
+    check_categorical : bool, default True
+        Whether to compare internal Categorical exactly.
+    check_like : bool, default False
+        If True, ignore the order of index & columns.
+        Note: index labels must match their respective
+        rows (same as in columns) - same labels must be with the same data.
+    rtol : float, default 1e-5
+        Relative tolerance. Only used when `check_exact` is False.
+    atol : float, default 1e-8
+        Absolute tolerance. Only used when `check_exact` is False.
+    obj : str, default 'DataFrame'
+        Specify object name being compared, internally used to
+        show appropriate assertion message.
+
+    Examples
+    --------
+    >>> import cudf
+    >>> df1 = cudf.DataFrame({"a":[1, 2], "b":[1.0, 2.0]}, index=[1, 2])
+    >>> df2 = cudf.DataFrame({"a":[1, 2], "b":[1.0, 2.0]}, index=[2, 3])
+    >>> cudf.testing.assert_frame_equal(df1, df2)
+    ......
+    ......
+    AssertionError: ColumnBase are different
+    <BLANKLINE>
+    values are different (100.0 %)
+    [left]:  [1 2]
+    [right]: [2 3]
+
+    >>> df2 = cudf.DataFrame({"a":[1, 2], "c":[1.0, 2.0]}, index=[1, 2])
+    >>> cudf.testing.assert_frame_equal(df1, df2)
+    ......
+    ......
+    AssertionError: DataFrame.columns are different
+    <BLANKLINE>
+    DataFrame.columns values are different (50.0 %)
+    [left]: Index(['a', 'b'], dtype='object')
+    right]: Index(['a', 'c'], dtype='object')
+
+    >>> df2 = cudf.DataFrame({"a":[1, 2], "b":[1.0, 3.0]}, index=[1, 2])
+    >>> cudf.testing.assert_frame_equal(df1, df2)
+    ......
+    ......
+    AssertionError: Column name="b" are different
+    <BLANKLINE>
+    values are different (50.0 %)
+    [left]:  [1. 2.]
+    [right]: [1. 3.]
+
+    This will pass without any hitch:
+
+    >>> df2 = cudf.DataFrame({"a":[1, 2], "b":[1.0, 2.0]}, index=[1, 2])
+    >>> cudf.testing.assert_frame_equal(df1, df2)
+    """
+    _check_isinstance(left, right, cudf.DataFrame)
+
+    if check_frame_type:
+        assert isinstance(left, type(right))
+
+    # shape comparison
+    if left.shape != right.shape:
+        raise AssertionError("left and right shape mismatch")
+
+    if check_like:
+        left, right = left.reindex(index=right.index), right
+        right = right[list(left._data.names)]
+
+    # index comparison
+    assert_index_equal(
+        left.index,
+        right.index,
+        exact=check_index_type,
+        check_names=check_names,
+        check_exact=check_exact,
+        check_categorical=check_categorical,
+        rtol=rtol,
+        atol=atol,
+        obj=f"{obj}.index",
+    )
+
+    pd.testing.assert_index_equal(
+        left._data.to_pandas_index(),
+        right._data.to_pandas_index(),
+        exact=check_column_type,
+        check_names=check_names,
+        check_exact=check_exact,
+        check_categorical=check_categorical,
+        rtol=rtol,
+        atol=atol,
+        obj=f"{obj}.columns",
+    )
+
+    for col in left._column_names:
+        assert_column_equal(
+            left._data[col],
+            right._data[col],
+            check_dtype=check_dtype,
+            check_exact=check_exact,
+            check_datetimelike_compat=check_datetimelike_compat,
+            check_categorical=check_categorical,
+            rtol=rtol,
+            atol=atol,
+            obj=f'Column name="{col}"',
+        )
diff --git a/python/cudf/cudf/tests/conftest.py b/python/cudf/cudf/tests/conftest.py
new file mode 100644
index 0000000..30d8f1c
--- /dev/null
+++ b/python/cudf/cudf/tests/conftest.py
@@ -0,0 +1,178 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import itertools
+import os
+import pathlib
+
+import cupy as cp
+import numpy as np
+import pytest
+
+import rmm  # noqa: F401
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+_CURRENT_DIRECTORY = str(pathlib.Path(__file__).resolve().parent)
+
+
+@pytest.fixture(scope="session")
+def datadir():
+    return pathlib.Path(__file__).parent / "data"
+
+
+@pytest.fixture(
+    params=itertools.product([0, 2, None], [0.3, None]),
+    ids=lambda arg: f"n={arg[0]}-frac={arg[1]}",
+)
+def sample_n_frac(request):
+    """
+    Specific to `test_sample*` tests.
+    """
+    n, frac = request.param
+    if n is not None and frac is not None:
+        pytest.skip("Cannot specify both n and frac.")
+    return n, frac
+
+
+def shape_checker(expected, got):
+    assert expected.shape == got.shape
+
+
+def exact_checker(expected, got):
+    assert_eq(expected, got)
+
+
+@pytest.fixture(
+    params=[
+        (None, None, shape_checker),
+        (42, 42, shape_checker),
+        (np.random.RandomState(42), np.random.RandomState(42), exact_checker),
+    ],
+    ids=["None", "IntSeed", "NumpyRandomState"],
+)
+def random_state_tuple_axis_1(request):
+    """
+    Specific to `test_sample*_axis_1` tests.
+    A pytest fixture of valid `random_state` parameter pairs for pandas
+    and cudf. Valid parameter combinations, and what to check for each pair
+    are listed below:
+
+    pandas:   None,   seed(int),  np.random.RandomState
+    cudf:     None,   seed(int),  np.random.RandomState
+    ------
+    check:    shape,  shape,      exact result
+
+    Each column above stands for one valid parameter combination and check.
+    """
+
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        (None, None, shape_checker),
+        (42, 42, shape_checker),
+        (np.random.RandomState(42), np.random.RandomState(42), exact_checker),
+        (np.random.RandomState(42), cp.random.RandomState(42), shape_checker),
+    ],
+    ids=["None", "IntSeed", "NumpyRandomState", "CupyRandomState"],
+)
+def random_state_tuple_axis_0(request):
+    """
+    Specific to `test_sample*_axis_0` tests.
+    A pytest fixture of valid `random_state` parameter pairs for pandas
+    and cudf. Valid parameter combinations, and what to check for each pair
+    are listed below:
+
+    pandas:   None,   seed(int),  np.random.RandomState,  np.random.RandomState
+    cudf:     None,   seed(int),  np.random.RandomState,  cp.random.RandomState
+    ------
+    check:    shape,  shape,      exact result,           shape
+
+    Each column above stands for one valid parameter combination and check.
+    """
+
+    return request.param
+
+
+@pytest.fixture(params=[None, "builtin_list", "ndarray"])
+def make_weights_axis_0(request):
+    """Specific to `test_sample*_axis_0` tests.
+    Only testing weights array that matches type with random state.
+    """
+
+    if request.param is None:
+        return lambda *_: (None, None)
+    elif request.param == "builtin-list":
+        return lambda size, _: ([1] * size, [1] * size)
+    else:
+
+        def wrapped(size, numpy_weights_for_cudf):
+            # Uniform distribution, non-normalized
+            if numpy_weights_for_cudf:
+                return np.ones(size), np.ones(size)
+            else:
+                return np.ones(size), cp.ones(size)
+
+        return wrapped
+
+
+# To set and remove the NO_EXTERNAL_ONLY_APIS environment variable we must use
+# the sessionstart and sessionfinish hooks rather than a simple autouse,
+# session-scope fixture because we need to set these variable before collection
+# occurs because the environment variable will be checked as soon as cudf is
+# imported anywhere.
+def pytest_sessionstart(session):
+    """
+    Called after the Session object has been created and
+    before performing collection and entering the run test loop.
+    """
+    os.environ["NO_EXTERNAL_ONLY_APIS"] = "1"
+    os.environ["_CUDF_TEST_ROOT"] = _CURRENT_DIRECTORY
+
+
+def pytest_sessionfinish(session, exitstatus):
+    """
+    Called after whole test run finished, right before
+    returning the exit status to the system.
+    """
+    try:
+        del os.environ["NO_EXTERNAL_ONLY_APIS"]
+        del os.environ["_CUDF_TEST_ROOT"]
+    except KeyError:
+        pass
+
+
+@pytest.fixture(params=[32, 64])
+def default_integer_bitwidth(request):
+    old_default = cudf.get_option("default_integer_bitwidth")
+    cudf.set_option("default_integer_bitwidth", request.param)
+    yield request.param
+    cudf.set_option("default_integer_bitwidth", old_default)
+
+
+@pytest.fixture(params=[32, 64])
+def default_float_bitwidth(request):
+    old_default = cudf.get_option("default_float_bitwidth")
+    cudf.set_option("default_float_bitwidth", request.param)
+    yield request.param
+    cudf.set_option("default_float_bitwidth", old_default)
+
+
+@pytest.hookimpl(tryfirst=True, hookwrapper=True)
+def pytest_runtest_makereport(item, call):
+    """Hook to make result information available in fixtures
+
+    This makes it possible for a pytest.fixture to access the current test
+    state through `request.node.report`.
+    See the `manager` fixture in `test_spilling.py` for an example.
+
+    Pytest doc: <https://docs.pytest.org/en/latest/example/simple.html>
+    """
+    outcome = yield
+    rep = outcome.get_result()
+
+    # Set a report attribute for each phase of a call, which can
+    # be "setup", "call", "teardown"
+    setattr(item, "report", {rep.when: rep})
diff --git a/python/cudf/cudf/tests/data/__init__.py b/python/cudf/cudf/tests/data/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/data/avro/__init__.py b/python/cudf/cudf/tests/data/avro/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/data/avro/example.avro b/python/cudf/cudf/tests/data/avro/example.avro
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diff --git a/python/cudf/cudf/tests/data/orc/TestOrcFile.decimal.runpos.issue.orc b/python/cudf/cudf/tests/data/orc/TestOrcFile.decimal.runpos.issue.orc
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diff --git a/python/cudf/cudf/tests/data/orc/TestOrcFile.decimal.same.values.orc b/python/cudf/cudf/tests/data/orc/TestOrcFile.decimal.same.values.orc
new file mode 100644
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diff --git a/python/cudf/cudf/tests/data/orc/TestOrcFile.emptyFile.orc b/python/cudf/cudf/tests/data/orc/TestOrcFile.emptyFile.orc
new file mode 100644
index 0000000000000000000000000000000000000000..ecdadcbff134615d7eefcb740d55fe710cee059b
GIT binary patch
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literal 0
HcmV?d00001

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diff --git a/python/cudf/cudf/tests/data/orc/TestOrcFile.testDate2038.orc b/python/cudf/cudf/tests/data/orc/TestOrcFile.testDate2038.orc
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diff --git a/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab.txt b/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab.txt
new file mode 100644
index 0000000..57c0877
--- /dev/null
+++ b/python/cudf/cudf/tests/data/subword_tokenizer_data/bert_base_cased_sampled/vocab.txt
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+oil
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+##at
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+##H
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diff --git a/python/cudf/cudf/tests/data/subword_tokenizer_data/test_sentences.txt b/python/cudf/cudf/tests/data/subword_tokenizer_data/test_sentences.txt
new file mode 100644
index 0000000..6111117
--- /dev/null
+++ b/python/cudf/cudf/tests/data/subword_tokenizer_data/test_sentences.txt
@@ -0,0 +1,100 @@
+This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত
+This sample text is public domain and was randomly selected from Project Guttenberg.
+The rain had only ceased with the gray streaks of morning at Blazing Star, and the settlement awoke to a moral sense of cleanliness, and the finding of forgotten knives, tin cups, and smaller camp utensils, where the heavy showers had washed away the debris and dust heaps before the cabin doors.
+Indeed, it was recorded in Blazing Star that a fortunate early riser had once picked up on the highway a solid chunk of gold quartz which the rain had freed from its incumbering soil, and washed into immediate and glittering popularity.
+Possibly this may have been the reason why early risers in that locality, during the rainy season, adopted a thoughtful habit of body, and seldom lifted their eyes to the rifted or india-ink washed skies above them.
+"Cass" Beard had risen early that morning, but not with a view to discovery.
+A leak in his cabin roof,--quite consistent with his careless, improvident habits,--had roused him at 4 A. M., with a flooded "bunk" and wet blankets.
+The chips from his wood pile refused to kindle a fire to dry his bed-clothes, and he had recourse to a more provident neighbor's to supply the deficiency.
+This was nearly opposite.
+Mr. Cassius crossed the highway, and stopped suddenly.
+But the Goblin could no longer sit quietly listening to the wisdom and intellect downstairs. No, as soon as the light shone in the evening from the attic it seemed to him as though its beams were strong ropes dragging him up, and he had to go and peep through the key-hole. There he felt the sort of feeling we have looking at the great rolling sea in a storm, and he burst into tears. He could not himself say why he wept, but in spite of his tears he felt quite happy. How beautiful it must be to sit under that tree with the student, but that he could not do; he had to content himself with the key-hole and be happy there!
+But, wonderful to relate, not an irregular, shapeless fragment of crude ore, fresh from Nature's crucible, but a bit of jeweler's handicraft in the form of a plain gold ring.
+Looking at it more attentively, he saw that it bore the inscription, "May to Cass."
+Like most of his fellow gold-seekers, Cass was superstitious.
+The fountain of classic wisdom, Hypatia herself.
+As the ancient sage--the name is unimportant to a monk--pumped water nightly that he might study by day, so I, the guardian of cloaks and parasols, at the sacred doors of her lecture-room, imbibe celestial knowledge.
+From my youth I felt in me a soul above the matter-entangled herd.
+She revealed to me the glorious fact, that I am a spark of Divinity itself.
+A fallen star, I am, sir!' continued he, pensively, stroking his lean stomach--'a fallen star!--fallen, if the dignity of philosophy will allow of the simile, among the hogs of the lower world--indeed, even into the hog-bucket itself. Well, after all, I will show you the way to the Archbishop's.
+There is a philosophic pleasure in opening one's treasures to the modest young.
+Perhaps you will assist me by carrying this basket of fruit?' And the little man jumped up, put his basket on Philammon's head, and trotted off up a neighbouring street.
+Philammon followed, half contemptuous, half wondering at what this philosophy might be, which could feed the self-conceit of anything so abject as his ragged little apish guide;
+but the novel roar and whirl of the street, the perpetual stream of busy faces, the line of curricles, palanquins, laden asses, camels, elephants, which met and passed him, and squeezed him up steps and into doorways, as they threaded their way through the great Moon-gate into the ample street beyond, drove everything from his mind but wondering curiosity, and a vague, helpless dread of that great living wilderness, more terrible than any dead wilderness of sand which he had left behind.
+Already he longed for the repose, the silence of the Laura--for faces which knew him and smiled upon him; but it was too late to turn back now.
+His guide held on for more than a mile up the great main street, crossed in the centre of the city, at right angles, by one equally magnificent, at each end of which, miles away, appeared, dim and distant over the heads of the living stream of passengers, the yellow sand-hills of the desert;
+while at the end of the vista in front of them gleamed the blue harbour, through a network of countless masts.
+At last they reached the quay at the opposite end of the street;
+and there burst on Philammon's astonished eyes a vast semicircle of blue sea, ringed with palaces and towers.
+He stopped involuntarily; and his little guide stopped also, and looked askance at the young monk, to watch the effect which that grand panorama should produce on him.
+Nana also troubled him in another way. He had sometimes a feeling that she did not admire him. “I know she admires you tremendously, George,”
+Mrs. Darling would assure him, and then she would sign to the children to be specially nice to father. Lovely dances followed, in which the only other servant, Liza, was sometimes allowed to join. 
+Such a midget she looked in her long skirt and maid's cap, though she had sworn, when engaged, that she would never see ten again. 
+The gaiety of those romps!
+And gayest of all was Mrs. Darling, who would pirouette so wildly that all you could see of her was the kiss, and then if you had dashed at her you might have got it. 
+There never was a simpler happier family until the coming of Peter Pan.
+Finally, I always go to sea as a sailor, because of the wholesome exercise and pure air of the fore-castle deck. 
+For as in this world, head winds are far more prevalent than winds from astern (that is, if you never violate the Pythagorean maxim), so for the most part the Commodore on the quarter-deck gets his atmosphere at second hand from the sailors on the forecastle. He thinks he breathes it first; but not so. 
+In much the same way do the commonalty lead their leaders in many other things, at the same time that the leaders little suspect it. 
+But wherefore it was that after having repeatedly smelt the sea as a merchant sailor, I should now take it into my head to go on a whaling voyage; this the invisible police officer of the Fates, who has the constant surveillance of me, and secretly dogs me, and influences me in some unaccountable way—he can better answer than any one else. 
+And, doubtless, my going on this whaling voyage, formed part of the grand programme of Providence that was drawn up a long time ago. 
+It came in as a sort of brief interlude and solo between more extensive performances. 
+I take it that this part of the bill must have run something like this:
+“_Grand Contested Election for the Presidency of the United States._
+“WHALING VOYAGE BY ONE ISHMAEL. “BLOODY BATTLE IN AFFGHANISTAN.”
+Amy followed, but she poked her hands out stiffly before her, and jerked herself along as if she went by machinery, and her "Ow!" was more suggestive of pins being run into her than of fear and anguish.
+Jo gave a despairing groan, and Meg laughed outright, while Beth let her bread burn as she watched the fun with interest. 
+"It's no use!  Do the best you can when the time comes, and if the audience laughs, don't blame me.  Come on, Meg."
+Then things went smoothly, for Don Pedro defied the world in a speech of two pages without a single break.  Hagar, the witch, chanted an awful incantation over her kettleful of simmering toads, with weird effect.  
+Roderigo rent his chains asunder manfully, and Hugo died in agonies of remorse and arsenic, with a wild, "Ha! Ha!"
+This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত
+This sample text is public domain and was randomly selected from Project Guttenberg.
+The rain had only ceased with the gray streaks of morning at Blazing Star, and the settlement awoke to a moral sense of cleanliness, and the finding of forgotten knives, tin cups, and smaller camp utensils, where the heavy showers had washed away the debris and dust heaps before the cabin doors.
+Indeed, it was recorded in Blazing Star that a fortunate early riser had once picked up on the highway a solid chunk of gold quartz which the rain had freed from its incumbering soil, and washed into immediate and glittering popularity.
+Possibly this may have been the reason why early risers in that locality, during the rainy season, adopted a thoughtful habit of body, and seldom lifted their eyes to the rifted or india-ink washed skies above them.
+"Cass" Beard had risen early that morning, but not with a view to discovery.
+A leak in his cabin roof,--quite consistent with his careless, improvident habits,--had roused him at 4 A. M., with a flooded "bunk" and wet blankets.
+The chips from his wood pile refused to kindle a fire to dry his bed-clothes, and he had recourse to a more provident neighbor's to supply the deficiency.
+This was nearly opposite.
+Mr. Cassius crossed the highway, and stopped suddenly.
+Something glittered in the nearest red pool before him.
+I had one experience with Master Philip before our visitors betook themselves back to Kent, which, unfortunate as it was, I cannot but relate here. My cousin would enter into none of those rough amusements in which I passed my time, for fear, I took it, of spoiling his fine broadcloths or of losing a gold buckle. He never could be got to wrestle, though I challenged him more than once. And he was a well-built lad, and might, with a little practice, have become skilled in that sport. He laughed at the homespun I wore about the farm, saying it was no costume for a gentleman's son, and begged me sneeringly to don leather breeches. He would have none of the company of those lads with whom I found pleasure, young Harvey, and Willis's son, who was being trained as Mr. Starkie's assistant. Nor indeed did I disdain to join in a game with Hugo, who had been given to me, and other negro lads. Philip saw no sport in a wrestle or a fight between two of the boys from the quarters, and marvelled that I could lower myself to bet with Harvey the younger. He took not a spark of interest in the gaming cocks we raised together to compete at the local contests and at the fair, and knew not a gaff from a cockspur. Being one day at my wits' end to amuse my cousin, I proposed to him a game of quoits on the green beside the spring-house, and thither we repaired, followed by Hugo, and young Harvey come to look on. Master Philip, not casting as well as he might, cries out suddenly to Hugo: "Begone, you black dog! What business have you here watching a game between gentlemen?"
+But, wonderful to relate, not an irregular, shapeless fragment of crude ore, fresh from Nature's crucible, but a bit of jeweler's handicraft in the form of a plain gold ring.
+Looking at it more attentively, he saw that it bore the inscription, "May to Cass."
+Like most of his fellow gold-seekers, Cass was superstitious.
+The fountain of classic wisdom, Hypatia herself.
+As the ancient sage--the name is unimportant to a monk--pumped water nightly that he might study by day, so I, the guardian of cloaks and parasols, at the sacred doors of her lecture-room, imbibe celestial knowledge.
+From my youth I felt in me a soul above the matter-entangled herd.
+She revealed to me the glorious fact, that I am a spark of Divinity itself.
+A fallen star, I am, sir!' continued he, pensively, stroking his lean stomach--'a fallen star!--fallen, if the dignity of philosophy will allow of the simile, among the hogs of the lower world--indeed, even into the hog-bucket itself. Well, after all, I will show you the way to the Archbishop's.
+There is a philosophic pleasure in opening one's treasures to the modest young.
+Perhaps you will assist me by carrying this basket of fruit?' And the little man jumped up, put his basket on Philammon's head, and trotted off up a neighbouring street.
+Philammon followed, half contemptuous, half wondering at what this philosophy might be, which could feed the self-conceit of anything so abject as his ragged little apish guide;
+but the novel roar and whirl of the street, the perpetual stream of busy faces, the line of curricles, palanquins, laden asses, camels, elephants, which met and passed him, and squeezed him up steps and into doorways, as they threaded their way through the great Moon-gate into the ample street beyond, drove everything from his mind but wondering curiosity, and a vague, helpless dread of that great living wilderness, more terrible than any dead wilderness of sand which he had left behind.
+Already he longed for the repose, the silence of the Laura--for faces which knew him and smiled upon him; but it was too late to turn back now.
+His guide held on for more than a mile up the great main street, crossed in the centre of the city, at right angles, by one equally magnificent, at each end of which, miles away, appeared, dim and distant over the heads of the living stream of passengers, the yellow sand-hills of the desert;
+while at the end of the vista in front of them gleamed the blue harbour, through a network of countless masts.
+and there burst on Philammon's astonished eyes a vast semicircle of blue sea, ringed with palaces and towers.
+He stopped involuntarily; and his little guide stopped also, and looked askance at the young monk, to watch the effect which that grand panorama should produce on him.
+Nana also troubled him in another way. He had sometimes a feeling that she did not admire him. “I know she admires you tremendously, George,”
+Mrs. Darling would assure him, and then she would sign to the children to be specially nice to father. Lovely dances followed, in which the only other servant, Liza, was sometimes allowed to join. 
+Such a midget she looked in her long skirt and maid's cap, though she had sworn, when engaged, that she would never see ten again. 
+In the Year 1676, the Prince of _Orange_ having, in concert with the _Spaniards_, resolv'd upon the important Siege of _Maestrich_ (the only Town in the _Dutch_ Provinces, then remaining in the Hands of the _French_) it was accordingly invested about the middle of _June_, with an Army of twenty Thousand Men, under the Command of his Highness Prince
+_Waldeck_, with the grand Army covering the Siege. It was some Time before the heavy Cannon, which we expected up the _Maes_, from _Holland_, arrived; which gave Occasion to a Piece of Raillery of Monsieur _Calvo_, the Governor, which was as handsomely repartec'd. That Governor, by a Messenger, intimating his Sorrow to find, we had pawn'd our Cannon for Ammunition Bread. Answer was made, That in a few Days we hoped to give him a Taste of the Loaves, which he should find would be sent him into the Town in extraordinary plenty. I remember another Piece of Raillery, which pass'd some Days after between the _Rhingrave_ and the same _Calvo_. The former sending Word, that he hoped within three Weeks to salute that Governor's Mistress within the Place. _Calvo_ reply'd, He'd give him leave to kiss her all over, if he kiss'd her anywhere in three Months.
+And gayest of all was Mrs. Darling, who would pirouette so wildly that all you could see of her was the kiss, and then if you had dashed at her you might have got it. 
+There never was a simpler happier family until the coming of Peter Pan.
+Finally, I always go to sea as a sailor, because of the wholesome exercise and pure air of the fore-castle deck. 
+For as in this world, head winds are far more prevalent than winds from astern (that is, if you never violate the Pythagorean maxim), so for the most part the Commodore on the quarter-deck gets his atmosphere at second hand from the sailors on the forecastle. He thinks he breathes it first; but not so. 
+In much the same way do the commonalty lead their leaders in many other things, at the same time that the leaders little suspect it. 
+But wherefore it was that after having repeatedly smelt the sea as a merchant sailor, I should now take it into my head to go on a whaling voyage; this the invisible police officer of the Fates, who has the constant surveillance of me, and secretly dogs me, and influences me in some unaccountable way—he can better answer than any one else. 
+And, doubtless, my going on this whaling voyage, formed part of the grand programme of Providence that was drawn up a long time ago. 
+It came in as a sort of brief interlude and solo between more extensive performances. 
+The British Isles have been ringing for the last few years with the word 'Art' in its German sense; with 'High Art,' 'Symbolic Art,' 'Ecclesiastical Art,' 'Dramatic Art,' 'Tragic Art,' and so forth; and every well-educated person is expected, nowadays, to know something about Art.  Yet in spite of all translations of German 'AEsthetic' treatises, and 'Kunstnovellen,' the mass of the British people cares very little about the matter, and sits contented under the imputation of 'bad taste.'  Our stage, long since dead, does not revive; our poetry is dying; our music, like our architecture, only reproduces the past; our painting is only first-rate when it handles landscapes and animals, and seems likely so to remain; but, meanwhile, nobody cares.  Some of the deepest and most earnest minds vote the question, in general, a 'sham and a snare,' and whisper to each other
+confidentially, that Gothic art is beginning to be a 'bore,' and that Sir Christopher Wren was a very good fellow after all; while the middle classes look on the Art movement half amused, as with a pretty toy, half sulkily suspicious of Popery and Paganism, and think,
+apparently, that Art is very well when it means nothing, and is merely used to beautify drawing-rooms and shawl patterns; not to mention that, if there were no painters, Mr. Smith could not hand down to posterity likenesses of himself, Mrs. Smith, and family.  But
+when 'Art' dares to be in earnest, and to mean something, much more to connect itself with religion, Smith's tone alters.  He will teach 'Art' to keep in what he considers its place, and if it refuses, take the law of it, and put it into the Ecclesiastical Court.  So he says, and what is more, he means what he says; and as all the world, from Hindostan to Canada, knows by most practical proof, what he means, he sooner or later does, perhaps not always in the wisest way, but still he does it.Ah!  It's pleasant to drop into my own easy-chair my dear though a little palpitating what with trotting up-stairs and what with trotting down, and why kitchen stairs should all be corner stairs is for the builders to justify though I do not think they fully understand their trade and never did, else why the sameness and why not more conveniences and fewer
+draughts and likewise making a practice of laying the plaster on too thick I am well convinced which holds the damp, and as to chimney-pots putting them on by guess-work like hats at a party and no more knowing what their effect will be upon the smoke bless you than I do if so much, except that it will mostly be either to send it down your throat in a straight form or give it a twist before it goes there.  And what I says speaking as I find of those new metal chimneys all manner of shapes (there's a row of 'em at Miss Wozenham's lodging-house lower down on the other side of the way) is that they only work your smoke into artificial patterns for you before you swallow it and that I'd quite as soon swallow mine plain, the flavour being the same, not to mention the conceit of putting up signs on the top of your house to show the forms in which you take your smoke into your inside
+Amy followed, but she poked her hands out stiffly before her, and jerked herself along as if she went by machinery, and her "Ow!" was more suggestive of pins being run into her than of fear and anguish.
+Jo gave a despairing groan, and Meg laughed outright, while Beth let her bread burn as she watched the fun with interest. 
+"It's no use!  Do the best you can when the time comes, and if the audience laughs, don't blame me.  Come on, Meg."
+Then things went smoothly, for Don Pedro defied the world in a speech of two pages without a single break.  Hagar, the witch, chanted an awful incantation over her kettleful of simmering toads, with weird effect.'''
\ No newline at end of file
diff --git a/python/cudf/cudf/tests/data/text/__init__.py b/python/cudf/cudf/tests/data/text/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/data/text/chess.pgn b/python/cudf/cudf/tests/data/text/chess.pgn
new file mode 100644
index 0000000..6f516e5
--- /dev/null
+++ b/python/cudf/cudf/tests/data/text/chess.pgn
@@ -0,0 +1,16 @@
+[Event "F/S Return Match"]
+[Site "Belgrade, Serbia JUG"]
+[Date "1992.11.04"]
+[Round "29"]
+[White "Fischer, Robert J."]
+[Black "Spassky, Boris V."]
+[Result "1/2-1/2"]
+
+1. e4 e5 2. Nf3 Nc6 3. Bb5 a6 {This opening is called the Ruy Lopez.}
+4. Ba4 Nf6 5. O-O Be7 6. Re1 b5 7. Bb3 d6 8. c3 O-O 9. h3 Nb8 10. d4 Nbd7
+11. c4 c6 12. cxb5 axb5 13. Nc3 Bb7 14. Bg5 b4 15. Nb1 h6 16. Bh4 c5 17. dxe5
+Nxe4 18. Bxe7 Qxe7 19. exd6 Qf6 20. Nbd2 Nxd6 21. Nc4 Nxc4 22. Bxc4 Nb6
+23. Ne5 Rae8 24. Bxf7+ Rxf7 25. Nxf7 Rxe1+ 26. Qxe1 Kxf7 27. Qe3 Qg5 28. Qxg5
+hxg5 29. b3 Ke6 30. a3 Kd6 31. axb4 cxb4 32. Ra5 Nd5 33. f3 Bc8 34. Kf2 Bf5
+35. Ra7 g6 36. Ra6+ Kc5 37. Ke1 Nf4 38. g3 Nxh3 39. Kd2 Kb5 40. Rd6 Kc5 41. Ra6
+Nf2 42. g4 Bd3 43. Re6 1/2-1/2
diff --git a/python/cudf/cudf/tests/data/text/chess.pgn.gz b/python/cudf/cudf/tests/data/text/chess.pgn.gz
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diff --git a/python/cudf/cudf/tests/dataframe/__init__.py b/python/cudf/cudf/tests/dataframe/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/dataframe/test_attributes.py b/python/cudf/cudf/tests/dataframe/test_attributes.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_attributes.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_binary_operations.py b/python/cudf/cudf/tests/dataframe/test_binary_operations.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_binary_operations.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_combining.py b/python/cudf/cudf/tests/dataframe/test_combining.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_combining.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_computation.py b/python/cudf/cudf/tests/dataframe/test_computation.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_computation.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_constructing.py b/python/cudf/cudf/tests/dataframe/test_constructing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_constructing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_conversion.py b/python/cudf/cudf/tests/dataframe/test_conversion.py
new file mode 100644
index 0000000..3673ea8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_conversion.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+import pandas as pd
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+def test_convert_dtypes():
+    data = {
+        "a": [1, 2, 3],
+        "b": [1, 2, 3],
+        "c": [1.1, 2.2, 3.3],
+        "d": [1.0, 2.0, 3.0],
+        "e": [1.0, 2.0, 3.0],
+        "f": ["a", "b", "c"],
+        "g": ["a", "b", "c"],
+        "h": ["2001-01-01", "2001-01-02", "2001-01-03"],
+    }
+    dtypes = [
+        "int8",
+        "int64",
+        "float32",
+        "float32",
+        "float64",
+        "str",
+        "category",
+        "datetime64[ns]",
+    ]
+    df = pd.DataFrame(
+        {
+            k: pd.Series(v, dtype=d)
+            for k, v, d in zip(data.keys(), data.values(), dtypes)
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(df)
+    expect = df.convert_dtypes()
+    got = gdf.convert_dtypes().to_pandas(nullable=True)
+    assert_eq(expect, got)
diff --git a/python/cudf/cudf/tests/dataframe/test_function_application.py b/python/cudf/cudf/tests/dataframe/test_function_application.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_function_application.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_indexing.py b/python/cudf/cudf/tests/dataframe/test_indexing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_indexing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_io_serialization.py b/python/cudf/cudf/tests/dataframe/test_io_serialization.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_io_serialization.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_missing.py b/python/cudf/cudf/tests/dataframe/test_missing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_missing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_reindexing.py b/python/cudf/cudf/tests/dataframe/test_reindexing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_reindexing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_reshaping.py b/python/cudf/cudf/tests/dataframe/test_reshaping.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_reshaping.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_selecting.py b/python/cudf/cudf/tests/dataframe/test_selecting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_selecting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_sorting.py b/python/cudf/cudf/tests/dataframe/test_sorting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_sorting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/dataframe/test_timeseries.py b/python/cudf/cudf/tests/dataframe/test_timeseries.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/dataframe/test_timeseries.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/general_functions/__init__.py b/python/cudf/cudf/tests/general_functions/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/general_functions/test_conversion.py b/python/cudf/cudf/tests/general_functions/test_conversion.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/general_functions/test_conversion.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/general_functions/test_data_manipulation.py b/python/cudf/cudf/tests/general_functions/test_data_manipulation.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/general_functions/test_data_manipulation.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/general_functions/test_datetimelike.py b/python/cudf/cudf/tests/general_functions/test_datetimelike.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/general_functions/test_datetimelike.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/general_utilities/__init__.py b/python/cudf/cudf/tests/general_utilities/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/general_utilities/test_testing.py b/python/cudf/cudf/tests/general_utilities/test_testing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/general_utilities/test_testing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/groupby/__init__.py b/python/cudf/cudf/tests/groupby/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/groupby/test_computation.py b/python/cudf/cudf/tests/groupby/test_computation.py
new file mode 100644
index 0000000..04c56ef
--- /dev/null
+++ b/python/cudf/cudf/tests/groupby/test_computation.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+def test_rank_return_type_compatible_mode(method):
+    # in compatible mode, rank() always returns floats
+    pdf = pd.DataFrame({"a": [1, 1, 1, 2, 2], "b": [1, 2, 3, 4, 5]})
+    with cudf.option_context("mode.pandas_compatible", True):
+        df = cudf.from_pandas(pdf)
+        result = df.groupby("a").rank(method=method)
+    expect = pdf.groupby("a").rank(method=method)
+    assert_eq(expect, result)
+    assert result["b"].dtype == "float64"
diff --git a/python/cudf/cudf/tests/groupby/test_function_application.py b/python/cudf/cudf/tests/groupby/test_function_application.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/groupby/test_function_application.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/groupby/test_indexing.py b/python/cudf/cudf/tests/groupby/test_indexing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/groupby/test_indexing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/groupby/test_stats.py b/python/cudf/cudf/tests/groupby/test_stats.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/groupby/test_stats.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/__init__.py b/python/cudf/cudf/tests/indexes/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/indexes/datetime/__init__.py b/python/cudf/cudf/tests/indexes/datetime/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/indexes/datetime/test_components.py b/python/cudf/cudf/tests/indexes/datetime/test_components.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/datetime/test_components.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/datetime/test_constructing.py b/python/cudf/cudf/tests/indexes/datetime/test_constructing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/datetime/test_constructing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/datetime/test_conversion.py b/python/cudf/cudf/tests/indexes/datetime/test_conversion.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/datetime/test_conversion.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/datetime/test_indexing.py b/python/cudf/cudf/tests/indexes/datetime/test_indexing.py
new file mode 100644
index 0000000..f2c2d9a
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/datetime/test_indexing.py
@@ -0,0 +1,19 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+import pandas as pd
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+def test_slice_datetimetz_index():
+    data = ["2001-01-01", "2001-01-02", None, None, "2001-01-03"]
+    pidx = pd.DatetimeIndex(data, dtype="datetime64[ns]").tz_localize(
+        "US/Eastern"
+    )
+    idx = cudf.DatetimeIndex(data, dtype="datetime64[ns]").tz_localize(
+        "US/Eastern"
+    )
+    expected = pidx[1:4]
+    got = idx[1:4]
+    assert_eq(expected, got)
diff --git a/python/cudf/cudf/tests/indexes/datetime/test_time_specific.py b/python/cudf/cudf/tests/indexes/datetime/test_time_specific.py
new file mode 100644
index 0000000..1ed1e23
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/datetime/test_time_specific.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+import pandas as pd
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+def test_tz_localize():
+    pidx = pd.date_range("2001-01-01", "2001-01-02", freq="1s")
+    pidx = pidx.astype("<M8[ns]")
+    idx = cudf.from_pandas(pidx)
+    assert pidx.dtype == idx.dtype
+    assert_eq(
+        pidx.tz_localize("America/New_York"),
+        idx.tz_localize("America/New_York"),
+    )
+
+
+def test_tz_convert():
+    pidx = pd.date_range("2023-01-01", periods=3, freq="H")
+    idx = cudf.from_pandas(pidx)
+    pidx = pidx.tz_localize("UTC")
+    idx = idx.tz_localize("UTC")
+    assert_eq(
+        pidx.tz_convert("America/New_York"), idx.tz_convert("America/New_York")
+    )
+
+
+def test_delocalize_naive():
+    pidx = pd.date_range("2023-01-01", periods=3, freq="H")
+    idx = cudf.from_pandas(pidx)
+    assert_eq(pidx.tz_localize(None), idx.tz_localize(None))
diff --git a/python/cudf/cudf/tests/indexes/multiindex/__init__.py b/python/cudf/cudf/tests/indexes/multiindex/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/indexes/multiindex/test_constructing.py b/python/cudf/cudf/tests/indexes/multiindex/test_constructing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/multiindex/test_constructing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/multiindex/test_properties.py b/python/cudf/cudf/tests/indexes/multiindex/test_properties.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/multiindex/test_properties.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/multiindex/test_selecting.py b/python/cudf/cudf/tests/indexes/multiindex/test_selecting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/multiindex/test_selecting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_categorical.py b/python/cudf/cudf/tests/indexes/test_categorical.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_categorical.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_combining.py b/python/cudf/cudf/tests/indexes/test_combining.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_combining.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_computation.py b/python/cudf/cudf/tests/indexes/test_computation.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_computation.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_constructing.py b/python/cudf/cudf/tests/indexes/test_constructing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_constructing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_conversion.py b/python/cudf/cudf/tests/indexes/test_conversion.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_conversion.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_interval.py b/python/cudf/cudf/tests/indexes/test_interval.py
new file mode 100644
index 0000000..52c49ae
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_interval.py
@@ -0,0 +1,317 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_210
+from cudf.core.index import IntervalIndex, interval_range
+from cudf.testing._utils import assert_eq
+
+
+def test_interval_constructor_default_closed():
+    idx = cudf.IntervalIndex([pd.Interval(0, 1)])
+    assert idx.closed == "right"
+    assert idx.dtype.closed == "right"
+
+
+def test_interval_to_arrow():
+    expect = pa.Array.from_pandas(pd.IntervalIndex([pd.Interval(0, 1)]))
+    got = cudf.IntervalIndex([pd.Interval(0, 1)]).to_arrow()
+    assert_eq(expect, got)
+
+
+INTERVAL_BOUNDARY_TYPES = [
+    int,
+    np.int8,
+    np.int16,
+    np.int32,
+    np.int64,
+    np.float32,
+    np.float64,
+    cudf.Scalar,
+]
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+@pytest.mark.parametrize("start", [0, 1, 2, 3])
+@pytest.mark.parametrize("end", [4, 5, 6, 7])
+def test_interval_range_basic(start, end, closed):
+    pindex = pd.interval_range(start=start, end=end, closed=closed)
+    gindex = cudf.interval_range(start=start, end=end, closed=closed)
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("start_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("end_t", INTERVAL_BOUNDARY_TYPES)
+def test_interval_range_dtype_basic(start_t, end_t):
+    start, end = start_t(24), end_t(42)
+    start_val = start.value if isinstance(start, cudf.Scalar) else start
+    end_val = end.value if isinstance(end, cudf.Scalar) else end
+    pindex = pd.interval_range(start=start_val, end=end_val, closed="left")
+    gindex = cudf.interval_range(start=start, end=end, closed="left")
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+@pytest.mark.parametrize("start", [0])
+@pytest.mark.parametrize("end", [0])
+def test_interval_range_empty(start, end, closed):
+    pindex = pd.interval_range(start=start, end=end, closed=closed)
+    gindex = cudf.interval_range(start=start, end=end, closed=closed)
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+@pytest.mark.parametrize("freq", [1, 2, 3])
+@pytest.mark.parametrize("start", [0, 1, 2, 3, 5])
+@pytest.mark.parametrize("end", [6, 8, 10, 43, 70])
+def test_interval_range_freq_basic(start, end, freq, closed):
+    pindex = pd.interval_range(start=start, end=end, freq=freq, closed=closed)
+    gindex = cudf.interval_range(
+        start=start, end=end, freq=freq, closed=closed
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("start_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("end_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("freq_t", INTERVAL_BOUNDARY_TYPES)
+def test_interval_range_freq_basic_dtype(start_t, end_t, freq_t):
+    start, end, freq = start_t(5), end_t(70), freq_t(3)
+    start_val = start.value if isinstance(start, cudf.Scalar) else start
+    end_val = end.value if isinstance(end, cudf.Scalar) else end
+    freq_val = freq.value if isinstance(freq, cudf.Scalar) else freq
+    pindex = pd.interval_range(
+        start=start_val, end=end_val, freq=freq_val, closed="left"
+    )
+    gindex = cudf.interval_range(
+        start=start, end=end, freq=freq, closed="left"
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+@pytest.mark.parametrize("periods", [1, 1.0, 2, 2.0, 3.0, 3])
+@pytest.mark.parametrize("start", [0, 0.0, 1.0, 1, 2, 2.0, 3.0, 3])
+@pytest.mark.parametrize("end", [4, 4.0, 5.0, 5, 6, 6.0, 7.0, 7])
+def test_interval_range_periods_basic(start, end, periods, closed):
+    pindex = pd.interval_range(
+        start=start, end=end, periods=periods, closed=closed
+    )
+    gindex = cudf.interval_range(
+        start=start, end=end, periods=periods, closed=closed
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("start_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("end_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("periods_t", INTERVAL_BOUNDARY_TYPES)
+def test_interval_range_periods_basic_dtype(start_t, end_t, periods_t):
+    start, end, periods = start_t(0), end_t(4), periods_t(1.0)
+    start_val = start.value if isinstance(start, cudf.Scalar) else start
+    end_val = end.value if isinstance(end, cudf.Scalar) else end
+    periods_val = (
+        periods.value if isinstance(periods, cudf.Scalar) else periods
+    )
+    pindex = pd.interval_range(
+        start=start_val, end=end_val, periods=periods_val, closed="left"
+    )
+    gindex = cudf.interval_range(
+        start=start, end=end, periods=periods, closed="left"
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+@pytest.mark.parametrize("periods", [1, 2, 3])
+@pytest.mark.parametrize("freq", [1, 2, 3, 4])
+@pytest.mark.parametrize("end", [4, 8, 9, 10])
+def test_interval_range_periods_freq_end(end, freq, periods, closed):
+    pindex = pd.interval_range(
+        end=end, freq=freq, periods=periods, closed=closed
+    )
+    gindex = cudf.interval_range(
+        end=end, freq=freq, periods=periods, closed=closed
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("periods_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("freq_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("end_t", INTERVAL_BOUNDARY_TYPES)
+def test_interval_range_periods_freq_end_dtype(periods_t, freq_t, end_t):
+    periods, freq, end = periods_t(2), freq_t(3), end_t(10)
+    freq_val = freq.value if isinstance(freq, cudf.Scalar) else freq
+    end_val = end.value if isinstance(end, cudf.Scalar) else end
+    periods_val = (
+        periods.value if isinstance(periods, cudf.Scalar) else periods
+    )
+    pindex = pd.interval_range(
+        end=end_val, freq=freq_val, periods=periods_val, closed="left"
+    )
+    gindex = cudf.interval_range(
+        end=end, freq=freq, periods=periods, closed="left"
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+@pytest.mark.parametrize("periods", [1, 2, 3])
+@pytest.mark.parametrize("freq", [1, 2, 3, 4])
+@pytest.mark.parametrize("start", [1, 4, 9, 12])
+def test_interval_range_periods_freq_start(start, freq, periods, closed):
+    pindex = pd.interval_range(
+        start=start, freq=freq, periods=periods, closed=closed
+    )
+    gindex = cudf.interval_range(
+        start=start, freq=freq, periods=periods, closed=closed
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("periods_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("freq_t", INTERVAL_BOUNDARY_TYPES)
+@pytest.mark.parametrize("start_t", INTERVAL_BOUNDARY_TYPES)
+def test_interval_range_periods_freq_start_dtype(periods_t, freq_t, start_t):
+    periods, freq, start = periods_t(2), freq_t(3), start_t(9)
+    freq_val = freq.value if isinstance(freq, cudf.Scalar) else freq
+    start_val = start.value if isinstance(start, cudf.Scalar) else start
+    periods_val = (
+        periods.value if isinstance(periods, cudf.Scalar) else periods
+    )
+    pindex = pd.interval_range(
+        start=start_val, freq=freq_val, periods=periods_val, closed="left"
+    )
+    gindex = cudf.interval_range(
+        start=start, freq=freq, periods=periods, closed="left"
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["right", "left", "both", "neither"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        ([pd.Interval(30, 50)]),
+        ([pd.Interval(0, 3), pd.Interval(1, 7)]),
+        ([pd.Interval(0.2, 60.3), pd.Interval(1, 7), pd.Interval(0, 0)]),
+        ([]),
+    ],
+)
+def test_interval_index_basic(data, closed):
+    pindex = pd.IntervalIndex(data, closed=closed)
+    gindex = IntervalIndex(data, closed=closed)
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["right", "left", "both", "neither"])
+def test_interval_index_empty(closed):
+    pindex = pd.IntervalIndex([], closed=closed)
+    gindex = IntervalIndex([], closed=closed)
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["right", "left", "both", "neither"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        ([pd.Interval(1, 6), pd.Interval(1, 10), pd.Interval(1, 3)]),
+        (
+            [
+                pd.Interval(3.5, 6.0),
+                pd.Interval(1.0, 7.0),
+                pd.Interval(0.0, 10.0),
+            ]
+        ),
+        (
+            [
+                pd.Interval(50, 100, closed="left"),
+                pd.Interval(1.0, 7.0, closed="left"),
+                pd.Interval(16, 322, closed="left"),
+            ]
+        ),
+        (
+            [
+                pd.Interval(50, 100, closed="right"),
+                pd.Interval(1.0, 7.0, closed="right"),
+                pd.Interval(16, 322, closed="right"),
+            ]
+        ),
+    ],
+)
+def test_interval_index_many_params(data, closed):
+    pindex = pd.IntervalIndex(data, closed=closed)
+    gindex = IntervalIndex(data, closed=closed)
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_interval_index_from_breaks(closed):
+    breaks = [0, 3, 6, 10]
+    pindex = pd.IntervalIndex.from_breaks(breaks, closed=closed)
+    gindex = IntervalIndex.from_breaks(breaks, closed=closed)
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize(
+    "start, stop, freq, periods",
+    [
+        (0.0, None, 0.2, 5),
+        (0.0, 1.0, None, 5),
+        pytest.param(
+            0.0,
+            1.0,
+            0.2,
+            None,
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_210,
+                reason="https://github.com/pandas-dev/pandas/pull/54477",
+            ),
+        ),
+        (None, 1.0, 0.2, 5),
+        pytest.param(
+            0.0,
+            1.0,
+            0.1,
+            None,
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_210,
+                reason="https://github.com/pandas-dev/pandas/pull/54477",
+            ),
+        ),
+        (0.0, 1.0, None, 10),
+        (0.0, None, 0.25, 4),
+        (1.0, None, 2.5, 2),
+    ],
+)
+def test_interval_range_floating(start, stop, freq, periods):
+    expected = pd.interval_range(
+        start=start, end=stop, freq=freq, periods=periods
+    )
+    got = interval_range(start=start, end=stop, freq=freq, periods=periods)
+    assert_eq(expected, got)
+
+
+def test_intervalindex_empty_typed_non_int():
+    data = np.array([], dtype="datetime64[ns]")
+    result = cudf.IntervalIndex(data)
+    expected = pd.IntervalIndex(data)
+    assert_eq(result, expected)
diff --git a/python/cudf/cudf/tests/indexes/test_memory_usage.py b/python/cudf/cudf/tests/indexes/test_memory_usage.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_memory_usage.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_missing.py b/python/cudf/cudf/tests/indexes/test_missing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_missing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_modifying.py b/python/cudf/cudf/tests/indexes/test_modifying.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_modifying.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_multiindex_compat.py b/python/cudf/cudf/tests/indexes/test_multiindex_compat.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_multiindex_compat.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_numeric.py b/python/cudf/cudf/tests/indexes/test_numeric.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_numeric.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_properties.py b/python/cudf/cudf/tests/indexes/test_properties.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_properties.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_selecting.py b/python/cudf/cudf/tests/indexes/test_selecting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_selecting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_sorting.py b/python/cudf/cudf/tests/indexes/test_sorting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_sorting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/test_time_specific.py b/python/cudf/cudf/tests/indexes/test_time_specific.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/test_time_specific.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/timedelta/__init__.py b/python/cudf/cudf/tests/indexes/timedelta/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/indexes/timedelta/test_components.py b/python/cudf/cudf/tests/indexes/timedelta/test_components.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/timedelta/test_components.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/timedelta/test_constructing.py b/python/cudf/cudf/tests/indexes/timedelta/test_constructing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/timedelta/test_constructing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/indexes/timedelta/test_conversion.py b/python/cudf/cudf/tests/indexes/timedelta/test_conversion.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/indexes/timedelta/test_conversion.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/__init__.py b/python/cudf/cudf/tests/input_output/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/input_output/test_avro.py b/python/cudf/cudf/tests/input_output/test_avro.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_avro.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_csv.py b/python/cudf/cudf/tests/input_output/test_csv.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_csv.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_feather.py b/python/cudf/cudf/tests/input_output/test_feather.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_feather.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_hdf5.py b/python/cudf/cudf/tests/input_output/test_hdf5.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_hdf5.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_json.py b/python/cudf/cudf/tests/input_output/test_json.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_json.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_orc.py b/python/cudf/cudf/tests/input_output/test_orc.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_orc.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_parquet.py b/python/cudf/cudf/tests/input_output/test_parquet.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_parquet.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/input_output/test_text.py b/python/cudf/cudf/tests/input_output/test_text.py
new file mode 100644
index 0000000..acba13b
--- /dev/null
+++ b/python/cudf/cudf/tests/input_output/test_text.py
@@ -0,0 +1,164 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from io import StringIO
+
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+@pytest.fixture(scope="module")
+def datadir(datadir):
+    return datadir / "text"
+
+
+def test_read_text(datadir):
+    chess_file = str(datadir) + "/chess.pgn"
+    delimiter = "1."
+
+    with open(chess_file) as f:
+        content = f.read().split(delimiter)
+
+    # Since Python split removes the delimiter and read_text does
+    # not we need to add it back to the 'content'
+    expected = cudf.Series(
+        [
+            c + delimiter if i < (len(content) - 1) else c
+            for i, c in enumerate(content)
+        ]
+    )
+
+    actual = cudf.read_text(chess_file, delimiter=delimiter)
+
+    assert_eq(expected, actual)
+
+
+def test_read_text_byte_range(datadir):
+    chess_file = str(datadir) + "/chess.pgn"
+    delimiter = "1."
+
+    with open(chess_file, "r") as f:
+        data = f.read()
+        content = data.split(delimiter)
+
+    # Since Python split removes the delimiter and read_text does
+    # not we need to add it back to the 'content'
+    expected = cudf.Series(
+        [
+            c + delimiter if i < (len(content) - 1) else c
+            for i, c in enumerate(content)
+        ]
+    )
+
+    byte_range_size = (len(data) // 3) + (len(data) % 3 != 0)
+
+    actual_0 = cudf.read_text(
+        chess_file,
+        delimiter=delimiter,
+        byte_range=[byte_range_size * 0, byte_range_size],
+    )
+    actual_1 = cudf.read_text(
+        chess_file,
+        delimiter=delimiter,
+        byte_range=[byte_range_size * 1, byte_range_size],
+    )
+    actual_2 = cudf.read_text(
+        chess_file,
+        delimiter=delimiter,
+        byte_range=[byte_range_size * 2, byte_range_size],
+    )
+
+    actual = cudf.concat([actual_0, actual_1, actual_2], ignore_index=True)
+
+    assert_eq(expected, actual)
+
+
+def test_read_text_byte_range_large(tmpdir):
+    content = "".join(("\n" if x % 5 == 4 else "x") for x in range(0, 3000))
+    delimiter = "\n"
+    temp_file = str(tmpdir) + "/temp.txt"
+
+    with open(temp_file, "w") as f:
+        f.write(content)
+
+    expected = cudf.Series(["xxxx\n" for i in range(0, 200)])
+
+    actual = cudf.read_text(
+        temp_file, delimiter=delimiter, byte_range=[1000, 1000]
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_read_text_in_memory(datadir):
+    # Since Python split removes the delimiter and read_text does
+    # not we need to add it back to the 'content'
+    expected = cudf.Series(["x::", "y::", "z"])
+
+    actual = cudf.read_text(StringIO("x::y::z"), delimiter="::")
+
+    assert_eq(expected, actual)
+
+
+def test_read_text_in_memory_strip_delimiter(datadir):
+    # Since Python split removes the delimiter and read_text does
+    # not we need to add it back to the 'content'
+    expected = cudf.Series(["x", "y", "z"])
+
+    actual = cudf.read_text(
+        StringIO("x::y::z"), delimiter="::", strip_delimiters=True
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_read_text_bgzip(datadir):
+    chess_file_compressed = str(datadir) + "/chess.pgn.gz"
+    chess_file = str(datadir) + "/chess.pgn"
+    delimiter = "1."
+
+    with open(chess_file) as f:
+        content = f.read().split(delimiter)
+
+    # Since Python split removes the delimiter and read_text does
+    # not we need to add it back to the 'content'
+    expected = cudf.Series(
+        [
+            c + delimiter if i < (len(content) - 1) else c
+            for i, c in enumerate(content)
+        ]
+    )
+
+    actual = cudf.read_text(
+        chess_file_compressed, compression="bgzip", delimiter=delimiter
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_read_text_bgzip_offsets(datadir):
+    chess_file_compressed = str(datadir) + "/chess.pgn.gz"
+    chess_file = str(datadir) + "/chess.pgn"
+    delimiter = "1."
+
+    with open(chess_file) as f:
+        content = f.read()[29:695].split(delimiter)
+
+    # Since Python split removes the delimiter and read_text does
+    # not we need to add it back to the 'content'
+    expected = cudf.Series(
+        [
+            c + delimiter if i < (len(content) - 1) else c
+            for i, c in enumerate(content)
+        ]
+    )
+
+    actual = cudf.read_text(
+        chess_file_compressed,
+        compression="bgzip",
+        compression_offsets=[58 * 2**16 + 2, 781 * 2**16 + 7],
+        delimiter=delimiter,
+    )
+
+    assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/lists/__init__.py b/python/cudf/cudf/tests/lists/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/lists/test_list_methods.py b/python/cudf/cudf/tests/lists/test_list_methods.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/lists/test_list_methods.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/options/__init__.py b/python/cudf/cudf/tests/options/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/options/test_options.py b/python/cudf/cudf/tests/options/test_options.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/options/test_options.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/pytest.ini b/python/cudf/cudf/tests/pytest.ini
new file mode 100644
index 0000000..1f38ffc
--- /dev/null
+++ b/python/cudf/cudf/tests/pytest.ini
@@ -0,0 +1,10 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+[pytest]
+markers =
+    spilling: mark benchmark a good candidate to run with `CUDF_SPILL=ON`
+xfail_strict = true
+filterwarnings =
+    error
+    ignore:::.*xdist.*
+    ignore:::.*pytest.*
diff --git a/python/cudf/cudf/tests/series/__init__.py b/python/cudf/cudf/tests/series/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/series/test_accessors.py b/python/cudf/cudf/tests/series/test_accessors.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_accessors.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_attributes.py b/python/cudf/cudf/tests/series/test_attributes.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_attributes.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_binary_operations.py b/python/cudf/cudf/tests/series/test_binary_operations.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_binary_operations.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_categorial.py b/python/cudf/cudf/tests/series/test_categorial.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_categorial.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_combining.py b/python/cudf/cudf/tests/series/test_combining.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_combining.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_computation.py b/python/cudf/cudf/tests/series/test_computation.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_computation.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_constructing.py b/python/cudf/cudf/tests/series/test_constructing.py
new file mode 100644
index 0000000..6600e99
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_constructing.py
@@ -0,0 +1,12 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+import numpy as np
+
+import cudf
+
+
+def test_construct_int_series_with_nulls_compat_mode():
+    # in compatibility mode, constructing a Series
+    # with nulls should result in a floating Series:
+    with cudf.option_context("mode.pandas_compatible", True):
+        s = cudf.Series([1, 2, None])
+    assert s.dtype == np.dtype("float64")
diff --git a/python/cudf/cudf/tests/series/test_conversion.py b/python/cudf/cudf/tests/series/test_conversion.py
new file mode 100644
index 0000000..08124a9
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_conversion.py
@@ -0,0 +1,34 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize(
+    "data, dtype",
+    [
+        ([1, 2, 3], "int8"),
+        ([1, 2, 3], "int64"),
+        ([1.1, 2.2, 3.3], "float32"),
+        ([1.0, 2.0, 3.0], "float32"),
+        ([1.0, 2.0, 3.0], "float64"),
+        (["a", "b", "c"], "str"),
+        (["a", "b", "c"], "category"),
+        (["2001-01-01", "2001-01-02", "2001-01-03"], "datetime64[ns]"),
+    ],
+)
+def test_convert_dtypes(data, dtype):
+    s = pd.Series(data, dtype=dtype)
+    gs = cudf.Series(data, dtype=dtype)
+    expect = s.convert_dtypes()
+
+    # because we don't have distinct nullable types, we check that we
+    # get the same result if we convert to nullable pandas types:
+    got = gs.convert_dtypes().to_pandas(nullable=True)
+    assert_eq(expect, got)
+
+
+# Now write the same test, but construct a DataFrame
+# as input instead of parametrizing:
diff --git a/python/cudf/cudf/tests/series/test_datetimelike.py b/python/cudf/cudf/tests/series/test_datetimelike.py
new file mode 100644
index 0000000..85da985
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_datetimelike.py
@@ -0,0 +1,192 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+import os
+
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import date_range
+from cudf.testing._utils import assert_eq
+
+
+def _get_all_zones():
+    zones = []
+    for root, dirs, files in os.walk("/usr/share/zoneinfo"):
+        for f in files:
+            zone_name = ("/".join([root, f])).lstrip("/usr/share/zoneinfo")
+            try:
+                _ = pd.DatetimeTZDtype("ns", zone_name)
+            except Exception:
+                continue
+            zones.append(zone_name)
+    return zones
+
+
+# NOTE: ALL_TIME_ZONES is a very large list; we likely do NOT want to
+# use it for more than a handful of tests
+ALL_TIME_ZONES = _get_all_zones()
+
+
+@pytest.fixture(params=["ns", "us", "ms", "s"])
+def unit(request):
+    return request.param
+
+
+@pytest.fixture(
+    params=["America/New_York", "Asia/Tokyo", "CET", "Etc/GMT+1", "UTC"]
+)
+def tz(request):
+    return request.param
+
+
+@pytest.mark.parametrize("zone_name", ALL_TIME_ZONES)
+def test_tz_localize(unit, zone_name):
+    s = cudf.Series(date_range("2001-01-01", "2001-01-02", freq="1s"))
+    s = s.astype(f"<M8[{unit}]")
+    s = s.dt.tz_localize(zone_name)
+    assert isinstance(s.dtype, pd.DatetimeTZDtype)
+    assert s.dtype.unit == unit
+    assert str(s.dtype.tz) == zone_name
+
+
+@pytest.mark.parametrize("zone_name", ALL_TIME_ZONES)
+def test_localize_ambiguous(request, unit, zone_name):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(zone_name == "America/Metlakatla"),
+            reason="https://www.timeanddate.com/news/time/metlakatla-quits-dst.html",  # noqa: E501
+        )
+    )
+    s = cudf.Series(
+        [
+            "2018-11-04 00:30:00",
+            "2018-11-04 01:00:00",
+            "2018-11-04 01:30:00",
+            "2018-11-04 02:00:00",
+            None,
+            "2018-11-04 02:30:00",
+        ],
+        dtype=f"datetime64[{unit}]",
+    )
+    expect = s.to_pandas().dt.tz_localize(
+        zone_name, ambiguous="NaT", nonexistent="NaT"
+    )
+    got = s.dt.tz_localize(zone_name)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("zone_name", ALL_TIME_ZONES)
+def test_localize_nonexistent(request, unit, zone_name):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(zone_name == "America/Grand_Turk"),
+            reason="https://www.worldtimezone.com/dst_news/dst_news_turkscaicos03.html",  # noqa: E501
+        )
+    )
+    s = cudf.Series(
+        [
+            "2018-03-11 01:30:00",
+            "2018-03-11 02:00:00",
+            "2018-03-11 02:30:00",
+            "2018-03-11 03:00:00",
+            None,
+            "2018-03-11 03:30:00",
+        ],
+        dtype=f"datetime64[{unit}]",
+    )
+    expect = s.to_pandas().dt.tz_localize(
+        zone_name, ambiguous="NaT", nonexistent="NaT"
+    )
+    got = s.dt.tz_localize(zone_name)
+    assert_eq(expect, got)
+
+
+def test_delocalize(unit, tz):
+    psr = pd.Series(
+        pd.date_range("2001-01-01", "2001-01-02", freq="1s")
+    ).astype(f"datetime64[{unit}]")
+    sr = cudf.from_pandas(psr)
+
+    expect = psr.dt.tz_localize(tz).dt.tz_localize(None)
+    got = sr.dt.tz_localize(tz).dt.tz_localize(None)
+    assert_eq(expect, got)
+
+
+def test_delocalize_naive():
+    # delocalizing naive datetimes should be a no-op
+    psr = pd.Series(["2001-01-01"], dtype="datetime64[ns]")
+    sr = cudf.from_pandas(psr)
+
+    expect = psr.dt.tz_localize(None)
+    got = sr.dt.tz_localize(None)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "from_tz", ["Europe/London", "America/Chicago", "UTC"]
+)
+@pytest.mark.parametrize(
+    "to_tz", ["Europe/London", "America/Chicago", "UTC", None]
+)
+def test_convert(from_tz, to_tz):
+    ps = pd.Series(pd.date_range("2023-01-01", periods=3, freq="H"))
+    gs = cudf.from_pandas(ps)
+    ps = ps.dt.tz_localize(from_tz)
+    gs = gs.dt.tz_localize(from_tz)
+    expect = ps.dt.tz_convert(to_tz)
+    got = gs.dt.tz_convert(to_tz)
+    assert_eq(expect, got)
+
+
+def test_convert_from_naive():
+    gs = cudf.Series(cudf.date_range("2023-01-01", periods=3, freq="H"))
+    with pytest.raises(TypeError):
+        gs.dt.tz_convert("America/New_York")
+
+
+@pytest.mark.parametrize(
+    "data,original_timezone,target_timezone",
+    [
+        # DST transition:
+        (["2023-03-12 01:30:00"], "America/New_York", "America/Los_Angeles"),
+        # crossing the international date line:
+        (["2023-05-17 23:30:00"], "Pacific/Auckland", "America/Los_Angeles"),
+        # timezone with non-integer offset:
+        (["2023-05-17 12:00:00"], "Asia/Kolkata", "Australia/Eucla"),
+        # timezone with negative offset:
+        (["2023-05-17 09:00:00"], "America/Los_Angeles", "Pacific/Auckland"),
+        # conversion across multiple days:
+        (["2023-05-16 23:30:00"], "America/New_York", "Asia/Kolkata"),
+        # timezone with half-hour offset:
+        (["2023-05-17 12:00:00"], "Asia/Kolkata", "Australia/Adelaide"),
+        # timezone conversion with a timestamp in the future:
+        (["2025-01-01 00:00:00"], "America/New_York", "Europe/London"),
+        # timezone conversion with a timestamp in the past:
+        (["2000-01-01 12:00:00"], "Europe/Paris", "America/Los_Angeles"),
+        # timezone conversion with a timestamp at midnight:
+        (["2023-05-17 00:00:00"], "Asia/Tokyo", "Europe/Paris"),
+    ],
+)
+def test_convert_edge_cases(data, original_timezone, target_timezone):
+    ps = pd.Series(data, dtype="datetime64[s]").dt.tz_localize(
+        original_timezone
+    )
+    gs = cudf.Series(data, dtype="datetime64[s]").dt.tz_localize(
+        original_timezone
+    )
+    expect = ps.dt.tz_convert(target_timezone)
+    got = gs.dt.tz_convert(target_timezone)
+    assert_eq(expect, got)
+
+
+def test_tz_aware_attributes_local():
+    data = [
+        "2008-05-12 13:50:00",
+        "2008-12-12 14:50:35",
+        "2009-05-12 13:50:32",
+    ]
+    dti = cudf.DatetimeIndex(data).tz_localize("UTC").tz_convert("US/Eastern")
+    result = dti.hour
+    expected = cudf.Index([9, 9, 9], dtype="int16")
+    assert_eq(result, expected)
diff --git a/python/cudf/cudf/tests/series/test_function_application.py b/python/cudf/cudf/tests/series/test_function_application.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_function_application.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_indexing.py b/python/cudf/cudf/tests/series/test_indexing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_indexing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_io_serialization.py b/python/cudf/cudf/tests/series/test_io_serialization.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_io_serialization.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_missing.py b/python/cudf/cudf/tests/series/test_missing.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_missing.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_reshaping.py b/python/cudf/cudf/tests/series/test_reshaping.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_reshaping.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_selecting.py b/python/cudf/cudf/tests/series/test_selecting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_selecting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_sorting.py b/python/cudf/cudf/tests/series/test_sorting.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_sorting.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/series/test_timeseries.py b/python/cudf/cudf/tests/series/test_timeseries.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/series/test_timeseries.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/strings/__init__.py b/python/cudf/cudf/tests/strings/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/strings/test_string_methods.py b/python/cudf/cudf/tests/strings/test_string_methods.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/strings/test_string_methods.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/structs/__init__.py b/python/cudf/cudf/tests/structs/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/structs/test_struct_methods.py b/python/cudf/cudf/tests/structs/test_struct_methods.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/structs/test_struct_methods.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/tests/test_api_types.py b/python/cudf/cudf/tests/test_api_types.py
new file mode 100644
index 0000000..1ebafbc
--- /dev/null
+++ b/python/cudf/cudf/tests/test_api_types.py
@@ -0,0 +1,1124 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+from pandas.api import types as pd_types
+
+import cudf
+from cudf.api import types
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), False),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, True),
+        (pd.CategoricalDtype, True),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), True),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, True),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), True),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), True),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        # TODO: Currently creating an empty Series of list type ignores the
+        # provided type and instead makes a float64 Series.
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        # TODO: Currently creating an empty Series of struct type fails because
+        # it uses a numpy utility that doesn't understand StructDtype.
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_categorical_dtype(obj, expect):
+    assert types.is_categorical_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, True),
+        (int, True),
+        (float, True),
+        (complex, True),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, True),
+        (np.int_, True),
+        (np.float64, True),
+        (np.complex128, True),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), True),
+        (np.int_(), True),
+        (np.float64(), True),
+        (np.complex128(), True),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), True),
+        (np.dtype("int"), True),
+        (np.dtype("float"), True),
+        (np.dtype("complex"), True),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), True),
+        (np.array([], dtype=np.int_), True),
+        (np.array([], dtype=np.float64), True),
+        (np.array([], dtype=np.complex128), True),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), True),
+        (pd.Series(dtype="int"), True),
+        (pd.Series(dtype="float"), True),
+        (pd.Series(dtype="complex"), True),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, True),
+        (cudf.Decimal64Dtype, True),
+        (cudf.Decimal32Dtype, True),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), True),
+        (cudf.Decimal64Dtype(5, 2), True),
+        (cudf.Decimal32Dtype(5, 2), True),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), True),
+        (cudf.Series(dtype="int"), True),
+        (cudf.Series(dtype="float"), True),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), True),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), True),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), True),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_numeric_dtype(obj, expect):
+    assert types.is_numeric_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, True),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, True),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), True),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), True),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), True),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), True),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), True),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_integer_dtype(obj, expect):
+    assert types.is_integer_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), True),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), True),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_integer(obj, expect):
+    assert types.is_integer(obj) == expect
+
+
+# TODO: Temporarily ignoring all cases of "object" until we decide what to do.
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, True),
+        # (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, True),
+        (np.unicode_, True),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), False),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), True),
+        (np.unicode_(), True),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), True),
+        (np.dtype("unicode"), True),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        # (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), True),
+        (np.array([], dtype=np.unicode_), True),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        # (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), True),
+        (pd.Series(dtype="unicode"), True),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        # (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), True),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_string_dtype(obj, expect):
+    assert types.is_string_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, True),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), False),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), True),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), True),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), True),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), True),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), True),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_datetime_dtype(obj, expect):
+    assert types.is_datetime_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), False),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, True),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), True),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), True),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_list_dtype(obj, expect):
+    assert types.is_list_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), False),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, True),
+        (cudf.Decimal128Dtype, False),
+        (cudf.Decimal64Dtype, False),
+        (cudf.Decimal32Dtype, False),
+        # (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), True),
+        (cudf.Decimal128Dtype(5, 2), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal32Dtype(5, 2), False),
+        # (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), True),
+        # (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_struct_dtype(obj, expect):
+    # TODO: All inputs of interval types are currently disabled due to
+    # inconsistent behavior of is_struct_dtype for interval types that will be
+    # fixed as part of the array refactor.
+    assert types.is_struct_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        # Base Python objects.
+        (bool(), False),
+        (int(), False),
+        (float(), False),
+        (complex(), False),
+        ("", False),
+        (object(), False),
+        # Base Python types.
+        (bool, False),
+        (int, False),
+        (float, False),
+        (complex, False),
+        (str, False),
+        (object, False),
+        # NumPy types.
+        (np.bool_, False),
+        (np.int_, False),
+        (np.float64, False),
+        (np.complex128, False),
+        (np.str_, False),
+        (np.unicode_, False),
+        (np.datetime64, False),
+        (np.timedelta64, False),
+        # NumPy scalars.
+        (np.bool_(), False),
+        (np.int_(), False),
+        (np.float64(), False),
+        (np.complex128(), False),
+        (np.str_(), False),
+        (np.unicode_(), False),
+        (np.datetime64(), False),
+        (np.timedelta64(), False),
+        # NumPy dtype objects.
+        (np.dtype("bool"), False),
+        (np.dtype("int"), False),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.dtype("str"), False),
+        (np.dtype("unicode"), False),
+        (np.dtype("datetime64"), False),
+        (np.dtype("timedelta64"), False),
+        (np.dtype("object"), False),
+        # NumPy arrays.
+        (np.array([], dtype=np.bool_), False),
+        (np.array([], dtype=np.int_), False),
+        (np.array([], dtype=np.float64), False),
+        (np.array([], dtype=np.complex128), False),
+        (np.array([], dtype=np.str_), False),
+        (np.array([], dtype=np.unicode_), False),
+        (np.array([], dtype=np.datetime64), False),
+        (np.array([], dtype=np.timedelta64), False),
+        (np.array([], dtype=object), False),
+        # Pandas dtypes.
+        (pd.CategoricalDtype.type, False),
+        (pd.CategoricalDtype, False),
+        # Pandas objects.
+        (pd.Series(dtype="bool"), False),
+        (pd.Series(dtype="int"), False),
+        (pd.Series(dtype="float"), False),
+        (pd.Series(dtype="complex"), False),
+        (pd.Series(dtype="str"), False),
+        (pd.Series(dtype="unicode"), False),
+        (pd.Series(dtype="datetime64[s]"), False),
+        (pd.Series(dtype="timedelta64[s]"), False),
+        (pd.Series(dtype="category"), False),
+        (pd.Series(dtype="object"), False),
+        # cuDF dtypes.
+        (cudf.CategoricalDtype, False),
+        (cudf.ListDtype, False),
+        (cudf.StructDtype, False),
+        (cudf.Decimal128Dtype, True),
+        (cudf.Decimal64Dtype, True),
+        (cudf.Decimal32Dtype, True),
+        (cudf.IntervalDtype, False),
+        # cuDF dtype instances.
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.ListDtype(int), False),
+        (cudf.StructDtype({"a": int}), False),
+        (cudf.Decimal128Dtype(5, 2), True),
+        (cudf.Decimal64Dtype(5, 2), True),
+        (cudf.Decimal32Dtype(5, 2), True),
+        (cudf.IntervalDtype(int), False),
+        # cuDF objects
+        (cudf.Series(dtype="bool"), False),
+        (cudf.Series(dtype="int"), False),
+        (cudf.Series(dtype="float"), False),
+        (cudf.Series(dtype="str"), False),
+        (cudf.Series(dtype="datetime64[s]"), False),
+        (cudf.Series(dtype="timedelta64[s]"), False),
+        (cudf.Series(dtype="category"), False),
+        (cudf.Series(dtype=cudf.Decimal128Dtype(5, 2)), True),
+        (cudf.Series(dtype=cudf.Decimal64Dtype(5, 2)), True),
+        (cudf.Series(dtype=cudf.Decimal32Dtype(5, 2)), True),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.Series(dtype=cudf.IntervalDtype(int)), False),
+    ),
+)
+def test_is_decimal_dtype(obj, expect):
+    assert types.is_decimal_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj",
+    (
+        # Base Python objects.
+        bool(),
+        int(),
+        float(),
+        complex(),
+        "",
+        object(),
+        # Base Python types.
+        bool,
+        int,
+        float,
+        complex,
+        str,
+        object,
+        # NumPy types.
+        np.bool_,
+        np.int_,
+        np.float64,
+        np.complex128,
+        np.str_,
+        np.unicode_,
+        np.datetime64,
+        np.timedelta64,
+        # NumPy scalars.
+        np.bool_(),
+        np.int_(),
+        np.float64(),
+        np.complex128(),
+        np.str_(),
+        np.unicode_(),
+        np.datetime64(),
+        np.timedelta64(),
+        # NumPy dtype objects.
+        np.dtype("bool"),
+        np.dtype("int"),
+        np.dtype("float"),
+        np.dtype("complex"),
+        np.dtype("str"),
+        np.dtype("unicode"),
+        np.dtype("datetime64"),
+        np.dtype("timedelta64"),
+        np.dtype("object"),
+        # NumPy arrays.
+        np.array([], dtype=np.bool_),
+        np.array([], dtype=np.int_),
+        np.array([], dtype=np.float64),
+        np.array([], dtype=np.complex128),
+        np.array([], dtype=np.str_),
+        np.array([], dtype=np.unicode_),
+        np.array([], dtype=np.datetime64),
+        np.array([], dtype=np.timedelta64),
+        np.array([], dtype=object),
+        # Pandas dtypes.
+        # TODO: pandas does not consider these to be categoricals.
+        # pd.CategoricalDtype.type,
+        # pd.CategoricalDtype,
+        # Pandas objects.
+        pd.Series(dtype="bool"),
+        pd.Series(dtype="int"),
+        pd.Series(dtype="float"),
+        pd.Series(dtype="complex"),
+        pd.Series(dtype="str"),
+        pd.Series(dtype="unicode"),
+        pd.Series(dtype="datetime64[s]"),
+        pd.Series(dtype="timedelta64[s]"),
+        pd.Series(dtype="category"),
+        pd.Series(dtype="object"),
+    ),
+)
+def test_pandas_agreement(obj):
+    assert types.is_categorical_dtype(obj) == pd_types.is_categorical_dtype(
+        obj
+    )
+    assert types.is_numeric_dtype(obj) == pd_types.is_numeric_dtype(obj)
+    assert types.is_integer_dtype(obj) == pd_types.is_integer_dtype(obj)
+    assert types.is_integer(obj) == pd_types.is_integer(obj)
+    assert types.is_string_dtype(obj) == pd_types.is_string_dtype(obj)
+
+
+@pytest.mark.parametrize(
+    "obj",
+    (
+        # Base Python objects.
+        bool(),
+        int(),
+        float(),
+        complex(),
+        "",
+        object(),
+        # Base Python types.
+        bool,
+        int,
+        float,
+        complex,
+        str,
+        object,
+        # NumPy types.
+        np.bool_,
+        np.int_,
+        np.float64,
+        np.complex128,
+        np.str_,
+        np.unicode_,
+        np.datetime64,
+        np.timedelta64,
+        # NumPy scalars.
+        np.bool_(),
+        np.int_(),
+        np.float64(),
+        np.complex128(),
+        np.str_(),
+        np.unicode_(),
+        np.datetime64(),
+        np.timedelta64(),
+        # NumPy dtype objects.
+        np.dtype("bool"),
+        np.dtype("int"),
+        np.dtype("float"),
+        np.dtype("complex"),
+        np.dtype("str"),
+        np.dtype("unicode"),
+        np.dtype("datetime64"),
+        np.dtype("timedelta64"),
+        np.dtype("object"),
+        # NumPy arrays.
+        np.array([], dtype=np.bool_),
+        np.array([], dtype=np.int_),
+        np.array([], dtype=np.float64),
+        np.array([], dtype=np.complex128),
+        np.array([], dtype=np.str_),
+        np.array([], dtype=np.unicode_),
+        np.array([], dtype=np.datetime64),
+        np.array([], dtype=np.timedelta64),
+        np.array([], dtype=object),
+        # Pandas dtypes.
+        # TODO: pandas does not consider these to be categoricals.
+        # pd.CategoricalDtype.type,
+        # pd.CategoricalDtype,
+        # Pandas objects.
+        pd.Series(dtype="bool"),
+        pd.Series(dtype="int"),
+        pd.Series(dtype="float"),
+        pd.Series(dtype="complex"),
+        pd.Series(dtype="str"),
+        pd.Series(dtype="unicode"),
+        pd.Series(dtype="datetime64[s]"),
+        pd.Series(dtype="timedelta64[s]"),
+        pd.Series(dtype="category"),
+        pd.Series(dtype="object"),
+    ),
+)
+def test_pandas_agreement_scalar(obj):
+    assert types.is_scalar(obj) == pd_types.is_scalar(obj)
+
+
+# TODO: Add test of interval.
+# TODO: Add test of Scalar.
diff --git a/python/cudf/cudf/tests/test_apply_rows.py b/python/cudf/cudf/tests/test_apply_rows.py
new file mode 100644
index 0000000..8870eb4
--- /dev/null
+++ b/python/cudf/cudf/tests/test_apply_rows.py
@@ -0,0 +1,54 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import pytest
+
+import cudf
+from cudf.core.column import column
+from cudf.testing._utils import assert_eq, gen_rand_series
+
+
+def _kernel_multiply(a, b, out):
+    for i, (x, y) in enumerate(zip(a, b)):
+        out[i] = x * y
+
+
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+@pytest.mark.parametrize("has_nulls", [False, True])
+@pytest.mark.parametrize("pessimistic", [False, True])
+def test_dataframe_apply_rows(dtype, has_nulls, pessimistic):
+    count = 1000
+    gdf_series_a = gen_rand_series(dtype, count, has_nulls=has_nulls)
+    gdf_series_b = gen_rand_series(dtype, count, has_nulls=has_nulls)
+    gdf_series_c = gen_rand_series(dtype, count, has_nulls=has_nulls)
+
+    if pessimistic:
+        # pessimistically combine the null masks
+        gdf_series_expected = gdf_series_a * gdf_series_b
+    else:
+        # optimistically ignore the null masks
+        a = cudf.Series(column.build_column(gdf_series_a.data, dtype))
+        b = cudf.Series(column.build_column(gdf_series_b.data, dtype))
+        gdf_series_expected = a * b
+
+    df_expected = cudf.DataFrame(
+        {
+            "a": gdf_series_a,
+            "b": gdf_series_b,
+            "c": gdf_series_c,
+            "out": gdf_series_expected,
+        }
+    )
+
+    df_original = cudf.DataFrame(
+        {"a": gdf_series_a, "b": gdf_series_b, "c": gdf_series_c}
+    )
+
+    df_actual = df_original.apply_rows(
+        _kernel_multiply,
+        ["a", "b"],
+        {"out": dtype},
+        {},
+        pessimistic_nulls=pessimistic,
+    )
+
+    assert_eq(df_expected, df_actual)
diff --git a/python/cudf/cudf/tests/test_applymap.py b/python/cudf/cudf/tests/test_applymap.py
new file mode 100644
index 0000000..32f3e39
--- /dev/null
+++ b/python/cudf/cudf/tests/test_applymap.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+import pytest
+
+from cudf import NA, DataFrame
+from cudf.testing import _utils as utils
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": [4, 5, 6]},
+        {"a": [1, 2, 3], "b": [1.0, 2.0, 3.0]},
+        {"a": [1, 2, 3], "b": [True, False, True]},
+        {"a": [1, NA, 2], "b": [NA, 4, NA]},
+    ],
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: x + 1,
+        lambda x: x - 0.5,
+        lambda x: 2 if x is NA else 2 + (x + 1) / 4.1,
+        lambda x: 42,
+    ],
+)
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_applymap_dataframe(data, func, na_action):
+    gdf = DataFrame(data)
+    pdf = gdf.to_pandas(nullable=True)
+
+    expect = pdf.applymap(func, na_action=na_action)
+    got = gdf.applymap(func, na_action=na_action)
+
+    utils.assert_eq(expect, got, check_dtype=False)
+
+
+def test_applymap_raise_cases():
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+    def f(x, some_kwarg=0):
+        return x + some_kwarg
+
+    with pytest.raises(NotImplementedError):
+        df.applymap(f, some_kwarg=1)
+
+    with pytest.raises(ValueError):
+        df.applymap(f, na_action="some_invalid_option")
diff --git a/python/cudf/cudf/tests/test_array_function.py b/python/cudf/cudf/tests/test_array_function.py
new file mode 100644
index 0000000..758a8cb
--- /dev/null
+++ b/python/cudf/cudf/tests/test_array_function.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+# To determine if NEP18 is available in the current version of NumPy we simply
+# attempt to concatenate an object with `__array_function__` defined and see if
+# NumPy invokes the protocol or not. Taken from dask array
+# https://github.com/dask/dask/blob/master/dask/array/utils.py#L352-L363
+# TODO: Unclear if this is still necessary. NEP 18 was introduced as the
+# default in 1.17 (https://github.com/numpy/numpy/releases/tag/v1.17.0) almost
+# 3 years ago, and it was originally introduced one version before in 1.16
+# (although not enabled by default then). Can we safely assume that testers
+# will have a sufficiently new version of numpy to run these tests?
+class _Test:
+    def __array_function__(self, *args, **kwargs):
+        return True
+
+
+try:
+    np.concatenate([_Test()])
+except ValueError:
+    missing_arrfunc_cond = True
+else:
+    missing_arrfunc_cond = False
+
+del _Test
+
+missing_arrfunc_reason = "NEP-18 support is not available in NumPy"
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+@pytest.mark.parametrize("np_ar", [np.random.random(100)])
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.mean(x),
+        lambda x: np.sum(x),
+        lambda x: np.var(x, ddof=1),
+        lambda x: np.unique(x),
+        lambda x: np.dot(x, x),
+        lambda x: np.linalg.norm(x),
+    ],
+)
+def test_array_func_cudf_series(np_ar, func):
+    cudf_ser = cudf.Series(np_ar)
+    expect = func(np_ar)
+    got = func(cudf_ser)
+    if np.isscalar(expect):
+        assert_eq(expect, got)
+    else:
+        assert_eq(expect, got.to_numpy())
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+@pytest.mark.parametrize(
+    "pd_df", [pd.DataFrame(np.random.uniform(size=(100, 10)))]
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.mean(x, axis=0),
+        lambda x: np.sum(x, axis=0),
+        lambda x: np.var(x, ddof=1),
+        lambda x: np.dot(x, x.transpose()),
+        lambda x: np.all(x),
+        lambda x: np.any(x),
+        lambda x: np.product(x),
+        lambda x: np.product(x, axis=0),
+        lambda x: np.product(x, axis=1),
+    ],
+)
+def test_array_func_cudf_dataframe(pd_df, func):
+    cudf_df = cudf.from_pandas(pd_df)
+    expect = func(pd_df)
+    got = func(cudf_df)
+    assert_eq(expect, got)
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+@pytest.mark.parametrize(
+    "pd_df", [pd.DataFrame(np.random.uniform(size=(100, 10)))]
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.cov(x, x),
+        lambda x: np.linalg.norm(x),
+        lambda x: np.linalg.det(x),
+    ],
+)
+def test_array_func_missing_cudf_dataframe(pd_df, func):
+    cudf_df = cudf.from_pandas(pd_df)
+    with pytest.raises(TypeError):
+        func(cudf_df)
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+@pytest.mark.parametrize("np_ar", [np.random.random(100)])
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.mean(x),
+        lambda x: np.sum(x),
+        lambda x: np.var(x, ddof=1),
+        lambda x: np.unique(x),
+        lambda x: np.dot(x, x),
+    ],
+)
+def test_array_func_cudf_index(np_ar, func):
+    cudf_index = cudf.core.index.as_index(cudf.Series(np_ar))
+    expect = func(np_ar)
+    got = func(cudf_index)
+    if np.isscalar(expect):
+        assert_eq(expect, got)
+    else:
+        assert_eq(expect, got.to_numpy())
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+@pytest.mark.parametrize("np_ar", [np.random.random(100)])
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.cov(x, x),
+        lambda x: np.linalg.norm(x),
+        lambda x: np.linalg.det(x),
+    ],
+)
+def test_array_func_missing_cudf_index(np_ar, func):
+    cudf_index = cudf.core.index.as_index(cudf.Series(np_ar))
+    with pytest.raises(TypeError):
+        func(cudf_index)
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: np.cov(x, x),
+        lambda x: np.dot(x, x),
+        lambda x: np.linalg.norm(x),
+        lambda x: np.linalg.det(x),
+    ],
+)
+def test_array_func_missing_cudf_multi_index(func):
+    levels = [["a", "b"], ["c", "d"]]
+    codes = [[0, 1], [1, 0]]
+
+    cudf_multi_index = cudf.MultiIndex(levels, codes)
+    with pytest.raises(TypeError):
+        func(cudf_multi_index)
+
+
+@pytest.mark.skipif(missing_arrfunc_cond, reason=missing_arrfunc_reason)
+def test_list_input_array_func():
+    ar = np.array([1, 2, 3])
+
+    s = cudf.Series(ar)
+    with pytest.raises(TypeError):
+        np.concatenate([s, s, s])
+
+    s = cudf.Series(ar, index=[1, 2, 3])
+    with pytest.raises(TypeError):
+        np.concatenate([s, s, s])
diff --git a/python/cudf/cudf/tests/test_array_ufunc.py b/python/cudf/cudf/tests/test_array_ufunc.py
new file mode 100644
index 0000000..81950bb
--- /dev/null
+++ b/python/cudf/cudf/tests/test_array_ufunc.py
@@ -0,0 +1,437 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import operator
+import warnings
+from contextlib import contextmanager
+from functools import reduce
+
+import cupy as cp
+import numpy as np
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_150
+from cudf.testing._utils import assert_eq, set_random_null_mask_inplace
+
+_UFUNCS = [
+    obj
+    for obj in (getattr(np, name) for name in dir(np))
+    if isinstance(obj, np.ufunc)
+]
+
+
+@contextmanager
+def _hide_ufunc_warnings(ufunc):
+    # pandas raises warnings for some inputs to the following ufuncs:
+    name = ufunc.__name__
+    if name in {
+        "arccos",
+        "arccosh",
+        "arcsin",
+        "arctanh",
+        "fmod",
+        "log",
+        "log10",
+        "log2",
+        "reciprocal",
+    }:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                f"invalid value encountered in {name}",
+                category=RuntimeWarning,
+            )
+            warnings.filterwarnings(
+                "ignore",
+                f"divide by zero encountered in {name}",
+                category=RuntimeWarning,
+            )
+            yield
+    else:
+        yield
+
+
+@pytest.mark.parametrize("ufunc", _UFUNCS)
+def test_ufunc_index(request, ufunc):
+    # Note: This test assumes that all ufuncs are unary or binary.
+    fname = ufunc.__name__
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                fname in {"bitwise_and", "bitwise_or", "bitwise_xor"}
+                and not PANDAS_GE_150
+            ),
+            reason="https://github.com/pandas-dev/pandas/issues/46769",
+        )
+    )
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=not hasattr(cp, fname),
+            reason=f"cupy has no support for '{fname}'",
+        )
+    )
+
+    N = 100
+    # Avoid zeros in either array to skip division by 0 errors. Also limit the
+    # scale to avoid issues with overflow, etc. We use ints because some
+    # operations (like bitwise ops) are not defined for floats.
+    pandas_args = args = [
+        cudf.Index(
+            cp.random.randint(low=1, high=10, size=N),
+        )
+        for _ in range(ufunc.nin)
+    ]
+
+    got = ufunc(*args)
+
+    with _hide_ufunc_warnings(ufunc):
+        expect = ufunc(*(arg.to_pandas() for arg in pandas_args))
+
+    if ufunc.nout > 1:
+        for g, e in zip(got, expect):
+            assert_eq(g, e, check_exact=False)
+    else:
+        assert_eq(got, expect, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "ufunc", [np.add, np.greater, np.greater_equal, np.logical_and]
+)
+@pytest.mark.parametrize("reflect", [True, False])
+def test_binary_ufunc_index_array(ufunc, reflect):
+    N = 100
+    # Avoid zeros in either array to skip division by 0 errors. Also limit the
+    # scale to avoid issues with overflow, etc. We use ints because some
+    # operations (like bitwise ops) are not defined for floats.
+    args = [cudf.Index(cp.random.rand(N)) for _ in range(ufunc.nin)]
+
+    arg1 = args[1].to_cupy()
+
+    if reflect:
+        got = ufunc(arg1, args[0])
+        expect = ufunc(args[1].to_numpy(), args[0].to_pandas())
+    else:
+        got = ufunc(args[0], arg1)
+        expect = ufunc(args[0].to_pandas(), args[1].to_numpy())
+
+    if ufunc.nout > 1:
+        for g, e in zip(got, expect):
+            if reflect:
+                assert (cp.asnumpy(g) == e).all()
+            else:
+                assert_eq(g, e, check_exact=False)
+    else:
+        if reflect:
+            assert (cp.asnumpy(got) == expect).all()
+        else:
+            assert_eq(got, expect, check_exact=False)
+
+
+@pytest.mark.parametrize("ufunc", _UFUNCS)
+@pytest.mark.parametrize("has_nulls", [True, False])
+@pytest.mark.parametrize("indexed", [True, False])
+def test_ufunc_series(request, ufunc, has_nulls, indexed):
+    # Note: This test assumes that all ufuncs are unary or binary.
+    fname = ufunc.__name__
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                indexed
+                and fname
+                in {
+                    "greater",
+                    "greater_equal",
+                    "less",
+                    "less_equal",
+                    "not_equal",
+                    "equal",
+                }
+            ),
+            reason="Comparison operators do not support misaligned indexes.",
+        )
+    )
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=ufunc == np.matmul and has_nulls,
+            reason="Can't call cupy on column with nulls",
+        )
+    )
+    # If we don't have explicit dispatch and cupy doesn't support the operator,
+    # we expect a failure
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=not hasattr(cp, fname),
+            reason=f"cupy has no support for '{fname}'",
+        )
+    )
+
+    N = 100
+    # Avoid zeros in either array to skip division by 0 errors. Also limit the
+    # scale to avoid issues with overflow, etc. We use ints because some
+    # operations (like bitwise ops) are not defined for floats.
+    pandas_args = args = [
+        cudf.Series(
+            cp.random.randint(low=1, high=10, size=N),
+            index=cp.random.choice(range(N), N, False) if indexed else None,
+        )
+        for _ in range(ufunc.nin)
+    ]
+
+    if has_nulls:
+        # Converting nullable integer cudf.Series to pandas will produce a
+        # float pd.Series, so instead we replace nulls with an arbitrary
+        # integer value, precompute the mask, and then reapply it afterwards.
+        for arg in args:
+            set_random_null_mask_inplace(arg)
+        pandas_args = [arg.fillna(0) for arg in args]
+
+        # Note: Different indexes must be aligned before the mask is computed.
+        # This requires using an internal function (_align_indices), and that
+        # is unlikely to change for the foreseeable future.
+        aligned = (
+            cudf.core.series._align_indices(args, allow_non_unique=True)
+            if indexed and ufunc.nin == 2
+            else args
+        )
+        mask = reduce(operator.or_, (a.isna() for a in aligned)).to_pandas()
+
+    got = ufunc(*args)
+
+    with _hide_ufunc_warnings(ufunc):
+        expect = ufunc(*(arg.to_pandas() for arg in pandas_args))
+
+    if ufunc.nout > 1:
+        for g, e in zip(got, expect):
+            if has_nulls:
+                e[mask] = np.nan
+            assert_eq(g, e, check_exact=False)
+    else:
+        if has_nulls:
+            expect[mask] = np.nan
+            assert_eq(got, expect, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "ufunc", [np.add, np.greater, np.greater_equal, np.logical_and]
+)
+@pytest.mark.parametrize("has_nulls", [True, False])
+@pytest.mark.parametrize("indexed", [True, False])
+@pytest.mark.parametrize("reflect", [True, False])
+def test_binary_ufunc_series_array(
+    request, ufunc, has_nulls, indexed, reflect
+):
+    fname = ufunc.__name__
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=reflect and has_nulls,
+            reason=(
+                "When cupy is the left operand there is no way for us to "
+                "avoid calling its binary operators, which cannot handle "
+                "cudf objects that contain nulls."
+            ),
+        )
+    )
+    # The way cudf casts nans in arrays to nulls during binops with cudf
+    # objects is currently incompatible with pandas.
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                fname in {"greater", "greater_equal", "logical_and"}
+                and has_nulls
+            ),
+            reason=(
+                "cudf and pandas incompatible casting nans "
+                "to nulls in binops"
+            ),
+        )
+    )
+    N = 100
+    # Avoid zeros in either array to skip division by 0 errors. Also limit the
+    # scale to avoid issues with overflow, etc. We use ints because some
+    # operations (like bitwise ops) are not defined for floats.
+    args = [
+        cudf.Series(
+            cp.random.rand(N),
+            index=cp.random.choice(range(N), N, False) if indexed else None,
+        )
+        for _ in range(ufunc.nin)
+    ]
+
+    if has_nulls:
+        # Converting nullable integer cudf.Series to pandas will produce a
+        # float pd.Series, so instead we replace nulls with an arbitrary
+        # integer value, precompute the mask, and then reapply it afterwards.
+        for arg in args:
+            set_random_null_mask_inplace(arg)
+
+        # Cupy doesn't support nulls, so we fill with nans before converting.
+        args[1] = args[1].fillna(cp.nan)
+        mask = args[0].isna().to_pandas()
+
+    arg1 = args[1].to_cupy()
+
+    if reflect:
+        got = ufunc(arg1, args[0])
+        expect = ufunc(args[1].to_numpy(), args[0].to_pandas())
+    else:
+        got = ufunc(args[0], arg1)
+        expect = ufunc(args[0].to_pandas(), args[1].to_numpy())
+
+    if ufunc.nout > 1:
+        for g, e in zip(got, expect):
+            if has_nulls:
+                e[mask] = np.nan
+            if reflect:
+                assert (cp.asnumpy(g) == e).all()
+            else:
+                assert_eq(g, e, check_exact=False)
+    else:
+        if has_nulls:
+            expect[mask] = np.nan
+        if reflect:
+            assert (cp.asnumpy(got) == expect).all()
+        else:
+            assert_eq(got, expect, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [np.add],
+)
+def test_ufunc_cudf_series_error_with_out_kwarg(func):
+    cudf_s1 = cudf.Series(data=[-1, 2, 3, 0])
+    cudf_s2 = cudf.Series(data=[-1, 2, 3, 0])
+    cudf_s3 = cudf.Series(data=[0, 0, 0, 0])
+    # this throws a value-error because of presence of out kwarg
+    with pytest.raises(TypeError):
+        func(x1=cudf_s1, x2=cudf_s2, out=cudf_s3)
+
+
+# Skip matmul since it requires aligned shapes.
+@pytest.mark.parametrize("ufunc", (uf for uf in _UFUNCS if uf != np.matmul))
+@pytest.mark.parametrize("has_nulls", [True, False])
+@pytest.mark.parametrize("indexed", [True, False])
+def test_ufunc_dataframe(request, ufunc, has_nulls, indexed):
+    # Note: This test assumes that all ufuncs are unary or binary.
+    fname = ufunc.__name__
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                indexed
+                and fname
+                in {
+                    "greater",
+                    "greater_equal",
+                    "less",
+                    "less_equal",
+                    "not_equal",
+                    "equal",
+                }
+            ),
+            reason="Comparison operators do not support misaligned indexes.",
+        )
+    )
+    # If we don't have explicit dispatch and cupy doesn't support the operator,
+    # we expect a failure
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=not hasattr(cp, fname),
+            reason=f"cupy has no support for '{fname}'",
+        )
+    )
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                indexed
+                and fname
+                in {
+                    "add",
+                    "arctan2",
+                    "bitwise_and",
+                    "bitwise_or",
+                    "bitwise_xor",
+                    "copysign",
+                    "divide",
+                    "divmod",
+                    "float_power",
+                    "floor_divide",
+                    "fmax",
+                    "fmin",
+                    "fmod",
+                    "heaviside",
+                    "gcd",
+                    "hypot",
+                    "lcm",
+                    "ldexp",
+                    "left_shift",
+                    "logaddexp",
+                    "logaddexp2",
+                    "logical_and",
+                    "logical_or",
+                    "logical_xor",
+                    "maximum",
+                    "minimum",
+                    "multiply",
+                    "nextafter",
+                    "power",
+                    "remainder",
+                    "right_shift",
+                    "subtract",
+                }
+            ),
+            reason=(
+                "pandas does not currently support misaligned "
+                "indexes in DataFrames"
+            ),
+        )
+    )
+
+    N = 100
+    # Avoid zeros in either array to skip division by 0 errors. Also limit the
+    # scale to avoid issues with overflow, etc. We use ints because some
+    # operations (like bitwise ops) are not defined for floats.
+    # TODO: Add tests of mismatched columns etc.
+    pandas_args = args = [
+        cudf.DataFrame(
+            {"foo": cp.random.randint(low=1, high=10, size=N)},
+            index=cp.random.choice(range(N), N, False) if indexed else None,
+        )
+        for _ in range(ufunc.nin)
+    ]
+
+    if has_nulls:
+        # Converting nullable integer cudf.Series to pandas will produce a
+        # float pd.Series, so instead we replace nulls with an arbitrary
+        # integer value, precompute the mask, and then reapply it afterwards.
+        for arg in args:
+            set_random_null_mask_inplace(arg["foo"])
+        pandas_args = [arg.copy() for arg in args]
+        for arg in pandas_args:
+            arg["foo"] = arg["foo"].fillna(0)
+
+        # Note: Different indexes must be aligned before the mask is computed.
+        # This requires using an internal function (_align_indices), and that
+        # is unlikely to change for the foreseeable future.
+        aligned = (
+            cudf.core.dataframe._align_indices(*args)
+            if indexed and ufunc.nin == 2
+            else args
+        )
+        mask = reduce(
+            operator.or_, (a["foo"].isna() for a in aligned)
+        ).to_pandas()
+
+    got = ufunc(*args)
+
+    with _hide_ufunc_warnings(ufunc):
+        expect = ufunc(*(arg.to_pandas() for arg in pandas_args))
+
+    if ufunc.nout > 1:
+        for g, e in zip(got, expect):
+            if has_nulls:
+                e[mask] = np.nan
+            assert_eq(g, e, check_exact=False)
+    else:
+        if has_nulls:
+            expect[mask] = np.nan
+        assert_eq(got, expect, check_exact=False)
diff --git a/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py b/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py
new file mode 100644
index 0000000..2272231
--- /dev/null
+++ b/python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py
@@ -0,0 +1,655 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import datetime
+import io
+import pathlib
+from typing import Optional
+
+import fastavro
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+from cudf.testing.dataset_generator import rand_dataframe
+
+
+def cudf_from_avro_util(schema: dict, records: list) -> cudf.DataFrame:
+    schema = [] if schema is None else fastavro.parse_schema(schema)
+    buffer = io.BytesIO()
+    fastavro.writer(buffer, schema, records)
+    buffer.seek(0)
+    return cudf.read_avro(buffer)
+
+
+avro_type_params = [
+    ("boolean", "bool"),
+    ("int", "int32"),
+    ("long", "int64"),
+    ("float", "float32"),
+    ("double", "float64"),
+    ("bytes", "str"),
+    ("string", "str"),
+]
+
+
+@pytest.mark.parametrize("avro_type, expected_dtype", avro_type_params)
+@pytest.mark.parametrize("namespace", [None, "root_ns"])
+@pytest.mark.parametrize("nullable", [True, False])
+def test_can_detect_dtype_from_avro_type(
+    avro_type, expected_dtype, namespace, nullable
+):
+    avro_type = avro_type if not nullable else ["null", avro_type]
+
+    schema = fastavro.parse_schema(
+        {
+            "type": "record",
+            "name": "test",
+            "namespace": namespace,
+            "fields": [{"name": "prop", "type": avro_type}],
+        }
+    )
+
+    actual = cudf_from_avro_util(schema, [])
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(None, None, expected_dtype)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("avro_type, expected_dtype", avro_type_params)
+@pytest.mark.parametrize("namespace", [None, "root_ns"])
+@pytest.mark.parametrize("nullable", [True, False])
+def test_can_detect_dtype_from_avro_type_nested(
+    avro_type, expected_dtype, namespace, nullable
+):
+    avro_type = avro_type if not nullable else ["null", avro_type]
+
+    schema_leaf = {
+        "name": "leaf",
+        "type": "record",
+        "fields": [{"name": "prop3", "type": avro_type}],
+    }
+
+    schema_child = {
+        "name": "child",
+        "type": "record",
+        "fields": [{"name": "prop2", "type": schema_leaf}],
+    }
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "namespace": namespace,
+        "fields": [{"name": "prop1", "type": schema_child}],
+    }
+
+    actual = cudf_from_avro_util(schema_root, [])
+
+    col_name = "{ns}child.{ns}leaf.prop3".format(
+        ns="" if namespace is None else namespace + "."
+    )
+
+    expected = cudf.DataFrame(
+        {col_name: cudf.Series(None, None, expected_dtype)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "avro_type, cudf_type, avro_val, cudf_val",
+    [
+        ("boolean", "bool", True, True),
+        ("boolean", "bool", False, False),
+        ("int", "int32", 1234, 1234),
+        ("long", "int64", 1234, 1234),
+        ("float", "float32", 12.34, 12.34),
+        ("double", "float64", 12.34, 12.34),
+        ("string", "str", "heyϴ", "heyϴ"),
+        # ("bytes", "str", "heyϴ", "heyϴ"),
+    ],
+)
+def test_can_parse_single_value(avro_type, cudf_type, avro_val, cudf_val):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [{"name": "prop", "type": ["null", avro_type]}],
+    }
+
+    records = [
+        {"prop": avro_val},
+    ]
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(data=[cudf_val], dtype=cudf_type)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("avro_type, cudf_type", avro_type_params)
+def test_can_parse_single_null(avro_type, cudf_type):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [{"name": "prop", "type": ["null", avro_type]}],
+    }
+
+    records = [{"prop": None}]
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(data=[None], dtype=cudf_type)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("avro_type, cudf_type", avro_type_params)
+def test_can_parse_no_data(avro_type, cudf_type):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [{"name": "prop", "type": ["null", avro_type]}],
+    }
+
+    records = []
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame({"prop": cudf.Series(data=[], dtype=cudf_type)})
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.xfail(
+    reason="cudf avro reader is unable to parse zero-field metadata."
+)
+@pytest.mark.parametrize("avro_type, cudf_type", avro_type_params)
+def test_can_parse_no_fields(avro_type, cudf_type):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [],
+    }
+
+    records = []
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame()
+
+    assert_eq(expected, actual)
+
+
+def test_can_parse_no_schema():
+
+    schema_root = None
+    records = []
+    actual = cudf_from_avro_util(schema_root, records)
+    expected = cudf.DataFrame()
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("rows", [0, 1, 10, 1000])
+@pytest.mark.parametrize("codec", ["null", "deflate", "snappy"])
+def test_avro_compression(rows, codec):
+    schema = {
+        "name": "root",
+        "type": "record",
+        "fields": [
+            {"name": "0", "type": "int"},
+            {"name": "1", "type": "string"},
+        ],
+    }
+
+    # N.B. rand_dataframe() is brutally slow for some reason.  Switching to
+    #      np.random() speeds things up by a factor of 10.
+    #      See also: https://github.com/rapidsai/cudf/issues/13128
+    df = rand_dataframe(
+        [
+            {"dtype": "int32", "null_frequency": 0, "cardinality": 1000},
+            {
+                "dtype": "str",
+                "null_frequency": 0,
+                "cardinality": 100,
+                "max_string_length": 10,
+            },
+        ],
+        rows,
+    )
+    expected_df = cudf.DataFrame.from_arrow(df)
+
+    records = df.to_pandas().to_dict(orient="records")
+
+    buffer = io.BytesIO()
+    fastavro.writer(buffer, schema, records, codec=codec)
+    buffer.seek(0)
+    got_df = cudf.read_avro(buffer)
+
+    assert_eq(expected_df, got_df)
+
+
+avro_logical_type_params = [
+    # (avro logical type, avro primitive type, cudf expected dtype)
+    ("date", "int", "datetime64[s]"),
+]
+
+
+@pytest.mark.parametrize(
+    "logical_type, primitive_type, expected_dtype", avro_logical_type_params
+)
+@pytest.mark.parametrize("namespace", [None, "root_ns"])
+@pytest.mark.parametrize("nullable", [True, False])
+@pytest.mark.parametrize("prepend_null", [True, False])
+def test_can_detect_dtypes_from_avro_logical_type(
+    logical_type,
+    primitive_type,
+    expected_dtype,
+    namespace,
+    nullable,
+    prepend_null,
+):
+    avro_type = [{"logicalType": logical_type, "type": primitive_type}]
+    if nullable:
+        if prepend_null:
+            avro_type.insert(0, "null")
+        else:
+            avro_type.append("null")
+
+    schema = fastavro.parse_schema(
+        {
+            "type": "record",
+            "name": "test",
+            "namespace": namespace,
+            "fields": [{"name": "prop", "type": avro_type}],
+        }
+    )
+
+    actual = cudf_from_avro_util(schema, [])
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(None, None, expected_dtype)}
+    )
+
+    assert_eq(expected, actual)
+
+
+def get_days_from_epoch(date: Optional[datetime.date]) -> Optional[int]:
+    if date is None:
+        return None
+    return (date - datetime.date(1970, 1, 1)).days
+
+
+@pytest.mark.parametrize("namespace", [None, "root_ns"])
+@pytest.mark.parametrize("nullable", [True, False])
+@pytest.mark.parametrize("prepend_null", [True, False])
+def test_can_parse_avro_date_logical_type(namespace, nullable, prepend_null):
+
+    avro_type = {"logicalType": "date", "type": "int"}
+    if nullable:
+        if prepend_null:
+            avro_type = ["null", avro_type]
+        else:
+            avro_type = [avro_type, "null"]
+
+    schema_dict = {
+        "type": "record",
+        "name": "test",
+        "fields": [
+            {"name": "o_date", "type": avro_type},
+        ],
+    }
+
+    if namespace:
+        schema_dict["namespace"] = namespace
+
+    schema = fastavro.parse_schema(schema_dict)
+
+    # Insert some None values in no particular order.  These will get converted
+    # into avro "nulls" by the fastavro writer (or filtered out if we're not
+    # nullable).  The first and last dates are epoch min/max values, the rest
+    # are arbitrarily chosen.
+    dates = [
+        None,
+        datetime.date(1970, 1, 1),
+        datetime.date(1970, 1, 2),
+        datetime.date(1981, 10, 25),
+        None,
+        None,
+        datetime.date(2012, 5, 18),
+        None,
+        datetime.date(2019, 9, 3),
+        None,
+        datetime.date(9999, 12, 31),
+    ]
+
+    if not nullable:
+        dates = [date for date in dates if date is not None]
+
+    days_from_epoch = [get_days_from_epoch(date) for date in dates]
+
+    records = [{"o_date": day} for day in days_from_epoch]
+
+    actual = cudf_from_avro_util(schema, records)
+
+    expected = cudf.DataFrame(
+        {"o_date": cudf.Series(dates, dtype="datetime64[s]")}
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_alltypes_plain_avro():
+    # During development of the logical type support, the Java avro tests were
+    # triggering CUDA kernel crashes (null pointer dereferences).  We were able
+    # to replicate the behavior in a C++ test case, and then subsequently came
+    # up with this Python unit test to also trigger the problematic code path.
+    #
+    # So, unlike the other tests, this test is inherently reactive in nature,
+    # added simply to verify we fixed the problematic code path that was
+    # causing CUDA kernel crashes.
+    #
+    # See https://github.com/rapidsai/cudf/pull/12788#issuecomment-1468822875
+    # for more information.
+    relpath = "../../../../java/src/test/resources/alltypes_plain.avro"
+    path = pathlib.Path(__file__).parent.joinpath(relpath).resolve()
+    assert path.is_file(), path
+    path = str(path)
+
+    with open(path, "rb") as f:
+        reader = fastavro.reader(f)
+        records = [record for record in reader]
+
+    # For reference:
+    #
+    # >>> from pprint import pprint
+    # >>> pprint(reader.writer_schema)
+    # {'fields': [{'name': 'id', 'type': ['int', 'null']},
+    #             {'name': 'bool_col', 'type': ['boolean', 'null']},
+    #             {'name': 'tinyint_col', 'type': ['int', 'null']},
+    #             {'name': 'smallint_col', 'type': ['int', 'null']},
+    #             {'name': 'int_col', 'type': ['int', 'null']},
+    #             {'name': 'bigint_col', 'type': ['long', 'null']},
+    #             {'name': 'float_col', 'type': ['float', 'null']},
+    #             {'name': 'double_col', 'type': ['double', 'null']},
+    #             {'name': 'date_string_col', 'type': ['bytes', 'null']},
+    #             {'name': 'string_col', 'type': ['bytes', 'null']},
+    #             {'name': 'timestamp_col',
+    #              'type': [{'logicalType': 'timestamp-micros',
+    #                        'type': 'long'},
+    #                       'null']}],
+    #  'name': 'topLevelRecord',
+    #  'type': 'record'}
+    #
+    # >>> pprint(records[0])
+    # {'bigint_col': 0,
+    #  'bool_col': True,
+    #  'date_string_col': b'03/01/09',
+    #  'double_col': 0.0,
+    #  'float_col': 0.0,
+    #  'id': 4,
+    #  'int_col': 0,
+    #  'smallint_col': 0,
+    #  'string_col': b'0',
+    #  'timestamp_col': datetime.datetime(2009, 3, 1, 0, 0,
+    #                                     tzinfo=datetime.timezone.utc),
+    #  'tinyint_col': 0}
+
+    # Nothing particularly special about these columns, other than them being
+    # the ones that @davidwendt used to coerce the crash.
+    columns = ["bool_col", "int_col", "timestamp_col"]
+
+    # This next line would trigger the fatal CUDA kernel crash.
+    actual = cudf.read_avro(path, columns=columns)
+
+    # If we get here, we haven't crashed, obviously.  Verify the returned data
+    # frame meets our expectations.  We need to fiddle with the dtypes of the
+    # expected data frame in order to correctly match the schema definition and
+    # our corresponding read_avro()-returned data frame.
+
+    data = [{column: row[column] for column in columns} for row in records]
+
+    # discard timezone information as we don't support it:
+    expected = pd.DataFrame(data)
+    expected["timestamp_col"].dt.tz_localize(None)
+
+    # The fastavro.reader supports the `'logicalType': 'timestamp-micros'` used
+    # by the 'timestamp_col' column, which is converted into Python
+    # datetime.datetime() objects (see output of pprint(records[0]) above).
+    # As we don't support that logical type yet in cudf, we need to convert to
+    # int64, then divide by 1000 to convert from nanoseconds to microseconds.
+    timestamps = expected["timestamp_col"].astype("int64")
+    timestamps //= 1000
+    expected["timestamp_col"] = timestamps
+
+    # Furthermore, we need to force the 'int_col' into an int32, per the schema
+    # definition.  (It ends up as an int64 due to cudf.DataFrame() defaulting
+    # all Python int values to int64 sans a dtype= override.)
+    expected["int_col"] = expected["int_col"].astype("int32")
+
+    assert_eq(actual, expected)
+
+
+def multiblock_testname_ids(param):
+    (total_rows, num_rows, skip_rows, sync_interval) = param
+    return f"{total_rows=}-{num_rows=}-{skip_rows=}-{sync_interval=}"
+
+
+# The following values are used to test various boundary conditions associated
+# with multiblock avro files.  Each tuple consists of four values: total number
+# of rows to generate, number of rows to limit the result set to, number of
+# rows to skip, and number of rows per block.  If the total number of rows and
+# number of rows (i.e. first and second tuple elements) are equal, it means
+# that all rows will be returned.  If the rows per block also equals the first
+# two numbers, it means that a single block will be used.
+@pytest.fixture(
+    ids=multiblock_testname_ids,
+    params=[
+        (10, 10, 9, 9),
+        (10, 10, 9, 5),
+        (10, 10, 9, 3),
+        (10, 10, 9, 2),
+        (10, 10, 9, 10),
+        (10, 10, 8, 2),
+        (10, 10, 5, 5),
+        (10, 10, 2, 9),
+        (10, 10, 2, 2),
+        (10, 10, 1, 9),
+        (10, 10, 1, 5),
+        (10, 10, 1, 2),
+        (10, 10, 1, 10),
+        (10, 10, 10, 9),
+        (10, 10, 10, 5),
+        (10, 10, 10, 2),
+        (10, 10, 10, 10),
+        (10, 10, 0, 9),
+        (10, 10, 0, 5),
+        (10, 10, 0, 2),
+        (10, 10, 0, 10),
+        (100, 100, 99, 10),
+        (100, 100, 90, 90),
+        (100, 100, 90, 89),
+        (100, 100, 90, 88),
+        (100, 100, 90, 87),
+        (100, 100, 90, 5),
+        (100, 100, 89, 90),
+        (100, 100, 87, 90),
+        (100, 100, 50, 7),
+        (100, 100, 50, 31),
+        (10, 1, 8, 9),
+        (100, 1, 99, 10),
+        (100, 1, 98, 10),
+        (100, 1, 97, 10),
+        (100, 3, 90, 87),
+        (100, 4, 90, 5),
+        (100, 2, 89, 90),
+        (100, 9, 87, 90),
+        (100, 20, 50, 7),
+        (100, 10, 50, 31),
+        (100, 20, 50, 31),
+        (100, 30, 50, 31),
+        (256, 256, 0, 256),
+        (256, 256, 0, 32),
+        (256, 256, 0, 31),
+        (256, 256, 0, 33),
+        (256, 256, 31, 32),
+        (256, 256, 32, 31),
+        (256, 256, 31, 33),
+        (512, 512, 0, 32),
+        (512, 512, 0, 31),
+        (512, 512, 0, 33),
+        (512, 512, 31, 32),
+        (512, 512, 32, 31),
+        (512, 512, 31, 33),
+        (1024, 1024, 0, 1),
+        (1024, 1024, 0, 3),
+        (1024, 1024, 0, 7),
+        (1024, 1024, 0, 8),
+        (1024, 1024, 0, 9),
+        (1024, 1024, 0, 15),
+        (1024, 1024, 0, 16),
+        (1024, 1024, 0, 17),
+        (1024, 1024, 0, 32),
+        (1024, 1024, 0, 31),
+        (1024, 1024, 0, 33),
+        (1024, 1024, 31, 32),
+        (1024, 1024, 32, 31),
+        (1024, 1024, 31, 33),
+        (16384, 16384, 0, 31),
+        (16384, 16384, 0, 32),
+        (16384, 16384, 0, 33),
+        (16384, 16384, 0, 16384),
+    ],
+)
+def total_rows_and_num_rows_and_skip_rows_and_rows_per_block(request):
+    return request.param
+
+
+# N.B. The float32 and float64 types are chosen specifically to exercise
+#      the only path in the avro reader GPU code that can process multiple
+#      rows in parallel (via warp-level parallelism).  See the logic around
+#      the line `if (cur + min_row_size * rows_remaining == end)` in
+#      gpuDecodeAvroColumnData().
+@pytest.mark.parametrize("dtype", ["str", "float32", "float64"])
+@pytest.mark.parametrize(
+    "use_sync_interval",
+    [True, False],
+    ids=["use_sync_interval", "ignore_sync_interval"],
+)
+@pytest.mark.parametrize("codec", ["null", "deflate", "snappy"])
+def test_avro_reader_multiblock(
+    dtype,
+    codec,
+    use_sync_interval,
+    total_rows_and_num_rows_and_skip_rows_and_rows_per_block,
+):
+    (
+        total_rows,
+        num_rows,
+        skip_rows,
+        rows_per_block,
+    ) = total_rows_and_num_rows_and_skip_rows_and_rows_per_block
+
+    assert total_rows >= num_rows
+    assert rows_per_block <= total_rows
+
+    limit_rows = num_rows != total_rows
+    if limit_rows:
+        assert total_rows >= num_rows + skip_rows
+
+    if dtype == "str":
+        avro_type = "string"
+
+        # Generate a list of strings, each of which is a 6-digit number, padded
+        # with leading zeros.  This data set was very useful during development
+        # of the multiblock avro reader logic, as you get implicit feedback as
+        # to what may have gone wrong when the test fails, based on the
+        # expected vs actual values.
+        values = [f"{i:0>6}" for i in range(0, total_rows)]
+
+        # Strings are encoded in avro with a zigzag-encoded length prefix, and
+        # then the string data.  As all of our strings are fixed at length 6,
+        # we only need one byte to encode the length prefix (0xc).  Thus, our
+        # bytes per row is 6 + 1 = 7.
+        bytes_per_row = len(values[0]) + 1
+        assert bytes_per_row == 7, bytes_per_row
+    else:
+        assert dtype in ("float32", "float64")
+        avro_type = "float" if dtype == "float32" else "double"
+
+        # We don't use rand_dataframe() here, because it increases the
+        # execution time of each test by a factor of 10 or more (it appears
+        # to use a very costly approach to generating random data).
+        # See also: https://github.com/rapidsai/cudf/issues/13128
+        values = np.random.rand(total_rows).astype(dtype)
+        bytes_per_row = values.dtype.itemsize
+
+    # The sync_interval is the number of bytes between sync blocks.  We know
+    # how many bytes we need per row, so we can calculate the number of bytes
+    # per block by multiplying the number of rows per block by the bytes per
+    # row.  This is the sync interval.
+    total_bytes_per_block = rows_per_block * bytes_per_row
+    sync_interval = total_bytes_per_block
+
+    source_df = cudf.DataFrame({"0": pd.Series(values)})
+
+    if limit_rows:
+        expected_df = source_df[skip_rows : skip_rows + num_rows].reset_index(
+            drop=True
+        )
+    else:
+        expected_df = source_df[skip_rows:].reset_index(drop=True)
+
+    records = source_df.to_pandas().to_dict(orient="records")
+
+    schema = {
+        "name": "root",
+        "type": "record",
+        "fields": [
+            {"name": "0", "type": avro_type},
+        ],
+    }
+
+    if use_sync_interval:
+        kwds = {"sync_interval": sync_interval}
+    else:
+        kwds = {}
+
+    kwds["codec"] = codec
+
+    buffer = io.BytesIO()
+    fastavro.writer(buffer, schema, records, **kwds)
+    buffer.seek(0)
+
+    if not limit_rows:
+        # Explicitly set num_rows to None if we want to read all rows.  This
+        # ensures we exercise the logic behind a read_avro() call where the
+        # caller doesn't specify the number of rows desired (which will be the
+        # most common use case).
+        num_rows = None
+    actual_df = cudf.read_avro(buffer, skiprows=skip_rows, num_rows=num_rows)
+
+    assert_eq(expected_df, actual_df)
diff --git a/python/cudf/cudf/tests/test_binops.py b/python/cudf/cudf/tests/test_binops.py
new file mode 100644
index 0000000..cd3e8f7
--- /dev/null
+++ b/python/cudf/cudf/tests/test_binops.py
@@ -0,0 +1,3337 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import decimal
+import operator
+import random
+import warnings
+from itertools import combinations_with_replacement, product
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Series
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.buffer.spill_manager import get_global_manager
+from cudf.core.index import as_index
+from cudf.testing import _utils as utils
+from cudf.utils.dtypes import (
+    BOOL_TYPES,
+    DATETIME_TYPES,
+    FLOAT_TYPES,
+    INTEGER_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+)
+
+STRING_TYPES = {"str"}
+
+_binops = [
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.floordiv,
+    operator.truediv,
+    operator.mod,
+    operator.pow,
+]
+
+_binops_compare = [
+    operator.eq,
+    operator.ne,
+    operator.lt,
+    operator.le,
+    operator.gt,
+    operator.ge,
+]
+
+_bitwise_binops = [operator.and_, operator.or_, operator.xor]
+
+_int_types = [
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "uint8",
+    "uint16",
+    "uint32",
+]
+
+_cmpops = [
+    operator.lt,
+    operator.gt,
+    operator.le,
+    operator.ge,
+    operator.eq,
+    operator.ne,
+]
+
+_reflected_ops = [
+    lambda x: 1 + x,
+    lambda x: 2 * x,
+    lambda x: 2 - x,
+    lambda x: 2 // x,
+    lambda x: 2 / x,
+    lambda x: 3 + x,
+    lambda x: 3 * x,
+    lambda x: 3 - x,
+    lambda x: 3 // x,
+    lambda x: 3 / x,
+    lambda x: 3 % x,
+    lambda x: -1 + x,
+    lambda x: -2 * x,
+    lambda x: -2 - x,
+    lambda x: -2 // x,
+    lambda x: -2 / x,
+    lambda x: -3 + x,
+    lambda x: -3 * x,
+    lambda x: -3 - x,
+    lambda x: -3 // x,
+    lambda x: -3 / x,
+    lambda x: -3 % x,
+    lambda x: 0 + x,
+    lambda x: 0 * x,
+    lambda x: 0 - x,
+    lambda x: 0 // x,
+    lambda x: 0 / x,
+]
+
+_operators_arithmetic = [
+    "add",
+    "radd",
+    "sub",
+    "rsub",
+    "mul",
+    "rmul",
+    "mod",
+    "rmod",
+    "pow",
+    "rpow",
+    "div",
+    "divide",
+    "floordiv",
+    "rfloordiv",
+    "truediv",
+    "rtruediv",
+]
+
+_operators_comparison = ["eq", "ne", "lt", "le", "gt", "ge"]
+
+
+_cudf_scalar_reflected_ops = [
+    lambda x: cudf.Scalar(1) + x,
+    lambda x: cudf.Scalar(2) * x,
+    lambda x: cudf.Scalar(2) - x,
+    lambda x: cudf.Scalar(2) // x,
+    lambda x: cudf.Scalar(2) / x,
+    lambda x: cudf.Scalar(3) + x,
+    lambda x: cudf.Scalar(3) * x,
+    lambda x: cudf.Scalar(3) - x,
+    lambda x: cudf.Scalar(3) // x,
+    lambda x: cudf.Scalar(3) / x,
+    lambda x: cudf.Scalar(3) % x,
+    lambda x: cudf.Scalar(-1) + x,
+    lambda x: cudf.Scalar(-2) * x,
+    lambda x: cudf.Scalar(-2) - x,
+    lambda x: cudf.Scalar(-2) // x,
+    lambda x: cudf.Scalar(-2) / x,
+    lambda x: cudf.Scalar(-3) + x,
+    lambda x: cudf.Scalar(-3) * x,
+    lambda x: cudf.Scalar(-3) - x,
+    lambda x: cudf.Scalar(-3) // x,
+    lambda x: cudf.Scalar(-3) / x,
+    lambda x: cudf.Scalar(-3) % x,
+    lambda x: cudf.Scalar(0) + x,
+    lambda x: cudf.Scalar(0) * x,
+    lambda x: cudf.Scalar(0) - x,
+    lambda x: cudf.Scalar(0) // x,
+    lambda x: cudf.Scalar(0) / x,
+]
+
+
+pytest_xfail = pytest.mark.xfail
+pytestmark = pytest.mark.spilling
+
+# If spilling is enabled globally, we skip many test permutations
+# to reduce running time.
+if get_global_manager() is not None:
+    _binops = _binops[:1]
+    _binops_compare = _binops_compare[:1]
+    _int_types = _int_types[-1:]
+    _cmpops = _cmpops[:1]
+    _reflected_ops = _reflected_ops[:1]
+    _operators_arithmetic = _operators_arithmetic[:1]
+    _operators_comparison = _operators_comparison[:1]
+    _cudf_scalar_reflected_ops = _cudf_scalar_reflected_ops[:1]
+    DATETIME_TYPES = {"datetime64[ms]"}  # noqa: F811
+    NUMERIC_TYPES = {"float32"}  # noqa: F811
+    FLOAT_TYPES = {"float64"}  # noqa: F811
+    INTEGER_TYPES = {"int16"}  # noqa: F811
+    TIMEDELTA_TYPES = {"timedelta64[s]"}  # noqa: F811
+    # To save time, we skip tests marked "pytest.mark.xfail"
+    pytest_xfail = pytest.mark.skipif
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize("binop", _binops)
+def test_series_binop(binop, obj_class):
+    nelem = 1000
+    arr1 = utils.gen_rand("float64", nelem) * 10000
+    # Keeping a low value because CUDA 'pow' has 2 full range error
+    arr2 = utils.gen_rand("float64", nelem) * 10
+
+    sr1 = Series(arr1)
+    sr2 = Series(arr2)
+
+    if obj_class == "Index":
+        sr1 = as_index(sr1)
+        sr2 = as_index(sr2)
+
+    result = binop(sr1, sr2)
+    expect = binop(pd.Series(arr1), pd.Series(arr2))
+
+    if obj_class == "Index":
+        result = Series(result)
+
+    utils.assert_eq(result, expect)
+
+
+@pytest.mark.parametrize("binop", _binops)
+def test_series_binop_concurrent(binop):
+    def func(index):
+        arr = np.random.random(100) * 10
+        sr = Series(arr)
+
+        result = binop(sr.astype("int32"), sr)
+        expect = binop(arr.astype("int32"), arr)
+
+        np.testing.assert_almost_equal(result.to_numpy(), expect, decimal=5)
+
+    from concurrent.futures import ThreadPoolExecutor
+
+    indices = range(10)
+    with ThreadPoolExecutor(4) as e:  # four processes
+        list(e.map(func, indices))
+
+
+@pytest.mark.parametrize("use_cudf_scalar", [False, True])
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize("nelem,binop", list(product([1, 2, 100], _binops)))
+def test_series_binop_scalar(nelem, binop, obj_class, use_cudf_scalar):
+    arr = np.random.random(nelem)
+    rhs = random.choice(arr).item()
+
+    sr = Series(arr)
+    if obj_class == "Index":
+        sr = as_index(sr)
+
+    if use_cudf_scalar:
+        result = binop(sr, rhs)
+    else:
+        result = binop(sr, cudf.Scalar(rhs))
+
+    if obj_class == "Index":
+        result = Series(result)
+
+    np.testing.assert_almost_equal(result.to_numpy(), binop(arr, rhs))
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize("binop", _bitwise_binops)
+@pytest.mark.parametrize(
+    "lhs_dtype,rhs_dtype", list(product(_int_types, _int_types))
+)
+def test_series_bitwise_binop(binop, obj_class, lhs_dtype, rhs_dtype):
+    arr1 = (np.random.random(100) * 100).astype(lhs_dtype)
+    sr1 = Series(arr1)
+
+    arr2 = (np.random.random(100) * 100).astype(rhs_dtype)
+    sr2 = Series(arr2)
+
+    if obj_class == "Index":
+        sr1 = as_index(sr1)
+        sr2 = as_index(sr2)
+
+    result = binop(sr1, sr2)
+
+    if obj_class == "Index":
+        result = Series(result)
+
+    np.testing.assert_almost_equal(result.to_numpy(), binop(arr1, arr2))
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize("cmpop", _cmpops)
+@pytest.mark.parametrize(
+    "dtype", ["int8", "int32", "int64", "float32", "float64", "datetime64[ms]"]
+)
+def test_series_compare(cmpop, obj_class, dtype):
+    arr1 = np.random.randint(0, 100, 100).astype(dtype)
+    arr2 = np.random.randint(0, 100, 100).astype(dtype)
+    sr1 = Series(arr1)
+    sr2 = Series(arr2)
+
+    if obj_class == "Index":
+        sr1 = as_index(sr1)
+        sr2 = as_index(sr2)
+
+    result1 = cmpop(sr1, sr1)
+    result2 = cmpop(sr2, sr2)
+    result3 = cmpop(sr1, sr2)
+
+    if obj_class == "Index":
+        result1 = Series(result1)
+        result2 = Series(result2)
+        result3 = Series(result3)
+
+    np.testing.assert_equal(result1.to_numpy(), cmpop(arr1, arr1))
+    np.testing.assert_equal(result2.to_numpy(), cmpop(arr2, arr2))
+    np.testing.assert_equal(result3.to_numpy(), cmpop(arr1, arr2))
+
+
+def _series_compare_nulls_typegen():
+    return [
+        *combinations_with_replacement(DATETIME_TYPES, 2),
+        *combinations_with_replacement(TIMEDELTA_TYPES, 2),
+        *combinations_with_replacement(NUMERIC_TYPES, 2),
+        *combinations_with_replacement(STRING_TYPES, 2),
+    ]
+
+
+@pytest.mark.parametrize("cmpop", _cmpops)
+@pytest.mark.parametrize("dtypes", _series_compare_nulls_typegen())
+def test_series_compare_nulls(cmpop, dtypes):
+    ltype, rtype = dtypes
+
+    ldata = [1, 2, None, None, 5]
+    rdata = [2, 1, None, 4, None]
+
+    lser = Series(ldata, dtype=ltype)
+    rser = Series(rdata, dtype=rtype)
+
+    lmask = ~lser.isnull()
+    rmask = ~rser.isnull()
+
+    expect_mask = np.logical_and(lmask, rmask)
+    expect = cudf.Series([None] * 5, dtype="bool")
+    expect[expect_mask] = cmpop(lser[expect_mask], rser[expect_mask])
+
+    got = cmpop(lser, rser)
+    utils.assert_eq(expect, got)
+
+
+@pytest.fixture
+def str_series_cmp_data():
+    return pd.Series(["a", "b", None, "d", "e", None], dtype="string")
+
+
+@pytest.fixture(ids=[op.__name__ for op in _cmpops], params=_cmpops)
+def str_series_compare_str_cmpop(request):
+    return request.param
+
+
+@pytest.fixture(ids=["eq", "ne"], params=[operator.eq, operator.ne])
+def str_series_compare_num_cmpop(request):
+    return request.param
+
+
+@pytest.fixture(ids=["int", "float", "bool"], params=[1, 1.5, True])
+def cmp_scalar(request):
+    return request.param
+
+
+def test_str_series_compare_str(
+    str_series_cmp_data, str_series_compare_str_cmpop
+):
+    expect = str_series_compare_str_cmpop(str_series_cmp_data, "a")
+    got = str_series_compare_str_cmpop(
+        Series.from_pandas(str_series_cmp_data), "a"
+    )
+
+    utils.assert_eq(expect, got.to_pandas(nullable=True))
+
+
+def test_str_series_compare_str_reflected(
+    str_series_cmp_data, str_series_compare_str_cmpop
+):
+    expect = str_series_compare_str_cmpop("a", str_series_cmp_data)
+    got = str_series_compare_str_cmpop(
+        "a", Series.from_pandas(str_series_cmp_data)
+    )
+
+    utils.assert_eq(expect, got.to_pandas(nullable=True))
+
+
+def test_str_series_compare_num(
+    str_series_cmp_data, str_series_compare_num_cmpop, cmp_scalar
+):
+    expect = str_series_compare_num_cmpop(str_series_cmp_data, cmp_scalar)
+    got = str_series_compare_num_cmpop(
+        Series.from_pandas(str_series_cmp_data), cmp_scalar
+    )
+
+    utils.assert_eq(expect, got.to_pandas(nullable=True))
+
+
+def test_str_series_compare_num_reflected(
+    str_series_cmp_data, str_series_compare_num_cmpop, cmp_scalar
+):
+    expect = str_series_compare_num_cmpop(cmp_scalar, str_series_cmp_data)
+    got = str_series_compare_num_cmpop(
+        cmp_scalar, Series.from_pandas(str_series_cmp_data)
+    )
+
+    utils.assert_eq(expect, got.to_pandas(nullable=True))
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize("nelem", [1, 2, 100])
+@pytest.mark.parametrize("cmpop", _cmpops)
+@pytest.mark.parametrize("dtype", utils.NUMERIC_TYPES + ["datetime64[ms]"])
+@pytest.mark.parametrize("use_cudf_scalar", [True, False])
+def test_series_compare_scalar(
+    nelem, cmpop, obj_class, dtype, use_cudf_scalar
+):
+    arr1 = np.random.randint(0, 100, 100).astype(dtype)
+    sr1 = Series(arr1)
+    rhs = random.choice(arr1).item()
+
+    if use_cudf_scalar:
+        rhs = cudf.Scalar(rhs)
+
+    if obj_class == "Index":
+        sr1 = as_index(sr1)
+
+    result1 = cmpop(sr1, rhs)
+    result2 = cmpop(rhs, sr1)
+
+    if obj_class == "Index":
+        result1 = Series(result1)
+        result2 = Series(result2)
+
+    np.testing.assert_equal(result1.to_numpy(), cmpop(arr1, rhs))
+    np.testing.assert_equal(result2.to_numpy(), cmpop(rhs, arr1))
+
+
+_nulls = ["none", "some"]
+
+
+@pytest.mark.parametrize("nelem", [1, 7, 8, 9, 32, 64, 128])
+@pytest.mark.parametrize("lhs_nulls,rhs_nulls", list(product(_nulls, _nulls)))
+def test_validity_add(nelem, lhs_nulls, rhs_nulls):
+    np.random.seed(0)
+    # LHS
+    lhs_data = np.random.random(nelem)
+    if lhs_nulls == "some":
+        lhs_mask = utils.random_bitmask(nelem)
+        lhs_bitmask = utils.expand_bits_to_bytes(lhs_mask)[:nelem]
+        lhs_null_count = utils.count_zero(lhs_bitmask)
+        assert lhs_null_count >= 0
+        lhs = Series.from_masked_array(lhs_data, lhs_mask)
+        assert lhs.null_count == lhs_null_count
+    else:
+        lhs = Series(lhs_data)
+    # RHS
+    rhs_data = np.random.random(nelem)
+    if rhs_nulls == "some":
+        rhs_mask = utils.random_bitmask(nelem)
+        rhs_bitmask = utils.expand_bits_to_bytes(rhs_mask)[:nelem]
+        rhs_null_count = utils.count_zero(rhs_bitmask)
+        assert rhs_null_count >= 0
+        rhs = Series.from_masked_array(rhs_data, rhs_mask)
+        assert rhs.null_count == rhs_null_count
+    else:
+        rhs = Series(rhs_data)
+    # Result
+    res = lhs + rhs
+    if lhs_nulls == "some" and rhs_nulls == "some":
+        res_mask = np.asarray(
+            utils.expand_bits_to_bytes(lhs_mask & rhs_mask), dtype=np.bool_
+        )[:nelem]
+    if lhs_nulls == "some" and rhs_nulls == "none":
+        res_mask = np.asarray(
+            utils.expand_bits_to_bytes(lhs_mask), dtype=np.bool_
+        )[:nelem]
+    if lhs_nulls == "none" and rhs_nulls == "some":
+        res_mask = np.asarray(
+            utils.expand_bits_to_bytes(rhs_mask), dtype=np.bool_
+        )[:nelem]
+    # Fill NA values
+    na_value = -10000
+    got = res.fillna(na_value).to_numpy()
+    expect = lhs_data + rhs_data
+    if lhs_nulls == "some" or rhs_nulls == "some":
+        expect[~res_mask] = na_value
+
+    np.testing.assert_array_equal(expect, got)
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize(
+    "binop,lhs_dtype,rhs_dtype",
+    list(
+        product(
+            [operator.add, operator.mul],
+            utils.NUMERIC_TYPES,
+            utils.NUMERIC_TYPES,
+        )
+    ),
+)
+def test_series_binop_mixed_dtype(binop, lhs_dtype, rhs_dtype, obj_class):
+    nelem = 10
+    lhs = (np.random.random(nelem) * nelem).astype(lhs_dtype)
+    rhs = (np.random.random(nelem) * nelem).astype(rhs_dtype)
+
+    sr1 = Series(lhs)
+    sr2 = Series(rhs)
+
+    if obj_class == "Index":
+        sr1 = as_index(sr1)
+        sr2 = as_index(sr2)
+
+    result = binop(Series(sr1), Series(sr2))
+
+    if obj_class == "Index":
+        result = Series(result)
+
+    np.testing.assert_almost_equal(result.to_numpy(), binop(lhs, rhs))
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize(
+    "cmpop,lhs_dtype,rhs_dtype",
+    list(product(_cmpops, utils.NUMERIC_TYPES, utils.NUMERIC_TYPES)),
+)
+def test_series_cmpop_mixed_dtype(cmpop, lhs_dtype, rhs_dtype, obj_class):
+    nelem = 5
+    lhs = (np.random.random(nelem) * nelem).astype(lhs_dtype)
+    rhs = (np.random.random(nelem) * nelem).astype(rhs_dtype)
+
+    sr1 = Series(lhs)
+    sr2 = Series(rhs)
+
+    if obj_class == "Index":
+        sr1 = as_index(sr1)
+        sr2 = as_index(sr2)
+
+    result = cmpop(Series(sr1), Series(sr2))
+
+    if obj_class == "Index":
+        result = Series(result)
+
+    np.testing.assert_array_equal(result.to_numpy(), cmpop(lhs, rhs))
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize(
+    "func, dtype", list(product(_reflected_ops, utils.NUMERIC_TYPES))
+)
+def test_series_reflected_ops_scalar(func, dtype, obj_class):
+    # create random series
+    np.random.seed(12)
+    random_series = utils.gen_rand(dtype, 100, low=10)
+
+    # gpu series
+    gs = Series(random_series)
+
+    # class typing
+    if obj_class == "Index":
+        gs = as_index(gs)
+
+    gs_result = func(gs)
+
+    # class typing
+    if obj_class == "Index":
+        gs = Series(gs)
+
+    # pandas
+    ps_result = func(random_series)
+
+    # verify
+    np.testing.assert_allclose(ps_result, gs_result.to_numpy())
+
+
+@pytest.mark.parametrize(
+    "func, dtype", list(product(_reflected_ops, utils.NUMERIC_TYPES))
+)
+def test_cudf_scalar_reflected_ops_scalar(func, dtype):
+    value = 42
+    scalar = cudf.Scalar(42)
+
+    expected = func(value)
+    actual = func(scalar).value
+
+    assert np.isclose(expected, actual)
+
+
+@pytest.mark.parametrize("obj_class", ["Series", "Index"])
+@pytest.mark.parametrize(
+    "funcs, dtype",
+    list(
+        product(
+            list(zip(_reflected_ops, _cudf_scalar_reflected_ops)),
+            utils.NUMERIC_TYPES,
+        )
+    ),
+)
+def test_series_reflected_ops_cudf_scalar(funcs, dtype, obj_class):
+    cpu_func, gpu_func = funcs
+
+    # create random series
+    np.random.seed(12)
+    random_series = utils.gen_rand(dtype, 100, low=10)
+
+    # gpu series
+    gs = Series(random_series)
+
+    # class typing
+    if obj_class == "Index":
+        gs = as_index(gs)
+
+    gs_result = gpu_func(gs)
+
+    # class typing
+    if obj_class == "Index":
+        gs = Series(gs)
+
+    # pandas
+    ps_result = cpu_func(random_series)
+
+    # verify
+    np.testing.assert_allclose(ps_result, gs_result.to_numpy())
+
+
+@pytest.mark.parametrize("binop", _binops)
+def test_different_shapes_and_columns(binop):
+
+    # TODO: support `pow()` on NaN values. Particularly, the cases:
+    #       `pow(1, NaN) == 1` and `pow(NaN, 0) == 1`
+    if binop is operator.pow:
+        return
+
+    # Empty frame on the right side
+    pd_frame = binop(pd.DataFrame({"x": [1, 2]}), pd.DataFrame({}))
+    cd_frame = binop(cudf.DataFrame({"x": [1, 2]}), cudf.DataFrame({}))
+    utils.assert_eq(cd_frame, pd_frame)
+
+    # Empty frame on the left side
+    pd_frame = pd.DataFrame({}) + pd.DataFrame({"x": [1, 2]})
+    cd_frame = cudf.DataFrame({}) + cudf.DataFrame({"x": [1, 2]})
+    utils.assert_eq(cd_frame, pd_frame)
+
+    # Note: the below rely on a discrepancy between cudf and pandas
+    # While pandas inserts columns in alphabetical order, cudf inserts in the
+    # order of whichever column comes first. So the following code will not
+    # work if the names of columns are reversed i.e. ('y', 'x') != ('x', 'y')
+
+    # More rows on the left side
+    pd_frame = pd.DataFrame({"x": [1, 2, 3]}) + pd.DataFrame({"y": [1, 2]})
+    cd_frame = cudf.DataFrame({"x": [1, 2, 3]}) + cudf.DataFrame({"y": [1, 2]})
+    utils.assert_eq(cd_frame, pd_frame)
+
+    # More rows on the right side
+    pd_frame = pd.DataFrame({"x": [1, 2]}) + pd.DataFrame({"y": [1, 2, 3]})
+    cd_frame = cudf.DataFrame({"x": [1, 2]}) + cudf.DataFrame({"y": [1, 2, 3]})
+    utils.assert_eq(cd_frame, pd_frame)
+
+
+@pytest.mark.parametrize("binop", _binops)
+def test_different_shapes_and_same_columns(binop):
+
+    # TODO: support `pow()` on NaN values. Particularly, the cases:
+    #       `pow(1, NaN) == 1` and `pow(NaN, 0) == 1`
+    if binop is operator.pow:
+        return
+
+    pd_frame = binop(
+        pd.DataFrame({"x": [1, 2]}), pd.DataFrame({"x": [1, 2, 3]})
+    )
+    cd_frame = binop(
+        cudf.DataFrame({"x": [1, 2]}), cudf.DataFrame({"x": [1, 2, 3]})
+    )
+    # cast x as float64 so it matches pandas dtype
+    cd_frame["x"] = cd_frame["x"].astype(np.float64)
+    utils.assert_eq(cd_frame, pd_frame)
+
+
+@pytest.mark.parametrize("binop", _binops)
+def test_different_shapes_and_columns_with_unaligned_indices(binop):
+
+    # TODO: support `pow()` on NaN values. Particularly, the cases:
+    #       `pow(1, NaN) == 1` and `pow(NaN, 0) == 1`
+    if binop is operator.pow:
+        return
+
+    # Test with a RangeIndex
+    pdf1 = pd.DataFrame({"x": [4, 3, 2, 1], "y": [7, 3, 8, 6]})
+    # Test with a GenericIndex
+    pdf2 = pd.DataFrame(
+        {"x": [1, 2, 3, 7], "y": [4, 5, 6, 7]}, index=[0, 1, 3, 4]
+    )
+    # Test with a GenericIndex in a different order
+    pdf3 = pd.DataFrame(
+        {"x": [4, 5, 6, 7], "y": [1, 2, 3, 7], "z": [0, 5, 3, 7]},
+        index=[0, 3, 5, 3],
+    )
+    gdf1 = cudf.DataFrame.from_pandas(pdf1)
+    gdf2 = cudf.DataFrame.from_pandas(pdf2)
+    gdf3 = cudf.DataFrame.from_pandas(pdf3)
+
+    pd_frame = binop(binop(pdf1, pdf2), pdf3)
+    cd_frame = binop(binop(gdf1, gdf2), gdf3)
+    # cast x and y as float64 so it matches pandas dtype
+    cd_frame["x"] = cd_frame["x"].astype(np.float64)
+    cd_frame["y"] = cd_frame["y"].astype(np.float64)
+    utils.assert_eq(cd_frame, pd_frame)
+
+    pdf1 = pd.DataFrame({"x": [1, 1]}, index=["a", "a"])
+    pdf2 = pd.DataFrame({"x": [2]}, index=["a"])
+    gdf1 = cudf.DataFrame.from_pandas(pdf1)
+    gdf2 = cudf.DataFrame.from_pandas(pdf2)
+    pd_frame = binop(pdf1, pdf2)
+    cd_frame = binop(gdf1, gdf2)
+    utils.assert_eq(pd_frame, cd_frame)
+
+
+@pytest.mark.parametrize(
+    "df2",
+    [
+        cudf.DataFrame({"a": [3, 2, 1]}, index=[3, 2, 1]),
+        cudf.DataFrame([3, 2]),
+    ],
+)
+@pytest.mark.parametrize("binop", [operator.eq, operator.ne])
+def test_df_different_index_shape(df2, binop):
+    df1 = cudf.DataFrame([1, 2, 3], index=[1, 2, 3])
+
+    pdf1 = df1.to_pandas()
+    pdf2 = df2.to_pandas()
+
+    utils.assert_exceptions_equal(
+        lfunc=binop,
+        rfunc=binop,
+        lfunc_args_and_kwargs=([pdf1, pdf2],),
+        rfunc_args_and_kwargs=([df1, df2],),
+    )
+
+
+@pytest.mark.parametrize("op", [operator.eq, operator.ne])
+def test_boolean_scalar_binop(op):
+    psr = pd.Series(np.random.choice([True, False], 10))
+    gsr = cudf.from_pandas(psr)
+    utils.assert_eq(op(psr, True), op(gsr, True))
+    utils.assert_eq(op(psr, False), op(gsr, False))
+
+    # cuDF scalar
+    utils.assert_eq(op(psr, True), op(gsr, cudf.Scalar(True)))
+    utils.assert_eq(op(psr, False), op(gsr, cudf.Scalar(False)))
+
+
+@pytest.mark.parametrize("func", _operators_arithmetic)
+@pytest.mark.parametrize("has_nulls", [True, False])
+@pytest.mark.parametrize("fill_value", [None, 27])
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+def test_operator_func_between_series(dtype, func, has_nulls, fill_value):
+    count = 1000
+    gdf_series_a = utils.gen_rand_series(
+        dtype, count, has_nulls=has_nulls, stride=10000
+    )
+    gdf_series_b = utils.gen_rand_series(
+        dtype, count, has_nulls=has_nulls, stride=100
+    )
+    pdf_series_a = gdf_series_a.to_pandas()
+    pdf_series_b = gdf_series_b.to_pandas()
+
+    gdf_result = getattr(gdf_series_a, func)(
+        gdf_series_b, fill_value=fill_value
+    )
+    pdf_result = getattr(pdf_series_a, func)(
+        pdf_series_b, fill_value=fill_value
+    )
+
+    utils.assert_eq(pdf_result, gdf_result)
+
+
+@pytest.mark.parametrize("func", _operators_arithmetic)
+@pytest.mark.parametrize("has_nulls", [True, False])
+@pytest.mark.parametrize("fill_value", [None, 27])
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+@pytest.mark.parametrize("use_cudf_scalar", [False, True])
+def test_operator_func_series_and_scalar(
+    dtype, func, has_nulls, fill_value, use_cudf_scalar
+):
+    count = 1000
+    scalar = 59
+    gdf_series = utils.gen_rand_series(
+        dtype, count, has_nulls=has_nulls, stride=10000
+    )
+    pdf_series = gdf_series.to_pandas()
+
+    gdf_series_result = getattr(gdf_series, func)(
+        cudf.Scalar(scalar) if use_cudf_scalar else scalar,
+        fill_value=fill_value,
+    )
+    pdf_series_result = getattr(pdf_series, func)(
+        scalar, fill_value=fill_value
+    )
+
+    utils.assert_eq(pdf_series_result, gdf_series_result)
+
+
+_permu_values = [0, 1, None, np.nan]
+
+
+@pytest.mark.parametrize("fill_value", _permu_values)
+@pytest.mark.parametrize("scalar_a", _permu_values)
+@pytest.mark.parametrize("scalar_b", _permu_values)
+@pytest.mark.parametrize("func", _operators_comparison)
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+def test_operator_func_between_series_logical(
+    dtype, func, scalar_a, scalar_b, fill_value
+):
+
+    gdf_series_a = Series([scalar_a], nan_as_null=False).astype(dtype)
+    gdf_series_b = Series([scalar_b], nan_as_null=False).astype(dtype)
+
+    pdf_series_a = gdf_series_a.to_pandas(nullable=True)
+    pdf_series_b = gdf_series_b.to_pandas(nullable=True)
+
+    gdf_series_result = getattr(gdf_series_a, func)(
+        gdf_series_b, fill_value=fill_value
+    )
+    pdf_series_result = getattr(pdf_series_a, func)(
+        pdf_series_b, fill_value=fill_value
+    )
+    expect = pdf_series_result
+    got = gdf_series_result.to_pandas(nullable=True)
+
+    # If fill_value is np.nan, things break down a bit,
+    # because setting a NaN into a pandas nullable float
+    # array still gets transformed to <NA>. As such,
+    # pd_series_with_nulls.fillna(np.nan) has no effect.
+    if (
+        (pdf_series_a.isnull().sum() != pdf_series_b.isnull().sum())
+        and np.isscalar(fill_value)
+        and np.isnan(fill_value)
+    ):
+        with pytest.raises(AssertionError):
+            utils.assert_eq(expect, got)
+        return
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+@pytest.mark.parametrize("func", _operators_comparison)
+@pytest.mark.parametrize("has_nulls", [True, False])
+@pytest.mark.parametrize("scalar", [-59.0, np.nan, 0, 59.0])
+@pytest.mark.parametrize("fill_value", [None, 1.0])
+@pytest.mark.parametrize("use_cudf_scalar", [False, True])
+def test_operator_func_series_and_scalar_logical(
+    dtype, func, has_nulls, scalar, fill_value, use_cudf_scalar
+):
+    gdf_series = utils.gen_rand_series(
+        dtype, 1000, has_nulls=has_nulls, stride=10000
+    )
+    pdf_series = gdf_series.to_pandas(nullable=True)
+    gdf_series_result = getattr(gdf_series, func)(
+        cudf.Scalar(scalar) if use_cudf_scalar else scalar,
+        fill_value=fill_value,
+    )
+    pdf_series_result = getattr(pdf_series, func)(
+        scalar, fill_value=fill_value
+    )
+
+    expect = pdf_series_result
+    got = gdf_series_result.to_pandas(nullable=True)
+
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("func", _operators_arithmetic)
+@pytest.mark.parametrize("nulls", _nulls)
+@pytest.mark.parametrize("fill_value", [None, 27])
+@pytest.mark.parametrize("other", ["df", "scalar"])
+def test_operator_func_dataframe(func, nulls, fill_value, other):
+    num_rows = 100
+    num_cols = 3
+
+    def gen_df():
+        pdf = pd.DataFrame()
+        from string import ascii_lowercase
+
+        cols = np.random.choice(num_cols + 5, num_cols, replace=False)
+
+        for i in range(num_cols):
+            colname = ascii_lowercase[cols[i]]
+            data = utils.gen_rand("float64", num_rows) * 10000
+            if nulls == "some":
+                idx = np.random.choice(
+                    num_rows, size=int(num_rows / 2), replace=False
+                )
+                data[idx] = np.nan
+            pdf[colname] = data
+        return pdf
+
+    pdf1 = gen_df()
+    pdf2 = gen_df() if other == "df" else 59.0
+    gdf1 = cudf.DataFrame.from_pandas(pdf1)
+    gdf2 = cudf.DataFrame.from_pandas(pdf2) if other == "df" else 59.0
+
+    got = getattr(gdf1, func)(gdf2, fill_value=fill_value)
+    expect = getattr(pdf1, func)(pdf2, fill_value=fill_value)[list(got._data)]
+
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("func", _operators_comparison)
+@pytest.mark.parametrize("nulls", _nulls)
+@pytest.mark.parametrize("other", ["df", "scalar"])
+def test_logical_operator_func_dataframe(func, nulls, other):
+    np.random.seed(0)
+    num_rows = 100
+    num_cols = 3
+
+    def gen_df():
+        pdf = pd.DataFrame()
+        from string import ascii_lowercase
+
+        cols = np.random.choice(num_cols + 5, num_cols, replace=False)
+
+        for i in range(num_cols):
+            colname = ascii_lowercase[cols[i]]
+            data = utils.gen_rand("float64", num_rows) * 10000
+            if nulls == "some":
+                idx = np.random.choice(
+                    num_rows, size=int(num_rows / 2), replace=False
+                )
+                data[idx] = np.nan
+            pdf[colname] = data
+        return pdf
+
+    pdf1 = gen_df()
+    pdf2 = gen_df() if other == "df" else 59.0
+    gdf1 = cudf.DataFrame.from_pandas(pdf1)
+    gdf2 = cudf.DataFrame.from_pandas(pdf2) if other == "df" else 59.0
+
+    got = getattr(gdf1, func)(gdf2)
+    expect = getattr(pdf1, func)(pdf2)[list(got._data)]
+
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [op for op in _operators_arithmetic if op not in {"rmod", "rfloordiv"}]
+    + _operators_comparison
+    + [
+        pytest.param(
+            "rmod",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/12162"
+            ),
+        ),
+        pytest.param(
+            "rfloordiv",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/12162"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("rhs", [0, 1, 2, 128])
+def test_binop_bool_uint(func, rhs):
+    psr = pd.Series([True, False, False])
+    gsr = cudf.from_pandas(psr)
+    utils.assert_eq(
+        getattr(psr, func)(rhs), getattr(gsr, func)(rhs), check_dtype=False
+    )
+
+
+@pytest.mark.parametrize(
+    "series_dtype", (np.int8, np.uint8, np.int64, np.uint64)
+)
+@pytest.mark.parametrize(
+    "divisor_dtype",
+    (
+        np.int8,
+        np.uint8,
+        np.int64,
+        np.uint64,
+    ),
+)
+@pytest.mark.parametrize("scalar_divisor", [False, True])
+def test_floordiv_zero_float64(series_dtype, divisor_dtype, scalar_divisor):
+    sr = pd.Series([1, 2, 3], dtype=series_dtype)
+    cr = cudf.from_pandas(sr)
+
+    if scalar_divisor:
+        pd_div = divisor_dtype(0)
+        cudf_div = cudf.Scalar(0, dtype=divisor_dtype)
+    else:
+        pd_div = pd.Series([0], dtype=divisor_dtype)
+        cudf_div = cudf.from_pandas(pd_div)
+    utils.assert_eq(sr // pd_div, cr // cudf_div)
+
+
+@pytest.mark.parametrize("scalar_divisor", [False, True])
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/12162")
+def test_floordiv_zero_bool(scalar_divisor):
+    sr = pd.Series([True, True, False], dtype=np.bool_)
+    cr = cudf.from_pandas(sr)
+
+    if scalar_divisor:
+        pd_div = np.bool_(0)
+        cudf_div = cudf.Scalar(0, dtype=np.bool_)
+    else:
+        pd_div = pd.Series([0], dtype=np.bool_)
+        cudf_div = cudf.from_pandas(pd_div)
+
+    with pytest.raises((NotImplementedError, ZeroDivisionError)):
+        # Pandas does raise
+        sr // pd_div
+    with pytest.raises((NotImplementedError, ZeroDivisionError)):
+        # Cudf does not
+        cr // cudf_div
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    (
+        pytest.param(
+            np.bool_,
+            marks=pytest_xfail(
+                reason=(
+                    "Pandas handling of division by zero-bool is too strange"
+                )
+            ),
+        ),
+        np.int8,
+        np.uint8,
+        np.int64,
+        np.uint64,
+        np.float32,
+        np.float64,
+    ),
+)
+def test_rmod_zero_nan(dtype):
+    sr = pd.Series([1, 1, 0], dtype=dtype)
+    cr = cudf.from_pandas(sr)
+    utils.assert_eq(1 % sr, 1 % cr)
+    expected_dtype = np.float64 if cr.dtype.kind != "f" else dtype
+    utils.assert_eq(1 % cr, cudf.Series([0, 0, None], dtype=expected_dtype))
+
+
+def test_series_misc_binop():
+    pds = pd.Series([1, 2, 4], name="abc xyz")
+    gds = cudf.Series([1, 2, 4], name="abc xyz")
+
+    utils.assert_eq(pds + 1, gds + 1)
+    utils.assert_eq(1 + pds, 1 + gds)
+
+    utils.assert_eq(pds + pds, gds + gds)
+
+    pds1 = pd.Series([1, 2, 4], name="hello world")
+    gds1 = cudf.Series([1, 2, 4], name="hello world")
+
+    utils.assert_eq(pds + pds1, gds + gds1)
+    utils.assert_eq(pds1 + pds, gds1 + gds)
+
+    utils.assert_eq(pds1 + pds + 5, gds1 + gds + 5)
+
+
+def test_int8_float16_binop():
+    a = cudf.Series([1], dtype="int8")
+    b = np.float16(2)
+    expect = cudf.Series([0.5])
+    got = a / b
+    utils.assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "float64", "str"])
+def test_vector_to_none_binops(dtype):
+    data = Series([1, 2, 3, None], dtype=dtype)
+
+    expect = Series([None] * 4).astype(dtype)
+    got = data + None
+
+    utils.assert_eq(expect, got)
+
+
+def dtype_scalar(val, dtype):
+    if dtype == "str":
+        return str(val)
+    dtype = cudf.dtype(dtype)
+    if dtype.type in {np.datetime64, np.timedelta64}:
+        res, _ = np.datetime_data(dtype)
+        return dtype.type(val, res)
+    else:
+        return dtype.type(val)
+
+
+def make_scalar_add_data():
+    valid = set()
+
+    # to any int, we may add any kind of
+    # other int, float, datetime timedelta, or bool
+    valid |= set(
+        product(
+            INTEGER_TYPES,
+            FLOAT_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # to any float, we may add any int, float, or bool
+    valid |= set(
+        product(FLOAT_TYPES, INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES)
+    )
+
+    # to any datetime, we may add any int, timedelta, or bool
+    valid |= set(
+        product(DATETIME_TYPES, INTEGER_TYPES | TIMEDELTA_TYPES | BOOL_TYPES)
+    )
+
+    # to any timedelta, we may add any int, datetime, other timedelta, or bool
+    valid |= set(
+        product(TIMEDELTA_TYPES, INTEGER_TYPES | DATETIME_TYPES | BOOL_TYPES)
+    )
+
+    # to any bool, we may add any int, float, datetime, timedelta, or bool
+    valid |= set(
+        product(
+            BOOL_TYPES,
+            INTEGER_TYPES
+            | FLOAT_TYPES
+            | DATETIME_TYPES
+            | TIMEDELTA_TYPES
+            | BOOL_TYPES,
+        )
+    )
+
+    # to any string, we may add any other string
+    valid |= {("str", "str")}
+
+    return sorted(list(valid))
+
+
+def make_invalid_scalar_add_data():
+    invalid = set()
+
+    # we can not add a datetime to a float
+    invalid |= set(product(FLOAT_TYPES, DATETIME_TYPES))
+
+    # We can not add a timedelta to a float
+    invalid |= set(product(FLOAT_TYPES, TIMEDELTA_TYPES))
+
+    # we can not add a float to any datetime
+    invalid |= set(product(DATETIME_TYPES, FLOAT_TYPES))
+
+    # can can not add a datetime to a datetime
+    invalid |= set(product(DATETIME_TYPES, DATETIME_TYPES))
+
+    # can not add a timedelta to a float
+    invalid |= set(product(FLOAT_TYPES, TIMEDELTA_TYPES))
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_add_data())
+def test_scalar_add(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    # expect = np.add(lval_host, rval_host)
+    expect = lval_host + rval_host
+    got = lval_gpu + rval_gpu
+
+    assert expect == got.value
+    if not dtype_l == dtype_r == "str":
+        assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_invalid_scalar_add_data())
+def test_scalar_add_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu + rval_gpu
+
+
+def make_scalar_difference_data():
+    valid = set()
+
+    # from an int, we may subtract any int, float, timedelta,
+    # or boolean value
+    valid |= set(
+        product(
+            INTEGER_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | TIMEDELTA_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # from any float, we may subtract any int, float, or bool
+    valid |= set(
+        product(FLOAT_TYPES, INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES)
+    )
+
+    # from any datetime we may subtract any int, datetime, timedelta, or bool
+    valid |= set(
+        product(
+            DATETIME_TYPES,
+            INTEGER_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # from any timedelta we may subtract any int, timedelta, or bool
+    valid |= set(
+        product(TIMEDELTA_TYPES, INTEGER_TYPES | TIMEDELTA_TYPES | BOOL_TYPES)
+    )
+
+    # from any bool we may subtract any int, float or timedelta
+    valid |= set(
+        product(BOOL_TYPES, INTEGER_TYPES | FLOAT_TYPES | TIMEDELTA_TYPES)
+    )
+
+    return sorted(list(valid))
+
+
+def make_scalar_difference_data_invalid():
+    invalid = set()
+
+    # we can't subtract a datetime from an int
+    invalid |= set(product(INTEGER_TYPES, DATETIME_TYPES))
+
+    # we can't subtract a datetime or timedelta from a float
+    invalid |= set(product(FLOAT_TYPES, DATETIME_TYPES | TIMEDELTA_TYPES))
+
+    # we can't subtract a float from a datetime or timedelta
+    invalid |= set(product(DATETIME_TYPES | TIMEDELTA_TYPES, FLOAT_TYPES))
+
+    # We can't subtract a datetime from a timedelta
+    invalid |= set(product(TIMEDELTA_TYPES, DATETIME_TYPES))
+
+    # we can't subtract a datetime or bool from a bool
+    invalid |= set(product(BOOL_TYPES, BOOL_TYPES | DATETIME_TYPES))
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_difference_data())
+def test_scalar_difference(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    expect = lval_host - rval_host
+    got = lval_gpu - rval_gpu
+
+    assert expect == got.value
+    assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize(
+    "dtype_l,dtype_r", make_scalar_difference_data_invalid()
+)
+def test_scalar_difference_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu - rval_gpu
+
+
+def make_scalar_product_data():
+    valid = set()
+
+    # we can multiply an int, or bool by any int, float, timedelta, or bool
+    valid |= set(
+        product(
+            INTEGER_TYPES | BOOL_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | TIMEDELTA_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # we can multiply any timedelta by any int, or bool
+    valid |= set(product(TIMEDELTA_TYPES, INTEGER_TYPES | BOOL_TYPES))
+
+    # we can multiply a float by any int, float, or bool
+    valid |= set(
+        product(FLOAT_TYPES, INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES)
+    )
+
+    return sorted(list(valid))
+
+
+def make_scalar_product_data_invalid():
+    invalid = set()
+
+    # can't multiply a ints, floats, datetimes, timedeltas,
+    # or bools by datetimes
+    invalid |= set(
+        product(
+            INTEGER_TYPES
+            | FLOAT_TYPES
+            | DATETIME_TYPES
+            | TIMEDELTA_TYPES
+            | BOOL_TYPES,
+            DATETIME_TYPES,
+        )
+    )
+
+    # can't multiply datetimes with anything really
+    invalid |= set(
+        product(
+            DATETIME_TYPES,
+            INTEGER_TYPES
+            | FLOAT_TYPES
+            | DATETIME_TYPES
+            | TIMEDELTA_TYPES
+            | BOOL_TYPES,
+        )
+    )
+
+    # can't multiply timedeltas by timedeltas
+    invalid |= set(product(TIMEDELTA_TYPES, TIMEDELTA_TYPES))
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_product_data())
+def test_scalar_product(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    expect = lval_host * rval_host
+    got = lval_gpu * rval_gpu
+
+    assert expect == got.value
+    assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_product_data_invalid())
+def test_scalar_product_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu * rval_gpu
+
+
+def make_scalar_floordiv_data():
+    valid = set()
+
+    # we can divide ints and floats by other ints, floats, or bools
+    valid |= set(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # we can divide timedeltas by ints, floats or other timedeltas
+    valid |= set(
+        product(TIMEDELTA_TYPES, INTEGER_TYPES | FLOAT_TYPES | TIMEDELTA_TYPES)
+    )
+
+    # we can divide bools by ints, floats or bools
+    valid |= set(product(BOOL_TYPES, INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES))
+
+    return sorted(list(valid))
+
+
+def make_scalar_floordiv_data_invalid():
+    invalid = set()
+
+    # we can't numeric types into datelike types
+    invalid |= set(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+            DATETIME_TYPES | TIMEDELTA_TYPES,
+        )
+    )
+
+    # we can't divide datetime types into anything
+    invalid |= set(
+        product(
+            DATETIME_TYPES,
+            INTEGER_TYPES
+            | FLOAT_TYPES
+            | DATETIME_TYPES
+            | TIMEDELTA_TYPES
+            | BOOL_TYPES,
+        )
+    )
+
+    # we can't divide timedeltas into bools, or datetimes
+    invalid |= set(product(TIMEDELTA_TYPES, BOOL_TYPES | DATETIME_TYPES))
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_floordiv_data())
+def test_scalar_floordiv(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    expect = lval_host // rval_host
+    got = lval_gpu // rval_gpu
+
+    assert expect == got.value
+    assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize(
+    "dtype_l,dtype_r", make_scalar_floordiv_data_invalid()
+)
+def test_scalar_floordiv_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu // rval_gpu
+
+
+def make_scalar_truediv_data():
+    valid = set()
+
+    # we can true divide ints, floats, or bools by other
+    # ints, floats or bools
+    valid |= set(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # we can true divide timedeltas by ints floats or timedeltas
+    valid |= set(product(TIMEDELTA_TYPES, INTEGER_TYPES | TIMEDELTA_TYPES))
+
+    return sorted(list(valid))
+
+
+def make_scalar_truediv_data_invalid():
+    invalid = set()
+
+    # we can't divide ints, floats or bools by datetimes
+    # or timedeltas
+    invalid |= set(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+            DATETIME_TYPES | TIMEDELTA_TYPES,
+        )
+    )
+
+    # we cant true divide datetime types by anything
+    invalid |= set(
+        product(
+            DATETIME_TYPES,
+            INTEGER_TYPES
+            | FLOAT_TYPES
+            | DATETIME_TYPES
+            | TIMEDELTA_TYPES
+            | BOOL_TYPES,
+        )
+    )
+
+    # we cant true divide timedeltas by datetimes or bools or floats
+    invalid |= set(
+        product(TIMEDELTA_TYPES, DATETIME_TYPES | BOOL_TYPES | FLOAT_TYPES)
+    )
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_truediv_data())
+def test_scalar_truediv(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    expect = np.true_divide(lval_host, rval_host)
+    got = lval_gpu / rval_gpu
+
+    assert expect == got.value
+
+    # numpy bug
+
+    if np.dtype(dtype_l).itemsize <= 2 and np.dtype(dtype_r).itemsize <= 2:
+        assert expect.dtype == "float64" and got.dtype == "float32"
+    else:
+        assert expect.dtype == got.dtype
+    # assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_truediv_data_invalid())
+def test_scalar_truediv_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu / rval_gpu
+
+
+def make_scalar_remainder_data():
+    valid = set()
+
+    # can mod numeric types with each other
+    valid |= set(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # can mod timedeltas by other timedeltas
+    valid |= set(product(TIMEDELTA_TYPES, TIMEDELTA_TYPES))
+
+    return sorted(list(valid))
+
+
+def make_scalar_remainder_data_invalid():
+    invalid = set()
+
+    # numeric types cant be modded against timedeltas
+    # or datetimes. Also, datetimes can't be modded
+    # against datetimes or timedeltas
+    invalid |= set(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES | DATETIME_TYPES,
+            DATETIME_TYPES | TIMEDELTA_TYPES,
+        )
+    )
+
+    # datetime and timedelta types cant be modded against
+    # any numeric types
+    invalid |= set(
+        product(
+            DATETIME_TYPES | TIMEDELTA_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+        )
+    )
+
+    # timedeltas cant mod with datetimes
+    invalid |= set(product(TIMEDELTA_TYPES, DATETIME_TYPES))
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_remainder_data())
+def test_scalar_remainder(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    expect = lval_host % rval_host
+    got = lval_gpu % rval_gpu
+
+    assert expect == got.value
+    assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize(
+    "dtype_l,dtype_r", make_scalar_remainder_data_invalid()
+)
+def test_scalar_remainder_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu % rval_gpu
+
+
+def make_scalar_power_data():
+    # only numeric values form valid operands for power
+    return sorted(
+        product(
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+            INTEGER_TYPES | FLOAT_TYPES | BOOL_TYPES,
+        )
+    )
+
+
+def make_scalar_power_data_invalid():
+    invalid = set()
+
+    # datetimes and timedeltas cant go in exponents
+    invalid |= set(
+        product(
+            INTEGER_TYPES
+            | FLOAT_TYPES
+            | TIMEDELTA_TYPES
+            | DATETIME_TYPES
+            | BOOL_TYPES,
+            DATETIME_TYPES | TIMEDELTA_TYPES,
+        )
+    )
+
+    # datetimes and timedeltas may not be raised to
+    # any exponent of any dtype
+    invalid |= set(
+        product(
+            DATETIME_TYPES | TIMEDELTA_TYPES,
+            DATETIME_TYPES
+            | TIMEDELTA_TYPES
+            | INTEGER_TYPES
+            | FLOAT_TYPES
+            | BOOL_TYPES,
+        )
+    )
+
+    return sorted(list(invalid))
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_power_data())
+def test_scalar_power(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_host = dtype_scalar(test_value, dtype=dtype_l)
+    rval_host = dtype_scalar(test_value, dtype=dtype_r)
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    expect = lval_host**rval_host
+    got = lval_gpu**rval_gpu
+
+    assert expect == got.value
+    assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize("dtype_l,dtype_r", make_scalar_power_data_invalid())
+def test_scalar_power_invalid(dtype_l, dtype_r):
+    test_value = 1
+
+    lval_gpu = cudf.Scalar(test_value, dtype=dtype_l)
+    rval_gpu = cudf.Scalar(test_value, dtype=dtype_r)
+
+    with pytest.raises(TypeError):
+        lval_gpu**rval_gpu
+
+
+def make_scalar_null_binops_data():
+    return (
+        [(operator.add, *dtypes) for dtypes in make_scalar_add_data()]
+        + [(operator.sub, *dtypes) for dtypes in make_scalar_difference_data()]
+        + [(operator.mul, *dtypes) for dtypes in make_scalar_product_data()]
+        + [(operator.add, *dtypes) for dtypes in make_scalar_add_data()]
+        + [
+            (operator.floordiv, *dtypes)
+            for dtypes in make_scalar_floordiv_data()
+        ]
+        + [
+            (operator.truediv, *dtypes)
+            for dtypes in make_scalar_truediv_data()
+        ]
+        + [(operator.mod, *dtypes) for dtypes in make_scalar_remainder_data()]
+        + [(operator.pow, *dtypes) for dtypes in make_scalar_power_data()]
+    )
+
+
+@pytest.mark.parametrize("op,dtype_l,dtype_r", make_scalar_null_binops_data())
+def test_scalar_null_binops(op, dtype_l, dtype_r):
+    lhs = cudf.Scalar(cudf.NA, dtype=dtype_l)
+    rhs = cudf.Scalar(cudf.NA, dtype=dtype_r)
+
+    result = op(lhs, rhs)
+    assert result.value is (
+        cudf.NaT
+        if cudf.api.types.is_datetime64_dtype(result.dtype)
+        or cudf.api.types.is_timedelta64_dtype(result.dtype)
+        else cudf.NA
+    )
+
+    # make sure dtype is the same as had there been a valid scalar
+    valid_lhs = cudf.Scalar(1, dtype=dtype_l)
+    valid_rhs = cudf.Scalar(1, dtype=dtype_r)
+
+    valid_result = op(valid_lhs, valid_rhs)
+    assert result.dtype == valid_result.dtype
+
+
+@pytest.mark.parametrize(
+    "date_col",
+    [
+        [
+            "2000-01-01 00:00:00.012345678",
+            "2000-01-31 00:00:00.012345678",
+            "2000-02-29 00:00:00.012345678",
+        ]
+    ],
+)
+@pytest.mark.parametrize("n_periods", [0, 1, -1, 12, -12])
+@pytest.mark.parametrize(
+    "frequency",
+    [
+        "months",
+        "years",
+        "days",
+        "hours",
+        "minutes",
+        "seconds",
+        "microseconds",
+        pytest.param(
+            "nanoseconds",
+            marks=pytest_xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/issues/36589",
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[ns]", "datetime64[us]", "datetime64[ms]", "datetime64[s]"],
+)
+@pytest.mark.parametrize("op", [operator.add, operator.sub])
+def test_datetime_dateoffset_binaryop(
+    date_col, n_periods, frequency, dtype, op
+):
+    gsr = cudf.Series(date_col, dtype=dtype)
+    psr = gsr.to_pandas()  # converts to nanos
+
+    kwargs = {frequency: n_periods}
+
+    goffset = cudf.DateOffset(**kwargs)
+    poffset = pd.DateOffset(**kwargs)
+
+    expect = op(psr, poffset)
+    got = op(gsr, goffset)
+
+    utils.assert_eq(expect, got)
+
+    expect = op(psr, -poffset)
+    got = op(gsr, -goffset)
+
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "date_col",
+    [
+        [
+            "2000-01-01 00:00:00.012345678",
+            "2000-01-31 00:00:00.012345678",
+            "2000-02-29 00:00:00.012345678",
+        ]
+    ],
+)
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"months": 2, "years": 5},
+        {"microseconds": 1, "seconds": 1},
+        {"months": 2, "years": 5, "seconds": 923, "microseconds": 481},
+        pytest.param(
+            {"milliseconds": 4},
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="Pandas gets the wrong answer for milliseconds",
+            ),
+        ),
+        pytest.param(
+            {"milliseconds": 4, "years": 2},
+            marks=pytest_xfail(
+                reason="https://github.com/pandas-dev/pandas/issues/49897"
+            ),
+        ),
+        pytest.param(
+            {"nanoseconds": 12},
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="Pandas gets the wrong answer for nanoseconds",
+            ),
+        ),
+        {"nanoseconds": 12},
+    ],
+)
+@pytest.mark.parametrize("op", [operator.add, operator.sub])
+def test_datetime_dateoffset_binaryop_multiple(date_col, kwargs, op):
+
+    gsr = cudf.Series(date_col, dtype="datetime64[ns]")
+    psr = gsr.to_pandas()
+
+    poffset = pd.DateOffset(**kwargs)
+    goffset = cudf.DateOffset(**kwargs)
+
+    expect = op(psr, poffset)
+    got = op(gsr, goffset)
+
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "date_col",
+    [
+        [
+            "2000-01-01 00:00:00.012345678",
+            "2000-01-31 00:00:00.012345678",
+            "2000-02-29 00:00:00.012345678",
+        ]
+    ],
+)
+@pytest.mark.parametrize("n_periods", [0, 1, -1, 12, -12])
+@pytest.mark.parametrize(
+    "frequency",
+    [
+        "months",
+        "years",
+        "days",
+        "hours",
+        "minutes",
+        "seconds",
+        "microseconds",
+        pytest.param(
+            "nanoseconds",
+            marks=pytest_xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/issues/36589",
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[ns]", "datetime64[us]", "datetime64[ms]", "datetime64[s]"],
+)
+def test_datetime_dateoffset_binaryop_reflected(
+    date_col, n_periods, frequency, dtype
+):
+    gsr = cudf.Series(date_col, dtype=dtype)
+    psr = gsr.to_pandas()  # converts to nanos
+
+    kwargs = {frequency: n_periods}
+
+    goffset = cudf.DateOffset(**kwargs)
+    poffset = pd.DateOffset(**kwargs)
+
+    expect = poffset + psr
+    got = goffset + gsr
+
+    utils.assert_eq(expect, got)
+
+    with pytest.raises(TypeError):
+        poffset - psr
+
+    with pytest.raises(TypeError):
+        goffset - gsr
+
+
+@pytest.mark.parametrize("frame", [cudf.Series, cudf.Index, cudf.DataFrame])
+@pytest.mark.parametrize(
+    "dtype", ["int", "str", "datetime64[s]", "timedelta64[s]", "category"]
+)
+def test_binops_with_lhs_numpy_scalar(frame, dtype):
+    data = [1, 2, 3, 4, 5]
+
+    data = (
+        frame({"a": data}, dtype=dtype)
+        if isinstance(frame, cudf.DataFrame)
+        else frame(data, dtype=dtype)
+    )
+
+    if dtype == "datetime64[s]":
+        val = cudf.dtype(dtype).type(4, "s")
+    elif dtype == "timedelta64[s]":
+        val = cudf.dtype(dtype).type(4, "s")
+    elif dtype == "category":
+        val = np.int64(4)
+    elif dtype == "str":
+        val = str(4)
+    else:
+        val = cudf.dtype(dtype).type(4)
+
+    # Compare equality with series on left side to dispatch to the pandas/cudf
+    # __eq__ operator and avoid a DeprecationWarning from numpy.
+    expected = data.to_pandas() == val
+    got = data == val
+
+    utils.assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+        "float32",
+        "float64",
+        "datetime64[ns]",
+        "datetime64[us]",
+        "datetime64[ms]",
+        "datetime64[s]",
+        "timedelta64[ns]",
+        "timedelta64[us]",
+        "timedelta64[ms]",
+        "timedelta64[s]",
+    ],
+)
+@pytest.mark.parametrize("op", _operators_comparison)
+def test_binops_with_NA_consistent(dtype, op):
+    data = [1, 2, 3]
+    sr = cudf.Series(data, dtype=dtype)
+
+    result = getattr(sr, op)(cudf.NA)
+    if dtype in NUMERIC_TYPES:
+        if op == "ne":
+            expect_all = True
+        else:
+            expect_all = False
+        assert (result == expect_all).all()
+    elif dtype in DATETIME_TYPES & TIMEDELTA_TYPES:
+        assert result._column.null_count == len(data)
+
+
+@pytest.mark.parametrize(
+    "op, lhs, l_dtype, rhs, r_dtype, expect, expect_dtype",
+    [
+        (
+            operator.add,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["3.0", "4.0"],
+            cudf.Decimal64Dtype(scale=2, precision=4),
+        ),
+        (
+            operator.add,
+            2,
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["3.5", "4.0"],
+            cudf.Decimal64Dtype(scale=2, precision=4),
+        ),
+        (
+            operator.add,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["2.25", "1.005"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["3.75", "3.005"],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=17),
+            ["0.1", "0.2"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["100.1", "200.2"],
+            cudf.Decimal128Dtype(scale=3, precision=23),
+        ),
+        (
+            operator.sub,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=1, precision=2),
+            ["2.25", "1.005"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["-0.75", "0.995"],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.sub,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=1, precision=2),
+            ["2.25", "1.005"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["-0.75", "0.995"],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=10),
+            ["0.1", "0.2"],
+            cudf.Decimal64Dtype(scale=6, precision=10),
+            ["99.9", "199.8"],
+            cudf.Decimal128Dtype(scale=6, precision=19),
+        ),
+        (
+            operator.sub,
+            2,
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["2.25", "1.005"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["-0.25", "0.995"],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.mul,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["1.5", "3.0"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["2.25", "6.0"],
+            cudf.Decimal64Dtype(scale=5, precision=8),
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["0.1", "0.2"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["10.0", "40.0"],
+            cudf.Decimal64Dtype(scale=1, precision=8),
+        ),
+        (
+            operator.mul,
+            ["1000", "2000"],
+            cudf.Decimal64Dtype(scale=-3, precision=4),
+            ["0.343", "0.500"],
+            cudf.Decimal64Dtype(scale=3, precision=3),
+            ["343.0", "1000.0"],
+            cudf.Decimal64Dtype(scale=0, precision=8),
+        ),
+        (
+            operator.mul,
+            200,
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            ["0.343", "0.500"],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            ["68.60", "100.0"],
+            cudf.Decimal64Dtype(scale=6, precision=13),
+        ),
+        (
+            operator.truediv,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=4),
+            ["1.5", "3.0"],
+            cudf.Decimal64Dtype(scale=1, precision=4),
+            ["1.0", "0.6"],
+            cudf.Decimal64Dtype(scale=7, precision=10),
+        ),
+        (
+            operator.truediv,
+            ["110", "200"],
+            cudf.Decimal64Dtype(scale=-1, precision=3),
+            ["0.1", "0.2"],
+            cudf.Decimal64Dtype(scale=2, precision=4),
+            ["1000.0", "1000.0"],
+            cudf.Decimal64Dtype(scale=6, precision=12),
+        ),
+        (
+            operator.truediv,
+            ["132.86", "15.25"],
+            cudf.Decimal64Dtype(scale=4, precision=14),
+            ["2.34", "8.50"],
+            cudf.Decimal64Dtype(scale=2, precision=8),
+            ["56.77", "1.79"],
+            cudf.Decimal128Dtype(scale=13, precision=25),
+        ),
+        (
+            operator.truediv,
+            20,
+            cudf.Decimal128Dtype(scale=2, precision=6),
+            ["20", "20"],
+            cudf.Decimal128Dtype(scale=2, precision=6),
+            ["1.0", "1.0"],
+            cudf.Decimal128Dtype(scale=9, precision=15),
+        ),
+        (
+            operator.add,
+            ["1.5", None, "2.0"],
+            cudf.Decimal64Dtype(scale=1, precision=2),
+            ["1.5", None, "2.0"],
+            cudf.Decimal64Dtype(scale=1, precision=2),
+            ["3.0", None, "4.0"],
+            cudf.Decimal64Dtype(scale=1, precision=3),
+        ),
+        (
+            operator.add,
+            ["1.5", None],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["2.25", "1.005"],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["3.75", None],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.sub,
+            ["1.5", None],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["2.25", None],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["-0.75", None],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.sub,
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["2.25", None],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["-0.75", None],
+            cudf.Decimal64Dtype(scale=3, precision=5),
+        ),
+        (
+            operator.mul,
+            ["1.5", None],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["1.5", None],
+            cudf.Decimal64Dtype(scale=3, precision=4),
+            ["2.25", None],
+            cudf.Decimal64Dtype(scale=5, precision=8),
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=10),
+            ["0.1", None],
+            cudf.Decimal64Dtype(scale=3, precision=12),
+            ["10.0", None],
+            cudf.Decimal128Dtype(scale=1, precision=23),
+        ),
+        (
+            operator.eq,
+            ["0.18", "0.42"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.18", "0.21"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            [True, False],
+            bool,
+        ),
+        (
+            operator.eq,
+            ["0.18", "0.42"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.1800", "0.2100"],
+            cudf.Decimal64Dtype(scale=4, precision=5),
+            [True, False],
+            bool,
+        ),
+        (
+            operator.eq,
+            ["100", None],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-1, precision=4),
+            [True, None],
+            bool,
+        ),
+        (
+            operator.ne,
+            ["0.06", "0.42"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.18", "0.42"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            [True, False],
+            bool,
+        ),
+        (
+            operator.ne,
+            ["1.33", "1.21"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.1899", "1.21"],
+            cudf.Decimal64Dtype(scale=4, precision=5),
+            [True, False],
+            bool,
+        ),
+        (
+            operator.ne,
+            ["300", None],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["110", "5500"],
+            cudf.Decimal64Dtype(scale=-1, precision=4),
+            [True, None],
+            bool,
+        ),
+        (
+            operator.lt,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.10", "0.87", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            [False, True, False],
+            bool,
+        ),
+        (
+            operator.lt,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.1000", "0.8700", "1.0000"],
+            cudf.Decimal64Dtype(scale=4, precision=5),
+            [False, True, False],
+            bool,
+        ),
+        (
+            operator.lt,
+            ["200", None, "100"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["100", "200", "100"],
+            cudf.Decimal64Dtype(scale=-1, precision=4),
+            [False, None, False],
+            bool,
+        ),
+        (
+            operator.gt,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.10", "0.87", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            [True, False, False],
+            bool,
+        ),
+        (
+            operator.gt,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.1000", "0.8700", "1.0000"],
+            cudf.Decimal64Dtype(scale=4, precision=5),
+            [True, False, False],
+            bool,
+        ),
+        (
+            operator.gt,
+            ["300", None, "100"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["100", "200", "100"],
+            cudf.Decimal64Dtype(scale=-1, precision=4),
+            [True, None, False],
+            bool,
+        ),
+        (
+            operator.le,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.10", "0.87", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            [False, True, True],
+            bool,
+        ),
+        (
+            operator.le,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.1000", "0.8700", "1.0000"],
+            cudf.Decimal64Dtype(scale=4, precision=5),
+            [False, True, True],
+            bool,
+        ),
+        (
+            operator.le,
+            ["300", None, "100"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["100", "200", "100"],
+            cudf.Decimal64Dtype(scale=-1, precision=4),
+            [False, None, True],
+            bool,
+        ),
+        (
+            operator.ge,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.10", "0.87", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            [True, False, True],
+            bool,
+        ),
+        (
+            operator.ge,
+            ["0.18", "0.42", "1.00"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["0.1000", "0.8700", "1.0000"],
+            cudf.Decimal64Dtype(scale=4, precision=5),
+            [True, False, True],
+            bool,
+        ),
+        (
+            operator.ge,
+            ["300", None, "100"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            ["100", "200", "100"],
+            cudf.Decimal64Dtype(scale=-1, precision=4),
+            [True, None, True],
+            bool,
+        ),
+    ],
+)
+def test_binops_decimal(op, lhs, l_dtype, rhs, r_dtype, expect, expect_dtype):
+
+    if isinstance(lhs, (int, float)):
+        a = cudf.Scalar(lhs, l_dtype)
+    else:
+        a = utils._decimal_series(lhs, l_dtype)
+    b = utils._decimal_series(rhs, r_dtype)
+    expect = (
+        utils._decimal_series(expect, expect_dtype)
+        if isinstance(
+            expect_dtype,
+            (cudf.Decimal64Dtype, cudf.Decimal32Dtype, cudf.Decimal128Dtype),
+        )
+        else cudf.Series(expect, dtype=expect_dtype)
+    )
+
+    got = op(a, b)
+    assert expect.dtype == got.dtype
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "op,lhs,l_dtype,rhs,r_dtype,expect,expect_dtype",
+    [
+        (
+            "radd",
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            ["3.0", "4.0"],
+            cudf.Decimal64Dtype(scale=2, precision=4),
+        ),
+        (
+            "rsub",
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=10),
+            ["0.1", "0.2"],
+            cudf.Decimal64Dtype(scale=6, precision=10),
+            ["-99.9", "-199.8"],
+            cudf.Decimal128Dtype(scale=6, precision=19),
+        ),
+        (
+            "rmul",
+            ["1000", "2000"],
+            cudf.Decimal64Dtype(scale=-3, precision=4),
+            ["0.343", "0.500"],
+            cudf.Decimal64Dtype(scale=3, precision=3),
+            ["343.0", "1000.0"],
+            cudf.Decimal64Dtype(scale=0, precision=8),
+        ),
+        (
+            "rtruediv",
+            ["1.5", "0.5"],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            ["1.5", "2.0"],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            ["1.0", "4.0"],
+            cudf.Decimal64Dtype(scale=10, precision=16),
+        ),
+    ],
+)
+def test_binops_reflect_decimal(
+    op, lhs, l_dtype, rhs, r_dtype, expect, expect_dtype
+):
+
+    a = utils._decimal_series(lhs, l_dtype)
+    b = utils._decimal_series(rhs, r_dtype)
+    expect = utils._decimal_series(expect, expect_dtype)
+
+    got = getattr(a, op)(b)
+    assert expect.dtype == got.dtype
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("powers", [0, 1, 2, 3])
+def test_binops_decimal_pow(powers):
+    s = cudf.Series(
+        [
+            decimal.Decimal("1.324324"),
+            None,
+            decimal.Decimal("2"),
+            decimal.Decimal("3"),
+            decimal.Decimal("5"),
+        ]
+    )
+    ps = s.to_pandas()
+
+    utils.assert_eq(s**powers, ps**powers, check_dtype=False)
+
+
+def test_binops_raise_error():
+    s = cudf.Series([decimal.Decimal("1.324324")])
+
+    with pytest.raises(TypeError):
+        s // 1
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        (
+            operator.eq,
+            ["100", "41", None],
+            cudf.Decimal64Dtype(scale=0, precision=5),
+            [100, 42, 12],
+            cudf.Series([True, False, None], dtype=bool),
+            cudf.Series([True, False, None], dtype=bool),
+        ),
+        (
+            operator.eq,
+            ["100.000", "42.001", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            [100, 42, 12],
+            cudf.Series([True, False, None], dtype=bool),
+            cudf.Series([True, False, None], dtype=bool),
+        ),
+        (
+            operator.eq,
+            ["100", "40", None],
+            cudf.Decimal64Dtype(scale=-1, precision=3),
+            [100, 42, 12],
+            cudf.Series([True, False, None], dtype=bool),
+            cudf.Series([True, False, None], dtype=bool),
+        ),
+        (
+            operator.ne,
+            ["100", "42", "24", None],
+            cudf.Decimal64Dtype(scale=0, precision=3),
+            [100, 40, 24, 12],
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, True, False, None], dtype=bool),
+        ),
+        (
+            operator.ne,
+            ["10.1", "88", "11", None],
+            cudf.Decimal64Dtype(scale=1, precision=3),
+            [10, 42, 11, 12],
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, True, False, None], dtype=bool),
+        ),
+        (
+            operator.ne,
+            ["100.000", "42", "23.999", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            [100, 42, 24, 12],
+            cudf.Series([False, False, True, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.lt,
+            ["100", "40", "28", None],
+            cudf.Decimal64Dtype(scale=0, precision=3),
+            [100, 42, 24, 12],
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.lt,
+            ["100.000", "42.002", "23.999", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            [100, 42, 24, 12],
+            cudf.Series([False, False, True, None], dtype=bool),
+            cudf.Series([False, True, False, None], dtype=bool),
+        ),
+        (
+            operator.lt,
+            ["100", "40", "10", None],
+            cudf.Decimal64Dtype(scale=-1, precision=3),
+            [100, 42, 8, 12],
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.gt,
+            ["100", "42", "20", None],
+            cudf.Decimal64Dtype(scale=0, precision=3),
+            [100, 40, 24, 12],
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.gt,
+            ["100.000", "42.002", "23.999", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            [100, 42, 24, 12],
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.gt,
+            ["100", "40", "10", None],
+            cudf.Decimal64Dtype(scale=-1, precision=3),
+            [100, 42, 8, 12],
+            cudf.Series([False, False, True, None], dtype=bool),
+            cudf.Series([False, True, False, None], dtype=bool),
+        ),
+        (
+            operator.le,
+            ["100", "40", "28", None],
+            cudf.Decimal64Dtype(scale=0, precision=3),
+            [100, 42, 24, 12],
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+        (
+            operator.le,
+            ["100.000", "42.002", "23.999", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            [100, 42, 24, 12],
+            cudf.Series([True, False, True, None], dtype=bool),
+            cudf.Series([True, True, False, None], dtype=bool),
+        ),
+        (
+            operator.le,
+            ["100", "40", "10", None],
+            cudf.Decimal64Dtype(scale=-1, precision=3),
+            [100, 42, 8, 12],
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+        (
+            operator.ge,
+            ["100", "42", "20", None],
+            cudf.Decimal64Dtype(scale=0, precision=3),
+            [100, 40, 24, 12],
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+        (
+            operator.ge,
+            ["100.000", "42.002", "23.999", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            [100, 42, 24, 12],
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+        (
+            operator.ge,
+            ["100", "40", "10", None],
+            cudf.Decimal64Dtype(scale=-1, precision=3),
+            [100, 42, 8, 12],
+            cudf.Series([True, False, True, None], dtype=bool),
+            cudf.Series([True, True, False, None], dtype=bool),
+        ),
+    ],
+)
+@pytest.mark.parametrize("integer_dtype", utils.INTEGER_TYPES)
+@pytest.mark.parametrize("reflected", [True, False])
+def test_binops_decimal_comp_mixed_integer(args, integer_dtype, reflected):
+    """
+    Tested compare operations:
+        eq, lt, gt, le, ge
+    Each operation has 3 decimal data setups, with scale from {==0, >0, <0}.
+    Decimal precisions are sufficient to hold the digits.
+    For each decimal data setup, there is at least one row that lead to one
+    of the following compare results: {True, False, None}.
+    """
+    if not reflected:
+        op, ldata, ldtype, rdata, expected, _ = args
+    else:
+        op, ldata, ldtype, rdata, _, expected = args
+
+    lhs = utils._decimal_series(ldata, ldtype)
+    rhs = cudf.Series(rdata, dtype=integer_dtype)
+
+    if reflected:
+        rhs, lhs = lhs, rhs
+
+    actual = op(lhs, rhs)
+
+    utils.assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal(1),
+            ["101", "201"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            False,
+        ),
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            1,
+            ["101", "201"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            False,
+        ),
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("1.5"),
+            ["101.5", "201.5"],
+            cudf.Decimal64Dtype(scale=1, precision=7),
+            False,
+        ),
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal(1),
+            ["101", "201"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            True,
+        ),
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            1,
+            ["101", "201"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            True,
+        ),
+        (
+            operator.add,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("1.5"),
+            ["101.5", "201.5"],
+            cudf.Decimal64Dtype(scale=1, precision=7),
+            True,
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            1,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=5),
+            False,
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal(2),
+            ["200", "400"],
+            cudf.Decimal64Dtype(scale=-2, precision=5),
+            False,
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("1.5"),
+            ["150", "300"],
+            cudf.Decimal64Dtype(scale=-1, precision=6),
+            False,
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            1,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=5),
+            True,
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal(2),
+            ["200", "400"],
+            cudf.Decimal64Dtype(scale=-2, precision=5),
+            True,
+        ),
+        (
+            operator.mul,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("1.5"),
+            ["150", "300"],
+            cudf.Decimal64Dtype(scale=-1, precision=6),
+            True,
+        ),
+        (
+            operator.truediv,
+            ["1000", "2000"],
+            cudf.Decimal64Dtype(scale=-2, precision=4),
+            1,
+            ["1000", "2000"],
+            cudf.Decimal64Dtype(scale=6, precision=12),
+            False,
+        ),
+        (
+            operator.truediv,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            decimal.Decimal(2),
+            ["50", "100"],
+            cudf.Decimal64Dtype(scale=6, precision=9),
+            False,
+        ),
+        (
+            operator.truediv,
+            ["35.23", "54.91"],
+            cudf.Decimal64Dtype(scale=2, precision=4),
+            decimal.Decimal("1.5"),
+            ["23.4", "36.6"],
+            cudf.Decimal64Dtype(scale=6, precision=9),
+            False,
+        ),
+        (
+            operator.truediv,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            1,
+            ["0", "0"],
+            cudf.Decimal64Dtype(scale=6, precision=9),
+            True,
+        ),
+        (
+            operator.truediv,
+            ["1.2", "0.5"],
+            cudf.Decimal64Dtype(scale=1, precision=6),
+            decimal.Decimal(20),
+            ["10", "40"],
+            cudf.Decimal64Dtype(scale=7, precision=10),
+            True,
+        ),
+        (
+            operator.truediv,
+            ["1.22", "5.24"],
+            cudf.Decimal64Dtype(scale=2, precision=3),
+            decimal.Decimal("8.55"),
+            ["7", "1"],
+            cudf.Decimal64Dtype(scale=6, precision=9),
+            True,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal(2),
+            ["98", "198"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            False,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("2.5"),
+            ["97.5", "197.5"],
+            cudf.Decimal64Dtype(scale=1, precision=7),
+            False,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            4,
+            ["96", "196"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            False,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal(2),
+            ["-98", "-198"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            True,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            4,
+            ["-96", "-196"],
+            cudf.Decimal64Dtype(scale=0, precision=6),
+            True,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("2.5"),
+            ["-97.5", "-197.5"],
+            cudf.Decimal64Dtype(scale=1, precision=7),
+            True,
+        ),
+        (
+            operator.sub,
+            ["100", "200"],
+            cudf.Decimal64Dtype(scale=-2, precision=3),
+            decimal.Decimal("2.5"),
+            ["-97.5", "-197.5"],
+            cudf.Decimal64Dtype(scale=1, precision=7),
+            True,
+        ),
+    ],
+)
+def test_binops_decimal_scalar(args):
+    op, lhs, l_dtype, rhs, expect, expect_dtype, reflect = args
+
+    def decimal_series(input, dtype):
+        return cudf.Series(
+            [x if x is None else decimal.Decimal(x) for x in input],
+            dtype=dtype,
+        )
+
+    lhs = decimal_series(lhs, l_dtype)
+    expect = decimal_series(expect, expect_dtype)
+
+    if reflect:
+        lhs, rhs = rhs, lhs
+
+    got = op(lhs, rhs)
+    assert expect.dtype == got.dtype
+    utils.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        (
+            operator.eq,
+            ["100.00", "41", None],
+            cudf.Decimal64Dtype(scale=0, precision=5),
+            100,
+            cudf.Series([True, False, None], dtype=bool),
+            cudf.Series([True, False, None], dtype=bool),
+        ),
+        (
+            operator.eq,
+            ["100.123", "41", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            decimal.Decimal("100.123"),
+            cudf.Series([True, False, None], dtype=bool),
+            cudf.Series([True, False, None], dtype=bool),
+        ),
+        (
+            operator.eq,
+            ["100.123", "41", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            cudf.Scalar(decimal.Decimal("100.123")),
+            cudf.Series([True, False, None], dtype=bool),
+            cudf.Series([True, False, None], dtype=bool),
+        ),
+        (
+            operator.ne,
+            ["100.00", "41", None],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            100,
+            cudf.Series([False, True, None], dtype=bool),
+            cudf.Series([False, True, None], dtype=bool),
+        ),
+        (
+            operator.ne,
+            ["100.123", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            decimal.Decimal("100.123"),
+            cudf.Series([False, True, None], dtype=bool),
+            cudf.Series([False, True, None], dtype=bool),
+        ),
+        (
+            operator.ne,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            cudf.Scalar(decimal.Decimal("100.123")),
+            cudf.Series([False, True, True, None], dtype=bool),
+            cudf.Series([False, True, True, None], dtype=bool),
+        ),
+        (
+            operator.gt,
+            ["100.00", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            100,
+            cudf.Series([False, False, True, None], dtype=bool),
+            cudf.Series([False, True, False, None], dtype=bool),
+        ),
+        (
+            operator.gt,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            decimal.Decimal("100.123"),
+            cudf.Series([False, False, True, None], dtype=bool),
+            cudf.Series([False, True, False, None], dtype=bool),
+        ),
+        (
+            operator.gt,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            cudf.Scalar(decimal.Decimal("100.123")),
+            cudf.Series([False, False, True, None], dtype=bool),
+            cudf.Series([False, True, False, None], dtype=bool),
+        ),
+        (
+            operator.ge,
+            ["100.00", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            100,
+            cudf.Series([True, False, True, None], dtype=bool),
+            cudf.Series([True, True, False, None], dtype=bool),
+        ),
+        (
+            operator.ge,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            decimal.Decimal("100.123"),
+            cudf.Series([True, False, True, None], dtype=bool),
+            cudf.Series([True, True, False, None], dtype=bool),
+        ),
+        (
+            operator.ge,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            cudf.Scalar(decimal.Decimal("100.123")),
+            cudf.Series([True, False, True, None], dtype=bool),
+            cudf.Series([True, True, False, None], dtype=bool),
+        ),
+        (
+            operator.lt,
+            ["100.00", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            100,
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.lt,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            decimal.Decimal("100.123"),
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.lt,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            cudf.Scalar(decimal.Decimal("100.123")),
+            cudf.Series([False, True, False, None], dtype=bool),
+            cudf.Series([False, False, True, None], dtype=bool),
+        ),
+        (
+            operator.le,
+            ["100.00", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=2, precision=5),
+            100,
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+        (
+            operator.le,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            decimal.Decimal("100.123"),
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+        (
+            operator.le,
+            ["100.123", "41", "120.21", None],
+            cudf.Decimal64Dtype(scale=3, precision=6),
+            cudf.Scalar(decimal.Decimal("100.123")),
+            cudf.Series([True, True, False, None], dtype=bool),
+            cudf.Series([True, False, True, None], dtype=bool),
+        ),
+    ],
+)
+@pytest.mark.parametrize("reflected", [True, False])
+def test_binops_decimal_scalar_compare(args, reflected):
+    """
+    Tested compare operations:
+        eq, lt, gt, le, ge
+    Each operation has 3 data setups: pyints, Decimal, and
+    decimal cudf.Scalar
+    For each data setup, there is at least one row that lead to one of the
+    following compare results: {True, False, None}.
+    """
+    if not reflected:
+        op, ldata, ldtype, rdata, expected, _ = args
+    else:
+        op, ldata, ldtype, rdata, _, expected = args
+
+    lhs = utils._decimal_series(ldata, ldtype)
+    rhs = rdata
+
+    if reflected:
+        rhs, lhs = lhs, rhs
+
+    actual = op(lhs, rhs)
+
+    utils.assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "float32",
+        "float64",
+        "str",
+        "datetime64[ns]",
+        "datetime64[us]",
+        "datetime64[ms]",
+        "datetime64[s]",
+        "timedelta64[ns]",
+        "timedelta64[us]",
+        "timedelta64[ms]",
+        "timedelta64[s]",
+    ],
+)
+@pytest.mark.parametrize("null_scalar", [None, cudf.NA, np.datetime64("NaT")])
+@pytest.mark.parametrize("cmpop", _cmpops)
+def test_column_null_scalar_comparison(dtype, null_scalar, cmpop):
+    # This test is meant to validate that comparing
+    # a series of any dtype with a null scalar produces
+    # a new series where all the elements are <NA>.
+
+    if isinstance(null_scalar, np.datetime64):
+        if cudf.dtype(dtype).kind not in "mM":
+            pytest.skip()
+        null_scalar = null_scalar.astype(dtype)
+
+    dtype = cudf.dtype(dtype)
+
+    data = [1, 2, 3, 4, 5]
+    sr = cudf.Series(data, dtype=dtype)
+    result = cmpop(sr, null_scalar)
+
+    assert result.isnull().all()
+
+
+@pytest.mark.parametrize("fn", ["eq", "ne", "lt", "gt", "le", "ge"])
+def test_equality_ops_index_mismatch(fn):
+    a = cudf.Series(
+        [1, 2, 3, None, None, 4], index=["a", "b", "c", "d", "e", "f"]
+    )
+    b = cudf.Series(
+        [-5, 4, 3, 2, 1, 0, 19, 11],
+        index=["aa", "b", "c", "d", "e", "f", "y", "z"],
+    )
+
+    pa = a.to_pandas(nullable=True)
+    pb = b.to_pandas(nullable=True)
+    expected = getattr(pa, fn)(pb)
+    actual = getattr(a, fn)(b).to_pandas(nullable=True)
+
+    utils.assert_eq(expected, actual)
+
+
+def generate_test_null_equals_columnops_data():
+    # Generate tuples of:
+    # (left_data, right_data, compare_bool
+    # where compare_bool is the correct answer to
+    # if the columns should compare as null equals
+
+    def set_null_cases(column_l, column_r, case):
+        if case == "neither":
+            return column_l, column_r
+        elif case == "left":
+            column_l[1] = None
+        elif case == "right":
+            column_r[1] = None
+        elif case == "both":
+            column_l[1] = None
+            column_r[1] = None
+        else:
+            raise ValueError("Unknown null case")
+        return column_l, column_r
+
+    null_cases = ["neither", "left", "right", "both"]
+    data = [1, 2, 3]
+
+    results = []
+    # TODO: Numeric types can be cross compared as null equal
+    for dtype in (
+        list(NUMERIC_TYPES)
+        + list(DATETIME_TYPES)
+        + list(TIMEDELTA_TYPES)
+        + list(STRING_TYPES)
+        + ["category"]
+    ):
+        for case in null_cases:
+            left = cudf.Series(data, dtype=dtype)
+            right = cudf.Series(data, dtype=dtype)
+            if case in {"left", "right"}:
+                answer = False
+            else:
+                answer = True
+            left, right = set_null_cases(left, right, case)
+            results.append((left._column, right._column, answer, case))
+
+    return results
+
+
+@pytest.mark.parametrize(
+    "lcol,rcol,ans,case", generate_test_null_equals_columnops_data()
+)
+def test_null_equals_columnops(lcol, rcol, ans, case):
+    assert lcol.equals(rcol).all() == ans
+
+
+def test_add_series_to_dataframe():
+    """Verify that missing columns result in NaNs, not NULLs."""
+    assert cp.all(
+        cp.isnan(
+            (
+                cudf.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+                + cudf.Series([1, 2, 3], index=["a", "b", "c"])
+            )["c"]
+        )
+    )
+
+
+@pytest.mark.parametrize("obj_class", [cudf.Series, cudf.Index])
+@pytest.mark.parametrize("binop", _binops)
+def test_binops_cupy_array(obj_class, binop):
+    # Skip 0 to not deal with NaNs from division.
+    data = range(1, 100)
+    lhs = obj_class(data)
+    rhs = cp.array(data)
+    assert (binop(lhs, rhs) == binop(lhs, lhs)).all()
+
+
+@pytest.mark.parametrize("binop", _binops + _binops_compare)
+@pytest.mark.parametrize("data", [None, [-9, 7], [5, -2], [12, 18]])
+@pytest.mark.parametrize("scalar", [1, 3, 12, np.nan])
+def test_empty_column(binop, data, scalar):
+    gdf = cudf.DataFrame(columns=["a", "b"])
+    if data is not None:
+        gdf["a"] = data
+
+    pdf = gdf.to_pandas()
+
+    got = binop(gdf, scalar)
+    expected = binop(pdf, scalar)
+
+    utils.assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame(
+            [[1, 2, 3, 4], [5, 6, 7, 8], [10, 11, 12, 13], [14, 15, 16, 17]]
+        ),
+        pytest.param(
+            cudf.DataFrame([[1, None, None, 4], [5, 6, 7, None]]),
+            marks=pytest_xfail(
+                reason="Cannot access Frame.values if frame contains nulls"
+            ),
+        ),
+        cudf.DataFrame(
+            [
+                [1.2, 2.3, 3.4, 4.5],
+                [5.6, 6.7, 7.8, 8.9],
+                [7.43, 4.2, 23.2, 23.2],
+                [9.1, 2.4, 4.5, 65.34],
+            ]
+        ),
+        cudf.Series([14, 15, 16, 17]),
+        cudf.Series([14.15, 15.16, 16.17, 17.18]),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        cudf.DataFrame([[9, 10], [11, 12], [13, 14], [15, 16]]),
+        cudf.DataFrame(
+            [[9.4, 10.5], [11.6, 12.7], [13.8, 14.9], [15.1, 16.2]]
+        ),
+        cudf.Series([5, 6, 7, 8]),
+        cudf.Series([5.6, 6.7, 7.8, 8.9]),
+        np.array([5, 6, 7, 8]),
+        [25.5, 26.6, 27.7, 28.8],
+    ],
+)
+def test_binops_dot(df, other):
+    pdf = df.to_pandas()
+    host_other = other.to_pandas() if hasattr(other, "to_pandas") else other
+
+    expected = pdf @ host_other
+    got = df @ other
+
+    utils.assert_eq(expected, got)
+
+
+def test_binop_dot_preserve_index():
+    ser = cudf.Series(range(2), index=["A", "B"])
+    df = cudf.DataFrame(np.eye(2), columns=["A", "B"], index=["A", "B"])
+    result = ser @ df
+    expected = ser.to_pandas() @ df.to_pandas()
+    utils.assert_eq(result, expected)
+
+
+def test_binop_series_with_repeated_index():
+    # GH: #11094
+    psr1 = pd.Series([1, 1], index=["a", "a"])
+    psr2 = pd.Series([1], index=["a"])
+    gsr1 = cudf.from_pandas(psr1)
+    gsr2 = cudf.from_pandas(psr2)
+    expected = psr1 - psr2
+    got = gsr1 - gsr2
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_series_series():
+    # GH: #10178
+    gs_base = cudf.Series([3, -3, 8, -8])
+    gs_exponent = cudf.Series([1, 1, 7, 7])
+    ps_base = gs_base.to_pandas()
+    ps_exponent = gs_exponent.to_pandas()
+    expected = ps_base**ps_exponent
+    got = gs_base**gs_exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_series_scalar():
+    # GH: #10178
+    gs_base = cudf.Series([3, -3, 8, -8])
+    exponent = cudf.Scalar(1)
+    ps_base = gs_base.to_pandas()
+    expected = ps_base**exponent.value
+    got = gs_base**exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_series_int():
+    # GH: #10178
+    gs_base = cudf.Series([3, -3, 8, -8])
+    exponent = 1
+    ps_base = gs_base.to_pandas()
+    expected = ps_base**exponent
+    got = gs_base**exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_scalar_series():
+    # GH: #10178
+    base = cudf.Scalar(3)
+    gs_exponent = cudf.Series([1, 1, 7, 7])
+    ps_exponent = gs_exponent.to_pandas()
+    expected = base.value**ps_exponent
+    got = base**gs_exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_scalar_scalar():
+    # GH: #10178
+    base = cudf.Scalar(3)
+    exponent = cudf.Scalar(1)
+    expected = base.value**exponent.value
+    got = base**exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_scalar_int():
+    # GH: #10178
+    base = cudf.Scalar(3)
+    exponent = 1
+    expected = base.value**exponent
+    got = base**exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_int_series():
+    # GH: #10178
+    base = 3
+    gs_exponent = cudf.Series([1, 1, 7, 7])
+    ps_exponent = gs_exponent.to_pandas()
+    expected = base**ps_exponent
+    got = base**gs_exponent
+    utils.assert_eq(expected, got)
+
+
+def test_binop_integer_power_int_scalar():
+    # GH: #10178
+    base = 3
+    exponent = cudf.Scalar(1)
+    expected = base**exponent.value
+    got = base**exponent
+    utils.assert_eq(expected, got)
+
+
+def test_numpy_int_scalar_binop():
+    assert (np.float32(1.0) - cudf.Scalar(1)) == 0.0
+
+
+@pytest.mark.parametrize("op", _binops)
+def test_binop_index_series(op):
+    gi = cudf.Index([10, 11, 12])
+    gs = cudf.Series([1, 2, 3])
+
+    actual = op(gi, gs)
+    expected = op(gi.to_pandas(), gs.to_pandas())
+
+    utils.assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("name1", utils.SERIES_OR_INDEX_NAMES)
+@pytest.mark.parametrize("name2", utils.SERIES_OR_INDEX_NAMES)
+def test_binop_index_dt_td_series_with_names(name1, name2):
+    gi = cudf.Index([1, 2, 3], dtype="datetime64[ns]", name=name1)
+    gs = cudf.Series([10, 11, 12], dtype="timedelta64[ns]", name=name2)
+    with warnings.catch_warnings():
+        # Numpy raises a deprecation warning:
+        # "elementwise comparison failed; this will raise an error "
+        warnings.simplefilter("ignore", (DeprecationWarning,))
+
+        expected = gi.to_pandas() + gs.to_pandas()
+    actual = gi + gs
+
+    utils.assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data1", [[1, 2, 3], [10, 11, None]])
+@pytest.mark.parametrize("data2", [[1, 2, 3], [10, 11, None]])
+def test_binop_eq_ne_index_series(data1, data2):
+    gi = cudf.Index(data1, dtype="datetime64[ns]", name=np.nan)
+    gs = cudf.Series(data2, dtype="timedelta64[ns]", name="abc")
+
+    actual = gi == gs
+    expected = gi.to_pandas() == gs.to_pandas()
+
+    utils.assert_eq(expected, actual)
+
+    actual = gi != gs
+    expected = gi.to_pandas() != gs.to_pandas()
+
+    utils.assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/test_buffer.py b/python/cudf/cudf/tests/test_buffer.py
new file mode 100644
index 0000000..1c9e747
--- /dev/null
+++ b/python/cudf/cudf/tests/test_buffer.py
@@ -0,0 +1,113 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import cupy as cp
+import pytest
+
+from cudf.core.buffer import Buffer, as_buffer
+
+pytestmark = pytest.mark.spilling
+
+arr_len = 10
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (cp.zeros(arr_len), True),
+        (cp.zeros((1, arr_len)), True),
+        (cp.zeros((1, arr_len, 1)), True),
+        (cp.zeros((arr_len, arr_len)), True),
+        (cp.zeros((arr_len, arr_len)).reshape(arr_len * arr_len), True),
+        (cp.zeros((arr_len, arr_len))[:, 0], False),
+    ],
+)
+def test_buffer_from_cuda_iface_contiguous(data):
+    data, expect_success = data
+    if expect_success:
+        as_buffer(data.view("|u1"))
+    else:
+        with pytest.raises(ValueError):
+            as_buffer(data.view("|u1"))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cp.arange(arr_len),
+        cp.arange(arr_len).reshape(1, arr_len),
+        cp.arange(arr_len).reshape(1, arr_len, 1),
+        cp.arange(arr_len**2).reshape(arr_len, arr_len),
+    ],
+)
+@pytest.mark.parametrize("dtype", ["uint8", "int8", "float32", "int32"])
+def test_buffer_from_cuda_iface_dtype(data, dtype):
+    data = data.astype(dtype)
+    buf = as_buffer(data)
+    got = cp.array(buf).reshape(-1).view("uint8")
+    expect = data.reshape(-1).view("uint8")
+    assert (expect == got).all()
+
+
+def test_buffer_creation_from_any():
+    ary = cp.arange(arr_len)
+    b = as_buffer(ary, exposed=True)
+    assert isinstance(b, Buffer)
+    assert ary.data.ptr == b.get_ptr(mode="read")
+    assert ary.nbytes == b.size
+
+    with pytest.raises(
+        ValueError, match="size must be specified when `data` is an integer"
+    ):
+        as_buffer(ary.data.ptr)
+
+    b = as_buffer(ary.data.ptr, size=ary.nbytes, owner=ary, exposed=True)
+    assert isinstance(b, Buffer)
+    assert ary.data.ptr == b.get_ptr(mode="read")
+    assert ary.nbytes == b.size
+    assert b.owner.owner is ary
+
+
+@pytest.mark.parametrize(
+    "size,expect", [(10, "10B"), (2**10 + 500, "1.49KiB"), (2**20, "1MiB")]
+)
+def test_buffer_repr(size, expect):
+    ary = cp.arange(size, dtype="uint8")
+    buf = as_buffer(ary)
+    assert f"size={expect}" in repr(buf)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        slice(0, 0),
+        slice(0, 1),
+        slice(-2, -1),
+        slice(0, arr_len),
+        slice(2, 3),
+        slice(2, -1),
+    ],
+)
+def test_buffer_slice(idx):
+    ary = cp.arange(arr_len, dtype="uint8")
+    buf = as_buffer(ary)
+    expect = ary[idx]
+    got = cp.array(buf[idx])
+    assert (expect == got).all()
+
+
+@pytest.mark.parametrize(
+    "idx, err_type, err_msg",
+    [
+        (1, TypeError, "Argument 'key' has incorrect type"),
+        (slice(3, 2), ValueError, "size cannot be negative"),
+        (slice(1, 2, 2), ValueError, "slice must be C-contiguous"),
+        (slice(1, 2, -1), ValueError, "slice must be C-contiguous"),
+        (slice(3, 2, -1), ValueError, "slice must be C-contiguous"),
+    ],
+)
+def test_buffer_slice_fail(idx, err_type, err_msg):
+    ary = cp.arange(arr_len, dtype="uint8")
+    buf = as_buffer(ary)
+
+    with pytest.raises(err_type, match=err_msg):
+        buf[idx]
diff --git a/python/cudf/cudf/tests/test_categorical.py b/python/cudf/cudf/tests/test_categorical.py
new file mode 100644
index 0000000..afbc0dc
--- /dev/null
+++ b/python/cudf/cudf/tests/test_categorical.py
@@ -0,0 +1,957 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import operator
+import string
+import warnings
+from contextlib import contextmanager
+from textwrap import dedent
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_134
+from cudf.testing._utils import (
+    NUMERIC_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+
+
+@contextmanager
+def _hide_deprecated_pandas_categorical_inplace_warnings(function_name):
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            (
+                "The `inplace` parameter in "
+                f"pandas.Categorical.{function_name} is deprecated and will "
+                "be removed in a future version."
+            ),
+            category=FutureWarning,
+        )
+        yield
+
+
+@contextmanager
+def _hide_cudf_safe_casting_warning():
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            "Can't safely cast column",
+            category=UserWarning,
+        )
+        yield
+
+
+@pytest.fixture
+def pd_str_cat():
+    categories = list("abc")
+    codes = [0, 0, 1, 0, 1, 2, 0, 1, 1, 2]
+    return pd.Categorical.from_codes(codes, categories=categories)
+
+
+def test_categorical_basic():
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    cudf_cat = cudf.Index(cat)
+
+    pdsr = pd.Series(cat, index=["p", "q", "r", "s", "t"])
+    sr = cudf.Series(cat, index=["p", "q", "r", "s", "t"])
+    assert_eq(pdsr.cat.codes, sr.cat.codes, check_dtype=False)
+
+    # Test attributes
+    assert_eq(pdsr.cat.categories, sr.cat.categories)
+    assert pdsr.cat.ordered == sr.cat.ordered
+
+    np.testing.assert_array_equal(
+        pdsr.cat.codes.values, sr.cat.codes.to_numpy()
+    )
+
+    string = str(sr)
+    expect_str = """
+p a
+q a
+r b
+s c
+t a
+"""
+    assert all(x == y for x, y in zip(string.split(), expect_str.split()))
+    assert_eq(cat.codes, cudf_cat.codes.to_numpy())
+
+
+def test_categorical_integer():
+    cat = pd.Categorical(["a", "_", "_", "c", "a"], categories=["a", "b", "c"])
+    pdsr = pd.Series(cat)
+    sr = cudf.Series(cat)
+    np.testing.assert_array_equal(
+        cat.codes, sr.cat.codes.astype(cat.codes.dtype).fillna(-1).to_numpy()
+    )
+    assert sr.null_count == 2
+
+    np.testing.assert_array_equal(
+        pdsr.cat.codes.values,
+        sr.cat.codes.astype(pdsr.cat.codes.dtype).fillna(-1).to_numpy(),
+    )
+
+    expect_str = dedent(
+        """\
+        0       a
+        1    <NA>
+        2    <NA>
+        3       c
+        4       a
+        dtype: category
+        Categories (3, object): ['a', 'b', 'c']"""
+    )
+    assert str(sr) == expect_str
+
+
+def test_categorical_compare_unordered():
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    pdsr = pd.Series(cat)
+
+    sr = cudf.Series(cat)
+
+    # test equal
+    out = sr == sr
+    assert out.dtype == np.bool_
+    assert type(out[0]) == np.bool_
+    assert np.all(out.to_numpy())
+    assert np.all(pdsr == pdsr)
+
+    # test inequality
+    out = sr != sr
+    assert not np.any(out.to_numpy())
+    assert not np.any(pdsr != pdsr)
+
+    assert not pdsr.cat.ordered
+    assert not sr.cat.ordered
+
+    # test using ordered operators
+    assert_exceptions_equal(
+        lfunc=operator.lt,
+        rfunc=operator.lt,
+        lfunc_args_and_kwargs=([pdsr, pdsr],),
+        rfunc_args_and_kwargs=([sr, sr],),
+    )
+
+
+def test_categorical_compare_ordered():
+    cat1 = pd.Categorical(
+        ["a", "a", "b", "c", "a"], categories=["a", "b", "c"], ordered=True
+    )
+    pdsr1 = pd.Series(cat1)
+    sr1 = cudf.Series(cat1)
+    cat2 = pd.Categorical(
+        ["a", "b", "a", "c", "b"], categories=["a", "b", "c"], ordered=True
+    )
+    pdsr2 = pd.Series(cat2)
+    sr2 = cudf.Series(cat2)
+
+    # test equal
+    out = sr1 == sr1
+    assert out.dtype == np.bool_
+    assert type(out[0]) == np.bool_
+    assert np.all(out.to_numpy())
+    assert np.all(pdsr1 == pdsr1)
+
+    # test inequality
+    out = sr1 != sr1
+    assert not np.any(out.to_numpy())
+    assert not np.any(pdsr1 != pdsr1)
+
+    assert pdsr1.cat.ordered
+    assert sr1.cat.ordered
+
+    # test using ordered operators
+    np.testing.assert_array_equal(pdsr1 < pdsr2, (sr1 < sr2).to_numpy())
+    np.testing.assert_array_equal(pdsr1 > pdsr2, (sr1 > sr2).to_numpy())
+
+
+def test_categorical_binary_add():
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    pdsr = pd.Series(cat)
+    sr = cudf.Series(cat)
+
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([pdsr, pdsr],),
+        rfunc_args_and_kwargs=([sr, sr],),
+    )
+
+
+def test_categorical_element_indexing():
+    """
+    Element indexing to a cat column must give the underlying object
+    not the numerical index.
+    """
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    pdsr = pd.Series(cat)
+    sr = cudf.Series(cat)
+    assert_eq(pdsr, sr)
+    assert_eq(pdsr.cat.codes, sr.cat.codes, check_dtype=False)
+
+
+def test_categorical_masking():
+    """
+    Test common operation for getting a all rows that matches a certain
+    category.
+    """
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    pdsr = pd.Series(cat)
+    sr = cudf.Series(cat)
+
+    # check scalar comparison
+    expect_matches = pdsr == "a"
+    got_matches = sr == "a"
+
+    np.testing.assert_array_equal(
+        expect_matches.values, got_matches.to_numpy()
+    )
+
+    # mask series
+    expect_masked = pdsr[expect_matches]
+    got_masked = sr[got_matches]
+
+    assert len(expect_masked) == len(got_masked)
+    assert len(expect_masked) == got_masked.valid_count
+    assert_eq(got_masked, expect_masked)
+
+
+def test_df_cat_set_index():
+    df = cudf.DataFrame()
+    df["a"] = pd.Categorical(list("aababcabbc"), categories=list("abc"))
+    df["b"] = np.arange(len(df))
+    got = df.set_index("a")
+
+    pddf = df.to_pandas(nullable_pd_dtype=False)
+    expect = pddf.set_index("a")
+
+    assert_eq(got, expect)
+
+
+def test_df_cat_sort_index():
+    df = cudf.DataFrame()
+    df["a"] = pd.Categorical(list("aababcabbc"), categories=list("abc"))
+    df["b"] = np.arange(len(df))
+
+    got = df.set_index("a").sort_index()
+    expect = df.to_pandas(nullable_pd_dtype=False).set_index("a").sort_index()
+
+    assert_eq(got, expect)
+
+
+def test_cat_series_binop_error():
+    df = cudf.DataFrame()
+    df["a"] = pd.Categorical(list("aababcabbc"), categories=list("abc"))
+    df["b"] = np.arange(len(df))
+
+    pdf = df.to_pandas()
+
+    # lhs is categorical
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([pdf["a"], pdf["b"]],),
+        rfunc_args_and_kwargs=([df["a"], df["b"]],),
+    )
+
+    # lhs is numerical
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([pdf["b"], pdf["a"]],),
+        rfunc_args_and_kwargs=([df["b"], df["a"]],),
+    )
+
+
+@pytest.mark.parametrize("num_elements", [10, 100, 1000])
+def test_categorical_unique(num_elements):
+    # create categorical series
+    np.random.seed(12)
+    pd_cat = pd.Categorical(
+        pd.Series(
+            np.random.choice(
+                list(string.ascii_letters + string.digits), num_elements
+            ),
+            dtype="category",
+        )
+    )
+
+    # gdf
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series.from_categorical(pd_cat)
+    gdf_unique_sorted = np.sort(gdf["a"].unique().to_pandas())
+
+    # pandas
+    pdf = pd.DataFrame()
+    pdf["a"] = pd_cat
+    pdf_unique_sorted = np.sort(pdf["a"].unique())
+
+    # verify
+    np.testing.assert_array_equal(pdf_unique_sorted, gdf_unique_sorted)
+
+
+@pytest.mark.parametrize("nelem", [20, 50, 100])
+def test_categorical_unique_count(nelem):
+    # create categorical series
+    np.random.seed(12)
+    pd_cat = pd.Categorical(
+        pd.Series(
+            np.random.choice(
+                list(string.ascii_letters + string.digits), nelem
+            ),
+            dtype="category",
+        )
+    )
+
+    # gdf
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series.from_categorical(pd_cat)
+    gdf_unique_count = gdf["a"].nunique()
+
+    # pandas
+    pdf = pd.DataFrame()
+    pdf["a"] = pd_cat
+    pdf_unique = pdf["a"].unique()
+
+    # verify
+    assert gdf_unique_count == len(pdf_unique)
+
+
+def test_categorical_empty():
+    cat = pd.Categorical([])
+    pdsr = pd.Series(cat)
+    sr = cudf.Series(cat)
+    np.testing.assert_array_equal(cat.codes, sr.cat.codes.to_numpy())
+
+    # Test attributes
+    assert_eq(pdsr.cat.categories, sr.cat.categories)
+    assert pdsr.cat.ordered == sr.cat.ordered
+
+    np.testing.assert_array_equal(
+        pdsr.cat.codes.values, sr.cat.codes.to_numpy()
+    )
+
+
+def test_categorical_set_categories():
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    psr = pd.Series(cat)
+    sr = cudf.Series.from_categorical(cat)
+
+    # adding category
+    expect = psr.cat.set_categories(["a", "b", "c", "d"])
+    got = sr.cat.set_categories(["a", "b", "c", "d"])
+    assert_eq(expect, got)
+
+    # removing category
+    expect = psr.cat.set_categories(["a", "b"])
+    got = sr.cat.set_categories(["a", "b"])
+    assert_eq(expect, got)
+
+
+def test_categorical_set_categories_preserves_order():
+    series = pd.Series([1, 0, 0, 0, 2]).astype("category")
+    # reassigning categories should preserve element ordering
+    assert_eq(
+        series.cat.set_categories([1, 2]),
+        cudf.Series(series).cat.set_categories([1, 2]),
+    )
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+def test_categorical_as_ordered(pd_str_cat, inplace):
+
+    pd_sr = pd.Series(pd_str_cat.copy().set_ordered(False))
+    cd_sr = cudf.Series(pd_str_cat.copy().set_ordered(False))
+
+    assert cd_sr.cat.ordered is False
+    assert cd_sr.cat.ordered == pd_sr.cat.ordered
+
+    # pandas internally uses a deprecated call to set_ordered(inplace=inplace)
+    # inside as_ordered.
+    with pytest.warns(FutureWarning):
+        pd_sr_1 = pd_sr.cat.as_ordered(inplace=inplace)
+    with expect_warning_if(inplace, FutureWarning):
+        cd_sr_1 = cd_sr.cat.as_ordered(inplace=inplace)
+    if inplace:
+        pd_sr_1 = pd_sr
+        cd_sr_1 = cd_sr
+
+    assert cd_sr_1.cat.ordered is True
+    assert cd_sr_1.cat.ordered == pd_sr_1.cat.ordered
+    assert str(cd_sr_1) == str(pd_sr_1)
+
+
+@pytest.mark.parametrize("inplace", [True, False])
+def test_categorical_as_unordered(pd_str_cat, inplace):
+
+    pd_sr = pd.Series(pd_str_cat.copy().set_ordered(True))
+    cd_sr = cudf.Series(pd_str_cat.copy().set_ordered(True))
+
+    assert cd_sr.cat.ordered is True
+    assert cd_sr.cat.ordered == pd_sr.cat.ordered
+
+    # pandas internally uses a deprecated call to set_ordered(inplace=inplace)
+    # inside as_unordered.
+    with pytest.warns(FutureWarning):
+        pd_sr_1 = pd_sr.cat.as_unordered(inplace=inplace)
+    with expect_warning_if(inplace, FutureWarning):
+        cd_sr_1 = cd_sr.cat.as_unordered(inplace=inplace)
+    if inplace:
+        pd_sr_1 = pd_sr
+        cd_sr_1 = cd_sr
+
+    assert cd_sr_1.cat.ordered is False
+    assert cd_sr_1.cat.ordered == pd_sr_1.cat.ordered
+    assert str(cd_sr_1) == str(pd_sr_1)
+
+
+@pytest.mark.parametrize("from_ordered", [True, False])
+@pytest.mark.parametrize("to_ordered", [True, False])
+@pytest.mark.parametrize(
+    "inplace",
+    [
+        pytest.param(
+            True,
+            marks=pytest.mark.skipif(
+                condition=not PANDAS_GE_134,
+                reason="https://github.com/pandas-dev/pandas/issues/43232",
+            ),
+        ),
+        False,
+    ],
+)
+def test_categorical_reorder_categories(
+    pd_str_cat, from_ordered, to_ordered, inplace
+):
+
+    pd_sr = pd.Series(pd_str_cat.copy().set_ordered(from_ordered))
+    cd_sr = cudf.Series(pd_str_cat.copy().set_ordered(from_ordered))
+
+    assert_eq(pd_sr, cd_sr)
+
+    assert str(pd_sr) == str(cd_sr)
+
+    kwargs = dict(ordered=to_ordered, inplace=inplace)
+
+    with _hide_deprecated_pandas_categorical_inplace_warnings(
+        "reorder_categories"
+    ):
+        pd_sr_1 = pd_sr.cat.reorder_categories(list("cba"), **kwargs)
+    if inplace:
+        with pytest.warns(FutureWarning):
+            cd_sr_1 = cd_sr.cat.reorder_categories(list("cba"), **kwargs)
+        pd_sr_1 = pd_sr
+        cd_sr_1 = cd_sr
+    else:
+        cd_sr_1 = cd_sr.cat.reorder_categories(list("cba"), **kwargs)
+
+    assert_eq(pd_sr_1, cd_sr_1)
+
+    assert str(cd_sr_1) == str(pd_sr_1)
+
+
+@pytest.mark.parametrize(
+    "inplace",
+    [
+        pytest.param(
+            True,
+            marks=pytest.mark.skipif(
+                condition=not PANDAS_GE_134,
+                reason="https://github.com/pandas-dev/pandas/issues/43232",
+            ),
+        ),
+        False,
+    ],
+)
+def test_categorical_add_categories(pd_str_cat, inplace):
+
+    pd_sr = pd.Series(pd_str_cat.copy())
+    cd_sr = cudf.Series(pd_str_cat.copy())
+
+    assert_eq(pd_sr, cd_sr)
+
+    assert str(pd_sr) == str(cd_sr)
+
+    with _hide_deprecated_pandas_categorical_inplace_warnings(
+        "add_categories"
+    ):
+        pd_sr_1 = pd_sr.cat.add_categories(["d"], inplace=inplace)
+
+    if inplace:
+        with pytest.warns(FutureWarning):
+            cd_sr_1 = cd_sr.cat.add_categories(["d"], inplace=inplace)
+        pd_sr_1 = pd_sr
+        cd_sr_1 = cd_sr
+    else:
+        cd_sr_1 = cd_sr.cat.add_categories(["d"], inplace=inplace)
+
+    assert "d" in pd_sr_1.cat.categories.to_list()
+    assert "d" in cd_sr_1.cat.categories.to_pandas().to_list()
+
+    assert_eq(pd_sr_1, cd_sr_1)
+
+
+@pytest.mark.parametrize(
+    "inplace",
+    [
+        pytest.param(
+            True,
+            marks=pytest.mark.skipif(
+                condition=not PANDAS_GE_134,
+                reason="https://github.com/pandas-dev/pandas/issues/43232",
+            ),
+        ),
+        False,
+    ],
+)
+def test_categorical_remove_categories(pd_str_cat, inplace):
+
+    pd_sr = pd.Series(pd_str_cat.copy())
+    cd_sr = cudf.Series(pd_str_cat.copy())
+
+    assert_eq(pd_sr, cd_sr)
+
+    assert str(pd_sr) == str(cd_sr)
+
+    with _hide_deprecated_pandas_categorical_inplace_warnings(
+        "remove_categories"
+    ):
+        pd_sr_1 = pd_sr.cat.remove_categories(["a"], inplace=inplace)
+
+    if inplace:
+        with pytest.warns(FutureWarning):
+            cd_sr_1 = cd_sr.cat.remove_categories(["a"], inplace=inplace)
+        pd_sr_1 = pd_sr
+        cd_sr_1 = cd_sr
+    else:
+        cd_sr_1 = cd_sr.cat.remove_categories(["a"], inplace=inplace)
+
+    assert "a" not in pd_sr_1.cat.categories.to_list()
+    assert "a" not in cd_sr_1.cat.categories.to_pandas().to_list()
+
+    assert_eq(pd_sr_1, cd_sr_1)
+
+    # test using ordered operators
+    with _hide_deprecated_pandas_categorical_inplace_warnings(
+        "remove_categories"
+    ) as _, pytest.warns(FutureWarning) as _:
+        assert_exceptions_equal(
+            lfunc=cd_sr.to_pandas().cat.remove_categories,
+            rfunc=cd_sr.cat.remove_categories,
+            lfunc_args_and_kwargs=([["a", "d"]], {"inplace": inplace}),
+            rfunc_args_and_kwargs=([["a", "d"]], {"inplace": inplace}),
+        )
+
+
+def test_categorical_dataframe_slice_copy():
+    pdf = pd.DataFrame({"g": pd.Series(["a", "b", "z"], dtype="category")})
+    gdf = cudf.from_pandas(pdf)
+
+    exp = pdf[1:].copy()
+    gdf = gdf[1:].copy()
+
+    assert_eq(exp, gdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([1, 2, 3, 89]),
+        pd.Series([1, 2, 3, 89, 3, 1, 89], dtype="category"),
+        pd.Series(["1", "2", "3", "4", "5"], dtype="category"),
+        pd.Series(["1.0", "2.5", "3.001", "9"], dtype="category"),
+        pd.Series(["1", "2", "3", None, "4", "5"], dtype="category"),
+        pd.Series(["1.0", "2.5", "3.001", None, "9"], dtype="category"),
+        pd.Series(["a", "b", "c", "c", "b", "a", "b", "b"]),
+        pd.Series(["aa", "b", "c", "c", "bb", "bb", "a", "b", "b"]),
+        pd.Series([1, 2, 3, 89, None, np.nan, np.NaN], dtype="float64"),
+        pd.Series([1, 2, 3, 89], dtype="float64"),
+        pd.Series([1, 2.5, 3.001, 89], dtype="float64"),
+        pd.Series([None, None, None]),
+        pd.Series([], dtype="float64"),
+    ],
+)
+@pytest.mark.parametrize(
+    "cat_type",
+    [
+        pd.CategoricalDtype(categories=["aa", "bb", "cc"]),
+        pd.CategoricalDtype(categories=[2, 4, 10, 100]),
+        pd.CategoricalDtype(categories=["aa", "bb", "c"]),
+        pd.CategoricalDtype(categories=["a", "bb", "c"]),
+        pd.CategoricalDtype(categories=["a", "b", "c"]),
+        pd.CategoricalDtype(categories=["1", "2", "3", "4"]),
+        pd.CategoricalDtype(categories=["1.0", "2.5", "3.001", "9"]),
+        pd.CategoricalDtype(categories=[]),
+    ],
+)
+def test_categorical_typecast(data, cat_type):
+    pd_data = data.copy()
+    gd_data = cudf.from_pandas(data)
+
+    assert_eq(pd_data.astype(cat_type), gd_data.astype(cat_type))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([1, 2, 3, 89]),
+        pd.Series(["a", "b", "c", "c", "b", "a", "b", "b"]),
+        pd.Series(["aa", "b", "c", "c", "bb", "bb", "a", "b", "b"]),
+        pd.Series([1, 2, 3, 89, None, np.nan, np.NaN], dtype="float64"),
+        pd.Series([1, 2, 3, 89], dtype="float64"),
+        pd.Series([1, 2.5, 3.001, 89], dtype="float64"),
+        pd.Series([None, None, None]),
+        pd.Series([], dtype="float64"),
+    ],
+)
+@pytest.mark.parametrize(
+    "new_categories",
+    [
+        ["aa", "bb", "cc"],
+        [2, 4, 10, 100],
+        ["aa", "bb", "c"],
+        ["a", "bb", "c"],
+        ["a", "b", "c"],
+        [],
+        pd.Series(["a", "b", "c"]),
+        pd.Series(["a", "b", "c"], dtype="category"),
+        pd.Series([-100, 10, 11, 0, 1, 2], dtype="category"),
+    ],
+)
+def test_categorical_set_categories_categoricals(data, new_categories):
+    pd_data = data.copy().astype("category")
+    gd_data = cudf.from_pandas(pd_data)
+
+    expected = pd_data.cat.set_categories(new_categories=new_categories)
+    with _hide_cudf_safe_casting_warning():
+        actual = gd_data.cat.set_categories(new_categories=new_categories)
+
+    assert_eq(expected, actual)
+
+    expected = pd_data.cat.set_categories(
+        new_categories=pd.Series(new_categories, dtype="category")
+    )
+    with _hide_cudf_safe_casting_warning():
+        actual = gd_data.cat.set_categories(
+            new_categories=cudf.Series(new_categories, dtype="category")
+        )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4],
+        ["a", "1", "2", "1", "a"],
+        pd.Series(["a", "1", "22", "1", "aa"]),
+        pd.Series(["a", "1", "22", "1", "aa"], dtype="category"),
+        pd.Series([1, 2, 3, -4], dtype="int64"),
+        pd.Series([1, 2, 3, 4], dtype="uint64"),
+        pd.Series([1, 2.3, 3, 4], dtype="float"),
+        np.asarray([0, 2, 1]),
+        [None, 1, None, 2, None],
+        [],
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        pd.CategoricalDtype(categories=["aa", "bb", "cc"]),
+        pd.CategoricalDtype(categories=[2, 4, 10, 100]),
+        pd.CategoricalDtype(categories=["aa", "bb", "c"]),
+        pd.CategoricalDtype(categories=["a", "bb", "c"]),
+        pd.CategoricalDtype(categories=["a", "b", "c"]),
+        pd.CategoricalDtype(categories=["22", "b", "c"]),
+        pd.CategoricalDtype(categories=[]),
+    ],
+)
+def test_categorical_creation(data, dtype):
+    expected = pd.Series(data, dtype=dtype)
+    got = cudf.Series(data, dtype=dtype)
+    assert_eq(expected, got)
+
+    got = cudf.Series(data, dtype=cudf.from_pandas(dtype))
+    assert_eq(expected, got)
+
+    expected = pd.Series(data, dtype="category")
+    got = cudf.Series(data, dtype="category")
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "categories",
+    [
+        [],
+        [1, 2, 3],
+        pd.Series(["a", "c", "b"], dtype="category"),
+        pd.Series([1, 2, 3, 4, -100], dtype="category"),
+    ],
+)
+@pytest.mark.parametrize("ordered", [True, False])
+def test_categorical_dtype(categories, ordered):
+    expected = pd.CategoricalDtype(categories=categories, ordered=ordered)
+    got = cudf.CategoricalDtype(categories=categories, ordered=ordered)
+    assert_eq(expected, got)
+
+    expected = pd.CategoricalDtype(categories=categories)
+    got = cudf.CategoricalDtype(categories=categories)
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    ("data", "expected"),
+    [
+        (cudf.Series([1]), np.uint8),
+        (cudf.Series([1, None]), np.uint8),
+        (cudf.Series(np.arange(np.iinfo(np.int8).max)), np.uint8),
+        (
+            cudf.Series(np.append(np.arange(np.iinfo(np.int8).max), [None])),
+            np.uint8,
+        ),
+        (cudf.Series(np.arange(np.iinfo(np.int16).max)), np.uint16),
+        (
+            cudf.Series(np.append(np.arange(np.iinfo(np.int16).max), [None])),
+            np.uint16,
+        ),
+        (cudf.Series(np.arange(np.iinfo(np.uint8).max)), np.uint8),
+        (
+            cudf.Series(np.append(np.arange(np.iinfo(np.uint8).max), [None])),
+            np.uint8,
+        ),
+        (cudf.Series(np.arange(np.iinfo(np.uint16).max)), np.uint16),
+        (
+            cudf.Series(np.append(np.arange(np.iinfo(np.uint16).max), [None])),
+            np.uint16,
+        ),
+    ],
+)
+def test_astype_dtype(data, expected):
+    got = data.astype("category").cat.codes.dtype
+    np.testing.assert_equal(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data,add",
+    [
+        ([1, 2, 3], [100, 11, 12]),
+        ([1, 2, 3], [0.01, 9.7, 15.0]),
+        ([0.0, 6.7, 10.0], [100, 11, 12]),
+        ([0.0, 6.7, 10.0], [0.01, 9.7, 15.0]),
+        (["a", "bd", "ef"], ["asdfsdf", "bddf", "eff"]),
+        ([1, 2, 3], []),
+        ([0.0, 6.7, 10.0], []),
+        (["a", "bd", "ef"], []),
+    ],
+)
+def test_add_categories(data, add):
+    pds = pd.Series(data, dtype="category")
+    gds = cudf.Series(data, dtype="category")
+
+    expected = pds.cat.add_categories(add)
+    with _hide_cudf_safe_casting_warning():
+        actual = gds.cat.add_categories(add)
+
+    assert_eq(
+        expected.cat.codes, actual.cat.codes.astype(expected.cat.codes.dtype)
+    )
+
+    # Need to type-cast pandas object to str due to mixed-type
+    # support in "object"
+    assert_eq(
+        expected.cat.categories.astype("str")
+        if (expected.cat.categories.dtype == "object")
+        else expected.cat.categories,
+        actual.cat.categories,
+    )
+
+
+@pytest.mark.parametrize(
+    "data,add",
+    [
+        ([1, 2, 3], [1, 3, 11]),
+        ([0.0, 6.7, 10.0], [1, 2, 0.0]),
+        (["a", "bd", "ef"], ["a", "bd", "a"]),
+    ],
+)
+def test_add_categories_error(data, add):
+    pds = pd.Series(data, dtype="category")
+    gds = cudf.Series(data, dtype="category")
+
+    assert_exceptions_equal(
+        pds.cat.add_categories,
+        gds.cat.add_categories,
+        ([add],),
+        ([add],),
+    )
+
+
+def test_add_categories_mixed_error():
+    gds = cudf.Series(["a", "bd", "ef"], dtype="category")
+
+    with pytest.raises(TypeError):
+        gds.cat.add_categories([1, 2, 3])
+
+    gds = cudf.Series([1, 2, 3], dtype="category")
+
+    with pytest.raises(TypeError):
+        gds.cat.add_categories(["a", "bd", "ef"])
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4],
+        ["a", "1", "2", "1", "a"],
+        pd.Series(["a", "1", "22", "1", "aa"]),
+        pd.Series(["a", "1", "22", "1", "aa"], dtype="category"),
+        pd.Series([1, 2, 3, 4], dtype="int64"),
+        pd.Series([1, 2.3, 3, 4], dtype="float"),
+        [None, 1, None, 2, None],
+        ["a"],
+    ],
+)
+@pytest.mark.parametrize(
+    "cat_dtype",
+    [
+        pd.CategoricalDtype(categories=["aa", "bb", "cc"]),
+        pd.CategoricalDtype(categories=[2, 4, 10, 100]),
+        pd.CategoricalDtype(categories=["aa", "bb", "c"]),
+        pd.CategoricalDtype(categories=["a", "bb", "c"]),
+        pd.CategoricalDtype(categories=["a", "b", "c"]),
+        pd.CategoricalDtype(categories=["22", "b", "c"]),
+        pd.CategoricalDtype(categories=["a"]),
+    ],
+)
+def test_categorical_assignment(data, cat_dtype):
+    pd_df = pd.DataFrame()
+    pd_df["a"] = np.ones(len(data))
+    cd_df = cudf.from_pandas(pd_df)
+
+    pd_cat_series = pd.Series(data, dtype=cat_dtype)
+    # assign categorical series
+    pd_df.assign(cat_col=pd_cat_series)
+    cd_df.assign(cat_col=pd_cat_series)
+    assert_eq(pd_df, cd_df)
+
+    # assign categorical array
+    # needed for dask_cudf support for including file name
+    # as a categorical column
+    # see issue: https://github.com/rapidsai/cudf/issues/2269
+    pd_df = pd.DataFrame()
+    pd_df["a"] = np.ones(len(data))
+    cd_df = cudf.from_pandas(pd_df)
+
+    pd_categorical = pd.Categorical(data, dtype=cat_dtype)
+    pd_df.assign(cat_col=pd_categorical)
+    cd_df.assign(cat_col=pd_categorical)
+    assert_eq(pd_df, cd_df)
+
+
+def test_categorical_allow_nan():
+    gs = cudf.Series([1, 2, np.nan, 10, np.nan, None], nan_as_null=False)
+    gs = gs.astype("category")
+    expected_codes = cudf.Series([0, 1, 3, 2, 3, None], dtype="uint8")
+    assert_eq(expected_codes, gs.cat.codes)
+
+    expected_categories = cudf.Index([1.0, 2.0, 10.0, np.nan], dtype="float64")
+    assert_eq(expected_categories, gs.cat.categories)
+
+    actual_ps = gs.to_pandas()
+    expected_ps = pd.Series(
+        [1.0, 2.0, np.nan, 10.0, np.nan, np.nan], dtype="category"
+    )
+    assert_eq(actual_ps, expected_ps)
+
+
+def test_categorical_setitem_with_nan():
+    gs = cudf.Series(
+        [1, 2, np.nan, 10, np.nan, None], nan_as_null=False
+    ).astype("category")
+    gs[[1, 3]] = np.nan
+
+    expected_series = cudf.Series(
+        [1, np.nan, np.nan, np.nan, np.nan, None], nan_as_null=False
+    ).astype(gs.dtype)
+    assert_eq(gs, expected_series)
+
+
+@pytest.mark.parametrize("dtype", list(NUMERIC_TYPES) + ["object"])
+@pytest.mark.parametrize("input_obj", [[1, cudf.NA, 3]])
+def test_series_construction_with_nulls(input_obj, dtype):
+    dtype = cudf.dtype(dtype)
+    input_obj = [
+        dtype.type(v) if v is not cudf.NA else cudf.NA for v in input_obj
+    ]
+
+    expect = pd.Series(input_obj, dtype="category")
+    got = cudf.Series(input_obj, dtype="category").to_pandas()
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": cudf.Series(["a", "b", "c", "a", "c", "b"]).astype("category")},
+        {
+            "a": cudf.Series(["a", "a", "b", "b"]).astype("category"),
+            "b": cudf.Series(["b", "b", "c", "c"]).astype("category"),
+            "c": cudf.Series(["c", "c", "a", "a"]).astype("category"),
+        },
+        {
+            "a": cudf.Series(["a", None, "b", "b"]).astype("category"),
+            "b": cudf.Series(["b", "b", None, "c"]).astype("category"),
+            "c": cudf.Series(["c", "c", "a", None]).astype("category"),
+        },
+    ],
+)
+def test_serialize_categorical_columns(data):
+    df = cudf.DataFrame(data)
+    recreated = df.__class__.deserialize(*df.serialize())
+    assert_eq(recreated, df)
+
+
+@pytest.mark.parametrize(
+    "data", [["$ 1", "$ 2", "hello"], ["($) 1", "( 2", "hello", "^1$"]]
+)
+@pytest.mark.parametrize("value", ["$ 1", "hello", "$", "^1$"])
+def test_categorical_string_index_contains(data, value):
+    idx = cudf.CategoricalIndex(data)
+    pidx = idx.to_pandas()
+
+    assert_eq(value in idx, value in pidx)
+
+
+def test_categorical_index_with_dtype():
+    dtype = cudf.CategoricalDtype(categories=["a", "z", "c"])
+    gi = cudf.Index(["z", "c", "a"], dtype=dtype)
+    pi = pd.Index(["z", "c", "a"], dtype=dtype.to_pandas())
+
+    assert_eq(gi, pi)
+    assert_eq(gi.dtype, pi.dtype)
+    assert_eq(gi.dtype.categories, pi.dtype.categories)
+
+
+def test_cat_iterate_error():
+    s = cudf.Series([1, 2, 3], dtype="category")
+    with pytest.raises(TypeError):
+        iter(s.cat)
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_empty_series_category_cast(ordered):
+    dtype = cudf.CategoricalDtype(ordered=ordered)
+    ps = pd.Series([], dtype="str")
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.astype(dtype.to_pandas())
+    actual = gs.astype(dtype)
+
+    assert_eq(expected, actual)
+    assert_eq(expected.dtype.ordered, actual.dtype.ordered)
diff --git a/python/cudf/cudf/tests/test_column.py b/python/cudf/cudf/tests/test_column.py
new file mode 100644
index 0000000..db0446d
--- /dev/null
+++ b/python/cudf/cudf/tests/test_column.py
@@ -0,0 +1,551 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf._lib.transform import mask_to_bools
+from cudf.core.column.column import as_column
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+from cudf.utils import dtypes as dtypeutils
+
+dtypes = sorted(
+    list(
+        dtypeutils.ALL_TYPES
+        - {
+            "datetime64[s]",
+            "datetime64[ms]",
+            "datetime64[us]",
+            "timedelta64[s]",
+            "timedelta64[ms]",
+            "timedelta64[us]",
+        }
+    )
+)
+
+
+@pytest.fixture(params=dtypes, ids=dtypes)
+def pandas_input(request):
+    dtype = request.param
+    rng = np.random.default_rng()
+    size = 100
+
+    def random_ints(dtype, size):
+        dtype_min = np.iinfo(dtype).min
+        dtype_max = np.iinfo(dtype).max
+        return rng.integers(dtype_min, dtype_max, size=size, dtype=dtype)
+
+    try:
+        dtype = np.dtype(dtype)
+    except TypeError:
+        if dtype == "category":
+            data = random_ints(np.int64, size)
+        else:
+            raise
+    else:
+        if dtype.kind == "b":
+            data = rng.choice([False, True], size=size)
+        elif dtype.kind in ("m", "M"):
+            # datetime or timedelta
+            data = random_ints(np.int64, size)
+        elif dtype.kind == "U":
+            # Unicode strings of integers like "12345"
+            data = random_ints(np.int64, size).astype(dtype.str)
+        elif dtype.kind == "f":
+            # floats in [0.0, 1.0)
+            data = rng.random(size=size, dtype=dtype)
+        else:
+            data = random_ints(dtype, size)
+    return pd.Series(data, dtype=dtype)
+
+
+def str_host_view(list_of_str, to_dtype):
+    return np.concatenate(
+        [np.frombuffer(s.encode("utf-8"), dtype=to_dtype) for s in list_of_str]
+    )
+
+
+@pytest.mark.parametrize("offset", [0, 1, 15])
+@pytest.mark.parametrize("size", [50, 10, 0])
+def test_column_offset_and_size(pandas_input, offset, size):
+    col = cudf.core.column.as_column(pandas_input)
+    col = cudf.core.column.build_column(
+        data=col.base_data,
+        dtype=col.dtype,
+        mask=col.base_mask,
+        size=size,
+        offset=offset,
+        children=col.base_children,
+    )
+
+    if cudf.api.types.is_categorical_dtype(col.dtype):
+        assert col.size == col.codes.size
+        assert col.size == (col.codes.data.size / col.codes.dtype.itemsize)
+    elif cudf.api.types.is_string_dtype(col.dtype):
+        if col.size > 0:
+            assert col.size == (col.children[0].size - 1)
+            assert col.size == (
+                (col.children[0].data.size / col.children[0].dtype.itemsize)
+                - 1
+            )
+    else:
+        assert col.size == (col.data.size / col.dtype.itemsize)
+
+    got = cudf.Series(col)
+
+    if offset is None:
+        offset = 0
+    if size is None:
+        size = 100
+    else:
+        size = size + offset
+
+    slicer = slice(offset, size)
+    expect = pandas_input.iloc[slicer].reset_index(drop=True)
+
+    assert_eq(expect, got)
+
+
+def column_slicing_test(col, offset, size, cast_to_float=False):
+    col_slice = col.slice(offset, offset + size)
+    series = cudf.Series(col)
+    sliced_series = cudf.Series(col_slice)
+
+    if cast_to_float:
+        pd_series = series.astype(float).to_pandas()
+        sliced_series = sliced_series.astype(float)
+    else:
+        pd_series = series.to_pandas()
+
+    if cudf.api.types.is_categorical_dtype(col.dtype):
+        # The cudf.Series is constructed from an already sliced column, whereas
+        # the pandas.Series is constructed from the unsliced series and then
+        # sliced, so the indexes should be different and we must ignore it.
+        # However, we must compare these as frames, not raw arrays,  because
+        # numpy comparison of categorical values won't work.
+        assert_eq(
+            pd_series[offset : offset + size].reset_index(drop=True),
+            sliced_series.reset_index(drop=True),
+        )
+    else:
+        assert_eq(
+            np.asarray(pd_series[offset : offset + size]),
+            sliced_series.to_numpy(),
+        )
+
+
+@pytest.mark.parametrize("offset", [0, 1, 15])
+@pytest.mark.parametrize("size", [50, 10, 0])
+def test_column_slicing(pandas_input, offset, size):
+    col = cudf.core.column.as_column(pandas_input)
+    column_slicing_test(col, offset, size)
+
+
+@pytest.mark.parametrize("offset", [0, 1, 15])
+@pytest.mark.parametrize("size", [50, 10, 0])
+@pytest.mark.parametrize("precision", [2, 3, 5])
+@pytest.mark.parametrize("scale", [0, 1, 2])
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal128Dtype, cudf.Decimal64Dtype, cudf.Decimal32Dtype],
+)
+def test_decimal_column_slicing(offset, size, precision, scale, decimal_type):
+    col = cudf.core.column.as_column(pd.Series(np.random.rand(1000)))
+    col = col.astype(decimal_type(precision, scale))
+    column_slicing_test(col, offset, size, True)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.array([[23, 68, 2, 38, 9, 83, 72, 6, 98, 30]]),
+        np.array([[1, 2], [7, 6]]),
+    ],
+)
+def test_column_series_multi_dim(data):
+    with pytest.raises(ValueError):
+        cudf.Series(data)
+
+    with pytest.raises(ValueError):
+        cudf.core.column.as_column(data)
+
+
+@pytest.mark.parametrize(
+    ("data", "error"),
+    [
+        ([1, "1.0", "2", -3], pa.lib.ArrowInvalid),
+        ([np.nan, 0, "null", cp.nan], pa.lib.ArrowInvalid),
+        (
+            [np.int32(4), np.float64(1.5), np.float32(1.290994), np.int8(0)],
+            None,
+        ),
+    ],
+)
+def test_column_mixed_dtype(data, error):
+    if error is None:
+        cudf.Series(data)
+    else:
+        with pytest.raises(error):
+            cudf.Series(data)
+
+
+@pytest.mark.parametrize("nan_as_null", [True, False])
+def test_as_column_scalar_with_nan(nan_as_null):
+    size = 10
+    scalar = np.nan
+
+    expected = (
+        cudf.Series([np.nan] * size, nan_as_null=nan_as_null)
+        .dropna()
+        .to_numpy()
+    )
+
+    got = (
+        cudf.Series(as_column(scalar, length=size, nan_as_null=nan_as_null))
+        .dropna()
+        .to_numpy()
+    )
+
+    np.testing.assert_equal(expected, got)
+
+
+@pytest.mark.parametrize("data", [[1.1, 2.2, 3.3, 4.4], [1, 2, 3, 4]])
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+def test_column_series_cuda_array_dtype(data, dtype):
+    psr = pd.Series(np.asarray(data), dtype=dtype)
+    sr = cudf.Series(cp.asarray(data), dtype=dtype)
+
+    assert_eq(psr, sr)
+
+    psr = pd.Series(data, dtype=dtype)
+    sr = cudf.Series(data, dtype=dtype)
+
+    assert_eq(psr, sr)
+
+
+def test_column_zero_length_slice():
+    # see https://github.com/rapidsai/cudf/pull/4777
+    from numba import cuda
+
+    x = cudf.DataFrame({"a": [1]})
+    the_column = x[1:]["a"]._column
+
+    expect = np.array([], dtype="int8")
+    got = cuda.as_cuda_array(the_column.data).copy_to_host()
+
+    np.testing.assert_array_equal(expect, got)
+
+
+def test_column_chunked_array_creation():
+    pyarrow_array = pa.array([1, 2, 3] * 1000)
+    chunked_array = pa.chunked_array(pyarrow_array)
+
+    actual_column = cudf.core.column.as_column(chunked_array, dtype="float")
+    expected_column = cudf.core.column.as_column(pyarrow_array, dtype="float")
+
+    assert_eq(cudf.Series(actual_column), cudf.Series(expected_column))
+
+    actual_column = cudf.core.column.as_column(chunked_array)
+    expected_column = cudf.core.column.as_column(pyarrow_array)
+
+    assert_eq(cudf.Series(actual_column), cudf.Series(expected_column))
+
+
+@pytest.mark.parametrize(
+    "data,from_dtype,to_dtype",
+    [
+        # equal size different kind
+        (np.arange(3), "int64", "float64"),
+        (np.arange(3), "float32", "int32"),
+        (np.arange(1), "int64", "datetime64[ns]"),
+        # size / 2^n should work for all n
+        (np.arange(3), "int64", "int32"),
+        (np.arange(3), "int64", "int16"),
+        (np.arange(3), "int64", "int8"),
+        (np.arange(3), "float64", "float32"),
+        # evenly divides into bigger type
+        (np.arange(8), "int8", "int64"),
+        (np.arange(16), "int8", "int64"),
+        (np.arange(128), "int8", "int64"),
+        (np.arange(2), "float32", "int64"),
+        (np.arange(8), "int8", "datetime64[ns]"),
+        (np.arange(16), "int8", "datetime64[ns]"),
+    ],
+)
+def test_column_view_valid_numeric_to_numeric(data, from_dtype, to_dtype):
+    cpu_data = np.asarray(data, dtype=from_dtype)
+    gpu_data = as_column(data, dtype=from_dtype)
+
+    cpu_data_view = cpu_data.view(to_dtype)
+    gpu_data_view = gpu_data.view(to_dtype)
+
+    expect = pd.Series(cpu_data_view, dtype=cpu_data_view.dtype)
+    got = cudf.Series(gpu_data_view, dtype=gpu_data_view.dtype)
+
+    gpu_ptr = gpu_data.data.get_ptr(mode="read")
+    assert gpu_ptr == got._column.data.get_ptr(mode="read")
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data,from_dtype,to_dtype",
+    [
+        (np.arange(9), "int8", "int64"),
+        (np.arange(3), "int8", "int16"),
+        (np.arange(6), "int8", "float32"),
+        (np.arange(1), "int8", "datetime64[ns]"),
+    ],
+)
+def test_column_view_invalid_numeric_to_numeric(data, from_dtype, to_dtype):
+    cpu_data = np.asarray(data, dtype=from_dtype)
+    gpu_data = as_column(data, dtype=from_dtype)
+
+    assert_exceptions_equal(
+        lfunc=cpu_data.view,
+        rfunc=gpu_data.view,
+        lfunc_args_and_kwargs=([to_dtype],),
+        rfunc_args_and_kwargs=([to_dtype],),
+    )
+
+
+@pytest.mark.parametrize(
+    "data,to_dtype",
+    [
+        (["a", "b", "c"], "int8"),
+        (["ab"], "int8"),
+        (["ab"], "int16"),
+        (["a", "ab", "a"], "int8"),
+        (["abcd", "efgh"], "float32"),
+        (["abcdefgh"], "datetime64[ns]"),
+    ],
+)
+def test_column_view_valid_string_to_numeric(data, to_dtype):
+    expect = cudf.Series(cudf.Series(data)._column.view(to_dtype))
+    got = cudf.Series(str_host_view(data, to_dtype))
+
+    assert_eq(expect, got)
+
+
+def test_column_view_nulls_widths_even():
+
+    data = [1, 2, None, 4, None]
+    expect_data = [
+        np.int32(val).view("float32") if val is not None else np.nan
+        for val in data
+    ]
+
+    sr = cudf.Series(data, dtype="int32")
+    expect = cudf.Series(expect_data, dtype="float32")
+    got = cudf.Series(sr._column.view("float32"))
+
+    assert_eq(expect, got)
+
+    data = [None, 2.1, None, 5.3, 8.8]
+    expect_data = [
+        np.float64(val).view("int64") if val is not None else val
+        for val in data
+    ]
+
+    sr = cudf.Series(data, dtype="float64")
+    expect = cudf.Series(expect_data, dtype="int64")
+    got = cudf.Series(sr._column.view("int64"))
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("slc", [slice(1, 5), slice(0, 4), slice(2, 4)])
+def test_column_view_numeric_slice(slc):
+
+    data = np.array([1, 2, 3, 4, 5], dtype="int32")
+    sr = cudf.Series(data)
+
+    expect = cudf.Series(data[slc].view("int64"))
+    got = cudf.Series(sr._column.slice(slc.start, slc.stop).view("int64"))
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "slc", [slice(3, 5), slice(0, 4), slice(2, 5), slice(1, 3)]
+)
+def test_column_view_string_slice(slc):
+    data = ["a", "bcde", "cd", "efg", "h"]
+
+    expect = cudf.Series(
+        cudf.Series(data)._column.slice(slc.start, slc.stop).view("int8")
+    )
+    got = cudf.Series(str_host_view(data[slc], "int8"))
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data,expected",
+    [
+        (
+            np.array([1, 2, 3, 4, 5], dtype="uint8"),
+            cudf.core.column.as_column([1, 2, 3, 4, 5], dtype="uint8"),
+        ),
+        (
+            cp.array([1, 2, 3, 4, 5], dtype="uint8"),
+            cudf.core.column.as_column([1, 2, 3, 4, 5], dtype="uint8"),
+        ),
+        (
+            cp.array([], dtype="uint8"),
+            cudf.core.column.as_column([], dtype="uint8"),
+        ),
+        (
+            cp.array([255], dtype="uint8"),
+            cudf.core.column.as_column([255], dtype="uint8"),
+        ),
+    ],
+)
+def test_as_column_buffer(data, expected):
+    actual_column = cudf.core.column.as_column(
+        cudf.core.buffer.as_buffer(data), dtype=data.dtype
+    )
+    assert_eq(cudf.Series(actual_column), cudf.Series(expected))
+
+
+@pytest.mark.parametrize(
+    "data,pyarrow_kwargs,cudf_kwargs",
+    [
+        (
+            [100, 200, 300],
+            {"type": pa.decimal128(3)},
+            {"dtype": cudf.core.dtypes.Decimal128Dtype(3, 0)},
+        ),
+        (
+            [{"a": 1, "b": 3}, {"c": 2, "d": 4}],
+            {},
+            {},
+        ),
+        (
+            [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]],
+            {},
+            {},
+        ),
+    ],
+)
+def test_as_column_arrow_array(data, pyarrow_kwargs, cudf_kwargs):
+    pyarrow_data = pa.array(data, **pyarrow_kwargs)
+    cudf_from_pyarrow = as_column(pyarrow_data)
+    expected = as_column(data, **cudf_kwargs)
+    assert_eq(cudf.Series(cudf_from_pyarrow), cudf.Series(expected))
+
+
+@pytest.mark.parametrize(
+    "pd_dtype,expect_dtype",
+    [
+        # TODO: Nullable float is coming
+        (pd.StringDtype(), np.dtype("O")),
+        (pd.UInt8Dtype(), np.dtype("uint8")),
+        (pd.UInt16Dtype(), np.dtype("uint16")),
+        (pd.UInt32Dtype(), np.dtype("uint32")),
+        (pd.UInt64Dtype(), np.dtype("uint64")),
+        (pd.Int8Dtype(), np.dtype("int8")),
+        (pd.Int16Dtype(), np.dtype("int16")),
+        (pd.Int32Dtype(), np.dtype("int32")),
+        (pd.Int64Dtype(), np.dtype("int64")),
+        (pd.BooleanDtype(), np.dtype("bool")),
+    ],
+)
+def test_build_df_from_nullable_pandas_dtype(pd_dtype, expect_dtype):
+    if pd_dtype == pd.StringDtype():
+        data = ["a", pd.NA, "c", pd.NA, "e"]
+    elif pd_dtype == pd.BooleanDtype():
+        data = [True, pd.NA, False, pd.NA, True]
+    else:
+        data = [1, pd.NA, 3, pd.NA, 5]
+
+    pd_data = pd.DataFrame.from_dict({"a": data}, dtype=pd_dtype)
+    gd_data = cudf.DataFrame.from_pandas(pd_data)
+
+    assert gd_data["a"].dtype == expect_dtype
+
+    # check mask
+    expect_mask = [x is not pd.NA for x in pd_data["a"]]
+    got_mask = mask_to_bools(
+        gd_data["a"]._column.base_mask, 0, len(gd_data)
+    ).values_host
+
+    np.testing.assert_array_equal(expect_mask, got_mask)
+
+
+@pytest.mark.parametrize(
+    "pd_dtype,expect_dtype",
+    [
+        # TODO: Nullable float is coming
+        (pd.StringDtype(), np.dtype("O")),
+        (pd.UInt8Dtype(), np.dtype("uint8")),
+        (pd.UInt16Dtype(), np.dtype("uint16")),
+        (pd.UInt32Dtype(), np.dtype("uint32")),
+        (pd.UInt64Dtype(), np.dtype("uint64")),
+        (pd.Int8Dtype(), np.dtype("int8")),
+        (pd.Int16Dtype(), np.dtype("int16")),
+        (pd.Int32Dtype(), np.dtype("int32")),
+        (pd.Int64Dtype(), np.dtype("int64")),
+        (pd.BooleanDtype(), np.dtype("bool")),
+    ],
+)
+def test_build_series_from_nullable_pandas_dtype(pd_dtype, expect_dtype):
+    if pd_dtype == pd.StringDtype():
+        data = ["a", pd.NA, "c", pd.NA, "e"]
+    elif pd_dtype == pd.BooleanDtype():
+        data = [True, pd.NA, False, pd.NA, True]
+    else:
+        data = [1, pd.NA, 3, pd.NA, 5]
+
+    pd_data = pd.Series(data, dtype=pd_dtype)
+    gd_data = cudf.Series.from_pandas(pd_data)
+
+    assert gd_data.dtype == expect_dtype
+
+    # check mask
+    expect_mask = [x is not pd.NA for x in pd_data]
+    got_mask = mask_to_bools(
+        gd_data._column.base_mask, 0, len(gd_data)
+    ).values_host
+
+    np.testing.assert_array_equal(expect_mask, got_mask)
+
+
+def test_concatenate_large_column_strings():
+    num_strings = 1_000_000
+    string_scale_f = 100
+
+    s_1 = cudf.Series(["very long string " * string_scale_f] * num_strings)
+    s_2 = cudf.Series(["very long string " * string_scale_f] * num_strings)
+
+    with pytest.raises(OverflowError):
+        cudf.concat([s_1, s_2])
+
+
+@pytest.mark.parametrize(
+    "alias,expect_dtype",
+    [
+        ("UInt8", "uint8"),
+        ("UInt16", "uint16"),
+        ("UInt32", "uint32"),
+        ("UInt64", "uint64"),
+        ("Int8", "int8"),
+        ("Int16", "int16"),
+        ("Int32", "int32"),
+        ("Int64", "int64"),
+        ("boolean", "bool"),
+        ("Float32", "float32"),
+        ("Float64", "float64"),
+    ],
+)
+@pytest.mark.parametrize(
+    "data",
+    [[1, 2, 0]],
+)
+def test_astype_with_aliases(alias, expect_dtype, data):
+    pd_data = pd.Series(data)
+    gd_data = cudf.Series.from_pandas(pd_data)
+
+    assert_eq(pd_data.astype(expect_dtype), gd_data.astype(alias))
diff --git a/python/cudf/cudf/tests/test_column_accessor.py b/python/cudf/cudf/tests/test_column_accessor.py
new file mode 100644
index 0000000..99d4bdd
--- /dev/null
+++ b/python/cudf/cudf/tests/test_column_accessor.py
@@ -0,0 +1,287 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core.column_accessor import ColumnAccessor
+from cudf.testing._utils import assert_eq
+
+simple_test_data = [
+    {},
+    {"a": []},
+    {"a": [1]},
+    {"a": ["a"]},
+    {"a": [1, 2, 3], "b": ["a", "b", "c"]},
+]
+
+mi_test_data = [
+    {("a", "b"): [1, 2, 4], ("a", "c"): [2, 3, 4]},
+    {("a", "b"): [1, 2, 3], ("a", ""): [2, 3, 4]},
+    {("a", "b"): [1, 2, 4], ("c", "d"): [2, 3, 4]},
+    {("a", "b"): [1, 2, 3], ("a", "c"): [2, 3, 4], ("b", ""): [4, 5, 6]},
+]
+
+
+def check_ca_equal(lhs, rhs):
+    assert lhs.level_names == rhs.level_names
+    assert lhs.multiindex == rhs.multiindex
+    for l_key, r_key in zip(lhs, rhs):
+        assert l_key == r_key
+        assert_eq(lhs[l_key], rhs[r_key])
+
+
+@pytest.fixture(params=simple_test_data)
+def simple_data(request):
+    return request.param
+
+
+@pytest.fixture(params=mi_test_data)
+def mi_data(request):
+    return request.param
+
+
+@pytest.fixture(params=simple_test_data + mi_test_data)
+def all_data(request):
+    return request.param
+
+
+def test_to_pandas_simple(simple_data):
+    """
+    Test that a ColumnAccessor converts to a correct pd.Index
+    """
+    ca = ColumnAccessor(simple_data)
+    assert_eq(ca.to_pandas_index(), pd.DataFrame(simple_data).columns)
+
+
+def test_to_pandas_multiindex(mi_data):
+    ca = ColumnAccessor(mi_data, multiindex=True)
+    assert_eq(ca.to_pandas_index(), pd.DataFrame(mi_data).columns)
+
+
+def test_to_pandas_multiindex_names():
+    ca = ColumnAccessor(
+        {("a", "b"): [1, 2, 3], ("c", "d"): [3, 4, 5]},
+        multiindex=True,
+        level_names=("foo", "bar"),
+    )
+    assert_eq(
+        ca.to_pandas_index(),
+        pd.MultiIndex.from_tuples(
+            (("a", "b"), ("c", "d")), names=("foo", "bar")
+        ),
+    )
+
+
+def test_iter(simple_data):
+    """
+    Test that iterating over the CA
+    yields column names.
+    """
+    ca = ColumnAccessor(simple_data)
+    for expect_key, got_key in zip(simple_data, ca):
+        assert expect_key == got_key
+
+
+def test_all_columns(simple_data):
+    """
+    Test that all values of the CA are
+    columns.
+    """
+    ca = ColumnAccessor(simple_data)
+    for col in ca.values():
+        assert isinstance(col, cudf.core.column.ColumnBase)
+
+
+def test_column_size_mismatch():
+    """
+    Test that constructing a CA from columns of
+    differing sizes throws an error.
+    """
+    with pytest.raises(ValueError):
+        ColumnAccessor({"a": [1], "b": [1, 2]})
+
+
+def test_select_by_label_simple():
+    """
+    Test getting a column by label
+    """
+    ca = ColumnAccessor({"a": [1, 2, 3], "b": [2, 3, 4]})
+    check_ca_equal(ca.select_by_label("a"), ColumnAccessor({"a": [1, 2, 3]}))
+    check_ca_equal(ca.select_by_label("b"), ColumnAccessor({"b": [2, 3, 4]}))
+
+
+def test_select_by_label_multiindex():
+    """
+    Test getting column(s) by label with MultiIndex
+    """
+    ca = ColumnAccessor(
+        {
+            ("a", "b", "c"): [1, 2, 3],
+            ("a", "b", "e"): [2, 3, 4],
+            ("b", "x", ""): [4, 5, 6],
+            ("a", "d", "e"): [3, 4, 5],
+        },
+        multiindex=True,
+    )
+
+    expect = ColumnAccessor(
+        {("b", "c"): [1, 2, 3], ("b", "e"): [2, 3, 4], ("d", "e"): [3, 4, 5]},
+        multiindex=True,
+    )
+    got = ca.select_by_label("a")
+    check_ca_equal(expect, got)
+
+    expect = ColumnAccessor({"c": [1, 2, 3], "e": [2, 3, 4]}, multiindex=False)
+    got = ca.select_by_label(("a", "b"))
+    check_ca_equal(expect, got)
+
+    expect = ColumnAccessor(
+        {("b", "c"): [1, 2, 3], ("b", "e"): [2, 3, 4], ("d", "e"): [3, 4, 5]},
+        multiindex=True,
+    )
+    got = ca.select_by_label("a")
+    check_ca_equal(expect, got)
+
+    expect = ColumnAccessor({"c": [1, 2, 3], "e": [2, 3, 4]}, multiindex=False)
+    got = ca.select_by_label(("a", "b"))
+    check_ca_equal(expect, got)
+
+
+def test_select_by_label_simple_slice():
+    ca = ColumnAccessor({"a": [1, 2, 3], "b": [2, 3, 4], "c": [3, 4, 5]})
+    expect = ColumnAccessor({"b": [2, 3, 4], "c": [3, 4, 5]})
+    got = ca.select_by_label(slice("b", "c"))
+    check_ca_equal(expect, got)
+
+
+def test_select_by_label_multiindex_slice():
+    ca = ColumnAccessor(
+        {
+            ("a", "b", "c"): [1, 2, 3],
+            ("a", "b", "e"): [2, 3, 4],
+            ("a", "d", "e"): [3, 4, 5],
+            ("b", "x", ""): [4, 5, 6],
+        },
+        multiindex=True,
+    )  # pandas needs columns to be sorted to do slicing with multiindex
+    expect = ca
+    got = ca.select_by_label(slice(None, None))
+    check_ca_equal(expect, got)
+
+    expect = ColumnAccessor(
+        {
+            ("a", "b", "e"): [2, 3, 4],
+            ("a", "d", "e"): [3, 4, 5],
+            ("b", "x", ""): [4, 5, 6],
+        },
+        multiindex=True,
+    )
+    got = ca.select_by_label(slice(("a", "b", "e"), ("b", "x", "")))
+    check_ca_equal(expect, got)
+
+
+def test_by_label_list():
+    ca = ColumnAccessor({"a": [1, 2, 3], "b": [2, 3, 4], "c": [3, 4, 5]})
+    expect = ColumnAccessor({"b": [2, 3, 4], "c": [3, 4, 5]})
+    got = ca.select_by_label(["b", "c"])
+    check_ca_equal(expect, got)
+
+
+def test_select_by_index_simple():
+    """
+    Test getting a column by label
+    """
+    ca = ColumnAccessor({"a": [1, 2, 3], "b": [2, 3, 4]})
+    check_ca_equal(ca.select_by_index(0), ColumnAccessor({"a": [1, 2, 3]}))
+    check_ca_equal(ca.select_by_index(1), ColumnAccessor({"b": [2, 3, 4]}))
+    check_ca_equal(ca.select_by_index([0, 1]), ca)
+    check_ca_equal(ca.select_by_index(slice(0, None)), ca)
+
+
+def test_select_by_index_multiindex():
+    """
+    Test getting column(s) by label with MultiIndex
+    """
+    ca = ColumnAccessor(
+        {
+            ("a", "b", "c"): [1, 2, 3],
+            ("a", "b", "e"): [2, 3, 4],
+            ("b", "x", ""): [4, 5, 6],
+            ("a", "d", "e"): [3, 4, 5],
+        },
+        multiindex=True,
+    )
+
+    expect = ColumnAccessor(
+        {
+            ("a", "b", "c"): [1, 2, 3],
+            ("a", "b", "e"): [2, 3, 4],
+            ("b", "x", ""): [4, 5, 6],
+        },
+        multiindex=True,
+    )
+    got = ca.select_by_index(slice(0, 3))
+    check_ca_equal(expect, got)
+
+    expect = ColumnAccessor(
+        {
+            ("a", "b", "c"): [1, 2, 3],
+            ("a", "b", "e"): [2, 3, 4],
+            ("a", "d", "e"): [3, 4, 5],
+        },
+        multiindex=True,
+    )
+    got = ca.select_by_index([0, 1, 3])
+    check_ca_equal(expect, got)
+
+
+def test_select_by_index_empty():
+    ca = ColumnAccessor(
+        {
+            ("a", "b", "c"): [1, 2, 3],
+            ("a", "b", "e"): [2, 3, 4],
+            ("b", "x", ""): [4, 5, 6],
+            ("a", "d", "e"): [3, 4, 5],
+        },
+        multiindex=True,
+    )
+    expect = ColumnAccessor(
+        {}, multiindex=True, level_names=((None, None, None))
+    )
+    got = ca.select_by_index(slice(None, 0))
+    check_ca_equal(expect, got)
+
+    got = ca.select_by_index([])
+    check_ca_equal(expect, got)
+
+
+def test_replace_level_values_RangeIndex():
+    ca = ColumnAccessor(
+        {("a"): [1, 2, 3], ("b"): [2, 3, 4], ("c"): [3, 4, 5]},
+        multiindex=False,
+    )
+
+    expect = ColumnAccessor(
+        {("f"): [1, 2, 3], ("b"): [2, 3, 4], ("c"): [3, 4, 5]},
+        multiindex=False,
+    )
+
+    got = ca.rename_levels(mapper={"a": "f"}, level=0)
+    check_ca_equal(expect, got)
+
+
+def test_replace_level_values_MultiColumn():
+    ca = ColumnAccessor(
+        {("a", 1): [1, 2, 3], ("a", 2): [2, 3, 4], ("b", 1): [3, 4, 5]},
+        multiindex=True,
+    )
+
+    expect = ColumnAccessor(
+        {("f", 1): [1, 2, 3], ("f", 2): [2, 3, 4], ("b", 1): [3, 4, 5]},
+        multiindex=True,
+    )
+
+    got = ca.rename_levels(mapper={"a": "f"}, level=0)
+    check_ca_equal(expect, got)
diff --git a/python/cudf/cudf/tests/test_compile_udf.py b/python/cudf/cudf/tests/test_compile_udf.py
new file mode 100644
index 0000000..d965f35
--- /dev/null
+++ b/python/cudf/cudf/tests/test_compile_udf.py
@@ -0,0 +1,67 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+from numba import types
+
+from cudf.utils import cudautils
+
+
+def setup_function():
+    cudautils._udf_code_cache.clear()
+
+
+def assert_cache_size(size):
+    assert cudautils._udf_code_cache.currsize == size
+
+
+def test_first_compile_sets_cache_entry():
+    # The first compilation should put an entry in the cache
+    cudautils.compile_udf(lambda x: x + 1, (types.float32,))
+    assert_cache_size(1)
+
+
+def test_code_cache_same_code_different_function_hit():
+    # Compilation of a distinct function with the same code and signature
+    # should reuse the cached entry
+
+    cudautils.compile_udf(lambda x: x + 1, (types.float32,))
+    assert_cache_size(1)
+
+    cudautils.compile_udf(lambda x: x + 1, (types.float32,))
+    assert_cache_size(1)
+
+
+def test_code_cache_different_types_miss():
+    # Compilation of a distinct function with the same code but different types
+    # should create an additional cache entry
+
+    cudautils.compile_udf(lambda x: x + 1, (types.float32,))
+    assert_cache_size(1)
+
+    cudautils.compile_udf(lambda x: x + 1, (types.float64,))
+    assert_cache_size(2)
+
+
+def test_code_cache_different_cvars_miss():
+    # Compilation of a distinct function with the same types and code as an
+    # existing entry but different closure variables should create an
+    # additional cache entry
+
+    def gen_closure(y):
+        return lambda x: x + y
+
+    cudautils.compile_udf(gen_closure(1), (types.float32,))
+    assert_cache_size(1)
+
+    cudautils.compile_udf(gen_closure(2), (types.float32,))
+    assert_cache_size(2)
+
+
+def test_lambda_in_loop_code_cached():
+    # Compiling a UDF defined in a loop should result in the code cache being
+    # reused for each loop iteration after the first. We check for this by
+    # ensuring that there is only one entry in the code cache after the loop.
+
+    for i in range(3):
+        cudautils.compile_udf(lambda x: x + 1, (types.float32,))
+
+    assert_cache_size(1)
diff --git a/python/cudf/cudf/tests/test_concat.py b/python/cudf/cudf/tests/test_concat.py
new file mode 100644
index 0000000..df743a9
--- /dev/null
+++ b/python/cudf/cudf/tests/test_concat.py
@@ -0,0 +1,1884 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf as gd
+from cudf.api.types import is_categorical_dtype
+from cudf.core._compat import PANDAS_GE_150, PANDAS_LT_140
+from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+def make_frames(index=None, nulls="none"):
+    df = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list(map(float, range(10))),
+            "z": list("abcde") * 2,
+        }
+    )
+    df.z = df.z.astype("category")
+    df2 = pd.DataFrame(
+        {
+            "x": range(10, 20),
+            "y": list(map(float, range(10, 20))),
+            "z": list("edcba") * 2,
+        }
+    )
+    df2.z = df2.z.astype("category")
+    if nulls == "all":
+        df.y = np.full_like(df.y, np.nan)
+        df2.y = np.full_like(df2.y, np.nan)
+    if nulls == "some":
+        mask = np.arange(10)
+        np.random.shuffle(mask)
+        mask = mask[:5]
+        df.loc[mask, "y"] = np.nan
+        df2.loc[mask, "y"] = np.nan
+    gdf = gd.DataFrame.from_pandas(df)
+    gdf2 = gd.DataFrame.from_pandas(df2)
+    if index:
+        df = df.set_index(index)
+        df2 = df2.set_index(index)
+        gdf = gdf.set_index(index)
+        gdf2 = gdf2.set_index(index)
+    return df, df2, gdf, gdf2
+
+
+@pytest.mark.parametrize("nulls", ["none", "some", "all"])
+@pytest.mark.parametrize("index", [False, "z", "y"])
+@pytest.mark.parametrize("axis", [0, "index"])
+def test_concat_dataframe(index, nulls, axis):
+    if index == "y" and nulls in ("some", "all"):
+        pytest.skip("nulls in columns, dont index")
+    df, df2, gdf, gdf2 = make_frames(index, nulls=nulls)
+    # Make empty frame
+    gdf_empty1 = gdf2[:0]
+    assert len(gdf_empty1) == 0
+    df_empty1 = gdf_empty1.to_pandas()
+
+    # DataFrame
+    res = gd.concat([gdf, gdf2, gdf, gdf_empty1], axis=axis).to_pandas()
+    sol = pd.concat([df, df2, df, df_empty1], axis=axis)
+    assert_eq(
+        res,
+        sol,
+        check_names=False,
+        check_categorical=False,
+        check_index_type=True,
+    )
+
+    # Series
+    for c in [i for i in ("x", "y", "z") if i != index]:
+        res = gd.concat([gdf[c], gdf2[c], gdf[c]], axis=axis).to_pandas()
+        sol = pd.concat([df[c], df2[c], df[c]], axis=axis)
+        assert_eq(
+            res,
+            sol,
+            check_names=False,
+            check_categorical=False,
+            check_index_type=True,
+        )
+
+    # Index
+    res = gd.concat([gdf.index, gdf2.index], axis=axis).to_pandas()
+    sol = df.index.append(df2.index)
+    assert_eq(res, sol, check_names=False, check_categorical=False)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [["foo", "bar"], [1.0, 2.0], pd.Series(["one", "two"], dtype="category")],
+)
+def test_concat_all_nulls(values):
+    pa = pd.Series(values)
+    pb = pd.Series([None])
+    ps = pd.concat([pa, pb])
+
+    ga = gd.Series(values)
+    gb = gd.Series([None])
+    gs = gd.concat([ga, gb])
+
+    assert_eq(
+        ps,
+        gs,
+        check_dtype=False,
+        check_categorical=False,
+        check_index_type=True,
+    )
+
+
+def test_concat_errors():
+    df, df2, gdf, gdf2 = make_frames()
+
+    # No objs
+    assert_exceptions_equal(
+        lfunc=pd.concat,
+        rfunc=gd.concat,
+        lfunc_args_and_kwargs=([], {"objs": []}),
+        rfunc_args_and_kwargs=([], {"objs": []}),
+    )
+
+    # All None
+    assert_exceptions_equal(
+        lfunc=pd.concat,
+        rfunc=gd.concat,
+        lfunc_args_and_kwargs=([], {"objs": [None, None]}),
+        rfunc_args_and_kwargs=([], {"objs": [None, None]}),
+    )
+
+    # Mismatched types
+    assert_exceptions_equal(
+        lfunc=pd.concat,
+        rfunc=gd.concat,
+        lfunc_args_and_kwargs=([], {"objs": [df, df.index, df.x]}),
+        rfunc_args_and_kwargs=([], {"objs": [gdf, gdf.index, gdf.x]}),
+    )
+
+    # Unknown type
+    assert_exceptions_equal(
+        lfunc=pd.concat,
+        rfunc=gd.concat,
+        lfunc_args_and_kwargs=([], {"objs": ["bar", "foo"]}),
+        rfunc_args_and_kwargs=([], {"objs": ["bar", "foo"]}),
+    )
+
+    # Mismatched index dtypes
+    gdf3 = gdf2.copy()
+    del gdf3["z"]
+    gdf4 = gdf2.set_index("z")
+
+    with pytest.raises(ValueError, match="All columns must be the same type"):
+        gd.concat([gdf3, gdf4])
+
+    # Bad axis value
+    assert_exceptions_equal(
+        lfunc=pd.concat,
+        rfunc=gd.concat,
+        lfunc_args_and_kwargs=(
+            [],
+            {"objs": [gdf.to_pandas(), gdf2.to_pandas()], "axis": "bad_value"},
+        ),
+        rfunc_args_and_kwargs=([], {"objs": [gdf, gdf2], "axis": "bad_value"}),
+    )
+
+
+def test_concat_misordered_columns():
+    df, df2, gdf, gdf2 = make_frames(False)
+    gdf2 = gdf2[["z", "x", "y"]]
+    df2 = df2[["z", "x", "y"]]
+
+    res = gd.concat([gdf, gdf2]).to_pandas()
+    sol = pd.concat([df, df2], sort=False)
+
+    assert_eq(
+        res,
+        sol,
+        check_names=False,
+        check_categorical=False,
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("axis", [1, "columns"])
+def test_concat_columns(axis):
+    pdf1 = pd.DataFrame(np.random.randint(10, size=(5, 3)), columns=[1, 2, 3])
+    pdf2 = pd.DataFrame(
+        np.random.randint(10, size=(5, 4)), columns=[4, 5, 6, 7]
+    )
+    gdf1 = gd.from_pandas(pdf1)
+    gdf2 = gd.from_pandas(pdf2)
+
+    expect = pd.concat([pdf1, pdf2], axis=axis)
+    got = gd.concat([gdf1, gdf2], axis=axis)
+
+    assert_eq(expect, got, check_index_type=True)
+
+
+def test_concat_multiindex_dataframe():
+    gdf = gd.DataFrame(
+        {
+            "w": np.arange(4),
+            "x": np.arange(4),
+            "y": np.arange(4),
+            "z": np.arange(4),
+        }
+    )
+    gdg = gdf.groupby(["w", "x"]).min()
+    pdg = gdg.to_pandas()
+    pdg1 = pdg.iloc[:, :1]
+    pdg2 = pdg.iloc[:, 1:]
+    gdg1 = gd.from_pandas(pdg1)
+    gdg2 = gd.from_pandas(pdg2)
+    assert_eq(
+        gd.concat([gdg1, gdg2]).astype("float64"),
+        pd.concat([pdg1, pdg2]),
+        check_index_type=True,
+    )
+    assert_eq(
+        gd.concat([gdg1, gdg2], axis=1),
+        pd.concat([pdg1, pdg2], axis=1),
+        check_index_type=True,
+    )
+
+
+def test_concat_multiindex_series():
+    gdf = gd.DataFrame(
+        {
+            "w": np.arange(4),
+            "x": np.arange(4),
+            "y": np.arange(4),
+            "z": np.arange(4),
+        }
+    )
+    gdg = gdf.groupby(["w", "x"]).min()
+    pdg = gdg.to_pandas()
+    pdg1 = pdg["y"]
+    pdg2 = pdg["z"]
+    gdg1 = gd.from_pandas(pdg1)
+    gdg2 = gd.from_pandas(pdg2)
+    assert_eq(
+        gd.concat([gdg1, gdg2]), pd.concat([pdg1, pdg2]), check_index_type=True
+    )
+    assert_eq(gd.concat([gdg1, gdg2], axis=1), pd.concat([pdg1, pdg2], axis=1))
+
+
+def test_concat_multiindex_dataframe_and_series():
+    gdf = gd.DataFrame(
+        {
+            "w": np.arange(4),
+            "x": np.arange(4),
+            "y": np.arange(4),
+            "z": np.arange(4),
+        }
+    )
+    gdg = gdf.groupby(["w", "x"]).min()
+    pdg = gdg.to_pandas()
+    pdg1 = pdg[["y", "z"]]
+    pdg2 = pdg["z"]
+    pdg2.name = "a"
+    gdg1 = gd.from_pandas(pdg1)
+    gdg2 = gd.from_pandas(pdg2)
+    assert_eq(
+        gd.concat([gdg1, gdg2], axis=1),
+        pd.concat([pdg1, pdg2], axis=1),
+        check_index_type=True,
+    )
+
+
+def test_concat_multiindex_series_and_dataframe():
+    gdf = gd.DataFrame(
+        {
+            "w": np.arange(4),
+            "x": np.arange(4),
+            "y": np.arange(4),
+            "z": np.arange(4),
+        }
+    )
+    gdg = gdf.groupby(["w", "x"]).min()
+    pdg = gdg.to_pandas()
+    pdg1 = pdg["z"]
+    pdg2 = pdg[["y", "z"]]
+    pdg1.name = "a"
+    gdg1 = gd.from_pandas(pdg1)
+    gdg2 = gd.from_pandas(pdg2)
+    assert_eq(
+        gd.concat([gdg1, gdg2], axis=1),
+        pd.concat([pdg1, pdg2], axis=1),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("myindex", ["a", "b"])
+def test_concat_string_index_name(myindex):
+    # GH-Issue #3420
+    data = {"a": [123, 456], "b": ["s1", "s2"]}
+    df1 = gd.DataFrame(data).set_index(myindex)
+    df2 = df1.copy()
+    df3 = gd.concat([df1, df2])
+
+    assert df3.index.name == myindex
+
+
+def test_pandas_concat_compatibility_axis1():
+    d1 = gd.datasets.randomdata(
+        3, dtypes={"a": float, "ind": float}
+    ).set_index("ind")
+    d2 = gd.datasets.randomdata(
+        3, dtypes={"b": float, "ind": float}
+    ).set_index("ind")
+    d3 = gd.datasets.randomdata(
+        3, dtypes={"c": float, "ind": float}
+    ).set_index("ind")
+    d4 = gd.datasets.randomdata(
+        3, dtypes={"d": float, "ind": float}
+    ).set_index("ind")
+    d5 = gd.datasets.randomdata(
+        3, dtypes={"e": float, "ind": float}
+    ).set_index("ind")
+
+    pd1 = d1.to_pandas()
+    pd2 = d2.to_pandas()
+    pd3 = d3.to_pandas()
+    pd4 = d4.to_pandas()
+    pd5 = d5.to_pandas()
+
+    expect = pd.concat([pd1, pd2, pd3, pd4, pd5], axis=1)
+    got = gd.concat([d1, d2, d3, d4, d5], axis=1)
+
+    assert_eq(
+        got.sort_index(),
+        expect.sort_index(),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("index", [[0, 1, 2], [2, 1, 0], [5, 9, 10]])
+@pytest.mark.parametrize("names", [False, (0, 1)])
+@pytest.mark.parametrize(
+    "data",
+    [
+        (["a", "b", "c"], ["a", "b", "c"]),
+        (["a", "b", "c"], ["XX", "YY", "ZZ"]),
+    ],
+)
+def test_pandas_concat_compatibility_axis1_overlap(index, names, data):
+    s1 = gd.Series(data[0], index=[0, 1, 2])
+    s2 = gd.Series(data[1], index=index)
+    if names:
+        s1.name = names[0]
+        s2.name = names[1]
+    ps1 = s1.to_pandas()
+    ps2 = s2.to_pandas()
+    got = gd.concat([s1, s2], axis=1)
+    expect = pd.concat([ps1, ps2], axis=1)
+    assert_eq(got, expect, check_index_type=True)
+
+
+def test_pandas_concat_compatibility_axis1_eq_index():
+    s1 = gd.Series(["a", "b", "c"], index=[0, 1, 2])
+    s2 = gd.Series(["a", "b", "c"], index=[1, 1, 1])
+    ps1 = s1.to_pandas()
+    ps2 = s2.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        assert_exceptions_equal(
+            lfunc=pd.concat,
+            rfunc=gd.concat,
+            lfunc_args_and_kwargs=([], {"objs": [ps1, ps2], "axis": 1}),
+            rfunc_args_and_kwargs=([], {"objs": [s1, s2], "axis": 1}),
+        )
+
+
+@pytest.mark.parametrize("name", [None, "a"])
+def test_pandas_concat_compatibility_axis1_single_column(name):
+    # Pandas renames series name `None` to 0
+    # and preserves anything else
+    s = gd.Series([1, 2, 3], name=name)
+    got = gd.concat([s], axis=1)
+    expected = pd.concat([s.to_pandas()], axis=1)
+    assert_eq(expected, got)
+
+
+def test_concat_duplicate_columns():
+    cdf = gd.DataFrame(
+        {
+            "id4": 4 * list(range(6)),
+            "id5": 4 * list(reversed(range(6))),
+            "v3": 6 * list(range(4)),
+        }
+    )
+    cdf_std = cdf.groupby(["id4", "id5"])[["v3"]].std()
+    cdf_med = cdf.groupby(["id4", "id5"])[["v3"]].quantile(q=0.5)
+    with pytest.raises(NotImplementedError):
+        gd.concat([cdf_med, cdf_std], axis=1)
+
+
+def test_concat_mixed_input():
+    pdf1 = pd.DataFrame({"a": [10, 20, 30]})
+    pdf2 = pd.DataFrame({"a": [11, 22, 33]})
+
+    gdf1 = gd.from_pandas(pdf1)
+    gdf2 = gd.from_pandas(pdf2)
+
+    assert_eq(
+        pd.concat([pdf1, None, pdf2, None]),
+        gd.concat([gdf1, None, gdf2, None]),
+        check_index_type=True,
+    )
+    assert_eq(
+        pd.concat([pdf1, None]), gd.concat([gdf1, None]), check_index_type=True
+    )
+    assert_eq(
+        pd.concat([None, pdf2]), gd.concat([None, gdf2]), check_index_type=True
+    )
+    assert_eq(
+        pd.concat([None, pdf2, pdf1]),
+        gd.concat([None, gdf2, gdf1]),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [pd.Series([1, 2, 3]), pd.DataFrame({"a": [1, 2]})],
+        [pd.Series([1, 2, 3]), pd.DataFrame({"a": []})],
+        [pd.Series([], dtype="float64"), pd.DataFrame({"a": []})],
+        [pd.Series([], dtype="float64"), pd.DataFrame({"a": [1, 2]})],
+        [pd.Series([1, 2, 3.0, 1.2], name="abc"), pd.DataFrame({"a": [1, 2]})],
+        [
+            pd.Series(
+                [1, 2, 3.0, 1.2], name="abc", index=[100, 110, 120, 130]
+            ),
+            pd.DataFrame({"a": [1, 2]}),
+        ],
+        [
+            pd.Series(
+                [1, 2, 3.0, 1.2], name="abc", index=["a", "b", "c", "d"]
+            ),
+            pd.DataFrame({"a": [1, 2]}, index=["a", "b"]),
+        ],
+        [
+            pd.Series(
+                [1, 2, 3.0, 1.2, 8, 100],
+                name="New name",
+                index=["a", "b", "c", "d", "e", "f"],
+            ),
+            pd.DataFrame(
+                {"a": [1, 2, 4, 10, 11, 12]},
+                index=["a", "b", "c", "d", "e", "f"],
+            ),
+        ],
+        [
+            pd.Series(
+                [1, 2, 3.0, 1.2, 8, 100],
+                name="New name",
+                index=["a", "b", "c", "d", "e", "f"],
+            ),
+            pd.DataFrame(
+                {"a": [1, 2, 4, 10, 11, 12]},
+                index=["a", "b", "c", "d", "e", "f"],
+            ),
+        ]
+        * 7,
+    ],
+)
+def test_concat_series_dataframe_input(objs):
+    pd_objs = objs
+    gd_objs = [gd.from_pandas(obj) for obj in objs]
+
+    expected = pd.concat(pd_objs)
+    actual = gd.concat(gd_objs)
+
+    assert_eq(
+        expected.fillna(-1),
+        actual.fillna(-1),
+        check_dtype=False,
+        check_index_type=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [
+            pd.Series(["a", "b", "c", "d"]),
+            pd.Series(["1", "2", "3", "4"]),
+            pd.DataFrame({"first col": ["10", "11", "12", "13"]}),
+        ],
+        [
+            pd.Series(["a", "b", "c", "d"]),
+            pd.Series(["1", "2", "3", "4"]),
+            pd.DataFrame(
+                {
+                    "first col": ["10", "11", "12", "13"],
+                    "second col": ["a", "b", "c", "d"],
+                }
+            ),
+        ],
+        [
+            pd.Series(["a", "b", "c"]),
+            pd.Series(["1", "2", "3", "4"]),
+            pd.DataFrame(
+                {
+                    "first col": ["10", "11", "12", "13"],
+                    "second col": ["a", "b", "c", "d"],
+                }
+            ),
+        ],
+    ],
+)
+def test_concat_series_dataframe_input_str(objs):
+    pd_objs = objs
+    gd_objs = [gd.from_pandas(obj) for obj in objs]
+
+    expected = pd.concat(pd_objs)
+    actual = gd.concat(gd_objs)
+    assert_eq(expected, actual, check_dtype=False, check_index_type=False)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[10, 20, 30]),
+        pd.DataFrame(
+            {"c": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+        ),
+        pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"l": [10]}, index=[200]),
+        pd.DataFrame([], index=[100]),
+        pd.DataFrame({"cat": pd.Series(["one", "two"], dtype="category")}),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()],
+        [
+            pd.DataFrame(
+                {"c": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+            ),
+            pd.DataFrame(),
+            pd.DataFrame(),
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        ],
+        [
+            pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+            pd.DataFrame({"l": [10]}),
+            pd.DataFrame({"l": [10]}, index=[200]),
+            pd.DataFrame(
+                {"cat": pd.Series(["two", "three"], dtype="category")}
+            ),
+        ],
+        [
+            pd.DataFrame([]),
+            pd.DataFrame([], index=[100]),
+            pd.DataFrame(
+                {"cat": pd.Series(["two", "three"], dtype="category")}
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_concat_empty_dataframes(df, other, ignore_index):
+    other_pd = [df] + other
+
+    gdf = gd.from_pandas(df)
+    other_gd = [gdf] + [gd.from_pandas(o) for o in other]
+
+    expected = pd.concat(other_pd, ignore_index=ignore_index)
+    actual = gd.concat(other_gd, ignore_index=ignore_index)
+    if expected.shape != df.shape:
+        for key, col in actual[actual.columns].items():
+            if is_categorical_dtype(col.dtype):
+                if not is_categorical_dtype(expected[key].dtype):
+                    # TODO: Pandas bug:
+                    # https://github.com/pandas-dev/pandas/issues/42840
+                    expected[key] = expected[key].fillna("-1").astype("str")
+                else:
+                    expected[key] = (
+                        expected[key]
+                        .cat.add_categories(["-1"])
+                        .fillna("-1")
+                        .astype("str")
+                    )
+                actual[key] = col.astype("str").fillna("-1")
+            else:
+                expected[key] = expected[key].fillna(-1)
+                actual[key] = col.fillna(-1)
+        assert_eq(expected, actual, check_dtype=False, check_index_type=True)
+    else:
+        assert_eq(expected, actual, check_index_type=not gdf.empty)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("axis", [0, "index"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        (["a", "b", "c"], ["a", "b", "c"]),
+        (["a", "b", "c"], ["XX", "YY", "ZZ"]),
+    ],
+)
+def test_concat_empty_and_nonempty_series(ignore_index, data, axis):
+    s1 = gd.Series()
+    s2 = gd.Series(data[0])
+    ps1 = s1.to_pandas()
+    ps2 = s2.to_pandas()
+    got = gd.concat([s1, s2], axis=axis, ignore_index=ignore_index)
+    expect = pd.concat([ps1, ps2], axis=axis, ignore_index=ignore_index)
+
+    assert_eq(got, expect, check_index_type=True)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("axis", [0, "index"])
+def test_concat_two_empty_series(ignore_index, axis):
+    s1 = gd.Series()
+    s2 = gd.Series()
+    ps1 = s1.to_pandas()
+    ps2 = s2.to_pandas()
+    got = gd.concat([s1, s2], axis=axis, ignore_index=ignore_index)
+    expect = pd.concat([ps1, ps2], axis=axis, ignore_index=ignore_index)
+
+    assert_eq(got, expect, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "df1,df2",
+    [
+        (
+            gd.DataFrame({"k1": [0, 1], "k2": [2, 3], "v1": [4, 5]}),
+            gd.DataFrame({"k1": [1, 0], "k2": [3, 2], "v2": [6, 7]}),
+        ),
+        (
+            gd.DataFrame({"k1": [0, 1], "k2": [2, 3], "v1": [4, 5]}),
+            gd.DataFrame({"k1": [0, 1], "k2": [3, 2], "v2": [6, 7]}),
+        ),
+    ],
+)
+def test_concat_dataframe_with_multiindex(df1, df2):
+    gdf1 = df1
+    gdf1 = gdf1.set_index(["k1", "k2"])
+
+    gdf2 = df2
+    gdf2 = gdf2.set_index(["k1", "k2"])
+
+    pdf1 = gdf1.to_pandas()
+    pdf2 = gdf2.to_pandas()
+
+    actual = gd.concat([gdf1, gdf2], axis=1)
+    expected = pd.concat([pdf1, pdf2], axis=1)
+
+    # Will need to sort_index before comparing as
+    # ordering is not deterministic in case of pandas
+    # multiIndex with concat.
+    assert_eq(
+        expected.sort_index(),
+        actual.sort_index(),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                }
+            ),
+            pd.DataFrame(
+                {"x": range(10, 20), "y": list(map(float, range(10, 20)))}
+            ),
+        ],
+        [
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                },
+                index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
+            ),
+            pd.DataFrame(
+                {"x": range(10, 20), "y": list(map(float, range(10, 20)))},
+                index=["k", "l", "m", "n", "o", "p", "q", "r", "s", "t"],
+            ),
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                },
+                index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
+            ),
+            pd.DataFrame(
+                {"x": range(10, 20), "y": list(map(float, range(10, 20)))},
+                index=["a", "b", "c", "d", "z", "f", "g", "h", "i", "w"],
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0])
+def test_concat_join(objs, ignore_index, sort, join, axis):
+    gpu_objs = [gd.from_pandas(o) for o in objs]
+
+    assert_eq(
+        pd.concat(
+            objs, sort=sort, join=join, ignore_index=ignore_index, axis=axis
+        ),
+        gd.concat(
+            gpu_objs,
+            sort=sort,
+            join=join,
+            ignore_index=ignore_index,
+            axis=axis,
+        ),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                }
+            ),
+            pd.DataFrame(
+                {"x": range(10, 20), "y": list(map(float, range(10, 20)))}
+            ),
+        ],
+    ],
+)
+def test_concat_join_axis_1_dup_error(objs):
+    gpu_objs = [gd.from_pandas(o) for o in objs]
+    # we do not support duplicate columns
+    with pytest.raises(NotImplementedError):
+        assert_eq(
+            pd.concat(
+                objs,
+                axis=1,
+            ),
+            gd.concat(
+                gpu_objs,
+                axis=1,
+            ),
+        )
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                }
+            ),
+            pd.DataFrame(
+                {"l": range(10, 20), "m": list(map(float, range(10, 20)))}
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [1])
+def test_concat_join_axis_1(objs, ignore_index, sort, join, axis):
+    # no duplicate columns
+    gpu_objs = [gd.from_pandas(o) for o in objs]
+    expected = pd.concat(
+        objs, sort=sort, join=join, ignore_index=ignore_index, axis=axis
+    )
+    actual = gd.concat(
+        gpu_objs,
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+
+    if PANDAS_GE_150:
+        assert_eq(expected, actual, check_index_type=True)
+    else:
+        # special handling of check_index_type below
+        # required because:
+        # https://github.com/pandas-dev/pandas/issues/47501
+        assert_eq(expected, actual, check_index_type=not (axis == 1 and sort))
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [1, 0])
+def test_concat_join_many_df_and_empty_df(ignore_index, sort, join, axis):
+    # no duplicate columns
+    pdf1 = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list(map(float, range(10))),
+            "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        }
+    )
+    pdf2 = pd.DataFrame(
+        {"l": range(10, 20), "m": list(map(float, range(10, 20)))}
+    )
+    pdf3 = pd.DataFrame({"j": [1, 2], "k": [1, 2], "s": [1, 2], "t": [1, 2]})
+    pdf_empty1 = pd.DataFrame()
+
+    gdf1 = gd.from_pandas(pdf1)
+    gdf2 = gd.from_pandas(pdf2)
+    gdf3 = gd.from_pandas(pdf3)
+    gdf_empty1 = gd.from_pandas(pdf_empty1)
+
+    assert_eq(
+        pd.concat(
+            [pdf1, pdf2, pdf3, pdf_empty1],
+            sort=sort,
+            join=join,
+            ignore_index=ignore_index,
+            axis=axis,
+        ),
+        gd.concat(
+            [gdf1, gdf2, gdf3, gdf_empty1],
+            sort=sort,
+            join=join,
+            ignore_index=ignore_index,
+            axis=axis,
+        ),
+        check_index_type=False,
+    )
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_concat_join_one_df(ignore_index, sort, join, axis):
+    pdf1 = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list(map(float, range(10))),
+            "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        }
+    )
+
+    gdf1 = gd.from_pandas(pdf1)
+    expected = pd.concat(
+        [pdf1], sort=sort, join=join, ignore_index=ignore_index, axis=axis
+    )
+    actual = gd.concat(
+        [gdf1], sort=sort, join=join, ignore_index=ignore_index, axis=axis
+    )
+
+    if PANDAS_GE_150:
+        assert_eq(expected, actual, check_index_type=True)
+    else:
+        # special handling of check_index_type below
+        # required because:
+        # https://github.com/pandas-dev/pandas/issues/47501
+        assert_eq(expected, actual, check_index_type=not (axis == 1 and sort))
+
+
+@pytest.mark.parametrize(
+    "pdf1,pdf2",
+    [
+        (
+            pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}),
+            pd.DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}),
+        ),
+        (
+            pd.DataFrame(
+                {"a": [1, 2, 3], "b": [4, 5, 6]}, index=["p", "q", "r"]
+            ),
+            pd.DataFrame(
+                {"c": [7, 8, 9], "d": [10, 11, 12]}, index=["r", "p", "z"]
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.xfail(
+    condition=PANDAS_LT_140,
+    reason="https://github.com/pandas-dev/pandas/issues/43584",
+)
+def test_concat_join_no_overlapping_columns(
+    pdf1, pdf2, ignore_index, sort, join, axis
+):
+    gdf1 = gd.from_pandas(pdf1)
+    gdf2 = gd.from_pandas(pdf2)
+
+    expected = pd.concat(
+        [pdf1, pdf2],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    actual = gd.concat(
+        [gdf1, gdf2],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+
+    if PANDAS_GE_150:
+        assert_eq(expected, actual, check_index_type=True)
+    else:
+        # special handling of check_index_type below
+        # required because:
+        # https://github.com/pandas-dev/pandas/issues/47501
+        assert_eq(expected, actual, check_index_type=not (axis == 1 and sort))
+
+
+@pytest.mark.parametrize("ignore_index", [False, True])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_concat_join_no_overlapping_columns_many_and_empty(
+    ignore_index, sort, join, axis
+):
+    pdf4 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    pdf5 = pd.DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]})
+    pdf6 = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list(map(float, range(10))),
+            "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        }
+    )
+    pdf_empty = pd.DataFrame()
+
+    gdf4 = gd.from_pandas(pdf4)
+    gdf5 = gd.from_pandas(pdf5)
+    gdf6 = gd.from_pandas(pdf6)
+    gdf_empty = gd.from_pandas(pdf_empty)
+
+    expected = pd.concat(
+        [pdf4, pdf5, pdf6, pdf_empty],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    actual = gd.concat(
+        [gdf4, gdf5, gdf6, gdf_empty],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    assert_eq(
+        expected,
+        actual,
+        check_index_type=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [
+            pd.DataFrame(
+                {"a": [1, 2, 3], "b": [4, 5, 6]}, index=["z", "t", "k"]
+            ),
+            pd.DataFrame(
+                {"c": [7, 8, 9], "d": [10, 11, 12]}, index=["z", "t", "k"]
+            ),
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                },
+                index=["z", "t", "k", "a", "b", "c", "d", "e", "f", "g"],
+            ),
+            pd.DataFrame(index=pd.Index([], dtype="str")),
+        ],
+        [
+            pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}),
+            pd.DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}),
+            pd.DataFrame(
+                {
+                    "x": range(10),
+                    "y": list(map(float, range(10))),
+                    "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+                }
+            ),
+            pd.DataFrame(index=pd.Index([], dtype="str")),
+        ],
+        pytest.param(
+            [
+                pd.DataFrame(
+                    {"a": [1, 2, 3], "nb": [10, 11, 12]}, index=["Q", "W", "R"]
+                ),
+                None,
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [False, True])
+@pytest.mark.parametrize("join", ["outer", "inner"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_concat_join_no_overlapping_columns_many_and_empty2(
+    objs, ignore_index, sort, join, axis
+):
+    objs_gd = [gd.from_pandas(o) if o is not None else o for o in objs]
+
+    expected = pd.concat(
+        objs,
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    actual = gd.concat(
+        objs_gd,
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    assert_eq(expected, actual, check_index_type=False)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_concat_join_no_overlapping_columns_empty_df_basic(
+    ignore_index, sort, join, axis
+):
+
+    pdf6 = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list(map(float, range(10))),
+            "z": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+        }
+    )
+    pdf_empty = pd.DataFrame()
+
+    gdf6 = gd.from_pandas(pdf6)
+    gdf_empty = gd.from_pandas(pdf_empty)
+
+    expected = pd.concat(
+        [pdf6, pdf_empty],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    actual = gd.concat(
+        [gdf6, gdf_empty],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    # TODO: change `check_index_type` to `True`
+    # after following bug from pandas is fixed:
+    # https://github.com/pandas-dev/pandas/issues/46675
+    assert_eq(expected, actual, check_index_type=False)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_concat_join_series(ignore_index, sort, join, axis):
+    s1 = gd.Series(["a", "b", "c"])
+    s2 = gd.Series(["a", "b"])
+    s3 = gd.Series(["a", "b", "c", "d"])
+    s4 = gd.Series()
+
+    ps1 = s1.to_pandas()
+    ps2 = s2.to_pandas()
+    ps3 = s3.to_pandas()
+    ps4 = s4.to_pandas()
+
+    expected = pd.concat(
+        [ps1, ps2, ps3, ps4],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+    actual = gd.concat(
+        [s1, s2, s3, s4],
+        sort=sort,
+        join=join,
+        ignore_index=ignore_index,
+        axis=axis,
+    )
+
+    if PANDAS_GE_150:
+        assert_eq(
+            expected,
+            actual,
+            check_index_type=True,
+        )
+    else:
+        # special handling of check_index_type required below:
+        # https://github.com/pandas-dev/pandas/issues/46675
+        # https://github.com/pandas-dev/pandas/issues/47501
+        assert_eq(
+            expected,
+            actual,
+            check_index_type=(axis == 0),
+        )
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[10, 20, 30]),
+        pd.DataFrame(
+            {"c": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+        ),
+        pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"l": [10]}, index=[200]),
+        pd.DataFrame([], index=[100]),
+        pd.DataFrame({"cat": pd.Series(["one", "two"], dtype="category")}),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()],
+        [
+            pd.DataFrame(
+                {"b": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+            ),
+            pd.DataFrame(),
+            pd.DataFrame(),
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        ],
+        [
+            pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+            pd.DataFrame({"l": [10]}),
+            pd.DataFrame({"k": [10]}, index=[200]),
+            pd.DataFrame(
+                {"cat": pd.Series(["two", "three"], dtype="category")}
+            ),
+        ],
+        [
+            pd.DataFrame([]),
+            pd.DataFrame([], index=[100]),
+            pd.DataFrame(
+                {"cat": pd.Series(["two", "three"], dtype="category")}
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("join", ["inner", "outer"])
+@pytest.mark.parametrize("axis", [0])
+def test_concat_join_empty_dataframes(
+    df, other, ignore_index, axis, join, sort
+):
+    other_pd = [df] + other
+    gdf = gd.from_pandas(df)
+    other_gd = [gdf] + [gd.from_pandas(o) for o in other]
+
+    expected = pd.concat(
+        other_pd, ignore_index=ignore_index, axis=axis, join=join, sort=sort
+    )
+    actual = gd.concat(
+        other_gd, ignore_index=ignore_index, axis=axis, join=join, sort=sort
+    )
+    if expected.shape != df.shape:
+        if axis == 0:
+            for key, col in actual[actual.columns].items():
+                if is_categorical_dtype(col.dtype):
+                    if not is_categorical_dtype(expected[key].dtype):
+                        # TODO: Pandas bug:
+                        # https://github.com/pandas-dev/pandas/issues/42840
+                        expected[key] = (
+                            expected[key].fillna("-1").astype("str")
+                        )
+                    else:
+                        expected[key] = (
+                            expected[key]
+                            .cat.add_categories(["-1"])
+                            .fillna("-1")
+                            .astype("str")
+                        )
+                    actual[key] = col.astype("str").fillna("-1")
+                else:
+                    expected[key] = expected[key].fillna(-1)
+                    actual[key] = col.fillna(-1)
+
+            assert_eq(
+                expected.fillna(-1),
+                actual.fillna(-1),
+                check_dtype=False,
+                check_index_type=False
+                if len(expected) == 0 or actual.empty
+                else True,
+                check_column_type=False,
+            )
+        else:
+            # no need to fill in if axis=1
+            assert_eq(
+                expected,
+                actual,
+                check_index_type=False,
+                check_column_type=False,
+            )
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+        check_index_type=False,
+        check_column_type=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[10, 20, 30]),
+        pd.DataFrame(
+            {"c": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+        ),
+        pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"m": [10]}, index=[200]),
+        pd.DataFrame([], index=[100]),
+        pd.DataFrame({"cat": pd.Series(["one", "two"], dtype="category")}),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()],
+        [
+            pd.DataFrame(
+                {"b": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+            ),
+            pd.DataFrame(),
+            pd.DataFrame(),
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("CD")),
+        ],
+        [
+            pd.DataFrame({"g": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+            pd.DataFrame({"h": [10]}),
+            pd.DataFrame({"k": [10]}, index=[200]),
+            pd.DataFrame(
+                {"dog": pd.Series(["two", "three"], dtype="category")}
+            ),
+        ],
+        [
+            pd.DataFrame([]),
+            pd.DataFrame([], index=[100]),
+            pd.DataFrame(
+                {"bird": pd.Series(["two", "three"], dtype="category")}
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize(
+    "join",
+    [
+        "inner",
+        pytest.param(
+            "outer",
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/issues/37937",
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("axis", [1])
+def test_concat_join_empty_dataframes_axis_1(
+    df, other, ignore_index, axis, join, sort
+):
+    # no duplicate columns
+    other_pd = [df] + other
+    gdf = gd.from_pandas(df)
+    other_gd = [gdf] + [gd.from_pandas(o) for o in other]
+
+    expected = pd.concat(
+        other_pd, ignore_index=ignore_index, axis=axis, join=join, sort=sort
+    )
+    actual = gd.concat(
+        other_gd, ignore_index=ignore_index, axis=axis, join=join, sort=sort
+    )
+    if expected.shape != df.shape:
+        if axis == 0:
+            for key, col in actual[actual.columns].items():
+                if is_categorical_dtype(col.dtype):
+                    expected[key] = expected[key].fillna("-1")
+                    actual[key] = col.astype("str").fillna("-1")
+            # if not expected.empty:
+            assert_eq(
+                expected.fillna(-1),
+                actual.fillna(-1),
+                check_dtype=False,
+                check_index_type=False
+                if len(expected) == 0 or actual.empty
+                else True,
+                check_column_type=False,
+            )
+        else:
+            # no need to fill in if axis=1
+            assert_eq(
+                expected,
+                actual,
+                check_index_type=False,
+                check_column_type=False,
+            )
+    assert_eq(
+        expected, actual, check_index_type=False, check_column_type=False
+    )
+
+
+def test_concat_preserve_order():
+    """Ensure that order is preserved on 'inner' concatenations."""
+    df = pd.DataFrame([["d", 3, 4.0], ["c", 4, 5.0]], columns=["c", "b", "a"])
+    dfs = [df, df]
+
+    assert_eq(
+        pd.concat(dfs, join="inner"),
+        gd.concat([gd.DataFrame(df) for df in dfs], join="inner"),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("typ", [gd.DataFrame, gd.Series])
+def test_concat_single_object(ignore_index, typ):
+    """Ensure that concat on a single object does not change it."""
+    obj = typ([1, 2, 3])
+    assert_eq(
+        gd.concat([obj], ignore_index=ignore_index, axis=0),
+        obj,
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize(
+    "ltype",
+    [Decimal64Dtype(3, 1), Decimal64Dtype(7, 2), Decimal64Dtype(8, 4)],
+)
+@pytest.mark.parametrize(
+    "rtype",
+    [
+        Decimal64Dtype(3, 2),
+        Decimal64Dtype(8, 4),
+        gd.Decimal128Dtype(3, 2),
+        gd.Decimal32Dtype(8, 4),
+    ],
+)
+def test_concat_decimal_dataframe(ltype, rtype):
+    gdf1 = gd.DataFrame(
+        {"id": np.random.randint(0, 10, 3), "val": ["22.3", "59.5", "81.1"]}
+    )
+    gdf2 = gd.DataFrame(
+        {"id": np.random.randint(0, 10, 3), "val": ["2.35", "5.59", "8.14"]}
+    )
+
+    gdf1["val"] = gdf1["val"].astype(ltype)
+    gdf2["val"] = gdf2["val"].astype(rtype)
+
+    pdf1 = gdf1.to_pandas()
+    pdf2 = gdf2.to_pandas()
+
+    got = gd.concat([gdf1, gdf2])
+    expected = pd.concat([pdf1, pdf2])
+
+    assert_eq(expected, got, check_index_type=True)
+
+
+@pytest.mark.parametrize("ltype", [Decimal64Dtype(4, 1), Decimal64Dtype(8, 2)])
+@pytest.mark.parametrize(
+    "rtype",
+    [
+        Decimal64Dtype(4, 3),
+        Decimal64Dtype(10, 4),
+        Decimal32Dtype(8, 3),
+        Decimal128Dtype(18, 3),
+    ],
+)
+def test_concat_decimal_series(ltype, rtype):
+    gs1 = gd.Series(["228.3", "559.5", "281.1"]).astype(ltype)
+    gs2 = gd.Series(["2.345", "5.259", "8.154"]).astype(rtype)
+
+    ps1 = gs1.to_pandas()
+    ps2 = gs2.to_pandas()
+
+    got = gd.concat([gs1, gs2])
+    expected = pd.concat([ps1, ps2])
+
+    assert_eq(expected, got, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "df1, df2, df3, expected",
+    [
+        (
+            gd.DataFrame(
+                {"val": [Decimal("42.5"), Decimal("8.7")]},
+                dtype=Decimal64Dtype(5, 2),
+            ),
+            gd.DataFrame(
+                {"val": [Decimal("9.23"), Decimal("-67.49")]},
+                dtype=Decimal64Dtype(6, 4),
+            ),
+            gd.DataFrame({"val": [8, -5]}, dtype="int32"),
+            gd.DataFrame(
+                {
+                    "val": [
+                        Decimal("42.5"),
+                        Decimal("8.7"),
+                        Decimal("9.23"),
+                        Decimal("-67.49"),
+                        Decimal("8"),
+                        Decimal("-5"),
+                    ]
+                },
+                dtype=Decimal32Dtype(7, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.DataFrame(
+                {"val": [Decimal("95.2"), Decimal("23.4")]},
+                dtype=Decimal64Dtype(5, 2),
+            ),
+            gd.DataFrame({"val": [54, 509]}, dtype="uint16"),
+            gd.DataFrame({"val": [24, -48]}, dtype="int32"),
+            gd.DataFrame(
+                {
+                    "val": [
+                        Decimal("95.2"),
+                        Decimal("23.4"),
+                        Decimal("54"),
+                        Decimal("509"),
+                        Decimal("24"),
+                        Decimal("-48"),
+                    ]
+                },
+                dtype=Decimal32Dtype(5, 2),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.DataFrame(
+                {"val": [Decimal("36.56"), Decimal("-59.24")]},
+                dtype=Decimal64Dtype(9, 4),
+            ),
+            gd.DataFrame({"val": [403.21, 45.13]}, dtype="float32"),
+            gd.DataFrame({"val": [52.262, -49.25]}, dtype="float64"),
+            gd.DataFrame(
+                {
+                    "val": [
+                        Decimal("36.56"),
+                        Decimal("-59.24"),
+                        Decimal("403.21"),
+                        Decimal("45.13"),
+                        Decimal("52.262"),
+                        Decimal("-49.25"),
+                    ]
+                },
+                dtype=Decimal32Dtype(9, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.DataFrame(
+                {"val": [Decimal("9563.24"), Decimal("236.633")]},
+                dtype=Decimal64Dtype(9, 4),
+            ),
+            gd.DataFrame({"val": [5393, -95832]}, dtype="int64"),
+            gd.DataFrame({"val": [-29.234, -31.945]}, dtype="float64"),
+            gd.DataFrame(
+                {
+                    "val": [
+                        Decimal("9563.24"),
+                        Decimal("236.633"),
+                        Decimal("5393"),
+                        Decimal("-95832"),
+                        Decimal("-29.234"),
+                        Decimal("-31.945"),
+                    ]
+                },
+                dtype=Decimal32Dtype(9, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.DataFrame(
+                {"val": [Decimal("95633.24"), Decimal("236.633")]},
+                dtype=Decimal128Dtype(19, 4),
+            ),
+            gd.DataFrame({"val": [5393, -95832]}, dtype="int64"),
+            gd.DataFrame({"val": [-29.234, -31.945]}, dtype="float64"),
+            gd.DataFrame(
+                {
+                    "val": [
+                        Decimal("95633.24"),
+                        Decimal("236.633"),
+                        Decimal("5393"),
+                        Decimal("-95832"),
+                        Decimal("-29.234"),
+                        Decimal("-31.945"),
+                    ]
+                },
+                dtype=Decimal128Dtype(19, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+    ],
+)
+def test_concat_decimal_numeric_dataframe(df1, df2, df3, expected):
+    df = gd.concat([df1, df2, df3])
+    assert_eq(df, expected, check_index_type=True)
+    assert_eq(df.val.dtype, expected.val.dtype)
+
+
+@pytest.mark.parametrize(
+    "s1, s2, s3, expected",
+    [
+        (
+            gd.Series(
+                [Decimal("32.8"), Decimal("-87.7")], dtype=Decimal64Dtype(6, 2)
+            ),
+            gd.Series(
+                [Decimal("101.243"), Decimal("-92.449")],
+                dtype=Decimal64Dtype(9, 6),
+            ),
+            gd.Series([94, -22], dtype="int32"),
+            gd.Series(
+                [
+                    Decimal("32.8"),
+                    Decimal("-87.7"),
+                    Decimal("101.243"),
+                    Decimal("-92.449"),
+                    Decimal("94"),
+                    Decimal("-22"),
+                ],
+                dtype=Decimal64Dtype(10, 6),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("7.2"), Decimal("122.1")], dtype=Decimal64Dtype(5, 2)
+            ),
+            gd.Series([33, 984], dtype="uint32"),
+            gd.Series([593, -702], dtype="int32"),
+            gd.Series(
+                [
+                    Decimal("7.2"),
+                    Decimal("122.1"),
+                    Decimal("33"),
+                    Decimal("984"),
+                    Decimal("593"),
+                    Decimal("-702"),
+                ],
+                dtype=Decimal32Dtype(5, 2),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("982.94"), Decimal("-493.626")],
+                dtype=Decimal64Dtype(9, 4),
+            ),
+            gd.Series([847.98, 254.442], dtype="float32"),
+            gd.Series([5299.262, -2049.25], dtype="float64"),
+            gd.Series(
+                [
+                    Decimal("982.94"),
+                    Decimal("-493.626"),
+                    Decimal("847.98"),
+                    Decimal("254.442"),
+                    Decimal("5299.262"),
+                    Decimal("-2049.25"),
+                ],
+                dtype=Decimal32Dtype(9, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("492.204"), Decimal("-72824.455")],
+                dtype=Decimal64Dtype(9, 4),
+            ),
+            gd.Series([8438, -27462], dtype="int64"),
+            gd.Series([-40.292, 49202.953], dtype="float64"),
+            gd.Series(
+                [
+                    Decimal("492.204"),
+                    Decimal("-72824.455"),
+                    Decimal("8438"),
+                    Decimal("-27462"),
+                    Decimal("-40.292"),
+                    Decimal("49202.953"),
+                ],
+                dtype=Decimal32Dtype(9, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("492.204"), Decimal("-72824.455")],
+                dtype=Decimal64Dtype(10, 4),
+            ),
+            gd.Series(
+                [Decimal("8438"), Decimal("-27462")],
+                dtype=Decimal32Dtype(9, 4),
+            ),
+            gd.Series(
+                [Decimal("-40.292"), Decimal("49202.953")],
+                dtype=Decimal128Dtype(19, 4),
+            ),
+            gd.Series(
+                [
+                    Decimal("492.204"),
+                    Decimal("-72824.455"),
+                    Decimal("8438"),
+                    Decimal("-27462"),
+                    Decimal("-40.292"),
+                    Decimal("49202.953"),
+                ],
+                dtype=Decimal128Dtype(19, 4),
+                index=[0, 1, 0, 1, 0, 1],
+            ),
+        ),
+    ],
+)
+def test_concat_decimal_numeric_series(s1, s2, s3, expected):
+    s = gd.concat([s1, s2, s3])
+    assert_eq(s, expected, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "s1, s2, expected",
+    [
+        (
+            gd.Series(
+                [Decimal("955.22"), Decimal("8.2")], dtype=Decimal64Dtype(5, 2)
+            ),
+            gd.Series(["2007-06-12", "2006-03-14"], dtype="datetime64[s]"),
+            gd.Series(
+                [
+                    "955.22",
+                    "8.20",
+                    "2007-06-12 00:00:00",
+                    "2006-03-14 00:00:00",
+                ],
+                index=[0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("-52.44"), Decimal("365.22")],
+                dtype=Decimal64Dtype(5, 2),
+            ),
+            gd.Series(
+                np.arange(
+                    "2005-02-01T12", "2005-02-01T15", dtype="datetime64[h]"
+                ),
+                dtype="datetime64[s]",
+            ),
+            gd.Series(
+                [
+                    "-52.44",
+                    "365.22",
+                    "2005-02-01 12:00:00",
+                    "2005-02-01 13:00:00",
+                    "2005-02-01 14:00:00",
+                ],
+                index=[0, 1, 0, 1, 2],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("753.0"), Decimal("94.22")],
+                dtype=Decimal64Dtype(5, 2),
+            ),
+            gd.Series([np.timedelta64(111, "s"), np.timedelta64(509, "s")]),
+            gd.Series(
+                ["753.00", "94.22", "0 days 00:01:51", "0 days 00:08:29"],
+                index=[0, 1, 0, 1],
+            ),
+        ),
+        (
+            gd.Series(
+                [Decimal("753.0"), Decimal("94.22")],
+                dtype=Decimal64Dtype(5, 2),
+            ),
+            gd.Series(
+                [np.timedelta64(940252, "s"), np.timedelta64(758385, "s")]
+            ),
+            gd.Series(
+                ["753.00", "94.22", "10 days 21:10:52", "8 days 18:39:45"],
+                index=[0, 1, 0, 1],
+            ),
+        ),
+    ],
+)
+def test_concat_decimal_non_numeric(s1, s2, expected):
+    s = gd.concat([s1, s2])
+    assert_eq(s, expected, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "s1, s2, expected",
+    [
+        (
+            gd.Series([{"a": 5}, {"c": "hello"}, {"b": 7}]),
+            gd.Series([{"a": 5, "c": "hello", "b": 7}]),
+            gd.Series(
+                [
+                    {"a": 5, "b": None, "c": None},
+                    {"a": None, "b": None, "c": "hello"},
+                    {"a": None, "b": 7, "c": None},
+                    {"a": 5, "b": 7, "c": "hello"},
+                ],
+                index=[0, 1, 2, 0],
+            ),
+        )
+    ],
+)
+def test_concat_struct_column(s1, s2, expected):
+    s = gd.concat([s1, s2])
+    assert_eq(s, expected, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "frame1, frame2, expected",
+    [
+        (
+            gd.Series([[{"b": 0}], [{"b": 1}], [{"b": 3}]]),
+            gd.Series([[{"b": 10}], [{"b": 12}], None]),
+            gd.Series(
+                [
+                    [{"b": 0}],
+                    [{"b": 1}],
+                    [{"b": 3}],
+                    [{"b": 10}],
+                    [{"b": 12}],
+                    None,
+                ],
+                index=[0, 1, 2, 0, 1, 2],
+            ),
+        ),
+        (
+            gd.DataFrame({"a": [[{"b": 0}], [{"b": 1}], [{"b": 3}]]}),
+            gd.DataFrame({"a": [[{"b": 10}], [{"b": 12}], None]}),
+            gd.DataFrame(
+                {
+                    "a": [
+                        [{"b": 0}],
+                        [{"b": 1}],
+                        [{"b": 3}],
+                        [{"b": 10}],
+                        [{"b": 12}],
+                        None,
+                    ]
+                },
+                index=[0, 1, 2, 0, 1, 2],
+            ),
+        ),
+    ],
+)
+def test_concat_list_column(frame1, frame2, expected):
+    actual = gd.concat([frame1, frame2])
+    assert_eq(actual, expected, check_index_type=True)
+
+
+def test_concat_categorical_ordering():
+    # https://github.com/rapidsai/cudf/issues/11486
+    sr = pd.Series(
+        ["a", "b", "c", "d", "e", "a", "b", "c", "d", "e"], dtype="category"
+    )
+    sr = sr.cat.set_categories(["d", "a", "b", "c", "e"])
+
+    df = pd.DataFrame({"a": sr})
+    gdf = gd.from_pandas(df)
+
+    expect = pd.concat([df, df, df])
+    got = gd.concat([gdf, gdf, gdf])
+
+    assert_eq(expect, got)
+
+
+@pytest.fixture(params=["rangeindex", "index"])
+def singleton_concat_index(request):
+    if request.param == "rangeindex":
+        return pd.RangeIndex(0, 4)
+    else:
+        return pd.Index(["a", "h", "g", "f"])
+
+
+@pytest.fixture(params=["dataframe", "series"])
+def singleton_concat_obj(request, singleton_concat_index):
+    if request.param == "dataframe":
+        return pd.DataFrame(
+            {
+                "b": [1, 2, 3, 4],
+                "d": [7, 8, 9, 10],
+                "a": [4, 5, 6, 7],
+                "c": [10, 11, 12, 13],
+            },
+            index=singleton_concat_index,
+        )
+    else:
+        return pd.Series([4, 5, 5, 6], index=singleton_concat_index)
+
+
+@pytest.mark.parametrize("axis", [0, 1, "columns", "index"])
+@pytest.mark.parametrize("sort", [False, True])
+@pytest.mark.parametrize("ignore_index", [False, True])
+def test_concat_singleton_sorting(
+    axis, sort, ignore_index, singleton_concat_obj
+):
+    gobj = gd.from_pandas(singleton_concat_obj)
+    gconcat = gd.concat(
+        [gobj], axis=axis, sort=sort, ignore_index=ignore_index
+    )
+    pconcat = pd.concat(
+        [singleton_concat_obj], axis=axis, sort=sort, ignore_index=ignore_index
+    )
+    assert_eq(pconcat, gconcat)
+
+
+@pytest.mark.parametrize("axis", [2, "invalid"])
+def test_concat_invalid_axis(axis):
+    s = gd.Series([1, 2, 3])
+    with pytest.raises(ValueError):
+        gd.concat([s], axis=axis)
+
+
+@pytest.mark.parametrize(
+    "s1,s2",
+    [
+        ([1, 2], [[1, 2], [3, 4]]),
+    ],
+)
+def test_concat_mixed_list_types_error(s1, s2):
+    s1, s2 = gd.Series(s1), gd.Series(s2)
+
+    with pytest.raises(NotImplementedError):
+        gd.concat([s1, s2], ignore_index=True)
diff --git a/python/cudf/cudf/tests/test_contains.py b/python/cudf/cudf/tests/test_contains.py
new file mode 100644
index 0000000..15dfa11
--- /dev/null
+++ b/python/cudf/cudf/tests/test_contains.py
@@ -0,0 +1,123 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Series
+from cudf.core.index import RangeIndex, as_index
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+    assert_eq,
+)
+
+
+def cudf_date_series(start, stop, freq):
+    return Series(pd.date_range(start, stop, freq=freq, name="times"))
+
+
+def cudf_num_series(start, stop, step=1):
+    return Series(range(start, stop, step))
+
+
+def get_categorical_series():
+    return Series(
+        pd.Categorical(
+            ["ab", "ac", "cd", "ab", "cd"], categories=["ab", "ac", "cd"]
+        )
+    )
+
+
+def get_string_series():
+    return Series(["ab", "ac", "ba", "cc", "ad"])
+
+
+# If the type being searched is different from type of series, exceptions
+# are thrown well within the python code, and needs to be handled.
+# Some of the test cases check this scenario. Example : String Vs Numerical
+testdata_all = [
+    (
+        cudf_date_series("20010101", "20020215", freq="400h"),
+        datetime.datetime.strptime("2001-01-01", "%Y-%m-%d"),
+        True,
+    ),
+    (
+        cudf_date_series("20010101", "20020215", freq="400h"),
+        datetime.datetime.strptime("2000-01-01", "%Y-%m-%d"),
+        False,
+    ),
+    (cudf_date_series("20010101", "20020215", freq="400h"), 20000101, False),
+    (get_categorical_series(), "cd", True),
+    (get_categorical_series(), "dc", False),
+    (get_categorical_series(), "c", False),
+    (get_categorical_series(), "c", False),
+    (get_categorical_series(), 1, False),
+    (get_string_series(), "ac", True),
+    (get_string_series(), "ca", False),
+    (get_string_series(), "c", False),
+    (get_string_series(), 97, False),
+    (cudf_num_series(0, 100, 5), 60, True),
+    (cudf_num_series(0, 100, 5), 71, False),
+    (cudf_num_series(0, 100, 5), "a", False),
+]
+
+
+@pytest.mark.parametrize("values, item, expected", testdata_all)
+def test_series_contains(values, item, expected):
+    assert_eq(expected, item in Series(index=values))
+
+
+@pytest.mark.parametrize("values, item, expected", testdata_all)
+def test_index_contains(values, item, expected):
+    index = as_index(values)
+    assert_eq(expected, item in index)
+
+
+def test_rangeindex_contains():
+    assert_eq(True, 9 in RangeIndex(start=0, stop=10, name="Index"))
+    assert_eq(False, 10 in RangeIndex(start=0, stop=10, name="Index"))
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+def test_lists_contains(dtype):
+    dtype = cudf.dtype(dtype)
+    inner_data = np.array([1, 2, 3], dtype=dtype)
+
+    data = Series([inner_data])
+
+    contained_scalar = inner_data.dtype.type(2)
+    not_contained_scalar = inner_data.dtype.type(42)
+
+    assert data.list.contains(contained_scalar)[0]
+    assert not data.list.contains(not_contained_scalar)[0]
+
+
+@pytest.mark.parametrize("dtype", DATETIME_TYPES + TIMEDELTA_TYPES)
+def test_lists_contains_datetime(dtype):
+    dtype = cudf.dtype(dtype)
+    inner_data = np.array([1, 2, 3])
+
+    unit, _ = np.datetime_data(dtype)
+
+    data = Series([inner_data])
+
+    contained_scalar = inner_data.dtype.type(2)
+    not_contained_scalar = inner_data.dtype.type(42)
+
+    assert data.list.contains(contained_scalar)[0]
+    assert not data.list.contains(not_contained_scalar)[0]
+
+
+def test_lists_contains_bool():
+    data = Series([[True, True, True]])
+
+    contained_scalar = True
+    not_contained_scalar = False
+
+    assert data.list.contains(contained_scalar)[0]
+    assert not data.list.contains(not_contained_scalar)[0]
diff --git a/python/cudf/cudf/tests/test_copying.py b/python/cudf/cudf/tests/test_copying.py
new file mode 100644
index 0000000..085774e
--- /dev/null
+++ b/python/cudf/cudf/tests/test_copying.py
@@ -0,0 +1,429 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Series
+from cudf.testing._utils import NUMERIC_TYPES, OTHER_TYPES, assert_eq
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + OTHER_TYPES)
+def test_repeat(dtype):
+    arr = np.random.rand(10) * 10
+    repeats = np.random.randint(10, size=10)
+    psr = pd.Series(arr).astype(dtype)
+    gsr = cudf.from_pandas(psr)
+
+    assert_eq(psr.repeat(repeats), gsr.repeat(repeats))
+
+
+def test_repeat_index():
+    arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]]
+    psr = pd.MultiIndex.from_arrays(arrays, names=("number", "color"))
+    gsr = cudf.from_pandas(psr)
+    repeats = np.random.randint(10, size=4)
+
+    assert_eq(psr.repeat(repeats), gsr.repeat(repeats))
+
+
+def test_repeat_dataframe():
+    psr = pd.DataFrame({"a": [1, 1, 2, 2]})
+    gsr = cudf.from_pandas(psr)
+    repeats = np.random.randint(10, size=4)
+
+    # pd.DataFrame doesn't have repeat() so as a workaround, we are
+    # comparing pd.Series.repeat() with cudf.DataFrame.repeat()['a']
+    assert_eq(psr["a"].repeat(repeats), gsr.repeat(repeats)["a"])
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+def test_repeat_scalar(dtype):
+    arr = np.random.rand(10) * 10
+    repeats = 10
+    psr = pd.Series(arr).astype(dtype)
+    gsr = cudf.from_pandas(psr)
+
+    assert_eq(psr.repeat(repeats), gsr.repeat(repeats))
+
+
+def test_null_copy():
+    col = Series(np.arange(2049))
+    col[:] = None
+    assert len(col) == 2049
+
+
+def test_series_setitem_cow_on():
+    with cudf.option_context("copy_on_write", True):
+        actual = cudf.Series([1, 2, 3, 4, 5])
+        new_copy = actual.copy(deep=False)
+
+        actual[1] = 100
+        assert_eq(actual, cudf.Series([1, 100, 3, 4, 5]))
+        assert_eq(new_copy, cudf.Series([1, 2, 3, 4, 5]))
+
+
+def test_series_setitem_cow_off():
+    with cudf.option_context("copy_on_write", False):
+        actual = cudf.Series([1, 2, 3, 4, 5])
+        new_copy = actual.copy(deep=False)
+
+        actual[1] = 100
+        assert_eq(actual, cudf.Series([1, 100, 3, 4, 5]))
+        assert_eq(new_copy, cudf.Series([1, 100, 3, 4, 5]))
+
+
+def test_series_setitem_both_slice_cow_on():
+    with cudf.option_context("copy_on_write", True):
+        actual = cudf.Series([1, 2, 3, 4, 5])
+        new_copy = actual.copy(deep=False)
+
+        actual[slice(0, 2, 1)] = 100
+        assert_eq(actual, cudf.Series([100, 100, 3, 4, 5]))
+        assert_eq(new_copy, cudf.Series([1, 2, 3, 4, 5]))
+
+        new_copy[slice(2, 4, 1)] = 300
+        assert_eq(actual, cudf.Series([100, 100, 3, 4, 5]))
+        assert_eq(new_copy, cudf.Series([1, 2, 300, 300, 5]))
+
+
+def test_series_setitem_both_slice_cow_off():
+    with cudf.option_context("copy_on_write", False):
+        actual = cudf.Series([1, 2, 3, 4, 5])
+        new_copy = actual.copy(deep=False)
+
+        actual[slice(0, 2, 1)] = 100
+        assert_eq(actual, cudf.Series([100, 100, 3, 4, 5]))
+        assert_eq(new_copy, cudf.Series([100, 100, 3, 4, 5]))
+
+        new_copy[slice(2, 4, 1)] = 300
+        assert_eq(actual, cudf.Series([100, 100, 300, 300, 5]))
+        assert_eq(new_copy, cudf.Series([100, 100, 300, 300, 5]))
+
+
+def test_series_setitem_partial_slice_cow_on():
+    with cudf.option_context("copy_on_write", True):
+        actual = cudf.Series([1, 2, 3, 4, 5])
+        new_copy = actual.copy(deep=False)
+
+        new_copy[slice(2, 4, 1)] = 300
+        assert_eq(actual, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(new_copy, cudf.Series([1, 2, 300, 300, 5]))
+
+        new_slice = actual[2:]
+        # TODO: when COW and spilling has been unified, find a clean way to
+        # test this without accessing the internal attributes _base and _ptr
+        assert (
+            new_slice._column.base_data._base._ptr
+            == actual._column.base_data._base._ptr
+        )
+        new_slice[0:2] = 10
+        assert_eq(new_slice, cudf.Series([10, 10, 5], index=[2, 3, 4]))
+        assert_eq(actual, cudf.Series([1, 2, 3, 4, 5]))
+
+
+def test_series_setitem_partial_slice_cow_off():
+    with cudf.option_context("copy_on_write", False):
+        actual = cudf.Series([1, 2, 3, 4, 5])
+        new_copy = actual.copy(deep=False)
+
+        new_copy[slice(2, 4, 1)] = 300
+        assert_eq(actual, cudf.Series([1, 2, 300, 300, 5]))
+        assert_eq(new_copy, cudf.Series([1, 2, 300, 300, 5]))
+
+        new_slice = actual[2:]
+        assert (
+            new_slice._column.base_data._ptr == actual._column.base_data._ptr
+        )
+        new_slice[0:2] = 10
+        assert_eq(new_slice, cudf.Series([10, 10, 5], index=[2, 3, 4]))
+        assert_eq(actual, cudf.Series([1, 2, 10, 10, 5]))
+
+
+def test_multiple_series_cow():
+    with cudf.option_context("copy_on_write", True):
+        # Verify constructing, modifying, deleting
+        # multiple copies of a series preserves
+        # the data appropriately when COW is enabled.
+        s = cudf.Series([10, 20, 30, 40, 50])
+        s1 = s.copy(deep=False)
+        s2 = s.copy(deep=False)
+        s3 = s.copy(deep=False)
+        s4 = s2.copy(deep=False)
+        s5 = s4.copy(deep=False)
+        s6 = s3.copy(deep=False)
+
+        s1[0:3] = 10000
+        # s1 will be unlinked from actual data in s,
+        # and then modified. Rest all should
+        # contain the original data.
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        for ser in [s, s2, s3, s4, s5, s6]:
+            assert_eq(ser, cudf.Series([10, 20, 30, 40, 50]))
+
+        s6[0:3] = 3000
+        # s6 will be unlinked from actual data in s,
+        # and then modified. Rest all should
+        # contain the original data.
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        for ser in [s2, s3, s4, s5]:
+            assert_eq(ser, cudf.Series([10, 20, 30, 40, 50]))
+
+        s2[1:4] = 4000
+        # s2 will be unlinked from actual data in s,
+        # and then modified. Rest all should
+        # contain the original data.
+        assert_eq(s2, cudf.Series([10, 4000, 4000, 4000, 50]))
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        for ser in [s3, s4, s5]:
+            assert_eq(ser, cudf.Series([10, 20, 30, 40, 50]))
+
+        s4[2:4] = 5000
+        # s4 will be unlinked from actual data in s,
+        # and then modified. Rest all should
+        # contain the original data.
+        assert_eq(s4, cudf.Series([10, 20, 5000, 5000, 50]))
+        assert_eq(s2, cudf.Series([10, 4000, 4000, 4000, 50]))
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        for ser in [s3, s5]:
+            assert_eq(ser, cudf.Series([10, 20, 30, 40, 50]))
+
+        s5[2:4] = 6000
+        # s5 will be unlinked from actual data in s,
+        # and then modified. Rest all should
+        # contain the original data.
+        assert_eq(s5, cudf.Series([10, 20, 6000, 6000, 50]))
+        assert_eq(s4, cudf.Series([10, 20, 5000, 5000, 50]))
+        assert_eq(s2, cudf.Series([10, 4000, 4000, 4000, 50]))
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        for ser in [s3]:
+            assert_eq(ser, cudf.Series([10, 20, 30, 40, 50]))
+
+        s7 = s5.copy(deep=False)
+        assert_eq(s7, cudf.Series([10, 20, 6000, 6000, 50]))
+        s7[1:3] = 55
+        # Making a copy of s5, i.e., s7 and modifying shouldn't
+        # be touching/modifying data in other series.
+        assert_eq(s7, cudf.Series([10, 55, 55, 6000, 50]))
+
+        assert_eq(s4, cudf.Series([10, 20, 5000, 5000, 50]))
+        assert_eq(s2, cudf.Series([10, 4000, 4000, 4000, 50]))
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        for ser in [s3]:
+            assert_eq(ser, cudf.Series([10, 20, 30, 40, 50]))
+
+        # Deleting any of the following series objects
+        # shouldn't delete rest of the weekly referenced data
+        # elsewhere.
+
+        del s2
+
+        assert_eq(s1, cudf.Series([10000, 10000, 10000, 40, 50]))
+        assert_eq(s3, cudf.Series([10, 20, 30, 40, 50]))
+        assert_eq(s4, cudf.Series([10, 20, 5000, 5000, 50]))
+        assert_eq(s5, cudf.Series([10, 20, 6000, 6000, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        assert_eq(s7, cudf.Series([10, 55, 55, 6000, 50]))
+
+        del s4
+        del s1
+
+        assert_eq(s3, cudf.Series([10, 20, 30, 40, 50]))
+        assert_eq(s5, cudf.Series([10, 20, 6000, 6000, 50]))
+        assert_eq(s6, cudf.Series([3000, 3000, 3000, 40, 50]))
+        assert_eq(s7, cudf.Series([10, 55, 55, 6000, 50]))
+
+        del s
+        del s6
+
+        assert_eq(s3, cudf.Series([10, 20, 30, 40, 50]))
+        assert_eq(s5, cudf.Series([10, 20, 6000, 6000, 50]))
+        assert_eq(s7, cudf.Series([10, 55, 55, 6000, 50]))
+
+        del s5
+
+        assert_eq(s3, cudf.Series([10, 20, 30, 40, 50]))
+        assert_eq(s7, cudf.Series([10, 55, 55, 6000, 50]))
+
+        del s3
+        assert_eq(s7, cudf.Series([10, 55, 55, 6000, 50]))
+
+
+def test_series_zero_copy_cow_on():
+    with cudf.option_context("copy_on_write", True):
+        s = cudf.Series([1, 2, 3, 4, 5])
+        s1 = s.copy(deep=False)
+        cp_array = cp.asarray(s)
+
+        # Ensure all original data & zero-copied
+        # data is same.
+        assert_eq(s, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(s1, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(cp_array, cp.array([1, 2, 3, 4, 5]))
+
+        cp_array[0:3] = 10
+        # Modifying a zero-copied array should only
+        # modify `s` and will leave rest of the copies
+        # untouched.
+
+        assert_eq(s, cudf.Series([10, 10, 10, 4, 5]))
+        assert_eq(s1, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(cp_array, cp.array([10, 10, 10, 4, 5]))
+
+        s2 = cudf.Series(cp_array)
+        assert_eq(s2, cudf.Series([10, 10, 10, 4, 5]))
+
+        s3 = s2.copy(deep=False)
+        cp_array[0] = 20
+        # Modifying a zero-copied array should modify
+        # `s2` and `s` only. Because `cp_array`
+        # is zero-copy shared with `s` & `s2`.
+
+        assert_eq(s, cudf.Series([20, 10, 10, 4, 5]))
+        assert_eq(s1, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(cp_array, cp.array([20, 10, 10, 4, 5]))
+        assert_eq(s2, cudf.Series([20, 10, 10, 4, 5]))
+        assert_eq(s3, cudf.Series([10, 10, 10, 4, 5]))
+
+        s4 = cudf.Series([10, 20, 30, 40, 50])
+        s5 = cudf.Series(s4)
+        assert_eq(s5, cudf.Series([10, 20, 30, 40, 50]))
+        s5[0:2] = 1
+        # Modifying `s5` should also modify `s4`
+        # because they are zero-copied.
+        assert_eq(s5, cudf.Series([1, 1, 30, 40, 50]))
+        assert_eq(s4, cudf.Series([1, 1, 30, 40, 50]))
+
+
+def test_series_zero_copy_cow_off():
+    with cudf.option_context("copy_on_write", False):
+        s = cudf.Series([1, 2, 3, 4, 5])
+        s1 = s.copy(deep=False)
+        cp_array = cp.asarray(s)
+
+        # Ensure all original data & zero-copied
+        # data is same.
+        assert_eq(s, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(s1, cudf.Series([1, 2, 3, 4, 5]))
+        assert_eq(cp_array, cp.array([1, 2, 3, 4, 5]))
+
+        cp_array[0:3] = 10
+        # When COW is off, modifying a zero-copied array
+        # will need to modify `s` & `s1` since they are
+        # shallow copied.
+
+        assert_eq(s, cudf.Series([10, 10, 10, 4, 5]))
+        assert_eq(s1, cudf.Series([10, 10, 10, 4, 5]))
+        assert_eq(cp_array, cp.array([10, 10, 10, 4, 5]))
+
+        s2 = cudf.Series(cp_array)
+        assert_eq(s2, cudf.Series([10, 10, 10, 4, 5]))
+        s3 = s2.copy(deep=False)
+        cp_array[0] = 20
+
+        # Modifying `cp_array`, will propagate the changes
+        # across all Series objects, because they are
+        # either shallow copied or zero-copied.
+
+        assert_eq(s, cudf.Series([20, 10, 10, 4, 5]))
+        assert_eq(s1, cudf.Series([20, 10, 10, 4, 5]))
+        assert_eq(cp_array, cp.array([20, 10, 10, 4, 5]))
+        assert_eq(s2, cudf.Series([20, 10, 10, 4, 5]))
+        assert_eq(s3, cudf.Series([20, 10, 10, 4, 5]))
+
+        s4 = cudf.Series([10, 20, 30, 40, 50])
+        s5 = cudf.Series(s4)
+        assert_eq(s5, cudf.Series([10, 20, 30, 40, 50]))
+        s5[0:2] = 1
+
+        # Modifying `s5` should also modify `s4`
+        # because they are zero-copied.
+        assert_eq(s5, cudf.Series([1, 1, 30, 40, 50]))
+        assert_eq(s4, cudf.Series([1, 1, 30, 40, 50]))
+
+
+@pytest.mark.parametrize("copy_on_write", [True, False])
+def test_series_str_copy(copy_on_write):
+    original_cow_setting = cudf.get_option("copy_on_write")
+    cudf.set_option("copy_on_write", copy_on_write)
+    s = cudf.Series(["a", "b", "c", "d", "e"])
+    s1 = s.copy(deep=True)
+    s2 = s.copy(deep=True)
+
+    assert_eq(s, cudf.Series(["a", "b", "c", "d", "e"]))
+    assert_eq(s1, cudf.Series(["a", "b", "c", "d", "e"]))
+    assert_eq(s2, cudf.Series(["a", "b", "c", "d", "e"]))
+
+    s[0:3] = "abc"
+
+    assert_eq(s, cudf.Series(["abc", "abc", "abc", "d", "e"]))
+    assert_eq(s1, cudf.Series(["a", "b", "c", "d", "e"]))
+    assert_eq(s2, cudf.Series(["a", "b", "c", "d", "e"]))
+
+    s2[1:4] = "xyz"
+
+    assert_eq(s, cudf.Series(["abc", "abc", "abc", "d", "e"]))
+    assert_eq(s1, cudf.Series(["a", "b", "c", "d", "e"]))
+    assert_eq(s2, cudf.Series(["a", "xyz", "xyz", "xyz", "e"]))
+    cudf.set_option("copy_on_write", original_cow_setting)
+
+
+@pytest.mark.parametrize("copy_on_write", [True, False])
+def test_series_cat_copy(copy_on_write):
+    original_cow_setting = cudf.get_option("copy_on_write")
+    cudf.set_option("copy_on_write", copy_on_write)
+    s = cudf.Series([10, 20, 30, 40, 50], dtype="category")
+    s1 = s.copy(deep=True)
+    s2 = s1.copy(deep=True)
+    s3 = s1.copy(deep=True)
+
+    s[0] = 50
+    assert_eq(s, cudf.Series([50, 20, 30, 40, 50], dtype=s.dtype))
+    assert_eq(s1, cudf.Series([10, 20, 30, 40, 50], dtype="category"))
+    assert_eq(s2, cudf.Series([10, 20, 30, 40, 50], dtype="category"))
+    assert_eq(s3, cudf.Series([10, 20, 30, 40, 50], dtype="category"))
+
+    s2[3] = 10
+    s3[2:5] = 20
+    assert_eq(s, cudf.Series([50, 20, 30, 40, 50], dtype=s.dtype))
+    assert_eq(s1, cudf.Series([10, 20, 30, 40, 50], dtype=s.dtype))
+    assert_eq(s2, cudf.Series([10, 20, 30, 10, 50], dtype=s.dtype))
+    assert_eq(s3, cudf.Series([10, 20, 20, 20, 20], dtype=s.dtype))
+    cudf.set_option("copy_on_write", original_cow_setting)
+
+
+def test_dataframe_cow_slice_setitem():
+    with cudf.option_context("copy_on_write", True):
+        df = cudf.DataFrame(
+            {"a": [10, 11, 12, 13, 14], "b": [20, 30, 40, 50, 60]}
+        )
+        slice_df = df[1:4]
+
+        assert_eq(
+            slice_df,
+            cudf.DataFrame(
+                {"a": [11, 12, 13], "b": [30, 40, 50]}, index=[1, 2, 3]
+            ),
+        )
+
+        slice_df["a"][2] = 1111
+
+        assert_eq(
+            slice_df,
+            cudf.DataFrame(
+                {"a": [11, 1111, 13], "b": [30, 40, 50]}, index=[1, 2, 3]
+            ),
+        )
+        assert_eq(
+            df,
+            cudf.DataFrame(
+                {"a": [10, 11, 12, 13, 14], "b": [20, 30, 40, 50, 60]}
+            ),
+        )
diff --git a/python/cudf/cudf/tests/test_csv.py b/python/cudf/cudf/tests/test_csv.py
new file mode 100644
index 0000000..318f81c
--- /dev/null
+++ b/python/cudf/cudf/tests/test_csv.py
@@ -0,0 +1,2250 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import codecs
+import gzip
+import os
+import re
+import shutil
+from collections import OrderedDict
+from io import BytesIO, StringIO
+from pathlib import Path
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+from pyarrow import fs as pa_fs
+
+import cudf
+from cudf import read_csv
+from cudf.core._compat import PANDAS_LT_140
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+def make_numeric_dataframe(nrows, dtype):
+    df = pd.DataFrame()
+    df["col1"] = np.arange(nrows, dtype=dtype)
+    df["col2"] = np.arange(1, 1 + nrows, dtype=dtype)
+    return df
+
+
+def make_datetime_dataframe(include_non_standard=False):
+    df = pd.DataFrame()
+    df["col1"] = np.array(
+        [
+            "31/10/2010",
+            "05/03/2001",
+            "20/10/1994",
+            "18/10/1990",
+            "1/1/1970",
+            "2016-04-30T01:02:03.000",
+            "2038-01-19 03:14:07",
+        ]
+    )
+    df["col2"] = np.array(
+        [
+            "18/04/1995",
+            "14 / 07 / 1994",
+            "07/06/2006",
+            "16/09/2005",
+            "2/2/1970",
+            "2007-4-30 1:6:40.000PM",
+            "2038-01-19 03:14:08",
+        ]
+    )
+    if include_non_standard:
+        # Last column contains non-standard date formats
+        df["col3"] = np.array(
+            [
+                "1 Jan",
+                "2 January 1994",
+                "Feb 2002",
+                "31-01-2000",
+                "1-1-1996",
+                "15-May-2009",
+                "21-Dec-3262",
+            ]
+        )
+    return df
+
+
+def make_numpy_mixed_dataframe():
+    df = pd.DataFrame()
+    df["Integer"] = np.array([2345, 11987, 9027, 9027])
+    df["Date"] = np.array(
+        ["18/04/1995", "14/07/1994", "07/06/2006", "16/09/2005"]
+    )
+    df["Float"] = np.array([9.001, 8.343, 6, 2.781])
+    df["Integer2"] = np.array([2345, 106, 2088, 789277])
+    df["Category"] = np.array(["M", "F", "F", "F"])
+    df["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    df["Boolean"] = np.array([True, False, True, False])
+    return df
+
+
+@pytest.fixture
+def pd_mixed_dataframe():
+    return make_numpy_mixed_dataframe()
+
+
+@pytest.fixture
+def cudf_mixed_dataframe():
+    return cudf.from_pandas(make_numpy_mixed_dataframe())
+
+
+def make_all_numeric_dataframe():
+    df = pd.DataFrame()
+
+    gdf_dtypes = [
+        "float",
+        "float32",
+        "double",
+        "float64",
+        "int8",
+        "short",
+        "int16",
+        "int",
+        "int32",
+        "long",
+        "int64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+    ]
+
+    np_dtypes = [
+        np.float32,
+        np.float32,
+        np.float64,
+        np.float64,
+        np.int8,
+        np.int16,
+        np.int16,
+        np.int32,
+        np.int32,
+        np.int64,
+        np.int64,
+        np.uint8,
+        np.uint16,
+        np.uint32,
+        np.uint64,
+    ]
+
+    for i in range(len(gdf_dtypes)):
+        df[gdf_dtypes[i]] = np.arange(10, dtype=np_dtypes[i])
+
+    return (
+        df,
+        OrderedDict(zip(gdf_dtypes, gdf_dtypes)),
+        OrderedDict(zip(gdf_dtypes, np_dtypes)),
+    )
+
+
+def make_all_numeric_extremes_dataframe():
+    # integers 0,+1,-1,min,max
+    # float 0.0, -0.0,+1,-1,min,max, nan, esp, espneg, tiny, [-ve values]
+    df, gdf_dtypes, pdf_dtypes = make_all_numeric_dataframe()
+    df = pd.DataFrame()
+
+    for gdf_dtype in gdf_dtypes:
+        np_type = pdf_dtypes[gdf_dtype]
+        if np.issubdtype(np_type, np.integer):
+            itype = np.iinfo(np_type)
+            extremes = [0, +1, -1, itype.min, itype.max]
+            df[gdf_dtype] = np.array(extremes * 4).astype(np_type)[:20]
+        else:
+            ftype = np.finfo(np_type)
+            extremes = [
+                0.0,
+                -0.0,
+                +1,
+                -1,
+                np.nan,
+                -np.nan,
+                # ftype.min, # TODO enable after fixing truncation issue #6235
+                # ftype.max, # TODO enable after fixing truncation issue #6235
+                np_type(np.inf),
+                -np_type(np.inf),
+                ftype.eps,
+                ftype.epsneg,
+                ftype.tiny,
+                -ftype.eps,
+                -ftype.epsneg,
+                -ftype.tiny,
+            ]
+            df[gdf_dtype] = np.array(extremes * 4, dtype=np_type)[:20]
+    return (
+        df,
+        gdf_dtypes,
+        pdf_dtypes,
+    )
+
+
+@pytest.fixture
+def pandas_extreme_numeric_dataframe():
+    return make_all_numeric_extremes_dataframe()[0]
+
+
+@pytest.fixture
+def cudf_extreme_numeric_dataframe(pandas_extreme_numeric_dataframe):
+    return cudf.from_pandas(pandas_extreme_numeric_dataframe)
+
+
+@pytest.fixture
+def path_or_buf(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_path_or_buf.csv")
+    df = make_numeric_dataframe(10, np.int32)
+
+    df.to_csv(fname, index=False, header=False)
+    buffer = df.to_csv(index=False, header=False)
+
+    def _make_path_or_buf(src):
+        if src == "filepath":
+            return str(fname)
+        if src == "pathobj":
+            return fname
+        if src == "bytes_io":
+            return BytesIO(buffer.encode())
+        if src == "string_io":
+            return StringIO(buffer)
+        if src == "url":
+            return Path(fname).as_uri()
+
+        raise ValueError("Invalid source type")
+
+    yield _make_path_or_buf
+
+
+dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint64, np.uint32]
+dtypes_dict = {"1": np.float64, "2": np.float32, "3": np.int64, "4": np.int32}
+nelem = [5, 25, 100]
+
+
+@pytest.mark.parametrize("dtype", dtypes)
+@pytest.mark.parametrize("nelem", nelem)
+def test_csv_reader_numeric_data(dtype, nelem, tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file1.csv")
+
+    df = make_numeric_dataframe(nelem, dtype)
+    df.to_csv(fname, index=False, header=False)
+
+    dtypes = [df[k].dtype for k in df.columns]
+    out = read_csv(str(fname), names=list(df.columns.values), dtype=dtypes)
+
+    assert len(out.columns) == len(df.columns)
+    assert_eq(df, out)
+
+
+@pytest.mark.parametrize("parse_dates", [["date2"], [0], ["date1", 1, "bad"]])
+def test_csv_reader_datetime(parse_dates):
+    df = make_datetime_dataframe(include_non_standard=True)
+    buffer = df.to_csv(index=False, header=False)
+
+    gdf = read_csv(
+        StringIO(buffer),
+        names=["date1", "date2", "bad"],
+        parse_dates=parse_dates,
+        dayfirst=True,
+    )
+    pdf = pd.read_csv(
+        StringIO(buffer),
+        names=["date1", "date2", "bad"],
+        parse_dates=parse_dates,
+        dayfirst=True,
+    )
+
+    assert_eq(gdf, pdf)
+
+
+@pytest.mark.parametrize("pandas_arg", [{"delimiter": "|"}, {"sep": "|"}])
+@pytest.mark.parametrize("cudf_arg", [{"sep": "|"}, {"delimiter": "|"}])
+def test_csv_reader_mixed_data_delimiter_sep(
+    tmpdir, pandas_arg, cudf_arg, pd_mixed_dataframe
+):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file3.csv")
+
+    pd_mixed_dataframe.to_csv(fname, sep="|", index=False, header=False)
+
+    gdf1 = read_csv(
+        str(fname),
+        names=["1", "2", "3", "4", "5", "6", "7"],
+        dtype=["int64", "date", "float64", "int64", "category", "str", "bool"],
+        dayfirst=True,
+        **cudf_arg,
+    )
+    gdf2 = read_csv(
+        str(fname),
+        names=["1", "2", "3", "4", "5", "6", "7"],
+        dtype=["int64", "date", "float64", "int64", "category", "str", "bool"],
+        dayfirst=True,
+        **pandas_arg,
+    )
+
+    pdf = pd.read_csv(
+        fname,
+        names=["1", "2", "3", "4", "5", "6", "7"],
+        parse_dates=[1],
+        dayfirst=True,
+        **pandas_arg,
+    )
+
+    assert len(gdf1.columns) == len(pdf.columns)
+    assert len(gdf2.columns) == len(pdf.columns)
+    assert_eq(gdf1, gdf2)
+
+
+@pytest.mark.parametrize("use_list", [False, True])
+def test_csv_reader_dtype_list(use_list):
+    df = make_numeric_dataframe(10, dtype=np.float32)
+    buffer = df.to_csv(index=False, header=False)
+
+    # PANDAS doesn't list but cudf does (treated as implied ordered dict)
+    # Select first column's dtype if non-list; expect the same dtype for all
+    if use_list:
+        dtypes = [df[k].dtype for k in df.columns]
+    else:
+        dtypes = df[df.columns[0]].dtype
+
+    gdf = read_csv(StringIO(buffer), dtype=dtypes, names=df.columns)
+
+    assert_eq(gdf, df)
+
+
+@pytest.mark.parametrize("use_names", [False, True])
+def test_csv_reader_dtype_dict(use_names):
+    # Save with the column header if not explicitly specifying a list of names
+    df, gdf_dtypes, pdf_dtypes = make_all_numeric_dataframe()
+    buffer = df.to_csv(index=False, header=(not use_names))
+    dtypes = df.dtypes.to_dict()
+    gdf_names = list(gdf_dtypes.keys()) if use_names else None
+    pdf_names = list(pdf_dtypes.keys()) if use_names else None
+    gdf = read_csv(StringIO(buffer), dtype=dtypes, names=gdf_names)
+    pdf = pd.read_csv(StringIO(buffer), dtype=dtypes, names=pdf_names)
+
+    assert_eq(gdf, pdf)
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
+@pytest.mark.parametrize("use_names", [True, False])
+def test_csv_reader_dtype_extremes(use_names):
+    # Save with the column header if not explicitly specifying a list of names
+    df, gdf_dtypes, pdf_dtypes = make_all_numeric_extremes_dataframe()
+    buffer = df.to_csv(index=False, header=(not use_names))
+    dtypes = df.dtypes.to_dict()
+    gdf_names = list(gdf_dtypes.keys()) if use_names else None
+    pdf_names = list(pdf_dtypes.keys()) if use_names else None
+
+    gdf = read_csv(StringIO(buffer), dtype=dtypes, names=gdf_names)
+    pdf = pd.read_csv(StringIO(buffer), dtype=dtypes, names=pdf_names)
+
+    assert_eq(gdf, pdf)
+
+
+def test_csv_reader_skiprows_skipfooter(tmpdir, pd_mixed_dataframe):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file5.csv")
+
+    pd_mixed_dataframe.to_csv(
+        fname, columns=["Integer", "Date", "Float"], index=False, header=False
+    )
+
+    # Using engine='python' to eliminate pandas warning of using python engine.
+    df_out = pd.read_csv(
+        fname,
+        names=["1", "2", "3"],
+        parse_dates=[1],
+        dayfirst=True,
+        skiprows=1,
+        skipfooter=1,
+        engine="python",
+    )
+    out = read_csv(
+        str(fname),
+        names=["1", "2", "3"],
+        dtype=["int64", "date", "float64"],
+        skiprows=1,
+        skipfooter=1,
+        dayfirst=True,
+    )
+
+    assert len(out.columns) == len(df_out.columns)
+    assert len(out) == len(df_out)
+    assert_eq(df_out, out)
+
+
+def test_csv_reader_negative_vals(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file6.csv")
+
+    names = ["0", "1", "2"]
+    dtypes = ["float32", "float32", "float32"]
+    lines = [
+        ",".join(names),
+        "-181.5060,-185.37000,-3",
+        "-127.6300,-230.54600,-9",
+    ]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    zero = [-181.5060, -127.6300]
+    one = [-185.370, -230.54600]
+    two = [-3, -9]
+
+    df = read_csv(str(fname), names=names, dtype=dtypes, skiprows=1)
+
+    np.testing.assert_allclose(zero, df["0"].to_numpy())
+    np.testing.assert_allclose(one, df["1"].to_numpy())
+    np.testing.assert_allclose(two, df["2"].to_numpy())
+
+
+def test_csv_reader_strings(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file7.csv")
+
+    names = ["text", "int"]
+    dtypes = ["str", "int"]
+    lines = [",".join(names), "a,0", "b,0", "c,0", "d,0"]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    df = read_csv(
+        str(fname),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        decimal=".",
+        thousands="'",
+    )
+
+    assert len(df.columns) == 2
+    assert df["text"].dtype == np.dtype("object")
+    assert df["int"].dtype == np.dtype("int64")
+    assert df["text"][0] == "a"
+    assert df["text"][1] == "b"
+    assert df["text"][2] == "c"
+    assert df["text"][3] == "d"
+
+
+def test_csv_reader_strings_quotechars(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file8.csv")
+
+    names = ["text", "int"]
+    dtypes = ["str", "int"]
+    lines = [",".join(names), '"a,\n",0', '"b ""c"" d",0', "e,0", '"f,,!.,",0']
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    df = read_csv(
+        str(fname),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        quotechar='"',
+        quoting=1,
+    )
+
+    assert len(df.columns) == 2
+    assert df["text"].dtype == np.dtype("object")
+    assert df["int"].dtype == np.dtype("int64")
+    assert df["text"][0] == "a,\n"
+    assert df["text"][1] == 'b "c" d'
+    assert df["text"][2] == "e"
+    assert df["text"][3] == "f,,!.,"
+
+
+def test_csv_reader_usecols_int_char(tmpdir, pd_mixed_dataframe):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file10.csv")
+    pd_mixed_dataframe.to_csv(
+        fname,
+        columns=["Integer", "Date", "Float", "Integer2"],
+        index=False,
+        header=False,
+    )
+
+    df_out = pd.read_csv(fname, usecols=[0, 1, 3])
+    out = read_csv(fname, usecols=[0, 1, 3])
+
+    assert len(out.columns) == len(df_out.columns)
+    assert len(out) == len(df_out)
+    assert_eq(df_out, out, check_names=False)
+
+
+@pytest.mark.parametrize(
+    "buffer",
+    [
+        "abc,ABC,abc,abcd,abc\n1,2,3,4,5\n",
+        "A,A,A.1,A,A.2,A,A.4,A,A\n1,2,3.1,4,a.2,a,a.4,a,a",
+        "A,A,A.1,,Unnamed: 4,A,A.4,A,A\n1,2,3.1,4,a.2,a,a.4,a,a",
+    ],
+)
+@pytest.mark.parametrize("mangle_dupe_cols", [True, False])
+def test_csv_reader_mangle_dupe_cols(tmpdir, buffer, mangle_dupe_cols):
+    # Default: mangle_dupe_cols=True
+    cu_df = read_csv(StringIO(buffer), mangle_dupe_cols=mangle_dupe_cols)
+    if mangle_dupe_cols:
+        pd_df = pd.read_csv(StringIO(buffer))
+    else:
+        # Pandas does not support mangle_dupe_cols=False
+        head = buffer.split("\n")[0].split(",")
+        first_cols = np.unique(head, return_index=True)[1]
+        pd_df = pd.read_csv(StringIO(buffer), usecols=first_cols)
+    assert_eq(cu_df, pd_df)
+
+
+def test_csv_reader_float_decimal(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file12.csv")
+
+    names = ["basic_32", "basic_64", "round", "decimal_only", "precision"]
+    dtypes = ["float32", "float64", "float64", "float32", "float64"]
+    lines = [
+        ";".join(names),
+        "1,2;1234,5678;12345;0,123;-73,98007199999998",
+        "3,4;3456,7890;67890;,456;1,7976931348623157e+307",
+        "5,6e0;0,5679e2;1,2e10;0,07e-001;0,0",
+    ]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    basic_32_ref = [1.2, 3.4, 5.6]
+    basic_64_ref = [1234.5678, 3456.7890, 56.79]
+    round_ref = [12345, 67890, 12000000000]
+    decimal_only_ref = [0.123, 0.456, 0.007]
+    precision_ref = [-73.98007199999998, 1.7976931348623157e307, 0.0]
+
+    df = read_csv(
+        str(fname),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        delimiter=";",
+        decimal=",",
+    )
+
+    np.testing.assert_allclose(basic_32_ref, df["basic_32"].to_numpy())
+    np.testing.assert_allclose(basic_64_ref, df["basic_64"].to_numpy())
+    np.testing.assert_allclose(round_ref, df["round"].to_numpy())
+    np.testing.assert_allclose(decimal_only_ref, df["decimal_only"].to_numpy())
+    np.testing.assert_allclose(precision_ref, df["precision"].to_numpy())
+
+
+def test_csv_reader_NaN_values():
+    names = dtypes = ["float32"]
+    empty_cells = '\n""\n'
+    default_na_cells = (
+        "#N/A\n#N/A N/A\n#NA\n-1.#IND\n"
+        "-1.#QNAN\n-NaN\n-nan\n1.#IND\n"
+        "1.#QNAN\nN/A\n<NA>\nNA\nNULL\n"
+        "NaN\nn/a\nnan\nnull\n"
+    )
+    custom_na_cells = "NV_NAN\nNotANumber\n"
+    all_cells = empty_cells + default_na_cells + custom_na_cells
+    custom_na_values = ["NV_NAN", "NotANumber"]
+
+    # test default NA values. empty cells should also yield NaNs
+    gdf = read_csv(
+        StringIO(default_na_cells + empty_cells), names=names, dtype=dtypes
+    )
+    pdf = pd.read_csv(
+        StringIO(default_na_cells + empty_cells), names=names, dtype=np.float32
+    )
+    assert_eq(pdf, gdf)
+
+    # custom NA values
+    gdf = read_csv(
+        StringIO(all_cells),
+        names=names,
+        dtype=dtypes,
+        na_values=custom_na_values,
+    )
+    pdf = pd.read_csv(
+        StringIO(all_cells),
+        names=names,
+        dtype=np.float32,
+        na_values=custom_na_values,
+    )
+    assert_eq(pdf, gdf)
+
+    # custom NA values
+    gdf = read_csv(
+        StringIO(empty_cells + default_na_cells + "_NAA_\n"),
+        names=names,
+        dtype=dtypes,
+        na_values="_NAA_",
+    )
+    pdf = pd.read_csv(
+        StringIO(empty_cells + default_na_cells + "_NAA_\n"),
+        names=names,
+        dtype=np.float32,
+        na_values="_NAA_",
+    )
+    assert_eq(pdf, gdf)
+
+    # data type detection should evaluate the column to int8 (all nulls)
+    gdf = read_csv(
+        StringIO(all_cells),
+        header=None,
+        na_values=custom_na_values,
+    )
+    assert gdf.dtypes[0] == "int8"
+    assert all(gdf["0"][idx] is cudf.NA for idx in range(len(gdf["0"])))
+
+    # data type detection should evaluate the column to object if some nulls
+    gdf = read_csv(StringIO(all_cells), header=None)
+    assert gdf.dtypes[0] == np.dtype("object")
+
+
+def test_csv_reader_thousands(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file13.csv")
+
+    names = dtypes = [
+        "float32",
+        "float64",
+        "int32",
+        "int64",
+        "uint32",
+        "uint64",
+    ]
+    lines = [
+        ",".join(names),
+        "1'234.5, 1'234.567, 1'234'567, 1'234'567'890,\
+                1'234'567, 1'234'567'890",
+        "12'345.6, 123'456.7, 12'345, 123'456'789, 12'345, 123'456'789",
+    ]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    f32_ref = [1234.5, 12345.6]
+    f64_ref = [1234.567, 123456.7]
+    int32_ref = [1234567, 12345]
+    int64_ref = [1234567890, 123456789]
+    uint32_ref = [1234567, 12345]
+    uint64_ref = [1234567890, 123456789]
+
+    df = read_csv(
+        str(fname), names=names, dtype=dtypes, skiprows=1, thousands="'"
+    )
+
+    np.testing.assert_allclose(f32_ref, df["float32"].to_numpy())
+    np.testing.assert_allclose(f64_ref, df["float64"].to_numpy())
+    np.testing.assert_allclose(int32_ref, df["int32"].to_numpy())
+    np.testing.assert_allclose(int64_ref, df["int64"].to_numpy())
+    np.testing.assert_allclose(uint32_ref, df["uint32"].to_numpy())
+    np.testing.assert_allclose(uint64_ref, df["uint64"].to_numpy())
+
+
+def test_csv_reader_buffer_strings():
+    names = ["text", "int"]
+    dtypes = ["str", "int"]
+    lines = [",".join(names), "a,0", "b,0", "c,0", "d,0"]
+
+    buffer = "\n".join(lines)
+
+    df = read_csv(StringIO(buffer), names=names, dtype=dtypes, skiprows=1)
+    assert len(df.columns) == 2
+    assert df["text"].dtype == np.dtype("object")
+    assert df["int"].dtype == np.dtype("int64")
+    assert df["text"][0] == "a"
+    assert df["text"][1] == "b"
+    assert df["text"][2] == "c"
+    assert df["text"][3] == "d"
+
+    df2 = read_csv(
+        BytesIO(str.encode(buffer)), names=names, dtype=dtypes, skiprows=1
+    )
+    assert len(df2.columns) == 2
+    assert df2["text"].dtype == np.dtype("object")
+    assert df2["int"].dtype == np.dtype("int64")
+    assert df2["text"][0] == "a"
+    assert df2["text"][1] == "b"
+    assert df2["text"][2] == "c"
+    assert df2["text"][3] == "d"
+
+
+@pytest.mark.parametrize(
+    "ext, out_comp, in_comp",
+    [
+        (".geez", "gzip", "gzip"),
+        (".beez", "bz2", "bz2"),
+        (".gz", "gzip", "infer"),
+        (".bz2", "bz2", "infer"),
+        (".beez", "bz2", np.str_("bz2")),
+        (".data", None, "infer"),
+        (".txt", None, None),
+        ("", None, None),
+    ],
+)
+def test_csv_reader_compression(
+    tmpdir, ext, out_comp, in_comp, pd_mixed_dataframe
+):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_compression" + ext)
+
+    df = pd_mixed_dataframe
+    df.to_csv(fname, index=False, header=False, compression=out_comp)
+
+    gdf = read_csv(fname, names=list(df.columns.values), compression=in_comp)
+    pdf = pd.read_csv(
+        fname, names=list(df.columns.values), compression=in_comp
+    )
+
+    assert_eq(gdf, pdf)
+
+
+@pytest.mark.parametrize(
+    "names, dtypes, data, trues, falses",
+    [
+        (
+            ["A", "B"],
+            ["bool", "bool"],
+            "True,True\nFalse,False\nTrue,False",
+            None,
+            None,
+        ),
+        (
+            ["A", "B"],
+            ["int32", "int32"],
+            "True,1\nFalse,2\nTrue,3",
+            None,
+            None,
+        ),
+        (
+            ["A", "B"],
+            ["int32", "int32"],
+            "YES,1\nno,2\nyes,3\nNo,4\nYes,5",
+            ["yes", "Yes", "YES"],
+            ["no", "NO", "No"],
+        ),
+        (["A", "B"], ["int32", "int32"], "foo,bar\nbar,foo", ["foo"], ["bar"]),
+        (["x", "y"], None, "True,1\nFalse,0", None, None),
+    ],
+)
+def test_csv_reader_bools(tmpdir, names, dtypes, data, trues, falses):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file11.csv")
+
+    lines = [",".join(names), data]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    # Usage of true_values and false_values makes that column into bool type
+    df_out = pd.read_csv(
+        fname,
+        names=names,
+        skiprows=1,
+        dtype=(dtypes[0] if dtypes else None),
+        true_values=trues,
+        false_values=falses,
+    )
+
+    out = read_csv(
+        fname,
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        true_values=trues,
+        false_values=falses,
+    )
+
+    assert_eq(df_out, out)
+
+
+def test_csv_reader_bools_custom():
+    names = ["text", "bool"]
+    dtypes = {"text": "str", "bool": "bool"}
+    trues = ["foo", "1"]
+    falses = ["bar", "0"]
+    lines = [
+        ",".join(names),
+        "true,true",
+        "false,false",
+        "foo,foo",
+        "bar,bar",
+        "0,0",
+        "1,1",
+    ]
+    buffer = "\n".join(lines)
+
+    df = read_csv(
+        StringIO(buffer),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        true_values=trues,
+        false_values=falses,
+    )
+
+    # Note: bool literals give parsing errors as int
+    # "0" and "1" give parsing errors as bool in pandas
+    expected = pd.read_csv(
+        StringIO(buffer),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        true_values=trues,
+        false_values=falses,
+    )
+    assert_eq(df, expected, check_dtype=True)
+
+
+def test_csv_reader_bools_NA():
+    names = ["text", "int"]
+    dtypes = ["str", "int"]
+    trues = ["foo"]
+    falses = ["bar"]
+    lines = [
+        ",".join(names),
+        "true,true",
+        "false,false",
+        "foo,foo",
+        "bar,bar",
+        "qux,qux",
+    ]
+
+    buffer = "\n".join(lines)
+
+    df = read_csv(
+        StringIO(buffer),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        true_values=trues,
+        false_values=falses,
+    )
+    assert len(df.columns) == 2
+    assert df["text"].dtype == np.dtype("object")
+    assert df["int"].dtype == np.dtype("int64")
+    expected = pd.DataFrame(
+        {
+            "text": ["true", "false", "foo", "bar", "qux"],
+            "int": [1.0, 0.0, 1.0, 0.0, np.nan],
+        }
+    )
+    assert_eq(df, expected)
+
+
+def test_csv_quotednumbers(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file12.csv")
+
+    names = ["integer", "decimal"]
+    dtypes = ["int32", "float32"]
+    lines = [
+        ",".join(names),
+        '1,"3.14"',
+        '"2","300"',
+        '"3",10101.0101',
+        '4,"6.28318"',
+    ]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    integer_ref = [1, 2, 3, 4]
+    decimal_ref = [3.14, 300, 10101.0101, 6.28318]
+
+    df1 = read_csv(str(fname), names=names, dtype=dtypes, skiprows=1)
+    df2 = read_csv(str(fname), names=names, dtype=dtypes, skiprows=1)
+
+    assert len(df2.columns) == 2
+    np.testing.assert_allclose(integer_ref, df1["integer"].to_numpy())
+    np.testing.assert_allclose(decimal_ref, df1["decimal"].to_numpy())
+    np.testing.assert_allclose(integer_ref, df2["integer"].to_numpy())
+    np.testing.assert_allclose(decimal_ref, df2["decimal"].to_numpy())
+
+
+def test_csv_reader_nrows(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file14.csv")
+
+    names = ["int1", "int2"]
+    dtypes = ["int32", "int32"]
+
+    rows = 4000000
+    read_rows = (rows * 3) // 4
+    skip_rows = (rows - read_rows) // 2
+    sample_skip = 1000
+
+    with open(str(fname), "w") as fp:
+        fp.write(",".join(names) + "\n")
+        for i in range(rows):
+            fp.write(str(i) + ", " + str(2 * i) + " \n")
+
+    # with specified names
+    df = read_csv(
+        str(fname),
+        names=names,
+        dtype=dtypes,
+        skiprows=skip_rows + 1,
+        nrows=read_rows,
+    )
+    assert df.shape == (read_rows, 2)
+    for row in range(0, read_rows // sample_skip, sample_skip):
+        assert df["int1"][row] == row + skip_rows
+        assert df["int2"][row] == 2 * (row + skip_rows)
+    assert df["int2"][read_rows - 1] == 2 * (read_rows - 1 + skip_rows)
+
+    # with column name inference
+    df = read_csv(
+        str(fname), dtype=dtypes, skiprows=skip_rows + 1, nrows=read_rows
+    )
+    assert df.shape == (read_rows, 2)
+    assert str(skip_rows) in list(df)[0]
+    assert str(2 * skip_rows) in list(df)[1]
+    for row in range(0, read_rows // sample_skip, sample_skip):
+        assert df[list(df)[0]][row] == row + skip_rows + 1
+        assert df[list(df)[1]][row] == 2 * (row + skip_rows + 1)
+    assert df[list(df)[1]][read_rows - 1] == 2 * (read_rows + skip_rows)
+
+    # nrows larger than the file
+    df = read_csv(str(fname), dtype=dtypes, nrows=rows * 2)
+    assert df.shape == (rows, 2)
+    for row in range(0, rows // sample_skip, sample_skip):
+        assert df["int1"][row] == row
+        assert df["int2"][row] == 2 * row
+    assert df["int2"][rows - 1] == 2 * (rows - 1)
+
+    # nrows + skiprows larger than the file
+    df = read_csv(
+        str(fname), dtype=dtypes, nrows=read_rows, skiprows=read_rows
+    )
+    assert df.shape == (rows - read_rows, 2)
+
+    # nrows equal to zero
+    df = read_csv(str(fname), dtype=dtypes, nrows=0)
+    assert df.shape == (0, 2)
+
+    # with both skipfooter and nrows - should throw
+    with pytest.raises(ValueError):
+        read_csv(str(fname), nrows=read_rows, skipfooter=1)
+
+
+def test_csv_reader_gzip_compression_strings(tmpdir):
+    fnamebase = tmpdir.mkdir("gdf_csv")
+    fname = fnamebase.join("tmp_csvreader_file15.csv")
+    fnamez = fnamebase.join("tmp_csvreader_file15.csv.gz")
+
+    names = ["text", "int"]
+    dtypes = ["str", "int"]
+    lines = [",".join(names), "a,0", "b,0", "c,0", "d,0"]
+
+    with open(str(fname), "w") as fp:
+        fp.write("\n".join(lines))
+
+    with open(str(fname), "rb") as f_in, gzip.open(str(fnamez), "wb") as f_out:
+        shutil.copyfileobj(f_in, f_out)
+
+    df = read_csv(
+        str(fnamez),
+        names=names,
+        dtype=dtypes,
+        skiprows=1,
+        decimal=".",
+        thousands="'",
+        compression="gzip",
+    )
+
+    assert len(df.columns) == 2
+    assert df["text"].dtype == np.dtype("object")
+    assert df["int"].dtype == np.dtype("int64")
+    assert df["text"][0] == "a"
+    assert df["text"][1] == "b"
+    assert df["text"][2] == "c"
+    assert df["text"][3] == "d"
+
+
+@pytest.mark.parametrize("skip_rows", [0, 2, 4])
+@pytest.mark.parametrize("header_row", [0, 2])
+def test_csv_reader_skiprows_header(skip_rows, header_row):
+    names = ["float_point", "integer"]
+    dtypes = ["float64", "int64"]
+    lines = [
+        ",".join(names),
+        "1.2, 1",
+        "2.3, 2",
+        "3.4, 3",
+        "4.5, 4",
+        "5.6, 5",
+        "6.7, 6",
+    ]
+    buffer = "\n".join(lines)
+
+    cu_df = read_csv(
+        StringIO(buffer), dtype=dtypes, skiprows=skip_rows, header=header_row
+    )
+    pd_df = pd.read_csv(
+        StringIO(buffer), skiprows=skip_rows, header=header_row
+    )
+
+    assert cu_df.shape == pd_df.shape
+    assert list(cu_df.columns.values) == list(pd_df.columns.values)
+
+
+def test_csv_reader_dtype_inference():
+    names = ["float_point", "integer"]
+    lines = [
+        ",".join(names),
+        "1.2,1",
+        "2.3,2",
+        "3.4,3",
+        "4.5,4",
+        "5.6,5",
+        "6.7,6",
+    ]
+    buffer = "\n".join(lines)
+    cu_df = read_csv(StringIO(buffer))
+    pd_df = pd.read_csv(StringIO(buffer))
+
+    assert cu_df.shape == pd_df.shape
+    assert list(cu_df.columns.values) == list(pd_df.columns.values)
+
+
+def test_csv_reader_dtype_inference_whitespace():
+    names = ["float_point", "integer"]
+    lines = [
+        ",".join(names),
+        "  1.2,    1",
+        "2.3,2    ",
+        "  3.4,   3",
+        " 4.5,4",
+        "5.6,  5",
+        " 6.7,6 ",
+    ]
+    buffer = "\n".join(lines)
+    cu_df = read_csv(StringIO(buffer))
+    pd_df = pd.read_csv(StringIO(buffer))
+
+    assert cu_df.shape == pd_df.shape
+    assert list(cu_df.columns.values) == list(pd_df.columns.values)
+
+
+def test_csv_reader_empty_dataframe():
+    dtypes = ["float64", "int64"]
+    buffer = "float_point, integer"
+
+    # should work fine with dtypes
+    df = read_csv(StringIO(buffer), dtype=dtypes)
+    assert df.shape == (0, 2)
+    assert all(df.dtypes == ["float64", "int64"])
+
+    # should default to string columns without dtypes
+    df = read_csv(StringIO(buffer))
+    assert df.shape == (0, 2)
+    assert all(df.dtypes == ["object", "object"])
+
+
+def test_csv_reader_filenotfound(tmpdir):
+    fname = "non-existing-filename.csv"
+
+    # should raise an error
+    with pytest.raises(FileNotFoundError):
+        read_csv(str(fname))
+
+    # should raise an error
+    dname = tmpdir.mkdir("gdf_csv")
+    with pytest.raises(FileNotFoundError):
+        read_csv(str(dname))
+
+
+@pytest.mark.parametrize(
+    "src", ["filepath", "pathobj", "bytes_io", "string_io", "url"]
+)
+def test_csv_reader_filepath_or_buffer(tmpdir, path_or_buf, src):
+    expect = pd.read_csv(path_or_buf("filepath"))
+    got = cudf.read_csv(path_or_buf(src))
+
+    assert_eq(expect, got)
+
+
+def test_csv_reader_arrow_nativefile(path_or_buf):
+    # Check that we can read a file opened with the
+    # Arrow FileSystem interface
+    expect = cudf.read_csv(path_or_buf("filepath"))
+    fs, path = pa_fs.FileSystem.from_uri(path_or_buf("filepath"))
+    with fs.open_input_file(path) as fil:
+        got = cudf.read_csv(fil)
+
+    assert_eq(expect, got)
+
+
+def test_small_zip(tmpdir):
+    df = pd.DataFrame(
+        {
+            "a": [1997] * 2,
+            "b": ["Ford"] * 2,
+            "c": ["Super, luxurious truck"] * 2,
+        }
+    )
+
+    fname = tmpdir.join("small_zip_file.zip")
+    df.to_csv(fname, index=False)
+
+    got = cudf.read_csv(fname)
+    assert_eq(df, got)
+
+
+def test_csv_reader_carriage_return(tmpdir):
+    rows = 1000
+    names = ["int_row", "int_double_row"]
+    buffer = ",".join(names) + "\r\n"
+    for row in range(rows):
+        buffer += str(row) + ", " + str(2 * row) + "\r\n"
+
+    df = read_csv(StringIO(buffer))
+    expect = cudf.DataFrame(
+        {"int_row": cp.arange(rows), "int_double_row": cp.arange(rows) * 2}
+    )
+
+    assert len(df) == rows
+    assert_eq(expect, df)
+
+
+def test_csv_reader_tabs():
+    names = ["float_point", "integer", "date"]
+    lines = [
+        ",".join(names),
+        "1.2,\t12,     \t11/22/1995",
+        "3.4\t,\t34\t,\t 01/01/2001",
+        "\t 5.6,56 \t, 12/12/1970",
+        "\t7.8 , 78\t,06/15/2018 \t",
+    ]
+    buffer = "\n".join(lines)
+
+    df = read_csv(StringIO(buffer), parse_dates=["date"])
+
+    assert df.shape == (4, 3)
+
+    floats = [1.2, 3.4, 5.6, 7.8]
+    ints = [12, 34, 56, 78]
+    dates = [
+        "1995-11-22T00:00:00.000000000",
+        "2001-01-01T00:00:00.000000000",
+        "1970-12-12T00:00:00.000000000",
+        "2018-06-15T00:00:00.000000000",
+    ]
+    np.testing.assert_allclose(floats, df["float_point"].to_numpy())
+    np.testing.assert_allclose(ints, df["integer"].to_numpy())
+    for row in range(4):
+        assert str(df["date"][row]) == dates[row]
+
+
+@pytest.mark.parametrize("segment_bytes", [10000, 19999, 30001, 36000])
+def test_csv_reader_byte_range(tmpdir, segment_bytes):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file16.csv")
+
+    names = ["int1", "int2"]
+
+    rows = 10000
+    with open(str(fname), "w") as fp:
+        for i in range(rows):
+            fp.write(str(i) + ", " + str(2 * i) + " \n")
+    file_size = os.stat(str(fname)).st_size
+
+    ref_df = read_csv(str(fname), names=names).to_pandas()
+
+    dfs = []
+    for segment in range((file_size + segment_bytes - 1) // segment_bytes):
+        dfs.append(
+            read_csv(
+                str(fname),
+                names=names,
+                byte_range=(segment * segment_bytes, segment_bytes),
+            )
+        )
+    df = cudf.concat(dfs).to_pandas()
+
+    assert list(df["int1"]) == list(ref_df["int1"])
+    assert list(df["int2"]) == list(ref_df["int2"])
+
+
+def test_csv_reader_byte_range_type_corner_case(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file17.csv")
+
+    cudf.datasets.timeseries(
+        start="2000-01-01",
+        end="2000-01-02",
+        dtypes={"name": str, "id": int, "x": float, "y": float},
+    ).to_csv(fname, chunksize=100000)
+
+    byte_range = (2_147_483_648, 0)
+    with pytest.raises(RuntimeError, match="Offset is past end of file"):
+        cudf.read_csv(fname, byte_range=byte_range, header=None)
+
+
+@pytest.mark.parametrize("segment_bytes", [10, 19, 31, 36])
+def test_csv_reader_byte_range_strings(segment_bytes):
+    names = ["strings"]
+    buffer = "\n".join('"' + str(x) + '"' for x in range(1, 100))
+    file_size = len(buffer)
+
+    ref_df = read_csv(StringIO(buffer), names=names).to_pandas()
+
+    dfs = []
+    for segment in range((file_size + segment_bytes - 1) // segment_bytes):
+        dfs.append(
+            read_csv(
+                StringIO(buffer),
+                names=names,
+                byte_range=(segment * segment_bytes, segment_bytes),
+            )
+        )
+    df = cudf.concat(dfs).to_pandas()
+
+    assert list(df["strings"]) == list(ref_df["strings"])
+
+
+@pytest.mark.parametrize(
+    "header_row, skip_rows, skip_blanks",
+    [
+        (1, 0, True),
+        ("infer", 2, True),
+        (1, 4, True),
+        (3, 0, False),
+        ("infer", 5, False),
+    ],
+)
+@pytest.mark.parametrize("lineterminator", ["\n", "\r\n"])
+def test_csv_reader_blanks_and_comments(
+    skip_rows, header_row, skip_blanks, lineterminator
+):
+    lines = [
+        "# first comment line",
+        lineterminator,
+        "# third comment line",
+        "1,2,3",
+        "4,5,6",
+        "7,8,9",
+        lineterminator,
+        "# last comment line",
+        lineterminator,
+        "1,1,1",
+    ]
+    buffer = lineterminator.join(lines)
+
+    cu_df = read_csv(
+        StringIO(buffer),
+        comment="#",
+        header=header_row,
+        skiprows=skip_rows,
+        skip_blank_lines=skip_blanks,
+    )
+    pd_df = pd.read_csv(
+        StringIO(buffer),
+        comment="#",
+        header=header_row,
+        skiprows=skip_rows,
+        skip_blank_lines=skip_blanks,
+    )
+
+    assert cu_df.shape == pd_df.shape
+    assert list(cu_df.columns.values) == list(pd_df.columns.values)
+
+
+def test_csv_reader_prefix():
+    lines = ["1, 1, 1, 1"]
+    buffer = "\n".join(lines)
+
+    prefix_str = "a_prefix"
+    df = read_csv(StringIO(buffer), header=None, prefix=prefix_str)
+
+    column_names = list(df.columns.values)
+    for col in range(len(column_names)):
+        assert column_names[col] == prefix_str + str(col)
+
+
+def test_csv_reader_delim_whitespace():
+    buffer = "1    2  3\n4  5 6"
+
+    # with header row
+    cu_df = read_csv(StringIO(buffer), delim_whitespace=True)
+    pd_df = pd.read_csv(StringIO(buffer), delim_whitespace=True)
+    assert_eq(pd_df, cu_df)
+
+    # without header row
+    cu_df = read_csv(StringIO(buffer), delim_whitespace=True, header=None)
+    pd_df = pd.read_csv(StringIO(buffer), delim_whitespace=True, header=None)
+    assert pd_df.shape == cu_df.shape
+
+    # should raise an error if used with delimiter or sep
+    with pytest.raises(ValueError):
+        read_csv(StringIO(buffer), delim_whitespace=True, delimiter=" ")
+    with pytest.raises(ValueError):
+        read_csv(StringIO(buffer), delim_whitespace=True, sep=" ")
+
+
+def test_csv_reader_unnamed_cols():
+    # first and last columns are unnamed
+    buffer = ",1,2,3,\n4,5,6,7,8"
+
+    cu_df = read_csv(StringIO(buffer))
+    pd_df = pd.read_csv(StringIO(buffer))
+
+    assert all(pd_df.columns == cu_df.columns)
+    assert pd_df.shape == cu_df.shape
+
+
+def test_csv_reader_header_quotation():
+    buffer = '"1,,1","2,\n,2",3\n+4,+5,+6'
+
+    cu_df = read_csv(StringIO(buffer))
+    pd_df = pd.read_csv(StringIO(buffer))
+    assert cu_df.shape == (1, 3)
+    assert_eq(pd_df, cu_df)
+
+    # test cases that fail with pandas
+    buffer_pd_fail = '"1,one," , ",2,two" ,3\n4,5,6'
+    cu_df = read_csv(StringIO(buffer_pd_fail))
+    assert cu_df.shape == (1, 3)
+
+
+def test_csv_reader_oversized_byte_range():
+    buffer = "a,b,c,d,e\n4,5,6,7,8"
+
+    cu_df = read_csv(StringIO(buffer), byte_range=(0, 1024))
+    pd_df = pd.read_csv(StringIO(buffer))
+
+    assert all(pd_df.columns == cu_df.columns)
+    assert pd_df.shape == cu_df.shape
+
+
+def test_csv_reader_index_col():
+    buffer = "0,1,2\n3,4,5\n6,7,8"
+    names = ["int1", "int2", "int3"]
+
+    # using a column name
+    cu_df = read_csv(StringIO(buffer), names=names, index_col="int1")
+    pd_df = pd.read_csv(StringIO(buffer), names=names, index_col="int1")
+    assert_eq(pd_df, cu_df)
+
+    # using a column index
+    cu_df = read_csv(StringIO(buffer), header=None, index_col=0)
+    pd_df = pd.read_csv(StringIO(buffer), header=None, index_col=0)
+    assert_eq(cu_df.index, pd_df.index)
+
+    # using a column index with names
+    cu_df = read_csv(StringIO(buffer), header=None, index_col=0, names=names)
+    pd_df = pd.read_csv(
+        StringIO(buffer), header=None, index_col=0, names=names
+    )
+    assert_eq(cu_df.index, pd_df.index)
+
+    # passing False to avoid using a column as index (no-op in cuDF)
+    cu_df = read_csv(StringIO(buffer), header=None, index_col=False)
+    pd_df = pd.read_csv(StringIO(buffer), header=None, index_col=False)
+    assert_eq(cu_df.index, pd_df.index)
+
+
+@pytest.mark.parametrize("index_name", [None, "custom name", 124])
+@pytest.mark.parametrize("index_col", [None, 0, "a"])
+def test_csv_reader_index_names(index_name, index_col):
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 3], "b": [10, 11, 12]}, index=["AB", "CD", "EF"]
+    )
+    pdf.index.name = index_name
+
+    buffer = pdf.to_csv()
+    actual = cudf.read_csv(StringIO(buffer), index_col=index_col)
+    expected = pd.read_csv(StringIO(buffer), index_col=index_col)
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "names", [["a", "b", "c"], [416, 905, 647], range(3), None]
+)
+def test_csv_reader_column_names(names):
+    buffer = "0,1,2\n3,4,5\n6,7,8"
+
+    df = read_csv(StringIO(buffer), names=names)
+    if names is None:
+        assert list(df) == ["0", "1", "2"]
+    else:
+        assert list(df) == list(names)
+
+
+@pytest.mark.xfail(
+    condition=PANDAS_LT_140,
+    reason="https://github.com/rapidsai/cudf/issues/10618",
+)
+def test_csv_reader_repeated_column_name():
+    buffer = """A,A,A.1,A,A.2,A,A.4,A,A
+                1,2,3.1,4,a.2,a,a.4,a,a
+                2,4,6.1,8,b.2,b,b.4,b,b"""
+
+    # pandas and cudf to have same repeated column names
+    pdf = pd.read_csv(StringIO(buffer))
+    gdf = cudf.read_csv(StringIO(buffer))
+    assert_eq(pdf.columns, gdf.columns)
+
+
+def test_csv_reader_bools_false_positives(tmpdir):
+    # values that are equal to ["True", "TRUE", "False", "FALSE"]
+    # when using ints to detect bool values
+    items = [3977, 4329, 24015, 27567]
+
+    buffer = "\n".join(str(i) for i in items)
+
+    df = read_csv(StringIO(buffer), header=None, dtype=["int32"])
+
+    np.testing.assert_array_equal(items, df["0"].to_numpy())
+
+
+def test_csv_reader_aligned_byte_range(tmpdir):
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file19.csv")
+    nelem = 1000
+
+    input_df = pd.DataFrame(
+        {"key": np.arange(0, nelem), "zeros": np.zeros(nelem)}
+    )
+    input_df.to_csv(fname)
+
+    df = cudf.read_csv(str(fname), byte_range=(0, 4096))
+    # read_csv call above used to crash; the assert below is not crucial
+    assert np.count_nonzero(df["zeros"].to_pandas().values) == 0
+
+
+@pytest.mark.parametrize(
+    "pdf_dtype, gdf_dtype",
+    [(None, None), ("int", "hex"), ("int32", "hex32"), ("int64", "hex64")],
+)
+def test_csv_reader_hexadecimals(pdf_dtype, gdf_dtype):
+    lines = ["0x0", "-0x1000", "0xfedcba", "0xABCDEF", "0xaBcDeF"]
+    values = [int(hex_int, 16) for hex_int in lines]
+
+    buffer = "\n".join(lines)
+
+    if gdf_dtype is not None:
+        # require explicit `hex` dtype to parse hexadecimals
+        pdf = pd.DataFrame(data=values, dtype=pdf_dtype, columns=["hex_int"])
+        gdf = read_csv(StringIO(buffer), dtype=[gdf_dtype], names=["hex_int"])
+        np.testing.assert_array_equal(
+            pdf["hex_int"], gdf["hex_int"].to_numpy()
+        )
+    else:
+        # otherwise, dtype inference returns as object (string)
+        pdf = pd.read_csv(StringIO(buffer), names=["hex_int"])
+        gdf = read_csv(StringIO(buffer), names=["hex_int"])
+        assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "np_dtype, gdf_dtype",
+    [("int", "hex"), ("int32", "hex32"), ("int64", "hex64")],
+)
+def test_csv_reader_hexadecimal_overflow(np_dtype, gdf_dtype):
+    # This tests values which cause an overflow warning that will become an
+    # error in pandas. NumPy wraps the overflow silently up to the bounds of a
+    # signed int64.
+    lines = [
+        "0x0",
+        "-0x1000",
+        "0xfedcba",
+        "0xABCDEF",
+        "0xaBcDeF",
+        "0x9512c20b",
+        "0x7fffffff",
+        "0x7fffffffffffffff",
+        "-0x8000000000000000",
+    ]
+    values = [int(hex_int, 16) for hex_int in lines]
+    buffer = "\n".join(lines)
+
+    gdf = read_csv(StringIO(buffer), dtype=[gdf_dtype], names=["hex_int"])
+
+    expected = np.array(values).astype(np_dtype)
+    actual = gdf["hex_int"].to_numpy()
+    np.testing.assert_array_equal(expected, actual)
+
+
+@pytest.mark.parametrize("quoting", [0, 1, 2, 3])
+def test_csv_reader_pd_consistent_quotes(quoting):
+    names = ["text"]
+    dtypes = ["str"]
+    lines = ['"a"', '"b ""c"" d"', '"f!\n."']
+
+    buffer = "\n".join(lines)
+
+    gd_df = read_csv(
+        StringIO(buffer), names=names, dtype=dtypes, quoting=quoting
+    )
+    pd_df = pd.read_csv(StringIO(buffer), names=names, quoting=quoting)
+
+    assert_eq(pd_df, gd_df)
+
+
+def test_read_csv_names_header_combination():
+    pdf = pd.DataFrame(
+        {
+            "firstname": ["Emma", "Ava", "Sophia"],
+            "lastname": ["Olivia", "Isabella", "Charlotte"],
+            "gender": ["F", "F", "F"],
+        }
+    )
+    buffer = pdf.to_csv(header=True, index=False)
+    names = pdf.columns
+
+    gdf = read_csv(StringIO(buffer), names=names, header=0)
+    assert_eq(pdf, gdf)
+
+    gdf = read_csv(StringIO(buffer), header=0)
+    assert_eq(pdf, gdf)
+
+    gdf = read_csv(StringIO(buffer))
+    assert_eq(pdf, gdf)
+
+
+def test_csv_reader_scientific_type_detection():
+    buffer = """1.,1.1,-1.1,1E1,1e1,-1e1,-1e-1,1e-1,1.1e1,1.1e-1,-1.1e-1,-1.1e1
+                +1.1,1E+1,1e+1,+1e1,+1e-1,1e-1,+1.1e1,1.1e+1,+1.1e+1,+1.1e1"""
+    expected = [
+        1.0,
+        1.1,
+        -1.1,
+        10.0,
+        10.0,
+        -10,
+        -0.1,
+        0.1,
+        11,
+        0.11,
+        -0.11,
+        -11,
+        1.1,
+        10.0,
+        10.0,
+        10,
+        0.1,
+        0.1,
+        11,
+        11,
+        11,
+        11,
+    ]
+
+    df = read_csv(StringIO(buffer), header=None)
+
+    for dt in df.dtypes:
+        assert dt == "float64"
+    for col in df:
+        assert np.isclose(df[col][0], expected[int(col)])
+
+
+@pytest.mark.parametrize("lineterminator", ["\n", "\r\n"])
+def test_csv_blank_first_row(lineterminator):
+    lines = ["colA,colB", "", "1, 1.1", "2, 2.2"]
+    buffer = lineterminator.join(lines)
+
+    cu_df = read_csv(StringIO(buffer))
+
+    assert cu_df.shape == (2, 2)
+    assert all(cu_df.columns == ["colA", "colB"])
+
+
+@pytest.mark.parametrize("contents", ["", "\n"])
+def test_csv_empty_file(tmpdir, contents):
+    fname = tmpdir.mkdir("gdf_csv").join("test_csv_empty_file.csv")
+    with open(fname, "w") as f:
+        f.write(contents)
+
+    col_names = ["col1", "col2", "col3", "col4"]
+    in_dtypes = ["int", "str", "float", "short"]
+    out_dtypes = ["int64", "object", "float64", "int16"]
+
+    # Empty dataframe if no columns names specified or inferred
+    df = read_csv(str(fname))
+    assert len(df.columns) == 0
+
+    # No row dataframe if columns names are specified or inferred
+    df = read_csv(str(fname), dtype=in_dtypes, names=col_names)
+    assert all(df.columns == col_names)
+    assert list(df.dtypes) == out_dtypes
+
+
+@pytest.mark.parametrize("contents", ["", "\n"])
+def test_csv_empty_buffer(tmpdir, contents):
+    col_names = ["col1", "col2", "col3", "col4"]
+    in_dtypes = ["int", "str", "float", "short"]
+    out_dtypes = ["int64", "object", "float64", "int16"]
+
+    # Empty dataframe if no columns names specified or inferred
+    df = read_csv(StringIO(contents))
+    assert len(df.columns) == 0
+
+    # No row dataframe if columns names are specified or inferred
+    df = read_csv(StringIO(contents), dtype=in_dtypes, names=col_names)
+    assert all(df.columns == col_names)
+    assert list(df.dtypes) == out_dtypes
+
+
+@pytest.mark.parametrize(
+    "dtype", [["short", "float", "int"], {"A": "short", "C": "int"}]
+)
+def test_csv_reader_partial_dtype(dtype):
+    names_df = read_csv(
+        StringIO("0,1,2"),
+        names=["A", "B", "C"],
+        dtype=dtype,
+        usecols=["A", "C"],
+    )
+    header_df = read_csv(
+        StringIO('"A","B","C"\n0,1,2'), dtype=dtype, usecols=["A", "C"]
+    )
+
+    assert names_df == header_df
+    assert all(names_df.dtypes == ["int16", "int64"])
+
+
+def test_csv_writer_file_handle(tmpdir):
+    df = pd.DataFrame({"a": [1, 2, 3], "b": ["xxx", "yyyy", "zzzzz"]})
+    gdf = cudf.from_pandas(df)
+
+    gdf_df_fname = tmpdir.join("gdf_df_1.csv")
+    with open(gdf_df_fname, "w") as f:
+        gdf.to_csv(path_or_buf=f, index=False)
+    assert os.path.exists(gdf_df_fname)
+
+    gdf2 = pd.read_csv(gdf_df_fname)
+    assert_eq(gdf, gdf2)
+
+
+def test_csv_writer_file_append(tmpdir):
+    gdf1 = cudf.DataFrame({"a": [1, 2, 3], "b": ["xxx", "yyyy", "zzzzz"]})
+    gdf2 = cudf.DataFrame({"a": [4, 5, 6], "b": ["foo", "bar", "baz"]})
+
+    gdf_df_fname = tmpdir.join("gdf_df_append.csv")
+    with open(gdf_df_fname, "w") as f:
+        gdf1.to_csv(f, index=False)
+    with open(gdf_df_fname, "a") as f:
+        gdf2.to_csv(f, header=False, index=False)
+
+    result = cudf.read_csv(gdf_df_fname)
+    expected = cudf.concat([gdf1, gdf2], ignore_index=True)
+    assert_eq(result, expected, check_index_type=True)
+
+
+def test_csv_writer_buffer(tmpdir):
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": ["xxx", "yyyy", "zzzzz"]})
+
+    buffer = BytesIO()
+    gdf.to_csv(buffer, index=False)
+
+    result = cudf.read_csv(buffer)
+    assert_eq(result, gdf)
+
+
+@pytest.mark.parametrize("dtype", dtypes)
+@pytest.mark.parametrize("nelem", nelem)
+def test_csv_writer_numeric_data(dtype, nelem, tmpdir):
+    pdf_df_fname = tmpdir.join("pdf_df_1.csv")
+    gdf_df_fname = tmpdir.join("gdf_df_1.csv")
+
+    df = make_numeric_dataframe(nelem, dtype)
+    gdf = cudf.from_pandas(df)
+    df.to_csv(path_or_buf=pdf_df_fname, index=False, lineterminator="\n")
+    gdf.to_csv(path_or_buf=gdf_df_fname, index=False)
+
+    assert os.path.exists(pdf_df_fname)
+    assert os.path.exists(gdf_df_fname)
+
+    expect = pd.read_csv(pdf_df_fname)
+    got = pd.read_csv(gdf_df_fname)
+    assert_eq(expect, got)
+
+
+def test_csv_writer_datetime_data(tmpdir):
+    pdf_df_fname = tmpdir.join("pdf_df_2.csv")
+    gdf_df_fname = tmpdir.join("gdf_df_2.csv")
+
+    df = make_datetime_dataframe()
+    gdf = cudf.from_pandas(df)
+    df.to_csv(path_or_buf=pdf_df_fname, index=False, lineterminator="\n")
+    gdf.to_csv(path_or_buf=gdf_df_fname, index=False)
+
+    assert os.path.exists(pdf_df_fname)
+    assert os.path.exists(gdf_df_fname)
+
+    expect = pd.read_csv(pdf_df_fname)
+    got = pd.read_csv(gdf_df_fname)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("lineterminator", ["\r", "\n", "\t", np.str_("\n")])
+@pytest.mark.parametrize("sep", [",", "/", np.str_(",")])
+def test_csv_writer_terminator_sep(lineterminator, sep, cudf_mixed_dataframe):
+    df = cudf_mixed_dataframe
+
+    buffer = BytesIO()
+    df.to_csv(buffer, lineterminator=lineterminator, sep=sep, index=False)
+
+    got = read_csv(buffer, lineterminator=lineterminator, sep=sep)
+    assert_eq(df, got)
+
+
+@pytest.mark.parametrize(
+    "lineterminator", ["\r\n", "ABC", "\t\t", np.str_("\r\n")]
+)
+def test_csv_writer_multichar_terminator(lineterminator, cudf_mixed_dataframe):
+    df = cudf_mixed_dataframe
+
+    default_terminator_csv = StringIO()
+    df.to_csv(default_terminator_csv)
+
+    # Need to check manually since readers don't support
+    # multicharacter line terminators
+    expected = default_terminator_csv.getvalue().replace("\n", lineterminator)
+
+    buffer = StringIO()
+    df.to_csv(buffer, lineterminator=lineterminator)
+    got = buffer.getvalue()
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "columns",
+    [
+        ["Date", "Float"],
+        ["Integer2", "Float", "Date", "Integer", "String", "Boolean"],
+        None,
+    ],
+)
+@pytest.mark.parametrize(
+    "header", [True, False, np.bool_(True), np.bool_(False)]
+)
+@pytest.mark.parametrize(
+    "index", [True, False, np.bool_(True), np.bool_(False)]
+)
+def test_csv_writer_column_and_header_options(
+    columns, header, index, pd_mixed_dataframe
+):
+    pdf = pd_mixed_dataframe
+    df = cudf.from_pandas(pdf)
+
+    cudf_buffer = BytesIO()
+    df.to_csv(cudf_buffer, columns=columns, header=header, index=index)
+    pd_buffer = BytesIO()
+    pdf.to_csv(pd_buffer, columns=columns, header=header, index=index)
+
+    expected = cudf.read_csv(pd_buffer, header=0 if header else None)
+    got = cudf.read_csv(cudf_buffer, header=0 if header else None)
+
+    expected_column_cnt = (1 if index else 0) + (
+        len(columns) if columns else pdf.shape[1]
+    )
+    assert_eq(expected_column_cnt, got.shape[1])
+    assert_eq(expected, got)
+
+
+def test_csv_writer_empty_columns_parameter(cudf_mixed_dataframe):
+    df = cudf_mixed_dataframe
+    write_str = df.to_csv(columns=[], index=False)
+    assert_eq(write_str, "\n")
+
+
+def test_csv_writer_multiindex(tmpdir):
+    pdf_df_fname = tmpdir.join("pdf_df_3.csv")
+    gdf_df_fname = tmpdir.join("gdf_df_3.csv")
+
+    np.random.seed(0)
+    gdf = cudf.DataFrame(
+        {
+            "a": np.random.randint(0, 5, 20),
+            "b": np.random.randint(0, 5, 20),
+            "c": range(20),
+            "d": np.random.random(20),
+        }
+    )
+    gdg = gdf.groupby(["a", "b"]).mean()
+    pdg = gdg.to_pandas()
+    pdg.to_csv(pdf_df_fname)
+    gdg.to_csv(gdf_df_fname)
+
+    assert os.path.exists(pdf_df_fname)
+    assert os.path.exists(gdf_df_fname)
+
+    expect = pd.read_csv(pdf_df_fname)
+    got = pd.read_csv(gdf_df_fname)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("chunksize", [None, 9, 1000])
+@pytest.mark.parametrize("dtype", dtypes)
+def test_csv_writer_chunksize(chunksize, dtype):
+    cu_df = cudf.from_pandas(make_numeric_dataframe(100, dtype))
+
+    buffer = BytesIO()
+    cu_df.to_csv(buffer, chunksize=chunksize, index=False)
+
+    got = cudf.read_csv(buffer, dtype=[dtype])
+    assert_eq(cu_df, got)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame({"vals": [1, 2, 3]}),
+        cudf.DataFrame(
+            {"vals1": [1, 2, 3], "vals2": ["hello", "rapids", "cudf"]}
+        ),
+        cudf.DataFrame(
+            {"vals1": [None, 2.0, 3.0], "vals2": ["hello", "rapids", None]}
+        ),
+    ],
+)
+def test_to_csv_empty_filename(df):
+    pdf = df.to_pandas()
+
+    actual = df.to_csv()
+    expected = pdf.to_csv()
+
+    assert actual == expected
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame({"vals": [1, 2, 3]}),
+        cudf.DataFrame(
+            {"vals1": [1, 2, 3], "vals2": ["hello", "rapids", "cudf"]}
+        ),
+        cudf.DataFrame(
+            {"vals1": [None, 2.0, 3.0], "vals2": ["hello", "rapids", None]}
+        ),
+    ],
+)
+def test_to_csv_StringIO(df):
+    cudf_io = StringIO()
+    pandas_io = StringIO()
+
+    pdf = df.to_pandas()
+
+    df.to_csv(cudf_io)
+    pdf.to_csv(pandas_io)
+
+    cudf_io.seek(0)
+    pandas_io.seek(0)
+
+    assert cudf_io.read() == pandas_io.read()
+
+
+def test_csv_writer_empty_dataframe(tmpdir):
+    df_fname = tmpdir.join("gdf_df_5.csv")
+    gdf = cudf.DataFrame({"float_point": [], "integer": []})
+    gdf["float_point"] = gdf["float_point"].astype("float")
+    gdf["integer"] = gdf["integer"].astype("int")
+
+    gdf.to_csv(df_fname, index=False)
+
+    df = cudf.read_csv(df_fname)
+
+    assert df.shape == (0, 2)
+    assert all(df.dtypes == ["object", "object"])
+
+
+def test_csv_write_chunksize_corner_case(tmpdir):
+    # With this num of rows and chunksize
+    # libcudf splits table such a way that it
+    # will end up creating an empty table slice
+    # which caused the issue 5588.
+    df_fname = tmpdir.join("gdf_df_17.csv")
+    df = cudf.DataFrame({"a": np.arange(10_000)})
+    df.to_csv(df_fname, chunksize=1000, index=False)
+    got = cudf.read_csv(df_fname)
+
+    assert_eq(df, got)
+
+
+def test_csv_write_no_caller_manipulation():
+    df = cudf.DataFrame({"a": [1, 2, 3]})
+    df_copy = df.copy(deep=True)
+    _ = df.to_csv(index=True)
+    assert_eq(df, df_copy)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame({"a": [1, 2, 3], "": [10, 20, 40]}),
+        cudf.DataFrame({"": [10, 20, 40], "a": [1, 2, 3]}),
+        cudf.DataFrame(
+            {"a": [1, 2, 3], "": [10, 20, 40]},
+            index=cudf.Index(["a", "z", "v"], name="custom name"),
+        ),
+    ],
+)
+@pytest.mark.parametrize("index", [True, False])
+@pytest.mark.parametrize("columns", [["a"], [""], None])
+def test_csv_write_empty_column_name(df, index, columns):
+    pdf = df.to_pandas()
+    expected = pdf.to_csv(index=index, columns=columns)
+    actual = df.to_csv(index=index, columns=columns)
+
+    assert expected == actual
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame(),
+        cudf.DataFrame(index=cudf.Index([], name="index name")),
+    ],
+)
+@pytest.mark.parametrize("index", [True, False])
+def test_csv_write_empty_dataframe(df, index):
+    pdf = df.to_pandas()
+
+    expected = pdf.to_csv(index=index)
+    actual = df.to_csv(index=index)
+
+    assert expected == actual
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(
+            {
+                "a": [1, 2, 3, None],
+                "": ["a", "v", None, None],
+                None: [12, 12, 32, 44],
+            }
+        ),
+        pd.DataFrame(
+            {
+                np.nan: [1, 2, 3, None],
+                "": ["a", "v", None, None],
+                None: [12, 12, 32, 44],
+            }
+        ),
+        pd.DataFrame({"": [1, None, 3, 4]}),
+        pd.DataFrame({None: [1, None, 3, 4]}),
+        pd.DataFrame(columns=[None, "", "a", "b"]),
+        pd.DataFrame(columns=[None]),
+        pd.DataFrame(columns=[""]),
+    ],
+)
+@pytest.mark.parametrize(
+    "na_rep", ["", "_NA_", "---", "_____CUSTOM_NA_REP______"]
+)
+def test_csv_write_dataframe_na_rep(df, na_rep):
+    gdf = cudf.from_pandas(df)
+
+    expected = df.to_csv(na_rep=na_rep)
+    actual = gdf.to_csv(na_rep=na_rep)
+
+    assert expected == actual
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "int",
+        "str",
+        "float",
+        np.int32,
+        np.dtype("float32"),
+        {"a": "int32", "b": "float64", "c": "uint8"},
+        int,
+        str,
+        object,
+    ],
+)
+def test_csv_reader_dtypes(dtype):
+    buf = "a,b,c\n1,10,111\n2,11,112\n3,12,113\n4,13,114\n"
+
+    expected = pd.read_csv(StringIO(buf), dtype=dtype)
+    actual = cudf.read_csv(StringIO(buf), dtype=dtype)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["Int64", "UInt32", {"a": "UInt64", "b": "Float64", "c": "Int32"}]
+)
+def test_csv_reader_nullable_dtypes(dtype):
+    buf = "a,b,c\n1,10,111\n2,11,112\n3,12,113\n4,13,114\n"
+
+    expected = pd.read_csv(StringIO(buf), dtype=dtype)
+    actual = cudf.read_csv(StringIO(buf), dtype=dtype)
+
+    assert_eq(expected, actual.to_pandas(nullable=True))
+
+
+@pytest.mark.parametrize(
+    "dtype", sorted(list(cudf.utils.dtypes.TIMEDELTA_TYPES))
+)
+def test_csv_reader_timedetla_dtypes(dtype):
+    buf = "a,b,c\n1,10,111\n2,11,112\n3,12,113\n43432423,13342,13243214\n"
+
+    expected = pd.read_csv(StringIO(buf)).astype(dtype)
+    actual = cudf.read_csv(StringIO(buf), dtype=dtype)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "dtype", sorted(list(cudf.utils.dtypes.DATETIME_TYPES))
+)
+def test_csv_reader_datetime_dtypes(dtype):
+    buf = "a,b,c\n1,10,111\n2,11,112\n3,12,113\n43432423,13342,13243214\n"
+
+    expected = pd.read_csv(StringIO(buf)).astype(dtype)
+    actual = cudf.read_csv(StringIO(buf), dtype=dtype)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame(
+            {
+                "a": cudf.Series([1, 2, 3, 1, 2], dtype="category"),
+                "b": cudf.Series(["a", "c", "a", "b", "a"], dtype="category"),
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "a": cudf.Series([1.1, 2, 3, 1.1, 2], dtype="category"),
+                "b": cudf.Series(
+                    [None, "c", None, "b", "a"], dtype="category"
+                ),
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "b": cudf.Series(
+                    [1.1, 2, 3, 1.1, 2],
+                    dtype="category",
+                    index=cudf.CategoricalIndex(
+                        ["abc", "def", "ghi", "jkl", "xyz"]
+                    ),
+                )
+            }
+        ),
+    ],
+)
+def test_csv_writer_category(df):
+    pdf = df.to_pandas()
+
+    expected = pdf.to_csv()
+    actual = df.to_csv()
+
+    assert expected == actual
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "category",
+        {"a": "category", "b": "str"},
+        {"b": "category"},
+        {"a": "category"},
+        {"a": pd.CategoricalDtype([1, 2])},
+        {"b": pd.CategoricalDtype([1, 2, 3])},
+        {"b": pd.CategoricalDtype(["b", "a"]), "a": "str"},
+        pd.CategoricalDtype(["a", "b"]),
+    ],
+)
+def test_csv_reader_category(dtype):
+    df = cudf.DataFrame({"a": [1, 2, 3, None], "b": ["a", "b", None, "c"]})
+    csv_buf = df.to_csv()
+
+    actual = cudf.read_csv(StringIO(csv_buf), dtype=dtype)
+    expected = pd.read_csv(StringIO(csv_buf), dtype=dtype)
+
+    assert_eq(expected, actual, check_dtype=True)
+
+
+def test_csv_writer_datetime_sep():
+    df = cudf.DataFrame(
+        {"a": cudf.Series([22343, 2323423, 234324234], dtype="datetime64[ns]")}
+    )
+    df["a"] = df["a"].astype("datetime64[s]")
+    expected = df.to_pandas().to_csv(date_format="%Y-%m-%dT%H:%M:%SZ", sep="-")
+    actual = df.to_csv(sep="-")
+    assert expected == actual
+
+
+def test_na_filter_empty_fields():
+    test_na = "TEST_NAN"
+    df = pd.DataFrame({"col0": ["valid", None, "also_valid", "", test_na]})
+    buffer = df.to_csv(index=False)
+
+    pdf = pd.read_csv(StringIO(buffer), na_filter=False)
+    gdf = cudf.read_csv(StringIO(buffer), na_filter=False)
+    assert_eq(pdf, gdf)
+
+    pdf = pd.read_csv(StringIO(buffer), keep_default_na=False)
+    gdf = cudf.read_csv(StringIO(buffer), keep_default_na=False)
+    assert_eq(pdf, gdf)
+
+    pdf = pd.read_csv(
+        StringIO(buffer), keep_default_na=False, na_values=test_na
+    )
+    gdf = cudf.read_csv(
+        StringIO(buffer), keep_default_na=False, na_values=test_na
+    )
+    assert_eq(pdf, gdf)
+
+
+def test_csv_sep_error():
+    pdf = pd.DataFrame({"a": [1, 2, 3]})
+    gdf = cudf.DataFrame({"a": [1, 2, 3]})
+    assert_exceptions_equal(
+        lfunc=pdf.to_csv,
+        rfunc=gdf.to_csv,
+        lfunc_args_and_kwargs=([], {"sep": "abc"}),
+        rfunc_args_and_kwargs=([], {"sep": "abc"}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.to_csv,
+        rfunc=gdf.to_csv,
+        lfunc_args_and_kwargs=([], {"sep": 1}),
+        rfunc_args_and_kwargs=([], {"sep": 1}),
+    )
+
+
+def test_to_csv_encoding_error():
+    # TODO: Remove this test once following
+    # issue is fixed: https://github.com/rapidsai/cudf/issues/2957
+    df = cudf.DataFrame({"a": ["你好", "test"]})
+    encoding = "utf-8-sig"
+    error_message = (
+        f"Encoding {encoding} is not supported. "
+        + "Currently, only utf-8 encoding is supported."
+    )
+    with pytest.raises(NotImplementedError, match=re.escape(error_message)):
+        df.to_csv("test.csv", encoding=encoding)
+
+
+def test_to_csv_compression_error():
+    df = cudf.DataFrame({"a": ["test"]})
+    compression = "snappy"
+    error_message = "Writing compressed csv is not currently supported in cudf"
+    with pytest.raises(NotImplementedError, match=re.escape(error_message)):
+        df.to_csv("test.csv", compression=compression)
+
+
+def test_empty_df_no_index():
+    actual = cudf.DataFrame({})
+    buffer = BytesIO()
+    actual.to_csv(buffer, index=False)
+
+    result = cudf.read_csv(buffer)
+
+    assert_eq(actual, result)
+
+
+def test_default_integer_bitwidth(
+    cudf_mixed_dataframe, default_integer_bitwidth
+):
+    # Test that integer columns in csv are _inferred_ as user specified
+    # bitwidth
+    buf = BytesIO()
+    cudf_mixed_dataframe.to_csv(buf)
+    buf.seek(0)
+    read = cudf.read_csv(buf)
+    assert read["Integer"].dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+    assert read["Integer2"].dtype == np.dtype(
+        f"i{default_integer_bitwidth//8}"
+    )
+
+
+def test_default_integer_bitwidth_partial(
+    cudf_mixed_dataframe, default_integer_bitwidth
+):
+    # Test that integer columns in csv are _inferred_ as user specified
+    # bitwidth
+    buf = BytesIO()
+    cudf_mixed_dataframe.to_csv(buf)
+    buf.seek(0)
+    read = cudf.read_csv(buf, dtype={"Integer": "int64"})
+    assert read["Integer"].dtype == np.dtype("i8")
+    assert read["Integer2"].dtype == np.dtype(
+        f"i{default_integer_bitwidth//8}"
+    )
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
+def test_default_integer_bitwidth_extremes(
+    cudf_extreme_numeric_dataframe, default_integer_bitwidth
+):
+    # Test that integer columns in csv are _inferred_ as user specified
+    # bitwidth
+    buf = BytesIO()
+    cudf_extreme_numeric_dataframe.to_csv(buf)
+    buf.seek(0)
+    read = cudf.read_csv(buf)
+
+    assert read["int64"].dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+    assert read["long"].dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+    assert read["uint64"].dtype == np.dtype(f"u{default_integer_bitwidth//8}")
+
+
+def test_default_float_bitwidth(cudf_mixed_dataframe, default_float_bitwidth):
+    # Test that float columns in csv are _inferred_ as user specified
+    # bitwidth
+    buf = BytesIO()
+    cudf_mixed_dataframe.to_csv(buf)
+    buf.seek(0)
+    read = cudf.read_csv(buf)
+    assert read["Float"].dtype == np.dtype(f"f{default_float_bitwidth//8}")
+
+
+def test_default_float_bitwidth_partial(default_float_bitwidth):
+    # Test that float columns in csv are _inferred_ as user specified
+    # bitwidth
+    read = cudf.read_csv(
+        StringIO("float1,float2\n1.0,2.0\n3.0,4.0"),
+        dtype={"float2": "float64"},
+    )
+    assert read["float1"].dtype == np.dtype(f"f{default_float_bitwidth//8}")
+    assert read["float2"].dtype == np.dtype("f8")
+
+
+@pytest.mark.parametrize(
+    "usecols,names",
+    [
+        # selection using indices; only names of selected columns are specified
+        ([1, 2], ["b", "c"]),
+        # selection using indices; names of all columns are specified
+        ([1, 2], ["a", "b", "c"]),
+        # selection using indices; duplicates
+        ([2, 2], ["a", "b", "c"]),
+        # selection using indices; out of order
+        ([2, 1], ["a", "b", "c"]),
+        # selection using names
+        (["b"], ["a", "b", "c"]),
+        # selection using names; multiple columns
+        (["b", "c"], ["a", "b", "c"]),
+        # selection using names; duplicates
+        (["c", "c"], ["a", "b", "c"]),
+        # selection using names; out of order
+        (["c", "b"], ["a", "b", "c"]),
+    ],
+)
+def test_column_selection_plus_column_names(usecols, names):
+    lines = [
+        "num,datetime,text",
+        "123,2018-11-13T12:00:00,abc",
+        "456,2018-11-14T12:35:01,def",
+        "789,2018-11-15T18:02:59,ghi",
+    ]
+
+    buffer = "\n".join(lines) + "\n"
+
+    assert_eq(
+        pd.read_csv(StringIO(buffer), usecols=usecols, names=names),
+        cudf.read_csv(StringIO(buffer), usecols=usecols, names=names),
+    )
+
+
+def test_read_compressed_BOM(tmpdir):
+    buffer = 'int, string\n1, "a"\n2, "b"\n3, "c"\n'
+
+    fname = tmpdir.mkdir("gdf_csv").join("tmp_csvreader_file20.gz")
+    with gzip.open(fname, "wt", encoding="utf-8") as f:
+        f.write(codecs.BOM_UTF8.decode("utf-8"))
+        f.write(buffer)
+
+    assert_eq(pd.read_csv(fname), cudf.read_csv(fname))
diff --git a/python/cudf/cudf/tests/test_cuda_apply.py b/python/cudf/cudf/tests/test_cuda_apply.py
new file mode 100644
index 0000000..7fdf975
--- /dev/null
+++ b/python/cudf/cudf/tests/test_cuda_apply.py
@@ -0,0 +1,224 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+"""
+Test method that apply GPU kernel to a frame.
+"""
+
+import numpy as np
+import pytest
+from numba import cuda
+
+from cudf import DataFrame
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize("nelem", [1, 2, 64, 128, 129])
+def test_df_apply_rows(nelem):
+    def kernel(in1, in2, in3, out1, out2, extra1, extra2):
+        for i, (x, y, z) in enumerate(zip(in1, in2, in3)):
+            out1[i] = extra2 * x - extra1 * y
+            out2[i] = y - extra1 * z
+
+    df = DataFrame()
+    df["in1"] = in1 = np.arange(nelem)
+    df["in2"] = in2 = np.arange(nelem)
+    df["in3"] = in3 = np.arange(nelem)
+
+    extra1 = 2.3
+    extra2 = 3.4
+
+    expect_out1 = extra2 * in1 - extra1 * in2
+    expect_out2 = in2 - extra1 * in3
+
+    outdf = df.apply_rows(
+        kernel,
+        incols=["in1", "in2", "in3"],
+        outcols=dict(out1=np.float64, out2=np.float64),
+        kwargs=dict(extra1=extra1, extra2=extra2),
+    )
+
+    got_out1 = outdf["out1"].to_numpy()
+    got_out2 = outdf["out2"].to_numpy()
+
+    np.testing.assert_array_almost_equal(got_out1, expect_out1)
+    np.testing.assert_array_almost_equal(got_out2, expect_out2)
+
+
+@pytest.mark.parametrize("nelem", [1, 2, 64, 128, 129])
+@pytest.mark.parametrize("chunksize", [1, 2, 3, 4, 23])
+def test_df_apply_chunks(nelem, chunksize):
+    def kernel(in1, in2, in3, out1, out2, extra1, extra2):
+        for i, (x, y, z) in enumerate(zip(in1, in2, in3)):
+            out1[i] = extra2 * x - extra1 * y + z
+            out2[i] = i
+
+    df = DataFrame()
+    df["in1"] = in1 = np.arange(nelem)
+    df["in2"] = in2 = np.arange(nelem)
+    df["in3"] = in3 = np.arange(nelem)
+
+    extra1 = 2.3
+    extra2 = 3.4
+
+    expect_out1 = extra2 * in1 - extra1 * in2 + in3
+    expect_out2 = np.arange(len(df)) % chunksize
+
+    outdf = df.apply_chunks(
+        kernel,
+        incols=["in1", "in2", "in3"],
+        outcols=dict(out1=np.float64, out2=np.int32),
+        kwargs=dict(extra1=extra1, extra2=extra2),
+        chunks=chunksize,
+    )
+
+    got_out1 = outdf["out1"]
+    got_out2 = outdf["out2"]
+
+    np.testing.assert_array_almost_equal(got_out1.to_numpy(), expect_out1)
+    np.testing.assert_array_almost_equal(got_out2.to_numpy(), expect_out2)
+
+
+@pytest.mark.parametrize("nelem", [1, 15, 30, 64, 128, 129])
+def test_df_apply_custom_chunks(nelem):
+    def kernel(in1, in2, in3, out1, out2, extra1, extra2):
+        for i, (x, y, z) in enumerate(zip(in1, in2, in3)):
+            out1[i] = extra2 * x - extra1 * y + z
+            out2[i] = i
+
+    df = DataFrame()
+    df["in1"] = in1 = np.arange(nelem)
+    df["in2"] = in2 = np.arange(nelem)
+    df["in3"] = in3 = np.arange(nelem)
+
+    chunks = [0, 7, 11, 29, 101, 777]
+    chunks = [c for c in chunks if c < nelem]
+
+    extra1 = 2.3
+    extra2 = 3.4
+
+    expect_out1 = extra2 * in1 - extra1 * in2 + in3
+    expect_out2 = np.hstack(
+        [np.arange(e - s) for s, e in zip(chunks, chunks[1:] + [len(df)])]
+    )
+
+    outdf = df.apply_chunks(
+        kernel,
+        incols=["in1", "in2", "in3"],
+        outcols=dict(out1=np.float64, out2=np.int32),
+        kwargs=dict(extra1=extra1, extra2=extra2),
+        chunks=chunks,
+    )
+
+    got_out1 = outdf["out1"]
+    got_out2 = outdf["out2"]
+
+    np.testing.assert_array_almost_equal(got_out1.to_numpy(), expect_out1)
+    np.testing.assert_array_almost_equal(got_out2.to_numpy(), expect_out2)
+
+
+@pytest.mark.parametrize("nelem", [1, 15, 30, 64, 128, 129])
+@pytest.mark.parametrize("blkct", [None, 1, 8])
+@pytest.mark.parametrize("tpb", [1, 8, 64])
+def test_df_apply_custom_chunks_blkct_tpb(nelem, blkct, tpb):
+    def kernel(in1, in2, in3, out1, out2, extra1, extra2):
+        for i in range(cuda.threadIdx.x, in1.size, cuda.blockDim.x):
+            x = in1[i]
+            y = in2[i]
+            z = in3[i]
+            out1[i] = extra2 * x - extra1 * y + z
+            out2[i] = i * cuda.blockDim.x
+
+    df = DataFrame()
+    df["in1"] = in1 = np.arange(nelem)
+    df["in2"] = in2 = np.arange(nelem)
+    df["in3"] = in3 = np.arange(nelem)
+
+    chunks = [0, 7, 11, 29, 101, 777]
+    chunks = [c for c in chunks if c < nelem]
+
+    extra1 = 2.3
+    extra2 = 3.4
+
+    expect_out1 = extra2 * in1 - extra1 * in2 + in3
+    expect_out2 = np.hstack(
+        [
+            tpb * np.arange(e - s)
+            for s, e in zip(chunks, chunks[1:] + [len(df)])
+        ]
+    )
+
+    outdf = df.apply_chunks(
+        kernel,
+        incols=["in1", "in2", "in3"],
+        outcols=dict(out1=np.float64, out2=np.int32),
+        kwargs=dict(extra1=extra1, extra2=extra2),
+        chunks=chunks,
+        blkct=blkct,
+        tpb=tpb,
+    )
+
+    got_out1 = outdf["out1"]
+    got_out2 = outdf["out2"]
+
+    np.testing.assert_array_almost_equal(got_out1.to_numpy(), expect_out1)
+    np.testing.assert_array_almost_equal(got_out2.to_numpy(), expect_out2)
+
+
+@pytest.mark.parametrize("nelem", [1, 2, 64, 128, 1000, 5000])
+def test_df_apply_rows_incols_mapping(nelem):
+    def kernel(x, y, z, out1, out2, extra1, extra2):
+        for i, (a, b, c) in enumerate(zip(x, y, z)):
+            out1[i] = extra2 * a - extra1 * b
+            out2[i] = b - extra1 * c
+
+    df = DataFrame()
+    df["in1"] = in1 = np.arange(nelem)
+    df["in2"] = in2 = np.arange(nelem)
+    df["in3"] = in3 = np.arange(nelem)
+
+    extra1 = 2.3
+    extra2 = 3.4
+
+    expected_out = DataFrame()
+    expected_out["out1"] = extra2 * in1 - extra1 * in2
+    expected_out["out2"] = in2 - extra1 * in3
+
+    outdf = df.apply_rows(
+        kernel,
+        incols={"in1": "x", "in2": "y", "in3": "z"},
+        outcols=dict(out1=np.float64, out2=np.float64),
+        kwargs=dict(extra1=extra1, extra2=extra2),
+    )
+
+    assert_eq(outdf[["out1", "out2"]], expected_out)
+
+
+@pytest.mark.parametrize("nelem", [1, 2, 64, 128, 129])
+@pytest.mark.parametrize("chunksize", [1, 2, 3, 4, 23])
+def test_df_apply_chunks_incols_mapping(nelem, chunksize):
+    def kernel(q, p, r, out1, out2, extra1, extra2):
+        for i, (a, b, c) in enumerate(zip(q, p, r)):
+            out1[i] = extra2 * a - extra1 * b + c
+            out2[i] = i
+
+    df = DataFrame()
+    df["in1"] = in1 = np.arange(nelem)
+    df["in2"] = in2 = np.arange(nelem)
+    df["in3"] = in3 = np.arange(nelem)
+
+    extra1 = 2.3
+    extra2 = 3.4
+
+    expected_out = DataFrame()
+    expected_out["out1"] = extra2 * in1 - extra1 * in2 + in3
+    expected_out["out2"] = np.arange(len(df)) % chunksize
+
+    outdf = df.apply_chunks(
+        kernel,
+        incols={"in1": "q", "in2": "p", "in3": "r"},
+        outcols=dict(out1=np.float64, out2=np.int64),
+        kwargs=dict(extra1=extra1, extra2=extra2),
+        chunks=chunksize,
+    )
+
+    assert_eq(outdf[["out1", "out2"]], expected_out)
diff --git a/python/cudf/cudf/tests/test_cuda_array_interface.py b/python/cudf/cudf/tests/test_cuda_array_interface.py
new file mode 100644
index 0000000..a9d1192
--- /dev/null
+++ b/python/cudf/cudf/tests/test_cuda_array_interface.py
@@ -0,0 +1,229 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import types
+from contextlib import ExitStack as does_not_raise
+
+import cupy
+import numpy as np
+import pandas as pd
+import pytest
+from numba import cuda
+
+import cudf
+from cudf.core.buffer.spill_manager import get_global_manager
+from cudf.testing._utils import DATETIME_TYPES, NUMERIC_TYPES, assert_eq
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES)
+@pytest.mark.parametrize("module", ["cupy", "numba"])
+def test_cuda_array_interface_interop_in(dtype, module):
+    np_data = np.arange(10).astype(dtype)
+
+    expectation = does_not_raise()
+    if module == "cupy":
+        module_constructor = cupy.array
+        if dtype in DATETIME_TYPES:
+            expectation = pytest.raises(ValueError)
+    elif module == "numba":
+        module_constructor = cuda.to_device
+
+    with expectation:
+        module_data = module_constructor(np_data)
+
+        pd_data = pd.Series(np_data)
+        # Test using a specific function for __cuda_array_interface__ here
+        cudf_data = cudf.Series(module_data)
+
+        assert_eq(pd_data, cudf_data)
+
+        gdf = cudf.DataFrame()
+        gdf["test"] = module_data
+        pd_data.name = "test"
+        assert_eq(pd_data, gdf["test"])
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES + ["str"])
+@pytest.mark.parametrize("module", ["cupy", "numba"])
+def test_cuda_array_interface_interop_out(dtype, module):
+    expectation = does_not_raise()
+    if dtype == "str":
+        expectation = pytest.raises(AttributeError)
+    if module == "cupy":
+        module_constructor = cupy.asarray
+
+        def to_host_function(x):
+            return cupy.asnumpy(x)
+
+    elif module == "numba":
+        module_constructor = cuda.as_cuda_array
+
+        def to_host_function(x):
+            return x.copy_to_host()
+
+    with expectation:
+        np_data = np.arange(10).astype(dtype)
+        cudf_data = cudf.Series(np_data)
+        assert isinstance(cudf_data.__cuda_array_interface__, dict)
+
+        module_data = module_constructor(cudf_data)
+        got = to_host_function(module_data)
+
+        expect = np_data
+
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES)
+@pytest.mark.parametrize("module", ["cupy", "numba"])
+def test_cuda_array_interface_interop_out_masked(dtype, module):
+    expectation = does_not_raise()
+    if module == "cupy":
+        pytest.skip(
+            "cupy doesn't support version 1 of "
+            "`__cuda_array_interface__` yet"
+        )
+        module_constructor = cupy.asarray
+
+        def to_host_function(x):
+            return cupy.asnumpy(x)
+
+    elif module == "numba":
+        expectation = pytest.raises(NotImplementedError)
+        module_constructor = cuda.as_cuda_array
+
+        def to_host_function(x):
+            return x.copy_to_host()
+
+    np_data = np.arange(10).astype("float64")
+    np_data[[0, 2, 4, 6, 8]] = np.nan
+
+    with expectation:
+        cudf_data = cudf.Series(np_data).astype(dtype)
+        assert isinstance(cudf_data.__cuda_array_interface__, dict)
+
+        module_data = module_constructor(cudf_data)  # noqa: F841
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES)
+@pytest.mark.parametrize("nulls", ["all", "some", "bools", "none"])
+@pytest.mark.parametrize("mask_type", ["bits", "bools"])
+def test_cuda_array_interface_as_column(dtype, nulls, mask_type):
+    sr = cudf.Series(np.arange(10))
+
+    if nulls == "some":
+        mask = [
+            True,
+            False,
+            True,
+            False,
+            False,
+            True,
+            True,
+            False,
+            True,
+            True,
+        ]
+        sr[sr[~np.asarray(mask)]] = None
+    elif nulls == "all":
+        sr[:] = None
+
+    sr = sr.astype(dtype)
+
+    obj = types.SimpleNamespace(
+        __cuda_array_interface__=sr.__cuda_array_interface__
+    )
+
+    if mask_type == "bools":
+        if nulls == "some":
+            obj.__cuda_array_interface__["mask"] = cuda.to_device(mask)
+        elif nulls == "all":
+            obj.__cuda_array_interface__["mask"] = cuda.to_device([False] * 10)
+
+    expect = sr
+    got = cudf.Series(obj)
+
+    assert_eq(expect, got)
+
+
+def test_column_from_ephemeral_cupy():
+    # Test that we keep a reference to the ephemeral
+    # CuPy array. If we didn't, then `a` would end
+    # up referring to the same memory as `b` due to
+    # CuPy's caching allocator
+    a = cudf.Series(cupy.asarray([1, 2, 3]))
+    b = cudf.Series(cupy.asarray([1, 1, 1]))
+    assert_eq(pd.Series([1, 2, 3]), a)
+    assert_eq(pd.Series([1, 1, 1]), b)
+
+
+def test_column_from_ephemeral_cupy_try_lose_reference():
+    # Try to lose the reference we keep to the ephemeral
+    # CuPy array
+    a = cudf.Series(cupy.asarray([1, 2, 3]))._column
+    a = cudf.core.column.as_column(a)
+    b = cupy.asarray([1, 1, 1])  # noqa: F841
+    assert_eq(pd.Series([1, 2, 3]), a.to_pandas())
+
+    a = cudf.Series(cupy.asarray([1, 2, 3]))._column
+    a.name = "b"
+    b = cupy.asarray([1, 1, 1])  # noqa: F841
+    assert_eq(pd.Series([1, 2, 3]), a.to_pandas())
+
+
+@pytest.mark.xfail(
+    get_global_manager() is not None,
+    reason=(
+        "spilling doesn't support PyTorch, see "
+        "`cudf.core.buffer.spillable_buffer.DelayedPointerTuple`"
+    ),
+)
+def test_cuda_array_interface_pytorch():
+    torch = pytest.importorskip("torch", minversion="1.6.0")
+    if not torch.cuda.is_available():
+        pytest.skip("need gpu version of pytorch to be installed")
+
+    series = cudf.Series([1, -1, 10, -56])
+    tensor = torch.tensor(series)
+    got = cudf.Series(tensor)
+
+    assert_eq(got, series)
+    buffer = cudf.core.buffer.as_buffer(cupy.ones(10, dtype=np.bool_))
+    tensor = torch.tensor(buffer)
+    got = cudf.Series(tensor, dtype=np.bool_)
+
+    assert_eq(got, cudf.Series(buffer, dtype=np.bool_))
+
+    index = cudf.Index([], dtype="float64")
+    tensor = torch.tensor(index)
+    got = cudf.Index(tensor)
+    assert_eq(got, index)
+
+    index = cudf.core.index.RangeIndex(start=0, stop=100)
+    tensor = torch.tensor(index)
+    got = cudf.Series(tensor)
+
+    assert_eq(got, cudf.Series(index))
+
+    index = cudf.Index([1, 2, 8, 6])
+    tensor = torch.tensor(index)
+    got = cudf.Index(tensor)
+
+    assert_eq(got, index)
+
+    str_series = cudf.Series(["a", "g"])
+
+    with pytest.raises(NotImplementedError):
+        str_series.__cuda_array_interface__
+
+    cat_series = str_series.astype("category")
+
+    with pytest.raises(TypeError):
+        cat_series.__cuda_array_interface__
+
+
+def test_cai_after_indexing():
+    df = cudf.DataFrame({"a": [1, 2, 3]})
+    cai1 = df["a"].__cuda_array_interface__
+    df[["a"]]
+    cai2 = df["a"].__cuda_array_interface__
+    assert cai1 == cai2
diff --git a/python/cudf/cudf/tests/test_custom_accessor.py b/python/cudf/cudf/tests/test_custom_accessor.py
new file mode 100644
index 0000000..325be95
--- /dev/null
+++ b/python/cudf/cudf/tests/test_custom_accessor.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import pandas as pd
+import pytest
+
+import cudf as gd
+from cudf.testing._utils import assert_eq
+
+
+@gd.api.extensions.register_dataframe_accessor("point")
+@pd.api.extensions.register_dataframe_accessor("point")
+class PointsAccessor:
+    def __init__(self, obj):
+        self._validate(obj)
+        self._obj = obj
+
+    @staticmethod
+    def _validate(obj):
+        cols = obj.columns
+        if not all(vertex in cols for vertex in ["x", "y"]):
+            raise AttributeError("Must have vertices 'x', 'y'.")
+
+    @property
+    def bounding_box(self):
+        xs, ys = self._obj["x"], self._obj["y"]
+        min_x, min_y, max_x, max_y = xs.min(), ys.min(), xs.max(), ys.max()
+
+        return (min_x, min_y, max_x, max_y)
+
+
+@pytest.mark.parametrize(
+    "gdf", [gd.datasets.randomdata(nrows=6, dtypes={"x": int, "y": int})]
+)
+def test_dataframe_accessor(gdf):
+    pdf = gdf.to_pandas()
+
+    assert_eq(gdf.point.bounding_box, pdf.point.bounding_box)
+
+
+@pytest.mark.parametrize(
+    "gdf1", [gd.datasets.randomdata(nrows=1, dtypes={"x": int, "y": int})]
+)
+@pytest.mark.parametrize(
+    "gdf2", [gd.datasets.randomdata(nrows=1, dtypes={"x": int, "y": int})]
+)
+def test_dataframe_accessor_idendity(gdf1, gdf2):
+    """Test for accessor identities
+    - An object should hold persistent reference to the same accessor
+    - Different objects should hold difference instances of the accessor
+    """
+
+    assert gdf1.point is gdf1.point
+    assert gdf1.point is not gdf2.point
+
+
+@pd.api.extensions.register_index_accessor("odd")
+@pd.api.extensions.register_series_accessor("odd")
+@gd.api.extensions.register_index_accessor("odd")
+@gd.api.extensions.register_series_accessor("odd")
+class OddRowAccessor:
+    def __init__(self, obj):
+        self._obj = obj
+
+    def __getitem__(self, i):
+        return self._obj[2 * i - 1]
+
+
+@pytest.mark.parametrize("gidx", [gd.Index(list(range(0, 50)))])
+def test_index_accessor(gidx):
+    pidx = gidx.to_pandas()
+
+    for i in range(1, 10):
+        assert_eq(gidx.odd[i], pidx.odd[i])
+
+
+@pytest.mark.parametrize("gs", [gd.Series(list(range(1, 50)))])
+def test_series_accessor(gs):
+    ps = gs.to_pandas()
+
+    for i in range(1, 10):
+        assert_eq(gs.odd[i], ps.odd[i])
+
+
+@pytest.mark.parametrize(
+    "gdf", [gd.datasets.randomdata(nrows=6, dtypes={"x": int, "y": int})]
+)
+@pytest.mark.parametrize("gidx", [gd.Index(list(range(1, 50)))])
+@pytest.mark.parametrize("gs", [gd.Series(list(range(1, 50)))])
+def test_accessor_space_separate(gdf, gidx, gs):
+    assert not id(gdf._accessors) == id(gidx._accessors)
+    assert not id(gidx._accessors) == id(gs._accessors)
+    assert not id(gdf._accessors) == id(gs._accessors)
diff --git a/python/cudf/cudf/tests/test_cut.py b/python/cudf/cudf/tests/test_cut.py
new file mode 100644
index 0000000..02e48f2
--- /dev/null
+++ b/python/cudf/cudf/tests/test_cut.py
@@ -0,0 +1,316 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+"""
+Test related to Cut
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from cudf.core.cut import cut
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize(
+    "x", [[1, 7, 5, 4, 6, 3], [1, 7], np.array([1, 7, 5, 4, 6, 3])]
+)
+@pytest.mark.parametrize("bins", [1, 2, 3])
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+@pytest.mark.parametrize(
+    "ordered", [True]
+)  # if ordered is False we need labels
+@pytest.mark.parametrize("precision", [1, 2, 3])
+def test_cut_basic(x, bins, right, include_lowest, ordered, precision):
+    # will test optional labels, retbins and duplicates separately
+    # they need more specific parameters to work
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+    pindex = pd.CategoricalIndex(pcat)
+    gindex = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("x", [[1, 7, 5, 4, 6, 3]])
+@pytest.mark.parametrize("bins", [3])  # labels must be the same len as bins
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+@pytest.mark.parametrize(
+    "ordered", [True, False]
+)  # labels must be unique if ordered=True
+@pytest.mark.parametrize("precision", [1, 2, 3])
+@pytest.mark.parametrize(
+    "labels", [["bad", "medium", "good"], ["A", "B", "C"], [1, 2, 3], False]
+)
+def test_cut_labels(
+    x, bins, right, include_lowest, ordered, precision, labels
+):
+
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+    pindex = pd.CategoricalIndex(pcat) if labels else pcat
+    gindex = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize("x", [[1, 7, 5, 4, 6, 3]])
+@pytest.mark.parametrize("bins", [3])  # labels must be the same len as bins
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+@pytest.mark.parametrize(
+    "ordered", [False]
+)  # labels must be unique if ordered=True
+@pytest.mark.parametrize("precision", [1, 2, 3])
+@pytest.mark.parametrize(
+    "labels", [["bad", "good", "good"], ["B", "A", "B"], [1, 2, 2], False]
+)
+def test_cut_labels_non_unique(
+    x, bins, right, include_lowest, ordered, precision, labels
+):
+
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+    pindex = pd.CategoricalIndex(pcat) if labels else pcat
+    gindex = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize(
+    "x",
+    [
+        [1, 7, 5, 4, 6, 3],
+        [1, 7],
+        np.array([1, 7, 5, 4, 6, 3]),
+        np.array([2, 4, 6, 8, 10]),
+    ],
+)
+@pytest.mark.parametrize(
+    "bins",
+    [1, 2, 3, [1, 2, 3], [0, 2, 4, 6, 10]],
+)
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("precision", [3])
+def test_cut_right(x, bins, right, precision):
+
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+    )
+    pindex = pd.CategoricalIndex(pcat)
+    gindex = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize(
+    "x",
+    [
+        [1, 7, 5, 4, 6, 3],
+        [1, 7],
+        np.array([1, 7, 5, 4, 6, 3]),
+        np.array([2, 4, 6, 8, 10]),
+    ],
+)
+@pytest.mark.parametrize(
+    "bins",
+    [[0, 2, 4, 6, 10, 10], [1, 2, 2, 3, 3]],
+)
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+@pytest.mark.parametrize("ordered", [True])
+@pytest.mark.parametrize("precision", [1, 2, 3])
+@pytest.mark.parametrize("duplicates", ["drop"])
+def test_cut_drop_duplicates(
+    x, bins, right, precision, duplicates, ordered, include_lowest
+):
+
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        duplicates=duplicates,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+    pindex = pd.CategoricalIndex(pcat)
+    gindex = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        duplicates=duplicates,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize(
+    "x",
+    [
+        [1, 7, 5, 4, 6, 3],
+        [1, 7],
+        np.array([1, 7, 5, 4, 6, 3]),
+        np.array([2, 4, 6, 8, 10]),
+    ],
+)
+@pytest.mark.parametrize(
+    "bins",
+    [[0, 2, 4, 6, 10, 10], [1, 2, 2, 3, 3]],
+)
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+@pytest.mark.parametrize("ordered", [True])
+@pytest.mark.parametrize("precision", [1, 2, 3])
+@pytest.mark.parametrize("duplicates", ["raises"])
+def test_cut_drop_duplicates_raises(
+    x, bins, right, precision, duplicates, ordered, include_lowest
+):
+    with pytest.raises(ValueError) as excgd:
+        cut(
+            x=x,
+            bins=bins,
+            right=right,
+            precision=precision,
+            duplicates=duplicates,
+            include_lowest=include_lowest,
+            ordered=ordered,
+        )
+    with pytest.raises(ValueError) as excpd:
+        pd.cut(
+            x=x,
+            bins=bins,
+            right=right,
+            precision=precision,
+            duplicates=duplicates,
+            include_lowest=include_lowest,
+            ordered=ordered,
+        )
+
+    assert_eq(str(excgd.value), str(excpd.value))
+
+
+@pytest.mark.parametrize(
+    "x",
+    [
+        [0, 0.5, 1.5, 2.5, 4.5],
+        [1, 7, 5, 4, 6, 3],
+        [1, 7],
+        np.array([1, 7, 5, 4, 6, 3]),
+        np.array([2, 4, 6, 8, 10]),
+    ],
+)
+@pytest.mark.parametrize(
+    "bins",
+    [pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])],
+)
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("precision", [1, 2, 3])
+@pytest.mark.parametrize("duplicates", ["drop", "raise"])
+def test_cut_intervalindex_bin(x, bins, right, precision, duplicates):
+
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        duplicates=duplicates,
+    )
+    pindex = pd.CategoricalIndex(pcat)
+    gindex = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        duplicates=duplicates,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize(
+    "x",
+    [pd.Series(np.array([2, 4, 6, 8, 10]), index=["a", "b", "c", "d", "e"])],
+)
+@pytest.mark.parametrize("bins", [1, 2, 3])
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("include_lowest", [True, False])
+@pytest.mark.parametrize("ordered", [True])
+@pytest.mark.parametrize("precision", [3])
+def test_cut_series(x, bins, right, include_lowest, ordered, precision):
+
+    pcat = pd.cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+
+    gcat = cut(
+        x=x,
+        bins=bins,
+        right=right,
+        precision=precision,
+        include_lowest=include_lowest,
+        ordered=ordered,
+    )
+
+    assert_eq(pcat, gcat)
diff --git a/python/cudf/cudf/tests/test_dask.py b/python/cudf/cudf/tests/test_dask.py
new file mode 100644
index 0000000..3af21b4
--- /dev/null
+++ b/python/cudf/cudf/tests/test_dask.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2019, NVIDIA CORPORATION.
+
+import pytest
+
+import cudf
+
+is_dataframe_like = pytest.importorskip(
+    "dask.dataframe.utils"
+).is_dataframe_like
+is_index_like = pytest.importorskip("dask.dataframe.utils").is_index_like
+is_series_like = pytest.importorskip("dask.dataframe.utils").is_series_like
+
+
+def test_is_dataframe_like():
+    df = cudf.DataFrame({"x": [1, 2, 3]})
+    assert is_dataframe_like(df)
+    assert is_series_like(df.x)
+    assert is_index_like(df.index)
+    assert not is_dataframe_like(df.x)
+    assert not is_series_like(df)
+    assert not is_index_like(df)
diff --git a/python/cudf/cudf/tests/test_dataframe.py b/python/cudf/cudf/tests/test_dataframe.py
new file mode 100644
index 0000000..5677f97
--- /dev/null
+++ b/python/cudf/cudf/tests/test_dataframe.py
@@ -0,0 +1,10737 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import array as arr
+import datetime
+import decimal
+import io
+import operator
+import random
+import re
+import string
+import textwrap
+import warnings
+from collections import OrderedDict, defaultdict, namedtuple
+from copy import copy
+
+import cupy
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+from numba import cuda
+from packaging import version
+
+import cudf
+from cudf.core._compat import (
+    PANDAS_GE_134,
+    PANDAS_GE_150,
+    PANDAS_GE_200,
+    PANDAS_LT_140,
+)
+from cudf.core.buffer.spill_manager import get_global_manager
+from cudf.core.column import column
+from cudf.testing import _utils as utils
+from cudf.testing._utils import (
+    ALL_TYPES,
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    _create_cudf_series_float64_default,
+    assert_eq,
+    assert_exceptions_equal,
+    assert_neq,
+    does_not_raise,
+    expect_warning_if,
+    gen_rand,
+)
+
+pytest_xfail = pytest.mark.xfail
+pytestmark = pytest.mark.spilling
+
+# Use this to "unmark" the module level spilling mark
+pytest_unmark_spilling = pytest.mark.skipif(
+    get_global_manager() is not None, reason="unmarked spilling"
+)
+
+# If spilling is enabled globally, we skip many test permutations
+# to reduce running time.
+if get_global_manager() is not None:
+    ALL_TYPES = ["float32"]  # noqa: F811
+    DATETIME_TYPES = ["datetime64[ms]"]  # noqa: F811
+    NUMERIC_TYPES = ["float32"]  # noqa: F811
+    # To save time, we skip tests marked "xfail"
+    pytest_xfail = pytest.mark.skipif
+
+
+def test_init_via_list_of_tuples():
+    data = [
+        (5, "cats", "jump", np.nan),
+        (2, "dogs", "dig", 7.5),
+        (3, "cows", "moo", -2.1, "occasionally"),
+    ]
+
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data)
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize("columns", [["a", "b"], pd.Series(["a", "b"])])
+def test_init_via_list_of_series(columns):
+    data = [pd.Series([1, 2]), pd.Series([3, 4])]
+
+    pdf = cudf.DataFrame(data, columns=columns)
+    gdf = cudf.DataFrame(data, columns=columns)
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize("index", [None, [0, 1, 2]])
+def test_init_with_missing_columns(index):
+    """Test initialization when columns and data keys are disjoint."""
+    data = {"a": [1, 2, 3], "b": [2, 3, 4]}
+    columns = ["c", "d"]
+
+    pdf = cudf.DataFrame(data, columns=columns, index=index)
+    gdf = cudf.DataFrame(data, columns=columns, index=index)
+
+    assert_eq(pdf, gdf)
+
+
+def _dataframe_na_data():
+    return [
+        pd.DataFrame(
+            {
+                "a": [0, 1, 2, np.nan, 4, None, 6],
+                "b": [np.nan, None, "u", "h", "d", "a", "m"],
+            },
+            index=["q", "w", "e", "r", "t", "y", "u"],
+        ),
+        pd.DataFrame({"a": [0, 1, 2, 3, 4], "b": ["a", "b", "u", "h", "d"]}),
+        pd.DataFrame(
+            {
+                "a": [None, None, np.nan, None],
+                "b": [np.nan, None, np.nan, None],
+            }
+        ),
+        pd.DataFrame({"a": []}),
+        pd.DataFrame({"a": [np.nan], "b": [None]}),
+        pd.DataFrame({"a": ["a", "b", "c", None, "e"]}),
+        pd.DataFrame({"a": ["a", "b", "c", "d", "e"]}),
+    ]
+
+
+@pytest.mark.parametrize(
+    "rows",
+    [
+        pytest.param(
+            0,
+            marks=pytest.mark.xfail(
+                not PANDAS_GE_200, reason=".column returns Index[object]"
+            ),
+        ),
+        1,
+        2,
+        100,
+    ],
+)
+def test_init_via_list_of_empty_tuples(rows):
+    data = [()] * rows
+
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data)
+
+    assert_eq(
+        pdf,
+        gdf,
+        check_like=True,
+        check_index_type=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "dict_of_series",
+    [
+        {"a": pd.Series([1.0, 2.0, 3.0])},
+        {"a": pd.Series([1.0, 2.0, 3.0], index=[4, 5, 6])},
+        {
+            "a": pd.Series([1.0, 2.0, 3.0], index=[4, 5, 6]),
+            "b": pd.Series([1.0, 2.0, 4.0], index=[1, 2, 3]),
+        },
+        {"a": [1, 2, 3], "b": pd.Series([1.0, 2.0, 3.0], index=[4, 5, 6])},
+        {
+            "a": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
+            "b": pd.Series([1.0, 2.0, 4.0], index=["c", "d", "e"]),
+        },
+        {
+            "a": pd.Series(
+                ["a", "b", "c"],
+                index=pd.MultiIndex.from_tuples([(1, 2), (1, 3), (2, 3)]),
+            ),
+            "b": pd.Series(
+                ["a", " b", "d"],
+                index=pd.MultiIndex.from_tuples([(1, 2), (1, 3), (2, 3)]),
+            ),
+        },
+    ],
+)
+def test_init_from_series_align(dict_of_series):
+    pdf = pd.DataFrame(dict_of_series)
+    gdf = cudf.DataFrame(dict_of_series)
+
+    assert_eq(pdf, gdf)
+
+    for key in dict_of_series:
+        if isinstance(dict_of_series[key], pd.Series):
+            dict_of_series[key] = cudf.Series(dict_of_series[key])
+
+    gdf = cudf.DataFrame(dict_of_series)
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    ("dict_of_series", "expectation"),
+    [
+        (
+            {
+                "a": pd.Series(["a", "b", "c"], index=[4, 4, 5]),
+                "b": pd.Series(["a", "b", "c"], index=[4, 5, 6]),
+            },
+            pytest.raises(
+                ValueError, match="Cannot align indices with non-unique values"
+            ),
+        ),
+        (
+            {
+                "a": pd.Series(["a", "b", "c"], index=[4, 4, 5]),
+                "b": pd.Series(["a", "b", "c"], index=[4, 4, 5]),
+            },
+            does_not_raise(),
+        ),
+    ],
+)
+def test_init_from_series_align_nonunique(dict_of_series, expectation):
+    with expectation:
+        gdf = cudf.DataFrame(dict_of_series)
+
+    if expectation == does_not_raise():
+        pdf = pd.DataFrame(dict_of_series)
+        assert_eq(pdf, gdf)
+
+
+def test_init_unaligned_with_index():
+    pdf = pd.DataFrame(
+        {
+            "a": pd.Series([1.0, 2.0, 3.0], index=[4, 5, 6]),
+            "b": pd.Series([1.0, 2.0, 3.0], index=[1, 2, 3]),
+        },
+        index=[7, 8, 9],
+    )
+    gdf = cudf.DataFrame(
+        {
+            "a": cudf.Series([1.0, 2.0, 3.0], index=[4, 5, 6]),
+            "b": cudf.Series([1.0, 2.0, 3.0], index=[1, 2, 3]),
+        },
+        index=[7, 8, 9],
+    )
+
+    assert_eq(pdf, gdf, check_dtype=False)
+
+
+def test_init_series_list_columns_unsort():
+    pseries = [
+        pd.Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)
+    ]
+    gseries = [
+        cudf.Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)
+    ]
+    pdf = pd.DataFrame(pseries)
+    gdf = cudf.DataFrame(gseries)
+    assert_eq(pdf, gdf)
+
+
+def test_series_basic():
+    # Make series from buffer
+    a1 = np.arange(10, dtype=np.float64)
+    series = cudf.Series(a1)
+    assert len(series) == 10
+    np.testing.assert_equal(series.to_numpy(), np.hstack([a1]))
+
+
+def test_series_from_cupy_scalars():
+    data = [0.1, 0.2, 0.3]
+    data_np = np.array(data)
+    data_cp = cupy.array(data)
+    s_np = cudf.Series([data_np[0], data_np[2]])
+    s_cp = cudf.Series([data_cp[0], data_cp[2]])
+    assert_eq(s_np, s_cp)
+
+
+@pytest.mark.parametrize("a", [[1, 2, 3], [1, 10, 30]])
+@pytest.mark.parametrize("b", [[4, 5, 6], [-11, -100, 30]])
+def test_append_index(a, b):
+    df = pd.DataFrame()
+    df["a"] = a
+    df["b"] = b
+
+    gdf = cudf.DataFrame()
+    gdf["a"] = a
+    gdf["b"] = b
+
+    # Check the default index after appending two columns(Series)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        expected = df.a.append(df.b)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        actual = gdf.a.append(gdf.b)
+
+    assert len(expected) == len(actual)
+    assert_eq(expected.index, actual.index)
+
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        expected = df.a.append(df.b, ignore_index=True)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        actual = gdf.a.append(gdf.b, ignore_index=True)
+
+    assert len(expected) == len(actual)
+    assert_eq(expected.index, actual.index)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2]},
+        {"a": [1, 2, 3], "b": [3, 4, 5]},
+        {"a": [1, 2, 3, 4], "b": [3, 4, 5, 6], "c": [1, 3, 5, 7]},
+        {"a": [np.nan, 2, 3, 4], "b": [3, 4, np.nan, 6], "c": [1, 3, 5, 7]},
+        {1: [1, 2, 3], 2: [3, 4, 5]},
+        {"a": [1, None, None], "b": [3, np.nan, np.nan]},
+        {1: ["a", "b", "c"], 2: ["q", "w", "u"]},
+        {1: ["a", np.nan, "c"], 2: ["q", None, "u"]},
+        pytest.param(
+            {},
+            marks=pytest_xfail(
+                reason="https://github.com/rapidsai/cudf/issues/11080"
+            ),
+        ),
+        pytest.param(
+            {1: [], 2: [], 3: []},
+            marks=pytest_xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/rapidsai/cudf/issues/11080",
+            ),
+        ),
+        pytest.param(
+            [1, 2, 3],
+            marks=pytest_xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/rapidsai/cudf/issues/11080",
+            ),
+        ),
+    ],
+)
+def test_axes(data):
+    csr = cudf.DataFrame(data)
+    psr = pd.DataFrame(data)
+
+    expected = psr.axes
+    actual = csr.axes
+
+    for e, a in zip(expected, actual):
+        assert_eq(e, a)
+
+
+def test_dataframe_truncate_axis_0():
+    df = cudf.DataFrame(
+        {
+            "A": ["a", "b", "c", "d", "e"],
+            "B": ["f", "g", "h", "i", "j"],
+            "C": ["k", "l", "m", "n", "o"],
+        },
+        index=[1, 2, 3, 4, 5],
+    )
+    pdf = df.to_pandas()
+
+    expected = pdf.truncate(before=2, after=4, axis="index")
+    actual = df.truncate(before=2, after=4, axis="index")
+    assert_eq(actual, expected)
+
+    expected = pdf.truncate(before=1, after=4, axis=0)
+    actual = df.truncate(before=1, after=4, axis=0)
+    assert_eq(expected, actual)
+
+
+def test_dataframe_truncate_axis_1():
+    df = cudf.DataFrame(
+        {
+            "A": ["a", "b", "c", "d", "e"],
+            "B": ["f", "g", "h", "i", "j"],
+            "C": ["k", "l", "m", "n", "o"],
+        },
+        index=[1, 2, 3, 4, 5],
+    )
+    pdf = df.to_pandas()
+
+    expected = pdf.truncate(before="A", after="B", axis="columns")
+    actual = df.truncate(before="A", after="B", axis="columns")
+    assert_eq(actual, expected)
+
+    expected = pdf.truncate(before="A", after="B", axis=1)
+    actual = df.truncate(before="A", after="B", axis=1)
+    assert_eq(actual, expected)
+
+
+def test_dataframe_truncate_datetimeindex():
+    dates = cudf.date_range(
+        "2021-01-01 23:45:00", "2021-01-01 23:46:00", freq="s"
+    )
+    df = cudf.DataFrame(data={"A": 1, "B": 2}, index=dates)
+    pdf = df.to_pandas()
+    expected = pdf.truncate(
+        before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
+    )
+    actual = df.truncate(
+        before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
+    )
+
+    assert_eq(actual, expected)
+
+
+def test_series_init_none():
+    # test for creating empty series
+    # 1: without initializing
+    sr1 = cudf.Series()
+    got = sr1.to_string()
+
+    expect = repr(sr1.to_pandas())
+    assert got == expect
+
+    # 2: Using `None` as an initializer
+    sr2 = cudf.Series(None)
+    got = sr2.to_string()
+
+    expect = repr(sr2.to_pandas())
+    assert got == expect
+
+
+def test_dataframe_basic():
+    np.random.seed(0)
+    df = cudf.DataFrame()
+
+    # Populate with cuda memory
+    df["keys"] = np.arange(10, dtype=np.float64)
+    np.testing.assert_equal(df["keys"].to_numpy(), np.arange(10))
+    assert len(df) == 10
+
+    # Populate with numpy array
+    rnd_vals = np.random.random(10)
+    df["vals"] = rnd_vals
+    np.testing.assert_equal(df["vals"].to_numpy(), rnd_vals)
+    assert len(df) == 10
+    assert tuple(df.columns) == ("keys", "vals")
+
+    # Make another dataframe
+    df2 = cudf.DataFrame()
+    df2["keys"] = np.array([123], dtype=np.float64)
+    df2["vals"] = np.array([321], dtype=np.float64)
+
+    # Concat
+    df = cudf.concat([df, df2])
+    assert len(df) == 11
+
+    hkeys = np.asarray(np.arange(10, dtype=np.float64).tolist() + [123])
+    hvals = np.asarray(rnd_vals.tolist() + [321])
+
+    np.testing.assert_equal(df["keys"].to_numpy(), hkeys)
+    np.testing.assert_equal(df["vals"].to_numpy(), hvals)
+
+    # As matrix
+    mat = df.values_host
+
+    expect = np.vstack([hkeys, hvals]).T
+
+    np.testing.assert_equal(mat, expect)
+
+    # test dataframe with tuple name
+    df_tup = cudf.DataFrame()
+    data = np.arange(10)
+    df_tup[(1, "foobar")] = data
+    np.testing.assert_equal(data, df_tup[(1, "foobar")].to_numpy())
+
+    df = cudf.DataFrame(pd.DataFrame({"a": [1, 2, 3], "c": ["a", "b", "c"]}))
+    pdf = pd.DataFrame(pd.DataFrame({"a": [1, 2, 3], "c": ["a", "b", "c"]}))
+    assert_eq(df, pdf)
+
+    gdf = cudf.DataFrame({"id": [0, 1], "val": [None, None]})
+    gdf["val"] = gdf["val"].astype("int")
+
+    assert gdf["val"].isnull().all()
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "c": range(1, 11)},
+            index=pd.Index(
+                ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
+                name="custom_name",
+            ),
+        ),
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "d": ["a", "v"] * 5}
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "columns",
+    [["a"], ["b"], "a", "b", ["a", "b"]],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_drop_columns(pdf, columns, inplace):
+    pdf = pdf.copy()
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.drop(columns=columns, inplace=inplace)
+    actual = gdf.drop(columns=columns, inplace=inplace)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "c": range(1, 11)},
+            index=pd.Index(list(range(10)), name="custom_name"),
+        ),
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "d": ["a", "v"] * 5}
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "labels",
+    [
+        [1],
+        [0],
+        1,
+        5,
+        [5, 9],
+        pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
+        pd.Index([0, 1, 8, 9], name="new name"),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_drop_labels_axis_0(pdf, labels, inplace):
+    pdf = pdf.copy()
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.drop(labels=labels, axis=0, inplace=inplace)
+    actual = gdf.drop(labels=labels, axis=0, inplace=inplace)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame({"a": range(10), "b": range(10, 20), "c": range(1, 11)}),
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "d": ["a", "v"] * 5}
+        ),
+        pd.DataFrame(
+            {
+                "a": range(10),
+                "b": range(10, 20),
+            },
+            index=pd.Index(list(range(10)), dtype="uint64"),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "index",
+    [[1], [0], 1, 5, [5, 9], pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_drop_index(pdf, index, inplace):
+    pdf = pdf.copy()
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.drop(index=index, inplace=inplace)
+    actual = gdf.drop(index=index, inplace=inplace)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "d": ["a", "v"] * 5},
+            index=pd.MultiIndex(
+                levels=[
+                    ["lama", "cow", "falcon"],
+                    ["speed", "weight", "length"],
+                ],
+                codes=[
+                    [0, 0, 0, 1, 1, 1, 2, 2, 2, 1],
+                    [0, 1, 2, 0, 1, 2, 0, 1, 2, 1],
+                ],
+            ),
+        )
+    ],
+)
+@pytest.mark.parametrize(
+    "index,level",
+    [
+        ("cow", 0),
+        ("lama", 0),
+        ("falcon", 0),
+        ("speed", 1),
+        ("weight", 1),
+        ("length", 1),
+        ("cow", None),
+        (
+            "lama",
+            None,
+        ),
+        (
+            "falcon",
+            None,
+        ),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_drop_multiindex(pdf, index, level, inplace):
+    pdf = pdf.copy()
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.drop(index=index, inplace=inplace, level=level)
+    actual = gdf.drop(index=index, inplace=inplace, level=level)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame({"a": range(10), "b": range(10, 20), "c": range(1, 11)}),
+        pd.DataFrame(
+            {"a": range(10), "b": range(10, 20), "d": ["a", "v"] * 5}
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "labels",
+    [["a"], ["b"], "a", "b", ["a", "b"]],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_drop_labels_axis_1(pdf, labels, inplace):
+    pdf = pdf.copy()
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.drop(labels=labels, axis=1, inplace=inplace)
+    actual = gdf.drop(labels=labels, axis=1, inplace=inplace)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(expected, actual)
+
+
+def test_dataframe_drop_error():
+    df = cudf.DataFrame({"a": [1], "b": [2], "c": [3]})
+    pdf = df.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([], {"columns": "d"}),
+        rfunc_args_and_kwargs=([], {"columns": "d"}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([], {"columns": ["a", "d", "b"]}),
+        rfunc_args_and_kwargs=([], {"columns": ["a", "d", "b"]}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=(["a"], {"columns": "a", "axis": 1}),
+        rfunc_args_and_kwargs=(["a"], {"columns": "a", "axis": 1}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([], {"axis": 1}),
+        rfunc_args_and_kwargs=([], {"axis": 1}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([[2, 0]],),
+        rfunc_args_and_kwargs=([[2, 0]],),
+    )
+
+
+def test_dataframe_swaplevel_axis_0():
+    midx = cudf.MultiIndex(
+        levels=[
+            ["Work"],
+            ["Final exam", "Coursework"],
+            ["History", "Geography"],
+            ["January", "February", "March", "April"],
+        ],
+        codes=[[0, 0, 0, 0], [0, 0, 1, 1], [0, 1, 0, 1], [0, 1, 2, 3]],
+        names=["a", "b", "c", "d"],
+    )
+    cdf = cudf.DataFrame(
+        {
+            "Grade": ["A", "B", "A", "C"],
+            "Percentage": ["95", "85", "95", "75"],
+        },
+        index=midx,
+    )
+    pdf = cdf.to_pandas()
+
+    assert_eq(pdf.swaplevel(), cdf.swaplevel())
+    assert_eq(pdf.swaplevel(), cdf.swaplevel(-2, -1, 0))
+    assert_eq(pdf.swaplevel(1, 2), cdf.swaplevel(1, 2))
+    assert_eq(cdf.swaplevel(2, 1), cdf.swaplevel(1, 2))
+    assert_eq(pdf.swaplevel(-1, -3), cdf.swaplevel(-1, -3))
+    assert_eq(pdf.swaplevel("a", "b", 0), cdf.swaplevel("a", "b", 0))
+    assert_eq(cdf.swaplevel("a", "b"), cdf.swaplevel("b", "a"))
+
+
+def test_dataframe_swaplevel_TypeError():
+    cdf = cudf.DataFrame(
+        {"a": [1, 2, 3], "c": [10, 20, 30]}, index=["x", "y", "z"]
+    )
+
+    with pytest.raises(TypeError):
+        cdf.swaplevel()
+
+
+def test_dataframe_swaplevel_axis_1():
+    midx = cudf.MultiIndex(
+        levels=[
+            ["b", "a"],
+            ["bb", "aa"],
+            ["bbb", "aaa"],
+        ],
+        codes=[[0, 0, 1, 1], [0, 1, 0, 1], [0, 1, 0, 1]],
+        names=[None, "a", "b"],
+    )
+    cdf = cudf.DataFrame(
+        data=[[45, 30, 100, 90], [200, 100, 50, 80]],
+        columns=midx,
+    )
+    pdf = cdf.to_pandas()
+
+    assert_eq(pdf.swaplevel(1, 2, 1), cdf.swaplevel(1, 2, 1))
+    assert_eq(pdf.swaplevel("a", "b", 1), cdf.swaplevel("a", "b", 1))
+    assert_eq(cdf.swaplevel(2, 1, 1), cdf.swaplevel(1, 2, 1))
+    assert_eq(pdf.swaplevel(0, 2, 1), cdf.swaplevel(0, 2, 1))
+    assert_eq(pdf.swaplevel(2, 0, 1), cdf.swaplevel(2, 0, 1))
+    assert_eq(cdf.swaplevel("a", "a", 1), cdf.swaplevel("b", "b", 1))
+
+
+def test_dataframe_drop_raises():
+    df = cudf.DataFrame(
+        {"a": [1, 2, 3], "c": [10, 20, 30]}, index=["x", "y", "z"]
+    )
+    pdf = df.to_pandas()
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=(["p"],),
+        rfunc_args_and_kwargs=(["p"],),
+    )
+
+    # label dtype mismatch
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([3],),
+        rfunc_args_and_kwargs=([3],),
+    )
+
+    expect = pdf.drop("p", errors="ignore")
+    actual = df.drop("p", errors="ignore")
+
+    assert_eq(actual, expect)
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([], {"columns": "p"}),
+        rfunc_args_and_kwargs=([], {"columns": "p"}),
+    )
+
+    expect = pdf.drop(columns="p", errors="ignore")
+    actual = df.drop(columns="p", errors="ignore")
+
+    assert_eq(actual, expect)
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop,
+        rfunc=df.drop,
+        lfunc_args_and_kwargs=([], {"labels": "p", "axis": 1}),
+        rfunc_args_and_kwargs=([], {"labels": "p", "axis": 1}),
+    )
+
+    expect = pdf.drop(labels="p", axis=1, errors="ignore")
+    actual = df.drop(labels="p", axis=1, errors="ignore")
+
+    assert_eq(actual, expect)
+
+
+def test_dataframe_column_add_drop_via_setitem():
+    df = cudf.DataFrame()
+    data = np.asarray(range(10))
+    df["a"] = data
+    df["b"] = data
+    assert tuple(df.columns) == ("a", "b")
+    del df["a"]
+    assert tuple(df.columns) == ("b",)
+    df["c"] = data
+    assert tuple(df.columns) == ("b", "c")
+    df["a"] = data
+    assert tuple(df.columns) == ("b", "c", "a")
+
+
+def test_dataframe_column_set_via_attr():
+    data_0 = np.asarray([0, 2, 4, 5])
+    data_1 = np.asarray([1, 4, 2, 3])
+    data_2 = np.asarray([2, 0, 3, 0])
+    df = cudf.DataFrame({"a": data_0, "b": data_1, "c": data_2})
+
+    for i in range(10):
+        df.c = df.a
+        assert assert_eq(df.c, df.a, check_names=False)
+        assert tuple(df.columns) == ("a", "b", "c")
+
+        df.c = df.b
+        assert assert_eq(df.c, df.b, check_names=False)
+        assert tuple(df.columns) == ("a", "b", "c")
+
+
+def test_dataframe_column_drop_via_attr():
+    df = cudf.DataFrame({"a": []})
+
+    with pytest.raises(AttributeError):
+        del df.a
+
+    assert tuple(df.columns) == tuple("a")
+
+
+@pytest.mark.parametrize("axis", [0, "index"])
+def test_dataframe_index_rename(axis):
+    pdf = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    expect = pdf.rename(mapper={1: 5, 2: 6}, axis=axis)
+    got = gdf.rename(mapper={1: 5, 2: 6}, axis=axis)
+
+    assert_eq(expect, got)
+
+    expect = pdf.rename(index={1: 5, 2: 6})
+    got = gdf.rename(index={1: 5, 2: 6})
+
+    assert_eq(expect, got)
+
+    expect = pdf.rename({1: 5, 2: 6})
+    got = gdf.rename({1: 5, 2: 6})
+
+    assert_eq(expect, got)
+
+    # `pandas` can support indexes with mixed values. We throw a
+    # `NotImplementedError`.
+    with pytest.raises(NotImplementedError):
+        gdf.rename(mapper={1: "x", 2: "y"}, axis=axis)
+
+
+def test_dataframe_MI_rename():
+    gdf = cudf.DataFrame(
+        {"a": np.arange(10), "b": np.arange(10), "c": np.arange(10)}
+    )
+    gdg = gdf.groupby(["a", "b"]).count()
+    pdg = gdg.to_pandas()
+
+    expect = pdg.rename(mapper={1: 5, 2: 6}, axis=0)
+    got = gdg.rename(mapper={1: 5, 2: 6}, axis=0)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("axis", [1, "columns"])
+def test_dataframe_column_rename(axis):
+    pdf = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    expect = pdf.rename(mapper=lambda name: 2 * name, axis=axis)
+    got = gdf.rename(mapper=lambda name: 2 * name, axis=axis)
+
+    assert_eq(expect, got)
+
+    expect = pdf.rename(columns=lambda name: 2 * name)
+    got = gdf.rename(columns=lambda name: 2 * name)
+
+    assert_eq(expect, got)
+
+    rename_mapper = {"a": "z", "b": "y", "c": "x"}
+    expect = pdf.rename(columns=rename_mapper)
+    got = gdf.rename(columns=rename_mapper)
+
+    assert_eq(expect, got)
+
+
+def test_dataframe_pop():
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 3], "b": ["x", "y", "z"], "c": [7.0, 8.0, 9.0]}
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    # Test non-existing column error
+    with pytest.raises(KeyError) as raises:
+        gdf.pop("fake_colname")
+    raises.match("fake_colname")
+
+    # check pop numeric column
+    pdf_pop = pdf.pop("a")
+    gdf_pop = gdf.pop("a")
+    assert_eq(pdf_pop, gdf_pop)
+    assert_eq(pdf, gdf)
+
+    # check string column
+    pdf_pop = pdf.pop("b")
+    gdf_pop = gdf.pop("b")
+    assert_eq(pdf_pop, gdf_pop)
+    assert_eq(pdf, gdf)
+
+    # check float column and empty dataframe
+    pdf_pop = pdf.pop("c")
+    gdf_pop = gdf.pop("c")
+    assert_eq(pdf_pop, gdf_pop)
+    assert_eq(pdf, gdf)
+
+    # check empty dataframe edge case
+    empty_pdf = pd.DataFrame(columns=["a", "b"])
+    empty_gdf = cudf.DataFrame(columns=["a", "b"])
+    pb = empty_pdf.pop("b")
+    gb = empty_gdf.pop("b")
+    assert len(pb) == len(gb)
+    assert empty_pdf.empty and empty_gdf.empty
+
+
+@pytest.mark.parametrize("nelem", [0, 3, 100, 1000])
+def test_dataframe_astype(nelem):
+    df = cudf.DataFrame()
+    data = np.asarray(range(nelem), dtype=np.int32)
+    df["a"] = data
+    assert df["a"].dtype is np.dtype(np.int32)
+    df["b"] = df["a"].astype(np.float32)
+    assert df["b"].dtype is np.dtype(np.float32)
+    np.testing.assert_equal(df["a"].to_numpy(), df["b"].to_numpy())
+
+
+def test_astype_dict():
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": ["1", "2", "3"]})
+    pdf = gdf.to_pandas()
+
+    assert_eq(pdf.astype({"a": "str"}), gdf.astype({"a": "str"}))
+    assert_eq(
+        pdf.astype({"a": "str", "b": np.int64}),
+        gdf.astype({"a": "str", "b": np.int64}),
+    )
+
+
+@pytest.mark.parametrize("nelem", [0, 100])
+def test_index_astype(nelem):
+    df = cudf.DataFrame()
+    data = np.asarray(range(nelem), dtype=np.int32)
+    df["a"] = data
+    assert df.index.dtype is np.dtype(np.int64)
+    df.index = df.index.astype(np.float32)
+    assert df.index.dtype is np.dtype(np.float32)
+    df["a"] = df["a"].astype(np.float32)
+    np.testing.assert_equal(df.index.to_numpy(), df["a"].to_numpy())
+    df["b"] = df["a"]
+    df = df.set_index("b")
+    df["a"] = df["a"].astype(np.int16)
+    df.index = df.index.astype(np.int16)
+    np.testing.assert_equal(df.index.to_numpy(), df["a"].to_numpy())
+
+
+def test_dataframe_to_string_with_skipped_rows():
+    # Test skipped rows
+    df = cudf.DataFrame(
+        {"a": [1, 2, 3, 4, 5, 6], "b": [11, 12, 13, 14, 15, 16]}
+    )
+
+    with pd.option_context("display.max_rows", 5):
+        got = df.to_string()
+
+    expect = textwrap.dedent(
+        """\
+            a   b
+        0   1  11
+        1   2  12
+        .. ..  ..
+        4   5  15
+        5   6  16
+
+        [6 rows x 2 columns]"""
+    )
+    assert got == expect
+
+
+def test_dataframe_to_string_with_skipped_rows_and_columns():
+    # Test skipped rows and skipped columns
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5, 6],
+            "b": [11, 12, 13, 14, 15, 16],
+            "c": [11, 12, 13, 14, 15, 16],
+            "d": [11, 12, 13, 14, 15, 16],
+        }
+    )
+
+    with pd.option_context("display.max_rows", 5, "display.max_columns", 3):
+        got = df.to_string()
+
+    expect = textwrap.dedent(
+        """\
+            a  ...   d
+        0   1  ...  11
+        1   2  ...  12
+        .. ..  ...  ..
+        4   5  ...  15
+        5   6  ...  16
+
+        [6 rows x 4 columns]"""
+    )
+    assert got == expect
+
+
+def test_dataframe_to_string_with_masked_data():
+    # Test masked data
+    df = cudf.DataFrame(
+        {"a": [1, 2, 3, 4, 5, 6], "b": [11, 12, 13, 14, 15, 16]}
+    )
+
+    data = np.arange(6)
+    mask = np.zeros(1, dtype=cudf.utils.utils.mask_dtype)
+    mask[0] = 0b00101101
+
+    masked = cudf.Series.from_masked_array(data, mask)
+    assert masked.null_count == 2
+    df["c"] = masked
+
+    # Check data
+    values = masked.copy()
+    validids = [0, 2, 3, 5]
+    densearray = masked.dropna().to_numpy()
+    np.testing.assert_equal(data[validids], densearray)
+    # Valid position is correct
+    for i in validids:
+        assert data[i] == values[i]
+    # Null position is correct
+    for i in range(len(values)):
+        if i not in validids:
+            assert values[i] is cudf.NA
+
+    with pd.option_context("display.max_rows", 10):
+        got = df.to_string()
+
+    expect = textwrap.dedent(
+        """\
+           a   b     c
+        0  1  11     0
+        1  2  12  <NA>
+        2  3  13     2
+        3  4  14     3
+        4  5  15  <NA>
+        5  6  16     5"""
+    )
+    assert got == expect
+
+
+def test_dataframe_to_string_wide(monkeypatch):
+    monkeypatch.setenv("COLUMNS", "79")
+    # Test basic
+    df = cudf.DataFrame({f"a{i}": [0, 1, 2] for i in range(100)})
+    with pd.option_context("display.max_columns", 0):
+        got = df.to_string()
+
+    expect = textwrap.dedent(
+        """\
+           a0  a1  a2  a3  a4  a5  a6  a7  ...  a92  a93  a94  a95  a96  a97  a98  a99
+        0   0   0   0   0   0   0   0   0  ...    0    0    0    0    0    0    0    0
+        1   1   1   1   1   1   1   1   1  ...    1    1    1    1    1    1    1    1
+        2   2   2   2   2   2   2   2   2  ...    2    2    2    2    2    2    2    2
+
+        [3 rows x 100 columns]"""  # noqa: E501
+    )
+    assert got == expect
+
+
+def test_dataframe_empty_to_string():
+    # Test for printing empty dataframe
+    df = cudf.DataFrame()
+    got = df.to_string()
+
+    expect = "Empty DataFrame\nColumns: []\nIndex: []"
+    assert got == expect
+
+
+def test_dataframe_emptycolumns_to_string():
+    # Test for printing dataframe having empty columns
+    df = cudf.DataFrame()
+    df["a"] = []
+    df["b"] = []
+    got = df.to_string()
+
+    expect = "Empty DataFrame\nColumns: [a, b]\nIndex: []"
+    assert got == expect
+
+
+def test_dataframe_copy():
+    # Test for copying the dataframe using python copy pkg
+    df = cudf.DataFrame()
+    df["a"] = [1, 2, 3]
+    df2 = copy(df)
+    df2["b"] = [4, 5, 6]
+    got = df.to_string()
+
+    expect = textwrap.dedent(
+        """\
+           a
+        0  1
+        1  2
+        2  3"""
+    )
+    assert got == expect
+
+
+def test_dataframe_copy_shallow():
+    # Test for copy dataframe using class method
+    df = cudf.DataFrame()
+    df["a"] = [1, 2, 3]
+    df2 = df.copy()
+    df2["b"] = [4, 2, 3]
+    got = df.to_string()
+
+    expect = textwrap.dedent(
+        """\
+           a
+        0  1
+        1  2
+        2  3"""
+    )
+    assert got == expect
+
+
+def test_dataframe_dtypes():
+    dtypes = pd.Series(
+        [np.int32, np.float32, np.float64], index=["c", "a", "b"]
+    )
+    df = cudf.DataFrame({k: np.ones(10, dtype=v) for k, v in dtypes.items()})
+    assert df.dtypes.equals(dtypes)
+
+
+def test_dataframe_add_col_to_object_dataframe():
+    # Test for adding column to an empty object dataframe
+    cols = ["a", "b", "c"]
+    df = pd.DataFrame(columns=cols, dtype="str")
+
+    data = {k: v for (k, v) in zip(cols, [["a"] for _ in cols])}
+
+    gdf = cudf.DataFrame(data)
+    gdf = gdf[:0]
+
+    assert gdf.dtypes.equals(df.dtypes)
+    gdf["a"] = [1]
+    df["a"] = [10]
+    assert gdf.dtypes.equals(df.dtypes)
+    gdf["b"] = [1.0]
+    df["b"] = [10.0]
+    assert gdf.dtypes.equals(df.dtypes)
+
+
+def test_dataframe_dir_and_getattr():
+    df = cudf.DataFrame(
+        {
+            "a": np.ones(10),
+            "b": np.ones(10),
+            "not an id": np.ones(10),
+            "oop$": np.ones(10),
+        }
+    )
+    o = dir(df)
+    assert {"a", "b"}.issubset(o)
+    assert "not an id" not in o
+    assert "oop$" not in o
+
+    # Getattr works
+    assert df.a.equals(df["a"])
+    assert df.b.equals(df["b"])
+    with pytest.raises(AttributeError):
+        df.not_a_column
+
+
+def test_empty_dataframe_to_cupy():
+    df = cudf.DataFrame()
+
+    # Check fully empty dataframe.
+    mat = df.to_cupy()
+    assert mat.shape == (0, 0)
+    mat = df.to_numpy()
+    assert mat.shape == (0, 0)
+
+    df = cudf.DataFrame()
+    nelem = 123
+    for k in "abc":
+        df[k] = np.random.random(nelem)
+
+    # Check all columns in empty dataframe.
+    mat = df.head(0).to_cupy()
+    assert mat.shape == (0, 3)
+
+
+def test_dataframe_to_cupy():
+    df = cudf.DataFrame()
+
+    nelem = 123
+    for k in "abcd":
+        df[k] = np.random.random(nelem)
+
+    # Check all columns
+    mat = df.to_cupy()
+    assert mat.shape == (nelem, 4)
+    assert mat.strides == (8, 984)
+
+    mat = df.to_numpy()
+    assert mat.shape == (nelem, 4)
+    assert mat.strides == (8, 984)
+    for i, k in enumerate(df.columns):
+        np.testing.assert_array_equal(df[k].to_numpy(), mat[:, i])
+
+    # Check column subset
+    mat = df[["a", "c"]].to_cupy().get()
+    assert mat.shape == (nelem, 2)
+
+    for i, k in enumerate("ac"):
+        np.testing.assert_array_equal(df[k].to_numpy(), mat[:, i])
+
+
+def test_dataframe_to_cupy_null_values():
+    df = cudf.DataFrame()
+
+    nelem = 123
+    na = -10000
+
+    refvalues = {}
+    for k in "abcd":
+        df[k] = data = np.random.random(nelem)
+        bitmask = utils.random_bitmask(nelem)
+        df[k] = df[k]._column.set_mask(bitmask)
+        boolmask = np.asarray(
+            utils.expand_bits_to_bytes(bitmask)[:nelem], dtype=np.bool_
+        )
+        data[~boolmask] = na
+        refvalues[k] = data
+
+    # Check null value causes error
+    with pytest.raises(ValueError):
+        df.to_cupy()
+    with pytest.raises(ValueError):
+        df.to_numpy()
+
+    for k in df.columns:
+        df[k] = df[k].fillna(na)
+
+    mat = df.to_numpy()
+    for i, k in enumerate(df.columns):
+        np.testing.assert_array_equal(refvalues[k], mat[:, i])
+
+
+def test_dataframe_append_empty():
+    pdf = pd.DataFrame(
+        {
+            "key": [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4],
+            "value": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    gdf["newcol"] = 100
+    pdf["newcol"] = 100
+
+    assert len(gdf["newcol"]) == len(pdf)
+    assert len(pdf["newcol"]) == len(pdf)
+    assert_eq(gdf, pdf)
+
+
+def test_dataframe_setitem_from_masked_object():
+    ary = np.random.randn(100)
+    mask = np.zeros(100, dtype=bool)
+    mask[:20] = True
+    np.random.shuffle(mask)
+    ary[mask] = np.nan
+
+    test1_null = cudf.Series(ary, nan_as_null=True)
+    assert test1_null.nullable
+    assert test1_null.null_count == 20
+    test1_nan = cudf.Series(ary, nan_as_null=False)
+    assert test1_nan.null_count == 0
+
+    test2_null = cudf.DataFrame.from_pandas(
+        pd.DataFrame({"a": ary}), nan_as_null=True
+    )
+    assert test2_null["a"].nullable
+    assert test2_null["a"].null_count == 20
+    test2_nan = cudf.DataFrame.from_pandas(
+        pd.DataFrame({"a": ary}), nan_as_null=False
+    )
+    assert test2_nan["a"].null_count == 0
+
+    gpu_ary = cupy.asarray(ary)
+    test3_null = cudf.Series(gpu_ary, nan_as_null=True)
+    assert test3_null.nullable
+    assert test3_null.null_count == 20
+    test3_nan = cudf.Series(gpu_ary, nan_as_null=False)
+    assert test3_nan.null_count == 0
+
+    test4 = cudf.DataFrame()
+    lst = [1, 2, None, 4, 5, 6, None, 8, 9]
+    test4["lst"] = lst
+    assert test4["lst"].nullable
+    assert test4["lst"].null_count == 2
+
+
+def test_dataframe_append_to_empty():
+    pdf = pd.DataFrame()
+    pdf["a"] = []
+    pdf["b"] = [1, 2, 3]
+
+    gdf = cudf.DataFrame()
+    gdf["a"] = []
+    gdf["b"] = [1, 2, 3]
+
+    assert_eq(gdf, pdf)
+
+
+def test_dataframe_setitem_index_len1():
+    gdf = cudf.DataFrame()
+    gdf["a"] = [1]
+    gdf["b"] = gdf.index._values
+
+    np.testing.assert_equal(gdf.b.to_numpy(), [0])
+
+
+def test_empty_dataframe_setitem_df():
+    gdf1 = cudf.DataFrame()
+    gdf2 = cudf.DataFrame({"a": [1, 2, 3, 4, 5]})
+    gdf1["a"] = gdf2["a"]
+    assert_eq(gdf1, gdf2)
+
+
+def test_assign():
+    gdf = cudf.DataFrame({"x": [1, 2, 3]})
+    gdf2 = gdf.assign(y=gdf.x + 1)
+    assert list(gdf.columns) == ["x"]
+    assert list(gdf2.columns) == ["x", "y"]
+
+    np.testing.assert_equal(gdf2.y.to_numpy(), [2, 3, 4])
+
+
+@pytest.mark.parametrize(
+    "mapping",
+    [
+        {"y": 1, "z": lambda df: df["x"] + df["y"]},
+        {
+            "x": lambda df: df["x"] * 2,
+            "y": lambda df: 2,
+            "z": lambda df: df["x"] / df["y"],
+        },
+    ],
+)
+def test_assign_callable(mapping):
+    df = pd.DataFrame({"x": [1, 2, 3]})
+    cdf = cudf.from_pandas(df)
+    expect = df.assign(**mapping)
+    actual = cdf.assign(**mapping)
+    assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize("nrows", [1, 8, 100, 1000])
+@pytest.mark.parametrize("method", ["murmur3", "md5"])
+@pytest.mark.parametrize("seed", [None, 42])
+def test_dataframe_hash_values(nrows, method, seed):
+    gdf = cudf.DataFrame()
+    data = np.arange(nrows)
+    data[0] = data[-1]  # make first and last the same
+    gdf["a"] = data
+    gdf["b"] = gdf.a + 100
+    out = gdf.hash_values()
+    assert isinstance(out, cudf.Series)
+    assert len(out) == nrows
+    assert out.dtype == np.uint32
+
+    warning_expected = (
+        True if seed is not None and method not in {"murmur3"} else False
+    )
+    # Check single column
+    if warning_expected:
+        with pytest.warns(
+            UserWarning, match="Provided seed value has no effect*"
+        ):
+            out_one = gdf[["a"]].hash_values(method=method, seed=seed)
+    else:
+        out_one = gdf[["a"]].hash_values(method=method, seed=seed)
+    # First matches last
+    assert out_one.iloc[0] == out_one.iloc[-1]
+    # Equivalent to the cudf.Series.hash_values()
+    if warning_expected:
+        with pytest.warns(
+            UserWarning, match="Provided seed value has no effect*"
+        ):
+            assert_eq(gdf["a"].hash_values(method=method, seed=seed), out_one)
+    else:
+        assert_eq(gdf["a"].hash_values(method=method, seed=seed), out_one)
+
+
+@pytest.mark.parametrize("method", ["murmur3"])
+def test_dataframe_hash_values_seed(method):
+    gdf = cudf.DataFrame()
+    data = np.arange(10)
+    data[0] = data[-1]  # make first and last the same
+    gdf["a"] = data
+    gdf["b"] = gdf.a + 100
+    out_one = gdf.hash_values(method=method, seed=0)
+    out_two = gdf.hash_values(method=method, seed=1)
+    assert out_one.iloc[0] == out_one.iloc[-1]
+    assert out_two.iloc[0] == out_two.iloc[-1]
+    assert_neq(out_one, out_two)
+
+
+@pytest.mark.parametrize("nrows", [3, 10, 100, 1000])
+@pytest.mark.parametrize("nparts", [1, 2, 8, 13])
+@pytest.mark.parametrize("nkeys", [1, 2])
+def test_dataframe_hash_partition(nrows, nparts, nkeys):
+    np.random.seed(123)
+    gdf = cudf.DataFrame()
+    keycols = []
+    for i in range(nkeys):
+        keyname = f"key{i}"
+        gdf[keyname] = np.random.randint(0, 7 - i, nrows)
+        keycols.append(keyname)
+    gdf["val1"] = np.random.randint(0, nrows * 2, nrows)
+
+    got = gdf.partition_by_hash(keycols, nparts=nparts)
+    # Must return a list
+    assert isinstance(got, list)
+    # Must have correct number of partitions
+    assert len(got) == nparts
+    # All partitions must be DataFrame type
+    assert all(isinstance(p, cudf.DataFrame) for p in got)
+    # Check that all partitions have unique keys
+    part_unique_keys = set()
+    for p in got:
+        if len(p):
+            # Take rows of the keycolumns and build a set of the key-values
+            unique_keys = set(map(tuple, p[keycols].values_host))
+            # Ensure that none of the key-values have occurred in other groups
+            assert not (unique_keys & part_unique_keys)
+            part_unique_keys |= unique_keys
+    assert len(part_unique_keys)
+
+
+@pytest.mark.parametrize("nrows", [3, 10, 50])
+def test_dataframe_hash_partition_masked_value(nrows):
+    gdf = cudf.DataFrame()
+    gdf["key"] = np.arange(nrows)
+    gdf["val"] = np.arange(nrows) + 100
+    bitmask = utils.random_bitmask(nrows)
+    bytemask = utils.expand_bits_to_bytes(bitmask)
+    gdf["val"] = gdf["val"]._column.set_mask(bitmask)
+    parted = gdf.partition_by_hash(["key"], nparts=3)
+    # Verify that the valid mask is correct
+    for p in parted:
+        df = p.to_pandas()
+        for row in df.itertuples():
+            valid = bool(bytemask[row.key])
+            expected_value = row.key + 100 if valid else np.nan
+            got_value = row.val
+            assert (expected_value == got_value) or (
+                np.isnan(expected_value) and np.isnan(got_value)
+            )
+
+
+@pytest.mark.parametrize("nrows", [3, 10, 50])
+def test_dataframe_hash_partition_masked_keys(nrows):
+    gdf = cudf.DataFrame()
+    gdf["key"] = np.arange(nrows)
+    gdf["val"] = np.arange(nrows) + 100
+    bitmask = utils.random_bitmask(nrows)
+    bytemask = utils.expand_bits_to_bytes(bitmask)
+    gdf["key"] = gdf["key"]._column.set_mask(bitmask)
+    parted = gdf.partition_by_hash(["key"], nparts=3, keep_index=False)
+    # Verify that the valid mask is correct
+    for p in parted:
+        df = p.to_pandas()
+        for row in df.itertuples():
+            valid = bool(bytemask[row.val - 100])
+            # val is key + 100
+            expected_value = row.val - 100 if valid else np.nan
+            got_value = row.key
+            assert (expected_value == got_value) or (
+                np.isnan(expected_value) and np.isnan(got_value)
+            )
+
+
+@pytest.mark.parametrize("keep_index", [True, False])
+def test_dataframe_hash_partition_keep_index(keep_index):
+    gdf = cudf.DataFrame(
+        {"val": [1, 2, 3, 4, 5], "key": [3, 2, 1, 4, 5]}, index=[5, 4, 3, 2, 1]
+    )
+
+    expected_df1 = cudf.DataFrame(
+        {"val": [1, 5], "key": [3, 5]}, index=[5, 1] if keep_index else None
+    )
+    expected_df2 = cudf.DataFrame(
+        {"val": [2, 3, 4], "key": [2, 1, 4]},
+        index=[4, 3, 2] if keep_index else None,
+    )
+    expected = [expected_df1, expected_df2]
+
+    parts = gdf.partition_by_hash(["key"], nparts=2, keep_index=keep_index)
+
+    for exp, got in zip(expected, parts):
+        assert_eq(exp, got)
+
+
+def test_dataframe_hash_partition_empty():
+    gdf = cudf.DataFrame({"val": [1, 2], "key": [3, 2]}, index=["a", "b"])
+    parts = gdf.iloc[:0].partition_by_hash(["key"], nparts=3)
+    assert len(parts) == 3
+    for part in parts:
+        assert_eq(gdf.iloc[:0], part)
+
+
+@pytest.mark.parametrize("dtype1", utils.supported_numpy_dtypes)
+@pytest.mark.parametrize("dtype2", utils.supported_numpy_dtypes)
+def test_dataframe_concat_different_numerical_columns(dtype1, dtype2):
+    df1 = pd.DataFrame(dict(x=pd.Series(np.arange(5)).astype(dtype1)))
+    df2 = pd.DataFrame(dict(x=pd.Series(np.arange(5)).astype(dtype2)))
+    if dtype1 != dtype2 and "datetime" in dtype1 or "datetime" in dtype2:
+        with pytest.raises(TypeError):
+            cudf.concat([df1, df2])
+    else:
+        pres = pd.concat([df1, df2])
+        gres = cudf.concat([cudf.from_pandas(df1), cudf.from_pandas(df2)])
+        assert_eq(pres, gres, check_dtype=False, check_index_type=True)
+
+
+def test_dataframe_concat_different_column_types():
+    df1 = cudf.Series([42], dtype=np.float64)
+    df2 = cudf.Series(["a"], dtype="category")
+    with pytest.raises(ValueError):
+        cudf.concat([df1, df2])
+
+    df2 = cudf.Series(["a string"])
+    with pytest.raises(TypeError):
+        cudf.concat([df1, df2])
+
+
+@pytest.mark.parametrize(
+    "df_1", [cudf.DataFrame({"a": [1, 2], "b": [1, 3]}), cudf.DataFrame({})]
+)
+@pytest.mark.parametrize(
+    "df_2", [cudf.DataFrame({"a": [], "b": []}), cudf.DataFrame({})]
+)
+def test_concat_empty_dataframe(df_1, df_2):
+    got = cudf.concat([df_1, df_2])
+    expect = pd.concat([df_1.to_pandas(), df_2.to_pandas()], sort=False)
+
+    # ignoring dtypes as pandas upcasts int to float
+    # on concatenation with empty dataframes
+
+    assert_eq(got, expect, check_dtype=False, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "df1_d",
+    [
+        {"a": [1, 2], "b": [1, 2], "c": ["s1", "s2"], "d": [1.0, 2.0]},
+        {"b": [1.9, 10.9], "c": ["s1", "s2"]},
+        {"c": ["s1"], "b": pd.Series([None], dtype="float"), "a": [False]},
+    ],
+)
+@pytest.mark.parametrize(
+    "df2_d",
+    [
+        {"a": [1, 2, 3]},
+        {"a": [1, None, 3], "b": [True, True, False], "c": ["s3", None, "s4"]},
+        {"a": [], "b": []},
+        {},
+    ],
+)
+def test_concat_different_column_dataframe(df1_d, df2_d):
+    got = cudf.concat(
+        [cudf.DataFrame(df1_d), cudf.DataFrame(df2_d), cudf.DataFrame(df1_d)],
+        sort=False,
+    )
+
+    pdf1 = pd.DataFrame(df1_d)
+    pdf2 = pd.DataFrame(df2_d)
+
+    # pandas warns when trying to concatenate any empty float columns (or float
+    # columns with all None values) with any non-empty bool columns.
+    def is_invalid_concat(left, right):
+        return (
+            pd.api.types.is_bool_dtype(left.dtype)
+            and pd.api.types.is_float_dtype(right.dtype)
+            and right.count() == 0
+        )
+
+    cond = any(
+        is_invalid_concat(pdf1[colname], pdf2[colname])
+        or is_invalid_concat(pdf2[colname], pdf1[colname])
+        for colname in set(pdf1) & set(pdf2)
+    )
+
+    with expect_warning_if(cond):
+        expect = pd.concat([pdf1, pdf2, pdf1], sort=False)
+
+    # numerical columns are upcasted to float in cudf.DataFrame.to_pandas()
+    # casts nan to 0 in non-float numerical columns
+
+    numeric_cols = got.dtypes[got.dtypes != "object"].index
+    for col in numeric_cols:
+        got[col] = got[col].astype(np.float64).fillna(np.nan)
+
+    assert_eq(got, expect, check_dtype=False, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "ser_1", [pd.Series([1, 2, 3]), pd.Series([], dtype="float64")]
+)
+@pytest.mark.parametrize("ser_2", [pd.Series([], dtype="float64")])
+def test_concat_empty_series(ser_1, ser_2):
+    got = cudf.concat([cudf.Series(ser_1), cudf.Series(ser_2)])
+    expect = pd.concat([ser_1, ser_2])
+
+    assert_eq(got, expect, check_index_type=True)
+
+
+def test_concat_with_axis():
+    df1 = pd.DataFrame(dict(x=np.arange(5), y=np.arange(5)))
+    df2 = pd.DataFrame(dict(a=np.arange(5), b=np.arange(5)))
+
+    concat_df = pd.concat([df1, df2], axis=1)
+    cdf1 = cudf.from_pandas(df1)
+    cdf2 = cudf.from_pandas(df2)
+
+    # concat only dataframes
+    concat_cdf = cudf.concat([cdf1, cdf2], axis=1)
+    assert_eq(concat_cdf, concat_df, check_index_type=True)
+
+    # concat only series
+    concat_s = pd.concat([df1.x, df1.y], axis=1)
+    cs1 = cudf.Series.from_pandas(df1.x)
+    cs2 = cudf.Series.from_pandas(df1.y)
+    concat_cdf_s = cudf.concat([cs1, cs2], axis=1)
+
+    assert_eq(concat_cdf_s, concat_s, check_index_type=True)
+
+    # concat series and dataframes
+    s3 = pd.Series(np.random.random(5))
+    cs3 = cudf.Series.from_pandas(s3)
+
+    concat_cdf_all = cudf.concat([cdf1, cs3, cdf2], axis=1)
+    concat_df_all = pd.concat([df1, s3, df2], axis=1)
+    assert_eq(concat_cdf_all, concat_df_all, check_index_type=True)
+
+    # concat manual multi index
+    midf1 = cudf.from_pandas(df1)
+    midf1.index = cudf.MultiIndex(
+        levels=[[0, 1, 2, 3], [0, 1]], codes=[[0, 1, 2, 3, 2], [0, 1, 0, 1, 0]]
+    )
+    midf2 = midf1[2:]
+    midf2.index = cudf.MultiIndex(
+        levels=[[3, 4, 5], [2, 0]], codes=[[0, 1, 2], [1, 0, 1]]
+    )
+    mipdf1 = midf1.to_pandas()
+    mipdf2 = midf2.to_pandas()
+
+    assert_eq(
+        cudf.concat([midf1, midf2]),
+        pd.concat([mipdf1, mipdf2]),
+        check_index_type=True,
+    )
+    assert_eq(
+        cudf.concat([midf2, midf1]),
+        pd.concat([mipdf2, mipdf1]),
+        check_index_type=True,
+    )
+    assert_eq(
+        cudf.concat([midf1, midf2, midf1]),
+        pd.concat([mipdf1, mipdf2, mipdf1]),
+        check_index_type=True,
+    )
+
+    # concat groupby multi index
+    gdf1 = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, 10, 10),
+            "y": np.random.randint(0, 10, 10),
+            "z": np.random.randint(0, 10, 10),
+            "v": np.random.randint(0, 10, 10),
+        }
+    )
+    gdf2 = gdf1[5:]
+    gdg1 = gdf1.groupby(["x", "y"]).min()
+    gdg2 = gdf2.groupby(["x", "y"]).min()
+    pdg1 = gdg1.to_pandas()
+    pdg2 = gdg2.to_pandas()
+
+    assert_eq(
+        cudf.concat([gdg1, gdg2]),
+        pd.concat([pdg1, pdg2]),
+        check_index_type=True,
+    )
+    assert_eq(
+        cudf.concat([gdg2, gdg1]),
+        pd.concat([pdg2, pdg1]),
+        check_index_type=True,
+    )
+
+    # series multi index concat
+    gdgz1 = gdg1.z
+    gdgz2 = gdg2.z
+    pdgz1 = gdgz1.to_pandas()
+    pdgz2 = gdgz2.to_pandas()
+
+    assert_eq(
+        cudf.concat([gdgz1, gdgz2]),
+        pd.concat([pdgz1, pdgz2]),
+        check_index_type=True,
+    )
+    assert_eq(
+        cudf.concat([gdgz2, gdgz1]),
+        pd.concat([pdgz2, pdgz1]),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("nrows", [0, 3, 10, 100, 1000])
+def test_nonmatching_index_setitem(nrows):
+    np.random.seed(0)
+
+    gdf = cudf.DataFrame()
+    gdf["a"] = np.random.randint(2147483647, size=nrows)
+    gdf["b"] = np.random.randint(2147483647, size=nrows)
+    gdf = gdf.set_index("b")
+
+    test_values = np.random.randint(2147483647, size=nrows)
+    gdf["c"] = test_values
+    assert len(test_values) == len(gdf["c"])
+    gdf_series = cudf.Series(test_values, index=gdf.index, name="c")
+    assert_eq(gdf["c"].to_pandas(), gdf_series.to_pandas())
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "int",
+        pytest.param(
+            "int64[pyarrow]",
+            marks=pytest.mark.skipif(
+                not PANDAS_GE_150, reason="pyarrow support only in >=1.5"
+            ),
+        ),
+    ],
+)
+def test_from_pandas(dtype):
+    df = pd.DataFrame({"x": [1, 2, 3]}, index=[4.0, 5.0, 6.0], dtype=dtype)
+    df.columns.name = "custom_column_name"
+    gdf = cudf.DataFrame.from_pandas(df)
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf, check_dtype="pyarrow" not in dtype)
+
+    s = df.x
+    gs = cudf.Series.from_pandas(s)
+    assert isinstance(gs, cudf.Series)
+
+    assert_eq(s, gs, check_dtype="pyarrow" not in dtype)
+
+
+@pytest.mark.parametrize("dtypes", [int, float])
+def test_from_records(dtypes):
+    h_ary = np.ndarray(shape=(10, 4), dtype=dtypes)
+    rec_ary = h_ary.view(np.recarray)
+
+    gdf = cudf.DataFrame.from_records(rec_ary, columns=["a", "b", "c", "d"])
+    df = pd.DataFrame.from_records(rec_ary, columns=["a", "b", "c", "d"])
+    assert isinstance(gdf, cudf.DataFrame)
+    assert_eq(df, gdf)
+
+    gdf = cudf.DataFrame.from_records(rec_ary)
+    df = pd.DataFrame.from_records(rec_ary)
+    assert isinstance(gdf, cudf.DataFrame)
+    assert_eq(df, gdf)
+
+
+@pytest.mark.parametrize("columns", [None, ["first", "second", "third"]])
+@pytest.mark.parametrize(
+    "index",
+    [
+        None,
+        ["first", "second"],
+        "name",
+        "age",
+        "weight",
+        [10, 11],
+        ["abc", "xyz"],
+    ],
+)
+def test_from_records_index(columns, index):
+    rec_ary = np.array(
+        [("Rex", 9, 81.0), ("Fido", 3, 27.0)],
+        dtype=[("name", "U10"), ("age", "i4"), ("weight", "f4")],
+    )
+    gdf = cudf.DataFrame.from_records(rec_ary, columns=columns, index=index)
+    df = pd.DataFrame.from_records(rec_ary, columns=columns, index=index)
+    assert isinstance(gdf, cudf.DataFrame)
+    assert_eq(df, gdf)
+
+
+def test_dataframe_construction_from_cupy_arrays():
+    h_ary = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
+    d_ary = cupy.asarray(h_ary)
+
+    gdf = cudf.DataFrame(d_ary, columns=["a", "b", "c"])
+    df = pd.DataFrame(h_ary, columns=["a", "b", "c"])
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf)
+
+    gdf = cudf.DataFrame(d_ary)
+    df = pd.DataFrame(h_ary)
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf)
+
+    gdf = cudf.DataFrame(d_ary, index=["a", "b"])
+    df = pd.DataFrame(h_ary, index=["a", "b"])
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf)
+
+    gdf = cudf.DataFrame(d_ary)
+    gdf = gdf.set_index(keys=0, drop=False)
+    df = pd.DataFrame(h_ary)
+    df = df.set_index(keys=0, drop=False)
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf)
+
+    gdf = cudf.DataFrame(d_ary)
+    gdf = gdf.set_index(keys=1, drop=False)
+    df = pd.DataFrame(h_ary)
+    df = df.set_index(keys=1, drop=False)
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf)
+
+
+def test_dataframe_cupy_wrong_dimensions():
+    d_ary = cupy.empty((2, 3, 4), dtype=np.int32)
+    with pytest.raises(
+        ValueError, match="records dimension expected 1 or 2 but found: 3"
+    ):
+        cudf.DataFrame(d_ary)
+
+
+def test_dataframe_cupy_array_wrong_index():
+    d_ary = cupy.empty((2, 3), dtype=np.int32)
+
+    with pytest.raises(ValueError):
+        cudf.DataFrame(d_ary, index=["a"])
+
+    with pytest.raises(ValueError):
+        cudf.DataFrame(d_ary, index="a")
+
+
+def test_index_in_dataframe_constructor():
+    a = pd.DataFrame({"x": [1, 2, 3]}, index=[4.0, 5.0, 6.0])
+    b = cudf.DataFrame({"x": [1, 2, 3]}, index=[4.0, 5.0, 6.0])
+
+    assert_eq(a, b)
+    assert_eq(a.loc[4:], b.loc[4:])
+
+
+dtypes = NUMERIC_TYPES + DATETIME_TYPES + ["bool"]
+
+
+@pytest.mark.parametrize("nelem", [0, 2, 3, 100, 1000])
+@pytest.mark.parametrize("data_type", dtypes)
+def test_from_arrow(nelem, data_type):
+    df = pd.DataFrame(
+        {
+            "a": np.random.randint(0, 1000, nelem).astype(data_type),
+            "b": np.random.randint(0, 1000, nelem).astype(data_type),
+        }
+    )
+    padf = pa.Table.from_pandas(
+        df, preserve_index=False
+    ).replace_schema_metadata(None)
+    gdf = cudf.DataFrame.from_arrow(padf)
+    assert isinstance(gdf, cudf.DataFrame)
+
+    assert_eq(df, gdf)
+
+    s = pa.Array.from_pandas(df.a)
+    gs = cudf.Series.from_arrow(s)
+    assert isinstance(gs, cudf.Series)
+
+    # For some reason PyArrow to_pandas() converts to numpy array and has
+    # better type compatibility
+    np.testing.assert_array_equal(s.to_pandas(), gs.to_numpy())
+
+
+@pytest.mark.parametrize("nelem", [0, 2, 3, 100, 1000])
+@pytest.mark.parametrize("data_type", dtypes)
+def test_to_arrow(nelem, data_type):
+    df = pd.DataFrame(
+        {
+            "a": np.random.randint(0, 1000, nelem).astype(data_type),
+            "b": np.random.randint(0, 1000, nelem).astype(data_type),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(df)
+
+    pa_df = pa.Table.from_pandas(
+        df, preserve_index=False
+    ).replace_schema_metadata(None)
+
+    pa_gdf = gdf.to_arrow(preserve_index=False).replace_schema_metadata(None)
+
+    assert isinstance(pa_gdf, pa.Table)
+    assert pa.Table.equals(pa_df, pa_gdf)
+
+    pa_s = pa.Array.from_pandas(df.a)
+    pa_gs = gdf["a"].to_arrow()
+
+    assert isinstance(pa_gs, pa.Array)
+    assert pa.Array.equals(pa_s, pa_gs)
+
+    pa_i = pa.Array.from_pandas(df.index)
+    pa_gi = gdf.index.to_arrow()
+
+    assert isinstance(pa_gi, pa.Array)
+    assert pa.Array.equals(pa_i, pa_gi)
+
+
+@pytest.mark.parametrize("data_type", dtypes)
+def test_to_from_arrow_nulls(data_type):
+    if data_type == "longlong":
+        data_type = "int64"
+    if data_type == "bool":
+        s1 = pa.array([True, None, False, None, True], type=data_type)
+    else:
+        dtype = np.dtype(data_type)
+        if dtype.type == np.datetime64:
+            time_unit, _ = np.datetime_data(dtype)
+            data_type = pa.timestamp(unit=time_unit)
+        s1 = pa.array([1, None, 3, None, 5], type=data_type)
+    gs1 = cudf.Series.from_arrow(s1)
+    assert isinstance(gs1, cudf.Series)
+    # We have 64B padded buffers for nulls whereas Arrow returns a minimal
+    # number of bytes, so only check the first byte in this case
+    np.testing.assert_array_equal(
+        np.asarray(s1.buffers()[0]).view("u1")[0],
+        gs1._column.mask_array_view(mode="read").copy_to_host().view("u1")[0],
+    )
+    assert pa.Array.equals(s1, gs1.to_arrow())
+
+    s2 = pa.array([None, None, None, None, None], type=data_type)
+    gs2 = cudf.Series.from_arrow(s2)
+    assert isinstance(gs2, cudf.Series)
+    # We have 64B padded buffers for nulls whereas Arrow returns a minimal
+    # number of bytes, so only check the first byte in this case
+    np.testing.assert_array_equal(
+        np.asarray(s2.buffers()[0]).view("u1")[0],
+        gs2._column.mask_array_view(mode="read").copy_to_host().view("u1")[0],
+    )
+    assert pa.Array.equals(s2, gs2.to_arrow())
+
+
+def test_to_arrow_categorical():
+    df = pd.DataFrame()
+    df["a"] = pd.Series(["a", "b", "c"], dtype="category")
+    gdf = cudf.DataFrame.from_pandas(df)
+
+    pa_df = pa.Table.from_pandas(
+        df, preserve_index=False
+    ).replace_schema_metadata(None)
+    pa_gdf = gdf.to_arrow(preserve_index=False).replace_schema_metadata(None)
+
+    assert isinstance(pa_gdf, pa.Table)
+    assert pa.Table.equals(pa_df, pa_gdf)
+
+    pa_s = pa.Array.from_pandas(df.a)
+    pa_gs = gdf["a"].to_arrow()
+
+    assert isinstance(pa_gs, pa.Array)
+    assert pa.Array.equals(pa_s, pa_gs)
+
+
+def test_from_arrow_missing_categorical():
+    pd_cat = pd.Categorical(["a", "b", "c"], categories=["a", "b"])
+    pa_cat = pa.array(pd_cat, from_pandas=True)
+    gd_cat = cudf.Series(pa_cat)
+
+    assert isinstance(gd_cat, cudf.Series)
+    assert_eq(
+        pd.Series(pa_cat.to_pandas()),  # PyArrow returns a pd.Categorical
+        gd_cat.to_pandas(),
+    )
+
+
+def test_to_arrow_missing_categorical():
+    pd_cat = pd.Categorical(["a", "b", "c"], categories=["a", "b"])
+    pa_cat = pa.array(pd_cat, from_pandas=True)
+    gd_cat = cudf.Series(pa_cat)
+
+    assert isinstance(gd_cat, cudf.Series)
+    assert pa.Array.equals(pa_cat, gd_cat.to_arrow())
+
+
+@pytest.mark.parametrize("data_type", dtypes)
+def test_from_scalar_typing(data_type):
+    if data_type == "datetime64[ms]":
+        scalar = (
+            np.dtype("int64")
+            .type(np.random.randint(0, 5))
+            .astype("datetime64[ms]")
+        )
+    elif data_type.startswith("datetime64"):
+        scalar = np.datetime64(datetime.date.today()).astype("datetime64[ms]")
+        data_type = "datetime64[ms]"
+    else:
+        scalar = np.dtype(data_type).type(np.random.randint(0, 5))
+
+    gdf = cudf.DataFrame()
+    gdf["a"] = [1, 2, 3, 4, 5]
+    gdf["b"] = scalar
+    assert gdf["b"].dtype == np.dtype(data_type)
+    assert len(gdf["b"]) == len(gdf["a"])
+
+
+@pytest.mark.parametrize("data_type", NUMERIC_TYPES)
+def test_from_python_array(data_type):
+    np_arr = np.random.randint(0, 100, 10).astype(data_type)
+    data = memoryview(np_arr)
+    data = arr.array(data.format, data)
+
+    gs = cudf.Series(data)
+
+    np.testing.assert_equal(gs.to_numpy(), np_arr)
+
+
+def test_series_shape():
+    ps = pd.Series([1, 2, 3, 4])
+    cs = cudf.Series([1, 2, 3, 4])
+
+    assert ps.shape == cs.shape
+
+
+def test_series_shape_empty():
+    ps = pd.Series([], dtype="float64")
+    cs = cudf.Series([], dtype="float64")
+
+    assert ps.shape == cs.shape
+
+
+def test_dataframe_shape():
+    pdf = pd.DataFrame({"a": [0, 1, 2, 3], "b": [0.1, 0.2, None, 0.3]})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    assert pdf.shape == gdf.shape
+
+
+def test_dataframe_shape_empty():
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+
+    assert pdf.shape == gdf.shape
+
+
+@pytest.mark.parametrize("num_cols", [1, 2, 10])
+@pytest.mark.parametrize("num_rows", [1, 2, 20])
+@pytest.mark.parametrize("dtype", dtypes + ["object"])
+@pytest.mark.parametrize("nulls", ["none", "some", "all"])
+def test_dataframe_transpose(nulls, num_cols, num_rows, dtype):
+    # In case of `bool` dtype: pandas <= 1.2.5 type-casts
+    # a boolean series to `float64` series if a `np.nan` is assigned to it:
+    # >>> s = pd.Series([True, False, True])
+    # >>> s
+    # 0     True
+    # 1    False
+    # 2     True
+    # dtype: bool
+    # >>> s[[2]] = np.nan
+    # >>> s
+    # 0    1.0
+    # 1    0.0
+    # 2    NaN
+    # dtype: float64
+    # In pandas >= 1.3.2 this behavior is fixed:
+    # >>> s = pd.Series([True, False, True])
+    # >>> s
+    # 0
+    # True
+    # 1
+    # False
+    # 2
+    # True
+    # dtype: bool
+    # >>> s[[2]] = np.nan
+    # >>> s
+    # 0
+    # True
+    # 1
+    # False
+    # 2
+    # NaN
+    # dtype: object
+    # In cudf we change `object` dtype to `str` type - for which there
+    # is no transpose implemented yet. Hence we need to test transpose
+    # against pandas nullable types as they are the ones that closely
+    # resemble `cudf` dtypes behavior.
+    pdf = pd.DataFrame()
+
+    null_rep = np.nan if dtype in ["float32", "float64"] else None
+    np_dtype = dtype
+    dtype = np.dtype(dtype)
+    dtype = cudf.utils.dtypes.np_dtypes_to_pandas_dtypes.get(dtype, dtype)
+    for i in range(num_cols):
+        colname = string.ascii_lowercase[i]
+        data = pd.Series(
+            np.random.randint(0, 26, num_rows).astype(np_dtype),
+            dtype=dtype,
+        )
+        if nulls == "some":
+            idx = np.random.choice(
+                num_rows, size=int(num_rows / 2), replace=False
+            )
+            if len(idx):
+                data[idx] = null_rep
+        elif nulls == "all":
+            data[:] = null_rep
+        pdf[colname] = data
+
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    got_function = gdf.transpose()
+    got_property = gdf.T
+
+    expect = pdf.transpose()
+
+    assert_eq(expect, got_function.to_pandas(nullable=True))
+    assert_eq(expect, got_property.to_pandas(nullable=True))
+
+
+@pytest.mark.parametrize("num_cols", [1, 2, 10])
+@pytest.mark.parametrize("num_rows", [1, 2, 20])
+def test_dataframe_transpose_category(num_cols, num_rows):
+    pdf = pd.DataFrame()
+
+    for i in range(num_cols):
+        colname = string.ascii_lowercase[i]
+        data = pd.Series(list(string.ascii_lowercase), dtype="category")
+        data = data.sample(num_rows, replace=True).reset_index(drop=True)
+        pdf[colname] = data
+
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    got_function = gdf.transpose()
+    got_property = gdf.T
+
+    expect = pdf.transpose()
+
+    assert_eq(expect, got_function.to_pandas())
+    assert_eq(expect, got_property.to_pandas())
+
+
+def test_generated_column():
+    gdf = cudf.DataFrame({"a": (i for i in range(5))})
+    assert len(gdf) == 5
+
+
+@pytest.fixture
+def pdf():
+    return pd.DataFrame({"x": range(10), "y": range(10)})
+
+
+@pytest.fixture
+def gdf(pdf):
+    return cudf.DataFrame.from_pandas(pdf)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "x": [np.nan, 2, 3, 4, 100, np.nan],
+            "y": [4, 5, 6, 88, 99, np.nan],
+            "z": [7, 8, 9, 66, np.nan, 77],
+        },
+        {"x": [1, 2, 3], "y": [4, 5, 6], "z": [7, 8, 9]},
+        {
+            "x": [np.nan, np.nan, np.nan],
+            "y": [np.nan, np.nan, np.nan],
+            "z": [np.nan, np.nan, np.nan],
+        },
+        pytest.param(
+            {"x": [], "y": [], "z": []},
+            marks=pytest_xfail(
+                condition=version.parse("11")
+                <= version.parse(cupy.__version__)
+                < version.parse("11.1"),
+                reason="Zero-sized array passed to cupy reduction, "
+                "https://github.com/cupy/cupy/issues/6937",
+            ),
+        ),
+        pytest.param(
+            {"x": []},
+            marks=pytest_xfail(
+                condition=version.parse("11")
+                <= version.parse(cupy.__version__)
+                < version.parse("11.1"),
+                reason="Zero-sized array passed to cupy reduction, "
+                "https://github.com/cupy/cupy/issues/6937",
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize(
+    "func",
+    [
+        "min",
+        "max",
+        "sum",
+        "prod",
+        "product",
+        "cummin",
+        "cummax",
+        "cumsum",
+        "cumprod",
+        "mean",
+        "median",
+        "sum",
+        "std",
+        "var",
+        "kurt",
+        "skew",
+        "all",
+        "any",
+    ],
+)
+@pytest.mark.parametrize("skipna", [True, False])
+def test_dataframe_reductions(data, axis, func, skipna):
+    pdf = pd.DataFrame(data=data)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    # Reductions can fail in numerous possible ways when attempting row-wise
+    # reductions, which are only partially supported. Catching the appropriate
+    # exception here allows us to detect API breakage in the form of changing
+    # exceptions.
+    expected_exception = None
+    if axis == 1:
+        if func in ("kurt", "skew"):
+            expected_exception = NotImplementedError
+        elif func not in cudf.core.dataframe._cupy_nan_methods_map:
+            if skipna is False:
+                expected_exception = NotImplementedError
+            elif any(col.nullable for name, col in gdf.items()):
+                expected_exception = ValueError
+            elif func in ("cummin", "cummax"):
+                expected_exception = AttributeError
+
+    # Test different degrees of freedom for var and std.
+    all_kwargs = [{"ddof": 1}, {"ddof": 2}] if func in ("var", "std") else [{}]
+    for kwargs in all_kwargs:
+        if expected_exception is not None:
+            with pytest.raises(expected_exception):
+                getattr(gdf, func)(axis=axis, skipna=skipna, **kwargs),
+        else:
+            expect = getattr(pdf, func)(axis=axis, skipna=skipna, **kwargs)
+            with expect_warning_if(
+                skipna
+                and func in {"min", "max"}
+                and axis == 1
+                and any(gdf.T[col].isna().all() for col in gdf.T),
+                RuntimeWarning,
+            ):
+                got = getattr(gdf, func)(axis=axis, skipna=skipna, **kwargs)
+            assert_eq(got, expect, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"x": [np.nan, 2, 3, 4, 100, np.nan], "y": [4, 5, 6, 88, 99, np.nan]},
+        {"x": [1, 2, 3], "y": [4, 5, 6]},
+        {"x": [np.nan, np.nan, np.nan], "y": [np.nan, np.nan, np.nan]},
+        {"x": [], "y": []},
+        {"x": []},
+    ],
+)
+@pytest.mark.parametrize("func", [lambda df: df.count()])
+def test_dataframe_count_reduction(data, func):
+    pdf = pd.DataFrame(data=data)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    assert_eq(func(pdf), func(gdf))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"x": [np.nan, 2, 3, 4, 100, np.nan], "y": [4, 5, 6, 88, 99, np.nan]},
+        {"x": [1, 2, 3], "y": [4, 5, 6]},
+        {"x": [np.nan, np.nan, np.nan], "y": [np.nan, np.nan, np.nan]},
+        {"x": pd.Series([], dtype="float"), "y": pd.Series([], dtype="float")},
+        {"x": pd.Series([], dtype="int")},
+    ],
+)
+@pytest.mark.parametrize("ops", ["sum", "product", "prod"])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [-10, -1, 0, 1, 2, 3, 10])
+def test_dataframe_min_count_ops(data, ops, skipna, min_count):
+    psr = pd.DataFrame(data)
+    gsr = cudf.from_pandas(psr)
+
+    assert_eq(
+        getattr(psr, ops)(skipna=skipna, min_count=min_count),
+        getattr(gsr, ops)(skipna=skipna, min_count=min_count),
+        check_dtype=False,
+    )
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "binop",
+    [
+        operator.add,
+        operator.mul,
+        operator.floordiv,
+        operator.truediv,
+        operator.mod,
+        operator.pow,
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        1.0,
+        pd.Series([1.0]),
+        pd.Series([1.0, 2.0]),
+        pd.Series([1.0, 2.0, 3.0]),
+        pd.Series([1.0], index=["x"]),
+        pd.Series([1.0, 2.0], index=["x", "y"]),
+        pd.Series([1.0, 2.0, 3.0], index=["x", "y", "z"]),
+        pd.DataFrame({"x": [1.0]}),
+        pd.DataFrame({"x": [1.0], "y": [2.0]}),
+        pd.DataFrame({"x": [1.0], "y": [2.0], "z": [3.0]}),
+    ],
+)
+def test_arithmetic_binops_df(pdf, gdf, binop, other):
+    # Avoid 1**NA cases: https://github.com/pandas-dev/pandas/issues/29997
+    pdf[pdf == 1.0] = 2
+    gdf[gdf == 1.0] = 2
+    try:
+        d = binop(pdf, other)
+    except Exception:
+        if isinstance(other, (pd.Series, pd.DataFrame)):
+            cudf_other = cudf.from_pandas(other)
+
+        # that returns before we enter this try-except.
+        assert_exceptions_equal(
+            lfunc=binop,
+            rfunc=binop,
+            lfunc_args_and_kwargs=([pdf, other], {}),
+            rfunc_args_and_kwargs=([gdf, cudf_other], {}),
+        )
+    else:
+        if isinstance(other, (pd.Series, pd.DataFrame)):
+            other = cudf.from_pandas(other)
+        g = binop(gdf, other)
+        assert_eq(d, g)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "binop",
+    [
+        operator.eq,
+        operator.lt,
+        operator.le,
+        operator.gt,
+        operator.ge,
+        operator.ne,
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        1.0,
+        pd.Series([1.0, 2.0], index=["x", "y"]),
+        pd.DataFrame({"x": [1.0]}),
+        pd.DataFrame({"x": [1.0], "y": [2.0]}),
+        pd.DataFrame({"x": [1.0], "y": [2.0], "z": [3.0]}),
+    ],
+)
+def test_comparison_binops_df(pdf, gdf, binop, other):
+    # Avoid 1**NA cases: https://github.com/pandas-dev/pandas/issues/29997
+    pdf[pdf == 1.0] = 2
+    gdf[gdf == 1.0] = 2
+    try:
+        d = binop(pdf, other)
+    except Exception:
+        if isinstance(other, (pd.Series, pd.DataFrame)):
+            cudf_other = cudf.from_pandas(other)
+
+        # that returns before we enter this try-except.
+        assert_exceptions_equal(
+            lfunc=binop,
+            rfunc=binop,
+            lfunc_args_and_kwargs=([pdf, other], {}),
+            rfunc_args_and_kwargs=([gdf, cudf_other], {}),
+        )
+    else:
+        if isinstance(other, (pd.Series, pd.DataFrame)):
+            other = cudf.from_pandas(other)
+        g = binop(gdf, other)
+        assert_eq(d, g)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "binop",
+    [
+        operator.eq,
+        operator.lt,
+        operator.le,
+        operator.gt,
+        operator.ge,
+        operator.ne,
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        pd.Series([1.0]),
+        pd.Series([1.0, 2.0]),
+        pd.Series([1.0, 2.0, 3.0]),
+        pd.Series([1.0], index=["x"]),
+        pd.Series([1.0, 2.0, 3.0], index=["x", "y", "z"]),
+    ],
+)
+def test_comparison_binops_df_reindexing(request, pdf, gdf, binop, other):
+    # Avoid 1**NA cases: https://github.com/pandas-dev/pandas/issues/29997
+    pdf[pdf == 1.0] = 2
+    gdf[gdf == 1.0] = 2
+    try:
+        with pytest.warns(FutureWarning):
+            d = binop(pdf, other)
+    except Exception:
+        if isinstance(other, (pd.Series, pd.DataFrame)):
+            cudf_other = cudf.from_pandas(other)
+
+        # that returns before we enter this try-except.
+        assert_exceptions_equal(
+            lfunc=binop,
+            rfunc=binop,
+            lfunc_args_and_kwargs=([pdf, other], {}),
+            rfunc_args_and_kwargs=([gdf, cudf_other], {}),
+        )
+    else:
+        request.applymarker(
+            pytest.mark.xfail(
+                condition=pdf.columns.difference(other.index).size > 0,
+                reason="""
+                Currently we will not match pandas for equality/inequality
+                operators when there are columns that exist in a Series but not
+                the DataFrame because pandas returns True/False values whereas
+                we return NA. However, this reindexing is deprecated in pandas
+                so we opt not to add support. This test should start passing
+                once pandas removes the deprecated behavior in 2.0.  When that
+                happens, this test can be merged with the two tests above into
+                a single test with common parameters.
+                """,
+            )
+        )
+
+        if isinstance(other, (pd.Series, pd.DataFrame)):
+            other = cudf.from_pandas(other)
+        g = binop(gdf, other)
+        assert_eq(d, g)
+
+
+def test_binops_df_invalid(gdf):
+    with pytest.raises(TypeError):
+        gdf + np.array([1, 2])
+
+
+@pytest.mark.parametrize("binop", [operator.and_, operator.or_, operator.xor])
+def test_bitwise_binops_df(pdf, gdf, binop):
+    d = binop(pdf, pdf + 1)
+    g = binop(gdf, gdf + 1)
+    assert_eq(d, g)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "binop",
+    [
+        operator.add,
+        operator.mul,
+        operator.floordiv,
+        operator.truediv,
+        operator.mod,
+        operator.pow,
+        operator.eq,
+        operator.lt,
+        operator.le,
+        operator.gt,
+        operator.ge,
+        operator.ne,
+    ],
+)
+def test_binops_series(pdf, gdf, binop):
+    pdf = pdf + 1.0
+    gdf = gdf + 1.0
+    d = binop(pdf.x, pdf.y)
+    g = binop(gdf.x, gdf.y)
+    assert_eq(d, g)
+
+
+@pytest.mark.parametrize("binop", [operator.and_, operator.or_, operator.xor])
+def test_bitwise_binops_series(pdf, gdf, binop):
+    d = binop(pdf.x, pdf.y + 1)
+    g = binop(gdf.x, gdf.y + 1)
+    assert_eq(d, g)
+
+
+@pytest.mark.parametrize("unaryop", [operator.neg, operator.inv, operator.abs])
+@pytest.mark.parametrize(
+    "col_name,assign_col_name", [(None, False), (None, True), ("abc", True)]
+)
+def test_unaryops_df(pdf, unaryop, col_name, assign_col_name):
+    pd_df = pdf.copy()
+    if assign_col_name:
+        pd_df.columns.name = col_name
+    gdf = cudf.from_pandas(pd_df)
+    d = unaryop(pd_df - 5)
+    g = unaryop(gdf - 5)
+    assert_eq(d, g)
+
+
+def test_df_abs(pdf):
+    np.random.seed(0)
+    disturbance = pd.Series(np.random.rand(10))
+    pdf = pdf - 5 + disturbance
+    d = pdf.apply(np.abs)
+    g = cudf.from_pandas(pdf).abs()
+    assert_eq(d, g)
+
+
+def test_scale_df(gdf):
+    got = (gdf - 5).scale()
+    expect = cudf.DataFrame(
+        {"x": np.linspace(0.0, 1.0, 10), "y": np.linspace(0.0, 1.0, 10)}
+    )
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df.empty,
+        lambda df: df.x.empty,
+        lambda df: df.x.fillna(123, limit=None, method=None, axis=None),
+        lambda df: df.drop("x", axis=1, errors="raise"),
+    ],
+)
+def test_unary_operators(func, pdf, gdf):
+    p = func(pdf)
+    g = func(gdf)
+    assert_eq(p, g)
+
+
+def test_is_monotonic(gdf):
+    pdf = pd.DataFrame({"x": [1, 2, 3]}, index=[3, 1, 2])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    with pytest.warns(FutureWarning):
+        assert not gdf.index.is_monotonic
+    assert not gdf.index.is_monotonic_increasing
+    assert not gdf.index.is_monotonic_decreasing
+
+
+def test_iter(pdf, gdf):
+    assert list(pdf) == list(gdf)
+
+
+def test_iteritems(gdf):
+    for k, v in gdf.items():
+        assert k in gdf.columns
+        assert isinstance(v, cudf.Series)
+        assert_eq(v, gdf[k])
+
+
+@pytest.mark.parametrize("q", [0.5, 1, 0.001, [0.5], [], [0.005, 0.5, 1]])
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_quantile(q, numeric_only):
+    ts = pd.date_range("2018-08-24", periods=5, freq="D")
+    td = pd.to_timedelta(np.arange(5), unit="h")
+    pdf = pd.DataFrame(
+        {"date": ts, "delta": td, "val": np.random.randn(len(ts))}
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    assert_eq(pdf["date"].quantile(q), gdf["date"].quantile(q))
+    assert_eq(pdf["delta"].quantile(q), gdf["delta"].quantile(q))
+    assert_eq(pdf["val"].quantile(q), gdf["val"].quantile(q))
+
+    q = q if isinstance(q, list) else [q]
+    assert_eq(
+        pdf.quantile(q, numeric_only=numeric_only),
+        gdf.quantile(q, numeric_only=numeric_only),
+    )
+
+
+@pytest.mark.parametrize("q", [0.2, 1, 0.001, [0.5], [], [0.005, 0.8, 0.03]])
+@pytest.mark.parametrize("interpolation", ["higher", "lower", "nearest"])
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+def test_decimal_quantile(q, interpolation, decimal_type):
+    data = ["244.8", "32.24", "2.22", "98.14", "453.23", "5.45"]
+    gdf = cudf.DataFrame(
+        {"id": np.random.randint(0, 10, size=len(data)), "val": data}
+    )
+    gdf["id"] = gdf["id"].astype("float64")
+    gdf["val"] = gdf["val"].astype(decimal_type(7, 2))
+    pdf = gdf.to_pandas()
+
+    got = gdf.quantile(q, numeric_only=False, interpolation=interpolation)
+    expected = pdf.quantile(
+        q if isinstance(q, list) else [q],
+        numeric_only=False,
+        interpolation=interpolation,
+    )
+
+    assert_eq(got, expected)
+
+
+def test_empty_quantile():
+    pdf = pd.DataFrame({"x": []})
+    df = cudf.DataFrame({"x": []})
+
+    actual = df.quantile()
+    expected = pdf.quantile()
+
+    assert_eq(actual, expected)
+
+
+def test_from_pandas_function(pdf):
+    gdf = cudf.from_pandas(pdf)
+    assert isinstance(gdf, cudf.DataFrame)
+    assert_eq(pdf, gdf)
+
+    gdf = cudf.from_pandas(pdf.x)
+    assert isinstance(gdf, cudf.Series)
+    assert_eq(pdf.x, gdf)
+
+    with pytest.raises(TypeError):
+        cudf.from_pandas(123)
+
+
+@pytest.mark.parametrize("preserve_index", [True, False])
+def test_arrow_pandas_compat(pdf, gdf, preserve_index):
+    pdf["z"] = range(10)
+    pdf = pdf.set_index("z")
+    gdf["z"] = range(10)
+    gdf = gdf.set_index("z")
+
+    pdf_arrow_table = pa.Table.from_pandas(pdf, preserve_index=preserve_index)
+    gdf_arrow_table = gdf.to_arrow(preserve_index=preserve_index)
+
+    assert pa.Table.equals(pdf_arrow_table, gdf_arrow_table)
+
+    gdf2 = cudf.DataFrame.from_arrow(pdf_arrow_table)
+    pdf2 = pdf_arrow_table.to_pandas()
+
+    assert_eq(pdf2, gdf2)
+    pdf.columns.name = "abc"
+    pdf_arrow_table = pa.Table.from_pandas(pdf, preserve_index=preserve_index)
+
+    gdf2 = cudf.DataFrame.from_arrow(pdf_arrow_table)
+    pdf2 = pdf_arrow_table.to_pandas()
+    assert_eq(pdf2, gdf2)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["bool"])
+def test_cuda_array_interface(dtype):
+    np_data = np.arange(10).astype(dtype)
+    cupy_data = cupy.array(np_data)
+    pd_data = pd.Series(np_data)
+
+    cudf_data = cudf.Series(cupy_data)
+    assert_eq(pd_data, cudf_data)
+
+    gdf = cudf.DataFrame()
+    gdf["test"] = cupy_data
+    pd_data.name = "test"
+    assert_eq(pd_data, gdf["test"])
+
+
+@pytest.mark.parametrize("nelem", [0, 2, 3, 100])
+@pytest.mark.parametrize("nchunks", [1, 2, 5, 10])
+@pytest.mark.parametrize("data_type", dtypes)
+def test_from_arrow_chunked_arrays(nelem, nchunks, data_type):
+    np_list_data = [
+        np.random.randint(0, 100, nelem).astype(data_type)
+        for i in range(nchunks)
+    ]
+    pa_chunk_array = pa.chunked_array(np_list_data)
+
+    expect = pd.Series(pa_chunk_array.to_pandas())
+    got = cudf.Series(pa_chunk_array)
+
+    assert_eq(expect, got)
+
+    np_list_data2 = [
+        np.random.randint(0, 100, nelem).astype(data_type)
+        for i in range(nchunks)
+    ]
+    pa_chunk_array2 = pa.chunked_array(np_list_data2)
+    pa_table = pa.Table.from_arrays(
+        [pa_chunk_array, pa_chunk_array2], names=["a", "b"]
+    )
+
+    expect = pa_table.to_pandas()
+    got = cudf.DataFrame.from_arrow(pa_table)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.skip(reason="Test was designed to be run in isolation")
+def test_gpu_memory_usage_with_boolmask():
+    ctx = cuda.current_context()
+
+    def query_GPU_memory(note=""):
+        memInfo = ctx.get_memory_info()
+        usedMemoryGB = (memInfo.total - memInfo.free) / 1e9
+        return usedMemoryGB
+
+    cuda.current_context().deallocations.clear()
+    nRows = int(1e8)
+    nCols = 2
+    dataNumpy = np.asfortranarray(np.random.rand(nRows, nCols))
+    colNames = ["col" + str(iCol) for iCol in range(nCols)]
+    pandasDF = pd.DataFrame(data=dataNumpy, columns=colNames, dtype=np.float32)
+    cudaDF = cudf.core.DataFrame.from_pandas(pandasDF)
+    boolmask = cudf.Series(np.random.randint(1, 2, len(cudaDF)).astype("bool"))
+
+    memory_used = query_GPU_memory()
+    cudaDF = cudaDF[boolmask]
+
+    assert (
+        cudaDF.index._values.data_array_view(mode="read").device_ctypes_pointer
+        == cudaDF["col0"].index._values.data_array_view.device_ctypes_pointer
+    )
+    assert (
+        cudaDF.index._values.data_array_view(mode="read").device_ctypes_pointer
+        == cudaDF["col1"].index._values.data_array_view.device_ctypes_pointer
+    )
+
+    assert memory_used == query_GPU_memory()
+
+
+def test_boolmask(pdf, gdf):
+    boolmask = np.random.randint(0, 2, len(pdf)) > 0
+    gdf = gdf[boolmask]
+    pdf = pdf[boolmask]
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "mask_shape",
+    [
+        (2, "ab"),
+        (2, "abc"),
+        (3, "ab"),
+        (3, "abc"),
+        (3, "abcd"),
+        (4, "abc"),
+        (4, "abcd"),
+    ],
+)
+def test_dataframe_boolmask(mask_shape):
+    pdf = pd.DataFrame()
+    for col in "abc":
+        pdf[col] = np.random.randint(0, 10, 3)
+    pdf_mask = pd.DataFrame()
+    for col in mask_shape[1]:
+        pdf_mask[col] = np.random.randint(0, 2, mask_shape[0]) > 0
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    gdf_mask = cudf.DataFrame.from_pandas(pdf_mask)
+    gdf = gdf[gdf_mask]
+    pdf = pdf[pdf_mask]
+
+    assert np.array_equal(gdf.columns, pdf.columns)
+    for col in gdf.columns:
+        assert np.array_equal(
+            gdf[col].fillna(-1).to_pandas().values, pdf[col].fillna(-1).values
+        )
+
+
+@pytest.mark.parametrize(
+    "mask",
+    [
+        [True, False, True],
+        pytest.param(
+            cudf.Series([True, False, True]),
+            marks=pytest_xfail(
+                reason="Pandas can't index a multiindex with a Series"
+            ),
+        ),
+    ],
+)
+def test_dataframe_multiindex_boolmask(mask):
+    gdf = cudf.DataFrame(
+        {"w": [3, 2, 1], "x": [1, 2, 3], "y": [0, 1, 0], "z": [1, 1, 1]}
+    )
+    gdg = gdf.groupby(["w", "x"]).count()
+    pdg = gdg.to_pandas()
+    assert_eq(gdg[mask], pdg[mask])
+
+
+def test_dataframe_assignment():
+    pdf = pd.DataFrame()
+    for col in "abc":
+        pdf[col] = np.array([0, 1, 1, -2, 10])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    gdf[gdf < 0] = 999
+    pdf[pdf < 0] = 999
+    assert_eq(gdf, pdf)
+
+
+def test_1row_arrow_table():
+    data = [pa.array([0]), pa.array([1])]
+    batch = pa.RecordBatch.from_arrays(data, ["f0", "f1"])
+    table = pa.Table.from_batches([batch])
+
+    expect = table.to_pandas()
+    got = cudf.DataFrame.from_arrow(table)
+    assert_eq(expect, got)
+
+
+def test_arrow_handle_no_index_name(pdf, gdf):
+    gdf_arrow = gdf.to_arrow()
+    pdf_arrow = pa.Table.from_pandas(pdf)
+    assert pa.Table.equals(pdf_arrow, gdf_arrow)
+
+    got = cudf.DataFrame.from_arrow(gdf_arrow)
+    expect = pdf_arrow.to_pandas()
+    assert_eq(expect, got)
+
+
+def test_pandas_non_contiguious():
+    arr1 = np.random.sample([5000, 10])
+    assert arr1.flags["C_CONTIGUOUS"] is True
+    df = pd.DataFrame(arr1)
+    for col in df.columns:
+        assert df[col].values.flags["C_CONTIGUOUS"] is False
+
+    gdf = cudf.DataFrame.from_pandas(df)
+    assert_eq(gdf.to_pandas(), df)
+
+
+@pytest.mark.parametrize("num_elements", [0, 2, 10, 100])
+@pytest.mark.parametrize("null_type", [np.nan, None, "mixed"])
+def test_series_all_null(num_elements, null_type):
+    if null_type == "mixed":
+        data = []
+        data1 = [np.nan] * int(num_elements / 2)
+        data2 = [None] * int(num_elements / 2)
+        for idx in range(len(data1)):
+            data.append(data1[idx])
+            data.append(data2[idx])
+    else:
+        data = [null_type] * num_elements
+
+    # Typecast Pandas because None will return `object` dtype
+    expect = pd.Series(data, dtype="float64")
+    got = cudf.Series(data, dtype="float64")
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("num_elements", [0, 2, 10, 100])
+def test_series_all_valid_nan(num_elements):
+    data = [np.nan] * num_elements
+    sr = _create_cudf_series_float64_default(data, nan_as_null=False)
+    np.testing.assert_equal(sr.null_count, 0)
+
+
+def test_series_rename():
+    pds = pd.Series([1, 2, 3], name="asdf")
+    gds = cudf.Series([1, 2, 3], name="asdf")
+
+    expect = pds.rename("new_name")
+    got = gds.rename("new_name")
+
+    assert_eq(expect, got)
+
+    pds = pd.Series(expect)
+    gds = cudf.Series(got)
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series(expect, name="name name")
+    gds = cudf.Series(got, name="name name")
+
+    assert_eq(pds, gds)
+
+
+@pytest.mark.parametrize("data_type", dtypes)
+@pytest.mark.parametrize("nelem", [0, 100])
+def test_head_tail(nelem, data_type):
+    def check_index_equality(left, right):
+        assert left.index.equals(right.index)
+
+    def check_values_equality(left, right):
+        if len(left) == 0 and len(right) == 0:
+            return None
+
+        np.testing.assert_array_equal(left.to_pandas(), right.to_pandas())
+
+    def check_frame_series_equality(left, right):
+        check_index_equality(left, right)
+        check_values_equality(left, right)
+
+    gdf = cudf.DataFrame(
+        {
+            "a": np.random.randint(0, 1000, nelem).astype(data_type),
+            "b": np.random.randint(0, 1000, nelem).astype(data_type),
+        }
+    )
+
+    check_frame_series_equality(gdf.head(), gdf[:5])
+    check_frame_series_equality(gdf.head(3), gdf[:3])
+    check_frame_series_equality(gdf.head(-2), gdf[:-2])
+    check_frame_series_equality(gdf.head(0), gdf[0:0])
+
+    check_frame_series_equality(gdf["a"].head(), gdf["a"][:5])
+    check_frame_series_equality(gdf["a"].head(3), gdf["a"][:3])
+    check_frame_series_equality(gdf["a"].head(-2), gdf["a"][:-2])
+
+    check_frame_series_equality(gdf.tail(), gdf[-5:])
+    check_frame_series_equality(gdf.tail(3), gdf[-3:])
+    check_frame_series_equality(gdf.tail(-2), gdf[2:])
+    check_frame_series_equality(gdf.tail(0), gdf[0:0])
+
+    check_frame_series_equality(gdf["a"].tail(), gdf["a"][-5:])
+    check_frame_series_equality(gdf["a"].tail(3), gdf["a"][-3:])
+    check_frame_series_equality(gdf["a"].tail(-2), gdf["a"][2:])
+
+
+def test_tail_for_string():
+    gdf = cudf.DataFrame()
+    gdf["id"] = cudf.Series(["a", "b"], dtype=np.object_)
+    gdf["v"] = cudf.Series([1, 2])
+    assert_eq(gdf.tail(3), gdf.to_pandas().tail(3))
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize("level", [None, 0, "l0", 1, ["l0", 1]])
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize(
+    "column_names",
+    [
+        ["v0", "v1"],
+        ["v0", "index"],
+        pd.MultiIndex.from_tuples([("x0", "x1"), ("y0", "y1")]),
+        pd.MultiIndex.from_tuples([(1, 2), (10, 11)], names=["ABC", "DEF"]),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("col_level", [0, 1])
+@pytest.mark.parametrize("col_fill", ["", "some_lv"])
+def test_reset_index(level, drop, column_names, inplace, col_level, col_fill):
+    midx = pd.MultiIndex.from_tuples(
+        [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=["l0", None]
+    )
+    pdf = pd.DataFrame(
+        [[1, 2], [3, 4], [5, 6], [7, 8]], index=midx, columns=column_names
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.reset_index(
+        level=level,
+        drop=drop,
+        inplace=inplace,
+        col_level=col_level,
+        col_fill=col_fill,
+    )
+    got = gdf.reset_index(
+        level=level,
+        drop=drop,
+        inplace=inplace,
+        col_level=col_level,
+        col_fill=col_fill,
+    )
+    if inplace:
+        expect = pdf
+        got = gdf
+
+    assert_eq(expect, got)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize("level", [None, 0, 1, [None]])
+@pytest.mark.parametrize("drop", [False, True])
+@pytest.mark.parametrize("inplace", [False, True])
+@pytest.mark.parametrize("col_level", [0, 1])
+@pytest.mark.parametrize("col_fill", ["", "some_lv"])
+def test_reset_index_dup_level_name(level, drop, inplace, col_level, col_fill):
+    # midx levels are named [None, None]
+    midx = pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)])
+    pdf = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=midx)
+    gdf = cudf.from_pandas(pdf)
+    if level == [None]:
+        assert_exceptions_equal(
+            lfunc=pdf.reset_index,
+            rfunc=gdf.reset_index,
+            lfunc_args_and_kwargs=(
+                [],
+                {"level": level, "drop": drop, "inplace": inplace},
+            ),
+            rfunc_args_and_kwargs=(
+                [],
+                {"level": level, "drop": drop, "inplace": inplace},
+            ),
+        )
+        return
+
+    expect = pdf.reset_index(
+        level=level,
+        drop=drop,
+        inplace=inplace,
+        col_level=col_level,
+        col_fill=col_fill,
+    )
+    got = gdf.reset_index(
+        level=level,
+        drop=drop,
+        inplace=inplace,
+        col_level=col_level,
+        col_fill=col_fill,
+    )
+    if inplace:
+        expect = pdf
+        got = gdf
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize("inplace", [False, True])
+@pytest.mark.parametrize("col_level", [0, 1])
+@pytest.mark.parametrize("col_fill", ["", "some_lv"])
+def test_reset_index_named(pdf, gdf, drop, inplace, col_level, col_fill):
+    pdf.index.name = "cudf"
+    gdf.index.name = "cudf"
+
+    expect = pdf.reset_index(
+        drop=drop, inplace=inplace, col_level=col_level, col_fill=col_fill
+    )
+    got = gdf.reset_index(
+        drop=drop, inplace=inplace, col_level=col_level, col_fill=col_fill
+    )
+    if inplace:
+        expect = pdf
+        got = gdf
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize("inplace", [False, True])
+@pytest.mark.parametrize("column_names", [["x", "y"], ["index", "y"]])
+@pytest.mark.parametrize("col_level", [0, 1])
+@pytest.mark.parametrize("col_fill", ["", "some_lv"])
+def test_reset_index_unnamed(
+    pdf, gdf, drop, inplace, column_names, col_level, col_fill
+):
+    pdf.columns = column_names
+    gdf.columns = column_names
+
+    expect = pdf.reset_index(
+        drop=drop, inplace=inplace, col_level=col_level, col_fill=col_fill
+    )
+    got = gdf.reset_index(
+        drop=drop, inplace=inplace, col_level=col_level, col_fill=col_fill
+    )
+    if inplace:
+        expect = pdf
+        got = gdf
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": [1, 2, 3, 4, 5],
+            "b": ["a", "b", "c", "d", "e"],
+            "c": [1.0, 2.0, 3.0, 4.0, 5.0],
+        }
+    ],
+)
+@pytest.mark.parametrize(
+    "index",
+    [
+        "a",
+        ["a", "b"],
+        pd.CategoricalIndex(["I", "II", "III", "IV", "V"]),
+        pd.Series(["h", "i", "k", "l", "m"]),
+        ["b", pd.Index(["I", "II", "III", "IV", "V"])],
+        ["c", [11, 12, 13, 14, 15]],
+        pd.MultiIndex(
+            levels=[
+                ["I", "II", "III", "IV", "V"],
+                ["one", "two", "three", "four", "five"],
+            ],
+            codes=[[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]],
+            names=["col1", "col2"],
+        ),
+        pd.RangeIndex(0, 5),  # corner case
+        [pd.Series(["h", "i", "k", "l", "m"]), pd.RangeIndex(0, 5)],
+        [
+            pd.MultiIndex(
+                levels=[
+                    ["I", "II", "III", "IV", "V"],
+                    ["one", "two", "three", "four", "five"],
+                ],
+                codes=[[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]],
+                names=["col1", "col2"],
+            ),
+            pd.RangeIndex(0, 5),
+        ],
+    ],
+)
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize("append", [True, False])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_set_index(data, index, drop, append, inplace):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expected = pdf.set_index(index, inplace=inplace, drop=drop, append=append)
+    actual = gdf.set_index(index, inplace=inplace, drop=drop, append=append)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": [1, 1, 2, 2, 5],
+            "b": ["a", "b", "c", "d", "e"],
+            "c": [1.0, 2.0, 3.0, 4.0, 5.0],
+        }
+    ],
+)
+@pytest.mark.parametrize("index", ["a", pd.Index([1, 1, 2, 2, 3])])
+@pytest.mark.parametrize("verify_integrity", [True])
+@pytest_xfail
+def test_set_index_verify_integrity(data, index, verify_integrity):
+    gdf = cudf.DataFrame(data)
+    gdf.set_index(index, verify_integrity=verify_integrity)
+
+
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize("nelem", [10, 200, 1333])
+def test_set_index_multi(drop, nelem):
+    np.random.seed(0)
+    a = np.arange(nelem)
+    np.random.shuffle(a)
+    df = pd.DataFrame(
+        {
+            "a": a,
+            "b": np.random.randint(0, 4, size=nelem),
+            "c": np.random.uniform(low=0, high=4, size=nelem),
+            "d": np.random.choice(["green", "black", "white"], nelem),
+        }
+    )
+    df["e"] = df["d"].astype("category")
+    gdf = cudf.DataFrame.from_pandas(df)
+
+    assert_eq(gdf.set_index("a", drop=drop), gdf.set_index(["a"], drop=drop))
+    assert_eq(
+        df.set_index(["b", "c"], drop=drop),
+        gdf.set_index(["b", "c"], drop=drop),
+    )
+    assert_eq(
+        df.set_index(["d", "b"], drop=drop),
+        gdf.set_index(["d", "b"], drop=drop),
+    )
+    assert_eq(
+        df.set_index(["b", "d", "e"], drop=drop),
+        gdf.set_index(["b", "d", "e"], drop=drop),
+    )
+
+
+@pytest.fixture()
+def reindex_data():
+    return cudf.datasets.randomdata(
+        nrows=6,
+        dtypes={
+            "a": "category",
+            "c": float,
+            "d": str,
+        },
+    )
+
+
+@pytest.fixture()
+def reindex_data_numeric():
+    return cudf.datasets.randomdata(
+        nrows=6,
+        dtypes={"a": float, "b": float, "c": float},
+    )
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize("copy", [True, False])
+@pytest.mark.parametrize(
+    "args,gd_kwargs",
+    [
+        ([], {}),
+        ([[-3, 0, 3, 0, -2, 1, 3, 4, 6]], {}),
+        ([[-3, 0, 3, 0, -2, 1, 3, 4, 6]], {}),
+        ([[-3, 0, 3, 0, -2, 1, 3, 4, 6]], {"axis": 0}),
+        ([["a", "b", "c", "d", "e"]], {"axis": 1}),
+        ([], {"labels": [-3, 0, 3, 0, -2, 1, 3, 4, 6], "axis": 0}),
+        ([], {"labels": ["a", "b", "c", "d", "e"], "axis": 1}),
+        ([], {"labels": [-3, 0, 3, 0, -2, 1, 3, 4, 6], "axis": "index"}),
+        ([], {"labels": ["a", "b", "c", "d", "e"], "axis": "columns"}),
+        ([], {"index": [-3, 0, 3, 0, -2, 1, 3, 4, 6]}),
+        ([], {"columns": ["a", "b", "c", "d", "e"]}),
+        (
+            [],
+            {
+                "index": [-3, 0, 3, 0, -2, 1, 3, 4, 6],
+                "columns": ["a", "b", "c", "d", "e"],
+            },
+        ),
+    ],
+)
+def test_dataframe_reindex(copy, reindex_data, args, gd_kwargs):
+    pdf, gdf = reindex_data.to_pandas(), reindex_data
+
+    gd_kwargs["copy"] = copy
+    pd_kwargs = gd_kwargs.copy()
+    pd_kwargs["copy"] = True
+    assert_eq(pdf.reindex(*args, **pd_kwargs), gdf.reindex(*args, **gd_kwargs))
+
+
+@pytest.mark.parametrize("fill_value", [-1.0, 0.0, 1.5])
+@pytest.mark.parametrize(
+    "args,kwargs",
+    [
+        ([], {}),
+        ([[-3, 0, 3, 0, -2, 1, 3, 4, 6]], {}),
+        ([[-3, 0, 3, 0, -2, 1, 3, 4, 6]], {}),
+        ([[-3, 0, 3, 0, -2, 1, 3, 4, 6]], {"axis": 0}),
+        ([["a", "b", "c", "d", "e"]], {"axis": 1}),
+        ([], {"labels": [-3, 0, 3, 0, -2, 1, 3, 4, 6], "axis": 0}),
+        ([], {"labels": ["a", "b", "c", "d", "e"], "axis": 1}),
+        ([], {"labels": [-3, 0, 3, 0, -2, 1, 3, 4, 6], "axis": "index"}),
+        ([], {"labels": ["a", "b", "c", "d", "e"], "axis": "columns"}),
+        ([], {"index": [-3, 0, 3, 0, -2, 1, 3, 4, 6]}),
+        ([], {"columns": ["a", "b", "c", "d", "e"]}),
+        (
+            [],
+            {
+                "index": [-3, 0, 3, 0, -2, 1, 3, 4, 6],
+                "columns": ["a", "b", "c", "d", "e"],
+            },
+        ),
+    ],
+)
+def test_dataframe_reindex_fill_value(
+    reindex_data_numeric, args, kwargs, fill_value
+):
+    pdf, gdf = reindex_data_numeric.to_pandas(), reindex_data_numeric
+    kwargs["fill_value"] = fill_value
+    assert_eq(pdf.reindex(*args, **kwargs), gdf.reindex(*args, **kwargs))
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_dataframe_reindex_change_dtype(copy):
+    index = pd.date_range("12/29/2009", periods=10, freq="D")
+    columns = ["a", "b", "c", "d", "e"]
+    gdf = cudf.datasets.randomdata(
+        nrows=6, dtypes={"a": "category", "c": float, "d": str}
+    )
+    pdf = gdf.to_pandas()
+    # Validate reindexes both labels and column names when
+    # index=index_labels and columns=column_labels
+    assert_eq(
+        pdf.reindex(index=index, columns=columns, copy=True),
+        gdf.reindex(index=index, columns=columns, copy=copy),
+        check_freq=False,
+    )
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_series_categorical_reindex(copy):
+    index = [-3, 0, 3, 0, -2, 1, 3, 4, 6]
+    gdf = cudf.datasets.randomdata(nrows=6, dtypes={"a": "category"})
+    pdf = gdf.to_pandas()
+    assert_eq(pdf["a"].reindex(copy=True), gdf["a"].reindex(copy=copy))
+    assert_eq(
+        pdf["a"].reindex(index, copy=True), gdf["a"].reindex(index, copy=copy)
+    )
+    assert_eq(
+        pdf["a"].reindex(index=index, copy=True),
+        gdf["a"].reindex(index=index, copy=copy),
+    )
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_series_float_reindex(copy):
+    index = [-3, 0, 3, 0, -2, 1, 3, 4, 6]
+    gdf = cudf.datasets.randomdata(nrows=6, dtypes={"c": float})
+    pdf = gdf.to_pandas()
+    assert_eq(pdf["c"].reindex(copy=True), gdf["c"].reindex(copy=copy))
+    assert_eq(
+        pdf["c"].reindex(index, copy=True), gdf["c"].reindex(index, copy=copy)
+    )
+    assert_eq(
+        pdf["c"].reindex(index=index, copy=True),
+        gdf["c"].reindex(index=index, copy=copy),
+    )
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_series_string_reindex(copy):
+    index = [-3, 0, 3, 0, -2, 1, 3, 4, 6]
+    gdf = cudf.datasets.randomdata(nrows=6, dtypes={"d": str})
+    pdf = gdf.to_pandas()
+    assert_eq(pdf["d"].reindex(copy=True), gdf["d"].reindex(copy=copy))
+    assert_eq(
+        pdf["d"].reindex(index, copy=True), gdf["d"].reindex(index, copy=copy)
+    )
+    assert_eq(
+        pdf["d"].reindex(index=index, copy=True),
+        gdf["d"].reindex(index=index, copy=copy),
+    )
+
+
+def test_to_frame(pdf, gdf):
+    assert_eq(pdf.x.to_frame(), gdf.x.to_frame())
+
+    name = "foo"
+    gdf_new_name = gdf.x.to_frame(name=name)
+    pdf_new_name = pdf.x.to_frame(name=name)
+    assert_eq(pdf.x.to_frame(), gdf.x.to_frame())
+
+    name = False
+    gdf_new_name = gdf.x.to_frame(name=name)
+    pdf_new_name = pdf.x.to_frame(name=name)
+    assert_eq(gdf_new_name, pdf_new_name)
+    assert gdf_new_name.columns[0] == name
+
+
+def test_dataframe_empty_sort_index():
+    pdf = pd.DataFrame({"x": []})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    expect = pdf.sort_index()
+    got = gdf.sort_index()
+
+    assert_eq(expect, got, check_index_type=True)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.RangeIndex(0, 3, 1),
+        [3.0, 1.0, np.nan],
+        # Test for single column MultiIndex
+        pd.MultiIndex.from_arrays(
+            [
+                [2, 0, 1],
+            ]
+        ),
+        pytest.param(
+            pd.RangeIndex(2, -1, -1),
+            marks=[
+                pytest_xfail(
+                    condition=PANDAS_LT_140,
+                    reason="https://github.com/pandas-dev/pandas/issues/43591",
+                )
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("axis", [0, 1, "index", "columns"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_dataframe_sort_index(
+    index, axis, ascending, inplace, ignore_index, na_position
+):
+    pdf = pd.DataFrame(
+        {"b": [1, 3, 2], "a": [1, 4, 3], "c": [4, 1, 5]},
+        index=index,
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    expected = pdf.sort_index(
+        axis=axis,
+        ascending=ascending,
+        ignore_index=ignore_index,
+        inplace=inplace,
+        na_position=na_position,
+    )
+    got = gdf.sort_index(
+        axis=axis,
+        ascending=ascending,
+        ignore_index=ignore_index,
+        inplace=inplace,
+        na_position=na_position,
+    )
+
+    if inplace is True:
+        assert_eq(pdf, gdf, check_index_type=True)
+    else:
+        assert_eq(expected, got, check_index_type=True)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize("axis", [0, 1, "index", "columns"])
+@pytest.mark.parametrize(
+    "level",
+    [
+        0,
+        "b",
+        1,
+        ["b"],
+        "a",
+        ["a", "b"],
+        ["b", "a"],
+        [0, 1],
+        [1, 0],
+        [0, 2],
+        None,
+    ],
+)
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_dataframe_mulitindex_sort_index(
+    axis, level, ascending, inplace, ignore_index, na_position
+):
+    pdf = pd.DataFrame(
+        {
+            "b": [1.0, 3.0, np.nan],
+            "a": [1, 4, 3],
+            1: ["a", "b", "c"],
+            "e": [3, 1, 4],
+            "d": [1, 2, 8],
+        }
+    ).set_index(["b", "a", 1])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    # ignore_index is supported in v.1.0
+
+    expected = pdf.sort_index(
+        axis=axis,
+        level=level,
+        ascending=ascending,
+        inplace=inplace,
+        na_position=na_position,
+    )
+    if ignore_index is True:
+        expected = expected
+    got = gdf.sort_index(
+        axis=axis,
+        level=level,
+        ascending=ascending,
+        ignore_index=ignore_index,
+        inplace=inplace,
+        na_position=na_position,
+    )
+
+    if inplace is True:
+        if ignore_index is True:
+            pdf = pdf.reset_index(drop=True)
+        assert_eq(pdf, gdf)
+    else:
+        if ignore_index is True:
+            expected = expected.reset_index(drop=True)
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("dtype", dtypes + ["category"])
+def test_dataframe_0_row_dtype(dtype):
+    if dtype == "category":
+        data = pd.Series(["a", "b", "c", "d", "e"], dtype="category")
+    else:
+        data = np.array([1, 2, 3, 4, 5], dtype=dtype)
+
+    expect = cudf.DataFrame()
+    expect["x"] = data
+    expect["y"] = data
+    got = expect.head(0)
+
+    for col_name in got.columns:
+        assert expect[col_name].dtype == got[col_name].dtype
+
+    expect = cudf.Series(data)
+    got = expect.head(0)
+
+    assert expect.dtype == got.dtype
+
+
+@pytest.mark.parametrize("nan_as_null", [True, False])
+def test_series_list_nanasnull(nan_as_null):
+    data = [1.0, 2.0, 3.0, np.nan, None]
+
+    expect = pa.array(data, from_pandas=nan_as_null)
+    got = cudf.Series(data, nan_as_null=nan_as_null).to_arrow()
+
+    # Bug in Arrow 0.14.1 where NaNs aren't handled
+    expect = expect.cast("int64", safe=False)
+    got = got.cast("int64", safe=False)
+
+    assert pa.Array.equals(expect, got)
+
+
+def test_column_assignment():
+    gdf = cudf.datasets.randomdata(
+        nrows=20, dtypes={"a": "category", "b": int, "c": float}
+    )
+    new_cols = ["q", "r", "s"]
+    gdf.columns = new_cols
+    assert list(gdf.columns) == new_cols
+
+
+def test_select_dtype():
+    gdf = cudf.datasets.randomdata(
+        nrows=20, dtypes={"a": "category", "b": int, "c": float, "d": str}
+    )
+    pdf = gdf.to_pandas()
+
+    assert_eq(pdf.select_dtypes("float64"), gdf.select_dtypes("float64"))
+    assert_eq(pdf.select_dtypes(np.float64), gdf.select_dtypes(np.float64))
+    assert_eq(
+        pdf.select_dtypes(include=["float64"]),
+        gdf.select_dtypes(include=["float64"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["object", "int", "category"]),
+        gdf.select_dtypes(include=["object", "int", "category"]),
+    )
+
+    assert_eq(
+        pdf.select_dtypes(include=["int64", "float64"]),
+        gdf.select_dtypes(include=["int64", "float64"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=np.number),
+        gdf.select_dtypes(include=np.number),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=[np.int64, np.float64]),
+        gdf.select_dtypes(include=[np.int64, np.float64]),
+    )
+
+    assert_eq(
+        pdf.select_dtypes(include=["category"]),
+        gdf.select_dtypes(include=["category"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(exclude=np.number),
+        gdf.select_dtypes(exclude=np.number),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.select_dtypes,
+        rfunc=gdf.select_dtypes,
+        lfunc_args_and_kwargs=([], {"includes": ["Foo"]}),
+        rfunc_args_and_kwargs=([], {"includes": ["Foo"]}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.select_dtypes,
+        rfunc=gdf.select_dtypes,
+        lfunc_args_and_kwargs=(
+            [],
+            {"exclude": np.number, "include": np.number},
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {"exclude": np.number, "include": np.number},
+        ),
+    )
+
+    gdf = cudf.DataFrame(
+        {"A": [3, 4, 5], "C": [1, 2, 3], "D": ["a", "b", "c"]}
+    )
+    pdf = gdf.to_pandas()
+    assert_eq(
+        pdf.select_dtypes(include=["object", "int", "category"]),
+        gdf.select_dtypes(include=["object", "int", "category"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["object"], exclude=["category"]),
+        gdf.select_dtypes(include=["object"], exclude=["category"]),
+    )
+
+    gdf = cudf.DataFrame({"a": range(10), "b": range(10, 20)})
+    pdf = gdf.to_pandas()
+    assert_eq(
+        pdf.select_dtypes(include=["category"]),
+        gdf.select_dtypes(include=["category"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["float"]),
+        gdf.select_dtypes(include=["float"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["object"]),
+        gdf.select_dtypes(include=["object"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["int"]), gdf.select_dtypes(include=["int"])
+    )
+    assert_eq(
+        pdf.select_dtypes(exclude=["float"]),
+        gdf.select_dtypes(exclude=["float"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(exclude=["object"]),
+        gdf.select_dtypes(exclude=["object"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["int"], exclude=["object"]),
+        gdf.select_dtypes(include=["int"], exclude=["object"]),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.select_dtypes,
+        rfunc=gdf.select_dtypes,
+    )
+
+    gdf = cudf.DataFrame(
+        {"a": cudf.Series([], dtype="int"), "b": cudf.Series([], dtype="str")}
+    )
+    pdf = gdf.to_pandas()
+    assert_eq(
+        pdf.select_dtypes(exclude=["object"]),
+        gdf.select_dtypes(exclude=["object"]),
+    )
+    assert_eq(
+        pdf.select_dtypes(include=["int"], exclude=["object"]),
+        gdf.select_dtypes(include=["int"], exclude=["object"]),
+    )
+
+    gdf = cudf.DataFrame(
+        {"int_col": [0, 1, 2], "list_col": [[1, 2], [3, 4], [5, 6]]}
+    )
+    pdf = gdf.to_pandas()
+    assert_eq(
+        pdf.select_dtypes("int64"),
+        gdf.select_dtypes("int64"),
+    )
+
+
+def test_select_dtype_datetime():
+    gdf = cudf.datasets.timeseries(
+        start="2000-01-01", end="2000-01-02", freq="3600s", dtypes={"x": int}
+    )
+    gdf = gdf.reset_index()
+    pdf = gdf.to_pandas()
+
+    assert_eq(pdf.select_dtypes("datetime64"), gdf.select_dtypes("datetime64"))
+    assert_eq(
+        pdf.select_dtypes(np.dtype("datetime64")),
+        gdf.select_dtypes(np.dtype("datetime64")),
+    )
+    assert_eq(
+        pdf.select_dtypes(include="datetime64"),
+        gdf.select_dtypes(include="datetime64"),
+    )
+
+
+def test_select_dtype_datetime_with_frequency():
+    gdf = cudf.datasets.timeseries(
+        start="2000-01-01", end="2000-01-02", freq="3600s", dtypes={"x": int}
+    )
+    gdf = gdf.reset_index()
+    pdf = gdf.to_pandas()
+
+    assert_exceptions_equal(
+        pdf.select_dtypes,
+        gdf.select_dtypes,
+        (["datetime64[ms]"],),
+        (["datetime64[ms]"],),
+    )
+
+
+def test_dataframe_describe_exclude():
+    np.random.seed(12)
+    data_length = 10000
+
+    df = cudf.DataFrame()
+    df["x"] = np.random.normal(10, 1, data_length)
+    df["x"] = df.x.astype("int64")
+    df["y"] = np.random.normal(10, 1, data_length)
+    pdf = df.to_pandas()
+    with pytest.warns(FutureWarning):
+        gdf_results = df.describe(exclude=["float"])
+    pdf_results = pdf.describe(exclude=["float"])
+
+    assert_eq(gdf_results, pdf_results)
+
+
+def test_dataframe_describe_include():
+    np.random.seed(12)
+    data_length = 10000
+
+    df = cudf.DataFrame()
+    df["x"] = np.random.normal(10, 1, data_length)
+    df["x"] = df.x.astype("int64")
+    df["y"] = np.random.normal(10, 1, data_length)
+    pdf = df.to_pandas()
+    with pytest.warns(FutureWarning):
+        gdf_results = df.describe(include=["int"])
+    pdf_results = pdf.describe(include=["int"])
+
+    assert_eq(gdf_results, pdf_results)
+
+
+def test_dataframe_describe_default():
+    np.random.seed(12)
+    data_length = 10000
+
+    df = cudf.DataFrame()
+    df["x"] = np.random.normal(10, 1, data_length)
+    df["y"] = np.random.normal(10, 1, data_length)
+    pdf = df.to_pandas()
+    with pytest.warns(FutureWarning):
+        gdf_results = df.describe()
+    pdf_results = pdf.describe()
+
+    assert_eq(pdf_results, gdf_results)
+
+
+def test_series_describe_include_all():
+    np.random.seed(12)
+    data_length = 10000
+
+    df = cudf.DataFrame()
+    df["x"] = np.random.normal(10, 1, data_length)
+    df["x"] = df.x.astype("int64")
+    df["y"] = np.random.normal(10, 1, data_length)
+    df["animal"] = np.random.choice(["dog", "cat", "bird"], data_length)
+
+    pdf = df.to_pandas()
+    with pytest.warns(FutureWarning):
+        gdf_results = df.describe(include="all")
+    pdf_results = pdf.describe(include="all")
+
+    assert_eq(gdf_results[["x", "y"]], pdf_results[["x", "y"]])
+    assert_eq(gdf_results.index, pdf_results.index)
+    assert_eq(gdf_results.columns, pdf_results.columns)
+    assert_eq(
+        gdf_results[["animal"]].fillna(-1).astype("str"),
+        pdf_results[["animal"]].fillna(-1).astype("str"),
+    )
+
+
+def test_dataframe_describe_percentiles():
+    np.random.seed(12)
+    data_length = 10000
+    sample_percentiles = [0.0, 0.1, 0.33, 0.84, 0.4, 0.99]
+
+    df = cudf.DataFrame()
+    df["x"] = np.random.normal(10, 1, data_length)
+    df["y"] = np.random.normal(10, 1, data_length)
+    pdf = df.to_pandas()
+    with pytest.warns(FutureWarning):
+        gdf_results = df.describe(percentiles=sample_percentiles)
+    pdf_results = pdf.describe(percentiles=sample_percentiles)
+
+    assert_eq(pdf_results, gdf_results)
+
+
+def test_get_numeric_data():
+    pdf = pd.DataFrame(
+        {"x": [1, 2, 3], "y": [1.0, 2.0, 3.0], "z": ["a", "b", "c"]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(pdf._get_numeric_data(), gdf._get_numeric_data())
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("period", [-15, -1, 0, 1, 15])
+@pytest.mark.parametrize("data_empty", [False, True])
+def test_shift(dtype, period, data_empty):
+    # TODO : this function currently tests for series.shift()
+    # but should instead test for dataframe.shift()
+    if data_empty:
+        data = None
+    else:
+        if dtype == np.int8:
+            # to keep data in range
+            data = gen_rand(dtype, 10, low=-2, high=2)
+        else:
+            data = gen_rand(dtype, 10)
+
+    gs = cudf.DataFrame({"a": cudf.Series(data, dtype=dtype)})
+    ps = pd.DataFrame({"a": pd.Series(data, dtype=dtype)})
+
+    shifted_outcome = gs.a.shift(period)
+    expected_outcome = ps.a.shift(period)
+
+    # pandas uses NaNs to signal missing value and force converts the
+    # results columns to float types
+    if data_empty:
+        assert_eq(
+            shifted_outcome,
+            expected_outcome,
+            check_index_type=False,
+            check_dtype=False,
+        )
+    else:
+        assert_eq(shifted_outcome, expected_outcome, check_dtype=False)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("period", [-1, -5, -10, -20, 0, 1, 5, 10, 20])
+@pytest.mark.parametrize("data_empty", [False, True])
+def test_diff(dtype, period, data_empty):
+    if data_empty:
+        data = None
+    else:
+        if dtype == np.int8:
+            # to keep data in range
+            data = gen_rand(dtype, 100000, low=-2, high=2)
+        else:
+            data = gen_rand(dtype, 100000)
+
+    gdf = cudf.DataFrame({"a": cudf.Series(data, dtype=dtype)})
+    pdf = pd.DataFrame({"a": pd.Series(data, dtype=dtype)})
+
+    expected_outcome = pdf.a.diff(period)
+    diffed_outcome = gdf.a.diff(period).astype(expected_outcome.dtype)
+
+    if data_empty:
+        assert_eq(diffed_outcome, expected_outcome, check_index_type=False)
+    else:
+        assert_eq(diffed_outcome, expected_outcome)
+
+
+@pytest.mark.parametrize("df", _dataframe_na_data())
+@pytest.mark.parametrize("nan_as_null", [True, False, None])
+def test_dataframe_isnull_isna(df, nan_as_null):
+    gdf = cudf.DataFrame.from_pandas(df, nan_as_null=nan_as_null)
+
+    assert_eq(df.isnull(), gdf.isnull())
+    assert_eq(df.isna(), gdf.isna())
+
+    # Test individual columns
+    for col in df:
+        assert_eq(df[col].isnull(), gdf[col].isnull())
+        assert_eq(df[col].isna(), gdf[col].isna())
+
+
+@pytest.mark.parametrize("df", _dataframe_na_data())
+@pytest.mark.parametrize("nan_as_null", [True, False, None])
+def test_dataframe_notna_notnull(df, nan_as_null):
+    gdf = cudf.DataFrame.from_pandas(df, nan_as_null=nan_as_null)
+
+    assert_eq(df.notnull(), gdf.notnull())
+    assert_eq(df.notna(), gdf.notna())
+
+    # Test individual columns
+    for col in df:
+        assert_eq(df[col].notnull(), gdf[col].notnull())
+        assert_eq(df[col].notna(), gdf[col].notna())
+
+
+def test_ndim():
+    pdf = pd.DataFrame({"x": range(5), "y": range(5, 10)})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert pdf.ndim == gdf.ndim
+    assert pdf.x.ndim == gdf.x.ndim
+
+    s = pd.Series(dtype="float64")
+    gs = cudf.Series()
+    assert s.ndim == gs.ndim
+
+
+@pytest.mark.parametrize(
+    "decimals",
+    [
+        -3,
+        0,
+        5,
+        pd.Series(
+            [1, 4, 3, -6],
+            index=["floats", "ints", "floats_with_nan", "floats_same"],
+        ),
+        cudf.Series(
+            [-4, -2, 12], index=["ints", "floats_with_nan", "floats_same"]
+        ),
+        {"floats": -1, "ints": 15, "floats_will_nan": 2},
+    ],
+)
+def test_dataframe_round(decimals):
+    gdf = cudf.DataFrame(
+        {
+            "floats": np.arange(0.5, 10.5, 1),
+            "ints": np.random.normal(-100, 100, 10),
+            "floats_with_na": np.array(
+                [
+                    14.123,
+                    2.343,
+                    np.nan,
+                    0.0,
+                    -8.302,
+                    np.nan,
+                    94.313,
+                    None,
+                    -8.029,
+                    np.nan,
+                ]
+            ),
+            "floats_same": np.repeat([-0.6459412758761901], 10),
+            "bools": np.random.choice([True, None, False], 10),
+            "strings": np.random.choice(["abc", "xyz", None], 10),
+            "struct": np.random.choice([{"abc": 1}, {"xyz": 2}, None], 10),
+            "list": [[1], [2], None, [4], [3]] * 2,
+        }
+    )
+    pdf = gdf.to_pandas()
+
+    if isinstance(decimals, cudf.Series):
+        pdecimals = decimals.to_pandas()
+    else:
+        pdecimals = decimals
+
+    result = gdf.round(decimals)
+    expected = pdf.round(pdecimals)
+
+    assert_eq(result, expected)
+
+
+def test_dataframe_round_dict_decimal_validation():
+    df = cudf.DataFrame({"A": [0.12], "B": [0.13]})
+    with pytest.raises(TypeError):
+        df.round({"A": 1, "B": 0.5})
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0, 1, 2, 3],
+        [-2, -1, 2, 3, 5],
+        [-2, -1, 0, 3, 5],
+        [True, False, False],
+        [True],
+        [False],
+        [],
+        [True, None, False],
+        [True, True, None],
+        [None, None],
+        [[0, 5], [1, 6], [2, 7], [3, 8], [4, 9]],
+        [[1, True], [2, False], [3, False]],
+        pytest.param(
+            [["a", True], ["b", False], ["c", False]],
+            marks=[
+                pytest_xfail(
+                    reason="NotImplementedError: all does not "
+                    "support columns of object dtype."
+                )
+            ],
+        ),
+    ],
+)
+def test_all(data):
+    # Provide a dtype when data is empty to avoid future pandas changes.
+    dtype = None if data else float
+    # Pandas treats `None` in object type columns as True for some reason, so
+    # replacing with `False`
+    if np.array(data).ndim <= 1:
+        pdata = pd.Series(data=data, dtype=dtype).replace([None], False)
+        gdata = cudf.Series.from_pandas(pdata)
+    else:
+        pdata = pd.DataFrame(data, columns=["a", "b"], dtype=dtype).replace(
+            [None], False
+        )
+        gdata = cudf.DataFrame.from_pandas(pdata)
+
+        # test bool_only
+        if pdata["b"].dtype == "bool":
+            got = gdata.all(bool_only=True)
+            expected = pdata.all(bool_only=True)
+            assert_eq(got, expected)
+        else:
+            with pytest.raises(NotImplementedError):
+                gdata.all(level="a")
+
+    got = gdata.all()
+    expected = pdata.all()
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0, 1, 2, 3],
+        [-2, -1, 2, 3, 5],
+        [-2, -1, 0, 3, 5],
+        [0, 0, 0, 0, 0],
+        [0, 0, None, 0],
+        [True, False, False],
+        [True],
+        [False],
+        [],
+        [True, None, False],
+        [True, True, None],
+        [None, None],
+        [[0, 5], [1, 6], [2, 7], [3, 8], [4, 9]],
+        [[1, True], [2, False], [3, False]],
+        pytest.param(
+            [["a", True], ["b", False], ["c", False]],
+            marks=[
+                pytest_xfail(
+                    reason="NotImplementedError: any does not "
+                    "support columns of object dtype."
+                )
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("axis", [0, 1])
+def test_any(data, axis):
+    # Provide a dtype when data is empty to avoid future pandas changes.
+    dtype = None if data else float
+    if np.array(data).ndim <= 1:
+        pdata = pd.Series(data=data, dtype=dtype)
+        gdata = cudf.Series(data=data, dtype=dtype)
+
+        if axis == 1:
+            with pytest.raises(NotImplementedError):
+                gdata.any(axis=axis)
+        else:
+            got = gdata.any(axis=axis)
+            expected = pdata.any(axis=axis)
+            assert_eq(got, expected)
+    else:
+        pdata = pd.DataFrame(data, columns=["a", "b"])
+        gdata = cudf.DataFrame.from_pandas(pdata)
+
+        # test bool_only
+        if pdata["b"].dtype == "bool":
+            got = gdata.any(bool_only=True)
+            expected = pdata.any(bool_only=True)
+            assert_eq(got, expected)
+        else:
+            with pytest.raises(NotImplementedError):
+                gdata.any(level="a")
+
+        got = gdata.any(axis=axis)
+        expected = pdata.any(axis=axis)
+        assert_eq(got, expected)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+def test_empty_dataframe_any(axis):
+    pdf = pd.DataFrame({}, columns=["a", "b"], dtype=float)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    got = gdf.any(axis=axis)
+    expected = pdf.any(axis=axis)
+    assert_eq(got, expected, check_index_type=False)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize("a", [[], ["123"]])
+@pytest.mark.parametrize("b", ["123", ["123"]])
+@pytest.mark.parametrize(
+    "misc_data",
+    ["123", ["123"] * 20, 123, [1, 2, 0.8, 0.9] * 50, 0.9, 0.00001],
+)
+@pytest.mark.parametrize("non_list_data", [123, "abc", "zyx", "rapids", 0.8])
+def test_create_dataframe_cols_empty_data(a, b, misc_data, non_list_data):
+    expected = pd.DataFrame({"a": a})
+    actual = cudf.DataFrame.from_pandas(expected)
+    expected["b"] = b
+    actual["b"] = b
+    assert_eq(actual, expected)
+
+    expected = pd.DataFrame({"a": []})
+    actual = cudf.DataFrame.from_pandas(expected)
+    expected["b"] = misc_data
+    actual["b"] = misc_data
+    assert_eq(actual, expected)
+
+    expected = pd.DataFrame({"a": a})
+    actual = cudf.DataFrame.from_pandas(expected)
+    expected["b"] = non_list_data
+    actual["b"] = non_list_data
+    assert_eq(actual, expected)
+
+
+def test_empty_dataframe_describe():
+    pdf = pd.DataFrame({"a": [], "b": []})
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.describe()
+    with pytest.warns(FutureWarning):
+        actual = gdf.describe()
+
+    assert_eq(expected, actual)
+
+
+def test_as_column_types():
+    col = column.as_column(cudf.Series([], dtype="float64"))
+    assert_eq(col.dtype, np.dtype("float64"))
+    gds = cudf.Series(col)
+    pds = pd.Series(pd.Series([], dtype="float64"))
+
+    assert_eq(pds, gds)
+
+    col = column.as_column(cudf.Series([], dtype="float64"), dtype="float32")
+    assert_eq(col.dtype, np.dtype("float32"))
+    gds = cudf.Series(col)
+    pds = pd.Series(pd.Series([], dtype="float32"))
+
+    assert_eq(pds, gds)
+
+    col = column.as_column(cudf.Series([], dtype="float64"), dtype="str")
+    assert_eq(col.dtype, np.dtype("object"))
+    gds = cudf.Series(col)
+    pds = pd.Series(pd.Series([], dtype="str"))
+
+    assert_eq(pds, gds)
+
+    col = column.as_column(cudf.Series([], dtype="float64"), dtype="object")
+    assert_eq(col.dtype, np.dtype("object"))
+    gds = cudf.Series(col)
+    pds = pd.Series(pd.Series([], dtype="object"))
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series(np.array([1, 2, 3]), dtype="float32")
+    gds = cudf.Series(column.as_column(np.array([1, 2, 3]), dtype="float32"))
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series([1, 2, 3], dtype="float32")
+    gds = cudf.Series([1, 2, 3], dtype="float32")
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series([], dtype="float64")
+    gds = cudf.Series(column.as_column(pds))
+    assert_eq(pds, gds)
+
+    pds = pd.Series([1, 2, 4], dtype="int64")
+    gds = cudf.Series(column.as_column(cudf.Series([1, 2, 4]), dtype="int64"))
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series([1.2, 18.0, 9.0], dtype="float32")
+    gds = cudf.Series(
+        column.as_column(cudf.Series([1.2, 18.0, 9.0]), dtype="float32")
+    )
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series([1.2, 18.0, 9.0], dtype="str")
+    gds = cudf.Series(
+        column.as_column(cudf.Series([1.2, 18.0, 9.0]), dtype="str")
+    )
+
+    assert_eq(pds, gds)
+
+    pds = pd.Series(pd.Index(["1", "18", "9"]), dtype="int")
+    gds = cudf.Series(cudf.Index(["1", "18", "9"]), dtype="int")
+
+    assert_eq(pds, gds)
+
+
+def test_one_row_head():
+    gdf = cudf.DataFrame({"name": ["carl"], "score": [100]}, index=[123])
+    pdf = gdf.to_pandas()
+
+    head_gdf = gdf.head()
+    head_pdf = pdf.head()
+
+    assert_eq(head_pdf, head_gdf)
+
+
+@pytest.mark.parametrize("dtype", ALL_TYPES)
+@pytest.mark.parametrize(
+    "np_dtype,pd_dtype",
+    [
+        tuple(item)
+        for item in cudf.utils.dtypes.np_dtypes_to_pandas_dtypes.items()
+    ],
+)
+def test_series_astype_pandas_nullable(dtype, np_dtype, pd_dtype):
+    source = cudf.Series([0, 1, None], dtype=dtype)
+
+    expect = source.astype(np_dtype)
+    got = source.astype(pd_dtype)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("as_dtype", NUMERIC_TYPES)
+def test_series_astype_numeric_to_numeric(dtype, as_dtype):
+    psr = pd.Series([1, 2, 4, 3], dtype=dtype)
+    gsr = cudf.from_pandas(psr)
+    assert_eq(psr.astype(as_dtype), gsr.astype(as_dtype))
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("as_dtype", NUMERIC_TYPES)
+def test_series_astype_numeric_to_numeric_nulls(dtype, as_dtype):
+    data = [1, 2, None, 3]
+    sr = cudf.Series(data, dtype=dtype)
+    got = sr.astype(as_dtype)
+    expect = cudf.Series([1, 2, None, 3], dtype=as_dtype)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "str",
+        "category",
+        "datetime64[s]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[ns]",
+    ],
+)
+def test_series_astype_numeric_to_other(dtype, as_dtype):
+    psr = pd.Series([1, 2, 3], dtype=dtype)
+    gsr = cudf.from_pandas(psr)
+    assert_eq(psr.astype(as_dtype), gsr.astype(as_dtype))
+
+
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "str",
+        "int32",
+        "uint32",
+        "float32",
+        "category",
+        "datetime64[s]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[ns]",
+    ],
+)
+def test_series_astype_string_to_other(as_dtype):
+    if "datetime64" in as_dtype:
+        data = ["2001-01-01", "2002-02-02", "2000-01-05"]
+    else:
+        data = ["1", "2", "3"]
+    psr = pd.Series(data)
+    gsr = cudf.from_pandas(psr)
+    assert_eq(psr.astype(as_dtype), gsr.astype(as_dtype))
+
+
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "category",
+        "datetime64[s]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[ns]",
+    ],
+)
+def test_series_astype_datetime_to_other(as_dtype):
+    data = ["2001-01-01", "2002-02-02", "2001-01-05"]
+    psr = pd.Series(data)
+    gsr = cudf.from_pandas(psr)
+    assert_eq(psr.astype(as_dtype), gsr.astype(as_dtype))
+
+
+@pytest.mark.parametrize(
+    "inp",
+    [
+        ("datetime64[ns]", "2011-01-01 00:00:00.000000000"),
+        ("datetime64[us]", "2011-01-01 00:00:00.000000"),
+        ("datetime64[ms]", "2011-01-01 00:00:00.000"),
+        ("datetime64[s]", "2011-01-01 00:00:00"),
+    ],
+)
+def test_series_astype_datetime_to_string(inp):
+    dtype, expect = inp
+    base_date = "2011-01-01"
+    sr = cudf.Series([base_date], dtype=dtype)
+    got = sr.astype(str)[0]
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "int32",
+        "uint32",
+        "float32",
+        "category",
+        "datetime64[s]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[ns]",
+        "str",
+    ],
+)
+def test_series_astype_categorical_to_other(as_dtype):
+    if "datetime64" in as_dtype:
+        data = ["2001-01-01", "2002-02-02", "2000-01-05", "2001-01-01"]
+    else:
+        data = [1, 2, 3, 1]
+    psr = pd.Series(data, dtype="category")
+    gsr = cudf.from_pandas(psr)
+    assert_eq(psr.astype(as_dtype), gsr.astype(as_dtype))
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_series_astype_to_categorical_ordered(ordered):
+    psr = pd.Series([1, 2, 3, 1], dtype="category")
+    gsr = cudf.from_pandas(psr)
+
+    ordered_dtype_pd = pd.CategoricalDtype(
+        categories=[1, 2, 3], ordered=ordered
+    )
+    ordered_dtype_gd = cudf.CategoricalDtype.from_pandas(ordered_dtype_pd)
+    assert_eq(
+        psr.astype("int32").astype(ordered_dtype_pd).astype("int32"),
+        gsr.astype("int32").astype(ordered_dtype_gd).astype("int32"),
+    )
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_series_astype_cat_ordered_to_unordered(ordered):
+    pd_dtype = pd.CategoricalDtype(categories=[1, 2, 3], ordered=ordered)
+    pd_to_dtype = pd.CategoricalDtype(
+        categories=[1, 2, 3], ordered=not ordered
+    )
+    gd_dtype = cudf.CategoricalDtype.from_pandas(pd_dtype)
+    gd_to_dtype = cudf.CategoricalDtype.from_pandas(pd_to_dtype)
+
+    psr = pd.Series([1, 2, 3], dtype=pd_dtype)
+    gsr = cudf.Series([1, 2, 3], dtype=gd_dtype)
+
+    expect = psr.astype(pd_to_dtype)
+    got = gsr.astype(gd_to_dtype)
+
+    assert_eq(expect, got)
+
+
+def test_series_astype_null_cases():
+    data = [1, 2, None, 3]
+
+    # numerical to other
+    assert_eq(cudf.Series(data, dtype="str"), cudf.Series(data).astype("str"))
+
+    assert_eq(
+        cudf.Series(data, dtype="category"),
+        cudf.Series(data).astype("category"),
+    )
+
+    assert_eq(
+        cudf.Series(data, dtype="float32"),
+        cudf.Series(data, dtype="int32").astype("float32"),
+    )
+
+    assert_eq(
+        cudf.Series(data, dtype="float32"),
+        cudf.Series(data, dtype="uint32").astype("float32"),
+    )
+
+    assert_eq(
+        cudf.Series(data, dtype="datetime64[ms]"),
+        cudf.Series(data).astype("datetime64[ms]"),
+    )
+
+    # categorical to other
+    assert_eq(
+        cudf.Series(data, dtype="str"),
+        cudf.Series(data, dtype="category").astype("str"),
+    )
+
+    assert_eq(
+        cudf.Series(data, dtype="float32"),
+        cudf.Series(data, dtype="category").astype("float32"),
+    )
+
+    assert_eq(
+        cudf.Series(data, dtype="datetime64[ms]"),
+        cudf.Series(data, dtype="category").astype("datetime64[ms]"),
+    )
+
+    # string to other
+    assert_eq(
+        cudf.Series([1, 2, None, 3], dtype="int32"),
+        cudf.Series(["1", "2", None, "3"]).astype("int32"),
+    )
+
+    assert_eq(
+        cudf.Series(
+            ["2001-01-01", "2001-02-01", None, "2001-03-01"],
+            dtype="datetime64[ms]",
+        ),
+        cudf.Series(["2001-01-01", "2001-02-01", None, "2001-03-01"]).astype(
+            "datetime64[ms]"
+        ),
+    )
+
+    assert_eq(
+        cudf.Series(["a", "b", "c", None], dtype="category").to_pandas(),
+        cudf.Series(["a", "b", "c", None]).astype("category").to_pandas(),
+    )
+
+    # datetime to other
+    data = [
+        "2001-01-01 00:00:00.000000",
+        "2001-02-01 00:00:00.000000",
+        None,
+        "2001-03-01 00:00:00.000000",
+    ]
+    assert_eq(
+        cudf.Series(data),
+        cudf.Series(data, dtype="datetime64[us]").astype("str"),
+    )
+
+    assert_eq(
+        pd.Series(data, dtype="datetime64[ns]").astype("category"),
+        cudf.from_pandas(pd.Series(data, dtype="datetime64[ns]")).astype(
+            "category"
+        ),
+    )
+
+
+def test_series_astype_null_categorical():
+    sr = cudf.Series([None, None, None], dtype="category")
+    expect = cudf.Series([None, None, None], dtype="int32")
+    got = sr.astype("int32")
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            pd.Series([3, 3.0]),
+            pd.Series([2.3, 3.9]),
+            pd.Series([1.5, 3.9]),
+            pd.Series([1.0, 2]),
+        ),
+        [
+            pd.Series([3, 3.0]),
+            pd.Series([2.3, 3.9]),
+            pd.Series([1.5, 3.9]),
+            pd.Series([1.0, 2]),
+        ],
+    ],
+)
+def test_create_dataframe_from_list_like(data):
+    pdf = pd.DataFrame(data, index=["count", "mean", "std", "min"])
+    gdf = cudf.DataFrame(data, index=["count", "mean", "std", "min"])
+
+    assert_eq(pdf, gdf)
+
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data)
+
+    assert_eq(pdf, gdf)
+
+
+def test_create_dataframe_column():
+    pdf = pd.DataFrame(columns=["a", "b", "c"], index=["A", "Z", "X"])
+    gdf = cudf.DataFrame(columns=["a", "b", "c"], index=["A", "Z", "X"])
+
+    assert_eq(pdf, gdf)
+
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 3], "b": [2, 3, 5]},
+        columns=["a", "b", "c"],
+        index=["A", "Z", "X"],
+    )
+    gdf = cudf.DataFrame(
+        {"a": [1, 2, 3], "b": [2, 3, 5]},
+        columns=["a", "b", "c"],
+        index=["A", "Z", "X"],
+    )
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.DataFrame(np.eye(2)),
+        cudf.DataFrame(np.eye(2)),
+        np.eye(2),
+        cupy.eye(2),
+        None,
+        [[1, 0], [0, 1]],
+        [cudf.Series([0, 1]), cudf.Series([1, 0])],
+    ],
+)
+@pytest.mark.parametrize(
+    "columns",
+    [None, range(2), pd.RangeIndex(2), cudf.RangeIndex(2)],
+)
+def test_dataframe_columns_returns_rangeindex(data, columns):
+    if data is None and columns is None:
+        pytest.skip(f"{data=} and {columns=} not relevant.")
+    result = cudf.DataFrame(data=data, columns=columns).columns
+    expected = pd.RangeIndex(range(2))
+    assert_eq(result, expected)
+
+
+def test_dataframe_columns_returns_rangeindex_single_col():
+    result = cudf.DataFrame([1, 2, 3]).columns
+    expected = pd.RangeIndex(range(1))
+    assert_eq(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 4],
+        [],
+        [5.0, 7.0, 8.0],
+        pd.Categorical(["a", "b", "c"]),
+        ["m", "a", "d", "v"],
+    ],
+)
+def test_series_values_host_property(data):
+    pds = pd.Series(data=data, dtype=None if data else float)
+    gds = _create_cudf_series_float64_default(data)
+
+    np.testing.assert_array_equal(pds.values, gds.values_host)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 4],
+        [],
+        [5.0, 7.0, 8.0],
+        pytest.param(
+            pd.Categorical(["a", "b", "c"]),
+            marks=pytest_xfail(raises=NotImplementedError),
+        ),
+        pytest.param(
+            ["m", "a", "d", "v"],
+            marks=pytest_xfail(raises=TypeError),
+        ),
+    ],
+)
+def test_series_values_property(data):
+    pds = pd.Series(data=data, dtype=None if data else float)
+    gds = _create_cudf_series_float64_default(data)
+    gds_vals = gds.values
+    assert isinstance(gds_vals, cupy.ndarray)
+    np.testing.assert_array_equal(gds_vals.get(), pds.values)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"A": [1, 2, 3], "B": [4, 5, 6]},
+        {"A": [1.0, 2.0, 3.0], "B": [4.0, 5.0, 6.0]},
+        {"A": [1, 2, 3], "B": [1.0, 2.0, 3.0]},
+        {"A": np.float32(np.arange(3)), "B": np.float64(np.arange(3))},
+        pytest.param(
+            {"A": [1, None, 3], "B": [1, 2, None]},
+            marks=pytest_xfail(
+                reason="Nulls not supported by values accessor"
+            ),
+        ),
+        pytest.param(
+            {"A": [None, None, None], "B": [None, None, None]},
+            marks=pytest_xfail(
+                reason="Nulls not supported by values accessor"
+            ),
+        ),
+        {"A": [], "B": []},
+        pytest.param(
+            {"A": [1, 2, 3], "B": ["a", "b", "c"]},
+            marks=pytest_xfail(
+                reason="str or categorical not supported by values accessor"
+            ),
+        ),
+        pytest.param(
+            {"A": pd.Categorical(["a", "b", "c"]), "B": ["d", "e", "f"]},
+            marks=pytest_xfail(
+                reason="str or categorical not supported by values accessor"
+            ),
+        ),
+    ],
+)
+def test_df_values_property(data):
+    pdf = pd.DataFrame.from_dict(data)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    pmtr = pdf.values
+    gmtr = gdf.values.get()
+
+    np.testing.assert_array_equal(pmtr, gmtr)
+
+
+def test_numeric_alpha_value_counts():
+    pdf = pd.DataFrame(
+        {
+            "numeric": [1, 2, 3, 4, 5, 6, 1, 2, 4] * 10,
+            "alpha": ["u", "h", "d", "a", "m", "u", "h", "d", "a"] * 10,
+        }
+    )
+
+    gdf = cudf.DataFrame(
+        {
+            "numeric": [1, 2, 3, 4, 5, 6, 1, 2, 4] * 10,
+            "alpha": ["u", "h", "d", "a", "m", "u", "h", "d", "a"] * 10,
+        }
+    )
+
+    assert_eq(
+        pdf.numeric.value_counts().sort_index(),
+        gdf.numeric.value_counts().sort_index(),
+        check_dtype=False,
+    )
+    assert_eq(
+        pdf.alpha.value_counts().sort_index(),
+        gdf.alpha.value_counts().sort_index(),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.DataFrame(
+            {
+                "num_legs": [2, 4],
+                "num_wings": [2, 0],
+                "bird_cats": pd.Series(
+                    ["sparrow", "pigeon"],
+                    dtype="category",
+                    index=["falcon", "dog"],
+                ),
+            },
+            index=["falcon", "dog"],
+        ),
+        pd.DataFrame(
+            {"num_legs": [8, 2], "num_wings": [0, 2]},
+            index=["spider", "falcon"],
+        ),
+        pd.DataFrame(
+            {
+                "num_legs": [8, 2, 1, 0, 2, 4, 5],
+                "num_wings": [2, 0, 2, 1, 2, 4, -1],
+            }
+        ),
+        pd.DataFrame({"a": ["a", "b", "c"]}, dtype="category"),
+        pd.DataFrame({"a": ["a", "b", "c"]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "values",
+    [
+        [0, 2],
+        {"num_wings": [0, 3]},
+        pd.DataFrame(
+            {"num_legs": [8, 2], "num_wings": [0, 2]},
+            index=["spider", "falcon"],
+        ),
+        pd.DataFrame(
+            {
+                "num_legs": [2, 4],
+                "num_wings": [2, 0],
+                "bird_cats": pd.Series(
+                    ["sparrow", "pigeon"],
+                    dtype="category",
+                    index=["falcon", "dog"],
+                ),
+            },
+            index=["falcon", "dog"],
+        ),
+        ["sparrow", "pigeon"],
+        pd.Series(["sparrow", "pigeon"], dtype="category"),
+        pd.Series([1, 2, 3, 4, 5]),
+        "abc",
+        123,
+        pd.Series(["a", "b", "c"]),
+        pd.Series(["a", "b", "c"], dtype="category"),
+        pd.DataFrame({"a": ["a", "b", "c"]}, dtype="category"),
+    ],
+)
+def test_isin_dataframe(data, values):
+    pdf = data
+    gdf = cudf.from_pandas(pdf)
+
+    if cudf.api.types.is_scalar(values):
+        assert_exceptions_equal(
+            lfunc=pdf.isin,
+            rfunc=gdf.isin,
+            lfunc_args_and_kwargs=([values],),
+            rfunc_args_and_kwargs=([values],),
+        )
+    else:
+        try:
+            expected = pdf.isin(values)
+        except TypeError as e:
+            # Can't do isin with different categories
+            if str(e) == (
+                "Categoricals can only be compared if 'categories' "
+                "are the same."
+            ):
+                return
+
+        if isinstance(values, (pd.DataFrame, pd.Series)):
+            values = cudf.from_pandas(values)
+
+        got = gdf.isin(values)
+        assert_eq(got, expected)
+
+
+def test_isin_axis_duplicated_error():
+    df = cudf.DataFrame(range(2))
+    with pytest.raises(ValueError):
+        df.isin(cudf.Series(range(2), index=[1, 1]))
+
+    with pytest.raises(ValueError):
+        df.isin(cudf.DataFrame(range(2), index=[1, 1]))
+
+    with pytest.raises(ValueError):
+        df.isin(cudf.DataFrame([[1, 2]], columns=[1, 1]))
+
+
+def test_constructor_properties():
+    df = cudf.DataFrame()
+    key1 = "a"
+    key2 = "b"
+    val1 = np.array([123], dtype=np.float64)
+    val2 = np.array([321], dtype=np.float64)
+    df[key1] = val1
+    df[key2] = val2
+
+    # Correct use of _constructor_sliced (for DataFrame)
+    assert_eq(df[key1], df._constructor_sliced(val1, name=key1))
+
+    # Correct use of _constructor_expanddim (for cudf.Series)
+    assert_eq(df, df[key2]._constructor_expanddim({key1: val1, key2: val2}))
+
+    # Incorrect use of _constructor_sliced (Raises for cudf.Series)
+    with pytest.raises(NotImplementedError):
+        df[key1]._constructor_sliced
+
+    # Incorrect use of _constructor_expanddim (Raises for DataFrame)
+    with pytest.raises(NotImplementedError):
+        df._constructor_expanddim
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("as_dtype", ALL_TYPES)
+def test_df_astype_numeric_to_all(dtype, as_dtype):
+    if "uint" in dtype:
+        data = [1, 2, None, 4, 7]
+    elif "int" in dtype or "longlong" in dtype:
+        data = [1, 2, None, 4, -7]
+    elif "float" in dtype:
+        data = [1.0, 2.0, None, 4.0, np.nan, -7.0]
+
+    gdf = cudf.DataFrame()
+
+    gdf["foo"] = cudf.Series(data, dtype=dtype)
+    gdf["bar"] = cudf.Series(data, dtype=dtype)
+
+    insert_data = cudf.Series(data, dtype=dtype)
+
+    expect = cudf.DataFrame()
+    expect["foo"] = insert_data.astype(as_dtype)
+    expect["bar"] = insert_data.astype(as_dtype)
+
+    got = gdf.astype(as_dtype)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "int32",
+        "float32",
+        "category",
+        "datetime64[s]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[ns]",
+    ],
+)
+def test_df_astype_string_to_other(as_dtype):
+    if "datetime64" in as_dtype:
+        # change None to "NaT" after this issue is fixed:
+        # https://github.com/rapidsai/cudf/issues/5117
+        data = ["2001-01-01", "2002-02-02", "2000-01-05", None]
+    elif as_dtype == "int32":
+        data = [1, 2, 3]
+    elif as_dtype == "category":
+        data = ["1", "2", "3", None]
+    elif "float" in as_dtype:
+        data = [1.0, 2.0, 3.0, np.nan]
+
+    insert_data = cudf.Series.from_pandas(pd.Series(data, dtype="str"))
+    expect_data = cudf.Series(data, dtype=as_dtype)
+
+    gdf = cudf.DataFrame()
+    expect = cudf.DataFrame()
+
+    gdf["foo"] = insert_data
+    gdf["bar"] = insert_data
+
+    expect["foo"] = expect_data
+    expect["bar"] = expect_data
+
+    got = gdf.astype(as_dtype)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "int64",
+        "datetime64[s]",
+        "datetime64[us]",
+        "datetime64[ns]",
+        "str",
+        "category",
+    ],
+)
+def test_df_astype_datetime_to_other(as_dtype):
+    data = [
+        "1991-11-20 00:00:00.000",
+        "2004-12-04 00:00:00.000",
+        "2016-09-13 00:00:00.000",
+        None,
+    ]
+
+    gdf = cudf.DataFrame()
+    expect = cudf.DataFrame()
+
+    gdf["foo"] = cudf.Series(data, dtype="datetime64[ms]")
+    gdf["bar"] = cudf.Series(data, dtype="datetime64[ms]")
+
+    if as_dtype == "int64":
+        expect["foo"] = cudf.Series(
+            [690595200000, 1102118400000, 1473724800000, None], dtype="int64"
+        )
+        expect["bar"] = cudf.Series(
+            [690595200000, 1102118400000, 1473724800000, None], dtype="int64"
+        )
+    elif as_dtype == "str":
+        expect["foo"] = cudf.Series(data, dtype="str")
+        expect["bar"] = cudf.Series(data, dtype="str")
+    elif as_dtype == "category":
+        expect["foo"] = cudf.Series(gdf["foo"], dtype="category")
+        expect["bar"] = cudf.Series(gdf["bar"], dtype="category")
+    else:
+        expect["foo"] = cudf.Series(data, dtype=as_dtype)
+        expect["bar"] = cudf.Series(data, dtype=as_dtype)
+
+    got = gdf.astype(as_dtype)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "as_dtype",
+    [
+        "int32",
+        "float32",
+        "category",
+        "datetime64[s]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[ns]",
+        "str",
+    ],
+)
+def test_df_astype_categorical_to_other(as_dtype):
+    if "datetime64" in as_dtype:
+        data = ["2001-01-01", "2002-02-02", "2000-01-05", "2001-01-01"]
+    else:
+        data = [1, 2, 3, 1]
+    psr = pd.Series(data, dtype="category")
+    pdf = pd.DataFrame()
+    pdf["foo"] = psr
+    pdf["bar"] = psr
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_eq(pdf.astype(as_dtype), gdf.astype(as_dtype))
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_df_astype_to_categorical_ordered(ordered):
+    psr = pd.Series([1, 2, 3, 1], dtype="category")
+    pdf = pd.DataFrame()
+    pdf["foo"] = psr
+    pdf["bar"] = psr
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    ordered_dtype_pd = pd.CategoricalDtype(
+        categories=[1, 2, 3], ordered=ordered
+    )
+    ordered_dtype_gd = cudf.CategoricalDtype.from_pandas(ordered_dtype_pd)
+
+    assert_eq(
+        pdf.astype(ordered_dtype_pd).astype("int32"),
+        gdf.astype(ordered_dtype_gd).astype("int32"),
+    )
+
+
+@pytest.mark.parametrize(
+    "dtype,args",
+    [(dtype, {}) for dtype in ALL_TYPES]
+    + [("category", {"ordered": True}), ("category", {"ordered": False})],
+)
+def test_empty_df_astype(dtype, args):
+    df = cudf.DataFrame()
+    kwargs = {}
+    kwargs.update(args)
+    assert_eq(df, df.astype(dtype=dtype, **kwargs))
+
+
+@pytest.mark.parametrize(
+    "errors",
+    [
+        pytest.param(
+            "raise", marks=pytest_xfail(reason="should raise error here")
+        ),
+        pytest.param("other", marks=pytest_xfail(raises=ValueError)),
+        "ignore",
+    ],
+)
+def test_series_astype_error_handling(errors):
+    sr = cudf.Series(["random", "words"])
+    got = sr.astype("datetime64", errors=errors)
+    assert_eq(sr, got)
+
+
+@pytest.mark.parametrize("dtype", ALL_TYPES)
+def test_df_constructor_dtype(dtype):
+    if "datetime" in dtype:
+        data = ["1991-11-20", "2004-12-04", "2016-09-13", None]
+    elif dtype == "str":
+        data = ["a", "b", "c", None]
+    elif "float" in dtype:
+        data = [1.0, 0.5, -1.1, np.nan, None]
+    elif "bool" in dtype:
+        data = [True, False, None]
+    else:
+        data = [1, 2, 3, None]
+
+    sr = cudf.Series(data, dtype=dtype)
+
+    expect = cudf.DataFrame()
+    expect["foo"] = sr
+    expect["bar"] = sr
+    got = cudf.DataFrame({"foo": data, "bar": data}, dtype=dtype)
+
+    assert_eq(expect, got)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.datasets.randomdata(
+            nrows=10, dtypes={"a": "category", "b": int, "c": float, "d": int}
+        ),
+        cudf.datasets.randomdata(
+            nrows=10, dtypes={"a": "category", "b": int, "c": float, "d": str}
+        ),
+        cudf.datasets.randomdata(
+            nrows=10, dtypes={"a": bool, "b": int, "c": float, "d": str}
+        ),
+        cudf.DataFrame(),
+        cudf.DataFrame({"a": [0, 1, 2], "b": [1, None, 3]}),
+        cudf.DataFrame(
+            {
+                "a": [1, 2, 3, 4],
+                "b": [7, np.NaN, 9, 10],
+                "c": [np.NaN, np.NaN, np.NaN, np.NaN],
+                "d": cudf.Series([None, None, None, None], dtype="int64"),
+                "e": [100, None, 200, None],
+                "f": cudf.Series([10, None, np.NaN, 11], nan_as_null=False),
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "a": [10, 11, 12, 13, 14, 15],
+                "b": cudf.Series(
+                    [10, None, np.NaN, 2234, None, np.NaN], nan_as_null=False
+                ),
+            }
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "op", ["max", "min", "sum", "product", "mean", "var", "std"]
+)
+@pytest.mark.parametrize("skipna", [True, False])
+def test_rowwise_ops(data, op, skipna):
+    gdf = data
+    pdf = gdf.to_pandas()
+
+    kwargs = {"axis": 1, "skipna": skipna}
+    if op in ("var", "std"):
+        kwargs["ddof"] = 0
+
+    with expect_warning_if(
+        not all(
+            (
+                (pdf[column].count() == 0)
+                if skipna
+                else (pdf[column].notna().count() == 0)
+            )
+            or cudf.api.types.is_numeric_dtype(pdf[column].dtype)
+            or cudf.api.types.is_bool_dtype(pdf[column].dtype)
+            for column in pdf
+        )
+    ):
+        expected = getattr(pdf, op)(**kwargs)
+    with expect_warning_if(
+        not all(
+            cudf.api.types.is_numeric_dtype(gdf[column].dtype)
+            or cudf.api.types.is_bool_dtype(gdf[column].dtype)
+            for column in gdf
+        ),
+        UserWarning,
+    ):
+        got = getattr(gdf, op)(**kwargs)
+
+    assert_eq(expected, got, check_exact=False)
+
+
+@pytest.mark.parametrize(
+    "op", ["max", "min", "sum", "product", "mean", "var", "std"]
+)
+def test_rowwise_ops_nullable_dtypes_all_null(op):
+    gdf = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4],
+            "b": [7, np.NaN, 9, 10],
+            "c": cudf.Series([np.NaN, np.NaN, np.NaN, np.NaN], dtype=float),
+            "d": cudf.Series([None, None, None, None], dtype="int64"),
+            "e": [100, None, 200, None],
+            "f": cudf.Series([10, None, np.NaN, 11], nan_as_null=False),
+        }
+    )
+
+    expected = cudf.Series([None, None, None, None], dtype="float64")
+
+    if op in ("var", "std"):
+        got = getattr(gdf, op)(axis=1, ddof=0, skipna=False)
+    else:
+        got = getattr(gdf, op)(axis=1, skipna=False)
+
+    assert_eq(got.null_count, expected.null_count)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "op,expected",
+    [
+        (
+            "max",
+            cudf.Series(
+                [10.0, None, np.NaN, 2234.0, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+        (
+            "min",
+            cudf.Series(
+                [10.0, None, np.NaN, 13.0, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+        (
+            "sum",
+            cudf.Series(
+                [20.0, None, np.NaN, 2247.0, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+        (
+            "product",
+            cudf.Series(
+                [100.0, None, np.NaN, 29042.0, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+        (
+            "mean",
+            cudf.Series(
+                [10.0, None, np.NaN, 1123.5, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+        (
+            "var",
+            cudf.Series(
+                [0.0, None, np.NaN, 1233210.25, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+        (
+            "std",
+            cudf.Series(
+                [0.0, None, np.NaN, 1110.5, None, np.NaN],
+                dtype="float64",
+                nan_as_null=False,
+            ),
+        ),
+    ],
+)
+def test_rowwise_ops_nullable_dtypes_partial_null(op, expected):
+    gdf = cudf.DataFrame(
+        {
+            "a": [10, 11, 12, 13, 14, 15],
+            "b": cudf.Series(
+                [10, None, np.NaN, 2234, None, np.NaN],
+                nan_as_null=False,
+            ),
+        }
+    )
+
+    if op in ("var", "std"):
+        got = getattr(gdf, op)(axis=1, ddof=0, skipna=False)
+    else:
+        got = getattr(gdf, op)(axis=1, skipna=False)
+
+    assert_eq(got.null_count, expected.null_count)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "op,expected",
+    [
+        (
+            "max",
+            cudf.Series(
+                [10, None, None, 2234, None, 453],
+                dtype="int64",
+            ),
+        ),
+        (
+            "min",
+            cudf.Series(
+                [10, None, None, 13, None, 15],
+                dtype="int64",
+            ),
+        ),
+        (
+            "sum",
+            cudf.Series(
+                [20, None, None, 2247, None, 468],
+                dtype="int64",
+            ),
+        ),
+        (
+            "product",
+            cudf.Series(
+                [100, None, None, 29042, None, 6795],
+                dtype="int64",
+            ),
+        ),
+        (
+            "mean",
+            cudf.Series(
+                [10.0, None, None, 1123.5, None, 234.0],
+                dtype="float32",
+            ),
+        ),
+        (
+            "var",
+            cudf.Series(
+                [0.0, None, None, 1233210.25, None, 47961.0],
+                dtype="float32",
+            ),
+        ),
+        (
+            "std",
+            cudf.Series(
+                [0.0, None, None, 1110.5, None, 219.0],
+                dtype="float32",
+            ),
+        ),
+    ],
+)
+def test_rowwise_ops_nullable_int_dtypes(op, expected):
+    gdf = cudf.DataFrame(
+        {
+            "a": [10, 11, None, 13, None, 15],
+            "b": cudf.Series(
+                [10, None, 323, 2234, None, 453],
+                nan_as_null=False,
+            ),
+        }
+    )
+
+    if op in ("var", "std"):
+        got = getattr(gdf, op)(axis=1, ddof=0, skipna=False)
+    else:
+        got = getattr(gdf, op)(axis=1, skipna=False)
+
+    assert_eq(got.null_count, expected.null_count)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "t2": cudf.Series(
+                ["1940-08-31 06:00:00", "2020-08-02 10:00:00"], dtype="<M8[ms]"
+            ),
+        },
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "t2": cudf.Series(
+                ["1940-08-31 06:00:00", "2020-08-02 10:00:00"], dtype="<M8[ns]"
+            ),
+            "t3": cudf.Series(
+                ["1960-08-31 06:00:00", "2030-08-02 10:00:00"], dtype="<M8[s]"
+            ),
+        },
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "t2": cudf.Series(
+                ["1940-08-31 06:00:00", "2020-08-02 10:00:00"], dtype="<M8[us]"
+            ),
+        },
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "t2": cudf.Series(
+                ["1940-08-31 06:00:00", "2020-08-02 10:00:00"], dtype="<M8[ms]"
+            ),
+            "i1": cudf.Series([1001, 2002], dtype="int64"),
+        },
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "t2": cudf.Series(["1940-08-31 06:00:00", None], dtype="<M8[ms]"),
+            "i1": cudf.Series([1001, 2002], dtype="int64"),
+        },
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "i1": cudf.Series([1001, 2002], dtype="int64"),
+            "f1": cudf.Series([-100.001, 123.456], dtype="float64"),
+        },
+        {
+            "t1": cudf.Series(
+                ["2020-08-01 09:00:00", "1920-05-01 10:30:00"], dtype="<M8[ms]"
+            ),
+            "i1": cudf.Series([1001, 2002], dtype="int64"),
+            "f1": cudf.Series([-100.001, 123.456], dtype="float64"),
+            "b1": cudf.Series([True, False], dtype="bool"),
+        },
+    ],
+)
+@pytest.mark.parametrize("op", ["max", "min"])
+@pytest.mark.parametrize("skipna", [True, False])
+def test_rowwise_ops_datetime_dtypes(data, op, skipna):
+    gdf = cudf.DataFrame(data)
+
+    pdf = gdf.to_pandas()
+
+    with expect_warning_if(
+        not all(cudf.api.types.is_datetime64_dtype(dt) for dt in gdf.dtypes),
+        UserWarning,
+    ):
+        got = getattr(gdf, op)(axis=1, skipna=skipna)
+    with expect_warning_if(
+        not all(pd.api.types.is_datetime64_dtype(dt) for dt in gdf.dtypes),
+        FutureWarning,
+    ):
+        expected = getattr(pdf, op)(axis=1, skipna=skipna)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data,op,skipna",
+    [
+        (
+            {
+                "t1": cudf.Series(
+                    ["2020-08-01 09:00:00", "1920-05-01 10:30:00"],
+                    dtype="<M8[ms]",
+                ),
+                "t2": cudf.Series(
+                    ["1940-08-31 06:00:00", None], dtype="<M8[ms]"
+                ),
+            },
+            "max",
+            True,
+        ),
+        (
+            {
+                "t1": cudf.Series(
+                    ["2020-08-01 09:00:00", "1920-05-01 10:30:00"],
+                    dtype="<M8[ms]",
+                ),
+                "t2": cudf.Series(
+                    ["1940-08-31 06:00:00", None], dtype="<M8[ms]"
+                ),
+            },
+            "min",
+            False,
+        ),
+        (
+            {
+                "t1": cudf.Series(
+                    ["2020-08-01 09:00:00", "1920-05-01 10:30:00"],
+                    dtype="<M8[ms]",
+                ),
+                "t2": cudf.Series(
+                    ["1940-08-31 06:00:00", None], dtype="<M8[ms]"
+                ),
+            },
+            "min",
+            True,
+        ),
+    ],
+)
+def test_rowwise_ops_datetime_dtypes_2(data, op, skipna):
+    gdf = cudf.DataFrame(data)
+
+    pdf = gdf.to_pandas()
+
+    got = getattr(gdf, op)(axis=1, skipna=skipna)
+    expected = getattr(pdf, op)(axis=1, skipna=skipna)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            {
+                "t1": pd.Series(
+                    ["2020-08-01 09:00:00", "1920-05-01 10:30:00"],
+                    dtype="<M8[ns]",
+                ),
+                "t2": pd.Series(
+                    ["1940-08-31 06:00:00", pd.NaT], dtype="<M8[ns]"
+                ),
+            }
+        )
+    ],
+)
+def test_rowwise_ops_datetime_dtypes_pdbug(data):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.max(axis=1, skipna=False)
+    got = gdf.max(axis=1, skipna=False)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [5.0, 6.0, 7.0],
+        "single value",
+        np.array(1, dtype="int64"),
+        np.array(0.6273643, dtype="float64"),
+    ],
+)
+def test_insert(data):
+    pdf = pd.DataFrame.from_dict({"A": [1, 2, 3], "B": ["a", "b", "c"]})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    # insertion by index
+
+    pdf.insert(0, "foo", data)
+    gdf.insert(0, "foo", data)
+
+    assert_eq(pdf, gdf)
+
+    pdf.insert(3, "bar", data)
+    gdf.insert(3, "bar", data)
+
+    assert_eq(pdf, gdf)
+
+    pdf.insert(1, "baz", data)
+    gdf.insert(1, "baz", data)
+
+    assert_eq(pdf, gdf)
+
+    # pandas insert doesn't support negative indexing
+    pdf.insert(len(pdf.columns), "qux", data)
+    gdf.insert(-1, "qux", data)
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [{"A": [1, 2, 3], "B": ["a", "b", "c"]}],
+)
+def test_insert_NA(data):
+    pdf = pd.DataFrame.from_dict(data)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    pdf["C"] = pd.NA
+    gdf["C"] = cudf.NA
+    assert_eq(pdf, gdf)
+
+
+def test_cov():
+    gdf = cudf.datasets.randomdata(10)
+    pdf = gdf.to_pandas()
+
+    assert_eq(pdf.cov(), gdf.cov())
+
+
+@pytest_xfail(reason="cupy-based cov does not support nulls")
+def test_cov_nans():
+    pdf = pd.DataFrame()
+    pdf["a"] = [None, None, None, 2.00758632, None]
+    pdf["b"] = [0.36403686, None, None, None, None]
+    pdf["c"] = [None, None, None, 0.64882227, None]
+    pdf["d"] = [None, -1.46863125, None, 1.22477948, -0.06031689]
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(pdf.cov(), gdf.cov())
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "gsr",
+    [
+        cudf.Series([4, 2, 3]),
+        cudf.Series([4, 2, 3], index=["a", "b", "c"]),
+        cudf.Series([4, 2, 3], index=["a", "b", "d"]),
+        cudf.Series([4, 2], index=["a", "b"]),
+        cudf.Series([4, 2, 3], index=cudf.core.index.RangeIndex(0, 3)),
+        cudf.Series([4, 2, 3, 4, 5], index=["a", "b", "d", "0", "12"]),
+    ],
+)
+@pytest.mark.parametrize("colnames", [["a", "b", "c"], [0, 1, 2]])
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.add,
+        operator.mul,
+        operator.floordiv,
+        operator.truediv,
+        operator.mod,
+        operator.pow,
+        operator.eq,
+        operator.lt,
+        operator.le,
+        operator.gt,
+        operator.ge,
+        operator.ne,
+    ],
+)
+def test_df_sr_binop(gsr, colnames, op):
+    # Anywhere that the column names of the DataFrame don't match the index
+    # names of the Series will trigger a deprecated reindexing. Since this
+    # behavior is deprecated in pandas, this test is temporarily silencing
+    # those warnings until cudf updates to pandas 2.0 as its compatibility
+    # target, at which point a large number of the parametrizations can be
+    # removed altogether (along with this warnings filter).
+    with warnings.catch_warnings():
+        assert version.parse(pd.__version__) < version.parse("2.0.0")
+        warnings.filterwarnings(
+            action="ignore",
+            category=FutureWarning,
+            message=(
+                "Automatic reindexing on DataFrame vs Series comparisons is "
+                "deprecated"
+            ),
+        )
+        data = [[3.0, 2.0, 5.0], [3.0, None, 5.0], [6.0, 7.0, np.nan]]
+        data = dict(zip(colnames, data))
+
+        gsr = gsr.astype("float64")
+
+        gdf = cudf.DataFrame(data)
+        pdf = gdf.to_pandas(nullable=True)
+
+        psr = gsr.to_pandas(nullable=True)
+
+        expect = op(pdf, psr)
+        got = op(gdf, gsr).to_pandas(nullable=True)
+        assert_eq(expect, got, check_dtype=False)
+
+        expect = op(psr, pdf)
+        got = op(gsr, gdf).to_pandas(nullable=True)
+        assert_eq(expect, got, check_dtype=False)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.add,
+        operator.mul,
+        operator.floordiv,
+        operator.truediv,
+        operator.mod,
+        operator.pow,
+        # comparison ops will temporarily XFAIL
+        # see PR  https://github.com/rapidsai/cudf/pull/7491
+        pytest.param(operator.eq, marks=pytest_xfail()),
+        pytest.param(operator.lt, marks=pytest_xfail()),
+        pytest.param(operator.le, marks=pytest_xfail()),
+        pytest.param(operator.gt, marks=pytest_xfail()),
+        pytest.param(operator.ge, marks=pytest_xfail()),
+        pytest.param(operator.ne, marks=pytest_xfail()),
+    ],
+)
+@pytest.mark.parametrize(
+    "gsr", [cudf.Series([1, 2, 3, 4, 5], index=["a", "b", "d", "0", "12"])]
+)
+def test_df_sr_binop_col_order(gsr, op):
+    colnames = [0, 1, 2]
+    data = [[0, 2, 5], [3, None, 5], [6, 7, np.nan]]
+    data = dict(zip(colnames, data))
+
+    gdf = cudf.DataFrame(data)
+    pdf = pd.DataFrame.from_dict(data)
+
+    psr = gsr.to_pandas()
+
+    with expect_warning_if(
+        op
+        in {
+            operator.eq,
+            operator.lt,
+            operator.le,
+            operator.gt,
+            operator.ge,
+            operator.ne,
+        },
+        FutureWarning,
+    ):
+        expect = op(pdf, psr).astype("float")
+    out = op(gdf, gsr).astype("float")
+    got = out[expect.columns]
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("set_index", [None, "A", "C", "D"])
+@pytest.mark.parametrize("index", [True, False])
+@pytest.mark.parametrize("deep", [True, False])
+def test_memory_usage(deep, index, set_index):
+    # Testing numerical/datetime by comparing with pandas
+    # (string and categorical columns will be different)
+    rows = int(100)
+    df = pd.DataFrame(
+        {
+            "A": np.arange(rows, dtype="int64"),
+            "B": np.arange(rows, dtype="int32"),
+            "C": np.arange(rows, dtype="float64"),
+        }
+    )
+    df["D"] = pd.to_datetime(df.A)
+    if set_index:
+        df = df.set_index(set_index)
+
+    gdf = cudf.from_pandas(df)
+
+    if index and set_index is None:
+        # Special Case: Assume RangeIndex size == 0
+        with expect_warning_if(deep, UserWarning):
+            assert gdf.index.memory_usage(deep=deep) == 0
+
+    else:
+        # Check for Series only
+        assert df["B"].memory_usage(index=index, deep=deep) == gdf[
+            "B"
+        ].memory_usage(index=index, deep=deep)
+
+        # Check for entire DataFrame
+        assert_eq(
+            df.memory_usage(index=index, deep=deep).sort_index(),
+            gdf.memory_usage(index=index, deep=deep).sort_index(),
+        )
+
+
+@pytest_xfail
+def test_memory_usage_string():
+    rows = int(100)
+    df = pd.DataFrame(
+        {
+            "A": np.arange(rows, dtype="int32"),
+            "B": np.random.choice(["apple", "banana", "orange"], rows),
+        }
+    )
+    gdf = cudf.from_pandas(df)
+
+    # Check deep=False (should match pandas)
+    assert gdf.B.memory_usage(deep=False, index=False) == df.B.memory_usage(
+        deep=False, index=False
+    )
+
+    # Check string column
+    assert gdf.B.memory_usage(deep=True, index=False) == df.B.memory_usage(
+        deep=True, index=False
+    )
+
+    # Check string index
+    assert gdf.set_index("B").index.memory_usage(
+        deep=True
+    ) == df.B.memory_usage(deep=True, index=False)
+
+
+def test_memory_usage_cat():
+    rows = int(100)
+    df = pd.DataFrame(
+        {
+            "A": np.arange(rows, dtype="int32"),
+            "B": np.random.choice(["apple", "banana", "orange"], rows),
+        }
+    )
+    df["B"] = df.B.astype("category")
+    gdf = cudf.from_pandas(df)
+
+    expected = (
+        gdf.B._column.categories.memory_usage
+        + gdf.B._column.codes.memory_usage
+    )
+
+    # Check cat column
+    assert gdf.B.memory_usage(deep=True, index=False) == expected
+
+    # Check cat index
+    assert gdf.set_index("B").index.memory_usage(deep=True) == expected
+
+
+def test_memory_usage_list():
+    df = cudf.DataFrame({"A": [[0, 1, 2, 3], [4, 5, 6], [7, 8], [9]]})
+    expected = (
+        df.A._column.offsets.memory_usage + df.A._column.elements.memory_usage
+    )
+    assert expected == df.A.memory_usage()
+
+
+@pytest.mark.parametrize("rows", [10, 100])
+def test_memory_usage_multi(rows):
+    # We need to sample without replacement to guarantee that the size of the
+    # levels are always the same.
+    df = pd.DataFrame(
+        {
+            "A": np.arange(rows, dtype="int32"),
+            "B": np.random.choice(
+                np.arange(rows, dtype="int64"), rows, replace=False
+            ),
+            "C": np.random.choice(
+                np.arange(rows, dtype="float64"), rows, replace=False
+            ),
+        }
+    ).set_index(["B", "C"])
+    gdf = cudf.from_pandas(df)
+    # Assume MultiIndex memory footprint is just that
+    # of the underlying columns, levels, and codes
+    expect = rows * 16  # Source Columns
+    expect += rows * 16  # Codes
+    expect += rows * 8  # Level 0
+    expect += rows * 8  # Level 1
+
+    assert expect == gdf.index.memory_usage(deep=True)
+
+
+@pytest.mark.parametrize(
+    "list_input",
+    [
+        pytest.param([1, 2, 3, 4], id="smaller"),
+        pytest.param([1, 2, 3, 4, 5, 6], id="larger"),
+    ],
+)
+@pytest.mark.parametrize(
+    "key",
+    [
+        pytest.param("list_test", id="new_column"),
+        pytest.param("id", id="existing_column"),
+    ],
+)
+def test_setitem_diff_size_list(list_input, key):
+    gdf = cudf.datasets.randomdata(5)
+    with pytest.raises(
+        ValueError, match=("All columns must be of equal length")
+    ):
+        gdf[key] = list_input
+
+
+@pytest.mark.parametrize(
+    "series_input",
+    [
+        pytest.param(cudf.Series([1, 2, 3, 4]), id="smaller_cudf"),
+        pytest.param(cudf.Series([1, 2, 3, 4, 5, 6]), id="larger_cudf"),
+        pytest.param(cudf.Series([1, 2, 3], index=[4, 5, 6]), id="index_cudf"),
+        pytest.param(pd.Series([1, 2, 3, 4]), id="smaller_pandas"),
+        pytest.param(pd.Series([1, 2, 3, 4, 5, 6]), id="larger_pandas"),
+        pytest.param(pd.Series([1, 2, 3], index=[4, 5, 6]), id="index_pandas"),
+    ],
+)
+@pytest.mark.parametrize(
+    "key",
+    [
+        pytest.param("list_test", id="new_column"),
+        pytest.param("id", id="existing_column"),
+    ],
+)
+def test_setitem_diff_size_series(series_input, key):
+    gdf = cudf.datasets.randomdata(5)
+    pdf = gdf.to_pandas()
+
+    pandas_input = series_input
+    if isinstance(pandas_input, cudf.Series):
+        pandas_input = pandas_input.to_pandas()
+
+    expect = pdf
+    expect[key] = pandas_input
+
+    got = gdf
+    got[key] = series_input
+
+    # Pandas uses NaN and typecasts to float64 if there's missing values on
+    # alignment, so need to typecast to float64 for equality comparison
+    expect = expect.astype("float64")
+    got = got.astype("float64")
+
+    assert_eq(expect, got)
+
+
+def test_tupleize_cols_False_set():
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    pdf[("a", "b")] = [1]
+    gdf[("a", "b")] = [1]
+    assert_eq(pdf, gdf)
+    assert_eq(pdf.columns, gdf.columns)
+
+
+def test_init_multiindex_from_dict():
+    pdf = pd.DataFrame({("a", "b"): [1]})
+    gdf = cudf.DataFrame({("a", "b"): [1]})
+    assert_eq(pdf, gdf)
+    assert_eq(pdf.columns, gdf.columns)
+
+
+def test_change_column_dtype_in_empty():
+    pdf = pd.DataFrame({"a": [], "b": []})
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(pdf, gdf)
+    pdf["b"] = pdf["b"].astype("int64")
+    gdf["b"] = gdf["b"].astype("int64")
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "str"])
+def test_dataframe_from_dictionary_series_same_name_index(dtype):
+    pd_idx1 = pd.Index([1, 2, 0], name="test_index").astype(dtype)
+    pd_idx2 = pd.Index([2, 0, 1], name="test_index").astype(dtype)
+    pd_series1 = pd.Series([1, 2, 3], index=pd_idx1)
+    pd_series2 = pd.Series([1, 2, 3], index=pd_idx2)
+
+    gd_idx1 = cudf.from_pandas(pd_idx1)
+    gd_idx2 = cudf.from_pandas(pd_idx2)
+    gd_series1 = cudf.Series([1, 2, 3], index=gd_idx1)
+    gd_series2 = cudf.Series([1, 2, 3], index=gd_idx2)
+
+    expect = pd.DataFrame({"a": pd_series1, "b": pd_series2})
+    got = cudf.DataFrame({"a": gd_series1, "b": gd_series2})
+
+    if dtype == "str":
+        # Pandas actually loses its index name erroneously here...
+        expect.index.name = "test_index"
+
+    assert_eq(expect, got)
+    assert expect.index.names == got.index.names
+
+
+@pytest.mark.parametrize(
+    "arg", [slice(2, 8, 3), slice(1, 20, 4), slice(-2, -6, -2)]
+)
+def test_dataframe_strided_slice(arg):
+    mul = pd.DataFrame(
+        {
+            "Index": [1, 2, 3, 4, 5, 6, 7, 8, 9],
+            "AlphaIndex": ["a", "b", "c", "d", "e", "f", "g", "h", "i"],
+        }
+    )
+    pdf = pd.DataFrame(
+        {"Val": [10, 9, 8, 7, 6, 5, 4, 3, 2]},
+        index=pd.MultiIndex.from_frame(mul),
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    expect = pdf[arg]
+    got = gdf[arg]
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data,condition,other,error",
+    [
+        (pd.Series(range(5)), pd.Series(range(5)) > 0, None, None),
+        (pd.Series(range(5)), pd.Series(range(5)) > 1, None, None),
+        (pd.Series(range(5)), pd.Series(range(5)) > 1, 10, None),
+        (
+            pd.Series(range(5)),
+            pd.Series(range(5)) > 1,
+            pd.Series(range(5, 10)),
+            None,
+        ),
+        (
+            pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"]),
+            (
+                pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"])
+                % 3
+            )
+            == 0,
+            -pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"]),
+            None,
+        ),
+        (
+            pd.DataFrame({"a": [1, 2, np.nan], "b": [4, np.nan, 6]}),
+            pd.DataFrame({"a": [1, 2, np.nan], "b": [4, np.nan, 6]}) == 4,
+            None,
+            None,
+        ),
+        (
+            pd.DataFrame({"a": [1, 2, np.nan], "b": [4, np.nan, 6]}),
+            pd.DataFrame({"a": [1, 2, np.nan], "b": [4, np.nan, 6]}) != 4,
+            None,
+            None,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            [True, True, True],
+            None,
+            ValueError,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            [True, True, True, False],
+            None,
+            ValueError,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            [[True, True, True, False], [True, True, True, False]],
+            None,
+            ValueError,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            [[True, True], [False, True], [True, False], [False, True]],
+            None,
+            None,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            cuda.to_device(
+                np.array(
+                    [[True, True], [False, True], [True, False], [False, True]]
+                )
+            ),
+            None,
+            None,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            cupy.array(
+                [[True, True], [False, True], [True, False], [False, True]]
+            ),
+            17,
+            None,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            [[True, True], [False, True], [True, False], [False, True]],
+            17,
+            None,
+        ),
+        (
+            pd.DataFrame({"p": [-2, 3, -4, -79], "k": [9, 10, 11, 12]}),
+            [
+                [True, True, False, True],
+                [True, True, False, True],
+                [True, True, False, True],
+                [True, True, False, True],
+            ],
+            None,
+            ValueError,
+        ),
+        (
+            pd.Series([1, 2, np.nan]),
+            pd.Series([1, 2, np.nan]) == 4,
+            None,
+            None,
+        ),
+        (
+            pd.Series([1, 2, np.nan]),
+            pd.Series([1, 2, np.nan]) != 4,
+            None,
+            None,
+        ),
+        (
+            pd.Series([4, np.nan, 6]),
+            pd.Series([4, np.nan, 6]) == 4,
+            None,
+            None,
+        ),
+        (
+            pd.Series([4, np.nan, 6]),
+            pd.Series([4, np.nan, 6]) != 4,
+            None,
+            None,
+        ),
+        (
+            pd.Series([4, np.nan, 6], dtype="category"),
+            pd.Series([4, np.nan, 6], dtype="category") != 4,
+            None,
+            None,
+        ),
+        (
+            pd.Series(["a", "b", "b", "d", "c", "s"], dtype="category"),
+            pd.Series(["a", "b", "b", "d", "c", "s"], dtype="category") == "b",
+            None,
+            None,
+        ),
+        (
+            pd.Series(["a", "b", "b", "d", "c", "s"], dtype="category"),
+            pd.Series(["a", "b", "b", "d", "c", "s"], dtype="category") == "b",
+            "s",
+            None,
+        ),
+        (
+            pd.Series([1, 2, 3, 2, 5]),
+            pd.Series([1, 2, 3, 2, 5]) == 2,
+            pd.DataFrame(
+                {
+                    "a": pd.Series([1, 2, 3, 2, 5]),
+                    "b": pd.Series([1, 2, 3, 2, 5]),
+                }
+            ),
+            NotImplementedError,
+        ),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_df_sr_mask_where(data, condition, other, error, inplace):
+    ps_where = data
+    gs_where = cudf.from_pandas(data)
+
+    ps_mask = ps_where.copy(deep=True)
+    gs_mask = gs_where.copy(deep=True)
+
+    if hasattr(condition, "__cuda_array_interface__"):
+        if type(condition).__module__.split(".")[0] == "cupy":
+            ps_condition = cupy.asnumpy(condition)
+        else:
+            ps_condition = np.array(condition).astype("bool")
+    else:
+        ps_condition = condition
+
+    if type(condition).__module__.split(".")[0] == "pandas":
+        gs_condition = cudf.from_pandas(condition)
+    else:
+        gs_condition = condition
+
+    ps_other = other
+    if type(other).__module__.split(".")[0] == "pandas":
+        gs_other = cudf.from_pandas(other)
+    else:
+        gs_other = other
+
+    if error is None:
+        expect_where = ps_where.where(
+            ps_condition, other=ps_other, inplace=inplace
+        )
+        got_where = gs_where.where(
+            gs_condition, other=gs_other, inplace=inplace
+        )
+
+        expect_mask = ps_mask.mask(
+            ps_condition, other=ps_other, inplace=inplace
+        )
+        got_mask = gs_mask.mask(gs_condition, other=gs_other, inplace=inplace)
+
+        if inplace:
+            expect_where = ps_where
+            got_where = gs_where
+
+            expect_mask = ps_mask
+            got_mask = gs_mask
+
+        if pd.api.types.is_categorical_dtype(expect_where):
+            np.testing.assert_array_equal(
+                expect_where.cat.codes,
+                got_where.cat.codes.astype(expect_where.cat.codes.dtype)
+                .fillna(-1)
+                .to_numpy(),
+            )
+            assert_eq(expect_where.cat.categories, got_where.cat.categories)
+
+            np.testing.assert_array_equal(
+                expect_mask.cat.codes,
+                got_mask.cat.codes.astype(expect_mask.cat.codes.dtype)
+                .fillna(-1)
+                .to_numpy(),
+            )
+            assert_eq(expect_mask.cat.categories, got_mask.cat.categories)
+        else:
+            assert_eq(
+                expect_where.fillna(-1),
+                got_where.fillna(-1),
+                check_dtype=False,
+            )
+            assert_eq(
+                expect_mask.fillna(-1), got_mask.fillna(-1), check_dtype=False
+            )
+    else:
+        assert_exceptions_equal(
+            lfunc=ps_where.where,
+            rfunc=gs_where.where,
+            lfunc_args_and_kwargs=(
+                [ps_condition],
+                {"other": ps_other, "inplace": inplace},
+            ),
+            rfunc_args_and_kwargs=(
+                [gs_condition],
+                {"other": gs_other, "inplace": inplace},
+            ),
+        )
+
+        assert_exceptions_equal(
+            lfunc=ps_mask.mask,
+            rfunc=gs_mask.mask,
+            lfunc_args_and_kwargs=(
+                [ps_condition],
+                {"other": ps_other, "inplace": inplace},
+            ),
+            rfunc_args_and_kwargs=(
+                [gs_condition],
+                {"other": gs_other, "inplace": inplace},
+            ),
+        )
+
+
+@pytest.mark.parametrize(
+    "data,condition,other,has_cat",
+    [
+        (
+            pd.DataFrame(
+                {
+                    "a": pd.Series(["a", "a", "b", "c", "a", "d", "d", "a"]),
+                    "b": pd.Series(["o", "p", "q", "e", "p", "p", "a", "a"]),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(["a", "a", "b", "c", "a", "d", "d", "a"]),
+                    "b": pd.Series(["o", "p", "q", "e", "p", "p", "a", "a"]),
+                }
+            )
+            != "a",
+            None,
+            None,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            )
+            != "a",
+            None,
+            True,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            )
+            == "a",
+            None,
+            True,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            )
+            != "a",
+            "a",
+            True,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        ["a", "a", "b", "c", "a", "d", "d", "a"],
+                        dtype="category",
+                    ),
+                    "b": pd.Series(
+                        ["o", "p", "q", "e", "p", "p", "a", "a"],
+                        dtype="category",
+                    ),
+                }
+            )
+            == "a",
+            "a",
+            True,
+        ),
+    ],
+)
+def test_df_string_cat_types_mask_where(data, condition, other, has_cat):
+    ps = data
+    gs = cudf.from_pandas(data)
+
+    ps_condition = condition
+    if type(condition).__module__.split(".")[0] == "pandas":
+        gs_condition = cudf.from_pandas(condition)
+    else:
+        gs_condition = condition
+
+    ps_other = other
+    if type(other).__module__.split(".")[0] == "pandas":
+        gs_other = cudf.from_pandas(other)
+    else:
+        gs_other = other
+
+    expect_where = ps.where(ps_condition, other=ps_other)
+    got_where = gs.where(gs_condition, other=gs_other)
+
+    expect_mask = ps.mask(ps_condition, other=ps_other)
+    got_mask = gs.mask(gs_condition, other=gs_other)
+
+    if has_cat is None:
+        assert_eq(
+            expect_where.fillna(-1).astype("str"),
+            got_where.fillna(-1),
+            check_dtype=False,
+        )
+        assert_eq(
+            expect_mask.fillna(-1).astype("str"),
+            got_mask.fillna(-1),
+            check_dtype=False,
+        )
+    else:
+        assert_eq(expect_where, got_where, check_dtype=False)
+        assert_eq(expect_mask, got_mask, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data,expected_upcast_type,error",
+    [
+        (
+            pd.Series([random.random() for _ in range(10)], dtype="float32"),
+            np.dtype("float32"),
+            None,
+        ),
+        (
+            pd.Series([random.random() for _ in range(10)], dtype="float16"),
+            np.dtype("float32"),
+            None,
+        ),
+        (
+            pd.Series([random.random() for _ in range(10)], dtype="float64"),
+            np.dtype("float64"),
+            None,
+        ),
+        (
+            pd.Series([random.random() for _ in range(10)], dtype="float128"),
+            None,
+            TypeError,
+        ),
+    ],
+)
+def test_from_pandas_unsupported_types(data, expected_upcast_type, error):
+    pdf = pd.DataFrame({"one_col": data})
+    if error is not None:
+        with pytest.raises(ValueError):
+            cudf.from_pandas(data)
+
+        with pytest.raises(ValueError):
+            cudf.Series(data)
+
+        with pytest.raises(error):
+            cudf.from_pandas(pdf)
+
+        with pytest.raises(error):
+            cudf.DataFrame(pdf)
+    else:
+        df = cudf.from_pandas(data)
+
+        assert_eq(data, df, check_dtype=False)
+        assert df.dtype == expected_upcast_type
+
+        df = cudf.Series(data)
+        assert_eq(data, df, check_dtype=False)
+        assert df.dtype == expected_upcast_type
+
+        df = cudf.from_pandas(pdf)
+        assert_eq(pdf, df, check_dtype=False)
+        assert df["one_col"].dtype == expected_upcast_type
+
+        df = cudf.DataFrame(pdf)
+        assert_eq(pdf, df, check_dtype=False)
+        assert df["one_col"].dtype == expected_upcast_type
+
+
+@pytest.mark.parametrize("nan_as_null", [True, False])
+@pytest.mark.parametrize("index", [None, "a", ["a", "b"]])
+def test_from_pandas_nan_as_null(nan_as_null, index):
+    data = [np.nan, 2.0, 3.0]
+
+    if index is None:
+        pdf = pd.DataFrame({"a": data, "b": data})
+        expected = cudf.DataFrame(
+            {
+                "a": column.as_column(data, nan_as_null=nan_as_null),
+                "b": column.as_column(data, nan_as_null=nan_as_null),
+            }
+        )
+    else:
+        pdf = pd.DataFrame({"a": data, "b": data}).set_index(index)
+        expected = cudf.DataFrame(
+            {
+                "a": column.as_column(data, nan_as_null=nan_as_null),
+                "b": column.as_column(data, nan_as_null=nan_as_null),
+            }
+        )
+        expected = cudf.DataFrame(
+            {
+                "a": column.as_column(data, nan_as_null=nan_as_null),
+                "b": column.as_column(data, nan_as_null=nan_as_null),
+            }
+        )
+        expected = expected.set_index(index)
+
+    got = cudf.from_pandas(pdf, nan_as_null=nan_as_null)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("nan_as_null", [True, False])
+def test_from_pandas_for_series_nan_as_null(nan_as_null):
+    data = [np.nan, 2.0, 3.0]
+    psr = pd.Series(data)
+
+    expected = cudf.Series(column.as_column(data, nan_as_null=nan_as_null))
+    got = cudf.from_pandas(psr, nan_as_null=nan_as_null)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_df_series_dataframe_astype_copy(copy):
+    gdf = cudf.DataFrame({"col1": [1, 2], "col2": [3, 4]})
+    pdf = gdf.to_pandas()
+
+    assert_eq(
+        gdf.astype(dtype="float", copy=copy),
+        pdf.astype(dtype="float", copy=copy),
+    )
+    assert_eq(gdf, pdf)
+
+    gsr = cudf.Series([1, 2])
+    psr = gsr.to_pandas()
+
+    assert_eq(
+        gsr.astype(dtype="float", copy=copy),
+        psr.astype(dtype="float", copy=copy),
+    )
+    assert_eq(gsr, psr)
+
+    gsr = cudf.Series([1, 2])
+    psr = gsr.to_pandas()
+
+    actual = gsr.astype(dtype="int64", copy=copy)
+    expected = psr.astype(dtype="int64", copy=copy)
+    assert_eq(expected, actual)
+    assert_eq(gsr, psr)
+    actual[0] = 3
+    expected[0] = 3
+    assert_eq(gsr, psr)
+
+
+@pytest.mark.parametrize("copy", [True, False])
+def test_df_series_dataframe_astype_dtype_dict(copy):
+    gdf = cudf.DataFrame({"col1": [1, 2], "col2": [3, 4]})
+    pdf = gdf.to_pandas()
+
+    assert_eq(
+        gdf.astype(dtype={"col1": "float"}, copy=copy),
+        pdf.astype(dtype={"col1": "float"}, copy=copy),
+    )
+    assert_eq(gdf, pdf)
+
+    gsr = cudf.Series([1, 2])
+    psr = gsr.to_pandas()
+
+    assert_eq(
+        gsr.astype(dtype={None: "float"}, copy=copy),
+        psr.astype(dtype={None: "float"}, copy=copy),
+    )
+    assert_eq(gsr, psr)
+
+    assert_exceptions_equal(
+        lfunc=psr.astype,
+        rfunc=gsr.astype,
+        lfunc_args_and_kwargs=([], {"dtype": {"a": "float"}, "copy": copy}),
+        rfunc_args_and_kwargs=([], {"dtype": {"a": "float"}, "copy": copy}),
+    )
+
+    gsr = cudf.Series([1, 2])
+    psr = gsr.to_pandas()
+
+    actual = gsr.astype({None: "int64"}, copy=copy)
+    expected = psr.astype({None: "int64"}, copy=copy)
+    assert_eq(expected, actual)
+    assert_eq(gsr, psr)
+
+    actual[0] = 3
+    expected[0] = 3
+    assert_eq(gsr, psr)
+
+
+@pytest.mark.parametrize(
+    "data,columns",
+    [
+        ([1, 2, 3, 100, 112, 35464], ["a"]),
+        (range(100), None),
+        pytest.param(
+            [],
+            None,
+            marks=pytest.mark.xfail(
+                not PANDAS_GE_200, reason=".column returns Index[object]"
+            ),
+        ),
+        ((-10, 21, 32, 32, 1, 2, 3), ["p"]),
+        pytest.param(
+            (),
+            None,
+            marks=pytest.mark.xfail(
+                not PANDAS_GE_200, reason=".column returns Index[object]"
+            ),
+        ),
+        ([[1, 2, 3], [1, 2, 3]], ["col1", "col2", "col3"]),
+        ([range(100), range(100)], ["range" + str(i) for i in range(100)]),
+        (((1, 2, 3), (1, 2, 3)), ["tuple0", "tuple1", "tuple2"]),
+        ([[1, 2, 3]], ["list col1", "list col2", "list col3"]),
+        ([[1, 2, 3]], pd.Index(["col1", "col2", "col3"], name="rapids")),
+        ([range(100)], ["range" + str(i) for i in range(100)]),
+        (((1, 2, 3),), ["k1", "k2", "k3"]),
+    ],
+)
+def test_dataframe_init_1d_list(data, columns):
+    expect = pd.DataFrame(data, columns=columns)
+    actual = cudf.DataFrame(data, columns=columns)
+
+    assert_eq(expect, actual, check_index_type=len(data) != 0)
+
+    expect = pd.DataFrame(data, columns=None)
+    actual = cudf.DataFrame(data, columns=None)
+
+    assert_eq(expect, actual, check_index_type=len(data) != 0)
+
+
+@pytest.mark.parametrize(
+    "data,cols,index",
+    [
+        (
+            np.ndarray(shape=(4, 2), dtype=float, order="F"),
+            ["a", "b"],
+            ["a", "b", "c", "d"],
+        ),
+        (
+            np.ndarray(shape=(4, 2), dtype=float, order="F"),
+            ["a", "b"],
+            [0, 20, 30, 10],
+        ),
+        (
+            np.ndarray(shape=(4, 2), dtype=float, order="F"),
+            ["a", "b"],
+            [0, 1, 2, 3],
+        ),
+        (np.array([11, 123, -2342, 232]), ["a"], [1, 2, 11, 12]),
+        (np.array([11, 123, -2342, 232]), ["a"], ["khsdjk", "a", "z", "kk"]),
+        (
+            cupy.ndarray(shape=(4, 2), dtype=float, order="F"),
+            ["a", "z"],
+            ["a", "z", "a", "z"],
+        ),
+        (cupy.array([11, 123, -2342, 232]), ["z"], [0, 1, 1, 0]),
+        (cupy.array([11, 123, -2342, 232]), ["z"], [1, 2, 3, 4]),
+        (cupy.array([11, 123, -2342, 232]), ["z"], ["a", "z", "d", "e"]),
+        (np.random.randn(2, 4), ["a", "b", "c", "d"], ["a", "b"]),
+        (np.random.randn(2, 4), ["a", "b", "c", "d"], [1, 0]),
+        (cupy.random.randn(2, 4), ["a", "b", "c", "d"], ["a", "b"]),
+        (cupy.random.randn(2, 4), ["a", "b", "c", "d"], [1, 0]),
+    ],
+)
+def test_dataframe_init_from_arrays_cols(data, cols, index):
+    gd_data = data
+    if isinstance(data, cupy.ndarray):
+        # pandas can't handle cupy arrays in general
+        pd_data = data.get()
+
+        # additional test for building DataFrame with gpu array whose
+        # cuda array interface has no `descr` attribute
+        numba_data = cuda.as_cuda_array(data)
+    else:
+        pd_data = data
+        numba_data = None
+
+    # verify with columns & index
+    pdf = pd.DataFrame(pd_data, columns=cols, index=index)
+    gdf = cudf.DataFrame(gd_data, columns=cols, index=index)
+
+    assert_eq(pdf, gdf, check_dtype=False)
+
+    # verify with columns
+    pdf = pd.DataFrame(pd_data, columns=cols)
+    gdf = cudf.DataFrame(gd_data, columns=cols)
+
+    assert_eq(pdf, gdf, check_dtype=False)
+
+    pdf = pd.DataFrame(pd_data)
+    gdf = cudf.DataFrame(gd_data)
+
+    assert_eq(pdf, gdf, check_dtype=False)
+
+    if numba_data is not None:
+        gdf = cudf.DataFrame(numba_data)
+        assert_eq(pdf, gdf, check_dtype=False)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "col_data",
+    [
+        range(5),
+        ["a", "b", "x", "y", "z"],
+        [1.0, 0.213, 0.34332],
+        ["a"],
+        [1],
+        [0.2323],
+        [],
+    ],
+)
+@pytest.mark.parametrize(
+    "assign_val",
+    [
+        1,
+        2,
+        np.array(2),
+        cupy.array(2),
+        0.32324,
+        np.array(0.34248),
+        cupy.array(0.34248),
+        "abc",
+        np.array("abc", dtype="object"),
+        np.array("abc", dtype="str"),
+        np.array("abc"),
+        None,
+    ],
+)
+def test_dataframe_assign_scalar(col_data, assign_val):
+    pdf = pd.DataFrame({"a": col_data})
+    gdf = cudf.DataFrame({"a": col_data})
+
+    pdf["b"] = (
+        cupy.asnumpy(assign_val)
+        if isinstance(assign_val, cupy.ndarray)
+        else assign_val
+    )
+    gdf["b"] = assign_val
+
+    assert_eq(pdf, gdf)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "col_data",
+    [
+        1,
+        2,
+        np.array(2),
+        cupy.array(2),
+        0.32324,
+        np.array(0.34248),
+        cupy.array(0.34248),
+        "abc",
+        np.array("abc", dtype="object"),
+        np.array("abc", dtype="str"),
+        np.array("abc"),
+        None,
+    ],
+)
+@pytest.mark.parametrize(
+    "assign_val",
+    [
+        1,
+        2,
+        np.array(2),
+        cupy.array(2),
+        0.32324,
+        np.array(0.34248),
+        cupy.array(0.34248),
+        "abc",
+        np.array("abc", dtype="object"),
+        np.array("abc", dtype="str"),
+        np.array("abc"),
+        None,
+    ],
+)
+def test_dataframe_assign_scalar_with_scalar_cols(col_data, assign_val):
+    pdf = pd.DataFrame(
+        {
+            "a": cupy.asnumpy(col_data)
+            if isinstance(col_data, cupy.ndarray)
+            else col_data
+        },
+        index=["dummy_mandatory_index"],
+    )
+    gdf = cudf.DataFrame({"a": col_data}, index=["dummy_mandatory_index"])
+
+    pdf["b"] = (
+        cupy.asnumpy(assign_val)
+        if isinstance(assign_val, cupy.ndarray)
+        else assign_val
+    )
+    gdf["b"] = assign_val
+
+    assert_eq(pdf, gdf)
+
+
+def test_dataframe_info_basic():
+    buffer = io.StringIO()
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    StringIndex: 10 entries, a to 1111
+    Data columns (total 10 columns):
+     #   Column  Non-Null Count  Dtype
+    ---  ------  --------------  -----
+     0   0       10 non-null     float64
+     1   1       10 non-null     float64
+     2   2       10 non-null     float64
+     3   3       10 non-null     float64
+     4   4       10 non-null     float64
+     5   5       10 non-null     float64
+     6   6       10 non-null     float64
+     7   7       10 non-null     float64
+     8   8       10 non-null     float64
+     9   9       10 non-null     float64
+    dtypes: float64(10)
+    memory usage: 859.0+ bytes
+    """
+    )
+    df = pd.DataFrame(
+        np.random.randn(10, 10),
+        index=["a", "2", "3", "4", "5", "6", "7", "8", "100", "1111"],
+    )
+    cudf.from_pandas(df).info(buf=buffer, verbose=True)
+    s = buffer.getvalue()
+    assert str_cmp == s
+
+
+def test_dataframe_info_verbose_mem_usage():
+    buffer = io.StringIO()
+    df = pd.DataFrame({"a": [1, 2, 3], "b": ["safdas", "assa", "asdasd"]})
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 3 entries, 0 to 2
+    Data columns (total 2 columns):
+     #   Column  Non-Null Count  Dtype
+    ---  ------  --------------  -----
+     0   a       3 non-null      int64
+     1   b       3 non-null      object
+    dtypes: int64(1), object(1)
+    memory usage: 56.0+ bytes
+    """
+    )
+    cudf.from_pandas(df).info(buf=buffer, verbose=True)
+    s = buffer.getvalue()
+    assert str_cmp == s
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 3 entries, 0 to 2
+    Columns: 2 entries, a to b
+    dtypes: int64(1), object(1)
+    memory usage: 56.0+ bytes
+    """
+    )
+    cudf.from_pandas(df).info(buf=buffer, verbose=False)
+    s = buffer.getvalue()
+    assert str_cmp == s
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    df = pd.DataFrame(
+        {"a": [1, 2, 3], "b": ["safdas", "assa", "asdasd"]},
+        index=["sdfdsf", "sdfsdfds", "dsfdf"],
+    )
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    StringIndex: 3 entries, sdfdsf to dsfdf
+    Data columns (total 2 columns):
+     #   Column  Non-Null Count  Dtype
+    ---  ------  --------------  -----
+     0   a       3 non-null      int64
+     1   b       3 non-null      object
+    dtypes: int64(1), object(1)
+    memory usage: 91.0 bytes
+    """
+    )
+    cudf.from_pandas(df).info(buf=buffer, verbose=True, memory_usage="deep")
+    s = buffer.getvalue()
+    assert str_cmp == s
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    int_values = [1, 2, 3, 4, 5]
+    text_values = ["alpha", "beta", "gamma", "delta", "epsilon"]
+    float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
+
+    df = cudf.DataFrame(
+        {
+            "int_col": int_values,
+            "text_col": text_values,
+            "float_col": float_values,
+        }
+    )
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 5 entries, 0 to 4
+    Data columns (total 3 columns):
+     #   Column     Non-Null Count  Dtype
+    ---  ------     --------------  -----
+     0   int_col    5 non-null      int64
+     1   text_col   5 non-null      object
+     2   float_col  5 non-null      float64
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 130.0 bytes
+    """
+    )
+    df.info(buf=buffer, verbose=True, memory_usage="deep")
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+
+def test_dataframe_info_null_counts():
+    int_values = [1, 2, 3, 4, 5]
+    text_values = ["alpha", "beta", "gamma", "delta", "epsilon"]
+    float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
+
+    df = cudf.DataFrame(
+        {
+            "int_col": int_values,
+            "text_col": text_values,
+            "float_col": float_values,
+        }
+    )
+    buffer = io.StringIO()
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 5 entries, 0 to 4
+    Data columns (total 3 columns):
+     #   Column     Dtype
+    ---  ------     -----
+     0   int_col    int64
+     1   text_col   object
+     2   float_col  float64
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 130.0+ bytes
+    """
+    )
+    df.info(buf=buffer, verbose=True, null_counts=False)
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    df.info(buf=buffer, verbose=True, max_cols=0)
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    df = cudf.DataFrame()
+
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 0 entries
+    Empty DataFrame"""
+    )
+    df.info(buf=buffer, verbose=True)
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, None, 10, 11, 12, None],
+            "b": ["a", "b", "c", "sd", "sdf", "sd", None, None],
+        }
+    )
+
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 8 entries, 0 to 7
+    Data columns (total 2 columns):
+     #   Column  Dtype
+    ---  ------  -----
+     0   a       int64
+     1   b       object
+    dtypes: int64(1), object(1)
+    memory usage: 238.0+ bytes
+    """
+    )
+    pd.options.display.max_info_rows = 2
+    df.info(buf=buffer, max_cols=2, null_counts=None)
+    pd.reset_option("display.max_info_rows")
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    str_cmp = textwrap.dedent(
+        """\
+    <class 'cudf.core.dataframe.DataFrame'>
+    RangeIndex: 8 entries, 0 to 7
+    Data columns (total 2 columns):
+     #   Column  Non-Null Count  Dtype
+    ---  ------  --------------  -----
+     0   a       6 non-null      int64
+     1   b       6 non-null      object
+    dtypes: int64(1), object(1)
+    memory usage: 238.0+ bytes
+    """
+    )
+
+    df.info(buf=buffer, max_cols=2, null_counts=None)
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+    buffer.truncate(0)
+    buffer.seek(0)
+
+    df.info(buf=buffer, null_counts=True)
+    actual_string = buffer.getvalue()
+    assert str_cmp == actual_string
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data1",
+    [
+        [1, 2, 3, 4, 5, 6, 7],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
+        [
+            1.9876543,
+            2.9876654,
+            3.9876543,
+            4.1234587,
+            5.23,
+            6.88918237,
+            7.00001,
+        ],
+        [
+            -1.9876543,
+            -2.9876654,
+            -3.9876543,
+            -4.1234587,
+            -5.23,
+            -6.88918237,
+            -7.00001,
+        ],
+        [
+            1.987654321,
+            2.987654321,
+            3.987654321,
+            0.1221,
+            2.1221,
+            0.112121,
+            -21.1212,
+        ],
+        [
+            -1.987654321,
+            -2.987654321,
+            -3.987654321,
+            -0.1221,
+            -2.1221,
+            -0.112121,
+            21.1212,
+        ],
+    ],
+)
+@pytest.mark.parametrize(
+    "data2",
+    [
+        [1, 2, 3, 4, 5, 6, 7],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
+        [
+            1.9876543,
+            2.9876654,
+            3.9876543,
+            4.1234587,
+            5.23,
+            6.88918237,
+            7.00001,
+        ],
+        [
+            -1.9876543,
+            -2.9876654,
+            -3.9876543,
+            -4.1234587,
+            -5.23,
+            -6.88918237,
+            -7.00001,
+        ],
+        [
+            1.987654321,
+            2.987654321,
+            3.987654321,
+            0.1221,
+            2.1221,
+            0.112121,
+            -21.1212,
+        ],
+        [
+            -1.987654321,
+            -2.987654321,
+            -3.987654321,
+            -0.1221,
+            -2.1221,
+            -0.112121,
+            21.1212,
+        ],
+    ],
+)
+@pytest.mark.parametrize("rtol", [0, 0.01, 1e-05, 1e-08, 5e-1, 50.12])
+@pytest.mark.parametrize("atol", [0, 0.01, 1e-05, 1e-08, 50.12])
+def test_cudf_isclose(data1, data2, rtol, atol):
+    array1 = cupy.array(data1)
+    array2 = cupy.array(data2)
+
+    expected = cudf.Series(cupy.isclose(array1, array2, rtol=rtol, atol=atol))
+
+    actual = cudf.isclose(
+        cudf.Series(data1), cudf.Series(data2), rtol=rtol, atol=atol
+    )
+
+    assert_eq(expected, actual)
+    actual = cudf.isclose(data1, data2, rtol=rtol, atol=atol)
+
+    assert_eq(expected, actual)
+
+    actual = cudf.isclose(
+        cupy.array(data1), cupy.array(data2), rtol=rtol, atol=atol
+    )
+
+    assert_eq(expected, actual)
+
+    actual = cudf.isclose(
+        np.array(data1), np.array(data2), rtol=rtol, atol=atol
+    )
+
+    assert_eq(expected, actual)
+
+    actual = cudf.isclose(
+        pd.Series(data1), pd.Series(data2), rtol=rtol, atol=atol
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data1",
+    [
+        [
+            -1.9876543,
+            -2.9876654,
+            np.nan,
+            -4.1234587,
+            -5.23,
+            -6.88918237,
+            -7.00001,
+        ],
+        [
+            1.987654321,
+            2.987654321,
+            3.987654321,
+            0.1221,
+            2.1221,
+            np.nan,
+            -21.1212,
+        ],
+    ],
+)
+@pytest.mark.parametrize(
+    "data2",
+    [
+        [
+            -1.9876543,
+            -2.9876654,
+            -3.9876543,
+            -4.1234587,
+            -5.23,
+            -6.88918237,
+            -7.00001,
+        ],
+        [
+            1.987654321,
+            2.987654321,
+            3.987654321,
+            0.1221,
+            2.1221,
+            0.112121,
+            -21.1212,
+        ],
+        [
+            -1.987654321,
+            -2.987654321,
+            -3.987654321,
+            np.nan,
+            np.nan,
+            np.nan,
+            21.1212,
+        ],
+    ],
+)
+@pytest.mark.parametrize("equal_nan", [True, False])
+def test_cudf_isclose_nulls(data1, data2, equal_nan):
+    array1 = cupy.array(data1)
+    array2 = cupy.array(data2)
+
+    expected = cudf.Series(cupy.isclose(array1, array2, equal_nan=equal_nan))
+
+    actual = cudf.isclose(
+        cudf.Series(data1), cudf.Series(data2), equal_nan=equal_nan
+    )
+    assert_eq(expected, actual, check_dtype=False)
+    actual = cudf.isclose(data1, data2, equal_nan=equal_nan)
+    assert_eq(expected, actual, check_dtype=False)
+
+
+def test_cudf_isclose_different_index():
+    s1 = cudf.Series(
+        [-1.9876543, -2.9876654, -3.9876543, -4.1234587, -5.23, -7.00001],
+        index=[0, 1, 2, 3, 4, 5],
+    )
+    s2 = cudf.Series(
+        [-1.9876543, -2.9876654, -7.00001, -4.1234587, -5.23, -3.9876543],
+        index=[0, 1, 5, 3, 4, 2],
+    )
+
+    expected = cudf.Series([True] * 6, index=s1.index)
+    assert_eq(expected, cudf.isclose(s1, s2))
+
+    s1 = cudf.Series(
+        [-1.9876543, -2.9876654, -3.9876543, -4.1234587, -5.23, -7.00001],
+        index=[0, 1, 2, 3, 4, 5],
+    )
+    s2 = cudf.Series(
+        [-1.9876543, -2.9876654, -7.00001, -4.1234587, -5.23, -3.9876543],
+        index=[0, 1, 5, 10, 4, 2],
+    )
+
+    expected = cudf.Series(
+        [True, True, True, False, True, True], index=s1.index
+    )
+    assert_eq(expected, cudf.isclose(s1, s2))
+
+    s1 = cudf.Series(
+        [-1.9876543, -2.9876654, -3.9876543, -4.1234587, -5.23, -7.00001],
+        index=[100, 1, 2, 3, 4, 5],
+    )
+    s2 = cudf.Series(
+        [-1.9876543, -2.9876654, -7.00001, -4.1234587, -5.23, -3.9876543],
+        index=[0, 1, 100, 10, 4, 2],
+    )
+
+    expected = cudf.Series(
+        [False, True, True, False, True, False], index=s1.index
+    )
+    assert_eq(expected, cudf.isclose(s1, s2))
+
+
+@pytest.mark.parametrize(
+    "orient", ["dict", "list", "split", "tight", "records", "index", "series"]
+)
+@pytest.mark.parametrize("into", [dict, OrderedDict, defaultdict(list)])
+def test_dataframe_to_dict(orient, into):
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": [9, 5, 3]}, index=[10, 11, 12])
+    pdf = df.to_pandas()
+
+    actual = df.to_dict(orient=orient, into=into)
+    expected = pdf.to_dict(orient=orient, into=into)
+    if orient == "series":
+        assert actual.keys() == expected.keys()
+        for key in actual.keys():
+            assert_eq(expected[key], actual[key])
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data, orient, dtype, columns",
+    [
+        (
+            {"col_1": [3, 2, 1, 0], "col_2": [3, 2, 1, 0]},
+            "columns",
+            None,
+            None,
+        ),
+        ({"col_1": [3, 2, 1, 0], "col_2": [3, 2, 1, 0]}, "index", None, None),
+        (
+            {"col_1": [None, 2, 1, 0], "col_2": [3, None, 1, 0]},
+            "index",
+            None,
+            ["A", "B", "C", "D"],
+        ),
+        (
+            {
+                "col_1": ["ab", "cd", "ef", "gh"],
+                "col_2": ["zx", "one", "two", "three"],
+            },
+            "index",
+            None,
+            ["A", "B", "C", "D"],
+        ),
+        (
+            {
+                "index": [("a", "b"), ("a", "c")],
+                "columns": [("x", 1), ("y", 2)],
+                "data": [[1, 3], [2, 4]],
+                "index_names": ["n1", "n2"],
+                "column_names": ["z1", "z2"],
+            },
+            "tight",
+            "float64",
+            None,
+        ),
+    ],
+)
+def test_dataframe_from_dict(data, orient, dtype, columns):
+    expected = pd.DataFrame.from_dict(
+        data=data, orient=orient, dtype=dtype, columns=columns
+    )
+
+    actual = cudf.DataFrame.from_dict(
+        data=data, orient=orient, dtype=dtype, columns=columns
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "str", None])
+def test_dataframe_from_dict_transposed(dtype):
+    pd_data = {"a": [3, 2, 1, 0], "col_2": [3, 2, 1, 0]}
+    gd_data = {key: cudf.Series(val) for key, val in pd_data.items()}
+
+    expected = pd.DataFrame.from_dict(pd_data, orient="index", dtype=dtype)
+    actual = cudf.DataFrame.from_dict(gd_data, orient="index", dtype=dtype)
+
+    gd_data = {key: cupy.asarray(val) for key, val in pd_data.items()}
+    actual = cudf.DataFrame.from_dict(gd_data, orient="index", dtype=dtype)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pd_data, gd_data, orient, dtype, columns",
+    [
+        (
+            {"col_1": np.array([3, 2, 1, 0]), "col_2": np.array([3, 2, 1, 0])},
+            {
+                "col_1": cupy.array([3, 2, 1, 0]),
+                "col_2": cupy.array([3, 2, 1, 0]),
+            },
+            "columns",
+            None,
+            None,
+        ),
+        (
+            {"col_1": np.array([3, 2, 1, 0]), "col_2": np.array([3, 2, 1, 0])},
+            {
+                "col_1": cupy.array([3, 2, 1, 0]),
+                "col_2": cupy.array([3, 2, 1, 0]),
+            },
+            "index",
+            None,
+            None,
+        ),
+        (
+            {
+                "col_1": np.array([None, 2, 1, 0]),
+                "col_2": np.array([3, None, 1, 0]),
+            },
+            {
+                "col_1": cupy.array([np.nan, 2, 1, 0]),
+                "col_2": cupy.array([3, np.nan, 1, 0]),
+            },
+            "index",
+            None,
+            ["A", "B", "C", "D"],
+        ),
+        (
+            {
+                "col_1": np.array(["ab", "cd", "ef", "gh"]),
+                "col_2": np.array(["zx", "one", "two", "three"]),
+            },
+            {
+                "col_1": np.array(["ab", "cd", "ef", "gh"]),
+                "col_2": np.array(["zx", "one", "two", "three"]),
+            },
+            "index",
+            None,
+            ["A", "B", "C", "D"],
+        ),
+        (
+            {
+                "index": [("a", "b"), ("a", "c")],
+                "columns": [("x", 1), ("y", 2)],
+                "data": [np.array([1, 3]), np.array([2, 4])],
+                "index_names": ["n1", "n2"],
+                "column_names": ["z1", "z2"],
+            },
+            {
+                "index": [("a", "b"), ("a", "c")],
+                "columns": [("x", 1), ("y", 2)],
+                "data": [cupy.array([1, 3]), cupy.array([2, 4])],
+                "index_names": ["n1", "n2"],
+                "column_names": ["z1", "z2"],
+            },
+            "tight",
+            "float64",
+            None,
+        ),
+    ],
+)
+def test_dataframe_from_dict_cp_np_arrays(
+    pd_data, gd_data, orient, dtype, columns
+):
+    expected = pd.DataFrame.from_dict(
+        data=pd_data, orient=orient, dtype=dtype, columns=columns
+    )
+
+    actual = cudf.DataFrame.from_dict(
+        data=gd_data, orient=orient, dtype=dtype, columns=columns
+    )
+
+    assert_eq(expected, actual, check_dtype=dtype is not None)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame({"a": [1, 2, 3, 4, 5, 10, 11, 12, 33, 55, 19]}),
+        pd.DataFrame(
+            {
+                "one": [1, 2, 3, 4, 5, 10],
+                "two": ["abc", "def", "ghi", "xyz", "pqr", "abc"],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "one": [1, 2, 3, 4, 5, 10],
+                "two": ["abc", "def", "ghi", "xyz", "pqr", "abc"],
+            },
+            index=[10, 20, 30, 40, 50, 60],
+        ),
+        pd.DataFrame(
+            {
+                "one": [1, 2, 3, 4, 5, 10],
+                "two": ["abc", "def", "ghi", "xyz", "pqr", "abc"],
+            },
+            index=["a", "b", "c", "d", "e", "f"],
+        ),
+        pd.DataFrame(index=["a", "b", "c", "d", "e", "f"]),
+        pd.DataFrame(columns=["a", "b", "c", "d", "e", "f"]),
+        pd.DataFrame(index=[10, 11, 12]),
+        pd.DataFrame(columns=[10, 11, 12]),
+        pd.DataFrame(),
+        pd.DataFrame({"one": [], "two": []}),
+        pd.DataFrame({2: [], 1: []}),
+        pd.DataFrame(
+            {
+                0: [1, 2, 3, 4, 5, 10],
+                1: ["abc", "def", "ghi", "xyz", "pqr", "abc"],
+                100: ["a", "b", "b", "x", "z", "a"],
+            },
+            index=[10, 20, 30, 40, 50, 60],
+        ),
+    ],
+)
+def test_dataframe_keys(df):
+    gdf = cudf.from_pandas(df)
+
+    assert_eq(df.keys(), gdf.keys())
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series([1, 2, 3, 4, 5, 10, 11, 12, 33, 55, 19]),
+        pd.Series(["abc", "def", "ghi", "xyz", "pqr", "abc"]),
+        pd.Series(
+            [1, 2, 3, 4, 5, 10],
+            index=["abc", "def", "ghi", "xyz", "pqr", "abc"],
+        ),
+        pd.Series(
+            ["abc", "def", "ghi", "xyz", "pqr", "abc"],
+            index=[1, 2, 3, 4, 5, 10],
+        ),
+        pd.Series(index=["a", "b", "c", "d", "e", "f"], dtype="float64"),
+        pd.Series(index=[10, 11, 12], dtype="float64"),
+        pd.Series(dtype="float64"),
+        pd.Series([], dtype="float64"),
+    ],
+)
+def test_series_keys(ps):
+    gds = cudf.from_pandas(ps)
+
+    assert_eq(ps.keys(), gds.keys())
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[10, 20, 30]),
+        pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB")),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[10, 20]),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[7, 8]),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            },
+            index=[7, 20, 11, 9],
+        ),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"l": [10]}, index=[100]),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame(
+            {"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]},
+            index=[100, 200, 300, 400, 500, 0],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        pd.DataFrame([[5, 6], [7, 8]], columns=list("BD")),
+        pd.DataFrame([[5, 6], [7, 8]], columns=list("DE")),
+        pd.DataFrame(),
+        pd.DataFrame(
+            {"c": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+        ),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"l": [10]}, index=[200]),
+        pd.DataFrame([]),
+        pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}),
+        pd.DataFrame([], index=[100]),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            },
+            index=[0, 100, 200, 300],
+        ),
+    ],
+)
+@pytest.mark.parametrize("sort", [False, True])
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_dataframe_append_dataframe(df, other, sort, ignore_index):
+    pdf = df
+    other_pd = other
+
+    gdf = cudf.from_pandas(df)
+    other_gd = cudf.from_pandas(other)
+
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        expected = pdf.append(other_pd, sort=sort, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        actual = gdf.append(other_gd, sort=sort, ignore_index=ignore_index)
+
+    if expected.shape != df.shape:
+        assert_eq(expected.fillna(-1), actual.fillna(-1), check_dtype=False)
+    else:
+        assert_eq(expected, actual, check_index_type=not gdf.empty)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[10, 20, 30]),
+        pd.DataFrame({12: [], 22: []}),
+        pd.DataFrame([[1, 2], [3, 4]], columns=[10, 20]),
+        pd.DataFrame([[1, 2], [3, 4]], columns=[0, 1], index=[10, 20]),
+        pd.DataFrame([[1, 2], [3, 4]], columns=[1, 0], index=[7, 8]),
+        pd.DataFrame(
+            {
+                23: [315.3324, 3243.32432, 3232.332, -100.32],
+                33: [0.3223, 0.32, 0.0000232, 0.32224],
+            }
+        ),
+        pd.DataFrame(
+            {
+                0: [315.3324, 3243.32432, 3232.332, -100.32],
+                1: [0.3223, 0.32, 0.0000232, 0.32224],
+            },
+            index=[7, 20, 11, 9],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        pd.Series([10, 11, 23, 234, 13]),
+        pytest.param(
+            pd.Series([10, 11, 23, 234, 13], index=[11, 12, 13, 44, 33]),
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="pandas bug: "
+                "https://github.com/pandas-dev/pandas/issues/35092",
+            ),
+        ),
+        {1: 1},
+        {0: 10, 1: 100, 2: 102},
+    ],
+)
+@pytest.mark.parametrize("sort", [False, True])
+def test_dataframe_append_series_dict(df, other, sort):
+    pdf = df
+    other_pd = other
+
+    gdf = cudf.from_pandas(df)
+    if isinstance(other, pd.Series):
+        other_gd = cudf.from_pandas(other)
+    else:
+        other_gd = other
+
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        expected = pdf.append(other_pd, ignore_index=True, sort=sort)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        actual = gdf.append(other_gd, ignore_index=True, sort=sort)
+
+    if expected.shape != df.shape:
+        # Ignore the column type comparison because pandas incorrectly
+        # returns pd.Index([1, 2, 3], dtype="object") instead
+        # of pd.Index([1, 2, 3], dtype="int64")
+        assert_eq(
+            expected.fillna(-1),
+            actual.fillna(-1),
+            check_dtype=False,
+            check_column_type=False,
+            check_index_type=True,
+        )
+    else:
+        assert_eq(expected, actual, check_index_type=not gdf.empty)
+
+
+def test_dataframe_append_series_mixed_index():
+    df = cudf.DataFrame({"first": [], "d": []})
+    sr = cudf.Series([1, 2, 3, 4])
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "cudf does not support mixed types, please type-cast "
+            "the column index of dataframe and index of series "
+            "to same dtypes."
+        ),
+    ):
+        with pytest.warns(FutureWarning, match="append method is deprecated"):
+            df.append(sr, ignore_index=True)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[10, 20, 30]),
+        pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB")),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[10, 20]),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[7, 8]),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            },
+            index=[7, 20, 11, 9],
+        ),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"l": [10]}, index=[100]),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame(
+            {"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]},
+            index=[100, 200, 300, 400, 500, 0],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [pd.DataFrame([[5, 6], [7, 8]], columns=list("AB"))],
+        [
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("BD")),
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("DE")),
+        ],
+        [pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()],
+        [
+            pd.DataFrame(
+                {"c": [10, 11, 22, 33, 44, 100]}, index=[7, 8, 9, 10, 11, 20]
+            ),
+            pd.DataFrame(),
+            pd.DataFrame(),
+            pd.DataFrame([[5, 6], [7, 8]], columns=list("AB")),
+        ],
+        [
+            pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+            pd.DataFrame({"l": [10]}),
+            pd.DataFrame({"l": [10]}, index=[200]),
+        ],
+        [pd.DataFrame([]), pd.DataFrame([], index=[100])],
+        [
+            pd.DataFrame([]),
+            pd.DataFrame([], index=[100]),
+            pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}),
+        ],
+        [
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                },
+                index=[0, 100, 200, 300],
+            ),
+        ],
+        [
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                },
+                index=[0, 100, 200, 300],
+            ),
+        ],
+        [
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                },
+                index=[0, 100, 200, 300],
+            ),
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                },
+                index=[0, 100, 200, 300],
+            ),
+        ],
+        [
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                },
+                index=[0, 100, 200, 300],
+            ),
+            pd.DataFrame(
+                {
+                    "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                    "z": [0.3223, 0.32, 0.0000232, 0.32224],
+                },
+                index=[0, 100, 200, 300],
+            ),
+            pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}),
+        ],
+    ],
+)
+@pytest.mark.parametrize("sort", [False, True])
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_dataframe_append_dataframe_lists(df, other, sort, ignore_index):
+    pdf = df
+    other_pd = other
+
+    gdf = cudf.from_pandas(df)
+    other_gd = [
+        cudf.from_pandas(o) if isinstance(o, pd.DataFrame) else o
+        for o in other
+    ]
+
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        expected = pdf.append(other_pd, sort=sort, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        actual = gdf.append(other_gd, sort=sort, ignore_index=ignore_index)
+    if expected.shape != df.shape:
+        assert_eq(expected.fillna(-1), actual.fillna(-1), check_dtype=False)
+    else:
+        assert_eq(expected, actual, check_index_type=not gdf.empty)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame({"A": [1, 2, 3, np.nan, None, 6]}),
+        pd.Series([1, 2, 3, None, np.nan, 5, 6, np.nan]),
+    ],
+)
+@pytest.mark.parametrize("alias", ["bfill", "backfill"])
+def test_dataframe_bfill(df, alias):
+    gdf = cudf.from_pandas(df)
+
+    actual = getattr(df, alias)()
+    with expect_warning_if(alias == "backfill"):
+        expected = getattr(gdf, alias)()
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame({"A": [1, 2, 3, np.nan, None, 6]}),
+        pd.Series([1, 2, 3, None, np.nan, 5, 6, np.nan]),
+    ],
+)
+@pytest.mark.parametrize("alias", ["ffill", "pad"])
+def test_dataframe_ffill(df, alias):
+    gdf = cudf.from_pandas(df)
+
+    actual = getattr(df, alias)()
+    with expect_warning_if(alias == "pad"):
+        expected = getattr(gdf, alias)()
+    assert_eq(expected, actual)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB")),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[10, 20]),
+        pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[7, 8]),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "a": [315.3324, 3243.32432, 3232.332, -100.32],
+                "z": [0.3223, 0.32, 0.0000232, 0.32224],
+            },
+            index=[7, 20, 11, 9],
+        ),
+        pd.DataFrame({"l": [10]}),
+        pd.DataFrame({"l": [10]}, index=[100]),
+        pd.DataFrame({"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]}),
+        pd.DataFrame(
+            {"f": [10.2, 11.2332, 0.22, 3.3, 44.23, 10.0]},
+            index=[100, 200, 300, 400, 500, 0],
+        ),
+        pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [[1, 2], [10, 100]],
+        [[1, 2, 10, 100, 0.1, 0.2, 0.0021]],
+        [[]],
+        [[], [], [], []],
+        [[0.23, 0.00023, -10.00, 100, 200, 1000232, 1232.32323]],
+    ],
+)
+@pytest.mark.parametrize("sort", [False, True])
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_dataframe_append_lists(df, other, sort, ignore_index):
+    pdf = df
+    other_pd = other
+
+    gdf = cudf.from_pandas(df)
+    other_gd = [
+        cudf.from_pandas(o) if isinstance(o, pd.DataFrame) else o
+        for o in other
+    ]
+
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        expected = pdf.append(other_pd, sort=sort, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning, match="append method is deprecated"):
+        actual = gdf.append(other_gd, sort=sort, ignore_index=ignore_index)
+
+    if expected.shape != df.shape:
+        assert_eq(
+            expected.fillna(-1),
+            actual.fillna(-1),
+            check_dtype=False,
+            check_column_type=not gdf.empty,
+        )
+    else:
+        assert_eq(expected, actual, check_index_type=not gdf.empty)
+
+
+def test_dataframe_append_error():
+    df = cudf.DataFrame({"a": [1, 2, 3]})
+    ps = cudf.Series([1, 2, 3])
+
+    with pytest.raises(
+        TypeError,
+        match="Can only append a Series if ignore_index=True "
+        "or if the Series has a name",
+    ):
+        with pytest.warns(FutureWarning, match="append method is deprecated"):
+            df.append(ps)
+
+
+def test_cudf_arrow_array_error():
+    df = cudf.DataFrame({"a": [1, 2, 3]})
+
+    with pytest.raises(
+        TypeError,
+        match="Implicit conversion to a host PyArrow object via "
+        "__arrow_array__ is not allowed. Consider using .to_arrow()",
+    ):
+        df.__arrow_array__()
+
+    sr = cudf.Series([1, 2, 3])
+
+    with pytest.raises(
+        TypeError,
+        match="Implicit conversion to a host PyArrow object via "
+        "__arrow_array__ is not allowed. Consider using .to_arrow()",
+    ):
+        sr.__arrow_array__()
+
+    sr = cudf.Series(["a", "b", "c"])
+    with pytest.raises(
+        TypeError,
+        match="Implicit conversion to a host PyArrow object via "
+        "__arrow_array__ is not allowed. Consider using .to_arrow()",
+    ):
+        sr.__arrow_array__()
+
+
+@pytest.mark.parametrize(
+    "make_weights_axis_1",
+    [lambda _: None, lambda s: [1] * s, lambda s: np.ones(s)],
+)
+def test_sample_axis_1(
+    sample_n_frac, random_state_tuple_axis_1, make_weights_axis_1
+):
+    n, frac = sample_n_frac
+    pd_random_state, gd_random_state, checker = random_state_tuple_axis_1
+
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5],
+            "float": [0.05, 0.2, 0.3, 0.2, 0.25],
+            "int": [1, 3, 5, 4, 2],
+        },
+    )
+    df = cudf.DataFrame.from_pandas(pdf)
+
+    weights = make_weights_axis_1(len(pdf.columns))
+
+    expected = pdf.sample(
+        n=n,
+        frac=frac,
+        replace=False,
+        random_state=pd_random_state,
+        weights=weights,
+        axis=1,
+    )
+    got = df.sample(
+        n=n,
+        frac=frac,
+        replace=False,
+        random_state=gd_random_state,
+        weights=weights,
+        axis=1,
+    )
+    checker(expected, got)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame(
+            {
+                "a": [1, 2, 3, 4, 5],
+                "float": [0.05, 0.2, 0.3, 0.2, 0.25],
+                "int": [1, 3, 5, 4, 2],
+            },
+        ),
+        pd.Series([1, 2, 3, 4, 5]),
+    ],
+)
+@pytest.mark.parametrize("replace", [True, False])
+def test_sample_axis_0(
+    pdf, sample_n_frac, replace, random_state_tuple_axis_0, make_weights_axis_0
+):
+    n, frac = sample_n_frac
+    pd_random_state, gd_random_state, checker = random_state_tuple_axis_0
+
+    df = cudf.from_pandas(pdf)
+
+    pd_weights, gd_weights = make_weights_axis_0(
+        len(pdf), isinstance(gd_random_state, np.random.RandomState)
+    )
+    if (
+        not replace
+        and not isinstance(gd_random_state, np.random.RandomState)
+        and gd_weights is not None
+    ):
+        pytest.skip(
+            "`cupy.random.RandomState` doesn't support weighted sampling "
+            "without replacement."
+        )
+
+    expected = pdf.sample(
+        n=n,
+        frac=frac,
+        replace=replace,
+        random_state=pd_random_state,
+        weights=pd_weights,
+        axis=0,
+    )
+
+    got = df.sample(
+        n=n,
+        frac=frac,
+        replace=replace,
+        random_state=gd_random_state,
+        weights=gd_weights,
+        axis=0,
+    )
+    checker(expected, got)
+
+
+@pytest.mark.parametrize("replace", [True, False])
+@pytest.mark.parametrize(
+    "random_state_lib", [cupy.random.RandomState, np.random.RandomState]
+)
+def test_sample_reproducibility(replace, random_state_lib):
+    df = cudf.DataFrame({"a": cupy.arange(0, 1024)})
+
+    n = 1024
+    expected = df.sample(n, replace=replace, random_state=random_state_lib(10))
+    out = df.sample(n, replace=replace, random_state=random_state_lib(10))
+
+    assert_eq(expected, out)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+def test_sample_invalid_n_frac_combo(axis):
+    n, frac = 2, 0.5
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5],
+            "float": [0.05, 0.2, 0.3, 0.2, 0.25],
+            "int": [1, 3, 5, 4, 2],
+        },
+    )
+    df = cudf.DataFrame.from_pandas(pdf)
+
+    assert_exceptions_equal(
+        lfunc=pdf.sample,
+        rfunc=df.sample,
+        lfunc_args_and_kwargs=([], {"n": n, "frac": frac, "axis": axis}),
+        rfunc_args_and_kwargs=([], {"n": n, "frac": frac, "axis": axis}),
+    )
+
+
+@pytest.mark.parametrize("n, frac", [(100, None), (None, 3)])
+@pytest.mark.parametrize("axis", [0, 1])
+def test_oversample_without_replace(n, frac, axis):
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
+    df = cudf.DataFrame.from_pandas(pdf)
+
+    assert_exceptions_equal(
+        lfunc=pdf.sample,
+        rfunc=df.sample,
+        lfunc_args_and_kwargs=(
+            [],
+            {"n": n, "frac": frac, "axis": axis, "replace": False},
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {"n": n, "frac": frac, "axis": axis, "replace": False},
+        ),
+    )
+
+
+@pytest.mark.parametrize("random_state", [None, cupy.random.RandomState(42)])
+def test_sample_unsupported_arguments(random_state):
+    df = cudf.DataFrame({"float": [0.05, 0.2, 0.3, 0.2, 0.25]})
+    with pytest.raises(
+        NotImplementedError,
+        match="Random sampling with cupy does not support these inputs.",
+    ):
+        df.sample(
+            n=2, replace=False, random_state=random_state, weights=[1] * 5
+        )
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[100, 10, 1, 0]),
+        pd.DataFrame(columns=["a", "b", "c", "d"]),
+        pd.DataFrame(columns=["a", "b", "c", "d"], index=[100]),
+        pd.DataFrame(
+            columns=["a", "b", "c", "d"], index=[100, 10000, 2131, 133]
+        ),
+        pd.DataFrame({"a": [1, 2, 3], "b": ["abc", "xyz", "klm"]}),
+    ],
+)
+def test_dataframe_empty(df):
+    pdf = df
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(pdf.empty, gdf.empty)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(index=[100, 10, 1, 0]),
+        pd.DataFrame(columns=["a", "b", "c", "d"]),
+        pd.DataFrame(columns=["a", "b", "c", "d"], index=[100]),
+        pd.DataFrame(
+            columns=["a", "b", "c", "d"], index=[100, 10000, 2131, 133]
+        ),
+        pd.DataFrame({"a": [1, 2, 3], "b": ["abc", "xyz", "klm"]}),
+    ],
+)
+def test_dataframe_size(df):
+    pdf = df
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(pdf.size, gdf.size)
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series(dtype="float64"),
+        pd.Series(index=[100, 10, 1, 0], dtype="float64"),
+        pd.Series([], dtype="float64"),
+        pd.Series(["a", "b", "c", "d"]),
+        pd.Series(["a", "b", "c", "d"], index=[0, 1, 10, 11]),
+    ],
+)
+def test_series_empty(ps):
+    ps = ps
+    gs = cudf.from_pandas(ps)
+
+    assert_eq(ps.empty, gs.empty)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        None,
+        [],
+        [1],
+        {"a": [10, 11, 12]},
+        {
+            "a": [10, 11, 12],
+            "another column name": [12, 22, 34],
+            "xyz": [0, 10, 11],
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "columns",
+    [["a"], ["another column name"], None, pd.Index(["a"], name="index name")],
+)
+def test_dataframe_init_with_columns(data, columns, request):
+    if data == [] and columns is None and not PANDAS_GE_200:
+        request.node.add_marker(
+            pytest.mark.xfail(reason=".column returns Index[object]")
+        )
+    pdf = pd.DataFrame(data, columns=columns)
+    gdf = cudf.DataFrame(data, columns=columns)
+
+    assert_eq(
+        pdf,
+        gdf,
+        check_index_type=len(pdf.index) != 0,
+        check_dtype=not (pdf.empty and len(pdf.columns)),
+    )
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data, ignore_dtype",
+    [
+        ([pd.Series([1, 2, 3])], False),
+        ([pd.Series(index=[1, 2, 3], dtype="float64")], False),
+        ([pd.Series(name="empty series name", dtype="float64")], False),
+        (
+            [pd.Series([1]), pd.Series([], dtype="float64"), pd.Series([3])],
+            False,
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], dtype="float64"),
+                pd.Series([3], name="series that is named"),
+            ],
+            False,
+        ),
+        ([pd.Series([1, 2, 3], name="hi")] * 10, False),
+        ([pd.Series([1, 2, 3], name=None, index=[10, 11, 12])] * 10, False),
+        (
+            [
+                pd.Series([1, 2, 3], name=None, index=[10, 11, 12]),
+                pd.Series([1, 2, 30], name=None, index=[13, 144, 15]),
+            ],
+            True,
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], dtype="float64"),
+                pd.Series(index=[10, 11, 12], dtype="float64"),
+            ],
+            False,
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], name="abc", dtype="float64"),
+                pd.Series(index=[10, 11, 12], dtype="float64"),
+            ],
+            False,
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([1, -100, 200, -399, 400], name="abc"),
+                pd.Series([111, 222, 333], index=[10, 11, 12]),
+            ],
+            False,
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "columns",
+    [
+        None,
+        ["0"],
+        [0],
+        ["abc"],
+        [144, 13],
+        [2, 1, 0],
+        pd.Index(["abc"], name="custom_name"),
+    ],
+)
+def test_dataframe_init_from_series_list(data, ignore_dtype, columns, request):
+    if columns is None and data[0].empty and not PANDAS_GE_200:
+        request.applymarker(
+            pytest.mark.xfail(reason=".column returns Index[object]")
+        )
+    gd_data = [cudf.from_pandas(obj) for obj in data]
+
+    expected = pd.DataFrame(data, columns=columns)
+    actual = cudf.DataFrame(gd_data, columns=columns)
+
+    if ignore_dtype:
+        # When a union is performed to generate columns,
+        # the order is never guaranteed. Hence sort by
+        # columns before comparison.
+        if not expected.columns.equals(actual.columns):
+            expected = expected.sort_index(axis=1)
+            actual = actual.sort_index(axis=1)
+        assert_eq(
+            expected.fillna(-1),
+            actual.fillna(-1),
+            check_dtype=False,
+            check_index_type=True,
+        )
+    else:
+        assert_eq(expected, actual, check_index_type=True)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data, ignore_dtype, index",
+    [
+        ([pd.Series([1, 2, 3])], False, ["a", "b", "c"]),
+        ([pd.Series(index=[1, 2, 3], dtype="float64")], False, ["a", "b"]),
+        (
+            [pd.Series(name="empty series name", dtype="float64")],
+            False,
+            ["index1"],
+        ),
+        (
+            [pd.Series([1]), pd.Series([], dtype="float64"), pd.Series([3])],
+            False,
+            ["0", "2", "1"],
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], dtype="float64"),
+                pd.Series([3], name="series that is named"),
+            ],
+            False,
+            ["_", "+", "*"],
+        ),
+        ([pd.Series([1, 2, 3], name="hi")] * 10, False, ["mean"] * 10),
+        (
+            [pd.Series([1, 2, 3], name=None, index=[10, 11, 12])] * 10,
+            False,
+            ["abc"] * 10,
+        ),
+        (
+            [
+                pd.Series([1, 2, 3], name=None, index=[10, 11, 12]),
+                pd.Series([1, 2, 30], name=None, index=[13, 144, 15]),
+            ],
+            True,
+            ["set_index_a", "set_index_b"],
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], dtype="float64"),
+                pd.Series(index=[10, 11, 12], dtype="float64"),
+            ],
+            False,
+            ["a", "b", "c"],
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], name="abc", dtype="float64"),
+                pd.Series(index=[10, 11, 12], dtype="float64"),
+            ],
+            False,
+            ["a", "v", "z"],
+        ),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([1, -100, 200, -399, 400], name="abc"),
+                pd.Series([111, 222, 333], index=[10, 11, 12]),
+            ],
+            False,
+            ["a", "v", "z"],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "columns", [None, ["0"], [0], ["abc"], [144, 13], [2, 1, 0]]
+)
+def test_dataframe_init_from_series_list_with_index(
+    data,
+    ignore_dtype,
+    index,
+    columns,
+    request,
+):
+    if columns is None and data[0].empty and not PANDAS_GE_200:
+        request.applymarker(
+            pytest.mark.xfail(reason=".column returns Index[object]")
+        )
+    gd_data = [cudf.from_pandas(obj) for obj in data]
+
+    expected = pd.DataFrame(data, columns=columns, index=index)
+    actual = cudf.DataFrame(gd_data, columns=columns, index=index)
+
+    if ignore_dtype:
+        # When a union is performed to generate columns,
+        # the order is never guaranteed. Hence sort by
+        # columns before comparison.
+        if not expected.columns.equals(actual.columns):
+            expected = expected.sort_index(axis=1)
+            actual = actual.sort_index(axis=1)
+        assert_eq(expected.fillna(-1), actual.fillna(-1), check_dtype=False)
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data, index",
+    [
+        ([pd.Series([1, 2]), pd.Series([1, 2])], ["a", "b", "c"]),
+        (
+            [
+                pd.Series([1, 0.324234, 32424.323, -1233, 34242]),
+                pd.Series([], dtype="float64"),
+                pd.Series([3], name="series that is named"),
+            ],
+            ["_", "+"],
+        ),
+        ([pd.Series([1, 2, 3], name="hi")] * 10, ["mean"] * 9),
+    ],
+)
+def test_dataframe_init_from_series_list_with_index_error(data, index):
+    gd_data = [cudf.from_pandas(obj) for obj in data]
+
+    assert_exceptions_equal(
+        pd.DataFrame,
+        cudf.DataFrame,
+        ([data], {"index": index}),
+        ([gd_data], {"index": index}),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [pd.Series([1, 2, 3], index=["a", "a", "a"])],
+        [pd.Series([1, 2, 3], index=["a", "a", "a"])] * 4,
+        [
+            pd.Series([1, 2, 3], index=["a", "b", "a"]),
+            pd.Series([1, 2, 3], index=["b", "b", "a"]),
+        ],
+        [
+            pd.Series([1, 2, 3], index=["a", "b", "z"]),
+            pd.Series([1, 2, 3], index=["u", "b", "a"]),
+            pd.Series([1, 2, 3], index=["u", "b", "u"]),
+        ],
+    ],
+)
+def test_dataframe_init_from_series_list_duplicate_index_error(data):
+    gd_data = [cudf.from_pandas(obj) for obj in data]
+
+    assert_exceptions_equal(
+        lfunc=pd.DataFrame,
+        rfunc=cudf.DataFrame,
+        lfunc_args_and_kwargs=([], {"data": data}),
+        rfunc_args_and_kwargs=([], {"data": gd_data}),
+        check_exception_type=False,
+    )
+
+
+def test_dataframe_iterrows_itertuples():
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "cuDF does not support iteration of DataFrame "
+            "via itertuples. Consider using "
+            "`.to_pandas().itertuples()` "
+            "if you wish to iterate over namedtuples."
+        ),
+    ):
+        df.itertuples()
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "cuDF does not support iteration of DataFrame "
+            "via iterrows. Consider using "
+            "`.to_pandas().iterrows()` "
+            "if you wish to iterate over each row."
+        ),
+    ):
+        df.iterrows()
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame(
+            {
+                "a": [1, 2, 3],
+                "b": [10, 22, 33],
+                "c": [0.3234, 0.23432, 0.0],
+                "d": ["hello", "world", "hello"],
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "a": [1, 2, 3],
+                "b": ["hello", "world", "hello"],
+                "c": [0.3234, 0.23432, 0.0],
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "int_data": [1, 2, 3],
+                "str_data": ["hello", "world", "hello"],
+                "float_data": [0.3234, 0.23432, 0.0],
+                "timedelta_data": cudf.Series(
+                    [1, 2, 1], dtype="timedelta64[ns]"
+                ),
+                "datetime_data": cudf.Series(
+                    [1, 2, 1], dtype="datetime64[ns]"
+                ),
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "int_data": [1, 2, 3],
+                "str_data": ["hello", "world", "hello"],
+                "float_data": [0.3234, 0.23432, 0.0],
+                "timedelta_data": cudf.Series(
+                    [1, 2, 1], dtype="timedelta64[ns]"
+                ),
+                "datetime_data": cudf.Series(
+                    [1, 2, 1], dtype="datetime64[ns]"
+                ),
+                "category_data": cudf.Series(
+                    ["a", "a", "b"], dtype="category"
+                ),
+            }
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "include",
+    [None, "all", ["object"], ["int"], ["object", "int", "category"]],
+)
+def test_describe_misc_include(df, include):
+    pdf = df.to_pandas()
+
+    expected = pdf.describe(include=include, datetime_is_numeric=True)
+    actual = df.describe(include=include, datetime_is_numeric=True)
+
+    for col in expected.columns:
+        if expected[col].dtype == np.dtype("object"):
+            expected[col] = expected[col].fillna(-1).astype("str")
+            actual[col] = actual[col].fillna(-1).astype("str")
+
+    assert_eq(expected, actual)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame(
+            {
+                "a": [1, 2, 3],
+                "b": [10, 22, 33],
+                "c": [0.3234, 0.23432, 0.0],
+                "d": ["hello", "world", "hello"],
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "a": [1, 2, 3],
+                "b": ["hello", "world", "hello"],
+                "c": [0.3234, 0.23432, 0.0],
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "int_data": [1, 2, 3],
+                "str_data": ["hello", "world", "hello"],
+                "float_data": [0.3234, 0.23432, 0.0],
+                "timedelta_data": cudf.Series(
+                    [1, 2, 1], dtype="timedelta64[ns]"
+                ),
+                "datetime_data": cudf.Series(
+                    [1, 2, 1], dtype="datetime64[ns]"
+                ),
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "int_data": [1, 2, 3],
+                "str_data": ["hello", "world", "hello"],
+                "float_data": [0.3234, 0.23432, 0.0],
+                "timedelta_data": cudf.Series(
+                    [1, 2, 1], dtype="timedelta64[ns]"
+                ),
+                "datetime_data": cudf.Series(
+                    [1, 2, 1], dtype="datetime64[ns]"
+                ),
+                "category_data": cudf.Series(
+                    ["a", "a", "b"], dtype="category"
+                ),
+            }
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "exclude", [None, ["object"], ["int"], ["object", "int", "category"]]
+)
+def test_describe_misc_exclude(df, exclude):
+    pdf = df.to_pandas()
+
+    expected = pdf.describe(exclude=exclude, datetime_is_numeric=True)
+    actual = df.describe(exclude=exclude, datetime_is_numeric=True)
+
+    for col in expected.columns:
+        if expected[col].dtype == np.dtype("object"):
+            expected[col] = expected[col].fillna(-1).astype("str")
+            actual[col] = actual[col].fillna(-1).astype("str")
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame({"a": [1, 2, 3]}),
+        cudf.DataFrame(
+            {"a": [1, 2, 3], "b": ["a", "z", "c"]}, index=["a", "z", "x"]
+        ),
+        cudf.DataFrame(
+            {
+                "a": [1, 2, 3, None, 2, 1, None],
+                "b": ["a", "z", "c", "a", "v", "z", "z"],
+            }
+        ),
+        cudf.DataFrame({"a": [], "b": []}),
+        cudf.DataFrame({"a": [None, None], "b": [None, None]}),
+        cudf.DataFrame(
+            {
+                "a": ["hello", "world", "rapids", "ai", "nvidia"],
+                "b": cudf.Series(
+                    [1, 21, 21, 11, 11],
+                    dtype="timedelta64[s]",
+                    index=["a", "b", "c", "d", " e"],
+                ),
+            },
+            index=["a", "b", "c", "d", " e"],
+        ),
+        cudf.DataFrame(
+            {
+                "a": ["hello", None, "world", "rapids", None, "ai", "nvidia"],
+                "b": cudf.Series(
+                    [1, 21, None, 11, None, 11, None], dtype="datetime64[s]"
+                ),
+            }
+        ),
+    ],
+)
+@pytest.mark.parametrize("numeric_only", [True, False])
+@pytest.mark.parametrize("dropna", [True, False])
+def test_dataframe_mode(df, numeric_only, dropna):
+    pdf = df.to_pandas()
+
+    expected = pdf.mode(numeric_only=numeric_only, dropna=dropna)
+    actual = df.mode(numeric_only=numeric_only, dropna=dropna)
+
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "lhs, rhs", [("a", "a"), ("a", "b"), (1, 1.0), (None, None), (None, "a")]
+)
+def test_equals_names(lhs, rhs):
+    lhs = cudf.DataFrame({lhs: [1, 2]})
+    rhs = cudf.DataFrame({rhs: [1, 2]})
+
+    got = lhs.equals(rhs)
+    expect = lhs.to_pandas().equals(rhs.to_pandas())
+
+    assert_eq(expect, got)
+
+
+def test_equals_dtypes():
+    lhs = cudf.DataFrame({"a": [1, 2.0]})
+    rhs = cudf.DataFrame({"a": [1, 2]})
+
+    got = lhs.equals(rhs)
+    expect = lhs.to_pandas().equals(rhs.to_pandas())
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "df1",
+    [
+        pd.DataFrame({"a": [10, 11, 12]}, index=["a", "b", "z"]),
+        pd.DataFrame({"z": ["a"]}),
+        pd.DataFrame({"a": [], "b": []}),
+    ],
+)
+@pytest.mark.parametrize(
+    "df2",
+    [
+        pd.DataFrame(),
+        pd.DataFrame({"a": ["a", "a", "c", "z", "A"], "z": [1, 2, 3, 4, 5]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.eq,
+        operator.ne,
+        operator.lt,
+        operator.gt,
+        operator.le,
+        operator.ge,
+    ],
+)
+def test_dataframe_error_equality(df1, df2, op):
+    gdf1 = cudf.from_pandas(df1)
+    gdf2 = cudf.from_pandas(df2)
+
+    assert_exceptions_equal(op, op, ([df1, df2],), ([gdf1, gdf2],))
+
+
+@pytest.mark.parametrize(
+    "df,expected_pdf",
+    [
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series([1, 2, None, 3], dtype="uint8"),
+                    "b": cudf.Series([23, None, None, 32], dtype="uint16"),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series([1, 2, None, 3], dtype=pd.UInt8Dtype()),
+                    "b": pd.Series(
+                        [23, None, None, 32], dtype=pd.UInt16Dtype()
+                    ),
+                }
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series([None, 123, None, 1], dtype="uint32"),
+                    "b": cudf.Series(
+                        [234, 2323, 23432, None, None, 224], dtype="uint64"
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        [None, 123, None, 1], dtype=pd.UInt32Dtype()
+                    ),
+                    "b": pd.Series(
+                        [234, 2323, 23432, None, None, 224],
+                        dtype=pd.UInt64Dtype(),
+                    ),
+                }
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [-10, 1, None, -1, None, 3], dtype="int8"
+                    ),
+                    "b": cudf.Series(
+                        [111, None, 222, None, 13], dtype="int16"
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        [-10, 1, None, -1, None, 3], dtype=pd.Int8Dtype()
+                    ),
+                    "b": pd.Series(
+                        [111, None, 222, None, 13], dtype=pd.Int16Dtype()
+                    ),
+                }
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [11, None, 22, 33, None, 2, None, 3], dtype="int32"
+                    ),
+                    "b": cudf.Series(
+                        [32431, None, None, 32322, 0, 10, -32324, None],
+                        dtype="int64",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        [11, None, 22, 33, None, 2, None, 3],
+                        dtype=pd.Int32Dtype(),
+                    ),
+                    "b": pd.Series(
+                        [32431, None, None, 32322, 0, 10, -32324, None],
+                        dtype=pd.Int64Dtype(),
+                    ),
+                }
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [True, None, False, None, False, True, True, False],
+                        dtype="bool_",
+                    ),
+                    "b": cudf.Series(
+                        [
+                            "abc",
+                            "a",
+                            None,
+                            "hello world",
+                            "foo buzz",
+                            "",
+                            None,
+                            "rapids ai",
+                        ],
+                        dtype="object",
+                    ),
+                    "c": cudf.Series(
+                        [0.1, None, 0.2, None, 3, 4, 1000, None],
+                        dtype="float64",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "a": pd.Series(
+                        [True, None, False, None, False, True, True, False],
+                        dtype=pd.BooleanDtype(),
+                    ),
+                    "b": pd.Series(
+                        [
+                            "abc",
+                            "a",
+                            None,
+                            "hello world",
+                            "foo buzz",
+                            "",
+                            None,
+                            "rapids ai",
+                        ],
+                        dtype=pd.StringDtype(),
+                    ),
+                    "c": pd.Series(
+                        [0.1, None, 0.2, None, 3, 4, 1000, None],
+                        dtype=pd.Float64Dtype(),
+                    ),
+                }
+            ),
+        ),
+    ],
+)
+def test_dataframe_to_pandas_nullable_dtypes(df, expected_pdf):
+    actual_pdf = df.to_pandas(nullable=True)
+
+    assert_eq(actual_pdf, expected_pdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [{"a": 1, "b": 2, "c": 3}, {"a": 4, "b": 5, "c": 6}],
+        [{"a": 1, "b": 2, "c": None}, {"a": None, "b": 5, "c": 6}],
+        [{"a": 1, "b": 2}, {"a": 1, "b": 5, "c": 6}],
+        [{"a": 1, "b": 2}, {"b": 5, "c": 6}],
+        [{}, {"a": 1, "b": 5, "c": 6}],
+        [{"a": 1, "b": 2, "c": 3}, {"a": 4.5, "b": 5.5, "c": 6.5}],
+    ],
+)
+def test_dataframe_init_from_list_of_dicts(data):
+    expect = pd.DataFrame(data)
+    got = cudf.DataFrame(data)
+
+    assert_eq(expect, got)
+
+
+def test_dataframe_pipe():
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+
+    def add_int_col(df, column):
+        df[column] = df._constructor_sliced([10, 20, 30, 40])
+        return df
+
+    def add_str_col(df, column):
+        df[column] = df._constructor_sliced(["a", "b", "xyz", "ai"])
+        return df
+
+    expected = (
+        pdf.pipe(add_int_col, "one")
+        .pipe(add_int_col, column="two")
+        .pipe(add_str_col, "three")
+    )
+    actual = (
+        gdf.pipe(add_int_col, "one")
+        .pipe(add_int_col, column="two")
+        .pipe(add_str_col, "three")
+    )
+
+    assert_eq(expected, actual)
+
+    expected = (
+        pdf.pipe((add_str_col, "df"), column="one")
+        .pipe(add_str_col, column="two")
+        .pipe(add_int_col, "three")
+    )
+    actual = (
+        gdf.pipe((add_str_col, "df"), column="one")
+        .pipe(add_str_col, column="two")
+        .pipe(add_int_col, "three")
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_dataframe_pipe_error():
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+
+    def custom_func(df, column):
+        df[column] = df._constructor_sliced([10, 20, 30, 40])
+        return df
+
+    assert_exceptions_equal(
+        lfunc=pdf.pipe,
+        rfunc=gdf.pipe,
+        lfunc_args_and_kwargs=([(custom_func, "columns")], {"columns": "d"}),
+        rfunc_args_and_kwargs=([(custom_func, "columns")], {"columns": "d"}),
+    )
+
+
+@pytest.mark.parametrize(
+    "op",
+    ["count", "kurt", "kurtosis", "skew"],
+)
+def test_dataframe_axis1_unsupported_ops(op):
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": [8, 9, 10]})
+
+    with pytest.raises(
+        NotImplementedError, match="Only axis=0 is currently supported."
+    ):
+        getattr(df, op)(axis=1)
+
+
+def test_dataframe_from_pandas_duplicate_columns():
+    pdf = pd.DataFrame(columns=["a", "b", "c", "a"])
+    pdf["a"] = [1, 2, 3]
+
+    with pytest.raises(
+        ValueError, match="Duplicate column names are not allowed"
+    ):
+        cudf.from_pandas(pdf)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(
+            {"a": [1, 2, 3], "b": [10, 11, 20], "c": ["a", "bcd", "xyz"]}
+        ),
+        pd.DataFrame(),
+    ],
+)
+@pytest.mark.parametrize(
+    "columns",
+    [
+        None,
+        ["a"],
+        ["c", "a"],
+        ["b", "a", "c"],
+        [],
+        pd.Index(["c", "a"]),
+        cudf.Index(["c", "a"]),
+        ["abc", "a"],
+        ["column_not_exists1", "column_not_exists2"],
+    ],
+)
+@pytest.mark.parametrize("index", [["abc", "def", "ghi"]])
+def test_dataframe_constructor_columns(df, columns, index, request):
+    def assert_local_eq(actual, df, expected, host_columns):
+        check_index_type = not expected.empty
+        if host_columns is not None and any(
+            col not in df.columns for col in host_columns
+        ):
+            assert_eq(
+                expected,
+                actual,
+                check_dtype=False,
+                check_index_type=check_index_type,
+            )
+        else:
+            assert_eq(expected, actual, check_index_type=check_index_type)
+
+    if df.empty and columns is None and not PANDAS_GE_200:
+        request.node.add_marker(
+            pytest.mark.xfail(
+                reason="pandas returns Index[object] instead of RangeIndex"
+            )
+        )
+    gdf = cudf.from_pandas(df)
+    host_columns = (
+        columns.to_pandas() if isinstance(columns, cudf.BaseIndex) else columns
+    )
+
+    expected = pd.DataFrame(df, columns=host_columns, index=index)
+    actual = cudf.DataFrame(gdf, columns=columns, index=index)
+
+    assert_local_eq(actual, df, expected, host_columns)
+
+
+def test_dataframe_constructor_column_index_only():
+    columns = ["a", "b", "c"]
+    index = ["r1", "r2", "r3"]
+
+    gdf = cudf.DataFrame(index=index, columns=columns)
+    assert not id(gdf["a"]._column) == id(gdf["b"]._column) and not id(
+        gdf["b"]._column
+    ) == id(gdf["c"]._column)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": [3.0, 4.0, 5.0], "c": [True, True, False]},
+        {"a": [1.0, 2.0, 3.0], "b": [3.0, 4.0, 5.0], "c": [True, True, False]},
+        {"a": [1, 2, 3], "b": [3, 4, 5], "c": [True, True, False]},
+        {"a": [1, 2, 3], "b": [True, True, False], "c": [False, True, False]},
+        {
+            "a": [1.0, 2.0, 3.0],
+            "b": [True, True, False],
+            "c": [False, True, False],
+        },
+        {"a": [1, 2, 3], "b": [3, 4, 5], "c": [2.0, 3.0, 4.0]},
+        {"a": [1, 2, 3], "b": [2.0, 3.0, 4.0], "c": [5.0, 6.0, 4.0]},
+    ],
+)
+@pytest.mark.parametrize(
+    "aggs",
+    [
+        ["min", "sum", "max"],
+        ("min", "sum", "max"),
+        {"min", "sum", "max"},
+        "sum",
+        {"a": "sum", "b": "min", "c": "max"},
+        {"a": ["sum"], "b": ["min"], "c": ["max"]},
+        {"a": ("sum"), "b": ("min"), "c": ("max")},
+        {"a": {"sum"}, "b": {"min"}, "c": {"max"}},
+        {"a": ["sum", "min"], "b": ["sum", "max"], "c": ["min", "max"]},
+        {"a": ("sum", "min"), "b": ("sum", "max"), "c": ("min", "max")},
+        {"a": {"sum", "min"}, "b": {"sum", "max"}, "c": {"min", "max"}},
+    ],
+)
+def test_agg_for_dataframes(data, aggs):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data)
+
+    expect = pdf.agg(aggs).sort_index()
+    got = gdf.agg(aggs).sort_index()
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("aggs", [{"a": np.sum, "b": np.min, "c": np.max}])
+def test_agg_for_unsupported_function(aggs):
+    gdf = cudf.DataFrame(
+        {"a": [1, 2, 3], "b": [3.0, 4.0, 5.0], "c": [True, True, False]}
+    )
+
+    with pytest.raises(NotImplementedError):
+        gdf.agg(aggs)
+
+
+@pytest.mark.parametrize("aggs", ["asdf"])
+def test_agg_for_dataframe_with_invalid_function(aggs):
+    gdf = cudf.DataFrame(
+        {"a": [1, 2, 3], "b": [3.0, 4.0, 5.0], "c": [True, True, False]}
+    )
+
+    with pytest.raises(
+        AttributeError,
+        match=f"{aggs} is not a valid function for 'DataFrame' object",
+    ):
+        gdf.agg(aggs)
+
+
+@pytest.mark.parametrize("aggs", [{"a": "asdf"}])
+def test_agg_for_series_with_invalid_function(aggs):
+    gdf = cudf.DataFrame(
+        {"a": [1, 2, 3], "b": [3.0, 4.0, 5.0], "c": [True, True, False]}
+    )
+
+    with pytest.raises(
+        AttributeError,
+        match=f"{aggs['a']} is not a valid function for 'Series' object",
+    ):
+        gdf.agg(aggs)
+
+
+@pytest.mark.parametrize(
+    "aggs",
+    [
+        "sum",
+        ["min", "sum", "max"],
+        {"a": {"sum", "min"}, "b": {"sum", "max"}, "c": {"min", "max"}},
+    ],
+)
+def test_agg_for_dataframe_with_string_columns(aggs):
+    gdf = cudf.DataFrame(
+        {"a": ["m", "n", "o"], "b": ["t", "u", "v"], "c": ["x", "y", "z"]},
+        index=["a", "b", "c"],
+    )
+
+    with pytest.raises(
+        NotImplementedError,
+        match=re.escape(
+            "DataFrame.agg() is not supported for "
+            "frames containing string columns"
+        ),
+    ):
+        gdf.agg(aggs)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "join",
+    ["left"],
+)
+@pytest.mark.parametrize(
+    "overwrite",
+    [True, False],
+)
+@pytest.mark.parametrize(
+    "errors",
+    ["ignore"],
+)
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": [3, 4, 5]},
+        {"e": [1.0, 2.0, 3.0], "d": [3.0, 4.0, 5.0]},
+        {"c": [True, False, False], "d": [False, True, True]},
+        {"g": [2.0, np.nan, 4.0], "n": [np.nan, np.nan, np.nan]},
+        {"d": [np.nan, np.nan, np.nan], "e": [np.nan, np.nan, np.nan]},
+        {"a": [1.0, 2, 3], "b": pd.Series([4.0, 8.0, 3.0], index=[1, 2, 3])},
+        {
+            "d": [1.0, 2.0, 3.0],
+            "c": pd.Series([np.nan, np.nan, np.nan], index=[1, 2, 3]),
+        },
+        {
+            "a": [False, True, False],
+            "b": pd.Series([1.0, 2.0, np.nan], index=[1, 2, 3]),
+        },
+        {
+            "a": [np.nan, np.nan, np.nan],
+            "e": pd.Series([np.nan, np.nan, np.nan], index=[1, 2, 3]),
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "data2",
+    [
+        {"b": [3, 5, 6], "e": [8, 2, 1]},
+        {"c": [True, False, True], "d": [3.0, 4.0, 5.0]},
+        {"e": [False, False, True], "g": [True, True, False]},
+        {"g": [np.nan, np.nan, np.nan], "c": [np.nan, np.nan, np.nan]},
+        {"a": [7, 5, 8], "b": pd.Series([2.0, 7.0, 9.0], index=[0, 1, 2])},
+        {
+            "b": [np.nan, 2.0, np.nan],
+            "c": pd.Series([2, np.nan, 5.0], index=[2, 3, 4]),
+        },
+        {
+            "a": pd.Series([True, None, True], dtype=pd.BooleanDtype()),
+            "d": pd.Series(
+                [False, True, None], index=[0, 1, 3], dtype=pd.BooleanDtype()
+            ),
+        },
+    ],
+)
+def test_update_for_dataframes(request, data, data2, join, overwrite, errors):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=request.node.name
+            in {
+                "test_update_for_dataframes[data21-data2-ignore-True-left]",
+                "test_update_for_dataframes[data24-data7-ignore-True-left]",
+                "test_update_for_dataframes[data25-data2-ignore-True-left]",
+            },
+            reason="mixing of bools & non-bools is not allowed.",
+        )
+    )
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data, nan_as_null=False)
+
+    other_pd = pd.DataFrame(data2)
+    other_gd = cudf.DataFrame(data2, nan_as_null=False)
+
+    pdf.update(other=other_pd, join=join, overwrite=overwrite, errors=errors)
+    gdf.update(other=other_gd, join=join, overwrite=overwrite, errors=errors)
+
+    assert_eq(pdf, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "join",
+    ["right"],
+)
+def test_update_for_right_join(join):
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": [3.0, 4.0, 5.0]})
+    other_gd = cudf.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, 2.0, 5.0]})
+
+    with pytest.raises(
+        NotImplementedError, match="Only left join is supported"
+    ):
+        gdf.update(other_gd, join)
+
+
+@pytest.mark.parametrize(
+    "errors",
+    ["raise"],
+)
+def test_update_for_data_overlap(errors):
+    pdf = pd.DataFrame({"a": [1, 2, 3], "b": [3.0, 4.0, 5.0]})
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": [3.0, 4.0, 5.0]})
+
+    other_pd = pd.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, 2.0, 5.0]})
+    other_gd = cudf.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, 2.0, 5.0]})
+
+    assert_exceptions_equal(
+        lfunc=pdf.update,
+        rfunc=gdf.update,
+        lfunc_args_and_kwargs=([other_pd, errors], {}),
+        rfunc_args_and_kwargs=([other_gd, errors], {}),
+    )
+
+
+@pytest.mark.parametrize(
+    "gdf",
+    [
+        cudf.DataFrame({"a": [[1], [2], [3]]}),
+        cudf.DataFrame(
+            {
+                "left-a": [0, 1, 2],
+                "a": [[1], None, [3]],
+                "right-a": ["abc", "def", "ghi"],
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "left-a": [[], None, None],
+                "a": [[1], None, [3]],
+                "right-a": ["abc", "def", "ghi"],
+            }
+        ),
+    ],
+)
+def test_dataframe_roundtrip_arrow_list_dtype(gdf):
+    table = gdf.to_arrow()
+    expected = cudf.DataFrame.from_arrow(table)
+
+    assert_eq(gdf, expected)
+
+
+@pytest.mark.parametrize(
+    "gdf",
+    [
+        cudf.DataFrame({"a": [{"one": 3, "two": 4, "three": 10}]}),
+        cudf.DataFrame(
+            {
+                "left-a": [0, 1, 2],
+                "a": [{"x": 0.23, "y": 43}, None, {"x": 23.9, "y": 4.3}],
+                "right-a": ["abc", "def", "ghi"],
+            }
+        ),
+        cudf.DataFrame(
+            {
+                "left-a": [{"a": 1}, None, None],
+                "a": [
+                    {"one": 324, "two": 23432, "three": 324},
+                    None,
+                    {"one": 3.24, "two": 1, "three": 324},
+                ],
+                "right-a": ["abc", "def", "ghi"],
+            }
+        ),
+    ],
+)
+def test_dataframe_roundtrip_arrow_struct_dtype(gdf):
+    table = gdf.to_arrow()
+    expected = cudf.DataFrame.from_arrow(table)
+
+    assert_eq(gdf, expected)
+
+
+def test_dataframe_setitem_cupy_array():
+    np.random.seed(0)
+    pdf = pd.DataFrame(np.random.randn(10, 2))
+    gdf = cudf.from_pandas(pdf)
+
+    gpu_array = cupy.array([True, False] * 5)
+    pdf[gpu_array.get()] = 1.5
+    gdf[gpu_array] = 1.5
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "data", [{"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}]
+)
+@pytest.mark.parametrize(
+    "index",
+    [{0: 123, 1: 4, 2: 6}],
+)
+@pytest.mark.parametrize(
+    "level",
+    ["x", 0],
+)
+def test_rename_for_level_MultiIndex_dataframe(data, index, level):
+    pdf = pd.DataFrame(
+        data,
+        index=pd.MultiIndex.from_tuples([(0, 1, 2), (1, 2, 3), (2, 3, 4)]),
+    )
+    pdf.index.names = ["x", "y", "z"]
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.rename(index=index, level=level)
+    got = gdf.rename(index=index, level=level)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data", [{"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}]
+)
+@pytest.mark.parametrize(
+    "columns",
+    [{"a": "f", "b": "g"}, {1: 3, 2: 4}, lambda s: 2 * s],
+)
+@pytest.mark.parametrize(
+    "level",
+    [0, 1],
+)
+def test_rename_for_level_MultiColumn_dataframe(data, columns, level):
+    gdf = cudf.DataFrame(data)
+    gdf.columns = pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1)])
+
+    pdf = gdf.to_pandas()
+
+    expect = pdf.rename(columns=columns, level=level)
+    got = gdf.rename(columns=columns, level=level)
+
+    assert_eq(expect, got)
+
+
+def test_rename_for_level_RangeIndex_dataframe():
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    pdf = gdf.to_pandas()
+
+    expect = pdf.rename(columns={"a": "f"}, index={0: 3, 1: 4}, level=0)
+    got = gdf.rename(columns={"a": "f"}, index={0: 3, 1: 4}, level=0)
+
+    assert_eq(expect, got)
+
+
+@pytest_xfail(reason="level=None not implemented yet")
+def test_rename_for_level_is_None_MC():
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+    gdf.columns = pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1)])
+    pdf = gdf.to_pandas()
+
+    expect = pdf.rename(columns={"a": "f"}, level=None)
+    got = gdf.rename(columns={"a": "f"}, level=None)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            [[1, 2, 3], 11, "a"],
+            [None, 22, "e"],
+            [[4], 33, "i"],
+            [[], 44, "o"],
+            [[5, 6], 55, "u"],
+        ],  # nested
+        [
+            [1, 11, "a"],
+            [2, 22, "e"],
+            [3, 33, "i"],
+            [4, 44, "o"],
+            [5, 55, "u"],
+        ],  # non-nested
+    ],
+)
+@pytest.mark.parametrize(
+    ("labels", "label_to_explode"),
+    [
+        (None, 0),
+        (pd.Index(["a", "b", "c"]), "a"),
+        (
+            pd.MultiIndex.from_tuples(
+                [(0, "a"), (0, "b"), (1, "a")], names=["l0", "l1"]
+            ),
+            (0, "a"),
+        ),
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize(
+    "p_index",
+    [
+        None,
+        ["ia", "ib", "ic", "id", "ie"],
+        pd.MultiIndex.from_tuples(
+            [(0, "a"), (0, "b"), (0, "c"), (1, "a"), (1, "b")]
+        ),
+    ],
+)
+def test_explode(data, labels, ignore_index, p_index, label_to_explode):
+    pdf = pd.DataFrame(data, index=p_index, columns=labels)
+    gdf = cudf.from_pandas(pdf)
+
+    if PANDAS_GE_134:
+        expect = pdf.explode(label_to_explode, ignore_index)
+    else:
+        # https://github.com/pandas-dev/pandas/issues/43314
+        if isinstance(label_to_explode, int):
+            pdlabel_to_explode = [label_to_explode]
+        else:
+            pdlabel_to_explode = label_to_explode
+        expect = pdf.explode(pdlabel_to_explode, ignore_index)
+
+    got = gdf.explode(label_to_explode, ignore_index)
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "df,ascending,expected",
+    [
+        (
+            cudf.DataFrame({"a": [10, 0, 2], "b": [-10, 10, 1]}),
+            True,
+            cupy.array([1, 2, 0], dtype="int32"),
+        ),
+        (
+            cudf.DataFrame({"a": [10, 0, 2], "b": [-10, 10, 1]}),
+            False,
+            cupy.array([0, 2, 1], dtype="int32"),
+        ),
+    ],
+)
+def test_dataframe_argsort(df, ascending, expected):
+    actual = df.argsort(ascending=ascending)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data,columns,index",
+    [
+        (pd.Series([1, 2, 3]), None, None),
+        (pd.Series(["a", "b", None, "c"], name="abc"), None, None),
+        (
+            pd.Series(["a", "b", None, "c"], name="abc"),
+            ["abc", "b"],
+            [1, 2, 3],
+        ),
+    ],
+)
+def test_dataframe_init_from_series(data, columns, index):
+    expected = pd.DataFrame(data, columns=columns, index=index)
+    actual = cudf.DataFrame(data, columns=columns, index=index)
+
+    assert_eq(
+        expected,
+        actual,
+        check_index_type=len(expected) != 0,
+    )
+
+
+def test_frame_series_where():
+    gdf = cudf.DataFrame(
+        {"a": [1.0, 2.0, None, 3.0, None], "b": [None, 10.0, 11.0, None, 23.0]}
+    )
+    pdf = gdf.to_pandas()
+    expected = gdf.where(gdf.notna(), gdf.mean())
+    actual = pdf.where(pdf.notna(), pdf.mean(), axis=1)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [{"a": [1, 2, 3], "b": [1, 1, 0]}],
+)
+def test_frame_series_where_other(data):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expected = gdf.where(gdf["b"] == 1, cudf.NA)
+    actual = pdf.where(pdf["b"] == 1, pd.NA)
+    assert_eq(
+        actual.fillna(-1).values,
+        expected.fillna(-1).values,
+        check_dtype=False,
+    )
+
+    expected = gdf.where(gdf["b"] == 1, 0)
+    actual = pdf.where(pdf["b"] == 1, 0)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data, gkey",
+    [
+        (
+            {
+                "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+                "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
+                "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+                "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+            },
+            ["id", "val1", "val2"],
+        ),
+        (
+            {
+                "id": [0] * 4 + [1] * 3,
+                "a": [10, 3, 4, 2, -3, 9, 10],
+                "b": [10, 23, -4, 2, -3, 9, 19],
+            },
+            ["id", "a"],
+        ),
+        (
+            {
+                "id": ["a", "a", "b", "b", "c", "c"],
+                "val": cudf.Series(
+                    [None, None, None, None, None, None], dtype="float64"
+                ),
+            },
+            ["id"],
+        ),
+        (
+            {
+                "id": ["a", "a", "b", "b", "c", "c"],
+                "val1": [None, 4, 6, 8, None, 2],
+                "val2": [4, 5, None, 2, 9, None],
+            },
+            ["id"],
+        ),
+        ({"id": [1.0], "val1": [2.0], "val2": [3.0]}, ["id"]),
+    ],
+)
+@pytest.mark.parametrize(
+    "min_per",
+    [0, 1, 2, 3, 4],
+)
+def test_pearson_corr_passing(data, gkey, min_per):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    actual = gdf.groupby(gkey).corr(method="pearson", min_periods=min_per)
+    expected = pdf.groupby(gkey).corr(method="pearson", min_periods=min_per)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("method", ["kendall", "spearman"])
+def test_pearson_corr_unsupported_methods(method):
+    gdf = cudf.DataFrame(
+        {
+            "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+            "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
+            "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+            "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+        }
+    )
+
+    with pytest.raises(
+        NotImplementedError,
+        match="Only pearson correlation is currently supported",
+    ):
+        gdf.groupby("id").corr(method)
+
+
+def test_pearson_corr_empty_columns():
+    gdf = cudf.DataFrame(columns=["id", "val1", "val2"])
+    pdf = gdf.to_pandas()
+
+    actual = gdf.groupby("id").corr("pearson")
+    expected = pdf.groupby("id").corr("pearson")
+
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+        check_index_type=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+            "val1": ["v", "n", "k", "l", "m", "i", "y", "r", "w"],
+            "val2": ["d", "d", "d", "e", "e", "e", "f", "f", "f"],
+        },
+        {
+            "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+            "val1": [1, 1, 1, 2, 2, 2, 3, 3, 3],
+            "val2": ["d", "d", "d", "e", "e", "e", "f", "f", "f"],
+        },
+    ],
+)
+@pytest.mark.parametrize("gkey", ["id", "val1", "val2"])
+def test_pearson_corr_invalid_column_types(data, gkey):
+    with pytest.raises(
+        TypeError,
+        match="Correlation accepts only numerical column-pairs",
+    ):
+        cudf.DataFrame(data).groupby(gkey).corr("pearson")
+
+
+def test_pearson_corr_multiindex_dataframe():
+    gdf = cudf.DataFrame(
+        {"a": [1, 1, 2, 2], "b": [1, 1, 2, 3], "c": [2, 3, 4, 5]}
+    ).set_index(["a", "b"])
+
+    actual = gdf.groupby(level="a").corr("pearson")
+    expected = gdf.to_pandas().groupby(level="a").corr("pearson")
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [np.nan, 1, 2], "b": [None, None, None]},
+        {"a": [1, 2, np.nan, 2], "b": [np.nan, np.nan, np.nan, np.nan]},
+        {
+            "a": [1, 2, np.nan, 2, None],
+            "b": [np.nan, np.nan, None, np.nan, np.nan],
+        },
+        {"a": [1, 2, 2, None, 1.1], "b": [1, 2.2, 3, None, 5]},
+    ],
+)
+@pytest.mark.parametrize("nan_as_null", [True, False])
+def test_dataframe_constructor_nan_as_null(data, nan_as_null):
+    actual = cudf.DataFrame(data, nan_as_null=nan_as_null)
+
+    if nan_as_null:
+        assert (
+            not (
+                actual.astype("float").replace(
+                    cudf.Series([np.nan], nan_as_null=False), cudf.Series([-1])
+                )
+                == -1
+            )
+            .any()
+            .any()
+        )
+    else:
+        actual = actual.select_dtypes(exclude=["object"])
+        assert (actual.replace(np.nan, -1) == -1).any().any()
+
+
+def test_dataframe_add_prefix():
+    cdf = cudf.DataFrame({"A": [1, 2, 3, 4], "B": [3, 4, 5, 6]})
+    pdf = cdf.to_pandas()
+
+    got = cdf.add_prefix("item_")
+    expected = pdf.add_prefix("item_")
+
+    assert_eq(got, expected)
+
+
+def test_dataframe_add_suffix():
+    cdf = cudf.DataFrame({"A": [1, 2, 3, 4], "B": [3, 4, 5, 6]})
+    pdf = cdf.to_pandas()
+
+    got = cdf.add_suffix("_item")
+    expected = pdf.add_suffix("_item")
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data, gkey",
+    [
+        (
+            {
+                "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+                "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
+                "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+                "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+            },
+            ["id"],
+        ),
+        (
+            {
+                "id": [0, 0, 0, 0, 1, 1, 1],
+                "a": [10.0, 3, 4, 2.0, -3.0, 9.0, 10.0],
+                "b": [10.0, 23, -4.0, 2, -3.0, 9, 19.0],
+            },
+            ["id", "a"],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "min_periods",
+    [0, 3],
+)
+@pytest.mark.parametrize(
+    "ddof",
+    [1, 2],
+)
+def test_groupby_covariance(data, gkey, min_periods, ddof):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    actual = gdf.groupby(gkey).cov(min_periods=min_periods, ddof=ddof)
+    # We observe a warning if there are too few observations to generate a
+    # non-singular covariance matrix _and_ there are enough that pandas will
+    # actually attempt to compute a value. Groups with fewer than min_periods
+    # inputs will be skipped altogether, so no warning occurs.
+    with expect_warning_if(
+        (pdf.groupby(gkey).count() < 2).all().all()
+        and (pdf.groupby(gkey).count() > min_periods).all().all(),
+        RuntimeWarning,
+    ):
+        expected = pdf.groupby(gkey).cov(min_periods=min_periods, ddof=ddof)
+
+    assert_eq(expected, actual)
+
+
+def test_groupby_covariance_multiindex_dataframe():
+    gdf = cudf.DataFrame(
+        {
+            "a": [1, 1, 2, 2],
+            "b": [1, 1, 2, 2],
+            "c": [2, 3, 4, 5],
+            "d": [6, 8, 9, 1],
+        }
+    ).set_index(["a", "b"])
+
+    actual = gdf.groupby(level=["a", "b"]).cov()
+    expected = gdf.to_pandas().groupby(level=["a", "b"]).cov()
+
+    assert_eq(expected, actual)
+
+
+def test_groupby_covariance_empty_columns():
+    gdf = cudf.DataFrame(columns=["id", "val1", "val2"])
+    pdf = gdf.to_pandas()
+
+    actual = gdf.groupby("id").cov()
+    expected = pdf.groupby("id").cov()
+
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+        check_index_type=False,
+    )
+
+
+def test_groupby_cov_invalid_column_types():
+    gdf = cudf.DataFrame(
+        {
+            "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+            "val1": ["v", "n", "k", "l", "m", "i", "y", "r", "w"],
+            "val2": ["d", "d", "d", "e", "e", "e", "f", "f", "f"],
+        },
+    )
+    with pytest.raises(
+        TypeError,
+        match="Covariance accepts only numerical column-pairs",
+    ):
+        gdf.groupby("id").cov()
+
+
+def test_groupby_cov_positive_semidefinite_matrix():
+    # Refer to discussions in PR #9889 re "pair-wise deletion" strategy
+    # being used in pandas to compute the covariance of a dataframe with
+    # rows containing missing values.
+    # Note: cuDF currently matches pandas behavior in that the covariance
+    # matrices are not guaranteed PSD (positive semi definite).
+    # https://github.com/rapidsai/cudf/pull/9889#discussion_r794158358
+    gdf = cudf.DataFrame(
+        [[1, 2], [None, 4], [5, None], [7, 8]], columns=["v0", "v1"]
+    )
+    actual = gdf.groupby(by=cudf.Series([1, 1, 1, 1])).cov()
+    actual.reset_index(drop=True, inplace=True)
+
+    pdf = gdf.to_pandas()
+    expected = pdf.groupby(by=pd.Series([1, 1, 1, 1])).cov()
+    expected.reset_index(drop=True, inplace=True)
+
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+    )
+
+
+@pytest_xfail
+def test_groupby_cov_for_pandas_bug_case():
+    # Handles case: pandas bug using ddof with missing data.
+    # Filed an issue in Pandas on GH, link below:
+    # https://github.com/pandas-dev/pandas/issues/45814
+    pdf = pd.DataFrame(
+        {"id": ["a", "a"], "val1": [1.0, 2.0], "val2": [np.nan, np.nan]}
+    )
+    expected = pdf.groupby("id").cov(ddof=2)
+
+    gdf = cudf.from_pandas(pdf)
+    actual = gdf.groupby("id").cov(ddof=2)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.random.RandomState(seed=10).randint(-50, 50, (25, 30)),
+        np.random.RandomState(seed=10).random_sample((4, 4)),
+        np.array([1.123, 2.343, 5.890, 0.0]),
+        [True, False, True, False, False],
+        {"a": [1.123, 2.343, np.nan, np.nan], "b": [None, 3, 9.08, None]},
+    ],
+)
+@pytest.mark.parametrize("periods", (-5, -1, 0, 1, 5))
+def test_diff_numeric_dtypes(data, periods):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    actual = gdf.diff(periods=periods, axis=0)
+    expected = pdf.diff(periods=periods, axis=0)
+
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    ("precision", "scale"),
+    [(5, 2), (8, 5)],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype],
+)
+def test_diff_decimal_dtypes(precision, scale, dtype):
+    gdf = cudf.DataFrame(
+        np.random.default_rng(seed=42).uniform(10.5, 75.5, (10, 6)),
+        dtype=dtype(precision=precision, scale=scale),
+    )
+    pdf = gdf.to_pandas()
+
+    actual = gdf.diff()
+    expected = pdf.diff()
+
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+    )
+
+
+def test_diff_invalid_axis():
+    gdf = cudf.DataFrame(np.array([1.123, 2.343, 5.890, 0.0]))
+    with pytest.raises(NotImplementedError, match="Only axis=0 is supported."):
+        gdf.diff(periods=1, axis=1)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "int_col": [1, 2, 3, 4, 5],
+            "float_col": [1.0, 2.0, 3.0, 4.0, 5.0],
+            "string_col": ["a", "b", "c", "d", "e"],
+        },
+        ["a", "b", "c", "d", "e"],
+    ],
+)
+def test_diff_unsupported_dtypes(data):
+    gdf = cudf.DataFrame(data)
+    with pytest.raises(
+        TypeError,
+        match=r"unsupported operand type\(s\)",
+    ):
+        gdf.diff()
+
+
+def test_diff_many_dtypes():
+    pdf = pd.DataFrame(
+        {
+            "dates": pd.date_range("2020-01-01", "2020-01-06", freq="D"),
+            "bools": [True, True, True, False, True, True],
+            "floats": [1.0, 2.0, 3.5, np.nan, 5.0, -1.7],
+            "ints": [1, 2, 3, 3, 4, 5],
+            "nans_nulls": [np.nan, None, None, np.nan, np.nan, None],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(pdf.diff(), gdf.diff())
+    assert_eq(pdf.diff(periods=2), gdf.diff(periods=2))
+
+
+def test_dataframe_assign_cp_np_array():
+    m, n = 5, 3
+    cp_ndarray = cupy.random.randn(m, n)
+    pdf = pd.DataFrame({f"f_{i}": range(m) for i in range(n)})
+    gdf = cudf.DataFrame({f"f_{i}": range(m) for i in range(n)})
+    pdf[[f"f_{i}" for i in range(n)]] = cupy.asnumpy(cp_ndarray)
+    gdf[[f"f_{i}" for i in range(n)]] = cp_ndarray
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [{"a": [1, 2, 3], "b": [1, 1, 0]}],
+)
+def test_dataframe_nunique(data):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    actual = gdf.nunique()
+    expected = pdf.nunique()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [{"key": [0, 1, 1, 0, 0, 1], "val": [1, 8, 3, 9, -3, 8]}],
+)
+def test_dataframe_nunique_index(data):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    actual = gdf.index.nunique()
+    expected = pdf.index.nunique()
+
+    assert_eq(expected, actual)
+
+
+def test_dataframe_rename_duplicate_column():
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]})
+    with pytest.raises(
+        ValueError, match="Duplicate column names are not allowed"
+    ):
+        gdf.rename(columns={"a": "b"}, inplace=True)
+
+
+@pytest_unmark_spilling
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.random.RandomState(seed=10).randint(-50, 50, (10, 10)),
+        np.random.RandomState(seed=10).random_sample((4, 4)),
+        np.array([1.123, 2.343, 5.890, 0.0]),
+        {"a": [1.123, 2.343, np.nan, np.nan], "b": [None, 3, 9.08, None]},
+    ],
+)
+@pytest.mark.parametrize("periods", [-5, -2, 0, 2, 5])
+@pytest.mark.parametrize("fill_method", ["ffill", "bfill", "pad", "backfill"])
+def test_dataframe_pct_change(data, periods, fill_method):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    actual = gdf.pct_change(periods=periods, fill_method=fill_method)
+    expected = pdf.pct_change(periods=periods, fill_method=fill_method)
+
+    assert_eq(expected, actual)
+
+
+def test_mean_timeseries():
+    gdf = cudf.datasets.timeseries()
+    pdf = gdf.to_pandas()
+
+    expected = pdf.mean(numeric_only=True)
+    actual = gdf.mean(numeric_only=True)
+
+    assert_eq(expected, actual)
+
+    with pytest.warns(FutureWarning):
+        expected = pdf.mean()
+    with pytest.warns(FutureWarning):
+        actual = gdf.mean()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": [1, 2, 3, 4, 5],
+            "b": ["a", "b", "c", "d", "e"],
+            "c": [1.0, 2.0, 3.0, 4.0, 5.0],
+        }
+    ],
+)
+def test_std_different_dtypes(data):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expected = pdf.std(numeric_only=True)
+    actual = gdf.std(numeric_only=True)
+
+    assert_eq(expected, actual)
+
+    with pytest.warns(FutureWarning):
+        expected = pdf.std()
+    with pytest.warns(FutureWarning):
+        actual = gdf.std()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+            "val1": ["v", "n", "k", "l", "m", "i", "y", "r", "w"],
+            "val2": ["d", "d", "d", "e", "e", "e", "f", "f", "f"],
+        }
+    ],
+)
+def test_empty_numeric_only(data):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+    expected = pdf.prod(numeric_only=True)
+    actual = gdf.prod(numeric_only=True)
+    assert_eq(expected, actual)
+
+
+@pytest.fixture(params=[0, 10], ids=["empty", "10"])
+def df_eval(request):
+    N = request.param
+    if N == 0:
+        value = np.zeros(0, dtype="int")
+        return cudf.DataFrame(
+            {
+                "a": value,
+                "b": value,
+                "c": value,
+                "d": value,
+            }
+        )
+    int_max = 10
+    rng = cupy.random.default_rng(0)
+    return cudf.DataFrame(
+        {
+            "a": rng.integers(N, size=int_max),
+            "b": rng.integers(N, size=int_max),
+            "c": rng.integers(N, size=int_max),
+            "d": rng.integers(N, size=int_max),
+        }
+    )
+
+
+# Note that for now expressions do not automatically handle casting, so inputs
+# need to be casted appropriately
+@pytest.mark.parametrize(
+    "expr, dtype",
+    [
+        ("a", int),
+        ("+a", int),
+        ("a + b", int),
+        ("a == b", int),
+        ("a / b", float),
+        ("a * b", int),
+        ("a > b", int),
+        ("a >= b", int),
+        ("a > b > c", int),
+        ("a > b < c", int),
+        ("a & b", int),
+        ("a & b | c", int),
+        ("sin(a)", float),
+        ("exp(sin(abs(a)))", float),
+        ("sqrt(floor(a))", float),
+        ("ceil(arctanh(a))", float),
+        ("(a + b) - (c * d)", int),
+        ("~a", int),
+        ("(a > b) and (c > d)", int),
+        ("(a > b) or (c > d)", int),
+        ("not (a > b)", int),
+        ("a + 1", int),
+        ("a + 1.0", float),
+        ("-a + 1", int),
+        ("+a + 1", int),
+        ("e = a + 1", int),
+        (
+            """
+            e = log(cos(a)) + 1.0
+            f = abs(c) - exp(d)
+            """,
+            float,
+        ),
+        ("a_b_are_equal = (a == b)", int),
+        ("a > b", str),
+        ("a < '1'", str),
+        ('a == "1"', str),
+    ],
+)
+def test_dataframe_eval(df_eval, expr, dtype):
+    df_eval = df_eval.astype(dtype)
+    expect = df_eval.to_pandas().eval(expr)
+    got = df_eval.eval(expr)
+    # In the specific case where the evaluated expression is a unary function
+    # of a single column with no nesting, pandas will retain the name. This
+    # level of compatibility is out of scope for now.
+    assert_eq(expect, got, check_names=False)
+
+    # Test inplace
+    if re.search("[^=><]=[^=]", expr) is not None:
+        pdf_eval = df_eval.to_pandas()
+        pdf_eval.eval(expr, inplace=True)
+        df_eval.eval(expr, inplace=True)
+        assert_eq(pdf_eval, df_eval)
+
+
+@pytest.mark.parametrize(
+    "expr",
+    [
+        """
+        e = a + b
+        a == b
+        """,
+        "a_b_are_equal = (a == b) = c",
+    ],
+)
+def test_dataframe_eval_errors(df_eval, expr):
+    with pytest.raises(ValueError):
+        df_eval.eval(expr)
+
+
+def test_dataframe_eval_misc():
+    df = cudf.DataFrame({"a": [1, 2, 3, None, 5]})
+    got = df.eval("isnull(a)")
+    assert_eq(got, cudf.Series.isnull(df["a"]), check_names=False)
+
+    df.eval("c = isnull(1)", inplace=True)
+    assert_eq(df["c"], cudf.Series([False] * len(df), name="c"))
+
+
+@pytest.mark.parametrize(
+    "gdf,subset",
+    [
+        (
+            cudf.DataFrame(
+                {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]},
+                index=["falcon", "dog", "cat", "ant"],
+            ),
+            ["num_legs"],
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "first_name": ["John", "Anne", "John", "Beth"],
+                    "middle_name": ["Smith", None, None, "Louise"],
+                }
+            ),
+            ["first_name"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("use_subset", [True, False])
+def test_value_counts(
+    gdf,
+    subset,
+    sort,
+    ascending,
+    normalize,
+    dropna,
+    use_subset,
+):
+    pdf = gdf.to_pandas()
+
+    got = gdf.value_counts(
+        subset=subset if (use_subset) else None,
+        sort=sort,
+        ascending=ascending,
+        normalize=normalize,
+        dropna=dropna,
+    )
+    expected = pdf.value_counts(
+        subset=subset if (use_subset) else None,
+        sort=sort,
+        ascending=ascending,
+        normalize=normalize,
+        dropna=dropna,
+    )
+
+    if not dropna:
+        # Convert the Pandas series to a cuDF one due to difference
+        # in the handling of NaNs between the two (<NA> in cuDF and
+        # NaN in Pandas) when dropna=False.
+        assert_eq(got.sort_index(), cudf.from_pandas(expected).sort_index())
+    else:
+        assert_eq(got.sort_index(), expected.sort_index())
+
+    with pytest.raises(KeyError):
+        gdf.value_counts(subset=["not_a_column_name"])
+
+
+@pytest.fixture
+def wildcard_df():
+    midx = cudf.MultiIndex.from_tuples(
+        [(c1, c2) for c1 in "abc" for c2 in "ab"]
+    )
+    df = cudf.DataFrame({f"{i}": [i] for i in range(6)})
+    df.columns = midx
+    return df
+
+
+def test_multiindex_wildcard_selection_all(wildcard_df):
+    expect = wildcard_df.to_pandas().loc[:, (slice(None), "b")]
+    got = wildcard_df.loc[:, (slice(None), "b")]
+    assert_eq(expect, got)
+
+
+@pytest_xfail(reason="Not yet properly supported.")
+def test_multiindex_wildcard_selection_partial(wildcard_df):
+    expect = wildcard_df.to_pandas().loc[:, (slice("a", "b"), "b")]
+    got = wildcard_df.loc[:, (slice("a", "b"), "b")]
+    assert_eq(expect, got)
+
+
+@pytest_xfail(reason="Not yet properly supported.")
+def test_multiindex_wildcard_selection_three_level_all():
+    midx = cudf.MultiIndex.from_tuples(
+        [(c1, c2, c3) for c1 in "abcd" for c2 in "abc" for c3 in "ab"]
+    )
+    df = cudf.DataFrame({f"{i}": [i] for i in range(24)})
+    df.columns = midx
+
+    expect = df.to_pandas().loc[:, (slice("a", "c"), slice("a", "b"), "b")]
+    got = df.loc[:, (slice(None), "b")]
+    assert_eq(expect, got)
+
+
+def test_dataframe_assign_scalar_to_empty_series():
+    expected = pd.DataFrame({"a": []})
+    actual = cudf.DataFrame({"a": []})
+    expected.a = 0
+    actual.a = 0
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {0: [1, 2, 3], 2: [10, 11, 23]},
+        {("a", "b"): [1, 2, 3], ("2",): [10, 11, 23]},
+    ],
+)
+def test_non_string_column_name_to_arrow(data):
+    df = cudf.DataFrame(data)
+
+    expected = df.to_arrow()
+    actual = pa.Table.from_pandas(df.to_pandas())
+
+    assert expected.equals(actual)
+
+
+def test_complex_types_from_arrow():
+    expected = pa.Table.from_arrays(
+        [
+            pa.array([1, 2, 3]),
+            pa.array([10, 20, 30]),
+            pa.array([{"a": 9}, {"b": 10}, {"c": 11}]),
+            pa.array([[{"a": 1}], [{"b": 2}], [{"c": 3}]]),
+            pa.array([10, 11, 12]).cast(pa.decimal128(21, 2)),
+            pa.array([{"a": 9}, {"b": 10, "c": {"g": 43}}, {"c": {"a": 10}}]),
+        ],
+        names=["a", "b", "c", "d", "e", "f"],
+    )
+
+    df = cudf.DataFrame.from_arrow(expected)
+    actual = df.to_arrow()
+
+    assert expected.equals(actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "brand": ["Yum Yum", "Yum Yum", "Indomie", "Indomie", "Indomie"],
+            "style": ["cup", "cup", "cup", "pack", "pack"],
+            "rating": [4, 4, 3.5, 15, 5],
+        },
+        {
+            "brand": ["Indomie", "Yum Yum", "Indomie", "Indomie", "Indomie"],
+            "style": ["cup", "cup", "cup", "cup", "pack"],
+            "rating": [4, 4, 3.5, 4, 5],
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "subset", [None, ["brand"], ["rating"], ["style", "rating"]]
+)
+@pytest.mark.parametrize("keep", ["first", "last", False])
+def test_dataframe_duplicated(data, subset, keep):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expected = pdf.duplicated(subset=subset, keep=keep)
+    actual = gdf.duplicated(subset=subset, keep=keep)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"col": [{"a": 1.1}, {"a": 2.1}, {"a": 10.0}, {"a": 11.2323}, None]},
+        {"a": [[{"b": 567}], None] * 10},
+        {"a": [decimal.Decimal(10), decimal.Decimal(20), None]},
+    ],
+)
+def test_dataframe_transpose_complex_types(data):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expected = pdf.T
+    actual = gdf.T
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"col": [{"a": 1.1}, {"a": 2.1}, {"a": 10.0}, {"a": 11.2323}, None]},
+        {"a": [[{"b": 567}], None] * 10},
+        {"a": [decimal.Decimal(10), decimal.Decimal(20), None]},
+    ],
+)
+def test_dataframe_values_complex_types(data):
+    gdf = cudf.DataFrame(data)
+    with pytest.raises(NotImplementedError):
+        gdf.values
+
+
+def test_dataframe_from_arrow_slice():
+    table = pa.Table.from_pandas(
+        pd.DataFrame.from_dict(
+            {"a": ["aa", "bb", "cc"] * 3, "b": [1, 2, 3] * 3}
+        )
+    )
+    table_slice = table.slice(3, 7)
+
+    expected = table_slice.to_pandas()
+    actual = cudf.DataFrame.from_arrow(table_slice)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": ["x", "y", "z"], "c": 4},
+        {"c": 4, "a": [1, 2, 3], "b": ["x", "y", "z"]},
+        {"a": [1, 2, 3], "c": 4},
+    ],
+)
+def test_dataframe_init_from_scalar_and_lists(data):
+    actual = cudf.DataFrame(data)
+    expected = pd.DataFrame(data)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data,index",
+    [
+        ({"a": [1, 2, 3], "b": ["x", "y", "z", "z"], "c": 4}, None),
+        (
+            {
+                "a": [1, 2, 3],
+                "b": ["x", "y", "z"],
+            },
+            [10, 11],
+        ),
+        (
+            {
+                "a": [1, 2, 3],
+                "b": ["x", "y", "z"],
+            },
+            [10, 11],
+        ),
+        ([[10, 11], [12, 13]], ["a", "b", "c"]),
+    ],
+)
+def test_dataframe_init_length_error(data, index):
+    assert_exceptions_equal(
+        lfunc=pd.DataFrame,
+        rfunc=cudf.DataFrame,
+        lfunc_args_and_kwargs=(
+            [],
+            {"data": data, "index": index},
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {"data": data, "index": index},
+        ),
+    )
+
+
+def test_dataframe_binop_with_mixed_date_types():
+    df = pd.DataFrame(
+        np.random.rand(2, 2),
+        columns=pd.Index(["2000-01-03", "2000-01-04"], dtype="datetime64[ns]"),
+    )
+    ser = pd.Series(np.random.rand(3), index=[0, 1, 2])
+    gdf = cudf.from_pandas(df)
+    gser = cudf.from_pandas(ser)
+    expected = df - ser
+    got = gdf - gser
+    assert_eq(expected, got)
+
+
+def test_dataframe_binop_with_mixed_string_types():
+    df1 = pd.DataFrame(np.random.rand(3, 3), columns=pd.Index([0, 1, 2]))
+    df2 = pd.DataFrame(
+        np.random.rand(6, 6),
+        columns=pd.Index([0, 1, 2, "VhDoHxRaqt", "X0NNHBIPfA", "5FbhPtS0D1"]),
+    )
+    gdf1 = cudf.from_pandas(df1)
+    gdf2 = cudf.from_pandas(df2)
+
+    expected = df2 + df1
+    got = gdf2 + gdf1
+
+    assert_eq(expected, got)
+
+
+def test_dataframe_binop_and_where():
+    df = pd.DataFrame(np.random.rand(2, 2), columns=pd.Index([True, False]))
+    gdf = cudf.from_pandas(df)
+
+    expected = df > 1
+    got = gdf > 1
+
+    assert_eq(expected, got)
+
+    expected = df[df > 1]
+    got = gdf[gdf > 1]
+
+    assert_eq(expected, got)
+
+
+def test_dataframe_binop_with_datetime_index():
+    df = pd.DataFrame(
+        np.random.rand(2, 2),
+        columns=pd.Index(["2000-01-03", "2000-01-04"], dtype="datetime64[ns]"),
+    )
+    ser = pd.Series(
+        np.random.rand(2),
+        index=pd.Index(
+            [
+                "2000-01-04",
+                "2000-01-03",
+            ],
+            dtype="datetime64[ns]",
+        ),
+    )
+    gdf = cudf.from_pandas(df)
+    gser = cudf.from_pandas(ser)
+    expected = df - ser
+    got = gdf - gser
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "columns",
+    (
+        [],
+        ["c", "a"],
+        ["a", "d", "b", "e", "c"],
+        ["a", "b", "c"],
+        pd.Index(["b", "a", "c"], name="custom_name"),
+    ),
+)
+@pytest.mark.parametrize("index", (None, [4, 5, 6]))
+def test_dataframe_dict_like_with_columns(columns, index):
+    data = {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
+    expect = pd.DataFrame(data, columns=columns, index=index)
+    actual = cudf.DataFrame(data, columns=columns, index=index)
+    if index is None and len(columns) == 0:
+        # We make an empty range index, pandas makes an empty index
+        expect = expect.reset_index(drop=True)
+    assert_eq(expect, actual)
+
+
+def test_dataframe_init_columns_named_multiindex():
+    np.random.seed(0)
+    data = np.random.randn(2, 2)
+    columns = cudf.MultiIndex.from_tuples(
+        [("A", "one"), ("A", "two")], names=["y", "z"]
+    )
+    gdf = cudf.DataFrame(data, columns=columns)
+    pdf = pd.DataFrame(data, columns=columns.to_pandas())
+
+    assert_eq(gdf, pdf)
+
+
+def test_dataframe_init_columns_named_index():
+    np.random.seed(0)
+    data = np.random.randn(2, 2)
+    columns = pd.Index(["a", "b"], name="custom_name")
+    gdf = cudf.DataFrame(data, columns=columns)
+    pdf = pd.DataFrame(data, columns=columns)
+
+    assert_eq(gdf, pdf)
+
+
+def test_dataframe_from_pandas_sparse():
+    pdf = pd.DataFrame(range(2), dtype=pd.SparseDtype(np.int64, 0))
+    with pytest.raises(NotImplementedError):
+        cudf.DataFrame(pdf)
+
+
+def test_dataframe_constructor_unbounded_sequence():
+    class A:
+        def __getitem__(self, key):
+            return 1
+
+    with pytest.raises(TypeError):
+        cudf.DataFrame([A()])
+
+    with pytest.raises(TypeError):
+        cudf.DataFrame({"a": A()})
+
+
+def test_dataframe_constructor_dataframe_list():
+    df = cudf.DataFrame(range(2))
+    with pytest.raises(ValueError):
+        cudf.DataFrame([df])
+
+
+def test_dataframe_constructor_from_namedtuple():
+    Point1 = namedtuple("Point1", ["a", "b", "c"])
+    Point2 = namedtuple("Point1", ["x", "y"])
+
+    data = [Point1(1, 2, 3), Point2(4, 5)]
+    idx = ["a", "b"]
+    gdf = cudf.DataFrame(data, index=idx)
+    pdf = pd.DataFrame(data, index=idx)
+
+    assert_eq(gdf, pdf)
+
+    data = [Point2(4, 5), Point1(1, 2, 3)]
+    with pytest.raises(ValueError):
+        cudf.DataFrame(data, index=idx)
+    with pytest.raises(ValueError):
+        pd.DataFrame(data, index=idx)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["datetime64[ns]", "timedelta64[ns]", "int64", "float32"]
+)
+def test_dataframe_mixed_dtype_error(dtype):
+    pdf = pd.Series([1, 2, 3], dtype=dtype).to_frame().astype(object)
+    with pytest.raises(TypeError):
+        cudf.from_pandas(pdf)
+
+
+@pytest.mark.parametrize(
+    "index_data,name",
+    [([10, 13], "a"), ([30, 40, 20], "b"), (["ef"], "c"), ([2, 3], "Z")],
+)
+def test_dataframe_reindex_with_index_names(index_data, name):
+    gdf = cudf.DataFrame(
+        {
+            "a": [10, 12, 13],
+            "b": [20, 30, 40],
+            "c": cudf.Series(["ab", "cd", "ef"], dtype="category"),
+        }
+    )
+    if name in gdf.columns:
+        gdf = gdf.set_index(name)
+    pdf = gdf.to_pandas()
+
+    gidx = cudf.Index(index_data, name=name)
+    actual = gdf.reindex(gidx)
+    expected = pdf.reindex(gidx.to_pandas())
+
+    assert_eq(actual, expected)
+
+    actual = gdf.reindex(index_data)
+    expected = pdf.reindex(index_data)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("attr", ["nlargest", "nsmallest"])
+def test_dataframe_nlargest_nsmallest_str_error(attr):
+    gdf = cudf.DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
+    pdf = gdf.to_pandas()
+
+    assert_exceptions_equal(
+        getattr(gdf, attr),
+        getattr(pdf, attr),
+        ([], {"n": 1, "columns": ["a", "b"]}),
+        ([], {"n": 1, "columns": ["a", "b"]}),
+    )
+
+
+def test_series_data_no_name_with_columns():
+    gdf = cudf.DataFrame(cudf.Series([1]), columns=[1])
+    pdf = pd.DataFrame(pd.Series([1]), columns=[1])
+    assert_eq(gdf, pdf)
+
+
+def test_series_data_no_name_with_columns_more_than_one_raises():
+    with pytest.raises(ValueError):
+        cudf.DataFrame(cudf.Series([1]), columns=[1, 2])
+    with pytest.raises(ValueError):
+        pd.DataFrame(pd.Series([1]), columns=[1, 2])
+
+
+def test_series_data_with_name_with_columns_matching():
+    gdf = cudf.DataFrame(cudf.Series([1], name=1), columns=[1])
+    pdf = pd.DataFrame(pd.Series([1], name=1), columns=[1])
+    assert_eq(gdf, pdf)
+
+
+@pytest.mark.xfail(
+    version.parse(pd.__version__) < version.parse("2.0"),
+    reason="pandas returns Index[object] instead of RangeIndex",
+)
+def test_series_data_with_name_with_columns_not_matching():
+    gdf = cudf.DataFrame(cudf.Series([1], name=2), columns=[1])
+    pdf = pd.DataFrame(pd.Series([1], name=2), columns=[1])
+    assert_eq(gdf, pdf)
+
+
+def test_series_data_with_name_with_columns_matching_align():
+    gdf = cudf.DataFrame(cudf.Series([1], name=2), columns=[1, 2])
+    pdf = pd.DataFrame(pd.Series([1], name=2), columns=[1, 2])
+    assert_eq(gdf, pdf)
+
+
+@pytest.mark.parametrize("digits", [0, 1, 3, 4, 10])
+def test_dataframe_round_builtin(digits):
+    pdf = pd.DataFrame(
+        {
+            "a": [1.2234242333234, 323432.3243423, np.nan],
+            "b": ["a", "b", "c"],
+            "c": pd.Series([34224, 324324, 324342], dtype="datetime64[ns]"),
+            "d": pd.Series([224.242, None, 2424.234324], dtype="category"),
+            "e": [
+                decimal.Decimal("342.3243234234242"),
+                decimal.Decimal("89.32432497687622"),
+                None,
+            ],
+        }
+    )
+    gdf = cudf.from_pandas(pdf, nan_as_null=False)
+
+    expected = round(pdf, digits)
+    actual = round(gdf, digits)
+
+    assert_eq(expected, actual)
+
+
+def test_dataframe_init_from_nested_dict():
+    ordered_dict = OrderedDict(
+        [
+            ("one", OrderedDict([("col_a", "foo1"), ("col_b", "bar1")])),
+            ("two", OrderedDict([("col_a", "foo2"), ("col_b", "bar2")])),
+            ("three", OrderedDict([("col_a", "foo3"), ("col_b", "bar3")])),
+        ]
+    )
+    pdf = pd.DataFrame(ordered_dict)
+    gdf = cudf.DataFrame(ordered_dict)
+
+    assert_eq(pdf, gdf)
+    regular_dict = {key: dict(value) for key, value in ordered_dict.items()}
+
+    pdf = pd.DataFrame(regular_dict)
+    gdf = cudf.DataFrame(regular_dict)
+    assert_eq(pdf, gdf)
+
+
+def test_init_from_2_categoricalindex_series_diff_categories():
+    s1 = cudf.Series(
+        [39, 6, 4], index=cudf.CategoricalIndex(["female", "male", "unknown"])
+    )
+    s2 = cudf.Series(
+        [2, 152, 2, 242, 150],
+        index=cudf.CategoricalIndex(["f", "female", "m", "male", "unknown"]),
+    )
+    result = cudf.DataFrame([s1, s2])
+    expected = pd.DataFrame([s1.to_pandas(), s2.to_pandas()])
+    assert_eq(result, expected, check_dtype=False)
+
+
+def test_data_frame_values_no_cols_but_index():
+    result = cudf.DataFrame(index=range(5)).values
+    expected = pd.DataFrame(index=range(5)).values
+    assert_eq(result, expected)
+
+
+def test_dataframe_reduction_error():
+    gdf = cudf.DataFrame(
+        {
+            "a": cudf.Series([1, 2, 3], dtype="float"),
+            "d": cudf.Series([10, 20, 30], dtype="timedelta64[ns]"),
+        }
+    )
+
+    with pytest.raises(TypeError):
+        gdf.sum()
+
+
+def test_dataframe_from_generator():
+    pdf = pd.DataFrame((i for i in range(5)))
+    gdf = cudf.DataFrame((i for i in range(5)))
+    assert_eq(pdf, gdf)
+
+
+def test_dataframe_from_ndarray_dup_columns():
+    with pytest.raises(ValueError):
+        cudf.DataFrame(np.eye(2), columns=["A", "A"])
+
+
+@pytest.mark.parametrize("name", ["a", 0, None, np.nan, cudf.NA])
+@pytest.mark.parametrize("contains", ["a", 0, None, np.nan, cudf.NA])
+@pytest.mark.parametrize("other_names", [[], ["b", "c"], [1, 2]])
+def test_dataframe_contains(name, contains, other_names):
+    column_names = [name] + other_names
+    gdf = cudf.DataFrame({c: [0] for c in column_names})
+    pdf = pd.DataFrame({c: [0] for c in column_names})
+
+    assert_eq(gdf, pdf)
+
+    if contains is cudf.NA or name is cudf.NA:
+        expectation = contains is cudf.NA and name is cudf.NA
+        assert (contains in pdf) == expectation
+        assert (contains in gdf) == expectation
+    elif pd.api.types.is_float_dtype(gdf.columns.dtype):
+        # In some cases, the columns are converted to a Float64Index based on
+        # the other column names. That casts name values from None to np.nan.
+        expectation = contains is np.nan and (name is None or name is np.nan)
+        assert (contains in pdf) == expectation
+        assert (contains in gdf) == expectation
+    else:
+        expectation = contains == name or (
+            contains is np.nan and name is np.nan
+        )
+        assert (contains in pdf) == expectation
+        assert (contains in gdf) == expectation
+
+    assert (contains in pdf) == (contains in gdf)
+
+
+def test_dataframe_series_dot():
+    pser = pd.Series(range(2))
+    gser = cudf.from_pandas(pser)
+
+    expected = pser @ pser
+    actual = gser @ gser
+
+    assert_eq(expected, actual)
+
+    pdf = pd.DataFrame([[1, 2], [3, 4]], columns=list("ab"))
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pser @ pdf
+    actual = gser @ gdf
+
+    assert_eq(expected, actual)
+
+    assert_exceptions_equal(
+        lfunc=pdf.dot,
+        rfunc=gdf.dot,
+        lfunc_args_and_kwargs=([pser], {}),
+        rfunc_args_and_kwargs=([gser], {}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.dot,
+        rfunc=gdf.dot,
+        lfunc_args_and_kwargs=([pdf], {}),
+        rfunc_args_and_kwargs=([gdf], {}),
+    )
+
+    pser = pd.Series(range(2), index=["a", "k"])
+    gser = cudf.from_pandas(pser)
+
+    pdf = pd.DataFrame([[1, 2], [3, 4]], columns=list("ab"), index=["a", "k"])
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pser @ pdf
+    actual = gser @ gdf
+
+    assert_eq(expected, actual)
+
+    actual = gdf @ [2, 3]
+    expected = pdf @ [2, 3]
+
+    assert_eq(expected, actual)
+
+    actual = pser @ [12, 13]
+    expected = gser @ [12, 13]
+
+    assert_eq(expected, actual)
+
+
+def test_dataframe_duplicate_index_reindex():
+    gdf = cudf.DataFrame({"a": [0, 1, 2, 3]}, index=[0, 0, 1, 1])
+    pdf = gdf.to_pandas()
+
+    assert_exceptions_equal(
+        gdf.reindex,
+        pdf.reindex,
+        lfunc_args_and_kwargs=([10, 11, 12, 13], {}),
+        rfunc_args_and_kwargs=([10, 11, 12, 13], {}),
+    )
diff --git a/python/cudf/cudf/tests/test_dataframe_copy.py b/python/cudf/cudf/tests/test_dataframe_copy.py
new file mode 100644
index 0000000..fec52d8
--- /dev/null
+++ b/python/cudf/cudf/tests/test_dataframe_copy.py
@@ -0,0 +1,175 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+from copy import copy, deepcopy
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+from cudf.core.dataframe import DataFrame
+from cudf.testing._utils import ALL_TYPES, assert_eq, assert_neq
+
+"""
+DataFrame copy expectations
+* A shallow copy constructs a new compound object and then (to the extent
+  possible) inserts references into it to the objects found in the original.
+* A deep copy constructs a new compound object and then, recursively, inserts
+  copies into it of the objects found in the original.
+
+  A cuDF DataFrame is a compound object containing a few members:
+  _index, _size, _cols, where _cols is an OrderedDict
+"""
+
+
+@pytest.mark.parametrize(
+    "copy_parameters",
+    [
+        {"fn": lambda x: x.copy(), "expected_equality": False},
+        {"fn": lambda x: x.copy(deep=True), "expected_equality": False},
+        {"fn": lambda x: copy(x), "expected_equality": False},
+        {"fn": lambda x: deepcopy(x), "expected_equality": False},
+    ],
+)
+def test_dataframe_deep_copy(copy_parameters):
+    pdf = pd.DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]
+    )
+    gdf = DataFrame.from_pandas(pdf)
+    copy_pdf = copy_parameters["fn"](pdf)
+    copy_gdf = copy_parameters["fn"](gdf)
+    copy_pdf["b"] = [0, 0, 0]
+    copy_gdf["b"] = [0, 0, 0]
+    pdf_is_equal = np.array_equal(pdf["b"].values, copy_pdf["b"].values)
+    gdf_is_equal = np.array_equal(
+        gdf["b"].to_numpy(), copy_gdf["b"].to_numpy()
+    )
+    assert pdf_is_equal == copy_parameters["expected_equality"]
+    assert gdf_is_equal == copy_parameters["expected_equality"]
+
+
+@pytest.mark.parametrize(
+    "copy_parameters",
+    [
+        {"fn": lambda x: x.copy(), "expected_equality": False},
+        {"fn": lambda x: x.copy(deep=True), "expected_equality": False},
+        {"fn": lambda x: copy(x), "expected_equality": False},
+        {"fn": lambda x: deepcopy(x), "expected_equality": False},
+    ],
+)
+def test_dataframe_deep_copy_and_insert(copy_parameters):
+    pdf = pd.DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]
+    )
+    gdf = DataFrame.from_pandas(pdf)
+    copy_pdf = copy_parameters["fn"](pdf)
+    copy_gdf = copy_parameters["fn"](gdf)
+    copy_pdf["b"] = [0, 0, 0]
+    copy_gdf["b"] = [0, 0, 0]
+    pdf_is_equal = np.array_equal(pdf["b"].values, copy_pdf["b"].values)
+    gdf_is_equal = np.array_equal(
+        gdf["b"].to_numpy(), copy_gdf["b"].to_numpy()
+    )
+    assert pdf_is_equal == copy_parameters["expected_equality"]
+    assert gdf_is_equal == copy_parameters["expected_equality"]
+
+
+"""
+DataFrame copy bounds checking - sizes 0 through 10 perform as
+expected_equality
+"""
+
+
+@pytest.mark.parametrize(
+    "copy_fn",
+    [
+        lambda x: x.copy(),
+        lambda x: x.copy(deep=True),
+        lambda x: copy(x),
+        lambda x: deepcopy(x),
+        lambda x: x.copy(deep=False),
+    ],
+)
+@pytest.mark.parametrize("ncols", [0, 1, 10])
+@pytest.mark.parametrize("data_type", ALL_TYPES)
+def test_cudf_dataframe_copy(copy_fn, ncols, data_type):
+    pdf = pd.DataFrame()
+    for i in range(ncols):
+        pdf[chr(i + ord("a"))] = pd.Series(
+            np.random.randint(0, 1000, 20)
+        ).astype(data_type)
+    df = DataFrame.from_pandas(pdf)
+    copy_df = copy_fn(df)
+    assert_eq(df, copy_df)
+
+
+@pytest.mark.parametrize(
+    "copy_fn",
+    [
+        lambda x: x.copy(),
+        lambda x: x.copy(deep=True),
+        lambda x: copy(x),
+        lambda x: deepcopy(x),
+        lambda x: x.copy(deep=False),
+    ],
+)
+@pytest.mark.parametrize("ncols", [0, 1, 10])
+@pytest.mark.parametrize("data_type", ALL_TYPES)
+def test_cudf_dataframe_copy_then_insert(copy_fn, ncols, data_type):
+    pdf = pd.DataFrame()
+    for i in range(ncols):
+        pdf[chr(i + ord("a"))] = pd.Series(
+            np.random.randint(0, 1000, 20)
+        ).astype(data_type)
+    df = DataFrame.from_pandas(pdf)
+    copy_df = copy_fn(df)
+    copy_pdf = copy_fn(pdf)
+    copy_df["aa"] = pd.Series(np.random.randint(0, 1000, 20)).astype(data_type)
+    copy_pdf["aa"] = pd.Series(np.random.randint(0, 1000, 20)).astype(
+        data_type
+    )
+    assert not copy_pdf.to_string().split() == pdf.to_string().split()
+    assert not copy_df.to_string().split() == df.to_string().split()
+
+
+def test_deep_copy_write_in_place():
+    pdf = pd.DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]
+    )
+    gdf = DataFrame.from_pandas(pdf)
+    cdf = gdf.copy(deep=True)
+    sr = gdf["b"]
+
+    # Write a value in-place on the deep copy.
+    # This should only affect the copy and not the original.
+    cp.asarray(sr._column)[1] = 42
+
+    assert_neq(gdf, cdf)
+
+
+def test_shallow_copy_write_in_place():
+    pdf = pd.DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]
+    )
+    gdf = DataFrame.from_pandas(pdf)
+    cdf = gdf.copy(deep=False)
+    sr = gdf["a"]
+
+    # Write a value in-place on the shallow copy.
+    # This should change the copy and original.
+    cp.asarray(sr._column)[1] = 42
+
+    assert_eq(gdf, cdf)
+
+
+@pytest.mark.xfail(reason="cudf column-wise shallow copy is immutable")
+def test_dataframe_copy_shallow():
+    pdf = pd.DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"]
+    )
+    gdf = DataFrame.from_pandas(pdf)
+    copy_pdf = pdf.copy(deep=False)
+    copy_gdf = gdf.copy(deep=False)
+    copy_pdf["b"] = [0, 0, 0]
+    copy_gdf["b"] = [0, 0, 0]
+    assert_eq(pdf["b"], copy_pdf["b"])
+    assert_eq(gdf["b"], copy_gdf["b"])
diff --git a/python/cudf/cudf/tests/test_datasets.py b/python/cudf/cudf/tests/test_datasets.py
new file mode 100644
index 0000000..98f801d
--- /dev/null
+++ b/python/cudf/cudf/tests/test_datasets.py
@@ -0,0 +1,57 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import numpy as np
+
+import cudf as gd
+from cudf.testing._utils import assert_eq
+
+
+def test_dataset_timeseries():
+    gdf1 = gd.datasets.timeseries(
+        dtypes={"x": int, "y": float}, freq="120s", nulls_frequency=0.3, seed=1
+    )
+    gdf2 = gd.datasets.timeseries(
+        dtypes={"x": int, "y": float}, freq="120s", nulls_frequency=0.3, seed=1
+    )
+
+    assert_eq(gdf1, gdf2)
+
+    assert gdf1["x"].head().dtype == int
+    assert gdf1["y"].head().dtype == float
+    assert gdf1.index.name == "timestamp"
+
+    gdf = gd.datasets.timeseries(
+        "2000",
+        "2010",
+        freq="2H",
+        dtypes={"value": float, "name": "category", "id": int},
+        nulls_frequency=0.7,
+        seed=1,
+    )
+
+    assert gdf["value"].head().dtype == float
+    assert gdf["id"].head().dtype == int
+    assert gdf["name"].head().dtype == "category"
+
+    gdf = gd.datasets.randomdata()
+    assert gdf["id"].head().dtype == int
+    assert gdf["x"].head().dtype == float
+    assert gdf["y"].head().dtype == float
+    assert len(gdf) == 10
+
+    gdf = gd.datasets.randomdata(
+        nrows=20, dtypes={"id": int, "a": int, "b": float}
+    )
+    assert gdf["id"].head().dtype == int
+    assert gdf["a"].head().dtype == int
+    assert gdf["b"].head().dtype == float
+    assert len(gdf) == 20
+
+
+def test_make_bool():
+    n = 10
+    state = np.random.RandomState(12)
+    arr = gd.datasets.make_bool(n, state)
+    assert np.alltrue(np.isin(arr, [True, False]))
+    assert arr.size == n
+    assert arr.dtype == bool
diff --git a/python/cudf/cudf/tests/test_datetime.py b/python/cudf/cudf/tests/test_datetime.py
new file mode 100644
index 0000000..ee0985a
--- /dev/null
+++ b/python/cudf/cudf/tests/test_datetime.py
@@ -0,0 +1,2299 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import datetime
+import operator
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+import cudf.testing.dataset_generator as dataset_generator
+from cudf import DataFrame, Series
+from cudf.core._compat import PANDAS_GE_150, PANDAS_LT_140
+from cudf.core.index import DatetimeIndex
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+
+_cmpops = [
+    operator.lt,
+    operator.gt,
+    operator.le,
+    operator.ge,
+    operator.eq,
+    operator.ne,
+]
+
+
+def data1():
+    return pd.date_range("20010101", "20020215", freq="400h", name="times")
+
+
+def data2():
+    return pd.date_range(
+        "20010101", freq="243434324423423234N", name="times", periods=10
+    )
+
+
+def timeseries_us_data():
+    return pd.date_range(
+        "2019-07-16 00:00:00",
+        "2019-07-16 00:00:01",
+        freq="5555us",
+        name="times",
+    )
+
+
+def timestamp_ms_data():
+    return pd.Series(
+        [
+            "2019-07-16 00:00:00.333",
+            "2019-07-16 00:00:00.666",
+            "2019-07-16 00:00:00.888",
+        ]
+    )
+
+
+def timestamp_us_data():
+    return pd.Series(
+        [
+            "2019-07-16 00:00:00.333333",
+            "2019-07-16 00:00:00.666666",
+            "2019-07-16 00:00:00.888888",
+        ]
+    )
+
+
+def timestamp_ns_data():
+    return pd.Series(
+        [
+            "2019-07-16 00:00:00.333333333",
+            "2019-07-16 00:00:00.666666666",
+            "2019-07-16 00:00:00.888888888",
+        ]
+    )
+
+
+def numerical_data():
+    return np.arange(1, 10)
+
+
+fields = [
+    "year",
+    "month",
+    "day",
+    "hour",
+    "minute",
+    "second",
+    "microsecond",
+    "nanosecond",
+    "weekday",
+    "dayofweek",
+    "dayofyear",
+    "day_of_year",
+]
+
+
+@pytest.mark.parametrize("data", [data1(), data2()])
+def test_series(data):
+    pd_data = pd.Series(data.copy())
+    gdf_data = Series(pd_data)
+    assert_eq(pd_data, gdf_data)
+
+
+@pytest.mark.parametrize(
+    "lhs_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+@pytest.mark.parametrize(
+    "rhs_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_datetime_series_binops_pandas(lhs_dtype, rhs_dtype):
+    pd_data_1 = pd.Series(
+        pd.date_range("20010101", "20020215", freq="400h", name="times")
+    )
+    pd_data_2 = pd.Series(
+        pd.date_range("20010101", "20020215", freq="401h", name="times")
+    )
+    gdf_data_1 = Series(pd_data_1).astype(lhs_dtype)
+    gdf_data_2 = Series(pd_data_2).astype(rhs_dtype)
+    assert_eq(pd_data_1, gdf_data_1.astype("datetime64[ns]"))
+    assert_eq(pd_data_2, gdf_data_2.astype("datetime64[ns]"))
+    assert_eq(pd_data_1 < pd_data_2, gdf_data_1 < gdf_data_2)
+    assert_eq(pd_data_1 > pd_data_2, gdf_data_1 > gdf_data_2)
+    assert_eq(pd_data_1 == pd_data_2, gdf_data_1 == gdf_data_2)
+    assert_eq(pd_data_1 <= pd_data_2, gdf_data_1 <= gdf_data_2)
+    assert_eq(pd_data_1 >= pd_data_2, gdf_data_1 >= gdf_data_2)
+
+
+@pytest.mark.parametrize(
+    "lhs_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+@pytest.mark.parametrize(
+    "rhs_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_datetime_series_binops_numpy(lhs_dtype, rhs_dtype):
+    pd_data_1 = pd.Series(
+        pd.date_range("20010101", "20020215", freq="400h", name="times")
+    )
+    pd_data_2 = pd.Series(
+        pd.date_range("20010101", "20020215", freq="401h", name="times")
+    )
+    gdf_data_1 = Series(pd_data_1).astype(lhs_dtype)
+    gdf_data_2 = Series(pd_data_2).astype(rhs_dtype)
+    np_data_1 = np.array(pd_data_1).astype(lhs_dtype)
+    np_data_2 = np.array(pd_data_2).astype(rhs_dtype)
+    np.testing.assert_equal(np_data_1, gdf_data_1.to_numpy())
+    np.testing.assert_equal(np_data_2, gdf_data_2.to_numpy())
+    np.testing.assert_equal(
+        np.less(np_data_1, np_data_2), (gdf_data_1 < gdf_data_2).to_numpy()
+    )
+    np.testing.assert_equal(
+        np.greater(np_data_1, np_data_2), (gdf_data_1 > gdf_data_2).to_numpy()
+    )
+    np.testing.assert_equal(
+        np.equal(np_data_1, np_data_2), (gdf_data_1 == gdf_data_2).to_numpy()
+    )
+    np.testing.assert_equal(
+        np.less_equal(np_data_1, np_data_2),
+        (gdf_data_1 <= gdf_data_2).to_numpy(),
+    )
+    np.testing.assert_equal(
+        np.greater_equal(np_data_1, np_data_2),
+        (gdf_data_1 >= gdf_data_2).to_numpy(),
+    )
+
+
+@pytest.mark.parametrize("data", [data1(), data2()])
+def test_dt_ops(data):
+    pd_data = pd.Series(data.copy())
+    gdf_data = Series(data.copy())
+
+    assert_eq(pd_data == pd_data, gdf_data == gdf_data)
+    assert_eq(pd_data < pd_data, gdf_data < gdf_data)
+    assert_eq(pd_data > pd_data, gdf_data > gdf_data)
+
+
+# libcudf doesn't respect timezones
+@pytest.mark.parametrize("data", [data1(), data2()])
+@pytest.mark.parametrize("field", fields)
+def test_dt_series(data, field):
+    pd_data = pd.Series(data.copy())
+    gdf_data = Series(pd_data)
+    base = getattr(pd_data.dt, field)
+    test = getattr(gdf_data.dt, field).to_pandas().astype("int64")
+    assert_eq(base, test)
+
+
+@pytest.mark.parametrize("data", [data1(), data2()])
+@pytest.mark.parametrize("field", fields)
+def test_dt_index(data, field):
+    pd_data = data.copy()
+    gdf_data = DatetimeIndex(pd_data)
+    assert_eq(getattr(gdf_data, field), getattr(pd_data, field))
+
+
+def test_setitem_datetime():
+    df = DataFrame()
+    df["date"] = pd.date_range("20010101", "20010105").values
+    assert np.issubdtype(df.date.dtype, np.datetime64)
+
+
+def test_sort_datetime():
+    df = pd.DataFrame()
+    df["date"] = np.array(
+        [
+            np.datetime64("2016-11-20"),
+            np.datetime64("2020-11-20"),
+            np.datetime64("2019-11-20"),
+            np.datetime64("1918-11-20"),
+            np.datetime64("2118-11-20"),
+        ]
+    )
+    df["vals"] = np.random.sample(len(df["date"]))
+
+    gdf = cudf.from_pandas(df)
+
+    s_df = df.sort_values(by="date")
+    s_gdf = gdf.sort_values(by="date")
+
+    assert_eq(s_df, s_gdf)
+
+
+def test_issue_165():
+    df_pandas = pd.DataFrame()
+    start_date = datetime.datetime.strptime("2000-10-21", "%Y-%m-%d")
+    data = [(start_date + datetime.timedelta(days=x)) for x in range(6)]
+    df_pandas["dates"] = data
+    df_pandas["num"] = [1, 2, 3, 4, 5, 6]
+    df_cudf = DataFrame.from_pandas(df_pandas)
+
+    base = df_pandas.query("dates==@start_date")
+    test = df_cudf.query("dates==@start_date")
+    assert_eq(base, test)
+    assert len(test) > 0
+
+    mask = df_cudf.dates == start_date
+    base_mask = df_pandas.dates == start_date
+    assert_eq(mask, base_mask, check_names=False)
+    assert mask.to_pandas().sum() > 0
+
+    start_date_ts = pd.Timestamp(start_date)
+    test = df_cudf.query("dates==@start_date_ts")
+    base = df_pandas.query("dates==@start_date_ts")
+    assert_eq(base, test)
+    assert len(test) > 0
+
+    mask = df_cudf.dates == start_date_ts
+    base_mask = df_pandas.dates == start_date_ts
+    assert_eq(mask, base_mask, check_names=False)
+    assert mask.to_pandas().sum() > 0
+
+    start_date_np = np.datetime64(start_date_ts, "ns")
+    test = df_cudf.query("dates==@start_date_np")
+    base = df_pandas.query("dates==@start_date_np")
+    assert_eq(base, test)
+    assert len(test) > 0
+
+    mask = df_cudf.dates == start_date_np
+    base_mask = df_pandas.dates == start_date_np
+    assert_eq(mask, base_mask, check_names=False)
+    assert mask.to_pandas().sum() > 0
+
+
+@pytest.mark.parametrize("data", [data1(), data2()])
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+def test_typecast_from_datetime(data, dtype):
+    pd_data = pd.Series(data.copy())
+    np_data = np.array(pd_data)
+    gdf_data = Series(pd_data)
+
+    np_casted = np_data.astype(dtype)
+    gdf_casted = gdf_data.astype(dtype)
+
+    np.testing.assert_equal(np_casted, gdf_casted.to_numpy())
+
+
+@pytest.mark.parametrize("data", [data1(), data2()])
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_typecast_from_datetime_to_int64_to_datetime(data, dtype):
+    pd_data = pd.Series(data.copy())
+    np_data = np.array(pd_data)
+    gdf_data = Series(pd_data)
+
+    np_casted = np_data.astype(np.int64).astype(dtype)
+    gdf_casted = gdf_data.astype(np.int64).astype(dtype)
+
+    np.testing.assert_equal(np_casted, gdf_casted.to_numpy())
+
+
+@pytest.mark.parametrize("data", [timeseries_us_data()])
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_typecast_to_different_datetime_resolutions(data, dtype):
+    pd_data = pd.Series(data.copy())
+    np_data = np.array(pd_data).astype(dtype)
+    gdf_series = Series(pd_data).astype(dtype)
+    np.testing.assert_equal(np_data, gdf_series.to_numpy())
+
+
+@pytest.mark.parametrize(
+    "data", [timestamp_ms_data(), timestamp_us_data(), timestamp_ns_data()]
+)
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_string_timstamp_typecast_to_different_datetime_resolutions(
+    data, dtype
+):
+    pd_sr = data
+    gdf_sr = cudf.Series.from_pandas(pd_sr)
+
+    expect = pd_sr.values.astype(dtype)
+    got = gdf_sr.astype(dtype).values_host
+
+    np.testing.assert_equal(expect, got)
+
+
+@pytest.mark.parametrize("data", [numerical_data()])
+@pytest.mark.parametrize("from_dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize(
+    "to_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_typecast_to_datetime(data, from_dtype, to_dtype):
+    np_data = data.astype(from_dtype)
+    gdf_data = Series(np_data)
+
+    np_casted = np_data.astype(to_dtype)
+    gdf_casted = gdf_data.astype(to_dtype)
+
+    np.testing.assert_equal(np_casted, gdf_casted.to_numpy())
+
+
+@pytest.mark.parametrize("data", [numerical_data()])
+@pytest.mark.parametrize("from_dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize(
+    "to_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_typecast_to_from_datetime(data, from_dtype, to_dtype):
+    np_data = data.astype(from_dtype)
+    gdf_data = Series(np_data)
+
+    np_casted = np_data.astype(to_dtype).astype(from_dtype)
+    gdf_casted = gdf_data.astype(to_dtype).astype(from_dtype)
+
+    np.testing.assert_equal(np_casted, gdf_casted.to_numpy())
+
+
+@pytest.mark.parametrize("data", [numerical_data()])
+@pytest.mark.parametrize(
+    "from_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+@pytest.mark.parametrize(
+    "to_dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_typecast_from_datetime_to_datetime(data, from_dtype, to_dtype):
+    np_data = data.astype(from_dtype)
+    ser = Series(np_data)
+
+    np_casted = np_data.astype(to_dtype)
+    ser_casted = ser.astype(to_dtype)
+
+    np.testing.assert_equal(np_casted, ser_casted.to_numpy())
+
+
+@pytest.mark.parametrize("data", [numerical_data()])
+@pytest.mark.parametrize("nulls", ["some", "all"])
+def test_to_from_pandas_nulls(data, nulls):
+    pd_data = pd.Series(data.copy().astype("datetime64[ns]"))
+    if nulls == "some":
+        # Fill half the values with NaT
+        pd_data[list(range(0, len(pd_data), 2))] = np.datetime64("nat", "ns")
+    elif nulls == "all":
+        # Fill all the values with NaT
+        pd_data[:] = np.datetime64("nat", "ns")
+    gdf_data = Series.from_pandas(pd_data)
+
+    expect = pd_data
+    got = gdf_data.to_pandas()
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_datetime_to_arrow(dtype):
+    timestamp = (
+        cudf.datasets.timeseries(
+            start="2000-01-01", end="2000-01-02", freq="3600s", dtypes={}
+        )
+        .reset_index()["timestamp"]
+        .reset_index(drop=True)
+    )
+    gdf = DataFrame({"timestamp": timestamp.astype(dtype)})
+    assert_eq(gdf, DataFrame.from_arrow(gdf.to_arrow(preserve_index=False)))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([], dtype="datetime64[ns]"),
+        pd.Series(pd.date_range("2010-01-01", "2010-02-01")),
+        pd.Series([None, None], dtype="datetime64[ns]"),
+    ],
+)
+@pytest.mark.parametrize("nulls", ["none", "some"])
+def test_datetime_unique(data, nulls):
+    psr = data.copy()
+
+    if len(data) > 0:
+        if nulls == "some":
+            p = np.random.randint(0, len(data), 2)
+            psr[p] = None
+
+    gsr = cudf.from_pandas(psr)
+    expected = psr.unique()
+    got = gsr.unique()
+
+    # Unique does not provide a guarantee on ordering.
+    assert_eq(
+        pd.Series(expected).sort_values(ignore_index=True),
+        got.sort_values(ignore_index=True).to_pandas(),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([], dtype="datetime64[ns]"),
+        pd.Series(pd.date_range("2010-01-01", "2010-02-01")),
+        pd.Series([None, None], dtype="datetime64[ns]"),
+    ],
+)
+@pytest.mark.parametrize("nulls", ["none", "some"])
+def test_datetime_nunique(data, nulls):
+    psr = data.copy()
+
+    if len(data) > 0:
+        if nulls == "some":
+            p = np.random.randint(0, len(data), 2)
+            psr[p] = None
+
+    gsr = cudf.from_pandas(psr)
+    expected = psr.nunique()
+    got = gsr.nunique()
+    assert_eq(got, expected)
+
+
+testdata = [
+    (
+        Series(
+            ["2018-01-01", None, "2019-01-31", None, "2018-01-01"],
+            dtype="datetime64[ms]",
+        ),
+        True,
+    ),
+    (
+        Series(
+            [
+                "2018-01-01",
+                "2018-01-02",
+                "2019-01-31",
+                "2018-03-01",
+                "2018-01-01",
+            ],
+            dtype="datetime64[ms]",
+        ),
+        False,
+    ),
+    (
+        Series(
+            np.array(
+                ["2018-01-01", None, "2019-12-30"], dtype="datetime64[ms]"
+            )
+        ),
+        True,
+    ),
+]
+
+
+@pytest.mark.parametrize("data, expected", testdata)
+def test_datetime_has_null_test(data, expected):
+    pd_data = data.to_pandas()
+    count = pd_data.notna().value_counts()
+    expected_count = 0
+    if False in count.keys():
+        expected_count = count[False]
+
+    assert_eq(expected, data.has_nulls)
+    assert_eq(expected_count, data.null_count)
+
+
+def test_datetime_has_null_test_pyarrow():
+    data = Series(
+        pa.array(
+            [0, np.iinfo("int64").min, np.iinfo("int64").max, None],
+            type=pa.timestamp("ns"),
+        )
+    )
+    expected = True
+    expected_count = 1
+
+    assert_eq(expected, data.has_nulls)
+    assert_eq(expected_count, data.null_count)
+
+
+def test_datetime_dataframe():
+    data = {
+        "timearray": np.array(
+            [0, 1, None, 2, 20, None, 897], dtype="datetime64[ms]"
+        )
+    }
+    gdf = cudf.DataFrame(data)
+    pdf = pd.DataFrame(data)
+
+    assert_eq(pdf, gdf)
+
+    assert_eq(pdf.dropna(), gdf.dropna())
+
+    assert_eq(pdf.isnull(), gdf.isnull())
+
+    data = np.array([0, 1, None, 2, 20, None, 897], dtype="datetime64[ms]")
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(ps, gs)
+
+    assert_eq(ps.dropna(), gs.dropna())
+
+    assert_eq(ps.isnull(), gs.isnull())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        None,
+        [],
+        pd.Series([], dtype="float64"),
+        pd.Index([]),
+        pd.Series([1, 2, 3]),
+        pd.Series([0, 1, -1]),
+        pd.Series([0, 1, -1, 100.3, 200, 47637289]),
+        pd.Series(["2012-10-11", "2010-01-01", "2016-07-07", "2014-02-02"]),
+        [1, 2, 3, 100, -123, -1, 0, 1000000000000679367],
+        pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]}),
+        pd.DataFrame(
+            {"year": ["2015", "2016"], "month": ["2", "3"], "day": [4, 5]}
+        ),
+        pd.DataFrame(
+            {
+                "year": [2015, 2016],
+                "month": [2, 3],
+                "day": [4, 5],
+                "minute": [1, 100],
+                "second": [90, 10],
+                "hour": [1, 0.5],
+            },
+            index=["a", "b"],
+        ),
+        pd.DataFrame(
+            {
+                "year": [],
+                "month": [],
+                "day": [],
+                "minute": [],
+                "second": [],
+                "hour": [],
+            },
+        ),
+        ["2012-10-11", "2010-01-01", "2016-07-07", "2014-02-02"],
+        pd.Index([1, 2, 3, 4]),
+        pd.DatetimeIndex(
+            ["1970-01-01 00:00:00.000000001", "1970-01-01 00:00:00.000000002"],
+            dtype="datetime64[ns]",
+            freq=None,
+        ),
+        pd.DatetimeIndex(
+            [],
+            dtype="datetime64[ns]",
+            freq=None,
+        ),
+        pd.Series([1, 2, 3]).astype("datetime64[ns]"),
+        pd.Series([1, 2, 3]).astype("datetime64[us]"),
+        pd.Series([1, 2, 3]).astype("datetime64[ms]"),
+        pd.Series([1, 2, 3]).astype("datetime64[s]"),
+        pd.Series([1, 2, 3]).astype("datetime64[D]"),
+        1,
+        100,
+        17,
+        53.638435454,
+        np.array([1, 10, 15, 478925, 2327623467]),
+        np.array([0.3474673, -10, 15, 478925.34345, 2327623467]),
+    ],
+)
+@pytest.mark.parametrize("dayfirst", [True, False])
+@pytest.mark.parametrize("infer_datetime_format", [True, False])
+def test_cudf_to_datetime(data, dayfirst, infer_datetime_format):
+    pd_data = data
+    is_string_data = False
+    if isinstance(pd_data, (pd.Series, pd.DataFrame, pd.Index)):
+        gd_data = cudf.from_pandas(pd_data)
+        is_string_data = (
+            gd_data.ndim == 1
+            and not gd_data.empty
+            and gd_data.dtype.kind == "O"
+        )
+    else:
+        if type(pd_data).__module__ == np.__name__:
+            gd_data = cp.array(pd_data)
+        else:
+            gd_data = pd_data
+            is_string_data = isinstance(gd_data, list) and isinstance(
+                next(iter(gd_data), None), str
+            )
+
+    if dayfirst and not infer_datetime_format and is_string_data:
+        # Note: pandas<2.0 also does not respect dayfirst=True correctly
+        # for object data
+        with pytest.raises(NotImplementedError):
+            cudf.to_datetime(
+                gd_data,
+                dayfirst=dayfirst,
+                infer_datetime_format=infer_datetime_format,
+            )
+    else:
+        expected = pd.to_datetime(
+            pd_data,
+            dayfirst=dayfirst,
+            infer_datetime_format=infer_datetime_format,
+        )
+        actual = cudf.to_datetime(
+            gd_data,
+            dayfirst=dayfirst,
+            infer_datetime_format=infer_datetime_format,
+        )
+        assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        "2",
+        ["1", "2", "3"],
+        ["1/1/1", "2/2/2", "1"],
+        pd.Series([1, 2, 3], dtype="timedelta64[ns]"),
+        pd.DataFrame(
+            {
+                "year": [2015, 2016],
+                "month": [2, 3],
+                "day": [4, 5],
+                "minute": [1, 100],
+                "second": [90, 10],
+                "hour": [1, 0],
+                "blablacol": [1, 1],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "month": [2, 3],
+                "day": [4, 5],
+                "minute": [1, 100],
+                "second": [90, 10],
+                "hour": [1, 0],
+            }
+        ),
+    ],
+)
+def test_to_datetime_errors(data):
+    pd_data = data
+    if isinstance(pd_data, (pd.Series, pd.DataFrame, pd.Index)):
+        gd_data = cudf.from_pandas(pd_data)
+    else:
+        gd_data = pd_data
+
+    assert_exceptions_equal(
+        pd.to_datetime,
+        cudf.to_datetime,
+        ([pd_data],),
+        ([gd_data],),
+    )
+
+
+def test_to_datetime_not_implemented():
+
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime([], exact=False)
+
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime([], origin="julian")
+
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime([], yearfirst=True)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        1,
+        [],
+        pd.Series([], dtype="float64"),
+        pd.Index([]),
+        pd.Series([1, 2, 3]),
+        pd.Series([1, 2.4, 3]),
+        pd.Series([0, 1, -1]),
+        pd.Series([0, 1, -1, 100, 200, 47637]),
+        [10, 12, 1200, 15003],
+        pd.DatetimeIndex(
+            [],
+            dtype="datetime64[ns]",
+            freq=None,
+        ),
+        pd.Index([1, 2, 3, 4]),
+    ],
+)
+@pytest.mark.parametrize("unit", ["D", "s", "ms", "us", "ns"])
+def test_to_datetime_units(data, unit):
+    pd_data = data
+    if isinstance(pd_data, (pd.Series, pd.DataFrame, pd.Index)):
+        gd_data = cudf.from_pandas(pd_data)
+    else:
+        gd_data = pd_data
+
+    expected = pd.to_datetime(pd_data, unit=unit)
+    actual = cudf.to_datetime(gd_data, unit=unit)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data,format",
+    [
+        ("2012-10-11", None),
+        ("2012-10-11", "%Y-%m-%d"),
+        ("2012-10-11", "%Y-%d-%m"),
+        (["2012-10-11", "2010-01-01", "2016-07-07", "2014-02-02"], None),
+        (["2012-10-11", "2010-01-01", "2016-07-07", "2014-02-02"], "%Y-%m-%d"),
+        (["2012-10-11", "2010-01-01", "2016-07-07", "2014-02-02"], "%Y-%d-%m"),
+        (["10-11-2012", "01-01-2010", "07-07-2016", "02-02-2014"], "%m-%d-%Y"),
+        (["10-11-2012", "01-01-2010", "07-07-2016", "02-02-2014"], "%d-%m-%Y"),
+        (["10-11-2012", "01-01-2010", "07-07-2016", "02-02-2014"], None),
+        (["2012/10/11", "2010/01/01", "2016/07/07", "2014/02/02"], None),
+        (["2012/10/11", "2010/01/01", "2016/07/07", "2014/02/02"], "%Y/%m/%d"),
+        (["2012/10/11", "2010/01/01", "2016/07/07", "2014/02/02"], "%Y/%d/%m"),
+        (["10/11/2012", "01/01/2010", "07/07/2016", "02/02/2014"], "%m/%d/%Y"),
+        (["10/11/2012", "01/01/2010", "07/07/2016", "02/02/2014"], "%d/%m/%Y"),
+        (["10/11/2012", "01/01/2010", "07/07/2016", "02/02/2014"], None),
+        (["2021-04-13 12:30:04.123456789"], "%Y-%m-%d %H:%M:%S.%f"),
+        (pd.Series([2015, 2020, 2021]), "%Y"),
+        pytest.param(
+            pd.Series(["1", "2", "1"]),
+            "%m",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/6109"
+                "https://github.com/pandas-dev/pandas/issues/35934"
+            ),
+        ),
+        pytest.param(
+            pd.Series(["14", "20", "10"]),
+            "%d",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/6109"
+                "https://github.com/pandas-dev/pandas/issues/35934"
+            ),
+        ),
+        (pd.Series([2015, 2020.0, 2021.2]), "%Y"),
+    ],
+)
+@pytest.mark.parametrize("infer_datetime_format", [True, False])
+def test_to_datetime_format(data, format, infer_datetime_format):
+    pd_data = data
+    if isinstance(pd_data, (pd.Series, pd.DataFrame, pd.Index)):
+        gd_data = cudf.from_pandas(pd_data)
+    else:
+        gd_data = pd_data
+
+    expected = pd.to_datetime(
+        pd_data, format=format, infer_datetime_format=infer_datetime_format
+    )
+    actual = cudf.to_datetime(
+        gd_data, format=format, infer_datetime_format=infer_datetime_format
+    )
+
+    assert_eq(actual, expected)
+
+
+def test_to_datetime_data_out_of_range_for_format():
+    with pytest.raises(ValueError):
+        cudf.to_datetime("2015-02-99", format="%Y-%m-%d")
+
+
+def test_to_datetime_different_formats_notimplemented():
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime(["2015-02-01", "2015-02-01 10:10:10"])
+
+
+def test_datetime_can_cast_safely():
+
+    sr = cudf.Series(
+        ["1679-01-01", "2000-01-31", "2261-01-01"], dtype="datetime64[ms]"
+    )
+    assert sr._column.can_cast_safely(np.dtype("datetime64[ns]"))
+
+    sr = cudf.Series(
+        ["1677-01-01", "2000-01-31", "2263-01-01"], dtype="datetime64[ms]"
+    )
+
+    assert sr._column.can_cast_safely(np.dtype("datetime64[ns]")) is False
+
+
+# Cudf autocasts unsupported time_units
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[D]", "datetime64[W]", "datetime64[M]", "datetime64[Y]"],
+)
+def test_datetime_array_timeunit_cast(dtype):
+    testdata = np.array(
+        [
+            np.datetime64("2016-11-20"),
+            np.datetime64("2020-11-20"),
+            np.datetime64("2019-11-20"),
+            np.datetime64("1918-11-20"),
+            np.datetime64("2118-11-20"),
+        ],
+        dtype=dtype,
+    )
+
+    gs = Series(testdata)
+    ps = pd.Series(testdata)
+
+    assert_eq(ps, gs)
+
+    gdf = DataFrame()
+    gdf["a"] = np.arange(5)
+    gdf["b"] = testdata
+
+    pdf = pd.DataFrame()
+    pdf["a"] = np.arange(5)
+    pdf["b"] = testdata
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize("timeunit", ["D", "W", "M", "Y"])
+def test_datetime_scalar_timeunit_cast(timeunit):
+    testscalar = np.datetime64("2016-11-20", timeunit)
+
+    gs = Series(testscalar)
+    ps = pd.Series(testscalar)
+    assert_eq(ps, gs)
+
+    gdf = DataFrame()
+    gdf["a"] = np.arange(5)
+    gdf["b"] = testscalar
+
+    pdf = pd.DataFrame()
+    pdf["a"] = np.arange(5)
+    pdf["b"] = testscalar
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["2001-01-01", "2002-02-02", "2000-01-05", "NaT"],
+        ["2001-01-01", "2002-02-02", "2000-01-05", None],
+        [None, None, None, None, None],
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+def test_str_null_to_datetime(data, dtype):
+    psr = pd.Series(data)
+    gsr = Series(data)
+
+    assert_eq(psr.astype(dtype), gsr.astype(dtype))
+
+
+def test_str_to_datetime_error():
+    psr = pd.Series(["2001-01-01", "2002-02-02", "2000-01-05", "None"])
+    gsr = Series(["2001-01-01", "2002-02-02", "2000-01-05", "None"])
+
+    assert_exceptions_equal(
+        lfunc=psr.astype,
+        rfunc=gsr.astype,
+        lfunc_args_and_kwargs=(["datetime64[s]"],),
+        rfunc_args_and_kwargs=(["datetime64[s]"],),
+        check_exception_type=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 10, 100, 20000],
+        [None] * 7,
+        [10, 20, 30, None, 100, 200, None],
+        [3223.234, 342.2332, 23423.23, 3343.23324, 23432.2323, 242.23, 233],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [1, 2, 3, 4, 10, 100, 20000],
+        [None] * 7,
+        [10, 20, 30, None, 100, 200, None],
+        [3223.234, 342.2332, 23423.23, 3343.23324, 23432.2323, 242.23, 233],
+        np.datetime64("2005-02"),
+        np.datetime64("2005-02-25"),
+        np.datetime64("2005-02-25T03:30"),
+        np.datetime64("nat"),
+    ],
+)
+@pytest.mark.parametrize("data_dtype", DATETIME_TYPES)
+@pytest.mark.parametrize("other_dtype", DATETIME_TYPES)
+def test_datetime_subtract(data, other, data_dtype, other_dtype):
+
+    gsr = cudf.Series(data, dtype=data_dtype)
+    psr = gsr.to_pandas()
+
+    if isinstance(other, np.datetime64):
+        gsr_other = other
+        psr_other = other
+    else:
+        gsr_other = cudf.Series(other, dtype=other_dtype)
+        psr_other = gsr_other.to_pandas()
+
+    expected = psr - psr_other
+    actual = gsr - gsr_other
+
+    assert_eq(expected, actual)
+
+    expected = psr_other - psr
+    actual = gsr_other - gsr
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [1],
+        [12, 11, 232, 223432411, 2343241, 234324, 23234],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    ],
+)
+@pytest.mark.parametrize(
+    "other_scalars",
+    [
+        datetime.timedelta(days=768),
+        datetime.timedelta(seconds=768),
+        datetime.timedelta(microseconds=7),
+        datetime.timedelta(minutes=447),
+        datetime.timedelta(hours=447),
+        datetime.timedelta(weeks=734),
+        np.timedelta64(4, "s"),
+        np.timedelta64(456, "D"),
+        np.timedelta64(46, "h"),
+        np.timedelta64("nat"),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64(1, "ns"),
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+@pytest.mark.parametrize(
+    "op",
+    ["add", "sub"],
+)
+def test_datetime_series_ops_with_scalars(data, other_scalars, dtype, op):
+    gsr = cudf.Series(data=data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    if op == "add":
+        expected = psr + other_scalars
+        actual = gsr + other_scalars
+    elif op == "sub":
+        expected = psr - other_scalars
+        actual = gsr - other_scalars
+
+    assert_eq(expected, actual)
+
+    if op == "add":
+        expected = other_scalars + psr
+        actual = other_scalars + gsr
+
+        assert_eq(expected, actual)
+
+    elif op == "sub":
+        assert_exceptions_equal(
+            lfunc=operator.sub,
+            rfunc=operator.sub,
+            lfunc_args_and_kwargs=([other_scalars, psr],),
+            rfunc_args_and_kwargs=([other_scalars, gsr],),
+        )
+
+
+@pytest.mark.parametrize("data", ["20110101", "20120101", "20130101"])
+@pytest.mark.parametrize("other_scalars", ["20110101", "20120101", "20130101"])
+@pytest.mark.parametrize("op", _cmpops)
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[ns]", "datetime64[us]", "datetime64[ms]", "datetime64[s]"],
+)
+def test_datetime_series_cmpops_with_scalars(data, other_scalars, dtype, op):
+    gsr = cudf.Series(data=data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    expect = op(psr, other_scalars)
+    got = op(gsr, other_scalars)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [1],
+        [12, 11, 232, 223432411, 2343241, 234324, 23234],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    ],
+)
+@pytest.mark.parametrize(
+    "scalar",
+    [
+        datetime.timedelta(days=768),
+        datetime.timedelta(seconds=768),
+        datetime.timedelta(microseconds=7),
+        pytest.param(np.timedelta64("nat"), marks=pytest.mark.xfail),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64(1, "ns"),
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+@pytest.mark.parametrize("op", [np.add, np.subtract])
+def test_datetime_series_ops_with_cudf_scalars(data, scalar, dtype, op):
+    gsr = cudf.Series(data=data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    expect = op(psr, scalar)
+    got = op(gsr, cudf.Scalar(scalar))
+
+    assert_eq(expect, got)
+
+
+def test_datetime_invalid_ops():
+    sr = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    psr = sr.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([psr, pd.Timestamp(1513393355.5, unit="s")],),
+        rfunc_args_and_kwargs=([sr, pd.Timestamp(1513393355.5, unit="s")],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.truediv,
+        rfunc=operator.truediv,
+        lfunc_args_and_kwargs=([psr, pd.Timestamp(1513393355.5, unit="s")],),
+        rfunc_args_and_kwargs=([sr, pd.Timestamp(1513393355.5, unit="s")],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([psr, psr],),
+        rfunc_args_and_kwargs=([sr, sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.floordiv,
+        rfunc=operator.floordiv,
+        lfunc_args_and_kwargs=([psr, psr],),
+        rfunc_args_and_kwargs=([sr, sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.floordiv,
+        rfunc=operator.floordiv,
+        lfunc_args_and_kwargs=([psr, pd.Timestamp(1513393355.5, unit="s")],),
+        rfunc_args_and_kwargs=([sr, pd.Timestamp(1513393355.5, unit="s")],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([psr, 1],),
+        rfunc_args_and_kwargs=([sr, 1],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.truediv,
+        rfunc=operator.truediv,
+        lfunc_args_and_kwargs=([psr, "a"],),
+        rfunc_args_and_kwargs=([sr, "a"],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.mul,
+        rfunc=operator.mul,
+        lfunc_args_and_kwargs=([psr, 1],),
+        rfunc_args_and_kwargs=([sr, 1],),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        [1, 2, 3],
+        [None, 1, 10, 11, None],
+        [None, None, None, None, None],
+        [None],
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        np.datetime64("2005-02"),
+        np.datetime64("2005-02-25"),
+        np.datetime64("2005-02-25T03:30"),
+        np.datetime64("nat"),
+        "NaT",
+    ],
+)
+def test_datetime_fillna(data, dtype, fill_value):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    expected = psr.dropna()
+    actual = sr.dropna()
+
+    assert_eq(expected, actual)
+
+    expected = psr.fillna(fill_value)
+    actual = sr.fillna(fill_value)
+
+    assert_eq(expected, actual)
+
+    expected = expected.dropna()
+    actual = actual.dropna()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data", [[1, 2, 3, None], [], [100121, 1221312, 321312321, 1232131223]]
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+@pytest.mark.parametrize(
+    "date_format",
+    [
+        "%d - %m",
+        "%y/%H",
+        "%Y",
+        "%I - %M / %S",
+        "%f",
+        "%j",
+        "%p",
+        "%w",
+        "%U",
+        "%W",
+        "%G",
+        "%u",
+        "%V",
+        "%b",
+        "%B",
+        "%a",
+        "%A",
+    ],
+)
+def test_datetime_strftime(data, dtype, date_format):
+    gsr = cudf.Series(data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    expected = psr.dt.strftime(date_format=date_format)
+    actual = gsr.dt.strftime(date_format=date_format)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("date_format", ["%c", "%x", "%X"])
+def test_datetime_strftime_not_implemented_formats(date_format):
+    gsr = cudf.Series([1, 2, 3], dtype="datetime64[ms]")
+
+    with pytest.raises(NotImplementedError):
+        gsr.dt.strftime(date_format=date_format)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], [], [1, 20, 1000, None]])
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+@pytest.mark.parametrize("stat", ["mean", "quantile"])
+def test_datetime_stats(data, dtype, stat):
+    gsr = cudf.Series(data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    expected = getattr(psr, stat)()
+    actual = getattr(gsr, stat)()
+
+    if len(data) == 0:
+        assert np.isnat(expected.to_numpy()) and np.isnat(actual.to_numpy())
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("op", ["max", "min", "std", "median"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        [1, 2, 3, 100],
+        [10, None, 100, None, None],
+        [None, None, None],
+        [1231],
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+def test_datetime_reductions(data, op, dtype):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    actual = getattr(sr, op)()
+    with expect_warning_if(
+        psr.size > 0 and psr.isnull().all() and op == "median", RuntimeWarning
+    ):
+        expected = getattr(psr, op)()
+
+    if (
+        expected is pd.NaT
+        and actual is pd.NaT
+        or (np.isnat(expected.to_numpy()) and np.isnat(actual))
+    ):
+        assert True
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("timezone", ["naive", "UTC"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.arange("2002-10-27T04:30", 4 * 60, 60, dtype="M8[m]"),
+        np.arange("2002-10-27T04:30", 10 * 60, 1, dtype="M8[m]"),
+        np.arange("2002-10-27T04:30", 10 * 60, 1, dtype="M8[ns]"),
+        np.arange("2002-10-27T04:30", 10 * 60, 1, dtype="M8[us]"),
+        np.arange("2002-10-27T04:30", 4 * 60, 60, dtype="M8[s]"),
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+def test_datetime_infer_format(data, timezone, dtype):
+    ts_data = np.datetime_as_string(data, timezone=timezone)
+    sr = cudf.Series(ts_data)
+    if timezone == "naive":
+        psr = pd.Series(ts_data)
+
+        expected = psr.astype(dtype)
+        actual = sr.astype(dtype)
+
+        assert_eq(expected, actual)
+    else:
+        with pytest.raises(NotImplementedError):
+            sr.astype(dtype)
+
+
+def test_dateoffset_instance_subclass_check():
+    assert not issubclass(pd.DateOffset, cudf.DateOffset)
+    assert not isinstance(pd.DateOffset(), cudf.DateOffset)
+
+
+def test_datetime_to_datetime_error():
+    assert_exceptions_equal(
+        lfunc=pd.to_datetime,
+        rfunc=cudf.to_datetime,
+        lfunc_args_and_kwargs=(["02-Oct-2017 09:30", "%d-%B-%Y %H:%M"],),
+        rfunc_args_and_kwargs=(["02-Oct-2017 09:30", "%d-%B-%Y %H:%M"],),
+        check_exception_type=False,
+    )
+
+
+def test_is_leap_year():
+    data = [
+        "2020-05-31 08:00:00",
+        None,
+        "1999-12-31 18:40:00",
+        "2000-12-31 04:00:00",
+        None,
+        "1900-02-28 07:00:00",
+        "1800-03-14 07:30:00",
+        "2100-03-14 07:30:00",
+        "1970-01-01 00:00:00",
+        "1969-12-31 12:59:00",
+    ]
+
+    # Series
+    ps = pd.Series(data, dtype="datetime64[s]")
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_leap_year
+    got = gs.dt.is_leap_year
+
+    assert_eq(expect, got)
+
+    # DatetimeIndex
+    pIndex = pd.DatetimeIndex(data)
+    gIndex = cudf.from_pandas(pIndex)
+
+    expect2 = pIndex.is_leap_year
+    got2 = gIndex.is_leap_year
+
+    assert_eq(expect2, got2)
+
+
+def test_quarter():
+    data = [
+        "2020-05-31 08:00:00",
+        "1999-12-31 18:40:00",
+        "2000-12-31 04:00:00",
+        "1900-02-28 07:00:00",
+        "1800-03-14 07:30:00",
+        "2100-03-14 07:30:00",
+        "1970-01-01 00:00:00",
+        "1969-12-31 12:59:00",
+    ]
+    dtype = "datetime64[s]"
+
+    # Series
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.quarter
+    got = gs.dt.quarter
+
+    assert_eq(expect, got, check_dtype=False)
+
+    # DatetimeIndex
+    pIndex = pd.DatetimeIndex(data)
+    gIndex = cudf.from_pandas(pIndex)
+
+    expect2 = pIndex.quarter
+    got2 = gIndex.quarter
+
+    assert_eq(expect2.values, got2.values)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([], dtype="datetime64[ns]"),
+        pd.Series(pd.date_range("2010-01-01", "2010-02-01")),
+        pd.Series([None, None], dtype="datetime64[ns]"),
+        pd.Series("2020-05-31 08:00:00", dtype="datetime64[s]"),
+        pd.Series(
+            pd.date_range(start="2021-07-25", end="2021-07-30"),
+            index=["a", "b", "c", "d", "e", "f"],
+        ),
+    ],
+)
+def test_isocalendar_series(data):
+    ps = data.copy()
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.isocalendar()
+    got = gs.dt.isocalendar()
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.DatetimeIndex([], dtype="datetime64[ns]"),
+        pd.DatetimeIndex([None, None], dtype="datetime64[ns]"),
+        pd.DatetimeIndex(
+            [
+                "2020-05-31 08:00:00",
+                "1999-12-31 18:40:00",
+                "2000-12-31 04:00:00",
+            ],
+            dtype="datetime64[ns]",
+        ),
+        pd.DatetimeIndex(["2100-03-14 07:30:00"], dtype="datetime64[ns]"),
+    ],
+)
+def test_isocalendar_index(data):
+    ps = data.copy()
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.isocalendar()
+    got = gs.isocalendar()
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+def test_days_in_months(dtype):
+    nrows = 1000
+
+    data = dataset_generator.rand_dataframe(
+        dtypes_meta=[
+            {"dtype": dtype, "null_frequency": 0.4, "cardinality": nrows}
+        ],
+        rows=nrows,
+        use_threads=False,
+        seed=23,
+    )
+
+    ps = data.to_pandas()["0"]
+    gs = cudf.from_pandas(ps)
+
+    assert_eq(ps.dt.days_in_month, gs.dt.days_in_month)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-05-31",
+            None,
+            "1999-12-01",
+            "2000-12-21",
+            None,
+            "1900-02-28",
+            "1800-03-14",
+            "2100-03-10",
+            "1970-01-01",
+            "1969-12-11",
+        ]
+    ],
+)
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_is_month_start(data, dtype):
+    # Series
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_month_start
+    got = gs.dt.is_month_start
+
+    assert_eq(expect, got)
+
+
+##################################################################
+#                       Date Range Tests                         #
+##################################################################
+
+date_range_test_dates_start = [
+    "2000-02-13 08:41:06",  # leap year
+    "1996-11-21 04:05:30",  # non leap year
+    "1970-01-01 00:00:00",  # unix epoch time 0
+    "1831-05-08 15:23:21",
+]
+date_range_test_dates_end = [
+    "2000-02-13 08:41:06",  # leap year
+    "1996-11-21 04:05:30",  # non leap year
+    "1970-01-01 00:00:00",  # unix epoch time 0
+    "1831-05-08 15:23:21",
+]
+date_range_test_periods = [1, 10, 100]
+date_range_test_freq = [
+    {"months": 3, "years": 1},
+    pytest.param(
+        {"hours": 10, "days": 57, "nanoseconds": 3},
+        marks=pytest.mark.xfail(
+            condition=PANDAS_LT_140,
+            reason="Pandas ignoring nanoseconds component. "
+            "https://github.com/pandas-dev/pandas/issues/44393",
+        ),
+    ),
+    "83D",
+    "17h",
+    "-680T",
+    "110546s",
+    pytest.param(
+        "110546789L",
+        marks=pytest.mark.xfail(
+            condition=not PANDAS_GE_150,
+            reason="Pandas DateOffset ignores milliseconds. "
+            "https://github.com/pandas-dev/pandas/issues/43371",
+        ),
+    ),
+    "110546789248U",
+]
+
+
+@pytest.fixture(params=date_range_test_dates_start[:])
+def start(request):
+    return request.param
+
+
+@pytest.fixture(params=date_range_test_dates_end[:])
+def end(request):
+    return request.param
+
+
+@pytest.fixture(params=date_range_test_periods[:])
+def periods(request):
+    return request.param
+
+
+@pytest.fixture(params=date_range_test_freq[:])
+def freq(request):
+    return request.param
+
+
+def test_date_range_start_end_periods(start, end, periods):
+    expect = pd.date_range(start=start, end=end, periods=periods, name="a")
+    got = cudf.date_range(start=start, end=end, periods=periods, name="a")
+
+    np.testing.assert_allclose(
+        expect.to_numpy().astype("int64"),
+        got.to_pandas().to_numpy().astype("int64"),
+    )
+
+
+def test_date_range_start_end_freq(request, start, end, freq):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                start == "1831-05-08 15:23:21"
+                and end == "1996-11-21 04:05:30"
+                and freq == "110546789L"
+            ),
+            reason="https://github.com/rapidsai/cudf/issues/12133",
+        )
+    )
+    if isinstance(freq, str):
+        _gfreq = _pfreq = freq
+    else:
+        _gfreq = cudf.DateOffset(**freq)
+        _pfreq = pd.DateOffset(**freq)
+
+    expect = pd.date_range(start=start, end=end, freq=_pfreq, name="a")
+    got = cudf.date_range(start=start, end=end, freq=_gfreq, name="a")
+
+    np.testing.assert_allclose(
+        expect.to_numpy().astype("int64"),
+        got.to_pandas().to_numpy().astype("int64"),
+    )
+
+
+def test_date_range_start_freq_periods(start, freq, periods):
+    if isinstance(freq, str):
+        _gfreq = _pfreq = freq
+    else:
+        _gfreq = cudf.DateOffset(**freq)
+        _pfreq = pd.DateOffset(**freq)
+
+    expect = pd.date_range(start=start, periods=periods, freq=_pfreq, name="a")
+    got = cudf.date_range(start=start, periods=periods, freq=_gfreq, name="a")
+
+    np.testing.assert_allclose(
+        expect.to_numpy().astype("int64"),
+        got.to_pandas().to_numpy().astype("int64"),
+    )
+
+
+def test_date_range_end_freq_periods(request, end, freq, periods):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                "nanoseconds" in freq
+                and periods != 1
+                and end == "1970-01-01 00:00:00"
+            ),
+            reason="https://github.com/pandas-dev/pandas/issues/46877",
+        )
+    )
+    if isinstance(freq, str):
+        _gfreq = _pfreq = freq
+    else:
+        _gfreq = cudf.DateOffset(**freq)
+        _pfreq = pd.DateOffset(**freq)
+
+    expect = pd.date_range(end=end, periods=periods, freq=_pfreq, name="a")
+    got = cudf.date_range(end=end, periods=periods, freq=_gfreq, name="a")
+
+    np.testing.assert_allclose(
+        expect.to_numpy().astype("int64"),
+        got.to_pandas().to_numpy().astype("int64"),
+    )
+
+
+def test_date_range_freq_does_not_divide_range():
+    expect = pd.date_range(
+        "2001-01-01 00:00:00.000000", "2001-01-01 00:00:00.000010", freq="3us"
+    )
+    got = cudf.date_range(
+        "2001-01-01 00:00:00.000000", "2001-01-01 00:00:00.000010", freq="3us"
+    )
+    np.testing.assert_allclose(
+        expect.to_numpy().astype("int64"),
+        got.to_pandas().to_numpy().astype("int64"),
+    )
+
+
+def test_date_range_raise_overflow():
+    # Fixed offset
+    start = np.datetime64(np.iinfo("int64").max, "ns")
+    periods = 2
+    freq = cudf.DateOffset(nanoseconds=1)
+    with pytest.raises(pd._libs.tslibs.np_datetime.OutOfBoundsDatetime):
+        cudf.date_range(start=start, periods=periods, freq=freq)
+
+    # Non-fixed offset
+    start = np.datetime64(np.iinfo("int64").max, "ns")
+    periods = 2
+    freq = cudf.DateOffset(months=1)
+    with pytest.raises(pd._libs.tslibs.np_datetime.OutOfBoundsDatetime):
+        # Extending beyond the max value will trigger a warning when pandas
+        # does an internal conversion to a Python built-in datetime.datetime
+        # object, which only supports down to microsecond resolution.
+        with pytest.warns(UserWarning):
+            cudf.date_range(start=start, periods=periods, freq=freq)
+
+
+@pytest.mark.parametrize(
+    "freqstr_unsupported",
+    [
+        "1M",
+        "2SM",
+        "3MS",
+        "4BM",
+        "5CBM",
+        "6SMS",
+        "7BMS",
+        "8CBMS",
+        "Q",
+        "2BQ",
+        "3BQS",
+        "10A",
+        "10Y",
+        "9BA",
+        "9BY",
+        "8AS",
+        "8YS",
+        "7BAS",
+        "7BYS",
+        "BH",
+        "B",
+    ],
+)
+def test_date_range_raise_unsupported(freqstr_unsupported):
+    s, e = "2001-01-01", "2008-01-31"
+    pd.date_range(start=s, end=e, freq=freqstr_unsupported)
+    with pytest.raises(ValueError, match="does not yet support"):
+        cudf.date_range(start=s, end=e, freq=freqstr_unsupported)
+
+    # We also check that these values are unsupported when using lowercase
+    # characters. We exclude the value 3MS (every 3 month starts) because 3ms
+    # is a valid frequency for every 3 milliseconds.
+    if freqstr_unsupported != "3MS":
+        freqstr_unsupported = freqstr_unsupported.lower()
+        pd.date_range(start=s, end=e, freq=freqstr_unsupported)
+        with pytest.raises(ValueError, match="does not yet support"):
+            cudf.date_range(start=s, end=e, freq=freqstr_unsupported)
+
+
+##################################################################
+#                    End of Date Range Test                      #
+##################################################################
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-05-31",
+            "2020-02-29",
+            None,
+            "1999-12-01",
+            "2000-12-21",
+            None,
+            "1900-02-28",
+            "1800-03-14",
+            "2100-03-10",
+            "1970-01-01",
+            "1969-12-11",
+        ]
+    ],
+)
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_is_month_end(data, dtype):
+    # Series
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_month_end
+    got = gs.dt.is_month_end
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-05-31",
+            None,
+            "1999-12-01",
+            "2000-12-21",
+            None,
+            "1900-01-01",
+            "1800-03-14",
+            "2100-03-10",
+            "1970-01-01",
+            "1969-12-11",
+            "2017-12-30",
+            "2017-12-31",
+            "2018-01-01",
+        ]
+    ],
+)
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_is_year_start(data, dtype):
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_year_start
+    got = gs.dt.is_year_start
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-05-31",
+            None,
+            "1999-12-01",
+            "2000-12-21",
+            None,
+            "1900-12-31",
+            "1800-03-14",
+            "2017-12-30",
+            "2017-12-31",
+            "2020-12-31 08:00:00",
+            None,
+            "1999-12-31 18:40:00",
+            "2000-12-31 04:00:00",
+            None,
+            "1800-12-14 07:30:00",
+            "2100-12-14 07:30:00",
+            "2020-05-31",
+        ]
+    ],
+)
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_is_year_end(data, dtype):
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_year_end
+    got = gs.dt.is_year_end
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-05-01",
+            "2020-05-31",
+            "2020-02-29",
+            None,
+            "1999-12-01",
+            "2000-12-21",
+            None,
+            "1900-02-28",
+            "1800-03-14",
+            "2100-03-10",
+            "1970-04-1",
+            "1970-01-01",
+            "1969-12-11",
+            "2020-12-31",
+        ]
+    ],
+)
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_is_quarter_start(data, dtype):
+    # Series
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_quarter_start
+    got = gs.dt.is_quarter_start
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-05-01",
+            "2020-05-31",
+            "2020-02-29",
+            None,
+            "1999-12-01",
+            "2000-12-21",
+            None,
+            "1900-02-28",
+            "1800-03-14",
+            "2100-03-10",
+            "1970-04-1",
+            "1970-01-01",
+            "1969-12-11",
+            "2020-12-31",
+        ]
+    ],
+)
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_is_quarter_end(data, dtype):
+    # Series
+    ps = pd.Series(data, dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.is_quarter_end
+    got = gs.dt.is_quarter_end
+
+    assert_eq(expect, got)
+
+
+def test_error_values():
+    s = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    with pytest.raises(
+        NotImplementedError,
+        match="DateTime Arrays is not yet implemented in cudf",
+    ):
+        s.values
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            [
+                "2020-05-31 08:00:00",
+                "1999-12-31 18:40:10",
+                "2000-12-31 04:00:05",
+                "1900-02-28 07:00:06",
+                "1800-03-14 07:30:20",
+                "2100-03-14 07:30:20",
+                "1970-01-01 00:00:09",
+                "1969-12-31 12:59:10",
+            ]
+        )
+    ],
+)
+@pytest.mark.parametrize("time_type", DATETIME_TYPES)
+@pytest.mark.parametrize(
+    "resolution", ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
+)
+def test_ceil(data, time_type, resolution):
+
+    gs = cudf.Series(data, dtype=time_type)
+    ps = gs.to_pandas()
+
+    expect = ps.dt.ceil(resolution)
+    got = gs.dt.ceil(resolution)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            [
+                "2020-05-31 08:00:00",
+                "1999-12-31 18:40:10",
+                "2000-12-31 04:00:05",
+                "1900-02-28 07:00:06",
+                "1800-03-14 07:30:20",
+                "2100-03-14 07:30:20",
+                "1970-01-01 00:00:09",
+                "1969-12-31 12:59:10",
+            ]
+        )
+    ],
+)
+@pytest.mark.parametrize("time_type", DATETIME_TYPES)
+@pytest.mark.parametrize(
+    "resolution", ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
+)
+def test_floor(data, time_type, resolution):
+
+    gs = cudf.Series(data, dtype=time_type)
+    ps = gs.to_pandas()
+
+    expect = ps.dt.floor(resolution)
+    got = gs.dt.floor(resolution)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            [
+                "2020-05-31 08:00:00",
+                "1999-12-31 18:40:10",
+                "2000-12-31 04:00:05",
+                "1900-02-28 07:00:06",
+                "1800-03-14 07:30:20",
+                "2100-03-14 07:30:20",
+                "1970-01-01 00:00:09",
+                "1969-12-31 12:59:10",
+            ]
+        )
+    ],
+)
+@pytest.mark.parametrize("time_type", DATETIME_TYPES)
+@pytest.mark.parametrize(
+    "resolution", ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
+)
+def test_round(data, time_type, resolution):
+
+    gs = cudf.Series(data, dtype=time_type)
+    ps = gs.to_pandas()
+
+    expect = ps.dt.round(resolution)
+    got = gs.dt.round(resolution)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.DatetimeIndex([]),
+        pd.DatetimeIndex(["2010-05-31"]),
+        pd.date_range("2000-01-01", "2000-12-31", periods=21),
+    ],
+)
+@pytest.mark.parametrize(
+    "offset",
+    [
+        "10Y",
+        "6M",
+        "M",
+        "31D",
+        "0H",
+        "44640T",
+        "44640min",
+        "2678000S",
+        "2678000000L",
+        "2678000000ms",
+        "2678000000000U",
+        "2678000000000us",
+        "2678000000000000N",
+        "2678000000000000ns",
+    ],
+)
+def test_first(idx, offset):
+    p = pd.Series(range(len(idx)), dtype="int64", index=idx)
+    g = cudf.from_pandas(p)
+
+    expect = p.first(offset=offset)
+    got = g.first(offset=offset)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    # This test case tests correctness when start is end of month
+    "idx, offset",
+    [
+        (
+            pd.DatetimeIndex(
+                [
+                    "2020-01-31",
+                    "2020-02-15",
+                    "2020-02-29",
+                    "2020-03-15",
+                    "2020-03-31",
+                    "2020-04-15",
+                    "2020-04-30",
+                ]
+            ),
+            "3M",
+        )
+    ],
+)
+def test_first_start_at_end_of_month(idx, offset):
+    p = pd.Series(range(len(idx)), index=idx)
+    g = cudf.from_pandas(p)
+
+    expect = p.first(offset=offset)
+    got = g.first(offset=offset)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.DatetimeIndex([]),
+        pd.DatetimeIndex(["2010-05-31"]),
+        pd.date_range("2000-01-01", "2000-12-31", periods=21),
+    ],
+)
+@pytest.mark.parametrize(
+    "offset",
+    [
+        "10Y",
+        "6M",
+        "M",
+        "31D",
+        "0H",
+        "44640T",
+        "44640min",
+        "2678000S",
+        "2678000000L",
+        "2678000000ms",
+        "2678000000000U",
+        "2678000000000us",
+        "2678000000000000N",
+        "2678000000000000ns",
+    ],
+)
+def test_last(idx, offset):
+    p = pd.Series(range(len(idx)), dtype="int64", index=idx)
+    g = cudf.from_pandas(p)
+
+    expect = p.last(offset=offset)
+    got = g.last(offset=offset)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2020-01-31",
+            "2020-02-15",
+            "2020-02-29",
+            "2020-03-15",
+            "2020-03-31",
+            "2020-04-15",
+            "2020-04-30",
+        ],
+        [43534, 43543, 37897, 2000],
+    ],
+)
+@pytest.mark.parametrize("dtype", [None, "datetime64[ns]"])
+def test_datetime_constructor(data, dtype):
+    expected = pd.DatetimeIndex(data=data, dtype=dtype)
+    actual = cudf.DatetimeIndex(data=data, dtype=dtype)
+
+    assert_eq(expected, actual)
+
+    expected = pd.DatetimeIndex(data=pd.Series(data), dtype=dtype)
+    actual = cudf.DatetimeIndex(data=cudf.Series(data), dtype=dtype)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [pd.Timestamp("2001-01-01", tz="America/New_York")],
+        pd.Series(["2001-01-01"], dtype="datetime64[ns, America/New_York]"),
+        pd.Index(["2001-01-01"], dtype="datetime64[ns, America/New_York]"),
+    ],
+)
+def test_construction_from_tz_timestamps(data):
+    with pytest.raises(NotImplementedError):
+        _ = cudf.Series(data)
+    with pytest.raises(NotImplementedError):
+        _ = cudf.Index(data)
+    with pytest.raises(NotImplementedError):
+        _ = cudf.DatetimeIndex(data)
+    with pytest.raises(NotImplementedError):
+        cudf.CategoricalIndex(data)
+
+
+@pytest.mark.parametrize("op", _cmpops)
+def test_datetime_binop_tz_timestamp(op):
+    s = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    pd_tz_timestamp = pd.Timestamp("1970-01-01 00:00:00.000000001", tz="utc")
+    with pytest.raises(NotImplementedError):
+        op(s, pd_tz_timestamp)
+
+    date_scalar = datetime.datetime.now(datetime.timezone.utc)
+    with pytest.raises(NotImplementedError):
+        op(s, date_scalar)
+
+
+@pytest.mark.parametrize(
+    "data1", [["20110101", "20120101", None, "20140101", None]]
+)
+@pytest.mark.parametrize(
+    "data2", [["20110101", "20120101", "20130101", None, None]]
+)
+@pytest.mark.parametrize("op", _cmpops)
+def test_datetime_series_cmpops_pandas_compatibility(data1, data2, op):
+    gsr1 = cudf.Series(data=data1, dtype="datetime64[ns]")
+    psr1 = gsr1.to_pandas()
+
+    gsr2 = cudf.Series(data=data2, dtype="datetime64[ns]")
+    psr2 = gsr2.to_pandas()
+
+    expect = op(psr1, psr2)
+    with cudf.option_context("mode.pandas_compatible", True):
+        got = op(gsr1, gsr2)
+
+    assert_eq(expect, got)
+
+
+def test_datetime_getitem_na():
+    s = cudf.Series([1, 2, None, 3], dtype="datetime64[ns]")
+    assert s[2] is cudf.NaT
+
+
+def test_daterange_pandas_compatibility():
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.date_range("20010101", "20020215", freq="400h", name="times")
+        expected = pd.date_range(
+            "2010-01-01", "2010-02-01", periods=10, name="times"
+        )
+        actual = cudf.date_range(
+            "2010-01-01", "2010-02-01", periods=10, name="times"
+        )
+    assert_eq(expected, actual)
+
+
+def test_strings_with_utc_offset_not_implemented():
+    with pytest.warns(DeprecationWarning, match="parsing timezone"):  # cupy
+        with pytest.raises(NotImplementedError):
+            DatetimeIndex(["2022-07-22 00:00:00+02:00"])
+
+
+@pytest.mark.parametrize("code", ["z", "Z"])
+def test_format_timezone_not_implemented(code):
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime(
+            ["2020-01-01 00:00:00 UTC"], format=f"%Y-%m-%d %H:%M:%S %{code}"
+        )
+
+
+@pytest.mark.parametrize("tz", ["Z", "UTC-3", "+01:00"])
+def test_no_format_timezone_not_implemented(tz):
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime([f"2020-01-01 00:00:00{tz}"])
+
+
+@pytest.mark.parametrize("arg", [True, False])
+def test_args_not_datetime_typerror(arg):
+    with pytest.raises(TypeError):
+        cudf.to_datetime([arg])
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "2000-01-01 00:00:00.000000000",
+            "2000-01-01 00:00:00.000000000",
+            "2000-01-01 00:00:00.000000000",
+        ],
+        [
+            "2000-01-01 00:00:00.000000000",
+            None,
+            "2000-01-01 00:00:00.000000000",
+        ],
+        [
+            "2000-01-01 00:00:00.001000000",
+            "2000-01-01 00:00:00.000000000",
+            "2000-01-01 00:00:00.000000000",
+        ],
+        [
+            "2000-01-01 00:00:00.010000000",
+            "2000-01-01 00:00:00.020000000",
+            "2000-01-01 00:00:00.030000000",
+        ],
+        [
+            "2000-01-01 00:00:00.010000000",
+            "2000-01-01 00:00:00.020000000",
+            None,
+        ],
+        [
+            "2000-01-01 00:00:00.000001000",
+            "2000-01-01 00:00:00.000000000",
+            "2000-01-01 00:00:00.000004000",
+        ],
+        [
+            None,
+            "2000-01-01 00:00:00.000000000",
+            "2000-01-01 00:00:00.000004000",
+        ],
+        [
+            "2000-01-01 00:00:00.000000010",
+            "2000-01-01 00:00:00.000000002",
+            "2000-01-01 00:00:00.000000000",
+        ],
+        [
+            "2000-01-01 00:00:00.000000010",
+            None,
+            "2000-01-01 00:00:00.000000000",
+        ],
+        [
+            "2000-01-01 00:00:01.000000000",
+            "2000-01-01 00:00:40.000000000",
+            "2000-01-01 00:00:59.000000000",
+        ],
+        [
+            "2000-01-01 00:10:00.000000000",
+            "2000-01-01 00:30:40.000000000",
+            "2000-01-01 00:59:00.000000000",
+        ],
+        [
+            "2000-01-01 07:00:00.000000000",
+            "2000-01-01 08:00:00.000000000",
+            None,
+        ],
+        [None, None, None],
+        [],
+        [
+            "2000-01-01 00:10:00.123456789",
+            "2000-01-01 00:30:40.123123456",
+            "2000-01-01 00:59:00.675347634",
+        ],
+    ],
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+def test_datetime_to_str(data, dtype):
+    gs = cudf.Series(data, dtype=dtype)
+    ps = gs.to_pandas()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        actual = gs.astype("str")
+
+    expected = ps.astype("string")
+
+    assert_eq(actual.to_pandas(nullable=True), expected)
+
+
+def test_dateimeindex_from_noniso_string():
+    data = ["20160920", "20160925"]
+    gdti = cudf.DatetimeIndex(data)
+    pdti = pd.DatetimeIndex(data)
+
+    assert_eq(gdti, pdti)
+
+
+@pytest.mark.parametrize("errors", ["coerce", "ignore"])
+def test_to_datetime_errors_non_scalar_not_implemented(errors):
+    with pytest.raises(NotImplementedError):
+        cudf.to_datetime([1, ""], unit="s", errors=errors)
diff --git a/python/cudf/cudf/tests/test_decimal.py b/python/cudf/cudf/tests/test_decimal.py
new file mode 100644
index 0000000..e4b2af9
--- /dev/null
+++ b/python/cudf/cudf/tests/test_decimal.py
@@ -0,0 +1,391 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import decimal
+from decimal import Decimal
+
+import numpy as np
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.core.column import Decimal32Column, Decimal64Column, NumericalColumn
+from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype
+from cudf.testing._utils import (
+    FLOAT_TYPES,
+    INTEGER_TYPES,
+    SIGNED_TYPES,
+    _decimal_series,
+    assert_eq,
+    expect_warning_if,
+)
+
+data_ = [
+    [Decimal("1.1"), Decimal("2.2"), Decimal("3.3"), Decimal("4.4")],
+    [Decimal("-1.1"), Decimal("2.2"), Decimal("3.3"), Decimal("4.4")],
+    [1],
+    [-1],
+    [1, 2, 3, 4],
+    [42, 17, 41],
+    [1, 2, None, 4],
+    [None, None, None],
+    [],
+]
+typ_ = [
+    pa.decimal128(precision=4, scale=2),
+    pa.decimal128(precision=5, scale=3),
+    pa.decimal128(precision=6, scale=4),
+]
+
+
+@pytest.mark.parametrize("data_", data_)
+@pytest.mark.parametrize("typ_", typ_)
+def test_round_trip_decimal64_column(data_, typ_):
+    pa_arr = pa.array(data_, type=typ_)
+    col_64 = Decimal64Column.from_arrow(pa_arr)
+    assert pa_arr.equals(col_64.to_arrow())
+
+
+@pytest.mark.parametrize("data_", data_)
+@pytest.mark.parametrize("typ_", typ_)
+def test_round_trip_decimal32_column(data_, typ_):
+    pa_arr = pa.array(data_, type=typ_)
+    col_32 = Decimal32Column.from_arrow(pa_arr)
+    assert pa_arr.equals(col_32.to_arrow())
+
+
+def test_from_arrow_max_precision_decimal64():
+    with pytest.raises(ValueError):
+        Decimal64Column.from_arrow(
+            pa.array([1, 2, 3], type=pa.decimal128(scale=0, precision=19))
+        )
+
+
+def test_from_arrow_max_precision_decimal32():
+    with pytest.raises(ValueError):
+        Decimal32Column.from_arrow(
+            pa.array([1, 2, 3], type=pa.decimal128(scale=0, precision=10))
+        )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.Series(
+            [
+                14.12302,
+                97938.2,
+                np.nan,
+                0.0,
+                -8.302014,
+                np.nan,
+                94.31304,
+                -112.2314,
+                0.3333333,
+                np.nan,
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize("from_dtype", FLOAT_TYPES)
+@pytest.mark.parametrize(
+    "to_dtype",
+    [Decimal64Dtype(7, 2), Decimal64Dtype(11, 4), Decimal64Dtype(18, 9)],
+)
+def test_typecast_from_float_to_decimal(data, from_dtype, to_dtype):
+    got = data.astype(from_dtype)
+
+    pa_arr = got.to_arrow().cast(
+        pa.decimal128(to_dtype.precision, to_dtype.scale)
+    )
+    expected = cudf.Series(Decimal64Column.from_arrow(pa_arr))
+
+    got = got.astype(to_dtype)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.Series(
+            [
+                14.12302,
+                38.2,
+                np.nan,
+                0.0,
+                -8.302014,
+                np.nan,
+                94.31304,
+                np.nan,
+                -112.2314,
+                0.3333333,
+                np.nan,
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize("from_dtype", INTEGER_TYPES)
+@pytest.mark.parametrize(
+    "to_dtype",
+    [Decimal64Dtype(9, 3), Decimal64Dtype(11, 4), Decimal64Dtype(18, 9)],
+)
+def test_typecast_from_int_to_decimal(data, from_dtype, to_dtype):
+    got = data.astype(from_dtype)
+
+    pa_arr = (
+        got.to_arrow()
+        .cast("float64")
+        .cast(pa.decimal128(to_dtype.precision, to_dtype.scale))
+    )
+    expected = cudf.Series(Decimal64Column.from_arrow(pa_arr))
+
+    got = got.astype(to_dtype)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.Series(
+            [
+                14.12309,
+                2.343942,
+                np.nan,
+                0.0,
+                -8.302082,
+                np.nan,
+                94.31308,
+                -112.2364,
+                -8.029972,
+                np.nan,
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "from_dtype",
+    [
+        Decimal64Dtype(7, 2),
+        Decimal64Dtype(11, 4),
+        Decimal64Dtype(18, 10),
+        Decimal32Dtype(7, 2),
+        Decimal32Dtype(5, 3),
+        Decimal32Dtype(9, 5),
+    ],
+)
+@pytest.mark.parametrize(
+    "to_dtype",
+    [
+        Decimal64Dtype(7, 2),
+        Decimal64Dtype(18, 10),
+        Decimal64Dtype(11, 4),
+        Decimal32Dtype(7, 2),
+        Decimal32Dtype(9, 5),
+        Decimal32Dtype(5, 3),
+    ],
+)
+def test_typecast_to_from_decimal(data, from_dtype, to_dtype):
+    if from_dtype.scale > to_dtype.MAX_PRECISION:
+        pytest.skip(
+            "This is supposed to overflow because the representation value in "
+            "the source exceeds the max representable in destination dtype."
+        )
+    s = data.astype(from_dtype)
+
+    pa_arr = s.to_arrow().cast(
+        pa.decimal128(to_dtype.precision, to_dtype.scale), safe=False
+    )
+    if isinstance(to_dtype, Decimal32Dtype):
+        expected = cudf.Series(Decimal32Column.from_arrow(pa_arr))
+    elif isinstance(to_dtype, Decimal64Dtype):
+        expected = cudf.Series(Decimal64Column.from_arrow(pa_arr))
+
+    with expect_warning_if(to_dtype.scale < s.dtype.scale, UserWarning):
+        got = s.astype(to_dtype)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.Series(
+            [
+                14.12309,
+                2.343942,
+                np.nan,
+                0.0,
+                -8.302082,
+                np.nan,
+                94.31308,
+                -112.2364,
+                -8.029972,
+                np.nan,
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "from_dtype",
+    [Decimal64Dtype(7, 2), Decimal64Dtype(11, 4), Decimal64Dtype(17, 10)],
+)
+@pytest.mark.parametrize("to_dtype", SIGNED_TYPES)
+def test_typecast_from_decimal(data, from_dtype, to_dtype):
+    got = data.astype(from_dtype)
+    pa_arr = got.to_arrow().cast(to_dtype, safe=False)
+
+    got = got.astype(to_dtype)
+    expected = cudf.Series(NumericalColumn.from_arrow(pa_arr))
+
+    assert_eq(got, expected)
+    assert_eq(got.dtype, expected.dtype)
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        # scatter to a single index
+        (
+            ["1", "2", "3"],
+            Decimal64Dtype(1, 0),
+            Decimal(5),
+            1,
+            ["1", "5", "3"],
+        ),
+        (
+            ["1.5", "2.5", "3.5"],
+            Decimal64Dtype(2, 1),
+            Decimal("5.5"),
+            1,
+            ["1.5", "5.5", "3.5"],
+        ),
+        (
+            ["1.0042", "2.0042", "3.0042"],
+            Decimal64Dtype(5, 4),
+            Decimal("5.0042"),
+            1,
+            ["1.0042", "5.0042", "3.0042"],
+        ),
+        # scatter via boolmask
+        (
+            ["1", "2", "3"],
+            Decimal64Dtype(1, 0),
+            Decimal(5),
+            cudf.Series([True, False, True]),
+            ["5", "2", "5"],
+        ),
+        (
+            ["1.5", "2.5", "3.5"],
+            Decimal64Dtype(2, 1),
+            Decimal("5.5"),
+            cudf.Series([True, True, True]),
+            ["5.5", "5.5", "5.5"],
+        ),
+        (
+            ["1.0042", "2.0042", "3.0042"],
+            Decimal64Dtype(5, 4),
+            Decimal("5.0042"),
+            cudf.Series([False, False, True]),
+            ["1.0042", "2.0042", "5.0042"],
+        ),
+        # We will allow assigning a decimal with less precision
+        (
+            ["1.00", "2.00", "3.00"],
+            Decimal64Dtype(3, 2),
+            Decimal(5),
+            1,
+            ["1.00", "5.00", "3.00"],
+        ),
+        # But not truncation
+        (
+            ["1", "2", "3"],
+            Decimal64Dtype(1, 0),
+            Decimal("5.5"),
+            1,
+            pa.lib.ArrowInvalid,
+        ),
+        # We will allow for setting scalars into decimal columns
+        (["1", "2", "3"], Decimal64Dtype(1, 0), 5, 1, ["1", "5", "3"]),
+        # But not if it has too many digits to fit the precision
+        (["1", "2", "3"], Decimal64Dtype(1, 0), 50, 1, pa.lib.ArrowInvalid),
+    ],
+)
+def test_series_setitem_decimal(args):
+    data, dtype, item, to, expect = args
+    data = _decimal_series(data, dtype)
+
+    if expect is pa.lib.ArrowInvalid:
+        with pytest.raises(expect):
+            data[to] = item
+        return
+    else:
+        expect = _decimal_series(expect, dtype)
+        data[to] = item
+        assert_eq(data, expect)
+
+
+@pytest.mark.parametrize(
+    "input_obj", [[decimal.Decimal(1), cudf.NA, decimal.Decimal(3)]]
+)
+def test_series_construction_with_nulls(input_obj):
+    expect = pa.array(input_obj, from_pandas=True)
+    got = cudf.Series(input_obj).to_arrow()
+
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": _decimal_series(
+                ["1", "2", "3"], dtype=cudf.Decimal64Dtype(1, 0)
+            )
+        },
+        {
+            "a": _decimal_series(
+                ["1", "2", "3"], dtype=cudf.Decimal64Dtype(1, 0)
+            ),
+            "b": _decimal_series(
+                ["1.0", "2.0", "3.0"], dtype=cudf.Decimal64Dtype(2, 1)
+            ),
+            "c": _decimal_series(
+                ["10.1", "20.2", "30.3"], dtype=cudf.Decimal64Dtype(3, 1)
+            ),
+        },
+        {
+            "a": _decimal_series(
+                ["1", None, "3"], dtype=cudf.Decimal64Dtype(1, 0)
+            ),
+            "b": _decimal_series(
+                ["1.0", "2.0", None], dtype=cudf.Decimal64Dtype(2, 1)
+            ),
+            "c": _decimal_series(
+                [None, "20.2", "30.3"], dtype=cudf.Decimal64Dtype(3, 1)
+            ),
+        },
+    ],
+)
+def test_serialize_decimal_columns(data):
+    df = cudf.DataFrame(data)
+    recreated = df.__class__.deserialize(*df.serialize())
+    assert_eq(recreated, df)
+
+
+def test_decimal_invalid_precision():
+    with pytest.raises(pa.ArrowInvalid):
+        _ = cudf.Series([10, 20, 30], dtype=cudf.Decimal64Dtype(2, 2))
+
+    with pytest.raises(pa.ArrowInvalid):
+        _ = cudf.Series([Decimal("300")], dtype=cudf.Decimal64Dtype(2, 1))
+
+
+def test_decimal_overflow():
+    s = cudf.Series([Decimal("0.0009384233522166997927180531650178250")])
+    result = s * s
+    assert_eq(cudf.Decimal128Dtype(precision=38, scale=37), result.dtype)
+
+    s = cudf.Series([1, 2], dtype=cudf.Decimal128Dtype(precision=38, scale=0))
+    result = s * Decimal("1.0")
+    assert_eq(cudf.Decimal128Dtype(precision=38, scale=1), result.dtype)
diff --git a/python/cudf/cudf/tests/test_df_protocol.py b/python/cudf/cudf/tests/test_df_protocol.py
new file mode 100644
index 0000000..d6134c7
--- /dev/null
+++ b/python/cudf/cudf/tests/test_df_protocol.py
@@ -0,0 +1,285 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from typing import Any, Tuple
+
+import cupy as cp
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.buffer import as_buffer
+from cudf.core.column import as_column, build_column
+from cudf.core.df_protocol import (
+    DataFrameObject,
+    _CuDFBuffer,
+    _CuDFColumn,
+    _DtypeKind,
+    _MaskKind,
+    _protocol_buffer_to_cudf_buffer,
+    from_dataframe,
+    protocol_dtype_to_cupy_dtype,
+)
+from cudf.testing._utils import assert_eq
+
+
+@pytest.fixture(
+    params=[
+        {"a": [1, 2, 3], "b": ["x", "y", "z"]},
+        {"a": [1, 2, None], "b": ["x", "y", "z"]},
+        {"a": [1, 2, 3], "b": pd.Categorical(["x", "y", None])},
+    ]
+)
+def pandas_df(request):
+    data = request.param
+    return pd.DataFrame(data)
+
+
+def assert_validity_equal(protocol_buffer, cudf_buffer, size, null, valid):
+    if null == _MaskKind.BYTEMASK:
+        protocol_mask = _protocol_buffer_to_cudf_buffer(protocol_buffer)
+        assert_eq(
+            as_column(protocol_mask, dtype="bool"),
+            as_column(cudf_buffer, dtype="bool"),
+        )
+    elif null == _MaskKind.BITMASK:
+        protocol_mask = _protocol_buffer_to_cudf_buffer(protocol_buffer)
+        cudf_mask = cudf_buffer
+        assert_eq(
+            build_column(
+                as_buffer(cp.zeros(10, dtype="int8")),
+                "int8",
+                size=size,
+                mask=protocol_mask,
+                children=(),
+            ),
+            build_column(
+                as_buffer(cp.zeros(10, dtype="int8")),
+                "int8",
+                size=size,
+                mask=cudf_mask,
+                children=(),
+            ),
+        )
+    else:
+        raise NotImplementedError()
+
+
+def assert_buffer_equal(buffer_and_dtype: Tuple[_CuDFBuffer, Any], cudfcol):
+    buf, dtype = buffer_and_dtype
+    device_id = cp.asarray(cudfcol.data).device.id
+    assert buf.__dlpack_device__() == (2, device_id)
+    col_from_buf = build_column(
+        _protocol_buffer_to_cudf_buffer(buf),
+        protocol_dtype_to_cupy_dtype(dtype),
+    )
+    # check that non null values are the equals as nulls are represented
+    # by sentinel values in the buffer.
+    # FIXME: In gh-10202 some minimal fixes were added to unblock CI. But
+    # currently only non-null values are compared, null positions are
+    # unchecked.
+    non_null_idxs = ~cudf.Series(cudfcol).isna()
+    assert_eq(
+        col_from_buf.apply_boolean_mask(non_null_idxs),
+        cudfcol.apply_boolean_mask(non_null_idxs),
+    )
+    array_from_dlpack = cp.from_dlpack(buf.__dlpack__()).get()
+    col_array = cp.asarray(cudfcol.data_array_view(mode="read")).get()
+    assert_eq(
+        array_from_dlpack[non_null_idxs.to_numpy()].flatten(),
+        col_array[non_null_idxs.to_numpy()].flatten(),
+    )
+
+
+def assert_column_equal(col: _CuDFColumn, cudfcol):
+    assert col.size() == cudfcol.size
+    assert col.offset == 0
+    assert col.null_count == cudfcol.null_count
+    assert col.num_chunks() == 1
+    if col.null_count == 0:
+        pytest.raises(RuntimeError, col._get_validity_buffer)
+        assert col.get_buffers()["validity"] is None
+    else:
+        assert_validity_equal(
+            col.get_buffers()["validity"][0],
+            cudfcol.mask,
+            cudfcol.size,
+            *col.describe_null,
+        )
+
+    if col.dtype[0] == _DtypeKind.CATEGORICAL:
+        assert_buffer_equal(col.get_buffers()["data"], cudfcol.codes)
+        assert col.get_buffers()["offsets"] is None
+
+    elif col.dtype[0] == _DtypeKind.STRING:
+        assert_buffer_equal(col.get_buffers()["data"], cudfcol.children[1])
+        assert_buffer_equal(col.get_buffers()["offsets"], cudfcol.children[0])
+
+    else:
+        assert_buffer_equal(col.get_buffers()["data"], cudfcol)
+        assert col.get_buffers()["offsets"] is None
+
+    if col.null_count == 0:
+        assert col.describe_null == (0, None)
+    else:
+        assert col.describe_null == (3, 0)
+
+
+def assert_dataframe_equal(dfo: DataFrameObject, df: cudf.DataFrame):
+    assert dfo.num_columns() == len(df.columns)
+    assert dfo.num_rows() == len(df)
+    assert dfo.num_chunks() == 1
+    assert dfo.column_names() == tuple(df.columns)
+    for col in df.columns:
+        assert_column_equal(dfo.get_column_by_name(col), df[col]._column)
+
+
+def assert_from_dataframe_equals(dfobj, allow_copy):
+    df2 = from_dataframe(dfobj, allow_copy=allow_copy)
+
+    assert_dataframe_equal(dfobj.__dataframe__(allow_copy), df2)
+    if isinstance(dfobj, cudf.DataFrame):
+        assert_eq(dfobj, df2)
+
+    elif isinstance(dfobj, pd.DataFrame):
+        assert_eq(cudf.DataFrame(dfobj), df2)
+
+    else:
+        raise TypeError(f"{type(dfobj)} not supported yet.")
+
+
+def test_from_dataframe_exception(pandas_df):
+    exception_msg = "This operation must copy data from CPU to GPU."
+    " Set `allow_copy=True` to allow it."
+    with pytest.raises(TypeError, match=exception_msg):
+        from_dataframe(pandas_df)
+
+
+def assert_df_unique_dtype_cols(data):
+    cdf = cudf.DataFrame(data=data)
+    assert_from_dataframe_equals(cdf, allow_copy=False)
+    assert_from_dataframe_equals(cdf, allow_copy=True)
+
+
+def test_from_dataframe():
+    data = dict(a=[1, 2, 3], b=[9, 10, 11])
+    df1 = cudf.DataFrame(data=data)
+    df2 = cudf.from_dataframe(df1)
+    assert_eq(df1, df2)
+
+    df3 = cudf.from_dataframe(df2)
+    assert_eq(df1, df3)
+
+
+def test_int_dtype():
+    data_int = dict(a=[1, 2, 3], b=[9, 10, 11])
+    assert_df_unique_dtype_cols(data_int)
+
+
+def test_float_dtype():
+    data_float = dict(a=[1.5, 2.5, 3.5], b=[9.2, 10.5, 11.8])
+    assert_df_unique_dtype_cols(data_float)
+
+
+def test_categorical_dtype():
+    cdf = cudf.DataFrame({"A": [1, 2, 5, 1]})
+    cdf["A"] = cdf["A"].astype("category")
+    col = cdf.__dataframe__().get_column_by_name("A")
+    assert col.dtype[0] == _DtypeKind.CATEGORICAL
+    assert col.describe_categorical == (False, True, {0: 1, 1: 2, 2: 5})
+    assert_from_dataframe_equals(cdf, allow_copy=False)
+    assert_from_dataframe_equals(cdf, allow_copy=True)
+
+
+def test_bool_dtype():
+    data_bool = dict(a=[True, True, False], b=[False, True, False])
+    assert_df_unique_dtype_cols(data_bool)
+
+
+def test_string_dtype():
+    data_string = dict(a=["a", "b", "cdef", "", "g"])
+    assert_df_unique_dtype_cols(data_string)
+
+
+def test_mixed_dtype():
+    data_mixed = dict(
+        int=[1, 2, 3],
+        float=[1.5, 2.5, 3.5],
+        bool=[True, False, True],
+        categorical=[5, 1, 5],
+        string=["rapidsai-cudf ", "", "df protocol"],
+    )
+    assert_df_unique_dtype_cols(data_mixed)
+
+
+def test_NA_int_dtype():
+    data_int = dict(
+        a=[1, None, 3, None, 5],
+        b=[9, 10, None, 7, 8],
+        c=[6, 19, 20, 100, 1000],
+    )
+    assert_df_unique_dtype_cols(data_int)
+
+
+def test_NA_float_dtype():
+    data_float = dict(
+        a=[1.4, None, 3.6, None, 5.2],
+        b=[9.7, 10.9, None, 7.8, 8.2],
+        c=[6.1, 19.2, 20.3, 100.4, 1000.5],
+    )
+    assert_df_unique_dtype_cols(data_float)
+
+
+def test_NA_categorical_dtype():
+    df = cudf.DataFrame({"A": [1, 2, 5, 1]})
+    df["B"] = df["A"].astype("category")
+    df.at[[1, 3], "B"] = None  # Set two items to null
+
+    # Some detailed testing for correctness of dtype and null handling:
+    col = df.__dataframe__().get_column_by_name("B")
+    assert col.dtype[0] == _DtypeKind.CATEGORICAL
+    assert col.null_count == 2
+    assert col.describe_null == (3, 0)
+    assert col.num_chunks() == 1
+    assert col.describe_categorical == (False, True, {0: 1, 1: 2, 2: 5})
+    assert_from_dataframe_equals(df, allow_copy=False)
+    assert_from_dataframe_equals(df, allow_copy=True)
+
+
+def test_NA_bool_dtype():
+    data_bool = dict(a=[None, True, False], b=[False, None, None])
+    assert_df_unique_dtype_cols(data_bool)
+
+
+def test_NA_string_dtype():
+    df = cudf.DataFrame({"A": ["a", "b", "cdef", "", "g"]})
+    df["B"] = df["A"].astype("object")
+    df.at[1, "B"] = cudf.NA  # Set one item to null
+
+    # Test for correctness and null handling:
+    col = df.__dataframe__().get_column_by_name("B")
+    assert col.dtype[0] == _DtypeKind.STRING
+    assert col.null_count == 1
+    assert col.describe_null == (3, 0)
+    assert col.num_chunks() == 1
+    assert_from_dataframe_equals(df, allow_copy=False)
+    assert_from_dataframe_equals(df, allow_copy=True)
+
+
+def test_NA_mixed_dtype():
+    data_mixed = dict(
+        int=[1, None, 2, 3, 1000],
+        float=[None, 1.5, 2.5, 3.5, None],
+        bool=[True, None, False, None, None],
+        categorical=[5, 1, 5, 3, None],
+        string=[None, None, None, "df protocol", None],
+    )
+    assert_df_unique_dtype_cols(data_mixed)
+
+
+@pytest.mark.skipif(
+    not PANDAS_GE_150,
+    reason="Pandas versions < 1.5.0 do not support interchange protocol",
+)
+def test_from_cpu_df(pandas_df):
+    cudf.from_dataframe(pandas_df, allow_copy=True)
diff --git a/python/cudf/cudf/tests/test_dlpack.py b/python/cudf/cudf/tests/test_dlpack.py
new file mode 100644
index 0000000..6e34817
--- /dev/null
+++ b/python/cudf/cudf/tests/test_dlpack.py
@@ -0,0 +1,216 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import itertools
+from contextlib import ExitStack as does_not_raise
+
+import cupy
+import numpy as np
+import pytest
+from packaging import version
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+nelems = [0, 3, 10]
+dtype = [np.uint16, np.int32, np.float64]
+nulls = ["some", "none"]
+params_1d = itertools.product(nelems, dtype, nulls)
+
+ncols = [0, 1, 2]
+params_2d = itertools.product(ncols, nelems, dtype, nulls)
+
+
+if version.parse(cupy.__version__) < version.parse("10"):
+    # fromDlpack deprecated in cupy version 10, replaced by from_dlpack
+    cupy_from_dlpack = cupy.fromDlpack
+else:
+    cupy_from_dlpack = cupy.from_dlpack
+
+
+def data_size_expectation_builder(data, nan_null_param=False):
+    if nan_null_param and np.isnan(data).any():
+        return pytest.raises((ValueError,))
+
+    if len(data.shape) == 2 and data.size == 0:
+        return pytest.raises((ValueError, IndexError))
+    else:
+        return does_not_raise()
+
+
+@pytest.fixture(params=params_1d)
+def data_1d(request):
+    nelems = request.param[0]
+    dtype = request.param[1]
+    nulls = request.param[2]
+    a = np.random.randint(10, size=nelems).astype(dtype)
+    if nulls == "some" and a.size != 0 and np.issubdtype(dtype, np.floating):
+        idx = np.random.choice(a.size, size=int(a.size * 0.2), replace=False)
+        a[idx] = np.nan
+    return a
+
+
+@pytest.fixture(params=params_2d)
+def data_2d(request):
+    ncols = request.param[0]
+    nrows = request.param[1]
+    dtype = request.param[2]
+    nulls = request.param[3]
+    a = np.random.randint(10, size=(nrows, ncols)).astype(dtype)
+    if nulls == "some" and a.size != 0 and np.issubdtype(dtype, np.floating):
+        idx = np.random.choice(a.size, size=int(a.size * 0.2), replace=False)
+        a.ravel()[idx] = np.nan
+    return np.ascontiguousarray(a)
+
+
+def test_to_dlpack_dataframe(data_2d):
+    expectation = data_size_expectation_builder(data_2d)
+
+    with expectation:
+        gdf = cudf.DataFrame.from_records(data_2d)
+        dlt = gdf.to_dlpack()
+
+        # PyCapsules are a C-API thing so couldn't come up with a better way
+        assert str(type(dlt)) == "<class 'PyCapsule'>"
+
+
+def test_to_dlpack_series(data_1d):
+    expectation = data_size_expectation_builder(data_1d, nan_null_param=False)
+
+    with expectation:
+        gs = cudf.Series(data_1d, nan_as_null=False)
+        dlt = gs.to_dlpack()
+
+        # PyCapsules are a C-API thing so couldn't come up with a better way
+        assert str(type(dlt)) == "<class 'PyCapsule'>"
+
+
+def test_to_dlpack_series_null(data_1d):
+    expectation = data_size_expectation_builder(data_1d, nan_null_param=True)
+
+    with expectation:
+        gs = cudf.Series(data_1d, nan_as_null=True)
+        dlt = gs.to_dlpack()
+
+        # PyCapsules are a C-API thing so couldn't come up with a better way
+        assert str(type(dlt)) == "<class 'PyCapsule'>"
+
+
+def test_to_dlpack_index(data_1d):
+    expectation = data_size_expectation_builder(data_1d)
+
+    with expectation:
+        if np.isnan(data_1d).any():
+            pytest.skip("Nulls not allowed in Index")
+        gi = cudf.core.index.as_index(data_1d)
+        dlt = gi.to_dlpack()
+
+        # PyCapsules are a C-API thing so couldn't come up with a better way
+        assert str(type(dlt)) == "<class 'PyCapsule'>"
+
+
+def test_to_dlpack_cupy_1d(data_1d):
+    expectation = data_size_expectation_builder(data_1d, False)
+    with expectation:
+        gs = cudf.Series(data_1d, nan_as_null=False)
+        cudf_host_array = gs.to_numpy(na_value=np.nan)
+        dlt = gs.to_dlpack()
+
+        cupy_array = cupy_from_dlpack(dlt)
+        cupy_host_array = cupy_array.get()
+
+        assert_eq(cudf_host_array, cupy_host_array)
+
+
+def test_to_dlpack_cupy_2d(data_2d):
+    expectation = data_size_expectation_builder(data_2d)
+
+    with expectation:
+        gdf = cudf.DataFrame.from_records(data_2d)
+        cudf_host_array = np.array(gdf.to_pandas()).flatten()
+        dlt = gdf.to_dlpack()
+
+        cupy_array = cupy_from_dlpack(dlt)
+        cupy_host_array = cupy_array.get().flatten()
+
+        assert_eq(cudf_host_array, cupy_host_array)
+
+
+def test_from_dlpack_cupy_1d(data_1d):
+    cupy_array = cupy.array(data_1d)
+    cupy_host_array = cupy_array.get()
+    dlt = cupy_array.toDlpack()
+
+    gs = cudf.from_dlpack(dlt)
+    cudf_host_array = gs.to_numpy(na_value=np.nan)
+
+    assert_eq(cudf_host_array, cupy_host_array)
+
+
+def test_from_dlpack_cupy_2d(data_2d):
+    cupy_array = cupy.array(data_2d, order="F")
+    cupy_host_array = cupy_array.get().flatten()
+    dlt = cupy_array.toDlpack()
+
+    gdf = cudf.from_dlpack(dlt)
+    cudf_host_array = np.array(gdf.to_pandas()).flatten()
+
+    assert_eq(cudf_host_array, cupy_host_array)
+
+
+def test_to_dlpack_cupy_2d_null(data_2d):
+    expectation = data_size_expectation_builder(data_2d, nan_null_param=True)
+
+    with expectation:
+        gdf = cudf.DataFrame.from_records(data_2d, nan_as_null=True)
+        cudf_host_array = np.array(gdf.to_pandas()).flatten()
+        dlt = gdf.to_dlpack()
+
+        cupy_array = cupy_from_dlpack(dlt)
+        cupy_host_array = cupy_array.get().flatten()
+
+        assert_eq(cudf_host_array, cupy_host_array)
+
+
+def test_to_dlpack_cupy_1d_null(data_1d):
+    expectation = data_size_expectation_builder(data_1d, nan_null_param=True)
+
+    with expectation:
+        gs = cudf.Series(data_1d)
+        cudf_host_array = gs.to_numpy(na_value=np.nan)
+        dlt = gs.to_dlpack()
+
+        cupy_array = cupy_from_dlpack(dlt)
+        cupy_host_array = cupy_array.get()
+
+        assert_eq(cudf_host_array, cupy_host_array)
+
+
+def test_to_dlpack_mixed_dtypes():
+    df = cudf.DataFrame({"a": [1, 2, 3, 4], "b": [10.32, 0.4, -0.2, -1000.32]})
+
+    cudf_host_array = df.to_numpy()
+    dlt = df.to_dlpack()
+
+    cupy_array = cupy_from_dlpack(dlt)
+    cupy_host_array = cupy_array.get()
+
+    assert_eq(cudf_host_array, cupy_host_array)
+
+
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (0, 3),
+        pytest.param(
+            (3, 0),
+            marks=pytest.mark.xfail(
+                reason="Index information not available via from_dlpack"
+            ),
+        ),
+        (0, 0),
+    ],
+)
+def test_from_dlpack_zero_sizes(shape):
+    arr = cupy.empty(shape, dtype=float)
+    df = cudf.io.dlpack.from_dlpack(arr.__dlpack__())
+    assert_eq(df, cudf.DataFrame(arr))
diff --git a/python/cudf/cudf/tests/test_doctests.py b/python/cudf/cudf/tests/test_doctests.py
new file mode 100644
index 0000000..0da5c6b
--- /dev/null
+++ b/python/cudf/cudf/tests/test_doctests.py
@@ -0,0 +1,112 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+import contextlib
+import doctest
+import inspect
+import io
+import itertools
+import os
+
+import numpy as np
+import pytest
+
+import cudf
+
+pytestmark = pytest.mark.filterwarnings("ignore::FutureWarning")
+
+# modules that will be searched for doctests
+tests = [cudf, cudf.core.groupby]
+
+
+def _name_in_all(parent, name):
+    return name in getattr(parent, "__all__", [])
+
+
+def _is_public_name(parent, name):
+    return not name.startswith("_")
+
+
+def _find_doctests_in_obj(obj, finder=None, criteria=None):
+    """Find all doctests in an object.
+
+    Parameters
+    ----------
+    obj : module or class
+        The object to search for docstring examples.
+    finder : doctest.DocTestFinder, optional
+        The DocTestFinder object to use. If not provided, a DocTestFinder is
+        constructed.
+    criteria : callable, optional
+        Callable indicating whether to recurse over members of the provided
+        object. If not provided, names not defined in the object's ``__all__``
+        property are ignored.
+
+    Yields
+    ------
+    doctest.DocTest
+        The next doctest found in the object.
+    """
+    if finder is None:
+        finder = doctest.DocTestFinder()
+    if criteria is None:
+        criteria = _name_in_all
+    for docstring in finder.find(obj):
+        if docstring.examples:
+            yield docstring
+    for name, member in inspect.getmembers(obj):
+        # Only recurse over members matching the criteria
+        if not criteria(obj, name):
+            continue
+        # Recurse over the public API of modules (objects defined in the
+        # module's __all__)
+        if inspect.ismodule(member):
+            yield from _find_doctests_in_obj(
+                member, finder, criteria=_name_in_all
+            )
+        # Recurse over the public API of classes (attributes not prefixed with
+        # an underscore)
+        if inspect.isclass(member):
+            yield from _find_doctests_in_obj(
+                member, finder, criteria=_is_public_name
+            )
+
+
+class TestDoctests:
+    @pytest.fixture(autouse=True)
+    def chdir_to_tmp_path(cls, tmp_path):
+        # Some doctests generate files, so this fixture runs the tests in a
+        # temporary directory.
+        original_directory = os.getcwd()
+        os.chdir(tmp_path)
+        yield
+        os.chdir(original_directory)
+
+    @pytest.mark.parametrize(
+        "docstring",
+        itertools.chain(*[_find_doctests_in_obj(mod) for mod in tests]),
+        ids=lambda docstring: docstring.name,
+    )
+    def test_docstring(self, docstring):
+        # We ignore differences in whitespace in the doctest output, and enable
+        # the use of an ellipsis "..." to match any string in the doctest
+        # output. An ellipsis is useful for, e.g., memory addresses or
+        # imprecise floating point values.
+        optionflags = doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE
+        runner = doctest.DocTestRunner(optionflags=optionflags)
+
+        # These global names are pre-defined and can be used in doctests
+        # without first importing them.
+        globals = dict(
+            cudf=cudf,
+            np=np,
+        )
+        docstring.globs = globals
+
+        # Capture stdout and include failing outputs in the traceback.
+        doctest_stdout = io.StringIO()
+        with contextlib.redirect_stdout(doctest_stdout):
+            runner.run(docstring)
+            results = runner.summarize()
+        assert not results.failed, (
+            f"{results.failed} of {results.attempted} doctests failed for "
+            f"{docstring.name}:\n{doctest_stdout.getvalue()}"
+        )
diff --git a/python/cudf/cudf/tests/test_dropna.py b/python/cudf/cudf/tests/test_dropna.py
new file mode 100644
index 0000000..1def659
--- /dev/null
+++ b/python/cudf/cudf/tests/test_dropna.py
@@ -0,0 +1,296 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import (
+    _create_pandas_series_float64_default,
+    assert_eq,
+)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        [1.0, 2, None, 4],
+        ["one", "two", "three", "four"],
+        pd.Series(["a", "b", "c", "d"], dtype="category"),
+        pd.Series(pd.date_range("2010-01-01", "2010-01-04")),
+    ],
+)
+@pytest.mark.parametrize("nulls", ["one", "some", "all", "none"])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dropna_series(data, nulls, inplace):
+
+    psr = _create_pandas_series_float64_default(data)
+
+    if len(data) > 0:
+        if nulls == "one":
+            p = np.random.randint(0, 4)
+            psr[p] = None
+        elif nulls == "some":
+            p1, p2 = np.random.randint(0, 4, (2,))
+            psr[p1] = None
+            psr[p2] = None
+        elif nulls == "all":
+            psr[:] = None
+
+    gsr = cudf.from_pandas(psr)
+
+    check_dtype = True
+    if gsr.null_count == len(gsr):
+        check_dtype = False
+
+    expected = psr.dropna()
+    actual = gsr.dropna()
+
+    if inplace:
+        expected = psr
+        actual = gsr
+
+    assert_eq(expected, actual, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, None]},
+        {"a": [1, 2, None], "b": [3, 4, 5]},
+        {"a": [1, 2, None], "b": [3, 4, None]},
+        {"a": [None, 1, 2], "b": [1, 2, None]},
+        {"a": [None, 1, None], "b": [None, 2, None]},
+        {"a": [None, None, 1], "b": [1, 2, None]},
+        {"a": ["d", "e", "f"], "b": ["a", None, "c"]},
+    ],
+)
+@pytest.mark.parametrize("how", ["all", "any"])
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dropna_dataframe(data, how, axis, inplace):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.dropna(axis=axis, how=how, inplace=inplace)
+    actual = gdf.dropna(axis=axis, how=how, inplace=inplace)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("how", ["all", "any"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": cudf.Series([None, None, None], dtype="float64"),
+            "b": cudf.Series([1, 2, None]),
+        },
+        {
+            "a": cudf.Series([np.nan, np.nan, np.nan], dtype="float64"),
+            "b": cudf.Series([1, 2, None]),
+        },
+        cudf.Series([None, None, None], dtype="object"),
+    ],
+)
+@pytest.mark.parametrize("axis", [0, 1])
+def test_dropna_with_all_nulls(how, data, axis):
+    gdf = cudf.DataFrame({"a": data})
+    pdf = gdf.to_pandas()
+
+    assert_eq(pdf.dropna(axis=axis, how=how), gdf.dropna(axis=axis, how=how))
+
+
+def test_dropna_nan_as_null():
+    sr = cudf.Series([1.0, 2.0, np.nan, None], nan_as_null=False)
+    assert_eq(sr.dropna(), sr[:2])
+    sr = sr.nans_to_nulls()
+    assert_eq(sr.dropna(), sr[:2])
+
+    df = cudf.DataFrame(
+        {
+            "a": cudf.Series([1.0, 2.0, np.nan, None], nan_as_null=False),
+            "b": cudf.Series([1, 2, 3, 4]),
+        }
+    )
+
+    got = df.dropna()
+    expected = df[:2]
+    assert_eq(expected, got)
+
+    df = df.nans_to_nulls()
+    got = df.dropna()
+    expected = df[:2]
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data,subset",
+    [
+        ({"a": [1, None], "b": [1, 2]}, ["a"]),
+        ({"a": [1, None], "b": [1, 2]}, ["b"]),
+        ({"a": [1, None], "b": [1, 2]}, []),
+        ({"a": [1, 2], "b": [1, 2]}, ["b"]),
+        ({"a": [1, 2, None], "b": [1, None, 2]}, ["a"]),
+        ({"a": [1, 2, None], "b": [1, None, 2]}, ["b"]),
+        ({"a": [1, 2, None], "b": [1, None, 2]}, ["a", "b"]),
+    ],
+)
+def test_dropna_subset_rows(data, subset):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(pdf.dropna(subset=subset), gdf.dropna(subset=subset))
+
+
+@pytest.mark.parametrize(
+    "data, subset",
+    [
+        ({"a": [1, None], "b": [1, 2]}, [0]),
+        ({"a": [1, None], "b": [1, 2]}, [1]),
+        ({"a": [1, None], "b": [1, 2]}, []),
+        ({"a": [1, 2], "b": [1, 2]}, [0]),
+        ({"a": [1, 2], "b": [None, 2], "c": [3, None]}, [0]),
+        ({"a": [1, 2], "b": [None, 2], "c": [3, None]}, [1]),
+        ({"a": [1, 2], "b": [None, 2], "c": [3, None]}, [0, 1]),
+    ],
+)
+def test_dropna_subset_cols(data, subset):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.dropna(axis=1, subset=subset), gdf.dropna(axis=1, subset=subset)
+    )
+
+
+# TODO: can't test with subset=[] below since Pandas
+# returns empty DF when both subset=[] and thresh are specified.
+@pytest.mark.parametrize("thresh", [0, 1, 2])
+@pytest.mark.parametrize("subset", [None, ["a"], ["b"], ["a", "b"]])
+def test_dropna_thresh(thresh, subset):
+    pdf = pd.DataFrame({"a": [1, 2, None, None], "b": [1, 2, 3, None]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.dropna(axis=0, thresh=thresh, subset=subset),
+        gdf.dropna(axis=0, thresh=thresh, subset=subset),
+    )
+
+
+@pytest.mark.parametrize("thresh", [0, 1, 2])
+@pytest.mark.parametrize("subset", [None, [0], [1], [0, 1]])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dropna_thresh_cols(thresh, subset, inplace):
+    pdf = pd.DataFrame(
+        {"a": [1, 2], "b": [3, 4], "c": [5, None], "d": [np.nan, np.nan]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.dropna(
+        axis=1, thresh=thresh, subset=subset, inplace=inplace
+    )
+    actual = gdf.dropna(axis=1, thresh=thresh, subset=subset, inplace=inplace)
+
+    if inplace:
+        expected = pdf
+        actual = gdf
+
+    assert_eq(
+        expected,
+        actual,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "key": [1, 2, 10],
+            "val": cudf.Series([np.nan, 3, 1], nan_as_null=False),
+            "abc": [np.nan, None, 1],
+        },
+        {
+            "key": [None, 2, 1],
+            "val": cudf.Series([3, np.nan, 0.1], nan_as_null=True),
+            "abc": [None, 1, None],
+        },
+    ],
+)
+@pytest.mark.parametrize("axis", [0, 1])
+def test_dropna_dataframe_np_nan(data, axis):
+    gdf = cudf.DataFrame(data)
+    pd_data = {
+        key: value.to_pandas() if isinstance(value, cudf.Series) else value
+        for key, value in data.items()
+    }
+    pdf = pd.DataFrame(pd_data)
+
+    assert_eq(pdf.dropna(axis=axis), gdf.dropna(axis=axis), check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data, dtype",
+    [
+        ([1, float("nan"), 2], "float64"),
+        (["x", None, "y"], "str"),
+        (["x", None, "y"], "category"),
+        (["2020-01-20", pd.NaT, "2020-03-15"], "datetime64[ns]"),
+        (["1s", pd.NaT, "3d"], "timedelta64[ns]"),
+    ],
+)
+def test_dropna_index(data, dtype):
+    pi = pd.Index(data, dtype=dtype)
+    gi = cudf.from_pandas(pi)
+
+    expect = pi.dropna()
+    got = gi.dropna()
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("data", [[[1, None, 2], [None, None, 2]]])
+@pytest.mark.parametrize("how", ["all", "any"])
+def test_dropna_multiindex(data, how):
+    pi = pd.MultiIndex.from_arrays(data)
+    gi = cudf.from_pandas(pi)
+
+    expect = pi.dropna(how)
+    got = gi.dropna(how)
+
+    with pytest.raises(AssertionError, match="different"):
+        # pandas-gh44792. Pandas infers the dtypes as (int64, int64), though
+        # int64 doesn't really store null/nans. The dtype propagates to the
+        # result of dropna. cuDF infers the dtypes as (float, float), which
+        # differs from pandas.
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            [pd.Timestamp("2020-01-01"), pd.NaT, pd.Timestamp("2020-02-01")],
+            [pd.NaT, pd.NaT, pd.Timestamp("2020-03-01")],
+        ],
+        [
+            [pd.Timestamp("2020-01-01"), pd.NaT, pd.Timestamp("2020-02-01")],
+            [np.nan, np.nan, 1.0],
+        ],
+        [[1.0, np.nan, 2.0], [np.nan, np.nan, 1.0]],
+    ],
+)
+@pytest.mark.parametrize("how", ["all", "any"])
+def test_dropna_multiindex_2(data, how):
+    pi = pd.MultiIndex.from_arrays(data)
+    gi = cudf.from_pandas(pi)
+
+    expect = pi.dropna(how)
+    got = gi.dropna(how)
+
+    assert_eq(expect, got)
diff --git a/python/cudf/cudf/tests/test_dtypes.py b/python/cudf/cudf/tests/test_dtypes.py
new file mode 100644
index 0000000..6e24099
--- /dev/null
+++ b/python/cudf/cudf/tests/test_dtypes.py
@@ -0,0 +1,370 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.column import ColumnBase
+from cudf.core.dtypes import (
+    CategoricalDtype,
+    Decimal32Dtype,
+    Decimal64Dtype,
+    Decimal128Dtype,
+    IntervalDtype,
+    ListDtype,
+    StructDtype,
+)
+from cudf.testing._utils import assert_eq
+from cudf.utils.dtypes import np_to_pa_dtype
+
+if PANDAS_GE_150:
+    from pandas.core.arrays.arrow.extension_types import ArrowIntervalType
+else:
+    from pandas.core.arrays._arrow_utils import ArrowIntervalType
+
+
+def test_cdt_basic():
+    psr = pd.Series(["a", "b", "a", "c"], dtype="category")
+    sr = cudf.Series(["a", "b", "a", "c"], dtype="category")
+    assert isinstance(sr.dtype, CategoricalDtype)
+    assert_eq(sr.dtype.categories, psr.dtype.categories)
+
+
+@pytest.mark.parametrize(
+    "data", [None, [], ["a"], [1], [1.0], ["a", "b", "c"]]
+)
+@pytest.mark.parametrize("ordered", [None, False, True])
+def test_cdt_eq(data, ordered):
+    dt = cudf.CategoricalDtype(categories=data, ordered=ordered)
+    assert dt == "category"
+    assert dt == dt
+    assert dt == cudf.CategoricalDtype(categories=None, ordered=ordered)
+    assert dt == cudf.CategoricalDtype(categories=data, ordered=ordered)
+    assert not dt == cudf.CategoricalDtype(
+        categories=data, ordered=not ordered
+    )
+
+
+@pytest.mark.parametrize(
+    "data", [None, [], ["a"], [1], [1.0], ["a", "b", "c"]]
+)
+@pytest.mark.parametrize("ordered", [None, False, True])
+def test_cdf_to_pandas(data, ordered):
+    assert (
+        pd.CategoricalDtype(data, ordered)
+        == cudf.CategoricalDtype(categories=data, ordered=ordered).to_pandas()
+    )
+
+
+@pytest.mark.parametrize(
+    "value_type",
+    [
+        int,
+        "int32",
+        np.int32,
+        "datetime64[ms]",
+        "datetime64[ns]",
+        "str",
+        "object",
+    ],
+)
+def test_list_dtype_pyarrow_round_trip(value_type):
+    pa_type = pa.list_(cudf.utils.dtypes.np_to_pa_dtype(np.dtype(value_type)))
+    expect = pa_type
+    got = ListDtype.from_arrow(expect).to_arrow()
+    assert expect.equals(got)
+
+
+def test_list_dtype_eq():
+    lhs = ListDtype("int32")
+    rhs = ListDtype("int32")
+    assert lhs == rhs
+    rhs = ListDtype("int64")
+    assert lhs != rhs
+
+
+def test_list_nested_dtype():
+    dt = ListDtype(ListDtype("int32"))
+    expect = ListDtype("int32")
+    got = dt.element_type
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "fields",
+    [
+        {},
+        {"a": "int64"},
+        {"a": "datetime64[ms]"},
+        {"a": "int32", "b": "int64"},
+    ],
+)
+def test_struct_dtype_pyarrow_round_trip(fields):
+    pa_type = pa.struct(
+        {
+            k: cudf.utils.dtypes.np_to_pa_dtype(np.dtype(v))
+            for k, v in fields.items()
+        }
+    )
+    expect = pa_type
+    got = StructDtype.from_arrow(expect).to_arrow()
+    assert expect.equals(got)
+
+
+def test_struct_dtype_eq():
+    lhs = StructDtype(
+        {"a": "int32", "b": StructDtype({"c": "int64", "ab": "int32"})}
+    )
+    rhs = StructDtype(
+        {"a": "int32", "b": StructDtype({"c": "int64", "ab": "int32"})}
+    )
+    assert lhs == rhs
+    rhs = StructDtype({"a": "int32", "b": "int64"})
+    assert lhs != rhs
+    lhs = StructDtype({"b": "int64", "a": "int32"})
+    assert lhs != rhs
+
+
+@pytest.mark.parametrize(
+    "fields",
+    [
+        {},
+        {"a": "int32"},
+        {"a": "object"},
+        {"a": "str"},
+        {"a": "datetime64[D]"},
+        {"a": "int32", "b": "int64"},
+        {"a": "int32", "b": StructDtype({"a": "int32", "b": "int64"})},
+    ],
+)
+def test_struct_dtype_fields(fields):
+    fields = {"a": "int32", "b": StructDtype({"c": "int64", "d": "int32"})}
+    dt = StructDtype(fields)
+    assert_eq(dt.fields, fields)
+
+
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+def test_decimal_dtype_arrow_roundtrip(decimal_type):
+    dt = decimal_type(4, 2)
+    assert dt.to_arrow() == pa.decimal128(4, 2)
+    assert dt == decimal_type.from_arrow(pa.decimal128(4, 2))
+
+
+@pytest.mark.parametrize(
+    "decimal_type,max_precision",
+    [
+        (cudf.Decimal32Dtype, 9),
+        (cudf.Decimal64Dtype, 18),
+        (cudf.Decimal128Dtype, 38),
+    ],
+)
+def test_max_precision(decimal_type, max_precision):
+    decimal_type(scale=0, precision=max_precision)
+    with pytest.raises(ValueError):
+        decimal_type(scale=0, precision=max_precision + 1)
+
+
+@pytest.fixture(params=["int64", "int32"])
+def subtype(request):
+    return request.param
+
+
+@pytest.fixture(params=["left", "right", "both", "neither"])
+def closed(request):
+    return request.param
+
+
+def test_interval_dtype_pyarrow_round_trip(subtype, closed):
+    pa_array = ArrowIntervalType(subtype, closed)
+    expect = pa_array
+    got = IntervalDtype.from_arrow(expect).to_arrow()
+    assert expect.equals(got)
+
+
+def test_interval_dtype_from_pandas(subtype, closed):
+    expect = cudf.IntervalDtype(subtype, closed=closed)
+    pd_type = pd.IntervalDtype(subtype, closed=closed)
+    got = cudf.IntervalDtype.from_pandas(pd_type)
+    assert expect == got
+
+
+def assert_column_array_dtype_equal(column: ColumnBase, array: pa.array):
+    """
+    In cudf, each column holds its dtype. And since column may have child
+    columns, child columns also holds their datatype. This method tests
+    that every level of `column` matches the type of the given `array`
+    recursively.
+    """
+
+    if isinstance(column.dtype, ListDtype):
+        return array.type.equals(
+            column.dtype.to_arrow()
+        ) and assert_column_array_dtype_equal(
+            column.base_children[1], array.values
+        )
+    elif isinstance(column.dtype, StructDtype):
+        return array.type.equals(column.dtype.to_arrow()) and all(
+            assert_column_array_dtype_equal(child, array.field(i))
+            for i, child in enumerate(column.base_children)
+        )
+    elif isinstance(
+        column.dtype, (Decimal128Dtype, Decimal64Dtype, Decimal32Dtype)
+    ):
+        return array.type.equals(column.dtype.to_arrow())
+    elif isinstance(column.dtype, CategoricalDtype):
+        raise NotImplementedError()
+    else:
+        return array.type.equals(np_to_pa_dtype(column.dtype))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[{"name": 123}]],
+        [
+            [
+                {
+                    "IsLeapYear": False,
+                    "data": {"Year": 1999, "Month": 7},
+                    "names": ["Mike", None],
+                },
+                {
+                    "IsLeapYear": True,
+                    "data": {"Year": 2004, "Month": 12},
+                    "names": None,
+                },
+                {
+                    "IsLeapYear": False,
+                    "data": {"Year": 1996, "Month": 2},
+                    "names": ["Rose", "Richard"],
+                },
+            ]
+        ],
+        [
+            [None, {"human?": True, "deets": {"weight": 2.4, "age": 27}}],
+            [
+                {"human?": None, "deets": {"weight": 5.3, "age": 25}},
+                {"human?": False, "deets": {"weight": 8.0, "age": 31}},
+                {"human?": False, "deets": None},
+            ],
+            [],
+            None,
+            [{"human?": None, "deets": {"weight": 6.9, "age": None}}],
+        ],
+        [
+            {
+                "name": "var0",
+                "val": [
+                    {"name": "var1", "val": None, "type": "optional<struct>"}
+                ],
+                "type": "list",
+            },
+            {},
+            {
+                "name": "var2",
+                "val": [
+                    {
+                        "name": "var3",
+                        "val": {"field": 42},
+                        "type": "optional<struct>",
+                    },
+                    {
+                        "name": "var4",
+                        "val": {"field": 3.14},
+                        "type": "optional<struct>",
+                    },
+                ],
+                "type": "list",
+            },
+            None,
+        ],
+    ],
+)
+def test_lists_of_structs_dtype(data):
+    got = cudf.Series(data)
+    expected = pa.array(data)
+
+    assert_column_array_dtype_equal(got._column, expected)
+    assert expected.equals(got._column.to_arrow())
+
+
+@pytest.mark.parametrize(
+    "in_dtype,expect",
+    [
+        (np.dtype("int8"), np.dtype("int8")),
+        (np.int8, np.dtype("int8")),
+        (pd.Int8Dtype(), np.dtype("int8")),
+        (pd.StringDtype(), np.dtype("object")),
+        ("int8", np.dtype("int8")),
+        ("boolean", np.dtype("bool")),
+        ("bool_", np.dtype("bool")),
+        (np.bool_, np.dtype("bool")),
+        (int, np.dtype("int64")),
+        (float, np.dtype("float64")),
+        (cudf.ListDtype("int64"), cudf.ListDtype("int64")),
+        (np.dtype("U"), np.dtype("object")),
+        ("timedelta64[ns]", np.dtype("<m8[ns]")),
+        ("timedelta64[ms]", np.dtype("<m8[ms]")),
+        ("<m8[s]", np.dtype("<m8[s]")),
+        ("datetime64[ns]", np.dtype("<M8[ns]")),
+        ("datetime64[ms]", np.dtype("<M8[ms]")),
+        ("<M8[s]", np.dtype("<M8[s]")),
+        (cudf.ListDtype("int64"), cudf.ListDtype("int64")),
+        ("category", cudf.CategoricalDtype()),
+        (
+            cudf.CategoricalDtype(categories=("a", "b", "c")),
+            cudf.CategoricalDtype(categories=("a", "b", "c")),
+        ),
+        (
+            pd.CategoricalDtype(categories=("a", "b", "c")),
+            cudf.CategoricalDtype(categories=("a", "b", "c")),
+        ),
+        (
+            # this is a pandas.core.arrays.numpy_.PandasDtype...
+            pd.array([1], dtype="int16").dtype,
+            np.dtype("int16"),
+        ),
+        (pd.IntervalDtype("int"), cudf.IntervalDtype("int64")),
+        (cudf.IntervalDtype("int"), cudf.IntervalDtype("int64")),
+        (pd.IntervalDtype("int64"), cudf.IntervalDtype("int64")),
+    ],
+)
+def test_dtype(in_dtype, expect):
+    assert_eq(cudf.dtype(in_dtype), expect)
+
+
+@pytest.mark.parametrize(
+    "in_dtype",
+    [
+        "complex",
+        np.complex128,
+        complex,
+        "S",
+        "a",
+        "V",
+        "float16",
+        np.float16,
+        "timedelta64",
+        "timedelta64[D]",
+        "datetime64[D]",
+        "datetime64",
+    ],
+)
+def test_dtype_raise(in_dtype):
+    with pytest.raises(TypeError):
+        cudf.dtype(in_dtype)
+
+
+def test_dtype_np_bool_to_pa_bool():
+    """This test case captures that utility np_to_pa_dtype
+    should map np.bool_ to pa.bool_, nuances on bit width
+    difference should be handled elsewhere.
+    """
+
+    assert np_to_pa_dtype(np.dtype("bool")) == pa.bool_()
diff --git a/python/cudf/cudf/tests/test_duplicates.py b/python/cudf/cudf/tests/test_duplicates.py
new file mode 100644
index 0000000..ddbfdf5
--- /dev/null
+++ b/python/cudf/cudf/tests/test_duplicates.py
@@ -0,0 +1,631 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import itertools
+import random
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import concat
+from cudf.testing._utils import (
+    _create_pandas_series_float64_default,
+    assert_eq,
+    assert_exceptions_equal,
+)
+
+# TODO: PANDAS 1.0 support
+# Revisit drop_duplicates() tests to update parameters like ignore_index.
+
+
+def assert_df(g, p):
+    # assert_eq() with sorted index of dataframes
+    g = g.sort_index()
+    p = p.sort_index()
+    return assert_eq(g, p)
+
+
+def assert_df2(g, p):
+    assert g.index.dtype == p.index.dtype
+    np.testing.assert_equal(g.index.to_numpy(), p.index)
+    assert tuple(g.columns) == tuple(p.columns)
+    for k in g.columns:
+        assert g[k].dtype == p[k].dtype
+        np.testing.assert_equal(g[k].to_numpy(), p[k])
+
+
+# most tests are similar to pandas drop_duplicates
+
+
+@pytest.mark.parametrize("subset", ["a", ["a"], ["a", "B"]])
+def test_duplicated_with_misspelled_column_name(subset):
+    df = pd.DataFrame({"A": [0, 0, 1], "B": [0, 0, 1], "C": [0, 0, 1]})
+    gdf = cudf.DataFrame.from_pandas(df)
+
+    assert_exceptions_equal(
+        lfunc=df.drop_duplicates,
+        rfunc=gdf.drop_duplicates,
+        lfunc_args_and_kwargs=([subset],),
+        rfunc_args_and_kwargs=([subset],),
+    )
+
+
+@pytest.mark.parametrize("keep", ["first", "last", False])
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 4, 5, 6, 6],
+        [],
+        ["a", "b", "s", "sd", "a", "b"],
+        pd.Series(["aaa"] * 10, dtype="object"),
+    ],
+)
+def test_drop_duplicates_series(data, keep):
+    pds = _create_pandas_series_float64_default(data)
+    gds = cudf.from_pandas(pds)
+
+    assert_df(pds.drop_duplicates(keep=keep), gds.drop_duplicates(keep=keep))
+    pds.drop_duplicates(keep=keep, inplace=True)
+    gds.drop_duplicates(keep=keep, inplace=True)
+    assert_df(pds, gds)
+
+
+def test_drop_duplicates():
+    pdf = pd.DataFrame(
+        {
+            "AAA": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1, 1, 2, 2, 2, 2, 1, 2],
+            "D": range(8),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    # single column
+    result = gdf.copy()
+    result.drop_duplicates("AAA", inplace=True)
+    expected = pdf.copy()
+    expected.drop_duplicates("AAA", inplace=True)
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates("AAA", keep="last")
+    expected = pdf.drop_duplicates("AAA", keep="last")
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates("AAA", keep=False)
+    expected = pdf.drop_duplicates("AAA", keep=False)
+    assert_df(result, expected)
+    assert len(result) == 0
+
+    # multi column
+    expected = pdf.loc[[0, 1, 2, 3]]
+    result = gdf.drop_duplicates(np.array(["AAA", "B"]))
+    assert_df(result, expected)
+    result = pdf.drop_duplicates(np.array(["AAA", "B"]))
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates(("AAA", "B"), keep="last")
+    expected = pdf.drop_duplicates(("AAA", "B"), keep="last")
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates(("AAA", "B"), keep=False)
+    expected = pdf.drop_duplicates(("AAA", "B"), keep=False)
+    assert_df(result, expected)
+
+    # consider everything
+    df2 = gdf.loc[:, ["AAA", "B", "C"]]
+
+    result = df2.drop_duplicates()
+    # in this case only
+    expected = df2.drop_duplicates(["AAA", "B"])
+    assert_df(result, expected)
+
+    result = df2.drop_duplicates(keep="last")
+    expected = df2.drop_duplicates(["AAA", "B"], keep="last")
+    assert_df(result, expected)
+
+    result = df2.drop_duplicates(keep=False)
+    expected = df2.drop_duplicates(["AAA", "B"], keep=False)
+    assert_df(result, expected)
+
+    # integers
+    result = gdf.drop_duplicates("C")
+    expected = pdf.drop_duplicates("C")
+    assert_df(result, expected)
+    result = gdf.drop_duplicates("C", keep="last")
+    expected = pdf.drop_duplicates("C", keep="last")
+    assert_df(result, expected)
+
+    gdf["E"] = gdf["C"].astype("int8")
+    result = gdf.drop_duplicates("E")
+    pdf["E"] = pdf["C"].astype("int8")
+    expected = pdf.drop_duplicates("E")
+    assert_df(result, expected)
+    result = gdf.drop_duplicates("E", keep="last")
+    expected = pdf.drop_duplicates("E", keep="last")
+    assert_df(result, expected)
+
+    pdf = pd.DataFrame(
+        {"x": [7, 6, 3, 3, 4, 8, 0], "y": [0, 6, 5, 5, 9, 1, 2]}
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    pdf = pd.DataFrame([[1, 0], [0, 2]])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    pdf = pd.DataFrame([[-2, 0], [0, -4]])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    x = np.iinfo(np.int64).max / 3 * 2
+    pdf = pd.DataFrame([[-x, x], [0, x + 4]])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    pdf = pd.DataFrame([[-x, x], [x, x + 4]])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    pdf = pd.DataFrame([i] * 9 for i in range(16))
+    pdf = pd.concat([pdf, pd.DataFrame([[1] + [0] * 8])], ignore_index=True)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+
+@pytest.mark.skip(reason="cudf does not support duplicate column names yet")
+def test_drop_duplicates_with_duplicate_column_names():
+    df = pd.DataFrame(
+        [[1, 2, 5], [3, 4, 6], [3, 4, 7]], columns=["a", "a", "b"]
+    )
+    df = cudf.DataFrame.from_pandas(df)
+
+    result0 = df.drop_duplicates()
+    assert_df(result0, df)
+
+    result1 = df.drop_duplicates("a")
+    expected1 = df[:2]
+    assert_df(result1, expected1)
+
+
+def test_drop_duplicates_for_take_all():
+    pdf = pd.DataFrame(
+        {
+            "AAA": ["foo", "bar", "baz", "bar", "foo", "bar", "qux", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1, 1, 2, 2, 2, 2, 1, 2],
+            "D": range(8),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    # single column
+    result = gdf.drop_duplicates("AAA")
+    expected = pdf.drop_duplicates("AAA")
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates("AAA", keep="last")
+    expected = pdf.drop_duplicates("AAA", keep="last")
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates("AAA", keep=False)
+    expected = pdf.drop_duplicates("AAA", keep=False)
+    assert_df(result, expected)
+
+    # multiple columns
+    result = gdf.drop_duplicates(["AAA", "B"])
+    expected = pdf.drop_duplicates(["AAA", "B"])
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates(["AAA", "B"], keep="last")
+    expected = pdf.drop_duplicates(["AAA", "B"], keep="last")
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates(["AAA", "B"], keep=False)
+    expected = pdf.drop_duplicates(["AAA", "B"], keep=False)
+    assert_df(result, expected)
+
+
+def test_drop_duplicates_tuple():
+    pdf = pd.DataFrame(
+        {
+            ("AA", "AB"): [
+                "foo",
+                "bar",
+                "foo",
+                "bar",
+                "foo",
+                "bar",
+                "bar",
+                "foo",
+            ],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1, 1, 2, 2, 2, 2, 1, 2],
+            "D": range(8),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    # single column
+    result = gdf.drop_duplicates(("AA", "AB"))
+    expected = pdf.drop_duplicates(("AA", "AB"))
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates(("AA", "AB"), keep="last")
+    expected = pdf.drop_duplicates(("AA", "AB"), keep="last")
+    assert_df(result, expected)
+
+    result = gdf.drop_duplicates(("AA", "AB"), keep=False)
+    expected = pdf.drop_duplicates(("AA", "AB"), keep=False)  # empty df
+    assert len(result) == 0
+    assert_df(result, expected)
+
+    # multi column
+    expected = pdf.drop_duplicates((("AA", "AB"), "B"))
+    result = gdf.drop_duplicates((("AA", "AB"), "B"))
+    assert_df(result, expected)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame(columns=[]),
+        pd.DataFrame(columns=["A", "B", "C"]),
+        pd.DataFrame(index=[]),
+        pd.DataFrame(index=["A", "B", "C"]),
+    ],
+)
+def test_drop_duplicates_empty(df):
+    df = cudf.DataFrame.from_pandas(df)
+    result = df.drop_duplicates()
+    assert_df(result, df)
+
+    result = df.copy()
+    result.drop_duplicates(inplace=True)
+    assert_df(result, df)
+
+
+@pytest.mark.parametrize("num_columns", [3, 4, 5])
+def test_dataframe_drop_duplicates_numeric_method(num_columns):
+    comb = list(itertools.permutations(range(num_columns), num_columns))
+    shuf = list(comb)
+    random.Random(num_columns).shuffle(shuf)
+
+    def get_pdf(n_dup):
+        # create dataframe with n_dup duplicate rows
+        rows = comb + shuf[:n_dup]
+        random.Random(n_dup).shuffle(rows)
+        return pd.DataFrame(rows)
+
+    for i in range(5):
+        pdf = get_pdf(i)
+        gdf = cudf.DataFrame.from_pandas(pdf)
+        assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    # subset columns, single columns
+    assert_df(
+        gdf.drop_duplicates(pdf.columns[:-1]),
+        pdf.drop_duplicates(pdf.columns[:-1]),
+    )
+    assert_df(
+        gdf.drop_duplicates(pdf.columns[-1]),
+        pdf.drop_duplicates(pdf.columns[-1]),
+    )
+    assert_df(
+        gdf.drop_duplicates(pdf.columns[0]),
+        pdf.drop_duplicates(pdf.columns[0]),
+    )
+
+    # subset columns shuffled
+    cols = list(pdf.columns)
+    random.Random(3).shuffle(cols)
+    assert_df(gdf.drop_duplicates(cols), pdf.drop_duplicates(cols))
+    random.Random(3).shuffle(cols)
+    assert_df(gdf.drop_duplicates(cols[:-1]), pdf.drop_duplicates(cols[:-1]))
+    random.Random(3).shuffle(cols)
+    assert_df(gdf.drop_duplicates(cols[-1]), pdf.drop_duplicates(cols[-1]))
+    assert_df(
+        gdf.drop_duplicates(cols, keep="last"),
+        pdf.drop_duplicates(cols, keep="last"),
+    )
+
+
+def test_dataframe_drop_duplicates_method():
+    pdf = pd.DataFrame(
+        [(1, 2, "a"), (2, 3, "b"), (3, 4, "c"), (2, 3, "d"), (3, 5, "c")],
+        columns=["n1", "n2", "s1"],
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates(), pdf.drop_duplicates())
+
+    assert_eq(
+        gdf.drop_duplicates("n1")["n1"].reset_index(drop=True),
+        pdf.drop_duplicates("n1")["n1"].reset_index(drop=True),
+    )
+    assert_eq(
+        gdf.drop_duplicates("n2")["n2"].reset_index(drop=True),
+        pdf.drop_duplicates("n2")["n2"].reset_index(drop=True),
+    )
+    assert_eq(
+        gdf.drop_duplicates("s1")["s1"].reset_index(drop=True),
+        pdf.drop_duplicates("s1")["s1"].reset_index(drop=True),
+    )
+    assert_eq(
+        gdf.drop_duplicates("s1", keep="last")["s1"]
+        .sort_index()
+        .reset_index(drop=True),
+        pdf.drop_duplicates("s1", keep="last")["s1"].reset_index(drop=True),
+    )
+    assert gdf.drop_duplicates("s1", inplace=True) is None
+
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_df(gdf.drop_duplicates("n1"), pdf.drop_duplicates("n1"))
+    assert_df(gdf.drop_duplicates("n2"), pdf.drop_duplicates("n2"))
+    assert_df(gdf.drop_duplicates("s1"), pdf.drop_duplicates("s1"))
+    assert_df(
+        gdf.drop_duplicates(["n1", "n2"]), pdf.drop_duplicates(["n1", "n2"])
+    )
+    assert_df(
+        gdf.drop_duplicates(["n1", "s1"]), pdf.drop_duplicates(["n1", "s1"])
+    )
+
+    # Test drop error
+    assert_exceptions_equal(
+        lfunc=pdf.drop_duplicates,
+        rfunc=gdf.drop_duplicates,
+        lfunc_args_and_kwargs=(["n3"],),
+        rfunc_args_and_kwargs=(["n3"],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.drop_duplicates,
+        rfunc=gdf.drop_duplicates,
+        lfunc_args_and_kwargs=([["n1", "n4", "n3"]],),
+        rfunc_args_and_kwargs=([["n1", "n4", "n3"]],),
+    )
+
+
+def test_datetime_drop_duplicates():
+
+    date_df = cudf.DataFrame()
+    date_df["date"] = pd.date_range("11/20/2018", periods=6, freq="D")
+    date_df["value"] = np.random.sample(len(date_df))
+
+    df = concat([date_df, date_df[:4]])
+    assert_df(df[:-4], df.drop_duplicates())
+
+    df2 = df.reset_index()
+    assert_df(df2[:-4], df2.drop_duplicates())
+
+    df3 = df.set_index("date")
+    assert_df(df3[:-4], df3.drop_duplicates())
+
+
+def test_drop_duplicates_NA():
+    # none
+    df = pd.DataFrame(
+        {
+            "A": [None, None, "foo", "bar", "foo", "bar", "bar", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1.0, np.nan, np.nan, np.nan, 1.0, 1.0, 1, 1.0],
+            "D": range(8),
+        }
+    )
+    df = cudf.DataFrame.from_pandas(df)
+    # single column
+    result = df.drop_duplicates("A")
+    expected = df.to_pandas().loc[[0, 2, 3]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates("A", keep="last")
+    expected = df.to_pandas().loc[[1, 6, 7]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates("A", keep=False)
+    expected = df.to_pandas().loc[[]]  # empty df
+    assert_df(result, expected)
+    assert len(result) == 0
+
+    # multi column
+    result = df.drop_duplicates(["A", "B"])
+    expected = df.to_pandas().loc[[0, 2, 3, 6]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates(["A", "B"], keep="last")
+    expected = df.to_pandas().loc[[1, 5, 6, 7]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates(["A", "B"], keep=False)
+    expected = df.to_pandas().loc[[6]]
+    assert_df(result, expected)
+
+    # nan
+    df = pd.DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1.0, np.nan, np.nan, np.nan, 1.0, 1.0, 1, 1.0],
+            "D": range(8),
+        }
+    )
+    df = cudf.DataFrame.from_pandas(df)
+    # single column
+    result = df.drop_duplicates("C")
+    expected = df[:2]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates("C", keep="last")
+    expected = df.to_pandas().loc[[3, 7]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates("C", keep=False)
+    expected = df.to_pandas().loc[[]]  # empty df
+    assert_df(result, expected)
+    assert len(result) == 0
+
+    # multi column
+    result = df.drop_duplicates(["C", "B"])
+    expected = df.to_pandas().loc[[0, 1, 2, 4]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates(["C", "B"], keep="last")
+    expected = df.to_pandas().loc[[1, 3, 6, 7]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates(["C", "B"], keep=False)
+    expected = df.to_pandas().loc[[1]]
+    assert_df(result, expected)
+
+
+def test_drop_duplicates_NA_for_take_all():
+    # TODO: PANDAS 1.0 support - add ignore_index for
+    # pandas drop_duplicates calls in this function.
+
+    # none
+    pdf = pd.DataFrame(
+        {
+            "A": [None, None, "foo", "bar", "foo", "baz", "bar", "qux"],
+            "C": [1.0, np.nan, np.nan, np.nan, 1.0, 2.0, 3, 1.0],
+        }
+    )
+
+    df = cudf.DataFrame.from_pandas(pdf)
+    # single column
+    result = df.drop_duplicates("A")
+    expected = pdf.iloc[[0, 2, 3, 5, 7]]
+    assert_df(result, expected)
+    assert_df(
+        df.drop_duplicates("A", ignore_index=True),
+        result.reset_index(drop=True),
+    )
+
+    result = df.drop_duplicates("A", keep="last")
+    expected = pdf.iloc[[1, 4, 5, 6, 7]]
+    assert_df(result, expected)
+    assert_df(
+        df.drop_duplicates("A", ignore_index=True, keep="last"),
+        result.reset_index(drop=True),
+    )
+
+    result = df.drop_duplicates("A", keep=False)
+    expected = pdf.iloc[[5, 7]]
+    assert_df(result, expected)
+    assert_df(
+        df.drop_duplicates("A", ignore_index=True, keep=False),
+        result.reset_index(drop=True),
+    )
+
+    # nan
+
+    # single column
+    result = df.drop_duplicates("C")
+    expected = pdf.iloc[[0, 1, 5, 6]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates("C", keep="last")
+    expected = pdf.iloc[[3, 5, 6, 7]]
+    assert_df(result, expected)
+
+    result = df.drop_duplicates("C", keep=False)
+    expected = pdf.iloc[[5, 6]]
+    assert_df(result, expected)
+
+
+def test_drop_duplicates_inplace():
+    orig = pd.DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": [1, 1, 2, 2, 2, 2, 1, 2],
+            "D": range(8),
+        }
+    )
+    orig = cudf.DataFrame.from_pandas(orig)
+    # single column
+    df = orig.copy()
+    df.drop_duplicates("A", inplace=True)
+    expected = orig[:2]
+    result = df
+    assert_df(result, expected)
+
+    df = orig.copy()
+    df.drop_duplicates("A", keep="last", inplace=True)
+    expected = orig.loc[[6, 7]]
+    result = df
+    assert_df(result, expected)
+
+    df = orig.copy()
+    df.drop_duplicates("A", keep=False, inplace=True)
+    expected = orig.loc[[]]
+    result = df
+    assert_df(result, expected)
+    assert len(df) == 0
+
+    # multi column
+    df = orig.copy()
+    df.drop_duplicates(["A", "B"], inplace=True)
+    expected = orig.loc[[0, 1, 2, 3]]
+    result = df
+    assert_df(result, expected)
+
+    df = orig.copy()
+    df.drop_duplicates(["A", "B"], keep="last", inplace=True)
+    expected = orig.loc[[0, 5, 6, 7]]
+    result = df
+    assert_df(result, expected)
+
+    df = orig.copy()
+    df.drop_duplicates(["A", "B"], keep=False, inplace=True)
+    expected = orig.loc[[0]]
+    result = df
+    assert_df(result, expected)
+
+    # consider everything
+    orig2 = orig.loc[:, ["A", "B", "C"]].copy()
+
+    df2 = orig2.copy()
+    df2.drop_duplicates(inplace=True)
+    # in this case only
+    expected = orig2.drop_duplicates(["A", "B"])
+    result = df2
+    assert_df(result, expected)
+
+    df2 = orig2.copy()
+    df2.drop_duplicates(keep="last", inplace=True)
+    expected = orig2.drop_duplicates(["A", "B"], keep="last")
+    result = df2
+    assert_df(result, expected)
+
+    df2 = orig2.copy()
+    df2.drop_duplicates(keep=False, inplace=True)
+    expected = orig2.drop_duplicates(["A", "B"], keep=False)
+    result = df2
+    assert_df(result, expected)
+
+
+def test_drop_duplicates_multi_index():
+    arrays = [
+        ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+        ["one", "two", "one", "two", "one", "two", "one", "two"],
+    ]
+
+    idx = pd.MultiIndex.from_tuples(list(zip(*arrays)), names=["a", "b"])
+    pdf = pd.DataFrame(np.random.randint(0, 2, (8, 4)), index=idx)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    expected = pdf.drop_duplicates()
+    result = gdf.drop_duplicates()
+    assert_df(result.to_pandas(), expected)
+    # FIXME: to_pandas needed until sort_index support for MultiIndex
+
+    for col in gdf.columns:
+        assert_df(
+            gdf[col].drop_duplicates().to_pandas(),
+            pdf[col].drop_duplicates(),
+        )
+
+
+def test_drop_duplicates_ignore_index_wrong_type():
+    gdf = cudf.DataFrame([1, 1, 2])
+    with pytest.raises(ValueError):
+        gdf.drop_duplicates(ignore_index="True")
diff --git a/python/cudf/cudf/tests/test_extension_compilation.py b/python/cudf/cudf/tests/test_extension_compilation.py
new file mode 100644
index 0000000..857cc11
--- /dev/null
+++ b/python/cudf/cudf/tests/test_extension_compilation.py
@@ -0,0 +1,335 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+import operator
+
+import cupy as cp
+import numpy as np
+import pytest
+from numba import cuda, types
+from numba.cuda import compile_ptx
+from numba.np.numpy_support import from_dtype
+
+from cudf import NA
+from cudf.core.udf.api import Masked
+from cudf.core.udf.masked_typing import MaskedType
+from cudf.testing._utils import parametrize_numeric_dtypes_pairwise
+from cudf.utils._numba import _CUDFNumbaConfig
+
+arith_ops = (
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.truediv,
+    operator.floordiv,
+    operator.mod,
+    operator.pow,
+)
+
+comparison_ops = (
+    operator.lt,
+    operator.le,
+    operator.eq,
+    operator.ne,
+    operator.ge,
+    operator.gt,
+)
+
+unary_ops = (operator.truth,)
+
+ops = arith_ops + comparison_ops
+
+number_types = (
+    types.float32,
+    types.float64,
+    types.int8,
+    types.int16,
+    types.int32,
+    types.int64,
+    types.uint8,
+    types.uint16,
+    types.uint32,
+    types.uint64,
+)
+
+QUICK = False
+
+if QUICK:
+    arith_ops = (operator.add, operator.truediv, operator.pow)
+    number_types = (types.int32, types.float32)
+
+
+number_ids = tuple(str(t) for t in number_types)
+
+
+@pytest.mark.parametrize("op", unary_ops)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+def test_compile_masked_unary(op, ty):
+    def func(x):
+        return op(x)
+
+    cc = (7, 5)
+    ptx, resty = compile_ptx(func, (MaskedType(ty),), cc=cc, device=True)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+def test_execute_masked_binary(op, ty):
+    @cuda.jit(device=True)
+    def func(x, y):
+        return op(x, y)
+
+    @cuda.jit
+    def test_kernel(x, y, err):
+        # Reference result with unmasked value
+        u = func(x, y)
+
+        # Construct masked values to test with
+        x0, y0 = Masked(x, False), Masked(y, False)
+        x1, y1 = Masked(x, True), Masked(y, True)
+
+        # Call with masked types
+        r0 = func(x0, y0)
+        r1 = func(x1, y1)
+
+        # Check masks are as expected, and unmasked result matches masked
+        # result
+        if r0.valid:
+            # TODO: ideally, we would raise an exception here rather
+            # than return an "error code", and that is what the
+            # previous version of this (and below) tests did. But,
+            # Numba kernels cannot currently use `debug=True` with
+            # CUDA enhanced compatibility.  Once a solution to that is
+            # reached, we should switch back to raising exceptions
+            # here.
+            err[0] = 1
+        if not r1.valid:
+            err[0] = 2
+        if u != r1.value:
+            err[0] = 3
+
+    err = cp.asarray([0], dtype="int8")
+    with _CUDFNumbaConfig():
+        test_kernel[1, 1](1, 2, err)
+    assert err[0] == 0
+
+
+@pytest.mark.parametrize("op", ops)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+@pytest.mark.parametrize("constant", [1, 1.5])
+def test_compile_arith_masked_vs_constant(op, ty, constant):
+    def func(x):
+        return op(x, constant)
+
+    cc = (7, 5)
+    ptx, resty = compile_ptx(func, (MaskedType(ty),), cc=cc, device=True)
+
+    assert isinstance(resty, MaskedType)
+
+    # Check that the masked typing matches that of the unmasked typing
+    um_ptx, um_resty = compile_ptx(func, (ty,), cc=cc, device=True)
+    assert resty.value_type == um_resty
+
+
+@pytest.mark.parametrize("op", ops)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+@pytest.mark.parametrize("constant", [1, 1.5])
+def test_compile_arith_constant_vs_masked(op, ty, constant):
+    def func(x):
+        return op(constant, x)
+
+    cc = (7, 5)
+    ptx, resty = compile_ptx(func, (MaskedType(ty),), cc=cc, device=True)
+
+    assert isinstance(resty, MaskedType)
+
+
+@pytest.mark.parametrize("op", ops)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+def test_compile_arith_masked_vs_na(op, ty):
+    def func(x):
+        return op(x, NA)
+
+    cc = (7, 5)
+    ptx, resty = compile_ptx(func, (MaskedType(ty),), cc=cc, device=True)
+
+    assert isinstance(resty, MaskedType)
+
+
+@pytest.mark.parametrize("op", ops)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+def test_compile_arith_na_vs_masked(op, ty):
+    def func(x):
+        return op(NA, x)
+
+    cc = (7, 5)
+    ptx, resty = compile_ptx(func, (MaskedType(ty),), cc=cc, device=True)
+
+
+@pytest.mark.parametrize("op", ops)
+@parametrize_numeric_dtypes_pairwise
+@pytest.mark.parametrize(
+    "masked",
+    ((False, True), (True, False), (True, True)),
+    ids=("um", "mu", "mm"),
+)
+def test_compile_arith_masked_ops(op, left_dtype, right_dtype, masked):
+    def func(x, y):
+        return op(x, y)
+
+    cc = (7, 5)
+
+    ty1 = from_dtype(np.dtype(left_dtype))
+    ty2 = from_dtype(np.dtype(right_dtype))
+
+    if masked[0]:
+        ty1 = MaskedType(ty1)
+    if masked[1]:
+        ty2 = MaskedType(ty2)
+
+    ptx, resty = compile_ptx(func, (ty1, ty2), cc=cc, device=True)
+
+
+def func_x_is_na(x):
+    return x is NA
+
+
+def func_na_is_x(x):
+    return NA is x
+
+
+@pytest.mark.parametrize("fn", (func_x_is_na, func_na_is_x))
+def test_is_na(fn):
+
+    valid = Masked(1, True)
+    invalid = Masked(1, False)
+
+    device_fn = cuda.jit(device=True)(fn)
+
+    @cuda.jit
+    def test_kernel(err):
+        valid_is_na = device_fn(valid)
+        invalid_is_na = device_fn(invalid)
+
+        if valid_is_na:
+            err[0] = 1
+
+        if not invalid_is_na:
+            err[0] = 2
+
+    err = cp.asarray([0], dtype="int8")
+    with _CUDFNumbaConfig():
+        test_kernel[1, 1](err)
+    assert err[0] == 0
+
+
+def func_lt_na(x):
+    return x < NA
+
+
+def func_gt_na(x):
+    return x > NA
+
+
+def func_eq_na(x):
+    return x == NA
+
+
+def func_ne_na(x):
+    return x != NA
+
+
+def func_ge_na(x):
+    return x >= NA
+
+
+def func_le_na(x):
+    return x <= NA
+
+
+def func_na_lt(x):
+    return x < NA
+
+
+def func_na_gt(x):
+    return x > NA
+
+
+def func_na_eq(x):
+    return x == NA
+
+
+def func_na_ne(x):
+    return x != NA
+
+
+def func_na_ge(x):
+    return x >= NA
+
+
+def func_na_le(x):
+    return x <= NA
+
+
+na_comparison_funcs = (
+    func_lt_na,
+    func_gt_na,
+    func_eq_na,
+    func_ne_na,
+    func_ge_na,
+    func_le_na,
+    func_na_lt,
+    func_na_gt,
+    func_na_eq,
+    func_na_ne,
+    func_na_ge,
+    func_na_le,
+)
+
+
+@pytest.mark.parametrize("fn", na_comparison_funcs)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+def test_na_masked_comparisons(fn, ty):
+
+    device_fn = cuda.jit(device=True)(fn)
+
+    @cuda.jit
+    def test_kernel(err):
+        unmasked = ty(1)
+        valid_masked = Masked(unmasked, True)
+        invalid_masked = Masked(unmasked, False)
+
+        valid_cmp_na = device_fn(valid_masked)
+        invalid_cmp_na = device_fn(invalid_masked)
+
+        if valid_cmp_na:
+            err[0] = 1
+
+        if invalid_cmp_na:
+            err[0] = 2
+
+    err = cp.asarray([0], dtype="int8")
+    with _CUDFNumbaConfig():
+        test_kernel[1, 1](err)
+    assert err[0] == 0
+
+
+# xfail because scalars do not yet cast for a comparison to NA
+@pytest.mark.xfail
+@pytest.mark.parametrize("fn", na_comparison_funcs)
+@pytest.mark.parametrize("ty", number_types, ids=number_ids)
+def test_na_scalar_comparisons(fn, ty):
+
+    device_fn = cuda.jit(device=True)(fn)
+
+    @cuda.jit
+    def test_kernel(err):
+        unmasked = ty(1)
+
+        unmasked_cmp_na = device_fn(unmasked)
+
+        if unmasked_cmp_na:
+            err[0] = 1
+
+    err = cp.asarray([0], dtype="int8")
+    with _CUDFNumbaConfig():
+        test_kernel[1, 1](err)
+    assert err[0] == 0
diff --git a/python/cudf/cudf/tests/test_factorize.py b/python/cudf/cudf/tests/test_factorize.py
new file mode 100644
index 0000000..bf409b3
--- /dev/null
+++ b/python/cudf/cudf/tests/test_factorize.py
@@ -0,0 +1,176 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import DataFrame, Index
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize("ncats,nelem", [(2, 2), (2, 10), (10, 100)])
+def test_factorize_series_obj(ncats, nelem):
+    df = DataFrame()
+    np.random.seed(0)
+
+    # initialize data frame
+    df["cats"] = arr = np.random.randint(2, size=10, dtype=np.int32)
+
+    uvals, labels = df["cats"].factorize()
+    np.testing.assert_array_equal(labels.to_numpy(), sorted(set(arr)))
+    assert isinstance(uvals, cp.ndarray)
+    assert isinstance(labels, Index)
+
+    encoder = {labels[idx]: idx for idx in range(len(labels))}
+    handcoded = [encoder[v] for v in arr]
+    np.testing.assert_array_equal(uvals.get(), handcoded)
+
+
+@pytest.mark.parametrize("ncats,nelem", [(2, 2), (2, 10), (10, 100)])
+def test_factorize_index_obj(ncats, nelem):
+    df = DataFrame()
+    np.random.seed(0)
+
+    # initialize data frame
+    df["cats"] = arr = np.random.randint(2, size=10, dtype=np.int32)
+    df = df.set_index("cats")
+
+    uvals, labels = df.index.factorize()
+    np.testing.assert_array_equal(labels.values.get(), sorted(set(arr)))
+    assert isinstance(uvals, cp.ndarray)
+    assert isinstance(labels, Index)
+
+    encoder = {labels[idx]: idx for idx in range(len(labels))}
+    handcoded = [encoder[v] for v in arr]
+    np.testing.assert_array_equal(uvals.get(), handcoded)
+
+
+def test_factorize_series_index():
+    df = DataFrame()
+    df["col1"] = ["C", "H", "C", "W", "W", "W", "W", "W", "C", "W"]
+    df["col2"] = [
+        2992443.0,
+        2992447.0,
+        2992466.0,
+        2992440.0,
+        2992441.0,
+        2992442.0,
+        2992444.0,
+        2992445.0,
+        2992446.0,
+        2992448.0,
+    ]
+    assert_eq(df.col1.factorize()[0].get(), df.to_pandas().col1.factorize()[0])
+    assert_eq(
+        df.col1.factorize()[1].to_pandas().values,
+        df.to_pandas().col1.factorize()[1].values,
+    )
+
+    df = df.set_index("col2")
+
+    assert_eq(df.col1.factorize()[0].get(), df.to_pandas().col1.factorize()[0])
+    assert_eq(
+        df.col1.factorize()[1].to_pandas().values,
+        df.to_pandas().col1.factorize()[1].values,
+    )
+
+
+def test_cudf_factorize_series():
+    data = [1, 2, 3, 4, 5]
+
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    expect = pd.factorize(psr)
+    got = cudf.factorize(gsr)
+
+    assert len(expect) == len(got)
+
+    np.testing.assert_array_equal(expect[0], got[0].get())
+    np.testing.assert_array_equal(expect[1], got[1].values.get())
+
+
+def test_cudf_factorize_index():
+    data = [1, 2, 3, 4, 5]
+
+    pi = pd.Index(data)
+    gi = cudf.Index(data)
+
+    expect = pd.factorize(pi)
+    got = cudf.factorize(gi)
+
+    assert len(expect) == len(got)
+
+    np.testing.assert_array_equal(expect[0], got[0].get())
+    np.testing.assert_array_equal(expect[1], got[1].values.get())
+
+
+def test_cudf_factorize_array():
+    data = [1, 2, 3, 4, 5]
+
+    parr = np.array(data)
+    garr = cp.array(data)
+
+    expect = pd.factorize(parr)
+    got = cudf.factorize(garr)
+
+    assert len(expect) == len(got)
+
+    np.testing.assert_array_equal(expect[0], got[0].get())
+    np.testing.assert_array_equal(expect[1], got[1].get())
+
+
+@pytest.mark.parametrize("pandas_compatibility", [True, False])
+def test_factorize_code_pandas_compatibility(pandas_compatibility):
+
+    psr = pd.Series([1, 2, 3, 4, 5])
+    gsr = cudf.from_pandas(psr)
+
+    expect = pd.factorize(psr)
+    with cudf.option_context("mode.pandas_compatible", pandas_compatibility):
+        got = cudf.factorize(gsr)
+    assert_eq(got[0], expect[0])
+    assert_eq(got[1], expect[1])
+    if pandas_compatibility:
+        assert got[0].dtype == expect[0].dtype
+    else:
+        assert got[0].dtype == cudf.dtype("int8")
+
+
+def test_factorize_result_classes():
+    data = [1, 2, 3]
+
+    labels, cats = cudf.factorize(cudf.Series(data))
+
+    assert isinstance(labels, cp.ndarray)
+    assert isinstance(cats, cudf.BaseIndex)
+
+    labels, cats = cudf.factorize(cudf.Index(data))
+
+    assert isinstance(labels, cp.ndarray)
+    assert isinstance(cats, cudf.BaseIndex)
+
+    labels, cats = cudf.factorize(cp.array(data))
+
+    assert isinstance(labels, cp.ndarray)
+    assert isinstance(cats, cp.ndarray)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "def", "abc", "a", "def", None],
+        [10, 20, 100, -10, 0, 1, None, 10, 100],
+    ],
+)
+def test_category_dtype_factorize(data):
+    gs = cudf.Series(data, dtype="category")
+    ps = gs.to_pandas()
+
+    actual_codes, actual_uniques = gs.factorize()
+    expected_codes, expected_uniques = ps.factorize()
+
+    assert_eq(actual_codes, expected_codes)
+    assert_eq(actual_uniques, expected_uniques)
diff --git a/python/cudf/cudf/tests/test_feather.py b/python/cudf/cudf/tests/test_feather.py
new file mode 100644
index 0000000..12a325f
--- /dev/null
+++ b/python/cudf/cudf/tests/test_feather.py
@@ -0,0 +1,85 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import os
+from string import ascii_letters
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.testing._utils import NUMERIC_TYPES, assert_eq
+
+
+@pytest.fixture(params=[0, 1, 10, 100])
+def pdf(request):
+    types = NUMERIC_TYPES + ["bool"]
+    nrows = request.param
+
+    # Create a pandas dataframe with random data of mixed types
+    test_pdf = pd.DataFrame(
+        {
+            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            for typ in types
+        }
+    )
+    # Delete the name of the column index, and rename the row index
+    test_pdf.columns.name = None
+    test_pdf.index.name = "index"
+
+    # Create non-numeric categorical data otherwise may get typecasted
+    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    test_pdf["col_category"] = pd.Series(data, dtype="category")
+
+    # Feather can't handle indexes properly
+    test_pdf = test_pdf.reset_index(drop=True)
+    test_pdf.index.name = None
+
+    return test_pdf
+
+
+@pytest.fixture
+def gdf(pdf):
+    return cudf.DataFrame.from_pandas(pdf)
+
+
+@pytest.fixture
+def feather_file(tmp_path_factory, pdf):
+    fname = tmp_path_factory.mktemp("feather") / "test.feather"
+    pdf.to_feather(fname)
+    return fname
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.filterwarnings("ignore:Strings are not yet supported")
+@pytest.mark.parametrize(
+    "columns",
+    [["col_int8"], ["col_category"], ["col_int32", "col_float32"], None],
+)
+def test_feather_reader(feather_file, columns):
+    expect = pa.feather.read_table(feather_file, columns=columns).to_pandas()
+    got = (
+        cudf.read_feather(feather_file, columns=columns)
+        .to_arrow(preserve_index=False)
+        .to_pandas()
+    )
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+def test_feather_writer(tmpdir, pdf, gdf):
+    pdf_fname = tmpdir.join("pdf.feather")
+    gdf_fname = tmpdir.join("gdf.feather")
+
+    pdf.to_feather(pdf_fname)
+    gdf.to_feather(gdf_fname)
+
+    assert os.path.exists(pdf_fname)
+    assert os.path.exists(gdf_fname)
+
+    expect = pa.feather.read_table(pdf_fname)
+    got = pa.feather.read_table(gdf_fname)
+
+    assert pa.Table.equals(expect, got)
diff --git a/python/cudf/cudf/tests/test_gcs.py b/python/cudf/cudf/tests/test_gcs.py
new file mode 100644
index 0000000..a677ace
--- /dev/null
+++ b/python/cudf/cudf/tests/test_gcs.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import io
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+gcsfs = pytest.importorskip("gcsfs")
+
+TEST_PROJECT = "cudf-gcs-test-project"
+TEST_BUCKET = "cudf-gcs-test-bucket"
+
+
+@pytest.fixture
+def pdf(scope="module"):
+    df = pd.DataFrame()
+    df["Integer"] = np.array([2345, 11987, 9027, 9027])
+    df["Float"] = np.array([9.001, 8.343, 6, 2.781])
+    df["Integer2"] = np.array([2345, 106, 2088, 789277])
+    df["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    df["Boolean"] = np.array([True, False, True, False])
+    return df
+
+
+def test_read_csv(pdf, monkeypatch, tmpdir):
+    # Write to buffer
+    fpath = TEST_BUCKET + "test_csv_reader.csv"
+    buffer = pdf.to_csv(index=False)
+
+    def mock_open(*args, **kwargs):
+        return io.BytesIO(buffer.encode())
+
+    def mock_size(*args):
+        return len(buffer.encode())
+
+    monkeypatch.setattr(gcsfs.core.GCSFileSystem, "open", mock_open)
+    monkeypatch.setattr(gcsfs.core.GCSFileSystem, "size", mock_size)
+
+    # Test read from explicit path.
+    # Since we are monkey-patching, we cannot use
+    # use_python_file_object=True, because the pyarrow
+    # `open_input_file` command will fail (since it doesn't
+    # use the monkey-patched `open` definition)
+    got = cudf.read_csv(f"gcs://{fpath}", use_python_file_object=False)
+    assert_eq(pdf, got)
+
+    # AbstractBufferedFile -> PythonFile conversion
+    # will work fine with the monkey-patched FS if we
+    # pass in an fsspec file object
+    fs = gcsfs.core.GCSFileSystem()
+    with fs.open(f"gcs://{fpath}") as f:
+        got = cudf.read_csv(f)
+    assert_eq(pdf, got)
+
+
+def test_write_orc(pdf, monkeypatch, tmpdir):
+    gcs_fname = TEST_BUCKET + "test_orc_writer.orc"
+    local_filepath = os.path.join(tmpdir, "test_orc.orc")
+    gdf = cudf.from_pandas(pdf)
+
+    def mock_open(*args, **kwargs):
+        return open(local_filepath, "wb")
+
+    monkeypatch.setattr(gcsfs.core.GCSFileSystem, "open", mock_open)
+    gdf.to_orc(f"gcs://{gcs_fname}")
+
+    got = pd.read_orc(local_filepath)
+    assert_eq(pdf, got)
diff --git a/python/cudf/cudf/tests/test_groupby.py b/python/cudf/cudf/tests/test_groupby.py
new file mode 100644
index 0000000..92df4c6
--- /dev/null
+++ b/python/cudf/cudf/tests/test_groupby.py
@@ -0,0 +1,3542 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import collections
+import datetime
+import itertools
+import operator
+import string
+import textwrap
+from decimal import Decimal
+from functools import partial
+
+import numpy as np
+import pandas as pd
+import pytest
+from numba import cuda
+from numpy.testing import assert_array_equal
+
+import rmm
+
+import cudf
+from cudf import DataFrame, Series
+from cudf.core._compat import PANDAS_GE_150, PANDAS_LT_140
+from cudf.core.udf.groupby_typing import SUPPORTED_GROUPBY_NUMPY_TYPES
+from cudf.core.udf.utils import precompiled
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    SIGNED_TYPES,
+    TIMEDELTA_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+from cudf.testing.dataset_generator import rand_dataframe
+
+_now = np.datetime64("now")
+_tomorrow = _now + np.timedelta64(1, "D")
+_now = np.int64(_now.astype("datetime64[ns]"))
+_tomorrow = np.int64(_tomorrow.astype("datetime64[ns]"))
+_index_type_aggs = {"count", "idxmin", "idxmax", "cumcount"}
+
+
+def assert_groupby_results_equal(
+    expect, got, sort=True, as_index=True, by=None, **kwargs
+):
+    # Because we don't sort by index by default in groupby,
+    # sort expect and got by index before comparing.
+    if sort:
+        if as_index:
+            expect = expect.sort_index()
+            got = got.sort_index()
+        else:
+            assert by is not None
+            if isinstance(expect, (pd.DataFrame, cudf.DataFrame)):
+                expect = expect.sort_values(by=by).reset_index(drop=True)
+            else:
+                expect = expect.sort_values().reset_index(drop=True)
+
+            if isinstance(got, cudf.DataFrame):
+                got = got.sort_values(by=by).reset_index(drop=True)
+            else:
+                got = got.sort_values().reset_index(drop=True)
+
+    assert_eq(expect, got, **kwargs)
+
+
+def make_frame(
+    dataframe_class,
+    nelem,
+    seed=0,
+    extra_levels=(),
+    extra_vals=(),
+    with_datetime=False,
+):
+    np.random.seed(seed)
+
+    df = dataframe_class()
+
+    df["x"] = np.random.randint(0, 5, nelem)
+    df["y"] = np.random.randint(0, 3, nelem)
+    for lvl in extra_levels:
+        df[lvl] = np.random.randint(0, 2, nelem)
+
+    df["val"] = np.random.random(nelem)
+    for val in extra_vals:
+        df[val] = np.random.random(nelem)
+
+    if with_datetime:
+        df["datetime"] = np.random.randint(
+            _now, _tomorrow, nelem, dtype=np.int64
+        ).astype("datetime64[ns]")
+
+    return df
+
+
+@pytest.fixture
+def gdf():
+    return DataFrame({"x": [1, 2, 3], "y": [0, 1, 1]})
+
+
+@pytest.fixture
+def pdf(gdf):
+    return gdf.to_pandas()
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+def test_groupby_mean(nelem):
+    got_df = make_frame(DataFrame, nelem=nelem).groupby(["x", "y"]).mean()
+    expect_df = (
+        make_frame(pd.DataFrame, nelem=nelem).groupby(["x", "y"]).mean()
+    )
+    assert_groupby_results_equal(got_df, expect_df)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+def test_groupby_mean_3level(nelem):
+    lvls = "z"
+    bys = list("xyz")
+    got_df = (
+        make_frame(DataFrame, nelem=nelem, extra_levels=lvls)
+        .groupby(bys)
+        .mean()
+    )
+    expect_df = (
+        make_frame(pd.DataFrame, nelem=nelem, extra_levels=lvls)
+        .groupby(bys)
+        .mean()
+    )
+    assert_groupby_results_equal(got_df, expect_df)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+def test_groupby_agg_mean_min(nelem):
+    got_df = (
+        make_frame(DataFrame, nelem=nelem)
+        .groupby(["x", "y"])
+        .agg(["mean", "min"])
+    )
+    expect_df = (
+        make_frame(pd.DataFrame, nelem=nelem)
+        .groupby(["x", "y"])
+        .agg(["mean", "min"])
+    )
+    assert_groupby_results_equal(got_df, expect_df)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+def test_groupby_agg_min_max_dictargs(nelem):
+    expect_df = (
+        make_frame(pd.DataFrame, nelem=nelem, extra_vals="ab")
+        .groupby(["x", "y"])
+        .agg({"a": "min", "b": "max"})
+    )
+    got_df = (
+        make_frame(DataFrame, nelem=nelem, extra_vals="ab")
+        .groupby(["x", "y"])
+        .agg({"a": "min", "b": "max"})
+    )
+    assert_groupby_results_equal(expect_df, got_df)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+def test_groupby_agg_min_max_dictlist(nelem):
+    expect_df = (
+        make_frame(pd.DataFrame, nelem=nelem, extra_vals="ab")
+        .groupby(["x", "y"])
+        .agg({"a": ["min", "max"], "b": ["min", "max"]})
+    )
+    got_df = (
+        make_frame(DataFrame, nelem=nelem, extra_vals="ab")
+        .groupby(["x", "y"])
+        .agg({"a": ["min", "max"], "b": ["min", "max"]})
+    )
+    assert_groupby_results_equal(got_df, expect_df)
+
+
+@pytest.mark.parametrize("as_index", [True, False])
+def test_groupby_as_index_single_agg(pdf, gdf, as_index):
+    gdf = gdf.groupby("y", as_index=as_index).agg({"x": "mean"})
+    pdf = pdf.groupby("y", as_index=as_index).agg({"x": "mean"})
+    assert_groupby_results_equal(pdf, gdf)
+
+
+@pytest.mark.parametrize("engine", ["cudf", "jit"])
+@pytest.mark.parametrize("as_index", [True, False])
+def test_groupby_as_index_apply(pdf, gdf, as_index, engine):
+    gdf = gdf.groupby("y", as_index=as_index).apply(
+        lambda df: df["x"].mean(), engine=engine
+    )
+    pdf = pdf.groupby("y", as_index=as_index).apply(lambda df: df["x"].mean())
+    assert_groupby_results_equal(pdf, gdf)
+
+
+@pytest.mark.parametrize("as_index", [True, False])
+def test_groupby_as_index_multiindex(pdf, gdf, as_index):
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 1], "b": [3, 3, 3], "c": [2, 2, 3], "d": [3, 1, 2]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    gdf = gdf.groupby(["a", "b"], as_index=as_index, sort=True).agg(
+        {"c": "mean"}
+    )
+    pdf = pdf.groupby(["a", "b"], as_index=as_index, sort=True).agg(
+        {"c": "mean"}
+    )
+
+    if as_index:
+        assert_eq(pdf, gdf)
+    else:
+        # column names don't match - check just the values
+        for gcol, pcol in zip(gdf, pdf):
+            assert_array_equal(gdf[gcol].to_numpy(), pdf[pcol].values)
+
+
+def test_groupby_default(pdf, gdf):
+    gdf = gdf.groupby("y").agg({"x": "mean"})
+    pdf = pdf.groupby("y").agg({"x": "mean"})
+    assert_groupby_results_equal(pdf, gdf)
+
+
+def test_group_keys_true(pdf, gdf):
+    gdf = gdf.groupby("y", group_keys=True).sum()
+    pdf = pdf.groupby("y", group_keys=True).sum()
+    assert_groupby_results_equal(pdf, gdf)
+
+
+@pytest.mark.parametrize("as_index", [True, False])
+def test_groupby_getitem_getattr(as_index):
+    pdf = pd.DataFrame({"x": [1, 3, 1], "y": [1, 2, 3], "z": [1, 4, 5]})
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        pdf.groupby("x", as_index=as_index)["y"].sum(),
+        gdf.groupby("x", as_index=as_index)["y"].sum(),
+        as_index=as_index,
+        by="x",
+    )
+    assert_groupby_results_equal(
+        pdf.groupby("x", as_index=as_index).y.sum(),
+        gdf.groupby("x", as_index=as_index).y.sum(),
+        as_index=as_index,
+        by="x",
+    )
+    assert_groupby_results_equal(
+        pdf.groupby("x", as_index=as_index)[["y"]].sum(),
+        gdf.groupby("x", as_index=as_index)[["y"]].sum(),
+        as_index=as_index,
+        by="x",
+    )
+    assert_groupby_results_equal(
+        pdf.groupby(["x", "y"], as_index=as_index).sum(),
+        gdf.groupby(["x", "y"], as_index=as_index).sum(),
+        as_index=as_index,
+        by=["x", "y"],
+    )
+
+
+def test_groupby_cats():
+    df = DataFrame()
+    df["cats"] = pd.Categorical(list("aabaacaab"))
+    df["vals"] = np.random.random(len(df))
+
+    cats = df["cats"].values_host
+    vals = df["vals"].to_numpy()
+
+    grouped = df.groupby(["cats"], as_index=False).mean()
+
+    got_vals = grouped["vals"]
+
+    got_cats = grouped["cats"]
+
+    for i in range(len(got_vals)):
+        expect = vals[cats == got_cats[i]].mean()
+        np.testing.assert_almost_equal(got_vals[i], expect)
+
+
+def test_groupby_iterate_groups():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 20
+    df["key1"] = np.random.randint(0, 3, nelem)
+    df["key2"] = np.random.randint(0, 2, nelem)
+    df["val1"] = np.random.random(nelem)
+    df["val2"] = np.random.random(nelem)
+
+    def assert_values_equal(arr):
+        np.testing.assert_array_equal(arr[0], arr)
+
+    for name, grp in df.groupby(["key1", "key2"]):
+        pddf = grp.to_pandas()
+        for k in "key1,key2".split(","):
+            assert_values_equal(pddf[k].values)
+
+
+def test_groupby_apply():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 20
+    df["key1"] = np.random.randint(0, 3, nelem)
+    df["key2"] = np.random.randint(0, 2, nelem)
+    df["val1"] = np.random.random(nelem)
+    df["val2"] = np.random.random(nelem)
+
+    expect_grpby = df.to_pandas().groupby(
+        ["key1", "key2"], as_index=False, group_keys=False
+    )
+    got_grpby = df.groupby(["key1", "key2"])
+
+    def foo(df):
+        df["out"] = df["val1"] + df["val2"]
+        return df
+
+    expect = expect_grpby.apply(foo)
+    got = got_grpby.apply(foo)
+    assert_groupby_results_equal(expect, got)
+
+
+def create_test_groupby_apply_args_params():
+    def f1(df, k):
+        df["out"] = df["val1"] + df["val2"] + k
+        return df
+
+    def f2(df, k, L):
+        df["out"] = df["val1"] - df["val2"] + (k / L)
+        return df
+
+    def f3(df, k, L, m):
+        df["out"] = ((k * df["val1"]) + (L * df["val2"])) / m
+        return df
+
+    return [(f1, (42,)), (f2, (42, 119)), (f3, (42, 119, 212.1))]
+
+
+@pytest.mark.parametrize("func,args", create_test_groupby_apply_args_params())
+def test_groupby_apply_args(func, args):
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 20
+    df["key1"] = np.random.randint(0, 3, nelem)
+    df["key2"] = np.random.randint(0, 2, nelem)
+    df["val1"] = np.random.random(nelem)
+    df["val2"] = np.random.random(nelem)
+
+    expect_grpby = df.to_pandas().groupby(
+        ["key1", "key2"], as_index=False, group_keys=False
+    )
+    got_grpby = df.groupby(["key1", "key2"])
+
+    expect = expect_grpby.apply(func, *args)
+    got = got_grpby.apply(func, *args)
+    assert_groupby_results_equal(expect, got)
+
+
+def test_groupby_apply_grouped():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 20
+    df["key1"] = np.random.randint(0, 3, nelem)
+    df["key2"] = np.random.randint(0, 2, nelem)
+    df["val1"] = np.random.random(nelem)
+    df["val2"] = np.random.random(nelem)
+
+    expect_grpby = df.to_pandas().groupby(
+        ["key1", "key2"], as_index=False, group_keys=False
+    )
+    got_grpby = df.groupby(["key1", "key2"])
+
+    def foo(key1, val1, com1, com2):
+        for i in range(cuda.threadIdx.x, len(key1), cuda.blockDim.x):
+            com1[i] = key1[i] * 10000 + val1[i]
+            com2[i] = i
+
+    got = got_grpby.apply_grouped(
+        foo,
+        incols=["key1", "val1"],
+        outcols={"com1": np.float64, "com2": np.int32},
+        tpb=8,
+    )
+
+    got = got.to_pandas()
+
+    # Get expected result by emulating the operation in pandas
+    def emulate(df):
+        df["com1"] = df.key1 * 10000 + df.val1
+        df["com2"] = np.arange(len(df), dtype=np.int32)
+        return df
+
+    expect = expect_grpby.apply(emulate)
+    expect = expect.sort_values(["key1", "key2"])
+
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.fixture(scope="module")
+def groupby_jit_data():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 20
+    df["key1"] = np.random.randint(0, 3, nelem)
+    df["key2"] = np.random.randint(0, 2, nelem)
+    df["val1"] = np.random.random(nelem)
+    df["val2"] = np.random.random(nelem)
+    df["val3"] = np.random.randint(0, 10, nelem)
+    df["val4"] = np.random.randint(0, 10, nelem)
+    return df
+
+
+def run_groupby_apply_jit_test(data, func, keys, *args):
+    expect_groupby_obj = data.to_pandas().groupby(keys)
+    got_groupby_obj = data.groupby(keys)
+
+    # compare cuDF jit to pandas
+    cudf_jit_result = got_groupby_obj.apply(func, *args, engine="jit")
+    pandas_result = expect_groupby_obj.apply(func, *args)
+    assert_groupby_results_equal(cudf_jit_result, pandas_result)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    SUPPORTED_GROUPBY_NUMPY_TYPES,
+    ids=[str(t) for t in SUPPORTED_GROUPBY_NUMPY_TYPES],
+)
+@pytest.mark.parametrize(
+    "func", ["min", "max", "sum", "mean", "var", "std", "idxmin", "idxmax"]
+)
+def test_groupby_apply_jit_reductions(func, groupby_jit_data, dtype):
+    # ideally we'd just have:
+    # lambda group: getattr(group, func)()
+    # but the current kernel caching mechanism relies on pickle which
+    # does not play nice with local functions. What's below uses
+    # exec as a workaround to write the test functions dynamically
+
+    funcstr = textwrap.dedent(
+        f"""
+        def func(df):
+            return df['val1'].{func}()
+        """
+    )
+    lcl = {}
+    exec(funcstr, lcl)
+    func = lcl["func"]
+
+    groupby_jit_data["val1"] = groupby_jit_data["val1"].astype(dtype)
+    groupby_jit_data["val2"] = groupby_jit_data["val2"].astype(dtype)
+
+    run_groupby_apply_jit_test(groupby_jit_data, func, ["key1"])
+
+
+@pytest.mark.parametrize("dtype", ["int32", "int64"])
+def test_groupby_apply_jit_correlation(groupby_jit_data, dtype):
+
+    groupby_jit_data["val3"] = groupby_jit_data["val3"].astype(dtype)
+    groupby_jit_data["val4"] = groupby_jit_data["val4"].astype(dtype)
+
+    keys = ["key1", "key2"]
+
+    def func(group):
+        return group["val3"].corr(group["val4"])
+
+    run_groupby_apply_jit_test(groupby_jit_data, func, keys)
+
+
+@pytest.mark.parametrize("dtype", ["int32", "int64"])
+def test_groupby_apply_jit_correlation_zero_variance(dtype):
+    # pearson correlation is undefined when the variance of either
+    # variable is zero. This test ensures that the jit implementation
+    # returns the same result as pandas in this case.
+    data = DataFrame(
+        {"a": [0, 0, 0, 0, 0], "b": [1, 1, 1, 1, 1], "c": [2, 2, 2, 2, 2]}
+    )
+
+    def func(group):
+        return group["b"].corr(group["c"])
+
+    run_groupby_apply_jit_test(data, func, ["a"])
+
+
+@pytest.mark.parametrize("dtype", ["int32"])
+def test_groupby_apply_jit_sum_integer_overflow(dtype):
+    max = np.iinfo(dtype).max
+
+    data = DataFrame(
+        {
+            "a": [0, 0, 0],
+            "b": [max, max, max],
+        }
+    )
+
+    def func(group):
+        return group["b"].sum()
+
+    run_groupby_apply_jit_test(data, func, ["a"])
+
+
+@pytest.mark.parametrize("dtype", ["float64"])
+@pytest.mark.parametrize("func", ["min", "max", "sum", "mean", "var", "std"])
+@pytest.mark.parametrize("special_val", [np.nan, np.inf, -np.inf])
+def test_groupby_apply_jit_reductions_special_vals(
+    func, groupby_jit_data, dtype, special_val
+):
+    # dynamically generate to avoid pickling error.
+    # see test_groupby_apply_jit_reductions for details.
+    funcstr = textwrap.dedent(
+        f"""
+            def func(df):
+                return df['val1'].{func}()
+        """
+    )
+    lcl = {}
+    exec(funcstr, lcl)
+    func = lcl["func"]
+
+    groupby_jit_data["val1"] = special_val
+    groupby_jit_data["val1"] = groupby_jit_data["val1"].astype(dtype)
+
+    run_groupby_apply_jit_test(groupby_jit_data, func, ["key1"])
+
+
+@pytest.mark.parametrize("dtype", ["float64"])
+@pytest.mark.parametrize("func", ["idxmax", "idxmin"])
+@pytest.mark.parametrize(
+    "special_val",
+    [
+        pytest.param(
+            np.nan,
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/13832"
+            ),
+        ),
+        np.inf,
+        -np.inf,
+    ],
+)
+def test_groupby_apply_jit_idx_reductions_special_vals(
+    func, groupby_jit_data, dtype, special_val
+):
+    # dynamically generate to avoid pickling error.
+    # see test_groupby_apply_jit_reductions for details.
+    funcstr = textwrap.dedent(
+        f"""
+            def func(df):
+                return df['val1'].{func}()
+        """
+    )
+    lcl = {}
+    exec(funcstr, lcl)
+    func = lcl["func"]
+
+    groupby_jit_data["val1"] = special_val
+    groupby_jit_data["val1"] = groupby_jit_data["val1"].astype(dtype)
+
+    expect = groupby_jit_data.to_pandas().groupby("key1").apply(func)
+    got = groupby_jit_data.groupby("key1").apply(func, engine="jit")
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df["val1"].max() + df["val2"].min(),
+        lambda df: df["val1"].sum() + df["val2"].var(),
+        lambda df: df["val1"].mean() + df["val2"].std(),
+    ],
+)
+def test_groupby_apply_jit_basic(func, groupby_jit_data):
+    run_groupby_apply_jit_test(groupby_jit_data, func, ["key1", "key2"])
+
+
+def create_test_groupby_apply_jit_args_params():
+    def f1(df, k):
+        return df["val1"].max() + df["val2"].min() + k
+
+    def f2(df, k, L):
+        return df["val1"].sum() - df["val2"].var() + (k / L)
+
+    def f3(df, k, L, m):
+        return ((k * df["val1"].mean()) + (L * df["val2"].std())) / m
+
+    return [(f1, (42,)), (f2, (42, 119)), (f3, (42, 119, 212.1))]
+
+
+@pytest.mark.parametrize(
+    "func,args", create_test_groupby_apply_jit_args_params()
+)
+def test_groupby_apply_jit_args(func, args, groupby_jit_data):
+    run_groupby_apply_jit_test(groupby_jit_data, func, ["key1", "key2"], *args)
+
+
+def test_groupby_apply_jit_block_divergence():
+    # https://github.com/rapidsai/cudf/issues/12686
+    df = cudf.DataFrame(
+        {
+            "a": [0, 0, 0, 1, 1, 1],
+            "b": [1, 1, 1, 2, 3, 4],
+        }
+    )
+
+    def diverging_block(grp_df):
+        if grp_df["a"].mean() > 0:
+            return grp_df["b"].mean()
+        return 0
+
+    run_groupby_apply_jit_test(df, diverging_block, ["a"])
+
+
+def test_groupby_apply_caching():
+    # Make sure similar functions that differ
+    # by simple things like constants actually
+    # recompile
+
+    # begin with a clear cache
+    precompiled.clear()
+    assert precompiled.currsize == 0
+
+    data = cudf.DataFrame({"a": [1, 1, 1, 2, 2, 2], "b": [1, 2, 3, 4, 5, 6]})
+
+    def f(group):
+        return group["b"].mean() * 2
+
+    # a single run should result in a cache size of 1
+    run_groupby_apply_jit_test(data, f, ["a"])
+    assert precompiled.currsize == 1
+
+    # a second run with f should not increase the count
+    run_groupby_apply_jit_test(data, f, ["a"])
+    assert precompiled.currsize == 1
+
+    # changing a constant value inside the UDF should miss
+    def f(group):
+        return group["b"].mean() * 3
+
+    run_groupby_apply_jit_test(data, f, ["a"])
+    assert precompiled.currsize == 2
+
+    # changing the dtypes of the columns should miss
+    data["b"] = data["b"].astype("float64")
+    run_groupby_apply_jit_test(data, f, ["a"])
+
+    assert precompiled.currsize == 3
+
+
+def test_groupby_apply_no_bytecode_fallback():
+    # tests that a function which contains no bytecode
+    # attribute, but would still be executable using
+    # the iterative groupby apply approach, still works.
+
+    gdf = cudf.DataFrame({"a": [0, 1, 1], "b": [1, 2, 3]})
+    pdf = gdf.to_pandas()
+
+    def f(group):
+        return group.sum()
+
+    part = partial(f)
+
+    expect = pdf.groupby("a").apply(part)
+    got = gdf.groupby("a").apply(part, engine="auto")
+
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize("func", [lambda group: group.x + group.y])
+def test_groupby_apply_return_col_from_df(func):
+    # tests a UDF that consists of purely colwise
+    # ops, such as `lambda group: group.x + group.y`
+    # which returns a column
+    df = cudf.datasets.randomdata()
+    pdf = df.to_pandas()
+
+    def func(df):
+        return df.x + df.y
+
+    expect = pdf.groupby("id").apply(func)
+    got = df.groupby("id").apply(func)
+
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize("func", [lambda group: group.sum()])
+def test_groupby_apply_return_df(func):
+    # tests a UDF that reduces over a dataframe
+    # and produces a series with the original column names
+    # as its index, such as lambda group: group.sum() + group.min()
+    df = cudf.DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 3, 4]})
+    pdf = df.to_pandas()
+
+    expect = pdf.groupby("a").apply(func)
+    got = df.groupby("a").apply(func)
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 500, 1000])
+@pytest.mark.parametrize(
+    "func",
+    [
+        "mean",
+        "std",
+        "var",
+        "min",
+        "max",
+        "idxmin",
+        "idxmax",
+        "count",
+        "sum",
+        "prod",
+    ],
+)
+def test_groupby_2keys_agg(nelem, func):
+    # gdf (Note: lack of multiIndex)
+    expect_df = (
+        make_frame(pd.DataFrame, nelem=nelem).groupby(["x", "y"]).agg(func)
+    )
+    got_df = make_frame(DataFrame, nelem=nelem).groupby(["x", "y"]).agg(func)
+
+    check_dtype = func not in _index_type_aggs
+    assert_groupby_results_equal(got_df, expect_df, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize("num_groups", [2, 3, 10, 50, 100])
+@pytest.mark.parametrize("nelem_per_group", [1, 10, 100])
+@pytest.mark.parametrize(
+    "func",
+    ["min", "max", "count", "sum"],
+    # TODO: Replace the above line with the one below once
+    # https://github.com/pandas-dev/pandas/issues/40685 is resolved.
+    # "func", ["min", "max", "idxmin", "idxmax", "count", "sum"],
+)
+@pytest.mark.xfail(
+    condition=PANDAS_LT_140,
+    reason="https://github.com/pandas-dev/pandas/issues/43209",
+)
+def test_groupby_agg_decimal(num_groups, nelem_per_group, func):
+    # The number of digits after the decimal to use.
+    decimal_digits = 2
+    # The number of digits before the decimal to use.
+    whole_digits = 2
+
+    scale = 10**whole_digits
+    nelem = num_groups * nelem_per_group
+
+    # The unique is necessary because otherwise if there are duplicates idxmin
+    # and idxmax may return different results than pandas (see
+    # https://github.com/rapidsai/cudf/issues/7756). This is not relevant to
+    # the current version of the test, because idxmin and idxmax simply don't
+    # work with pandas Series composed of Decimal objects (see
+    # https://github.com/pandas-dev/pandas/issues/40685). However, if that is
+    # ever enabled, then this issue will crop up again so we may as well have
+    # it fixed now.
+    x = np.unique((np.random.rand(nelem) * scale).round(decimal_digits))
+    y = np.unique((np.random.rand(nelem) * scale).round(decimal_digits))
+
+    if x.size < y.size:
+        total_elements = x.size
+        y = y[: x.size]
+    else:
+        total_elements = y.size
+        x = x[: y.size]
+
+    # Note that this filtering can lead to one group with fewer elements, but
+    # that shouldn't be a problem and is probably useful to test.
+    idx_col = np.tile(np.arange(num_groups), nelem_per_group)[:total_elements]
+
+    decimal_x = pd.Series([Decimal(str(d)) for d in x])
+    decimal_y = pd.Series([Decimal(str(d)) for d in y])
+
+    pdf = pd.DataFrame({"idx": idx_col, "x": decimal_x, "y": decimal_y})
+    gdf = DataFrame(
+        {
+            "idx": idx_col,
+            "x": cudf.Series(decimal_x),
+            "y": cudf.Series(decimal_y),
+        }
+    )
+
+    expect_df = pdf.groupby("idx", sort=True).agg(func)
+    if rmm._cuda.gpu.runtimeGetVersion() < 11000:
+        with pytest.raises(RuntimeError):
+            got_df = gdf.groupby("idx", sort=True).agg(func)
+    else:
+        got_df = gdf.groupby("idx", sort=True).agg(func)
+        assert_eq(expect_df["x"], got_df["x"], check_dtype=False)
+        assert_eq(expect_df["y"], got_df["y"], check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "agg", ["min", "max", "idxmin", "idxmax", "count", "sum", "prod", "mean"]
+)
+def test_series_groupby(agg):
+    s = pd.Series([1, 2, 3])
+    g = Series([1, 2, 3])
+    sg = s.groupby(s // 2)
+    gg = g.groupby(g // 2)
+    sa = getattr(sg, agg)()
+    ga = getattr(gg, agg)()
+    check_dtype = agg not in _index_type_aggs
+    assert_groupby_results_equal(sa, ga, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize(
+    "agg", ["min", "max", "idxmin", "idxmax", "count", "sum", "prod", "mean"]
+)
+def test_series_groupby_agg(agg):
+    s = pd.Series([1, 2, 3])
+    g = Series([1, 2, 3])
+    sg = s.groupby(s // 2).agg(agg)
+    gg = g.groupby(g // 2).agg(agg)
+    check_dtype = agg not in _index_type_aggs
+    assert_groupby_results_equal(sg, gg, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize(
+    "agg",
+    [
+        "min",
+        "max",
+        "count",
+        "sum",
+        "prod",
+        "mean",
+        pytest.param(
+            "idxmin",
+            marks=pytest.mark.xfail(reason="gather needed for idxmin"),
+        ),
+        pytest.param(
+            "idxmax",
+            marks=pytest.mark.xfail(reason="gather needed for idxmax"),
+        ),
+    ],
+)
+def test_groupby_level_zero(agg):
+    pdf = pd.DataFrame({"x": [1, 2, 3]}, index=[2, 5, 5])
+    gdf = DataFrame.from_pandas(pdf)
+    pdg = pdf.groupby(level=0)
+    gdg = gdf.groupby(level=0)
+    pdresult = getattr(pdg, agg)()
+    gdresult = getattr(gdg, agg)()
+    check_dtype = agg not in _index_type_aggs
+    assert_groupby_results_equal(pdresult, gdresult, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize(
+    "agg",
+    [
+        "min",
+        "max",
+        "count",
+        "sum",
+        "prod",
+        "mean",
+        pytest.param(
+            "idxmin",
+            marks=pytest.mark.xfail(reason="gather needed for idxmin"),
+        ),
+        pytest.param(
+            "idxmax",
+            marks=pytest.mark.xfail(reason="gather needed for idxmax"),
+        ),
+    ],
+)
+def test_groupby_series_level_zero(agg):
+    pdf = pd.Series([1, 2, 3], index=[2, 5, 5])
+    gdf = Series.from_pandas(pdf)
+    pdg = pdf.groupby(level=0)
+    gdg = gdf.groupby(level=0)
+    pdresult = getattr(pdg, agg)()
+    gdresult = getattr(gdg, agg)()
+    check_dtype = agg not in _index_type_aggs
+    assert_groupby_results_equal(pdresult, gdresult, check_dtype=check_dtype)
+
+
+def test_groupby_column_name():
+    pdf = pd.DataFrame({"xx": [1.0, 2.0, 3.0], "yy": [1, 2, 3]})
+    gdf = DataFrame.from_pandas(pdf)
+    g = gdf.groupby("yy")
+    p = pdf.groupby("yy")
+    gxx = g["xx"].sum()
+    pxx = p["xx"].sum()
+    assert_groupby_results_equal(pxx, gxx)
+
+    gxx = g["xx"].count()
+    pxx = p["xx"].count()
+    assert_groupby_results_equal(pxx, gxx, check_dtype=False)
+
+    gxx = g["xx"].min()
+    pxx = p["xx"].min()
+    assert_groupby_results_equal(pxx, gxx)
+
+    gxx = g["xx"].max()
+    pxx = p["xx"].max()
+    assert_groupby_results_equal(pxx, gxx)
+
+    gxx = g["xx"].idxmin()
+    pxx = p["xx"].idxmin()
+    assert_groupby_results_equal(pxx, gxx, check_dtype=False)
+
+    gxx = g["xx"].idxmax()
+    pxx = p["xx"].idxmax()
+    assert_groupby_results_equal(pxx, gxx, check_dtype=False)
+
+    gxx = g["xx"].mean()
+    pxx = p["xx"].mean()
+    assert_groupby_results_equal(pxx, gxx)
+
+
+def test_groupby_column_numeral():
+    pdf = pd.DataFrame({0: [1.0, 2.0, 3.0], 1: [1, 2, 3]})
+    gdf = DataFrame.from_pandas(pdf)
+    p = pdf.groupby(1)
+    g = gdf.groupby(1)
+    pxx = p[0].sum()
+    gxx = g[0].sum()
+    assert_groupby_results_equal(pxx, gxx)
+
+    pdf = pd.DataFrame({0.5: [1.0, 2.0, 3.0], 1.5: [1, 2, 3]})
+    gdf = DataFrame.from_pandas(pdf)
+    p = pdf.groupby(1.5)
+    g = gdf.groupby(1.5)
+    pxx = p[0.5].sum()
+    gxx = g[0.5].sum()
+    assert_groupby_results_equal(pxx, gxx)
+
+
+@pytest.mark.parametrize(
+    "series",
+    [
+        [0, 1, 0],
+        [1, 1, 1],
+        [0, 1, 1],
+        [1, 2, 3],
+        [4, 3, 2],
+        [0, 2, 0],
+        pd.Series([0, 2, 0]),
+        pd.Series([0, 2, 0], index=[0, 2, 1]),
+    ],
+)  # noqa: E501
+def test_groupby_external_series(series):
+    pdf = pd.DataFrame({"x": [1.0, 2.0, 3.0], "y": [1, 2, 1]})
+    gdf = DataFrame.from_pandas(pdf)
+    pxx = pdf.groupby(pd.Series(series)).x.sum()
+    gxx = gdf.groupby(cudf.Series(series)).x.sum()
+    assert_groupby_results_equal(pxx, gxx)
+
+
+@pytest.mark.parametrize("series", [[0.0, 1.0], [1.0, 1.0, 1.0, 1.0]])
+def test_groupby_external_series_incorrect_length(series):
+    pdf = pd.DataFrame({"x": [1.0, 2.0, 3.0], "y": [1, 2, 1]})
+    gdf = DataFrame.from_pandas(pdf)
+    pxx = pdf.groupby(pd.Series(series)).x.sum()
+    gxx = gdf.groupby(cudf.Series(series)).x.sum()
+    assert_groupby_results_equal(pxx, gxx)
+
+
+@pytest.mark.parametrize(
+    "level", [0, 1, "a", "b", [0, 1], ["a", "b"], ["a", 1], -1, [-1, -2]]
+)
+def test_groupby_levels(level):
+    idx = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (2, 2)], names=("a", "b"))
+    pdf = pd.DataFrame({"c": [1, 2, 3], "d": [2, 3, 4]}, index=idx)
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        pdf.groupby(level=level).sum(),
+        gdf.groupby(level=level).sum(),
+    )
+
+
+def test_advanced_groupby_levels():
+    pdf = pd.DataFrame({"x": [1, 2, 3], "y": [1, 2, 1], "z": [1, 1, 1]})
+    gdf = cudf.from_pandas(pdf)
+    pdg = pdf.groupby(["x", "y"]).sum()
+    gdg = gdf.groupby(["x", "y"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdh = pdg.groupby(level=1).sum()
+    gdh = gdg.groupby(level=1).sum()
+    assert_groupby_results_equal(pdh, gdh)
+    pdg = pdf.groupby(["x", "y", "z"]).sum()
+    gdg = gdf.groupby(["x", "y", "z"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdg = pdf.groupby(["z"]).sum()
+    gdg = gdf.groupby(["z"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdg = pdf.groupby(["y", "z"]).sum()
+    gdg = gdf.groupby(["y", "z"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdg = pdf.groupby(["x", "z"]).sum()
+    gdg = gdf.groupby(["x", "z"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdg = pdf.groupby(["y"]).sum()
+    gdg = gdf.groupby(["y"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdg = pdf.groupby(["x"]).sum()
+    gdg = gdf.groupby(["x"]).sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdh = pdg.groupby(level=0).sum()
+    gdh = gdg.groupby(level=0).sum()
+    assert_groupby_results_equal(pdh, gdh)
+    pdg = pdf.groupby(["x", "y"]).sum()
+    gdg = gdf.groupby(["x", "y"]).sum()
+    pdh = pdg.groupby(level=[0, 1]).sum()
+    gdh = gdg.groupby(level=[0, 1]).sum()
+    assert_groupby_results_equal(pdh, gdh)
+    pdh = pdg.groupby(level=[1, 0]).sum()
+    gdh = gdg.groupby(level=[1, 0]).sum()
+    assert_groupby_results_equal(pdh, gdh)
+    pdg = pdf.groupby(["x", "y"]).sum()
+    gdg = gdf.groupby(["x", "y"]).sum()
+
+    assert_exceptions_equal(
+        lfunc=pdg.groupby,
+        rfunc=gdg.groupby,
+        lfunc_args_and_kwargs=([], {"level": 2}),
+        rfunc_args_and_kwargs=([], {"level": 2}),
+    )
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        pytest.param(
+            lambda df: df.groupby(["x", "y", "z"]).sum(),
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/issues/32464",
+            ),
+        ),
+        lambda df: df.groupby(["x", "y"]).sum(),
+        lambda df: df.groupby(["x", "y"]).agg("sum"),
+        lambda df: df.groupby(["y"]).sum(),
+        lambda df: df.groupby(["y"]).agg("sum"),
+        lambda df: df.groupby(["x"]).sum(),
+        lambda df: df.groupby(["x"]).agg("sum"),
+        lambda df: df.groupby(["x", "y"]).z.sum(),
+        lambda df: df.groupby(["x", "y"]).z.agg("sum"),
+    ],
+)
+def test_empty_groupby(func):
+    pdf = pd.DataFrame({"x": [], "y": [], "z": []})
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(func(pdf), func(gdf), check_index_type=False)
+
+
+def test_groupby_unsupported_columns():
+    np.random.seed(12)
+    pd_cat = pd.Categorical(
+        pd.Series(np.random.choice(["a", "b", 1], 3), dtype="category")
+    )
+    pdf = pd.DataFrame(
+        {
+            "x": [1, 2, 3],
+            "y": ["a", "b", "c"],
+            "z": ["d", "e", "f"],
+            "a": [3, 4, 5],
+        }
+    )
+    pdf["b"] = pd_cat
+    gdf = cudf.from_pandas(pdf)
+    with pytest.warns(FutureWarning):
+        pdg = pdf.groupby("x").sum()
+    # cudf does not yet support numeric_only, so our default is False (unlike
+    # pandas, which defaults to inferring and throws a warning about it).
+    gdg = gdf.groupby("x").sum()
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_list_of_series():
+    pdf = pd.DataFrame({"x": [1, 2, 3], "y": [1, 2, 1]})
+    gdf = cudf.from_pandas(pdf)
+    pdg = pdf.groupby([pdf.x]).y.sum()
+    gdg = gdf.groupby([gdf.x]).y.sum()
+    assert_groupby_results_equal(pdg, gdg)
+    pdg = pdf.groupby([pdf.x, pdf.y]).y.sum()
+    gdg = gdf.groupby([gdf.x, gdf.y]).y.sum()
+    pytest.skip()
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_use_agg_column_as_index():
+    pdf = pd.DataFrame()
+    pdf["a"] = [1, 1, 1, 3, 5]
+    gdf = cudf.DataFrame()
+    gdf["a"] = [1, 1, 1, 3, 5]
+    pdg = pdf.groupby("a").agg({"a": "count"})
+    gdg = gdf.groupby("a").agg({"a": "count"})
+    assert_groupby_results_equal(pdg, gdg, check_dtype=False)
+
+
+def test_groupby_list_then_string():
+    gdf = cudf.DataFrame()
+    gdf["a"] = [0, 1, 0, 1, 2]
+    gdf["b"] = [11, 2, 15, 12, 2]
+    gdf["c"] = [6, 7, 6, 7, 6]
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby("a", as_index=True).agg(
+        {"b": ["min", "max"], "c": "max"}
+    )
+    pdg = pdf.groupby("a", as_index=True).agg(
+        {"b": ["min", "max"], "c": "max"}
+    )
+    assert_groupby_results_equal(gdg, pdg)
+
+
+def test_groupby_different_unequal_length_column_aggregations():
+    gdf = cudf.DataFrame()
+    gdf["a"] = [0, 1, 0, 1, 2]
+    gdf["b"] = [11, 2, 15, 12, 2]
+    gdf["c"] = [11, 2, 15, 12, 2]
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby("a", as_index=True).agg(
+        {"b": "min", "c": ["max", "min"]}
+    )
+    pdg = pdf.groupby("a", as_index=True).agg(
+        {"b": "min", "c": ["max", "min"]}
+    )
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_single_var_two_aggs():
+    gdf = cudf.DataFrame()
+    gdf["a"] = [0, 1, 0, 1, 2]
+    gdf["b"] = [11, 2, 15, 12, 2]
+    gdf["c"] = [11, 2, 15, 12, 2]
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby("a", as_index=True).agg({"b": ["min", "max"]})
+    pdg = pdf.groupby("a", as_index=True).agg({"b": ["min", "max"]})
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_double_var_two_aggs():
+    gdf = cudf.DataFrame()
+    gdf["a"] = [0, 1, 0, 1, 2]
+    gdf["b"] = [11, 2, 15, 12, 2]
+    gdf["c"] = [11, 2, 15, 12, 2]
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["a", "b"], as_index=True).agg({"c": ["min", "max"]})
+    pdg = pdf.groupby(["a", "b"], as_index=True).agg({"c": ["min", "max"]})
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_apply_basic_agg_single_column():
+    gdf = DataFrame()
+    gdf["key"] = [0, 0, 1, 1, 2, 2, 0]
+    gdf["val"] = [0, 1, 2, 3, 4, 5, 6]
+    gdf["mult"] = gdf["key"] * gdf["val"]
+    pdf = gdf.to_pandas()
+
+    gdg = gdf.groupby(["key", "val"]).mult.sum()
+    pdg = pdf.groupby(["key", "val"]).mult.sum()
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_multi_agg_single_groupby_series():
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=10000),
+            "y": np.random.normal(size=10000),
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    pdg = pdf.groupby("x").y.agg(["sum", "max"])
+    gdg = gdf.groupby("x").y.agg(["sum", "max"])
+
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_multi_agg_multi_groupby():
+    pdf = pd.DataFrame(
+        {
+            "a": np.random.randint(0, 5, 10),
+            "b": np.random.randint(0, 5, 10),
+            "c": np.random.randint(0, 5, 10),
+            "d": np.random.randint(0, 5, 10),
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    pdg = pdf.groupby(["a", "b"]).agg(["sum", "max"])
+    gdg = gdf.groupby(["a", "b"]).agg(["sum", "max"])
+    assert_groupby_results_equal(pdg, gdg)
+
+
+def test_groupby_datetime_multi_agg_multi_groupby():
+    pdf = pd.DataFrame(
+        {
+            "a": pd.date_range(
+                datetime.datetime.now(),
+                datetime.datetime.now() + datetime.timedelta(9),
+                freq="D",
+            ),
+            "b": np.random.randint(0, 5, 10),
+            "c": np.random.randint(0, 5, 10),
+            "d": np.random.randint(0, 5, 10),
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    pdg = pdf.groupby(["a", "b"]).agg(["sum", "max"])
+    gdg = gdf.groupby(["a", "b"]).agg(["sum", "max"])
+
+    assert_groupby_results_equal(pdg, gdg)
+
+
+@pytest.mark.parametrize(
+    "agg",
+    [
+        ["min", "max", "count", "mean"],
+        ["mean", "var", "std"],
+        ["count", "mean", "var", "std"],
+    ],
+)
+def test_groupby_multi_agg_hash_groupby(agg):
+    alphabets = "abcdefghijklmnopqrstuvwxyz"
+    prefixes = alphabets[:10]
+    coll_dict = dict()
+    for prefix in prefixes:
+        for this_name in alphabets:
+            coll_dict[prefix + this_name] = float
+    coll_dict["id"] = int
+    gdf = cudf.datasets.timeseries(
+        start="2000",
+        end="2000-01-2",
+        dtypes=coll_dict,
+        freq="1s",
+        seed=1,
+    ).reset_index(drop=True)
+    pdf = gdf.to_pandas()
+    check_dtype = "count" not in agg
+    pdg = pdf.groupby("id").agg(agg)
+    gdg = gdf.groupby("id").agg(agg)
+    assert_groupby_results_equal(pdg, gdg, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize(
+    "agg", ["min", "max", "idxmax", "idxmax", "sum", "prod", "count", "mean"]
+)
+def test_groupby_nulls_basic(agg):
+    check_dtype = agg not in _index_type_aggs
+
+    pdf = pd.DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": [1, 2, 1, 2, 1, None]})
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        getattr(pdf.groupby("a"), agg)(),
+        getattr(gdf.groupby("a"), agg)(),
+        check_dtype=check_dtype,
+    )
+
+    pdf = pd.DataFrame(
+        {
+            "a": [0, 0, 1, 1, 2, 2],
+            "b": [1, 2, 1, 2, 1, None],
+            "c": [1, 2, 1, None, 1, 2],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        getattr(pdf.groupby("a"), agg)(),
+        getattr(gdf.groupby("a"), agg)(),
+        check_dtype=check_dtype,
+    )
+
+    pdf = pd.DataFrame(
+        {
+            "a": [0, 0, 1, 1, 2, 2],
+            "b": [1, 2, 1, 2, 1, None],
+            "c": [1, 2, None, None, 1, 2],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    # TODO: fillna() used here since we don't follow
+    # Pandas' null semantics. Should we change it?
+    assert_groupby_results_equal(
+        getattr(pdf.groupby("a"), agg)().fillna(0),
+        getattr(gdf.groupby("a"), agg)().fillna(0 if agg != "prod" else 1),
+        check_dtype=check_dtype,
+    )
+
+
+def test_groupby_nulls_in_index():
+    pdf = pd.DataFrame({"a": [None, 2, 1, 1], "b": [1, 2, 3, 4]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").sum(), gdf.groupby("a").sum()
+    )
+
+
+def test_groupby_all_nulls_index():
+    gdf = cudf.DataFrame(
+        {
+            "a": cudf.Series([None, None, None, None], dtype="object"),
+            "b": [1, 2, 3, 4],
+        }
+    )
+    pdf = gdf.to_pandas()
+    assert_groupby_results_equal(
+        pdf.groupby("a").sum(), gdf.groupby("a").sum()
+    )
+
+    gdf = cudf.DataFrame(
+        {"a": cudf.Series([np.nan, np.nan, np.nan, np.nan]), "b": [1, 2, 3, 4]}
+    )
+    pdf = gdf.to_pandas()
+    assert_groupby_results_equal(
+        pdf.groupby("a").sum(), gdf.groupby("a").sum()
+    )
+
+
+@pytest.mark.parametrize("sort", [True, False])
+def test_groupby_sort(sort):
+    pdf = pd.DataFrame({"a": [2, 2, 1, 1], "b": [1, 2, 3, 4]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.groupby("a", sort=sort).sum(),
+        gdf.groupby("a", sort=sort).sum(),
+        check_like=not sort,
+    )
+
+    pdf = pd.DataFrame(
+        {"c": [-1, 2, 1, 4], "b": [1, 2, 3, 4], "a": [2, 2, 1, 1]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.groupby(["c", "b"], sort=sort).sum(),
+        gdf.groupby(["c", "b"], sort=sort).sum(),
+        check_like=not sort,
+    )
+
+    ps = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=[2, 2, 2, 3, 3, 1, 1, 1])
+    gs = cudf.from_pandas(ps)
+
+    assert_eq(
+        ps.groupby(level=0, sort=sort).sum().to_frame(),
+        gs.groupby(level=0, sort=sort).sum().to_frame(),
+        check_like=not sort,
+    )
+
+    ps = pd.Series(
+        [1, 2, 3, 4, 5, 6, 7, 8],
+        index=pd.MultiIndex.from_product([(1, 2), ("a", "b"), (42, 84)]),
+    )
+    gs = cudf.from_pandas(ps)
+
+    assert_eq(
+        ps.groupby(level=0, sort=sort).sum().to_frame(),
+        gs.groupby(level=0, sort=sort).sum().to_frame(),
+        check_like=not sort,
+    )
+
+
+def test_groupby_cat():
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 2], "b": pd.Series(["b", "b", "a"], dtype="category")}
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        pdf.groupby("a").count(),
+        gdf.groupby("a").count(),
+        check_dtype=False,
+    )
+
+
+def test_groupby_index_type():
+    df = cudf.DataFrame()
+    df["string_col"] = ["a", "b", "c"]
+    df["counts"] = [1, 2, 3]
+    res = df.groupby(by="string_col").counts.sum()
+    assert isinstance(res.index, cudf.StringIndex)
+
+
+@pytest.mark.parametrize(
+    "interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
+)
+@pytest.mark.parametrize("q", [0.25, 0.4, 0.5, 0.7, 1])
+def test_groupby_quantile(request, interpolation, q):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(q == 0.5 and interpolation == "nearest"),
+            reason=(
+                "Pandas NaN Rounding will fail nearest interpolation at 0.5"
+            ),
+        )
+    )
+
+    raw_data = {
+        "y": [None, 1, 2, 3, 4, None, 6, 7, 8, 9],
+        "x": [1, 2, 3, 1, 2, 2, 1, None, 3, 2],
+    }
+    # Pandas>0.25 now casts NaN in quantile operations as a float64
+    # # so we are filling with zeros.
+    pdf = pd.DataFrame(raw_data).fillna(0)
+    gdf = DataFrame.from_pandas(pdf)
+
+    pdg = pdf.groupby("x")
+    gdg = gdf.groupby("x")
+
+    pdresult = pdg.quantile(q, interpolation=interpolation)
+    gdresult = gdg.quantile(q, interpolation=interpolation)
+
+    assert_groupby_results_equal(pdresult, gdresult)
+
+
+def test_groupby_std():
+    raw_data = {
+        "x": [1, 2, 3, 1, 2, 2, 1, None, 3, 2],
+        "y": [None, 1, 2, 3, 4, None, 6, 7, 8, 9],
+    }
+    pdf = pd.DataFrame(raw_data)
+    gdf = DataFrame.from_pandas(pdf)
+    pdg = pdf.groupby("x")
+    gdg = gdf.groupby("x")
+    pdresult = pdg.std()
+    gdresult = gdg.std()
+
+    assert_groupby_results_equal(pdresult, gdresult)
+
+
+def test_groupby_size():
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 3, 4],
+            "b": ["bob", "bob", "alice", "cooper"],
+            "c": [1, 2, 3, 4],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").size(),
+        gdf.groupby("a").size(),
+        check_dtype=False,
+    )
+
+    assert_groupby_results_equal(
+        pdf.groupby(["a", "b", "c"]).size(),
+        gdf.groupby(["a", "b", "c"]).size(),
+        check_dtype=False,
+    )
+
+    sr = pd.Series(range(len(pdf)))
+    assert_groupby_results_equal(
+        pdf.groupby(sr).size(),
+        gdf.groupby(sr).size(),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize("index", [None, [1, 2, 3, 4]])
+def test_groupby_cumcount(index):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 3, 4],
+            "b": ["bob", "bob", "alice", "cooper"],
+            "c": [1, 2, 3, 4],
+        },
+        index=index,
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").cumcount(),
+        gdf.groupby("a").cumcount(),
+        check_dtype=False,
+    )
+
+    assert_groupby_results_equal(
+        pdf.groupby(["a", "b", "c"]).cumcount(),
+        gdf.groupby(["a", "b", "c"]).cumcount(),
+        check_dtype=False,
+    )
+
+    sr = pd.Series(range(len(pdf)), index=index)
+    assert_groupby_results_equal(
+        pdf.groupby(sr).cumcount(),
+        gdf.groupby(sr).cumcount(),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 1000])
+@pytest.mark.parametrize("as_index", [True, False])
+@pytest.mark.parametrize(
+    "agg", ["min", "max", "idxmin", "idxmax", "mean", "count"]
+)
+def test_groupby_datetime(nelem, as_index, agg):
+    if agg == "mean" and as_index is True:
+        return
+    check_dtype = agg not in ("mean", "count", "idxmin", "idxmax")
+    pdf = make_frame(pd.DataFrame, nelem=nelem, with_datetime=True)
+    gdf = make_frame(cudf.DataFrame, nelem=nelem, with_datetime=True)
+    pdg = pdf.groupby("datetime", as_index=as_index)
+    gdg = gdf.groupby("datetime", as_index=as_index)
+    if as_index is False:
+        pdres = getattr(pdg, agg)()
+        gdres = getattr(gdg, agg)()
+    else:
+        pdres = pdg.agg({"datetime": agg})
+        gdres = gdg.agg({"datetime": agg})
+    assert_groupby_results_equal(
+        pdres,
+        gdres,
+        check_dtype=check_dtype,
+        as_index=as_index,
+        by=["datetime"],
+    )
+
+
+def test_groupby_dropna():
+    df = cudf.DataFrame({"a": [1, 1, None], "b": [1, 2, 3]})
+    expect = cudf.DataFrame(
+        {"b": [3, 3]}, index=cudf.Series([1, None], name="a")
+    )
+    got = df.groupby("a", dropna=False).sum()
+    assert_groupby_results_equal(expect, got)
+
+    df = cudf.DataFrame(
+        {"a": [1, 1, 1, None], "b": [1, None, 1, None], "c": [1, 2, 3, 4]}
+    )
+    idx = cudf.MultiIndex.from_frame(
+        df[["a", "b"]].drop_duplicates().sort_values(["a", "b"]),
+        names=["a", "b"],
+    )
+    expect = cudf.DataFrame({"c": [4, 2, 4]}, index=idx)
+    got = df.groupby(["a", "b"], dropna=False).sum()
+
+    assert_groupby_results_equal(expect, got)
+
+
+def test_groupby_dropna_getattr():
+    df = cudf.DataFrame()
+    df["id"] = [0, 1, 1, None, None, 3, 3]
+    df["val"] = [0, 1, 1, 2, 2, 3, 3]
+    got = df.groupby("id", dropna=False).val.sum()
+
+    expect = cudf.Series(
+        [0, 2, 6, 4], name="val", index=cudf.Series([0, 1, 3, None], name="id")
+    )
+
+    assert_groupby_results_equal(expect, got)
+
+
+def test_groupby_categorical_from_string():
+    gdf = cudf.DataFrame()
+    gdf["id"] = ["a", "b", "c"]
+    gdf["val"] = [0, 1, 2]
+    gdf["id"] = gdf["id"].astype("category")
+    assert_groupby_results_equal(
+        cudf.DataFrame({"val": gdf["val"]}).set_index(keys=gdf["id"]),
+        gdf.groupby("id").sum(),
+    )
+
+
+def test_groupby_arbitrary_length_series():
+    gdf = cudf.DataFrame({"a": [1, 1, 2], "b": [2, 3, 4]}, index=[4, 5, 6])
+    gsr = cudf.Series([1.0, 2.0, 2.0], index=[3, 4, 5])
+
+    pdf = gdf.to_pandas()
+    psr = gsr.to_pandas()
+
+    expect = pdf.groupby(psr).sum()
+    got = gdf.groupby(gsr).sum()
+
+    assert_groupby_results_equal(expect, got)
+
+
+def test_groupby_series_same_name_as_dataframe_column():
+    gdf = cudf.DataFrame({"a": [1, 1, 2], "b": [2, 3, 4]}, index=[4, 5, 6])
+    gsr = cudf.Series([1.0, 2.0, 2.0], name="a", index=[3, 4, 5])
+
+    pdf = gdf.to_pandas()
+    psr = gsr.to_pandas()
+
+    expect = pdf.groupby(psr).sum()
+    got = gdf.groupby(gsr).sum()
+
+    assert_groupby_results_equal(expect, got)
+
+
+def test_group_by_series_and_column_name_in_by():
+    gdf = cudf.DataFrame(
+        {"x": [1.0, 2.0, 3.0], "y": [1, 2, 1]}, index=[1, 2, 3]
+    )
+    gsr0 = cudf.Series([0.0, 1.0, 2.0], name="a", index=[1, 2, 3])
+    gsr1 = cudf.Series([0.0, 1.0, 3.0], name="b", index=[3, 4, 5])
+
+    pdf = gdf.to_pandas()
+    psr0 = gsr0.to_pandas()
+    psr1 = gsr1.to_pandas()
+
+    expect = pdf.groupby(["x", psr0, psr1]).sum()
+    got = gdf.groupby(["x", gsr0, gsr1]).sum()
+
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize(
+    "grouper",
+    [
+        "a",
+        ["a"],
+        ["a", "b"],
+        np.array([0, 1, 1, 2, 3, 2]),
+        {0: "a", 1: "a", 2: "b", 3: "a", 4: "b", 5: "c"},
+        lambda x: x + 1,
+        ["a", np.array([0, 1, 1, 2, 3, 2])],
+    ],
+)
+def test_grouping(grouper):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 2, 3],
+            "b": [1, 2, 1, 2, 1, 2],
+            "c": [1, 2, 3, 4, 5, 6],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    # There's no easy way to validate that the same warning is thrown by both
+    # cudf and pandas here because it's only thrown upon iteration, so we
+    # settle for catching warnings on the whole block.
+    with expect_warning_if(isinstance(grouper, list) and len(grouper) == 1):
+        for pdf_group, gdf_group in zip(
+            pdf.groupby(grouper), gdf.groupby(grouper)
+        ):
+            assert pdf_group[0] == gdf_group[0]
+            assert_eq(pdf_group[1], gdf_group[1])
+
+
+@pytest.mark.parametrize("agg", [lambda x: x.count(), "count"])
+@pytest.mark.parametrize("by", ["a", ["a", "b"], ["a", "c"]])
+def test_groupby_count(agg, by):
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 1, 2, 3], "b": [1, 2, 2, 2, 1], "c": [1, 2, None, 4, 5]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.groupby(by).agg(agg)
+    got = gdf.groupby(by).agg(agg)
+
+    assert_groupby_results_equal(expect, got, check_dtype=True)
+
+
+@pytest.mark.parametrize("agg", [lambda x: x.median(), "median"])
+@pytest.mark.parametrize("by", ["a", ["a", "b"], ["a", "c"]])
+def test_groupby_median(agg, by):
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 1, 2, 3], "b": [1, 2, 2, 2, 1], "c": [1, 2, None, 4, 5]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.groupby(by).agg(agg)
+    got = gdf.groupby(by).agg(agg)
+
+    assert_groupby_results_equal(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("agg", [lambda x: x.nunique(), "nunique"])
+@pytest.mark.parametrize("by", ["a", ["a", "b"], ["a", "c"]])
+def test_groupby_nunique(agg, by):
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 1, 2, 3], "b": [1, 2, 2, 2, 1], "c": [1, 2, None, 4, 5]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.groupby(by).nunique()
+    got = gdf.groupby(by).nunique()
+
+    assert_groupby_results_equal(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "n",
+    [0, 1, 2, 10],
+)
+@pytest.mark.parametrize("by", ["a", ["a", "b"], ["a", "c"]])
+def test_groupby_nth(n, by):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 3],
+            "b": [1, 2, 2, 2, 1],
+            "c": [1, 2, None, 4, 5],
+            "d": ["a", "b", "c", "d", "e"],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.groupby(by).nth(n)
+    got = gdf.groupby(by).nth(n)
+
+    assert_groupby_results_equal(expect, got, check_dtype=False)
+
+
+@pytest.mark.xfail(
+    reason="https://github.com/pandas-dev/pandas/issues/43209",
+)
+def test_raise_data_error():
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_exceptions_equal(
+        pdf.groupby("a").mean,
+        gdf.groupby("a").mean,
+    )
+
+
+def test_drop_unsupported_multi_agg():
+    gdf = cudf.DataFrame(
+        {"a": [1, 1, 2, 2], "b": [1, 2, 3, 4], "c": ["a", "b", "c", "d"]}
+    )
+    assert_groupby_results_equal(
+        gdf.groupby("a").agg(["count", "mean"]),
+        gdf.groupby("a").agg({"b": ["count", "mean"], "c": ["count"]}),
+    )
+
+
+@pytest.mark.parametrize(
+    "agg",
+    (
+        list(itertools.combinations(["count", "max", "min", "nunique"], 2))
+        + [
+            {"b": "min", "c": "mean"},
+            {"b": "max", "c": "mean"},
+            {"b": "count", "c": "mean"},
+            {"b": "nunique", "c": "mean"},
+        ]
+    ),
+)
+def test_groupby_agg_combinations(agg):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 2, 2, 3],
+            "b": ["a", "a", "b", "c", "d"],
+            "c": [1, 2, 3, 4, 5],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").agg(agg),
+        gdf.groupby("a").agg(agg),
+        check_dtype=False,
+    )
+
+
+def test_groupby_apply_noempty_group():
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 2, 2], "b": [1, 2, 1, 2], "c": [1, 2, 3, 4]}
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        pdf.groupby("a", group_keys=False)
+        .apply(lambda x: x.iloc[[0, 1]])
+        .reset_index(drop=True),
+        gdf.groupby("a")
+        .apply(lambda x: x.iloc[[0, 1]])
+        .reset_index(drop=True),
+    )
+
+
+def test_reset_index_after_empty_groupby():
+    # GH #5475
+    pdf = pd.DataFrame({"a": [1, 2, 3]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").sum().reset_index(),
+        gdf.groupby("a").sum().reset_index(),
+        as_index=False,
+        by="a",
+    )
+
+
+def test_groupby_attribute_error():
+    err_msg = "Test error message"
+
+    class TestGroupBy(cudf.core.groupby.GroupBy):
+        @property
+        def _groupby(self):
+            raise AttributeError(err_msg)
+
+    a = cudf.DataFrame({"a": [1, 2], "b": [2, 3]})
+    gb = TestGroupBy(a, a["a"])
+
+    with pytest.raises(AttributeError, match=err_msg):
+        gb.sum()
+
+
+@pytest.mark.parametrize(
+    "by",
+    [
+        "a",
+        "b",
+        ["a"],
+        ["b"],
+        ["a", "b"],
+        ["b", "a"],
+        np.array([0, 0, 0, 1, 1, 1, 2]),
+    ],
+)
+def test_groupby_groups(by):
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 1, 2, 1, 2, 3], "b": [1, 2, 3, 4, 5, 6, 7]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    pdg = pdf.groupby(by)
+    gdg = gdf.groupby(by)
+
+    for key in pdg.groups:
+        assert key in gdg.groups
+        assert_eq(pdg.groups[key], gdg.groups[key])
+
+
+@pytest.mark.parametrize(
+    "by",
+    [
+        "a",
+        "b",
+        ["a"],
+        ["b"],
+        ["a", "b"],
+        ["b", "a"],
+        ["a", "c"],
+        ["a", "b", "c"],
+    ],
+)
+def test_groupby_groups_multi(by):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 2, 1, 2, 1, 2, 3],
+            "b": ["a", "b", "a", "b", "b", "c", "c"],
+            "c": [1, 2, 3, 4, 5, 6, 7],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    pdg = pdf.groupby(by)
+    gdg = gdf.groupby(by)
+
+    for key in pdg.groups:
+        assert key in gdg.groups
+        assert_eq(pdg.groups[key], gdg.groups[key])
+
+
+def test_groupby_nunique_series():
+    pdf = pd.DataFrame({"a": [1, 1, 1, 2, 2, 2], "b": [1, 2, 3, 1, 1, 2]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a")["b"].nunique(),
+        gdf.groupby("a")["b"].nunique(),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize("list_agg", [list, "collect"])
+def test_groupby_list_simple(list_agg):
+    pdf = pd.DataFrame({"a": [1, 1, 1, 2, 2, 2], "b": [1, 2, None, 4, 5, 6]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").agg({"b": list}),
+        gdf.groupby("a").agg({"b": list_agg}),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize("list_agg", [list, "collect"])
+def test_groupby_list_of_lists(list_agg):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 2, 2],
+            "b": [[1, 2], [3, None, 5], None, [], [7, 8], [9]],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").agg({"b": list}),
+        gdf.groupby("a").agg({"b": list_agg}),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize("list_agg", [list, "collect"])
+def test_groupby_list_of_structs(list_agg):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 2, 2],
+            "b": [
+                {"c": "1", "d": 1},
+                {"c": "2", "d": 2},
+                {"c": "3", "d": 3},
+                {"c": "4", "d": 4},
+                {"c": "5", "d": 5},
+                {"c": "6", "d": 6},
+            ],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    grouped = gdf.groupby("a").agg({"b": list_agg})
+    assert_groupby_results_equal(
+        pdf.groupby("a").agg({"b": list}),
+        grouped,
+        check_dtype=True,
+    )
+    assert grouped["b"].dtype.element_type == gdf["b"].dtype
+
+
+@pytest.mark.parametrize("list_agg", [list, "collect"])
+def test_groupby_list_single_element(list_agg):
+    pdf = pd.DataFrame({"a": [1, 2], "b": [3, None]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").agg({"b": list}),
+        gdf.groupby("a").agg({"b": list_agg}),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "agg", [list, [list, "count"], {"b": list, "c": "sum"}]
+)
+def test_groupby_list_strings(agg):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 2],
+            "b": ["b", "a", None, "e", "d"],
+            "c": [1, 2, 3, 4, 5],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    assert_groupby_results_equal(
+        pdf.groupby("a").agg(agg),
+        gdf.groupby("a").agg(agg),
+        check_dtype=False,
+    )
+
+
+def test_groupby_list_columns_excluded():
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 2, 2],
+            "b": [1, 2, 3, 4],
+            "c": [[1, 2], [3, 4], [5, 6], [7, 8]],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    # cudf does not yet support numeric_only, so our default is False, but
+    # pandas defaults to inferring and throws a warning about it, so we need to
+    # catch that. pandas future behavior will match ours by default (at which
+    # point supporting numeric_only=True will be the open feature request).
+    with pytest.warns(FutureWarning):
+        pandas_result = pdf.groupby("a").mean()
+    with pytest.warns(FutureWarning):
+        pandas_agg_result = pdf.groupby("a").agg("mean")
+
+    assert_groupby_results_equal(
+        pandas_result, gdf.groupby("a").mean(), check_dtype=False
+    )
+
+    assert_groupby_results_equal(
+        pandas_agg_result,
+        gdf.groupby("a").agg("mean"),
+        check_dtype=False,
+    )
+
+
+def test_groupby_pipe():
+    pdf = pd.DataFrame({"A": "a b a b".split(), "B": [1, 2, 3, 4]})
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.groupby("A").pipe(lambda x: x.max() - x.min())
+    actual = gdf.groupby("A").pipe(lambda x: x.max() - x.min())
+
+    assert_groupby_results_equal(expected, actual)
+
+
+def create_test_groupby_apply_return_scalars_params():
+    def f0(x):
+        x = x[~x["B"].isna()]
+        ticker = x.shape[0]
+        full = ticker / 10
+        return full
+
+    def f1(x, k):
+        x = x[~x["B"].isna()]
+        ticker = x.shape[0]
+        full = ticker / k
+        return full
+
+    def f2(x, k, L):
+        x = x[~x["B"].isna()]
+        ticker = x.shape[0]
+        full = L * (ticker / k)
+        return full
+
+    def f3(x, k, L, m):
+        x = x[~x["B"].isna()]
+        ticker = x.shape[0]
+        full = L * (ticker / k) % m
+        return full
+
+    return [(f0, ()), (f1, (42,)), (f2, (42, 119)), (f3, (42, 119, 212.1))]
+
+
+@pytest.mark.parametrize(
+    "func,args", create_test_groupby_apply_return_scalars_params()
+)
+def test_groupby_apply_return_scalars(func, args):
+    pdf = pd.DataFrame(
+        {
+            "A": [1, 1, 2, 2, 3, 3, 4, 4, 5, 5],
+            "B": [
+                0.01,
+                np.nan,
+                0.03,
+                0.04,
+                np.nan,
+                0.06,
+                0.07,
+                0.08,
+                0.09,
+                1.0,
+            ],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.groupby("A").apply(func, *args)
+    actual = gdf.groupby("A").apply(func, *args)
+
+    assert_groupby_results_equal(expected, actual)
+
+
+def create_test_groupby_apply_return_series_dataframe_params():
+    def f0(x):
+        return x - x.max()
+
+    def f1(x):
+        return x.min() - x.max()
+
+    def f2(x):
+        return x.min()
+
+    def f3(x, k):
+        return x - x.max() + k
+
+    def f4(x, k, L):
+        return x.min() - x.max() + (k / L)
+
+    def f5(x, k, L, m):
+        return m * x.min() + (k / L)
+
+    return [
+        (f0, ()),
+        (f1, ()),
+        (f2, ()),
+        (f3, (42,)),
+        (f4, (42, 119)),
+        (f5, (41, 119, 212.1)),
+    ]
+
+
+@pytest.mark.parametrize(
+    "func,args", create_test_groupby_apply_return_series_dataframe_params()
+)
+def test_groupby_apply_return_series_dataframe(func, args):
+    pdf = pd.DataFrame(
+        {"key": [0, 0, 1, 1, 2, 2, 2], "val": [0, 1, 2, 3, 4, 5, 6]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.groupby(["key"], group_keys=False).apply(func, *args)
+    actual = gdf.groupby(["key"]).apply(func, *args)
+
+    assert_groupby_results_equal(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [pd.DataFrame(), pd.DataFrame({"a": []}), pd.Series([], dtype="float64")],
+)
+def test_groupby_no_keys(pdf):
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        pdf.groupby([]).max(),
+        gdf.groupby([]).max(),
+        check_dtype=False,
+        check_index_type=False,  # Int64Index v/s Float64Index
+    )
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [pd.DataFrame(), pd.DataFrame({"a": []}), pd.Series([], dtype="float64")],
+)
+def test_groupby_apply_no_keys(pdf):
+    gdf = cudf.from_pandas(pdf)
+    assert_groupby_results_equal(
+        pdf.groupby([], group_keys=False).apply(lambda x: x.max()),
+        gdf.groupby([]).apply(lambda x: x.max()),
+        check_index_type=False,  # Int64Index v/s Float64Index
+    )
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [pd.DataFrame({"a": [1, 2]}), pd.DataFrame({"a": [1, 2], "b": [2, 3]})],
+)
+def test_groupby_nonempty_no_keys(pdf):
+    gdf = cudf.from_pandas(pdf)
+    assert_exceptions_equal(
+        lambda: pdf.groupby([]),
+        lambda: gdf.groupby([]),
+    )
+
+
+@pytest.mark.parametrize(
+    "by,data",
+    [
+        # ([], []),  # error?
+        ([1, 1, 2, 2], [0, 0, 1, 1]),
+        ([1, 2, 3, 4], [0, 0, 0, 0]),
+        ([1, 2, 1, 2], [0, 1, 1, 1]),
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    SIGNED_TYPES + DATETIME_TYPES + TIMEDELTA_TYPES + ["string", "category"],
+)
+def test_groupby_unique(by, data, dtype):
+    pdf = pd.DataFrame({"by": by, "data": data})
+    pdf["data"] = pdf["data"].astype(dtype)
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.groupby("by")["data"].unique()
+    got = gdf.groupby("by")["data"].unique()
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+@pytest.mark.parametrize("func", ["cummin", "cummax", "cumcount", "cumsum"])
+def test_groupby_2keys_scan(nelem, func):
+    pdf = make_frame(pd.DataFrame, nelem=nelem)
+    expect_df = pdf.groupby(["x", "y"], sort=True).agg(func)
+    got_df = (
+        make_frame(DataFrame, nelem=nelem)
+        .groupby(["x", "y"], sort=True)
+        .agg(func)
+    )
+    # pd.groupby.cumcount returns a series.
+    if isinstance(expect_df, pd.Series):
+        expect_df = expect_df.to_frame("val")
+
+    check_dtype = func not in _index_type_aggs
+    assert_groupby_results_equal(got_df, expect_df, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize("nelem", [100, 1000])
+@pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_option", ["keep", "top", "bottom"])
+@pytest.mark.parametrize("pct", [False, True])
+def test_groupby_2keys_rank(nelem, method, ascending, na_option, pct):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    pdf = t.to_pandas()
+    pdf.columns = ["x", "y", "z"]
+    gdf = cudf.from_pandas(pdf)
+    expect_df = pdf.groupby(["x", "y"], sort=True).rank(
+        method=method, ascending=ascending, na_option=na_option, pct=pct
+    )
+    got_df = gdf.groupby(["x", "y"], sort=True).rank(
+        method=method, ascending=ascending, na_option=na_option, pct=pct
+    )
+
+    assert_groupby_results_equal(got_df, expect_df, check_dtype=False)
+
+
+def test_groupby_rank_fails():
+    gdf = cudf.DataFrame(
+        {"x": [1, 2, 3, 4], "y": [1, 2, 3, 4], "z": [1, 2, 3, 4]}
+    )
+    with pytest.raises(NotImplementedError):
+        gdf.groupby(["x", "y"]).rank(method="min", axis=1)
+    gdf = cudf.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 2, 2],
+            "b": [[1, 2], [3, None, 5], None, [], [7, 8], [9]],
+        }
+    )
+    with pytest.raises(NotImplementedError):
+        gdf.groupby(["a"]).rank(method="min", axis=1)
+
+
+@pytest.mark.parametrize(
+    "with_nan", [False, True], ids=["just-NA", "also-NaN"]
+)
+@pytest.mark.parametrize("dropna", [False, True], ids=["keepna", "dropna"])
+@pytest.mark.parametrize(
+    "duplicate_index", [False, True], ids=["rangeindex", "dupindex"]
+)
+def test_groupby_scan_null_keys(with_nan, dropna, duplicate_index):
+    key_col = [None, 1, 2, None, 3, None, 3, 1, None, 1]
+    if with_nan:
+        df = pd.DataFrame(
+            {"key": pd.Series(key_col, dtype="float32"), "value": range(10)}
+        )
+    else:
+        df = pd.DataFrame(
+            {"key": pd.Series(key_col, dtype="Int32"), "value": range(10)}
+        )
+
+    if duplicate_index:
+        # Non-default index with duplicates
+        df.index = [1, 2, 3, 1, 3, 2, 4, 1, 6, 10]
+
+    cdf = cudf.from_pandas(df)
+
+    expect = df.groupby("key", dropna=dropna).cumsum()
+    got = cdf.groupby("key", dropna=dropna).cumsum()
+    assert_eq(expect, got)
+
+
+def test_groupby_mix_agg_scan():
+    err_msg = "Cannot perform both aggregation and scan in one operation"
+    func = ["cumsum", "sum"]
+    gb = make_frame(DataFrame, nelem=10).groupby(["x", "y"], sort=True)
+
+    gb.agg(func[0])
+    gb.agg(func[1])
+    gb.agg(func[1:])
+    with pytest.raises(NotImplementedError, match=err_msg):
+        gb.agg(func)
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+@pytest.mark.parametrize("shift_perc", [0.5, 1.0, 1.5])
+@pytest.mark.parametrize("direction", [1, -1])
+@pytest.mark.parametrize("fill_value", [None, np.nan, 42])
+def test_groupby_shift_row(nelem, shift_perc, direction, fill_value):
+    pdf = make_frame(pd.DataFrame, nelem=nelem, extra_vals=["val2"])
+    gdf = cudf.from_pandas(pdf)
+    n_shift = int(nelem * shift_perc) * direction
+
+    expected = pdf.groupby(["x", "y"]).shift(
+        periods=n_shift, fill_value=fill_value
+    )
+    got = gdf.groupby(["x", "y"]).shift(periods=n_shift, fill_value=fill_value)
+
+    assert_groupby_results_equal(
+        expected[["val", "val2"]], got[["val", "val2"]]
+    )
+
+
+@pytest.mark.parametrize("nelem", [10, 50, 100, 1000])
+@pytest.mark.parametrize("shift_perc", [0.5, 1.0, 1.5])
+@pytest.mark.parametrize("direction", [1, -1])
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        None,
+        pytest.param(
+            0,
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/10608"
+            ),
+        ),
+        pytest.param(
+            42,
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/10608"
+            ),
+        ),
+    ],
+)
+def test_groupby_shift_row_mixed_numerics(
+    nelem, shift_perc, direction, fill_value
+):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+    n_shift = int(nelem * shift_perc) * direction
+
+    expected = pdf.groupby(["0"]).shift(periods=n_shift, fill_value=fill_value)
+    got = gdf.groupby(["0"]).shift(periods=n_shift, fill_value=fill_value)
+
+    assert_groupby_results_equal(
+        expected[["1", "2", "3", "4"]], got[["1", "2", "3", "4"]]
+    )
+
+
+# TODO: Shifting list columns is currently unsupported because we cannot
+# construct a null list scalar in python. Support once it is added.
+@pytest.mark.parametrize("nelem", [10, 50, 100, 1000])
+@pytest.mark.parametrize("shift_perc", [0.5, 1.0, 1.5])
+@pytest.mark.parametrize("direction", [1, -1])
+def test_groupby_shift_row_mixed(nelem, shift_perc, direction):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "str", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+    n_shift = int(nelem * shift_perc) * direction
+
+    expected = pdf.groupby(["0"]).shift(periods=n_shift)
+    got = gdf.groupby(["0"]).shift(periods=n_shift)
+
+    assert_groupby_results_equal(
+        expected[["1", "2", "3", "4"]], got[["1", "2", "3", "4"]]
+    )
+
+
+@pytest.mark.parametrize("nelem", [10, 50, 100, 1000])
+@pytest.mark.parametrize("shift_perc", [0.5, 1.0, 1.5])
+@pytest.mark.parametrize("direction", [1, -1])
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        [
+            42,
+            "fill",
+            np.datetime64(123, "ns"),
+            cudf.Scalar(456, dtype="timedelta64[ns]"),
+        ]
+    ],
+)
+def test_groupby_shift_row_mixed_fill(
+    nelem, shift_perc, direction, fill_value
+):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "str", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+    n_shift = int(nelem * shift_perc) * direction
+
+    # Pandas does not support specifying different fill_value by column, so we
+    # simulate it column by column
+    expected = pdf.copy()
+    for col, single_fill in zip(pdf.iloc[:, 1:], fill_value):
+        if isinstance(single_fill, cudf.Scalar):
+            single_fill = single_fill._host_value
+        expected[col] = (
+            pdf[col]
+            .groupby(pdf["0"])
+            .shift(periods=n_shift, fill_value=single_fill)
+        )
+
+    got = gdf.groupby(["0"]).shift(periods=n_shift, fill_value=fill_value)
+
+    assert_groupby_results_equal(
+        expected[["1", "2", "3", "4"]], got[["1", "2", "3", "4"]]
+    )
+
+
+@pytest.mark.parametrize("nelem", [10, 50, 100, 1000])
+@pytest.mark.parametrize("fill_value", [None, 0, 42])
+def test_groupby_shift_row_zero_shift(nelem, fill_value):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    gdf = cudf.from_pandas(t.to_pandas())
+
+    expected = gdf
+    got = gdf.groupby(["0"]).shift(periods=0, fill_value=fill_value)
+
+    assert_groupby_results_equal(
+        expected[["1", "2", "3", "4"]], got[["1", "2", "3", "4"]]
+    )
+
+
+@pytest.mark.parametrize("nelem", [2, 3, 100, 1000])
+@pytest.mark.parametrize("shift_perc", [0.5, 1.0, 1.5])
+@pytest.mark.parametrize("direction", [1, -1])
+def test_groupby_diff_row(nelem, shift_perc, direction):
+    pdf = make_frame(pd.DataFrame, nelem=nelem, extra_vals=["val2"])
+    gdf = cudf.from_pandas(pdf)
+    n_shift = int(nelem * shift_perc) * direction
+
+    expected = pdf.groupby(["x", "y"]).diff(periods=n_shift)
+    got = gdf.groupby(["x", "y"]).diff(periods=n_shift)
+
+    assert_groupby_results_equal(
+        expected[["val", "val2"]], got[["val", "val2"]]
+    )
+
+
+@pytest.mark.parametrize("nelem", [10, 50, 100, 1000])
+@pytest.mark.parametrize("shift_perc", [0.5, 1.0, 1.5])
+@pytest.mark.parametrize("direction", [1, -1])
+def test_groupby_diff_row_mixed_numerics(nelem, shift_perc, direction):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "decimal64", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+    n_shift = int(nelem * shift_perc) * direction
+
+    expected = pdf.groupby(["0"]).diff(periods=n_shift)
+    got = gdf.groupby(["0"]).diff(periods=n_shift)
+
+    assert_groupby_results_equal(
+        expected[["1", "2", "3", "4", "5"]], got[["1", "2", "3", "4", "5"]]
+    )
+
+
+@pytest.mark.parametrize("nelem", [10, 50, 100, 1000])
+def test_groupby_diff_row_zero_shift(nelem):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+        ],
+        rows=nelem,
+        use_threads=False,
+    )
+    gdf = cudf.from_pandas(t.to_pandas())
+
+    expected = gdf
+    got = gdf.groupby(["0"]).shift(periods=0)
+
+    assert_groupby_results_equal(
+        expected[["1", "2", "3", "4"]], got[["1", "2", "3", "4"]]
+    )
+
+
+# TODO: test for category columns when cudf.Scalar supports category type
+@pytest.mark.parametrize("nelem", [10, 100, 1000])
+def test_groupby_fillna_multi_value(nelem):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ms]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {"dtype": "decimal64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "str", "null_frequency": 0.4, "cardinality": 10},
+        ],
+        rows=nelem,
+        use_threads=False,
+        seed=0,
+    )
+    key_col = "0"
+    value_cols = ["1", "2", "3", "4", "5", "6"]
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+
+    # fill the dataframe with the first non-null item in the column
+    fill_values = {
+        name: pdf[name].loc[pdf[name].first_valid_index()]
+        for name in value_cols
+    }
+    # cudf can't fillna with a pandas.Timedelta type
+    fill_values["4"] = fill_values["4"].to_numpy()
+
+    expect = pdf.groupby(key_col).fillna(value=fill_values)
+
+    got = gdf.groupby(key_col).fillna(value=fill_values)
+
+    assert_groupby_results_equal(expect[value_cols], got[value_cols])
+
+
+# TODO: test for category columns when cudf.Scalar supports category type
+# TODO: cudf.fillna does not support decimal column to column fill yet
+@pytest.mark.parametrize("nelem", [10, 100, 1000])
+def test_groupby_fillna_multi_value_df(nelem):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ms]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {"dtype": "str", "null_frequency": 0.4, "cardinality": 10},
+        ],
+        rows=nelem,
+        use_threads=False,
+        seed=0,
+    )
+    key_col = "0"
+    value_cols = ["1", "2", "3", "4", "5"]
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+
+    # fill the dataframe with the first non-null item in the column
+    fill_values = {
+        name: pdf[name].loc[pdf[name].first_valid_index()]
+        for name in value_cols
+    }
+    # cudf can't fillna with a pandas.Timedelta type
+    fill_values["4"] = fill_values["4"].to_numpy()
+    fill_values = pd.DataFrame(fill_values, index=pdf.index)
+
+    expect = pdf.groupby(key_col).fillna(value=fill_values)
+
+    fill_values = cudf.from_pandas(fill_values)
+    got = gdf.groupby(key_col).fillna(value=fill_values)
+
+    assert_groupby_results_equal(expect[value_cols], got[value_cols])
+
+
+@pytest.mark.parametrize(
+    "by",
+    [pd.Series([1, 1, 2, 2, 3, 4]), lambda x: x % 2 == 0, pd.Grouper(level=0)],
+)
+@pytest.mark.parametrize(
+    "data", [[1, None, 2, None, 3, None], [1, 2, 3, 4, 5, 6]]
+)
+@pytest.mark.parametrize("args", [{"value": 42}, {"method": "ffill"}])
+def test_groupby_various_by_fillna(by, data, args):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.groupby(by).fillna(**args)
+    if isinstance(by, pd.Grouper):
+        by = cudf.Grouper(level=by.level)
+    got = gs.groupby(by).fillna(**args)
+
+    assert_groupby_results_equal(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("nelem", [10, 100, 1000])
+@pytest.mark.parametrize("method", ["pad", "ffill", "backfill", "bfill"])
+def test_groupby_fillna_method(nelem, method):
+    t = rand_dataframe(
+        dtypes_meta=[
+            {"dtype": "int64", "null_frequency": 0, "cardinality": 10},
+            {"dtype": "int64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "float32", "null_frequency": 0.4, "cardinality": 10},
+            {
+                "dtype": "datetime64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "timedelta64[ns]",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+            },
+            {
+                "dtype": "list",
+                "null_frequency": 0.4,
+                "cardinality": 10,
+                "lists_max_length": 10,
+                "nesting_max_depth": 3,
+                "value_type": "int64",
+            },
+            {"dtype": "category", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "decimal64", "null_frequency": 0.4, "cardinality": 10},
+            {"dtype": "str", "null_frequency": 0.4, "cardinality": 10},
+        ],
+        rows=nelem,
+        use_threads=False,
+        seed=0,
+    )
+    key_col = "0"
+    value_cols = ["1", "2", "3", "4", "5", "6", "7", "8"]
+    pdf = t.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.groupby(key_col).fillna(method=method)
+    with expect_warning_if(method in {"pad", "backfill"}):
+        got = gdf.groupby(key_col).fillna(method=method)
+
+    assert_groupby_results_equal(
+        expect[value_cols], got[value_cols], sort=False
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"Speed": [380.0, 370.0, 24.0, 26.0], "Score": [50, 30, 90, 80]},
+        {
+            "Speed": [380.0, 370.0, 24.0, 26.0],
+            "Score": [50, 30, 90, 80],
+            "Other": [10, 20, 30, 40],
+        },
+    ],
+)
+@pytest.mark.parametrize("group", ["Score", "Speed"])
+def test_groupby_describe(data, group):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    got = gdf.groupby(group).describe()
+    expect = pdf.groupby(group).describe()
+
+    assert_groupby_results_equal(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [], "b": []},
+        {"a": [2, 1, 2, 1, 1, 3], "b": [None, 1, 2, None, 2, None]},
+        {"a": [None], "b": [None]},
+        {"a": [2, 1, 1], "b": [None, 1, 0], "c": [None, 0, 1]},
+    ],
+)
+@pytest.mark.parametrize("agg", ["first", "last", ["first", "last"]])
+def test_groupby_first(data, agg):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.groupby("a").agg(agg)
+    got = gdf.groupby("a").agg(agg)
+    assert_groupby_results_equal(expect, got, check_dtype=False)
+
+
+def test_groupby_apply_series():
+    def foo(x):
+        return x.sum()
+
+    got = make_frame(DataFrame, 100).groupby("x").y.apply(foo)
+    expect = make_frame(pd.DataFrame, 100).groupby("x").y.apply(foo)
+
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize(
+    "func,args",
+    [
+        (lambda x, k: x + k, (42,)),
+        (lambda x, k, L: x + k - L, (42, 191)),
+        (lambda x, k, L, m: (x + k) / (L * m), (42, 191, 99.9)),
+    ],
+)
+def test_groupby_apply_series_args(func, args):
+    got = make_frame(DataFrame, 100).groupby("x").y.apply(func, *args)
+    expect = (
+        make_frame(pd.DataFrame, 100)
+        .groupby("x", group_keys=False)
+        .y.apply(func, *args)
+    )
+
+    assert_groupby_results_equal(expect, got)
+
+
+@pytest.mark.parametrize("label", [None, "left", "right"])
+@pytest.mark.parametrize("closed", [None, "left", "right"])
+def test_groupby_freq_week(label, closed):
+    pdf = pd.DataFrame(
+        {
+            "Publish date": [
+                pd.Timestamp("2000-01-03"),
+                pd.Timestamp("2000-01-01"),
+                pd.Timestamp("2000-01-09"),
+                pd.Timestamp("2000-01-02"),
+                pd.Timestamp("2000-01-07"),
+                pd.Timestamp("2000-01-16"),
+            ],
+            "ID": [0, 1, 2, 3, 4, 5],
+            "Price": [10, 20, 30, 40, 50, 60],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.groupby(
+        pd.Grouper(key="Publish date", freq="1W", label=label, closed=closed)
+    ).mean()
+    got = gdf.groupby(
+        cudf.Grouper(key="Publish date", freq="1W", label=label, closed=closed)
+    ).mean()
+    assert_eq(expect, got, check_like=True, check_dtype=False)
+
+
+@pytest.mark.parametrize("label", [None, "left", "right"])
+@pytest.mark.parametrize("closed", [None, "left", "right"])
+def test_groupby_freq_day(label, closed):
+    pdf = pd.DataFrame(
+        {
+            "Publish date": [
+                pd.Timestamp("2000-01-03"),
+                pd.Timestamp("2000-01-01"),
+                pd.Timestamp("2000-01-09"),
+                pd.Timestamp("2000-01-02"),
+                pd.Timestamp("2000-01-07"),
+                pd.Timestamp("2000-01-16"),
+            ],
+            "ID": [0, 1, 2, 3, 4, 5],
+            "Price": [10, 20, 30, 40, 50, 60],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.groupby(
+        pd.Grouper(key="Publish date", freq="3D", label=label, closed=closed)
+    ).mean()
+    got = gdf.groupby(
+        cudf.Grouper(key="Publish date", freq="3D", label=label, closed=closed)
+    ).mean()
+    assert_eq(expect, got, check_like=True, check_dtype=False)
+
+
+@pytest.mark.parametrize("label", [None, "left", "right"])
+@pytest.mark.parametrize("closed", [None, "left", "right"])
+def test_groupby_freq_min(label, closed):
+    pdf = pd.DataFrame(
+        {
+            "Publish date": [
+                pd.Timestamp("2000-01-01 12:01:00"),
+                pd.Timestamp("2000-01-01 12:05:00"),
+                pd.Timestamp("2000-01-01 15:30:00"),
+                pd.Timestamp("2000-01-02 00:00:00"),
+                pd.Timestamp("2000-01-01 23:47:00"),
+                pd.Timestamp("2000-01-02 00:05:00"),
+            ],
+            "ID": [0, 1, 2, 3, 4, 5],
+            "Price": [10, 20, 30, 40, 50, 60],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.groupby(
+        pd.Grouper(key="Publish date", freq="1h", label=label, closed=closed)
+    ).mean()
+    got = gdf.groupby(
+        cudf.Grouper(key="Publish date", freq="1h", label=label, closed=closed)
+    ).mean()
+    assert_eq(expect, got, check_like=True, check_dtype=False)
+
+
+@pytest.mark.parametrize("label", [None, "left", "right"])
+@pytest.mark.parametrize("closed", [None, "left", "right"])
+def test_groupby_freq_s(label, closed):
+    pdf = pd.DataFrame(
+        {
+            "Publish date": [
+                pd.Timestamp("2000-01-01 00:00:02"),
+                pd.Timestamp("2000-01-01 00:00:07"),
+                pd.Timestamp("2000-01-01 00:00:02"),
+                pd.Timestamp("2000-01-02 00:00:15"),
+                pd.Timestamp("2000-01-01 00:00:05"),
+                pd.Timestamp("2000-01-02 00:00:09"),
+            ],
+            "ID": [0, 1, 2, 3, 4, 5],
+            "Price": [10, 20, 30, 40, 50, 60],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.groupby(
+        pd.Grouper(key="Publish date", freq="3s", label=label, closed=closed)
+    ).mean()
+    got = gdf.groupby(
+        cudf.Grouper(key="Publish date", freq="3s", label=label, closed=closed)
+    ).mean()
+    assert_eq(expect, got, check_like=True, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "pdf, group, name, obj",
+    [
+        (
+            pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]}),
+            "X",
+            "A",
+            None,
+        ),
+        (
+            pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]}),
+            "X",
+            "B",
+            None,
+        ),
+        (
+            pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]}),
+            "X",
+            "A",
+            pd.DataFrame({"a": [1, 2, 4, 5, 10, 11]}),
+        ),
+        (
+            pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]}),
+            "Y",
+            1,
+            pd.DataFrame({"a": [1, 2, 4, 5, 10, 11]}),
+        ),
+        (
+            pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]}),
+            "Y",
+            3,
+            pd.DataFrame({"a": [1, 2, 0, 11]}),
+        ),
+    ],
+)
+def test_groupby_get_group(pdf, group, name, obj):
+    gdf = cudf.from_pandas(pdf)
+
+    if isinstance(obj, pd.DataFrame):
+        gobj = cudf.from_pandas(obj)
+    else:
+        gobj = obj
+
+    expected = pdf.groupby(group).get_group(name=name, obj=obj)
+    actual = gdf.groupby(group).get_group(name=name, obj=gobj)
+
+    assert_groupby_results_equal(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "by",
+    [
+        "a",
+        ["a", "b"],
+        pd.Series([2, 1, 1, 2, 2]),
+        pd.Series(["b", "a", "a", "b", "b"]),
+    ],
+)
+@pytest.mark.parametrize("agg", ["sum", "mean", lambda df: df.mean()])
+def test_groupby_transform_aggregation(by, agg):
+    gdf = cudf.DataFrame(
+        {"a": [2, 2, 1, 2, 1], "b": [1, 1, 1, 2, 2], "c": [1, 2, 3, 4, 5]}
+    )
+    pdf = gdf.to_pandas()
+
+    expected = pdf.groupby(by).transform(agg)
+    actual = gdf.groupby(by).transform(agg)
+
+    assert_groupby_results_equal(expected, actual)
+
+
+def test_groupby_select_then_ffill():
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 2, 2],
+            "b": [1, None, None, 2, None],
+            "c": [3, None, None, 4, None],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.groupby("a")["c"].ffill()
+    actual = gdf.groupby("a")["c"].ffill()
+
+    assert_groupby_results_equal(expected, actual)
+
+
+def test_groupby_select_then_shift():
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 1, 2, 2], "b": [1, 2, 3, 4, 5], "c": [3, 4, 5, 6, 7]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.groupby("a")["c"].shift(1)
+    actual = gdf.groupby("a")["c"].shift(1)
+
+    assert_groupby_results_equal(expected, actual)
+
+
+def test_groupby_select_then_diff():
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 1, 2, 2], "b": [1, 2, 3, 4, 5], "c": [3, 4, 5, 6, 7]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.groupby("a")["c"].diff(1)
+    actual = gdf.groupby("a")["c"].diff(1)
+
+    assert_groupby_results_equal(expected, actual)
+
+
+# TODO: Add a test including datetime64[ms] column in input data
+
+
+@pytest.mark.parametrize("by", ["a", ["a", "b"], pd.Series([1, 2, 1, 3])])
+def test_groupby_transform_maintain_index(by):
+    # test that we maintain the index after a groupby transform
+    gdf = cudf.DataFrame(
+        {"a": [1, 1, 1, 2], "b": [1, 2, 1, 2]}, index=[3, 2, 1, 0]
+    )
+    pdf = gdf.to_pandas()
+    assert_groupby_results_equal(
+        pdf.groupby(by).transform("max"), gdf.groupby(by).transform("max")
+    )
+
+
+@pytest.mark.parametrize(
+    "data, gkey",
+    [
+        (
+            {
+                "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+                "val1": [5, 4, 6, 4, 8, 7, 4, 5, 2],
+                "val2": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+                "val3": [4, 5, 6, 1, 2, 9, 8, 5, 1],
+            },
+            ["id"],
+        ),
+        (
+            {
+                "id": [0, 0, 0, 0, 1, 1, 1],
+                "a": [1, 3, 4, 2.0, -3.0, 9.0, 10.0],
+                "b": [10.0, 23, -4.0, 2, -3.0, None, 19.0],
+            },
+            ["id", "a"],
+        ),
+        (
+            {
+                "id": ["a", "a", "b", "b", "c", "c"],
+                "val1": [None, None, None, None, None, None],
+            },
+            ["id"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("periods", [-5, -2, 0, 2, 5])
+@pytest.mark.parametrize("fill_method", ["ffill", "bfill", "pad", "backfill"])
+def test_groupby_pct_change(data, gkey, periods, fill_method):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    with expect_warning_if(fill_method in ("pad", "backfill")):
+        actual = gdf.groupby(gkey).pct_change(
+            periods=periods, fill_method=fill_method
+        )
+    with expect_warning_if(fill_method in ("pad", "backfill")):
+        expected = pdf.groupby(gkey).pct_change(
+            periods=periods, fill_method=fill_method
+        )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/11259")
+@pytest.mark.parametrize("periods", [-5, 5])
+def test_groupby_pct_change_multiindex_dataframe(periods):
+    gdf = cudf.DataFrame(
+        {
+            "a": [1, 1, 2, 2],
+            "b": [1, 1, 2, 3],
+            "c": [2, 3, 4, 5],
+            "d": [6, 8, 9, 1],
+        }
+    ).set_index(["a", "b"])
+
+    actual = gdf.groupby(level=["a", "b"]).pct_change(periods)
+    expected = gdf.to_pandas().groupby(level=["a", "b"]).pct_change(periods)
+
+    assert_eq(expected, actual)
+
+
+def test_groupby_pct_change_empty_columns():
+    gdf = cudf.DataFrame(columns=["id", "val1", "val2"])
+    pdf = gdf.to_pandas()
+
+    actual = gdf.groupby("id").pct_change()
+    expected = pdf.groupby("id").pct_change()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "group_keys",
+    [
+        None,
+        pytest.param(
+            True,
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/pull/34998",
+            ),
+        ),
+        False,
+    ],
+)
+@pytest.mark.parametrize("by", ["A", ["A", "B"]])
+def test_groupby_group_keys(group_keys, by):
+    gdf = cudf.DataFrame(
+        {
+            "A": "a a a a b b".split(),
+            "B": [1, 1, 2, 2, 3, 3],
+            "C": [4, 6, 5, 9, 8, 7],
+        }
+    )
+    pdf = gdf.to_pandas()
+
+    g_group = gdf.groupby(by, group_keys=group_keys)
+    p_group = pdf.groupby(by, group_keys=group_keys)
+
+    actual = g_group[["B", "C"]].apply(lambda x: x / x.sum())
+    expected = p_group[["B", "C"]].apply(lambda x: x / x.sum())
+    assert_eq(actual, expected)
+
+
+@pytest.fixture
+def df_ngroup():
+    df = cudf.DataFrame(
+        {
+            "a": [2, 2, 1, 1, 2, 3],
+            "b": [1, 2, 1, 2, 1, 2],
+            "c": ["a", "a", "b", "c", "d", "c"],
+        },
+        index=[1, 3, 5, 7, 4, 2],
+    )
+    df.index.name = "foo"
+    return df
+
+
+@pytest.mark.parametrize(
+    "by",
+    [
+        lambda: "a",
+        lambda: "b",
+        lambda: ["a", "b"],
+        lambda: "c",
+        lambda: pd.Series([1, 2, 1, 2, 1, 2]),
+        lambda: pd.Series(["x", "y", "y", "x", "z", "x"]),
+    ],
+)
+@pytest.mark.parametrize("ascending", [True, False])
+def test_groupby_ngroup(by, ascending, df_ngroup):
+    by = by()
+    expected = df_ngroup.to_pandas().groupby(by).ngroup(ascending=ascending)
+    actual = df_ngroup.groupby(by).ngroup(ascending=ascending)
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "groups", ["a", "b", "c", ["a", "c"], ["a", "b", "c"]]
+)
+def test_groupby_dtypes(groups):
+    df = cudf.DataFrame(
+        {"a": [1, 2, 3, 3], "b": ["x", "y", "z", "a"], "c": [10, 11, 12, 12]}
+    )
+    pdf = df.to_pandas()
+
+    assert_eq(pdf.groupby(groups).dtypes, df.groupby(groups).dtypes)
+
+
+@pytest.mark.parametrize("index_names", ["a", "b", "c", ["b", "c"]])
+def test_groupby_by_index_names(index_names):
+    gdf = cudf.DataFrame(
+        {"a": [1, 2, 3, 4], "b": ["a", "b", "a", "a"], "c": [1, 1, 2, 1]}
+    ).set_index(index_names)
+    pdf = gdf.to_pandas()
+
+    assert_groupby_results_equal(
+        pdf.groupby(index_names).min(), gdf.groupby(index_names).min()
+    )
+
+
+@pytest.mark.parametrize(
+    "groups", ["a", "b", "c", ["a", "c"], ["a", "b", "c"]]
+)
+def test_group_by_pandas_compat(groups):
+    with cudf.option_context("mode.pandas_compatible", True):
+        df = cudf.DataFrame(
+            {
+                "a": [1, 3, 2, 3, 3],
+                "b": ["x", "a", "y", "z", "a"],
+                "c": [10, 13, 11, 12, 12],
+            }
+        )
+        pdf = df.to_pandas()
+
+        assert_eq(pdf.groupby(groups).max(), df.groupby(groups).max())
+
+
+class TestSample:
+    @pytest.fixture(params=["default", "rangeindex", "intindex", "strindex"])
+    def index(self, request):
+        n = 12
+        if request.param == "rangeindex":
+            return cudf.RangeIndex(2, n + 2)
+        elif request.param == "intindex":
+            return cudf.Index(
+                [2, 3, 4, 1, 0, 5, 6, 8, 7, 9, 10, 13], dtype="int32"
+            )
+        elif request.param == "strindex":
+            return cudf.Index(list(string.ascii_lowercase[:n]))
+        elif request.param == "default":
+            return None
+
+    @pytest.fixture(
+        params=[
+            ["a", "a", "b", "b", "c", "c", "c", "d", "d", "d", "d", "d"],
+            [1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4],
+        ],
+        ids=["str-group", "int-group"],
+    )
+    def df(self, index, request):
+        return cudf.DataFrame(
+            {"a": request.param, "b": request.param, "v": request.param},
+            index=index,
+        )
+
+    @pytest.fixture(params=["a", ["a", "b"]], ids=["single-col", "two-col"])
+    def by(self, request):
+        return request.param
+
+    def expected(self, df, *, n=None, frac=None):
+        value_counts = collections.Counter(df.a.values_host)
+        if n is not None:
+            values = list(
+                itertools.chain.from_iterable(
+                    itertools.repeat(v, n) for v in value_counts.keys()
+                )
+            )
+        elif frac is not None:
+            values = list(
+                itertools.chain.from_iterable(
+                    itertools.repeat(v, round(count * frac))
+                    for v, count in value_counts.items()
+                )
+            )
+        else:
+            raise ValueError("Must provide either n or frac")
+        values = cudf.Series(sorted(values), dtype=df.a.dtype)
+        return cudf.DataFrame({"a": values, "b": values, "v": values})
+
+    @pytest.mark.parametrize("n", [None, 0, 1, 2])
+    def test_constant_n_no_replace(self, df, by, n):
+        result = df.groupby(by).sample(n=n).sort_values("a")
+        n = 1 if n is None else n
+        assert_eq(self.expected(df, n=n), result.reset_index(drop=True))
+
+    def test_constant_n_no_replace_too_large_raises(self, df):
+        with pytest.raises(ValueError):
+            df.groupby("a").sample(n=3)
+
+    @pytest.mark.parametrize("n", [1, 2, 3])
+    def test_constant_n_replace(self, df, by, n):
+        result = df.groupby(by).sample(n=n, replace=True).sort_values("a")
+        assert_eq(self.expected(df, n=n), result.reset_index(drop=True))
+
+    def test_invalid_arguments(self, df):
+        with pytest.raises(ValueError):
+            df.groupby("a").sample(n=1, frac=0.1)
+
+    def test_not_implemented_arguments(self, df):
+        with pytest.raises(NotImplementedError):
+            # These are valid weights, but we don't implement this yet.
+            df.groupby("a").sample(n=1, weights=[1 / len(df)] * len(df))
+
+    @pytest.mark.parametrize("frac", [0, 1 / 3, 1 / 2, 2 / 3, 1])
+    @pytest.mark.parametrize("replace", [False, True])
+    def test_fraction_rounding(self, df, by, frac, replace):
+        result = (
+            df.groupby(by).sample(frac=frac, replace=replace).sort_values("a")
+        )
+        assert_eq(self.expected(df, frac=frac), result.reset_index(drop=True))
+
+
+class TestHeadTail:
+    @pytest.fixture(params=[-3, -2, -1, 0, 1, 2, 3], ids=lambda n: f"{n=}")
+    def n(self, request):
+        return request.param
+
+    @pytest.fixture(
+        params=[False, True], ids=["no-preserve-order", "preserve-order"]
+    )
+    def preserve_order(self, request):
+        return request.param
+
+    @pytest.fixture
+    def df(self):
+        return cudf.DataFrame(
+            {
+                "a": [1, 0, 1, 2, 2, 1, 3, 2, 3, 3, 3],
+                "b": [0, 1, 2, 4, 3, 5, 6, 7, 9, 8, 10],
+            }
+        )
+
+    @pytest.fixture(params=[True, False], ids=["head", "tail"])
+    def take_head(self, request):
+        return request.param
+
+    @pytest.fixture
+    def expected(self, df, n, take_head, preserve_order):
+        if n == 0:
+            # We'll get an empty dataframe in this case
+            return df._empty_like(keep_index=True)
+        else:
+            if preserve_order:
+                # Should match pandas here
+                g = df.to_pandas().groupby("a")
+                if take_head:
+                    return g.head(n=n)
+                else:
+                    return g.tail(n=n)
+            else:
+                # We groupby "a" which is the first column. This
+                # possibly relies on an implementation detail that for
+                # integer group keys, cudf produces groups in sorted
+                # (ascending) order.
+                keyfunc = operator.itemgetter(0)
+                if take_head or n == 0:
+                    # Head does group[:n] as does tail for n == 0
+                    slicefunc = operator.itemgetter(slice(None, n))
+                else:
+                    # Tail does group[-n:] except when n == 0
+                    slicefunc = operator.itemgetter(
+                        slice(-n, None) if n else slice(0)
+                    )
+                values_to_sort = np.hstack(
+                    [df.values_host, np.arange(len(df)).reshape(-1, 1)]
+                )
+                expect_a, expect_b, index = zip(
+                    *itertools.chain.from_iterable(
+                        slicefunc(list(group))
+                        for _, group in itertools.groupby(
+                            sorted(values_to_sort.tolist(), key=keyfunc),
+                            key=keyfunc,
+                        )
+                    )
+                )
+                return cudf.DataFrame(
+                    {"a": expect_a, "b": expect_b}, index=index
+                )
+
+    def test_head_tail(self, df, n, take_head, expected, preserve_order):
+        if take_head:
+            actual = df.groupby("a").head(n=n, preserve_order=preserve_order)
+        else:
+            actual = df.groupby("a").tail(n=n, preserve_order=preserve_order)
+        assert_eq(actual, expected)
+
+
+def test_head_tail_empty():
+    # GH #13397
+
+    values = [1, 2, 3]
+    pdf = pd.DataFrame({}, index=values)
+    df = cudf.DataFrame({}, index=values)
+
+    expected = pdf.groupby(pd.Series(values)).head()
+    got = df.groupby(cudf.Series(values)).head()
+    assert_eq(expected, got)
+
+    expected = pdf.groupby(pd.Series(values)).tail()
+    got = df.groupby(cudf.Series(values)).tail()
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "groups", ["a", "b", "c", ["a", "c"], ["a", "b", "c"]]
+)
+@pytest.mark.parametrize("sort", [True, False])
+def test_group_by_pandas_sort_order(groups, sort):
+    with cudf.option_context("mode.pandas_compatible", True):
+        df = cudf.DataFrame(
+            {
+                "a": [10, 1, 10, 3, 2, 1, 3, 3],
+                "b": [5, 6, 7, 1, 2, 3, 4, 9],
+                "c": [20, 20, 10, 11, 13, 11, 12, 12],
+            }
+        )
+        pdf = df.to_pandas()
+
+        assert_eq(
+            pdf.groupby(groups, sort=sort).sum(),
+            df.groupby(groups, sort=sort).sum(),
+        )
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["int32", "int64", "float64", "datetime64[ns]", "timedelta64[ns]", "bool"],
+)
+@pytest.mark.parametrize(
+    "reduce_op",
+    [
+        "min",
+        "max",
+        "idxmin",
+        "idxmax",
+        "first",
+        "last",
+    ],
+)
+def test_group_by_empty_reduction(dtype, reduce_op):
+    gdf = cudf.DataFrame({"a": [], "b": [], "c": []}, dtype=dtype)
+    pdf = gdf.to_pandas()
+
+    gg = gdf.groupby("a")["c"]
+    pg = pdf.groupby("a")["c"]
+
+    assert_eq(
+        getattr(gg, reduce_op)(), getattr(pg, reduce_op)(), check_dtype=True
+    )
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["int32", "int64", "float64", "datetime64[ns]", "timedelta64[ns]", "bool"],
+)
+@pytest.mark.parametrize(
+    "apply_op",
+    ["sum", "min", "max", "idxmax"],
+)
+def test_group_by_empty_apply(request, dtype, apply_op):
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(dtype == "datetime64[ns]" and apply_op == "sum"),
+            reason=("sum isn't supported for datetime64[ns]"),
+        )
+    )
+
+    gdf = cudf.DataFrame({"a": [], "b": [], "c": []}, dtype=dtype)
+    pdf = gdf.to_pandas()
+
+    gg = gdf.groupby("a")["c"]
+    pg = pdf.groupby("a")["c"]
+
+    assert_eq(
+        gg.apply(apply_op),
+        pg.apply(apply_op),
+        check_dtype=True,
+        check_index_type=True,
+    )
+
+
+def test_groupby_consecutive_operations():
+    df = cudf.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
+    pdf = df.to_pandas()
+
+    gg = df.groupby("A")
+    pg = pdf.groupby("A")
+
+    actual = gg.nth(-1)
+    expected = pg.nth(-1)
+
+    assert_groupby_results_equal(actual, expected, check_dtype=False)
+
+    actual = gg.nth(0)
+    expected = pg.nth(0)
+
+    assert_groupby_results_equal(actual, expected, check_dtype=False)
+
+    actual = gg.cumsum()
+    expected = pg.cumsum()
+
+    assert_groupby_results_equal(actual, expected, check_dtype=False)
+
+    actual = gg.cumcount()
+    expected = pg.cumcount()
+
+    assert_groupby_results_equal(actual, expected, check_dtype=False)
+
+    actual = gg.cumsum()
+    expected = pg.cumsum()
+
+    assert_groupby_results_equal(actual, expected, check_dtype=False)
+
+
+def test_categorical_grouping_pandas_compatibility():
+    gdf = cudf.DataFrame(
+        {
+            "key": cudf.Series([2, 1, 3, 1, 1], dtype="category"),
+            "a": [0, 1, 3, 2, 3],
+        }
+    )
+    pdf = gdf.to_pandas()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        actual = gdf.groupby("key", sort=False).sum()
+    expected = pdf.groupby("key", sort=False).sum()
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("as_index", [True, False])
+def test_group_by_value_counts(normalize, sort, ascending, dropna, as_index):
+    # From Issue#12789
+    df = cudf.DataFrame(
+        {
+            "gender": ["male", "male", "female", "male", "female", "male"],
+            "education": ["low", "medium", np.nan, "low", "high", "low"],
+            "country": ["US", "FR", "US", "FR", "FR", "FR"],
+        }
+    )
+    pdf = df.to_pandas()
+
+    actual = df.groupby("gender", as_index=as_index).value_counts(
+        normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+    )
+    expected = pdf.groupby("gender", as_index=as_index).value_counts(
+        normalize=normalize, sort=sort, ascending=ascending, dropna=dropna
+    )
+
+    # TODO: Remove `check_names=False` once testing against `pandas>=2.0.0`
+    assert_groupby_results_equal(
+        actual, expected, check_names=False, check_index_type=False
+    )
+
+
+def test_group_by_value_counts_subset():
+    # From Issue#12789
+    df = cudf.DataFrame(
+        {
+            "gender": ["male", "male", "female", "male", "female", "male"],
+            "education": ["low", "medium", "high", "low", "high", "low"],
+            "country": ["US", "FR", "US", "FR", "FR", "FR"],
+        }
+    )
+    pdf = df.to_pandas()
+
+    actual = df.groupby("gender").value_counts(["education"])
+    expected = pdf.groupby("gender").value_counts(["education"])
+
+    # TODO: Remove `check_names=False` once testing against `pandas>=2.0.0`
+    assert_groupby_results_equal(
+        actual, expected, check_names=False, check_index_type=False
+    )
+
+
+def test_group_by_value_counts_clash_with_subset():
+    df = cudf.DataFrame({"a": [1, 5, 3], "b": [2, 5, 2]})
+    with pytest.raises(ValueError):
+        df.groupby("a").value_counts(["a"])
+
+
+def test_group_by_value_counts_subset_not_exists():
+    df = cudf.DataFrame({"a": [1, 5, 3], "b": [2, 5, 2]})
+    with pytest.raises(ValueError):
+        df.groupby("a").value_counts(["c"])
+
+
+def test_group_by_value_counts_with_count_column():
+    df = cudf.DataFrame({"a": [1, 5, 3], "count": [2, 5, 2]})
+    with pytest.raises(ValueError):
+        df.groupby("a", as_index=False).value_counts()
diff --git a/python/cudf/cudf/tests/test_hash_vocab.py b/python/cudf/cudf/tests/test_hash_vocab.py
new file mode 100644
index 0000000..e081119
--- /dev/null
+++ b/python/cudf/cudf/tests/test_hash_vocab.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+import filecmp
+import os
+import warnings
+
+import pytest
+
+from cudf.utils.hash_vocab_utils import hash_vocab
+
+
+@pytest.fixture(scope="module")
+def datadir(datadir):
+    return os.path.join(
+        datadir, "subword_tokenizer_data", "bert_base_cased_sampled"
+    )
+
+
+def test_correct_bert_base_vocab_hash(datadir, tmpdir):
+    # The vocabulary is drawn from bert-base-cased
+    vocab_path = os.path.join(datadir, "vocab.txt")
+
+    groundtruth_path = os.path.join(datadir, "vocab-hash.txt")
+    output_path = tmpdir.join("cudf-vocab-hash.txt")
+    with warnings.catch_warnings():
+        # See https://github.com/rapidsai/cudf/issues/12403
+        warnings.simplefilter(action="ignore", category=RuntimeWarning)
+        hash_vocab(vocab_path, output_path)
+
+    assert filecmp.cmp(output_path, groundtruth_path, shallow=False)
diff --git a/python/cudf/cudf/tests/test_hdf.py b/python/cudf/cudf/tests/test_hdf.py
new file mode 100644
index 0000000..71c9485
--- /dev/null
+++ b/python/cudf/cudf/tests/test_hdf.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import os
+from string import ascii_letters
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import DATETIME_TYPES, NUMERIC_TYPES, assert_eq
+
+try:
+    import tables  # noqa F401
+except ImportError:
+    pytest.skip(
+        "PyTables is not installed and is required for HDF reading/writing",
+        allow_module_level=True,
+    )
+
+
+@pytest.fixture(params=[0, 1, 10, 100])
+def pdf(request):
+    types = NUMERIC_TYPES + DATETIME_TYPES + ["bool"]
+    typer = {"col_" + val: val for val in types}
+    ncols = len(types)
+    nrows = request.param
+
+    # Create a pandas dataframe with random data of mixed types
+    test_pdf = pd.DataFrame(
+        [list(range(ncols * i, ncols * (i + 1))) for i in range(nrows)],
+        columns=pd.Index([f"col_{typ}" for typ in types], name="foo"),
+    )
+    # Delete the name of the column index, and rename the row index
+    test_pdf.columns.name = None
+    test_pdf.index.name = "test_index"
+
+    # Cast all the column dtypes to objects, rename them, and then cast to
+    # appropriate types
+    test_pdf = (
+        test_pdf.astype("object")
+        .astype(typer)
+        .rename({"col_datetime64[ms]": "col_datetime64"}, axis=1)
+    )
+
+    # Create non-numeric categorical data otherwise may be typecasted
+    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    test_pdf["col_category"] = pd.Series(data, dtype="category")
+
+    return (test_pdf, nrows)
+
+
+@pytest.fixture
+def gdf(pdf):
+    pdf, nrows = pdf
+    return (cudf.DataFrame.from_pandas(pdf), nrows)
+
+
+@pytest.fixture(params=["fixed", "table"])
+def hdf_files(request, tmp_path_factory, pdf):
+    pdf, nrows = pdf
+    if request.param == "fixed":
+        pdf = pdf.drop("col_category", axis=1)
+
+    fname_df = tmp_path_factory.mktemp("hdf") / "test_df.hdf"
+    pdf.to_hdf(fname_df, "hdf_df_tests", format=request.param)
+
+    fname_series = {}
+    for column in pdf.columns:
+        fname_series[column] = (
+            tmp_path_factory.mktemp("hdf") / "test_series.hdf"
+        )
+        pdf[column].to_hdf(
+            fname_series[column], "hdf_series_tests", format=request.param
+        )
+    return (fname_df, fname_series, request.param, nrows)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.filterwarnings("ignore:Strings are not yet supported")
+@pytest.mark.parametrize(
+    "columns",
+    [["col_int8"], ["col_category"], ["col_int32", "col_float32"], None],
+)
+def test_hdf_reader(hdf_files, columns):
+    hdf_df_file, hdf_series, format, nrows = hdf_files
+    if format == "fixed" and columns is not None:
+        pytest.skip("Can't use columns with format 'fixed'")
+    if format == "table" and nrows == 0:
+        pytest.skip("Can't read 0 row table with format 'table'")
+    expect_df = pd.read_hdf(hdf_df_file, columns=columns)
+    got_df = cudf.read_hdf(hdf_df_file, columns=columns)
+
+    assert_eq(
+        expect_df, got_df, check_categorical=False, check_index_type=False
+    )
+
+    for column in hdf_series.keys():
+
+        expect_series = pd.read_hdf(hdf_series[column])
+        got_series = cudf.read_hdf(hdf_series[column])
+
+        assert_eq(expect_series, got_series, check_index_type=False)
+
+
+@pytest.mark.parametrize("format", ["fixed", "table"])
+@pytest.mark.parametrize("complib", ["zlib", "bzip2", "lzo", "blosc"])
+@pytest.mark.filterwarnings("ignore:Using CPU")
+def test_hdf_writer(tmpdir, pdf, gdf, complib, format):
+    pdf, nrows = pdf
+    gdf, _ = gdf
+
+    if format == "fixed":
+        pdf = pdf.drop("col_category", axis=1)
+        gdf = gdf.drop("col_category", axis=1)
+
+    pdf_df_fname = tmpdir.join("pdf_df.hdf")
+    gdf_df_fname = tmpdir.join("gdf_df.hdf")
+
+    pdf.to_hdf(pdf_df_fname, "hdf_tests", format=format, complib=complib)
+    gdf.to_hdf(gdf_df_fname, "hdf_tests", format=format, complib=complib)
+
+    assert os.path.exists(pdf_df_fname)
+    assert os.path.exists(gdf_df_fname)
+
+    if format == "table" and nrows == 0:
+        pytest.skip("Can't read 0 row table with format 'table'")
+
+    expect = pd.read_hdf(pdf_df_fname)
+    got = pd.read_hdf(gdf_df_fname)
+
+    assert_eq(expect, got, check_index_type=False)
+
+    for column in pdf.columns:
+        pdf_series_fname = tmpdir.join(column + "_" + "pdf_series.hdf")
+        gdf_series_fname = tmpdir.join(column + "_" + "gdf_series.hdf")
+
+        pdf[column].to_hdf(
+            pdf_series_fname, "hdf_tests", format=format, complib=complib
+        )
+        gdf[column].to_hdf(
+            gdf_series_fname, "hdf_tests", format=format, complib=complib
+        )
+
+        assert os.path.exists(pdf_series_fname)
+        assert os.path.exists(gdf_series_fname)
+
+        expect_series = pd.read_hdf(pdf_series_fname)
+        got_series = pd.read_hdf(gdf_series_fname)
+
+        assert_eq(expect_series, got_series, check_index_type=False)
diff --git a/python/cudf/cudf/tests/test_hdfs.py b/python/cudf/cudf/tests/test_hdfs.py
new file mode 100644
index 0000000..f8de16f
--- /dev/null
+++ b/python/cudf/cudf/tests/test_hdfs.py
@@ -0,0 +1,300 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import os
+from io import BytesIO
+
+import fastavro
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+if not os.environ.get("RUN_HDFS_TESTS"):
+    pytestmark = pytest.mark.skip("Env not configured to run HDFS tests")
+
+
+basedir = "/tmp/test-hdfs"
+host = "localhost"  # hadoop hostname
+port = 9000  # hadoop rpc port
+
+
+@pytest.fixture
+def hdfs(scope="module"):
+    # Default Rpc port can be 8020/9000 depending on the hdfs config
+    fs = pa.hdfs.connect(host=host, port=port)
+    try:
+        if not fs.exists(basedir):
+            fs.mkdir(basedir)
+    except pa.lib.ArrowIOError:
+        pytest.skip("hdfs config probably incorrect")
+
+    return fs
+
+
+@pytest.fixture
+def pdf(scope="module"):
+    df = pd.DataFrame()
+    df["Integer"] = np.array([2345, 11987, 9027, 9027])
+    df["Float"] = np.array([9.001, 8.343, 6, 2.781])
+    df["Integer2"] = np.array([2345, 106, 2088, 789277], dtype="uint64")
+    df["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    df["Boolean"] = np.array([True, False, True, False])
+    return df
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_read_csv(tmpdir, pdf, hdfs, test_url):
+    fname = tmpdir.mkdir("csv").join("file.csv")
+    # Write to local file system
+    pdf.to_csv(fname)
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/test_csv_reader.csv", buffer)
+
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_csv_reader.csv".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_csv_reader.csv"
+
+    got = cudf.read_csv(hd_fpath)
+
+    # Read pandas from byte buffer
+    with hdfs.open(basedir + "/test_csv_reader.csv") as f:
+        expect = pd.read_csv(f)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_write_csv(pdf, hdfs, test_url):
+    gdf = cudf.from_pandas(pdf)
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_csv_writer.csv".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_csv_writer.csv"
+
+    gdf.to_csv(hd_fpath, index=False)
+
+    assert hdfs.exists(f"{basedir}/test_csv_writer.csv")
+    with hdfs.open(f"{basedir}/test_csv_writer.csv", mode="rb") as f:
+        got = pd.read_csv(f, dtype=dict(pdf.dtypes))
+    assert_eq(pdf, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_read_parquet(tmpdir, pdf, hdfs, test_url):
+    fname = tmpdir.mkdir("parquet").join("test_parquet_reader.parquet")
+    # Write to local file system
+    pdf.to_parquet(fname)
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/test_parquet_reader.parquet", buffer)
+
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_parquet_reader.parquet".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_parquet_reader.parquet"
+
+    got = cudf.read_parquet(hd_fpath)
+
+    # Read pandas from byte buffer
+    with hdfs.open(basedir + "/test_parquet_reader.parquet") as f:
+        expect = pd.read_parquet(f)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_write_parquet(pdf, hdfs, test_url):
+    gdf = cudf.from_pandas(pdf)
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_parquet_writer.parquet".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_parquet_writer.parquet"
+
+    gdf.to_parquet(hd_fpath)
+
+    assert hdfs.exists(f"{basedir}/test_parquet_writer.parquet")
+    with hdfs.open(f"{basedir}/test_parquet_writer.parquet", mode="rb") as f:
+        got = pd.read_parquet(f)
+
+    assert_eq(pdf, got)
+
+
+@pytest.mark.xfail(
+    reason="Writing string columns with parition_cols is incorrect"
+)
+@pytest.mark.parametrize("test_url", [False, True])
+def test_write_parquet_partitioned(tmpdir, pdf, hdfs, test_url):
+    pdf.to_parquet(
+        path=tmpdir.join("pandas_parquet_writer_partitioned.parquet"),
+        index=False,
+        partition_cols=["Integer", "Boolean"],
+    )
+    gdf = cudf.from_pandas(pdf)
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_parquet_partitioned.parquet".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_parquet_partitioned.parquet"
+    # Clear data written from previous runs
+    hdfs.rm(f"{basedir}/test_parquet_partitioned.parquet", recursive=True)
+    gdf.to_parquet(
+        hd_fpath, index=False, partition_cols=["Integer", "Boolean"]
+    )
+
+    assert hdfs.exists(f"{basedir}/test_parquet_partitioned.parquet")
+    got = pd.read_parquet(hd_fpath)
+    expect = pd.read_parquet(
+        tmpdir.join("pandas_parquet_writer_partitioned.parquet")
+    )
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_read_json(tmpdir, pdf, hdfs, test_url):
+    fname = tmpdir.mkdir("json").join("test_json_reader.json")
+    # Write to local file system
+    # Sorting by col_name now as pandas sorts by col name while reading json
+
+    pdf.sort_index(axis=1).to_json(fname, orient="records", lines=True)
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/test_json_reader.json", buffer)
+
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_json_reader.json".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_json_reader.json"
+
+    got = cudf.read_json(hd_fpath, engine="cudf", orient="records", lines=True)
+
+    # Read pandas from byte buffer
+    with hdfs.open(basedir + "/test_json_reader.json") as f:
+        expect = pd.read_json(f, lines=True)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_read_orc(datadir, hdfs, test_url):
+    fname = datadir / "orc" / "TestOrcFile.testSnappy.orc"
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/file.orc", buffer)
+
+    if test_url:
+        hd_fpath = f"hdfs://{host}:{port}{basedir}/file.orc"
+    else:
+        hd_fpath = f"hdfs://{basedir}/file.orc"
+
+    got = cudf.read_orc(hd_fpath)
+    expect = pd.read_orc(buffer)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_write_orc(pdf, hdfs, test_url):
+    # Orc writer doesn't support writing unsigned ints
+    pdf["Integer2"] = pdf["Integer2"].astype("int64")
+    gdf = cudf.from_pandas(pdf)
+    if test_url:
+        hd_fpath = "hdfs://{}:{}{}/test_orc_writer.orc".format(
+            host, port, basedir
+        )
+    else:
+        hd_fpath = f"hdfs://{basedir}/test_orc_writer.orc"
+
+    gdf.to_orc(hd_fpath)
+
+    assert hdfs.exists(f"{basedir}/test_orc_writer.orc")
+    with hdfs.open(f"{basedir}/test_orc_writer.orc", mode="rb") as f:
+        got = pd.read_orc(f)
+
+    assert_eq(pdf, got)
+
+
+@pytest.mark.parametrize("test_url", [False, True])
+def test_read_avro(datadir, hdfs, test_url):
+    fname = datadir / "avro" / "example.avro"
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/file.avro", buffer)
+
+    if test_url:
+        hd_fpath = f"hdfs://{host}:{port}{basedir}/file.avro"
+    else:
+        hd_fpath = f"hdfs://{basedir}/file.avro"
+
+    got = cudf.read_avro(hd_fpath)
+    with open(fname, mode="rb") as f:
+        expect = pd.DataFrame.from_records(fastavro.reader(f))
+
+    for col in expect.columns:
+        expect[col] = expect[col].astype(got[col].dtype)
+    assert_eq(expect, got)
+
+
+def test_storage_options(tmpdir, pdf, hdfs):
+    fname = tmpdir.mkdir("csv").join("file.csv")
+    # Write to local file system
+    pdf.to_csv(fname)
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/file.csv", buffer)
+
+    hd_fpath = f"hdfs://{basedir}/file.csv"
+
+    storage_options = {"host": host, "port": port}
+
+    got = cudf.read_csv(hd_fpath, storage_options=storage_options)
+
+    # Read pandas from byte buffer
+    with hdfs.open(basedir + "/file.csv") as f:
+        expect = pd.read_csv(f)
+
+    assert_eq(expect, got)
+
+
+def test_storage_options_error(tmpdir, pdf, hdfs):
+    fname = tmpdir.mkdir("csv").join("file.csv")
+    # Write to local file system
+    pdf.to_csv(fname)
+    # Read from local file system as buffer
+    with open(fname, mode="rb") as f:
+        buffer = BytesIO(f.read())
+    # Write to hdfs
+    hdfs.upload(basedir + "/file.csv", buffer)
+
+    hd_fpath = f"hdfs://{host}:{port}{basedir}/file.avro"
+
+    storage_options = {"host": host, "port": port}
+
+    with pytest.raises(KeyError):
+        cudf.read_csv(hd_fpath, storage_options=storage_options)
diff --git a/python/cudf/cudf/tests/test_index.py b/python/cudf/cudf/tests/test_index.py
new file mode 100644
index 0000000..087b93f
--- /dev/null
+++ b/python/cudf/cudf/tests/test_index.py
@@ -0,0 +1,2951 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+"""
+Test related to Index
+"""
+import operator
+import re
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.api.extensions import no_default
+from cudf.api.types import is_bool_dtype
+from cudf.core._compat import PANDAS_GE_133, PANDAS_GE_200
+from cudf.core.index import (
+    CategoricalIndex,
+    DatetimeIndex,
+    GenericIndex,
+    RangeIndex,
+    as_index,
+)
+from cudf.testing._utils import (
+    ALL_TYPES,
+    FLOAT_TYPES,
+    NUMERIC_TYPES,
+    OTHER_TYPES,
+    SERIES_OR_INDEX_NAMES,
+    SIGNED_INTEGER_TYPES,
+    SIGNED_TYPES,
+    UNSIGNED_TYPES,
+    _create_cudf_series_float64_default,
+    _create_pandas_series_float64_default,
+    assert_column_memory_eq,
+    assert_column_memory_ne,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+from cudf.utils.utils import search_range
+
+
+def test_df_set_index_from_series():
+    df = cudf.DataFrame()
+    df["a"] = list(range(10))
+    df["b"] = list(range(0, 20, 2))
+
+    # Check set_index(Series)
+    df2 = df.set_index(df["b"])
+    assert list(df2.columns) == ["a", "b"]
+    sliced_strided = df2.loc[2:6]
+    assert len(sliced_strided) == 3
+    assert list(sliced_strided.index.values) == [2, 4, 6]
+
+
+def test_df_set_index_from_name():
+    df = cudf.DataFrame()
+    df["a"] = list(range(10))
+    df["b"] = list(range(0, 20, 2))
+
+    # Check set_index(column_name)
+    df2 = df.set_index("b")
+    # 1 less column because 'b' is used as index
+    assert list(df2.columns) == ["a"]
+    sliced_strided = df2.loc[2:6]
+    assert len(sliced_strided) == 3
+    assert list(sliced_strided.index.values) == [2, 4, 6]
+
+
+def test_df_slice_empty_index():
+    df = cudf.DataFrame()
+    assert isinstance(df.index, RangeIndex)
+    assert isinstance(df.index[:1], RangeIndex)
+    with pytest.raises(IndexError):
+        df.index[1]
+
+
+def test_index_find_label_range_genericindex():
+    # Monotonic Index
+    idx = cudf.Index(np.asarray([4, 5, 6, 10]))
+    assert idx.find_label_range(slice(4, 6)) == slice(0, 3, 1)
+    assert idx.find_label_range(slice(5, 10)) == slice(1, 4, 1)
+    assert idx.find_label_range(slice(0, 6)) == slice(0, 3, 1)
+    assert idx.find_label_range(slice(4, 11)) == slice(0, 4, 1)
+
+    # Non-monotonic Index
+    idx_nm = cudf.Index(np.asarray([5, 4, 6, 10]))
+    assert idx_nm.find_label_range(slice(4, 6)) == slice(1, 3, 1)
+    assert idx_nm.find_label_range(slice(5, 10)) == slice(0, 4, 1)
+    # Last value not found
+    with pytest.raises(KeyError) as raises:
+        idx_nm.find_label_range(slice(0, 6))
+    raises.match("not in index")
+    # Last value not found
+    with pytest.raises(KeyError) as raises:
+        idx_nm.find_label_range(slice(4, 11))
+    raises.match("not in index")
+
+
+def test_index_find_label_range_rangeindex():
+    """Cudf specific"""
+    # step > 0
+    # 3, 8, 13, 18
+    ridx = RangeIndex(3, 20, 5)
+    assert ridx.find_label_range(slice(3, 8)) == slice(0, 2, 1)
+    assert ridx.find_label_range(slice(0, 7)) == slice(0, 1, 1)
+    assert ridx.find_label_range(slice(3, 19)) == slice(0, 4, 1)
+    assert ridx.find_label_range(slice(2, 21)) == slice(0, 4, 1)
+
+    # step < 0
+    # 20, 15, 10, 5
+    ridx = RangeIndex(20, 3, -5)
+    assert ridx.find_label_range(slice(15, 10)) == slice(1, 3, 1)
+    assert ridx.find_label_range(slice(10, 15, -1)) == slice(2, 0, -1)
+    assert ridx.find_label_range(slice(10, 0)) == slice(2, 4, 1)
+    assert ridx.find_label_range(slice(30, 13)) == slice(0, 2, 1)
+    assert ridx.find_label_range(slice(30, 0)) == slice(0, 4, 1)
+
+
+def test_index_comparision():
+    start, stop = 10, 34
+    rg = cudf.RangeIndex(start, stop)
+    gi = cudf.Index(np.arange(start, stop))
+    assert rg.equals(gi)
+    assert gi.equals(rg)
+    assert not rg[:-1].equals(gi)
+    assert rg[:-1].equals(gi[:-1])
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: x.min(),
+        lambda x: x.max(),
+        lambda x: x.sum(),
+        lambda x: x.mean(),
+        lambda x: x.any(),
+        lambda x: x.all(),
+        lambda x: x.prod(),
+    ],
+)
+def test_reductions(func):
+    x = np.asarray([4, 5, 6, 10])
+    idx = cudf.Index(np.asarray([4, 5, 6, 10]))
+
+    assert func(x) == func(idx)
+
+
+def test_name():
+    idx = cudf.Index(np.asarray([4, 5, 6, 10]), name="foo")
+    assert idx.name == "foo"
+
+
+def test_index_immutable():
+    start, stop = 10, 34
+    rg = RangeIndex(start, stop)
+    with pytest.raises(TypeError):
+        rg[1] = 5
+    gi = cudf.Index(np.arange(start, stop))
+    with pytest.raises(TypeError):
+        gi[1] = 5
+
+
+def test_categorical_index():
+    pdf = pd.DataFrame()
+    pdf["a"] = [1, 2, 3]
+    pdf["index"] = pd.Categorical(["a", "b", "c"])
+    initial_df = cudf.from_pandas(pdf)
+    pdf = pdf.set_index("index")
+    gdf1 = cudf.from_pandas(pdf)
+    gdf2 = cudf.DataFrame()
+    gdf2["a"] = [1, 2, 3]
+    gdf2["index"] = pd.Categorical(["a", "b", "c"])
+    assert_eq(initial_df.index, gdf2.index)
+    gdf2 = gdf2.set_index("index")
+
+    assert isinstance(gdf1.index, CategoricalIndex)
+    assert_eq(pdf, gdf1)
+    assert_eq(pdf.index, gdf1.index)
+    assert_eq(
+        pdf.index.codes,
+        gdf1.index.codes.astype(pdf.index.codes.dtype).to_numpy(),
+    )
+
+    assert isinstance(gdf2.index, CategoricalIndex)
+    assert_eq(pdf, gdf2)
+    assert_eq(pdf.index, gdf2.index)
+    assert_eq(
+        pdf.index.codes,
+        gdf2.index.codes.astype(pdf.index.codes.dtype).to_numpy(),
+    )
+
+
+def test_pandas_as_index():
+    # Define Pandas Indexes
+    pdf_int_index = pd.Index([1, 2, 3, 4, 5])
+    pdf_uint_index = pd.Index([1, 2, 3, 4, 5])
+    pdf_float_index = pd.Index([1.0, 2.0, 3.0, 4.0, 5.0])
+    pdf_datetime_index = pd.DatetimeIndex(
+        [1000000, 2000000, 3000000, 4000000, 5000000]
+    )
+    pdf_category_index = pd.CategoricalIndex(["a", "b", "c", "b", "a"])
+
+    # Define cudf Indexes
+    gdf_int_index = as_index(pdf_int_index)
+    gdf_uint_index = as_index(pdf_uint_index)
+    gdf_float_index = as_index(pdf_float_index)
+    gdf_datetime_index = as_index(pdf_datetime_index)
+    gdf_category_index = as_index(pdf_category_index)
+
+    # Check instance types
+    assert isinstance(gdf_int_index, GenericIndex)
+    assert isinstance(gdf_uint_index, GenericIndex)
+    assert isinstance(gdf_float_index, GenericIndex)
+    assert isinstance(gdf_datetime_index, DatetimeIndex)
+    assert isinstance(gdf_category_index, CategoricalIndex)
+
+    # Check equality
+    assert_eq(pdf_int_index, gdf_int_index)
+    assert_eq(pdf_uint_index, gdf_uint_index)
+    assert_eq(pdf_float_index, gdf_float_index)
+    assert_eq(pdf_datetime_index, gdf_datetime_index)
+    assert_eq(pdf_category_index, gdf_category_index)
+
+    assert_eq(
+        pdf_category_index.codes,
+        gdf_category_index.codes.astype(
+            pdf_category_index.codes.dtype
+        ).to_numpy(),
+    )
+
+
+@pytest.mark.parametrize("initial_name", SERIES_OR_INDEX_NAMES)
+@pytest.mark.parametrize("name", SERIES_OR_INDEX_NAMES)
+def test_index_rename(initial_name, name):
+    pds = pd.Index([1, 2, 3], name=initial_name)
+    gds = as_index(pds)
+
+    assert_eq(pds, gds)
+
+    expect = pds.rename(name)
+    got = gds.rename(name)
+
+    assert_eq(expect, got)
+    """
+    From here on testing recursive creation
+    and if name is being handles in recursive creation.
+    """
+    pds = pd.Index(expect)
+    gds = as_index(got)
+
+    assert_eq(pds, gds)
+
+    pds = pd.Index(pds, name="abc")
+    gds = as_index(gds, name="abc")
+    assert_eq(pds, gds)
+
+
+def test_index_rename_inplace():
+    pds = pd.Index([1, 2, 3], name="asdf")
+    gds = as_index(pds)
+
+    # inplace=False should yield a deep copy
+    gds_renamed_deep = gds.rename("new_name", inplace=False)
+
+    assert gds_renamed_deep._values.data_ptr != gds._values.data_ptr
+
+    # inplace=True returns none
+    expected_ptr = gds._values.data_ptr
+    gds.rename("new_name", inplace=True)
+
+    assert expected_ptr == gds._values.data_ptr
+
+
+def test_index_rename_preserves_arg():
+    idx1 = cudf.Index([1, 2, 3], name="orig_name")
+
+    # this should be an entirely new object
+    idx2 = idx1.rename("new_name", inplace=False)
+
+    assert idx2.name == "new_name"
+    assert idx1.name == "orig_name"
+
+    # a new object but referencing the same data
+    idx3 = as_index(idx1, name="last_name")
+
+    assert idx3.name == "last_name"
+    assert idx1.name == "orig_name"
+
+
+def test_set_index_as_property():
+    cdf = cudf.DataFrame()
+    col1 = np.arange(10)
+    col2 = np.arange(0, 20, 2)
+    cdf["a"] = col1
+    cdf["b"] = col2
+
+    # Check set_index(Series)
+    cdf.index = cdf["b"]
+
+    assert_eq(cdf.index.to_numpy(), col2)
+
+    with pytest.raises(ValueError):
+        cdf.index = [list(range(10))]
+
+    idx = pd.Index(np.arange(0, 1000, 100))
+    cdf.index = idx
+    assert_eq(cdf.index.to_pandas(), idx)
+
+    df = cdf.to_pandas()
+    assert_eq(df.index, idx)
+
+    head = cdf.head().to_pandas()
+    assert_eq(head.index, idx[:5])
+
+
+@pytest.mark.parametrize("name", ["x"])
+@pytest.mark.parametrize("dtype", SIGNED_INTEGER_TYPES)
+def test_index_copy_range(name, dtype, deep=True):
+    cidx = cudf.RangeIndex(1, 5)
+    pidx = cidx.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
+    with pytest.warns(FutureWarning):
+        cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
+
+    assert_eq(pidx_copy, cidx_copy)
+
+
+@pytest.mark.parametrize("name", ["x"])
+@pytest.mark.parametrize("dtype,", ["datetime64[ns]", "int64"])
+def test_index_copy_datetime(name, dtype, deep=True):
+    cidx = cudf.DatetimeIndex(["2001", "2002", "2003"])
+    pidx = cidx.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
+    with pytest.warns(FutureWarning):
+        cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
+
+    assert_eq(pidx_copy, cidx_copy)
+
+
+@pytest.mark.parametrize("name", ["x"])
+@pytest.mark.parametrize("dtype", ["category", "object"])
+def test_index_copy_string(name, dtype, deep=True):
+    cidx = cudf.Index(["a", "b", "c"])
+    pidx = cidx.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
+    with pytest.warns(FutureWarning):
+        cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
+
+    assert_eq(pidx_copy, cidx_copy)
+
+
+@pytest.mark.parametrize("name", ["x"])
+@pytest.mark.parametrize(
+    "dtype",
+    NUMERIC_TYPES + ["datetime64[ns]", "timedelta64[ns]"] + OTHER_TYPES,
+)
+def test_index_copy_integer(name, dtype, deep=True):
+    """Test for NumericIndex Copy Casts"""
+    cidx = cudf.Index([1, 2, 3])
+    pidx = cidx.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
+    with pytest.warns(FutureWarning):
+        cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
+
+    assert_eq(pidx_copy, cidx_copy)
+
+
+@pytest.mark.parametrize("name", ["x"])
+@pytest.mark.parametrize("dtype", SIGNED_TYPES)
+def test_index_copy_float(name, dtype, deep=True):
+    """Test for NumericIndex Copy Casts"""
+    cidx = cudf.Index([1.0, 2.0, 3.0])
+    pidx = cidx.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
+    with pytest.warns(FutureWarning):
+        cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
+
+    assert_eq(pidx_copy, cidx_copy)
+
+
+@pytest.mark.parametrize("name", ["x"])
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["category"])
+def test_index_copy_category(name, dtype, deep=True):
+    cidx = cudf.core.index.CategoricalIndex([1, 2, 3])
+    pidx = cidx.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        pidx_copy = pidx.copy(name=name, deep=deep, dtype=dtype)
+    with pytest.warns(FutureWarning):
+        cidx_copy = cidx.copy(name=name, deep=deep, dtype=dtype)
+
+    assert_column_memory_ne(cidx._values, cidx_copy._values)
+    assert_eq(pidx_copy, cidx_copy)
+
+
+@pytest.mark.parametrize("deep", [True, False])
+@pytest.mark.parametrize(
+    "idx",
+    [
+        cudf.DatetimeIndex(["2001", "2002", "2003"]),
+        cudf.Index(["a", "b", "c"]),
+        cudf.Index([1, 2, 3]),
+        cudf.Index([1.0, 2.0, 3.0]),
+        cudf.CategoricalIndex([1, 2, 3]),
+        cudf.CategoricalIndex(["a", "b", "c"]),
+    ],
+)
+@pytest.mark.parametrize("copy_on_write", [True, False])
+def test_index_copy_deep(idx, deep, copy_on_write):
+    """Test if deep copy creates a new instance for device data."""
+    idx_copy = idx.copy(deep=deep)
+    original_cow_setting = cudf.get_option("copy_on_write")
+    cudf.set_option("copy_on_write", copy_on_write)
+    if (
+        isinstance(idx, cudf.StringIndex)
+        or not deep
+        or (cudf.get_option("copy_on_write") and not deep)
+    ):
+        # StringColumn is immutable hence, deep copies of a
+        # StringIndex will share the same StringColumn.
+
+        # When `copy_on_write` is turned on, Index objects will
+        # have unique column object but they all point to same
+        # data pointers.
+        assert_column_memory_eq(idx._values, idx_copy._values)
+    else:
+        assert_column_memory_ne(idx._values, idx_copy._values)
+    cudf.set_option("copy_on_write", original_cow_setting)
+
+
+@pytest.mark.parametrize("idx", [[1, None, 3, None, 5]])
+def test_index_isna(idx):
+    pidx = pd.Index(idx, name="idx")
+    gidx = cudf.Index(idx, name="idx")
+    assert_eq(gidx.isna(), pidx.isna())
+
+
+@pytest.mark.parametrize("idx", [[1, None, 3, None, 5]])
+def test_index_notna(idx):
+    pidx = pd.Index(idx, name="idx")
+    gidx = cudf.Index(idx, name="idx")
+    assert_eq(gidx.notna(), pidx.notna())
+
+
+def test_rangeindex_slice_attr_name():
+    start, stop = 0, 10
+    rg = RangeIndex(start, stop, name="myindex")
+    sliced_rg = rg[0:9]
+    assert_eq(rg.name, sliced_rg.name)
+
+
+def test_from_pandas_str():
+    idx = ["a", "b", "c"]
+    pidx = pd.Index(idx, name="idx")
+    gidx_1 = cudf.Index(idx, name="idx")
+    gidx_2 = cudf.from_pandas(pidx)
+
+    assert_eq(gidx_1, gidx_2)
+
+
+def test_from_pandas_gen():
+    idx = [2, 4, 6]
+    pidx = pd.Index(idx, name="idx")
+    gidx_1 = cudf.Index(idx, name="idx")
+    gidx_2 = cudf.from_pandas(pidx)
+
+    assert_eq(gidx_1, gidx_2)
+
+
+def test_index_names():
+    idx = cudf.core.index.as_index([1, 2, 3], name="idx")
+    assert idx.names == ("idx",)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        range(0),
+        range(1),
+        range(0, 1),
+        range(0, 5),
+        range(1, 10),
+        range(1, 10, 1),
+        range(1, 10, 3),
+        range(10, 1, -3),
+        range(-5, 10),
+    ],
+)
+def test_range_index_from_range(data):
+    assert_eq(pd.Index(data), cudf.Index(data))
+
+
+@pytest.mark.parametrize(
+    "n",
+    [-10, -5, -2, 0, 1, 0, 2, 5, 10],
+)
+def test_empty_df_head_tail_index(n):
+    df = cudf.DataFrame()
+    pdf = pd.DataFrame()
+    assert_eq(df.head(n).index.values, pdf.head(n).index.values)
+    assert_eq(df.tail(n).index.values, pdf.tail(n).index.values)
+
+    df = cudf.DataFrame({"a": [11, 2, 33, 44, 55]})
+    pdf = pd.DataFrame({"a": [11, 2, 33, 44, 55]})
+    assert_eq(df.head(n).index.values, pdf.head(n).index.values)
+    assert_eq(df.tail(n).index.values, pdf.tail(n).index.values)
+
+    df = cudf.DataFrame(index=[1, 2, 3])
+    pdf = pd.DataFrame(index=[1, 2, 3])
+    assert_eq(df.head(n).index.values, pdf.head(n).index.values)
+    assert_eq(df.tail(n).index.values, pdf.tail(n).index.values)
+
+
+@pytest.mark.parametrize(
+    "data,condition,other,error",
+    [
+        (pd.Index(range(5)), pd.Index(range(5)) > 0, None, None),
+        (pd.Index([1, 2, 3]), pd.Index([1, 2, 3]) != 2, None, None),
+        (pd.Index(list("abc")), pd.Index(list("abc")) == "c", None, None),
+        (
+            pd.Index(list("abc")),
+            pd.Index(list("abc")) == "c",
+            pd.Index(list("xyz")),
+            None,
+        ),
+        (pd.Index(range(5)), pd.Index(range(4)) > 0, None, ValueError),
+        pytest.param(
+            pd.Index(range(5)),
+            pd.Index(range(5)) > 1,
+            10,
+            None,
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_133,
+                reason="https://github.com/pandas-dev/pandas/issues/43240",
+            ),
+        ),
+        (
+            pd.Index(np.arange(10)),
+            (pd.Index(np.arange(10)) % 3) == 0,
+            -pd.Index(np.arange(10)),
+            None,
+        ),
+        (
+            pd.Index([1, 2, np.nan]),
+            pd.Index([1, 2, np.nan]) == 4,
+            None,
+            None,
+        ),
+        (
+            pd.Index([1, 2, np.nan]),
+            pd.Index([1, 2, np.nan]) != 4,
+            None,
+            None,
+        ),
+        (
+            pd.Index([-2, 3, -4, -79]),
+            [True, True, True],
+            None,
+            ValueError,
+        ),
+        (
+            pd.Index([-2, 3, -4, -79]),
+            [True, True, True, False],
+            None,
+            None,
+        ),
+        (
+            pd.Index([-2, 3, -4, -79]),
+            [True, True, True, False],
+            17,
+            None,
+        ),
+        (pd.Index(list("abcdgh")), pd.Index(list("abcdgh")) != "g", "3", None),
+        (
+            pd.Index(list("abcdgh")),
+            pd.Index(list("abcdg")) != "g",
+            "3",
+            ValueError,
+        ),
+        (
+            pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"]),
+            pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"]) != "a",
+            "a",
+            None,
+        ),
+        (
+            pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"]),
+            pd.CategoricalIndex(["a", "b", "c", "a", "b", "c"]) != "a",
+            "b",
+            None,
+        ),
+        (
+            pd.MultiIndex.from_tuples(
+                list(
+                    zip(
+                        *[
+                            [
+                                "bar",
+                                "bar",
+                                "baz",
+                                "baz",
+                                "foo",
+                                "foo",
+                                "qux",
+                                "qux",
+                            ],
+                            [
+                                "one",
+                                "two",
+                                "one",
+                                "two",
+                                "one",
+                                "two",
+                                "one",
+                                "two",
+                            ],
+                        ]
+                    )
+                )
+            ),
+            pd.MultiIndex.from_tuples(
+                list(
+                    zip(
+                        *[
+                            [
+                                "bar",
+                                "bar",
+                                "baz",
+                                "baz",
+                                "foo",
+                                "foo",
+                                "qux",
+                                "qux",
+                            ],
+                            [
+                                "one",
+                                "two",
+                                "one",
+                                "two",
+                                "one",
+                                "two",
+                                "one",
+                                "two",
+                            ],
+                        ]
+                    )
+                )
+            )
+            != "a",
+            None,
+            NotImplementedError,
+        ),
+    ],
+)
+def test_index_where(data, condition, other, error):
+    ps = data
+    gs = cudf.from_pandas(data)
+
+    ps_condition = condition
+    if type(condition).__module__.split(".")[0] == "pandas":
+        gs_condition = cudf.from_pandas(condition)
+    else:
+        gs_condition = condition
+
+    ps_other = other
+    if type(other).__module__.split(".")[0] == "pandas":
+        gs_other = cudf.from_pandas(other)
+    else:
+        gs_other = other
+
+    if error is None:
+        if pd.api.types.is_categorical_dtype(ps):
+            expect = ps.where(ps_condition, other=ps_other)
+            got = gs.where(gs_condition, other=gs_other)
+            np.testing.assert_array_equal(
+                expect.codes,
+                got.codes.astype(expect.codes.dtype).fillna(-1).to_numpy(),
+            )
+            assert_eq(expect.categories, got.categories)
+        else:
+            assert_eq(
+                ps.where(ps_condition, other=ps_other),
+                gs.where(gs_condition, other=gs_other).to_pandas(),
+            )
+    else:
+        assert_exceptions_equal(
+            lfunc=ps.where,
+            rfunc=gs.where,
+            lfunc_args_and_kwargs=([ps_condition], {"other": ps_other}),
+            rfunc_args_and_kwargs=([gs_condition], {"other": gs_other}),
+        )
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + OTHER_TYPES)
+@pytest.mark.parametrize("copy", [True, False])
+def test_index_astype(dtype, copy):
+    pdi = pd.Index([1, 2, 3])
+    gdi = cudf.from_pandas(pdi)
+
+    actual = gdi.astype(dtype=dtype, copy=copy)
+    expected = pdi.astype(dtype=dtype, copy=copy)
+
+    assert_eq(expected, actual)
+    assert_eq(pdi, gdi)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 10, 2, 100, -10],
+        ["z", "x", "a", "c", "b"],
+        [-10.2, 100.1, -100.2, 0.0, 0.23],
+    ],
+)
+def test_index_argsort(data):
+    pdi = pd.Index(data)
+    gdi = cudf.from_pandas(pdi)
+
+    assert_eq(pdi.argsort(), gdi.argsort())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Index([1, 10, 2, 100, -10], name="abc"),
+        pd.Index(["z", "x", "a", "c", "b"]),
+        pd.Index(["z", "x", "a", "c", "b"], dtype="category"),
+        pd.Index(
+            [-10.2, 100.1, -100.2, 0.0, 0.23], name="this is a float index"
+        ),
+        pd.Index([102, 1001, 1002, 0.0, 23], dtype="datetime64[ns]"),
+        pd.Index([13240.2, 1001, 100.2, 0.0, 23], dtype="datetime64[ns]"),
+        pd.RangeIndex(0, 10, 1),
+        pd.RangeIndex(0, -100, -2),
+        pd.Index([-10.2, 100.1, -100.2, 0.0, 23], dtype="timedelta64[ns]"),
+    ],
+)
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("return_indexer", [True, False])
+def test_index_sort_values(data, ascending, return_indexer):
+    pdi = data
+    gdi = cudf.from_pandas(pdi)
+
+    expected = pdi.sort_values(
+        ascending=ascending, return_indexer=return_indexer
+    )
+    actual = gdi.sort_values(
+        ascending=ascending, return_indexer=return_indexer
+    )
+
+    if return_indexer:
+        expected_indexer = expected[1]
+        actual_indexer = actual[1]
+
+        assert_eq(expected_indexer, actual_indexer)
+
+        expected = expected[0]
+        actual = actual[0]
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 10, 2, 100, -10],
+        ["z", "x", "a", "c", "b"],
+        [-10.2, 100.1, -100.2, 0.0, 0.23],
+    ],
+)
+def test_index_to_series(data):
+    pdi = pd.Index(data)
+    gdi = cudf.from_pandas(pdi)
+
+    assert_eq(pdi.to_series(), gdi.to_series())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [4, 5, 6, 10, 20, 30],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        ["5", "6", "2", "a", "b", "c"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        [1.0, 5.0, 6.0, 0.0, 1.3],
+        ["ab", "cd", "ef"],
+        pd.Series(["1", "2", "a", "3", None], dtype="category"),
+        range(0, 10),
+        [],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [4, 5, 6, 10, 20, 30],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        ["5", "6", "2", "a", "b", "c"],
+        ["ab", "ef", None],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        [1.0, 5.0, 6.0, 0.0, 1.3],
+        range(2, 4),
+        pd.Series(["1", "a", "3", None], dtype="category"),
+        [],
+    ],
+)
+@pytest.mark.parametrize("sort", [None, False])
+@pytest.mark.parametrize(
+    "name_data,name_other",
+    [("abc", "c"), (None, "abc"), ("abc", pd.NA), ("abc", "abc")],
+)
+def test_index_difference(data, other, sort, name_data, name_other):
+    pd_data = pd.Index(data, name=name_data)
+    pd_other = pd.Index(other, name=name_other)
+
+    gd_data = cudf.from_pandas(pd_data)
+    gd_other = cudf.from_pandas(pd_other)
+
+    expected = pd_data.difference(pd_other, sort=sort)
+    actual = gd_data.difference(gd_other, sort=sort)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("other", ["a", 1, None])
+def test_index_difference_invalid_inputs(other):
+    pdi = pd.Index([1, 2, 3])
+    gdi = cudf.Index([1, 2, 3])
+
+    assert_exceptions_equal(
+        pdi.difference,
+        gdi.difference,
+        ([other], {}),
+        ([other], {}),
+    )
+
+
+def test_index_difference_sort_error():
+    pdi = pd.Index([1, 2, 3])
+    gdi = cudf.Index([1, 2, 3])
+
+    assert_exceptions_equal(
+        pdi.difference,
+        gdi.difference,
+        ([pdi], {"sort": True}),
+        ([gdi], {"sort": True}),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        [],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+def test_index_equals(data, other):
+    pd_data = pd.Index(data)
+    pd_other = pd.Index(other)
+
+    gd_data = cudf.core.index.as_index(data)
+    gd_other = cudf.core.index.as_index(other)
+
+    expected = pd_data.equals(pd_other)
+    actual = gd_data.equals(gd_other)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+def test_index_categories_equal(data, other):
+    pd_data = pd.Index(data).astype("category")
+    pd_other = pd.Index(other)
+
+    gd_data = cudf.core.index.as_index(data).astype("category")
+    gd_other = cudf.core.index.as_index(other)
+
+    expected = pd_data.equals(pd_other)
+    actual = gd_data.equals(gd_other)
+    assert_eq(expected, actual)
+
+    expected = pd_other.equals(pd_data)
+    actual = gd_other.equals(gd_data)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+def test_index_equal_misc(data, other):
+    pd_data = pd.Index(data)
+    pd_other = other
+
+    gd_data = cudf.core.index.as_index(data)
+    gd_other = other
+
+    expected = pd_data.equals(pd_other)
+    actual = gd_data.equals(gd_other)
+    assert_eq(expected, actual)
+
+    expected = pd_data.equals(np.array(pd_other))
+    actual = gd_data.equals(np.array(gd_other))
+    assert_eq(expected, actual)
+
+    expected = pd_data.equals(_create_pandas_series_float64_default(pd_other))
+    actual = gd_data.equals(_create_cudf_series_float64_default(gd_other))
+    assert_eq(expected, actual)
+
+    expected = pd_data.astype("category").equals(pd_other)
+    actual = gd_data.astype("category").equals(gd_other)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        ["1", "2", "3", "4", "5", "6"],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        ["a"],
+        ["b", "c", "d"],
+        [1],
+        [2, 3, 4],
+        [],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+def test_index_append(data, other):
+    pd_data = pd.Index(data)
+    pd_other = pd.Index(other)
+
+    gd_data = cudf.core.index.as_index(data)
+    gd_other = cudf.core.index.as_index(other)
+
+    if cudf.utils.dtypes.is_mixed_with_object_dtype(gd_data, gd_other):
+        gd_data = gd_data.astype("str")
+        gd_other = gd_other.astype("str")
+
+    expected = pd_data.append(pd_other)
+
+    actual = gd_data.append(gd_other)
+    if len(data) == 0 and len(other) == 0:
+        # Pandas default dtype to "object" for empty list
+        # cudf default dtype to "float" for empty list
+        assert_eq(expected, actual.astype("str"))
+    elif actual.dtype == "object":
+        assert_eq(expected.astype("str"), actual)
+    else:
+        assert_eq(expected, actual)
+
+
+def test_index_empty_append_name_conflict():
+    empty = cudf.Index([], name="foo")
+    non_empty = cudf.Index([1], name="bar")
+    expected = cudf.Index([1])
+
+    result = non_empty.append(empty)
+    assert_eq(result, expected)
+
+    result = empty.append(non_empty)
+    assert_eq(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4, 5, 6],
+        [10, 20, 30, 40, 50, 60],
+        [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+        [1],
+        [2, 3, 4],
+        [10.0],
+        [1100.112, 2323.2322, 2323.2322],
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        ["1", "2", "3", "4", "5", "6"],
+        ["a"],
+        ["b", "c", "d"],
+        ["abcd", "defgh", "werty", "poiu"],
+    ],
+)
+def test_index_append_error(data, other):
+    gd_data = cudf.core.index.as_index(data)
+    gd_other = cudf.core.index.as_index(other)
+
+    got_dtype = (
+        gd_other.dtype
+        if gd_data.dtype == np.dtype("object")
+        else gd_data.dtype
+    )
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"cudf does not support appending an Index of "
+            f"dtype `{np.dtype('object')}` with an Index "
+            f"of dtype `{got_dtype}`, please type-cast "
+            f"either one of them to same dtypes."
+        ),
+    ):
+        gd_data.append(gd_other)
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"cudf does not support appending an Index of "
+            f"dtype `{np.dtype('object')}` with an Index "
+            f"of dtype `{got_dtype}`, please type-cast "
+            f"either one of them to same dtypes."
+        ),
+    ):
+        gd_other.append(gd_data)
+
+    sr = gd_other.to_series()
+
+    assert_exceptions_equal(
+        lfunc=gd_data.to_pandas().append,
+        rfunc=gd_data.append,
+        lfunc_args_and_kwargs=([[sr.to_pandas()]],),
+        rfunc_args_and_kwargs=([[sr]],),
+    )
+
+
+@pytest.mark.parametrize(
+    "data,other",
+    [
+        (
+            pd.Index([1, 2, 3, 4, 5, 6]),
+            [
+                pd.Index([1, 2, 3, 4, 5, 6]),
+                pd.Index([1, 2, 3, 4, 5, 6, 10]),
+                pd.Index([]),
+            ],
+        ),
+        (
+            pd.Index([]),
+            [
+                pd.Index([1, 2, 3, 4, 5, 6]),
+                pd.Index([1, 2, 3, 4, 5, 6, 10]),
+                pd.Index([1, 4, 5, 6]),
+            ],
+        ),
+        (
+            pd.Index([10, 20, 30, 40, 50, 60]),
+            [
+                pd.Index([10, 20, 30, 40, 50, 60]),
+                pd.Index([10, 20, 30]),
+                pd.Index([40, 50, 60]),
+                pd.Index([10, 60]),
+                pd.Index([60]),
+            ],
+        ),
+        (
+            pd.Index([]),
+            [
+                pd.Index([10, 20, 30, 40, 50, 60]),
+                pd.Index([10, 20, 30]),
+                pd.Index([40, 50, 60]),
+                pd.Index([10, 60]),
+                pd.Index([60]),
+            ],
+        ),
+        (
+            pd.Index(["1", "2", "3", "4", "5", "6"]),
+            [
+                pd.Index(["1", "2", "3", "4", "5", "6"]),
+                pd.Index(["1", "2", "3"]),
+                pd.Index(["6"]),
+                pd.Index(["1", "6"]),
+            ],
+        ),
+        (
+            pd.Index([]),
+            [
+                pd.Index(["1", "2", "3", "4", "5", "6"]),
+                pd.Index(["1", "2", "3"]),
+                pd.Index(["6"]),
+                pd.Index(["1", "6"]),
+            ],
+        ),
+        (
+            pd.Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]),
+            [
+                pd.Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]),
+                pd.Index([1.0, 6.0]),
+                pd.Index([]),
+                pd.Index([6.0]),
+            ],
+        ),
+        (
+            pd.Index([]),
+            [
+                pd.Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]),
+                pd.Index([1.0, 6.0]),
+                pd.Index([1.0, 2.0, 6.0]),
+                pd.Index([6.0]),
+            ],
+        ),
+        (
+            pd.Index(["a"]),
+            [
+                pd.Index(["a"]),
+                pd.Index(["a", "b", "c"]),
+                pd.Index(["c"]),
+                pd.Index(["d"]),
+                pd.Index(["ae", "hello", "world"]),
+            ],
+        ),
+        (
+            pd.Index([]),
+            [
+                pd.Index(["a"]),
+                pd.Index(["a", "b", "c"]),
+                pd.Index(["c"]),
+                pd.Index(["d"]),
+                pd.Index(["ae", "hello", "world"]),
+                pd.Index([]),
+            ],
+        ),
+    ],
+)
+def test_index_append_list(data, other):
+    pd_data = data
+    pd_other = other
+
+    gd_data = cudf.from_pandas(data)
+    gd_other = [cudf.from_pandas(i) for i in other]
+
+    expected = pd_data.append(pd_other)
+    actual = gd_data.append(gd_other)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+@pytest.mark.parametrize("name", [1, "a", None])
+def test_index_basic(data, dtype, name):
+    pdi = pd.Index(data, dtype=dtype, name=name)
+    gdi = cudf.Index(data, dtype=dtype, name=name)
+
+    assert_eq(pdi, gdi)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize("name", [1, "a", None])
+@pytest.mark.parametrize("dtype", SIGNED_INTEGER_TYPES)
+def test_integer_index_apis(data, name, dtype):
+    with pytest.warns(FutureWarning):
+        pindex = pd.Int64Index(data, dtype=dtype, name=name)
+    # Int8Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.Int8Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("int8")
+
+    # Int16Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.Int16Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("int16")
+
+    # Int32Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.Int32Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("int32")
+
+    # Int64Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.Int64Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("int64")
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize("name", [1, "a", None])
+@pytest.mark.parametrize("dtype", UNSIGNED_TYPES)
+def test_unsigned_integer_index_apis(data, name, dtype):
+    with pytest.warns(FutureWarning):
+        pindex = pd.UInt64Index(data, dtype=dtype, name=name)
+    # UInt8Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.UInt8Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("uint8")
+
+    # UInt16Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.UInt16Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("uint16")
+
+    # UInt32Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.UInt32Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("uint32")
+
+    # UInt64Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.UInt64Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("uint64")
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize("name", [1, "a", None])
+@pytest.mark.parametrize("dtype", FLOAT_TYPES)
+def test_float_index_apis(data, name, dtype):
+    with pytest.warns(FutureWarning):
+        pindex = pd.Float64Index(data, dtype=dtype, name=name)
+    # Float32Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.Float32Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("float32")
+
+    # Float64Index
+    with pytest.warns(FutureWarning):
+        gindex = cudf.Float64Index(data, dtype=dtype, name=name)
+
+    assert_eq(pindex, gindex)
+    assert gindex.dtype == np.dtype("float64")
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize("categories", [[1, 2], None])
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        pd.CategoricalDtype([1, 2, 3], ordered=True),
+        pd.CategoricalDtype([1, 2, 3], ordered=False),
+        None,
+    ],
+)
+@pytest.mark.parametrize("ordered", [True, False])
+@pytest.mark.parametrize("name", [1, "a", None])
+def test_categorical_index_basic(data, categories, dtype, ordered, name):
+    # can't have both dtype and categories/ordered
+    if dtype is not None:
+        categories = None
+        ordered = None
+    pindex = pd.CategoricalIndex(
+        data=data,
+        categories=categories,
+        dtype=dtype,
+        ordered=ordered,
+        name=name,
+    )
+    gindex = CategoricalIndex(
+        data=data,
+        categories=categories,
+        dtype=dtype,
+        ordered=ordered,
+        name=name,
+    )
+
+    assert_eq(pindex, gindex)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.MultiIndex.from_arrays(
+            [[1, 1, 2, 2], ["red", "blue", "red", "blue"]],
+            names=("number", "color"),
+        ),
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 4], ["yellow", "violet", "pink", "white"]],
+            names=("number1", "color2"),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        pd.MultiIndex.from_arrays(
+            [[1, 1, 2, 2], ["red", "blue", "red", "blue"]],
+            names=("number", "color"),
+        ),
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 4], ["yellow", "violet", "pink", "white"]],
+            names=("number1", "color2"),
+        ),
+    ],
+)
+def test_multiindex_append(data, other):
+    pdi = data
+    other_pd = other
+
+    gdi = cudf.from_pandas(data)
+    other_gd = cudf.from_pandas(other)
+
+    expected = pdi.append(other_pd)
+    actual = gdi.append(other_gd)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+def test_index_empty(data, dtype):
+    pdi = pd.Index(data, dtype=dtype)
+    gdi = cudf.Index(data, dtype=dtype)
+
+    assert_eq(pdi.empty, gdi.empty)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 4], []])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+def test_index_size(data, dtype):
+    pdi = pd.Index(data, dtype=dtype)
+    gdi = cudf.Index(data, dtype=dtype)
+
+    assert_eq(pdi.size, gdi.size)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 1, 2, 3, 4], [], [1], [1, 2, 3]])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+def test_index_drop_duplicates(data, dtype):
+    pdi = pd.Index(data, dtype=dtype)
+    gdi = cudf.Index(data, dtype=dtype)
+
+    assert_eq(pdi.drop_duplicates(), gdi.drop_duplicates())
+
+
+def test_dropna_bad_how():
+    with pytest.raises(ValueError):
+        cudf.Index([1]).dropna(how="foo")
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3, 1, 2, 3, 4], []])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+def test_index_tolist(data, dtype):
+    gdi = cudf.Index(data, dtype=dtype)
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            r"cuDF does not support conversion to host memory "
+            r"via the `tolist()` method. Consider using "
+            r"`.to_arrow().to_pylist()` to construct a Python list."
+        ),
+    ):
+        gdi.tolist()
+
+
+@pytest.mark.parametrize("data", [[], [1], [1, 2, 3]])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+def test_index_iter_error(data, dtype):
+    gdi = cudf.Index(data, dtype=dtype)
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"{gdi.__class__.__name__} object is not iterable. "
+            f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
+            f"if you wish to iterate over the values."
+        ),
+    ):
+        iter(gdi)
+
+
+@pytest.mark.parametrize("data", [[], [1], [1, 2, 3, 4, 5]])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + ["str", "category", "datetime64[ns]"]
+)
+def test_index_values_host(data, dtype):
+    gdi = cudf.Index(data, dtype=dtype)
+    pdi = pd.Index(data, dtype=dtype)
+
+    np.testing.assert_array_equal(gdi.values_host, pdi.values)
+
+
+@pytest.mark.parametrize(
+    "data,fill_value",
+    [
+        ([1, 2, 3, 1, None, None], 1),
+        ([None, None, 3.2, 1, None, None], 10.0),
+        ([None, "a", "3.2", "z", None, None], "helloworld"),
+        (pd.Series(["a", "b", None], dtype="category"), "b"),
+        (pd.Series([None, None, 1.0], dtype="category"), 1.0),
+        (
+            np.array([1, 2, 3, None], dtype="datetime64[s]"),
+            np.datetime64("2005-02-25"),
+        ),
+        (
+            np.array(
+                [None, None, 122, 3242234, None, 6237846],
+                dtype="datetime64[ms]",
+            ),
+            np.datetime64("2005-02-25"),
+        ),
+    ],
+)
+def test_index_fillna(data, fill_value):
+    pdi = pd.Index(data)
+    gdi = cudf.Index(data)
+
+    assert_eq(
+        pdi.fillna(fill_value), gdi.fillna(fill_value), exact=False
+    )  # Int64Index v/s Float64Index
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 1, None, None],
+        [None, None, 3.2, 1, None, None],
+        [None, "a", "3.2", "z", None, None],
+        pd.Series(["a", "b", None], dtype="category"),
+        np.array([1, 2, 3, None], dtype="datetime64[s]"),
+    ],
+)
+def test_index_to_arrow(data):
+    pdi = pd.Index(data)
+    gdi = cudf.Index(data)
+
+    expected_arrow_array = pa.Array.from_pandas(pdi)
+    got_arrow_array = gdi.to_arrow()
+
+    assert_eq(expected_arrow_array, got_arrow_array)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [None, None, 3.2, 1, None, None],
+        [None, "a", "3.2", "z", None, None],
+        pd.Series(["a", "b", None], dtype="category"),
+        np.array([1, 2, 3, None], dtype="datetime64[s]"),
+    ],
+)
+def test_index_from_arrow(data):
+    pdi = pd.Index(data)
+
+    arrow_array = pa.Array.from_pandas(pdi)
+    expected_index = pd.Index(arrow_array.to_pandas())
+    gdi = cudf.Index.from_arrow(arrow_array)
+
+    assert_eq(expected_index, gdi)
+
+
+def test_multiindex_to_arrow():
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 2, 1, 2, 3],
+            "b": [1.0, 2.0, 3.0, 4.0, 5.0],
+            "c": np.array([1, 2, 3, None, 5], dtype="datetime64[s]"),
+            "d": ["a", "b", "c", "d", "e"],
+        }
+    )
+    pdf["a"] = pdf["a"].astype("category")
+    df = cudf.from_pandas(pdf)
+    gdi = cudf.MultiIndex.from_frame(df)
+
+    expected = pa.Table.from_pandas(pdf)
+    got = gdi.to_arrow()
+
+    assert_eq(expected, got)
+
+
+def test_multiindex_from_arrow():
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 2, 1, 2, 3],
+            "b": [1.0, 2.0, 3.0, 4.0, 5.0],
+            "c": np.array([1, 2, 3, None, 5], dtype="datetime64[s]"),
+            "d": ["a", "b", "c", "d", "e"],
+        }
+    )
+    pdf["a"] = pdf["a"].astype("category")
+    ptb = pa.Table.from_pandas(pdf)
+    gdi = cudf.MultiIndex.from_arrow(ptb)
+    pdi = pd.MultiIndex.from_frame(pdf)
+
+    assert_eq(pdi, gdi)
+
+
+def test_index_equals_categories():
+    lhs = cudf.CategoricalIndex(
+        ["a", "b", "c", "b", "a"], categories=["a", "b", "c"]
+    )
+    rhs = cudf.CategoricalIndex(
+        ["a", "b", "c", "b", "a"], categories=["a", "b", "c", "_"]
+    )
+
+    got = lhs.equals(rhs)
+    expect = lhs.to_pandas().equals(rhs.to_pandas())
+
+    assert_eq(expect, got)
+
+
+def test_rangeindex_arg_validation():
+    with pytest.raises(TypeError):
+        RangeIndex("1")
+
+    with pytest.raises(TypeError):
+        RangeIndex(1, "2")
+
+    with pytest.raises(TypeError):
+        RangeIndex(1, 3, "1")
+
+    with pytest.raises(ValueError):
+        RangeIndex(1, dtype="float64")
+
+    with pytest.raises(ValueError):
+        RangeIndex(1, dtype="uint64")
+
+
+def test_rangeindex_name_not_hashable():
+    with pytest.raises(ValueError):
+        RangeIndex(range(2), name=["foo"])
+
+    with pytest.raises(ValueError):
+        RangeIndex(range(2)).copy(name=["foo"])
+
+
+def test_index_rangeindex_search_range():
+    # step > 0
+    ridx = RangeIndex(-13, 17, 4)
+    ri = ridx.as_range
+    for i in range(len(ridx)):
+        assert i == search_range(ridx[i], ri, side="left")
+        assert i + 1 == search_range(ridx[i], ri, side="right")
+
+
+@pytest.mark.parametrize(
+    "rge",
+    [(1, 10, 1), (1, 10, 3), (10, -17, -1), (10, -17, -3)],
+)
+def test_index_rangeindex_get_item_basic(rge):
+    pridx = pd.RangeIndex(*rge)
+    gridx = cudf.RangeIndex(*rge)
+
+    for i in range(-len(pridx), len(pridx)):
+        assert pridx[i] == gridx[i]
+
+
+@pytest.mark.parametrize(
+    "rge",
+    [(1, 10, 3), (10, 1, -3)],
+)
+def test_index_rangeindex_get_item_out_of_bounds(rge):
+    gridx = cudf.RangeIndex(*rge)
+    with pytest.raises(IndexError):
+        _ = gridx[4]
+
+
+@pytest.mark.parametrize(
+    "rge",
+    [(10, 1, 1), (-17, 10, -3)],
+)
+def test_index_rangeindex_get_item_null_range(rge):
+    gridx = cudf.RangeIndex(*rge)
+
+    with pytest.raises(IndexError):
+        gridx[0]
+
+
+@pytest.mark.parametrize(
+    "rge", [(-17, 21, 2), (21, -17, -3), (0, 0, 1), (0, 1, -3), (10, 0, 5)]
+)
+@pytest.mark.parametrize(
+    "sl",
+    [
+        slice(1, 7, 1),
+        slice(1, 7, 2),
+        slice(-1, 7, 1),
+        slice(-1, 7, 2),
+        slice(-3, 7, 2),
+        slice(7, 1, -2),
+        slice(7, -3, -2),
+        slice(None, None, 1),
+        slice(0, None, 2),
+        slice(0, None, 3),
+        slice(0, 0, 3),
+    ],
+)
+def test_index_rangeindex_get_item_slices(rge, sl):
+    pridx = pd.RangeIndex(*rge)
+    gridx = cudf.RangeIndex(*rge)
+
+    assert_eq(pridx[sl], gridx[sl])
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.Index([1, 2, 3]),
+        pd.Index(["abc", "def", "ghi"]),
+        pd.RangeIndex(0, 10, 1),
+        pd.Index([0.324, 0.234, 1.3], name="abc"),
+    ],
+)
+@pytest.mark.parametrize("names", [None, "a", "new name", ["another name"]])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_index_set_names(idx, names, inplace):
+    pi = idx.copy()
+    gi = cudf.from_pandas(idx)
+
+    expected = pi.set_names(names=names, inplace=inplace)
+    actual = gi.set_names(names=names, inplace=inplace)
+
+    if inplace:
+        expected, actual = pi, gi
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("idx", [pd.Index([1, 2, 3], name="abc")])
+@pytest.mark.parametrize("level", [1, [0], "abc"])
+@pytest.mark.parametrize("names", [None, "a"])
+def test_index_set_names_error(idx, level, names):
+    pi = idx.copy()
+    gi = cudf.from_pandas(idx)
+
+    assert_exceptions_equal(
+        lfunc=pi.set_names,
+        rfunc=gi.set_names,
+        lfunc_args_and_kwargs=([], {"names": names, "level": level}),
+        rfunc_args_and_kwargs=([], {"names": names, "level": level}),
+    )
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [pd.Index([1, 3, 6]), pd.Index([6, 1, 3])],  # monotonic  # non-monotonic
+)
+@pytest.mark.parametrize("key", list(range(0, 8)))
+@pytest.mark.parametrize("method", [None, "ffill", "bfill", "nearest"])
+def test_get_loc_single_unique_numeric(idx, key, method):
+    pi = idx
+    gi = cudf.from_pandas(pi)
+
+    if (
+        (key not in pi and method is None)
+        # `method` only applicable to monotonic index
+        or (not pi.is_monotonic_increasing and method is not None)
+        # Get key before the first element is KeyError
+        or (key == 0 and method in "ffill")
+        # Get key after the last element is KeyError
+        or (key == 7 and method in "bfill")
+    ):
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [pd.RangeIndex(3, 100, 4)],
+)
+@pytest.mark.parametrize("key", list(range(1, 110, 3)))
+@pytest.mark.parametrize("method", [None, "ffill"])
+def test_get_loc_rangeindex(idx, key, method):
+    pi = idx
+    gi = cudf.from_pandas(pi)
+
+    if (
+        (key not in pi and method is None)
+        # Get key before the first element is KeyError
+        or (key < pi.start and method in "ffill")
+        # Get key after the last element is KeyError
+        or (key >= pi.stop and method in "bfill")
+    ):
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.Index([1, 3, 3, 6]),  # monotonic
+        pd.Index([6, 1, 3, 3]),  # non-monotonic
+    ],
+)
+@pytest.mark.parametrize("key", [0, 3, 6, 7])
+@pytest.mark.parametrize("method", [None])
+def test_get_loc_single_duplicate_numeric(idx, key, method):
+    pi = idx
+    gi = cudf.from_pandas(pi)
+
+    if key not in pi:
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx", [pd.Index(["b", "f", "m", "q"]), pd.Index(["m", "f", "b", "q"])]
+)
+@pytest.mark.parametrize("key", ["a", "f", "n", "z"])
+@pytest.mark.parametrize("method", [None, "ffill", "bfill"])
+def test_get_loc_single_unique_string(idx, key, method):
+    pi = idx
+    gi = cudf.from_pandas(pi)
+
+    if (
+        (key not in pi and method is None)
+        # `method` only applicable to monotonic index
+        or (not pi.is_monotonic_increasing and method is not None)
+        # Get key before the first element is KeyError
+        or (key == "a" and method == "ffill")
+        # Get key after the last element is KeyError
+        or (key == "z" and method == "bfill")
+    ):
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx", [pd.Index(["b", "m", "m", "q"]), pd.Index(["m", "f", "m", "q"])]
+)
+@pytest.mark.parametrize("key", ["a", "f", "n", "z"])
+@pytest.mark.parametrize("method", [None])
+def test_get_loc_single_duplicate_string(idx, key, method):
+    pi = idx
+    gi = cudf.from_pandas(pi)
+
+    if key not in pi:
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.MultiIndex.from_tuples(
+            [(1, 1, 1), (1, 1, 2), (1, 2, 1), (1, 2, 3), (2, 1, 1), (2, 2, 1)]
+        ),
+        pd.MultiIndex.from_tuples(
+            [(2, 1, 1), (1, 2, 3), (1, 2, 1), (1, 1, 2), (2, 2, 1), (1, 1, 1)]
+        ),
+        pd.MultiIndex.from_tuples(
+            [(1, 1, 1), (1, 1, 2), (1, 1, 2), (1, 2, 3), (2, 1, 1), (2, 2, 1)]
+        ),
+    ],
+)
+@pytest.mark.parametrize("key", [1, (1, 2), (1, 2, 3), (2, 1, 1), (9, 9, 9)])
+@pytest.mark.parametrize("method", [None])
+def test_get_loc_multi_numeric(idx, key, method):
+    pi = idx.sort_values()
+    gi = cudf.from_pandas(pi)
+
+    if key not in pi:
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.MultiIndex.from_tuples(
+            [(2, 1, 1), (1, 2, 3), (1, 2, 1), (1, 1, 1), (1, 1, 1), (2, 2, 1)]
+        )
+    ],
+)
+@pytest.mark.parametrize(
+    "key, result",
+    [
+        (1, slice(1, 5, 1)),  # deviates
+        ((1, 2), slice(1, 3, 1)),
+        ((1, 2, 3), slice(1, 2, None)),
+        ((2, 1, 1), slice(0, 1, None)),
+        ((9, 9, 9), None),
+    ],
+)
+@pytest.mark.parametrize("method", [None])
+def test_get_loc_multi_numeric_deviate(idx, key, result, method):
+    pi = idx
+    gi = cudf.from_pandas(pi)
+
+    with expect_warning_if(
+        isinstance(key, tuple), pd.errors.PerformanceWarning
+    ):
+        key_flag = key not in pi
+
+    if key_flag:
+        with expect_warning_if(
+            isinstance(key, tuple), pd.errors.PerformanceWarning
+        ):
+            assert_exceptions_equal(
+                lfunc=pi.get_loc,
+                rfunc=gi.get_loc,
+                lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+                rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            )
+    else:
+        expected = result
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.MultiIndex.from_tuples(
+            [
+                ("a", "a", "a"),
+                ("a", "a", "b"),
+                ("a", "b", "a"),
+                ("a", "b", "c"),
+                ("b", "a", "a"),
+                ("b", "c", "a"),
+            ]
+        ),
+        pd.MultiIndex.from_tuples(
+            [
+                ("a", "a", "b"),
+                ("a", "b", "c"),
+                ("b", "a", "a"),
+                ("a", "a", "a"),
+                ("a", "b", "a"),
+                ("b", "c", "a"),
+            ]
+        ),
+        pd.MultiIndex.from_tuples(
+            [
+                ("a", "a", "a"),
+                ("a", "b", "c"),
+                ("b", "a", "a"),
+                ("a", "a", "b"),
+                ("a", "b", "a"),
+                ("b", "c", "a"),
+            ]
+        ),
+        pd.MultiIndex.from_tuples(
+            [
+                ("a", "a", "a"),
+                ("a", "a", "b"),
+                ("a", "a", "b"),
+                ("a", "b", "c"),
+                ("b", "a", "a"),
+                ("b", "c", "a"),
+            ]
+        ),
+        pd.MultiIndex.from_tuples(
+            [
+                ("a", "a", "b"),
+                ("b", "a", "a"),
+                ("b", "a", "a"),
+                ("a", "a", "a"),
+                ("a", "b", "a"),
+                ("b", "c", "a"),
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "key", ["a", ("a", "a"), ("a", "b", "c"), ("b", "c", "a"), ("z", "z", "z")]
+)
+@pytest.mark.parametrize("method", [None])
+def test_get_loc_multi_string(idx, key, method):
+    pi = idx.sort_values()
+    gi = cudf.from_pandas(pi)
+
+    if key not in pi:
+        assert_exceptions_equal(
+            lfunc=pi.get_loc,
+            rfunc=gi.get_loc,
+            lfunc_args_and_kwargs=([], {"key": key, "method": method}),
+            rfunc_args_and_kwargs=([], {"key": key, "method": method}),
+        )
+    else:
+        with expect_warning_if(method is not None):
+            expected = pi.get_loc(key, method=method)
+        with expect_warning_if(method is not None):
+            got = gi.get_loc(key, method=method)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "objs",
+    [
+        [pd.RangeIndex(0, 10), pd.RangeIndex(10, 20)],
+        [pd.RangeIndex(10, 20), pd.RangeIndex(22, 40), pd.RangeIndex(50, 60)],
+        [pd.RangeIndex(10, 20, 2), pd.RangeIndex(20, 40, 2)],
+    ],
+)
+def test_range_index_concat(objs):
+    cudf_objs = [cudf.from_pandas(obj) for obj in objs]
+
+    actual = cudf.concat(cudf_objs)
+
+    expected = objs[0]
+    for obj in objs[1:]:
+        expected = expected.append(obj)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx1, idx2",
+    [
+        (pd.RangeIndex(0, 10), pd.RangeIndex(3, 7)),
+        (pd.RangeIndex(0, 10), pd.RangeIndex(10, 20)),
+        (pd.RangeIndex(0, 10, 2), pd.RangeIndex(1, 5, 3)),
+        (pd.RangeIndex(1, 5, 3), pd.RangeIndex(0, 10, 2)),
+        (pd.RangeIndex(1, 10, 3), pd.RangeIndex(1, 5, 2)),
+        (pd.RangeIndex(1, 5, 2), pd.RangeIndex(1, 10, 3)),
+        (pd.RangeIndex(1, 100, 3), pd.RangeIndex(1, 50, 3)),
+        (pd.RangeIndex(1, 100, 3), pd.RangeIndex(1, 50, 6)),
+        (pd.RangeIndex(1, 100, 6), pd.RangeIndex(1, 50, 3)),
+        (pd.RangeIndex(0, 10, name="a"), pd.RangeIndex(90, 100, name="b")),
+        (pd.Index([0, 1, 2, 30], name="a"), pd.Index([90, 100])),
+        (pd.Index([0, 1, 2, 30], name="a"), [90, 100]),
+        (pd.Index([0, 1, 2, 30]), pd.Index([0, 10, 1.0, 11])),
+        (pd.Index(["a", "b", "c", "d", "c"]), pd.Index(["a", "c", "z"])),
+        (
+            pd.IntervalIndex.from_tuples([(0, 2), (0, 2), (2, 4)]),
+            pd.IntervalIndex.from_tuples([(0, 2), (2, 4)]),
+        ),
+        (pd.RangeIndex(0, 10), pd.Index([8, 1, 2, 4])),
+        (pd.Index([8, 1, 2, 4], name="a"), pd.Index([8, 1, 2, 4], name="b")),
+        (
+            pd.Index([8, 1, 2, 4], name="a"),
+            pd.Index([], name="b", dtype="int64"),
+        ),
+        (pd.Index([], dtype="int64", name="a"), pd.Index([10, 12], name="b")),
+        (pd.Index([True, True, True], name="a"), pd.Index([], dtype="bool")),
+        (
+            pd.Index([True, True, True]),
+            pd.Index([False, True], dtype="bool", name="b"),
+        ),
+    ],
+)
+@pytest.mark.parametrize("sort", [None, False])
+def test_union_index(idx1, idx2, sort):
+    expected = idx1.union(idx2, sort=sort)
+
+    idx1 = cudf.from_pandas(idx1) if isinstance(idx1, pd.Index) else idx1
+    idx2 = cudf.from_pandas(idx2) if isinstance(idx2, pd.Index) else idx2
+
+    actual = idx1.union(idx2, sort=sort)
+
+    assert_eq(expected, actual)
+
+
+def test_union_bool_with_other():
+    idx1 = cudf.Index([True, True, True])
+    idx2 = cudf.Index([0, 1], name="b")
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(cudf.errors.MixedTypeError):
+            idx1.union(idx2)
+
+
+@pytest.mark.parametrize("dtype1", ["int8", "int32", "int32"])
+@pytest.mark.parametrize("dtype2", ["uint32", "uint64"])
+def test_union_unsigned_vs_signed(dtype1, dtype2):
+    idx1 = cudf.Index([10, 20, 30], dtype=dtype1)
+    idx2 = cudf.Index([0, 1], dtype=dtype2)
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(cudf.errors.MixedTypeError):
+            idx1.union(idx2)
+
+
+@pytest.mark.parametrize(
+    "idx1, idx2",
+    [
+        (pd.RangeIndex(0, 10), pd.RangeIndex(3, 7)),
+        (pd.RangeIndex(0, 10), pd.RangeIndex(-10, 20)),
+        (pd.RangeIndex(0, 10, name="a"), pd.RangeIndex(90, 100, name="b")),
+        (pd.Index([0, 1, 2, 30], name=pd.NA), pd.Index([30, 0, 90, 100])),
+        (pd.Index([0, 1, 2, 30], name="a"), [90, 100]),
+        (pd.Index([0, 1, 2, 30]), pd.Index([0, 10, 1.0, 11])),
+        (
+            pd.Index(["a", "b", "c", "d", "c"]),
+            pd.Index(["a", "c", "z"], name="abc"),
+        ),
+        (
+            pd.Index(["a", "b", "c", "d", "c"]),
+            pd.Index(["a", "b", "c", "d", "c"]),
+        ),
+        (pd.Index([True, False, True, True]), pd.Index([10, 11, 12, 0, 1, 2])),
+        (pd.Index([True, False, True, True]), pd.Index([True, True])),
+        (pd.RangeIndex(0, 10, name="a"), pd.Index([5, 6, 7], name="b")),
+        (pd.Index(["a", "b", "c"], dtype="category"), pd.Index(["a", "b"])),
+        (pd.Index(["a", "b", "c"], dtype="category"), pd.Index([1, 2, 3])),
+        (pd.Index([0, 1, 2], dtype="category"), pd.RangeIndex(0, 10)),
+        (pd.Index(["a", "b", "c"], name="abc"), []),
+        (pd.Index([], name="abc"), pd.RangeIndex(0, 4)),
+        (pd.Index([1, 2, 3]), pd.Index([1, 2], dtype="category")),
+        (pd.Index([]), pd.Index([1, 2], dtype="category")),
+    ],
+)
+@pytest.mark.parametrize("sort", [None, False])
+@pytest.mark.parametrize("pandas_compatible", [True, False])
+def test_intersection_index(idx1, idx2, sort, pandas_compatible):
+    expected = idx1.intersection(idx2, sort=sort)
+
+    with cudf.option_context("mode.pandas_compatible", pandas_compatible):
+        idx1 = cudf.from_pandas(idx1) if isinstance(idx1, pd.Index) else idx1
+        idx2 = cudf.from_pandas(idx2) if isinstance(idx2, pd.Index) else idx2
+
+        actual = idx1.intersection(idx2, sort=sort)
+
+        # TODO: Resolve the bool vs ints mixed issue
+        # once pandas has a direction on this issue
+        # https://github.com/pandas-dev/pandas/issues/44000
+        assert_eq(
+            expected,
+            actual,
+            exact=False
+            if (is_bool_dtype(idx1.dtype) and not is_bool_dtype(idx2.dtype))
+            or (not is_bool_dtype(idx1.dtype) or is_bool_dtype(idx2.dtype))
+            else True,
+        )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3],
+        ["a", "v", "d"],
+        [234.243, 2432.3, None],
+        [True, False, True],
+        pd.Series(["a", " ", "v"], dtype="category"),
+        pd.IntervalIndex.from_breaks([0, 1, 2, 3]),
+    ],
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        "is_numeric",
+        "is_boolean",
+        "is_integer",
+        "is_floating",
+        "is_object",
+        "is_categorical",
+        "is_interval",
+    ],
+)
+def test_index_type_methods(data, func):
+    pidx = pd.Index(data)
+    gidx = cudf.from_pandas(pidx)
+
+    if PANDAS_GE_200:
+        with pytest.warns(FutureWarning):
+            expected = getattr(pidx, func)()
+    else:
+        expected = getattr(pidx, func)()
+    with pytest.warns(FutureWarning):
+        actual = getattr(gidx, func)()
+
+    if gidx.dtype == np.dtype("bool") and func == "is_object":
+        assert_eq(False, actual)
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "resolution", ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
+)
+def test_index_datetime_ceil(resolution):
+    cuidx = cudf.DatetimeIndex([1000000, 2000000, 3000000, 4000000, 5000000])
+    pidx = cuidx.to_pandas()
+
+    pidx_ceil = pidx.ceil(resolution)
+    cuidx_ceil = cuidx.ceil(resolution)
+
+    assert_eq(pidx_ceil, cuidx_ceil)
+
+
+@pytest.mark.parametrize(
+    "resolution", ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
+)
+def test_index_datetime_floor(resolution):
+    cuidx = cudf.DatetimeIndex([1000000, 2000000, 3000000, 4000000, 5000000])
+    pidx = cuidx.to_pandas()
+
+    pidx_floor = pidx.floor(resolution)
+    cuidx_floor = cuidx.floor(resolution)
+
+    assert_eq(pidx_floor, cuidx_floor)
+
+
+@pytest.mark.parametrize(
+    "resolution", ["D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"]
+)
+def test_index_datetime_round(resolution):
+    cuidx = cudf.DatetimeIndex([1000000, 2000000, 3000000, 4000000, 5000000])
+    pidx = cuidx.to_pandas()
+
+    pidx_floor = pidx.round(resolution)
+    cuidx_floor = cuidx.round(resolution)
+
+    assert_eq(pidx_floor, cuidx_floor)
+
+
+@pytest.mark.parametrize(
+    "data,nan_idx,NA_idx",
+    [([1, 2, 3, None], None, 3), ([2, 3, np.nan, None], 2, 3)],
+)
+@pytest.mark.parametrize("nan_as_null", [True, False])
+def test_index_nan_as_null(data, nan_idx, NA_idx, nan_as_null):
+    idx = cudf.Index(data, nan_as_null=nan_as_null)
+
+    if nan_as_null:
+        if nan_idx is not None:
+            assert idx[nan_idx] is cudf.NA
+    else:
+        if nan_idx is not None:
+            assert np.isnan(idx[nan_idx])
+
+    if NA_idx is not None:
+        assert idx[NA_idx] is cudf.NA
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        pd.Series(
+            ["this", "is", None, "a", "test"], index=["a", "b", "c", "d", "e"]
+        ),
+        pd.Series([0, 15, 10], index=[0, None, 9]),
+        pd.Series(
+            range(25),
+            index=pd.date_range(
+                start="2019-01-01", end="2019-01-02", freq="H"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "values",
+    [
+        [],
+        ["this", "is"],
+        [0, 19, 13],
+        ["2019-01-01 04:00:00", "2019-01-01 06:00:00", "2018-03-02 10:00:00"],
+    ],
+)
+def test_isin_index(data, values):
+    psr = _create_pandas_series_float64_default(data)
+    gsr = cudf.Series.from_pandas(psr)
+
+    got = gsr.index.isin(values)
+    expected = psr.index.isin(values)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3], ["red", "blue", "green"]], names=("number", "color")
+        ),
+        pd.MultiIndex.from_arrays([[], []], names=("number", "color")),
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 10, 100], ["red", "blue", "green", "pink", "white"]],
+            names=("number", "color"),
+        ),
+        pd.MultiIndex.from_product(
+            [[0, 1], ["red", "blue", "green"]], names=("number", "color")
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "values,level,err",
+    [
+        ([(1, "red"), (2, "blue"), (0, "green")], None, None),
+        (["red", "orange", "yellow"], "color", None),
+        (["red", "white", "yellow"], "color", None),
+        ([0, 1, 2, 10, 11, 15], "number", None),
+        ([0, 1, 2, 10, 11, 15], None, TypeError),
+        (pd.Series([0, 1, 2, 10, 11, 15]), None, TypeError),
+        (pd.Index([0, 1, 2, 10, 11, 15]), None, TypeError),
+        (pd.Index([0, 1, 2, 8, 11, 15]), "number", None),
+        (pd.Index(["red", "white", "yellow"]), "color", None),
+        ([(1, "red"), (3, "red")], None, None),
+        (((1, "red"), (3, "red")), None, None),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3], ["red", "blue", "green"]],
+                names=("number", "color"),
+            ),
+            None,
+            None,
+        ),
+        (
+            pd.MultiIndex.from_arrays([[], []], names=("number", "color")),
+            None,
+            None,
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [
+                    [1, 2, 3, 10, 100],
+                    ["red", "blue", "green", "pink", "white"],
+                ],
+                names=("number", "color"),
+            ),
+            None,
+            None,
+        ),
+    ],
+)
+def test_isin_multiindex(data, values, level, err):
+    pmdx = data
+    gmdx = cudf.from_pandas(data)
+
+    if err is None:
+        expected = pmdx.isin(values, level=level)
+        if isinstance(values, pd.MultiIndex):
+            values = cudf.from_pandas(values)
+        got = gmdx.isin(values, level=level)
+
+        assert_eq(got, expected)
+    else:
+        assert_exceptions_equal(
+            lfunc=pmdx.isin,
+            rfunc=gmdx.isin,
+            lfunc_args_and_kwargs=([values], {"level": level}),
+            rfunc_args_and_kwargs=([values], {"level": level}),
+            check_exception_type=False,
+        )
+
+
+range_data = [
+    range(np.random.randint(0, 100)),
+    range(9, 12, 2),
+    range(20, 30),
+    range(100, 1000, 10),
+    range(0, 10, -2),
+    range(0, -10, 2),
+    range(0, -10, -2),
+]
+
+
+@pytest.fixture(params=range_data)
+def rangeindex(request):
+    """Create a cudf RangeIndex of different `nrows`"""
+    return RangeIndex(request.param)
+
+
+@pytest.mark.parametrize(
+    "func",
+    ["nunique", "min", "max", "any", "values"],
+)
+def test_rangeindex_methods(rangeindex, func):
+    gidx = rangeindex
+    pidx = gidx.to_pandas()
+
+    if func == "values":
+        expected = pidx.values
+        actual = gidx.values
+    else:
+        expected = getattr(pidx, func)()
+        actual = getattr(gidx, func)()
+
+    assert_eq(expected, actual)
+
+
+def test_index_constructor_integer(default_integer_bitwidth):
+    got = cudf.Index([1, 2, 3])
+    expect = cudf.Index([1, 2, 3], dtype=f"int{default_integer_bitwidth}")
+
+    assert_eq(expect, got)
+
+
+def test_index_constructor_float(default_float_bitwidth):
+    got = cudf.Index([1.0, 2.0, 3.0])
+    expect = cudf.Index(
+        [1.0, 2.0, 3.0], dtype=f"float{default_float_bitwidth}"
+    )
+
+    assert_eq(expect, got)
+
+
+def test_rangeindex_union_default_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for union operation.
+    idx1 = cudf.RangeIndex(0, 2)
+    idx2 = cudf.RangeIndex(5, 6)
+
+    expected = cudf.Index([0, 1, 5], dtype=f"int{default_integer_bitwidth}")
+    actual = idx1.union(idx2)
+
+    assert_eq(expected, actual)
+
+
+def test_rangeindex_intersection_default_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for intersection operation.
+    idx1 = cudf.RangeIndex(0, 100)
+    # Intersecting two RangeIndex will _always_ result in a RangeIndex, use
+    # regular index here to force materializing.
+    idx2 = cudf.Index([50, 102])
+
+    expected = cudf.Index([50], dtype=f"int{default_integer_bitwidth}")
+    actual = idx1.intersection(idx2)
+
+    assert_eq(expected, actual)
+
+
+def test_rangeindex_take_default_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for take operation.
+    idx = cudf.RangeIndex(0, 100)
+    actual = idx.take([0, 3, 7, 62])
+    expected = cudf.Index(
+        [0, 3, 7, 62], dtype=f"int{default_integer_bitwidth}"
+    )
+    assert_eq(expected, actual)
+
+
+def test_rangeindex_apply_boolean_mask_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for apply boolean mask operation.
+    idx = cudf.RangeIndex(0, 8)
+    mask = [True, True, True, False, False, False, True, False]
+    actual = idx[mask]
+    expected = cudf.Index([0, 1, 2, 6], dtype=f"int{default_integer_bitwidth}")
+    assert_eq(expected, actual)
+
+
+def test_rangeindex_repeat_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for repeat operation.
+    idx = cudf.RangeIndex(0, 3)
+    actual = idx.repeat(3)
+    expected = cudf.Index(
+        [0, 0, 0, 1, 1, 1, 2, 2, 2], dtype=f"int{default_integer_bitwidth}"
+    )
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "op, expected, expected_kind",
+    [
+        (lambda idx: 2**idx, [2, 4, 8, 16], "int"),
+        (lambda idx: idx**2, [1, 4, 9, 16], "int"),
+        (lambda idx: idx / 2, [0.5, 1, 1.5, 2], "float"),
+        (lambda idx: 2 / idx, [2, 1, 2 / 3, 0.5], "float"),
+        (lambda idx: idx % 3, [1, 2, 0, 1], "int"),
+        (lambda idx: 3 % idx, [0, 1, 0, 3], "int"),
+    ],
+)
+def test_rangeindex_binops_user_option(
+    op, expected, expected_kind, default_integer_bitwidth
+):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for binary operation.
+    idx = cudf.RangeIndex(1, 5)
+    actual = op(idx)
+    expected = cudf.Index(
+        expected, dtype=f"{expected_kind}{default_integer_bitwidth}"
+    )
+    assert_eq(
+        expected,
+        actual,
+    )
+
+
+@pytest.mark.parametrize(
+    "op", [operator.add, operator.sub, operator.mul, operator.truediv]
+)
+def test_rangeindex_binop_diff_names_none(op):
+    idx1 = cudf.RangeIndex(10, 13, name="foo")
+    idx2 = cudf.RangeIndex(13, 16, name="bar")
+    result = op(idx1, idx2)
+    expected = op(idx1.to_pandas(), idx2.to_pandas())
+    assert_eq(result, expected)
+    assert result.name is None
+
+
+def test_rangeindex_join_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for join.
+    idx1 = cudf.RangeIndex(0, 10, name="a")
+    idx2 = cudf.RangeIndex(5, 15, name="b")
+
+    actual = idx1.join(idx2, how="inner", sort=True)
+    expected = idx1.to_pandas().join(idx2.to_pandas(), how="inner", sort=True)
+    assert actual.dtype == cudf.dtype(f"int{default_integer_bitwidth}")
+    assert_eq(expected, actual)
+
+
+def test_rangeindex_where_user_option(default_integer_bitwidth):
+    # Test that RangeIndex is materialized into 32 bit index under user
+    # configuration for where operation.
+    idx = cudf.RangeIndex(0, 10)
+    mask = [True, False, True, False, True, False, True, False, True, False]
+    actual = idx.where(mask, -1)
+    expected = cudf.Index(
+        [0, -1, 2, -1, 4, -1, 6, -1, 8, -1],
+        dtype=f"int{default_integer_bitwidth}",
+    )
+    assert_eq(expected, actual)
+
+
+def test_rangeindex_append_return_rangeindex():
+    idx = cudf.RangeIndex(0, 10)
+    result = idx.append([])
+    assert_eq(idx, result)
+
+    result = idx.append(cudf.Index([10]))
+    expected = cudf.RangeIndex(0, 11)
+    assert_eq(result, expected)
+
+
+index_data = [
+    range(np.random.randint(0, 100)),
+    range(0, 10, -2),
+    range(0, -10, 2),
+    range(0, -10, -2),
+    range(0, 1),
+    [1, 2, 3, 1, None, None],
+    [None, None, 3.2, 1, None, None],
+    [None, "a", "3.2", "z", None, None],
+    pd.Series(["a", "b", None], dtype="category"),
+    np.array([1, 2, 3, None], dtype="datetime64[s]"),
+]
+
+
+@pytest.fixture(params=index_data)
+def index(request):
+    """Create a cudf Index of different dtypes"""
+    return cudf.Index(request.param)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        "to_series",
+        "isna",
+        "notna",
+        "append",
+    ],
+)
+def test_index_methods(index, func):
+    gidx = index
+    pidx = gidx.to_pandas()
+
+    if func == "append":
+        expected = pidx.append(other=pidx)
+        actual = gidx.append(other=gidx)
+    else:
+        expected = getattr(pidx, func)()
+        actual = getattr(gidx, func)()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx, values",
+    [
+        (range(100, 1000, 10), [200, 600, 800]),
+        ([None, "a", "3.2", "z", None, None], ["a", "z"]),
+        (pd.Series(["a", "b", None], dtype="category"), [10, None]),
+    ],
+)
+def test_index_isin_values(idx, values):
+    gidx = cudf.Index(idx)
+    pidx = gidx.to_pandas()
+
+    actual = gidx.isin(values)
+    expected = pidx.isin(values)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx, scalar",
+    [
+        (range(0, -10, -2), -4),
+        ([None, "a", "3.2", "z", None, None], "x"),
+        (pd.Series(["a", "b", None], dtype="category"), 10),
+    ],
+)
+def test_index_isin_scalar_values(idx, scalar):
+    gidx = cudf.Index(idx)
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"only list-like objects are allowed to be passed "
+            f"to isin(), you passed a {type(scalar).__name__}"
+        ),
+    ):
+        gidx.isin(scalar)
+
+
+def test_index_any():
+    gidx = cudf.Index([1, 2, 3])
+    pidx = gidx.to_pandas()
+
+    assert_eq(pidx.any(), gidx.any())
+
+
+def test_index_values():
+    gidx = cudf.Index([1, 2, 3])
+    pidx = gidx.to_pandas()
+
+    assert_eq(pidx.values, gidx.values)
+
+
+def test_index_null_values():
+    gidx = cudf.Index([1.0, None, 3, 0, None])
+    with pytest.raises(ValueError):
+        gidx.values
+
+
+def test_index_error_list_index():
+    s = cudf.Series([[1, 2], [2], [4]])
+    with pytest.raises(
+        NotImplementedError,
+        match=re.escape(
+            "Unsupported column type passed to create an "
+            "Index: <class 'cudf.core.column.lists.ListColumn'>"
+        ),
+    ):
+        cudf.Index(s)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3],
+        pytest.param(
+            [np.nan, 10, 15, 16],
+            marks=pytest.mark.xfail(
+                reason="https://github.com/pandas-dev/pandas/issues/49818"
+            ),
+        ),
+        range(0, 10),
+        [np.nan, None, 10, 20],
+        ["ab", "zx", "pq"],
+        ["ab", "zx", None, "pq"],
+    ],
+)
+def test_index_hasnans(data):
+    gs = cudf.Index(data, nan_as_null=False)
+    ps = gs.to_pandas(nullable=True)
+
+    # Check type to avoid mixing Python bool and NumPy bool
+    assert isinstance(gs.hasnans, bool)
+    assert gs.hasnans == ps.hasnans
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 1, 1, 3, 2, 3],
+        [np.nan, 10, 15, 16, np.nan, 10, 16],
+        range(0, 10),
+        ["ab", "zx", None, "pq", "ab", None, "zx", None],
+    ],
+)
+@pytest.mark.parametrize("keep", ["first", "last", False])
+def test_index_duplicated(data, keep):
+    gs = cudf.Index(data)
+    ps = gs.to_pandas()
+
+    expected = ps.duplicated(keep=keep)
+    actual = gs.duplicated(keep=keep)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data,expected_dtype",
+    [
+        ([10, 11, 12], pd.Int64Dtype()),
+        ([0.1, 10.2, 12.3], pd.Float64Dtype()),
+        (["abc", None, "def"], pd.StringDtype()),
+    ],
+)
+def test_index_to_pandas_nullable(data, expected_dtype):
+    gi = cudf.Index(data)
+    pi = gi.to_pandas(nullable=True)
+    expected = pd.Index(data, dtype=expected_dtype)
+
+    assert_eq(pi, expected)
+
+
+class TestIndexScalarGetItem:
+    @pytest.fixture(
+        params=[range(1, 10, 2), [1, 2, 3], ["a", "b", "c"], [1.5, 2.5, 3.5]]
+    )
+    def index_values(self, request):
+        return request.param
+
+    @pytest.fixture(params=[int, np.int8, np.int32, np.int64])
+    def i(self, request):
+        return request.param(1)
+
+    def test_scalar_getitem(self, index_values, i):
+        index = cudf.Index(index_values)
+
+        assert not isinstance(index[i], cudf.Index)
+        assert index[i] == index_values[i]
+        assert_eq(index, index.to_pandas())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            pd.Timestamp("1970-01-01 00:00:00.000000001"),
+            pd.Timestamp("1970-01-01 00:00:00.000000002"),
+            12,
+            20,
+        ],
+        [
+            pd.Timedelta(10),
+            pd.Timedelta(20),
+            12,
+            20,
+        ],
+        [1, 2, 3, 4],
+    ],
+)
+def test_index_mixed_dtype_error(data):
+    pi = pd.Index(data, dtype="object")
+    with pytest.raises(TypeError):
+        cudf.Index(pi)
+
+
+@pytest.mark.parametrize("cls", [pd.DatetimeIndex, pd.TimedeltaIndex])
+def test_index_date_duration_freq_error(cls):
+    s = cls([1, 2, 3], freq="infer")
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.Index(s)
+
+
+@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+def test_index_getitem_time_duration(dtype):
+    gidx = cudf.Index([1, 2, 3, 4, None], dtype=dtype)
+    pidx = gidx.to_pandas()
+    with cudf.option_context("mode.pandas_compatible", True):
+        for i in range(len(gidx)):
+            if i == 4:
+                assert gidx[i] is pidx[i]
+            else:
+                assert_eq(gidx[i], pidx[i])
+
+
+@pytest.mark.parametrize("dtype", ALL_TYPES)
+def test_index_empty_from_pandas(request, dtype):
+    request.node.add_marker(
+        pytest.mark.xfail(
+            condition=not PANDAS_GE_200
+            and dtype
+            in {
+                "datetime64[ms]",
+                "datetime64[s]",
+                "datetime64[us]",
+                "timedelta64[ms]",
+                "timedelta64[s]",
+                "timedelta64[us]",
+            },
+            reason="Fixed in pandas-2.0",
+        )
+    )
+    pidx = pd.Index([], dtype=dtype)
+    gidx = cudf.from_pandas(pidx)
+
+    assert_eq(pidx, gidx)
+
+
+def test_empty_index_init():
+    pidx = pd.Index([])
+    gidx = cudf.Index([])
+
+    assert_eq(pidx, gidx)
+
+
+@pytest.mark.parametrize(
+    "data", [[1, 2, 3], ["ab", "cd", "e", None], range(0, 10)]
+)
+@pytest.mark.parametrize("data_name", [None, 1, "abc"])
+@pytest.mark.parametrize("index", [True, False])
+@pytest.mark.parametrize("name", [None, no_default, 1, "abc"])
+def test_index_to_frame(data, data_name, index, name):
+    pidx = pd.Index(data, name=data_name)
+    gidx = cudf.from_pandas(pidx)
+
+    with expect_warning_if(name is None):
+        expected = pidx.to_frame(index=index, name=name)
+    with expect_warning_if(name is None):
+        actual = gidx.to_frame(index=index, name=name)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], range(0, 10)])
+@pytest.mark.parametrize("dtype", ["str", "int64", "float64"])
+def test_index_with_index_dtype(data, dtype):
+    pidx = pd.Index(data)
+    gidx = cudf.Index(data)
+
+    expected = pd.Index(pidx, dtype=dtype)
+    actual = cudf.Index(gidx, dtype=dtype)
+
+    assert_eq(expected, actual)
+
+
+def test_period_index_error():
+    pidx = pd.PeriodIndex(year=[2000, 2002], quarter=[1, 3])
+    with pytest.raises(NotImplementedError):
+        cudf.from_pandas(pidx)
+    with pytest.raises(NotImplementedError):
+        cudf.Index(pidx)
+    with pytest.raises(NotImplementedError):
+        cudf.Series(pidx)
+    with pytest.raises(NotImplementedError):
+        cudf.Series(pd.Series(pidx))
+    with pytest.raises(NotImplementedError):
+        cudf.Series(pd.array(pidx))
+
+
+def test_index_from_dataframe_valueerror():
+    with pytest.raises(ValueError):
+        cudf.Index(cudf.DataFrame(range(1)))
+
+
+def test_index_from_scalar_valueerror():
+    with pytest.raises(ValueError):
+        cudf.Index(11)
+
+
+@pytest.mark.parametrize("idx", [0, np.int64(0)])
+def test_index_getitem_from_int(idx):
+    result = cudf.Index([1, 2])[idx]
+    assert result == 1
+
+
+@pytest.mark.parametrize("idx", [1.5, True, "foo"])
+def test_index_getitem_from_nonint_raises(idx):
+    with pytest.raises(ValueError):
+        cudf.Index([1, 2])[idx]
diff --git a/python/cudf/cudf/tests/test_indexing.py b/python/cudf/cudf/tests/test_indexing.py
new file mode 100644
index 0000000..27e84f1
--- /dev/null
+++ b/python/cudf/cudf/tests/test_indexing.py
@@ -0,0 +1,2197 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+from datetime import datetime
+from itertools import combinations
+
+import cupy
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing import _utils as utils
+from cudf.testing._utils import (
+    INTEGER_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+)
+
+index_dtypes = INTEGER_TYPES
+
+
+@pytest.fixture
+def pdf_gdf():
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 3], "b": ["c", "d", "e"]}, index=["one", "two", "three"]
+    )
+    gdf = cudf.from_pandas(pdf)
+    return pdf, gdf
+
+
+@pytest.fixture
+def pdf_gdf_multi():
+    pdf = pd.DataFrame(np.random.rand(7, 5))
+    pdfIndex = pd.MultiIndex(
+        [
+            ["a", "b", "c"],
+            ["house", "store", "forest"],
+            ["clouds", "clear", "storm"],
+            ["fire", "smoke", "clear"],
+        ],
+        [
+            [0, 0, 0, 0, 1, 1, 2],
+            [1, 1, 1, 1, 0, 0, 2],
+            [0, 0, 2, 2, 2, 0, 1],
+            [0, 0, 0, 1, 2, 0, 1],
+        ],
+    )
+    pdfIndex.names = ["alpha", "location", "weather", "sign"]
+    pdf.index = pdfIndex
+    gdf = cudf.from_pandas(pdf)
+    return pdf, gdf
+
+
+@pytest.mark.parametrize(
+    "i1, i2, i3",
+    (
+        [
+            (slice(None, 12), slice(3, None), slice(None, None, 2)),
+            (range(12), range(3, 12), range(0, 9, 2)),
+            (np.arange(12), np.arange(3, 12), np.arange(0, 9, 2)),
+            (list(range(12)), list(range(3, 12)), list(range(0, 9, 2))),
+            (
+                pd.Series(range(12)),
+                pd.Series(range(3, 12)),
+                pd.Series(range(0, 9, 2)),
+            ),
+            (
+                cudf.Series(range(12)),
+                cudf.Series(range(3, 12)),
+                cudf.Series(range(0, 9, 2)),
+            ),
+            (
+                [i in range(12) for i in range(20)],
+                [i in range(3, 12) for i in range(12)],
+                [i in range(0, 9, 2) for i in range(9)],
+            ),
+            (
+                np.array([i in range(12) for i in range(20)], dtype=bool),
+                np.array([i in range(3, 12) for i in range(12)], dtype=bool),
+                np.array([i in range(0, 9, 2) for i in range(9)], dtype=bool),
+            ),
+        ]
+        + [
+            (
+                np.arange(12, dtype=t),
+                np.arange(3, 12, dtype=t),
+                np.arange(0, 9, 2, dtype=t),
+            )
+            for t in index_dtypes
+        ]
+    ),
+    ids=(
+        [
+            "slice",
+            "range",
+            "numpy.array",
+            "list",
+            "pandas.Series",
+            "Series",
+            "list[bool]",
+            "numpy.array[bool]",
+        ]
+        + ["numpy.array[%s]" % np.dtype(t).type.__name__ for t in index_dtypes]
+    ),
+)
+def test_series_indexing(i1, i2, i3):
+    a1 = np.arange(20)
+    series = cudf.Series(a1)
+
+    # Indexing
+    sr1 = series.iloc[i1]
+    assert sr1.null_count == 0
+    np.testing.assert_equal(sr1.to_numpy(), a1[:12])
+
+    sr2 = sr1.iloc[i2]
+    assert sr2.null_count == 0
+    np.testing.assert_equal(sr2.to_numpy(), a1[3:12])
+
+    # Index with stride
+    sr3 = sr2.iloc[i3]
+    assert sr3.null_count == 0
+    np.testing.assert_equal(sr3.to_numpy(), a1[3:12:2])
+
+    # Integer indexing
+    if isinstance(i1, range):
+        for i in i1:  # Python int-s
+            assert series[i] == a1[i]
+    if isinstance(i1, np.ndarray) and i1.dtype in index_dtypes:
+        for i in i1:  # numpy integers
+            assert series[i] == a1[i]
+
+
+@pytest.mark.parametrize(
+    "arg",
+    [
+        1,
+        -1,
+        "b",
+        np.int32(1),
+        np.uint32(1),
+        np.int8(1),
+        np.uint8(1),
+        np.int16(1),
+        np.uint16(1),
+        np.int64(1),
+        np.uint64(1),
+    ],
+)
+def test_series_get_item_iloc_defer(arg):
+    # Indexing for non-numeric dtype Index
+    ps = pd.Series([1, 2, 3], index=pd.Index(["a", "b", "c"]))
+    gs = cudf.from_pandas(ps)
+
+    expect = ps[arg]
+    got = gs[arg]
+
+    assert_eq(expect, got)
+
+
+def test_series_iloc_defer_cudf_scalar():
+    ps = pd.Series([1, 2, 3], index=pd.Index(["a", "b", "c"]))
+    gs = cudf.from_pandas(ps)
+
+    for t in index_dtypes:
+        arg = cudf.Scalar(1, dtype=t)
+        got = gs[arg]
+        expect = 2
+        assert_eq(expect, got)
+
+
+def test_series_indexing_large_size():
+    n_elem = 100_000
+    gsr = cudf.Series(cupy.ones(n_elem))
+    gsr[0] = None
+    got = gsr[gsr.isna()]
+    expect = cudf.Series([None], dtype="float64")
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("psr", [pd.Series([1, 2, 3], index=["a", "b", "c"])])
+@pytest.mark.parametrize(
+    "arg", ["b", ["a", "c"], slice(1, 2, 1), [True, False, True]]
+)
+def test_series_get_item(psr, arg):
+    gsr = cudf.from_pandas(psr)
+
+    expect = psr[arg]
+    got = gsr[arg]
+
+    assert_eq(expect, got)
+
+
+def test_dataframe_column_name_indexing():
+    df = cudf.DataFrame()
+    data = np.asarray(range(10), dtype=np.int32)
+    df["a"] = data
+    df[1] = data
+    np.testing.assert_equal(
+        df["a"].to_numpy(), np.asarray(range(10), dtype=np.int32)
+    )
+    np.testing.assert_equal(
+        df[1].to_numpy(), np.asarray(range(10), dtype=np.int32)
+    )
+
+    pdf = pd.DataFrame()
+    nelem = 10
+    pdf["key1"] = np.random.randint(0, 5, nelem)
+    pdf["key2"] = np.random.randint(0, 3, nelem)
+    pdf[1] = np.arange(1, 1 + nelem)
+    pdf[2] = np.random.random(nelem)
+    df = cudf.from_pandas(pdf)
+
+    assert_eq(df[df.columns], df)
+    assert_eq(df[df.columns[:1]], df[["key1"]])
+
+    for i in range(1, len(pdf.columns) + 1):
+        for idx in combinations(pdf.columns, i):
+            assert pdf[list(idx)].equals(df[list(idx)].to_pandas())
+
+    # test for only numeric columns
+    df = pd.DataFrame()
+    for i in range(0, 10):
+        df[i] = range(nelem)
+    gdf = cudf.DataFrame.from_pandas(df)
+    assert_eq(gdf, df)
+
+    assert_eq(gdf[gdf.columns], gdf)
+    assert_eq(gdf[gdf.columns[:3]], gdf[[0, 1, 2]])
+
+
+def test_dataframe_slicing():
+    df = cudf.DataFrame()
+    size = 123
+    df["a"] = ha = np.random.randint(low=0, high=100, size=size).astype(
+        np.int32
+    )
+    df["b"] = hb = np.random.random(size).astype(np.float32)
+    df["c"] = hc = np.random.randint(low=0, high=100, size=size).astype(
+        np.int64
+    )
+    df["d"] = hd = np.random.random(size).astype(np.float64)
+
+    # Row slice first 10
+    first_10 = df[:10]
+    assert len(first_10) == 10
+    assert tuple(first_10.columns) == ("a", "b", "c", "d")
+    np.testing.assert_equal(first_10["a"].to_numpy(), ha[:10])
+    np.testing.assert_equal(first_10["b"].to_numpy(), hb[:10])
+    np.testing.assert_equal(first_10["c"].to_numpy(), hc[:10])
+    np.testing.assert_equal(first_10["d"].to_numpy(), hd[:10])
+    del first_10
+
+    # Row slice last 10
+    last_10 = df[-10:]
+    assert len(last_10) == 10
+    assert tuple(last_10.columns) == ("a", "b", "c", "d")
+    np.testing.assert_equal(last_10["a"].to_numpy(), ha[-10:])
+    np.testing.assert_equal(last_10["b"].to_numpy(), hb[-10:])
+    np.testing.assert_equal(last_10["c"].to_numpy(), hc[-10:])
+    np.testing.assert_equal(last_10["d"].to_numpy(), hd[-10:])
+    del last_10
+
+    # Row slice [begin:end]
+    begin = 7
+    end = 121
+    subrange = df[begin:end]
+    assert len(subrange) == end - begin
+    assert tuple(subrange.columns) == ("a", "b", "c", "d")
+    np.testing.assert_equal(subrange["a"].to_numpy(), ha[begin:end])
+    np.testing.assert_equal(subrange["b"].to_numpy(), hb[begin:end])
+    np.testing.assert_equal(subrange["c"].to_numpy(), hc[begin:end])
+    np.testing.assert_equal(subrange["d"].to_numpy(), hd[begin:end])
+    del subrange
+
+
+@pytest.mark.parametrize("step", [1, 2, 5])
+@pytest.mark.parametrize("scalar", [0, 20, 100])
+def test_dataframe_loc(scalar, step):
+    size = 123
+    pdf = pd.DataFrame(
+        {
+            "a": np.random.randint(low=0, high=100, size=size),
+            "b": np.random.random(size).astype(np.float32),
+            "c": np.random.random(size).astype(np.float64),
+            "d": np.random.random(size).astype(np.float64),
+        }
+    )
+    pdf.index.name = "index"
+
+    df = cudf.DataFrame.from_pandas(pdf)
+
+    assert_eq(df.loc[:, ["a"]], pdf.loc[:, ["a"]])
+
+    assert_eq(df.loc[:, "d"], pdf.loc[:, "d"])
+
+    # Scalar label
+    assert_eq(df.loc[scalar], pdf.loc[scalar])
+
+    # Full slice
+    assert_eq(df.loc[:, "c"], pdf.loc[:, "c"])
+
+    # Repeat with at[]
+    assert_eq(df.loc[:, ["a"]], df.at[:, ["a"]])
+    assert_eq(df.loc[:, "d"], df.at[:, "d"])
+    assert_eq(df.loc[scalar], df.at[scalar])
+    assert_eq(df.loc[:, "c"], df.at[:, "c"])
+
+    begin = 110
+    end = 122
+
+    assert_eq(
+        df.loc[begin:end:step, ["c", "d", "a"]],
+        pdf.loc[begin:end:step, ["c", "d", "a"]],
+    )
+
+    assert_eq(df.loc[begin:end, ["c", "d"]], pdf.loc[begin:end, ["c", "d"]])
+
+    # Slicing on columns:
+    assert_eq(
+        df.loc[begin:end:step, "a":"c"], pdf.loc[begin:end:step, "a":"c"]
+    )
+
+    # Slicing of size 1:
+    assert_eq(df.loc[begin:begin, "a"], pdf.loc[begin:begin, "a"])
+
+    # TODO: Pandas changes the dtype here when it shouldn't
+    assert_eq(
+        df.loc[begin, "a":"a"], pdf.loc[begin, "a":"a"], check_dtype=False
+    )
+
+    # Repeat with at[]
+    assert_eq(
+        df.loc[begin:end:step, ["c", "d", "a"]],
+        df.at[begin:end:step, ["c", "d", "a"]],
+    )
+    assert_eq(df.loc[begin:end, ["c", "d"]], df.at[begin:end, ["c", "d"]])
+    assert_eq(df.loc[begin:end:step, "a":"c"], df.at[begin:end:step, "a":"c"])
+    assert_eq(df.loc[begin:begin, "a"], df.at[begin:begin, "a"])
+    assert_eq(df.loc[begin, "a":"a"], df.at[begin, "a":"a"], check_dtype=False)
+
+    # Make int64 index
+    offset = 50
+    df2 = df[offset:]
+    pdf2 = pdf[offset:]
+    begin = 117
+    end = 122
+    assert_eq(
+        df2.loc[begin:end, ["c", "d", "a"]],
+        pdf2.loc[begin:end, ["c", "d", "a"]],
+    )
+
+    # loc with list like indexing
+    assert_eq(df.loc[[0]], pdf.loc[[0]])
+    # loc with column like indexing
+    assert_eq(df.loc[cudf.Series([0])], pdf.loc[pd.Series([0])])
+    assert_eq(df.loc[cudf.Series([0])._column], pdf.loc[pd.Series([0])])
+    assert_eq(df.loc[np.array([0])], pdf.loc[np.array([0])])
+
+
+def test_dataframe_loc_duplicate_index_scalar():
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4, 5]}, index=[1, 2, 1, 4, 2])
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    pdf_sorted = pdf.sort_values(by=list(pdf.columns), axis=0)
+    gdf_sorted = gdf.sort_values(by=list(gdf.columns), axis=0)
+
+    assert_eq(pdf_sorted, gdf_sorted)
+
+
+@pytest.mark.parametrize(
+    "mask",
+    [[True, False, False, False, False], [True, False, True, False, True]],
+)
+@pytest.mark.parametrize("arg", ["a", slice("a", "a"), slice("a", "b")])
+def test_dataframe_loc_mask(mask, arg):
+    pdf = pd.DataFrame(
+        {"a": ["a", "b", "c", "d", "e"], "b": ["f", "g", "h", "i", "j"]}
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    assert_eq(pdf.loc[mask, arg], gdf.loc[mask, arg])
+
+
+def test_dataframe_loc_outbound():
+    df = cudf.DataFrame()
+    size = 10
+    df["a"] = ha = np.random.randint(low=0, high=100, size=size).astype(
+        np.int32
+    )
+    df["b"] = hb = np.random.random(size).astype(np.float32)
+
+    pdf = pd.DataFrame()
+    pdf["a"] = ha
+    pdf["b"] = hb
+
+    assert_exceptions_equal(lambda: pdf.loc[11], lambda: df.loc[11])
+
+
+def test_series_loc_numerical():
+    ps = pd.Series([1, 2, 3, 4, 5], index=[5, 6, 7, 8, 9])
+    gs = cudf.Series.from_pandas(ps)
+
+    assert_eq(ps.loc[5], gs.loc[5])
+    assert_eq(ps.loc[6], gs.loc[6])
+    assert_eq(ps.loc[6:8], gs.loc[6:8])
+    assert_eq(ps.loc[:8], gs.loc[:8])
+    assert_eq(ps.loc[6:], gs.loc[6:])
+    assert_eq(ps.loc[::2], gs.loc[::2])
+    assert_eq(ps.loc[[5, 8, 9]], gs.loc[[5, 8, 9]])
+    assert_eq(
+        ps.loc[[True, False, True, False, True]],
+        gs.loc[[True, False, True, False, True]],
+    )
+    assert_eq(ps.loc[[5, 8, 9]], gs.loc[cupy.array([5, 8, 9])])
+
+
+def test_series_loc_float_index():
+    ps = pd.Series([1, 2, 3, 4, 5], index=[5.43, 6.34, 7.34, 8.0, 9.1])
+    gs = cudf.Series.from_pandas(ps)
+
+    assert_eq(ps.loc[5.43], gs.loc[5.43])
+    assert_eq(ps.loc[8], gs.loc[8])
+    assert_eq(ps.loc[6.1:8], gs.loc[6.1:8])
+    assert_eq(ps.loc[:7.1], gs.loc[:7.1])
+    assert_eq(ps.loc[6.345:], gs.loc[6.345:])
+    assert_eq(ps.loc[::2], gs.loc[::2])
+    assert_eq(
+        ps.loc[[True, False, True, False, True]],
+        gs.loc[[True, False, True, False, True]],
+    )
+
+
+def test_series_loc_string():
+    ps = pd.Series(
+        [1, 2, 3, 4, 5], index=["one", "two", "three", "four", "five"]
+    )
+    gs = cudf.Series.from_pandas(ps)
+
+    assert_eq(ps.loc["one"], gs.loc["one"])
+    assert_eq(ps.loc["five"], gs.loc["five"])
+    assert_eq(ps.loc["two":"four"], gs.loc["two":"four"])
+    assert_eq(ps.loc[:"four"], gs.loc[:"four"])
+    assert_eq(ps.loc["two":], gs.loc["two":])
+    assert_eq(ps.loc[::2], gs.loc[::2])
+    assert_eq(ps.loc[["one", "four", "five"]], gs.loc[["one", "four", "five"]])
+    assert_eq(
+        ps.loc[[True, False, True, False, True]],
+        gs.loc[[True, False, True, False, True]],
+    )
+
+
+def test_series_loc_datetime():
+    ps = pd.Series(
+        [1, 2, 3, 4, 5], index=pd.date_range("20010101", "20010105")
+    )
+    gs = cudf.Series.from_pandas(ps)
+
+    # a few different ways of specifying a datetime label:
+    assert_eq(ps.loc["20010101"], gs.loc["20010101"])
+    assert_eq(ps.loc["2001-01-01"], gs.loc["2001-01-01"])
+    assert_eq(
+        ps.loc[pd.to_datetime("2001-01-01")],
+        gs.loc[pd.to_datetime("2001-01-01")],
+    )
+    assert_eq(
+        ps.loc[np.datetime64("2001-01-01")],
+        gs.loc[np.datetime64("2001-01-01")],
+    )
+
+    assert_eq(
+        ps.loc["2001-01-02":"2001-01-05"],
+        gs.loc["2001-01-02":"2001-01-05"],
+        check_freq=False,
+    )
+    assert_eq(ps.loc["2001-01-02":], gs.loc["2001-01-02":], check_freq=False)
+    assert_eq(ps.loc[:"2001-01-04"], gs.loc[:"2001-01-04"], check_freq=False)
+    assert_eq(ps.loc[::2], gs.loc[::2], check_freq=False)
+
+    assert_eq(
+        ps.loc[["2001-01-01", "2001-01-04", "2001-01-05"]],
+        gs.loc[["2001-01-01", "2001-01-04", "2001-01-05"]],
+    )
+
+    assert_eq(
+        ps.loc[
+            [
+                pd.to_datetime("2001-01-01"),
+                pd.to_datetime("2001-01-04"),
+                pd.to_datetime("2001-01-05"),
+            ]
+        ],
+        gs.loc[
+            [
+                pd.to_datetime("2001-01-01"),
+                pd.to_datetime("2001-01-04"),
+                pd.to_datetime("2001-01-05"),
+            ]
+        ],
+    )
+    assert_eq(
+        ps.loc[[True, False, True, False, True]],
+        gs.loc[[True, False, True, False, True]],
+        check_freq=False,
+    )
+
+    just_less_than_max = ps.index.max() - pd.Timedelta("5m")
+
+    assert_eq(
+        ps.loc[:just_less_than_max],
+        gs.loc[:just_less_than_max],
+        check_freq=False,
+    )
+
+
+def test_series_loc_categorical():
+    ps = pd.Series(
+        [1, 2, 3, 4, 5], index=pd.Categorical(["a", "b", "c", "d", "e"])
+    )
+    gs = cudf.Series.from_pandas(ps)
+
+    assert_eq(ps.loc["a"], gs.loc["a"])
+    assert_eq(ps.loc["e"], gs.loc["e"])
+    assert_eq(ps.loc["b":"d"], gs.loc["b":"d"])
+    assert_eq(ps.loc[:"d"], gs.loc[:"d"])
+    assert_eq(ps.loc["b":], gs.loc["b":])
+    assert_eq(ps.loc[::2], gs.loc[::2])
+
+    # order of categories changes, so we can only
+    # compare values:
+    assert_eq(
+        ps.loc[["a", "d", "e"]].values, gs.loc[["a", "d", "e"]].to_numpy()
+    )
+
+    assert_eq(
+        ps.loc[[True, False, True, False, True]],
+        gs.loc[[True, False, True, False, True]],
+    )
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        pd.DataFrame(
+            {"a": [1, 2, 3, 4]},
+            index=pd.MultiIndex.from_frame(
+                pd.DataFrame(
+                    {"A": [2, 3, 1, 4], "B": ["low", "high", "high", "low"]}
+                )
+            ),
+        ),
+        pd.Series(
+            [1, 2, 3, 4],
+            index=pd.MultiIndex.from_frame(
+                pd.DataFrame(
+                    {"A": [2, 3, 1, 4], "B": ["low", "high", "high", "low"]}
+                )
+            ),
+        ),
+    ],
+)
+def test_dataframe_series_loc_multiindex(obj):
+    pindex = pd.MultiIndex.from_frame(
+        pd.DataFrame({"A": [3, 2], "B": ["high", "low"]})
+    )
+
+    gobj = cudf.from_pandas(obj)
+    gindex = cudf.MultiIndex.from_pandas(pindex)
+
+    # cudf MultiIndex as arg
+    expected = obj.loc[pindex]
+    got = gobj.loc[gindex]
+    assert_eq(expected, got)
+
+    # pandas MultiIndex as arg
+    expected = obj.loc[pindex]
+    got = gobj.loc[pindex]
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("nelem", [2, 5, 20, 100])
+def test_series_iloc(nelem):
+    # create random cudf.Series
+    np.random.seed(12)
+    ps = pd.Series(np.random.sample(nelem))
+
+    # gpu cudf.Series
+    gs = cudf.Series(ps)
+
+    # positive tests for indexing
+    np.testing.assert_allclose(gs.iloc[-1 * nelem], ps.iloc[-1 * nelem])
+    np.testing.assert_allclose(gs.iloc[-1], ps.iloc[-1])
+    np.testing.assert_allclose(gs.iloc[0], ps.iloc[0])
+    np.testing.assert_allclose(gs.iloc[1], ps.iloc[1])
+    np.testing.assert_allclose(gs.iloc[nelem - 1], ps.iloc[nelem - 1])
+
+    # positive tests for slice
+    np.testing.assert_allclose(gs.iloc[-1:1].to_numpy(), ps.iloc[-1:1])
+    np.testing.assert_allclose(
+        gs.iloc[nelem - 1 : -1].to_numpy(), ps.iloc[nelem - 1 : -1]
+    )
+    np.testing.assert_allclose(
+        gs.iloc[0 : nelem - 1].to_pandas(), ps.iloc[0 : nelem - 1]
+    )
+    np.testing.assert_allclose(gs.iloc[0:nelem].to_pandas(), ps.iloc[0:nelem])
+    np.testing.assert_allclose(gs.iloc[1:1].to_pandas(), ps.iloc[1:1])
+    np.testing.assert_allclose(gs.iloc[1:2].to_pandas(), ps.iloc[1:2].values)
+    np.testing.assert_allclose(
+        gs.iloc[nelem - 1 : nelem + 1].to_pandas(),
+        ps.iloc[nelem - 1 : nelem + 1],
+    )
+    np.testing.assert_allclose(
+        gs.iloc[nelem : nelem * 2].to_pandas(), ps.iloc[nelem : nelem * 2]
+    )
+
+
+@pytest.mark.parametrize("nelem", [2, 5, 20, 100])
+def test_dataframe_iloc(nelem):
+    gdf = cudf.DataFrame()
+
+    gdf["a"] = ha = np.random.randint(low=0, high=100, size=nelem).astype(
+        np.int32
+    )
+    gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
+
+    pdf = pd.DataFrame()
+    pdf["a"] = ha
+    pdf["b"] = hb
+
+    gdf.index.name = "index"
+    pdf.index.name = "index"
+
+    assert_eq(gdf.iloc[-1:1], pdf.iloc[-1:1])
+    assert_eq(gdf.iloc[nelem - 1 : -1], pdf.iloc[nelem - 1 : -1])
+    assert_eq(gdf.iloc[0 : nelem - 1], pdf.iloc[0 : nelem - 1])
+    assert_eq(gdf.iloc[0:nelem], pdf.iloc[0:nelem])
+    assert_eq(gdf.iloc[1:1], pdf.iloc[1:1])
+    assert_eq(gdf.iloc[1:2], pdf.iloc[1:2])
+    assert_eq(gdf.iloc[nelem - 1 : nelem + 1], pdf.iloc[nelem - 1 : nelem + 1])
+    assert_eq(gdf.iloc[nelem : nelem * 2], pdf.iloc[nelem : nelem * 2])
+
+    assert_eq(gdf.iloc[-1 * nelem], pdf.iloc[-1 * nelem])
+    assert_eq(gdf.iloc[-1], pdf.iloc[-1])
+    assert_eq(gdf.iloc[0], pdf.iloc[0])
+    assert_eq(gdf.iloc[1], pdf.iloc[1])
+    assert_eq(gdf.iloc[nelem - 1], pdf.iloc[nelem - 1])
+
+    # Repeat the above with iat[]
+    assert_eq(gdf.iloc[-1:1], gdf.iat[-1:1])
+    assert_eq(gdf.iloc[nelem - 1 : -1], gdf.iat[nelem - 1 : -1])
+    assert_eq(gdf.iloc[0 : nelem - 1], gdf.iat[0 : nelem - 1])
+    assert_eq(gdf.iloc[0:nelem], gdf.iat[0:nelem])
+    assert_eq(gdf.iloc[1:1], gdf.iat[1:1])
+    assert_eq(gdf.iloc[1:2], gdf.iat[1:2])
+    assert_eq(gdf.iloc[nelem - 1 : nelem + 1], gdf.iat[nelem - 1 : nelem + 1])
+    assert_eq(gdf.iloc[nelem : nelem * 2], gdf.iat[nelem : nelem * 2])
+
+    assert_eq(gdf.iloc[-1 * nelem], gdf.iat[-1 * nelem])
+    assert_eq(gdf.iloc[-1], gdf.iat[-1])
+    assert_eq(gdf.iloc[0], gdf.iat[0])
+    assert_eq(gdf.iloc[1], gdf.iat[1])
+    assert_eq(gdf.iloc[nelem - 1], gdf.iat[nelem - 1])
+
+    # iloc with list like indexing
+    assert_eq(gdf.iloc[[0]], pdf.iloc[[0]])
+    # iloc with column like indexing
+    assert_eq(gdf.iloc[cudf.Series([0])], pdf.iloc[pd.Series([0])])
+    assert_eq(gdf.iloc[cudf.Series([0])._column], pdf.iloc[pd.Series([0])])
+    assert_eq(gdf.iloc[np.array([0])], pdf.loc[np.array([0])])
+
+
+def test_dataframe_iloc_tuple():
+    gdf = cudf.DataFrame()
+    nelem = 123
+    gdf["a"] = ha = np.random.randint(low=0, high=100, size=nelem).astype(
+        np.int32
+    )
+    gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
+
+    pdf = pd.DataFrame()
+    pdf["a"] = ha
+    pdf["b"] = hb
+
+    assert_eq(gdf.iloc[1, [1]], pdf.iloc[1, [1]], check_dtype=False)
+    assert_eq(gdf.iloc[:, -1], pdf.iloc[:, -1])
+
+
+def test_dataframe_iloc_index_error():
+    gdf = cudf.DataFrame()
+    nelem = 123
+    gdf["a"] = ha = np.random.randint(low=0, high=100, size=nelem).astype(
+        np.int32
+    )
+    gdf["b"] = hb = np.random.random(nelem).astype(np.float32)
+
+    pdf = pd.DataFrame()
+    pdf["a"] = ha
+    pdf["b"] = hb
+
+    with pytest.raises(IndexError):
+        pdf.iloc[nelem * 2]
+    with pytest.raises(IndexError):
+        gdf.iloc[nelem * 2]
+
+
+@pytest.mark.parametrize("ntake", [0, 1, 10, 123, 122, 200])
+def test_dataframe_take(ntake):
+    np.random.seed(0)
+    df = cudf.DataFrame()
+
+    nelem = 123
+    df["ii"] = np.random.randint(0, 20, nelem)
+    df["ff"] = np.random.random(nelem)
+
+    take_indices = np.random.randint(0, len(df), ntake)
+
+    actual = df.take(take_indices)
+    expected = df.to_pandas().take(take_indices)
+
+    assert actual.ii.null_count == 0
+    assert actual.ff.null_count == 0
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("ntake", [1, 2, 8, 9])
+def test_dataframe_take_with_multiindex(ntake):
+    np.random.seed(0)
+    df = cudf.DataFrame(
+        index=cudf.MultiIndex(
+            levels=[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
+            codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
+        )
+    )
+
+    nelem = 9
+    df["ii"] = np.random.randint(0, 20, nelem)
+    df["ff"] = np.random.random(nelem)
+
+    take_indices = np.random.randint(0, len(df), ntake)
+
+    actual = df.take(take_indices)
+    expected = df.to_pandas().take(take_indices)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("ntake", [0, 1, 10, 123, 122, 200])
+def test_series_take(ntake):
+    np.random.seed(0)
+    nelem = 123
+
+    psr = pd.Series(np.random.randint(0, 20, nelem))
+    gsr = cudf.Series(psr)
+
+    take_indices = np.random.randint(0, len(gsr), ntake)
+
+    actual = gsr.take(take_indices)
+    expected = psr.take(take_indices)
+
+    assert_eq(actual, expected)
+
+
+def test_series_take_positional():
+    psr = pd.Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"])
+
+    gsr = cudf.Series.from_pandas(psr)
+
+    take_indices = [1, 2, 0, 3]
+
+    expect = psr.take(take_indices)
+    got = gsr.take(take_indices)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("nelem", [0, 1, 5, 20, 100])
+@pytest.mark.parametrize("slice_start", [None, 0, 1, 3, 10, -10])
+@pytest.mark.parametrize("slice_end", [None, 0, 1, 30, 50, -1])
+def test_dataframe_masked_slicing(nelem, slice_start, slice_end):
+    gdf = cudf.DataFrame()
+    gdf["a"] = list(range(nelem))
+    gdf["b"] = list(range(nelem, 2 * nelem))
+    gdf["a"] = gdf["a"]._column.set_mask(utils.random_bitmask(nelem))
+    gdf["b"] = gdf["b"]._column.set_mask(utils.random_bitmask(nelem))
+
+    def do_slice(x):
+        return x[slice_start:slice_end]
+
+    expect = do_slice(gdf.to_pandas())
+    got = do_slice(gdf).to_pandas()
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("dtype", [int, float, str])
+def test_empty_boolean_mask(dtype):
+    gdf = cudf.datasets.randomdata(nrows=0, dtypes={"a": dtype})
+    pdf = gdf.to_pandas()
+
+    compare_val = dtype(1)
+
+    expected = pdf[pdf.a == compare_val]
+    got = gdf[gdf.a == compare_val]
+    assert_eq(expected, got)
+
+    expected = pdf.a[pdf.a == compare_val]
+    got = gdf.a[gdf.a == compare_val]
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4],
+        [1.0, 2.0, 3.0, 4.0],
+        ["one", "two", "three", "four"],
+        pd.Series(["a", "b", "c", "d"], dtype="category"),
+        pd.Series(pd.date_range("2010-01-01", "2010-01-04")),
+    ],
+)
+@pytest.mark.parametrize(
+    "mask",
+    [
+        [True, True, True, True],
+        [False, False, False, False],
+        [True, False, True, False],
+        [True, False, False, True],
+        np.array([True, False, True, False]),
+        pd.Series([True, False, True, False]),
+        cudf.Series([True, False, True, False]),
+    ],
+)
+@pytest.mark.parametrize("nulls", ["one", "some", "all", "none"])
+def test_series_apply_boolean_mask(data, mask, nulls):
+    psr = pd.Series(data)
+
+    if len(data) > 0:
+        if nulls == "one":
+            p = np.random.randint(0, 4)
+            psr[p] = None
+        elif nulls == "some":
+            p1, p2 = np.random.randint(0, 4, (2,))
+            psr[p1] = None
+            psr[p2] = None
+        elif nulls == "all":
+            psr[:] = None
+
+    gsr = cudf.from_pandas(psr)
+
+    # TODO: from_pandas(psr) has dtype "float64"
+    # when psr has dtype "object" and is all None
+    if psr.dtype == "object" and nulls == "all":
+        gsr = cudf.Series([None, None, None, None], dtype="object")
+
+    if isinstance(mask, cudf.Series):
+        expect = psr[mask.to_pandas()]
+    else:
+        expect = psr[mask]
+    got = gsr[mask]
+
+    assert_eq(expect, got)
+
+
+def test_dataframe_apply_boolean_mask():
+    pdf = pd.DataFrame(
+        {
+            "a": [0, 1, 2, 3],
+            "b": [0.1, 0.2, None, 0.3],
+            "c": ["a", None, "b", "c"],
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    assert_eq(pdf[[True, False, True, False]], gdf[[True, False, True, False]])
+
+
+"""
+This test compares cudf and Pandas DataFrame boolean indexing.
+"""
+
+
+@pytest.mark.parametrize(
+    "mask_fn", [lambda x: x, lambda x: np.array(x), lambda x: pd.Series(x)]
+)
+def test_dataframe_boolean_mask(mask_fn):
+    mask_base = [
+        True,
+        False,
+        True,
+        False,
+        True,
+        False,
+        True,
+        False,
+        True,
+        False,
+    ]
+    pdf = pd.DataFrame({"x": range(10), "y": range(10)})
+    gdf = cudf.from_pandas(pdf)
+    mask = mask_fn(mask_base)
+    assert len(mask) == gdf.shape[0]
+    pdf_masked = pdf[mask]
+    gdf_masked = gdf[mask]
+    assert pdf_masked.to_string().split() == gdf_masked.to_string().split()
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        (0, 4),
+        (1, 4),
+        ([0, 1], 4),
+        ([0, 1], [4, 5]),
+        (slice(0, 2), [4, 5]),
+        (slice(1, None), [4, 5, 6, 7]),
+        ([], 1),
+        ([], []),
+        (slice(None, None), 1),
+        (slice(-1, -3), 7),
+    ],
+)
+@pytest.mark.parametrize("nulls", ["none", "some", "all"])
+def test_series_setitem_basics(key, value, nulls):
+    psr = pd.Series([1, 2, 3, 4, 5])
+    if nulls == "some":
+        psr[[0, 4]] = None
+    elif nulls == "all":
+        psr[:] = None
+    gsr = cudf.from_pandas(psr)
+    psr[key] = value
+    gsr[key] = value
+    assert_eq(psr, gsr, check_dtype=False)
+
+
+def test_series_setitem_null():
+    gsr = cudf.Series([1, 2, 3, 4])
+    gsr[0] = None
+
+    expect = cudf.Series([None, 2, 3, 4])
+    got = gsr
+    assert_eq(expect, got)
+
+    gsr = cudf.Series([None, 2, 3, 4])
+    gsr[0] = 1
+
+    expect = cudf.Series([1, 2, 3, 4])
+    got = gsr
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        (0, 4),
+        (1, 4),
+        ([0, 1], 4),
+        ([0, 1], [4, 5]),
+        (slice(0, 2), [4, 5]),
+        (slice(1, None), [4, 5, 6, 7]),
+        ([], 1),
+        ([], []),
+        (slice(None, None), 1),
+        (slice(-1, -3), 7),
+    ],
+)
+@pytest.mark.parametrize("nulls", ["none", "some", "all"])
+def test_series_setitem_iloc(key, value, nulls):
+    psr = pd.Series([1, 2, 3, 4, 5])
+    if nulls == "some":
+        psr[[0, 4]] = None
+    elif nulls == "all":
+        psr[:] = None
+    gsr = cudf.from_pandas(psr)
+    psr.iloc[key] = value
+    gsr.iloc[key] = value
+    assert_eq(psr, gsr, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        pytest.param(
+            0,
+            0.5,
+        ),
+        ([0, 1], 0.5),
+        ([0, 1], [0.5, 2.5]),
+        (slice(0, 2), [0.5, 0.25]),
+    ],
+)
+def test_series_setitem_dtype(key, value):
+    psr = pd.Series([1, 2, 3], dtype="int32")
+    gsr = cudf.from_pandas(psr)
+    psr[key] = value
+    gsr[key] = value
+    assert_eq(psr, gsr)
+
+
+def test_series_setitem_datetime():
+    psr = pd.Series(["2001", "2002", "2003"], dtype="datetime64[ns]")
+    gsr = cudf.from_pandas(psr)
+
+    psr[0] = np.datetime64("2005")
+    gsr[0] = np.datetime64("2005")
+
+    assert_eq(psr, gsr)
+
+
+def test_series_setitem_datetime_coerced():
+    psr = pd.Series(["2001", "2002", "2003"], dtype="datetime64[ns]")
+    gsr = cudf.from_pandas(psr)
+
+    psr[0] = "2005"
+    gsr[0] = "2005"
+
+    assert_eq(psr, gsr)
+
+
+def test_series_setitem_categorical():
+    psr = pd.Series(["a", "b", "a", "c", "d"], dtype="category")
+    gsr = cudf.from_pandas(psr)
+
+    psr[0] = "d"
+    gsr[0] = "d"
+    assert_eq(psr, gsr)
+
+    psr = psr.cat.add_categories(["e"])
+    gsr = gsr.cat.add_categories(["e"])
+    psr[0] = "e"
+    gsr[0] = "e"
+    assert_eq(psr, gsr)
+
+    psr[[0, 1]] = "b"
+    gsr[[0, 1]] = "b"
+    assert_eq(psr, gsr)
+
+    psr[0:3] = "e"
+    gsr[0:3] = "e"
+    assert_eq(psr, gsr)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        (0, "d"),
+        (0, "g"),
+        ([0, 1], "g"),
+        ([0, 1], None),
+        (slice(None, 2), "g"),
+        (slice(None, 2), ["g", None]),
+    ],
+)
+def test_series_setitem_string(key, value):
+    psr = pd.Series(["a", "b", "c", "d", "e"])
+    gsr = cudf.from_pandas(psr)
+    psr[key] = value
+    gsr[key] = value
+    assert_eq(psr, gsr)
+
+    psr = pd.Series(["a", None, "c", "d", "e"])
+    gsr = cudf.from_pandas(psr)
+    psr[key] = value
+    gsr[key] = value
+    assert_eq(psr, gsr)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        ("a", 4),
+        ("b", 4),
+        ("b", np.int8(8)),
+        ("d", 4),
+        ("d", np.int8(16)),
+        ("d", np.float32(16)),
+        (["a", "b"], 4),
+        (["a", "b"], [4, 5]),
+        ([True, False, True], 4),
+        ([False, False, False], 4),
+        ([True, False, True], [4, 5]),
+    ],
+)
+def test_series_setitem_loc(key, value):
+    psr = pd.Series([1, 2, 3], ["a", "b", "c"])
+    gsr = cudf.from_pandas(psr)
+    psr.loc[key] = value
+    gsr.loc[key] = value
+    assert_eq(psr, gsr)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        (1, "d"),
+        (2, "e"),
+        (4, "f"),
+        ([1, 3], "g"),
+        ([1, 3], ["g", "h"]),
+        ([True, False, True], "i"),
+        ([False, False, False], "j"),
+        ([True, False, True], ["k", "l"]),
+    ],
+)
+def test_series_setitem_loc_numeric_index(key, value):
+    psr = pd.Series(["a", "b", "c"], [1, 2, 3])
+    gsr = cudf.from_pandas(psr)
+    psr.loc[key] = value
+    gsr.loc[key] = value
+    assert_eq(psr, gsr)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        ((0, 0), 5),
+        ((slice(None), 0), 5),
+        ((slice(None), 0), range(3)),
+        ((slice(None, -1), 0), range(2)),
+        (([0, 1], 0), 5),
+    ],
+)
+def test_dataframe_setitem_iloc(key, value, pdf_gdf):
+    pdf, gdf = pdf_gdf
+    pdf.iloc[key] = value
+    gdf.iloc[key] = value
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        (("one", "a"), 5),
+        ((slice(None), "a"), 5),
+        ((slice(None), "a"), range(3)),
+        ((slice(None), "a"), [3, 2, 1]),
+        ((slice(None, "two"), "a"), range(2)),
+        ((slice(None, "two"), "a"), [4, 5]),
+        ((["one", "two"], "a"), 5),
+        (("one", "c"), 5),
+        ((["one", "two"], "c"), 5),
+        ((slice(None), "c"), 5),
+        ((slice(None), "c"), range(3)),
+        ((slice(None), "c"), [3, 2, 1]),
+        ((slice(None, "two"), "c"), range(2)),
+        ((slice(None, "two"), "c"), [4, 5]),
+    ],
+)
+def test_dataframe_setitem_loc(key, value, pdf_gdf):
+    pdf, gdf = pdf_gdf
+    pdf.loc[key] = value
+    gdf.loc[key] = value
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "key, value",
+    [
+        (("one", "a"), 5),
+        ((slice(None), "a"), range(3)),
+        ((slice(None), "a"), [3, 2, 1]),
+    ],
+)
+def test_dataframe_setitem_loc_empty_df(key, value):
+    pdf, gdf = pd.DataFrame(), cudf.DataFrame()
+    pdf.loc[key] = value
+    gdf.loc[key] = value
+    assert_eq(pdf, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "key,value",
+    [
+        ((0, 0), 5.0),
+        ((slice(None), 0), 5.0),
+        ((slice(None), 0), np.arange(7, dtype="float64")),
+    ],
+)
+def test_dataframe_setitem_iloc_multiindex(key, value, pdf_gdf_multi):
+    pdf, gdf = pdf_gdf_multi
+
+    pdf.iloc[key] = value
+    gdf.iloc[key] = value
+
+    assert_eq(pdf, gdf)
+
+
+def test_boolean_indexing_single_row(pdf_gdf):
+    pdf, gdf = pdf_gdf
+    assert_eq(
+        pdf.loc[[True, False, False], :], gdf.loc[[True, False, False], :]
+    )
+
+
+def test_iloc_negative_indices():
+    psr = pd.Series([1, 2, 3, 4, 5])
+    gsr = cudf.from_pandas(psr)
+    assert_eq(psr.iloc[[-1, -2, -4]], gsr.iloc[[-1, -2, -4]])
+
+
+def test_out_of_bounds_indexing():
+    psr = pd.Series([1, 2, 3])
+    gsr = cudf.from_pandas(psr)
+
+    assert_exceptions_equal(
+        lambda: psr[[0, 1, 9]],
+        lambda: gsr[[0, 1, 9]],
+    )
+    assert_exceptions_equal(
+        lambda: psr[[0, 1, -4]],
+        lambda: gsr[[0, 1, -4]],
+    )
+    assert_exceptions_equal(
+        lambda: psr.__setitem__([0, 1, 9], 2),
+        lambda: gsr.__setitem__([0, 1, 9], 2),
+    )
+    assert_exceptions_equal(
+        lambda: psr.__setitem__([0, 1, -4], 2),
+        lambda: gsr.__setitem__([0, 1, -4], 2),
+    )
+    assert_exceptions_equal(
+        lambda: psr[4:6].iloc.__setitem__(-1, 2),
+        lambda: gsr[4:6].iloc.__setitem__(-1, 2),
+    )
+    assert_exceptions_equal(
+        lambda: psr[4:6].iloc.__setitem__(1, 2),
+        lambda: gsr[4:6].iloc.__setitem__(1, 2),
+    )
+
+
+def test_sliced_indexing():
+    a = list(range(4, 4 + 150))
+    b = list(range(0, 0 + 150))
+    pdf = pd.DataFrame({"a": a, "b": b})
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    pdf = pdf.set_index("a")
+    gdf = gdf.set_index("a")
+    pidx = pdf.index[:75]
+    gidx = gdf.index[:75]
+
+    assert_eq(pdf.loc[pidx], gdf.loc[gidx])
+
+
+@pytest.mark.parametrize("index", [["a"], ["a", "a"], ["a", "a", "b", "c"]])
+def test_iloc_categorical_index(index):
+    gdf = cudf.DataFrame({"data": range(len(index))}, index=index)
+    gdf.index = gdf.index.astype("category")
+    pdf = gdf.to_pandas()
+    expect = pdf.iloc[:, 0]
+    got = gdf.iloc[:, 0]
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "sli",
+    [
+        slice("2001", "2020"),
+        slice("2001", "2002"),
+        slice("2002", "2001"),
+        slice(None, "2020"),
+        slice("2001", None),
+    ],
+)
+@pytest.mark.parametrize("is_dataframe", [True, False])
+def test_loc_datetime_index(sli, is_dataframe):
+    if is_dataframe is True:
+        pd_data = pd.DataFrame(
+            {"a": [1, 2, 3]},
+            index=pd.Series(["2001", "2009", "2002"], dtype="datetime64[ns]"),
+        )
+    else:
+        pd_data = pd.Series(
+            [1, 2, 3],
+            pd.Series(["2001", "2009", "2002"], dtype="datetime64[ns]"),
+        )
+
+    gd_data = cudf.from_pandas(pd_data)
+
+    expect = pd_data.loc[sli]
+    got = gd_data.loc[sli]
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "gdf_kwargs",
+    [
+        {"data": {"a": range(1000)}},
+        {"data": {"a": range(1000), "b": range(1000)}},
+        {
+            "data": {
+                "a": range(20),
+                "b": range(20),
+                "c": ["abc", "def", "xyz", "def", "pqr"] * 4,
+            }
+        },
+        {"index": [1, 2, 3]},
+        {"index": range(1000)},
+        {"columns": ["a", "b", "c", "d"]},
+        {"columns": ["a"], "index": range(1000)},
+        {"columns": ["a", "col2", "...col n"], "index": range(1000)},
+        {"index": cudf.Series(range(1000)).astype("str")},
+        {
+            "columns": ["a", "b", "c", "d"],
+            "index": cudf.Series(range(1000)).astype("str"),
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "slice",
+    [
+        slice(6, None),  # start but no stop, [6:]
+        slice(None, None, 3),  # only step, [::3]
+        slice(1, 10, 2),  # start, stop, step
+        slice(3, -5, 2),  # negative stop
+        slice(-2, -4),  # slice is empty
+        slice(-10, -20, -1),  # reversed slice
+        slice(None),  # slices everything, same as [:]
+        slice(250, 500),
+        slice(250, 251),
+        slice(50),
+        slice(1, 10),
+        slice(10, 20),
+        slice(15, 24),
+        slice(6),
+    ],
+)
+def test_dataframe_sliced(gdf_kwargs, slice):
+    gdf = cudf.DataFrame(**gdf_kwargs)
+    pdf = gdf.to_pandas()
+
+    actual = gdf[slice]
+    expected = pdf[slice]
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "gdf",
+    [
+        cudf.DataFrame({"a": range(10000)}),
+        cudf.DataFrame(
+            {
+                "a": range(10000),
+                "b": range(10000),
+                "c": range(10000),
+                "d": range(10000),
+                "e": range(10000),
+                "f": range(10000),
+            }
+        ),
+        cudf.DataFrame({"a": range(20), "b": range(20)}),
+        cudf.DataFrame(
+            {
+                "a": range(20),
+                "b": range(20),
+                "c": ["abc", "def", "xyz", "def", "pqr"] * 4,
+            }
+        ),
+        cudf.DataFrame(index=[1, 2, 3]),
+        cudf.DataFrame(index=range(10000)),
+        cudf.DataFrame(columns=["a", "b", "c", "d"]),
+        cudf.DataFrame(columns=["a"], index=range(10000)),
+        cudf.DataFrame(columns=["a", "col2", "...col n"], index=range(10000)),
+        cudf.DataFrame(index=cudf.Series(range(10000)).astype("str")),
+        cudf.DataFrame(
+            columns=["a", "b", "c", "d"],
+            index=cudf.Series(range(10000)).astype("str"),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "slice",
+    [slice(6), slice(1), slice(7), slice(1, 3)],
+)
+def test_dataframe_iloc_index(gdf, slice):
+    pdf = gdf.to_pandas()
+
+    actual = gdf.iloc[:, slice]
+    expected = pdf.iloc[:, slice]
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[0], [1], [2]],
+        [[0, 1], [2, 3], [4, 5]],
+        [[[0, 1], [2]], [[3, 4]], [[5, 6]]],
+        [None, [[0, 1], [2]], [[3, 4], [5, 6]]],
+        [[], [[0, 1], [2]], [[3, 4], [5, 6]]],
+        [[], [["a", "b"], None], [["c", "d"], []]],
+    ],
+)
+@pytest.mark.parametrize(
+    "key", [[], [0], [0, 1], [0, 1, 0], slice(None), slice(0, 2), slice(1, 3)]
+)
+def test_iloc_with_lists(data, key):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+    assert_eq(psr.iloc[key], gsr.iloc[key])
+
+    pdf = pd.DataFrame({"a": data, "b": data})
+    gdf = cudf.DataFrame({"a": data, "b": data})
+    assert_eq(pdf.iloc[key], gdf.iloc[key])
+
+
+@pytest.mark.parametrize("key", [5, -10, "0", "a", np.array(5), np.array("a")])
+def test_loc_bad_key_type(key):
+    psr = pd.Series([1, 2, 3])
+    gsr = cudf.from_pandas(psr)
+    assert_exceptions_equal(lambda: psr[key], lambda: gsr[key])
+    assert_exceptions_equal(lambda: psr.loc[key], lambda: gsr.loc[key])
+
+
+@pytest.mark.parametrize("key", ["b", 1.0, np.array("b")])
+def test_loc_bad_key_type_string_index(key):
+    psr = pd.Series([1, 2, 3], index=["a", "1", "c"])
+    gsr = cudf.from_pandas(psr)
+    assert_exceptions_equal(lambda: psr[key], lambda: gsr[key])
+    assert_exceptions_equal(lambda: psr.loc[key], lambda: gsr.loc[key])
+
+
+def test_loc_zero_dim_array():
+    psr = pd.Series([1, 2, 3])
+    gsr = cudf.from_pandas(psr)
+
+    assert_eq(psr[np.array(0)], gsr[np.array(0)])
+    assert_eq(psr[np.array([0])[0]], gsr[np.array([0])[0]])
+
+
+@pytest.mark.parametrize(
+    "arg",
+    [
+        slice(None),
+        slice((1, 2), None),
+        slice(None, (1, 2)),
+        (1, 1),
+        pytest.param(
+            (1, slice(None)),
+            marks=pytest.mark.xfail(
+                reason="https://github.com/pandas-dev/pandas/issues/46704"
+            ),
+        ),
+        1,
+        2,
+    ],
+)
+def test_loc_series_multiindex(arg):
+    gsr = cudf.DataFrame(
+        {"a": [1, 1, 2], "b": [1, 2, 3], "c": ["a", "b", "c"]}
+    ).set_index(["a", "b"])["c"]
+    psr = gsr.to_pandas()
+    assert_eq(psr.loc[arg], gsr.loc[arg])
+
+
+@pytest.mark.parametrize(
+    "arg",
+    [
+        slice(None, None, -1),
+        slice(None, -1, -1),
+        slice(4, -1, -1),
+        slice(None, None, -3),
+        slice(None, -1, -3),
+        slice(4, -1, -3),
+    ],
+)
+@pytest.mark.parametrize(
+    "pobj", [pd.DataFrame({"a": [1, 2, 3, 4, 5]}), pd.Series([1, 2, 3, 4, 5])]
+)
+def test_iloc_before_zero_terminate(arg, pobj):
+    gobj = cudf.from_pandas(pobj)
+
+    assert_eq(pobj.iloc[arg], gobj.iloc[arg])
+
+
+def test_iloc_decimal():
+    sr = cudf.Series(["1.00", "2.00", "3.00", "4.00"]).astype(
+        cudf.Decimal64Dtype(scale=2, precision=3)
+    )
+    got = sr.iloc[[3, 2, 1, 0]]
+    expect = cudf.Series(
+        ["4.00", "3.00", "2.00", "1.00"],
+    ).astype(cudf.Decimal64Dtype(scale=2, precision=3))
+    assert_eq(expect.reset_index(drop=True), got.reset_index(drop=True))
+
+
+@pytest.mark.parametrize(
+    ("key, value"),
+    [
+        (
+            ([0], ["x", "y"]),
+            [10, 20],
+        ),
+        (
+            ([0, 2], ["x", "y"]),
+            [[10, 30], [20, 40]],
+        ),
+        (
+            (0, ["x", "y"]),
+            [10, 20],
+        ),
+        (
+            ([0, 2], "x"),
+            [10, 20],
+        ),
+    ],
+)
+def test_dataframe_loc_inplace_update(key, value):
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    pdf = gdf.to_pandas()
+
+    actual = gdf.loc[key] = value
+    expected = pdf.loc[key] = value
+
+    assert_eq(expected, actual)
+
+
+def test_dataframe_loc_inplace_update_string_index():
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}, index=list("abc"))
+    pdf = gdf.to_pandas()
+
+    actual = gdf.loc[["a"], ["x", "y"]] = [10, 20]
+    expected = pdf.loc[["a"], ["x", "y"]] = [10, 20]
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    ("key, value"),
+    [
+        ([0], [10, 20]),
+        ([0, 2], [[10, 30], [20, 40]]),
+        (([0, 2], [0, 1]), [[10, 30], [20, 40]]),
+        (([0, 2], 0), [10, 30]),
+        ((0, [0, 1]), [20, 40]),
+    ],
+)
+def test_dataframe_iloc_inplace_update(key, value):
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    pdf = gdf.to_pandas()
+
+    actual = gdf.iloc[key] = value
+    expected = pdf.iloc[key] = value
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "loc_key",
+    [([0, 2], ["x", "y"])],
+)
+@pytest.mark.parametrize(
+    "iloc_key",
+    [[0, 2]],
+)
+@pytest.mark.parametrize(
+    ("data, index"),
+    [
+        (
+            {"x": [10, 20], "y": [30, 40]},
+            [0, 2],
+        )
+    ],
+)
+def test_dataframe_loc_iloc_inplace_update_with_RHS_dataframe(
+    loc_key, iloc_key, data, index
+):
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    pdf = gdf.to_pandas()
+
+    actual = gdf.loc[loc_key] = cudf.DataFrame(data, index=cudf.Index(index))
+    expected = pdf.loc[loc_key] = pd.DataFrame(data, index=pd.Index(index))
+    assert_eq(expected, actual)
+
+    actual = gdf.iloc[iloc_key] = cudf.DataFrame(data, index=cudf.Index(index))
+    expected = pdf.iloc[iloc_key] = pd.DataFrame(data, index=pd.Index(index))
+    assert_eq(expected, actual)
+
+
+def test_dataframe_loc_inplace_update_with_invalid_RHS_df_columns():
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    pdf = gdf.to_pandas()
+
+    actual = gdf.loc[[0, 2], ["x", "y"]] = cudf.DataFrame(
+        {"b": [10, 20], "y": [30, 40]}, index=cudf.Index([0, 2])
+    )
+    expected = pdf.loc[[0, 2], ["x", "y"]] = pd.DataFrame(
+        {"b": [10, 20], "y": [30, 40]}, index=pd.Index([0, 2])
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    ("key, value"),
+    [
+        (([0, 2], ["x", "y"]), [[10, 30, 50], [20, 40, 60]]),
+        (([0], ["x", "y"]), [[10], [20]]),
+    ],
+)
+def test_dataframe_loc_inplace_update_shape_mismatch(key, value):
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    with pytest.raises(ValueError, match="shape mismatch:"):
+        gdf.loc[key] = value
+
+
+@pytest.mark.parametrize(
+    ("key, value"),
+    [
+        ([0, 2], [[10, 30, 50], [20, 40, 60]]),
+        ([0], [[10], [20]]),
+    ],
+)
+def test_dataframe_iloc_inplace_update_shape_mismatch(key, value):
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    with pytest.raises(ValueError, match="shape mismatch:"):
+        gdf.iloc[key] = value
+
+
+def test_dataframe_loc_inplace_update_shape_mismatch_RHS_df():
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    with pytest.raises(ValueError, match="shape mismatch:"):
+        gdf.loc[([0, 2], ["x", "y"])] = cudf.DataFrame(
+            {"x": [10, 20]}, index=cudf.Index([0, 2])
+        )
+
+
+def test_dataframe_iloc_inplace_update_shape_mismatch_RHS_df():
+    gdf = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    with pytest.raises(ValueError, match="shape mismatch:"):
+        gdf.iloc[[0, 2]] = cudf.DataFrame(
+            {"x": [10, 20]}, index=cudf.Index([0, 2])
+        )
+
+
+@pytest.mark.parametrize(
+    "array,is_error",
+    [
+        (cupy.arange(20, 40).reshape(-1, 2), False),
+        (cupy.arange(20, 50).reshape(-1, 3), True),
+        (np.arange(20, 40).reshape(-1, 2), False),
+        (np.arange(20, 30).reshape(-1, 1), False),
+        (cupy.arange(20, 30).reshape(-1, 1), False),
+    ],
+)
+def test_dataframe_indexing_setitem_np_cp_array(array, is_error):
+    gdf = cudf.DataFrame({"a": range(10), "b": range(10)})
+    pdf = gdf.to_pandas()
+    if not is_error:
+        gdf.loc[:, ["a", "b"]] = array
+        pdf.loc[:, ["a", "b"]] = cupy.asnumpy(array)
+
+        assert_eq(gdf, pdf)
+    else:
+        assert_exceptions_equal(
+            lfunc=pdf.loc.__setitem__,
+            rfunc=gdf.loc.__setitem__,
+            lfunc_args_and_kwargs=(
+                [(slice(None, None, None), ["a", "b"]), cupy.asnumpy(array)],
+                {},
+            ),
+            rfunc_args_and_kwargs=(
+                [(slice(None, None, None), ["a", "b"]), array],
+                {},
+            ),
+        )
+
+
+def test_iloc_single_row_with_nullable_column():
+    # see https://github.com/rapidsai/cudf/issues/11349
+    pdf = pd.DataFrame({"a": [0, 1, 2, 3], "b": [0.1, 0.2, None, 0.4]})
+    df = cudf.from_pandas(pdf)
+
+    df.iloc[0]  # before the fix for #11349 this would segfault
+    assert_eq(pdf.iloc[0], df.iloc[0])
+
+
+def test_loc_single_row_from_slice():
+    # see https://github.com/rapidsai/cudf/issues/11930
+    pdf = pd.DataFrame({"a": [10, 20, 30], "b": [1, 2, 3]}).set_index("a")
+    df = cudf.from_pandas(pdf)
+    assert_eq(pdf.loc[5:10], df.loc[5:10])
+
+
+@pytest.mark.parametrize("indexer", ["loc", "iloc"])
+@pytest.mark.parametrize(
+    "mask",
+    [[False, True], [False, False, True, True, True]],
+    ids=["too-short", "too-long"],
+)
+def test_boolean_mask_wrong_length(indexer, mask):
+    s = pd.Series([1, 2, 3, 4])
+
+    indexee = getattr(s, indexer)
+    with pytest.raises(IndexError):
+        indexee[mask]
+
+    c = cudf.from_pandas(s)
+    indexee = getattr(c, indexer)
+    with pytest.raises(IndexError):
+        indexee[mask]
+
+
+@pytest.mark.parametrize("indexer", ["loc", "iloc"])
+def test_boolean_mask_columns(indexer):
+    df = pd.DataFrame(np.zeros((3, 3)))
+    cdf = cudf.from_pandas(df)
+    mask = [True, False, True]
+    expect = getattr(df, indexer)[:, mask]
+    got = getattr(cdf, indexer)[:, mask]
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("indexer", ["loc", "iloc"])
+@pytest.mark.parametrize(
+    "mask",
+    [[False, True], [False, False, True, True, True]],
+    ids=["too-short", "too-long"],
+)
+def test_boolean_mask_columns_wrong_length(indexer, mask):
+    df = pd.DataFrame(np.zeros((3, 3)))
+    cdf = cudf.from_pandas(df)
+
+    with pytest.raises(IndexError):
+        getattr(df, indexer)[:, mask]
+    with pytest.raises(IndexError):
+        getattr(cdf, indexer)[:, mask]
+
+
+def test_boolean_mask_columns_iloc_series():
+    df = pd.DataFrame(np.zeros((3, 3)))
+    cdf = cudf.from_pandas(df)
+
+    mask = pd.Series([True, False, True], dtype=bool)
+    with pytest.raises(NotImplementedError):
+        df.iloc[:, mask]
+
+    with pytest.raises(NotImplementedError):
+        cdf.iloc[:, mask]
+
+
+@pytest.mark.parametrize("index_type", ["single", "slice"])
+def test_loc_timestamp_issue_8585(index_type):
+    # https://github.com/rapidsai/cudf/issues/8585
+    start = pd.Timestamp(
+        datetime.strptime("2021-03-12 00:00", "%Y-%m-%d %H:%M")
+    )
+    end = pd.Timestamp(datetime.strptime("2021-03-12 11:00", "%Y-%m-%d %H:%M"))
+    timestamps = pd.date_range(start, end, periods=12)
+    value = np.random.normal(size=12)
+    df = pd.DataFrame(value, index=timestamps, columns=["value"])
+    cdf = cudf.from_pandas(df)
+    if index_type == "single":
+        index = pd.Timestamp(
+            datetime.strptime("2021-03-12 03:00", "%Y-%m-%d %H:%M")
+        )
+    elif index_type == "slice":
+        index = slice(start, end, None)
+    else:
+        raise ValueError("Invalid index type")
+    expect = df.loc[index]
+    actual = cdf.loc[index]
+    assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize(
+    "index_type",
+    [
+        "single",
+        pytest.param(
+            "slice",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/8585"
+            ),
+        ),
+        pytest.param(
+            "date_range",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/8585"
+            ),
+        ),
+    ],
+)
+def test_loc_multiindex_timestamp_issue_8585(index_type):
+    # https://github.com/rapidsai/cudf/issues/8585
+    start = pd.Timestamp(
+        datetime.strptime("2021-03-12 00:00", "%Y-%m-%d %H:%M")
+    )
+    end = pd.Timestamp(datetime.strptime("2021-03-12 03:00", "%Y-%m-%d %H:%M"))
+    timestamps = pd.date_range(start, end, periods=4)
+    labels = ["A", "B", "C"]
+    index = pd.MultiIndex.from_product(
+        [timestamps, labels], names=["timestamp", "label"]
+    )
+    value = np.random.normal(size=12)
+    df = pd.DataFrame(value, index=index, columns=["value"])
+    cdf = cudf.from_pandas(df)
+    start = pd.Timestamp(
+        datetime.strptime("2021-03-12 01:00", "%Y-%m-%d %H:%M")
+    )
+    end = pd.Timestamp(datetime.strptime("2021-03-12 02:00", "%Y-%m-%d %H:%M"))
+    if index_type == "single":
+        index = pd.Timestamp(
+            datetime.strptime("2021-03-12 03:00", "%Y-%m-%d %H:%M")
+        )
+    elif index_type == "slice":
+        index = slice(start, end, None)
+    elif index_type == "date_range":
+        index = pd.date_range(start, end, periods=2)
+    else:
+        raise ValueError("Invalid index type")
+    expect = df.loc[index]
+    actual = cdf.loc[index]
+    assert_eq(expect, actual)
+
+
+def test_loc_repeated_index_label_issue_8693():
+    # https://github.com/rapidsai/cudf/issues/8693
+    s = pd.Series([1, 2, 3, 4], index=[0, 1, 1, 2])
+    cs = cudf.from_pandas(s)
+    expect = s.loc[1]
+    actual = cs.loc[1]
+    assert_eq(expect, actual)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/13268")
+@pytest.mark.parametrize(
+    "indexer", [(..., 0), (0, ...)], ids=["row_ellipsis", "column_ellipsis"]
+)
+def test_loc_ellipsis_as_slice_issue_13268(indexer):
+    # https://github.com/rapidsai/cudf/issues/13268
+    df = pd.DataFrame(np.arange(4).reshape(2, 2))
+    cdf = cudf.from_pandas(df)
+
+    expect = df.loc[indexer]
+    actual = cdf.loc[indexer]
+    assert_eq(expect, actual)
+
+
+@pytest.mark.xfail(
+    reason="https://github.com/rapidsai/cudf/issues/13269 "
+    "and https://github.com/rapidsai/cudf/issues/13273"
+)
+def test_loc_repeated_column_label_issue_13269():
+    # https://github.com/rapidsai/cudf/issues/13269
+    # https://github.com/rapidsai/cudf/issues/13273
+    df = pd.DataFrame(np.arange(4).reshape(2, 2))
+    cdf = cudf.from_pandas(df)
+
+    expect = df.loc[:, [0, 1, 0]]
+    actual = cdf.loc[:, [0, 1, 0]]
+    assert_eq(expect, actual)
+
+
+def test_loc_column_boolean_mask_issue_13270():
+    # https://github.com/rapidsai/cudf/issues/13270
+    df = pd.DataFrame(np.arange(4).reshape(2, 2))
+    cdf = cudf.from_pandas(df)
+    expect = df.loc[:, [True, True]]
+    actual = cdf.loc[:, [True, True]]
+    assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize("indexer", [[1], [0, 2]])
+def test_iloc_integer_categorical_issue_13013(indexer):
+    # https://github.com/rapidsai/cudf/issues/13013
+    s = pd.Series([0, 1, 2])
+    index = pd.Categorical(indexer)
+    expect = s.iloc[index]
+    c = cudf.from_pandas(s)
+    actual = c.iloc[index]
+    assert_eq(expect, actual)
+
+
+def test_iloc_incorrect_boolean_mask_length_issue_13015():
+    # https://github.com/rapidsai/cudf/issues/13015
+    s = pd.Series([0, 1, 2])
+    with pytest.raises(IndexError):
+        s.iloc[[True, False]]
+    c = cudf.from_pandas(s)
+    with pytest.raises(IndexError):
+        c.iloc[[True, False]]
+
+
+def test_iloc_column_boolean_mask_issue_13265():
+    # https://github.com/rapidsai/cudf/issues/13265
+    df = pd.DataFrame(np.arange(4).reshape(2, 2))
+    cdf = cudf.from_pandas(df)
+    expect = df.iloc[:, [True, True]]
+    actual = cdf.iloc[:, [True, True]]
+    assert_eq(expect, actual)
+
+
+def test_iloc_repeated_column_label_issue_13266():
+    # https://github.com/rapidsai/cudf/issues/13266
+    # https://github.com/rapidsai/cudf/issues/13273
+    df = pd.DataFrame(np.arange(4).reshape(2, 2))
+    cdf = cudf.from_pandas(df)
+
+    with pytest.raises(NotImplementedError):
+        cdf.iloc[:, [0, 1, 0]]
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [
+        (..., 0),
+        (0, ...),
+    ],
+    ids=["row_ellipsis", "column_ellipsis"],
+)
+def test_iloc_ellipsis_as_slice_issue_13267(indexer):
+    # https://github.com/rapidsai/cudf/issues/13267
+    df = pd.DataFrame(np.arange(4).reshape(2, 2))
+    cdf = cudf.from_pandas(df)
+
+    expect = df.iloc[indexer]
+    actual = cdf.iloc[indexer]
+    assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [
+        0,
+        (slice(None), 0),
+        ([0, 2], 1),
+        (slice(None), slice(None)),
+        (slice(None), [1, 0]),
+        (0, 0),
+        (1, [1, 0]),
+        ([1, 0], 0),
+        ([1, 2], [0, 1]),
+    ],
+)
+def test_iloc_multiindex_lookup_as_label_issue_13515(indexer):
+    # https://github.com/rapidsai/cudf/issues/13515
+    df = pd.DataFrame(
+        {"a": [1, 1, 3], "b": [2, 3, 4], "c": [1, 6, 7], "d": [1, 8, 9]}
+    ).set_index(["a", "b"])
+    cdf = cudf.from_pandas(df)
+
+    expect = df.iloc[indexer]
+    actual = cdf.iloc[indexer]
+    assert_eq(expect, actual)
+
+
+def test_loc_unsorted_index_slice_lookup_keyerror_issue_12833():
+    # https://github.com/rapidsai/cudf/issues/12833
+    df = pd.DataFrame({"a": [1, 2, 3]}, index=[7, 0, 4])
+    cdf = cudf.from_pandas(df)
+
+    # Check that pandas don't change their mind
+    with pytest.raises(KeyError):
+        df.loc[1:5]
+
+    with pytest.raises(KeyError):
+        cdf.loc[1:5]
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/13379")
+@pytest.mark.parametrize("index", [range(5), list(range(5))])
+def test_loc_missing_label_keyerror_issue_13379(index):
+    # https://github.com/rapidsai/cudf/issues/13379
+    df = pd.DataFrame({"a": index}, index=index)
+    cdf = cudf.from_pandas(df)
+    # Check that pandas don't change their mind
+    with pytest.raises(KeyError):
+        df.loc[[0, 5]]
+
+    with pytest.raises(KeyError):
+        cdf.loc[[0, 5]]
+
+
+@pytest.mark.parametrize("series", [True, False], ids=["Series", "DataFrame"])
+def test_loc_repeated_label_ordering_issue_13658(series):
+    # https://github.com/rapidsai/cudf/issues/13658
+    values = range(2048)
+    index = [1 for _ in values]
+    if series:
+        frame = cudf.Series(values, index=index)
+    else:
+        frame = cudf.DataFrame({"a": values}, index=index)
+    expect = frame.to_pandas().loc[[1]]
+    actual = frame.loc[[1]]
+    assert_eq(actual, expect)
+
+
+@pytest.mark.parametrize("index", [None, [2, 1, 3, 5, 4]])
+def test_loc_bool_key_numeric_index_raises(index):
+    ser = cudf.Series(range(5), index=index)
+    with pytest.raises(KeyError):
+        ser.loc[True]
+
+
+class TestLocIndexWithOrder:
+    # https://github.com/rapidsai/cudf/issues/12833
+    @pytest.fixture(params=["increasing", "decreasing", "neither"])
+    def order(self, request):
+        return request.param
+
+    @pytest.fixture(params=[-1, 1], ids=["reverse", "forward"])
+    def take_order(self, request):
+        return request.param
+
+    @pytest.fixture(params=["float", "int", "string", "range"])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def index(self, order, dtype):
+        if dtype == "string":
+            index = ["a", "h", "f", "z"]
+        elif dtype == "int":
+            index = [-1, 10, 7, 14]
+        elif dtype == "float":
+            index = [-1.5, 7.10, 2.4, 11.2]
+        elif dtype == "range":
+            if order == "increasing":
+                return cudf.RangeIndex(2, 10, 3)
+            elif order == "decreasing":
+                return cudf.RangeIndex(10, 1, -3)
+            else:
+                return cudf.RangeIndex(10, 20, 3)
+        else:
+            raise ValueError(f"Unhandled index dtype {dtype}")
+        if order == "decreasing":
+            return sorted(index, reverse=True)
+        elif order == "increasing":
+            return sorted(index)
+        elif order == "neither":
+            return index
+        else:
+            raise ValueError(f"Unhandled index order {order}")
+
+    @pytest.fixture
+    def df(self, index):
+        return cudf.DataFrame({"a": range(len(index))}, index=index)
+
+    def test_loc_index_inindex_slice(self, df, take_order):
+        pdf = df.to_pandas()
+        lo = pdf.index[1]
+        hi = pdf.index[-2]
+        expect = pdf.loc[lo:hi:take_order]
+        actual = df.loc[lo:hi:take_order]
+        assert_eq(expect, actual)
+
+    def test_loc_index_inindex_subset(self, df, take_order):
+        pdf = df.to_pandas()
+        vals = [pdf.index[0], pdf.index[2]][::take_order]
+        expect = pdf.loc[vals]
+        actual = df.loc[vals]
+        assert_eq(expect, actual)
+
+    def test_loc_index_notinindex_slice(
+        self, request, df, order, dtype, take_order
+    ):
+        pdf = df.to_pandas()
+        lo = pdf.index[1]
+        hi = pdf.index[-2]
+        if isinstance(lo, str):
+            lo = chr(ord(lo) - 1)
+            hi = chr(ord(hi) + 1)
+        else:
+            lo -= 1
+            hi += 1
+        if order == "neither" and dtype != "range":
+            with pytest.raises(KeyError):
+                pdf.loc[lo:hi:take_order]
+            with pytest.raises(KeyError):
+                df.loc[lo:hi:take_order]
+        else:
+            expect = pdf.loc[lo:hi:take_order]
+            actual = df.loc[lo:hi:take_order]
+            assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize(
+    "arg",
+    [
+        (2, ("one", "second")),
+        (slice(None, None, None), ("two", "first")),
+        (1, ("one", "first")),
+        (slice(None, None, None), ("two", "second")),
+        (slice(None, None, None), ("two", "first", "three")),
+        (3, ("two", "first", "three")),
+        (slice(None, None, None), ("two",)),
+        (0, ("two",)),
+    ],
+)
+def test_loc_dataframe_column_multiindex(arg):
+    gdf = cudf.DataFrame(
+        [list("abcd"), list("efgh"), list("ijkl"), list("mnop")],
+        columns=cudf.MultiIndex.from_product(
+            [["one", "two"], ["first", "second"], ["three"]]
+        ),
+    )
+    pdf = gdf.to_pandas()
+
+    assert_eq(gdf.loc[arg], pdf.loc[arg])
+
+
+@pytest.mark.parametrize(
+    "arg", [slice(2, 4), slice(2, 5), slice(2.3, 5), slice(4.6, 6)]
+)
+def test_series_iloc_float_int(arg):
+    gs = cudf.Series(range(4), index=[2.0, 3.0, 4.5, 5.5])
+    ps = gs.to_pandas()
+
+    actual = gs.loc[arg]
+    expected = ps.loc[arg]
+
+    assert_eq(actual, expected)
+
+
+def test_iloc_loc_mixed_dtype():
+    df = cudf.DataFrame({"a": ["a", "b"], "b": [0, 1]})
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(TypeError):
+            df.iloc[0]
+        with pytest.raises(TypeError):
+            df.loc[0]
+    df = df.astype("str")
+    pdf = df.to_pandas()
+
+    assert_eq(df.iloc[0], pdf.iloc[0])
+    assert_eq(df.loc[0], pdf.loc[0])
+
+
+def test_loc_setitem_categorical_integer_not_position_based():
+    gdf = cudf.DataFrame(range(3), index=cudf.CategoricalIndex([1, 2, 3]))
+    pdf = gdf.to_pandas()
+    gdf.loc[1] = 10
+    pdf.loc[1] = 10
+    assert_eq(gdf, pdf)
+
+
+@pytest.mark.parametrize("typ", ["datetime64[ns]", "timedelta64[ns]"])
+@pytest.mark.parametrize("idx_method, key", [["iloc", 0], ["loc", "a"]])
+def test_series_iloc_scalar_datetimelike_return_pd_scalar(
+    typ, idx_method, key
+):
+    obj = cudf.Series([1, 2, 3], index=list("abc"), dtype=typ)
+    with cudf.option_context("mode.pandas_compatible", True):
+        result = getattr(obj, idx_method)[key]
+    expected = getattr(obj.to_pandas(), idx_method)[key]
+    assert result == expected
+
+
+@pytest.mark.parametrize("typ", ["datetime64[ns]", "timedelta64[ns]"])
+@pytest.mark.parametrize(
+    "idx_method, row_key, col_key", [["iloc", 0, 0], ["loc", "a", "a"]]
+)
+def test_dataframe_iloc_scalar_datetimelike_return_pd_scalar(
+    typ, idx_method, row_key, col_key
+):
+    obj = cudf.DataFrame(
+        [1, 2, 3], index=list("abc"), columns=["a"], dtype=typ
+    )
+    with cudf.option_context("mode.pandas_compatible", True):
+        result = getattr(obj, idx_method)[row_key, col_key]
+    expected = getattr(obj.to_pandas(), idx_method)[row_key, col_key]
+    assert result == expected
+
+
+@pytest.mark.parametrize("idx_method, key", [["iloc", 0], ["loc", "a"]])
+def test_series_iloc_scalar_interval_return_pd_scalar(idx_method, key):
+    iidx = cudf.IntervalIndex.from_breaks([1, 2, 3])
+    obj = cudf.Series(iidx, index=list("ab"))
+    with cudf.option_context("mode.pandas_compatible", True):
+        result = getattr(obj, idx_method)[key]
+    expected = getattr(obj.to_pandas(), idx_method)[key]
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "idx_method, row_key, col_key", [["iloc", 0, 0], ["loc", "a", "a"]]
+)
+def test_dataframe_iloc_scalar_interval_return_pd_scalar(
+    idx_method, row_key, col_key
+):
+    iidx = cudf.IntervalIndex.from_breaks([1, 2, 3])
+    obj = cudf.DataFrame({"a": iidx}, index=list("ab"))
+    with cudf.option_context("mode.pandas_compatible", True):
+        result = getattr(obj, idx_method)[row_key, col_key]
+    expected = getattr(obj.to_pandas(), idx_method)[row_key, col_key]
+    assert result == expected
+
+
+def test_scalar_loc_row_categoricalindex():
+    df = cudf.DataFrame(
+        range(4), index=cudf.CategoricalIndex(["a", "a", "b", "c"])
+    )
+    result = df.loc["a"]
+    expected = df.to_pandas().loc["a"]
+    assert_eq(result, expected)
diff --git a/python/cudf/cudf/tests/test_interpolate.py b/python/cudf/cudf/tests/test_interpolate.py
new file mode 100644
index 0000000..c0b085a
--- /dev/null
+++ b/python/cudf/cudf/tests/test_interpolate.py
@@ -0,0 +1,121 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # basics
+        {"A": [1.0, 2.0, 3.0], "B": [4.0, 5.0, 6.0]},
+        {"A": [1.0, None, 3.0], "B": [4.0, None, 6.0]},
+        {"A": [None, 2.0, 3.0], "B": [4.0, 5.0, None]},
+    ],
+)
+@pytest.mark.parametrize("method", ["linear"])
+@pytest.mark.parametrize("axis", [0])
+def test_interpolate_dataframe(data, method, axis):
+    # Pandas interpolate methods do not seem to work
+    # with nullable dtypes yet, so this method treats
+    # NAs as NaNs
+    # https://github.com/pandas-dev/pandas/issues/40252
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expect = pdf.interpolate(method=method, axis=axis)
+    got = gdf.interpolate(method=method, axis=axis)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.0, 2.0, 3.0],
+        [1.0, None, 3.0],
+        [None, 2.0, None, 4.0],
+        [1.0, None, 3.0, None],
+        [None, None, 3.0, 4.0],
+        [1.0, 2.0, None, None],
+        [None, None, None, None],
+        [0.1, 0.2, 0.3],
+    ],
+)
+@pytest.mark.parametrize("method", ["linear"])
+@pytest.mark.parametrize("axis", [0])
+def test_interpolate_series(data, method, axis):
+    gsr = cudf.Series(data)
+    psr = gsr.to_pandas()
+
+    expect = psr.interpolate(method=method, axis=axis)
+    got = gsr.interpolate(method=method, axis=axis)
+
+    assert_eq(expect, got, check_dtype=psr.dtype != "object")
+
+
+@pytest.mark.parametrize(
+    "data,index", [([2.0, None, 4.0, None, 2.0], [1, 2, 3, 2, 1])]
+)
+def test_interpolate_series_unsorted_index(data, index):
+    gsr = cudf.Series(data, index=index)
+    psr = gsr.to_pandas()
+
+    expect = psr.interpolate(method="values")
+    got = gsr.interpolate(method="values")
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.0, 2.0, 3.0, 4.0],
+        [None, 2.0, 3.0, 4.0],
+        [1.0, 2.0, 3.0, None],
+        [None, None, 3.0, 4.0],
+        [1.0, 2.0, None, None],
+        [1.0, None, 3.0, None],
+        [None, 2.0, None, 4.0],
+        [None, None, None, None],
+    ],
+)
+@pytest.mark.parametrize("index", [[0, 1, 2, 3], [0, 2, 4, 6], [0, 3, 4, 9]])
+@pytest.mark.parametrize("method", ["index", "values"])
+def test_interpolate_series_values_or_index(data, index, method):
+    gsr = cudf.Series(data, index=index)
+    psr = gsr.to_pandas()
+
+    expect = psr.interpolate(method=method)
+    got = gsr.interpolate(method=method)
+
+    assert_eq(expect, got, check_dtype=psr.dtype != "object")
+
+
+@pytest.mark.parametrize(
+    "data,kwargs",
+    [
+        (
+            {"A": ["a", "b", "c"], "B": ["d", "e", "f"]},
+            {"axis": 0, "method": "linear"},
+        ),
+        ({"A": [1, 2, 3]}, {"method": "pad", "limit_direction": "backward"}),
+        ({"A": [1, 2, 3]}, {"method": "ffill", "limit_direction": "backward"}),
+        ({"A": [1, 2, 3]}, {"method": "bfill", "limit_direction": "forward"}),
+        (
+            {"A": [1, 2, 3]},
+            {"method": "backfill", "limit_direction": "forward"},
+        ),
+    ],
+)
+def test_interpolate_dataframe_error_cases(data, kwargs):
+    gsr = cudf.DataFrame(data)
+    psr = gsr.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=psr.interpolate,
+        rfunc=gsr.interpolate,
+        lfunc_args_and_kwargs=([], kwargs),
+        rfunc_args_and_kwargs=([], kwargs),
+    )
diff --git a/python/cudf/cudf/tests/test_interval.py b/python/cudf/cudf/tests/test_interval.py
new file mode 100644
index 0000000..a27de60
--- /dev/null
+++ b/python/cudf/cudf/tests/test_interval.py
@@ -0,0 +1,184 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize(
+    "data1, data2",
+    [(1, 2), (1.0, 2.0), (3, 4.0)],
+)
+@pytest.mark.parametrize("data3, data4", [(6, 10), (5.0, 9.0), (2, 6.0)])
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_create_interval_series(data1, data2, data3, data4, closed):
+
+    expect = pd.Series(pd.Interval(data1, data2, closed), dtype="interval")
+    got = cudf.Series(pd.Interval(data1, data2, closed), dtype="interval")
+    assert_eq(expect, got)
+
+    expect_two = pd.Series(
+        [pd.Interval(data1, data2, closed), pd.Interval(data3, data4, closed)],
+        dtype="interval",
+    )
+    got_two = cudf.Series(
+        [pd.Interval(data1, data2, closed), pd.Interval(data3, data4, closed)],
+        dtype="interval",
+    )
+    assert_eq(expect_two, got_two)
+
+    expect_three = pd.Series(
+        [
+            pd.Interval(data1, data2, closed),
+            pd.Interval(data3, data4, closed),
+            pd.Interval(data1, data2, closed),
+        ],
+        dtype="interval",
+    )
+    got_three = cudf.Series(
+        [
+            pd.Interval(data1, data2, closed),
+            pd.Interval(data3, data4, closed),
+            pd.Interval(data1, data2, closed),
+        ],
+        dtype="interval",
+    )
+    assert_eq(expect_three, got_three)
+
+
+@pytest.mark.parametrize(
+    "data1, data2",
+    [(1, 2), (1.0, 2.0), (3, 4.0)],
+)
+@pytest.mark.parametrize("data3, data4", [(6, 10), (5.0, 9.0), (2, 6.0)])
+@pytest.mark.parametrize("closed", ["left", "right", "both", "neither"])
+def test_create_interval_df(data1, data2, data3, data4, closed):
+    # df for both pandas and cudf only works when interval is in a list
+    expect = pd.DataFrame(
+        [pd.Interval(data1, data2, closed)], dtype="interval"
+    )
+    got = cudf.DataFrame([pd.Interval(data1, data2, closed)], dtype="interval")
+    assert_eq(expect, got)
+
+    expect_two = pd.DataFrame(
+        {
+            "a": [
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+            ],
+            "b": [
+                pd.Interval(data3, data4, closed),
+                pd.Interval(data1, data2, closed),
+            ],
+        },
+        dtype="interval",
+    )
+    got_two = cudf.DataFrame(
+        {
+            "a": [
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+            ],
+            "b": [
+                pd.Interval(data3, data4, closed),
+                pd.Interval(data1, data2, closed),
+            ],
+        },
+        dtype="interval",
+    )
+    assert_eq(expect_two, got_two)
+
+    expect_three = pd.DataFrame(
+        {
+            "a": [
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+                pd.Interval(data1, data2, closed),
+            ],
+            "b": [
+                pd.Interval(data3, data4, closed),
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+            ],
+            "c": [
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+            ],
+        },
+        dtype="interval",
+    )
+
+    got_three = cudf.DataFrame(
+        {
+            "a": [
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+                pd.Interval(data1, data2, closed),
+            ],
+            "b": [
+                pd.Interval(data3, data4, closed),
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+            ],
+            "c": [
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data1, data2, closed),
+                pd.Interval(data3, data4, closed),
+            ],
+        },
+        dtype="interval",
+    )
+    assert_eq(expect_three, got_three)
+
+
+def test_create_interval_index_from_list():
+    interval_list = [
+        np.nan,
+        pd.Interval(2.0, 3.0, closed="right"),
+        pd.Interval(3.0, 4.0, closed="right"),
+    ]
+
+    expected = pd.Index(interval_list)
+    actual = cudf.Index(interval_list)
+
+    assert_eq(expected, actual)
+
+
+def test_interval_index_unique():
+    interval_list = [
+        np.nan,
+        pd.Interval(2.0, 3.0, closed="right"),
+        pd.Interval(3.0, 4.0, closed="right"),
+        np.nan,
+        pd.Interval(3.0, 4.0, closed="right"),
+        pd.Interval(3.0, 4.0, closed="right"),
+    ]
+    pi = pd.Index(interval_list)
+    gi = cudf.from_pandas(pi)
+
+    expected = pi.unique()
+    actual = gi.unique()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("box", [pd.Series, pd.IntervalIndex])
+@pytest.mark.parametrize("tz", ["US/Eastern", None])
+def test_interval_with_datetime(tz, box):
+    dti = pd.date_range(
+        start=pd.Timestamp("20180101", tz=tz),
+        end=pd.Timestamp("20181231", tz=tz),
+        freq="M",
+    )
+    pobj = box(pd.IntervalIndex.from_breaks(dti))
+    if tz is None:
+        gobj = cudf.from_pandas(pobj)
+        assert_eq(pobj, gobj)
+    else:
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pobj)
diff --git a/python/cudf/cudf/tests/test_joining.py b/python/cudf/cudf/tests/test_joining.py
new file mode 100644
index 0000000..9c9c99a
--- /dev/null
+++ b/python/cudf/cudf/tests/test_joining.py
@@ -0,0 +1,2272 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from itertools import combinations, product, repeat
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core.dtypes import CategoricalDtype, Decimal64Dtype, Decimal128Dtype
+from cudf.testing._utils import (
+    INTEGER_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+
+_JOIN_TYPES = ("left", "inner", "outer", "right", "leftanti", "leftsemi")
+
+
+def make_params():
+    np.random.seed(0)
+
+    hows = _JOIN_TYPES
+
+    # Test specific cases (1)
+    aa = [0, 0, 4, 5, 5]
+    bb = [0, 0, 2, 3, 5]
+    for how in hows:
+        yield (aa, bb, how)
+
+    # Test specific cases (2)
+    aa = [0, 0, 1, 2, 3]
+    bb = [0, 1, 2, 2, 3]
+    for how in hows:
+        yield (aa, bb, how)
+
+    # Test large random integer inputs
+    aa = np.random.randint(0, 50, 100)
+    bb = np.random.randint(0, 50, 100)
+    for how in hows:
+        yield (aa, bb, how)
+
+    # Test floating point inputs
+    aa = np.random.random(50)
+    bb = np.random.random(50)
+    for how in hows:
+        yield (aa, bb, how)
+
+
+def pd_odd_joins(left, right, join_type):
+    if join_type == "leftanti":
+        return left[~left.index.isin(right.index)][left.columns]
+    elif join_type == "leftsemi":
+        return left[left.index.isin(right.index)][left.columns]
+
+
+def assert_join_results_equal(expect, got, how, **kwargs):
+    if how not in _JOIN_TYPES:
+        raise ValueError(f"Unrecognized join type {how}")
+    if how == "right":
+        got = got[expect.columns]
+
+    if isinstance(expect, (pd.Series, cudf.Series)):
+        return assert_eq(
+            expect.sort_values().reset_index(drop=True),
+            got.sort_values().reset_index(drop=True),
+            **kwargs,
+        )
+    elif isinstance(expect, (pd.DataFrame, cudf.DataFrame)):
+        if not len(
+            expect.columns
+        ):  # can't sort_values() on a df without columns
+            return assert_eq(expect, got, **kwargs)
+
+        assert_eq(
+            expect.sort_values(expect.columns.to_list()).reset_index(
+                drop=True
+            ),
+            got.sort_values(got.columns.to_list()).reset_index(drop=True),
+            **kwargs,
+        )
+    elif isinstance(expect, (pd.Index, cudf.Index)):
+        return assert_eq(expect.sort_values(), got.sort_values(), **kwargs)
+    else:
+        raise ValueError(f"Not a join result: {type(expect).__name__}")
+
+
+@pytest.mark.parametrize("aa,bb,how", make_params())
+def test_dataframe_join_how(aa, bb, how):
+    df = cudf.DataFrame()
+    df["a"] = aa
+    df["b"] = bb
+
+    def work_pandas(df, how):
+        df1 = df.set_index("a")
+        df2 = df.set_index("b")
+        if how == "leftanti":
+            joined = pd_odd_joins(df1, df2, "leftanti")
+        elif how == "leftsemi":
+            joined = pd_odd_joins(df1, df2, "leftsemi")
+        else:
+            joined = df1.join(df2, how=how, sort=True)
+        return joined
+
+    def work_gdf(df):
+        df1 = df.set_index("a")
+        df2 = df.set_index("b")
+        joined = df1.join(df2, how=how, sort=True)
+        return joined
+
+    expect = work_pandas(df.to_pandas(), how)
+    got = work_gdf(df)
+    expecto = expect.copy()
+    goto = got.copy()
+
+    expect = expect.astype(np.float64).fillna(np.nan)[expect.columns]
+    got = got.astype(np.float64).fillna(np.nan)[expect.columns]
+
+    assert got.index.name is None
+
+    assert list(expect.columns) == list(got.columns)
+    if how in {"left", "inner", "right", "leftanti", "leftsemi"}:
+        assert_eq(sorted(expect.index.values), sorted(got.index.values))
+        if how != "outer":
+            # Newly introduced ambiguous ValueError thrown when
+            # an index and column have the same name. Rename the
+            # index so sorts work.
+            # TODO: What is the less hacky way?
+            expect.index.name = "bob"
+            got.index.name = "mary"
+            assert_join_results_equal(expect, got, how=how)
+        # if(how=='right'):
+        #     _sorted_check_series(expect['a'], expect['b'],
+        #                          got['a'], got['b'])
+        # else:
+        #     _sorted_check_series(expect['b'], expect['a'], got['b'],
+        #                          got['a'])
+        else:
+            for c in expecto.columns:
+                _check_series(expecto[c].fillna(-1), goto[c].fillna(-1))
+
+
+def _check_series(expect, got):
+    magic = 0xDEADBEAF
+
+    direct_equal = np.all(expect.values == got.to_numpy())
+    nanfilled_equal = np.all(
+        expect.fillna(magic).values == got.fillna(magic).to_numpy()
+    )
+    msg = "direct_equal={}, nanfilled_equal={}".format(
+        direct_equal, nanfilled_equal
+    )
+    assert direct_equal or nanfilled_equal, msg
+
+
+def test_dataframe_join_suffix():
+    np.random.seed(0)
+
+    df = cudf.DataFrame()
+    for k in "abc":
+        df[k] = np.random.randint(0, 5, 5)
+
+    left = df.set_index("a")
+    right = df.set_index("c")
+    with pytest.raises(ValueError) as raises:
+        left.join(right)
+    raises.match(
+        "there are overlapping columns but lsuffix"
+        " and rsuffix are not defined"
+    )
+
+    got = left.join(right, lsuffix="_left", rsuffix="_right", sort=True)
+    # Get expected value
+    pddf = df.to_pandas()
+    expect = pddf.set_index("a").join(
+        pddf.set_index("c"), lsuffix="_left", rsuffix="_right"
+    )
+    # Check
+    assert list(expect.columns) == list(got.columns)
+    assert_eq(expect.index.values, got.index.values)
+
+    got_sorted = got.sort_values(by=list(got.columns), axis=0)
+    expect_sorted = expect.sort_values(by=list(expect.columns), axis=0)
+    for k in expect_sorted.columns:
+        _check_series(expect_sorted[k].fillna(-1), got_sorted[k].fillna(-1))
+
+
+def test_dataframe_join_cats():
+    lhs = cudf.DataFrame()
+    lhs["a"] = pd.Categorical(list("aababcabbc"), categories=list("abc"))
+    lhs["b"] = bb = np.arange(len(lhs))
+    lhs = lhs.set_index("a")
+
+    rhs = cudf.DataFrame()
+    rhs["a"] = pd.Categorical(list("abcac"), categories=list("abc"))
+    rhs["c"] = cc = np.arange(len(rhs))
+    rhs = rhs.set_index("a")
+
+    got = lhs.join(rhs)
+    expect = lhs.to_pandas().join(rhs.to_pandas())
+
+    # Note: pandas make an object Index after joining
+    assert_join_results_equal(expect, got, how="inner")
+
+    # Just do some rough checking here.
+    assert list(got.columns) == ["b", "c"]
+    assert len(got) > 0
+    assert set(got.index.to_pandas()) & set("abc")
+    assert set(got["b"].to_numpy()) & set(bb)
+    assert set(got["c"].to_numpy()) & set(cc)
+
+
+def test_dataframe_join_combine_cats():
+    lhs = cudf.DataFrame({"join_index": ["a", "b", "c"], "data_x": [1, 2, 3]})
+    rhs = cudf.DataFrame({"join_index": ["b", "c", "d"], "data_y": [2, 3, 4]})
+
+    lhs["join_index"] = lhs["join_index"].astype("category")
+    rhs["join_index"] = rhs["join_index"].astype("category")
+
+    lhs = lhs.set_index("join_index")
+    rhs = rhs.set_index("join_index")
+
+    lhs_pd = lhs.to_pandas()
+    rhs_pd = rhs.to_pandas()
+
+    lhs_pd.index = lhs_pd.index.astype("object")
+    rhs_pd.index = rhs_pd.index.astype("object")
+
+    expect = lhs_pd.join(rhs_pd, how="outer")
+    expect.index = expect.index.astype("category")
+    got = lhs.join(rhs, how="outer")
+
+    assert_eq(expect.index.sort_values(), got.index.sort_values())
+
+
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+def test_dataframe_join_mismatch_cats(how):
+    pdf1 = pd.DataFrame(
+        {
+            "join_col": ["a", "b", "c", "d", "e"],
+            "data_col_left": [10, 20, 30, 40, 50],
+        }
+    )
+    pdf2 = pd.DataFrame(
+        {"join_col": ["c", "e", "f"], "data_col_right": [6, 7, 8]}
+    )
+
+    pdf1["join_col"] = pdf1["join_col"].astype("category")
+    pdf2["join_col"] = pdf2["join_col"].astype("category")
+
+    gdf1 = cudf.from_pandas(pdf1)
+    gdf2 = cudf.from_pandas(pdf2)
+
+    gdf1 = gdf1.set_index("join_col")
+    gdf2 = gdf2.set_index("join_col")
+
+    pdf1 = pdf1.set_index("join_col")
+    pdf2 = pdf2.set_index("join_col")
+    join_gdf = gdf1.join(gdf2, how=how, sort=True)
+    join_pdf = pdf1.join(pdf2, how=how)
+
+    got = join_gdf.fillna(-1).to_pandas()
+    expect = join_pdf.fillna(-1)  # note: cudf join doesn't mask NA
+
+    # We yield a categorical here whereas pandas gives Object.
+    expect.index = expect.index.astype("category")
+    # cudf creates the columns in different order than pandas for right join
+    if how == "right":
+        got = got[["data_col_left", "data_col_right"]]
+
+    expect.data_col_right = expect.data_col_right.astype(np.int64)
+    expect.data_col_left = expect.data_col_left.astype(np.int64)
+
+    assert_join_results_equal(expect, got, how=how, check_categorical=False)
+
+
+@pytest.mark.parametrize("on", ["key1", ["key1", "key2"], None])
+def test_dataframe_merge_on(on):
+    np.random.seed(0)
+
+    # Make cuDF
+    df_left = cudf.DataFrame()
+    nelem = 500
+    df_left["key1"] = np.random.randint(0, 40, nelem)
+    df_left["key2"] = np.random.randint(0, 50, nelem)
+    df_left["left_val"] = np.arange(nelem)
+
+    df_right = cudf.DataFrame()
+    nelem = 500
+    df_right["key1"] = np.random.randint(0, 30, nelem)
+    df_right["key2"] = np.random.randint(0, 50, nelem)
+    df_right["right_val"] = np.arange(nelem)
+
+    # Make pandas DF
+    pddf_left = df_left.to_pandas()
+    pddf_right = df_right.to_pandas()
+
+    # Expected result (from pandas)
+    pddf_joined = pddf_left.merge(pddf_right, on=on, how="left")
+
+    # Test (from cuDF; doesn't check for ordering)
+    join_result = df_left.merge(df_right, on=on, how="left")
+    join_result_cudf = cudf.merge(df_left, df_right, on=on, how="left")
+
+    join_result["right_val"] = (
+        join_result["right_val"].astype(np.float64).fillna(np.nan)
+    )
+
+    join_result_cudf["right_val"] = (
+        join_result_cudf["right_val"].astype(np.float64).fillna(np.nan)
+    )
+
+    for col in list(pddf_joined.columns):
+        if col.count("_y") > 0:
+            join_result[col] = (
+                join_result[col].astype(np.float64).fillna(np.nan)
+            )
+            join_result_cudf[col] = (
+                join_result_cudf[col].astype(np.float64).fillna(np.nan)
+            )
+
+    # Test dataframe equality (ignore order of rows and columns)
+    cdf_result = (
+        join_result.to_pandas()
+        .sort_values(list(pddf_joined.columns))
+        .reset_index(drop=True)
+    )
+
+    pdf_result = pddf_joined.sort_values(
+        list(pddf_joined.columns)
+    ).reset_index(drop=True)
+
+    assert_join_results_equal(cdf_result, pdf_result, how="left")
+
+    merge_func_result_cdf = (
+        join_result_cudf.to_pandas()
+        .sort_values(list(pddf_joined.columns))
+        .reset_index(drop=True)
+    )
+
+    assert_join_results_equal(merge_func_result_cdf, cdf_result, how="left")
+
+
+def test_dataframe_merge_on_unknown_column():
+    np.random.seed(0)
+
+    # Make cuDF
+    df_left = cudf.DataFrame()
+    nelem = 500
+    df_left["key1"] = np.random.randint(0, 40, nelem)
+    df_left["key2"] = np.random.randint(0, 50, nelem)
+    df_left["left_val"] = np.arange(nelem)
+
+    df_right = cudf.DataFrame()
+    nelem = 500
+    df_right["key1"] = np.random.randint(0, 30, nelem)
+    df_right["key2"] = np.random.randint(0, 50, nelem)
+    df_right["right_val"] = np.arange(nelem)
+
+    with pytest.raises(KeyError) as raises:
+        df_left.merge(df_right, on="bad_key", how="left")
+    raises.match("bad_key")
+
+
+def test_dataframe_merge_no_common_column():
+    np.random.seed(0)
+
+    # Make cuDF
+    df_left = cudf.DataFrame()
+    nelem = 500
+    df_left["key1"] = np.random.randint(0, 40, nelem)
+    df_left["key2"] = np.random.randint(0, 50, nelem)
+    df_left["left_val"] = np.arange(nelem)
+
+    df_right = cudf.DataFrame()
+    nelem = 500
+    df_right["key3"] = np.random.randint(0, 30, nelem)
+    df_right["key4"] = np.random.randint(0, 50, nelem)
+    df_right["right_val"] = np.arange(nelem)
+
+    with pytest.raises(ValueError) as raises:
+        df_left.merge(df_right, how="left")
+    raises.match("No common columns to perform merge on")
+
+
+def test_dataframe_empty_merge():
+    gdf1 = cudf.DataFrame({"a": [], "b": []})
+    gdf2 = cudf.DataFrame({"a": [], "c": []})
+
+    expect = cudf.DataFrame({"a": [], "b": [], "c": []})
+    got = gdf1.merge(gdf2, how="left", on=["a"])
+
+    assert_join_results_equal(expect, got, how="left")
+
+
+def test_dataframe_merge_order():
+    gdf1 = cudf.DataFrame()
+    gdf2 = cudf.DataFrame()
+    gdf1["id"] = [10, 11]
+    gdf1["timestamp"] = [1, 2]
+    gdf1["a"] = [3, 4]
+
+    gdf2["id"] = [4, 5]
+    gdf2["a"] = [7, 8]
+
+    gdf = gdf1.merge(gdf2, how="left", on=["id", "a"])
+
+    df1 = pd.DataFrame()
+    df2 = pd.DataFrame()
+    df1["id"] = [10, 11]
+    df1["timestamp"] = [1, 2]
+    df1["a"] = [3, 4]
+
+    df2["id"] = [4, 5]
+    df2["a"] = [7, 8]
+
+    df = df1.merge(df2, how="left", on=["id", "a"])
+    assert_join_results_equal(df, gdf, how="left")
+
+
+@pytest.mark.parametrize(
+    "pairs",
+    [
+        ("", ""),
+        ("", "a"),
+        ("", "ab"),
+        ("", "abc"),
+        ("", "b"),
+        ("", "bcd"),
+        ("", "cde"),
+        ("a", "a"),
+        ("a", "ab"),
+        ("a", "abc"),
+        ("a", "b"),
+        ("a", "bcd"),
+        ("a", "cde"),
+        ("ab", "ab"),
+        ("ab", "abc"),
+        ("ab", "b"),
+        ("ab", "bcd"),
+        ("ab", "cde"),
+        ("abc", "abc"),
+        ("abc", "b"),
+        ("abc", "bcd"),
+        ("abc", "cde"),
+        ("b", "b"),
+        ("b", "bcd"),
+        ("b", "cde"),
+        ("bcd", "bcd"),
+        ("bcd", "cde"),
+        ("cde", "cde"),
+    ],
+)
+@pytest.mark.parametrize("max", [5, 1000])
+@pytest.mark.parametrize("rows", [1, 5, 100])
+@pytest.mark.parametrize("how", ["left", "inner", "outer"])
+def test_dataframe_pairs_of_triples(pairs, max, rows, how):
+    np.random.seed(0)
+
+    pdf_left = pd.DataFrame()
+    pdf_right = pd.DataFrame()
+    for left_column in pairs[0]:
+        pdf_left[left_column] = np.random.randint(0, max, rows)
+    for right_column in pairs[1]:
+        pdf_right[right_column] = np.random.randint(0, max, rows)
+    gdf_left = cudf.from_pandas(pdf_left)
+    gdf_right = cudf.from_pandas(pdf_right)
+    if not set(pdf_left.columns).intersection(pdf_right.columns):
+        with pytest.raises(
+            pd.errors.MergeError,
+            match="No common columns to perform merge on",
+        ):
+            pdf_left.merge(pdf_right)
+        with pytest.raises(
+            ValueError, match="No common columns to perform merge on"
+        ):
+            gdf_left.merge(gdf_right)
+    elif not [value for value in pdf_left if value in pdf_right]:
+        with pytest.raises(
+            pd.errors.MergeError,
+            match="No common columns to perform merge on",
+        ):
+            pdf_left.merge(pdf_right)
+        with pytest.raises(
+            ValueError, match="No common columns to perform merge on"
+        ):
+            gdf_left.merge(gdf_right)
+    else:
+        pdf_result = pdf_left.merge(pdf_right, how=how)
+        gdf_result = gdf_left.merge(gdf_right, how=how)
+        assert np.array_equal(gdf_result.columns, pdf_result.columns)
+        for column in gdf_result:
+            gdf_col_result_sorted = gdf_result[column].fillna(-1).sort_values()
+            pd_col_result_sorted = pdf_result[column].fillna(-1).sort_values()
+            assert np.array_equal(
+                gdf_col_result_sorted.to_pandas().values,
+                pd_col_result_sorted.values,
+            )
+
+
+def test_safe_merging_with_left_empty():
+
+    np.random.seed(0)
+
+    pairs = ("bcd", "b")
+    pdf_left = pd.DataFrame()
+    pdf_right = pd.DataFrame()
+    for left_column in pairs[0]:
+        pdf_left[left_column] = np.random.randint(0, 10, 0)
+    for right_column in pairs[1]:
+        pdf_right[right_column] = np.random.randint(0, 10, 5)
+    gdf_left = cudf.from_pandas(pdf_left)
+    gdf_right = cudf.from_pandas(pdf_right)
+
+    pdf_result = pdf_left.merge(pdf_right)
+    gdf_result = gdf_left.merge(gdf_right)
+    # Simplify test because pandas does not consider empty Index and RangeIndex
+    # to be equivalent. TODO: Allow empty Index objects to have equivalence.
+    assert len(pdf_result) == len(gdf_result)
+
+
+@pytest.mark.parametrize("how", ["left", "inner", "outer"])
+@pytest.mark.parametrize("left_empty", [True, False])
+@pytest.mark.parametrize("right_empty", [True, False])
+def test_empty_joins(how, left_empty, right_empty):
+    pdf = pd.DataFrame({"x": [1, 2, 3]})
+
+    if left_empty:
+        left = pdf.head(0)
+    else:
+        left = pdf
+    if right_empty:
+        right = pdf.head(0)
+    else:
+        right = pdf
+
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+
+    expected = left.merge(right, how=how)
+    result = gleft.merge(gright, how=how)
+    assert len(expected) == len(result)
+
+
+def test_merge_left_index_zero():
+    left = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6]}, index=[0, 1, 2, 3, 4, 5])
+    right = pd.DataFrame(
+        {"y": [10, 20, 30, 6, 5, 4]}, index=[0, 1, 2, 3, 4, 6]
+    )
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    pd_merge = left.merge(right, left_on="x", right_on="y")
+    gd_merge = gleft.merge(gright, left_on="x", right_on="y")
+
+    assert_join_results_equal(pd_merge, gd_merge, how="left")
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left_index": True, "right_on": "y"},
+        {"right_index": True, "left_on": "x"},
+        {"left_on": "x", "right_on": "y"},
+        {"left_index": True, "right_index": True},
+    ],
+)
+def test_merge_left_right_index_left_right_on_zero_kwargs(kwargs):
+    left = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6]}, index=[0, 1, 2, 3, 4, 5])
+    right = pd.DataFrame(
+        {"y": [10, 20, 30, 6, 5, 4]}, index=[0, 1, 2, 3, 4, 6]
+    )
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    pd_merge = left.merge(right, **kwargs)
+    gd_merge = gleft.merge(gright, **kwargs)
+    assert_join_results_equal(pd_merge, gd_merge, how="left")
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left_index": True, "right_on": "y"},
+        {"right_index": True, "left_on": "x"},
+        {"left_on": "x", "right_on": "y"},
+        {"left_index": True, "right_index": True},
+    ],
+)
+def test_merge_left_right_index_left_right_on_kwargs(kwargs):
+    left = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6]}, index=[1, 2, 3, 4, 5, 6])
+    right = pd.DataFrame(
+        {"y": [10, 20, 30, 6, 5, 4]}, index=[1, 2, 3, 4, 5, 7]
+    )
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    pd_merge = left.merge(right, **kwargs)
+    gd_merge = gleft.merge(gright, **kwargs)
+    assert_join_results_equal(pd_merge, gd_merge, how="left")
+
+
+def test_indicator():
+    gdf = cudf.DataFrame({"x": [1, 2, 1]})
+    gdf.merge(gdf, indicator=False)
+
+    with pytest.raises(NotImplementedError) as info:
+        gdf.merge(gdf, indicator=True)
+
+    assert "indicator=False" in str(info.value)
+
+
+def test_merge_suffixes():
+    pdf = cudf.DataFrame({"x": [1, 2, 1]})
+    gdf = cudf.DataFrame({"x": [1, 2, 1]})
+    assert_join_results_equal(
+        gdf.merge(gdf, suffixes=("left", "right")),
+        pdf.merge(pdf, suffixes=("left", "right")),
+        how="left",
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf.merge,
+        rfunc=gdf.merge,
+        lfunc_args_and_kwargs=([pdf], {"lsuffix": "left", "rsuffix": "right"}),
+        rfunc_args_and_kwargs=([gdf], {"lsuffix": "left", "rsuffix": "right"}),
+    )
+
+
+def test_merge_left_on_right_on():
+    left = pd.DataFrame({"xx": [1, 2, 3, 4, 5, 6]})
+    right = pd.DataFrame({"xx": [10, 20, 30, 6, 5, 4]})
+
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+
+    assert_join_results_equal(
+        left.merge(right, on="xx"), gleft.merge(gright, on="xx"), how="left"
+    )
+
+    assert_join_results_equal(
+        left.merge(right, left_on="xx", right_on="xx"),
+        gleft.merge(gright, left_on="xx", right_on="xx"),
+        how="left",
+    )
+
+
+def test_merge_on_index_retained():
+    df = cudf.DataFrame()
+    df["a"] = [1, 2, 3, 4, 5]
+    df["b"] = ["a", "b", "c", "d", "e"]
+    df.index = [5, 3, 4, 2, 1]
+
+    df2 = cudf.DataFrame()
+    df2["a2"] = [1, 2, 3, 4, 5]
+    df2["res"] = ["a", "b", "c", "d", "e"]
+
+    pdf = df.to_pandas()
+    pdf2 = df2.to_pandas()
+
+    gdm = df.merge(df2, left_index=True, right_index=True, how="left")
+    pdm = pdf.merge(pdf2, left_index=True, right_index=True, how="left")
+    gdm["a2"] = gdm["a2"].astype("float64")
+    assert_eq(gdm.sort_index(), pdm.sort_index())
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left_index": True, "right_on": "y"},
+        {"right_index": True, "left_on": "x"},
+        {"left_on": "x", "right_on": "y"},
+    ],
+)
+def test_merge_left_right_index_left_right_on_kwargs2(kwargs):
+    left = pd.DataFrame({"x": [1, 2, 3]}, index=[10, 20, 30])
+    right = pd.DataFrame({"y": [10, 20, 30]}, index=[1, 2, 30])
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    gd_merge = gleft.merge(gright, **kwargs)
+    pd_merge = left.merge(right, **kwargs)
+    if pd_merge.empty:
+        assert gd_merge.empty
+
+
+@pytest.mark.parametrize(
+    "hows", [{"how": "inner"}, {"how": "left"}, {"how": "outer"}]
+)
+@pytest.mark.parametrize(
+    "ons",
+    [
+        {"on": "a"},
+        {"on": ["a", "b"]},
+        {"on": ["b", "a"]},
+        {"on": ["a", "aa", "b"]},
+        {"on": ["b", "a", "aa"]},
+    ],
+)
+def test_merge_sort(ons, hows):
+    kwargs = {}
+    kwargs.update(hows)
+    kwargs.update(ons)
+    kwargs["sort"] = True
+    a = [4, 6, 9, 5, 2, 4, 1, 8, 1]
+    b = [9, 8, 7, 8, 3, 9, 7, 9, 2]
+    aa = [8, 9, 2, 9, 3, 1, 2, 3, 4]
+    left = pd.DataFrame({"a": a, "b": b, "aa": aa})
+    right = left.copy(deep=True)
+
+    left.index = [6, 5, 4, 7, 5, 5, 5, 4, 4]
+    right.index = [5, 4, 1, 9, 4, 3, 5, 4, 4]
+
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    gd_merge = gleft.merge(gright, **kwargs)
+
+    pd_merge = left.merge(right, **kwargs)
+    # require the join keys themselves to be sorted correctly
+    # the non-key columns will NOT match pandas ordering
+    assert_join_results_equal(
+        pd_merge[kwargs["on"]], gd_merge[kwargs["on"]], how="left"
+    )
+    pd_merge = pd_merge.drop(kwargs["on"], axis=1)
+    gd_merge = gd_merge.drop(kwargs["on"], axis=1)
+    if not pd_merge.empty:
+        # check to make sure the non join key columns are the same
+        pd_merge = pd_merge.sort_values(list(pd_merge.columns)).reset_index(
+            drop=True
+        )
+        gd_merge = gd_merge.sort_values(list(gd_merge.columns)).reset_index(
+            drop=True
+        )
+
+    assert_join_results_equal(pd_merge, gd_merge, how="left")
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left_on": ["a"], "left_index": False, "right_index": True},
+        {"right_on": ["b"], "left_index": True, "right_index": False},
+    ],
+)
+def test_merge_sort_on_indexes(kwargs):
+    left_index = kwargs["left_index"]
+    right_index = kwargs["right_index"]
+    kwargs["sort"] = True
+    a = [4, 6, 9, 5, 2, 4, 1, 8, 1]
+    left = pd.DataFrame({"a": a})
+    right = pd.DataFrame({"b": a})
+
+    left.index = [6, 5, 4, 7, 5, 5, 5, 4, 4]
+    right.index = [5, 4, 1, 9, 4, 3, 5, 4, 4]
+
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    gd_merge = gleft.merge(gright, **kwargs)
+
+    if left_index and right_index:
+        check_if_sorted = gd_merge[["a", "b"]].to_pandas()
+        check_if_sorted.index.name = "index"
+        definitely_sorted = check_if_sorted.sort_values(["index", "a", "b"])
+        definitely_sorted.index.name = None
+        assert_eq(gd_merge, definitely_sorted)
+    elif left_index:
+        assert gd_merge["b"].is_monotonic_increasing
+    elif right_index:
+        assert gd_merge["a"].is_monotonic_increasing
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_join_datetimes_index(dtype):
+    datetimes = pd.Series(pd.date_range("20010101", "20010102", freq="12h"))
+    pdf_lhs = pd.DataFrame(index=[1, 0, 1, 2, 0, 0, 1])
+    pdf_rhs = pd.DataFrame({"d": datetimes})
+    gdf_lhs = cudf.from_pandas(pdf_lhs)
+    gdf_rhs = cudf.from_pandas(pdf_rhs)
+
+    gdf_rhs["d"] = gdf_rhs["d"].astype(dtype)
+
+    pdf = pdf_lhs.join(pdf_rhs, sort=True)
+    gdf = gdf_lhs.join(gdf_rhs, sort=True)
+
+    assert gdf["d"].dtype == cudf.dtype(dtype)
+
+    assert_join_results_equal(pdf, gdf, how="inner")
+
+
+def test_join_with_different_names():
+    left = pd.DataFrame({"a": [0, 1, 2.0, 3, 4, 5, 9]})
+    right = pd.DataFrame({"b": [12, 5, 3, 9.0, 5], "c": [1, 2, 3, 4, 5.0]})
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    pd_merge = left.merge(right, how="outer", left_on=["a"], right_on=["b"])
+    gd_merge = gleft.merge(gright, how="outer", left_on=["a"], right_on=["b"])
+    assert_join_results_equal(pd_merge, gd_merge, how="outer")
+
+
+def test_join_same_name_different_order():
+    left = pd.DataFrame({"a": [0, 0], "b": [1, 2]})
+    right = pd.DataFrame({"a": [1, 2], "b": [0, 0]})
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    pd_merge = left.merge(right, left_on=["a", "b"], right_on=["b", "a"])
+    gd_merge = gleft.merge(gright, left_on=["a", "b"], right_on=["b", "a"])
+    assert_join_results_equal(pd_merge, gd_merge, how="left")
+
+
+def test_join_empty_table_dtype():
+    left = pd.DataFrame({"a": []})
+    right = pd.DataFrame({"b": [12, 5, 3, 9.0, 5], "c": [1, 2, 3, 4, 5.0]})
+    gleft = cudf.from_pandas(left)
+    gright = cudf.from_pandas(right)
+    pd_merge = left.merge(right, how="left", left_on=["a"], right_on=["b"])
+    gd_merge = gleft.merge(gright, how="left", left_on=["a"], right_on=["b"])
+    assert_eq(pd_merge["a"].dtype, gd_merge["a"].dtype)
+
+
+@pytest.mark.parametrize("how", ["outer", "inner", "left", "right"])
+@pytest.mark.parametrize(
+    "column_a",
+    [
+        (
+            pd.Series([None, 1, 2, 3, 4, 5, 6, 7], dtype=np.float64),
+            pd.Series([8, 9, 10, 11, 12, None, 14, 15], dtype=np.float64),
+        )
+    ],
+)
+@pytest.mark.parametrize(
+    "column_b",
+    [
+        (
+            pd.Series([0, 1, 0, None, 1, 0, 0, 0], dtype=np.float64),
+            pd.Series([None, 1, 2, 1, 2, 2, 0, 0], dtype=np.float64),
+        )
+    ],
+)
+@pytest.mark.parametrize(
+    "column_c",
+    [
+        (
+            pd.Series(["dog", "cat", "fish", "bug"] * 2),
+            pd.Series(["bird", "cat", "mouse", "snake"] * 2),
+        ),
+        (
+            pd.Series(["dog", "cat", "fish", "bug"] * 2).astype("category"),
+            pd.Series(["bird", "cat", "mouse", "snake"] * 2).astype(
+                "category"
+            ),
+        ),
+    ],
+)
+def test_join_multi(how, column_a, column_b, column_c):
+    index = ["b", "c"]
+    df1 = pd.DataFrame()
+    df1["a1"] = column_a[0]
+    df1["b"] = column_b[0]
+    df1["c"] = column_c[0]
+    df1 = df1.set_index(index)
+    gdf1 = cudf.from_pandas(df1)
+
+    df2 = pd.DataFrame()
+    df2["a2"] = column_a[1]
+    df2["b"] = column_b[1]
+    df2["c"] = column_c[1]
+    df2 = df2.set_index(index)
+    gdf2 = cudf.from_pandas(df2)
+
+    gdf_result = gdf1.join(gdf2, how=how, sort=True)
+    pdf_result = df1.join(df2, how=how, sort=True)
+
+    # Make sure columns are in the same order
+    columns = pdf_result.columns.values
+    gdf_result = gdf_result[columns]
+    pdf_result = pdf_result[columns]
+
+    assert_join_results_equal(pdf_result, gdf_result, how="inner")
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {
+            "left_on": ["a", "b"],
+            "right_on": ["a", "b"],
+            "left_index": False,
+            "right_index": False,
+        },  # left and right on, no indices
+        {
+            "left_on": None,
+            "right_on": None,
+            "left_index": True,
+            "right_index": True,
+        },  # left_index and right_index, no on
+        {
+            "left_on": ["a", "b"],
+            "right_on": None,
+            "left_index": False,
+            "right_index": True,
+        },  # left on and right_index
+        {
+            "left_on": None,
+            "right_on": ["a", "b"],
+            "left_index": True,
+            "right_index": False,
+        },  # right_on and left_index
+    ],
+)
+def test_merge_multi(kwargs):
+
+    left = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 3, 5, 6],
+            "b": [1, 3, 5, 7, 5, 9, 0],
+            "c": ["o", "p", "q", "r", "s", "t", "u"],
+            "d": ["v", "w", "x", "y", "z", "1", "2"],
+        }
+    )
+    right = cudf.DataFrame(
+        {
+            "a": [0, 9, 3, 4, 3, 7, 8],
+            "b": [2, 4, 5, 7, 5, 6, 8],
+            "c": ["a", "b", "c", "d", "e", "f", "g"],
+            "d": ["j", "i", "j", "k", "l", "m", "n"],
+        }
+    )
+
+    if (
+        kwargs["left_on"] is not None
+        and kwargs["right_on"] is not None
+        and kwargs["left_index"] is False
+        and kwargs["right_index"] is False
+    ):
+        left = left.set_index(["c", "d"])
+        right = right.set_index(["c", "d"])
+    elif (
+        kwargs["left_on"] is None
+        and kwargs["right_on"] is None
+        and kwargs["left_index"] is True
+        and kwargs["right_index"] is True
+    ):
+        left = left.set_index(["a", "b"])
+        right = right.set_index(["a", "b"])
+    elif kwargs["left_on"] is not None and kwargs["right_index"] is True:
+        left = left.set_index(["c", "d"])
+        right = right.set_index(["a", "b"])
+    elif kwargs["right_on"] is not None and kwargs["left_index"] is True:
+        left = left.set_index(["a", "b"])
+        right = right.set_index(["c", "d"])
+
+    gleft = left.to_pandas()
+    gright = right.to_pandas()
+
+    kwargs["sort"] = True
+    expect = gleft.merge(gright, **kwargs)
+    got = left.merge(right, **kwargs)
+
+    assert_eq(expect.sort_index().index, got.sort_index().index)
+
+    expect.index = range(len(expect))
+    got.index = range(len(got))
+    expect = expect.sort_values(list(expect.columns))
+    got = got.sort_values(list(got.columns))
+    expect.index = range(len(expect))
+    got.index = range(len(got))
+
+    assert_join_results_equal(expect, got, how="left")
+
+
+@pytest.mark.parametrize("dtype_l", INTEGER_TYPES)
+@pytest.mark.parametrize("dtype_r", INTEGER_TYPES)
+def test_typecast_on_join_int_to_int(dtype_l, dtype_r):
+    other_data = ["a", "b", "c"]
+
+    join_data_l = cudf.Series([1, 2, 3], dtype=dtype_l)
+    join_data_r = cudf.Series([1, 2, 4], dtype=dtype_r)
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_dtype = np.find_common_type([], [np.dtype(dtype_l), np.dtype(dtype_r)])
+
+    exp_join_data = [1, 2]
+    exp_other_data = ["a", "b"]
+    exp_join_col = cudf.Series(exp_join_data, dtype=exp_dtype)
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+@pytest.mark.parametrize("dtype_l", ["float32", "float64"])
+@pytest.mark.parametrize("dtype_r", ["float32", "float64"])
+def test_typecast_on_join_float_to_float(dtype_l, dtype_r):
+    other_data = ["a", "b", "c", "d", "e", "f"]
+
+    join_data_l = cudf.Series([1, 2, 3, 0.9, 4.5, 6], dtype=dtype_l)
+    join_data_r = cudf.Series([1, 2, 3, 0.9, 4.5, 7], dtype=dtype_r)
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_dtype = np.find_common_type([], [np.dtype(dtype_l), np.dtype(dtype_r)])
+
+    if dtype_l != dtype_r:
+        exp_join_data = [1, 2, 3, 4.5]
+        exp_other_data = ["a", "b", "c", "e"]
+    else:
+        exp_join_data = [1, 2, 3, 0.9, 4.5]
+        exp_other_data = ["a", "b", "c", "d", "e"]
+
+    exp_join_col = cudf.Series(exp_join_data, dtype=exp_dtype)
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+@pytest.mark.parametrize("dtype_l", NUMERIC_TYPES)
+@pytest.mark.parametrize("dtype_r", NUMERIC_TYPES)
+def test_typecast_on_join_mixed_int_float(dtype_l, dtype_r):
+    if (
+        ("int" in dtype_l or "long" in dtype_l)
+        and ("int" in dtype_r or "long" in dtype_r)
+    ) or ("float" in dtype_l and "float" in dtype_r):
+        pytest.skip("like types not tested in this function")
+
+    other_data = ["a", "b", "c", "d", "e", "f"]
+
+    join_data_l = cudf.Series([1, 2, 3, 0.9, 4.5, 6], dtype=dtype_l)
+    join_data_r = cudf.Series([1, 2, 3, 0.9, 4.5, 7], dtype=dtype_r)
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_dtype = np.find_common_type([], [np.dtype(dtype_l), np.dtype(dtype_r)])
+
+    exp_join_data = [1, 2, 3]
+    exp_other_data = ["a", "b", "c"]
+    exp_join_col = cudf.Series(exp_join_data, dtype=exp_dtype)
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_typecast_on_join_no_float_round():
+
+    other_data = ["a", "b", "c", "d", "e"]
+
+    join_data_l = cudf.Series([1, 2, 3, 4, 5], dtype="int8")
+    join_data_r = cudf.Series([1, 2, 3, 4.01, 4.99], dtype="float32")
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_join_data = [1, 2, 3, 4, 5]
+    exp_Bx = ["a", "b", "c", "d", "e"]
+    exp_By = ["a", "b", "c", None, None]
+    exp_join_col = cudf.Series(exp_join_data, dtype="float32")
+
+    expect = cudf.DataFrame(
+        {"join_col": exp_join_col, "B_x": exp_Bx, "B_y": exp_By}
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="left")
+
+    assert_join_results_equal(expect, got, how="left")
+
+
+@pytest.mark.parametrize(
+    "dtypes",
+    [
+        (np.dtype("int8"), np.dtype("int16")),
+        (np.dtype("int16"), np.dtype("int32")),
+        (np.dtype("int32"), np.dtype("int64")),
+        (np.dtype("uint8"), np.dtype("uint16")),
+        (np.dtype("uint16"), np.dtype("uint32")),
+        (np.dtype("uint32"), np.dtype("uint64")),
+        (np.dtype("float32"), np.dtype("float64")),
+        (np.dtype("int32"), np.dtype("float32")),
+        (np.dtype("uint32"), np.dtype("float32")),
+    ],
+)
+def test_typecast_on_join_overflow_unsafe(dtypes):
+    dtype_l, dtype_r = dtypes
+    if dtype_l.kind in {"i", "u"}:
+        dtype_l_max = np.iinfo(dtype_l).max
+    elif dtype_l.kind == "f":
+        dtype_l_max = np.finfo(dtype_r).max
+
+    lhs = cudf.DataFrame({"a": [1, 2, 3, 4, 5]}, dtype=dtype_l)
+    rhs = cudf.DataFrame({"a": [1, 2, 3, 4, dtype_l_max + 1]}, dtype=dtype_r)
+
+    p_lhs = lhs.to_pandas()
+    p_rhs = rhs.to_pandas()
+
+    with expect_warning_if(
+        (dtype_l.kind == "f" and dtype_r.kind in {"i", "u"})
+        or (dtype_l.kind in {"i", "u"} and dtype_r.kind == "f"),
+        UserWarning,
+    ):
+        expect = p_lhs.merge(p_rhs, on="a", how="left")
+    got = lhs.merge(rhs, on="a", how="left")
+
+    # The dtypes here won't match exactly because pandas does some unsafe
+    # conversions (with a warning that we are catching above) that we don't
+    # want to match.
+    assert_join_results_equal(expect, got, how="left", check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(5, 2),
+        Decimal64Dtype(7, 5),
+        Decimal64Dtype(12, 7),
+        Decimal128Dtype(20, 5),
+    ],
+)
+def test_decimal_typecast_inner(dtype):
+    other_data = ["a", "b", "c", "d", "e"]
+
+    join_data_l = cudf.Series(["1.6", "9.5", "7.2", "8.7", "2.3"]).astype(
+        dtype
+    )
+    join_data_r = cudf.Series(["1.6", "9.5", "7.2", "4.5", "2.3"]).astype(
+        dtype
+    )
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_join_data = ["1.6", "9.5", "7.2", "2.3"]
+    exp_other_data = ["a", "b", "c", "e"]
+
+    exp_join_col = cudf.Series(exp_join_data).astype(dtype)
+
+    expected = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+    assert_join_results_equal(expected, got, how="inner")
+    assert_eq(dtype, got["join_col"].dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(7, 3),
+        Decimal64Dtype(9, 5),
+        Decimal64Dtype(14, 10),
+        Decimal128Dtype(21, 9),
+    ],
+)
+def test_decimal_typecast_left(dtype):
+    other_data = ["a", "b", "c", "d"]
+
+    join_data_l = cudf.Series(["95.05", "384.26", "74.22", "1456.94"]).astype(
+        dtype
+    )
+    join_data_r = cudf.Series(
+        ["95.05", "62.4056", "74.22", "1456.9472"]
+    ).astype(dtype)
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_join_data = ["95.05", "74.22", "384.26", "1456.94"]
+    exp_other_data_x = ["a", "c", "b", "d"]
+    exp_other_data_y = ["a", "c", None, None]
+
+    exp_join_col = cudf.Series(exp_join_data).astype(dtype)
+
+    expected = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data_x,
+            "B_y": exp_other_data_y,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="left")
+
+    assert_join_results_equal(expected, got, how="left")
+    assert_eq(dtype, got["join_col"].dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(7, 3),
+        Decimal64Dtype(10, 5),
+        Decimal64Dtype(18, 9),
+        Decimal128Dtype(22, 8),
+    ],
+)
+def test_decimal_typecast_outer(dtype):
+    other_data = ["a", "b", "c"]
+    join_data_l = cudf.Series(["741.248", "1029.528", "3627.292"]).astype(
+        dtype
+    )
+    join_data_r = cudf.Series(["9284.103", "1029.528", "948.637"]).astype(
+        dtype
+    )
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+    exp_join_data = ["9284.103", "948.637", "1029.528", "741.248", "3627.292"]
+    exp_other_data_x = [None, None, "b", "a", "c"]
+    exp_other_data_y = ["a", "c", "b", None, None]
+    exp_join_col = cudf.Series(exp_join_data).astype(dtype)
+    expected = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data_x,
+            "B_y": exp_other_data_y,
+        }
+    )
+    got = gdf_l.merge(gdf_r, on="join_col", how="outer")
+
+    assert_join_results_equal(expected, got, how="outer")
+    assert_eq(dtype, got["join_col"].dtype)
+
+
+@pytest.mark.parametrize(
+    "dtype_l",
+    [Decimal64Dtype(7, 3), Decimal64Dtype(9, 5)],
+)
+@pytest.mark.parametrize(
+    "dtype_r",
+    [Decimal64Dtype(8, 3), Decimal64Dtype(11, 6)],
+)
+def test_mixed_decimal_typecast(dtype_l, dtype_r):
+    other_data = ["a", "b", "c", "d"]
+
+    join_data_l = cudf.Series(["95.05", "34.6", "74.22", "14.94"]).astype(
+        dtype_r
+    )
+    join_data_r = cudf.Series(["95.05", "62.4056", "74.22", "1.42"]).astype(
+        dtype_l
+    )
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    with pytest.raises(
+        TypeError,
+        match="Decimal columns can only be merged with decimal columns "
+        "of the same precision and scale",
+    ):
+        gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+
+@pytest.mark.parametrize(
+    "dtype_l",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+@pytest.mark.parametrize(
+    "dtype_r",
+    ["datetime64[s]", "datetime64[ms]", "datetime64[us]", "datetime64[ns]"],
+)
+def test_typecast_on_join_dt_to_dt(dtype_l, dtype_r):
+    other_data = ["a", "b", "c", "d", "e"]
+    join_data_l = cudf.Series(
+        ["1991-11-20", "1999-12-31", "2004-12-04", "2015-01-01", "2019-08-15"]
+    ).astype(dtype_l)
+    join_data_r = cudf.Series(
+        ["1991-11-20", "1999-12-31", "2004-12-04", "2015-01-01", "2019-08-16"]
+    ).astype(dtype_r)
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_dtype = max(np.dtype(dtype_l), np.dtype(dtype_r))
+
+    exp_join_data = ["1991-11-20", "1999-12-31", "2004-12-04", "2015-01-01"]
+    exp_other_data = ["a", "b", "c", "d"]
+    exp_join_col = cudf.Series(exp_join_data, dtype=exp_dtype)
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+@pytest.mark.parametrize("dtype_l", ["category", "str", "int32", "float32"])
+@pytest.mark.parametrize("dtype_r", ["category", "str", "int32", "float32"])
+def test_typecast_on_join_categorical(dtype_l, dtype_r):
+    if not (dtype_l == "category" or dtype_r == "category"):
+        pytest.skip("at least one side must be category for this set of tests")
+    if dtype_l == "category" and dtype_r == "category":
+        pytest.skip("Can't determine which categorical to use")
+
+    other_data = ["a", "b", "c", "d", "e"]
+    join_data_l = cudf.Series([1, 2, 3, 4, 5], dtype=dtype_l)
+    join_data_r = cudf.Series([1, 2, 3, 4, 6], dtype=dtype_r)
+    if dtype_l == "category":
+        exp_dtype = join_data_l.dtype.categories.dtype
+    elif dtype_r == "category":
+        exp_dtype = join_data_r.dtype.categories.dtype
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    exp_join_data = [1, 2, 3, 4]
+    exp_other_data = ["a", "b", "c", "d"]
+    exp_join_col = cudf.Series(exp_join_data, dtype=exp_dtype)
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_col,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+
+    got = gdf_l.merge(gdf_r, on="join_col", how="inner")
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def make_categorical_dataframe(categories, ordered=False):
+    dtype = CategoricalDtype(categories=categories, ordered=ordered)
+    data = cudf.Series(categories).astype(dtype)
+    return cudf.DataFrame({"key": data})
+
+
+def test_categorical_typecast_inner():
+    # Inner join casting rules for categoricals
+
+    # Equal categories, equal ordering -> common categorical
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([1, 2, 3], ordered=False)
+    result = left.merge(right, how="inner", on="key")
+
+    expect_dtype = CategoricalDtype(categories=[1, 2, 3], ordered=False)
+    expect_data = cudf.Series([1, 2, 3], dtype=expect_dtype, name="key")
+
+    assert_join_results_equal(
+        expect_data, result["key"], how="inner", check_categorical=False
+    )
+
+    # Equal categories, unequal ordering -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([1, 2, 3], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, how="inner", on="key")
+
+    # Unequal categories
+    # Neither ordered -> unordered categorical with intersection
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([2, 3, 4], ordered=False)
+
+    result = left.merge(right, how="inner", on="key")
+
+    expect_dtype = cudf.CategoricalDtype(categories=[2, 3], ordered=False)
+    expect_data = cudf.Series([2, 3], dtype=expect_dtype, name="key")
+    assert_join_results_equal(
+        expect_data, result["key"], how="inner", check_categorical=False
+    )
+
+    # One is ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([2, 3, 4], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, how="inner", on="key")
+
+    # Both are ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=True)
+    right = make_categorical_dataframe([2, 3, 4], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, how="inner", on="key")
+
+
+def test_categorical_typecast_left():
+    # TODO: generalize to right or write another test
+    # Left join casting rules for categoricals
+
+    # equal categories, neither ordered -> common dtype
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([1, 2, 3], ordered=False)
+
+    result = left.merge(right, on="key", how="left")
+
+    expect_dtype = CategoricalDtype(categories=[1, 2, 3], ordered=False)
+    expect_data = cudf.Series([1, 2, 3], dtype=expect_dtype, name="key")
+
+    assert_join_results_equal(expect_data, result["key"], how="left")
+
+    # equal categories, unequal ordering -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=True)
+    right = make_categorical_dataframe([1, 2, 3], ordered=False)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, on="key", how="left")
+    with pytest.raises(TypeError):
+        result = right.merge(left, on="key", how="left")
+
+    # unequal categories neither ordered -> left dtype
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([2, 3, 4], ordered=False)
+
+    result = left.merge(right, on="key", how="left")
+    expect_dtype = CategoricalDtype(categories=[1, 2, 3], ordered=False)
+    expect_data = cudf.Series([1, 2, 3], dtype=expect_dtype, name="key")
+
+    assert_join_results_equal(expect_data, result["key"], how="left")
+
+    # unequal categories, unequal ordering -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=True)
+    right = make_categorical_dataframe([2, 3, 4], ordered=False)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, on="key", how="left")
+
+    # unequal categories, right ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([2, 3, 4], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, on="key", how="left")
+
+    # unequal categories, both ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=True)
+    right = make_categorical_dataframe([2, 3, 4], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, on="key", how="left")
+
+
+def test_categorical_typecast_outer():
+    # Outer join casting rules for categoricals
+
+    # equal categories, neither ordered -> common dtype
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([1, 2, 3], ordered=False)
+    result = left.merge(right, on="key", how="outer")
+
+    expect_dtype = CategoricalDtype(categories=[1, 2, 3], ordered=False)
+    expect_data = cudf.Series([1, 2, 3], dtype=expect_dtype, name="key")
+
+    assert_join_results_equal(expect_data, result["key"], how="outer")
+
+    # equal categories, both ordered -> common dtype
+    left = make_categorical_dataframe([1, 2, 3], ordered=True)
+    right = make_categorical_dataframe([1, 2, 3], ordered=True)
+    result = left.merge(right, on="key", how="outer")
+
+    expect_dtype = CategoricalDtype(categories=[1, 2, 3], ordered=True)
+    expect_data = cudf.Series([1, 2, 3], dtype=expect_dtype, name="key")
+
+    assert_join_results_equal(expect_data, result["key"], how="outer")
+
+    # equal categories, one ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([1, 2, 3], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, how="outer", on="key")
+    with pytest.raises(TypeError):
+        result = right.merge(left, how="outer", on="key")
+
+    # unequal categories, neither ordered -> superset
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([2, 3, 4], ordered=False)
+    result = left.merge(right, on="key", how="outer")
+
+    expect_dtype = CategoricalDtype(categories=[1, 2, 3, 4], ordered=False)
+    expect_data = cudf.Series([1, 2, 3, 4], dtype=expect_dtype, name="key")
+
+    assert_join_results_equal(expect_data, result["key"], how="outer")
+
+    # unequal categories, one ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=False)
+    right = make_categorical_dataframe([2, 3, 4], ordered=True)
+
+    with pytest.raises(TypeError):
+        result = left.merge(right, how="outer", on="key")
+    with pytest.raises(TypeError):
+        result = right.merge(left, how="outer", on="key")
+
+    # unequal categories, both ordered -> error
+    left = make_categorical_dataframe([1, 2, 3], ordered=True)
+    right = make_categorical_dataframe([2, 3, 4], ordered=True)
+    with pytest.raises(TypeError):
+        result = left.merge(right, how="outer", on="key")
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["object"])
+def test_categorical_typecast_inner_one_cat(dtype):
+
+    data = np.array([1, 2, 3], dtype=dtype)
+
+    left = make_categorical_dataframe(data)
+    right = left.astype(left["key"].dtype.categories.dtype)
+
+    result = left.merge(right, on="key", how="inner")
+    assert result["key"].dtype == left["key"].dtype.categories.dtype
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["object"])
+def test_categorical_typecast_left_one_cat(dtype):
+
+    data = np.array([1, 2, 3], dtype=dtype)
+
+    left = make_categorical_dataframe(data)
+    right = left.astype(left["key"].dtype.categories.dtype)
+
+    result = left.merge(right, on="key", how="left")
+    assert result["key"].dtype == left["key"].dtype
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["object"])
+def test_categorical_typecast_outer_one_cat(dtype):
+
+    data = np.array([1, 2, 3], dtype=dtype)
+
+    left = make_categorical_dataframe(data)
+    right = left.astype(left["key"].dtype.categories.dtype)
+
+    result = left.merge(right, on="key", how="outer")
+    assert result["key"].dtype == left["key"].dtype.categories.dtype
+
+
+@pytest.mark.parametrize(
+    ("lhs", "rhs"),
+    [
+        (["a", "b"], ["a"]),
+        (["a"], ["a", "b"]),
+        (["a", "b"], ["b"]),
+        (["b"], ["a", "b"]),
+        (["a"], ["a"]),
+    ],
+)
+@pytest.mark.parametrize("how", ["left", "right", "outer", "inner"])
+@pytest.mark.parametrize("level", ["a", "b", 0, 1])
+def test_index_join(lhs, rhs, how, level):
+    l_pdf = pd.DataFrame({"a": [2, 3, 1, 4], "b": [3, 7, 8, 1]})
+    r_pdf = pd.DataFrame({"a": [1, 5, 4, 0], "b": [3, 9, 8, 4]})
+    l_df = cudf.from_pandas(l_pdf)
+    r_df = cudf.from_pandas(r_pdf)
+    p_lhs = l_pdf.set_index(lhs).index
+    p_rhs = r_pdf.set_index(rhs).index
+    g_lhs = l_df.set_index(lhs).index
+    g_rhs = r_df.set_index(rhs).index
+
+    expected = p_lhs.join(p_rhs, level=level, how=how).to_frame(index=False)
+    got = g_lhs.join(g_rhs, level=level, how=how).to_frame(index=False)
+
+    assert_join_results_equal(expected, got, how=how)
+
+
+def test_index_join_corner_cases():
+    l_pdf = pd.DataFrame({"a": [2, 3, 1, 4], "b": [3, 7, 8, 1]})
+    r_pdf = pd.DataFrame(
+        {"a": [1, 5, 4, 0], "b": [3, 9, 8, 4], "c": [2, 3, 6, 0]}
+    )
+    l_df = cudf.from_pandas(l_pdf)
+    r_df = cudf.from_pandas(r_pdf)
+
+    # Join when column name doesn't match with level
+    lhs = ["a", "b"]
+    # level and rhs don't match
+    rhs = ["c"]
+    level = "b"
+    how = "outer"
+    p_lhs = l_pdf.set_index(lhs).index
+    p_rhs = r_pdf.set_index(rhs).index
+    g_lhs = l_df.set_index(lhs).index
+    g_rhs = r_df.set_index(rhs).index
+    expected = p_lhs.join(p_rhs, level=level, how=how).to_frame(index=False)
+    got = g_lhs.join(g_rhs, level=level, how=how).to_frame(index=False)
+
+    assert_join_results_equal(expected, got, how=how)
+
+    # sort is supported only in case of two non-MultiIndex join
+    # Join when column name doesn't match with level
+    lhs = ["a"]
+    # level and rhs don't match
+    rhs = ["a"]
+    level = "b"
+    how = "left"
+    p_lhs = l_pdf.set_index(lhs).index
+    p_rhs = r_pdf.set_index(rhs).index
+    g_lhs = l_df.set_index(lhs).index
+    g_rhs = r_df.set_index(rhs).index
+    expected = p_lhs.join(p_rhs, how=how, sort=True)
+    got = g_lhs.join(g_rhs, how=how, sort=True)
+
+    assert_join_results_equal(expected, got, how=how)
+
+    # Pandas Index.join on categorical column returns generic column
+    # but cudf will be returning a categorical column itself.
+    lhs = ["a", "b"]
+    rhs = ["a"]
+    level = "a"
+    how = "inner"
+    l_df["a"] = l_df["a"].astype("category")
+    r_df["a"] = r_df["a"].astype("category")
+    p_lhs = l_pdf.set_index(lhs).index
+    p_rhs = r_pdf.set_index(rhs).index
+    g_lhs = l_df.set_index(lhs).index
+    g_rhs = r_df.set_index(rhs).index
+    expected = p_lhs.join(p_rhs, level=level, how=how).to_frame(index=False)
+    got = g_lhs.join(g_rhs, level=level, how=how).to_frame(index=False)
+
+    got["a"] = got["a"].astype(expected["a"].dtype)
+
+    assert_join_results_equal(expected, got, how=how)
+
+
+def test_index_join_exception_cases():
+    l_df = cudf.DataFrame({"a": [2, 3, 1, 4], "b": [3, 7, 8, 1]})
+    r_df = cudf.DataFrame(
+        {"a": [1, 5, 4, 0], "b": [3, 9, 8, 4], "c": [2, 3, 6, 0]}
+    )
+
+    # Join between two MultiIndex
+    lhs = ["a", "b"]
+    rhs = ["a", "c"]
+    level = "a"
+    how = "outer"
+    g_lhs = l_df.set_index(lhs).index
+    g_rhs = r_df.set_index(rhs).index
+
+    with pytest.raises(TypeError):
+        g_lhs.join(g_rhs, level=level, how=how)
+
+    # Improper level value, level should be an int or scalar value
+    level = ["a"]
+    rhs = ["a"]
+    g_lhs = l_df.set_index(lhs).index
+    g_rhs = r_df.set_index(rhs).index
+    with pytest.raises(ValueError):
+        g_lhs.join(g_rhs, level=level, how=how)
+
+
+def test_typecast_on_join_indexes():
+    join_data_l = cudf.Series([1, 2, 3, 4, 5], dtype="int8")
+    join_data_r = cudf.Series([1, 2, 3, 4, 6], dtype="int32")
+    other_data = ["a", "b", "c", "d", "e"]
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    gdf_l = gdf_l.set_index("join_col")
+    gdf_r = gdf_r.set_index("join_col")
+
+    exp_join_data = [1, 2, 3, 4]
+    exp_other_data = ["a", "b", "c", "d"]
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_data,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+    expect = expect.set_index("join_col")
+
+    got = gdf_l.join(gdf_r, how="inner", lsuffix="_x", rsuffix="_y")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_typecast_on_join_multiindices():
+    join_data_l_0 = cudf.Series([1, 2, 3, 4, 5], dtype="int8")
+    join_data_l_1 = cudf.Series([2, 3, 4.1, 5.9, 6], dtype="float32")
+    join_data_l_2 = cudf.Series([7, 8, 9, 0, 1], dtype="float32")
+
+    join_data_r_0 = cudf.Series([1, 2, 3, 4, 5], dtype="int32")
+    join_data_r_1 = cudf.Series([2, 3, 4, 5, 6], dtype="int32")
+    join_data_r_2 = cudf.Series([7, 8, 9, 0, 0], dtype="float64")
+
+    other_data = ["a", "b", "c", "d", "e"]
+
+    gdf_l = cudf.DataFrame(
+        {
+            "join_col_0": join_data_l_0,
+            "join_col_1": join_data_l_1,
+            "join_col_2": join_data_l_2,
+            "B": other_data,
+        }
+    )
+    gdf_r = cudf.DataFrame(
+        {
+            "join_col_0": join_data_r_0,
+            "join_col_1": join_data_r_1,
+            "join_col_2": join_data_r_2,
+            "B": other_data,
+        }
+    )
+
+    gdf_l = gdf_l.set_index(["join_col_0", "join_col_1", "join_col_2"])
+    gdf_r = gdf_r.set_index(["join_col_0", "join_col_1", "join_col_2"])
+
+    exp_join_data_0 = cudf.Series([1, 2], dtype="int32")
+    exp_join_data_1 = cudf.Series([2, 3], dtype="float64")
+    exp_join_data_2 = cudf.Series([7, 8], dtype="float64")
+    exp_other_data = cudf.Series(["a", "b"])
+
+    expect = cudf.DataFrame(
+        {
+            "join_col_0": exp_join_data_0,
+            "join_col_1": exp_join_data_1,
+            "join_col_2": exp_join_data_2,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+    expect = expect.set_index(["join_col_0", "join_col_1", "join_col_2"])
+    got = gdf_l.join(gdf_r, how="inner", lsuffix="_x", rsuffix="_y")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_typecast_on_join_indexes_matching_categorical():
+    join_data_l = cudf.Series(["a", "b", "c", "d", "e"], dtype="category")
+    join_data_r = cudf.Series(["a", "b", "c", "d", "e"], dtype="str")
+    other_data = [1, 2, 3, 4, 5]
+
+    gdf_l = cudf.DataFrame({"join_col": join_data_l, "B": other_data})
+    gdf_r = cudf.DataFrame({"join_col": join_data_r, "B": other_data})
+
+    gdf_l = gdf_l.set_index("join_col")
+    gdf_r = gdf_r.set_index("join_col")
+
+    exp_join_data = ["a", "b", "c", "d", "e"]
+    exp_other_data = [1, 2, 3, 4, 5]
+
+    expect = cudf.DataFrame(
+        {
+            "join_col": exp_join_data,
+            "B_x": exp_other_data,
+            "B_y": exp_other_data,
+        }
+    )
+    expect = expect.set_index("join_col")
+    got = gdf_l.join(gdf_r, how="inner", lsuffix="_x", rsuffix="_y")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+@pytest.mark.parametrize(
+    "lhs",
+    [
+        cudf.Series([1, 2, 3], name="a"),
+        cudf.DataFrame({"a": [2, 3, 4], "c": [4, 5, 6]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "rhs",
+    [
+        cudf.Series([1, 2, 3], name="b"),
+        cudf.DataFrame({"b": [2, 3, 4], "c": [4, 5, 6]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "how", ["left", "inner", "outer", "leftanti", "leftsemi"]
+)
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left_on": "a", "right_on": "b"},
+        {"left_index": True, "right_on": "b"},
+        {"left_on": "a", "right_index": True},
+        {"left_index": True, "right_index": True},
+    ],
+)
+def test_series_dataframe_mixed_merging(lhs, rhs, how, kwargs):
+
+    if how in ("leftsemi", "leftanti") and (
+        kwargs.get("left_index") or kwargs.get("right_index")
+    ):
+        pytest.skip("Index joins not compatible with leftsemi and leftanti")
+
+    check_lhs = lhs.copy()
+    check_rhs = rhs.copy()
+    if isinstance(lhs, cudf.Series):
+        check_lhs = lhs.to_frame()
+    if isinstance(rhs, cudf.Series):
+        check_rhs = rhs.to_frame()
+
+    expect = cudf.merge(check_lhs, check_rhs, how=how, **kwargs)
+    got = cudf.merge(lhs, rhs, how=how, **kwargs)
+
+    assert_join_results_equal(expect, got, how=how)
+
+
+@pytest.mark.xfail(reason="Cannot sort values of list dtype")
+@pytest.mark.parametrize(
+    "how", ["left", "inner", "right", "leftanti", "leftsemi"]
+)
+def test_merge_with_lists(how):
+    pd_left = pd.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5, 6],
+            "b": [[1, 2, 3], [4, 5], None, [6], [7, 8, None], []],
+            "c": ["a", "b", "c", "d", "e", "f"],
+        }
+    )
+    pd_right = pd.DataFrame(
+        {
+            "a": [4, 3, 2, 1, 0, -1],
+            "d": [[[1, 2], None], [], [[3, 4]], None, [[5], [6, 7]], [[8]]],
+        }
+    )
+
+    gd_left = cudf.from_pandas(pd_left)
+    gd_right = cudf.from_pandas(pd_right)
+
+    expect = pd_left.merge(pd_right, on="a")
+    got = gd_left.merge(gd_right, on="a")
+
+    assert_join_results_equal(expect, got, how=how)
+
+
+def test_join_renamed_index():
+    df = cudf.DataFrame(
+        {0: [1, 2, 3, 4, 5], 1: [1, 2, 3, 4, 5], "c": [1, 2, 3, 4, 5]}
+    ).set_index([0, 1])
+    df.index.names = ["a", "b"]  # doesn't actually change df._index._data
+
+    expect = df.to_pandas().merge(
+        df.to_pandas(), left_index=True, right_index=True
+    )
+    got = df.merge(df, left_index=True, right_index=True, how="inner")
+    assert_join_results_equal(expect, got, how="inner")
+
+
+@pytest.mark.parametrize(
+    "lhs_col, lhs_idx, rhs_col, rhs_idx, on",
+    [
+        (["A", "B"], "L0", ["B", "C"], "L0", ["B"]),
+        (["A", "B"], "L0", ["B", "C"], "L0", ["L0"]),
+        (["A", "B"], "L0", ["B", "C"], "L0", ["B", "L0"]),
+        (["A", "B"], "L0", ["C", "L0"], "A", ["A"]),
+        (["A", "B"], "L0", ["C", "L0"], "A", ["L0"]),
+        (["A", "B"], "L0", ["C", "L0"], "A", ["A", "L0"]),
+    ],
+)
+@pytest.mark.parametrize(
+    "how", ["left", "inner", "right", "outer", "leftanti", "leftsemi"]
+)
+def test_join_merge_with_on(lhs_col, lhs_idx, rhs_col, rhs_idx, on, how):
+    lhs_data = {col_name: [4, 5, 6] for col_name in lhs_col}
+    lhs_index = cudf.Index([0, 1, 2], name=lhs_idx)
+
+    rhs_data = {col_name: [4, 5, 6] for col_name in rhs_col}
+    rhs_index = cudf.Index([2, 3, 4], name=rhs_idx)
+
+    gd_left = cudf.DataFrame(lhs_data, lhs_index)
+    gd_right = cudf.DataFrame(rhs_data, rhs_index)
+    pd_left = gd_left.to_pandas()
+    pd_right = gd_right.to_pandas()
+
+    expect = pd_left.merge(pd_right, on=on).sort_index(axis=1, ascending=False)
+    got = gd_left.merge(gd_right, on=on).sort_index(axis=1, ascending=False)
+
+    assert_join_results_equal(expect, got, how=how)
+
+
+@pytest.mark.parametrize(
+    "on",
+    ["A", "L0"],
+)
+@pytest.mark.parametrize(
+    "how", ["left", "inner", "right", "outer", "leftanti", "leftsemi"]
+)
+def test_join_merge_invalid_keys(on, how):
+    gd_left = cudf.DataFrame(
+        {"A": [1, 2, 3], "B": [4, 5, 6]}, index=cudf.Index([0, 1, 2], name="C")
+    )
+    gd_right = cudf.DataFrame(
+        {"D": [2, 3, 4], "E": [7, 8, 0]}, index=cudf.Index([0, 2, 4], name="F")
+    )
+    pd_left = gd_left.to_pandas()
+    pd_right = gd_right.to_pandas()
+
+    with pytest.raises(KeyError):
+        pd_left.merge(pd_right, on=on)
+        gd_left.merge(gd_right, on=on)
+
+
+@pytest.mark.parametrize(
+    "str_data",
+    [[], ["a", "b", "c", "d", "e"], [None, None, None, None, None]],
+)
+@pytest.mark.parametrize("num_keys", [1, 2, 3])
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+def test_string_join_key(str_data, num_keys, how):
+    other_data = [1, 2, 3, 4, 5][: len(str_data)]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    for i in range(num_keys):
+        pdf[i] = pd.Series(str_data, dtype="str")
+        gdf[i] = cudf.Series(str_data, dtype="str")
+    pdf["a"] = other_data
+    gdf["a"] = other_data
+
+    pdf2 = pdf.copy()
+    gdf2 = gdf.copy()
+
+    expect = pdf.merge(pdf2, on=list(range(num_keys)), how=how)
+    got = gdf.merge(gdf2, on=list(range(num_keys)), how=how)
+
+    if len(expect) == 0 and len(got) == 0:
+        expect = expect.reset_index(drop=True)
+        got = got[expect.columns]  # reorder columns
+
+    if how == "right":
+        got = got[expect.columns]  # reorder columns
+
+    assert_join_results_equal(expect, got, how=how)
+
+
+@pytest.mark.parametrize(
+    "str_data_nulls",
+    [
+        ["a", "b", "c"],
+        ["a", "b", "f", "g"],
+        ["f", "g", "h", "i", "j"],
+        ["f", "g", "h"],
+        [None, None, None, None, None],
+        [],
+    ],
+)
+def test_string_join_key_nulls(str_data_nulls):
+    str_data = ["a", "b", "c", "d", "e"]
+    other_data = [1, 2, 3, 4, 5]
+
+    other_data_nulls = [6, 7, 8, 9, 10][: len(str_data_nulls)]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    pdf["key"] = pd.Series(str_data, dtype="str")
+    gdf["key"] = cudf.Series(str_data, dtype="str")
+    pdf["vals"] = other_data
+    gdf["vals"] = other_data
+
+    pdf2 = pd.DataFrame()
+    gdf2 = cudf.DataFrame()
+    pdf2["key"] = pd.Series(str_data_nulls, dtype="str")
+    gdf2["key"] = cudf.Series(str_data_nulls, dtype="str")
+    pdf2["vals"] = pd.Series(other_data_nulls, dtype="int64")
+    gdf2["vals"] = cudf.Series(other_data_nulls, dtype="int64")
+
+    expect = pdf.merge(pdf2, on="key", how="left")
+    got = gdf.merge(gdf2, on="key", how="left")
+    got["vals_y"] = got["vals_y"].fillna(-1)
+
+    if len(expect) == 0 and len(got) == 0:
+        expect = expect.reset_index(drop=True)
+        got = got[expect.columns]
+
+    expect["vals_y"] = expect["vals_y"].fillna(-1).astype("int64")
+
+    assert_join_results_equal(expect, got, how="left")
+
+
+@pytest.mark.parametrize(
+    "str_data", [[], ["a", "b", "c", "d", "e"], [None, None, None, None, None]]
+)
+@pytest.mark.parametrize("num_cols", [1, 2, 3])
+@pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
+def test_string_join_non_key(str_data, num_cols, how):
+    other_data = [1, 2, 3, 4, 5][: len(str_data)]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    for i in range(num_cols):
+        pdf[i] = pd.Series(str_data, dtype="str")
+        gdf[i] = cudf.Series(str_data, dtype="str")
+    pdf["a"] = other_data
+    gdf["a"] = other_data
+
+    pdf2 = pdf.copy()
+    gdf2 = gdf.copy()
+
+    expect = pdf.merge(pdf2, on=["a"], how=how)
+    got = gdf.merge(gdf2, on=["a"], how=how)
+
+    if len(expect) == 0 and len(got) == 0:
+        expect = expect.reset_index(drop=True)
+        got = got[expect.columns]
+
+    if how == "right":
+        got = got[expect.columns]  # reorder columns
+
+    assert_join_results_equal(expect, got, how=how)
+
+
+@pytest.mark.parametrize(
+    "str_data_nulls",
+    [
+        ["a", "b", "c"],
+        ["a", "b", "f", "g"],
+        ["f", "g", "h", "i", "j"],
+        ["f", "g", "h"],
+        [None, None, None, None, None],
+        [],
+    ],
+)
+def test_string_join_non_key_nulls(str_data_nulls):
+    str_data = ["a", "b", "c", "d", "e"]
+    other_data = [1, 2, 3, 4, 5]
+
+    other_data_nulls = [6, 7, 8, 9, 10][: len(str_data_nulls)]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    pdf["vals"] = pd.Series(str_data, dtype="str")
+    gdf["vals"] = cudf.Series(str_data, dtype="str")
+    pdf["key"] = other_data
+    gdf["key"] = other_data
+
+    pdf2 = pd.DataFrame()
+    gdf2 = cudf.DataFrame()
+    pdf2["vals"] = pd.Series(str_data_nulls, dtype="str")
+    gdf2["vals"] = cudf.Series(str_data_nulls, dtype="str")
+    pdf2["key"] = pd.Series(other_data_nulls, dtype="int64")
+    gdf2["key"] = cudf.Series(other_data_nulls, dtype="int64")
+
+    expect = pdf.merge(pdf2, on="key", how="left")
+    got = gdf.merge(gdf2, on="key", how="left")
+
+    if len(expect) == 0 and len(got) == 0:
+        expect = expect.reset_index(drop=True)
+        got = got[expect.columns]
+
+    assert_join_results_equal(expect, got, how="left")
+
+
+def test_string_join_values_nulls():
+    left_dict = [
+        {"b": "MATCH 1", "a": 1.0},
+        {"b": "MATCH 1", "a": 1.0},
+        {"b": "LEFT NO MATCH 1", "a": -1.0},
+        {"b": "MATCH 2", "a": 2.0},
+        {"b": "MATCH 2", "a": 2.0},
+        {"b": "MATCH 1", "a": 1.0},
+        {"b": "MATCH 1", "a": 1.0},
+        {"b": "MATCH 2", "a": 2.0},
+        {"b": "MATCH 2", "a": 2.0},
+        {"b": "LEFT NO MATCH 2", "a": -2.0},
+        {"b": "MATCH 3", "a": 3.0},
+        {"b": "MATCH 3", "a": 3.0},
+    ]
+
+    right_dict = [
+        {"b": "RIGHT NO MATCH 1", "c": -1.0},
+        {"b": "MATCH 3", "c": 3.0},
+        {"b": "MATCH 2", "c": 2.0},
+        {"b": "RIGHT NO MATCH 2", "c": -2.0},
+        {"b": "RIGHT NO MATCH 3", "c": -3.0},
+        {"b": "MATCH 1", "c": 1.0},
+    ]
+
+    left_pdf = pd.DataFrame(left_dict)
+    right_pdf = pd.DataFrame(right_dict)
+
+    left_gdf = cudf.DataFrame.from_pandas(left_pdf)
+    right_gdf = cudf.DataFrame.from_pandas(right_pdf)
+
+    expect = left_pdf.merge(right_pdf, how="left", on="b")
+    got = left_gdf.merge(right_gdf, how="left", on="b")
+
+    expect = expect.sort_values(by=["a", "b", "c"]).reset_index(drop=True)
+    got = got.sort_values(by=["a", "b", "c"]).reset_index(drop=True)
+
+    assert_join_results_equal(expect, got, how="left")
+
+
+@pytest.mark.parametrize(
+    "left_on,right_on",
+    [
+        *product(["a", "b", "c"], ["a", "b"]),
+        *zip(combinations(["a", "b", "c"], 2), repeat(["a", "b"])),
+    ],
+)
+def test_merge_mixed_index_columns(left_on, right_on):
+    left = pd.DataFrame({"a": [1, 2, 1, 2], "b": [2, 3, 3, 4]}).set_index("a")
+    right = pd.DataFrame({"a": [1, 2, 1, 3], "b": [2, 30, 3, 4]}).set_index(
+        "a"
+    )
+
+    left["c"] = 10
+
+    expect = left.merge(right, left_on=left_on, right_on=right_on, how="outer")
+    cleft = cudf.from_pandas(left)
+    cright = cudf.from_pandas(right)
+    got = cleft.merge(cright, left_on=left_on, right_on=right_on, how="outer")
+    assert_join_results_equal(expect, got, how="outer")
+
+
+def test_merge_multiindex_columns():
+    lhs = pd.DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
+    lhs.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
+    rhs = pd.DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
+    rhs.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "z")])
+    expect = lhs.merge(rhs, on=[("a", "x")], how="inner")
+
+    lhs = cudf.from_pandas(lhs)
+    rhs = cudf.from_pandas(rhs)
+    got = lhs.merge(rhs, on=[("a", "x")], how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_join_multiindex_empty():
+    lhs = pd.DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]}, index=["a", "b", "c"])
+    lhs.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])
+    rhs = pd.DataFrame(index=["a", "c", "d"])
+    with pytest.warns(FutureWarning):
+        expect = lhs.join(rhs, how="inner")
+
+    lhs = cudf.from_pandas(lhs)
+    rhs = cudf.from_pandas(rhs)
+    with pytest.warns(FutureWarning):
+        got = lhs.join(rhs, how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_join_on_index_with_duplicate_names():
+    # although index levels with duplicate names are poorly supported
+    # overall, we *should* be able to join on them:
+    lhs = pd.DataFrame({"a": [1, 2, 3]})
+    rhs = pd.DataFrame({"b": [1, 2, 3]})
+    lhs.index = pd.MultiIndex.from_tuples(
+        [(1, 1), (1, 2), (2, 1)], names=["x", "x"]
+    )
+    rhs.index = pd.MultiIndex.from_tuples(
+        [(1, 1), (1, 3), (2, 1)], names=["x", "x"]
+    )
+    expect = lhs.join(rhs, how="inner")
+
+    lhs = cudf.from_pandas(lhs)
+    rhs = cudf.from_pandas(rhs)
+    got = lhs.join(rhs, how="inner")
+
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_join_redundant_params():
+    lhs = cudf.DataFrame(
+        {"a": [1, 2, 3], "c": [2, 3, 4]}, index=cudf.Index([0, 1, 2], name="c")
+    )
+    rhs = cudf.DataFrame(
+        {"b": [1, 2, 3]}, index=cudf.Index([0, 1, 2], name="a")
+    )
+    with pytest.raises(ValueError):
+        lhs.merge(rhs, on="a", left_index=True)
+    with pytest.raises(ValueError):
+        lhs.merge(rhs, left_on="a", left_index=True, right_index=True)
+    with pytest.raises(ValueError):
+        lhs.merge(rhs, right_on="a", left_index=True, right_index=True)
+    with pytest.raises(ValueError):
+        lhs.merge(rhs, left_on="c", right_on="b")
+
+
+def test_join_multiindex_index():
+    # test joining a MultiIndex with an Index with overlapping name
+    lhs = (
+        cudf.DataFrame({"a": [2, 3, 1], "b": [3, 4, 2]})
+        .set_index(["a", "b"])
+        .index
+    )
+    rhs = cudf.DataFrame({"a": [1, 4, 3]}).set_index("a").index
+    expect = lhs.to_pandas().join(rhs.to_pandas(), how="inner")
+    got = lhs.join(rhs, how="inner")
+    assert_join_results_equal(expect, got, how="inner")
+
+
+def test_dataframe_join_on():
+    """Verify that specifying the on parameter gives a NotImplementedError."""
+    df = cudf.DataFrame({"a": [1, 2, 3]})
+    with pytest.raises(NotImplementedError):
+        df.join(df, on="a")
+
+
+def test_index_join_return_indexers_notimplemented():
+    index = cudf.RangeIndex(start=0, stop=20, step=2)
+    other = cudf.Index([4, 4, 3, 3])
+    with pytest.raises(NotImplementedError):
+        index.join(other, how="left", return_indexers=True)
+
+
+@pytest.mark.parametrize("how", ["inner", "outer"])
+def test_index_join_names(how):
+    idx1 = cudf.Index([10, 1, 2, 4, 2, 1], name="a")
+    idx2 = cudf.Index([-10, 2, 3, 1, 2], name="b")
+
+    expected = idx1.to_pandas().join(idx2.to_pandas(), how=how)
+    actual = idx1.join(idx2, how=how)
+    assert_join_results_equal(actual, expected, how=how)
+
+
+@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+def test_join_datetime_timedelta_error(dtype):
+    df1 = cudf.DataFrame({"a": cudf.Series([10, 20, 30], dtype=dtype)})
+    df2 = df1.astype("int")
+
+    with pytest.raises(TypeError):
+        df1.merge(df2)
+
+
+@pytest.mark.parametrize("dtype1", TIMEDELTA_TYPES)
+@pytest.mark.parametrize("dtype2", TIMEDELTA_TYPES)
+def test_merge_timedelta_types(dtype1, dtype2):
+    df1 = cudf.DataFrame({"a": cudf.Series([10, 20, 30], dtype=dtype1)})
+    df2 = cudf.DataFrame({"a": cudf.Series([20, 500, 33240], dtype=dtype2)})
+
+    pdf1 = df1.to_pandas()
+    pdf2 = df2.to_pandas()
+    actual = df1.merge(df2)
+    expected = pdf1.merge(pdf2)
+
+    # Pandas is materializing the index, which is unnecessary
+    # hence the special handling.
+    assert_eq(
+        actual,
+        expected,
+        check_index_type=False
+        if isinstance(actual.index, cudf.RangeIndex)
+        and isinstance(expected.index, pd.Index)
+        else True,
+        check_dtype=True,
+    )
diff --git a/python/cudf/cudf/tests/test_json.py b/python/cudf/cudf/tests/test_json.py
new file mode 100644
index 0000000..47f5b99
--- /dev/null
+++ b/python/cudf/cudf/tests/test_json.py
@@ -0,0 +1,1372 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import copy
+import gzip
+import itertools
+import os
+from io import BytesIO, StringIO
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+    assert_eq,
+)
+
+
+def make_numeric_dataframe(nrows, dtype):
+    df = pd.DataFrame()
+    df["col1"] = np.arange(nrows, dtype=dtype)
+    df["col2"] = np.arange(1, 1 + nrows, dtype=dtype)
+    return df
+
+
+@pytest.fixture(params=[0, 1, 10, 100])
+def pdf(request):
+    types = NUMERIC_TYPES + DATETIME_TYPES + ["bool"]
+    nrows = request.param
+
+    # Create a pandas dataframe with random data of mixed types
+    test_pdf = pd.DataFrame(
+        {
+            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            for typ in types
+        }
+    )
+    # Delete the name of the column index, and rename the row index
+    test_pdf.columns.name = None
+    test_pdf.index.name = "test_index"
+
+    return test_pdf
+
+
+@pytest.fixture
+def gdf(pdf):
+    return cudf.DataFrame.from_pandas(pdf)
+
+
+@pytest.fixture(params=[0, 1, 10, 100])
+def gdf_writer_types(request):
+    # datetime64[us], datetime64[ns] are unsupported due to a bug in parser
+    types = (
+        NUMERIC_TYPES
+        + ["datetime64[s]", "datetime64[ms]"]
+        + TIMEDELTA_TYPES
+        + ["bool", "str"]
+    )
+    typer = {"col_" + val: val for val in types}
+    ncols = len(types)
+    nrows = request.param
+
+    # Create a pandas dataframe with random data of mixed types
+    test_pdf = cudf.DataFrame(
+        [list(range(ncols * i, ncols * (i + 1))) for i in range(nrows)],
+        columns=pd.Index([f"col_{typ}" for typ in types]),
+    )
+
+    # Cast all the column dtypes to objects, rename them, and then cast to
+    # appropriate types
+    test_pdf = test_pdf.astype(typer)
+
+    return test_pdf
+
+
+index_params = [True, False]
+compression_params = ["gzip", "bz2", "zip", "xz", None]
+orient_params = ["columns", "records", "table", "split"]
+params = itertools.product(index_params, compression_params, orient_params)
+
+
+@pytest.fixture(params=params)
+def json_files(request, tmp_path_factory, pdf):
+    index, compression, orient = request.param
+    if index is False and orient not in ("split", "table"):
+        pytest.skip(
+            "'index=False' is only valid when 'orient' is 'split' or "
+            "'table'"
+        )
+    if index is False and orient == "table":
+        pytest.skip("'index=False' isn't valid when 'orient' is 'table'")
+    fname_df = tmp_path_factory.mktemp("json") / "test_df.json"
+    fname_series = tmp_path_factory.mktemp("json") / "test_series.json"
+    pdf.to_json(fname_df, index=index, compression=compression, orient=orient)
+    pdf["col_int32"].to_json(
+        fname_series, index=index, compression=compression, orient=orient
+    )
+    return (fname_df, fname_series, orient, compression)
+
+
+@pytest.mark.filterwarnings("ignore:Strings are not yet supported")
+@pytest.mark.filterwarnings("ignore:Using CPU")
+def test_json_reader(json_files):
+    path_df, path_series, orient, compression = json_files
+    expect_df = pd.read_json(path_df, orient=orient, compression=compression)
+    got_df = cudf.read_json(path_df, orient=orient, compression=compression)
+    if len(expect_df) == 0:
+        expect_df = expect_df.reset_index(drop=True)
+        expect_df.columns = expect_df.columns.astype("object")
+    if len(got_df) == 0:
+        got_df = got_df.reset_index(drop=True)
+
+    assert_eq(expect_df, got_df, check_categorical=False)
+
+    # Only these orients are allowed for Series, but isn't enforced by Pandas
+    if orient in ("split", "records", "index"):
+        expect_series = pd.read_json(
+            path_series, orient=orient, compression=compression, typ="series"
+        )
+        got_series = cudf.read_json(
+            path_series, orient=orient, compression=compression, typ="series"
+        )
+        if len(expect_series) == 0:
+            expect_series = expect_series.reset_index(drop=True)
+        if len(got_df) == 0:
+            got_series = got_series.reset_index(drop=True)
+
+        assert_eq(expect_series, got_series)
+
+
+@pytest.mark.filterwarnings("ignore:Can't infer compression")
+@pytest.mark.filterwarnings("ignore:Using CPU")
+def test_json_writer(tmpdir, pdf, gdf):
+    pdf_df_fname = tmpdir.join("pdf_df.json")
+    gdf_df_fname = tmpdir.join("gdf_df.json")
+
+    pdf.to_json(pdf_df_fname)
+    gdf.to_json(gdf_df_fname)
+
+    assert os.path.exists(pdf_df_fname)
+    assert os.path.exists(gdf_df_fname)
+
+    expect_df = pd.read_json(pdf_df_fname)
+    got_df = pd.read_json(gdf_df_fname)
+
+    assert_eq(expect_df, got_df)
+
+    for column in pdf.columns:
+        pdf_series_fname = tmpdir.join(column + "_" + "pdf_series.json")
+        gdf_series_fname = tmpdir.join(column + "_" + "gdf_series.json")
+
+        pdf[column].to_json(pdf_series_fname)
+        gdf[column].to_json(gdf_series_fname)
+
+        assert os.path.exists(pdf_series_fname)
+        assert os.path.exists(gdf_series_fname)
+
+        expect_series = pd.read_json(pdf_series_fname, typ="series")
+        got_series = pd.read_json(gdf_series_fname, typ="series")
+
+        assert_eq(expect_series, got_series)
+
+        # Make sure results align for regular strings, not just files
+        pdf_string = pdf[column].to_json()
+        gdf_string = pdf[column].to_json()
+        assert_eq(pdf_string, gdf_string)
+
+
+@pytest.mark.parametrize(
+    "lines", [True, False], ids=["lines=True", "lines=False"]
+)
+def test_cudf_json_writer(pdf, lines):
+    # removing datetime column because pandas doesn't support it
+    for col_name in pdf.columns:
+        if "datetime" in col_name:
+            pdf.drop(col_name, axis=1, inplace=True)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    pdf_string = pdf.to_json(orient="records", lines=lines)
+    gdf_string = gdf.to_json(orient="records", lines=lines, engine="cudf")
+
+    assert_eq(pdf_string, gdf_string)
+
+    gdf_string = gdf.to_json(
+        orient="records", lines=lines, engine="cudf", rows_per_chunk=8
+    )
+
+    assert_eq(pdf_string, gdf_string)
+
+
+def test_cudf_json_writer_read(gdf_writer_types):
+    dtypes = {
+        col_name: col_name[len("col_") :]
+        for col_name in gdf_writer_types.columns
+    }
+    gdf_string = gdf_writer_types.to_json(
+        orient="records", lines=True, engine="cudf"
+    )
+    gdf2 = cudf.read_json(
+        StringIO(gdf_string),
+        lines=True,
+        engine="cudf",
+        dtype=dict(dtypes),
+    )
+    pdf2 = pd.read_json(StringIO(gdf_string), lines=True, dtype=dict(dtypes))
+
+    # Bug in pandas https://github.com/pandas-dev/pandas/issues/28558
+    if pdf2.empty:
+        pdf2.reset_index(drop=True, inplace=True)
+        pdf2.columns = pdf2.columns.astype("object")
+    assert_eq(pdf2, gdf2)
+
+
+@pytest.mark.parametrize(
+    "jsonl_string, expected",
+    [
+        # fixed width
+        ("""{"a":10, "b":1.1}\n {"a":20, "b":2.1}\n""", None),
+        # simple list
+        ("""{"a":[1, 2, 3], "b":1.1}\n {"a":[]}\n""", None),
+        # simple struct
+        ("""{"a":{"c": 123 }, "b":1.1}\n {"a": {"c": 456}}\n""", None),
+        # list of lists
+        ("""{"a":[[], [1, 2], [3, 4]], "b":1.1}\n""", None),
+        ("""{"a":[null, [1, 2], [null, 4]], "b":1.1}\n""", None),
+        # list of structs
+        # error ("""{"a":[null, {}], "b":1.1}\n""", None),
+        (
+            """{"a":[null, {"L": 123}], "b":1.0}\n {"b":1.1}\n {"b":2.1}\n""",
+            None,
+        ),
+        (
+            """{"a":[{"L": 123}, null], "b":1.0}\n {"b":1.1}\n {"b":2.1}\n""",
+            None,
+        ),
+        # struct of lists
+        (
+            """{"a":{"L": [1, 2, 3]}, "b":1.1}\n {"a": {"L": [4, 5, 6]}}\n""",
+            None,
+        ),
+        ("""{"a":{"L": [1, 2, null]}, "b":1.1}\n {"a": {"L": []}}\n""", None),
+        # struct of structs
+        (
+            """{"a":{"L": {"M": 123}}, "b":1.1}
+               {"a": {"L": {"M": 456}}}\n""",
+            None,
+        ),
+        (
+            """{"a":{"L": {"M": null}}, "b":1.1}\n {"a": {"L": {}}}\n""",
+            """{"a":{"L": {}}, "b":1.1}\n {"a": {"L": {}}}\n""",
+        ),
+        # list of structs of lists
+        ("""{"a":[{"L": [1, 2, 3]}, {"L": [4, 5, 6]}], "b":1.1}\n""", None),
+        ("""{"a":[{"L": [1, 2, null]}, {"L": []}], "b":1.1}\n""", None),
+        # struct of lists of structs
+        ("""{"a":{"L": [{"M": 123}, {"M": 456}]}, "b":1.1}\n""", None),
+        (
+            """{"a":{"L": [{"M": null}, {}]}, "b":1.1}\n""",
+            """{"a":{"L": [{}, {}]}, "b":1.1}\n""",
+        ),
+    ],
+)
+def test_cudf_json_roundtrip(jsonl_string, expected):
+    gdf = cudf.read_json(
+        StringIO(jsonl_string),
+        lines=True,
+        engine="cudf",
+        # dtype=dict(dtypes),
+    )
+    expected = jsonl_string if expected is None else expected
+    gdf_string = gdf.to_json(
+        orient="records", lines=True, engine="cudf", include_nulls=False
+    )
+    assert_eq(gdf_string, expected.replace(" ", ""))
+
+
+@pytest.mark.parametrize("sink", ["string", "file"])
+def test_cudf_json_writer_sinks(sink, tmp_path_factory):
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    target = None
+    if sink == "string":
+        target = StringIO()
+    elif sink == "file":
+        target = tmp_path_factory.mktemp("json") / "test_df.json"
+    df.to_json(target, engine="cudf")
+    if sink == "string":
+        assert (
+            target.getvalue() == '[{"a":1,"b":4},{"a":2,"b":5},{"a":3,"b":6}]'
+        )
+    elif sink == "file":
+        assert os.path.exists(target)
+        with open(target, "r") as f:
+            assert f.read() == '[{"a":1,"b":4},{"a":2,"b":5},{"a":3,"b":6}]'
+
+
+@pytest.fixture(
+    params=["string", "filepath", "pathobj", "bytes_io", "string_io", "url"]
+)
+def json_input(request, tmp_path_factory):
+    input_type = request.param
+    buffer = "[1, 2, 3]\n[4, 5, 6]\n[7, 8, 9]\n"
+    fname = tmp_path_factory.mktemp("json") / "test_df.json"
+    if not os.path.isfile(fname):
+        with open(str(fname), "w") as fp:
+            fp.write(buffer)
+
+    if input_type == "string":
+        return buffer
+    if input_type == "filepath":
+        return str(fname)
+    if input_type == "pathobj":
+        return Path(fname)
+    if input_type == "bytes_io":
+        return BytesIO(buffer.encode())
+    if input_type == "string_io":
+        return StringIO(buffer)
+    if input_type == "url":
+        return Path(fname).as_uri()
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["auto", "cudf", "pandas"])
+def test_json_lines_basic(json_input, engine):
+    cu_df = cudf.read_json(json_input, engine=engine, lines=True)
+    pd_df = pd.read_json(json_input, lines=True)
+
+    assert all(cu_df.dtypes == ["int64", "int64", "int64"])
+    for cu_col, pd_col in zip(cu_df.columns, pd_df.columns):
+        assert str(cu_col) == str(pd_col)
+        np.testing.assert_array_equal(pd_df[pd_col], cu_df[cu_col].to_numpy())
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["auto", "cudf"])
+def test_json_lines_multiple(tmpdir, json_input, engine):
+    tmp_file1 = tmpdir.join("MultiInputs1.json")
+    tmp_file2 = tmpdir.join("MultiInputs2.json")
+
+    pdf = pd.read_json(json_input, lines=True)
+    pdf.to_json(tmp_file1, compression="infer", lines=True, orient="records")
+    pdf.to_json(tmp_file2, compression="infer", lines=True, orient="records")
+
+    cu_df = cudf.read_json([tmp_file1, tmp_file2], engine=engine, lines=True)
+    pd_df = pd.concat([pdf, pdf])
+
+    assert all(cu_df.dtypes == ["int64", "int64", "int64"])
+    for cu_col, pd_col in zip(cu_df.columns, pd_df.columns):
+        assert str(cu_col) == str(pd_col)
+        np.testing.assert_array_equal(pd_df[pd_col], cu_df[cu_col].to_numpy())
+
+
+@pytest.mark.parametrize("engine", ["auto", "cudf"])
+def test_json_read_directory(tmpdir, json_input, engine):
+    pdf = pd.read_json(json_input, lines=True)
+    pdf.to_json(
+        tmpdir.join("MultiInputs1.json"),
+        compression="infer",
+        lines=True,
+        orient="records",
+    )
+    pdf.to_json(
+        tmpdir.join("MultiInputs2.json"),
+        compression="infer",
+        lines=True,
+        orient="records",
+    )
+    pdf.to_json(
+        tmpdir.join("MultiInputs3.json"),
+        compression="infer",
+        lines=True,
+        orient="records",
+    )
+
+    cu_df = cudf.read_json(tmpdir, engine=engine, lines=True)
+    pd_df = pd.concat([pdf, pdf, pdf])
+
+    assert all(cu_df.dtypes == ["int64", "int64", "int64"])
+    for cu_col, pd_col in zip(cu_df.columns, pd_df.columns):
+        assert str(cu_col) == str(pd_col)
+        np.testing.assert_array_equal(pd_df[pd_col], cu_df[cu_col].to_numpy())
+
+
+def test_json_lines_byte_range(json_input):
+    # include the first row and half of the second row
+    # should parse the first two rows
+    df = cudf.read_json(
+        copy.deepcopy(json_input), lines=True, byte_range=(0, 15)
+    )
+    assert df.shape == (2, 3)
+
+    # include half of the second row and half of the third row
+    # should parse only the third row
+    df = cudf.read_json(
+        copy.deepcopy(json_input), lines=True, byte_range=(15, 10)
+    )
+    assert df.shape == (1, 3)
+
+    # include half of the second row and entire third row
+    # should parse only the third row
+    df = cudf.read_json(
+        copy.deepcopy(json_input), lines=True, byte_range=(15, 0)
+    )
+    assert df.shape == (1, 3)
+
+    # include half of the second row till past the end of the file
+    # should parse only the third row
+    df = cudf.read_json(
+        copy.deepcopy(json_input), lines=True, byte_range=(10, 50)
+    )
+    assert df.shape == (1, 3)
+
+
+def test_json_lines_dtypes(json_input):
+    df = cudf.read_json(
+        json_input, lines=True, dtype={1: "int", 2: "short", 0: "float"}
+    )
+    assert all(df.dtypes == ["float64", "int64", "int16"])
+
+
+@pytest.mark.parametrize(
+    "ext, out_comp, in_comp",
+    [
+        (".geez", "gzip", "gzip"),
+        (".beez", "bz2", "bz2"),
+        (".gz", "gzip", "infer"),
+        (".bz2", "bz2", "infer"),
+        (".data", None, "infer"),
+        (".txt", None, None),
+        ("", None, None),
+    ],
+)
+def test_json_lines_compression(tmpdir, ext, out_comp, in_comp):
+    fname = tmpdir.mkdir("gdf_json").join("tmp_json_compression" + ext)
+
+    nrows = 20
+    pd_df = make_numeric_dataframe(nrows, np.int32)
+    pd_df.to_json(fname, compression=out_comp, lines=True, orient="records")
+
+    cu_df = cudf.read_json(
+        str(fname),
+        compression=in_comp,
+        lines=True,
+        dtype={"col1": "int32", "col2": "int32"},
+    )
+    assert_eq(pd_df, cu_df)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.filterwarnings(
+    "ignore:engine='cudf_legacy' is a deprecated engine."
+)
+def test_json_engine_selection():
+    json = "[1, 2, 3]"
+
+    # should use the cudf engine
+    df = cudf.read_json(json, lines=True)
+    # column names are strings when parsing with cudf
+    for col_name in df.columns:
+        assert isinstance(col_name, str)
+
+    # should use the pandas engine
+    df = cudf.read_json(json, lines=False, engine="pandas")
+    # column names are ints when parsing with pandas
+    for col_name in df.columns:
+        assert isinstance(col_name, int)
+
+    # should use the pandas engine
+    df = cudf.read_json(json, lines=True, engine="pandas")
+    # column names are ints when parsing with pandas
+    for col_name in df.columns:
+        assert isinstance(col_name, int)
+
+    # should raise an exception
+    with pytest.raises(ValueError):
+        cudf.read_json(json, lines=False, engine="cudf_legacy")
+
+
+def test_json_bool_values():
+    buffer = "[true,1]\n[false,false]\n[true,true]"
+    cu_df = cudf.read_json(buffer, lines=True)
+    pd_df = pd.read_json(buffer, lines=True)
+
+    # types should be ['bool', 'int64']
+    np.testing.assert_array_equal(pd_df.dtypes, cu_df.dtypes)
+    np.testing.assert_array_equal(pd_df[0], cu_df["0"].to_numpy())
+    # boolean values should be converted to 0/1
+    np.testing.assert_array_equal(pd_df[1], cu_df["1"].to_numpy())
+
+    cu_df = cudf.read_json(
+        buffer, lines=True, dtype={"0": "bool", "1": "long"}
+    )
+    np.testing.assert_array_equal(pd_df.dtypes, cu_df.dtypes)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:engine='cudf_legacy' is a deprecated engine."
+)
+@pytest.mark.parametrize(
+    "buffer",
+    [
+        "[1.0,]\n[null, ]",
+        '{"0":1.0,"1":}\n{"0":null,"1": }',
+        '{ "0" : 1.0 , "1" : }\n{ "0" : null , "1" : }',
+        '{"0":1.0}\n{"1":}',
+    ],
+)
+def test_json_null_literal(buffer):
+    df = cudf.read_json(buffer, lines=True, engine="cudf_legacy")
+
+    # first column contains a null field, type should be set to float
+    # second column contains only empty fields, type should be set to int8
+    np.testing.assert_array_equal(df.dtypes, ["float64", "int8"])
+    np.testing.assert_array_equal(
+        df["0"].to_numpy(na_value=np.nan), [1.0, np.nan]
+    )
+    np.testing.assert_array_equal(df["1"].to_numpy(na_value=0), [0, 0])
+
+
+def test_json_bad_protocol_string():
+    test_string = '{"field": "s3://path"}'
+
+    expect = pd.DataFrame([{"field": "s3://path"}])
+    got = cudf.read_json(test_string, lines=True)
+
+    assert_eq(expect, got)
+
+
+def test_json_corner_case_with_escape_and_double_quote_char_with_pandas(
+    tmpdir,
+):
+    fname = tmpdir.mkdir("gdf_json").join("tmp_json_escape_double_quote")
+
+    pdf = pd.DataFrame(
+        {
+            "a": ['ab"cd', "\\\b", "\r\\", "'"],
+            "b": ["a\tb\t", "\\", '\\"', "\t"],
+            "c": ["aeiou", "try", "json", "cudf"],
+        }
+    )
+    pdf.to_json(fname, compression="infer", lines=True, orient="records")
+
+    df = cudf.read_json(
+        fname, compression="infer", lines=True, orient="records"
+    )
+    pdf = pd.read_json(
+        fname, compression="infer", lines=True, orient="records"
+    )
+
+    assert_eq(cudf.DataFrame(pdf), df)
+
+
+def test_json_corner_case_with_escape_and_double_quote_char_with_strings():
+    str_buffer = StringIO(
+        """{"a":"ab\\"cd","b":"a\\tb\\t","c":"aeiou"}
+           {"a":"\\\\\\b","b":"\\\\","c":"try"}
+           {"a":"\\r\\\\","b":"\\\\\\"","c":"json"}
+           {"a":"\'","b":"\\t","c":"cudf"}"""
+    )
+
+    df = cudf.read_json(
+        str_buffer, compression="infer", lines=True, orient="records"
+    )
+
+    expected = {
+        "a": ['ab"cd', "\\\b", "\r\\", "'"],
+        "b": ["a\tb\t", "\\", '\\"', "\t"],
+        "c": ["aeiou", "try", "json", "cudf"],
+    }
+
+    num_rows = df.shape[0]
+    for col_name in df._data:
+        for i in range(num_rows):
+            assert expected[col_name][i] == df[col_name][i]
+
+
+def test_json_to_json_special_characters():
+    df = cudf.DataFrame(
+        {
+            "'a'": ['ab"cd', "\\\b", "\r\\", "'"],
+            "b": ["a\tb\t", "\\", '\\"', "\t"],
+            "c": ["aeiou", "try", "json", "cudf"],
+        }
+    )
+
+    actual = StringIO()
+    df.to_json(actual, engine="cudf", lines=True, orient="records")
+    expected = StringIO()
+    df.to_pandas().to_json(expected, lines=True, orient="records")
+    assert expected.getvalue() == actual.getvalue()
+
+
+@pytest.mark.parametrize(
+    "gdf,pdf",
+    [
+        (
+            cudf.DataFrame(
+                {
+                    "int col": cudf.Series(
+                        [1, 2, None, 2, 2323, 234, None], dtype="int64"
+                    )
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "int col": pd.Series(
+                        [1, 2, None, 2, 2323, 234, None], dtype=pd.Int64Dtype()
+                    )
+                }
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "int64 col": cudf.Series(
+                        [1, 2, None, 2323, None], dtype="int64"
+                    ),
+                    "string col": cudf.Series(
+                        ["abc", "a", None, "", None], dtype="str"
+                    ),
+                    "float col": cudf.Series(
+                        [0.234, None, 234234.2343, None, 0.0], dtype="float64"
+                    ),
+                    "bool col": cudf.Series(
+                        [None, True, False, None, True], dtype="bool"
+                    ),
+                    "categorical col": cudf.Series(
+                        [1, 2, 1, None, 2], dtype="category"
+                    ),
+                    "datetime col": cudf.Series(
+                        [1231233, None, 2323234, None, 1],
+                        dtype="datetime64[ns]",
+                    ),
+                    "timedelta col": cudf.Series(
+                        [None, 34687236, 2323234, 1, None],
+                        dtype="timedelta64[ns]",
+                    ),
+                }
+            ),
+            pd.DataFrame(
+                {
+                    "int64 col": pd.Series(
+                        [1, 2, None, 2323, None], dtype=pd.Int64Dtype()
+                    ),
+                    "string col": pd.Series(
+                        ["abc", "a", None, "", None], dtype=pd.StringDtype()
+                    ),
+                    "float col": pd.Series(
+                        [0.234, None, 234234.2343, None, 0.0], dtype="float64"
+                    ),
+                    "bool col": pd.Series(
+                        [None, True, False, None, True],
+                        dtype=pd.BooleanDtype(),
+                    ),
+                    "categorical col": pd.Series(
+                        [1, 2, 1, None, 2], dtype="category"
+                    ),
+                    "datetime col": pd.Series(
+                        [1231233, None, 2323234, None, 1],
+                        dtype="datetime64[ns]",
+                    ),
+                    "timedelta col": pd.Series(
+                        [None, 34687236, 2323234, 1, None],
+                        dtype="timedelta64[ns]",
+                    ),
+                }
+            ),
+        ),
+    ],
+)
+def test_json_to_json_compare_contents(gdf, pdf):
+    expected_json = pdf.to_json(lines=True, orient="records")
+    with pytest.warns(UserWarning):
+        actual_json = gdf.to_json(lines=True, orient="records")
+
+    assert expected_json == actual_json
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["cudf", "pandas"])
+def test_default_integer_bitwidth(default_integer_bitwidth, engine):
+    buf = BytesIO()
+    pd.DataFrame({"a": range(10)}).to_json(buf, lines=True, orient="records")
+    buf.seek(0)
+    df = cudf.read_json(buf, engine=engine, lines=True, orient="records")
+
+    assert df["a"].dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize(
+    "engine",
+    [
+        pytest.param(
+            "cudf_legacy",
+            marks=pytest.mark.skip(
+                reason="cannot partially set dtypes for cudf json engine"
+            ),
+        ),
+        "pandas",
+        "cudf",
+    ],
+)
+def test_default_integer_bitwidth_partial(default_integer_bitwidth, engine):
+    buf = BytesIO()
+    pd.DataFrame({"a": range(10), "b": range(10, 20)}).to_json(
+        buf, lines=True, orient="records"
+    )
+    buf.seek(0)
+    df = cudf.read_json(
+        buf, engine=engine, lines=True, orient="records", dtype={"b": "i8"}
+    )
+
+    assert df["a"].dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+    assert df["b"].dtype == np.dtype("i8")
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["cudf", "pandas"])
+def test_default_integer_bitwidth_extremes(default_integer_bitwidth, engine):
+    # Test that integer columns in json are _inferred_ as 32 bit columns.
+    buf = StringIO(
+        '{"u8":18446744073709551615, "i8":9223372036854775807}\n'
+        '{"u8": 0, "i8": -9223372036854775808}'
+    )
+    df = cudf.read_json(buf, engine=engine, lines=True, orient="records")
+
+    assert df["u8"].dtype == np.dtype(f"u{default_integer_bitwidth//8}")
+    assert df["i8"].dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+
+
+def test_default_float_bitwidth(default_float_bitwidth):
+    # Test that float columns in json are _inferred_ as 32 bit columns.
+    df = cudf.read_json(
+        '{"a": 1.0, "b": 2.5}\n{"a": 3.5, "b": 4.0}',
+        engine="cudf",
+        lines=True,
+        orient="records",
+    )
+    assert df["a"].dtype == np.dtype(f"f{default_float_bitwidth//8}")
+    assert df["b"].dtype == np.dtype(f"f{default_float_bitwidth//8}")
+
+
+def test_json_nested_basic():
+    bytes_obj = BytesIO()
+    data = {
+        "c1": [{"f1": "sf11", "f2": "sf21"}, {"f1": "sf12", "f2": "sf22"}],
+        "c2": [["l11", "l21"], ["l12", "l22"]],
+    }
+    pdf = pd.DataFrame(data)
+    pdf.to_json(bytes_obj, orient="records")
+
+    df = cudf.read_json(bytes_obj, engine="cudf", orient="records")
+    bytes_obj.seek(0)
+    pdf = pd.read_json(bytes_obj, orient="records")
+
+    assert_eq(pdf, df)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "c1": [{"f1": "sf11", "f2": "sf21"}, {"f1": "sf12", "f2": "sf22"}],
+            "c2": [["l11", "l21"], ["l12", "l22"]],
+        },
+        # Essential test case to handle omissions
+        {
+            "c1": [{"f2": "sf21"}, {"f1": "sf12"}],
+            "c2": [["l11", "l21"], []],
+        },
+        # empty input
+        {},
+    ],
+)
+@pytest.mark.parametrize("lines", [True, False])
+def test_json_nested_lines(data, lines):
+    bytes = BytesIO()
+    pdf = pd.DataFrame(data)
+    pdf.to_json(bytes, orient="records", lines=lines)
+    bytes.seek(0)
+    df = cudf.read_json(bytes, engine="cudf", orient="records", lines=lines)
+    bytes.seek(0)
+    pdf = pd.read_json(bytes, orient="records", lines=lines)
+    # In the second test-case we need to take a detour via pyarrow
+    # Pandas omits "f1" in first row, so we have to enforce a common schema,
+    # such that pandas would have the f1 member with null
+    # Also, pyarrow chooses to select different ordering of a nested column
+    # children though key-value pairs are correct.
+    pa_table_pdf = pa.Table.from_pandas(
+        pdf, schema=df.to_arrow().schema, safe=False
+    )
+    assert df.to_arrow().equals(pa_table_pdf)
+
+
+def test_json_nested_data():
+    json_str = (
+        '[{"0":{},"2":{}},{"1":[[""],[]],"2":{"2":""}},'
+        '{"0":{"a":"1"},"2":{"0":"W&RR=+I","1":""}}]'
+    )
+    df = cudf.read_json(StringIO(json_str), engine="cudf", orient="records")
+    pdf = pd.read_json(StringIO(json_str), orient="records")
+    pdf.columns = pdf.columns.astype("str")
+    pa_table_pdf = pa.Table.from_pandas(
+        pdf, schema=df.to_arrow().schema, safe=False
+    )
+    assert df.to_arrow().equals(pa_table_pdf)
+
+
+def test_json_empty_types():
+    json_str = """ {}
+    {"a": [], "b": {}}
+    {"a": []}
+    {"b": {}}
+    {"c": {"d": []}}
+    {"e": [{}]}
+    """
+    df = cudf.read_json(StringIO(json_str), orient="records", lines=True)
+    pdf = pd.read_json(StringIO(json_str), orient="records", lines=True)
+    assert_eq(df, pdf)
+
+
+def test_json_types_data():
+    # 0:<0:string,1:float>
+    # 1:list<int>
+    # 2:<0:bool>
+    json_str = (
+        '[{"0":null,"2":{}},'
+        '{"1":[123],"0":{"0":"foo","1":123.4},"2":{"0":false}},'
+        '{"0":{},"1":[],"2":{"0":null}}]'
+    )
+    df = cudf.read_json(StringIO(json_str), engine="cudf", orient="records")
+    pdf = pd.read_json(StringIO(json_str), orient="records")
+    pdf.columns = pdf.columns.astype("str")
+    pa_table_pdf = pa.Table.from_pandas(
+        pdf, schema=df.to_arrow().schema, safe=False
+    )
+    assert df.to_arrow().equals(pa_table_pdf)
+
+
+@pytest.mark.parametrize(
+    "col_type,json_str,expected_data",
+    [
+        # without quotes
+        ("int", '[{"k": 1}, {"k": 2}, {"k": 3}, {"k": 4}]', [1, 2, 3, 4]),
+        # with quotes
+        ("int", '[{"k": "1"}, {"k": "2"}]', [1, 2]),
+        # with quotes, mixed
+        ("int", '[{"k": "1"}, {"k": "2"}, {"k": 3}, {"k": 4}]', [1, 2, 3, 4]),
+        # with quotes, null, mixed
+        (
+            "int",
+            '[{"k": "1"}, {"k": "2"}, {"k": null}, {"k": 4}]',
+            [1, 2, None, 4],
+        ),
+        # without quotes, null
+        (
+            "int",
+            '[{"k": 1}, {"k": 2}, {"k": null}, {"k": 4}]',
+            [1, 2, None, 4],
+        ),
+        # without quotes
+        ("float", '[{"k": 1}, {"k": 2}, {"k": 3}, {"k": 4}]', [1, 2, 3, 4]),
+        # with quotes
+        ("float", '[{"k": "1"}, {"k": "2"}]', [1, 2]),
+        # with quotes, mixed
+        (
+            "float",
+            '[{"k": "1"}, {"k": "2"}, {"k": 3}, {"k": 4}]',
+            [1, 2, 3, 4],
+        ),
+        # with quotes, null, mixed
+        (
+            "float",
+            '[{"k": "1"}, {"k": "2"}, {"k": null}, {"k": 4}]',
+            [1, 2, None, 4],
+        ),
+        # with quotes, NAN
+        (
+            "float",
+            '[{"k": "1"}, {"k": "2"}, {"k": NaN}, {"k": "4"}]',
+            [1, 2, np.nan, 4],
+        ),
+        # without quotes
+        ("str", '[{"k": 1}, {"k": 2}, {"k": 3}, {"k": 4}]', [1, 2, 3, 4]),
+        # with quotes
+        ("str", '[{"k": "1"}, {"k": "2"}]', [1, 2]),
+        # with quotes, mixed
+        ("str", '[{"k": "1"}, {"k": "2"}, {"k": 3}, {"k": 4}]', [1, 2, 3, 4]),
+        # with quotes, null, mixed
+        (
+            "str",
+            '[{"k": "1"}, {"k": "2"}, {"k": null}, {"k": 4}]',
+            [1, 2, None, 4],
+        ),
+        # without quotes, null
+        (
+            "str",
+            '[{"k": 1}, {"k": 2}, {"k": null}, {"k": 4}]',
+            [1, 2, None, 4],
+        ),
+    ],
+)
+def test_json_quoted_values_with_schema(col_type, json_str, expected_data):
+    actual = cudf.read_json(
+        StringIO(json_str),
+        engine="cudf",
+        orient="records",
+        dtype={"k": col_type},
+    )
+    expected = cudf.DataFrame({"k": expected_data}, dtype=col_type)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "col_type,json_str,expected_data",
+    [
+        # with quotes, mixed
+        ("int", '[{"k": "1"}, {"k": "2"}, {"k": 3}, {"k": 4}]', [1, 2, 3, 4]),
+        # with quotes, null, mixed
+        (
+            "int",
+            '[{"k": "1"}, {"k": "2"}, {"k": null}, {"k": 4}]',
+            [1, 2, None, 4],
+        ),
+        # with quotes, mixed
+        (
+            "str",
+            '[{"k": "1"}, {"k": "2"}, {"k": 3}, {"k": 4}]',
+            ["1", "2", "3", "4"],
+        ),
+        # with quotes, null, mixed
+        (
+            "str",
+            '[{"k": "1"}, {"k": "2"}, {"k": null}, {"k": 4}]',
+            ["1", "2", None, "4"],
+        ),
+    ],
+)
+def test_json_quoted_values(col_type, json_str, expected_data):
+    actual = cudf.read_json(
+        StringIO(json_str),
+        engine="cudf",
+        orient="records",
+        dtype={"k": col_type},
+    )
+    expected = cudf.DataFrame({"k": expected_data}, dtype=col_type)
+
+    assert_eq(expected, actual)
+    assert_eq(expected_data, actual.k.to_arrow().to_pylist())
+
+
+@pytest.mark.parametrize(
+    "keep_quotes,result",
+    [
+        (
+            True,
+            {
+                "c1": [
+                    {"f1": '"sf11"', "f2": '"sf21"'},
+                    {"f1": '"sf12"', "f2": '"sf22"'},
+                ],
+                "c2": [['"l11"', '"l21"'], ['"l12"', '"l22"']],
+            },
+        ),
+        (
+            False,
+            {
+                "c1": [
+                    {"f1": "sf11", "f2": "sf21"},
+                    {"f1": "sf12", "f2": "sf22"},
+                ],
+                "c2": [["l11", "l21"], ["l12", "l22"]],
+            },
+        ),
+    ],
+)
+def test_json_keep_quotes(keep_quotes, result):
+    bytes_file = BytesIO()
+    data = {
+        "c1": [{"f1": "sf11", "f2": "sf21"}, {"f1": "sf12", "f2": "sf22"}],
+        "c2": [["l11", "l21"], ["l12", "l22"]],
+    }
+    pdf = pd.DataFrame(data)
+    pdf.to_json(bytes_file, orient="records", lines=True)
+
+    actual = cudf.read_json(
+        bytes_file,
+        orient="records",
+        lines=True,
+        keep_quotes=keep_quotes,
+    )
+    expected = pd.DataFrame(result)
+
+    assert_eq(actual, expected)
+
+
+def test_json_dtypes_nested_data():
+    # a: StructDtype({'a': StructDtype({'b': dtype('float64')}),
+    #                 'b': dtype('int64')})
+    # b: ListDtype(ListDtype(float64))
+    actual_json_str = (
+        '{"a":{"a":{"b":10.0},"b":11},"b":[[10.0,1.1],[12.0,23.0]]}\n'
+        '{"a":{"a":{"b":107.0},"b":5},"b":[[10.0,11.2],[12.0,0.23]]}\n'
+        '{"a":{"a":{"b":50.7},"b":2},"b":[[10.0,11.3],[12.0,2.3]]}\n'
+        '{"a":{"a":{"b":1.2},"b":67},"b":[[6.0,7.0]]}\n'
+        '{"a":{"a":{"b":40.1},"b":1090},"b":null}\n'
+    )
+
+    """
+    In [3]: df
+    Out[3]:
+                                   a                             b
+    0    {'a': {'b': 10.0}, 'b': 11}   [[10.0, 1.1], [12.0, 23.0]]
+    1    {'a': {'b': 107.0}, 'b': 5}  [[10.0, 11.2], [12.0, 0.23]]
+    2     {'a': {'b': 50.7}, 'b': 2}   [[10.0, 11.3], [12.0, 2.3]]
+    3     {'a': {'b': 1.2}, 'b': 67}                  [[6.0, 7.0]]
+    4  {'a': {'b': 40.1}, 'b': 1090}                          None
+    """
+
+    # a: StructDtype({'a': StructDtype({'b': dtype('int64')}),
+    #                 'b': dtype('float64')})
+    # b: ListDtype(ListDtype(int64))
+    expected_json_str = (
+        '{"a":{"a":{"b":10},"b":11.0},"b":[[10,1],[12,23]]}\n'
+        '{"a":{"a":{"b":107},"b":5.0},"b":[[10,11],[12,0]]}\n'
+        '{"a":{"a":{"b":50},"b":2.0},"b":[[10,11],[12,2]]}\n'
+        '{"a":{"a":{"b":1},"b":67.0},"b":[[6,7]]}\n'
+        '{"a":{"a":{"b":40},"b":1090.0},"b":null}\n'
+    )
+
+    """
+    In [7]: df
+    Out[7]:
+                                  a                    b
+    0    {'a': {'b': 10}, 'b': 11.0}  [[10, 1], [12, 23]]
+    1    {'a': {'b': 107}, 'b': 5.0}  [[10, 11], [12, 0]]
+    2     {'a': {'b': 50}, 'b': 2.0}  [[10, 11], [12, 2]]
+    3     {'a': {'b': 1}, 'b': 67.0}             [[6, 7]]
+    4  {'a': {'b': 40}, 'b': 1090.0}                 None
+    """
+
+    df = cudf.read_json(
+        StringIO(actual_json_str),
+        engine="cudf",
+        orient="records",
+        lines=True,
+        dtype={
+            "a": cudf.StructDtype(
+                {
+                    "a": cudf.StructDtype({"b": cudf.dtype("int64")}),
+                    "b": cudf.dtype("float64"),
+                }
+            ),
+            "b": cudf.ListDtype(cudf.ListDtype("int64")),
+        },
+    )
+
+    pdf = pd.read_json(
+        StringIO(expected_json_str), orient="records", lines=True
+    )
+    pdf.columns = pdf.columns.astype("str")
+    pa_table_pdf = pa.Table.from_pandas(
+        pdf, schema=df.to_arrow().schema, safe=False
+    )
+    assert df.to_arrow().equals(pa_table_pdf)
+
+
+@pytest.mark.parametrize(
+    "tag, data",
+    [
+        (
+            "normal",
+            """\
+{"a": 1, "b": 2}
+{"a": 3, "b": 4}""",
+        ),
+        (
+            "multiple",
+            """\
+    { "a": { "y" : 6}, "b" : [1, 2, 3], "c": 11 }
+    { "a": { "y" : 6}, "b" : [4, 5   ], "c": 12 }
+    { "a": { "y" : 6}, "b" : [6      ], "c": 13 }
+    { "a": { "y" : 6}, "b" : [7      ], "c": 14 }""",
+        ),
+        (
+            "reordered",
+            """\
+    { "a": { "y" : 6}, "b" : [1, 2, 3], "c": 11 }
+    { "a": { "y" : 6}, "c": 12 , "b" : [4, 5   ]}
+    { "b" : [6      ],  "a": { "y" : 6}, "c": 13}
+    { "c" : 14, "a": { "y" : 6}, "b" : [7      ]}
+""",
+        ),
+        (
+            "missing",
+            """
+    { "a": { "y" : 6}, "b" : [1, 2, 3], "c": 11 }
+    { "a": { "y" : 6}, "b" : [4, 5   ]          }
+    { "a": { "y" : 6}, "c": 13                  }
+    { "a": { "y" : 6}, "b" : [7      ], "c": 14 }
+""",
+        ),
+        pytest.param(
+            "dtype_mismatch",
+            """\
+    { "a": { "y" : 6}, "b" : [1, 2, 3], "c": 11 }
+    { "a": { "y" : 6}, "b" : [4, 5   ], "c": 12 }
+    { "a": { "y" : 6}, "b" : [6      ], "c": 13 }
+    { "a": { "y" : 6}, "b" : [7      ], "c": 14.0 }""",
+        ),
+    ],
+)
+class TestNestedJsonReaderCommon:
+    @pytest.mark.parametrize("chunk_size", [10, 100, 1024, 1024 * 1024])
+    def test_chunked_nested_json_reader(self, tag, data, chunk_size):
+        expected = cudf.read_json(StringIO(data), lines=True)
+
+        source_size = len(data)
+        chunks = []
+        for chunk_start in range(0, source_size, chunk_size):
+            chunks.append(
+                cudf.read_json(
+                    StringIO(data),
+                    byte_range=[chunk_start, chunk_size],
+                    lines=True,
+                )
+            )
+        df = cudf.concat(chunks, ignore_index=True)
+        assert expected.to_arrow().equals(df.to_arrow())
+
+    def test_order_nested_json_reader(self, tag, data):
+        expected = pd.read_json(StringIO(data), lines=True)
+        target = cudf.read_json(StringIO(data), lines=True)
+        if tag == "dtype_mismatch":
+            with pytest.raises(AssertionError):
+                # pandas parses integer values in float representation
+                # as integer
+                assert pa.Table.from_pandas(expected).equals(target.to_arrow())
+        elif tag == "missing":
+            with pytest.raises(AssertionError):
+                # pandas inferences integer with nulls as float64
+                assert pa.Table.from_pandas(expected).equals(target.to_arrow())
+        else:
+            assert pa.Table.from_pandas(expected).equals(target.to_arrow())
+
+
+def test_json_round_trip_gzip():
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": ["abc", "def", "ghi"]})
+    bytes = BytesIO()
+    with gzip.open(bytes, mode="wb") as fo:
+        with pytest.warns(UserWarning):
+            df.to_json(fo, orient="records", lines=True)
+    bytes.seek(0)
+    with gzip.open(bytes, mode="rb") as fo:
+        written_df = cudf.read_json(fo, orient="records", lines=True)
+    assert_eq(written_df, df)
+
+    # Testing writing from middle of the file.
+    loc = bytes.tell()
+
+    with gzip.open(bytes, mode="wb") as fo:
+        fo.seek(loc)
+        with pytest.warns(UserWarning):
+            df.to_json(fo, orient="records", lines=True)
+    bytes.seek(loc)
+    with gzip.open(bytes, mode="rb") as fo:
+        fo.seek(loc)
+        written_df = cudf.read_json(fo, orient="records", lines=True)
+    assert_eq(written_df, df)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # # empty input
+        # assert failing due to missing index size information
+        "",
+        "[]",
+        "[]\n[]\n[]",
+        # simple values
+        """[1]\n[2]\n[3]""",
+        """[1, 2, 3]\n[4, 5, 6]\n[7, 8, 9]""",
+        # nulls
+        """[1, 2, 3]\n[4, 5, null]\n[7, 8, 9]""",
+        """[1, 2, 3]\n[4, 5, null]\n[7, 8, 9]\n[null, null, null]""",
+        """[1, 2, 3]\n[4, 5, null]\n[]""",
+        # missing
+        """[1, 2, 3]\n[4, 5   ]\n[7, 8, 9]""",
+        """[1, 2, 3]\n[4, 5, 6]\n[7, 8, 9, 10]""",
+        """[1, 2, 3]\n[4, 5, 6, {}]\n[7, 8, 9]""",
+        """[1, 2, 3]\n[4, 5, 6, []]\n[7, 8, 9]""",
+        """[1, 2, 3]\n[4, 5, 6, {"a": 10}]\n[7, 8, 9]""",
+        """[1, 2, 3]\n[4, 5, 6, [10]]\n[7, 8, 9]""",
+        # mixed
+        """[1, 2, 3]\n[4, 5, {}]\n[7, 8, 9]""",
+        """[1, 2, {}]\n[4, 5, 6]\n[7, 8, 9]""",
+        """[1, 2, 3]\n[4, 5, [6]]\n[7, 8, 9]""",
+        """[1, 2, [3]]\n[4, 5, 6]\n[7, 8, 9]""",
+        # nested
+        """[1, 2, [3]]\n[4, 5, [6]]\n[7, 8, [9]]""",
+        """[1, 2, {"a": 3}]\n[4, 5, {"b": 6}]\n[7, 8, {"c": 9}]""",
+        """[1, 2, [{"a": 3}, {"a": 3}]]
+           [4, 5, [{"b": 6}, {"b": 6}, {}, {"b": 6}]]
+           [7, 8, [{}]]""",
+        """[1, 2, {"a": [3, 3, 3]}]
+           [4, 5, {"b": [6, 6]}]
+           [7, 8, {"c": 9}]""",
+        """[1, 2, [{"a": 3}, {"a": null}]]
+           [4, 5, [{"b": [6.0, 6, 06]}, {"b": [6]}, {}, {"b": null}]]
+           [7, 8, [{}]]""",
+    ],
+)
+@pytest.mark.parametrize("lines", [True, False])
+def test_json_array_of_arrays(data, lines):
+    data = data if lines else "[" + data.replace("\n", ",") + "]"
+    pdf = pd.read_json(data, orient="values", lines=lines)
+    df = cudf.read_json(
+        StringIO(data),
+        engine="cudf",
+        orient="values",
+        lines=lines,
+    )
+    # if mixed with dict/list type, replace other types with None.
+    if 2 in pdf.columns and any(
+        pdf[2].apply(lambda x: isinstance(x, dict) or isinstance(x, list))
+    ):
+        pdf[2] = pdf[2].apply(
+            lambda x: x if isinstance(x, dict) or isinstance(x, list) else None
+        )
+    # TODO: Replace string column names with integer column names
+    # for values orient in cudf json reader
+    pdf.rename(columns={name: str(name) for name in pdf.columns}, inplace=True)
+    # assert_eq(pdf, df)
+    pa_table_pdf = pa.Table.from_pandas(
+        pdf, schema=df.to_arrow().schema, safe=False
+    )
+    assert df.to_arrow().equals(pa_table_pdf)
+
+
+@pytest.mark.parametrize(
+    "jsonl_string",
+    [
+        # simple list with mixed types
+        """{"a":[123, {}], "b":1.1}""",
+        """{"a":[123, {"0": 123}], "b":1.0}\n {"b":1.1}\n {"b":2.1}""",
+        """{"a":[{"L": 123}, 123], "b":1.0}\n {"b":1.1}\n {"b":2.1}""",
+        """{"a":[123, {"0": 123}, 12.3], "b":1.0}\n {"b":1.1}\n {"b":2.1}""",
+        """{"a":[123, {"0": 123}, null], "b":1.0}\n {"b":1.1}\n {"b":2.1}""",
+        """{"a":["123", {"0": 123}], "b":1.0}\n {"b":1.1}\n {"b":2.1}""",
+        """{"a":[{"0": 123}, "123"], "b":1.0}\n {"b":1.1}\n {"b":2.1}""",
+        """{"a":["123", {"0": 123}, "123"], "b":1.0}\n {"b":1.1}""",
+        """{"a":[123]}\n {"a":[{"0": 123}], "b":1.0}\n {"b":1.1}""",
+        """{"a":[{"0": 123}]}\n {"a":[123], "b":1.0}\n {"b":1.1}""",
+        """{"a":[{"0": 123}]}\n {"a": []}\n {"a":[123], "b":1.0}\n{"b":1.1}""",
+        """{"b":1.0, "a":[{"0": 123}]}\n {"a":[123]}\n {"b":1.1}\n{"a": []}""",
+        """{"a": []}\n {"a":[{"0": 123}]}\n {"a":[123], "b":1.0}\n{"b":1.1}""",
+        """{"a": []}\n {"a":[123], "b":1.0}\n {"a":[{"0": 123}]}\n{"b":1.1}""",
+        # nested list with mixed types
+        """{"a":[123, [{"0": 123}, {}]], "b":1.0}
+           {"b":1.1}
+           {"a":[]}
+           {"a":[123]}
+           {"a":[[123], []]}""",
+        """{"a":[], "b":1.0}
+           {"a":[[[456]]]}
+           {"a":[[123]]}
+           {"a":[123]}""",
+        """{"a":[123], "b":1.0}
+           {"b":1.1}
+           {"b":2.1}
+           {"a":[[[[[[]]]]]]}""",
+        """{"a":[123], "b":1.0}
+           {"a":[[[[[[]]]]]]}
+           {"a":[[[[[[]]]]], [[[[[]]]]]]}
+           {"a":[[[[[[]]]], [[[[]]]]]]}
+           {"a":[[[[[[]]], [[[]]]]]]}
+           {"a":[[[[[[]], [[]]]]]]}
+           {"a":[[[[[[], 123, []]]]]]}""",
+        # mixed elements in multiple columns
+        """{"a":[123, {"0": 123}], "b":1.0}
+           {"c": ["abc"], "b":1.1}
+           {"c": ["abc", []] }""",
+    ],
+)
+def test_json_nested_mixed_types_in_list(jsonl_string):
+    # utility function for this test:
+    # replace list elements with None if it has dict and non-dict (ignore None)
+    def _replace_in_list(list_to_replace, replace_items):
+        return [
+            _replace_in_list(x, replace_items)
+            if isinstance(x, list)
+            else None
+            if x in replace_items
+            else x
+            for x in list_to_replace
+        ]
+
+    def _replace_with_nulls(df, replace_items):
+        for col in df.columns:
+            if df[col].dtype == "object":
+                df[col] = df[col].apply(
+                    lambda x: _replace_in_list(x, replace_items)
+                    if isinstance(x, list)
+                    else x
+                )
+        return df
+
+    # both json lines and json string tested.
+    json_string = "[" + jsonl_string.replace("\n", ",") + "]"
+    pdf = pd.read_json(jsonl_string, orient="records", lines=True)
+    pdf2 = pd.read_json(json_string, orient="records", lines=False)
+    assert_eq(pdf, pdf2)
+    # replace list elements with None if it has dict and non-dict
+    # in above test cases, these items are mixed with dict/list items
+    # so, replace them with None.
+    pdf = _replace_with_nulls(pdf, [123, "123", 12.3, "abc"])
+    gdf = cudf.read_json(
+        StringIO(jsonl_string),
+        orient="records",
+        lines=True,
+    )
+    gdf2 = cudf.read_json(
+        StringIO(json_string),
+        engine="cudf",
+        orient="records",
+        lines=False,
+    )
+    if """[{"0": 123}, {}]""" not in jsonl_string:
+        # {} in pandas is represented as {"0": None} in cudf
+        assert_eq(gdf, pdf)
+        assert_eq(gdf2, pdf)
+    pa_table_pdf = pa.Table.from_pandas(
+        pdf, schema=gdf.to_arrow().schema, safe=False
+    )
+    assert gdf.to_arrow().equals(pa_table_pdf)
+    assert gdf2.to_arrow().equals(pa_table_pdf)
+
+
+@pytest.mark.parametrize(
+    "jsonl_string",
+    [
+        # mixed type in list (in different order)
+        """{"a":[[{"0": 123}, {}], {"1": 321}], "b":1.0}""",
+        """{"a":[{"1": 321}, [{"0": 123}, {}], ], "b":1.0}""",
+        """{"a":[123, [{"0": 123}, {}], {"1": 321}], "b":1.0}""",
+        """{"a":[null, [{"0": 123}, {}], {"1": 321}], "b":1.0}""",
+        # mixed type in struct (in different order)
+        """{"a": {"b": {"0": 123}, "c": {"1": 321}}, "d":1.0}
+           {"a": {"b": {"0": 123}, "c": [123, 123]}, "d":1.0}""",
+        """{"a": {"b": {"0": 123}, "c": [123, 123]}, "d":1.0}
+           {"a": {"b": {"0": 123}, "c": {"1": 321}}, "d":1.0}""",
+        """{"a": {"b": {"0": 123}, "c": null}, "d":1.0}
+           {"a": {"b": {"0": 123}, "c": {"1": 321}}, "d":1.0}
+           {"a": {"b": {"0": 123}, "c": [123, 123]}, "d":1.0}""",
+        """{"a": {"b": {"0": 123}, "c": 123}, "d":1.0}
+           {"a": {"b": {"0": 123}, "c": {"1": 321}}, "d":1.0}
+           {"a": {"b": {"0": 123}, "c": [123, 123]}, "d":1.0}""",
+    ],
+)
+def test_json_nested_mixed_types_error(jsonl_string):
+    # mixing list and struct should raise an exception
+    with pytest.raises(RuntimeError):
+        cudf.read_json(
+            StringIO(jsonl_string),
+            orient="records",
+            lines=True,
+        )
diff --git a/python/cudf/cudf/tests/test_list.py b/python/cudf/cudf/tests/test_list.py
new file mode 100644
index 0000000..8149188
--- /dev/null
+++ b/python/cudf/cudf/tests/test_list.py
@@ -0,0 +1,932 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import functools
+import operator
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf import NA
+from cudf._lib.copying import get_element
+from cudf.api.types import is_scalar
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+    assert_eq,
+)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[]],
+        [[[]]],
+        [[0]],
+        [[0, 1]],
+        [[0, 1], [2, 3]],
+        [[[0, 1], [2]], [[3, 4]]],
+        [[None]],
+        [[[None]]],
+        [[None], None],
+        [[1, None], [1]],
+        [[1, None], None],
+        [[[1, None], None], None],
+    ],
+)
+def test_create_list_series(data):
+    expect = pd.Series(data)
+    got = cudf.Series(data)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [[]]},
+        {"a": [[None]]},
+        {"a": [[1, 2, 3]]},
+        {"a": [[1, 2, 3]], "b": [[2, 3, 4]]},
+        {"a": [[1, 2, 3, None], [None]], "b": [[2, 3, 4], [5]], "c": None},
+        {"a": [[1]], "b": [[1, 2, 3]]},
+        pd.DataFrame({"a": [[1, 2, 3]]}),
+    ],
+)
+def test_df_list_dtypes(data):
+    expect = pd.DataFrame(data)
+    got = cudf.DataFrame(data)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[]],
+        [[[]]],
+        [[0]],
+        [[0, 1]],
+        [[0, 1], [2, 3]],
+        [[[0, 1], [2]], [[3, 4]]],
+        [[[0, 1, None], None], None, [[3, 2, None], None]],
+        [[["a", "c", None], None], None, [["b", "d", None], None]],
+    ],
+)
+def test_leaves(data):
+    pa_array = pa.array(data)
+    while hasattr(pa_array, "flatten"):
+        pa_array = pa_array.flatten()
+
+    expect = cudf.Series(pa_array)
+    got = cudf.Series(data).list.leaves
+    assert_eq(
+        expect,
+        got,
+        check_dtype=not isinstance(pa_array, pa.NullArray),
+    )
+
+
+def test_list_to_pandas_nullable_true():
+    df = cudf.DataFrame({"a": cudf.Series([[1, 2, 3]])})
+    actual = df.to_pandas(nullable=True)
+    expected = pd.DataFrame({"a": pd.Series([[1, 2, 3]])})
+
+    assert_eq(actual, expected)
+
+
+def test_listdtype_hash():
+    a = cudf.core.dtypes.ListDtype("int64")
+    b = cudf.core.dtypes.ListDtype("int64")
+
+    assert hash(a) == hash(b)
+
+    c = cudf.core.dtypes.ListDtype("int32")
+
+    assert hash(a) != hash(c)
+
+
+@pytest.fixture(params=["int", "float", "datetime", "timedelta"])
+def leaf_value(request):
+    if request.param == "int":
+        return np.int32(1)
+    elif request.param == "float":
+        return np.float64(1)
+    elif request.param == "datetime":
+        return pd.to_datetime("1900-01-01")
+    elif request.param == "timedelta":
+        return pd.to_timedelta("10d")
+    else:
+        raise ValueError("Unhandled data type")
+
+
+@pytest.fixture(params=["list", "struct"])
+def list_or_struct(request, leaf_value):
+    if request.param == "list":
+        return [[leaf_value], [leaf_value]]
+    elif request.param == "struct":
+        return {"a": leaf_value, "b": [leaf_value], "c": {"d": [leaf_value]}}
+    else:
+        raise ValueError("Unhandled data type")
+
+
+@pytest.fixture(params=["list", "struct"])
+def nested_list(request, list_or_struct, leaf_value):
+    if request.param == "list":
+        return [list_or_struct, list_or_struct]
+    elif request.param == "struct":
+        return [
+            {
+                "a": list_or_struct,
+                "b": leaf_value,
+                "c": {"d": list_or_struct, "e": leaf_value},
+            }
+        ]
+    else:
+        raise ValueError("Unhandled data type")
+
+
+def test_list_dtype_explode(nested_list):
+    sr = cudf.Series([nested_list])
+    assert sr.dtype.element_type == sr.explode().dtype
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[]],
+        [[1, 2, 3], [4, 5]],
+        [[1, 2, 3], [], [4, 5]],
+        [[1, 2, 3], None, [4, 5]],
+        [[None, None], [None]],
+        [[[[[[1, 2, 3]]]]]],
+        cudf.Series([[1, 2]]).iloc[0:0],
+        cudf.Series([None, [1, 2]]).iloc[0:1],
+    ],
+)
+def test_len(data):
+    gsr = cudf.Series(data)
+    psr = gsr.to_pandas()
+
+    expect = psr.map(lambda x: len(x) if x is not None else None)
+    got = gsr.list.len()
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    ("data", "idx"),
+    [
+        ([[1, 2, 3], [3, 4, 5], [4, 5, 6]], [[0, 1], [2], [1, 2]]),
+        ([[1, 2, 3], [3, 4, 5], [4, 5, 6]], [[1, 2, 0], [1, 0, 2], [0, 1, 2]]),
+        ([[1, 2, 3], []], [[0, 1], []]),
+        ([[1, 2, 3], [None]], [[0, 1], []]),
+        ([[1, None, 3], None], [[0, 1], []]),
+    ],
+)
+def test_take(data, idx):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.Series(zip(ps, idx)).map(
+        lambda x: [x[0][i] for i in x[1]] if x[0] is not None else None
+    )
+    got = gs.list.take(idx)
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    ("invalid", "exception"),
+    [
+        ([[0]], pytest.raises(ValueError, match="different size")),
+        ([1, 2, 3, 4], pytest.raises(ValueError, match="should be list type")),
+        (
+            [["a", "b"], ["c"]],
+            pytest.raises(
+                TypeError, match="should be column of values of index types"
+            ),
+        ),
+        (
+            [[[1], [0]], [[0]]],
+            pytest.raises(
+                TypeError, match="should be column of values of index types"
+            ),
+        ),
+        ([[0, 1], None], pytest.raises(ValueError, match="contains null")),
+    ],
+)
+def test_take_invalid(invalid, exception):
+    gs = cudf.Series([[0, 1], [2, 3]])
+    with exception:
+        gs.list.take(invalid)
+
+
+@pytest.mark.parametrize(
+    ("data", "expected"),
+    [
+        ([[1, 1, 2, 2], [], None, [3, 4, 5]], [[1, 2], [], None, [3, 4, 5]]),
+        (
+            [[1.233, np.nan, 1.234, 3.141, np.nan, 1.234]],
+            [[1.233, 1.234, np.nan, 3.141]],
+        ),  # duplicate nans
+        ([[1, 1, 2, 2, None, None]], [[1, 2, None]]),  # duplicate nulls
+        (
+            [[1.233, np.nan, None, 1.234, 3.141, np.nan, 1.234, None]],
+            [[1.233, 1.234, np.nan, None, 3.141]],
+        ),  # duplicate nans and nulls
+        ([[2, None, 1, None, 2]], [[1, 2, None]]),
+        ([[], []], [[], []]),
+        ([[], None], [[], None]),
+    ],
+)
+def test_unique(data, expected):
+    """
+    Pandas de-duplicates nans and nulls respectively in Series.unique.
+    `expected` is setup to mimic such behavior
+    """
+    gs = cudf.Series(data, nan_as_null=False)
+
+    got = gs.list.unique()
+    expected = cudf.Series(expected, nan_as_null=False).list.sort_values()
+
+    got = got.list.sort_values()
+
+    assert_eq(expected, got)
+
+
+def key_func_builder(x, na_position):
+    if x is None:
+        if na_position == "first":
+            return -1e8
+        else:
+            return 1e8
+    else:
+        return x
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[4, 2, None, 9], [8, 8, 2], [2, 1]],
+        [[4, 2, None, 9], [8, 8, 2], None],
+        [[4, 2, None, 9], [], None],
+    ],
+)
+@pytest.mark.parametrize(
+    "index",
+    [
+        None,
+        pd.Index(["a", "b", "c"]),
+        pd.MultiIndex.from_tuples(
+            [(0, "a"), (0, "b"), (1, "a")], names=["l0", "l1"]
+        ),
+    ],
+)
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_sort_values(data, index, ascending, na_position, ignore_index):
+    key_func = functools.partial(key_func_builder, na_position=na_position)
+
+    ps = pd.Series(data, index=index)
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.apply(
+        lambda x: sorted(x, key=key_func, reverse=not ascending)
+        if x is not None
+        else None
+    )
+    if ignore_index:
+        expected.reset_index(drop=True, inplace=True)
+    got = gs.list.sort_values(
+        ascending=ascending, na_position=na_position, ignore_index=ignore_index
+    )
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data, index, expect",
+    [
+        ([[None, None], [None, None]], 0, [None, None]),
+        ([[1, 2], [3, 4]], 0, [1, 3]),
+        ([["a", "b"], ["c", "d"]], 1, ["b", "d"]),
+        ([[1, None], [None, 2]], 1, [None, 2]),
+        ([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], 1, [[3, 4], [7, 8]]),
+    ],
+)
+def test_get(data, index, expect):
+    sr = cudf.Series(data)
+    expect = cudf.Series(expect)
+    got = sr.list.get(index)
+
+    assert_eq(expect, got, check_dtype=not expect.isnull().all())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [{"k": "v1"}, {"k": "v2"}],
+        [[{"k": "v1", "b": "v2"}], [{"k": "v3", "b": "v4"}]],
+        [
+            [{"k": "v1", "b": [{"c": 10, "d": "v5"}]}],
+            [{"k": "v3", "b": [{"c": 14, "d": "v6"}]}],
+        ],
+    ],
+)
+@pytest.mark.parametrize("index", [0, 1])
+def test_get_nested_struct_dtype_transfer(data, index):
+    sr = cudf.Series([data])
+    expect = cudf.Series(data[index : index + 1])
+    assert_eq(expect, sr.list.get(index))
+
+
+def test_get_nested_lists():
+    sr = cudf.Series(
+        [
+            [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [], [[3, 4], [7, 8]]],
+            [[], [[9, 10]], [[11, 12], [13, 14]]],
+        ]
+    )
+    expect = cudf.Series([[[1, 2], [3, 4]], []])
+    got = sr.list.get(0)
+    assert_eq(expect, got)
+
+
+def test_get_default():
+    sr = cudf.Series([[1, 2], [3, 4, 5], [6, 7, 8, 9]])
+
+    assert_eq(cudf.Series([cudf.NA, 5, 8]), sr.list.get(2))
+    assert_eq(cudf.Series([cudf.NA, 5, 8]), sr.list.get(2, default=cudf.NA))
+    assert_eq(cudf.Series([0, 5, 8]), sr.list.get(2, default=0))
+    assert_eq(cudf.Series([0, 3, 7]), sr.list.get(-3, default=0))
+    assert_eq(cudf.Series([2, 5, 9]), sr.list.get(-1))
+
+    string_sr = cudf.Series(
+        [["apple", "banana"], ["carrot", "daffodil", "elephant"]]
+    )
+    assert_eq(
+        cudf.Series(["default", "elephant"]),
+        string_sr.list.get(2, default="default"),
+    )
+
+    sr_with_null = cudf.Series([[0, cudf.NA], [1]])
+    assert_eq(cudf.Series([cudf.NA, 0]), sr_with_null.list.get(1, default=0))
+
+    sr_nested = cudf.Series([[[1, 2], [3, 4], [5, 6]], [[5, 6], [7, 8]]])
+    assert_eq(cudf.Series([[3, 4], [7, 8]]), sr_nested.list.get(1))
+    assert_eq(cudf.Series([[5, 6], cudf.NA]), sr_nested.list.get(2))
+    assert_eq(
+        cudf.Series([[5, 6], [0, 0]]), sr_nested.list.get(2, default=[0, 0])
+    )
+
+
+def test_get_ind_sequence():
+    # test .list.get() when `index` is a sequence
+    sr = cudf.Series([[1, 2], [3, 4, 5], [6, 7, 8, 9]])
+    assert_eq(cudf.Series([1, 4, 8]), sr.list.get([0, 1, 2]))
+    assert_eq(cudf.Series([1, 4, 8]), sr.list.get(cudf.Series([0, 1, 2])))
+    assert_eq(cudf.Series([cudf.NA, 5, cudf.NA]), sr.list.get([2, 2, -5]))
+    assert_eq(cudf.Series([0, 5, 0]), sr.list.get([2, 2, -5], default=0))
+    sr_nested = cudf.Series([[[1, 2], [3, 4], [5, 6]], [[5, 6], [7, 8]]])
+    assert_eq(cudf.Series([[1, 2], [7, 8]]), sr_nested.list.get([0, 1]))
+
+
+@pytest.mark.parametrize(
+    "data, scalar, expect",
+    [
+        (
+            [[1, 2, 3], []],
+            1,
+            [True, False],
+        ),
+        (
+            [[1, 2, 3], [], [3, 4, 5]],
+            6,
+            [False, False, False],
+        ),
+        (
+            [[1.0, 2.0, 3.0], None, []],
+            2.0,
+            [True, None, False],
+        ),
+        (
+            [[None, "b", "c"], [], ["b", "e", "f"]],
+            "b",
+            [True, False, True],
+        ),
+        ([[None, 2, 3], None, []], 1, [False, None, False]),
+        (
+            [[None, "b", "c"], [], ["b", "e", "f"]],
+            "d",
+            [False, False, False],
+        ),
+    ],
+)
+def test_contains_scalar(data, scalar, expect):
+    sr = cudf.Series(data)
+    expect = cudf.Series(expect)
+    got = sr.list.contains(cudf.Scalar(scalar, sr.dtype.element_type))
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data, expect",
+    [
+        (
+            [[1, 2, 3], []],
+            [None, None],
+        ),
+        (
+            [[1.0, 2.0, 3.0], None, []],
+            [None, None, None],
+        ),
+        (
+            [[None, 2, 3], [], None],
+            [None, None, None],
+        ),
+        (
+            [[1, 2, 3], [3, 4, 5]],
+            [None, None],
+        ),
+        (
+            [[], [], []],
+            [None, None, None],
+        ),
+    ],
+)
+def test_contains_null_search_key(data, expect):
+    sr = cudf.Series(data)
+    expect = cudf.Series(expect, dtype="bool")
+    got = sr.list.contains(cudf.Scalar(cudf.NA, sr.dtype.element_type))
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data, scalar",
+    [
+        (
+            [[9, 0, 2], [], [1, None, 0]],
+            "x",
+        ),
+        (
+            [["z", "y", None], None, [None, "x"]],
+            5,
+        ),
+    ],
+)
+def test_contains_invalid(data, scalar):
+    sr = cudf.Series(data)
+    with pytest.raises(
+        TypeError,
+        match="Type/Scale of search key does not "
+        "match list column element type.",
+    ):
+        sr.list.contains(scalar)
+
+
+@pytest.mark.parametrize(
+    "data, search_key, expect",
+    [
+        (
+            [[1, 2, 3], [], [3, 4, 5]],
+            3,
+            [2, -1, 0],
+        ),
+        (
+            [[1.0, 2.0, 3.0], None, [2.0, 5.0]],
+            2.0,
+            [1, None, 0],
+        ),
+        (
+            [[None, "b", "c"], [], ["b", "e", "f"]],
+            "f",
+            [-1, -1, 2],
+        ),
+        ([[-5, None, 8], None, []], -5, [0, None, -1]),
+        (
+            [[None, "x", None, "y"], ["z", "i", "j"]],
+            "y",
+            [3, -1],
+        ),
+        (
+            [["h", "a", None], ["t", "g"]],
+            ["a", "b"],
+            [1, -1],
+        ),
+        (
+            [None, ["h", "i"], ["p", "k", "z"]],
+            ["x", None, "z"],
+            [None, None, 2],
+        ),
+        (
+            [["d", None, "e"], [None, "f"], []],
+            cudf.Scalar(cudf.NA, "O"),
+            [None, None, None],
+        ),
+        (
+            [None, [10, 9, 8], [5, 8, None]],
+            cudf.Scalar(cudf.NA, "int64"),
+            [None, None, None],
+        ),
+    ],
+)
+def test_index(data, search_key, expect):
+    sr = cudf.Series(data)
+    expect = cudf.Series(expect, dtype="int32")
+    if is_scalar(search_key):
+        got = sr.list.index(cudf.Scalar(search_key, sr.dtype.element_type))
+    else:
+        got = sr.list.index(
+            cudf.Series(search_key, dtype=sr.dtype.element_type)
+        )
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data, search_key",
+    [
+        (
+            [[9, None, 8], [], [7, 6, 5]],
+            "c",
+        ),
+        (
+            [["a", "b", "c"], None, [None, "d"]],
+            2,
+        ),
+        (
+            [["e", "s"], ["t", "w"]],
+            [5, 6],
+        ),
+    ],
+)
+def test_index_invalid_type(data, search_key):
+    sr = cudf.Series(data)
+    with pytest.raises(
+        TypeError,
+        match="Type/Scale of search key does not "
+        "match list column element type.",
+    ):
+        sr.list.index(search_key)
+
+
+@pytest.mark.parametrize(
+    "data, search_key",
+    [
+        (
+            [[5, 8], [2, 6]],
+            [8, 2, 4],
+        ),
+        (
+            [["h", "j"], ["p", None], ["t", "z"]],
+            ["j", "a"],
+        ),
+    ],
+)
+def test_index_invalid_length(data, search_key):
+    sr = cudf.Series(data)
+    with pytest.raises(
+        RuntimeError,
+        match="Number of search keys must match list column size.",
+    ):
+        sr.list.index(search_key)
+
+
+@pytest.mark.parametrize(
+    "row",
+    [
+        [[]],
+        [[1]],
+        [[1, 2]],
+        [[1, 2], [3, 4, 5]],
+        [[1, 2], [], [3, 4, 5]],
+        [[1, 2, None], [3, 4, 5]],
+        [[1, 2, None], None, [3, 4, 5]],
+        [[1, 2, None], None, [], [3, 4, 5]],
+        [[[1, 2], [3, 4]], [[5, 6, 7], [8, 9]]],
+        [[["a", "c", "de", None], None, ["fg"]], [["abc", "de"], None]],
+    ],
+)
+@pytest.mark.parametrize("dropna", [True, False])
+def test_concat_elements(row, dropna):
+    if any(x is None for x in row):
+        if dropna:
+            row = [x for x in row if x is not None]
+            result = functools.reduce(operator.add, row)
+        else:
+            result = None
+    else:
+        result = functools.reduce(operator.add, row)
+
+    expect = pd.Series([result])
+    got = cudf.Series([row]).list.concat(dropna=dropna)
+    assert_eq(expect, got)
+
+
+def test_concat_elements_raise():
+    s = cudf.Series([[1, 2, 3]])  # no nesting
+    with pytest.raises(ValueError):
+        s.list.concat()
+
+
+def test_concatenate_rows_of_lists():
+    pdf = pd.DataFrame({"val": [["a", "a"], ["b"], ["c"]]})
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf["val"] + pdf["val"]
+    got = gdf["val"] + gdf["val"]
+
+    assert_eq(expect, got)
+
+
+def test_concatenate_list_with_nonlist():
+    with pytest.raises(TypeError):
+        gdf1 = cudf.DataFrame({"A": [["a", "c"], ["b", "d"], ["c", "d"]]})
+        gdf2 = cudf.DataFrame({"A": ["a", "b", "c"]})
+        gdf1["A"] + gdf2["A"]
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1],
+        [1, 2, 3],
+        [[1, 2, 3], [4, 5, 6]],
+        [NA],
+        [1, NA, 3],
+        [[1, NA, 3], [NA, 5, 6]],
+    ],
+)
+def test_list_getitem(data):
+    list_sr = cudf.Series([data])
+    assert list_sr[0] == data
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3],
+        [[1, 2, 3], [4, 5, 6]],
+        ["a", "b", "c"],
+        [["a", "b", "c"], ["d", "e", "f"]],
+        [1.1, 2.2, 3.3],
+        [[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]],
+        [1, NA, 3],
+        [[1, NA, 3], [4, 5, NA]],
+        ["a", NA, "c"],
+        [["a", NA, "c"], ["d", "e", NA]],
+        [1.1, NA, 3.3],
+        [[1.1, NA, 3.3], [4.4, 5.5, NA]],
+    ],
+)
+def test_list_scalar_host_construction(data):
+    slr = cudf.Scalar(data)
+    assert slr.value == data
+    assert slr.device_value.value == data
+
+
+@pytest.mark.parametrize(
+    "elem_type", NUMERIC_TYPES + DATETIME_TYPES + TIMEDELTA_TYPES + ["str"]
+)
+@pytest.mark.parametrize("nesting_level", [1, 2, 3])
+def test_list_scalar_host_construction_null(elem_type, nesting_level):
+    dtype = cudf.ListDtype(elem_type)
+    for level in range(nesting_level - 1):
+        dtype = cudf.ListDtype(dtype)
+
+    slr = cudf.Scalar(None, dtype=dtype)
+    assert slr.value is (
+        cudf.NaT
+        if cudf.api.types.is_datetime64_dtype(slr.dtype)
+        or cudf.api.types.is_timedelta64_dtype(slr.dtype)
+        else cudf.NA
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3],
+        [[1, 2, 3], [4, 5, 6]],
+        ["a", "b", "c"],
+        [["a", "b", "c"], ["d", "e", "f"]],
+        [1.1, 2.2, 3.3],
+        [[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]],
+        [1, NA, 3],
+        [[1, NA, 3], [4, 5, NA]],
+        ["a", NA, "c"],
+        [["a", NA, "c"], ["d", "e", NA]],
+        [1.1, NA, 3.3],
+        [[1.1, NA, 3.3], [4.4, 5.5, NA]],
+    ],
+)
+def test_list_scalar_device_construction(data):
+    col = cudf.Series([data])._column
+    slr = get_element(col, 0)
+    assert slr.value == data
+
+
+@pytest.mark.parametrize("nesting_level", [1, 2, 3])
+def test_list_scalar_device_construction_null(nesting_level):
+    data = [[]]
+    for i in range(nesting_level - 1):
+        data = [data]
+
+    arrow_type = pa.infer_type(data)
+    arrow_arr = pa.array([None], type=arrow_type)
+
+    col = cudf.Series(arrow_arr)._column
+    slr = get_element(col, 0)
+
+    assert slr.value is cudf.NA
+
+
+@pytest.mark.parametrize("input_obj", [[[1, NA, 3]], [[1, NA, 3], [4, 5, NA]]])
+def test_construction_series_with_nulls(input_obj):
+    expect = pa.array(input_obj, from_pandas=True)
+    got = cudf.Series(input_obj).to_arrow()
+
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [[]]},
+        {"a": [[1, 2, None, 4]]},
+        {"a": [["cat", None, "dog"]]},
+        {
+            "a": [[1, 2, 3, None], [4, None, 5]],
+            "b": [None, ["fish", "bird"]],
+            "c": [[], []],
+        },
+        {"a": [[1, 2, 3, None], [4, None, 5], None, [6, 7]]},
+    ],
+)
+def test_serialize_list_columns(data):
+    df = cudf.DataFrame(data)
+    recreated = df.__class__.deserialize(*df.serialize())
+    assert_eq(recreated, df)
+
+
+@pytest.mark.parametrize(
+    "data,item",
+    [
+        (
+            # basic list into a list column
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            [0, 0, 0],
+        ),
+        (
+            # nested list into nested list column
+            [
+                [[1, 2, 3], [4, 5, 6]],
+                [[1, 2, 3], [4, 5, 6]],
+                [[1, 2, 3], [4, 5, 6]],
+            ],
+            [[0, 0, 0], [0, 0, 0]],
+        ),
+        (
+            # NA into a list column
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            NA,
+        ),
+        (
+            # NA into nested list column
+            [
+                [[1, 2, 3], [4, 5, 6]],
+                [[1, 2, 3], [4, 5, 6]],
+                [[1, 2, 3], [4, 5, 6]],
+            ],
+            NA,
+        ),
+    ],
+)
+def test_listcol_setitem(data, item):
+    sr = cudf.Series(data)
+
+    sr[1] = item
+    data[1] = item
+    expect = cudf.Series(data)
+
+    assert_eq(expect, sr)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+        [
+            [[1, 2, 3], [4, 5, 6]],
+            [[1, 2, 3], [4, 5, 6]],
+            [[1, 2, 3], [4, 5, 6]],
+        ],
+        [[[1, 2, 3], [4, None, 6]], [], None, [[7, 8], [], None, [9]]],
+        [[1, 2, 3], [4, None, 6], [7, 8], [], None, [9]],
+        [[1.0, 2.0, 3.0], [4.0, None, 6.0], [7.0, 8.0], [], None, [9.0]],
+    ],
+)
+def test_listcol_as_string(data):
+    got = cudf.Series(data).astype("str")
+    expect = pd.Series(data).astype("str")
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data,item,error",
+    [
+        (
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            [[1, 2, 3], [4, 5, 6]],
+            "list nesting level mismatch",
+        ),
+        (
+            [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+            0,
+            "Can not set 0 into ListColumn",
+        ),
+    ],
+)
+def test_listcol_setitem_error_cases(data, item, error):
+    sr = cudf.Series(data)
+    with pytest.raises(BaseException, match=error):
+        sr[1] = item
+
+
+def test_listcol_setitem_retain_dtype():
+    df = cudf.DataFrame(
+        {"a": cudf.Series([["a", "b"], []]), "b": [1, 2], "c": [123, 321]}
+    )
+    df1 = df.head(0)
+    # Performing a setitem on `b` triggers a `column.column_empty_like` call
+    # which tries to create an empty ListColumn.
+    df1["b"] = df1["c"]
+    # Performing a copy to trigger a copy dtype which is obtained by accessing
+    # `ListColumn.children` that would have been corrupted in previous call
+    # prior to this fix: https://github.com/rapidsai/cudf/pull/10151/
+    df2 = df1.copy()
+    assert df2["a"].dtype == df["a"].dtype
+
+
+def test_list_astype():
+    s = cudf.Series([[1, 2], [3, 4]])
+    s2 = s.list.astype("float64")
+    assert s2.dtype == cudf.ListDtype("float64")
+    assert_eq(s.list.leaves.astype("float64"), s2.list.leaves)
+
+    s = cudf.Series([[[1, 2], [3]], [[5, 6], None]])
+    s2 = s.list.astype("string")
+    assert s2.dtype == cudf.ListDtype(cudf.ListDtype("string"))
+    assert_eq(s.list.leaves.astype("string"), s2.list.leaves)
+
+
+def test_memory_usage():
+    s1 = cudf.Series([[1, 2], [3, 4]])
+    assert s1.memory_usage() == 44
+    s2 = cudf.Series([[[[1, 2]]], [[[3, 4]]]])
+    assert s2.memory_usage() == 68
+    s3 = cudf.Series([[{"b": 1, "a": 10}, {"b": 2, "a": 100}]])
+    assert s3.memory_usage() == 40
+
+
+@pytest.mark.parametrize(
+    "data, idx",
+    [
+        (
+            [[{"f2": {"a": 100}, "f1": "a"}, {"f1": "sf12", "f2": NA}]],
+            0,
+        ),
+        (
+            [
+                [
+                    {"f2": {"a": 100, "c": 90, "f2": 10}, "f1": "a"},
+                    {"f1": "sf12", "f2": NA},
+                ]
+            ],
+            0,
+        ),
+        (
+            [[[[1, 2]], [[2], [3]]], [[[2]]], [[[3]]]],
+            0,
+        ),
+        ([[[[1, 2]], [[2], [3]]], [[[2]]], [[[3]]]], 2),
+        ([[[{"a": 1, "b": 2, "c": 10}]]], 0),
+    ],
+)
+def test_nested_list_extract_host_scalars(data, idx):
+    series = cudf.Series(data)
+
+    assert series[idx] == data[idx]
+
+
+def test_list_iterate_error():
+    s = cudf.Series([[[[1, 2]], [[2], [3]]], [[[2]]], [[[3]]]])
+    with pytest.raises(TypeError):
+        iter(s.list)
+
+
+def test_list_struct_list_memory_usage():
+    df = cudf.DataFrame({"a": [[{"b": [1]}]]})
+    assert df.memory_usage().sum() == 16
diff --git a/python/cudf/cudf/tests/test_monotonic.py b/python/cudf/cudf/tests/test_monotonic.py
new file mode 100644
index 0000000..53d972d
--- /dev/null
+++ b/python/cudf/cudf/tests/test_monotonic.py
@@ -0,0 +1,394 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+"""
+Tests related to is_unique and is_monotonic attributes
+"""
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Index, MultiIndex, Series
+from cudf.core.index import (
+    CategoricalIndex,
+    DatetimeIndex,
+    GenericIndex,
+    RangeIndex,
+)
+from cudf.testing._utils import assert_eq, expect_warning_if
+
+
+@pytest.mark.parametrize("testrange", [(10, 20, 1), (0, -10, -1), (5, 5, 1)])
+def test_range_index(testrange):
+    index = RangeIndex(
+        start=testrange[0], stop=testrange[1], step=testrange[2]
+    )
+    index_pd = pd.RangeIndex(
+        start=testrange[0], stop=testrange[1], step=testrange[2]
+    )
+
+    assert index.is_unique == index_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = index_pd.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = index.is_monotonic
+    assert got == expect
+    assert index.is_monotonic_increasing == index_pd.is_monotonic_increasing
+    assert index.is_monotonic_decreasing == index_pd.is_monotonic_decreasing
+
+
+@pytest.mark.parametrize(
+    "testlist",
+    [
+        [1, 2, 3, 4],
+        [1, 2, 3, 3, 4],
+        [10, 9, 8, 7],
+        [10, 9, 8, 8, 7],
+        ["c", "d", "e", "f"],
+        ["c", "d", "e", "e", "f"],
+        ["z", "y", "x", "r"],
+        ["z", "y", "x", "x", "r"],
+    ],
+)
+def test_generic_index(testlist):
+    index = GenericIndex(testlist)
+    index_pd = pd.Index(testlist)
+
+    assert index.is_unique == index_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = index_pd.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = index.is_monotonic
+    assert got == expect
+    assert index.is_monotonic_increasing == index_pd.is_monotonic_increasing
+    assert index.is_monotonic_decreasing == index_pd.is_monotonic_decreasing
+
+
+@pytest.mark.parametrize(
+    "testlist",
+    [
+        ["c", "d", "e", "f"],
+        ["c", "d", "e", "e", "f"],
+        ["z", "y", "x", "r"],
+        ["z", "y", "x", "x", "r"],
+    ],
+)
+def test_string_index(testlist):
+    index = cudf.Index(testlist)
+    index_pd = pd.Index(testlist)
+
+    assert index.is_unique == index_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = index_pd.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = index.is_monotonic
+    assert got == expect
+    assert index.is_monotonic_increasing == index_pd.is_monotonic_increasing
+    assert index.is_monotonic_decreasing == index_pd.is_monotonic_decreasing
+
+
+@pytest.mark.parametrize(
+    "testlist", [["c", "d", "e", "f"], ["z", "y", "x", "r"]]
+)
+def test_categorical_index(testlist):
+    # Assuming unordered categorical data cannot be "monotonic"
+    raw_cat = pd.Categorical(testlist, ordered=True)
+    index = CategoricalIndex(raw_cat)
+    index_pd = pd.CategoricalIndex(raw_cat)
+
+    assert index.is_unique == index_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = index_pd.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = index.is_monotonic
+    assert got == expect
+    assert index.is_monotonic_increasing == index_pd.is_monotonic_increasing
+    assert index.is_monotonic_decreasing == index_pd.is_monotonic_decreasing
+
+
+@pytest.mark.parametrize(
+    "testlist",
+    [
+        [
+            "2001-01-01 00:00:00",
+            "2001-02-03 08:00:00",
+            "2001-03-08 16:00:00",
+            "2001-04-11 00:00:00",
+        ],
+        [
+            "2001-04-11 00:00:00",
+            "2001-03-08 16:00:00",
+            "2001-02-03 08:00:00",
+            "2001-01-01 00:00:00",
+        ],
+        [
+            "2001-04-11 00:00:00",
+            "2001-02-03 08:00:00",
+            "2001-03-08 16:00:00",
+            "2001-01-01 00:00:00",
+        ],
+        [
+            "2001-04-11 00:00:00",
+            "2001-01-01 00:00:00",
+            "2001-02-03 08:00:00",
+            "2001-03-08 16:00:00",
+            "2001-01-01 00:00:00",
+        ],
+    ],
+)
+def test_datetime_index(testlist):
+    index = DatetimeIndex(testlist)
+    index_pd = pd.DatetimeIndex(testlist)
+
+    assert index.is_unique == index_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = index_pd.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = index.is_monotonic
+    assert got == expect
+    assert index.is_monotonic_increasing == index_pd.is_monotonic_increasing
+    assert index.is_monotonic_decreasing == index_pd.is_monotonic_decreasing
+
+
+@pytest.mark.parametrize(
+    "testlist",
+    [
+        [1, 2, 3, 4],
+        [1, 2, 3, 3, 4],
+        [10, 9, 8, 7],
+        [10, 9, 8, 8, 7],
+        ["c", "d", "e", "f"],
+        ["c", "d", "e", "e", "f"],
+        ["z", "y", "x", "r"],
+        ["z", "y", "x", "x", "r"],
+    ],
+)
+def test_series(testlist):
+    series = Series(testlist)
+    series_pd = pd.Series(testlist)
+
+    assert series.is_unique == series_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = series_pd.index.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = series.index.is_monotonic
+    assert got == expect
+    assert series.is_monotonic_increasing == series_pd.is_monotonic_increasing
+    assert series.is_monotonic_decreasing == series_pd.is_monotonic_decreasing
+
+
+def test_multiindex():
+    pdf = pd.DataFrame(np.random.rand(7, 5))
+    pdf.index = pd.MultiIndex(
+        [
+            ["a", "b", "c"],
+            ["house", "store", "forest"],
+            ["clouds", "clear", "storm"],
+            ["fire", "smoke", "clear"],
+        ],
+        [
+            [0, 0, 0, 0, 1, 1, 2],
+            [1, 1, 1, 1, 0, 0, 2],
+            [0, 0, 2, 2, 2, 0, 1],
+            [0, 0, 0, 1, 2, 0, 1],
+        ],
+    )
+    pdf.index.names = ["alpha", "location", "weather", "sign"]
+    gdf = cudf.from_pandas(pdf)
+
+    assert pdf.index.is_unique == gdf.index.is_unique
+    with pytest.warns(FutureWarning):
+        expect = pdf.index.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = gdf.index.is_monotonic
+    assert got == expect
+    assert (
+        pdf.index.is_monotonic_increasing == gdf.index.is_monotonic_increasing
+    )
+    assert (
+        pdf.index.is_monotonic_decreasing == gdf.index.is_monotonic_decreasing
+    )
+
+
+@pytest.mark.parametrize(
+    "testarr",
+    [
+        (
+            [
+                ["bar", "bar", "foo", "foo", "qux", "qux", "qux"],
+                ["one", "two", "one", "two", "one", "two", "two"],
+            ],
+            ["first", "second"],
+        ),
+        (
+            [
+                ["bar", "bar", "foo", "foo", "qux", "qux"],
+                ["one", "two", "one", "two", "one", "two"],
+            ],
+            ["first", "second"],
+        ),
+    ],
+)
+def test_multiindex_tuples(testarr):
+    tuples = list(zip(*testarr[0]))
+
+    index = MultiIndex.from_tuples(tuples, names=testarr[1])
+    index_pd = pd.MultiIndex.from_tuples(tuples, names=testarr[1])
+
+    assert index.is_unique == index_pd.is_unique
+    with pytest.warns(FutureWarning):
+        expect = index_pd.is_monotonic
+    with pytest.warns(FutureWarning):
+        got = index.is_monotonic
+    assert got == expect
+    assert index.is_monotonic_increasing == index_pd.is_monotonic_increasing
+    assert index.is_monotonic_decreasing == index_pd.is_monotonic_decreasing
+
+
+@pytest.mark.parametrize(
+    "testlist",
+    [
+        [10, 9, 8, 8, 7],
+        [2.0, 5.0, 4.0, 3.0, 7.0],
+        ["b", "d", "e", "a", "c"],
+        ["frog", "cat", "bat", "dog"],
+    ],
+)
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("kind", ["loc", "getitem", None])
+def test_get_slice_bound(testlist, side, kind):
+    index = GenericIndex(testlist)
+    index_pd = pd.Index(testlist)
+    for label in testlist:
+        with pytest.warns(FutureWarning):
+            expect = index_pd.get_slice_bound(label, side, kind)
+        with expect_warning_if(kind is not None, FutureWarning):
+            got = index.get_slice_bound(label, side, kind)
+        assert got == expect
+
+
+@pytest.mark.parametrize("bounds", [(0, 10), (0, 1), (3, 4), (0, 0), (3, 3)])
+@pytest.mark.parametrize(
+    "indices",
+    [[-1, 0, 5, 10, 11], [-1, 0, 1, 2], [2, 3, 4, 5], [-1, 0, 1], [2, 3, 4]],
+)
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("kind", ["getitem", "loc"])
+def test_rangeindex_get_slice_bound_basic(bounds, indices, side, kind):
+    start, stop = bounds
+    pd_index = pd.RangeIndex(start, stop)
+    cudf_index = RangeIndex(start, stop)
+    for idx in indices:
+        with pytest.warns(FutureWarning):
+            expect = pd_index.get_slice_bound(idx, side, kind)
+        with expect_warning_if(kind is not None, FutureWarning):
+            got = cudf_index.get_slice_bound(idx, side, kind)
+        assert expect == got
+
+
+@pytest.mark.parametrize(
+    "bounds",
+    [(3, 20, 5), (20, 3, -5), (20, 3, 5), (3, 20, -5), (0, 0, 2), (3, 3, 2)],
+)
+@pytest.mark.parametrize(
+    "label",
+    [3, 8, 13, 18, 20, 15, 10, 5, -1, 0, 19, 21, 6, 11, 17],
+)
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("kind", ["getitem", "loc"])
+def test_rangeindex_get_slice_bound_step(bounds, label, side, kind):
+    start, stop, step = bounds
+    pd_index = pd.RangeIndex(start, stop, step)
+    cudf_index = RangeIndex(start, stop, step)
+
+    with pytest.warns(FutureWarning):
+        expect = pd_index.get_slice_bound(label, side, kind)
+    with expect_warning_if(kind is not None, FutureWarning):
+        got = cudf_index.get_slice_bound(label, side, kind)
+    assert expect == got
+
+
+@pytest.mark.parametrize("label", [1, 3, 5, 7, 9, 11])
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("kind", ["loc", "getitem", None])
+def test_get_slice_bound_missing(label, side, kind):
+    mylist = [2, 4, 6, 8, 10]
+    index = GenericIndex(mylist)
+    index_pd = pd.Index(mylist)
+
+    with pytest.warns(FutureWarning):
+        expect = index_pd.get_slice_bound(label, side, kind)
+    with expect_warning_if(kind is not None, FutureWarning):
+        got = index.get_slice_bound(label, side, kind)
+    assert got == expect
+
+
+@pytest.mark.parametrize("label", ["a", "c", "e", "g"])
+@pytest.mark.parametrize("side", ["left", "right"])
+def test_get_slice_bound_missing_str(label, side):
+    mylist = ["b", "d", "f"]
+    index = GenericIndex(mylist)
+    index_pd = pd.Index(mylist)
+    with pytest.warns(FutureWarning):
+        got = index.get_slice_bound(label, side, "getitem")
+    with pytest.warns(FutureWarning):
+        expect = index_pd.get_slice_bound(label, side, "getitem")
+    assert got == expect
+
+
+testdata = [
+    (
+        Series(["2018-01-01", "2019-01-31", None], dtype="datetime64[ms]"),
+        False,
+    ),
+    (Series([1, 2, 3, None]), False),
+    (Series([None, 1, 2, 3]), False),
+    (Series(["a", "b", "c", None]), False),
+    (Series([None, "a", "b", "c"]), False),
+]
+
+
+@pytest.mark.parametrize("data, expected", testdata)
+def test_is_monotonic_always_falls_for_null(data, expected):
+    assert_eq(expected, data.is_monotonic_increasing)
+    assert_eq(expected, data.is_monotonic_decreasing)
+
+
+@pytest.mark.parametrize("box", [Series, Index])
+@pytest.mark.parametrize(
+    "value,na_like",
+    [
+        [1, None],
+        [np.datetime64("2020-01-01", "ns"), np.datetime64("nat", "ns")],
+        ["s", None],
+        [1.0, np.nan],
+    ],
+    ids=repr,
+)
+def test_is_unique(box, value, na_like):
+    obj = box([value], nan_as_null=False)
+    assert obj.is_unique
+
+    obj = box([value, value], nan_as_null=False)
+    assert not obj.is_unique
+
+    obj = box([None, value], nan_as_null=False)
+    assert obj.is_unique
+
+    obj = box([None, None, value], nan_as_null=False)
+    assert not obj.is_unique
+
+    if na_like is not None:
+        obj = box([na_like, value], nan_as_null=False)
+        assert obj.is_unique
+
+        obj = box([na_like, na_like], nan_as_null=False)
+        assert not obj.is_unique
+
+        try:
+            if not np.isnat(na_like):
+                # pyarrow coerces nat to null
+                obj = box([None, na_like, value], nan_as_null=False)
+                assert obj.is_unique
+        except TypeError:
+            pass
diff --git a/python/cudf/cudf/tests/test_multiindex.py b/python/cudf/cudf/tests/test_multiindex.py
new file mode 100644
index 0000000..c07f4e7
--- /dev/null
+++ b/python/cudf/cudf/tests/test_multiindex.py
@@ -0,0 +1,2081 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+"""
+Test related to MultiIndex
+"""
+import itertools
+import operator
+import pickle
+import re
+from contextlib import contextmanager
+from io import BytesIO
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.api.extensions import no_default
+from cudf.core._compat import PANDAS_GE_200
+from cudf.core.column import as_column
+from cudf.core.index import as_index
+from cudf.testing._utils import (
+    assert_eq,
+    assert_exceptions_equal,
+    assert_neq,
+    expect_warning_if,
+)
+
+
+@contextmanager
+def expect_pandas_performance_warning(idx):
+    with expect_warning_if(
+        (not isinstance(idx[0], tuple) and len(idx) > 2)
+        or (isinstance(idx[0], tuple) and len(idx[0]) > 2),
+        pd.errors.PerformanceWarning,
+    ):
+        yield
+
+
+def test_multiindex_levels_codes_validation():
+    levels = [["a", "b"], ["c", "d"]]
+
+    # Codes not a sequence of sequences
+    assert_exceptions_equal(
+        lfunc=pd.MultiIndex,
+        rfunc=cudf.MultiIndex,
+        lfunc_args_and_kwargs=([levels, [0, 1]],),
+        rfunc_args_and_kwargs=([levels, [0, 1]],),
+    )
+
+    # Codes don't match levels
+    assert_exceptions_equal(
+        lfunc=pd.MultiIndex,
+        rfunc=cudf.MultiIndex,
+        lfunc_args_and_kwargs=([levels, [[0], [1], [1]]],),
+        rfunc_args_and_kwargs=([levels, [[0], [1], [1]]],),
+    )
+
+    # Largest code greater than number of levels
+    assert_exceptions_equal(
+        lfunc=pd.MultiIndex,
+        rfunc=cudf.MultiIndex,
+        lfunc_args_and_kwargs=([levels, [[0, 1], [0, 2]]],),
+        rfunc_args_and_kwargs=([levels, [[0, 1], [0, 2]]],),
+    )
+
+    # Unequal code lengths
+    assert_exceptions_equal(
+        lfunc=pd.MultiIndex,
+        rfunc=cudf.MultiIndex,
+        lfunc_args_and_kwargs=([levels, [[0, 1], [0]]],),
+        rfunc_args_and_kwargs=([levels, [[0, 1], [0]]],),
+    )
+    # Didn't pass levels and codes
+    assert_exceptions_equal(lfunc=pd.MultiIndex, rfunc=cudf.MultiIndex)
+
+    # Didn't pass non zero levels and codes
+    assert_exceptions_equal(
+        lfunc=pd.MultiIndex,
+        rfunc=cudf.MultiIndex,
+        lfunc_args_and_kwargs=([[], []],),
+        rfunc_args_and_kwargs=([[], []],),
+    )
+
+
+def test_multiindex_construction():
+    levels = [["a", "b"], ["c", "d"]]
+    codes = [[0, 1], [1, 0]]
+    pmi = pd.MultiIndex(levels, codes)
+    mi = cudf.MultiIndex(levels, codes)
+    assert_eq(pmi, mi)
+    pmi = pd.MultiIndex(levels, codes)
+    mi = cudf.MultiIndex(levels=levels, codes=codes)
+    assert_eq(pmi, mi)
+
+
+def test_multiindex_types():
+    codes = [[0, 1], [1, 0]]
+    levels = [[0, 1], [2, 3]]
+    pmi = pd.MultiIndex(levels, codes)
+    mi = cudf.MultiIndex(levels, codes)
+    assert_eq(pmi, mi)
+    levels = [[1.2, 2.1], [1.3, 3.1]]
+    pmi = pd.MultiIndex(levels, codes)
+    mi = cudf.MultiIndex(levels, codes)
+    assert_eq(pmi, mi)
+    levels = [["a", "b"], ["c", "d"]]
+    pmi = pd.MultiIndex(levels, codes)
+    mi = cudf.MultiIndex(levels, codes)
+    assert_eq(pmi, mi)
+
+
+def test_multiindex_df_assignment():
+    pdf = pd.DataFrame({"x": [1, 2, 3]})
+    gdf = cudf.from_pandas(pdf)
+    pdf.index = pd.MultiIndex([["a", "b"], ["c", "d"]], [[0, 1, 0], [1, 0, 1]])
+    gdf.index = cudf.MultiIndex(
+        levels=[["a", "b"], ["c", "d"]], codes=[[0, 1, 0], [1, 0, 1]]
+    )
+    assert_eq(pdf, gdf)
+
+
+def test_multiindex_series_assignment():
+    ps = pd.Series([1, 2, 3])
+    gs = cudf.from_pandas(ps)
+    ps.index = pd.MultiIndex([["a", "b"], ["c", "d"]], [[0, 1, 0], [1, 0, 1]])
+    gs.index = cudf.MultiIndex(
+        levels=[["a", "b"], ["c", "d"]], codes=[[0, 1, 0], [1, 0, 1]]
+    )
+    assert_eq(ps, gs)
+
+
+def test_multiindex_swaplevel():
+    midx = cudf.MultiIndex(
+        levels=[
+            ["lama", "cow", "falcon"],
+            ["speed", "weight", "length"],
+            ["first", "second"],
+        ],
+        codes=[
+            [0, 0, 0, 1, 1, 1, 2, 2, 2],
+            [0, 1, 2, 0, 1, 2, 0, 1, 2],
+            [0, 0, 0, 0, 0, 0, 1, 1, 1],
+        ],
+        names=["Col1", "Col2", "Col3"],
+    )
+    pd_midx = midx.to_pandas()
+
+    assert_eq(pd_midx.swaplevel(-1, -2), midx.swaplevel(-1, -2))
+    assert_eq(pd_midx.swaplevel(2, 1), midx.swaplevel(2, 1))
+    assert_eq(midx.swaplevel(2, 1), midx.swaplevel(1, 2))
+    assert_eq(pd_midx.swaplevel(0, 2), midx.swaplevel(0, 2))
+    assert_eq(pd_midx.swaplevel(2, 0), midx.swaplevel(2, 0))
+    assert_eq(midx.swaplevel(1, 1), midx.swaplevel(1, 1))
+
+
+def test_string_index():
+    from cudf.core.index import Index
+
+    pdf = pd.DataFrame(np.random.rand(5, 5))
+    gdf = cudf.from_pandas(pdf)
+    stringIndex = ["a", "b", "c", "d", "e"]
+    pdf.index = stringIndex
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+    stringIndex = np.array(["a", "b", "c", "d", "e"])
+    pdf.index = stringIndex
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+    stringIndex = Index(["a", "b", "c", "d", "e"], name="name")
+    pdf.index = stringIndex.to_pandas()
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+    stringIndex = as_index(as_column(["a", "b", "c", "d", "e"]), name="name")
+    pdf.index = stringIndex.to_pandas()
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+
+
+def test_multiindex_row_shape():
+    pdf = pd.DataFrame(np.random.rand(0, 5))
+    gdf = cudf.from_pandas(pdf)
+    pdfIndex = pd.MultiIndex([["a", "b", "c"]], [[0]])
+    pdfIndex.names = ["alpha"]
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+
+    assert_exceptions_equal(
+        lfunc=operator.setitem,
+        rfunc=operator.setitem,
+        lfunc_args_and_kwargs=([], {"a": pdf, "b": "index", "c": pdfIndex}),
+        rfunc_args_and_kwargs=([], {"a": gdf, "b": "index", "c": gdfIndex}),
+    )
+
+
+@pytest.fixture
+def pdf():
+    return pd.DataFrame(np.random.rand(7, 5))
+
+
+@pytest.fixture
+def gdf(pdf):
+    return cudf.from_pandas(pdf)
+
+
+@pytest.fixture
+def pdfIndex():
+    pdfIndex = pd.MultiIndex(
+        [
+            ["a", "b", "c"],
+            ["house", "store", "forest"],
+            ["clouds", "clear", "storm"],
+            ["fire", "smoke", "clear"],
+            [
+                np.datetime64("2001-01-01", "ns"),
+                np.datetime64("2002-01-01", "ns"),
+                np.datetime64("2003-01-01", "ns"),
+            ],
+        ],
+        [
+            [0, 0, 0, 0, 1, 1, 2],
+            [1, 1, 1, 1, 0, 0, 2],
+            [0, 0, 2, 2, 2, 0, 1],
+            [0, 0, 0, 1, 2, 0, 1],
+            [1, 0, 1, 2, 0, 0, 1],
+        ],
+    )
+    pdfIndex.names = ["alpha", "location", "weather", "sign", "timestamp"]
+    return pdfIndex
+
+
+@pytest.fixture
+def pdfIndexNulls():
+    pdfIndex = pd.MultiIndex(
+        [
+            ["a", "b", "c"],
+            ["house", "store", "forest"],
+            ["clouds", "clear", "storm"],
+        ],
+        [
+            [0, 0, 0, -1, 1, 1, 2],
+            [1, -1, 1, 1, 0, 0, -1],
+            [-1, 0, 2, 2, 2, 0, 1],
+        ],
+    )
+    pdfIndex.names = ["alpha", "location", "weather"]
+    return pdfIndex
+
+
+def test_from_pandas(pdf, pdfIndex):
+    pdf.index = pdfIndex
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(pdf, gdf)
+
+
+def test_multiindex_transpose(pdf, pdfIndex):
+    pdf.index = pdfIndex
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(pdf.transpose(), gdf.transpose())
+
+
+def test_from_pandas_series():
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
+    ).set_index(["a", "b"])
+
+    result = cudf.from_pandas(pdf)
+    assert_eq(pdf, result)
+
+    test_pdf = pdf["c"]
+    result = cudf.from_pandas(test_pdf)
+    assert_eq(test_pdf, result)
+
+
+def test_series_multiindex(pdfIndex):
+    ps = pd.Series(np.random.rand(7))
+    gs = cudf.from_pandas(ps)
+    ps.index = pdfIndex
+    gs.index = cudf.from_pandas(pdfIndex)
+    assert_eq(ps, gs)
+
+
+def test_multiindex_take(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    assert_eq(pdf.index.take([0]), gdf.index.take([0]))
+    assert_eq(pdf.index.take(np.array([0])), gdf.index.take(np.array([0])))
+    from cudf import Series
+
+    assert_eq(pdf.index.take(pd.Series([0])), gdf.index.take(Series([0])))
+    assert_eq(pdf.index.take([0, 1]), gdf.index.take([0, 1]))
+    assert_eq(
+        pdf.index.take(np.array([0, 1])), gdf.index.take(np.array([0, 1]))
+    )
+    assert_eq(
+        pdf.index.take(pd.Series([0, 1])), gdf.index.take(Series([0, 1]))
+    )
+
+
+def test_multiindex_getitem(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    assert_eq(pdf.index[0], gdf.index[0])
+
+
+@pytest.mark.parametrize(
+    "key_tuple",
+    [
+        # return 2 rows, 0 remaining keys = dataframe with entire index
+        ("a", "store", "clouds", "fire"),
+        (("a", "store", "clouds", "fire"), slice(None)),
+        # return 2 rows, 1 remaining key = dataframe with n-k index columns
+        ("a", "store", "storm"),
+        (("a", "store", "storm"), slice(None)),
+        # return 2 rows, 2 remaining keys = dataframe with n-k index columns
+        ("a", "store"),
+        (("a", "store"), slice(None)),
+        # return 2 rows, n-1 remaining keys = dataframe with n-k index columns
+        ("a",),
+        "a",
+        "b",
+        "c",
+        (("a",), slice(None)),
+        # return 1 row, 0 remaining keys = dataframe with entire index
+        ("a", "store", "storm", "smoke"),
+        (("a", "store", "storm", "smoke"), slice(None)),
+        # return 1 row and 1 remaining key = series
+        ("c", "forest", "clear"),
+        (("c", "forest", "clear"), slice(None)),
+    ],
+)
+def test_multiindex_loc(pdf, gdf, pdfIndex, key_tuple):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    # The index is unsorted, which makes things slow but is fine for testing.
+    with expect_pandas_performance_warning(key_tuple):
+        expected = pdf.loc[key_tuple]
+    got = gdf.loc[key_tuple].sort_index()
+    assert_eq(expected.sort_index(), got)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        got = gdf.loc[key_tuple]
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [
+        (([1, 1], [0, 1]), slice(None)),
+        (([1, 1], [1, 0]), slice(None)),
+    ],
+)
+def test_multiindex_compatible_ordering(indexer):
+    df = pd.DataFrame(
+        {"a": [1, 1, 2, 3], "b": [1, 0, 1, 1], "c": [1, 2, 3, 4]}
+    ).set_index(["a", "b"])
+    cdf = cudf.from_pandas(df)
+    expect = df.loc[indexer]
+    with cudf.option_context("mode.pandas_compatible", True):
+        actual = cdf.loc[indexer]
+    assert_eq(actual, expect)
+
+
+@pytest.mark.parametrize(
+    "arg",
+    [
+        slice(("a", "store"), ("b", "house")),
+        slice(None, ("b", "house")),
+        slice(("a", "store"), None),
+        slice(None),
+    ],
+)
+def test_multiindex_loc_slice(pdf, gdf, pdfIndex, arg):
+    gdf = cudf.from_pandas(pdf)
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    assert_eq(pdf.loc[arg], gdf.loc[arg])
+
+
+def test_multiindex_loc_errors(pdf, gdf, pdfIndex):
+    gdf = cudf.from_pandas(pdf)
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    gdf.index = gdfIndex
+
+    with pytest.raises(KeyError):
+        gdf.loc[("a", "store", "clouds", "foo")]
+    with pytest.raises(IndexError):
+        gdf.loc[
+            ("a", "store", "clouds", "fire", "x", "y")
+        ]  # too many indexers
+    with pytest.raises(IndexError):
+        gdf.loc[slice(None, ("a", "store", "clouds", "fire", "x", "y"))]
+
+
+def test_multiindex_loc_then_column(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    # The index is unsorted, which makes things slow but is fine for testing.
+    with pytest.warns(pd.errors.PerformanceWarning):
+        expected = pdf.loc[("a", "store", "clouds", "fire"), :][0]
+    got = gdf.loc[("a", "store", "clouds", "fire"), :][0]
+    assert_eq(expected, got)
+
+
+def test_multiindex_loc_rows_0(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+
+    assert_exceptions_equal(
+        lfunc=pdf.loc.__getitem__,
+        rfunc=gdf.loc.__getitem__,
+        lfunc_args_and_kwargs=([(("d",), slice(None, None, None))],),
+        rfunc_args_and_kwargs=([(("d",), slice(None, None, None))],),
+    )
+
+
+def test_multiindex_loc_rows_1_2_key(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    print(pdf.loc[("c", "forest"), :])
+    print(gdf.loc[("c", "forest"), :].to_pandas())
+    assert_eq(pdf.loc[("c", "forest"), :], gdf.loc[("c", "forest"), :])
+
+
+def test_multiindex_loc_rows_1_1_key(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    print(pdf.loc[("c",), :])
+    print(gdf.loc[("c",), :].to_pandas())
+    assert_eq(pdf.loc[("c",), :], gdf.loc[("c",), :])
+
+
+def test_multiindex_column_shape():
+    pdf = pd.DataFrame(np.random.rand(5, 0))
+    gdf = cudf.from_pandas(pdf)
+    pdfIndex = pd.MultiIndex([["a", "b", "c"]], [[0]])
+    pdfIndex.names = ["alpha"]
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+
+    assert_exceptions_equal(
+        lfunc=operator.setitem,
+        rfunc=operator.setitem,
+        lfunc_args_and_kwargs=([], {"a": pdf, "b": "columns", "c": pdfIndex}),
+        rfunc_args_and_kwargs=([], {"a": gdf, "b": "columns", "c": gdfIndex}),
+    )
+
+
+@pytest.mark.parametrize(
+    "query",
+    [
+        ("a", "store", "clouds", "fire"),
+        ("a", "store", "storm", "smoke"),
+        ("a", "store"),
+        ("b", "house"),
+        ("a", "store", "storm"),
+        ("a",),
+        ("c", "forest", "clear"),
+    ],
+)
+def test_multiindex_columns(pdf, gdf, pdfIndex, query):
+    pdf = pdf.T
+    gdf = cudf.from_pandas(pdf)
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+    pdf.columns = pdfIndex
+    gdf.columns = gdfIndex
+    # The index is unsorted, which makes things slow but is fine for testing.
+    with expect_pandas_performance_warning(query):
+        expected = pdf[query]
+    got = gdf[query]
+    assert_eq(expected, got)
+
+
+def test_multiindex_from_tuples():
+    arrays = [["a", "a", "b", "b"], ["house", "store", "house", "store"]]
+    tuples = list(zip(*arrays))
+    pmi = pd.MultiIndex.from_tuples(tuples)
+    gmi = cudf.MultiIndex.from_tuples(tuples)
+    assert_eq(pmi, gmi)
+
+
+def test_multiindex_from_dataframe():
+    if not hasattr(pd.MultiIndex([[]], [[]]), "codes"):
+        pytest.skip()
+    pdf = pd.DataFrame(
+        [["a", "house"], ["a", "store"], ["b", "house"], ["b", "store"]]
+    )
+    gdf = cudf.from_pandas(pdf)
+    pmi = pd.MultiIndex.from_frame(pdf, names=["alpha", "location"])
+    gmi = cudf.MultiIndex.from_frame(gdf, names=["alpha", "location"])
+    assert_eq(pmi, gmi)
+
+
+@pytest.mark.parametrize(
+    "arrays",
+    [
+        [["a", "a", "b", "b"], ["house", "store", "house", "store"]],
+        [["a", "n", "n"] * 1000, ["house", "store", "house", "store"]],
+        [
+            ["a", "n", "n"],
+            ["house", "store", "house", "store", "store"] * 1000,
+        ],
+        [
+            ["a", "a", "n"] * 50,
+            ["house", "store", "house", "store", "store"] * 100,
+        ],
+    ],
+)
+def test_multiindex_from_product(arrays):
+    pmi = pd.MultiIndex.from_product(arrays, names=["alpha", "location"])
+    gmi = cudf.MultiIndex.from_product(arrays, names=["alpha", "location"])
+    assert_eq(pmi, gmi)
+
+
+def test_multiindex_index_and_columns():
+    gdf = cudf.DataFrame()
+    gdf["x"] = np.random.randint(0, 5, 5)
+    gdf["y"] = np.random.randint(0, 5, 5)
+    pdf = gdf.to_pandas()
+    mi = cudf.MultiIndex(
+        levels=[[0, 1, 2], [3, 4]],
+        codes=[[0, 0, 1, 1, 2], [0, 1, 0, 1, 1]],
+        names=["x", "y"],
+    )
+    gdf.index = mi
+    mc = cudf.MultiIndex(
+        levels=[["val"], ["mean", "min"]], codes=[[0, 0], [0, 1]]
+    )
+    gdf.columns = mc
+    pdf.index = mi.to_pandas()
+    pdf.columns = mc.to_pandas()
+    assert_eq(pdf, gdf)
+
+
+def test_multiindex_multiple_groupby():
+    pdf = pd.DataFrame(
+        {
+            "a": [4, 17, 4, 9, 5],
+            "b": [1, 4, 4, 3, 2],
+            "x": np.random.normal(size=5),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    pdg = pdf.groupby(["a", "b"], sort=True).sum()
+    gdg = gdf.groupby(["a", "b"], sort=True).sum()
+    assert_eq(pdg, gdg)
+    pdg = pdf.groupby(["a", "b"], sort=True).x.sum()
+    gdg = gdf.groupby(["a", "b"], sort=True).x.sum()
+    assert_eq(pdg, gdg)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df.groupby(["x", "y"], sort=True).z.sum(),
+        lambda df: df.groupby(["x", "y"], sort=True).sum(),
+    ],
+)
+def test_multi_column(func):
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=1000),
+            "y": np.random.randint(0, 10, size=1000),
+            "z": np.random.normal(size=1000),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    a = func(pdf)
+    b = func(gdf)
+
+    assert_eq(a, b)
+
+
+def test_multiindex_equality():
+    # mi made from groupby
+    # mi made manually to be identical
+    # are they equal?
+    gdf = cudf.DataFrame(
+        {"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5], "z": [0, 1, 0, 1, 0]}
+    )
+    mi1 = gdf.groupby(["x", "y"], sort=True).mean().index
+    mi2 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    assert_eq(mi1, mi2)
+
+    # mi made from two groupbys, are they equal?
+    mi2 = gdf.groupby(["x", "y"], sort=True).max().index
+    assert_eq(mi1, mi2)
+
+    # mi made manually twice are they equal?
+    mi1 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    mi2 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    assert_eq(mi1, mi2)
+
+    # mi made from different groupbys are they not equal?
+    mi1 = gdf.groupby(["x", "y"]).mean().index
+    mi2 = gdf.groupby(["x", "z"]).mean().index
+    assert_neq(mi1, mi2)
+
+    # mi made from different manuals are they not equal?
+    mi1 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    mi2 = cudf.MultiIndex(
+        levels=[[0, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    assert_neq(mi1, mi2)
+
+
+def test_multiindex_equals():
+    # mi made from groupby
+    # mi made manually to be identical
+    # are they equal?
+    gdf = cudf.DataFrame(
+        {"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5], "z": [0, 1, 0, 1, 0]}
+    )
+    mi1 = gdf.groupby(["x", "y"], sort=True).mean().index
+    mi2 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    assert_eq(mi1.equals(mi2), True)
+
+    # mi made from two groupbys, are they equal?
+    mi2 = gdf.groupby(["x", "y"], sort=True).max().index
+    assert_eq(mi1.equals(mi2), True)
+
+    # mi made manually twice are they equal?
+    mi1 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    mi2 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    assert_eq(mi1.equals(mi2), True)
+
+    # mi made from different groupbys are they not equal?
+    mi1 = gdf.groupby(["x", "y"], sort=True).mean().index
+    mi2 = gdf.groupby(["x", "z"], sort=True).mean().index
+    assert_eq(mi1.equals(mi2), False)
+
+    # mi made from different manuals are they not equal?
+    mi1 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    mi2 = cudf.MultiIndex(
+        levels=[[0, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    assert_eq(mi1.equals(mi2), False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "Date": [
+                "2020-08-27",
+                "2020-08-28",
+                "2020-08-31",
+                "2020-08-27",
+                "2020-08-28",
+                "2020-08-31",
+                "2020-08-27",
+                "2020-08-28",
+                "2020-08-31",
+            ],
+            "Close": [
+                3400.00,
+                3401.80,
+                3450.96,
+                226.58,
+                228.91,
+                225.53,
+                505.13,
+                525.91,
+                534.98,
+            ],
+            "Symbol": [
+                "AMZN",
+                "AMZN",
+                "AMZN",
+                "MSFT",
+                "MSFT",
+                "MSFT",
+                "NVDA",
+                "NVDA",
+                "NVDA",
+            ],
+        }
+    ],
+)
+@pytest.mark.parametrize(
+    "levels",
+    [[["2000-01-01", "2000-01-02", "2000-01-03"], ["A", "B", "C"]], None],
+)
+@pytest.mark.parametrize(
+    "codes", [[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], None]
+)
+@pytest.mark.parametrize("names", [["X", "Y"]])
+def test_multiindex_copy_sem(data, levels, codes, names):
+    """Test semantic equality for MultiIndex.copy"""
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    gdf = gdf.groupby(["Date", "Symbol"], sort=True).mean()
+    pdf = pdf.groupby(["Date", "Symbol"], sort=True).mean()
+
+    gmi = gdf.index
+    with expect_warning_if(levels is not None or codes is not None):
+        gmi_copy = gmi.copy(levels=levels, codes=codes, names=names)
+
+    pmi = pdf.index
+    with expect_warning_if(levels is not None or codes is not None):
+        pmi_copy = pmi.copy(levels=levels, codes=codes, names=names)
+
+    for glv, plv in zip(gmi_copy.levels, pmi_copy.levels):
+        assert all(glv.values_host == plv.values)
+    for gval, pval in zip(gmi.codes, pmi.codes):
+        assert_eq(gval, pval)
+    assert_eq(gmi_copy.names, pmi_copy.names)
+
+    # Test same behavior when used on DataFrame
+    gdf.index = gmi_copy
+    pdf.index = pmi_copy
+    assert repr(gdf) == repr(pdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "Date": [
+                "2020-08-27",
+                "2020-08-28",
+                "2020-08-31",
+                "2020-08-27",
+                "2020-08-28",
+                "2020-08-31",
+                "2020-08-27",
+                "2020-08-28",
+                "2020-08-31",
+            ],
+            "Close": [
+                3400.00,
+                3401.80,
+                3450.96,
+                226.58,
+                228.91,
+                225.53,
+                505.13,
+                525.91,
+                534.98,
+            ],
+            "Symbol": [
+                "AMZN",
+                "AMZN",
+                "AMZN",
+                "MSFT",
+                "MSFT",
+                "MSFT",
+                "NVDA",
+                "NVDA",
+                "NVDA",
+            ],
+        },
+        cudf.MultiIndex(
+            levels=[[1001, 1002], [2001, 2002]],
+            codes=[[1, 1, 0, 0], [0, 1, 0, 1]],
+            names=["col1", "col2"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("copy_on_write", [True, False])
+@pytest.mark.parametrize("deep", [True, False])
+def test_multiindex_copy_deep(data, copy_on_write, deep):
+    """Test memory identity for deep copy
+    Case1: Constructed from GroupBy, StringColumns
+    Case2: Constructed from MultiIndex, NumericColumns
+    """
+    original_cow_setting = cudf.get_option("copy_on_write")
+    cudf.set_option("copy_on_write", copy_on_write)
+
+    if isinstance(data, dict):
+        import operator
+        from functools import reduce
+
+        gdf = cudf.DataFrame(data)
+        mi1 = gdf.groupby(["Date", "Symbol"]).mean().index
+        mi2 = mi1.copy(deep=deep)
+
+        lchildren = [col.children for _, col in mi1._data.items()]
+        rchildren = [col.children for _, col in mi2._data.items()]
+
+        # Flatten
+        lchildren = reduce(operator.add, lchildren)
+        rchildren = reduce(operator.add, rchildren)
+
+        lptrs = [child.base_data.get_ptr(mode="read") for child in lchildren]
+        rptrs = [child.base_data.get_ptr(mode="read") for child in rchildren]
+
+        assert all((x == y) for x, y in zip(lptrs, rptrs))
+
+    elif isinstance(data, cudf.MultiIndex):
+        same_ref = (not deep) or (
+            cudf.get_option("copy_on_write") and not deep
+        )
+        mi1 = data
+        mi2 = mi1.copy(deep=deep)
+
+        # Assert ._levels identity
+        lptrs = [
+            lv._data._data[None].base_data.get_ptr(mode="read")
+            for lv in mi1._levels
+        ]
+        rptrs = [
+            lv._data._data[None].base_data.get_ptr(mode="read")
+            for lv in mi2._levels
+        ]
+
+        assert all((x == y) == same_ref for x, y in zip(lptrs, rptrs))
+
+        # Assert ._codes identity
+        lptrs = [
+            c.base_data.get_ptr(mode="read")
+            for _, c in mi1._codes._data.items()
+        ]
+        rptrs = [
+            c.base_data.get_ptr(mode="read")
+            for _, c in mi2._codes._data.items()
+        ]
+
+        assert all((x == y) == same_ref for x, y in zip(lptrs, rptrs))
+
+        # Assert ._data identity
+        lptrs = [
+            d.base_data.get_ptr(mode="read") for _, d in mi1._data.items()
+        ]
+        rptrs = [
+            d.base_data.get_ptr(mode="read") for _, d in mi2._data.items()
+        ]
+
+        assert all((x == y) == same_ref for x, y in zip(lptrs, rptrs))
+    cudf.set_option("copy_on_write", original_cow_setting)
+
+
+@pytest.mark.parametrize(
+    "iloc_rows",
+    [
+        0,
+        1,
+        slice(None, 0),
+        slice(None, 1),
+        slice(0, 1),
+        slice(1, 2),
+        slice(0, 2),
+        slice(0, None),
+        slice(1, None),
+    ],
+)
+@pytest.mark.parametrize(
+    "iloc_columns",
+    [
+        0,
+        1,
+        slice(None, 0),
+        slice(None, 1),
+        slice(0, 1),
+        slice(1, 2),
+        slice(0, 2),
+        slice(0, None),
+        slice(1, None),
+    ],
+)
+def test_multiindex_iloc(pdf, gdf, pdfIndex, iloc_rows, iloc_columns):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    presult = pdf.iloc[iloc_rows, iloc_columns]
+    gresult = gdf.iloc[iloc_rows, iloc_columns]
+    if isinstance(gresult, cudf.DataFrame):
+        assert_eq(
+            presult, gresult, check_index_type=False, check_column_type=False
+        )
+    else:
+        assert_eq(presult, gresult, check_index_type=False, check_dtype=False)
+
+
+def test_multiindex_iloc_scalar():
+    arrays = [["a", "a", "b", "b"], [1, 2, 3, 4]]
+    tuples = list(zip(*arrays))
+    idx = cudf.MultiIndex.from_tuples(tuples)
+    gdf = cudf.DataFrame(
+        {"first": cp.random.rand(4), "second": cp.random.rand(4)}
+    )
+    gdf.index = idx
+
+    pdf = gdf.to_pandas()
+    assert_eq(pdf.iloc[3], gdf.iloc[3])
+
+
+@pytest.mark.parametrize(
+    "iloc_rows",
+    [
+        0,
+        1,
+        slice(None, 0),
+        slice(None, 1),
+        slice(0, 1),
+        slice(1, 2),
+        slice(0, 2),
+        slice(0, None),
+        slice(1, None),
+    ],
+)
+@pytest.mark.parametrize(
+    "iloc_columns",
+    [
+        0,
+        1,
+        slice(None, 0),
+        slice(None, 1),
+        slice(0, 1),
+        slice(1, 2),
+        slice(0, 2),
+        slice(0, None),
+        slice(1, None),
+    ],
+)
+def test_multicolumn_iloc(pdf, gdf, pdfIndex, iloc_rows, iloc_columns):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex, gdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    pdf = pdf.T
+    gdf = gdf.T
+    presult = pdf.iloc[iloc_rows, iloc_columns]
+    gresult = gdf.iloc[iloc_rows, iloc_columns]
+    if hasattr(gresult, "name") and isinstance(gresult.name, tuple):
+        name = gresult.name[len(gresult.name) - 1]
+        if isinstance(name, str) and "cudf" in name:
+            gresult.name = name
+    if isinstance(presult, pd.DataFrame):
+        assert_eq(
+            presult, gresult, check_index_type=False, check_column_type=False
+        )
+    else:
+        assert_eq(presult, gresult, check_index_type=False, check_dtype=False)
+
+
+def test_multicolumn_item():
+    gdf = cudf.DataFrame(
+        {"x": np.arange(10), "y": np.arange(10), "z": np.arange(10)}
+    )
+    gdg = gdf.groupby(["x", "y"]).min()
+    gdgT = gdg.T
+    pdgT = gdgT.to_pandas()
+    assert_eq(gdgT[(0, 0)], pdgT[(0, 0)])
+
+
+def test_multiindex_to_frame(pdfIndex, pdfIndexNulls):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex.to_frame(), gdfIndex.to_frame())
+
+    gdfIndex = cudf.from_pandas(pdfIndexNulls)
+    assert_eq(
+        pdfIndexNulls.to_frame().fillna("nan"),
+        gdfIndex.to_frame().fillna("nan"),
+    )
+
+
+def test_multiindex_groupby_to_frame():
+    gdf = cudf.DataFrame(
+        {"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5], "z": [0, 1, 0, 1, 0]}
+    )
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["x", "y"], sort=True).count()
+    pdg = pdf.groupby(["x", "y"], sort=True).count()
+    assert_eq(pdg.index.to_frame(), gdg.index.to_frame())
+
+
+def test_multiindex_reset_index(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    assert_eq(pdf.reset_index(), gdf.reset_index())
+
+
+def test_multiindex_groupby_reset_index():
+    gdf = cudf.DataFrame(
+        {"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5], "z": [0, 1, 0, 1, 0]}
+    )
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["x", "y"], sort=True).sum()
+    pdg = pdf.groupby(["x", "y"], sort=True).sum()
+    assert_eq(pdg.reset_index(), gdg.reset_index())
+
+
+def test_multicolumn_reset_index():
+    gdf = cudf.DataFrame({"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5]})
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["x"], sort=True).agg({"y": ["count", "mean"]})
+    pdg = pdf.groupby(["x"], sort=True).agg({"y": ["count", "mean"]})
+    assert_eq(pdg.reset_index(), gdg.reset_index(), check_dtype=False)
+    gdg = gdf.groupby(["x"], sort=True).agg({"y": ["count"]})
+    pdg = pdf.groupby(["x"], sort=True).agg({"y": ["count"]})
+    assert_eq(pdg.reset_index(), gdg.reset_index(), check_dtype=False)
+    gdg = gdf.groupby(["x"], sort=True).agg({"y": "count"})
+    pdg = pdf.groupby(["x"], sort=True).agg({"y": "count"})
+    assert_eq(pdg.reset_index(), gdg.reset_index(), check_dtype=False)
+
+
+def test_multiindex_multicolumn_reset_index():
+    gdf = cudf.DataFrame(
+        {"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5], "z": [1, 2, 3, 4, 5]}
+    )
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["x", "y"], sort=True).agg({"y": ["count", "mean"]})
+    pdg = pdf.groupby(["x", "y"], sort=True).agg({"y": ["count", "mean"]})
+    assert_eq(pdg.reset_index(), gdg.reset_index(), check_dtype=False)
+    gdg = gdf.groupby(["x", "z"], sort=True).agg({"y": ["count", "mean"]})
+    pdg = pdf.groupby(["x", "z"], sort=True).agg({"y": ["count", "mean"]})
+    assert_eq(pdg.reset_index(), gdg.reset_index(), check_dtype=False)
+
+
+def test_groupby_multiindex_columns_from_pandas(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    assert_eq(gdf, pdf)
+    assert_eq(gdf.T, pdf.T)
+
+
+def test_multiindex_rows_with_wildcard(pdf, gdf, pdfIndex):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    pdf.index = pdfIndex
+    gdf.index = gdfIndex
+    # The index is unsorted, which makes things slow but is fine for testing.
+    with pytest.warns(pd.errors.PerformanceWarning):
+        assert_eq(
+            pdf.loc[("a",), :].sort_index(), gdf.loc[("a",), :].sort_index()
+        )
+        assert_eq(
+            pdf.loc[(("a"), ("store")), :].sort_index(),
+            gdf.loc[(("a"), ("store")), :].sort_index(),
+        )
+        assert_eq(
+            pdf.loc[(("a"), ("store"), ("storm")), :].sort_index(),
+            gdf.loc[(("a"), ("store"), ("storm")), :].sort_index(),
+        )
+        assert_eq(
+            pdf.loc[(("a"), ("store"), ("storm"), ("smoke")), :].sort_index(),
+            gdf.loc[(("a"), ("store"), ("storm"), ("smoke")), :].sort_index(),
+        )
+        assert_eq(
+            pdf.loc[(slice(None), "store"), :].sort_index(),
+            gdf.loc[(slice(None), "store"), :].sort_index(),
+        )
+        assert_eq(
+            pdf.loc[(slice(None), slice(None), "storm"), :].sort_index(),
+            gdf.loc[(slice(None), slice(None), "storm"), :].sort_index(),
+        )
+        assert_eq(
+            pdf.loc[
+                (slice(None), slice(None), slice(None), "smoke"), :
+            ].sort_index(),
+            gdf.loc[
+                (slice(None), slice(None), slice(None), "smoke"), :
+            ].sort_index(),
+        )
+
+
+def test_multiindex_multicolumn_zero_row_slice():
+    gdf = cudf.DataFrame(
+        {"x": [1, 5, 3, 4, 1], "y": [1, 1, 2, 2, 5], "z": [1, 2, 3, 4, 5]}
+    )
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["x", "y"]).agg({"z": ["count"]}).iloc[:0]
+    pdg = pdf.groupby(["x", "y"]).agg({"z": ["count"]}).iloc[:0]
+    assert_eq(pdg, gdg, check_dtype=False)
+
+
+def test_multicolumn_loc(pdf, pdfIndex):
+    pdf = pdf.T
+    pdf.columns = pdfIndex
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(pdf.loc[:, "a"], gdf.loc[:, "a"])
+    assert_eq(pdf.loc[:, ("a", "store")], gdf.loc[:, ("a", "store")])
+    assert_eq(pdf.loc[:, "a":"b"], gdf.loc[:, "a":"b"])
+    assert_eq(pdf.loc[:, ["a", "b"]], gdf.loc[:, ["a", "b"]])
+
+
+@pytest.mark.xfail(
+    reason="https://github.com/pandas-dev/pandas/issues/43351",
+)
+def test_multicolumn_set_item(pdf, pdfIndex):
+    pdf = pdf.T
+    pdf.columns = pdfIndex
+    gdf = cudf.from_pandas(pdf)
+    pdf["d"] = [1, 2, 3, 4, 5]
+    gdf["d"] = [1, 2, 3, 4, 5]
+    assert_eq(pdf, gdf)
+
+
+def test_multiindex_iter_error():
+    midx = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"{midx.__class__.__name__} object is not iterable. "
+            f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
+            f"if you wish to iterate over the values."
+        ),
+    ):
+        iter(midx)
+
+
+def test_multiindex_values():
+    midx = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+
+    result = midx.values
+
+    assert isinstance(result, cp.ndarray)
+    np.testing.assert_array_equal(
+        result.get(), np.array([[1, 1], [1, 5], [3, 2], [4, 2], [5, 1]])
+    )
+
+
+def test_multiindex_values_host():
+    midx = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    pmidx = midx.to_pandas()
+
+    assert_eq(midx.values_host, pmidx.values)
+
+
+def test_multiindex_to_numpy():
+    midx = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    pmidx = midx.to_pandas()
+
+    assert_eq(midx.to_numpy(), pmidx.to_numpy())
+
+
+@pytest.mark.parametrize(
+    "gdi, fill_value, expected",
+    [
+        (
+            cudf.MultiIndex(
+                levels=[[1, 3, 4, None], [1, 2, 5]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+            5,
+            cudf.MultiIndex(
+                levels=[[1, 3, 4, 5], [1, 2, 5]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+        ),
+        (
+            cudf.MultiIndex(
+                levels=[[1, 3, 4, None], [1, None, 5]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+            100,
+            cudf.MultiIndex(
+                levels=[[1, 3, 4, 100], [1, 100, 5]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+        ),
+        (
+            cudf.MultiIndex(
+                levels=[["a", "b", "c", None], ["1", None, "5"]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+            "100",
+            cudf.MultiIndex(
+                levels=[["a", "b", "c", "100"], ["1", "100", "5"]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+        ),
+    ],
+)
+def test_multiindex_fillna(gdi, fill_value, expected):
+    assert_eq(expected, gdi.fillna(fill_value))
+
+
+@pytest.mark.parametrize(
+    "pdi",
+    [
+        pd.MultiIndex(
+            levels=[[], [], []],
+            codes=[[], [], []],
+            names=["one", "two", "three"],
+        ),
+        pd.MultiIndex.from_tuples(
+            list(
+                zip(
+                    *[
+                        [
+                            "bar",
+                            "bar",
+                            "baz",
+                            "baz",
+                            "foo",
+                            "foo",
+                            "qux",
+                            "qux",
+                        ],
+                        [
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                        ],
+                    ]
+                )
+            )
+        ),
+    ],
+)
+def test_multiindex_empty(pdi):
+    gdi = cudf.from_pandas(pdi)
+
+    assert_eq(pdi.empty, gdi.empty)
+
+
+@pytest.mark.parametrize(
+    "pdi",
+    [
+        pd.MultiIndex(
+            levels=[[], [], []],
+            codes=[[], [], []],
+            names=["one", "two", "three"],
+        ),
+        pd.MultiIndex.from_tuples(
+            list(
+                zip(
+                    *[
+                        [
+                            "bar",
+                            "bar",
+                            "baz",
+                            "baz",
+                            "foo",
+                            "foo",
+                            "qux",
+                            "qux",
+                        ],
+                        [
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                        ],
+                    ]
+                )
+            )
+        ),
+    ],
+)
+def test_multiindex_size(pdi):
+    gdi = cudf.from_pandas(pdi)
+
+    assert_eq(pdi.size, gdi.size)
+
+
+@pytest.mark.parametrize(
+    "level",
+    [
+        [],
+        "alpha",
+        "location",
+        "weather",
+        0,
+        1,
+        [0, 1],
+        -1,
+        [-1, -2],
+        [-1, "weather"],
+    ],
+)
+def test_multiindex_droplevel_simple(pdfIndex, level):
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex.droplevel(level), gdfIndex.droplevel(level))
+
+
+@pytest.mark.parametrize(
+    "level",
+    itertools.chain(
+        *(
+            itertools.combinations(
+                ("alpha", "location", "weather", "sign", "timestamp"), r
+            )
+            for r in range(5)
+        )
+    ),
+)
+def test_multiindex_droplevel_name(pdfIndex, level):
+    level = list(level)
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex.droplevel(level), gdfIndex.droplevel(level))
+
+
+@pytest.mark.parametrize(
+    "level",
+    itertools.chain(*(itertools.combinations(range(5), r) for r in range(5))),
+)
+def test_multiindex_droplevel_index(pdfIndex, level):
+    level = list(level)
+    gdfIndex = cudf.from_pandas(pdfIndex)
+    assert_eq(pdfIndex.droplevel(level), gdfIndex.droplevel(level))
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("return_indexer", [True, False])
+@pytest.mark.parametrize(
+    "pmidx",
+    [
+        pd.MultiIndex(
+            levels=[[1, 3, 4, 5], [1, 2, 5]],
+            codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+            names=["x", "y"],
+        ),
+        pd.MultiIndex.from_product(
+            [["bar", "baz", "foo", "qux"], ["one", "two"]],
+            names=["first", "second"],
+        ),
+        pd.MultiIndex(
+            levels=[[], [], []],
+            codes=[[], [], []],
+            names=["one", "two", "three"],
+        ),
+        pd.MultiIndex.from_tuples(
+            list(
+                zip(
+                    *[
+                        [
+                            "bar",
+                            "bar",
+                            "baz",
+                            "baz",
+                            "foo",
+                            "foo",
+                            "qux",
+                            "qux",
+                        ],
+                        [
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                            "one",
+                            "two",
+                        ],
+                    ]
+                )
+            )
+        ),
+    ],
+)
+def test_multiindex_sort_values(pmidx, ascending, return_indexer):
+    pmidx = pmidx
+    midx = cudf.from_pandas(pmidx)
+
+    expected = pmidx.sort_values(
+        ascending=ascending, return_indexer=return_indexer
+    )
+    actual = midx.sort_values(
+        ascending=ascending, return_indexer=return_indexer
+    )
+
+    if return_indexer:
+        expected_indexer = expected[1]
+        actual_indexer = actual[1]
+
+        assert_eq(expected_indexer, actual_indexer)
+
+        expected = expected[0]
+        actual = actual[0]
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "pdi",
+    [
+        pd.MultiIndex(
+            levels=[[1, 3.0, 4, 5], [1, 2.3, 5]],
+            codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+            names=["x", "y"],
+        ),
+        pd.MultiIndex(
+            levels=[[1, 3, 4, -10], [1, 11, 5]],
+            codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+            names=["x", "y"],
+        ),
+        pd.MultiIndex(
+            levels=[["a", "b", "c", "100"], ["1", "100", "5"]],
+            codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+            names=["x", "y"],
+        ),
+        pytest.param(
+            pd.MultiIndex(
+                levels=[[None, "b", "c", "a"], ["1", None, "5"]],
+                codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+                names=["x", "y"],
+            ),
+            marks=[
+                pytest.mark.xfail(
+                    reason="https://github.com/pandas-dev/pandas/issues/35584"
+                )
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("ascending", [True, False])
+def test_multiindex_argsort(pdi, ascending):
+    gdi = cudf.from_pandas(pdi)
+
+    if not ascending:
+        expected = pdi.argsort()[::-1]
+    else:
+        expected = pdi.argsort()
+
+    actual = gdi.argsort(ascending=ascending)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx", [pd.MultiIndex.from_product([["python", "cobra"], [2018, 2019]])]
+)
+@pytest.mark.parametrize(
+    "names", [[None, None], ["a", None], ["new name", "another name"]]
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_multiindex_set_names(idx, names, inplace):
+    pi = idx.copy()
+    gi = cudf.from_pandas(idx)
+
+    expected = pi.set_names(names=names, inplace=inplace)
+    actual = gi.set_names(names=names, inplace=inplace)
+
+    if inplace:
+        expected, actual = pi, gi
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.MultiIndex.from_product(
+            [["python", "cobra"], [2018, 2019], ["aab", "bcd"]]
+        ),
+        pd.MultiIndex.from_product(
+            [["python", "cobra"], [2018, 2019], ["aab", "bcd"]],
+            names=[1, 0, 2],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "level, names",
+    [
+        (0, "abc"),
+        (1, "xyz"),
+        ([2, 1], ["a", "b"]),
+        ([0, 1], ["aa", "bb"]),
+        (None, ["a", "b", "c"]),
+        (None, ["a", None, "c"]),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_multiindex_set_names_default_and_int_names(
+    idx, level, names, inplace
+):
+    pi = idx.copy()
+    gi = cudf.from_pandas(idx)
+
+    expected = pi.set_names(names=names, level=level, inplace=inplace)
+    actual = gi.set_names(names=names, level=level, inplace=inplace)
+
+    if inplace:
+        expected, actual = pi, gi
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.MultiIndex.from_product(
+            [["python", "cobra"], [2018, 2019], ["aab", "bcd"]],
+            names=["one", None, "three"],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "level, names",
+    [
+        ([None], "abc"),
+        (["three", "one"], ["a", "b"]),
+        (["three", 1], ["a", "b"]),
+        ([0, "three", 1], ["a", "b", "z"]),
+        (["one", 1, "three"], ["a", "b", "z"]),
+        (["one", None, "three"], ["a", "b", "z"]),
+        ([2, 1], ["a", "b"]),
+        (1, "xyz"),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_multiindex_set_names_string_names(idx, level, names, inplace):
+    pi = idx.copy()
+    gi = cudf.from_pandas(idx)
+
+    expected = pi.set_names(names=names, level=level, inplace=inplace)
+    actual = gi.set_names(names=names, level=level, inplace=inplace)
+
+    if inplace:
+        expected, actual = pi, gi
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "level, names", [(1, ["a"]), (None, "a"), ([1, 2], ["a"]), (None, ["a"])]
+)
+def test_multiindex_set_names_error(level, names):
+    pi = pd.MultiIndex.from_product(
+        [["python", "cobra"], [2018, 2019], ["aab", "bcd"]]
+    )
+    gi = cudf.from_pandas(pi)
+
+    assert_exceptions_equal(
+        lfunc=pi.set_names,
+        rfunc=gi.set_names,
+        lfunc_args_and_kwargs=([], {"names": names, "level": level}),
+        rfunc_args_and_kwargs=([], {"names": names, "level": level}),
+    )
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        pd.MultiIndex.from_product([["python", "cobra"], [2018, 2019]]),
+        pd.MultiIndex.from_product(
+            [["python", "cobra"], [2018, 2019]], names=["old name", None]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "names",
+    [
+        [None, None],
+        ["a", None],
+        ["new name", "another name"],
+        [1, None],
+        [2, 3],
+        [42, "name"],
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_multiindex_rename(idx, names, inplace):
+    pi = idx.copy()
+    gi = cudf.from_pandas(idx)
+
+    expected = pi.rename(names=names, inplace=inplace)
+    actual = gi.rename(names=names, inplace=inplace)
+
+    if inplace:
+        expected, actual = pi, gi
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "names", ["plain string", 123, ["str"], ["l1", "l2", "l3"]]
+)
+def test_multiindex_rename_error(names):
+    pi = pd.MultiIndex.from_product([["python", "cobra"], [2018, 2019]])
+    gi = cudf.from_pandas(pi)
+
+    assert_exceptions_equal(
+        lfunc=pi.rename,
+        rfunc=gi.rename,
+        lfunc_args_and_kwargs=([], {"names": names}),
+        rfunc_args_and_kwargs=([], {"names": names}),
+    )
+
+
+@pytest.mark.parametrize(
+    "key",
+    [0, 1, [], [0, 1], slice(None), slice(0, 0), slice(0, 1), slice(0, 2)],
+)
+def test_multiindex_indexing(key):
+    gi = cudf.MultiIndex.from_frame(
+        cudf.DataFrame({"a": [1, 2, 3], "b": [True, False, False]})
+    )
+    pi = gi.to_pandas()
+
+    assert_eq(gi[key], pi[key], exact=False)
+
+
+def test_multiindex_duplicate_names():
+    gi = cudf.MultiIndex(
+        levels=[["a", "b"], ["b", "a"]],
+        codes=[[0, 0], [0, 1]],
+        names=["a", "a"],
+    )
+    pi = pd.MultiIndex(
+        levels=[["a", "b"], ["b", "a"]],
+        codes=[[0, 0], [0, 1]],
+        names=["a", "a"],
+    )
+
+    assert_eq(gi, pi)
+
+
+def test_difference():
+    midx = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3], [0, 2, 1, 1, 0]],
+        names=["x", "y"],
+    )
+    midx2 = cudf.MultiIndex(
+        levels=[[1, 3, 4, 5], [1, 2, 5]],
+        codes=[[0, 0, 1, 2, 3, 3], [0, 2, 1, 1, 0, 2]],
+        names=["x", "y"],
+    )
+
+    expected = midx2.to_pandas().difference(midx.to_pandas())
+    actual = midx2.difference(midx)
+    assert isinstance(actual, cudf.MultiIndex)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx1, idx2",
+    [
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+            ),
+            pd.MultiIndex.from_arrays(
+                [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
+            ),
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], ["Red", "Blue", "Red", "Blue"]],
+                names=["a", "b"],
+            ),
+            pd.MultiIndex.from_arrays(
+                [[3, 3, 2, 4], ["Red", "Green", "Red", "Green"]],
+                names=["x", "y"],
+            ),
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+                names=["a", "b", "c"],
+            ),
+            pd.MultiIndex.from_arrays(
+                [[3, 3, 2, 4], [0.2, 0.4, 1.4, 10], [3, 3, 2, 4]]
+            ),
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+                names=["a", "b", "c"],
+            ),
+            [(2, 6, 12)],
+        ),
+    ],
+)
+@pytest.mark.parametrize("sort", [None, False])
+def test_union_mulitIndex(idx1, idx2, sort):
+    expected = idx1.union(idx2, sort=sort)
+
+    idx1 = cudf.from_pandas(idx1) if isinstance(idx1, pd.MultiIndex) else idx1
+    idx2 = cudf.from_pandas(idx2) if isinstance(idx2, pd.MultiIndex) else idx2
+
+    actual = idx1.union(idx2, sort=sort)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "idx1, idx2",
+    [
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+            ),
+            pd.MultiIndex.from_arrays(
+                [[1, 3, 2, 2], ["Red", "Green", "Red", "Green"]]
+            ),
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], ["Red", "Blue", "Red", "Blue"]],
+                names=["a", "b"],
+            ),
+            pd.MultiIndex.from_arrays(
+                [[3, 3, 2, 4], ["Red", "Green", "Red", "Green"]],
+                names=["x", "y"],
+            ),
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+                names=["a", "b", "c"],
+            ),
+            pd.MultiIndex.from_arrays(
+                [[3, 3, 2, 4], [0.2, 0.4, 1.4, 10], [3, 3, 2, 4]]
+            ),
+        ),
+        (
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+                names=["a", "b", "c"],
+            ),
+            pd.MultiIndex.from_arrays(
+                [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("sort", [None, False])
+def test_intersection_mulitIndex(idx1, idx2, sort):
+    expected = idx1.intersection(idx2, sort=sort)
+
+    idx1 = cudf.from_pandas(idx1)
+    idx2 = cudf.from_pandas(idx2)
+
+    actual = idx1.intersection(idx2, sort=sort)
+    assert_eq(expected, actual, exact=False)
+
+
+@pytest.mark.parametrize(
+    "names",
+    [
+        ["a", "b", "c"],
+        [None, None, None],
+        ["aa", "aa", "aa"],
+        ["bb", "aa", "aa"],
+        None,
+    ],
+)
+def test_pickle_roundtrip_multiindex(names):
+    df = cudf.DataFrame(
+        {
+            "one": [1, 2, 3],
+            "two": [True, False, True],
+            "three": ["ab", "cd", "ef"],
+            "four": [0.2, 0.1, -10.2],
+        }
+    )
+    expected_df = df.set_index(["one", "two", "three"])
+    expected_df.index.names = names
+    local_file = BytesIO()
+
+    pickle.dump(expected_df, local_file)
+    local_file.seek(0)
+    actual_df = pickle.load(local_file)
+    assert_eq(expected_df, actual_df)
+
+
+@pytest.mark.parametrize(
+    "pidx",
+    [
+        pd.MultiIndex.from_arrays(
+            [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]]
+        ),
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+            names=["a", "b", "c"],
+        ),
+        pd.MultiIndex.from_arrays(
+            [[1.0, 2, 3, 4], [5, 6, 7.8, 10], [11, 12, 12, 13]],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        "is_numeric",
+        "is_boolean",
+        "is_integer",
+        "is_floating",
+        "is_object",
+        "is_categorical",
+        "is_interval",
+    ],
+)
+def test_multiindex_type_methods(pidx, func):
+    gidx = cudf.from_pandas(pidx)
+
+    if PANDAS_GE_200:
+        with pytest.warns(FutureWarning):
+            expected = getattr(pidx, func)()
+    else:
+        expected = getattr(pidx, func)()
+
+    with pytest.warns(FutureWarning):
+        actual = getattr(gidx, func)()
+
+    if func == "is_object":
+        assert_eq(False, actual)
+    else:
+        assert_eq(expected, actual)
+
+
+def test_multiindex_index_single_row():
+    arrays = [["a", "a", "b", "b"], [1, 2, 3, 4]]
+    tuples = list(zip(*arrays))
+    idx = cudf.MultiIndex.from_tuples(tuples)
+    gdf = cudf.DataFrame(
+        {"first": cp.random.rand(4), "second": cp.random.rand(4)}
+    )
+    gdf.index = idx
+    pdf = gdf.to_pandas()
+    assert_eq(pdf.loc[("b", 3)], gdf.loc[("b", 3)])
+
+
+def test_multiindex_levels():
+    gidx = cudf.MultiIndex.from_product(
+        [range(3), ["one", "two"]], names=["first", "second"]
+    )
+    pidx = gidx.to_pandas()
+
+    assert_eq(gidx.levels[0], pidx.levels[0])
+    assert_eq(gidx.levels[1], pidx.levels[1])
+
+
+def test_multiindex_empty_slice_pandas_compatibility():
+    expected = pd.MultiIndex.from_tuples([("a", "b")])[:0]
+    with cudf.option_context("mode.pandas_compatible", True):
+        actual = cudf.from_pandas(expected)
+    assert_eq(expected, actual, exact=False)
+
+
+@pytest.mark.parametrize(
+    "levels",
+    itertools.chain.from_iterable(
+        itertools.permutations(range(3), n) for n in range(1, 4)
+    ),
+    ids=str,
+)
+def test_multiindex_sort_index_partial(levels):
+    df = pd.DataFrame(
+        {
+            "a": [3, 3, 3, 1, 1, 1, 2, 2],
+            "b": [4, 2, 7, -1, 11, -2, 7, 7],
+            "c": [4, 4, 2, 3, 3, 3, 1, 1],
+            "val": [1, 2, 3, 4, 5, 6, 7, 8],
+        }
+    ).set_index(["a", "b", "c"])
+    cdf = cudf.from_pandas(df)
+
+    expect = df.sort_index(level=levels, sort_remaining=True)
+    got = cdf.sort_index(level=levels, sort_remaining=True)
+    assert_eq(expect, got)
+
+
+def test_multiindex_to_series_error():
+    midx = cudf.MultiIndex.from_tuples([("a", "b")])
+    with pytest.raises(NotImplementedError):
+        midx.to_series()
+
+
+@pytest.mark.parametrize(
+    "pidx",
+    [
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+            names=["a", "b", "c"],
+        ),
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+            names=["a", "a", "a"],
+        ),
+        pd.MultiIndex.from_arrays(
+            [[1, 2, 3, 4], [5, 6, 7, 10], [11, 12, 12, 13]],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "name", [None, no_default, ["x", "y", "z"], ["rapids", "rapids", "rapids"]]
+)
+@pytest.mark.parametrize("allow_duplicates", [True, False])
+@pytest.mark.parametrize("index", [True, False])
+def test_multiindex_to_frame_allow_duplicates(
+    pidx, name, allow_duplicates, index
+):
+    gidx = cudf.from_pandas(pidx)
+
+    if (
+        (
+            len(pidx.names) != len(set(pidx.names))
+            and not all(x is None for x in pidx.names)
+        )
+        and not allow_duplicates
+        and (name is None or name is no_default)
+    ):
+        assert_exceptions_equal(
+            pidx.to_frame,
+            gidx.to_frame,
+            lfunc_args_and_kwargs=(
+                [],
+                {
+                    "index": index,
+                    "name": name,
+                    "allow_duplicates": allow_duplicates,
+                },
+            ),
+            rfunc_args_and_kwargs=(
+                [],
+                {
+                    "index": index,
+                    "name": name,
+                    "allow_duplicates": allow_duplicates,
+                },
+            ),
+        )
+    else:
+        if (
+            len(pidx.names) != len(set(pidx.names))
+            and not all(x is None for x in pidx.names)
+            and not isinstance(name, list)
+        ) or (isinstance(name, list) and len(name) != len(set(name))):
+            # cudf doesn't have the ability to construct dataframes
+            # with duplicate column names
+            with expect_warning_if(name is None):
+                with pytest.raises(ValueError):
+                    gidx.to_frame(
+                        index=index,
+                        name=name,
+                        allow_duplicates=allow_duplicates,
+                    )
+        else:
+            with expect_warning_if(name is None):
+                expected = pidx.to_frame(
+                    index=index, name=name, allow_duplicates=allow_duplicates
+                )
+            with expect_warning_if(name is None):
+                actual = gidx.to_frame(
+                    index=index, name=name, allow_duplicates=allow_duplicates
+                )
+
+            assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("bad", ["foo", ["foo"]])
+def test_multiindex_set_names_validation(bad):
+    mi = cudf.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0), (1, 1)])
+    with pytest.raises(ValueError):
+        mi.names = bad
+
+
+def test_multiindex_values_pandas_compatible():
+    midx = cudf.MultiIndex.from_tuples([(10, 12), (8, 9), (3, 4)])
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            midx.values
+
+
+def test_multiindex_dtype_error():
+    midx = cudf.MultiIndex.from_tuples([(10, 12), (8, 9), (3, 4)])
+    with pytest.raises(TypeError):
+        cudf.Index(midx, dtype="int64")
+    with pytest.raises(TypeError):
+        cudf.Index(midx.to_pandas(), dtype="int64")
+
+
+def test_multiindex_codes():
+    midx = cudf.MultiIndex.from_tuples(
+        [("a", "b"), ("a", "c"), ("b", "c")], names=["A", "Z"]
+    )
+
+    for p_array, g_array in zip(midx.to_pandas().codes, midx.codes):
+        assert_eq(p_array, g_array)
+
+
+def test_multiindex_union_error():
+    midx = cudf.MultiIndex.from_tuples([(10, 12), (8, 9), (3, 4)])
+    pidx = midx.to_pandas()
+
+    assert_exceptions_equal(
+        midx.union,
+        pidx.union,
+        lfunc_args_and_kwargs=(["a"],),
+        rfunc_args_and_kwargs=(["b"],),
+    )
+
+
+@pytest.mark.parametrize("idx_get", [(0, 0), (0, 1), (1, 0), (1, 1)])
+@pytest.mark.parametrize("cols_get", [0, 1, [0, 1], [1, 0], [1], [0]])
+def test_multiindex_loc_scalar(idx_get, cols_get):
+    idx = cudf.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0), (1, 1)])
+    df = cudf.DataFrame({0: range(4), 1: range(10, 50, 10)}, index=idx)
+    pdf = df.to_pandas()
+
+    actual = df.loc[idx_get, cols_get]
+    expected = pdf.loc[idx_get, cols_get]
+
+    assert_eq(actual, expected)
+
+
+def test_multiindex_eq_other_multiindex():
+    idx = cudf.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0), (1, 1)])
+    result = idx == idx
+    expected = np.array([True, True])
+    assert_eq(result, expected)
diff --git a/python/cudf/cudf/tests/test_no_cuinit.py b/python/cudf/cudf/tests/test_no_cuinit.py
new file mode 100644
index 0000000..b142b0d
--- /dev/null
+++ b/python/cudf/cudf/tests/test_no_cuinit.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+import os
+import subprocess
+import sys
+from shutil import which
+
+import pytest
+
+GDB_COMMANDS = """
+set confirm off
+set breakpoint pending on
+break cuInit
+run
+exit
+"""
+
+
+@pytest.fixture(scope="module")
+def cuda_gdb(request):
+    gdb = which("cuda-gdb")
+    if gdb is None:
+        request.applymarker(
+            pytest.mark.xfail(reason="No cuda-gdb found, can't detect cuInit"),
+        )
+        return gdb
+    else:
+        output = subprocess.run(
+            [gdb, "--version"], capture_output=True, text=True, cwd="/"
+        )
+        if output.returncode != 0:
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=(
+                        "cuda-gdb not working on this platform, "
+                        f"can't detect cuInit: {output.stderr}"
+                    )
+                ),
+            )
+        return gdb
+
+
+def test_cudf_import_no_cuinit(cuda_gdb):
+    # When RAPIDS_NO_INITIALIZE is set, importing cudf should _not_
+    # create a CUDA context (i.e. cuInit should not be called).
+    # Intercepting the call to cuInit programmatically is tricky since
+    # the way it is resolved from dynamic libraries by
+    # cuda-python/numba/cupy is multitudinous (see discussion at
+    # https://github.com/rapidsai/cudf/pull/12361 which does this, but
+    # needs provide hooks that override dlsym, cuGetProcAddress, and
+    # cuInit.
+    # Instead, we just run under GDB and see if we hit a breakpoint
+    env = os.environ.copy()
+    env["RAPIDS_NO_INITIALIZE"] = "1"
+    output = subprocess.run(
+        [
+            cuda_gdb,
+            "-x",
+            "-",
+            "--args",
+            sys.executable,
+            "-c",
+            "import cudf",
+        ],
+        input=GDB_COMMANDS,
+        env=env,
+        capture_output=True,
+        text=True,
+    )
+
+    cuInit_called = output.stdout.find("in cuInit ()")
+    print("Command output:\n")
+    print("*** STDOUT ***")
+    print(output.stdout)
+    print("*** STDERR ***")
+    print(output.stderr)
+    assert output.returncode == 0
+    assert cuInit_called < 0
+
+
+def test_cudf_create_series_cuinit(cuda_gdb):
+    # This tests that our gdb scripting correctly identifies cuInit
+    # when it definitely should have been called.
+    env = os.environ.copy()
+    env["RAPIDS_NO_INITIALIZE"] = "1"
+    output = subprocess.run(
+        [
+            cuda_gdb,
+            "-x",
+            "-",
+            "--args",
+            sys.executable,
+            "-c",
+            "import cudf; cudf.Series([1])",
+        ],
+        input=GDB_COMMANDS,
+        env=env,
+        capture_output=True,
+        text=True,
+        cwd="/",
+    )
+
+    cuInit_called = output.stdout.find("in cuInit ()")
+    print("Command output:\n")
+    print("*** STDOUT ***")
+    print(output.stdout)
+    print("*** STDERR ***")
+    print(output.stderr)
+    assert output.returncode == 0
+    assert cuInit_called >= 0
diff --git a/python/cudf/cudf/tests/test_numba_import.py b/python/cudf/cudf/tests/test_numba_import.py
new file mode 100644
index 0000000..238a32a
--- /dev/null
+++ b/python/cudf/cudf/tests/test_numba_import.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+import subprocess
+import sys
+
+import pytest
+
+IS_CUDA_11 = False
+try:
+    from ptxcompiler.patch import NO_DRIVER, safe_get_versions
+
+    versions = safe_get_versions()
+    if versions != NO_DRIVER:
+        driver_version, runtime_version = versions
+        if driver_version < (12, 0):
+            IS_CUDA_11 = True
+except ModuleNotFoundError:
+    pass
+
+TEST_NUMBA_MVC_ENABLED = """
+import numba.cuda
+import cudf
+from cudf.utils._numba import _CUDFNumbaConfig, _patch_numba_mvc
+
+
+_patch_numba_mvc()
+
+@numba.cuda.jit
+def test_kernel(x):
+    id = numba.cuda.grid(1)
+    if id < len(x):
+        x[id] += 1
+
+s = cudf.Series([1, 2, 3])
+with _CUDFNumbaConfig():
+    test_kernel.forall(len(s))(s)
+"""
+
+
+@pytest.mark.skipif(
+    not IS_CUDA_11, reason="Minor Version Compatibility test for CUDA 11"
+)
+def test_numba_mvc_enabled_cuda_11():
+    cp = subprocess.run(
+        [sys.executable, "-c", TEST_NUMBA_MVC_ENABLED],
+        capture_output=True,
+        cwd="/",
+    )
+    assert cp.returncode == 0
diff --git a/python/cudf/cudf/tests/test_numerical.py b/python/cudf/cudf/tests/test_numerical.py
new file mode 100644
index 0000000..5bb55c1
--- /dev/null
+++ b/python/cudf/cudf/tests/test_numerical.py
@@ -0,0 +1,427 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_150
+from cudf.testing._utils import NUMERIC_TYPES, assert_eq
+from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes
+
+
+def test_can_cast_safely_same_kind():
+    # 'i' -> 'i'
+    data = cudf.Series([1, 2, 3], dtype="int32")._column
+    to_dtype = np.dtype("int64")
+
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1, 2, 3], dtype="int64")._column
+    to_dtype = np.dtype("int32")
+
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1, 2, 2**31], dtype="int64")._column
+    assert not data.can_cast_safely(to_dtype)
+
+    # 'u' -> 'u'
+    data = cudf.Series([1, 2, 3], dtype="uint32")._column
+    to_dtype = np.dtype("uint64")
+
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1, 2, 3], dtype="uint64")._column
+    to_dtype = np.dtype("uint32")
+
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1, 2, 2**33], dtype="uint64")._column
+    assert not data.can_cast_safely(to_dtype)
+
+    # 'f' -> 'f'
+    data = cudf.Series([np.inf, 1.0], dtype="float64")._column
+    to_dtype = np.dtype("float32")
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series(
+        [np.finfo("float32").max * 2, 1.0], dtype="float64"
+    )._column
+    to_dtype = np.dtype("float32")
+    assert not data.can_cast_safely(to_dtype)
+
+
+def test_can_cast_safely_mixed_kind():
+    data = cudf.Series([1, 2, 3], dtype="int32")._column
+    to_dtype = np.dtype("float32")
+    assert data.can_cast_safely(to_dtype)
+
+    # too big to fit into f32 exactly
+    data = cudf.Series([1, 2, 2**24 + 1], dtype="int32")._column
+    assert not data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1, 2, 3], dtype="uint32")._column
+    to_dtype = np.dtype("float32")
+    assert data.can_cast_safely(to_dtype)
+
+    # too big to fit into f32 exactly
+    data = cudf.Series([1, 2, 2**24 + 1], dtype="uint32")._column
+    assert not data.can_cast_safely(to_dtype)
+
+    to_dtype = np.dtype("float64")
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1.0, 2.0, 3.0], dtype="float32")._column
+    to_dtype = np.dtype("int32")
+    assert data.can_cast_safely(to_dtype)
+
+    # not integer float
+    data = cudf.Series([1.0, 2.0, 3.5], dtype="float32")._column
+    assert not data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([10.0, 11.0, 2000.0], dtype="float64")._column
+    assert data.can_cast_safely(to_dtype)
+
+    # float out of int range
+    data = cudf.Series([1.0, 2.0, 1.0 * (2**31)], dtype="float32")._column
+    assert not data.can_cast_safely(to_dtype)
+
+    # negative signed integers casting to unsigned integers
+    data = cudf.Series([-1, 0, 1], dtype="int32")._column
+    to_dtype = np.dtype("uint32")
+    assert not data.can_cast_safely(to_dtype)
+
+
+def test_to_pandas_nullable_integer():
+    gsr_not_null = cudf.Series([1, 2, 3])
+    gsr_has_null = cudf.Series([1, 2, None])
+
+    psr_not_null = pd.Series([1, 2, 3], dtype="int64")
+    psr_has_null = pd.Series([1, 2, None], dtype="Int64")
+
+    assert_eq(gsr_not_null.to_pandas(), psr_not_null)
+    assert_eq(gsr_has_null.to_pandas(nullable=True), psr_has_null)
+
+
+def test_to_pandas_nullable_bool():
+    gsr_not_null = cudf.Series([True, False, True])
+    gsr_has_null = cudf.Series([True, False, None])
+
+    psr_not_null = pd.Series([True, False, True], dtype="bool")
+    psr_has_null = pd.Series([True, False, None], dtype="boolean")
+
+    assert_eq(gsr_not_null.to_pandas(), psr_not_null)
+    assert_eq(gsr_has_null.to_pandas(nullable=True), psr_has_null)
+
+
+def test_can_cast_safely_has_nulls():
+    data = cudf.Series([1, 2, 3, None], dtype="float32")._column
+    to_dtype = np.dtype("int64")
+
+    assert data.can_cast_safely(to_dtype)
+
+    data = cudf.Series([1, 2, 3.1, None], dtype="float32")._column
+    assert not data.can_cast_safely(to_dtype)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3],
+        (1.0, 2.0, 3.0),
+        [float("nan"), None],
+        np.array([1, 2.0, -3, float("nan")]),
+        pd.Series(["123", "2.0"]),
+        pd.Series(["1.0", "2.", "-.3", "1e6"]),
+        pd.Series(
+            ["1", "2", "3"],
+            dtype=pd.CategoricalDtype(categories=["1", "2", "3"]),
+        ),
+        pd.Series(
+            ["1.0", "2.0", "3.0"],
+            dtype=pd.CategoricalDtype(categories=["1.0", "2.0", "3.0"]),
+        ),
+        # Categories with nulls
+        pd.Series([1, 2, 3], dtype=pd.CategoricalDtype(categories=[1, 2])),
+        pd.Series(
+            [5.0, 6.0], dtype=pd.CategoricalDtype(categories=[5.0, 6.0])
+        ),
+        pd.Series(
+            ["2020-08-01 08:00:00", "1960-08-01 08:00:00"],
+            dtype=np.dtype("<M8[ns]"),
+        ),
+        pd.Series(
+            [pd.Timedelta(days=1, seconds=1), pd.Timedelta("-3 seconds 4ms")],
+            dtype=np.dtype("<m8[ns]"),
+        ),
+        [
+            "inf",
+            "-inf",
+            "+inf",
+            "infinity",
+            "-infinity",
+            "+infinity",
+            "inFInity",
+        ],
+    ],
+)
+def test_to_numeric_basic_1d(data):
+    expected = pd.to_numeric(data)
+    got = cudf.to_numeric(data)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2**11],
+        [1, 2**33],
+        [1, 2**63],
+        [np.iinfo(np.int64).max, np.iinfo(np.int64).min],
+    ],
+)
+@pytest.mark.parametrize(
+    "downcast", ["integer", "signed", "unsigned", "float"]
+)
+def test_to_numeric_downcast_int(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.0, 2.0**11],
+        [-1.0, -(2.0**11)],
+        [1.0, 2.0**33],
+        [-1.0, -(2.0**33)],
+        [1.0, 2.0**65],
+        [-1.0, -(2.0**65)],
+        [1.0, float("inf")],
+        [1.0, float("-inf")],
+        [1.0, float("nan")],
+        [1.0, 2.0, 3.0, 4.0],
+        [1.0, 1.5, 2.6, 3.4],
+    ],
+)
+@pytest.mark.parametrize(
+    "downcast", ["signed", "integer", "unsigned", "float"]
+)
+def test_to_numeric_downcast_float(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.0, 2.0**129],
+        [1.0, 2.0**257],
+        [1.0, 1.79e308],
+        [-1.0, -(2.0**129)],
+        [-1.0, -(2.0**257)],
+        [-1.0, -1.79e308],
+    ],
+)
+@pytest.mark.parametrize("downcast", ["signed", "integer", "unsigned"])
+def test_to_numeric_downcast_large_float(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.filterwarnings("ignore:overflow encountered in cast")
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.0, 2.0**129],
+        [1.0, 2.0**257],
+        [1.0, 1.79e308],
+        [-1.0, -(2.0**129)],
+        [-1.0, -(2.0**257)],
+        [-1.0, -1.79e308],
+    ],
+)
+@pytest.mark.parametrize("downcast", ["float"])
+def test_to_numeric_downcast_large_float_pd_bug(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    if PANDAS_GE_150:
+        assert_eq(expected, got)
+    else:
+        # Pandas bug: https://github.com/pandas-dev/pandas/issues/19729
+        with pytest.raises(AssertionError, match="Series are different"):
+            assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["1", "2", "3"],
+        [str(np.iinfo(np.int64).max), str(np.iinfo(np.int64).min)],
+    ],
+)
+@pytest.mark.parametrize(
+    "downcast", ["signed", "integer", "unsigned", "float"]
+)
+def test_to_numeric_downcast_string_int(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [""],  # pure empty strings
+        ["10.0", "11.0", "2e3"],
+        ["1.0", "2e3"],
+        ["1", "10", "1.0", "2e3"],  # int-float mixed
+        ["1", "10", "1.0", "2e3", "2e+3", "2e-3"],
+        ["1", "10", "1.0", "2e3", "", ""],  # mixed empty strings
+    ],
+)
+@pytest.mark.parametrize(
+    "downcast", ["signed", "integer", "unsigned", "float"]
+)
+def test_to_numeric_downcast_string_float(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expected = pd.to_numeric(ps, downcast=downcast)
+
+    if downcast in {"signed", "integer", "unsigned"}:
+        with pytest.warns(
+            UserWarning,
+            match="Downcasting from float to int "
+            "will be limited by float32 precision.",
+        ):
+            got = cudf.to_numeric(gs, downcast=downcast)
+    else:
+        got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.filterwarnings("ignore:overflow encountered in cast")
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["2e128", "-2e128"],
+        [
+            "1.79769313486231e308",
+            "-1.79769313486231e308",
+        ],  # 2 digits relaxed from np.finfo(np.float64).min/max
+    ],
+)
+@pytest.mark.parametrize(
+    "downcast", ["signed", "integer", "unsigned", "float"]
+)
+def test_to_numeric_downcast_string_large_float(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    if downcast == "float":
+        expected = pd.to_numeric(ps, downcast=downcast)
+        got = cudf.to_numeric(gs, downcast=downcast)
+
+        if PANDAS_GE_150:
+            assert_eq(expected, got)
+        else:
+            # Pandas bug: https://github.com/pandas-dev/pandas/issues/19729
+            with pytest.raises(AssertionError, match="Series are different"):
+                assert_eq(expected, got)
+    else:
+        expected = pd.Series([np.inf, -np.inf])
+        with pytest.warns(
+            UserWarning,
+            match="Downcasting from float to int "
+            "will be limited by float32 precision.",
+        ):
+            got = cudf.to_numeric(gs, downcast=downcast)
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series(["1", "a", "3"]),
+        pd.Series(["1", "a", "3", ""]),  # mix of unconvertible and empty str
+    ],
+)
+@pytest.mark.parametrize("errors", ["ignore", "raise", "coerce"])
+def test_to_numeric_error(data, errors):
+    if errors == "raise":
+        with pytest.raises(
+            ValueError, match="Unable to convert some strings to numerics."
+        ):
+            cudf.to_numeric(data, errors=errors)
+    else:
+        expect = pd.to_numeric(data, errors=errors)
+        got = cudf.to_numeric(data, errors=errors)
+
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("input_obj", [[1, cudf.NA, 3]])
+def test_series_construction_with_nulls(dtype, input_obj):
+    dtype = cudf.dtype(dtype)
+    # numpy case
+
+    expect = pd.Series(input_obj, dtype=np_dtypes_to_pandas_dtypes[dtype])
+    got = cudf.Series(input_obj, dtype=dtype).to_pandas(nullable=True)
+
+    assert_eq(expect, got)
+
+    # Test numpy array of objects case
+    np_data = [
+        dtype.type(v) if v is not cudf.NA else cudf.NA for v in input_obj
+    ]
+
+    expect = pd.Series(np_data, dtype=np_dtypes_to_pandas_dtypes[dtype])
+    got = cudf.Series(np_data, dtype=dtype).to_pandas(nullable=True)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [[True, False, True]],
+)
+@pytest.mark.parametrize(
+    "downcast", ["signed", "integer", "unsigned", "float"]
+)
+def test_series_to_numeric_bool(data, downcast):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expect = pd.to_numeric(ps, downcast=downcast)
+    got = cudf.to_numeric(gs, downcast=downcast)
+
+    assert_eq(expect, got)
diff --git a/python/cudf/cudf/tests/test_numpy_interop.py b/python/cudf/cudf/tests/test_numpy_interop.py
new file mode 100644
index 0000000..46324a8
--- /dev/null
+++ b/python/cudf/cudf/tests/test_numpy_interop.py
@@ -0,0 +1,95 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pytest
+
+from cudf import DataFrame, Series
+from cudf.testing._utils import assert_eq
+
+
+def test_to_records_noindex():
+    df = DataFrame()
+    df["a"] = aa = np.arange(10, dtype=np.int32)
+    df["b"] = bb = np.arange(10, 20, dtype=np.float64)
+
+    rec = df.to_records(index=False)
+    assert rec.dtype.names == ("a", "b")
+    np.testing.assert_array_equal(rec["a"], aa)
+    np.testing.assert_array_equal(rec["b"], bb)
+
+
+def test_to_records_withindex():
+    df = DataFrame()
+    df["a"] = aa = np.arange(10, dtype=np.int32)
+    df["b"] = bb = np.arange(10, 20, dtype=np.float64)
+
+    rec_indexed = df.to_records(index=True)
+    assert rec_indexed.size == len(aa)
+    assert rec_indexed.dtype.names == ("index", "a", "b")
+    np.testing.assert_array_equal(rec_indexed["a"], aa)
+    np.testing.assert_array_equal(rec_indexed["b"], bb)
+    np.testing.assert_array_equal(rec_indexed["index"], np.arange(10))
+
+
+@pytest.mark.parametrize("columns", [None, ("a", "b"), ("a",), ("b",)])
+def test_from_records_noindex(columns):
+    recdtype = np.dtype([("a", np.int32), ("b", np.float64)])
+    rec = np.recarray(10, dtype=recdtype)
+    rec.a = aa = np.arange(10, dtype=np.int32)
+    rec.b = bb = np.arange(10, 20, dtype=np.float64)
+    df = DataFrame.from_records(rec, columns=columns)
+
+    if columns and "a" in columns:
+        assert_eq(aa, df["a"].values)
+    if columns and "b" in columns:
+        assert_eq(bb, df["b"].values)
+    assert_eq(np.arange(10), df.index.values)
+
+
+@pytest.mark.parametrize("columns", [None, ("a", "b"), ("a",), ("b",)])
+def test_from_records_withindex(columns):
+    recdtype = np.dtype(
+        [("index", np.int64), ("a", np.int32), ("b", np.float64)]
+    )
+    rec = np.recarray(10, dtype=recdtype)
+    rec.index = ii = np.arange(30, 40)
+    rec.a = aa = np.arange(10, dtype=np.int32)
+    rec.b = bb = np.arange(10, 20, dtype=np.float64)
+    df = DataFrame.from_records(rec, index="index")
+
+    if columns and "a" in columns:
+        assert_eq(aa, df["a"].values)
+    if columns and "b" in columns:
+        assert_eq(bb, df["b"].values)
+    assert_eq(ii, df.index.values)
+
+
+def test_numpy_non_contiguious():
+    recdtype = np.dtype([("index", np.int64), ("a", np.int32)])
+    rec = np.recarray(10, dtype=recdtype)
+    rec.index = np.arange(30, 40)
+    rec.a = aa = np.arange(20, dtype=np.int32)[::2]
+    assert rec.a.flags["C_CONTIGUOUS"] is False
+
+    gdf = DataFrame.from_records(rec, index="index")
+    assert_eq(aa, gdf["a"].values)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        Series([1, 2, 3, -12, 12, 44]),
+        Series([1, 2, 3, -12, 12, 44], dtype="str"),
+        Series([1, 2, 3, -12, 12, 44]).index,
+        DataFrame({"a": [1, 2, 3, -1234], "b": [0.1, 0.2222, 0.4, -3.14]}),
+        DataFrame(
+            {"a": [1, 2, 3, -1234], "b": [0.1, 0.2222, 0.4, -3.14]}
+        ).index,
+    ],
+)
+@pytest.mark.parametrize("dtype", [None, "float", "int", "str"])
+def test_series_dataframe__array__(data, dtype):
+    gs = data
+
+    with pytest.raises(TypeError):
+        gs.__array__(dtype=dtype)
diff --git a/python/cudf/cudf/tests/test_offset.py b/python/cudf/cudf/tests/test_offset.py
new file mode 100644
index 0000000..1ed0461
--- /dev/null
+++ b/python/cudf/cudf/tests/test_offset.py
@@ -0,0 +1,61 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+import re
+
+import numpy as np
+import pytest
+
+from cudf import DateOffset
+
+INT64MAX = np.iinfo("int64").max
+
+
+@pytest.mark.parametrize("period", [1.5, 0.5, "string", "1", "1.0"])
+@pytest.mark.parametrize("freq", ["years", "months"])
+def test_construction_invalid(period, freq):
+    kwargs = {freq: period}
+    with pytest.raises(ValueError):
+        DateOffset(**kwargs)
+
+
+@pytest.mark.parametrize(
+    "unit", ["nanoseconds", "microseconds", "milliseconds", "seconds"]
+)
+def test_construct_max_offset(unit):
+    DateOffset(**{unit: np.iinfo("int64").max})
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"seconds": INT64MAX + 1},
+        {"seconds": INT64MAX, "minutes": 1},
+        {"minutes": INT64MAX},
+    ],
+)
+def test_offset_construction_overflow(kwargs):
+    with pytest.raises(NotImplementedError):
+        DateOffset(**kwargs)
+
+
+@pytest.mark.parametrize(
+    "unit",
+    [
+        "years",
+        "months",
+        "weeks",
+        "days",
+        "hours",
+        "minutes",
+        "seconds",
+        "milliseconds",
+        "microseconds",
+        "nanoseconds",
+    ],
+)
+@pytest.mark.parametrize("period", [0.5, -0.5, 0.71])
+def test_offset_no_fractional_periods(unit, period):
+    with pytest.raises(
+        ValueError, match=re.escape("Non-integer periods not supported")
+    ):
+        DateOffset(**{unit: period})
diff --git a/python/cudf/cudf/tests/test_onehot.py b/python/cudf/cudf/tests/test_onehot.py
new file mode 100644
index 0000000..baaca0b
--- /dev/null
+++ b/python/cudf/cudf/tests/test_onehot.py
@@ -0,0 +1,174 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from string import ascii_lowercase
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+pytestmark = pytest.mark.spilling
+
+
+@pytest.mark.parametrize(
+    "data, index",
+    [
+        (np.arange(10), None),
+        (["abc", "zyx", "pppp"], None),
+        ([], None),
+        (pd.Series(["cudf", "hello", "pandas"] * 10, dtype="category"), None),
+        (range(10), [1, 2, 3, 4, 5] * 2),
+    ],
+)
+def test_get_dummies(data, index):
+    gdf = cudf.DataFrame({"x": data}, index=index)
+    pdf = pd.DataFrame({"x": data}, index=index)
+
+    encoded_expected = pd.get_dummies(pdf, prefix="test")
+    with pytest.warns(FutureWarning):
+        encoded_actual = cudf.get_dummies(gdf, prefix="test")
+
+    assert_eq(
+        encoded_expected,
+        encoded_actual,
+        check_dtype=len(data) != 0,
+    )
+    encoded_actual = cudf.get_dummies(gdf, prefix="test", dtype=np.uint8)
+
+    assert_eq(
+        encoded_expected,
+        encoded_actual,
+        check_dtype=len(data) != 0,
+    )
+
+
+@pytest.mark.parametrize("n_cols", [5, 10, 20])
+def test_onehot_get_dummies_multicol(n_cols):
+    n_categories = 5
+    data = dict(
+        zip(ascii_lowercase, (np.arange(n_categories) for _ in range(n_cols)))
+    )
+
+    gdf = cudf.DataFrame(data)
+    pdf = pd.DataFrame(data)
+
+    encoded_expected = pd.get_dummies(pdf, prefix="test")
+    with pytest.warns(FutureWarning):
+        encoded_actual = cudf.get_dummies(gdf, prefix="test")
+
+    assert_eq(encoded_expected, encoded_actual)
+
+
+@pytest.mark.parametrize("nan_as_null", [True, False])
+@pytest.mark.parametrize("dummy_na", [True, False])
+def test_onehost_get_dummies_dummy_na(nan_as_null, dummy_na):
+    pdf = pd.DataFrame({"a": [0, 1, np.nan]})
+    df = cudf.DataFrame.from_pandas(pdf, nan_as_null=nan_as_null)
+
+    expected = pd.get_dummies(pdf, dummy_na=dummy_na, columns=["a"])
+    with pytest.warns(FutureWarning):
+        actual = cudf.get_dummies(df, dummy_na=dummy_na, columns=["a"])
+
+    if dummy_na and nan_as_null:
+        actual = actual.rename(columns={"a_<NA>": "a_nan"})[expected.columns]
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "prefix",
+    [
+        ["a", "b", "c"],
+        "",
+        None,
+        {"first": "one", "second": "two", "third": "three"},
+        "--",
+    ],
+)
+@pytest.mark.parametrize(
+    "prefix_sep",
+    [
+        ["a", "b", "c"],
+        "",
+        "++",
+        {"first": "*******", "second": "__________", "third": "#########"},
+    ],
+)
+def test_get_dummies_prefix_sep(prefix, prefix_sep):
+    data = {
+        "first": ["1", "2", "3"],
+        "second": ["abc", "def", "ghi"],
+        "third": ["ji", "ji", "ji"],
+    }
+
+    gdf = cudf.DataFrame(data)
+    pdf = pd.DataFrame(data)
+
+    encoded_expected = pd.get_dummies(
+        pdf, prefix=prefix, prefix_sep=prefix_sep
+    )
+    with pytest.warns(FutureWarning):
+        encoded_actual = cudf.get_dummies(
+            gdf, prefix=prefix, prefix_sep=prefix_sep
+        )
+
+    assert_eq(encoded_expected, encoded_actual)
+
+
+def test_get_dummies_with_nan():
+    df = cudf.DataFrame(
+        {"a": cudf.Series([1, 2, np.nan, None], nan_as_null=False)}
+    )
+
+    expected = pd.get_dummies(
+        df.to_pandas(nullable=True), dummy_na=True, columns=["a"]
+    )
+
+    with pytest.warns(FutureWarning):
+        actual = cudf.get_dummies(df, dummy_na=True, columns=["a"])
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        lambda: cudf.Series(["abc", "l", "a", "abc", "z", "xyz"]),
+        lambda: cudf.Index([None, 1, 2, 3.3, None, 0.2]),
+        lambda: cudf.Series([0.1, 2, 3, None, np.nan]),
+        lambda: cudf.Series([23678, 324, 1, 324], name="abc"),
+    ],
+)
+@pytest.mark.parametrize("prefix_sep", ["-", "#"])
+@pytest.mark.parametrize("prefix", [None, "hi"])
+@pytest.mark.parametrize("dtype", ["uint8", "int16"])
+def test_get_dummies_array_like(data, prefix_sep, prefix, dtype):
+    data = data()
+    pd_data = data.to_pandas()
+
+    expected = pd.get_dummies(
+        pd_data, prefix=prefix, prefix_sep=prefix_sep, dtype=dtype
+    )
+
+    actual = cudf.get_dummies(
+        data, prefix=prefix, prefix_sep=prefix_sep, dtype=dtype
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_get_dummies_array_like_with_nan():
+    ser = cudf.Series([0.1, 2, 3, None, np.nan], nan_as_null=False)
+
+    expected = pd.get_dummies(
+        ser.to_pandas(nullable=True), dummy_na=True, prefix="a", prefix_sep="_"
+    )
+
+    with pytest.warns(FutureWarning):
+        actual = cudf.get_dummies(
+            ser, dummy_na=True, prefix="a", prefix_sep="_"
+        )
+
+    assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/test_options.py b/python/cudf/cudf/tests/test_options.py
new file mode 100644
index 0000000..ef9c7ec
--- /dev/null
+++ b/python/cudf/cudf/tests/test_options.py
@@ -0,0 +1,129 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+from contextlib import redirect_stdout
+from io import StringIO
+
+import pytest
+
+import cudf
+
+
+@pytest.fixture(scope="class", autouse=False)
+def empty_option_environment():
+    old_option_environment = cudf.options._OPTIONS
+    cudf.options._OPTIONS = {}
+    yield
+    cudf.options._OPTIONS = old_option_environment
+
+
+@pytest.fixture(scope="function")
+def odd_option(empty_option_environment):
+    def validator(x):
+        if not x % 2 == 1:
+            raise ValueError(f"Invalid option value {x}")
+
+    cudf.options._register_option(
+        "odd_option",
+        1,
+        "An odd option.",
+        validator,
+    )
+    yield
+    del cudf.options._OPTIONS["odd_option"]
+
+
+@pytest.fixture(scope="function")
+def even_option(empty_option_environment):
+    def validator(x):
+        if not x % 2 == 0:
+            raise ValueError(f"Invalid option value {x}")
+
+    cudf.options._register_option(
+        "even_option", 0, "An even option.", validator
+    )
+    yield
+    del cudf.options._OPTIONS["even_option"]
+
+
+@pytest.mark.usefixtures("odd_option", "even_option")
+class TestCleanOptions:
+    def test_option_get_set(odd_option):
+        assert cudf.get_option("odd_option") == 1
+        cudf.set_option("odd_option", 101)
+        assert cudf.get_option("odd_option") == 101
+
+    def test_option_set_invalid(odd_option):
+        with pytest.raises(ValueError, match="Invalid option value 0"):
+            cudf.set_option("odd_option", 0)
+
+    def test_option_description(odd_option):
+        s = StringIO()
+        with redirect_stdout(s):
+            cudf.describe_option("odd_option")
+        s.seek(0)
+        expected = (
+            "odd_option:\n\tAn odd option.\n\t[Default: 1] [Current: 1]\n"
+        )
+        assert expected == s.read()
+
+    def test_option_description_all(odd_option, even_option):
+        s = StringIO()
+        with redirect_stdout(s):
+            cudf.describe_option()
+        s.seek(0)
+        expected = (
+            "odd_option:\n\tAn odd option.\n\t[Default: 1] [Current: 1]\n"
+            "even_option:\n\tAn even option.\n\t[Default: 0] [Current: 0]\n"
+        )
+        assert expected == s.read()
+
+
+@pytest.mark.parametrize("default_integer_bitwidth", [32, 64, None])
+def test_empty_option_context(default_integer_bitwidth):
+    prev_setting = cudf.get_option("default_integer_bitwidth")
+    cudf.set_option("default_integer_bitwidth", default_integer_bitwidth)
+    with cudf.option_context():
+        assert (
+            cudf.get_option("default_integer_bitwidth")
+            == default_integer_bitwidth
+        )
+
+    assert (
+        cudf.get_option("default_integer_bitwidth") == default_integer_bitwidth
+    )
+    cudf.set_option("default_integer_bitwidth", prev_setting)
+
+
+@pytest.mark.parametrize("pandas_compatible", [True, False])
+@pytest.mark.parametrize("default_integer_bitwidth", [32, 64])
+def test_option_context(pandas_compatible, default_integer_bitwidth):
+    prev_pandas_compatible_setting = cudf.get_option("mode.pandas_compatible")
+    prev_width_setting = cudf.get_option("default_integer_bitwidth")
+
+    with cudf.option_context(
+        "mode.pandas_compatible",
+        pandas_compatible,
+        "default_integer_bitwidth",
+        default_integer_bitwidth,
+    ):
+        assert cudf.get_option("mode.pandas_compatible") is pandas_compatible
+        assert (
+            cudf.get_option("default_integer_bitwidth")
+            is default_integer_bitwidth
+        )
+
+    assert (
+        cudf.get_option("mode.pandas_compatible")
+        is prev_pandas_compatible_setting
+    )
+    assert cudf.get_option("default_integer_bitwidth") is prev_width_setting
+
+
+def test_options_context_error():
+    with pytest.raises(ValueError):
+        with cudf.option_context("mode.pandas_compatible"):
+            pass
+
+    with pytest.raises(ValueError):
+        with cudf.option_context("mode.pandas_compatible", 1, 2):
+            pass
diff --git a/python/cudf/cudf/tests/test_orc.py b/python/cudf/cudf/tests/test_orc.py
new file mode 100644
index 0000000..07aa543
--- /dev/null
+++ b/python/cudf/cudf/tests/test_orc.py
@@ -0,0 +1,1939 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import datetime
+import decimal
+import os
+import random
+from io import BytesIO
+from string import ascii_lowercase
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.orc
+import pyorc
+import pytest
+
+import cudf
+from cudf.io.orc import ORCWriter
+from cudf.testing import assert_frame_equal
+from cudf.testing._utils import (
+    assert_eq,
+    expect_warning_if,
+    gen_rand_series,
+    supported_numpy_dtypes,
+)
+
+# Removal of these deprecated features is no longer imminent. They will not be
+# removed until a suitable alternative has been implemented. As a result, we
+# also do not want to stop testing them yet.
+# https://github.com/rapidsai/cudf/issues/11519
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:(num_rows|skiprows) is deprecated and will be removed."
+)
+
+
+@pytest.fixture(scope="module")
+def datadir(datadir):
+    return datadir / "orc"
+
+
+@pytest.fixture
+def path_or_buf(datadir):
+    fname = datadir / "TestOrcFile.test1.orc"
+    try:
+        with open(fname, "rb") as f:
+            buffer = BytesIO(f.read())
+    except Exception as excpr:
+        if type(excpr).__name__ == "FileNotFoundError":
+            pytest.skip(".parquet file is not found")
+        else:
+            print(type(excpr).__name__)
+
+    def _make_path_or_buf(src):
+        if src == "filepath":
+            return str(fname)
+        if src == "pathobj":
+            return fname
+        if src == "bytes_io":
+            return buffer
+        if src == "bytes":
+            return buffer.getvalue()
+        if src == "url":
+            return fname.as_uri()
+
+        raise ValueError("Invalid source type")
+
+    yield _make_path_or_buf
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["pyarrow", "cudf"])
+@pytest.mark.parametrize("use_index", [False, True])
+@pytest.mark.parametrize(
+    "inputfile, columns",
+    [
+        ("TestOrcFile.emptyFile.orc", ["boolean1"]),
+        (
+            "TestOrcFile.test1.orc",
+            [
+                "boolean1",
+                "byte1",
+                "short1",
+                "int1",
+                "long1",
+                "float1",
+                "double1",
+            ],
+        ),
+        ("TestOrcFile.RLEv2.orc", ["x", "y"]),
+        ("TestOrcFile.testSnappy.orc", None),
+        ("TestOrcFile.demo-12-zlib.orc", ["_col2", "_col3", "_col4", "_col5"]),
+    ],
+)
+def test_orc_reader_basic(datadir, inputfile, columns, use_index, engine):
+    path = datadir / inputfile
+
+    expect = pd.read_orc(path, columns=columns)
+    got = cudf.read_orc(
+        path, engine=engine, columns=columns, use_index=use_index
+    )
+
+    assert_frame_equal(cudf.from_pandas(expect), got, check_categorical=False)
+
+
+def test_orc_reader_filenotfound(tmpdir):
+    with pytest.raises(FileNotFoundError):
+        cudf.read_orc("TestMissingFile.orc")
+
+    with pytest.raises(FileNotFoundError):
+        cudf.read_orc(tmpdir.mkdir("cudf_orc"))
+
+
+def test_orc_reader_local_filepath():
+    path = "~/TestLocalFile.orc"
+    if not os.path.isfile(path):
+        pytest.skip("Local .orc file is not found")
+
+    cudf.read_orc(path)
+
+
+@pytest.mark.parametrize(
+    "src", ["filepath", "pathobj", "bytes_io", "bytes", "url"]
+)
+def test_orc_reader_filepath_or_buffer(path_or_buf, src):
+    cols = ["int1", "long1", "float1", "double1"]
+
+    expect = pd.read_orc(path_or_buf("filepath"), columns=cols)
+    got = cudf.read_orc(path_or_buf(src), columns=cols)
+
+    assert_eq(expect, got)
+
+
+def test_orc_reader_trailing_nulls(datadir):
+    path = datadir / "TestOrcFile.nulls-at-end-snappy.orc"
+
+    expect = pd.read_orc(path).fillna(0)
+    got = cudf.read_orc(path).fillna(0)
+
+    # PANDAS uses NaN to represent invalid data, which forces float dtype
+    # For comparison, we can replace NaN with 0 and cast to the cuDF dtype
+    for col in expect.columns:
+        expect[col] = expect[col].astype(got[col].dtype)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+@pytest.mark.parametrize("use_index", [False, True])
+@pytest.mark.parametrize(
+    "inputfile",
+    ["TestOrcFile.testDate1900.orc", "TestOrcFile.testDate2038.orc"],
+)
+def test_orc_reader_datetimestamp(datadir, inputfile, use_index):
+    path = datadir / inputfile
+    try:
+        orcfile = pa.orc.ORCFile(path)
+    except pa.ArrowIOError as e:
+        pytest.skip(".orc file is not found: %s" % e)
+
+    pdf = orcfile.read().to_pandas(date_as_object=False)
+    gdf = cudf.read_orc(path, use_index=use_index)
+
+    assert_eq(pdf, gdf, check_categorical=False)
+
+
+def test_orc_reader_strings(datadir):
+    path = datadir / "TestOrcFile.testStringAndBinaryStatistics.orc"
+
+    expect = pd.read_orc(path, columns=["string1"])
+    got = cudf.read_orc(path, columns=["string1"])
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_orc_read_statistics(datadir):
+    # Read in file containing 2 columns ("int1" and "string1") and 3 stripes
+    # (sizes 5000, 5000 and 1000 respectively). Each stripe has the same value
+    # in every one of its rows. The values the stripes have are 1, 2, and 3 in
+    # "int1" and "one", "two", and "three" in "string1".
+    path = datadir / "TestOrcFile.testStripeLevelStats.orc"
+    try:
+        (
+            file_statistics,
+            stripes_statistics,
+        ) = cudf.io.orc.read_orc_statistics([path, path])
+    except pa.ArrowIOError as e:
+        pytest.skip(".orc file is not found: %s" % e)
+
+    # Check numberOfValues
+    assert_eq(file_statistics[0]["int1"]["number_of_values"], 11_000)
+    assert_eq(
+        file_statistics[0]["int1"]["number_of_values"],
+        sum(
+            [
+                stripes_statistics[0]["int1"]["number_of_values"],
+                stripes_statistics[1]["int1"]["number_of_values"],
+                stripes_statistics[2]["int1"]["number_of_values"],
+            ]
+        ),
+    )
+    assert_eq(
+        stripes_statistics[1]["int1"]["number_of_values"],
+        stripes_statistics[1]["string1"]["number_of_values"],
+    )
+    assert_eq(stripes_statistics[2]["string1"]["number_of_values"], 1_000)
+
+    # Check other statistics
+    assert_eq(stripes_statistics[2]["string1"]["has_null"], False)
+    assert_eq(
+        file_statistics[0]["int1"]["minimum"],
+        min(
+            stripes_statistics[0]["int1"]["minimum"],
+            stripes_statistics[1]["int1"]["minimum"],
+            stripes_statistics[2]["int1"]["minimum"],
+        ),
+    )
+    assert_eq(file_statistics[0]["int1"]["minimum"], 1)
+    assert_eq(file_statistics[0]["string1"]["minimum"], "one")
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+@pytest.mark.parametrize(
+    "predicate,expected_len",
+    [
+        ([[("int1", "==", 1)]], 5000),
+        ([[("int1", "<=", 2)]], 10000),
+        ([[("int1", "==", -1)]], 0),
+        ([[("int1", "in", range(3))]], 10000),
+        ([[("int1", "in", {1, 3})]], 6000),
+        ([[("int1", "not in", {1, 3})]], 5000),
+    ],
+)
+def test_orc_read_filtered(datadir, engine, predicate, expected_len):
+    path = datadir / "TestOrcFile.testStripeLevelStats.orc"
+    try:
+        df_filtered = cudf.read_orc(path, engine=engine, filters=predicate)
+    except pa.ArrowIOError as e:
+        pytest.skip(".orc file is not found: %s" % e)
+
+    # Assert # of rows after filtering
+    assert len(df_filtered) == expected_len
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+def test_orc_read_stripes(datadir, engine):
+    path = datadir / "TestOrcFile.testDate1900.orc"
+    try:
+        pdf = cudf.read_orc(path, engine=engine)
+    except pa.ArrowIOError as e:
+        pytest.skip(".orc file is not found: %s" % e)
+
+    num_rows, stripes, col_names = cudf.io.read_orc_metadata(path)
+
+    # Read stripes one at a time
+    gdf = [
+        cudf.read_orc(path, engine=engine, stripes=[[i]])
+        for i in range(stripes)
+    ]
+    gdf = cudf.concat(gdf).reset_index(drop=True)
+    assert_eq(pdf, gdf, check_categorical=False, check_index_type=True)
+
+    # Read stripes all at once
+    gdf = cudf.read_orc(
+        path, engine=engine, stripes=[[int(x) for x in range(stripes)]]
+    )
+    assert_eq(pdf, gdf, check_categorical=False)
+
+    # Read only some stripes
+    gdf = cudf.read_orc(path, engine=engine, stripes=[[0, 1]])
+    assert_eq(gdf, pdf.head(25000))
+    gdf = cudf.read_orc(path, engine=engine, stripes=[[0, stripes - 1]])
+    assert_eq(
+        gdf,
+        cudf.concat([pdf.head(15000), pdf.tail(10000)], ignore_index=True),
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("num_rows", [1, 100, 3000])
+@pytest.mark.parametrize("skiprows", [0, 1, 3000])
+def test_orc_read_rows(datadir, skiprows, num_rows):
+    path = datadir / "TestOrcFile.decimal.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path, skiprows=skiprows, num_rows=num_rows)
+
+    # Slice rows out of the whole dataframe for comparison as PyArrow doesn't
+    # have an API to read a subsection of rows from the file
+    pdf = pdf[skiprows : skiprows + num_rows]
+    pdf = pdf.reset_index(drop=True)
+
+    assert_eq(pdf, gdf)
+
+
+def test_orc_read_skiprows():
+    buff = BytesIO()
+    data = [
+        True,
+        False,
+        True,
+        False,
+        None,
+        True,
+        True,
+        True,
+        False,
+        None,
+        False,
+        False,
+        True,
+        True,
+        True,
+        True,
+    ]
+    writer = pyorc.Writer(buff, pyorc.Struct(a=pyorc.Boolean()))
+    writer.writerows([(d,) for d in data])
+    writer.close()
+
+    # testing 10 skiprows due to a boolean specific bug fix that didn't
+    # repro for other sizes of data
+    skiprows = 10
+
+    expected = (
+        pd.read_orc(buff)[skiprows:].reset_index(drop=True).astype("bool")
+    )
+    got = cudf.read_orc(buff, skiprows=skiprows)
+    assert_eq(expected, got)
+
+
+def test_orc_reader_uncompressed_block(datadir):
+    path = datadir / "uncompressed_snappy.orc"
+
+    expect = pd.read_orc(path)
+    got = cudf.read_orc(path)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_orc_reader_nodata_block(datadir):
+    path = datadir / "nodata.orc"
+
+    expect = pd.read_orc(path)
+    got = cudf.read_orc(path, num_rows=1)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+@pytest.mark.parametrize("compression", [None, "snappy"])
+@pytest.mark.parametrize(
+    "reference_file, columns",
+    [
+        (
+            "TestOrcFile.test1.orc",
+            [
+                "boolean1",
+                "byte1",
+                "short1",
+                "int1",
+                "long1",
+                "float1",
+                "double1",
+            ],
+        ),
+        ("TestOrcFile.demo-12-zlib.orc", ["_col1", "_col3", "_col5"]),
+    ],
+)
+def test_orc_writer(datadir, tmpdir, reference_file, columns, compression):
+    pdf_fname = datadir / reference_file
+    gdf_fname = tmpdir.join("gdf.orc")
+
+    expect = cudf.from_pandas(pd.read_orc(pdf_fname, columns=columns))
+    expect.to_orc(gdf_fname.strpath, compression=compression)
+    got = cudf.from_pandas(pd.read_orc(gdf_fname, columns=columns))
+
+    assert_frame_equal(expect, got)
+
+
+@pytest.mark.parametrize("stats_freq", ["NONE", "STRIPE", "ROWGROUP"])
+def test_orc_writer_statistics_frequency(datadir, tmpdir, stats_freq):
+    reference_file = "TestOrcFile.demo-12-zlib.orc"
+    pdf_fname = datadir / reference_file
+    gdf_fname = tmpdir.join("gdf.orc")
+
+    expect = cudf.from_pandas(pd.read_orc(pdf_fname))
+    expect.to_orc(gdf_fname.strpath, statistics=stats_freq)
+    got = cudf.from_pandas(pd.read_orc(gdf_fname))
+
+    assert_frame_equal(expect, got)
+
+
+@pytest.mark.parametrize("stats_freq", ["NONE", "STRIPE", "ROWGROUP"])
+def test_chunked_orc_writer_statistics_frequency(datadir, tmpdir, stats_freq):
+    reference_file = "TestOrcFile.test1.orc"
+    pdf_fname = datadir / reference_file
+    gdf_fname = tmpdir.join("chunked_gdf.orc")
+
+    columns = [
+        "boolean1",
+        "byte1",
+        "short1",
+        "int1",
+        "long1",
+        "float1",
+        "double1",
+    ]
+    pdf = pd.read_orc(pdf_fname, columns=columns)
+    gdf = cudf.from_pandas(pdf)
+    expect = pd.concat([pdf, pdf]).reset_index(drop=True)
+
+    writer = ORCWriter(gdf_fname, statistics=stats_freq)
+    writer.write_table(gdf)
+    writer.write_table(gdf)
+    writer.close()
+
+    got = pd.read_orc(gdf_fname)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("compression", [None, "snappy"])
+@pytest.mark.parametrize(
+    "reference_file, columns",
+    [
+        (
+            "TestOrcFile.test1.orc",
+            [
+                "boolean1",
+                "byte1",
+                "short1",
+                "int1",
+                "long1",
+                "float1",
+                "double1",
+            ],
+        ),
+        ("TestOrcFile.demo-12-zlib.orc", ["_col1", "_col3", "_col5"]),
+    ],
+)
+def test_chunked_orc_writer(
+    datadir, tmpdir, reference_file, columns, compression
+):
+    pdf_fname = datadir / reference_file
+    gdf_fname = tmpdir.join("chunked_gdf.orc")
+
+    pdf = pd.read_orc(pdf_fname, columns=columns)
+    gdf = cudf.from_pandas(pdf)
+    expect = pd.concat([pdf, pdf]).reset_index(drop=True)
+
+    writer = ORCWriter(gdf_fname, compression=compression)
+    writer.write_table(gdf)
+    writer.write_table(gdf)
+    writer.close()
+
+    got = pd.read_orc(gdf_fname, columns=columns)
+    assert_frame_equal(cudf.from_pandas(expect), cudf.from_pandas(got))
+
+
+@pytest.mark.parametrize(
+    "dtypes",
+    [
+        {"c": str, "a": int},
+        {"c": int, "a": str},
+        {"c": int, "a": str, "b": float},
+        {"c": str, "a": object},
+    ],
+)
+def test_orc_writer_strings(tmpdir, dtypes):
+    gdf_fname = tmpdir.join("gdf_strings.orc")
+
+    expect = cudf.datasets.randomdata(nrows=10, dtypes=dtypes, seed=1)
+    expect.to_orc(gdf_fname)
+    got = pd.read_orc(gdf_fname)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "dtypes",
+    [
+        {"c": str, "a": int},
+        {"c": int, "a": str},
+        {"c": int, "a": str, "b": float},
+        {"c": str, "a": object},
+    ],
+)
+def test_chunked_orc_writer_strings(tmpdir, dtypes):
+    gdf_fname = tmpdir.join("chunked_gdf_strings.orc")
+
+    gdf = cudf.datasets.randomdata(nrows=10, dtypes=dtypes, seed=1)
+    pdf = gdf.to_pandas()
+    expect = pd.concat([pdf, pdf]).reset_index(drop=True)
+    writer = ORCWriter(gdf_fname)
+    writer.write_table(gdf)
+    writer.write_table(gdf)
+    writer.close()
+
+    got = pd.read_orc(gdf_fname)
+
+    assert_eq(expect, got)
+
+
+def test_orc_writer_sliced(tmpdir):
+    cudf_path = tmpdir.join("cudf.orc")
+
+    df = pd.DataFrame()
+    df["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    df = cudf.from_pandas(df)
+
+    df_select = df.iloc[1:3]
+
+    df_select.to_orc(cudf_path)
+    assert_eq(cudf.read_orc(cudf_path), df_select)
+
+
+@pytest.mark.parametrize(
+    "orc_file",
+    [
+        "TestOrcFile.decimal.orc",
+        "TestOrcFile.decimal.same.values.orc",
+        "TestOrcFile.decimal.multiple.values.orc",
+        # For additional information take look at PR 7034
+        "TestOrcFile.decimal.runpos.issue.orc",
+    ],
+)
+def test_orc_reader_decimal_type(datadir, orc_file):
+    file_path = datadir / orc_file
+
+    pdf = pd.read_orc(file_path)
+    df = cudf.read_orc(file_path)
+
+    assert_eq(pdf, df)
+
+
+def test_orc_decimal_precision_fail(datadir):
+    file_path = datadir / "TestOrcFile.int_decimal.precision_19.orc"
+
+    # Shouldn't cause failure if decimal column is not chosen to be read.
+    pdf = pd.read_orc(file_path, columns=["int"])
+    gdf = cudf.read_orc(file_path, columns=["int"])
+
+    assert_eq(pdf, gdf)
+
+
+# For additional information take look at PR 6636 and 6702
+@pytest.mark.parametrize(
+    "orc_file",
+    [
+        "TestOrcFile.boolean_corruption_PR_6636.orc",
+        "TestOrcFile.boolean_corruption_PR_6702.orc",
+    ],
+)
+def test_orc_reader_boolean_type(datadir, orc_file):
+    file_path = datadir / orc_file
+
+    pdf = pd.read_orc(file_path)
+    df = cudf.read_orc(file_path).to_pandas()
+
+    assert_eq(pdf, df)
+
+
+def test_orc_reader_tzif_timestamps(datadir):
+    # Contains timstamps in the range covered by the TZif file
+    # Other timedate tests only cover "future" times
+    path = datadir / "TestOrcFile.lima_timezone.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path)
+
+    assert_eq(pdf, gdf)
+
+
+def test_int_overflow(tmpdir):
+    file_path = tmpdir.join("gdf_overflow.orc")
+
+    # The number of rows and the large element trigger delta encoding
+    num_rows = 513
+    df = cudf.DataFrame({"a": [None] * num_rows}, dtype="int32")
+    df["a"][0] = 1024 * 1024 * 1024
+    df["a"][num_rows - 1] = 1
+    df.to_orc(file_path)
+
+    assert_eq(cudf.read_orc(file_path), df)
+
+
+def normalized_equals(value1, value2):
+    # need naive time object for numpy to convert to datetime64
+    if isinstance(value1, datetime.datetime):
+        value1 = value1.replace(tzinfo=None)
+    if isinstance(value2, datetime.datetime):
+        value2 = value2.replace(tzinfo=None)
+
+    if isinstance(value1, (datetime.datetime, np.datetime64)):
+        value1 = np.datetime64(value1, "ms")
+    if isinstance(value2, (datetime.datetime, np.datetime64)):
+        value2 = np.datetime64(value2, "ms")
+
+    # Compare integers with floats now
+    if isinstance(value1, float) or isinstance(value2, float):
+        return np.isclose(value1, value2)
+
+    return value1 == value2
+
+
+@pytest.mark.parametrize("stats_freq", ["STRIPE", "ROWGROUP"])
+@pytest.mark.parametrize("nrows", [1, 100, 6000000])
+def test_orc_write_statistics(tmpdir, datadir, nrows, stats_freq):
+    supported_stat_types = supported_numpy_dtypes + ["str"]
+    # Can't write random bool columns until issue #6763 is fixed
+    if nrows == 6000000:
+        supported_stat_types.remove("bool")
+
+    # Make a dataframe
+    gdf = cudf.DataFrame(
+        {
+            "col_" + str(dtype): gen_rand_series(dtype, nrows, has_nulls=True)
+            for dtype in supported_stat_types
+        }
+    )
+    fname = tmpdir.join("gdf.orc")
+
+    # Write said dataframe to ORC with cuDF
+    gdf.to_orc(fname.strpath, statistics=stats_freq)
+
+    # Read back written ORC's statistics
+    orc_file = pa.orc.ORCFile(fname)
+    (
+        file_stats,
+        stripes_stats,
+    ) = cudf.io.orc.read_orc_statistics([fname])
+
+    # check file stats
+    for col in gdf:
+        if "minimum" in file_stats[0][col]:
+            stats_min = file_stats[0][col]["minimum"]
+            if stats_min is not None:
+                actual_min = gdf[col].min()
+                assert normalized_equals(actual_min, stats_min)
+        if "maximum" in file_stats[0][col]:
+            stats_max = file_stats[0][col]["maximum"]
+            if stats_max is not None:
+                actual_max = gdf[col].max()
+                assert normalized_equals(actual_max, stats_max)
+        if "number_of_values" in file_stats[0][col]:
+            stats_num_vals = file_stats[0][col]["number_of_values"]
+            if stats_num_vals is not None:
+                actual_num_vals = gdf[col].count()
+                assert stats_num_vals == actual_num_vals
+
+    # compare stripe statistics with actual min/max
+    for stripe_idx in range(0, orc_file.nstripes):
+        stripe = orc_file.read_stripe(stripe_idx)
+        # pandas is unable to handle min/max of string col with nulls
+        stripe_df = cudf.DataFrame(stripe.to_pandas())
+        for col in stripe_df:
+            if "minimum" in stripes_stats[stripe_idx][col]:
+                stats_min = stripes_stats[stripe_idx][col]["minimum"]
+                if stats_min is not None:
+                    actual_min = stripe_df[col].min()
+                    assert normalized_equals(actual_min, stats_min)
+
+            if "maximum" in stripes_stats[stripe_idx][col]:
+                stats_max = stripes_stats[stripe_idx][col]["maximum"]
+                if stats_max is not None:
+                    actual_max = stripe_df[col].max()
+                    assert normalized_equals(actual_max, stats_max)
+
+            if "number_of_values" in stripes_stats[stripe_idx][col]:
+                stats_num_vals = stripes_stats[stripe_idx][col][
+                    "number_of_values"
+                ]
+                if stats_num_vals is not None:
+                    actual_num_vals = stripe_df[col].count()
+                    assert stats_num_vals == actual_num_vals
+
+
+@pytest.mark.parametrize("stats_freq", ["STRIPE", "ROWGROUP"])
+@pytest.mark.parametrize("nrows", [2, 100, 6000000])
+def test_orc_chunked_write_statistics(tmpdir, datadir, nrows, stats_freq):
+    np.random.seed(0)
+    supported_stat_types = supported_numpy_dtypes + ["str"]
+    # Can't write random bool columns until issue #6763 is fixed
+    if nrows == 6000000:
+        supported_stat_types.remove("bool")
+
+    gdf_fname = tmpdir.join("chunked_stats.orc")
+    writer = ORCWriter(gdf_fname)
+
+    max_char_length = 1000 if nrows < 10000 else 100
+
+    # Make a dataframe
+    gdf = cudf.DataFrame(
+        {
+            "col_"
+            + str(dtype): gen_rand_series(
+                dtype,
+                int(nrows / 2),
+                has_nulls=True,
+                low=0,
+                high=max_char_length,
+            )
+            for dtype in supported_stat_types
+        }
+    )
+
+    pdf1 = gdf.to_pandas()
+    writer.write_table(gdf)
+    # gdf is specifically being reused here to ensure the data is destroyed
+    # before the next write_table call to ensure the data is persisted inside
+    # write and no pointers are saved into the original table
+    gdf = cudf.DataFrame(
+        {
+            "col_"
+            + str(dtype): gen_rand_series(
+                dtype,
+                int(nrows / 2),
+                has_nulls=True,
+                low=0,
+                high=max_char_length,
+            )
+            for dtype in supported_stat_types
+        }
+    )
+    pdf2 = gdf.to_pandas()
+    writer.write_table(gdf)
+    writer.close()
+
+    # pandas is unable to handle min/max of string col with nulls
+    expect = cudf.DataFrame(pd.concat([pdf1, pdf2]).reset_index(drop=True))
+
+    # Read back written ORC's statistics
+    orc_file = pa.orc.ORCFile(gdf_fname)
+    (
+        file_stats,
+        stripes_stats,
+    ) = cudf.io.orc.read_orc_statistics([gdf_fname])
+
+    # check file stats
+    for col in expect:
+        if "minimum" in file_stats[0][col]:
+            stats_min = file_stats[0][col]["minimum"]
+            if stats_min is not None:
+                actual_min = expect[col].min()
+                assert normalized_equals(actual_min, stats_min)
+        if "maximum" in file_stats[0][col]:
+            stats_max = file_stats[0][col]["maximum"]
+            if stats_max is not None:
+                actual_max = expect[col].max()
+                assert normalized_equals(actual_max, stats_max)
+        if "number_of_values" in file_stats[0][col]:
+            stats_num_vals = file_stats[0][col]["number_of_values"]
+            if stats_num_vals is not None:
+                actual_num_vals = expect[col].count()
+                assert stats_num_vals == actual_num_vals
+
+    # compare stripe statistics with actual min/max
+    for stripe_idx in range(0, orc_file.nstripes):
+        stripe = orc_file.read_stripe(stripe_idx)
+        # pandas is unable to handle min/max of string col with nulls
+        stripe_df = cudf.DataFrame(stripe.to_pandas())
+        for col in stripe_df:
+            if "minimum" in stripes_stats[stripe_idx][col]:
+                stats_min = stripes_stats[stripe_idx][col]["minimum"]
+                if stats_min is not None:
+                    actual_min = stripe_df[col].min()
+                    assert normalized_equals(actual_min, stats_min)
+
+            if "maximum" in stripes_stats[stripe_idx][col]:
+                stats_max = stripes_stats[stripe_idx][col]["maximum"]
+                if stats_max is not None:
+                    actual_max = stripe_df[col].max()
+                    assert normalized_equals(actual_max, stats_max)
+
+            if "number_of_values" in stripes_stats[stripe_idx][col]:
+                stats_num_vals = stripes_stats[stripe_idx][col][
+                    "number_of_values"
+                ]
+                if stats_num_vals is not None:
+                    actual_num_vals = stripe_df[col].count()
+                    assert stats_num_vals == actual_num_vals
+
+
+@pytest.mark.parametrize("nrows", [1, 100, 6000000])
+def test_orc_write_bool_statistics(tmpdir, datadir, nrows):
+    # Make a dataframe
+    gdf = cudf.DataFrame({"col_bool": gen_rand_series("bool", nrows)})
+    fname = tmpdir.join("gdf.orc")
+
+    # Write said dataframe to ORC with cuDF
+    gdf.to_orc(fname.strpath)
+
+    # Read back written ORC's statistics
+    orc_file = pa.orc.ORCFile(fname)
+    (
+        file_stats,
+        stripes_stats,
+    ) = cudf.io.orc.read_orc_statistics([fname])
+
+    # check file stats
+    col = "col_bool"
+    if "true_count" in file_stats[0][col]:
+        stats_true_count = file_stats[0][col]["true_count"]
+        actual_true_count = gdf[col].sum()
+        assert normalized_equals(actual_true_count, stats_true_count)
+
+    if "number_of_values" in file_stats[0][col]:
+        stats_valid_count = file_stats[0][col]["number_of_values"]
+        actual_valid_count = gdf[col].valid_count
+        assert normalized_equals(actual_valid_count, stats_valid_count)
+
+    # compare stripe statistics with actual min/max
+    for stripe_idx in range(0, orc_file.nstripes):
+        stripe = orc_file.read_stripe(stripe_idx)
+        # pandas is unable to handle min/max of string col with nulls
+        stripe_df = cudf.DataFrame(stripe.to_pandas())
+
+        if "true_count" in stripes_stats[stripe_idx][col]:
+            actual_true_count = stripe_df[col].sum()
+            stats_true_count = stripes_stats[stripe_idx][col]["true_count"]
+            assert normalized_equals(actual_true_count, stats_true_count)
+
+        if "number_of_values" in stripes_stats[stripe_idx][col]:
+            actual_valid_count = stripe_df[col].valid_count
+            stats_valid_count = stripes_stats[stripe_idx][col][
+                "number_of_values"
+            ]
+            assert normalized_equals(actual_valid_count, stats_valid_count)
+
+
+def test_orc_reader_gmt_timestamps(datadir):
+    path = datadir / "TestOrcFile.gmt.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path)
+    assert_eq(pdf, gdf)
+
+
+def test_orc_bool_encode_fail():
+    np.random.seed(0)
+    buffer = BytesIO()
+
+    # Generate a boolean column longer than a single stripe
+    fail_df = cudf.DataFrame({"col": gen_rand_series("bool", 600000)})
+    # Invalidate the first row in the second stripe to break encoding
+    fail_df["col"][500000] = None
+
+    # Should throw instead of generating a file that is incompatible
+    # with other readers (see issue #6763)
+    with pytest.raises(RuntimeError):
+        fail_df.to_orc(buffer)
+
+    # Generate a boolean column that fits into a single stripe
+    okay_df = cudf.DataFrame({"col": gen_rand_series("bool", 500000)})
+    okay_df["col"][500000 - 1] = None
+    # Invalid row is in the last row group of the stripe;
+    # encoding is assumed to be correct
+    okay_df.to_orc(buffer)
+
+    # Also validate data
+    pdf = pd.read_orc(buffer)
+
+    assert_eq(okay_df.to_pandas(nullable=True), pdf)
+
+
+def test_nanoseconds_overflow():
+    buffer = BytesIO()
+    # Use nanosecond values that take more than 32 bits to encode
+    s = cudf.Series([710424008, -1338482640], dtype="datetime64[ns]")
+    expected = cudf.DataFrame({"s": s})
+    expected.to_orc(buffer)
+
+    cudf_got = cudf.read_orc(buffer)
+    assert_eq(expected, cudf_got)
+
+    pandas_got = pd.read_orc(buffer)
+    assert_eq(expected, pandas_got)
+
+
+def test_empty_dataframe():
+    buffer = BytesIO()
+    expected = cudf.DataFrame()
+    expected.to_orc(buffer)
+
+    # Raise error if column name is mentioned, but it doesn't exist.
+    with pytest.raises(RuntimeError):
+        cudf.read_orc(buffer, columns=["a"])
+
+    got_df = cudf.read_orc(buffer)
+    expected_pdf = pd.read_orc(buffer)
+
+    assert_eq(expected, got_df)
+    assert_eq(expected_pdf, got_df)
+
+
+@pytest.mark.parametrize(
+    "data", [[None, ""], ["", None], [None, None], ["", ""]]
+)
+def test_empty_string_columns(data):
+    buffer = BytesIO()
+
+    expected = cudf.DataFrame({"string": data}, dtype="str")
+    expected.to_orc(buffer)
+
+    expected_pdf = pd.read_orc(buffer)
+    got_df = cudf.read_orc(buffer)
+
+    assert_eq(expected, got_df)
+    assert_eq(
+        expected_pdf,
+        got_df.to_pandas(nullable=True)
+        if expected_pdf["string"].dtype == pd.StringDtype()
+        else got_df,
+    )
+
+
+@pytest.mark.parametrize("scale", [-3, 0, 3])
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+def test_orc_writer_decimal(tmpdir, scale, decimal_type):
+    np.random.seed(0)
+    fname = tmpdir / "decimal.orc"
+
+    expected = cudf.DataFrame({"dec_val": gen_rand_series("i", 100)})
+    expected["dec_val"] = expected["dec_val"].astype(decimal_type(7, scale))
+
+    expected.to_orc(fname)
+
+    got = pd.read_orc(fname)
+    assert_eq(expected.to_pandas()["dec_val"], got["dec_val"])
+
+
+@pytest.mark.parametrize("num_rows", [1, 100, 3000])
+def test_orc_reader_multiple_files(datadir, num_rows):
+    path = datadir / "TestOrcFile.testSnappy.orc"
+
+    df_1 = pd.read_orc(path)
+    df_2 = pd.read_orc(path)
+    df = pd.concat([df_1, df_2], ignore_index=True)
+
+    gdf = cudf.read_orc([path, path], num_rows=num_rows).to_pandas()
+
+    # Slice rows out of the whole dataframe for comparison as PyArrow doesn't
+    # have an API to read a subsection of rows from the file
+    df = df[:num_rows]
+    df = df.reset_index(drop=True)
+
+    assert_eq(df, gdf)
+
+
+def test_orc_reader_multi_file_single_stripe(datadir):
+    path = datadir / "TestOrcFile.testSnappy.orc"
+
+    # should raise an exception
+    with pytest.raises(ValueError):
+        cudf.read_orc([path, path], stripes=[0])
+
+
+def test_orc_reader_multi_file_multi_stripe(datadir):
+    path = datadir / "TestOrcFile.testStripeLevelStats.orc"
+    gdf = cudf.read_orc([path, path], stripes=[[0, 1], [2]])
+    pdf = pd.read_orc(path)
+    assert_eq(pdf, gdf)
+
+
+def test_orc_string_stream_offset_issue():
+    size = 30000
+    vals = {
+        str(x): [decimal.Decimal(1)] * size if x != 0 else ["XYZ"] * size
+        for x in range(0, 5)
+    }
+    df = cudf.DataFrame(vals)
+
+    buffer = BytesIO()
+    df.to_orc(buffer)
+
+    assert_eq(df, cudf.read_orc(buffer))
+
+
+# Data is generated using pyorc module
+def generate_list_struct_buff(size=100_000):
+    rd = random.Random(1)
+    np.random.seed(seed=1)
+
+    buff = BytesIO()
+
+    schema = {
+        "lvl3_list": pyorc.Array(pyorc.Array(pyorc.Array(pyorc.BigInt()))),
+        "lvl1_list": pyorc.Array(pyorc.BigInt()),
+        "lvl1_struct": pyorc.Struct(
+            **{"a": pyorc.BigInt(), "b": pyorc.BigInt()}
+        ),
+        "lvl2_struct": pyorc.Struct(
+            **{
+                "a": pyorc.BigInt(),
+                "lvl1_struct": pyorc.Struct(
+                    **{"c": pyorc.BigInt(), "d": pyorc.BigInt()}
+                ),
+            }
+        ),
+        "list_nests_struct": pyorc.Array(
+            pyorc.Array(
+                pyorc.Struct(**{"a": pyorc.BigInt(), "b": pyorc.BigInt()})
+            )
+        ),
+        "struct_nests_list": pyorc.Struct(
+            **{
+                "struct": pyorc.Struct(
+                    **{"a": pyorc.BigInt(), "b": pyorc.BigInt()}
+                ),
+                "list": pyorc.Array(pyorc.BigInt()),
+            }
+        ),
+    }
+
+    schema = pyorc.Struct(**schema)
+
+    lvl3_list = [
+        rd.choice(
+            [
+                None,
+                [
+                    [
+                        [
+                            rd.choice([None, np.random.randint(1, 3)])
+                            for z in range(np.random.randint(1, 3))
+                        ]
+                        for z in range(np.random.randint(0, 3))
+                    ]
+                    for y in range(np.random.randint(0, 3))
+                ],
+            ]
+        )
+        for x in range(size)
+    ]
+    lvl1_list = [
+        [
+            rd.choice([None, np.random.randint(0, 3)])
+            for y in range(np.random.randint(1, 4))
+        ]
+        for x in range(size)
+    ]
+    lvl1_struct = [
+        rd.choice([None, (np.random.randint(0, 3), np.random.randint(0, 3))])
+        for x in range(size)
+    ]
+    lvl2_struct = [
+        rd.choice(
+            [
+                None,
+                (
+                    rd.choice([None, np.random.randint(0, 3)]),
+                    (
+                        rd.choice([None, np.random.randint(0, 3)]),
+                        np.random.randint(0, 3),
+                    ),
+                ),
+            ]
+        )
+        for x in range(size)
+    ]
+    list_nests_struct = [
+        [
+            [rd.choice(lvl1_struct), rd.choice(lvl1_struct)]
+            for y in range(np.random.randint(1, 4))
+        ]
+        for x in range(size)
+    ]
+    struct_nests_list = [(lvl1_struct[x], lvl1_list[x]) for x in range(size)]
+
+    df = pd.DataFrame(
+        {
+            "lvl3_list": lvl3_list,
+            "lvl1_list": lvl1_list,
+            "lvl1_struct": lvl1_struct,
+            "lvl2_struct": lvl2_struct,
+            "list_nests_struct": list_nests_struct,
+            "struct_nests_list": struct_nests_list,
+        }
+    )
+
+    writer = pyorc.Writer(buff, schema, stripe_size=1024)
+    tuples = list(
+        map(
+            lambda x: (None,) if x[0] is pd.NA else x,
+            list(df.itertuples(index=False, name=None)),
+        )
+    )
+    writer.writerows(tuples)
+    writer.close()
+
+    return buff
+
+
+@pytest.fixture(scope="module")
+def list_struct_buff():
+    return generate_list_struct_buff()
+
+
+@pytest.mark.parametrize(
+    "columns",
+    [
+        None,
+        ["lvl3_list", "list_nests_struct", "lvl2_struct", "struct_nests_list"],
+        ["lvl2_struct", "lvl1_struct"],
+    ],
+)
+@pytest.mark.parametrize("num_rows", [0, 15, 1005, 10561, 100_000])
+@pytest.mark.parametrize("use_index", [True, False])
+def test_lists_struct_nests(columns, num_rows, use_index, list_struct_buff):
+    gdf = cudf.read_orc(
+        list_struct_buff,
+        columns=columns,
+        num_rows=num_rows,
+        use_index=use_index,
+    )
+
+    pyarrow_tbl = pyarrow.orc.ORCFile(list_struct_buff).read()
+
+    pyarrow_tbl = (
+        pyarrow_tbl[:num_rows]
+        if columns is None
+        else pyarrow_tbl.select(columns)[:num_rows]
+    )
+
+    if num_rows > 0:
+        assert pyarrow_tbl.equals(gdf.to_arrow())
+    else:
+        assert_eq(pyarrow_tbl.to_pandas(), gdf)
+
+
+@pytest.mark.parametrize("columns", [None, ["lvl1_struct"], ["lvl1_list"]])
+def test_skip_rows_for_nested_types(columns, list_struct_buff):
+    with pytest.raises(
+        RuntimeError, match="skip_rows is not supported by nested column"
+    ):
+        cudf.read_orc(
+            list_struct_buff,
+            columns=columns,
+            use_index=True,
+            skiprows=5,
+        )
+
+
+def test_pyspark_struct(datadir):
+    path = datadir / "TestOrcFile.testPySparkStruct.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path)
+
+    assert_eq(pdf, gdf)
+
+
+def gen_map_buff(size=10000):
+    from string import ascii_letters as al
+
+    rd = random.Random(1)
+    np.random.seed(seed=1)
+
+    buff = BytesIO()
+
+    schema = {
+        "lvl1_map": pyorc.Map(key=pyorc.String(), value=pyorc.BigInt()),
+        "lvl2_map": pyorc.Map(
+            key=pyorc.String(), value=pyorc.Array(pyorc.BigInt())
+        ),
+        "lvl2_struct_map": pyorc.Map(
+            key=pyorc.String(),
+            value=pyorc.Struct(**{"a": pyorc.BigInt(), "b": pyorc.BigInt()}),
+        ),
+    }
+
+    schema = pyorc.Struct(**schema)
+
+    lvl1_map = [
+        rd.choice(
+            [
+                None,
+                [
+                    (
+                        rd.choice(al),
+                        rd.choice([None, np.random.randint(1, 1500)]),
+                    )
+                    for y in range(2)
+                ],
+            ]
+        )
+        for x in range(size)
+    ]
+    lvl2_map = [
+        rd.choice(
+            [
+                None,
+                [
+                    (
+                        rd.choice(al),
+                        rd.choice(
+                            [
+                                None,
+                                [
+                                    rd.choice(
+                                        [None, np.random.randint(1, 1500)]
+                                    )
+                                    for z in range(5)
+                                ],
+                            ]
+                        ),
+                    )
+                    for y in range(2)
+                ],
+            ]
+        )
+        for x in range(size)
+    ]
+    lvl2_struct_map = [
+        rd.choice(
+            [
+                None,
+                [
+                    (
+                        rd.choice(al),
+                        rd.choice(
+                            [
+                                None,
+                                (
+                                    rd.choice(
+                                        [None, np.random.randint(1, 1500)]
+                                    ),
+                                    rd.choice(
+                                        [None, np.random.randint(1, 1500)]
+                                    ),
+                                ),
+                            ]
+                        ),
+                    )
+                    for y in range(2)
+                ],
+            ]
+        )
+        for x in range(size)
+    ]
+
+    pdf = pd.DataFrame(
+        {
+            "lvl1_map": lvl1_map,
+            "lvl2_map": lvl2_map,
+            "lvl2_struct_map": lvl2_struct_map,
+        }
+    )
+    writer = pyorc.Writer(
+        buff, schema, stripe_size=1024, compression=pyorc.CompressionKind.NONE
+    )
+    tuples = list(
+        map(
+            lambda x: (None,) if x[0] is pd.NA else x,
+            list(pdf.itertuples(index=False, name=None)),
+        )
+    )
+
+    writer.writerows(tuples)
+    writer.close()
+
+    return buff
+
+
+map_buff = gen_map_buff(size=100000)
+
+
+@pytest.mark.parametrize(
+    "columns",
+    [None, ["lvl1_map", "lvl2_struct_map"], ["lvl2_struct_map", "lvl2_map"]],
+)
+@pytest.mark.parametrize("num_rows", [0, 15, 1005, 10561, 100000])
+@pytest.mark.parametrize("use_index", [True, False])
+def test_map_type_read(columns, num_rows, use_index):
+    tbl = pa.orc.ORCFile(map_buff).read()
+
+    lvl1_map = (
+        tbl["lvl1_map"]
+        .combine_chunks()
+        .view(pa.list_(pa.struct({"key": pa.string(), "value": pa.int64()})))
+    )
+    lvl2_map = (
+        tbl["lvl2_map"]
+        .combine_chunks()
+        .view(
+            pa.list_(
+                pa.struct({"key": pa.string(), "value": pa.list_(pa.int64())})
+            )
+        )
+    )
+    lvl2_struct_map = (
+        tbl["lvl2_struct_map"]
+        .combine_chunks()
+        .view(
+            pa.list_(
+                pa.struct(
+                    {
+                        "key": pa.string(),
+                        "value": pa.struct({"a": pa.int64(), "b": pa.int64()}),
+                    }
+                )
+            )
+        )
+    )
+
+    expected_tbl = pa.table(
+        {
+            "lvl1_map": lvl1_map,
+            "lvl2_map": lvl2_map,
+            "lvl2_struct_map": lvl2_struct_map,
+        }
+    )
+    gdf = cudf.read_orc(
+        map_buff, columns=columns, num_rows=num_rows, use_index=use_index
+    )
+
+    expected_tbl = (
+        expected_tbl[:num_rows]
+        if columns is None
+        else expected_tbl.select(columns)[:num_rows]
+    )
+
+    if num_rows > 0:
+        assert expected_tbl.equals(gdf.to_arrow())
+    else:
+        assert_eq(expected_tbl.to_pandas(), gdf)
+
+
+def test_orc_reader_decimal(datadir):
+    path = datadir / "TestOrcFile.decimal.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path)
+
+    assert_eq(pdf, gdf)
+
+
+# This test case validates the issue raised in #8665,
+# please check the issue for more details.
+def test_orc_timestamp_read(datadir):
+    path = datadir / "TestOrcFile.timestamp.issue.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path)
+
+    assert_eq(pdf, gdf)
+
+
+def dec(num):
+    return decimal.Decimal(str(num))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # basic + nested strings
+        {
+            "lls": [[["a"], ["bb"]] * 5 for i in range(12345)],
+            "lls2": [[["ccc", "dddd"]] * 6 for i in range(12345)],
+            "ls_dict": [["X"] * 7 for i in range(12345)],
+            "ls_direct": [[str(i)] * 9 for i in range(12345)],
+            "li": [[i] * 11 for i in range(12345)],
+            "lf": [[i * 0.5] * 13 for i in range(12345)],
+            "ld": [[dec(i / 2)] * 15 for i in range(12345)],
+        },
+        # with nulls
+        {
+            "ls": [
+                [str(i) if i % 5 else None, str(2 * i)] if i % 2 else None
+                for i in range(12345)
+            ],
+            "li": [[i, i * i, i % 2] if i % 3 else None for i in range(12345)],
+            "ld": [
+                [dec(i), dec(i / 2) if i % 7 else None] if i % 5 else None
+                for i in range(12345)
+            ],
+        },
+        # with empty elements
+        {
+            "ls": [
+                [str(i), str(2 * i)] if i % 2 else [] for i in range(12345)
+            ],
+            "lls": [
+                [[str(i), str(2 * i)]] if i % 2 else [[], []]
+                for i in range(12345)
+            ],
+            "li": [[i, i * i, i % 2] if i % 3 else [] for i in range(12345)],
+            "lli": [
+                [[i], [i * i], [i % 2]] if i % 3 else [[]]
+                for i in range(12345)
+            ],
+            "ld": [
+                [dec(i), dec(i / 2)] if i % 5 else [] for i in range(12345)
+            ],
+        },
+        # variable list lengths
+        {
+            "ls": [[str(i)] * i for i in range(123)],
+            "li": [[i, i * i] * i for i in range(123)],
+            "ld": [[dec(i), dec(i / 2)] * i for i in range(123)],
+        },
+        # many child elements (more that max_stripe_rows)
+        {"li": [[i] * 1100 for i in range(11000)]},
+    ],
+)
+def test_orc_writer_lists(data):
+    pdf_in = pd.DataFrame(data)
+
+    buffer = BytesIO()
+    cudf.from_pandas(pdf_in).to_orc(
+        buffer, stripe_size_rows=2048, row_index_stride=512
+    )
+
+    pdf_out = pd.read_orc(buffer)
+    assert_eq(pdf_out, pdf_in)
+
+
+def test_chunked_orc_writer_lists():
+    num_rows = 12345
+    pdf_in = pd.DataFrame(
+        {
+            "ls": [[str(i), str(2 * i)] for i in range(num_rows)],
+            "ld": [[dec(i / 2)] * 5 for i in range(num_rows)],
+        }
+    )
+
+    gdf = cudf.from_pandas(pdf_in)
+    expect = pd.concat([pdf_in, pdf_in]).reset_index(drop=True)
+
+    buffer = BytesIO()
+    writer = ORCWriter(buffer)
+    writer.write_table(gdf)
+    writer.write_table(gdf)
+    writer.close()
+
+    got = pd.read_orc(buffer)
+    assert_eq(expect, got)
+
+
+def test_writer_timestamp_stream_size(datadir, tmpdir):
+    pdf_fname = datadir / "TestOrcFile.largeTimestamps.orc"
+    gdf_fname = tmpdir.join("gdf.orc")
+
+    expect = pd.read_orc(pdf_fname)
+    cudf.from_pandas(expect).to_orc(gdf_fname.strpath)
+    got = pd.read_orc(gdf_fname)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "fname",
+    [
+        "TestOrcFile.NoIndStrm.StructWithNoNulls.orc",
+        "TestOrcFile.NoIndStrm.StructAndIntWithNulls.orc",
+        "TestOrcFile.NoIndStrm.StructAndIntWithNulls.TwoStripes.orc",
+        "TestOrcFile.NoIndStrm.IntWithNulls.orc",
+    ],
+)
+def test_no_row_group_index_orc_read(datadir, fname):
+    fpath = datadir / fname
+
+    expect = pa.orc.ORCFile(fpath).read()
+    got = cudf.read_orc(fpath)
+
+    assert expect.equals(got.to_arrow())
+
+
+def test_names_in_struct_dtype_nesting(datadir):
+    fname = datadir / "TestOrcFile.NestedStructDataFrame.orc"
+
+    expect = pa.orc.ORCFile(fname).read()
+    got = cudf.read_orc(fname)
+
+    # test dataframes
+    assert expect.equals(got.to_arrow())
+
+    edf = cudf.DataFrame(expect.to_pandas())
+    # test schema
+    assert edf.dtypes.equals(got.dtypes)
+
+
+def test_writer_lists_structs(list_struct_buff):
+    df_in = cudf.read_orc(list_struct_buff)
+
+    buff = BytesIO()
+    df_in.to_orc(buff)
+
+    pyarrow_tbl = pyarrow.orc.ORCFile(buff).read()
+
+    assert pyarrow_tbl.equals(df_in.to_arrow())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "with_pd": [
+                [i if i % 3 else None] if i < 9999 or i > 20001 else None
+                for i in range(21000)
+            ],
+            "no_pd": [
+                [i if i % 3 else None] if i < 9999 or i > 20001 else []
+                for i in range(21000)
+            ],
+        },
+    ],
+)
+def test_orc_writer_lists_empty_rg(data):
+    pdf_in = pd.DataFrame(data)
+    buffer = BytesIO()
+    cudf_in = cudf.from_pandas(pdf_in)
+
+    cudf_in.to_orc(buffer)
+
+    df = cudf.read_orc(buffer)
+    assert_eq(df, cudf_in)
+
+    pdf_out = pd.read_orc(buffer)
+    assert_eq(pdf_in, pdf_out)
+
+
+def test_statistics_sum_overflow():
+    maxint64 = np.iinfo(np.int64).max
+    minint64 = np.iinfo(np.int64).min
+
+    buff = BytesIO()
+    with pyorc.Writer(
+        buff,
+        pyorc.Struct(a=pyorc.BigInt(), b=pyorc.BigInt(), c=pyorc.BigInt()),
+    ) as writer:
+        writer.write((maxint64, minint64, minint64))
+        writer.write((1, -1, 1))
+
+    file_stats, stripe_stats = cudf.io.orc.read_orc_statistics([buff])
+    assert file_stats[0]["a"].get("sum") is None
+    assert file_stats[0]["b"].get("sum") is None
+    assert file_stats[0]["c"].get("sum") == minint64 + 1
+
+    assert stripe_stats[0]["a"].get("sum") is None
+    assert stripe_stats[0]["b"].get("sum") is None
+    assert stripe_stats[0]["c"].get("sum") == minint64 + 1
+
+
+def test_empty_statistics():
+    buff = BytesIO()
+    orc_schema = pyorc.Struct(
+        a=pyorc.BigInt(),
+        b=pyorc.Double(),
+        c=pyorc.String(),
+        d=pyorc.Decimal(11, 2),
+        e=pyorc.Date(),
+        f=pyorc.Timestamp(),
+        g=pyorc.Boolean(),
+        h=pyorc.Binary(),
+        i=pyorc.BigInt(),
+        # One column with non null value, else cudf/pyorc readers crash
+    )
+    data = tuple([None] * (len(orc_schema.fields) - 1) + [1])
+    with pyorc.Writer(buff, orc_schema) as writer:
+        writer.write(data)
+
+    got = cudf.io.orc.read_orc_statistics([buff])
+
+    # Check for both file and stripe stats
+    for stats in got:
+        # Similar expected stats for the first 6 columns in this case
+        for col_name in ascii_lowercase[:6]:
+            assert stats[0][col_name].get("number_of_values") == 0
+            assert stats[0][col_name].get("has_null") is True
+            assert stats[0][col_name].get("minimum") is None
+            assert stats[0][col_name].get("maximum") is None
+        for col_name in ascii_lowercase[:3]:
+            assert stats[0][col_name].get("sum") == 0
+        # Sum for decimal column is a string
+        assert stats[0]["d"].get("sum") == "0"
+
+        assert stats[0]["g"].get("number_of_values") == 0
+        assert stats[0]["g"].get("has_null") is True
+        assert stats[0]["g"].get("true_count") == 0
+        assert stats[0]["g"].get("false_count") == 0
+
+        assert stats[0]["h"].get("number_of_values") == 0
+        assert stats[0]["h"].get("has_null") is True
+        assert stats[0]["h"].get("sum") == 0
+
+        assert stats[0]["i"].get("number_of_values") == 1
+        assert stats[0]["i"].get("has_null") is False
+        assert stats[0]["i"].get("minimum") == 1
+        assert stats[0]["i"].get("maximum") == 1
+        assert stats[0]["i"].get("sum") == 1
+
+
+@pytest.mark.parametrize(
+    "equivalent_columns",
+    [
+        (["lvl1_struct.a", "lvl1_struct.b"], ["lvl1_struct"]),
+        (["lvl1_struct", "lvl1_struct.a"], ["lvl1_struct"]),
+        (["lvl1_struct.a", "lvl1_struct"], ["lvl1_struct"]),
+        (["lvl1_struct.b", "lvl1_struct.a"], ["lvl1_struct.b", "lvl1_struct"]),
+        (["lvl2_struct.lvl1_struct", "lvl2_struct"], ["lvl2_struct"]),
+        (
+            ["lvl2_struct.a", "lvl2_struct.lvl1_struct.c", "lvl2_struct"],
+            ["lvl2_struct"],
+        ),
+    ],
+)
+def test_select_nested(list_struct_buff, equivalent_columns):
+    # The two column selections should be equivalent
+    df_cols1 = cudf.read_orc(list_struct_buff, columns=equivalent_columns[0])
+    df_cols2 = cudf.read_orc(list_struct_buff, columns=equivalent_columns[1])
+    assert_eq(df_cols1, df_cols2)
+
+
+def test_orc_writer_rle_stream_size(datadir, tmpdir):
+    original = datadir / "TestOrcFile.int16.rle.size.orc"
+    reencoded = tmpdir.join("int16_map.orc")
+
+    df = cudf.read_orc(original)
+    df.to_orc(reencoded)
+
+    # Segfaults when RLE stream sizes don't account for varint length
+    pa_out = pa.orc.ORCFile(reencoded).read()
+    assert df.to_arrow().equals(pa_out)
+
+
+def test_empty_columns():
+    buffer = BytesIO()
+    # string and decimal columns have additional steps that need to be skipped
+    expected = cudf.DataFrame(
+        {
+            "string": cudf.Series([], dtype="str"),
+            "decimal": cudf.Series([], dtype=cudf.Decimal64Dtype(10, 1)),
+        }
+    )
+    expected.to_orc(buffer, compression="snappy")
+
+    got_df = cudf.read_orc(buffer)
+    assert_eq(expected, got_df)
+
+
+def test_orc_reader_zstd_compression(list_struct_buff):
+    expected = cudf.read_orc(list_struct_buff)
+    # save with ZSTD compression
+    buffer = BytesIO()
+    pyarrow_tbl = pyarrow.orc.ORCFile(list_struct_buff).read()
+    writer = pyarrow.orc.ORCWriter(buffer, compression="zstd")
+    writer.write(pyarrow_tbl)
+    writer.close()
+    try:
+        got = cudf.read_orc(buffer)
+        assert_eq(expected, got)
+    except RuntimeError:
+        pytest.mark.xfail(reason="zstd support is not enabled")
+
+
+def test_writer_protobuf_large_rowindexentry():
+    s = [
+        "Length of the two strings needs to add up to at least ~120",
+        "So that the encoded statistics are larger than 128 bytes",
+    ] * 5001  # generate more than 10K rows to have two row groups
+    df = cudf.DataFrame({"s1": s})
+
+    buff = BytesIO()
+    df.to_orc(buff)
+
+    got = cudf.read_orc(buff)
+    assert_frame_equal(df, got)
+
+
+@pytest.mark.parametrize("compression", ["ZLIB", "ZSTD"])
+def test_orc_writer_nvcomp(compression):
+    expected = cudf.datasets.randomdata(
+        nrows=12345, dtypes={"a": int, "b": str, "c": float}, seed=1
+    )
+
+    buff = BytesIO()
+    try:
+        expected.to_orc(buff, compression=compression)
+    except RuntimeError:
+        pytest.mark.xfail(reason="Newer nvCOMP version is required")
+    else:
+        got = pd.read_orc(buff)
+        assert_eq(expected, got)
+
+
+def run_orc_columns_and_index_param(index_obj, index, columns):
+    buffer = BytesIO()
+    df = cudf.DataFrame(
+        {"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=index_obj
+    )
+    df.to_orc(buffer, index=index)
+
+    expected = pd.read_orc(buffer, columns=columns)
+    got = cudf.read_orc(buffer, columns=columns)
+
+    if columns:
+        # TODO: Remove workaround after this issue is fixed:
+        # https://github.com/pandas-dev/pandas/issues/47944
+        assert_eq(
+            expected.sort_index(axis=1),
+            got.sort_index(axis=1),
+            check_index_type=True,
+        )
+    else:
+        assert_eq(expected, got, check_index_type=True)
+
+
+@pytest.mark.parametrize("index_obj", [None, [10, 11, 12], ["x", "y", "z"]])
+@pytest.mark.parametrize("index", [True, False, None])
+@pytest.mark.parametrize(
+    "columns",
+    [
+        None,
+        [],
+    ],
+)
+def test_orc_columns_and_index_param(index_obj, index, columns):
+    run_orc_columns_and_index_param(index_obj, index, columns)
+
+
+@pytest.mark.parametrize(
+    "columns,index,index_obj",
+    [
+        (
+            ["a", "b"],
+            True,
+            None,
+        ),
+        (
+            ["a", "b"],
+            True,
+            [10, 11, 12],
+        ),
+        (
+            ["a", "b"],
+            True,
+            ["x", "y", "z"],
+        ),
+        (
+            ["a", "b"],
+            None,
+            [10, 11, 12],
+        ),
+        (
+            ["a", "b"],
+            None,
+            ["x", "y", "z"],
+        ),
+    ],
+)
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/12026")
+def test_orc_columns_and_index_param_read_index(index_obj, index, columns):
+    run_orc_columns_and_index_param(index_obj, index, columns)
+
+
+@pytest.mark.parametrize(
+    "columns,index,index_obj",
+    [
+        (["a", "b"], False, None),
+        (["a", "b"], False, [10, 11, 12]),
+        (["a", "b"], False, ["x", "y", "z"]),
+        (["a", "b"], None, None),
+    ],
+)
+def test_orc_columns_and_index_param_no_read_index(index_obj, index, columns):
+    run_orc_columns_and_index_param(index_obj, index, columns)
+
+
+@pytest.mark.parametrize(
+    "df_data,cols_as_map_type,expected_data",
+    [
+        (
+            {"a": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]]},
+            ["a"],
+            {"a": [[(10, 20)], [(1, 21)]]},
+        ),
+        (
+            {
+                "a": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]],
+                "b": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]],
+            },
+            ["b"],
+            {
+                "a": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]],
+                "b": [[(10, 20)], [(1, 21)]],
+            },
+        ),
+        (
+            {
+                "a": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]],
+                "b": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]],
+                "c": [
+                    [{"a": {"a": 10}, "b": 20}],
+                    [{"a": {"a": 12}, "b": 21}],
+                ],
+            },
+            ["b", "c"],
+            {
+                "a": [[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]],
+                "b": [[(10, 20)], [(1, 21)]],
+                "c": [[({"a": 10}, 20)], [({"a": 12}, 21)]],
+            },
+        ),
+    ],
+)
+def test_orc_writer_cols_as_map_type(df_data, cols_as_map_type, expected_data):
+    df = cudf.DataFrame(df_data)
+    buffer = BytesIO()
+    df.to_orc(buffer, cols_as_map_type=cols_as_map_type)
+
+    got = pd.read_orc(buffer)
+    expected = pd.DataFrame(expected_data)
+
+    assert_eq(got, expected)
+
+
+def test_orc_writer_cols_as_map_type_error():
+    df = cudf.DataFrame(
+        {"a": cudf.Series([[{"a": 10, "b": 20}], [{"a": 1, "b": 21}]])}
+    )
+    buffer = BytesIO()
+    with pytest.raises(
+        TypeError, match="cols_as_map_type must be a list of column names."
+    ):
+        df.to_orc(buffer, cols_as_map_type=1)
+
+
+@pytest.fixture
+def negative_timestamp_df():
+    return cudf.DataFrame(
+        {
+            "a": [
+                pd.Timestamp("1969-12-31 23:59:59.000123"),
+                pd.Timestamp("1969-12-31 23:59:58.000999"),
+                pd.Timestamp("1969-12-31 23:59:58.001001"),
+                pd.Timestamp("1839-12-24 03:58:56.000826"),
+            ]
+        }
+    )
+
+
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+def test_orc_reader_negative_timestamp(negative_timestamp_df, engine):
+    buffer = BytesIO()
+    pyorc_table = pa.Table.from_pandas(
+        negative_timestamp_df.to_pandas(), preserve_index=False
+    )
+    pyarrow.orc.write_table(pyorc_table, buffer)
+
+    # We warn the user that this function will fall back to the CPU for reading
+    # when the engine is pyarrow.
+    with expect_warning_if(engine == "pyarrow", UserWarning):
+        got = cudf.read_orc(buffer, engine=engine)
+
+    assert_eq(negative_timestamp_df, got)
+
+
+def test_orc_writer_negative_timestamp(negative_timestamp_df):
+    buffer = BytesIO()
+    negative_timestamp_df.to_orc(buffer)
+
+    assert_eq(negative_timestamp_df, pd.read_orc(buffer))
+    assert_eq(negative_timestamp_df, pyarrow.orc.ORCFile(buffer).read())
+
+
+def test_orc_reader_apache_negative_timestamp(datadir):
+    path = datadir / "TestOrcFile.apache_timestamp.orc"
+
+    pdf = pd.read_orc(path)
+    gdf = cudf.read_orc(path)
+
+    assert_eq(pdf, gdf)
+
+
+def test_statistics_string_sum():
+    strings = ["a string", "another string!"]
+    buff = BytesIO()
+    df = cudf.DataFrame({"str": strings})
+    df.to_orc(buff)
+
+    file_stats, stripe_stats = cudf.io.orc.read_orc_statistics([buff])
+    assert_eq(file_stats[0]["str"].get("sum"), sum(len(s) for s in strings))
+
+
+@pytest.mark.parametrize(
+    "fname",
+    [
+        "TestOrcFile.Hive.OneEmptyMap.orc",
+        "TestOrcFile.Hive.OneEmptyList.orc",
+        "TestOrcFile.Hive.OneNullStruct.orc",
+        "TestOrcFile.Hive.EmptyListStripe.orc",
+        "TestOrcFile.Hive.NullStructStripe.orc",
+        "TestOrcFile.Hive.AllNulls.orc",
+    ],
+)
+def test_reader_empty_stripe(datadir, fname):
+    path = datadir / fname
+
+    expected = pd.read_orc(path)
+    got = cudf.read_orc(path)
+    assert_eq(expected, got)
+
+
+# needs enough data for multiple row groups
+@pytest.mark.parametrize("data", [["*"] * 10001, ["**", None] * 5001])
+def test_reader_row_index_order(data):
+    expected = cudf.DataFrame({"str": data}, dtype="string")
+
+    buffer = BytesIO()
+    expected.to_pandas().to_orc(buffer)
+    got = cudf.read_orc(buffer)
+    assert_eq(expected, got)
+
+
+# Test the corner case where empty blocks are compressed
+# Decompressed data size is zero, even though compressed data size is non-zero
+# For more information see https://github.com/rapidsai/cudf/issues/13608
+def test_orc_reader_empty_decomp_data(datadir):
+    path = datadir / "TestOrcFile.Spark.EmptyDecompData.orc"
+
+    expect = pd.read_orc(path)
+    got = cudf.read_orc(path)
+
+    assert_eq(expect, got)
+
+
+def test_orc_reader_empty_deeply_nested_level(datadir):
+    # Test the case where top level struct has nulls, but the nested struct is
+    # not nullable. In this case there is no data in the second level, but we
+    # still need to pass the parent null mask to the third level.
+    path = datadir / "TestOrcFile.Spark.NestedNotNullableStruct.orc"
+
+    expect = pd.read_orc(path)
+    got = cudf.read_orc(path)
+
+    assert_eq(expect, got)
diff --git a/python/cudf/cudf/tests/test_pack.py b/python/cudf/cudf/tests/test_pack.py
new file mode 100644
index 0000000..9b5a8c1
--- /dev/null
+++ b/python/cudf/cudf/tests/test_pack.py
@@ -0,0 +1,317 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pickle
+import sys
+
+import numpy as np
+import pandas as pd
+
+from cudf import DataFrame, GenericIndex, Series
+from cudf._lib.copying import pack, unpack
+from cudf.testing._utils import assert_eq
+
+
+def test_sizeof_packed_dataframe():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 1000
+    df["keys"] = hkeys = np.arange(nelem, dtype=np.float64)
+    df["vals"] = hvals = np.random.random(nelem)
+    packed = pack(df)
+
+    nbytes = hkeys.nbytes + hvals.nbytes
+    sizeof = sys.getsizeof(packed)
+    assert sizeof < nbytes
+
+    serialized_nbytes = len(
+        pickle.dumps(packed, protocol=pickle.HIGHEST_PROTOCOL)
+    )
+
+    # assert at least sizeof bytes were serialized
+    assert serialized_nbytes >= sizeof
+
+
+def check_packed_equality(df):
+    # basic
+    assert_packed_frame_equality(df)
+    # sliced
+    assert_packed_frame_equality(df[:-1])
+    assert_packed_frame_equality(df[1:])
+    assert_packed_frame_equality(df[2:-2])
+    # sorted
+    sortvaldf = df.sort_values("vals")
+    assert isinstance(sortvaldf.index, GenericIndex)
+    assert_packed_frame_equality(sortvaldf)
+
+
+def assert_packed_frame_equality(df):
+    pdf = df.to_pandas()
+
+    packed = pack(df)
+    del df
+    unpacked = unpack(packed)
+
+    assert_eq(unpacked, pdf)
+
+
+def test_packed_dataframe_equality_numeric():
+    np.random.seed(0)
+
+    df = DataFrame()
+    nelem = 10
+    df["keys"] = np.arange(nelem, dtype=np.float64)
+    df["vals"] = np.random.random(nelem)
+
+    check_packed_equality(df)
+
+
+def test_packed_dataframe_equality_categorical():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = pd.Categorical(
+        ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_equality(df)
+
+
+def test_packed_dataframe_equality_list():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_equality(df)
+
+
+def test_packed_dataframe_equality_struct():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(
+        list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_equality(df)
+
+
+def check_packed_unique_pointers(df):
+    # basic
+    assert_packed_frame_unique_pointers(df)
+    # sliced
+    assert_packed_frame_unique_pointers(df[:-1])
+    assert_packed_frame_unique_pointers(df[1:])
+    assert_packed_frame_unique_pointers(df[2:-2])
+    # sorted
+    sortvaldf = df.sort_values("vals")
+    assert isinstance(sortvaldf.index, GenericIndex)
+    assert_packed_frame_unique_pointers(sortvaldf)
+
+
+def assert_packed_frame_unique_pointers(df):
+    unpacked = unpack(pack(df))
+
+    for col in df:
+        if df._data[col].data:
+            assert df._data[col].data.get_ptr(mode="read") != unpacked._data[
+                col
+            ].data.get_ptr(mode="read")
+
+
+def test_packed_dataframe_unique_pointers_numeric():
+    np.random.seed(0)
+
+    df = DataFrame()
+    nelem = 10
+    df["keys"] = np.arange(nelem, dtype=np.float64)
+    df["vals"] = np.random.random(nelem)
+
+    check_packed_unique_pointers(df)
+
+
+def test_packed_dataframe_unique_pointers_categorical():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = pd.Categorical(
+        ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_unique_pointers(df)
+
+
+def test_packed_dataframe_unique_pointers_list():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_unique_pointers(df)
+
+
+def test_packed_dataframe_unique_pointers_struct():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(
+        list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_unique_pointers(df)
+
+
+def check_packed_pickled_equality(df):
+    # basic
+    assert_packed_frame_picklable(df)
+    # sliced
+    assert_packed_frame_picklable(df[:-1])
+    assert_packed_frame_picklable(df[1:])
+    assert_packed_frame_picklable(df[2:-2])
+    # sorted
+    sortvaldf = df.sort_values("vals")
+    assert isinstance(sortvaldf.index, GenericIndex)
+    assert_packed_frame_picklable(sortvaldf)
+    # out-of-band
+    buffers = []
+    serialbytes = pickle.dumps(
+        pack(df), protocol=5, buffer_callback=buffers.append
+    )
+    for b in buffers:
+        assert isinstance(b, pickle.PickleBuffer)
+    loaded = unpack(pickle.loads(serialbytes, buffers=buffers))
+    assert_eq(loaded, df)
+
+
+def assert_packed_frame_picklable(df):
+    serialbytes = pickle.dumps(pack(df))
+    loaded = unpack(pickle.loads(serialbytes))
+    assert_eq(loaded, df)
+
+
+def test_pickle_packed_dataframe_numeric():
+    np.random.seed(0)
+
+    df = DataFrame()
+    nelem = 10
+    df["keys"] = np.arange(nelem, dtype=np.float64)
+    df["vals"] = np.random.random(nelem)
+
+    check_packed_pickled_equality(df)
+
+
+def test_pickle_packed_dataframe_categorical():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = pd.Categorical(
+        ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_pickled_equality(df)
+
+
+def test_pickle_packed_dataframe_list():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_pickled_equality(df)
+
+
+def test_pickle_packed_dataframe_struct():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(
+        list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_pickled_equality(df)
+
+
+def check_packed_serialized_equality(df):
+    # basic
+    assert_packed_frame_serializable(df)
+    # sliced
+    assert_packed_frame_serializable(df[:-1])
+    assert_packed_frame_serializable(df[1:])
+    assert_packed_frame_serializable(df[2:-2])
+    # sorted
+    sortvaldf = df.sort_values("vals")
+    assert isinstance(sortvaldf.index, GenericIndex)
+    assert_packed_frame_serializable(sortvaldf)
+
+
+def assert_packed_frame_serializable(df):
+    packed = pack(df)
+    header, frames = packed.serialize()
+    loaded = unpack(packed.deserialize(header, frames))
+    assert_eq(loaded, df)
+
+
+def test_serialize_packed_dataframe_numeric():
+    np.random.seed(0)
+
+    df = DataFrame()
+    nelem = 10
+    df["keys"] = np.arange(nelem, dtype=np.float64)
+    df["vals"] = np.random.random(nelem)
+
+    check_packed_serialized_equality(df)
+
+
+def test_serialize_packed_dataframe_categorical():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = pd.Categorical(
+        ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_serialized_equality(df)
+
+
+def test_serialize_packed_dataframe_list():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(list([i, i + 1, i + 2] for i in range(10)))
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_serialized_equality(df)
+
+
+def test_serialize_packed_dataframe_struct():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = Series(
+        list({"0": i, "1": i + 1, "2": i + 2} for i in range(10))
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_packed_serialized_equality(df)
diff --git a/python/cudf/cudf/tests/test_pandas_interop.py b/python/cudf/cudf/tests/test_pandas_interop.py
new file mode 100644
index 0000000..78cf5b9
--- /dev/null
+++ b/python/cudf/cudf/tests/test_pandas_interop.py
@@ -0,0 +1,90 @@
+# Copyright (c) 2018-2021, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf import DataFrame
+from cudf.testing._utils import assert_eq
+
+
+def test_to_pandas():
+    df = DataFrame()
+    df["a"] = np.arange(5, dtype=np.int32)
+    df["b"] = np.arange(10, 15, dtype=np.float64)
+    df["c"] = np.array([True, False, None, True, True])
+
+    pdf = df.to_pandas()
+
+    assert tuple(df.columns) == tuple(pdf.columns)
+
+    assert df["a"].dtype == pdf["a"].dtype
+    assert df["b"].dtype == pdf["b"].dtype
+
+    # Notice, the dtype differ when Pandas and cudf boolean series
+    # contains None/NaN
+    assert df["c"].dtype == np.bool_
+    assert pdf["c"].dtype == np.object_
+
+    assert len(df["a"]) == len(pdf["a"])
+    assert len(df["b"]) == len(pdf["b"])
+    assert len(df["c"]) == len(pdf["c"])
+
+
+def test_from_pandas():
+    pdf = pd.DataFrame()
+    pdf["a"] = np.arange(10, dtype=np.int32)
+    pdf["b"] = np.arange(10, 20, dtype=np.float64)
+
+    df = DataFrame.from_pandas(pdf)
+
+    assert tuple(df.columns) == tuple(pdf.columns)
+
+    assert df["a"].dtype == pdf["a"].dtype
+    assert df["b"].dtype == pdf["b"].dtype
+
+    assert len(df["a"]) == len(pdf["a"])
+    assert len(df["b"]) == len(pdf["b"])
+
+
+def test_from_pandas_ex1():
+    pdf = pd.DataFrame({"a": [0, 1, 2, 3], "b": [0.1, 0.2, None, 0.3]})
+    df = DataFrame.from_pandas(pdf)
+
+    assert tuple(df.columns) == tuple(pdf.columns)
+    assert np.all(df["a"].to_numpy() == pdf["a"])
+    matches = df["b"].to_numpy(na_value=np.nan) == pdf["b"]
+    # the 3d element is False due to (nan == nan) == False
+    assert np.all(matches == [True, True, False, True])
+    assert np.isnan(df["b"].to_numpy(na_value=np.nan)[2])
+    assert np.isnan(pdf["b"][2])
+
+
+def test_from_pandas_with_index():
+    pdf = pd.DataFrame({"a": [0, 1, 2, 3], "b": [0.1, 0.2, None, 0.3]})
+    pdf = pdf.set_index(np.asarray([4, 3, 2, 1]))
+    df = DataFrame.from_pandas(pdf)
+
+    # Check columns
+    assert_eq(df.a, pdf.a)
+    assert_eq(df.b, pdf.b)
+    # Check index
+    assert_eq(df.index.values, pdf.index.values)
+    # Check again using pandas testing tool on frames
+    assert_eq(df, pdf)
+
+
+def test_from_pandas_rangeindex():
+    idx1 = pd.RangeIndex(start=0, stop=4, step=1, name="myindex")
+    idx2 = cudf.from_pandas(idx1)
+
+    # Check index
+    assert_eq(idx1.values, idx2.values)
+    assert idx1.name == idx2.name
+
+
+def test_from_pandas_rangeindex_step():
+    expected = pd.RangeIndex(start=0, stop=8, step=2, name="myindex")
+    actual = cudf.from_pandas(expected)
+
+    assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/test_parquet.py b/python/cudf/cudf/tests/test_parquet.py
new file mode 100644
index 0000000..9349e8c
--- /dev/null
+++ b/python/cudf/cudf/tests/test_parquet.py
@@ -0,0 +1,2882 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import datetime
+import glob
+import math
+import os
+import pathlib
+import random
+from contextlib import contextmanager
+from io import BytesIO
+from string import ascii_letters
+
+import cupy
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+from fsspec.core import get_fs_token_paths
+from packaging import version
+from pyarrow import fs as pa_fs, parquet as pq
+
+import cudf
+from cudf.core._compat import PANDAS_LT_153
+from cudf.io.parquet import (
+    ParquetDatasetWriter,
+    ParquetWriter,
+    merge_parquet_filemetadata,
+)
+from cudf.testing import dataset_generator as dg
+from cudf.testing._utils import (
+    TIMEDELTA_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+    set_random_null_mask_inplace,
+)
+
+
+@contextmanager
+def _hide_pyarrow_parquet_cpu_warnings(engine):
+    if engine == "pyarrow":
+        with pytest.warns(
+            UserWarning,
+            match="Using CPU via PyArrow to read Parquet dataset. This option "
+            "is both inefficient and unstable!",
+        ):
+            yield
+    else:
+        yield
+
+
+@pytest.fixture(scope="module")
+def datadir(datadir):
+    return datadir / "parquet"
+
+
+@pytest.fixture(params=[1, 5, 10, 100000])
+def simple_pdf(request):
+    types = [
+        "bool",
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "uint8",
+        "uint16",
+        # "uint32", pandas promotes uint32 to int64
+        # https://issues.apache.org/jira/browse/ARROW-9215
+        "uint64",
+        "float32",
+        "float64",
+    ]
+    nrows = request.param
+
+    # Create a pandas dataframe with random data of mixed types
+    test_pdf = pd.DataFrame(
+        {
+            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            for typ in types
+        },
+        # Need to ensure that this index is not a RangeIndex to get the
+        # expected round-tripping behavior from Parquet reader/writer.
+        index=pd.Index(list(range(nrows))),
+    )
+    # Delete the name of the column index, and rename the row index
+    test_pdf.columns.name = None
+    test_pdf.index.name = "test_index"
+
+    return test_pdf
+
+
+@pytest.fixture
+def simple_gdf(simple_pdf):
+    return cudf.DataFrame.from_pandas(simple_pdf)
+
+
+def build_pdf(num_columns, day_resolution_timestamps):
+    types = [
+        "bool",
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "uint8",
+        "uint16",
+        # "uint32", pandas promotes uint32 to int64
+        # https://issues.apache.org/jira/browse/ARROW-9215
+        "uint64",
+        "float32",
+        "float64",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "str",
+    ]
+    nrows = num_columns.param
+
+    # Create a pandas dataframe with random data of mixed types
+    test_pdf = pd.DataFrame(
+        {
+            f"col_{typ}": np.random.randint(0, nrows, nrows).astype(typ)
+            for typ in types
+        },
+        # Need to ensure that this index is not a RangeIndex to get the
+        # expected round-tripping behavior from Parquet reader/writer.
+        index=pd.Index(list(range(nrows))),
+    )
+    # Delete the name of the column index, and rename the row index
+    test_pdf.columns.name = None
+    test_pdf.index.name = "test_index"
+
+    # make datetime64's a little more interesting by increasing the range of
+    # dates note that pandas will convert these to ns timestamps, so care is
+    # taken to avoid overflowing a ns timestamp. There is also the ability to
+    # request timestamps be whole days only via `day_resolution_timestamps`.
+    for t in [
+        {
+            "name": "datetime64[ms]",
+            "nsDivisor": 1000000,
+            "dayModulus": 86400000,
+        },
+        {
+            "name": "datetime64[us]",
+            "nsDivisor": 1000,
+            "dayModulus": 86400000000,
+        },
+    ]:
+        data = [
+            np.random.randint(0, (0x7FFFFFFFFFFFFFFF / t["nsDivisor"]))
+            for i in range(nrows)
+        ]
+        if day_resolution_timestamps:
+            data = [int(d / t["dayModulus"]) * t["dayModulus"] for d in data]
+        test_pdf["col_" + t["name"]] = pd.Series(
+            np.asarray(data, dtype=t["name"])
+        )
+
+    # Create non-numeric categorical data otherwise parquet may typecast it
+    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    test_pdf["col_category"] = pd.Series(data, dtype="category")
+
+    # Create non-numeric str data
+    data = [ascii_letters[np.random.randint(0, 52)] for i in range(nrows)]
+    test_pdf["col_str"] = pd.Series(data, dtype="str")
+
+    return test_pdf
+
+
+@pytest.fixture(params=[0, 1, 10, 10000])
+def pdf(request):
+    return build_pdf(request, False)
+
+
+@pytest.fixture(params=[0, 1, 10, 10000])
+def pdf_day_timestamps(request):
+    return build_pdf(request, True)
+
+
+@pytest.fixture
+def gdf(pdf):
+    return cudf.DataFrame.from_pandas(pdf)
+
+
+@pytest.fixture
+def gdf_day_timestamps(pdf_day_timestamps):
+    return cudf.DataFrame.from_pandas(pdf_day_timestamps)
+
+
+@pytest.fixture(params=["snappy", "gzip", "brotli", None, np.str_("snappy")])
+def parquet_file(request, tmp_path_factory, pdf):
+    fname = tmp_path_factory.mktemp("parquet") / (
+        str(request.param) + "_test.parquet"
+    )
+    pdf.to_parquet(fname, engine="pyarrow", compression=request.param)
+    return fname
+
+
+@pytest.fixture(scope="module")
+def rdg_seed():
+    return int(os.environ.get("TEST_CUDF_RDG_SEED", "42"))
+
+
+def make_pdf(nrows, ncolumns=1, nvalids=0, dtype=np.int64):
+    test_pdf = pd.DataFrame(
+        [list(range(ncolumns * i, ncolumns * (i + 1))) for i in range(nrows)],
+        columns=pd.Index(["foo"], name="bar"),
+        # Need to ensure that this index is not a RangeIndex to get the
+        # expected round-tripping behavior from Parquet reader/writer.
+        index=pd.Index(list(range(nrows))),
+    )
+    test_pdf.columns.name = None
+
+    # Randomly but reproducibly mark subset of rows as invalid
+    random.seed(1337)
+    mask = random.sample(range(nrows), nvalids)
+    test_pdf[test_pdf.index.isin(mask)] = np.NaN
+
+    return test_pdf
+
+
+@pytest.fixture
+def parquet_path_or_buf(datadir):
+    fname = datadir / "spark_timestamp.snappy.parquet"
+    try:
+        with open(fname, "rb") as f:
+            buffer = BytesIO(f.read())
+    except Exception as excpr:
+        if type(excpr).__name__ == "FileNotFoundError":
+            pytest.skip(".parquet file is not found")
+        else:
+            print(type(excpr).__name__)
+
+    def _make_parquet_path_or_buf(src):
+        if src == "filepath":
+            return str(fname)
+        if src == "pathobj":
+            return fname
+        if src == "bytes_io":
+            return buffer
+        if src == "bytes":
+            return buffer.getvalue()
+        if src == "url":
+            return fname.as_uri()
+
+        raise ValueError("Invalid source type")
+
+    yield _make_parquet_path_or_buf
+
+
+@pytest.fixture(scope="module")
+def large_int64_gdf():
+    return cudf.DataFrame.from_pandas(pd.DataFrame({"col": range(0, 1 << 20)}))
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["pyarrow", "cudf"])
+@pytest.mark.parametrize(
+    "columns",
+    [
+        ["col_int8"],
+        ["col_category"],
+        ["col_int32", "col_float32"],
+        ["col_int16", "col_float64", "col_int8"],
+        None,
+    ],
+)
+def test_parquet_reader_basic(parquet_file, columns, engine):
+    expect = pd.read_parquet(parquet_file, columns=columns)
+    got = cudf.read_parquet(parquet_file, engine=engine, columns=columns)
+
+    # PANDAS returns category objects whereas cuDF returns hashes
+    if engine == "cudf":
+        if "col_category" in expect.columns:
+            expect = expect.drop(columns=["col_category"])
+        if "col_category" in got.columns:
+            got = got.drop(columns=["col_category"])
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("engine", ["cudf"])
+def test_parquet_reader_empty_pandas_dataframe(tmpdir, engine):
+    df = pd.DataFrame()
+    fname = tmpdir.join("test_pq_reader_empty_pandas_dataframe.parquet")
+    df.to_parquet(fname)
+    assert os.path.exists(fname)
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname, engine=engine)
+    expect = expect.reset_index(drop=True)
+    got = got.reset_index(drop=True)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("has_null", [False, True])
+def test_parquet_reader_strings(tmpdir, has_null):
+    df = pd.DataFrame(
+        [(1, "aaa", 9.0), (2, "bbb", 8.0), (3, "ccc", 7.0)],
+        columns=pd.Index(list("abc")),
+    )
+    if has_null:
+        df.at[1, "b"] = None
+    fname = tmpdir.join("test_pq_reader_strings.parquet")
+    df.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    gdf = cudf.read_parquet(fname, engine="cudf")
+
+    assert gdf["b"].dtype == np.dtype("object")
+    assert_eq(gdf["b"], df["b"])
+
+
+@pytest.mark.parametrize("columns", [None, ["b"]])
+@pytest.mark.parametrize("index_col", ["b", "Nameless", None])
+def test_parquet_reader_index_col(tmpdir, index_col, columns):
+    df = pd.DataFrame({"a": range(3), "b": range(3, 6), "c": range(6, 9)})
+
+    if index_col is None:
+        # No index column
+        df.reset_index(drop=True, inplace=True)
+    elif index_col == "Nameless":
+        # Index column but no name
+        df.set_index("a", inplace=True)
+        df.index.name = None
+    else:
+        # Index column as normal
+        df.set_index(index_col, inplace=True)
+
+    fname = tmpdir.join("test_pq_reader_index_col.parquet")
+
+    # PANDAS' PyArrow backend always writes the index unless disabled
+    df.to_parquet(fname, index=(index_col is not None))
+    assert os.path.exists(fname)
+
+    pdf = pd.read_parquet(fname, columns=columns)
+    gdf = cudf.read_parquet(fname, engine="cudf", columns=columns)
+
+    assert_eq(pdf, gdf, check_categorical=False)
+
+
+@pytest.mark.parametrize("pandas_compat", [True, False])
+@pytest.mark.parametrize(
+    "columns", [["a"], ["d"], ["a", "b"], ["a", "d"], None]
+)
+def test_parquet_reader_pandas_metadata(tmpdir, columns, pandas_compat):
+    df = pd.DataFrame(
+        {
+            "a": range(6, 9),
+            "b": range(3, 6),
+            "c": range(6, 9),
+            "d": ["abc", "def", "xyz"],
+        }
+    )
+    df.set_index("b", inplace=True)
+
+    fname = tmpdir.join("test_pq_reader_pandas_metadata.parquet")
+    df.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    # PANDAS `read_parquet()` and PyArrow `read_pandas()` always includes index
+    # Instead, directly use PyArrow to optionally omit the index
+    expect = pa.parquet.read_table(
+        fname, columns=columns, use_pandas_metadata=pandas_compat
+    ).to_pandas()
+    got = cudf.read_parquet(
+        fname, columns=columns, use_pandas_metadata=pandas_compat
+    )
+
+    if pandas_compat or columns is None or "b" in columns:
+        assert got.index.name == "b"
+    else:
+        assert got.index.name is None
+    assert_eq(expect, got, check_categorical=False)
+
+
+@pytest.mark.parametrize("pandas_compat", [True, False])
+@pytest.mark.parametrize("as_bytes", [True, False])
+def test_parquet_range_index_pandas_metadata(tmpdir, pandas_compat, as_bytes):
+    df = pd.DataFrame(
+        {"a": range(6, 9), "b": ["abc", "def", "xyz"]},
+        index=pd.RangeIndex(3, 6, 1, name="c"),
+    )
+
+    fname = tmpdir.join("test_parquet_range_index_pandas_metadata")
+    df.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    # PANDAS `read_parquet()` and PyArrow `read_pandas()` always includes index
+    # Instead, directly use PyArrow to optionally omit the index
+    expect = pa.parquet.read_table(
+        fname, use_pandas_metadata=pandas_compat
+    ).to_pandas()
+    if as_bytes:
+        # Make sure we can handle RangeIndex parsing
+        # in pandas when the input is `bytes`
+        with open(fname, "rb") as f:
+            got = cudf.read_parquet(
+                f.read(), use_pandas_metadata=pandas_compat
+            )
+    else:
+        got = cudf.read_parquet(fname, use_pandas_metadata=pandas_compat)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_read_metadata(tmpdir, pdf):
+    if len(pdf) > 100:
+        pytest.skip("Skipping long setup test")
+
+    def num_row_groups(rows, group_size):
+        return max(1, (rows + (group_size - 1)) // group_size)
+
+    fname = tmpdir.join("metadata.parquet")
+    row_group_size = 5
+    pdf.to_parquet(fname, compression="snappy", row_group_size=row_group_size)
+
+    num_rows, row_groups, col_names = cudf.io.read_parquet_metadata(fname)
+
+    assert num_rows == len(pdf.index)
+    assert row_groups == num_row_groups(num_rows, row_group_size)
+    for a, b in zip(col_names, pdf.columns):
+        assert a == b
+
+
+def test_parquet_read_filtered(tmpdir, rdg_seed):
+    # Generate data
+    fname = tmpdir.join("filtered.parquet")
+    dg.generate(
+        fname,
+        dg.Parameters(
+            num_rows=2048,
+            column_parameters=[
+                dg.ColumnParameters(
+                    cardinality=40,
+                    null_frequency=0.05,
+                    generator=lambda g: [g.address.city() for _ in range(40)],
+                    is_sorted=False,
+                ),
+                dg.ColumnParameters(
+                    40,
+                    0.2,
+                    lambda g: [g.person.age() for _ in range(40)],
+                    True,
+                ),
+            ],
+            seed=rdg_seed,
+        ),
+        format={"name": "parquet", "row_group_size": 64},
+    )
+
+    # Get dataframes to compare
+    df = cudf.read_parquet(fname)
+    df_filtered = cudf.read_parquet(fname, filters=[("1", ">", 60)])
+    # PyArrow's read_table function does row-group-level filtering in addition
+    # to applying given filters once the table has been read into memory.
+    # Because of this, we aren't using PyArrow as a reference for testing our
+    # row-group selection method since the only way to only select row groups
+    # with PyArrow is with the method we use and intend to test.
+    tbl_filtered = pq.read_table(
+        fname, filters=[("1", ">", 60)], use_legacy_dataset=False
+    )
+
+    assert_eq(cudf.io.read_parquet_metadata(fname)[1], 2048 / 64)
+    print(len(df_filtered))
+    print(len(tbl_filtered))
+    assert len(df_filtered) < len(df)
+    assert len(tbl_filtered) <= len(df_filtered)
+
+
+def test_parquet_read_filtered_everything(tmpdir):
+    # Generate data
+    fname = tmpdir.join("filtered_everything.parquet")
+    df = pd.DataFrame({"x": range(10), "y": list("aabbccddee")})
+    df.to_parquet(fname, row_group_size=2)
+
+    # Check filter
+    df_filtered = cudf.read_parquet(fname, filters=[("x", "==", 12)])
+    assert_eq(len(df_filtered), 0)
+    assert_eq(df_filtered["x"].dtype, "int64")
+    assert_eq(df_filtered["y"].dtype, "object")
+
+
+def test_parquet_read_filtered_multiple_files(tmpdir):
+    # Generate data
+    fname_0 = tmpdir.join("filtered_multiple_files_0.parquet")
+    df = pd.DataFrame({"x": range(10), "y": list("aabbccddee")})
+    df.to_parquet(fname_0, row_group_size=2)
+    fname_1 = tmpdir.join("filtered_multiple_files_1.parquet")
+    df = pd.DataFrame({"x": range(10), "y": list("aaccccddee")})
+    df.to_parquet(fname_1, row_group_size=2)
+    fname_2 = tmpdir.join("filtered_multiple_files_2.parquet")
+    df = pd.DataFrame(
+        {"x": [0, 1, 9, 9, 4, 5, 6, 7, 8, 9], "y": list("aabbzzddee")}
+    )
+    df.to_parquet(fname_2, row_group_size=2)
+
+    # Check filter
+    filtered_df = cudf.read_parquet(
+        [fname_0, fname_1, fname_2], filters=[("x", "==", 2)]
+    )
+    assert_eq(
+        filtered_df,
+        cudf.DataFrame({"x": [2, 2], "y": list("bc")}, index=[2, 2]),
+    )
+
+
+@pytest.mark.skipif(
+    version.parse(pa.__version__) < version.parse("1.0.1"),
+    reason="pyarrow 1.0.0 needed for various operators and operand types",
+)
+@pytest.mark.parametrize(
+    "predicate,expected_len",
+    [
+        ([[("x", "==", 0)], [("z", "==", 0)]], 2),
+        ([("x", "==", 0), ("z", "==", 0)], 0),
+        ([("x", "==", 0), ("z", "!=", 0)], 1),
+        ([("y", "==", "c"), ("x", ">", 8)], 0),
+        ([("y", "==", "c"), ("x", ">=", 5)], 1),
+        ([[("y", "==", "c")], [("x", "<", 3)]], 5),
+        ([[("x", "not in", (0, 9)), ("z", "not in", (4, 5))]], 6),
+        ([[("y", "==", "c")], [("x", "in", (0, 9)), ("z", "in", (0, 9))]], 4),
+        ([[("x", "==", 0)], [("x", "==", 1)], [("x", "==", 2)]], 3),
+        ([[("x", "==", 0), ("z", "==", 9), ("y", "==", "a")]], 1),
+    ],
+)
+def test_parquet_read_filtered_complex_predicate(
+    tmpdir, predicate, expected_len
+):
+    # Generate data
+    fname = tmpdir.join("filtered_complex_predicate.parquet")
+    df = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list("aabbccddee"),
+            "z": reversed(range(10)),
+        }
+    )
+    df.to_parquet(fname, row_group_size=2)
+
+    # Check filters
+    df_filtered = cudf.read_parquet(fname, filters=predicate)
+    assert_eq(cudf.io.read_parquet_metadata(fname)[1], 10 / 2)
+    assert_eq(len(df_filtered), expected_len)
+
+
+@pytest.mark.parametrize("row_group_size", [1, 5, 100])
+def test_parquet_read_row_groups(tmpdir, pdf, row_group_size):
+    if len(pdf) > 100:
+        pytest.skip("Skipping long setup test")
+
+    if "col_category" in pdf.columns:
+        pdf = pdf.drop(columns=["col_category"])
+    fname = tmpdir.join("row_group.parquet")
+    pdf.to_parquet(fname, compression="gzip", row_group_size=row_group_size)
+
+    num_rows, row_groups, col_names = cudf.io.read_parquet_metadata(fname)
+
+    gdf = [cudf.read_parquet(fname, row_groups=[i]) for i in range(row_groups)]
+    gdf = cudf.concat(gdf)
+    assert_eq(pdf.reset_index(drop=True), gdf.reset_index(drop=True))
+
+    # first half rows come from the first source, rest from the second
+    gdf = cudf.read_parquet(
+        [fname, fname],
+        row_groups=[
+            list(range(row_groups // 2)),
+            list(range(row_groups // 2, row_groups)),
+        ],
+    )
+    assert_eq(pdf.reset_index(drop=True), gdf.reset_index(drop=True))
+
+
+@pytest.mark.parametrize("row_group_size", [1, 5, 100])
+def test_parquet_read_row_groups_non_contiguous(tmpdir, pdf, row_group_size):
+    if len(pdf) > 100:
+        pytest.skip("Skipping long setup test")
+
+    fname = tmpdir.join("row_group.parquet")
+    pdf.to_parquet(fname, compression="gzip", row_group_size=row_group_size)
+
+    num_rows, row_groups, col_names = cudf.io.read_parquet_metadata(fname)
+
+    # alternate rows between the two sources
+    gdf = cudf.read_parquet(
+        [fname, fname],
+        row_groups=[
+            list(range(0, row_groups, 2)),
+            list(range(1, row_groups, 2)),
+        ],
+    )
+
+    ref_df = [
+        cudf.read_parquet(fname, row_groups=i)
+        for i in list(range(0, row_groups, 2)) + list(range(1, row_groups, 2))
+    ]
+    ref_df = cudf.concat(ref_df)
+
+    assert_eq(ref_df, gdf)
+
+
+def test_parquet_reader_spark_timestamps(datadir):
+    fname = datadir / "spark_timestamp.snappy.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_spark_decimals(datadir):
+    fname = datadir / "spark_decimal.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("columns", [["a"], ["b", "a"], None])
+def test_parquet_reader_decimal128(datadir, columns):
+    fname = datadir / "nested_decimal128_file.parquet"
+    got = cudf.read_parquet(fname, columns=columns)
+    expect = cudf.read_parquet(fname, columns=columns)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_microsecond_timestamps(datadir):
+    fname = datadir / "usec_timestamp.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_mixedcompression(datadir):
+    fname = datadir / "mixed_compression.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_select_columns(datadir):
+    fname = datadir / "nested_column_map.parquet"
+
+    expect = cudf.read_parquet(fname).to_pandas()[["value"]]
+    got = cudf.read_parquet(fname, columns=["value"])
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_invalids(tmpdir):
+    test_pdf = make_pdf(nrows=1000, nvalids=1000 // 4, dtype=np.int64)
+
+    fname = tmpdir.join("invalids.parquet")
+    test_pdf.to_parquet(fname, engine="pyarrow")
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_filenotfound(tmpdir):
+    with pytest.raises(FileNotFoundError):
+        cudf.read_parquet("TestMissingFile.parquet")
+
+    with pytest.raises(FileNotFoundError):
+        cudf.read_parquet(tmpdir.mkdir("cudf_parquet"))
+
+
+def test_parquet_reader_local_filepath():
+    fname = "~/TestLocalFile.parquet"
+    if not os.path.isfile(fname):
+        pytest.skip("Local .parquet file is not found")
+
+    cudf.read_parquet(fname)
+
+
+@pytest.mark.parametrize(
+    "src", ["filepath", "pathobj", "bytes_io", "bytes", "url"]
+)
+def test_parquet_reader_filepath_or_buffer(parquet_path_or_buf, src):
+    expect = pd.read_parquet(parquet_path_or_buf("filepath"))
+    got = cudf.read_parquet(parquet_path_or_buf(src))
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_arrow_nativefile(parquet_path_or_buf):
+    # Check that we can read a file opened with the
+    # Arrow FileSystem interface
+    expect = cudf.read_parquet(parquet_path_or_buf("filepath"))
+    fs, path = pa_fs.FileSystem.from_uri(parquet_path_or_buf("filepath"))
+    with fs.open_input_file(path) as fil:
+        got = cudf.read_parquet(fil)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("use_python_file_object", [True, False])
+def test_parquet_reader_use_python_file_object(
+    parquet_path_or_buf, use_python_file_object
+):
+    # Check that the non-default `use_python_file_object=True`
+    # option works as expected
+    expect = cudf.read_parquet(parquet_path_or_buf("filepath"))
+    fs, _, paths = get_fs_token_paths(parquet_path_or_buf("filepath"))
+
+    # Pass open fsspec file
+    with fs.open(paths[0], mode="rb") as fil:
+        got1 = cudf.read_parquet(
+            fil, use_python_file_object=use_python_file_object
+        )
+    assert_eq(expect, got1)
+
+    # Pass path only
+    got2 = cudf.read_parquet(
+        paths[0], use_python_file_object=use_python_file_object
+    )
+    assert_eq(expect, got2)
+
+
+def create_parquet_source(df, src_type, fname):
+    if src_type == "filepath":
+        df.to_parquet(fname, engine="pyarrow")
+        return str(fname)
+    if src_type == "pathobj":
+        df.to_parquet(fname, engine="pyarrow")
+        return fname
+    if src_type == "bytes_io":
+        buffer = BytesIO()
+        df.to_parquet(buffer, engine="pyarrow")
+        return buffer
+    if src_type == "bytes":
+        buffer = BytesIO()
+        df.to_parquet(buffer, engine="pyarrow")
+        return buffer.getvalue()
+    if src_type == "url":
+        df.to_parquet(fname, engine="pyarrow")
+        return pathlib.Path(fname).as_uri()
+
+
+@pytest.mark.parametrize(
+    "src", ["filepath", "pathobj", "bytes_io", "bytes", "url"]
+)
+def test_parquet_reader_multiple_files(tmpdir, src):
+    test_pdf1 = make_pdf(nrows=1000, nvalids=1000 // 2)
+    test_pdf2 = make_pdf(nrows=500)
+    expect = pd.concat([test_pdf1, test_pdf2])
+
+    src1 = create_parquet_source(test_pdf1, src, tmpdir.join("multi1.parquet"))
+    src2 = create_parquet_source(test_pdf2, src, tmpdir.join("multi2.parquet"))
+    got = cudf.read_parquet([src1, src2])
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_reordered_columns(tmpdir):
+    src = pd.DataFrame(
+        {"name": ["cow", None, "duck", "fish", None], "id": [0, 1, 2, 3, 4]}
+    )
+    fname = tmpdir.join("test_parquet_reader_reordered_columns.parquet")
+    src.to_parquet(fname)
+    assert os.path.exists(fname)
+    expect = pd.DataFrame(
+        {"id": [0, 1, 2, 3, 4], "name": ["cow", None, "duck", "fish", None]}
+    )
+    got = cudf.read_parquet(fname, columns=["id", "name"])
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_parquet_reader_reordered_columns_mixed(tmpdir):
+    src = pd.DataFrame(
+        {
+            "name": ["cow", None, "duck", "fish", None],
+            "list0": [
+                [[1, 2], [3, 4]],
+                None,
+                [[5, 6], None],
+                [[1]],
+                [[5], [6, None, 8]],
+            ],
+            "id": [0, 1, 2, 3, 4],
+            "list1": [
+                [[1, 2], [3, 4]],
+                [[0, 0]],
+                [[5, 6], [10, 12]],
+                [[1]],
+                [[5], [6, 8]],
+            ],
+        }
+    )
+    fname = tmpdir.join("test_parquet_reader_reordered_columns.parquet")
+    src.to_parquet(fname)
+    assert os.path.exists(fname)
+    expect = pd.DataFrame(
+        {
+            "list1": [
+                [[1, 2], [3, 4]],
+                [[0, 0]],
+                [[5, 6], [10, 12]],
+                [[1]],
+                [[5], [6, 8]],
+            ],
+            "id": [0, 1, 2, 3, 4],
+            "list0": [
+                [[1, 2], [3, 4]],
+                None,
+                [[5, 6], None],
+                [[1]],
+                [[5], [6, None, 8]],
+            ],
+            "name": ["cow", None, "duck", "fish", None],
+        }
+    )
+    got = cudf.read_parquet(fname, columns=["list1", "id", "list0", "name"])
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_parquet_reader_list_basic(tmpdir):
+    expect = pd.DataFrame({"a": [[[1, 2], [3, 4]], None, [[5, 6], None]]})
+    fname = tmpdir.join("test_parquet_reader_list_basic.parquet")
+    expect.to_parquet(fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_list_table(tmpdir):
+    expect = pd.DataFrame(
+        {
+            "a": [[[1, 2], [3, 4]], None, [[5, 6], None]],
+            "b": [[None, None], None, [None, None]],
+            "c": [[[1, 2, 3]], [[None]], [[], None]],
+            "d": [[[]], [[None]], [[1, 2, 3], None]],
+            "e": [[["cows"]], [["dogs"]], [["cats", "birds", "owls"], None]],
+        }
+    )
+    fname = tmpdir.join("test_parquet_reader_list_table.parquet")
+    expect.to_parquet(fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert pa.Table.from_pandas(expect).equals(got.to_arrow())
+
+
+def int_gen(first_val, i):
+    """
+    Returns an integer based on an absolute index and a starting value. Used
+    as input to `list_gen`.
+    """
+    return int(i + first_val)
+
+
+strings = [
+    "cats",
+    "dogs",
+    "cows",
+    "birds",
+    "fish",
+    "sheep",
+    "owls",
+    "bears",
+    "ants",
+]
+
+
+def string_gen(first_val, i):
+    """
+    Returns a string based on an absolute index and a starting value. Used as
+    input to `list_gen`.
+    """
+    return strings[int_gen(first_val, i) % len(strings)]
+
+
+def list_row_gen(
+    gen, first_val, list_size, lists_per_row, include_validity=False
+):
+    """
+    Generate a single row for a List<List<>> column based on input parameters.
+
+    Parameters
+    ----------
+    gen : A callable which generates an individual leaf element based on an
+        absolute index.
+    first_val : Generate the column as if it had started at 'first_val'
+        instead of 0.
+    list_size : Size of each generated list.
+    lists_per_row : Number of lists to generate per row.
+    include_validity : Whether or not to include nulls as part of the
+        column. If true, it will add a selection of nulls at both the
+        topmost row level and at the leaf level.
+
+    Returns
+    -------
+    The generated list column.
+    """
+
+    def L(list_size, first_val):
+        return [
+            (gen(first_val, i) if i % 2 == 0 else None)
+            if include_validity
+            else (gen(first_val, i))
+            for i in range(list_size)
+        ]
+
+    return [
+        (L(list_size, first_val + (list_size * i)) if i % 2 == 0 else None)
+        if include_validity
+        else L(list_size, first_val + (list_size * i))
+        for i in range(lists_per_row)
+    ]
+
+
+def list_gen(gen, num_rows, lists_per_row, list_size, include_validity=False):
+    """
+    Generate a list column based on input parameters.
+
+    Parameters
+    ----------
+    gen : A callable which generates an individual leaf element based on an
+        absolute index.
+    num_rows : Number of rows to generate.
+    lists_per_row : Number of lists to generate per row.
+    list_size : Size of each generated list.
+    include_validity : Whether or not to include nulls as part of the
+        column. If true, it will add a selection of nulls at both the
+        topmost row level and at the leaf level.
+
+    Returns
+    -------
+    The generated list column.
+    """
+
+    def L(list_size, first_val):
+        return [
+            (gen(first_val, i) if i % 2 == 0 else None)
+            if include_validity
+            else (gen(first_val, i))
+            for i in range(list_size)
+        ]
+
+    def R(first_val, lists_per_row, list_size):
+        return [
+            L(list_size, first_val + (list_size * i))
+            for i in range(lists_per_row)
+        ]
+
+    return [
+        (
+            R(
+                lists_per_row * list_size * i,
+                lists_per_row,
+                list_size,
+            )
+            if i % 2 == 0
+            else None
+        )
+        if include_validity
+        else R(
+            lists_per_row * list_size * i,
+            lists_per_row,
+            list_size,
+        )
+        for i in range(num_rows)
+    ]
+
+
+def test_parquet_reader_list_large(tmpdir):
+    expect = pd.DataFrame({"a": list_gen(int_gen, 256, 80, 50)})
+    fname = tmpdir.join("test_parquet_reader_list_large.parquet")
+    expect.to_parquet(fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_parquet_reader_list_validity(tmpdir):
+    expect = pd.DataFrame(
+        {"a": list_gen(int_gen, 256, 80, 50, include_validity=True)}
+    )
+    fname = tmpdir.join("test_parquet_reader_list_validity.parquet")
+    expect.to_parquet(fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_parquet_reader_list_large_mixed(tmpdir):
+    expect = pd.DataFrame(
+        {
+            "a": list_gen(string_gen, 128, 80, 50),
+            "b": list_gen(int_gen, 128, 80, 50),
+            "c": list_gen(int_gen, 128, 80, 50, include_validity=True),
+            "d": list_gen(string_gen, 128, 80, 50, include_validity=True),
+        }
+    )
+    fname = tmpdir.join("test_parquet_reader_list_large_mixed.parquet")
+    expect.to_parquet(fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert pa.Table.from_pandas(expect).equals(got.to_arrow())
+
+
+def test_parquet_reader_list_large_multi_rowgroup(tmpdir):
+    # > 40 row groups
+    num_rows = 100000
+    num_docs = num_rows / 2
+    num_categories = 1_000
+    row_group_size = 1000
+
+    cupy.random.seed(0)
+
+    # generate a random pairing of doc: category
+    documents = cudf.DataFrame(
+        {
+            "document_id": cupy.random.randint(num_docs, size=num_rows),
+            "category_id": cupy.random.randint(num_categories, size=num_rows),
+        }
+    )
+
+    # group categories by document_id to create a list column
+    expect = documents.groupby("document_id").agg({"category_id": ["collect"]})
+    expect.columns = expect.columns.get_level_values(0)
+    expect.reset_index(inplace=True)
+
+    # round trip the dataframe to/from parquet
+    fname = tmpdir.join(
+        "test_parquet_reader_list_large_multi_rowgroup.parquet"
+    )
+    expect.to_pandas().to_parquet(fname, row_group_size=row_group_size)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_list_large_multi_rowgroup_nulls(tmpdir):
+    # 25 row groups
+    num_rows = 25000
+    row_group_size = 1000
+
+    expect = cudf.DataFrame(
+        {"a": list_gen(int_gen, num_rows, 3, 2, include_validity=True)}
+    )
+
+    # round trip the dataframe to/from parquet
+    fname = tmpdir.join(
+        "test_parquet_reader_list_large_multi_rowgroup_nulls.parquet"
+    )
+    expect.to_pandas().to_parquet(fname, row_group_size=row_group_size)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+def struct_gen(gen, skip_rows, num_rows, include_validity=False):
+    """
+    Generate a struct column based on input parameters.
+
+    Parameters
+    ----------
+    gen : A array of callables which generate an individual row based on an
+        absolute index.
+    skip_rows : Generate the column as if it had started at 'skip_rows'
+        instead of 0. The intent here is to emulate the skip_rows
+        parameter of the parquet reader.
+    num_fields : Number of fields in the struct.
+    include_validity : Whether or not to include nulls as part of the
+        column. If true, it will add a selection of nulls at both the
+        field level and at the value level.
+
+    Returns
+    -------
+    The generated struct column.
+    """
+
+    def R(first_val, num_fields):
+        return {
+            "col"
+            + str(f): (gen[f](first_val, first_val) if f % 4 != 0 else None)
+            if include_validity
+            else (gen[f](first_val, first_val))
+            for f in range(len(gen))
+        }
+
+    return [
+        (R((i + skip_rows), len(gen)) if (i + skip_rows) % 4 != 0 else None)
+        if include_validity
+        else R((i + skip_rows), len(gen))
+        for i in range(num_rows)
+    ]
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # struct
+        [
+            {"a": 1, "b": 2},
+            {"a": 10, "b": 20},
+            {"a": None, "b": 22},
+            {"a": None, "b": None},
+            {"a": 15, "b": None},
+        ],
+        # struct-of-list
+        [
+            {"a": 1, "b": 2, "c": [1, 2, 3]},
+            {"a": 10, "b": 20, "c": [4, 5]},
+            {"a": None, "b": 22, "c": [6]},
+            {"a": None, "b": None, "c": None},
+            {"a": 15, "b": None, "c": [-1, -2]},
+            None,
+            {"a": 100, "b": 200, "c": [-10, None, -20]},
+        ],
+        # list-of-struct
+        [
+            [{"a": 1, "b": 2}, {"a": 2, "b": 3}, {"a": 4, "b": 5}],
+            None,
+            [{"a": 10, "b": 20}],
+            [{"a": 100, "b": 200}, {"a": None, "b": 300}, None],
+        ],
+        # struct-of-struct
+        [
+            {"a": 1, "b": {"inner_a": 10, "inner_b": 20}, "c": 2},
+            {"a": 3, "b": {"inner_a": 30, "inner_b": 40}, "c": 4},
+            {"a": 5, "b": {"inner_a": 50, "inner_b": None}, "c": 6},
+            {"a": 7, "b": None, "c": 8},
+            {"a": None, "b": {"inner_a": None, "inner_b": None}, "c": None},
+            None,
+            {"a": None, "b": {"inner_a": None, "inner_b": 100}, "c": 10},
+        ],
+    ],
+)
+def test_parquet_reader_struct_basic(tmpdir, data):
+    expect = pa.Table.from_pydict({"struct": data})
+    fname = tmpdir.join("test_parquet_reader_struct_basic.parquet")
+    pa.parquet.write_table(expect, fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert expect.equals(got.to_arrow())
+
+
+def select_columns_params():
+    dfs = [
+        # struct
+        (
+            [
+                {"a": 1, "b": 2},
+                {"a": 10, "b": 20},
+                {"a": None, "b": 22},
+                {"a": None, "b": None},
+                {"a": 15, "b": None},
+            ],
+            [["struct"], ["struct.a"], ["struct.b"], ["c"]],
+        ),
+        # struct-of-list
+        (
+            [
+                {"a": 1, "b": 2, "c": [1, 2, 3]},
+                {"a": 10, "b": 20, "c": [4, 5]},
+                {"a": None, "b": 22, "c": [6]},
+                {"a": None, "b": None, "c": None},
+                {"a": 15, "b": None, "c": [-1, -2]},
+                None,
+                {"a": 100, "b": 200, "c": [-10, None, -20]},
+            ],
+            [
+                ["struct"],
+                ["struct.c"],
+                ["struct.c.list"],
+                ["struct.c.list.item"],
+                ["struct.b", "struct.c"],
+                ["struct.b", "struct.d", "struct.c"],
+            ],
+        ),
+        # list-of-struct
+        (
+            [
+                [{"a": 1, "b": 2}, {"a": 2, "b": 3}, {"a": 4, "b": 5}],
+                None,
+                [{"a": 10, "b": 20}],
+                [{"a": 100, "b": 200}, {"a": None, "b": 300}, None],
+            ],
+            [
+                ["struct"],
+                ["struct.list"],
+                ["struct.list.item"],
+                ["struct.list.item.a", "struct.list.item.b"],
+                ["struct.list.item.c"],
+            ],
+        ),
+        # struct with "." in field names
+        (
+            [
+                {"a.b": 1, "b.a": 2},
+                {"a.b": 10, "b.a": 20},
+                {"a.b": None, "b.a": 22},
+                {"a.b": None, "b.a": None},
+                {"a.b": 15, "b.a": None},
+            ],
+            [["struct"], ["struct.a"], ["struct.b.a"]],
+        ),
+    ]
+    for df_col_pair in dfs:
+        for cols in df_col_pair[1]:
+            yield df_col_pair[0], cols
+
+
+@pytest.mark.parametrize("data, columns", select_columns_params())
+def test_parquet_reader_struct_select_columns(tmpdir, data, columns):
+    table = pa.Table.from_pydict({"struct": data})
+    buff = BytesIO()
+
+    pa.parquet.write_table(table, buff)
+
+    expect = pq.ParquetFile(buff).read(columns=columns)
+    got = cudf.read_parquet(buff, columns=columns)
+    assert expect.equals(got.to_arrow())
+
+
+def test_parquet_reader_struct_los_large(tmpdir):
+    num_rows = 256
+    list_size = 64
+    data = [
+        struct_gen([string_gen, int_gen, string_gen], 0, list_size, False)
+        if i % 2 == 0
+        else None
+        for i in range(num_rows)
+    ]
+    expect = pa.Table.from_pydict({"los": data})
+    fname = tmpdir.join("test_parquet_reader_struct_los_large.parquet")
+    pa.parquet.write_table(expect, fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert expect.equals(got.to_arrow())
+
+
+@pytest.mark.parametrize(
+    "params", [[3, 4, 32, False], [3, 4, 32, True], [100, 25, 256, True]]
+)
+def test_parquet_reader_struct_sol_table(tmpdir, params):
+    # Struct<List<List>>
+    lists_per_row = params[0]
+    list_size = params[1]
+    num_rows = params[2]
+    include_validity = params[3]
+
+    def list_gen_wrapped(x, y):
+        return list_row_gen(
+            int_gen, x * list_size * lists_per_row, list_size, lists_per_row
+        )
+
+    def string_list_gen_wrapped(x, y):
+        return list_row_gen(
+            string_gen,
+            x * list_size * lists_per_row,
+            list_size,
+            lists_per_row,
+            include_validity,
+        )
+
+    data = struct_gen(
+        [int_gen, string_gen, list_gen_wrapped, string_list_gen_wrapped],
+        0,
+        num_rows,
+        include_validity,
+    )
+    expect = pa.Table.from_pydict({"sol": data})
+    fname = tmpdir.join("test_parquet_reader_struct_sol_table.parquet")
+    pa.parquet.write_table(expect, fname)
+    assert os.path.exists(fname)
+    got = cudf.read_parquet(fname)
+    assert expect.equals(got.to_arrow())
+
+
+def test_parquet_reader_v2(tmpdir, simple_pdf):
+    pdf_fname = tmpdir.join("pdfv2.parquet")
+    simple_pdf.to_parquet(pdf_fname, data_page_version="2.0")
+    assert_eq(cudf.read_parquet(pdf_fname), simple_pdf)
+
+
+@pytest.mark.parametrize("nrows", [1, 100000])
+@pytest.mark.parametrize("add_nulls", [True, False])
+def test_delta_binary(nrows, add_nulls, tmpdir):
+    null_frequency = 0.25 if add_nulls else 0
+
+    # Create a pandas dataframe with random data of mixed types
+    arrow_table = dg.rand_dataframe(
+        dtypes_meta=[
+            {
+                "dtype": "int8",
+                "null_frequency": null_frequency,
+                "cardinality": nrows,
+            },
+            {
+                "dtype": "int16",
+                "null_frequency": null_frequency,
+                "cardinality": nrows,
+            },
+            {
+                "dtype": "int32",
+                "null_frequency": null_frequency,
+                "cardinality": nrows,
+            },
+            {
+                "dtype": "int64",
+                "null_frequency": null_frequency,
+                "cardinality": nrows,
+            },
+        ],
+        rows=nrows,
+        seed=0,
+        use_threads=False,
+    )
+    # Roundabout conversion to pandas to preserve nulls/data types
+    cudf_table = cudf.DataFrame.from_arrow(arrow_table)
+    test_pdf = cudf_table.to_pandas(nullable=True)
+    pdf_fname = tmpdir.join("pdfv2.parquet")
+    test_pdf.to_parquet(
+        pdf_fname,
+        version="2.6",
+        column_encoding="DELTA_BINARY_PACKED",
+        data_page_version="2.0",
+        engine="pyarrow",
+        use_dictionary=False,
+    )
+    cdf = cudf.read_parquet(pdf_fname)
+    pcdf = cudf.from_pandas(test_pdf)
+    assert_eq(cdf, pcdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # Structs
+        {
+            "being": [
+                None,
+                {"human?": True, "Deets": {"Name": "Carrot", "Age": 27}},
+                {"human?": None, "Deets": {"Name": "Angua", "Age": 25}},
+                {"human?": False, "Deets": {"Name": "Cheery", "Age": 31}},
+                {"human?": False, "Deets": None},
+                {"human?": None, "Deets": {"Name": "Mr", "Age": None}},
+            ]
+        },
+        # List of Structs
+        {
+            "family": [
+                [None, {"human?": True, "deets": {"weight": 2.4, "age": 27}}],
+                [
+                    {"human?": None, "deets": {"weight": 5.3, "age": 25}},
+                    {"human?": False, "deets": {"weight": 8.0, "age": 31}},
+                    {"human?": False, "deets": None},
+                ],
+                [],
+                [{"human?": None, "deets": {"weight": 6.9, "age": None}}],
+            ]
+        },
+        # Struct of Lists
+        {
+            "Real estate records": [
+                None,
+                {
+                    "Status": "NRI",
+                    "Ownerships": {
+                        "land_unit": [None, 2, None],
+                        "flats": [[1, 2, 3], [], [4, 5], [], [0, 6, 0]],
+                    },
+                },
+                {
+                    "Status": None,
+                    "Ownerships": {
+                        "land_unit": [4, 5],
+                        "flats": [[7, 8], []],
+                    },
+                },
+                {
+                    "Status": "RI",
+                    "Ownerships": {"land_unit": None, "flats": [[]]},
+                },
+                {"Status": "RI", "Ownerships": None},
+                {
+                    "Status": None,
+                    "Ownerships": {
+                        "land_unit": [7, 8, 9],
+                        "flats": [[], [], []],
+                    },
+                },
+            ]
+        },
+    ],
+)
+def test_parquet_reader_nested_v2(tmpdir, data):
+    expect = pd.DataFrame(data)
+    pdf_fname = tmpdir.join("pdfv2.parquet")
+    expect.to_parquet(pdf_fname, data_page_version="2.0")
+    assert_eq(cudf.read_parquet(pdf_fname), expect)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+def test_parquet_writer_cpu_pyarrow(
+    tmpdir, pdf_day_timestamps, gdf_day_timestamps
+):
+    pdf_fname = tmpdir.join("pdf.parquet")
+    gdf_fname = tmpdir.join("gdf.parquet")
+
+    if len(pdf_day_timestamps) == 0:
+        pdf_day_timestamps = pdf_day_timestamps.reset_index(drop=True)
+        gdf_day_timestamps = pdf_day_timestamps.reset_index(drop=True)
+
+    pdf_day_timestamps.to_parquet(pdf_fname.strpath)
+    gdf_day_timestamps.to_parquet(gdf_fname.strpath, engine="pyarrow")
+
+    assert os.path.exists(pdf_fname)
+    assert os.path.exists(gdf_fname)
+
+    expect = pa.parquet.read_pandas(pdf_fname)
+    got = pa.parquet.read_pandas(gdf_fname)
+
+    assert_eq(expect, got)
+
+    def clone_field(table, name, datatype):
+        f = table.schema.field(name)
+        return pa.field(f.name, datatype, f.nullable, f.metadata)
+
+    # Pandas uses a datetime64[ns] while we use a datetime64[ms]
+    for t in [expect, got]:
+        for t_col in ["col_datetime64[ms]", "col_datetime64[us]"]:
+            idx = t.schema.get_field_index(t_col)
+            field = clone_field(t, t_col, pa.timestamp("ms"))
+            t = t.set_column(idx, field, t.column(idx).cast(field.type))
+            t = t.replace_schema_metadata()
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+def test_parquet_writer_int96_timestamps(tmpdir, pdf, gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+
+    if len(pdf) == 0:
+        pdf = pdf.reset_index(drop=True)
+        gdf = gdf.reset_index(drop=True)
+
+    if "col_category" in pdf.columns:
+        pdf = pdf.drop(columns=["col_category"])
+    if "col_category" in gdf.columns:
+        gdf = gdf.drop(columns=["col_category"])
+
+    assert_eq(pdf, gdf)
+
+    # Write out the gdf using the GPU accelerated writer with INT96 timestamps
+    gdf.to_parquet(gdf_fname.strpath, index=None, int96_timestamps=True)
+
+    assert os.path.exists(gdf_fname)
+
+    expect = pdf
+    got = pd.read_parquet(gdf_fname)
+
+    # verify INT96 timestamps were converted back to the same data.
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_multifile_parquet_folder(tmpdir):
+
+    test_pdf1 = make_pdf(nrows=10, nvalids=10 // 2)
+    test_pdf2 = make_pdf(nrows=20)
+    expect = pd.concat([test_pdf1, test_pdf2])
+
+    tmpdir.mkdir("multi_part")
+
+    create_parquet_source(
+        test_pdf1, "filepath", tmpdir.join("multi_part/multi1.parquet")
+    )
+    create_parquet_source(
+        test_pdf2, "filepath", tmpdir.join("multi_part/multi2.parquet")
+    )
+
+    got1 = cudf.read_parquet(tmpdir.join("multi_part/*.parquet"))
+    assert_eq(expect, got1)
+
+    got2 = cudf.read_parquet(tmpdir.join("multi_part"))
+    assert_eq(expect, got2)
+
+
+# Validates the metadata return path of the parquet writer
+def test_parquet_writer_return_metadata(tmpdir, simple_gdf):
+    gdf_fname = tmpdir.join("data1.parquet")
+
+    # Write out the gdf using the GPU accelerated writer
+    df_metadata = simple_gdf.to_parquet(
+        gdf_fname.strpath, index=None, metadata_file_path="test/data1.parquet"
+    )
+    # Verify that we got a valid parquet signature in the initial metadata blob
+    assert df_metadata.tobytes()[0:4] == b"PAR1"
+
+    df_metadata_list1 = [df_metadata]
+    df_metadata_list2 = [df_metadata, df_metadata]
+    merged_metadata1 = merge_parquet_filemetadata(df_metadata_list1)
+    merged_metadata2 = merge_parquet_filemetadata(df_metadata_list2)
+
+    # Verify that we got a valid parquet signature in the final metadata blob
+    assert merged_metadata1.tobytes()[0:4] == b"PAR1"
+    assert merged_metadata2.tobytes()[0:4] == b"PAR1"
+
+    # Make sure aggregation is combining metadata correctly
+    fmd1 = pa.parquet.ParquetFile(BytesIO(merged_metadata1.tobytes())).metadata
+    fmd2 = pa.parquet.ParquetFile(BytesIO(merged_metadata2.tobytes())).metadata
+    assert fmd2.num_columns == fmd1.num_columns
+    assert fmd2.num_rows == 2 * fmd1.num_rows
+    assert fmd2.num_row_groups == 2 * fmd1.num_row_groups
+
+
+# Validates the integrity of the GPU accelerated parquet writer.
+def test_parquet_writer_gpu_none_index(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+    pdf_fname = tmpdir.join("pdf.parquet")
+
+    assert_eq(simple_pdf, simple_gdf)
+
+    # Write out the gdf using the GPU accelerated writer
+    simple_gdf.to_parquet(gdf_fname.strpath, index=None)
+    simple_pdf.to_parquet(pdf_fname.strpath, index=None)
+
+    assert os.path.exists(gdf_fname)
+    assert os.path.exists(pdf_fname)
+
+    expect = pd.read_parquet(pdf_fname)
+    got = pd.read_parquet(gdf_fname)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_parquet_writer_gpu_true_index(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+    pdf_fname = tmpdir.join("pdf.parquet")
+
+    assert_eq(simple_pdf, simple_gdf)
+
+    # Write out the gdf using the GPU accelerated writer
+    simple_gdf.to_parquet(gdf_fname.strpath, index=True)
+    simple_pdf.to_parquet(pdf_fname.strpath, index=True)
+
+    assert os.path.exists(gdf_fname)
+    assert os.path.exists(pdf_fname)
+
+    expect = pd.read_parquet(pdf_fname)
+    got = pd.read_parquet(gdf_fname)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_parquet_writer_gpu_false_index(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+    pdf_fname = tmpdir.join("pdf.parquet")
+
+    assert_eq(simple_pdf, simple_gdf)
+
+    # Write out the gdf using the GPU accelerated writer
+    simple_gdf.to_parquet(gdf_fname.strpath, index=False)
+    simple_pdf.to_parquet(pdf_fname.strpath, index=False)
+
+    assert os.path.exists(gdf_fname)
+    assert os.path.exists(pdf_fname)
+
+    expect = pd.read_parquet(pdf_fname)
+    got = pd.read_parquet(gdf_fname)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_parquet_writer_gpu_multi_index(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+    pdf_fname = tmpdir.join("pdf.parquet")
+
+    simple_pdf = simple_pdf.set_index(["col_bool", "col_int8"])
+    simple_gdf = simple_gdf.set_index(["col_bool", "col_int8"])
+
+    assert_eq(simple_pdf, simple_gdf)
+
+    print("PDF Index Type: " + str(type(simple_pdf.index)))
+    print("GDF Index Type: " + str(type(simple_gdf.index)))
+
+    # Write out the gdf using the GPU accelerated writer
+    simple_gdf.to_parquet(gdf_fname.strpath, index=None)
+    simple_pdf.to_parquet(pdf_fname.strpath, index=None)
+
+    assert os.path.exists(gdf_fname)
+    assert os.path.exists(pdf_fname)
+
+    expect = pd.read_parquet(pdf_fname)
+    got = pd.read_parquet(gdf_fname)
+
+    assert_eq(expect, got, check_categorical=False)
+
+
+def test_parquet_writer_gpu_chunked(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+
+    writer = ParquetWriter(gdf_fname)
+    writer.write_table(simple_gdf)
+    writer.write_table(simple_gdf)
+    writer.close()
+
+    assert_eq(pd.read_parquet(gdf_fname), pd.concat([simple_pdf, simple_pdf]))
+
+
+def test_parquet_writer_gpu_chunked_context(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+
+    with ParquetWriter(gdf_fname) as writer:
+        writer.write_table(simple_gdf)
+        writer.write_table(simple_gdf)
+
+    got = pd.read_parquet(gdf_fname)
+    expect = pd.concat([simple_pdf, simple_pdf])
+    assert_eq(got, expect)
+
+
+def test_parquet_write_bytes_io(simple_gdf):
+    output = BytesIO()
+    simple_gdf.to_parquet(output)
+    assert_eq(cudf.read_parquet(output), simple_gdf)
+
+
+def test_parquet_writer_bytes_io(simple_gdf):
+    output = BytesIO()
+
+    writer = ParquetWriter(output)
+    writer.write_table(simple_gdf)
+    writer.write_table(simple_gdf)
+    writer.close()
+
+    assert_eq(cudf.read_parquet(output), cudf.concat([simple_gdf, simple_gdf]))
+
+
+@pytest.mark.parametrize(
+    "row_group_size_kwargs",
+    [
+        {"row_group_size_bytes": 4 * 1024},
+        {"row_group_size_rows": 5000},
+    ],
+)
+def test_parquet_writer_row_group_size(tmpdir, row_group_size_kwargs):
+    # Check that row_group_size options are exposed in Python
+    # See https://github.com/rapidsai/cudf/issues/10978
+
+    size = 20000
+    gdf = cudf.DataFrame({"a": range(size), "b": [1] * size})
+
+    fname = tmpdir.join("gdf.parquet")
+    with ParquetWriter(fname, **row_group_size_kwargs) as writer:
+        writer.write_table(gdf)
+
+    # Simple check for multiple row-groups
+    nrows, nrow_groups, columns = cudf.io.parquet.read_parquet_metadata(fname)
+    assert nrows == size
+    assert nrow_groups > 1
+    assert columns == ["a", "b"]
+
+    # Know the specific row-group count for row_group_size_rows
+    if "row_group_size_rows" in row_group_size_kwargs:
+        assert (
+            nrow_groups == size // row_group_size_kwargs["row_group_size_rows"]
+        )
+
+    assert_eq(cudf.read_parquet(fname), gdf)
+
+
+def test_parquet_writer_column_index(tmpdir):
+    # Simple test for presence of indices. validity is checked
+    # in libcudf tests.
+    # Write 2 files, one with column index set, one without.
+    # Make sure the former is larger in size.
+
+    size = 20000
+    gdf = cudf.DataFrame({"a": range(size), "b": [1] * size})
+
+    fname = tmpdir.join("gdf.parquet")
+    with ParquetWriter(fname, statistics="ROWGROUP") as writer:
+        writer.write_table(gdf)
+    s1 = os.path.getsize(fname)
+
+    fname = tmpdir.join("gdfi.parquet")
+    with ParquetWriter(fname, statistics="COLUMN") as writer:
+        writer.write_table(gdf)
+    s2 = os.path.getsize(fname)
+    assert s2 > s1
+
+
+@pytest.mark.parametrize(
+    "max_page_size_kwargs",
+    [
+        {"max_page_size_bytes": 4 * 1024},
+        {"max_page_size_rows": 5000},
+    ],
+)
+def test_parquet_writer_max_page_size(tmpdir, max_page_size_kwargs):
+    # Check that max_page_size options are exposed in Python
+    # Since we don't have access to page metadata, instead check that
+    # file written with more pages will be slightly larger
+
+    size = 20000
+    gdf = cudf.DataFrame({"a": range(size), "b": [1] * size})
+
+    fname = tmpdir.join("gdf.parquet")
+    with ParquetWriter(fname, **max_page_size_kwargs) as writer:
+        writer.write_table(gdf)
+    s1 = os.path.getsize(fname)
+
+    assert_eq(cudf.read_parquet(fname), gdf)
+
+    fname = tmpdir.join("gdf0.parquet")
+    with ParquetWriter(fname) as writer:
+        writer.write_table(gdf)
+    s2 = os.path.getsize(fname)
+
+    assert_eq(cudf.read_parquet(fname), gdf)
+    assert s1 > s2
+
+
+@pytest.mark.parametrize("filename", ["myfile.parquet", None])
+@pytest.mark.parametrize("cols", [["b"], ["c", "b"]])
+def test_parquet_partitioned(tmpdir_factory, cols, filename):
+    # Checks that write_to_dataset is wrapping to_parquet
+    # as expected
+    gdf_dir = str(tmpdir_factory.mktemp("gdf_dir"))
+    pdf_dir = str(tmpdir_factory.mktemp("pdf_dir"))
+    size = 100
+    pdf = pd.DataFrame(
+        {
+            "a": np.arange(0, stop=size, dtype="int64"),
+            "b": np.random.choice(list("abcd"), size=size),
+            "c": np.random.choice(np.arange(4), size=size),
+        }
+    )
+    pdf.to_parquet(pdf_dir, index=False, partition_cols=cols)
+    gdf = cudf.from_pandas(pdf)
+    gdf.to_parquet(
+        gdf_dir, index=False, partition_cols=cols, partition_file_name=filename
+    )
+
+    # Read back with pandas to compare
+    expect_pd = pd.read_parquet(pdf_dir)
+    got_pd = pd.read_parquet(gdf_dir)
+    assert_eq(expect_pd, got_pd)
+
+    # Check that cudf and pd return the same read
+    got_cudf = cudf.read_parquet(gdf_dir)
+    assert_eq(got_pd, got_cudf)
+
+    # If filename is specified, check that it is correct
+    if filename:
+        for _, _, files in os.walk(gdf_dir):
+            for fn in files:
+                assert fn == filename
+
+
+@pytest.mark.parametrize("return_meta", [True, False])
+def test_parquet_writer_chunked_partitioned(tmpdir_factory, return_meta):
+    pdf_dir = str(tmpdir_factory.mktemp("pdf_dir"))
+    gdf_dir = str(tmpdir_factory.mktemp("gdf_dir"))
+
+    df1 = cudf.DataFrame({"a": [1, 1, 2, 2, 1], "b": [9, 8, 7, 6, 5]})
+    df2 = cudf.DataFrame({"a": [1, 3, 3, 1, 3], "b": [4, 3, 2, 1, 0]})
+
+    cw = ParquetDatasetWriter(gdf_dir, partition_cols=["a"], index=False)
+    cw.write_table(df1)
+    cw.write_table(df2)
+    meta_byte_array = cw.close(return_metadata=return_meta)
+    pdf = cudf.concat([df1, df2]).to_pandas()
+    pdf.to_parquet(pdf_dir, index=False, partition_cols=["a"])
+
+    if return_meta:
+        fmd = pq.ParquetFile(BytesIO(meta_byte_array)).metadata
+        assert fmd.num_rows == len(pdf)
+        assert fmd.num_row_groups == 4
+        files = {
+            os.path.join(directory, files[0])
+            for directory, _, files in os.walk(gdf_dir)
+            if files
+        }
+        meta_files = {
+            os.path.join(gdf_dir, fmd.row_group(i).column(c).file_path)
+            for i in range(fmd.num_row_groups)
+            for c in range(fmd.row_group(i).num_columns)
+        }
+        assert files == meta_files
+
+    # Read back with pandas to compare
+    expect_pd = pd.read_parquet(pdf_dir)
+    got_pd = pd.read_parquet(gdf_dir)
+    assert_eq(expect_pd, got_pd)
+
+    # Check that cudf and pd return the same read
+    got_cudf = cudf.read_parquet(gdf_dir)
+    assert_eq(got_pd, got_cudf)
+
+
+@pytest.mark.parametrize(
+    "max_file_size,max_file_size_in_bytes",
+    [("500KB", 500000), ("MB", 1000000)],
+)
+def test_parquet_writer_chunked_max_file_size(
+    tmpdir_factory, max_file_size, max_file_size_in_bytes
+):
+    pdf_dir = str(tmpdir_factory.mktemp("pdf_dir"))
+    gdf_dir = str(tmpdir_factory.mktemp("gdf_dir"))
+
+    df1 = cudf.DataFrame({"a": [1, 1, 2, 2, 1] * 10000, "b": range(0, 50000)})
+    df2 = cudf.DataFrame(
+        {"a": [1, 3, 3, 1, 3] * 10000, "b": range(50000, 100000)}
+    )
+
+    cw = ParquetDatasetWriter(
+        gdf_dir,
+        partition_cols=["a"],
+        max_file_size=max_file_size,
+        file_name_prefix="sample",
+    )
+    cw.write_table(df1)
+    cw.write_table(df2)
+    cw.close()
+    pdf = cudf.concat([df1, df2]).to_pandas()
+    pdf.to_parquet(pdf_dir, index=False, partition_cols=["a"])
+
+    expect_pd = pd.read_parquet(pdf_dir)
+    got_pd = pd.read_parquet(gdf_dir)
+
+    assert_eq(
+        expect_pd.sort_values(["b"]).reset_index(drop=True),
+        got_pd.sort_values(["b"]).reset_index(drop=True),
+    )
+
+    # Check that cudf and pd return the same read
+    got_cudf = cudf.read_parquet(gdf_dir)
+
+    assert_eq(
+        got_pd.sort_values(["b"]).reset_index(drop=True),
+        got_cudf.sort_values(["b"]).reset_index(drop=True),
+    )
+
+    all_files = glob.glob(gdf_dir + "/**/*.parquet", recursive=True)
+    for each_file in all_files:
+        # Validate file sizes with some extra 1000
+        # bytes buffer to spare
+        assert os.path.getsize(each_file) <= (
+            max_file_size_in_bytes
+        ), "File exceeded max_file_size"
+
+
+def test_parquet_writer_chunked_max_file_size_error():
+    with pytest.raises(
+        ValueError,
+        match="file_name_prefix cannot be None if max_file_size is passed",
+    ):
+        ParquetDatasetWriter("sample", partition_cols=["a"], max_file_size=100)
+
+
+def test_parquet_writer_chunked_partitioned_context(tmpdir_factory):
+    pdf_dir = str(tmpdir_factory.mktemp("pdf_dir"))
+    gdf_dir = str(tmpdir_factory.mktemp("gdf_dir"))
+
+    df1 = cudf.DataFrame({"a": [1, 1, 2, 2, 1], "b": [9, 8, 7, 6, 5]})
+    df2 = cudf.DataFrame({"a": [1, 3, 3, 1, 3], "b": [4, 3, 2, 1, 0]})
+
+    with ParquetDatasetWriter(
+        gdf_dir, partition_cols=["a"], index=False
+    ) as cw:
+        cw.write_table(df1)
+        cw.write_table(df2)
+
+    pdf = cudf.concat([df1, df2]).to_pandas()
+    pdf.to_parquet(pdf_dir, index=False, partition_cols=["a"])
+
+    # Read back with pandas to compare
+    expect_pd = pd.read_parquet(pdf_dir)
+    got_pd = pd.read_parquet(gdf_dir)
+    assert_eq(expect_pd, got_pd)
+
+    # Check that cudf and pd return the same read
+    got_cudf = cudf.read_parquet(gdf_dir)
+    assert_eq(got_pd, got_cudf)
+
+
+@pytest.mark.parametrize("cols", [None, ["b"]])
+def test_parquet_write_to_dataset(tmpdir_factory, cols):
+    dir1 = tmpdir_factory.mktemp("dir1")
+    dir2 = tmpdir_factory.mktemp("dir2")
+    if cols is None:
+        dir1 = dir1.join("file.pq")
+        dir2 = dir2.join("file.pq")
+    dir1 = str(dir1)
+    dir2 = str(dir2)
+
+    size = 100
+    gdf = cudf.DataFrame(
+        {
+            "a": np.arange(0, stop=size),
+            "b": np.random.choice(np.arange(4), size=size),
+        }
+    )
+    gdf.to_parquet(dir1, partition_cols=cols)
+    cudf.io.write_to_dataset(gdf, dir2, partition_cols=cols)
+
+    # Read back with cudf
+    expect = cudf.read_parquet(dir1)
+    got = cudf.read_parquet(dir2)
+    assert_eq(expect, got)
+
+    gdf = cudf.DataFrame(
+        {
+            "a": cudf.Series([1, 2, 3]),
+            "b": cudf.Series([1, 2, 3]),
+            "c": cudf.Series(["a", "b", "c"], dtype="category"),
+        }
+    )
+    with pytest.raises(ValueError):
+        gdf.to_parquet(dir1, partition_cols=cols)
+
+
+@pytest.mark.parametrize(
+    "pfilters",
+    [[("b", "==", "b")], [("b", "==", "a"), ("c", "==", 1)]],
+)
+@pytest.mark.parametrize("selection", ["directory", "files", "row-groups"])
+@pytest.mark.parametrize("use_cat", [True, False])
+def test_read_parquet_partitioned_filtered(
+    tmpdir, pfilters, selection, use_cat
+):
+    path = str(tmpdir)
+    size = 100
+    df = cudf.DataFrame(
+        {
+            "a": np.arange(0, stop=size, dtype="int64"),
+            "b": np.random.choice(list("abcd"), size=size),
+            "c": np.random.choice(np.arange(4), size=size),
+        }
+    )
+    df.to_parquet(path, partition_cols=["c", "b"])
+
+    if selection == "files":
+        # Pass in a list of paths
+        fs = get_fs_token_paths(path)[0]
+        read_path = fs.find(path)
+        row_groups = None
+    elif selection == "row-groups":
+        # Pass in a list of paths AND row-group ids
+        fs = get_fs_token_paths(path)[0]
+        read_path = fs.find(path)
+        row_groups = [[0] for p in read_path]
+    else:
+        # Pass in a directory path
+        # (row-group selection not allowed in this case)
+        read_path = path
+        row_groups = None
+
+    # Filter on partitioned columns
+    expect = pd.read_parquet(read_path, filters=pfilters)
+    got = cudf.read_parquet(
+        read_path,
+        filters=pfilters,
+        row_groups=row_groups,
+        categorical_partitions=use_cat,
+    )
+    expect["b"] = expect["b"].astype(str)
+    expect["c"] = expect["c"].astype(int)
+    if use_cat:
+        assert got.dtypes["b"] == "category"
+        assert got.dtypes["c"] == "category"
+        got["b"] = got["b"].astype(str)
+        got["c"] = got["c"].astype(int)
+    else:
+        # Check that we didn't get categorical
+        # columns, but convert back to categorical
+        # for comparison with pandas
+        assert got.dtypes["b"] == "object"
+        assert got.dtypes["c"] == "int"
+    assert_eq(expect, got)
+
+    # Filter on non-partitioned column
+    filters = [("a", "==", 10)]
+    got = cudf.read_parquet(read_path, filters=filters)
+    expect = pd.read_parquet(read_path, filters=filters)
+
+    # Filter on both kinds of columns
+    filters = [[("a", "==", 10)], [("c", "==", 1)]]
+    got = cudf.read_parquet(read_path, filters=filters)
+    expect = pd.read_parquet(read_path, filters=filters)
+    assert_eq(expect, got)
+
+
+def test_parquet_writer_chunked_metadata(tmpdir, simple_pdf, simple_gdf):
+    gdf_fname = tmpdir.join("gdf.parquet")
+    test_path = "test/path"
+
+    writer = ParquetWriter(gdf_fname)
+    writer.write_table(simple_gdf)
+    writer.write_table(simple_gdf)
+    meta_byte_array = writer.close(metadata_file_path=test_path)
+    fmd = pq.ParquetFile(BytesIO(meta_byte_array)).metadata
+
+    assert fmd.num_rows == 2 * len(simple_gdf)
+    assert fmd.num_row_groups == 2
+
+    for r in range(fmd.num_row_groups):
+        for c in range(fmd.num_columns):
+            assert fmd.row_group(r).column(c).file_path == test_path
+
+
+def test_write_read_cudf(tmpdir, pdf):
+    file_path = tmpdir.join("cudf.parquet")
+    if "col_category" in pdf.columns:
+        pdf = pdf.drop(columns=["col_category"])
+
+    gdf = cudf.from_pandas(pdf)
+    gdf.to_parquet(file_path)
+    gdf = cudf.read_parquet(file_path)
+
+    assert_eq(gdf, pdf, check_index_type=not pdf.empty)
+
+
+def test_write_cudf_read_pandas_pyarrow(tmpdir, pdf):
+    cudf_path = tmpdir.join("cudf.parquet")
+    pandas_path = tmpdir.join("pandas.parquet")
+
+    if "col_category" in pdf.columns:
+        pdf = pdf.drop(columns=["col_category"])
+
+    df = cudf.from_pandas(pdf)
+
+    df.to_parquet(cudf_path)
+    pdf.to_parquet(pandas_path)
+
+    cudf_res = pd.read_parquet(cudf_path)
+    pd_res = pd.read_parquet(pandas_path)
+
+    assert_eq(pd_res, cudf_res, check_index_type=not pdf.empty)
+
+    cudf_res = pa.parquet.read_table(
+        cudf_path, use_pandas_metadata=True
+    ).to_pandas()
+    pd_res = pa.parquet.read_table(
+        pandas_path, use_pandas_metadata=True
+    ).to_pandas()
+
+    assert_eq(cudf_res, pd_res, check_index_type=not pdf.empty)
+
+
+def test_parquet_writer_criteo(tmpdir):
+    # To run this test, download the day 0 of criteo dataset from
+    # http://labs.criteo.com/2013/12/download-terabyte-click-logs/
+    # and place the uncompressed dataset in the home directory
+    fname = os.path.expanduser("~/day_0")
+    if not os.path.isfile(fname):
+        pytest.skip("Local criteo day 0 tsv file is not found")
+
+    cudf_path = tmpdir.join("cudf.parquet")
+
+    cont_names = ["I" + str(x) for x in range(1, 14)]
+    cat_names = ["C" + str(x) for x in range(1, 27)]
+    cols = ["label"] + cont_names + cat_names
+
+    df = cudf.read_csv(fname, sep="\t", names=cols, byte_range=(0, 1000000000))
+    df = df.drop(columns=cont_names)
+
+    df.to_parquet(cudf_path)
+
+
+def test_trailing_nans(datadir, tmpdir):
+    fname = "trailing_nans.parquet"
+    file_path = datadir / fname
+    cu_df = cudf.read_parquet(file_path)
+
+    tmp_file_path = tmpdir.join(fname)
+    cu_df.to_parquet(tmp_file_path)
+
+    pd.read_parquet(tmp_file_path)
+
+
+def test_parquet_writer_sliced(tmpdir):
+    cudf_path = tmpdir.join("cudf.parquet")
+
+    df = pd.DataFrame()
+    df["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    df = cudf.from_pandas(df)
+
+    df_select = df.iloc[1:3]
+
+    df_select.to_parquet(cudf_path)
+    assert_eq(cudf.read_parquet(cudf_path), df_select)
+
+
+def test_parquet_writer_list_basic(tmpdir):
+    expect = pd.DataFrame({"a": [[[1, 2], [3, 4]], None, [[5, 6], None]]})
+    fname = tmpdir.join("test_parquet_writer_list_basic.parquet")
+
+    gdf = cudf.from_pandas(expect)
+
+    gdf.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    got = pd.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+def test_parquet_writer_list_large(tmpdir):
+    expect = pd.DataFrame({"a": list_gen(int_gen, 256, 80, 50)})
+    fname = tmpdir.join("test_parquet_writer_list_large.parquet")
+
+    gdf = cudf.from_pandas(expect)
+
+    gdf.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    got = pd.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+def test_parquet_writer_list_large_mixed(tmpdir):
+    expect = pd.DataFrame(
+        {
+            "a": list_gen(string_gen, 128, 80, 50),
+            "b": list_gen(int_gen, 128, 80, 50),
+            "c": list_gen(int_gen, 128, 80, 50, include_validity=True),
+            "d": list_gen(string_gen, 128, 80, 50, include_validity=True),
+        }
+    )
+    fname = tmpdir.join("test_parquet_writer_list_large_mixed.parquet")
+    gdf = cudf.from_pandas(expect)
+
+    gdf.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    got = pd.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+def test_parquet_writer_list_chunked(tmpdir):
+    table1 = cudf.DataFrame(
+        {
+            "a": list_gen(string_gen, 128, 80, 50),
+            "b": list_gen(int_gen, 128, 80, 50),
+            "c": list_gen(int_gen, 128, 80, 50, include_validity=True),
+            "d": list_gen(string_gen, 128, 80, 50, include_validity=True),
+        }
+    )
+    table2 = cudf.DataFrame(
+        {
+            "a": list_gen(string_gen, 128, 80, 50),
+            "b": list_gen(int_gen, 128, 80, 50),
+            "c": list_gen(int_gen, 128, 80, 50, include_validity=True),
+            "d": list_gen(string_gen, 128, 80, 50, include_validity=True),
+        }
+    )
+    fname = tmpdir.join("test_parquet_writer_list_chunked.parquet")
+    expect = cudf.concat([table1, table2])
+    expect = expect.reset_index(drop=True)
+
+    writer = ParquetWriter(fname)
+    writer.write_table(table1)
+    writer.write_table(table2)
+    writer.close()
+
+    assert os.path.exists(fname)
+
+    got = pd.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+def test_parquet_nullable_boolean(tmpdir, engine):
+    pandas_path = tmpdir.join("pandas_bools.parquet")
+
+    pdf = pd.DataFrame(
+        {
+            "a": pd.Series(
+                [True, False, None, True, False], dtype=pd.BooleanDtype()
+            )
+        }
+    )
+    expected_gdf = cudf.DataFrame({"a": [True, False, None, True, False]})
+
+    pdf.to_parquet(pandas_path)
+    with _hide_pyarrow_parquet_cpu_warnings(engine):
+        actual_gdf = cudf.read_parquet(pandas_path, engine=engine)
+
+    assert_eq(actual_gdf, expected_gdf)
+
+
+def run_parquet_index(pdf, index):
+    pandas_buffer = BytesIO()
+    cudf_buffer = BytesIO()
+
+    gdf = cudf.from_pandas(pdf)
+
+    pdf.to_parquet(pandas_buffer, index=index)
+    gdf.to_parquet(cudf_buffer, index=index)
+
+    expected = pd.read_parquet(cudf_buffer)
+    actual = cudf.read_parquet(pandas_buffer)
+
+    assert_eq(expected, actual, check_index_type=True)
+
+    expected = pd.read_parquet(pandas_buffer)
+    actual = cudf.read_parquet(cudf_buffer)
+
+    assert_eq(expected, actual, check_index_type=True)
+
+
+@pytest.mark.parametrize(
+    "pdf",
+    [
+        pd.DataFrame(index=[1, 2, 3]),
+        pd.DataFrame({"a": [1, 2, 3]}, index=[0.43534, 345, 0.34534]),
+        pd.DataFrame(
+            {"b": [11, 22, 33], "c": ["a", "b", "c"]},
+            index=pd.Index(["a", "b", "c"], name="custom name"),
+        ),
+        pd.DataFrame(
+            {"a": [10, 11, 12], "b": [99, 88, 77]},
+            index=pd.RangeIndex(12, 17, 2),
+        ),
+        pd.DataFrame(
+            {"b": [99, 88, 77]},
+            index=pd.RangeIndex(22, 27, 2, name="hello index"),
+        ),
+        pd.DataFrame(index=pd.Index(["a", "b", "c"], name="custom name")),
+        pd.DataFrame(
+            {"a": ["a", "bb", "cc"], "b": [10, 21, 32]},
+            index=pd.MultiIndex.from_tuples([[1, 2], [10, 11], [15, 16]]),
+        ),
+        pd.DataFrame(
+            {"a": ["a", "bb", "cc"], "b": [10, 21, 32]},
+            index=pd.MultiIndex.from_tuples(
+                [[1, 2], [10, 11], [15, 16]], names=["first", "second"]
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("index", [None, True, False])
+def test_parquet_index(pdf, index):
+    run_parquet_index(pdf, index)
+
+
+@pytest.mark.parametrize("index", [None, True])
+@pytest.mark.xfail(
+    reason="https://github.com/rapidsai/cudf/issues/12243",
+)
+def test_parquet_index_empty(index):
+    pdf = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
+    run_parquet_index(pdf, index)
+
+
+def test_parquet_no_index_empty():
+    pdf = pd.DataFrame(index=pd.RangeIndex(0, 10, 1))
+    run_parquet_index(pdf, index=False)
+
+
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+def test_parquet_allnull_str(tmpdir, engine):
+    pandas_path = tmpdir.join("pandas_allnulls.parquet")
+
+    pdf = pd.DataFrame(
+        {"a": pd.Series([None, None, None, None, None], dtype="str")}
+    )
+    expected_gdf = cudf.DataFrame(
+        {"a": cudf.Series([None, None, None, None, None], dtype="str")}
+    )
+
+    pdf.to_parquet(pandas_path)
+    with _hide_pyarrow_parquet_cpu_warnings(engine):
+        actual_gdf = cudf.read_parquet(pandas_path, engine=engine)
+
+    assert_eq(actual_gdf, expected_gdf)
+
+
+def normalized_equals(value1, value2):
+    if value1 is pd.NA or value1 is pd.NaT:
+        value1 = None
+    if value2 is pd.NA or value2 is pd.NaT:
+        value2 = None
+    if isinstance(value1, pd.Timestamp):
+        value1 = value1.to_pydatetime()
+    if isinstance(value2, pd.Timestamp):
+        value2 = value2.to_pydatetime()
+    if isinstance(value1, datetime.datetime):
+        value1 = value1.replace(tzinfo=None)
+    if isinstance(value2, datetime.datetime):
+        value2 = value2.replace(tzinfo=None)
+
+    # if one is datetime then both values are datetimes now
+    if isinstance(value1, datetime.datetime):
+        return value1 == value2
+
+    # Compare integers with floats now
+    if isinstance(value1, float) or isinstance(value2, float):
+        return math.isclose(value1, value2)
+
+    return value1 == value2
+
+
+@pytest.mark.parametrize("add_nulls", [True, False])
+def test_parquet_writer_statistics(tmpdir, pdf, add_nulls):
+    file_path = tmpdir.join("cudf.parquet")
+    if "col_category" in pdf.columns:
+        pdf = pdf.drop(columns=["col_category", "col_bool"])
+
+    if not add_nulls:
+        # Timedelta types convert NaT to None when reading from parquet into
+        # pandas which interferes with series.max()/min()
+        for t in TIMEDELTA_TYPES:
+            pdf["col_" + t] = pd.Series(np.arange(len(pdf.index))).astype(t)
+        # pyarrow can't read values with non-zero nanoseconds
+        pdf["col_timedelta64[ns]"] = pdf["col_timedelta64[ns]"] * 1000
+
+    gdf = cudf.from_pandas(pdf)
+    if add_nulls:
+        for col in gdf:
+            set_random_null_mask_inplace(gdf[col])
+    gdf.to_parquet(file_path, index=False)
+
+    # Read back from pyarrow
+    pq_file = pq.ParquetFile(file_path)
+    # verify each row group's statistics
+    for rg in range(0, pq_file.num_row_groups):
+        pd_slice = pq_file.read_row_group(rg).to_pandas()
+
+        # statistics are per-column. So need to verify independently
+        for i, col in enumerate(pd_slice):
+            stats = pq_file.metadata.row_group(rg).column(i).statistics
+
+            actual_min = pd_slice[col].min()
+            stats_min = stats.min
+            assert normalized_equals(actual_min, stats_min)
+
+            actual_max = pd_slice[col].max()
+            stats_max = stats.max
+            assert normalized_equals(actual_max, stats_max)
+
+            assert stats.null_count == pd_slice[col].isna().sum()
+            assert stats.num_values == pd_slice[col].count()
+
+
+def test_parquet_writer_list_statistics(tmpdir):
+    df = pd.DataFrame(
+        {
+            "a": list_gen(string_gen, 128, 80, 50),
+            "b": list_gen(int_gen, 128, 80, 50),
+            "c": list_gen(int_gen, 128, 80, 50, include_validity=True),
+            "d": list_gen(string_gen, 128, 80, 50, include_validity=True),
+        }
+    )
+    fname = tmpdir.join("test_parquet_writer_list_statistics.parquet")
+    gdf = cudf.from_pandas(df)
+
+    gdf.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    # Read back from pyarrow
+    pq_file = pq.ParquetFile(fname)
+    # verify each row group's statistics
+    for rg in range(0, pq_file.num_row_groups):
+        pd_slice = pq_file.read_row_group(rg).to_pandas()
+
+        # statistics are per-column. So need to verify independently
+        for i, col in enumerate(pd_slice):
+            stats = pq_file.metadata.row_group(rg).column(i).statistics
+
+            actual_min = pd_slice[col].explode().explode().dropna().min()
+            stats_min = stats.min
+            assert normalized_equals(actual_min, stats_min)
+
+            actual_max = pd_slice[col].explode().explode().dropna().max()
+            stats_max = stats.max
+            assert normalized_equals(actual_max, stats_max)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # Structs
+        {
+            "being": [
+                None,
+                {"human?": True, "Deets": {"Name": "Carrot", "Age": 27}},
+                {"human?": None, "Deets": {"Name": "Angua", "Age": 25}},
+                {"human?": False, "Deets": {"Name": "Cheery", "Age": 31}},
+                {"human?": False, "Deets": None},
+                {"human?": None, "Deets": {"Name": "Mr", "Age": None}},
+            ]
+        },
+        # List of Structs
+        {
+            "family": [
+                [None, {"human?": True, "deets": {"weight": 2.4, "age": 27}}],
+                [
+                    {"human?": None, "deets": {"weight": 5.3, "age": 25}},
+                    {"human?": False, "deets": {"weight": 8.0, "age": 31}},
+                    {"human?": False, "deets": None},
+                ],
+                [],
+                [{"human?": None, "deets": {"weight": 6.9, "age": None}}],
+            ]
+        },
+        # Struct of Lists
+        pytest.param(
+            {
+                "Real estate records": [
+                    None,
+                    {
+                        "Status": "NRI",
+                        "Ownerships": {
+                            "land_unit": [None, 2, None],
+                            "flats": [[1, 2, 3], [], [4, 5], [], [0, 6, 0]],
+                        },
+                    },
+                    {
+                        "Status": None,
+                        "Ownerships": {
+                            "land_unit": [4, 5],
+                            "flats": [[7, 8], []],
+                        },
+                    },
+                    {
+                        "Status": "RI",
+                        "Ownerships": {"land_unit": None, "flats": [[]]},
+                    },
+                    {"Status": "RI", "Ownerships": None},
+                    {
+                        "Status": None,
+                        "Ownerships": {
+                            "land_unit": [7, 8, 9],
+                            "flats": [[], [], []],
+                        },
+                    },
+                ]
+            },
+            marks=pytest.mark.xfail(
+                condition=PANDAS_LT_153,
+                reason="pandas assertion fixed in pandas 1.5.3",
+            ),
+        ),
+    ],
+)
+def test_parquet_writer_nested(tmpdir, data):
+    expect = pd.DataFrame(data)
+    gdf = cudf.from_pandas(expect)
+
+    fname = tmpdir.join("test_parquet_writer_nested.parquet")
+    gdf.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    got = pd.read_parquet(fname)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+@pytest.mark.parametrize("data", [[1, 2, 3], [0.00, 0.01, None, 0.5]])
+def test_parquet_writer_decimal(decimal_type, data):
+    gdf = cudf.DataFrame({"val": data})
+
+    gdf["dec_val"] = gdf["val"].astype(decimal_type(7, 2))
+
+    buff = BytesIO()
+    gdf.to_parquet(buff)
+
+    got = pd.read_parquet(buff, use_nullable_dtypes=True)
+    assert_eq(gdf.to_pandas(nullable=True), got)
+
+
+def test_parquet_writer_column_validation():
+    df = cudf.DataFrame({1: [1, 2, 3], "1": ["a", "b", "c"]})
+    pdf = df.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=df.to_parquet,
+        rfunc=pdf.to_parquet,
+        lfunc_args_and_kwargs=(["cudf.parquet"],),
+        rfunc_args_and_kwargs=(["pandas.parquet"],),
+    )
+
+
+def test_parquet_writer_nulls_pandas_read(tmpdir, pdf):
+    if "col_bool" in pdf.columns:
+        pdf.drop(columns="col_bool", inplace=True)
+    if "col_category" in pdf.columns:
+        pdf.drop(columns="col_category", inplace=True)
+    gdf = cudf.from_pandas(pdf)
+
+    num_rows = len(gdf)
+
+    if num_rows > 0:
+        for col in gdf.columns:
+            gdf[col][random.randint(0, num_rows - 1)] = None
+
+    fname = tmpdir.join("test_parquet_writer_nulls_pandas_read.parquet")
+    gdf.to_parquet(fname)
+    assert os.path.exists(fname)
+
+    got = pd.read_parquet(fname)
+    nullable = num_rows > 0
+    assert_eq(gdf.to_pandas(nullable=nullable), got)
+
+
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+def test_parquet_decimal_precision(tmpdir, decimal_type):
+    df = cudf.DataFrame({"val": ["3.5", "4.2"]}).astype(decimal_type(5, 2))
+    assert df.val.dtype.precision == 5
+
+    fname = tmpdir.join("decimal_test.parquet")
+    df.to_parquet(fname)
+    df = cudf.read_parquet(fname)
+    assert df.val.dtype.precision == 5
+
+
+def test_parquet_decimal_precision_empty(tmpdir):
+    df = (
+        cudf.DataFrame({"val": ["3.5", "4.2"]})
+        .astype(cudf.Decimal64Dtype(5, 2))
+        .iloc[:0]
+    )
+    assert df.val.dtype.precision == 5
+
+    fname = tmpdir.join("decimal_test.parquet")
+    df.to_parquet(fname)
+    df = cudf.read_parquet(fname)
+    assert df.val.dtype.precision == 5
+
+
+def test_parquet_reader_brotli(datadir):
+    fname = datadir / "brotli_int16.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname).to_pandas(nullable=True)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_one_level_list(datadir):
+    fname = datadir / "one_level_list.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname).to_pandas(nullable=True)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_binary_decimal(datadir):
+    fname = datadir / "binary_decimal.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname).to_pandas()
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_fixed_bin(datadir):
+    fname = datadir / "fixed_len_byte_array.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+def test_parquet_reader_rle_boolean(datadir):
+    fname = datadir / "rle_boolean_encoding.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got)
+
+
+# testing a specific bug-fix/edge case.
+# specifically:  int a parquet file containing a particular way of representing
+#                a list column in a schema, the cudf reader was confusing
+#                nesting information between a list column and a subsequent
+#                string column, ultimately causing a crash.
+def test_parquet_reader_one_level_list2(datadir):
+    # we are reading in a file containing binary types, but cudf returns
+    # those as strings. so we have to massage the pandas data to get
+    # them to compare correctly.
+    def postprocess(val):
+        if isinstance(val, bytes):
+            return val.decode()
+        elif isinstance(val, np.ndarray):
+            return np.array([v.decode() for v in val])
+        else:
+            return val
+
+    fname = datadir / "one_level_list2.parquet"
+
+    expect = pd.read_parquet(fname)
+    expect = expect.applymap(postprocess)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+# testing a specific bug-fix/edge case.
+# specifically:  in a parquet file containing a particular way of representing
+#                a list column in a schema, the cudf reader was confusing
+#                nesting information and building a list of list of int instead
+#                of a list of int
+def test_parquet_reader_one_level_list3(datadir):
+    fname = datadir / "one_level_list3.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname)
+
+    assert_eq(expect, got, check_dtype=True)
+
+
+@pytest.mark.parametrize("size_bytes", [4_000_000, 1_000_000, 600_000])
+@pytest.mark.parametrize("size_rows", [1_000_000, 100_000, 10_000])
+def test_to_parquet_row_group_size(
+    tmpdir, large_int64_gdf, size_bytes, size_rows
+):
+    fname = tmpdir.join("row_group_size.parquet")
+    large_int64_gdf.to_parquet(
+        fname, row_group_size_bytes=size_bytes, row_group_size_rows=size_rows
+    )
+
+    num_rows, row_groups, col_names = cudf.io.read_parquet_metadata(fname)
+    # 8 bytes per row, as the column is int64
+    expected_num_rows = max(
+        math.ceil(num_rows / size_rows), math.ceil(8 * num_rows / size_bytes)
+    )
+    assert expected_num_rows == row_groups
+
+
+def test_parquet_reader_decimal_columns():
+    df = cudf.DataFrame(
+        {
+            "col1": cudf.Series([1, 2, 3], dtype=cudf.Decimal64Dtype(10, 2)),
+            "col2": [10, 11, 12],
+            "col3": [12, 13, 14],
+            "col4": ["a", "b", "c"],
+        }
+    )
+    buffer = BytesIO()
+    df.to_parquet(buffer)
+
+    actual = cudf.read_parquet(buffer, columns=["col3", "col2", "col1"])
+    expected = pd.read_parquet(buffer, columns=["col3", "col2", "col1"])
+
+    assert_eq(actual, expected)
+
+
+def test_parquet_reader_zstd_compression(datadir):
+    fname = datadir / "spark_zstd.parquet"
+    try:
+        df = cudf.read_parquet(fname)
+        pdf = pd.read_parquet(fname)
+        assert_eq(df, pdf)
+    except RuntimeError:
+        pytest.mark.xfail(reason="zstd support is not enabled")
+
+
+def test_read_parquet_multiple_files(tmpdir):
+    df_1_path = tmpdir / "df_1.parquet"
+    df_2_path = tmpdir / "df_2.parquet"
+    df_1 = cudf.DataFrame({"id": range(100), "a": [1] * 100})
+    df_1.to_parquet(df_1_path)
+
+    df_2 = cudf.DataFrame({"id": range(200, 2200), "a": [2] * 2000})
+    df_2.to_parquet(df_2_path)
+
+    expected = pd.read_parquet([df_1_path, df_2_path])
+    actual = cudf.read_parquet([df_1_path, df_2_path])
+    assert_eq(expected, actual)
+
+    expected = pd.read_parquet([df_2_path, df_1_path])
+    actual = cudf.read_parquet([df_2_path, df_1_path])
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("index", [True, False, None])
+@pytest.mark.parametrize("columns", [None, [], ["b", "a"]])
+def test_parquet_columns_and_index_param(index, columns):
+    buffer = BytesIO()
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
+    df.to_parquet(buffer, index=index)
+
+    expected = pd.read_parquet(buffer, columns=columns)
+    got = cudf.read_parquet(buffer, columns=columns)
+
+    assert_eq(expected, got, check_index_type=True)
+
+
+@pytest.mark.parametrize("columns", [None, ["b", "a"]])
+def test_parquet_columns_and_range_index(columns):
+    buffer = BytesIO()
+    df = cudf.DataFrame(
+        {"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=pd.RangeIndex(2, 5)
+    )
+    df.to_parquet(buffer)
+
+    expected = pd.read_parquet(buffer, columns=columns)
+    got = cudf.read_parquet(buffer, columns=columns)
+
+    assert_eq(expected, got, check_index_type=True)
+
+
+def test_parquet_nested_struct_list():
+    buffer = BytesIO()
+    data = {
+        "payload": {
+            "Domain": {
+                "Name": "abc",
+                "Id": {"Name": "host", "Value": "127.0.0.8"},
+            },
+            "StreamId": "12345678",
+            "Duration": 10,
+            "Offset": 12,
+            "Resource": [{"Name": "ZoneName", "Value": "RAPIDS"}],
+        }
+    }
+    df = cudf.DataFrame({"a": cudf.Series(data)})
+
+    df.to_parquet(buffer)
+    expected = pd.read_parquet(buffer)
+    actual = cudf.read_parquet(buffer)
+    assert_eq(expected, actual)
+    assert_eq(actual.a.dtype, df.a.dtype)
+
+
+def test_parquet_writer_zstd():
+    size = 12345
+    expected = cudf.DataFrame(
+        {
+            "a": np.arange(0, stop=size, dtype="float64"),
+            "b": np.random.choice(list("abcd"), size=size),
+            "c": np.random.choice(np.arange(4), size=size),
+        }
+    )
+
+    buff = BytesIO()
+    try:
+        expected.to_parquet(buff, compression="ZSTD")
+    except RuntimeError:
+        pytest.mark.xfail(reason="Newer nvCOMP version is required")
+    else:
+        got = pd.read_parquet(buff)
+        assert_eq(expected, got)
+
+
+def test_parquet_writer_time_delta_physical_type():
+    df = cudf.DataFrame(
+        {
+            "s": cudf.Series([1], dtype="timedelta64[s]"),
+            "ms": cudf.Series([2], dtype="timedelta64[ms]"),
+            "us": cudf.Series([3], dtype="timedelta64[us]"),
+            # 4K because Pandas/pyarrow don't support non-zero nanoseconds
+            # in Parquet files
+            "ns": cudf.Series([4000], dtype="timedelta64[ns]"),
+        }
+    )
+    buffer = BytesIO()
+    df.to_parquet(buffer)
+
+    got = pd.read_parquet(buffer)
+    expected = pd.DataFrame(
+        {
+            "s": ["00:00:01"],
+            "ms": ["00:00:00.002000"],
+            "us": ["00:00:00.000003"],
+            "ns": ["00:00:00.000004"],
+        },
+        dtype="str",
+    )
+    assert_eq(got.astype("str"), expected)
+
+
+def test_parquet_roundtrip_time_delta():
+    num_rows = 12345
+    df = cudf.DataFrame(
+        {
+            "s": cudf.Series(
+                random.sample(range(0, 200000), num_rows),
+                dtype="timedelta64[s]",
+            ),
+            "ms": cudf.Series(
+                random.sample(range(0, 200000), num_rows),
+                dtype="timedelta64[ms]",
+            ),
+            "us": cudf.Series(
+                random.sample(range(0, 200000), num_rows),
+                dtype="timedelta64[us]",
+            ),
+            "ns": cudf.Series(
+                random.sample(range(0, 200000), num_rows),
+                dtype="timedelta64[ns]",
+            ),
+        }
+    )
+    buffer = BytesIO()
+    df.to_parquet(buffer)
+    assert_eq(df, cudf.read_parquet(buffer))
+
+
+def test_parquet_reader_malformed_file(datadir):
+    fname = datadir / "nested-unsigned-malformed.parquet"
+
+    # expect a failure when reading the whole file
+    with pytest.raises(RuntimeError):
+        cudf.read_parquet(fname)
+
+
+def test_parquet_reader_unsupported_page_encoding(datadir):
+    fname = datadir / "delta_encoding.parquet"
+
+    # expect a failure when reading the whole file
+    with pytest.raises(RuntimeError):
+        cudf.read_parquet(fname)
+
+
+@pytest.mark.parametrize("data", [{"a": [1, 2, 3, 4]}, {"b": [1, None, 2, 3]}])
+@pytest.mark.parametrize("force_nullable_schema", [True, False])
+def test_parquet_writer_schema_nullability(data, force_nullable_schema):
+    df = cudf.DataFrame(data)
+    file_obj = BytesIO()
+
+    df.to_parquet(file_obj, force_nullable_schema=force_nullable_schema)
+
+    assert pa.parquet.read_schema(file_obj).field(0).nullable == (
+        force_nullable_schema or df.isnull().any().any()
+    )
+
+
+def test_parquet_read_filter_and_project():
+    # Filter on columns that are not included
+    # in the current column projection
+
+    with BytesIO() as buffer:
+        # Write parquet data
+        df = cudf.DataFrame(
+            {
+                "a": [1, 2, 3, 4, 5] * 10,
+                "b": [0, 1, 2, 3, 4] * 10,
+                "c": range(50),
+                "d": [6, 7] * 25,
+                "e": [8, 9] * 25,
+            }
+        )
+        df.to_parquet(buffer)
+
+        # Read back with filter and projection
+        columns = ["b"]
+        filters = [[("a", "==", 5), ("c", ">", 20)]]
+        got = cudf.read_parquet(buffer, columns=columns, filters=filters)
+
+    # Check result
+    expected = df[(df.a == 5) & (df.c > 20)][columns].reset_index(drop=True)
+    assert_eq(got, expected)
+
+
+def test_parquet_reader_multiindex():
+    expected = pd.DataFrame(
+        {"A": [1, 2, 3]},
+        index=pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1)]),
+    )
+    file_obj = BytesIO()
+    expected.to_parquet(file_obj, engine="pyarrow")
+    with pytest.warns(UserWarning):
+        actual = cudf.read_parquet(file_obj, engine="pyarrow")
+    assert_eq(actual, expected)
+
+
+def test_parquet_reader_engine_error():
+    with pytest.raises(ValueError):
+        cudf.read_parquet(BytesIO(), engine="abc")
diff --git a/python/cudf/cudf/tests/test_pickling.py b/python/cudf/cudf/tests/test_pickling.py
new file mode 100644
index 0000000..71c1f20
--- /dev/null
+++ b/python/cudf/cudf/tests/test_pickling.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import pickle
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from cudf import DataFrame, GenericIndex, RangeIndex, Series
+from cudf.core.buffer import as_buffer
+from cudf.testing._utils import assert_eq
+
+pytestmark = pytest.mark.spilling
+
+
+def check_serialization(df):
+    # basic
+    assert_frame_picklable(df)
+    # sliced
+    assert_frame_picklable(df[:-1])
+    assert_frame_picklable(df[1:])
+    assert_frame_picklable(df[2:-2])
+    # sorted
+    sortvaldf = df.sort_values("vals")
+    assert isinstance(sortvaldf.index, (GenericIndex, RangeIndex))
+    assert_frame_picklable(sortvaldf)
+    # out-of-band
+    buffers = []
+    serialbytes = pickle.dumps(df, protocol=5, buffer_callback=buffers.append)
+    for b in buffers:
+        assert isinstance(b, pickle.PickleBuffer)
+    loaded = pickle.loads(serialbytes, buffers=buffers)
+    assert_eq(loaded, df)
+
+
+def assert_frame_picklable(df):
+    serialbytes = pickle.dumps(df)
+    loaded = pickle.loads(serialbytes)
+    assert_eq(loaded, df)
+
+
+def test_pickle_dataframe_numeric():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 10
+    df["keys"] = np.arange(nelem, dtype=np.float64)
+    df["vals"] = np.random.random(nelem)
+
+    check_serialization(df)
+
+
+def test_pickle_dataframe_categorical():
+    np.random.seed(0)
+
+    df = DataFrame()
+    df["keys"] = pd.Categorical(
+        ["a", "a", "a", "b", "a", "b", "a", "b", "a", "c"]
+    )
+    df["vals"] = np.random.random(len(df))
+
+    check_serialization(df)
+
+
+def test_memory_usage_dataframe():
+    np.random.seed(0)
+    df = DataFrame()
+    nelem = 1000
+    df["keys"] = hkeys = np.arange(nelem, dtype=np.float64)
+    df["vals"] = hvals = np.random.random(nelem)
+
+    nbytes = hkeys.nbytes + hvals.nbytes
+    sizeof = df.memory_usage().sum()
+    assert sizeof >= nbytes
+
+    serialized_nbytes = len(pickle.dumps(df, protocol=pickle.HIGHEST_PROTOCOL))
+
+    # assert at least sizeof bytes were serialized
+    assert serialized_nbytes >= sizeof
+
+
+def test_pickle_index():
+    nelem = 10
+    idx = GenericIndex(np.arange(nelem), name="a")
+    pickled = pickle.dumps(idx)
+    out = pickle.loads(pickled)
+    assert (idx == out).all()
+
+
+def test_pickle_buffer():
+    arr = np.arange(10).view("|u1")
+    buf = as_buffer(arr)
+    assert buf.size == arr.nbytes
+    pickled = pickle.dumps(buf)
+    unpacked = pickle.loads(pickled)
+    # Check that unpacked capacity equals buf.size
+    assert unpacked.size == arr.nbytes
+
+
+@pytest.mark.parametrize("named", [True, False])
+def test_pickle_series(named):
+    np.random.seed(0)
+    if named:
+        ser = Series(np.random.random(10), name="a")
+    else:
+        ser = Series(np.random.random(10))
+
+    pickled = pickle.dumps(ser)
+    out = pickle.loads(pickled)
+    assert (ser == out).all()
+
+
+@pytest.mark.parametrize(
+    "slices",
+    [
+        slice(None, None, None),
+        slice(1, 3, 1),
+        slice(0, 3, 1),
+        slice(3, 5, 1),
+        slice(10, 12, 1),
+    ],
+)
+def test_pickle_categorical_column(slices):
+    sr = Series(["a", "b", None, "a", "c", "b"]).astype("category")
+    sliced_sr = sr.iloc[slices]
+    input_col = sliced_sr._column
+
+    pickled = pickle.dumps(input_col)
+    out = pickle.loads(pickled)
+
+    assert_eq(Series(out), Series(input_col))
+
+
+@pytest.mark.parametrize(
+    "slices",
+    [
+        slice(None, None, None),
+        slice(1, 3, 1),
+        slice(0, 3, 1),
+        slice(3, 5, 1),
+        slice(10, 12, 1),
+    ],
+)
+def test_pickle_string_column(slices):
+    sr = Series(["a", "b", None, "a", "c", "b"])
+    sliced_sr = sr.iloc[slices]
+    input_col = sliced_sr._column
+
+    pickled = pickle.dumps(input_col)
+    out = pickle.loads(pickled)
+
+    assert_eq(Series(out), Series(input_col))
diff --git a/python/cudf/cudf/tests/test_quantiles.py b/python/cudf/cudf/tests/test_quantiles.py
new file mode 100644
index 0000000..8b12607
--- /dev/null
+++ b/python/cudf/cudf/tests/test_quantiles.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import re
+
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+def test_single_q():
+    q = 0.5
+
+    pdf = pd.DataFrame({"a": [4, 24, 13, 8, 7]})
+    gdf = cudf.from_pandas(pdf)
+
+    pdf_q = pdf.quantile(q, interpolation="nearest")
+    gdf_q = gdf.quantile(q, interpolation="nearest", method="table")
+
+    assert_eq(pdf_q, gdf_q, check_index_type=False)
+
+
+def test_with_index():
+    q = [0, 0.5, 1]
+
+    pdf = pd.DataFrame({"a": [7, 4, 4, 9, 13]}, index=[0, 4, 3, 2, 7])
+    gdf = cudf.from_pandas(pdf)
+
+    pdf_q = pdf.quantile(q, interpolation="nearest")
+    gdf_q = gdf.quantile(q, interpolation="nearest", method="table")
+
+    assert_eq(pdf_q, gdf_q, check_index_type=False)
+
+
+def test_with_multiindex():
+    q = [0, 0.5, 1]
+
+    pdf = pd.DataFrame(
+        {
+            "index_1": [3, 1, 9, 7, 5],
+            "index_2": [2, 4, 3, 5, 1],
+            "a": [8, 4, 2, 3, 8],
+        }
+    )
+    pdf.set_index(["index_1", "index_2"], inplace=True)
+
+    gdf = cudf.from_pandas(pdf)
+
+    pdf_q = pdf.quantile(q, interpolation="nearest")
+    gdf_q = gdf.quantile(q, interpolation="nearest", method="table")
+
+    assert_eq(pdf_q, gdf_q, check_index_type=False)
+
+
+@pytest.mark.parametrize("q", [2, [1, 2, 3]])
+def test_quantile_range_error(q):
+    ps = pd.Series([1, 2, 3])
+    gs = cudf.from_pandas(ps)
+    assert_exceptions_equal(
+        lfunc=ps.quantile,
+        rfunc=gs.quantile,
+        lfunc_args_and_kwargs=([q],),
+        rfunc_args_and_kwargs=([q],),
+    )
+
+
+def test_quantile_q_type():
+    gs = cudf.Series([1, 2, 3])
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "q must be a scalar or array-like, got <class "
+            "'cudf.core.dataframe.DataFrame'>"
+        ),
+    ):
+        gs.quantile(cudf.DataFrame())
+
+
+@pytest.mark.parametrize(
+    "interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
+)
+def test_quantile_type_int_float(interpolation):
+    data = [1, 3, 4]
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    expected = psr.quantile(0.5, interpolation=interpolation)
+    actual = gsr.quantile(0.5, interpolation=interpolation)
+
+    assert expected == actual
+    assert type(expected) == type(actual)
diff --git a/python/cudf/cudf/tests/test_query.py b/python/cudf/cudf/tests/test_query.py
new file mode 100644
index 0000000..cf9e70d
--- /dev/null
+++ b/python/cudf/cudf/tests/test_query.py
@@ -0,0 +1,233 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+
+import datetime
+import inspect
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import DataFrame
+from cudf.testing._utils import assert_eq
+from cudf.utils import queryutils
+
+_params_query_parser = []
+_params_query_parser.append(("a > @b", ("a", "__CUDF_ENVREF__b")))
+_params_query_parser.append(("(a + b) <= @c", ("a", "b", "__CUDF_ENVREF__c")))
+_params_query_parser.append(("a > b if a > 0 else b > a", ("a", "b")))
+
+
+@pytest.mark.parametrize("text,expect_args", _params_query_parser)
+def test_query_parser(text, expect_args):
+    info = queryutils.query_parser(text)
+    fn = queryutils.query_builder(info, "myfoo")
+    assert callable(fn)
+    argspec = inspect.getfullargspec(fn)
+    assert tuple(argspec.args) == tuple(expect_args)
+
+
+params_query_data = list(product([1, 2, 7, 8, 9, 16, 100, 129], range(2)))
+params_query_fn = [
+    (lambda a, b: a < b, "a < b"),
+    (lambda a, b: a * 2 >= b, "a * 2 >= b"),
+    (lambda a, b: 2 * (a + b) > (a + b) / 2, "2 * (a + b) > (a + b) / 2"),
+]
+nulls = [True, False]
+
+
+@pytest.mark.parametrize(
+    "data,fn,nulls", product(params_query_data, params_query_fn, nulls)
+)
+def test_query(data, fn, nulls):
+    # prepare
+    nelem, seed = data
+    expect_fn, query_expr = fn
+    np.random.seed(seed)
+    pdf = pd.DataFrame()
+    pdf["a"] = np.arange(nelem)
+    pdf["b"] = np.random.random(nelem) * nelem
+    if nulls:
+        pdf.loc[::2, "a"] = None
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(pdf.query(query_expr), gdf.query(query_expr))
+
+
+params_query_env_fn = [
+    (lambda a, b, c, d: a * c > b + d, "a * @c > b + @d"),
+    (
+        lambda a, b, c, d: ((a / c) < d) | ((b**c) > d),
+        "((a / @c) < @d) | ((b ** @c) > @d)",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "data,fn", product(params_query_data, params_query_env_fn)
+)
+def test_query_ref_env(data, fn):
+    # prepare
+    nelem, seed = data
+    expect_fn, query_expr = fn
+    np.random.seed(seed)
+    df = DataFrame()
+    df["a"] = aa = np.arange(nelem)
+    df["b"] = bb = np.random.random(nelem) * nelem
+    c = 2.3
+    d = 1.2
+    # udt
+    expect_mask = expect_fn(aa, bb, c, d)
+    print(expect_mask)
+    df2 = df.query(query_expr)
+    # check
+    assert len(df2) == np.count_nonzero(expect_mask)
+    np.testing.assert_array_almost_equal(df2["a"].to_numpy(), aa[expect_mask])
+    np.testing.assert_array_almost_equal(df2["b"].to_numpy(), bb[expect_mask])
+
+
+def test_query_env_changing():
+    df = DataFrame()
+    df["a"] = aa = np.arange(100)
+    expr = "a < @c"
+    # first attempt
+    c = 10
+    got = df.query(expr)
+    np.testing.assert_array_equal(aa[aa < c], got["a"].to_numpy())
+    # change env
+    c = 50
+    got = df.query(expr)
+    np.testing.assert_array_equal(aa[aa < c], got["a"].to_numpy())
+
+
+def test_query_local_dict():
+    df = DataFrame()
+    df["a"] = aa = np.arange(100)
+    expr = "a < @val"
+
+    got = df.query(expr, local_dict={"val": 10})
+    np.testing.assert_array_equal(aa[aa < 10], got["a"].to_numpy())
+
+    # test for datetime
+    df = DataFrame()
+    data = np.array(["2018-10-07", "2018-10-08"], dtype="datetime64")
+    df["datetimes"] = data
+    search_date = datetime.datetime.strptime("2018-10-08", "%Y-%m-%d")
+    expr = "datetimes==@search_date"
+
+    got = df.query(expr, local_dict={"search_date": search_date})
+    np.testing.assert_array_equal(data[1], got["datetimes"].to_numpy())
+
+
+def test_query_splitted_combine():
+    np.random.seed(0)
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=10), "y": np.random.normal(size=10)}
+    )
+    gdf = DataFrame.from_pandas(df)
+
+    # Split the GDF
+    s1 = gdf[:5]
+    s2 = gdf[5:]
+
+    # Do the query
+    expr = "x > 2"
+    q1 = s1.query(expr)
+    q2 = s2.query(expr)
+    # Combine
+    got = cudf.concat([q1, q2]).to_pandas()
+
+    # Should equal to just querying the original GDF
+    expect = gdf.query(expr).to_pandas()
+    assert_eq(got, expect, check_index_type=True)
+
+
+def test_query_empty_frames():
+    empty_pdf = pd.DataFrame({"a": [], "b": []})
+    empty_gdf = DataFrame.from_pandas(empty_pdf)
+    # Do the query
+    expr = "a > 2"
+    got = empty_gdf.query(expr).to_pandas()
+    expect = empty_pdf.query(expr)
+
+    # assert equal results
+    assert_eq(got, expect)
+
+
+@pytest.mark.parametrize(("a_val", "b_val", "c_val"), [(4, 3, 15)])
+@pytest.mark.parametrize("index", ["a", ["a", "b"]])
+@pytest.mark.parametrize(
+    "query",
+    [
+        "a < @a_val",
+        "a < @a_val and b > @b_val",
+        "(a < @a_val and b >@b_val) or c >@c_val",
+    ],
+)
+def test_query_with_index_name(index, query, a_val, b_val, c_val):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, None, 3, 4, 5],
+            "b": [5, 4, 3, 2, 1],
+            "c": [12, 15, 17, 19, 27],
+        }
+    )
+    pdf.set_index(index)
+
+    gdf = DataFrame.from_pandas(pdf)
+
+    out = gdf.query(query)
+    expect = pdf.query(query)
+
+    assert_eq(out, expect)
+
+
+@pytest.mark.parametrize(("a_val", "b_val", "c_val"), [(4, 3, 15)])
+@pytest.mark.parametrize(
+    "query",
+    [
+        "index < @a_val",
+        "index < @a_val and b > @b_val",
+        "(index < @a_val and b >@b_val) or c >@c_val",
+    ],
+)
+def test_query_with_index_keyword(query, a_val, b_val, c_val):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, None, 3, 4, 5],
+            "b": [5, 4, 3, 2, 1],
+            "c": [12, 15, 17, 19, 27],
+        }
+    )
+    pdf.set_index("a")
+
+    gdf = DataFrame.from_pandas(pdf)
+
+    out = gdf.query(query)
+    expect = pdf.query(query)
+
+    assert_eq(out, expect)
+
+
+@pytest.mark.parametrize(
+    "data, query",
+    [
+        # Only need to test the dtypes that pandas
+        # supports but that we do not
+        (["a", "b", "c"], "data == 'a'"),
+    ],
+)
+def test_query_unsupported_dtypes(data, query):
+    gdf = cudf.DataFrame({"data": data})
+
+    # make sure the query works in pandas
+    pdf = gdf.to_pandas()
+    pdf_result = pdf.query(query)
+
+    expect = pd.DataFrame({"data": ["a"]})
+    assert_eq(expect, pdf_result)
+
+    # but fails in cuDF
+    with pytest.raises(TypeError):
+        gdf.query(query)
diff --git a/python/cudf/cudf/tests/test_query_mask.py b/python/cudf/cudf/tests/test_query_mask.py
new file mode 100644
index 0000000..ae5171f
--- /dev/null
+++ b/python/cudf/cudf/tests/test_query_mask.py
@@ -0,0 +1,71 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+_data = [
+    {"a": [0, 1.0, 2.0, None, np.nan, None, 3, 5]},
+    {"a": [0, 1.0, 2.0, None, 3, np.nan, None, 4]},
+    {"a": [0, 1.0, 2.0, None, 3, np.nan, None, 4, None, 9]},
+]
+_queries = [
+    "a == 3",
+    # "a != 3", # incompatible with pandas
+    "a < 3",
+    "a <= 3",
+    "a < 3",
+    "a >= 3",
+]
+
+
+@pytest.mark.parametrize("data", _data)
+@pytest.mark.parametrize("query", _queries)
+def test_mask_0(data, query):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    pdf_q_res = pdf.query(query)
+    gdf_q_res = gdf.query(query)
+
+    assert_eq(pdf_q_res, gdf_q_res)
+
+
+@pytest.mark.parametrize("data", _data)
+@pytest.mark.parametrize("nan_as_null", [False, True])
+@pytest.mark.parametrize("query", _queries)
+def test_mask_1(data, nan_as_null, query):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame.from_pandas(pdf, nan_as_null=nan_as_null)
+
+    pdf_q_res = pdf.query(query)
+    gdf_q_res = gdf.query(query)
+
+    assert_eq(pdf_q_res, gdf_q_res)
+
+
+@pytest.mark.parametrize("data", _data)
+@pytest.mark.parametrize("query", _queries)
+def test_mask_2(data, query):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data)
+
+    pdf_q_res = pdf.query(query)
+    gdf_q_res = gdf.query(query)
+
+    assert_eq(pdf_q_res, gdf_q_res)
+
+
+@pytest.mark.parametrize("data", _data)
+@pytest.mark.parametrize("query", _queries)
+def test_dataframe_initializer(data, query):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame(data)
+
+    pdf_q_res = pdf.query(query)
+    gdf_q_res = gdf.query(query)
+
+    assert_eq(pdf_q_res, gdf_q_res)
diff --git a/python/cudf/cudf/tests/test_rank.py b/python/cudf/cudf/tests/test_rank.py
new file mode 100644
index 0000000..f8a8903
--- /dev/null
+++ b/python/cudf/cudf/tests/test_rank.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from itertools import chain, combinations_with_replacement, product
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from cudf import DataFrame
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+@pytest.fixture
+def pdf():
+    return pd.DataFrame(
+        {
+            "col1": np.array([5, 4, 3, 5, 8, 5, 2, 1, 6, 6]),
+            "col2": np.array(
+                [5, 4, np.nan, 5, 8, 5, np.inf, np.nan, 6, -np.inf]
+            ),
+        },
+        index=np.array([5, 4, 3, 2, 1, 6, 7, 8, 9, 10]),
+    )
+
+
+@pytest.mark.parametrize("dtype", ["O", "f8", "i4"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+@pytest.mark.parametrize("na_option", ["keep", "top", "bottom"])
+@pytest.mark.parametrize("pct", [True, False])
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_rank_all_arguments(
+    pdf, dtype, ascending, method, na_option, pct, numeric_only
+):
+    if method == "first" and dtype == "O":
+        # not supported by pandas
+        return
+
+    pdf = pdf.copy(deep=True)  # for parallel pytest
+    if numeric_only:
+        pdf["str"] = np.array(
+            ["a", "b", "c", "d", "e", "1", "2", "3", "4", "5"]
+        )
+    gdf = DataFrame.from_pandas(pdf)
+
+    kwargs = {
+        "method": method,
+        "na_option": na_option,
+        "ascending": ascending,
+        "pct": pct,
+        "numeric_only": numeric_only,
+    }
+
+    # Series
+    assert_eq(gdf["col1"].rank(**kwargs), pdf["col1"].rank(**kwargs))
+    assert_eq(gdf["col2"].rank(**kwargs), pdf["col2"].rank(**kwargs))
+    if numeric_only:
+        with pytest.warns(FutureWarning):
+            expect = pdf["str"].rank(**kwargs)
+        got = gdf["str"].rank(**kwargs)
+        assert expect.empty == got.empty
+        expected = pdf.select_dtypes(include=np.number)
+    else:
+        expected = pdf.copy(deep=True)
+
+    actual = gdf.rank(**kwargs)
+    expected = pdf.rank(**kwargs)
+
+    assert_eq(expected, actual)
+
+
+def test_rank_error_arguments(pdf):
+    gdf = DataFrame.from_pandas(pdf)
+
+    assert_exceptions_equal(
+        lfunc=pdf["col1"].rank,
+        rfunc=gdf["col1"].rank,
+        lfunc_args_and_kwargs=(
+            [],
+            {
+                "method": "randomname",
+                "na_option": "keep",
+                "ascending": True,
+                "pct": True,
+            },
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {
+                "method": "randomname",
+                "na_option": "keep",
+                "ascending": True,
+                "pct": True,
+            },
+        ),
+    )
+
+    assert_exceptions_equal(
+        lfunc=pdf["col1"].rank,
+        rfunc=gdf["col1"].rank,
+        lfunc_args_and_kwargs=(
+            [],
+            {
+                "method": "first",
+                "na_option": "randomname",
+                "ascending": True,
+                "pct": True,
+            },
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {
+                "method": "first",
+                "na_option": "randomname",
+                "ascending": True,
+                "pct": True,
+            },
+        ),
+    )
+
+
+sort_group_args = [
+    np.full((3,), np.nan),
+    100 * np.random.random(10),
+    np.full((3,), np.inf),
+    np.full((3,), -np.inf),
+]
+sort_dtype_args = [np.int32, np.int64, np.float32, np.float64]
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered in cast")
+@pytest.mark.parametrize(
+    "elem,dtype",
+    list(
+        product(
+            combinations_with_replacement(sort_group_args, 4),
+            sort_dtype_args,
+        )
+    ),
+)
+def test_series_rank_combinations(elem, dtype):
+    np.random.seed(0)
+    aa = np.fromiter(chain.from_iterable(elem), np.float64).astype(dtype)
+    gdf = DataFrame()
+    df = pd.DataFrame()
+    gdf["a"] = aa
+    df["a"] = aa
+    ranked_gs = gdf["a"].rank(method="first")
+    ranked_ps = df["a"].rank(method="first")
+    # Check
+    assert_eq(ranked_ps, ranked_gs)
diff --git a/python/cudf/cudf/tests/test_reductions.py b/python/cudf/cudf/tests/test_reductions.py
new file mode 100644
index 0000000..47968ec
--- /dev/null
+++ b/python/cudf/cudf/tests/test_reductions.py
@@ -0,0 +1,366 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+
+from decimal import Decimal
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Series
+from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype
+from cudf.testing import _utils as utils
+from cudf.testing._utils import (
+    NUMERIC_TYPES,
+    assert_eq,
+    expect_warning_if,
+    gen_rand,
+)
+
+params_dtype = NUMERIC_TYPES
+
+params_sizes = [1, 2, 3, 127, 128, 129, 200, 10000]
+
+params = list(product(params_dtype, params_sizes))
+
+
+@pytest.mark.parametrize("dtype,nelem", params)
+def test_sum(dtype, nelem):
+    dtype = cudf.dtype(dtype).type
+    data = gen_rand(dtype, nelem)
+    sr = Series(data)
+
+    got = sr.sum()
+    expect = data.sum()
+    significant = 4 if dtype == np.float32 else 6
+    np.testing.assert_approx_equal(expect, got, significant=significant)
+
+
+def test_sum_string():
+    s = Series(["Hello", "there", "World"])
+
+    got = s.sum()
+    expected = "HellothereWorld"
+
+    assert got == expected
+
+    s = Series(["Hello", None, "World"])
+
+    got = s.sum()
+    expected = "HelloWorld"
+
+    assert got == expected
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(6, 3),
+        Decimal64Dtype(10, 6),
+        Decimal64Dtype(16, 7),
+        Decimal32Dtype(6, 3),
+        Decimal128Dtype(20, 7),
+    ],
+)
+@pytest.mark.parametrize("nelem", params_sizes)
+def test_sum_decimal(dtype, nelem):
+    np.random.seed(0)
+    data = [str(x) for x in gen_rand("int64", nelem) / 100]
+
+    expected = pd.Series([Decimal(x) for x in data]).sum()
+    got = cudf.Series(data).astype(dtype).sum()
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("dtype,nelem", params)
+def test_product(dtype, nelem):
+    np.random.seed(0)
+    dtype = cudf.dtype(dtype).type
+    if cudf.dtype(dtype).kind in {"u", "i"}:
+        data = np.ones(nelem, dtype=dtype)
+        # Set at most 30 items to [0..2) to keep the value within 2^32
+        for _ in range(30):
+            data[np.random.randint(low=0, high=nelem, size=1)] = (
+                np.random.uniform() * 2
+            )
+    else:
+        data = gen_rand(dtype, nelem)
+
+    sr = Series(data)
+
+    got = sr.product()
+    expect = pd.Series(data).product()
+    significant = 4 if dtype == np.float32 else 6
+    np.testing.assert_approx_equal(expect, got, significant=significant)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(6, 2),
+        Decimal64Dtype(8, 4),
+        Decimal64Dtype(10, 5),
+        Decimal32Dtype(6, 2),
+        Decimal128Dtype(20, 5),
+    ],
+)
+def test_product_decimal(dtype):
+    np.random.seed(0)
+    data = [str(x) for x in gen_rand("int8", 3) / 10]
+
+    expected = pd.Series([Decimal(x) for x in data]).product()
+    got = cudf.Series(data).astype(dtype).product()
+
+    assert_eq(expected, got)
+
+
+accuracy_for_dtype = {np.float64: 6, np.float32: 5}
+
+
+@pytest.mark.parametrize("dtype,nelem", params)
+def test_sum_of_squares(dtype, nelem):
+    dtype = cudf.dtype(dtype).type
+    data = gen_rand(dtype, nelem)
+    sr = Series(data)
+    df = cudf.DataFrame(sr)
+
+    got = (sr**2).sum()
+    got_df = (df**2).sum()
+    expect = (data**2).sum()
+
+    if cudf.dtype(dtype).kind in {"u", "i"}:
+        if 0 <= expect <= np.iinfo(dtype).max:
+            np.testing.assert_array_almost_equal(expect, got)
+            np.testing.assert_array_almost_equal(expect, got_df.iloc[0])
+        else:
+            print("overflow, passing")
+    else:
+        np.testing.assert_approx_equal(
+            expect, got, significant=accuracy_for_dtype[dtype]
+        )
+        np.testing.assert_approx_equal(
+            expect, got_df.iloc[0], significant=accuracy_for_dtype[dtype]
+        )
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(6, 2),
+        Decimal64Dtype(8, 4),
+        Decimal64Dtype(10, 5),
+        Decimal128Dtype(20, 7),
+        Decimal32Dtype(6, 2),
+    ],
+)
+def test_sum_of_squares_decimal(dtype):
+    np.random.seed(0)
+    data = [str(x) for x in gen_rand("int8", 3) / 10]
+
+    expected = pd.Series([Decimal(x) for x in data]).pow(2).sum()
+    got = (cudf.Series(data).astype(dtype) ** 2).sum()
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("dtype,nelem", params)
+def test_min(dtype, nelem):
+    dtype = cudf.dtype(dtype).type
+    data = gen_rand(dtype, nelem)
+    sr = Series(data)
+
+    got = sr.min()
+    expect = dtype(data.min())
+
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(6, 3),
+        Decimal64Dtype(10, 6),
+        Decimal64Dtype(16, 7),
+        Decimal32Dtype(6, 3),
+        Decimal128Dtype(20, 7),
+    ],
+)
+@pytest.mark.parametrize("nelem", params_sizes)
+def test_min_decimal(dtype, nelem):
+    np.random.seed(0)
+    data = [str(x) for x in gen_rand("int64", nelem) / 100]
+
+    expected = pd.Series([Decimal(x) for x in data]).min()
+    got = cudf.Series(data).astype(dtype).min()
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("dtype,nelem", params)
+def test_max(dtype, nelem):
+    dtype = cudf.dtype(dtype).type
+    data = gen_rand(dtype, nelem)
+    sr = Series(data)
+
+    got = sr.max()
+    expect = dtype(data.max())
+
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(6, 3),
+        Decimal64Dtype(10, 6),
+        Decimal64Dtype(16, 7),
+        Decimal32Dtype(6, 3),
+        Decimal128Dtype(20, 7),
+    ],
+)
+@pytest.mark.parametrize("nelem", params_sizes)
+def test_max_decimal(dtype, nelem):
+    np.random.seed(0)
+    data = [str(x) for x in gen_rand("int64", nelem) / 100]
+
+    expected = pd.Series([Decimal(x) for x in data]).max()
+    got = cudf.Series(data).astype(dtype).max()
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("nelem", params_sizes)
+def test_sum_masked(nelem):
+    dtype = np.float64
+    data = gen_rand(dtype, nelem)
+
+    mask = utils.random_bitmask(nelem)
+    bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
+    null_count = utils.count_zero(bitmask)
+
+    sr = Series.from_masked_array(data, mask, null_count)
+
+    got = sr.sum()
+    res_mask = np.asarray(bitmask, dtype=np.bool_)[: data.size]
+    expect = data[res_mask].sum()
+
+    significant = 4 if dtype == np.float32 else 6
+    np.testing.assert_approx_equal(expect, got, significant=significant)
+
+
+def test_sum_boolean():
+    s = Series(np.arange(100000))
+    got = (s > 1).sum(dtype=np.int32)
+    expect = 99998
+
+    assert expect == got
+
+    got = (s > 1).sum(dtype=np.bool_)
+    expect = True
+
+    assert expect == got
+
+
+def test_date_minmax():
+    np_data = np.random.normal(size=10**3)
+    gdf_data = Series(np_data)
+
+    np_casted = np_data.astype("datetime64[ms]")
+    gdf_casted = gdf_data.astype("datetime64[ms]")
+
+    np_min = np_casted.min()
+    gdf_min = gdf_casted.min()
+    assert np_min == gdf_min
+
+    np_max = np_casted.max()
+    gdf_max = gdf_casted.max()
+    assert np_max == gdf_max
+
+
+@pytest.mark.parametrize(
+    "op",
+    ["sum", "product", "var", "kurt", "kurtosis", "skew"],
+)
+def test_datetime_unsupported_reductions(op):
+    gsr = cudf.Series([1, 2, 3, None], dtype="datetime64[ns]")
+    psr = gsr.to_pandas()
+
+    utils.assert_exceptions_equal(
+        lfunc=getattr(psr, op),
+        rfunc=getattr(gsr, op),
+    )
+
+
+@pytest.mark.parametrize("op", ["product", "var", "kurt", "kurtosis", "skew"])
+def test_timedelta_unsupported_reductions(op):
+    gsr = cudf.Series([1, 2, 3, None], dtype="timedelta64[ns]")
+    psr = gsr.to_pandas()
+
+    utils.assert_exceptions_equal(
+        lfunc=getattr(psr, op),
+        rfunc=getattr(gsr, op),
+    )
+
+
+@pytest.mark.parametrize("op", ["sum", "product", "std", "var"])
+def test_categorical_reductions(op):
+    gsr = cudf.Series([1, 2, 3, None], dtype="category")
+    psr = gsr.to_pandas()
+
+    utils.assert_exceptions_equal(getattr(psr, op), getattr(gsr, op))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": [10, 11, 12]},
+        {"a": [1, 0, 3], "b": [10, 11, 12]},
+        {"a": [1, 2, 3], "b": [10, 11, None]},
+        {
+            "a": [],
+        },
+        {},
+    ],
+)
+@pytest.mark.parametrize("op", ["all", "any"])
+def test_any_all_axis_none(data, op):
+    gdf = cudf.DataFrame(data)
+    pdf = gdf.to_pandas()
+
+    expected = getattr(pdf, op)(axis=None)
+    actual = getattr(gdf, op)(axis=None)
+
+    assert expected == actual
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        "sum",
+        "product",
+        "std",
+        "var",
+        "kurt",
+        "kurtosis",
+        "skew",
+        "min",
+        "max",
+        "mean",
+        "median",
+    ],
+)
+def test_reductions_axis_none_warning(op):
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": [10, 2, 3]})
+    pdf = df.to_pandas()
+    with pytest.warns(FutureWarning):
+        actual = getattr(df, op)(axis=None)
+    with expect_warning_if(
+        op in {"kurt", "kurtosis", "skew", "min", "max", "mean", "median"},
+        FutureWarning,
+    ):
+        expected = getattr(pdf, op)(axis=None)
+
+    assert_eq(expected, actual, check_dtype=False)
diff --git a/python/cudf/cudf/tests/test_replace.py b/python/cudf/cudf/tests/test_replace.py
new file mode 100644
index 0000000..13e44e7
--- /dev/null
+++ b/python/cudf/cudf/tests/test_replace.py
@@ -0,0 +1,1353 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import re
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_134, PANDAS_GE_150
+from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype
+from cudf.testing._utils import (
+    INTEGER_TYPES,
+    NUMERIC_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+)
+
+
+@pytest.mark.parametrize(
+    "gsr",
+    [
+        cudf.Series([5, 1, 2, 3, None, 243, None, 4]),
+        cudf.Series(["one", "two", "three", None, "one"], dtype="category"),
+        cudf.Series(list(range(400)) + [None]),
+    ],
+)
+@pytest.mark.parametrize(
+    "to_replace,value",
+    [
+        (0, 5),
+        ("one", "two"),
+        ("one", "five"),
+        ("abc", "hello"),
+        ([0, 1], [5, 6]),
+        ([22, 323, 27, 0], -1),
+        ([1, 2, 3], cudf.Series([10, 11, 12])),
+        (cudf.Series([1, 2, 3]), None),
+        ({1: 10, 2: 22}, None),
+        (np.inf, 4),
+    ],
+)
+def test_series_replace_all(gsr, to_replace, value):
+    psr = gsr.to_pandas()
+
+    gd_to_replace = to_replace
+    if isinstance(to_replace, cudf.Series):
+        pd_to_replace = to_replace.to_pandas()
+    else:
+        pd_to_replace = to_replace
+
+    gd_value = value
+    if isinstance(value, cudf.Series):
+        pd_value = value.to_pandas()
+    else:
+        pd_value = value
+
+    actual = gsr.replace(to_replace=gd_to_replace, value=gd_value)
+    if pd_value is None:
+        # TODO: Remove this workaround once cudf
+        # introduces `no_default` values
+        expected = psr.replace(to_replace=pd_to_replace)
+    else:
+        expected = psr.replace(to_replace=pd_to_replace, value=pd_value)
+
+    assert_eq(
+        expected.sort_values().reset_index(drop=True),
+        actual.sort_values().reset_index(drop=True),
+    )
+
+
+def test_series_replace():
+    a1 = np.array([0, 1, 2, 3, 4])
+
+    # Numerical
+    a2 = np.array([5, 1, 2, 3, 4])
+    sr1 = cudf.Series(a1)
+    sr2 = sr1.replace(0, 5)
+    assert_eq(a2, sr2.to_numpy())
+
+    # Categorical
+    psr3 = pd.Series(["one", "two", "three"], dtype="category")
+    psr4 = psr3.replace("one", "two")
+    sr3 = cudf.from_pandas(psr3)
+    sr4 = sr3.replace("one", "two")
+    assert_eq(
+        psr4.sort_values().reset_index(drop=True),
+        sr4.sort_values().reset_index(drop=True),
+    )
+
+    psr5 = psr3.replace("one", "five")
+    sr5 = sr3.replace("one", "five")
+
+    assert_eq(psr5, sr5)
+
+    # List input
+    a6 = np.array([5, 6, 2, 3, 4])
+    sr6 = sr1.replace([0, 1], [5, 6])
+    assert_eq(a6, sr6.to_numpy())
+
+    with pytest.raises(TypeError):
+        sr1.replace([0, 1], [5.5, 6.5])
+
+    # Series input
+    a8 = np.array([5, 5, 5, 3, 4])
+    sr8 = sr1.replace(sr1[:3].to_numpy(), 5)
+    assert_eq(a8, sr8.to_numpy())
+
+    # large input containing null
+    sr9 = cudf.Series(list(range(400)) + [None])
+    sr10 = sr9.replace([22, 323, 27, 0], None)
+    assert sr10.null_count == 5
+    assert len(sr10.dropna().to_numpy()) == (401 - 5)
+
+    sr11 = sr9.replace([22, 323, 27, 0], -1)
+    assert sr11.null_count == 1
+    assert len(sr11.dropna().to_numpy()) == (401 - 1)
+
+    # large input not containing nulls
+    sr9 = sr9.fillna(-11)
+    sr12 = sr9.replace([22, 323, 27, 0], None)
+    assert sr12.null_count == 4
+    assert len(sr12.dropna().to_numpy()) == (401 - 4)
+
+    sr13 = sr9.replace([22, 323, 27, 0], -1)
+    assert sr13.null_count == 0
+    assert len(sr13.to_numpy()) == 401
+
+
+def test_series_replace_with_nulls():
+    a1 = np.array([0, 1, 2, 3, 4])
+
+    # Numerical
+    a2 = np.array([-10, 1, 2, 3, 4])
+    sr1 = cudf.Series(a1)
+    sr2 = sr1.replace(0, None).fillna(-10)
+    assert_eq(a2, sr2.to_numpy())
+
+    # List input
+    a6 = np.array([-10, 6, 2, 3, 4])
+    sr6 = sr1.replace([0, 1], [None, 6]).fillna(-10)
+    assert_eq(a6, sr6.to_numpy())
+
+    sr1 = cudf.Series([0, 1, 2, 3, 4, None])
+    with pytest.raises(TypeError):
+        sr1.replace([0, 1], [5.5, 6.5]).fillna(-10)
+
+    # Series input
+    a8 = np.array([-10, -10, -10, 3, 4, -10])
+    sr8 = sr1.replace(cudf.Series([-10] * 3, index=sr1[:3]), None).fillna(-10)
+    assert_eq(a8, sr8.to_numpy())
+
+    a9 = np.array([-10, 6, 2, 3, 4, -10])
+    sr9 = sr1.replace([0, 1], [None, 6]).fillna(-10)
+    assert_eq(a9, sr9.to_numpy())
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        cudf.DataFrame(
+            {
+                "a": [0, 1, None, 2, 3],
+                "b": [3, 2, 2, 3, None],
+                "c": ["abc", "def", ".", None, None],
+            }
+        ),
+        pytest.param(
+            cudf.DataFrame(
+                {
+                    "a": ["one", "two", None, "three"],
+                    "b": ["one", None, "two", "three"],
+                },
+                dtype="category",
+            ),
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/issues/46672",
+            ),
+        ),
+        cudf.DataFrame(
+            {
+                "col one": [None, 10, 11, None, 1000, 500, 600],
+                "col two": ["abc", "def", "ghi", None, "pp", None, "a"],
+                "a": [0.324, 0.234, 324.342, 23.32, 9.9, None, None],
+            }
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "to_replace,value",
+    [
+        (0, 4),
+        ([0, 1], [4, 5]),
+        ([0, 1], 4),
+        ({"a": 0, "b": 0}, {"a": 4, "b": 5}),
+        ({"a": 0}, {"a": 4}),
+        ("abc", "---"),
+        ([".", "gh"], "hi"),
+        ([".", "def"], ["_", None]),
+        ({"c": 0}, {"a": 4, "b": 5}),
+        ({"a": 2}, {"c": "a"}),
+        ("two", "three"),
+        ([1, 2], pd.Series([10, 11])),
+        (pd.Series([10, 11], index=[3, 2]), None),
+        (
+            pd.Series(["a+", "+c", "p", "---"], index=["abc", "gh", "l", "z"]),
+            None,
+        ),
+        (
+            pd.Series([10, 11], index=[3, 2]),
+            {"a": [-10, -30], "l": [-111, -222]},
+        ),
+        (pd.Series([10, 11], index=[3, 2]), 555),
+        (
+            pd.Series([10, 11], index=["a", "b"]),
+            pd.Series([555, 1111], index=["a", "b"]),
+        ),
+        ({"a": "2", "b": "3", "zzz": "hi"}, None),
+        ({"a": 2, "b": 3, "zzz": "hi"}, 324353),
+        (
+            {"a": 2, "b": 3, "zzz": "hi"},
+            pd.Series([5, 6, 10], index=["a", "b", "col one"]),
+        ),
+    ],
+)
+def test_dataframe_replace(df, to_replace, value):
+    gdf = df
+    pdf = gdf.to_pandas()
+
+    pd_value = value
+    if isinstance(value, pd.Series):
+        gd_value = cudf.from_pandas(value)
+    else:
+        gd_value = value
+
+    pd_to_replace = to_replace
+    if isinstance(to_replace, pd.Series):
+        gd_to_replace = cudf.from_pandas(to_replace)
+    else:
+        gd_to_replace = to_replace
+
+    if pd_value is None:
+        expected = pdf.replace(to_replace=pd_to_replace)
+    else:
+        expected = pdf.replace(to_replace=pd_to_replace, value=pd_value)
+    actual = gdf.replace(to_replace=gd_to_replace, value=gd_value)
+
+    expected_sorted = expected.sort_values(by=list(expected.columns), axis=0)
+    actual_sorted = actual.sort_values(by=list(actual.columns), axis=0)
+
+    assert_eq(expected_sorted, actual_sorted)
+
+
+def test_dataframe_replace_with_nulls():
+    # numerical
+    pdf1 = pd.DataFrame({"a": [0, 1, 2, 3], "b": [0, 1, 2, 3]})
+    gdf1 = cudf.from_pandas(pdf1)
+    pdf2 = pdf1.replace(0, 4)
+    gdf2 = gdf1.replace(0, None).fillna(4)
+    assert_eq(gdf2, pdf2)
+
+    # list input
+    pdf6 = pdf1.replace([0, 1], [4, 5])
+    gdf6 = gdf1.replace([0, 1], [4, None]).fillna(5)
+    assert_eq(gdf6, pdf6)
+
+    pdf7 = pdf1.replace([0, 1], 4)
+    gdf7 = gdf1.replace([0, 1], None).fillna(4)
+    assert_eq(gdf7, pdf7)
+
+    # dict input:
+    pdf8 = pdf1.replace({"a": 0, "b": 0}, {"a": 4, "b": 5})
+    gdf8 = gdf1.replace({"a": 0, "b": 0}, {"a": None, "b": 5}).fillna(4)
+    assert_eq(gdf8, pdf8)
+
+    gdf1 = cudf.DataFrame({"a": [0, 1, 2, 3], "b": [0, 1, 2, None]})
+    gdf9 = gdf1.replace([0, 1], [4, 5]).fillna(3)
+    assert_eq(gdf9, pdf6)
+
+
+@pytest.mark.parametrize(
+    "psr",
+    [
+        pd.Series([0, 1, None, 2, None], dtype=pd.Int8Dtype()),
+        pd.Series([0, 1, np.nan, 2, np.nan]),
+    ],
+)
+@pytest.mark.parametrize("data_dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("fill_value", [10, pd.Series([10, 20, 30, 40, 50])])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_series_fillna_numerical(psr, data_dtype, fill_value, inplace):
+    test_psr = psr.copy(deep=True)
+    # TODO: These tests should use Pandas' nullable int type
+    # when we support a recent enough version of Pandas
+    # https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
+    if np.dtype(data_dtype).kind not in ("f") and test_psr.dtype.kind == "i":
+        test_psr = test_psr.astype(
+            cudf.utils.dtypes.np_dtypes_to_pandas_dtypes[np.dtype(data_dtype)]
+        )
+
+    gsr = cudf.from_pandas(test_psr)
+
+    if isinstance(fill_value, pd.Series):
+        fill_value_cudf = cudf.from_pandas(fill_value)
+    else:
+        fill_value_cudf = fill_value
+
+    expected = test_psr.fillna(fill_value, inplace=inplace)
+    actual = gsr.fillna(fill_value_cudf, inplace=inplace)
+
+    if inplace:
+        expected = test_psr
+        actual = gsr
+
+    # TODO: Remove check_dtype when we have support
+    # to compare with pandas nullable dtypes
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, None, None, 2, 3, 4],
+        [None, None, 1, 2, None, 3, 4],
+        [1, 2, None, 3, 4, None, None],
+        [0] + [None] * 14,
+        [None] * 14 + [0],
+    ],
+)
+@pytest.mark.parametrize("container", [pd.Series, pd.DataFrame])
+@pytest.mark.parametrize("data_dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("method", ["ffill", "bfill"])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_method_numerical(data, container, data_dtype, method, inplace):
+    if container == pd.DataFrame:
+        data = {"a": data, "b": data, "c": data}
+
+    pdata = container(data)
+
+    if np.dtype(data_dtype).kind not in ("f"):
+        data_dtype = cudf.utils.dtypes.np_dtypes_to_pandas_dtypes[
+            np.dtype(data_dtype)
+        ]
+    pdata = pdata.astype(data_dtype)
+
+    # Explicitly using nans_as_nulls=True
+    gdata = cudf.from_pandas(pdata, nan_as_null=True)
+
+    expected = pdata.fillna(method=method, inplace=inplace)
+    actual = gdata.fillna(method=method, inplace=inplace)
+
+    if inplace:
+        expected = pdata
+        actual = gdata
+
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "gsr_data",
+    [
+        cudf.Series(["2.34", "5.2", "7.47", None, "92.29", None]).astype(
+            Decimal64Dtype(7, 2)
+        ),
+        cudf.Series(["-74.56", None, "-23.73", "34.55", "2.89", None]).astype(
+            Decimal32Dtype(7, 2)
+        ),
+        cudf.Series(
+            ["85.955", np.nan, "-3.243", np.nan, "29.492", np.nan]
+        ).astype(Decimal64Dtype(8, 3)),
+        cudf.Series(
+            ["2.964", None, "57.432", "-989.330", None, "56.444"]
+        ).astype(Decimal64Dtype(8, 3)),
+        cudf.Series(
+            [np.nan, "55.2498", np.nan, "-5.2965", "-28.9423", np.nan]
+        ).astype(Decimal64Dtype(10, 4)),
+        cudf.Series(
+            ["2.964", None, "54347.432", "-989.330", None, "56.444"]
+        ).astype(Decimal128Dtype(20, 7)),
+    ],
+)
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        42,
+        -123,
+        Decimal("8.2"),
+        Decimal("-12.87"),
+        cudf.Series([None, -854, 9533, -274, -845, 7924], dtype="int32"),
+        cudf.Series(["-53.5", "13.4", "-64.3", None, "42.42", None]).astype(
+            Decimal64Dtype(7, 2)
+        ),
+        cudf.Series(
+            ["57.45", np.nan, np.nan, "686.49", "-55.5", "73.24"],
+        ).astype(Decimal64Dtype(7, 2)),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_decimal(gsr_data, fill_value, inplace):
+    gsr = gsr_data.copy(deep=True)
+    psr = gsr.to_pandas()
+
+    if isinstance(fill_value, cudf.Series):
+        p_fill_value = fill_value.to_pandas()
+    else:
+        p_fill_value = fill_value
+
+    expected = psr.fillna(p_fill_value, inplace=inplace)
+    got = gsr.fillna(fill_value, inplace=inplace)
+
+    assert_eq(expected, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "psr_data",
+    [
+        pd.Series(["a", "b", "a", None, "c", None], dtype="category"),
+        pd.Series(
+            ["a", "b", "a", None, "c", None],
+            dtype="category",
+            index=["q", "r", "z", "a", "b", "c"],
+        ),
+        pd.Series(
+            ["a", "b", "a", None, "c", None],
+            dtype="category",
+            index=["x", "t", "p", "q", "r", "z"],
+        ),
+        pd.Series(["a", "b", "a", np.nan, "c", np.nan], dtype="category"),
+        pd.Series(
+            [None, None, None, None, None, None, "a", "b", "c"],
+            dtype="category",
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        "c",
+        pd.Series(["c", "c", "c", "c", "c", "a"], dtype="category"),
+        pd.Series(
+            ["a", "b", "a", None, "c", None],
+            dtype="category",
+            index=["x", "t", "p", "q", "r", "z"],
+        ),
+        pd.Series(
+            ["a", "b", "a", None, "c", None],
+            dtype="category",
+            index=["q", "r", "z", "a", "b", "c"],
+        ),
+        pd.Series(["a", "b", "a", None, "c", None], dtype="category"),
+        pd.Series(["a", "b", "a", np.nan, "c", np.nan], dtype="category"),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_categorical(psr_data, fill_value, inplace):
+    psr = psr_data.copy(deep=True)
+    gsr = cudf.from_pandas(psr)
+
+    if isinstance(fill_value, pd.Series):
+        fill_value_cudf = cudf.from_pandas(fill_value)
+    else:
+        fill_value_cudf = fill_value
+
+    if (
+        isinstance(fill_value_cudf, cudf.Series)
+        and gsr.dtype != fill_value_cudf.dtype
+    ):
+        assert_exceptions_equal(
+            lfunc=psr.fillna,
+            rfunc=gsr.fillna,
+            lfunc_args_and_kwargs=([fill_value], {"inplace": inplace}),
+            rfunc_args_and_kwargs=([fill_value_cudf], {"inplace": inplace}),
+        )
+    else:
+        expected = psr.fillna(fill_value, inplace=inplace)
+        got = gsr.fillna(fill_value_cudf, inplace=inplace)
+
+        if inplace:
+            expected = psr
+            got = gsr
+
+        assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "psr_data",
+    [
+        pd.Series(pd.date_range("2010-01-01", "2020-01-10", freq="1y")),
+        pd.Series(["2010-01-01", None, "2011-10-10"], dtype="datetime64[ns]"),
+        pd.Series(
+            [
+                None,
+                None,
+                None,
+                None,
+                None,
+                None,
+                "2011-10-10",
+                "2010-01-01",
+                "2010-01-02",
+                "2010-01-04",
+                "2010-11-01",
+            ],
+            dtype="datetime64[ns]",
+        ),
+        pd.Series(
+            [
+                None,
+                None,
+                None,
+                None,
+                None,
+                None,
+                "2011-10-10",
+                "2010-01-01",
+                "2010-01-02",
+                "2010-01-04",
+                "2010-11-01",
+            ],
+            dtype="datetime64[ns]",
+            index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        pd.Timestamp("2010-01-02"),
+        pd.Series(pd.date_range("2010-01-01", "2020-01-10", freq="1y"))
+        + pd.Timedelta("1d"),
+        pd.Series(["2010-01-01", None, "2011-10-10"], dtype="datetime64[ns]"),
+        pd.Series(
+            [
+                None,
+                None,
+                None,
+                None,
+                None,
+                None,
+                "2011-10-10",
+                "2010-01-01",
+                "2010-01-02",
+                "2010-01-04",
+                "2010-11-01",
+            ],
+            dtype="datetime64[ns]",
+        ),
+        pd.Series(
+            [
+                None,
+                None,
+                None,
+                None,
+                None,
+                None,
+                "2011-10-10",
+                "2010-01-01",
+                "2010-01-02",
+                "2010-01-04",
+                "2010-11-01",
+            ],
+            dtype="datetime64[ns]",
+            index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_datetime(psr_data, fill_value, inplace):
+    psr = psr_data.copy(deep=True)
+    gsr = cudf.from_pandas(psr)
+
+    if isinstance(fill_value, pd.Series):
+        fill_value_cudf = cudf.from_pandas(fill_value)
+    else:
+        fill_value_cudf = fill_value
+
+    expected = psr.fillna(fill_value, inplace=inplace)
+    got = gsr.fillna(fill_value_cudf, inplace=inplace)
+
+    if inplace:
+        got = gsr
+        expected = psr
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # Categorical
+        pd.Categorical([1, 2, None, None, 3, 4]),
+        pd.Categorical([None, None, 1, None, 3, 4]),
+        pd.Categorical([1, 2, None, 3, 4, None, None]),
+        pd.Categorical(["1", "20", None, None, "3", "40"]),
+        pd.Categorical([None, None, "10", None, "30", "4"]),
+        pd.Categorical(["1", "20", None, "30", "4", None, None]),
+        # Datetime
+        np.array(
+            [
+                "2020-01-01 08:00:00",
+                "2020-01-01 09:00:00",
+                None,
+                "2020-01-01 10:00:00",
+                None,
+                "2020-01-01 10:00:00",
+            ],
+            dtype="datetime64[ns]",
+        ),
+        np.array(
+            [
+                None,
+                None,
+                "2020-01-01 09:00:00",
+                "2020-01-01 10:00:00",
+                None,
+                "2020-01-01 10:00:00",
+            ],
+            dtype="datetime64[ns]",
+        ),
+        np.array(
+            [
+                "2020-01-01 09:00:00",
+                None,
+                None,
+                "2020-01-01 10:00:00",
+                None,
+                None,
+            ],
+            dtype="datetime64[ns]",
+        ),
+        # Timedelta
+        np.array(
+            [10, 100, 1000, None, None, 10, 100, 1000], dtype="datetime64[ns]"
+        ),
+        np.array(
+            [None, None, 10, None, 1000, 100, 10], dtype="datetime64[ns]"
+        ),
+        np.array(
+            [10, 100, None, None, 1000, None, None], dtype="datetime64[ns]"
+        ),
+        # String
+        np.array(
+            ["10", "100", "1000", None, None, "10", "100", "1000"],
+            dtype="object",
+        ),
+        np.array(
+            [None, None, "1000", None, "10", "100", "10"], dtype="object"
+        ),
+        np.array(
+            ["10", "100", None, None, "1000", None, None], dtype="object"
+        ),
+    ],
+)
+@pytest.mark.parametrize("container", [pd.Series, pd.DataFrame])
+@pytest.mark.parametrize("method", ["ffill", "bfill"])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_method_fixed_width_non_num(data, container, method, inplace):
+    if container == pd.DataFrame:
+        data = {"a": data, "b": data, "c": data}
+
+    pdata = container(data)
+
+    # Explicitly using nans_as_nulls=True
+    gdata = cudf.from_pandas(pdata, nan_as_null=True)
+
+    expected = pdata.fillna(method=method, inplace=inplace)
+    actual = gdata.fillna(method=method, inplace=inplace)
+
+    if inplace:
+        expected = pdata
+        actual = gdata
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame({"a": [1, 2, None], "b": [None, None, 5]}),
+        pd.DataFrame(
+            {"a": [1, 2, None], "b": [None, None, 5]}, index=["a", "p", "z"]
+        ),
+        pd.DataFrame({"a": [1, 2, 3]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "value",
+    [
+        10,
+        pd.Series([10, 20, 30]),
+        pd.Series([3, 4, 5]),
+        pd.Series([10, 20, 30], index=["z", "a", "p"]),
+        {"a": 5, "b": pd.Series([3, 4, 5])},
+        {"a": 5001},
+        {"b": pd.Series([11, 22, 33], index=["a", "p", "z"])},
+        {"a": 5, "b": pd.Series([3, 4, 5], index=["a", "p", "z"])},
+        {"c": 100},
+        np.nan,
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_dataframe(df, value, inplace):
+    pdf = df.copy(deep=True)
+    gdf = cudf.from_pandas(pdf)
+
+    fill_value_pd = value
+    if isinstance(fill_value_pd, (pd.Series, pd.DataFrame)):
+        fill_value_cudf = cudf.from_pandas(fill_value_pd)
+    elif isinstance(fill_value_pd, dict):
+        fill_value_cudf = {}
+        for key in fill_value_pd:
+            temp_val = fill_value_pd[key]
+            if isinstance(temp_val, pd.Series):
+                temp_val = cudf.from_pandas(temp_val)
+            fill_value_cudf[key] = temp_val
+    else:
+        fill_value_cudf = value
+
+    expect = pdf.fillna(fill_value_pd, inplace=inplace)
+    got = gdf.fillna(fill_value_cudf, inplace=inplace)
+
+    if inplace:
+        got = gdf
+        expect = pdf
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "ps_data",
+    [
+        pd.Series(["a", "b", "c", "d"]),
+        pd.Series([None] * 4, dtype="object"),
+        pd.Series(["z", None, "z", None]),
+        pd.Series(["x", "y", None, None, None]),
+        pd.Series([None, None, None, "i", "P"]),
+    ],
+)
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        "a",
+        pd.Series(["a", "b", "c", "d"]),
+        pd.Series(["z", None, "z", None]),
+        pd.Series([None] * 4, dtype="object"),
+        pd.Series(["x", "y", None, None, None]),
+        pd.Series([None, None, None, "i", "P"]),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_fillna_string(ps_data, fill_value, inplace):
+    psr = ps_data.copy(deep=True)
+    gsr = cudf.from_pandas(psr)
+
+    if isinstance(fill_value, pd.Series):
+        fill_value_cudf = cudf.from_pandas(fill_value)
+    else:
+        fill_value_cudf = fill_value
+
+    expected = psr.fillna(fill_value, inplace=inplace)
+    got = gsr.fillna(fill_value_cudf, inplace=inplace)
+
+    if inplace:
+        expected = psr
+        got = gsr
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("data_dtype", INTEGER_TYPES)
+def test_series_fillna_invalid_dtype(data_dtype):
+    gdf = cudf.Series([1, 2, None, 3], dtype=data_dtype)
+    fill_value = 2.5
+    with pytest.raises(TypeError) as raises:
+        gdf.fillna(fill_value)
+    raises.match(
+        f"Cannot safely cast non-equivalent"
+        f" {type(fill_value).__name__} to {gdf.dtype.type.__name__}"
+    )
+
+
+@pytest.mark.parametrize("data_dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("fill_value", [100, 100.0, 128.5])
+def test_series_where(data_dtype, fill_value):
+    psr = pd.Series(list(range(10)), dtype=data_dtype)
+    sr = cudf.from_pandas(psr)
+
+    if sr.dtype.type(fill_value) != fill_value:
+        with pytest.raises(TypeError):
+            sr.where(sr > 0, fill_value)
+    else:
+        # Cast back to original dtype as pandas automatically upcasts
+        expect = psr.where(psr > 0, fill_value)
+        got = sr.where(sr > 0, fill_value)
+        # pandas returns 'float16' dtype, which is not supported in cudf
+        assert_eq(
+            expect,
+            got,
+            check_dtype=expect.dtype.kind not in ("f"),
+        )
+
+    if sr.dtype.type(fill_value) != fill_value:
+        with pytest.raises(TypeError):
+            sr.where(sr < 0, fill_value)
+    else:
+        expect = psr.where(psr < 0, fill_value)
+        got = sr.where(sr < 0, fill_value)
+        # pandas returns 'float16' dtype, which is not supported in cudf
+        assert_eq(
+            expect,
+            got,
+            check_dtype=expect.dtype.kind not in ("f"),
+        )
+
+    if sr.dtype.type(fill_value) != fill_value:
+        with pytest.raises(TypeError):
+            sr.where(sr == 0, fill_value)
+    else:
+        expect = psr.where(psr == 0, fill_value)
+        got = sr.where(sr == 0, fill_value)
+        # pandas returns 'float16' dtype, which is not supported in cudf
+        assert_eq(
+            expect,
+            got,
+            check_dtype=expect.dtype.kind not in ("f"),
+        )
+
+
+@pytest.mark.parametrize("fill_value", [100, 100.0, 100.5])
+def test_series_with_nulls_where(fill_value):
+    psr = pd.Series([None] * 3 + list(range(5)))
+    sr = cudf.from_pandas(psr)
+
+    expect = psr.where(psr > 0, fill_value)
+    got = sr.where(sr > 0, fill_value)
+    assert_eq(expect, got)
+
+    expect = psr.where(psr < 0, fill_value)
+    got = sr.where(sr < 0, fill_value)
+    assert_eq(expect, got)
+
+    expect = psr.where(psr == 0, fill_value)
+    got = sr.where(sr == 0, fill_value)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("fill_value", [[888, 999]])
+def test_dataframe_with_nulls_where_with_scalars(fill_value):
+    pdf = pd.DataFrame(
+        {
+            "A": [-1, 2, -3, None, 5, 6, -7, 0],
+            "B": [4, -2, 3, None, 7, 6, 8, 0],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.where(pdf % 3 == 0, fill_value)
+    got = gdf.where(gdf % 3 == 0, fill_value)
+
+    assert_eq(expect, got)
+
+
+def test_dataframe_with_different_types():
+
+    # Testing for int and float
+    pdf = pd.DataFrame(
+        {"A": [111, 22, 31, 410, 56], "B": [-10.12, 121.2, 45.7, 98.4, 87.6]}
+    )
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.where(pdf > 50, -pdf)
+    got = gdf.where(gdf > 50, -gdf)
+
+    assert_eq(expect, got)
+
+    # Testing for string
+    pdf = pd.DataFrame({"A": ["a", "bc", "cde", "fghi"]})
+    gdf = cudf.from_pandas(pdf)
+    pdf_mask = pd.DataFrame({"A": [True, False, True, False]})
+    gdf_mask = cudf.from_pandas(pdf_mask)
+    expect = pdf.where(pdf_mask, ["cudf"])
+    got = gdf.where(gdf_mask, ["cudf"])
+
+    assert_eq(expect, got)
+
+    # Testing for categoriacal
+    pdf = pd.DataFrame({"A": ["a", "b", "b", "c"]})
+    pdf["A"] = pdf["A"].astype("category")
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.where(pdf_mask, "c")
+    got = gdf.where(gdf_mask, ["c"])
+
+    assert_eq(expect, got)
+
+
+def test_dataframe_where_with_different_options():
+    pdf = pd.DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]})
+    gdf = cudf.from_pandas(pdf)
+
+    # numpy array
+    boolean_mask = np.array([[False, True], [True, False], [False, True]])
+
+    expect = pdf.where(boolean_mask, -pdf)
+    got = gdf.where(boolean_mask, -gdf)
+
+    assert_eq(expect, got)
+
+    # with single scalar
+    expect = pdf.where(boolean_mask, 8)
+    got = gdf.where(boolean_mask, 8)
+
+    assert_eq(expect, got)
+
+    # with multi scalar
+    expect = pdf.where(boolean_mask, [8, 9])
+    got = gdf.where(boolean_mask, [8, 9])
+
+    assert_eq(expect, got)
+
+
+def test_series_multiple_times_with_nulls():
+    sr = cudf.Series([1, 2, 3, None])
+    expected = cudf.Series([None, None, None, None], dtype=np.int64)
+
+    for i in range(3):
+        got = sr.replace([1, 2, 3], None)
+        assert_eq(expected, got)
+        # BUG: #2695
+        # The following series will acquire a chunk of memory and update with
+        # values, but these values may still linger even after the memory
+        # gets released. This memory space might get used for replace in
+        # subsequent calls and the memory used for mask may have junk values.
+        # So, if it is not updated properly, the result would be wrong.
+        # So, this will help verify that scenario.
+        cudf.Series([1, 1, 1, None])
+
+
+@pytest.mark.parametrize("series_dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize(
+    "replacement", [128, 128.0, 128.5, 32769, 32769.0, 32769.5]
+)
+def test_numeric_series_replace_dtype(series_dtype, replacement):
+    psr = pd.Series([0, 1, 2, 3, 4, 5], dtype=series_dtype)
+    sr = cudf.from_pandas(psr)
+
+    if sr.dtype.kind in "ui":
+        can_replace = np.array([replacement])[0].is_integer() and np.can_cast(
+            int(replacement), sr.dtype
+        )
+    else:
+        can_replace = np.can_cast(replacement, sr.dtype)
+
+    # Both Scalar
+    if not can_replace:
+        with pytest.raises(TypeError):
+            sr.replace(1, replacement)
+    else:
+        expect = psr.replace(1, replacement).astype(psr.dtype)
+        got = sr.replace(1, replacement)
+        assert_eq(expect, got)
+
+    # to_replace is a list, replacement is a scalar
+    if not can_replace:
+        with pytest.raises(TypeError):
+
+            sr.replace([2, 3], replacement)
+    else:
+        expect = psr.replace([2, 3], replacement).astype(psr.dtype)
+        got = sr.replace([2, 3], replacement)
+        assert_eq(expect, got)
+
+    # If to_replace is a scalar and replacement is a list
+    with pytest.raises(TypeError):
+        sr.replace(0, [replacement, 2])
+
+    # Both list of unequal length
+    with pytest.raises(ValueError):
+        sr.replace([0, 1], [replacement])
+
+    # Both lists of equal length
+    if (
+        np.dtype(type(replacement)).kind == "f" and sr.dtype.kind in {"i", "u"}
+    ) or (not can_replace):
+        with pytest.raises(TypeError):
+            sr.replace([2, 3], [replacement, replacement])
+    else:
+        expect = psr.replace([2, 3], [replacement, replacement]).astype(
+            psr.dtype
+        )
+        got = sr.replace([2, 3], [replacement, replacement])
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "pframe, replace_args",
+    [
+        (
+            pd.Series([5, 1, 2, 3, 4]),
+            {"to_replace": 5, "value": 0, "inplace": True},
+        ),
+        (
+            pd.Series([5, 1, 2, 3, 4]),
+            {"to_replace": {5: 0, 3: -5}, "inplace": True},
+        ),
+        (pd.Series([5, 1, 2, 3, 4]), {}),
+        pytest.param(
+            pd.Series(["one", "two", "three"], dtype="category"),
+            {"to_replace": "one", "value": "two", "inplace": True},
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_134,
+                reason="https://github.com/pandas-dev/pandas/issues/43232",
+            ),
+        ),
+        (
+            pd.DataFrame({"A": [0, 1, 2, 3, 4], "B": [5, 6, 7, 8, 9]}),
+            {"to_replace": 5, "value": 0, "inplace": True},
+        ),
+        (
+            pd.Series([1, 2, 3, 45]),
+            {
+                "to_replace": np.array([]).astype(int),
+                "value": 77,
+                "inplace": True,
+            },
+        ),
+        (
+            pd.Series([1, 2, 3, 45]),
+            {
+                "to_replace": np.array([]).astype(int),
+                "value": 77,
+                "inplace": False,
+            },
+        ),
+        (
+            pd.DataFrame({"a": [1, 2, 3, 4, 5, 666]}),
+            {"to_replace": {"a": 2}, "value": {"a": -33}, "inplace": True},
+        ),
+        (
+            pd.DataFrame({"a": [1, 2, 3, 4, 5, 666]}),
+            {
+                "to_replace": {"a": [2, 5]},
+                "value": {"a": [9, 10]},
+                "inplace": True,
+            },
+        ),
+        (
+            pd.DataFrame({"a": [1, 2, 3, 4, 5, 666]}),
+            {"to_replace": [], "value": [], "inplace": True},
+        ),
+    ],
+)
+def test_replace_inplace(pframe, replace_args):
+    gpu_frame = cudf.from_pandas(pframe)
+    pandas_frame = pframe.copy()
+
+    gpu_copy = gpu_frame.copy()
+    cpu_copy = pandas_frame.copy()
+
+    assert_eq(gpu_frame, pandas_frame)
+    assert_eq(gpu_copy, cpu_copy)
+    gpu_frame.replace(**replace_args)
+    pandas_frame.replace(**replace_args)
+    assert_eq(gpu_frame, pandas_frame)
+    assert_eq(gpu_copy, cpu_copy)
+
+
+def test_replace_df_error():
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4, 5, 666]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_exceptions_equal(
+        lfunc=pdf.replace,
+        rfunc=gdf.replace,
+        lfunc_args_and_kwargs=([], {"to_replace": -1, "value": []}),
+        rfunc_args_and_kwargs=([], {"to_replace": -1, "value": []}),
+    )
+
+
+@pytest.mark.parametrize(
+    ("lower", "upper"),
+    [
+        ([2, 7.4], [4, 7.9]),
+        ([2, 7.4], None),
+        (
+            None,
+            [4, 7.9],
+        ),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_clip(lower, upper, inplace):
+    pdf = pd.DataFrame(
+        {"a": [1, 2, 3, 4, 5], "b": [7.1, 7.24, 7.5, 7.8, 8.11]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    got = gdf.clip(lower=lower, upper=upper, inplace=inplace)
+    expect = pdf.clip(lower=lower, upper=upper, axis=1)
+
+    if inplace is True:
+        assert_eq(expect, gdf)
+    else:
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    ("lower", "upper"),
+    [("b", "d"), ("b", None), (None, "c"), (None, None)],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_dataframe_category_clip(lower, upper, inplace):
+    data = ["a", "b", "c", "d", "e"]
+    pdf = pd.DataFrame({"a": data})
+    gdf = cudf.from_pandas(pdf)
+    gdf["a"] = gdf["a"].astype("category")
+
+    expect = pdf.clip(lower=lower, upper=upper)
+    got = gdf.clip(lower=lower, upper=upper, inplace=inplace)
+
+    if inplace is True:
+        assert_eq(expect, gdf.astype("str"))
+    else:
+        assert_eq(expect, got.astype("str"))
+
+
+@pytest.mark.parametrize(
+    ("lower", "upper"),
+    [([2, 7.4], [4, 7.9, "d"]), ([2, 7.4, "a"], [4, 7.9, "d"])],
+)
+def test_dataframe_exceptions_for_clip(lower, upper):
+    gdf = cudf.DataFrame(
+        {"a": [1, 2, 3, 4, 5], "b": [7.1, 7.24, 7.5, 7.8, 8.11]}
+    )
+
+    with pytest.raises(ValueError):
+        gdf.clip(lower=lower, upper=upper)
+
+
+@pytest.mark.parametrize(
+    ("data", "lower", "upper"),
+    [
+        ([1, 2, 3, 4, 5], 2, 4),
+        ([1, 2, 3, 4, 5], 2, None),
+        ([1, 2, 3, 4, 5], None, 4),
+        ([1, 2, 3, 4, 5], None, None),
+        ([1, 2, 3, 4, 5], 4, 2),
+        ([1.0, 2.0, 3.0, 4.0, 5.0], 4, 2),
+        (pd.Series([1, 2, 3, 4, 5], dtype="int32"), 4, 2),
+        (["a", "b", "c", "d", "e"], "b", "d"),
+        (["a", "b", "c", "d", "e"], "b", None),
+        (["a", "b", "c", "d", "e"], None, "d"),
+        (["a", "b", "c", "d", "e"], "d", "b"),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_series_clip(data, lower, upper, inplace):
+    psr = pd.Series(data)
+    gsr = cudf.from_pandas(psr)
+
+    expect = psr.clip(lower=lower, upper=upper)
+    got = gsr.clip(lower=lower, upper=upper, inplace=inplace)
+
+    if inplace is True:
+        assert_eq(expect, gsr)
+    else:
+        assert_eq(expect, got)
+
+
+def test_series_exceptions_for_clip():
+
+    with pytest.raises(ValueError):
+        cudf.Series([1, 2, 3, 4]).clip([1, 2], [2, 3])
+
+    with pytest.raises(NotImplementedError):
+        cudf.Series([1, 2, 3, 4]).clip(1, 2, axis=0)
+
+
+@pytest.mark.parametrize(
+    "data", [[1, 2.0, 3, 4, None, 1, None, 10, None], ["a", "b", "c"]]
+)
+@pytest.mark.parametrize(
+    "index",
+    [
+        None,
+        [1, 2, 3],
+        ["a", "b", "z"],
+        ["a", "b", "c", "d", "e", "f", "g", "l", "m"],
+    ],
+)
+@pytest.mark.parametrize("value", [[1, 2, 3, 4, None, 1, None, 10, None]])
+def test_series_fillna(data, index, value):
+    psr = pd.Series(
+        data,
+        index=index if index is not None and len(index) == len(data) else None,
+    )
+    gsr = cudf.Series(
+        data,
+        index=index if index is not None and len(index) == len(data) else None,
+    )
+
+    expect = psr.fillna(pd.Series(value))
+    got = gsr.fillna(cudf.Series(value))
+    assert_eq(expect, got)
+
+
+def test_series_fillna_error():
+    psr = pd.Series([1, 2, None, 3, None])
+    gsr = cudf.from_pandas(psr)
+
+    assert_exceptions_equal(
+        psr.fillna,
+        gsr.fillna,
+        ([pd.DataFrame({"a": [1, 2, 3]})],),
+        ([cudf.DataFrame({"a": [1, 2, 3]})],),
+    )
+
+
+def test_series_replace_errors():
+    gsr = cudf.Series([1, 2, None, 3, None])
+    psr = gsr.to_pandas()
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "to_replace and value should be of same types,"
+            "got to_replace dtype: int64 and "
+            "value dtype: object"
+        ),
+    ):
+        gsr.replace(1, "a")
+
+    gsr = cudf.Series(["a", "b", "c"])
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "to_replace and value should be of same types,"
+            "got to_replace dtype: int64 and "
+            "value dtype: object"
+        ),
+    ):
+        gsr.replace([1, 2], ["a", "b"])
+
+    assert_exceptions_equal(
+        psr.replace,
+        gsr.replace,
+        ([{"a": 1}, 1],),
+        ([{"a": 1}, 1],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=psr.replace,
+        rfunc=gsr.replace,
+        lfunc_args_and_kwargs=([[1, 2], [1]],),
+        rfunc_args_and_kwargs=([[1, 2], [1]],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=psr.replace,
+        rfunc=gsr.replace,
+        lfunc_args_and_kwargs=([object(), [1]],),
+        rfunc_args_and_kwargs=([object(), [1]],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=psr.replace,
+        rfunc=gsr.replace,
+        lfunc_args_and_kwargs=([{"a": 1}, object()],),
+        rfunc_args_and_kwargs=([{"a": 1}, object()],),
+    )
+
+
+@pytest.mark.parametrize(
+    "gsr,old,new,expected",
+    [
+        (
+            cudf.Series(["a", "b", "c", None]),
+            None,
+            "a",
+            cudf.Series(["a", "b", "c", "a"]),
+        ),
+        (
+            cudf.Series(["a", "b", "c", None]),
+            [None, "a", "a"],
+            ["c", "b", "d"],
+            cudf.Series(["d", "b", "c", "c"]),
+        ),
+        (
+            cudf.Series(["a", "b", "c", None]),
+            [None, "a"],
+            ["b", None],
+            cudf.Series([None, "b", "c", "b"]),
+        ),
+        (
+            cudf.Series(["a", "b", "c", None]),
+            [None, None],
+            [None, None],
+            cudf.Series(["a", "b", "c", None]),
+        ),
+        (cudf.Series([1, 2, None, 3]), None, 10, cudf.Series([1, 2, 10, 3])),
+        (
+            cudf.Series([1, 2, None, 3]),
+            [None, 1, 1],
+            [3, 2, 4],
+            cudf.Series([4, 2, 3, 3]),
+        ),
+        (
+            cudf.Series([1, 2, None, 3]),
+            [None, 1],
+            [2, None],
+            cudf.Series([None, 2, 2, 3]),
+        ),
+        (
+            cudf.Series(["a", "q", "t", None], dtype="category"),
+            None,
+            "z",
+            cudf.Series(["a", "q", "t", "z"], dtype="category"),
+        ),
+        (
+            cudf.Series(["a", "q", "t", None], dtype="category"),
+            [None, "a", "q"],
+            ["z", None, None],
+            cudf.Series([None, None, "t", "z"], dtype="category"),
+        ),
+        (
+            cudf.Series(["a", None, "t", None], dtype="category"),
+            [None, "t"],
+            ["p", None],
+            cudf.Series(["a", "p", None, "p"], dtype="category"),
+        ),
+    ],
+)
+def test_replace_nulls(gsr, old, new, expected):
+
+    actual = gsr.replace(old, new)
+    assert_eq(
+        expected.sort_values().reset_index(drop=True),
+        actual.sort_values().reset_index(drop=True),
+    )
+
+
+def test_fillna_columns_multiindex():
+    columns = pd.MultiIndex.from_tuples([("a", "b"), ("d", "e")])
+    pdf = pd.DataFrame(
+        {"0": [1, 2, None, 3, None], "1": [None, None, None, None, 4]}
+    )
+    pdf.columns = columns
+    gdf = cudf.from_pandas(pdf)
+
+    expected = pdf.fillna(10)
+    actual = gdf.fillna(10)
+
+    assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/test_repr.py b/python/cudf/cudf/tests/test_repr.py
new file mode 100644
index 0000000..a36cc1b
--- /dev/null
+++ b/python/cudf/cudf/tests/test_repr.py
@@ -0,0 +1,1484 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import textwrap
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import given, settings, strategies as st
+
+import cudf
+from cudf.testing import _utils as utils
+from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes
+
+repr_categories = [
+    "uint16",
+    "int64",
+    "float64",
+    "str",
+    "category",
+    "datetime64[ns]",
+]
+
+
+@pytest.mark.parametrize("dtype", repr_categories)
+@pytest.mark.parametrize("nrows", [0, 5, 10])
+def test_null_series(nrows, dtype):
+    size = 5
+    sr = cudf.Series(np.random.randint(1, 9, size)).astype(dtype)
+    sr[np.random.choice([False, True], size=size)] = None
+    if dtype != "category" and cudf.dtype(dtype).kind in {"u", "i"}:
+        ps = pd.Series(
+            sr._column.data_array_view(mode="read").copy_to_host(),
+            dtype=np_dtypes_to_pandas_dtypes.get(
+                cudf.dtype(dtype), cudf.dtype(dtype)
+            ),
+        )
+        ps[sr.isnull().to_pandas()] = pd.NA
+    else:
+        ps = sr.to_pandas()
+
+    pd.options.display.max_rows = int(nrows)
+    psrepr = repr(ps).replace("NaN", "<NA>").replace("None", "<NA>")
+    if "UInt" in psrepr:
+        psrepr = psrepr.replace("UInt", "uint")
+    elif "Int" in psrepr:
+        psrepr = psrepr.replace("Int", "int")
+    assert psrepr.split() == repr(sr).split()
+    pd.reset_option("display.max_rows")
+
+
+dtype_categories = [
+    "float32",
+    "float64",
+    "datetime64[ns]",
+    "str",
+    "category",
+]
+
+
+@pytest.mark.parametrize("ncols", [1, 2, 3, 4, 5, 10])
+def test_null_dataframe(ncols):
+    size = 20
+    gdf = cudf.DataFrame()
+    for idx, dtype in enumerate(dtype_categories):
+        sr = cudf.Series(np.random.randint(0, 128, size)).astype(dtype)
+        sr[np.random.choice([False, True], size=size)] = None
+        gdf[dtype] = sr
+    pdf = gdf.to_pandas()
+    pd.options.display.max_columns = int(ncols)
+    pdf_repr = repr(pdf).replace("NaN", "<NA>").replace("None", "<NA>")
+    assert pdf_repr.split() == repr(gdf).split()
+    pd.reset_option("display.max_columns")
+
+
+@pytest.mark.parametrize("dtype", repr_categories)
+@pytest.mark.parametrize("nrows", [None, 0, 1, 2, 9, 10, 11, 19, 20, 21])
+def test_full_series(nrows, dtype):
+    size = 20
+    ps = pd.Series(np.random.randint(0, 100, size)).astype(dtype)
+    sr = cudf.from_pandas(ps)
+    pd.options.display.max_rows = nrows
+    assert repr(ps) == repr(sr)
+    pd.reset_option("display.max_rows")
+
+
+@pytest.mark.parametrize("nrows", [5, 10, 15])
+@pytest.mark.parametrize("ncols", [5, 10, 15])
+@pytest.mark.parametrize("size", [20, 21])
+@pytest.mark.parametrize("dtype", repr_categories)
+def test_full_dataframe_20(dtype, size, nrows, ncols):
+    pdf = pd.DataFrame(
+        {idx: np.random.randint(0, 100, size) for idx in range(size)}
+    ).astype(dtype)
+    gdf = cudf.from_pandas(pdf)
+
+    with pd.option_context(
+        "display.max_rows", int(nrows), "display.max_columns", int(ncols)
+    ):
+        assert repr(pdf) == repr(gdf)
+        assert pdf._repr_html_() == gdf._repr_html_()
+        assert pdf._repr_latex_() == gdf._repr_latex_()
+
+
+@given(
+    st.lists(
+        st.integers(-9223372036854775808, 9223372036854775807),
+        min_size=1,
+        max_size=10000,
+    )
+)
+@settings(deadline=None)
+def test_integer_dataframe(x):
+    gdf = cudf.DataFrame({"x": x})
+    pdf = gdf.to_pandas()
+    pd.options.display.max_columns = 1
+    assert repr(gdf) == repr(pdf)
+    assert repr(gdf.T) == repr(pdf.T)
+    pd.reset_option("display.max_columns")
+
+
+@given(
+    st.lists(
+        st.integers(-9223372036854775808, 9223372036854775807), max_size=10000
+    )
+)
+@settings(deadline=None)
+def test_integer_series(x):
+    sr = cudf.Series(x, dtype=int)
+    ps = pd.Series(data=x, dtype=int)
+
+    assert repr(sr) == repr(ps)
+
+
+@given(st.lists(st.floats()))
+@settings(deadline=None)
+def test_float_dataframe(x):
+    gdf = cudf.DataFrame({"x": cudf.Series(x, dtype=float, nan_as_null=False)})
+    pdf = gdf.to_pandas()
+    assert repr(gdf) == repr(pdf)
+
+
+@given(st.lists(st.floats()))
+@settings(deadline=None)
+def test_float_series(x):
+    sr = cudf.Series(x, dtype=float, nan_as_null=False)
+    ps = pd.Series(data=x, dtype=float)
+    assert repr(sr) == repr(ps)
+
+
+@pytest.fixture
+def mixed_pdf():
+    pdf = pd.DataFrame()
+    pdf["Integer"] = np.array([2345, 11987, 9027, 9027])
+    pdf["Date"] = np.array(
+        ["18/04/1995", "14/07/1994", "07/06/2006", "16/09/2005"]
+    )
+    pdf["Float"] = np.array([9.001, 8.343, 6, 2.781])
+    pdf["Integer2"] = np.array([2345, 106, 2088, 789277])
+    pdf["Category"] = np.array(["M", "F", "F", "F"])
+    pdf["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    pdf["Boolean"] = np.array([True, False, True, False])
+    return pdf
+
+
+@pytest.fixture
+def mixed_gdf(mixed_pdf):
+    return cudf.from_pandas(mixed_pdf)
+
+
+def test_mixed_dataframe(mixed_pdf, mixed_gdf):
+    assert repr(mixed_gdf) == repr(mixed_pdf)
+
+
+def test_mixed_series(mixed_pdf, mixed_gdf):
+    for col in mixed_gdf.columns:
+        assert repr(mixed_gdf[col]) == repr(mixed_pdf[col])
+
+
+def test_MI():
+    gdf = cudf.DataFrame(
+        {
+            "a": np.random.randint(0, 4, 10),
+            "b": np.random.randint(0, 4, 10),
+            "c": np.random.randint(0, 4, 10),
+        }
+    )
+    levels = [["a", "b", "c", "d"], ["w", "x", "y", "z"], ["m", "n"]]
+    codes = cudf.DataFrame(
+        {
+            "a": [0, 0, 0, 0, 1, 1, 2, 2, 3, 3],
+            "b": [0, 1, 2, 3, 0, 1, 2, 3, 0, 1],
+            "c": [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
+        }
+    )
+    pd.options.display.max_rows = 999
+    pd.options.display.max_columns = 0
+    gdf = gdf.set_index(cudf.MultiIndex(levels=levels, codes=codes))
+    pdf = gdf.to_pandas()
+    assert repr(gdf) == repr(pdf)
+    assert repr(gdf.index) == repr(pdf.index)
+    assert repr(gdf.T) == repr(pdf.T)
+    pd.reset_option("display.max_rows")
+    pd.reset_option("display.max_columns")
+
+
+@pytest.mark.parametrize("nrows", [0, 1, 3, 5, 10])
+@pytest.mark.parametrize("ncols", [0, 1, 2, 3])
+def test_groupby_MI(nrows, ncols):
+    gdf = cudf.DataFrame(
+        {"a": np.arange(10), "b": np.arange(10), "c": np.arange(10)}
+    )
+    pdf = gdf.to_pandas()
+    gdg = gdf.groupby(["a", "b"], sort=True).count()
+    pdg = pdf.groupby(["a", "b"], sort=True).count()
+    pd.options.display.max_rows = nrows
+    pd.options.display.max_columns = ncols
+    assert repr(gdg) == repr(pdg)
+    assert repr(gdg.index) == repr(pdg.index)
+    assert repr(gdg.T) == repr(pdg.T)
+    pd.reset_option("display.max_rows")
+    pd.reset_option("display.max_columns")
+
+
+@pytest.mark.parametrize("dtype", utils.NUMERIC_TYPES)
+@pytest.mark.parametrize("length", [0, 1, 10, 100, 1000])
+def test_generic_index(length, dtype):
+    psr = pd.Series(
+        range(length),
+        index=np.random.randint(0, high=100, size=length).astype(dtype),
+        dtype="float64" if length == 0 else None,
+    )
+    gsr = cudf.Series.from_pandas(psr)
+
+    assert repr(psr.index) == repr(gsr.index)
+
+
+@pytest.mark.parametrize(
+    "gdf",
+    [
+        cudf.DataFrame({"a": range(10000)}),
+        cudf.DataFrame({"a": range(10000), "b": range(10000)}),
+        cudf.DataFrame({"a": range(20), "b": range(20)}),
+        cudf.DataFrame(
+            {
+                "a": range(20),
+                "b": range(20),
+                "c": ["abc", "def", "xyz", "def", "pqr"] * 4,
+            }
+        ),
+        cudf.DataFrame(index=[1, 2, 3]),
+        cudf.DataFrame(index=range(10000)),
+        cudf.DataFrame(columns=["a", "b", "c", "d"]),
+        cudf.DataFrame(columns=["a"], index=range(10000)),
+        cudf.DataFrame(columns=["a", "col2", "...col n"], index=range(10000)),
+        cudf.DataFrame(index=cudf.Series(range(10000)).astype("str")),
+        cudf.DataFrame(
+            columns=["a", "b", "c", "d"],
+            index=cudf.Series(range(10000)).astype("str"),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "slice",
+    [
+        slice(2500, 5000),
+        slice(2500, 2501),
+        slice(5000),
+        slice(1, 10),
+        slice(10, 20),
+        slice(15, 2400),
+    ],
+)
+@pytest.mark.parametrize("max_seq_items", [1, 10, 60, 10000, None])
+@pytest.mark.parametrize("max_rows", [1, 10, 60, 10000, None])
+def test_dataframe_sliced(gdf, slice, max_seq_items, max_rows):
+    pd.options.display.max_seq_items = max_seq_items
+    pd.options.display.max_rows = max_rows
+    pdf = gdf.to_pandas()
+
+    sliced_gdf = gdf[slice]
+    sliced_pdf = pdf[slice]
+
+    expected_repr = repr(sliced_pdf).replace("None", "<NA>")
+    actual_repr = repr(sliced_gdf)
+
+    assert expected_repr == actual_repr
+    pd.reset_option("display.max_rows")
+    pd.reset_option("display.max_seq_items")
+
+
+@pytest.mark.parametrize(
+    "index,expected_repr",
+    [
+        (
+            cudf.Index([1, 2, 3, None]),
+            "Int64Index([1, 2, 3, <NA>], dtype='int64')",
+        ),
+        (
+            cudf.Index([None, 2.2, 3.324342, None]),
+            "Float64Index([<NA>, 2.2, 3.324342, <NA>], dtype='float64')",
+        ),
+        (
+            cudf.Index([None, None, None], name="hello"),
+            "StringIndex([None None None], dtype='object', name='hello')",
+        ),
+        (
+            cudf.Index([None, None, None], dtype="float", name="hello"),
+            "Float64Index([<NA>, <NA>, <NA>], dtype='float64', name='hello')",
+        ),
+        (
+            cudf.Index([None], dtype="float64", name="hello"),
+            "Float64Index([<NA>], dtype='float64', name='hello')",
+        ),
+        (
+            cudf.Index([None], dtype="int8", name="hello"),
+            "Int8Index([<NA>], dtype='int8', name='hello')",
+        ),
+        (
+            cudf.Index([None] * 50, dtype="object"),
+            "StringIndex([None None None None None None None None "
+            "None None None None None None\n None None None None None None "
+            "None None None None None None None None\n None None None None "
+            "None None None None None None None None None None\n None None "
+            "None None None None None None], dtype='object')",
+        ),
+        (
+            cudf.Index([None] * 20, dtype="uint32"),
+            "UInt32Index([<NA>, <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, "
+            "<NA>,\n       <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, <NA>, "
+            "<NA>,\n       <NA>, <NA>],\n      dtype='uint32')",
+        ),
+        (
+            cudf.Index(
+                [None, 111, 22, 33, None, 23, 34, 2343, None], dtype="int16"
+            ),
+            "Int16Index([<NA>, 111, 22, 33, <NA>, 23, 34, 2343, <NA>], "
+            "dtype='int16')",
+        ),
+        (
+            cudf.Index([1, 2, 3, None], dtype="category"),
+            "CategoricalIndex([1, 2, 3, <NA>], categories=[1, 2, 3], "
+            "ordered=False, dtype='category')",
+        ),
+        (
+            cudf.Index([None, None], dtype="category"),
+            "CategoricalIndex([<NA>, <NA>], categories=[], ordered=False, "
+            "dtype='category')",
+        ),
+        (
+            cudf.Index(np.array([10, 20, 30, None], dtype="datetime64[ns]")),
+            "DatetimeIndex([1970-01-01 00:00:00.000000010, "
+            "1970-01-01 00:00:00.000000020,"
+            "\n       1970-01-01 00:00:00.000000030, NaT],\n      "
+            "dtype='datetime64[ns]')",
+        ),
+        (
+            cudf.Index(np.array([10, 20, 30, None], dtype="datetime64[s]")),
+            "DatetimeIndex([1970-01-01 00:00:10, "
+            "1970-01-01 00:00:20, 1970-01-01 00:00:30,\n"
+            "       NaT],\n      dtype='datetime64[s]')",
+        ),
+        (
+            cudf.Index(np.array([10, 20, 30, None], dtype="datetime64[us]")),
+            "DatetimeIndex([1970-01-01 00:00:00.000010, "
+            "1970-01-01 00:00:00.000020,\n       "
+            "1970-01-01 00:00:00.000030, NaT],\n      "
+            "dtype='datetime64[us]')",
+        ),
+        (
+            cudf.Index(np.array([10, 20, 30, None], dtype="datetime64[ms]")),
+            "DatetimeIndex([1970-01-01 00:00:00.010, "
+            "1970-01-01 00:00:00.020,\n       "
+            "1970-01-01 00:00:00.030, NaT],\n      "
+            "dtype='datetime64[ms]')",
+        ),
+        (
+            cudf.Index(np.array([None] * 10, dtype="datetime64[ms]")),
+            "DatetimeIndex([NaT, NaT, NaT, NaT, NaT, NaT, NaT, NaT, "
+            "NaT, NaT], dtype='datetime64[ms]')",
+        ),
+    ],
+)
+def test_generic_index_null(index, expected_repr):
+
+    actual_repr = repr(index)
+
+    assert expected_repr == actual_repr
+
+
+@pytest.mark.parametrize(
+    "df,pandas_special_case",
+    [
+        (pd.DataFrame({"a": [1, 2, 3]}, index=[10, 20, None]), False),
+        (
+            pd.DataFrame(
+                {
+                    "a": [1, None, 3],
+                    "string_col": ["hello", "world", "rapids"],
+                },
+                index=[None, "a", "b"],
+            ),
+            True,
+        ),
+        (pd.DataFrame([], index=[None, "a", "b"]), False),
+        (pd.DataFrame({"aa": [None, None]}, index=[None, None]), False),
+        (pd.DataFrame({"aa": [1, 2, 3]}, index=[None, None, None]), False),
+        (
+            pd.DataFrame(
+                {"aa": [None, 2, 3]},
+                index=np.array([1, None, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {"aa": [None, 2, 3]},
+                index=np.array([100, None, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {"aa": [None, None, None]},
+                index=np.array([None, None, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {"aa": [1, None, 3]},
+                index=np.array([10, 15, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {"a": [1, 2, None], "v": [10, None, 22], "p": [100, 200, 300]}
+            ).set_index(["a", "v"]),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": [1, 2, None],
+                    "v": ["n", "c", "a"],
+                    "p": [None, None, None],
+                }
+            ).set_index(["a", "v"]),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": np.array([1, None, None], dtype="datetime64[ns]"),
+                    "v": ["n", "c", "a"],
+                    "p": [None, None, None],
+                }
+            ).set_index(["a", "v"]),
+            False,
+        ),
+    ],
+)
+def test_dataframe_null_index_repr(df, pandas_special_case):
+    pdf = df
+    gdf = cudf.from_pandas(pdf)
+
+    expected_repr = repr(pdf).replace("NaN", "<NA>").replace("None", "<NA>")
+    actual_repr = repr(gdf)
+
+    if pandas_special_case:
+        # Pandas inconsistently print StringIndex null values
+        # as `None` at some places and `NaN` at few other places
+        # Whereas cudf is consistent with strings `null` values
+        # to be printed as `None` everywhere.
+        actual_repr = repr(gdf).replace("None", "<NA>")
+
+    assert expected_repr.split() == actual_repr.split()
+
+
+@pytest.mark.parametrize(
+    "sr,pandas_special_case",
+    [
+        (pd.Series([1, 2, 3], index=[10, 20, None]), False),
+        (pd.Series([1, None, 3], name="a", index=[None, "a", "b"]), True),
+        (pd.Series(None, index=[None, "a", "b"], dtype="float"), True),
+        (pd.Series([None, None], name="aa", index=[None, None]), False),
+        (pd.Series([1, 2, 3], index=[None, None, None]), False),
+        (
+            pd.Series(
+                [None, 2, 3],
+                index=np.array([1, None, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.Series(
+                [None, None, None],
+                index=np.array([None, None, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.Series(
+                [1, None, 3],
+                index=np.array([10, 15, None], dtype="datetime64[ns]"),
+            ),
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {"a": [1, 2, None], "v": [10, None, 22], "p": [100, 200, 300]}
+            ).set_index(["a", "v"])["p"],
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": [1, 2, None],
+                    "v": ["n", "c", "a"],
+                    "p": [None, None, None],
+                }
+            ).set_index(["a", "v"])["p"],
+            False,
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": np.array([1, None, None], dtype="datetime64[ns]"),
+                    "v": ["n", "c", "a"],
+                    "p": [None, None, None],
+                }
+            ).set_index(["a", "v"])["p"],
+            False,
+        ),
+    ],
+)
+def test_series_null_index_repr(sr, pandas_special_case):
+    psr = sr
+    gsr = cudf.from_pandas(psr)
+
+    expected_repr = repr(psr).replace("NaN", "<NA>").replace("None", "<NA>")
+    actual_repr = repr(gsr)
+
+    if pandas_special_case:
+        # Pandas inconsistently print StringIndex null values
+        # as `None` at some places and `NaN` at few other places
+        # Whereas cudf is consistent with strings `null` values
+        # to be printed as `None` everywhere.
+        actual_repr = repr(gsr).replace("None", "<NA>")
+    assert expected_repr.split() == actual_repr.split()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [1],
+        [12, 11, 232, 223432411, 2343241, 234324, 23234],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+        [
+            136457654,
+            134736784,
+            245345345,
+            223432411,
+            2343241,
+            3634548734,
+            23234,
+        ],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    ],
+)
+@pytest.mark.parametrize("dtype", ["timedelta64[s]", "timedelta64[us]"])
+def test_timedelta_series_s_us_repr(data, dtype):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    expected = repr(psr).replace("timedelta64[ns]", dtype)
+    actual = repr(sr)
+
+    assert expected.split() == actual.split()
+
+
+@pytest.mark.parametrize(
+    "ser, expected_repr",
+    [
+        (
+            cudf.Series([], dtype="timedelta64[ns]"),
+            textwrap.dedent(
+                """
+            Series([], dtype: timedelta64[ns])
+            """
+            ),
+        ),
+        (
+            cudf.Series([], dtype="timedelta64[ms]"),
+            textwrap.dedent(
+                """
+            Series([], dtype: timedelta64[ms])
+            """
+            ),
+        ),
+        (
+            cudf.Series([1000000, 200000, 3000000], dtype="timedelta64[ns]"),
+            textwrap.dedent(
+                """
+            0    0 days 00:00:00.001000000
+            1    0 days 00:00:00.000200000
+            2    0 days 00:00:00.003000000
+            dtype: timedelta64[ns]
+            """
+            ),
+        ),
+        (
+            cudf.Series([1000000, 200000, 3000000], dtype="timedelta64[ms]"),
+            textwrap.dedent(
+                """
+            0    0 days 00:16:40
+            1    0 days 00:03:20
+            2    0 days 00:50:00
+            dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series([1000000, 200000, None], dtype="timedelta64[ns]"),
+            textwrap.dedent(
+                """
+            0    0 days 00:00:00.001000000
+            1    0 days 00:00:00.000200000
+            2                          NaT
+            dtype: timedelta64[ns]
+            """
+            ),
+        ),
+        (
+            cudf.Series([1000000, 200000, None], dtype="timedelta64[ms]"),
+            textwrap.dedent(
+                """
+            0    0 days 00:16:40
+            1    0 days 00:03:20
+            2                NaT
+            dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [None, None, None, None, None], dtype="timedelta64[ns]"
+            ),
+            textwrap.dedent(
+                """
+            0    NaT
+            1    NaT
+            2    NaT
+            3    NaT
+            4    NaT
+            dtype: timedelta64[ns]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [None, None, None, None, None], dtype="timedelta64[ms]"
+            ),
+            textwrap.dedent(
+                """
+            0    NaT
+            1    NaT
+            2    NaT
+            3    NaT
+            4    NaT
+            dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [12, 12, 22, 343, 4353534, 435342], dtype="timedelta64[ns]"
+            ),
+            textwrap.dedent(
+                """
+            0    0 days 00:00:00.000000012
+            1    0 days 00:00:00.000000012
+            2    0 days 00:00:00.000000022
+            3    0 days 00:00:00.000000343
+            4    0 days 00:00:00.004353534
+            5    0 days 00:00:00.000435342
+            dtype: timedelta64[ns]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [12, 12, 22, 343, 4353534, 435342], dtype="timedelta64[ms]"
+            ),
+            textwrap.dedent(
+                """
+            0    0 days 00:00:00.012
+            1    0 days 00:00:00.012
+            2    0 days 00:00:00.022
+            3    0 days 00:00:00.343
+            4    0 days 01:12:33.534
+            5    0 days 00:07:15.342
+            dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+                dtype="timedelta64[ns]",
+            ),
+            textwrap.dedent(
+                """
+            0    0 days 00:00:00.000000001
+            1    0 days 00:00:00.000001132
+            2    0 days 00:00:00.023223231
+            3    0 days 00:00:00.000000233
+            4              0 days 00:00:00
+            5    0 days 00:00:00.000000332
+            6    0 days 00:00:00.000000323
+            dtype: timedelta64[ns]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+                dtype="timedelta64[ms]",
+            ),
+            textwrap.dedent(
+                """
+            0    0 days 00:00:00.001
+            1    0 days 00:00:01.132
+            2    0 days 06:27:03.231
+            3    0 days 00:00:00.233
+            4        0 days 00:00:00
+            5    0 days 00:00:00.332
+            6    0 days 00:00:00.323
+            dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    13645765432432,
+                    134736784,
+                    245345345,
+                    223432411,
+                    999992343241,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[ms]",
+            ),
+            textwrap.dedent(
+                """
+            0    157937 days 02:23:52.432
+            1         1 days 13:25:36.784
+            2         2 days 20:09:05.345
+            3         2 days 14:03:52.411
+            4     11573 days 23:39:03.241
+            5        42 days 01:35:48.734
+            6         0 days 00:00:23.234
+            dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    13645765432432,
+                    134736784,
+                    245345345,
+                    223432411,
+                    999992343241,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[ns]",
+            ),
+            textwrap.dedent(
+                """
+            0    0 days 03:47:25.765432432
+            1    0 days 00:00:00.134736784
+            2    0 days 00:00:00.245345345
+            3    0 days 00:00:00.223432411
+            4    0 days 00:16:39.992343241
+            5    0 days 00:00:03.634548734
+            6    0 days 00:00:00.000023234
+            dtype: timedelta64[ns]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    13645765432432,
+                    134736784,
+                    245345345,
+                    223432411,
+                    999992343241,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[ms]",
+                name="abc",
+            ),
+            textwrap.dedent(
+                """
+            0    157937 days 02:23:52.432
+            1         1 days 13:25:36.784
+            2         2 days 20:09:05.345
+            3         2 days 14:03:52.411
+            4     11573 days 23:39:03.241
+            5        42 days 01:35:48.734
+            6         0 days 00:00:23.234
+            Name: abc, dtype: timedelta64[ms]
+            """
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    13645765432432,
+                    134736784,
+                    245345345,
+                    223432411,
+                    999992343241,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[ns]",
+                index=["a", "b", "z", "x", "y", "l", "m"],
+                name="hello",
+            ),
+            textwrap.dedent(
+                """
+            a    0 days 03:47:25.765432432
+            b    0 days 00:00:00.134736784
+            z    0 days 00:00:00.245345345
+            x    0 days 00:00:00.223432411
+            y    0 days 00:16:39.992343241
+            l    0 days 00:00:03.634548734
+            m    0 days 00:00:00.000023234
+            Name: hello, dtype: timedelta64[ns]
+            """
+            ),
+        ),
+    ],
+)
+def test_timedelta_series_ns_ms_repr(ser, expected_repr):
+    expected = expected_repr
+    actual = repr(ser)
+
+    assert expected.split() == actual.split()
+
+
+@pytest.mark.parametrize(
+    "df,expected_repr",
+    [
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [1000000, 200000, 3000000], dtype="timedelta64[s]"
+                    )
+                }
+            ),
+            textwrap.dedent(
+                """
+                                  a
+                0  11 days 13:46:40
+                1   2 days 07:33:20
+                2  34 days 17:20:00
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [
+                            136457654,
+                            None,
+                            245345345,
+                            223432411,
+                            None,
+                            3634548734,
+                            23234,
+                        ],
+                        dtype="timedelta64[s]",
+                    ),
+                    "b": [10, 11, 22, 33, 44, 55, 66],
+                }
+            ),
+            textwrap.dedent(
+                """
+                                     a   b
+                0   1579 days 08:54:14  10
+                1                  NaT  11
+                2   2839 days 15:29:05  22
+                3   2586 days 00:33:31  33
+                4                  NaT  44
+                5  42066 days 12:52:14  55
+                6      0 days 06:27:14  66
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [
+                            136457654,
+                            None,
+                            245345345,
+                            223432411,
+                            None,
+                            3634548734,
+                            23234,
+                        ],
+                        dtype="timedelta64[s]",
+                        index=["a", "b", "c", "d", "e", "f", "g"],
+                    )
+                }
+            ),
+            textwrap.dedent(
+                """
+                                     a
+                a   1579 days 08:54:14
+                b                  NaT
+                c   2839 days 15:29:05
+                d   2586 days 00:33:31
+                e                  NaT
+                f  42066 days 12:52:14
+                g      0 days 06:27:14
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        [1, 2, 3, 4, 5, 6, 7],
+                        index=cudf.Index(
+                            [
+                                136457654,
+                                None,
+                                245345345,
+                                223432411,
+                                None,
+                                3634548734,
+                                23234,
+                            ],
+                            dtype="timedelta64[ms]",
+                        ),
+                    )
+                }
+            ),
+            textwrap.dedent(
+                """
+                                      a
+                1 days 13:54:17.654   1
+                NaT                   2
+                2 days 20:09:05.345   3
+                2 days 14:03:52.411   4
+                NaT                   5
+                42 days 01:35:48.734  6
+                0 days 00:00:23.234   7
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series(
+                        ["a", "f", "q", "e", "w", "e", "t"],
+                        index=cudf.Index(
+                            [
+                                136457654,
+                                None,
+                                245345345,
+                                223432411,
+                                None,
+                                3634548734,
+                                23234,
+                            ],
+                            dtype="timedelta64[ns]",
+                        ),
+                    )
+                }
+            ),
+            textwrap.dedent(
+                """
+                                    a
+                0 days 00:00:00.136457654  a
+                NaT                 f
+                0 days 00:00:00.245345345  q
+                0 days 00:00:00.223432411  e
+                NaT                 w
+                0 days 00:00:03.634548734  e
+                0 days 00:00:00.000023234  t
+                """
+            ),
+        ),
+    ],
+)
+def test_timedelta_dataframe_repr(df, expected_repr):
+    actual_repr = repr(df)
+
+    assert actual_repr.split() == expected_repr.split()
+
+
+@pytest.mark.parametrize(
+    "index, expected_repr",
+    [
+        (
+            cudf.Index([1000000, 200000, 3000000], dtype="timedelta64[ms]"),
+            "TimedeltaIndex(['0 days 00:16:40', "
+            "'0 days 00:03:20', '0 days 00:50:00'], "
+            "dtype='timedelta64[ms]')",
+        ),
+        (
+            cudf.Index(
+                [None, None, None, None, None], dtype="timedelta64[us]"
+            ),
+            "TimedeltaIndex([NaT, NaT, NaT, NaT, NaT], "
+            "dtype='timedelta64[us]')",
+        ),
+        (
+            cudf.Index(
+                [
+                    136457654,
+                    None,
+                    245345345,
+                    223432411,
+                    None,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[us]",
+            ),
+            "TimedeltaIndex([0 days 00:02:16.457654, NaT, "
+            "0 days 00:04:05.345345, "
+            "0 days 00:03:43.432411, NaT,"
+            "       0 days 01:00:34.548734, 0 days 00:00:00.023234],"
+            "      dtype='timedelta64[us]')",
+        ),
+        (
+            cudf.Index(
+                [
+                    136457654,
+                    None,
+                    245345345,
+                    223432411,
+                    None,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[s]",
+            ),
+            "TimedeltaIndex([1579 days 08:54:14, NaT, 2839 days 15:29:05,"
+            "       2586 days 00:33:31, NaT, 42066 days 12:52:14, "
+            "0 days 06:27:14],"
+            "      dtype='timedelta64[s]')",
+        ),
+    ],
+)
+def test_timedelta_index_repr(index, expected_repr):
+    actual_repr = repr(index)
+
+    assert actual_repr.split() == expected_repr.split()
+
+
+@pytest.mark.parametrize(
+    "pmi",
+    [
+        pd.MultiIndex.from_tuples(
+            [(1, "red"), (1, "blue"), (2, "red"), (2, "blue")]
+        ),
+        pd.MultiIndex.from_tuples(
+            [(1, "red"), (1, "blue"), (2, "red"), (2, "blue")] * 10
+        ),
+        pd.MultiIndex.from_tuples([(1, "red", 102, "sdf")]),
+        pd.MultiIndex.from_tuples(
+            [
+                ("abc", 0.234, 1),
+                ("a", -0.34, 0),
+                ("ai", 111, 4385798),
+                ("rapids", 0, 34534534),
+            ],
+            names=["alphabets", "floats", "ints"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("max_seq_items", [None, 1, 2, 5, 10, 100])
+def test_multiindex_repr(pmi, max_seq_items):
+    pd.set_option("display.max_seq_items", max_seq_items)
+    gmi = cudf.from_pandas(pmi)
+
+    assert repr(gmi) == repr(pmi)
+    pd.reset_option("display.max_seq_items")
+
+
+@pytest.mark.parametrize(
+    "gdi, expected_repr",
+    [
+        (
+            cudf.DataFrame(
+                {
+                    "a": [None, 1, 2, 3],
+                    "b": ["abc", None, "xyz", None],
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["a", "b"])
+            .index,
+            textwrap.dedent(
+                """
+                MultiIndex([(<NA>, 'abc'),
+                            (   1,  <NA>),
+                            (   2, 'xyz'),
+                            (   3,  <NA>)],
+                        names=['a', 'b'])
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series([None, np.nan, 2, 3], nan_as_null=False),
+                    "b": ["abc", None, "xyz", None],
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["a", "b"])
+            .index,
+            textwrap.dedent(
+                """
+            MultiIndex([(<NA>, 'abc'),
+                        ( nan,  <NA>),
+                        ( 2.0, 'xyz'),
+                        ( 3.0,  <NA>)],
+                    names=['a', 'b'])
+            """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series([None, 1, 2, 3], dtype="datetime64[ns]"),
+                    "b": ["abc", None, "xyz", None],
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["a", "b"])
+            .index,
+            textwrap.dedent(
+                """
+            MultiIndex([(                          'NaT', 'abc'),
+                        ('1970-01-01 00:00:00.000000001',  <NA>),
+                        ('1970-01-01 00:00:00.000000002', 'xyz'),
+                        ('1970-01-01 00:00:00.000000003',  <NA>)],
+                    names=['a', 'b'])
+            """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": cudf.Series([None, 1, 2, 3], dtype="datetime64[ns]"),
+                    "b": ["abc", None, "xyz", None],
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["a", "b", "c"])
+            .index,
+            textwrap.dedent(
+                """
+                MultiIndex([(                          'NaT', 'abc', 0.345),
+                            ('1970-01-01 00:00:00.000000001',  <NA>,  <NA>),
+                            ('1970-01-01 00:00:00.000000002', 'xyz', 100.0),
+                            ('1970-01-01 00:00:00.000000003',  <NA>,  10.0)],
+                        names=['a', 'b', 'c'])
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": ["abc", None, "xyz", None],
+                    "b": cudf.Series([None, 1, 2, 3], dtype="timedelta64[ns]"),
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["a", "b", "c"])
+            .index,
+            textwrap.dedent(
+                """
+                MultiIndex([('abc',                       'NaT', 0.345),
+                            ( <NA>, '0 days 00:00:00.000000001',  <NA>),
+                            ('xyz', '0 days 00:00:00.000000002', 100.0),
+                            ( <NA>, '0 days 00:00:00.000000003',  10.0)],
+                        names=['a', 'b', 'c'])
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": ["abc", None, "xyz", None],
+                    "b": cudf.Series([None, 1, 2, 3], dtype="timedelta64[ns]"),
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["c", "a"])
+            .index,
+            textwrap.dedent(
+                """
+                MultiIndex([(0.345, 'abc'),
+                            ( <NA>,  <NA>),
+                            (100.0, 'xyz'),
+                            ( 10.0,  <NA>)],
+                        names=['c', 'a'])
+                """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": [None, None, None, None],
+                    "b": cudf.Series(
+                        [None, None, None, None], dtype="timedelta64[ns]"
+                    ),
+                    "c": [0.345, np.nan, 100, 10],
+                }
+            )
+            .set_index(["b", "a"])
+            .index,
+            textwrap.dedent(
+                """
+            MultiIndex([('NaT', <NA>),
+                        ('NaT', <NA>),
+                        ('NaT', <NA>),
+                        ('NaT', <NA>)],
+                    names=['b', 'a'])
+            """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": [1, 2, None, 3, 5],
+                    "b": [
+                        "abc",
+                        "def, hi, bye",
+                        None,
+                        ", one, two, three, four",
+                        None,
+                    ],
+                    "c": cudf.Series(
+                        [0.3232, np.nan, 1, None, -0.34534], nan_as_null=False
+                    ),
+                    "d": [None, 100, 2000324, None, None],
+                }
+            )
+            .set_index(["a", "b", "c", "d"])
+            .index,
+            textwrap.dedent(
+                """
+    MultiIndex([(   1,                     'abc',   0.3232,    <NA>),
+                (   2,            'def, hi, bye',      nan,     100),
+                (<NA>,                      <NA>,      1.0, 2000324),
+                (   3, ', one, two, three, four',     <NA>,    <NA>),
+                (   5,                      <NA>, -0.34534,    <NA>)],
+            names=['a', 'b', 'c', 'd'])
+    """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": [1, 2, None, 3, 5],
+                    "b": [
+                        "abc",
+                        "def, hi, bye",
+                        None,
+                        ", one, two, three, four",
+                        None,
+                    ],
+                    "c": cudf.Series(
+                        [0.3232, np.nan, 1, None, -0.34534], nan_as_null=False
+                    ),
+                    "d": [None, 100, 2000324, None, None],
+                }
+            )
+            .set_index(["b", "a", "c", "d"])
+            .index,
+            textwrap.dedent(
+                """
+    MultiIndex([(                    'abc',    1,   0.3232,    <NA>),
+                (           'def, hi, bye',    2,      nan,     100),
+                (                     <NA>, <NA>,      1.0, 2000324),
+                (', one, two, three, four',    3,     <NA>,    <NA>),
+                (                     <NA>,    5, -0.34534,    <NA>)],
+            names=['b', 'a', 'c', 'd'])
+    """
+            ),
+        ),
+        (
+            cudf.DataFrame(
+                {
+                    "a": ["(abc", "2", None, "3", "5"],
+                    "b": [
+                        "abc",
+                        "def, hi, bye",
+                        None,
+                        ", one, two, three, four",
+                        None,
+                    ],
+                    "c": cudf.Series(
+                        [0.3232, np.nan, 1, None, -0.34534], nan_as_null=False
+                    ),
+                    "d": [None, 100, 2000324, None, None],
+                }
+            )
+            .set_index(["a", "b", "c", "d"])
+            .index,
+            textwrap.dedent(
+                """
+    MultiIndex([('(abc',                     'abc',   0.3232,    <NA>),
+                (   '2',            'def, hi, bye',      nan,     100),
+                (  <NA>,                      <NA>,      1.0, 2000324),
+                (   '3', ', one, two, three, four',     <NA>,    <NA>),
+                (   '5',                      <NA>, -0.34534,    <NA>)],
+            names=['a', 'b', 'c', 'd'])
+    """
+            ),
+        ),
+    ],
+)
+def test_multiindex_null_repr(gdi, expected_repr):
+    actual_repr = repr(gdi)
+
+    assert actual_repr.split() == expected_repr.split()
+
+
+def test_categorical_series_with_nan_repr():
+    series = cudf.Series(
+        [1, 2, np.nan, 10, np.nan, None], nan_as_null=False
+    ).astype("category")
+
+    expected_repr = textwrap.dedent(
+        """
+    0     1.0
+    1     2.0
+    2     NaN
+    3    10.0
+    4     NaN
+    5    <NA>
+    dtype: category
+    Categories (4, float64): [1.0, 2.0, 10.0, NaN]
+    """
+    )
+
+    assert repr(series).split() == expected_repr.split()
+
+    sliced_expected_repr = textwrap.dedent(
+        """
+        2     NaN
+        3    10.0
+        4     NaN
+        5    <NA>
+        dtype: category
+        Categories (4, float64): [1.0, 2.0, 10.0, NaN]
+        """
+    )
+
+    assert repr(series[2:]).split() == sliced_expected_repr.split()
+
+
+def test_categorical_dataframe_with_nan_repr():
+    series = cudf.Series(
+        [1, 2, np.nan, 10, np.nan, None], nan_as_null=False
+    ).astype("category")
+    df = cudf.DataFrame({"a": series})
+    expected_repr = textwrap.dedent(
+        """
+          a
+    0   1.0
+    1   2.0
+    2   NaN
+    3  10.0
+    4   NaN
+    5  <NA>
+    """
+    )
+
+    assert repr(df).split() == expected_repr.split()
+
+
+def test_categorical_index_with_nan_repr():
+    cat_index = cudf.Index(
+        cudf.Series(
+            [1, 2, np.nan, 10, np.nan, None], nan_as_null=False
+        ).astype("category")
+    )
+
+    expected_repr = (
+        "CategoricalIndex([1.0, 2.0, NaN, 10.0, NaN, <NA>], "
+        "categories=[1.0, 2.0, 10.0, NaN], ordered=False, dtype='category')"
+    )
+
+    assert repr(cat_index) == expected_repr
+
+    sliced_expected_repr = (
+        "CategoricalIndex([NaN, 10.0, NaN, <NA>], "
+        "categories=[1.0, 2.0, 10.0, NaN], ordered=False, dtype='category')"
+    )
+
+    assert repr(cat_index[2:]) == sliced_expected_repr
+
+
+def test_empty_series_name():
+    ps = pd.Series([], name="abc", dtype="int")
+    gs = cudf.from_pandas(ps)
+
+    assert repr(ps) == repr(gs)
+
+
+def test_repr_struct_after_concat():
+    df = cudf.DataFrame(
+        {
+            "a": cudf.Series(
+                [
+                    {"sa": 2056831253},
+                    {"sa": -1463792165},
+                    {"sa": 1735783038},
+                    {"sa": 103774433},
+                    {"sa": -1413247520},
+                ]
+                * 13
+            ),
+            "b": cudf.Series(
+                [
+                    {"sa": {"ssa": 1140062029}},
+                    None,
+                    {"sa": {"ssa": 1998862860}},
+                    {"sa": None},
+                    {"sa": {"ssa": -395088502}},
+                ]
+                * 13
+            ),
+        }
+    )
+    pdf = df.to_pandas()
+
+    assert repr(df) == repr(pdf)
+
+
+def test_interval_index_repr():
+    pi = pd.Index(
+        [
+            np.nan,
+            pd.Interval(2.0, 3.0, closed="right"),
+            pd.Interval(3.0, 4.0, closed="right"),
+        ]
+    )
+    gi = cudf.from_pandas(pi)
+
+    assert repr(pi) == repr(gi)
diff --git a/python/cudf/cudf/tests/test_resampling.py b/python/cudf/cudf/tests/test_resampling.py
new file mode 100644
index 0000000..f010180
--- /dev/null
+++ b/python/cudf/cudf/tests/test_resampling.py
@@ -0,0 +1,160 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+
+def assert_resample_results_equal(lhs, rhs, **kwargs):
+    assert_eq(
+        lhs.sort_index(),
+        rhs.sort_index(),
+        check_dtype=False,
+        check_freq=False,
+        **kwargs,
+    )
+
+
+@pytest.mark.parametrize("ts_resolution", ["ns", "s", "ms"])
+def test_series_downsample_simple(ts_resolution):
+    # Series with and index of 5min intervals:
+
+    index = pd.date_range(start="2001-01-01", periods=10, freq="1T")
+    psr = pd.Series(range(10), index=index)
+    gsr = cudf.from_pandas(psr)
+    gsr.index = gsr.index.astype(f"datetime64[{ts_resolution}]")
+    assert_resample_results_equal(
+        psr.resample("3T").sum(),
+        gsr.resample("3T").sum(),
+    )
+
+
+def test_series_upsample_simple():
+    # Series with and index of 5min intervals:
+
+    index = pd.date_range(start="2001-01-01", periods=10, freq="1T")
+    psr = pd.Series(range(10), index=index)
+    gsr = cudf.from_pandas(psr)
+    assert_resample_results_equal(
+        psr.resample("3T").sum(),
+        gsr.resample("3T").sum(),
+    )
+
+
+@pytest.mark.parametrize("rule", ["2S", "10S"])
+def test_series_resample_ffill(rule):
+    rng = pd.date_range("1/1/2012", periods=10, freq="5S")
+    ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
+    gts = cudf.from_pandas(ts)
+    assert_resample_results_equal(
+        ts.resample(rule).ffill(), gts.resample(rule).ffill()
+    )
+
+
+@pytest.mark.parametrize("rule", ["2S", "10S"])
+def test_series_resample_bfill(rule):
+    rng = pd.date_range("1/1/2012", periods=10, freq="5S")
+    ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
+    gts = cudf.from_pandas(ts)
+    assert_resample_results_equal(
+        ts.resample(rule).bfill(), gts.resample(rule).bfill()
+    )
+
+
+@pytest.mark.parametrize("rule", ["2S", "10S"])
+def test_series_resample_asfreq(rule):
+    rng = pd.date_range("1/1/2012", periods=100, freq="5S")
+    ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
+    gts = cudf.from_pandas(ts)
+    assert_resample_results_equal(
+        ts.resample(rule).asfreq(), gts.resample(rule).asfreq()
+    )
+
+
+def test_dataframe_resample_aggregation_simple():
+    pdf = pd.DataFrame(
+        np.random.randn(1000, 3),
+        index=pd.date_range("1/1/2012", freq="S", periods=1000),
+        columns=["A", "B", "C"],
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_resample_results_equal(
+        pdf.resample("3T").mean(), gdf.resample("3T").mean()
+    )
+
+
+def test_dataframe_resample_multiagg():
+    pdf = pd.DataFrame(
+        np.random.randn(1000, 3),
+        index=pd.date_range("1/1/2012", freq="S", periods=1000),
+        columns=["A", "B", "C"],
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_resample_results_equal(
+        pdf.resample("3T").agg(["sum", "mean", "std"]),
+        gdf.resample("3T").agg(["sum", "mean", "std"]),
+    )
+
+
+def test_dataframe_resample_on():
+    # test resampling on a specified column
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randn(1000),
+            "y": pd.date_range("1/1/2012", freq="S", periods=1000),
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_resample_results_equal(
+        pdf.resample("3T", on="y").mean(), gdf.resample("3T", on="y").mean()
+    )
+
+
+def test_dataframe_resample_level():
+    # test resampling on a specific level of a MultIndex
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randn(1000),
+            "y": pd.date_range("1/1/2012", freq="S", periods=1000),
+        }
+    )
+    pdi = pd.MultiIndex.from_frame(pdf)
+    pdf = pd.DataFrame({"a": np.random.randn(1000)}, index=pdi)
+    gdf = cudf.from_pandas(pdf)
+    assert_resample_results_equal(
+        pdf.resample("3T", level="y").mean(),
+        gdf.resample("3T", level="y").mean(),
+    )
+
+
+@pytest.mark.parametrize(
+    "in_freq, sampling_freq, out_freq",
+    [
+        ("1ns", "1us", "us"),
+        ("1us", "10us", "us"),
+        ("ms", "100us", "us"),
+        ("ms", "1s", "s"),
+        ("s", "1T", "s"),
+        ("1T", "30s", "s"),
+        ("1D", "10D", "s"),
+        ("10D", "1D", "s"),
+    ],
+)
+def test_resampling_frequency_conversion(in_freq, sampling_freq, out_freq):
+    # test that we cast to the appropriate frequency
+    # when resampling:
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randn(100),
+            "y": pd.date_range("1/1/2012", freq=in_freq, periods=100),
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    expect = pdf.resample(sampling_freq, on="y").mean()
+    got = gdf.resample(sampling_freq, on="y").mean()
+    assert_resample_results_equal(expect, got)
+
+    assert got.index.dtype == np.dtype(f"datetime64[{out_freq}]")
diff --git a/python/cudf/cudf/tests/test_reshape.py b/python/cudf/cudf/tests/test_reshape.py
new file mode 100644
index 0000000..0a07eec
--- /dev/null
+++ b/python/cudf/cudf/tests/test_reshape.py
@@ -0,0 +1,808 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import re
+from itertools import chain
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import melt as cudf_melt
+from cudf.core.buffer.spill_manager import get_global_manager
+from cudf.testing._utils import (
+    ALL_TYPES,
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    assert_eq,
+)
+
+pytest_xfail = pytest.mark.xfail
+pytestmark = pytest.mark.spilling
+
+# If spilling is enabled globally, we skip many test permutations
+# to reduce running time.
+if get_global_manager() is not None:
+    ALL_TYPES = ["float32"]  # noqa: F811
+    DATETIME_TYPES = ["datetime64[ms]"]  # noqa: F811
+    NUMERIC_TYPES = ["float32"]  # noqa: F811
+    # To save time, we skip tests marked "pytest.mark.xfail"
+    pytest_xfail = pytest.mark.skipif
+
+
+@pytest.mark.parametrize("num_id_vars", [0, 1, 2])
+@pytest.mark.parametrize("num_value_vars", [0, 1, 2])
+@pytest.mark.parametrize("num_rows", [1, 2, 100])
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES)
+@pytest.mark.parametrize("nulls", ["none", "some", "all"])
+def test_melt(nulls, num_id_vars, num_value_vars, num_rows, dtype):
+    if dtype not in ["float32", "float64"] and nulls in ["some", "all"]:
+        pytest.skip(reason="nulls not supported in dtype: " + dtype)
+
+    pdf = pd.DataFrame()
+    id_vars = []
+    for i in range(num_id_vars):
+        colname = "id" + str(i)
+        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        if nulls == "some":
+            idx = np.random.choice(
+                num_rows, size=int(num_rows / 2), replace=False
+            )
+            data[idx] = np.nan
+        elif nulls == "all":
+            data[:] = np.nan
+        pdf[colname] = data
+        id_vars.append(colname)
+
+    value_vars = []
+    for i in range(num_value_vars):
+        colname = "val" + str(i)
+        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        if nulls == "some":
+            idx = np.random.choice(
+                num_rows, size=int(num_rows / 2), replace=False
+            )
+            data[idx] = np.nan
+        elif nulls == "all":
+            data[:] = np.nan
+        pdf[colname] = data
+        value_vars.append(colname)
+
+    gdf = cudf.from_pandas(pdf)
+
+    got = cudf_melt(frame=gdf, id_vars=id_vars, value_vars=value_vars)
+    got_from_melt_method = gdf.melt(id_vars=id_vars, value_vars=value_vars)
+
+    expect = pd.melt(frame=pdf, id_vars=id_vars, value_vars=value_vars)
+    # pandas' melt makes the 'variable' column of 'object' type (string)
+    # cuDF's melt makes it Categorical because it doesn't support strings
+    expect["variable"] = expect["variable"].astype("category")
+
+    assert_eq(expect, got)
+
+    assert_eq(expect, got_from_melt_method)
+
+
+def test_melt_many_columns():
+    mydict = {"id": ["foobar"]}
+    for i in range(1, 1942):
+        mydict[f"d_{i}"] = i
+
+    df = pd.DataFrame(mydict)
+    grid_df = pd.melt(df, id_vars=["id"], var_name="d", value_name="sales")
+
+    df_d = cudf.DataFrame(mydict)
+    grid_df_d = cudf.melt(
+        df_d, id_vars=["id"], var_name="d", value_name="sales"
+    )
+    grid_df_d["d"] = grid_df_d["d"].astype("str")
+
+    assert_eq(grid_df, grid_df_d)
+
+
+@pytest.mark.parametrize("num_cols", [1, 2, 10])
+@pytest.mark.parametrize("num_rows", [1, 2, 1000])
+@pytest.mark.parametrize(
+    "dtype", list(chain(NUMERIC_TYPES, DATETIME_TYPES, ["str"]))
+)
+@pytest.mark.parametrize("nulls", ["none", "some"])
+def test_df_stack(nulls, num_cols, num_rows, dtype):
+    if dtype not in ["float32", "float64"] and nulls in ["some"]:
+        pytest.skip(reason="nulls not supported in dtype: " + dtype)
+
+    pdf = pd.DataFrame()
+    for i in range(num_cols):
+        colname = str(i)
+        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        if nulls == "some":
+            idx = np.random.choice(
+                num_rows, size=int(num_rows / 2), replace=False
+            )
+            data[idx] = np.nan
+        pdf[colname] = data
+
+    gdf = cudf.from_pandas(pdf)
+
+    got = gdf.stack()
+    expect = pdf.stack()
+
+    assert_eq(expect, got)
+
+
+def test_df_stack_reset_index():
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4],
+            "b": [10, 11, 12, 13],
+            "c": ["ab", "cd", None, "gh"],
+        }
+    )
+    df = df.set_index(["a", "b"])
+    pdf = df.to_pandas()
+
+    expected = pdf.stack()
+    actual = df.stack()
+
+    assert_eq(expected, actual)
+
+    expected = expected.reset_index()
+    actual = actual.reset_index()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "columns",
+    [
+        pd.MultiIndex.from_tuples(
+            [("A", "cat"), ("A", "dog"), ("B", "cat"), ("B", "dog")],
+            names=["letter", "animal"],
+        ),
+        pd.MultiIndex.from_tuples(
+            [("A", "cat"), ("B", "bird"), ("A", "dog"), ("B", "dog")],
+            names=["letter", "animal"],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "level",
+    [
+        -1,
+        0,
+        1,
+        "letter",
+        "animal",
+        [0, 1],
+        [1, 0],
+        ["letter", "animal"],
+        ["animal", "letter"],
+    ],
+)
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.RangeIndex(2, name="range"),
+        pd.Index([9, 8], name="myindex"),
+        pd.MultiIndex.from_arrays(
+            [
+                ["A", "B"],
+                [101, 102],
+            ],
+            names=["first", "second"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("dropna", [True, False])
+def test_df_stack_multiindex_column_axis(columns, index, level, dropna):
+    if isinstance(level, list) and len(level) > 1 and not dropna:
+        pytest.skip(
+            "Stacking multiple levels with dropna==False is unsupported."
+        )
+
+    pdf = pd.DataFrame(
+        data=[[1, 2, 3, 4], [2, 4, 6, 8]], columns=columns, index=index
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    got = gdf.stack(level=level, dropna=dropna)
+    expect = pdf.stack(level=level, dropna=dropna)
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_df_stack_mixed_dtypes():
+    pdf = pd.DataFrame(
+        {
+            "A": pd.Series([1, 2, 3], dtype="f4"),
+            "B": pd.Series([4, 5, 6], dtype="f8"),
+        }
+    )
+
+    gdf = cudf.from_pandas(pdf)
+
+    got = gdf.stack()
+    expect = pdf.stack()
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("level", [["animal", "hair_length"], [1, 2]])
+def test_df_stack_multiindex_column_axis_pd_example(level):
+    columns = pd.MultiIndex.from_tuples(
+        [
+            ("A", "cat", "long"),
+            ("B", "cat", "long"),
+            ("A", "dog", "short"),
+            ("B", "dog", "short"),
+        ],
+        names=["exp", "animal", "hair_length"],
+    )
+
+    df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
+
+    expect = df.stack(level=level)
+    got = cudf.from_pandas(df).stack(level=level)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("num_rows", [1, 2, 10, 1000])
+@pytest.mark.parametrize("num_cols", [1, 2, 10])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + DATETIME_TYPES + ["category"]
+)
+@pytest.mark.parametrize("nulls", ["none", "some"])
+def test_interleave_columns(nulls, num_cols, num_rows, dtype):
+
+    if dtype not in ["float32", "float64"] and nulls in ["some"]:
+        pytest.skip(reason="nulls not supported in dtype: " + dtype)
+
+    pdf = pd.DataFrame(dtype=dtype)
+    for i in range(num_cols):
+        colname = str(i)
+        data = pd.Series(np.random.randint(0, 26, num_rows)).astype(dtype)
+
+        if nulls == "some":
+            idx = np.random.choice(
+                num_rows, size=int(num_rows / 2), replace=False
+            )
+            data[idx] = np.nan
+        pdf[colname] = data
+
+    gdf = cudf.from_pandas(pdf)
+
+    if dtype == "category":
+        with pytest.raises(ValueError):
+            assert gdf.interleave_columns()
+    else:
+        got = gdf.interleave_columns()
+
+        expect = pd.Series(np.vstack(pdf.to_numpy()).reshape((-1,))).astype(
+            dtype
+        )
+
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("num_cols", [1, 2, 10])
+@pytest.mark.parametrize("num_rows", [1, 2, 1000])
+@pytest.mark.parametrize("count", [1, 2, 10])
+@pytest.mark.parametrize("dtype", ALL_TYPES)
+@pytest.mark.parametrize("nulls", ["none", "some"])
+def test_tile(nulls, num_cols, num_rows, dtype, count):
+
+    if dtype not in ["float32", "float64"] and nulls in ["some"]:
+        pytest.skip(reason="nulls not supported in dtype: " + dtype)
+
+    pdf = pd.DataFrame(dtype=dtype)
+    for i in range(num_cols):
+        colname = str(i)
+        data = pd.Series(np.random.randint(num_cols, 26, num_rows)).astype(
+            dtype
+        )
+
+        if nulls == "some":
+            idx = np.random.choice(
+                num_rows, size=int(num_rows / 2), replace=False
+            )
+            data[idx] = np.nan
+        pdf[colname] = data
+
+    gdf = cudf.from_pandas(pdf)
+
+    got = gdf.tile(count)
+    expect = pd.DataFrame(pd.concat([pdf] * count))
+
+    assert_eq(expect, got)
+
+
+def _prepare_merge_sorted_test(
+    size,
+    nparts,
+    keys,
+    add_null=False,
+    na_position="last",
+    ascending=True,
+    series=False,
+    index=False,
+):
+    if index:
+        df = (
+            cudf.datasets.timeseries()[:size]
+            .reset_index(drop=False)
+            .set_index(keys, drop=True)
+        )
+    else:
+        df = cudf.datasets.timeseries()[:size].reset_index(drop=False)
+        if add_null:
+            df.iloc[1, df.columns.get_loc(keys[0])] = None
+    chunk = int(size / nparts)
+    indices = [i * chunk for i in range(0, nparts)] + [size]
+    if index:
+        dfs = [
+            df.iloc[indices[i] : indices[i + 1]]
+            .copy()
+            .sort_index(ascending=ascending)
+            for i in range(nparts)
+        ]
+    elif series:
+        df = df[keys[0]]
+        dfs = [
+            df.iloc[indices[i] : indices[i + 1]]
+            .copy()
+            .sort_values(na_position=na_position, ascending=ascending)
+            for i in range(nparts)
+        ]
+    else:
+        dfs = [
+            df.iloc[indices[i] : indices[i + 1]]
+            .copy()
+            .sort_values(keys, na_position=na_position, ascending=ascending)
+            for i in range(nparts)
+        ]
+    return df, dfs
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("keys", [None, ["id"], ["name", "timestamp"]])
+@pytest.mark.parametrize("nparts", [2, 10])
+def test_df_merge_sorted(nparts, keys, na_position, ascending):
+    size = 100
+    keys_1 = keys or ["timestamp"]
+    # Null values NOT currently supported with Categorical data
+    # or when `ascending=False`
+    add_null = keys_1[0] not in ("name")
+    df, dfs = _prepare_merge_sorted_test(
+        size,
+        nparts,
+        keys_1,
+        add_null=add_null,
+        na_position=na_position,
+        ascending=ascending,
+    )
+
+    expect = df.sort_values(
+        keys_1, na_position=na_position, ascending=ascending
+    )
+    result = cudf.core.reshape._merge_sorted(
+        dfs, keys=keys, na_position=na_position, ascending=ascending
+    )
+    if keys:
+        expect = expect[keys]
+        result = result[keys]
+
+    assert expect.index.dtype == result.index.dtype
+    assert_eq(expect.reset_index(drop=True), result.reset_index(drop=True))
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("index", ["id", "x"])
+@pytest.mark.parametrize("nparts", [2, 10])
+def test_df_merge_sorted_index(nparts, index, ascending):
+    size = 100
+    df, dfs = _prepare_merge_sorted_test(
+        size, nparts, index, ascending=ascending, index=True
+    )
+
+    expect = df.sort_index(ascending=ascending)
+    result = cudf.core.reshape._merge_sorted(
+        dfs, by_index=True, ascending=ascending
+    )
+
+    assert_eq(expect.index, result.index)
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("keys", [None, ["name", "timestamp"]])
+def test_df_merge_sorted_ignore_index(keys, na_position, ascending):
+    size = 100
+    nparts = 3
+    keys_1 = keys or ["timestamp"]
+    # Null values NOT currently supported with Categorical data
+    # or when `ascending=False`
+    add_null = keys_1[0] not in ("name")
+    df, dfs = _prepare_merge_sorted_test(
+        size,
+        nparts,
+        keys_1,
+        add_null=add_null,
+        na_position=na_position,
+        ascending=ascending,
+    )
+
+    expect = df.sort_values(
+        keys_1, na_position=na_position, ascending=ascending
+    )
+    result = cudf.core.reshape._merge_sorted(
+        dfs,
+        keys=keys,
+        na_position=na_position,
+        ascending=ascending,
+        ignore_index=True,
+    )
+    if keys:
+        expect = expect[keys]
+        result = result[keys]
+
+    assert_eq(expect.reset_index(drop=True), result)
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("key", ["id", "name", "timestamp"])
+@pytest.mark.parametrize("nparts", [2, 10])
+def test_series_merge_sorted(nparts, key, na_position, ascending):
+    size = 100
+    df, dfs = _prepare_merge_sorted_test(
+        size,
+        nparts,
+        [key],
+        na_position=na_position,
+        ascending=ascending,
+        series=True,
+    )
+
+    expect = df.sort_values(na_position=na_position, ascending=ascending)
+    result = cudf.core.reshape._merge_sorted(
+        dfs, na_position=na_position, ascending=ascending
+    )
+
+    assert_eq(expect.reset_index(drop=True), result.reset_index(drop=True))
+
+
+@pytest.mark.parametrize(
+    "index, column, data",
+    [
+        ([], [], []),
+        ([0], [0], [0]),
+        ([0, 0], [0, 1], [1, 2.0]),
+        ([0, 1], [0, 0], [1, 2.0]),
+        ([0, 1], [0, 1], [1, 2.0]),
+        (["a", "a", "b", "b"], ["c", "d", "c", "d"], [1, 2, 3, 4]),
+        (
+            ["a", "a", "b", "b", "a"],
+            ["c", "d", "c", "d", "e"],
+            [1, 2, 3, 4, 5],
+        ),
+    ],
+)
+def test_pivot_simple(index, column, data):
+    pdf = pd.DataFrame({"index": index, "column": column, "data": data})
+    gdf = cudf.from_pandas(pdf)
+
+    # In pandas 2.0 this will be a failure because pandas will require all of
+    # these as keyword arguments. Matching that check in cudf is a bit
+    # cumbersome and not worth the effort to match the warning, so this code
+    # just catches pandas's warning (rather than updating the signature) so
+    # that when it starts failing we know to update our impl of pivot.
+    with pytest.warns(FutureWarning):
+        expect = pdf.pivot("index", "column")
+    got = gdf.pivot("index", "column")
+
+    check_index_and_columns = expect.shape != (0, 0)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+        check_index_type=check_index_and_columns,
+        check_column_type=check_index_and_columns,
+    )
+
+
+def test_pivot_multi_values():
+    # from Pandas docs:
+    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html
+    pdf = pd.DataFrame(
+        {
+            "foo": ["one", "one", "one", "two", "two", "two"],
+            "bar": ["A", "B", "C", "A", "B", "C"],
+            "baz": [1, 2, 3, 4, 5, 6],
+            "zoo": ["x", "y", "z", "q", "w", "t"],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(
+        pdf.pivot(index="foo", columns="bar", values=["baz", "zoo"]),
+        gdf.pivot(index="foo", columns="bar", values=["baz", "zoo"]),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "values", ["z", "z123", ["z123"], ["z", "z123", "123z"]]
+)
+def test_pivot_values(values):
+    data = [
+        ["A", "a", 0, 0, 0],
+        ["A", "b", 1, 1, 1],
+        ["A", "c", 2, 2, 2],
+        ["B", "a", 0, 0, 0],
+        ["B", "b", 1, 1, 1],
+        ["B", "c", 2, 2, 2],
+        ["C", "a", 0, 0, 0],
+        ["C", "b", 1, 1, 1],
+        ["C", "c", 2, 2, 2],
+    ]
+    columns = ["x", "y", "z", "z123", "123z"]
+    pdf = pd.DataFrame(data, columns=columns)
+    cdf = cudf.DataFrame(data, columns=columns)
+    expected = pd.pivot(pdf, index="x", columns="y", values=values)
+    actual = cudf.pivot(cdf, index="x", columns="y", values=values)
+    assert_eq(
+        expected,
+        actual,
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "level",
+    [
+        0,
+        pytest.param(
+            1,
+            marks=pytest_xfail(
+                reason="Categorical column indexes not supported"
+            ),
+        ),
+        2,
+        "foo",
+        pytest.param(
+            "bar",
+            marks=pytest_xfail(
+                reason="Categorical column indexes not supported"
+            ),
+        ),
+        "baz",
+        [],
+        pytest.param(
+            [0, 1],
+            marks=pytest_xfail(
+                reason="Categorical column indexes not supported"
+            ),
+        ),
+        ["foo"],
+        pytest.param(
+            ["foo", "bar"],
+            marks=pytest_xfail(
+                reason="Categorical column indexes not supported"
+            ),
+        ),
+        pytest.param(
+            [0, 1, 2],
+            marks=pytest_xfail(reason="Pandas behaviour unclear"),
+        ),
+        pytest.param(
+            ["foo", "bar", "baz"],
+            marks=pytest_xfail(reason="Pandas behaviour unclear"),
+        ),
+    ],
+)
+def test_unstack_multiindex(level):
+    pdf = pd.DataFrame(
+        {
+            "foo": ["one", "one", "one", "two", "two", "two"],
+            "bar": pd.Categorical(["A", "B", "C", "A", "B", "C"]),
+            "baz": [1, 2, 3, 4, 5, 6],
+            "zoo": ["x", "y", "z", "q", "w", "t"],
+        }
+    ).set_index(["foo", "bar", "baz"])
+    gdf = cudf.from_pandas(pdf)
+    assert_eq(
+        pdf.unstack(level=level),
+        gdf.unstack(level=level),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [{"A": [1.0, 2.0, 3.0, 4.0, 5.0], "B": [11.0, 12.0, 13.0, 14.0, 15.0]}],
+)
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.Index(range(0, 5), name=None),
+        pd.Index(range(0, 5), name="row_index"),
+        pytest.param(
+            pd.CategoricalIndex(["d", "e", "f", "g", "h"]),
+            marks=pytest_xfail(
+                reason="Categorical column indexes not supported"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "col_idx",
+    [
+        pd.Index(["a", "b"], name=None),
+        pd.Index(["a", "b"], name="col_index"),
+        pd.MultiIndex.from_tuples([("c", 1), ("c", 2)], names=[None, None]),
+        pd.MultiIndex.from_tuples(
+            [("c", 1), ("c", 2)], names=["col_index1", "col_index2"]
+        ),
+    ],
+)
+def test_unstack_index(data, index, col_idx):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    pdf.index = index
+    pdf.columns = col_idx
+
+    gdf.index = cudf.from_pandas(index)
+    gdf.columns = cudf.from_pandas(col_idx)
+
+    assert_eq(pdf.unstack(), gdf.unstack())
+
+
+def test_unstack_index_invalid():
+    gdf = cudf.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Calling unstack() on single index dataframe with "
+            "different column datatype is not supported."
+        ),
+    ):
+        gdf.unstack()
+
+
+def test_pivot_duplicate_error():
+    gdf = cudf.DataFrame(
+        {"a": [0, 1, 2, 2], "b": [1, 2, 3, 3], "d": [1, 2, 3, 4]}
+    )
+    with pytest.raises(ValueError):
+        gdf.pivot(index="a", columns="b")
+    with pytest.raises(ValueError):
+        gdf.pivot(index="b", columns="a")
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "A": ["one", "one", "two", "three"] * 6,
+            "B": ["A", "B", "C"] * 8,
+            "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+            "D": np.random.randn(24),
+            "E": np.random.randn(24),
+        }
+    ],
+)
+@pytest.mark.parametrize(
+    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
+)
+@pytest.mark.parametrize("fill_value", [0])
+def test_pivot_table_simple(data, aggfunc, fill_value):
+    pdf = pd.DataFrame(data)
+    expected = pd.pivot_table(
+        pdf,
+        values=["D", "E"],
+        index=["A", "B"],
+        columns=["C"],
+        aggfunc=aggfunc,
+        fill_value=fill_value,
+    )
+    cdf = cudf.DataFrame(data)
+    actual = cudf.pivot_table(
+        cdf,
+        values=["D", "E"],
+        index=["A", "B"],
+        columns=["C"],
+        aggfunc=aggfunc,
+        fill_value=fill_value,
+    )
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "A": ["one", "one", "two", "three"] * 6,
+            "B": ["A", "B", "C"] * 8,
+            "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 4,
+            "D": np.random.randn(24),
+            "E": np.random.randn(24),
+        }
+    ],
+)
+@pytest.mark.parametrize(
+    "aggfunc", ["mean", "count", {"D": "sum", "E": "count"}]
+)
+@pytest.mark.parametrize("fill_value", [0])
+def test_dataframe_pivot_table_simple(data, aggfunc, fill_value):
+    pdf = pd.DataFrame(data)
+    expected = pdf.pivot_table(
+        values=["D", "E"],
+        index=["A", "B"],
+        columns=["C"],
+        aggfunc=aggfunc,
+        fill_value=fill_value,
+    )
+    cdf = cudf.DataFrame(data)
+    actual = cdf.pivot_table(
+        values=["D", "E"],
+        index=["A", "B"],
+        columns=["C"],
+        aggfunc=aggfunc,
+        fill_value=fill_value,
+    )
+    assert_eq(expected, actual, check_dtype=False)
+
+
+def test_crosstab_simple():
+    a = np.array(
+        [
+            "foo",
+            "foo",
+            "foo",
+            "foo",
+            "bar",
+            "bar",
+            "bar",
+            "bar",
+            "foo",
+            "foo",
+            "foo",
+        ],
+        dtype=object,
+    )
+    b = np.array(
+        [
+            "one",
+            "one",
+            "one",
+            "two",
+            "one",
+            "one",
+            "one",
+            "two",
+            "two",
+            "two",
+            "one",
+        ],
+        dtype=object,
+    )
+    c = np.array(
+        [
+            "dull",
+            "dull",
+            "shiny",
+            "dull",
+            "dull",
+            "shiny",
+            "shiny",
+            "dull",
+            "shiny",
+            "shiny",
+            "shiny",
+        ],
+        dtype=object,
+    )
+    expected = pd.crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"])
+    actual = cudf.crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"])
+    assert_eq(expected, actual, check_dtype=False)
diff --git a/python/cudf/cudf/tests/test_rolling.py b/python/cudf/cudf/tests/test_rolling.py
new file mode 100644
index 0000000..43fa83e
--- /dev/null
+++ b/python/cudf/cudf/tests/test_rolling.py
@@ -0,0 +1,562 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import math
+from contextlib import contextmanager
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_150, PANDAS_LT_140
+from cudf.testing._utils import (
+    _create_pandas_series_float64_default,
+    assert_eq,
+)
+from cudf.testing.dataset_generator import rand_dataframe
+
+
+@contextmanager
+def _hide_pandas_rolling_min_periods_warning(agg):
+    if agg == "count":
+        with pytest.warns(
+            FutureWarning,
+            match="min_periods=None will default to the size of window "
+            "consistent with other methods in a future version. Specify "
+            "min_periods=0 instead.",
+        ):
+            yield
+    else:
+        yield
+
+
+@pytest.mark.parametrize(
+    "data,index",
+    [
+        ([], []),
+        ([1, 1, 1, 1], None),
+        ([1, 2, 3, 4], pd.date_range("2001-01-01", "2001-01-04")),
+        ([1, 2, 4, 9, 9, 4], ["a", "b", "c", "d", "e", "f"]),
+    ],
+)
+@pytest.mark.parametrize(
+    "agg", ["sum", "min", "max", "mean", "count", "std", "var"]
+)
+@pytest.mark.parametrize("nulls", ["none", "one", "some", "all"])
+@pytest.mark.parametrize("center", [True, False])
+def test_rolling_series_basic(data, index, agg, nulls, center):
+    rng = np.random.default_rng(1)
+
+    if len(data) > 0:
+        if nulls == "one":
+            p = rng.integers(0, len(data))
+            data[p] = np.nan
+        elif nulls == "some":
+            p1, p2 = rng.integers(0, len(data), (2,))
+            data[p1] = np.nan
+            data[p2] = np.nan
+        elif nulls == "all":
+            data = [np.nan] * len(data)
+
+    psr = _create_pandas_series_float64_default(data, index=index)
+    gsr = cudf.Series(psr)
+    for window_size in range(1, len(data) + 1):
+        for min_periods in range(1, window_size + 1):
+            expect = getattr(
+                psr.rolling(window_size, min_periods, center), agg
+            )().fillna(-1)
+            got = getattr(
+                gsr.rolling(window_size, min_periods, center), agg
+            )().fillna(-1)
+            assert_eq(expect, got, check_dtype=False, check_freq=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [], "b": []},
+        {"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]},
+        {"a": [1, 2, 4, 9, 9, 4], "b": [1, 2, 4, 9, 9, 4]},
+        {
+            "a": np.array([1, 2, 4, 9, 9, 4]),
+            "b": np.array([1.5, 2.2, 2.2, 8.0, 9.1, 4.2]),
+        },
+    ],
+)
+@pytest.mark.parametrize(
+    "agg", ["sum", "min", "max", "mean", "count", "std", "var"]
+)
+@pytest.mark.parametrize("nulls", ["none", "one", "some", "all"])
+@pytest.mark.parametrize("center", [True, False])
+def test_rolling_dataframe_basic(data, agg, nulls, center):
+    rng = np.random.default_rng(0)
+    pdf = pd.DataFrame(data)
+
+    if len(pdf) > 0:
+        for col_idx in range(len(pdf.columns)):
+            if nulls == "one":
+                p = rng.integers(0, len(data))
+                pdf.iloc[p, col_idx] = np.nan
+            elif nulls == "some":
+                p1, p2 = rng.integers(0, len(data), (2,))
+                pdf.iloc[p1, col_idx] = np.nan
+                pdf.iloc[p2, col_idx] = np.nan
+            elif nulls == "all":
+                pdf.iloc[:, col_idx] = np.nan
+
+    gdf = cudf.from_pandas(pdf)
+    for window_size in range(1, len(data) + 1):
+        for min_periods in range(1, window_size + 1):
+            expect = getattr(
+                pdf.rolling(window_size, min_periods, center), agg
+            )().fillna(-1)
+            got = getattr(
+                gdf.rolling(window_size, min_periods, center), agg
+            )().fillna(-1)
+            assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "agg",
+    [
+        pytest.param("sum"),
+        pytest.param("min"),
+        pytest.param("max"),
+        pytest.param("mean"),
+        pytest.param("count"),
+        pytest.param("std"),
+        pytest.param("var"),
+    ],
+)
+def test_rolling_with_offset(agg):
+    psr = pd.Series(
+        [1, 2, 4, 4, np.nan, 9],
+        index=[
+            pd.Timestamp("20190101 09:00:00"),
+            pd.Timestamp("20190101 09:00:01"),
+            pd.Timestamp("20190101 09:00:02"),
+            pd.Timestamp("20190101 09:00:04"),
+            pd.Timestamp("20190101 09:00:07"),
+            pd.Timestamp("20190101 09:00:08"),
+        ],
+    )
+    gsr = cudf.from_pandas(psr)
+    assert_eq(
+        getattr(psr.rolling("2s"), agg)().fillna(-1),
+        getattr(gsr.rolling("2s"), agg)().fillna(-1),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize("agg", ["std", "var"])
+@pytest.mark.parametrize("ddof", [0, 1])
+@pytest.mark.parametrize("center", [True, False])
+@pytest.mark.parametrize("seed", [100, 2000])
+@pytest.mark.parametrize("window_size", [2, 10, 100])
+def test_rolling_var_std_large(agg, ddof, center, seed, window_size):
+
+    iupper_bound = math.sqrt(np.iinfo(np.int64).max / window_size)
+    ilower_bound = -math.sqrt(abs(np.iinfo(np.int64).min) / window_size)
+
+    fupper_bound = math.sqrt(np.finfo(np.float64).max / window_size)
+    flower_bound = -math.sqrt(abs(np.finfo(np.float64).min) / window_size)
+
+    n_rows = 1_000
+    data = rand_dataframe(
+        dtypes_meta=[
+            {
+                "dtype": "int64",
+                "null_frequency": 0.4,
+                "cardinality": n_rows,
+                "min_bound": ilower_bound,
+                "max_bound": iupper_bound,
+            },
+            {
+                "dtype": "float64",
+                "null_frequency": 0.4,
+                "cardinality": n_rows,
+                "min_bound": flower_bound,
+                "max_bound": fupper_bound,
+            },
+            {
+                "dtype": "decimal64",
+                "null_frequency": 0.4,
+                "cardinality": n_rows,
+                "min_bound": ilower_bound,
+                "max_bound": iupper_bound,
+            },
+        ],
+        rows=n_rows,
+        use_threads=False,
+        seed=seed,
+    )
+    pdf = data.to_pandas()
+    gdf = cudf.from_pandas(pdf)
+
+    expect = getattr(pdf.rolling(window_size, 1, center), agg)(ddof=ddof)
+    got = getattr(gdf.rolling(window_size, 1, center), agg)(ddof=ddof)
+
+    import platform
+
+    if platform.machine() == "aarch64":
+        # Due to pandas-37051, pandas rolling var/std on uniform window is
+        # not reliable. Skipping these rows when comparing.
+        for col in expect:
+            mask = (got[col].fillna(-1) != 0).to_pandas()
+            expect[col] = expect[col][mask]
+            got[col] = got[col][mask]
+            assert_eq(expect[col], got[col], check_freq=False)
+    else:
+        assert_eq(expect, got, check_freq=False)
+
+
+def test_rolling_var_uniform_window():
+    """
+    Pandas adopts an online variance calculation algorithm. This gives a
+    floating point artifact.
+
+    In cudf, each window is computed independently from the previous window,
+    this gives better numeric precision.
+    """
+
+    s = pd.Series([1e8, 5, 5, 5])
+    expected = s.rolling(3).var()
+    got = cudf.from_pandas(s).rolling(3).var()
+
+    assert_eq(expected, got)
+
+
+def test_rolling_count_with_offset():
+    """
+    This test covers the xfail case from test_rolling_with_offset["count"].
+    It is expected that count should return a non-Nan value, even if
+    the counted value is a Nan, unless the min-periods condition
+    is not met.
+    This behaviour is consistent with counts for rolling-windows,
+    in the non-offset window case.
+    """
+    psr = pd.Series(
+        [1, 2, 4, 4, np.nan, 9],
+        index=[
+            pd.Timestamp("20190101 09:00:00"),
+            pd.Timestamp("20190101 09:00:01"),
+            pd.Timestamp("20190101 09:00:02"),
+            pd.Timestamp("20190101 09:00:04"),
+            pd.Timestamp("20190101 09:00:07"),
+            pd.Timestamp("20190101 09:00:08"),
+        ],
+    )
+    gsr = cudf.from_pandas(psr)
+    assert_eq(
+        getattr(gsr.rolling("2s"), "count")().fillna(-1),
+        pd.Series(
+            [1, 2, 2, 1, 0, 1],
+            index=[
+                pd.Timestamp("20190101 09:00:00"),
+                pd.Timestamp("20190101 09:00:01"),
+                pd.Timestamp("20190101 09:00:02"),
+                pd.Timestamp("20190101 09:00:04"),
+                pd.Timestamp("20190101 09:00:07"),
+                pd.Timestamp("20190101 09:00:08"),
+            ],
+        ),
+        check_dtype=False,
+    )
+
+
+def test_rolling_getattr():
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.rolling(2).a.sum().fillna(-1),
+        gdf.rolling(2).a.sum().fillna(-1),
+        check_dtype=False,
+    )
+
+
+def test_rolling_getitem():
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]})
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.rolling(2)["a"].sum().fillna(-1),
+        gdf.rolling(2)["a"].sum().fillna(-1),
+        check_dtype=False,
+    )
+    assert_eq(
+        pdf.rolling(2)["a", "b"].sum().fillna(-1),
+        gdf.rolling(2)["a", "b"].sum().fillna(-1),
+        check_dtype=False,
+    )
+    assert_eq(
+        pdf.rolling(2)[["a", "b"]].sum().fillna(-1),
+        gdf.rolling(2)["a", "b"].sum().fillna(-1),
+        check_dtype=False,
+    )
+
+
+def test_rolling_getitem_window():
+    index = pd.DatetimeIndex(
+        pd.date_range("2000-01-01", "2000-01-02", freq="1h")
+    )
+    pdf = pd.DataFrame({"x": np.arange(len(index))}, index=index)
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(
+        pdf.rolling("2h").x.mean(),
+        gdf.rolling("2h").x.mean(),
+        check_freq=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data,index", [([1.2, 4.5, 5.9, 2.4, 9.3, 7.1], None), ([], [])]
+)
+@pytest.mark.parametrize("center", [True, False])
+def test_rollling_series_numba_udf_basic(data, index, center):
+
+    psr = _create_pandas_series_float64_default(data, index=index)
+    gsr = cudf.from_pandas(psr)
+
+    def some_func(A):
+        b = 0
+        for a in A:
+            b = max(b, math.sqrt(a))
+        return b
+
+    for window_size in range(1, len(data) + 1):
+        for min_periods in range(1, window_size + 1):
+            assert_eq(
+                psr.rolling(window_size, min_periods, center)
+                .apply(some_func)
+                .fillna(-1),
+                gsr.rolling(window_size, min_periods, center)
+                .apply(some_func)
+                .fillna(-1),
+                check_dtype=False,
+            )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [], "b": []},
+        {"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]},
+        {"a": [1, 2, 4, 9, 9, 4], "b": [1, 2, 4, 9, 9, 4]},
+        {
+            "a": np.array([1, 2, 4, 9, 9, 4]),
+            "b": np.array([1.5, 2.2, 2.2, 8.0, 9.1, 4.2]),
+        },
+    ],
+)
+@pytest.mark.parametrize("center", [True, False])
+def test_rolling_dataframe_numba_udf_basic(data, center):
+
+    pdf = pd.DataFrame(data)
+    gdf = cudf.from_pandas(pdf)
+
+    def some_func(A):
+        b = 0
+        for a in A:
+            b = b + a**2
+        return b / len(A)
+
+    for window_size in range(1, len(data) + 1):
+        for min_periods in range(1, window_size + 1):
+            assert_eq(
+                pdf.rolling(window_size, min_periods, center)
+                .apply(some_func)
+                .fillna(-1),
+                gdf.rolling(window_size, min_periods, center)
+                .apply(some_func)
+                .fillna(-1),
+                check_dtype=False,
+            )
+
+
+def test_rolling_numba_udf_with_offset():
+    psr = pd.Series(
+        [1, 2, 4, 4, 8, 9],
+        index=[
+            pd.Timestamp("20190101 09:00:00"),
+            pd.Timestamp("20190101 09:00:01"),
+            pd.Timestamp("20190101 09:00:02"),
+            pd.Timestamp("20190101 09:00:04"),
+            pd.Timestamp("20190101 09:00:07"),
+            pd.Timestamp("20190101 09:00:08"),
+        ],
+    )
+    gsr = cudf.from_pandas(psr)
+
+    def some_func(A):
+        b = 0
+        for a in A:
+            b = b + a
+        return b / len(A)
+
+    assert_eq(
+        psr.rolling("2s").apply(some_func).fillna(-1),
+        gsr.rolling("2s").apply(some_func).fillna(-1),
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "agg", ["sum", "min", "max", "mean", "count", "var", "std"]
+)
+def test_rolling_groupby_simple(agg):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [1, 2, 3, 1, 2, 3, 1, 2, 3, 1],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    for window_size in range(1, len(pdf) + 1):
+        with _hide_pandas_rolling_min_periods_warning(agg):
+            expect = getattr(
+                pdf.groupby("a").rolling(window_size), agg
+            )().fillna(-1)
+        got = getattr(gdf.groupby("a").rolling(window_size), agg)().fillna(-1)
+        assert_eq(expect, got, check_dtype=False)
+
+    pdf = pd.DataFrame(
+        {"a": [1, 1, 1, 2, 2], "b": [1, 1, 2, 2, 3], "c": [1, 2, 3, 4, 5]}
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    for window_size in range(1, len(pdf) + 1):
+        with _hide_pandas_rolling_min_periods_warning(agg):
+            expect = getattr(
+                pdf.groupby("a").rolling(window_size), agg
+            )().fillna(-1)
+        got = getattr(gdf.groupby("a").rolling(window_size), agg)().fillna(-1)
+        assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "agg", ["sum", "min", "max", "mean", "count", "var", "std"]
+)
+def test_rolling_groupby_multi(agg):
+    pdf = pd.DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [0, 0, 1, 1, 0, 1, 2, 1, 1, 0],
+            "c": [1, 2, 3, 1, 2, 3, 1, 2, 3, 1],
+        }
+    )
+    gdf = cudf.from_pandas(pdf)
+
+    for window_size in range(1, len(pdf) + 1):
+        with _hide_pandas_rolling_min_periods_warning(agg):
+            expect = getattr(
+                pdf.groupby(["a", "b"], sort=True).rolling(window_size), agg
+            )().fillna(-1)
+        got = getattr(
+            gdf.groupby(["a", "b"], sort=True).rolling(window_size), agg
+        )().fillna(-1)
+        assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "agg", ["sum", "min", "max", "mean", "count", "var", "std"]
+)
+@pytest.mark.parametrize(
+    "window_size", ["1d", "2d", "3d", "4d", "5d", "6d", "7d"]
+)
+def test_rolling_groupby_offset(agg, window_size):
+    pdf = pd.DataFrame(
+        {
+            "date": pd.date_range(start="2016-01-01", periods=7, freq="D"),
+            "group": [1, 2, 2, 1, 1, 2, 1],
+            "val": [5, 6, 7, 8, 1, 2, 3],
+        }
+    ).set_index("date")
+    gdf = cudf.from_pandas(pdf)
+    expect = getattr(pdf.groupby("group").rolling(window_size), agg)().fillna(
+        -1
+    )
+    got = getattr(gdf.groupby("group").rolling(window_size), agg)().fillna(-1)
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_rolling_custom_index_support():
+    from pandas.api.indexers import BaseIndexer
+
+    class CustomIndexer(BaseIndexer):
+        def custom_get_window_bounds(
+            self, num_values, min_periods, center, closed, step=None
+        ):
+            start = np.empty(num_values, dtype=np.int64)
+            end = np.empty(num_values, dtype=np.int64)
+
+            for i in range(num_values):
+                if self.use_expanding[i]:
+                    start[i] = 0
+                    end[i] = i + 1
+                else:
+                    start[i] = i
+                    end[i] = i + self.window_size
+
+            return start, end
+
+        if PANDAS_GE_150:
+
+            def get_window_bounds(
+                self, num_values, min_periods, center, closed, step
+            ):
+                return self.custom_get_window_bounds(
+                    num_values, min_periods, center, closed, step
+                )
+
+        else:
+
+            def get_window_bounds(
+                self, num_values, min_periods, center, closed
+            ):
+                return self.custom_get_window_bounds(
+                    num_values, min_periods, center, closed
+                )
+
+    use_expanding = [True, False, True, False, True]
+    indexer = CustomIndexer(window_size=1, use_expanding=use_expanding)
+
+    df = pd.DataFrame({"values": range(5)})
+    gdf = cudf.from_pandas(df)
+
+    expected = df.rolling(window=indexer).sum()
+    actual = gdf.rolling(window=indexer).sum()
+
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "indexer",
+    [
+        pd.api.indexers.FixedForwardWindowIndexer(window_size=2),
+        pd.core.window.expanding.ExpandingIndexer(),
+        pd.core.window.indexers.FixedWindowIndexer(window_size=3)
+        if PANDAS_LT_140
+        else pd.core.indexers.objects.FixedWindowIndexer(window_size=3),
+    ],
+)
+def test_rolling_indexer_support(indexer):
+    df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
+    gdf = cudf.from_pandas(df)
+
+    expected = df.rolling(window=indexer, min_periods=2).sum()
+    actual = gdf.rolling(window=indexer, min_periods=2).sum()
+
+    assert_eq(expected, actual)
+
+
+def test_rolling_series():
+    df = cudf.DataFrame({"a": range(0, 100), "b": [10, 20, 30, 40, 50] * 20})
+    pdf = df.to_pandas()
+
+    expected = pdf.groupby("b")["a"].rolling(5).mean()
+    actual = df.groupby("b")["a"].rolling(5).mean()
+
+    assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/test_s3.py b/python/cudf/cudf/tests/test_s3.py
new file mode 100644
index 0000000..d54a2ea
--- /dev/null
+++ b/python/cudf/cudf/tests/test_s3.py
@@ -0,0 +1,535 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import os
+import socket
+from contextlib import contextmanager
+from io import BytesIO
+
+import numpy as np
+import pandas as pd
+import pyarrow.fs as pa_fs
+import pytest
+from fsspec.core import get_fs_token_paths
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+moto = pytest.importorskip("moto", minversion="3.1.6")
+boto3 = pytest.importorskip("boto3")
+s3fs = pytest.importorskip("s3fs")
+
+ThreadedMotoServer = pytest.importorskip("moto.server").ThreadedMotoServer
+
+
+@pytest.fixture(scope="session")
+def endpoint_ip():
+    return "127.0.0.1"
+
+
+@pytest.fixture(scope="session")
+def endpoint_port():
+    # Return a free port per worker session.
+    sock = socket.socket()
+    sock.bind(("127.0.0.1", 0))
+    port = sock.getsockname()[1]
+    sock.close()
+    return port
+
+
+@contextmanager
+def ensure_safe_environment_variables():
+    """
+    Get a context manager to safely set environment variables
+    All changes will be undone on close, hence environment variables set
+    within this contextmanager will neither persist nor change global state.
+    """
+    saved_environ = dict(os.environ)
+    try:
+        yield
+    finally:
+        os.environ.clear()
+        os.environ.update(saved_environ)
+
+
+@pytest.fixture(scope="session")
+def s3_base(endpoint_ip, endpoint_port):
+    """
+    Fixture to set up moto server in separate process
+    """
+    with ensure_safe_environment_variables():
+        # Fake aws credentials exported to prevent botocore looking for
+        # system aws credentials, https://github.com/spulec/moto/issues/1793
+        os.environ["AWS_ACCESS_KEY_ID"] = "foobar_key"
+        os.environ["AWS_SECRET_ACCESS_KEY"] = "foobar_secret"
+        os.environ["S3FS_LOGGING_LEVEL"] = "DEBUG"
+        os.environ["AWS_SECURITY_TOKEN"] = "foobar_security_token"
+        os.environ["AWS_SESSION_TOKEN"] = "foobar_session_token"
+        os.environ["AWS_DEFAULT_REGION"] = "us-east-1"
+
+        # Launching moto in server mode, i.e., as a separate process
+        # with an S3 endpoint on localhost
+
+        endpoint_uri = f"http://{endpoint_ip}:{endpoint_port}/"
+
+        server = ThreadedMotoServer(ip_address=endpoint_ip, port=endpoint_port)
+        server.start()
+        yield endpoint_uri
+        server.stop()
+
+
+@pytest.fixture()
+def s3so(endpoint_ip, endpoint_port):
+    """
+    Returns s3 storage options to pass to fsspec
+    """
+    endpoint_uri = f"http://{endpoint_ip}:{endpoint_port}/"
+
+    return {"client_kwargs": {"endpoint_url": endpoint_uri}}
+
+
+@contextmanager
+def s3_context(s3_base, bucket, files=None):
+    if files is None:
+        files = {}
+    with ensure_safe_environment_variables():
+        client = boto3.client("s3", endpoint_url=s3_base)
+        client.create_bucket(Bucket=bucket, ACL="public-read-write")
+        for f, data in files.items():
+            client.put_object(Bucket=bucket, Key=f, Body=data)
+
+        yield s3fs.S3FileSystem(client_kwargs={"endpoint_url": s3_base})
+
+        for f, data in files.items():
+            try:
+                client.delete_object(Bucket=bucket, Key=f)
+            except Exception:
+                pass
+
+
+@pytest.fixture
+def pdf(scope="module"):
+    df = pd.DataFrame()
+    df["Integer"] = np.array([2345, 11987, 9027, 9027])
+    df["Float"] = np.array([9.001, 8.343, 6, 2.781])
+    df["Integer2"] = np.array([2345, 106, 2088, 789277])
+    df["String"] = np.array(["Alpha", "Beta", "Gamma", "Delta"])
+    df["Boolean"] = np.array([True, False, True, False])
+    return df
+
+
+@pytest.fixture
+def pdf_ext(scope="module"):
+    size = 100
+    df = pd.DataFrame()
+    df["Integer"] = np.array([i for i in range(size)])
+    df["List"] = [[i] for i in range(size)]
+    df["Struct"] = [{"a": i} for i in range(size)]
+    df["String"] = (["Alpha", "Beta", "Gamma", "Delta"] * (-(size // -4)))[
+        :size
+    ]
+    return df
+
+
+@pytest.mark.parametrize("bytes_per_thread", [32, 1024])
+def test_read_csv(s3_base, s3so, pdf, bytes_per_thread):
+    # Write to buffer
+    fname = "test_csv_reader.csv"
+    bucket = "csv"
+    buffer = pdf.to_csv(index=False)
+
+    # Use fsspec file object
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got = cudf.read_csv(
+            f"s3://{bucket}/{fname}",
+            storage_options=s3so,
+            bytes_per_thread=bytes_per_thread,
+            use_python_file_object=False,
+        )
+    assert_eq(pdf, got)
+
+    # Use Arrow PythonFile object
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got = cudf.read_csv(
+            f"s3://{bucket}/{fname}",
+            storage_options=s3so,
+            use_python_file_object=True,
+        )
+    assert_eq(pdf, got)
+
+
+def test_read_csv_arrow_nativefile(s3_base, s3so, pdf):
+    # Write to buffer
+    fname = "test_csv_reader_arrow_nativefile.csv"
+    bucket = "csv"
+    buffer = pdf.to_csv(index=False)
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        fs = pa_fs.S3FileSystem(
+            endpoint_override=s3so["client_kwargs"]["endpoint_url"],
+        )
+        with fs.open_input_file(f"{bucket}/{fname}") as fil:
+            got = cudf.read_csv(fil)
+
+    assert_eq(pdf, got)
+
+
+@pytest.mark.parametrize("bytes_per_thread", [32, 1024])
+@pytest.mark.parametrize("use_python_file_object", [True, False])
+def test_read_csv_byte_range(
+    s3_base, s3so, pdf, bytes_per_thread, use_python_file_object
+):
+    # Write to buffer
+    fname = "test_csv_reader_byte_range.csv"
+    bucket = "csv"
+    buffer = pdf.to_csv(index=False)
+
+    # Use fsspec file object
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got = cudf.read_csv(
+            f"s3://{bucket}/{fname}",
+            storage_options=s3so,
+            byte_range=(74, 73),
+            bytes_per_thread=bytes_per_thread
+            if not use_python_file_object
+            else None,
+            header=None,
+            names=["Integer", "Float", "Integer2", "String", "Boolean"],
+            use_python_file_object=use_python_file_object,
+        )
+
+    assert_eq(pdf.iloc[-2:].reset_index(drop=True), got)
+
+
+@pytest.mark.parametrize("chunksize", [None, 3])
+def test_write_csv(s3_base, s3so, pdf, chunksize):
+    # Write to buffer
+    fname = "test_csv_writer.csv"
+    bucket = "csv"
+    gdf = cudf.from_pandas(pdf)
+    with s3_context(s3_base=s3_base, bucket=bucket) as s3fs:
+        gdf.to_csv(
+            f"s3://{bucket}/{fname}",
+            index=False,
+            chunksize=chunksize,
+            storage_options=s3so,
+        )
+        assert s3fs.exists(f"s3://{bucket}/{fname}")
+
+        # TODO: Update to use `storage_options` from pandas v1.2.0
+        got = pd.read_csv(s3fs.open(f"s3://{bucket}/{fname}"))
+
+    assert_eq(pdf, got)
+
+
+@pytest.mark.parametrize("bytes_per_thread", [32, 1024])
+@pytest.mark.parametrize("columns", [None, ["Float", "String"]])
+@pytest.mark.parametrize("precache", [None, "parquet"])
+@pytest.mark.parametrize("use_python_file_object", [True, False])
+def test_read_parquet(
+    s3_base,
+    s3so,
+    pdf,
+    bytes_per_thread,
+    columns,
+    precache,
+    use_python_file_object,
+):
+    fname = "test_parquet_reader.parquet"
+    bucket = "parquet"
+    buffer = BytesIO()
+    pdf.to_parquet(path=buffer)
+
+    # Check direct path handling
+    buffer.seek(0)
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got1 = cudf.read_parquet(
+            f"s3://{bucket}/{fname}",
+            open_file_options=(
+                {"precache_options": {"method": precache}}
+                if use_python_file_object
+                else None
+            ),
+            storage_options=s3so,
+            bytes_per_thread=bytes_per_thread,
+            columns=columns,
+            use_python_file_object=use_python_file_object,
+        )
+    expect = pdf[columns] if columns else pdf
+    assert_eq(expect, got1)
+
+    # Check fsspec file-object handling
+    buffer.seek(0)
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        fs = get_fs_token_paths(
+            f"s3://{bucket}/{fname}", storage_options=s3so
+        )[0]
+        with fs.open(f"s3://{bucket}/{fname}", mode="rb") as f:
+            got2 = cudf.read_parquet(
+                f,
+                bytes_per_thread=bytes_per_thread,
+                columns=columns,
+                use_python_file_object=use_python_file_object,
+            )
+    assert_eq(expect, got2)
+
+
+@pytest.mark.parametrize("bytes_per_thread", [32, 1024])
+@pytest.mark.parametrize("columns", [None, ["List", "Struct"]])
+@pytest.mark.parametrize("index", [None, "Integer"])
+def test_read_parquet_ext(
+    s3_base,
+    s3so,
+    pdf_ext,
+    bytes_per_thread,
+    columns,
+    index,
+):
+    fname = "test_parquet_reader_ext.parquet"
+    bucket = "parquet"
+    buffer = BytesIO()
+
+    if index:
+        pdf_ext.set_index(index).to_parquet(path=buffer)
+    else:
+        pdf_ext.to_parquet(path=buffer)
+
+    # Check direct path handling
+    buffer.seek(0)
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got1 = cudf.read_parquet(
+            f"s3://{bucket}/{fname}",
+            storage_options=s3so,
+            bytes_per_thread=bytes_per_thread,
+            columns=columns,
+        )
+    if index:
+        expect = (
+            pdf_ext.set_index(index)[columns]
+            if columns
+            else pdf_ext.set_index(index)
+        )
+    else:
+        expect = pdf_ext[columns] if columns else pdf_ext
+    assert_eq(expect, got1)
+
+
+def test_read_parquet_multi_file(s3_base, s3so, pdf):
+    fname_1 = "test_parquet_reader_multi_file_1.parquet"
+    buffer_1 = BytesIO()
+    pdf.to_parquet(path=buffer_1)
+    buffer_1.seek(0)
+
+    fname_2 = "test_parquet_reader_multi_file_2.parquet"
+    buffer_2 = BytesIO()
+    pdf.to_parquet(path=buffer_2)
+    buffer_2.seek(0)
+
+    bucket = "parquet"
+    with s3_context(
+        s3_base=s3_base,
+        bucket=bucket,
+        files={
+            fname_1: buffer_1,
+            fname_2: buffer_2,
+        },
+    ):
+        got = cudf.read_parquet(
+            [
+                f"s3://{bucket}/{fname_1}",
+                f"s3://{bucket}/{fname_2}",
+            ],
+            storage_options=s3so,
+        ).reset_index(drop=True)
+
+    expect = pd.concat([pdf, pdf], ignore_index=True)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("columns", [None, ["Float", "String"]])
+def test_read_parquet_arrow_nativefile(s3_base, s3so, pdf, columns):
+    # Write to buffer
+    fname = "test_parquet_reader_arrow_nativefile.parquet"
+    bucket = "parquet"
+    buffer = BytesIO()
+    pdf.to_parquet(path=buffer)
+    buffer.seek(0)
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        fs = pa_fs.S3FileSystem(
+            endpoint_override=s3so["client_kwargs"]["endpoint_url"],
+        )
+        with fs.open_input_file(f"{bucket}/{fname}") as fil:
+            got = cudf.read_parquet(fil, columns=columns)
+
+    expect = pdf[columns] if columns else pdf
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("precache", [None, "parquet"])
+def test_read_parquet_filters(s3_base, s3so, pdf_ext, precache):
+    fname = "test_parquet_reader_filters.parquet"
+    bucket = "parquet"
+    buffer = BytesIO()
+    pdf_ext.to_parquet(path=buffer)
+    buffer.seek(0)
+    filters = [("String", "==", "Omega")]
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got = cudf.read_parquet(
+            f"s3://{bucket}/{fname}",
+            storage_options=s3so,
+            filters=filters,
+            open_file_options={"precache_options": {"method": precache}},
+        )
+
+    # All row-groups should be filtered out
+    assert_eq(pdf_ext.iloc[:0], got.reset_index(drop=True))
+
+
+@pytest.mark.parametrize("partition_cols", [None, ["String"]])
+def test_write_parquet(s3_base, s3so, pdf, partition_cols):
+    fname_cudf = "test_parquet_writer_cudf"
+    fname_pandas = "test_parquet_writer_pandas"
+    bucket = "parquet"
+    gdf = cudf.from_pandas(pdf)
+
+    with s3_context(s3_base=s3_base, bucket=bucket) as s3fs:
+        gdf.to_parquet(
+            f"s3://{bucket}/{fname_cudf}",
+            partition_cols=partition_cols,
+            storage_options=s3so,
+        )
+        assert s3fs.exists(f"s3://{bucket}/{fname_cudf}")
+        pdf.to_parquet(
+            f"s3://{bucket}/{fname_pandas}",
+            partition_cols=partition_cols,
+            storage_options=s3so,
+        )
+        assert s3fs.exists(f"s3://{bucket}/{fname_pandas}")
+
+        got = pd.read_parquet(
+            f"s3://{bucket}/{fname_pandas}", storage_options=s3so
+        )
+        expect = cudf.read_parquet(
+            f"s3://{bucket}/{fname_cudf}", storage_options=s3so
+        )
+
+    assert_eq(expect, got)
+
+
+def test_read_json(s3_base, s3so):
+    fname = "test_json_reader.json"
+    bucket = "json"
+    buffer = (
+        '{"amount": 100, "name": "Alice"}\n'
+        '{"amount": 200, "name": "Bob"}\n'
+        '{"amount": 300, "name": "Charlie"}\n'
+        '{"amount": 400, "name": "Dennis"}\n'
+    )
+
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got = cudf.read_json(
+            f"s3://{bucket}/{fname}",
+            engine="cudf",
+            orient="records",
+            lines=True,
+            storage_options=s3so,
+        )
+
+    expect = pd.read_json(buffer, lines=True)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("use_python_file_object", [False, True])
+@pytest.mark.parametrize("columns", [None, ["string1"]])
+def test_read_orc(s3_base, s3so, datadir, use_python_file_object, columns):
+    source_file = str(datadir / "orc" / "TestOrcFile.testSnappy.orc")
+    fname = "test_orc_reader.orc"
+    bucket = "orc"
+    expect = pd.read_orc(source_file)
+
+    with open(source_file, "rb") as f:
+        buffer = f.read()
+
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        got = cudf.read_orc(
+            f"s3://{bucket}/{fname}",
+            columns=columns,
+            storage_options=s3so,
+            use_python_file_object=use_python_file_object,
+        )
+
+    if columns:
+        expect = expect[columns]
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("columns", [None, ["string1"]])
+def test_read_orc_arrow_nativefile(s3_base, s3so, datadir, columns):
+    source_file = str(datadir / "orc" / "TestOrcFile.testSnappy.orc")
+    fname = "test_orc_reader.orc"
+    bucket = "orc"
+    expect = pd.read_orc(source_file)
+
+    with open(source_file, "rb") as f:
+        buffer = f.read()
+
+    with s3_context(s3_base=s3_base, bucket=bucket, files={fname: buffer}):
+        fs = pa_fs.S3FileSystem(
+            endpoint_override=s3so["client_kwargs"]["endpoint_url"],
+        )
+        with fs.open_input_file(f"{bucket}/{fname}") as fil:
+            got = cudf.read_orc(fil, columns=columns)
+
+    if columns:
+        expect = expect[columns]
+    assert_eq(expect, got)
+
+
+def test_write_orc(s3_base, s3so, pdf):
+    fname = "test_orc_writer.orc"
+    bucket = "orc"
+    gdf = cudf.from_pandas(pdf)
+    with s3_context(s3_base=s3_base, bucket=bucket) as s3fs:
+        gdf.to_orc(f"s3://{bucket}/{fname}", storage_options=s3so)
+        assert s3fs.exists(f"s3://{bucket}/{fname}")
+
+        with s3fs.open(f"s3://{bucket}/{fname}") as f:
+            got = pd.read_orc(f)
+
+    assert_eq(pdf, got)
+
+
+def test_write_chunked_parquet(s3_base, s3so):
+    df1 = cudf.DataFrame({"b": [10, 11, 12], "a": [1, 2, 3]})
+    df2 = cudf.DataFrame({"b": [20, 30, 50], "a": [3, 2, 1]})
+    dirname = "chunked_writer_directory"
+    bucket = "parquet"
+    from cudf.io.parquet import ParquetDatasetWriter
+
+    with s3_context(
+        s3_base=s3_base, bucket=bucket, files={dirname: BytesIO()}
+    ) as s3fs:
+        with ParquetDatasetWriter(
+            f"s3://{bucket}/{dirname}",
+            partition_cols=["a"],
+            storage_options=s3so,
+        ) as cw:
+            cw.write_table(df1)
+            cw.write_table(df2)
+
+        # TODO: Replace following workaround with:
+        # expect = cudf.read_parquet(f"s3://{bucket}/{dirname}/",
+        # storage_options=s3so)
+        # after the following bug is fixed:
+        # https://issues.apache.org/jira/browse/ARROW-16438
+
+        dfs = []
+        for folder in {"a=1", "a=2", "a=3"}:
+            assert s3fs.exists(f"s3://{bucket}/{dirname}/{folder}")
+            for file in s3fs.ls(f"s3://{bucket}/{dirname}/{folder}"):
+                df = cudf.read_parquet("s3://" + file, storage_options=s3so)
+                dfs.append(df)
+
+        actual = cudf.concat(dfs).astype("int64")
+        assert_eq(
+            actual.sort_values(["b"]).reset_index(drop=True),
+            cudf.concat([df1, df2]).sort_values(["b"]).reset_index(drop=True),
+        )
diff --git a/python/cudf/cudf/tests/test_scalar.py b/python/cudf/cudf/tests/test_scalar.py
new file mode 100644
index 0000000..d73a1d4
--- /dev/null
+++ b/python/cudf/cudf/tests/test_scalar.py
@@ -0,0 +1,472 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import datetime
+import re
+from decimal import Decimal
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import rmm
+
+import cudf
+from cudf._lib.copying import get_element
+from cudf.testing._utils import (
+    ALL_TYPES,
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+)
+
+
+@pytest.fixture(autouse=True)
+def clear_scalar_cache():
+    cudf.Scalar._clear_instance_cache()
+    yield
+
+
+TEST_DECIMAL_TYPES = [
+    cudf.Decimal64Dtype(1, 1),
+    cudf.Decimal64Dtype(4, 2),
+    cudf.Decimal64Dtype(4, -2),
+    cudf.Decimal32Dtype(3, 1),
+    cudf.Decimal128Dtype(28, 3),
+]
+
+SCALAR_VALUES = [
+    0,
+    -1,
+    42,
+    0.0,
+    1.0,
+    np.int8(0),
+    np.int8(1),
+    np.int8(-1),
+    np.iinfo(np.int8).min,
+    np.iinfo(np.int8).max,
+    np.int16(1),
+    np.iinfo(np.int16).min,
+    np.iinfo(np.int16).max,
+    np.int32(42),
+    np.int32(-42),
+    np.iinfo(np.int32).min,
+    np.iinfo(np.int32).max,
+    np.int64(42),
+    np.iinfo(np.int64).min,
+    np.iinfo(np.int64).max,
+    np.uint8(0),
+    np.uint8(1),
+    np.uint8(255),
+    np.iinfo(np.uint8).min,
+    np.iinfo(np.uint8).max,
+    np.uint16(1),
+    np.iinfo(np.uint16).min,
+    np.iinfo(np.uint16).max,
+    np.uint32(42),
+    np.uint32(4294967254),
+    np.iinfo(np.uint32).min,
+    np.iinfo(np.uint32).max,
+    np.uint64(42),
+    np.iinfo(np.uint64).min,
+    np.uint64(np.iinfo(np.uint64).max),
+    np.float32(1),
+    np.float32(-1),
+    np.finfo(np.float32).min,
+    np.finfo(np.float32).max,
+    np.float64(1),
+    np.float64(-1),
+    np.finfo(np.float64).min,
+    np.finfo(np.float64).max,
+    np.float32("NaN"),
+    np.float64("NaN"),
+    np.datetime64(0, "s"),
+    np.datetime64(1, "s"),
+    np.datetime64(-1, "s"),
+    np.datetime64(42, "s"),
+    np.datetime64(np.iinfo(np.int64).max, "s"),
+    np.datetime64(np.iinfo(np.int64).min + 1, "s"),
+    np.datetime64(42, "ms"),
+    np.datetime64(np.iinfo(np.int64).max, "ms"),
+    np.datetime64(np.iinfo(np.int64).min + 1, "ms"),
+    np.datetime64(42, "us"),
+    np.datetime64(np.iinfo(np.int64).max, "us"),
+    np.datetime64(np.iinfo(np.int64).min + 1, "us"),
+    np.datetime64(42, "ns"),
+    np.datetime64(np.iinfo(np.int64).max, "ns"),
+    np.datetime64(np.iinfo(np.int64).min + 1, "ns"),
+    np.timedelta64(0, "s"),
+    np.timedelta64(1, "s"),
+    np.timedelta64(-1, "s"),
+    np.timedelta64(42, "s"),
+    np.timedelta64(np.iinfo(np.int64).max, "s"),
+    np.timedelta64(np.iinfo(np.int64).min + 1, "s"),
+    np.timedelta64(42, "ms"),
+    np.timedelta64(np.iinfo(np.int64).max, "ms"),
+    np.timedelta64(np.iinfo(np.int64).min + 1, "ms"),
+    np.timedelta64(42, "us"),
+    np.timedelta64(np.iinfo(np.int64).max, "us"),
+    np.timedelta64(np.iinfo(np.int64).min + 1, "us"),
+    np.timedelta64(42, "ns"),
+    np.timedelta64(np.iinfo(np.int64).max, "ns"),
+    np.timedelta64(np.iinfo(np.int64).min + 1, "ns"),
+    "",
+    "one",
+    "1",
+    True,
+    False,
+    np.bool_(True),
+    np.bool_(False),
+    np.str_("asdf"),
+    np.object_("asdf"),
+]
+
+DECIMAL_VALUES = [
+    Decimal("100"),
+    Decimal("0.0042"),
+    Decimal("1.0042"),
+]
+
+
+@pytest.mark.parametrize("value", SCALAR_VALUES + DECIMAL_VALUES)
+def test_scalar_host_initialization(value):
+    s = cudf.Scalar(value)
+
+    np.testing.assert_equal(s.value, value)
+    assert s.is_valid() is True
+    assert s._is_host_value_current
+    assert not s._is_device_value_current
+
+
+@pytest.mark.parametrize("value", SCALAR_VALUES)
+def test_scalar_device_initialization(value):
+    column = cudf.Series([value], nan_as_null=False)._column
+    dev_slr = get_element(column, 0)
+
+    s = cudf.Scalar.from_device_scalar(dev_slr)
+
+    assert s._is_device_value_current
+    assert not s._is_host_value_current
+
+    assert s.value == value or np.isnan(s.value) and np.isnan(value)
+
+    assert s._is_device_value_current
+    assert s._is_host_value_current
+
+
+@pytest.mark.parametrize("value", DECIMAL_VALUES)
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+def test_scalar_device_initialization_decimal(value, decimal_type):
+    dtype = decimal_type._from_decimal(value)
+    column = cudf.Series([str(value)]).astype(dtype)._column
+    dev_slr = get_element(column, 0)
+
+    s = cudf.Scalar.from_device_scalar(dev_slr)
+
+    assert s._is_device_value_current
+    assert not s._is_host_value_current
+
+    assert s.value == value
+
+    assert s._is_device_value_current
+    assert s._is_host_value_current
+
+
+@pytest.mark.parametrize("value", SCALAR_VALUES + DECIMAL_VALUES)
+def test_scalar_roundtrip(value):
+
+    s = cudf.Scalar(value)
+
+    assert s._is_host_value_current
+    assert not s._is_device_value_current
+
+    # call this property to sync the scalar
+    s.device_value
+
+    assert s._is_host_value_current
+    assert s._is_device_value_current
+
+    # invalidate the host cache
+    s._host_value = None
+    s._host_dtype = None
+
+    assert not s._is_host_value_current
+    assert s._is_device_value_current
+
+    # this should trigger a host copy
+
+    assert s.value == value or np.isnan(s.value) and np.isnan(value)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    NUMERIC_TYPES
+    + DATETIME_TYPES
+    + TIMEDELTA_TYPES
+    + ["object"]
+    + TEST_DECIMAL_TYPES,
+)
+def test_null_scalar(dtype):
+    s = cudf.Scalar(None, dtype=dtype)
+    if cudf.api.types.is_datetime64_dtype(
+        dtype
+    ) or cudf.api.types.is_timedelta64_dtype(dtype):
+        assert s.value is cudf.NaT
+    else:
+        assert s.value is cudf.NA
+    assert s.dtype == (
+        cudf.dtype(dtype)
+        if not isinstance(dtype, cudf.core.dtypes.DecimalDtype)
+        else dtype
+    )
+    assert s.is_valid() is False
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        np.datetime64("NaT", "ns"),
+        np.datetime64("NaT", "us"),
+        np.datetime64("NaT", "ms"),
+        np.datetime64("NaT", "s"),
+        np.timedelta64("NaT", "ns"),
+        np.timedelta64("NaT", "us"),
+        np.timedelta64("NaT", "ms"),
+        np.timedelta64("NaT", "s"),
+    ],
+)
+def test_nat_to_null_scalar_succeeds(value):
+    s = cudf.Scalar(value)
+    assert s.value is cudf.NaT
+    assert not s.is_valid()
+    assert s.dtype == value.dtype
+
+
+@pytest.mark.parametrize(
+    "value", [None, np.datetime64("NaT"), np.timedelta64("NaT")]
+)
+def test_generic_null_scalar_construction_fails(value):
+    with pytest.raises(TypeError):
+        cudf.Scalar(value)
+
+
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + DATETIME_TYPES + TIMEDELTA_TYPES + ["object"]
+)
+def test_scalar_dtype_and_validity(dtype):
+    s = cudf.Scalar(1, dtype=dtype)
+
+    assert s.dtype == cudf.dtype(dtype)
+    assert s.is_valid() is True
+
+
+@pytest.mark.parametrize(
+    "slr,dtype,expect",
+    [
+        (1, cudf.Decimal64Dtype(1, 0), Decimal("1")),
+        (Decimal(1), cudf.Decimal64Dtype(1, 0), Decimal("1")),
+        (Decimal("1.1"), cudf.Decimal64Dtype(2, 1), Decimal("1.1")),
+        (Decimal("1.1"), cudf.Decimal64Dtype(4, 3), Decimal("1.100")),
+        (Decimal("41.123"), cudf.Decimal32Dtype(5, 3), Decimal("41.123")),
+        (
+            Decimal("41345435344353535344373628492731234.123"),
+            cudf.Decimal128Dtype(38, 3),
+            Decimal("41345435344353535344373628492731234.123"),
+        ),
+        (Decimal("1.11"), cudf.Decimal64Dtype(2, 2), pa.lib.ArrowInvalid),
+    ],
+)
+def test_scalar_dtype_and_validity_decimal(slr, dtype, expect):
+    if expect is pa.lib.ArrowInvalid:
+        with pytest.raises(expect):
+            cudf.Scalar(slr, dtype=dtype)
+        return
+    else:
+        result = cudf.Scalar(slr, dtype=dtype)
+        assert result.dtype == dtype
+        assert result.is_valid
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        datetime.timedelta(seconds=76),
+        datetime.timedelta(microseconds=7),
+        datetime.timedelta(minutes=47),
+        datetime.timedelta(hours=4427),
+        datetime.timedelta(weeks=7134),
+        pd.Timestamp(15133.5, unit="s"),
+        pd.Timestamp(15133.5, unit="D"),
+        pd.Timedelta(1513393355.5, unit="s"),
+        pd.Timedelta(34765, unit="D"),
+    ],
+)
+def test_date_duration_scalars(value):
+    s = cudf.Scalar(value)
+
+    actual = s.value
+
+    if isinstance(value, datetime.datetime):
+        expected = np.datetime64(value)
+    elif isinstance(value, datetime.timedelta):
+        expected = np.timedelta64(value)
+    elif isinstance(value, pd.Timestamp):
+        expected = value.to_datetime64()
+    elif isinstance(value, pd.Timedelta):
+        expected = value.to_timedelta64()
+
+    np.testing.assert_equal(actual, expected)
+    assert s.is_valid() is True
+
+
+def test_scalar_implicit_bool_conversion():
+    assert cudf.Scalar(True)
+    assert not cudf.Scalar(False)
+    assert cudf.Scalar(0) == cudf.Scalar(0)
+    assert cudf.Scalar(1) <= cudf.Scalar(2)
+    assert cudf.Scalar(1) <= 2
+
+
+@pytest.mark.parametrize("value", [1, -1, 1.5, 0, "1.5", "1", True, False])
+def test_scalar_implicit_float_conversion(value):
+    expect = float(value)
+    got = float(cudf.Scalar(value))
+
+    assert expect == got
+    assert type(expect) == type(got)
+
+
+@pytest.mark.parametrize("value", [1, -1, 1.5, 0, "1", True, False])
+def test_scalar_implicit_int_conversion(value):
+    expect = int(value)
+    got = int(cudf.Scalar(value))
+
+    assert expect == got
+    assert type(expect) == type(got)
+
+
+@pytest.mark.parametrize("cls", [int, float, bool])
+@pytest.mark.parametrize("dtype", sorted(set(ALL_TYPES) - {"category"}))
+def test_scalar_invalid_implicit_conversion(cls, dtype):
+
+    try:
+        cls(
+            pd.NaT
+            if cudf.api.types.is_datetime64_dtype(dtype)
+            or cudf.api.types.is_timedelta64_dtype(dtype)
+            else pd.NA
+        )
+    except TypeError as e:
+        with pytest.raises(TypeError, match=re.escape(str(e))):
+            slr = cudf.Scalar(None, dtype=dtype)
+            cls(slr)
+
+
+@pytest.mark.parametrize("value", SCALAR_VALUES + DECIMAL_VALUES)
+@pytest.mark.parametrize(
+    "decimal_type",
+    [cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype],
+)
+def test_device_scalar_direct_construction(value, decimal_type):
+    value = cudf.utils.dtypes.to_cudf_compatible_scalar(value)
+
+    dtype = (
+        value.dtype
+        if not isinstance(value, Decimal)
+        else decimal_type._from_decimal(value)
+    )
+
+    s = cudf._lib.scalar.DeviceScalar(value, dtype)
+
+    assert s.value == value or np.isnan(s.value) and np.isnan(value)
+    if isinstance(
+        dtype, (cudf.Decimal64Dtype, cudf.Decimal128Dtype, cudf.Decimal32Dtype)
+    ):
+        assert s.dtype.precision == dtype.precision
+        assert s.dtype.scale == dtype.scale
+    elif dtype.char == "U":
+        assert s.dtype == "object"
+    else:
+        assert s.dtype == dtype
+
+
+@pytest.mark.parametrize("value", SCALAR_VALUES + DECIMAL_VALUES)
+def test_construct_from_scalar(value):
+    value = cudf.utils.dtypes.to_cudf_compatible_scalar(value)
+    x = cudf.Scalar(
+        value, value.dtype if not isinstance(value, Decimal) else None
+    )
+    y = cudf.Scalar(x)
+    assert x.value == y.value or np.isnan(x.value) and np.isnan(y.value)
+
+    # check that this works:
+    y.device_value
+
+    x._is_host_value_current == y._is_host_value_current
+    x._is_device_value_current == y._is_device_value_current
+
+
+@pytest.mark.parametrize(
+    "data", ["20000101", "2000-01-01", "2000-01-01T00:00:00.000000000", "2000"]
+)
+@pytest.mark.parametrize("dtype", DATETIME_TYPES)
+def test_datetime_scalar_from_string(data, dtype):
+    slr = cudf.Scalar(data, dtype)
+
+    expected = np.datetime64(datetime.datetime(2000, 1, 1)).astype(dtype)
+
+    assert expected == slr.value
+
+
+def test_scalar_cache():
+    s = cudf.Scalar(1)
+    s2 = cudf.Scalar(1)
+
+    assert s is s2
+
+
+def test_scalar_cache_rmm_hook():
+    # test that reinitializing rmm clears the cuDF scalar cache, as we
+    # register a hook with RMM that does that on reinitialization
+    s = cudf.Scalar(1)
+    s2 = cudf.Scalar(1)
+
+    assert s is s2
+
+    rmm.reinitialize()
+
+    s3 = cudf.Scalar(1)
+    assert s3 is not s
+
+
+def test_default_integer_bitwidth_scalar(default_integer_bitwidth):
+    # Test that integer scalars are default to 32 bits under user options.
+    slr = cudf.Scalar(128)
+    assert slr.dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+
+
+def test_default_float_bitwidth_scalar(default_float_bitwidth):
+    # Test that float scalars are default to 32 bits under user options.
+    slr = cudf.Scalar(128.0)
+    assert slr.dtype == np.dtype(f"f{default_float_bitwidth//8}")
+
+
+def test_scalar_numpy_casting():
+    # binop should upcast to wider type
+    s1 = cudf.Scalar(1, dtype=np.int32)
+    s2 = np.int64(2)
+    assert s1 < s2
+
+
+def test_construct_timezone_scalar_error():
+    pd_scalar = pd.Timestamp("1970-01-01 00:00:00.000000001", tz="utc")
+    with pytest.raises(NotImplementedError):
+        cudf.utils.dtypes.to_cudf_compatible_scalar(pd_scalar)
+
+    date_scalar = datetime.datetime.now(datetime.timezone.utc)
+    with pytest.raises(NotImplementedError):
+        cudf.utils.dtypes.to_cudf_compatible_scalar(date_scalar)
diff --git a/python/cudf/cudf/tests/test_scan.py b/python/cudf/cudf/tests/test_scan.py
new file mode 100644
index 0000000..4cbc219
--- /dev/null
+++ b/python/cudf/cudf/tests/test_scan.py
@@ -0,0 +1,279 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype
+from cudf.testing._utils import (
+    INTEGER_TYPES,
+    NUMERIC_TYPES,
+    assert_eq,
+    gen_rand,
+)
+
+params_sizes = [0, 1, 2, 5]
+
+
+def _gen_params():
+    for t, n in product(NUMERIC_TYPES, params_sizes):
+        if (t == np.int8 or t == np.int16) and n > 20:
+            # to keep data in range
+            continue
+        yield t, n
+
+
+@pytest.mark.parametrize("dtype,nelem", list(_gen_params()))
+def test_cumsum(dtype, nelem):
+    if dtype == np.int8:
+        # to keep data in range
+        data = gen_rand(dtype, nelem, low=-2, high=2)
+    else:
+        data = gen_rand(dtype, nelem)
+
+    decimal = 4 if dtype == np.float32 else 6
+
+    # series
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gs.cumsum().to_numpy(), ps.cumsum(), decimal=decimal
+    )
+
+    # dataframe series (named series)
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series(data)
+    pdf = pd.DataFrame()
+    pdf["a"] = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gdf.a.cumsum().to_numpy(), pdf.a.cumsum(), decimal=decimal
+    )
+
+
+def test_cumsum_masked():
+    data = [1, 2, None, 4, 5]
+    float_types = ["float32", "float64"]
+
+    for type_ in float_types:
+        gs = cudf.Series(data).astype(type_)
+        ps = pd.Series(data).astype(type_)
+        assert_eq(gs.cumsum(), ps.cumsum())
+
+    for type_ in INTEGER_TYPES:
+        gs = cudf.Series(data).astype(type_)
+        got = gs.cumsum()
+        expected = pd.Series([1, 3, np.nan, 7, 12], dtype="float64")
+        assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(8, 4),
+        Decimal64Dtype(10, 5),
+        Decimal64Dtype(12, 7),
+        Decimal32Dtype(8, 5),
+        Decimal128Dtype(13, 6),
+    ],
+)
+def test_cumsum_decimal(dtype):
+    data = ["243.32", "48.245", "-7234.298", np.nan, "-467.2"]
+    gser = cudf.Series(data).astype(dtype)
+    pser = pd.Series(data, dtype="float64")
+
+    got = gser.cumsum()
+    expected = cudf.Series.from_pandas(pser.cumsum()).astype(dtype)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize("dtype,nelem", list(_gen_params()))
+def test_cummin(dtype, nelem):
+    if dtype == np.int8:
+        # to keep data in range
+        data = gen_rand(dtype, nelem, low=-2, high=2)
+    else:
+        data = gen_rand(dtype, nelem)
+
+    decimal = 4 if dtype == np.float32 else 6
+
+    # series
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gs.cummin().to_numpy(), ps.cummin(), decimal=decimal
+    )
+
+    # dataframe series (named series)
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series(data)
+    pdf = pd.DataFrame()
+    pdf["a"] = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gdf.a.cummin().to_numpy(), pdf.a.cummin(), decimal=decimal
+    )
+
+
+def test_cummin_masked():
+    data = [1, 2, None, 4, 5]
+    float_types = ["float32", "float64"]
+
+    for type_ in float_types:
+        gs = cudf.Series(data).astype(type_)
+        ps = pd.Series(data).astype(type_)
+        assert_eq(gs.cummin(), ps.cummin())
+
+    for type_ in INTEGER_TYPES:
+        gs = cudf.Series(data).astype(type_)
+        expected = pd.Series([1, 1, np.nan, 1, 1]).astype("float64")
+        assert_eq(gs.cummin(), expected)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(8, 4),
+        Decimal64Dtype(11, 6),
+        Decimal64Dtype(14, 7),
+        Decimal32Dtype(8, 4),
+        Decimal128Dtype(11, 6),
+    ],
+)
+def test_cummin_decimal(dtype):
+    data = ["8394.294", np.nan, "-9940.444", np.nan, "-23.928"]
+    gser = cudf.Series(data).astype(dtype)
+    pser = pd.Series(data, dtype="float64")
+
+    got = gser.cummin()
+    expected = cudf.Series.from_pandas(pser.cummin()).astype(dtype)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize("dtype,nelem", list(_gen_params()))
+def test_cummax(dtype, nelem):
+    if dtype == np.int8:
+        # to keep data in range
+        data = gen_rand(dtype, nelem, low=-2, high=2)
+    else:
+        data = gen_rand(dtype, nelem)
+
+    decimal = 4 if dtype == np.float32 else 6
+
+    # series
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gs.cummax().to_numpy(), ps.cummax(), decimal=decimal
+    )
+
+    # dataframe series (named series)
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series(data)
+    pdf = pd.DataFrame()
+    pdf["a"] = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gdf.a.cummax().to_numpy(), pdf.a.cummax(), decimal=decimal
+    )
+
+
+def test_cummax_masked():
+    data = [1, 2, None, 4, 5]
+    float_types = ["float32", "float64"]
+
+    for type_ in float_types:
+        gs = cudf.Series(data).astype(type_)
+        ps = pd.Series(data).astype(type_)
+        assert_eq(gs.cummax(), ps.cummax())
+
+    for type_ in INTEGER_TYPES:
+        gs = cudf.Series(data).astype(type_)
+        expected = pd.Series([1, 2, np.nan, 4, 5]).astype("float64")
+        assert_eq(gs.cummax(), expected)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        Decimal64Dtype(8, 4),
+        Decimal64Dtype(11, 6),
+        Decimal64Dtype(14, 7),
+        Decimal32Dtype(8, 4),
+        Decimal128Dtype(11, 6),
+    ],
+)
+def test_cummax_decimal(dtype):
+    data = [np.nan, "54.203", "8.222", "644.32", "-562.272"]
+    gser = cudf.Series(data).astype(dtype)
+    pser = pd.Series(data, dtype="float64")
+
+    got = gser.cummax()
+    expected = cudf.Series.from_pandas(pser.cummax()).astype(dtype)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize("dtype,nelem", list(_gen_params()))
+def test_cumprod(dtype, nelem):
+    if dtype == np.int8:
+        # to keep data in range
+        data = gen_rand(dtype, nelem, low=-2, high=2)
+    else:
+        data = gen_rand(dtype, nelem)
+
+    decimal = 4 if dtype == np.float32 else 6
+
+    # series
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gs.cumprod().to_numpy(), ps.cumprod(), decimal=decimal
+    )
+
+    # dataframe series (named series)
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series(data)
+    pdf = pd.DataFrame()
+    pdf["a"] = pd.Series(data)
+    np.testing.assert_array_almost_equal(
+        gdf.a.cumprod().to_numpy(), pdf.a.cumprod(), decimal=decimal
+    )
+
+
+def test_cumprod_masked():
+    data = [1, 2, None, 4, 5]
+    float_types = ["float32", "float64"]
+
+    for type_ in float_types:
+        gs = cudf.Series(data).astype(type_)
+        ps = pd.Series(data).astype(type_)
+        assert_eq(gs.cumprod(), ps.cumprod())
+
+    for type_ in INTEGER_TYPES:
+        gs = cudf.Series(data).astype(type_)
+        got = gs.cumprod()
+        expected = pd.Series([1, 2, np.nan, 8, 40], dtype="float64")
+        assert_eq(got, expected)
+
+
+def test_scan_boolean_cumsum():
+    s = cudf.Series([0, -1, -300, 23, 4, -3, 0, 0, 100])
+
+    # cumsum test
+    got = (s > 0).cumsum()
+    expect = (s > 0).to_pandas().cumsum()
+
+    assert_eq(expect, got)
+
+
+def test_scan_boolean_cumprod():
+    s = cudf.Series([0, -1, -300, 23, 4, -3, 0, 0, 100])
+
+    # cumprod test
+    got = (s > 0).cumprod()
+    expect = (s > 0).to_pandas().cumprod()
+
+    assert_eq(expect, got)
diff --git a/python/cudf/cudf/tests/test_search.py b/python/cudf/cudf/tests/test_search.py
new file mode 100644
index 0000000..b0eacb1
--- /dev/null
+++ b/python/cudf/cudf/tests/test_search.py
@@ -0,0 +1,170 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+import cupy
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing._utils import assert_eq, gen_rand, random_bitmask
+
+
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("obj_class", ["series", "index", "column"])
+@pytest.mark.parametrize("vals_class", ["series", "index"])
+def test_searchsorted(side, obj_class, vals_class):
+    nelem = 1000
+    column_data = gen_rand("float64", nelem)
+    column_mask = random_bitmask(nelem)
+
+    values_data = gen_rand("float64", nelem)
+    values_mask = random_bitmask(nelem)
+
+    sr = cudf.Series.from_masked_array(column_data, column_mask)
+    vals = cudf.Series.from_masked_array(values_data, values_mask)
+
+    sr = sr.sort_values()
+
+    # Reference object can be Series, Index, or Column
+    if obj_class == "index":
+        sr.reset_index(drop=True)
+    elif obj_class == "column":
+        sr = sr._column
+
+    # Values can be Series or Index
+    if vals_class == "index":
+        vals.reset_index(drop=True)
+
+    psr = sr.to_pandas()
+    pvals = vals.to_pandas()
+
+    expect = psr.searchsorted(pvals, side)
+    got = sr.searchsorted(vals, side)
+
+    assert_eq(expect, cupy.asnumpy(got))
+
+
+@pytest.mark.parametrize("side", ["left", "right"])
+@pytest.mark.parametrize("multiindex", [True, False])
+def test_searchsorted_dataframe(side, multiindex):
+    values = cudf.DataFrame(
+        {
+            "a": [1, 0, 5, 1],
+            "b": [-0.998, 0.031, -0.888, -0.998],
+            "c": ["C", "A", "G", "B"],
+        }
+    )
+    base = cudf.DataFrame(
+        {
+            "a": [1, 1, 1, 5],
+            "b": [-0.999, -0.998, -0.997, -0.888],
+            "c": ["A", "C", "E", "G"],
+        }
+    )
+
+    if multiindex:
+        base = base.set_index(["a", "b", "c"]).index
+        values = values.set_index(["a", "b", "c"]).index
+
+    result = base.searchsorted(values, side=side).tolist()
+
+    if side == "left":
+        assert result == [1, 0, 3, 1]
+    else:
+        assert result == [2, 0, 4, 1]
+
+
+def test_search_sorted_dataframe_unequal_number_of_columns():
+    values = cudf.DataFrame({"a": [1, 0, 5, 1]})
+    base = cudf.DataFrame({"a": [1, 0, 5, 1], "b": ["x", "z", "w", "a"]})
+
+    with pytest.raises(ValueError, match="Mismatch number of columns"):
+        base.searchsorted(values)
+
+
+@pytest.mark.parametrize("side", ["left", "right"])
+def test_searchsorted_categorical(side):
+
+    cat1 = pd.Categorical(
+        ["a", "a", "b", "c", "a"], categories=["a", "b", "c"], ordered=True
+    )
+    psr1 = pd.Series(cat1).sort_values()
+    sr1 = cudf.Series(cat1).sort_values()
+    cat2 = pd.Categorical(
+        ["a", "b", "a", "c", "b"], categories=["a", "b", "c"], ordered=True
+    )
+    psr2 = pd.Series(cat2)
+    sr2 = cudf.Series(cat2)
+
+    expect = psr1.searchsorted(psr2, side)
+    got = sr1.searchsorted(sr2, side)
+
+    assert_eq(expect, cupy.asnumpy(got))
+
+
+@pytest.mark.parametrize("side", ["left", "right"])
+def test_searchsorted_datetime(side):
+
+    psr1 = pd.Series(
+        pd.date_range("20190101", "20200101", freq="400h", name="times")
+    )
+    sr1 = cudf.from_pandas(psr1)
+
+    psr2 = pd.Series(
+        np.array(
+            [
+                np.datetime64("2019-11-20"),
+                np.datetime64("2019-04-15"),
+                np.datetime64("2019-02-20"),
+                np.datetime64("2019-05-31"),
+                np.datetime64("2020-01-02"),
+            ]
+        )
+    )
+
+    sr2 = cudf.from_pandas(psr2)
+
+    expect = psr1.searchsorted(psr2, side)
+    got = sr1.searchsorted(sr2, side)
+
+    assert_eq(expect, cupy.asnumpy(got))
+
+
+def test_searchsorted_misc():
+    psr = pd.Series([1, 2, 3.4, 6])
+    sr = cudf.from_pandas(psr)
+
+    assert_eq(psr.searchsorted(1), sr.searchsorted(1))
+    assert_eq(psr.searchsorted(0), sr.searchsorted(0))
+    assert_eq(psr.searchsorted(4), sr.searchsorted(4))
+    assert_eq(psr.searchsorted(5), sr.searchsorted(5))
+    assert_eq(
+        psr.searchsorted([-100, 3.4, 2.2, 2.0, 2.000000001]),
+        sr.searchsorted([-100, 3.4, 2.2, 2.0, 2.000000001]),
+    )
+
+    psr = pd.Series([1, 2, 3])
+    sr = cudf.from_pandas(psr)
+    assert_eq(psr.searchsorted(1), sr.searchsorted(1))
+    assert_eq(
+        psr.searchsorted([0, 1, 2, 3, 4, -4, -3, -2, -1, 0, -120]),
+        sr.searchsorted([0, 1, 2, 3, 4, -4, -3, -2, -1, 0, -120]),
+    )
+    assert_eq(psr.searchsorted(1.5), sr.searchsorted(1.5))
+    assert_eq(psr.searchsorted(1.99), sr.searchsorted(1.99))
+    assert_eq(psr.searchsorted(3.00001), sr.searchsorted(3.00001))
+    assert_eq(
+        psr.searchsorted([-100, 3.00001, 2.2, 2.0, 2.000000001]),
+        sr.searchsorted([-100, 3.00001, 2.2, 2.0, 2.000000001]),
+    )
+
+
+@pytest.mark.xfail(reason="https://github.com/pandas-dev/pandas/issues/54668")
+def test_searchsorted_mixed_str_int():
+    psr = pd.Series([1, 2, 3], dtype="int")
+    sr = cudf.from_pandas(psr)
+
+    with pytest.raises(ValueError):
+        actual = sr.searchsorted("a")
+    with pytest.raises(ValueError):
+        expect = psr.searchsorted("a")
+    assert_eq(expect, actual)
diff --git a/python/cudf/cudf/tests/test_serialize.py b/python/cudf/cudf/tests/test_serialize.py
new file mode 100644
index 0000000..e2788e4
--- /dev/null
+++ b/python/cudf/cudf/tests/test_serialize.py
@@ -0,0 +1,403 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import pickle
+
+import msgpack
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_200
+from cudf.testing import _utils as utils
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        lambda: cudf.Index([1, 2, 3]),
+        lambda: cudf.Index([1.0, 2.0, 3.0]),
+        lambda: cudf.Series([1, 2, 3]),
+        lambda: cudf.Series([1, 2, 3], index=[4, 5, 6]),
+        lambda: cudf.Series([1, None, 3]),
+        lambda: cudf.Series([1, 2, 3], index=[4, 5, None]),
+        lambda: cudf.Series([1, 2, 3])[:2],
+        lambda: cudf.Series([1, 2, 3])[:2]._column,
+        lambda: cudf.Series(["a", "bb", "ccc"]),
+        lambda: cudf.Series(["a", None, "ccc"]),
+        lambda: cudf.Series(
+            [
+                {"a": ({"b": [1, 2, 3], "c": [4, 5, 6]}, {"d": [2, 4, 6]})},
+                {"e": ({"b": [0, 2, 4], "c": [-1, -2, -3]}, {"d": [1, 1, 1]})},
+            ]
+        ),
+        lambda: cudf.Series(
+            [
+                14.12302,
+                97938.2,
+                np.nan,
+                0.0,
+                -8.302014,
+                np.nan,
+                -112.2314,
+            ]
+        ).astype(cudf.Decimal64Dtype(7, 2)),
+        lambda: cudf.DataFrame({"x": [1, 2, 3]}),
+        lambda: cudf.DataFrame({"x": [1, 2, 3], "y": [1.0, None, 3.0]}),
+        lambda: cudf.DataFrame(
+            {"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]}, index=[1, None, 3]
+        ),
+        lambda: cudf.DataFrame(
+            {"x": [1, 2, 3], "y": [1.0, None, 3.0]}, index=[1, None, 3]
+        ),
+        lambda: cudf.DataFrame(
+            {"x": ["a", "bb", "ccc"], "y": [1.0, None, 3.0]},
+            index=[1, None, 3],
+        ),
+        lambda: pd.Index([True, False] * 5),
+        lambda: pd.CategoricalIndex(["a", "b", "a", "b"], ["a", "b", "c"]),
+        lambda: (
+            cudf.DataFrame(
+                {
+                    "a": [1, 2, 3],
+                    "b": ["c", "e", "g"],
+                    "d": [True, False, True],
+                },
+                index=cudf.MultiIndex.from_tuples(
+                    [("i1", "i2"), ("i3", "i4"), ("i5", "i6")],
+                    names=["foo", "bar"],
+                ),
+            )
+        ),
+        lambda: cudf.Index(
+            cudf.date_range(start="2011-01-01", end="2012-01-01", periods=13)
+        ),
+        lambda: cudf.Index([1.2, 3.4, 5.6]),
+        lambda: cudf.Series([1.2, 3.4, 5.6]),
+        lambda: pd.IntervalIndex.from_breaks(range(10)),
+        lambda: cudf.MultiIndex.from_tuples(
+            [("i1", "i2"), ("i3", "i4"), ("i5", "i6")], names=["foo", "bar"]
+        ),
+        lambda: cudf.RangeIndex(10),
+        lambda: cudf.DataFrame(
+            {"a": list(range(13)), "b": [float(x) for x in range(13)]},
+            index=cudf.Index(
+                cudf.date_range(
+                    start="2011-01-01", end="2012-01-01", periods=13
+                )
+            ),
+        ),
+        lambda: cudf.Series(
+            list(range(13)),
+            index=cudf.Index(
+                cudf.date_range(
+                    start="2011-01-01", end="2012-01-01", periods=13
+                )
+            ),
+        ),
+        lambda: cudf.TimedeltaIndex(
+            [1132223, 2023232, 342234324, 4234324],
+            dtype="timedelta64[ns]",
+            name="foo",
+        ),
+        lambda: cudf.Index(
+            [
+                "y7ssMP1PWJ",
+                "rZDLbzIQsX",
+                "NrPwYMsxNw",
+                "4zja1Vw9Rq",
+                "Y9TNDhjXgR",
+                "Ryjt7up2hT",
+                "dxYKtRGHkb",
+                "nMCWj5yhMu",
+                "Rt7S362FNX",
+                "OGbssOJLUI",
+            ]
+        ),
+    ],
+)
+@pytest.mark.parametrize("to_host", [True, False])
+def test_serialize(df, to_host):
+    """This should hopefully replace all functions below"""
+    a = df()
+    if "cudf" not in type(a).__module__:
+        a = cudf.from_pandas(a)
+    if to_host:
+        header, frames = a.host_serialize()
+    else:
+        header, frames = a.device_serialize()
+    msgpack.dumps(header)  # ensure that header is msgpack serializable
+    ndevice = 0
+    for frame in frames:
+        if hasattr(frame, "__cuda_array_interface__"):
+            ndevice += 1
+    # Indices etc. will not be DeviceNDArray
+    # but data should be...
+    if to_host:
+        assert ndevice == 0
+    elif hasattr(df, "_cols"):
+        assert ndevice >= len(df._data)
+    else:
+        # If there are frames, something should be on the device
+        assert ndevice > 0 or not frames
+
+    typ = type(a)
+    b = typ.deserialize(header, frames)
+    assert_eq(a, b)
+
+
+def test_serialize_dtype_error_checking():
+    dtype = cudf.IntervalDtype("float", "right")
+    header, frames = dtype.serialize()
+    with pytest.raises(AssertionError):
+        # Invalid number of frames
+        type(dtype).deserialize(header, [None] * (header["frame_count"] + 1))
+    with pytest.raises(AssertionError):
+        # mismatching class
+        cudf.StructDtype.deserialize(header, frames)
+
+
+def test_serialize_dataframe():
+    df = cudf.DataFrame()
+    df["a"] = np.arange(100)
+    df["b"] = np.arange(100, dtype=np.float32)
+    df["c"] = pd.Categorical(
+        ["a", "b", "c", "_", "_"] * 20, categories=["a", "b", "c"]
+    )
+    outdf = cudf.DataFrame.deserialize(*df.serialize())
+    assert_eq(df, outdf)
+
+
+def test_serialize_dataframe_with_index():
+    df = cudf.DataFrame()
+    df["a"] = np.arange(100)
+    df["b"] = np.random.random(100)
+    df["c"] = pd.Categorical(
+        ["a", "b", "c", "_", "_"] * 20, categories=["a", "b", "c"]
+    )
+    df = df.sort_values("b")
+    outdf = cudf.DataFrame.deserialize(*df.serialize())
+    assert_eq(df, outdf)
+
+
+def test_serialize_series():
+    sr = cudf.Series(np.arange(100))
+    outsr = cudf.Series.deserialize(*sr.serialize())
+    assert_eq(sr, outsr)
+
+
+def test_serialize_range_index():
+    index = cudf.core.index.RangeIndex(10, 20)
+    outindex = cudf.core.index.RangeIndex.deserialize(*index.serialize())
+    assert_eq(index, outindex)
+
+
+def test_serialize_generic_index():
+    index = cudf.core.index.GenericIndex(cudf.Series(np.arange(10)))
+    outindex = cudf.core.index.GenericIndex.deserialize(*index.serialize())
+    assert_eq(index, outindex)
+
+
+def test_serialize_multi_index():
+    pdf = pd.DataFrame(
+        {
+            "a": [4, 17, 4, 9, 5],
+            "b": [1, 4, 4, 3, 2],
+            "x": np.random.normal(size=5),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    gdg = gdf.groupby(["a", "b"]).sum()
+    multiindex = gdg.index
+    outindex = cudf.core.multiindex.MultiIndex.deserialize(
+        *multiindex.serialize()
+    )
+    assert_eq(multiindex, outindex)
+
+
+def test_serialize_masked_series():
+    nelem = 50
+    data = np.random.random(nelem)
+    mask = utils.random_bitmask(nelem)
+    bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
+    null_count = utils.count_zero(bitmask)
+    assert null_count >= 0
+    sr = cudf.Series.from_masked_array(data, mask, null_count=null_count)
+    outsr = cudf.Series.deserialize(*sr.serialize())
+    assert_eq(sr, outsr)
+
+
+def test_serialize_groupby_df():
+    df = cudf.DataFrame()
+    df["key_1"] = np.random.randint(0, 20, 100)
+    df["key_2"] = np.random.randint(0, 20, 100)
+    df["val"] = np.arange(100, dtype=np.float32)
+    gb = df.groupby(["key_1", "key_2"], sort=True)
+    outgb = gb.deserialize(*gb.serialize())
+    expect = gb.mean()
+    got = outgb.mean()
+    assert_eq(got.sort_index(), expect.sort_index())
+
+
+def test_serialize_groupby_external():
+    df = cudf.DataFrame()
+    df["val"] = np.arange(100, dtype=np.float32)
+    gb = df.groupby(cudf.Series(np.random.randint(0, 20, 100)))
+    outgb = gb.deserialize(*gb.serialize())
+    expect = gb.mean()
+    got = outgb.mean()
+    assert_eq(got.sort_index(), expect.sort_index())
+
+
+def test_serialize_groupby_level():
+    idx = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (2, 2)], names=("a", "b"))
+    pdf = pd.DataFrame({"c": [1, 2, 3], "d": [2, 3, 4]}, index=idx)
+    df = cudf.from_pandas(pdf)
+    gb = df.groupby(level="a")
+    expect = gb.mean()
+    outgb = gb.deserialize(*gb.serialize())
+    got = outgb.mean()
+    assert_eq(expect.sort_index(), got.sort_index())
+
+
+def test_serialize_groupby_sr():
+    sr = cudf.Series(np.random.randint(0, 20, 100))
+    gb = sr.groupby(sr // 2)
+    outgb = gb.deserialize(*gb.serialize())
+    got = gb.mean()
+    expect = outgb.mean()
+    assert_eq(got.sort_index(), expect.sort_index())
+
+
+def test_serialize_datetime():
+    # Make frame with datetime column
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+    )
+    ts = np.arange(0, len(df), dtype=np.dtype("datetime64[ms]"))
+    df["timestamp"] = ts
+    gdf = cudf.DataFrame.from_pandas(df)
+    # (De)serialize roundtrip
+    recreated = cudf.DataFrame.deserialize(*gdf.serialize())
+    # Check
+    assert_eq(recreated, df)
+
+
+def test_serialize_string():
+    # Make frame with string column
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=5), "y": np.random.normal(size=5)}
+    )
+    str_data = ["a", "bc", "def", "ghij", "klmno"]
+    df["timestamp"] = str_data
+    gdf = cudf.DataFrame.from_pandas(df)
+    # (De)serialize roundtrip
+    recreated = cudf.DataFrame.deserialize(*gdf.serialize())
+    # Check
+    assert_eq(recreated, df)
+
+
+@pytest.mark.parametrize(
+    "frames",
+    [
+        (cudf.Series([], dtype="str"), pd.Series([], dtype="str")),
+        pytest.param(
+            (cudf.DataFrame([]), pd.DataFrame([])),
+            marks=pytest.mark.xfail(
+                not PANDAS_GE_200, reason=".column returns Index[object]"
+            ),
+        ),
+        (cudf.DataFrame([1]).head(0), pd.DataFrame([1]).head(0)),
+        (cudf.DataFrame({"a": []}), pd.DataFrame({"a": []})),
+        (
+            cudf.DataFrame({"a": ["a"]}).head(0),
+            pd.DataFrame({"a": ["a"]}).head(0),
+        ),
+        (
+            cudf.DataFrame({"a": [1.0]}).head(0),
+            pd.DataFrame({"a": [1.0]}).head(0),
+        ),
+    ],
+)
+def test_serialize_empty(frames):
+    gdf, pdf = frames
+
+    typ = type(gdf)
+    res = typ.deserialize(*gdf.serialize())
+    assert_eq(res, gdf)
+
+
+def test_serialize_all_null_string():
+    data = [None, None, None, None, None]
+    pd_series = pd.Series(data, dtype="str")
+    gd_series = cudf.Series(data, dtype="str")
+
+    recreated = cudf.Series.deserialize(*gd_series.serialize())
+    assert_eq(recreated, pd_series)
+
+
+def test_serialize_named_series():
+    gdf = cudf.DataFrame({"a": [1, 2, 3, 4], "b": [5, 1, 2, 5]})
+
+    ser = gdf["b"]
+    recreated = cudf.Series.deserialize(*ser.serialize())
+    assert_eq(recreated, ser)
+
+
+def test_serialize_seriesgroupby():
+    gdf = cudf.DataFrame({"a": [1, 2, 3, 4], "b": [5, 1, 2, 5]})
+
+    gb = gdf.groupby(["a"]).b
+    recreated = gb.__class__.deserialize(*gb.serialize())
+    assert_eq(recreated.sum(), gb.sum())
+
+
+def test_serialize_seriesresampler():
+    index = cudf.date_range(start="2001-01-01", periods=10, freq="1T")
+    sr = cudf.Series(range(10), index=index)
+    re_sampler = sr.resample("3T")
+    actual = re_sampler.sum()
+    recreated = re_sampler.__class__.deserialize(*re_sampler.serialize())
+    expected = recreated.sum()
+
+    assert_eq(actual, expected)
+
+
+def test_serialize_string_check_buffer_sizes():
+    df = cudf.DataFrame({"a": ["a", "b", "cd", None]})
+    expect = df.memory_usage(deep=True).loc["a"]
+    header, frames = df.serialize()
+    got = sum(b.nbytes for b in frames)
+    assert expect == got
+
+
+def test_deserialize_cudf_0_16(datadir):
+    fname = datadir / "pkl" / "stringColumnWithRangeIndex_cudf_0.16.pkl"
+
+    expected = cudf.DataFrame({"a": ["hi", "hello", "world", None]})
+    with open(fname, "rb") as f:
+        actual = pickle.load(f)
+
+    assert_eq(expected, actual)
+
+
+def test_serialize_sliced_string():
+    # https://github.com/rapidsai/cudf/issues/7735
+    data = ["hi", "hello", None]
+    pd_series = pd.Series(data, dtype=pd.StringDtype())
+    gd_series = cudf.Series(data, dtype="str")
+    sliced = gd_series[0:3]
+    serialized_gd_series = gd_series.serialize()
+    serialized_sliced = sliced.serialize()
+
+    # validate frames are equal or not
+    # because both should be identical
+    for i in range(3):
+        assert_eq(
+            serialized_gd_series[1][i].memoryview(),
+            serialized_sliced[1][i].memoryview(),
+        )
+
+    recreated = cudf.Series.deserialize(*sliced.serialize())
+    assert_eq(recreated.to_pandas(nullable=True), pd_series)
diff --git a/python/cudf/cudf/tests/test_series.py b/python/cudf/cudf/tests/test_series.py
new file mode 100644
index 0000000..c15a797
--- /dev/null
+++ b/python/cudf/cudf/tests/test_series.py
@@ -0,0 +1,2652 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import decimal
+import hashlib
+import operator
+import re
+from collections import OrderedDict, defaultdict
+from string import ascii_letters, digits
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_LT_140
+from cudf.testing._utils import (
+    NUMERIC_TYPES,
+    SERIES_OR_INDEX_NAMES,
+    TIMEDELTA_TYPES,
+    _create_cudf_series_float64_default,
+    _create_pandas_series_float64_default,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+    gen_rand,
+)
+
+
+def _series_na_data():
+    return [
+        pd.Series([0, 1, 2, np.nan, 4, None, 6]),
+        pd.Series(
+            [0, 1, 2, np.nan, 4, None, 6],
+            index=["q", "w", "e", "r", "t", "y", "u"],
+            name="a",
+        ),
+        pd.Series([0, 1, 2, 3, 4]),
+        pd.Series(["a", "b", "u", "h", "d"]),
+        pd.Series([None, None, np.nan, None, np.inf, -np.inf]),
+        pd.Series([], dtype="float64"),
+        pd.Series(
+            [pd.NaT, pd.Timestamp("1939-05-27"), pd.Timestamp("1940-04-25")]
+        ),
+        pd.Series([np.nan]),
+        pd.Series([None]),
+        pd.Series(["a", "b", "", "c", None, "e"]),
+    ]
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": 1, "b": 2, "c": 24, "d": 1010},
+        {"a": 1},
+        {1: "a", 2: "b", 24: "c", 1010: "d"},
+        {1: "a"},
+    ],
+)
+def test_series_init_dict(data):
+    pandas_series = pd.Series(data)
+    cudf_series = cudf.Series(data)
+
+    assert_eq(pandas_series, cudf_series)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": [1, 2, 3],
+            "b": [2, 3, 5],
+            "c": [24, 12212, 22233],
+            "d": [1010, 101010, 1111],
+        },
+        {"a": [1]},
+    ],
+)
+def test_series_init_dict_lists(data):
+    assert_eq(pd.Series(data), cudf.Series(data))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4],
+        [1.0, 12.221, 12.34, 13.324, 324.3242],
+        [-10, -1111, 100, 11, 133],
+    ],
+)
+@pytest.mark.parametrize(
+    "others",
+    [
+        [10, 11, 12, 13],
+        [0.1, 0.002, 324.2332, 0.2342],
+        [-10, -1111, 100, 11, 133],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_series_append_basic(data, others, ignore_index):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    other_ps = pd.Series(others)
+    other_gs = cudf.Series(others)
+
+    with pytest.warns(FutureWarning):
+        expected = psr.append(other_ps, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning):
+        actual = gsr.append(other_gs, ignore_index=ignore_index)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "abc",
+            "def",
+            "this is a string",
+            "this is another string",
+            "a",
+            "b",
+            "c",
+        ],
+        ["a"],
+    ],
+)
+@pytest.mark.parametrize(
+    "others",
+    [
+        [
+            "abc",
+            "def",
+            "this is a string",
+            "this is another string",
+            "a",
+            "b",
+            "c",
+        ],
+        ["a"],
+        ["1", "2", "3", "4", "5"],
+        ["+", "-", "!", "_", "="],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_series_append_basic_str(data, others, ignore_index):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    other_ps = pd.Series(others)
+    other_gs = cudf.Series(others)
+
+    with pytest.warns(FutureWarning):
+        expected = psr.append(other_ps, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning):
+        actual = gsr.append(other_gs, ignore_index=ignore_index)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series(
+            [
+                "abc",
+                "def",
+                "this is a string",
+                "this is another string",
+                "a",
+                "b",
+                "c",
+            ],
+            index=[10, 20, 30, 40, 50, 60, 70],
+        ),
+        pd.Series(["a"], index=[2]),
+    ],
+)
+@pytest.mark.parametrize(
+    "others",
+    [
+        pd.Series(
+            [
+                "abc",
+                "def",
+                "this is a   string",
+                "this is another string",
+                "a",
+                "b",
+                "c",
+            ],
+            index=[10, 20, 30, 40, 50, 60, 70],
+        ),
+        pd.Series(["a"], index=[133]),
+        pd.Series(["1", "2", "3", "4", "5"], index=[-10, 22, 33, 44, 49]),
+        pd.Series(["+", "-", "!", "_", "="], index=[11, 22, 33, 44, 2]),
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_series_append_series_with_index(data, others, ignore_index):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    other_ps = others
+    other_gs = cudf.from_pandas(others)
+
+    with pytest.warns(FutureWarning):
+        expected = psr.append(other_ps, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning):
+        actual = gsr.append(other_gs, ignore_index=ignore_index)
+    assert_eq(expected, actual)
+
+
+def test_series_append_error_mixed_types():
+    gsr = cudf.Series([1, 2, 3, 4])
+    other = cudf.Series(["a", "b", "c", "d"])
+
+    with pytest.raises(
+        TypeError,
+        match="cudf does not support mixed types, please type-cast "
+        "both series to same dtypes.",
+    ):
+        with pytest.warns(FutureWarning):
+            gsr.append(other)
+
+    with pytest.raises(
+        TypeError,
+        match="cudf does not support mixed types, please type-cast "
+        "both series to same dtypes.",
+    ):
+        with pytest.warns(FutureWarning):
+            gsr.append([gsr, other, gsr, other])
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"]),
+        pd.Series(
+            [1.0, 12.221, 12.34, 13.324, 324.3242],
+            index=[
+                "float one",
+                "float two",
+                "float three",
+                "float four",
+                "float five",
+            ],
+        ),
+        pd.Series(
+            [-10, -1111, 100, 11, 133],
+            index=["one", "two", "three", "four", "five"],
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "others",
+    [
+        [
+            pd.Series([10, 11, 12, 13], index=["a", "b", "c", "d"]),
+            pd.Series([12, 14, 15, 27], index=["d", "e", "z", "x"]),
+        ],
+        [
+            pd.Series([10, 11, 12, 13], index=["a", "b", "c", "d"]),
+            pd.Series([12, 14, 15, 27], index=["d", "e", "z", "x"]),
+        ]
+        * 25,
+        [
+            pd.Series(
+                [0.1, 0.002, 324.2332, 0.2342], index=["-", "+", "%", "#"]
+            ),
+            pd.Series([12, 14, 15, 27], index=["d", "e", "z", "x"]),
+        ]
+        * 46,
+        [
+            pd.Series(
+                [-10, -1111, 100, 11, 133],
+                index=["aa", "vv", "bb", "dd", "ll"],
+            )
+        ],
+    ],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_series_append_list_series_with_index(data, others, ignore_index):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    other_ps = others
+    other_gs = [cudf.from_pandas(obj) for obj in others]
+
+    with pytest.warns(FutureWarning):
+        expected = psr.append(other_ps, ignore_index=ignore_index)
+    with pytest.warns(FutureWarning):
+        actual = gsr.append(other_gs, ignore_index=ignore_index)
+    assert_eq(expected, actual)
+
+
+def test_series_append_existing_buffers():
+    a1 = np.arange(10, dtype=np.float64)
+    gs = cudf.Series(a1)
+
+    # Add new buffer
+    a2 = cudf.Series(np.arange(5))
+    with pytest.warns(FutureWarning):
+        gs = gs.append(a2)
+    assert len(gs) == 15
+    np.testing.assert_equal(gs.to_numpy(), np.hstack([a1, a2.to_numpy()]))
+
+    # Ensure appending to previous buffer
+    a3 = cudf.Series(np.arange(3))
+    with pytest.warns(FutureWarning):
+        gs = gs.append(a3)
+    assert len(gs) == 18
+    a4 = np.hstack([a1, a2.to_numpy(), a3.to_numpy()])
+    np.testing.assert_equal(gs.to_numpy(), a4)
+
+    # Appending different dtype
+    a5 = cudf.Series(np.array([1, 2, 3], dtype=np.int32))
+    a6 = cudf.Series(np.array([4.5, 5.5, 6.5], dtype=np.float64))
+    with pytest.warns(FutureWarning):
+        gs = a5.append(a6)
+    np.testing.assert_equal(
+        gs.to_numpy(), np.hstack([a5.to_numpy(), a6.to_numpy()])
+    )
+    with pytest.warns(FutureWarning):
+        gs = cudf.Series(a6).append(a5)
+    np.testing.assert_equal(
+        gs.to_numpy(), np.hstack([a6.to_numpy(), a5.to_numpy()])
+    )
+
+
+def test_series_column_iter_error():
+    gs = cudf.Series([1, 2, 3])
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"{gs.__class__.__name__} object is not iterable. "
+            f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
+            f"if you wish to iterate over the values."
+        ),
+    ):
+        iter(gs)
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"{gs.__class__.__name__} object is not iterable. "
+            f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
+            f"if you wish to iterate over the values."
+        ),
+    ):
+        gs.items()
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            f"{gs.__class__.__name__} object is not iterable. "
+            f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
+            f"if you wish to iterate over the values."
+        ),
+    ):
+        gs.iteritems()
+
+    with pytest.raises(TypeError):
+        iter(gs._column)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.0, 2.0, None, 4.0, 5.0],
+        ["a", "b", "c", "d", "e"],
+        ["a", "b", None, "d", "e"],
+        [None, None, None, None, None],
+        np.array(["1991-11-20", "2004-12-04"], dtype=np.datetime64),
+        np.array(["1991-11-20", None], dtype=np.datetime64),
+        np.array(
+            ["1991-11-20 05:15:00", "2004-12-04 10:00:00"], dtype=np.datetime64
+        ),
+        np.array(["1991-11-20 05:15:00", None], dtype=np.datetime64),
+    ],
+)
+def test_series_tolist(data):
+    psr = pd.Series(data)
+    gsr = cudf.from_pandas(psr)
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            r"cuDF does not support conversion to host memory "
+            r"via the `tolist()` method. Consider using "
+            r"`.to_arrow().to_pylist()` to construct a Python list."
+        ),
+    ):
+        gsr.tolist()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [[], [None, None], ["a"], ["a", "b", "c"] * 500, [1.0, 2.0, 0.3] * 57],
+)
+def test_series_size(data):
+    psr = _create_pandas_series_float64_default(data)
+    gsr = _create_cudf_series_float64_default(data)
+
+    assert_eq(psr.size, gsr.size)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+def test_series_describe_numeric(dtype):
+    ps = pd.Series([0, 1, 2, 3, 1, 2, 3], dtype=dtype)
+    gs = cudf.from_pandas(ps)
+    with pytest.warns(FutureWarning):
+        actual = gs.describe()
+    expected = ps.describe()
+
+    assert_eq(expected, actual, check_dtype=True)
+
+
+@pytest.mark.parametrize("dtype", ["datetime64[ns]"])
+def test_series_describe_datetime(dtype):
+    # Note that other datetime units are not tested because pandas does not
+    # support them. When specified coarser units, cuDF datetime columns cannot
+    # represent fractional time for quantiles of the column, which may require
+    # interpolation, this differs from pandas which always stay in [ns] unit.
+    gs = cudf.Series([0, 1, 2, 3, 1, 2, 3], dtype=dtype)
+    ps = gs.to_pandas()
+
+    # Treating datetimes as categoricals is deprecated in pandas and will
+    # be removed in future. Future behavior is treating datetime as numeric.
+    expected = ps.describe(datetime_is_numeric=True)
+    with pytest.warns(FutureWarning):
+        actual = gs.describe()
+
+    assert_eq(expected.astype("str"), actual)
+
+
+@pytest.mark.parametrize("dtype", TIMEDELTA_TYPES)
+def test_series_describe_timedelta(dtype):
+    ps = pd.Series([0, 1, 2, 3, 1, 2, 3], dtype=dtype)
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.describe()
+    with pytest.warns(FutureWarning):
+        actual = gs.describe()
+
+    assert_eq(actual, expected.astype("str"))
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series(["a", "b", "c", "d", "e", "a"]),
+        pd.Series([True, False, True, True, False]),
+        pd.Series([], dtype="str"),
+        pd.Series(["a", "b", "c", "a"], dtype="category"),
+        pd.Series(["d", "e", "f"], dtype="category"),
+        pd.Series(pd.Categorical(["d", "e", "f"], categories=["f", "e", "d"])),
+        pd.Series(
+            pd.Categorical(
+                ["d", "e", "f"], categories=["f", "e", "d"], ordered=True
+            )
+        ),
+    ],
+)
+def test_series_describe_other_types(ps):
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.describe()
+    with pytest.warns(FutureWarning):
+        actual = gs.describe()
+
+    if len(ps) == 0:
+        assert_eq(expected.fillna("a").astype("str"), actual.fillna("a"))
+    else:
+        assert_eq(expected.astype("str"), actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 2, 1],
+        [1, 2, None, 3, 1, 1],
+        [],
+        ["a", "b", "c", None, "z", "a"],
+    ],
+)
+@pytest.mark.parametrize("na_sentinel", [99999, 11, -1, 0])
+def test_series_factorize(data, na_sentinel):
+    gsr = _create_cudf_series_float64_default(data)
+    psr = gsr.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        expected_labels, expected_cats = psr.factorize(na_sentinel=na_sentinel)
+    with pytest.warns(FutureWarning):
+        actual_labels, actual_cats = gsr.factorize(na_sentinel=na_sentinel)
+
+    assert_eq(expected_labels, actual_labels.get())
+    assert_eq(expected_cats.values, actual_cats.to_pandas().values)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 2, 1],
+        [1, 2, None, 3, 1, 1],
+        [],
+        ["a", "b", "c", None, "z", "a"],
+    ],
+)
+@pytest.mark.parametrize("use_na_sentinel", [True, False])
+def test_series_factorize_use_na_sentinel(data, use_na_sentinel):
+    gsr = _create_cudf_series_float64_default(data)
+    psr = gsr.to_pandas(nullable=True)
+
+    expected_labels, expected_cats = psr.factorize(
+        use_na_sentinel=use_na_sentinel, sort=True
+    )
+    actual_labels, actual_cats = gsr.factorize(
+        use_na_sentinel=use_na_sentinel, sort=True
+    )
+    assert_eq(expected_labels, actual_labels.get())
+    assert_eq(expected_cats, actual_cats.to_pandas(nullable=True))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 2, 1],
+        [1, 2, None, 3, 1, 1],
+        [],
+        ["a", "b", "c", None, "z", "a"],
+    ],
+)
+@pytest.mark.parametrize("sort", [True, False])
+def test_series_factorize_sort(data, sort):
+    gsr = _create_cudf_series_float64_default(data)
+    psr = gsr.to_pandas(nullable=True)
+
+    expected_labels, expected_cats = psr.factorize(sort=sort)
+    actual_labels, actual_cats = gsr.factorize(sort=sort)
+    assert_eq(expected_labels, actual_labels.get())
+    assert_eq(expected_cats, actual_cats.to_pandas(nullable=True))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([], dtype="datetime64[ns]"),
+        pd.Series(pd.date_range("2010-01-01", "2010-02-01")),
+        pd.Series([None, None], dtype="datetime64[ns]"),
+    ],
+)
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("nulls", ["none", "some"])
+def test_series_datetime_value_counts(data, nulls, normalize, dropna):
+    psr = data.copy()
+
+    if len(data) > 0:
+        if nulls == "one":
+            p = np.random.randint(0, len(data))
+            psr[p] = None
+        elif nulls == "some":
+            p = np.random.randint(0, len(data), 2)
+            psr[p] = None
+
+    gsr = cudf.from_pandas(psr)
+    expected = psr.value_counts(dropna=dropna, normalize=normalize)
+    got = gsr.value_counts(dropna=dropna, normalize=normalize)
+
+    assert_eq(expected.sort_index(), got.sort_index(), check_dtype=False)
+    assert_eq(
+        expected.reset_index(drop=True),
+        got.reset_index(drop=True),
+        check_dtype=False,
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("num_elements", [10, 100, 1000])
+def test_categorical_value_counts(dropna, normalize, num_elements):
+    # create categorical series
+    np.random.seed(12)
+    pd_cat = pd.Categorical(
+        pd.Series(
+            np.random.choice(list(ascii_letters + digits), num_elements),
+            dtype="category",
+        )
+    )
+
+    # gdf
+    gdf = cudf.DataFrame()
+    gdf["a"] = cudf.Series.from_categorical(pd_cat)
+    gdf_value_counts = gdf["a"].value_counts(
+        dropna=dropna, normalize=normalize
+    )
+
+    # pandas
+    pdf = pd.DataFrame()
+    pdf["a"] = pd_cat
+    pdf_value_counts = pdf["a"].value_counts(
+        dropna=dropna, normalize=normalize
+    )
+
+    # verify
+    assert_eq(
+        pdf_value_counts.sort_index(),
+        gdf_value_counts.sort_index(),
+        check_dtype=False,
+        check_index_type=True,
+    )
+    assert_eq(
+        pdf_value_counts.reset_index(drop=True),
+        gdf_value_counts.reset_index(drop=True),
+        check_dtype=False,
+        check_index_type=True,
+    )
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("normalize", [True, False])
+def test_series_value_counts(dropna, normalize):
+    for size in [10**x for x in range(5)]:
+        arr = np.random.randint(low=-1, high=10, size=size)
+        mask = arr != -1
+        sr = cudf.Series.from_masked_array(
+            arr, cudf.Series(mask)._column.as_mask()
+        )
+        sr.name = "col"
+
+        expect = (
+            sr.to_pandas()
+            .value_counts(dropna=dropna, normalize=normalize)
+            .sort_index()
+        )
+        got = sr.value_counts(dropna=dropna, normalize=normalize).sort_index()
+
+        assert_eq(expect, got, check_dtype=True, check_index_type=False)
+
+
+@pytest.mark.parametrize("bins", [1, 2, 3])
+def test_series_value_counts_bins(bins):
+    psr = pd.Series([1.0, 2.0, 2.0, 3.0, 3.0, 3.0])
+    gsr = cudf.from_pandas(psr)
+
+    expected = psr.value_counts(bins=bins)
+    got = gsr.value_counts(bins=bins)
+
+    assert_eq(expected.sort_index(), got.sort_index(), check_dtype=True)
+
+
+@pytest.mark.parametrize("bins", [1, 2, 3])
+@pytest.mark.parametrize("dropna", [True, False])
+def test_series_value_counts_bins_dropna(bins, dropna):
+    psr = pd.Series([1.0, 2.0, 2.0, 3.0, 3.0, 3.0, np.nan])
+    gsr = cudf.from_pandas(psr)
+
+    expected = psr.value_counts(bins=bins, dropna=dropna)
+    got = gsr.value_counts(bins=bins, dropna=dropna)
+
+    assert_eq(expected.sort_index(), got.sort_index(), check_dtype=True)
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("normalize", [True, False])
+def test_series_value_counts_optional_arguments(ascending, dropna, normalize):
+    psr = pd.Series([1.0, 2.0, 2.0, 3.0, 3.0, 3.0, None])
+    gsr = cudf.from_pandas(psr)
+
+    expected = psr.value_counts(
+        ascending=ascending, dropna=dropna, normalize=normalize
+    )
+    got = gsr.value_counts(
+        ascending=ascending, dropna=dropna, normalize=normalize
+    )
+
+    assert_eq(expected.sort_index(), got.sort_index(), check_dtype=True)
+    assert_eq(
+        expected.reset_index(drop=True),
+        got.reset_index(drop=True),
+        check_dtype=True,
+    )
+
+
+@pytest.mark.parametrize(
+    "gs",
+    [
+        cudf.Series([1, 2, 3]),
+        cudf.Series([None]),
+        cudf.Series([4]),
+        cudf.Series([2, 3, -1, 0, 1], name="test name"),
+        cudf.Series(
+            [1, 2, 3, None, 2, 1], index=["a", "v", "d", "e", "f", "g"]
+        ),
+        cudf.Series([1, 2, 3, None, 2, 1, None], name="abc"),
+        cudf.Series(["ab", "bc", "ab", None, "bc", None, None]),
+        cudf.Series([None, None, None, None, None], dtype="str"),
+        cudf.Series([None, None, None, None, None]),
+        cudf.Series(
+            [
+                123213,
+                23123,
+                123123,
+                12213123,
+                12213123,
+                12213123,
+                23123,
+                2312323123,
+                None,
+                None,
+            ],
+            dtype="timedelta64[ns]",
+        ),
+        cudf.Series(
+            [
+                None,
+                1,
+                2,
+                3242434,
+                3233243,
+                1,
+                2,
+                1023,
+                None,
+                12213123,
+                None,
+                2312323123,
+                None,
+                None,
+            ],
+            dtype="datetime64[ns]",
+        ),
+        cudf.Series(name="empty series", dtype="float64"),
+        cudf.Series(["a", "b", "c", " ", "a", "b", "z"], dtype="category"),
+    ],
+)
+@pytest.mark.parametrize("dropna", [True, False])
+def test_series_mode(gs, dropna):
+    ps = gs.to_pandas()
+
+    expected = ps.mode(dropna=dropna)
+    actual = gs.mode(dropna=dropna)
+
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "arr",
+    [
+        np.random.normal(-100, 100, 1000),
+        np.random.randint(-50, 50, 1000),
+        np.zeros(100),
+        np.repeat([-0.6459412758761901], 100),
+        np.repeat(np.nan, 100),
+        np.array([1.123, 2.343, np.nan, 0.0]),
+        np.arange(-100.5, 101.5, 1),
+    ],
+)
+@pytest.mark.parametrize("decimals", [-5, -3, -1, 0, 1, 4, 12, np.int8(1)])
+def test_series_round(arr, decimals):
+    pser = pd.Series(arr)
+    ser = cudf.Series(arr)
+    result = ser.round(decimals)
+    expected = pser.round(decimals)
+
+    assert_eq(result, expected)
+
+    # with nulls, maintaining existing null mask
+    arr = arr.astype("float64")  # for pandas nulls
+    arr.ravel()[
+        np.random.choice(arr.shape[0], arr.shape[0] // 2, replace=False)
+    ] = np.nan
+
+    pser = pd.Series(arr)
+    ser = cudf.Series(arr)
+    result = ser.round(decimals)
+    expected = pser.round(decimals)
+
+    assert_eq(result, expected)
+
+
+def test_series_round_half_up():
+    s = cudf.Series([0.0, 1.0, 1.2, 1.7, 0.5, 1.5, 2.5, None])
+    expect = cudf.Series([0.0, 1.0, 1.0, 2.0, 1.0, 2.0, 3.0, None])
+    got = s.round(how="half_up")
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "series",
+    [
+        cudf.Series([1.0, None, np.nan, 4.0], nan_as_null=False),
+        cudf.Series([1.24430, None, np.nan, 4.423530], nan_as_null=False),
+        cudf.Series([1.24430, np.nan, 4.423530], nan_as_null=False),
+        cudf.Series([-1.24430, np.nan, -4.423530], nan_as_null=False),
+        cudf.Series(np.repeat(np.nan, 100)),
+    ],
+)
+@pytest.mark.parametrize("decimal", [0, 1, 2, 3])
+def test_round_nan_as_null_false(series, decimal):
+    pser = series.to_pandas()
+    result = series.round(decimal)
+    expected = pser.round(decimal)
+    assert_eq(result, expected, atol=1e-10)
+
+
+@pytest.mark.parametrize("ps", _series_na_data())
+@pytest.mark.parametrize("nan_as_null", [True, False, None])
+def test_series_isnull_isna(ps, nan_as_null):
+
+    gs = cudf.Series.from_pandas(ps, nan_as_null=nan_as_null)
+
+    assert_eq(ps.isnull(), gs.isnull())
+    assert_eq(ps.isna(), gs.isna())
+
+
+@pytest.mark.parametrize("ps", _series_na_data())
+@pytest.mark.parametrize("nan_as_null", [True, False, None])
+def test_series_notnull_notna(ps, nan_as_null):
+
+    gs = cudf.Series.from_pandas(ps, nan_as_null=nan_as_null)
+
+    assert_eq(ps.notnull(), gs.notnull())
+    assert_eq(ps.notna(), gs.notna())
+
+
+@pytest.mark.parametrize(
+    "sr1", [pd.Series([10, 11, 12], index=["a", "b", "z"]), pd.Series(["a"])]
+)
+@pytest.mark.parametrize(
+    "sr2",
+    [pd.Series([], dtype="float64"), pd.Series(["a", "a", "c", "z", "A"])],
+)
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.eq,
+        operator.ne,
+        operator.lt,
+        operator.gt,
+        operator.le,
+        operator.ge,
+    ],
+)
+def test_series_error_equality(sr1, sr2, op):
+    gsr1 = cudf.from_pandas(sr1)
+    gsr2 = cudf.from_pandas(sr2)
+
+    assert_exceptions_equal(op, op, ([sr1, sr2],), ([gsr1, gsr2],))
+
+
+def test_series_memory_usage():
+    sr = cudf.Series([1, 2, 3, 4], dtype="int64")
+    assert sr.memory_usage() == 32
+
+    sliced_sr = sr[2:]
+    assert sliced_sr.memory_usage() == 16
+
+    sliced_sr[3] = None
+    assert sliced_sr.memory_usage() == 80
+
+    sr = cudf.Series(["hello world", "rapids ai", "abc", "z"])
+    assert sr.memory_usage() == 44
+
+    assert sr[3:].memory_usage() == 9  # z
+    assert sr[:1].memory_usage() == 19  # hello world
+
+
+@pytest.mark.parametrize(
+    "sr,expected_psr",
+    [
+        (
+            cudf.Series([1, 2, None, 3], dtype="uint8"),
+            pd.Series([1, 2, None, 3], dtype=pd.UInt8Dtype()),
+        ),
+        (
+            cudf.Series([23, None, None, 32], dtype="uint16"),
+            pd.Series([23, None, None, 32], dtype=pd.UInt16Dtype()),
+        ),
+        (
+            cudf.Series([None, 123, None, 1], dtype="uint32"),
+            pd.Series([None, 123, None, 1], dtype=pd.UInt32Dtype()),
+        ),
+        (
+            cudf.Series([234, 2323, 23432, None, None, 224], dtype="uint64"),
+            pd.Series(
+                [234, 2323, 23432, None, None, 224], dtype=pd.UInt64Dtype()
+            ),
+        ),
+        (
+            cudf.Series([-10, 1, None, -1, None, 3], dtype="int8"),
+            pd.Series([-10, 1, None, -1, None, 3], dtype=pd.Int8Dtype()),
+        ),
+        (
+            cudf.Series([111, None, 222, None, 13], dtype="int16"),
+            pd.Series([111, None, 222, None, 13], dtype=pd.Int16Dtype()),
+        ),
+        (
+            cudf.Series([11, None, 22, 33, None, 2, None, 3], dtype="int32"),
+            pd.Series(
+                [11, None, 22, 33, None, 2, None, 3], dtype=pd.Int32Dtype()
+            ),
+        ),
+        (
+            cudf.Series(
+                [32431, None, None, 32322, 0, 10, -32324, None], dtype="int64"
+            ),
+            pd.Series(
+                [32431, None, None, 32322, 0, 10, -32324, None],
+                dtype=pd.Int64Dtype(),
+            ),
+        ),
+        (
+            cudf.Series(
+                [True, None, False, None, False, True, True, False],
+                dtype="bool_",
+            ),
+            pd.Series(
+                [True, None, False, None, False, True, True, False],
+                dtype=pd.BooleanDtype(),
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    "abc",
+                    "a",
+                    None,
+                    "hello world",
+                    "foo buzz",
+                    "",
+                    None,
+                    "rapids ai",
+                ],
+                dtype="object",
+            ),
+            pd.Series(
+                [
+                    "abc",
+                    "a",
+                    None,
+                    "hello world",
+                    "foo buzz",
+                    "",
+                    None,
+                    "rapids ai",
+                ],
+                dtype=pd.StringDtype(),
+            ),
+        ),
+        (
+            cudf.Series(
+                [1, 2, None, 10.2, None],
+                dtype="float32",
+            ),
+            pd.Series(
+                [1, 2, None, 10.2, None],
+                dtype=pd.Float32Dtype(),
+            ),
+        ),
+    ],
+)
+def test_series_to_pandas_nullable_dtypes(sr, expected_psr):
+    actual_psr = sr.to_pandas(nullable=True)
+
+    assert_eq(actual_psr, expected_psr)
+
+
+def test_series_pipe():
+    psr = pd.Series([10, 20, 30, 40])
+    gsr = cudf.Series([10, 20, 30, 40])
+
+    def custom_add_func(sr, val):
+        new_sr = sr + val
+        return new_sr
+
+    def custom_to_str_func(sr, val):
+        new_sr = sr.astype("str") + val
+        return new_sr
+
+    expected = (
+        psr.pipe(custom_add_func, 11)
+        .pipe(custom_add_func, val=12)
+        .pipe(custom_to_str_func, "rapids")
+    )
+    actual = (
+        gsr.pipe(custom_add_func, 11)
+        .pipe(custom_add_func, val=12)
+        .pipe(custom_to_str_func, "rapids")
+    )
+
+    assert_eq(expected, actual)
+
+    expected = (
+        psr.pipe((custom_add_func, "sr"), val=11)
+        .pipe(custom_add_func, val=1)
+        .pipe(custom_to_str_func, "rapids-ai")
+    )
+    actual = (
+        gsr.pipe((custom_add_func, "sr"), val=11)
+        .pipe(custom_add_func, val=1)
+        .pipe(custom_to_str_func, "rapids-ai")
+    )
+
+    assert_eq(expected, actual)
+
+
+def test_series_pipe_error():
+    psr = pd.Series([10, 20, 30, 40])
+    gsr = cudf.Series([10, 20, 30, 40])
+
+    def custom_add_func(sr, val):
+        new_sr = sr + val
+        return new_sr
+
+    assert_exceptions_equal(
+        lfunc=psr.pipe,
+        rfunc=gsr.pipe,
+        lfunc_args_and_kwargs=([(custom_add_func, "val")], {"val": 11}),
+        rfunc_args_and_kwargs=([(custom_add_func, "val")], {"val": 11}),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [cudf.Series([1, 2, 3]), cudf.Series([10, 11, 12], index=[1, 2, 3])],
+)
+@pytest.mark.parametrize(
+    "other",
+    [
+        cudf.Series([4, 5, 6]),
+        cudf.Series([4, 5, 6, 7, 8]),
+        cudf.Series([4, np.nan, 6], nan_as_null=False),
+        [4, np.nan, 6],
+        {1: 9},
+    ],
+)
+def test_series_update(data, other):
+    gs = data.copy(deep=True)
+    if isinstance(other, cudf.Series):
+        g_other = other.copy(deep=True)
+        p_other = g_other.to_pandas()
+    else:
+        g_other = other
+        p_other = other
+
+    ps = gs.to_pandas()
+
+    ps.update(p_other)
+    with expect_warning_if(
+        isinstance(other, cudf.Series) and other.isna().any(), UserWarning
+    ):
+        gs.update(g_other)
+    assert_eq(gs, ps)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, None, 11, 2.0, np.nan],
+        [np.nan],
+        [None, None, None],
+        [np.nan, 1, 10, 393.32, np.nan],
+    ],
+)
+@pytest.mark.parametrize("nan_as_null", [True, False])
+@pytest.mark.parametrize("fill_value", [1.2, 332, np.nan])
+def test_fillna_with_nan(data, nan_as_null, fill_value):
+    gs = cudf.Series(data, dtype="float64", nan_as_null=nan_as_null)
+    ps = gs.to_pandas()
+
+    expected = ps.fillna(fill_value)
+    actual = gs.fillna(fill_value)
+
+    assert_eq(expected, actual)
+
+
+def test_series_mask_mixed_dtypes_error():
+    s = cudf.Series(["a", "b", "c"])
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "cudf does not support mixed types, please type-cast "
+            "the column of dataframe/series and other "
+            "to same dtypes."
+        ),
+    ):
+        s.where([True, False, True], [1, 2, 3])
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series(["a"] * 20, index=range(0, 20)),
+        pd.Series(["b", None] * 10, index=range(0, 20), name="ASeries"),
+        pd.Series(
+            ["b", None] * 5,
+            index=pd.Index(list(range(10)), dtype="uint64"),
+            name="BSeries",
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "labels",
+    [
+        [1],
+        [0],
+        1,
+        5,
+        [5, 9],
+        pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
+        pd.Index([0, 1, 2, 3, 4], dtype="float32"),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_series_drop_labels(ps, labels, inplace):
+    ps = ps.copy()
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.drop(labels=labels, axis=0, inplace=inplace)
+    actual = gs.drop(labels=labels, axis=0, inplace=inplace)
+
+    if inplace:
+        expected = ps
+        actual = gs
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series(["a"] * 20, index=range(0, 20)),
+        pd.Series(["b", None] * 10, index=range(0, 20), name="ASeries"),
+    ],
+)
+@pytest.mark.parametrize(
+    "index",
+    [[1], [0], 1, 5, [5, 9], pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_series_drop_index(ps, index, inplace):
+    ps = ps.copy()
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.drop(index=index, inplace=inplace)
+    actual = gs.drop(index=index, inplace=inplace)
+
+    if inplace:
+        expected = ps
+        actual = gs
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series(
+            ["a" if i % 2 == 0 else "b" for i in range(0, 10)],
+            index=pd.MultiIndex(
+                levels=[
+                    ["lama", "cow", "falcon"],
+                    ["speed", "weight", "length"],
+                ],
+                codes=[
+                    [0, 0, 0, 1, 1, 1, 2, 2, 2, 1],
+                    [0, 1, 2, 0, 1, 2, 0, 1, 2, 1],
+                ],
+            ),
+            name="abc",
+        )
+    ],
+)
+@pytest.mark.parametrize(
+    "index,level",
+    [
+        ("cow", 0),
+        ("lama", 0),
+        ("falcon", 0),
+        ("speed", 1),
+        ("weight", 1),
+        ("length", 1),
+        (
+            "cow",
+            None,
+        ),
+        (
+            "lama",
+            None,
+        ),
+        (
+            "falcon",
+            None,
+        ),
+    ],
+)
+@pytest.mark.parametrize("inplace", [True, False])
+def test_series_drop_multiindex(ps, index, level, inplace):
+    ps = ps.copy()
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.drop(index=index, inplace=inplace, level=level)
+    actual = gs.drop(index=index, inplace=inplace, level=level)
+
+    if inplace:
+        expected = ps
+        actual = gs
+
+    assert_eq(expected, actual)
+
+
+def test_series_drop_edge_inputs():
+    gs = cudf.Series([42], name="a")
+    ps = gs.to_pandas()
+
+    assert_eq(ps.drop(columns=["b"]), gs.drop(columns=["b"]))
+
+    assert_eq(ps.drop(columns="b"), gs.drop(columns="b"))
+
+    assert_exceptions_equal(
+        lfunc=ps.drop,
+        rfunc=gs.drop,
+        lfunc_args_and_kwargs=(["a"], {"columns": "a", "axis": 1}),
+        rfunc_args_and_kwargs=(["a"], {"columns": "a", "axis": 1}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=ps.drop,
+        rfunc=gs.drop,
+        lfunc_args_and_kwargs=([], {}),
+        rfunc_args_and_kwargs=([], {}),
+    )
+
+    assert_exceptions_equal(
+        lfunc=ps.drop,
+        rfunc=gs.drop,
+        lfunc_args_and_kwargs=(["b"], {"axis": 1}),
+        rfunc_args_and_kwargs=(["b"], {"axis": 1}),
+    )
+
+
+def test_series_drop_raises():
+    gs = cudf.Series([10, 20, 30], index=["x", "y", "z"], name="c")
+    ps = gs.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=ps.drop,
+        rfunc=gs.drop,
+        lfunc_args_and_kwargs=(["p"],),
+        rfunc_args_and_kwargs=(["p"],),
+    )
+
+    # dtype specified mismatch
+    assert_exceptions_equal(
+        lfunc=ps.drop,
+        rfunc=gs.drop,
+        lfunc_args_and_kwargs=([3],),
+        rfunc_args_and_kwargs=([3],),
+    )
+
+    expect = ps.drop("p", errors="ignore")
+    actual = gs.drop("p", errors="ignore")
+
+    assert_eq(actual, expect)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [[[1, 2, 3], None, [4], [], [5, 6]], [1, 2, 3, 4, 5]],
+)
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize(
+    "p_index",
+    [
+        None,
+        ["ia", "ib", "ic", "id", "ie"],
+        pd.MultiIndex.from_tuples(
+            [(0, "a"), (0, "b"), (0, "c"), (1, "a"), (1, "b")]
+        ),
+    ],
+)
+def test_explode(data, ignore_index, p_index):
+    pdf = pd.Series(data, index=p_index, name="someseries")
+    gdf = cudf.from_pandas(pdf)
+
+    expect = pdf.explode(ignore_index)
+    got = gdf.explode(ignore_index)
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data, expected",
+    [
+        (
+            [cudf.Series([1, 2, 3]), cudf.Series([10, 20])],
+            cudf.Series([[1, 2, 3], [10, 20]]),
+        ),
+        (
+            [cudf.Series([1, 2, 3]), None, cudf.Series([10, 20, np.nan])],
+            cudf.Series([[1, 2, 3], None, [10, 20, np.nan]]),
+        ),
+        (
+            [cp.array([5, 6]), cudf.NA, cp.array([1])],
+            cudf.Series([[5, 6], None, [1]]),
+        ),
+        (
+            [None, None, None, None, None, cudf.Series([10, 20])],
+            cudf.Series([None, None, None, None, None, [10, 20]]),
+        ),
+    ],
+)
+def test_nested_series_from_sequence_data(data, expected):
+    actual = cudf.Series(data)
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cp.ones(5, dtype=cp.float16),
+        np.ones(5, dtype="float16"),
+        pd.Series([0.1, 1.2, 3.3], dtype="float16"),
+        pytest.param(
+            pa.array(np.ones(5, dtype="float16")),
+            marks=pytest.mark.xfail(
+                reason="https://issues.apache.org/jira/browse/ARROW-13762"
+            ),
+        ),
+    ],
+)
+def test_series_upcast_float16(data):
+    actual_series = cudf.Series(data)
+    expected_series = cudf.Series(data, dtype="float32")
+    assert_eq(actual_series, expected_series)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.RangeIndex(0, 3, 1),
+        [3.0, 1.0, np.nan],
+        ["a", "z", None],
+        pytest.param(
+            pd.RangeIndex(4, -1, -2),
+            marks=[
+                pytest.mark.xfail(
+                    condition=PANDAS_LT_140,
+                    reason="https://github.com/pandas-dev/pandas/issues/43591",
+                )
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("axis", [0, "index"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_series_sort_index(
+    index, axis, ascending, inplace, ignore_index, na_position
+):
+    ps = pd.Series([10, 3, 12], index=index)
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.sort_index(
+        axis=axis,
+        ascending=ascending,
+        ignore_index=ignore_index,
+        inplace=inplace,
+        na_position=na_position,
+    )
+    got = gs.sort_index(
+        axis=axis,
+        ascending=ascending,
+        ignore_index=ignore_index,
+        inplace=inplace,
+        na_position=na_position,
+    )
+
+    if inplace is True:
+        assert_eq(ps, gs, check_index_type=True)
+    else:
+        assert_eq(expected, got, check_index_type=True)
+
+
+@pytest.mark.parametrize("method", ["md5"])
+def test_series_hash_values(method):
+    inputs = cudf.Series(
+        [
+            "",
+            "0",
+            "A 56 character string to test message padding algorithm.",
+            "A 63 character string to test message padding algorithm, again.",
+            "A 64 character string to test message padding algorithm, again!!",
+            (
+                "A very long (greater than 128 bytes/char string) to execute "
+                "a multi hash-step data point in the hash function being "
+                "tested. This string needed to be longer."
+            ),
+            "All work and no play makes Jack a dull boy",
+            "!\"#$%&'()*+,-./0123456789:;<=>?@[\\]^_`{|}~",
+            "\x00\x00\x00\x10\x00\x00\x00\x00",
+            "\x00\x00\x00\x00",
+        ]
+    )
+
+    def hashlib_compute_digest(data):
+        hasher = getattr(hashlib, method)()
+        hasher.update(data.encode("utf-8"))
+        return hasher.hexdigest()
+
+    hashlib_validation = inputs.to_pandas().apply(hashlib_compute_digest)
+    validation_results = cudf.Series(hashlib_validation)
+    hash_values = inputs.hash_values(method=method)
+    assert_eq(hash_values, validation_results)
+
+
+def test_series_hash_values_invalid_method():
+    inputs = cudf.Series(["", "0"])
+    with pytest.raises(ValueError):
+        inputs.hash_values(method="invalid_method")
+
+
+def test_set_index_unequal_length():
+    s = cudf.Series(dtype="float64")
+    with pytest.raises(ValueError):
+        s.index = [1, 2, 3]
+
+
+@pytest.mark.parametrize(
+    "lhs, rhs", [("a", "a"), ("a", "b"), (1, 1.0), (None, None), (None, "a")]
+)
+def test_equals_names(lhs, rhs):
+    lhs = cudf.Series([1, 2], name=lhs)
+    rhs = cudf.Series([1, 2], name=rhs)
+
+    got = lhs.equals(rhs)
+    expect = lhs.to_pandas().equals(rhs.to_pandas())
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data", [[True, False, None, True, False], [None, None], []]
+)
+@pytest.mark.parametrize("bool_dtype", ["bool", "boolean", pd.BooleanDtype()])
+def test_nullable_bool_dtype_series(data, bool_dtype):
+    psr = pd.Series(data, dtype=pd.BooleanDtype())
+    gsr = cudf.Series(data, dtype=bool_dtype)
+
+    assert_eq(psr, gsr.to_pandas(nullable=True))
+
+
+@pytest.mark.parametrize("level", [None, 0, "l0", 1, ["l0", 1]])
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize("original_name", [None, "original_ser"])
+@pytest.mark.parametrize("name", [None, "ser"])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_reset_index(level, drop, inplace, original_name, name):
+    midx = pd.MultiIndex.from_tuples(
+        [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=["l0", None]
+    )
+    ps = pd.Series(range(4), index=midx, name=original_name)
+    gs = cudf.from_pandas(ps)
+
+    if not drop and inplace:
+        pytest.skip(
+            "For exception checks, see "
+            "test_reset_index_dup_level_name_exceptions"
+        )
+
+    with expect_warning_if(name is None and not drop):
+        expect = ps.reset_index(
+            level=level, drop=drop, name=name, inplace=inplace
+        )
+    got = gs.reset_index(level=level, drop=drop, name=name, inplace=inplace)
+    if inplace:
+        expect = ps
+        got = gs
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("level", [None, 0, 1, [None]])
+@pytest.mark.parametrize("drop", [False, True])
+@pytest.mark.parametrize("inplace", [False, True])
+@pytest.mark.parametrize("original_name", [None, "original_ser"])
+@pytest.mark.parametrize("name", [None, "ser"])
+def test_reset_index_dup_level_name(level, drop, inplace, original_name, name):
+    # midx levels are named [None, None]
+    midx = pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)])
+    ps = pd.Series(range(4), index=midx, name=original_name)
+    gs = cudf.from_pandas(ps)
+    if level == [None] or not drop and inplace:
+        pytest.skip(
+            "For exception checks, see "
+            "test_reset_index_dup_level_name_exceptions"
+        )
+
+    with expect_warning_if(name is None and not drop):
+        expect = ps.reset_index(
+            level=level, drop=drop, inplace=inplace, name=name
+        )
+    got = gs.reset_index(level=level, drop=drop, inplace=inplace, name=name)
+    if inplace:
+        expect = ps
+        got = gs
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("drop", [True, False])
+@pytest.mark.parametrize("inplace", [True, False])
+@pytest.mark.parametrize("original_name", [None, "original_ser"])
+@pytest.mark.parametrize("name", [None, "ser"])
+def test_reset_index_named(drop, inplace, original_name, name):
+    ps = pd.Series(range(4), index=["x", "y", "z", "w"], name=original_name)
+    gs = cudf.from_pandas(ps)
+
+    ps.index.name = "cudf"
+    gs.index.name = "cudf"
+
+    if not drop and inplace:
+        pytest.skip(
+            "For exception checks, see "
+            "test_reset_index_dup_level_name_exceptions"
+        )
+
+    with expect_warning_if(name is None and not drop):
+        expect = ps.reset_index(drop=drop, inplace=inplace, name=name)
+    got = gs.reset_index(drop=drop, inplace=inplace, name=name)
+
+    if inplace:
+        expect = ps
+        got = gs
+
+    assert_eq(expect, got)
+
+
+def test_reset_index_dup_level_name_exceptions():
+    midx = pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)])
+    ps = pd.Series(range(4), index=midx)
+    gs = cudf.from_pandas(ps)
+
+    # Should specify duplicate level names with level number.
+    assert_exceptions_equal(
+        lfunc=ps.reset_index,
+        rfunc=gs.reset_index,
+        lfunc_args_and_kwargs=(
+            [],
+            {"level": [None]},
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {"level": [None]},
+        ),
+    )
+
+    # Cannot use drop=False and inplace=True to turn a series into dataframe.
+    assert_exceptions_equal(
+        lfunc=ps.reset_index,
+        rfunc=gs.reset_index,
+        lfunc_args_and_kwargs=(
+            [],
+            {"drop": False, "inplace": True},
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {"drop": False, "inplace": True},
+        ),
+    )
+
+    # Pandas raises the above exception should these two inputs crosses.
+    assert_exceptions_equal(
+        lfunc=ps.reset_index,
+        rfunc=gs.reset_index,
+        lfunc_args_and_kwargs=(
+            [],
+            {"level": [None], "drop": False, "inplace": True},
+        ),
+        rfunc_args_and_kwargs=(
+            [],
+            {"level": [None], "drop": False, "inplace": True},
+        ),
+    )
+
+
+def test_series_add_prefix():
+    cd_s = cudf.Series([1, 2, 3, 4])
+    pd_s = cd_s.to_pandas()
+
+    got = cd_s.add_prefix("item_")
+    expected = pd_s.add_prefix("item_")
+
+    assert_eq(got, expected)
+
+
+def test_series_add_suffix():
+    cd_s = cudf.Series([1, 2, 3, 4])
+    pd_s = cd_s.to_pandas()
+
+    got = cd_s.add_suffix("_item")
+    expected = pd_s.add_suffix("_item")
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "cudf_series",
+    [
+        cudf.Series([0.25, 0.5, 0.2, -0.05]),
+        cudf.Series([0, 1, 2, np.nan, 4, cudf.NA, 6]),
+    ],
+)
+@pytest.mark.parametrize("lag", [1, 2, 3, 4])
+def test_autocorr(cudf_series, lag):
+    psr = cudf_series.to_pandas()
+
+    cudf_corr = cudf_series.autocorr(lag=lag)
+
+    # autocorrelation is undefined (nan) for less than two entries, but pandas
+    # short-circuits when there are 0 entries and bypasses the numpy function
+    # call that generates an error.
+    num_both_valid = (psr.notna() & psr.shift(lag).notna()).sum()
+    with expect_warning_if(num_both_valid == 1, RuntimeWarning):
+        pd_corr = psr.autocorr(lag=lag)
+
+    assert_eq(pd_corr, cudf_corr)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0, 1, 2, 3],
+        ["abc", "a", None, "hello world", "foo buzz", "", None, "rapids ai"],
+    ],
+)
+def test_series_transpose(data):
+    psr = pd.Series(data=data)
+    csr = cudf.Series(data=data)
+
+    cudf_transposed = csr.transpose()
+    pd_transposed = psr.transpose()
+    cudf_property = csr.T
+    pd_property = psr.T
+
+    assert_eq(pd_transposed, cudf_transposed)
+    assert_eq(pd_property, cudf_property)
+    assert_eq(cudf_transposed, csr)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [1, 3, 5, 7, 7],
+)
+def test_series_nunique(data):
+    cd_s = cudf.Series(data)
+    pd_s = cd_s.to_pandas()
+
+    actual = cd_s.nunique()
+    expected = pd_s.nunique()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [1, 3, 5, 7, 7],
+)
+def test_series_nunique_index(data):
+    cd_s = cudf.Series(data)
+    pd_s = cd_s.to_pandas()
+
+    actual = cd_s.index.nunique()
+    expected = pd_s.index.nunique()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        [1, 2, 3, 4],
+        ["a", "b", "c"],
+        [1.2, 2.2, 4.5],
+        [np.nan, np.nan],
+        [None, None, None],
+    ],
+)
+def test_axes(data):
+    csr = _create_cudf_series_float64_default(data)
+    psr = csr.to_pandas()
+
+    expected = psr.axes
+    actual = csr.axes
+
+    for e, a in zip(expected, actual):
+        assert_eq(e, a)
+
+
+def test_series_truncate():
+    csr = cudf.Series([1, 2, 3, 4])
+    psr = csr.to_pandas()
+
+    assert_eq(csr.truncate(), psr.truncate())
+    assert_eq(csr.truncate(1, 2), psr.truncate(1, 2))
+    assert_eq(csr.truncate(before=1, after=2), psr.truncate(before=1, after=2))
+
+
+def test_series_truncate_errors():
+    csr = cudf.Series([1, 2, 3, 4])
+    with pytest.raises(ValueError):
+        csr.truncate(axis=1)
+    with pytest.raises(ValueError):
+        csr.truncate(copy=False)
+
+    csr.index = [3, 2, 1, 6]
+    psr = csr.to_pandas()
+    assert_exceptions_equal(
+        lfunc=csr.truncate,
+        rfunc=psr.truncate,
+    )
+
+
+def test_series_truncate_datetimeindex():
+    dates = cudf.date_range(
+        "2021-01-01 23:45:00", "2021-01-02 23:46:00", freq="s"
+    )
+    csr = cudf.Series(range(len(dates)), index=dates)
+    psr = csr.to_pandas()
+
+    assert_eq(
+        csr.truncate(
+            before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
+        ),
+        psr.truncate(
+            before="2021-01-01 23:45:18", after="2021-01-01 23:45:27"
+        ),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        [0, 12, 14],
+        [0, 14, 12, 12, 3, 10, 12, 14],
+        np.random.randint(-100, 100, 200),
+        pd.Series([0.0, 1.0, None, 10.0]),
+        [None, None, None, None],
+        [np.nan, None, -1, 2, 3],
+    ],
+)
+@pytest.mark.parametrize(
+    "values",
+    [
+        np.random.randint(-100, 100, 10),
+        [],
+        [np.nan, None, -1, 2, 3],
+        [1.0, 12.0, None, None, 120],
+        [0, 14, 12, 12, 3, 10, 12, 14, None],
+        [None, None, None],
+        ["0", "12", "14"],
+        ["0", "12", "14", "a"],
+    ],
+)
+def test_isin_numeric(data, values):
+    index = np.random.randint(0, 100, len(data))
+    psr = _create_pandas_series_float64_default(data, index=index)
+    gsr = cudf.Series.from_pandas(psr, nan_as_null=False)
+
+    expected = psr.isin(values)
+    got = gsr.isin(values)
+
+    assert_eq(got, expected)
+
+
+@pytest.mark.xfail(raises=TypeError)
+def test_fill_new_category():
+    gs = cudf.Series(pd.Categorical(["a", "b", "c"]))
+    gs[0:1] = "d"
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        pd.Series(
+            ["2018-01-01", "2019-04-03", None, "2019-12-30"],
+            dtype="datetime64[ns]",
+        ),
+        pd.Series(
+            [
+                "2018-01-01",
+                "2019-04-03",
+                None,
+                "2019-12-30",
+                "2018-01-01",
+                "2018-01-01",
+            ],
+            dtype="datetime64[ns]",
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "values",
+    [
+        [],
+        [1514764800000000000, 1577664000000000000],
+        [
+            1514764800000000000,
+            1577664000000000000,
+            1577664000000000000,
+            1577664000000000000,
+            1514764800000000000,
+        ],
+        ["2019-04-03", "2019-12-30", "2012-01-01"],
+        [
+            "2012-01-01",
+            "2012-01-01",
+            "2012-01-01",
+            "2019-04-03",
+            "2019-12-30",
+            "2012-01-01",
+        ],
+    ],
+)
+def test_isin_datetime(data, values):
+    psr = _create_pandas_series_float64_default(data)
+    gsr = cudf.Series.from_pandas(psr)
+
+    got = gsr.isin(values)
+    expected = psr.isin(values)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        pd.Series(["this", "is", None, "a", "test"]),
+        pd.Series(["test", "this", "test", "is", None, "test", "a", "test"]),
+        pd.Series(["0", "12", "14"]),
+    ],
+)
+@pytest.mark.parametrize(
+    "values",
+    [
+        [],
+        ["this", "is"],
+        [None, None, None],
+        ["12", "14", "19"],
+        [12, 14, 19],
+        ["is", "this", "is", "this", "is"],
+    ],
+)
+def test_isin_string(data, values):
+    psr = _create_pandas_series_float64_default(data)
+    gsr = cudf.Series.from_pandas(psr)
+
+    got = gsr.isin(values)
+    expected = psr.isin(values)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        pd.Series(["a", "b", "c", "c", "c", "d", "e"], dtype="category"),
+        pd.Series(["a", "b", None, "c", "d", "e"], dtype="category"),
+        pd.Series([0, 3, 10, 12], dtype="category"),
+        pd.Series([0, 3, 10, 12, 0, 10, 3, 0, 0, 3, 3], dtype="category"),
+    ],
+)
+@pytest.mark.parametrize(
+    "values",
+    [
+        [],
+        ["a", "b", None, "f", "words"],
+        ["0", "12", None, "14"],
+        [0, 10, 12, None, 39, 40, 1000],
+        [0, 0, 0, 0, 3, 3, 3, None, 1, 2, 3],
+    ],
+)
+def test_isin_categorical(data, values):
+    psr = _create_pandas_series_float64_default(data)
+    gsr = cudf.Series.from_pandas(psr)
+
+    got = gsr.isin(values)
+    expected = psr.isin(values)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+@pytest.mark.parametrize("period", [-1, -5, -10, -20, 0, 1, 5, 10, 20])
+@pytest.mark.parametrize("data_empty", [False, True])
+def test_diff(dtype, period, data_empty):
+    if data_empty:
+        data = None
+    else:
+        if dtype == np.int8:
+            # to keep data in range
+            data = gen_rand(dtype, 100000, low=-2, high=2)
+        else:
+            data = gen_rand(dtype, 100000)
+
+    gs = cudf.Series(data, dtype=dtype)
+    ps = pd.Series(data, dtype=dtype)
+
+    expected_outcome = ps.diff(period)
+    diffed_outcome = gs.diff(period).astype(expected_outcome.dtype)
+
+    if data_empty:
+        assert_eq(diffed_outcome, expected_outcome, check_index_type=False)
+    else:
+        assert_eq(diffed_outcome, expected_outcome)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["a", "b", "c", "d", "e"],
+    ],
+)
+def test_diff_unsupported_dtypes(data):
+    gs = cudf.Series(data)
+    with pytest.raises(
+        TypeError,
+        match=r"unsupported operand type\(s\)",
+    ):
+        gs.diff()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.date_range("2020-01-01", "2020-01-06", freq="D"),
+        [True, True, True, False, True, True],
+        [1.0, 2.0, 3.5, 4.0, 5.0, -1.7],
+        [1, 2, 3, 3, 4, 5],
+        [np.nan, None, None, np.nan, np.nan, None],
+    ],
+)
+def test_diff_many_dtypes(data):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+    assert_eq(ps.diff(), gs.diff())
+    assert_eq(ps.diff(periods=2), gs.diff(periods=2))
+
+
+@pytest.mark.parametrize("num_rows", [1, 100])
+@pytest.mark.parametrize("num_bins", [1, 10])
+@pytest.mark.parametrize("right", [True, False])
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + ["bool"])
+@pytest.mark.parametrize("series_bins", [True, False])
+def test_series_digitize(num_rows, num_bins, right, dtype, series_bins):
+    data = np.random.randint(0, 100, num_rows).astype(dtype)
+    bins = np.unique(np.sort(np.random.randint(2, 95, num_bins).astype(dtype)))
+    s = cudf.Series(data)
+    if series_bins:
+        s_bins = cudf.Series(bins)
+        indices = s.digitize(s_bins, right)
+    else:
+        indices = s.digitize(bins, right)
+    np.testing.assert_array_equal(
+        np.digitize(data, bins, right), indices.to_numpy()
+    )
+
+
+def test_series_digitize_invalid_bins():
+    s = cudf.Series(np.random.randint(0, 30, 80), dtype="int32")
+    bins = cudf.Series([2, None, None, 50, 90], dtype="int32")
+
+    with pytest.raises(
+        ValueError, match="`bins` cannot contain null entries."
+    ):
+        _ = s.digitize(bins)
+
+
+@pytest.mark.parametrize(
+    "data,left,right",
+    [
+        ([0, 1, 2, 3, 4, 5, 10], 0, 5),
+        ([0, 1, 2, 3, 4, 5, 10], 10, 1),
+        ([0, 1, 2, 3, 4, 5], [0, 10, 11] * 2, [1, 2, 5] * 2),
+        (["a", "few", "set", "of", "strings", "xyz", "abc"], "banana", "few"),
+        (["a", "few", "set", "of", "strings", "xyz", "abc"], "phone", "hello"),
+        (
+            ["a", "few", "set", "of", "strings", "xyz", "abc"],
+            ["a", "hello", "rapids", "ai", "world", "chars", "strs"],
+            ["yes", "no", "hi", "bye", "test", "pass", "fail"],
+        ),
+        ([0, 1, 2, np.nan, 4, np.nan, 10], 10, 1),
+    ],
+)
+@pytest.mark.parametrize("inclusive", ["both", "neither", "left", "right"])
+def test_series_between(data, left, right, inclusive):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps, nan_as_null=False)
+
+    expected = ps.between(left, right, inclusive=inclusive)
+    actual = gs.between(left, right, inclusive=inclusive)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data,left,right",
+    [
+        ([0, 1, 2, None, 4, 5, 10], 0, 5),
+        ([0, 1, 2, 3, None, 5, 10], 10, 1),
+        ([None, 1, 2, 3, 4, None], [0, 10, 11] * 2, [1, 2, 5] * 2),
+        (
+            ["a", "few", "set", None, "strings", "xyz", "abc"],
+            ["a", "hello", "rapids", "ai", "world", "chars", "strs"],
+            ["yes", "no", "hi", "bye", "test", "pass", "fail"],
+        ),
+    ],
+)
+@pytest.mark.parametrize("inclusive", ["both", "neither", "left", "right"])
+def test_series_between_with_null(data, left, right, inclusive):
+    gs = cudf.Series(data)
+    ps = gs.to_pandas(nullable=True)
+
+    expected = ps.between(left, right, inclusive=inclusive)
+    actual = gs.between(left, right, inclusive=inclusive)
+
+    assert_eq(expected, actual.to_pandas(nullable=True))
+
+
+def test_default_construction():
+    s = cudf.Series([np.int8(8), np.int16(128)])
+    assert s.dtype == np.dtype("i2")
+
+
+@pytest.mark.parametrize(
+    "data", [[0, 1, 2, 3, 4], range(5), [np.int8(8), np.int16(128)]]
+)
+def test_default_integer_bitwidth_construction(default_integer_bitwidth, data):
+    s = cudf.Series(data)
+    assert s.dtype == np.dtype(f"i{default_integer_bitwidth//8}")
+
+
+@pytest.mark.parametrize("data", [[1.5, 2.5, 4.5], [1000, 2000, 4000, 3.14]])
+def test_default_float_bitwidth_construction(default_float_bitwidth, data):
+    s = cudf.Series(data)
+    assert s.dtype == np.dtype(f"f{default_float_bitwidth//8}")
+
+
+def test_series_ordered_dedup():
+    # part of https://github.com/rapidsai/cudf/issues/11486
+    sr = cudf.Series(np.random.randint(0, 100, 1000))
+    # pandas unique() preserves order
+    expect = pd.Series(sr.to_pandas().unique())
+    got = cudf.Series(sr._column.unique())
+    assert_eq(expect.values, got.values)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "float64"])
+@pytest.mark.parametrize("bool_scalar", [True, False])
+def test_set_bool_error(dtype, bool_scalar):
+    sr = cudf.Series([1, 2, 3], dtype=dtype)
+    psr = sr.to_pandas(nullable=True)
+
+    assert_exceptions_equal(
+        lfunc=sr.__setitem__,
+        rfunc=psr.__setitem__,
+        lfunc_args_and_kwargs=([bool_scalar],),
+        rfunc_args_and_kwargs=([bool_scalar],),
+    )
+
+
+def test_int64_equality():
+    s = cudf.Series(np.asarray([2**63 - 10, 2**63 - 100], dtype=np.int64))
+    assert (s != np.int64(2**63 - 1)).all()
+    assert (s != cudf.Scalar(2**63 - 1, dtype=np.int64)).all()
+
+
+@pytest.mark.parametrize("into", [dict, OrderedDict, defaultdict(list)])
+def test_series_to_dict(into):
+    gs = cudf.Series(["ab", "de", "zx"], index=[10, 20, 100])
+    ps = gs.to_pandas()
+
+    actual = gs.to_dict(into=into)
+    expected = ps.to_dict(into=into)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3],
+        pytest.param(
+            [np.nan, 10, 15, 16],
+            marks=pytest.mark.xfail(
+                reason="https://github.com/pandas-dev/pandas/issues/49818"
+            ),
+        ),
+        [np.nan, None, 10, 20],
+        ["ab", "zx", "pq"],
+        ["ab", "zx", None, "pq"],
+        [],
+    ],
+)
+def test_series_hasnans(data):
+    gs = _create_cudf_series_float64_default(data, nan_as_null=False)
+    ps = gs.to_pandas(nullable=True)
+
+    # Check type to avoid mixing Python bool and NumPy bool
+    assert isinstance(gs.hasnans, bool)
+    assert gs.hasnans == ps.hasnans
+
+
+@pytest.mark.parametrize(
+    "data,index",
+    [
+        ([1, 2, 3], [10, 11, 12]),
+        ([1, 2, 3, 1, 1, 2, 3, 2], [10, 20, 23, 24, 25, 26, 27, 28]),
+        ([1, None, 2, None, 3, None, 3, 1], [5, 6, 7, 8, 9, 10, 11, 12]),
+        ([np.nan, 1.0, np.nan, 5.4, 5.4, 1.0], ["a", "b", "c", "d", "e", "f"]),
+        (
+            ["lama", "cow", "lama", None, "beetle", "lama", None, None],
+            [1, 4, 10, 11, 2, 100, 200, 400],
+        ),
+    ],
+)
+@pytest.mark.parametrize("keep", ["first", "last", False])
+def test_series_duplicated(data, index, keep):
+    gs = cudf.Series(data, index=index)
+    ps = gs.to_pandas()
+
+    assert_eq(gs.duplicated(keep=keep), ps.duplicated(keep=keep))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 2, 3, 4],
+        [10, 20, None, None],
+    ],
+)
+@pytest.mark.parametrize("copy", [True, False])
+def test_series_copy(data, copy):
+    psr = pd.Series(data)
+    gsr = cudf.from_pandas(psr)
+
+    new_psr = pd.Series(psr, copy=copy)
+    new_gsr = cudf.Series(gsr, copy=copy)
+
+    new_psr.iloc[0] = 999
+    new_gsr.iloc[0] = 999
+
+    assert_eq(psr, gsr)
+    assert_eq(new_psr, new_gsr)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": 1, "b": 2, "c": 24, "d": 1010},
+        {"a": 1},
+    ],
+)
+@pytest.mark.parametrize(
+    "index", [None, ["b", "c"], ["d", "a", "c", "b"], ["a"]]
+)
+def test_series_init_dict_with_index(data, index):
+    pandas_series = pd.Series(data, index=index)
+    cudf_series = cudf.Series(data, index=index)
+
+    assert_eq(pandas_series, cudf_series)
+
+
+@pytest.mark.parametrize("data", ["abc", None, 1, 3.7])
+@pytest.mark.parametrize(
+    "index", [None, ["b", "c"], ["d", "a", "c", "b"], ["a"]]
+)
+def test_series_init_scalar_with_index(data, index):
+    pandas_series = _create_pandas_series_float64_default(data, index=index)
+    cudf_series = _create_cudf_series_float64_default(data, index=index)
+
+    assert_eq(
+        pandas_series,
+        cudf_series,
+        check_index_type=False if data is None and index is None else True,
+    )
+
+
+def test_series_init_error():
+    assert_exceptions_equal(
+        lfunc=pd.Series,
+        rfunc=cudf.Series,
+        lfunc_args_and_kwargs=([], {"data": [11], "index": [10, 11]}),
+        rfunc_args_and_kwargs=([], {"data": [11], "index": [10, 11]}),
+    )
+
+
+def test_series_init_from_series_and_index():
+    ser = cudf.Series([4, 7, -5, 3], index=["d", "b", "a", "c"])
+    result = cudf.Series(ser, index=list("abcd"))
+    expected = cudf.Series([-5, 7, 3, 4], index=list("abcd"))
+    assert_eq(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["datetime64[ns]", "timedelta64[ns]", "object", "str"]
+)
+def test_series_mixed_dtype_error(dtype):
+    ps = pd.concat([pd.Series([1, 2, 3], dtype=dtype), pd.Series([10, 11])])
+    with pytest.raises(TypeError):
+        cudf.Series(ps)
+    with pytest.raises(TypeError):
+        cudf.Series(ps.array)
+
+
+@pytest.mark.parametrize("data", [[True, False, None], [10, 200, 300]])
+@pytest.mark.parametrize("index", [None, [10, 20, 30]])
+def test_series_contains(data, index):
+    ps = pd.Series(data, index=index)
+    gs = cudf.Series(data, index=index)
+
+    assert_eq(1 in ps, 1 in gs)
+    assert_eq(10 in ps, 10 in gs)
+    assert_eq(True in ps, True in gs)
+    assert_eq(False in ps, False in gs)
+
+
+def test_series_from_pandas_sparse():
+    pser = pd.Series(range(2), dtype=pd.SparseDtype(np.int64, 0))
+    with pytest.raises(NotImplementedError):
+        cudf.Series(pser)
+
+
+def test_series_constructor_unbounded_sequence():
+    class A:
+        def __getitem__(self, key):
+            return 1
+
+    with pytest.raises(TypeError):
+        cudf.Series(A())
+
+
+def test_series_constructor_error_mixed_type():
+    with pytest.raises(pa.ArrowTypeError):
+        cudf.Series(["abc", np.nan, "123"], nan_as_null=False)
+
+
+def test_series_typecast_to_object_error():
+    actual = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(ValueError):
+            actual.astype(object)
+        with pytest.raises(ValueError):
+            actual.astype(np.dtype("object"))
+        new_series = actual.astype("str")
+        assert new_series[0] == "1970-01-01 00:00:00.000000001"
+
+
+def test_series_typecast_to_object():
+    actual = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    with cudf.option_context("mode.pandas_compatible", False):
+        new_series = actual.astype(object)
+        assert new_series[0] == "1970-01-01 00:00:00.000000001"
+        new_series = actual.astype(np.dtype("object"))
+        assert new_series[0] == "1970-01-01 00:00:00.000000001"
+
+
+@pytest.mark.parametrize("attr", ["nlargest", "nsmallest"])
+def test_series_nlargest_nsmallest_str_error(attr):
+    gs = cudf.Series(["a", "b", "c", "d", "e"])
+    ps = gs.to_pandas()
+
+    assert_exceptions_equal(
+        getattr(gs, attr), getattr(ps, attr), ([], {"n": 1}), ([], {"n": 1})
+    )
+
+
+def test_series_unique_pandas_compatibility():
+    gs = cudf.Series([10, 11, 12, 11, 10])
+    ps = gs.to_pandas()
+    with cudf.option_context("mode.pandas_compatible", True):
+        actual = gs.unique()
+    expected = ps.unique()
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("initial_name", SERIES_OR_INDEX_NAMES)
+@pytest.mark.parametrize("name", SERIES_OR_INDEX_NAMES)
+def test_series_rename(initial_name, name):
+    gsr = cudf.Series([1, 2, 3], name=initial_name)
+    psr = pd.Series([1, 2, 3], name=initial_name)
+
+    assert_eq(gsr, psr)
+
+    actual = gsr.rename(name)
+    expected = psr.rename(name)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1.2234242333234, 323432.3243423, np.nan],
+        pd.Series([34224, 324324, 324342], dtype="datetime64[ns]"),
+        pd.Series([224.242, None, 2424.234324], dtype="category"),
+        [
+            decimal.Decimal("342.3243234234242"),
+            decimal.Decimal("89.32432497687622"),
+            None,
+        ],
+    ],
+)
+@pytest.mark.parametrize("digits", [0, 1, 3, 4, 10])
+def test_series_round_builtin(data, digits):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps, nan_as_null=False)
+
+    # TODO: Remove `to_frame` workaround
+    # after following issue is fixed:
+    # https://github.com/pandas-dev/pandas/issues/55114
+    expected = round(ps.to_frame(), digits)[0]
+    expected.name = None
+    actual = round(gs, digits)
+
+    assert_eq(expected, actual)
+
+
+def test_series_empty_warning():
+    with pytest.warns(FutureWarning):
+        expected = pd.Series([])
+    with pytest.warns(FutureWarning):
+        actual = cudf.Series([])
+    assert_eq(expected, actual)
+
+
+@pytest.mark.filterwarnings("ignore::FutureWarning")  # tested above
+@pytest.mark.parametrize("data", [None, {}, []])
+def test_series_empty_index_rangeindex(data):
+    expected = cudf.RangeIndex(0)
+    result = cudf.Series(data).index
+    assert_eq(result, expected)
+
+
+def test_series_count_invalid_param():
+    s = cudf.Series([], dtype="float64")
+    with pytest.raises(TypeError):
+        s.count(skipna=True)
+
+
+@pytest.mark.parametrize(
+    "data", [[0, 1, 2], ["a", "b", "c"], [0.324, 32.32, 3243.23]]
+)
+def test_series_setitem_nat_with_non_datetimes(data):
+    s = cudf.Series(data)
+    with pytest.raises(TypeError):
+        s[0] = cudf.NaT
+
+
+def test_series_string_setitem():
+    gs = cudf.Series(["abc", "def", "ghi", "xyz", "pqr"])
+    ps = gs.to_pandas()
+
+    gs[0] = "NaT"
+    gs[1] = "NA"
+    gs[2] = "<NA>"
+    gs[3] = "NaN"
+
+    ps[0] = "NaT"
+    ps[1] = "NA"
+    ps[2] = "<NA>"
+    ps[3] = "NaN"
+
+    assert_eq(gs, ps)
+
+
+def test_multi_dim_series_error():
+    arr = cp.array([(1, 2), (3, 4)])
+    with pytest.raises(ValueError):
+        cudf.Series(arr)
+
+
+def test_bool_series_mixed_dtype_error():
+    ps = pd.Series([True, False, None])
+    # ps now has `object` dtype, which
+    # isn't supported by `cudf`.
+    with pytest.raises(TypeError):
+        cudf.Series(ps)
+    with pytest.raises(TypeError):
+        cudf.from_pandas(ps)
+
+
+@pytest.mark.parametrize(
+    "pandas_type",
+    [
+        pd.ArrowDtype(pa.int8()),
+        pd.ArrowDtype(pa.int16()),
+        pd.ArrowDtype(pa.int32()),
+        pd.ArrowDtype(pa.int64()),
+        pd.ArrowDtype(pa.uint8()),
+        pd.ArrowDtype(pa.uint16()),
+        pd.ArrowDtype(pa.uint32()),
+        pd.ArrowDtype(pa.uint64()),
+        pd.ArrowDtype(pa.float32()),
+        pd.ArrowDtype(pa.float64()),
+        pd.Int8Dtype(),
+        pd.Int16Dtype(),
+        pd.Int32Dtype(),
+        pd.Int64Dtype(),
+        pd.UInt8Dtype(),
+        pd.UInt16Dtype(),
+        pd.UInt32Dtype(),
+        pd.UInt64Dtype(),
+        pd.Float32Dtype(),
+        pd.Float64Dtype(),
+    ],
+)
+def test_series_arrow_numeric_types_roundtrip(pandas_type):
+    ps = pd.Series([1, 2, 3], dtype=pandas_type)
+    pi = pd.Index(ps)
+    pdf = ps.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pi)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+@pytest.mark.parametrize(
+    "pandas_type", [pd.ArrowDtype(pa.bool_()), pd.BooleanDtype()]
+)
+def test_series_arrow_bool_types_roundtrip(pandas_type):
+    ps = pd.Series([True, False, None], dtype=pandas_type)
+    pi = pd.Index(ps)
+    pdf = ps.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pi)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+@pytest.mark.parametrize(
+    "pandas_type", [pd.ArrowDtype(pa.string()), pd.StringDtype()]
+)
+def test_series_arrow_string_types_roundtrip(pandas_type):
+    ps = pd.Series(["abc", None, "xyz"], dtype=pandas_type)
+    pi = pd.Index(ps)
+    pdf = ps.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pi)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+def test_series_arrow_category_types_roundtrip():
+    pa_array = pa.array(pd.Series([1, 2, 3], dtype="category"))
+    ps = pd.Series([1, 2, 3], dtype=pd.ArrowDtype(pa_array.type))
+    pi = pd.Index(ps)
+    pdf = pi.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pi)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+@pytest.mark.parametrize(
+    "pa_type",
+    [pa.decimal128(10, 2), pa.decimal128(5, 2), pa.decimal128(20, 2)],
+)
+def test_series_arrow_decimal_types_roundtrip(pa_type):
+    ps = pd.Series(
+        [
+            decimal.Decimal("1.2"),
+            decimal.Decimal("20.56"),
+            decimal.Decimal("3"),
+        ],
+        dtype=pd.ArrowDtype(pa_type),
+    )
+    pdf = ps.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+def test_series_arrow_struct_types_roundtrip():
+    ps = pd.Series(
+        [{"a": 1}, {"b": "abc"}],
+        dtype=pd.ArrowDtype(pa.struct({"a": pa.int64(), "b": pa.string()})),
+    )
+    pdf = ps.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+def test_series_arrow_list_types_roundtrip():
+    ps = pd.Series([[1], [2], [4]], dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+    pdf = ps.to_frame()
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(ps)
+
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            cudf.from_pandas(pdf)
+
+
+@pytest.mark.parametrize("reso", ["M", "ps"])
+@pytest.mark.parametrize("typ", ["M", "m"])
+def test_series_invalid_reso_dtype(reso, typ):
+    with pytest.raises(NotImplementedError):
+        cudf.Series([], dtype=f"{typ}8[{reso}]")
+
+
+def test_series_categorical_missing_value_count():
+    ps = pd.Series(pd.Categorical(list("abcccb"), categories=list("cabd")))
+    gs = cudf.from_pandas(ps)
+
+    expected = ps.value_counts()
+    actual = gs.value_counts()
+
+    assert_eq(expected, actual, check_dtype=False)
+
+
+def test_series_error_nan_mixed_types():
+    ps = pd.Series([np.nan, "ab", "cd"])
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(pa.ArrowInvalid):
+            cudf.from_pandas(ps)
+
+
+def test_series_error_nan_non_float_dtypes():
+    s = cudf.Series(["a", "b", "c"])
+    with pytest.raises(TypeError):
+        s[0] = np.nan
+
+    s = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    with pytest.raises(TypeError):
+        s[0] = np.nan
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        pd.ArrowDtype(pa.int8()),
+        pd.ArrowDtype(pa.int16()),
+        pd.ArrowDtype(pa.int32()),
+        pd.ArrowDtype(pa.int64()),
+        pd.ArrowDtype(pa.uint8()),
+        pd.ArrowDtype(pa.uint16()),
+        pd.ArrowDtype(pa.uint32()),
+        pd.ArrowDtype(pa.uint64()),
+        pd.ArrowDtype(pa.float32()),
+        pd.ArrowDtype(pa.float64()),
+        pd.Int8Dtype(),
+        pd.Int16Dtype(),
+        pd.Int32Dtype(),
+        pd.Int64Dtype(),
+        pd.UInt8Dtype(),
+        pd.UInt16Dtype(),
+        pd.UInt32Dtype(),
+        pd.UInt64Dtype(),
+        pd.Float32Dtype(),
+        pd.Float64Dtype(),
+    ],
+)
+@pytest.mark.parametrize("klass", [cudf.Series, cudf.DataFrame, cudf.Index])
+@pytest.mark.parametrize("kind", [lambda x: x, str], ids=["obj", "string"])
+def test_astype_pandas_nullable_pandas_compat(dtype, klass, kind):
+    ser = klass([1, 2, 3])
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(NotImplementedError):
+            ser.astype(kind(dtype))
+
+
+def test_series_where_mixed_bool_dtype():
+    s = cudf.Series([True, False, True])
+    with pytest.raises(TypeError):
+        s.where(~s, 10)
+
+
+def test_series_setitem_mixed_bool_dtype():
+    s = cudf.Series([True, False, True])
+    with pytest.raises(TypeError):
+        s[0] = 10
+
+
+def test_series_duplicate_index_reindex():
+    gs = cudf.Series([0, 1, 2, 3], index=[0, 0, 1, 1])
+    ps = gs.to_pandas()
+
+    assert_exceptions_equal(
+        gs.reindex,
+        ps.reindex,
+        lfunc_args_and_kwargs=([10, 11, 12, 13], {}),
+        rfunc_args_and_kwargs=([10, 11, 12, 13], {}),
+    )
diff --git a/python/cudf/cudf/tests/test_seriesmap.py b/python/cudf/cudf/tests/test_seriesmap.py
new file mode 100644
index 0000000..9da08e4
--- /dev/null
+++ b/python/cudf/cudf/tests/test_seriesmap.py
@@ -0,0 +1,95 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from itertools import product
+from math import floor
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Series
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+def test_series_map_basic():
+    gd1 = cudf.Series(["cat", np.nan, "rabbit", "dog"])
+    pdf1 = gd1.to_pandas()
+
+    expected_dict = pdf1.map({"cat": "kitten", "dog": "puppy"})
+    actual_dict = gd1.map({"cat": "kitten", "dog": "puppy"})
+
+    assert_eq(expected_dict, actual_dict)
+
+
+@pytest.mark.parametrize("name", ["a", None, 2])
+def test_series_map_series_input(name):
+    gd1 = cudf.Series(["cat", "dog", np.nan, "rabbit"], name=name)
+    pdf1 = gd1.to_pandas()
+
+    expected_series = pdf1.map(pd.Series({"cat": "kitten", "dog": "puppy"}))
+    actual_series = gd1.map(cudf.Series({"cat": "kitten", "dog": "puppy"}))
+
+    assert_eq(expected_series, actual_series)
+
+
+def test_series_map_callable_numeric_basic():
+    gd2 = cudf.Series([1, 2, 3, 4, np.nan])
+    pdf2 = gd2.to_pandas()
+
+    expected_function = pdf2.map(lambda x: x**2)
+    actual_function = gd2.map(lambda x: x**2)
+
+    assert_eq(expected_function, actual_function)
+
+
+@pytest.mark.parametrize("nelem", list(product([2, 10, 100, 1000])))
+def test_series_map_callable_numeric_random(nelem):
+    # Generate data
+    np.random.seed(0)
+    data = np.random.random(nelem) * 100
+
+    sr = Series(data)
+    pdsr = pd.Series(data)
+
+    # Call map
+    got = sr.map(lambda x: (floor(x) + 1 if x - floor(x) >= 0.5 else floor(x)))
+    expect = pdsr.map(
+        lambda x: (floor(x) + 1 if x - floor(x) >= 0.5 else floor(x))
+    )
+
+    # Check
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_series_map_callable_numeric_random_dtype_change():
+    # Test for changing the out_dtype using map
+
+    data = list(range(10))
+
+    sr = Series(data)
+    pdsr = pd.Series(data)
+
+    got = sr.map(lambda x: float(x))
+    expect = pdsr.map(lambda x: float(x))
+
+    # Check
+    assert_eq(expect, got)
+
+
+def test_series_map_non_unique_index():
+    # test for checking correct error is produced
+
+    gd1 = cudf.Series([1, 2, 3, 4, np.nan])
+    pd1 = pd.Series([1, 2, 3, 4, np.nan])
+
+    gd_map_series = cudf.Series(["a", "b", "c"], index=[1, 1, 2])
+    pd_map_series = pd.Series(["a", "b", "c"], index=[1, 1, 2])
+
+    assert_exceptions_equal(
+        lfunc=pd1.map,
+        rfunc=gd1.map,
+        check_exception_type=False,
+        lfunc_args_and_kwargs=([pd_map_series],),
+        rfunc_args_and_kwargs=([gd_map_series],),
+    )
diff --git a/python/cudf/cudf/tests/test_setitem.py b/python/cudf/cudf/tests/test_setitem.py
new file mode 100644
index 0000000..6e1e53f
--- /dev/null
+++ b/python/cudf/cudf/tests/test_setitem.py
@@ -0,0 +1,506 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.core._compat import PANDAS_GE_150
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+
+@pytest.mark.parametrize("df", [pd.DataFrame({"a": [1, 2, 3]})])
+@pytest.mark.parametrize("arg", [[True, False, True], [True, True, True]])
+@pytest.mark.parametrize("value", [0, -1])
+def test_dataframe_setitem_bool_mask_scaler(df, arg, value):
+    gdf = cudf.from_pandas(df)
+
+    df[arg] = value
+    gdf[arg] = value
+    assert_eq(df, gdf)
+
+
+def test_dataframe_setitem_scaler_bool():
+    df = pd.DataFrame({"a": [1, 2, 3]})
+    df[[True, False, True]] = pd.DataFrame({"a": [-1, -2]})
+
+    gdf = cudf.DataFrame({"a": [1, 2, 3]})
+    gdf[[True, False, True]] = cudf.DataFrame({"a": [-1, -2]})
+    assert_eq(df, gdf)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [pd.DataFrame({"a": [1, 2, 3]}), pd.DataFrame({"a": ["x", "y", "z"]})],
+)
+@pytest.mark.parametrize("arg", [["a"], "a", "b"])
+@pytest.mark.parametrize(
+    "value", [-10, pd.DataFrame({"a": [-1, -2, -3]}), "abc"]
+)
+def test_dataframe_setitem_columns(df, arg, value):
+    gdf = cudf.from_pandas(df)
+    cudf_replace_value = value
+
+    if isinstance(cudf_replace_value, pd.DataFrame):
+        cudf_replace_value = cudf.from_pandas(value)
+
+    df[arg] = value
+    gdf[arg] = cudf_replace_value
+    assert_eq(df, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("df", [pd.DataFrame({"a": [1, 2, 3]})])
+@pytest.mark.parametrize("arg", [["b", "c"]])
+@pytest.mark.parametrize(
+    "value",
+    [
+        pd.DataFrame({"0": [-1, -2, -3], "1": [-0, -10, -1]}),
+        10,
+        20,
+        30,
+        "rapids",
+        "ai",
+        0.32234,
+        np.datetime64(1324232423423342, "ns"),
+        np.timedelta64(34234324234324234, "ns"),
+    ],
+)
+def test_dataframe_setitem_new_columns(df, arg, value):
+    gdf = cudf.from_pandas(df)
+    cudf_replace_value = value
+
+    if isinstance(cudf_replace_value, pd.DataFrame):
+        cudf_replace_value = cudf.from_pandas(value)
+
+    df[arg] = value
+    gdf[arg] = cudf_replace_value
+    assert_eq(df, gdf, check_dtype=True)
+
+
+# set_item_series inconsistency
+def test_series_setitem_index():
+    df = pd.DataFrame(
+        data={"b": [-1, -2, -3], "c": [1, 2, 3]}, index=[1, 2, 3]
+    )
+
+    df["b"] = pd.Series(data=[12, 11, 10], index=[3, 2, 1])
+    gdf = cudf.DataFrame(
+        data={"b": [-1, -2, -3], "c": [1, 2, 3]}, index=[1, 2, 3]
+    )
+    gdf["b"] = cudf.Series(data=[12, 11, 10], index=[3, 2, 1])
+    assert_eq(df, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("psr", [pd.Series([1, 2, 3], index=["a", "b", "c"])])
+@pytest.mark.parametrize(
+    "arg", ["b", ["a", "c"], slice(1, 2, 1), [True, False, True]]
+)
+def test_series_set_item(psr, arg):
+    gsr = cudf.from_pandas(psr)
+
+    psr[arg] = 11
+    gsr[arg] = 11
+
+    assert_eq(psr, gsr)
+
+
+def test_series_setitem_singleton_range():
+    sr = cudf.Series([1, 2, 3], dtype=np.int64)
+    psr = sr.to_pandas()
+    value = np.asarray([7], dtype=np.int64)
+    sr.iloc[:1] = value
+    psr.iloc[:1] = value
+    assert_eq(sr, cudf.Series([7, 2, 3], dtype=np.int64))
+    assert_eq(sr, psr, check_dtype=True)
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(
+            {"a": [1, 2, 3]},
+            index=pd.MultiIndex.from_frame(
+                pd.DataFrame({"b": [3, 2, 1], "c": ["a", "b", "c"]})
+            ),
+        ),
+        pd.DataFrame({"a": [1, 2, 3]}, index=["a", "b", "c"]),
+    ],
+)
+def test_setitem_dataframe_series_inplace(df):
+    pdf = df.copy(deep=True)
+    gdf = cudf.from_pandas(pdf)
+
+    pdf["a"].replace(1, 500, inplace=True)
+    gdf["a"].replace(1, 500, inplace=True)
+
+    assert_eq(pdf, gdf)
+
+    psr_a = pdf["a"]
+    gsr_a = gdf["a"]
+
+    psr_a.replace(500, 501, inplace=True)
+    gsr_a.replace(500, 501, inplace=True)
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "replace_data",
+    [
+        [100, 200, 300, 400, 500],
+        cudf.Series([100, 200, 300, 400, 500]),
+        cudf.Series([100, 200, 300, 400, 500], index=[2, 3, 4, 5, 6]),
+    ],
+)
+def test_series_set_equal_length_object_by_mask(replace_data):
+    psr = pd.Series([1, 2, 3, 4, 5], dtype="Int64")
+    gsr = cudf.from_pandas(psr)
+
+    # Lengths match in trivial case
+    pd_bool_col = pd.Series([True] * len(psr), dtype="boolean")
+    gd_bool_col = cudf.from_pandas(pd_bool_col)
+    psr[pd_bool_col] = (
+        replace_data.to_pandas(nullable=True)
+        if hasattr(replace_data, "to_pandas")
+        else pd.Series(replace_data)
+    )
+    gsr[gd_bool_col] = replace_data
+
+    assert_eq(psr.astype("float"), gsr.astype("float"))
+
+    # Test partial masking
+    psr[psr > 1] = (
+        replace_data.to_pandas()
+        if hasattr(replace_data, "to_pandas")
+        else pd.Series(replace_data)
+    )
+    gsr[gsr > 1] = replace_data
+
+    assert_eq(psr.astype("float"), gsr.astype("float"))
+
+
+def test_column_set_equal_length_object_by_mask():
+    # Series.__setitem__ might bypass some of the cases
+    # handled in column.__setitem__ so this test is needed
+
+    data = cudf.Series([0, 0, 1, 1, 1])._column
+    replace_data = cudf.Series([100, 200, 300, 400, 500])._column
+    bool_col = cudf.Series([True, True, True, True, True])._column
+
+    data[bool_col] = replace_data
+    assert_eq(cudf.Series(data), cudf.Series(replace_data))
+
+    data = cudf.Series([0, 0, 1, 1, 1])._column
+    bool_col = cudf.Series([True, False, True, False, True])._column
+    data[bool_col] = replace_data
+
+    assert_eq(cudf.Series(data), cudf.Series([100, 0, 300, 1, 500]))
+
+
+def test_column_set_unequal_length_object_by_mask():
+    data = [1, 2, 3, 4, 5]
+    replace_data_1 = [8, 9]
+    replace_data_2 = [8, 9, 10, 11]
+    mask = [True, True, False, True, False]
+
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+    assert_exceptions_equal(
+        psr.__setitem__,
+        gsr.__setitem__,
+        ([mask, replace_data_1], {}),
+        ([mask, replace_data_1], {}),
+    )
+
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+    assert_exceptions_equal(
+        psr.__setitem__,
+        gsr.__setitem__,
+        ([mask, replace_data_2], {}),
+        ([mask, replace_data_2], {}),
+    )
+
+
+def test_categorical_setitem_invalid():
+    ps = pd.Series([1, 2, 3], dtype="category")
+    gs = cudf.Series([1, 2, 3], dtype="category")
+
+    if PANDAS_GE_150:
+        assert_exceptions_equal(
+            lfunc=ps.__setitem__,
+            rfunc=gs.__setitem__,
+            lfunc_args_and_kwargs=([0, 5], {}),
+            rfunc_args_and_kwargs=([0, 5], {}),
+        )
+    else:
+        # Following workaround is needed because:
+        # https://github.com/pandas-dev/pandas/issues/46646
+        with pytest.raises(
+            ValueError,
+            match="Cannot setitem on a Categorical with a new category, set "
+            "the categories first",
+        ):
+            gs[0] = 5
+
+
+def test_series_slice_setitem_list():
+    actual = cudf.Series([[[1, 2], [2, 3]], [[3, 4]], [[4, 5]], [[6, 7]]])
+    actual[slice(0, 3, 1)] = [[10, 11], [12, 23]]
+    expected = cudf.Series(
+        [
+            [[10, 11], [12, 23]],
+            [[10, 11], [12, 23]],
+            [[10, 11], [12, 23]],
+            [[6, 7]],
+        ]
+    )
+    assert_eq(actual, expected)
+
+    actual = cudf.Series([[[1, 2], [2, 3]], [[3, 4]], [[4, 5]], [[6, 7]]])
+    actual[0:3] = cudf.Scalar([[10, 11], [12, 23]])
+
+    assert_eq(actual, expected)
+
+
+def test_series_slice_setitem_struct():
+    actual = cudf.Series(
+        [
+            {"a": {"b": 10}, "b": 11},
+            {"a": {"b": 100}, "b": 5},
+            {"a": {"b": 50}, "b": 2},
+            {"a": {"b": 1000}, "b": 67},
+            {"a": {"b": 4000}, "b": 1090},
+        ]
+    )
+    actual[slice(0, 3, 1)] = {"a": {"b": 5050}, "b": 101}
+    expected = cudf.Series(
+        [
+            {"a": {"b": 5050}, "b": 101},
+            {"a": {"b": 5050}, "b": 101},
+            {"a": {"b": 5050}, "b": 101},
+            {"a": {"b": 1000}, "b": 67},
+            {"a": {"b": 4000}, "b": 1090},
+        ]
+    )
+    assert_eq(actual, expected)
+
+    actual = cudf.Series(
+        [
+            {"a": {"b": 10}, "b": 11},
+            {"a": {"b": 100}, "b": 5},
+            {"a": {"b": 50}, "b": 2},
+            {"a": {"b": 1000}, "b": 67},
+            {"a": {"b": 4000}, "b": 1090},
+        ]
+    )
+    actual[0:3] = cudf.Scalar({"a": {"b": 5050}, "b": 101})
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
+@pytest.mark.parametrize("indices", [0, [1, 2]])
+def test_series_setitem_upcasting(dtype, indices):
+    sr = pd.Series([0, 0, 0], dtype=dtype)
+    cr = cudf.from_pandas(sr)
+    assert_eq(sr, cr)
+    # Must be a non-integral floating point value that can't be losslessly
+    # converted to float32, otherwise pandas will try and match the source
+    # column dtype.
+    new_value = np.float64(np.pi)
+    col_ref = cr._column
+    sr[indices] = new_value
+    cr[indices] = new_value
+    if PANDAS_GE_150:
+        assert_eq(sr, cr)
+    else:
+        # pandas bug, incorrectly fails to upcast from float32 to float64
+        assert_eq(sr.values, cr.values)
+    if dtype == np.float64:
+        # no-op type cast should not modify backing column
+        assert col_ref == cr._column
+
+
+# TODO: these two tests could perhaps be changed once specifics of
+# pandas compat wrt upcasting are decided on; this is just baking in
+# status-quo.
+def test_series_setitem_upcasting_string_column():
+    sr = pd.Series([0, 0, 0], dtype=str)
+    cr = cudf.from_pandas(sr)
+    new_value = np.float64(10.5)
+    sr[0] = str(new_value)
+    cr[0] = new_value
+    assert_eq(sr, cr)
+
+
+def test_series_setitem_upcasting_string_value():
+    sr = cudf.Series([0, 0, 0], dtype=int)
+    # This is a distinction with pandas, which lets you instead make an
+    # object column with ["10", 0, 0]
+    sr[0] = "10"
+    assert_eq(pd.Series([10, 0, 0], dtype=int), sr)
+    with pytest.raises(ValueError):
+        sr[0] = "non-integer"
+
+
+def test_scatter_by_slice_with_start_and_step():
+    source = pd.Series([1, 2, 3, 4, 5])
+    csource = cudf.from_pandas(source)
+    target = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+    ctarget = cudf.from_pandas(target)
+    target[1::2] = source
+    ctarget[1::2] = csource
+    assert_eq(target, ctarget)
+
+
+@pytest.mark.parametrize("n", [1, 3])
+def test_setitem_str_trailing_null(n):
+    trailing_nulls = "\x00" * n
+    s = cudf.Series(["a", "b", "c" + trailing_nulls])
+    assert s[2] == "c" + trailing_nulls
+    s[0] = "a" + trailing_nulls
+    assert s[0] == "a" + trailing_nulls
+    s[1] = trailing_nulls
+    assert s[1] == trailing_nulls
+    s[0] = ""
+    assert s[0] == ""
+    s[0] = "\x00"
+    assert s[0] == "\x00"
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/7448")
+def test_iloc_setitem_7448():
+    index = pd.MultiIndex.from_product([(1, 2), (3, 4)])
+    expect = cudf.Series([1, 2, 3, 4], index=index)
+    actual = cudf.from_pandas(expect)
+    expect[(1, 3)] = 101
+    actual[(1, 3)] = 101
+    assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        "7",
+        pytest.param(
+            ["7", "8"],
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/11298"
+            ),
+        ),
+    ],
+)
+def test_loc_setitem_string_11298(value):
+    df = pd.DataFrame({"a": ["a", "b", "c"]})
+    cdf = cudf.from_pandas(df)
+
+    df.loc[:1, "a"] = value
+
+    cdf.loc[:1, "a"] = value
+
+    assert_eq(df, cdf)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/11944")
+def test_loc_setitem_list_11944():
+    df = pd.DataFrame(
+        data={"a": ["yes", "no"], "b": [["l1", "l2"], ["c", "d"]]}
+    )
+    cdf = cudf.from_pandas(df)
+    df.loc[df.a == "yes", "b"] = [["hello"]]
+    cdf.loc[df.a == "yes", "b"] = [["hello"]]
+    assert_eq(df, cdf)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/12504")
+def test_loc_setitem_extend_empty_12504():
+    df = pd.DataFrame(columns=["a"])
+    cdf = cudf.from_pandas(df)
+
+    df.loc[0] = [1]
+
+    cdf.loc[0] = [1]
+
+    assert_eq(df, cdf)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/12505")
+def test_loc_setitem_extend_existing_12505():
+    df = pd.DataFrame({"a": [0]})
+    cdf = cudf.from_pandas(df)
+
+    df.loc[1] = 1
+
+    cdf.loc[1] = 1
+
+    assert_eq(df, cdf)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/12801")
+def test_loc_setitem_add_column_partial_12801():
+    df = pd.DataFrame({"a": [0, 1, 2]})
+    cdf = cudf.from_pandas(df)
+
+    df.loc[df.a < 2, "b"] = 1
+
+    cdf.loc[cdf.a < 2, "b"] = 1
+
+    assert_eq(df, cdf)
+
+
+@pytest.mark.xfail(reason="https://github.com/rapidsai/cudf/issues/13031")
+@pytest.mark.parametrize("other_index", [["1", "3", "2"], [1, 2, 3]])
+def test_loc_setitem_series_index_alignment_13031(other_index):
+    s = pd.Series([1, 2, 3], index=["1", "2", "3"])
+    other = pd.Series([5, 6, 7], index=other_index)
+
+    cs = cudf.from_pandas(s)
+    cother = cudf.from_pandas(other)
+
+    s.loc[["1", "3"]] = other
+
+    cs.loc[["1", "3"]] = cother
+
+    assert_eq(s, cs)
+
+
+@pytest.mark.parametrize(
+    "ps",
+    [
+        pd.Series([1, 2, 3], index=pd.RangeIndex(0, 3)),
+        pd.Series([1, 2, 3], index=pd.RangeIndex(start=2, stop=-1, step=-1)),
+        pd.Series([1, 2, 3], index=pd.RangeIndex(start=1, stop=6, step=2)),
+        pd.Series(
+            [1, 2, 3, 4, 5], index=pd.RangeIndex(start=1, stop=-9, step=-2)
+        ),
+        pd.Series(
+            [1, 2, 3, 4, 5], index=pd.RangeIndex(start=1, stop=-12, step=-3)
+        ),
+        pd.Series([1, 2, 3, 4], index=pd.RangeIndex(start=1, stop=14, step=4)),
+        pd.Series(
+            [1, 2, 3, 4], index=pd.RangeIndex(start=1, stop=-14, step=-4)
+        ),
+    ],
+)
+@pytest.mark.parametrize("arg", list(range(-20, 20)) + [5.6, 3.1])
+def test_series_set_item_range_index(ps, arg):
+    gsr = cudf.from_pandas(ps)
+    psr = ps.copy(deep=True)
+    psr[arg] = 11
+    gsr[arg] = 11
+
+    assert_eq(psr, gsr, check_index_type=True)
+
+
+def test_series_set_item_index_reference():
+    gs1 = cudf.Series([1], index=[7])
+    gs2 = cudf.Series([2], index=gs1.index)
+    gs1.loc[11] = 2
+
+    ps1 = pd.Series([1], index=[7])
+    ps2 = pd.Series([2], index=ps1.index)
+    ps1.loc[11] = 2
+
+    assert_eq(ps1, gs1)
+    assert_eq(ps2, gs2)
diff --git a/python/cudf/cudf/tests/test_sorting.py b/python/cudf/cudf/tests/test_sorting.py
new file mode 100644
index 0000000..b3db131
--- /dev/null
+++ b/python/cudf/cudf/tests/test_sorting.py
@@ -0,0 +1,399 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import string
+from itertools import product
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from cudf import DataFrame, Series
+from cudf.core.column import NumericalColumn
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+
+sort_nelem_args = [2, 257]
+sort_dtype_args = [
+    np.int32,
+    np.int64,
+    np.uint32,
+    np.uint64,
+    np.float32,
+    np.float64,
+]
+sort_slice_args = [slice(1, None), slice(None, -1), slice(1, -1)]
+
+
+@pytest.mark.parametrize(
+    "nelem,dtype", list(product(sort_nelem_args, sort_dtype_args))
+)
+def test_dataframe_sort_values(nelem, dtype):
+    np.random.seed(0)
+    df = DataFrame()
+    df["a"] = aa = (100 * np.random.random(nelem)).astype(dtype)
+    df["b"] = bb = (100 * np.random.random(nelem)).astype(dtype)
+    sorted_df = df.sort_values(by="a")
+    # Check
+    sorted_index = np.argsort(aa, kind="mergesort")
+    assert_eq(sorted_df.index.values, sorted_index)
+    assert_eq(sorted_df["a"].values, aa[sorted_index])
+    assert_eq(sorted_df["b"].values, bb[sorted_index])
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+@pytest.mark.parametrize("index", ["a", "b", ["a", "b"]])
+def test_dataframe_sort_values_ignore_index(index, ignore_index):
+    gdf = DataFrame(
+        {"a": [1, 3, 5, 2, 4], "b": [1, 1, 2, 2, 3], "c": [9, 7, 7, 7, 1]}
+    )
+    gdf = gdf.set_index(index)
+
+    pdf = gdf.to_pandas()
+
+    expect = pdf.sort_values(list(pdf.columns), ignore_index=ignore_index)
+    got = gdf.sort_values((gdf.columns), ignore_index=ignore_index)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("ignore_index", [True, False])
+def test_series_sort_values_ignore_index(ignore_index):
+    gsr = Series([1, 3, 5, 2, 4])
+    psr = gsr.to_pandas()
+
+    expect = psr.sort_values(ignore_index=ignore_index)
+    got = gsr.sort_values(ignore_index=ignore_index)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "nelem,sliceobj", list(product([10, 100], sort_slice_args))
+)
+def test_dataframe_sort_values_sliced(nelem, sliceobj):
+    np.random.seed(0)
+    df = pd.DataFrame()
+    df["a"] = np.random.random(nelem)
+
+    expect = df[sliceobj]["a"].sort_values()
+    gdf = DataFrame.from_pandas(df)
+    got = gdf[sliceobj]["a"].sort_values()
+    assert (got.to_pandas() == expect).all()
+
+
+@pytest.mark.parametrize(
+    "nelem,dtype,asc",
+    list(product(sort_nelem_args, sort_dtype_args, [True, False])),
+)
+def test_series_argsort(nelem, dtype, asc):
+    np.random.seed(0)
+    sr = Series((100 * np.random.random(nelem)).astype(dtype))
+    res = sr.argsort(ascending=asc)
+
+    if asc:
+        expected = np.argsort(sr.to_numpy(), kind="mergesort")
+    else:
+        expected = np.argsort(sr.to_numpy() * -1, kind="mergesort")
+    np.testing.assert_array_equal(expected, res.to_numpy())
+
+
+@pytest.mark.parametrize(
+    "nelem,asc", list(product(sort_nelem_args, [True, False]))
+)
+def test_series_sort_index(nelem, asc):
+    np.random.seed(0)
+    sr = Series(100 * np.random.random(nelem))
+    psr = sr.to_pandas()
+
+    expected = psr.sort_index(ascending=asc)
+    got = sr.sort_index(ascending=asc)
+
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize("data", [[0, 1, 1, 2, 2, 2, 3, 3], [0], [1, 2, 3]])
+@pytest.mark.parametrize("n", [-100, -50, -12, -2, 0, 1, 2, 3, 4, 7])
+def test_series_nlargest(data, n):
+    """Indirectly tests Series.sort_values()"""
+    sr = Series(data)
+    psr = pd.Series(data)
+    assert_eq(sr.nlargest(n), psr.nlargest(n))
+    assert_eq(sr.nlargest(n, keep="last"), psr.nlargest(n, keep="last"))
+
+    assert_exceptions_equal(
+        lfunc=psr.nlargest,
+        rfunc=sr.nlargest,
+        lfunc_args_and_kwargs=([], {"n": 3, "keep": "what"}),
+        rfunc_args_and_kwargs=([], {"n": 3, "keep": "what"}),
+    )
+
+
+@pytest.mark.parametrize("data", [[0, 1, 1, 2, 2, 2, 3, 3], [0], [1, 2, 3]])
+@pytest.mark.parametrize("n", [-100, -50, -12, -2, 0, 1, 2, 3, 4, 9])
+def test_series_nsmallest(data, n):
+    """Indirectly tests Series.sort_values()"""
+    sr = Series(data)
+    psr = pd.Series(data)
+    assert_eq(sr.nsmallest(n), psr.nsmallest(n))
+    assert_eq(
+        sr.nsmallest(n, keep="last").sort_index(),
+        psr.nsmallest(n, keep="last").sort_index(),
+    )
+
+    assert_exceptions_equal(
+        lfunc=psr.nsmallest,
+        rfunc=sr.nsmallest,
+        lfunc_args_and_kwargs=([], {"n": 3, "keep": "what"}),
+        rfunc_args_and_kwargs=([], {"n": 3, "keep": "what"}),
+    )
+
+
+@pytest.mark.parametrize("nelem,n", [(1, 1), (100, 100), (10, 5), (100, 10)])
+@pytest.mark.parametrize("op", ["nsmallest", "nlargest"])
+@pytest.mark.parametrize("columns", ["a", ["b", "a"]])
+def test_dataframe_nlargest_nsmallest(nelem, n, op, columns):
+    np.random.seed(0)
+    aa = np.random.random(nelem)
+    bb = np.random.random(nelem)
+
+    df = DataFrame({"a": aa, "b": bb})
+    pdf = df.to_pandas()
+    assert_eq(getattr(df, op)(n, columns), getattr(pdf, op)(n, columns))
+
+
+@pytest.mark.parametrize(
+    "counts,sliceobj", list(product([(10, 5), (100, 10)], sort_slice_args))
+)
+def test_dataframe_nlargest_sliced(counts, sliceobj):
+    nelem, n = counts
+    np.random.seed(0)
+    df = pd.DataFrame()
+    df["a"] = np.random.random(nelem)
+    df["b"] = np.random.random(nelem)
+
+    expect = df[sliceobj].nlargest(n, "a")
+    gdf = DataFrame.from_pandas(df)
+    got = gdf[sliceobj].nlargest(n, "a")
+    assert (got.to_pandas() == expect).all().all()
+
+
+@pytest.mark.parametrize(
+    "counts,sliceobj", list(product([(10, 5), (100, 10)], sort_slice_args))
+)
+def test_dataframe_nsmallest_sliced(counts, sliceobj):
+    nelem, n = counts
+    np.random.seed(0)
+    df = pd.DataFrame()
+    df["a"] = np.random.random(nelem)
+    df["b"] = np.random.random(nelem)
+
+    expect = df[sliceobj].nsmallest(n, "a")
+    gdf = DataFrame.from_pandas(df)
+    got = gdf[sliceobj].nsmallest(n, "a")
+    assert (got.to_pandas() == expect).all().all()
+
+
+@pytest.mark.parametrize("num_cols", [1, 2, 3, 5])
+@pytest.mark.parametrize("num_rows", [0, 1, 2, 1000])
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES)
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_dataframe_multi_column(
+    num_cols, num_rows, dtype, ascending, na_position
+):
+
+    np.random.seed(0)
+    by = list(string.ascii_lowercase[:num_cols])
+    pdf = pd.DataFrame()
+
+    for i in range(5):
+        colname = string.ascii_lowercase[i]
+        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        pdf[colname] = data
+
+    gdf = DataFrame.from_pandas(pdf)
+
+    got = gdf.sort_values(by, ascending=ascending, na_position=na_position)
+    expect = pdf.sort_values(by, ascending=ascending, na_position=na_position)
+
+    assert_eq(
+        got[by].reset_index(drop=True), expect[by].reset_index(drop=True)
+    )
+
+
+@pytest.mark.parametrize("num_cols", [1, 2, 3])
+@pytest.mark.parametrize("num_rows", [0, 1, 2, 3, 5])
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+@pytest.mark.parametrize("nulls", ["some", "all"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_dataframe_multi_column_nulls(
+    num_cols, num_rows, dtype, nulls, ascending, na_position
+):
+
+    np.random.seed(0)
+    by = list(string.ascii_lowercase[:num_cols])
+    pdf = pd.DataFrame()
+
+    for i in range(3):
+        colname = string.ascii_lowercase[i]
+        data = np.random.randint(0, 26, num_rows).astype(dtype)
+        if nulls == "some":
+            idx = np.array([], dtype="int64")
+            if num_rows > 0:
+                idx = np.random.choice(
+                    num_rows, size=int(num_rows / 4), replace=False
+                )
+            data[idx] = np.nan
+        elif nulls == "all":
+            data[:] = np.nan
+        pdf[colname] = data
+
+    gdf = DataFrame.from_pandas(pdf)
+
+    got = gdf.sort_values(by, ascending=ascending, na_position=na_position)
+    expect = pdf.sort_values(by, ascending=ascending, na_position=na_position)
+
+    assert_eq(
+        got[by].reset_index(drop=True), expect[by].reset_index(drop=True)
+    )
+
+
+@pytest.mark.parametrize(
+    "ascending", list(product((True, False), (True, False)))
+)
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_dataframe_multi_column_nulls_multiple_ascending(
+    ascending, na_position
+):
+    pdf = pd.DataFrame(
+        {"a": [3, 1, None, 2, 2, None, 1], "b": [1, 2, 3, 4, 5, 6, 7]}
+    )
+    gdf = DataFrame.from_pandas(pdf)
+    expect = pdf.sort_values(
+        by=["a", "b"], ascending=ascending, na_position=na_position
+    )
+    actual = gdf.sort_values(
+        by=["a", "b"], ascending=ascending, na_position=na_position
+    )
+
+    assert_eq(actual, expect)
+
+
+@pytest.mark.parametrize("nelem", [1, 100])
+def test_series_nlargest_nelem(nelem):
+    np.random.seed(0)
+    elems = np.random.random(nelem)
+    gds = Series(elems).nlargest(nelem)
+    pds = pd.Series(elems).nlargest(nelem)
+
+    assert (pds == gds.to_pandas()).all().all()
+
+
+@pytest.mark.parametrize("map_size", [1, 2, 8])
+@pytest.mark.parametrize("nelem", [1, 10, 100])
+@pytest.mark.parametrize("keep", [True, False])
+def test_dataframe_scatter_by_map(map_size, nelem, keep):
+
+    strlist = ["dog", "cat", "fish", "bird", "pig", "fox", "cow", "goat"]
+    np.random.seed(0)
+    df = DataFrame()
+    df["a"] = np.random.choice(strlist[:map_size], nelem)
+    df["b"] = np.random.uniform(low=0, high=map_size, size=nelem)
+    df["c"] = np.random.randint(map_size, size=nelem)
+    df["d"] = df["a"].astype("category")
+
+    def _check_scatter_by_map(dfs, col):
+        assert len(dfs) == map_size
+        nrows = 0
+        # print(col._column)
+        name = col.name
+        for i, df in enumerate(dfs):
+            nrows += len(df)
+            if len(df) > 0:
+                # Make sure the column types were preserved
+                assert isinstance(df[name]._column, type(col._column))
+            try:
+                sr = df[name].astype(np.int32)
+            except ValueError:
+                sr = df[name]
+            assert sr.nunique() <= 1
+            if sr.nunique() == 1:
+                if isinstance(df[name]._column, NumericalColumn):
+                    assert sr.iloc[0] == i
+        assert nrows == nelem
+
+    with pytest.warns(UserWarning):
+        _check_scatter_by_map(
+            df.scatter_by_map("a", map_size, keep_index=keep), df["a"]
+        )
+    _check_scatter_by_map(
+        df.scatter_by_map("b", map_size, keep_index=keep), df["b"]
+    )
+    _check_scatter_by_map(
+        df.scatter_by_map("c", map_size, keep_index=keep), df["c"]
+    )
+    with pytest.warns(UserWarning):
+        _check_scatter_by_map(
+            df.scatter_by_map("d", map_size, keep_index=keep), df["d"]
+        )
+
+    if map_size == 2 and nelem == 100:
+        with pytest.warns(UserWarning):
+            df.scatter_by_map("a")  # Auto-detect map_size
+        with pytest.raises(ValueError):
+            with pytest.warns(UserWarning):
+                df.scatter_by_map("a", map_size=1, debug=True)  # Bad map_size
+
+    # Test GenericIndex
+    df2 = df.set_index("c")
+    generic_result = df2.scatter_by_map("b", map_size, keep_index=keep)
+    _check_scatter_by_map(generic_result, df2["b"])
+    if keep:
+        for frame in generic_result:
+            isinstance(frame.index, type(df2.index))
+
+    # Test MultiIndex
+    df2 = df.set_index(["a", "c"])
+    multiindex_result = df2.scatter_by_map("b", map_size, keep_index=keep)
+    _check_scatter_by_map(multiindex_result, df2["b"])
+    if keep:
+        for frame in multiindex_result:
+            isinstance(frame.index, type(df2.index))
+
+
+@pytest.mark.parametrize(
+    "nelem,dtype", list(product(sort_nelem_args, sort_dtype_args))
+)
+@pytest.mark.parametrize(
+    "kind", ["quicksort", "mergesort", "heapsort", "stable"]
+)
+def test_dataframe_sort_values_kind(nelem, dtype, kind):
+    np.random.seed(0)
+    df = DataFrame()
+    df["a"] = aa = (100 * np.random.random(nelem)).astype(dtype)
+    df["b"] = bb = (100 * np.random.random(nelem)).astype(dtype)
+    with expect_warning_if(kind != "quicksort", UserWarning):
+        sorted_df = df.sort_values(by="a", kind=kind)
+    # Check
+    sorted_index = np.argsort(aa, kind="mergesort")
+    assert_eq(sorted_df.index.values, sorted_index)
+    assert_eq(sorted_df["a"].values, aa[sorted_index])
+    assert_eq(sorted_df["b"].values, bb[sorted_index])
+
+
+@pytest.mark.parametrize("ids", [[-1, 0, 1, 0], [0, 2, 3, 0]])
+def test_dataframe_scatter_by_map_7513(ids):
+    df = DataFrame({"id": ids, "val": [0, 1, 2, 3]})
+    with pytest.raises(ValueError):
+        df.scatter_by_map(df["id"])
+
+
+def test_dataframe_scatter_by_map_empty():
+    df = DataFrame({"a": [], "b": []})
+    scattered = df.scatter_by_map(df["a"])
+    assert len(scattered) == 0
diff --git a/python/cudf/cudf/tests/test_sparse_df.py b/python/cudf/cudf/tests/test_sparse_df.py
new file mode 100644
index 0000000..3248e7f
--- /dev/null
+++ b/python/cudf/cudf/tests/test_sparse_df.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import numpy as np
+
+from cudf import Series
+
+
+def test_to_dense_array():
+    data = np.random.random(8)
+    mask = np.asarray([0b11010110]).astype(np.byte)
+
+    sr = Series.from_masked_array(data=data, mask=mask, null_count=3)
+    assert sr.has_nulls
+    assert sr.null_count != len(sr)
+    filled = sr.to_numpy(na_value=np.nan)
+    dense = sr.dropna().to_numpy()
+    assert dense.size < filled.size
+    assert filled.size == len(sr)
diff --git a/python/cudf/cudf/tests/test_spilling.py b/python/cudf/cudf/tests/test_spilling.py
new file mode 100644
index 0000000..88ce908
--- /dev/null
+++ b/python/cudf/cudf/tests/test_spilling.py
@@ -0,0 +1,661 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import importlib
+import random
+import time
+import warnings
+import weakref
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Tuple
+
+import cupy
+import numpy as np
+import pandas
+import pandas.testing
+import pytest
+
+import rmm
+
+import cudf
+import cudf.core.buffer.spill_manager
+import cudf.options
+from cudf.core.abc import Serializable
+from cudf.core.buffer import (
+    Buffer,
+    acquire_spill_lock,
+    as_buffer,
+    get_spill_lock,
+)
+from cudf.core.buffer.spill_manager import (
+    SpillManager,
+    get_global_manager,
+    get_rmm_memory_resource_stack,
+    set_global_manager,
+)
+from cudf.core.buffer.spillable_buffer import (
+    SpillableBuffer,
+    SpillableBufferSlice,
+    SpillLock,
+)
+from cudf.testing._utils import assert_eq
+
+if get_global_manager() is not None:
+    pytest.skip(
+        "cannot test spilling when enabled globally, set `CUDF_SPILL=off`",
+        allow_module_level=True,
+    )
+
+
+def single_column_df(target="gpu") -> cudf.DataFrame:
+    """Create a standard single column dataframe used for testing
+
+    Use `single_column_df_data`, `single_column_df_base_data`,
+    `gen_df_data_nbytes` for easy access to the buffer of the column.
+
+    Notice, this is just for convenience, there is nothing special
+    about this dataframe.
+
+    Parameters
+    ----------
+    target : str, optional
+        Set the spill state of the dataframe
+
+    Return
+    ------
+    DataFrame
+        A standard dataframe with a single column
+    """
+    ret = cudf.DataFrame({"a": [1, 2, 3]})
+    if target != "gpu":
+        single_column_df_data(ret).spill(target=target)
+    return ret
+
+
+def single_column_df_data(df: cudf.DataFrame) -> SpillableBuffer:
+    """Access `.data` of the column of a standard dataframe"""
+    ret = df._data._data["a"].data
+    assert isinstance(ret, SpillableBuffer)
+    return ret
+
+
+def single_column_df_base_data(df: cudf.DataFrame) -> SpillableBuffer:
+    """Access `.base_data` of the column of a standard dataframe"""
+    ret = df._data._data["a"].base_data
+    assert isinstance(ret, SpillableBuffer)
+    return ret
+
+
+# Get number of bytes of the column of a standard dataframe
+gen_df_data_nbytes = single_column_df()._data._data["a"].data.nbytes
+
+
+def spilled_and_unspilled(manager: SpillManager) -> Tuple[int, int]:
+    """Get bytes spilled and unspilled known by the manager"""
+    spilled = sum(buf.size for buf in manager.buffers() if buf.is_spilled)
+    unspilled = sum(
+        buf.size for buf in manager.buffers() if not buf.is_spilled
+    )
+    return spilled, unspilled
+
+
+@pytest.fixture
+def manager(request):
+    """Fixture to enable and make a spilling manager availabe"""
+    kwargs = dict(getattr(request, "param", {}))
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        set_global_manager(manager=SpillManager(**kwargs))
+        yield get_global_manager()
+        # Retrieving the test result using the `pytest_runtest_makereport`
+        # hook from conftest.py
+        if request.node.report["call"].failed:
+            # Ignore `overwriting non-empty manager` errors when
+            # test is failing.
+            warnings.simplefilter("ignore")
+        set_global_manager(manager=None)
+
+
+def test_spillable_buffer(manager: SpillManager):
+    buf = as_buffer(data=rmm.DeviceBuffer(size=10), exposed=False)
+    assert isinstance(buf, SpillableBuffer)
+    assert buf.spillable
+    buf.mark_exposed()
+    assert buf.exposed
+    assert not buf.spillable
+    buf = as_buffer(data=rmm.DeviceBuffer(size=10), exposed=False)
+    # Notice, accessing `__cuda_array_interface__` itself doesn't
+    # expose the pointer, only accessing the "data" field exposes
+    # the pointer.
+    iface = buf.__cuda_array_interface__
+    assert not buf.exposed
+    assert buf.spillable
+    iface["data"][0]  # Expose pointer
+    assert buf.exposed
+    assert not buf.spillable
+
+
+@pytest.mark.parametrize(
+    "attribute",
+    [
+        "get_ptr",
+        "memoryview",
+        "is_spilled",
+        "exposed",
+        "spillable",
+        "spill_lock",
+        "spill",
+        "memory_info",
+    ],
+)
+def test_spillable_buffer_view_attributes(manager: SpillManager, attribute):
+    base = as_buffer(data=rmm.DeviceBuffer(size=10), exposed=False)
+    view = base[:]
+    attr_base = getattr(base, attribute)
+    attr_view = getattr(view, attribute)
+    if callable(attr_view):
+        pass
+    else:
+        assert attr_base == attr_view
+
+
+@pytest.mark.parametrize("target", ["gpu", "cpu"])
+def test_memory_info(manager: SpillManager, target):
+    if target == "gpu":
+        mem = rmm.DeviceBuffer(size=10)
+        ptr = mem.ptr
+    elif target == "cpu":
+        mem = np.empty(10, dtype="u1")
+        ptr = mem.__array_interface__["data"][0]
+    b = as_buffer(data=mem, exposed=False)
+    assert b.memory_info() == (ptr, mem.size, target)
+    assert b[:].memory_info() == (ptr, mem.size, target)
+    assert b[:-1].memory_info() == (ptr, mem.size - 1, target)
+    assert b[1:].memory_info() == (ptr + 1, mem.size - 1, target)
+    assert b[2:4].memory_info() == (ptr + 2, 2, target)
+
+
+def test_from_pandas(manager: SpillManager):
+    pdf1 = pandas.DataFrame({"a": [1, 2, 3]})
+    df = cudf.from_pandas(pdf1)
+    assert single_column_df_data(df).spillable
+    pdf2 = df.to_pandas()
+    pandas.testing.assert_frame_equal(pdf1, pdf2)
+
+
+def test_creations(manager: SpillManager):
+    df = single_column_df()
+    assert single_column_df_data(df).spillable
+
+    df = cudf.datasets.timeseries(dtypes={"a": float})
+    assert single_column_df_data(df).spillable
+
+    df = cudf.datasets.randomdata(dtypes={"a": float})
+    assert single_column_df_data(df).spillable
+
+
+def test_spillable_df_groupby(manager: SpillManager):
+    df = cudf.DataFrame({"a": [1, 1, 1]})
+    gb = df.groupby("a")
+    assert len(single_column_df_base_data(df)._spill_locks) == 0
+    gb._groupby
+    # `gb._groupby`, which is cached on `gb`, holds a spill lock
+    assert len(single_column_df_base_data(df)._spill_locks) == 1
+    assert not single_column_df_data(df).spillable
+    del gb
+    assert single_column_df_data(df).spillable
+
+
+def test_spilling_buffer(manager: SpillManager):
+    buf = as_buffer(rmm.DeviceBuffer(size=10), exposed=False)
+    buf.spill(target="cpu")
+    assert buf.is_spilled
+    buf.mark_exposed()  # Expose pointer and trigger unspill
+    assert not buf.is_spilled
+    with pytest.raises(ValueError, match="unspillable buffer"):
+        buf.spill(target="cpu")
+
+
+def test_environment_variables(monkeypatch):
+    def reload_options():
+        # In order to enabling monkey patching of the environment variables
+        # mark the global manager as uninitialized.
+        set_global_manager(None)
+        cudf.core.buffer.spill_manager._global_manager_uninitialized = True
+        importlib.reload(cudf.options)
+
+    monkeypatch.setenv("CUDF_SPILL_ON_DEMAND", "off")
+    monkeypatch.setenv("CUDF_SPILL", "off")
+    reload_options()
+    assert get_global_manager() is None
+
+    monkeypatch.setenv("CUDF_SPILL", "on")
+    reload_options()
+    manager = get_global_manager()
+    assert isinstance(manager, SpillManager)
+    assert manager._spill_on_demand is False
+    assert manager._device_memory_limit is None
+    assert manager.statistics.level == 0
+
+    monkeypatch.setenv("CUDF_SPILL_DEVICE_LIMIT", "1000")
+    reload_options()
+    manager = get_global_manager()
+    assert isinstance(manager, SpillManager)
+    assert manager._device_memory_limit == 1000
+    assert manager.statistics.level == 0
+
+    monkeypatch.setenv("CUDF_SPILL_STATS", "1")
+    reload_options()
+    manager = get_global_manager()
+    assert isinstance(manager, SpillManager)
+    assert manager.statistics.level == 1
+
+    monkeypatch.setenv("CUDF_SPILL_STATS", "2")
+    reload_options()
+    manager = get_global_manager()
+    assert isinstance(manager, SpillManager)
+    assert manager.statistics.level == 2
+
+
+def test_spill_device_memory(manager: SpillManager):
+    df = single_column_df()
+    assert spilled_and_unspilled(manager) == (0, gen_df_data_nbytes)
+    manager.spill_device_memory(nbytes=1)
+    assert spilled_and_unspilled(manager) == (gen_df_data_nbytes, 0)
+    del df
+    assert spilled_and_unspilled(manager) == (0, 0)
+    df1 = single_column_df()
+    df2 = single_column_df()
+    manager.spill_device_memory(nbytes=1)
+    assert single_column_df_data(df1).is_spilled
+    assert not single_column_df_data(df2).is_spilled
+    manager.spill_device_memory(nbytes=1)
+    assert single_column_df_data(df1).is_spilled
+    assert single_column_df_data(df2).is_spilled
+    df3 = df1 + df2
+    assert not single_column_df_data(df1).is_spilled
+    assert not single_column_df_data(df2).is_spilled
+    assert not single_column_df_data(df3).is_spilled
+    manager.spill_device_memory(nbytes=1)
+    assert single_column_df_data(df1).is_spilled
+    assert not single_column_df_data(df2).is_spilled
+    assert not single_column_df_data(df3).is_spilled
+    df2.abs()  # Should change the access time
+    manager.spill_device_memory(nbytes=1)
+    assert single_column_df_data(df1).is_spilled
+    assert not single_column_df_data(df2).is_spilled
+    assert single_column_df_data(df3).is_spilled
+
+
+def test_spill_to_device_limit(manager: SpillManager):
+    df1 = single_column_df()
+    df2 = single_column_df()
+    assert spilled_and_unspilled(manager) == (0, gen_df_data_nbytes * 2)
+    manager.spill_to_device_limit(device_limit=0)
+    assert spilled_and_unspilled(manager) == (gen_df_data_nbytes * 2, 0)
+    df3 = df1 + df2
+    manager.spill_to_device_limit(device_limit=0)
+    assert spilled_and_unspilled(manager) == (gen_df_data_nbytes * 3, 0)
+    assert single_column_df_data(df1).is_spilled
+    assert single_column_df_data(df2).is_spilled
+    assert single_column_df_data(df3).is_spilled
+
+
+@pytest.mark.parametrize(
+    "manager", [{"device_memory_limit": 0}], indirect=True
+)
+def test_zero_device_limit(manager: SpillManager):
+    assert manager._device_memory_limit == 0
+    df1 = single_column_df()
+    df2 = single_column_df()
+    assert spilled_and_unspilled(manager) == (gen_df_data_nbytes * 2, 0)
+    df1 + df2
+    # Notice, while performing the addintion both df1 and df2 are unspillable
+    assert spilled_and_unspilled(manager) == (0, gen_df_data_nbytes * 2)
+    manager.spill_to_device_limit()
+    assert spilled_and_unspilled(manager) == (gen_df_data_nbytes * 2, 0)
+
+
+def test_spill_df_index(manager: SpillManager):
+    df = single_column_df()
+    df.index = [1, 3, 2]  # use a materialized index
+    assert spilled_and_unspilled(manager) == (0, gen_df_data_nbytes * 2)
+
+    manager.spill_to_device_limit(gen_df_data_nbytes)
+    assert spilled_and_unspilled(manager) == (
+        gen_df_data_nbytes,
+        gen_df_data_nbytes,
+    )
+
+    manager.spill_to_device_limit(0)
+    assert spilled_and_unspilled(manager) == (gen_df_data_nbytes * 2, 0)
+
+
+def test_external_memory(manager):
+    cupy.cuda.set_allocator()  # uses default allocator
+    cpy = cupy.asarray([1, 2, 3])
+    s = cudf.Series(cpy)
+    # Check that the cupy array is still alive after overwriting `cpy`
+    cpy = weakref.ref(cpy)
+    assert cpy() is not None
+    # Check that the series is spillable and known by the spill manager
+    assert len(manager.buffers()) == 1
+    assert s._data[None].data.spillable
+
+
+def test_spilling_df_views(manager):
+    df = single_column_df(target="cpu")
+    assert single_column_df_data(df).is_spilled
+    df_view = df.loc[1:]
+    assert single_column_df_data(df_view).spillable
+    assert single_column_df_data(df).spillable
+
+
+def test_modify_spilled_views(manager):
+    df = single_column_df()
+    df_view = df.iloc[1:]
+    buf = single_column_df_data(df)
+    buf.spill(target="cpu")
+
+    # modify the spilled df and check that the changes are reflected
+    # in the view
+    df.iloc[1:] = 0
+    assert_eq(df_view, df.iloc[1:])
+
+    # now, modify the view and check that the changes are reflected in
+    # the df
+    df_view.iloc[:] = -1
+    assert_eq(df_view, df.iloc[1:])
+
+
+@pytest.mark.parametrize("target", ["gpu", "cpu"])
+def test_get_ptr(manager: SpillManager, target):
+    if target == "gpu":
+        mem = rmm.DeviceBuffer(size=10)
+    elif target == "cpu":
+        mem = np.empty(10, dtype="u1")
+    buf = as_buffer(data=mem, exposed=False)
+    assert buf.spillable
+    assert len(buf._spill_locks) == 0
+    with acquire_spill_lock():
+        buf.get_ptr(mode="read")
+        assert not buf.spillable
+        with acquire_spill_lock():
+            buf.get_ptr(mode="read")
+            assert not buf.spillable
+        assert not buf.spillable
+    assert buf.spillable
+
+
+def test_get_spill_lock(manager: SpillManager):
+    @acquire_spill_lock()
+    def f(sleep=False, nest=0):
+        if sleep:
+            time.sleep(random.random() / 100)
+        if nest:
+            return f(nest=nest - 1)
+        return get_spill_lock()
+
+    assert get_spill_lock() is None
+    slock = f()
+    assert isinstance(slock, SpillLock)
+    assert get_spill_lock() is None
+    slock = f(nest=2)
+    assert isinstance(slock, SpillLock)
+    assert get_spill_lock() is None
+
+    with ThreadPoolExecutor(max_workers=2) as executor:
+        futures_with_spill_lock = []
+        futures_without_spill_lock = []
+        for _ in range(100):
+            futures_with_spill_lock.append(
+                executor.submit(f, sleep=True, nest=1)
+            )
+            futures_without_spill_lock.append(
+                executor.submit(f, sleep=True, nest=1)
+            )
+        all(isinstance(f.result(), SpillLock) for f in futures_with_spill_lock)
+        all(f is None for f in futures_without_spill_lock)
+
+
+def test_get_spill_lock_no_manager():
+    """When spilling is disabled, get_spill_lock() should return None always"""
+
+    @acquire_spill_lock()
+    def f():
+        return get_spill_lock()
+
+    assert get_spill_lock() is None
+    assert f() is None
+
+
+@pytest.mark.parametrize("target", ["gpu", "cpu"])
+@pytest.mark.parametrize("view", [None, slice(0, 2), slice(1, 3)])
+def test_serialize_device(manager, target, view):
+    df1 = single_column_df()
+    if view is not None:
+        df1 = df1.iloc[view]
+    single_column_df_data(df1).spill(target=target)
+
+    header, frames = df1.device_serialize()
+    assert len(frames) == 1
+    if target == "gpu":
+        assert isinstance(frames[0], Buffer)
+        assert not single_column_df_data(df1).is_spilled
+        assert not single_column_df_data(df1).spillable
+        frames[0] = cupy.array(frames[0], copy=True)
+    else:
+        assert isinstance(frames[0], memoryview)
+        assert single_column_df_data(df1).is_spilled
+        assert single_column_df_data(df1).spillable
+
+    df2 = Serializable.device_deserialize(header, frames)
+    assert_eq(df1, df2)
+
+
+@pytest.mark.parametrize("target", ["gpu", "cpu"])
+@pytest.mark.parametrize("view", [None, slice(0, 2), slice(1, 3)])
+def test_serialize_host(manager, target, view):
+    df1 = single_column_df()
+    if view is not None:
+        df1 = df1.iloc[view]
+    single_column_df_data(df1).spill(target=target)
+
+    # Unspilled df becomes spilled after host serialization
+    header, frames = df1.host_serialize()
+    assert all(isinstance(f, memoryview) for f in frames)
+    df2 = Serializable.host_deserialize(header, frames)
+    assert single_column_df_data(df2).is_spilled
+    assert_eq(df1, df2)
+
+
+def test_serialize_dask_dataframe(manager: SpillManager):
+    protocol = pytest.importorskip("distributed.protocol")
+
+    df1 = single_column_df(target="gpu")
+    header, frames = protocol.serialize(
+        df1, serializers=("dask",), on_error="raise"
+    )
+    buf = single_column_df_data(df1)
+    assert len(frames) == 1
+    assert isinstance(frames[0], memoryview)
+    # Check that the memoryview and frames is the same memory
+    assert (
+        np.array(buf.memoryview()).__array_interface__["data"]
+        == np.array(frames[0]).__array_interface__["data"]
+    )
+
+    df2 = protocol.deserialize(header, frames)
+    assert single_column_df_data(df2).is_spilled
+    assert_eq(df1, df2)
+
+
+def test_serialize_cuda_dataframe(manager: SpillManager):
+    protocol = pytest.importorskip("distributed.protocol")
+
+    df1 = single_column_df(target="gpu")
+    header, frames = protocol.serialize(
+        df1, serializers=("cuda",), on_error="raise"
+    )
+    buf: SpillableBufferSlice = single_column_df_data(df1)
+    assert len(buf._base._spill_locks) == 1
+    assert len(frames) == 1
+    assert isinstance(frames[0], Buffer)
+    assert frames[0].get_ptr(mode="read") == buf.get_ptr(mode="read")
+
+    frames[0] = cupy.array(frames[0], copy=True)
+    df2 = protocol.deserialize(header, frames)
+    assert_eq(df1, df2)
+
+
+def test_get_rmm_memory_resource_stack():
+    mr1 = rmm.mr.get_current_device_resource()
+    assert all(
+        not isinstance(m, rmm.mr.FailureCallbackResourceAdaptor)
+        for m in get_rmm_memory_resource_stack(mr1)
+    )
+
+    mr2 = rmm.mr.FailureCallbackResourceAdaptor(mr1, lambda x: False)
+    assert get_rmm_memory_resource_stack(mr2)[0] is mr2
+    assert get_rmm_memory_resource_stack(mr2)[1] is mr1
+
+    mr3 = rmm.mr.FixedSizeMemoryResource(mr2)
+    assert get_rmm_memory_resource_stack(mr3)[0] is mr3
+    assert get_rmm_memory_resource_stack(mr3)[1] is mr2
+    assert get_rmm_memory_resource_stack(mr3)[2] is mr1
+
+    mr4 = rmm.mr.FailureCallbackResourceAdaptor(mr3, lambda x: False)
+    assert get_rmm_memory_resource_stack(mr4)[0] is mr4
+    assert get_rmm_memory_resource_stack(mr4)[1] is mr3
+    assert get_rmm_memory_resource_stack(mr4)[2] is mr2
+    assert get_rmm_memory_resource_stack(mr4)[3] is mr1
+
+
+def test_df_transpose(manager: SpillManager):
+    df1 = cudf.DataFrame({"a": [1, 2]})
+    df2 = df1.transpose()
+    # For now, all buffers are marked as exposed
+    assert df1._data._data["a"].data.exposed
+    assert df2._data._data[0].data.exposed
+    assert df2._data._data[1].data.exposed
+
+
+def test_as_buffer_of_spillable_buffer(manager: SpillManager):
+    data = cupy.arange(10, dtype="u1")
+    b1 = as_buffer(data, exposed=False)
+    assert isinstance(b1, SpillableBuffer)
+    assert b1.owner is data
+    b2 = as_buffer(b1)
+    assert b1 is b2
+
+    with pytest.raises(
+        ValueError,
+        match="buffer must either be exposed or spilled locked",
+    ):
+        # Use `memory_info` to access device point _without_ making
+        # the buffer unspillable.
+        b3 = as_buffer(b1.memory_info()[0], size=b1.size, owner=b1)
+
+    with acquire_spill_lock():
+        b3 = as_buffer(b1.get_ptr(mode="read"), size=b1.size, owner=b1)
+    assert isinstance(b3, SpillableBufferSlice)
+    assert b3.owner is b1
+
+    b4 = as_buffer(
+        b1.get_ptr(mode="write") + data.itemsize,
+        size=b1.size - data.itemsize,
+        owner=b3,
+    )
+    assert isinstance(b4, SpillableBufferSlice)
+    assert b4.owner is b1
+    assert all(cupy.array(b4.memoryview()) == data[1:])
+
+    b5 = as_buffer(b4.get_ptr(mode="write"), size=b4.size - 1, owner=b4)
+    assert isinstance(b5, SpillableBufferSlice)
+    assert b5.owner is b1
+    assert all(cupy.array(b5.memoryview()) == data[1:-1])
+
+
+@pytest.mark.parametrize("dtype", ["uint8", "uint64"])
+def test_memoryview_slice(manager: SpillManager, dtype):
+    """Check .memoryview() of a sliced spillable buffer"""
+
+    data = np.arange(10, dtype=dtype)
+    # memoryview of a sliced spillable buffer
+    m1 = as_buffer(data=data)[1:-1].memoryview()
+    # sliced memoryview of data as bytes
+    m2 = memoryview(data).cast("B")[1:-1]
+    assert m1 == m2
+
+
+@pytest.mark.parametrize(
+    "manager", [{"statistic_level": 0}, {"statistic_level": 1}], indirect=True
+)
+def test_statistics(manager: SpillManager):
+    assert len(manager.statistics.spill_totals) == 0
+
+    buf: SpillableBuffer = as_buffer(
+        data=rmm.DeviceBuffer(size=10), exposed=False
+    )
+    buf.spill(target="cpu")
+
+    if manager.statistics.level == 0:
+        assert len(manager.statistics.spill_totals) == 0
+        return
+
+    assert len(manager.statistics.spill_totals) == 1
+    nbytes, time = manager.statistics.spill_totals[("gpu", "cpu")]
+    assert nbytes == buf.size
+    assert time > 0
+
+    buf.spill(target="gpu")
+    assert len(manager.statistics.spill_totals) == 2
+    nbytes, time = manager.statistics.spill_totals[("cpu", "gpu")]
+    assert nbytes == buf.size
+    assert time > 0
+
+
+@pytest.mark.parametrize("manager", [{"statistic_level": 2}], indirect=True)
+def test_statistics_expose(manager: SpillManager):
+    assert len(manager.statistics.spill_totals) == 0
+
+    buffers: List[SpillableBuffer] = [
+        as_buffer(data=rmm.DeviceBuffer(size=10), exposed=False)
+        for _ in range(10)
+    ]
+
+    # Expose the first buffer
+    buffers[0].mark_exposed()
+    assert len(manager.statistics.exposes) == 1
+    stat = list(manager.statistics.exposes.values())[0]
+    assert stat.count == 1
+    assert stat.total_nbytes == buffers[0].nbytes
+    assert stat.spilled_nbytes == 0
+
+    # Expose all 10 buffers
+    for i in range(10):
+        buffers[i].mark_exposed()
+
+    # The rest of the ptr accesses should accumulate to a single stat
+    # because they resolve to the same traceback.
+    assert len(manager.statistics.exposes) == 2
+    stat = list(manager.statistics.exposes.values())[1]
+    assert stat.count == 9
+    assert stat.total_nbytes == buffers[0].nbytes * 9
+    assert stat.spilled_nbytes == 0
+
+    # Create and spill 10 new buffers
+    buffers: List[SpillableBuffer] = [
+        as_buffer(data=rmm.DeviceBuffer(size=10), exposed=False)
+        for _ in range(10)
+    ]
+
+    manager.spill_to_device_limit(0)
+
+    # Expose the new buffers and check that they are counted as spilled
+    for i in range(10):
+        buffers[i].mark_exposed()
+    assert len(manager.statistics.exposes) == 3
+    stat = list(manager.statistics.exposes.values())[2]
+    assert stat.count == 10
+    assert stat.total_nbytes == buffers[0].nbytes * 10
+    assert stat.spilled_nbytes == buffers[0].nbytes * 10
diff --git a/python/cudf/cudf/tests/test_stats.py b/python/cudf/cudf/tests/test_stats.py
new file mode 100644
index 0000000..5f01066
--- /dev/null
+++ b/python/cudf/cudf/tests/test_stats.py
@@ -0,0 +1,650 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from concurrent.futures import ThreadPoolExecutor
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.datasets import randomdata
+from cudf.testing._utils import (
+    _create_cudf_series_float64_default,
+    _create_pandas_series_float64_default,
+    assert_eq,
+    assert_exceptions_equal,
+    expect_warning_if,
+)
+
+params_dtypes = [np.int32, np.uint32, np.float32, np.float64]
+methods = ["min", "max", "sum", "mean", "var", "std"]
+
+interpolation_methods = ["linear", "lower", "higher", "midpoint", "nearest"]
+
+
+@pytest.mark.parametrize("method", methods)
+@pytest.mark.parametrize("dtype", params_dtypes)
+@pytest.mark.parametrize("skipna", [True, False])
+def test_series_reductions(method, dtype, skipna):
+    np.random.seed(0)
+    arr = np.random.random(100)
+    if np.issubdtype(dtype, np.integer):
+        arr *= 100
+        mask = arr > 10
+    else:
+        mask = arr > 0.5
+
+    arr = arr.astype(dtype)
+    if dtype in (np.float32, np.float64):
+        arr[[2, 5, 14, 19, 50, 70]] = np.nan
+    sr = cudf.Series(arr)
+    sr[~mask] = None
+    psr = sr.to_pandas()
+    psr[~mask] = np.nan
+
+    def call_test(sr, skipna):
+        fn = getattr(sr, method)
+        if method in ["std", "var"]:
+            return fn(ddof=1, skipna=skipna)
+        else:
+            return fn(skipna=skipna)
+
+    expect, got = call_test(psr, skipna=skipna), call_test(sr, skipna=skipna)
+
+    np.testing.assert_approx_equal(expect, got)
+
+
+@pytest.mark.parametrize("method", methods)
+def test_series_reductions_concurrency(method):
+    e = ThreadPoolExecutor(10)
+
+    np.random.seed(0)
+    srs = [cudf.Series(np.random.random(10000)) for _ in range(1)]
+
+    def call_test(sr):
+        fn = getattr(sr, method)
+        if method in ["std", "var"]:
+            return fn(ddof=1)
+        else:
+            return fn()
+
+    def f(sr):
+        return call_test(sr + 1)
+
+    list(e.map(f, srs * 50))
+
+
+@pytest.mark.parametrize("ddof", range(3))
+def test_series_std(ddof):
+    np.random.seed(0)
+    arr = np.random.random(100) - 0.5
+    sr = cudf.Series(arr)
+    pd = sr.to_pandas()
+    got = sr.std(ddof=ddof)
+    expect = pd.std(ddof=ddof)
+    np.testing.assert_approx_equal(expect, got)
+
+
+def test_series_unique():
+    for size in [10**x for x in range(5)]:
+        arr = np.random.randint(low=-1, high=10, size=size)
+        mask = arr != -1
+        sr = cudf.Series(arr)
+        sr[~mask] = None
+        assert set(arr[mask]) == set(sr.unique().dropna().to_numpy())
+        assert len(set(arr[mask])) == sr.nunique()
+
+
+@pytest.mark.parametrize(
+    "nan_as_null, dropna",
+    [(True, True), (True, False), (False, True), (False, False)],
+)
+def test_series_nunique(nan_as_null, dropna):
+    # We remove nulls as opposed to NaNs using the dropna parameter,
+    # so to test against pandas we replace NaN with another discrete value
+    cudf_series = cudf.Series([1, 2, 2, 3, 3], nan_as_null=nan_as_null)
+    pd_series = pd.Series([1, 2, 2, 3, 3])
+    expect = pd_series.nunique(dropna=dropna)
+    got = cudf_series.nunique(dropna=dropna)
+    assert expect == got
+
+    cudf_series = cudf.Series(
+        [1.0, 2.0, 3.0, np.nan, None], nan_as_null=nan_as_null
+    )
+    if nan_as_null is True:
+        pd_series = pd.Series([1.0, 2.0, 3.0, np.nan, None])
+    else:
+        pd_series = pd.Series([1.0, 2.0, 3.0, -1.0, None])
+
+    expect = pd_series.nunique(dropna=dropna)
+    got = cudf_series.nunique(dropna=dropna)
+    assert expect == got
+
+    cudf_series = cudf.Series([1.0, np.nan, np.nan], nan_as_null=nan_as_null)
+    if nan_as_null is True:
+        pd_series = pd.Series([1.0, np.nan, np.nan])
+    else:
+        pd_series = pd.Series([1.0, -1.0, -1.0])
+    expect = pd_series.nunique(dropna=dropna)
+    got = cudf_series.nunique(dropna=dropna)
+    assert expect == got
+
+
+def test_series_scale():
+    arr = pd.Series(np.random.randint(low=-10, high=10, size=100))
+    sr = cudf.Series(arr)
+
+    vmin = arr.min()
+    vmax = arr.max()
+    scaled = (arr - vmin) / (vmax - vmin)
+    assert scaled.min() == 0
+    assert scaled.max() == 1
+    assert_eq(sr.scale(), scaled)
+
+
+@pytest.mark.parametrize("int_method", interpolation_methods)
+def test_exact_quantiles(int_method):
+    arr = np.asarray([6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.01, 0.8, 5.7])
+    quant_values = [0.0, 0.25, 0.33, 0.5, 1.0]
+
+    df = pd.DataFrame(arr)
+    gdf_series = cudf.Series(arr)
+
+    q1 = gdf_series.quantile(
+        quant_values, interpolation=int_method, exact=True
+    )
+
+    q2 = df.quantile(quant_values, interpolation=int_method)
+
+    np.testing.assert_allclose(
+        q1.to_pandas().values, np.array(q2.values).T.flatten(), rtol=1e-10
+    )
+
+
+@pytest.mark.parametrize("int_method", interpolation_methods)
+def test_exact_quantiles_int(int_method):
+    arr = np.asarray([7, 0, 3, 4, 2, 1, -1, 1, 6])
+    quant_values = [0.0, 0.25, 0.33, 0.5, 1.0]
+
+    df = pd.DataFrame(arr)
+    gdf_series = cudf.Series(arr)
+
+    q1 = gdf_series.quantile(
+        quant_values, interpolation=int_method, exact=True
+    )
+
+    q2 = df.quantile(quant_values, interpolation=int_method)
+
+    np.testing.assert_allclose(
+        q1.to_pandas().values, np.array(q2.values).T.flatten(), rtol=1e-10
+    )
+
+
+def test_approx_quantiles():
+
+    arr = np.asarray([6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.01, 0.8, 5.7])
+    quant_values = [0.0, 0.25, 0.33, 0.5, 1.0]
+
+    gdf_series = cudf.Series(arr)
+    pdf_series = pd.Series(arr)
+
+    q1 = gdf_series.quantile(quant_values, exact=False)
+    q2 = pdf_series.quantile(quant_values)
+
+    assert_eq(q1, q2)
+
+
+def test_approx_quantiles_int():
+    arr = np.asarray([1, 2, 3])
+    quant_values = [0.5]
+    approx_results = [2]
+
+    gdf_series = cudf.Series(arr)
+
+    q1 = gdf_series.quantile(quant_values, exact=False)
+
+    assert approx_results == q1.to_pandas().values
+
+
+@pytest.mark.parametrize("data", [[], [1, 2, 3, 10, 326497]])
+@pytest.mark.parametrize(
+    "q",
+    [
+        [],
+        0.5,
+        1,
+        0.234,
+        [0.345],
+        [0.243, 0.5, 1],
+        np.array([0.5, 1]),
+        cp.array([0.5, 1]),
+    ],
+)
+def test_misc_quantiles(data, q):
+
+    pdf_series = _create_pandas_series_float64_default(data)
+    gdf_series = _create_cudf_series_float64_default(data)
+
+    expected = pdf_series.quantile(q.get() if isinstance(q, cp.ndarray) else q)
+    actual = gdf_series.quantile(q)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.Series(np.random.normal(-100, 100, 1000)),
+        cudf.Series(np.random.randint(-50, 50, 1000)),
+        cudf.Series(np.zeros(100)),
+        cudf.Series(np.repeat(np.nan, 100)),
+        cudf.Series(np.array([1.123, 2.343, np.nan, 0.0])),
+        cudf.Series(
+            [5, 10, 53, None, np.nan, None, 12, 43, -423], nan_as_null=False
+        ),
+        cudf.Series([1.1032, 2.32, 43.4, 13, -312.0], index=[0, 4, 3, 19, 6]),
+        cudf.Series([], dtype="float64"),
+        cudf.Series([-3]),
+    ],
+)
+@pytest.mark.parametrize("null_flag", [False, True])
+@pytest.mark.parametrize("numeric_only", [False, True])
+def test_kurtosis_series(data, null_flag, numeric_only):
+    pdata = data.to_pandas()
+
+    if null_flag and len(data) > 2:
+        data.iloc[[0, 2]] = None
+        pdata.iloc[[0, 2]] = None
+
+    got = data.kurtosis(numeric_only=numeric_only)
+    got = got if np.isscalar(got) else got.to_numpy()
+    expected = pdata.kurtosis(numeric_only=numeric_only)
+    np.testing.assert_array_almost_equal(got, expected)
+
+    got = data.kurt(numeric_only=numeric_only)
+    got = got if np.isscalar(got) else got.to_numpy()
+    expected = pdata.kurt(numeric_only=numeric_only)
+    np.testing.assert_array_almost_equal(got, expected)
+
+
+@pytest.mark.parametrize("op", ["skew", "kurt"])
+def test_kurt_skew_error(op):
+    gs = cudf.Series(["ab", "cd"])
+    ps = gs.to_pandas()
+
+    with pytest.warns(FutureWarning):
+        assert_exceptions_equal(
+            getattr(gs, op),
+            getattr(ps, op),
+            lfunc_args_and_kwargs=([], {"numeric_only": True}),
+            rfunc_args_and_kwargs=([], {"numeric_only": True}),
+        )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.Series(np.random.normal(-100, 100, 1000)),
+        cudf.Series(np.random.randint(-50, 50, 1000)),
+        cudf.Series(np.zeros(100)),
+        cudf.Series(np.repeat(np.nan, 100)),
+        cudf.Series(np.array([1.123, 2.343, np.nan, 0.0])),
+        cudf.Series(
+            [5, 10, 53, None, np.nan, None, 12, 43, -423], nan_as_null=False
+        ),
+        cudf.Series([1.1032, 2.32, 43.4, 13, -312.0], index=[0, 4, 3, 19, 6]),
+        cudf.Series([], dtype="float64"),
+        cudf.Series([-3]),
+    ],
+)
+@pytest.mark.parametrize("null_flag", [False, True])
+@pytest.mark.parametrize("numeric_only", [False, True])
+def test_skew_series(data, null_flag, numeric_only):
+    pdata = data.to_pandas()
+
+    if null_flag and len(data) > 2:
+        data.iloc[[0, 2]] = None
+        pdata.iloc[[0, 2]] = None
+
+    got = data.skew(numeric_only=numeric_only)
+    expected = pdata.skew(numeric_only=numeric_only)
+    got = got if np.isscalar(got) else got.to_numpy()
+    np.testing.assert_array_almost_equal(got, expected)
+
+
+@pytest.mark.parametrize("dtype", params_dtypes)
+@pytest.mark.parametrize("num_na", [0, 1, 50, 99, 100])
+def test_series_median(dtype, num_na):
+    np.random.seed(0)
+    arr = np.random.random(100)
+    if np.issubdtype(dtype, np.integer):
+        arr *= 100
+    mask = np.arange(100) >= num_na
+
+    arr = arr.astype(dtype)
+    sr = cudf.Series(arr)
+    sr[~mask] = None
+    arr2 = arr[mask]
+    ps = pd.Series(arr2, dtype=dtype)
+
+    actual = sr.median(skipna=True)
+    desired = ps.median(skipna=True)
+
+    np.testing.assert_approx_equal(actual, desired)
+
+    # only for float until integer null supported convert to pandas in cudf
+    # eg. pd.Int64Dtype
+    if np.issubdtype(dtype, np.floating):
+        ps = sr.to_pandas()
+        actual = sr.median(skipna=False)
+        desired = ps.median(skipna=False)
+        np.testing.assert_approx_equal(actual, desired)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.random.normal(-100, 100, 1000),
+        np.random.randint(-50, 50, 1000),
+        np.zeros(100),
+        np.array([1.123, 2.343, np.nan, 0.0]),
+        np.array([-2, 3.75, 6, None, None, None, -8.5, None, 4.2]),
+        cudf.Series([], dtype="float64"),
+        cudf.Series([-3]),
+    ],
+)
+@pytest.mark.parametrize("periods", range(-5, 5))
+@pytest.mark.parametrize("fill_method", ["ffill", "bfill", "pad", "backfill"])
+def test_series_pct_change(data, periods, fill_method):
+    cs = cudf.Series(data)
+    ps = cs.to_pandas()
+
+    if np.abs(periods) <= len(cs):
+        got = cs.pct_change(periods=periods, fill_method=fill_method)
+        expected = ps.pct_change(periods=periods, fill_method=fill_method)
+        np.testing.assert_array_almost_equal(
+            got.to_numpy(na_value=np.nan), expected
+        )
+
+
+@pytest.mark.parametrize(
+    "data1",
+    [
+        np.random.normal(-100, 100, 1000),
+        np.random.randint(-50, 50, 1000),
+        np.zeros(100),
+        np.repeat(np.nan, 100),
+        np.array([1.123, 2.343, np.nan, 0.0]),
+        cudf.Series([5, 10, 53, None, np.nan, None], nan_as_null=False),
+        cudf.Series([1.1, 2.32, 43.4], index=[0, 4, 3]),
+        cudf.Series([], dtype="float64"),
+        cudf.Series([-3]),
+    ],
+)
+@pytest.mark.parametrize(
+    "data2",
+    [
+        np.random.normal(-100, 100, 1000),
+        np.random.randint(-50, 50, 1000),
+        np.zeros(100),
+        np.repeat(np.nan, 100),
+        np.array([1.123, 2.343, np.nan, 0.0]),
+        cudf.Series([1.1, 2.32, 43.4], index=[0, 500, 4000]),
+        cudf.Series([5]),
+    ],
+)
+def test_cov1d(data1, data2):
+    gs1 = cudf.Series(data1)
+    gs2 = cudf.Series(data2)
+
+    ps1 = gs1.to_pandas()
+    ps2 = gs2.to_pandas()
+
+    got = gs1.cov(gs2)
+    ps1_align, ps2_align = ps1.align(ps2, join="inner")
+    with expect_warning_if(
+        (len(ps1_align.dropna()) == 1 and len(ps2_align.dropna()) > 0)
+        or (len(ps2_align.dropna()) == 1 and len(ps1_align.dropna()) > 0),
+        RuntimeWarning,
+    ):
+        expected = ps1.cov(ps2)
+    np.testing.assert_approx_equal(got, expected, significant=8)
+
+
+@pytest.mark.parametrize(
+    "data1",
+    [
+        np.random.normal(-100, 100, 1000),
+        np.random.randint(-50, 50, 1000),
+        np.zeros(100),
+        np.repeat(np.nan, 100),
+        np.array([1.123, 2.343, np.nan, 0.0]),
+        cudf.Series([5, 10, 53, None, np.nan, None], nan_as_null=False),
+        cudf.Series([1.1032, 2.32, 43.4], index=[0, 4, 3]),
+        cudf.Series([], dtype="float64"),
+        cudf.Series([-3]),
+    ],
+)
+@pytest.mark.parametrize(
+    "data2",
+    [
+        np.random.normal(-100, 100, 1000),
+        np.random.randint(-50, 50, 1000),
+        np.zeros(100),
+        np.repeat(np.nan, 100),
+        np.array([1.123, 2.343, np.nan, 0.0]),
+        cudf.Series([1.1, 2.32, 43.4], index=[0, 500, 4000]),
+        cudf.Series([5]),
+    ],
+)
+@pytest.mark.parametrize("method", ["spearman", "pearson"])
+def test_corr1d(data1, data2, method):
+    if method == "spearman":
+        # Pandas uses scipy.stats.spearmanr code-path
+        pytest.importorskip("scipy")
+
+    gs1 = cudf.Series(data1)
+    gs2 = cudf.Series(data2)
+
+    ps1 = gs1.to_pandas()
+    ps2 = gs2.to_pandas()
+
+    got = gs1.corr(gs2, method)
+
+    ps1_align, ps2_align = ps1.align(ps2, join="inner")
+
+    is_singular = (
+        len(ps1_align.dropna()) == 1 and len(ps2_align.dropna()) > 0
+    ) or (len(ps2_align.dropna()) == 1 and len(ps1_align.dropna()) > 0)
+    is_identical = (
+        len(ps1_align.dropna().unique()) == 1 and len(ps2_align.dropna()) > 0
+    ) or (
+        len(ps2_align.dropna().unique()) == 1 and len(ps1_align.dropna()) > 0
+    )
+
+    # Pearson correlation leads to division by 0 when either sample size is 1.
+    # Spearman allows for size 1 samples, but will error if all data in a
+    # sample is identical since the covariance is zero and so the correlation
+    # coefficient is not defined.
+    cond = (is_singular and method == "pearson") or (
+        is_identical and not is_singular and method == "spearman"
+    )
+    if method == "spearman":
+        # SciPy has shuffled around the warning it throws a couple of times.
+        # It's not worth the effort of conditionally importing the appropriate
+        # warning based on the scipy version, just catching a base Warning is
+        # good enough validation.
+        expected_warning = Warning
+    elif method == "pearson":
+        expected_warning = RuntimeWarning
+
+    with expect_warning_if(cond, expected_warning):
+        expected = ps1.corr(ps2, method)
+    np.testing.assert_approx_equal(got, expected, significant=8)
+
+
+@pytest.mark.parametrize("method", ["spearman", "pearson"])
+def test_df_corr(method):
+
+    gdf = randomdata(100, {str(x): float for x in range(50)})
+    pdf = gdf.to_pandas()
+    got = gdf.corr(method)
+    expected = pdf.corr(method)
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0.0, 1, 3, 6, np.NaN, 7, 5.0, np.nan, 5, 2, 3, -100],
+        [np.nan] * 3,
+        [1, 5, 3],
+        [],
+    ],
+)
+@pytest.mark.parametrize(
+    "ops",
+    [
+        "mean",
+        "min",
+        "max",
+        "sum",
+        "product",
+        "var",
+        "std",
+        "prod",
+        "kurtosis",
+        "skew",
+        "any",
+        "all",
+        "cummin",
+        "cummax",
+        "cumsum",
+        "cumprod",
+    ],
+)
+@pytest.mark.parametrize("skipna", [True, False])
+def test_nans_stats(data, ops, skipna):
+    psr = _create_pandas_series_float64_default(data)
+    gsr = _create_cudf_series_float64_default(data, nan_as_null=False)
+
+    assert_eq(
+        getattr(psr, ops)(skipna=skipna), getattr(gsr, ops)(skipna=skipna)
+    )
+
+    gsr = _create_cudf_series_float64_default(data, nan_as_null=False)
+    # Since there is no concept of `nan_as_null` in pandas,
+    # nulls will be returned in the operations. So only
+    # testing for `skipna=True` when `nan_as_null=False`
+    assert_eq(getattr(psr, ops)(skipna=True), getattr(gsr, ops)(skipna=True))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [0.0, 1, 3, 6, np.NaN, 7, 5.0, np.nan, 5, 2, 3, -100],
+        [np.nan] * 3,
+        [1, 5, 3],
+    ],
+)
+@pytest.mark.parametrize("ops", ["sum", "product", "prod"])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [-10, -1, 0, 1, 2, 3, 5, 10])
+def test_min_count_ops(data, ops, skipna, min_count):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data, nan_as_null=False)
+
+    assert_eq(
+        getattr(psr, ops)(skipna=skipna, min_count=min_count),
+        getattr(gsr, ops)(skipna=skipna, min_count=min_count),
+    )
+
+
+@pytest.mark.parametrize(
+    "gsr",
+    [
+        cudf.Series([1, 2, 3, 4], dtype="datetime64[ns]"),
+        cudf.Series([1, 2, 3, 4], dtype="timedelta64[ns]"),
+    ],
+)
+def test_cov_corr_invalid_dtypes(gsr):
+    psr = gsr.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=psr.corr,
+        rfunc=gsr.corr,
+        lfunc_args_and_kwargs=([psr],),
+        rfunc_args_and_kwargs=([gsr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=psr.cov,
+        rfunc=gsr.cov,
+        lfunc_args_and_kwargs=([psr],),
+        rfunc_args_and_kwargs=([gsr],),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        randomdata(
+            nrows=1000, dtypes={"a": float, "b": int, "c": float, "d": str}
+        ),
+    ],
+)
+@pytest.mark.parametrize("null_flag", [False, True])
+def test_kurtosis_df(data, null_flag):
+    pdata = data.to_pandas()
+
+    if null_flag and len(data) > 2:
+        data.iloc[[0, 2]] = None
+        pdata.iloc[[0, 2]] = None
+
+    with pytest.warns(FutureWarning):
+        got = data.kurtosis()
+    got = got if np.isscalar(got) else got.to_numpy()
+    with pytest.warns(FutureWarning):
+        expected = pdata.kurtosis()
+    np.testing.assert_array_almost_equal(got, expected)
+
+    with pytest.warns(FutureWarning):
+        got = data.kurt()
+    got = got if np.isscalar(got) else got.to_numpy()
+    with pytest.warns(FutureWarning):
+        expected = pdata.kurt()
+    np.testing.assert_array_almost_equal(got, expected)
+
+    got = data.kurt(numeric_only=True)
+    got = got if np.isscalar(got) else got.to_numpy()
+    expected = pdata.kurt(numeric_only=True)
+    np.testing.assert_array_almost_equal(got, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        randomdata(
+            nrows=1000, dtypes={"a": float, "b": int, "c": float, "d": str}
+        ),
+    ],
+)
+@pytest.mark.parametrize("null_flag", [False, True])
+def test_skew_df(data, null_flag):
+    pdata = data.to_pandas()
+
+    if null_flag and len(data) > 2:
+        data.iloc[[0, 2]] = None
+        pdata.iloc[[0, 2]] = None
+
+    with pytest.warns(FutureWarning):
+        got = data.skew()
+    with pytest.warns(FutureWarning):
+        expected = pdata.skew()
+    got = got if np.isscalar(got) else got.to_numpy()
+    np.testing.assert_array_almost_equal(got, expected)
+
+    got = data.skew(numeric_only=True)
+    expected = pdata.skew(numeric_only=True)
+    got = got if np.isscalar(got) else got.to_numpy()
+    np.testing.assert_array_almost_equal(got, expected)
diff --git a/python/cudf/cudf/tests/test_string.py b/python/cudf/cudf/tests/test_string.py
new file mode 100644
index 0000000..9c895a0
--- /dev/null
+++ b/python/cudf/cudf/tests/test_string.py
@@ -0,0 +1,3476 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import json
+import re
+import urllib.parse
+from contextlib import ExitStack as does_not_raise
+from decimal import Decimal
+from sys import getsizeof
+
+import cupy
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf import concat
+from cudf.core._compat import PANDAS_GE_150
+from cudf.core.column.string import StringColumn
+from cudf.core.index import StringIndex, as_index
+from cudf.testing._utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    assert_eq,
+    assert_exceptions_equal,
+)
+from cudf.utils import dtypes as dtypeutils
+
+data_list = [
+    ["AbC", "de", "FGHI", "j", "kLm"],
+    ["nOPq", None, "RsT", None, "uVw"],
+    [None, None, None, None, None],
+]
+
+data_id_list = ["no_nulls", "some_nulls", "all_nulls"]
+
+idx_list = [None, [10, 11, 12, 13, 14]]
+
+idx_id_list = ["None_index", "Set_index"]
+
+
+def raise_builder(flags, exceptions):
+    if any(flags):
+        return pytest.raises(exceptions)
+    else:
+        return does_not_raise()
+
+
+@pytest.fixture(params=data_list, ids=data_id_list)
+def data(request):
+    return request.param
+
+
+@pytest.fixture(params=idx_list, ids=idx_id_list)
+def index(request):
+    return request.param
+
+
+@pytest.fixture
+def ps_gs(data, index):
+    ps = pd.Series(data, index=index, dtype="str", name="nice name")
+    gs = cudf.Series(data, index=index, dtype="str", name="nice name")
+    return (ps, gs)
+
+
+@pytest.mark.parametrize("construct", [list, np.array, pd.Series, pa.array])
+def test_string_ingest(construct):
+    expect = ["a", "a", "b", "c", "a"]
+    data = construct(expect)
+    got = cudf.Series(data)
+    assert got.dtype == np.dtype("object")
+    assert len(got) == 5
+    for idx, val in enumerate(expect):
+        assert expect[idx] == got[idx]
+
+
+def test_string_export(ps_gs):
+    ps, gs = ps_gs
+
+    expect = ps
+    got = gs.to_pandas()
+    assert_eq(expect, got)
+
+    expect = np.array(ps)
+    got = gs.to_numpy()
+    assert_eq(expect, got)
+
+    expect = pa.Array.from_pandas(ps)
+    got = gs.to_arrow()
+
+    assert pa.Array.equals(expect, got)
+
+
+@pytest.mark.parametrize(
+    "item",
+    [
+        0,
+        2,
+        4,
+        slice(1, 3),
+        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+        [0, 1, 2, 3, 4, 4, 3, 2, 1, 0],
+        np.array([0, 1, 2, 3, 4]),
+        cupy.asarray(np.array([0, 1, 2, 3, 4])),
+    ],
+)
+def test_string_get_item(ps_gs, item):
+    ps, gs = ps_gs
+
+    got = gs.iloc[item]
+    if isinstance(got, cudf.Series):
+        got = got.to_arrow()
+
+    if isinstance(item, cupy.ndarray):
+        item = cupy.asnumpy(item)
+
+    expect = ps.iloc[item]
+    if isinstance(expect, pd.Series):
+        expect = pa.Array.from_pandas(expect)
+        pa.Array.equals(expect, got)
+    else:
+        if got is cudf.NA and expect is None:
+            return
+        assert expect == got
+
+
+@pytest.mark.parametrize(
+    "item",
+    [
+        [True] * 5,
+        [False] * 5,
+        np.array([True] * 5),
+        np.array([False] * 5),
+        cupy.asarray(np.array([True] * 5)),
+        cupy.asarray(np.array([False] * 5)),
+        np.random.randint(0, 2, 5).astype("bool").tolist(),
+        np.random.randint(0, 2, 5).astype("bool"),
+        cupy.asarray(np.random.randint(0, 2, 5).astype("bool")),
+    ],
+)
+def test_string_bool_mask(ps_gs, item):
+    ps, gs = ps_gs
+
+    got = gs.iloc[item]
+    if isinstance(got, cudf.Series):
+        got = got.to_arrow()
+
+    if isinstance(item, cupy.ndarray):
+        item = cupy.asnumpy(item)
+
+    expect = ps[item]
+    if isinstance(expect, pd.Series):
+        expect = pa.Array.from_pandas(expect)
+        pa.Array.equals(expect, got)
+    else:
+        assert expect == got
+
+
+@pytest.mark.parametrize("item", [0, slice(1, 3), slice(5)])
+def test_string_repr(ps_gs, item):
+    ps, gs = ps_gs
+
+    got_out = gs.iloc[item]
+    expect_out = ps.iloc[item]
+
+    expect = str(expect_out)
+    got = str(got_out)
+
+    if got_out is not cudf.NA and len(got_out) > 1:
+        expect = expect.replace("None", "<NA>")
+
+    assert expect == got or (expect == "None" and got == "<NA>")
+
+
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + DATETIME_TYPES + ["bool", "object", "str"]
+)
+def test_string_astype(dtype):
+    if (
+        dtype.startswith("int")
+        or dtype.startswith("uint")
+        or dtype.startswith("long")
+    ):
+        data = ["1", "2", "3", "4", "5"]
+    elif dtype.startswith("float"):
+        data = [
+            "1.0",
+            "2.0",
+            "3.0",
+            "4.0",
+            None,
+            "5.0",
+            "nan",
+            "-INF",
+            "NaN",
+            "inF",
+            "NAn",
+        ]
+    elif dtype.startswith("bool"):
+        data = ["True", "False", "True", "False", "False"]
+    elif dtype.startswith("datetime64"):
+        data = [
+            "2019-06-04T00:00:00",
+            "2019-06-04T12:12:12",
+            "2019-06-03T00:00:00",
+            "2019-05-04T00:00:00",
+            "2018-06-04T00:00:00",
+            "1922-07-21T01:02:03",
+        ]
+    elif dtype == "str" or dtype == "object":
+        data = ["ab", "cd", "ef", "gh", "ij"]
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    expect = ps.astype(dtype)
+    got = gs.astype(dtype)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data, scale, precision",
+    [
+        (["1.11", "2.22", "3.33"], 2, 3),
+        (["111", "222", "33"], 0, 3),
+        (["111000", "22000", "3000"], -3, 3),
+        ([None, None, None], 0, 5),
+        ([None, "-2345", None], 0, 5),
+        ([], 0, 5),
+    ],
+)
+@pytest.mark.parametrize(
+    "decimal_dtype",
+    [cudf.Decimal128Dtype, cudf.Decimal64Dtype, cudf.Decimal32Dtype],
+)
+def test_string_to_decimal(data, scale, precision, decimal_dtype):
+    gs = cudf.Series(data, dtype="str")
+    fp = gs.astype(decimal_dtype(scale=scale, precision=precision))
+    got = fp.astype("str")
+    assert_eq(gs, got)
+
+
+def test_string_empty_to_decimal():
+    gs = cudf.Series(["", "-85", ""], dtype="str")
+    got = gs.astype(cudf.Decimal64Dtype(scale=0, precision=5))
+    expected = cudf.Series(
+        [0, -85, 0],
+        dtype=cudf.Decimal64Dtype(scale=0, precision=5),
+    )
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data, scale, precision",
+    [
+        (["1.23", "-2.34", "3.45"], 2, 3),
+        (["123", "-234", "345"], 0, 3),
+        (["12300", "-400", "5000.0"], -2, 5),
+        ([None, None, None], 0, 5),
+        ([None, "-100", None], 0, 5),
+        ([], 0, 5),
+    ],
+)
+@pytest.mark.parametrize(
+    "decimal_dtype",
+    [cudf.Decimal128Dtype, cudf.Decimal32Dtype, cudf.Decimal64Dtype],
+)
+def test_string_from_decimal(data, scale, precision, decimal_dtype):
+    decimal_data = []
+    for d in data:
+        if d is None:
+            decimal_data.append(None)
+        else:
+            decimal_data.append(Decimal(d))
+    fp = cudf.Series(
+        decimal_data,
+        dtype=decimal_dtype(scale=scale, precision=precision),
+    )
+    gs = fp.astype("str")
+    got = gs.astype(decimal_dtype(scale=scale, precision=precision))
+    assert_eq(fp, got)
+
+
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + DATETIME_TYPES + ["bool", "object", "str"]
+)
+def test_string_empty_astype(dtype):
+    data = []
+    ps = pd.Series(data, dtype="str")
+    gs = cudf.Series(data, dtype="str")
+
+    expect = ps.astype(dtype)
+    got = gs.astype(dtype)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES + ["bool"])
+def test_string_numeric_astype(dtype):
+    if dtype.startswith("bool"):
+        data = [1, 0, 1, 0, 1]
+    elif (
+        dtype.startswith("int")
+        or dtype.startswith("uint")
+        or dtype.startswith("long")
+    ):
+        data = [1, 2, 3, 4, 5]
+    elif dtype.startswith("float"):
+        data = [1.0, 2.0, 3.0, 4.0, 5.0]
+    elif dtype.startswith("datetime64"):
+        # pandas rounds the output format based on the data
+        # Use numpy instead
+        # but fix '2011-01-01T00:00:00' -> '2011-01-01 00:00:00'
+        data = [1000000001, 2000000001, 3000000001, 4000000001, 5000000001]
+        ps = np.asarray(data, dtype=dtype).astype(str)
+        ps = np.array([i.replace("T", " ") for i in ps])
+
+    if not dtype.startswith("datetime64"):
+        ps = pd.Series(data, dtype=dtype)
+
+    gs = cudf.Series(data, dtype=dtype)
+
+    expect = pd.Series(ps.astype("str"))
+    got = gs.astype("str")
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES + DATETIME_TYPES + ["bool"])
+def test_string_empty_numeric_astype(dtype):
+    data = []
+
+    if dtype.startswith("datetime64"):
+        ps = pd.Series(data, dtype="datetime64[ns]")
+    else:
+        ps = pd.Series(data, dtype=dtype)
+    gs = cudf.Series(data, dtype=dtype)
+
+    expect = ps.astype("str")
+    got = gs.astype("str")
+
+    assert_eq(expect, got)
+
+
+def test_string_concat():
+    data1 = ["a", "b", "c", "d", "e"]
+    data2 = ["f", "g", "h", "i", "j"]
+    index = [1, 2, 3, 4, 5]
+
+    ps1 = pd.Series(data1, index=index)
+    ps2 = pd.Series(data2, index=index)
+    gs1 = cudf.Series(data1, index=index)
+    gs2 = cudf.Series(data2, index=index)
+
+    expect = pd.concat([ps1, ps2])
+    got = concat([gs1, gs2])
+
+    assert_eq(expect, got)
+
+    expect = ps1.str.cat(ps2)
+    got = gs1.str.cat(gs2)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+def test_string_sort(ps_gs, ascending):
+    ps, gs = ps_gs
+
+    expect = ps.sort_values(ascending=ascending)
+    got = gs.sort_values(ascending=ascending)
+
+    assert_eq(expect, got)
+
+
+def test_string_len(ps_gs):
+    ps, gs = ps_gs
+
+    expect = ps.str.len()
+    got = gs.str.len()
+
+    # Can't handle nulls in Pandas so use PyArrow instead
+    # Pandas will return as a float64 so need to typecast to int32
+    expect = pa.array(expect, from_pandas=True).cast(pa.int32())
+    got = got.to_arrow()
+    assert pa.Array.equals(expect, got)
+
+
+def _cat_convert_seq_to_cudf(others):
+    pd_others = others
+    if isinstance(pd_others, (pd.Series, pd.Index)):
+        gd_others = cudf.from_pandas(pd_others)
+    else:
+        gd_others = pd_others
+    if isinstance(gd_others, (list, tuple)):
+        temp_tuple = [
+            cudf.from_pandas(elem)
+            if isinstance(elem, (pd.Series, pd.Index))
+            else elem
+            for elem in gd_others
+        ]
+
+        if isinstance(gd_others, tuple):
+            gd_others = tuple(temp_tuple)
+        else:
+            gd_others = list(temp_tuple)
+    return gd_others
+
+
+@pytest.mark.parametrize(
+    "others",
+    [
+        None,
+        ["f", "g", "h", "i", "j"],
+        ("f", "g", "h", "i", "j"),
+        pd.Series(["f", "g", "h", "i", "j"]),
+        pd.Series(["AbC", "de", "FGHI", "j", "kLm"]),
+        pd.Index(["f", "g", "h", "i", "j"]),
+        pd.Index(["AbC", "de", "FGHI", "j", "kLm"]),
+        (
+            np.array(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ),
+        [
+            np.array(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ],
+        [
+            pd.Series(["f", "g", "h", "i", "j"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+        ],
+        (
+            pd.Series(["f", "g", "h", "i", "j"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+        ),
+        [
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ],
+        (
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ),
+        (
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["1", "2", "3", "4", "5"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["f", "g", "h", "i", "j"]),
+        ),
+        [
+            pd.Index(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["f", "g", "h", "i", "j"]),
+        ],
+        [
+            pd.Series(["hello", "world", "abc", "xyz", "pqr"]),
+            pd.Series(["abc", "xyz", "hello", "pqr", "world"]),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=[10, 11, 12, 13, 14],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=[10, 15, 11, 13, 14],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["10", "11", "12", "13", "14"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["10", "11", "12", "13", "14"],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["10", "11", "12", "13", "14"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["10", "15", "11", "13", "14"],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["1", "2", "3", "4", "5"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["10", "11", "12", "13", "14"],
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("sep", [None, "", " ", "|", ",", "|||"])
+@pytest.mark.parametrize("na_rep", [None, "", "null", "a"])
+@pytest.mark.parametrize(
+    "index",
+    [["1", "2", "3", "4", "5"]],
+)
+def test_string_cat(ps_gs, others, sep, na_rep, index):
+    ps, gs = ps_gs
+
+    pd_others = others
+    gd_others = _cat_convert_seq_to_cudf(others)
+
+    expect = ps.str.cat(others=pd_others, sep=sep, na_rep=na_rep)
+    got = gs.str.cat(others=gd_others, sep=sep, na_rep=na_rep)
+    assert_eq(expect, got)
+
+    ps.index = index
+    gs.index = index
+
+    expect = ps.str.cat(others=ps.index, sep=sep, na_rep=na_rep)
+    got = gs.str.cat(others=gs.index, sep=sep, na_rep=na_rep)
+
+    assert_eq(expect, got)
+
+    expect = ps.str.cat(others=[ps.index] + [ps.index], sep=sep, na_rep=na_rep)
+    got = gs.str.cat(others=[gs.index] + [gs.index], sep=sep, na_rep=na_rep)
+
+    assert_eq(expect, got)
+
+    expect = ps.str.cat(others=(ps.index, ps.index), sep=sep, na_rep=na_rep)
+    got = gs.str.cat(others=(gs.index, gs.index), sep=sep, na_rep=na_rep)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["1", "2", "3", "4", "5"],
+        ["a", "b", "c", "d", "e"],
+        ["a", "b", "c", None, "e"],
+    ],
+)
+@pytest.mark.parametrize(
+    "others",
+    [
+        None,
+        ["f", "g", "h", "i", "j"],
+        ("f", "g", "h", "i", "j"),
+        pd.Series(["f", "g", "h", "i", "j"]),
+        pd.Series(["AbC", "de", "FGHI", "j", "kLm"]),
+        pd.Index(["f", "g", "h", "i", "j"]),
+        pd.Index(["AbC", "de", "FGHI", "j", "kLm"]),
+        (
+            np.array(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ),
+        [
+            np.array(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ],
+        [
+            pd.Series(["f", "g", "h", "i", "j"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+        ],
+        (
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["1", "2", "3", "4", "5"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["f", "g", "h", "i", "j"]),
+        ),
+        [
+            pd.Index(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["f", "g", "h", "i", "j"]),
+            np.array(["f", "a", "b", "f", "a"]),
+            pd.Index(["f", "g", "h", "i", "j"]),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["a", "b", "c", "d", "e"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["a", "b", "c", "d", "e"],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=[10, 11, 12, 13, 14],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=[10, 15, 11, 13, 14],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["1", "2", "3", "4", "5"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["1", "2", "3", "4", "5"],
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("sep", [None, "", " ", "|", ",", "|||"])
+@pytest.mark.parametrize("na_rep", [None, "", "null", "a"])
+@pytest.mark.parametrize("name", [None, "This is the name"])
+def test_string_index_str_cat(data, others, sep, na_rep, name):
+    pi, gi = pd.Index(data, name=name), cudf.Index(data, name=name)
+
+    pd_others = others
+    gd_others = _cat_convert_seq_to_cudf(others)
+
+    expect = pi.str.cat(others=pd_others, sep=sep, na_rep=na_rep)
+    got = gi.str.cat(others=gd_others, sep=sep, na_rep=na_rep)
+
+    assert_eq(
+        expect,
+        got,
+        exact=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [["a", None, "c", None, "e"], ["a", "b", "c", "d", "a"]],
+)
+@pytest.mark.parametrize(
+    "others",
+    [
+        None,
+        ["f", "g", "h", "i", "j"],
+        pd.Series(["AbC", "de", "FGHI", "j", "kLm"]),
+        pd.Index(["f", "g", "h", "i", "j"]),
+        pd.Index(["AbC", "de", "FGHI", "j", "kLm"]),
+        [
+            np.array(["f", "g", "h", "i", "j"]),
+            np.array(["f", "g", "h", "i", "j"]),
+        ],
+        [
+            pd.Series(["f", "g", "h", "i", "j"]),
+            pd.Series(["f", "g", "h", "i", "j"]),
+        ],
+        pytest.param(
+            [
+                pd.Series(["f", "g", "h", "i", "j"]),
+                np.array(["f", "g", "h", "i", "j"]),
+            ],
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/5862"
+            ),
+        ),
+        pytest.param(
+            (
+                pd.Series(["f", "g", "h", "i", "j"]),
+                np.array(["f", "a", "b", "f", "a"]),
+                pd.Series(["f", "g", "h", "i", "j"]),
+                np.array(["f", "a", "b", "f", "a"]),
+                np.array(["f", "a", "b", "f", "a"]),
+                pd.Index(["1", "2", "3", "4", "5"]),
+                np.array(["f", "a", "b", "f", "a"]),
+                pd.Index(["f", "g", "h", "i", "j"]),
+            ),
+            marks=pytest.mark.xfail(
+                reason="https://github.com/pandas-dev/pandas/issues/33436"
+            ),
+        ),
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["a", "b", "c", "d", "e"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["a", "b", "c", "d", "e"],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=[10, 11, 12, 13, 14],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=[10, 15, 11, 13, 14],
+            ),
+        ],
+        [
+            pd.Series(
+                ["hello", "world", "abc", "xyz", "pqr"],
+                index=["1", "2", "3", "4", "5"],
+            ),
+            pd.Series(
+                ["abc", "xyz", "hello", "pqr", "world"],
+                index=["1", "2", "3", "4", "5"],
+            ),
+        ],
+    ],
+)
+@pytest.mark.parametrize("sep", [None, "", " ", "|", ",", "|||"])
+@pytest.mark.parametrize("na_rep", [None, "", "null", "a"])
+@pytest.mark.parametrize("name", [None, "This is the name"])
+def test_string_index_duplicate_str_cat(data, others, sep, na_rep, name):
+    pi, gi = pd.Index(data, name=name), cudf.Index(data, name=name)
+
+    pd_others = others
+    gd_others = _cat_convert_seq_to_cudf(others)
+
+    got = gi.str.cat(others=gd_others, sep=sep, na_rep=na_rep)
+    expect = pi.str.cat(others=pd_others, sep=sep, na_rep=na_rep)
+
+    # TODO: Remove got.sort_values call once we have `join` param support
+    # in `.str.cat`
+    # https://github.com/rapidsai/cudf/issues/5862
+
+    assert_eq(
+        expect.sort_values() if not isinstance(expect, str) else expect,
+        got.sort_values() if not isinstance(got, str) else got,
+        exact=False,
+    )
+
+
+def test_string_cat_str_error():
+    gs = cudf.Series(["a", "v", "s"])
+    # https://github.com/pandas-dev/pandas/issues/28277
+    # ability to pass StringMethods is being removed in future.
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "others must be Series, Index, DataFrame, np.ndarrary "
+            "or list-like (either containing only strings or "
+            "containing only objects of type Series/Index/"
+            "np.ndarray[1-dim])"
+        ),
+    ):
+        gs.str.cat(gs.str)
+
+
+@pytest.mark.parametrize("sep", ["", " ", "|", ",", "|||"])
+def test_string_join(ps_gs, sep):
+    ps, gs = ps_gs
+
+    expect = ps.str.join(sep)
+    got = gs.str.join(sep)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("pat", [r"(a)", r"(f)", r"([a-z])", r"([A-Z])"])
+@pytest.mark.parametrize("expand", [True, False])
+@pytest.mark.parametrize(
+    "flags,flags_raise", [(0, 0), (re.M | re.S, 0), (re.I, 1)]
+)
+def test_string_extract(ps_gs, pat, expand, flags, flags_raise):
+    ps, gs = ps_gs
+    expectation = raise_builder([flags_raise], NotImplementedError)
+
+    with expectation:
+        expect = ps.str.extract(pat, flags=flags, expand=expand)
+        got = gs.str.extract(pat, flags=flags, expand=expand)
+
+        assert_eq(expect, got)
+
+
+def test_string_invalid_regex():
+    gs = cudf.Series(["a"])
+    with pytest.raises(RuntimeError):
+        gs.str.extract(r"{\}")
+
+
+@pytest.mark.parametrize(
+    "pat,regex",
+    [
+        ("a", False),
+        ("a", True),
+        ("f", False),
+        (r"[a-z]", True),
+        (r"[A-Z]", True),
+        ("hello", False),
+        ("FGHI", False),
+    ],
+)
+@pytest.mark.parametrize(
+    "flags,flags_raise",
+    [(0, 0), (re.MULTILINE | re.DOTALL, 0), (re.I, 1), (re.I | re.DOTALL, 1)],
+)
+@pytest.mark.parametrize("na,na_raise", [(np.nan, 0), (None, 1), ("", 1)])
+def test_string_contains(ps_gs, pat, regex, flags, flags_raise, na, na_raise):
+    ps, gs = ps_gs
+
+    expectation = does_not_raise()
+    if flags_raise or na_raise:
+        expectation = pytest.raises(NotImplementedError)
+
+    with expectation:
+        expect = ps.str.contains(pat, flags=flags, na=na, regex=regex)
+        got = gs.str.contains(pat, flags=flags, na=na, regex=regex)
+        assert_eq(expect, got)
+
+
+def test_string_contains_case(ps_gs):
+    ps, gs = ps_gs
+    with pytest.raises(NotImplementedError):
+        gs.str.contains("A", case=False)
+    expected = ps.str.contains("A", regex=False, case=False)
+    got = gs.str.contains("A", regex=False, case=False)
+    assert_eq(expected, got)
+    got = gs.str.contains("a", regex=False, case=False)
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "pat,esc,expect",
+    [
+        ("abc", "", [True, False, False, False, False, False]),
+        ("b%", "/", [False, True, False, False, False, False]),
+        ("%b", ":", [False, True, False, False, False, False]),
+        ("%b%", "*", [True, True, False, False, False, False]),
+        ("___", "", [True, True, True, False, False, False]),
+        ("__/%", "/", [False, False, True, False, False, False]),
+        ("55/____", "/", [False, False, False, True, False, False]),
+        ("%:%%", ":", [False, False, True, False, False, False]),
+        ("55*_100", "*", [False, False, False, True, False, False]),
+        ("abc", "abc", [True, False, False, False, False, False]),
+    ],
+)
+def test_string_like(pat, esc, expect):
+
+    expectation = does_not_raise()
+    if len(esc) > 1:
+        expectation = pytest.raises(ValueError)
+
+    with expectation:
+        gs = cudf.Series(["abc", "bab", "99%", "55_100", "", "556100"])
+        got = gs.str.like(pat, esc)
+        expect = cudf.Series(expect)
+        assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [["hello", "world", None, "", "!"]],
+)
+@pytest.mark.parametrize(
+    "repeats",
+    [
+        2,
+        0,
+        -3,
+        [5, 4, 3, 2, 6],
+        [5, None, 3, 2, 6],
+        [0, 0, 0, 0, 0],
+        [-1, -2, -3, -4, -5],
+        [None, None, None, None, None],
+    ],
+)
+def test_string_repeat(data, repeats):
+    ps = pd.Series(data)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.str.repeat(repeats)
+    got = gs.str.repeat(repeats)
+
+    assert_eq(expect, got)
+
+
+# Pandas doesn't respect the `n` parameter so ignoring it in test parameters
+@pytest.mark.parametrize(
+    "pat,regex",
+    [("a", False), ("f", False), (r"[a-z]", True), (r"[A-Z]", True)],
+)
+@pytest.mark.parametrize("repl", ["qwerty", "", " "])
+@pytest.mark.parametrize("case,case_raise", [(None, 0), (True, 1), (False, 1)])
+@pytest.mark.parametrize("flags,flags_raise", [(0, 0), (1, 1)])
+def test_string_replace(
+    ps_gs, pat, repl, case, case_raise, flags, flags_raise, regex
+):
+    ps, gs = ps_gs
+
+    expectation = raise_builder([case_raise, flags_raise], NotImplementedError)
+
+    with expectation:
+        expect = ps.str.replace(pat, repl, case=case, flags=flags, regex=regex)
+        got = gs.str.replace(pat, repl, case=case, flags=flags, regex=regex)
+
+        assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("pat", ["A*", "F?H?"])
+def test_string_replace_zero_length(ps_gs, pat):
+    ps, gs = ps_gs
+
+    expect = ps.str.replace(pat, "_", regex=True)
+    got = gs.str.replace(pat, "_", regex=True)
+
+    assert_eq(expect, got)
+
+
+def test_string_lower(ps_gs):
+    ps, gs = ps_gs
+
+    expect = ps.str.lower()
+    got = gs.str.lower()
+
+    assert_eq(expect, got)
+
+
+def test_string_upper(ps_gs):
+    ps, gs = ps_gs
+
+    expect = ps.str.upper()
+    got = gs.str.upper()
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["a b", " c ", "   d", "e   ", "f"],
+        ["a-b", "-c-", "---d", "e---", "f"],
+        ["ab", "c", "d", "e", "f"],
+        [None, None, None, None, None],
+    ],
+)
+@pytest.mark.parametrize("pat", [None, " ", "-"])
+@pytest.mark.parametrize("n", [-1, 0, 1, 3, 10])
+@pytest.mark.parametrize("expand", [True, False])
+def test_string_split(data, pat, n, expand):
+    ps = pd.Series(data, dtype="str")
+    gs = cudf.Series(data, dtype="str")
+
+    expect = ps.str.split(pat=pat, n=n, expand=expand)
+    got = gs.str.split(pat=pat, n=n, expand=expand)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["a b", " c ", "   d", "e   ", "f"],
+        ["a-b", "-c-", "---d", "e---", "f"],
+        ["ab", "c", "d", "e", "f"],
+        [None, None, None, None, None],
+    ],
+)
+@pytest.mark.parametrize("pat", [None, " ", "\\-+", "\\s+"])
+@pytest.mark.parametrize("n", [-1, 0, 1, 3, 10])
+@pytest.mark.parametrize("expand", [True, False])
+def test_string_split_re(data, pat, n, expand):
+    ps = pd.Series(data, dtype="str")
+    gs = cudf.Series(data, dtype="str")
+
+    # Pandas does not support the regex parameter until 1.4.0
+    expect = ps.str.split(pat=pat, n=n, expand=expand)
+    got = gs.str.split(pat=pat, n=n, expand=expand, regex=True)
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "str_data", [[], ["a", "b", "c", "d", "e"], [None, None, None, None, None]]
+)
+@pytest.mark.parametrize("num_keys", [1, 2, 3])
+def test_string_groupby_key(str_data, num_keys):
+    other_data = [1, 2, 3, 4, 5][: len(str_data)]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    for i in range(num_keys):
+        pdf[i] = pd.Series(str_data, dtype="str")
+        gdf[i] = cudf.Series(str_data, dtype="str")
+    pdf["a"] = other_data
+    gdf["a"] = other_data
+
+    expect = pdf.groupby(list(range(num_keys)), as_index=False).count()
+    got = gdf.groupby(list(range(num_keys)), as_index=False).count()
+
+    expect = expect.sort_values([0]).reset_index(drop=True)
+    got = got.sort_values([0]).reset_index(drop=True)
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "str_data", [[], ["a", "b", "c", "d", "e"], [None, None, None, None, None]]
+)
+@pytest.mark.parametrize("num_cols", [1, 2, 3])
+@pytest.mark.parametrize("agg", ["count", "max", "min"])
+def test_string_groupby_non_key(str_data, num_cols, agg):
+    other_data = [1, 2, 3, 4, 5][: len(str_data)]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    for i in range(num_cols):
+        pdf[i] = pd.Series(str_data, dtype="str")
+        gdf[i] = cudf.Series(str_data, dtype="str")
+    pdf["a"] = other_data
+    gdf["a"] = other_data
+
+    expect = getattr(pdf.groupby("a", as_index=False), agg)()
+    got = getattr(gdf.groupby("a", as_index=False), agg)()
+
+    expect = expect.sort_values(["a"]).reset_index(drop=True)
+    got = got.sort_values(["a"]).reset_index(drop=True)
+
+    if agg in ["min", "max"] and len(expect) == 0 and len(got) == 0:
+        for i in range(num_cols):
+            expect[i] = expect[i].astype("str")
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_string_groupby_key_index():
+    str_data = ["a", "b", "c", "d", "e"]
+    other_data = [1, 2, 3, 4, 5]
+
+    pdf = pd.DataFrame()
+    gdf = cudf.DataFrame()
+    pdf["a"] = pd.Series(str_data, dtype="str")
+    gdf["a"] = cudf.Series(str_data, dtype="str")
+    pdf["b"] = other_data
+    gdf["b"] = other_data
+
+    expect = pdf.groupby("a", sort=True).count()
+    got = gdf.groupby("a", sort=True).count()
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize("scalar", ["a", None])
+def test_string_set_scalar(scalar):
+    pdf = pd.DataFrame()
+    pdf["a"] = [1, 2, 3, 4, 5]
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    pdf["b"] = "a"
+    gdf["b"] = "a"
+
+    assert_eq(pdf["b"], gdf["b"])
+    assert_eq(pdf, gdf)
+
+
+def test_string_index():
+    pdf = pd.DataFrame(np.random.rand(5, 5))
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    stringIndex = ["a", "b", "c", "d", "e"]
+    pdf.index = stringIndex
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+    stringIndex = np.array(["a", "b", "c", "d", "e"])
+    pdf.index = stringIndex
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+    with pytest.warns(FutureWarning):
+        stringIndex = StringIndex(["a", "b", "c", "d", "e"], name="name")
+    pdf.index = stringIndex.to_pandas()
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+    stringIndex = cudf.Index(
+        cudf.core.column.as_column(["a", "b", "c", "d", "e"]), name="name"
+    )
+    pdf.index = stringIndex.to_pandas()
+    gdf.index = stringIndex
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "item",
+    [
+        ["Cbe", "cbe", "CbeD", "Cb", "ghi", "Cb"],
+        ["a", "a", "a", "a", "A"],
+        ["A"],
+        ["abc", "xyz", None, "ab", "123"],
+        [None, None, "abc", None, "abc"],
+    ],
+)
+def test_string_unique(item):
+    ps = pd.Series(item)
+    gs = cudf.Series(item)
+    # Pandas `unique` returns a numpy array
+    pres = pd.Series(ps.unique())
+    # cudf returns a cudf.Series
+    gres = gs.unique()
+    assert_eq(pres, gres)
+
+
+def test_string_slice():
+    df = cudf.DataFrame({"a": ["hello", "world"]})
+    pdf = pd.DataFrame({"a": ["hello", "world"]})
+    a_slice_got = df.a.str.slice(0, 2)
+    a_slice_expected = pdf.a.str.slice(0, 2)
+
+    assert isinstance(a_slice_got, cudf.Series)
+    assert_eq(a_slice_expected, a_slice_got)
+
+
+def test_string_equality():
+    data1 = ["b", "c", "d", "a", "c"]
+    data2 = ["a", None, "c", "a", "c"]
+
+    ps1 = pd.Series(data1)
+    ps2 = pd.Series(data2)
+    gs1 = cudf.Series(data1)
+    gs2 = cudf.Series(data2)
+
+    expect = ps1 == ps2
+    got = gs1 == gs2
+
+    assert_eq(expect, got.fillna(False))
+
+    expect = ps1 == "m"
+    got = gs1 == "m"
+
+    assert_eq(expect, got.fillna(False))
+
+    ps1 = pd.Series(["a"])
+    gs1 = cudf.Series(["a"])
+
+    expect = ps1 == "m"
+    got = gs1 == "m"
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "lhs",
+    [
+        ["Cbe", "cbe", "CbeD", "Cb", "ghi", "Cb"],
+        ["abc", "xyz", "a", "ab", "123", "097"],
+    ],
+)
+@pytest.mark.parametrize(
+    "rhs",
+    [
+        ["Cbe", "cbe", "CbeD", "Cb", "ghi", "Cb"],
+        ["a", "a", "a", "a", "A", "z"],
+    ],
+)
+def test_string_binary_op_add(lhs, rhs):
+    pds = pd.Series(lhs) + pd.Series(rhs)
+    gds = cudf.Series(lhs) + cudf.Series(rhs)
+
+    assert_eq(pds, gds)
+
+
+@pytest.mark.parametrize("name", [None, "new name", 123])
+def test_string_misc_name(ps_gs, name):
+    ps, gs = ps_gs
+    ps.name = name
+    gs.name = name
+
+    expect = ps.str.slice(0, 1)
+    got = gs.str.slice(0, 1)
+
+    assert_eq(expect, got)
+    assert_eq(ps + ps, gs + gs)
+    assert_eq(ps + "RAPIDS", gs + "RAPIDS")
+    assert_eq("RAPIDS" + ps, "RAPIDS" + gs)
+
+
+def test_string_no_children_properties():
+    empty_col = StringColumn(children=())
+    assert empty_col.base_children == ()
+    assert empty_col.base_size == 0
+
+    assert empty_col.children == ()
+    assert empty_col.size == 0
+
+    assert getsizeof(empty_col) >= 0  # Accounts for Python GC overhead
+
+
+@pytest.mark.parametrize(
+    "string",
+    [
+        ["Cbe", "cbe", "CbeD", "Cb", "ghi", "Cb"],
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""],
+    ],
+)
+@pytest.mark.parametrize(
+    "index", [-100, -5, -2, -6, -1, 0, 1, 2, 3, 9, 10, 100]
+)
+def test_string_get(string, index):
+    pds = pd.Series(string)
+    gds = cudf.Series(string)
+
+    assert_eq(
+        pds.str.get(index).fillna(""),
+        gds.str.get(index).fillna(""),
+    )
+
+
+@pytest.mark.parametrize(
+    "string",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""],
+        ["koala", "fox", "chameleon"],
+    ],
+)
+@pytest.mark.parametrize(
+    "number",
+    [-10, 0, 1, 3, 10],
+)
+@pytest.mark.parametrize(
+    "diff",
+    [0, 2, 5, 9],
+)
+def test_string_slice_str(string, number, diff):
+    pds = pd.Series(string)
+    gds = cudf.Series(string)
+
+    assert_eq(pds.str.slice(start=number), gds.str.slice(start=number))
+    assert_eq(pds.str.slice(stop=number), gds.str.slice(stop=number))
+    assert_eq(pds.str.slice(), gds.str.slice())
+    assert_eq(
+        pds.str.slice(start=number, stop=number + diff),
+        gds.str.slice(start=number, stop=number + diff),
+    )
+    if diff != 0:
+        assert_eq(pds.str.slice(step=diff), gds.str.slice(step=diff))
+        assert_eq(
+            pds.str.slice(start=number, stop=number + diff, step=diff),
+            gds.str.slice(start=number, stop=number + diff, step=diff),
+        )
+
+
+def test_string_slice_from():
+    gs = cudf.Series(["hello world", "holy accéntéd", "batman", None, ""])
+    d_starts = cudf.Series([2, 3, 0, -1, -1], dtype=np.int32)
+    d_stops = cudf.Series([-1, -1, 0, -1, -1], dtype=np.int32)
+    got = gs.str.slice_from(starts=d_starts._column, stops=d_stops._column)
+    expected = cudf.Series(["llo world", "y accéntéd", "", None, ""])
+    assert_eq(got, expected)
+
+
+@pytest.mark.parametrize(
+    "string",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""],
+        ["koala", "fox", "chameleon"],
+    ],
+)
+@pytest.mark.parametrize("number", [0, 1, 10])
+@pytest.mark.parametrize("diff", [0, 2, 9])
+@pytest.mark.parametrize("repr", ["2", "!!"])
+def test_string_slice_replace(string, number, diff, repr):
+    pds = pd.Series(string)
+    gds = cudf.Series(string)
+
+    assert_eq(
+        pds.str.slice_replace(start=number, repl=repr),
+        gds.str.slice_replace(start=number, repl=repr),
+        check_dtype=False,
+    )
+    assert_eq(
+        pds.str.slice_replace(stop=number, repl=repr),
+        gds.str.slice_replace(stop=number, repl=repr),
+    )
+    assert_eq(pds.str.slice_replace(), gds.str.slice_replace())
+    assert_eq(
+        pds.str.slice_replace(start=number, stop=number + diff),
+        gds.str.slice_replace(start=number, stop=number + diff),
+    )
+    assert_eq(
+        pds.str.slice_replace(start=number, stop=number + diff, repl=repr),
+        gds.str.slice_replace(start=number, stop=number + diff, repl=repr),
+        check_dtype=False,
+    )
+
+
+def test_string_slice_replace_fail():
+    gs = cudf.Series(["abc", "xyz", ""])
+    with pytest.raises(TypeError):
+        gs.str.slice_replace(0, 1, ["_"])
+
+
+def test_string_insert():
+    gs = cudf.Series(["hello world", "holy accéntéd", "batman", None, ""])
+
+    ps = pd.Series(["hello world", "holy accéntéd", "batman", None, ""])
+
+    assert_eq(gs.str.insert(0, ""), gs)
+    assert_eq(gs.str.insert(0, "+"), "+" + ps)
+    assert_eq(gs.str.insert(-1, "---"), ps + "---")
+    assert_eq(
+        gs.str.insert(5, "---"),
+        ps.str.slice(stop=5) + "---" + ps.str.slice(start=5),
+    )
+
+    with pytest.raises(TypeError):
+        gs.str.insert(0, ["+"])
+
+
+_string_char_types_data = [
+    ["abc", "xyz", "a", "ab", "123", "097"],
+    ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""],
+    ["koala", "fox", "chameleon"],
+    [
+        "1234567890",
+        "de",
+        "1.75",
+        "-34",
+        "+9.8",
+        "7¼",
+        "x³",
+        "2³",
+        "12⅝",
+        "",
+        "\t\r\n ",
+    ],
+    ["one", "one1", "1", ""],
+    ["A B", "1.5", "3,000"],
+    ["23", "³", "⅕", ""],
+    [" ", "\t\r\n ", ""],
+    ["leopard", "Golden Eagle", "SNAKE", ""],
+    [r"¯\_(ツ)_/¯", "(╯°□°)╯︵ ┻━┻", "┬─┬ノ( º _ ºノ)"],
+    ["a1", "A1", "a!", "A!", "!1", "aA"],
+]
+
+
+@pytest.mark.parametrize(
+    "type_op",
+    [
+        "isdecimal",
+        "isalnum",
+        "isalpha",
+        "isdigit",
+        "isnumeric",
+        "isupper",
+        "islower",
+    ],
+)
+@pytest.mark.parametrize("data", _string_char_types_data)
+def test_string_char_types(type_op, data):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(getattr(gs.str, type_op)(), getattr(ps.str, type_op)())
+
+
+def test_string_filter_alphanum():
+    data = ["1234567890", "!@#$%^&*()", ",./<>?;:[]}{|+=", "abc DEF"]
+    expected = []
+    for st in data:
+        rs = ""
+        for c in st:
+            if str.isalnum(c):
+                rs = rs + c
+        expected.append(rs)
+
+    gs = cudf.Series(data)
+    assert_eq(gs.str.filter_alphanum(), cudf.Series(expected))
+
+    expected = []
+    for st in data:
+        rs = ""
+        for c in st:
+            if not str.isalnum(c):
+                rs = rs + c
+        expected.append(rs)
+    assert_eq(gs.str.filter_alphanum(keep=False), cudf.Series(expected))
+
+    expected = []
+    for st in data:
+        rs = ""
+        for c in st:
+            if str.isalnum(c):
+                rs = rs + c
+            else:
+                rs = rs + "*"
+        expected.append(rs)
+    assert_eq(gs.str.filter_alphanum("*"), cudf.Series(expected))
+
+    expected = []
+    for st in data:
+        rs = ""
+        for c in st:
+            if not str.isalnum(c):
+                rs = rs + c
+            else:
+                rs = rs + "*"
+        expected.append(rs)
+    assert_eq(gs.str.filter_alphanum("*", keep=False), cudf.Series(expected))
+
+    with pytest.raises(TypeError):
+        gs.str.filter_alphanum(["a"])
+
+
+@pytest.mark.parametrize(
+    "case_op", ["title", "capitalize", "lower", "upper", "swapcase"]
+)
+@pytest.mark.parametrize(
+    "data",
+    [
+        *_string_char_types_data,
+        [
+            None,
+            "The quick bRoWn fox juMps over the laze DOG",
+            '123nr98nv9rev!$#INF4390v03n1243<>?}{:-"',
+            "accénted",
+        ],
+    ],
+)
+def test_string_char_case(case_op, data):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    s = gs.str
+    a = getattr(s, case_op)
+
+    assert_eq(a(), getattr(ps.str, case_op)())
+
+    assert_eq(gs.str.capitalize(), ps.str.capitalize())
+    assert_eq(gs.str.isdecimal(), ps.str.isdecimal())
+    assert_eq(gs.str.isalnum(), ps.str.isalnum())
+    assert_eq(gs.str.isalpha(), ps.str.isalpha())
+    assert_eq(gs.str.isdigit(), ps.str.isdigit())
+    assert_eq(gs.str.isnumeric(), ps.str.isnumeric())
+    assert_eq(gs.str.isspace(), ps.str.isspace())
+
+    assert_eq(gs.str.isempty(), ps == "")
+
+
+def test_string_is_title():
+    data = [
+        "leopard",
+        "Golden Eagle",
+        "SNAKE",
+        "",
+        "!A",
+        "hello World",
+        "A B C",
+        "#",
+        "AƻB",
+        "Ⓑⓖ",
+        "Art of War",
+    ]
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(gs.str.istitle(), ps.str.istitle())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["koala", "fox", "chameleon"],
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+    ],
+)
+def test_strings_rpartition(data):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(ps.str.rpartition(), gs.str.rpartition())
+    assert_eq(ps.str.rpartition("-"), gs.str.rpartition("-"))
+    assert_eq(ps.str.rpartition(","), gs.str.rpartition(","))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["koala", "fox", "chameleon"],
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+    ],
+)
+def test_strings_partition(data):
+    gs = cudf.Series(data, name="str_name")
+    ps = pd.Series(data, name="str_name")
+
+    assert_eq(ps.str.partition(), gs.str.partition())
+    assert_eq(ps.str.partition(","), gs.str.partition(","))
+    assert_eq(ps.str.partition("-"), gs.str.partition("-"))
+
+    gi = as_index(data, name="new name")
+    pi = pd.Index(data, name="new name")
+    assert_eq(pi.str.partition(), gi.str.partition())
+    assert_eq(pi.str.partition(","), gi.str.partition(","))
+    assert_eq(pi.str.partition("-"), gi.str.partition("-"))
+
+
+def test_string_partition_fail():
+    gs = cudf.Series(["abc", "aa", "cba"])
+    with pytest.raises(TypeError):
+        gs.str.partition(["a"])
+    with pytest.raises(TypeError):
+        gs.str.rpartition(["a"])
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["koala", "fox", "chameleon"],
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        [
+            "this is a regular sentence",
+            "https://docs.python.org/3/tutorial/index.html",
+            None,
+        ],
+    ],
+)
+@pytest.mark.parametrize("n", [-1, 2, 1, 9])
+@pytest.mark.parametrize("expand", [True, False])
+def test_strings_rsplit(data, n, expand):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        ps.str.rsplit(n=n, expand=expand).reset_index(),
+        gs.str.rsplit(n=n, expand=expand).reset_index(),
+        check_index_type=False,
+    )
+    assert_eq(
+        ps.str.rsplit(",", n=n, expand=expand),
+        gs.str.rsplit(",", n=n, expand=expand),
+    )
+    assert_eq(
+        ps.str.rsplit("-", n=n, expand=expand),
+        gs.str.rsplit("-", n=n, expand=expand),
+    )
+
+
+@pytest.mark.parametrize("n", [-1, 0, 1, 3, 10])
+@pytest.mark.parametrize("expand", [True, False])
+def test_string_rsplit_re(n, expand):
+    data = ["a b", " c ", "   d", "e   ", "f"]
+    ps = pd.Series(data, dtype="str")
+    gs = cudf.Series(data, dtype="str")
+
+    # Pandas does not yet support the regex parameter for rsplit
+    import inspect
+
+    assert (
+        "regex"
+        not in inspect.signature(pd.Series.str.rsplit).parameters.keys()
+    )
+
+    expect = ps.str.rsplit(pat=" ", n=n, expand=expand)
+    got = gs.str.rsplit(pat="\\s", n=n, expand=expand, regex=True)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["koala", "fox", "chameleon"],
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        [
+            "this is a regular sentence",
+            "https://docs.python.org/3/tutorial/index.html",
+            None,
+        ],
+    ],
+)
+@pytest.mark.parametrize("n", [-1, 2, 1, 9])
+@pytest.mark.parametrize("expand", [True, False])
+def test_strings_split(data, n, expand):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        ps.str.split(n=n, expand=expand).reset_index(),
+        gs.str.split(n=n, expand=expand).reset_index(),
+        check_index_type=False,
+    )
+
+    assert_eq(
+        ps.str.split(",", n=n, expand=expand),
+        gs.str.split(",", n=n, expand=expand),
+    )
+    assert_eq(
+        ps.str.split("-", n=n, expand=expand),
+        gs.str.split("-", n=n, expand=expand),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["koala", "fox", "chameleon"],
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        [
+            "this is a regular sentence",
+            "https://docs.python.org/3/tutorial/index.html",
+            None,
+        ],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize(
+    "to_strip", ["⅕", None, "123.", ".!? \n\t", "123.!? \n\t", " ", ".", ","]
+)
+def test_strings_strip_tests(data, to_strip):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(ps.str.strip(to_strip=to_strip), gs.str.strip(to_strip=to_strip))
+    assert_eq(
+        ps.str.rstrip(to_strip=to_strip), gs.str.rstrip(to_strip=to_strip)
+    )
+    assert_eq(
+        ps.str.lstrip(to_strip=to_strip), gs.str.lstrip(to_strip=to_strip)
+    )
+
+    gi = as_index(data)
+    pi = pd.Index(data)
+
+    assert_eq(pi.str.strip(to_strip=to_strip), gi.str.strip(to_strip=to_strip))
+    assert_eq(
+        pi.str.rstrip(to_strip=to_strip), gi.str.rstrip(to_strip=to_strip)
+    )
+    assert_eq(
+        pi.str.lstrip(to_strip=to_strip), gi.str.lstrip(to_strip=to_strip)
+    )
+
+
+def test_string_strip_fail():
+    gs = cudf.Series(["a", "aa", ""])
+    with pytest.raises(TypeError):
+        gs.str.strip(["a"])
+    with pytest.raises(TypeError):
+        gs.str.lstrip(["a"])
+    with pytest.raises(TypeError):
+        gs.str.rstrip(["a"])
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["koala", "fox", "chameleon"],
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        [
+            "this is a regular sentence",
+            "https://docs.python.org/3/tutorial/index.html",
+            None,
+        ],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize("width", [0, 1, 4, 9, 100])
+@pytest.mark.parametrize("fillchar", ["⅕", "1", ".", "t", " ", ","])
+def test_strings_filling_tests(data, width, fillchar):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        ps.str.center(width=width, fillchar=fillchar),
+        gs.str.center(width=width, fillchar=fillchar),
+    )
+    assert_eq(
+        ps.str.ljust(width=width, fillchar=fillchar),
+        gs.str.ljust(width=width, fillchar=fillchar),
+    )
+    assert_eq(
+        ps.str.rjust(width=width, fillchar=fillchar),
+        gs.str.rjust(width=width, fillchar=fillchar),
+    )
+
+    gi = as_index(data)
+    pi = pd.Index(data)
+
+    assert_eq(
+        pi.str.center(width=width, fillchar=fillchar),
+        gi.str.center(width=width, fillchar=fillchar),
+    )
+    assert_eq(
+        pi.str.ljust(width=width, fillchar=fillchar),
+        gi.str.ljust(width=width, fillchar=fillchar),
+    )
+    assert_eq(
+        pi.str.rjust(width=width, fillchar=fillchar),
+        gi.str.rjust(width=width, fillchar=fillchar),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["³", "⅕", ""],
+        pytest.param(
+            ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+            marks=pytest.mark.xfail(
+                condition=not PANDAS_GE_150,
+                reason="https://github.com/pandas-dev/pandas/issues/20868",
+            ),
+        ),
+        [" ", "\t\r\n ", ""],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize("width", [0, 1, 4, 6, 9, 100])
+def test_strings_zfill_tests(data, width):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(ps.str.zfill(width=width), gs.str.zfill(width=width))
+
+    gi = as_index(data)
+    pi = pd.Index(data)
+
+    assert_eq(pi.str.zfill(width=width), gi.str.zfill(width=width))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["A,,B", "1,,5", "3,00,0"],
+        ["Linda van der Berg", "George Pitt-Rivers"],
+        ["+23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize("width", [0, 1, 4, 9, 100])
+@pytest.mark.parametrize(
+    "side",
+    ["left", "right", "both"],
+)
+@pytest.mark.parametrize("fillchar", [" ", ".", "\n", "+", "\t"])
+def test_strings_pad_tests(data, width, side, fillchar):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        ps.str.pad(width=width, side=side, fillchar=fillchar),
+        gs.str.pad(width=width, side=side, fillchar=fillchar),
+    )
+
+    gi = as_index(data)
+    pi = pd.Index(data)
+
+    assert_eq(
+        pi.str.pad(width=width, side=side, fillchar=fillchar),
+        gi.str.pad(width=width, side=side, fillchar=fillchar),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["A B", "1.5", "3,000"],
+        ["23", "³", "⅕", ""],
+        # [" ", "\t\r\n ", ""],
+        ["leopard", "Golden Eagle", "SNAKE", ""],
+        ["line to be wrapped", "another line to be wrapped"],
+    ],
+)
+@pytest.mark.parametrize("width", [1, 4, 8, 12, 100])
+def test_string_wrap(data, width):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        gs.str.wrap(
+            width=width,
+            break_long_words=False,
+            expand_tabs=False,
+            replace_whitespace=True,
+            drop_whitespace=True,
+            break_on_hyphens=False,
+        ),
+        ps.str.wrap(
+            width=width,
+            break_long_words=False,
+            expand_tabs=False,
+            replace_whitespace=True,
+            drop_whitespace=True,
+            break_on_hyphens=False,
+        ),
+    )
+
+    gi = as_index(data)
+    pi = pd.Index(data)
+
+    assert_eq(
+        gi.str.wrap(
+            width=width,
+            break_long_words=False,
+            expand_tabs=False,
+            replace_whitespace=True,
+            drop_whitespace=True,
+            break_on_hyphens=False,
+        ),
+        pi.str.wrap(
+            width=width,
+            break_long_words=False,
+            expand_tabs=False,
+            replace_whitespace=True,
+            drop_whitespace=True,
+            break_on_hyphens=False,
+        ),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["A B", "1.5", "3,000"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["$", "B", "Aab$", "$$ca", "C$B$", "cat", "cat\ndog"],
+        ["line\nto be wrapped", "another\nline\nto be wrapped"],
+    ],
+)
+@pytest.mark.parametrize(
+    "pat",
+    ["a", " ", "\t", "another", "0", r"\$", "^line$", "line.*be", "cat$"],
+)
+@pytest.mark.parametrize("flags", [0, re.MULTILINE, re.DOTALL])
+def test_string_count(data, pat, flags):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        gs.str.count(pat=pat, flags=flags),
+        ps.str.count(pat=pat, flags=flags),
+        check_dtype=False,
+    )
+    assert_eq(as_index(gs).str.count(pat=pat), pd.Index(ps).str.count(pat=pat))
+
+
+@pytest.mark.parametrize(
+    "pat, flags",
+    [
+        ("Monkey", 0),
+        ("on", 0),
+        ("b", 0),
+        ("on$", 0),
+        ("on$", re.MULTILINE),
+        ("o.*k", re.DOTALL),
+    ],
+)
+def test_string_findall(pat, flags):
+    test_data = ["Lion", "Monkey", "Rabbit", "Don\nkey"]
+    ps = pd.Series(test_data)
+    gs = cudf.Series(test_data)
+
+    expected = ps.str.findall(pat, flags)
+    actual = gs.str.findall(pat, flags)
+    assert_eq(expected, actual)
+
+
+def test_string_replace_multi():
+    ps = pd.Series(["hello", "goodbye"])
+    gs = cudf.Series(["hello", "goodbye"])
+    expect = ps.str.replace("e", "E").str.replace("o", "O")
+    got = gs.str.replace(["e", "o"], ["E", "O"])
+
+    assert_eq(expect, got)
+
+    ps = pd.Series(["foo", "fuz", np.nan])
+    gs = cudf.Series.from_pandas(ps)
+
+    expect = ps.str.replace("f.", "ba", regex=True)
+    got = gs.str.replace(["f."], ["ba"], regex=True)
+    assert_eq(expect, got)
+
+    ps = pd.Series(["f.o", "fuz", np.nan])
+    gs = cudf.Series.from_pandas(ps)
+
+    expect = ps.str.replace("f.", "ba", regex=False)
+    got = gs.str.replace(["f."], ["ba"], regex=False)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "find",
+    [
+        "(\\d)(\\d)",
+        "(\\d)(\\d)",
+        "(\\d)(\\d)",
+        "(\\d)(\\d)",
+        "([a-z])-([a-z])",
+        "([a-z])-([a-zé])",
+        "([a-z])-([a-z])",
+        "([a-z])-([a-zé])",
+        re.compile("([A-Z])(\\d)"),
+    ],
+)
+@pytest.mark.parametrize(
+    "replace",
+    ["\\1-\\2", "V\\2-\\1", "\\1 \\2", "\\2 \\1", "X\\1+\\2Z", "X\\1+\\2Z"],
+)
+def test_string_replace_with_backrefs(find, replace):
+    s = [
+        "A543",
+        "Z756",
+        "",
+        None,
+        "tést-string",
+        "two-thréé four-fivé",
+        "abcd-éfgh",
+        "tést-string-again",
+    ]
+    ps = pd.Series(s)
+    gs = cudf.Series(s)
+    got = gs.str.replace_with_backrefs(find, replace)
+    expected = ps.str.replace(find, replace, regex=True)
+    assert_eq(got, expected)
+
+    got = as_index(gs).str.replace_with_backrefs(find, replace)
+    expected = pd.Index(ps).str.replace(find, replace, regex=True)
+    assert_eq(got, expected)
+
+
+def test_string_table_view_creation():
+    data = ["hi"] * 25 + [None] * 2027
+    psr = pd.Series(data)
+    gsr = cudf.Series.from_pandas(psr)
+
+    expect = psr[:1]
+    got = gsr[:1]
+
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["A B", "1.5", "3,000"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+        ["line to be wrapped", "another line to be wrapped"],
+        ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize(
+    "pat",
+    ["", None, " ", "a", "abc", "cat", "$", "\n"],
+)
+def test_string_starts_ends(data, pat):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    if pat is None:
+        assert_exceptions_equal(
+            lfunc=ps.str.startswith,
+            rfunc=gs.str.startswith,
+            lfunc_args_and_kwargs=([pat],),
+            rfunc_args_and_kwargs=([pat],),
+        )
+        assert_exceptions_equal(
+            lfunc=ps.str.endswith,
+            rfunc=gs.str.endswith,
+            lfunc_args_and_kwargs=([pat],),
+            rfunc_args_and_kwargs=([pat],),
+        )
+    else:
+        assert_eq(
+            ps.str.startswith(pat), gs.str.startswith(pat), check_dtype=False
+        )
+        assert_eq(
+            ps.str.endswith(pat), gs.str.endswith(pat), check_dtype=False
+        )
+
+
+@pytest.mark.parametrize(
+    "data,pat",
+    [
+        (
+            ["abc", "xyz", "a", "ab", "123", "097"],
+            ["abc", "x", "a", "b", "3", "7"],
+        ),
+        (["A B", "1.5", "3,000"], ["A ", ".", ","]),
+        (["23", "³", "⅕", ""], ["23", "³", "⅕", ""]),
+        ([" ", "\t\r\n ", ""], ["d", "\n ", ""]),
+        (
+            ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+            ["$", "$", "a", "<", "(", "#"],
+        ),
+        (
+            ["line to be wrapped", "another line to be wrapped"],
+            ["another", "wrapped"],
+        ),
+        (
+            ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+            ["hsdjfk", None, "ll", "+", "-", "w", "-", "én"],
+        ),
+        (
+            ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+            ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+        ),
+    ],
+)
+def test_string_starts_ends_list_like_pat(data, pat):
+    gs = cudf.Series(data)
+
+    starts_expected = []
+    ends_expected = []
+    for i in range(len(pat)):
+        if data[i] is None:
+            starts_expected.append(None)
+            ends_expected.append(None)
+        else:
+            if pat[i] is None:
+                starts_expected.append(False)
+                ends_expected.append(False)
+            else:
+                starts_expected.append(data[i].startswith(pat[i]))
+                ends_expected.append(data[i].endswith(pat[i]))
+    starts_expected = pd.Series(starts_expected)
+    ends_expected = pd.Series(ends_expected)
+    assert_eq(starts_expected, gs.str.startswith(pat), check_dtype=False)
+    assert_eq(ends_expected, gs.str.endswith(pat), check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["str_foo", "str_bar", "no_prefix", "", None],
+        ["foo_str", "bar_str", "no_suffix", "", None],
+    ],
+)
+def test_string_remove_suffix_prefix(data):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    got = gs.str.removeprefix("str_")
+    expect = ps.str.removeprefix("str_")
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+    got = gs.str.removesuffix("_str")
+    expect = ps.str.removesuffix("_str")
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["A B", "1.5", "3,000"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+        ["line to be wrapped", "another line to be wrapped"],
+        ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize(
+    "sub",
+    ["", " ", "a", "abc", "cat", "$", "\n"],
+)
+def test_string_find(data, sub):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    got = gs.str.find(sub)
+    expect = ps.str.find(sub)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.find(sub, start=1)
+    expect = ps.str.find(sub, start=1)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.find(sub, end=10)
+    expect = ps.str.find(sub, end=10)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.find(sub, start=2, end=10)
+    expect = ps.str.find(sub, start=2, end=10)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.rfind(sub)
+    expect = ps.str.rfind(sub)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.rfind(sub, start=1)
+    expect = ps.str.rfind(sub, start=1)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.rfind(sub, end=10)
+    expect = ps.str.rfind(sub, end=10)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+    got = gs.str.rfind(sub, start=2, end=10)
+    expect = ps.str.rfind(sub, start=2, end=10)
+    assert_eq(
+        expect,
+        got,
+        check_dtype=False,
+    )
+
+
+@pytest.mark.parametrize(
+    "data,sub,er",
+    [
+        (["abc", "xyz", "a", "ab", "123", "097"], "a", ValueError),
+        (["A B", "1.5", "3,000"], "abc", ValueError),
+        (["23", "³", "⅕", ""], "⅕", ValueError),
+        ([" ", "\t\r\n ", ""], "\n", ValueError),
+        (["$", "B", "Aab$", "$$ca", "C$B$", "cat"], "$", ValueError),
+        (["line to be wrapped", "another line to be wrapped"], " ", None),
+        (
+            ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+            "+",
+            ValueError,
+        ),
+        (["line to be wrapped", "another line to be wrapped"], "", None),
+    ],
+)
+def test_string_str_index(data, sub, er):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    if er is None:
+        assert_eq(ps.str.index(sub), gs.str.index(sub), check_dtype=False)
+
+    try:
+        ps.str.index(sub)
+    except er:
+        pass
+    else:
+        assert not er
+
+    try:
+        gs.str.index(sub)
+    except er:
+        pass
+    else:
+        assert not er
+
+
+@pytest.mark.parametrize(
+    "data,sub,er",
+    [
+        (["abc", "xyz", "a", "ab", "123", "097"], "a", ValueError),
+        (["A B", "1.5", "3,000"], "abc", ValueError),
+        (["23", "³", "⅕", ""], "⅕", ValueError),
+        ([" ", "\t\r\n ", ""], "\n", ValueError),
+        (["$", "B", "Aab$", "$$ca", "C$B$", "cat"], "$", ValueError),
+        (["line to be wrapped", "another line to be wrapped"], " ", None),
+        (
+            ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+            "+",
+            ValueError,
+        ),
+        (["line to be wrapped", "another line to be wrapped"], "", None),
+    ],
+)
+def test_string_str_rindex(data, sub, er):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    if er is None:
+        assert_eq(ps.str.rindex(sub), gs.str.rindex(sub), check_dtype=False)
+        assert_eq(pd.Index(ps).str.rindex(sub), as_index(gs).str.rindex(sub))
+
+    try:
+        ps.str.rindex(sub)
+    except er:
+        pass
+    else:
+        assert not er
+
+    try:
+        gs.str.rindex(sub)
+    except er:
+        pass
+    else:
+        assert not er
+
+
+@pytest.mark.parametrize(
+    "data,sub,expect",
+    [
+        (
+            ["abc", "xyz", "a", "ab", "123", "097"],
+            ["b", "y", "a", "c", "4", "8"],
+            [True, True, True, False, False, False],
+        ),
+        (
+            ["A B", "1.5", "3,000", "23", "³", "⅕"],
+            ["A B", ".", ",", "1", " ", " "],
+            [True, True, True, False, False, False],
+        ),
+        (
+            [" ", "\t", "\r", "\f ", "\n", ""],
+            ["", "\t", "\r", "xx", "yy", "zz"],
+            [True, True, True, False, False, False],
+        ),
+        (
+            ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+            ["$", "B", "ab", "*", "@", "dog"],
+            [True, True, True, False, False, False],
+        ),
+        (
+            ["hello", "there", "world", "-1234", None, "accént"],
+            ["lo", "e", "o", "+1234", " ", "e"],
+            [True, True, True, False, None, False],
+        ),
+        (
+            ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", "", "x", None],
+            ["A", "B", "C", " ", "y", "e"],
+            [True, True, True, False, False, None],
+        ),
+    ],
+)
+def test_string_contains_multi(data, sub, expect):
+    gs = cudf.Series(data)
+    sub = cudf.Series(sub)
+    got = gs.str.contains(sub)
+    expect = cudf.Series(expect)
+    assert_eq(expect, got, check_dtype=False)
+
+
+# Pandas does not allow 'case' or 'flags' if 'pat' is re.Pattern
+# This covers contains, match, count, and replace
+@pytest.mark.parametrize(
+    "pat",
+    [re.compile("[n-z]"), re.compile("[A-Z]"), re.compile("de"), "A"],
+)
+@pytest.mark.parametrize("repl", ["xyz", "", " "])
+def test_string_compiled_re(ps_gs, pat, repl):
+    ps, gs = ps_gs
+
+    expect = ps.str.contains(pat, regex=True)
+    got = gs.str.contains(pat, regex=True)
+    assert_eq(expect, got)
+
+    expect = ps.str.match(pat)
+    got = gs.str.match(pat)
+    assert_eq(expect, got)
+
+    expect = ps.str.count(pat)
+    got = gs.str.count(pat)
+    assert_eq(expect, got, check_dtype=False)
+
+    expect = ps.str.replace(pat, repl, regex=True)
+    got = gs.str.replace(pat, repl, regex=True)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["A B", "1.5", "3,000"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+        ["line to be wrapped", "another line to be wrapped"],
+        ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+@pytest.mark.parametrize("pat", ["", " ", "a", "abc", "cat", "$", "\n"])
+def test_string_str_match(data, pat):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    assert_eq(ps.str.match(pat), gs.str.match(pat))
+    assert_eq(
+        pd.Index(pd.Index(ps).str.match(pat)), as_index(gs).str.match(pat)
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "a", "ab", "123", "097"],
+        ["A B", "1.5", "3,000"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+        ["line to be wrapped", "another line to be wrapped"],
+        ["hello", "there", "world", "+1234", "-1234", None, "accént", ""],
+        ["1. Ant.  ", "2. Bee!\n", "3. Cat?\t", None],
+    ],
+)
+def test_string_str_translate(data):
+    ps = pd.Series(data)
+    gs = cudf.Series(data)
+
+    assert_eq(
+        ps.str.translate(str.maketrans({"a": "z"})),
+        gs.str.translate(str.maketrans({"a": "z"})),
+    )
+    assert_eq(
+        pd.Index(ps).str.translate(str.maketrans({"a": "z"})),
+        as_index(gs).str.translate(str.maketrans({"a": "z"})),
+    )
+    assert_eq(
+        ps.str.translate(str.maketrans({"a": "z", "i": "$", "z": "1"})),
+        gs.str.translate(str.maketrans({"a": "z", "i": "$", "z": "1"})),
+    )
+    assert_eq(
+        pd.Index(ps).str.translate(
+            str.maketrans({"a": "z", "i": "$", "z": "1"})
+        ),
+        as_index(gs).str.translate(
+            str.maketrans({"a": "z", "i": "$", "z": "1"})
+        ),
+    )
+    assert_eq(
+        ps.str.translate(
+            str.maketrans({"+": "-", "-": "$", "?": "!", "B": "."})
+        ),
+        gs.str.translate(
+            str.maketrans({"+": "-", "-": "$", "?": "!", "B": "."})
+        ),
+    )
+    assert_eq(
+        pd.Index(ps).str.translate(
+            str.maketrans({"+": "-", "-": "$", "?": "!", "B": "."})
+        ),
+        as_index(gs).str.translate(
+            str.maketrans({"+": "-", "-": "$", "?": "!", "B": "."})
+        ),
+    )
+    assert_eq(
+        ps.str.translate(str.maketrans({"é": "É"})),
+        gs.str.translate(str.maketrans({"é": "É"})),
+    )
+
+
+def test_string_str_filter_characters():
+
+    data = [
+        "hello world",
+        "A+B+C+D",
+        "?!@#$%^&*()",
+        "accént",
+        None,
+        "$1.50",
+        "",
+    ]
+    gs = cudf.Series(data)
+    expected = cudf.Series(
+        ["helloworld", "ABCD", "", "accnt", None, "150", ""]
+    )
+    filter = {"a": "z", "A": "Z", "0": "9"}
+    assert_eq(expected, gs.str.filter_characters(filter))
+
+    expected = cudf.Series([" ", "+++", "?!@#$%^&*()", "é", None, "$.", ""])
+    assert_eq(expected, gs.str.filter_characters(filter, False))
+
+    expected = cudf.Series(
+        ["hello world", "A B C D", "           ", "acc nt", None, " 1 50", ""]
+    )
+    assert_eq(expected, gs.str.filter_characters(filter, True, " "))
+
+    with pytest.raises(TypeError):
+        gs.str.filter_characters(filter, True, ["a"])
+
+
+def test_string_str_code_points():
+
+    data = [
+        "abc",
+        "Def",
+        None,
+        "jLl",
+        "dog and cat",
+        "accénted",
+        "",
+        " 1234 ",
+        "XYZ",
+    ]
+    gs = cudf.Series(data)
+    expected = [
+        97,
+        98,
+        99,
+        68,
+        101,
+        102,
+        106,
+        76,
+        108,
+        100,
+        111,
+        103,
+        32,
+        97,
+        110,
+        100,
+        32,
+        99,
+        97,
+        116,
+        97,
+        99,
+        99,
+        50089,
+        110,
+        116,
+        101,
+        100,
+        32,
+        49,
+        50,
+        51,
+        52,
+        32,
+        88,
+        89,
+        90,
+    ]
+    expected = cudf.Series(expected)
+
+    assert_eq(expected, gs.str.code_points(), check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["http://www.hellow.com", "/home/nvidia/nfs", "123.45 ~ABCDEF"],
+        ["23", "³", "⅕", ""],
+        [" ", "\t\r\n ", ""],
+        ["$", "B", "Aab$", "$$ca", "C$B$", "cat"],
+    ],
+)
+def test_string_str_url_encode(data):
+    gs = cudf.Series(data)
+
+    got = gs.str.url_encode()
+    expected = pd.Series([urllib.parse.quote(url, safe="~") for url in data])
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            "http://www.hellow.com?k1=acc%C3%A9nted&k2=a%2F/b.c",
+            "%2Fhome%2fnfs",
+            "987%20ZYX",
+        ]
+    ],
+)
+def test_string_str_decode_url(data):
+    gs = cudf.Series(data)
+
+    got = gs.str.url_decode()
+    expected = pd.Series([urllib.parse.unquote(url) for url in data])
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "data,dtype",
+    [
+        (["0.1", "10.2", "10.876"], "float"),
+        (["-0.1", "10.2", "+10.876"], "float"),
+        (["1", "10.2", "10.876"], "float32"),
+        (["+123", "6344556789", "0"], "int"),
+        (["+123", "6344556789", "0"], "uint64"),
+        (["+123", "6344556789", "0"], "float"),
+        (["0.1", "-10.2", "10.876", None], "float"),
+    ],
+)
+@pytest.mark.parametrize("obj_type", [None, "str", "category"])
+def test_string_typecast(data, obj_type, dtype):
+    psr = pd.Series(data, dtype=obj_type)
+    gsr = cudf.Series(data, dtype=obj_type)
+
+    expect = psr.astype(dtype=dtype)
+    actual = gsr.astype(dtype=dtype)
+    assert_eq(expect, actual)
+
+
+@pytest.mark.parametrize(
+    "data,dtype",
+    [
+        (["0.1", "10.2", "10.876"], "int"),
+        (["1", "10.2", "+10.876"], "int"),
+        (["abc", "1", "2", " "], "int"),
+        (["0.1", "10.2", "10.876"], "uint64"),
+        (["1", "10.2", "+10.876"], "uint64"),
+        (["abc", "1", "2", " "], "uint64"),
+        ([" ", "0.1", "2"], "float"),
+        ([""], "int"),
+        ([""], "uint64"),
+        ([" "], "float"),
+        (["\n"], "int"),
+        (["\n"], "uint64"),
+        (["0.1", "-10.2", "10.876", None], "int"),
+        (["0.1", "-10.2", "10.876", None], "uint64"),
+        (["0.1", "-10.2", "10.876", None, "ab"], "float"),
+        (["+", "-"], "float"),
+        (["+", "-"], "int"),
+        (["+", "-"], "uint64"),
+        (["1++++", "--2"], "float"),
+        (["1++++", "--2"], "int"),
+        (["1++++", "--2"], "uint64"),
+        (["++++1", "--2"], "float"),
+        (["++++1", "--2"], "int"),
+        (["++++1", "--2"], "uint64"),
+    ],
+)
+@pytest.mark.parametrize("obj_type", [None, "str", "category"])
+def test_string_typecast_error(data, obj_type, dtype):
+    psr = pd.Series(data, dtype=obj_type)
+    gsr = cudf.Series(data, dtype=obj_type)
+
+    assert_exceptions_equal(
+        lfunc=psr.astype,
+        rfunc=gsr.astype,
+        lfunc_args_and_kwargs=([dtype],),
+        rfunc_args_and_kwargs=([dtype],),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["f0:18:98:22:c2:e4", "00:00:00:00:00:00", "ff:ff:ff:ff:ff:ff"],
+        ["f0189822c2e4", "000000000000", "ffffffffffff"],
+        ["0xf0189822c2e4", "0x000000000000", "0xffffffffffff"],
+        ["0Xf0189822c2e4", "0X000000000000", "0Xffffffffffff"],
+    ],
+)
+def test_string_hex_to_int(data):
+
+    gsr = cudf.Series(data)
+
+    expected = cudf.Series([263988422296292, 0, 281474976710655])
+
+    got = gsr.str.htoi()
+    assert_eq(expected, got)
+
+    got = gsr.str.hex_to_int()  # alias
+    assert_eq(expected, got)
+
+
+def test_string_ishex():
+    gsr = cudf.Series(["", None, "0x01a2b3c4d5e6f", "0789", "ABCDEF0"])
+    got = gsr.str.ishex()
+    expected = cudf.Series([False, None, True, True, True])
+    assert_eq(expected, got)
+
+
+def test_string_istimestamp():
+    gsr = cudf.Series(
+        [
+            "",
+            None,
+            "20201009 123456.987654AM+0100",
+            "1920111 012345.000001",
+            "18201235 012345.1",
+            "20201009 250001.2",
+            "20201009 129901.3",
+            "20201009 123499.4",
+            "20201009 000000.500000PM-0130",
+            "20201009:000000.600000",
+            "20201009 010203.700000PM-2500",
+            "20201009 010203.800000AM+0590",
+            "20201009 010203.900000AP-0000",
+        ]
+    )
+    got = gsr.str.istimestamp(r"%Y%m%d %H%M%S.%f%p%z")
+    expected = cudf.Series(
+        [
+            False,
+            None,
+            True,
+            False,
+            False,
+            False,
+            False,
+            False,
+            True,
+            False,
+            False,
+            False,
+            False,
+        ]
+    )
+    assert_eq(expected, got)
+
+
+def test_string_ip4_to_int():
+    gsr = cudf.Series(
+        ["", None, "hello", "41.168.0.1", "127.0.0.1", "41.197.0.1"]
+    )
+    expected = cudf.Series([0, None, 0, 698875905, 2130706433, 700776449])
+
+    got = gsr.str.ip2int()
+    assert_eq(expected, got)
+
+    got = gsr.str.ip_to_int()  # alias
+    assert_eq(expected, got)
+
+
+def test_string_int_to_ipv4():
+    gsr = cudf.Series([0, None, 0, 698875905, 2130706433, 700776449])
+    expected = cudf.Series(
+        ["0.0.0.0", None, "0.0.0.0", "41.168.0.1", "127.0.0.1", "41.197.0.1"]
+    )
+
+    got = cudf.Series(gsr._column.int2ip())
+
+    assert_eq(expected, got)
+
+
+def test_string_isipv4():
+    gsr = cudf.Series(
+        [
+            "",
+            None,
+            "1...1",
+            "141.168.0.1",
+            "127.0.0.1",
+            "1.255.0.1",
+            "256.27.28.26",
+            "25.257.28.26",
+            "25.27.258.26",
+            "25.27.28.256",
+            "-1.0.0.0",
+        ]
+    )
+    got = gsr.str.isipv4()
+    expected = cudf.Series(
+        [
+            False,
+            None,
+            False,
+            True,
+            True,
+            True,
+            False,
+            False,
+            False,
+            False,
+            False,
+        ]
+    )
+    assert_eq(expected, got)
+
+
+@pytest.mark.parametrize(
+    "dtype", sorted(list(dtypeutils.NUMERIC_TYPES - {"int64", "uint64"}))
+)
+def test_string_int_to_ipv4_dtype_fail(dtype):
+    gsr = cudf.Series([1, 2, 3, 4, 5]).astype(dtype)
+    with pytest.raises(TypeError):
+        gsr._column.int2ip()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["abc", "xyz", "pqr", "tuv"],
+        ["aaaaaaaaaaaa"],
+        ["aaaaaaaaaaaa", "bdfeqwert", "poiuytre"],
+    ],
+)
+@pytest.mark.parametrize(
+    "index",
+    [
+        0,
+        1,
+        2,
+        slice(0, 1, 2),
+        slice(0, 5, 2),
+        slice(-1, -2, 1),
+        slice(-1, -2, -1),
+        slice(-2, -1, -1),
+        slice(-2, -1, 1),
+        slice(0),
+        slice(None),
+    ],
+)
+def test_string_str_subscriptable(data, index):
+    psr = pd.Series(data)
+    gsr = cudf.Series(data)
+
+    assert_eq(psr.str[index], gsr.str[index])
+
+    psi = pd.Index(data)
+    gsi = cudf.Index(data)
+
+    assert_eq(psi.str[index], gsi.str[index])
+
+
+@pytest.mark.parametrize(
+    "data,expected",
+    [
+        (["abc", "xyz", "pqr", "tuv"], [3, 3, 3, 3]),
+        (["aaaaaaaaaaaa"], [12]),
+        (["aaaaaaaaaaaa", "bdfeqwert", "poiuytre"], [12, 9, 8]),
+        (["abc", "d", "ef"], [3, 1, 2]),
+        (["Hello", "Bye", "Thanks 😊"], [5, 3, 11]),
+        (["\n\t", "Bye", "Thanks 😊"], [2, 3, 11]),
+    ],
+)
+def test_string_str_byte_count(data, expected):
+    sr = cudf.Series(data)
+    expected = cudf.Series(expected, dtype="int32")
+    actual = sr.str.byte_count()
+    assert_eq(expected, actual)
+
+    si = as_index(data)
+    expected = as_index(expected, dtype="int32")
+    actual = si.str.byte_count()
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data,expected",
+    [
+        (["1", "2", "3", "4", "5"], [True, True, True, True, True]),
+        (
+            ["1.1", "2.0", "3.2", "4.3", "5."],
+            [False, False, False, False, False],
+        ),
+        (
+            [".12312", "213123.", ".3223.", "323423.."],
+            [False, False, False, False],
+        ),
+        ([""], [False]),
+        (
+            ["1..1", "+2", "++3", "4++", "-5"],
+            [False, True, False, False, True],
+        ),
+        (
+            [
+                "24313345435345 ",
+                "+2632726478",
+                "++367293674326",
+                "4382493264392746.237649274692++",
+                "-578239479238469264",
+            ],
+            [False, True, False, False, True],
+        ),
+        (
+            ["2a2b", "a+b", "++a", "a.b++", "-b"],
+            [False, False, False, False, False],
+        ),
+        (
+            ["2a2b", "1+3", "9.0++a", "+", "-"],
+            [False, False, False, False, False],
+        ),
+    ],
+)
+def test_str_isinteger(data, expected):
+    sr = cudf.Series(data, dtype="str")
+    expected = cudf.Series(expected)
+    actual = sr.str.isinteger()
+    assert_eq(expected, actual)
+
+    sr = as_index(data)
+    expected = as_index(expected)
+    actual = sr.str.isinteger()
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data,expected",
+    [
+        (["1", "2", "3", "4", "5"], [True, True, True, True, True]),
+        (["1.1", "2.0", "3.2", "4.3", "5."], [True, True, True, True, True]),
+        ([""], [False]),
+        (
+            [".12312", "213123.", ".3223.", "323423.."],
+            [True, True, False, False],
+        ),
+        (
+            ["1.00.323.1", "+2.1", "++3.30", "4.9991++", "-5.3"],
+            [False, True, False, False, True],
+        ),
+        (
+            [
+                "24313345435345 ",
+                "+2632726478",
+                "++367293674326",
+                "4382493264392746.237649274692++",
+                "-578239479238469264",
+            ],
+            [False, True, False, False, True],
+        ),
+        (
+            [
+                "24313345435345.32732 ",
+                "+2632726478.3627638276",
+                "++0.326294632367293674326",
+                "4382493264392746.237649274692++",
+                "-57823947923.8469264",
+            ],
+            [False, True, False, False, True],
+        ),
+        (
+            ["2a2b", "a+b", "++a", "a.b++", "-b"],
+            [False, False, False, False, False],
+        ),
+        (
+            ["2a2b", "1+3", "9.0++a", "+", "-"],
+            [False, False, False, False, False],
+        ),
+    ],
+)
+def test_str_isfloat(data, expected):
+    sr = cudf.Series(data, dtype="str")
+    expected = cudf.Series(expected)
+    actual = sr.str.isfloat()
+    assert_eq(expected, actual)
+
+    sr = as_index(data)
+    expected = as_index(expected)
+    actual = sr.str.isfloat()
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["a", "b", "c", "d", "e"],
+        ["a", "z", ".", '"', "aa", "zz"],
+        ["aa", "zz"],
+        ["z", "a", "zz", "aa"],
+        ["1", "2", "3", "4", "5"],
+        [""],
+        ["a"],
+        ["hello"],
+        ["small text", "this is a larger text......"],
+        ["👋🏻", "🔥", "🥇"],
+        ["This is 💯", "here is a calendar", "📅"],
+        ["", ".", ";", "[", "]"],
+        ["\t", ".", "\n", "\n\t", "\t\n"],
+    ],
+)
+def test_str_min(data):
+    psr = pd.Series(data)
+    sr = cudf.Series(data)
+
+    assert_eq(psr.min(), sr.min())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["a", "b", "c", "d", "e"],
+        ["a", "z", ".", '"', "aa", "zz"],
+        ["aa", "zz"],
+        ["z", "a", "zz", "aa"],
+        ["1", "2", "3", "4", "5"],
+        [""],
+        ["a"],
+        ["hello"],
+        ["small text", "this is a larger text......"],
+        ["👋🏻", "🔥", "🥇"],
+        ["This is 💯", "here is a calendar", "📅"],
+        ["", ".", ";", "[", "]"],
+        ["\t", ".", "\n", "\n\t", "\t\n"],
+    ],
+)
+def test_str_max(data):
+    psr = pd.Series(data)
+    sr = cudf.Series(data)
+
+    assert_eq(psr.max(), sr.max())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["a", "b", "c", "d", "e"],
+        ["a", "z", ".", '"', "aa", "zz"],
+        ["aa", "zz"],
+        ["z", "a", "zz", "aa"],
+        ["1", "2", "3", "4", "5"],
+        [""],
+        ["a"],
+        ["hello"],
+        ["small text", "this is a larger text......"],
+        ["👋🏻", "🔥", "🥇"],
+        ["This is 💯", "here is a calendar", "📅"],
+        ["", ".", ";", "[", "]"],
+        ["\t", ".", "\n", "\n\t", "\t\n"],
+    ],
+)
+def test_str_sum(data):
+    psr = pd.Series(data)
+    sr = cudf.Series(data)
+
+    assert_eq(psr.sum(), sr.sum())
+
+
+def test_str_mean():
+    sr = cudf.Series(["a", "b", "c", "d", "e"])
+
+    with pytest.raises(TypeError):
+        sr.mean()
+
+
+def test_string_product():
+    psr = pd.Series(["1", "2", "3", "4", "5"])
+    sr = cudf.Series(["1", "2", "3", "4", "5"])
+
+    assert_exceptions_equal(
+        lfunc=psr.product,
+        rfunc=sr.product,
+    )
+
+
+def test_string_var():
+    psr = pd.Series(["1", "2", "3", "4", "5"])
+    sr = cudf.Series(["1", "2", "3", "4", "5"])
+
+    assert_exceptions_equal(lfunc=psr.var, rfunc=sr.var)
+
+
+def test_string_std():
+    psr = pd.Series(["1", "2", "3", "4", "5"])
+    sr = cudf.Series(["1", "2", "3", "4", "5"])
+
+    assert_exceptions_equal(lfunc=psr.std, rfunc=sr.std)
+
+
+def test_string_slice_with_mask():
+    actual = cudf.Series(["hi", "hello", None])
+    expected = actual[0:3]
+
+    assert actual._column.base_size == 3
+    assert_eq(actual._column.base_size, expected._column.base_size)
+    assert_eq(actual._column.null_count, expected._column.null_count)
+
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            "category":"reference",
+                            "author":"Nigel Rees",
+                            "title":"Sayings of the Century",
+                            "price":8.95
+                        },
+                        {
+                            "category":"fiction",
+                            "author":"Evelyn Waugh",
+                            "title":"Sword of Honour",
+                            "price":12.99
+                        }
+                    ]
+                }
+            }
+            """
+        ],
+        [
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            "category":"reference",
+                            "author":"Nigel Rees",
+                            "title":"Sayings of the Century",
+                            "price":8.95
+                        }
+                    ]
+                }
+            }
+            """,
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            "category":"fiction",
+                            "author":"Evelyn Waugh",
+                            "title":"Sword of Honour",
+                            "price":12.99
+                        }
+                    ]
+                }
+            }
+            """,
+        ],
+    ],
+)
+def test_string_get_json_object_n(data):
+    gs = cudf.Series(data)
+    ps = pd.Series(data)
+
+    assert_eq(
+        json.loads(gs.str.get_json_object("$.store")[0]),
+        ps.apply(lambda x: json.loads(x)["store"])[0],
+    )
+    assert_eq(
+        json.loads(gs.str.get_json_object("$.store.book")[0]),
+        ps.apply(lambda x: json.loads(x)["store"]["book"])[0],
+    )
+    assert_eq(
+        gs.str.get_json_object("$.store.book[0].category"),
+        ps.apply(lambda x: json.loads(x)["store"]["book"][0]["category"]),
+    )
+
+
+@pytest.mark.parametrize(
+    "json_path", ["$.store", "$.store.book", "$.store.book[*].category", " "]
+)
+def test_string_get_json_object_empty_json_strings(json_path):
+    gs = cudf.Series(
+        [
+            """
+            {
+                "":{
+                    "":[
+                        {
+                            "":"",
+                            "":"",
+                            "":""
+                        },
+                        {
+                            "":"fiction",
+                            "":"",
+                            "title":""
+                        }
+                    ]
+                }
+            }
+            """
+        ]
+    )
+
+    got = gs.str.get_json_object(json_path)
+    expect = cudf.Series([None], dtype="object")
+
+    assert_eq(got, expect)
+
+
+@pytest.mark.parametrize("json_path", ["a", ".", "/.store"])
+def test_string_get_json_object_invalid_JSONPath(json_path):
+    gs = cudf.Series(
+        [
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            "category":"reference",
+                            "author":"Nigel Rees",
+                            "title":"Sayings of the Century",
+                            "price":8.95
+                        },
+                        {
+                            "category":"fiction",
+                            "author":"Evelyn Waugh",
+                            "title":"Sword of Honour",
+                            "price":12.99
+                        }
+                    ]
+                }
+            }
+            """
+        ]
+    )
+
+    with pytest.raises(ValueError):
+        gs.str.get_json_object(json_path)
+
+
+def test_string_get_json_object_allow_single_quotes():
+    gs = cudf.Series(
+        [
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            'author':"Nigel Rees",
+                            "title":'Sayings of the Century',
+                            "price":8.95
+                        },
+                        {
+                            "category":"fiction",
+                            "author":"Evelyn Waugh",
+                            'title':"Sword of Honour",
+                            "price":12.99
+                        }
+                    ]
+                }
+            }
+            """
+        ]
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[0].author", allow_single_quotes=True
+        ),
+        cudf.Series(["Nigel Rees"]),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[*].title", allow_single_quotes=True
+        ),
+        cudf.Series(["['Sayings of the Century',\"Sword of Honour\"]"]),
+    )
+
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[0].author", allow_single_quotes=False
+        ),
+        cudf.Series([None]),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[*].title", allow_single_quotes=False
+        ),
+        cudf.Series([None]),
+    )
+
+
+def test_string_get_json_object_strip_quotes_from_single_strings():
+    gs = cudf.Series(
+        [
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            "author":"Nigel Rees",
+                            "title":"Sayings of the Century",
+                            "price":8.95
+                        },
+                        {
+                            "category":"fiction",
+                            "author":"Evelyn Waugh",
+                            "title":"Sword of Honour",
+                            "price":12.99
+                        }
+                    ]
+                }
+            }
+            """
+        ]
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[0].author", strip_quotes_from_single_strings=True
+        ),
+        cudf.Series(["Nigel Rees"]),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[*].title", strip_quotes_from_single_strings=True
+        ),
+        cudf.Series(['["Sayings of the Century","Sword of Honour"]']),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[0].author", strip_quotes_from_single_strings=False
+        ),
+        cudf.Series(['"Nigel Rees"']),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[*].title", strip_quotes_from_single_strings=False
+        ),
+        cudf.Series(['["Sayings of the Century","Sword of Honour"]']),
+    )
+
+
+def test_string_get_json_object_missing_fields_as_nulls():
+    gs = cudf.Series(
+        [
+            """
+            {
+                "store":{
+                    "book":[
+                        {
+                            "author":"Nigel Rees",
+                            "title":"Sayings of the Century",
+                            "price":8.95
+                        },
+                        {
+                            "category":"fiction",
+                            "author":"Evelyn Waugh",
+                            "title":"Sword of Honour",
+                            "price":12.99
+                        }
+                    ]
+                }
+            }
+            """
+        ]
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[0].category", missing_fields_as_nulls=True
+        ),
+        cudf.Series(["null"]),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[*].category", missing_fields_as_nulls=True
+        ),
+        cudf.Series(['[null,"fiction"]']),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[0].category", missing_fields_as_nulls=False
+        ),
+        cudf.Series([None]),
+    )
+    assert_eq(
+        gs.str.get_json_object(
+            "$.store.book[*].category", missing_fields_as_nulls=False
+        ),
+        cudf.Series(['["fiction"]']),
+    )
+
+
+def test_str_join_lists_error():
+    sr = cudf.Series([["a", "a"], ["b"], ["c"]])
+
+    with pytest.raises(
+        ValueError, match="sep_na_rep cannot be defined when `sep` is scalar."
+    ):
+        sr.str.join(sep="-", sep_na_rep="-")
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "string_na_rep should be a string scalar, got [10, 20] of type "
+            ": <class 'list'>"
+        ),
+    ):
+        sr.str.join(string_na_rep=[10, 20])
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "sep should be of similar size to the series, got: 2, expected: 3"
+        ),
+    ):
+        sr.str.join(sep=["=", "-"])
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "sep_na_rep should be a string scalar, got "
+            "['na'] of type: <class 'list'>"
+        ),
+    ):
+        sr.str.join(sep=["-", "+", "."], sep_na_rep=["na"])
+
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "sep should be an str, array-like or Series object, "
+            "found <class 'cudf.core.dataframe.DataFrame'>"
+        ),
+    ):
+        sr.str.join(sep=cudf.DataFrame())
+
+
+@pytest.mark.parametrize(
+    "sr,sep,string_na_rep,sep_na_rep,expected",
+    [
+        (
+            cudf.Series([["a", "a"], ["b"], ["c"]]),
+            "-",
+            None,
+            None,
+            cudf.Series(["a-a", "b", "c"]),
+        ),
+        (
+            cudf.Series([["a", "b"], [None], [None, "hello", None, "world"]]),
+            "__",
+            "=",
+            None,
+            cudf.Series(["a__b", None, "=__hello__=__world"]),
+        ),
+        (
+            cudf.Series(
+                [
+                    ["a", None, "b"],
+                    [None],
+                    [None, "hello", None, "world"],
+                    None,
+                ]
+            ),
+            ["-", "_", "**", "!"],
+            None,
+            None,
+            cudf.Series(["a--b", None, "**hello****world", None]),
+        ),
+        (
+            cudf.Series(
+                [
+                    ["a", None, "b"],
+                    [None],
+                    [None, "hello", None, "world"],
+                    None,
+                ]
+            ),
+            ["-", "_", "**", None],
+            "rep_str",
+            "sep_str",
+            cudf.Series(
+                ["a-rep_str-b", None, "rep_str**hello**rep_str**world", None]
+            ),
+        ),
+        (
+            cudf.Series([[None, "a"], [None], None]),
+            ["-", "_", None],
+            "rep_str",
+            None,
+            cudf.Series(["rep_str-a", None, None]),
+        ),
+        (
+            cudf.Series([[None, "a"], [None], None]),
+            ["-", "_", None],
+            None,
+            "sep_str",
+            cudf.Series(["-a", None, None]),
+        ),
+    ],
+)
+def test_str_join_lists(sr, sep, string_na_rep, sep_na_rep, expected):
+    actual = sr.str.join(
+        sep=sep, string_na_rep=string_na_rep, sep_na_rep=sep_na_rep
+    )
+    assert_eq(actual, expected)
+
+
+@pytest.mark.parametrize(
+    "patterns, expected",
+    [
+        (
+            lambda: ["a", "s", "g", "i", "o", "r"],
+            [
+                [-1, 0, 5, 3, -1, 2],
+                [-1, -1, -1, -1, 1, -1],
+                [2, 0, -1, -1, -1, 3],
+                [-1, -1, -1, 0, -1, -1],
+            ],
+        ),
+        (
+            lambda: cudf.Series(["a", "string", "g", "inn", "o", "r", "sea"]),
+            [
+                [-1, 0, 5, -1, -1, 2, -1],
+                [-1, -1, -1, -1, 1, -1, -1],
+                [2, -1, -1, -1, -1, 3, 0],
+                [-1, -1, -1, -1, -1, -1, -1],
+            ],
+        ),
+    ],
+)
+def test_str_find_multiple(patterns, expected):
+    s = cudf.Series(["strings", "to", "search", "in"])
+    t = patterns()
+
+    expected = cudf.Series(expected)
+
+    # We convert to pandas because find_multiple returns ListDtype(int32)
+    # and expected is ListDtype(int64).
+    # Currently there is no easy way to type-cast these to match.
+    assert_eq(s.str.find_multiple(t).to_pandas(), expected.to_pandas())
+
+    s = cudf.Index(s)
+    t = cudf.Index(t)
+
+    expected.index = s
+
+    assert_eq(s.str.find_multiple(t).to_pandas(), expected.to_pandas())
+
+
+def test_str_find_multiple_error():
+    s = cudf.Series(["strings", "to", "search", "in"])
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "patterns should be an array-like or a Series object, found "
+            "<class 'str'>"
+        ),
+    ):
+        s.str.find_multiple("a")
+
+    t = cudf.Series([1, 2, 3])
+    with pytest.raises(
+        TypeError,
+        match=re.escape("patterns can only be of 'string' dtype, got: int64"),
+    ):
+        s.str.find_multiple(t)
+
+
+def test_str_iterate_error():
+    s = cudf.Series(["abc", "xyz"])
+    with pytest.raises(TypeError):
+        iter(s.str)
diff --git a/python/cudf/cudf/tests/test_string_udfs.py b/python/cudf/cudf/tests/test_string_udfs.py
new file mode 100644
index 0000000..88c73cc
--- /dev/null
+++ b/python/cudf/cudf/tests/test_string_udfs.py
@@ -0,0 +1,371 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+
+import numba
+import numpy as np
+import pandas as pd
+import pytest
+from numba import cuda
+from numba.core.typing import signature as nb_signature
+from numba.types import CPointer, void
+
+import rmm
+
+import cudf
+from cudf._lib.strings_udf import (
+    column_from_udf_string_array,
+    column_to_string_view_array,
+)
+from cudf.core.udf.strings_typing import (
+    str_view_arg_handler,
+    string_view,
+    udf_string,
+)
+from cudf.core.udf.utils import _PTX_FILE, _get_extensionty_size
+from cudf.testing._utils import assert_eq, sv_to_udf_str
+from cudf.utils._numba import _CUDFNumbaConfig
+
+
+def get_kernels(func, dtype, size):
+    """
+    Create two kernels for testing a single scalar string function.
+    The first tests the function's action on a string_view object and
+    the second tests the same except using a udf_string object.
+    Allocates an output vector with a dtype specified by the caller
+    The returned kernels execute the input function on each data
+    element of the input and returns the output into the output vector
+    """
+
+    func = cuda.jit(device=True)(func)
+
+    if dtype == "str":
+        outty = CPointer(udf_string)
+    else:
+        outty = numba.np.numpy_support.from_dtype(dtype)[::1]
+    sig = nb_signature(void, CPointer(string_view), outty)
+
+    @cuda.jit(sig, link=[_PTX_FILE], extensions=[str_view_arg_handler])
+    def string_view_kernel(input_strings, output_col):
+        id = cuda.grid(1)
+        if id < size:
+            st = input_strings[id]
+            result = func(st)
+            output_col[id] = result
+
+    @cuda.jit(sig, link=[_PTX_FILE], extensions=[str_view_arg_handler])
+    def udf_string_kernel(input_strings, output_col):
+        # test the string function with a udf_string as input
+        id = cuda.grid(1)
+        if id < size:
+            st = input_strings[id]
+            st = sv_to_udf_str(st)
+            result = func(st)
+            output_col[id] = result
+
+    return string_view_kernel, udf_string_kernel
+
+
+def run_udf_test(data, func, dtype):
+    """
+    Run a test kernel on a set of input data
+    Converts the input data to a cuDF column and subsequently
+    to an array of cudf::string_view objects. It then creates
+    a CUDA kernel using get_kernel which calls the input function,
+    and then assembles the result back into a cuDF series before
+    comparing it with the equivalent pandas result
+    """
+    if dtype == "str":
+        output = rmm.DeviceBuffer(
+            size=len(data) * _get_extensionty_size(udf_string)
+        )
+    else:
+        dtype = np.dtype(dtype)
+        output = cudf.core.column.column_empty(len(data), dtype=dtype)
+
+    cudf_column = cudf.core.column.as_column(data)
+    str_views = column_to_string_view_array(cudf_column)
+    sv_kernel, udf_str_kernel = get_kernels(func, dtype, len(data))
+
+    expect = pd.Series(data).apply(func)
+    with _CUDFNumbaConfig():
+        sv_kernel.forall(len(data))(str_views, output)
+    if dtype == "str":
+        result = column_from_udf_string_array(output)
+    else:
+        result = output
+
+    got = cudf.Series(result, dtype=dtype)
+    assert_eq(expect, got, check_dtype=False)
+    with _CUDFNumbaConfig():
+        udf_str_kernel.forall(len(data))(str_views, output)
+    if dtype == "str":
+        result = column_from_udf_string_array(output)
+    else:
+        result = output
+
+    got = cudf.Series(result, dtype=dtype)
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.fixture(scope="module")
+def data():
+    return [
+        "abc",
+        "ABC",
+        "AbC",
+        "123",
+        "123aBc",
+        "123@.!",
+        "",
+        "rapids ai",
+        "gpu",
+        "True",
+        "False",
+        "1.234",
+        ".123a",
+        "0.013",
+        "1.0",
+        "01",
+        "20010101",
+        "cudf",
+        "cuda",
+        "gpu",
+        "This Is A Title",
+        "This is Not a Title",
+        "Neither is This a Title",
+        "NoT a TiTlE",
+        "123 Title Works",
+    ]
+
+
+@pytest.fixture(params=["cudf", "cuda", "gpucudf", "abc"])
+def rhs(request):
+    return request.param
+
+
+@pytest.fixture(params=["c", "cu", "2", "abc", "", "gpu"])
+def substr(request):
+    return request.param
+
+
+def test_string_udf_eq(data, rhs):
+    def func(st):
+        return st == rhs
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_ne(data, rhs):
+    def func(st):
+        return st != rhs
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_ge(data, rhs):
+    def func(st):
+        return st >= rhs
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_le(data, rhs):
+    def func(st):
+        return st <= rhs
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_gt(data, rhs):
+    def func(st):
+        return st > rhs
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_lt(data, rhs):
+    def func(st):
+        return st < rhs
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_contains(data, substr):
+    def func(st):
+        return substr in st
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_count(data, substr):
+    def func(st):
+        return st.count(substr)
+
+    run_udf_test(data, func, "int32")
+
+
+def test_string_udf_find(data, substr):
+    def func(st):
+        return st.find(substr)
+
+    run_udf_test(data, func, "int32")
+
+
+def test_string_udf_endswith(data, substr):
+    def func(st):
+        return st.endswith(substr)
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isalnum(data):
+    def func(st):
+        return st.isalnum()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isalpha(data):
+    def func(st):
+        return st.isalpha()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isdecimal(data):
+    def func(st):
+        return st.isdecimal()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isdigit(data):
+    def func(st):
+        return st.isdigit()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_islower(data):
+    def func(st):
+        return st.islower()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isnumeric(data):
+    def func(st):
+        return st.isnumeric()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isspace(data):
+    def func(st):
+        return st.isspace()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_isupper(data):
+    def func(st):
+        return st.isupper()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_istitle(data):
+    def func(st):
+        return st.istitle()
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_len(data):
+    def func(st):
+        return len(st)
+
+    run_udf_test(data, func, "int64")
+
+
+def test_string_udf_rfind(data, substr):
+    def func(st):
+        return st.rfind(substr)
+
+    run_udf_test(data, func, "int32")
+
+
+def test_string_udf_startswith(data, substr):
+    def func(st):
+        return st.startswith(substr)
+
+    run_udf_test(data, func, "bool")
+
+
+def test_string_udf_return_string(data):
+    def func(st):
+        return st
+
+    run_udf_test(data, func, "str")
+
+
+@pytest.mark.parametrize("strip_char", ["1", "a", "12", " ", "", ".", "@"])
+def test_string_udf_strip(data, strip_char):
+    def func(st):
+        return st.strip(strip_char)
+
+    run_udf_test(data, func, "str")
+
+
+@pytest.mark.parametrize("strip_char", ["1", "a", "12", " ", "", ".", "@"])
+def test_string_udf_lstrip(data, strip_char):
+    def func(st):
+        return st.lstrip(strip_char)
+
+    run_udf_test(data, func, "str")
+
+
+@pytest.mark.parametrize("strip_char", ["1", "a", "12", " ", "", ".", "@"])
+def test_string_udf_rstrip(data, strip_char):
+    def func(st):
+        return st.rstrip(strip_char)
+
+    run_udf_test(data, func, "str")
+
+
+def test_string_udf_upper(data):
+    def func(st):
+        return st.upper()
+
+    run_udf_test(data, func, "str")
+
+
+def test_string_udf_lower(data):
+    def func(st):
+        return st.lower()
+
+    run_udf_test(data, func, "str")
+
+
+@pytest.mark.parametrize("concat_char", ["1", "a", "12", " ", "", ".", "@"])
+def test_string_udf_concat(data, concat_char):
+    def func(st):
+        return st + concat_char
+
+    run_udf_test(data, func, "str")
+
+
+@pytest.mark.parametrize("concat_char", ["1", "a", "12", " ", "", ".", "@"])
+def test_string_udf_concat_reflected(data, concat_char):
+    def func(st):
+        return concat_char + st
+
+    run_udf_test(data, func, "str")
+
+
+@pytest.mark.parametrize("to_replace", ["a", "1", "", "@"])
+@pytest.mark.parametrize("replacement", ["a", "1", "", "@"])
+def test_string_udf_replace(data, to_replace, replacement):
+    def func(st):
+        return st.replace(to_replace, replacement)
+
+    run_udf_test(data, func, "str")
diff --git a/python/cudf/cudf/tests/test_struct.py b/python/cudf/cudf/tests/test_struct.py
new file mode 100644
index 0000000..60d9516
--- /dev/null
+++ b/python/cudf/cudf/tests/test_struct.py
@@ -0,0 +1,450 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.core.dtypes import StructDtype
+from cudf.testing._utils import DATETIME_TYPES, TIMEDELTA_TYPES, assert_eq
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [{}],
+        [{"a": None}],
+        [{"a": 1}],
+        [{"a": "one"}],
+        [{"a": 1}, {"a": 2}],
+        [{"a": 1, "b": "one"}, {"a": 2, "b": "two"}],
+        [{"b": "two", "a": None}, None, {"a": "one", "b": "two"}],
+    ],
+)
+def test_create_struct_series(data):
+    expect = pd.Series(data)
+    got = cudf.Series(data)
+    assert_eq(expect, got, check_dtype=False)
+
+
+def test_struct_of_struct_copy():
+    sr = cudf.Series([{"a": {"b": 1}}])
+    assert_eq(sr, sr.copy())
+
+
+def test_struct_of_struct_loc():
+    df = cudf.DataFrame({"col": [{"a": {"b": 1}}]})
+    expect = cudf.Series([{"a": {"b": 1}}], name="col")
+    assert_eq(expect, df["col"])
+
+
+@pytest.mark.parametrize(
+    "key, expect", [(0, [1, 3]), (1, [2, 4]), ("a", [1, 3]), ("b", [2, 4])]
+)
+def test_struct_for_field(key, expect):
+    sr = cudf.Series([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
+    expect = cudf.Series(expect)
+    got = sr.struct.field(key)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("input_obj", [[{"a": 1, "b": cudf.NA, "c": 3}]])
+def test_series_construction_with_nulls(input_obj):
+    expect = pa.array(input_obj, from_pandas=True)
+    got = cudf.Series(input_obj).to_arrow()
+
+    assert expect == got
+
+
+@pytest.mark.parametrize(
+    "fields",
+    [
+        {"a": np.dtype(np.int64)},
+        {"a": np.dtype(np.int64), "b": None},
+        {
+            "a": cudf.ListDtype(np.dtype(np.int64)),
+            "b": cudf.Decimal64Dtype(1, 0),
+        },
+        {
+            "a": cudf.ListDtype(cudf.StructDtype({"b": np.dtype(np.int64)})),
+            "b": cudf.ListDtype(cudf.ListDtype(np.dtype(np.int64))),
+        },
+    ],
+)
+def test_serialize_struct_dtype(fields):
+    dtype = cudf.StructDtype(fields)
+    recreated = dtype.__class__.deserialize(*dtype.serialize())
+    assert recreated == dtype
+
+
+@pytest.mark.parametrize(
+    "series, expected",
+    [
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": 1},
+                {},
+            ],
+            {"a": "Hello world", "b": [], "c": cudf.NA},
+        ),
+        ([{}], {}),
+        (
+            [{"b": True}, {"a": 1, "c": [1, 2, 3], "d": "1", "b": False}],
+            {"a": cudf.NA, "c": cudf.NA, "d": cudf.NA, "b": True},
+        ),
+    ],
+)
+def test_struct_getitem(series, expected):
+    sr = cudf.Series(series)
+    assert sr[0] == expected
+
+
+@pytest.mark.parametrize(
+    "data, item",
+    [
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": cudf.NA},
+                {"a": "abcde", "b": [4, 5, 6], "c": 9},
+            ],
+            {"a": "Hello world", "b": [], "c": cudf.NA},
+        ),
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": cudf.NA},
+                {"a": "abcde", "b": [4, 5, 6], "c": 9},
+            ],
+            {},
+        ),
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": cudf.NA},
+                {"a": "abcde", "b": [4, 5, 6], "c": 9},
+            ],
+            cudf.NA,
+        ),
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": cudf.NA},
+                {"a": "abcde", "b": [4, 5, 6], "c": 9},
+            ],
+            {"a": "Second element", "b": [1, 2], "c": 1000},
+        ),
+    ],
+)
+def test_struct_setitem(data, item):
+    sr = cudf.Series(data)
+    sr[1] = item
+    data[1] = item
+    expected = cudf.Series(data)
+    assert sr.to_arrow() == expected.to_arrow()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": 1, "b": "rapids", "c": [1, 2, 3, 4]},
+        {"a": "Hello"},
+    ],
+)
+def test_struct_scalar_host_construction(data):
+    slr = cudf.Scalar(data)
+    assert slr.value == data
+    assert list(slr.device_value.value.values()) == list(data.values())
+
+
+@pytest.mark.parametrize(
+    ("data", "dtype"),
+    [
+        (
+            {"a": 1, "b": "rapids", "c": [1, 2, 3, 4], "d": cudf.NA},
+            cudf.StructDtype(
+                {
+                    "a": np.dtype(np.int64),
+                    "b": np.dtype(np.str_),
+                    "c": cudf.ListDtype(np.dtype(np.int64)),
+                    "d": np.dtype(np.int64),
+                }
+            ),
+        ),
+        (
+            {"b": [], "c": [1, 2, 3]},
+            cudf.StructDtype(
+                {
+                    "b": cudf.ListDtype(np.dtype(np.int64)),
+                    "c": cudf.ListDtype(np.dtype(np.int64)),
+                }
+            ),
+        ),
+    ],
+)
+def test_struct_scalar_host_construction_no_dtype_inference(data, dtype):
+    # cudf cannot infer the dtype of the scalar when it contains only nulls or
+    # is empty.
+    slr = cudf.Scalar(data, dtype=dtype)
+    assert slr.value == data
+    assert list(slr.device_value.value.values()) == list(data.values())
+
+
+def test_struct_scalar_null():
+    slr = cudf.Scalar(cudf.NA, dtype=StructDtype)
+    assert slr.device_value.value is cudf.NA
+
+
+def test_struct_explode():
+    s = cudf.Series([], dtype=cudf.StructDtype({}))
+    expect = cudf.DataFrame({})
+    assert_eq(expect, s.struct.explode())
+
+    s = cudf.Series(
+        [
+            {"a": 1, "b": "x"},
+            {"a": 2, "b": "y"},
+            {"a": 3, "b": "z"},
+            {"a": 4, "b": "a"},
+        ]
+    )
+    expect = cudf.DataFrame({"a": [1, 2, 3, 4], "b": ["x", "y", "z", "a"]})
+    got = s.struct.explode()
+    assert_eq(expect, got)
+
+    # check that a copy was made:
+    got["a"][0] = 5
+    assert_eq(s.struct.explode(), expect)
+
+
+def test_dataframe_to_struct():
+    df = cudf.DataFrame()
+    expect = cudf.Series(dtype=cudf.StructDtype({}))
+    got = df.to_struct()
+    assert_eq(expect, got)
+
+    df = cudf.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
+    expect = cudf.Series(
+        [{"a": 1, "b": "x"}, {"a": 2, "b": "y"}, {"a": 3, "b": "z"}]
+    )
+    got = df.to_struct()
+    assert_eq(expect, got)
+
+    # check that a copy was made:
+    df["a"][0] = 5
+    assert_eq(got, expect)
+
+    # check that a non-string (but convertible to string) named column can be
+    # converted to struct
+    df = cudf.DataFrame([[1, 2], [3, 4]], columns=[(1, "b"), 0])
+    expect = cudf.Series([{"(1, 'b')": 1, "0": 2}, {"(1, 'b')": 3, "0": 4}])
+    with pytest.warns(UserWarning, match="will be casted"):
+        got = df.to_struct()
+    assert_eq(got, expect)
+
+
+@pytest.mark.parametrize(
+    "series, slce",
+    [
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": 1},
+                {},
+                None,
+            ],
+            slice(1, None),
+        ),
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": 1},
+                {},
+                None,
+                {"d": ["Hello", "rapids"]},
+                None,
+                cudf.NA,
+            ],
+            slice(1, 5),
+        ),
+        (
+            [
+                {"a": "Hello world", "b": []},
+                {"a": "CUDF", "b": [1, 2, 3], "c": 1},
+                {},
+                None,
+                {"c": 5},
+                None,
+                cudf.NA,
+            ],
+            slice(None, 4),
+        ),
+        ([{"a": {"b": 42, "c": -1}}, {"a": {"b": 0, "c": None}}], slice(0, 1)),
+    ],
+)
+def test_struct_slice(series, slce):
+    got = cudf.Series(series)[slce]
+    expected = cudf.Series(series[slce])
+    assert got.to_arrow() == expected.to_arrow()
+
+
+def test_struct_slice_nested_struct():
+    data = [
+        {"a": {"b": 42, "c": "abc"}},
+        {"a": {"b": 42, "c": "hello world"}},
+    ]
+
+    got = cudf.Series(data)[0:1]
+    expect = cudf.Series(data[0:1])
+    assert got.to_arrow() == expect.to_arrow()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [{}],
+        [{"a": None}],
+        [{"a": 1}],
+        [{"a": "one"}],
+        [{"a": 1}, {"a": 2}],
+        [{"a": 1, "b": "one"}, {"a": 2, "b": "two"}],
+        [{"b": "two", "a": None}, None, {"a": "one", "b": "two"}],
+    ],
+)
+def test_struct_field_errors(data):
+    got = cudf.Series(data)
+
+    with pytest.raises(KeyError):
+        got.struct.field("notWithinFields")
+
+    with pytest.raises(IndexError):
+        got.struct.field(100)
+
+
+@pytest.mark.parametrize("dtype", DATETIME_TYPES + TIMEDELTA_TYPES)
+def test_struct_with_datetime_and_timedelta(dtype):
+    df = cudf.DataFrame(
+        {
+            "a": [12, 232, 2334],
+            "datetime": cudf.Series([23432, 3432423, 324324], dtype=dtype),
+        }
+    )
+    series = df.to_struct()
+    a_array = np.array([12, 232, 2334])
+    datetime_array = np.array([23432, 3432423, 324324]).astype(dtype)
+
+    actual = series.to_pandas()
+    values_list = []
+    for i, val in enumerate(a_array):
+        values_list.append({"a": val, "datetime": datetime_array[i]})
+
+    expected = pd.Series(values_list)
+    assert_eq(expected, actual)
+
+
+def test_struct_int_values():
+    series = cudf.Series(
+        [{"a": 1, "b": 2}, {"a": 10, "b": None}, {"a": 5, "b": 6}]
+    )
+    actual_series = series.to_pandas()
+
+    assert isinstance(actual_series[0]["b"], int)
+    assert isinstance(actual_series[1]["b"], type(None))
+    assert isinstance(actual_series[2]["b"], int)
+
+
+def test_nested_struct_from_pandas_empty():
+    # tests constructing nested structs columns that would result in
+    # libcudf EMPTY type child columns inheriting their parent's null
+    # mask. See GH PR: #10761
+    pdf = pd.Series([[{"c": {"x": None}}], [{"c": None}]])
+    gdf = cudf.from_pandas(pdf)
+
+    assert_eq(pdf, gdf)
+
+
+def _nested_na_replace(struct_scalar):
+    """
+    Replace `cudf.NA` with `None` in the dict
+    """
+    for key, value in struct_scalar.items():
+        if value is cudf.NA:
+            struct_scalar[key] = None
+    return struct_scalar
+
+
+@pytest.mark.parametrize(
+    "data, idx, expected",
+    [
+        (
+            [{"f2": {"a": "sf21"}, "f1": "a"}, {"f1": "sf12", "f2": None}],
+            0,
+            {"f1": "a", "f2": {"a": "sf21"}},
+        ),
+        (
+            [
+                {"f2": {"a": "sf21"}},
+                {"f1": "sf12", "f2": None},
+            ],
+            0,
+            {"f1": cudf.NA, "f2": {"a": "sf21"}},
+        ),
+        (
+            [{"a": "123"}, {"a": "sf12", "b": {"a": {"b": "c"}}}],
+            1,
+            {"a": "sf12", "b": {"a": {"b": "c"}}},
+        ),
+    ],
+)
+def test_nested_struct_extract_host_scalars(data, idx, expected):
+    series = cudf.Series(data)
+
+    assert _nested_na_replace(series[idx]) == _nested_na_replace(expected)
+
+
+def test_struct_memory_usage():
+    s = cudf.Series([{"a": 1, "b": 10}, {"a": 2, "b": 20}, {"a": 3, "b": 30}])
+    df = s.struct.explode()
+
+    assert_eq(s.memory_usage(), df.memory_usage().sum())
+
+
+def test_struct_with_null_memory_usage():
+    df = cudf.DataFrame(
+        {
+            "a": cudf.Series([1, 2, -1, -1, 3], dtype="int64"),
+            "b": cudf.Series([10, 20, -1, -1, 30], dtype="int64"),
+        }
+    )
+    s = df.to_struct()
+    assert s.memory_usage() == 80
+
+    s[2:4] = None
+    assert s.memory_usage() == 272
+
+
+@pytest.mark.parametrize(
+    "indices",
+    [slice(0, 3), slice(1, 4), slice(None, None, 2), slice(1, None, 2)],
+    ids=[":3", "1:4", "0::2", "1::2"],
+)
+@pytest.mark.parametrize(
+    "values",
+    [[None, {}, {}, None], [{}, {}, {}, {}]],
+    ids=["nulls", "no_nulls"],
+)
+def test_struct_empty_children_slice(indices, values):
+    s = cudf.Series(values)
+    actual = s.iloc[indices]
+    expect = cudf.Series(values[indices], index=range(len(values))[indices])
+    assert_eq(actual, expect)
+
+
+def test_struct_iterate_error():
+    s = cudf.Series(
+        [{"f2": {"a": "sf21"}, "f1": "a"}, {"f1": "sf12", "f2": None}]
+    )
+    with pytest.raises(TypeError):
+        iter(s.struct)
diff --git a/python/cudf/cudf/tests/test_testing.py b/python/cudf/cudf/tests/test_testing.py
new file mode 100644
index 0000000..e665804
--- /dev/null
+++ b/python/cudf/cudf/tests/test_testing.py
@@ -0,0 +1,439 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pytest
+
+import cudf
+from cudf.core.column.column import as_column, full
+from cudf.testing import (
+    assert_frame_equal,
+    assert_index_equal,
+    assert_series_equal,
+)
+from cudf.testing._utils import (
+    NUMERIC_TYPES,
+    OTHER_TYPES,
+    assert_column_memory_eq,
+    assert_column_memory_ne,
+    assert_eq,
+)
+from cudf.testing.testing import assert_column_equal
+
+
+@pytest.fixture(
+    params=[
+        pa.array([*range(10)]),
+        pa.array(["hello", "world", "rapids", "AI"]),
+        pa.array([[1, 2, 3], [4, 5], [6], [], [7]]),
+        pa.array([{"f0": "hello", "f1": 42}, {"f0": "world", "f1": 3}]),
+    ]
+)
+def arrow_arrays(request):
+    return request.param
+
+
+@pytest.mark.parametrize("rdata", [[1, 2, 5], [1, 2, 6], [1, 2, 5, 6]])
+@pytest.mark.parametrize("exact", ["equiv", True, False])
+@pytest.mark.parametrize("check_names", [True, False])
+@pytest.mark.parametrize("rname", ["a", "b"])
+@pytest.mark.parametrize("check_categorical", [True, False])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + OTHER_TYPES + ["datetime64[ns]"]
+)
+def test_basic_assert_index_equal(
+    rdata,
+    exact,
+    check_names,
+    rname,
+    check_categorical,
+    dtype,
+):
+    p_left = pd.Index([1, 2, 3], name="a", dtype=dtype)
+    p_right = pd.Index(rdata, name=rname, dtype=dtype)
+
+    left = cudf.from_pandas(p_left)
+    right = cudf.from_pandas(p_right)
+
+    kind = None
+    try:
+        pd.testing.assert_index_equal(
+            p_left,
+            p_right,
+            exact=exact,
+            check_names=check_names,
+            check_categorical=check_categorical,
+        )
+    except BaseException as e:
+        kind = type(e)
+        msg = str(e)
+
+    if kind is not None:
+        if (kind == TypeError) and (
+            msg
+            == (
+                "Categoricals can only be compared "
+                "if 'categories' are the same."
+            )
+        ):
+            kind = AssertionError
+        with pytest.raises(kind):
+            assert_index_equal(
+                left,
+                right,
+                exact=exact,
+                check_names=check_names,
+                check_categorical=check_categorical,
+            )
+    else:
+        assert_index_equal(
+            left,
+            right,
+            exact=exact,
+            check_names=check_names,
+            check_categorical=check_categorical,
+        )
+
+
+@pytest.mark.parametrize("rdata", [[1, 2, 5], [1, 2, 6], [1, 2, 5, 6]])
+@pytest.mark.parametrize("check_names", [True, False])
+@pytest.mark.parametrize("rname", ["a", "b"])
+@pytest.mark.parametrize("check_category_order", [True, False])
+@pytest.mark.parametrize("check_categorical", [True, False])
+@pytest.mark.parametrize(
+    "dtype", NUMERIC_TYPES + OTHER_TYPES + ["datetime64[ns]"]
+)
+def test_basic_assert_series_equal(
+    rdata,
+    rname,
+    check_names,
+    check_category_order,
+    check_categorical,
+    dtype,
+):
+
+    p_left = pd.Series([1, 2, 3], name="a", dtype=dtype)
+    p_right = pd.Series(rdata, name=rname, dtype=dtype)
+
+    left = cudf.from_pandas(p_left)
+    right = cudf.from_pandas(p_right)
+
+    kind = None
+    try:
+        pd.testing.assert_series_equal(
+            p_left,
+            p_right,
+            check_names=check_names,
+            check_categorical=check_categorical,
+            check_category_order=check_category_order,
+        )
+    except BaseException as e:
+        kind = type(e)
+
+    if kind is not None:
+        with pytest.raises(kind):
+            assert_series_equal(
+                left,
+                right,
+                check_names=check_names,
+                check_categorical=check_categorical,
+                check_category_order=check_category_order,
+            )
+    else:
+        assert_series_equal(
+            left,
+            right,
+            check_names=check_names,
+            check_categorical=check_categorical,
+            check_category_order=check_category_order,
+        )
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        as_column(["1", "2", "3"]),
+        as_column([[1], [2], [3]]),
+        as_column([{"a": 1}, {"a": 2}, {"a": 3}]),
+    ],
+)
+def test_assert_column_equal_dtype_edge_cases(other):
+    # string series should be 100% different
+    # even when the elements are the same
+    base = as_column([1, 2, 3])
+
+    # for these dtypes, the diff should always be 100% regardless of the values
+    with pytest.raises(
+        AssertionError, match=r".*values are different \(100.0 %\).*"
+    ):
+        assert_column_equal(base, other, check_dtype=False)
+
+    # the exceptions are the empty and all null cases
+    assert_column_equal(base.slice(0, 0), other.slice(0, 0), check_dtype=False)
+    assert_column_equal(other.slice(0, 0), base.slice(0, 0), check_dtype=False)
+
+    base = full(len(base), fill_value=cudf.NA, dtype=base.dtype)
+    other = full(len(other), fill_value=cudf.NA, dtype=other.dtype)
+
+    assert_column_equal(base, other, check_dtype=False)
+    assert_column_equal(other, base, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "rdtype", [["int8", "int16", "int64"], ["int64", "int16", "int8"]]
+)
+@pytest.mark.parametrize("rname", [["a", "b", "c"], ["b", "c", "a"]])
+@pytest.mark.parametrize("index", [[1, 2, 3], [3, 2, 1]])
+@pytest.mark.parametrize("check_exact", [True, False])
+@pytest.mark.parametrize("check_dtype", [True, False])
+@pytest.mark.parametrize("check_names", [True, False])
+@pytest.mark.parametrize("check_like", [True, False])
+@pytest.mark.parametrize("mismatch", [True, False])
+def test_basic_assert_frame_equal(
+    rdtype,
+    rname,
+    index,
+    check_exact,
+    check_dtype,
+    check_names,
+    check_like,
+    mismatch,
+):
+    data = [1, 2, 1]
+    p_left = pd.DataFrame(index=[1, 2, 3])
+    p_left["a"] = np.array(data, dtype="int8")
+    p_left["b"] = np.array(data, dtype="int16")
+    if mismatch:
+        p_left["c"] = np.array([1, 2, 3], dtype="int64")
+    else:
+        p_left["c"] = np.array(data, dtype="int64")
+
+    p_right = pd.DataFrame(index=index)
+    for dtype, name in zip(rdtype, rname):
+        p_right[name] = np.array(data, dtype=dtype)
+
+    left = cudf.from_pandas(p_left)
+    right = cudf.from_pandas(p_right)
+
+    kind = None
+    try:
+        pd.testing.assert_frame_equal(
+            p_left,
+            p_right,
+            check_exact=check_exact,
+            check_dtype=check_dtype,
+            check_names=check_names,
+            check_like=check_like,
+        )
+    except BaseException as e:
+        kind = type(e)
+
+    if kind is not None:
+        with pytest.raises(kind):
+            assert_frame_equal(
+                left,
+                right,
+                check_exact=check_exact,
+                check_dtype=check_dtype,
+                check_names=check_names,
+                check_like=check_like,
+            )
+    else:
+        assert_frame_equal(
+            left,
+            right,
+            check_exact=check_exact,
+            check_dtype=check_dtype,
+            check_names=check_names,
+            check_like=check_like,
+        )
+
+
+@pytest.mark.parametrize("rdata", [[0, 1, 2, 3], [0, 1, 2, 4]])
+@pytest.mark.parametrize("check_datetimelike_compat", [True, False])
+def test_datetime_like_compaibility(rdata, check_datetimelike_compat):
+    psr1 = pd.Series([0, 1, 2, 3], dtype="datetime64[ns]")
+    psr2 = pd.Series(rdata, dtype="datetime64[ns]").astype("str")
+
+    sr1 = cudf.from_pandas(psr1)
+    sr2 = cudf.from_pandas(psr2)
+
+    kind = None
+    try:
+        pd.testing.assert_series_equal(
+            psr1, psr2, check_datetimelike_compat=check_datetimelike_compat
+        )
+    except BaseException as e:
+        kind = type(e)
+
+    if kind is not None:
+        with pytest.raises(kind):
+            assert_series_equal(
+                sr1, sr2, check_datetimelike_compat=check_datetimelike_compat
+            )
+    else:
+        assert_series_equal(
+            sr1, sr2, check_datetimelike_compat=check_datetimelike_compat
+        )
+
+
+@pytest.mark.parametrize(
+    "rdata",
+    [
+        [[0, 1, 2, 3], ["G", "O", "N", "E"]],
+        [[0, 1, 2, 4], ["G", "O", "N", "E"]],
+    ],
+)
+def test_multiindex_equal(rdata):
+    pidx1 = pd.MultiIndex.from_arrays(
+        [[0, 1, 2, 3], ["G", "O", "N", "E"]], names=("n", "id")
+    )
+    pidx2 = pd.MultiIndex.from_arrays(rdata, names=("n", "id"))
+
+    idx1 = cudf.from_pandas(pidx1)
+    idx2 = cudf.from_pandas(pidx2)
+
+    kind = None
+    try:
+        pd.testing.assert_index_equal(pidx1, pidx2)
+    except BaseException as e:
+        kind = type(e)
+
+    if kind is not None:
+        with pytest.raises(kind):
+            assert_index_equal(idx1, idx2)
+    else:
+        assert_index_equal(idx1, idx2)
+
+
+@pytest.mark.parametrize("dtype", ["int8", "uint8", "float32"])
+@pytest.mark.parametrize("check_exact", [True, False])
+@pytest.mark.parametrize("check_dtype", [True, False])
+def test_series_different_type_cases(dtype, check_exact, check_dtype):
+    data = [0, 1, 2, 3]
+
+    psr1 = pd.Series(data, dtype="uint8")
+    psr2 = pd.Series(data, dtype=dtype)
+
+    sr1 = cudf.from_pandas(psr1)
+    sr2 = cudf.from_pandas(psr2)
+
+    kind = None
+    try:
+        pd.testing.assert_series_equal(
+            psr1, psr2, check_exact=check_exact, check_dtype=check_dtype
+        )
+    except BaseException as e:
+        kind = type(e)
+
+    if kind is not None:
+        with pytest.raises(kind):
+            assert_series_equal(
+                sr1, sr2, check_exact=check_exact, check_dtype=check_dtype
+            )
+    else:
+        assert_series_equal(
+            sr1, sr2, check_exact=check_exact, check_dtype=check_dtype
+        )
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["int8", "int16", "int32", "int64"],
+)
+@pytest.mark.parametrize("exact", ["equiv", True, False])
+def test_range_index_and_int_index_eqaulity(dtype, exact):
+    pidx1 = pd.RangeIndex(0, stop=5, step=1)
+    pidx2 = pd.Index([0, 1, 2, 3, 4])
+    idx1 = cudf.from_pandas(pidx1)
+    idx2 = cudf.Index([0, 1, 2, 3, 4], dtype=dtype)
+
+    kind = None
+    try:
+        pd.testing.assert_index_equal(pidx1, pidx2, exact=exact)
+    except BaseException as e:
+        kind = type(e)
+
+    if kind is not None:
+        with pytest.raises(kind):
+            assert_index_equal(idx1, idx2, exact=exact)
+    else:
+        assert_index_equal(idx1, idx2, exact=exact)
+
+
+@pytest.mark.parametrize(
+    "left, right",
+    [
+        (1493282, 1493282),
+        (1493282.0, 1493282.0 + 1e-8),
+        ("abc", "abc"),
+        (0, np.array(0)),
+        (
+            np.datetime64(123456, "ns"),
+            pd.Timestamp(np.datetime64(123456, "ns")),
+        ),
+        ("int64", np.dtype("int64")),
+        (np.nan, np.nan),
+    ],
+)
+def test_basic_scalar_equality(left, right):
+    assert_eq(left, right)
+
+
+@pytest.mark.parametrize(
+    "left, right",
+    [
+        (1493282, 1493274),
+        (1493282.0, 1493282.0 + 1e-6),
+        ("abc", "abd"),
+        (0, np.array(1)),
+        (
+            np.datetime64(123456, "ns"),
+            pd.Timestamp(np.datetime64(123457, "ns")),
+        ),
+        ("int64", np.dtype("int32")),
+    ],
+)
+def test_basic_scalar_inequality(left, right):
+    with pytest.raises(AssertionError, match=r".*not (almost )?equal.*"):
+        assert_eq(left, right)
+
+
+def test_assert_column_memory_basic(arrow_arrays):
+    left = cudf.core.column.ColumnBase.from_arrow(arrow_arrays)
+    right = cudf.core.column.ColumnBase.from_arrow(arrow_arrays)
+
+    with pytest.raises(AssertionError):
+        assert_column_memory_eq(left, right)
+    assert_column_memory_ne(left, right)
+
+
+def test_assert_column_memory_slice(arrow_arrays):
+    col = cudf.core.column.ColumnBase.from_arrow(arrow_arrays)
+    left = col.slice(0, 1)
+    right = col.slice(1, 2)
+
+    with pytest.raises(AssertionError):
+        assert_column_memory_eq(left, right)
+    assert_column_memory_ne(left, right)
+
+    with pytest.raises(AssertionError):
+        assert_column_memory_eq(left, col)
+    assert_column_memory_ne(left, col)
+
+    with pytest.raises(AssertionError):
+        assert_column_memory_eq(right, col)
+    assert_column_memory_ne(right, col)
+
+
+def test_assert_column_memory_basic_same(arrow_arrays):
+    data = cudf.core.column.ColumnBase.from_arrow(arrow_arrays)
+    buf = cudf.core.buffer.as_buffer(data.base_data)
+
+    left = cudf.core.column.build_column(buf, dtype=np.int32)
+    right = cudf.core.column.build_column(buf, dtype=np.int32)
+
+    assert_column_memory_eq(left, right)
+    with pytest.raises(AssertionError):
+        assert_column_memory_ne(left, right)
diff --git a/python/cudf/cudf/tests/test_timedelta.py b/python/cudf/cudf/tests/test_timedelta.py
new file mode 100644
index 0000000..139ce1c
--- /dev/null
+++ b/python/cudf/cudf/tests/test_timedelta.py
@@ -0,0 +1,1471 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import datetime
+import operator
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf.testing import _utils as utils
+from cudf.testing._utils import assert_eq, assert_exceptions_equal
+
+_TIMEDELTA_DATA = [
+    [1000000, 200000, 3000000],
+    [1000000, 200000, None],
+    [],
+    [None],
+    [None, None, None, None, None],
+    [12, 12, 22, 343, 4353534, 435342],
+    np.array([10, 20, 30, None, 100]),
+    cp.asarray([10, 20, 30, 100]),
+    [1000000, 200000, 3000000],
+    [1000000, 200000, None],
+    [1],
+    [12, 11, 232, 223432411, 2343241, 234324, 23234],
+    [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+    [
+        136457654736252,
+        134736784364431,
+        245345345545332,
+        223432411,
+        2343241,
+        3634548734,
+        23234,
+    ],
+    [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+]
+
+_TIMEDELTA_DATA_NON_OVERFLOW = [
+    [1000000, 200000, 3000000],
+    [1000000, 200000, None],
+    [],
+    [None],
+    [None, None, None, None, None],
+    [12, 12, 22, 343, 4353534, 435342],
+    np.array([10, 20, 30, None, 100]),
+    cp.asarray([10, 20, 30, 100]),
+    [1000000, 200000, 3000000],
+    [1000000, 200000, None],
+    [1],
+    [12, 11, 232, 223432411, 2343241, 234324, 23234],
+    [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+    [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+]
+
+_cmpops = [
+    operator.lt,
+    operator.gt,
+    operator.le,
+    operator.ge,
+    operator.eq,
+    operator.ne,
+]
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        [0.3534, 12, 22, 343, 43.53534, 4353.42],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_series_create(data, dtype):
+    if dtype not in ("timedelta64[ns]"):
+        pytest.skip(
+            "Bug in pandas : https://github.com/pandas-dev/pandas/issues/35465"
+        )
+    psr = pd.Series(
+        cp.asnumpy(data) if isinstance(data, cp.ndarray) else data, dtype=dtype
+    )
+    gsr = cudf.Series(data, dtype=dtype)
+
+    assert_eq(psr, gsr)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [12, 12, 22, 343, 4353534, 435342],
+        [0.3534, 12, 22, 343, 43.53534, 4353.42],
+        cp.asarray([10, 20, 30, 100]),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize("cast_dtype", ["int64", "category"])
+def test_timedelta_from_typecast(data, dtype, cast_dtype):
+    if dtype not in ("timedelta64[ns]"):
+        pytest.skip(
+            "Bug in pandas : https://github.com/pandas-dev/pandas/issues/35465"
+        )
+    psr = pd.Series(
+        cp.asnumpy(data) if isinstance(data, cp.ndarray) else data, dtype=dtype
+    )
+    gsr = cudf.Series(data, dtype=dtype)
+
+    if cast_dtype == "int64":
+        assert_eq(psr.values.view(cast_dtype), gsr.astype(cast_dtype).values)
+    else:
+        assert_eq(psr.astype(cast_dtype), gsr.astype(cast_dtype))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [12, 12, 22, 343, 4353534, 435342],
+        [0.3534, 12, 22, 343, 43.53534, 4353.42],
+        cp.asarray([10, 20, 30, 100]),
+    ],
+)
+@pytest.mark.parametrize("cast_dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_to_typecast(data, cast_dtype):
+    psr = pd.Series(cp.asnumpy(data) if isinstance(data, cp.ndarray) else data)
+    gsr = cudf.Series(data)
+
+    assert_eq(psr.astype(cast_dtype), gsr.astype(cast_dtype))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        [0.3534, 12, 22, 343, 43.53534, 4353.42],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_from_pandas(data, dtype):
+    psr = pd.Series(
+        cp.asnumpy(data) if isinstance(data, cp.ndarray) else data, dtype=dtype
+    )
+    gsr = cudf.from_pandas(psr)
+
+    assert_eq(psr, gsr)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_series_to_numpy(data, dtype):
+    gsr = cudf.Series(data, dtype=dtype)
+
+    expected = np.array(
+        cp.asnumpy(data) if isinstance(data, cp.ndarray) else data, dtype=dtype
+    )
+    expected = expected[~np.isnan(expected)]
+
+    actual = gsr.dropna().to_numpy()
+
+    np.testing.assert_array_equal(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_series_to_pandas(data, dtype):
+    gsr = cudf.Series(data, dtype=dtype)
+
+    expected = np.array(
+        cp.asnumpy(data) if isinstance(data, cp.ndarray) else data, dtype=dtype
+    )
+
+    expected = pd.Series(expected)
+    actual = gsr.to_pandas()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data,other",
+    [
+        ([1000000, 200000, 3000000], [1000000, 200000, 3000000]),
+        ([1000000, 200000, None], [1000000, 200000, None]),
+        ([], []),
+        ([None], [None]),
+        ([None, None, None, None, None], [None, None, None, None, None]),
+        (
+            [12, 12, 22, 343, 4353534, 435342],
+            [12, 12, 22, 343, 4353534, 435342],
+        ),
+        (np.array([10, 20, 30, None, 100]), np.array([10, 20, 30, None, 100])),
+        (cp.asarray([10, 20, 30, 100]), cp.asarray([10, 20, 30, 100])),
+        ([1000000, 200000, 3000000], [200000, 34543, 3000000]),
+        ([1000000, 200000, None], [1000000, 200000, 3000000]),
+        ([None], [1]),
+        (
+            [12, 12, 22, 343, 4353534, 435342],
+            [None, 1, 220, 3, 34, 4353423287],
+        ),
+        (np.array([10, 20, 30, None, 100]), np.array([10, 20, 30, None, 100])),
+        (cp.asarray([10, 20, 30, 100]), cp.asarray([10, 20, 30, 100])),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "ops",
+    [
+        "eq",
+        "ne",
+        "lt",
+        "gt",
+        "le",
+        "ge",
+        "add",
+        "radd",
+        "sub",
+        "rsub",
+        "floordiv",
+        "truediv",
+        "mod",
+    ],
+)
+def test_timedelta_ops_misc_inputs(data, other, dtype, ops):
+    gsr = cudf.Series(data, dtype=dtype)
+    other_gsr = cudf.Series(other, dtype=dtype)
+
+    psr = gsr.to_pandas()
+    other_psr = other_gsr.to_pandas()
+
+    expected = getattr(psr, ops)(other_psr)
+    actual = getattr(gsr, ops)(other_gsr)
+    if ops in ("eq", "lt", "gt", "le", "ge"):
+        actual = actual.fillna(False)
+    elif ops == "ne":
+        actual = actual.fillna(True)
+
+    if ops == "floordiv":
+        expected[actual.isna().to_pandas()] = np.nan
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "datetime_data,timedelta_data",
+    [
+        ([1000000, 200000, 3000000], [1000000, 200000, 3000000]),
+        ([1000000, 200000, None], [1000000, 200000, None]),
+        ([], []),
+        ([None], [None]),
+        ([None, None, None, None, None], [None, None, None, None, None]),
+        (
+            [12, 12, 22, 343, 4353534, 435342],
+            [12, 12, 22, 343, 4353534, 435342],
+        ),
+        (np.array([10, 20, 30, None, 100]), np.array([10, 20, 30, None, 100])),
+        (cp.asarray([10, 20, 30, 100]), cp.asarray([10, 20, 30, 100])),
+        ([1000000, 200000, 3000000], [200000, 34543, 3000000]),
+        ([1000000, 200000, None], [1000000, 200000, 3000000]),
+        ([None], [1]),
+        (
+            [12, 12, 22, 343, 4353534, 435342],
+            [None, 1, 220, 3, 34, 4353423287],
+        ),
+        (np.array([10, 20, 30, None, 100]), np.array([10, 20, 30, None, 100])),
+        (cp.asarray([10, 20, 30, 100]), cp.asarray([10, 20, 30, 100])),
+        (
+            [12, 11, 232, 223432411, 2343241, 234324, 23234],
+            [11, 1132324, 2322323111, 23341, 2434, 332, 323],
+        ),
+        (
+            [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+            [11, 1132324, 2322323111, 23341, 2434, 332, 323],
+        ),
+        (
+            [11, 1132324, 2322323111, 23341, 2434, 332, 323],
+            [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        ),
+        (
+            [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+            [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        ),
+    ],
+)
+@pytest.mark.parametrize("datetime_dtype", utils.DATETIME_TYPES)
+@pytest.mark.parametrize("timedelta_dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "ops",
+    ["add", "sub"],
+)
+def test_timedelta_ops_datetime_inputs(
+    datetime_data, timedelta_data, datetime_dtype, timedelta_dtype, ops
+):
+    gsr_datetime = cudf.Series(datetime_data, dtype=datetime_dtype)
+    gsr_timedelta = cudf.Series(timedelta_data, dtype=timedelta_dtype)
+
+    psr_datetime = gsr_datetime.to_pandas()
+    psr_timedelta = gsr_timedelta.to_pandas()
+
+    expected = getattr(psr_datetime, ops)(psr_timedelta)
+    actual = getattr(gsr_datetime, ops)(gsr_timedelta)
+
+    assert_eq(expected, actual)
+
+    if ops == "add":
+        expected = getattr(psr_timedelta, ops)(psr_datetime)
+        actual = getattr(gsr_timedelta, ops)(gsr_datetime)
+
+        assert_eq(expected, actual)
+    elif ops == "sub":
+        assert_exceptions_equal(
+            lfunc=operator.sub,
+            rfunc=operator.sub,
+            lfunc_args_and_kwargs=([psr_timedelta, psr_datetime],),
+            rfunc_args_and_kwargs=([gsr_timedelta, gsr_datetime],),
+        )
+
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(
+            {
+                "A": pd.Series(pd.date_range("2012-1-1", periods=3, freq="D")),
+                "B": pd.Series([pd.Timedelta(days=i) for i in range(3)]),
+            }
+        ),
+        pd.DataFrame(
+            {
+                "A": pd.Series(
+                    pd.date_range("1994-1-1", periods=50, freq="D")
+                ),
+                "B": pd.Series([pd.Timedelta(days=i) for i in range(50)]),
+            }
+        ),
+    ],
+)
+@pytest.mark.parametrize("op", ["add", "sub"])
+def test_timedelta_dataframe_ops(df, op):
+    pdf = df
+    gdf = cudf.from_pandas(pdf)
+
+    if op == "add":
+        pdf["C"] = pdf["A"] + pdf["B"]
+        gdf["C"] = gdf["A"] + gdf["B"]
+    elif op == "sub":
+        pdf["C"] = pdf["A"] - pdf["B"]
+        gdf["C"] = gdf["A"] - gdf["B"]
+
+    assert_eq(pdf, gdf)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [1],
+        [12, 11, 232, 223432411, 2343241, 234324, 23234],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        [1.321, 1132.324, 23223231.11, 233.41, 332, 323],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    ],
+)
+@pytest.mark.parametrize(
+    "other_scalars",
+    [
+        datetime.timedelta(days=768),
+        datetime.timedelta(seconds=768),
+        datetime.timedelta(microseconds=7),
+        datetime.timedelta(minutes=447),
+        datetime.timedelta(hours=447),
+        datetime.timedelta(weeks=734),
+        np.timedelta64(4, "s"),
+        np.timedelta64(456, "D"),
+        np.timedelta64(46, "h"),
+        np.timedelta64("nat"),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64(1, "ns"),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "op",
+    [
+        "add",
+        "sub",
+        "truediv",
+        "mod",
+        "floordiv",
+    ],
+)
+def test_timedelta_series_ops_with_scalars(data, other_scalars, dtype, op):
+    gsr = cudf.Series(data=data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    if op == "add":
+        expected = psr + other_scalars
+        actual = gsr + other_scalars
+    elif op == "sub":
+        expected = psr - other_scalars
+        actual = gsr - other_scalars
+    elif op == "truediv":
+        expected = psr / other_scalars
+        actual = gsr / other_scalars
+    elif op == "floordiv":
+        expected = psr // other_scalars
+        actual = gsr // other_scalars
+    elif op == "mod":
+        expected = psr % other_scalars
+        actual = gsr % other_scalars
+
+    assert_eq(expected, actual)
+
+    if op == "add":
+        expected = other_scalars + psr
+        actual = other_scalars + gsr
+    elif op == "sub":
+        expected = other_scalars - psr
+        actual = other_scalars - gsr
+    elif op == "truediv":
+        expected = other_scalars / psr
+        actual = other_scalars / gsr
+    elif op == "floordiv":
+        expected = other_scalars // psr
+        actual = other_scalars // gsr
+    elif op == "mod":
+        expected = other_scalars % psr
+        actual = other_scalars % gsr
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "reverse",
+    [
+        False,
+        pytest.param(
+            True,
+            marks=pytest.mark.xfail(
+                strict=True,
+                reason=(
+                    "timedelta modulo by zero is dubiously defined in "
+                    "both pandas and cuDF "
+                    "(see https://github.com/rapidsai/cudf/issues/5938)"
+                ),
+            ),
+        ),
+    ],
+)
+def test_timedelta_series_mod_with_scalar_zero(reverse):
+    gsr = cudf.Series(data=[0.2434], dtype=np.timedelta64(1, "ns"))
+    psr = gsr.to_pandas()
+    scalar = datetime.timedelta(days=768)
+    if reverse:
+        expected = scalar % psr
+        actual = scalar % gsr
+    else:
+        expected = psr % scalar
+        actual = gsr % scalar
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [1],
+        [12, 11, 232, 223432411, 2343241, 234324, 23234],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        [1.321, 1132.324, 23223231.11, 233.41, 332, 323],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    ],
+)
+@pytest.mark.parametrize(
+    "cpu_scalar",
+    [
+        datetime.timedelta(seconds=768),
+        datetime.timedelta(microseconds=7),
+        np.timedelta64(4, "s"),
+        np.timedelta64("nat", "s"),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64("nat", "ns"),
+        np.timedelta64(1, "ns"),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "op",
+    [
+        "add",
+        "sub",
+        "truediv",
+        "mod",
+        "floordiv",
+    ],
+)
+def test_timedelta_series_ops_with_cudf_scalars(data, cpu_scalar, dtype, op):
+    gpu_scalar = cudf.Scalar(cpu_scalar)
+
+    gsr = cudf.Series(data=data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    if op == "add":
+        expected = psr + cpu_scalar
+        actual = gsr + gpu_scalar
+    elif op == "sub":
+        expected = psr - cpu_scalar
+        actual = gsr - gpu_scalar
+    elif op == "truediv":
+        expected = psr / cpu_scalar
+        actual = gsr / gpu_scalar
+    elif op == "floordiv":
+        expected = psr // cpu_scalar
+        actual = gsr // gpu_scalar
+    elif op == "mod":
+        expected = psr % cpu_scalar
+        actual = gsr % gpu_scalar
+
+    assert_eq(expected, actual)
+
+    if op == "add":
+        expected = cpu_scalar + psr
+        actual = gpu_scalar + gsr
+    elif op == "sub":
+        expected = cpu_scalar - psr
+        actual = gpu_scalar - gsr
+    elif op == "truediv":
+        expected = cpu_scalar / psr
+        actual = gpu_scalar / gsr
+    elif op == "floordiv":
+        expected = cpu_scalar // psr
+        actual = gpu_scalar // gsr
+    elif op == "mod":
+        expected = cpu_scalar % psr
+        actual = gpu_scalar % gsr
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "reverse",
+    [
+        False,
+        pytest.param(
+            True,
+            marks=pytest.mark.xfail(
+                strict=True,
+                reason=(
+                    "timedelta modulo by zero is dubiously defined in "
+                    "both pandas and cuDF "
+                    "(see https://github.com/rapidsai/cudf/issues/5938)"
+                ),
+            ),
+        ),
+    ],
+)
+def test_timedelta_series_mod_with_cudf_scalar_zero(reverse):
+    gsr = cudf.Series(data=[0.2434], dtype=np.timedelta64(1, "ns"))
+    psr = gsr.to_pandas()
+    scalar = datetime.timedelta(days=768)
+    gpu_scalar = cudf.Scalar(scalar)
+    if reverse:
+        expected = scalar % psr
+        actual = gpu_scalar % gsr
+    else:
+        expected = psr % scalar
+        actual = gsr % gpu_scalar
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [],
+        [None],
+        [None, None, None, None, None],
+        [12, 12, 22, 343, 4353534, 435342],
+        np.array([10, 20, 30, None, 100]),
+        cp.asarray([10, 20, 30, 100]),
+        [1000000, 200000, 3000000],
+        [1000000, 200000, None],
+        [1],
+        [12, 11, 232, 223432411, 2343241, 234324, 23234],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+        [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize("reduction_op", ["sum", "mean", "median", "quantile"])
+def test_timedelta_reduction_ops(data, dtype, reduction_op):
+    gsr = cudf.Series(data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    if len(psr) > 0 and psr.isnull().all() and reduction_op == "median":
+        with pytest.warns(RuntimeWarning, match="Mean of empty slice"):
+            expected = getattr(psr, reduction_op)()
+    else:
+        expected = getattr(psr, reduction_op)()
+    actual = getattr(gsr, reduction_op)()
+    if pd.isna(expected) and pd.isna(actual):
+        pass
+    elif isinstance(expected, pd.Timedelta) and isinstance(
+        actual, pd.Timedelta
+    ):
+        assert (
+            expected.round(gsr._column.time_unit).value
+            == actual.round(gsr._column.time_unit).value
+        )
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    _TIMEDELTA_DATA,
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_dt_components(data, dtype):
+    gsr = cudf.Series(data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    expected = psr.dt.components
+    actual = gsr.dt.components
+
+    if gsr.isnull().any():
+        assert_eq(expected, actual.astype("float"))
+    else:
+        assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "data",
+    _TIMEDELTA_DATA,
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_dt_properties(data, dtype):
+    gsr = cudf.Series(data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    def local_assert(expected, actual):
+        if gsr.isnull().any():
+            assert_eq(expected, actual.astype("float"))
+        else:
+            assert_eq(expected, actual)
+
+    expected_days = psr.dt.days
+    actual_days = gsr.dt.days
+
+    local_assert(expected_days, actual_days)
+
+    expected_seconds = psr.dt.seconds
+    actual_seconds = gsr.dt.seconds
+
+    local_assert(expected_seconds, actual_seconds)
+
+    expected_microseconds = psr.dt.microseconds
+    actual_microseconds = gsr.dt.microseconds
+
+    local_assert(expected_microseconds, actual_microseconds)
+
+    expected_nanoseconds = psr.dt.nanoseconds
+    actual_nanoseconds = gsr.dt.nanoseconds
+
+    local_assert(expected_nanoseconds, actual_nanoseconds)
+
+
+@pytest.mark.parametrize(
+    "data",
+    _TIMEDELTA_DATA,
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_index(data, dtype):
+    gdi = cudf.Index(data, dtype=dtype)
+    pdi = gdi.to_pandas()
+
+    assert_eq(pdi, gdi)
+
+
+@pytest.mark.parametrize("data", _TIMEDELTA_DATA_NON_OVERFLOW)
+@pytest.mark.parametrize("datetime_dtype", utils.DATETIME_TYPES)
+@pytest.mark.parametrize("timedelta_dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_index_datetime_index_ops(
+    data, datetime_dtype, timedelta_dtype
+):
+    gdt = cudf.Index(data, dtype=datetime_dtype)
+    gtd = cudf.Index(data, dtype=timedelta_dtype)
+
+    pdt = gdt.to_pandas()
+    ptd = gtd.to_pandas()
+
+    assert_eq(gdt - gtd, pdt - ptd)
+    assert_eq(gdt + gtd, pdt + ptd)
+
+
+@pytest.mark.parametrize(
+    "datetime_data,timedelta_data",
+    [
+        ([1000000, 200000, 3000000], [1000000, 200000, 3000000]),
+        ([1000000, 200000, None], [1000000, 200000, None]),
+        ([], []),
+        ([None], [None]),
+        ([None, None, None, None, None], [None, None, None, None, None]),
+        (
+            [12, 12, 22, 343, 4353534, 435342],
+            [12, 12, 22, 343, 4353534, 435342],
+        ),
+        (np.array([10, 20, 30, None, 100]), np.array([10, 20, 30, None, 100])),
+        (cp.asarray([10, 20, 30, 100]), cp.asarray([10, 20, 30, 100])),
+        ([1000000, 200000, 3000000], [200000, 34543, 3000000]),
+        ([1000000, 200000, None], [1000000, 200000, 3000000]),
+        ([None], [1]),
+        (
+            [12, 12, 22, 343, 4353534, 435342],
+            [None, 1, 220, 3, 34, 4353423287],
+        ),
+        (np.array([10, 20, 30, None, 100]), np.array([10, 20, 30, None, 100])),
+        (cp.asarray([10, 20, 30, 100]), cp.asarray([10, 20, 30, 100])),
+        (
+            [12, 11, 232, 223432411, 2343241, 234324, 23234],
+            [11, 1132324, 2322323111, 23341, 2434, 332, 323],
+        ),
+        (
+            [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+            [11, 1132324, 2322323111, 23341, 2434, 332, 323],
+        ),
+        (
+            [11, 1132324, 2322323111, 23341, 2434, 332, 323],
+            [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        ),
+        (
+            [1.321, 1132.324, 23223231.11, 233.41, 0.2434, 332, 323],
+            [12, 11, 2.32, 2234.32411, 2343.241, 23432.4, 23234],
+        ),
+    ],
+)
+@pytest.mark.parametrize("datetime_dtype", utils.DATETIME_TYPES)
+@pytest.mark.parametrize("timedelta_dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_datetime_index_ops_misc(
+    datetime_data, timedelta_data, datetime_dtype, timedelta_dtype
+):
+    gdt = cudf.Index(datetime_data, dtype=datetime_dtype)
+    gtd = cudf.Index(timedelta_data, dtype=timedelta_dtype)
+
+    pdt = gdt.to_pandas()
+    ptd = gtd.to_pandas()
+
+    assert_eq(gdt - gtd, pdt - ptd)
+    assert_eq(gdt + gtd, pdt + ptd)
+
+
+@pytest.mark.parametrize("data", _TIMEDELTA_DATA_NON_OVERFLOW)
+@pytest.mark.parametrize(
+    "other_scalars",
+    [
+        pd.Timedelta(1513393355.5, unit="s"),
+        pd.Timedelta(34765, unit="D"),
+        datetime.timedelta(days=768),
+        datetime.timedelta(seconds=768),
+        datetime.timedelta(microseconds=7),
+        datetime.timedelta(minutes=447),
+        datetime.timedelta(hours=447),
+        datetime.timedelta(weeks=734),
+        np.timedelta64(4, "s"),
+        np.timedelta64(456, "D"),
+        np.timedelta64(46, "h"),
+        np.timedelta64("nat"),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64(1, "ns"),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "op",
+    [
+        "add",
+        "sub",
+        "truediv",
+        "floordiv",
+    ],
+)
+@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning:pandas")
+def test_timedelta_index_ops_with_scalars(
+    request, data, other_scalars, dtype, op
+):
+    gtdi = cudf.Index(data=data, dtype=dtype)
+    ptdi = gtdi.to_pandas()
+
+    if op == "add":
+        expected = ptdi + other_scalars
+        actual = gtdi + other_scalars
+    elif op == "sub":
+        expected = ptdi - other_scalars
+        actual = gtdi - other_scalars
+    elif op == "truediv":
+        expected = ptdi / other_scalars
+        actual = gtdi / other_scalars
+    elif op == "floordiv":
+        expected = ptdi // other_scalars
+        actual = gtdi // other_scalars
+
+    assert_eq(expected, actual)
+
+    if op == "add":
+        expected = other_scalars + ptdi
+        actual = other_scalars + gtdi
+    elif op == "sub":
+        expected = other_scalars - ptdi
+        actual = other_scalars - gtdi
+    elif op == "truediv":
+        expected = other_scalars / ptdi
+        actual = other_scalars / gtdi
+    elif op == "floordiv":
+        expected = other_scalars // ptdi
+        actual = other_scalars // gtdi
+
+    # Division by zero for datetime or timedelta is
+    # dubiously defined in both pandas (Any // 0 -> 0 in
+    # pandas) and cuDF (undefined behaviour)
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                op == "floordiv"
+                and 0 in ptdi.astype("int")
+                and np.timedelta64(other_scalars).item() is not None
+            ),
+            reason="Related to https://github.com/rapidsai/cudf/issues/5938",
+        )
+    )
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", _TIMEDELTA_DATA_NON_OVERFLOW)
+@pytest.mark.parametrize(
+    "cpu_scalar",
+    [
+        pd.Timedelta(1513393355.5, unit="s"),
+        datetime.timedelta(seconds=768),
+        datetime.timedelta(microseconds=7),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64(1, "ns"),
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "op",
+    [
+        "add",
+        "sub",
+        "truediv",
+        "floordiv",
+    ],
+)
+def test_timedelta_index_ops_with_cudf_scalars(
+    request, data, cpu_scalar, dtype, op
+):
+    gtdi = cudf.Index(data=data, dtype=dtype)
+    ptdi = gtdi.to_pandas()
+
+    gpu_scalar = cudf.Scalar(cpu_scalar)
+
+    if op == "add":
+        expected = ptdi + cpu_scalar
+        actual = gtdi + gpu_scalar
+    elif op == "sub":
+        expected = ptdi - cpu_scalar
+        actual = gtdi - gpu_scalar
+    elif op == "truediv":
+        expected = ptdi / cpu_scalar
+        actual = gtdi / gpu_scalar
+    elif op == "floordiv":
+        expected = ptdi // cpu_scalar
+        actual = gtdi // gpu_scalar
+
+    assert_eq(expected, actual)
+
+    if op == "add":
+        expected = cpu_scalar + ptdi
+        actual = gpu_scalar + gtdi
+    elif op == "sub":
+        expected = cpu_scalar - ptdi
+        actual = gpu_scalar - gtdi
+    elif op == "truediv":
+        expected = cpu_scalar / ptdi
+        actual = gpu_scalar / gtdi
+    elif op == "floordiv":
+        expected = cpu_scalar // ptdi
+        actual = gpu_scalar // gtdi
+
+    # Division by zero for datetime or timedelta is
+    # dubiously defined in both pandas (Any // 0 -> 0 in
+    # pandas) and cuDF (undefined behaviour)
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                op == "floordiv"
+                and 0 in ptdi.astype("int")
+                and np.timedelta64(cpu_scalar).item() is not None
+            ),
+            reason="https://github.com/rapidsai/cudf/issues/5938",
+        )
+    )
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", _TIMEDELTA_DATA)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize("name", ["abcd", None])
+def test_timedelta_index_properties(data, dtype, name):
+    gdi = cudf.Index(data, dtype=dtype, name=name)
+    pdi = gdi.to_pandas()
+
+    def local_assert(expected, actual):
+        if actual._values.null_count:
+            assert_eq(expected, actual.astype("float64"))
+        else:
+            assert_eq(expected, actual)
+
+    expected_days = pdi.days
+    actual_days = gdi.days
+
+    local_assert(expected_days, actual_days)
+
+    expected_seconds = pdi.seconds
+    actual_seconds = gdi.seconds
+
+    local_assert(expected_seconds, actual_seconds)
+
+    expected_microseconds = pdi.microseconds
+    actual_microseconds = gdi.microseconds
+
+    local_assert(expected_microseconds, actual_microseconds)
+
+    expected_nanoseconds = pdi.nanoseconds
+    actual_nanoseconds = gdi.nanoseconds
+
+    local_assert(expected_nanoseconds, actual_nanoseconds)
+
+    expected_components = pdi.components
+    actual_components = gdi.components
+
+    if actual_components.isnull().any().any():
+        assert_eq(expected_components, actual_components.astype("float"))
+    else:
+        assert_eq(
+            expected_components,
+            actual_components,
+            check_index_type=not actual_components.empty,
+        )
+
+
+@pytest.mark.parametrize("data", _TIMEDELTA_DATA)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        np.timedelta64(4, "s"),
+        np.timedelta64(456, "D"),
+        np.timedelta64(46, "h"),
+        np.timedelta64("nat"),
+        np.timedelta64(1, "s"),
+        np.timedelta64(1, "ms"),
+        np.timedelta64(1, "us"),
+        np.timedelta64(1, "ns"),
+        "NaT",
+    ],
+)
+def test_timedelta_fillna(data, dtype, fill_value):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    expected = psr.dropna()
+    actual = sr.dropna()
+
+    assert_eq(expected, actual)
+
+    expected = psr.fillna(fill_value)
+    actual = sr.fillna(fill_value)
+    assert_eq(expected, actual)
+
+    expected = expected.dropna()
+    actual = actual.dropna()
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "gsr,expected_series",
+    [
+        (
+            cudf.Series([1, 2, 3], dtype="timedelta64[ns]"),
+            cudf.Series(
+                [
+                    "0 days 00:00:00.000000001",
+                    "0 days 00:00:00.000000002",
+                    "0 days 00:00:00.000000003",
+                ]
+            ),
+        ),
+        (
+            cudf.Series([1000000, 200000, 3000000], dtype="timedelta64[ms]"),
+            cudf.Series(
+                ["0 days 00:16:40", "0 days 00:03:20", "0 days 00:50:00"]
+            ),
+        ),
+        (
+            cudf.Series([1000000, 200000, 3000000], dtype="timedelta64[s]"),
+            cudf.Series(
+                ["11 days 13:46:40", "2 days 07:33:20", "34 days 17:20:00"]
+            ),
+        ),
+        (
+            cudf.Series(
+                [None, None, None, None, None], dtype="timedelta64[us]"
+            ),
+            cudf.Series([None, None, None, None, None], dtype="str"),
+        ),
+        (
+            cudf.Series(
+                [
+                    136457654,
+                    None,
+                    245345345,
+                    223432411,
+                    None,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[us]",
+            ),
+            cudf.Series(
+                [
+                    "0 days 00:02:16.457654",
+                    None,
+                    "0 days 00:04:05.345345",
+                    "0 days 00:03:43.432411",
+                    None,
+                    "0 days 01:00:34.548734",
+                    "0 days 00:00:00.023234",
+                ]
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    136457654,
+                    None,
+                    245345345,
+                    223432411,
+                    None,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[ms]",
+            ),
+            cudf.Series(
+                [
+                    "1 days 13:54:17.654",
+                    None,
+                    "2 days 20:09:05.345",
+                    "2 days 14:03:52.411",
+                    None,
+                    "42 days 01:35:48.734",
+                    "0 days 00:00:23.234",
+                ]
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    136457654,
+                    None,
+                    245345345,
+                    223432411,
+                    None,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[s]",
+            ),
+            cudf.Series(
+                [
+                    "1579 days 08:54:14",
+                    None,
+                    "2839 days 15:29:05",
+                    "2586 days 00:33:31",
+                    None,
+                    "42066 days 12:52:14",
+                    "0 days 06:27:14",
+                ]
+            ),
+        ),
+        (
+            cudf.Series(
+                [
+                    136457654,
+                    None,
+                    245345345,
+                    223432411,
+                    None,
+                    3634548734,
+                    23234,
+                ],
+                dtype="timedelta64[ns]",
+            ),
+            cudf.Series(
+                [
+                    "0 days 00:00:00.136457654",
+                    None,
+                    "0 days 00:00:00.245345345",
+                    "0 days 00:00:00.223432411",
+                    None,
+                    "0 days 00:00:03.634548734",
+                    "0 days 00:00:00.000023234",
+                ]
+            ),
+        ),
+    ],
+)
+def test_timedelta_str_roundtrip(gsr, expected_series):
+
+    actual_series = gsr.astype("str")
+
+    assert_eq(expected_series, actual_series)
+
+    assert_eq(gsr, actual_series.astype(gsr.dtype))
+
+
+def test_timedelta_invalid_ops():
+    sr = cudf.Series([1, 2, 3], dtype="timedelta64[ns]")
+    psr = sr.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([psr, 1],),
+        rfunc_args_and_kwargs=([sr, 1],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.add,
+        rfunc=operator.add,
+        lfunc_args_and_kwargs=([psr, "a"],),
+        rfunc_args_and_kwargs=([sr, "a"],),
+    )
+
+    dt_sr = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    dt_psr = dt_sr.to_pandas()
+
+    assert_exceptions_equal(
+        lfunc=operator.mod,
+        rfunc=operator.mod,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.mod,
+        rfunc=operator.mod,
+        lfunc_args_and_kwargs=([psr, "a"],),
+        rfunc_args_and_kwargs=([sr, "a"],),
+        check_exception_type=False,
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.gt,
+        rfunc=operator.gt,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.lt,
+        rfunc=operator.lt,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.ge,
+        rfunc=operator.ge,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.le,
+        rfunc=operator.le,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.truediv,
+        rfunc=operator.truediv,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.floordiv,
+        rfunc=operator.floordiv,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.mul,
+        rfunc=operator.mul,
+        lfunc_args_and_kwargs=([psr, dt_psr],),
+        rfunc_args_and_kwargs=([sr, dt_sr],),
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.mul,
+        rfunc=operator.mul,
+        lfunc_args_and_kwargs=([psr, psr],),
+        rfunc_args_and_kwargs=([sr, sr],),
+        check_exception_type=False,
+    )
+
+    assert_exceptions_equal(
+        lfunc=operator.xor,
+        rfunc=operator.xor,
+        lfunc_args_and_kwargs=([psr, psr],),
+        rfunc_args_and_kwargs=([sr, sr],),
+    )
+
+
+def test_timedelta_datetime_cast_invalid():
+    sr = cudf.Series([1, 2, 3], dtype="timedelta64[ns]")
+    psr = sr.to_pandas()
+
+    assert_exceptions_equal(
+        psr.astype,
+        sr.astype,
+        (["datetime64[ns]"],),
+        (["datetime64[ns]"],),
+    )
+
+    sr = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    psr = sr.to_pandas()
+
+    assert_exceptions_equal(
+        psr.astype,
+        sr.astype,
+        (["timedelta64[ns]"],),
+        (["timedelta64[ns]"],),
+    )
+
+
+@pytest.mark.parametrize("data", [[], [1, 2, 3, 4, 5]])
+@pytest.mark.parametrize("dtype", utils.NUMERIC_TYPES)
+@pytest.mark.parametrize("timedelta_dtype", utils.TIMEDELTA_TYPES)
+def test_numeric_to_timedelta(data, dtype, timedelta_dtype):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    actual = sr.astype(timedelta_dtype)
+    expected = pd.Series(psr.to_numpy().astype(timedelta_dtype))
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", [[], [1, 2, 3, 4, 5]])
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize(
+    "scalar",
+    [
+        1,
+        2,
+        3,
+        "a",
+        np.timedelta64(1, "s"),
+        np.timedelta64(2, "s"),
+        np.timedelta64(2, "D"),
+        np.timedelta64(3, "ms"),
+        np.timedelta64(4, "us"),
+        np.timedelta64(5, "ns"),
+        np.timedelta64(6, "ns"),
+        np.datetime64(6, "s"),
+    ],
+)
+def test_timedelta_contains(data, dtype, scalar):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    expected = scalar in sr
+    actual = scalar in psr
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], [], [1, 20, 1000, None]])
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize("ddof", [1, 2, 3])
+def test_timedelta_std(data, dtype, ddof):
+    gsr = cudf.Series(data, dtype=dtype)
+    psr = gsr.to_pandas()
+
+    expected = psr.std(ddof=ddof)
+    actual = gsr.std(ddof=ddof)
+
+    if np.isnat(expected.to_numpy()) and np.isnat(actual.to_numpy()):
+        assert True
+    else:
+        np.testing.assert_allclose(
+            expected.to_numpy().astype("float64"),
+            actual.to_numpy().astype("float64"),
+            rtol=1e-5,
+            atol=0,
+        )
+
+
+@pytest.mark.parametrize("op", ["max", "min"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        [],
+        [1, 2, 3, 100],
+        [10, None, 100, None, None],
+        [None, None, None],
+        [1231],
+    ],
+)
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+def test_timedelta_reductions(data, op, dtype):
+    sr = cudf.Series(data, dtype=dtype)
+    psr = sr.to_pandas()
+
+    actual = getattr(sr, op)()
+    expected = getattr(psr, op)()
+
+    if np.isnat(expected.to_numpy()) and np.isnat(actual):
+        assert True
+    else:
+        assert_eq(expected.to_numpy(), actual)
+
+
+def test_error_values():
+    s = cudf.Series([1, 2, 3], dtype="timedelta64[ns]")
+    with pytest.raises(
+        NotImplementedError,
+        match="TimeDelta Arrays is not yet implemented in cudf",
+    ):
+        s.values
+
+
+@pytest.mark.parametrize("dtype", utils.TIMEDELTA_TYPES)
+@pytest.mark.parametrize("name", [None, "delta-index"])
+def test_create_TimedeltaIndex(dtype, name):
+    gdi = cudf.TimedeltaIndex(
+        [1132223, 2023232, 342234324, 4234324], dtype=dtype, name=name
+    )
+    pdi = gdi.to_pandas()
+    assert_eq(pdi, gdi)
+
+
+@pytest.mark.parametrize("data", [[43534, 43543, 37897, 2000]])
+@pytest.mark.parametrize("dtype", ["timedelta64[ns]"])
+def test_timedelta_constructor(data, dtype):
+    expected = pd.TimedeltaIndex(data=data, dtype=dtype)
+    actual = cudf.TimedeltaIndex(data=data, dtype=dtype)
+
+    assert_eq(expected, actual)
+
+    expected = pd.TimedeltaIndex(data=pd.Series(data), dtype=dtype)
+    actual = cudf.TimedeltaIndex(data=cudf.Series(data), dtype=dtype)
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("op", [operator.add, operator.sub])
+def test_timdelta_binop_tz_timestamp(op):
+    s = cudf.Series([1, 2, 3], dtype="timedelta64[ns]")
+    pd_tz_timestamp = pd.Timestamp("1970-01-01 00:00:00.000000001", tz="utc")
+    with pytest.raises(NotImplementedError):
+        op(s, pd_tz_timestamp)
+    date_tz_scalar = datetime.datetime.now(datetime.timezone.utc)
+    with pytest.raises(NotImplementedError):
+        op(s, date_tz_scalar)
+
+
+def test_timedelta_getitem_na():
+    s = cudf.Series([1, 2, None, 3], dtype="timedelta64[ns]")
+    assert s[2] is cudf.NaT
+
+
+@pytest.mark.parametrize("data1", [[123, 456, None, 321, None]])
+@pytest.mark.parametrize("data2", [[123, 456, 789, None, None]])
+@pytest.mark.parametrize("op", _cmpops)
+def test_timedelta_series_cmpops_pandas_compatibility(data1, data2, op):
+    gsr1 = cudf.Series(data=data1, dtype="timedelta64[ns]")
+    psr1 = gsr1.to_pandas()
+
+    gsr2 = cudf.Series(data=data2, dtype="timedelta64[ns]")
+    psr2 = gsr2.to_pandas()
+
+    expect = op(psr1, psr2)
+    with cudf.option_context("mode.pandas_compatible", True):
+        got = op(gsr1, gsr2)
+
+    assert_eq(expect, got)
diff --git a/python/cudf/cudf/tests/test_transform.py b/python/cudf/cudf/tests/test_transform.py
new file mode 100644
index 0000000..723bbdf
--- /dev/null
+++ b/python/cudf/cudf/tests/test_transform.py
@@ -0,0 +1,34 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+
+import numpy as np
+import pytest
+
+from cudf import Series
+from cudf.testing._utils import NUMERIC_TYPES
+
+supported_types = NUMERIC_TYPES
+
+
+def _generic_function(a):
+    return a**3
+
+
+@pytest.mark.parametrize("dtype", supported_types)
+@pytest.mark.parametrize(
+    "udf,testfunc",
+    [
+        (_generic_function, lambda ser: ser**3),
+        (lambda x: x in [1, 2, 3, 4], lambda ser: np.isin(ser, [1, 2, 3, 4])),
+    ],
+)
+def test_apply_python_lambda(dtype, udf, testfunc):
+
+    size = 500
+
+    lhs_arr = np.random.random(size).astype(dtype)
+    lhs_ser = Series(lhs_arr)
+
+    out_ser = lhs_ser.apply(udf)
+    result = testfunc(lhs_arr)
+    np.testing.assert_almost_equal(result, out_ser.to_numpy())
diff --git a/python/cudf/cudf/tests/test_udf_binops.py b/python/cudf/cudf/tests/test_udf_binops.py
new file mode 100644
index 0000000..1ad45e7
--- /dev/null
+++ b/python/cudf/cudf/tests/test_udf_binops.py
@@ -0,0 +1,51 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pytest
+from numba.cuda import compile_ptx
+from numba.np import numpy_support
+
+import rmm
+
+import cudf
+from cudf import Series, _lib as libcudf
+from cudf.utils import dtypes as dtypeutils
+
+_driver_version = rmm._cuda.gpu.driverGetVersion()
+_runtime_version = rmm._cuda.gpu.runtimeGetVersion()
+_CUDA_JIT128INT_SUPPORTED = (_driver_version >= 11050) and (
+    _runtime_version >= 11050
+)
+
+
+@pytest.mark.skipif(not _CUDA_JIT128INT_SUPPORTED, reason="requires CUDA 11.5")
+@pytest.mark.parametrize(
+    "dtype", sorted(list(dtypeutils.NUMERIC_TYPES - {"int8"}))
+)
+def test_generic_ptx(dtype):
+
+    size = 500
+
+    lhs_arr = np.random.random(size).astype(dtype)
+    lhs_col = Series(lhs_arr)._column
+
+    rhs_arr = np.random.random(size).astype(dtype)
+    rhs_col = Series(rhs_arr)._column
+
+    def generic_function(a, b):
+        return a**3 + b
+
+    nb_type = numpy_support.from_dtype(cudf.dtype(dtype))
+    type_signature = (nb_type, nb_type)
+
+    ptx_code, output_type = compile_ptx(
+        generic_function, type_signature, device=True
+    )
+
+    dtype = numpy_support.as_dtype(output_type).type
+
+    out_col = libcudf.binaryop.binaryop_udf(lhs_col, rhs_col, ptx_code, dtype)
+
+    result = lhs_arr**3 + rhs_arr
+
+    np.testing.assert_almost_equal(result, out_col.values_host)
diff --git a/python/cudf/cudf/tests/test_udf_masked_ops.py b/python/cudf/cudf/tests/test_udf_masked_ops.py
new file mode 100644
index 0000000..85531f8
--- /dev/null
+++ b/python/cudf/cudf/tests/test_udf_masked_ops.py
@@ -0,0 +1,994 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+import math
+import operator
+
+import numpy as np
+import pytest
+from numba import cuda
+
+import cudf
+from cudf.core.missing import NA
+from cudf.core.udf._ops import (
+    arith_ops,
+    bitwise_ops,
+    comparison_ops,
+    unary_ops,
+)
+from cudf.core.udf.api import Masked
+from cudf.core.udf.utils import precompiled
+from cudf.testing._utils import (
+    _decimal_series,
+    assert_eq,
+    parametrize_numeric_dtypes_pairwise,
+    sv_to_udf_str,
+)
+
+
+@pytest.fixture(scope="module")
+def str_udf_data():
+    return cudf.DataFrame(
+        {
+            "str_col": [
+                "abc",
+                "ABC",
+                "AbC",
+                "123",
+                "123aBc",
+                "123@.!",
+                "",
+                "rapids ai",
+                "gpu",
+                "True",
+                "False",
+                "1.234",
+                ".123a",
+                "0.013",
+                "1.0",
+                "01",
+                "20010101",
+                "cudf",
+                "cuda",
+                "gpu",
+                "This Is A Title",
+                "This is Not a Title",
+                "Neither is This a Title",
+                "NoT a TiTlE",
+                "123 Title Works",
+            ]
+        }
+    )
+
+
+@pytest.fixture(params=["a", "cu", "2", "gpu", "", " "])
+def substr(request):
+    return request.param
+
+
+def run_masked_udf_test(func, data, args=(), **kwargs):
+    gdf = data
+    pdf = data.to_pandas(nullable=True)
+
+    expect = pdf.apply(func, args=args, axis=1)
+    obtain = gdf.apply(func, args=args, axis=1)
+    assert_eq(expect, obtain, **kwargs)
+
+
+def run_masked_string_udf_test(func, data, args=(), **kwargs):
+
+    gdf = data
+    pdf = data.to_pandas(nullable=True)
+
+    def row_wrapper(row):
+        st = row["str_col"]
+        return func(st)
+
+    expect = pdf.apply(row_wrapper, args=args, axis=1)
+
+    func = cuda.jit(device=True)(func)
+    obtain = gdf.apply(row_wrapper, args=args, axis=1)
+    assert_eq(expect, obtain, **kwargs)
+
+    # strings that come directly from input columns are backed by
+    # MaskedType(string_view) types. But new strings that are returned
+    # from functions or operators are backed by MaskedType(udf_string)
+    # types. We need to make sure all of our methods work on both kind
+    # of MaskedType. This function promotes the former to the latter
+    # prior to running the input function
+    def udf_string_wrapper(row):
+        masked_udf_str = Masked(
+            sv_to_udf_str(row["str_col"].value), row["str_col"].valid
+        )
+        return func(masked_udf_str)
+
+    obtain = gdf.apply(udf_string_wrapper, args=args, axis=1)
+    assert_eq(expect, obtain, **kwargs)
+
+
+def run_masked_udf_series(func, data, args=(), **kwargs):
+    gsr = data
+    psr = data.to_pandas(nullable=True)
+
+    expect = psr.apply(func, args=args)
+    obtain = gsr.apply(func, args=args)
+    assert_eq(expect, obtain, **kwargs)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+def test_arith_masked_vs_masked(op):
+    # This test should test all the typing
+    # and lowering for arithmetic ops between
+    # two columns
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        return op(x, y)
+
+    gdf = cudf.DataFrame({"a": [1, None, 3, None], "b": [4, 5, None, None]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", bitwise_ops)
+def test_bitwise_masked_vs_masked(op):
+    # This test should test all the typing
+    # and lowering for bitwise ops between
+    # two columns
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        return op(x, y)
+
+    gdf = cudf.DataFrame(
+        {
+            "a": [1, 0, 1, 0, 0b1011, 42, None],
+            "b": [1, 1, 0, 0, 0b1100, -42, 5],
+        }
+    )
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "dtype_l",
+    ["datetime64[ns]", "datetime64[us]", "datetime64[ms]", "datetime64[s]"],
+)
+@pytest.mark.parametrize(
+    "dtype_r",
+    [
+        "timedelta64[ns]",
+        "timedelta64[us]",
+        "timedelta64[ms]",
+        "timedelta64[s]",
+        "datetime64[ns]",
+        "datetime64[ms]",
+        "datetime64[us]",
+        "datetime64[s]",
+    ],
+)
+@pytest.mark.parametrize("op", [operator.add, operator.sub])
+def test_arith_masked_vs_masked_datelike(op, dtype_l, dtype_r):
+    # Datetime version of the above
+    # does not test all dtype combinations for now
+    if "datetime" in dtype_l and "datetime" in dtype_r and op is operator.add:
+        # don't try adding datetimes to datetimes.
+        pytest.skip("Adding datetime to datetime is not valid")
+
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        return op(x, y)
+
+    gdf = cudf.DataFrame(
+        {
+            "a": ["2011-01-01", cudf.NA, "2011-03-01", cudf.NA],
+            "b": [4, 5, cudf.NA, cudf.NA],
+        }
+    )
+    gdf["a"] = gdf["a"].astype(dtype_l)
+    gdf["b"] = gdf["b"].astype(dtype_r)
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", comparison_ops)
+def test_compare_masked_vs_masked(op):
+    # this test should test all the
+    # typing and lowering for comparisons
+    # between columns
+
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        return op(x, y)
+
+    # we should get:
+    # [?, ?, <NA>, <NA>, <NA>]
+    gdf = cudf.DataFrame(
+        {"a": [1, 0, None, 1, None], "b": [0, 1, 0, None, None]}
+    )
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+@pytest.mark.parametrize("constant", [1, 1.5, True, False])
+@pytest.mark.parametrize("data", [[1, 2, cudf.NA]])
+def test_arith_masked_vs_constant(op, constant, data):
+    def func(row):
+        x = row["data"]
+        return op(x, constant)
+
+    gdf = cudf.DataFrame({"data": data})
+
+    if constant is False and op in {
+        operator.mod,
+        operator.pow,
+        operator.truediv,
+        operator.floordiv,
+        operator.imod,
+        operator.ipow,
+        operator.itruediv,
+        operator.ifloordiv,
+    }:
+        # The following tests cases yield undefined behavior:
+        # - truediv(x, False) because its dividing by zero
+        # - floordiv(x, False) because its dividing by zero
+        # - mod(x, False) because its mod by zero,
+        # - pow(x, False) because we have an NA in the series and pandas
+        #   insists that (NA**0 == 1) where we do not
+        pytest.skip()
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+@pytest.mark.parametrize("constant", [1, 1.5, True, False])
+@pytest.mark.parametrize("data", [[2, 3, cudf.NA], [1, cudf.NA, 1]])
+def test_arith_masked_vs_constant_reflected(request, op, constant, data):
+    def func(row):
+        x = row["data"]
+        return op(constant, x)
+
+    # Just a single column -> result will be all NA
+    gdf = cudf.DataFrame({"data": data})
+
+    # cudf differs from pandas for 1**NA
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(constant == 1 and op in {operator.pow, operator.ipow}),
+            reason="https://github.com/rapidsai/cudf/issues/7478",
+        )
+    )
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+@pytest.mark.parametrize("data", [[1, cudf.NA, 3], [2, 3, cudf.NA]])
+def test_arith_masked_vs_null(request, op, data):
+    def func(row):
+        x = row["data"]
+        return op(x, NA)
+
+    gdf = cudf.DataFrame({"data": data})
+
+    # In pandas, 1**NA == 1.
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                (gdf["data"] == 1).any()
+                and op in {operator.pow, operator.ipow}
+            ),
+            reason="https://github.com/rapidsai/cudf/issues/7478",
+        )
+    )
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+def test_arith_masked_vs_null_reflected(op):
+    def func(row):
+        x = row["data"]
+        return op(NA, x)
+
+    gdf = cudf.DataFrame({"data": [1, None, 3]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", unary_ops)
+def test_unary_masked(op):
+    # This test should test all the typing
+    # and lowering for unary ops
+
+    def func(row):
+        x = row["a"]
+        return op(x) if x is not NA else NA
+
+    if "log" in op.__name__:
+        gdf = cudf.DataFrame({"a": [0.1, 1.0, None, 3.5, 1e8]})
+    elif op.__name__ in {"asin", "acos"}:
+        gdf = cudf.DataFrame({"a": [0.0, 0.5, None, 1.0]})
+    elif op.__name__ in {"atanh"}:
+        gdf = cudf.DataFrame({"a": [0.0, -0.5, None, 0.8]})
+    elif op.__name__ in {"acosh", "sqrt", "lgamma"}:
+        gdf = cudf.DataFrame({"a": [1.0, 2.0, None, 11.0]})
+    elif op.__name__ in {"gamma"}:
+        gdf = cudf.DataFrame({"a": [0.1, 2, None, 4]})
+    elif op.__name__ in {"invert"}:
+        gdf = cudf.DataFrame({"a": [-100, 128, None, 0]}, dtype="int64")
+    else:
+        gdf = cudf.DataFrame({"a": [-125.60, 395.2, 0.0, None]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+def test_masked_is_null_conditional():
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        if x is NA:
+            return y
+        else:
+            return x + y
+
+    gdf = cudf.DataFrame({"a": [1, None, 3, None], "b": [4, 5, None, None]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+def test_apply_contains():
+    def func(row):
+        x = row["a"]
+        return x in [1, 2]
+
+    gdf = cudf.DataFrame({"a": [1, 3]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@parametrize_numeric_dtypes_pairwise
+@pytest.mark.parametrize("op", [operator.add, operator.and_, operator.eq])
+def test_apply_mixed_dtypes(left_dtype, right_dtype, op):
+    """
+    Test that operations can be performed between columns
+    of different dtypes and return a column with the correct
+    values and nulls
+    """
+
+    # First perform the op on two dummy data on host, if numpy can
+    # safely type cast, we should expect it to work in udf too.
+    try:
+        op(np.dtype(left_dtype).type(0), np.dtype(right_dtype).type(42))
+    except TypeError:
+        pytest.skip("Operation is unsupported for corresponding dtype.")
+
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        return op(x, y)
+
+    gdf = cudf.DataFrame({"a": [1.5, None, 3, None], "b": [4, 5, None, None]})
+    gdf["a"] = gdf["a"].astype(left_dtype)
+    gdf["b"] = gdf["b"].astype(right_dtype)
+
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+@pytest.mark.parametrize("val", [5, 5.5])
+def test_apply_return_literal(val):
+    """
+    Test unification codepath for scalars and MaskedType
+    makes sure that numba knows how to cast a scalar value
+    to a MaskedType
+    """
+
+    def func(row):
+        x = row["a"]
+        y = row["b"]
+        if x is not NA and x < 2:
+            return val
+        else:
+            return x + y
+
+    gdf = cudf.DataFrame({"a": [1, None, 3, None], "b": [4, 5, None, None]})
+
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+def test_apply_return_null():
+    """
+    Tests casting / unification of Masked and NA
+    """
+
+    def func(row):
+        x = row["a"]
+        if x is NA:
+            return NA
+        else:
+            return x
+
+    gdf = cudf.DataFrame({"a": [1, None, 3]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+def test_apply_return_either_null_or_literal():
+    def func(row):
+        x = row["a"]
+        if x > 5:
+            return 2
+        else:
+            return NA
+
+    gdf = cudf.DataFrame({"a": [1, 3, 6]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+def test_apply_return_literal_only():
+    def func(x):
+        return 5
+
+    gdf = cudf.DataFrame({"a": [1, None, 3]})
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+def test_apply_everything():
+    def func(row):
+        w = row["a"]
+        x = row["b"]
+        y = row["c"]
+        z = row["d"]
+        if x is NA:
+            return w + y - z
+        elif ((z > y) is not NA) and z > y:
+            return x
+        elif ((x + y) is not NA) and x + y == 0:
+            return z / x
+        elif x + y is NA:
+            return 2.5
+        elif w > 100:
+            return (
+                math.sin(x)
+                + math.sqrt(y)
+                - (-z)
+                + math.lgamma(x) * math.fabs(-0.8) / math.radians(3.14)
+            )
+        else:
+            return y > 2
+
+    gdf = cudf.DataFrame(
+        {
+            "a": [1, 3, 6, 0, None, 5, None, 101],
+            "b": [3.0, 2.5, None, 5.0, 1.0, 5.0, 11.0, 1.0],
+            "c": [2, 3, 6, 0, None, 5, None, 6],
+            "d": [4, None, 6, 0, None, 5, None, 7.5],
+        }
+    )
+    run_masked_udf_test(func, gdf, check_dtype=False)
+
+
+###
+
+
+@pytest.mark.parametrize(
+    "data,name",
+    [([1, 2, 3], None), ([1, cudf.NA, 3], None), ([1, 2, 3], "test_name")],
+)
+def test_series_apply_basic(data, name):
+    data = cudf.Series(data, name=name)
+
+    def func(x):
+        return x + 1
+
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+def test_series_apply_null_conditional():
+    def func(x):
+        if x is NA:
+            return 42
+        else:
+            return x - 1
+
+    data = cudf.Series([1, cudf.NA, 3])
+
+    run_masked_udf_series(func, data)
+
+
+###
+
+
+@pytest.mark.parametrize("op", arith_ops)
+def test_series_arith_masked_vs_masked(op):
+    def func(x):
+        return op(x, x)
+
+    data = cudf.Series([1, cudf.NA, 3])
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", comparison_ops)
+def test_series_compare_masked_vs_masked(op):
+    """
+    In the series case, only one other MaskedType to compare with
+    - itself
+    """
+
+    def func(x):
+        return op(x, x)
+
+    data = cudf.Series([1, cudf.NA, 3])
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+@pytest.mark.parametrize("constant", [1, 1.5, cudf.NA])
+def test_series_arith_masked_vs_constant(request, op, constant):
+    def func(x):
+        return op(x, constant)
+
+    # Just a single column -> result will be all NA
+    data = cudf.Series([1, 2, cudf.NA])
+    # in pandas, 1**NA == 1. In cudf, 1**NA == NA.
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                constant is cudf.NA and op in {operator.pow, operator.ipow}
+            ),
+            reason="https://github.com/rapidsai/cudf/issues/7478",
+        )
+    )
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops)
+@pytest.mark.parametrize("constant", [1, 1.5, cudf.NA])
+def test_series_arith_masked_vs_constant_reflected(request, op, constant):
+    def func(x):
+        return op(constant, x)
+
+    # Just a single column -> result will be all NA
+    data = cudf.Series([1, 2, cudf.NA])
+    # Using in {1} since bool(NA == 1) raises a TypeError since NA is
+    # neither truthy nor falsy
+    # in pandas, 1**NA == 1. In cudf, 1**NA == NA.
+    request.applymarker(
+        pytest.mark.xfail(
+            condition=(
+                constant in {1} and op in {operator.pow, operator.ipow}
+            ),
+            reason="https://github.com/rapidsai/cudf/issues/7478",
+        )
+    )
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+def test_series_masked_is_null_conditional():
+    def func(x):
+        if x is NA:
+            return 42
+        else:
+            return x
+
+    data = cudf.Series([1, cudf.NA, 3, cudf.NA])
+
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops + comparison_ops)
+def test_masked_udf_lambda_support(op):
+    func = lambda row: op(row["a"], row["b"])  # noqa: E731
+
+    data = cudf.DataFrame(
+        {"a": [1, cudf.NA, 3, cudf.NA], "b": [1, 2, cudf.NA, cudf.NA]}
+    )
+
+    run_masked_udf_test(func, data, check_dtype=False)
+
+
+@pytest.mark.parametrize("op", arith_ops + comparison_ops)
+def test_masked_udf_nested_function_support(op):
+    """
+    Nested functions need to be explicitly jitted by the user
+    for numba to recognize them. Unfortunately the object
+    representing the jitted function can not itself be used in
+    pandas udfs.
+    """
+
+    def inner(x, y):
+        return op(x, y)
+
+    def outer(row):
+        x = row["a"]
+        y = row["b"]
+        return inner(x, y)
+
+    gdf = cudf.DataFrame(
+        {"a": [1, cudf.NA, 3, cudf.NA], "b": [1, 2, cudf.NA, cudf.NA]}
+    )
+
+    with pytest.raises(ValueError):
+        gdf.apply(outer, axis=1)
+
+    pdf = gdf.to_pandas(nullable=True)
+    inner_gpu = cuda.jit(device=True)(inner)
+
+    def outer_gpu(row):
+        x = row["a"]
+        y = row["b"]
+        return inner_gpu(x, y)
+
+    got = gdf.apply(outer_gpu, axis=1)
+    expect = pdf.apply(outer, axis=1)
+    assert_eq(expect, got, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]},
+        {"a": [1, 2, 3], "c": [4, 5, 6], "b": [7, 8, 9]},
+        {"a": [1, 2, 3], "b": [4, 5, 6], "c": ["a", "b", "c"]},
+    ],
+)
+def test_masked_udf_subset_selection(data):
+    def func(row):
+        return row["a"] + row["b"]
+
+    data = cudf.DataFrame(data)
+    run_masked_udf_test(func, data)
+
+
+@pytest.mark.parametrize(
+    "unsupported_col",
+    [
+        _decimal_series(
+            ["1.0", "2.0", "3.0"], dtype=cudf.Decimal64Dtype(2, 1)
+        ),
+        cudf.Series([1, 2, 3], dtype="category"),
+        cudf.interval_range(start=0, end=3, closed=True),
+        [[1, 2], [3, 4], [5, 6]],
+        [{"a": 1}, {"a": 2}, {"a": 3}],
+    ],
+)
+def test_masked_udf_unsupported_dtype(unsupported_col):
+    data = cudf.DataFrame()
+    data["unsupported_col"] = unsupported_col
+
+    def func(row):
+        return row["unsupported_col"]
+
+    # check that we fail when an unsupported type is used within a function
+    with pytest.raises(ValueError):
+        data.apply(func, axis=1)
+
+    # also check that a DF containing unsupported dtypes can still run a
+    # function that does NOT involve any of the unsupported dtype columns
+    data["supported_col"] = 1
+
+    def other_func(row):
+        return row["supported_col"]
+
+    expect = cudf.Series(np.ones(len(data)))
+    got = data.apply(other_func, axis=1)
+
+    assert_eq(expect, got, check_dtype=False)
+
+
+# tests for `DataFrame.apply(f, args=(x,y,z))`
+# testing the whole space of possibilities is intractable
+# these test the most rudimentary guaranteed functionality
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, cudf.NA, 3]},
+        {"a": [0.5, 2.0, cudf.NA, cudf.NA, 5.0]},
+        {"a": [True, False, cudf.NA]},
+    ],
+)
+@pytest.mark.parametrize("op", arith_ops + comparison_ops)
+def test_masked_udf_scalar_args_binops(data, op):
+    data = cudf.DataFrame(data)
+
+    def func(row, c):
+        return op(row["a"], c)
+
+    run_masked_udf_test(func, data, args=(1,), check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [1, cudf.NA, 3]},
+        {"a": [0.5, 2.0, cudf.NA, cudf.NA, 5.0]},
+        {"a": [True, False, cudf.NA]},
+    ],
+)
+@pytest.mark.parametrize("op", arith_ops + comparison_ops)
+def test_masked_udf_scalar_args_binops_multiple(data, op):
+    data = cudf.DataFrame(data)
+
+    def func(row, c, k):
+        x = op(row["a"], c)
+        y = op(x, k)
+        return y
+
+    run_masked_udf_test(func, data, args=(1, 2), check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, cudf.NA, 3],
+        [0.5, 2.0, cudf.NA, cudf.NA, 5.0],
+        [True, False, cudf.NA],
+    ],
+)
+@pytest.mark.parametrize("op", arith_ops + comparison_ops)
+def test_mask_udf_scalar_args_binops_series(data, op):
+    data = cudf.Series(data)
+
+    def func(x, c):
+        return x + c
+
+    run_masked_udf_series(func, data, args=(1,), check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, cudf.NA, 3],
+        [0.5, 2.0, cudf.NA, cudf.NA, 5.0],
+        [True, False, cudf.NA],
+    ],
+)
+@pytest.mark.parametrize("op", arith_ops + comparison_ops)
+def test_masked_udf_scalar_args_binops_multiple_series(data, op):
+    data = cudf.Series(data)
+
+    def func(data, c, k):
+        x = op(data, c)
+        y = op(x, k)
+        return y
+
+    run_masked_udf_series(func, data, args=(1, 2), check_dtype=False)
+
+
+def test_masked_udf_caching():
+    # Make sure similar functions that differ
+    # by simple things like constants actually
+    # recompile
+
+    data = cudf.Series([1, 2, 3])
+
+    expect = data**2
+    got = data.apply(lambda x: x**2)
+    assert_eq(expect, got, check_dtype=False)
+
+    # update the constant value being used and make sure
+    # it does not result in a cache hit
+
+    expect = data**3
+    got = data.apply(lambda x: x**3)
+    assert_eq(expect, got, check_dtype=False)
+
+    # make sure we get a hit when reapplying
+    def f(x):
+        return x + 1
+
+    precompiled.clear()
+    assert precompiled.currsize == 0
+    data.apply(f)
+
+    assert precompiled.currsize == 1
+    data.apply(f)
+
+    assert precompiled.currsize == 1
+
+    # validate that changing the type of a scalar arg
+    # results in a miss
+    precompiled.clear()
+
+    def f(x, c):
+        return x + c
+
+    data.apply(f, args=(1,))
+    assert precompiled.currsize == 1
+
+    data.apply(f, args=(1.5,))
+    assert precompiled.currsize == 2
+
+
+@pytest.mark.parametrize(
+    "data", [[1.0, 0.0, 1.5], [1, 0, 2], [True, False, True]]
+)
+@pytest.mark.parametrize("operator", [float, int, bool])
+def test_masked_udf_casting(operator, data):
+    data = cudf.Series(data)
+
+    def func(x):
+        return operator(x)
+
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        np.array(
+            [0, 1, -1, 0, np.iinfo("int64").min, np.iinfo("int64").max],
+            dtype="int64",
+        ),
+        np.array([0, 0, 1, np.iinfo("uint64").max], dtype="uint64"),
+        np.array(
+            [
+                0,
+                0.0,
+                -1.0,
+                1.5,
+                -1.5,
+                np.finfo("float64").min,
+                np.finfo("float64").max,
+                np.nan,
+                np.inf,
+                -np.inf,
+            ],
+            dtype="float64",
+        ),
+        [False, True, False, cudf.NA],
+    ],
+)
+def test_masked_udf_abs(data):
+    data = cudf.Series(data)
+    data[0] = cudf.NA
+
+    def func(x):
+        return abs(x)
+
+    run_masked_udf_series(func, data, check_dtype=False)
+
+
+class TestStringUDFs:
+    def test_string_udf_len(self, str_udf_data):
+        def func(row):
+            return len(row["str_col"])
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_startswith(self, str_udf_data, substr):
+        def func(row):
+            return row["str_col"].startswith(substr)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_endswith(self, str_udf_data, substr):
+        def func(row):
+            return row["str_col"].endswith(substr)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_find(self, str_udf_data, substr):
+        def func(row):
+            return row["str_col"].find(substr)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_rfind(self, str_udf_data, substr):
+        def func(row):
+            return row["str_col"].rfind(substr)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_contains(self, str_udf_data, substr):
+        def func(row):
+            return substr in row["str_col"]
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    @pytest.mark.parametrize("other", ["cudf", "123", "", " "])
+    @pytest.mark.parametrize("cmpop", comparison_ops)
+    def test_string_udf_cmpops(self, str_udf_data, other, cmpop):
+        def func(row):
+            return cmpop(row["str_col"], other)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_isalnum(self, str_udf_data):
+        def func(row):
+            return row["str_col"].isalnum()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_isalpha(self, str_udf_data):
+        def func(row):
+            return row["str_col"].isalpha()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_isdigit(self, str_udf_data):
+        def func(row):
+            return row["str_col"].isdigit()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_isdecimal(self, str_udf_data):
+        def func(row):
+            return row["str_col"].isdecimal()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_isupper(self, str_udf_data):
+        def func(row):
+            return row["str_col"].isupper()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_islower(self, str_udf_data):
+        def func(row):
+            return row["str_col"].islower()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_isspace(self, str_udf_data):
+        def func(row):
+            return row["str_col"].isspace()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_istitle(self, str_udf_data):
+        def func(row):
+            return row["str_col"].istitle()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_count(self, str_udf_data, substr):
+        def func(row):
+            return row["str_col"].count(substr)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_return_string(self, str_udf_data):
+        def func(row):
+            return row["str_col"]
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    @pytest.mark.parametrize("strip_char", ["1", "a", "12", " ", "", ".", "@"])
+    def test_string_udf_strip(self, str_udf_data, strip_char):
+        def func(row):
+            return row["str_col"].strip(strip_char)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    @pytest.mark.parametrize("strip_char", ["1", "a", "12", " ", "", ".", "@"])
+    def test_string_udf_lstrip(self, str_udf_data, strip_char):
+        def func(row):
+            return row["str_col"].lstrip(strip_char)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    @pytest.mark.parametrize("strip_char", ["1", "a", "12", " ", "", ".", "@"])
+    def test_string_udf_rstrip(self, str_udf_data, strip_char):
+        def func(row):
+            return row["str_col"].rstrip(strip_char)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_upper(self, str_udf_data):
+        def func(row):
+            return row["str_col"].upper()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    def test_string_udf_lower(self, str_udf_data):
+        def func(row):
+            return row["str_col"].lower()
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    @pytest.mark.parametrize(
+        "concat_char", ["1", "a", "12", " ", "", ".", "@"]
+    )
+    def test_string_udf_concat(self, str_udf_data, concat_char):
+        def func(row):
+            return row["str_col"] + concat_char
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
+
+    @pytest.mark.parametrize("to_replace", ["a", "1", "", "@"])
+    @pytest.mark.parametrize("replacement", ["a", "1", "", "@"])
+    def test_string_udf_replace(self, str_udf_data, to_replace, replacement):
+        def func(row):
+            return row["str_col"].replace(to_replace, replacement)
+
+        run_masked_udf_test(func, str_udf_data, check_dtype=False)
diff --git a/python/cudf/cudf/tests/test_unaops.py b/python/cudf/cudf/tests/test_unaops.py
new file mode 100644
index 0000000..15d9d03
--- /dev/null
+++ b/python/cudf/cudf/tests/test_unaops.py
@@ -0,0 +1,131 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import itertools
+import operator
+import re
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import cudf
+from cudf import Series
+from cudf.testing import _utils as utils
+
+_unaops = [operator.abs, operator.invert, operator.neg, np.ceil, np.floor]
+
+
+@pytest.mark.parametrize("dtype", utils.NUMERIC_TYPES)
+def test_series_abs(dtype):
+    arr = (np.random.random(1000) * 100).astype(dtype)
+    sr = Series(arr)
+    np.testing.assert_equal(sr.abs().to_numpy(), np.abs(arr))
+    np.testing.assert_equal(abs(sr).to_numpy(), abs(arr))
+
+
+@pytest.mark.parametrize("dtype", utils.INTEGER_TYPES)
+def test_series_invert(dtype):
+    arr = (np.random.random(1000) * 100).astype(dtype)
+    sr = Series(arr)
+    np.testing.assert_equal((~sr).to_numpy(), np.invert(arr))
+    np.testing.assert_equal((~sr).to_numpy(), ~arr)
+
+
+def test_series_neg():
+    arr = np.random.random(100) * 100
+    sr = Series(arr)
+    np.testing.assert_equal((-sr).to_numpy(), -arr)
+
+
+@pytest.mark.parametrize("mth", ["min", "max", "sum", "product"])
+def test_series_pandas_methods(mth):
+    np.random.seed(0)
+    arr = (1 + np.random.random(5) * 100).astype(np.int64)
+    sr = Series(arr)
+    psr = pd.Series(arr)
+    np.testing.assert_equal(getattr(sr, mth)(), getattr(psr, mth)())
+
+
+@pytest.mark.parametrize("mth", ["min", "max", "sum", "product", "quantile"])
+def test_series_pandas_methods_empty(mth):
+    arr = np.array([])
+    sr = Series(arr)
+    psr = pd.Series(arr)
+    np.testing.assert_equal(getattr(sr, mth)(), getattr(psr, mth)())
+
+
+def generate_valid_scalar_unaop_combos():
+    results = []
+
+    # All ops valid for integer values
+    int_values = [0, 1, -1]
+    int_dtypes = utils.INTEGER_TYPES
+    int_ops = _unaops
+
+    results += list(itertools.product(int_values, int_dtypes, int_ops))
+
+    float_values = [0.0, 1.0, -1.1]
+    float_dtypes = utils.FLOAT_TYPES
+    float_ops = [op for op in _unaops if op is not operator.invert]
+    results += list(itertools.product(float_values, float_dtypes, float_ops))
+
+    bool_values = [True, False]
+    bool_dtypes = ["bool"]
+    bool_ops = [op for op in _unaops if op is not operator.neg]
+    results += list(itertools.product(bool_values, bool_dtypes, bool_ops))
+
+    return results
+
+
+@pytest.mark.filterwarnings("ignore:overflow encountered in scalar negative")
+@pytest.mark.parametrize("slr,dtype,op", generate_valid_scalar_unaop_combos())
+def test_scalar_unary_operations(slr, dtype, op):
+    slr_host = np.array([slr])[0].astype(cudf.dtype(dtype))
+    slr_device = cudf.Scalar(slr, dtype=dtype)
+
+    expect = op(slr_host)
+    got = op(slr_device)
+
+    assert expect == got.value
+
+    # f16 for small ints with ceil and float
+    if expect.dtype == np.dtype("float16"):
+        assert got.dtype == np.dtype("float32")
+    else:
+        assert expect.dtype == got.dtype
+
+
+def test_scalar_logical():
+    T = cudf.Scalar(True)
+    F = cudf.Scalar(False)
+
+    assert T
+    assert not F
+
+    assert T and T
+    assert not (T and F)
+    assert not (F and T)
+    assert not (F and F)
+
+    assert T or T
+    assert T or F
+    assert F or T
+    assert not (F or F)
+
+
+def test_scalar_no_negative_bools():
+    x = cudf.Scalar(True)
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "Boolean scalars in cuDF do not "
+            "support negation, use logical not"
+        ),
+    ):
+        -x
+
+
+def test_series_bool_neg():
+    sr = Series([True, False, True, None, False, None, True, True])
+    psr = sr.to_pandas(nullable=True)
+    utils.assert_eq((-sr).to_pandas(nullable=True), -psr, check_dtype=True)
diff --git a/python/cudf/cudf/tests/text/__init__.py b/python/cudf/cudf/tests/text/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/text/test_subword_tokenizer.py b/python/cudf/cudf/tests/text/test_subword_tokenizer.py
new file mode 100644
index 0000000..ac17daa
--- /dev/null
+++ b/python/cudf/cudf/tests/text/test_subword_tokenizer.py
@@ -0,0 +1,238 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+import os
+
+import cupy
+import numpy as np
+import pytest
+
+import cudf
+from cudf.core.subword_tokenizer import SubwordTokenizer
+from cudf.testing._utils import assert_eq
+
+
+@pytest.fixture(scope="module")
+def datadir(datadir):
+    return os.path.join(datadir, "subword_tokenizer_data")
+
+
+def assert_equal_tokenization_outputs(hf_output, cudf_output):
+    assert (
+        np.sum(hf_output["input_ids"] != cudf_output["input_ids"].get()) == 0
+    )
+    assert (
+        np.sum(
+            hf_output["attention_mask"] != cudf_output["attention_mask"].get()
+        )
+        == 0
+    )
+
+
+@pytest.mark.parametrize("seq_len", [32, 64])
+@pytest.mark.parametrize("stride", [0, 15, 30])
+@pytest.mark.parametrize("add_special_tokens", [True, False])
+@pytest.mark.parametrize("do_lower_case", [True, False])
+def test_subword_tokenize(
+    seq_len, stride, add_special_tokens, do_lower_case, datadir
+):
+    with open(
+        os.path.join(datadir, "test_sentences.txt"), encoding="utf-8"
+    ) as file:
+        input_sentence_ls = [line.strip() for line in file]
+
+    vocab_dir = os.path.join(datadir, "bert_base_cased_sampled")
+
+    transformers = pytest.importorskip("transformers")
+
+    hf_tokenizer = transformers.BertTokenizer.from_pretrained(
+        vocab_dir, do_lower_case=do_lower_case
+    )
+
+    hf_output = hf_tokenizer(
+        input_sentence_ls,
+        max_length=seq_len,
+        stride=stride,
+        padding="max_length",
+        return_tensors="np",
+        truncation=True,
+        add_special_tokens=add_special_tokens,
+    )
+
+    vocab_hash = os.path.join(vocab_dir, "vocab-hash.txt")
+    str_series = cudf.Series(input_sentence_ls)
+    cudf_tokenizer = SubwordTokenizer(vocab_hash, do_lower_case=do_lower_case)
+    cudf_output = cudf_tokenizer(
+        str_series,
+        max_length=seq_len,
+        max_num_rows=len(str_series),
+        stride=stride,
+        padding="max_length",
+        return_tensors="cp",
+        truncation=True,
+        add_special_tokens=add_special_tokens,
+    )
+    assert_equal_tokenization_outputs(hf_output, cudf_output)
+
+
+def test_subword_tokenize_with_truncation(datadir):
+    vocab_dir = os.path.join(datadir, "bert_base_cased_sampled")
+    vocab_hash = os.path.join(vocab_dir, "vocab-hash.txt")
+    str_series = cudf.Series(["Test error"])
+    cudf_tokenizer = SubwordTokenizer(vocab_hash)
+
+    error_msg = (
+        "Adding special tokens is not supported with truncation = False. "
+        "Custom Cupy kernel can potentially "
+        "be used to add it. For reference "
+        "see: _bert_add_special_tokens"
+    )
+
+    with pytest.raises(NotImplementedError, match=error_msg):
+        cudf_tokenizer(
+            str_series,
+            max_length=64,
+            max_num_rows=len(str_series),
+            truncation=False,
+            add_special_tokens=True,
+        )
+
+
+def test_text_subword_tokenize(tmpdir):
+    sr = cudf.Series(
+        [
+            "This is a test",
+            "A test this is",
+            "Is test a this",
+            "Test   test",
+            "this   This",
+        ]
+    )
+    hash_file = tmpdir.mkdir("nvtext").join("tmp_hashed_vocab.txt")
+    content = "1\n0\n23\n"
+    coefficients = [65559] * 23
+    for c in coefficients:
+        content = content + str(c) + " 0\n"
+    # based on values from the bert_hash_table.txt file for the
+    # test words used here: 'this' 'is' 'a' test'
+    table = [0] * 23
+    table[0] = 3015668
+    table[1] = 6205475701751155871
+    table[5] = 6358029
+    table[16] = 451412625363
+    table[20] = 6206321707968235495
+    content = content + "23\n"
+    for v in table:
+        content = content + str(v) + "\n"
+    content = content + "100\n101\n102\n\n"
+    hash_file.write(content)
+
+    cudf_tokenizer = SubwordTokenizer(hash_file)
+
+    token_d = cudf_tokenizer(
+        sr, 8, 8, add_special_tokens=False, truncation=True
+    )
+    tokens, masks, metadata = (
+        token_d["input_ids"],
+        token_d["attention_mask"],
+        token_d["metadata"],
+    )
+    expected_tokens = cupy.asarray(
+        [
+            2023,
+            2003,
+            1037,
+            3231,
+            0,
+            0,
+            0,
+            0,
+            1037,
+            3231,
+            2023,
+            2003,
+            0,
+            0,
+            0,
+            0,
+            2003,
+            3231,
+            1037,
+            2023,
+            0,
+            0,
+            0,
+            0,
+            3231,
+            3231,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            2023,
+            2023,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ],
+        dtype=np.uint32,
+    )
+    expected_tokens = expected_tokens.reshape(-1, 8)
+    assert_eq(expected_tokens, tokens)
+
+    expected_masks = cupy.asarray(
+        [
+            1,
+            1,
+            1,
+            1,
+            0,
+            0,
+            0,
+            0,
+            1,
+            1,
+            1,
+            1,
+            0,
+            0,
+            0,
+            0,
+            1,
+            1,
+            1,
+            1,
+            0,
+            0,
+            0,
+            0,
+            1,
+            1,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            1,
+            1,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+        ],
+        dtype=np.uint32,
+    )
+    expected_masks = expected_masks.reshape(-1, 8)
+    assert_eq(expected_masks, masks)
+
+    expected_metadata = cupy.asarray(
+        [0, 0, 3, 1, 0, 3, 2, 0, 3, 3, 0, 1, 4, 0, 1], dtype=np.uint32
+    )
+    expected_metadata = expected_metadata.reshape(-1, 3)
+    assert_eq(expected_metadata, metadata)
diff --git a/python/cudf/cudf/tests/text/test_text_methods.py b/python/cudf/cudf/tests/text/test_text_methods.py
new file mode 100644
index 0000000..e565df8
--- /dev/null
+++ b/python/cudf/cudf/tests/text/test_text_methods.py
@@ -0,0 +1,1026 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import random
+import string
+
+import numpy as np
+import pytest
+
+import cudf
+from cudf.core.tokenize_vocabulary import TokenizeVocabulary
+from cudf.testing._utils import assert_eq
+
+
+def test_tokenize():
+    strings = cudf.Series(
+        [
+            "the quick fox jumped over the lazy dog",
+            "the siamésé cat jumped under the sofa",
+            None,
+            "",
+        ]
+    )
+
+    expected_values = [
+        "the",
+        "quick",
+        "fox",
+        "jumped",
+        "over",
+        "the",
+        "lazy",
+        "dog",
+        "the",
+        "siamésé",
+        "cat",
+        "jumped",
+        "under",
+        "the",
+        "sofa",
+    ]
+    expected_index = strings.index.repeat(strings.str.token_count())
+    expected = cudf.Series(expected_values, index=expected_index)
+
+    actual = strings.str.tokenize()
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_tokenize_delimiter():
+    strings = cudf.Series(
+        [
+            "the quick fox jumped over the lazy dog",
+            "the siamésé cat jumped under the sofa",
+            None,
+            "",
+        ]
+    )
+
+    expected_values = [
+        "the quick f",
+        "x jumped ",
+        "ver the lazy d",
+        "g",
+        "the siamésé cat jumped under the s",
+        "fa",
+    ]
+    expected_index = strings.index.repeat(strings.str.token_count("o"))
+    expected = cudf.Series(expected_values, index=expected_index)
+
+    actual = strings.str.tokenize(delimiter="o")
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_detokenize():
+    strings = cudf.Series(
+        [
+            "the",
+            "quick",
+            "fox",
+            "jumped",
+            "over",
+            "the",
+            "lazy",
+            "dog",
+            "the",
+            "siamésé",
+            "cat",
+            "jumped",
+            "under",
+            "the",
+            "sofa",
+        ]
+    )
+
+    indices = cudf.Series([0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3])
+    actual = strings.str.detokenize(indices)
+    expected = cudf.Series(
+        [
+            "the quick fox",
+            "jumped over",
+            "the lazy dog",
+            "the siamésé cat jumped under the sofa",
+        ]
+    )
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+    indices = cudf.Series(
+        [4, 0, 0, 0, 0, 4, 1, 1, 4, 2, 2, 2, 2, 4, 3], dtype=np.int8
+    )
+    actual = strings.str.detokenize(indices, "+")
+    expected = cudf.Series(
+        [
+            "quick+fox+jumped+over",
+            "lazy+dog",
+            "siamésé+cat+jumped+under",
+            "sofa",
+            "the+the+the+the",
+        ]
+    )
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "delimiter, expected_token_counts",
+    [
+        ("", [10, 9, 0, 0, 5]),
+        ("o", [6, 3, 0, 0, 1]),
+        (["a", "e", "i", "o", "u"], [13, 13, 0, 0, 6]),
+        (["a", "e", "i", "o"], [12, 11, 0, 0, 6]),
+    ],
+)
+def test_token_count(delimiter, expected_token_counts):
+    strings = cudf.Series(
+        [
+            "the quick brown fox jumped over the lazy brown dog",
+            "the sable siamésé cat jumped under the brown sofa",
+            None,
+            "",
+            "test_str\x01test_str\x02test_str\x03test_str\x04test_str\x05",
+        ]
+    )
+
+    expected = cudf.Series(expected_token_counts)
+
+    actual = strings.str.token_count(delimiter)
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+    "delimiter, input, default_id, results",
+    [
+        (
+            "",
+            "the quick brown fox jumps over the lazy brown dog",
+            99,
+            [0, 1, 2, 3, 4, 5, 0, 99, 2, 6],
+        ),
+        (
+            " ",
+            " the sable siamésé cat jumps under the brown sofa ",
+            -1,
+            [0, 7, 8, 9, 4, 10, 0, 2, 11],
+        ),
+        (
+            "_",
+            "the_quick_brown_fox_jumped__over_the_lazy_brown_dog",
+            -99,
+            [0, 1, 2, 3, -99, 5, 0, -99, 2, 6],
+        ),
+    ],
+)
+def test_tokenize_with_vocabulary(delimiter, input, default_id, results):
+    vocabulary = cudf.Series(
+        [
+            "the",
+            "quick",
+            "brown",
+            "fox",
+            "jumps",
+            "over",
+            "dog",
+            "sable",
+            "siamésé",
+            "cat",
+            "under",
+            "sofa",
+        ]
+    )
+    tokenizer = TokenizeVocabulary(vocabulary)
+
+    strings = cudf.Series([input, None, "", input])
+
+    expected = cudf.Series(
+        [
+            cudf.Series(results, dtype=np.int32),
+            None,
+            cudf.Series([], dtype=np.int32),
+            cudf.Series(results, dtype=np.int32),
+        ]
+    )
+
+    actual = tokenizer.tokenize(strings, delimiter, default_id)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_normalize_spaces():
+    strings = cudf.Series(
+        [
+            " the\t quick fox  jumped over the lazy dog",
+            "the siamésé cat\f jumped\t\tunder the sofa  ",
+            None,
+            "",
+        ]
+    )
+    expected = cudf.Series(
+        [
+            "the quick fox jumped over the lazy dog",
+            "the siamésé cat jumped under the sofa",
+            None,
+            "",
+        ]
+    )
+
+    actual = strings.str.normalize_spaces()
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_normalize_characters():
+    strings = cudf.Series(
+        ["乾 \t 乿", "ĂĆCĖÑTÜATE", "âscénd, Descend", "", None, "Stock^ $1"]
+    )
+    expected = cudf.Series(
+        [
+            " 乾     乿 ",
+            "accentuate",
+            "ascend ,  descend",
+            "",
+            None,
+            "stock ^   $ 1",
+        ]
+    )
+
+    actual = strings.str.normalize_characters()
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+    expected = cudf.Series(
+        [
+            " 乾     乿 ",
+            "ĂĆCĖÑTÜATE",
+            "âscénd ,  Descend",
+            "",
+            None,
+            "Stock ^   $ 1",
+        ]
+    )
+    actual = strings.str.normalize_characters(do_lower=False)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "n, separator, expected_values",
+    [
+        (
+            2,
+            "_",
+            [
+                "this_is",
+                "is_my",
+                "my_favorite",
+                "favorite_book",
+                "book_on",
+                "on_my",
+                "my_bookshelf",
+            ],
+        ),
+        (
+            3,
+            "-",
+            [
+                "this-is-my",
+                "is-my-favorite",
+                "my-favorite-book",
+                "favorite-book-on",
+                "book-on-my",
+                "on-my-bookshelf",
+            ],
+        ),
+    ],
+)
+def test_ngrams(n, separator, expected_values):
+    strings = cudf.Series(
+        ["this", "is", "my", "favorite", "book", "on", "my", "bookshelf"]
+    )
+
+    expected = cudf.Series(expected_values)
+
+    actual = strings.str.ngrams(n=n, separator=separator)
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "n, expected_values, expected_index, as_list",
+    [
+        (
+            2,
+            [
+                "th",
+                "hi",
+                "is",
+                "is",
+                "my",
+                "bo",
+                "oo",
+                "ok",
+                "he",
+                "er",
+                "re",
+                cudf.NA,
+            ],
+            [1, 1, 1, 2, 3, 4, 4, 4, 5, 5, 5, 6],
+            False,
+        ),
+        (
+            3,
+            [
+                "thi",
+                "his",
+                cudf.NA,
+                cudf.NA,
+                "boo",
+                "ook",
+                "her",
+                "ere",
+                cudf.NA,
+            ],
+            [1, 1, 2, 3, 4, 4, 5, 5, 6],
+            False,
+        ),
+        (
+            3,
+            [["thi", "his"], [], [], ["boo", "ook"], ["her", "ere"], []],
+            [1, 2, 3, 4, 5, 6],
+            True,
+        ),
+    ],
+)
+def test_character_ngrams(n, expected_values, expected_index, as_list):
+    strings = cudf.Series(
+        ["this", "is", "my", "book", "here", ""], index=[1, 2, 3, 4, 5, 6]
+    )
+
+    expected = cudf.Series(expected_values, index=expected_index)
+
+    actual = strings.str.character_ngrams(n=n, as_list=as_list)
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_hash_character_ngrams():
+    strings = cudf.Series(["abcdefg", "stuvwxyz"])
+    expected = cudf.Series(
+        [
+            cudf.Series([3902511862, 570445242, 4202475763], dtype=np.uint32),
+            cudf.Series(
+                [556054766, 3166857694, 3760633458, 192452857], dtype=np.uint32
+            ),
+        ]
+    )
+    actual = strings.str.hash_character_ngrams(5, True)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+    actual = strings.str.hash_character_ngrams(5)
+    expected = expected.explode()
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "n, separator, expected_values",
+    [
+        (
+            2,
+            "_",
+            [
+                "this_is",
+                "is_my",
+                "my_favorite",
+                "book_on",
+                "on_my",
+                "my_bookshelf",
+            ],
+        ),
+        (
+            3,
+            "-",
+            ["this-is-my", "is-my-favorite", "book-on-my", "on-my-bookshelf"],
+        ),
+    ],
+)
+def test_ngrams_tokenize(n, separator, expected_values):
+    strings = cudf.Series(["this is my favorite", "book on my bookshelf"])
+
+    expected = cudf.Series(expected_values)
+
+    actual = strings.str.ngrams_tokenize(n=n, separator=separator)
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_character_tokenize_series():
+    sr = cudf.Series(
+        [
+            "hello world",
+            "sdf",
+            None,
+            (
+                "goodbye, one-two:three~four+five_six@sev"
+                "en#eight^nine heŒŽ‘•™œ$µ¾ŤƠé DŽ"
+            ),
+        ]
+    )
+    expected_values = [
+        "h",
+        "e",
+        "l",
+        "l",
+        "o",
+        " ",
+        "w",
+        "o",
+        "r",
+        "l",
+        "d",
+        "s",
+        "d",
+        "f",
+        "g",
+        "o",
+        "o",
+        "d",
+        "b",
+        "y",
+        "e",
+        ",",
+        " ",
+        "o",
+        "n",
+        "e",
+        "-",
+        "t",
+        "w",
+        "o",
+        ":",
+        "t",
+        "h",
+        "r",
+        "e",
+        "e",
+        "~",
+        "f",
+        "o",
+        "u",
+        "r",
+        "+",
+        "f",
+        "i",
+        "v",
+        "e",
+        "_",
+        "s",
+        "i",
+        "x",
+        "@",
+        "s",
+        "e",
+        "v",
+        "e",
+        "n",
+        "#",
+        "e",
+        "i",
+        "g",
+        "h",
+        "t",
+        "^",
+        "n",
+        "i",
+        "n",
+        "e",
+        " ",
+        "h",
+        "e",
+        "Œ",
+        "Ž",
+        "‘",
+        "•",
+        "™",
+        "œ",
+        "$",
+        "µ",
+        "¾",
+        "Ť",
+        "Ơ",
+        "é",
+        " ",
+        "DŽ",
+    ]
+    expected_index = sr.index.repeat(sr.str.len().fillna(0))
+    expected = cudf.Series(expected_values, index=expected_index)
+
+    actual = sr.str.character_tokenize()
+    assert_eq(expected, actual)
+
+    sr = cudf.Series([""])
+    expected = cudf.Series([], dtype="object")
+
+    actual = sr.str.character_tokenize()
+    assert_eq(expected, actual)
+
+    sr = cudf.Series(["a"])
+    expected = cudf.Series(["a"])
+
+    actual = sr.str.character_tokenize()
+    assert_eq(expected, actual)
+
+
+def test_character_tokenize_index():
+    sr = cudf.core.index.as_index(
+        [
+            "hello world",
+            "sdf",
+            None,
+            (
+                "goodbye, one-two:three~four+five_six@sev"
+                "en#eight^nine heŒŽ‘•™œ$µ¾ŤƠé DŽ"
+            ),
+        ]
+    )
+    expected = cudf.core.index.as_index(
+        [
+            "h",
+            "e",
+            "l",
+            "l",
+            "o",
+            " ",
+            "w",
+            "o",
+            "r",
+            "l",
+            "d",
+            "s",
+            "d",
+            "f",
+            "g",
+            "o",
+            "o",
+            "d",
+            "b",
+            "y",
+            "e",
+            ",",
+            " ",
+            "o",
+            "n",
+            "e",
+            "-",
+            "t",
+            "w",
+            "o",
+            ":",
+            "t",
+            "h",
+            "r",
+            "e",
+            "e",
+            "~",
+            "f",
+            "o",
+            "u",
+            "r",
+            "+",
+            "f",
+            "i",
+            "v",
+            "e",
+            "_",
+            "s",
+            "i",
+            "x",
+            "@",
+            "s",
+            "e",
+            "v",
+            "e",
+            "n",
+            "#",
+            "e",
+            "i",
+            "g",
+            "h",
+            "t",
+            "^",
+            "n",
+            "i",
+            "n",
+            "e",
+            " ",
+            "h",
+            "e",
+            "Œ",
+            "Ž",
+            "‘",
+            "•",
+            "™",
+            "œ",
+            "$",
+            "µ",
+            "¾",
+            "Ť",
+            "Ơ",
+            "é",
+            " ",
+            "DŽ",
+        ]
+    )
+
+    actual = sr.str.character_tokenize()
+    assert_eq(expected, actual)
+
+    sr = cudf.Index([""])
+    expected = cudf.Index([], dtype="object")
+
+    actual = sr.str.character_tokenize()
+    assert_eq(expected, actual)
+
+    sr = cudf.core.index.as_index(["a"])
+    expected = cudf.core.index.as_index(["a"])
+
+    actual = sr.str.character_tokenize()
+    assert_eq(expected, actual)
+
+
+def test_text_replace_tokens():
+    sr = cudf.Series(["this is me", "theme music", ""])
+    targets = cudf.Series(["is", "me"])
+
+    expected = cudf.Series(["this _ _", "theme music", ""])
+    actual = sr.str.replace_tokens(targets, "_")
+
+    assert_eq(expected, actual)
+
+    replacements = cudf.Series(["IS", "ME"])
+    expected = cudf.Series(["this IS ME", "theme music", ""])
+    actual = sr.str.replace_tokens(targets, replacements)
+
+    assert_eq(expected, actual)
+
+    sr = cudf.Series(
+        [
+            "this is a small text ☕",
+            "this \t\t is ; ; - + a looooooooooonnnnnnnggggggg text \n\t",
+            "emptyme",
+        ],
+    )
+    targets = cudf.Series(
+        ["a", "☕", "\t", "looooooooooonnnnnnnggggggg", "emptyme"]
+    )
+    replacements = cudf.Series(["the", "🚒", "🚒🚒🚒🚒", "🔥🔥", ""])
+
+    expected = cudf.Series(
+        [
+            "this is the small text 🚒",
+            "this \t\t is ; ; - + the 🔥🔥 text \n\t",
+            "",
+        ]
+    )
+    actual = sr.str.replace_tokens(targets, replacements)
+
+    assert_eq(expected, actual)
+
+    sr = cudf.Series(
+        ["All-we-need;is;🔥", "\tall-we-need0is;🌊", "all;we:need+is;🌬"]
+    )
+    targets = cudf.Series(["🌬", "🔥", "🌊"])
+    replacements = "🚰"
+
+    expected = cudf.Series(
+        ["All-we-need;is;🚰", "\tall-we-need0is;🚰", "all;we:need+is;🚰"]
+    )
+    actual = sr.str.replace_tokens(targets, replacements, delimiter=";")
+
+    assert_eq(expected, actual)
+    assert_eq(sr, sr.str.replace_tokens(targets, replacements))
+    assert_eq(sr, sr.str.replace_tokens([""], [""]))
+
+
+def test_text_replace_tokens_error_cases():
+    sr = cudf.Series(["this is me", "theme music", ""])
+
+    with pytest.raises(
+        TypeError,
+        match="targets should be an array-like or a Series object, "
+        "found <class 'str'>",
+    ):
+        sr.str.replace_tokens("me", ["a"])
+
+    with pytest.raises(
+        ValueError,
+        match="targets and replacements should be same size"
+        " sequences unless replacements is a string.",
+    ):
+        sr.str.replace_tokens(["a"], ["me", "ki"])
+
+    with pytest.raises(
+        TypeError,
+        match="replacements should be an str, array-like or Series object,"
+        " found <class 'set'>",
+    ):
+        sr.str.replace_tokens(["a"], {"s"})
+
+    with pytest.raises(
+        TypeError,
+        match="Type of delimiter should be a string, found <class 'list'>",
+    ):
+        sr.str.replace_tokens(["a"], ["s"], delimiter=["a", "b"])
+
+
+def test_text_filter_tokens():
+    sr = cudf.Series(["the quick brown fox jumped", "over the lazy dog", ""])
+
+    expected = cudf.Series([" quick brown  jumped", "   ", ""])
+    actual = sr.str.filter_tokens(5)
+    assert_eq(expected, actual)
+
+    expected = cudf.Series(["🔥 quick brown 🔥 jumped", "🔥 🔥 🔥 🔥", ""])
+    actual = sr.str.filter_tokens(5, "🔥")
+    assert_eq(expected, actual)
+
+    sr = cudf.Series(
+        ["All-we-need;is;🔥", "\tall-we-need0is;🌊", "all;we:need+is;🌬"]
+    )
+    expected = cudf.Series(
+        ["All-we-need;is;--", "\tall-we-need0is;--", "all;we:need+is;--"]
+    )
+    actual = sr.str.filter_tokens(2, "--", ";")
+    assert_eq(expected, actual)
+
+    assert_eq(sr, sr.str.filter_tokens(1))
+
+
+def test_text_filter_tokens_error_cases():
+    sr = cudf.Series(["abc", "def", ""])
+
+    with pytest.raises(
+        TypeError,
+        match="Type of replacement should be a string, found <class 'list'>",
+    ):
+        sr.str.filter_tokens(3, replacement=["a", "b"])
+
+    with pytest.raises(
+        TypeError,
+        match="Type of delimiter should be a string, found <class 'list'>",
+    ):
+        sr.str.filter_tokens(3, delimiter=["a", "b"])
+
+
+def test_edit_distance():
+    sr = cudf.Series(["kitten", "saturday", "address", "book"])
+    tg = cudf.Series(["sitting", "sunday", "addressee", "back"])
+
+    expected = cudf.Series([3, 3, 2, 2], dtype=np.int32)
+    actual = sr.str.edit_distance(tg)
+    assert_eq(expected, actual)
+
+    expected = cudf.Series([0, 7, 6, 6], dtype=np.int32)
+    actual = sr.str.edit_distance("kitten")
+    assert_eq(expected, actual)
+
+
+def test_edit_distance_matrix():
+    # normal
+    sr = cudf.Series(["rounded", "bounded", "bounce", "trounce", "ounce"])
+
+    expected = cudf.Series(
+        [
+            [0, 1, 3, 3, 3],
+            [1, 0, 2, 4, 3],
+            [3, 2, 0, 2, 1],
+            [3, 4, 2, 0, 2],
+            [3, 3, 1, 2, 0],
+        ]
+    )
+    got = sr.str.edit_distance_matrix()
+
+    assert_eq(expected, got, check_dtype=False)
+
+    # 1-row series
+    sr2 = cudf.Series(["x"])
+    with pytest.raises(ValueError, match="Require size >= 2"):
+        sr2.str.edit_distance_matrix()
+
+    # null rows
+    sr3 = cudf.Series(["rounded", None, "bounce", "trounce", "ounce"])
+    with pytest.raises(ValueError, match="Cannot compute"):
+        sr3.str.edit_distance_matrix()
+
+
+def test_porter_stemmer_measure():
+    strings = cudf.Series(
+        [
+            "tr",
+            "ee",
+            "tree",
+            "y",
+            "by",
+            "trouble",
+            "oats",
+            "trees",
+            "ivy",
+            "troubles",
+            "private",
+            "oaten",
+            "orrery",
+            None,
+            "",
+        ]
+    )
+    expected = cudf.Series(
+        [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, None, 0], dtype=np.int32
+    )
+
+    actual = strings.str.porter_stemmer_measure()
+
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_is_vowel_consonant():
+    strings = cudf.Series(
+        ["tr", "ee", "tree", "y", "by", "oats", "ivy", "orrery", None, ""]
+    )
+    expected = cudf.Series(
+        [False, False, True, False, False, False, True, False, None, False]
+    )
+    actual = strings.str.is_vowel(2)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+    expected = cudf.Series(
+        [True, False, True, False, False, False, True, True, None, False]
+    )
+    actual = strings.str.is_consonant(1)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+    indices = cudf.Series([2, 1, 0, 0, 1, 2, 0, 3, 0, 0])
+    expected = cudf.Series(
+        [False, True, False, False, True, False, True, True, None, False]
+    )
+    actual = strings.str.is_vowel(indices)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+    expected = cudf.Series(
+        [False, False, True, True, False, True, False, False, None, False]
+    )
+    actual = strings.str.is_consonant(indices)
+    assert type(expected) == type(actual)
+    assert_eq(expected, actual)
+
+
+def test_minhash():
+    strings = cudf.Series(["this is my", "favorite book", None, ""])
+
+    expected = cudf.Series(
+        [
+            cudf.Series([21141582], dtype=np.uint32),
+            cudf.Series([962346254], dtype=np.uint32),
+            None,
+            cudf.Series([0], dtype=np.uint32),
+        ]
+    )
+    actual = strings.str.minhash()
+    assert_eq(expected, actual)
+    seeds = cudf.Series([0, 1, 2], dtype=np.uint32)
+    expected = cudf.Series(
+        [
+            cudf.Series([1305480167, 668155704, 34311509], dtype=np.uint32),
+            cudf.Series([32665384, 3470118, 363147162], dtype=np.uint32),
+            None,
+            cudf.Series([0, 0, 0], dtype=np.uint32),
+        ]
+    )
+    actual = strings.str.minhash(seeds=seeds, width=5)
+    assert_eq(expected, actual)
+
+    expected = cudf.Series(
+        [
+            cudf.Series([3232308021562742685], dtype=np.uint64),
+            cudf.Series([23008204270530356], dtype=np.uint64),
+            None,
+            cudf.Series([0], dtype=np.uint64),
+        ]
+    )
+    actual = strings.str.minhash64()
+    assert_eq(expected, actual)
+    seeds = cudf.Series([0, 1, 2], dtype=np.uint64)
+    expected = cudf.Series(
+        [
+            cudf.Series(
+                [7082801294247314046, 185949556058924788, 167570629329462454],
+                dtype=np.uint64,
+            ),
+            cudf.Series(
+                [382665377781028452, 86243762733551437, 7688750597953083512],
+                dtype=np.uint64,
+            ),
+            None,
+            cudf.Series([0, 0, 0], dtype=np.uint64),
+        ]
+    )
+    actual = strings.str.minhash64(seeds=seeds, width=5)
+    assert_eq(expected, actual)
+
+    # test wrong seed types
+    with pytest.raises(ValueError):
+        strings.str.minhash(seeds="a")
+    with pytest.raises(ValueError):
+        seeds = cudf.Series([0, 1, 2], dtype=np.int32)
+        strings.str.minhash(seeds=seeds)
+    with pytest.raises(ValueError):
+        seeds = cudf.Series([0, 1, 2], dtype=np.uint32)
+        strings.str.minhash64(seeds=seeds)
+
+
+def test_jaccard_index():
+    str1 = cudf.Series(["the brown dog", "jumped about"])
+    str2 = cudf.Series(["the black cat", "jumped around"])
+
+    expected = cudf.Series([0.058824, 0.307692], dtype=np.float32)
+    actual = str1.str.jaccard_index(str2, 5)
+    assert_eq(expected, actual)
+
+    actual = str2.str.jaccard_index(str1, 5)
+    assert_eq(expected, actual)
+
+    with pytest.raises(ValueError):
+        str1.str.jaccard_index(str2, 1)
+    with pytest.raises(ValueError):
+        str3 = cudf.Series(["not enough rows"])
+        str1.str.jaccard_index(str3, 5)
+
+
+def _make_list_of_strings_of_random_length(
+    num_strings, min_length, max_length
+):
+    return [
+        "".join(
+            random.choice(string.ascii_lowercase)
+            for _ in range(random.randint(min_length, max_length))
+        )
+        for _ in range(num_strings)
+    ]
+
+
+def test_jaccard_index_random_strings():
+    # Seed the rng before random string generation.
+    random.seed(42)
+    num_strings = 100
+    jaccard_width = 5
+    common_strings = _make_list_of_strings_of_random_length(
+        num_strings, jaccard_width, 50
+    )
+    uncommon_strings1 = _make_list_of_strings_of_random_length(
+        num_strings, jaccard_width, 10
+    )
+    uncommon_strings2 = _make_list_of_strings_of_random_length(
+        num_strings, jaccard_width, 20
+    )
+    str1 = cudf.Series(uncommon_strings1).str.cat(cudf.Series(common_strings))
+    str2 = cudf.Series(uncommon_strings2).str.cat(cudf.Series(common_strings))
+
+    # adopted from https://github.com/rapidsai/rapids-deduplication/issues/36
+    da = str1.str.character_ngrams(jaccard_width, True)
+    db = str2.str.character_ngrams(jaccard_width, True)
+    da = da.list.unique()
+    db = db.list.unique()
+    da = da.explode()
+    db = db.explode()
+    da = da.to_frame()
+    db = db.to_frame()
+    da = da.reset_index()
+    db = db.reset_index()
+    da = da.rename(columns={0: "token"})
+    db = db.rename(columns={0: "token"})
+    db["match"] = 1
+    inter = da.merge(db, on=["index", "token"], how="left")
+    inter = inter.groupby("index")["match"].sum()
+    union = da.merge(db, on=["index", "token"], how="outer")
+    union = union.groupby("index").size()
+    res = inter / union
+    res.fillna(0, inplace=True)
+    res = res.sort_index()
+    res = res.values.astype("float32")
+    expected = cudf.Series(res)
+
+    actual = str1.str.jaccard_index(str2, jaccard_width)
+    assert_eq(expected, actual)
diff --git a/python/cudf/cudf/tests/window/__init__.py b/python/cudf/cudf/tests/window/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf/cudf/tests/window/test_rolling.py b/python/cudf/cudf/tests/window/test_rolling.py
new file mode 100644
index 0000000..06777c8
--- /dev/null
+++ b/python/cudf/cudf/tests/window/test_rolling.py
@@ -0,0 +1 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/utils/__init__.py b/python/cudf/cudf/utils/__init__.py
new file mode 100644
index 0000000..ccbb162
--- /dev/null
+++ b/python/cudf/cudf/utils/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/utils/_numba.py b/python/cudf/cudf/utils/_numba.py
new file mode 100644
index 0000000..09afb56
--- /dev/null
+++ b/python/cudf/cudf/utils/_numba.py
@@ -0,0 +1,191 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+
+import glob
+import os
+import sys
+import warnings
+
+from numba import config as numba_config
+
+CC_60_PTX_FILE = os.path.join(
+    os.path.dirname(__file__), "../core/udf/shim_60.ptx"
+)
+
+
+def _get_best_ptx_file(archs, max_compute_capability):
+    """
+    Determine of the available PTX files which one is
+    the most recent up to and including the device compute capability.
+    """
+    filtered_archs = [x for x in archs if x[0] <= max_compute_capability]
+    if filtered_archs:
+        return max(filtered_archs, key=lambda x: x[0])
+    else:
+        return None
+
+
+def _get_ptx_file(path, prefix):
+    if "RAPIDS_NO_INITIALIZE" in os.environ:
+        # cc=60 ptx is always built
+        cc = int(os.environ.get("STRINGS_UDF_CC", "60"))
+    else:
+        from numba import cuda
+
+        dev = cuda.get_current_device()
+
+        # Load the highest compute capability file available that is less than
+        # the current device's.
+        cc = int("".join(str(x) for x in dev.compute_capability))
+    files = glob.glob(os.path.join(path, f"{prefix}*.ptx"))
+    if len(files) == 0:
+        raise RuntimeError(f"Missing PTX files for cc={cc}")
+    regular_sms = []
+
+    for f in files:
+        file_name = os.path.basename(f)
+        sm_number = file_name.rstrip(".ptx").lstrip(prefix)
+        if sm_number.endswith("a"):
+            processed_sm_number = int(sm_number.rstrip("a"))
+            if processed_sm_number == cc:
+                return f
+        else:
+            regular_sms.append((int(sm_number), f))
+
+    regular_result = None
+
+    if regular_sms:
+        regular_result = _get_best_ptx_file(regular_sms, cc)
+
+    if regular_result is None:
+        raise RuntimeError(
+            "This cuDF installation is missing the necessary PTX "
+            f"files that are <={cc}."
+        )
+    else:
+        return regular_result[1]
+
+
+def _patch_numba_mvc():
+    # Enable the config option for minor version compatibility
+    numba_config.CUDA_ENABLE_MINOR_VERSION_COMPATIBILITY = 1
+
+    if "numba.cuda" in sys.modules:
+        # Patch numba for version 0.57.0 MVC support, which must know the
+        # config value at import time. We cannot guarantee the order of imports
+        # between cudf and numba.cuda so we patch numba to ensure it has these
+        # names available.
+        # See https://github.com/numba/numba/issues/8977 for details.
+        import numba.cuda
+        from cubinlinker import CubinLinker, CubinLinkerError
+        from ptxcompiler import compile_ptx
+
+        numba.cuda.cudadrv.driver.compile_ptx = compile_ptx
+        numba.cuda.cudadrv.driver.CubinLinker = CubinLinker
+        numba.cuda.cudadrv.driver.CubinLinkerError = CubinLinkerError
+
+
+def _setup_numba():
+    """
+    Configure the numba linker for use with cuDF. This consists of
+    potentially putting numba into enhanced compatibility mode
+    based on the user driver and runtime versions as well as the
+    version of the CUDA Toolkit used to build the PTX files shipped
+    with the user cuDF package.
+    """
+    # ptxcompiler is a requirement for cuda 11.x packages but not
+    # cuda 12.x packages. However its version checking machinery
+    # is still necessary. If a user happens to have ptxcompiler
+    # in a cuda 12 environment, it's use for the purposes of
+    # checking the driver and runtime versions is harmless
+    try:
+        from ptxcompiler.patch import NO_DRIVER, safe_get_versions
+    except ModuleNotFoundError:
+        # use vendored version
+        from cudf.utils._ptxcompiler import NO_DRIVER, safe_get_versions
+
+    versions = safe_get_versions()
+    if versions != NO_DRIVER:
+        driver_version, runtime_version = versions
+        if driver_version >= (12, 0) and runtime_version > driver_version:
+            warnings.warn(
+                f"Using CUDA toolkit version {runtime_version} with CUDA "
+                f"driver version {driver_version} requires minor version "
+                "compatibility, which is not yet supported for CUDA "
+                "driver versions 12.0 and above. It is likely that many "
+                "cuDF operations will not work in this state. Please "
+                f"install CUDA toolkit version {driver_version} to "
+                "continue using cuDF."
+            )
+        else:
+            # Support MVC for all CUDA versions in the 11.x range
+            ptx_toolkit_version = _get_cuda_version_from_ptx_file(
+                CC_60_PTX_FILE
+            )
+            # Numba thinks cubinlinker is only needed if the driver is older
+            # than the CUDA runtime, but when PTX files are present, it might
+            # also need to patch because those PTX files may be compiled by
+            # a CUDA version that is newer than the driver as well
+            if (driver_version < ptx_toolkit_version) or (
+                driver_version < runtime_version
+            ):
+                _patch_numba_mvc()
+
+
+def _get_cuda_version_from_ptx_file(path):
+    """
+    https://docs.nvidia.com/cuda/parallel-thread-execution/
+    Each PTX module must begin with a .version
+    directive specifying the PTX language version
+
+    example header:
+    //
+    // Generated by NVIDIA NVVM Compiler
+    //
+    // Compiler Build ID: CL-31057947
+    // Cuda compilation tools, release 11.6, V11.6.124
+    // Based on NVVM 7.0.1
+    //
+
+    .version 7.6
+    .target sm_52
+    .address_size 64
+
+    """
+    with open(path) as ptx_file:
+        for line in ptx_file:
+            if line.startswith(".version"):
+                ver_line = line
+                break
+        else:
+            raise ValueError("Could not read CUDA version from ptx file.")
+    version = ver_line.strip("\n").split(" ")[1]
+    # This dictionary maps from supported versions of NVVM to the
+    # PTX version it produces. The lowest value should be the minimum
+    # CUDA version required to compile the library. Currently CUDA 11.5
+    # or higher is required to build cudf. New CUDA versions should
+    # be added to this dictionary when officially supported.
+    ver_map = {
+        "7.5": (11, 5),
+        "7.6": (11, 6),
+        "7.7": (11, 7),
+        "7.8": (11, 8),
+        "8.0": (12, 0),
+        "8.1": (12, 1),
+    }
+
+    cuda_ver = ver_map.get(version)
+    if cuda_ver is None:
+        raise ValueError(
+            f"Could not map PTX version {version} to a CUDA version"
+        )
+
+    return cuda_ver
+
+
+class _CUDFNumbaConfig:
+    def __enter__(self):
+        self.enter_val = numba_config.CUDA_LOW_OCCUPANCY_WARNINGS
+        numba_config.CUDA_LOW_OCCUPANCY_WARNINGS = 0
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        numba_config.CUDA_LOW_OCCUPANCY_WARNINGS = self.enter_val
diff --git a/python/cudf/cudf/utils/_ptxcompiler.py b/python/cudf/cudf/utils/_ptxcompiler.py
new file mode 100644
index 0000000..54f5ea0
--- /dev/null
+++ b/python/cudf/cudf/utils/_ptxcompiler.py
@@ -0,0 +1,107 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import os
+import subprocess
+import sys
+import warnings
+
+NO_DRIVER = (math.inf, math.inf)
+
+NUMBA_CHECK_VERSION_CMD = """\
+from ctypes import c_int, byref
+from numba import cuda
+dv = c_int(0)
+cuda.cudadrv.driver.driver.cuDriverGetVersion(byref(dv))
+drv_major = dv.value // 1000
+drv_minor = (dv.value - (drv_major * 1000)) // 10
+run_major, run_minor = cuda.runtime.get_version()
+print(f'{drv_major} {drv_minor} {run_major} {run_minor}')
+"""
+
+
+def check_disabled_in_env():
+    # We should avoid checking whether the patch is
+    # needed if the user requested that we don't check
+    # (e.g. in a non-fork-safe environment)
+    check = os.getenv("PTXCOMPILER_CHECK_NUMBA_CODEGEN_PATCH_NEEDED")
+    if check is not None:
+        try:
+            check = int(check)
+        except ValueError:
+            check = False
+    else:
+        check = True
+
+    return not check
+
+
+def get_versions():
+    cp = subprocess.run(
+        [sys.executable, "-c", NUMBA_CHECK_VERSION_CMD], capture_output=True
+    )
+    if cp.returncode:
+        msg = (
+            f"Error getting driver and runtime versions:\n\nstdout:\n\n"
+            f"{cp.stdout.decode()}\n\nstderr:\n\n{cp.stderr.decode()}\n\n"
+            "Not patching Numba"
+        )
+        warnings.warn(msg, UserWarning)
+        return NO_DRIVER
+
+    versions = [int(s) for s in cp.stdout.strip().split()]
+    driver_version = tuple(versions[:2])
+    runtime_version = tuple(versions[2:])
+
+    return driver_version, runtime_version
+
+
+def safe_get_versions():
+    """
+    Return a 2-tuple of deduced driver and runtime versions.
+
+    To ensure that this function does not initialize a CUDA context,
+    calls to the runtime and driver are made in a subprocess.
+
+    If PTXCOMPILER_CHECK_NUMBA_CODEGEN_PATCH_NEEDED is set
+    in the environment, then this subprocess call is not launched.
+    To specify the driver and runtime versions of the environment
+    in this case, set PTXCOMPILER_KNOWN_DRIVER_VERSION and
+    PTXCOMPILER_KNOWN_RUNTIME_VERSION appropriately.
+    """
+    if check_disabled_in_env():
+        try:
+            # allow user to specify driver/runtime
+            # versions manually, if necessary
+            driver_version = os.environ[
+                "PTXCOMPILER_KNOWN_DRIVER_VERSION"
+            ].split(".")
+            runtime_version = os.environ[
+                "PTXCOMPILER_KNOWN_RUNTIME_VERSION"
+            ].split(".")
+            driver_version, runtime_version = (
+                tuple(map(int, driver_version)),
+                tuple(map(int, runtime_version)),
+            )
+        except (KeyError, ValueError):
+            warnings.warn(
+                "No way to determine driver and runtime versions for "
+                "patching, set PTXCOMPILER_KNOWN_DRIVER_VERSION and "
+                "PTXCOMPILER_KNOWN_RUNTIME_VERSION"
+            )
+            return NO_DRIVER
+    else:
+        driver_version, runtime_version = get_versions()
+    return driver_version, runtime_version
diff --git a/python/cudf/cudf/utils/applyutils.py b/python/cudf/cudf/utils/applyutils.py
new file mode 100644
index 0000000..66dbd73
--- /dev/null
+++ b/python/cudf/cudf/utils/applyutils.py
@@ -0,0 +1,375 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import functools
+from typing import Any, Dict
+
+import cupy as cp
+from numba import cuda
+from numba.core.utils import pysignature
+
+import cudf
+from cudf import _lib as libcudf
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.column import column
+from cudf.utils import utils
+from cudf.utils._numba import _CUDFNumbaConfig
+from cudf.utils.docutils import docfmt_partial
+
+_doc_applyparams = """
+df : DataFrame
+    The source dataframe.
+func : function
+    The transformation function that will be executed on the CUDA GPU.
+incols: list or dict
+    A list of names of input columns that match the function arguments.
+    Or, a dictionary mapping input column names to their corresponding
+    function arguments such as {'col1': 'arg1'}.
+outcols: dict
+    A dictionary of output column names and their dtype.
+kwargs: dict
+    name-value of extra arguments.  These values are passed
+    directly into the function.
+pessimistic_nulls : bool
+    Whether or not apply_rows output should be null when any corresponding
+    input is null. If False, all outputs will be non-null, but will be the
+    result of applying func against the underlying column data, which
+    may be garbage.
+"""
+
+_doc_applychunkparams = """
+chunks : int or Series-like
+    If it is an ``int``, it is the chunksize.
+    If it is an array, it contains integer offset for the start of each chunk.
+    The span of a chunk for chunk i-th is ``data[chunks[i] : chunks[i + 1]]``
+    for any ``i + 1 < chunks.size``; or, ``data[chunks[i]:]`` for the
+    ``i == len(chunks) - 1``.
+tpb : int; optional
+    The threads-per-block for the underlying kernel.
+    If not specified (Default), uses Numba ``.forall(...)`` built-in to query
+    the CUDA Driver API to determine optimal kernel launch configuration.
+    Specify 1 to emulate serial execution for each chunk.  It is a good
+    starting point but inefficient.
+    Its maximum possible value is limited by the available CUDA GPU resources.
+blkct : int; optional
+    The number of blocks for the underlying kernel.
+    If not specified (Default) and ``tpb`` is not specified (Default), uses
+    Numba ``.forall(...)`` built-in to query the CUDA Driver API to determine
+    optimal kernel launch configuration.
+    If not specified (Default) and ``tpb`` is specified, uses ``chunks`` as the
+    number of blocks.
+"""
+
+doc_apply = docfmt_partial(params=_doc_applyparams)
+doc_applychunks = docfmt_partial(
+    params=_doc_applyparams, params_chunks=_doc_applychunkparams
+)
+
+
+@doc_apply()
+def apply_rows(
+    df, func, incols, outcols, kwargs, pessimistic_nulls, cache_key
+):
+    """Row-wise transformation
+
+    Parameters
+    ----------
+    {params}
+    """
+    applyrows = ApplyRowsCompiler(
+        func, incols, outcols, kwargs, pessimistic_nulls, cache_key=cache_key
+    )
+    return applyrows.run(df)
+
+
+@doc_applychunks()
+def apply_chunks(
+    df,
+    func,
+    incols,
+    outcols,
+    kwargs,
+    pessimistic_nulls,
+    chunks,
+    blkct=None,
+    tpb=None,
+):
+    """Chunk-wise transformation
+
+    Parameters
+    ----------
+    {params}
+    {params_chunks}
+    """
+    applychunks = ApplyChunksCompiler(
+        func, incols, outcols, kwargs, pessimistic_nulls, cache_key=None
+    )
+    return applychunks.run(df, chunks=chunks, tpb=tpb)
+
+
+@acquire_spill_lock()
+def make_aggregate_nullmask(df, columns=None, op="__and__"):
+
+    out_mask = None
+    for k in columns or df._data:
+        col = cudf.core.dataframe.extract_col(df, k)
+        if not col.nullable:
+            continue
+        nullmask = column.as_column(df[k]._column.nullmask)
+
+        if out_mask is None:
+            out_mask = column.as_column(
+                nullmask.copy(), dtype=utils.mask_dtype
+            )
+        else:
+            out_mask = libcudf.binaryop.binaryop(
+                nullmask, out_mask, op, out_mask.dtype
+            )
+
+    return out_mask
+
+
+class ApplyKernelCompilerBase:
+    def __init__(
+        self, func, incols, outcols, kwargs, pessimistic_nulls, cache_key
+    ):
+        # Get signature of user function
+        sig = pysignature(func)
+        self.sig = sig
+        self.incols = incols
+        self.outcols = outcols
+        self.kwargs = kwargs
+        self.pessimistic_nulls = pessimistic_nulls
+        self.cache_key = cache_key
+        self.kernel = self.compile(func, sig.parameters.keys(), kwargs.keys())
+
+    @acquire_spill_lock()
+    def run(self, df, **launch_params):
+        # Get input columns
+        if isinstance(self.incols, dict):
+            inputs = {
+                v: df[k]._column.data_array_view(mode="read")
+                for (k, v) in self.incols.items()
+            }
+        else:
+            inputs = {
+                k: df[k]._column.data_array_view(mode="read")
+                for k in self.incols
+            }
+        # Allocate output columns
+        outputs = {}
+        for k, dt in self.outcols.items():
+            outputs[k] = column.column_empty(
+                len(df), dt, False
+            ).data_array_view(mode="write")
+        # Bind argument
+        args = {}
+        for dct in [inputs, outputs, self.kwargs]:
+            args.update(dct)
+        bound = self.sig.bind(**args)
+        # Launch kernel
+        self.launch_kernel(df, bound.args, **launch_params)
+        # Prepare pessimistic nullmask
+        if self.pessimistic_nulls:
+            out_mask = make_aggregate_nullmask(df, columns=self.incols)
+        else:
+            out_mask = None
+        # Prepare output frame
+        outdf = df.copy()
+        for k in sorted(self.outcols):
+            outdf[k] = cudf.Series(
+                outputs[k], index=outdf.index, nan_as_null=False
+            )
+            if out_mask is not None:
+                outdf._data[k] = outdf[k]._column.set_mask(
+                    out_mask.data_array_view(mode="write")
+                )
+
+        return outdf
+
+
+class ApplyRowsCompiler(ApplyKernelCompilerBase):
+    def compile(self, func, argnames, extra_argnames):
+        # Compile kernel
+        kernel = _load_cache_or_make_row_wise_kernel(
+            self.cache_key, func, argnames, extra_argnames
+        )
+        return kernel
+
+    def launch_kernel(self, df, args):
+        with _CUDFNumbaConfig():
+            self.kernel.forall(len(df))(*args)
+
+
+class ApplyChunksCompiler(ApplyKernelCompilerBase):
+    def compile(self, func, argnames, extra_argnames):
+        # Compile kernel
+        kernel = _load_cache_or_make_chunk_wise_kernel(
+            func, argnames, extra_argnames
+        )
+        return kernel
+
+    def launch_kernel(self, df, args, chunks, blkct=None, tpb=None):
+        chunks = self.normalize_chunks(len(df), chunks)
+        if blkct is None and tpb is None:
+            with _CUDFNumbaConfig():
+                self.kernel.forall(len(df))(len(df), chunks, *args)
+        else:
+            assert tpb is not None
+            if blkct is None:
+                blkct = chunks.size
+            with _CUDFNumbaConfig():
+                self.kernel[blkct, tpb](len(df), chunks, *args)
+
+    def normalize_chunks(self, size, chunks):
+        if isinstance(chunks, int):
+            # *chunks* is the chunksize
+            return cuda.as_cuda_array(
+                cp.arange(start=0, stop=size, step=chunks)
+            ).view("int64")
+        else:
+            # *chunks* is an array of chunk leading offset
+            return cuda.as_cuda_array(cp.asarray(chunks)).view("int64")
+
+
+def _make_row_wise_kernel(func, argnames, extras):
+    """
+    Make a kernel that does a stride loop over the input rows.
+
+    Each thread is responsible for a row in each iteration.
+    Several iteration may be needed to handling a large number of rows.
+
+    The resulting kernel can be used with any 1D grid size and 1D block size.
+    """
+    # Build kernel source
+    argnames = list(map(_mangle_user, argnames))
+    extras = list(map(_mangle_user, extras))
+    source = """
+def row_wise_kernel({args}):
+{body}
+"""
+
+    args = ", ".join(argnames)
+    body = []
+
+    body.append("tid = cuda.grid(1)")
+    body.append("ntid = cuda.gridsize(1)")
+
+    for a in argnames:
+        if a not in extras:
+            start = "tid"
+            stop = ""
+            stride = "ntid"
+            srcidx = "{a} = {a}[{start}:{stop}:{stride}]"
+            body.append(
+                srcidx.format(a=a, start=start, stop=stop, stride=stride)
+            )
+
+    body.append(f"inner({args})")
+
+    indented = ["{}{}".format(" " * 4, ln) for ln in body]
+    # Finalize source
+    concrete = source.format(args=args, body="\n".join(indented))
+    # Get bytecode
+    glbs = {"inner": cuda.jit(device=True)(func), "cuda": cuda}
+    exec(concrete, glbs)
+    # Compile as CUDA kernel
+    kernel = cuda.jit(glbs["row_wise_kernel"])
+    return kernel
+
+
+def _make_chunk_wise_kernel(func, argnames, extras):
+    """
+    Make a kernel that does a stride loop over the input chunks.
+
+    Each block is responsible for a chunk in each iteration.
+    Several iteration may be needed to handling a large number of chunks.
+
+    The user function *func* will have all threads in the block for its
+    computation.
+
+    The resulting kernel can be used with any 1D grid size and 1D block size.
+    """
+
+    # Build kernel source
+    argnames = list(map(_mangle_user, argnames))
+    extras = list(map(_mangle_user, extras))
+    source = """
+def chunk_wise_kernel(nrows, chunks, {args}):
+{body}
+"""
+
+    args = ", ".join(argnames)
+    body = []
+
+    body.append("blkid = cuda.blockIdx.x")
+    body.append("nblkid = cuda.gridDim.x")
+    body.append("tid = cuda.threadIdx.x")
+    body.append("ntid = cuda.blockDim.x")
+
+    # Stride loop over the block
+    body.append("for curblk in range(blkid, chunks.size, nblkid):")
+    indent = " " * 4
+
+    body.append(indent + "start = chunks[curblk]")
+    body.append(
+        indent
+        + "stop = chunks[curblk + 1]"
+        + " if curblk + 1 < chunks.size else nrows"
+    )
+
+    slicedargs = {}
+    for a in argnames:
+        if a not in extras:
+            slicedargs[a] = f"{a}[start:stop]"
+        else:
+            slicedargs[a] = str(a)
+    body.append(
+        "{}inner({})".format(
+            indent, ", ".join(slicedargs[k] for k in argnames)
+        )
+    )
+
+    indented = ["{}{}".format(" " * 4, ln) for ln in body]
+    # Finalize source
+    concrete = source.format(args=args, body="\n".join(indented))
+    # Get bytecode
+    glbs = {"inner": cuda.jit(device=True)(func), "cuda": cuda}
+    exec(concrete, glbs)
+    # Compile as CUDA kernel
+    kernel = cuda.jit(glbs["chunk_wise_kernel"])
+    return kernel
+
+
+_cache: Dict[Any, Any] = dict()
+
+
+@functools.wraps(_make_row_wise_kernel)
+def _load_cache_or_make_row_wise_kernel(cache_key, func, *args, **kwargs):
+    """Caching version of ``_make_row_wise_kernel``."""
+    if cache_key is None:
+        cache_key = func
+    try:
+        out = _cache[cache_key]
+        # print("apply cache loaded", cache_key)
+        return out
+    except KeyError:
+        # print("apply cache NOT loaded", cache_key)
+        kernel = _make_row_wise_kernel(func, *args, **kwargs)
+        _cache[cache_key] = kernel
+        return kernel
+
+
+@functools.wraps(_make_chunk_wise_kernel)
+def _load_cache_or_make_chunk_wise_kernel(func, *args, **kwargs):
+    """Caching version of ``_make_row_wise_kernel``."""
+    try:
+        return _cache[func]
+    except KeyError:
+        kernel = _make_chunk_wise_kernel(func, *args, **kwargs)
+        _cache[func] = kernel
+        return kernel
+
+
+def _mangle_user(name):
+    """Mangle user variable name"""
+    return f"__user_{name}"
diff --git a/python/cudf/cudf/utils/cudautils.py b/python/cudf/cudf/utils/cudautils.py
new file mode 100755
index 0000000..020c32d
--- /dev/null
+++ b/python/cudf/cudf/utils/cudautils.py
@@ -0,0 +1,138 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from pickle import dumps
+
+import cachetools
+from numba import cuda
+from numba.np import numpy_support
+
+from cudf.utils._numba import _CUDFNumbaConfig
+
+#
+# Misc kernels
+#
+
+
+@cuda.jit
+def gpu_window_sizes_from_offset(arr, window_sizes, offset):
+    i = cuda.grid(1)
+    j = i
+    if i < arr.size:
+        while j > -1:
+            if (arr[i] - arr[j]) >= offset:
+                break
+            j -= 1
+        window_sizes[i] = i - j
+
+
+def window_sizes_from_offset(arr, offset):
+    window_sizes = cuda.device_array(shape=(arr.shape), dtype="int32")
+    if arr.size > 0:
+        with _CUDFNumbaConfig():
+            gpu_window_sizes_from_offset.forall(arr.size)(
+                arr, window_sizes, offset
+            )
+    return window_sizes
+
+
+@cuda.jit
+def gpu_grouped_window_sizes_from_offset(
+    arr, window_sizes, group_starts, offset
+):
+    i = cuda.grid(1)
+    j = i
+    if i < arr.size:
+        while j > (group_starts[i] - 1):
+            if (arr[i] - arr[j]) >= offset:
+                break
+            j -= 1
+        window_sizes[i] = i - j
+
+
+def grouped_window_sizes_from_offset(arr, group_starts, offset):
+    window_sizes = cuda.device_array(shape=(arr.shape), dtype="int32")
+    if arr.size > 0:
+        with _CUDFNumbaConfig():
+            gpu_grouped_window_sizes_from_offset.forall(arr.size)(
+                arr, window_sizes, group_starts, offset
+            )
+    return window_sizes
+
+
+# This cache is keyed on the (signature, code, closure variables) of UDFs, so
+# it can hit for distinct functions that are similar. The lru_cache wrapping
+# compile_udf misses for these similar functions, but doesn't need to serialize
+# closure variables to check for a hit.
+_udf_code_cache: cachetools.LRUCache = cachetools.LRUCache(maxsize=32)
+
+
+def make_cache_key(udf, sig):
+    """
+    Build a cache key for a user defined function. Used to avoid
+    recompiling the same function for the same set of types
+    """
+    codebytes = udf.__code__.co_code
+    constants = udf.__code__.co_consts
+    names = udf.__code__.co_names
+
+    if udf.__closure__ is not None:
+        cvars = tuple(x.cell_contents for x in udf.__closure__)
+        cvarbytes = dumps(cvars)
+    else:
+        cvarbytes = b""
+
+    return names, constants, codebytes, cvarbytes, sig
+
+
+def compile_udf(udf, type_signature):
+    """Compile ``udf`` with `numba`
+
+    Compile a python callable function ``udf`` with
+    `numba.cuda.compile_ptx_for_current_device(device=True)` using
+    ``type_signature`` into CUDA PTX together with the generated output type.
+
+    The output is expected to be passed to the PTX parser in `libcudf`
+    to generate a CUDA device function to be inlined into CUDA kernels,
+    compiled at runtime and launched.
+
+    Parameters
+    ----------
+    udf:
+      a python callable function
+
+    type_signature:
+      a tuple that specifies types of each of the input parameters of ``udf``.
+      The types should be one in `numba.types` and could be converted from
+      numpy types with `numba.numpy_support.from_dtype(...)`.
+
+    Returns
+    -------
+    ptx_code:
+      The compiled CUDA PTX
+
+    output_type:
+      An numpy type
+
+    """
+    import cudf.core.udf
+
+    key = make_cache_key(udf, type_signature)
+    res = _udf_code_cache.get(key)
+    if res:
+        return res
+
+    # We haven't compiled a function like this before, so need to fall back to
+    # compilation with Numba
+    ptx_code, return_type = cuda.compile_ptx_for_current_device(
+        udf, type_signature, device=True
+    )
+    if not isinstance(return_type, cudf.core.udf.masked_typing.MaskedType):
+        output_type = numpy_support.as_dtype(return_type).type
+    else:
+        output_type = return_type
+
+    # Populate the cache for this function
+    res = (ptx_code, output_type)
+    _udf_code_cache[key] = res
+
+    return res
diff --git a/python/cudf/cudf/utils/docutils.py b/python/cudf/cudf/utils/docutils.py
new file mode 100644
index 0000000..5a7b8ba
--- /dev/null
+++ b/python/cudf/cudf/utils/docutils.py
@@ -0,0 +1,352 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+"""
+Helper functions for parameterized docstring
+"""
+import functools
+import re
+import string
+
+_regex_whitespaces = re.compile(r"^\s+$")
+
+
+def _only_spaces(s):
+    return bool(_regex_whitespaces.match(s))
+
+
+_wrapopts = {"width": 78, "replace_whitespace": False}
+
+
+def docfmt(**kwargs):
+    """Format docstring.
+
+    Similar to saving the result of ``__doc__.format(**kwargs)`` as the
+    function's docstring.
+    """
+    kwargs = {k: v.lstrip() for k, v in kwargs.items()}
+
+    def outer(fn):
+        buf = []
+        if fn.__doc__ is None:
+            return fn
+        formatsiter = string.Formatter().parse(fn.__doc__)
+        for literal, field, fmtspec, conv in formatsiter:
+            assert conv is None
+            assert not fmtspec
+            buf.append(literal)
+            if field is not None:
+                # get indentation
+                lines = literal.rsplit("\n", 1)
+                if _only_spaces(lines[-1]):
+                    indent = " " * len(lines[-1])
+                    valuelines = kwargs[field].splitlines(True)
+                    # first line
+                    buf.append(valuelines[0])
+                    # subsequent lines are indented
+                    buf.extend([indent + ln for ln in valuelines[1:]])
+                else:
+                    buf.append(kwargs[field])
+        fn.__doc__ = "".join(buf)
+        return fn
+
+    return outer
+
+
+def docfmt_partial(**kwargs):
+    return functools.partial(docfmt, **kwargs)
+
+
+def copy_docstring(other):
+    """
+    Decorator that sets ``__doc__`` to ``other.__doc___``.
+    """
+
+    def wrapper(func):
+        func.__doc__ = other.__doc__
+        return func
+
+    return wrapper
+
+
+def doc_apply(doc):
+    """Set `__doc__` attribute of `func` to `doc`."""
+
+    def wrapper(func):
+        func.__doc__ = doc
+        return func
+
+    return wrapper
+
+
+doc_describe = docfmt_partial(
+    docstring="""
+        Generate descriptive statistics.
+
+        Descriptive statistics include those that summarize the
+        central tendency, dispersion and shape of a dataset's
+        distribution, excluding ``NaN`` values.
+
+        Analyzes both numeric and object series, as well as
+        ``DataFrame`` column sets of mixed data types. The
+        output will vary depending on what is provided.
+        Refer to the notes below for more detail.
+
+        Parameters
+        ----------
+        percentiles : list-like of numbers, optional
+            The percentiles to include in the output.
+            All should fall between 0 and 1. The default is
+            ``[.25, .5, .75]``, which returns the 25th, 50th,
+            and 75th percentiles.
+
+        include : 'all', list-like of dtypes or None(default), optional
+            A list of data types to include in the result.
+            Ignored for ``Series``. Here are the options:
+
+            - 'all' : All columns of the input will be included in the output.
+            - A list-like of dtypes : Limits the results to the
+              provided data types.
+              To limit the result to numeric types submit
+              ``numpy.number``. To limit it instead to object columns submit
+              the ``numpy.object`` data type. Strings
+              can also be used in the style of
+              ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
+              select pandas categorical columns, use ``'category'``
+            - None (default) : The result will include all numeric columns.
+
+        exclude : list-like of dtypes or None (default), optional,
+            A list of data types to omit from the result. Ignored
+            for ``Series``. Here are the options:
+
+            - A list-like of dtypes : Excludes the provided data types
+              from the result. To exclude numeric types submit
+              ``numpy.number``. To exclude object columns submit the data
+              type ``numpy.object``. Strings can also be used in the style of
+              ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
+              exclude pandas categorical columns, use ``'category'``
+            - None (default) : The result will exclude nothing.
+
+        datetime_is_numeric : bool, default False
+            For DataFrame input, this also controls whether datetime columns
+            are included by default.
+
+            .. deprecated:: 23.04
+
+               `datetime_is_numeric` is deprecated and will be removed in
+               a future version of cudf.
+
+        Returns
+        -------
+        output_frame : Series or DataFrame
+            Summary statistics of the Series or Dataframe provided.
+
+        Notes
+        -----
+        For numeric data, the result's index will include ``count``,
+        ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
+        upper percentiles. By default the lower percentile is ``25`` and the
+        upper percentile is ``75``. The ``50`` percentile is the
+        same as the median.
+
+        For strings dtype or datetime dtype, the result's index
+        will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
+        is the most common value. The ``freq`` is the most common value's
+        frequency. Timestamps also include the ``first`` and ``last`` items.
+
+        If multiple object values have the highest count, then the
+        ``count`` and ``top`` results will be arbitrarily chosen from
+        among those with the highest count.
+
+        For mixed data types provided via a ``DataFrame``, the default is to
+        return only an analysis of numeric columns. If the dataframe consists
+        only of object and categorical data without any numeric columns, the
+        default is to return an analysis of both the object and categorical
+        columns. If ``include='all'`` is provided as an option, the result
+        will include a union of attributes of each type.
+
+        The ``include`` and ``exclude`` parameters can be used to limit
+        which columns in a ``DataFrame`` are analyzed for the output.
+        The parameters are ignored when analyzing a ``Series``.
+
+        Examples
+        --------
+        Describing a ``Series`` containing numeric values.
+
+        >>> import cudf
+        >>> s = cudf.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        >>> s
+        0     1
+        1     2
+        2     3
+        3     4
+        4     5
+        5     6
+        6     7
+        7     8
+        8     9
+        9    10
+        dtype: int64
+        >>> s.describe()
+        count    10.00000
+        mean      5.50000
+        std       3.02765
+        min       1.00000
+        25%       3.25000
+        50%       5.50000
+        75%       7.75000
+        max      10.00000
+        dtype: float64
+
+        Describing a categorical ``Series``.
+
+        >>> s = cudf.Series(['a', 'b', 'a', 'b', 'c', 'a'], dtype='category')
+        >>> s
+        0    a
+        1    b
+        2    a
+        3    b
+        4    c
+        5    a
+        dtype: category
+        Categories (3, object): ['a', 'b', 'c']
+        >>> s.describe()
+        count     6
+        unique    3
+        top       a
+        freq      3
+        dtype: object
+
+        Describing a timestamp ``Series``.
+
+        >>> import numpy as np
+        >>> s = cudf.Series([
+        ...   np.datetime64("2000-01-01"),
+        ...   np.datetime64("2010-01-01"),
+        ...   np.datetime64("2010-01-01")
+        ... ])
+        >>> s
+        0   2000-01-01
+        1   2010-01-01
+        2   2010-01-01
+        dtype: datetime64[s]
+        >>> s.describe()
+        count                     3
+        mean    2006-09-01 08:00:00
+        min     2000-01-01 00:00:00
+        25%     2004-12-31 12:00:00
+        50%     2010-01-01 00:00:00
+        75%     2010-01-01 00:00:00
+        max     2010-01-01 00:00:00
+        dtype: object
+
+        Describing a ``DataFrame``. By default only numeric fields are
+        returned.
+
+        >>> df = cudf.DataFrame({"categorical": cudf.Series(['d', 'e', 'f'],
+        ...                         dtype='category'),
+        ...                      "numeric": [1, 2, 3],
+        ...                      "object": ['a', 'b', 'c']
+        ... })
+        >>> df
+          categorical  numeric object
+        0           d        1      a
+        1           e        2      b
+        2           f        3      c
+        >>> df.describe()
+               numeric
+        count      3.0
+        mean       2.0
+        std        1.0
+        min        1.0
+        25%        1.5
+        50%        2.0
+        75%        2.5
+        max        3.0
+
+        Describing all columns of a ``DataFrame`` regardless of data type.
+
+        >>> df.describe(include='all')
+               categorical numeric object
+        count            3     3.0      3
+        unique           3    <NA>      3
+        top              d    <NA>      a
+        freq             1    <NA>      1
+        mean          <NA>     2.0   <NA>
+        std           <NA>     1.0   <NA>
+        min           <NA>     1.0   <NA>
+        25%           <NA>     1.5   <NA>
+        50%           <NA>     2.0   <NA>
+        75%           <NA>     2.5   <NA>
+        max           <NA>     3.0   <NA>
+
+        Describing a column from a ``DataFrame`` by accessing it as an
+        attribute.
+
+        >>> df.numeric.describe()
+        count    3.0
+        mean     2.0
+        std      1.0
+        min      1.0
+        25%      1.5
+        50%      2.0
+        75%      2.5
+        max      3.0
+        Name: numeric, dtype: float64
+
+        Including only numeric columns in a ``DataFrame`` description.
+
+        >>> df.describe(include=[np.number])
+               numeric
+        count      3.0
+        mean       2.0
+        std        1.0
+        min        1.0
+        25%        1.5
+        50%        2.0
+        75%        2.5
+        max        3.0
+
+        Including only string columns in a ``DataFrame`` description.
+
+        >>> df.describe(include=[object])
+               object
+        count       3
+        unique      3
+        top         a
+        freq        1
+
+        Including only categorical columns from a ``DataFrame`` description.
+
+        >>> df.describe(include=['category'])
+               categorical
+        count            3
+        unique           3
+        top              d
+        freq             1
+
+        Excluding numeric columns from a ``DataFrame`` description.
+
+        >>> df.describe(exclude=[np.number])
+               categorical object
+        count            3      3
+        unique           3      3
+        top              d      a
+        freq             1      1
+
+        Excluding object columns from a ``DataFrame`` description.
+
+        >>> df.describe(exclude=[object])
+               categorical numeric
+        count            3     3.0
+        unique           3    <NA>
+        top              d    <NA>
+        freq             1    <NA>
+        mean          <NA>     2.0
+        std           <NA>     1.0
+        min           <NA>     1.0
+        25%           <NA>     1.5
+        50%           <NA>     2.0
+        75%           <NA>     2.5
+        max           <NA>     3.0
+"""
+)
diff --git a/python/cudf/cudf/utils/dtypes.py b/python/cudf/cudf/utils/dtypes.py
new file mode 100644
index 0000000..73ea8e2
--- /dev/null
+++ b/python/cudf/cudf/utils/dtypes.py
@@ -0,0 +1,857 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import datetime
+from collections import namedtuple
+from decimal import Decimal
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from pandas.core.dtypes.common import infer_dtype_from_object
+
+import cudf
+from cudf._typing import DtypeObj
+from cudf.api.types import is_bool, is_float, is_integer
+
+"""Map numpy dtype to pyarrow types.
+Note that np.bool_ bitwidth (8) is different from pa.bool_ (1). Special
+handling is required when converting a Boolean column into arrow.
+"""
+_np_pa_dtypes = {
+    np.float64: pa.float64(),
+    np.float32: pa.float32(),
+    np.int64: pa.int64(),
+    np.longlong: pa.int64(),
+    np.int32: pa.int32(),
+    np.int16: pa.int16(),
+    np.int8: pa.int8(),
+    np.bool_: pa.bool_(),
+    np.uint64: pa.uint64(),
+    np.uint32: pa.uint32(),
+    np.uint16: pa.uint16(),
+    np.uint8: pa.uint8(),
+    np.datetime64: pa.date64(),
+    np.object_: pa.string(),
+    np.str_: pa.string(),
+}
+
+np_dtypes_to_pandas_dtypes = {
+    np.dtype("uint8"): pd.UInt8Dtype(),
+    np.dtype("uint16"): pd.UInt16Dtype(),
+    np.dtype("uint32"): pd.UInt32Dtype(),
+    np.dtype("uint64"): pd.UInt64Dtype(),
+    np.dtype("int8"): pd.Int8Dtype(),
+    np.dtype("int16"): pd.Int16Dtype(),
+    np.dtype("int32"): pd.Int32Dtype(),
+    np.dtype("int64"): pd.Int64Dtype(),
+    np.dtype("bool_"): pd.BooleanDtype(),
+    np.dtype("object"): pd.StringDtype(),
+}
+
+pyarrow_dtypes_to_pandas_dtypes = {
+    pa.uint8(): pd.UInt8Dtype(),
+    pa.uint16(): pd.UInt16Dtype(),
+    pa.uint32(): pd.UInt32Dtype(),
+    pa.uint64(): pd.UInt64Dtype(),
+    pa.int8(): pd.Int8Dtype(),
+    pa.int16(): pd.Int16Dtype(),
+    pa.int32(): pd.Int32Dtype(),
+    pa.int64(): pd.Int64Dtype(),
+    pa.bool_(): pd.BooleanDtype(),
+    pa.string(): pd.StringDtype(),
+}
+
+pandas_dtypes_to_np_dtypes = {
+    pd.UInt8Dtype(): np.dtype("uint8"),
+    pd.UInt16Dtype(): np.dtype("uint16"),
+    pd.UInt32Dtype(): np.dtype("uint32"),
+    pd.UInt64Dtype(): np.dtype("uint64"),
+    pd.Int8Dtype(): np.dtype("int8"),
+    pd.Int16Dtype(): np.dtype("int16"),
+    pd.Int32Dtype(): np.dtype("int32"),
+    pd.Int64Dtype(): np.dtype("int64"),
+    pd.BooleanDtype(): np.dtype("bool_"),
+    pd.StringDtype(): np.dtype("object"),
+}
+
+pandas_dtypes_alias_to_cudf_alias = {
+    "UInt8": "uint8",
+    "UInt16": "uint16",
+    "UInt32": "uint32",
+    "UInt64": "uint64",
+    "Int8": "int8",
+    "Int16": "int16",
+    "Int32": "int32",
+    "Int64": "int64",
+    "boolean": "bool",
+}
+
+
+np_dtypes_to_pandas_dtypes[np.dtype("float32")] = pd.Float32Dtype()
+np_dtypes_to_pandas_dtypes[np.dtype("float64")] = pd.Float64Dtype()
+pandas_dtypes_to_np_dtypes[pd.Float32Dtype()] = np.dtype("float32")
+pandas_dtypes_to_np_dtypes[pd.Float64Dtype()] = np.dtype("float64")
+pandas_dtypes_alias_to_cudf_alias["Float32"] = "float32"
+pandas_dtypes_alias_to_cudf_alias["Float64"] = "float64"
+
+SIGNED_INTEGER_TYPES = {"int8", "int16", "int32", "int64"}
+UNSIGNED_TYPES = {"uint8", "uint16", "uint32", "uint64"}
+INTEGER_TYPES = SIGNED_INTEGER_TYPES | UNSIGNED_TYPES
+FLOAT_TYPES = {"float32", "float64"}
+SIGNED_TYPES = SIGNED_INTEGER_TYPES | FLOAT_TYPES
+NUMERIC_TYPES = SIGNED_TYPES | UNSIGNED_TYPES
+DATETIME_TYPES = {
+    "datetime64[s]",
+    "datetime64[ms]",
+    "datetime64[us]",
+    "datetime64[ns]",
+}
+TIMEDELTA_TYPES = {
+    "timedelta64[s]",
+    "timedelta64[ms]",
+    "timedelta64[us]",
+    "timedelta64[ns]",
+}
+OTHER_TYPES = {"bool", "category", "str"}
+STRING_TYPES = {"object"}
+BOOL_TYPES = {"bool"}
+ALL_TYPES = NUMERIC_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | OTHER_TYPES
+
+
+def np_to_pa_dtype(dtype):
+    """Util to convert numpy dtype to PyArrow dtype."""
+    # special case when dtype is np.datetime64
+    if dtype.kind == "M":
+        time_unit, _ = np.datetime_data(dtype)
+        if time_unit in ("s", "ms", "us", "ns"):
+            # return a pa.Timestamp of the appropriate unit
+            return pa.timestamp(time_unit)
+        # default is int64_t UNIX ms
+        return pa.date64()
+    elif dtype.kind == "m":
+        time_unit, _ = np.datetime_data(dtype)
+        if time_unit in ("s", "ms", "us", "ns"):
+            # return a pa.Duration of the appropriate unit
+            return pa.duration(time_unit)
+        # default fallback unit is ns
+        return pa.duration("ns")
+    return _np_pa_dtypes[cudf.dtype(dtype).type]
+
+
+def get_numeric_type_info(dtype):
+    _TypeMinMax = namedtuple("_TypeMinMax", "min,max")
+    if dtype.kind in {"i", "u"}:
+        info = np.iinfo(dtype)
+        return _TypeMinMax(info.min, info.max)
+    elif dtype.kind == "f":
+        return _TypeMinMax(dtype.type("-inf"), dtype.type("+inf"))
+    else:
+        raise TypeError(dtype)
+
+
+def numeric_normalize_types(*args):
+    """Cast all args to a common type using numpy promotion logic"""
+    dtype = np.result_type(*[a.dtype for a in args])
+    return [a.astype(dtype) for a in args]
+
+
+def _find_common_type_decimal(dtypes):
+    # Find the largest scale and the largest difference between
+    # precision and scale of the columns to be concatenated
+    s = max(dtype.scale for dtype in dtypes)
+    lhs = max(dtype.precision - dtype.scale for dtype in dtypes)
+    # Combine to get the necessary precision and clip at the maximum
+    # precision
+    p = s + lhs
+
+    if p > cudf.Decimal64Dtype.MAX_PRECISION:
+        return cudf.Decimal128Dtype(
+            min(cudf.Decimal128Dtype.MAX_PRECISION, p), s
+        )
+    elif p > cudf.Decimal32Dtype.MAX_PRECISION:
+        return cudf.Decimal64Dtype(
+            min(cudf.Decimal64Dtype.MAX_PRECISION, p), s
+        )
+    else:
+        return cudf.Decimal32Dtype(
+            min(cudf.Decimal32Dtype.MAX_PRECISION, p), s
+        )
+
+
+def cudf_dtype_from_pydata_dtype(dtype):
+    """Given a numpy or pandas dtype, converts it into the equivalent cuDF
+    Python dtype.
+    """
+
+    if cudf.api.types.is_categorical_dtype(dtype):
+        return cudf.core.dtypes.CategoricalDtype
+    elif cudf.api.types.is_decimal32_dtype(dtype):
+        return cudf.core.dtypes.Decimal32Dtype
+    elif cudf.api.types.is_decimal64_dtype(dtype):
+        return cudf.core.dtypes.Decimal64Dtype
+    elif cudf.api.types.is_decimal128_dtype(dtype):
+        return cudf.core.dtypes.Decimal128Dtype
+    elif dtype in cudf._lib.types.SUPPORTED_NUMPY_TO_LIBCUDF_TYPES:
+        return dtype.type
+
+    return infer_dtype_from_object(dtype)
+
+
+def cudf_dtype_to_pa_type(dtype):
+    """Given a cudf pandas dtype, converts it into the equivalent cuDF
+    Python dtype.
+    """
+    if cudf.api.types.is_categorical_dtype(dtype):
+        raise NotImplementedError()
+    elif (
+        cudf.api.types.is_list_dtype(dtype)
+        or cudf.api.types.is_struct_dtype(dtype)
+        or cudf.api.types.is_decimal_dtype(dtype)
+    ):
+        return dtype.to_arrow()
+    else:
+        return np_to_pa_dtype(cudf.dtype(dtype))
+
+
+def cudf_dtype_from_pa_type(typ):
+    """Given a cuDF pyarrow dtype, converts it into the equivalent
+    cudf pandas dtype.
+    """
+    if pa.types.is_list(typ):
+        return cudf.core.dtypes.ListDtype.from_arrow(typ)
+    elif pa.types.is_struct(typ):
+        return cudf.core.dtypes.StructDtype.from_arrow(typ)
+    elif pa.types.is_decimal(typ):
+        return cudf.core.dtypes.Decimal128Dtype.from_arrow(typ)
+    else:
+        return cudf.api.types.pandas_dtype(typ.to_pandas_dtype())
+
+
+def to_cudf_compatible_scalar(val, dtype=None):
+    """
+    Converts the value `val` to a numpy/Pandas scalar,
+    optionally casting to `dtype`.
+
+    If `val` is None, returns None.
+    """
+
+    if cudf._lib.scalar._is_null_host_scalar(val) or isinstance(
+        val, cudf.Scalar
+    ):
+        return val
+
+    if not cudf.api.types._is_scalar_or_zero_d_array(val):
+        raise ValueError(
+            f"Cannot convert value of type {type(val).__name__} "
+            "to cudf scalar"
+        )
+
+    if isinstance(val, Decimal):
+        return val
+
+    if isinstance(val, (np.ndarray, cp.ndarray)) and val.ndim == 0:
+        val = val.item()
+
+    if (
+        (dtype is None) and isinstance(val, str)
+    ) or cudf.api.types.is_string_dtype(dtype):
+        dtype = "str"
+
+        if isinstance(val, str) and val.endswith("\x00"):
+            # Numpy string dtypes are fixed width and use NULL to
+            # indicate the end of the string, so they cannot
+            # distinguish between "abc\x00" and "abc".
+            # https://github.com/numpy/numpy/issues/20118
+            # In this case, don't try going through numpy and just use
+            # the string value directly (cudf.DeviceScalar will DTRT)
+            return val
+
+    tz_error_msg = (
+        "Cannot covert a timezone-aware timestamp to timezone-naive scalar."
+    )
+    if isinstance(val, pd.Timestamp):
+        if val.tz is not None:
+            raise NotImplementedError(tz_error_msg)
+        val = val.to_datetime64()
+    elif isinstance(val, pd.Timedelta):
+        val = val.to_timedelta64()
+    elif isinstance(val, datetime.datetime):
+        if val.tzinfo is not None:
+            raise NotImplementedError(tz_error_msg)
+        val = np.datetime64(val)
+    elif isinstance(val, datetime.timedelta):
+        val = np.timedelta64(val)
+
+    val = _maybe_convert_to_default_type(
+        cudf.api.types.pandas_dtype(type(val))
+    ).type(val)
+
+    if dtype is not None:
+        if isinstance(val, str) and np.dtype(dtype).kind == "M":
+            # pd.Timestamp can handle str, but not np.str_
+            val = pd.Timestamp(str(val)).to_datetime64().astype(dtype)
+        else:
+            val = val.astype(dtype)
+
+    if val.dtype.type is np.datetime64:
+        time_unit, _ = np.datetime_data(val.dtype)
+        if time_unit in ("D", "W", "M", "Y"):
+            val = val.astype("datetime64[s]")
+    elif val.dtype.type is np.timedelta64:
+        time_unit, _ = np.datetime_data(val.dtype)
+        if time_unit in ("D", "W", "M", "Y"):
+            val = val.astype("timedelta64[ns]")
+
+    return val
+
+
+def is_column_like(obj):
+    """
+    This function checks if the given `obj`
+    is a column-like (Series, Index...)
+    type or not.
+
+    Parameters
+    ----------
+    obj : object of any type which needs to be validated.
+
+    Returns
+    -------
+    Boolean: True or False depending on whether the
+    input `obj` is column-like or not.
+    """
+    return (
+        isinstance(
+            obj,
+            (
+                cudf.core.column.ColumnBase,
+                cudf.Series,
+                cudf.Index,
+                pd.Series,
+                pd.Index,
+            ),
+        )
+        or (
+            hasattr(obj, "__cuda_array_interface__")
+            and len(obj.__cuda_array_interface__["shape"]) == 1
+        )
+        or (
+            hasattr(obj, "__array_interface__")
+            and len(obj.__array_interface__["shape"]) == 1
+        )
+    )
+
+
+def can_convert_to_column(obj):
+    """
+    This function checks if the given `obj`
+    can be used to create a column or not.
+
+    Parameters
+    ----------
+    obj : object of any type which needs to be validated.
+
+    Returns
+    -------
+    Boolean: True or False depending on whether the
+    input `obj` is column-compatible or not.
+    """
+    return is_column_like(obj) or cudf.api.types.is_list_like(obj)
+
+
+def min_scalar_type(a, min_size=8):
+    return min_signed_type(a, min_size=min_size)
+
+
+def min_signed_type(x, min_size=8):
+    """
+    Return the smallest *signed* integer dtype
+    that can represent the integer ``x``
+    """
+    for int_dtype in np.sctypes["int"]:
+        if (cudf.dtype(int_dtype).itemsize * 8) >= min_size:
+            if np.iinfo(int_dtype).min <= x <= np.iinfo(int_dtype).max:
+                return int_dtype
+    # resort to using `int64` and let numpy raise appropriate exception:
+    return np.int64(x).dtype
+
+
+def min_unsigned_type(x, min_size=8):
+    """
+    Return the smallest *unsigned* integer dtype
+    that can represent the integer ``x``
+    """
+    for int_dtype in np.sctypes["uint"]:
+        if (cudf.dtype(int_dtype).itemsize * 8) >= min_size:
+            if 0 <= x <= np.iinfo(int_dtype).max:
+                return int_dtype
+    # resort to using `uint64` and let numpy raise appropriate exception:
+    return np.uint64(x).dtype
+
+
+def min_column_type(x, expected_type):
+    """
+    Return the smallest dtype which can represent all
+    elements of the `NumericalColumn` `x`
+    If the column is not a subtype of `np.signedinteger` or `np.floating`
+    returns the same dtype as the dtype of `x` without modification
+    """
+
+    if not isinstance(x, cudf.core.column.NumericalColumn):
+        raise TypeError("Argument x must be of type column.NumericalColumn")
+    if x.valid_count == 0:
+        return x.dtype
+
+    if np.issubdtype(x.dtype, np.floating):
+        return get_min_float_dtype(x)
+
+    elif np.issubdtype(expected_type, np.integer):
+        max_bound_dtype = np.min_scalar_type(x.max())
+        min_bound_dtype = np.min_scalar_type(x.min())
+        result_type = np.promote_types(max_bound_dtype, min_bound_dtype)
+    else:
+        result_type = x.dtype
+
+    return cudf.dtype(result_type)
+
+
+def get_min_float_dtype(col):
+    max_bound_dtype = np.min_scalar_type(float(col.max()))
+    min_bound_dtype = np.min_scalar_type(float(col.min()))
+    result_type = np.promote_types(
+        "float32", np.promote_types(max_bound_dtype, min_bound_dtype)
+    )
+    return cudf.dtype(result_type)
+
+
+def is_mixed_with_object_dtype(lhs, rhs):
+    if cudf.api.types.is_categorical_dtype(lhs.dtype):
+        return is_mixed_with_object_dtype(lhs.dtype.categories, rhs)
+    elif cudf.api.types.is_categorical_dtype(rhs.dtype):
+        return is_mixed_with_object_dtype(lhs, rhs.dtype.categories)
+
+    return (lhs.dtype == "object" and rhs.dtype != "object") or (
+        rhs.dtype == "object" and lhs.dtype != "object"
+    )
+
+
+def get_time_unit(obj):
+    if isinstance(
+        obj,
+        (
+            cudf.core.column.datetime.DatetimeColumn,
+            cudf.core.column.timedelta.TimeDeltaColumn,
+        ),
+    ):
+        return obj.time_unit
+
+    time_unit, _ = np.datetime_data(obj.dtype)
+    return time_unit
+
+
+def _get_nan_for_dtype(dtype):
+    dtype = cudf.dtype(dtype)
+    if pd.api.types.is_datetime64_dtype(
+        dtype
+    ) or pd.api.types.is_timedelta64_dtype(dtype):
+        time_unit, _ = np.datetime_data(dtype)
+        return dtype.type("nat", time_unit)
+    elif dtype.kind == "f":
+        return dtype.type("nan")
+    else:
+        return np.float64("nan")
+
+
+def get_allowed_combinations_for_operator(dtype_l, dtype_r, op):
+    error = TypeError(
+        f"{op} not supported between {dtype_l} and {dtype_r} scalars"
+    )
+
+    to_numpy_ops = {
+        "__add__": _ADD_TYPES,
+        "__radd__": _ADD_TYPES,
+        "__sub__": _SUB_TYPES,
+        "__rsub__": _SUB_TYPES,
+        "__mul__": _MUL_TYPES,
+        "__rmul__": _MUL_TYPES,
+        "__floordiv__": _FLOORDIV_TYPES,
+        "__rfloordiv__": _FLOORDIV_TYPES,
+        "__truediv__": _TRUEDIV_TYPES,
+        "__rtruediv__": _TRUEDIV_TYPES,
+        "__mod__": _MOD_TYPES,
+        "__rmod__": _MOD_TYPES,
+        "__pow__": _POW_TYPES,
+        "__rpow__": _POW_TYPES,
+    }
+    allowed = to_numpy_ops.get(op, op)
+
+    # special rules for string
+    if dtype_l == "object" or dtype_r == "object":
+        if (dtype_l == dtype_r == "object") and op == "__add__":
+            return "str"
+        else:
+            raise error
+
+    # Check if we can directly operate
+
+    for valid_combo in allowed:
+        ltype, rtype, outtype = valid_combo
+        if np.can_cast(dtype_l.char, ltype) and np.can_cast(
+            dtype_r.char, rtype
+        ):
+            return outtype
+
+    raise error
+
+
+def find_common_type(dtypes):
+    """
+    Wrapper over np.find_common_type to handle special cases
+
+    Corner cases:
+    1. "M8", "M8" -> "M8" | "m8", "m8" -> "m8"
+
+    Parameters
+    ----------
+    dtypes : iterable, sequence of dtypes to find common types
+
+    Returns
+    -------
+    dtype : np.dtype optional, the result from np.find_common_type,
+    None if input is empty
+
+    """
+
+    if len(dtypes) == 0:
+        return None
+
+    # Early exit for categoricals since they're not hashable and therefore
+    # can't be put in a set.
+    if any(cudf.api.types.is_categorical_dtype(dtype) for dtype in dtypes):
+        if all(
+            (
+                cudf.api.types.is_categorical_dtype(dtype)
+                and (not dtype.ordered if hasattr(dtype, "ordered") else True)
+            )
+            for dtype in dtypes
+        ):
+            if len({dtype._categories.dtype for dtype in dtypes}) == 1:
+                return cudf.CategoricalDtype(
+                    cudf.core.column.concat_columns(
+                        [dtype._categories for dtype in dtypes]
+                    ).unique()
+                )
+            else:
+                raise ValueError(
+                    "Only unordered categories of the same underlying type "
+                    "may be coerced to a common type."
+                )
+        else:
+            # TODO: Should this be an error case (mixing categorical with other
+            # dtypes) or should this return object? Unclear if we have enough
+            # information to decide right now, may have to come back to this as
+            # usage of find_common_type increases.
+            return cudf.dtype("O")
+
+    # Aggregate same types
+    dtypes = set(dtypes)
+
+    if any(cudf.api.types.is_decimal_dtype(dtype) for dtype in dtypes):
+        if all(
+            cudf.api.types.is_decimal_dtype(dtype)
+            or cudf.api.types.is_numeric_dtype(dtype)
+            for dtype in dtypes
+        ):
+            return _find_common_type_decimal(
+                [
+                    dtype
+                    for dtype in dtypes
+                    if cudf.api.types.is_decimal_dtype(dtype)
+                ]
+            )
+        else:
+            return cudf.dtype("O")
+    if any(cudf.api.types.is_list_dtype(dtype) for dtype in dtypes):
+        if len(dtypes) == 1:
+            return dtypes.get(0)
+        else:
+            # TODO: As list dtypes allow casting
+            # to identical types, improve this logic of returning a
+            # common dtype, for example:
+            # ListDtype(int64) & ListDtype(int32) common
+            # dtype could be ListDtype(int64).
+            raise NotImplementedError(
+                "Finding a common type for `ListDtype` is currently "
+                "not supported"
+            )
+    if any(cudf.api.types.is_struct_dtype(dtype) for dtype in dtypes):
+        if len(dtypes) == 1:
+            return dtypes.get(0)
+        else:
+            raise NotImplementedError(
+                "Finding a common type for `StructDtype` is currently "
+                "not supported"
+            )
+
+    # Corner case 1:
+    # Resort to np.result_type to handle "M" and "m" types separately
+    dt_dtypes = set(
+        filter(lambda t: cudf.api.types.is_datetime_dtype(t), dtypes)
+    )
+    if len(dt_dtypes) > 0:
+        dtypes = dtypes - dt_dtypes
+        dtypes.add(np.result_type(*dt_dtypes))
+
+    td_dtypes = set(
+        filter(lambda t: pd.api.types.is_timedelta64_dtype(t), dtypes)
+    )
+    if len(td_dtypes) > 0:
+        dtypes = dtypes - td_dtypes
+        dtypes.add(np.result_type(*td_dtypes))
+
+    common_dtype = np.find_common_type(list(dtypes), [])
+    if common_dtype == np.dtype("float16"):
+        return cudf.dtype("float32")
+    return cudf.dtype(common_dtype)
+
+
+def _dtype_pandas_compatible(dtype):
+    """
+    A utility function, that returns `str` instead of `object`
+    dtype when pandas comptibility mode is enabled.
+    """
+    if cudf.get_option("mode.pandas_compatible") and dtype == cudf.dtype("O"):
+        return "str"
+    return dtype
+
+
+def _can_cast(from_dtype, to_dtype):
+    """
+    Utility function to determine if we can cast
+    from `from_dtype` to `to_dtype`. This function primarily calls
+    `np.can_cast` but with some special handling around
+    cudf specific dtypes.
+    """
+    if cudf.utils.utils.is_na_like(from_dtype):
+        return True
+    if isinstance(from_dtype, type):
+        from_dtype = cudf.dtype(from_dtype)
+    if isinstance(to_dtype, type):
+        to_dtype = cudf.dtype(to_dtype)
+
+    # TODO : Add precision & scale checking for
+    # decimal types in future
+
+    if isinstance(from_dtype, cudf.core.dtypes.DecimalDtype):
+        if isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):
+            return True
+        elif isinstance(to_dtype, np.dtype):
+            if to_dtype.kind in {"i", "f", "u", "U", "O"}:
+                return True
+            else:
+                return False
+    elif isinstance(from_dtype, np.dtype):
+        if isinstance(to_dtype, np.dtype):
+            return np.can_cast(from_dtype, to_dtype)
+        elif isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):
+            if from_dtype.kind in {"i", "f", "u", "U", "O"}:
+                return True
+            else:
+                return False
+        elif isinstance(to_dtype, cudf.core.types.CategoricalDtype):
+            return True
+        else:
+            return False
+    elif isinstance(from_dtype, cudf.core.dtypes.ListDtype):
+        # TODO: Add level based checks too once casting of
+        # list columns is supported
+        if isinstance(to_dtype, cudf.core.dtypes.ListDtype):
+            return np.can_cast(from_dtype.leaf_type, to_dtype.leaf_type)
+        else:
+            return False
+    elif isinstance(from_dtype, cudf.core.dtypes.CategoricalDtype):
+        if isinstance(to_dtype, cudf.core.dtypes.CategoricalDtype):
+            return True
+        elif isinstance(to_dtype, np.dtype):
+            return np.can_cast(from_dtype._categories.dtype, to_dtype)
+        else:
+            return False
+    else:
+        return np.can_cast(from_dtype, to_dtype)
+
+
+def _maybe_convert_to_default_type(dtype):
+    """Convert `dtype` to default if specified by user.
+
+    If not specified, return as is.
+    """
+    if cudf.get_option("default_integer_bitwidth"):
+        if cudf.api.types.is_signed_integer_dtype(dtype):
+            return cudf.dtype(
+                f'i{cudf.get_option("default_integer_bitwidth")//8}'
+            )
+        elif cudf.api.types.is_unsigned_integer_dtype(dtype):
+            return cudf.dtype(
+                f'u{cudf.get_option("default_integer_bitwidth")//8}'
+            )
+    if cudf.get_option(
+        "default_float_bitwidth"
+    ) and cudf.api.types.is_float_dtype(dtype):
+        return cudf.dtype(f'f{cudf.get_option("default_float_bitwidth")//8}')
+
+    return dtype
+
+
+def _dtype_can_hold_range(rng: range, dtype: np.dtype) -> bool:
+    if not len(rng):
+        return True
+    return np.can_cast(rng[0], dtype) and np.can_cast(rng[-1], dtype)
+
+
+def _dtype_can_hold_element(dtype: np.dtype, element) -> bool:
+    if dtype.kind in {"i", "u"}:
+        if isinstance(element, range):
+            if _dtype_can_hold_range(element, dtype):
+                return True
+            return False
+
+        elif is_integer(element) or (
+            is_float(element) and element.is_integer()
+        ):
+            info = np.iinfo(dtype)
+            if info.min <= element <= info.max:
+                return True
+            return False
+
+    elif dtype.kind == "f":
+        if is_integer(element) or is_float(element):
+            casted = dtype.type(element)
+            if np.isnan(casted) or casted == element:
+                return True
+            # otherwise e.g. overflow see TestCoercionFloat32
+            return False
+
+    elif dtype.kind == "b":
+        if is_bool(element):
+            return True
+        return False
+
+    raise NotImplementedError(f"Unsupported dtype: {dtype}")
+
+
+def _get_base_dtype(dtype: DtypeObj) -> DtypeObj:
+    # TODO: replace the use of this function with just `dtype.base`
+    # when Pandas 2.1.0 is the minimum version we support:
+    # https://github.com/pandas-dev/pandas/pull/52706
+    if isinstance(dtype, pd.DatetimeTZDtype):
+        return np.dtype(f"<M8[{dtype.unit}]")
+    else:
+        return dtype.base
+
+
+# Type dispatch loops similar to what are found in `np.add.types`
+# In NumPy, whether or not an op can be performed between two
+# operands is determined by checking to see if NumPy has a c/c++
+# loop specifically for adding those two operands built in. If
+# not it will search lists like these for a loop for types that
+# the operands can be safely cast to. These are those lookups,
+# modified slightly for cuDF's rules
+_ADD_TYPES = [
+    "???",
+    "BBB",
+    "HHH",
+    "III",
+    "LLL",
+    "bbb",
+    "hhh",
+    "iii",
+    "lll",
+    "fff",
+    "ddd",
+    "mMM",
+    "MmM",
+    "mmm",
+    "LMM",
+    "MLM",
+    "Lmm",
+    "mLm",
+]
+_SUB_TYPES = [
+    "BBB",
+    "HHH",
+    "III",
+    "LLL",
+    "bbb",
+    "hhh",
+    "iii",
+    "lll",
+    "fff",
+    "ddd",
+    "???",
+    "MMm",
+    "mmm",
+    "MmM",
+    "MLM",
+    "mLm",
+    "Lmm",
+]
+_MUL_TYPES = [
+    "???",
+    "BBB",
+    "HHH",
+    "III",
+    "LLL",
+    "bbb",
+    "hhh",
+    "iii",
+    "lll",
+    "fff",
+    "ddd",
+    "mLm",
+    "Lmm",
+    "mlm",
+    "lmm",
+]
+_FLOORDIV_TYPES = [
+    "bbb",
+    "BBB",
+    "HHH",
+    "III",
+    "LLL",
+    "hhh",
+    "iii",
+    "lll",
+    "fff",
+    "ddd",
+    "???",
+    "mqm",
+    "mdm",
+    "mmq",
+]
+_TRUEDIV_TYPES = ["fff", "ddd", "mqm", "mmd", "mLm"]
+_MOD_TYPES = [
+    "bbb",
+    "BBB",
+    "hhh",
+    "HHH",
+    "iii",
+    "III",
+    "lll",
+    "LLL",
+    "fff",
+    "ddd",
+    "mmm",
+]
+_POW_TYPES = [
+    "bbb",
+    "BBB",
+    "hhh",
+    "HHH",
+    "iii",
+    "III",
+    "lll",
+    "LLL",
+    "fff",
+    "ddd",
+]
diff --git a/python/cudf/cudf/utils/gpu_utils.py b/python/cudf/cudf/utils/gpu_utils.py
new file mode 100644
index 0000000..ab3adc1
--- /dev/null
+++ b/python/cudf/cudf/utils/gpu_utils.py
@@ -0,0 +1,148 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+
+
+def validate_setup():
+    import os
+
+    # TODO: Remove the following check once we arrive at a solution for #4827
+    # This is a temporary workaround to unblock internal testing
+    # related issue: https://github.com/rapidsai/cudf/issues/4827
+    if (
+        "RAPIDS_NO_INITIALIZE" in os.environ
+        or "CUDF_NO_INITIALIZE" in os.environ
+    ):
+        return
+
+    import warnings
+
+    from cuda.cudart import cudaDeviceAttr, cudaError_t
+
+    from rmm._cuda.gpu import (
+        CUDARuntimeError,
+        deviceGetName,
+        driverGetVersion,
+        getDeviceAttribute,
+        getDeviceCount,
+        runtimeGetVersion,
+    )
+
+    from cudf.errors import UnsupportedCUDAError
+
+    notify_caller_errors = {
+        cudaError_t.cudaErrorInitializationError,
+        cudaError_t.cudaErrorInsufficientDriver,
+        cudaError_t.cudaErrorInvalidDeviceFunction,
+        cudaError_t.cudaErrorInvalidDevice,
+        cudaError_t.cudaErrorStartupFailure,
+        cudaError_t.cudaErrorInvalidKernelImage,
+        cudaError_t.cudaErrorAlreadyAcquired,
+        cudaError_t.cudaErrorOperatingSystem,
+        cudaError_t.cudaErrorNotPermitted,
+        cudaError_t.cudaErrorNotSupported,
+        cudaError_t.cudaErrorSystemNotReady,
+        cudaError_t.cudaErrorSystemDriverMismatch,
+        cudaError_t.cudaErrorCompatNotSupportedOnDevice,
+        cudaError_t.cudaErrorDeviceUninitialized,
+        cudaError_t.cudaErrorTimeout,
+        cudaError_t.cudaErrorUnknown,
+        cudaError_t.cudaErrorApiFailureBase,
+    }
+
+    try:
+        gpus_count = getDeviceCount()
+    except CUDARuntimeError as e:
+        if e.status in notify_caller_errors:
+            raise e
+        # If there is no GPU detected, set `gpus_count` to -1
+        gpus_count = -1
+    except RuntimeError as e:
+        # getDeviceCount() can raise a RuntimeError
+        # when ``libcuda.so`` is missing.
+        # We don't want this to propagate up to the user.
+        warnings.warn(str(e))
+        return
+
+    if gpus_count > 0:
+        # Cupy throws RunTimeException to get GPU count,
+        # hence obtaining GPU count by in-house cpp api above
+
+        major_version = getDeviceAttribute(
+            cudaDeviceAttr.cudaDevAttrComputeCapabilityMajor, 0
+        )
+
+        if major_version < 6:
+            # A GPU with NVIDIA Pascal™ architecture or newer is required.
+            # Reference: https://developer.nvidia.com/cuda-gpus
+            # Hardware Generation	Compute Capability
+            #    Ampere	                8.x
+            #    Turing	                7.5
+            #    Volta	                7.0, 7.2
+            #    Pascal	                6.x
+            #    Maxwell                5.x
+            #    Kepler	                3.x
+            #    Fermi	                2.x
+            device_name = deviceGetName(0)
+            minor_version = getDeviceAttribute(
+                cudaDeviceAttr.cudaDevAttrComputeCapabilityMinor, 0
+            )
+            warnings.warn(
+                "A GPU with NVIDIA Pascal™ (Compute Capability 6.0) "
+                "or newer architecture is required.\n"
+                f"Detected GPU 0: {device_name}\n"
+                f"Detected Compute Capability: {major_version}.{minor_version}"
+            )
+
+        cuda_runtime_version = runtimeGetVersion()
+
+        if cuda_runtime_version < 11000:
+            # Require CUDA Runtime version 11.0 or greater.
+            major_version = cuda_runtime_version // 1000
+            minor_version = (cuda_runtime_version % 1000) // 10
+            raise UnsupportedCUDAError(
+                "Detected CUDA Runtime version is "
+                f"{major_version}.{minor_version}. "
+                "Please update your CUDA Runtime to 11.0 or above."
+            )
+
+        cuda_driver_supported_rt_version = driverGetVersion()
+
+        # Though Yes, Externally driver version is represented like `418.39`
+        # and cuda runtime version like `10.1`. It is not the similar case
+        # at cuda api's level. Coming down to APIs they follow a uniform
+        # convention of an integer which corresponds to the versioning
+        # like (1000 major + 10 minor) for 10.1 Driver version API doesn't
+        # actually indicate driver version, it indicates only the latest
+        # CUDA version supported by the driver.
+        # For reference :
+        # https://docs.nvidia.com/deploy/cuda-compatibility/index.html
+
+        if cuda_driver_supported_rt_version == 0:
+            raise UnsupportedCUDAError(
+                "We couldn't detect the GPU driver properly. Please follow "
+                "the installation guide to ensure your driver is properly "
+                "installed: "
+                "https://docs.nvidia.com/cuda/cuda-installation-guide-linux/"
+            )
+        elif cuda_driver_supported_rt_version >= cuda_runtime_version:
+            # CUDA Driver Version Check:
+            # Driver Runtime version is >= Runtime version
+            pass
+        elif (
+            cuda_driver_supported_rt_version >= 11000
+            and cuda_runtime_version >= 11000
+        ):
+            # With cuda enhanced compatibility any code compiled
+            # with 11.x version of cuda can now run on any
+            # driver >= 450.80.02. 11000 is the minimum cuda
+            # version 450.80.02 supports.
+            pass
+        else:
+            raise UnsupportedCUDAError(
+                "Please update your NVIDIA GPU Driver to support CUDA "
+                "Runtime.\n"
+                f"Detected CUDA Runtime version : {cuda_runtime_version}\n"
+                "Latest version of CUDA supported by current "
+                f"NVIDIA GPU Driver : {cuda_driver_supported_rt_version}"
+            )
+    else:
+        warnings.warn("No NVIDIA GPU detected")
diff --git a/python/cudf/cudf/utils/hash_vocab_utils.py b/python/cudf/cudf/utils/hash_vocab_utils.py
new file mode 100644
index 0000000..a091595
--- /dev/null
+++ b/python/cudf/cudf/utils/hash_vocab_utils.py
@@ -0,0 +1,299 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# This function is from the rapidsai/clx repo at below link
+# https://github.com/rapidsai/clx/blob/267c6d30805c9dcbf80840f222bf31c5c4b7068a/python/clx/analytics/_perfect_hash.py
+import numpy as np
+
+PRIME = np.uint64(281474976710677)
+
+# Coefficients ranges for inner hash - This are important to set to be
+# large so that we have randomness in the bottom bits when modding
+A_SECOND_LEVEL_POW = np.uint8(48)
+B_SECOND_LEVEL_POW = np.uint8(7)
+
+A_LBOUND_SECOND_LEVEL_HASH = 2**16
+A_HBOUND_SECOND_LEVEL_HASH = 2**A_SECOND_LEVEL_POW
+
+B_LBOUND_SECOND_LEVEL_HASH = 0
+B_HBOUND_SECOND_LEVEL_HASH = 2**B_SECOND_LEVEL_POW
+
+# Extremely generous and should not ever happen. This limit is imposed
+# To ensure we can bit pack all the information needed for the bin hash
+# functions - a, b and table size
+MAX_SIZE_FOR_INITIAL_BIN = 2**8 - 1
+
+
+# Shifts for bit packing
+A_SECOND_LEVEL_SHIFT_AMT = np.uint8(64 - A_SECOND_LEVEL_POW)
+B_SECOND_LEVEL_SHIFT_AMT = np.uint8(
+    64 - A_SECOND_LEVEL_POW - B_SECOND_LEVEL_POW
+)
+BITS_FOR_INNER_TABLE_SIZE = np.uint8(8)
+
+NOT_FOUND = -1
+
+
+def _sdbm_hash(string):
+    hv = 0
+    mask = (1 << 48) - 1
+    for c in string:
+        hv = ord(c) + (hv << 6) + (hv << 16) - hv
+        hv &= mask
+    return hv
+
+
+def _hash_func(k, a, b, size):
+    k = np.uint64(k)
+    a = np.uint64(a)
+    b = np.uint64(b)
+    size = np.uint64(size)
+    return ((a * k + b) % PRIME) % size
+
+
+def _longest_bin_length(bins):
+    return len(max(bins, key=len))
+
+
+def _make_bins(data, num_bins, a, b):
+    bins = [[] for i in range(num_bins)]
+
+    for item in data:
+        bins[_hash_func(item, a, b, num_bins)].append(item)
+    return bins
+
+
+def _new_bin_length(orig_length):
+    return int(orig_length)
+
+
+def _get_space_util(bins, init_bins):
+    return sum(_new_bin_length(len(b)) for b in bins) + 2 * init_bins
+
+
+def _pick_initial_a_b(data, max_constant, init_bins):
+    while True:
+        a = np.random.randint(2**12, 2**15)
+        b = np.random.randint(2**12, 2**15)
+        bins = _make_bins(data, init_bins, a, b)
+        score = _get_space_util(bins, init_bins) / len(data)
+
+        longest = _new_bin_length(_longest_bin_length(bins))
+
+        if score <= max_constant and longest <= MAX_SIZE_FOR_INITIAL_BIN:
+            print(f"Attempting to build table using {score:.6f}n space")
+            print(f"Longest bin was {longest}")
+            break
+
+    return bins, a, b
+
+
+def _find_hash_for_internal(hash_bin):
+    if not hash_bin:
+        return [[], 0, 0]
+
+    new_length = _new_bin_length(len(hash_bin))
+
+    while True:
+        a = np.random.randint(
+            A_LBOUND_SECOND_LEVEL_HASH, A_HBOUND_SECOND_LEVEL_HASH
+        )
+        b = np.random.randint(
+            B_LBOUND_SECOND_LEVEL_HASH, B_HBOUND_SECOND_LEVEL_HASH
+        )
+        bins = _make_bins(hash_bin, new_length, a, b)
+
+        max_length = len(max(bins, key=len))
+        if max_length == 1:
+            bins = [b[0] if b else 0 for b in bins]
+            return bins, a, b
+
+
+def _perfect_hash(integers, max_constant):
+    num_top_level_bins = len(integers) // 4
+
+    init_bins, init_a, init_b = _pick_initial_a_b(
+        integers, max_constant, num_top_level_bins
+    )
+    flattened_bins = []
+
+    internal_table_coeffs = np.zeros(
+        shape=[num_top_level_bins], dtype=np.uint64
+    )
+    offset_into_flattened_table = np.zeros(
+        shape=[num_top_level_bins + 1], dtype=np.uint64
+    )
+
+    max_bin_length = 0
+    for i, b in enumerate(init_bins):
+        if i % 500 == 0:
+            print(f"Processing bin {i} / {len(init_bins)} of size = {len(b)}")
+        internal_table, coeff_a, coeff_b = _find_hash_for_internal(b)
+        bin_length = len(internal_table)
+        max_bin_length = max(bin_length, max_bin_length)
+        internal_table_coeffs[i] = (
+            coeff_a << A_SECOND_LEVEL_SHIFT_AMT
+            | coeff_b << B_SECOND_LEVEL_SHIFT_AMT
+            | bin_length
+        )
+        offset_into_flattened_table[i + 1] = (
+            offset_into_flattened_table[i] + bin_length
+        )
+        flattened_bins.extend(internal_table)
+
+    print(
+        "Final table size {} elements compared to {} for original".format(
+            len(flattened_bins), len(integers)
+        )
+    )
+
+    print("Max bin length was", max_bin_length)
+
+    return (
+        init_a,
+        init_b,
+        num_top_level_bins,
+        flattened_bins,
+        internal_table_coeffs,
+        offset_into_flattened_table,
+    )
+
+
+def _pack_keys_and_values(flattened_hash_table, original_dict):
+
+    for i in range(len(flattened_hash_table)):
+        if flattened_hash_table[i] in original_dict:
+            value = original_dict[flattened_hash_table[i]]
+            flattened_hash_table[i] <<= 16
+            flattened_hash_table[i] |= value
+
+
+def _load_vocab_dict(path):
+    vocab = {}
+    with open(path, encoding="utf-8") as f:
+        counter = 0
+        for line in f:
+            vocab[line.strip()] = counter
+            counter += 1
+
+    return vocab
+
+
+def _store_func(
+    out_name,
+    outer_a,
+    outer_b,
+    num_outer_bins,
+    hash_table,
+    inner_table_coeffs,
+    offsets_into_ht,
+    unk_tok_id,
+    first_token_id,
+    sep_token_id,
+):
+
+    with open(out_name, mode="w+") as f:
+        f.write(f"{outer_a}\n")
+        f.write(f"{outer_b}\n")
+        f.write(f"{num_outer_bins}\n")
+        f.writelines(
+            f"{coeff} {offset}\n"
+            for coeff, offset in zip(inner_table_coeffs, offsets_into_ht)
+        )
+        f.write(f"{len(hash_table)}\n")
+        f.writelines(f"{kv}\n" for kv in hash_table)
+        f.writelines(
+            f"{tok_id}\n"
+            for tok_id in [unk_tok_id, first_token_id, sep_token_id]
+        )
+
+
+def _retrieve(
+    k,
+    outer_a,
+    outer_b,
+    num_outer_bins,
+    hash_table,
+    inner_table_coeffs,
+    offsets_into_ht,
+):
+
+    bin_hash = _hash_func(k, outer_a, outer_b, num_outer_bins)
+    start_offset_in_ht = offsets_into_ht[bin_hash]
+    inner_table_values = inner_table_coeffs[bin_hash]
+
+    one = np.uint64(1)
+
+    inner_a = inner_table_values >> A_SECOND_LEVEL_SHIFT_AMT
+    inner_b = (inner_table_values >> B_SECOND_LEVEL_SHIFT_AMT) & (
+        (one << B_SECOND_LEVEL_POW) - one
+    )
+    size = inner_table_values & ((one << BITS_FOR_INNER_TABLE_SIZE) - one)
+
+    inner_offset = _hash_func(k, inner_a, inner_b, size)
+    kv = hash_table[start_offset_in_ht + inner_offset]
+
+    key, value = kv >> 16, kv & ((1 << 16) - 1)
+    indicator = key == k
+
+    return indicator * value + (not indicator) * NOT_FOUND
+
+
+def hash_vocab(
+    vocab_path,
+    output_path,
+    unk_tok="[UNK]",
+    first_token="[CLS]",
+    sep_token="[SEP]",
+):
+    """
+    Write the vocab vocabulary hashtable to the output_path
+    """
+    np.random.seed(1243342)
+    vocab = _load_vocab_dict(vocab_path)
+    keys = list(map(_sdbm_hash, vocab.keys()))
+
+    hashed_vocab = {_sdbm_hash(key): value for key, value in vocab.items()}
+
+    error_message = (
+        "A collision occurred and only sdbm token hash is currently "
+        "supported. This can be extended to use random hashes if needed."
+    )
+    assert len(hashed_vocab) == len(vocab), error_message
+
+    (
+        outer_a,
+        outer_b,
+        num_outer_bins,
+        hash_table,
+        inner_table_coeffs,
+        offsets_into_ht,
+    ) = _perfect_hash(keys, 10)
+
+    _pack_keys_and_values(hash_table, hashed_vocab)
+    _store_func(
+        output_path,
+        outer_a,
+        outer_b,
+        num_outer_bins,
+        hash_table,
+        inner_table_coeffs,
+        offsets_into_ht,
+        vocab[unk_tok],
+        vocab[first_token],
+        vocab[sep_token],
+    )
+
+    for key, value in hashed_vocab.items():
+        val = _retrieve(
+            key,
+            outer_a,
+            outer_b,
+            num_outer_bins,
+            hash_table,
+            inner_table_coeffs,
+            offsets_into_ht,
+        )
+        assert (
+            val == value
+        ), f"Incorrect value found. Got {val} expected {value}"
+
+    print("All present tokens return correct value.")
diff --git a/python/cudf/cudf/utils/ioutils.py b/python/cudf/cudf/utils/ioutils.py
new file mode 100644
index 0000000..91925bf
--- /dev/null
+++ b/python/cudf/cudf/utils/ioutils.py
@@ -0,0 +1,2068 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import datetime
+import os
+import urllib
+import warnings
+from io import BufferedWriter, BytesIO, IOBase, TextIOWrapper
+from threading import Thread
+
+import fsspec
+import fsspec.implementations.local
+import numpy as np
+import pandas as pd
+from fsspec.core import get_fs_token_paths
+from pyarrow import PythonFile as ArrowPythonFile
+from pyarrow.fs import FSSpecHandler, PyFileSystem
+from pyarrow.lib import NativeFile
+
+from cudf.utils.docutils import docfmt_partial
+
+try:
+    import fsspec.parquet as fsspec_parquet
+
+except ImportError:
+    fsspec_parquet = None
+
+
+_BYTES_PER_THREAD_DEFAULT = 256 * 1024 * 1024
+_ROW_GROUP_SIZE_BYTES_DEFAULT = 128 * 1024 * 1024
+
+_docstring_remote_sources = """
+- cuDF supports local and remote data stores. See configuration details for
+  available sources
+  `here <https://docs.dask.org/en/latest/remote-data-services.html>`__.
+"""
+
+_docstring_read_avro = """
+Load an Avro dataset into a DataFrame
+
+Parameters
+----------
+filepath_or_buffer : str, path object, bytes, or file-like object
+    Either a path to a file (a `str`, `pathlib.Path`, or
+    `py._path.local.LocalPath`), URL (including http, ftp, and S3 locations),
+    Python bytes of raw binary data, or any object with a `read()` method
+    (such as builtin `open()` file handler function or `BytesIO`).
+columns : list, default None
+    If not None, only these columns will be read.
+skiprows : int, default None
+    If not None, the number of rows to skip from the start of the file.
+num_rows : int, default None
+    If not None, the total number of rows to read.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+
+Returns
+-------
+DataFrame
+
+Notes
+-----
+{remote_data_sources}
+
+Examples
+--------
+>>> import pandavro
+>>> import pandas as pd
+>>> import cudf
+>>> pandas_df = pd.DataFrame()
+>>> pandas_df['numbers'] = [10, 20, 30]
+>>> pandas_df['text'] = ["hello", "rapids", "ai"]
+>>> pandas_df
+   numbers    text
+0       10   hello
+1       20  rapids
+2       30      ai
+>>> pandavro.to_avro("data.avro", pandas_df)
+>>> cudf.read_avro("data.avro")
+   numbers    text
+0       10   hello
+1       20  rapids
+2       30      ai
+""".format(
+    remote_data_sources=_docstring_remote_sources
+)
+doc_read_avro = docfmt_partial(docstring=_docstring_read_avro)
+
+_docstring_read_parquet_metadata = """
+Read a Parquet file's metadata and schema
+
+Parameters
+----------
+path : string or path object
+    Path of file to be read
+
+Returns
+-------
+Total number of rows
+Number of row groups
+List of column names
+
+Examples
+--------
+>>> import cudf
+>>> num_rows, num_row_groups, names = cudf.io.read_parquet_metadata(filename)
+>>> df = [cudf.read_parquet(fname, row_group=i) for i in range(row_groups)]
+>>> df = cudf.concat(df)
+>>> df
+  num1                datetime text
+0  123 2018-11-13T12:00:00.000 5451
+1  456 2018-11-14T12:35:01.000 5784
+2  789 2018-11-15T18:02:59.000 6117
+
+See Also
+--------
+cudf.read_parquet
+"""
+doc_read_parquet_metadata = docfmt_partial(
+    docstring=_docstring_read_parquet_metadata
+)
+
+_docstring_read_parquet = """
+Load a Parquet dataset into a DataFrame
+
+Parameters
+----------
+filepath_or_buffer : str, path object, bytes, file-like object, or a list
+    of such objects.
+    Contains one or more of the following: either a path to a file (a `str`,
+    `pathlib.Path`, or `py._path.local.LocalPath`), URL (including http, ftp,
+    and S3 locations), Python bytes of raw binary data, or any object with a
+    `read()` method (such as builtin `open()` file handler function or
+    `BytesIO`).
+engine : {{ 'cudf', 'pyarrow' }}, default 'cudf'
+    Parser engine to use.
+columns : list, default None
+    If not None, only these columns will be read.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+filters : list of tuple, list of lists of tuples, default None
+    If not None, specifies a filter predicate used to filter out row groups
+    using statistics stored for each row group as Parquet metadata. Row groups
+    that do not match the given filter predicate are not read. The filters
+    will also be applied to the rows of the in-memory DataFrame after IO.
+    The predicate is expressed in disjunctive normal form (DNF) like
+    `[[('x', '=', 0), ...], ...]`. DNF allows arbitrary boolean logical
+    combinations of single column predicates. The innermost tuples each
+    describe a single column predicate. The list of inner predicates is
+    interpreted as a conjunction (AND), forming a more selective and
+    multiple column predicate. Finally, the most outer list combines
+    these filters as a disjunction (OR). Predicates may also be passed
+    as a list of tuples. This form is interpreted as a single conjunction.
+    To express OR in predicates, one must use the (preferred) notation of
+    list of lists of tuples.
+row_groups : int, or list, or a list of lists default None
+    If not None, specifies, for each input file, which row groups to read.
+    If reading multiple inputs, a list of lists should be passed, one list
+    for each input.
+categorical_partitions : boolean, default True
+    Whether directory-partitioned columns should be interpreted as categorical
+    or raw dtypes.
+use_pandas_metadata : boolean, default True
+    If True and dataset has custom PANDAS schema metadata, ensure that index
+    columns are also loaded.
+use_python_file_object : boolean, default True
+    If True, Arrow-backed PythonFile objects will be used in place of fsspec
+    AbstractBufferedFile objects at IO time. Setting this argument to `False`
+    will require the entire file to be copied to host memory, and is highly
+    discouraged.
+open_file_options : dict, optional
+    Dictionary of key-value pairs to pass to the function used to open remote
+    files. By default, this will be `fsspec.parquet.open_parquet_file`. To
+    deactivate optimized precaching, set the "method" to `None` under the
+    "precache_options" key. Note that the `open_file_func` key can also be
+    used to specify a custom file-open function.
+bytes_per_thread : int, default None
+    Determines the number of bytes to be allocated per thread to read the
+    files in parallel. When there is a file of large size, we get slightly
+    better throughput by decomposing it and transferring multiple "blocks"
+    in parallel (using a python thread pool). Default allocation is
+    {bytes_per_thread} bytes.
+    This parameter is functional only when `use_python_file_object=False`.
+
+Returns
+-------
+DataFrame
+
+Notes
+-----
+{remote_data_sources}
+
+Examples
+--------
+>>> import cudf
+>>> df = cudf.read_parquet(filename)
+>>> df
+  num1                datetime text
+0  123 2018-11-13T12:00:00.000 5451
+1  456 2018-11-14T12:35:01.000 5784
+2  789 2018-11-15T18:02:59.000 6117
+
+See Also
+--------
+cudf.io.parquet.read_parquet_metadata
+cudf.DataFrame.to_parquet
+cudf.read_orc
+""".format(
+    remote_data_sources=_docstring_remote_sources,
+    bytes_per_thread=_BYTES_PER_THREAD_DEFAULT,
+)
+doc_read_parquet = docfmt_partial(docstring=_docstring_read_parquet)
+
+_docstring_to_parquet = """
+Write a DataFrame to the parquet format.
+
+Parameters
+----------
+path : str or list of str
+    File path or Root Directory path. Will be used as Root Directory path
+    while writing a partitioned dataset. Use list of str with partition_offsets
+    to write parts of the dataframe to different files.
+compression : {{'snappy', 'ZSTD', None}}, default 'snappy'
+    Name of the compression to use. Use ``None`` for no compression.
+index : bool, default None
+    If ``True``, include the dataframe's index(es) in the file output.
+    If ``False``, they will not be written to the file.
+    If ``None``, similar to ``True`` the dataframe's index(es) will
+    be saved, however, instead of being saved as values any
+    ``RangeIndex`` will be stored as a range in the metadata so it
+    doesn't require much space and is faster. Other indexes will
+    be included as columns in the file output.
+partition_cols : list, optional, default None
+    Column names by which to partition the dataset
+    Columns are partitioned in the order they are given
+partition_file_name : str, optional, default None
+    File name to use for partitioned datasets. Different partitions
+    will be written to different directories, but all files will
+    have this name.  If nothing is specified, a random uuid4 hex string
+    will be used for each file.
+partition_offsets : list, optional, default None
+    Offsets to partition the dataframe by. Should be used when path is list
+    of str. Should be a list of integers of size ``len(path) + 1``
+statistics : {{'ROWGROUP', 'PAGE', 'COLUMN', 'NONE'}}, default 'ROWGROUP'
+    Level at which column statistics should be included in file.
+metadata_file_path : str, optional, default None
+    If specified, this function will return a binary blob containing the footer
+    metadata of the written parquet file. The returned blob will have the
+    ``chunk.file_path`` field set to the ``metadata_file_path`` for each chunk.
+    When using with ``partition_offsets``, should be same size as ``len(path)``
+int96_timestamps : bool, default False
+    If ``True``, write timestamps in int96 format. This will convert
+    timestamps from timestamp[ns], timestamp[ms], timestamp[s], and
+    timestamp[us] to the int96 format, which is the number of Julian
+    days and the number of nanoseconds since midnight of 1970-01-01.
+    If ``False``, timestamps will not be altered.
+row_group_size_bytes: integer, default {row_group_size_bytes_val}
+    Maximum size of each stripe of the output.
+    If None, {row_group_size_bytes_val}
+    ({row_group_size_bytes_val_in_mb} MB) will be used.
+row_group_size_rows: integer or None, default None
+    Maximum number of rows of each stripe of the output.
+    If None, 1000000 will be used.
+max_page_size_bytes: integer or None, default None
+    Maximum uncompressed size of each page of the output.
+    If None, 524288 (512KB) will be used.
+max_page_size_rows: integer or None, default None
+    Maximum number of rows of each page of the output.
+    If None, 20000 will be used.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+return_metadata : bool, default False
+    Return parquet metadata for written data. Returned metadata will
+    include the file path metadata (relative to `root_path`).
+    To request metadata binary blob when using with ``partition_cols``, Pass
+    ``return_metadata=True`` instead of specifying ``metadata_file_path``
+force_nullable_schema : bool, default False.
+    If True, writes all columns as `null` in schema.
+    If False, columns are written as `null` if they contain null values,
+    otherwise as `not null`.
+**kwargs
+    Additional parameters will be passed to execution engines other
+    than ``cudf``.
+
+
+See Also
+--------
+cudf.read_parquet
+""".format(
+    row_group_size_bytes_val=_ROW_GROUP_SIZE_BYTES_DEFAULT,
+    row_group_size_bytes_val_in_mb=_ROW_GROUP_SIZE_BYTES_DEFAULT / 1024 / 1024,
+)
+doc_to_parquet = docfmt_partial(docstring=_docstring_to_parquet)
+
+_docstring_merge_parquet_filemetadata = """
+Merge multiple parquet metadata blobs
+
+Parameters
+----------
+metadata_list : list
+    List of buffers returned by to_parquet
+
+Returns
+-------
+Combined parquet metadata blob
+
+See Also
+--------
+cudf.DataFrame.to_parquet
+"""
+doc_merge_parquet_filemetadata = docfmt_partial(
+    docstring=_docstring_merge_parquet_filemetadata
+)
+
+
+_docstring_read_orc_metadata = """
+Read an ORC file's metadata and schema
+
+Parameters
+----------
+path : string or path object
+    Path of file to be read
+
+Returns
+-------
+Total number of rows
+Number of stripes
+List of column names
+
+Notes
+-----
+{remote_data_sources}
+
+Examples
+--------
+>>> import cudf
+>>> num_rows, stripes, names = cudf.io.read_orc_metadata(filename)
+>>> df = [cudf.read_orc(fname, stripes=i) for i in range(stripes)]
+>>> df = cudf.concat(df)
+>>> df
+  num1                datetime text
+0  123 2018-11-13T12:00:00.000 5451
+1  456 2018-11-14T12:35:01.000 5784
+2  789 2018-11-15T18:02:59.000 6117
+
+See Also
+--------
+cudf.read_orc
+"""
+doc_read_orc_metadata = docfmt_partial(docstring=_docstring_read_orc_metadata)
+
+
+_docstring_read_orc_statistics = """
+Read an ORC file's file-level and stripe-level statistics
+
+Parameters
+----------
+filepath_or_buffer : str, path object, bytes, or file-like object
+    Either a path to a file (a `str`, `pathlib.Path`, or
+    `py._path.local.LocalPath`), URL (including http, ftp, and S3 locations),
+    Python bytes of raw binary data, or any object with a `read()` method
+    (such as builtin `open()` file handler function or `BytesIO`).
+columns : list, default None
+    If not None, statistics for only these columns will be read from the file.
+
+
+Returns
+-------
+Statistics for each column of given file
+Statistics for each column for each stripe of given file
+
+See Also
+--------
+cudf.read_orc
+"""
+doc_read_orc_statistics = docfmt_partial(
+    docstring=_docstring_read_orc_statistics
+)
+
+_docstring_read_orc = """
+Load an ORC dataset into a DataFrame
+
+Parameters
+----------
+filepath_or_buffer : str, path object, bytes, or file-like object
+    Either a path to a file (a `str`, `pathlib.Path`, or
+    `py._path.local.LocalPath`), URL (including http, ftp, and S3 locations),
+    Python bytes of raw binary data, or any object with a `read()` method
+    (such as builtin `open()` file handler function or `BytesIO`).
+engine : {{ 'cudf', 'pyarrow' }}, default 'cudf'
+    Parser engine to use.
+columns : list, default None
+    If not None, only these columns will be read from the file.
+filters : list of tuple, list of lists of tuples default None
+    If not None, specifies a filter predicate used to filter out row groups
+    using statistics stored for each row group as Parquet metadata. Row groups
+    that do not match the given filter predicate are not read. The
+    predicate is expressed in disjunctive normal form (DNF) like
+    `[[('x', '=', 0), ...], ...]`. DNF allows arbitrary boolean logical
+    combinations of single column predicates. The innermost tuples each
+    describe a single column predicate. The list of inner predicates is
+    interpreted as a conjunction (AND), forming a more selective and
+    multiple column predicate. Finally, the outermost list combines
+    these filters as a disjunction (OR). Predicates may also be passed
+    as a list of tuples. This form is interpreted as a single conjunction.
+    To express OR in predicates, one must use the (preferred) notation of
+    list of lists of tuples.
+stripes: list, default None
+    If not None, only these stripe will be read from the file. Stripes are
+    concatenated with index ignored.
+skiprows : int, default None
+    If not None, the number of rows to skip from the start of the file.
+    This parameter is deprecated.
+num_rows : int, default None
+    If not None, the total number of rows to read.
+    This parameter is deprecated.
+use_index : bool, default True
+    If True, use row index if available for faster seeking.
+use_python_file_object : boolean, default True
+    If True, Arrow-backed PythonFile objects will be used in place of fsspec
+    AbstractBufferedFile objects at IO time. This option is likely to improve
+    performance when making small reads from larger ORC files.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+bytes_per_thread : int, default None
+    Determines the number of bytes to be allocated per thread to read the
+    files in parallel. When there is a file of large size, we get slightly
+    better throughput by decomposing it and transferring multiple "blocks"
+    in parallel (using a python thread pool). Default allocation is
+    {bytes_per_thread} bytes.
+    This parameter is functional only when `use_python_file_object=False`.
+
+Returns
+-------
+DataFrame
+
+Notes
+-----
+{remote_data_sources}
+
+Examples
+--------
+>>> import cudf
+>>> df = cudf.read_orc(filename)
+>>> df
+  num1                datetime text
+0  123 2018-11-13T12:00:00.000 5451
+1  456 2018-11-14T12:35:01.000 5784
+2  789 2018-11-15T18:02:59.000 6117
+
+See Also
+--------
+cudf.DataFrame.to_orc
+""".format(
+    remote_data_sources=_docstring_remote_sources,
+    bytes_per_thread=_BYTES_PER_THREAD_DEFAULT,
+)
+doc_read_orc = docfmt_partial(docstring=_docstring_read_orc)
+
+_docstring_to_orc = """
+Write a DataFrame to the ORC format.
+
+Parameters
+----------
+fname : str
+    File path or object where the ORC dataset will be stored.
+compression : {{ 'snappy', 'ZSTD', None }}, default 'snappy'
+    Name of the compression to use. Use None for no compression.
+statistics: str {{ "ROWGROUP", "STRIPE", None }}, default "ROWGROUP"
+    The granularity with which column statistics must
+    be written to the file.
+stripe_size_bytes: integer or None, default None
+    Maximum size of each stripe of the output.
+    If None, 67108864 (64MB) will be used.
+stripe_size_rows: integer or None, default None
+    Maximum number of rows of each stripe of the output.
+    If None, 1000000 will be used.
+row_index_stride: integer or None, default None
+    Row index stride (maximum number of rows in each row group).
+    If None, 10000 will be used.
+cols_as_map_type : list of column names or None, default None
+    A list of column names which should be written as map type in the ORC file.
+    Note that this option only affects columns of ListDtype. Names of other
+    column types will be ignored.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+index : bool, default None
+    If ``True``, include the dataframe's index(es) in the file output.
+    If ``False``, they will not be written to the file.
+    If ``None``, similar to ``True`` the dataframe's index(es) will
+    be saved, however, instead of being saved as values any
+    ``RangeIndex`` will be stored as a range in the metadata so it
+    doesn't require much space and is faster. Other indexes will
+    be included as columns in the file output.
+
+See Also
+--------
+cudf.read_orc
+"""
+doc_to_orc = docfmt_partial(docstring=_docstring_to_orc)
+
+_docstring_read_json = r"""
+Load a JSON dataset into a DataFrame
+
+Parameters
+----------
+path_or_buf : list, str, path object, or file-like object
+    Either JSON data in a `str`, path to a file (a `str`, `pathlib.Path`, or
+    `py._path.local.LocalPath`), URL (including http, ftp, and S3 locations),
+    or any object with a `read()` method (such as builtin `open()` file handler
+    function or `StringIO`). Multiple inputs may be provided as a list. If a
+    list is specified each list entry may be of a different input type as long
+    as each input is of a valid type and all input JSON schema(s) match.
+engine : {{ 'auto', 'cudf', 'cudf_legacy', 'pandas' }}, default 'auto'
+    Parser engine to use. If 'auto' is passed, the engine will be
+    automatically selected based on the other parameters. See notes below.
+orient : string
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    Indication of expected JSON string format.
+    Compatible JSON strings can be produced by ``to_json()`` with a
+    corresponding orient value.
+    The set of possible orients is:
+
+    - ``'split'`` : dict like
+      ``{index -> [index], columns -> [columns], data -> [values]}``
+    - ``'records'`` : list like
+      ``[{column -> value}, ... , {column -> value}]``
+    - ``'index'`` : dict like ``{index -> {column -> value}}``
+    - ``'columns'`` : dict like ``{column -> {index -> value}}``
+    - ``'values'`` : just the values array
+
+    The allowed and default values depend on the value
+    of the `typ` parameter.
+
+    * when ``typ == 'series'``,
+
+      - allowed orients are ``{'split','records','index'}``
+      - default is ``'index'``
+      - The Series index must be unique for orient ``'index'``.
+    * when ``typ == 'frame'``,
+
+      - allowed orients are ``{'split','records','index',
+        'columns','values', 'table'}``
+      - default is ``'columns'``
+      - The DataFrame index must be unique for orients ``'index'`` and
+        ``'columns'``.
+      - The DataFrame columns must be unique for orients ``'index'``,
+        ``'columns'``, and ``'records'``.
+typ : type of object to recover (series or frame), default 'frame'
+    With cudf engine, only frame output is supported.
+dtype : boolean or dict, default None
+    If True, infer dtypes for all columns; if False, then don't infer dtypes at all,
+    if a dict, provide a mapping from column names to their respective dtype (any missing
+    columns will have their dtype inferred). Applies only to the data.
+    For all ``orient`` values except ``'table'``, default is ``True``.
+convert_axes : boolean, default True
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    Try to convert the axes to the proper dtypes.
+convert_dates : boolean, default True
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    List of columns to parse for dates; If True, then try
+    to parse datelike columns default is True; a column label is datelike if
+
+    * it ends with ``'_at'``,
+    * it ends with ``'_time'``,
+    * it begins with ``'timestamp'``,
+    * it is ``'modified'``, or
+    * it is ``'date'``
+keep_default_dates : boolean, default True
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    If parsing dates, parse the default datelike columns.
+numpy : boolean, default False
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    Direct decoding to numpy arrays. Supports numeric
+    data only, but non-numeric column and index labels are supported. Note
+    also that the JSON ordering MUST be the same for each term if numpy=True.
+precise_float : boolean, default False
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    Set to enable usage of higher precision (strtod) function when
+    decoding string to double values (pandas engine only). Default (False)
+    is to use fast but less precise builtin functionality
+date_unit : string, default None
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    The timestamp unit to detect if converting dates.
+    The default behavior is to try and detect the correct precision, but if
+    this is not desired then pass one of 's', 'ms', 'us' or 'ns' to force
+    parsing only seconds, milliseconds, microseconds or nanoseconds.
+encoding : str, default is 'utf-8'
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    The encoding to use to decode py3 bytes.
+    With cudf engine, only utf-8 is supported.
+lines : boolean, default False
+    Read the file as a json object per line.
+chunksize : integer, default None
+
+    .. admonition:: Not GPU-accelerated
+
+       This parameter is only supported with ``engine='pandas'``.
+
+    Return JsonReader object for iteration.
+    See the `line-delimited json docs
+    <http://pandas.pydata.org/pandas-docs/stable/io.html#io-jsonl>`_
+    for more information on ``chunksize``.
+    This can only be passed if `lines=True`.
+    If this is None, the file will be read into memory all at once.
+compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
+    For on-the-fly decompression of on-disk data. If 'infer', then use
+    gzip, bz2, zip or xz if path_or_buf is a string ending in
+    '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression
+    otherwise. If using 'zip', the ZIP file must contain only one data
+    file to be read in. Set to None for no decompression.
+byte_range : list or tuple, default None
+
+    .. admonition:: GPU-accelerated
+
+       This parameter is only supported with ``engine='cudf'``.
+
+    Byte range within the input file to be read.
+    The first number is the offset in bytes, the second number is the range
+    size in bytes. Set the size to zero to read all data after the offset
+    location. Reads the row that starts before or at the end of the range,
+    even if it ends after the end of the range.
+keep_quotes : bool, default False
+
+    .. admonition:: GPU-accelerated feature
+
+       This parameter is only supported with ``engine='cudf'``.
+
+    This parameter is only supported in ``cudf`` engine.
+    If `True`, any string values are read literally (and wrapped in an
+    additional set of quotes).
+    If `False` string values are parsed into Python strings.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+
+Returns
+-------
+result : Series or DataFrame, depending on the value of `typ`.
+
+Notes
+-----
+When `engine='auto'`, and `line=False`, the `pandas` json
+reader will be used. To override the selection, please
+use `engine='cudf'`.
+
+See Also
+--------
+cudf.DataFrame.to_json
+
+Examples
+--------
+>>> import cudf
+>>> df = cudf.DataFrame({'a': ["hello", "rapids"], 'b': ["hello", "worlds"]})
+>>> df
+        a       b
+0   hello   hello
+1  rapids  worlds
+>>> json_str = df.to_json(orient='records', lines=True)
+>>> json_str
+'{"a":"hello","b":"hello"}\n{"a":"rapids","b":"worlds"}\n'
+>>> cudf.read_json(json_str,  engine="cudf", lines=True)
+        a       b
+0   hello   hello
+1  rapids  worlds
+
+To read the strings with additional set of quotes:
+
+>>> cudf.read_json(json_str,  engine="cudf", lines=True,
+...                keep_quotes=True)
+          a         b
+0   "hello"   "hello"
+1  "rapids"  "worlds"
+
+Reading a JSON string containing ordered lists and name/value pairs:
+
+>>> json_str = '[{"list": [0,1,2], "struct": {"k":"v1"}}, {"list": [3,4,5], "struct": {"k":"v2"}}]'
+>>> cudf.read_json(json_str, engine='cudf')
+        list       struct
+0  [0, 1, 2]  {'k': 'v1'}
+1  [3, 4, 5]  {'k': 'v2'}
+
+Reading JSON Lines data containing ordered lists and name/value pairs:
+
+>>> json_str = '{"a": [{"k1": "v1"}]}\n{"a": [{"k1":"v2"}]}'
+>>> cudf.read_json(json_str, engine='cudf', lines=True)
+                a
+0  [{'k1': 'v1'}]
+1  [{'k1': 'v2'}]
+
+Using the `dtype` argument to specify type casting:
+
+>>> json_str = '{"k1": 1, "k2":[1.5]}'
+>>> cudf.read_json(json_str, engine='cudf', lines=True, dtype={'k1':float, 'k2':cudf.ListDtype(int)})
+    k1   k2
+0  1.0  [1]
+"""  # noqa: E501
+doc_read_json = docfmt_partial(docstring=_docstring_read_json)
+
+_docstring_to_json = """
+Convert the cuDF object to a JSON string.
+Note nulls and NaNs will be converted to null and datetime objects
+will be converted to UNIX timestamps.
+
+Parameters
+----------
+path_or_buf : string or file handle, optional
+    File path or object. If not specified, the result is returned as a string.
+engine : {{ 'auto', 'cudf', 'pandas' }}, default 'auto'
+    Parser engine to use. If 'auto' is passed, the `pandas` engine
+    will be selected.
+orient : string
+    Indication of expected JSON string format.
+
+    * Series
+        - default is 'index'
+        - allowed values are: {'split','records','index','table'}
+    * DataFrame
+        - default is 'columns'
+        - allowed values are:
+          {'split','records','index','columns','values','table'}
+    * The format of the JSON string
+        - 'split' : dict like {'index' -> [index],
+          'columns' -> [columns], 'data' -> [values]}
+        - 'records' : list like
+          [{column -> value}, ... , {column -> value}]
+        - 'index' : dict like {index -> {column -> value}}
+        - 'columns' : dict like {column -> {index -> value}}
+        - 'values' : just the values array
+        - 'table' : dict like {'schema': {schema}, 'data': {data}}
+          describing the data, and the data component is
+          like ``orient='records'``.
+date_format : {None, 'epoch', 'iso'}
+    Type of date conversion. 'epoch' = epoch milliseconds,
+    'iso' = ISO8601. The default depends on the `orient`. For
+    ``orient='table'``, the default is 'iso'. For all other orients,
+    the default is 'epoch'.
+double_precision : int, default 10
+    The number of decimal places to use when encoding
+    floating point values.
+force_ascii : bool, default True
+    Force encoded string to be ASCII.
+date_unit : string, default 'ms' (milliseconds)
+    The time unit to encode to, governs timestamp and ISO8601
+    precision.  One of 's', 'ms', 'us', 'ns' for second, millisecond,
+    microsecond, and nanosecond respectively.
+default_handler : callable, default None
+    Handler to call if object cannot otherwise be converted to a
+    suitable format for JSON. Should receive a single argument which is
+    the object to convert and return a serializable object.
+lines : bool, default False
+    If 'orient' is 'records' write out line delimited json format. Will
+    throw ValueError if incorrect 'orient' since others are not list
+    like.
+compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}
+    A string representing the compression to use in the output file,
+    only used when the first argument is a filename. By default, the
+    compression is inferred from the filename.
+index : bool, default True
+    Whether to include the index values in the JSON string. Not
+    including the index (``index=False``) is only supported when
+    orient is 'split' or 'table'.
+
+See Also
+--------
+cudf.read_json
+"""
+doc_to_json = docfmt_partial(docstring=_docstring_to_json)
+
+_docstring_read_hdf = """
+Read from the store, close it if we opened it.
+
+Retrieve pandas object stored in file, optionally based on where
+criteria
+
+Parameters
+----------
+path_or_buf : string, buffer or path object
+    Path to the file to open, or an open `HDFStore
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables>`_.
+    object.
+    Supports any object implementing the ``__fspath__`` protocol.
+    This includes :class:`pathlib.Path` and py._path.local.LocalPath
+    objects.
+key : object, optional
+    The group identifier in the store. Can be omitted if the HDF file
+    contains a single pandas object.
+mode : {'r', 'r+', 'a'}, optional
+    Mode to use when opening the file. Ignored if path_or_buf is a
+    `Pandas HDFS
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables>`_.
+    Default is 'r'.
+where : list, optional
+    A list of Term (or convertible) objects.
+start : int, optional
+    Row number to start selection.
+stop  : int, optional
+    Row number to stop selection.
+columns : list, optional
+    A list of columns names to return.
+iterator : bool, optional
+    Return an iterator object.
+chunksize : int, optional
+    Number of rows to include in an iteration when using an iterator.
+errors : str, default 'strict'
+    Specifies how encoding and decoding errors are to be handled.
+    See the errors argument for :func:`open` for a full list
+    of options.
+**kwargs
+    Additional keyword arguments passed to HDFStore.
+
+Returns
+-------
+item : object
+    The selected object. Return type depends on the object stored.
+
+See Also
+--------
+cudf.DataFrame.to_hdf : Write a HDF file from a DataFrame.
+"""
+doc_read_hdf = docfmt_partial(docstring=_docstring_read_hdf)
+
+_docstring_to_hdf = """
+Write the contained data to an HDF5 file using HDFStore.
+
+Hierarchical Data Format (HDF) is self-describing, allowing an
+application to interpret the structure and contents of a file with
+no outside information. One HDF file can hold a mix of related objects
+which can be accessed as a group or as individual objects.
+
+In order to add another DataFrame or Series to an existing HDF file
+please use append mode and a different a key.
+
+For more information see the `user guide
+<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables>`_.
+
+Parameters
+----------
+path_or_buf : str or pandas.HDFStore
+    File path or HDFStore object.
+key : str
+    Identifier for the group in the store.
+mode : {'a', 'w', 'r+'}, default 'a'
+    Mode to open file:
+
+    - 'w': write, a new file is created (an existing file with the same name
+      would be deleted).
+    - 'a': append, an existing file is opened for reading and writing, and if
+      the file does not exist it is created.
+    - 'r+': similar to 'a', but the file must already exist.
+format : {'fixed', 'table'}, default 'fixed'
+    Possible values:
+
+    - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
+      nor searchable.
+    - 'table': Table format. Write as a PyTables Table structure
+      which may perform worse but allow more flexible operations
+      like searching / selecting subsets of the data.
+append : bool, default False
+    For Table formats, append the input data to the existing.
+data_columns :  list of columns or True, optional
+    List of columns to create as indexed data columns for on-disk
+    queries, or True to use all columns. By default only the axes
+    of the object are indexed. See `Query via Data Columns
+    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-hdf5-query-data-columns>`_.
+    Applicable only to format='table'.
+complevel : {0-9}, optional
+    Specifies a compression level for data.
+    A value of 0 disables compression.
+complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
+    Specifies the compression library to be used.
+    As of v0.20.2 these additional compressors for Blosc are supported
+    (default if no compressor specified: 'blosc:blosclz'):
+    {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
+    'blosc:zlib', 'blosc:zstd'}.
+    Specifying a compression library which is not available issues
+    a ValueError.
+fletcher32 : bool, default False
+    If applying compression use the fletcher32 checksum.
+dropna : bool, default False
+    If true, ALL nan rows will not be written to store.
+errors : str, default 'strict'
+    Specifies how encoding and decoding errors are to be handled.
+    See the errors argument for :func:`open` for a full list
+    of options.
+
+See Also
+--------
+cudf.read_hdf : Read from HDF file.
+cudf.DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
+cudf.DataFrame.to_feather : Write out feather-format for DataFrames.
+"""
+doc_to_hdf = docfmt_partial(docstring=_docstring_to_hdf)
+
+_docstring_read_feather = """
+Load an feather object from the file path, returning a DataFrame.
+
+Parameters
+----------
+path : string
+    File path
+columns : list, default=None
+    If not None, only these columns will be read from the file.
+
+Returns
+-------
+DataFrame
+
+Examples
+--------
+>>> import cudf
+>>> df = cudf.read_feather(filename)
+>>> df
+  num1                datetime text
+0  123 2018-11-13T12:00:00.000 5451
+1  456 2018-11-14T12:35:01.000 5784
+2  789 2018-11-15T18:02:59.000 6117
+
+See Also
+--------
+cudf.DataFrame.to_feather
+"""
+doc_read_feather = docfmt_partial(docstring=_docstring_read_feather)
+
+_docstring_to_feather = """
+Write a DataFrame to the feather format.
+
+Parameters
+----------
+path : str
+    File path
+
+See Also
+--------
+cudf.read_feather
+"""
+doc_to_feather = docfmt_partial(docstring=_docstring_to_feather)
+
+_docstring_to_dlpack = """
+Converts a cuDF object into a DLPack tensor.
+
+DLPack is an open-source memory tensor structure:
+`dmlc/dlpack <https://github.com/dmlc/dlpack>`_.
+
+This function takes a cuDF object and converts it to a PyCapsule object
+which contains a pointer to a DLPack tensor. This function deep copies the
+data into the DLPack tensor from the cuDF object.
+
+Parameters
+----------
+cudf_obj : DataFrame, Series, Index, or Column
+
+Returns
+-------
+pycapsule_obj : PyCapsule
+    Output DLPack tensor pointer which is encapsulated in a PyCapsule
+    object.
+"""
+doc_to_dlpack = docfmt_partial(docstring=_docstring_to_dlpack)
+
+_docstring_read_csv = """
+Load a comma-separated-values (CSV) dataset into a DataFrame
+
+Parameters
+----------
+filepath_or_buffer : str, path object, or file-like object
+    Either a path to a file (a `str`, `pathlib.Path`, or
+    `py._path.local.LocalPath`), URL (including http, ftp, and S3 locations),
+    or any object with a `read()` method (such as builtin `open()` file handler
+    function or `StringIO`).
+sep : char, default ','
+    Delimiter to be used.
+delimiter : char, default None
+    Alternative argument name for sep.
+header : int, default 'infer'
+    Row number to use as the column names. Default behavior is to infer
+    the column names: if no names are passed, header=0;
+    if column names are passed explicitly, header=None.
+names : list of str, default None
+    List of column names to be used. Needs to include names of all columns in
+    the file, or names of all columns selected using `usecols` (only when
+    `usecols` holds integer indices). When `usecols` is not used to select
+    column indices, `names` can contain more names than there are columns i.n
+    the file. In this case the extra columns will only contain null rows.
+index_col : int, string or False, default None
+    Column to use as the row labels of the DataFrame. Passing `index_col=False`
+    explicitly disables index column inference and discards the last column.
+usecols : list of int or str, default None
+    Returns subset of the columns given in the list. All elements must be
+    either integer indices (column number) or strings that correspond to
+    column names. When an integer index is passed for each name in the `names`
+    parameter, the names are interpreted as names in the output table, not as
+    names in the input file.
+prefix : str, default None
+    Prefix to add to column numbers when parsing without a header row.
+mangle_dupe_cols : boolean, default True
+    Duplicate columns will be specified as 'X','X.1',...'X.N'.
+dtype : type, str, list of types, or dict of column -> type, default None
+    Data type(s) for data or columns. If `dtype` is a type/str, all columns
+    are mapped to the particular type passed. If list, types are applied in
+    the same order as the column names. If dict, types are mapped to the
+    column names.
+    E.g. {{'a': np.float64, 'b': int32, 'c': 'float'}}
+    If `None`, dtypes are inferred from the dataset. Use `str` to preserve data
+    and not infer or interpret to dtype.
+true_values : list, default None
+    Values to consider as boolean True
+false_values : list, default None
+    Values to consider as boolean False
+skipinitialspace : bool, default False
+    Skip spaces after delimiter.
+skiprows : int, default 0
+    Number of rows to be skipped from the start of file.
+skipfooter : int, default 0
+    Number of rows to be skipped at the bottom of file.
+nrows : int, default None
+    If specified, maximum number of rows to read
+na_values : scalar, str, or list-like, optional
+    Additional strings to recognize as nulls.
+    By default the following values are interpreted as
+    nulls: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND',
+    '-1.#QNAN', '-NaN', '-nan', '1.#IND', '1.#QNAN',
+    '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan',
+    'null'.
+keep_default_na : bool, default True
+    Whether or not to include the default NA values when parsing the data.
+na_filter : bool, default True
+    Detect missing values (empty strings and the values in na_values).
+    Passing False can improve performance.
+skip_blank_lines : bool, default True
+    If True, discard and do not parse empty lines
+    If False, interpret empty lines as NaN values
+parse_dates : list of int or names, default None
+    If list of columns, then attempt to parse each entry as a date.
+    Columns may not always be recognized as dates, for instance due to
+    unusual or non-standard formats. To guarantee a date and increase parsing
+    speed, explicitly specify `dtype='date'` for the desired columns.
+dayfirst : bool, default False
+    DD/MM format dates, international and European format.
+compression : {{'infer', 'gzip', 'zip', None}}, default 'infer'
+    For on-the-fly decompression of on-disk data. If 'infer', then detect
+    compression from the following extensions: '.gz','.zip' (otherwise no
+    decompression). If using 'zip', the ZIP file must contain only one
+    data file to be read in, otherwise the first non-zero-sized file will
+    be used. Set to None for no decompression.
+thousands : char, default None
+    Character used as a thousands delimiter.
+decimal : char, default '.'
+    Character used as a decimal point.
+lineterminator : char, default '\\n'
+    Character to indicate end of line.
+quotechar : char, default '"'
+    Character to indicate start and end of quote item.
+quoting : str or int, default 0
+    Controls quoting behavior. Set to one of
+    0 (csv.QUOTE_MINIMAL), 1 (csv.QUOTE_ALL),
+    2 (csv.QUOTE_NONNUMERIC) or 3 (csv.QUOTE_NONE).
+    Quoting is enabled with all values except 3.
+doublequote : bool, default True
+    When quoting is enabled, indicates whether to interpret two
+    consecutive quotechar inside fields as single quotechar
+comment : char, default None
+    Character used as a comments indicator. If found at the beginning of a
+    line, the line will be ignored altogether.
+delim_whitespace : bool, default False
+    Determines whether to use whitespace as delimiter.
+byte_range : list or tuple, default None
+    Byte range within the input file to be read. The first number is the
+    offset in bytes, the second number is the range size in bytes. Set the
+    size to zero to read all data after the offset location. Reads the row
+    that starts before or at the end of the range, even if it ends after
+    the end of the range.
+use_python_file_object : boolean, default True
+    If True, Arrow-backed PythonFile objects will be used in place of fsspec
+    AbstractBufferedFile objects at IO time. This option is likely to improve
+    performance when making small reads from larger CSV files.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+bytes_per_thread : int, default None
+    Determines the number of bytes to be allocated per thread to read the
+    files in parallel. When there is a file of large size, we get slightly
+    better throughput by decomposing it and transferring multiple "blocks"
+    in parallel (using a python thread pool). Default allocation is
+    {bytes_per_thread} bytes.
+    This parameter is functional only when `use_python_file_object=False`.
+Returns
+-------
+GPU ``DataFrame`` object.
+
+Notes
+-----
+{remote_data_sources}
+
+Examples
+--------
+
+Create a test csv file
+
+>>> import cudf
+>>> filename = 'foo.csv'
+>>> lines = [
+...   "num1,datetime,text",
+...   "123,2018-11-13T12:00:00,abc",
+...   "456,2018-11-14T12:35:01,def",
+...   "789,2018-11-15T18:02:59,ghi"
+... ]
+>>> with open(filename, 'w') as fp:
+...     fp.write('\\n'.join(lines)+'\\n')
+
+Read the file with ``cudf.read_csv``
+
+>>> cudf.read_csv(filename)
+  num1                datetime text
+0  123 2018-11-13T12:00:00.000 5451
+1  456 2018-11-14T12:35:01.000 5784
+2  789 2018-11-15T18:02:59.000 6117
+
+See Also
+--------
+cudf.DataFrame.to_csv
+""".format(
+    remote_data_sources=_docstring_remote_sources,
+    bytes_per_thread=_BYTES_PER_THREAD_DEFAULT,
+)
+doc_read_csv = docfmt_partial(docstring=_docstring_read_csv)
+
+_to_csv_example = """
+
+Write a dataframe to csv.
+
+>>> import cudf
+>>> filename = 'foo.csv'
+>>> df = cudf.DataFrame({'x': [0, 1, 2, 3],
+...                      'y': [1.0, 3.3, 2.2, 4.4],
+...                      'z': ['a', 'b', 'c', 'd']})
+>>> df = df.set_index(cudf.Series([3, 2, 1, 0]))
+>>> df.to_csv(filename)
+
+"""
+_docstring_to_csv = """
+
+Write a dataframe to csv file format.
+
+Parameters
+----------
+{df_param}
+path_or_buf : str or file handle, default None
+    File path or object, if None is provided
+    the result is returned as a string.
+sep : char, default ','
+    Delimiter to be used.
+na_rep : str, default ''
+    String to use for null entries
+columns : list of str, optional
+    Columns to write
+header : bool, default True
+    Write out the column names
+index : bool, default True
+    Write out the index as a column
+encoding : str, default 'utf-8'
+    A string representing the encoding to use in the output file
+    Only 'utf-8' is currently supported
+compression : str, None
+    A string representing the compression scheme to use in the output file
+    Compression while writing csv is not supported currently
+lineterminator : str, optional
+    The newline character or character sequence to use in the output file.
+    Defaults to :data:`os.linesep`.
+chunksize : int or None, default None
+    Rows to write at a time
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+
+Returns
+-------
+None or str
+    If `path_or_buf` is None, returns the resulting csv format as a string.
+    Otherwise returns None.
+
+Notes
+-----
+- Follows the standard of Pandas csv.QUOTE_NONNUMERIC for all output.
+- The default behaviour is to write all rows of the dataframe at once.
+  This can lead to memory or overflow errors for large tables. If this
+  happens, consider setting the ``chunksize`` argument to some
+  reasonable fraction of the total rows in the dataframe.
+
+Examples
+--------
+{example}
+
+See Also
+--------
+cudf.read_csv
+"""
+doc_to_csv = docfmt_partial(
+    docstring=_docstring_to_csv.format(
+        df_param="""
+df : DataFrame
+    DataFrame object to be written to csv
+""",
+        example=_to_csv_example,
+    )
+)
+
+doc_dataframe_to_csv = docfmt_partial(
+    docstring=_docstring_to_csv.format(df_param="", example=_to_csv_example)
+)
+
+_docstring_kafka_datasource = """
+Configuration object for a Kafka Datasource
+
+Parameters
+----------
+kafka_configs : dict, key/value pairs of librdkafka configuration values.
+    The complete list of valid configurations can be found at
+    https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
+topic : string, case sensitive name of the Kafka topic that contains the
+    source data.
+partition : int,
+    Zero-based identifier of the Kafka partition that the underlying consumer
+    should consume messages from. Valid values are 0 - (N-1)
+start_offset : int, Kafka Topic/Partition offset that consumption
+    should begin at. Inclusive.
+end_offset : int, Kafka Topic/Partition offset that consumption
+    should end at. Inclusive.
+batch_timeout : int, default 10000
+    Maximum number of milliseconds that will be spent trying to
+    consume messages between the specified 'start_offset' and 'end_offset'.
+delimiter : string, default None, optional delimiter to insert into the
+    output between kafka messages, Ex: "\n"
+
+"""
+doc_kafka_datasource = docfmt_partial(docstring=_docstring_kafka_datasource)
+
+
+_docstring_text_datasource = """
+Configuration object for a text Datasource
+
+Parameters
+----------
+filepath_or_buffer : str, path object, or file-like object
+    Either a path to a file (a `str`, `pathlib.Path`, or
+    `py._path.local.LocalPath`), URL (including http, ftp, and S3 locations),
+    or any object with a `read()` method (such as builtin `open()` file handler
+    function or `StringIO`).
+delimiter : string, default None
+    The delimiter that should be used for splitting text chunks into
+    separate cudf column rows. The delimiter may be one or more characters.
+byte_range : list or tuple, default None
+    Byte range within the input file to be read. The first number is the
+    offset in bytes, the second number is the range size in bytes.
+    The output contains all rows that start inside the byte range
+    (i.e. at or after the offset, and before the end at `offset + size`),
+    which may include rows that continue past the end.
+strip_delimiters : boolean, default False
+    Unlike the `str.split()` function, `read_text` preserves the delimiter
+    at the end of a field in output by default, meaning `a;b;c` will turn into
+    `['a;','b;','c']` when using `;` as a delimiter.
+    Setting this option to `True` will strip these trailing delimiters,
+    leaving only the contents between delimiters in the resulting column:
+    `['a','b','c']`
+compression : string, default None
+    Which compression type is the input compressed with.
+    Currently supports only `bgzip`, and requires the path to a file as input.
+compression_offsets: list or tuple, default None
+    The virtual begin and end offset associated with the provided compression.
+    For `bgzip`, they are composed of a local uncompressed offset inside a
+    BGZIP block (lower 16 bits) and the start offset of this BGZIP block in the
+    compressed file (upper 48 bits).
+    The start offset points to the first byte to be read, the end offset points
+    one past the last byte to be read.
+storage_options : dict, optional, default None
+    Extra options that make sense for a particular storage connection,
+    e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details.
+
+Returns
+-------
+result : Series
+
+"""
+doc_read_text = docfmt_partial(docstring=_docstring_text_datasource)
+
+
+_docstring_get_reader_filepath_or_buffer = """
+Return either a filepath string to data, or a memory buffer of data.
+If filepath, then the source filepath is expanded to user's environment.
+If buffer, then data is returned in-memory as bytes or a ByteIO object.
+
+Parameters
+----------
+path_or_data : str, file-like object, bytes, ByteIO
+    Path to data or the data itself.
+compression : str
+    Type of compression algorithm for the content
+mode : str
+    Mode in which file is opened
+iotypes : (), default (BytesIO)
+    Object type to exclude from file-like check
+use_python_file_object : boolean, default False
+    If True, Arrow-backed PythonFile objects will be used in place
+    of fsspec AbstractBufferedFile objects.
+open_file_options : dict, optional
+    Optional dictionary of keyword arguments to pass to
+    `_open_remote_files` (used for remote storage only).
+allow_raw_text_input : boolean, default False
+    If True, this indicates the input `path_or_data` could be a raw text
+    input and will not check for its existence in the filesystem. If False,
+    the input must be a path and an error will be raised if it does not
+    exist.
+storage_options : dict, optional
+    Extra options that make sense for a particular storage connection, e.g.
+    host, port, username, password, etc. For HTTP(S) URLs the key-value
+    pairs are forwarded to ``urllib.request.Request`` as header options.
+    For other URLs (e.g. starting with "s3://", and "gcs://") the key-value
+    pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and
+    ``urllib`` for more details, and for more examples on storage options
+    refer `here <https://pandas.pydata.org/docs/user_guide/io.html?
+    highlight=storage_options#reading-writing-remote-files>`__.
+bytes_per_thread : int, default None
+    Determines the number of bytes to be allocated per thread to read the
+    files in parallel. When there is a file of large size, we get slightly
+    better throughput by decomposing it and transferring multiple "blocks"
+    in parallel (using a Python thread pool). Default allocation is
+    {bytes_per_thread} bytes.
+    This parameter is functional only when `use_python_file_object=False`.
+
+Returns
+-------
+filepath_or_buffer : str, bytes, BytesIO, list
+    Filepath string or in-memory buffer of data or a
+    list of Filepath strings or in-memory buffers of data.
+compression : str
+    Type of compression algorithm for the content
+    """.format(
+    bytes_per_thread=_BYTES_PER_THREAD_DEFAULT
+)
+
+
+doc_get_reader_filepath_or_buffer = docfmt_partial(
+    docstring=_docstring_get_reader_filepath_or_buffer
+)
+
+
+def is_url(url):
+    """Check if a string is a valid URL to a network location.
+
+    Parameters
+    ----------
+    url : str
+        String containing a possible URL
+
+    Returns
+    -------
+    bool : bool
+        If `url` has a valid protocol return True otherwise False.
+    """
+    # Do not include the empty ('') scheme in the check
+    schemes = urllib.parse.uses_netloc[1:]
+    try:
+        return urllib.parse.urlparse(url).scheme in schemes
+    except Exception:
+        return False
+
+
+def is_file_like(obj):
+    """Check if the object is a file-like object, per PANDAS' definition.
+    An object is considered file-like if it has an iterator AND has a either or
+    both `read()` / `write()` methods as attributes.
+
+    Parameters
+    ----------
+    obj : object
+        Object to check for file-like properties
+
+    Returns
+    -------
+    is_file_like : bool
+        If `obj` is file-like returns True otherwise False
+    """
+    if not (hasattr(obj, "read") or hasattr(obj, "write")):
+        return False
+    elif not hasattr(obj, "__iter__"):
+        return False
+    else:
+        return True
+
+
+def _is_local_filesystem(fs):
+    return isinstance(fs, fsspec.implementations.local.LocalFileSystem)
+
+
+def ensure_single_filepath_or_buffer(path_or_data, storage_options=None):
+    """Return False if `path_or_data` resolves to multiple filepaths or
+    buffers.
+    """
+    path_or_data = stringify_pathlike(path_or_data)
+    if isinstance(path_or_data, str):
+        path_or_data = os.path.expanduser(path_or_data)
+        try:
+            fs, _, paths = get_fs_token_paths(
+                path_or_data, mode="rb", storage_options=storage_options
+            )
+        except ValueError as e:
+            if str(e).startswith("Protocol not known"):
+                return True
+            else:
+                raise e
+
+        if len(paths) > 1:
+            return False
+    elif isinstance(path_or_data, (list, tuple)) and len(path_or_data) > 1:
+        return False
+
+    return True
+
+
+def is_directory(path_or_data, storage_options=None):
+    """Returns True if the provided filepath is a directory"""
+    path_or_data = stringify_pathlike(path_or_data)
+    if isinstance(path_or_data, str):
+        path_or_data = os.path.expanduser(path_or_data)
+        try:
+            fs = get_fs_token_paths(
+                path_or_data, mode="rb", storage_options=storage_options
+            )[0]
+        except ValueError as e:
+            if str(e).startswith("Protocol not known"):
+                return False
+            else:
+                raise e
+
+        return fs.isdir(path_or_data)
+
+    return False
+
+
+def _get_filesystem_and_paths(path_or_data, storage_options):
+    # Returns a filesystem object and the filesystem-normalized
+    # paths. If `path_or_data` does not correspond to a path or
+    # list of paths (or if the protocol is not supported), the
+    # return will be `None` for the fs and `[]` for the paths.
+
+    fs = None
+    return_paths = path_or_data
+    if isinstance(path_or_data, str) or (
+        isinstance(path_or_data, list)
+        and isinstance(stringify_pathlike(path_or_data[0]), str)
+    ):
+        # Ensure we are always working with a list
+        if isinstance(path_or_data, list):
+            path_or_data = [
+                os.path.expanduser(stringify_pathlike(source))
+                for source in path_or_data
+            ]
+        else:
+            path_or_data = [path_or_data]
+
+        try:
+            fs, _, fs_paths = get_fs_token_paths(
+                path_or_data, mode="rb", storage_options=storage_options
+            )
+            return_paths = fs_paths
+        except ValueError as e:
+            if str(e).startswith("Protocol not known"):
+                return None, []
+            else:
+                raise e
+
+    return fs, return_paths
+
+
+def _set_context(obj, stack):
+    # Helper function to place open file on context stack
+    if stack is None:
+        return obj
+    return stack.enter_context(obj)
+
+
+def _open_remote_files(
+    paths,
+    fs,
+    context_stack=None,
+    open_file_func=None,
+    precache_options=None,
+    **kwargs,
+):
+    """Return a list of open file-like objects given
+    a list of remote file paths.
+
+    Parameters
+    ----------
+    paths : list(str)
+        List of file-path strings.
+    fs : fsspec.AbstractFileSystem
+        Fsspec file-system object.
+    context_stack : contextlib.ExitStack, Optional
+        Context manager to use for open files.
+    open_file_func : Callable, Optional
+        Call-back function to use for opening. If this argument
+        is specified, all other arguments will be ignored.
+    precache_options : dict, optional
+        Dictionary of key-word arguments to pass to use for
+        precaching. Unless the input contains ``{"method": None}``,
+        ``fsspec.parquet.open_parquet_file`` will be used for remote
+        storage.
+    **kwargs :
+        Key-word arguments to be passed to format-specific
+        open functions.
+    """
+
+    # Just use call-back function if one was specified
+    if open_file_func is not None:
+        return [
+            _set_context(open_file_func(path, **kwargs), context_stack)
+            for path in paths
+        ]
+
+    # Check if the "precache" option is supported.
+    # In the future, fsspec should do this check for us
+    precache_options = (precache_options or {}).copy()
+    precache = precache_options.pop("method", None)
+    if precache not in ("parquet", None):
+        raise ValueError(f"{precache} not a supported `precache` option.")
+
+    # Check that "parts" caching (used for all format-aware file handling)
+    # is supported by the installed fsspec/s3fs version
+    if precache == "parquet" and not fsspec_parquet:
+        warnings.warn(
+            f"This version of fsspec ({fsspec.__version__}) does "
+            f"not support parquet-optimized precaching. Please upgrade "
+            f"to the latest fsspec version for better performance."
+        )
+        precache = None
+
+    if precache == "parquet":
+        # Use fsspec.parquet module.
+        # TODO: Use `cat_ranges` to collect "known"
+        # parts for all files at once.
+        row_groups = precache_options.pop("row_groups", None) or (
+            [None] * len(paths)
+        )
+        return [
+            ArrowPythonFile(
+                _set_context(
+                    fsspec_parquet.open_parquet_file(
+                        path,
+                        fs=fs,
+                        row_groups=rgs,
+                        **precache_options,
+                        **kwargs,
+                    ),
+                    context_stack,
+                )
+            )
+            for path, rgs in zip(paths, row_groups)
+        ]
+
+    # Default open - Use pyarrow filesystem API
+    pa_fs = PyFileSystem(FSSpecHandler(fs))
+    return [
+        _set_context(pa_fs.open_input_file(fpath), context_stack)
+        for fpath in paths
+    ]
+
+
+@doc_get_reader_filepath_or_buffer()
+def get_reader_filepath_or_buffer(
+    path_or_data,
+    compression,
+    mode="rb",
+    fs=None,
+    iotypes=(BytesIO, NativeFile),
+    use_python_file_object=False,
+    open_file_options=None,
+    allow_raw_text_input=False,
+    storage_options=None,
+    bytes_per_thread=_BYTES_PER_THREAD_DEFAULT,
+):
+    """{docstring}"""
+
+    path_or_data = stringify_pathlike(path_or_data)
+
+    if isinstance(path_or_data, str):
+        # Get a filesystem object if one isn't already available
+        paths = [path_or_data]
+        if fs is None:
+            fs, paths = _get_filesystem_and_paths(
+                path_or_data, storage_options
+            )
+            if fs is None:
+                return path_or_data, compression
+
+        if _is_local_filesystem(fs):
+            # Doing this as `read_json` accepts a json string
+            # path_or_data need not be a filepath like string
+            if len(paths):
+                if fs.exists(paths[0]):
+                    path_or_data = paths if len(paths) > 1 else paths[0]
+                elif not allow_raw_text_input:
+                    raise FileNotFoundError(
+                        f"{path_or_data} could not be resolved to any files"
+                    )
+
+        else:
+            if len(paths) == 0:
+                raise FileNotFoundError(
+                    f"{path_or_data} could not be resolved to any files"
+                )
+            if use_python_file_object:
+                path_or_data = _open_remote_files(
+                    paths,
+                    fs,
+                    **(open_file_options or {}),
+                )
+            else:
+                path_or_data = [
+                    BytesIO(
+                        _fsspec_data_transfer(
+                            fpath,
+                            fs=fs,
+                            mode=mode,
+                            bytes_per_thread=bytes_per_thread,
+                        )
+                    )
+                    for fpath in paths
+                ]
+            if len(path_or_data) == 1:
+                path_or_data = path_or_data[0]
+
+    elif not isinstance(path_or_data, iotypes) and is_file_like(path_or_data):
+        if isinstance(path_or_data, TextIOWrapper):
+            path_or_data = path_or_data.buffer
+        if use_python_file_object:
+            path_or_data = ArrowPythonFile(path_or_data)
+        else:
+            path_or_data = BytesIO(
+                _fsspec_data_transfer(
+                    path_or_data, mode=mode, bytes_per_thread=bytes_per_thread
+                )
+            )
+
+    return path_or_data, compression
+
+
+def get_writer_filepath_or_buffer(path_or_data, mode, storage_options=None):
+    """
+    Return either a filepath string to data,
+    or a open file object to the output filesystem
+
+    Parameters
+    ----------
+    path_or_data : str, file-like object, bytes, ByteIO
+        Path to data or the data itself.
+    mode : str
+        Mode in which file is opened
+    storage_options : dict, optional, default None
+        Extra options that make sense for a particular storage connection,
+        e.g. host, port, username, password, etc. For HTTP(S) URLs the
+        key-value pairs are forwarded to ``urllib.request.Request`` as
+        header options. For other URLs (e.g. starting with "s3://", and
+        "gcs://") the key-value pairs are forwarded to ``fsspec.open``.
+        Please see ``fsspec`` and ``urllib`` for more details.
+
+    Returns
+    -------
+    filepath_or_buffer : str,
+        Filepath string or buffer of data
+    """
+    if storage_options is None:
+        storage_options = {}
+
+    if isinstance(path_or_data, str):
+        path_or_data = os.path.expanduser(path_or_data)
+        fs = get_fs_token_paths(
+            path_or_data, mode=mode or "w", storage_options=storage_options
+        )[0]
+
+        if not _is_local_filesystem(fs):
+            filepath_or_buffer = fsspec.open(
+                path_or_data, mode=mode or "w", **(storage_options)
+            )
+            return filepath_or_buffer
+
+    return path_or_data
+
+
+def get_IOBase_writer(file_obj):
+    """
+    Parameters
+    ----------
+    file_obj : file-like object
+        Open file object for writing to any filesystem
+
+    Returns
+    -------
+    iobase_file_obj : file-like object
+        Open file object inheriting from io.IOBase
+    """
+    if not isinstance(file_obj, IOBase):
+        if "b" in file_obj.mode:
+            iobase_file_obj = BufferedWriter(file_obj)
+        else:
+            iobase_file_obj = TextIOWrapper(file_obj)
+        return iobase_file_obj
+
+    return file_obj
+
+
+def is_fsspec_open_file(file_obj):
+    if isinstance(file_obj, fsspec.core.OpenFile):
+        return True
+    return False
+
+
+def stringify_pathlike(pathlike):
+    """
+    Convert any object that implements the fspath protocol
+    to a string. Leaves other objects unchanged
+    Parameters
+    ----------
+    pathlike
+        Pathlike object that implements the fspath protocol
+
+    Returns
+    -------
+    maybe_pathlike_str
+        String version of the object if possible
+    """
+    maybe_pathlike_str = (
+        pathlike.__fspath__() if hasattr(pathlike, "__fspath__") else pathlike
+    )
+
+    return maybe_pathlike_str
+
+
+def buffer_write_lines(buf, lines):
+    """
+    Appends lines to a buffer.
+
+    Parameters
+    ----------
+    buf
+        The buffer to write to
+    lines
+        The lines to append.
+    """
+    if any(isinstance(x, str) for x in lines):
+        lines = [str(x) for x in lines]
+    buf.write("\n".join(lines))
+
+
+def _apply_filter_bool_eq(val, col_stats):
+    if "true_count" in col_stats and "false_count" in col_stats:
+        if val is True:
+            if (col_stats["true_count"] == 0) or (
+                col_stats["false_count"] == col_stats["number_of_values"]
+            ):
+                return False
+        elif val is False:
+            if (col_stats["false_count"] == 0) or (
+                col_stats["true_count"] == col_stats["number_of_values"]
+            ):
+                return False
+    return True
+
+
+def _apply_filter_not_eq(val, col_stats):
+    return ("minimum" in col_stats and val < col_stats["minimum"]) or (
+        "maximum" in col_stats and val > col_stats["maximum"]
+    )
+
+
+def _apply_predicate(op, val, col_stats):
+    # Sanitize operator
+    if op not in {"=", "==", "!=", "<", "<=", ">", ">=", "in", "not in"}:
+        raise ValueError(f"'{op}' is not a valid operator in predicates.")
+
+    col_min = col_stats.get("minimum", None)
+    col_max = col_stats.get("maximum", None)
+    col_sum = col_stats.get("sum", None)
+
+    # Apply operator
+    if op == "=" or op == "==":
+        if _apply_filter_not_eq(val, col_stats):
+            return False
+        # TODO: Replace pd.isnull with
+        # cudf.isnull once it is implemented
+        if pd.isnull(val) and not col_stats["has_null"]:
+            return False
+        if not _apply_filter_bool_eq(val, col_stats):
+            return False
+    elif op == "!=":
+        if (
+            col_min is not None
+            and col_max is not None
+            and val == col_min
+            and val == col_max
+        ):
+            return False
+        if _apply_filter_bool_eq(val, col_stats):
+            return False
+    elif col_min is not None and (
+        (op == "<" and val <= col_min) or (op == "<=" and val < col_min)
+    ):
+        return False
+    elif col_max is not None and (
+        (op == ">" and val >= col_max) or (op == ">=" and val > col_max)
+    ):
+        return False
+    elif (
+        col_sum is not None
+        and op == ">"
+        and (
+            (col_min is not None and col_min >= 0 and col_sum <= val)
+            or (col_max is not None and col_max <= 0 and col_sum >= val)
+        )
+    ):
+        return False
+    elif (
+        col_sum is not None
+        and op == ">="
+        and (
+            (col_min is not None and col_min >= 0 and col_sum < val)
+            or (col_max is not None and col_max <= 0 and col_sum > val)
+        )
+    ):
+        return False
+    elif op == "in":
+        if (col_max is not None and col_max < min(val)) or (
+            col_min is not None and col_min > max(val)
+        ):
+            return False
+        if all(_apply_filter_not_eq(elem, col_stats) for elem in val):
+            return False
+    elif op == "not in" and col_min is not None and col_max is not None:
+        if any(elem == col_min == col_max for elem in val):
+            return False
+        col_range = None
+        if isinstance(col_min, int):
+            col_range = range(col_min, col_max)
+        elif isinstance(col_min, datetime.datetime):
+            col_range = pd.date_range(col_min, col_max)
+        if col_range and all(elem in val for elem in col_range):
+            return False
+    return True
+
+
+def _apply_filters(filters, stats):
+    for conjunction in filters:
+        if all(
+            _apply_predicate(op, val, stats[col])
+            for col, op, val in conjunction
+        ):
+            return True
+    return False
+
+
+def _prepare_filters(filters):
+    # Coerce filters into list of lists of tuples
+    if isinstance(filters[0][0], str):
+        filters = [filters]
+
+    return filters
+
+
+def _ensure_filesystem(passed_filesystem, path, storage_options):
+    if passed_filesystem is None:
+        return get_fs_token_paths(
+            path[0] if isinstance(path, list) else path,
+            storage_options={} if storage_options is None else storage_options,
+        )[0]
+    return passed_filesystem
+
+
+#
+# Fsspec Data-transfer Optimization Code
+#
+
+
+def _fsspec_data_transfer(
+    path_or_fob,
+    fs=None,
+    file_size=None,
+    bytes_per_thread=_BYTES_PER_THREAD_DEFAULT,
+    max_gap=64_000,
+    mode="rb",
+):
+    if bytes_per_thread is None:
+        bytes_per_thread = _BYTES_PER_THREAD_DEFAULT
+
+    # Require `fs` if `path_or_fob` is not file-like
+    file_like = is_file_like(path_or_fob)
+    if fs is None and not file_like:
+        raise ValueError(
+            "fs must be defined if `path_or_fob` is not file-like"
+        )
+
+    # Calculate total file size
+    if file_like:
+        try:
+            file_size = path_or_fob.size
+        except AttributeError:
+            # If we cannot find the size of path_or_fob
+            # just read it.
+            return path_or_fob.read()
+    file_size = file_size or fs.size(path_or_fob)
+
+    # Check if a direct read makes the most sense
+    if bytes_per_thread >= file_size:
+        if file_like:
+            return path_or_fob.read()
+        else:
+            return fs.open(path_or_fob, mode=mode, cache_type="all").read()
+
+    # Threaded read into "local" buffer
+    buf = np.zeros(file_size, dtype="b")
+
+    byte_ranges = [
+        (b, min(bytes_per_thread, file_size - b))
+        for b in range(0, file_size, bytes_per_thread)
+    ]
+    _read_byte_ranges(
+        path_or_fob,
+        byte_ranges,
+        buf,
+        fs=fs,
+    )
+
+    return buf.tobytes()
+
+
+def _merge_ranges(byte_ranges, max_block=256_000_000, max_gap=64_000):
+    # Simple utility to merge small/adjacent byte ranges
+    new_ranges = []
+    if not byte_ranges:
+        # Early return
+        return new_ranges
+
+    offset, size = byte_ranges[0]
+    for (new_offset, new_size) in byte_ranges[1:]:
+        gap = new_offset - (offset + size)
+        if gap > max_gap or (size + new_size + gap) > max_block:
+            # Gap is too large or total read is too large
+            new_ranges.append((offset, size))
+            offset = new_offset
+            size = new_size
+            continue
+        size += new_size + gap
+    new_ranges.append((offset, size))
+    return new_ranges
+
+
+def _assign_block(fs, path_or_fob, local_buffer, offset, nbytes):
+    if fs is None:
+        # We have an open fsspec file object
+        path_or_fob.seek(offset)
+        local_buffer[offset : offset + nbytes] = np.frombuffer(
+            path_or_fob.read(nbytes),
+            dtype="b",
+        )
+    else:
+        # We have an fsspec filesystem and a path
+        with fs.open(path_or_fob, mode="rb", cache_type="none") as fob:
+            fob.seek(offset)
+            local_buffer[offset : offset + nbytes] = np.frombuffer(
+                fob.read(nbytes),
+                dtype="b",
+            )
+
+
+def _read_byte_ranges(
+    path_or_fob,
+    ranges,
+    local_buffer,
+    fs=None,
+):
+    # Simple utility to copy remote byte ranges
+    # into a local buffer for IO in libcudf
+    workers = []
+    for (offset, nbytes) in ranges:
+        if len(ranges) > 1:
+            workers.append(
+                Thread(
+                    target=_assign_block,
+                    args=(fs, path_or_fob, local_buffer, offset, nbytes),
+                )
+            )
+            workers[-1].start()
+        else:
+            _assign_block(fs, path_or_fob, local_buffer, offset, nbytes)
+
+    for worker in workers:
+        worker.join()
diff --git a/python/cudf/cudf/utils/metadata/__init__.py b/python/cudf/cudf/utils/metadata/__init__.py
new file mode 100644
index 0000000..ccbb162
--- /dev/null
+++ b/python/cudf/cudf/utils/metadata/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
diff --git a/python/cudf/cudf/utils/metadata/orc_column_statistics.proto b/python/cudf/cudf/utils/metadata/orc_column_statistics.proto
new file mode 100644
index 0000000..1bc0fa6
--- /dev/null
+++ b/python/cudf/cudf/utils/metadata/orc_column_statistics.proto
@@ -0,0 +1,62 @@
+syntax = "proto2";
+
+message IntegerStatistics  {
+  optional sint64 minimum = 1;
+  optional sint64 maximum = 2;
+  optional sint64 sum = 3;
+}
+
+message DoubleStatistics {
+  optional double minimum = 1;
+  optional double maximum = 2;
+  optional double sum = 3;
+}
+
+message StringStatistics {
+  optional string minimum = 1;
+  optional string maximum = 2;
+  // sum will store the total length of all strings in a stripe
+  optional sint64 sum = 3;
+}
+
+message BucketStatistics {
+  repeated uint64 count = 1 [packed=true];
+}
+
+message DecimalStatistics {
+  optional string minimum = 1;
+  optional string maximum = 2;
+  optional string sum = 3;
+}
+
+message DateStatistics {
+  // min,max values saved as days since epoch
+  optional sint32 minimum = 1;
+  optional sint32 maximum = 2;
+}
+
+message TimestampStatistics {
+  // min,max values saved as milliseconds since epoch
+  optional sint64 minimum = 1;
+  optional sint64 maximum = 2;
+  optional sint64 minimumUtc = 3;
+  optional sint64 maximumUtc = 4;
+}
+
+message BinaryStatistics {
+  // sum will store the total binary blob length in a stripe
+  optional sint64 sum = 1;
+}
+
+message ColumnStatistics {
+  optional uint64 numberOfValues = 1;
+  optional IntegerStatistics intStatistics = 2;
+  optional DoubleStatistics doubleStatistics = 3;
+  optional StringStatistics stringStatistics = 4;
+  optional BucketStatistics bucketStatistics = 5;
+  optional DecimalStatistics decimalStatistics = 6;
+  optional DateStatistics dateStatistics = 7;
+  optional BinaryStatistics binaryStatistics = 8;
+  optional TimestampStatistics timestampStatistics = 9;
+  optional bool hasNull = 10;
+}
diff --git a/python/cudf/cudf/utils/queryutils.py b/python/cudf/cudf/utils/queryutils.py
new file mode 100644
index 0000000..239438a
--- /dev/null
+++ b/python/cudf/cudf/utils/queryutils.py
@@ -0,0 +1,255 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import ast
+import datetime
+from typing import Any, Dict
+
+import numpy as np
+from numba import cuda
+
+import cudf
+from cudf.core.buffer import acquire_spill_lock
+from cudf.core.column import column_empty
+from cudf.utils import applyutils
+from cudf.utils._numba import _CUDFNumbaConfig
+from cudf.utils.dtypes import (
+    BOOL_TYPES,
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+)
+
+ENVREF_PREFIX = "__CUDF_ENVREF__"
+
+SUPPORTED_QUERY_TYPES = {
+    np.dtype(dt)
+    for dt in NUMERIC_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | BOOL_TYPES
+}
+
+
+class QuerySyntaxError(ValueError):
+    pass
+
+
+class _NameExtractor(ast.NodeVisitor):
+    def __init__(self):
+        self.colnames = set()
+        self.refnames = set()
+
+    def visit_Name(self, node):
+        if not isinstance(node.ctx, ast.Load):
+            raise QuerySyntaxError("assignment is not allowed")
+
+        name = node.id
+        chosen = (
+            self.refnames if name.startswith(ENVREF_PREFIX) else self.colnames
+        )
+        chosen.add(name)
+
+
+def query_parser(text):
+    """The query expression parser.
+
+    See https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.query.html
+
+    * names with '@' prefix are global reference.
+    * other names must be column names of the dataframe.
+
+    Parameters
+    ----------
+    text: str
+        The query string
+
+    Returns
+    -------
+    info: a `dict` of the parsed info
+    """  # noqa
+    # convert any '@' to
+    text = text.replace("@", ENVREF_PREFIX)
+    tree = ast.parse(text)
+    _check_error(tree)
+    [expr] = tree.body
+    extractor = _NameExtractor()
+    extractor.visit(expr)
+    colnames = sorted(extractor.colnames)
+    refnames = sorted(extractor.refnames)
+    info = {
+        "source": text,
+        "args": colnames + refnames,
+        "colnames": colnames,
+        "refnames": refnames,
+    }
+    return info
+
+
+def query_builder(info, funcid):
+    """Function builder for the query expression
+
+    Parameters
+    ----------
+    info: dict
+        From the `query_parser()`
+    funcid: str
+        The name for the function being generated
+
+    Returns
+    -------
+    func: a python function of the query
+    """
+    args = info["args"]
+    def_line = "def {funcid}({args}):".format(
+        funcid=funcid, args=", ".join(args)
+    )
+    lines = [def_line, "    return {}".format(info["source"])]
+    source = "\n".join(lines)
+    glbs = {}
+    exec(source, glbs)
+    return glbs[funcid]
+
+
+def _check_error(tree):
+    if not isinstance(tree, ast.Module):
+        raise QuerySyntaxError("top level should be of ast.Module")
+    if len(tree.body) != 1:
+        raise QuerySyntaxError("too many expressions")
+
+
+_cache: Dict[Any, Any] = {}
+
+
+def query_compile(expr):
+    """Compile the query expression.
+
+    This generates a CUDA Kernel for the query expression.  The kernel is
+    cached for reuse.  All variable names, including both references to
+    columns and references to variables in the calling environment, in the
+    expression are passed as argument to the kernel. Thus, the kernel is
+    reusable on any dataframe and in any environment.
+
+    Parameters
+    ----------
+    expr : str
+        The boolean expression
+
+    Returns
+    -------
+    compiled: dict
+        key "kernel" is the cuda kernel for the query.
+        key "args" is a sequence of name of the arguments.
+    """
+
+    # hash returns in the semi-open interval [-2**63, 2**63)
+    funcid = f"queryexpr_{(hash(expr) + 2**63):x}"
+    # Load cache
+    compiled = _cache.get(funcid)
+    # Cache not found
+    if compiled is None:
+        info = query_parser(expr)
+        fn = query_builder(info, funcid)
+        args = info["args"]
+        # compile
+        devicefn = cuda.jit(device=True)(fn)
+
+        kernelid = f"kernel_{funcid}"
+        kernel = _wrap_query_expr(kernelid, devicefn, args)
+
+        compiled = info.copy()
+        compiled["kernel"] = kernel
+        # Store cache
+        _cache[funcid] = compiled
+    return compiled
+
+
+_kernel_source = """
+@cuda.jit
+def {kernelname}(out, {args}):
+    idx = cuda.grid(1)
+    if idx < out.size:
+        out[idx] = queryfn({indiced_args})
+"""
+
+
+def _wrap_query_expr(name, fn, args):
+    """Wrap the query expression in a cuda kernel."""
+
+    def _add_idx(arg):
+        if arg.startswith(ENVREF_PREFIX):
+            return arg
+        else:
+            return f"{arg}[idx]"
+
+    def _add_prefix(arg):
+        return f"_args_{arg}"
+
+    glbls = {"queryfn": fn, "cuda": cuda}
+    kernargs = map(_add_prefix, args)
+    indiced_args = map(_add_prefix, map(_add_idx, args))
+    src = _kernel_source.format(
+        kernelname=name,
+        args=", ".join(kernargs),
+        indiced_args=", ".join(indiced_args),
+    )
+    exec(src, glbls)
+    kernel = glbls[name]
+    return kernel
+
+
+@acquire_spill_lock()
+def query_execute(df, expr, callenv):
+    """Compile & execute the query expression
+
+    Note: the expression is compiled and cached for future reuse.
+
+    Parameters
+    ----------
+    df : DataFrame
+    expr : str
+        boolean expression
+    callenv : dict
+        Contains keys 'local_dict', 'locals' and 'globals' which are all dict.
+        They represent the arg, local and global dictionaries of the caller.
+    """
+
+    # compile
+    compiled = query_compile(expr)
+    columns = compiled["colnames"]
+
+    # prepare col args
+    colarrays = [cudf.core.dataframe.extract_col(df, col) for col in columns]
+
+    # wait to check the types until we know which cols are used
+    if any(col.dtype not in SUPPORTED_QUERY_TYPES for col in colarrays):
+        raise TypeError(
+            "query only supports numeric, datetime, timedelta, "
+            "or bool dtypes."
+        )
+
+    colarrays = [col.data_array_view(mode="read") for col in colarrays]
+
+    kernel = compiled["kernel"]
+    # process env args
+    envargs = []
+    envdict = callenv["globals"].copy()
+    envdict.update(callenv["locals"])
+    envdict.update(callenv["local_dict"])
+    for name in compiled["refnames"]:
+        name = name[len(ENVREF_PREFIX) :]
+        try:
+            val = envdict[name]
+            if isinstance(val, datetime.datetime):
+                val = np.datetime64(val)
+        except KeyError:
+            msg = "{!r} not defined in the calling environment"
+            raise NameError(msg.format(name))
+        else:
+            envargs.append(val)
+
+    # allocate output buffer
+    nrows = len(df)
+    out = column_empty(nrows, dtype=np.bool_)
+    # run kernel
+    args = [out] + colarrays + envargs
+    with _CUDFNumbaConfig():
+        kernel.forall(nrows)(*args)
+    out_mask = applyutils.make_aggregate_nullmask(df, columns=columns)
+    return out.set_mask(out_mask).fillna(False)
diff --git a/python/cudf/cudf/utils/string.py b/python/cudf/cudf/utils/string.py
new file mode 100644
index 0000000..9c02d1d
--- /dev/null
+++ b/python/cudf/cudf/utils/string.py
@@ -0,0 +1,13 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+
+def format_bytes(nbytes: int) -> str:
+    """Format `nbytes` to a human readable string"""
+    n = float(nbytes)
+    for unit in ["B", "KiB", "MiB", "GiB", "TiB"]:
+        if abs(n) < 1024:
+            if n.is_integer():
+                return f"{int(n)}{unit}"
+            return f"{n:.2f}{unit}"
+        n /= 1024
+    return f"{n:.2f} PiB"
diff --git a/python/cudf/cudf/utils/utils.py b/python/cudf/cudf/utils/utils.py
new file mode 100644
index 0000000..ffc3c29
--- /dev/null
+++ b/python/cudf/cudf/utils/utils.py
@@ -0,0 +1,431 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import decimal
+import functools
+import hashlib
+import os
+import traceback
+import warnings
+from functools import partial
+from typing import FrozenSet, Set, Union
+
+import numpy as np
+import pandas as pd
+from nvtx import annotate
+
+import rmm
+
+import cudf
+import cudf.api.types
+from cudf.core import column
+from cudf.core.buffer import as_buffer
+
+# The size of the mask in bytes
+mask_dtype = cudf.api.types.dtype(np.int32)
+mask_bitsize = mask_dtype.itemsize * 8
+
+# Mapping from ufuncs to the corresponding binary operators.
+_ufunc_binary_operations = {
+    # Arithmetic binary operations.
+    "add": "add",
+    "subtract": "sub",
+    "multiply": "mul",
+    "matmul": "matmul",
+    "divide": "truediv",
+    "true_divide": "truediv",
+    "floor_divide": "floordiv",
+    "power": "pow",
+    "float_power": "pow",
+    "remainder": "mod",
+    "mod": "mod",
+    "fmod": "mod",
+    # Bitwise binary operations.
+    "bitwise_and": "and",
+    "bitwise_or": "or",
+    "bitwise_xor": "xor",
+    # Comparison binary operators
+    "greater": "gt",
+    "greater_equal": "ge",
+    "less": "lt",
+    "less_equal": "le",
+    "not_equal": "ne",
+    "equal": "eq",
+}
+
+# These operators need to be mapped to their inverses when performing a
+# reflected ufunc operation because no reflected version of the operators
+# themselves exist. When these operators are invoked directly (not via
+# __array_ufunc__) Python takes care of calling the inverse operation.
+_ops_without_reflection = {
+    "gt": "lt",
+    "ge": "le",
+    "lt": "gt",
+    "le": "ge",
+    # ne and eq are symmetric, so they are their own inverse op
+    "ne": "ne",
+    "eq": "eq",
+}
+
+
+# This is the implementation of __array_ufunc__ used for Frame and Column.
+# For more detail on this function and how it should work, see
+# https://numpy.org/doc/stable/reference/ufuncs.html
+def _array_ufunc(obj, ufunc, method, inputs, kwargs):
+    # We don't currently support reduction, accumulation, etc. We also
+    # don't support any special kwargs or higher arity ufuncs than binary.
+    if method != "__call__" or kwargs or ufunc.nin > 2:
+        return NotImplemented
+
+    fname = ufunc.__name__
+    if fname in _ufunc_binary_operations:
+        reflect = obj is not inputs[0]
+        other = inputs[0] if reflect else inputs[1]
+
+        op = _ufunc_binary_operations[fname]
+        if reflect and op in _ops_without_reflection:
+            op = _ops_without_reflection[op]
+            reflect = False
+        op = f"__{'r' if reflect else ''}{op}__"
+
+        # float_power returns float irrespective of the input type.
+        # TODO: Do not get the attribute directly, get from the operator module
+        # so that we can still exploit reflection.
+        if fname == "float_power":
+            return getattr(obj, op)(other).astype(float)
+        return getattr(obj, op)(other)
+
+    # Special handling for various unary operations.
+    if fname == "negative":
+        return obj * -1
+    if fname == "positive":
+        return obj.copy(deep=True)
+    if fname == "invert":
+        return ~obj
+    if fname == "absolute":
+        # TODO: Make sure all obj (mainly Column) implement abs.
+        return abs(obj)
+    if fname == "fabs":
+        return abs(obj).astype(np.float64)
+
+    # None is a sentinel used by subclasses to trigger cupy dispatch.
+    return None
+
+
+_EQUALITY_OPS = {
+    "__eq__",
+    "__ne__",
+    "__lt__",
+    "__gt__",
+    "__le__",
+    "__ge__",
+}
+
+_NVTX_COLORS = ["green", "blue", "purple", "rapids"]
+
+# The test root is set by pytest to support situations where tests are run from
+# a source tree on a built version of cudf.
+NO_EXTERNAL_ONLY_APIS = os.getenv("NO_EXTERNAL_ONLY_APIS")
+
+_cudf_root = os.path.dirname(cudf.__file__)
+# If the environment variable for the test root is not set, we default to
+# using the path relative to the cudf root directory.
+_tests_root = os.getenv("_CUDF_TEST_ROOT") or os.path.join(_cudf_root, "tests")
+
+
+def _external_only_api(func, alternative=""):
+    """Decorator to indicate that a function should not be used internally.
+
+    cudf contains many APIs that exist for pandas compatibility but are
+    intrinsically inefficient. For some of these cudf has internal
+    equivalents that are much faster. Usage of the slow public APIs inside
+    our implementation can lead to unnecessary performance bottlenecks.
+    Applying this decorator to such functions and setting the environment
+    variable NO_EXTERNAL_ONLY_APIS will cause such functions to raise
+    exceptions if they are called from anywhere inside cudf, making it easy
+    to identify and excise such usage.
+
+    The `alternative` should be a complete phrase or sentence since it will
+    be used verbatim in error messages.
+    """
+
+    # If the first arg is a string then an alternative function to use in
+    # place of this API was provided, so we pass that to a subsequent call.
+    # It would be cleaner to implement this pattern by using a class
+    # decorator with a factory method, but there is no way to generically
+    # wrap docstrings on a class (we would need the docstring to be on the
+    # class itself, not instances, because that's what `help` looks at) and
+    # there is also no way to make mypy happy with that approach.
+    if isinstance(func, str):
+        return lambda actual_func: _external_only_api(actual_func, func)
+
+    if not NO_EXTERNAL_ONLY_APIS:
+        return func
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        # Check the immediately preceding frame to see if it's in cudf.
+        frame, lineno = next(traceback.walk_stack(None))
+        fn = frame.f_code.co_filename
+        if _cudf_root in fn and _tests_root not in fn:
+            raise RuntimeError(
+                f"External-only API called in {fn} at line {lineno}. "
+                f"{alternative}"
+            )
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+def initfunc(f):
+    """
+    Decorator for initialization functions that should
+    be run exactly once.
+    """
+
+    @functools.wraps(f)
+    def wrapper(*args, **kwargs):
+        if wrapper.initialized:
+            return
+        wrapper.initialized = True
+        return f(*args, **kwargs)
+
+    wrapper.initialized = False
+    return wrapper
+
+
+def clear_cache():
+    """Clear all internal caches"""
+    cudf.Scalar._clear_instance_cache()
+
+
+class GetAttrGetItemMixin:
+    """This mixin changes `__getattr__` to attempt a `__getitem__` call.
+
+    Classes that include this mixin gain enhanced functionality for the
+    behavior of attribute access like `obj.foo`: if `foo` is not an attribute
+    of `obj`, obj['foo'] will be attempted, and the result returned.  To make
+    this behavior safe, classes that include this mixin must define a class
+    attribute `_PROTECTED_KEYS` that defines the attributes that are accessed
+    within `__getitem__`. For example, if `__getitem__` is defined as
+    `return self._data[key]`, we must define `_PROTECTED_KEYS={'_data'}`.
+    """
+
+    # Tracking of protected keys by each subclass is necessary to make the
+    # `__getattr__`->`__getitem__` call safe. See
+    # https://nedbatchelder.com/blog/201010/surprising_getattr_recursion.html  # noqa: E501
+    # for an explanation. In brief, defining the `_PROTECTED_KEYS` allows this
+    # class to avoid calling `__getitem__` inside `__getattr__` when
+    # `__getitem__` will internally again call `__getattr__`, resulting in an
+    # infinite recursion.
+    # This problem only arises when the copy protocol is invoked (e.g. by
+    # `copy.copy` or `pickle.dumps`), and could also be avoided by redefining
+    # methods involved with the copy protocol such as `__reduce__` or
+    # `__setstate__`, but this class may be used in complex multiple
+    # inheritance hierarchies that might also override serialization.  The
+    # solution here is a minimally invasive change that avoids such conflicts.
+    _PROTECTED_KEYS: Union[FrozenSet[str], Set[str]] = frozenset()
+
+    def __getattr__(self, key):
+        if key in self._PROTECTED_KEYS:
+            raise AttributeError
+        try:
+            return self[key]
+        except KeyError:
+            raise AttributeError(
+                f"{type(self).__name__} object has no attribute {key}"
+            )
+
+
+class NotIterable:
+    def __iter__(self):
+        """
+        Iteration is unsupported.
+
+        See :ref:`iteration <pandas-comparison/iteration>` for more
+        information.
+        """
+        raise TypeError(
+            f"{self.__class__.__name__} object is not iterable. "
+            f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
+            f"if you wish to iterate over the values."
+        )
+
+
+def pa_mask_buffer_to_mask(mask_buf, size):
+    """
+    Convert PyArrow mask buffer to cuDF mask buffer
+    """
+    mask_size = cudf._lib.null_mask.bitmask_allocation_size_bytes(size)
+    if mask_buf.size < mask_size:
+        dbuf = rmm.DeviceBuffer(size=mask_size)
+        dbuf.copy_from_host(np.asarray(mask_buf).view("u1"))
+        return as_buffer(dbuf)
+    return as_buffer(mask_buf)
+
+
+def _isnat(val):
+    """Wraps np.isnat to return False instead of error on invalid inputs."""
+    if val is pd.NaT:
+        return True
+    elif not isinstance(val, (np.datetime64, np.timedelta64, str)):
+        return False
+    else:
+        try:
+            return val in {"NaT", "NAT"} or np.isnat(val)
+        except TypeError:
+            return False
+
+
+def search_range(x: int, ri: range, *, side: str) -> int:
+    """
+
+    Find insertion point in a range to maintain sorted order
+
+    Parameters
+    ----------
+    x
+        Integer to insert
+    ri
+        Range to insert into
+    side
+        Tie-breaking decision for the case that `x` is a member of the
+        range. If `"left"` then the insertion point is before the
+        entry, otherwise it is after.
+
+    Returns
+    -------
+    int
+        The insertion point
+
+    See Also
+    --------
+    numpy.searchsorted
+
+    Notes
+    -----
+    Let ``p`` be the return value, then if ``side="left"`` the
+    following invariants are maintained::
+
+        all(x < n for n in ri[:p])
+        all(x >= n for n in ri[p:])
+
+    Conversely, if ``side="right"`` then we have::
+
+        all(x <= n for n in ri[:p])
+        all(x > n for n in ri[p:])
+
+    Examples
+    --------
+    For series: 1 4 7
+    >>> search_range(4, range(1, 10, 3), side="left")
+    1
+    >>> search_range(4, range(1, 10, 3), side="right")
+    2
+    """
+    assert side in {"left", "right"}
+    if flip := (ri.step < 0):
+        ri = ri[::-1]
+        shift = int(side == "right")
+    else:
+        shift = int(side == "left")
+
+    offset = (x - ri.start - shift) // ri.step + 1
+    if flip:
+        offset = len(ri) - offset
+    return max(min(len(ri), offset), 0)
+
+
+def is_na_like(obj):
+    """
+    Check if `obj` is a cudf NA value,
+    i.e., None, cudf.NA or cudf.NaT
+    """
+    return obj is None or obj is cudf.NA or obj is cudf.NaT
+
+
+def _get_color_for_nvtx(name):
+    m = hashlib.sha256()
+    m.update(name.encode())
+    hash_value = int(m.hexdigest(), 16)
+    idx = hash_value % len(_NVTX_COLORS)
+    return _NVTX_COLORS[idx]
+
+
+def _cudf_nvtx_annotate(func, domain="cudf_python"):
+    """Decorator for applying nvtx annotations to methods in cudf."""
+    return annotate(
+        message=func.__qualname__,
+        color=_get_color_for_nvtx(func.__qualname__),
+        domain=domain,
+    )(func)
+
+
+_dask_cudf_nvtx_annotate = partial(
+    _cudf_nvtx_annotate, domain="dask_cudf_python"
+)
+
+
+def _warn_no_dask_cudf(fn):
+    @functools.wraps(fn)
+    def wrapper(self):
+        # try import
+        try:
+            # Import dask_cudf (if available) in case
+            # this is being called within Dask Dataframe
+            import dask_cudf  # noqa: F401
+
+        except ImportError:
+            warnings.warn(
+                f"Using dask to tokenize a {type(self)} object, "
+                "but `dask_cudf` is not installed. Please install "
+                "`dask_cudf` for proper dispatching."
+            )
+        return fn(self)
+
+    return wrapper
+
+
+def _is_same_name(left_name, right_name):
+    # Internal utility to compare if two names are same.
+    with warnings.catch_warnings():
+        # numpy throws warnings while comparing
+        # NaT values with non-NaT values.
+        warnings.simplefilter("ignore")
+        try:
+            same = (left_name is right_name) or (left_name == right_name)
+            if not same:
+                if isinstance(left_name, decimal.Decimal) and isinstance(
+                    right_name, decimal.Decimal
+                ):
+                    return left_name.is_nan() and right_name.is_nan()
+                if isinstance(left_name, float) and isinstance(
+                    right_name, float
+                ):
+                    return np.isnan(left_name) and np.isnan(right_name)
+                if isinstance(left_name, np.datetime64) and isinstance(
+                    right_name, np.datetime64
+                ):
+                    return np.isnan(left_name) and np.isnan(right_name)
+            return same
+        except TypeError:
+            return False
+
+
+def _all_bools_with_nulls(lhs, rhs, bool_fill_value):
+    # Internal utility to construct a boolean column
+    # by combining nulls from `lhs` & `rhs`.
+    if lhs.has_nulls() and rhs.has_nulls():
+        result_mask = lhs._get_mask_as_column() & rhs._get_mask_as_column()
+    elif lhs.has_nulls():
+        result_mask = lhs._get_mask_as_column()
+    elif rhs.has_nulls():
+        result_mask = rhs._get_mask_as_column()
+    else:
+        result_mask = None
+
+    result_col = column.full(
+        size=len(lhs), fill_value=bool_fill_value, dtype=cudf.dtype(np.bool_)
+    )
+    if result_mask is not None:
+        result_col = result_col.set_mask(result_mask.as_mask())
+    return result_col
diff --git a/python/cudf/cudf_pandas_tests/_magics_cpu_test.py b/python/cudf/cudf_pandas_tests/_magics_cpu_test.py
new file mode 100644
index 0000000..053040e
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/_magics_cpu_test.py
@@ -0,0 +1,30 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+
+def ipython_magics_cpu_test():
+    import warnings
+
+    from IPython.core.interactiveshell import InteractiveShell
+
+    # Use in-memory history file to avoid file handles leaking
+    # https://github.com/pandas-dev/pandas/pull/35711
+    from traitlets.config import Config  # isort:skip
+
+    warnings.filterwarnings("ignore", category=UserWarning)
+
+    c = Config()
+    c.HistoryManager.hist_file = ":memory:"
+
+    ip = InteractiveShell(config=c)
+    ip.run_line_magic("load_ext", "cudf.pandas")
+
+    # confirm pandas is not aliased
+    ip.run_cell("import pandas as pd; s = pd.Series(range(5))")
+    result = ip.run_cell("assert not hasattr(s, '_fsproxy_state')")
+    result.raise_error()
+
+
+if __name__ == "__main__":
+    ipython_magics_cpu_test()
diff --git a/python/cudf/cudf_pandas_tests/_magics_gpu_test.py b/python/cudf/cudf_pandas_tests/_magics_gpu_test.py
new file mode 100755
index 0000000..2c92ec3
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/_magics_gpu_test.py
@@ -0,0 +1,26 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+
+def ipython_magics_gpu_test():
+    from IPython.core.interactiveshell import InteractiveShell
+
+    # Use in-memory history file to avoid file handles leaking
+    # https://github.com/pandas-dev/pandas/pull/35711
+    from traitlets.config import Config  # isort:skip
+
+    c = Config()
+    c.HistoryManager.hist_file = ":memory:"
+
+    ip = InteractiveShell(config=c)
+    ip.run_line_magic("load_ext", "cudf.pandas")
+
+    # Directly check for private proxy attribute
+    ip.run_cell("import pandas as pd; s = pd.Series(range(5))")
+    result = ip.run_cell("assert hasattr(s, '_fsproxy_state')")
+    result.raise_error()
+
+
+if __name__ == "__main__":
+    ipython_magics_gpu_test()
diff --git a/python/cudf/cudf_pandas_tests/test_array_function.py b/python/cudf/cudf_pandas_tests/test_array_function.py
new file mode 100644
index 0000000..8805da3
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/test_array_function.py
@@ -0,0 +1,91 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from cudf.pandas import LOADED
+from cudf.pandas._wrappers.common import array_function_method
+
+if not LOADED:
+    raise ImportError("These tests must be run with cudf.pandas loaded")
+
+import pandas._testing as tm
+
+from cudf.pandas.fast_slow_proxy import make_final_proxy_type
+
+
+class Slow:
+    def __array__(self):
+        return np.array([1, 1, 1, 2, 2, 3])
+
+
+class Slow2:
+    def __array_function__(self, func, types, args, kwargs):
+        return "slow"
+
+
+class Fast:
+    def __array_function__(self, func, types, args, kwargs):
+        return "fast"
+
+
+class Fast2:
+    def __array_function__(self, func, types, args, kwargs):
+        return NotImplemented
+
+
+def test_array_function():
+    # test that fast dispatch to __array_function__ works
+    Proxy = make_final_proxy_type(
+        "Proxy",
+        Fast,
+        Slow2,
+        fast_to_slow=lambda fast: Slow2(),
+        slow_to_fast=lambda slow: Fast(),
+        additional_attributes={"__array_function__": array_function_method},
+    )
+    tm.assert_equal(np.unique(Proxy()), "fast")
+
+
+def test_array_function_fallback():
+    # test that slow dispatch works when the fast dispatch fails
+    Proxy = make_final_proxy_type(
+        "Proxy",
+        Fast2,
+        Slow2,
+        fast_to_slow=lambda fast: Slow2(),
+        slow_to_fast=lambda slow: Fast2(),
+        additional_attributes={"__array_function__": array_function_method},
+    )
+    tm.assert_equal(np.unique(Proxy()), "slow")
+
+
+def test_array_function_fallback_array():
+    # test that dispatch to slow __array__ works when
+    # fast __array_function__ fails
+    Proxy = make_final_proxy_type(
+        "Proxy",
+        Fast2,
+        Slow,
+        fast_to_slow=lambda fast: Slow(),
+        slow_to_fast=lambda slow: Fast2(),
+        additional_attributes={"__array_function__": array_function_method},
+    )
+    tm.assert_equal(np.unique(Proxy()), np.unique(np.asarray(Slow())))
+
+
+def test_array_function_notimplemented():
+    # tests that when neither Fast nor Slow implement __array_function__,
+    # we get a TypeError
+    Proxy = make_final_proxy_type(
+        "Proxy",
+        Fast2,
+        Fast2,
+        fast_to_slow=lambda fast: Fast2(),
+        slow_to_fast=lambda slow: Fast2(),
+        additional_attributes={"__array_function__": array_function_method},
+    )
+    with pytest.raises(TypeError):
+        np.unique(Proxy())
diff --git a/python/cudf/cudf_pandas_tests/test_cudf_pandas.py b/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
new file mode 100644
index 0000000..2500ba0
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/test_cudf_pandas.py
@@ -0,0 +1,1228 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import collections
+import copy
+import datetime
+import operator
+import pathlib
+import pickle
+import tempfile
+import types
+from io import BytesIO, StringIO
+
+import numpy as np
+import pyarrow as pa
+import pytest
+from numba import NumbaDeprecationWarning
+
+from cudf.pandas import LOADED, Profiler
+from cudf.pandas.fast_slow_proxy import _Unusable
+
+if not LOADED:
+    raise ImportError("These tests must be run with cudf.pandas loaded")
+
+import pandas as xpd
+import pandas._testing as tm
+
+# Accelerated pandas has the real pandas module as an attribute
+pd = xpd._fsproxy_slow
+
+
+@pytest.fixture
+def dataframe():
+    pdf = pd.DataFrame({"a": [1, 1, 1, 2, 3], "b": [1, 2, 3, 4, 5]})
+    df = xpd.DataFrame(pdf)
+    return (pdf, df)
+
+
+@pytest.fixture
+def series(dataframe):
+    pdf, df = dataframe
+    return (pdf["a"], df["a"])
+
+
+@pytest.fixture
+def index():
+    return (
+        pd.Index(["a", "b", "c", "d", "e"]),
+        xpd.Index(["a", "b", "c", "d", "e"]),
+    )
+
+
+@pytest.fixture
+def multiindex(dataframe):
+    pdf, df = dataframe
+    pmi = pd.MultiIndex.from_frame(pdf)
+    mi = xpd.MultiIndex.from_frame(df)
+    return (pmi, mi)
+
+
+@pytest.fixture
+def array(series):
+    arr, xarr = series
+    return (arr.values, xarr.values)
+
+
+@pytest.fixture(
+    params=[
+        lambda group: group["a"].sum(),
+        lambda group: group.sum().apply(lambda val: [val]),
+    ]
+)
+def groupby_udf(request):
+    return request.param
+
+
+def test_assert_equal():
+    tm.assert_frame_equal(
+        pd.DataFrame({"a": [1, 2, 3]}), xpd.DataFrame({"a": [1, 2, 3]})
+    )
+
+    with pytest.raises(AssertionError):
+        tm.assert_frame_equal(
+            pd.DataFrame({"a": [1, 2, 3]}), xpd.DataFrame({"a": [1, 2, 4]})
+        )
+
+
+def test_construction():
+    # test that constructing a DataFrame returns an DataFrame
+    data = {"a": [1, 2, 3], "b": ["x", "y", "z"]}
+    pdf = pd.DataFrame(data)
+    df = xpd.DataFrame(data)
+    tm.assert_frame_equal(pdf, df)
+
+
+def test_construction_object():
+    # test that we can construct a Series with `object` dtype
+    psr = pd.Series([1, "a", [1, 2, 3]])
+    sr = xpd.Series([1, "a", [1, 2, 3]])
+    tm.assert_series_equal(psr, sr)
+
+
+def test_construction_from_frame(dataframe):
+    pdf, _ = dataframe
+    df = xpd.DataFrame(pdf)
+    tm.assert_frame_equal(pdf, df)
+
+
+def test_groupby(dataframe):
+    pdf, df = dataframe
+    expected = pdf.groupby("a", sort=True).max()
+    gb = df.groupby("a", sort=True)
+    got = gb.max()
+    tm.assert_frame_equal(expected, got)
+
+
+def test_repr(dataframe):
+    pdf, df = dataframe
+    assert df.__repr__() == pdf.__repr__()
+
+
+def test_binops_series(series):
+    psr, sr = series
+    expected = psr + psr
+    got = sr + sr
+    tm.assert_series_equal(expected, got)
+
+
+def test_binops_df(dataframe):
+    pdf, df = dataframe
+    expected = pdf + pdf
+    got = df + df
+    tm.assert_frame_equal(expected, got)
+
+
+def test_attribute(dataframe):
+    pdf, df = dataframe
+    assert pdf.shape == df.shape
+
+
+def test_tz_localize():
+    psr = pd.Series(["2001-01-01", "2002-02-02"], dtype="datetime64[ms]")
+    sr = xpd.Series(psr)
+    tm.assert_series_equal(
+        psr.dt.tz_localize("America/New_York"),
+        sr.dt.tz_localize("America/New_York"),
+        check_dtype=False,
+    )
+
+
+def test_index_tz_localize():
+    pti = pd.Index(pd.date_range("2020-01-01", periods=3, freq="D"))
+    xti = xpd.Index(xpd.date_range("2020-01-01", periods=3, freq="D"))
+    pti = pti.tz_localize("UTC")
+    xti = xti.tz_localize("UTC")
+    tm.assert_equal(pti, xti)
+
+
+def test_index_generator():
+    pi = pd.Index(iter(range(10)))
+    xi = xpd.Index(iter(range(10)))
+    tm.assert_equal(pi, xi)
+
+
+def test_groupby_apply_fallback(dataframe, groupby_udf):
+    pdf, df = dataframe
+    tm.assert_equal(
+        pdf.groupby("a", sort=True, group_keys=True).apply(groupby_udf),
+        df.groupby("a", sort=True, group_keys=True).apply(groupby_udf),
+    )
+
+
+def test_groupby_external_series_apply_fallback(dataframe, groupby_udf):
+    pdf, df = dataframe
+    tm.assert_equal(
+        pdf.groupby(
+            pd.Series([1, 2, 1, 2, 1]), sort=True, group_keys=True
+        ).apply(groupby_udf),
+        df.groupby(
+            xpd.Series([1, 2, 1, 2, 1]), sort=True, group_keys=True
+        ).apply(groupby_udf),
+    )
+
+
+def test_read_csv():
+    data = "1,2,3\n4,5,6"
+    expected = pd.read_csv(StringIO(data))
+    got = xpd.read_csv(StringIO(data))
+    tm.assert_frame_equal(expected, got)
+
+
+def test_iloc(dataframe):
+    pdf, df = dataframe
+    tm.assert_frame_equal(pdf.iloc[:, :], df.iloc[:, :])
+
+
+def test_neg(dataframe):
+    pdf, df = dataframe
+    tm.assert_frame_equal(-pdf, -df)
+
+
+def test_groupby_filter(dataframe):
+    pdf, df = dataframe
+    expected = pdf.groupby("a").filter(lambda df: len(df) > 2)
+    got = df.groupby("a").filter(lambda df: len(df) > 2)
+    tm.assert_frame_equal(expected, got)
+
+
+def test_groupby_rolling(dataframe):
+    pdf, df = dataframe
+    expected = pdf.groupby("a").rolling(2).sum()
+    got = df.groupby("a").rolling(2).sum()
+    tm.assert_frame_equal(expected, got)
+
+
+def test_groupby_rolling_window(dataframe):
+    pdf, df = dataframe
+    expected = pdf.groupby("a").rolling(2, win_type="triang").mean()
+    got = df.groupby("a").rolling(2, win_type="triang").mean()
+    tm.assert_frame_equal(expected, got)
+
+
+def test_ewm():
+    pdf = pd.DataFrame(range(5))
+    df = xpd.DataFrame(range(5))
+    result = df.ewm(0.5).mean()
+    expected = pdf.ewm(0.5).mean()
+    tm.assert_equal(result, expected)
+
+
+def test_setitem_frame(dataframe):
+    pdf, df = dataframe
+    pdf[pdf > 1] = -pdf
+    df[df > 1] = -df
+    tm.assert_frame_equal(pdf, df)
+
+
+def test_concat(dataframe):
+    pdf, df = dataframe
+    expected = pd.concat([pdf, pdf])
+    got = xpd.concat([df, df])
+    tm.assert_frame_equal(expected, got)
+
+
+def test_attribute_error():
+    df = xpd.DataFrame()
+    with pytest.raises(AttributeError):
+        df.blah
+
+
+def test_df_from_series(series):
+    psr, sr = series
+    tm.assert_frame_equal(pd.DataFrame(psr), xpd.DataFrame(sr))
+
+
+def test_iloc_change_type(series):
+    psr, sr = series
+    psr.iloc[0] = "a"
+    sr.iloc[0] = "a"
+    tm.assert_series_equal(psr, sr)
+
+
+def test_rename_categories():
+    psr = pd.Series([1, 2, 3], dtype="category")
+    sr = xpd.Series([1, 2, 3], dtype="category")
+    psr = psr.cat.rename_categories({1: 5})
+    sr = sr.cat.rename_categories({1: 5})
+    tm.assert_series_equal(psr, sr)
+
+
+def test_rename_categories_inplace():
+    psr = pd.Series([1, 2, 3], dtype="category")
+    sr = xpd.Series([1, 2, 3], dtype="category")
+    with pytest.warns(FutureWarning):
+        psr.cat.rename_categories({1: 5}, inplace=True)
+        sr.cat.rename_categories({1: 5}, inplace=True)
+    tm.assert_series_equal(psr, sr)
+
+
+def test_rename_categories_inplace_after_copying_parent():
+    s = xpd.Series([1, 2, 3], dtype="category")
+    # cudf does not define "rename_categories",
+    # so this copies `s` from device to host:
+    rename_categories = s.cat.rename_categories
+    _ = len(s)  # trigger a copy of `s` from host to device:
+    with pytest.warns(FutureWarning):
+        rename_categories([5, 2, 3], inplace=True)
+    assert s.cat.categories.tolist() == [5, 2, 3]
+
+
+def test_column_rename(dataframe):
+    pdf, df = dataframe
+    pdf.columns = ["x", "y"]
+    df.columns = ["x", "y"]
+    tm.assert_frame_equal(pdf, df)
+
+
+def test_shape(dataframe):
+    pdf, df = dataframe
+    assert pdf.shape == df.shape
+    pdf["c"] = range(5)
+    df["c"] = range(5)
+    assert pdf.shape == df.shape
+
+
+def test_isnull():
+    psr = pd.Series([1, 2, 3])
+    sr = xpd.Series(psr)
+    # Test that invoking `Pandas` functions works.
+    tm.assert_series_equal(pd.isnull(psr), xpd.isnull(sr))
+
+
+def test_copy_deepcopy_recursion(dataframe):
+    # test that we don't recurse when calling the copy/deepcopy
+    # methods, which can happen due to
+    # https://nedbatchelder.com/blog/201010/surprising_getattr_recursion.html
+    import copy
+
+    pdf, df = dataframe
+    copy.copy(df)
+    copy.deepcopy(df)
+
+
+@pytest.mark.parametrize("copier", [copy.copy, copy.deepcopy])
+def test_copy_deepcopy(copier):
+    s = xpd.Series([1, 2, 3])
+    s2 = copier(s)
+    assert isinstance(s2, s.__class__)
+    tm.assert_equal(s, s2)
+
+    df = xpd.DataFrame({"a": [1, 2, 3]})
+    df2 = copier(df)
+    assert isinstance(df2, df.__class__)
+    tm.assert_equal(df, df2)
+
+    idx = xpd.Index([1, 2, 3])
+    idx2 = copier(idx)
+    assert isinstance(idx2, idx.__class__)
+    tm.assert_equal(idx, idx2)
+
+
+def test_classmethod():
+    pdf = pd.DataFrame.from_dict({"a": [1, 2, 3]})
+    df = xpd.DataFrame.from_dict({"a": [1, 2, 3]})
+    tm.assert_frame_equal(pdf, df)
+
+
+def test_rolling(dataframe):
+    pdf, df = dataframe
+    tm.assert_frame_equal(pdf.rolling(2).agg("sum"), df.rolling(2).agg("sum"))
+
+
+def test_array_function_series(series):
+    psr, sr = series
+    np.testing.assert_allclose(np.average(psr), np.average(sr))
+
+
+def test_array_function_ndarray(array):
+    arr, xarr = array
+    np.isclose(np.average(arr), np.average(xarr))
+
+
+def test_histogram_ndarray(array):
+    arr, xarr = array
+    expected_hist, expected_edges = np.histogram(arr, bins="auto")
+    got_hist, got_edges = np.histogram(xarr, bins="auto")
+    tm.assert_almost_equal(expected_hist, got_hist)
+    tm.assert_almost_equal(expected_edges, got_edges)
+
+
+def test_pickle_round_trip(dataframe):
+    pdf, df = dataframe
+    pickled_pdf = BytesIO()
+    pickled_cudf_pandas = BytesIO()
+    pdf.to_pickle(pickled_pdf)
+    df.to_pickle(pickled_cudf_pandas)
+
+    pickled_pdf.seek(0)
+    pickled_cudf_pandas.seek(0)
+
+    tm.assert_frame_equal(
+        pd.read_pickle(pickled_pdf), xpd.read_pickle(pickled_cudf_pandas)
+    )
+
+
+def test_excel_round_trip(dataframe):
+    pdf, df = dataframe
+    excel_pdf = BytesIO()
+    excel_cudf_pandas = BytesIO()
+    pdf.to_excel(excel_pdf)
+    df.to_excel(excel_cudf_pandas)
+
+    excel_pdf.seek(0)
+    excel_cudf_pandas.seek(0)
+
+    tm.assert_frame_equal(
+        pd.read_excel(excel_pdf), xpd.read_excel(excel_cudf_pandas)
+    )
+
+
+def test_hash_array(series):
+    ps, xs = series
+    expected = pd.util.hash_array(ps.values)
+    actual = xpd.util.hash_array(xs.values)
+
+    tm.assert_almost_equal(expected, actual)
+
+
+def test_is_sparse():
+    psa = pd.arrays.SparseArray([0, 0, 1, 0])
+    xsa = xpd.arrays.SparseArray([0, 0, 1, 0])
+
+    assert pd.api.types.is_sparse(psa) == xpd.api.types.is_sparse(xsa)
+
+
+def test_is_file_like():
+    assert pd.api.types.is_file_like("a") == xpd.api.types.is_file_like("a")
+    assert pd.api.types.is_file_like(BytesIO()) == xpd.api.types.is_file_like(
+        BytesIO()
+    )
+    assert pd.api.types.is_file_like(
+        StringIO("abc")
+    ) == xpd.api.types.is_file_like(StringIO("abc"))
+
+
+def test_is_re_compilable():
+    assert pd.api.types.is_re_compilable(
+        ".^"
+    ) == xpd.api.types.is_re_compilable(".^")
+    assert pd.api.types.is_re_compilable(
+        ".*"
+    ) == xpd.api.types.is_re_compilable(".*")
+
+
+def test_module_attribute_types():
+    assert isinstance(xpd.read_csv, types.FunctionType)
+    assert isinstance(xpd.tseries.frequencies.Day, type)
+    assert isinstance(xpd.api, types.ModuleType)
+
+
+def test_infer_freq():
+    expected = pd.infer_freq(
+        pd.date_range(start="2020/12/01", end="2020/12/30", periods=30)
+    )
+    got = xpd.infer_freq(
+        xpd.date_range(start="2020/12/01", end="2020/12/30", periods=30)
+    )
+    assert expected == got
+
+
+def test_groupby_grouper_fallback(dataframe, groupby_udf):
+    pdf, df = dataframe
+    tm.assert_equal(
+        pdf.groupby(pd.Grouper("a"), sort=True, group_keys=True).apply(
+            groupby_udf
+        ),
+        df.groupby(xpd.Grouper("a"), sort=True, group_keys=True).apply(
+            groupby_udf
+        ),
+    )
+
+
+def test_options_mode():
+    assert xpd.options.mode.copy_on_write == pd.options.mode.copy_on_write
+
+
+def test_profiler():
+    pytest.importorskip("cudf")
+
+    # test that the profiler correctly reports
+    # when we use the GPU v/s CPU
+    with Profiler() as p:
+        df = xpd.DataFrame({"a": [1, 2, 3], "b": "b"})
+        df.groupby("a").max()
+
+    assert len(p.per_line_stats) == 2
+    for line_no, line, gpu_time, cpu_time in p.per_line_stats:
+        assert gpu_time
+        assert not cpu_time
+
+    with Profiler() as p:
+        s = xpd.Series([1, "a"])
+        s = s + s
+
+    assert len(p.per_line_stats) == 2
+    for line_no, line, gpu_time, cpu_time in p.per_line_stats:
+        assert cpu_time
+
+
+def test_column_access_as_attribute():
+    pdf = pd.DataFrame({"fast": [1, 2, 3], "slow": [2, 3, 4]})
+    df = xpd.DataFrame({"fast": [1, 2, 3], "slow": [2, 3, 4]})
+
+    tm.assert_series_equal(pdf.fast, df.fast)
+    tm.assert_series_equal(pdf.slow, df.slow)
+
+
+def test_binop_dataframe_list(dataframe):
+    pdf, df = dataframe
+    expect = pdf[["a"]] == [[1, 2, 3, 4, 5]]
+    got = df[["a"]] == [[1, 2, 3, 4, 5]]
+    tm.assert_frame_equal(expect, got)
+
+
+def test_binop_array_series(series):
+    psr, sr = series
+    arr = psr.array
+    expect = arr + psr
+    got = arr + sr
+    tm.assert_series_equal(expect, got)
+
+
+def test_array_ufunc_reduction(series):
+    psr, sr = series
+    expect = np.ufunc.reduce(np.subtract, psr)
+    got = np.ufunc.reduce(np.subtract, sr)
+    tm.assert_equal(expect, got)
+
+
+def test_array_ufunc(series):
+    psr, sr = series
+    expect = np.subtract(psr, psr)
+    got = np.subtract(sr, sr)
+    assert isinstance(got, sr.__class__)
+    tm.assert_equal(expect, got)
+
+
+def test_groupby_apply_func_returns_series(dataframe):
+    pdf, df = dataframe
+    expect = pdf.groupby("a").apply(lambda group: pd.Series({"x": 1}))
+    got = df.groupby("a").apply(lambda group: xpd.Series({"x": 1}))
+    tm.assert_equal(expect, got)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], ["a", None, "b"]])
+def test_pyarrow_array_construction(data):
+    cudf_pandas_series = xpd.Series(data)
+    actual_pa_array = pa.array(cudf_pandas_series)
+    expected_pa_array = pa.array(data)
+    assert actual_pa_array.equals(expected_pa_array)
+
+
+@pytest.mark.parametrize(
+    "op", [">", "<", "==", "<=", ">=", "+", "%", "-", "*", "/"]
+)
+def test_cudf_pandas_eval_series(op):
+    lhs = xpd.Series([10, 11, 12])  # noqa: F841
+    rhs = xpd.Series([100, 1, 12])  # noqa: F841
+
+    actual = xpd.eval(f"lhs {op} rhs")
+
+    pd_lhs = pd.Series([10, 11, 12])  # noqa: F841
+    pd_rhs = pd.Series([100, 1, 12])  # noqa: F841
+
+    expected = pd.eval(f"pd_lhs {op} pd_rhs")
+
+    tm.assert_series_equal(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "op", [">", "<", "==", "<=", ">=", "+", "%", "-", "*", "/"]
+)
+def test_cudf_pandas_eval_dataframe(op):
+    lhs = xpd.DataFrame({"a": [10, 11, 12], "b": [1, 2, 3]})  # noqa: F841
+    rhs = xpd.DataFrame({"a": [100, 1, 12], "b": [15, -10, 3]})  # noqa: F841
+
+    actual = xpd.eval(f"lhs {op} rhs")
+
+    pd_lhs = pd.DataFrame({"a": [10, 11, 12], "b": [1, 2, 3]})  # noqa: F841
+    pd_rhs = pd.DataFrame({"a": [100, 1, 12], "b": [15, -10, 3]})  # noqa: F841
+
+    expected = pd.eval(f"pd_lhs {op} pd_rhs")
+
+    tm.assert_frame_equal(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "expr", ["((a + b) * c % d) > e", "((a + b) * c % d)"]
+)
+def test_cudf_pandas_eval_complex(expr):
+    data = {
+        "a": [10, 11, 12],
+        "b": [1, 2, 3],
+        "c": [100, 1, 12],
+        "d": [15, -10, 3],
+        "e": [100, 200, 300],
+    }
+    cudf_pandas_frame = xpd.DataFrame(data)
+    pd_frame = pd.DataFrame(data)
+    actual = cudf_pandas_frame.eval(expr)
+    expected = pd_frame.eval(expr)
+    tm.assert_series_equal(expected, actual)
+
+
+def test_array_function_series_fallback(series):
+    psr, sr = series
+    expect = np.unique(psr, return_counts=True)
+    got = np.unique(sr, return_counts=True)
+    tm.assert_equal(expect, got)
+
+
+def test_timedeltaproperties(series):
+    psr, sr = series
+    psr, sr = psr.astype("timedelta64[ns]"), sr.astype("timedelta64[ns]")
+    tm.assert_equal(psr.dt.days, sr.dt.days)
+    tm.assert_equal(psr.dt.components, sr.dt.components)
+    tm.assert_equal(psr.dt.total_seconds(), sr.dt.total_seconds())
+
+
+@pytest.mark.parametrize("scalar_type", [int, float, complex, bool])
+@pytest.mark.parametrize("scalar", [1, 1.0, True, 0])
+def test_coerce_zero_d_array_to_scalar(scalar_type, scalar):
+    expected = scalar_type(pd.Series([scalar]).values[0])
+    got = scalar_type(xpd.Series([scalar]).values[0])
+    tm.assert_equal(expected, got)
+
+
+def test_cupy_asarray_zero_copy():
+    cp = pytest.importorskip("cupy")
+
+    sr = xpd.Series([1, 2, 3])
+    cpary = cp.asarray(sr.values)
+
+    assert (
+        sr.__cuda_array_interface__["data"][0]
+        == cpary.__cuda_array_interface__["data"][0]
+    )
+
+
+def test_pipe(dataframe):
+    pdf, df = dataframe
+
+    def func(df, x):
+        return df + x
+
+    expect = pdf.pipe(func, 1)
+    got = df.pipe(func, 1)
+    tm.assert_frame_equal(expect, got)
+
+
+def test_pipe_tuple(dataframe):
+    pdf, df = dataframe
+
+    def func(x, df):
+        return df + x
+
+    expect = pdf.pipe((func, "df"), 1)
+    got = df.pipe((func, "df"), 1)
+    tm.assert_frame_equal(expect, got)
+
+
+def test_maintain_container_subclasses(multiindex):
+    # pandas Frozenlist is a list subclass
+    pmi, mi = multiindex
+    got = mi.names.difference(["b"])
+    expect = pmi.names.difference(["b"])
+    assert got == expect
+    assert isinstance(got, xpd.core.indexes.frozen.FrozenList)
+
+
+def test_rolling_win_type():
+    pdf = pd.DataFrame(range(5))
+    df = xpd.DataFrame(range(5))
+    result = df.rolling(2, win_type="boxcar").mean()
+    with pytest.warns(DeprecationWarning):
+        expected = pdf.rolling(2, win_type="boxcar").mean()
+    tm.assert_equal(result, expected)
+
+
+def test_rolling_apply_numba_engine():
+    def weighted_mean(x):
+        arr = np.ones((1, x.shape[1]))
+        arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum()
+        return arr
+
+    pdf = pd.DataFrame([[1, 2, 0.6], [2, 3, 0.4], [3, 4, 0.2], [4, 5, 0.7]])
+    df = xpd.DataFrame([[1, 2, 0.6], [2, 3, 0.4], [3, 4, 0.2], [4, 5, 0.7]])
+
+    with pytest.warns(NumbaDeprecationWarning):
+        expect = pdf.rolling(2, method="table", min_periods=0).apply(
+            weighted_mean, raw=True, engine="numba"
+        )
+    got = df.rolling(2, method="table", min_periods=0).apply(
+        weighted_mean, raw=True, engine="numba"
+    )
+    tm.assert_equal(expect, got)
+
+
+def test_expanding():
+    pdf = pd.DataFrame(range(5))
+    df = xpd.DataFrame(range(5))
+    result = df.expanding().mean()
+    expected = pdf.expanding().mean()
+    tm.assert_equal(result, expected)
+
+
+def test_pipe_with_data_creating_func():
+    def pandas_func(df):
+        df2 = pd.DataFrame({"b": np.arange(len(df))})
+        return df.join(df2)
+
+    def cudf_pandas_func(df):
+        df2 = xpd.DataFrame({"b": np.arange(len(df))})
+        return df.join(df2)
+
+    pdf = pd.DataFrame({"a": [1, 2, 3]})
+    df = xpd.DataFrame({"a": [1, 2, 3]})
+
+    tm.assert_frame_equal(pdf.pipe(pandas_func), df.pipe(cudf_pandas_func))
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        '{"a": 1, "b": 2, "c": 3}',
+        '{"a": 1, "b": 2, "c": 3}\n{"a": 4, "b": 5, "c": 6}',
+    ],
+)
+def test_chunked_json_reader(tmpdir, data):
+    file_path = tmpdir / "test.json"
+    with open(file_path, "w") as f:
+        f.write(data)
+
+    with (
+        pd.read_json(file_path, lines=True, chunksize=1) as pd_reader,
+        xpd.read_json(file_path, lines=True, chunksize=1) as xpd_reader,
+    ):
+        for pd_chunk, xpd_chunk in zip(pd_reader, xpd_reader):
+            tm.assert_equal(pd_chunk, xpd_chunk)
+
+    with (
+        pd.read_json(StringIO(data), lines=True, chunksize=1) as pd_reader,
+        xpd.read_json(StringIO(data), lines=True, chunksize=1) as xpd_reader,
+    ):
+        for pd_chunk, xpd_chunk in zip(pd_reader, xpd_reader):
+            tm.assert_equal(pd_chunk, xpd_chunk)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        "1,2,3",
+        "1,2,3\n4,5,6",
+    ],
+)
+def test_chunked_csv_reader(tmpdir, data):
+    file_path = tmpdir / "test.json"
+    with open(file_path, "w") as f:
+        f.write(data)
+
+    with (
+        pd.read_csv(file_path, chunksize=1) as pd_reader,
+        xpd.read_csv(file_path, chunksize=1) as xpd_reader,
+    ):
+        for pd_chunk, xpd_chunk in zip(pd_reader, xpd_reader):
+            tm.assert_equal(pd_chunk, xpd_chunk, check_index_type=False)
+
+    with (
+        pd.read_json(StringIO(data), lines=True, chunksize=1) as pd_reader,
+        xpd.read_json(StringIO(data), lines=True, chunksize=1) as xpd_reader,
+    ):
+        for pd_chunk, xpd_chunk in zip(pd_reader, xpd_reader):
+            tm.assert_equal(pd_chunk, xpd_chunk, check_index_type=False)
+
+
+@pytest.mark.parametrize(
+    "data", [(), (1,), (1, 2, 3), ("a", "b", "c"), (1, 2, "test")]
+)
+def test_construct_from_generator(data):
+    expect = pd.Series((x for x in data))
+    got = xpd.Series((x for x in data))
+    tm.assert_series_equal(expect, got)
+
+
+def test_read_csv_stringio_usecols():
+    data = "col1,col2,col3\na,b,1\na,b,2\nc,d,3"
+    expect = pd.read_csv(StringIO(data), usecols=lambda x: x.upper() != "COL3")
+    got = xpd.read_csv(StringIO(data), usecols=lambda x: x.upper() != "COL3")
+    tm.assert_frame_equal(expect, got)
+
+
+def test_construct_datetime_index():
+    expect = pd.DatetimeIndex([10, 20, 30], dtype="datetime64[ns]")
+    got = xpd.DatetimeIndex([10, 20, 30], dtype="datetime64[ns]")
+    tm.assert_index_equal(expect, got)
+
+
+def test_construct_timedelta_index():
+    expect = pd.TimedeltaIndex([10, 20, 30], dtype="timedelta64[ns]")
+    got = xpd.TimedeltaIndex([10, 20, 30], dtype="timedelta64[ns]")
+    tm.assert_index_equal(expect, got)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.eq,
+        operator.sub,
+        operator.lt,
+        operator.gt,
+        operator.le,
+        operator.ge,
+    ],
+)
+def test_datetime_ops(op):
+    pd_dt_idx1 = pd.DatetimeIndex([10, 20, 30], dtype="datetime64[ns]")
+    cudf_pandas_dt_idx = xpd.DatetimeIndex(
+        [10, 20, 30], dtype="datetime64[ns]"
+    )
+
+    tm.assert_equal(
+        op(pd_dt_idx1, pd_dt_idx1), op(cudf_pandas_dt_idx, cudf_pandas_dt_idx)
+    )
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.eq,
+        operator.add,
+        operator.sub,
+        operator.lt,
+        operator.gt,
+        operator.le,
+        operator.ge,
+    ],
+)
+def test_timedelta_ops(op):
+    pd_td_idx1 = pd.TimedeltaIndex([10, 20, 30], dtype="timedelta64[ns]")
+    cudf_pandas_td_idx = xpd.TimedeltaIndex(
+        [10, 20, 30], dtype="timedelta64[ns]"
+    )
+
+    tm.assert_equal(
+        op(pd_td_idx1, pd_td_idx1), op(cudf_pandas_td_idx, cudf_pandas_td_idx)
+    )
+
+
+@pytest.mark.parametrize("op", [operator.add, operator.sub])
+def test_datetime_timedelta_ops(op):
+    pd_dt_idx1 = pd.DatetimeIndex([10, 20, 30], dtype="datetime64[ns]")
+    cudf_pandas_dt_idx = xpd.DatetimeIndex(
+        [10, 20, 30], dtype="datetime64[ns]"
+    )
+
+    pd_td_idx1 = pd.TimedeltaIndex([10, 20, 30], dtype="timedelta64[ns]")
+    cudf_pandas_td_idx = xpd.TimedeltaIndex(
+        [10, 20, 30], dtype="timedelta64[ns]"
+    )
+
+    tm.assert_equal(
+        op(pd_dt_idx1, pd_td_idx1), op(cudf_pandas_dt_idx, cudf_pandas_td_idx)
+    )
+
+
+def test_itertuples():
+    df = xpd.DataFrame(range(1))
+    result = next(iter(df.itertuples()))
+    tup = collections.namedtuple("Pandas", ["Index", "1"], rename=True)
+    expected = tup(0, 0)
+    assert result == expected
+    assert result._fields == expected._fields
+
+
+def test_namedagg_namedtuple():
+    df = xpd.DataFrame(
+        {
+            "kind": ["cat", "dog", "cat", "dog"],
+            "height": [9.1, 6.0, 9.5, 34.0],
+            "weight": [7.9, 7.5, 9.9, 198.0],
+        }
+    )
+    result = df.groupby("kind").agg(
+        min_height=pd.NamedAgg(column="height", aggfunc="min"),
+        max_height=pd.NamedAgg(column="height", aggfunc="max"),
+        average_weight=pd.NamedAgg(column="weight", aggfunc=np.mean),
+    )
+    expected = xpd.DataFrame(
+        {
+            "min_height": [9.1, 6.0],
+            "max_height": [9.5, 34.0],
+            "average_weight": [8.90, 102.75],
+        },
+        index=xpd.Index(["cat", "dog"], name="kind"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dataframe_dir(dataframe):
+    """Test that column names are present in the dataframe dir
+
+    We do not test direct dir equality because pandas does some runtime
+    modifications of dir that we cannot replicate without forcing D2H
+    conversions (e.g. modifying what elements are visible based on the contents
+    of a DataFrame instance).
+    """
+    _, df = dataframe
+    assert "a" in dir(df)
+    assert "b" in dir(df)
+
+    # Only string types are added to dir
+    df[1] = [1] * len(df)
+    assert 1 not in dir(df)
+
+
+def test_array_copy(array):
+    arr, xarr = array
+    tm.assert_equal(copy.copy(arr), copy.copy(xarr))
+
+
+def test_datetime_values_dtype_roundtrip():
+    s = pd.Series([1, 2, 3], dtype="datetime64[ns]")
+    xs = xpd.Series([1, 2, 3], dtype="datetime64[ns]")
+    expected = np.asarray(s.values)
+    actual = np.asarray(xs.values)
+    assert expected.dtype == actual.dtype
+    tm.assert_equal(expected, actual)
+
+
+def test_resample():
+    ser = pd.Series(
+        range(3), index=pd.date_range("2020-01-01", freq="D", periods=3)
+    )
+    xser = xpd.Series(
+        range(3), index=xpd.date_range("2020-01-01", freq="D", periods=3)
+    )
+    expected = ser.resample("D").max()
+    result = xser.resample("D").max()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("accessor", ["str", "dt", "cat"])
+def test_accessor_types(accessor):
+    assert isinstance(getattr(xpd.Series, accessor), type)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [operator.itemgetter(1), operator.methodcaller("sum")],
+    ids=["getitem[1]", ".sum()"],
+)
+def test_values_zero_dim_result_is_scalar(op):
+    s = pd.Series([1, 2, 3])
+    x = xpd.Series([1, 2, 3])
+    expect = op(s.values)
+    got = op(x.values)
+    assert expect == got
+    assert type(expect) is type(got)
+
+
+@pytest.mark.parametrize("box", ["DataFrame", "Series"])
+def test_round_builtin(box):
+    xobj = getattr(xpd, box)([1.23])
+    pobj = getattr(pd, box)([1.23])
+    result = round(xobj, 1)
+    expected = round(pobj, 1)
+    tm.assert_equal(result, expected)
+
+
+def test_hash():
+    xobj = xpd.Timedelta(1)
+    pobj = pd.Timedelta(1)
+    assert hash(xobj) == hash(pobj)
+
+
+def test_non_hashable_object():
+    with pytest.raises(TypeError):
+        hash(pd.DataFrame(range(1)))
+
+    with pytest.raises(TypeError):
+        hash(xpd.DataFrame(range(1)))
+
+
+@pytest.mark.parametrize("offset", ["DateOffset", "Day", "BDay"])
+def test_timestamp_offset_binop(offset):
+    ts = xpd.Timestamp(2020, 1, 1)
+    result = ts + getattr(xpd.offsets, offset)()
+    expected = pd.Timestamp(2020, 1, 2)
+    tm.assert_equal(result, expected)
+
+
+def test_string_dtype():
+    xobj = xpd.StringDtype()
+    pobj = pd.StringDtype()
+    tm.assert_equal(xobj, pobj)
+
+
+def test_string_array():
+    data = np.array(["1"], dtype=object)
+    xobj = xpd.arrays.StringArray(data)
+    pobj = pd.arrays.StringArray(data)
+    tm.assert_extension_array_equal(xobj, pobj)
+
+
+def test_subclass_series():
+    class foo(pd.Series):
+        def __init__(self, myinput):
+            super().__init__(myinput)
+
+    s1 = pd.Series([1, 2, 3])
+    s2 = foo(myinput=[1, 2, 3])
+
+    tm.assert_equal(s1, s2, check_series_type=False)
+
+
+@pytest.mark.parametrize(
+    "index_type",
+    [
+        xpd.RangeIndex,
+        xpd.CategoricalIndex,
+        xpd.DatetimeIndex,
+        xpd.TimedeltaIndex,
+        xpd.PeriodIndex,
+        xpd.MultiIndex,
+        xpd.IntervalIndex,
+        xpd.UInt64Index,
+        xpd.Int64Index,
+        xpd.Float64Index,
+        xpd.core.indexes.numeric.UInt64Index,
+        xpd.core.indexes.numeric.Int64Index,
+        xpd.core.indexes.numeric.Float64Index,
+    ],
+)
+def test_index_subclass(index_type):
+    # test that proxy index types are derived
+    # from Index
+    assert issubclass(index_type, xpd.Index)
+    assert not issubclass(xpd.Index, index_type)
+
+
+def test_index_internal_subclass():
+    # test that proxy index types that are not related by inheritance
+    # still appear to be so if the underlying slow types are related
+    # by inheritance:
+    assert issubclass(
+        xpd.Int64Index,
+        xpd.core.indexes.numeric.NumericIndex,
+    ) == issubclass(
+        pd.Int64Index,
+        pd.core.indexes.numeric.NumericIndex,
+    )
+    assert isinstance(
+        xpd.Index([1, 2, 3]), xpd.core.indexes.numeric.NumericIndex
+    ) == isinstance(pd.Index([1, 2, 3]), pd.core.indexes.numeric.NumericIndex)
+
+
+def test_np_array_of_timestamps():
+    expected = np.array([pd.Timestamp(1)]) + pd.tseries.offsets.MonthEnd()
+    got = np.array([xpd.Timestamp(1)]) + xpd.tseries.offsets.MonthEnd()
+    tm.assert_equal(expected, got)
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        # Basic types
+        xpd.Series(dtype="float64"),
+        xpd.Series([1, 2, 3]),
+        xpd.DataFrame(dtype="float64"),
+        xpd.DataFrame({"a": [1, 2, 3]}),
+        xpd.Series([1, 2, 3]),
+        # Index (doesn't support nullary construction)
+        xpd.Index([1, 2, 3]),
+        xpd.Index(["a", "b", "c"]),
+        # Complex index
+        xpd.to_datetime(
+            [
+                "1/1/2018",
+                np.datetime64("2018-01-01"),
+                datetime.datetime(2018, 1, 1),
+            ]
+        ),
+        # Objects where the underlying store is the slow type.
+        xpd.Series(["a", 2, 3]),
+        xpd.Index(["a", 2, 3]),
+        # Other types
+        xpd.tseries.offsets.BDay(5),
+    ],
+)
+def test_pickle(obj):
+    with tempfile.TemporaryFile() as f:
+        pickle.dump(obj, f)
+        f.seek(0)
+        copy = pickle.load(f)
+
+    tm.assert_equal(obj, copy)
+
+
+def test_dataframe_query():
+    cudf_pandas_df = xpd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
+    pd_df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
+
+    actual = cudf_pandas_df.query("foo > 2")
+    expected = pd_df.query("foo > 2")
+
+    tm.assert_equal(actual, expected)
+
+    bizz = 2  # noqa: F841
+    actual = cudf_pandas_df.query("foo > @bizz")
+    expected = pd_df.query("foo > @bizz")
+
+    tm.assert_equal(actual, expected)
+
+
+def test_numpy_var():
+    np.random.seed(42)
+    data = np.random.rand(1000)
+    psr = pd.Series(data)
+    sr = xpd.Series(data)
+
+    tm.assert_almost_equal(np.var(psr), np.var(sr))
+
+
+def test_index_new():
+    expected = pd.Index.__new__(pd.Index, [1, 2, 3])
+    got = xpd.Index.__new__(xpd.Index, [1, 2, 3])
+    tm.assert_equal(expected, got)
+
+    expected = pd.Index.__new__(pd.Index, [1, 2, 3], dtype="int8")
+    got = xpd.Index.__new__(xpd.Index, [1, 2, 3], dtype="int8")
+    tm.assert_equal(expected, got)
+
+    expected = pd.RangeIndex.__new__(pd.RangeIndex, 0, 10, 2)
+    got = xpd.RangeIndex.__new__(xpd.RangeIndex, 0, 10, 2)
+    tm.assert_equal(expected, got)
+
+
+@pytest.mark.xfail(not LOADED, reason="Should not fail in accelerated mode")
+def test_groupby_apply_callable_referencing_pandas(dataframe):
+
+    pdf, df = dataframe
+
+    class Callable1:
+        def __call__(self, df):
+            if not isinstance(df, pd.DataFrame):
+                raise TypeError
+            return 1
+
+    class Callable2:
+        def __call__(self, df):
+            if not isinstance(df, xpd.DataFrame):
+                raise TypeError
+            return 1
+
+    expect = pdf.groupby("a").apply(Callable1())
+    got = df.groupby("a").apply(Callable2())
+
+    tm.assert_equal(expect, got)
+
+
+def test_constructor_properties(dataframe, series, index):
+    _, df = dataframe
+    _, sr = series
+    _, idx = index
+
+    assert df._constructor is xpd.DataFrame
+    assert sr._constructor is xpd.Series
+    assert idx._constructor is xpd.Index
+    assert sr._constructor_expanddim is xpd.DataFrame
+    assert df._constructor_sliced is xpd.Series
+
+
+def test_pos():
+    xser = +xpd.Series([-1])
+    ser = +pd.Series([-1])
+    tm.assert_equal(xser, ser)
+
+
+def test_intermediates_are_proxied():
+    df = xpd.DataFrame({"a": [1, 2, 3]})
+    grouper = df.groupby("a")
+    assert isinstance(grouper, xpd.core.groupby.generic.DataFrameGroupBy)
+
+
+def test_from_dataframe():
+    cudf = pytest.importorskip("cudf")
+    from cudf.testing._utils import assert_eq
+
+    data = {"foo": [1, 2, 3], "bar": [4, 5, 6]}
+
+    cudf_pandas_df = xpd.DataFrame(data)
+    cudf_df = cudf.DataFrame(data)
+
+    # test construction of a cuDF DataFrame from an cudf_pandas DataFrame
+    assert_eq(cudf_df, cudf.DataFrame.from_pandas(cudf_pandas_df))
+    assert_eq(cudf_df, cudf.from_dataframe(cudf_pandas_df))
+
+    # ideally the below would work as well, but currently segfaults
+
+    # pd_df = pd.DataFrame(data)
+    # assert_eq(pd_df, pd.api.interchange.from_dataframe(cudf_pandas_df))
+
+
+def test_multiindex_values_returns_1d_tuples():
+    mi = xpd.MultiIndex.from_tuples([(1, 2), (3, 4)])
+    result = mi.values
+    expected = np.empty(2, dtype=object)
+    expected[...] = [(1, 2), (3, 4)]
+    tm.assert_equal(result, expected)
+
+
+def test_read_sas_context():
+    cudf_path = pathlib.Path(__file__).parent.parent
+    path = cudf_path / "cudf" / "tests" / "data" / "sas" / "cars.sas7bdat"
+    with xpd.read_sas(path, format="sas7bdat", iterator=True) as reader:
+        df = reader.read()
+    assert isinstance(df, xpd.DataFrame)
+
+
+@pytest.mark.parametrize(
+    "idx_obj", ["Float64Index", "Int64Index", "UInt64Index"]
+)
+def test_pandas_module_getattr_objects(idx_obj):
+    # Objects that are behind pandas.__getattr__ (version 1.5 specific)
+    idx = getattr(xpd, idx_obj)([1, 2, 3])
+    assert isinstance(idx, xpd.Index)
+
+
+def test_concat_fast():
+    pytest.importorskip("cudf")
+
+    assert type(xpd.concat._fsproxy_fast) is not _Unusable
+
+
+def test_func_namespace():
+    # note: this test is sensitive to Pandas' internal module layout
+    assert xpd.concat is xpd.core.reshape.concat.concat
diff --git a/python/cudf/cudf_pandas_tests/test_cudf_pandas_cudf_interop.py b/python/cudf/cudf_pandas_tests/test_cudf_pandas_cudf_interop.py
new file mode 100644
index 0000000..a3010f9
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/test_cudf_pandas_cudf_interop.py
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import cudf
+from cudf.pandas import LOADED
+
+if not LOADED:
+    raise ImportError("These tests must be run with cudf.pandas loaded")
+
+import pandas as pd
+
+
+def test_cudf_pandas_loaded_to_cudf():
+    hybrid_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    cudf_df = cudf.from_pandas(hybrid_df)
+    pd.testing.assert_frame_equal(hybrid_df, cudf_df.to_pandas())
diff --git a/python/cudf/cudf_pandas_tests/test_fast_slow_proxy.py b/python/cudf/cudf_pandas_tests/test_fast_slow_proxy.py
new file mode 100644
index 0000000..b964dfd
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/test_fast_slow_proxy.py
@@ -0,0 +1,522 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import inspect
+from functools import partial
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from cudf.pandas.fast_slow_proxy import (
+    _fast_arg,
+    _FunctionProxy,
+    _slow_arg,
+    _transform_arg,
+    _Unusable,
+    make_final_proxy_type,
+    make_intermediate_proxy_type,
+)
+
+
+@pytest.fixture
+def final_proxy():
+    class Fast:
+        def __init__(self, x):
+            self.x = x
+
+        def to_slow(self):
+            return Slow(self.x)
+
+        @classmethod
+        def from_slow(cls, slow):
+            return cls(slow)
+
+        def __eq__(self, other):
+            return self.x == other.x
+
+        def method(self):
+            return "fast method"
+
+    class Slow:
+        def __init__(self, x):
+            self.x = x
+
+        def __eq__(self, other):
+            return self.x == other.x
+
+        def method(self):
+            return "slow method"
+
+    Pxy = make_final_proxy_type(
+        "Pxy",
+        Fast,
+        Slow,
+        fast_to_slow=lambda fast: fast.to_slow(),
+        slow_to_fast=lambda slow: Fast.from_slow(slow),
+    )
+    return Fast(1), Slow(1), Pxy(1)
+
+
+@pytest.fixture
+def function_proxy():
+    def fast_func():
+        """
+        Fast doc
+        """
+        return "fast func"
+
+    def slow_func():
+        """
+        Slow doc
+        """
+        return "slow_func"
+
+    return fast_func, slow_func, _FunctionProxy(fast_func, slow_func)
+
+
+def test_repr_no_fast_object():
+    # test that __repr__ falls back to slow object
+    # when we don't have a corresponding fast object:
+    class Slow:
+        def __repr__(self):
+            return "slow object"
+
+    Pxy = make_final_proxy_type(
+        "Pxy",
+        _Unusable,
+        Slow,
+        fast_to_slow=lambda fast: Slow(),
+        slow_to_fast=lambda slow: _Unusable(),
+    )
+    assert repr(Pxy()) == repr(Slow())
+
+
+def test_fast_slow_arg_function_basic():
+    def func1():
+        return 1
+
+    assert _fast_arg(func1)() == _slow_arg(func1)() == 1
+
+    def func2(x, y):
+        return x + y
+
+    assert _fast_arg(func2)(1, 2) == _slow_arg(func2)(1, 2) == 3
+
+
+def test_fast_slow_arg_function_closure(function_proxy, final_proxy):
+    fast_x, slow_x, x = function_proxy
+    fast_y, slow_y, y = final_proxy
+
+    def func():
+        return x, y.method()
+
+    assert _slow_arg(func)() == (slow_x, slow_y.method())
+    assert _fast_arg(func)() == (fast_x, fast_y.method())
+
+
+def test_fast_slow_arg_function_global(
+    monkeypatch, function_proxy, final_proxy
+):
+    fast_x, slow_x, x = function_proxy
+    fast_y, slow_y, y = final_proxy
+
+    # temporarily set x, y as globals
+    monkeypatch.setitem(globals(), "__x", x)
+    monkeypatch.setitem(globals(), "__y", y)
+
+    def func():
+        global __x, __y
+        return __x, __y.method()
+
+    assert _slow_arg(func)() == (slow_x, slow_y.method())
+    assert _fast_arg(func)() == (fast_x, fast_y.method())
+
+
+def test_fast_slow_arg_function_np():
+    # test that _fast_arg() and _slow_arg() return "externally"
+    # defined functions like numpy functions as-is:
+    assert _slow_arg(np.mean) is np.mean
+    assert _slow_arg(np.unique) is np.unique
+    assert _fast_arg(np.mean) is np.mean
+    assert _fast_arg(np.unique) is np.unique
+
+
+def test_fast_slow_arg_builtins(function_proxy):
+    # test that builtins are accessible in the result of
+    # _fast_arg() and _slow_arg()
+
+    _, _, x = function_proxy
+
+    def func():
+        x  # nonlocal x ensures _fast_arg() makes a copy
+        return len([1])
+
+    assert _slow_arg(func)() == 1
+    assert _fast_arg(func)() == 1
+
+
+def test_function_proxy_decorating_super_method():
+    # test that we can use a function proxy as a decorator to a method
+    # that invokes super() (GH: #254)
+    deco = _FunctionProxy(_Unusable(), lambda func: func)
+
+    class Foo:
+        @deco
+        def method(self):
+            super()
+
+
+@pytest.mark.xfail(
+    reason="Mutually recursive functions are known to be handled incorrectly."
+)
+def test_fast_slow_arg_recursion(final_proxy):
+    fast_x, slow_x, x = final_proxy
+
+    def foo(n):
+        if n <= 0:
+            return x
+        else:
+            return bar(n - 1)
+
+    def bar(n):
+        return foo(n - 1)
+
+    assert _slow_arg(foo)(0) == slow_x
+    assert _slow_arg(bar)(1) == slow_x
+    assert _slow_arg(foo)(1) == slow_x
+    assert _slow_arg(bar)(2) == slow_x
+    assert _fast_arg(foo)(0) == fast_x
+    assert _fast_arg(bar)(1) == fast_x
+    assert _fast_arg(foo)(1) == fast_x
+    assert _fast_arg(bar)(2) == fast_x
+
+
+def test_fallback_with_stringio():
+    def slow(s):
+        return s.read()
+
+    def fast(s):
+        s.read()
+        raise ValueError()
+
+    pxy = _FunctionProxy(fast=fast, slow=slow)
+    assert pxy(StringIO("hello")) == "hello"
+
+
+def test_access_class():
+    def func():
+        pass
+
+    pxy = _FunctionProxy(fast=_Unusable(), slow=func)
+    pxy.__class__
+
+
+def test_class_attribute_error(final_proxy, function_proxy):
+    _, _, x = final_proxy
+    _, _, y = function_proxy
+    # Test that an attribute error is raised when attempting to
+    # access undefined class attributes:
+    with pytest.raises(AttributeError):
+        x.foo
+
+    with pytest.raises(AttributeError):
+        y.foo
+
+    with pytest.raises(AttributeError):
+        y.__abs__
+
+
+def test_function_proxy_doc(function_proxy):
+    _, slow, pxy = function_proxy
+    assert pxy.__doc__ == slow.__doc__
+
+
+def test_special_methods():
+    class Fast:
+        def __abs__(self):
+            pass
+
+        def __gt__(self):
+            pass
+
+    class Slow:
+        def __gt__(self):
+            pass
+
+    Pxy = make_final_proxy_type(
+        "Pxy",
+        Fast,
+        Slow,
+        fast_to_slow=lambda _: Slow(),
+        slow_to_fast=lambda _: Fast(),
+    )
+
+    # test that special methods defined _only_ on the
+    # fast type are not accessible on the proxy:
+    assert not hasattr(Pxy, "__abs__")
+    assert not hasattr(Pxy(), "__abs__")
+
+    # test that special methods defined on the
+    # slow type are accessible on the proxy:
+    assert hasattr(Pxy, "__gt__")
+    assert hasattr(Pxy(), "__gt__")
+
+
+@pytest.fixture(scope="module")
+def fast_and_intermediate_with_doc():
+    class FastIntermediate:
+        """The fast intermediate docstring."""
+
+        def method(self):
+            """The fast intermediate method docstring."""
+
+    class Fast:
+        """The fast docstring."""
+
+        @property
+        def prop(self):
+            """The fast property docstring."""
+
+        def method(self):
+            """The fast method docstring."""
+
+        def intermediate(self):
+            """The fast intermediate docstring."""
+            return FastIntermediate()
+
+    return Fast, FastIntermediate
+
+
+@pytest.fixture(scope="module")
+def slow_and_intermediate_with_doc():
+    class SlowIntermediate:
+        """The slow intermediate docstring."""
+
+        def method(self):
+            """The slow intermediate method docstring."""
+
+    class Slow:
+        """The slow docstring."""
+
+        @property
+        def prop(self):
+            """The slow property docstring."""
+
+        def method(self):
+            """The slow method docstring."""
+
+        def intermediate(self):
+            """The slow intermediate docstring."""
+            return SlowIntermediate()
+
+    return Slow, SlowIntermediate
+
+
+def test_doc(fast_and_intermediate_with_doc, slow_and_intermediate_with_doc):
+    Fast, FastIntermediate = fast_and_intermediate_with_doc
+    Slow, SlowIntermediate = slow_and_intermediate_with_doc
+
+    Pxy = make_final_proxy_type(
+        "Pxy",
+        Fast,
+        Slow,
+        fast_to_slow=lambda _: Slow(),
+        slow_to_fast=lambda _: Fast(),
+    )
+
+    IntermediatePxy = make_intermediate_proxy_type(  # noqa: F841
+        "IntermediatePxy",
+        FastIntermediate,
+        SlowIntermediate,
+    )
+
+    assert inspect.getdoc(Pxy) == inspect.getdoc(Slow)
+    assert inspect.getdoc(Pxy()) == inspect.getdoc(Slow())
+    assert inspect.getdoc(Pxy.prop) == inspect.getdoc(Slow.prop)
+    assert inspect.getdoc(Pxy().prop) == inspect.getdoc(Slow().prop)
+    assert inspect.getdoc(Pxy.method) == inspect.getdoc(Slow.method)
+    assert inspect.getdoc(Pxy().method) == inspect.getdoc(Slow().method)
+    assert inspect.getdoc(Pxy().intermediate()) == inspect.getdoc(
+        Slow().intermediate()
+    )
+    assert inspect.getdoc(Pxy().intermediate().method) == inspect.getdoc(
+        Slow().intermediate().method
+    )
+
+
+def test_dir(fast_and_intermediate_with_doc, slow_and_intermediate_with_doc):
+    Fast, FastIntermediate = fast_and_intermediate_with_doc
+    Slow, SlowIntermediate = slow_and_intermediate_with_doc
+
+    Pxy = make_final_proxy_type(
+        "Pxy",
+        Fast,
+        Slow,
+        fast_to_slow=lambda _: Slow(),
+        slow_to_fast=lambda _: Fast(),
+    )
+
+    IntermediatePxy = make_intermediate_proxy_type(  # noqa: F841
+        "IntermediatePxy",
+        FastIntermediate,
+        SlowIntermediate,
+    )
+
+    assert dir(Pxy) == dir(Slow)
+    assert dir(Pxy()) == dir(Slow())
+    assert dir(Pxy.prop) == dir(Slow.prop)
+    assert dir(Pxy().prop) == dir(Slow().prop)
+    assert dir(Pxy.method) == dir(Slow.method)
+    assert dir(Pxy().intermediate()) == dir(Slow().intermediate())
+
+
+@pytest.mark.xfail
+@pytest.mark.parametrize(
+    "check",
+    [
+        lambda Pxy, Slow: dir(Pxy().method) == dir(Slow().method),
+        lambda Pxy, Slow: dir(Pxy().intermediate().method)
+        == dir(Slow().intermediate().method),
+    ],
+)
+def test_dir_bound_method(
+    fast_and_intermediate_with_doc, slow_and_intermediate_with_doc, check
+):
+    """This test will fail because dir for bound methods is currently
+    incorrect, but we have no way to fix it without materializing the slow
+    type, which is unnecessarily expensive."""
+    Fast, FastIntermediate = fast_and_intermediate_with_doc
+    Slow, SlowIntermediate = slow_and_intermediate_with_doc
+
+    Pxy = make_final_proxy_type(
+        "Pxy",
+        Fast,
+        Slow,
+        fast_to_slow=lambda _: Slow(),
+        slow_to_fast=lambda _: Fast(),
+    )
+
+    IntermediatePxy = make_intermediate_proxy_type(  # noqa: F841
+        "IntermediatePxy",
+        FastIntermediate,
+        SlowIntermediate,
+    )
+    assert check(Pxy, Slow)
+
+
+def test_proxy_binop():
+    class Foo:
+        pass
+
+    class Bar:
+        def __add__(self, other):
+            if isinstance(other, Foo):
+                return "sum"
+            return NotImplemented
+
+        def __radd__(self, other):
+            return self.__add__(other)
+
+    FooProxy = make_final_proxy_type(
+        "FooProxy",
+        _Unusable,
+        Foo,
+        fast_to_slow=Foo(),
+        slow_to_fast=_Unusable(),
+    )
+
+    BarProxy = make_final_proxy_type(
+        "BarProxy",
+        _Unusable,
+        Bar,
+        fast_to_slow=Bar(),
+        slow_to_fast=_Unusable(),
+    )
+
+    assert Foo() + Bar() == "sum"
+    assert Bar() + Foo() == "sum"
+    assert FooProxy() + BarProxy() == "sum"
+    assert BarProxy() + FooProxy() == "sum"
+    assert FooProxy() + Bar() == "sum"
+    assert Bar() + FooProxy() == "sum"
+    assert Foo() + BarProxy() == "sum"
+    assert BarProxy() + Foo() == "sum"
+
+
+def tuple_with_attrs(name, fields: list[str], extra_fields: set[str]):
+    # Build a tuple-like class with some extra attributes and a custom
+    # pickling scheme with __getnewargs_ex__
+    args = ", ".join(fields)
+    kwargs = ", ".join(sorted(extra_fields))
+    code = f"""
+def __new__(cls, {args}, *, {kwargs}):
+    return tuple.__new__(cls, ({args}, ))
+
+def __init__(self, {args}, *, {kwargs}):
+    for key, val in zip({sorted(extra_fields)}, [{kwargs}]):
+        self.__dict__[key] = val
+
+def __eq__(self, other):
+    return (
+        type(other) is type(self)
+        and tuple.__eq__(self, other)
+        and all(getattr(self, k) == getattr(other, k) for k in self._fields)
+    )
+
+def __ne__(self, other):
+    return not (self == other)
+
+def __getnewargs_ex__(self):
+    return tuple(self), self.__dict__
+"""
+    namespace = {
+        "__builtins__": {
+            "AttributeError": AttributeError,
+            "tuple": tuple,
+            "zip": zip,
+            "super": super,
+            "frozenset": frozenset,
+            "type": type,
+            "all": all,
+            "getattr": getattr,
+        }
+    }
+    exec(code, namespace)
+    return type(
+        name,
+        (tuple,),
+        {
+            "_fields": frozenset(extra_fields),
+            "__eq__": namespace["__eq__"],
+            "__getnewargs_ex__": namespace["__getnewargs_ex__"],
+            "__init__": namespace["__init__"],
+            "__ne__": namespace["__ne__"],
+            "__new__": namespace["__new__"],
+        },
+    )
+
+
+def test_tuple_with_attrs_transform():
+    Bunch = tuple_with_attrs("Bunch", ["a", "b"], {"c", "d"})
+    Bunch2 = tuple_with_attrs("Bunch", ["a", "b"], {"c", "d"})
+    a = Bunch(1, 2, c=3, d=4)
+    b = (1, 2)
+    c = Bunch(1, 2, c=4, d=3)
+    d = Bunch2(1, 2, c=3, d=4)
+    assert a != c
+    assert a != b
+    assert b != c
+    assert a != d
+    transform = partial(
+        _transform_arg, attribute_name="_fsproxy_fast", seen=set()
+    )
+    aprime = transform(a)
+    bprime = transform(b)
+    cprime = transform(c)
+    dprime = transform(d)
+    assert a == aprime and a is not aprime
+    assert b == bprime and b is not bprime
+    assert c == cprime and c is not cprime
+    assert d == dprime and d is not dprime
diff --git a/python/cudf/cudf_pandas_tests/test_magics.py b/python/cudf/cudf_pandas_tests/test_magics.py
new file mode 100644
index 0000000..c86125d
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/test_magics.py
@@ -0,0 +1,50 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import pathlib
+import subprocess
+import sys
+
+import pytest
+
+
+# Proxy check for whether the proxy could be referencing GPU objects without
+# trying to import cuDF, which could poison the global environment
+def _gpu_available():
+    try:
+        import rmm
+
+        return rmm._cuda.gpu.getDeviceCount() >= 1
+    except ImportError:
+        return False
+
+
+LOCATION = pathlib.Path(__file__).absolute().parent
+
+
+@pytest.mark.skipif(
+    not _gpu_available(), reason="Skipping test if a GPU isn't available."
+)
+def test_magics_gpu():
+    sp_completed = subprocess.run(
+        [sys.executable, LOCATION / "_magics_gpu_test.py"], capture_output=True
+    )
+    assert sp_completed.stderr.decode() == ""
+
+
+@pytest.mark.skip(
+    "This test was viable when cudf.pandas was separate from cudf, but now "
+    "that it is a subpackage we always require a GPU to be present and cannot "
+    "run this test."
+)
+def test_magics_cpu():
+    env = os.environ.copy()
+    env["CUDA_VISIBLE_DEVICES"] = ""
+    sp_completed = subprocess.run(
+        [sys.executable, LOCATION / "_magics_cpu_test.py"],
+        capture_output=True,
+        env=env,
+    )
+    assert sp_completed.stderr.decode() == ""
diff --git a/python/cudf/cudf_pandas_tests/test_profiler.py b/python/cudf/cudf_pandas_tests/test_profiler.py
new file mode 100644
index 0000000..a947d67
--- /dev/null
+++ b/python/cudf/cudf_pandas_tests/test_profiler.py
@@ -0,0 +1,70 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from cudf.pandas import LOADED, Profiler
+
+if not LOADED:
+    raise ImportError("These tests must be run with cudf.pandas loaded")
+
+import numpy as np
+import pandas as pd
+
+
+def test_profiler():
+    np.random.seed(42)
+    with Profiler() as profiler:
+        df = pd.DataFrame(
+            {
+                "idx": np.random.randint(0, 10, 1000),
+                "data": np.random.rand(1000),
+            }
+        )
+        sums = df.groupby("idx").sum()
+        total = df.sum()["data"]
+        assert np.isclose(total, sums.sum()["data"])
+        _ = pd.Timestamp(2020, 1, 1) + pd.Timedelta(1)
+
+    per_function_stats = profiler.per_function_stats
+    assert set(per_function_stats) == {
+        "DataFrame",
+        "DataFrame.groupby",
+        "DataFrameGroupBy.sum",
+        "DataFrame.sum",
+        "Series.__getitem__",
+    }
+    for name, func in per_function_stats.items():
+        assert (
+            len(func["cpu"]) == 0
+            if "Time" not in name
+            else len(func["gpu"]) == 0
+        )
+
+    per_line_stats = profiler.per_line_stats
+    calls = [
+        "pd.DataFrame",
+        "",
+        "np.random.randint",
+        "np.random.rand",
+        'df.groupby("idx").sum',
+        'df.sum()["data"]',
+        "np.isclose",
+        "pd.Timestamp",
+    ]
+    for line_stats, call in zip(per_line_stats, calls):
+        # Check that the expected function calls were recorded.
+        assert call in line_stats[1]
+        # No CPU time
+        assert line_stats[3] == 0 if "Time" not in call else line_stats[2] == 0
+
+
+def test_profiler_hasattr_exception():
+    with Profiler():
+        df = pd.DataFrame({"data": [1, 2, 3]})
+        hasattr(df, "this_does_not_exist")
+
+
+def test_profiler_fast_slow_name_mismatch():
+    with Profiler():
+        df = pd.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]})
+        df.iloc[0, 1] = "foo"
diff --git a/python/cudf/pyproject.toml b/python/cudf/pyproject.toml
new file mode 100644
index 0000000..a1fec83
--- /dev/null
+++ b/python/cudf/pyproject.toml
@@ -0,0 +1,171 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+[build-system]
+build-backend = "setuptools.build_meta"
+requires = [
+    "cmake>=3.26.4",
+    "cython>=3.0.0",
+    "ninja",
+    "numpy>=1.21,<1.25",
+    "protoc-wheel",
+    "pyarrow==12.0.1.*",
+    "rmm==23.10.*",
+    "scikit-build>=0.13.1",
+    "setuptools",
+    "wheel",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project]
+name = "cudf"
+version = "23.10.00"
+description = "cuDF - GPU Dataframe"
+readme = { file = "README.md", content-type = "text/markdown" }
+authors = [
+    { name = "NVIDIA Corporation" },
+]
+license = { text = "Apache 2.0" }
+requires-python = ">=3.9"
+dependencies = [
+    "cachetools",
+    "cubinlinker",
+    "cuda-python>=11.7.1,<12.0a0",
+    "cupy-cuda11x>=12.0.0",
+    "fsspec>=0.6.0",
+    "numba>=0.57,<0.58",
+    "numpy>=1.21,<1.25",
+    "nvtx>=0.2.1",
+    "packaging",
+    "pandas>=1.3,<1.6.0dev0",
+    "protobuf>=4.21,<5",
+    "ptxcompiler",
+    "pyarrow==12.*",
+    "rich",
+    "rmm==23.10.*",
+    "typing_extensions>=4.0.0",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+classifiers = [
+    "Intended Audience :: Developers",
+    "Topic :: Database",
+    "Topic :: Scientific/Engineering",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+]
+
+[project.optional-dependencies]
+test = [
+    "cramjam",
+    "fastavro>=0.22.9",
+    "hypothesis",
+    "mimesis>=4.1.0",
+    "msgpack",
+    "pyorc",
+    "pytest",
+    "pytest-benchmark",
+    "pytest-cases",
+    "pytest-cov",
+    "pytest-xdist",
+    "python-snappy>=0.6.0",
+    "scipy",
+    "tokenizers==0.13.1",
+    "transformers==4.24.0",
+    "tzdata",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+pandas_tests = [
+    "beautifulsoup4",
+    "blosc",
+    "boto3",
+    "botocore>=1.24.21",
+    "bottleneck",
+    "brotlipy",
+    "fastparquet",
+    "flask",
+    "fsspec",
+    "gcsfs",
+    "html5lib",
+    "hypothesis",
+    "ipython",
+    "jinja2",
+    "lxml",
+    "matplotlib",
+    "moto",
+    "numba",
+    "numexpr",
+    "odfpy",
+    "openpyxl",
+    "pandas-gbq",
+    "psycopg2-binary",
+    "py",
+    "pyarrow",
+    "pymysql",
+    "pyreadstat",
+    "pytest-asyncio",
+    "pytest-reportlog",
+    "python-snappy",
+    "pyxlsb",
+    "s3fs",
+    "scipy",
+    "sqlalchemy",
+    "tables",
+    "tabulate",
+    "xarray",
+    "xlrd",
+    "xlsxwriter",
+    "xlwt",
+    "zstandard",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+cudf_pandas_tests = [
+    "ipython",
+    "openpyxl",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project.urls]
+Homepage = "https://github.com/rapidsai/cudf"
+Documentation = "https://docs.rapids.ai/api/cudf/stable/"
+
+[tool.setuptools]
+license-files = ["LICENSE"]
+
+[tool.isort]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+order_by_type = true
+known_dask = [
+    "dask",
+    "distributed",
+    "dask_cuda",
+]
+known_rapids = [
+    "rmm",
+]
+known_first_party = [
+    "cudf",
+]
+default_section = "THIRDPARTY"
+sections = [
+    "FUTURE",
+    "STDLIB",
+    "THIRDPARTY",
+    "DASK",
+    "RAPIDS",
+    "FIRSTPARTY",
+    "LOCALFOLDER",
+]
+skip = [
+    "thirdparty",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "__init__.py",
+]
diff --git a/python/cudf/setup.py b/python/cudf/setup.py
new file mode 100644
index 0000000..96b91b4
--- /dev/null
+++ b/python/cudf/setup.py
@@ -0,0 +1,11 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from setuptools import find_packages
+from skbuild import setup
+
+packages = find_packages(include=["cudf*", "udf_cpp*"])
+setup(
+    packages=packages,
+    package_data={key: ["*.pxd", "*.hpp", "*.cuh"] for key in packages},
+    zip_safe=False,
+)
diff --git a/python/cudf/udf_cpp/CMakeLists.txt b/python/cudf/udf_cpp/CMakeLists.txt
new file mode 100644
index 0000000..7d6dc84
--- /dev/null
+++ b/python/cudf/udf_cpp/CMakeLists.txt
@@ -0,0 +1,113 @@
+# =============================================================================
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+cmake_minimum_required(VERSION 3.26.4)
+
+include(rapids-cmake)
+include(rapids-cpm)
+include(rapids-find)
+
+rapids_cpm_init()
+
+rapids_find_package(
+  CUDAToolkit REQUIRED
+  BUILD_EXPORT_SET udf-exports
+  INSTALL_EXPORT_SET udf-exports
+)
+
+include(${rapids-cmake-dir}/cpm/libcudacxx.cmake)
+rapids_cpm_libcudacxx(BUILD_EXPORT_SET udf-exports INSTALL_EXPORT_SET udf-exports)
+
+add_library(cudf_strings_udf SHARED strings/src/strings/udf/udf_apis.cu)
+target_include_directories(
+  cudf_strings_udf PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/strings/include>"
+)
+
+set_target_properties(
+  cudf_strings_udf
+  PROPERTIES BUILD_RPATH "\$ORIGIN/../"
+             INSTALL_RPATH "\$ORIGIN/../"
+             CXX_STANDARD 17
+             CXX_STANDARD_REQUIRED ON
+             CUDA_STANDARD 17
+             CUDA_STANDARD_REQUIRED ON
+             POSITION_INDEPENDENT_CODE ON
+             INTERFACE_POSITION_INDEPENDENT_CODE ON
+)
+
+set(UDF_CXX_FLAGS)
+set(UDF_CUDA_FLAGS --expt-extended-lambda --expt-relaxed-constexpr)
+target_compile_options(
+  cudf_strings_udf PRIVATE "$<$<COMPILE_LANGUAGE:CXX>:${UDF_CXX_FLAGS}>"
+                           "$<$<COMPILE_LANGUAGE:CUDA>:${UDF_CUDA_FLAGS}>"
+)
+target_link_libraries(cudf_strings_udf PUBLIC cudf::cudf)
+install(TARGETS cudf_strings_udf DESTINATION ./cudf/_lib/)
+
+# This function will copy the generated PTX file from its generator-specific location in the build
+# tree into a specified location in the build tree from which we can install it.
+function(copy_ptx_to_location target destination new_name)
+  set(cmake_generated_file
+      "${CMAKE_CURRENT_BINARY_DIR}/cmake/cp_${target}_$<LOWER_CASE:$<CONFIG>>_ptx.cmake"
+  )
+  file(
+    GENERATE
+    OUTPUT "${cmake_generated_file}"
+    CONTENT
+      "
+set(ptx_path \"$<TARGET_OBJECTS:${target}>\")
+file(MAKE_DIRECTORY \"${destination}\")
+file(COPY_FILE \${ptx_path} \"${destination}/${new_name}\")"
+  )
+
+  add_custom_target(
+    ${target}_cp_ptx ALL
+    COMMAND ${CMAKE_COMMAND} -P "${cmake_generated_file}"
+    DEPENDS $<TARGET_OBJECTS:${target}>
+    COMMENT "Copying PTX files to '${destination}'"
+  )
+endfunction()
+
+# Create the shim library for each architecture.
+set(SHIM_CUDA_FLAGS --expt-relaxed-constexpr -rdc=true)
+
+# always build a default PTX file in case RAPIDS_NO_INITIALIZE is set and the device cc can't be
+# safely queried through a context
+list(INSERT CMAKE_CUDA_ARCHITECTURES 0 "60")
+
+list(TRANSFORM CMAKE_CUDA_ARCHITECTURES REPLACE "-real" "")
+list(TRANSFORM CMAKE_CUDA_ARCHITECTURES REPLACE "-virtual" "")
+list(SORT CMAKE_CUDA_ARCHITECTURES)
+list(REMOVE_DUPLICATES CMAKE_CUDA_ARCHITECTURES)
+
+foreach(arch IN LISTS CMAKE_CUDA_ARCHITECTURES)
+  set(tgt shim_${arch})
+
+  add_library(${tgt} OBJECT shim.cu)
+
+  set_target_properties(${tgt} PROPERTIES CUDA_ARCHITECTURES ${arch} CUDA_PTX_COMPILATION ON)
+
+  target_include_directories(
+    ${tgt} PUBLIC "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/strings/include>"
+  )
+  target_compile_options(${tgt} PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:${SHIM_CUDA_FLAGS}>")
+  target_link_libraries(${tgt} PUBLIC cudf::cudf)
+
+  copy_ptx_to_location(${tgt} "${CMAKE_CURRENT_BINARY_DIR}/../udf" ${tgt}.ptx)
+  install(
+    FILES $<TARGET_OBJECTS:${tgt}>
+    DESTINATION ./cudf/core/udf/
+    RENAME ${tgt}.ptx
+  )
+endforeach()
diff --git a/python/cudf/udf_cpp/shim.cu b/python/cudf/udf_cpp/shim.cu
new file mode 100644
index 0000000..cabca31
--- /dev/null
+++ b/python/cudf/udf_cpp/shim.cu
@@ -0,0 +1,723 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/detail/utilities/device_operators.cuh>
+#include <cudf/strings/udf/case.cuh>
+#include <cudf/strings/udf/char_types.cuh>
+#include <cudf/strings/udf/replace.cuh>
+#include <cudf/strings/udf/search.cuh>
+#include <cudf/strings/udf/starts_with.cuh>
+#include <cudf/strings/udf/strip.cuh>
+#include <cudf/strings/udf/udf_string.cuh>
+
+#include <cuda/atomic>
+
+#include <cooperative_groups.h>
+
+#include <limits>
+#include <type_traits>
+
+using namespace cudf::strings::udf;
+
+extern "C" __device__ int len(int* nb_retval, void const* str)
+{
+  auto sv    = reinterpret_cast<cudf::string_view const*>(str);
+  *nb_retval = sv->length();
+  return 0;
+}
+
+extern "C" __device__ int startswith(bool* nb_retval, void const* str, void const* substr)
+{
+  auto str_view    = reinterpret_cast<cudf::string_view const*>(str);
+  auto substr_view = reinterpret_cast<cudf::string_view const*>(substr);
+
+  *nb_retval = starts_with(*str_view, *substr_view);
+  return 0;
+}
+
+extern "C" __device__ int endswith(bool* nb_retval, void const* str, void const* substr)
+{
+  auto str_view    = reinterpret_cast<cudf::string_view const*>(str);
+  auto substr_view = reinterpret_cast<cudf::string_view const*>(substr);
+
+  *nb_retval = ends_with(*str_view, *substr_view);
+  return 0;
+}
+
+extern "C" __device__ int contains(bool* nb_retval, void const* str, void const* substr)
+{
+  auto str_view    = reinterpret_cast<cudf::string_view const*>(str);
+  auto substr_view = reinterpret_cast<cudf::string_view const*>(substr);
+
+  *nb_retval = (str_view->find(*substr_view) != cudf::string_view::npos);
+  return 0;
+}
+
+extern "C" __device__ int find(int* nb_retval, void const* str, void const* substr)
+{
+  auto str_view    = reinterpret_cast<cudf::string_view const*>(str);
+  auto substr_view = reinterpret_cast<cudf::string_view const*>(substr);
+
+  *nb_retval = str_view->find(*substr_view);
+  return 0;
+}
+
+extern "C" __device__ int rfind(int* nb_retval, void const* str, void const* substr)
+{
+  auto str_view    = reinterpret_cast<cudf::string_view const*>(str);
+  auto substr_view = reinterpret_cast<cudf::string_view const*>(substr);
+
+  *nb_retval = str_view->rfind(*substr_view);
+  return 0;
+}
+
+extern "C" __device__ int eq(bool* nb_retval, void const* str, void const* rhs)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+  auto rhs_view = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  *nb_retval = (*str_view == *rhs_view);
+  return 0;
+}
+
+extern "C" __device__ int ne(bool* nb_retval, void const* str, void const* rhs)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+  auto rhs_view = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  *nb_retval = (*str_view != *rhs_view);
+  return 0;
+}
+
+extern "C" __device__ int ge(bool* nb_retval, void const* str, void const* rhs)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+  auto rhs_view = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  *nb_retval = (*str_view >= *rhs_view);
+  return 0;
+}
+
+extern "C" __device__ int le(bool* nb_retval, void const* str, void const* rhs)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+  auto rhs_view = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  *nb_retval = (*str_view <= *rhs_view);
+  return 0;
+}
+
+extern "C" __device__ int gt(bool* nb_retval, void const* str, void const* rhs)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+  auto rhs_view = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  *nb_retval = (*str_view > *rhs_view);
+  return 0;
+}
+
+extern "C" __device__ int lt(bool* nb_retval, void const* str, void const* rhs)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+  auto rhs_view = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  *nb_retval = (*str_view < *rhs_view);
+  return 0;
+}
+
+extern "C" __device__ int pyislower(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_lower(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisupper(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_upper(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisspace(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_space(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisdecimal(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_decimal(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisnumeric(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_numeric(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisdigit(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_digit(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisalnum(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_alpha_numeric(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyisalpha(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_alpha(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pyistitle(bool* nb_retval, void const* str, std::uintptr_t chars_table)
+{
+  auto str_view = reinterpret_cast<cudf::string_view const*>(str);
+
+  *nb_retval = is_title(
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(chars_table), *str_view);
+  return 0;
+}
+
+extern "C" __device__ int pycount(int* nb_retval, void const* str, void const* substr)
+{
+  auto str_view    = reinterpret_cast<cudf::string_view const*>(str);
+  auto substr_view = reinterpret_cast<cudf::string_view const*>(substr);
+
+  *nb_retval = count(*str_view, *substr_view);
+  return 0;
+}
+
+extern "C" __device__ int udf_string_from_string_view(int* nb_retbal,
+                                                      void const* str,
+                                                      void* udf_str)
+{
+  auto str_view_ptr = reinterpret_cast<cudf::string_view const*>(str);
+  auto udf_str_ptr  = new (udf_str) udf_string;
+  *udf_str_ptr      = udf_string(*str_view_ptr);
+
+  return 0;
+}
+
+extern "C" __device__ int string_view_from_udf_string(int* nb_retval,
+                                                      void const* udf_str,
+                                                      void* str)
+{
+  auto udf_str_ptr = reinterpret_cast<udf_string const*>(udf_str);
+  auto sv_ptr      = new (str) cudf::string_view;
+  *sv_ptr          = cudf::string_view(*udf_str_ptr);
+
+  return 0;
+}
+
+extern "C" __device__ int strip(int* nb_retval,
+                                void* udf_str,
+                                void* const* to_strip,
+                                void* const* strip_str)
+{
+  auto to_strip_ptr  = reinterpret_cast<cudf::string_view const*>(to_strip);
+  auto strip_str_ptr = reinterpret_cast<cudf::string_view const*>(strip_str);
+  auto udf_str_ptr   = new (udf_str) udf_string;
+
+  *udf_str_ptr = strip(*to_strip_ptr, *strip_str_ptr);
+
+  return 0;
+}
+
+extern "C" __device__ int lstrip(int* nb_retval,
+                                 void* udf_str,
+                                 void* const* to_strip,
+                                 void* const* strip_str)
+{
+  auto to_strip_ptr  = reinterpret_cast<cudf::string_view const*>(to_strip);
+  auto strip_str_ptr = reinterpret_cast<cudf::string_view const*>(strip_str);
+  auto udf_str_ptr   = new (udf_str) udf_string;
+
+  *udf_str_ptr = strip(*to_strip_ptr, *strip_str_ptr, cudf::strings::side_type::LEFT);
+
+  return 0;
+}
+
+extern "C" __device__ int rstrip(int* nb_retval,
+                                 void* udf_str,
+                                 void* const* to_strip,
+                                 void* const* strip_str)
+{
+  auto to_strip_ptr  = reinterpret_cast<cudf::string_view const*>(to_strip);
+  auto strip_str_ptr = reinterpret_cast<cudf::string_view const*>(strip_str);
+  auto udf_str_ptr   = new (udf_str) udf_string;
+
+  *udf_str_ptr = strip(*to_strip_ptr, *strip_str_ptr, cudf::strings::side_type::RIGHT);
+
+  return 0;
+}
+extern "C" __device__ int upper(int* nb_retval,
+                                void* udf_str,
+                                void const* st,
+                                std::uintptr_t flags_table,
+                                std::uintptr_t cases_table,
+                                std::uintptr_t special_table)
+{
+  auto udf_str_ptr = new (udf_str) udf_string;
+  auto st_ptr      = reinterpret_cast<cudf::string_view const*>(st);
+
+  auto flags_table_ptr =
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(flags_table);
+  auto cases_table_ptr =
+    reinterpret_cast<cudf::strings::detail::character_cases_table_type*>(cases_table);
+  auto special_table_ptr =
+    reinterpret_cast<cudf::strings::detail::special_case_mapping*>(special_table);
+
+  cudf::strings::udf::chars_tables tables{flags_table_ptr, cases_table_ptr, special_table_ptr};
+
+  *udf_str_ptr = to_upper(tables, *st_ptr);
+
+  return 0;
+}
+
+extern "C" __device__ int lower(int* nb_retval,
+                                void* udf_str,
+                                void const* st,
+                                std::uintptr_t flags_table,
+                                std::uintptr_t cases_table,
+                                std::uintptr_t special_table)
+{
+  auto udf_str_ptr = new (udf_str) udf_string;
+  auto st_ptr      = reinterpret_cast<cudf::string_view const*>(st);
+
+  auto flags_table_ptr =
+    reinterpret_cast<cudf::strings::detail::character_flags_table_type*>(flags_table);
+  auto cases_table_ptr =
+    reinterpret_cast<cudf::strings::detail::character_cases_table_type*>(cases_table);
+  auto special_table_ptr =
+    reinterpret_cast<cudf::strings::detail::special_case_mapping*>(special_table);
+
+  cudf::strings::udf::chars_tables tables{flags_table_ptr, cases_table_ptr, special_table_ptr};
+  *udf_str_ptr = to_lower(tables, *st_ptr);
+  return 0;
+}
+
+extern "C" __device__ int concat(int* nb_retval, void* udf_str, void* const* lhs, void* const* rhs)
+{
+  auto lhs_ptr = reinterpret_cast<cudf::string_view const*>(lhs);
+  auto rhs_ptr = reinterpret_cast<cudf::string_view const*>(rhs);
+
+  auto udf_str_ptr = new (udf_str) udf_string;
+
+  udf_string result;
+  result.append(*lhs_ptr).append(*rhs_ptr);
+  *udf_str_ptr = result;
+  return 0;
+}
+
+extern "C" __device__ int replace(
+  int* nb_retval, void* udf_str, void* const src, void* const to_replace, void* const replacement)
+{
+  auto src_ptr         = reinterpret_cast<cudf::string_view const*>(src);
+  auto to_replace_ptr  = reinterpret_cast<cudf::string_view const*>(to_replace);
+  auto replacement_ptr = reinterpret_cast<cudf::string_view const*>(replacement);
+
+  auto udf_str_ptr = new (udf_str) udf_string;
+  *udf_str_ptr     = replace(*src_ptr, *to_replace_ptr, *replacement_ptr);
+
+  return 0;
+}
+
+// Groupby Shim Functions
+template <typename T>
+__device__ bool are_all_nans(cooperative_groups::thread_block const& block,
+                             T const* data,
+                             int64_t size)
+{
+  // TODO: to be refactored with CG vote functions once
+  // block size is known at build time
+  __shared__ int64_t count;
+
+  if (block.thread_rank() == 0) { count = 0; }
+  block.sync();
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    if (not std::isnan(data[idx])) {
+      cuda::atomic_ref<int64_t, cuda::thread_scope_block> ref{count};
+      ref.fetch_add(1, cuda::std::memory_order_relaxed);
+      break;
+    }
+  }
+
+  block.sync();
+  return count == 0;
+}
+
+template <typename T, typename AccumT = std::conditional_t<std::is_integral_v<T>, int64_t, T>>
+__device__ AccumT device_sum(cooperative_groups::thread_block const& block,
+                             T const* data,
+                             int64_t size)
+{
+  __shared__ AccumT block_sum;
+  if (block.thread_rank() == 0) { block_sum = 0; }
+  block.sync();
+
+  AccumT local_sum = 0;
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    local_sum += static_cast<AccumT>(data[idx]);
+  }
+
+  cuda::atomic_ref<AccumT, cuda::thread_scope_block> ref{block_sum};
+  ref.fetch_add(local_sum, cuda::std::memory_order_relaxed);
+
+  block.sync();
+  return block_sum;
+}
+
+template <typename T, typename AccumT = std::conditional_t<std::is_integral_v<T>, int64_t, T>>
+__device__ AccumT BlockSum(T const* data, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  if constexpr (std::is_floating_point_v<T>) {
+    if (are_all_nans(block, data, size)) { return 0; }
+  }
+
+  auto block_sum = device_sum<T>(block, data, size);
+  return block_sum;
+}
+
+template <typename T>
+__device__ double BlockMean(T const* data, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  auto block_sum = device_sum<T>(block, data, size);
+  return static_cast<double>(block_sum) / static_cast<double>(size);
+}
+
+template <typename T>
+__device__ double BlockCoVar(T const* lhs, T const* rhs, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  __shared__ double block_covar;
+
+  if (block.thread_rank() == 0) { block_covar = 0; }
+  block.sync();
+
+  auto block_sum_lhs = device_sum<T>(block, lhs, size);
+
+  auto const mu_l = static_cast<double>(block_sum_lhs) / static_cast<double>(size);
+  auto const mu_r = [=]() {
+    if (lhs == rhs) {
+      // If the lhs and rhs are the same, this is calculating variance.
+      // Thus we can assume mu_r = mu_l.
+      return mu_l;
+    } else {
+      auto block_sum_rhs = device_sum<T>(block, rhs, size);
+      return static_cast<double>(block_sum_rhs) / static_cast<double>(size);
+    }
+  }();
+
+  double local_covar = 0;
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    local_covar += (static_cast<double>(lhs[idx]) - mu_l) * (static_cast<double>(rhs[idx]) - mu_r);
+  }
+
+  cuda::atomic_ref<double, cuda::thread_scope_block> ref{block_covar};
+  ref.fetch_add(local_covar, cuda::std::memory_order_relaxed);
+  block.sync();
+
+  if (block.thread_rank() == 0) { block_covar /= static_cast<double>(size - 1); }
+  block.sync();
+
+  return block_covar;
+}
+
+template <typename T>
+__device__ double BlockVar(T const* data, int64_t size)
+{
+  return BlockCoVar<T>(data, data, size);
+}
+
+template <typename T>
+__device__ double BlockStd(T const* data, int64_t size)
+{
+  auto const var = BlockVar(data, size);
+  return sqrt(var);
+}
+
+template <typename T>
+__device__ T BlockMax(T const* data, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  if constexpr (std::is_floating_point_v<T>) {
+    if (are_all_nans(block, data, size)) { return std::numeric_limits<T>::quiet_NaN(); }
+  }
+
+  auto local_max = cudf::DeviceMax::identity<T>();
+  __shared__ T block_max;
+  if (block.thread_rank() == 0) { block_max = local_max; }
+  block.sync();
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    local_max = max(local_max, data[idx]);
+  }
+
+  cuda::atomic_ref<T, cuda::thread_scope_block> ref{block_max};
+  ref.fetch_max(local_max, cuda::std::memory_order_relaxed);
+
+  block.sync();
+
+  return block_max;
+}
+
+template <typename T>
+__device__ T BlockMin(T const* data, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  if constexpr (std::is_floating_point_v<T>) {
+    if (are_all_nans(block, data, size)) { return std::numeric_limits<T>::quiet_NaN(); }
+  }
+
+  auto local_min = cudf::DeviceMin::identity<T>();
+
+  __shared__ T block_min;
+  if (block.thread_rank() == 0) { block_min = local_min; }
+  block.sync();
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    local_min = min(local_min, data[idx]);
+  }
+
+  cuda::atomic_ref<T, cuda::thread_scope_block> ref{block_min};
+  ref.fetch_min(local_min, cuda::std::memory_order_relaxed);
+
+  block.sync();
+
+  return block_min;
+}
+
+template <typename T>
+__device__ int64_t BlockIdxMax(T const* data, int64_t* index, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  __shared__ T block_max;
+  __shared__ int64_t block_idx_max;
+  __shared__ bool found_max;
+
+  auto local_max     = cudf::DeviceMax::identity<T>();
+  auto local_idx_max = cudf::DeviceMin::identity<int64_t>();
+
+  if (block.thread_rank() == 0) {
+    block_max     = local_max;
+    block_idx_max = local_idx_max;
+    found_max     = false;
+  }
+  block.sync();
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    auto const current_data = data[idx];
+    if (current_data > local_max) {
+      local_max     = current_data;
+      local_idx_max = index[idx];
+      found_max     = true;
+    }
+  }
+
+  cuda::atomic_ref<T, cuda::thread_scope_block> ref{block_max};
+  ref.fetch_max(local_max, cuda::std::memory_order_relaxed);
+  block.sync();
+
+  if (found_max) {
+    if (local_max == block_max) {
+      cuda::atomic_ref<int64_t, cuda::thread_scope_block> ref_idx{block_idx_max};
+      ref_idx.fetch_min(local_idx_max, cuda::std::memory_order_relaxed);
+    }
+  } else {
+    if (block.thread_rank() == 0) { block_idx_max = index[0]; }
+  }
+  block.sync();
+
+  return block_idx_max;
+}
+
+template <typename T>
+__device__ int64_t BlockIdxMin(T const* data, int64_t* index, int64_t size)
+{
+  auto block = cooperative_groups::this_thread_block();
+
+  __shared__ T block_min;
+  __shared__ int64_t block_idx_min;
+  __shared__ bool found_min;
+
+  auto local_min     = cudf::DeviceMin::identity<T>();
+  auto local_idx_min = cudf::DeviceMin::identity<int64_t>();
+
+  if (block.thread_rank() == 0) {
+    block_min     = local_min;
+    block_idx_min = local_idx_min;
+    found_min     = false;
+  }
+  block.sync();
+
+  for (int64_t idx = block.thread_rank(); idx < size; idx += block.size()) {
+    auto const current_data = data[idx];
+    if (current_data < local_min) {
+      local_min     = current_data;
+      local_idx_min = index[idx];
+      found_min     = true;
+    }
+  }
+
+  cuda::atomic_ref<T, cuda::thread_scope_block> ref{block_min};
+  ref.fetch_min(local_min, cuda::std::memory_order_relaxed);
+  block.sync();
+
+  if (found_min) {
+    if (local_min == block_min) {
+      cuda::atomic_ref<int64_t, cuda::thread_scope_block> ref_idx{block_idx_min};
+      ref_idx.fetch_min(local_idx_min, cuda::std::memory_order_relaxed);
+    }
+  } else {
+    if (block.thread_rank() == 0) { block_idx_min = index[0]; }
+  }
+  block.sync();
+
+  return block_idx_min;
+}
+
+template <typename T>
+__device__ double BlockCorr(T* const lhs_ptr, T* const rhs_ptr, int64_t size)
+{
+  auto numerator   = BlockCoVar(lhs_ptr, rhs_ptr, size);
+  auto denominator = BlockStd(lhs_ptr, size) * BlockStd<T>(rhs_ptr, size);
+  if (denominator == 0.0) {
+    return std::numeric_limits<double>::quiet_NaN();
+  } else {
+    return numerator / denominator;
+  }
+}
+
+extern "C" {
+#define make_definition(name, cname, type, return_type)                                          \
+  __device__ int name##_##cname(return_type* numba_return_value, type* const data, int64_t size) \
+  {                                                                                              \
+    return_type const res = name<type>(data, size);                                              \
+    *numba_return_value   = res;                                                                 \
+    __syncthreads();                                                                             \
+    return 0;                                                                                    \
+  }
+
+make_definition(BlockSum, int32, int32_t, int64_t);
+make_definition(BlockSum, int64, int64_t, int64_t);
+make_definition(BlockSum, float32, float, float);
+make_definition(BlockSum, float64, double, double);
+
+make_definition(BlockMean, int32, int32_t, double);
+make_definition(BlockMean, int64, int64_t, double);
+make_definition(BlockMean, float32, float, float);
+make_definition(BlockMean, float64, double, double);
+
+make_definition(BlockStd, int32, int32_t, double);
+make_definition(BlockStd, int64, int64_t, double);
+make_definition(BlockStd, float32, float, float);
+make_definition(BlockStd, float64, double, double);
+
+make_definition(BlockVar, int64, int64_t, double);
+make_definition(BlockVar, int32, int32_t, double);
+make_definition(BlockVar, float32, float, float);
+make_definition(BlockVar, float64, double, double);
+
+make_definition(BlockMin, int32, int32_t, int32_t);
+make_definition(BlockMin, int64, int64_t, int64_t);
+make_definition(BlockMin, float32, float, float);
+make_definition(BlockMin, float64, double, double);
+
+make_definition(BlockMax, int32, int32_t, int32_t);
+make_definition(BlockMax, int64, int64_t, int64_t);
+make_definition(BlockMax, float32, float, float);
+make_definition(BlockMax, float64, double, double);
+#undef make_definition
+}
+
+extern "C" {
+#define make_definition_idx(name, cname, type)                                   \
+  __device__ int name##_##cname(                                                 \
+    int64_t* numba_return_value, type* const data, int64_t* index, int64_t size) \
+  {                                                                              \
+    auto const res      = name<type>(data, index, size);                         \
+    *numba_return_value = res;                                                   \
+    __syncthreads();                                                             \
+    return 0;                                                                    \
+  }
+
+make_definition_idx(BlockIdxMin, int32, int32_t);
+make_definition_idx(BlockIdxMin, int64, int64_t);
+make_definition_idx(BlockIdxMin, float32, float);
+make_definition_idx(BlockIdxMin, float64, double);
+
+make_definition_idx(BlockIdxMax, int32, int32_t);
+make_definition_idx(BlockIdxMax, int64, int64_t);
+make_definition_idx(BlockIdxMax, float32, float);
+make_definition_idx(BlockIdxMax, float64, double);
+#undef make_definition_idx
+}
+
+extern "C" {
+#define make_definition_corr(name, cname, type)                                 \
+  __device__ int name##_##cname##_##cname(                                      \
+    double* numba_return_value, type* const lhs, type* const rhs, int64_t size) \
+  {                                                                             \
+    double const res    = name<type>(lhs, rhs, size);                           \
+    *numba_return_value = res;                                                  \
+    __syncthreads();                                                            \
+    return 0;                                                                   \
+  }
+
+make_definition_corr(BlockCorr, int32, int32_t);
+make_definition_corr(BlockCorr, int64, int64_t);
+
+#undef make_definition_corr
+}
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/case.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/case.cuh
new file mode 100644
index 0000000..7647708
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/case.cuh
@@ -0,0 +1,211 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "udf_string.cuh"
+
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Global variables for character-type flags and case conversion
+ */
+struct chars_tables {
+  cudf::strings::detail::character_flags_table_type* flags_table;
+  cudf::strings::detail::character_cases_table_type* cases_table;
+  struct cudf::strings::detail::special_case_mapping* special_case_mapping_table;
+};
+
+namespace detail {
+
+/**
+ * @brief Utility for converting a single character
+ *
+ * There are special cases where the conversion may result in multiple characters.
+ *
+ * @param tables The char tables required for conversion
+ * @param result String to append the converted character
+ * @param code_point The code-point of the character to convert
+ * @param flag The char-type flag of the character to convert
+ */
+__device__ inline void convert_char(chars_tables const tables,
+                                    udf_string& result,
+                                    uint32_t code_point,
+                                    uint8_t flag)
+{
+  if (!cudf::strings::detail::IS_SPECIAL(flag)) {
+    result.append(cudf::strings::detail::codepoint_to_utf8(tables.cases_table[code_point]));
+    return;
+  }
+
+  // handle special case
+  auto const map =
+    tables
+      .special_case_mapping_table[cudf::strings::detail::get_special_case_hash_index(code_point)];
+  auto const output_count =
+    cudf::strings::detail::IS_LOWER(flag) ? map.num_upper_chars : map.num_lower_chars;
+  auto const* output_chars = cudf::strings::detail::IS_LOWER(flag) ? map.upper : map.lower;
+  for (uint16_t idx = 0; idx < output_count; idx++) {
+    result.append(cudf::strings::detail::codepoint_to_utf8(output_chars[idx]));
+  }
+}
+
+/**
+ * @brief Converts the given string to either upper or lower case
+ *
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @param case_flag Identifies upper/lower case conversion
+ * @return New string containing the converted characters
+ */
+__device__ inline udf_string convert_case(
+  chars_tables const tables,
+  string_view d_str,
+  cudf::strings::detail::character_flags_table_type case_flag)
+{
+  udf_string result;
+  for (auto const chr : d_str) {
+    auto const code_point = cudf::strings::detail::utf8_to_codepoint(chr);
+    auto const flag       = code_point <= 0x00FFFF ? tables.flags_table[code_point] : 0;
+
+    if ((flag & case_flag) || (cudf::strings::detail::IS_SPECIAL(flag) &&
+                               !cudf::strings::detail::IS_UPPER_OR_LOWER(flag))) {
+      convert_char(tables, result, code_point, flag);
+    } else {
+      result.append(chr);
+    }
+  }
+
+  return result;
+}
+
+/**
+ * @brief Utility for capitalize and title functions
+ *
+ * @tparam CapitalizeNextFn returns true if the next candidate character should be capitalized
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @param next_fn Function for next character capitalized
+ * @return New string containing the converted characters
+ */
+template <typename CapitalizeNextFn>
+__device__ inline udf_string capitalize(chars_tables const tables,
+                                        string_view d_str,
+                                        CapitalizeNextFn next_fn)
+{
+  udf_string result;
+  bool capitalize = true;
+  for (auto const chr : d_str) {
+    auto const code_point = cudf::strings::detail::utf8_to_codepoint(chr);
+    auto const flag       = code_point <= 0x00FFFF ? tables.flags_table[code_point] : 0;
+    auto const change_case =
+      capitalize ? cudf::strings::detail::IS_LOWER(flag) : cudf::strings::detail::IS_UPPER(flag);
+    if (change_case) {
+      detail::convert_char(tables, result, code_point, flag);
+    } else {
+      result.append(chr);
+    }
+    capitalize = next_fn(flag);
+  }
+  return result;
+}
+}  // namespace detail
+
+/**
+ * @brief Converts the given string to lower case
+ *
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @return New string containing the converted characters
+ */
+__device__ inline udf_string to_lower(chars_tables const tables, string_view d_str)
+{
+  cudf::strings::detail::character_flags_table_type case_flag = cudf::strings::detail::IS_UPPER(
+    cudf::strings::detail::ALL_FLAGS);  // convert only upper case characters
+  return detail::convert_case(tables, d_str, case_flag);
+}
+
+/**
+ * @brief Converts the given string to upper case
+ *
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @return New string containing the converted characters
+ */
+__device__ inline udf_string to_upper(chars_tables const tables, string_view d_str)
+{
+  cudf::strings::detail::character_flags_table_type case_flag = cudf::strings::detail::IS_LOWER(
+    cudf::strings::detail::ALL_FLAGS);  // convert only lower case characters
+  return detail::convert_case(tables, d_str, case_flag);
+}
+
+/**
+ * @brief Converts the given string to lower/upper case
+ *
+ * All lower case characters are converted to upper case and
+ * all upper case characters are converted to lower case.
+ *
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @return New string containing the converted characters
+ */
+__device__ inline udf_string swap_case(chars_tables const tables, string_view d_str)
+{
+  cudf::strings::detail::character_flags_table_type case_flag =
+    cudf::strings::detail::IS_LOWER(cudf::strings::detail::ALL_FLAGS) |
+    cudf::strings::detail::IS_UPPER(cudf::strings::detail::ALL_FLAGS);
+  return detail::convert_case(tables, d_str, case_flag);
+}
+
+/**
+ * @brief Capitalize the first character of the given string
+ *
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @return New string containing the converted characters
+ */
+__device__ inline udf_string capitalize(chars_tables const tables, string_view d_str)
+{
+  auto next_fn = [](cudf::strings::detail::character_flags_table_type) -> bool { return false; };
+  return detail::capitalize(tables, d_str, next_fn);
+}
+
+/**
+ * @brief Converts the given string to title case
+ *
+ * The first character after a non-character is converted to upper case.
+ * All other characters are converted to lower case.
+ *
+ * @param tables The char tables required for conversion
+ * @param d_str Input string to convert
+ * @return New string containing the converted characters
+ */
+__device__ inline udf_string title(chars_tables const tables, string_view d_str)
+{
+  auto next_fn = [](cudf::strings::detail::character_flags_table_type flag) -> bool {
+    return !cudf::strings::detail::IS_ALPHA(flag);
+  };
+  return detail::capitalize(tables, d_str, next_fn);
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/char_types.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/char_types.cuh
new file mode 100644
index 0000000..d03de2c
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/char_types.cuh
@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/char_types/char_types_enum.hpp>
+#include <cudf/strings/detail/char_tables.hpp>
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Returns true if all characters in the string are of the type specified.
+ *
+ * The output will be false if the string is empty or has at least one character
+ * not of the specified type. If all characters fit the type then true is returned.
+ *
+ * To ignore all but specific types, set the `verify_types` to those types
+ * which should be checked. Otherwise, the default `ALL_TYPES` will verify all
+ * characters match `types`.
+ *
+ * @code{.pseudo}
+ * Examples:
+ * s = ['ab', 'a b', 'a7', 'a B']
+ * all_characters_of_type('ab', LOWER) => true
+ * all_characters_of_type('a b', LOWER) => false
+ * all_characters_of_type('a7b', LOWER) => false
+ * all_characters_of_type('aB', LOWER) => false
+ * all_characters_of_type('ab', LOWER, LOWER|UPPER) => true
+ * all_characters_of_type('a b', LOWER, LOWER|UPPER) => true
+ * all_characters_of_type('a7', LOWER, LOWER|UPPER) => true
+ * all_characters_of_type('a B', LOWER, LOWER|UPPER) => false
+ * @endcode
+ *
+ * @param flags_table Table of character-type flags
+ * @param d_str String for this operation
+ * @param types The character types to check in the string
+ * @param verify_types Only verify against these character types.
+ *                     Default `ALL_TYPES` means return `true`
+ *                     iff all characters match `types`.
+ * @return True if all characters match the type conditions
+ */
+__device__ inline bool all_characters_of_type(
+  cudf::strings::detail::character_flags_table_type* flags_table,
+  string_view d_str,
+  string_character_types types,
+  string_character_types verify_types = string_character_types::ALL_TYPES)
+{
+  bool check            = !d_str.empty();  // require at least one character
+  size_type check_count = 0;
+  for (auto itr = d_str.begin(); check && (itr != d_str.end()); ++itr) {
+    auto code_point = cudf::strings::detail::utf8_to_codepoint(*itr);
+    // lookup flags in table by code-point
+    auto flag = code_point <= 0x00FFFF ? flags_table[code_point] : 0;
+    if ((verify_types & flag) ||                   // should flag be verified
+        (flag == 0 && verify_types == ALL_TYPES))  // special edge case
+    {
+      check = (types & flag) > 0;
+      ++check_count;
+    }
+  }
+  return check && (check_count > 0);
+}
+
+/**
+ * @brief Returns true if all characters are alphabetic only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters alphabetic
+ */
+__device__ inline bool is_alpha(cudf::strings::detail::character_flags_table_type* flags_table,
+                                string_view d_str)
+{
+  return all_characters_of_type(flags_table, d_str, string_character_types::ALPHA);
+}
+
+/**
+ * @brief Returns true if all characters are alphanumeric only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters are alphanumeric
+ */
+__device__ inline bool is_alpha_numeric(
+  cudf::strings::detail::character_flags_table_type* flags_table, string_view d_str)
+{
+  return all_characters_of_type(flags_table, d_str, string_character_types::ALPHANUM);
+}
+
+/**
+ * @brief Returns true if all characters are numeric only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters are numeric
+ */
+__device__ inline bool is_numeric(cudf::strings::detail::character_flags_table_type* flags_table,
+                                  string_view d_str)
+{
+  return all_characters_of_type(flags_table, d_str, string_character_types::NUMERIC);
+}
+
+/**
+ * @brief Returns true if all characters are digits only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters are digits
+ */
+__device__ inline bool is_digit(cudf::strings::detail::character_flags_table_type* flags_table,
+                                string_view d_str)
+{
+  return all_characters_of_type(flags_table, d_str, string_character_types::DIGIT);
+}
+
+/**
+ * @brief Returns true if all characters are decimal only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters are decimal
+ */
+__device__ inline bool is_decimal(cudf::strings::detail::character_flags_table_type* flags_table,
+                                  string_view d_str)
+{
+  return all_characters_of_type(flags_table, d_str, string_character_types::DECIMAL);
+}
+
+/**
+ * @brief Returns true if all characters are spaces only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters spaces
+ */
+__device__ inline bool is_space(cudf::strings::detail::character_flags_table_type* flags_table,
+                                string_view d_str)
+{
+  return all_characters_of_type(flags_table, d_str, string_character_types::SPACE);
+}
+
+/**
+ * @brief Returns true if all characters are upper case only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters are upper case
+ */
+__device__ inline bool is_upper(cudf::strings::detail::character_flags_table_type* flags_table,
+                                string_view d_str)
+{
+  return all_characters_of_type(
+    flags_table, d_str, string_character_types::UPPER, string_character_types::CASE_TYPES);
+}
+
+/**
+ * @brief Returns true if all characters are lower case only
+ *
+ * @param flags_table Table required for checking character types
+ * @param d_str Input string to check
+ * @return True if characters are lower case
+ */
+__device__ inline bool is_lower(cudf::strings::detail::character_flags_table_type* flags_table,
+                                string_view d_str)
+{
+  return all_characters_of_type(
+    flags_table, d_str, string_character_types::LOWER, string_character_types::CASE_TYPES);
+}
+
+/**
+ * @brief Returns true if string is in title case
+ *
+ * @param tables The char tables required for checking characters
+ * @param d_str Input string to check
+ * @return True if string is in title case
+ */
+__device__ inline bool is_title(cudf::strings::detail::character_flags_table_type* flags_table,
+                                string_view d_str)
+{
+  auto valid                 = false;  // requires one or more cased characters
+  auto should_be_capitalized = true;   // current character should be upper-case
+  for (auto const chr : d_str) {
+    auto const code_point = cudf::strings::detail::utf8_to_codepoint(chr);
+    auto const flag       = code_point <= 0x00FFFF ? flags_table[code_point] : 0;
+    if (cudf::strings::detail::IS_UPPER_OR_LOWER(flag)) {
+      if (should_be_capitalized == !cudf::strings::detail::IS_UPPER(flag)) return false;
+      valid = true;
+    }
+    should_be_capitalized = !cudf::strings::detail::IS_UPPER_OR_LOWER(flag);
+  }
+  return valid;
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/numeric.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/numeric.cuh
new file mode 100644
index 0000000..ecd952a
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/numeric.cuh
@@ -0,0 +1,72 @@
+
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "udf_string.cuh"
+
+#include <cudf/strings/detail/convert/int_to_string.cuh>
+#include <cudf/strings/detail/convert/string_to_float.cuh>
+#include <cudf/strings/detail/convert/string_to_int.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Converts a string into an integer
+ *
+ * The '+' and '-' are allowed but only at the beginning of the string.
+ * The string is expected to contain base-10 [0-9] characters only.
+ * Any other character will end the parse.
+ * Overflow of the int64 type is not detected.
+ */
+__device__ inline int64_t stoi(string_view const& d_str)
+{
+  return cudf::strings::detail::string_to_integer(d_str);
+}
+
+/**
+ * @brief Converts an integer into string
+ *
+ * @param value integer value to convert
+ */
+__device__ inline udf_string to_string(int64_t value)
+{
+  udf_string result;
+  if (value == 0) {
+    result.append("0");
+    return result;
+  }
+  result.resize(cudf::strings::detail::count_digits(value));
+  cudf::strings::detail::integer_to_string(value, result.data());
+  return result;
+}
+
+/**
+ * @brief Converts a string into a double
+ *
+ * This function supports scientific notation.
+ * Overflow goes to inf or -inf and underflow may go to 0.
+ */
+__device__ inline double stod(string_view const& d_str)
+{
+  return cudf::strings::detail::stod(d_str);
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/pad.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/pad.cuh
new file mode 100644
index 0000000..2f3b4fe
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/pad.cuh
@@ -0,0 +1,72 @@
+
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "udf_string.cuh"
+
+#include <cudf/strings/detail/pad_impl.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Pad beginning and/or end of a string with the given fill character
+ *
+ * The side_type::BOTH will attempt to center the text using the `fill_char`.
+ * If `width <= d_str.length()` no change occurs and the input `d_str` is returned.
+ *
+ * @tparam side Specify where the padding should occur
+ * @param d_str String to pad
+ * @param width Minimum length in characters of the output string
+ * @param fill_char Character used for padding
+ */
+template <side_type side = side_type::RIGHT>
+__device__ udf_string pad(cudf::string_view const d_str,
+                          cudf::size_type width,
+                          cudf::string_view fill_char = cudf::string_view{" ", 1})
+{
+  if (fill_char.empty()) { return udf_string{d_str}; }
+
+  udf_string result;
+  result.resize(cudf::strings::detail::compute_padded_size(d_str, width, fill_char.size_bytes()));
+  cudf::strings::detail::pad_impl<side>(d_str, width, *fill_char.begin(), result.data());
+  return result;
+}
+
+/**
+ * @brief Pad beginning of a string with zero '0'
+ *
+ * If the `width` is smaller than the length of `d_str` no change occurs.
+ *
+ * If `d_str` starts with a sign character ('-' or '+') then '0' padding
+ * starts after the sign.
+ *
+ * @param d_str String to fill
+ * @param width Minimum length in characters of the output string (including the sign character)
+ */
+__device__ udf_string zfill(cudf::string_view const d_str, cudf::size_type width)
+{
+  udf_string result;
+  result.resize(cudf::strings::detail::compute_padded_size(d_str, width, 1));
+  cudf::strings::detail::zfill_impl(d_str, width, result.data());
+  return result;
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/replace.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/replace.cuh
new file mode 100644
index 0000000..6d21352
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/replace.cuh
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+#include <cudf/strings/udf/udf_string.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Returns new string replacing all occurrences of target with replacement
+ *
+ * If target is empty then replacement is inserted between every character.
+ *
+ * @param source Source string to search
+ * @param target String to match within source
+ * @param replacement String to replace the target within the source
+ * @return Resulting string
+ */
+__device__ inline udf_string replace(string_view source,
+                                     string_view target,
+                                     string_view replacement)
+{
+  udf_string result;
+  auto const tgt_length   = target.length();
+  auto const src_length   = source.length();
+  size_type last_position = 0;
+  size_type position      = 0;
+  while (position != string_view::npos) {
+    position = source.find(target, last_position);
+    if (position != string_view::npos) {
+      result.append(source.substr(last_position, position - last_position));
+      result.append(replacement);
+      last_position = position + tgt_length;
+      if ((tgt_length == 0) && (++last_position <= src_length)) {
+        result.append(source.substr(position, 1));
+      }
+    }
+  }
+  if (last_position < src_length) {
+    result.append(source.substr(last_position, src_length - last_position));
+  }
+
+  return result;
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/search.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/search.cuh
new file mode 100644
index 0000000..3300c85
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/search.cuh
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Returns the number of times that the target string appears
+ * in the source string.
+ *
+ * If `start <= 0` the search begins at the beginning of the `source` string.
+ * If `end <=0` or `end` is greater the length of the `source` string,
+ * the search stops at the end of the string.
+ *
+ * @param source Source string to search
+ * @param target String to match within source
+ * @param start First character position within source to start the search
+ * @param end Last character position (exclusive) within source to search
+ * @return Number of matches
+ */
+__device__ inline cudf::size_type count(string_view const source,
+                                        string_view const target,
+                                        cudf::size_type start = 0,
+                                        cudf::size_type end   = -1)
+{
+  auto const tgt_length = target.length();
+  auto const src_length = source.length();
+
+  start = start < 0 ? 0 : start;
+  end   = (end < 0 || end > src_length) ? src_length : end;
+
+  if (tgt_length == 0) { return (end - start) + 1; }
+  cudf::size_type count = 0;
+  cudf::size_type pos   = start;
+  while (pos != cudf::string_view::npos) {
+    pos = source.find(target, pos, end - pos);
+    if (pos != cudf::string_view::npos) {
+      ++count;
+      pos += tgt_length;
+    }
+  }
+  return count;
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/split.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/split.cuh
new file mode 100644
index 0000000..d313146
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/split.cuh
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "udf_string.cuh"
+
+#include <cudf/strings/detail/split_utils.cuh>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+namespace detail {
+
+/**
+ * @brief Split string using given string
+ *
+ * The caller must allocate an array of cudf::string_view to be filled
+ * in by this function. This function can be called with a `result=nullptr`
+ * to compute the number of tokens.
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{"the best  of times ", 19};
+ * auto tgt = cudf::string_view{" ", 1};
+ * auto token_count = split(d_str, tgt, nullptr);
+ * auto result = new cudf::string_view[token_count];
+ * split(d_str, tgt, result);
+ * // result is array like ["the", "best", "", "of", "times", ""]
+ * @endcode
+ *
+ * @param d_str String to split
+ * @param tgt String to split on
+ * @param result Empty array to populate with output objects.
+ *               Pass `nullptr` to just get the token count.
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type split(cudf::string_view const d_str,
+                                        cudf::string_view const tgt,
+                                        cudf::string_view* result)
+{
+  auto const nchars     = d_str.length();
+  cudf::size_type count = 0;
+
+  cudf::size_type last_pos = 0;
+  while (last_pos <= nchars) {
+    cudf::size_type const pos = d_str.find(tgt, last_pos);
+    auto const length         = (pos < 0 ? nchars : pos) - last_pos;
+    if (result) { *result++ = d_str.substr(last_pos, length); }
+    last_pos = pos + tgt.length();
+    ++count;
+    if (pos < 0) { break; }
+  }
+
+  return count;
+}
+}  // namespace detail
+
+/**
+ * @brief Count tokens in a string without performing the split
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{"the best  of times ", 19};
+ * auto tgt = cudf::string_view{" ", 1};
+ * auto token_count = count_tokens(d_str, tgt);
+ * // token_count is 6
+ * @endcode
+ *
+ * @param d_str String to split
+ * @param tgt String to split on
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type count_tokens(cudf::string_view const d_str,
+                                               cudf::string_view const tgt)
+{
+  return detail::split(d_str, tgt, nullptr);
+}
+
+/**
+ * @brief Split string using given string
+ *
+ * The caller must allocate an array of cudf::string_view to be filled
+ * in by this function.
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{"the best  of times ", 19};
+ * auto tgt = cudf::string_view{" ", 1};
+ * auto token_count = count_tokens(d_str, tgt);
+ * auto result = new cudf::string_view[token_count];
+ * split(d_str, tgt, result);
+ * // result is array like ["the", "best", "", "of", "times", ""]
+ * @endcode
+ *
+ * @param d_str String to split
+ * @param tgt String to split on
+ * @param result Empty array to populate with output objects.
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type split(cudf::string_view const d_str,
+                                        cudf::string_view const tgt,
+                                        cudf::string_view* result)
+{
+  return detail::split(d_str, tgt, result);
+}
+
+/**
+ * @brief Split string using given target array
+ *
+ * @param d_str String to split
+ * @param tgt Character array encoded in UTF-8 used for identifying split points
+ * @param bytes Number of bytes to read from `tgt`
+ * @param result Empty array to populate with output objects
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type split(cudf::string_view const d_str,
+                                        char const* tgt,
+                                        cudf::size_type bytes,
+                                        cudf::string_view* result)
+{
+  return detail::split(d_str, cudf::string_view{tgt, bytes}, result);
+}
+
+/**
+ * @brief Split string using given target array
+ *
+ * @param d_str String to split
+ * @param tgt Null-terminated character array encoded in UTF-8 used for identifying split points
+ * @param result Empty array to populate with output objects
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type split(cudf::string_view const d_str,
+                                        char const* tgt,
+                                        cudf::string_view* result)
+{
+  return split(d_str, tgt, detail::bytes_in_null_terminated_string(tgt), result);
+}
+
+namespace detail {
+/**
+ * @brief Split string on whitespace
+ *
+ * The caller must allocate an array of cudf::string_view to be filled
+ * in by this function. This function can be called with a `result=nullptr`
+ * to compute the number of tokens.
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{"the best  of times ", 19};
+ * auto token_count = split(d_str, nullptr);
+ * auto result = new cudf::string_view[token_count];
+ * split(d_str, result);
+ * // result is array like ["the", "best", "of", "times"]
+ * @endcode
+ *
+ * @param d_str String to split
+ * @param result Empty array to populate with output objects.
+ *               Pass `nullptr` to just get the token count.
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type split(cudf::string_view const d_str, cudf::string_view* result)
+{
+  cudf::strings::detail::whitespace_string_tokenizer tokenizer{d_str};
+  cudf::size_type count = 0;
+  while (tokenizer.next_token()) {
+    auto token = tokenizer.get_token();
+    if (result) { *result++ = d_str.substr(token.first, token.second - token.first); }
+    ++count;
+  }
+  return count;
+}
+}  // namespace detail
+
+/**
+ * @brief Count tokens in a string without performing the split on whitespace
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{"the best  of times ", 19};
+ * auto token_count = count_tokens(d_str);
+ * // token_count is 4
+ * @endcode
+ *
+ * @param d_str String to split
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type count_tokens(cudf::string_view const d_str)
+{
+  return detail::split(d_str, nullptr);
+}
+
+/**
+ * @brief Split string on whitespace
+ *
+ * This will create tokens by splitting on one or more consecutive whitespace characters
+ * found in `d_str`.
+ *
+ * @param d_str String to split
+ * @param result Empty array to populate with output objects.
+ * @return Number of tokens returned
+ */
+__device__ inline cudf::size_type split(cudf::string_view const d_str, cudf::string_view* result)
+{
+  return detail::split(d_str, result);
+}
+
+/**
+ * @brief Join an array of strings with a separator
+ *
+ * @code{.cpp}
+ * auto separator = cudf::string_view{"::", 2};
+ * cudf::string_view input[] = {
+ *   cudf::string_view{"hello", 5},
+ *   cudf::string_view{"goodbye", 7},
+ *   cudf::string_view{"world", 5} };
+ *
+ * auto result = join(separator, input, 3);
+ * // result is "hello::goodbye::world"
+ * @endcode
+ *
+ * @param separator Separator string
+ * @param input An array of strings to join
+ * @param count Number of elements in `input`
+ * @return New string
+ */
+__device__ inline udf_string join(cudf::string_view const separator,
+                                  cudf::string_view* input,
+                                  cudf::size_type count)
+{
+  udf_string result{""};
+  while (count-- > 0) {
+    result += *input++;
+    if (count > 0) { result += separator; }
+  }
+  return result;
+}
+
+/**
+ * @brief Join an array of strings with a separator
+ *
+ * @param separator Null-terminated UTF-8 string
+ * @param bytes Number of bytes to read from `separator`
+ * @param input An array of strings to join
+ * @param count Number of elements in `input`
+ * @return New string
+ */
+__device__ inline udf_string join(char const* separator,
+                                  cudf::size_type bytes,
+                                  cudf::string_view* input,
+                                  cudf::size_type count)
+{
+  return join(cudf::string_view{separator, bytes}, input, count);
+}
+
+/**
+ * @brief Join an array of strings with a separator
+ *
+ * @param separator Null-terminated UTF-8 string
+ * @param input An array of strings to join
+ * @param count Number of elements in `input`
+ * @return New string
+ */
+__device__ inline udf_string join(char const* separator,
+                                  cudf::string_view* input,
+                                  cudf::size_type count)
+{
+  return join(separator, detail::bytes_in_null_terminated_string(separator), input, count);
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/starts_with.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/starts_with.cuh
new file mode 100644
index 0000000..1c7beac
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/starts_with.cuh
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Returns true if the beginning of the specified string
+ * matches the given character array.
+ *
+ * @param dstr String to check
+ * @param tgt Character array encoded in UTF-8
+ * @param bytes Number of bytes to read from `tgt`
+ * @return true if `tgt` matches the beginning of `dstr`
+ */
+__device__ inline bool starts_with(cudf::string_view const dstr,
+                                   char const* tgt,
+                                   cudf::size_type bytes)
+{
+  if (bytes > dstr.size_bytes()) { return false; }
+  auto const start_str = cudf::string_view{dstr.data(), bytes};
+  return start_str.compare(tgt, bytes) == 0;
+}
+
+/**
+ * @brief Returns true if the beginning of the specified string
+ * matches the given target string.
+ *
+ * @param dstr String to check
+ * @param tgt String to match
+ * @return true if `tgt` matches the beginning of `dstr`
+ */
+__device__ inline bool starts_with(cudf::string_view const dstr, cudf::string_view const& tgt)
+{
+  return starts_with(dstr, tgt.data(), tgt.size_bytes());
+}
+
+/**
+ * @brief Returns true if the end of the specified string
+ * matches the given character array.
+ *
+ * @param dstr String to check
+ * @param tgt Character array encoded in UTF-8
+ * @param bytes Number of bytes to read from `tgt`
+ * @return true if `tgt` matches the end of `dstr`
+ */
+__device__ inline bool ends_with(cudf::string_view const dstr,
+                                 char const* tgt,
+                                 cudf::size_type bytes)
+{
+  if (bytes > dstr.size_bytes()) { return false; }
+  auto const end_str = cudf::string_view{dstr.data() + dstr.size_bytes() - bytes, bytes};
+  return end_str.compare(tgt, bytes) == 0;
+}
+
+/**
+ * @brief Returns true if the end of the specified string
+ * matches the given target` string.
+ *
+ * @param dstr String to check
+ * @param tgt String to match
+ * @return true if `tgt` matches the end of `dstr`
+ */
+__device__ inline bool ends_with(cudf::string_view const dstr, cudf::string_view const& tgt)
+{
+  return ends_with(dstr, tgt.data(), tgt.size_bytes());
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/strip.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/strip.cuh
new file mode 100644
index 0000000..521b578
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/strip.cuh
@@ -0,0 +1,80 @@
+
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include "udf_string.cuh"
+
+#include <cudf/strings/detail/strip.cuh>
+#include <cudf/strings/string_view.cuh>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Strip characters from the beginning and/or end of the given string
+ *
+ * The `d_to_strip` is interpreted as an array of characters to be removed.
+ * If `d_to_strip` is an empty string, whitespace characters are stripped.
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{" aba ", 5};
+ * auto d_to_strip = cudf::string_view{}; // empty string
+ * auto result = strip(d_str, d_to_strip);
+ * // result is "aba"
+ * d_to_strip = cudf::string_view{" a", 2}; // space and 'a'
+ * result = strip(d_str, d_to_strip);
+ * // result is "b" ('a' or ' ' removed from the ends)
+ * @endcode
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{" aba ", 5};
+ * auto d_to_strip = cudf::string_view{}; // empty string
+ * auto result = strip(d_str, d_to_strip, side_type::LEFT);
+ * // result is "aba "
+ * d_to_strip = cudf::string_view{"a ", 2}; // 'a' and space
+ * result = strip(d_str, d_to_strip, side_type::LEFT);
+ * // result is "ba " ('a' or ' ' removed from the beginning)
+ * @endcode
+ *
+ * @code{.cpp}
+ * auto d_str = cudf::string_view{" aba ", 5};
+ * auto d_to_strip = cudf::string_view{}; // empty string
+ * auto result = strip(d_str, d_to_strip, side_type::RIGHT);
+ * // result is " aba"
+ * d_to_strip = cudf::string_view{" a", 2}; // space and 'a'
+ * result = rstrip(d_str, d_to_strip, side_type::RIGHT);
+ * // result is " ab" ('a' or ' ' removed from the end)
+ * @endcode
+ *
+ * @param d_str String to strip characters from
+ * @param d_to_strip Characters to remove
+ * @param stype From where to strip the characters;
+ *              Default `BOTH` indicates stripping characters from the
+ *              beginning and the end of the input string `d_str`
+ * @return New string with characters removed
+ */
+__device__ udf_string strip(cudf::string_view const d_str,
+                            cudf::string_view const d_to_strip,
+                            side_type stype = side_type::BOTH)
+{
+  return udf_string{cudf::strings::detail::strip(d_str, d_to_strip, stype)};
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_apis.hpp b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_apis.hpp
new file mode 100644
index 0000000..219dbe2
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_apis.hpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+
+#include <rmm/device_buffer.hpp>
+
+#include <memory>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+class udf_string;
+
+/**
+ * @brief Return a cudf::string_view array for the given strings column
+ *
+ * No string data is copied so the input column controls the lifetime of the
+ * underlying strings.
+ *
+ * New device memory is allocated and returned to hold just the string_view instances.
+ *
+ * @param input Strings column to convert to a string_view array.
+ * @return Array of string_view objects in device memory
+ */
+std::unique_ptr<rmm::device_buffer> to_string_view_array(cudf::column_view const input);
+
+/**
+ * @brief Return a STRINGS column given an array of udf_string objects
+ *
+ * This will make a copy of the strings in d_string in order to build
+ * the output column.
+ * The individual udf_strings are also cleared freeing each of their internal
+ * device memory buffers.
+ *
+ * @param d_strings Pointer to device memory of udf_string objects
+ * @param size The number of elements in the d_strings array
+ * @return A strings column copy of the udf_string objects
+ */
+std::unique_ptr<cudf::column> column_from_udf_string_array(udf_string* d_strings,
+                                                           cudf::size_type size);
+
+/**
+ * @brief Frees a vector of udf_string objects
+ *
+ * The individual udf_strings are cleared freeing each of their internal
+ * device memory buffers.
+ *
+ * @param d_strings Pointer to device memory of udf_string objects
+ * @param size The number of elements in the d_strings array
+ */
+void free_udf_string_array(udf_string* d_strings, cudf::size_type size);
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.cuh b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.cuh
new file mode 100644
index 0000000..a8970c0
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.cuh
@@ -0,0 +1,458 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+
+#include "udf_string.hpp"
+
+#include <cudf/strings/detail/utf8.hpp>
+#include <cudf/strings/string_view.cuh>
+
+#include <algorithm>
+#include <limits>
+#include <string>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+namespace detail {
+
+/**
+ * @brief Count the bytes in a null-terminated character array
+ *
+ * @param str Null-terminated string
+ * @return Number of bytes in `str` up to but not including the null-terminator
+ */
+__device__ inline static cudf::size_type bytes_in_null_terminated_string(char const* str)
+{
+  if (!str) return 0;
+  cudf::size_type bytes = 0;
+  while (*str++)
+    ++bytes;
+  return bytes;
+}
+
+}  // namespace detail
+
+/**
+ * @brief Allocate memory for strings operation
+ *
+ * @param bytes Number of bytes in to allocate
+ * @return Pointer to allocated memory
+ */
+__device__ inline char* udf_string::allocate(cudf::size_type bytes)
+{
+  char* data  = static_cast<char*>(malloc(bytes + 1));
+  data[bytes] = '\0';  // add null-terminator so we can printf strings in device code
+  return data;
+}
+
+/**
+ * @brief Free memory created by allocate()
+ *
+ * @param data Pointer to allocated memory
+ */
+__device__ inline void udf_string::deallocate(char* data)
+{
+  if (data) free(data);
+}
+
+/**
+ * @brief Allocate memory for strings operation
+ *
+ * Reallocates memory for `m_data` with new size `bytes`
+ * The original data in `m_data` is preserved up to `min(bytes,m_bytes)`
+ *
+ * @param bytes Number of bytes in to allocate
+ * @return Pointer to allocated memory
+ */
+__device__ void udf_string::reallocate(cudf::size_type bytes)
+{
+  m_capacity    = bytes;
+  auto new_data = allocate(m_capacity);
+  memcpy(new_data, m_data, std::min(m_bytes, bytes));
+  deallocate(m_data);
+  m_data = new_data;
+}
+
+__device__ inline udf_string::udf_string(char const* data, cudf::size_type bytes)
+  : m_bytes(bytes), m_capacity(bytes)
+{
+  m_data = allocate(m_capacity);
+  memcpy(m_data, data, bytes);
+}
+
+__device__ udf_string::udf_string(cudf::size_type count, cudf::char_utf8 chr)
+{
+  if (count <= 0) { return; }
+  m_bytes = m_capacity = cudf::strings::detail::bytes_in_char_utf8(chr) * count;
+  m_data               = allocate(m_capacity);
+  auto out_ptr         = m_data;
+  for (cudf::size_type idx = 0; idx < count; ++idx) {
+    out_ptr += cudf::strings::detail::from_char_utf8(chr, out_ptr);
+  }
+}
+
+__device__ inline udf_string::udf_string(char const* data)
+  : udf_string(data, detail::bytes_in_null_terminated_string(data))
+{
+}
+
+__device__ inline udf_string::udf_string(udf_string const& src)
+  : udf_string(src.m_data, src.m_bytes)
+{
+}
+
+__device__ inline udf_string::udf_string(udf_string&& src) noexcept
+  : m_data(src.m_data), m_bytes(src.m_bytes), m_capacity(src.m_capacity)
+{
+  src.m_data     = nullptr;
+  src.m_bytes    = 0;
+  src.m_capacity = 0;
+}
+
+__device__ inline udf_string::udf_string(cudf::string_view str)
+  : udf_string(str.data(), str.size_bytes())
+{
+}
+
+__device__ inline udf_string::~udf_string() { deallocate(m_data); }
+
+__device__ inline udf_string& udf_string::operator=(udf_string const& str) { return assign(str); }
+
+__device__ inline udf_string& udf_string::operator=(udf_string&& str) noexcept
+{
+  return assign(std::move(str));
+}
+
+__device__ inline udf_string& udf_string::operator=(cudf::string_view str) { return assign(str); }
+
+__device__ inline udf_string& udf_string::operator=(char const* str) { return assign(str); }
+
+__device__ udf_string& udf_string::assign(udf_string&& str) noexcept
+{
+  if (this == &str) { return *this; }
+  deallocate(m_data);
+  m_data         = str.m_data;
+  m_bytes        = str.m_bytes;
+  m_capacity     = str.m_capacity;
+  str.m_data     = nullptr;
+  str.m_bytes    = 0;
+  str.m_capacity = 0;
+  return *this;
+}
+
+__device__ udf_string& udf_string::assign(cudf::string_view str)
+{
+  return assign(str.data(), str.size_bytes());
+}
+
+__device__ udf_string& udf_string::assign(char const* str)
+{
+  return assign(str, detail::bytes_in_null_terminated_string(str));
+}
+
+__device__ udf_string& udf_string::assign(char const* str, cudf::size_type bytes)
+{
+  if (bytes >= m_capacity) {
+    deallocate(m_data);
+    m_capacity = bytes;
+    m_data     = allocate(m_capacity);
+  }
+  m_bytes = bytes;
+  memcpy(m_data, str, bytes);
+  m_data[m_bytes] = '\0';
+  return *this;
+}
+
+__device__ inline cudf::size_type udf_string::size_bytes() const noexcept { return m_bytes; }
+
+__device__ inline cudf::size_type udf_string::length() const noexcept
+{
+  return cudf::strings::detail::characters_in_string(m_data, m_bytes);
+}
+
+__device__ constexpr cudf::size_type udf_string::max_size() const noexcept
+{
+  return std::numeric_limits<cudf::size_type>::max() - 1;
+}
+
+__device__ inline char* udf_string::data() noexcept { return m_data; }
+
+__device__ inline char const* udf_string::data() const noexcept { return m_data; }
+
+__device__ inline bool udf_string::is_empty() const noexcept { return m_bytes == 0; }
+
+__device__ inline cudf::string_view::const_iterator udf_string::begin() const noexcept
+{
+  return cudf::string_view::const_iterator(cudf::string_view(m_data, m_bytes), 0);
+}
+
+__device__ inline cudf::string_view::const_iterator udf_string::end() const noexcept
+{
+  return cudf::string_view::const_iterator(cudf::string_view(m_data, m_bytes), length());
+}
+
+__device__ inline cudf::char_utf8 udf_string::at(cudf::size_type pos) const
+{
+  auto const offset = byte_offset(pos);
+  auto chr          = cudf::char_utf8{0};
+  if (offset < m_bytes) { cudf::strings::detail::to_char_utf8(data() + offset, chr); }
+  return chr;
+}
+
+__device__ inline cudf::char_utf8 udf_string::operator[](cudf::size_type pos) const
+{
+  return at(pos);
+}
+
+__device__ inline cudf::size_type udf_string::byte_offset(cudf::size_type pos) const
+{
+  cudf::size_type offset = 0;
+
+  auto start = m_data;
+  auto end   = start + m_bytes;
+  while ((pos > 0) && (start < end)) {
+    auto const byte       = static_cast<uint8_t>(*start++);
+    auto const char_bytes = cudf::strings::detail::bytes_in_utf8_byte(byte);
+    if (char_bytes) { --pos; }
+    offset += char_bytes;
+  }
+  return offset;
+}
+
+__device__ inline int udf_string::compare(cudf::string_view in) const noexcept
+{
+  return compare(in.data(), in.size_bytes());
+}
+
+__device__ inline int udf_string::compare(char const* data, cudf::size_type bytes) const
+{
+  auto const view = static_cast<cudf::string_view>(*this);
+  return view.compare(data, bytes);
+}
+
+__device__ inline bool udf_string::operator==(cudf::string_view rhs) const noexcept
+{
+  return m_bytes == rhs.size_bytes() && compare(rhs) == 0;
+}
+
+__device__ inline bool udf_string::operator!=(cudf::string_view rhs) const noexcept
+{
+  return compare(rhs) != 0;
+}
+
+__device__ inline bool udf_string::operator<(cudf::string_view rhs) const noexcept
+{
+  return compare(rhs) < 0;
+}
+
+__device__ inline bool udf_string::operator>(cudf::string_view rhs) const noexcept
+{
+  return compare(rhs) > 0;
+}
+
+__device__ inline bool udf_string::operator<=(cudf::string_view rhs) const noexcept
+{
+  return compare(rhs) <= 0;
+}
+
+__device__ inline bool udf_string::operator>=(cudf::string_view rhs) const noexcept
+{
+  return compare(rhs) >= 0;
+}
+
+__device__ inline void udf_string::clear() noexcept
+{
+  deallocate(m_data);
+  m_data     = nullptr;
+  m_bytes    = 0;
+  m_capacity = 0;
+}
+
+__device__ inline void udf_string::resize(cudf::size_type count)
+{
+  if (count > max_size()) { return; }
+  if (count > m_capacity) { reallocate(count); }
+
+  // add padding if necessary (null chars)
+  if (count > m_bytes) { memset(m_data + m_bytes, 0, count - m_bytes); }
+
+  m_bytes         = count;
+  m_data[m_bytes] = '\0';
+}
+
+__device__ void udf_string::reserve(cudf::size_type count)
+{
+  if (count < max_size() && count > m_capacity) { reallocate(count); }
+}
+
+__device__ cudf::size_type udf_string::capacity() const noexcept { return m_capacity; }
+
+__device__ void udf_string::shrink_to_fit()
+{
+  if (m_bytes < m_capacity) { reallocate(m_bytes); }
+}
+
+__device__ inline udf_string& udf_string::append(char const* str, cudf::size_type bytes)
+{
+  if (bytes <= 0) { return *this; }
+  auto const nbytes = m_bytes + bytes;
+  if (nbytes > m_capacity) { reallocate(2 * nbytes); }
+  memcpy(m_data + m_bytes, str, bytes);
+  m_bytes         = nbytes;
+  m_data[m_bytes] = '\0';
+  return *this;
+}
+
+__device__ inline udf_string& udf_string::append(char const* str)
+{
+  return append(str, detail::bytes_in_null_terminated_string(str));
+}
+
+__device__ inline udf_string& udf_string::append(cudf::char_utf8 chr, cudf::size_type count)
+{
+  auto d_str = udf_string(count, chr);
+  return append(d_str);
+}
+
+__device__ inline udf_string& udf_string::append(cudf::string_view in)
+{
+  return append(in.data(), in.size_bytes());
+}
+
+__device__ inline udf_string& udf_string::operator+=(cudf::string_view in) { return append(in); }
+
+__device__ inline udf_string& udf_string::operator+=(cudf::char_utf8 chr) { return append(chr); }
+
+__device__ inline udf_string& udf_string::operator+=(char const* str) { return append(str); }
+
+__device__ inline udf_string& udf_string::insert(cudf::size_type pos,
+                                                 char const* str,
+                                                 cudf::size_type in_bytes)
+{
+  return replace(pos, 0, str, in_bytes);
+}
+
+__device__ inline udf_string& udf_string::insert(cudf::size_type pos, char const* str)
+{
+  return insert(pos, str, detail::bytes_in_null_terminated_string(str));
+}
+
+__device__ inline udf_string& udf_string::insert(cudf::size_type pos, cudf::string_view in)
+{
+  return insert(pos, in.data(), in.size_bytes());
+}
+
+__device__ inline udf_string& udf_string::insert(cudf::size_type pos,
+                                                 cudf::size_type count,
+                                                 cudf::char_utf8 chr)
+{
+  return replace(pos, 0, count, chr);
+}
+
+__device__ inline udf_string udf_string::substr(cudf::size_type pos, cudf::size_type count) const
+{
+  if (pos < 0) { return udf_string{"", 0}; }
+  auto const start_pos = byte_offset(pos);
+  if (start_pos >= m_bytes) { return udf_string{"", 0}; }
+  auto const end_pos = count < 0 ? m_bytes : std::min(byte_offset(pos + count), m_bytes);
+  return udf_string{data() + start_pos, end_pos - start_pos};
+}
+
+// utility for replace()
+__device__ void udf_string::shift_bytes(cudf::size_type start_pos,
+                                        cudf::size_type end_pos,
+                                        cudf::size_type nbytes)
+{
+  if (nbytes < m_bytes) {
+    // shift bytes to the left [...wxyz] -> [wxyzxyz]
+    auto src = end_pos;
+    auto tgt = start_pos;
+    while (tgt < nbytes) {
+      m_data[tgt++] = m_data[src++];
+    }
+  } else if (nbytes > m_bytes) {
+    // shift bytes to the right [abcd...] -> [abcabcd]
+    auto src = m_bytes;
+    auto tgt = nbytes;
+    while (src > end_pos) {
+      m_data[--tgt] = m_data[--src];
+    }
+  }
+}
+
+__device__ inline udf_string& udf_string::replace(cudf::size_type pos,
+                                                  cudf::size_type count,
+                                                  char const* str,
+                                                  cudf::size_type in_bytes)
+{
+  if (pos < 0 || in_bytes < 0) { return *this; }
+  auto const start_pos = byte_offset(pos);
+  if (start_pos > m_bytes) { return *this; }
+  auto const end_pos = count < 0 ? m_bytes : std::min(byte_offset(pos + count), m_bytes);
+
+  // compute new size
+  auto const nbytes = m_bytes + in_bytes - (end_pos - start_pos);
+  if (nbytes > m_capacity) { reallocate(2 * nbytes); }
+
+  // move bytes -- make room for replacement
+  shift_bytes(start_pos + in_bytes, end_pos, nbytes);
+
+  // insert the replacement
+  memcpy(m_data + start_pos, str, in_bytes);
+
+  m_bytes         = nbytes;
+  m_data[m_bytes] = '\0';
+  return *this;
+}
+
+__device__ inline udf_string& udf_string::replace(cudf::size_type pos,
+                                                  cudf::size_type count,
+                                                  char const* str)
+{
+  return replace(pos, count, str, detail::bytes_in_null_terminated_string(str));
+}
+
+__device__ inline udf_string& udf_string::replace(cudf::size_type pos,
+                                                  cudf::size_type count,
+                                                  cudf::string_view in)
+{
+  return replace(pos, count, in.data(), in.size_bytes());
+}
+
+__device__ inline udf_string& udf_string::replace(cudf::size_type pos,
+                                                  cudf::size_type count,
+                                                  cudf::size_type chr_count,
+                                                  cudf::char_utf8 chr)
+{
+  auto d_str = udf_string(chr_count, chr);
+  return replace(pos, count, d_str);
+}
+
+__device__ udf_string& udf_string::erase(cudf::size_type pos, cudf::size_type count)
+{
+  return replace(pos, count, nullptr, 0);
+}
+
+__device__ inline cudf::size_type udf_string::char_offset(cudf::size_type byte_pos) const
+{
+  return cudf::strings::detail::characters_in_string(data(), byte_pos);
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.hpp b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.hpp
new file mode 100644
index 0000000..59cb35d
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/include/cudf/strings/udf/udf_string.hpp
@@ -0,0 +1,550 @@
+/*
+ * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#pragma once
+
+#include <cudf/strings/string_view.hpp>
+
+#include <cuda_runtime.h>
+
+// This header contains all class and function declarations so that it
+// can be included in a .cpp file which only has declaration requirements
+// (i.e. sizeof, conditionally-comparable, explicit conversions, etc).
+// The definitions are coded in udf_string.cuh which is to be included
+// in .cu files that use this class in kernel calls.
+
+namespace cudf {
+namespace strings {
+namespace udf {
+
+/**
+ * @brief Device string class for use with user-defined functions
+ *
+ * This class manages a device buffer of UTF-8 encoded characters
+ * for string manipulation in a device kernel.
+ *
+ * Its methods and behavior are modelled after std::string but
+ * with special consideration for UTF-8 encoded strings and for
+ * use within a cuDF UDF.
+ */
+class udf_string {
+ public:
+  /**
+   * @brief Represents unknown character position or length
+   */
+  static constexpr cudf::size_type npos = static_cast<cudf::size_type>(-1);
+
+  /**
+   * @brief Cast to cudf::string_view operator
+   */
+  __device__ operator cudf::string_view() const { return cudf::string_view(m_data, m_bytes); }
+
+  /**
+   * @brief Create an empty string.
+   */
+  udf_string() = default;
+
+  /**
+   * @brief Create a string using existing device memory
+   *
+   * The given memory is copied into the instance returned.
+   *
+   * @param data Device pointer to UTF-8 encoded string
+   * @param bytes Number of bytes in `data`
+   */
+  __device__ udf_string(char const* data, cudf::size_type bytes);
+
+  /**
+   * @brief Create a string object from a null-terminated character array
+   *
+   * The given memory is copied into the instance returned.
+   *
+   * @param data Device pointer to UTF-8 encoded null-terminated
+   *             character array.
+   */
+  __device__ udf_string(char const* data);
+
+  /**
+   * @brief Create a string object from a cudf::string_view
+   *
+   * The input string data is copied into the instance returned.
+   *
+   * @param str String to copy
+   */
+  __device__ udf_string(cudf::string_view str);
+
+  /**
+   * @brief Create a string object with `count` copies of character `chr`
+   *
+   * @param count Number of times to copy `chr`
+   * @param chr Character from which to create the string
+   */
+  __device__ udf_string(cudf::size_type count, cudf::char_utf8 chr);
+
+  /**
+   * @brief Create a string object from another instance
+   *
+   * The string data is copied from the `src` into the instance returned.
+   *
+   * @param src String to copy
+   */
+  __device__ udf_string(udf_string const& src);
+
+  /**
+   * @brief Move a string object from an rvalue reference
+   *
+   * The string data is moved from `src` into the instance returned.
+   * The `src` will have no content.
+   *
+   * @param src String to copy
+   */
+  __device__ udf_string(udf_string&& src) noexcept;
+
+  __device__ ~udf_string();
+
+  __device__ udf_string& operator=(udf_string const&);
+  __device__ udf_string& operator=(udf_string&&) noexcept;
+  __device__ udf_string& operator=(cudf::string_view const);
+  __device__ udf_string& operator=(char const*);
+
+  /**
+   * @brief Return the number of bytes in this string
+   */
+  __device__ cudf::size_type size_bytes() const noexcept;
+
+  /**
+   * @brief Return the number of characters in this string
+   */
+  __device__ cudf::size_type length() const noexcept;
+
+  /**
+   * @brief Return the maximum number of bytes a udf_string can hold
+   */
+  __device__ constexpr cudf::size_type max_size() const noexcept;
+
+  /**
+   * @brief Return the internal pointer to the character array for this object
+   */
+  __device__ char* data() noexcept;
+  __device__ char const* data() const noexcept;
+
+  /**
+   * @brief Returns true if there are no characters in this string
+   */
+  __device__ bool is_empty() const noexcept;
+
+  /**
+   * @brief Returns an iterator that can be used to navigate through
+   *        the UTF-8 characters in this string
+   *
+   * This returns a `cudf::string_view::const_iterator` which is read-only.
+   */
+  __device__ cudf::string_view::const_iterator begin() const noexcept;
+  __device__ cudf::string_view::const_iterator end() const noexcept;
+
+  /**
+   * @brief Returns the character at the specified position
+   *
+   * This will return 0 if `pos >= length()`.
+   *
+   * @param pos Index position of character to return
+   * @return Character at position `pos`
+   */
+  __device__ cudf::char_utf8 at(cudf::size_type pos) const;
+
+  /**
+   * @brief Returns the character at the specified index
+   *
+   * This will return 0 if `pos >= length()`.
+   * Note this is read-only. Use replace() to modify a character.
+   *
+   * @param pos Index position of character to return
+   * @return Character at position `pos`
+   */
+  __device__ cudf::char_utf8 operator[](cudf::size_type pos) const;
+
+  /**
+   * @brief Return the byte offset for a given character position
+   *
+   * The byte offset for the character at `pos` such that
+   * `data() + byte_offset(pos)` points to the memory location
+   * the character at position `pos`.
+   *
+   * The behavior is undefined if `pos < 0 or pos >= length()`
+   *
+   * @param pos Index position of character to return byte offset.
+   * @return Byte offset for character at `pos`
+   */
+  __device__ cudf::size_type byte_offset(cudf::size_type pos) const;
+
+  /**
+   * @brief Comparing target string with this string
+   *
+   * @param str Target string to compare with this string
+   * @return 0  If they compare equal
+   *         <0 Either the value of the first character of this string that does
+   *            not match is ordered before the corresponding character in `str`,
+   *            or all compared characters match but the `str` string is shorter.
+   *         >0 Either the value of the first character of this string that does
+   *            not match is ordered after the corresponding character in `str`,
+   *            or all compared characters match but the `str` string is longer.
+   */
+  __device__ int compare(cudf::string_view str) const noexcept;
+
+  /**
+   * @brief Comparing target character array with this string
+   *
+   * @param str Target array of UTF-8 characters.
+   * @param bytes Number of bytes in `str`.
+   * @return 0  If they compare equal
+   *         <0 Either the value of the first character of this string that does
+   *            not match is ordered before the corresponding character in `str`,
+   *            or all compared characters match but `bytes < size_bytes()`.
+   *         >0 Either the value of the first character of this string that does
+   *            not match is ordered after the corresponding character in `str`,
+   *            or all compared characters match but `bytes > size_bytes()`.
+   */
+  __device__ int compare(char const* str, cudf::size_type bytes) const;
+
+  /**
+   * @brief Returns true if `rhs` matches this string exactly
+   */
+  __device__ bool operator==(cudf::string_view rhs) const noexcept;
+
+  /**
+   * @brief Returns true if `rhs` does not match this string
+   */
+  __device__ bool operator!=(cudf::string_view rhs) const noexcept;
+
+  /**
+   * @brief Returns true if this string is ordered before `rhs`
+   */
+  __device__ bool operator<(cudf::string_view rhs) const noexcept;
+
+  /**
+   * @brief Returns true if `rhs` is ordered before this string
+   */
+  __device__ bool operator>(cudf::string_view rhs) const noexcept;
+
+  /**
+   * @brief Returns true if this string matches or is ordered before `rhs`
+   */
+  __device__ bool operator<=(cudf::string_view rhs) const noexcept;
+
+  /**
+   * @brief Returns true if `rhs` matches or is ordered before this string
+   */
+  __device__ bool operator>=(cudf::string_view rhs) const noexcept;
+
+  /**
+   * @brief Remove all bytes from this string
+   *
+   * All pointers, references, and iterators are invalidated.
+   */
+  __device__ void clear() noexcept;
+
+  /**
+   * @brief Resizes string to contain `count` bytes
+   *
+   * If `count > size_bytes()` then zero-padding is added.
+   * If `count < size_bytes()` then the string is truncated to size `count`.
+   *
+   * All pointers, references, and iterators may be invalidated.
+   *
+   * The behavior is undefined if `count > max_size()`
+   *
+   * @param count Size in bytes of this string.
+   */
+  __device__ void resize(cudf::size_type count);
+
+  /**
+   * @brief Reserve `count` bytes in this string
+   *
+   * If `count > capacity()`, new memory is allocated and `capacity()` will
+   * be greater than or equal to `count`.
+   * There is no effect if `count <= capacity()`.
+   *
+   * @param count Total number of bytes to reserve for this string
+   */
+  __device__ void reserve(cudf::size_type count);
+
+  /**
+   * @brief Returns the number of bytes that the string has allocated
+   */
+  __device__ cudf::size_type capacity() const noexcept;
+
+  /**
+   * @brief Reduces internal allocation to just `size_bytes()`
+   *
+   * All pointers, references, and iterators may be invalidated.
+   */
+  __device__ void shrink_to_fit();
+
+  /**
+   * @brief Moves the contents of `str` into this string instance
+   *
+   * On return, the `str` will have no contents.
+   *
+   * @param str String to move
+   * @return This string with new contents
+   */
+  __device__ udf_string& assign(udf_string&& str) noexcept;
+
+  /**
+   * @brief Replaces the contents of this string with contents of `str`
+   *
+   * @param str String to copy
+   * @return This string with new contents
+   */
+  __device__ udf_string& assign(cudf::string_view str);
+
+  /**
+   * @brief Replaces the contents of this string with contents of `str`
+   *
+   * @param str Null-terminated UTF-8 character array
+   * @return This string with new contents
+   */
+  __device__ udf_string& assign(char const* str);
+
+  /**
+   * @brief Replaces the contents of this string with contents of `str`
+   *
+   * @param str UTF-8 character array
+   * @param bytes Number of bytes to copy from `str`
+   * @return This string with new contents
+   */
+  __device__ udf_string& assign(char const* str, cudf::size_type bytes);
+
+  /**
+   * @brief Append a string to the end of this string
+   *
+   * @param str String to append
+   * @return This string with the appended argument
+   */
+  __device__ udf_string& operator+=(cudf::string_view str);
+
+  /**
+   * @brief Append a character to the end of this string
+   *
+   * @param str Character to append
+   * @return This string with the appended argument
+   */
+  __device__ udf_string& operator+=(cudf::char_utf8 chr);
+
+  /**
+   * @brief Append a null-terminated device memory character array
+   * to the end of this string
+   *
+   * @param str String to append
+   * @return This string with the appended argument
+   */
+  __device__ udf_string& operator+=(char const* str);
+
+  /**
+   * @brief Append a null-terminated character array to the end of this string
+   *
+   * @param str String to append
+   * @return This string with the appended argument
+   */
+  __device__ udf_string& append(char const* str);
+
+  /**
+   * @brief Append a character array to the end of this string
+   *
+   * @param str Character array to append
+   * @param bytes Number of bytes from `str` to append.
+   * @return This string with the appended argument
+   */
+  __device__ udf_string& append(char const* str, cudf::size_type bytes);
+
+  /**
+   * @brief Append a string to the end of this string
+   *
+   * @param str String to append
+   * @return This string with the appended argument
+   */
+  __device__ udf_string& append(cudf::string_view str);
+
+  /**
+   * @brief Append a character to the end of this string
+   * a specified number of times.
+   *
+   * @param chr Character to append
+   * @param count Number of times to append `chr`
+   * @return This string with the append character(s)
+   */
+  __device__ udf_string& append(cudf::char_utf8 chr, cudf::size_type count = 1);
+
+  /**
+   * @brief Insert a string into the character position specified
+   *
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * @param pos Character position to begin insert
+   * @param str String to insert into this one
+   * @return This string with the inserted argument
+   */
+  __device__ udf_string& insert(cudf::size_type pos, cudf::string_view str);
+
+  /**
+   * @brief Insert a null-terminated character array into the character position specified
+   *
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * @param pos Character position to begin insert
+   * @param data Null-terminated character array to insert
+   * @return This string with the inserted argument
+   */
+  __device__ udf_string& insert(cudf::size_type pos, char const* data);
+
+  /**
+   * @brief Insert a character array into the character position specified
+   *
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * @param pos Character position to begin insert
+   * @param data Character array to insert
+   * @param bytes Number of bytes from `data` to insert
+   * @return This string with the inserted argument
+   */
+  __device__ udf_string& insert(cudf::size_type pos, char const* data, cudf::size_type bytes);
+
+  /**
+   * @brief Insert a character one or more times into the character position specified
+   *
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * @param pos Character position to begin insert
+   * @param count Number of times to insert `chr`
+   * @param chr Character to insert
+   * @return This string with the inserted argument
+   */
+  __device__ udf_string& insert(cudf::size_type pos, cudf::size_type count, cudf::char_utf8 chr);
+
+  /**
+   * @brief Returns a substring of this string
+   *
+   * An empty string is returned if `pos < 0 or pos >= length()`.
+   *
+   * @param pos Character position to start the substring
+   * @param count Number of characters for the substring;
+   *              This can be greater than the number of available characters.
+   *              Default npos returns characters in range `[pos, length())`.
+   * @return New string with the specified characters
+   */
+  __device__ udf_string substr(cudf::size_type pos, cudf::size_type count = npos) const;
+
+  /**
+   * @brief Replace a range of characters with a given string
+   *
+   * Replaces characters in range `[pos, pos + count]` with `str`.
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * If `count==0` then `str` is inserted starting at `pos`.
+   * If `count==npos` then the replacement range is `[pos,length())`.
+   *
+   * @param pos Position of first character to replace
+   * @param count Number of characters to replace
+   * @param str String to replace the given range
+   * @return This string modified with the replacement
+   */
+  __device__ udf_string& replace(cudf::size_type pos, cudf::size_type count, cudf::string_view str);
+
+  /**
+   * @brief Replace a range of characters with a null-terminated character array
+   *
+   * Replaces characters in range `[pos, pos + count)` with `data`.
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * If `count==0` then `data` is inserted starting at `pos`.
+   * If `count==npos` then the replacement range is `[pos,length())`.
+   *
+   * @param pos Position of first character to replace
+   * @param count Number of characters to replace
+   * @param data Null-terminated character array to replace the given range
+   * @return This string modified with the replacement
+   */
+  __device__ udf_string& replace(cudf::size_type pos, cudf::size_type count, char const* data);
+
+  /**
+   * @brief Replace a range of characters with a given character array
+   *
+   * Replaces characters in range `[pos, pos + count)` with `[data, data + bytes)`.
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * If `count==0` then `data` is inserted starting at `pos`.
+   * If `count==npos` then the replacement range is `[pos,length())`.
+   *
+   * @param pos Position of first character to replace
+   * @param count Number of characters to replace
+   * @param data String to replace the given range
+   * @param bytes Number of bytes from data to use for replacement
+   * @return This string modified with the replacement
+   */
+  __device__ udf_string& replace(cudf::size_type pos,
+                                 cudf::size_type count,
+                                 char const* data,
+                                 cudf::size_type bytes);
+
+  /**
+   * @brief Replace a range of characters with a character one or more times
+   *
+   * Replaces characters in range `[pos, pos + count)` with `chr` `chr_count` times.
+   * There is no effect if `pos < 0 or pos > length()`.
+   *
+   * If `count==0` then `chr` is inserted starting at `pos`.
+   * If `count==npos` then the replacement range is `[pos,length())`.
+   *
+   * @param pos Position of first character to replace
+   * @param count Number of characters to replace
+   * @param chr_count Number of times `chr` will repeated
+   * @param chr Character to use for replacement
+   * @return This string modified with the replacement
+   */
+  __device__ udf_string& replace(cudf::size_type pos,
+                                 cudf::size_type count,
+                                 cudf::size_type chr_count,
+                                 cudf::char_utf8 chr);
+
+  /**
+   * @brief Removes specified characters from this string
+   *
+   * Removes `min(count, length() - pos)` characters starting at `pos`.
+   * There is no effect if `pos < 0 or pos >= length()`.
+   *
+   * @param pos Character position to begin insert
+   * @param count Number of characters to remove starting at `pos`
+   * @return This string with remove characters
+   */
+  __device__ udf_string& erase(cudf::size_type pos, cudf::size_type count = npos);
+
+ private:
+  char* m_data{};
+  cudf::size_type m_bytes{};
+  cudf::size_type m_capacity{};
+
+  // utilities
+  __device__ char* allocate(cudf::size_type bytes);
+  __device__ void deallocate(char* data);
+  __device__ void reallocate(cudf::size_type bytes);
+  __device__ cudf::size_type char_offset(cudf::size_type byte_pos) const;
+  __device__ void shift_bytes(cudf::size_type start_pos,
+                              cudf::size_type end_pos,
+                              cudf::size_type nbytes);
+};
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf/udf_cpp/strings/src/strings/udf/udf_apis.cu b/python/cudf/udf_cpp/strings/src/strings/udf/udf_apis.cu
new file mode 100644
index 0000000..bedaa8e
--- /dev/null
+++ b/python/cudf/udf_cpp/strings/src/strings/udf/udf_apis.cu
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cudf/strings/udf/udf_apis.hpp>
+#include <cudf/strings/udf/udf_string.cuh>
+
+#include <cudf/column/column_factories.hpp>
+#include <cudf/strings/detail/utilities.hpp>
+#include <cudf/strings/string_view.cuh>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace strings {
+namespace udf {
+namespace detail {
+namespace {
+
+/**
+ * @brief Functor wraps string_view objects around udf_string objects
+ *
+ * No string data is copied.
+ */
+struct udf_string_to_string_view_transform_fn {
+  __device__ cudf::string_view operator()(cudf::strings::udf::udf_string const& dstr)
+  {
+    return dstr.data() == nullptr ? cudf::string_view{}
+                                  : cudf::string_view{dstr.data(), dstr.size_bytes()};
+  }
+};
+
+}  // namespace
+
+/**
+ * @copydoc to_string_view_array
+ *
+ * @param stream CUDA stream used for allocating/copying device memory and launching kernels
+ */
+std::unique_ptr<rmm::device_buffer> to_string_view_array(cudf::column_view const input,
+                                                         rmm::cuda_stream_view stream)
+{
+  return std::make_unique<rmm::device_buffer>(
+    std::move(cudf::strings::detail::create_string_vector_from_column(
+                cudf::strings_column_view(input), stream, rmm::mr::get_current_device_resource())
+                .release()));
+}
+
+/**
+ * @copydoc column_from_udf_string_array
+ *
+ * @param stream CUDA stream used for allocating/copying device memory and launching kernels
+ */
+std::unique_ptr<cudf::column> column_from_udf_string_array(udf_string* d_strings,
+                                                           cudf::size_type size,
+                                                           rmm::cuda_stream_view stream)
+{
+  // create string_views of the udf_strings
+  auto indices = rmm::device_uvector<cudf::string_view>(size, stream);
+  thrust::transform(rmm::exec_policy(stream),
+                    d_strings,
+                    d_strings + size,
+                    indices.data(),
+                    udf_string_to_string_view_transform_fn{});
+
+  return cudf::make_strings_column(indices, cudf::string_view(nullptr, 0), stream);
+}
+
+/**
+ * @copydoc free_udf_string_array
+ *
+ * @param stream CUDA stream used for allocating/copying device memory and launching kernels
+ */
+void free_udf_string_array(cudf::strings::udf::udf_string* d_strings,
+                           cudf::size_type size,
+                           rmm::cuda_stream_view stream)
+{
+  thrust::for_each_n(rmm::exec_policy(stream),
+                     thrust::make_counting_iterator(0),
+                     size,
+                     [d_strings] __device__(auto idx) { d_strings[idx].clear(); });
+}
+
+}  // namespace detail
+
+// external APIs
+
+std::unique_ptr<rmm::device_buffer> to_string_view_array(cudf::column_view const input)
+{
+  return detail::to_string_view_array(input, cudf::get_default_stream());
+}
+
+std::unique_ptr<cudf::column> column_from_udf_string_array(udf_string* d_strings,
+                                                           cudf::size_type size)
+{
+  return detail::column_from_udf_string_array(d_strings, size, cudf::get_default_stream());
+}
+
+void free_udf_string_array(udf_string* d_strings, cudf::size_type size)
+{
+  detail::free_udf_string_array(d_strings, size, cudf::get_default_stream());
+}
+
+}  // namespace udf
+}  // namespace strings
+}  // namespace cudf
diff --git a/python/cudf_kafka/cudf_kafka/__init__.py b/python/cudf_kafka/cudf_kafka/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf_kafka/cudf_kafka/_lib/__init__.pxd b/python/cudf_kafka/cudf_kafka/_lib/__init__.pxd
new file mode 100644
index 0000000..e69de29
diff --git a/python/cudf_kafka/cudf_kafka/_lib/kafka.pxd b/python/cudf_kafka/cudf_kafka/_lib/kafka.pxd
new file mode 100644
index 0000000..ca729c6
--- /dev/null
+++ b/python/cudf_kafka/cudf_kafka/_lib/kafka.pxd
@@ -0,0 +1,84 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, int64_t
+from libcpp cimport bool
+from libcpp.map cimport map
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.vector cimport vector
+
+from cudf._lib.cpp.io.datasource cimport datasource
+from cudf._lib.io.datasource cimport Datasource
+
+
+cdef extern from "kafka_callback.hpp" \
+        namespace "cudf::io::external::kafka" nogil:
+    ctypedef object (*python_callable_type)()
+
+
+cdef extern from "kafka_consumer.hpp" \
+        namespace "cudf::io::external::kafka" nogil:
+
+    cpdef cppclass kafka_consumer:
+
+        kafka_consumer(map[string, string] configs,
+                       python_callable_type python_callable) except +
+
+        kafka_consumer(map[string, string] configs,
+                       python_callable_type python_callable,
+                       string topic_name,
+                       int32_t partition,
+                       int64_t start_offset,
+                       int64_t end_offset,
+                       int32_t batch_timeout,
+                       string delimiter) except +
+
+        bool assign(vector[string] topics, vector[int32_t] partitions) except +
+
+        void commit_offset(string topic,
+                           int32_t partition,
+                           int64_t offset) except +
+
+        int64_t get_committed_offset(string topic,
+                                     int32_t partition) except +
+
+        map[string, vector[int32_t]] list_topics(string topic) except +
+
+        map[string, int64_t] get_watermark_offset(string topic,
+                                                  int32_t partition,
+                                                  int32_t timeout,
+                                                  bool cached) except +
+
+        void unsubscribe() except +
+
+        void close(int32_t timeout) except +
+
+cdef class KafkaDatasource(Datasource):
+
+    cdef unique_ptr[datasource] c_datasource
+    cdef string topic
+    cdef int32_t partition
+    cdef int64_t start_offset
+    cdef int64_t end_offset
+    cdef int32_t batch_timeout
+    cdef string delimiter
+
+    cdef datasource* get_datasource(self) nogil
+
+    cpdef void commit_offset(self,
+                             string topic,
+                             int32_t partition,
+                             int64_t offset)
+
+    cpdef int64_t get_committed_offset(self, string topic, int32_t partition)
+
+    cpdef map[string, vector[int32_t]] list_topics(self, string tp) except *
+
+    cpdef map[string, int64_t] get_watermark_offset(self, string topic,
+                                                    int32_t partition,
+                                                    int32_t timeout,
+                                                    bool cached)
+
+    cpdef void unsubscribe(self)
+
+    cpdef void close(self, int32_t timeout)
diff --git a/python/cudf_kafka/cudf_kafka/_lib/kafka.pyx b/python/cudf_kafka/cudf_kafka/_lib/kafka.pyx
new file mode 100644
index 0000000..4d73247
--- /dev/null
+++ b/python/cudf_kafka/cudf_kafka/_lib/kafka.pyx
@@ -0,0 +1,102 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from libc.stdint cimport int32_t, int64_t
+from libcpp cimport bool, nullptr
+from libcpp.map cimport map
+from libcpp.memory cimport unique_ptr
+from libcpp.string cimport string
+from libcpp.utility cimport move
+
+from cudf._lib.cpp.io.datasource cimport datasource
+from cudf._lib.cpp.libcpp.memory cimport make_unique
+
+from cudf_kafka._lib.kafka cimport kafka_consumer
+
+
+# To avoid including <python.h> in libcudf_kafka
+# we introduce this wrapper in Cython
+cdef map[string, string] oauth_callback_wrapper(void *ctx):
+    resp = (<object>(ctx))()
+    cdef map[string, string] c_resp
+    c_resp[str.encode("token")] = str.encode(resp["token"])
+    c_resp[str.encode("token_expiration_in_epoch")] \
+        = str(resp["token_expiration_in_epoch"]).encode()
+    return c_resp
+
+
+cdef class KafkaDatasource(Datasource):
+
+    def __cinit__(self,
+                  object kafka_configs,
+                  string topic=b"",
+                  int32_t partition=-1,
+                  int64_t start_offset=0,
+                  int64_t end_offset=0,
+                  int32_t batch_timeout=10000,
+                  string delimiter=b"",):
+
+        cdef map[string, string] configs
+        cdef void* python_callable = nullptr
+        cdef map[string, string] (*python_callable_wrapper)(void *)
+
+        for key in kafka_configs:
+            if key == 'oauth_cb':
+                if callable(kafka_configs[key]):
+                    python_callable = <void *>kafka_configs[key]
+                    python_callable_wrapper = &oauth_callback_wrapper
+                else:
+                    raise TypeError("'oauth_cb' configuration must \
+                                      be a Python callable object")
+            else:
+                configs[key.encode()] = kafka_configs[key].encode()
+
+        if topic != b"" and partition != -1:
+            self.c_datasource = <unique_ptr[datasource]> \
+                move(make_unique[kafka_consumer](configs,
+                                                 python_callable,
+                                                 python_callable_wrapper,
+                                                 topic,
+                                                 partition,
+                                                 start_offset,
+                                                 end_offset,
+                                                 batch_timeout,
+                                                 delimiter))
+        else:
+            self.c_datasource = <unique_ptr[datasource]> \
+                move(make_unique[kafka_consumer](configs,
+                                                 python_callable,
+                                                 python_callable_wrapper))
+
+    cdef datasource* get_datasource(self) nogil:
+        return <datasource *> self.c_datasource.get()
+
+    cpdef void commit_offset(self,
+                             string topic,
+                             int32_t partition,
+                             int64_t offset):
+        (<kafka_consumer *> self.c_datasource.get()).commit_offset(
+            topic, partition, offset)
+
+    cpdef int64_t get_committed_offset(self,
+                                       string topic,
+                                       int32_t partition):
+        return (<kafka_consumer *> self.c_datasource.get()). \
+            get_committed_offset(topic, partition)
+
+    cpdef map[string, vector[int32_t]] list_topics(self,
+                                                   string topic) except *:
+        return (<kafka_consumer *> self.c_datasource.get()). \
+            list_topics(topic)
+
+    cpdef map[string, int64_t] get_watermark_offset(self, string topic,
+                                                    int32_t partition,
+                                                    int32_t timeout,
+                                                    bool cached):
+        return (<kafka_consumer *> self.c_datasource.get()). \
+            get_watermark_offset(topic, partition, timeout, cached)
+
+    cpdef void unsubscribe(self):
+        (<kafka_consumer *> self.c_datasource.get()).unsubscribe()
+
+    cpdef void close(self, int32_t timeout):
+        (<kafka_consumer *> self.c_datasource.get()).close(timeout)
diff --git a/python/cudf_kafka/pyproject.toml b/python/cudf_kafka/pyproject.toml
new file mode 100644
index 0000000..386cdc3
--- /dev/null
+++ b/python/cudf_kafka/pyproject.toml
@@ -0,0 +1,85 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+[build-system]
+
+requires = [
+    "cython>=3.0.0",
+    "numpy>=1.21,<1.25",
+    "pyarrow==12.0.1.*",
+    "setuptools",
+    "wheel",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project]
+name = "cudf_kafka"
+version = "23.10.00"
+description = "cuDF Kafka Datasource"
+readme = { file = "README.md", content-type = "text/markdown" }
+authors = [
+    { name = "NVIDIA Corporation" },
+]
+license = { text = "Apache 2.0" }
+requires-python = ">=3.9"
+dependencies = [
+    "cudf==23.10.*",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project.optional-dependencies]
+test = [
+    "pytest",
+    "pytest-cov",
+    "pytest-xdist",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project.urls]
+Homepage = "https://github.com/rapidsai/cudf"
+Documentation = "https://docs.rapids.ai/api/cudf/stable/"
+
+[tool.setuptools]
+license-files = ["LICENSE"]
+
+[tool.isort]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+order_by_type = true
+known_dask = [
+    "dask",
+    "distributed",
+    "dask_cuda",
+    "streamz",
+]
+known_rapids = [
+    "rmm",
+    "cudf",
+    "dask_cudf",
+]
+known_first_party = [
+    "cudf_kafka",
+]
+default_section = "THIRDPARTY"
+sections = [
+    "FUTURE",
+    "STDLIB",
+    "THIRDPARTY",
+    "DASK",
+    "RAPIDS",
+    "FIRSTPARTY",
+    "LOCALFOLDER",
+]
+skip = [
+    "thirdparty",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "__init__.py",
+]
diff --git a/python/cudf_kafka/setup.py b/python/cudf_kafka/setup.py
new file mode 100644
index 0000000..d955d95
--- /dev/null
+++ b/python/cudf_kafka/setup.py
@@ -0,0 +1,96 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+import os
+import shutil
+import sysconfig
+from distutils.sysconfig import get_python_lib
+
+import numpy as np
+import pyarrow as pa
+from Cython.Build import cythonize
+from setuptools import find_packages, setup
+from setuptools.extension import Extension
+
+cython_files = ["cudf_kafka/_lib/*.pyx"]
+
+CUDA_HOME = os.environ.get("CUDA_HOME", False)
+if not CUDA_HOME:
+    path_to_cuda_gdb = shutil.which("cuda-gdb")
+    if path_to_cuda_gdb is None:
+        raise OSError(
+            "Could not locate CUDA. "
+            "Please set the environment variable "
+            "CUDA_HOME to the path to the CUDA installation "
+            "and try again."
+        )
+    CUDA_HOME = os.path.dirname(os.path.dirname(path_to_cuda_gdb))
+
+if not os.path.isdir(CUDA_HOME):
+    raise OSError(f"Invalid CUDA_HOME: directory does not exist: {CUDA_HOME}")
+
+cuda_include_dir = os.path.join(CUDA_HOME, "include")
+
+CUDF_ROOT = os.environ.get(
+    "CUDF_ROOT",
+    os.path.abspath(
+        os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), "../../cpp/build/"
+        )
+    ),
+)
+CUDF_KAFKA_ROOT = os.environ.get(
+    "CUDF_KAFKA_ROOT", "../../cpp/libcudf_kafka/build"
+)
+
+try:
+    nthreads = int(os.environ.get("PARALLEL_LEVEL", "0") or "0")
+except Exception:
+    nthreads = 0
+
+extensions = [
+    Extension(
+        "*",
+        sources=cython_files,
+        include_dirs=[
+            os.path.abspath(os.path.join(CUDF_ROOT, "../include/cudf")),
+            os.path.abspath(os.path.join(CUDF_ROOT, "../include")),
+            os.path.abspath(
+                os.path.join(CUDF_ROOT, "../libcudf_kafka/include/cudf_kafka")
+            ),
+            os.path.join(CUDF_ROOT, "include"),
+            os.path.join(CUDF_ROOT, "_deps/libcudacxx-src/include"),
+            os.path.join(
+                os.path.dirname(sysconfig.get_path("include")),
+                "rapids/libcudacxx",
+            ),
+            os.path.dirname(sysconfig.get_path("include")),
+            np.get_include(),
+            pa.get_include(),
+            cuda_include_dir,
+        ],
+        library_dirs=(
+            [
+                get_python_lib(),
+                os.path.join(os.sys.prefix, "lib"),
+                CUDF_KAFKA_ROOT,
+            ]
+        ),
+        libraries=["cudf", "cudf_kafka"],
+        language="c++",
+        extra_compile_args=["-std=c++17", "-DFMT_HEADER_ONLY=1"],
+    )
+]
+
+packages = find_packages(include=["cudf_kafka*"])
+setup(
+    # Include the separately-compiled shared library
+    ext_modules=cythonize(
+        extensions,
+        nthreads=nthreads,
+        compiler_directives=dict(
+            profile=False, language_level=3, embedsignature=True
+        ),
+    ),
+    packages=packages,
+    package_data={key: ["*.pxd"] for key in packages},
+    zip_safe=False,
+)
diff --git a/python/custreamz/.coveragerc b/python/custreamz/.coveragerc
new file mode 100644
index 0000000..26bed78
--- /dev/null
+++ b/python/custreamz/.coveragerc
@@ -0,0 +1,3 @@
+# Configuration file for Python coverage tests
+[run]
+source = custreamz
diff --git a/python/custreamz/LICENSE b/python/custreamz/LICENSE
new file mode 120000
index 0000000..30cff74
--- /dev/null
+++ b/python/custreamz/LICENSE
@@ -0,0 +1 @@
+../../LICENSE
\ No newline at end of file
diff --git a/python/custreamz/README.md b/python/custreamz/README.md
new file mode 100644
index 0000000..a1d9842
--- /dev/null
+++ b/python/custreamz/README.md
@@ -0,0 +1,69 @@
+# <div align="left"><img src="../../img/rapids_logo.png" width="90px"/>&nbsp;custreamz - GPU Accelerated Streaming</div>
+
+Built as an extension to [python streamz](https://github.com/python-streamz/streamz), cuStreamz provides GPU accelerated abstractions for streaming data. CuStreamz can be used along side python streamz or as a standalone library for ingesting streaming data to cudf dataframes.
+
+The most common use for cuStreamz is accelerated data ingestion to a cudf dataframe. CuStreamz currently supports ingestion from Apache Kafka in the following message formats; Avro, CSV, JSON, Parquet, and ORC.
+
+For example, the following snippet consumes CSV data from a Kafka topic named `custreamz_tips` and generates a cudf dataframe.
+
+Users can visit [Apache Kafka Quickstart](https://kafka.apache.org/quickstart) to learn how to install, create `custreamz_tips` topic, and insert the [tips](https://github.com/plotly/datasets/raw/master/tips.csv) data into Kafka.
+
+
+```python
+from custreamz import kafka
+
+# Full list of configurations can be found at: https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
+kafka_configs = {
+	"metadata.broker.list": "localhost:9092",
+	"group.id": "custreamz-client",
+}
+
+# Create a reusable Kafka Consumer client; "datasource"
+consumer = kafka.Consumer(kafka_configs)
+
+# Read 10,000 messages from `custreamz_tips` topic in CSV format.
+tips_df = consumer.read_gdf(topic="custreamz_tips",
+                        partition=0,
+                        start=0,
+                        end=10000,
+                        message_format="CSV")
+
+print(tips_df.head())
+tips_df['tip_percentage'] = tips_df['tip'] / tips_df['total_bill'] * 100
+
+# display average tip by dining party size
+print(tips_df.groupby('size').tip_percentage.mean())
+```
+
+A "hello world" of using cuStreamz with python streamz can be found [here](https://github.com/rapidsai-community/notebooks-contrib/blob/main/getting_started_materials/hello_worlds/hello_streamz.ipynb)
+
+A more detailed example of [parsing haproxy logs](https://github.com/rapidsai-community/notebooks-contrib/blob/branch-0.14/intermediate_notebooks/examples/custreamz/parsing_haproxy_logs.ipynb) is also available.
+
+## Quick Start
+
+Please see the [Demo Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you're running. This provides a ready to run Docker container with cuStreamz already installed.
+
+## Installation
+
+
+### CUDA/GPU requirements
+
+* CUDA 11.0+
+* NVIDIA driver 450.80.02+
+* Pascal architecture or better (Compute Capability >=6.0)
+
+### Conda
+
+cuStreamz is installed with conda ([miniconda](https://conda.io/miniconda.html), or the full [Anaconda distribution](https://www.anaconda.com/download)) from the `rapidsai` or `rapidsai-nightly` channel:
+
+Release:
+```bash
+conda install -c rapidsai cudf_kafka custreamz
+```
+
+Nightly:
+```bash
+conda install -c rapidsai-nightly cudf_kafka custreamz
+```
+
+See the [Get RAPIDS version picker](https://rapids.ai/start.html) for more OS and version info.
diff --git a/python/custreamz/custreamz/__init__.py b/python/custreamz/custreamz/__init__.py
new file mode 100644
index 0000000..52be76a
--- /dev/null
+++ b/python/custreamz/custreamz/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+
+from .kafka import Consumer
diff --git a/python/custreamz/custreamz/kafka.py b/python/custreamz/custreamz/kafka.py
new file mode 100644
index 0000000..0def0ba
--- /dev/null
+++ b/python/custreamz/custreamz/kafka.py
@@ -0,0 +1,291 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+import confluent_kafka as ck
+from cudf_kafka._lib.kafka import KafkaDatasource
+
+import cudf
+
+
+# Base class for anything class that needs to interact with Apache Kafka
+class CudfKafkaClient:
+    def __init__(self, kafka_configs):
+        """
+        Base object for any client that wants to interact with a Kafka broker.
+        This object creates the underlying KafkaDatasource connection which
+        is used to read data from Kafka and create cudf Dataframes.
+        This class should not be directly instantiated.
+
+        Parameters
+        ----------
+        kafka_configs : dict,
+            Dict of Key/Value pairs of librdkafka
+            configuration values. Full list of valid configuration
+            options can be found at
+            https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
+        """
+
+        self.kafka_configs = kafka_configs
+        self.kafka_meta_client = KafkaDatasource(kafka_configs)
+        self.ck_consumer = ck.Consumer(kafka_configs)
+
+    def list_topics(self, specific_topic=None):
+        """
+        List the topics associated with the underlying Kafka Broker connection.
+
+        Parameters
+        ----------
+        specific_topic : str,
+            If specified this is the only topic that metadata information will
+            be retrieved for. Otherwise metadata for all topics in the
+            broker will be retrieved.
+        """
+
+        return self.kafka_meta_client.list_topics(
+            b"" if specific_topic is None else specific_topic.encode()
+        )
+
+    def unsubscribe(self):
+        """
+        Stop all active consumption and remove consumer subscriptions
+        to topic/partition instances
+        """
+
+        self.kafka_meta_client.unsubscribe()
+
+    def close(self, timeout=10000):
+        """
+        Close the underlying socket connection to Kafka and
+        clean up system resources
+        """
+
+        self.kafka_meta_client.close(timeout)
+
+
+# Apache Kafka Consumer implementation
+class Consumer(CudfKafkaClient):
+    def __init__(self, kafka_configs):
+        """
+        Creates a KafkaConsumer object which allows for all valid Kafka
+        consumer type operations such as reading messages, committing
+        offsets, and retrieving the current consumption offsets.
+
+        Parameters
+        ----------
+        kafka_configs : dict,
+            Dict of Key/Value pairs of librdkafka
+            configuration values. Full list of valid configuration
+            options can be found at
+            https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
+        """
+
+        super().__init__(kafka_configs)
+
+    def read_gdf(
+        self,
+        topic=None,
+        partition=0,
+        lines=True,
+        start=0,
+        end=0,
+        batch_timeout=10000,
+        delimiter="\n",
+        message_format="json",
+    ):
+        r"""
+        Read messages from the underlying KafkaDatasource connection and create
+        a cudf Dataframe
+
+        Parameters
+        ----------
+        topic : str,
+            Name of the Kafka topic that the messages
+            should be read from
+        partition : int,
+            Partition number on the specified topic that
+            should be read from
+        lines : {{ True, False }}, default True,
+            Whether messages should be treated as individual lines
+        start : int, default 0,
+            The beginning offset that should be used when
+            reading a range of messages
+        end : int, default 0,
+            The last offset that will be read when
+            reading a range of messages
+        batch_timeout : int, default 10000,
+            Amount of time to wait on the
+            reading of the messages from Kafka in Milliseconds
+        delimiter : str, default "\n",
+            If lines=True this is the delimiter that
+            will be placed between all messages that are read from Kafka
+        message_format : {{ 'avro', 'csv', 'json', 'orc', 'parquet' }},
+        default 'json',
+            Format of the messages that will be read from Kafka.
+            This dictates which underlying cudf reader will be invoked the
+            create the Dataframe.
+
+        Returns
+        -------
+        DataFrame
+        """
+
+        if topic is None:
+            raise ValueError(
+                "ERROR: You must specify the topic "
+                "that you want to consume from"
+            )
+
+        kafka_datasource = KafkaDatasource(
+            self.kafka_configs,
+            topic.encode(),
+            partition,
+            start,
+            end,
+            batch_timeout,
+            delimiter.encode(),
+        )
+
+        cudf_readers = {
+            "json": cudf.io.read_json,
+            "csv": cudf.io.read_csv,
+            "orc": cudf.io.read_orc,
+            "avro": cudf.io.read_avro,
+            "parquet": cudf.io.read_parquet,
+        }
+
+        result = cudf_readers[message_format](
+            kafka_datasource, engine="cudf", lines=True
+        )
+
+        # Close up the cudf datasource instance
+        # TODO: Ideally the C++ destructor should handle the
+        # unsubscribe and closing the socket connection.
+        kafka_datasource.unsubscribe()
+        kafka_datasource.close(batch_timeout)
+
+        if result is not None:
+            if isinstance(result, cudf.DataFrame):
+                return result
+            else:
+                return cudf.DataFrame._from_data(result)
+        else:
+            # empty Dataframe
+            return cudf.DataFrame()
+
+    def committed(self, partitions, timeout=10000):
+        """
+        Retrieves the last successfully committed Kafka offset of the
+        underlying KafkaDatasource connection.
+
+        Parameters
+        ----------
+        partitions : list,
+            Topic/Partition instances that specify the TOPPAR
+            instances the offsets should be retrieved for
+        timeout : int, default 10000,
+            Max time to wait on the response from
+            the Kafka broker in milliseconds
+
+        Returns
+        -------
+        tuple
+            Tuple of ck.TopicPartition objects
+        """
+
+        toppars = [
+            ck.TopicPartition(
+                part.topic,
+                part.partition,
+                self.kafka_meta_client.get_committed_offset(
+                    part.topic.encode(), part.partition
+                ),
+            )
+            for part in partitions
+        ]
+
+        return toppars
+
+    def get_watermark_offsets(self, partition, timeout=10000, cached=False):
+        """
+        Retrieve the low and high watermark offsets from the Kafka consumer
+
+        Returns
+        -------
+        Tuple with a [low, high] value
+
+        Examples
+        --------
+        >>> from custream import kafka
+        >>> kafka_configs = {
+        ... "metadata.broker.list": "localhost:9092",
+        ... "enable.partition.eof": "true",
+        ... "group.id": "groupid",
+        ... "auto.offset.reset": "earliest",
+        ... "enable.auto.commit": "false"
+        ... }
+        >>> consumer = kafka.KafkaHandle(kafka_configs,
+        ... topics=["kafka-topic"], partitions=[0]))
+        >>> low, high = consumer.get_watermark_offsets("kafka-topic", 0)
+        """
+
+        offsets = ()
+
+        try:
+            offsets = self.kafka_meta_client.get_watermark_offset(
+                topic=partition.topic.encode(),
+                partition=partition.partition,
+                timeout=timeout,
+                cached=cached,
+            )
+        except RuntimeError:
+            raise RuntimeError("Unable to connect to Kafka broker")
+
+        if len(offsets) != 2:
+            raise RuntimeError(
+                f"Multiple watermark offsets encountered. "
+                f"Only 2 were expected and {len(offsets)} encountered"
+            )
+
+        if offsets[b"low"] < 0:
+            offsets[b"low"] = 0
+
+        if offsets[b"high"] < 0:
+            offsets[b"high"] = 0
+
+        return offsets[b"low"], offsets[b"high"]
+
+    def commit(self, offsets=None, asynchronous=True):
+        """
+        Takes a list of ck.TopicPartition objects and commits their
+        offset values to the KafkaDatasource connection
+
+        Parameters
+        ----------
+        offsets : list,
+            ck.TopicPartition objects containing the
+            Topic/Partition/Offset values to be committed to the Kafka broker
+        asynchronous : {{ True, False }}, default True,
+            True to wait on
+            Kafka broker response to commit request and False otherwise
+        """
+
+        for offs in offsets:
+            self.kafka_meta_client.commit_offset(
+                offs.topic.encode(), offs.partition, offs.offset
+            )
+
+    def poll(self, timeout=None):
+        """
+        Consumes a single message, calls callbacks and returns events.
+
+        The application must check the returned Message object's
+        Message.error() method to distinguish between proper messages
+        (error() returns None), or an event or error
+        (see error().code() for specifics).
+
+        Parameters
+        ----------
+        timeout : float
+            Maximum time to block waiting for message, event or callback
+            (default: infinite (None translated into -1 in the
+            library)). (Seconds)
+        """
+        return self.ck.poll(timeout)
diff --git a/python/custreamz/custreamz/tests/__init__.py b/python/custreamz/custreamz/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/custreamz/custreamz/tests/conftest.py b/python/custreamz/custreamz/tests/conftest.py
new file mode 100644
index 0000000..5840ff7
--- /dev/null
+++ b/python/custreamz/custreamz/tests/conftest.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+import socket
+
+import pytest
+from custreamz import kafka
+
+
+@pytest.fixture(scope="session")
+def kafka_client():
+
+    # Check for the existence of a kafka broker
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        s.connect_ex(("localhost", 9092))
+        s.shutdown(2)
+        s.close()
+    except Exception:
+        pytest.skip(
+            "A running Kafka instance must be available to run these tests"
+        )
+
+    kafka_configs = {
+        "metadata.broker.list": "localhost:9092",
+        "enable.partition.eof": "true",
+        "group.id": "groupid",
+        "auto.offset.reset": "earliest",
+        "enable.auto.commit": "false",
+    }
+
+    return kafka.Consumer(kafka_configs)
diff --git a/python/custreamz/custreamz/tests/test_dataframes.py b/python/custreamz/custreamz/tests/test_dataframes.py
new file mode 100644
index 0000000..1a1fc84
--- /dev/null
+++ b/python/custreamz/custreamz/tests/test_dataframes.py
@@ -0,0 +1,940 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+"""
+Tests for Streamz Dataframes (SDFs) built on top of cuDF DataFrames.
+*** Borrowed from streamz.dataframe.tests | License at thirdparty/LICENSE ***
+"""
+
+import json
+import operator
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from dask.dataframe.utils import assert_eq
+from distributed import Client
+
+from streamz import Stream
+from streamz.dask import DaskStream
+from streamz.dataframe import Aggregation, DataFrame, DataFrames, Series
+
+cudf = pytest.importorskip("cudf")
+
+
+@pytest.fixture(scope="module")
+def client():
+    client = Client(processes=False, asynchronous=False)
+    try:
+        yield client
+    finally:
+        client.close()
+
+
+@pytest.fixture(params=["core", "dask"])
+def stream(request, client):
+    if request.param == "core":
+        return Stream()
+    else:
+        return DaskStream()
+
+
+def test_identity(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+    L = sdf.stream.gather().sink_to_list()
+
+    sdf.emit(df)
+
+    assert L[0] is df
+    assert list(sdf.example.columns) == ["x", "y"]
+
+    x = sdf.x
+    assert isinstance(x, Series)
+    L2 = x.stream.gather().sink_to_list()
+    assert not L2
+
+    sdf.emit(df)
+    assert isinstance(L2[0], cudf.Series)
+    assert_eq(L2[0], df.x)
+
+
+def test_dtype(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+
+    assert str(sdf.dtypes) == str(df.dtypes)
+    assert sdf.x.dtype == df.x.dtype
+    assert sdf.index.dtype == df.index.dtype
+
+
+def test_attributes():
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df)
+
+    assert getattr(sdf, "x", -1) != -1
+    assert getattr(sdf, "z", -1) == -1
+
+    sdf.x
+    with pytest.raises(AttributeError):
+        sdf.z
+
+
+def test_exceptions(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+    with pytest.raises(TypeError):
+        sdf.emit(1)
+
+    with pytest.raises(IndexError):
+        sdf.emit(cudf.DataFrame())
+
+
+@pytest.mark.parametrize(
+    "func", [lambda x: x.sum(), lambda x: x.mean(), lambda x: x.count()]
+)
+def test_reductions(stream, func):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    for example in [df, df.iloc[:0]]:
+        sdf = DataFrame(example=example, stream=stream)
+
+        df_out = func(sdf).stream.gather().sink_to_list()
+
+        x = sdf.x
+        x_out = func(x).stream.gather().sink_to_list()
+
+        sdf.emit(df)
+        sdf.emit(df)
+
+        assert_eq(df_out[-1], func(cudf.concat([df, df])))
+        assert_eq(x_out[-1], func(cudf.concat([df, df]).x))
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.add,
+        operator.and_,
+        operator.eq,
+        operator.floordiv,
+        operator.ge,
+        operator.gt,
+        operator.le,
+        operator.lshift,
+        operator.lt,
+        operator.mod,
+        operator.mul,
+        operator.ne,
+        operator.or_,
+        operator.pow,
+        operator.rshift,
+        operator.sub,
+        operator.truediv,
+        operator.xor,
+    ],
+)
+@pytest.mark.parametrize("getter", [lambda df: df, lambda df: df.x])
+def test_binary_operators(op, getter, stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    try:
+        left = op(getter(df), 2)
+        right = op(2, getter(df))
+    except Exception:
+        return
+
+    a = DataFrame(example=df, stream=stream)
+    li = op(getter(a), 2).stream.gather().sink_to_list()
+    r = op(2, getter(a)).stream.gather().sink_to_list()
+
+    a.emit(df)
+
+    assert_eq(li[0], left)
+    assert_eq(r[0], right)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        operator.abs,
+        operator.inv,
+        operator.invert,
+        operator.neg,
+        lambda x: x.map(lambda x: x + 1),
+        lambda x: x.reset_index(),
+        lambda x: x.astype(float),
+    ],
+)
+@pytest.mark.parametrize("getter", [lambda df: df, lambda df: df.x])
+def test_unary_operators(op, getter):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    try:
+        expected = op(getter(df))
+    except Exception:
+        return
+
+    a = DataFrame(example=df)
+    b = op(getter(a)).stream.sink_to_list()
+
+    a.emit(df)
+
+    assert_eq(b[0], expected)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df.query("x > 1 and x < 4"),
+        pytest.param(
+            lambda df: df.x.value_counts().nlargest(2).astype(int),
+            marks=pytest.mark.xfail(reason="Index name lost in _getattr_"),
+        ),
+    ],
+)
+def test_dataframe_simple(func):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    expected = func(df)
+
+    a = DataFrame(example=df)
+    L = func(a).stream.sink_to_list()
+
+    a.emit(df)
+
+    assert_eq(L[0], expected)
+
+
+def test_set_index():
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+
+    a = DataFrame(example=df)
+
+    b = a.set_index("x").stream.sink_to_list()
+    a.emit(df)
+    assert_eq(b[0], df.set_index("x"))
+
+    b = a.set_index(a.y + 1).stream.sink_to_list()
+    a.emit(df)
+    assert_eq(b[0], df.set_index(df.y + 1))
+
+
+def test_binary_stream_operators(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+
+    expected = df.x + df.y
+
+    a = DataFrame(example=df, stream=stream)
+    b = (a.x + a.y).stream.gather().sink_to_list()
+
+    a.emit(df)
+
+    assert_eq(b[0], expected)
+
+
+def test_index(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    a = DataFrame(example=df, stream=stream)
+    b = a.index + 5
+    L = b.stream.gather().sink_to_list()
+
+    a.emit(df)
+    a.emit(df)
+
+    assert_eq(L[0], df.index + 5)
+    assert_eq(L[1], df.index + 5)
+
+
+def test_pair_arithmetic(stream):
+    df = cudf.DataFrame({"x": list(range(10)), "y": [1] * 10})
+
+    a = DataFrame(example=df.iloc[:0], stream=stream)
+    L = ((a.x + a.y) * 2).stream.gather().sink_to_list()
+
+    a.emit(df.iloc[:5])
+    a.emit(df.iloc[5:])
+
+    assert len(L) == 2
+    assert_eq(cudf.concat(L), (df.x + df.y) * 2)
+
+
+def test_getitem(stream):
+    df = cudf.DataFrame({"x": list(range(10)), "y": [1] * 10})
+
+    a = DataFrame(example=df.iloc[:0], stream=stream)
+    L = a[a.x > 4].stream.gather().sink_to_list()
+
+    a.emit(df.iloc[:5])
+    a.emit(df.iloc[5:])
+
+    assert len(L) == 2
+    assert_eq(cudf.concat(L), df[df.x > 4])
+
+
+@pytest.mark.parametrize("agg", [lambda x: x.sum(), lambda x: x.mean()])
+@pytest.mark.parametrize(
+    "grouper",
+    [lambda a: a.x % 3, lambda a: "x", lambda a: a.index % 2, lambda a: ["x"]],
+)
+@pytest.mark.parametrize(
+    "indexer", [lambda g: g, lambda g: g[["y"]], lambda g: g[["x", "y"]]]
+)
+def test_groupby_aggregate(agg, grouper, indexer, stream):
+    df = cudf.DataFrame(
+        {"x": (np.arange(10) // 2).astype(float), "y": [1.0, 2.0] * 5}
+    )
+
+    a = DataFrame(example=df.iloc[:0], stream=stream)
+
+    def f(x):
+        return agg(indexer(x.groupby(grouper(x))))
+
+    L = f(a).stream.gather().sink_to_list()
+
+    a.emit(df.iloc[:3])
+    a.emit(df.iloc[3:7])
+    a.emit(df.iloc[7:])
+
+    first = df.iloc[:3]
+    g = f(first)
+
+    h = f(df)
+
+    assert_eq(L[0], g)
+    assert_eq(L[-1], h)
+
+
+def test_repr(stream):
+    df = cudf.DataFrame(
+        {"x": (np.arange(10) // 2).astype(float), "y": [1.0] * 10}
+    )
+    a = DataFrame(example=df, stream=stream)
+
+    text = repr(a)
+    assert type(a).__name__ in text
+    assert "x" in text
+    assert "y" in text
+
+    text = repr(a.x)
+    assert type(a.x).__name__ in text
+    assert "x" in text
+
+    text = repr(a.x.sum())
+    assert type(a.x.sum()).__name__ in text
+
+
+def test_repr_html(stream):
+    df = cudf.DataFrame(
+        {"x": (np.arange(10) // 2).astype(float), "y": [1.0] * 10}
+    )
+    a = DataFrame(example=df, stream=stream)
+
+    for x in [a, a.y, a.y.mean()]:
+        html = x._repr_html_()
+        assert type(x).__name__ in html
+        assert "1" in html
+
+
+def test_setitem(stream):
+    df = cudf.DataFrame({"x": list(range(10)), "y": [1] * 10})
+
+    sdf = DataFrame(example=df.iloc[:0], stream=stream)
+    stream = sdf.stream
+
+    sdf["z"] = sdf["x"] * 2
+    sdf["a"] = 10
+    sdf[["c", "d"]] = sdf[["x", "y"]]
+
+    L = sdf.mean().stream.gather().sink_to_list()
+
+    stream.emit(df.iloc[:3])
+    stream.emit(df.iloc[3:7])
+    stream.emit(df.iloc[7:])
+
+    df["z"] = df["x"] * 2
+    df["a"] = 10
+    df["c"] = df["x"]
+    df["d"] = df["y"]
+
+    assert_eq(L[-1], df.mean())
+
+
+def test_setitem_overwrites(stream):
+    df = cudf.DataFrame({"x": list(range(10))})
+    sdf = DataFrame(example=df.iloc[:0], stream=stream)
+    stream = sdf.stream
+
+    sdf["x"] = sdf["x"] * 2
+
+    L = sdf.stream.gather().sink_to_list()
+
+    stream.emit(df.iloc[:3])
+    stream.emit(df.iloc[3:7])
+    stream.emit(df.iloc[7:])
+
+    assert_eq(L[-1], df.iloc[7:] * 2)
+
+
+@pytest.mark.parametrize(
+    "kwargs,op",
+    [
+        ({}, "sum"),
+        ({}, "mean"),
+        pytest.param({}, "min"),
+        pytest.param(
+            {},
+            "median",
+            marks=pytest.mark.xfail(reason="Unavailable for rolling objects"),
+        ),
+        pytest.param({}, "max"),
+        pytest.param(
+            {},
+            "var",
+            marks=pytest.mark.xfail(reason="Unavailable for rolling objects"),
+        ),
+        pytest.param({}, "count"),
+        pytest.param(
+            {"ddof": 0},
+            "std",
+            marks=pytest.mark.xfail(reason="Unavailable for rolling objects"),
+        ),
+        pytest.param(
+            {"quantile": 0.5},
+            "quantile",
+            marks=pytest.mark.xfail(reason="Unavailable for rolling objects"),
+        ),
+        pytest.param(
+            {"arg": {"A": "sum", "B": "min"}},
+            "aggregate",
+            marks=pytest.mark.xfail(reason="Unavailable for rolling objects"),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "window",
+    [pytest.param(2), 7, pytest.param("3h"), pd.Timedelta("200 minutes")],
+)
+@pytest.mark.parametrize("m", [2, pytest.param(5)])
+@pytest.mark.parametrize(
+    "pre_get,post_get",
+    [
+        (lambda df: df, lambda df: df),
+        (lambda df: df.x, lambda x: x),
+        (lambda df: df, lambda df: df.x),
+    ],
+)
+def test_rolling_count_aggregations(
+    op, window, m, pre_get, post_get, kwargs, stream
+):
+    index = pd.DatetimeIndex(
+        pd.date_range("2000-01-01", "2000-01-03", freq="1h")
+    )
+    df = cudf.DataFrame({"x": np.arange(len(index))}, index=index)
+
+    expected = getattr(post_get(pre_get(df).rolling(window)), op)(**kwargs)
+
+    sdf = DataFrame(example=df, stream=stream)
+    roll = getattr(post_get(pre_get(sdf).rolling(window)), op)(**kwargs)
+    L = roll.stream.gather().sink_to_list()
+    assert len(L) == 0
+
+    for i in range(0, len(df), m):
+        sdf.emit(df.iloc[i : i + m])
+
+    assert len(L) > 1
+
+    assert_eq(cudf.concat(L), expected)
+
+
+def test_stream_to_dataframe(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    source = stream
+    L = source.to_dataframe(example=df).x.sum().stream.gather().sink_to_list()
+
+    source.emit(df)
+    source.emit(df)
+    source.emit(df)
+
+    assert L == [6, 12, 18]
+
+
+def test_integration_from_stream(stream):
+    source = stream
+    sdf = (
+        source.partition(4)
+        .to_batch(example=['{"x": 0, "y": 0}'])
+        .map(json.loads)
+        .to_dataframe()
+    )
+    result = sdf.groupby(sdf.x).y.sum().mean()
+    L = result.stream.gather().sink_to_list()
+
+    for i in range(12):
+        source.emit(json.dumps({"x": i % 3, "y": i}))
+
+    assert L == [2, 28 / 3, 22.0]
+
+
+def test_to_frame(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+
+    assert sdf.to_frame() is sdf
+
+    a = sdf.x.to_frame()
+    assert isinstance(a, DataFrame)
+    assert list(a.columns) == ["x"]
+
+
+@pytest.mark.parametrize("op", ["cumsum", "cummax", "cumprod", "cummin"])
+@pytest.mark.parametrize("getter", [lambda df: df, lambda df: df.x])
+def test_cumulative_aggregations(op, getter, stream):
+    df = cudf.DataFrame({"x": list(range(10)), "y": [1] * 10})
+    expected = getattr(getter(df), op)()
+
+    sdf = DataFrame(example=df, stream=stream)
+
+    L = getattr(getter(sdf), op)().stream.gather().sink_to_list()
+
+    for i in range(0, 10, 3):
+        sdf.emit(df.iloc[i : i + 3])
+    sdf.emit(df.iloc[:0])
+
+    assert len(L) > 1
+
+    assert_eq(cudf.concat(L), expected)
+
+
+@pytest.mark.xfail(
+    reason="IPyWidgets 8.0 broke streamz 0.6.4. "
+    "We should remove this xfail when this is fixed in streamz."
+)
+def test_display(stream):
+    pytest.importorskip("ipywidgets")
+    pytest.importorskip("IPython")
+
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+
+    s = sdf.x.sum()
+
+    s._ipython_display_()
+
+
+def test_tail(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+
+    L = sdf.tail(2).stream.gather().sink_to_list()
+
+    sdf.emit(df)
+    sdf.emit(df)
+
+    assert_eq(L[0], df.tail(2))
+    assert_eq(L[1], df.tail(2))
+
+
+def test_example_type_error_message():
+    try:
+        DataFrame(example=[123])
+    except Exception as e:
+        assert "DataFrame" in str(e)
+        assert "[123]" in str(e)
+
+
+def test_dataframes(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrames(example=df, stream=stream)
+    L = sdf.x.sum().stream.gather().sink_to_list()
+
+    sdf.emit(df)
+    sdf.emit(df)
+
+    assert L == [6, 6]
+
+
+def test_groupby_aggregate_updating(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+
+    assert sdf.groupby("x").y.mean()._stream_type == "updating"
+    assert sdf.x.sum()._stream_type == "updating"
+    assert (sdf.x.sum() + 1)._stream_type == "updating"
+
+
+def test_window_sum(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+    L = sdf.window(n=4).x.sum().stream.gather().sink_to_list()
+
+    sdf.emit(df)
+    assert L == [6]
+    sdf.emit(df)
+    assert L == [6, 9]
+    sdf.emit(df)
+    assert L == [6, 9, 9]
+
+
+def test_window_sum_dataframe(stream):
+    df = cudf.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
+    sdf = DataFrame(example=df, stream=stream)
+    L = sdf.window(n=4).sum().stream.gather().sink_to_list()
+
+    sdf.emit(df)
+    assert_eq(L[0], cudf.Series([6, 15], index=["x", "y"]))
+    sdf.emit(df)
+    assert_eq(L[0], cudf.Series([6, 15], index=["x", "y"]))
+    assert_eq(L[1], cudf.Series([9, 21], index=["x", "y"]))
+    sdf.emit(df)
+    assert_eq(L[0], cudf.Series([6, 15], index=["x", "y"]))
+    assert_eq(L[1], cudf.Series([9, 21], index=["x", "y"]))
+    assert_eq(L[2], cudf.Series([9, 21], index=["x", "y"]))
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: x.sum(),
+        lambda x: x.mean(),
+        lambda x: x.count(),
+        lambda x: x.var(ddof=1),
+        lambda x: x.std(ddof=1),
+        lambda x: x.var(ddof=0),
+    ],
+)
+@pytest.mark.parametrize("n", [2, 4])
+@pytest.mark.parametrize("getter", [lambda df: df.x])
+def test_windowing_n(func, n, getter):
+    df = cudf.DataFrame({"x": list(range(10)), "y": [1, 2] * 5})
+
+    sdf = DataFrame(example=df)
+    L = func(getter(sdf).window(n=n)).stream.gather().sink_to_list()
+
+    for i in range(0, 10, 3):
+        sdf.emit(df.iloc[i : i + 3])
+    sdf.emit(df.iloc[:0])
+
+    assert len(L) == 5
+
+    assert_eq(L[0], func(getter(df).iloc[max(0, 3 - n) : 3]))
+    assert_eq(L[-1], func(getter(df).iloc[len(df) - n :]))
+
+
+@pytest.mark.parametrize("func", [lambda x: x.sum(), lambda x: x.mean()])
+@pytest.mark.parametrize("value", ["10h", "1d"])
+@pytest.mark.parametrize("getter", [lambda df: df, lambda df: df.x])
+@pytest.mark.parametrize(
+    "grouper", [lambda a: "y", lambda a: a.index, lambda a: ["y"]]
+)
+@pytest.mark.parametrize(
+    "indexer", [lambda g: g, lambda g: g[["x"]], lambda g: g[["x", "y"]]]
+)
+def test_groupby_windowing_value(func, value, getter, grouper, indexer):
+    index = pd.DatetimeIndex(
+        pd.date_range("2000-01-01", "2000-01-03", freq="1h")
+    )
+    df = cudf.DataFrame(
+        {
+            "x": np.arange(len(index), dtype=float),
+            "y": np.arange(len(index), dtype=float) % 2,
+        },
+        index=index,
+    )
+
+    value = pd.Timedelta(value)
+
+    sdf = DataFrame(example=df)
+
+    def f(x):
+        return func(indexer(x.groupby(grouper(x))))
+
+    L = f(sdf.window(value)).stream.gather().sink_to_list()
+
+    diff = 13
+    for i in range(0, len(index), diff):
+        sdf.emit(df.iloc[i : i + diff])
+
+    assert len(L) == 4
+
+    first = df.iloc[:diff]
+    lost = first.loc[first.index.min() + value :]
+    first = first.iloc[len(lost) :]
+
+    g = f(first)
+    assert_eq(L[0], g)
+
+    last = df.loc[index.max() - value + pd.Timedelta("1s") :]
+    h = f(last)
+    assert_eq(L[-1], h)
+
+
+@pytest.mark.parametrize("func", [lambda x: x.sum(), lambda x: x.mean()])
+@pytest.mark.parametrize("n", [1, 4])
+@pytest.mark.parametrize("getter", [lambda df: df, lambda df: df.x])
+@pytest.mark.parametrize(
+    "grouper",
+    [lambda a: a.x % 3, lambda a: "y", lambda a: a.index % 2, lambda a: ["y"]],
+)
+@pytest.mark.parametrize("indexer", [lambda g: g, lambda g: g[["x", "y"]]])
+def test_groupby_windowing_n(func, n, getter, grouper, indexer):
+    df = cudf.DataFrame({"x": np.arange(10, dtype=float), "y": [1.0, 2.0] * 5})
+
+    sdf = DataFrame(example=df)
+
+    def f(x):
+        return func(indexer(x.groupby(grouper(x))))
+
+    L = f(sdf.window(n=n)).stream.gather().sink_to_list()
+
+    diff = 3
+    for i in range(0, 10, diff):
+        sdf.emit(df.iloc[i : i + diff])
+    sdf.emit(df.iloc[:0])
+
+    assert len(L) == 5
+
+    first = df.iloc[max(0, diff - n) : diff]
+
+    g = f(first)
+    assert_eq(L[0], g)
+
+    last = df.iloc[len(df) - n :]
+    h = f(last)
+    assert_eq(L[-1], h)
+
+
+def test_window_full():
+    df = cudf.DataFrame({"x": np.arange(10, dtype=float), "y": [1.0, 2.0] * 5})
+
+    sdf = DataFrame(example=df)
+
+    L = sdf.window(n=4).apply(lambda x: x).stream.sink_to_list()
+
+    sdf.emit(df.iloc[:3])
+    sdf.emit(df.iloc[3:8])
+    sdf.emit(df.iloc[8:])
+
+    assert_eq(L[0], df.iloc[:3])
+    assert_eq(L[1], df.iloc[4:8])
+    assert_eq(L[2], df.iloc[-4:])
+
+
+def test_custom_aggregation():
+    df = cudf.DataFrame({"x": np.arange(10, dtype=float), "y": [1.0, 2.0] * 5})
+
+    class Custom(Aggregation):
+        def initial(self, new):
+            return 0
+
+        def on_new(self, state, new):
+            return state + 1, state
+
+        def on_old(self, state, new):
+            return state - 100, state
+
+    sdf = DataFrame(example=df)
+    L = sdf.aggregate(Custom()).stream.sink_to_list()
+
+    sdf.emit(df)
+    sdf.emit(df)
+    sdf.emit(df)
+
+    assert L == [0, 1, 2]
+
+    sdf = DataFrame(example=df)
+    L = sdf.window(n=5).aggregate(Custom()).stream.sink_to_list()
+
+    sdf.emit(df)
+    sdf.emit(df)
+    sdf.emit(df)
+
+    assert L == [1, -198, -397]
+
+
+def test_groupby_aggregate_with_start_state(stream):
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example).groupby(["name"])
+    output0 = sdf.amount.sum(start=None).stream.gather().sink_to_list()
+    output1 = (
+        sdf.amount.mean(with_state=True, start=None)
+        .stream.gather()
+        .sink_to_list()
+    )
+    output2 = sdf.amount.count(start=None).stream.gather().sink_to_list()
+
+    df = cudf.DataFrame({"name": ["Alice", "Tom"], "amount": [50, 100]})
+    stream.emit(df)
+
+    out_df0 = cudf.DataFrame({"name": ["Alice", "Tom"], "amount": [50, 100]})
+    out_df1 = cudf.DataFrame(
+        {"name": ["Alice", "Tom"], "amount": [50.0, 100.0]}
+    )
+    out_df2 = cudf.DataFrame({"name": ["Alice", "Tom"], "amount": [1, 1]})
+    assert assert_eq(output0[0].reset_index(), out_df0)
+    assert assert_eq(output1[0][1].reset_index(), out_df1)
+    assert assert_eq(output2[0].reset_index(), out_df2)
+
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example).groupby(["name"])
+    output3 = sdf.amount.sum(start=output0[0]).stream.gather().sink_to_list()
+    output4 = (
+        sdf.amount.mean(with_state=True, start=output1[0][0])
+        .stream.gather()
+        .sink_to_list()
+    )
+    output5 = sdf.amount.count(start=output2[0]).stream.gather().sink_to_list()
+    df = cudf.DataFrame(
+        {"name": ["Alice", "Tom", "Linda"], "amount": [50, 100, 200]}
+    )
+    stream.emit(df)
+
+    out_df2 = cudf.DataFrame(
+        {"name": ["Alice", "Linda", "Tom"], "amount": [100, 200, 200]}
+    )
+    out_df3 = cudf.DataFrame(
+        {"name": ["Alice", "Linda", "Tom"], "amount": [50.0, 200.0, 100.0]}
+    )
+    out_df4 = cudf.DataFrame(
+        {"name": ["Alice", "Linda", "Tom"], "amount": [2, 1, 2]}
+    )
+    assert assert_eq(output3[0].reset_index(), out_df2)
+    assert assert_eq(output4[0][1].reset_index(), out_df3)
+    assert assert_eq(output5[0].reset_index(), out_df4)
+
+
+def test_reductions_with_start_state(stream):
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output0 = sdf.amount.mean(start=(10, 2)).stream.gather().sink_to_list()
+    output1 = sdf.amount.count(start=3).stream.gather().sink_to_list()
+    output2 = sdf.amount.sum(start=10).stream.gather().sink_to_list()
+
+    df = cudf.DataFrame(
+        {"name": ["Alice", "Tom", "Linda"], "amount": [50, 100, 200]}
+    )
+    stream.emit(df)
+
+    assert output0[0] == 72.0
+    assert output1[0] == 6
+    assert output2[0] == 360
+
+
+def test_rolling_aggs_with_start_state(stream):
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output0 = (
+        sdf.rolling(2, with_state=True, start=())
+        .amount.sum()
+        .stream.gather()
+        .sink_to_list()
+    )
+
+    df = cudf.DataFrame(
+        {"name": ["Alice", "Tom", "Linda"], "amount": [50, 100, 200]}
+    )
+    stream.emit(df)
+    df = cudf.DataFrame({"name": ["Bob"], "amount": [250]})
+    stream.emit(df)
+    assert assert_eq(
+        output0[-1][0].reset_index(drop=True),
+        cudf.Series([200, 250], name="amount"),
+    )
+    assert assert_eq(
+        output0[-1][1].reset_index(drop=True),
+        cudf.Series([450], name="amount"),
+    )
+
+    stream = Stream()
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output1 = (
+        sdf.rolling(2, with_state=True, start=output0[-1][0])
+        .amount.sum()
+        .stream.gather()
+        .sink_to_list()
+    )
+    df = cudf.DataFrame({"name": ["Alice"], "amount": [50]})
+    stream.emit(df)
+    assert assert_eq(
+        output1[-1][0].reset_index(drop=True),
+        cudf.Series([250, 50], name="amount"),
+    )
+    assert assert_eq(
+        output1[-1][1].reset_index(drop=True),
+        cudf.Series([300], name="amount"),
+    )
+
+
+def test_window_aggs_with_start_state(stream):
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output0 = (
+        sdf.window(2, with_state=True, start=None)
+        .amount.sum()
+        .stream.gather()
+        .sink_to_list()
+    )
+
+    df = cudf.DataFrame(
+        {"name": ["Alice", "Tom", "Linda"], "amount": [50, 100, 200]}
+    )
+    stream.emit(df)
+    df = cudf.DataFrame({"name": ["Bob"], "amount": [250]})
+    stream.emit(df)
+    assert output0[-1][1] == 450
+
+    stream = Stream()
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output1 = (
+        sdf.window(2, with_state=True, start=output0[-1][0])
+        .amount.sum()
+        .stream.gather()
+        .sink_to_list()
+    )
+    df = cudf.DataFrame({"name": ["Alice"], "amount": [50]})
+    stream.emit(df)
+    assert output1[-1][1] == 300
+
+
+def test_windowed_groupby_aggs_with_start_state(stream):
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output0 = (
+        sdf.window(5, with_state=True, start=None)
+        .groupby(["name"])
+        .amount.sum()
+        .stream.gather()
+        .sink_to_list()
+    )
+
+    df = cudf.DataFrame(
+        {"name": ["Alice", "Tom", "Linda"], "amount": [50, 100, 200]}
+    )
+    stream.emit(df)
+    df = cudf.DataFrame(
+        {"name": ["Alice", "Linda", "Bob"], "amount": [250, 300, 350]}
+    )
+    stream.emit(df)
+
+    stream = Stream()
+    example = cudf.DataFrame({"name": [], "amount": []})
+    sdf = DataFrame(stream, example=example)
+    output1 = (
+        sdf.window(5, with_state=True, start=output0[-1][0])
+        .groupby(["name"])
+        .amount.sum()
+        .stream.gather()
+        .sink_to_list()
+    )
+    df = cudf.DataFrame(
+        {
+            "name": ["Alice", "Linda", "Tom", "Bob"],
+            "amount": [50, 100, 150, 200],
+        }
+    )
+    stream.emit(df)
+    out_df1 = cudf.DataFrame(
+        {
+            "name": ["Alice", "Bob", "Linda", "Tom"],
+            "amount": [50, 550, 100, 150],
+        }
+    )
+    assert_eq(output1[-1][1].reset_index(), out_df1)
diff --git a/python/custreamz/custreamz/tests/test_kafka.py b/python/custreamz/custreamz/tests/test_kafka.py
new file mode 100644
index 0000000..ad3b829
--- /dev/null
+++ b/python/custreamz/custreamz/tests/test_kafka.py
@@ -0,0 +1,20 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.
+import confluent_kafka as ck
+import pytest
+
+from cudf.testing._utils import assert_eq
+
+
+@pytest.mark.parametrize("commit_offset", [1, 45, 100, 22, 1000, 10])
+@pytest.mark.parametrize("topic", ["cudf-kafka-test-topic"])
+def test_kafka_offset(kafka_client, topic, commit_offset):
+    offsets = [ck.TopicPartition(topic, 0, commit_offset)]
+    kafka_client.commit(offsets=offsets)
+
+    # Get the offsets that were just committed to Kafka
+    retrieved_offsets = kafka_client.committed(offsets)
+
+    for off in retrieved_offsets:
+        assert_eq(off.topic, offsets[0].topic)
+        assert_eq(off.partition, offsets[0].partition)
+        assert_eq(off.offset, offsets[0].offset)
diff --git a/python/custreamz/pyproject.toml b/python/custreamz/pyproject.toml
new file mode 100644
index 0000000..47ade91
--- /dev/null
+++ b/python/custreamz/pyproject.toml
@@ -0,0 +1,100 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+[build-system]
+build-backend = "setuptools.build_meta"
+requires = [
+    "setuptools",
+    "wheel",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project]
+name = "custreamz"
+version = "23.10.00"
+description = "cuStreamz - GPU Accelerated Streaming"
+readme = { file = "README.md", content-type = "text/markdown" }
+authors = [
+    { name = "NVIDIA Corporation" },
+]
+license = { text = "Apache 2.0" }
+requires-python = ">=3.9"
+dependencies = [
+    "confluent-kafka>=1.9.0,<1.10.0a0",
+    "cudf==23.10.*",
+    "cudf_kafka==23.10.*",
+    "streamz",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+classifiers = [
+    "Intended Audience :: Developers",
+    "Topic :: Streaming",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Apache Kafka",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+]
+
+[project.optional-dependencies]
+test = [
+    "pytest",
+    "pytest-cov",
+    "pytest-xdist",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project.urls]
+Homepage = "https://github.com/rapidsai/cudf"
+
+[tool.setuptools]
+license-files = ["LICENSE"]
+zip-safe = false
+
+[tools.setuptools.packages.find]
+include = [
+    "custreamz",
+    "custreamz.*",
+]
+
+[tool.isort]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+order_by_type = true
+known_dask = [
+    "dask",
+    "distributed",
+    "dask_cuda",
+]
+known_rapids = [
+    "rmm",
+    "cudf",
+    "dask_cudf",
+]
+known_first_party = [
+    "streamz",
+]
+default_section = "THIRDPARTY"
+sections = [
+    "FUTURE",
+    "STDLIB",
+    "THIRDPARTY",
+    "DASK",
+    "RAPIDS",
+    "FIRSTPARTY",
+    "LOCALFOLDER",
+]
+skip = [
+    "thirdparty",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "__init__.py",
+]
diff --git a/python/custreamz/setup.py b/python/custreamz/setup.py
new file mode 100644
index 0000000..2fa45ac
--- /dev/null
+++ b/python/custreamz/setup.py
@@ -0,0 +1,5 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from setuptools import setup
+
+setup()
diff --git a/python/dask_cudf/.coveragerc b/python/dask_cudf/.coveragerc
new file mode 100644
index 0000000..10a8ce8
--- /dev/null
+++ b/python/dask_cudf/.coveragerc
@@ -0,0 +1,3 @@
+# Configuration file for Python coverage tests
+[run]
+source = dask_cudf
diff --git a/python/dask_cudf/LICENSE b/python/dask_cudf/LICENSE
new file mode 120000
index 0000000..30cff74
--- /dev/null
+++ b/python/dask_cudf/LICENSE
@@ -0,0 +1 @@
+../../LICENSE
\ No newline at end of file
diff --git a/python/dask_cudf/README.md b/python/dask_cudf/README.md
new file mode 120000
index 0000000..fe84005
--- /dev/null
+++ b/python/dask_cudf/README.md
@@ -0,0 +1 @@
+../../README.md
\ No newline at end of file
diff --git a/python/dask_cudf/dask_cudf/DASK_LICENSE.txt b/python/dask_cudf/dask_cudf/DASK_LICENSE.txt
new file mode 100644
index 0000000..09b1cf5
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/DASK_LICENSE.txt
@@ -0,0 +1,31 @@
+This library contains modified code from the Dask library
+(https://github.com/dask/dask). The original Dask license is below.
+
+Copyright (c) 2014-2017, Continuum Analytics, Inc. and contributors
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice,
+this list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation
+and/or other materials provided with the distribution.
+
+Neither the name of Continuum Analytics nor the names of any contributors
+may be used to endorse or promote products derived from this software
+without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/python/dask_cudf/dask_cudf/__init__.py b/python/dask_cudf/dask_cudf/__init__.py
new file mode 100644
index 0000000..6952c3d
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/__init__.py
@@ -0,0 +1,30 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+from dask.dataframe import from_delayed
+
+import cudf
+
+from . import backends
+from .core import DataFrame, Series, concat, from_cudf, from_dask_dataframe
+from .groupby import groupby_agg
+from .io import read_csv, read_json, read_orc, read_text, to_orc
+
+try:
+    from .io import read_parquet
+except ImportError:
+    pass
+
+__version__ = "23.10.00"
+
+__all__ = [
+    "DataFrame",
+    "Series",
+    "from_cudf",
+    "from_dask_dataframe",
+    "concat",
+    "from_delayed",
+]
+
+if not hasattr(cudf.DataFrame, "mean"):
+    cudf.DataFrame.mean = None
+del cudf
diff --git a/python/dask_cudf/dask_cudf/accessors.py b/python/dask_cudf/dask_cudf/accessors.py
new file mode 100644
index 0000000..1c21fca
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/accessors.py
@@ -0,0 +1,286 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+
+class StructMethods:
+    def __init__(self, d_series):
+        self.d_series = d_series
+
+    def field(self, key):
+        """
+        Extract children of the specified struct column
+        in the Series
+        Parameters
+        ----------
+        key: int or str
+            index/position or field name of the respective
+            struct column
+        Returns
+        -------
+        Series
+        Examples
+        --------
+        >>> s = cudf.Series([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds.struct.field(0).compute()
+        0    1
+        1    3
+        dtype: int64
+        >>> ds.struct.field('a').compute()
+        0    1
+        1    3
+        dtype: int64
+        """
+        typ = self.d_series._meta.struct.field(key).dtype
+
+        return self.d_series.map_partitions(
+            lambda s: s.struct.field(key),
+            meta=self.d_series._meta._constructor([], dtype=typ),
+        )
+
+    def explode(self):
+        """
+        Creates a dataframe view of the struct column, one column per field.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> import cudf, dask_cudf
+        >>> ds = dask_cudf.from_cudf(cudf.Series(
+        ...     [{'a': 42, 'b': 'str1', 'c': [-1]},
+        ...      {'a': 0,  'b': 'str2', 'c': [400, 500]},
+        ...      {'a': 7,  'b': '',     'c': []}]), npartitions=2)
+        >>> ds.struct.explode().compute()
+            a     b           c
+        0  42  str1        [-1]
+        1   0  str2  [400, 500]
+        2   7                []
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.struct.explode(),
+            meta=self.d_series._meta.struct.explode(),
+        )
+
+
+class ListMethods:
+    def __init__(self, d_series):
+        self.d_series = d_series
+
+    def len(self):
+        """
+        Computes the length of each element in the Series/Index.
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds
+        0    [1, 2, 3]
+        1         None
+        2       [4, 5]
+        dtype: list
+        >>> ds.list.len().compute()
+        0       3
+        1    <NA>
+        2       2
+        dtype: int32
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.len(), meta=self.d_series._meta
+        )
+
+    def contains(self, search_key):
+        """
+        Creates a column of bool values indicating whether the specified scalar
+        is an element of each row of a list column.
+
+        Parameters
+        ----------
+        search_key : scalar
+            element being searched for in each row of the list column
+
+        Returns
+        -------
+        Column
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], [3, 4, 5], [4, 5, 6]])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds.list.contains(4).compute()
+        Series([False, True, True])
+        dtype: bool
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.contains(search_key), meta=self.d_series._meta
+        )
+
+    def get(self, index):
+        """
+        Extract element at the given index from each component
+        Extract element from lists, tuples, or strings in
+        each element in the Series/Index.
+
+        Parameters
+        ----------
+        index : int
+
+        Returns
+        -------
+        Series or Index
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], [3, 4, 5], [4, 5, 6]])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds.list.get(-1).compute()
+        0    3
+        1    5
+        2    6
+        dtype: int64
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.get(index), meta=self.d_series._meta
+        )
+
+    @property
+    def leaves(self):
+        """
+        From a Series of (possibly nested) lists, obtain the elements from
+        the innermost lists as a flat Series (one value per row).
+
+        Returns
+        -------
+        Series
+
+        Examples
+        --------
+        >>> s = cudf.Series([[[1, None], [3, 4]], None, [[5, 6]]])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds.list.leaves.compute()
+        0       1
+        1    <NA>
+        2       3
+        3       4
+        4       5
+        5       6
+        dtype: int64
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.leaves, meta=self.d_series._meta
+        )
+
+    def take(self, lists_indices):
+        """
+        Collect list elements based on given indices.
+
+        Parameters
+        ----------
+        lists_indices: List type arrays
+            Specifies what to collect from each row
+
+        Returns
+        -------
+        ListColumn
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds
+        0    [1, 2, 3]
+        1         None
+        2       [4, 5]
+        dtype: list
+        >>> ds.list.take([[0, 1], [], []]).compute()
+        0    [1, 2]
+        1      None
+        2        []
+        dtype: list
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.take(lists_indices), meta=self.d_series._meta
+        )
+
+    def unique(self):
+        """
+        Returns unique element for each list in the column, order for each
+        unique element is not guaranteed.
+
+        Returns
+        -------
+        ListColumn
+
+        Examples
+        --------
+        >>> s = cudf.Series([[1, 1, 2, None, None], None, [4, 4], []])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds
+        0    [1.0, 1.0, 2.0, nan, nan]
+        1                         None
+        2                   [4.0, 4.0]
+        3                           []
+        dtype: list
+        >>> ds.list.unique().compute() # Order of elements not guaranteed
+        0              [1.0, 2.0, nan]
+        1                         None
+        2                        [4.0]
+        3                           []
+        dtype: list
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.unique(), meta=self.d_series._meta
+        )
+
+    def sort_values(
+        self,
+        ascending=True,
+        inplace=False,
+        kind="quicksort",
+        na_position="last",
+        ignore_index=False,
+    ):
+        """
+        Sort each list by the values.
+        Sort the lists in ascending or descending order by some criterion.
+
+        Parameters
+        ----------
+        ascending : bool, default True
+            If True, sort values in ascending order, otherwise descending.
+        na_position : {'first', 'last'}, default 'last'
+            'first' puts nulls at the beginning, 'last' puts nulls at the end.
+        ignore_index : bool, default False
+            If True, the resulting axis will be labeled 0, 1, ..., n - 1.
+
+        Returns
+        -------
+        ListColumn with each list sorted
+
+        Notes
+        -----
+        Difference from pandas:
+          * Not supporting: `inplace`, `kind`
+
+        Examples
+        --------
+        >>> s = cudf.Series([[4, 2, None, 9], [8, 8, 2], [2, 1]])
+        >>> ds = dask_cudf.from_cudf(s, 2)
+        >>> ds.list.sort_values(ascending=True, na_position="last").compute()
+        0    [2.0, 4.0, 9.0, nan]
+        1         [2.0, 8.0, 8.0]
+        2              [1.0, 2.0]
+        dtype: list
+        """
+        return self.d_series.map_partitions(
+            lambda s: s.list.sort_values(
+                ascending, inplace, kind, na_position, ignore_index
+            ),
+            meta=self.d_series._meta,
+        )
diff --git a/python/dask_cudf/dask_cudf/backends.py b/python/dask_cudf/dask_cudf/backends.py
new file mode 100644
index 0000000..2be256f
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/backends.py
@@ -0,0 +1,625 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import warnings
+from collections.abc import Iterator
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+from pandas.api.types import is_scalar
+from pandas.core.tools.datetimes import is_datetime64tz_dtype
+
+import dask.dataframe as dd
+from dask import config
+from dask.dataframe.backends import (
+    DataFrameBackendEntrypoint,
+    PandasBackendEntrypoint,
+)
+from dask.dataframe.core import get_parallel_type, meta_nonempty
+from dask.dataframe.dispatch import (
+    categorical_dtype_dispatch,
+    concat_dispatch,
+    from_pyarrow_table_dispatch,
+    group_split_dispatch,
+    grouper_dispatch,
+    hash_object_dispatch,
+    is_categorical_dtype_dispatch,
+    make_meta_dispatch,
+    pyarrow_schema_dispatch,
+    to_pyarrow_table_dispatch,
+    tolist_dispatch,
+    union_categoricals_dispatch,
+)
+from dask.dataframe.utils import (
+    UNKNOWN_CATEGORIES,
+    _nonempty_scalar,
+    _scalar_from_dtype,
+    make_meta_obj,
+)
+from dask.sizeof import sizeof as sizeof_dispatch
+from dask.utils import Dispatch, is_arraylike
+
+import cudf
+from cudf.api.types import is_string_dtype
+from cudf.utils.utils import _dask_cudf_nvtx_annotate
+
+from .core import DataFrame, Index, Series
+
+get_parallel_type.register(cudf.DataFrame, lambda _: DataFrame)
+get_parallel_type.register(cudf.Series, lambda _: Series)
+get_parallel_type.register(cudf.BaseIndex, lambda _: Index)
+
+
+@meta_nonempty.register(cudf.BaseIndex)
+@_dask_cudf_nvtx_annotate
+def _nonempty_index(idx):
+    if isinstance(idx, cudf.core.index.RangeIndex):
+        return cudf.core.index.RangeIndex(2, name=idx.name)
+    elif isinstance(idx, cudf.core.index.DatetimeIndex):
+        start = "1970-01-01"
+        data = np.array([start, "1970-01-02"], dtype=idx.dtype)
+        values = cudf.core.column.as_column(data)
+        return cudf.core.index.DatetimeIndex(values, name=idx.name)
+    elif isinstance(idx, cudf.StringIndex):
+        return cudf.StringIndex(["cat", "dog"], name=idx.name)
+    elif isinstance(idx, cudf.core.index.CategoricalIndex):
+        key = tuple(idx._data.keys())
+        assert len(key) == 1
+        categories = idx._data[key[0]].categories
+        codes = [0, 0]
+        ordered = idx._data[key[0]].ordered
+        values = cudf.core.column.build_categorical_column(
+            categories=categories, codes=codes, ordered=ordered
+        )
+        return cudf.core.index.CategoricalIndex(values, name=idx.name)
+    elif isinstance(idx, cudf.core.index.GenericIndex):
+        return cudf.core.index.GenericIndex(
+            np.arange(2, dtype=idx.dtype), name=idx.name
+        )
+    elif isinstance(idx, cudf.core.multiindex.MultiIndex):
+        levels = [meta_nonempty(lev) for lev in idx.levels]
+        codes = [[0, 0] for i in idx.levels]
+        return cudf.core.multiindex.MultiIndex(
+            levels=levels, codes=codes, names=idx.names
+        )
+
+    raise TypeError(f"Don't know how to handle index of type {type(idx)}")
+
+
+def _nest_list_data(data, leaf_type):
+    """
+    Helper for _get_non_empty_data which creates
+    nested list data
+    """
+    data = [data]
+    while isinstance(leaf_type, cudf.ListDtype):
+        leaf_type = leaf_type.element_type
+        data = [data]
+    return data
+
+
+@_dask_cudf_nvtx_annotate
+def _get_non_empty_data(s):
+    if isinstance(s, cudf.core.column.CategoricalColumn):
+        categories = (
+            s.categories if len(s.categories) else [UNKNOWN_CATEGORIES]
+        )
+        codes = cudf.core.column.full(
+            size=2, fill_value=0, dtype=cudf._lib.types.size_type_dtype
+        )
+        ordered = s.ordered
+        data = cudf.core.column.build_categorical_column(
+            categories=categories, codes=codes, ordered=ordered
+        )
+    elif isinstance(s, cudf.core.column.ListColumn):
+        leaf_type = s.dtype.leaf_type
+        if is_string_dtype(leaf_type):
+            data = ["cat", "dog"]
+        else:
+            data = np.array([0, 1], dtype=leaf_type).tolist()
+        data = _nest_list_data(data, s.dtype) * 2
+        data = cudf.core.column.as_column(data, dtype=s.dtype)
+    elif isinstance(s, cudf.core.column.StructColumn):
+        struct_dtype = s.dtype
+        data = [{key: None for key in struct_dtype.fields.keys()}] * 2
+        data = cudf.core.column.as_column(data, dtype=s.dtype)
+    elif is_string_dtype(s.dtype):
+        data = pa.array(["cat", "dog"])
+    elif is_datetime64tz_dtype(s.dtype):
+        from cudf.utils.dtypes import get_time_unit
+
+        data = cudf.date_range("2001-01-01", periods=2, freq=get_time_unit(s))
+        data = data.tz_localize(str(s.dtype.tz))._column
+    else:
+        if pd.api.types.is_numeric_dtype(s.dtype):
+            data = cudf.core.column.as_column(
+                cp.arange(start=0, stop=2, dtype=s.dtype)
+            )
+        else:
+            data = cudf.core.column.as_column(
+                cp.arange(start=0, stop=2, dtype="int64")
+            ).astype(s.dtype)
+    return data
+
+
+@meta_nonempty.register(cudf.Series)
+@_dask_cudf_nvtx_annotate
+def _nonempty_series(s, idx=None):
+    if idx is None:
+        idx = _nonempty_index(s.index)
+    data = _get_non_empty_data(s._column)
+
+    return cudf.Series(data, name=s.name, index=idx)
+
+
+@meta_nonempty.register(cudf.DataFrame)
+@_dask_cudf_nvtx_annotate
+def meta_nonempty_cudf(x):
+    idx = meta_nonempty(x.index)
+    columns_with_dtype = dict()
+    res = cudf.DataFrame(index=idx)
+    for col in x._data.names:
+        dtype = str(x._data[col].dtype)
+        if dtype in ("list", "struct", "category"):
+            # 1. Not possible to hash and store list & struct types
+            #    as they can contain different levels of nesting or
+            #    fields.
+            # 2. Not possible to has `category` types as
+            #    they often contain an underlying types to them.
+            res._data[col] = _get_non_empty_data(x._data[col])
+        else:
+            if dtype not in columns_with_dtype:
+                columns_with_dtype[dtype] = cudf.core.column.as_column(
+                    _get_non_empty_data(x._data[col])
+                )
+            res._data[col] = columns_with_dtype[dtype]
+
+    return res
+
+
+@make_meta_dispatch.register((cudf.Series, cudf.DataFrame))
+@_dask_cudf_nvtx_annotate
+def make_meta_cudf(x, index=None):
+    return x.head(0)
+
+
+@make_meta_dispatch.register(cudf.BaseIndex)
+@_dask_cudf_nvtx_annotate
+def make_meta_cudf_index(x, index=None):
+    return x[:0]
+
+
+@_dask_cudf_nvtx_annotate
+def _empty_series(name, dtype, index=None):
+    if isinstance(dtype, str) and dtype == "category":
+        return cudf.Series(
+            [UNKNOWN_CATEGORIES], dtype=dtype, name=name, index=index
+        ).iloc[:0]
+    return cudf.Series([], dtype=dtype, name=name, index=index)
+
+
+@make_meta_obj.register(object)
+@_dask_cudf_nvtx_annotate
+def make_meta_object_cudf(x, index=None):
+    """Create an empty cudf object containing the desired metadata.
+
+    Parameters
+    ----------
+    x : dict, tuple, list, cudf.Series, cudf.DataFrame, cudf.Index,
+        dtype, scalar
+        To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or
+        an iterable of `(name, dtype)` tuples. To create a `Series`, provide a
+        tuple of `(name, dtype)`. If a cudf object, names, dtypes, and index
+        should match the desired output. If a dtype or scalar, a scalar of the
+        same dtype is returned.
+    index :  cudf.Index, optional
+        Any cudf index to use in the metadata. If none provided, a
+        `RangeIndex` will be used.
+
+    Examples
+    --------
+    >>> make_meta([('a', 'i8'), ('b', 'O')])
+    Empty DataFrame
+    Columns: [a, b]
+    Index: []
+    >>> make_meta(('a', 'f8'))
+    Series([], Name: a, dtype: float64)
+    >>> make_meta('i8')
+    1
+    """
+    if hasattr(x, "_meta"):
+        return x._meta
+    elif is_arraylike(x) and x.shape:
+        return x[:0]
+
+    if index is not None:
+        index = make_meta_dispatch(index)
+
+    if isinstance(x, dict):
+        return cudf.DataFrame(
+            {c: _empty_series(c, d, index=index) for (c, d) in x.items()},
+            index=index,
+        )
+    if isinstance(x, tuple) and len(x) == 2:
+        return _empty_series(x[0], x[1], index=index)
+    elif isinstance(x, (list, tuple)):
+        if not all(isinstance(i, tuple) and len(i) == 2 for i in x):
+            raise ValueError(
+                f"Expected iterable of tuples of (name, dtype), got {x}"
+            )
+        return cudf.DataFrame(
+            {c: _empty_series(c, d, index=index) for (c, d) in x},
+            columns=[c for c, d in x],
+            index=index,
+        )
+    elif not hasattr(x, "dtype") and x is not None:
+        # could be a string, a dtype object, or a python type. Skip `None`,
+        # because it is implicitly converted to `dtype('f8')`, which we don't
+        # want here.
+        try:
+            dtype = np.dtype(x)
+            return _scalar_from_dtype(dtype)
+        except Exception:
+            # Continue on to next check
+            pass
+
+    if is_scalar(x):
+        return _nonempty_scalar(x)
+
+    raise TypeError(f"Don't know how to create metadata from {x}")
+
+
+@concat_dispatch.register((cudf.DataFrame, cudf.Series, cudf.BaseIndex))
+@_dask_cudf_nvtx_annotate
+def concat_cudf(
+    dfs,
+    axis=0,
+    join="outer",
+    uniform=False,
+    filter_warning=True,
+    sort=None,
+    ignore_index=False,
+    **kwargs,
+):
+    assert join == "outer"
+
+    ignore_order = kwargs.get("ignore_order", False)
+    if ignore_order:
+        raise NotImplementedError(
+            "ignore_order parameter is not yet supported in dask-cudf"
+        )
+
+    return cudf.concat(dfs, axis=axis, ignore_index=ignore_index)
+
+
+@categorical_dtype_dispatch.register(
+    (cudf.DataFrame, cudf.Series, cudf.BaseIndex)
+)
+@_dask_cudf_nvtx_annotate
+def categorical_dtype_cudf(categories=None, ordered=False):
+    return cudf.CategoricalDtype(categories=categories, ordered=ordered)
+
+
+@tolist_dispatch.register((cudf.Series, cudf.BaseIndex))
+@_dask_cudf_nvtx_annotate
+def tolist_cudf(obj):
+    return obj.to_arrow().to_pylist()
+
+
+@is_categorical_dtype_dispatch.register(
+    (cudf.Series, cudf.BaseIndex, cudf.CategoricalDtype, Series)
+)
+@_dask_cudf_nvtx_annotate
+def is_categorical_dtype_cudf(obj):
+    return cudf.api.types.is_categorical_dtype(obj)
+
+
+@grouper_dispatch.register((cudf.Series, cudf.DataFrame))
+def get_grouper_cudf(obj):
+    return cudf.core.groupby.Grouper
+
+
+try:
+    try:
+        from dask.array.dispatch import percentile_lookup
+    except ImportError:
+        from dask.dataframe.dispatch import (
+            percentile_dispatch as percentile_lookup,
+        )
+
+    @percentile_lookup.register((cudf.Series, cp.ndarray, cudf.BaseIndex))
+    @_dask_cudf_nvtx_annotate
+    def percentile_cudf(a, q, interpolation="linear"):
+        # Cudf dispatch to the equivalent of `np.percentile`:
+        # https://numpy.org/doc/stable/reference/generated/numpy.percentile.html
+        a = cudf.Series(a)
+        # a is series.
+        n = len(a)
+        if not len(a):
+            return None, n
+        if isinstance(q, Iterator):
+            q = list(q)
+
+        if cudf.api.types.is_categorical_dtype(a.dtype):
+            result = cp.percentile(a.cat.codes, q, interpolation=interpolation)
+
+            return (
+                pd.Categorical.from_codes(
+                    result, a.dtype.categories, a.dtype.ordered
+                ),
+                n,
+            )
+        if np.issubdtype(a.dtype, np.datetime64):
+            result = a.quantile(
+                [i / 100.0 for i in q], interpolation=interpolation
+            )
+
+            if q[0] == 0:
+                # https://github.com/dask/dask/issues/6864
+                result[0] = min(result[0], a.min())
+            return result.to_pandas(), n
+        if not np.issubdtype(a.dtype, np.number):
+            interpolation = "nearest"
+        return (
+            a.quantile(
+                [i / 100.0 for i in q], interpolation=interpolation
+            ).to_pandas(),
+            n,
+        )
+
+except ImportError:
+    pass
+
+
+@pyarrow_schema_dispatch.register((cudf.DataFrame,))
+def _get_pyarrow_schema_cudf(obj, preserve_index=None, **kwargs):
+    if kwargs:
+        warnings.warn(
+            "Ignoring the following arguments to "
+            f"`pyarrow_schema_dispatch`: {list(kwargs)}"
+        )
+
+    return _cudf_to_table(
+        meta_nonempty(obj), preserve_index=preserve_index
+    ).schema
+
+
+@to_pyarrow_table_dispatch.register(cudf.DataFrame)
+def _cudf_to_table(obj, preserve_index=None, **kwargs):
+    if kwargs:
+        warnings.warn(
+            "Ignoring the following arguments to "
+            f"`to_pyarrow_table_dispatch`: {list(kwargs)}"
+        )
+
+    # TODO: Remove this logic when cudf#14159 is resolved
+    # (see: https://github.com/rapidsai/cudf/issues/14159)
+    if preserve_index and isinstance(obj.index, cudf.RangeIndex):
+        obj = obj.copy()
+        obj.index.name = (
+            obj.index.name
+            if obj.index.name is not None
+            else "__index_level_0__"
+        )
+        obj.index = obj.index._as_int_index()
+
+    return obj.to_arrow(preserve_index=preserve_index)
+
+
+@from_pyarrow_table_dispatch.register(cudf.DataFrame)
+def _table_to_cudf(obj, table, self_destruct=None, **kwargs):
+    # cudf ignores self_destruct.
+    kwargs.pop("self_destruct", None)
+    if kwargs:
+        warnings.warn(
+            f"Ignoring the following arguments to "
+            f"`from_pyarrow_table_dispatch`: {list(kwargs)}"
+        )
+    result = obj.from_arrow(table)
+
+    # TODO: Remove this logic when cudf#14159 is resolved
+    # (see: https://github.com/rapidsai/cudf/issues/14159)
+    if "__index_level_0__" in result.index.names:
+        assert len(result.index.names) == 1
+        result.index.name = None
+
+    return result
+
+
+@union_categoricals_dispatch.register((cudf.Series, cudf.BaseIndex))
+@_dask_cudf_nvtx_annotate
+def union_categoricals_cudf(
+    to_union, sort_categories=False, ignore_order=False
+):
+    return cudf.api.types._union_categoricals(
+        to_union, sort_categories=False, ignore_order=False
+    )
+
+
+@hash_object_dispatch.register((cudf.DataFrame, cudf.Series))
+@_dask_cudf_nvtx_annotate
+def hash_object_cudf(frame, index=True):
+    if index:
+        frame = frame.reset_index()
+    return frame.hash_values()
+
+
+@hash_object_dispatch.register(cudf.BaseIndex)
+@_dask_cudf_nvtx_annotate
+def hash_object_cudf_index(ind, index=None):
+
+    if isinstance(ind, cudf.MultiIndex):
+        return ind.to_frame(index=False).hash_values()
+
+    col = cudf.core.column.as_column(ind)
+    return cudf.Series(col).hash_values()
+
+
+@group_split_dispatch.register((cudf.Series, cudf.DataFrame))
+@_dask_cudf_nvtx_annotate
+def group_split_cudf(df, c, k, ignore_index=False):
+    return dict(
+        zip(
+            range(k),
+            df.scatter_by_map(
+                c.astype(np.int32, copy=False),
+                map_size=k,
+                keep_index=not ignore_index,
+            ),
+        )
+    )
+
+
+@sizeof_dispatch.register(cudf.DataFrame)
+@_dask_cudf_nvtx_annotate
+def sizeof_cudf_dataframe(df):
+    return int(
+        sum(col.memory_usage for col in df._data.columns)
+        + df._index.memory_usage()
+    )
+
+
+@sizeof_dispatch.register((cudf.Series, cudf.BaseIndex))
+@_dask_cudf_nvtx_annotate
+def sizeof_cudf_series_index(obj):
+    return obj.memory_usage()
+
+
+def _default_backend(func, *args, **kwargs):
+    # Utility to call a dask.dataframe function with
+    # the default ("pandas") backend
+
+    # NOTE: Some `CudfBackendEntrypoint` methods need to
+    # invoke the "pandas"-version of the same method, but
+    # with custom kwargs (e.g. `engine`). In these cases,
+    # an explicit "pandas" config context is needed to
+    # avoid a recursive loop
+    with config.set({"dataframe.backend": "pandas"}):
+        return func(*args, **kwargs)
+
+
+def _unsupported_kwargs(old, new, kwargs):
+    # Utility to raise a meaningful error when
+    # unsupported kwargs are encountered within
+    # ``to_backend_dispatch``
+    if kwargs:
+        raise ValueError(
+            f"Unsupported key-word arguments used in `to_backend` "
+            f"for {old}-to-{new} conversion: {kwargs}"
+        )
+
+
+# Register cudf->pandas
+to_pandas_dispatch = PandasBackendEntrypoint.to_backend_dispatch()
+
+
+@to_pandas_dispatch.register((cudf.DataFrame, cudf.Series, cudf.Index))
+def to_pandas_dispatch_from_cudf(data, nullable=False, **kwargs):
+    _unsupported_kwargs("cudf", "pandas", kwargs)
+    return data.to_pandas(nullable=nullable)
+
+
+# Register pandas->cudf
+to_cudf_dispatch = Dispatch("to_cudf_dispatch")
+
+
+@to_cudf_dispatch.register((pd.DataFrame, pd.Series, pd.Index))
+def to_cudf_dispatch_from_pandas(data, nan_as_null=None, **kwargs):
+    _unsupported_kwargs("pandas", "cudf", kwargs)
+    return cudf.from_pandas(data, nan_as_null=nan_as_null)
+
+
+# Define "cudf" backend engine to be registered with Dask
+class CudfBackendEntrypoint(DataFrameBackendEntrypoint):
+    """Backend-entrypoint class for Dask-DataFrame
+
+    This class is registered under the name "cudf" for the
+    ``dask.dataframe.backends`` entrypoint in ``setup.cfg``.
+    Dask-DataFrame will use the methods defined in this class
+    in place of ``dask.dataframe.<creation-method>`` when the
+    "dataframe.backend" configuration is set to "cudf":
+
+    Examples
+    --------
+    >>> import dask
+    >>> import dask.dataframe as dd
+    >>> with dask.config.set({"dataframe.backend": "cudf"}):
+    ...     ddf = dd.from_dict({"a": range(10)})
+    >>> type(ddf)
+    <class 'dask_cudf.core.DataFrame'>
+    """
+
+    @classmethod
+    def to_backend_dispatch(cls):
+        return to_cudf_dispatch
+
+    @classmethod
+    def to_backend(cls, data: dd.core._Frame, **kwargs):
+        if isinstance(data._meta, (cudf.DataFrame, cudf.Series, cudf.Index)):
+            # Already a cudf-backed collection
+            _unsupported_kwargs("cudf", "cudf", kwargs)
+            return data
+        return data.map_partitions(cls.to_backend_dispatch(), **kwargs)
+
+    @staticmethod
+    def from_dict(
+        data,
+        npartitions,
+        orient="columns",
+        dtype=None,
+        columns=None,
+        constructor=cudf.DataFrame,
+    ):
+
+        return _default_backend(
+            dd.from_dict,
+            data,
+            npartitions=npartitions,
+            orient=orient,
+            dtype=dtype,
+            columns=columns,
+            constructor=constructor,
+        )
+
+    @staticmethod
+    def read_parquet(*args, engine=None, **kwargs):
+        from dask_cudf.io.parquet import CudfEngine
+
+        return _default_backend(
+            dd.read_parquet,
+            *args,
+            engine=CudfEngine,
+            **kwargs,
+        )
+
+    @staticmethod
+    def read_json(*args, **kwargs):
+        from dask_cudf.io.json import read_json
+
+        return read_json(*args, **kwargs)
+
+    @staticmethod
+    def read_orc(*args, **kwargs):
+        from dask_cudf.io import read_orc
+
+        return read_orc(*args, **kwargs)
+
+    @staticmethod
+    def read_csv(*args, **kwargs):
+        from dask_cudf.io import read_csv
+
+        return read_csv(*args, **kwargs)
+
+    @staticmethod
+    def read_hdf(*args, **kwargs):
+        from dask_cudf import from_dask_dataframe
+
+        # HDF5 reader not yet implemented in cudf
+        warnings.warn(
+            "read_hdf is not yet implemented in cudf/dask_cudf. "
+            "Moving to cudf from pandas. Expect poor performance!"
+        )
+        return from_dask_dataframe(
+            _default_backend(dd.read_hdf, *args, **kwargs)
+        )
diff --git a/python/dask_cudf/dask_cudf/core.py b/python/dask_cudf/dask_cudf/core.py
new file mode 100644
index 0000000..5b37e6e
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/core.py
@@ -0,0 +1,743 @@
+# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+
+import math
+import textwrap
+import warnings
+
+import numpy as np
+import pandas as pd
+from tlz import partition_all
+
+from dask import dataframe as dd
+from dask.base import normalize_token, tokenize
+from dask.dataframe.core import (
+    Scalar,
+    handle_out,
+    make_meta as dask_make_meta,
+    map_partitions,
+)
+from dask.dataframe.utils import raise_on_meta_error
+from dask.highlevelgraph import HighLevelGraph
+from dask.utils import M, OperatorMethodMixin, apply, derived_from, funcname
+
+import cudf
+from cudf import _lib as libcudf
+from cudf.utils.utils import _dask_cudf_nvtx_annotate
+
+from dask_cudf import sorting
+from dask_cudf.accessors import ListMethods, StructMethods
+from dask_cudf.sorting import _get_shuffle_type
+
+
+class _Frame(dd.core._Frame, OperatorMethodMixin):
+    """Superclass for DataFrame and Series
+
+    Parameters
+    ----------
+    dsk : dict
+        The dask graph to compute this DataFrame
+    name : str
+        The key prefix that specifies which keys in the dask comprise this
+        particular DataFrame / Series
+    meta : cudf.DataFrame, cudf.Series, or cudf.Index
+        An empty cudf object with names, dtypes, and indices matching the
+        expected output.
+    divisions : tuple of index values
+        Values along which we partition our blocks on the index
+    """
+
+    def _is_partition_type(self, meta):
+        return isinstance(meta, self._partition_type)
+
+    def __repr__(self):
+        s = "<dask_cudf.%s | %d tasks | %d npartitions>"
+        return s % (type(self).__name__, len(self.dask), self.npartitions)
+
+    @_dask_cudf_nvtx_annotate
+    def to_dask_dataframe(self, **kwargs):
+        """Create a dask.dataframe object from a dask_cudf object"""
+        nullable_pd_dtype = kwargs.get("nullable_pd_dtype", False)
+        return self.map_partitions(
+            M.to_pandas, nullable_pd_dtype=nullable_pd_dtype
+        )
+
+
+concat = dd.concat
+
+
+normalize_token.register(_Frame, lambda a: a._name)
+
+
+class DataFrame(_Frame, dd.core.DataFrame):
+    """
+    A distributed Dask DataFrame where the backing dataframe is a
+    :class:`cuDF DataFrame <cudf:cudf.DataFrame>`.
+
+    Typically you would not construct this object directly, but rather
+    use one of Dask-cuDF's IO routines.
+
+    Most operations on :doc:`Dask DataFrames <dask:dataframe>` are
+    supported, with many of the same caveats.
+
+    """
+
+    _partition_type = cudf.DataFrame
+
+    @_dask_cudf_nvtx_annotate
+    def _assign_column(self, k, v):
+        def assigner(df, k, v):
+            out = df.copy()
+            out[k] = v
+            return out
+
+        meta = assigner(self._meta, k, dask_make_meta(v))
+        return self.map_partitions(assigner, k, v, meta=meta)
+
+    @_dask_cudf_nvtx_annotate
+    def apply_rows(self, func, incols, outcols, kwargs=None, cache_key=None):
+        import uuid
+
+        if kwargs is None:
+            kwargs = {}
+
+        if cache_key is None:
+            cache_key = uuid.uuid4()
+
+        def do_apply_rows(df, func, incols, outcols, kwargs):
+            return df.apply_rows(
+                func, incols, outcols, kwargs, cache_key=cache_key
+            )
+
+        meta = do_apply_rows(self._meta, func, incols, outcols, kwargs)
+        return self.map_partitions(
+            do_apply_rows, func, incols, outcols, kwargs, meta=meta
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def merge(self, other, shuffle=None, **kwargs):
+        on = kwargs.pop("on", None)
+        if isinstance(on, tuple):
+            on = list(on)
+        return super().merge(
+            other, on=on, shuffle=_get_shuffle_type(shuffle), **kwargs
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def join(self, other, shuffle=None, **kwargs):
+        # CuDF doesn't support "right" join yet
+        how = kwargs.pop("how", "left")
+        if how == "right":
+            return other.join(other=self, how="left", **kwargs)
+
+        on = kwargs.pop("on", None)
+        if isinstance(on, tuple):
+            on = list(on)
+        return super().join(
+            other, how=how, on=on, shuffle=_get_shuffle_type(shuffle), **kwargs
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def set_index(
+        self, other, sorted=False, divisions=None, shuffle=None, **kwargs
+    ):
+
+        pre_sorted = sorted
+        del sorted
+
+        if (
+            divisions == "quantile"
+            or isinstance(divisions, (cudf.DataFrame, cudf.Series))
+            or (
+                isinstance(other, str)
+                and cudf.api.types.is_string_dtype(self[other].dtype)
+            )
+        ):
+
+            # Let upstream-dask handle "pre-sorted" case
+            if pre_sorted:
+                return dd.shuffle.set_sorted_index(
+                    self, other, divisions=divisions, **kwargs
+                )
+
+            by = other
+            if not isinstance(other, list):
+                by = [by]
+            if len(by) > 1:
+                raise ValueError("Dask does not support MultiIndex (yet).")
+            if divisions == "quantile":
+                divisions = None
+
+            # Use dask_cudf's sort_values
+            df = self.sort_values(
+                by,
+                max_branch=kwargs.get("max_branch", None),
+                divisions=divisions,
+                set_divisions=True,
+                ignore_index=True,
+                shuffle=shuffle,
+            )
+
+            # Ignore divisions if its a dataframe
+            if isinstance(divisions, cudf.DataFrame):
+                divisions = None
+
+            # Set index and repartition
+            df2 = df.map_partitions(
+                sorting.set_index_post,
+                index_name=other,
+                drop=kwargs.get("drop", True),
+                column_dtype=df.columns.dtype,
+            )
+            npartitions = kwargs.get("npartitions", self.npartitions)
+            partition_size = kwargs.get("partition_size", None)
+            if partition_size:
+                return df2.repartition(partition_size=partition_size)
+            if not divisions and df2.npartitions != npartitions:
+                return df2.repartition(npartitions=npartitions)
+            if divisions and df2.npartitions != len(divisions) - 1:
+                return df2.repartition(divisions=divisions)
+            return df2
+
+        return super().set_index(
+            other,
+            sorted=pre_sorted,
+            shuffle=_get_shuffle_type(shuffle),
+            divisions=divisions,
+            **kwargs,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def sort_values(
+        self,
+        by,
+        ignore_index=False,
+        max_branch=None,
+        divisions=None,
+        set_divisions=False,
+        ascending=True,
+        na_position="last",
+        sort_function=None,
+        sort_function_kwargs=None,
+        shuffle=None,
+        **kwargs,
+    ):
+        if kwargs:
+            raise ValueError(
+                f"Unsupported input arguments passed : {list(kwargs.keys())}"
+            )
+
+        df = sorting.sort_values(
+            self,
+            by,
+            max_branch=max_branch,
+            divisions=divisions,
+            set_divisions=set_divisions,
+            ignore_index=ignore_index,
+            ascending=ascending,
+            na_position=na_position,
+            shuffle=shuffle,
+            sort_function=sort_function,
+            sort_function_kwargs=sort_function_kwargs,
+        )
+
+        if ignore_index:
+            return df.reset_index(drop=True)
+        return df
+
+    @_dask_cudf_nvtx_annotate
+    def to_parquet(self, path, *args, **kwargs):
+        """Calls dask.dataframe.io.to_parquet with CudfEngine backend"""
+        from dask_cudf.io import to_parquet
+
+        return to_parquet(self, path, *args, **kwargs)
+
+    @_dask_cudf_nvtx_annotate
+    def to_orc(self, path, **kwargs):
+        """Calls dask_cudf.io.to_orc"""
+        from dask_cudf.io import to_orc
+
+        return to_orc(self, path, **kwargs)
+
+    @derived_from(pd.DataFrame)
+    @_dask_cudf_nvtx_annotate
+    def var(
+        self,
+        axis=None,
+        skipna=True,
+        ddof=1,
+        split_every=False,
+        dtype=None,
+        out=None,
+        naive=False,
+    ):
+        axis = self._validate_axis(axis)
+        meta = self._meta_nonempty.var(axis=axis, skipna=skipna)
+        if axis == 1:
+            result = map_partitions(
+                M.var,
+                self,
+                meta=meta,
+                token=self._token_prefix + "var",
+                axis=axis,
+                skipna=skipna,
+                ddof=ddof,
+            )
+            return handle_out(out, result)
+        elif naive:
+            return _naive_var(self, meta, skipna, ddof, split_every, out)
+        else:
+            return _parallel_var(self, meta, skipna, split_every, out)
+
+    @_dask_cudf_nvtx_annotate
+    def shuffle(self, *args, shuffle=None, **kwargs):
+        """Wraps dask.dataframe DataFrame.shuffle method"""
+        return super().shuffle(
+            *args, shuffle=_get_shuffle_type(shuffle), **kwargs
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def groupby(self, by=None, **kwargs):
+        from .groupby import CudfDataFrameGroupBy
+
+        return CudfDataFrameGroupBy(self, by=by, **kwargs)
+
+
+@_dask_cudf_nvtx_annotate
+def sum_of_squares(x):
+    x = x.astype("f8")._column
+    outcol = libcudf.reduce.reduce("sum_of_squares", x)
+    return cudf.Series(outcol)
+
+
+@_dask_cudf_nvtx_annotate
+def var_aggregate(x2, x, n, ddof):
+    try:
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter("always")
+            result = (x2 / n) - (x / n) ** 2
+        if ddof != 0:
+            result = result * n / (n - ddof)
+        return result
+    except ZeroDivisionError:
+        return np.float64(np.nan)
+
+
+@_dask_cudf_nvtx_annotate
+def nlargest_agg(x, **kwargs):
+    return cudf.concat(x).nlargest(**kwargs)
+
+
+@_dask_cudf_nvtx_annotate
+def nsmallest_agg(x, **kwargs):
+    return cudf.concat(x).nsmallest(**kwargs)
+
+
+class Series(_Frame, dd.core.Series):
+    _partition_type = cudf.Series
+
+    @_dask_cudf_nvtx_annotate
+    def count(self, split_every=False):
+        return reduction(
+            [self],
+            chunk=M.count,
+            aggregate=np.sum,
+            split_every=split_every,
+            meta="i8",
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def mean(self, split_every=False):
+        sum = self.sum(split_every=split_every)
+        n = self.count(split_every=split_every)
+        return sum / n
+
+    @derived_from(pd.DataFrame)
+    @_dask_cudf_nvtx_annotate
+    def var(
+        self,
+        axis=None,
+        skipna=True,
+        ddof=1,
+        split_every=False,
+        dtype=None,
+        out=None,
+        naive=False,
+    ):
+        axis = self._validate_axis(axis)
+        meta = self._meta_nonempty.var(axis=axis, skipna=skipna)
+        if axis == 1:
+            result = map_partitions(
+                M.var,
+                self,
+                meta=meta,
+                token=self._token_prefix + "var",
+                axis=axis,
+                skipna=skipna,
+                ddof=ddof,
+            )
+            return handle_out(out, result)
+        elif naive:
+            return _naive_var(self, meta, skipna, ddof, split_every, out)
+        else:
+            return _parallel_var(self, meta, skipna, split_every, out)
+
+    @_dask_cudf_nvtx_annotate
+    def groupby(self, *args, **kwargs):
+        from .groupby import CudfSeriesGroupBy
+
+        return CudfSeriesGroupBy(self, *args, **kwargs)
+
+    @property  # type: ignore
+    @_dask_cudf_nvtx_annotate
+    def list(self):
+        return ListMethods(self)
+
+    @property  # type: ignore
+    @_dask_cudf_nvtx_annotate
+    def struct(self):
+        return StructMethods(self)
+
+
+class Index(Series, dd.core.Index):
+    _partition_type = cudf.Index  # type: ignore
+
+
+@_dask_cudf_nvtx_annotate
+def _naive_var(ddf, meta, skipna, ddof, split_every, out):
+    num = ddf._get_numeric_data()
+    x = 1.0 * num.sum(skipna=skipna, split_every=split_every)
+    x2 = 1.0 * (num**2).sum(skipna=skipna, split_every=split_every)
+    n = num.count(split_every=split_every)
+    name = ddf._token_prefix + "var"
+    result = map_partitions(
+        var_aggregate, x2, x, n, token=name, meta=meta, ddof=ddof
+    )
+    if isinstance(ddf, DataFrame):
+        result.divisions = (min(ddf.columns), max(ddf.columns))
+    return handle_out(out, result)
+
+
+@_dask_cudf_nvtx_annotate
+def _parallel_var(ddf, meta, skipna, split_every, out):
+    def _local_var(x, skipna):
+        if skipna:
+            n = x.count()
+            avg = x.mean(skipna=skipna)
+        else:
+            # Not skipping nulls, so might as well
+            # avoid the full `count` operation
+            n = len(x)
+            avg = x.sum(skipna=skipna) / n
+        m2 = ((x - avg) ** 2).sum(skipna=skipna)
+        return n, avg, m2
+
+    def _aggregate_var(parts):
+        n, avg, m2 = parts[0]
+        for i in range(1, len(parts)):
+            n_a, avg_a, m2_a = n, avg, m2
+            n_b, avg_b, m2_b = parts[i]
+            n = n_a + n_b
+            avg = (n_a * avg_a + n_b * avg_b) / n
+            delta = avg_b - avg_a
+            m2 = m2_a + m2_b + delta**2 * n_a * n_b / n
+        return n, avg, m2
+
+    def _finalize_var(vals):
+        n, _, m2 = vals
+        return m2 / (n - 1)
+
+    # Build graph
+    nparts = ddf.npartitions
+    if not split_every:
+        split_every = nparts
+    name = "var-" + tokenize(skipna, split_every, out)
+    local_name = "local-" + name
+    num = ddf._get_numeric_data()
+    dsk = {
+        (local_name, n, 0): (_local_var, (num._name, n), skipna)
+        for n in range(nparts)
+    }
+
+    # Use reduction tree
+    widths = [nparts]
+    while nparts > 1:
+        nparts = math.ceil(nparts / split_every)
+        widths.append(nparts)
+    height = len(widths)
+    for depth in range(1, height):
+        for group in range(widths[depth]):
+            p_max = widths[depth - 1]
+            lstart = split_every * group
+            lstop = min(lstart + split_every, p_max)
+            node_list = [
+                (local_name, p, depth - 1) for p in range(lstart, lstop)
+            ]
+            dsk[(local_name, group, depth)] = (_aggregate_var, node_list)
+    if height == 1:
+        group = depth = 0
+    dsk[(name, 0)] = (_finalize_var, (local_name, group, depth))
+
+    graph = HighLevelGraph.from_collections(name, dsk, dependencies=[num, ddf])
+    result = dd.core.new_dd_object(graph, name, meta, (None, None))
+    if isinstance(ddf, DataFrame):
+        result.divisions = (min(ddf.columns), max(ddf.columns))
+    return handle_out(out, result)
+
+
+@_dask_cudf_nvtx_annotate
+def _extract_meta(x):
+    """
+    Extract internal cache data (``_meta``) from dask_cudf objects
+    """
+    if isinstance(x, (Scalar, _Frame)):
+        return x._meta
+    elif isinstance(x, list):
+        return [_extract_meta(_x) for _x in x]
+    elif isinstance(x, tuple):
+        return tuple(_extract_meta(_x) for _x in x)
+    elif isinstance(x, dict):
+        return {k: _extract_meta(v) for k, v in x.items()}
+    return x
+
+
+@_dask_cudf_nvtx_annotate
+def _emulate(func, *args, **kwargs):
+    """
+    Apply a function using args / kwargs. If arguments contain dd.DataFrame /
+    dd.Series, using internal cache (``_meta``) for calculation
+    """
+    with raise_on_meta_error(funcname(func)):
+        return func(*_extract_meta(args), **_extract_meta(kwargs))
+
+
+@_dask_cudf_nvtx_annotate
+def align_partitions(args):
+    """Align partitions between dask_cudf objects.
+
+    Note that if all divisions are unknown, but have equal npartitions, then
+    they will be passed through unchanged.
+    """
+    dfs = [df for df in args if isinstance(df, _Frame)]
+    if not dfs:
+        return args
+
+    divisions = dfs[0].divisions
+    if not all(df.divisions == divisions for df in dfs):
+        raise NotImplementedError("Aligning mismatched partitions")
+    return args
+
+
+@_dask_cudf_nvtx_annotate
+def reduction(
+    args,
+    chunk=None,
+    aggregate=None,
+    combine=None,
+    meta=None,
+    token=None,
+    chunk_kwargs=None,
+    aggregate_kwargs=None,
+    combine_kwargs=None,
+    split_every=None,
+    **kwargs,
+):
+    """Generic tree reduction operation.
+
+    Parameters
+    ----------
+    args :
+        Positional arguments for the `chunk` function. All `dask.dataframe`
+        objects should be partitioned and indexed equivalently.
+    chunk : function [block-per-arg] -> block
+        Function to operate on each block of data
+    aggregate : function list-of-blocks -> block
+        Function to operate on the list of results of chunk
+    combine : function list-of-blocks -> block, optional
+        Function to operate on intermediate lists of results of chunk
+        in a tree-reduction. If not provided, defaults to aggregate.
+    $META
+    token : str, optional
+        The name to use for the output keys.
+    chunk_kwargs : dict, optional
+        Keywords for the chunk function only.
+    aggregate_kwargs : dict, optional
+        Keywords for the aggregate function only.
+    combine_kwargs : dict, optional
+        Keywords for the combine function only.
+    split_every : int, optional
+        Group partitions into groups of this size while performing a
+        tree-reduction. If set to False, no tree-reduction will be used,
+        and all intermediates will be concatenated and passed to ``aggregate``.
+        Default is 8.
+    kwargs :
+        All remaining keywords will be passed to ``chunk``, ``aggregate``, and
+        ``combine``.
+    """
+    if chunk_kwargs is None:
+        chunk_kwargs = dict()
+    if aggregate_kwargs is None:
+        aggregate_kwargs = dict()
+    chunk_kwargs.update(kwargs)
+    aggregate_kwargs.update(kwargs)
+
+    if combine is None:
+        if combine_kwargs:
+            raise ValueError("`combine_kwargs` provided with no `combine`")
+        combine = aggregate
+        combine_kwargs = aggregate_kwargs
+    else:
+        if combine_kwargs is None:
+            combine_kwargs = dict()
+        combine_kwargs.update(kwargs)
+
+    if not isinstance(args, (tuple, list)):
+        args = [args]
+
+    npartitions = {arg.npartitions for arg in args if isinstance(arg, _Frame)}
+    if len(npartitions) > 1:
+        raise ValueError("All arguments must have same number of partitions")
+    npartitions = npartitions.pop()
+
+    if split_every is None:
+        split_every = 8
+    elif split_every is False:
+        split_every = npartitions
+    elif split_every < 2 or not isinstance(split_every, int):
+        raise ValueError("split_every must be an integer >= 2")
+
+    token_key = tokenize(
+        token or (chunk, aggregate),
+        meta,
+        args,
+        chunk_kwargs,
+        aggregate_kwargs,
+        combine_kwargs,
+        split_every,
+    )
+
+    # Chunk
+    a = f"{token or funcname(chunk)}-chunk-{token_key}"
+    if len(args) == 1 and isinstance(args[0], _Frame) and not chunk_kwargs:
+        dsk = {
+            (a, 0, i): (chunk, key)
+            for i, key in enumerate(args[0].__dask_keys__())
+        }
+    else:
+        dsk = {
+            (a, 0, i): (
+                apply,
+                chunk,
+                [(x._name, i) if isinstance(x, _Frame) else x for x in args],
+                chunk_kwargs,
+            )
+            for i in range(args[0].npartitions)
+        }
+
+    # Combine
+    b = f"{token or funcname(combine)}-combine-{token_key}"
+    k = npartitions
+    depth = 0
+    while k > split_every:
+        for part_i, inds in enumerate(partition_all(split_every, range(k))):
+            conc = (list, [(a, depth, i) for i in inds])
+            dsk[(b, depth + 1, part_i)] = (
+                (apply, combine, [conc], combine_kwargs)
+                if combine_kwargs
+                else (combine, conc)
+            )
+        k = part_i + 1
+        a = b
+        depth += 1
+
+    # Aggregate
+    b = f"{token or funcname(aggregate)}-agg-{token_key}"
+    conc = (list, [(a, depth, i) for i in range(k)])
+    if aggregate_kwargs:
+        dsk[(b, 0)] = (apply, aggregate, [conc], aggregate_kwargs)
+    else:
+        dsk[(b, 0)] = (aggregate, conc)
+
+    if meta is None:
+        meta_chunk = _emulate(apply, chunk, args, chunk_kwargs)
+        meta = _emulate(apply, aggregate, [[meta_chunk]], aggregate_kwargs)
+    meta = dask_make_meta(meta)
+
+    graph = HighLevelGraph.from_collections(b, dsk, dependencies=args)
+    return dd.core.new_dd_object(graph, b, meta, (None, None))
+
+
+@_dask_cudf_nvtx_annotate
+def from_cudf(data, npartitions=None, chunksize=None, sort=True, name=None):
+    if isinstance(getattr(data, "index", None), cudf.MultiIndex):
+        raise NotImplementedError(
+            "dask_cudf does not support MultiIndex Dataframes."
+        )
+
+    name = name or ("from_cudf-" + tokenize(data, npartitions or chunksize))
+    return dd.from_pandas(
+        data,
+        npartitions=npartitions,
+        chunksize=chunksize,
+        sort=sort,
+        name=name,
+    )
+
+
+from_cudf.__doc__ = (
+    textwrap.dedent(
+        """
+        Create a :class:`.DataFrame` from a :class:`cudf.DataFrame`.
+
+        This function is a thin wrapper around
+        :func:`dask.dataframe.from_pandas`, accepting the same
+        arguments (described below) excepting that it operates on cuDF
+        rather than pandas objects.\n
+        """
+    )
+    + textwrap.dedent(dd.from_pandas.__doc__)
+)
+
+
+@_dask_cudf_nvtx_annotate
+def from_dask_dataframe(df):
+    """
+    Convert a Dask :class:`dask.dataframe.DataFrame` to a Dask-cuDF
+    one.
+
+    Parameters
+    ----------
+    df : dask.dataframe.DataFrame
+        The Dask dataframe to convert
+
+    Returns
+    -------
+    dask_cudf.DataFrame : A new Dask collection backed by cuDF objects
+    """
+    return df.map_partitions(cudf.from_pandas)
+
+
+for name in (
+    "add",
+    "sub",
+    "mul",
+    "truediv",
+    "floordiv",
+    "mod",
+    "pow",
+    "radd",
+    "rsub",
+    "rmul",
+    "rtruediv",
+    "rfloordiv",
+    "rmod",
+    "rpow",
+):
+    meth = getattr(cudf.DataFrame, name)
+    DataFrame._bind_operator_method(name, meth, original=cudf.Series)
+
+    meth = getattr(cudf.Series, name)
+    Series._bind_operator_method(name, meth, original=cudf.Series)
+
+for name in ("lt", "gt", "le", "ge", "ne", "eq"):
+    meth = getattr(cudf.Series, name)
+    Series._bind_comparison_method(name, meth, original=cudf.Series)
diff --git a/python/dask_cudf/dask_cudf/groupby.py b/python/dask_cudf/dask_cudf/groupby.py
new file mode 100644
index 0000000..f4bbcaf
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/groupby.py
@@ -0,0 +1,892 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from functools import wraps
+from typing import Set
+
+import numpy as np
+import pandas as pd
+
+from dask.dataframe.core import (
+    DataFrame as DaskDataFrame,
+    aca,
+    split_out_on_cols,
+)
+from dask.dataframe.groupby import DataFrameGroupBy, SeriesGroupBy
+from dask.utils import funcname
+
+import cudf
+from cudf.utils.utils import _dask_cudf_nvtx_annotate
+
+# aggregations that are dask-cudf optimized
+OPTIMIZED_AGGS = (
+    "count",
+    "mean",
+    "std",
+    "var",
+    "sum",
+    "min",
+    "max",
+    "collect",
+    "first",
+    "last",
+)
+
+
+def _check_groupby_optimized(func):
+    """
+    Decorator for dask-cudf's groupby methods that returns the dask-cudf
+    optimized method if the groupby object is supported, otherwise
+    reverting to the upstream Dask method
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        gb = args[0]
+        if _groupby_optimized(gb):
+            return func(*args, **kwargs)
+        # note that we use upstream Dask's default kwargs for this call if
+        # none are specified; this shouldn't be an issue as those defaults are
+        # consistent with dask-cudf
+        return getattr(super(type(gb), gb), func.__name__)(*args[1:], **kwargs)
+
+    return wrapper
+
+
+class CudfDataFrameGroupBy(DataFrameGroupBy):
+    @_dask_cudf_nvtx_annotate
+    def __init__(self, *args, sort=None, **kwargs):
+        self.sep = kwargs.pop("sep", "___")
+        self.as_index = kwargs.pop("as_index", True)
+        super().__init__(*args, sort=sort, **kwargs)
+
+    @_dask_cudf_nvtx_annotate
+    def __getitem__(self, key):
+        if isinstance(key, list):
+            g = CudfDataFrameGroupBy(
+                self.obj,
+                by=self.by,
+                slice=key,
+                sort=self.sort,
+                **self.dropna,
+            )
+        else:
+            g = CudfSeriesGroupBy(
+                self.obj,
+                by=self.by,
+                slice=key,
+                sort=self.sort,
+                **self.dropna,
+            )
+
+        g._meta = g._meta[key]
+        return g
+
+    @_dask_cudf_nvtx_annotate
+    def _make_groupby_method_aggs(self, agg_name):
+        """Create aggs dictionary for aggregation methods"""
+
+        if isinstance(self.by, list):
+            return {c: agg_name for c in self.obj.columns if c not in self.by}
+        return {c: agg_name for c in self.obj.columns if c != self.by}
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def count(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("count"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def mean(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("mean"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def std(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("std"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def var(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("var"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def sum(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("sum"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def min(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("min"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def max(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("max"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def collect(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("collect"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def first(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("first"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def last(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            self._make_groupby_method_aggs("last"),
+            split_every,
+            split_out,
+        )
+
+    @_dask_cudf_nvtx_annotate
+    def aggregate(self, arg, split_every=None, split_out=1, shuffle=None):
+        if arg == "size":
+            return self.size()
+
+        arg = _redirect_aggs(arg)
+
+        if _groupby_optimized(self) and _aggs_optimized(arg, OPTIMIZED_AGGS):
+            if isinstance(self._meta.grouping.keys, cudf.MultiIndex):
+                keys = self._meta.grouping.keys.names
+            else:
+                keys = self._meta.grouping.keys.name
+
+            return groupby_agg(
+                self.obj,
+                keys,
+                arg,
+                split_every=split_every,
+                split_out=split_out,
+                sep=self.sep,
+                sort=self.sort,
+                as_index=self.as_index,
+                shuffle=shuffle,
+                **self.dropna,
+            )
+
+        return super().aggregate(
+            arg,
+            split_every=split_every,
+            split_out=split_out,
+            shuffle=shuffle,
+        )
+
+
+class CudfSeriesGroupBy(SeriesGroupBy):
+    @_dask_cudf_nvtx_annotate
+    def __init__(self, *args, sort=None, **kwargs):
+        self.sep = kwargs.pop("sep", "___")
+        self.as_index = kwargs.pop("as_index", True)
+        super().__init__(*args, sort=sort, **kwargs)
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def count(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "count"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def mean(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "mean"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def std(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "std"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def var(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "var"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def sum(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "sum"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def min(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "min"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def max(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "max"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def collect(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "collect"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def first(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "first"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    @_check_groupby_optimized
+    def last(self, split_every=None, split_out=1):
+        return _make_groupby_agg_call(
+            self,
+            {self._slice: "last"},
+            split_every,
+            split_out,
+        )[self._slice]
+
+    @_dask_cudf_nvtx_annotate
+    def aggregate(self, arg, split_every=None, split_out=1, shuffle=None):
+        if arg == "size":
+            return self.size()
+
+        arg = _redirect_aggs(arg)
+
+        if not isinstance(arg, dict):
+            arg = {self._slice: arg}
+
+        if _groupby_optimized(self) and _aggs_optimized(arg, OPTIMIZED_AGGS):
+            return _make_groupby_agg_call(
+                self, arg, split_every, split_out, shuffle
+            )[self._slice]
+
+        return super().aggregate(
+            arg,
+            split_every=split_every,
+            split_out=split_out,
+            shuffle=shuffle,
+        )
+
+
+def _shuffle_aggregate(
+    ddf,
+    gb_cols,
+    chunk,
+    chunk_kwargs,
+    aggregate,
+    aggregate_kwargs,
+    split_every,
+    split_out,
+    token=None,
+    sort=None,
+    shuffle=None,
+):
+    # Shuffle-based groupby aggregation
+    # NOTE: This function is the dask_cudf version of
+    # dask.dataframe.groupby._shuffle_aggregate
+
+    # Step 1 - Chunkwise groupby operation
+    chunk_name = f"{token or funcname(chunk)}-chunk"
+    chunked = ddf.map_partitions(
+        chunk,
+        meta=chunk(ddf._meta, **chunk_kwargs),
+        token=chunk_name,
+        **chunk_kwargs,
+    )
+
+    # Step 2 - Perform global sort or shuffle
+    shuffle_npartitions = max(
+        chunked.npartitions // split_every,
+        split_out,
+    )
+    if sort and split_out > 1:
+        # Sort-based code path
+        result = (
+            chunked.repartition(npartitions=shuffle_npartitions)
+            .sort_values(
+                gb_cols,
+                ignore_index=True,
+                shuffle=shuffle,
+            )
+            .map_partitions(
+                aggregate,
+                meta=aggregate(chunked._meta, **aggregate_kwargs),
+                **aggregate_kwargs,
+            )
+        )
+    else:
+        # Hash-based code path
+        result = chunked.shuffle(
+            gb_cols,
+            npartitions=shuffle_npartitions,
+            ignore_index=True,
+            shuffle=shuffle,
+        ).map_partitions(
+            aggregate,
+            meta=aggregate(chunked._meta, **aggregate_kwargs),
+            **aggregate_kwargs,
+        )
+
+    # Step 3 - Repartition and return
+    if split_out < result.npartitions:
+        return result.repartition(npartitions=split_out)
+    return result
+
+
+@_dask_cudf_nvtx_annotate
+def groupby_agg(
+    ddf,
+    gb_cols,
+    aggs_in,
+    split_every=None,
+    split_out=None,
+    dropna=True,
+    sep="___",
+    sort=False,
+    as_index=True,
+    shuffle=None,
+):
+    """Optimized groupby aggregation for Dask-CuDF.
+
+    Parameters
+    ----------
+    ddf : DataFrame
+        DataFrame object to perform grouping on.
+    gb_cols : str or list[str]
+        Column names to group by.
+    aggs_in : str, list, or dict
+        Aggregations to perform.
+    split_every : int (optional)
+        How to group intermediate aggregates.
+    dropna : bool
+        Drop grouping key values corresponding to NA values.
+    as_index : bool
+        Currently ignored.
+    sort : bool
+        Sort the group keys, better performance is obtained when
+        not sorting.
+    shuffle : str (optional)
+        Control how shuffling of the DataFrame is performed.
+    sep : str
+        Internal usage.
+
+
+    Notes
+    -----
+    This "optimized" approach is more performant than the algorithm in
+    implemented in :meth:`DataFrame.apply` because it allows the cuDF
+    backend to perform multiple aggregations at once.
+
+    This aggregation algorithm only supports the following options
+
+    * "collect"
+    * "count"
+    * "first"
+    * "last"
+    * "max"
+    * "mean"
+    * "min"
+    * "std"
+    * "sum"
+    * "var"
+
+
+    See Also
+    --------
+    DataFrame.groupby : generic groupby of a DataFrame
+    dask.dataframe.apply_concat_apply : for more description of the
+        split_every argument.
+
+    """
+    # Assert that aggregations are supported
+    aggs = _redirect_aggs(aggs_in)
+    if not _aggs_optimized(aggs, OPTIMIZED_AGGS):
+        raise ValueError(
+            f"Supported aggs include {OPTIMIZED_AGGS} for groupby_agg API. "
+            f"Aggregations must be specified with dict or list syntax."
+        )
+
+    # If split_every is False, we use an all-to-one reduction
+    if split_every is False:
+        split_every = max(ddf.npartitions, 2)
+
+    # Deal with default split_out and split_every params
+    split_every = split_every or 8
+    split_out = split_out or 1
+
+    # Standardize `gb_cols`, `columns`, and `aggs`
+    if isinstance(gb_cols, str):
+        gb_cols = [gb_cols]
+    columns = [c for c in ddf.columns if c not in gb_cols]
+    if not isinstance(aggs, dict):
+        aggs = {col: aggs for col in columns}
+
+    # Assert if our output will have a MultiIndex; this will be the case if
+    # any value in the `aggs` dict is not a string (i.e. multiple/named
+    # aggregations per column)
+    str_cols_out = True
+    aggs_renames = {}
+    for col in aggs:
+        if isinstance(aggs[col], str) or callable(aggs[col]):
+            aggs[col] = [aggs[col]]
+        elif isinstance(aggs[col], dict):
+            str_cols_out = False
+            col_aggs = []
+            for k, v in aggs[col].items():
+                aggs_renames[col, v] = k
+                col_aggs.append(v)
+            aggs[col] = col_aggs
+        else:
+            str_cols_out = False
+        if col in gb_cols:
+            columns.append(col)
+
+    # Construct meta
+    _aggs = aggs.copy()
+    if str_cols_out:
+        # Metadata should use `str` for dict values if that is
+        # what the user originally specified (column names will
+        # be str, rather than tuples).
+        for col in aggs:
+            _aggs[col] = _aggs[col][0]
+    _meta = ddf._meta.groupby(gb_cols, as_index=as_index).agg(_aggs)
+    if aggs_renames:
+        col_array = []
+        agg_array = []
+        for col, agg in _meta.columns:
+            col_array.append(col)
+            agg_array.append(aggs_renames.get((col, agg), agg))
+        _meta.columns = pd.MultiIndex.from_arrays([col_array, agg_array])
+
+    chunk = _groupby_partition_agg
+    chunk_kwargs = {
+        "gb_cols": gb_cols,
+        "aggs": aggs,
+        "columns": columns,
+        "dropna": dropna,
+        "sort": sort,
+        "sep": sep,
+    }
+
+    combine = _tree_node_agg
+    combine_kwargs = {
+        "gb_cols": gb_cols,
+        "dropna": dropna,
+        "sort": sort,
+        "sep": sep,
+    }
+
+    aggregate = _finalize_gb_agg
+    aggregate_kwargs = {
+        "gb_cols": gb_cols,
+        "aggs": aggs,
+        "columns": columns,
+        "final_columns": _meta.columns,
+        "as_index": as_index,
+        "dropna": dropna,
+        "sort": sort,
+        "sep": sep,
+        "str_cols_out": str_cols_out,
+        "aggs_renames": aggs_renames,
+    }
+
+    # Use shuffle=True for split_out>1
+    if sort and split_out > 1 and shuffle is None:
+        shuffle = "tasks"
+
+    # Check if we are using the shuffle-based algorithm
+    if shuffle:
+        # Shuffle-based aggregation
+        return _shuffle_aggregate(
+            ddf,
+            gb_cols,
+            chunk,
+            chunk_kwargs,
+            aggregate,
+            aggregate_kwargs,
+            split_every,
+            split_out,
+            token="cudf-aggregate",
+            sort=sort,
+            shuffle=shuffle if isinstance(shuffle, str) else None,
+        )
+
+    # Deal with sort/shuffle defaults
+    if split_out > 1 and sort:
+        raise ValueError(
+            "dask-cudf's groupby algorithm does not yet support "
+            "`sort=True` when `split_out>1`, unless a shuffle-based "
+            "algorithm is used. Please use `split_out=1`, group "
+            "with `sort=False`, or set `shuffle=True`."
+        )
+
+    # Determine required columns to enable column projection
+    required_columns = list(
+        set(gb_cols).union(aggs.keys()).intersection(ddf.columns)
+    )
+
+    return aca(
+        [ddf[required_columns]],
+        chunk=chunk,
+        chunk_kwargs=chunk_kwargs,
+        combine=combine,
+        combine_kwargs=combine_kwargs,
+        aggregate=aggregate,
+        aggregate_kwargs=aggregate_kwargs,
+        token="cudf-aggregate",
+        split_every=split_every,
+        split_out=split_out,
+        split_out_setup=split_out_on_cols,
+        split_out_setup_kwargs={"cols": gb_cols},
+        sort=sort,
+        ignore_index=True,
+    )
+
+
+@_dask_cudf_nvtx_annotate
+def _make_groupby_agg_call(gb, aggs, split_every, split_out, shuffle=None):
+    """Helper method to consolidate the common `groupby_agg` call for all
+    aggregations in one place
+    """
+
+    return groupby_agg(
+        gb.obj,
+        gb.by,
+        aggs,
+        split_every=split_every,
+        split_out=split_out,
+        sep=gb.sep,
+        sort=gb.sort,
+        as_index=gb.as_index,
+        shuffle=shuffle,
+        **gb.dropna,
+    )
+
+
+@_dask_cudf_nvtx_annotate
+def _redirect_aggs(arg):
+    """Redirect aggregations to their corresponding name in cuDF"""
+    redirects = {
+        sum: "sum",
+        max: "max",
+        min: "min",
+        list: "collect",
+        "list": "collect",
+    }
+    if isinstance(arg, dict):
+        new_arg = dict()
+        for col in arg:
+            if isinstance(arg[col], list):
+                new_arg[col] = [redirects.get(agg, agg) for agg in arg[col]]
+            elif isinstance(arg[col], dict):
+                new_arg[col] = {
+                    k: redirects.get(v, v) for k, v in arg[col].items()
+                }
+            else:
+                new_arg[col] = redirects.get(arg[col], arg[col])
+        return new_arg
+    if isinstance(arg, list):
+        return [redirects.get(agg, agg) for agg in arg]
+    return redirects.get(arg, arg)
+
+
+@_dask_cudf_nvtx_annotate
+def _aggs_optimized(arg, supported: set):
+    """Check that aggregations in `arg` are a subset of `supported`"""
+    if isinstance(arg, (list, dict)):
+        if isinstance(arg, dict):
+            _global_set: Set[str] = set()
+            for col in arg:
+                if isinstance(arg[col], list):
+                    _global_set = _global_set.union(set(arg[col]))
+                elif isinstance(arg[col], dict):
+                    _global_set = _global_set.union(set(arg[col].values()))
+                else:
+                    _global_set.add(arg[col])
+        else:
+            _global_set = set(arg)
+
+        return bool(_global_set.issubset(supported))
+    elif isinstance(arg, str):
+        return arg in supported
+    return False
+
+
+@_dask_cudf_nvtx_annotate
+def _groupby_optimized(gb):
+    """Check that groupby input can use dask-cudf optimized codepath"""
+    return isinstance(gb.obj, DaskDataFrame) and (
+        isinstance(gb.by, str)
+        or (isinstance(gb.by, list) and all(isinstance(x, str) for x in gb.by))
+    )
+
+
+def _make_name(col_name, sep="_"):
+    """Combine elements of `col_name` into a single string, or no-op if
+    `col_name` is already a string
+    """
+    if isinstance(col_name, str):
+        return col_name
+    return sep.join(name for name in col_name if name != "")
+
+
+@_dask_cudf_nvtx_annotate
+def _groupby_partition_agg(df, gb_cols, aggs, columns, dropna, sort, sep):
+    """Initial partition-level aggregation task.
+
+    This is the first operation to be executed on each input
+    partition in `groupby_agg`.  Depending on `aggs`, four possible
+    groupby aggregations ("count", "sum", "min", and "max") are
+    performed.  The result is then partitioned (by hashing `gb_cols`)
+    into a number of distinct dictionary elements.  The number of
+    elements in the output dictionary (`split_out`) corresponds to
+    the number of partitions in the final output of `groupby_agg`.
+    """
+
+    # Modify dict for initial (partition-wise) aggregations
+    _agg_dict = {}
+    for col, agg_list in aggs.items():
+        _agg_dict[col] = set()
+        for agg in agg_list:
+            if agg in ("mean", "std", "var"):
+                _agg_dict[col].add("count")
+                _agg_dict[col].add("sum")
+            else:
+                _agg_dict[col].add(agg)
+        _agg_dict[col] = list(_agg_dict[col])
+        if set(agg_list).intersection({"std", "var"}):
+            pow2_name = _make_name((col, "pow2"), sep=sep)
+            df[pow2_name] = df[col].astype("float64").pow(2)
+            _agg_dict[pow2_name] = ["sum"]
+
+    gb = df.groupby(gb_cols, dropna=dropna, as_index=False, sort=sort).agg(
+        _agg_dict
+    )
+    output_columns = [_make_name(name, sep=sep) for name in gb.columns]
+    gb.columns = output_columns
+    # Return with deterministic column ordering
+    return gb[sorted(output_columns)]
+
+
+@_dask_cudf_nvtx_annotate
+def _tree_node_agg(df, gb_cols, dropna, sort, sep):
+    """Node in groupby-aggregation reduction tree.
+
+    The input DataFrame (`df`) corresponds to the
+    concatenated output of one or more `_groupby_partition_agg`
+    tasks. In this function, "sum", "min" and/or "max" groupby
+    aggregations will be used to combine the statistics for
+    duplicate keys.
+    """
+
+    agg_dict = {}
+    for col in df.columns:
+        if col in gb_cols:
+            continue
+        agg = col.split(sep)[-1]
+        if agg in ("count", "sum"):
+            agg_dict[col] = ["sum"]
+        elif agg in OPTIMIZED_AGGS:
+            agg_dict[col] = [agg]
+        else:
+            raise ValueError(f"Unexpected aggregation: {agg}")
+
+    gb = df.groupby(gb_cols, dropna=dropna, as_index=False, sort=sort).agg(
+        agg_dict
+    )
+
+    # Don't include the last aggregation in the column names
+    output_columns = [
+        _make_name(name[:-1] if isinstance(name, tuple) else name, sep=sep)
+        for name in gb.columns
+    ]
+    gb.columns = output_columns
+    # Return with deterministic column ordering
+    return gb[sorted(output_columns)]
+
+
+@_dask_cudf_nvtx_annotate
+def _var_agg(df, col, count_name, sum_name, pow2_sum_name, ddof=1):
+    """Calculate variance (given count, sum, and sum-squared columns)."""
+
+    # Select count, sum, and sum-squared
+    n = df[count_name]
+    x = df[sum_name]
+    x2 = df[pow2_sum_name]
+
+    # Use sum-squared approach to get variance
+    var = x2 - x**2 / n
+    div = n - ddof
+    div[div < 1] = 1  # Avoid division by 0
+    var /= div
+
+    # Set appropriate NaN elements
+    # (since we avoided 0-division)
+    var[(n - ddof) == 0] = np.nan
+
+    return var
+
+
+@_dask_cudf_nvtx_annotate
+def _finalize_gb_agg(
+    gb_in,
+    gb_cols,
+    aggs,
+    columns,
+    final_columns,
+    as_index,
+    dropna,
+    sort,
+    sep,
+    str_cols_out,
+    aggs_renames,
+):
+    """Final aggregation task.
+
+    This is the final operation on each output partitions
+    of the `groupby_agg` algorithm.  This function must
+    take care of higher-order aggregations, like "mean",
+    "std" and "var".  We also need to deal with the column
+    index, the row index, and final sorting behavior.
+    """
+
+    gb = _tree_node_agg(gb_in, gb_cols, dropna, sort, sep)
+
+    # Deal with higher-order aggregations
+    for col in columns:
+        agg_list = aggs.get(col, [])
+        agg_set = set(agg_list)
+        if agg_set.intersection({"mean", "std", "var"}):
+            count_name = _make_name((col, "count"), sep=sep)
+            sum_name = _make_name((col, "sum"), sep=sep)
+            if agg_set.intersection({"std", "var"}):
+                pow2_sum_name = _make_name((col, "pow2", "sum"), sep=sep)
+                var = _var_agg(gb, col, count_name, sum_name, pow2_sum_name)
+                if "var" in agg_list:
+                    name_var = _make_name((col, "var"), sep=sep)
+                    gb[name_var] = var
+                if "std" in agg_list:
+                    name_std = _make_name((col, "std"), sep=sep)
+                    gb[name_std] = np.sqrt(var)
+                gb.drop(columns=[pow2_sum_name], inplace=True)
+            if "mean" in agg_list:
+                mean_name = _make_name((col, "mean"), sep=sep)
+                gb[mean_name] = gb[sum_name] / gb[count_name]
+            if "sum" not in agg_list:
+                gb.drop(columns=[sum_name], inplace=True)
+            if "count" not in agg_list:
+                gb.drop(columns=[count_name], inplace=True)
+        if "collect" in agg_list:
+            collect_name = _make_name((col, "collect"), sep=sep)
+            gb[collect_name] = gb[collect_name].list.concat()
+
+    # Ensure sorted keys if `sort=True`
+    if sort:
+        gb = gb.sort_values(gb_cols)
+
+    # Set index if necessary
+    if as_index:
+        gb.set_index(gb_cols, inplace=True)
+
+    # Unflatten column names
+    col_array = []
+    agg_array = []
+    for col in gb.columns:
+        if col in gb_cols:
+            col_array.append(col)
+            agg_array.append("")
+        else:
+            name, agg = col.split(sep)
+            col_array.append(name)
+            agg_array.append(aggs_renames.get((name, agg), agg))
+    if str_cols_out:
+        gb.columns = col_array
+    else:
+        gb.columns = pd.MultiIndex.from_arrays([col_array, agg_array])
+
+    return gb[final_columns]
diff --git a/python/dask_cudf/dask_cudf/io/__init__.py b/python/dask_cudf/dask_cudf/io/__init__.py
new file mode 100644
index 0000000..76bb2ea
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+from .csv import read_csv
+from .json import read_json
+from .orc import read_orc, to_orc
+from .text import read_text
+
+try:
+    from .parquet import read_parquet, to_parquet
+except ImportError:
+    pass
diff --git a/python/dask_cudf/dask_cudf/io/csv.py b/python/dask_cudf/dask_cudf/io/csv.py
new file mode 100644
index 0000000..fa54003
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/csv.py
@@ -0,0 +1,222 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import os
+from glob import glob
+from warnings import warn
+
+from fsspec.utils import infer_compression
+
+from dask import dataframe as dd
+from dask.base import tokenize
+from dask.dataframe.io.csv import make_reader
+from dask.utils import apply, parse_bytes
+
+import cudf
+
+
+def read_csv(path, blocksize="default", **kwargs):
+    """
+    Read CSV files into a :class:`.DataFrame`.
+
+    This API parallelizes the :func:`cudf:cudf.read_csv` function in
+    the following ways:
+
+    It supports loading many files at once using globstrings:
+
+    >>> import dask_cudf
+    >>> df = dask_cudf.read_csv("myfiles.*.csv")
+
+    In some cases it can break up large files:
+
+    >>> df = dask_cudf.read_csv("largefile.csv", blocksize="256 MiB")
+
+    It can read CSV files from external resources (e.g. S3, HTTP, FTP)
+
+    >>> df = dask_cudf.read_csv("s3://bucket/myfiles.*.csv")
+    >>> df = dask_cudf.read_csv("https://www.mycloud.com/sample.csv")
+
+    Internally ``read_csv`` uses :func:`cudf:cudf.read_csv` and
+    supports many of the same keyword arguments with the same
+    performance guarantees. See the docstring for
+    :func:`cudf:cudf.read_csv` for more information on available
+    keyword arguments.
+
+    Parameters
+    ----------
+    path : str, path object, or file-like object
+        Either a path to a file (a str, :py:class:`pathlib.Path`, or
+        py._path.local.LocalPath), URL (including http, ftp, and S3
+        locations), or any object with a read() method (such as
+        builtin :py:func:`open` file handler function or
+        :py:class:`~io.StringIO`).
+    blocksize : int or str, default "256 MiB"
+        The target task partition size. If ``None``, a single block
+        is used for each file.
+    **kwargs : dict
+        Passthrough key-word arguments that are sent to
+        :func:`cudf:cudf.read_csv`.
+
+    Notes
+    -----
+    If any of `skipfooter`/`skiprows`/`nrows` are passed,
+    `blocksize` will default to None.
+
+    Examples
+    --------
+    >>> import dask_cudf
+    >>> ddf = dask_cudf.read_csv("sample.csv", usecols=["a", "b"])
+    >>> ddf.compute()
+       a      b
+    0  1     hi
+    1  2  hello
+    2  3     ai
+
+    """
+
+    # Handle `chunksize` deprecation
+    if "chunksize" in kwargs:
+        chunksize = kwargs.pop("chunksize", "default")
+        warn(
+            "`chunksize` is deprecated and will be removed in the future. "
+            "Please use `blocksize` instead.",
+            FutureWarning,
+        )
+        if blocksize == "default":
+            blocksize = chunksize
+
+    # Set default `blocksize`
+    if blocksize == "default":
+        if (
+            kwargs.get("skipfooter", 0) != 0
+            or kwargs.get("skiprows", 0) != 0
+            or kwargs.get("nrows", None) is not None
+        ):
+            # Cannot read in blocks if skipfooter,
+            # skiprows or nrows is passed.
+            blocksize = None
+        else:
+            blocksize = "256 MiB"
+
+    if "://" in str(path):
+        func = make_reader(cudf.read_csv, "read_csv", "CSV")
+        return func(path, blocksize=blocksize, **kwargs)
+    else:
+        return _internal_read_csv(path=path, blocksize=blocksize, **kwargs)
+
+
+def _internal_read_csv(path, blocksize="256 MiB", **kwargs):
+    if isinstance(blocksize, str):
+        blocksize = parse_bytes(blocksize)
+
+    if isinstance(path, list):
+        filenames = path
+    elif isinstance(path, str):
+        filenames = sorted(glob(path))
+    elif hasattr(path, "__fspath__"):
+        filenames = sorted(glob(path.__fspath__()))
+    else:
+        raise TypeError(f"Path type not understood:{type(path)}")
+
+    if not filenames:
+        msg = f"A file in: {filenames} does not exist."
+        raise FileNotFoundError(msg)
+
+    name = "read-csv-" + tokenize(
+        path, tokenize, **kwargs
+    )  # TODO: get last modified time
+
+    compression = kwargs.get("compression", "infer")
+
+    if compression == "infer":
+        # Infer compression from first path by default
+        compression = infer_compression(filenames[0])
+
+    if compression and blocksize:
+        # compressed CSVs reading must read the entire file
+        kwargs.pop("byte_range", None)
+        warn(
+            "Warning %s compression does not support breaking apart files\n"
+            "Please ensure that each individual file can fit in memory and\n"
+            "use the keyword ``blocksize=None to remove this message``\n"
+            "Setting ``blocksize=(size of file)``" % compression
+        )
+        blocksize = None
+
+    if blocksize is None:
+        return read_csv_without_blocksize(path, **kwargs)
+
+    # Let dask.dataframe generate meta
+    dask_reader = make_reader(cudf.read_csv, "read_csv", "CSV")
+    kwargs1 = kwargs.copy()
+    usecols = kwargs1.pop("usecols", None)
+    dtype = kwargs1.pop("dtype", None)
+    meta = dask_reader(filenames[0], **kwargs1)._meta
+    names = meta.columns
+    if usecols or dtype:
+        # Regenerate meta with original kwargs if
+        # `usecols` or `dtype` was specified
+        meta = dask_reader(filenames[0], **kwargs)._meta
+
+    dsk = {}
+    i = 0
+    dtypes = meta.dtypes.values
+
+    for fn in filenames:
+        size = os.path.getsize(fn)
+        for start in range(0, size, blocksize):
+            kwargs2 = kwargs.copy()
+            kwargs2["byte_range"] = (
+                start,
+                blocksize,
+            )  # specify which chunk of the file we care about
+            if start != 0:
+                kwargs2["names"] = names  # no header in the middle of the file
+                kwargs2["header"] = None
+            dsk[(name, i)] = (apply, _read_csv, [fn, dtypes], kwargs2)
+
+            i += 1
+
+    divisions = [None] * (len(dsk) + 1)
+    return dd.core.new_dd_object(dsk, name, meta, divisions)
+
+
+def _read_csv(fn, dtypes=None, **kwargs):
+    return cudf.read_csv(fn, **kwargs)
+
+
+def read_csv_without_blocksize(path, **kwargs):
+    """Read entire CSV with optional compression (gzip/zip)
+
+    Parameters
+    ----------
+    path : str
+        path to files (support for glob)
+    """
+    if isinstance(path, list):
+        filenames = path
+    elif isinstance(path, str):
+        filenames = sorted(glob(path))
+    elif hasattr(path, "__fspath__"):
+        filenames = sorted(glob(path.__fspath__()))
+    else:
+        raise TypeError(f"Path type not understood:{type(path)}")
+
+    name = "read-csv-" + tokenize(path, **kwargs)
+
+    meta_kwargs = kwargs.copy()
+    if "skipfooter" in meta_kwargs:
+        meta_kwargs.pop("skipfooter")
+    if "nrows" in meta_kwargs:
+        meta_kwargs.pop("nrows")
+    # Read "head" of first file (first 5 rows).
+    # Convert to empty df for metadata.
+    meta = cudf.read_csv(filenames[0], nrows=5, **meta_kwargs).iloc[:0]
+
+    graph = {
+        (name, i): (apply, cudf.read_csv, [fn], kwargs)
+        for i, fn in enumerate(filenames)
+    }
+
+    divisions = [None] * (len(filenames) + 1)
+
+    return dd.core.new_dd_object(graph, name, meta, divisions)
diff --git a/python/dask_cudf/dask_cudf/io/json.py b/python/dask_cudf/dask_cudf/io/json.py
new file mode 100644
index 0000000..2a6ad60
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/json.py
@@ -0,0 +1,75 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from functools import partial
+
+import dask
+
+import cudf
+
+from dask_cudf.backends import _default_backend
+
+
+def read_json(url_path, engine="auto", **kwargs):
+    """Read JSON data into a :class:`.DataFrame`.
+
+    This function wraps :func:`dask.dataframe.read_json`, and passes
+    ``engine=partial(cudf.read_json, engine="auto")`` by default.
+
+    Parameters
+    ----------
+    url_path : str, list of str
+        Location to read from. If a string, can include a glob character to
+        find a set of file names.
+        Supports protocol specifications such as ``"s3://"``.
+    engine : str or Callable, default "auto"
+
+        If str, this value will be used as the ``engine`` argument
+        when :func:`cudf.read_json` is used to create each partition.
+        If a :obj:`~typing.Callable`, this value will be used as the
+        underlying function used to create each partition from JSON
+        data. The default value is "auto", so that
+        ``engine=partial(cudf.read_json, engine="auto")`` will be
+        passed to :func:`dask.dataframe.read_json` by default.
+
+    **kwargs :
+        Key-word arguments to pass through to :func:`dask.dataframe.read_json`.
+
+    Returns
+    -------
+    :class:`.DataFrame`
+
+    Examples
+    --------
+    Load single file
+
+    >>> from dask_cudf import read_json
+    >>> read_json('myfile.json')  # doctest: +SKIP
+
+    Load large line-delimited JSON files using partitions of approx
+    256MB size
+
+    >>> read_json('data/file*.csv', blocksize=2**28)  # doctest: +SKIP
+
+    Load nested JSON data
+
+    >>> read_json('myfile.json')  # doctest: +SKIP
+
+    See Also
+    --------
+    dask.dataframe.read_json
+
+    """
+
+    # TODO: Add optimized code path to leverage the
+    # `byte_range` argument in `cudf.read_json` for
+    # local storage (see `dask_cudf.read_csv`)
+    return _default_backend(
+        dask.dataframe.read_json,
+        url_path,
+        engine=(
+            partial(cudf.read_json, engine=engine)
+            if isinstance(engine, str)
+            else engine
+        ),
+        **kwargs,
+    )
diff --git a/python/dask_cudf/dask_cudf/io/orc.py b/python/dask_cudf/dask_cudf/io/orc.py
new file mode 100644
index 0000000..49fea0d
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/orc.py
@@ -0,0 +1,199 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from io import BufferedWriter, IOBase
+
+from fsspec.core import get_fs_token_paths
+from fsspec.utils import stringify_path
+from pyarrow import orc as orc
+
+from dask import dataframe as dd
+from dask.base import tokenize
+from dask.dataframe.io.utils import _get_pyarrow_dtypes
+
+import cudf
+
+
+def _read_orc_stripe(fs, path, stripe, columns, kwargs=None):
+    """Pull out specific columns from specific stripe"""
+    if kwargs is None:
+        kwargs = {}
+    with fs.open(path, "rb") as f:
+        df_stripe = cudf.read_orc(
+            f, stripes=[stripe], columns=columns, **kwargs
+        )
+    return df_stripe
+
+
+def read_orc(path, columns=None, filters=None, storage_options=None, **kwargs):
+    """Read ORC files into a :class:`.DataFrame`.
+
+    Note that this function is mostly borrowed from upstream Dask.
+
+    Parameters
+    ----------
+    path : str or list[str]
+        Location of file(s), which can be a full URL with protocol specifier,
+        and may include glob character if a single string.
+    columns : None or list[str]
+        Columns to load. If None, loads all.
+    filters : None or list of tuple or list of lists of tuples
+        If not None, specifies a filter predicate used to filter out
+        row groups using statistics stored for each row group as
+        Parquet metadata. Row groups that do not match the given
+        filter predicate are not read. The predicate is expressed in
+        `disjunctive normal form (DNF)
+        <https://en.wikipedia.org/wiki/Disjunctive_normal_form>`__
+        like ``[[('x', '=', 0), ...], ...]``. DNF allows arbitrary
+        boolean logical combinations of single column predicates. The
+        innermost tuples each describe a single column predicate. The
+        list of inner predicates is interpreted as a conjunction
+        (AND), forming a more selective and multiple column predicate.
+        Finally, the outermost list combines these filters as a
+        disjunction (OR). Predicates may also be passed as a list of
+        tuples. This form is interpreted as a single conjunction. To
+        express OR in predicates, one must use the (preferred)
+        notation of list of lists of tuples.
+    storage_options : None or dict
+        Further parameters to pass to the bytes backend.
+
+    See Also
+    --------
+    dask.dataframe.read_orc
+
+    Returns
+    -------
+    dask_cudf.DataFrame
+
+    """
+
+    storage_options = storage_options or {}
+    fs, fs_token, paths = get_fs_token_paths(
+        path, mode="rb", storage_options=storage_options
+    )
+    schema = None
+    nstripes_per_file = []
+    for path in paths:
+        with fs.open(path, "rb") as f:
+            o = orc.ORCFile(f)
+            if schema is None:
+                schema = o.schema
+            elif schema != o.schema:
+                raise ValueError(
+                    "Incompatible schemas while parsing ORC files"
+                )
+            nstripes_per_file.append(o.nstripes)
+    schema = _get_pyarrow_dtypes(schema, categories=None)
+    if columns is not None:
+        ex = set(columns) - set(schema)
+        if ex:
+            raise ValueError(
+                f"Requested columns ({ex}) not in schema ({set(schema)})"
+            )
+    else:
+        columns = list(schema)
+
+    with fs.open(paths[0], "rb") as f:
+        meta = cudf.read_orc(
+            f,
+            stripes=[0] if nstripes_per_file[0] else None,
+            columns=columns,
+            **kwargs,
+        )
+
+    name = "read-orc-" + tokenize(fs_token, path, columns, **kwargs)
+    dsk = {}
+    N = 0
+    for path, n in zip(paths, nstripes_per_file):
+        for stripe in (
+            range(n)
+            if filters is None
+            else cudf.io.orc._filter_stripes(filters, path)
+        ):
+            dsk[(name, N)] = (
+                _read_orc_stripe,
+                fs,
+                path,
+                stripe,
+                columns,
+                kwargs,
+            )
+            N += 1
+
+    divisions = [None] * (len(dsk) + 1)
+    return dd.core.new_dd_object(dsk, name, meta, divisions)
+
+
+def write_orc_partition(df, path, fs, filename, compression="snappy"):
+    full_path = fs.sep.join([path, filename])
+    with fs.open(full_path, mode="wb") as out_file:
+        if not isinstance(out_file, IOBase):
+            out_file = BufferedWriter(out_file)
+        cudf.io.to_orc(df, out_file, compression=compression)
+    return full_path
+
+
+def to_orc(
+    df,
+    path,
+    write_index=True,
+    storage_options=None,
+    compression="snappy",
+    compute=True,
+    **kwargs,
+):
+    """
+    Write a :class:`.DataFrame` to ORC file(s) (one file per partition).
+
+    Parameters
+    ----------
+    df : DataFrame
+    path : str or pathlib.Path
+        Destination directory for data.  Prepend with protocol like ``s3://``
+        or ``hdfs://`` for remote data.
+    write_index : boolean, optional
+        Whether or not to write the index. Defaults to True.
+    storage_options : None or dict
+        Further parameters to pass to the bytes backend.
+    compression : string or dict, optional
+    compute : bool, optional
+        If True (default) then the result is computed immediately. If
+        False then a :class:`~dask.delayed.Delayed` object is returned
+        for future computation.
+
+    """
+
+    from dask import compute as dask_compute, delayed
+
+    # TODO: Use upstream dask implementation once available
+    #       (see: Dask Issue#5596)
+
+    if hasattr(path, "name"):
+        path = stringify_path(path)
+    fs, _, _ = get_fs_token_paths(
+        path, mode="wb", storage_options=storage_options
+    )
+    # Trim any protocol information from the path before forwarding
+    path = fs._strip_protocol(path)
+
+    if write_index:
+        df = df.reset_index()
+    else:
+        # Not writing index - might as well drop it
+        df = df.reset_index(drop=True)
+
+    fs.mkdirs(path, exist_ok=True)
+
+    # Use i_offset and df.npartitions to define file-name list
+    filenames = ["part.%i.orc" % i for i in range(df.npartitions)]
+
+    # write parts
+    dwrite = delayed(write_orc_partition)
+    parts = [
+        dwrite(d, path, fs, filename, compression=compression)
+        for d, filename in zip(df.to_delayed(), filenames)
+    ]
+
+    if compute:
+        return dask_compute(*parts)
+
+    return delayed(list)(parts)
diff --git a/python/dask_cudf/dask_cudf/io/parquet.py b/python/dask_cudf/dask_cudf/io/parquet.py
new file mode 100644
index 0000000..d82d539
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/parquet.py
@@ -0,0 +1,531 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+import itertools
+import warnings
+from contextlib import ExitStack
+from functools import partial
+from io import BufferedWriter, BytesIO, IOBase
+
+import numpy as np
+from pyarrow import dataset as pa_ds, parquet as pq
+
+from dask import dataframe as dd
+from dask.dataframe.io.parquet.arrow import ArrowDatasetEngine
+
+try:
+    from dask.dataframe.io.parquet import (
+        create_metadata_file as create_metadata_file_dd,
+    )
+except ImportError:
+    create_metadata_file_dd = None
+
+import cudf
+from cudf.core.column import as_column, build_categorical_column
+from cudf.io import write_to_dataset
+from cudf.io.parquet import (
+    _apply_post_filters,
+    _default_open_file_options,
+    _normalize_filters,
+)
+from cudf.utils.dtypes import cudf_dtype_from_pa_type
+from cudf.utils.ioutils import (
+    _ROW_GROUP_SIZE_BYTES_DEFAULT,
+    _is_local_filesystem,
+    _open_remote_files,
+)
+
+
+class CudfEngine(ArrowDatasetEngine):
+    @classmethod
+    def _create_dd_meta(cls, dataset_info, **kwargs):
+        # Start with pandas-version of meta
+        meta_pd = super()._create_dd_meta(dataset_info, **kwargs)
+
+        # Convert to cudf
+        meta_cudf = cudf.from_pandas(meta_pd)
+
+        # Re-set "object" dtypes to align with pa schema
+        kwargs = dataset_info.get("kwargs", {})
+        set_object_dtypes_from_pa_schema(
+            meta_cudf,
+            kwargs.get("schema", None),
+        )
+
+        return meta_cudf
+
+    @classmethod
+    def multi_support(cls):
+        # Assert that this class is CudfEngine
+        # and that multi-part reading is supported
+        return cls == CudfEngine
+
+    @classmethod
+    def _read_paths(
+        cls,
+        paths,
+        fs,
+        columns=None,
+        row_groups=None,
+        filters=None,
+        partitions=None,
+        partitioning=None,
+        partition_keys=None,
+        open_file_options=None,
+        dataset_kwargs=None,
+        **kwargs,
+    ):
+
+        # Simplify row_groups if all None
+        if row_groups == [None for path in paths]:
+            row_groups = None
+
+        # Make sure we read in the columns needed for row-wise
+        # filtering after IO. This means that one or more columns
+        # will be dropped almost immediately after IO. However,
+        # we do NEED these columns for accurate filtering.
+        filters = _normalize_filters(filters)
+        projected_columns = None
+        if columns and filters:
+            projected_columns = [c for c in columns if c is not None]
+            columns = sorted(
+                set(v[0] for v in itertools.chain.from_iterable(filters))
+                | set(projected_columns)
+            )
+
+        dataset_kwargs = dataset_kwargs or {}
+        dataset_kwargs["partitioning"] = partitioning or "hive"
+        with ExitStack() as stack:
+
+            # Non-local filesystem handling
+            paths_or_fobs = paths
+            if not _is_local_filesystem(fs):
+                paths_or_fobs = _open_remote_files(
+                    paths_or_fobs,
+                    fs,
+                    context_stack=stack,
+                    **_default_open_file_options(
+                        open_file_options, columns, row_groups
+                    ),
+                )
+
+            # Use cudf to read in data
+            try:
+                df = cudf.read_parquet(
+                    paths_or_fobs,
+                    engine="cudf",
+                    columns=columns,
+                    row_groups=row_groups if row_groups else None,
+                    dataset_kwargs=dataset_kwargs,
+                    categorical_partitions=False,
+                    **kwargs,
+                )
+            except RuntimeError as err:
+                # TODO: Remove try/except after null-schema issue is resolved
+                # (See: https://github.com/rapidsai/cudf/issues/12702)
+                if len(paths_or_fobs) > 1:
+                    df = cudf.concat(
+                        [
+                            cudf.read_parquet(
+                                pof,
+                                engine="cudf",
+                                columns=columns,
+                                row_groups=row_groups[i]
+                                if row_groups
+                                else None,
+                                dataset_kwargs=dataset_kwargs,
+                                categorical_partitions=False,
+                                **kwargs,
+                            )
+                            for i, pof in enumerate(paths_or_fobs)
+                        ]
+                    )
+                else:
+                    raise err
+
+        # Apply filters (if any are defined)
+        df = _apply_post_filters(df, filters)
+
+        if projected_columns:
+            # Elements of `projected_columns` may now be in the index.
+            # We must filter these names from our projection
+            projected_columns = [
+                col for col in projected_columns if col in df._column_names
+            ]
+            df = df[projected_columns]
+
+        if partitions and partition_keys is None:
+
+            # Use `HivePartitioning` by default
+            ds = pa_ds.dataset(
+                paths,
+                filesystem=fs,
+                **dataset_kwargs,
+            )
+            frag = next(ds.get_fragments())
+            if frag:
+                # Extract hive-partition keys, and make sure they
+                # are ordered the same as they are in `partitions`
+                raw_keys = pa_ds._get_partition_keys(frag.partition_expression)
+                partition_keys = [
+                    (hive_part.name, raw_keys[hive_part.name])
+                    for hive_part in partitions
+                ]
+
+        if partition_keys:
+            if partitions is None:
+                raise ValueError("Must pass partition sets")
+
+            for i, (name, index2) in enumerate(partition_keys):
+
+                if len(partitions[i].keys):
+                    # Build a categorical column from `codes` directly
+                    # (since the category is often a larger dtype)
+                    codes = as_column(
+                        partitions[i].keys.get_loc(index2),
+                        length=len(df),
+                    )
+                    df[name] = build_categorical_column(
+                        categories=partitions[i].keys,
+                        codes=codes,
+                        size=codes.size,
+                        offset=codes.offset,
+                        ordered=False,
+                    )
+                elif name not in df.columns:
+                    # Add non-categorical partition column
+                    df[name] = as_column(index2, length=len(df))
+
+        return df
+
+    @classmethod
+    def read_partition(
+        cls,
+        fs,
+        pieces,
+        columns,
+        index,
+        categories=(),
+        partitions=(),
+        filters=None,
+        partitioning=None,
+        schema=None,
+        open_file_options=None,
+        **kwargs,
+    ):
+
+        if columns is not None:
+            columns = [c for c in columns]
+        if isinstance(index, list):
+            columns += index
+
+        dataset_kwargs = kwargs.get("dataset", {})
+        partitioning = partitioning or dataset_kwargs.get("partitioning", None)
+        if isinstance(partitioning, dict):
+            partitioning = pa_ds.partitioning(**partitioning)
+
+        # Check if we are actually selecting any columns
+        read_columns = columns
+        if schema and columns:
+            ignored = set(schema.names) - set(columns)
+            if not ignored:
+                read_columns = None
+
+        if not isinstance(pieces, list):
+            pieces = [pieces]
+
+        # Extract supported kwargs from `kwargs`
+        read_kwargs = kwargs.get("read", {})
+        read_kwargs.update(open_file_options or {})
+        check_file_size = read_kwargs.pop("check_file_size", None)
+
+        # Wrap reading logic in a `try` block so that we can
+        # inform the user that the `read_parquet` partition
+        # size is too large for the available memory
+        try:
+
+            # Assume multi-piece read
+            paths = []
+            rgs = []
+            last_partition_keys = None
+            dfs = []
+
+            for i, piece in enumerate(pieces):
+
+                (path, row_group, partition_keys) = piece
+                row_group = None if row_group == [None] else row_group
+
+                # File-size check to help "protect" users from change
+                # to up-stream `split_row_groups` default. We only
+                # check the file size if this partition corresponds
+                # to a full file, and `check_file_size` is defined
+                if check_file_size and len(pieces) == 1 and row_group is None:
+                    file_size = fs.size(path)
+                    if file_size > check_file_size:
+                        warnings.warn(
+                            f"A large parquet file ({file_size}B) is being "
+                            f"used to create a DataFrame partition in "
+                            f"read_parquet. This may cause out of memory "
+                            f"exceptions in operations downstream. See the "
+                            f"notes on split_row_groups in the read_parquet "
+                            f"documentation. Setting split_row_groups "
+                            f"explicitly will silence this warning."
+                        )
+
+                if i > 0 and partition_keys != last_partition_keys:
+                    dfs.append(
+                        cls._read_paths(
+                            paths,
+                            fs,
+                            columns=read_columns,
+                            row_groups=rgs if rgs else None,
+                            filters=filters,
+                            partitions=partitions,
+                            partitioning=partitioning,
+                            partition_keys=last_partition_keys,
+                            dataset_kwargs=dataset_kwargs,
+                            **read_kwargs,
+                        )
+                    )
+                    paths = []
+                    rgs = []
+                    last_partition_keys = None
+                paths.append(path)
+                rgs.append(
+                    [row_group]
+                    if not isinstance(row_group, list)
+                    and row_group is not None
+                    else row_group
+                )
+                last_partition_keys = partition_keys
+
+            dfs.append(
+                cls._read_paths(
+                    paths,
+                    fs,
+                    columns=read_columns,
+                    row_groups=rgs if rgs else None,
+                    filters=filters,
+                    partitions=partitions,
+                    partitioning=partitioning,
+                    partition_keys=last_partition_keys,
+                    dataset_kwargs=dataset_kwargs,
+                    **read_kwargs,
+                )
+            )
+            df = cudf.concat(dfs) if len(dfs) > 1 else dfs[0]
+
+            # Re-set "object" dtypes align with pa schema
+            set_object_dtypes_from_pa_schema(df, schema)
+
+            if index and (index[0] in df.columns):
+                df = df.set_index(index[0])
+            elif index is False and df.index.names != (None,):
+                # If index=False, we shouldn't have a named index
+                df.reset_index(inplace=True)
+
+        except MemoryError as err:
+            raise MemoryError(
+                "Parquet data was larger than the available GPU memory!\n\n"
+                "See the notes on split_row_groups in the read_parquet "
+                "documentation.\n\n"
+                "Original Error: " + str(err)
+            )
+            raise err
+
+        return df
+
+    @staticmethod
+    def write_partition(
+        df,
+        path,
+        fs,
+        filename,
+        partition_on,
+        return_metadata,
+        fmd=None,
+        compression="snappy",
+        index_cols=None,
+        **kwargs,
+    ):
+        preserve_index = False
+        if len(index_cols) and set(index_cols).issubset(set(df.columns)):
+            df.set_index(index_cols, drop=True, inplace=True)
+            preserve_index = True
+        if partition_on:
+            md = write_to_dataset(
+                df=df,
+                root_path=path,
+                compression=compression,
+                filename=filename,
+                partition_cols=partition_on,
+                fs=fs,
+                preserve_index=preserve_index,
+                return_metadata=return_metadata,
+                statistics=kwargs.get("statistics", "ROWGROUP"),
+                int96_timestamps=kwargs.get("int96_timestamps", False),
+                row_group_size_bytes=kwargs.get(
+                    "row_group_size_bytes", _ROW_GROUP_SIZE_BYTES_DEFAULT
+                ),
+                row_group_size_rows=kwargs.get("row_group_size_rows", None),
+                max_page_size_bytes=kwargs.get("max_page_size_bytes", None),
+                max_page_size_rows=kwargs.get("max_page_size_rows", None),
+                storage_options=kwargs.get("storage_options", None),
+            )
+        else:
+            with fs.open(fs.sep.join([path, filename]), mode="wb") as out_file:
+                if not isinstance(out_file, IOBase):
+                    out_file = BufferedWriter(out_file)
+                md = df.to_parquet(
+                    path=out_file,
+                    engine=kwargs.get("engine", "cudf"),
+                    index=kwargs.get("index", None),
+                    partition_cols=kwargs.get("partition_cols", None),
+                    partition_file_name=kwargs.get(
+                        "partition_file_name", None
+                    ),
+                    partition_offsets=kwargs.get("partition_offsets", None),
+                    statistics=kwargs.get("statistics", "ROWGROUP"),
+                    int96_timestamps=kwargs.get("int96_timestamps", False),
+                    row_group_size_bytes=kwargs.get(
+                        "row_group_size_bytes", _ROW_GROUP_SIZE_BYTES_DEFAULT
+                    ),
+                    row_group_size_rows=kwargs.get(
+                        "row_group_size_rows", None
+                    ),
+                    storage_options=kwargs.get("storage_options", None),
+                    metadata_file_path=filename if return_metadata else None,
+                )
+        # Return the schema needed to write the metadata
+        if return_metadata:
+            return [{"meta": md}]
+        else:
+            return []
+
+    @staticmethod
+    def write_metadata(parts, fmd, fs, path, append=False, **kwargs):
+        if parts:
+            # Aggregate metadata and write to _metadata file
+            metadata_path = fs.sep.join([path, "_metadata"])
+            _meta = []
+            if append and fmd is not None:
+                _meta = [fmd]
+            _meta.extend([parts[i][0]["meta"] for i in range(len(parts))])
+            _meta = (
+                cudf.io.merge_parquet_filemetadata(_meta)
+                if len(_meta) > 1
+                else _meta[0]
+            )
+            with fs.open(metadata_path, "wb") as fil:
+                fil.write(memoryview(_meta))
+
+    @classmethod
+    def collect_file_metadata(cls, path, fs, file_path):
+        with fs.open(path, "rb") as f:
+            meta = pq.ParquetFile(f).metadata
+        if file_path:
+            meta.set_file_path(file_path)
+        with BytesIO() as myio:
+            meta.write_metadata_file(myio)
+            myio.seek(0)
+            meta = np.frombuffer(myio.read(), dtype="uint8")
+        return meta
+
+    @classmethod
+    def aggregate_metadata(cls, meta_list, fs, out_path):
+        meta = (
+            cudf.io.merge_parquet_filemetadata(meta_list)
+            if len(meta_list) > 1
+            else meta_list[0]
+        )
+        if out_path:
+            metadata_path = fs.sep.join([out_path, "_metadata"])
+            with fs.open(metadata_path, "wb") as fil:
+                fil.write(memoryview(meta))
+            return None
+        else:
+            return meta
+
+
+def set_object_dtypes_from_pa_schema(df, schema):
+    # Simple utility to modify cudf DataFrame
+    # "object" dtypes to agree with a specific
+    # pyarrow schema.
+    if schema:
+        for col_name, col in df._data.items():
+            if col_name is None:
+                # Pyarrow cannot handle `None` as a field name.
+                # However, this should be a simple range index that
+                # we can ignore anyway
+                continue
+            typ = cudf_dtype_from_pa_type(schema.field(col_name).type)
+            if (
+                col_name in schema.names
+                and not isinstance(typ, (cudf.ListDtype, cudf.StructDtype))
+                and isinstance(col, cudf.core.column.StringColumn)
+            ):
+                df._data[col_name] = col.astype(typ)
+
+
+def read_parquet(path, columns=None, **kwargs):
+    """
+    Read parquet files into a :class:`.DataFrame`.
+
+    Calls :func:`dask.dataframe.read_parquet` with ``engine=CudfEngine``
+    to coordinate the execution of :func:`cudf.read_parquet`, and to
+    ultimately create a :class:`.DataFrame` collection.
+
+    See the :func:`dask.dataframe.read_parquet` documentation for
+    all available options.
+
+    Examples
+    --------
+    >>> from dask_cudf import read_parquet
+    >>> df = read_parquet("/path/to/dataset/")  # doctest: +SKIP
+
+    When dealing with one or more large parquet files having an
+    in-memory footprint >15% device memory, the ``split_row_groups``
+    argument should be used to map Parquet **row-groups** to DataFrame
+    partitions (instead of **files** to partitions). For example, the
+    following code will map each row-group to a distinct partition:
+
+    >>> df = read_parquet(..., split_row_groups=True)  # doctest: +SKIP
+
+    To map **multiple** row-groups to each partition, an integer can be
+    passed to ``split_row_groups`` to specify the **maximum** number of
+    row-groups allowed in each output partition:
+
+    >>> df = read_parquet(..., split_row_groups=10)  # doctest: +SKIP
+
+    See Also
+    --------
+    cudf.read_parquet
+    dask.dataframe.read_parquet
+    """
+    if isinstance(columns, str):
+        columns = [columns]
+
+    # Set "check_file_size" option to determine whether we
+    # should check the parquet-file size. This check is meant
+    # to "protect" users from `split_row_groups` default changes
+    check_file_size = kwargs.pop("check_file_size", 500_000_000)
+    if (
+        check_file_size
+        and ("split_row_groups" not in kwargs)
+        and ("chunksize" not in kwargs)
+    ):
+        # User is not specifying `split_row_groups` or `chunksize`,
+        # so we should warn them if/when a file is ~>0.5GB on disk.
+        # They can set `split_row_groups` explicitly to silence/skip
+        # this check
+        if "read" not in kwargs:
+            kwargs["read"] = {}
+        kwargs["read"]["check_file_size"] = check_file_size
+
+    return dd.read_parquet(path, columns=columns, engine=CudfEngine, **kwargs)
+
+
+to_parquet = partial(dd.to_parquet, engine=CudfEngine)
+
+if create_metadata_file_dd is None:
+    create_metadata_file = create_metadata_file_dd
+else:
+    create_metadata_file = partial(create_metadata_file_dd, engine=CudfEngine)
diff --git a/python/dask_cudf/dask_cudf/io/tests/__init__.py b/python/dask_cudf/dask_cudf/io/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/dask_cudf/dask_cudf/io/tests/data/orc/sample.orc b/python/dask_cudf/dask_cudf/io/tests/data/orc/sample.orc
new file mode 100644
index 0000000000000000000000000000000000000000..f51ffdbd03a43fadbedce302ffa8e5967a30ad59
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A-v9sr

literal 0
HcmV?d00001

diff --git a/python/dask_cudf/dask_cudf/io/tests/data/text/sample.pgn b/python/dask_cudf/dask_cudf/io/tests/data/text/sample.pgn
new file mode 100644
index 0000000..e927c24
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/data/text/sample.pgn
@@ -0,0 +1,53 @@
+[Event "Rated Bullet tournament https://lichess.org/tournament/IaRkDsvp"]
+[Site "https://lichess.org/r0cYFhsy"]
+[White "GreatGig"]
+[Black "hackattack"]
+[Result "0-1"]
+[UTCDate "2016.04.30"]
+[UTCTime "22:00:03"]
+[WhiteElo "1777"]
+[BlackElo "1809"]
+[WhiteRatingDiff "-11"]
+[BlackRatingDiff "+11"]
+[ECO "B01"]
+[Opening "Scandinavian Defense: Mieses-Kotroc Variation"]
+[TimeControl "60+0"]
+[Termination "Time forfeit"]
+
+1. e4 d5 2. exd5 Qxd5 3. Nc3 Qd8 4. d4 Nf6 5. Nf3 Bg4 6. h3 Bxf3 7. gxf3 c6 8. Bg2 Nbd7 9. Be3 e6 10. Qd2 Nd5 11. Nxd5 cxd5 12. O-O-O Be7 13. c3 Qc7 14. Kb1 O-O-O 15. f4 Kb8 16. Rhg1 Ka8 17. Bh1 g6 18. h4 Bxh4 19. f3 Be7 20. Qc2 Nf6 21. Bg2 Nh5 22. Bh3 Nxf4 23. Bxf4 Qxf4 24. Rdf1 Qd6 25. Rg4 Rdf8 26. Rfg1 f5 27. R4g2 Bf6 28. Rg3 Rfg8 29. Bf1 Rg7 30. Bd3 Rhg8 31. Qh2 Qb8 32. Qg2 Qc8 33. f4 Qc6 34. Qf2 Bh4 35. Rxg6 Bxf2 36. Rxg7 Rxg7 37. Rxg7 a6 38. Rg8+ Ka7 39. Rh8 Qd7 40. Rxh7 Qxh7 0-1
+
+[Event "Rated Bullet tournament https://lichess.org/tournament/IaRkDsvp"]
+[Site "https://lichess.org/s7lpBNiu"]
+[White "kh447"]
+[Black "blueskyminer23"]
+[Result "0-1"]
+[UTCDate "2016.04.30"]
+[UTCTime "22:00:03"]
+[WhiteElo "2025"]
+[BlackElo "2046"]
+[WhiteRatingDiff "-12"]
+[BlackRatingDiff "+11"]
+[ECO "D94"]
+[Opening "Gruenfeld Defense: Three Knights Variation, Paris Variation"]
+[TimeControl "60+0"]
+[Termination "Time forfeit"]
+
+1. d4 Nf6 2. c4 g6 3. Nc3 d5 4. Nf3 Bg7 5. e3 O-O 6. Bd3 c5 7. cxd5 Nxd5 8. O-O Nc6 9. Qe2 Bg4 10. dxc5 Nxc3 11. bxc3 Bxf3 12. gxf3 Bxc3 13. Rb1 Rb8 14. Kh1 Qd5 15. Rg1 Qxc5 16. Qc2 Qa5 17. Qb3 Bg7 18. Qc2 Ne5 19. Be4 Rfc8 20. Qe2 f5 21. Bc2 Qd5 22. e4 Qc6 23. Bb3+ Kh8 24. Bf4 fxe4 25. fxe4 Nd7 26. Rbc1 Qf6 27. Bxb8 Rxb8 28. Rg3 Ne5 29. Rcg1 h6 30. f4 Qxf4 31. Rxg6 Nxg6 32. Qg2 Qe5 33. Bc2 Kg8 34. Qe2 Rf8 35. Qf3 Rf7 36. Qe2 Rf6 37. Qg2 Re6 0-1
+
+[Event "Rated Bullet tournament https://lichess.org/tournament/IaRkDsvp"]
+[Site "https://lichess.org/9CTXrWUB"]
+[White "Demis115"]
+[Black "churrosagogo"]
+[Result "1-0"]
+[UTCDate "2016.04.30"]
+[UTCTime "22:00:03"]
+[WhiteElo "1944"]
+[BlackElo "2007"]
+[WhiteRatingDiff "+14"]
+[BlackRatingDiff "-13"]
+[ECO "C28"]
+[Opening "Bishop's Opening: Vienna Hybrid"]
+[TimeControl "60+0"]
+[Termination "Normal"]
+
+1. e4 e5 2. Nc3 Nc6 3. Bc4 Nf6 4. d3 Bc5 5. Be3 O-O 6. Bxc5 d6 7. Be3 a6 8. Nge2 b5 9. Bb3 b4 10. Nd5 Na5 11. Bg5 Nxb3 12. axb3 Be6 13. Bxf6 gxf6 14. Ng3 Bxd5 15. exd5 Qd7 16. O-O Kh8 17. Qe2 f5 18. Qh5 f4 19. Nf5 Rg8 20. Nh6 Rg6 21. Rfe1 Rag8 22. g3 f5 23. Qxf5 Qg7 24. Nf7+ Qxf7 25. Qxf7 fxg3 26. hxg3 R6g7 27. Qf6 1-0
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_csv.py b/python/dask_cudf/dask_cudf/io/tests/test_csv.py
new file mode 100644
index 0000000..4ff630a
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/test_csv.py
@@ -0,0 +1,255 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import gzip
+import os
+import warnings
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import dask
+from dask import dataframe as dd
+
+import cudf
+
+import dask_cudf
+
+
+@pytest.fixture
+def csv_begin_bad_lines(tmp_path):
+    lines = """x
+    x
+    x
+    A, B, C, D
+    1, 2, 3, 4
+    2, 3, 5, 1
+    4, 5, 2, 5"""
+
+    file = tmp_path / "test_read_csv_begin.csv"
+
+    with open(file, "w") as fp:
+        fp.write(lines)
+
+    return file
+
+
+@pytest.fixture
+def csv_end_bad_lines(tmp_path):
+    lines = """A, B, C, D
+    1, 2, 3, 4
+    2, 3, 5, 1
+    4, 5, 2, 5
+    x
+    x
+    x"""
+
+    file = tmp_path / "test_read_csv_end.csv"
+
+    with open(file, "w") as fp:
+        fp.write(lines)
+
+    return file
+
+
+def test_csv_roundtrip_backend_dispatch(tmp_path):
+    # Test ddf.read_csv cudf-backend dispatch
+    df = cudf.DataFrame({"x": [1, 2, 3, 4], "id": ["a", "b", "c", "d"]})
+    ddf = dask_cudf.from_cudf(df, npartitions=2)
+    csv_path = str(tmp_path / "data-*.csv")
+    ddf.to_csv(csv_path, index=False)
+    with dask.config.set({"dataframe.backend": "cudf"}):
+        ddf2 = dd.read_csv(csv_path)
+    assert isinstance(ddf2, dask_cudf.DataFrame)
+    dd.assert_eq(ddf, ddf2, check_divisions=False, check_index=False)
+
+
+def test_csv_roundtrip(tmp_path):
+    df = cudf.DataFrame({"x": [1, 2, 3, 4], "id": ["a", "b", "c", "d"]})
+    ddf = dask_cudf.from_cudf(df, npartitions=2)
+    csv_path = str(tmp_path / "data-*.csv")
+    ddf.to_csv(csv_path, index=False)
+
+    ddf2 = dask_cudf.read_csv(csv_path)
+    dd.assert_eq(ddf, ddf2, check_divisions=False, check_index=False)
+
+
+def test_csv_roundtrip_filepath(tmp_path):
+    df = cudf.DataFrame({"x": [1, 2, 3, 4], "id": ["a", "b", "c", "d"]})
+    ddf = dask_cudf.from_cudf(df, npartitions=2)
+    stmp_path = str(tmp_path / "data-*.csv")
+
+    ddf.to_csv(f"file://{stmp_path}", index=False)
+
+    ddf2 = dask_cudf.read_csv(f"file://{stmp_path}")
+    dd.assert_eq(ddf, ddf2, check_divisions=False, check_index=False)
+
+
+def test_read_csv(tmp_path):
+    df = dask.datasets.timeseries(
+        dtypes={"x": int, "y": int}, freq="120s"
+    ).reset_index(drop=True)
+
+    csv_path = str(tmp_path / "data-*.csv")
+    df.to_csv(csv_path, index=False)
+
+    df2 = dask_cudf.read_csv(csv_path)
+    dd.assert_eq(df, df2)
+
+    # file path test
+    stmp_path = str(csv_path)
+    df3 = dask_cudf.read_csv(f"file://{stmp_path}")
+    dd.assert_eq(df2, df3)
+
+    # file list test
+    list_paths = [
+        os.path.join(tmp_path, fname) for fname in sorted(os.listdir(tmp_path))
+    ]
+    df4 = dask_cudf.read_csv(list_paths)
+    dd.assert_eq(df, df4)
+
+
+def test_raises_FileNotFoundError():
+    with pytest.raises(FileNotFoundError):
+        dask_cudf.read_csv("foo.csv")
+
+
+def test_read_csv_w_bytes(tmp_path):
+    df = dask.datasets.timeseries(
+        dtypes={"x": int, "y": int}, freq="120s"
+    ).reset_index(drop=True)
+    df = pd.DataFrame(dict(x=np.arange(20), y=np.arange(20)))
+    df.to_csv(tmp_path / "data-*.csv", index=False)
+
+    df2 = dask_cudf.read_csv(tmp_path / "*.csv", blocksize="50 B")
+    assert df2.npartitions == 3
+    dd.assert_eq(df2, df, check_index=False)
+
+
+def test_read_csv_compression(tmp_path):
+    df = pd.DataFrame(dict(x=np.arange(20), y=np.arange(20)))
+    df.to_csv(tmp_path / "data.csv.gz", index=False)
+
+    with pytest.warns(UserWarning) as w:
+        df2 = dask_cudf.read_csv(tmp_path / "*.csv.gz", blocksize="50 B")
+
+    assert len(w) == 1
+    msg = str(w[0].message)
+    assert "gzip" in msg
+
+    assert df2.npartitions == 1
+    dd.assert_eq(df2, df, check_index=False)
+
+    with warnings.catch_warnings(record=True) as record:
+        df2 = dask_cudf.read_csv(tmp_path / "*.csv.gz", blocksize=None)
+
+        assert not record
+
+
+def test_read_csv_compression_file_list(tmp_path):
+    # Repro from Issue#3412
+    lines = """col1,col2
+    0,1
+    2,3"""
+
+    files = [tmp_path / "test1.csv", tmp_path / "test2.csv"]
+
+    for fn in files:
+        with gzip.open(fn, "wb") as fp:
+            fp.write(lines.encode("utf-8"))
+
+    ddf_cpu = dd.read_csv(files, compression="gzip").compute()
+    ddf_gpu = dask_cudf.read_csv(files, compression="gzip").compute()
+
+    dd.assert_eq(ddf_cpu, ddf_gpu)
+
+
+@pytest.mark.parametrize("size", [0, 3, 20])
+@pytest.mark.parametrize("compression", ["gzip", None])
+def test_read_csv_blocksize_none(tmp_path, compression, size):
+    df = pd.DataFrame(dict(x=np.arange(size), y=np.arange(size)))
+
+    path = (
+        tmp_path / "data.csv.gz"
+        if compression == "gzip"
+        else tmp_path / "data.csv"
+    )
+
+    # Types need to be specified for empty csv files
+    if size == 0:
+        typ = {"x": df.x.dtype, "y": df.y.dtype}
+    else:
+        typ = None
+
+    df.to_csv(path, index=False, compression=compression)
+    df2 = dask_cudf.read_csv(path, blocksize=None, dtype=typ)
+    dd.assert_eq(df, df2)
+
+    # Test chunksize deprecation
+    with pytest.warns(FutureWarning, match="deprecated"):
+        df3 = dask_cudf.read_csv(path, chunksize=None, dtype=typ)
+    dd.assert_eq(df, df3)
+
+
+@pytest.mark.parametrize("dtype", [{"b": str, "c": int}, None])
+def test_csv_reader_usecols(tmp_path, dtype):
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4] * 100,
+            "b": ["a", "b", "c", "d"] * 100,
+            "c": [10, 11, 12, 13] * 100,
+        }
+    )
+    csv_path = str(tmp_path / "usecols_data.csv")
+    df.to_csv(csv_path, index=False)
+    ddf = dask_cudf.from_cudf(df[["b", "c"]], npartitions=5)
+    ddf2 = dask_cudf.read_csv(csv_path, usecols=["b", "c"], dtype=dtype)
+
+    dd.assert_eq(ddf, ddf2, check_divisions=False, check_index=False)
+
+
+def test_read_csv_skiprows(csv_begin_bad_lines):
+    # Repro from Issue#13552
+    ddf_cpu = dd.read_csv(csv_begin_bad_lines, skiprows=3).compute()
+    ddf_gpu = dask_cudf.read_csv(csv_begin_bad_lines, skiprows=3).compute()
+
+    dd.assert_eq(ddf_cpu, ddf_gpu)
+
+
+def test_read_csv_skiprows_error(csv_begin_bad_lines):
+    # Repro from Issue#13552
+    with pytest.raises(ValueError):
+        dask_cudf.read_csv(
+            csv_begin_bad_lines, skiprows=3, blocksize="100 MiB"
+        ).compute()
+
+
+def test_read_csv_skipfooter(csv_end_bad_lines):
+    # Repro from Issue#13552
+
+    ddf_cpu = dd.read_csv(csv_end_bad_lines, skipfooter=3).compute()
+    ddf_gpu = dask_cudf.read_csv(csv_end_bad_lines, skipfooter=3).compute()
+
+    dd.assert_eq(ddf_cpu, ddf_gpu, check_dtype=False)
+
+
+def test_read_csv_skipfooter_error(csv_end_bad_lines):
+    with pytest.raises(ValueError):
+        dask_cudf.read_csv(
+            csv_end_bad_lines, skipfooter=3, blocksize="100 MiB"
+        ).compute()
+
+
+def test_read_csv_nrows(csv_end_bad_lines):
+    ddf_cpu = pd.read_csv(csv_end_bad_lines, nrows=2)
+    ddf_gpu = dask_cudf.read_csv(csv_end_bad_lines, nrows=2).compute()
+
+    dd.assert_eq(ddf_cpu, ddf_gpu)
+
+
+def test_read_csv_nrows_error(csv_end_bad_lines):
+
+    with pytest.raises(ValueError):
+        dask_cudf.read_csv(
+            csv_end_bad_lines, nrows=2, blocksize="100 MiB"
+        ).compute()
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_json.py b/python/dask_cudf/dask_cudf/io/tests/test_json.py
new file mode 100644
index 0000000..fddbfb1
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/test_json.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import os
+
+import pandas as pd
+import pytest
+
+import dask
+import dask.dataframe as dd
+from dask.utils import tmpfile
+
+import dask_cudf
+
+
+def test_read_json_backend_dispatch(tmp_path):
+    # Test ddf.read_json cudf-backend dispatch
+    df1 = dask.datasets.timeseries(
+        dtypes={"x": int, "y": int}, freq="120s"
+    ).reset_index(drop=True)
+    json_path = str(tmp_path / "data-*.json")
+    df1.to_json(json_path)
+    with dask.config.set({"dataframe.backend": "cudf"}):
+        df2 = dd.read_json(json_path)
+    assert isinstance(df2, dask_cudf.DataFrame)
+    dd.assert_eq(df1, df2)
+
+
+def test_read_json(tmp_path):
+    df1 = dask.datasets.timeseries(
+        dtypes={"x": int, "y": int}, freq="120s"
+    ).reset_index(drop=True)
+    json_path = str(tmp_path / "data-*.json")
+    df1.to_json(json_path)
+    df2 = dask_cudf.read_json(json_path)
+    dd.assert_eq(df1, df2)
+
+    # file path test
+    stmp_path = str(tmp_path / "data-*.json")
+    df3 = dask_cudf.read_json(f"file://{stmp_path}")
+    dd.assert_eq(df1, df3)
+
+    # file list test
+    list_paths = [
+        os.path.join(tmp_path, fname) for fname in sorted(os.listdir(tmp_path))
+    ]
+    df4 = dask_cudf.read_json(list_paths)
+    dd.assert_eq(df1, df4)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("orient", ["split", "index", "columns", "values"])
+def test_read_json_basic(orient):
+    df = pd.DataFrame({"x": ["a", "b", "c", "d"], "y": [1, 2, 3, 4]})
+    with tmpfile("json") as f:
+        df.to_json(f, orient=orient, lines=False)
+        actual = dask_cudf.read_json(f, orient=orient, lines=False)
+        actual_pd = pd.read_json(f, orient=orient, lines=False)
+        dd.assert_eq(actual, actual_pd)
+
+
+@pytest.mark.filterwarnings("ignore:Using CPU")
+@pytest.mark.parametrize("lines", [True, False])
+def test_read_json_lines(lines):
+    df = pd.DataFrame({"x": ["a", "b", "c", "d"], "y": [1, 2, 3, 4]})
+    with tmpfile("json") as f:
+        df.to_json(f, orient="records", lines=lines)
+        actual = dask_cudf.read_json(f, orient="records", lines=lines)
+        actual_pd = pd.read_json(f, orient="records", lines=lines)
+        dd.assert_eq(actual, actual_pd)
+
+
+def test_read_json_nested(tmp_path):
+    # Check that `engine="cudf"` can
+    # be used to support nested data
+    df = pd.DataFrame(
+        {
+            "a": [{"y": 2}, {"y": 4}, {"y": 6}, {"y": 8}],
+            "b": [[1, 2, 3], [4, 5], [6], [7]],
+            "c": [1, 3, 5, 7],
+        }
+    )
+    kwargs = dict(orient="records", lines=True)
+    with tmp_path / "data.json" as f:
+        df.to_json(f, **kwargs)
+        # Ensure engine='cudf' is tested.
+        actual = dask_cudf.read_json(f, engine="cudf", **kwargs)
+        actual_pd = pd.read_json(f, **kwargs)
+        dd.assert_eq(actual, actual_pd)
+        # Ensure not passing engine='cudf'(default i.e., auto) is tested.
+        actual = dask_cudf.read_json(f, **kwargs)
+        dd.assert_eq(actual, actual_pd)
+        # Ensure not passing kwargs also reads the file.
+        actual = dask_cudf.read_json(f)
+        dd.assert_eq(actual, actual_pd)
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_orc.py b/python/dask_cudf/dask_cudf/io/tests/test_orc.py
new file mode 100644
index 0000000..5565a44
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/test_orc.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2018-2022, NVIDIA CORPORATION.
+
+import glob
+import os
+from datetime import datetime, timezone
+
+import pytest
+
+import dask
+from dask import dataframe as dd
+
+import cudf
+
+import dask_cudf
+
+cur_dir = os.path.dirname(__file__)
+sample_orc = os.path.join(cur_dir, "data/orc/sample.orc")
+
+
+def test_read_orc_backend_dispatch():
+    # Test ddf.read_orc cudf-backend dispatch
+    df1 = cudf.read_orc(sample_orc)
+    with dask.config.set({"dataframe.backend": "cudf"}):
+        df2 = dd.read_orc(sample_orc)
+    assert isinstance(df2, dask_cudf.DataFrame)
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+def test_read_orc_defaults():
+    df1 = cudf.read_orc(sample_orc)
+    df2 = dask_cudf.read_orc(sample_orc)
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+def test_filepath_read_orc_defaults():
+    path = "file://%s" % sample_orc
+    df1 = cudf.read_orc(path)
+    df2 = dask_cudf.read_orc(path)
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+def test_filelist_read_orc_defaults():
+    path = [sample_orc]
+    df1 = cudf.read_orc(path[0])
+    df2 = dask_cudf.read_orc(path)
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+@pytest.mark.parametrize("columns", [["time", "date"], ["time"]])
+def test_read_orc_cols(engine, columns):
+    df1 = cudf.read_orc(sample_orc, engine=engine, columns=columns)
+
+    df2 = dask_cudf.read_orc(sample_orc, engine=engine, columns=columns)
+
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+@pytest.mark.parametrize("engine", ["cudf", "pyarrow"])
+@pytest.mark.parametrize(
+    "predicate,expected_len",
+    [
+        (None, 70_000),
+        (
+            [("date", "==", datetime(1900, 12, 25, tzinfo=timezone.utc))],
+            15_000,
+        ),
+        (
+            [("date", "<=", datetime(1928, 12, 25, tzinfo=timezone.utc))],
+            30_000,
+        ),
+        (
+            [
+                [("date", ">", datetime(1950, 12, 25, tzinfo=timezone.utc))],
+                [("date", "<=", datetime(1928, 12, 25, tzinfo=timezone.utc))],
+            ],
+            55_000,
+        ),
+    ],
+)
+def test_read_orc_filtered(tmpdir, engine, predicate, expected_len):
+    df = dask_cudf.read_orc(sample_orc, engine=engine, filters=predicate)
+
+    dd.assert_eq(len(df), expected_len)
+
+
+def test_read_orc_first_file_empty(tmpdir):
+
+    # Write a 3-file dataset where the first file is empty
+    # See: https://github.com/rapidsai/cudf/issues/8011
+    path = str(tmpdir)
+    os.makedirs(path, exist_ok=True)
+    df1 = cudf.DataFrame({"id": [1, 2], "float": [1.0, 2.0]})
+    df1.iloc[:0].to_orc(os.path.join(path, "data.0"))
+    df1.iloc[:1].to_orc(os.path.join(path, "data.1"))
+    df1.iloc[1:].to_orc(os.path.join(path, "data.2"))
+
+    # Read back the files with dask_cudf,
+    # and check the result.
+    df2 = dask_cudf.read_orc(os.path.join(path, "*"))
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+@pytest.mark.parametrize("compute", [True, False])
+@pytest.mark.parametrize("compression", [None, "snappy"])
+@pytest.mark.parametrize(
+    "dtypes",
+    [
+        {"index": int, "c": int, "a": str},
+        {"index": int, "c": int, "a": str, "b": float},
+        {"index": int, "c": str, "a": object},
+    ],
+)
+def test_to_orc(tmpdir, dtypes, compression, compute):
+
+    # Create cudf and dask_cudf dataframes
+    df = cudf.datasets.randomdata(nrows=10, dtypes=dtypes, seed=1)
+    df = df.set_index("index").sort_index()
+    ddf = dask_cudf.from_cudf(df, npartitions=3)
+
+    # Write cudf dataframe as single file
+    # (preserve index by setting to column)
+    fname = tmpdir.join("test.orc")
+    df.reset_index().to_orc(fname, compression=compression)
+
+    # Write dask_cudf dataframe as multiple files
+    # (preserve index by `write_index=True`)
+    to = ddf.to_orc(
+        str(tmpdir), write_index=True, compression=compression, compute=compute
+    )
+
+    if not compute:
+        to.compute()
+
+    # Read back cudf dataframe
+    df_read = cudf.read_orc(fname).set_index("index")
+
+    # Read back dask_cudf dataframe
+    paths = glob.glob(str(tmpdir) + "/part.*.orc")
+    ddf_read = dask_cudf.read_orc(paths).set_index("index")
+
+    # Make sure the dask_cudf dataframe matches
+    # the cudf dataframes (df and df_read)
+    dd.assert_eq(df, ddf_read)
+    dd.assert_eq(df_read, ddf_read)
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_parquet.py b/python/dask_cudf/dask_cudf/io/tests/test_parquet.py
new file mode 100644
index 0000000..85ec36c
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/test_parquet.py
@@ -0,0 +1,597 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import glob
+import math
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import dask
+from dask import dataframe as dd
+from dask.utils import natural_sort_key
+
+import cudf
+
+import dask_cudf
+
+# Check if create_metadata_file is supported by
+# the current dask.dataframe version
+need_create_meta = pytest.mark.skipif(
+    dask_cudf.io.parquet.create_metadata_file is None,
+    reason="Need create_metadata_file support in dask.dataframe.",
+)
+
+nrows = 40
+npartitions = 15
+df = pd.DataFrame(
+    {
+        "x": [i * 7 % 5 for i in range(nrows)],  # Not sorted
+        "y": [i * 2.5 for i in range(nrows)],
+    },
+    index=pd.Index(range(nrows), name="index"),
+)  # Sorted
+ddf = dd.from_pandas(df, npartitions=npartitions)
+
+
+def test_roundtrip_backend_dispatch(tmpdir):
+    # Test ddf.read_parquet cudf-backend dispatch
+    tmpdir = str(tmpdir)
+    ddf.to_parquet(tmpdir, engine="pyarrow")
+    with dask.config.set({"dataframe.backend": "cudf"}):
+        ddf2 = dd.read_parquet(tmpdir, index=False)
+    assert isinstance(ddf2, dask_cudf.DataFrame)
+    dd.assert_eq(ddf.reset_index(drop=False), ddf2)
+
+
+@pytest.mark.parametrize("write_metadata_file", [True, False])
+@pytest.mark.parametrize("divisions", [True, False])
+def test_roundtrip_from_dask(tmpdir, divisions, write_metadata_file):
+    tmpdir = str(tmpdir)
+    ddf.to_parquet(
+        tmpdir, write_metadata_file=write_metadata_file, engine="pyarrow"
+    )
+    files = sorted(
+        (os.path.join(tmpdir, f) for f in os.listdir(tmpdir)),
+        key=natural_sort_key,
+    )
+
+    # Read list of parquet files
+    ddf2 = dask_cudf.read_parquet(files, calculate_divisions=divisions)
+    dd.assert_eq(ddf, ddf2, check_divisions=divisions)
+
+    # Specify columns=['x']
+    ddf2 = dask_cudf.read_parquet(
+        files, columns=["x"], calculate_divisions=divisions
+    )
+    dd.assert_eq(ddf[["x"]], ddf2, check_divisions=divisions)
+
+    # Specify columns='y'
+    ddf2 = dask_cudf.read_parquet(
+        files, columns="y", calculate_divisions=divisions
+    )
+    dd.assert_eq(ddf[["y"]], ddf2, check_divisions=divisions)
+
+    # Now include metadata
+    ddf2 = dask_cudf.read_parquet(tmpdir, calculate_divisions=divisions)
+    dd.assert_eq(ddf, ddf2, check_divisions=divisions)
+
+    # Specify columns=['x'] (with metadata)
+    ddf2 = dask_cudf.read_parquet(
+        tmpdir, columns=["x"], calculate_divisions=divisions
+    )
+    dd.assert_eq(ddf[["x"]], ddf2, check_divisions=divisions)
+
+    # Specify columns='y' (with metadata)
+    ddf2 = dask_cudf.read_parquet(
+        tmpdir, columns="y", calculate_divisions=divisions
+    )
+    dd.assert_eq(ddf[["y"]], ddf2, check_divisions=divisions)
+
+
+def test_roundtrip_from_dask_index_false(tmpdir):
+    tmpdir = str(tmpdir)
+    ddf.to_parquet(tmpdir, engine="pyarrow")
+
+    ddf2 = dask_cudf.read_parquet(tmpdir, index=False)
+    dd.assert_eq(ddf.reset_index(drop=False), ddf2)
+
+
+def test_roundtrip_from_dask_none_index_false(tmpdir):
+    tmpdir = str(tmpdir)
+    path = os.path.join(tmpdir, "test.parquet")
+
+    df2 = ddf.reset_index(drop=True).compute()
+    df2.to_parquet(path, engine="pyarrow")
+
+    ddf3 = dask_cudf.read_parquet(path, index=False)
+    dd.assert_eq(df2, ddf3)
+
+
+@pytest.mark.parametrize("write_meta", [True, False])
+def test_roundtrip_from_dask_cudf(tmpdir, write_meta):
+    tmpdir = str(tmpdir)
+    gddf = dask_cudf.from_dask_dataframe(ddf)
+    gddf.to_parquet(tmpdir, write_metadata_file=write_meta)
+
+    gddf2 = dask_cudf.read_parquet(tmpdir, calculate_divisions=True)
+    dd.assert_eq(gddf, gddf2)
+
+
+def test_roundtrip_none_rangeindex(tmpdir):
+    fn = str(tmpdir.join("test.parquet"))
+    gdf = cudf.DataFrame(
+        {"id": [0, 1, 2, 3], "val": [None, None, 0, 1]},
+        index=pd.RangeIndex(start=5, stop=9),
+    )
+    dask_cudf.from_cudf(gdf, npartitions=2).to_parquet(fn)
+    ddf2 = dask_cudf.read_parquet(fn)
+    dd.assert_eq(gdf, ddf2, check_index=True)
+
+
+def test_roundtrip_from_pandas(tmpdir):
+    fn = str(tmpdir.join("test.parquet"))
+
+    # First without specifying an index
+    dfp = df.copy()
+    dfp.to_parquet(fn, engine="pyarrow", index=False)
+    dfp = dfp.reset_index(drop=True)
+    ddf2 = dask_cudf.read_parquet(fn)
+    dd.assert_eq(dfp, ddf2, check_index=True)
+
+    # Now, specifying an index
+    dfp = df.copy()
+    dfp.to_parquet(fn, engine="pyarrow", index=True)
+    ddf2 = dask_cudf.read_parquet(fn, index=["index"])
+    dd.assert_eq(dfp, ddf2, check_index=True)
+
+
+def test_strings(tmpdir):
+
+    fn = str(tmpdir)
+    dfp = pd.DataFrame(
+        {"a": ["aa", "bbb", "cccc"], "b": ["hello", "dog", "man"]}
+    )
+    dfp.set_index("a", inplace=True, drop=True)
+    ddf2 = dd.from_pandas(dfp, npartitions=2)
+    ddf2.to_parquet(fn, engine="pyarrow")
+    read_df = dask_cudf.read_parquet(fn, index=["a"])
+    dd.assert_eq(ddf2, read_df.compute().to_pandas())
+
+
+def test_dask_timeseries_from_pandas(tmpdir):
+
+    fn = str(tmpdir.join("test.parquet"))
+    ddf2 = dask.datasets.timeseries(freq="D")
+    pdf = ddf2.compute()
+    pdf.to_parquet(fn, engine="pyarrow")
+    read_df = dask_cudf.read_parquet(fn)
+    dd.assert_eq(ddf2, read_df.compute())
+
+
+@pytest.mark.parametrize("index", [False, None])
+@pytest.mark.parametrize("divisions", [False, True])
+def test_dask_timeseries_from_dask(tmpdir, index, divisions):
+
+    fn = str(tmpdir)
+    ddf2 = dask.datasets.timeseries(freq="D")
+    ddf2.to_parquet(fn, engine="pyarrow", write_index=index)
+    read_df = dask_cudf.read_parquet(
+        fn, index=index, calculate_divisions=divisions
+    )
+    dd.assert_eq(
+        ddf2, read_df, check_divisions=(divisions and index), check_index=index
+    )
+
+
+@pytest.mark.parametrize("index", [False, None])
+@pytest.mark.parametrize("divisions", [False, True])
+def test_dask_timeseries_from_daskcudf(tmpdir, index, divisions):
+
+    fn = str(tmpdir)
+    ddf2 = dask_cudf.from_cudf(
+        cudf.datasets.timeseries(freq="D"), npartitions=4
+    )
+    ddf2.name = ddf2.name.astype("object")
+    ddf2.to_parquet(fn, write_index=index)
+    read_df = dask_cudf.read_parquet(
+        fn, index=index, calculate_divisions=divisions
+    )
+    dd.assert_eq(
+        ddf2, read_df, check_divisions=(divisions and index), check_index=index
+    )
+
+
+@pytest.mark.parametrize("index", [False, True])
+def test_empty(tmpdir, index):
+
+    fn = str(tmpdir)
+    dfp = pd.DataFrame({"a": [11.0, 12.0, 12.0], "b": [4, 5, 6]})[:0]
+    if index:
+        dfp.set_index("a", inplace=True, drop=True)
+    ddf2 = dd.from_pandas(dfp, npartitions=2)
+
+    ddf2.to_parquet(fn, write_index=index, engine="pyarrow")
+    read_df = dask_cudf.read_parquet(fn)
+    dd.assert_eq(ddf2, read_df.compute())
+
+
+def test_filters(tmpdir):
+
+    tmp_path = str(tmpdir)
+    df = pd.DataFrame({"x": range(10), "y": list("aabbccddee")})
+    ddf = dd.from_pandas(df, npartitions=5)
+    assert ddf.npartitions == 5
+
+    ddf.to_parquet(tmp_path, engine="pyarrow")
+
+    a = dask_cudf.read_parquet(
+        tmp_path, filters=[("x", ">", 4)], split_row_groups=True
+    )
+    assert a.npartitions == 3
+    assert (a.x > 3).all().compute()
+
+    b = dask_cudf.read_parquet(
+        tmp_path, filters=[("y", "==", "c")], split_row_groups=True
+    )
+    assert b.npartitions == 1
+    b = b.compute().to_pandas()
+    assert (b.y == "c").all()
+
+    c = dask_cudf.read_parquet(
+        tmp_path,
+        filters=[("y", "==", "c"), ("x", ">", 6)],
+        split_row_groups=True,
+    )
+    assert c.npartitions <= 1
+    assert not len(c)
+
+
+@pytest.mark.parametrize("numeric", [True, False])
+@pytest.mark.parametrize("null", [np.nan, None])
+def test_isna_filters(tmpdir, null, numeric):
+
+    tmp_path = str(tmpdir)
+    df = pd.DataFrame(
+        {
+            "x": range(10),
+            "y": list("aabbccddee"),
+            "i": [0] * 4 + [np.nan] * 2 + [0] * 4,
+            "j": [""] * 4 + [None] * 2 + [""] * 4,
+        }
+    )
+    ddf = dd.from_pandas(df, npartitions=5)
+    assert ddf.npartitions == 5
+    ddf.to_parquet(tmp_path, engine="pyarrow")
+
+    # Test "is"
+    col = "i" if numeric else "j"
+    filters = [(col, "is", null)]
+    out = dask_cudf.read_parquet(
+        tmp_path, filters=filters, split_row_groups=True
+    )
+    assert len(out) == 2
+    assert list(out.x.compute().values) == [4, 5]
+
+    # Test "is not"
+    filters = [(col, "is not", null)]
+    out = dask_cudf.read_parquet(
+        tmp_path, filters=filters, split_row_groups=True
+    )
+    assert len(out) == 8
+    assert list(out.x.compute().values) == [0, 1, 2, 3, 6, 7, 8, 9]
+
+
+def test_filters_at_row_group_level(tmpdir):
+
+    tmp_path = str(tmpdir)
+    df = pd.DataFrame({"x": range(10), "y": list("aabbccddee")})
+    ddf = dd.from_pandas(df, npartitions=5)
+    assert ddf.npartitions == 5
+
+    ddf.to_parquet(tmp_path, engine="pyarrow", row_group_size=10 / 5)
+
+    a = dask_cudf.read_parquet(
+        tmp_path, filters=[("x", "==", 1)], split_row_groups=True
+    )
+    assert a.npartitions == 1
+    assert (a.shape[0] == 1).compute()
+
+    ddf.to_parquet(tmp_path, engine="pyarrow", row_group_size=1)
+
+    b = dask_cudf.read_parquet(
+        tmp_path, filters=[("x", "==", 1)], split_row_groups=True
+    )
+    assert b.npartitions == 1
+    assert (b.shape[0] == 1).compute()
+
+
+@pytest.mark.parametrize("metadata", [True, False])
+@pytest.mark.parametrize("daskcudf", [True, False])
+@pytest.mark.parametrize(
+    "parts", [["year", "month", "day"], ["year", "month"], ["year"]]
+)
+def test_roundtrip_from_dask_partitioned(tmpdir, parts, daskcudf, metadata):
+    tmpdir = str(tmpdir)
+
+    df = pd.DataFrame()
+    df["year"] = [2018, 2019, 2019, 2019, 2020, 2021]
+    df["month"] = [1, 2, 3, 3, 3, 2]
+    df["day"] = [1, 1, 1, 2, 2, 1]
+    df["data"] = [0, 0, 0, 0, 0, 0]
+    df.index.name = "index"
+    if daskcudf:
+        ddf2 = dask_cudf.from_cudf(cudf.from_pandas(df), npartitions=2)
+        ddf2.to_parquet(
+            tmpdir, write_metadata_file=metadata, partition_on=parts
+        )
+    else:
+        ddf2 = dd.from_pandas(df, npartitions=2)
+        ddf2.to_parquet(
+            tmpdir,
+            engine="pyarrow",
+            write_metadata_file=metadata,
+            partition_on=parts,
+        )
+    df_read = dd.read_parquet(tmpdir, engine="pyarrow")
+    gdf_read = dask_cudf.read_parquet(tmpdir)
+
+    # TODO: Avoid column selection after `CudfEngine`
+    # can be aligned with dask/dask#6534
+    columns = list(df_read.columns)
+    assert set(df_read.columns) == set(gdf_read.columns)
+    dd.assert_eq(
+        df_read.compute(scheduler=dask.get)[columns],
+        gdf_read.compute(scheduler=dask.get)[columns],
+    )
+
+    assert gdf_read.index.name == "index"
+
+    # Check that we don't have uuid4 file names
+    for _, _, files in os.walk(tmpdir):
+        for fn in files:
+            if not fn.startswith("_"):
+                assert "part" in fn
+
+    # Check that we can aggregate files by a partition name
+    df_read = dd.read_parquet(
+        tmpdir, engine="pyarrow", aggregate_files="year", split_row_groups=2
+    )
+    gdf_read = dask_cudf.read_parquet(
+        tmpdir, aggregate_files="year", split_row_groups=2
+    )
+    dd.assert_eq(df_read, gdf_read)
+
+
+@pytest.mark.parametrize("row_groups", [1, 3, 10, 12])
+@pytest.mark.parametrize("index", [False, True])
+def test_split_row_groups(tmpdir, row_groups, index):
+    nparts = 2
+    df_size = 100
+    row_group_size = 5
+    file_row_groups = 10  # Known apriori
+    npartitions_expected = math.ceil(file_row_groups / row_groups) * 2
+
+    df = pd.DataFrame(
+        {
+            "a": np.random.choice(["apple", "banana", "carrot"], size=df_size),
+            "b": np.random.random(size=df_size),
+            "c": np.random.randint(1, 5, size=df_size),
+            "index": np.arange(0, df_size),
+        }
+    )
+    if index:
+        df = df.set_index("index")
+
+    ddf1 = dd.from_pandas(df, npartitions=nparts)
+    ddf1.to_parquet(
+        str(tmpdir),
+        engine="pyarrow",
+        row_group_size=row_group_size,
+        write_metadata_file=True,
+    )
+
+    ddf2 = dask_cudf.read_parquet(
+        str(tmpdir),
+        split_row_groups=row_groups,
+    )
+
+    dd.assert_eq(ddf1, ddf2, check_divisions=False)
+
+    assert ddf2.npartitions == npartitions_expected
+
+
+@need_create_meta
+@pytest.mark.parametrize("partition_on", [None, "a"])
+def test_create_metadata_file(tmpdir, partition_on):
+
+    tmpdir = str(tmpdir)
+
+    # Write ddf without a _metadata file
+    df1 = cudf.DataFrame({"b": range(100), "a": ["A", "B", "C", "D"] * 25})
+    df1.index.name = "myindex"
+    ddf1 = dask_cudf.from_cudf(df1, npartitions=10)
+    ddf1.to_parquet(
+        tmpdir,
+        write_metadata_file=False,
+        partition_on=partition_on,
+    )
+
+    # Add global _metadata file
+    if partition_on:
+        fns = glob.glob(os.path.join(tmpdir, partition_on + "=*/*.parquet"))
+    else:
+        fns = glob.glob(os.path.join(tmpdir, "*.parquet"))
+    dask_cudf.io.parquet.create_metadata_file(
+        fns,
+        split_every=3,  # Force tree reduction
+    )
+
+    # Check that we can now read the ddf
+    # with the _metadata file present
+    ddf2 = dask_cudf.read_parquet(
+        tmpdir,
+        split_row_groups=False,
+        index="myindex",
+        calculate_divisions=True,
+    )
+    if partition_on:
+        ddf1 = df1.sort_values("b")
+        ddf2 = ddf2.compute().sort_values("b")
+        ddf2.a = ddf2.a.astype("object")
+    dd.assert_eq(ddf1, ddf2)
+
+
+@need_create_meta
+def test_create_metadata_file_inconsistent_schema(tmpdir):
+
+    # NOTE: This test demonstrates that the CudfEngine
+    # can be used to generate a global `_metadata` file
+    # even if there are inconsistent schemas in the dataset.
+
+    # Write file 0
+    df0 = pd.DataFrame({"a": [None] * 10, "b": range(10)})
+    p0 = os.path.join(tmpdir, "part.0.parquet")
+    df0.to_parquet(p0, engine="pyarrow")
+
+    # Write file 1
+    b = list(range(10))
+    b[1] = None
+    df1 = pd.DataFrame({"a": range(10), "b": b})
+    p1 = os.path.join(tmpdir, "part.1.parquet")
+    df1.to_parquet(p1, engine="pyarrow")
+
+    # New pyarrow-dataset base can handle an inconsistent
+    # schema (even without a _metadata file), but computing
+    # and dtype validation may fail
+    ddf1 = dask_cudf.read_parquet(str(tmpdir), calculate_divisions=True)
+
+    # Add global metadata file.
+    # Dask-CuDF can do this without requiring schema
+    # consistency.
+    dask_cudf.io.parquet.create_metadata_file([p0, p1])
+
+    # Check that we can still read the ddf
+    # with the _metadata file present
+    ddf2 = dask_cudf.read_parquet(str(tmpdir), calculate_divisions=True)
+
+    # Check that the result is the same with and
+    # without the _metadata file.  Note that we must
+    # call `compute` on `ddf1`, because the dtype of
+    # the inconsistent column ("a") may be "object"
+    # before computing, and "int" after
+    dd.assert_eq(ddf1.compute(), ddf2)
+    dd.assert_eq(ddf1.compute(), ddf2.compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["dog", "cat", "fish"],
+        [[0], [1, 2], [3]],
+        [None, [1, 2], [3]],
+        [{"f1": 1}, {"f1": 0, "f2": "dog"}, {"f2": "cat"}],
+        [None, {"f1": 0, "f2": "dog"}, {"f2": "cat"}],
+    ],
+)
+def test_cudf_dtypes_from_pandas(tmpdir, data):
+    # Simple test that we can read in list and struct types
+    fn = str(tmpdir.join("test.parquet"))
+    dfp = pd.DataFrame({"data": data})
+    dfp.to_parquet(fn, engine="pyarrow", index=True)
+    # Use `split_row_groups=True` to avoid "fast path" where
+    # schema is not is passed through in older Dask versions
+    ddf2 = dask_cudf.read_parquet(fn, split_row_groups=True)
+    dd.assert_eq(cudf.from_pandas(dfp), ddf2)
+
+
+def test_cudf_list_struct_write(tmpdir):
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3],
+            "b": [[[1, 2]], [[2, 3]], None],
+            "c": [[[["a", "z"]]], [[["b", "d", "e"]]], None],
+        }
+    )
+    df["d"] = df.to_struct()
+
+    ddf = dask_cudf.from_cudf(df, 3)
+    temp_file = str(tmpdir.join("list_struct.parquet"))
+
+    ddf.to_parquet(temp_file)
+    new_ddf = dask_cudf.read_parquet(temp_file)
+    dd.assert_eq(df, new_ddf)
+
+
+def test_check_file_size(tmpdir):
+    # Test simple file-size check to help warn users
+    # of upstream change to `split_row_groups` default
+    fn = str(tmpdir.join("test.parquet"))
+    cudf.DataFrame({"a": np.arange(1000)}).to_parquet(fn)
+    with pytest.warns(match="large parquet file"):
+        dask_cudf.read_parquet(fn, check_file_size=1).compute()
+
+
+def test_null_partition(tmpdir):
+    import pyarrow as pa
+    from pyarrow.dataset import HivePartitioning
+
+    ids = pd.Series([0, 1, None], dtype="Int64")
+    df = pd.DataFrame({"id": ids, "x": [1, 2, 3]})
+    ddf = dd.from_pandas(df, npartitions=1).to_backend("cudf")
+    ddf.to_parquet(str(tmpdir), partition_on="id")
+    fns = glob.glob(os.path.join(tmpdir, "id" + "=*/*.parquet"))
+    assert len(fns) == 3
+
+    partitioning = HivePartitioning(pa.schema([("id", pa.int64())]))
+    ddf_read = dask_cudf.read_parquet(
+        str(tmpdir),
+        dataset={"partitioning": partitioning},
+    )
+    dd.assert_eq(
+        ddf[["x", "id"]],
+        ddf_read[["x", "id"]],
+        check_divisions=False,
+    )
+
+
+def test_nullable_schema_mismatch(tmpdir):
+    # See: https://github.com/rapidsai/cudf/issues/12702
+    path0 = str(tmpdir.join("test.0.parquet"))
+    path1 = str(tmpdir.join("test.1.parquet"))
+    cudf.DataFrame.from_dict({"a": [1, 2, 3]}).to_parquet(path0)
+    cudf.DataFrame.from_dict({"a": [4, 5, None]}).to_parquet(path1)
+    with dask.config.set({"dataframe.backend": "cudf"}):
+        ddf = dd.read_parquet(
+            [path0, path1], split_row_groups=2, aggregate_files=True
+        )
+        expect = pd.read_parquet([path0, path1])
+    dd.assert_eq(ddf, expect, check_index=False)
+
+
+def test_parquet_read_filter_and_project(tmpdir):
+    # Filter on columns that are not included
+    # in the current column projection
+
+    # Write parquet data
+    path = str(tmpdir.join("test.parquet"))
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4, 5] * 10,
+            "b": [0, 1, 2, 3, 4] * 10,
+            "c": range(50),
+            "d": [6, 7] * 25,
+            "e": [8, 9] * 25,
+        }
+    )
+    df.to_parquet(path)
+
+    # Read back with filter and projection
+    columns = ["b"]
+    filters = [[("a", "==", 5), ("c", ">", 20)]]
+    got = dask_cudf.read_parquet(path, columns=columns, filters=filters)
+
+    # Check result
+    expected = df[(df.a == 5) & (df.c > 20)][columns].reset_index(drop=True)
+    dd.assert_eq(got, expected)
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_s3.py b/python/dask_cudf/dask_cudf/io/tests/test_s3.py
new file mode 100644
index 0000000..7614ea3
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/test_s3.py
@@ -0,0 +1,142 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import os
+import socket
+from contextlib import contextmanager
+from io import BytesIO
+
+import pandas as pd
+import pyarrow.fs as pa_fs
+import pytest
+
+import dask_cudf
+
+moto = pytest.importorskip("moto", minversion="3.1.6")
+boto3 = pytest.importorskip("boto3")
+s3fs = pytest.importorskip("s3fs")
+ThreadedMotoServer = pytest.importorskip("moto.server").ThreadedMotoServer
+
+
+@pytest.fixture(scope="session")
+def endpoint_ip():
+    return "127.0.0.1"
+
+
+@pytest.fixture(scope="session")
+def endpoint_port():
+    # Return a free port per worker session.
+    sock = socket.socket()
+    sock.bind(("127.0.0.1", 0))
+    port = sock.getsockname()[1]
+    sock.close()
+    return port
+
+
+@contextmanager
+def ensure_safe_environment_variables():
+    """
+    Get a context manager to safely set environment variables
+    All changes will be undone on close, hence environment variables set
+    within this contextmanager will neither persist nor change global state.
+    """
+    saved_environ = dict(os.environ)
+    try:
+        yield
+    finally:
+        os.environ.clear()
+        os.environ.update(saved_environ)
+
+
+@pytest.fixture(scope="session")
+def s3_base(endpoint_ip, endpoint_port):
+    """
+    Fixture to set up moto server in separate process
+    """
+    with ensure_safe_environment_variables():
+        # Fake aws credentials exported to prevent botocore looking for
+        # system aws credentials, https://github.com/spulec/moto/issues/1793
+        os.environ["AWS_ACCESS_KEY_ID"] = "foobar_key"
+        os.environ["AWS_SECRET_ACCESS_KEY"] = "foobar_secret"
+        os.environ["S3FS_LOGGING_LEVEL"] = "DEBUG"
+        os.environ["AWS_SECURITY_TOKEN"] = "foobar_security_token"
+        os.environ["AWS_SESSION_TOKEN"] = "foobar_session_token"
+        os.environ["AWS_DEFAULT_REGION"] = "us-east-1"
+
+        # Launching moto in server mode, i.e., as a separate process
+        # with an S3 endpoint on localhost
+
+        endpoint_uri = f"http://{endpoint_ip}:{endpoint_port}/"
+
+        server = ThreadedMotoServer(ip_address=endpoint_ip, port=endpoint_port)
+        server.start()
+        yield endpoint_uri
+        server.stop()
+
+
+@pytest.fixture()
+def s3so(endpoint_ip, endpoint_port):
+    """
+    Returns s3 storage options to pass to fsspec
+    """
+    endpoint_uri = f"http://{endpoint_ip}:{endpoint_port}/"
+
+    return {"client_kwargs": {"endpoint_url": endpoint_uri}}
+
+
+@contextmanager
+def s3_context(s3_base, bucket, files=None):
+    if files is None:
+        files = {}
+    with ensure_safe_environment_variables():
+        client = boto3.client("s3", endpoint_url=s3_base)
+        client.create_bucket(Bucket=bucket, ACL="public-read-write")
+        for f, data in files.items():
+            client.put_object(Bucket=bucket, Key=f, Body=data)
+
+        yield s3fs.S3FileSystem(client_kwargs={"endpoint_url": s3_base})
+
+        for f, data in files.items():
+            try:
+                client.delete_object(Bucket=bucket, Key=f)
+            except Exception:
+                pass
+
+
+def test_read_csv(s3_base, s3so):
+    with s3_context(
+        s3_base=s3_base, bucket="daskcsv", files={"a.csv": b"a,b\n1,2\n3,4\n"}
+    ):
+        df = dask_cudf.read_csv(
+            "s3://daskcsv/*.csv", chunksize="50 B", storage_options=s3so
+        )
+        assert df.a.sum().compute() == 4
+
+
+@pytest.mark.parametrize(
+    "open_file_options",
+    [
+        {"precache_options": {"method": None}},
+        {"precache_options": {"method": "parquet"}},
+        {"open_file_func": None},
+    ],
+)
+def test_read_parquet(s3_base, s3so, open_file_options):
+    pdf = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2.1, 2.2, 2.3, 2.4]})
+    buffer = BytesIO()
+    pdf.to_parquet(path=buffer)
+    buffer.seek(0)
+    with s3_context(
+        s3_base=s3_base, bucket="daskparquet", files={"file.parq": buffer}
+    ):
+        if "open_file_func" in open_file_options:
+            fs = pa_fs.S3FileSystem(
+                endpoint_override=s3so["client_kwargs"]["endpoint_url"],
+            )
+            open_file_options["open_file_func"] = fs.open_input_file
+        df = dask_cudf.read_parquet(
+            "s3://daskparquet/*.parq",
+            storage_options=s3so,
+            open_file_options=open_file_options,
+        )
+        assert df.a.sum().compute() == 10
+        assert df.b.sum().compute() == 9
diff --git a/python/dask_cudf/dask_cudf/io/tests/test_text.py b/python/dask_cudf/dask_cudf/io/tests/test_text.py
new file mode 100644
index 0000000..a14eec1
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/tests/test_text.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import os
+
+import pytest
+
+import dask.dataframe as dd
+
+import cudf
+
+import dask_cudf
+
+cur_dir = os.path.dirname(__file__)
+text_file = os.path.join(cur_dir, "data/text/sample.pgn")
+
+
+@pytest.mark.parametrize("file", [text_file, [text_file]])
+@pytest.mark.parametrize("chunksize", [12, "50 B", None])
+def test_read_text(file, chunksize):
+    df1 = cudf.read_text(text_file, delimiter='"]')
+    df2 = dask_cudf.read_text(file, chunksize=chunksize, delimiter='"]')
+    dd.assert_eq(df1, df2, check_index=False)
+
+
+@pytest.mark.parametrize("offset", [0, 100, 250, 500, 1000])
+@pytest.mark.parametrize("size", [64, 128, 256])
+def test_read_text_byte_range(offset, size):
+    df1 = cudf.read_text(text_file, delimiter=".", byte_range=(offset, size))
+    df2 = dask_cudf.read_text(
+        text_file, chunksize=None, delimiter=".", byte_range=(offset, size)
+    )
+    dd.assert_eq(df1, df2, check_index=False)
diff --git a/python/dask_cudf/dask_cudf/io/text.py b/python/dask_cudf/dask_cudf/io/text.py
new file mode 100644
index 0000000..2adace5
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/io/text.py
@@ -0,0 +1,55 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import os
+from glob import glob
+
+import dask.dataframe as dd
+from dask.base import tokenize
+from dask.utils import apply, parse_bytes
+
+import cudf
+
+
+def read_text(path, chunksize="256 MiB", **kwargs):
+
+    if isinstance(chunksize, str):
+        chunksize = parse_bytes(chunksize)
+
+    if isinstance(path, list):
+        filenames = path
+    elif isinstance(path, str):
+        filenames = sorted(glob(path))
+    elif hasattr(path, "__fspath__"):
+        filenames = sorted(glob(path.__fspath__()))
+    else:
+        raise TypeError(f"Path type not understood:{type(path)}")
+
+    if not filenames:
+        msg = f"A file in: {filenames} does not exist."
+        raise FileNotFoundError(msg)
+
+    name = "read-text-" + tokenize(path, tokenize, **kwargs)
+
+    if chunksize:
+        dsk = {}
+        i = 0
+        for fn in filenames:
+            size = os.path.getsize(fn)
+            for start in range(0, size, chunksize):
+                kwargs1 = kwargs.copy()
+                kwargs1["byte_range"] = (
+                    start,
+                    chunksize,
+                )  # specify which chunk of the file we care about
+
+                dsk[(name, i)] = (apply, cudf.read_text, [fn], kwargs1)
+                i += 1
+    else:
+        dsk = {
+            (name, i): (apply, cudf.read_text, [fn], kwargs)
+            for i, fn in enumerate(filenames)
+        }
+
+    meta = cudf.Series([], dtype="O")
+    divisions = [None] * (len(dsk) + 1)
+    return dd.core.new_dd_object(dsk, name, meta, divisions)
diff --git a/python/dask_cudf/dask_cudf/sorting.py b/python/dask_cudf/dask_cudf/sorting.py
new file mode 100644
index 0000000..d6c9c1b
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/sorting.py
@@ -0,0 +1,333 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+from collections.abc import Iterator
+
+import cupy
+import numpy as np
+import tlz as toolz
+
+from dask import config
+from dask.base import tokenize
+from dask.dataframe import methods
+from dask.dataframe.core import DataFrame, Index, Series
+from dask.dataframe.shuffle import rearrange_by_column
+from dask.highlevelgraph import HighLevelGraph
+from dask.utils import M
+
+import cudf as gd
+from cudf.api.types import is_categorical_dtype
+from cudf.utils.utils import _dask_cudf_nvtx_annotate
+
+_SHUFFLE_SUPPORT = ("tasks", "p2p")  # "disk" not supported
+
+
+@_dask_cudf_nvtx_annotate
+def set_index_post(df, index_name, drop, column_dtype):
+    df2 = df.set_index(index_name, drop=drop)
+    df2.columns = df2.columns.astype(column_dtype)
+    return df2
+
+
+@_dask_cudf_nvtx_annotate
+def _set_partitions_pre(s, divisions, ascending=True, na_position="last"):
+    if ascending:
+        partitions = divisions.searchsorted(s, side="right") - 1
+    else:
+        partitions = (
+            len(divisions) - divisions.searchsorted(s, side="right") - 1
+        )
+    partitions[(partitions < 0) | (partitions >= len(divisions) - 1)] = (
+        0 if ascending else (len(divisions) - 2)
+    )
+    partitions[s._columns[0].isnull().values] = (
+        len(divisions) - 2 if na_position == "last" else 0
+    )
+    return partitions
+
+
+@_dask_cudf_nvtx_annotate
+def _quantile(a, q):
+    n = len(a)
+    if not len(a):
+        return None, n
+    return (
+        a.quantile(q=q.tolist(), interpolation="nearest", method="table"),
+        n,
+    )
+
+
+@_dask_cudf_nvtx_annotate
+def merge_quantiles(finalq, qs, vals):
+    """Combine several quantile calculations of different data.
+    [NOTE: Same logic as dask.array merge_percentiles]
+    """
+    if isinstance(finalq, Iterator):
+        finalq = list(finalq)
+    finalq = np.array(finalq)
+    qs = list(map(list, qs))
+    vals = list(vals)
+    vals, Ns = zip(*vals)
+    Ns = list(Ns)
+
+    L = list(zip(*[(q, val, N) for q, val, N in zip(qs, vals, Ns) if N]))
+    if not L:
+        raise ValueError("No non-trivial arrays found")
+    qs, vals, Ns = L
+
+    if len(vals) != len(qs) or len(Ns) != len(qs):
+        raise ValueError("qs, vals, and Ns parameters must be the same length")
+
+    # transform qs and Ns into number of observations between quantiles
+    counts = []
+    for q, N in zip(qs, Ns):
+        count = np.empty(len(q))
+        count[1:] = np.diff(q)
+        count[0] = q[0]
+        count *= N
+        counts.append(count)
+
+    def _append_counts(val, count):
+        val["_counts"] = count
+        return val
+
+    # Sort by calculated quantile values, then number of observations.
+    combined_vals_counts = gd.core.reshape._merge_sorted(
+        [*map(_append_counts, vals, counts)]
+    )
+    combined_counts = cupy.asnumpy(combined_vals_counts["_counts"].values)
+    combined_vals = combined_vals_counts.drop(columns=["_counts"])
+
+    # quantile-like, but scaled by total number of observations
+    combined_q = np.cumsum(combined_counts)
+
+    # rescale finalq quantiles to match combined_q
+    desired_q = finalq * sum(Ns)
+
+    # TODO: Support other interpolation methods
+    # For now - Always use "nearest" for interpolation
+    left = np.searchsorted(combined_q, desired_q, side="left")
+    right = np.searchsorted(combined_q, desired_q, side="right") - 1
+    np.minimum(left, len(combined_vals) - 1, left)  # don't exceed max index
+    lower = np.minimum(left, right)
+    upper = np.maximum(left, right)
+    lower_residual = np.abs(combined_q[lower] - desired_q)
+    upper_residual = np.abs(combined_q[upper] - desired_q)
+    mask = lower_residual > upper_residual
+    index = lower  # alias; we no longer need lower
+    index[mask] = upper[mask]
+    rv = combined_vals.iloc[index]
+    return rv.reset_index(drop=True)
+
+
+@_dask_cudf_nvtx_annotate
+def _approximate_quantile(df, q):
+    """Approximate quantiles of DataFrame or Series.
+    [NOTE: Same logic as dask.dataframe Series quantile]
+    """
+    # current implementation needs q to be sorted so
+    # sort if array-like, otherwise leave it alone
+    q_ndarray = np.array(q)
+    if q_ndarray.ndim > 0:
+        q_ndarray.sort(kind="mergesort")
+        q = q_ndarray
+
+    # Lets assume we are dealing with a DataFrame throughout
+    if isinstance(df, (Series, Index)):
+        df = df.to_frame()
+    assert isinstance(df, DataFrame)
+    final_type = df._meta._constructor
+
+    # Create metadata
+    meta = df._meta_nonempty.quantile(q=q, method="table")
+
+    # Define final action (create df with quantiles as index)
+    def finalize_tsk(tsk):
+        return (final_type, tsk)
+
+    return_type = df.__class__
+
+    # pandas/cudf uses quantile in [0, 1]
+    # numpy / cupy uses [0, 100]
+    qs = np.asarray(q)
+    token = tokenize(df, qs)
+
+    if len(qs) == 0:
+        name = "quantiles-" + token
+        empty_index = gd.Index([], dtype=float)
+        return Series(
+            {
+                (name, 0): final_type(
+                    {col: [] for col in df.columns},
+                    name=df.name,
+                    index=empty_index,
+                )
+            },
+            name,
+            df._meta,
+            [None, None],
+        )
+    else:
+        new_divisions = [np.min(q), np.max(q)]
+
+    name = "quantiles-1-" + token
+    val_dsk = {
+        (name, i): (_quantile, key, qs)
+        for i, key in enumerate(df.__dask_keys__())
+    }
+
+    name2 = "quantiles-2-" + token
+    merge_dsk = {
+        (name2, 0): finalize_tsk(
+            (merge_quantiles, qs, [qs] * df.npartitions, sorted(val_dsk))
+        )
+    }
+    dsk = toolz.merge(val_dsk, merge_dsk)
+    graph = HighLevelGraph.from_collections(name2, dsk, dependencies=[df])
+    df = return_type(graph, name2, meta, new_divisions)
+
+    def set_quantile_index(df):
+        df.index = q
+        return df
+
+    df = df.map_partitions(set_quantile_index, meta=meta)
+    return df
+
+
+@_dask_cudf_nvtx_annotate
+def quantile_divisions(df, by, npartitions):
+    qn = np.linspace(0.0, 1.0, npartitions + 1).tolist()
+    divisions = _approximate_quantile(df[by], qn).compute()
+    columns = divisions.columns
+
+    # TODO: Make sure divisions are correct for all dtypes..
+    if (
+        len(columns) == 1
+        and df[columns[0]].dtype != "object"
+        and not is_categorical_dtype(df[columns[0]].dtype)
+    ):
+        dtype = df[columns[0]].dtype
+        divisions = divisions[columns[0]].astype("int64")
+        divisions.iloc[-1] += 1
+        divisions = sorted(
+            divisions.drop_duplicates().astype(dtype).to_arrow().tolist(),
+            key=lambda x: (x is None, x),
+        )
+    else:
+        for col in columns:
+            dtype = df[col].dtype
+            if dtype != "object":
+                divisions[col] = divisions[col].astype("int64")
+                divisions[col].iloc[-1] += 1
+                divisions[col] = divisions[col].astype(dtype)
+            else:
+                if last := divisions[col].iloc[-1]:
+                    val = chr(ord(last[0]) + 1)
+                else:
+                    val = "this string intentionally left empty"  # any but ""
+                divisions[col].iloc[-1] = val
+        divisions = divisions.drop_duplicates().sort_index()
+    return divisions
+
+
+@_dask_cudf_nvtx_annotate
+def sort_values(
+    df,
+    by,
+    max_branch=None,
+    divisions=None,
+    set_divisions=False,
+    ignore_index=False,
+    ascending=True,
+    na_position="last",
+    shuffle=None,
+    sort_function=None,
+    sort_function_kwargs=None,
+):
+    """Sort by the given list/tuple of column names."""
+
+    if not isinstance(ascending, bool):
+        raise ValueError("ascending must be either True or False")
+    if na_position not in ("first", "last"):
+        raise ValueError("na_position must be either 'first' or 'last'")
+
+    npartitions = df.npartitions
+    if isinstance(by, tuple):
+        by = list(by)
+    elif not isinstance(by, list):
+        by = [by]
+
+    # parse custom sort function / kwargs if provided
+    sort_kwargs = {
+        "by": by,
+        "ascending": ascending,
+        "na_position": na_position,
+    }
+    if sort_function is None:
+        sort_function = M.sort_values
+    if sort_function_kwargs is not None:
+        sort_kwargs.update(sort_function_kwargs)
+
+    # handle single partition case
+    if npartitions == 1:
+        return df.map_partitions(sort_function, **sort_kwargs)
+
+    # Step 1 - Calculate new divisions (if necessary)
+    if divisions is None:
+        divisions = quantile_divisions(df, by, npartitions)
+
+    # Step 2 - Perform repartitioning shuffle
+    meta = df._meta._constructor_sliced([0])
+    if not isinstance(divisions, (gd.Series, gd.DataFrame)):
+        dtype = df[by[0]].dtype
+        divisions = df._meta._constructor_sliced(divisions, dtype=dtype)
+
+    partitions = df[by].map_partitions(
+        _set_partitions_pre,
+        divisions=divisions,
+        ascending=ascending,
+        na_position=na_position,
+        meta=meta,
+    )
+
+    df2 = df.assign(_partitions=partitions)
+    df3 = rearrange_by_column(
+        df2,
+        "_partitions",
+        max_branch=max_branch,
+        npartitions=len(divisions) - 1,
+        shuffle=_get_shuffle_type(shuffle),
+        ignore_index=ignore_index,
+    ).drop(columns=["_partitions"])
+    df3.divisions = (None,) * (df3.npartitions + 1)
+
+    # Step 3 - Return final sorted df
+    df4 = df3.map_partitions(sort_function, **sort_kwargs)
+    if not isinstance(divisions, gd.DataFrame) and set_divisions:
+        # Can't have multi-column divisions elsewhere in dask (yet)
+        df4.divisions = tuple(methods.tolist(divisions))
+
+    return df4
+
+
+def get_default_shuffle_method():
+    # Note that `dask.utils.get_default_shuffle_method`
+    # will return "p2p" by default when a distributed
+    # client is present. Dask-cudf supports "p2p", but
+    # will not use it by default (yet)
+    default = config.get("dataframe.shuffle.method", "tasks")
+    if default not in _SHUFFLE_SUPPORT:
+        default = "tasks"
+    return default
+
+
+def _get_shuffle_type(shuffle):
+    # Utility to set the shuffle-kwarg default
+    # and to validate user-specified options
+    shuffle = shuffle or get_default_shuffle_method()
+    if shuffle not in _SHUFFLE_SUPPORT:
+        raise ValueError(
+            "Dask-cudf only supports the following shuffle "
+            f"methods: {_SHUFFLE_SUPPORT}. Got shuffle={shuffle}"
+        )
+
+    return shuffle
diff --git a/python/dask_cudf/dask_cudf/tests/__init__.py b/python/dask_cudf/dask_cudf/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/python/dask_cudf/dask_cudf/tests/test_accessor.py b/python/dask_cudf/dask_cudf/tests/test_accessor.py
new file mode 100644
index 0000000..bea0cbb
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_accessor.py
@@ -0,0 +1,559 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+from pandas.testing import assert_series_equal
+
+from dask import dataframe as dd
+
+from cudf import DataFrame, Series, date_range
+from cudf.testing._utils import assert_eq, does_not_raise
+
+import dask_cudf as dgd
+
+#############################################################################
+#                        Datetime Accessor                                  #
+#############################################################################
+
+
+def data_dt_1():
+    return pd.date_range("20010101", "20020215", freq="400h")
+
+
+def data_dt_2():
+    return np.random.randn(100)
+
+
+dt_fields = ["year", "month", "day", "hour", "minute", "second"]
+
+
+@pytest.mark.parametrize("data", [data_dt_2()])
+def test_datetime_accessor_initialization(data):
+    pdsr = pd.Series(data.copy())
+    sr = Series(pdsr)
+    dsr = dgd.from_cudf(sr, npartitions=5)
+    with pytest.raises(AttributeError):
+        dsr.dt
+
+
+@pytest.mark.parametrize("data", [data_dt_1()])
+def test_series(data):
+    pdsr = pd.Series(data.copy())
+    sr = Series(pdsr)
+    dsr = dgd.from_cudf(sr, npartitions=5)
+
+    np.testing.assert_equal(np.array(pdsr), dsr.compute().values_host)
+
+
+@pytest.mark.parametrize("data", [data_dt_1()])
+@pytest.mark.parametrize("field", dt_fields)
+def test_dt_series(data, field):
+    pdsr = pd.Series(data.copy())
+    sr = Series(pdsr)
+    dsr = dgd.from_cudf(sr, npartitions=5)
+    base = getattr(pdsr.dt, field)
+    test = getattr(dsr.dt, field).compute().to_pandas().astype("int64")
+    assert_series_equal(base, test)
+
+
+@pytest.mark.parametrize("data", [data_dt_1()])
+def test_dt_accessor(data):
+    df = DataFrame({"dt_col": data.copy()})
+    ddf = dgd.from_cudf(df, npartitions=5)
+
+    for i in ["year", "month", "day", "hour", "minute", "second", "weekday"]:
+        assert i in dir(ddf.dt_col.dt)
+        assert_series_equal(
+            getattr(ddf.dt_col.dt, i).compute().to_pandas(),
+            getattr(df.dt_col.dt, i).to_pandas(),
+        )
+
+
+#############################################################################
+#                        Categorical Accessor                               #
+#############################################################################
+
+
+def data_cat_1():
+    cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
+    return cat
+
+
+def data_cat_2():
+    return pd.Series([1, 2, 3])
+
+
+def data_cat_3():
+    cat1 = pd.Categorical(
+        ["a", "a", "b", "c", "a"], categories=["a", "b", "c"], ordered=True
+    )
+    cat2 = pd.Categorical(
+        ["a", "b", "a", "c", "b"], categories=["a", "b", "c"], ordered=True
+    )
+    return cat1, cat2
+
+
+@pytest.mark.parametrize("data", [data_cat_1()])
+def test_categorical_accessor_initialization1(data):
+    sr = Series(data.copy())
+    dsr = dgd.from_cudf(sr, npartitions=5)
+    dsr.cat
+
+
+@pytest.mark.parametrize("data", [data_cat_2()])
+def test_categorical_accessor_initialization2(data):
+    sr = Series(data.copy())
+    dsr = dgd.from_cudf(sr, npartitions=5)
+    with pytest.raises(AttributeError):
+        dsr.cat
+
+
+@pytest.mark.parametrize("data", [data_cat_1()])
+def test_categorical_basic(data):
+    cat = data.copy()
+    pdsr = pd.Series(cat)
+    sr = Series(cat)
+    dsr = dgd.from_cudf(sr, npartitions=2)
+    result = dsr.compute()
+    np.testing.assert_array_equal(cat.codes, result.cat.codes.values_host)
+
+    assert dsr.dtype.to_pandas() == pdsr.dtype
+    # Test attributes
+    assert pdsr.cat.ordered == dsr.cat.ordered
+
+    assert_eq(pdsr.cat.categories, dsr.cat.categories)
+
+    np.testing.assert_array_equal(
+        pdsr.cat.codes.values, result.cat.codes.values_host
+    )
+
+    string = str(result)
+    expect_str = """
+0 a
+1 a
+2 b
+3 c
+4 a
+"""
+    assert all(x == y for x, y in zip(string.split(), expect_str.split()))
+
+    df = DataFrame()
+    df["a"] = ["xyz", "abc", "def"] * 10
+
+    pdf = df.to_pandas()
+    cddf = dgd.from_cudf(df, 1)
+    cddf["b"] = cddf["a"].astype("category")
+
+    ddf = dd.from_pandas(pdf, 1)
+    ddf["b"] = ddf["a"].astype("category")
+
+    assert_eq(ddf._meta_nonempty["b"], cddf._meta_nonempty["b"])
+
+    with pytest.raises(NotImplementedError):
+        cddf["b"].cat.categories
+
+    with pytest.raises(NotImplementedError):
+        ddf["b"].cat.categories
+
+    cddf = cddf.categorize()
+    ddf = ddf.categorize()
+
+    assert_eq(ddf["b"].cat.categories, cddf["b"].cat.categories)
+    assert_eq(ddf["b"].cat.ordered, cddf["b"].cat.ordered)
+
+
+@pytest.mark.parametrize("data", [data_cat_1()])
+def test_categorical_compare_unordered(data):
+    cat = data.copy()
+    pdsr = pd.Series(cat)
+    sr = Series(cat)
+    dsr = dgd.from_cudf(sr, npartitions=2)
+
+    # Test equality
+    out = dsr == dsr
+    assert out.dtype == np.bool_
+    assert np.all(out.compute())
+    assert np.all(pdsr == pdsr)
+
+    # Test inequality
+    out = dsr != dsr
+    assert not np.any(out.compute())
+    assert not np.any(pdsr != pdsr)
+
+    assert not dsr.cat.ordered
+    assert not pdsr.cat.ordered
+
+    with pytest.raises(
+        (TypeError, ValueError),
+        match="Unordered Categoricals can only compare equality or not",
+    ):
+        pdsr < pdsr
+
+    with pytest.raises(
+        (TypeError, ValueError),
+        match=(
+            "The only binary operations supported by unordered categorical "
+            "columns are equality and inequality."
+        ),
+    ):
+        dsr < dsr
+
+
+@pytest.mark.parametrize("data", [data_cat_3()])
+def test_categorical_compare_ordered(data):
+    cat1 = data[0]
+    cat2 = data[1]
+    pdsr1 = pd.Series(cat1)
+    pdsr2 = pd.Series(cat2)
+    sr1 = Series(cat1)
+    sr2 = Series(cat2)
+    dsr1 = dgd.from_cudf(sr1, npartitions=2)
+    dsr2 = dgd.from_cudf(sr2, npartitions=2)
+
+    # Test equality
+    out = dsr1 == dsr1
+    assert out.dtype == np.bool_
+    assert np.all(out.compute().values_host)
+    assert np.all(pdsr1 == pdsr1)
+
+    # Test inequality
+    out = dsr1 != dsr1
+    assert not np.any(out.compute().values_host)
+    assert not np.any(pdsr1 != pdsr1)
+
+    assert dsr1.cat.ordered
+    assert pdsr1.cat.ordered
+
+    # Test ordered operators
+    np.testing.assert_array_equal(
+        pdsr1 < pdsr2, (dsr1 < dsr2).compute().values_host
+    )
+    np.testing.assert_array_equal(
+        pdsr1 > pdsr2, (dsr1 > dsr2).compute().values_host
+    )
+
+
+#############################################################################
+#                        String Accessor                                    #
+#############################################################################
+
+
+def data_str_1():
+    return pd.Series(["20190101", "20190102", "20190103"])
+
+
+@pytest.mark.parametrize("data", [data_str_1()])
+def test_string_slicing(data):
+    pdsr = pd.Series(data.copy())
+    sr = Series(pdsr)
+    dsr = dgd.from_cudf(sr, npartitions=2)
+    base = pdsr.str.slice(0, 4)
+    test = dsr.str.slice(0, 4).compute()
+    assert_eq(base, test)
+
+
+def test_categorical_categories():
+
+    df = DataFrame(
+        {"a": ["a", "b", "c", "d", "e", "e", "a", "d"], "b": range(8)}
+    )
+    df["a"] = df["a"].astype("category")
+    pdf = df.to_pandas(nullable_pd_dtype=False)
+
+    ddf = dgd.from_cudf(df, 2)
+    dpdf = dd.from_pandas(pdf, 2)
+
+    dd.assert_eq(
+        ddf.a.cat.categories.to_series().to_pandas(nullable_pd_dtype=False),
+        dpdf.a.cat.categories.to_series(),
+        check_index=False,
+    )
+
+
+def test_categorical_as_known():
+    df = dgd.from_cudf(DataFrame({"col_1": [0, 1, 2, 3]}), npartitions=2)
+    df["col_1"] = df["col_1"].astype("category")
+    actual = df["col_1"].cat.as_known()
+
+    pdf = dd.from_pandas(pd.DataFrame({"col_1": [0, 1, 2, 3]}), npartitions=2)
+    pdf["col_1"] = pdf["col_1"].astype("category")
+    expected = pdf["col_1"].cat.as_known()
+    dd.assert_eq(expected, actual)
+
+
+def test_str_slice():
+
+    df = DataFrame({"a": ["abc,def,123", "xyz,hi,bye"]})
+
+    ddf = dgd.from_cudf(df, 1)
+    pdf = df.to_pandas()
+
+    dd.assert_eq(
+        pdf.a.str.split(",", expand=True, n=1),
+        ddf.a.str.split(",", expand=True, n=1),
+    )
+    dd.assert_eq(
+        pdf.a.str.split(",", expand=True, n=2),
+        ddf.a.str.split(",", expand=True, n=2),
+    )
+
+
+#############################################################################
+#                              List Accessor                                #
+#############################################################################
+
+
+def data_test_1():
+    return [list(range(100)) for _ in range(100)]
+
+
+def data_test_2():
+    return [list(i for _ in range(i)) for i in range(500)]
+
+
+def data_test_non_numeric():
+    return [list(chr(97 + i % 20) for _ in range(i)) for i in range(500)]
+
+
+def data_test_nested():
+    return [
+        list(list(y for y in range(x % 5)) for x in range(i))
+        for i in range(40)
+    ]
+
+
+def data_test_sort():
+    return [[1, 2, 3, 1, 2, 5] for _ in range(20)]
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [[]],
+        [[[]]],
+        [[0]],
+        [[0, 1]],
+        [[0, 1], [2, 3]],
+        [[[0, 1], [2]], [[3, 4]]],
+        [[None]],
+        [[[None]]],
+        [[None], None],
+        [[1, None], [1]],
+        [[1, None], None],
+        [[[1, None], None], None],
+    ],
+)
+def test_create_list_series(data):
+    expect = pd.Series(data)
+    ds_got = dgd.from_cudf(Series(data), 4)
+    assert_eq(expect, ds_got.compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [data_test_1(), data_test_2(), data_test_non_numeric()],
+)
+def test_unique(data):
+    expect = Series(data).list.unique()
+    ds = dgd.from_cudf(Series(data), 5)
+    assert_eq(expect, ds.list.unique().compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [data_test_2(), data_test_non_numeric()],
+)
+def test_len(data):
+    expect = Series(data).list.len()
+    ds = dgd.from_cudf(Series(data), 5)
+    assert_eq(expect, ds.list.len().compute())
+
+
+@pytest.mark.parametrize(
+    "data, search_key",
+    [(data_test_2(), 1)],
+)
+def test_contains(data, search_key):
+    expect = Series(data).list.contains(search_key)
+    ds = dgd.from_cudf(Series(data), 5)
+    assert_eq(expect, ds.list.contains(search_key).compute())
+
+
+@pytest.mark.parametrize(
+    "data, index",
+    [
+        (data_test_1(), 1),
+        (data_test_2(), 2),
+    ],
+)
+def test_get(data, index):
+    expect = Series(data).list.get(index)
+    ds = dgd.from_cudf(Series(data), 5)
+    assert_eq(expect, ds.list.get(index).compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    [data_test_1(), data_test_2(), data_test_nested()],
+)
+def test_leaves(data):
+    expect = Series(data).list.leaves
+    ds = dgd.from_cudf(Series(data), 5)
+    got = ds.list.leaves.compute().reset_index(drop=True)
+    assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data, list_indices, expectation",
+    [
+        (
+            data_test_1(),
+            [[0, 1] for _ in range(len(data_test_1()))],
+            does_not_raise(),
+        ),
+        (data_test_2(), [[0]], pytest.raises(ValueError)),
+    ],
+)
+def test_take(data, list_indices, expectation):
+    with expectation:
+        expect = Series(data).list.take(list_indices)
+
+    if expectation == does_not_raise():
+        ds = dgd.from_cudf(Series(data), 5)
+        assert_eq(expect, ds.list.take(list_indices).compute())
+
+
+@pytest.mark.parametrize(
+    "data, ascending, na_position, ignore_index",
+    [
+        (data_test_sort(), True, "first", False),
+        (data_test_sort(), False, "last", True),
+    ],
+)
+def test_sorting(data, ascending, na_position, ignore_index):
+    expect = Series(data).list.sort_values(
+        ascending=ascending, na_position=na_position, ignore_index=ignore_index
+    )
+    got = (
+        dgd.from_cudf(Series(data), 5)
+        .list.sort_values(
+            ascending=ascending,
+            na_position=na_position,
+            ignore_index=ignore_index,
+        )
+        .compute()
+        .reset_index(drop=True)
+    )
+    assert_eq(expect, got)
+
+
+#############################################################################
+#                            Struct Accessor                                #
+#############################################################################
+struct_accessor_data_params = [
+    [{"a": 5, "b": 10}, {"a": 3, "b": 7}, {"a": -3, "b": 11}],
+    [{"a": None, "b": 1}, {"a": None, "b": 0}, {"a": -3, "b": None}],
+    [{"a": 1, "b": 2}],
+    [{"a": 1, "b": 3, "c": 4}],
+]
+
+
+@pytest.mark.parametrize(
+    "data",
+    struct_accessor_data_params,
+)
+def test_create_struct_series(data):
+    expect = pd.Series(data)
+    ds_got = dgd.from_cudf(Series(data), 2)
+    assert_eq(expect, ds_got.compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    struct_accessor_data_params,
+)
+def test_struct_field_str(data):
+    for test_key in ["a", "b"]:
+        expect = Series(data).struct.field(test_key)
+        ds_got = dgd.from_cudf(Series(data), 2).struct.field(test_key)
+        assert_eq(expect, ds_got.compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    struct_accessor_data_params,
+)
+def test_struct_field_integer(data):
+    for test_key in [0, 1]:
+        expect = Series(data).struct.field(test_key)
+        ds_got = dgd.from_cudf(Series(data), 2).struct.field(test_key)
+        assert_eq(expect, ds_got.compute())
+
+
+@pytest.mark.parametrize(
+    "data",
+    struct_accessor_data_params,
+)
+def test_dask_struct_field_Key_Error(data):
+    got = dgd.from_cudf(Series(data), 2)
+
+    with pytest.raises(KeyError):
+        got.struct.field("notakey").compute()
+
+
+@pytest.mark.parametrize(
+    "data",
+    struct_accessor_data_params,
+)
+def test_dask_struct_field_Int_Error(data):
+    # breakpoint()
+    got = dgd.from_cudf(Series(data), 2)
+
+    with pytest.raises(IndexError):
+        got.struct.field(1000).compute()
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [{}, {}, {}],
+        [{"a": 100, "b": "abc"}, {"a": 42, "b": "def"}, {"a": -87, "b": ""}],
+        [{"a": [1, 2, 3], "b": {"c": 101}}, {"a": [4, 5], "b": {"c": 102}}],
+    ],
+)
+def test_struct_explode(data):
+    expect = Series(data).struct.explode()
+    got = dgd.from_cudf(Series(data), 2).struct.explode()
+    # Output index will not agree for >1 partitions
+    assert_eq(expect, got.compute().reset_index(drop=True))
+
+
+def test_tz_localize():
+    data = Series(date_range("2000-04-01", "2000-04-03", freq="H"))
+    expect = data.dt.tz_localize(
+        "US/Eastern", ambiguous="NaT", nonexistent="NaT"
+    )
+    got = dgd.from_cudf(data, 2).dt.tz_localize(
+        "US/Eastern", ambiguous="NaT", nonexistent="NaT"
+    )
+    dd.assert_eq(expect, got)
+
+    expect = expect.dt.tz_localize(None)
+    got = got.dt.tz_localize(None)
+    dd.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        date_range("2000-04-01", "2000-04-03", freq="H").tz_localize("UTC"),
+        date_range("2000-04-01", "2000-04-03", freq="H").tz_localize(
+            "US/Eastern"
+        ),
+    ],
+)
+def test_tz_convert(data):
+    expect = Series(data).dt.tz_convert("US/Pacific")
+    got = dgd.from_cudf(Series(data), 2).dt.tz_convert("US/Pacific")
+    dd.assert_eq(expect, got)
diff --git a/python/dask_cudf/dask_cudf/tests/test_applymap.py b/python/dask_cudf/dask_cudf/tests/test_applymap.py
new file mode 100644
index 0000000..929f00e
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_applymap.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import pytest
+from pandas import NA
+
+from dask import dataframe as dd
+
+from dask_cudf.tests.utils import _make_random_frame
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: x + 1,
+        lambda x: x - 0.5,
+        lambda x: 2 if x is NA else 2 + (x + 1) / 4.1,
+        lambda x: 42,
+    ],
+)
+@pytest.mark.parametrize("has_na", [True, False])
+def test_applymap_basic(func, has_na):
+    size = 2000
+    pdf, dgdf = _make_random_frame(size, include_na=False)
+
+    dpdf = dd.from_pandas(pdf, npartitions=dgdf.npartitions)
+
+    expect = dpdf.applymap(func)
+    got = dgdf.applymap(func)
+    dd.assert_eq(expect, got, check_dtype=False)
diff --git a/python/dask_cudf/dask_cudf/tests/test_binops.py b/python/dask_cudf/dask_cudf/tests/test_binops.py
new file mode 100644
index 0000000..87bd401
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_binops.py
@@ -0,0 +1,90 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import operator
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from dask import dataframe as dd
+
+import cudf
+
+from dask_cudf.tests.utils import _make_random_frame
+
+
+def _make_empty_frame(npartitions=2):
+    df = pd.DataFrame({"x": [], "y": []})
+    gdf = cudf.DataFrame.from_pandas(df)
+    dgf = dd.from_pandas(gdf, npartitions=npartitions)
+    return dgf
+
+
+def _make_random_frame_float(nelem, npartitions=2):
+    df = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=nelem),
+            "y": np.random.normal(size=nelem) + 1,
+        }
+    )
+    gdf = cudf.from_pandas(df)
+    dgf = dd.from_pandas(gdf, npartitions=npartitions)
+    return df, dgf
+
+
+_binops = [
+    operator.add,
+    operator.sub,
+    operator.mul,
+    operator.truediv,
+    operator.floordiv,
+    operator.mod,
+    operator.pow,
+    operator.eq,
+    operator.ne,
+    operator.gt,
+    operator.ge,
+    operator.lt,
+    operator.le,
+]
+
+
+@pytest.mark.parametrize("binop", _binops)
+def test_series_binops_integer(binop):
+    np.random.seed(0)
+    size = 1000
+    lhs_df, lhs_gdf = _make_random_frame(size)
+    rhs_df, rhs_gdf = _make_random_frame(size)
+    got = binop(lhs_gdf.x, rhs_gdf.y)
+    exp = binop(lhs_df.x, rhs_df.y)
+    dd.assert_eq(got, exp)
+
+
+@pytest.mark.parametrize("binop", _binops)
+def test_series_binops_float(binop):
+    np.random.seed(0)
+    size = 1000
+    lhs_df, lhs_gdf = _make_random_frame_float(size)
+    rhs_df, rhs_gdf = _make_random_frame_float(size)
+    got = binop(lhs_gdf.x, rhs_gdf.y)
+    exp = binop(lhs_df.x, rhs_df.y)
+    dd.assert_eq(got, exp)
+
+
+@pytest.mark.parametrize("operator", _binops)
+def test_df_series_bind_ops(operator):
+    np.random.seed(0)
+    size = 1000
+    lhs_df, lhs_gdf = _make_random_frame_float(size)
+    rhs = np.random.rand()
+
+    for col in lhs_gdf.columns:
+        got = getattr(lhs_gdf[col], operator.__name__)(rhs)
+        exp = getattr(lhs_df[col], operator.__name__)(rhs)
+        dd.assert_eq(got, exp)
+
+    if operator.__name__ not in ["eq", "ne", "lt", "gt", "le", "ge"]:
+        got = getattr(lhs_gdf, operator.__name__)(rhs)
+        exp = getattr(lhs_df, operator.__name__)(rhs)
+
+        dd.assert_eq(got, exp)
diff --git a/python/dask_cudf/dask_cudf/tests/test_core.py b/python/dask_cudf/dask_cudf/tests/test_core.py
new file mode 100644
index 0000000..7f8876c
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_core.py
@@ -0,0 +1,946 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import random
+
+import cupy as cp
+import numpy as np
+import pandas as pd
+import pytest
+from packaging import version
+
+import dask
+from dask import dataframe as dd
+from dask.dataframe.core import make_meta as dask_make_meta, meta_nonempty
+from dask.utils import M
+
+import cudf
+
+import dask_cudf as dgd
+
+
+def test_from_dict_backend_dispatch():
+    # Test ddf.from_dict cudf-backend dispatch
+    np.random.seed(0)
+    data = {
+        "x": np.random.randint(0, 5, size=10000),
+        "y": np.random.normal(size=10000),
+    }
+    expect = cudf.DataFrame(data)
+    with dask.config.set({"dataframe.backend": "cudf"}):
+        ddf = dd.from_dict(data, npartitions=2)
+    assert isinstance(ddf, dgd.DataFrame)
+    dd.assert_eq(expect, ddf)
+
+
+def test_to_backend():
+    np.random.seed(0)
+    data = {
+        "x": np.random.randint(0, 5, size=10000),
+        "y": np.random.normal(size=10000),
+    }
+    with dask.config.set({"dataframe.backend": "pandas"}):
+        ddf = dd.from_dict(data, npartitions=2)
+        assert isinstance(ddf._meta, pd.DataFrame)
+
+        gdf = ddf.to_backend("cudf")
+        assert isinstance(gdf, dgd.DataFrame)
+        dd.assert_eq(cudf.DataFrame(data), ddf)
+
+        assert isinstance(gdf.to_backend()._meta, pd.DataFrame)
+
+
+def test_to_backend_kwargs():
+    data = {"x": [0, 2, np.nan, 3, 4, 5]}
+    with dask.config.set({"dataframe.backend": "pandas"}):
+        dser = dd.from_dict(data, npartitions=2)["x"]
+        assert isinstance(dser._meta, pd.Series)
+
+        # Using `nan_as_null=False` will result in a cudf-backed
+        # Series with a NaN element (ranther than <NA>)
+        gser_nan = dser.to_backend("cudf", nan_as_null=False)
+        assert isinstance(gser_nan, dgd.Series)
+        assert np.isnan(gser_nan.compute()).sum() == 1
+
+        # Using `nan_as_null=True` will result in a cudf-backed
+        # Series with a <NA> element (ranther than NaN)
+        gser_null = dser.to_backend("cudf", nan_as_null=True)
+        assert isinstance(gser_null, dgd.Series)
+        assert np.isnan(gser_null.compute()).sum() == 0
+
+        # Check `nullable` argument for `cudf.Series.to_pandas`
+        dser_null = gser_null.to_backend("pandas", nullable=False)
+        assert dser_null.compute().dtype == "float"
+        dser_null = gser_null.to_backend("pandas", nullable=True)
+        assert isinstance(dser_null.compute().dtype, pd.Float64Dtype)
+
+        # Check unsupported arguments
+        with pytest.raises(ValueError, match="pandas-to-cudf"):
+            dser.to_backend("cudf", bad_arg=True)
+
+        with pytest.raises(ValueError, match="cudf-to-cudf"):
+            gser_null.to_backend("cudf", bad_arg=True)
+
+        with pytest.raises(ValueError, match="cudf-to-pandas"):
+            gser_null.to_backend("pandas", bad_arg=True)
+
+
+def test_from_cudf():
+    np.random.seed(0)
+
+    df = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=10000),
+            "y": np.random.normal(size=10000),
+        }
+    )
+
+    gdf = cudf.DataFrame.from_pandas(df)
+
+    # Test simple around to/from dask
+    ingested = dd.from_pandas(gdf, npartitions=2)
+    dd.assert_eq(ingested, df)
+
+    # Test conversion to dask.dataframe
+    ddf = ingested.to_dask_dataframe()
+    dd.assert_eq(ddf, df)
+
+
+def test_from_cudf_multiindex_raises():
+
+    df = cudf.DataFrame({"x": list("abc"), "y": [1, 2, 3], "z": [1, 2, 3]})
+
+    with pytest.raises(NotImplementedError):
+        # dask_cudf does not support MultiIndex yet
+        dgd.from_cudf(df.set_index(["x", "y"]))
+
+
+def test_from_cudf_with_generic_idx():
+
+    cdf = cudf.DataFrame(
+        {
+            "a": list(range(20)),
+            "b": list(reversed(range(20))),
+            "c": list(range(20)),
+        }
+    )
+
+    ddf = dgd.from_cudf(cdf, npartitions=2)
+
+    assert isinstance(ddf.index.compute(), cudf.RangeIndex)
+    dd.assert_eq(ddf.loc[1:2, ["a"]], cdf.loc[1:2, ["a"]])
+
+
+def _fragmented_gdf(df, nsplit):
+    n = len(df)
+
+    # Split dataframe in *nsplit*
+    subdivsize = n // nsplit
+    starts = [i * subdivsize for i in range(nsplit)]
+    ends = starts[1:] + [None]
+    frags = [df[s:e] for s, e in zip(starts, ends)]
+    return frags
+
+
+def test_query():
+    np.random.seed(0)
+
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=10), "y": np.random.normal(size=10)}
+    )
+    gdf = cudf.DataFrame.from_pandas(df)
+    expr = "x > 2"
+
+    dd.assert_eq(gdf.query(expr), df.query(expr))
+
+    queried = dd.from_pandas(gdf, npartitions=2).query(expr)
+
+    got = queried
+    expect = gdf.query(expr)
+
+    dd.assert_eq(got, expect)
+
+
+def test_query_local_dict():
+    np.random.seed(0)
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=10), "y": np.random.normal(size=10)}
+    )
+    gdf = cudf.DataFrame.from_pandas(df)
+    ddf = dgd.from_cudf(gdf, npartitions=2)
+
+    val = 2
+
+    gdf_queried = gdf.query("x > @val")
+    ddf_queried = ddf.query("x > @val", local_dict={"val": val})
+
+    dd.assert_eq(gdf_queried, ddf_queried)
+
+
+def test_head():
+    np.random.seed(0)
+    df = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=100),
+            "y": np.random.normal(size=100),
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(df)
+    dgf = dd.from_pandas(gdf, npartitions=2)
+
+    dd.assert_eq(dgf.head(), df.head())
+
+
+def test_from_dask_dataframe():
+    np.random.seed(0)
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+    )
+    ddf = dd.from_pandas(df, npartitions=2)
+    dgdf = ddf.map_partitions(cudf.from_pandas)
+    got = dgdf.compute().to_pandas()
+    expect = df
+
+    dd.assert_eq(got, expect)
+
+
+@pytest.mark.parametrize("nelem", [10, 200, 1333])
+@pytest.mark.parametrize("divisions", [None, "quantile"])
+def test_set_index(nelem, divisions):
+    with dask.config.set(scheduler="single-threaded"):
+        np.random.seed(0)
+        # Use unique index range as the sort may not be stable-ordering
+        x = np.arange(nelem)
+        np.random.shuffle(x)
+        df = pd.DataFrame(
+            {"x": x, "y": np.random.randint(0, nelem, size=nelem)}
+        )
+        ddf = dd.from_pandas(df, npartitions=2)
+        dgdf = ddf.map_partitions(cudf.from_pandas)
+
+        expect = ddf.set_index("x")
+        got = dgdf.set_index("x", divisions=divisions)
+
+        dd.assert_eq(expect, got, check_index=False, check_divisions=False)
+
+
+@pytest.mark.parametrize("by", ["a", "b"])
+@pytest.mark.parametrize("nelem", [10, 500])
+@pytest.mark.parametrize("nparts", [1, 10])
+def test_set_index_quantile(nelem, nparts, by):
+    df = cudf.DataFrame()
+    df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
+    df["b"] = np.random.choice(cudf.datasets.names, size=nelem)
+    ddf = dd.from_pandas(df, npartitions=nparts)
+
+    got = ddf.set_index(by, divisions="quantile")
+    expect = df.sort_values(by=by).set_index(by)
+    dd.assert_eq(got, expect)
+
+
+def assert_frame_equal_by_index_group(expect, got):
+    assert sorted(expect.columns) == sorted(got.columns)
+    assert sorted(set(got.index)) == sorted(set(expect.index))
+    # Note the set_index sort is not stable,
+    unique_values = sorted(set(got.index))
+    for iv in unique_values:
+        sr_expect = expect.loc[[iv]]
+        sr_got = got.loc[[iv]]
+
+        for k in expect.columns:
+            # Sort each column before we compare them
+            sorted_expect = sr_expect.sort_values(k)[k]
+            sorted_got = sr_got.sort_values(k)[k]
+            np.testing.assert_array_equal(sorted_expect, sorted_got)
+
+
+@pytest.mark.parametrize("nelem", [10, 200, 1333])
+def test_set_index_2(nelem):
+    with dask.config.set(scheduler="single-threaded"):
+        np.random.seed(0)
+        df = pd.DataFrame(
+            {
+                "x": 100 + np.random.randint(0, nelem // 2, size=nelem),
+                "y": np.random.normal(size=nelem),
+            }
+        )
+        expect = df.set_index("x").sort_index()
+
+        dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=4)
+        res = dgf.set_index("x")  # sort by default
+        got = res.compute().to_pandas()
+
+        assert_frame_equal_by_index_group(expect, got)
+
+
+@pytest.mark.xfail(reason="dask's index name '__dask_cudf.index' is correct")
+def test_set_index_w_series():
+    with dask.config.set(scheduler="single-threaded"):
+        nelem = 20
+        np.random.seed(0)
+        df = pd.DataFrame(
+            {
+                "x": 100 + np.random.randint(0, nelem // 2, size=nelem),
+                "y": np.random.normal(size=nelem),
+            }
+        )
+        expect = df.set_index(df.x).sort_index()
+
+        dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=4)
+        res = dgf.set_index(dgf.x)  # sort by default
+        got = res.compute().to_pandas()
+
+        dd.assert_eq(expect, got)
+
+
+def test_set_index_sorted():
+    with dask.config.set(scheduler="single-threaded"):
+        df1 = pd.DataFrame({"val": [4, 3, 2, 1, 0], "id": [0, 1, 3, 5, 7]})
+        ddf1 = dd.from_pandas(df1, npartitions=2)
+
+        gdf1 = cudf.from_pandas(df1)
+        gddf1 = dgd.from_cudf(gdf1, npartitions=2)
+
+        expect = ddf1.set_index("id", sorted=True)
+        got = gddf1.set_index("id", sorted=True)
+
+        dd.assert_eq(expect, got)
+
+        with pytest.raises(ValueError):
+            # Cannot set `sorted=True` for non-sorted column
+            gddf1.set_index("val", sorted=True)
+
+
+@pytest.mark.parametrize("nelem", [10, 200, 1333])
+@pytest.mark.parametrize("index", [None, "myindex"])
+def test_rearrange_by_divisions(nelem, index):
+    with dask.config.set(scheduler="single-threaded"):
+        np.random.seed(0)
+        df = pd.DataFrame(
+            {
+                "x": np.random.randint(0, 20, size=nelem),
+                "y": np.random.normal(size=nelem),
+                "z": np.random.choice(["dog", "cat", "bird"], nelem),
+            }
+        )
+        df["z"] = df["z"].astype("category")
+
+        ddf1 = dd.from_pandas(df, npartitions=4)
+        gdf1 = dgd.from_cudf(cudf.DataFrame.from_pandas(df), npartitions=4)
+        ddf1.index.name = index
+        gdf1.index.name = index
+        divisions = (0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
+
+        expect = dd.shuffle.rearrange_by_divisions(
+            ddf1, "x", divisions=divisions, shuffle="tasks"
+        )
+        result = dd.shuffle.rearrange_by_divisions(
+            gdf1, "x", divisions=divisions, shuffle="tasks"
+        )
+        dd.assert_eq(expect, result)
+
+
+def test_assign():
+    np.random.seed(0)
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+    )
+
+    dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
+    pdcol = pd.Series(np.arange(20) + 1000)
+    newcol = dd.from_pandas(cudf.Series(pdcol), npartitions=dgf.npartitions)
+    got = dgf.assign(z=newcol)
+
+    dd.assert_eq(got.loc[:, ["x", "y"]], df)
+    np.testing.assert_array_equal(got["z"].compute().values_host, pdcol)
+
+
+@pytest.mark.parametrize("data_type", ["int8", "int16", "int32", "int64"])
+def test_setitem_scalar_integer(data_type):
+    np.random.seed(0)
+    scalar = np.random.randint(0, 100, dtype=data_type)
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+    )
+    dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
+
+    df["z"] = scalar
+    dgf["z"] = scalar
+
+    got = dgf.compute().to_pandas()
+    np.testing.assert_array_equal(got["z"], df["z"])
+
+
+@pytest.mark.parametrize("data_type", ["float32", "float64"])
+def test_setitem_scalar_float(data_type):
+    np.random.seed(0)
+    scalar = np.random.randn(1).astype(data_type)[0]
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+    )
+    dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
+
+    df["z"] = scalar
+    dgf["z"] = scalar
+
+    got = dgf.compute().to_pandas()
+    np.testing.assert_array_equal(got["z"], df["z"])
+
+
+def test_setitem_scalar_datetime():
+    np.random.seed(0)
+    scalar = np.int64(np.random.randint(0, 100)).astype("datetime64[ms]")
+    df = pd.DataFrame(
+        {"x": np.random.randint(0, 5, size=20), "y": np.random.normal(size=20)}
+    )
+    dgf = dd.from_pandas(cudf.DataFrame.from_pandas(df), npartitions=2)
+
+    df["z"] = scalar
+    dgf["z"] = scalar
+
+    got = dgf.compute().to_pandas()
+    np.testing.assert_array_equal(got["z"], df["z"])
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda: pd.DataFrame(
+            {"A": np.random.rand(10), "B": np.random.rand(10)},
+            index=list("abcdefghij"),
+        ),
+        lambda: pd.DataFrame(
+            {
+                "A": np.random.rand(10),
+                "B": list("a" * 10),
+                "C": pd.Series(
+                    [str(20090101 + i) for i in range(10)],
+                    dtype="datetime64[ns]",
+                ),
+            },
+            index=list("abcdefghij"),
+        ),
+        lambda: pd.Series(list("abcdefghijklmnop")),
+        lambda: pd.Series(
+            np.random.rand(10),
+            index=pd.Index(
+                [str(20090101 + i) for i in range(10)], dtype="datetime64[ns]"
+            ),
+        ),
+    ],
+)
+def test_repr(func):
+    pdf = func()
+    gdf = cudf.from_pandas(pdf)
+    gddf = dd.from_pandas(gdf, npartitions=3, sort=False)
+
+    assert repr(gddf)
+    if hasattr(pdf, "_repr_html_"):
+        assert gddf._repr_html_()
+
+
+@pytest.mark.skip(reason="datetime indexes not fully supported in cudf")
+@pytest.mark.parametrize("start", ["1d", "5d", "1w", "12h"])
+@pytest.mark.parametrize("stop", ["1d", "3d", "8h"])
+def test_repartition_timeseries(start, stop):
+    # This test is currently absurdly slow.  It should not be unskipped without
+    # slimming it down.
+    pdf = dask.datasets.timeseries(
+        "2000-01-01",
+        "2000-01-31",
+        freq="1s",
+        partition_freq=start,
+        dtypes={"x": int, "y": float},
+    )
+    gdf = pdf.map_partitions(cudf.DataFrame.from_pandas)
+
+    a = pdf.repartition(freq=stop)
+    b = gdf.repartition(freq=stop)
+    assert a.divisions == b.divisions
+
+    dd.utils.assert_eq(a, b)
+
+
+@pytest.mark.parametrize("start", [1, 2, 5])
+@pytest.mark.parametrize("stop", [1, 3, 7])
+def test_repartition_simple_divisions(start, stop):
+    pdf = pd.DataFrame({"x": range(100)})
+
+    pdf = dd.from_pandas(pdf, npartitions=start)
+    gdf = pdf.map_partitions(cudf.DataFrame.from_pandas)
+
+    a = pdf.repartition(npartitions=stop)
+    b = gdf.repartition(npartitions=stop)
+    assert a.divisions == b.divisions
+
+    dd.assert_eq(a, b)
+
+
+@pytest.mark.parametrize("npartitions", [2, 17, 20])
+def test_repartition_hash_staged(npartitions):
+    by = ["b"]
+    datarange = 35
+    size = 100
+    gdf = cudf.DataFrame(
+        {
+            "a": np.arange(size, dtype="int64"),
+            "b": np.random.randint(datarange, size=size),
+        }
+    )
+    # WARNING: Specific npartitions-max_branch combination
+    # was specifically chosen to cover changes in #4676
+    npartitions_initial = 17
+    ddf = dgd.from_cudf(gdf, npartitions=npartitions_initial)
+    ddf_new = ddf.shuffle(
+        on=by, ignore_index=True, npartitions=npartitions, max_branch=4
+    )
+
+    # Make sure we are getting a dask_cudf dataframe
+    assert type(ddf_new) == type(ddf)
+
+    # Check that the length was preserved
+    assert len(ddf_new) == len(ddf)
+
+    # Check that the partitions have unique keys,
+    # and that the key values are preserved
+    expect_unique = gdf[by].drop_duplicates().sort_values(by)
+    got_unique = cudf.concat(
+        [
+            part[by].compute().drop_duplicates()
+            for part in ddf_new[by].partitions
+        ],
+        ignore_index=True,
+    ).sort_values(by)
+    dd.assert_eq(got_unique, expect_unique, check_index=False)
+
+
+@pytest.mark.parametrize("by", [["b"], ["c"], ["d"], ["b", "c"]])
+@pytest.mark.parametrize("npartitions", [3, 4, 5])
+@pytest.mark.parametrize("max_branch", [3, 32])
+def test_repartition_hash(by, npartitions, max_branch):
+    npartitions_i = 4
+    datarange = 26
+    size = 100
+    gdf = cudf.DataFrame(
+        {
+            "a": np.arange(0, stop=size, dtype="int64"),
+            "b": np.random.randint(datarange, size=size),
+            "c": np.random.choice(list("abcdefgh"), size=size),
+            "d": np.random.choice(np.arange(26), size=size),
+        }
+    )
+    gdf.d = gdf.d.astype("datetime64[ms]")
+    ddf = dgd.from_cudf(gdf, npartitions=npartitions_i)
+    ddf_new = ddf.shuffle(
+        on=by,
+        ignore_index=True,
+        npartitions=npartitions,
+        max_branch=max_branch,
+    )
+
+    # Check that the length was preserved
+    assert len(ddf_new) == len(ddf)
+
+    # Check that the partitions have unique keys,
+    # and that the key values are preserved
+    expect_unique = gdf[by].drop_duplicates().sort_values(by)
+    got_unique = cudf.concat(
+        [
+            part[by].compute().drop_duplicates()
+            for part in ddf_new[by].partitions
+        ],
+        ignore_index=True,
+    ).sort_values(by)
+    dd.assert_eq(got_unique, expect_unique, check_index=False)
+
+
+def test_repartition_no_extra_row():
+    # see https://github.com/rapidsai/cudf/issues/11930
+    gdf = cudf.DataFrame({"a": [10, 20, 30], "b": [1, 2, 3]}).set_index("a")
+    ddf = dgd.from_cudf(gdf, npartitions=1)
+    ddf_new = ddf.repartition([0, 5, 10, 30], force=True)
+    dd.assert_eq(ddf, ddf_new)
+    dd.assert_eq(gdf, ddf_new)
+
+
+@pytest.fixture
+def pdf():
+    return pd.DataFrame(
+        {"x": [1, 2, 3, 4, 5, 6], "y": [11.0, 12.0, 13.0, 14.0, 15.0, 16.0]}
+    )
+
+
+@pytest.fixture
+def gdf(pdf):
+    return cudf.from_pandas(pdf)
+
+
+@pytest.fixture
+def ddf(pdf):
+    return dd.from_pandas(pdf, npartitions=3)
+
+
+@pytest.fixture
+def gddf(gdf):
+    return dd.from_pandas(gdf, npartitions=3)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df + 1,
+        lambda df: df.index,
+        lambda df: df.x.sum(),
+        lambda df: df.x.astype(float),
+        lambda df: df.assign(z=df.x.astype("int")),
+    ],
+)
+def test_unary_ops(func, gdf, gddf):
+    p = func(gdf)
+    g = func(gddf)
+
+    # Fixed in https://github.com/dask/dask/pull/4657
+    if isinstance(p, cudf.Index):
+        if version.parse(dask.__version__) < version.parse("1.1.6"):
+            pytest.skip(
+                "dask.dataframe assert_eq index check hardcoded to "
+                "pandas prior to 1.1.6 release"
+            )
+
+    dd.assert_eq(p, g, check_names=False)
+
+
+@pytest.mark.parametrize("series", [True, False])
+def test_concat(gdf, gddf, series):
+    if series:
+        gdf = gdf.x
+        gddf = gddf.x
+        a = (
+            cudf.concat([gdf, gdf + 1, gdf + 2])
+            .sort_values()
+            .reset_index(drop=True)
+        )
+        b = (
+            dd.concat([gddf, gddf + 1, gddf + 2], interleave_partitions=True)
+            .compute()
+            .sort_values()
+            .reset_index(drop=True)
+        )
+    else:
+        a = (
+            cudf.concat([gdf, gdf + 1, gdf + 2])
+            .sort_values("x")
+            .reset_index(drop=True)
+        )
+        b = (
+            dd.concat([gddf, gddf + 1, gddf + 2], interleave_partitions=True)
+            .compute()
+            .sort_values("x")
+            .reset_index(drop=True)
+        )
+    dd.assert_eq(a, b)
+
+
+def test_boolean_index(gdf, gddf):
+
+    gdf2 = gdf[gdf.x > 2]
+    gddf2 = gddf[gddf.x > 2]
+
+    dd.assert_eq(gdf2, gddf2)
+
+
+def test_drop(gdf, gddf):
+    gdf2 = gdf.drop(columns="x")
+    gddf2 = gddf.drop(columns="x").compute()
+
+    dd.assert_eq(gdf2, gddf2)
+
+
+@pytest.mark.parametrize("deep", [True, False])
+@pytest.mark.parametrize("index", [True, False])
+def test_memory_usage(gdf, gddf, index, deep):
+
+    dd.assert_eq(
+        gdf.memory_usage(deep=deep, index=index),
+        gddf.memory_usage(deep=deep, index=index),
+    )
+
+
+@pytest.mark.parametrize("index", [True, False])
+def test_hash_object_dispatch(index):
+    obj = cudf.DataFrame(
+        {"x": ["a", "b", "c"], "y": [1, 2, 3], "z": [1, 1, 0]}, index=[2, 4, 6]
+    )
+
+    # DataFrame
+    result = dd.core.hash_object_dispatch(obj, index=index)
+    expected = dgd.backends.hash_object_cudf(obj, index=index)
+    assert isinstance(result, cudf.Series)
+    dd.assert_eq(result, expected)
+
+    # Series
+    result = dd.core.hash_object_dispatch(obj["x"], index=index)
+    expected = dgd.backends.hash_object_cudf(obj["x"], index=index)
+    assert isinstance(result, cudf.Series)
+    dd.assert_eq(result, expected)
+
+    # DataFrame with MultiIndex
+    obj_multi = obj.set_index(["x", "z"], drop=True)
+    result = dd.core.hash_object_dispatch(obj_multi, index=index)
+    expected = dgd.backends.hash_object_cudf(obj_multi, index=index)
+    assert isinstance(result, cudf.Series)
+    dd.assert_eq(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        "int8",
+        "int32",
+        "int64",
+        "float64",
+        "strings",
+        "cats",
+        "time_s",
+        "time_ms",
+        "time_ns",
+        ["int32", "int64"],
+        ["int8", "float64", "strings"],
+        ["cats", "int8", "float64"],
+        ["time_ms", "cats"],
+    ],
+)
+def test_make_meta_backends(index):
+
+    dtypes = ["int8", "int32", "int64", "float64"]
+    df = cudf.DataFrame(
+        {dt: np.arange(start=0, stop=3, dtype=dt) for dt in dtypes}
+    )
+    df["strings"] = ["cat", "dog", "fish"]
+    df["cats"] = df["strings"].astype("category")
+    df["time_s"] = np.array(
+        ["2018-10-07", "2018-10-08", "2018-10-09"], dtype="datetime64[s]"
+    )
+    df["time_ms"] = df["time_s"].astype("datetime64[ms]")
+    df["time_ns"] = df["time_s"].astype("datetime64[ns]")
+    df = df.set_index(index)
+
+    # Check "empty" metadata types
+    chk_meta = dask_make_meta(df)
+    dd.assert_eq(chk_meta.dtypes, df.dtypes)
+
+    # Check "non-empty" metadata types
+    chk_meta_nonempty = meta_nonempty(df)
+    dd.assert_eq(chk_meta.dtypes, chk_meta_nonempty.dtypes)
+
+    # Check dask code path if not MultiIndex
+    if not isinstance(df.index, cudf.MultiIndex):
+
+        ddf = dgd.from_cudf(df, npartitions=1)
+
+        # Check "empty" metadata types
+        dd.assert_eq(ddf._meta.dtypes, df.dtypes)
+
+        # Check "non-empty" metadata types
+        dd.assert_eq(ddf._meta.dtypes, ddf._meta_nonempty.dtypes)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.Series([], dtype="float64"),
+        pd.DataFrame({"abc": [], "xyz": []}),
+        pd.Series([1, 2, 10, 11]),
+        pd.DataFrame({"abc": [1, 2, 10, 11], "xyz": [100, 12, 120, 1]}),
+    ],
+)
+def test_dataframe_series_replace(data):
+    pdf = data.copy()
+    gdf = cudf.from_pandas(pdf)
+
+    ddf = dgd.from_cudf(gdf, npartitions=5)
+
+    dd.assert_eq(ddf.replace(1, 2), pdf.replace(1, 2))
+
+
+def test_dataframe_assign_col():
+    df = cudf.DataFrame(list(range(100)))
+    pdf = pd.DataFrame(list(range(100)))
+
+    ddf = dgd.from_cudf(df, npartitions=4)
+    ddf["fold"] = 0
+    ddf["fold"] = ddf["fold"].map_partitions(
+        lambda cudf_df: cp.random.randint(0, 4, len(cudf_df))
+    )
+
+    pddf = dd.from_pandas(pdf, npartitions=4)
+    pddf["fold"] = 0
+    pddf["fold"] = pddf["fold"].map_partitions(
+        lambda p_df: np.random.randint(0, 4, len(p_df))
+    )
+
+    dd.assert_eq(ddf[0], pddf[0])
+    dd.assert_eq(len(ddf["fold"]), len(pddf["fold"]))
+
+
+def test_dataframe_set_index():
+    random.seed(0)
+    df = cudf.datasets.randomdata(26, dtypes={"a": float, "b": int})
+    df["str"] = list("abcdefghijklmnopqrstuvwxyz")
+    pdf = df.to_pandas()
+
+    ddf = dgd.from_cudf(df, npartitions=4)
+    ddf = ddf.set_index("str")
+
+    pddf = dd.from_pandas(pdf, npartitions=4)
+    pddf = pddf.set_index("str")
+    from cudf.testing._utils import assert_eq
+
+    assert_eq(ddf.compute(), pddf.compute())
+
+
+def test_series_describe():
+    random.seed(0)
+    sr = cudf.datasets.randomdata(20)["x"]
+    psr = sr.to_pandas()
+
+    dsr = dgd.from_cudf(sr, npartitions=4)
+    pdsr = dd.from_pandas(psr, npartitions=4)
+
+    dd.assert_eq(
+        dsr.describe(),
+        pdsr.describe(),
+        check_less_precise=3,
+    )
+
+
+def test_dataframe_describe():
+    random.seed(0)
+    df = cudf.datasets.randomdata(20)
+    pdf = df.to_pandas()
+
+    ddf = dgd.from_cudf(df, npartitions=4)
+    pddf = dd.from_pandas(pdf, npartitions=4)
+
+    dd.assert_eq(
+        ddf.describe(), pddf.describe(), check_exact=False, atol=0.0001
+    )
+
+
+def test_zero_std_describe():
+    num = 84886781
+    df = cudf.DataFrame(
+        {
+            "x": np.full((20,), num, dtype=np.float64),
+            "y": np.full((20,), num, dtype=np.float64),
+        }
+    )
+    pdf = df.to_pandas()
+    ddf = dgd.from_cudf(df, npartitions=4)
+    pddf = dd.from_pandas(pdf, npartitions=4)
+
+    dd.assert_eq(ddf.describe(), pddf.describe(), check_less_precise=3)
+
+
+def test_large_numbers_var():
+    num = 8488678001
+    df = cudf.DataFrame(
+        {
+            "x": np.arange(num, num + 1000, dtype=np.float64),
+            "y": np.arange(num, num + 1000, dtype=np.float64),
+        }
+    )
+    pdf = df.to_pandas()
+    ddf = dgd.from_cudf(df, npartitions=4)
+    pddf = dd.from_pandas(pdf, npartitions=4)
+
+    dd.assert_eq(ddf.var(), pddf.var(), check_less_precise=3)
+
+
+def test_index_map_partitions():
+    # https://github.com/rapidsai/cudf/issues/6738
+
+    ddf = dd.from_pandas(pd.DataFrame({"a": range(10)}), npartitions=2)
+    mins_pd = ddf.index.map_partitions(M.min, meta=ddf.index).compute()
+
+    gddf = dgd.from_cudf(cudf.DataFrame({"a": range(10)}), npartitions=2)
+    mins_gd = gddf.index.map_partitions(M.min, meta=gddf.index).compute()
+
+    dd.assert_eq(mins_pd, mins_gd)
+
+
+def test_merging_categorical_columns():
+    try:
+        from dask.dataframe.dispatch import (  # noqa: F401
+            union_categoricals_dispatch,
+        )
+    except ImportError:
+        pytest.skip(
+            "need a version of dask that has union_categoricals_dispatch"
+        )
+
+    df_1 = cudf.DataFrame(
+        {"id_1": [0, 1, 2, 3], "cat_col": ["a", "b", "f", "f"]}
+    )
+
+    ddf_1 = dgd.from_cudf(df_1, npartitions=2)
+
+    ddf_1 = dd.categorical.categorize(ddf_1, columns=["cat_col"])
+
+    df_2 = cudf.DataFrame(
+        {"id_2": [111, 112, 113], "cat_col": ["g", "h", "f"]}
+    )
+
+    ddf_2 = dgd.from_cudf(df_2, npartitions=2)
+
+    ddf_2 = dd.categorical.categorize(ddf_2, columns=["cat_col"])
+    expected = cudf.DataFrame(
+        {
+            "id_1": [2, 3],
+            "cat_col": cudf.Series(
+                ["f", "f"],
+                dtype=cudf.CategoricalDtype(
+                    categories=["a", "b", "f", "g", "h"], ordered=False
+                ),
+            ),
+            "id_2": [113, 113],
+        }
+    )
+    dd.assert_eq(ddf_1.merge(ddf_2), expected)
+
+
+def test_correct_meta():
+    try:
+        from dask.dataframe.dispatch import make_meta_obj  # noqa: F401
+    except ImportError:
+        pytest.skip("need make_meta_obj to be preset")
+
+    # Need these local imports in this specific order.
+    # For context: https://github.com/rapidsai/cudf/issues/7946
+    import pandas as pd
+
+    from dask import dataframe as dd
+
+    import dask_cudf  # noqa: F401
+
+    df = pd.DataFrame({"a": [3, 4], "b": [1, 2]})
+    ddf = dd.from_pandas(df, npartitions=1)
+    emb = ddf["a"].apply(pd.Series, meta={"c0": "int64", "c1": "int64"})
+
+    assert isinstance(emb, dd.DataFrame)
+    assert isinstance(emb._meta, pd.DataFrame)
+
+
+def test_categorical_dtype_round_trip():
+    s = cudf.Series(4 * ["foo"], dtype="category")
+    assert s.dtype.ordered is False
+
+    ds = dgd.from_cudf(s, npartitions=2)
+    pds = dd.from_pandas(s.to_pandas(), npartitions=2)
+    dd.assert_eq(ds, pds)
+    assert ds.dtype.ordered is False
+
+    # Below validations are required, see:
+    # https://github.com/rapidsai/cudf/issues/11487#issuecomment-1208912383
+    actual = ds.compute()
+    expected = pds.compute()
+    assert actual.dtype.ordered == expected.dtype.ordered
diff --git a/python/dask_cudf/dask_cudf/tests/test_delayed_io.py b/python/dask_cudf/dask_cudf/tests/test_delayed_io.py
new file mode 100644
index 0000000..6c68d92
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_delayed_io.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+"""
+Test IO with dask.delayed API
+"""
+
+import numpy as np
+import pytest
+from pandas.testing import assert_frame_equal
+
+from dask.delayed import delayed
+
+import cudf as gd
+
+import dask_cudf as dgd
+
+
+@delayed
+def load_data(nelem, ident):
+    df = gd.DataFrame()
+    df["x"] = np.arange(nelem)
+    df["ident"] = np.asarray([ident] * nelem)
+    return df
+
+
+@delayed
+def get_combined_column(df):
+    return df.x * df.ident
+
+
+def test_dataframe_from_delayed():
+    delays = [load_data(10 * i, i) for i in range(1, 3)]
+    out = dgd.from_delayed(delays)
+    res = out.compute()
+    assert isinstance(res, gd.DataFrame)
+
+    expected = gd.concat([d.compute() for d in delays])
+    assert_frame_equal(res.to_pandas(), expected.to_pandas())
+
+
+def test_series_from_delayed():
+    delays = [get_combined_column(load_data(10 * i, i)) for i in range(1, 3)]
+    out = dgd.from_delayed(delays)
+    res = out.compute()
+    assert isinstance(res, gd.Series)
+
+    expected = gd.concat([d.compute() for d in delays])
+    np.testing.assert_array_equal(res.to_pandas(), expected.to_pandas())
+
+
+def test_dataframe_to_delayed():
+    nelem = 100
+
+    df = gd.DataFrame()
+    df["x"] = np.arange(nelem)
+    df["y"] = np.random.randint(nelem, size=nelem)
+
+    ddf = dgd.from_cudf(df, npartitions=5)
+
+    delays = ddf.to_delayed()
+
+    assert len(delays) == 5
+
+    # Concat the delayed partitions
+    got = gd.concat([d.compute() for d in delays])
+    assert_frame_equal(got.to_pandas(), df.to_pandas())
+
+    # Check individual partitions
+    divs = ddf.divisions
+    assert len(divs) == len(delays) + 1
+
+    for i, part in enumerate(delays):
+        s = divs[i]
+        # The last divisions in the last index
+        e = None if i + 1 == len(delays) else divs[i + 1]
+        expect = df[s:e].to_pandas()
+        got = part.compute().to_pandas()
+        assert_frame_equal(got, expect)
+
+
+def test_series_to_delayed():
+    nelem = 100
+
+    sr = gd.Series(np.random.randint(nelem, size=nelem))
+
+    dsr = dgd.from_cudf(sr, npartitions=5)
+
+    delays = dsr.to_delayed()
+
+    assert len(delays) == 5
+
+    # Concat the delayed partitions
+    got = gd.concat([d.compute() for d in delays])
+    assert isinstance(got, gd.Series)
+    np.testing.assert_array_equal(got.to_pandas(), sr.to_pandas())
+
+    # Check individual partitions
+    divs = dsr.divisions
+    assert len(divs) == len(delays) + 1
+
+    for i, part in enumerate(delays):
+        s = divs[i]
+        # The last divisions in the last index
+        e = None if i + 1 == len(delays) else divs[i + 1]
+        expect = sr[s:e].to_pandas()
+        got = part.compute().to_pandas()
+        np.testing.assert_array_equal(got, expect)
+
+
+def test_mixing_series_frame_error():
+    nelem = 20
+
+    df = gd.DataFrame()
+    df["x"] = np.arange(nelem)
+    df["y"] = np.random.randint(nelem, size=nelem)
+
+    ddf = dgd.from_cudf(df, npartitions=5)
+
+    delay_frame = ddf.to_delayed()
+    delay_series = ddf.x.to_delayed()
+    combined = dgd.from_delayed(delay_frame + delay_series)
+
+    with pytest.raises(ValueError) as raises:
+        combined.compute()
+
+    raises.match(r"^Metadata mismatch found in `from_delayed`.")
+
+
+def test_frame_extra_columns_error():
+    nelem = 20
+
+    df = gd.DataFrame()
+    df["x"] = np.arange(nelem)
+    df["y"] = np.random.randint(nelem, size=nelem)
+    ddf1 = dgd.from_cudf(df, npartitions=5)
+
+    df["z"] = np.arange(nelem)
+    ddf2 = dgd.from_cudf(df, npartitions=5)
+
+    combined = dgd.from_delayed(ddf1.to_delayed() + ddf2.to_delayed())
+
+    with pytest.raises(ValueError) as raises:
+        combined.compute()
+
+    raises.match(r"^Metadata mismatch found in `from_delayed`.")
+    raises.match(r"z")
+
+
+@pytest.mark.xfail(reason="")
+def test_frame_dtype_error():
+    nelem = 20
+
+    df1 = gd.DataFrame()
+    df1["bad"] = np.arange(nelem)
+    df1["bad"] = np.arange(nelem, dtype=np.float64)
+
+    df2 = gd.DataFrame()
+    df2["bad"] = np.arange(nelem)
+    df2["bad"] = np.arange(nelem, dtype=np.float32)
+
+    ddf1 = dgd.from_cudf(df1, npartitions=5)
+    ddf2 = dgd.from_cudf(df2, npartitions=5)
+
+    combined = dgd.from_delayed(ddf1.to_delayed() + ddf2.to_delayed())
+
+    with pytest.raises(ValueError) as raises:
+        combined.compute()
+
+    raises.match(r"same type")
diff --git a/python/dask_cudf/dask_cudf/tests/test_dispatch.py b/python/dask_cudf/dask_cudf/tests/test_dispatch.py
new file mode 100644
index 0000000..c64e25f
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_dispatch.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from dask.base import tokenize
+from dask.dataframe import assert_eq
+from dask.dataframe.methods import is_categorical_dtype
+
+import cudf
+
+
+def test_is_categorical_dispatch():
+    assert is_categorical_dtype(pd.CategoricalDtype([1, 2, 3]))
+    assert is_categorical_dtype(cudf.CategoricalDtype([1, 2, 3]))
+
+    assert is_categorical_dtype(cudf.Series([1, 2, 3], dtype="category"))
+    assert is_categorical_dtype(pd.Series([1, 2, 3], dtype="category"))
+
+    assert is_categorical_dtype(pd.Index([1, 2, 3], dtype="category"))
+    assert is_categorical_dtype(cudf.Index([1, 2, 3], dtype="category"))
+
+
+@pytest.mark.parametrize("preserve_index", [True, False])
+def test_pyarrow_conversion_dispatch(preserve_index):
+    from dask.dataframe.dispatch import (
+        from_pyarrow_table_dispatch,
+        to_pyarrow_table_dispatch,
+    )
+
+    df1 = cudf.DataFrame(np.random.randn(10, 3), columns=list("abc"))
+    df2 = from_pyarrow_table_dispatch(
+        df1, to_pyarrow_table_dispatch(df1, preserve_index=preserve_index)
+    )
+
+    assert type(df1) == type(df2)
+    assert_eq(df1, df2)
+
+    # Check that preserve_index does not produce a RangeIndex
+    if preserve_index:
+        assert not isinstance(df2.index, cudf.RangeIndex)
+
+
+@pytest.mark.parametrize("index", [None, [1, 2] * 5])
+def test_deterministic_tokenize(index):
+    # Checks that `dask.base.normalize_token` correctly
+    # dispatches to the logic defined in `backends.py`
+    # (making `tokenize(<cudf-data>)` deterministic).
+    df = cudf.DataFrame(
+        {"A": range(10), "B": ["dog", "cat"] * 5, "C": range(10, 0, -1)},
+        index=index,
+    )
+
+    # Matching data should produce the same token
+    assert tokenize(df) == tokenize(df)
+    assert tokenize(df.A) == tokenize(df.A)
+    assert tokenize(df.index) == tokenize(df.index)
+    assert tokenize(df) == tokenize(df.copy(deep=True))
+    assert tokenize(df.A) == tokenize(df.A.copy(deep=True))
+    assert tokenize(df.index) == tokenize(df.index.copy(deep=True))
+
+    # Modifying a column element should change the token
+    original_token = tokenize(df)
+    original_token_a = tokenize(df.A)
+    df.A.iloc[2] = 10
+    assert original_token != tokenize(df)
+    assert original_token_a != tokenize(df.A)
+
+    # Modifying an index element should change the token
+    original_token = tokenize(df)
+    original_token_index = tokenize(df.index)
+    new_index = df.index.values
+    new_index[2] = 10
+    df.index = new_index
+    assert original_token != tokenize(df)
+    assert original_token_index != tokenize(df.index)
+
+    # Check MultiIndex case
+    df2 = df.set_index(["B", "C"], drop=False)
+    assert tokenize(df) != tokenize(df2)
+    assert tokenize(df2) == tokenize(df2)
+
+
+@pytest.mark.parametrize("preserve_index", [True, False])
+def test_pyarrow_schema_dispatch(preserve_index):
+    from dask.dataframe.dispatch import (
+        pyarrow_schema_dispatch,
+        to_pyarrow_table_dispatch,
+    )
+
+    df = cudf.DataFrame(np.random.randn(10, 3), columns=list("abc"))
+    df["d"] = cudf.Series(["cat", "dog"] * 5)
+    table = to_pyarrow_table_dispatch(df, preserve_index=preserve_index)
+    schema = pyarrow_schema_dispatch(df, preserve_index=preserve_index)
+
+    assert schema.equals(table.schema)
diff --git a/python/dask_cudf/dask_cudf/tests/test_distributed.py b/python/dask_cudf/dask_cudf/tests/test_distributed.py
new file mode 100644
index 0000000..db3f369
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_distributed.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+
+import numba.cuda
+import pytest
+
+import dask
+from dask import dataframe as dd
+from dask.distributed import Client
+from distributed.utils_test import cleanup, loop, loop_in_thread  # noqa: F401
+
+import cudf
+from cudf.testing._utils import assert_eq
+
+import dask_cudf
+
+dask_cuda = pytest.importorskip("dask_cuda")
+
+
+def more_than_two_gpus():
+    ngpus = len(numba.cuda.gpus)
+    return ngpus >= 2
+
+
+@pytest.mark.parametrize("delayed", [True, False])
+def test_basic(loop, delayed):  # noqa: F811
+    with dask_cuda.LocalCUDACluster(loop=loop) as cluster:
+        with Client(cluster):
+            pdf = dask.datasets.timeseries(dtypes={"x": int}).reset_index()
+            gdf = pdf.map_partitions(cudf.DataFrame.from_pandas)
+            if delayed:
+                gdf = dd.from_delayed(gdf.to_delayed())
+            assert_eq(pdf.head(), gdf.head())
+
+
+def test_merge():
+    # Repro Issue#3366
+    with dask_cuda.LocalCUDACluster(n_workers=1) as cluster:
+        with Client(cluster):
+            r1 = cudf.DataFrame()
+            r1["a1"] = range(4)
+            r1["a2"] = range(4, 8)
+            r1["a3"] = range(4)
+
+            r2 = cudf.DataFrame()
+            r2["b0"] = range(4)
+            r2["b1"] = range(4)
+            r2["b1"] = r2.b1.astype("str")
+
+            d1 = dask_cudf.from_cudf(r1, 2)
+            d2 = dask_cudf.from_cudf(r2, 2)
+
+            res = d1.merge(d2, left_on=["a3"], right_on=["b0"])
+            assert len(res) == 4
+
+
+@pytest.mark.skipif(
+    not more_than_two_gpus(), reason="Machine does not have more than two GPUs"
+)
+def test_ucx_seriesgroupby():
+    pytest.importorskip("ucp")
+
+    # Repro Issue#3913
+    with dask_cuda.LocalCUDACluster(n_workers=2, protocol="ucx") as cluster:
+        with Client(cluster):
+            df = cudf.DataFrame({"a": [1, 2, 3, 4], "b": [5, 1, 2, 5]})
+            dask_df = dask_cudf.from_cudf(df, npartitions=2)
+            dask_df_g = dask_df.groupby(["a"]).b.sum().compute()
+
+            assert dask_df_g.name == "b"
+
+
+def test_str_series_roundtrip():
+    with dask_cuda.LocalCUDACluster(n_workers=1) as cluster:
+        with Client(cluster):
+            expected = cudf.Series(["hi", "hello", None])
+            dask_series = dask_cudf.from_cudf(expected, npartitions=2)
+
+            actual = dask_series.compute()
+            assert_eq(actual, expected)
+
+
+def test_p2p_shuffle():
+    # Check that we can use `shuffle="p2p"`
+    with dask_cuda.LocalCUDACluster(n_workers=1) as cluster:
+        with Client(cluster):
+            ddf = (
+                dask.datasets.timeseries(
+                    start="2000-01-01",
+                    end="2000-01-08",
+                    dtypes={"x": int},
+                )
+                .reset_index(drop=True)
+                .to_backend("cudf")
+            )
+            dd.assert_eq(
+                ddf.sort_values("x", shuffle="p2p").compute(),
+                ddf.compute().sort_values("x"),
+                check_index=False,
+            )
diff --git a/python/dask_cudf/dask_cudf/tests/test_groupby.py b/python/dask_cudf/dask_cudf/tests/test_groupby.py
new file mode 100644
index 0000000..84a821a
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_groupby.py
@@ -0,0 +1,859 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import dask
+from dask import dataframe as dd
+from dask.utils_test import hlg_layer
+
+import cudf
+
+import dask_cudf
+from dask_cudf.groupby import OPTIMIZED_AGGS, _aggs_optimized
+
+
+def assert_cudf_groupby_layers(ddf):
+    for prefix in ("cudf-aggregate-chunk", "cudf-aggregate-agg"):
+        try:
+            hlg_layer(ddf.dask, prefix)
+        except KeyError:
+            raise AssertionError(
+                "Expected Dask dataframe to contain groupby layer with "
+                f"prefix {prefix}"
+            )
+
+
+@pytest.fixture(params=["non_null", "null"])
+def pdf(request):
+    np.random.seed(0)
+
+    # note that column name "x" is a substring of the groupby key;
+    # this gives us coverage for cudf#10829
+    pdf = pd.DataFrame(
+        {
+            "xx": np.random.randint(0, 5, size=10000),
+            "x": np.random.normal(size=10000),
+            "y": np.random.normal(size=10000),
+        }
+    )
+
+    # insert nulls into dataframe at random
+    if request.param == "null":
+        pdf = pdf.mask(np.random.choice([True, False], size=pdf.shape))
+
+    return pdf
+
+
+@pytest.mark.parametrize("aggregation", OPTIMIZED_AGGS)
+@pytest.mark.parametrize("series", [False, True])
+def test_groupby_basic(series, aggregation, pdf):
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    gdf_grouped = gdf.groupby("xx")
+    ddf_grouped = dask_cudf.from_cudf(gdf, npartitions=5).groupby("xx")
+
+    if series:
+        gdf_grouped = gdf_grouped.xx
+        ddf_grouped = ddf_grouped.xx
+
+    check_dtype = aggregation != "count"
+
+    expect = getattr(gdf_grouped, aggregation)()
+    actual = getattr(ddf_grouped, aggregation)()
+
+    assert_cudf_groupby_layers(actual)
+
+    dd.assert_eq(expect, actual, check_dtype=check_dtype)
+
+    expect = gdf_grouped.agg({"xx": aggregation})
+    actual = ddf_grouped.agg({"xx": aggregation})
+
+    assert_cudf_groupby_layers(actual)
+
+    dd.assert_eq(expect, actual, check_dtype=check_dtype)
+
+
+# TODO: explore adding support with `.agg()`
+@pytest.mark.parametrize("series", [True, False])
+@pytest.mark.parametrize(
+    "aggregation",
+    [
+        "cumsum",
+        pytest.param(
+            "cumcount",
+            marks=pytest.mark.xfail(
+                reason="https://github.com/rapidsai/cudf/issues/13390"
+            ),
+        ),
+    ],
+)
+def test_groupby_cumulative(aggregation, pdf, series):
+    gdf = cudf.DataFrame.from_pandas(pdf)
+    ddf = dask_cudf.from_cudf(gdf, npartitions=5)
+
+    gdf_grouped = gdf.groupby("xx")
+    ddf_grouped = ddf.groupby("xx")
+
+    if series:
+        gdf_grouped = gdf_grouped.xx
+        ddf_grouped = ddf_grouped.xx
+
+    a = getattr(gdf_grouped, aggregation)()
+    b = getattr(ddf_grouped, aggregation)()
+
+    dd.assert_eq(a, b)
+
+
+@pytest.mark.parametrize("aggregation", OPTIMIZED_AGGS)
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df, agg: df.groupby("xx").agg({"y": agg}),
+        lambda df, agg: df.groupby("xx").y.agg({"y": agg}),
+        lambda df, agg: df.groupby("xx").agg([agg]),
+        lambda df, agg: df.groupby("xx").y.agg([agg]),
+        lambda df, agg: df.groupby("xx").agg(agg),
+        lambda df, agg: df.groupby("xx").y.agg(agg),
+    ],
+)
+def test_groupby_agg(func, aggregation, pdf):
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    ddf = dask_cudf.from_cudf(gdf, npartitions=5)
+
+    actual = func(ddf, aggregation)
+    expect = func(gdf, aggregation)
+
+    check_dtype = aggregation != "count"
+
+    assert_cudf_groupby_layers(actual)
+
+    # groupby.agg should add an explicit getitem layer
+    # to improve/enable column projection
+    assert hlg_layer(actual.dask, "getitem")
+
+    dd.assert_eq(expect, actual, check_names=False, check_dtype=check_dtype)
+
+
+@pytest.mark.parametrize("split_out", [1, 3])
+def test_groupby_agg_empty_partition(tmpdir, split_out):
+    # Write random and empty cudf DataFrames
+    # to two distinct files.
+    df = cudf.datasets.randomdata()
+    df.to_parquet(str(tmpdir.join("f0.parquet")))
+    cudf.DataFrame(
+        columns=["id", "x", "y"],
+        dtype={"id": "int64", "x": "float64", "y": "float64"},
+    ).to_parquet(str(tmpdir.join("f1.parquet")))
+
+    # Read back our two partitions as a single
+    # dask_cudf DataFrame (one partition is now empty)
+    ddf = dask_cudf.read_parquet(str(tmpdir))
+    gb = ddf.groupby(["id"]).agg({"x": ["sum"]}, split_out=split_out)
+
+    expect = df.groupby(["id"]).agg({"x": ["sum"]}).sort_index()
+    dd.assert_eq(gb.compute().sort_index(), expect)
+
+
+# reason gotattr in cudf
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df.groupby(["a", "b"]).x.sum(),
+        lambda df: df.groupby(["a", "b"]).sum(),
+        pytest.param(
+            lambda df: df.groupby(["a", "b"]).agg({"x", "sum"}),
+            marks=pytest.mark.xfail,
+        ),
+    ],
+)
+def test_groupby_multi_column(func):
+    pdf = pd.DataFrame(
+        {
+            "a": np.random.randint(0, 20, size=1000),
+            "b": np.random.randint(0, 5, size=1000),
+            "x": np.random.normal(size=1000),
+        }
+    )
+
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    ddf = dask_cudf.from_cudf(gdf, npartitions=5)
+
+    a = func(gdf).to_pandas()
+    b = func(ddf).compute().to_pandas()
+
+    dd.assert_eq(a, b)
+
+
+def test_reset_index_multiindex():
+    df = cudf.DataFrame()
+    df["id_1"] = ["a", "a", "b"]
+    df["id_2"] = [0, 0, 1]
+    df["val"] = [1, 2, 3]
+
+    df_lookup = cudf.DataFrame()
+    df_lookup["id_1"] = ["a", "b"]
+    df_lookup["metadata"] = [0, 1]
+
+    gddf = dask_cudf.from_cudf(df, npartitions=2)
+    gddf_lookup = dask_cudf.from_cudf(df_lookup, npartitions=2)
+
+    ddf = dd.from_pandas(df.to_pandas(), npartitions=2)
+    ddf_lookup = dd.from_pandas(df_lookup.to_pandas(), npartitions=2)
+
+    # Note: 'id_2' has wrong type (object) until after compute
+    dd.assert_eq(
+        gddf.groupby(by=["id_1", "id_2"])
+        .val.sum()
+        .reset_index()
+        .merge(gddf_lookup, on="id_1")
+        .compute(),
+        ddf.groupby(by=["id_1", "id_2"])
+        .val.sum()
+        .reset_index()
+        .merge(ddf_lookup, on="id_1"),
+    )
+
+
+@pytest.mark.parametrize("split_out", [1, 2, 3])
+@pytest.mark.parametrize(
+    "column", ["c", "d", "e", ["b", "c"], ["b", "d"], ["b", "e"]]
+)
+def test_groupby_split_out(split_out, column):
+    df = pd.DataFrame(
+        {
+            "a": np.arange(8),
+            "b": [1, 0, 0, 2, 1, 1, 2, 0],
+            "c": [0, 1] * 4,
+            "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
+        }
+    ).fillna(0)
+    df["e"] = df["d"].astype("category")
+
+    gdf = cudf.from_pandas(df)
+
+    ddf = dd.from_pandas(df, npartitions=3)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=3)
+
+    ddf_result = (
+        ddf.groupby(column)
+        .a.mean(split_out=split_out)
+        .compute()
+        .sort_values()
+        .dropna()
+    )
+    gddf_result = (
+        gddf.groupby(column)
+        .a.mean(split_out=split_out)
+        .compute()
+        .sort_values()
+    )
+
+    dd.assert_eq(gddf_result, ddf_result, check_index=False)
+
+
+@pytest.mark.parametrize("dropna", [False, True, None])
+@pytest.mark.parametrize(
+    "by", ["a", "b", "c", "d", ["a", "b"], ["a", "c"], ["a", "d"]]
+)
+def test_groupby_dropna_cudf(dropna, by):
+    # NOTE: This test is borrowed from upstream dask
+    #       (dask/dask/dataframe/tests/test_groupby.py)
+    df = cudf.DataFrame(
+        {
+            "a": [1, 2, 3, 4, None, None, 7, 8],
+            "b": [1, None, 1, 3, None, 3, 1, 3],
+            "c": ["a", "b", None, None, "e", "f", "g", "h"],
+            "e": [4, 5, 6, 3, 2, 1, 0, 0],
+        }
+    )
+    df["b"] = df["b"].astype("datetime64[ns]")
+    df["d"] = df["c"].astype("category")
+    ddf = dask_cudf.from_cudf(df, npartitions=3)
+
+    if dropna is None:
+        dask_result = ddf.groupby(by).e.sum()
+        cudf_result = df.groupby(by).e.sum()
+    else:
+        dask_result = ddf.groupby(by, dropna=dropna).e.sum()
+        cudf_result = df.groupby(by, dropna=dropna).e.sum()
+    if by in ["c", "d"]:
+        # Loose string/category index name in cudf...
+        dask_result = dask_result.compute()
+        dask_result.index.name = cudf_result.index.name
+
+    dd.assert_eq(dask_result, cudf_result)
+
+
+@pytest.mark.parametrize(
+    "dropna,by",
+    [
+        (False, "a"),
+        (False, "b"),
+        (False, "c"),
+        pytest.param(
+            False,
+            "d",
+            marks=pytest.mark.xfail(
+                reason="dropna=False is broken in Dask CPU for groupbys on "
+                "categorical columns"
+            ),
+        ),
+        pytest.param(
+            False,
+            ["a", "b"],
+            marks=pytest.mark.xfail(
+                reason="https://github.com/dask/dask/issues/8817"
+            ),
+        ),
+        pytest.param(
+            False,
+            ["a", "c"],
+            marks=pytest.mark.xfail(
+                reason="https://github.com/dask/dask/issues/8817"
+            ),
+        ),
+        pytest.param(
+            False,
+            ["a", "d"],
+            marks=pytest.mark.xfail(
+                reason="multi-col groupbys on categorical columns are broken "
+                "in Dask CPU"
+            ),
+        ),
+        (True, "a"),
+        (True, "b"),
+        (True, "c"),
+        (True, "d"),
+        (True, ["a", "b"]),
+        (True, ["a", "c"]),
+        pytest.param(
+            True,
+            ["a", "d"],
+            marks=pytest.mark.xfail(
+                reason="multi-col groupbys on categorical columns are broken "
+                "in Dask CPU"
+            ),
+        ),
+        (None, "a"),
+        (None, "b"),
+        (None, "c"),
+        (None, "d"),
+        (None, ["a", "b"]),
+        (None, ["a", "c"]),
+        pytest.param(
+            None,
+            ["a", "d"],
+            marks=pytest.mark.xfail(
+                reason="multi-col groupbys on categorical columns are broken "
+                "in Dask CPU"
+            ),
+        ),
+    ],
+)
+def test_groupby_dropna_dask(dropna, by):
+    # NOTE: This test is borrowed from upstream dask
+    #       (dask/dask/dataframe/tests/test_groupby.py)
+    df = pd.DataFrame(
+        {
+            "a": [1, 2, 3, 4, None, None, 7, 8],
+            "b": [1, None, 1, 3, None, 3, 1, 3],
+            "c": ["a", "b", None, None, "e", "f", "g", "h"],
+            "e": [4, 5, 6, 3, 2, 1, 0, 0],
+        }
+    )
+    df["b"] = df["b"].astype("datetime64[ns]")
+    df["d"] = df["c"].astype("category")
+
+    gdf = cudf.from_pandas(df)
+    ddf = dd.from_pandas(df, npartitions=3)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=3)
+
+    if dropna is None:
+        dask_cudf_result = gddf.groupby(by).e.sum()
+        dask_result = ddf.groupby(by).e.sum()
+    else:
+        dask_cudf_result = gddf.groupby(by, dropna=dropna).e.sum()
+        dask_result = ddf.groupby(by, dropna=dropna).e.sum()
+
+    dd.assert_eq(dask_cudf_result, dask_result)
+
+
+@pytest.mark.parametrize("myindex", [[1, 2] * 4, ["s1", "s2"] * 4])
+def test_groupby_string_index_name(myindex):
+    # GH-Issue #3420
+    data = {"index": myindex, "data": [0, 1] * 4}
+    df = cudf.DataFrame(data=data)
+    ddf = dask_cudf.from_cudf(df, npartitions=2)
+    gdf = ddf.groupby("index").agg({"data": "count"})
+
+    assert gdf.compute().index.name == gdf.index.name
+
+
+@pytest.mark.parametrize(
+    "agg_func",
+    [
+        lambda gb: gb.agg({"c": ["count"]}, split_out=2),
+        lambda gb: gb.agg({"c": "count"}, split_out=2),
+        lambda gb: gb.agg({"c": ["count", "sum"]}, split_out=2),
+        lambda gb: gb.count(split_out=2),
+        lambda gb: gb.c.count(split_out=2),
+    ],
+)
+def test_groupby_split_out_multiindex(agg_func):
+    df = cudf.DataFrame(
+        {
+            "a": np.random.randint(0, 10, 100),
+            "b": np.random.randint(0, 5, 100),
+            "c": np.random.random(100),
+        }
+    )
+    ddf = dask_cudf.from_cudf(df, 5)
+    pddf = dd.from_pandas(df.to_pandas(), 5)
+    gr = agg_func(ddf.groupby(["a", "b"]))
+    pr = agg_func(pddf.groupby(["a", "b"]))
+    dd.assert_eq(gr.compute(), pr.compute())
+
+
+@pytest.mark.parametrize("npartitions", [1, 2])
+def test_groupby_multiindex_reset_index(npartitions):
+    df = cudf.DataFrame(
+        {"a": [1, 1, 2, 3, 4], "b": [5, 2, 1, 2, 5], "c": [1, 2, 2, 3, 5]}
+    )
+    ddf = dask_cudf.from_cudf(df, npartitions=npartitions)
+    pddf = dd.from_pandas(df.to_pandas(), npartitions=npartitions)
+    gr = ddf.groupby(["a", "c"]).agg({"b": ["count"]}).reset_index()
+    pr = pddf.groupby(["a", "c"]).agg({"b": ["count"]}).reset_index()
+
+    # CuDF uses "int32" for count. Pandas uses "int64"
+    gr_out = gr.compute().sort_values(by=["a", "c"]).reset_index(drop=True)
+    gr_out[("b", "count")] = gr_out[("b", "count")].astype("int64")
+
+    dd.assert_eq(
+        gr_out,
+        pr.compute().sort_values(by=["a", "c"]).reset_index(drop=True),
+    )
+
+
+@pytest.mark.parametrize(
+    "groupby_keys", [["a"], ["a", "b"], ["a", "b", "dd"], ["a", "dd", "b"]]
+)
+@pytest.mark.parametrize(
+    "agg_func",
+    [
+        lambda gb: gb.agg({"c": ["count"]}),
+        lambda gb: gb.agg({"c": "count"}),
+        lambda gb: gb.agg({"c": ["count", "sum"]}),
+        lambda gb: gb.count(),
+        lambda gb: gb.c.count(),
+    ],
+)
+def test_groupby_reset_index_multiindex(groupby_keys, agg_func):
+    df = cudf.DataFrame(
+        {
+            "a": np.random.randint(0, 10, 10),
+            "b": np.random.randint(0, 5, 10),
+            "c": np.random.randint(0, 5, 10),
+            "dd": np.random.randint(0, 5, 10),
+        }
+    )
+    ddf = dask_cudf.from_cudf(df, 5)
+    pddf = dd.from_pandas(df.to_pandas(), 5)
+    gr = agg_func(ddf.groupby(groupby_keys)).reset_index()
+    pr = agg_func(pddf.groupby(groupby_keys)).reset_index()
+    gf = gr.compute().sort_values(groupby_keys).reset_index(drop=True)
+    pf = pr.compute().sort_values(groupby_keys).reset_index(drop=True)
+    dd.assert_eq(gf, pf)
+
+
+def test_groupby_reset_index_drop_True():
+    df = cudf.DataFrame(
+        {"a": np.random.randint(0, 10, 10), "b": np.random.randint(0, 5, 10)}
+    )
+    ddf = dask_cudf.from_cudf(df, 5)
+    pddf = dd.from_pandas(df.to_pandas(), 5)
+    gr = ddf.groupby(["a"]).agg({"b": ["count"]}).reset_index(drop=True)
+    pr = pddf.groupby(["a"]).agg({"b": ["count"]}).reset_index(drop=True)
+    gf = gr.compute().sort_values(by=["b"]).reset_index(drop=True)
+    pf = pr.compute().sort_values(by=[("b", "count")]).reset_index(drop=True)
+    dd.assert_eq(gf, pf)
+
+
+def test_groupby_mean_sort_false():
+    df = cudf.datasets.randomdata(nrows=150, dtypes={"a": int, "b": int})
+    ddf = dask_cudf.from_cudf(df, 1)
+    pddf = dd.from_pandas(df.to_pandas(), 1)
+
+    gr = ddf.groupby(["a"]).agg({"b": "mean"})
+    pr = pddf.groupby(["a"]).agg({"b": "mean"})
+    assert pr.index.name == gr.index.name
+    assert pr.head(0).index.name == gr.head(0).index.name
+
+    gf = gr.compute().sort_values(by=["b"]).reset_index(drop=True)
+    pf = pr.compute().sort_values(by=["b"]).reset_index(drop=True)
+    dd.assert_eq(gf, pf)
+
+
+def test_groupby_reset_index_dtype():
+    # Make sure int8 dtype is properly preserved
+    # Through various cudf/dask_cudf ops
+    #
+    # Note: GitHub Issue#4090 reproducer
+
+    df = cudf.DataFrame()
+    df["a"] = np.arange(10, dtype="int8")
+    df["b"] = np.arange(10, dtype="int8")
+    df = dask_cudf.from_cudf(df, 1)
+
+    a = df.groupby("a").agg({"b": ["count"]})
+
+    assert a.index.dtype == "int8"
+    assert a.reset_index().dtypes[0] == "int8"
+
+
+def test_groupby_reset_index_names():
+    df = cudf.datasets.randomdata(
+        nrows=10, dtypes={"a": str, "b": int, "c": int}
+    )
+    pdf = df.to_pandas()
+
+    gddf = dask_cudf.from_cudf(df, 2)
+    pddf = dd.from_pandas(pdf, 2)
+
+    g_res = gddf.groupby("a", sort=True).sum()
+    p_res = pddf.groupby("a", sort=True).sum()
+
+    got = g_res.reset_index().compute().sort_values(["a", "b", "c"])
+    expect = p_res.reset_index().compute().sort_values(["a", "b", "c"])
+
+    dd.assert_eq(got, expect)
+
+
+def test_groupby_reset_index_string_name():
+    df = cudf.DataFrame({"value": range(5), "key": ["a", "a", "b", "a", "c"]})
+    pdf = df.to_pandas()
+
+    gddf = dask_cudf.from_cudf(df, npartitions=1)
+    pddf = dd.from_pandas(pdf, npartitions=1)
+
+    g_res = (
+        gddf.groupby(["key"]).agg({"value": "mean"}).reset_index(drop=False)
+    )
+    p_res = (
+        pddf.groupby(["key"]).agg({"value": "mean"}).reset_index(drop=False)
+    )
+
+    got = g_res.compute().sort_values(["key", "value"]).reset_index(drop=True)
+    expect = (
+        p_res.compute().sort_values(["key", "value"]).reset_index(drop=True)
+    )
+
+    dd.assert_eq(got, expect)
+    assert len(g_res) == len(p_res)
+
+
+def test_groupby_categorical_key():
+    # See https://github.com/rapidsai/cudf/issues/4608
+    df = dask.datasets.timeseries()
+    gddf = dask_cudf.from_dask_dataframe(df)
+    gddf["name"] = gddf["name"].astype("category")
+    ddf = gddf.to_dask_dataframe()
+
+    got = gddf.groupby("name", sort=True).agg(
+        {"x": ["mean", "max"], "y": ["mean", "count"]}
+    )
+    # Use `compute` to avoid upstream issue for now
+    # (See: https://github.com/dask/dask/issues/9515)
+    expect = (
+        ddf.compute()
+        .groupby("name", sort=True)
+        .agg({"x": ["mean", "max"], "y": ["mean", "count"]})
+    )
+    dd.assert_eq(expect, got)
+
+
+@pytest.mark.parametrize("as_index", [True, False])
+@pytest.mark.parametrize("split_out", ["use_dask_default", 1, 2])
+@pytest.mark.parametrize("split_every", [False, 4])
+@pytest.mark.parametrize("npartitions", [1, 10])
+def test_groupby_agg_params(npartitions, split_every, split_out, as_index):
+    df = cudf.datasets.randomdata(
+        nrows=150,
+        dtypes={"name": str, "a": int, "b": int, "c": float},
+    )
+    df["a"] = [0, 1, 2] * 50
+    ddf = dask_cudf.from_cudf(df, npartitions)
+    pddf = dd.from_pandas(df.to_pandas(), npartitions)
+
+    agg_dict = {
+        "a": "sum",
+        "b": ["min", "max", "mean"],
+        "c": ["mean", "std", "var"],
+    }
+
+    split_kwargs = {"split_every": split_every, "split_out": split_out}
+    if split_out == "use_dask_default":
+        split_kwargs.pop("split_out")
+
+    # Check `sort=True` behavior
+    if split_out == 1:
+        gf = (
+            ddf.groupby(["name", "a"], sort=True, as_index=as_index)
+            .aggregate(
+                agg_dict,
+                **split_kwargs,
+            )
+            .compute()
+        )
+        if as_index:
+            # Groupby columns became the index.
+            # Sorting the index should not change anything.
+            dd.assert_eq(gf.index, gf.sort_index().index)
+        else:
+            # Groupby columns are did NOT become the index.
+            # Sorting by these columns should not change anything.
+            sort_cols = [("name", ""), ("a", "")]
+            dd.assert_eq(
+                gf[sort_cols],
+                gf[sort_cols].sort_values(sort_cols),
+                check_index=False,
+            )
+
+    # Full check (`sort=False`)
+    gr = ddf.groupby(["name", "a"], sort=False, as_index=as_index).aggregate(
+        agg_dict,
+        **split_kwargs,
+    )
+    pr = pddf.groupby(["name", "a"], sort=False).agg(
+        agg_dict,
+        **split_kwargs,
+    )
+
+    # Test `as_index` argument
+    if as_index:
+        # Groupby columns should NOT be in columns
+        assert ("name", "") not in gr.columns and ("a", "") not in gr.columns
+    else:
+        # Groupby columns SHOULD be in columns
+        assert ("name", "") in gr.columns and ("a", "") in gr.columns
+
+    # Check `split_out` argument
+    assert gr.npartitions == (
+        1 if split_out == "use_dask_default" else split_out
+    )
+
+    # Compute for easier multiindex handling
+    gf = gr.compute()
+    pf = pr.compute()
+
+    # Reset index and sort by groupby columns
+    if as_index:
+        gf = gf.reset_index(drop=False)
+    sort_cols = [("name", ""), ("a", ""), ("c", "mean")]
+    gf = gf.sort_values(sort_cols).reset_index(drop=True)
+    pf = (
+        pf.reset_index(drop=False)
+        .sort_values(sort_cols)
+        .reset_index(drop=True)
+    )
+
+    dd.assert_eq(gf, pf)
+
+
+@pytest.mark.parametrize(
+    "aggregations", [(sum, "sum"), (max, "max"), (min, "min")]
+)
+def test_groupby_agg_redirect(aggregations):
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=10000),
+            "y": np.random.normal(size=10000),
+        }
+    )
+
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    ddf = dask_cudf.from_cudf(gdf, npartitions=5)
+
+    a = ddf.groupby("x").agg({"x": aggregations[0]}).compute()
+    b = ddf.groupby("x").agg({"x": aggregations[1]}).compute()
+
+    dd.assert_eq(a, b)
+
+
+@pytest.mark.parametrize(
+    "arg,supported",
+    [
+        ("sum", True),
+        (["sum"], True),
+        ({"a": "sum"}, True),
+        ({"a": ["sum"]}, True),
+        ("not_supported", False),
+        (["not_supported"], False),
+        ({"a": "not_supported"}, False),
+        ({"a": ["not_supported"]}, False),
+    ],
+)
+def test_is_supported(arg, supported):
+    assert _aggs_optimized(arg, OPTIMIZED_AGGS) is supported
+
+
+def test_groupby_unique_lists():
+    df = pd.DataFrame({"a": [0, 0, 0, 1, 1, 1], "b": [10, 10, 10, 7, 8, 9]})
+    ddf = dd.from_pandas(df, 2)
+    gdf = cudf.from_pandas(df)
+    gddf = dask_cudf.from_cudf(gdf, 2)
+    dd.assert_eq(
+        ddf.groupby("a").b.unique().compute(),
+        gddf.groupby("a").b.unique().compute(),
+    )
+    dd.assert_eq(
+        gdf.groupby("a").b.unique(),
+        gddf.groupby("a").b.unique().compute(),
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        {"a": [], "b": []},
+        {"a": [2, 1, 2, 1, 1, 3], "b": [None, 1, 2, None, 2, None]},
+        {"a": [None], "b": [None]},
+        {"a": [2, 1, 1], "b": [None, 1, 0], "c": [None, 0, 1]},
+    ],
+)
+@pytest.mark.parametrize("agg", ["first", "last"])
+def test_groupby_first_last(data, agg):
+    pdf = pd.DataFrame(data)
+    gdf = cudf.DataFrame.from_pandas(pdf)
+
+    ddf = dd.from_pandas(pdf, npartitions=2)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=2)
+
+    dd.assert_eq(
+        ddf.groupby("a").agg(agg),
+        gddf.groupby("a").agg(agg),
+    )
+
+    dd.assert_eq(
+        getattr(ddf.groupby("a"), agg)(),
+        getattr(gddf.groupby("a"), agg)(),
+    )
+
+    dd.assert_eq(gdf.groupby("a").agg(agg), gddf.groupby("a").agg(agg))
+
+    dd.assert_eq(
+        getattr(gdf.groupby("a"), agg)(),
+        getattr(gddf.groupby("a"), agg)(),
+    )
+
+
+def test_groupby_with_list_of_series():
+    df = cudf.DataFrame({"a": [1, 2, 3, 4, 5]})
+    gdf = dask_cudf.from_cudf(df, npartitions=2)
+    gs = cudf.Series([1, 1, 1, 2, 2], name="id")
+    ggs = dask_cudf.from_cudf(gs, npartitions=2)
+
+    ddf = dd.from_pandas(df.to_pandas(), npartitions=2)
+    pgs = dd.from_pandas(gs.to_pandas(), npartitions=2)
+
+    dd.assert_eq(
+        gdf.groupby([ggs]).agg(["sum"]), ddf.groupby([pgs]).agg(["sum"])
+    )
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df.groupby("x").agg({"y": {"foo": "sum"}}),
+        lambda df: df.groupby("x").agg({"y": {"foo": "sum", "bar": "count"}}),
+    ],
+)
+def test_groupby_nested_dict(func):
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=10000),
+            "y": np.random.normal(size=10000),
+        }
+    )
+
+    ddf = dd.from_pandas(pdf, npartitions=5)
+    c_ddf = ddf.map_partitions(cudf.from_pandas)
+
+    a = func(ddf).compute()
+    b = func(c_ddf).compute().to_pandas()
+
+    a.index.name = None
+    a.name = None
+    b.index.name = None
+    b.name = None
+
+    dd.assert_eq(a, b)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda df: df.groupby(["x", "y"]).min(),
+        pytest.param(
+            lambda df: df.groupby(["x", "y"]).agg("min"),
+            marks=pytest.mark.skip(
+                reason="https://github.com/dask/dask/issues/9093"
+            ),
+        ),
+        lambda df: df.groupby(["x", "y"]).y.min(),
+        lambda df: df.groupby(["x", "y"]).y.agg("min"),
+    ],
+)
+def test_groupby_all_columns(func):
+    pdf = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=10000),
+            "y": np.random.normal(size=10000),
+        }
+    )
+
+    ddf = dd.from_pandas(pdf, npartitions=5)
+    gddf = ddf.map_partitions(cudf.from_pandas)
+
+    expect = func(ddf)
+    actual = func(gddf)
+
+    dd.assert_eq(expect, actual)
+
+
+def test_groupby_shuffle():
+    df = cudf.datasets.randomdata(
+        nrows=640, dtypes={"a": str, "b": int, "c": int}
+    )
+    gddf = dask_cudf.from_cudf(df, 8)
+    spec = {"b": "mean", "c": "max"}
+    expect = df.groupby("a", sort=True).agg(spec)
+
+    # Sorted aggregation, single-partition output
+    # (sort=True, split_out=1)
+    got = gddf.groupby("a", sort=True).agg(spec, shuffle=True, split_out=1)
+    dd.assert_eq(expect, got)
+
+    # Sorted aggregation, multi-partition output
+    # (sort=True, split_out=2)
+    got = gddf.groupby("a", sort=True).agg(spec, shuffle=True, split_out=2)
+    dd.assert_eq(expect, got)
+
+    # Un-sorted aggregation, single-partition output
+    # (sort=False, split_out=1)
+    got = gddf.groupby("a", sort=False).agg(spec, shuffle=True, split_out=1)
+    dd.assert_eq(expect.sort_index(), got.compute().sort_index())
+
+    # Un-sorted aggregation, multi-partition output
+    # (sort=False, split_out=2)
+    # NOTE: `shuffle=True` should be default
+    got = gddf.groupby("a", sort=False).agg(spec, split_out=2)
+    dd.assert_eq(expect, got.compute().sort_index())
+
+    # Sorted aggregation fails with split_out>1 when shuffle is False
+    # (sort=True, split_out=2, shuffle=False)
+    with pytest.raises(ValueError):
+        gddf.groupby("a", sort=True).agg(spec, shuffle=False, split_out=2)
diff --git a/python/dask_cudf/dask_cudf/tests/test_join.py b/python/dask_cudf/dask_cudf/tests/test_join.py
new file mode 100644
index 0000000..15e383a
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_join.py
@@ -0,0 +1,361 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+from functools import partial
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from dask import dataframe as dd
+
+import cudf
+
+import dask_cudf as dgd
+
+param_nrows = [5, 10, 50, 100]
+
+
+@pytest.mark.parametrize("left_nrows", param_nrows)
+@pytest.mark.parametrize("right_nrows", param_nrows)
+@pytest.mark.parametrize("left_nkeys", [4, 5])
+@pytest.mark.parametrize("right_nkeys", [4, 5])
+def test_join_inner(left_nrows, right_nrows, left_nkeys, right_nkeys):
+    chunksize = 50
+
+    np.random.seed(0)
+
+    # cuDF
+    left = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "a": np.arange(left_nrows),
+        }
+    )
+    right = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "a": 1000 * np.arange(right_nrows),
+        }
+    )
+
+    expect = left.set_index("x").join(
+        right.set_index("x"), how="inner", sort=True, lsuffix="l", rsuffix="r"
+    )
+    expect = expect.to_pandas()
+
+    # dask_cudf
+    left = dgd.from_cudf(left, chunksize=chunksize)
+    right = dgd.from_cudf(right, chunksize=chunksize)
+
+    joined = left.set_index("x").join(
+        right.set_index("x"), how="inner", lsuffix="l", rsuffix="r"
+    )
+    got = joined.compute().to_pandas()
+
+    if len(got.columns):
+        got = got.sort_values(list(got.columns))
+        expect = expect.sort_values(list(expect.columns))
+
+    # Check index
+    np.testing.assert_array_equal(expect.index.values, got.index.values)
+
+    # Check rows in each groups
+    expect_rows = {}
+    got_rows = {}
+
+    def gather(df, grows):
+        grows[df["x"].values[0]] = (set(df.al), set(df.ar))
+
+    expect.reset_index().groupby("x").apply(partial(gather, grows=expect_rows))
+    expect.reset_index().groupby("x").apply(partial(gather, grows=got_rows))
+
+    assert got_rows == expect_rows
+
+
+@pytest.mark.parametrize("left_nrows", param_nrows)
+@pytest.mark.parametrize("right_nrows", param_nrows)
+@pytest.mark.parametrize("left_nkeys", [4, 5])
+@pytest.mark.parametrize("right_nkeys", [4, 5])
+@pytest.mark.parametrize("how", ["left", "right"])
+def test_join_left(left_nrows, right_nrows, left_nkeys, right_nkeys, how):
+    chunksize = 50
+
+    np.random.seed(0)
+
+    # cuDF
+    left = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "a": np.arange(left_nrows, dtype=np.float64),
+        }
+    )
+    right = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "a": 1000 * np.arange(right_nrows, dtype=np.float64),
+        }
+    )
+
+    expect = left.set_index("x").join(
+        right.set_index("x"), how=how, sort=True, lsuffix="l", rsuffix="r"
+    )
+    expect = expect.to_pandas()
+
+    # dask_cudf
+    left = dgd.from_cudf(left, chunksize=chunksize)
+    right = dgd.from_cudf(right, chunksize=chunksize)
+
+    joined = left.set_index("x").join(
+        right.set_index("x"), how=how, lsuffix="l", rsuffix="r"
+    )
+    got = joined.compute().to_pandas()
+
+    if len(got.columns):
+        got = got.sort_values(list(got.columns))
+        expect = expect.sort_values(list(expect.columns))
+
+    # Check index
+    np.testing.assert_array_equal(expect.index.values, got.index.values)
+
+    # Check rows in each groups
+    expect_rows = {}
+    got_rows = {}
+
+    def gather(df, grows):
+        cola = np.sort(np.asarray(df.al))
+        colb = np.sort(np.asarray(df.ar))
+
+        grows[df["x"].values[0]] = (cola, colb)
+
+    expect.reset_index().groupby("x").apply(partial(gather, grows=expect_rows))
+
+    expect.reset_index().groupby("x").apply(partial(gather, grows=got_rows))
+
+    for k in expect_rows:
+        np.testing.assert_array_equal(expect_rows[k][0], got_rows[k][0])
+        np.testing.assert_array_equal(expect_rows[k][1], got_rows[k][1])
+
+
+@pytest.mark.parametrize("left_nrows", param_nrows)
+@pytest.mark.parametrize("right_nrows", param_nrows)
+@pytest.mark.parametrize("left_nkeys", [4, 5])
+@pytest.mark.parametrize("right_nkeys", [4, 5])
+def test_merge_left(
+    left_nrows, right_nrows, left_nkeys, right_nkeys, how="left"
+):
+    chunksize = 3
+
+    np.random.seed(0)
+
+    # cuDF
+    left = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "y": np.random.randint(0, left_nkeys, size=left_nrows),
+            "a": np.arange(left_nrows, dtype=np.float64),
+        }
+    )
+    right = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "y": np.random.randint(0, right_nkeys, size=right_nrows),
+            "a": 1000 * np.arange(right_nrows, dtype=np.float64),
+        }
+    )
+
+    expect = left.merge(right, on=("x", "y"), how=how)
+
+    def normalize(df):
+        return (
+            df.to_pandas()
+            .sort_values(["x", "y", "a_x", "a_y"])
+            .reset_index(drop=True)
+        )
+
+    # dask_cudf
+    left = dgd.from_cudf(left, chunksize=chunksize)
+    right = dgd.from_cudf(right, chunksize=chunksize)
+
+    result = left.merge(right, on=("x", "y"), how=how).compute(
+        scheduler="single-threaded"
+    )
+
+    dd.assert_eq(normalize(expect), normalize(result))
+
+
+@pytest.mark.parametrize("left_nrows", [2, 5])
+@pytest.mark.parametrize("right_nrows", [5, 10])
+@pytest.mark.parametrize("left_nkeys", [4])
+@pytest.mark.parametrize("right_nkeys", [4])
+def test_merge_1col_left(
+    left_nrows, right_nrows, left_nkeys, right_nkeys, how="left"
+):
+    chunksize = 3
+
+    np.random.seed(0)
+
+    # cuDF
+    left = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, left_nkeys, size=left_nrows),
+            "a": np.arange(left_nrows, dtype=np.float64),
+        }
+    )
+    right = cudf.DataFrame(
+        {
+            "x": np.random.randint(0, right_nkeys, size=right_nrows),
+            "a": 1000 * np.arange(right_nrows, dtype=np.float64),
+        }
+    )
+
+    expect = left.merge(right, on=["x"], how=how)
+    expect = (
+        expect.to_pandas()
+        .sort_values(["x", "a_x", "a_y"])
+        .reset_index(drop=True)
+    )
+
+    # dask_cudf
+    left = dgd.from_cudf(left, chunksize=chunksize)
+    right = dgd.from_cudf(right, chunksize=chunksize)
+
+    joined = left.merge(right, on=["x"], how=how)
+
+    got = joined.compute().to_pandas()
+
+    got = got.sort_values(["x", "a_x", "a_y"]).reset_index(drop=True)
+
+    dd.assert_eq(expect, got)
+
+
+def test_merge_should_fail():
+    # Expected failure cases described in #2694
+    df1 = cudf.DataFrame()
+    df1["a"] = [1, 2, 3, 4, 5, 6] * 2
+    df1["b"] = np.random.randint(0, 12, 12)
+
+    df2 = cudf.DataFrame()
+    df2["a"] = [7, 2, 3, 8, 5, 9] * 2
+    df2["c"] = np.random.randint(0, 12, 12)
+
+    left = dgd.from_cudf(df1, 1).groupby("a").b.min().to_frame()
+    right = dgd.from_cudf(df2, 1).groupby("a").c.min().to_frame()
+
+    with pytest.raises(KeyError):
+        left.merge(right, how="left", on=["nonCol"])
+    with pytest.raises(KeyError):
+        left.merge(right, how="left", on=["b"])
+    with pytest.raises(KeyError):
+        left.merge(right, how="left", on=["c"])
+
+    # Same column names
+    df2["b"] = np.random.randint(0, 12, 12)
+    right = dgd.from_cudf(df2, 1).groupby("a").b.min().to_frame()
+
+    with pytest.raises(KeyError):
+        left.merge(right, how="left", on="NonCol")
+
+
+@pytest.mark.parametrize("how", ["inner", "left"])
+def test_indexed_join(how):
+    p_left = pd.DataFrame({"x": np.arange(10)}, index=np.arange(10) * 2)
+    p_right = pd.DataFrame({"y": 1}, index=np.arange(15))
+
+    g_left = cudf.from_pandas(p_left)
+    g_right = cudf.from_pandas(p_right)
+
+    dg_left = dd.from_pandas(g_left, npartitions=4)
+    dg_right = dd.from_pandas(g_right, npartitions=5)
+
+    d = g_left.merge(g_right, left_index=True, right_index=True, how=how)
+    dg = dg_left.merge(dg_right, left_index=True, right_index=True, how=how)
+
+    # occasionally order is not correct (possibly do to hashing in the merge)
+    d = d.sort_values("x")  # index is preserved
+    dg = dg.sort_values(
+        "x"
+    )  # index is reset -- sort_values will slow test down
+
+    dd.assert_eq(d, dg, check_index=False)
+
+
+@pytest.mark.parametrize("how", ["left", "inner"])
+def test_how(how):
+    left = cudf.DataFrame(
+        {"x": [1, 2, 3, 4, None], "y": [1.0, 2.0, 3.0, 4.0, 0.0]}
+    )
+    right = cudf.DataFrame({"x": [2, 3, None, 2], "y": [20, 30, 0, 20]})
+
+    dleft = dd.from_pandas(left, npartitions=2)
+    dright = dd.from_pandas(right, npartitions=3)
+
+    expected = left.merge(right, how=how, on="x")
+    result = dleft.merge(dright, how=how, on="x")
+
+    dd.assert_eq(
+        result.compute().to_pandas().sort_values("x"),
+        expected.to_pandas().sort_values("x"),
+        check_index=False,
+    )
+
+
+@pytest.mark.parametrize("daskify", [True, False])
+def test_single_dataframe_merge(daskify):
+    right = cudf.DataFrame({"x": [1, 2, 1, 2], "y": [1, 2, 3, 4]})
+    left = cudf.DataFrame({"x": np.arange(100) % 10, "z": np.arange(100)})
+
+    dleft = dd.from_pandas(left, npartitions=10)
+
+    if daskify:
+        dright = dd.from_pandas(right, npartitions=1)
+    else:
+        dright = right
+
+    expected = left.merge(right, how="inner")
+    result = dd.merge(dleft, dright, how="inner")
+    assert len(result.dask) < 25
+
+    dd.assert_eq(
+        result.compute().to_pandas().sort_values(["z", "y"]),
+        expected.to_pandas().sort_values(["z", "y"]),
+        check_index=False,
+    )
+
+
+@pytest.mark.parametrize("how", ["inner", "left"])
+@pytest.mark.parametrize("on", ["id_1", ["id_1"], ["id_1", "id_2"]])
+def test_on(how, on):
+    left = cudf.DataFrame(
+        {"id_1": [1, 2, 3, 4, 5], "id_2": [1.0, 2.0, 3.0, 4.0, 0.0]}
+    )
+    right = cudf.DataFrame(
+        {"id_1": [2, 3, None, 2], "id_2": [2.0, 3.0, 4.0, 20]}
+    )
+
+    dleft = dd.from_pandas(left, npartitions=2)
+    dright = dd.from_pandas(right, npartitions=3)
+
+    expected = left.merge(right, how=how, on=on)
+    result = dleft.merge(dright, how=how, on=on)
+
+    dd.assert_eq(
+        result.compute().to_pandas().sort_values(on),
+        expected.to_pandas().sort_values(on),
+        check_index=False,
+    )
+
+
+def test_single_partition():
+    left = cudf.DataFrame({"x": range(200), "y": range(200)})
+    right = cudf.DataFrame({"x": range(100), "z": range(100)})
+
+    dleft = dd.from_pandas(left, npartitions=1)
+    dright = dd.from_pandas(right, npartitions=10)
+
+    m = dleft.merge(dright, how="inner")
+    assert len(m.dask) < len(dleft.dask) + len(dright.dask) * 3
+
+    dleft = dd.from_pandas(left, npartitions=5)
+    m2 = dleft.merge(right, how="inner")
+    assert len(m2.dask) < len(dleft.dask) * 3
+    assert len(m2) == 100
diff --git a/python/dask_cudf/dask_cudf/tests/test_onehot.py b/python/dask_cudf/dask_cudf/tests/test_onehot.py
new file mode 100644
index 0000000..6453d84
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_onehot.py
@@ -0,0 +1,125 @@
+# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+
+import pandas as pd
+import pytest
+
+from dask import dataframe as dd
+
+import cudf
+
+import dask_cudf
+
+
+def test_get_dummies_cat():
+    df = pd.DataFrame({"C": [], "A": []})
+    ddf = dd.from_pandas(df, npartitions=10)
+    dd.assert_eq(dd.get_dummies(ddf).compute(), pd.get_dummies(df))
+    gdf = cudf.from_pandas(df)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=10)
+    dd.assert_eq(
+        dd.get_dummies(ddf).compute(),
+        dd.get_dummies(gddf).compute(),
+        check_dtype=False,
+    )
+
+    df = pd.DataFrame({"A": ["a", "b", "c", "a", "z"], "C": [1, 2, 3, 4, 5]})
+    df["B"] = df["A"].astype("category")
+    df["A"] = df["A"].astype("category")
+    ddf = dd.from_pandas(df, npartitions=10)
+    dd.assert_eq(dd.get_dummies(ddf).compute(), pd.get_dummies(df))
+    gdf = cudf.from_pandas(df)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=10)
+    dd.assert_eq(
+        dd.get_dummies(ddf).compute(),
+        dd.get_dummies(gddf).compute(),
+        check_dtype=False,
+    )
+
+    df = pd.DataFrame(
+        {
+            "A": ["a", "b", "c", "a", "z"],
+            "C": pd.Series([1, 2, 3, 4, 5], dtype="category"),
+        }
+    )
+    df["B"] = df["A"].astype("category")
+    df["A"] = df["A"].astype("category")
+    ddf = dd.from_pandas(df, npartitions=10)
+    dd.assert_eq(dd.get_dummies(ddf).compute(), pd.get_dummies(df))
+    gdf = cudf.from_pandas(df)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=10)
+    dd.assert_eq(
+        dd.get_dummies(ddf).compute(),
+        dd.get_dummies(gddf).compute(),
+        check_dtype=False,
+    )
+
+
+def test_get_dummies_non_cat():
+    df = pd.DataFrame({"C": pd.Series([1, 2, 3, 4, 5])})
+    ddf = dd.from_pandas(df, npartitions=10)
+    dd.assert_eq(dd.get_dummies(ddf).compute(), pd.get_dummies(df))
+    with pytest.raises(NotImplementedError):
+        dd.get_dummies(ddf, columns=["C"]).compute()
+    gdf = cudf.from_pandas(df)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=10)
+    with pytest.raises(NotImplementedError):
+        dd.get_dummies(gddf, columns=["C"]).compute()
+    dd.assert_eq(
+        dd.get_dummies(ddf).compute(),
+        dd.get_dummies(gddf).compute(),
+        check_dtype=False,
+    )
+
+
+def test_get_dummies_cat_index():
+    df = pd.DataFrame({"C": pd.CategoricalIndex([1, 2, 3, 4, 5])})
+    ddf = dd.from_pandas(df, npartitions=10)
+    dd.assert_eq(dd.get_dummies(ddf).compute(), pd.get_dummies(df))
+    gdf = cudf.from_pandas(df)
+    gddf = dask_cudf.from_cudf(gdf, npartitions=10)
+    dd.assert_eq(
+        dd.get_dummies(ddf).compute(),
+        dd.get_dummies(gddf).compute(),
+        check_dtype=False,
+    )
+
+
+def test_get_dummies_large():
+    gdf = cudf.datasets.randomdata(
+        nrows=200000,
+        dtypes={
+            "C": int,
+            "first": "category",
+            "b": float,
+            "second": "category",
+        },
+    )
+    df = gdf.to_pandas()
+    ddf = dd.from_pandas(df, npartitions=25)
+    dd.assert_eq(dd.get_dummies(ddf).compute(), pd.get_dummies(df))
+    gddf = dask_cudf.from_cudf(gdf, npartitions=25)
+    dd.assert_eq(
+        dd.get_dummies(ddf).compute(),
+        dd.get_dummies(gddf).compute(),
+        check_dtype=False,
+    )
+
+
+def test_get_dummies_categorical():
+    # https://github.com/rapidsai/cudf/issues/7111
+    gdf = cudf.DataFrame({"A": ["a", "b", "b"], "B": [1, 2, 3]})
+    pdf = gdf.to_pandas()
+
+    gddf = dask_cudf.from_cudf(gdf, npartitions=1)
+    gddf = gddf.categorize(columns=["B"])
+
+    pddf = dd.from_pandas(pdf, npartitions=1)
+    pddf = pddf.categorize(columns=["B"])
+
+    expect = dd.get_dummies(pddf, columns=["B"])
+    got = dd.get_dummies(gddf, columns=["B"])
+
+    dd.assert_eq(
+        expect,
+        got,
+    )
diff --git a/python/dask_cudf/dask_cudf/tests/test_reductions.py b/python/dask_cudf/dask_cudf/tests/test_reductions.py
new file mode 100644
index 0000000..c34fbc3
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_reductions.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from dask import dataframe as dd
+
+import cudf
+
+import dask_cudf as dgd
+
+
+def _make_random_frame(nelem, npartitions=2):
+    df = pd.DataFrame(
+        {
+            "x": np.random.randint(0, 5, size=nelem),
+            "y": np.random.normal(size=nelem) + 1,
+        }
+    )
+    gdf = cudf.DataFrame.from_pandas(df)
+    dgf = dgd.from_cudf(gdf, npartitions=npartitions)
+    return df, dgf
+
+
+_reducers = ["sum", "count", "mean", "var", "std", "min", "max"]
+
+
+def _get_reduce_fn(name):
+    def wrapped(series):
+        fn = getattr(series, name)
+        return fn()
+
+    return wrapped
+
+
+@pytest.mark.parametrize("reducer", _reducers)
+def test_series_reduce(reducer):
+    reducer = _get_reduce_fn(reducer)
+    np.random.seed(0)
+    size = 10
+    df, gdf = _make_random_frame(size)
+
+    got = reducer(gdf.x)
+    exp = reducer(df.x)
+    dd.assert_eq(got, exp)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        cudf.datasets.randomdata(
+            nrows=10000,
+            dtypes={"a": "category", "b": int, "c": float, "d": int},
+        ),
+        cudf.datasets.randomdata(
+            nrows=10000,
+            dtypes={"a": "category", "b": int, "c": float, "d": str},
+        ),
+        cudf.datasets.randomdata(
+            nrows=10000, dtypes={"a": bool, "b": int, "c": float, "d": str}
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "op", ["max", "min", "sum", "prod", "mean", "var", "std"]
+)
+def test_rowwise_reductions(data, op):
+
+    gddf = dgd.from_cudf(data, npartitions=10)
+    pddf = gddf.to_dask_dataframe()
+
+    if op in ("var", "std"):
+        expected = getattr(pddf, op)(axis=1, ddof=0)
+        got = getattr(gddf, op)(axis=1, ddof=0)
+    else:
+        expected = getattr(pddf, op)(axis=1)
+        got = getattr(pddf, op)(axis=1)
+
+    dd.assert_eq(expected.compute(), got.compute(), check_exact=False)
diff --git a/python/dask_cudf/dask_cudf/tests/test_sort.py b/python/dask_cudf/dask_cudf/tests/test_sort.py
new file mode 100644
index 0000000..94609b1
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_sort.py
@@ -0,0 +1,116 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+import cupy as cp
+import numpy as np
+import pytest
+
+import dask
+from dask import dataframe as dd
+
+import cudf
+
+import dask_cudf
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("by", ["a", "b", "c", "d", ["a", "b"], ["c", "d"]])
+@pytest.mark.parametrize("nelem", [10, 500])
+@pytest.mark.parametrize("nparts", [1, 10])
+def test_sort_values(nelem, nparts, by, ascending):
+    np.random.seed(0)
+    df = cudf.DataFrame()
+    df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
+    df["b"] = np.arange(100, nelem + 100)
+    df["c"] = df["b"].astype("str")
+    df["d"] = df["a"].astype("category")
+    ddf = dd.from_pandas(df, npartitions=nparts)
+
+    with dask.config.set(scheduler="single-threaded"):
+        got = ddf.sort_values(by=by, ascending=ascending)
+    expect = df.sort_values(by=by, ascending=ascending)
+    dd.assert_eq(got, expect, check_index=False)
+
+
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
+def test_sort_values_single_partition(by, ascending):
+    df = cudf.DataFrame()
+    nelem = 1000
+    df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
+    df["b"] = np.arange(100, nelem + 100)
+    ddf = dd.from_pandas(df, npartitions=1)
+
+    with dask.config.set(scheduler="single-threaded"):
+        got = ddf.sort_values(by=by, ascending=ascending)
+    expect = df.sort_values(by=by, ascending=ascending)
+    dd.assert_eq(got, expect)
+
+
+def test_sort_repartition():
+    ddf = dask_cudf.from_cudf(
+        cudf.DataFrame({"a": [0, 0, 1, 2, 3, 4, 2]}), npartitions=2
+    )
+
+    new_ddf = ddf.shuffle(on="a", ignore_index=True, npartitions=3)
+
+    dd.assert_eq(len(new_ddf), len(ddf))
+
+
+@pytest.mark.parametrize("na_position", ["first", "last"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
+@pytest.mark.parametrize(
+    "data",
+    [
+        {
+            "a": [None] * 100 + list(range(100, 150)),
+            "b": list(range(50)) + [None] * 50 + list(range(50, 100)),
+        },
+        {"a": list(range(15)) + [None] * 5, "b": list(reversed(range(20)))},
+    ],
+)
+def test_sort_values_with_nulls(data, by, ascending, na_position):
+    np.random.seed(0)
+    cp.random.seed(0)
+    df = cudf.DataFrame(data)
+    ddf = dd.from_pandas(df, npartitions=5)
+
+    with dask.config.set(scheduler="single-threaded"):
+        got = ddf.sort_values(
+            by=by, ascending=ascending, na_position=na_position
+        )
+        expect = df.sort_values(
+            by=by, ascending=ascending, na_position=na_position
+        )
+
+    # cudf ordering for nulls is non-deterministic
+    dd.assert_eq(got[by], expect[by], check_index=False)
+
+
+@pytest.mark.parametrize("by", [["a", "b"], ["b", "a"]])
+@pytest.mark.parametrize("nparts", [1, 10])
+def test_sort_values_custom_function(by, nparts):
+    df = cudf.DataFrame({"a": [1, 2, 3] * 20, "b": [4, 5, 6, 7] * 15})
+    ddf = dd.from_pandas(df, npartitions=nparts)
+
+    def f(partition, by_columns, ascending, na_position, **kwargs):
+        return partition.sort_values(
+            by_columns, ascending=ascending, na_position=na_position
+        )
+
+    with dask.config.set(scheduler="single-threaded"):
+        got = ddf.sort_values(
+            by=by[0], sort_function=f, sort_function_kwargs={"by_columns": by}
+        )
+    expect = df.sort_values(by=by)
+    dd.assert_eq(got, expect, check_index=False)
+
+
+@pytest.mark.parametrize("by", ["a", "b", ["a", "b"], ["b", "a"]])
+def test_sort_values_empty_string(by):
+    df = cudf.DataFrame({"a": [3, 2, 1, 4], "b": [""] * 4})
+    ddf = dd.from_pandas(df, npartitions=2)
+    got = ddf.sort_values(by)
+    if "a" in by:
+        expect = df.sort_values(by)
+        assert dd.assert_eq(got, expect, check_index=False)
diff --git a/python/dask_cudf/dask_cudf/tests/test_struct.py b/python/dask_cudf/dask_cudf/tests/test_struct.py
new file mode 100644
index 0000000..6abac4c
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/test_struct.py
@@ -0,0 +1,61 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
+import pytest
+
+import cudf
+
+import dask_cudf
+
+
+@pytest.mark.parametrize(
+    "data, column",
+    [
+        (
+            {
+                "a": [{"a": [1, 2, 3, 4], "b": "Hello world"}, {}, {"a": []}],
+                "b": [1, 2, 3],
+                "c": ["rapids", "cudf", "hi"],
+            },
+            "a",
+        ),
+        (
+            {"a": [{}, {}, {}], "b": [1, 2, 3], "c": ["rapids", "cudf", "hi"]},
+            "a",
+        ),
+        (
+            {
+                "a": [{}, {}, {}],
+                "b": [{"a": 1}, {"b": 5}, {"c": "Hello"}],
+                "c": ["rapids", "cudf", "hi"],
+            },
+            "b",
+        ),
+        (
+            {
+                "a": [{}, {}, {}, None],
+                "b": [{"a": 1}, {"b": 5}, {"c": "Hello"}, None],
+                "c": ["rapids", "cudf", "hi", "cool"],
+            },
+            "b",
+        ),
+        (
+            {
+                "a": [{}, {}, {}, None, {}, {"a": 5}],
+                "b": [
+                    {"a": 1},
+                    {"b": 5},
+                    {"c": "Hello"},
+                    None,
+                    {"a": 10, "b": 5},
+                    {},
+                ],
+                "c": ["rapids", "cudf", "hi", "cool", "hello", "world"],
+            },
+            "b",
+        ),
+    ],
+)
+def test_select_struct(data, column):
+    df = cudf.DataFrame(data)
+    ddf = dask_cudf.from_cudf(df, 2)
+    assert df[column].to_arrow() == ddf[column].compute().to_arrow()
diff --git a/python/dask_cudf/dask_cudf/tests/utils.py b/python/dask_cudf/dask_cudf/tests/utils.py
new file mode 100644
index 0000000..88a2116
--- /dev/null
+++ b/python/dask_cudf/dask_cudf/tests/utils.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.
+
+import numpy as np
+import pandas as pd
+
+import dask.dataframe as dd
+
+import cudf
+
+
+def _make_random_frame(nelem, npartitions=2, include_na=False):
+    df = pd.DataFrame(
+        {"x": np.random.random(size=nelem), "y": np.random.random(size=nelem)}
+    )
+
+    if include_na:
+        df["x"][::2] = pd.NA
+
+    gdf = cudf.DataFrame.from_pandas(df)
+    dgf = dd.from_pandas(gdf, npartitions=npartitions)
+    return df, dgf
diff --git a/python/dask_cudf/pyproject.toml b/python/dask_cudf/pyproject.toml
new file mode 100644
index 0000000..41b57b7
--- /dev/null
+++ b/python/dask_cudf/pyproject.toml
@@ -0,0 +1,98 @@
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+
+[build-system]
+build-backend = "setuptools.build_meta"
+requires = [
+    "setuptools",
+    "wheel",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project]
+name = "dask_cudf"
+version = "23.10.00"
+description = "Utilities for Dask and cuDF interactions"
+readme = { file = "README.md", content-type = "text/markdown" }
+authors = [
+    { name = "NVIDIA Corporation" },
+]
+license = { text = "Apache 2.0" }
+requires-python = ">=3.9"
+dependencies = [
+    "cudf==23.10.*",
+    "cupy-cuda11x>=12.0.0",
+    "dask==2023.9.2",
+    "distributed==2023.9.2",
+    "fsspec>=0.6.0",
+    "numpy>=1.21,<1.25",
+    "pandas>=1.3,<1.6.0dev0",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+classifiers = [
+    "Intended Audience :: Developers",
+    "Topic :: Database",
+    "Topic :: Scientific/Engineering",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+]
+dynamic = ["entry-points"]
+
+[project.optional-dependencies]
+test = [
+    "dask-cuda==23.10.*",
+    "numba>=0.57,<0.58",
+    "pytest",
+    "pytest-cov",
+    "pytest-xdist",
+] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
+
+[project.urls]
+Homepage = "https://github.com/rapidsai/cudf"
+
+[tool.setuptools]
+license-files = ["LICENSE"]
+
+[tool.isort]
+line_length = 79
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+order_by_type = true
+
+known_dask = [
+    "dask",
+    "distributed",
+    "dask_cuda",
+]
+known_rapids = [
+    "rmm",
+    "cudf",
+]
+known_first_party = [
+    "dask_cudf",
+]
+
+default_section = "THIRDPARTY"
+sections = [
+    "FUTURE",
+    "STDLIB",
+    "THIRDPARTY",
+    "DASK",
+    "RAPIDS",
+    "FIRSTPARTY",
+    "LOCALFOLDER",
+]
+skip = [
+    "thirdparty",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".tox",
+    ".venv",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+]
diff --git a/python/dask_cudf/setup.py b/python/dask_cudf/setup.py
new file mode 100644
index 0000000..3fa0f25
--- /dev/null
+++ b/python/dask_cudf/setup.py
@@ -0,0 +1,14 @@
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+
+from setuptools import find_packages, setup
+
+setup(
+    include_package_data=True,
+    packages=find_packages(exclude=["tests", "tests.*"]),
+    entry_points={
+        "dask.dataframe.backends": [
+            "cudf = dask_cudf.backends:CudfBackendEntrypoint",
+        ]
+    },
+    zip_safe=False,
+)